1D_CNN_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning - *SS19 - 27.06.2019*\n",
    "# 1D-CNN Tutorial 02: Sequential in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "- [Introduction](#introduction)<br>\n",
    "- [1D-CNN sequential classification](#cnn_sequential_classification)<br>\n",
    "    - [Load packages](#load_packages)<br>  \n",
    "    - [The dataset](#the_dataset)<br>  \n",
    "    - [Load the data](#load_the_data)<br> \n",
    "    - [Data exploration](#data_exploration)\n",
    "    - [Build model](#build_model)<br>\n",
    "    - [Evaluation data](#evaluation_data)<br>\n",
    "    - [Prediction](#prediction)<br>\n",
    "- [Compromise](#compromise)<br>\n",
    "- [Summary](#summary)<br>\n",
    "- [References](#references)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a id=\"introduction\"></a>\n",
    "Most use cases for 1D-CNN models are those where sequential data sets are used. These are often sensor data measured in a defined time interval. For example, temperature, humidity or wind force are read in and analyzed, but also texts, as shown in Notebook \"1D-CNN Tutorial 01: Simple 1D-CNN in Keras\". The used texts have to be converted into a suitable format (one-hot vectors) before being used in a 1D-CNN, after that they can be used like other sequential data. In this second notebook to 1D-CNN we will concentrate on the classical use of 1D-CNN, i.e. the analysis of numerical values. \n",
    "The example, which we will examine in more detail in the following, as well as the corresponding data sets are taken from a Kaggle competition. Kaggle is an online community of data scientists and enables users to find or publish data sets. However, the platform also offers the opportunity to explore or create models in a web-based data science environment, to collaborate with other data scientists and engineers, and to participate in competitions to solve data science challenges. Klagge offers its users a wide range of interesting content in this area, including competitions for machine learning, a public data platform, a cloud-based workbench for data science and a short form of AI-training. On March 8, 2017, Google announced that they would acquire Kaggle. \n",
    "\n",
    "\n",
    "The competition, which is interesting for us, deals with the subject of earthquakes. Current scientific studies in connection with earthquake forecasting concentrate on three key points: When the event will occur, where it will occur and how big it will be. The analysis of seismic signals is used to predict the time of an earthquake. Due to their devastating consequences, the prediction of earthquakes is one of the most important problems in the geosciences. \n",
    "The aim of the Klagge competition is to create a model that can predict the time span until an earthquake from real-time data. In this notebook, only the training and test data provided will be discussed. The data we will use originates from a known experimental setup for the investigation of earthquake physics. More information about the competition can be found at the following link: https://www.kaggle.com/c/LANL-Earthquake-Prediction (The last check of the link took place on 8. June 2019)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D_CNN_text_classification 1D-CNN sequential classification<a id=\"cnn_sequential_classification\"></a>\n",
    "\n",
    "### Load packages<a id=\"load_packages\"></a>\n",
    "Load all required packages for data manipulation, feature engineering and model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "#data preprocessing\n",
    "import pandas as pd\n",
    "#math operations\n",
    "import numpy as np\n",
    "#data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "#host-os operations\n",
    "import os\n",
    "from os import listdir, makedirs, path\n",
    "from os.path import isfile, join, basename, splitext, isfile, exists\n",
    "from pathlib import Path\n",
    "#import keras.utils as utils\n",
    "from tqdm import tqdm_notebook\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Embedding, GlobalAveragePooling1D\n",
    "from keras.optimizers import adam\n",
    "from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from keras.utils import plot_model\n",
    "from keras import optimizers\n",
    "# Graphic output\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset<a id=\"the_dataset\"></a>\n",
    "\n",
    "The training data is a single, continuous segment of seismic signals. The test data consists of a folder with many small segments. The data within each test file is continuous, but the test files do not represent a continuous segment of the experiment. Thus, it cannot be assumed that the predictions follow the same regular pattern as in the training file.\n",
    "\n",
    "**Notice that:** The training and test data are in the zip file \"LANL-Earthquake-Prediction\" and must be **incorporated into the project structure and unpacked**! After unpacking the test data, the following files or folders should be available: ['test', 'sample_submission.csv', 'train.csv']. The folder \"test\" should contain at least 2.624 files. Another note: From time to time Jupiter also creates a checkpoint file, so there may be 2.625 files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already available\n"
     ]
    }
   ],
   "source": [
    "Earthquake_Prediction = Path('LANL-Earthquake-Prediction')\n",
    "Test = Path('LANL-Earthquake-Prediction/test/')\n",
    "if Earthquake_Prediction.is_dir() and Test.is_dir():\n",
    "    print('Data already available')\n",
    "else:\n",
    "    import zipfile\n",
    "    zip_ref = zipfile.ZipFile('LANL-Earthquake-Prediction.zip', 'r')\n",
    "    zip_ref.extractall('LANL-Earthquake-Prediction')\n",
    "    zip_ref.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then check that all the data is correct. The project directory should now contain a folder called \"LANL-Earthquake-Prediction\". In this directory you should find the following folders/files:\n",
    "- **sample_submission.csv**: Example prediction file  \n",
    "- **test**: Folder with the test sequences\n",
    "- **train.csv**: File with the training data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sample_submission.csv', 'test', 'train.csv']\n",
      "There are 2624 files in test folder\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir('LANL-Earthquake-Prediction'))\n",
    "print(\"There are {} files in test folder\".format(len(os.listdir(os.path.join('LANL-Earthquake-Prediction', 'test' )))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File descriptions:\n",
    "- **train.csv:** A single, continuous training segment for experimental data\n",
    "    <br>Data fields:\n",
    "    - `acoustic_data` - The seismic signal [int16]\n",
    "    - `time_to_failure` - Time (in seconds) to the next laboratory earthquake [float64]\n",
    "<br><br>\n",
    "- **test:** A folder with many small segments of test data\n",
    "    - `seg_id` - The test segment for which predictions are to be made (one prediction per segment)\n",
    "<br><br>\n",
    "- **sample_sumbission.csv:** An example file (prediction) in the correct format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data <a id=\"load_the_data\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sequences = 629000000\n",
    "sequence_length= 50000\n",
    "num_features = 1\n",
    "batch_size = 128\n",
    "epochs = 100\n",
    "path_trainData = 'LANL-Earthquake-Prediction/'\n",
    "path_testData = 'LANL-Earthquake-Prediction/test/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanation:\n",
    "- **num_sequences:** Number of data sets to be loaded from the training data (the larger, the more training data sets we have, but the longer it takes to train the model)\n",
    "- **sequence_length:** Length of the training sequences (The huge sequence is divided into x blocks of the length `sequence_length`)\n",
    "- **num_features:** Number of channels of our model (1: Since there is only one \"huge\" sequence)\n",
    "- **path_***: Path specifications\n",
    "\n",
    "If an out-of-memory error occurs, the `num_sequences` and `sequence_length` can be set to a smaller value at this point. This reduces the recognition rate of the model, but it can also be trained on weaker machines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_data = pd.read_csv(path_trainData+'train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file train.csv contains our training data. It consists of more than 600 million data rows and two columns (`acoustic_data` and `time_to_failure`). Importing the file may take a few minutes (about five minutes). To see if the dataset has been imported correctly, we will display the first and last ten data fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   acoustic_data  time_to_failure\n",
      "0             12           1.4691\n",
      "1              6           1.4691\n",
      "2              8           1.4691\n",
      "3              5           1.4691\n",
      "4              8           1.4691\n",
      "5              8           1.4691\n",
      "6              9           1.4691\n",
      "7              7           1.4691\n",
      "8             -5           1.4691\n",
      "9              3           1.4691\n",
      "\n",
      "\n",
      "           acoustic_data  time_to_failure\n",
      "629145470              6         9.759796\n",
      "629145471              2         9.759796\n",
      "629145472              3         9.759796\n",
      "629145473              6         9.759796\n",
      "629145474              5         9.759796\n",
      "629145475              7         9.759796\n",
      "629145476              9         9.759796\n",
      "629145477             10         9.759796\n",
      "629145478              6         9.759796\n",
      "629145479              5         9.759796\n"
     ]
    }
   ],
   "source": [
    "print(load_data.head(10))\n",
    "print('\\n')\n",
    "print(load_data.tail(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data exploration <a id=\"data_exploration\"></a>\n",
    "\n",
    "The `acoustic_data` (red) represent the seismic data, the `time_to_failure` (blue) the time until an earthquake occurs. From this diagram we can clearly see that if an earthquake has occurred, the `acoustic_data` has extremely deflected shortly before. It is also possible to see that the higher the `time_to_failure` deflection after an earthquake, the longer it takes for the next earthquake to occur. The plot represents only every 100 data sets of the data set. [[1]](#r1),[[5]](#r5),[[7]](#r7)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_step = 100\n",
    "train_ad_sample_df = load_data['acoustic_data'].values[::n_step]\n",
    "train_ttf_sample_df = load_data['time_to_failure'].values[::n_step]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('Acoustic data:')\n",
    "plt.plot(train_ad_sample_df, color='r')\n",
    "plt.legend(['acoustic data'], loc=(0.01, 0.95))\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the data first. Initially we look at the `acoustic_data` data. These are the individual swings of seismic activity and there are no conspicuous features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('time to failure:')\n",
    "plt.plot(train_ttf_sample_df, color='b')\n",
    "plt.legend(['time to failure'], loc=(0.01, 0.9))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the second step we want to look at `time_to_failure`. We see that it is a sequence of values, which are minimized by a positive start value step by step, until they reach the value 0. Then a positive start value is set again and the cycle starts again.\n",
    "`time_to_failure` specifies the time at one point of the sequence until an earthquake occurs from this point on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3vZhvuukmPvrRjzJ27FimTp3KySef3OyxpPc3atQoxo4dy4wZM5rcTkRERERERESkLVQsvwVUkD136X8jIiIiIiIikjtULF9ERERERERERCQHKBEmIiKSAM75HxERERGRJFMiTEREpJAdfzxcfDH9+sFHPhI6GBERERFJOjObYWabzGxZg/VfMrNXzGy5mf0oU/tXIqyFklpLLZfpfyIi0gI7d8ITT7BlCzz4YOhgRERERES4G7ik7gozi4APAKOccyOA6ZnauRJhLdCpUyeqqqqUeMkhzjmqqqro1KlT6FBERPLKxo2hIxARERGRJHPOzQaqG6z+N+AW59y+eJtNmdp/caYeONf17t2bioqKFm3rnOPAgQPU1tZmNihplQ4dOlBSUsIbb7wROhQRkdw1PW5Mu9YvbrttOVG0OVw8IiIiIlLois1sYZ2/73DO3XGU+wwDJpvZfwI1wLXOueczElwmHjQfVFdXk0qlQochIiKSWVEEQN++X2fLFti0aQS6/ImIiIhIBh10zp3byvsUA72ACcA44EEzO9VlYGiehkaKSPv7wQ/ADA4cCB2JiMSK46avp58OG4eIiIiISCMqgT867zmgFuibiR0ltkdYaxw4cIDKykpqampChyLN6NSpE4MGDaKkpCR0KPKDH/jl3r2g/4dITnnlFdiwAQYMCB2JiIiIiMhhjwDlQIWZDQOOA7ZkYkdKhLVAZWUl3bt3p7S0FDMLHY40Il08v7KykiFDhoQOR0QkJ40dC4sX+15hH/tY6GhEREREJInM7PdACuhrZpXATcAMYIaZLQP2A9MyMSwSlAhrkZqaGiXBcpyZ0adPHzZvVgFoEZGmjBkDa9ZARYUSYSIiIiIShnPuo03cdGU29q8aYS2kJFju0/9IRKR5RUUwZYrqhImIiIhIcikRJiIikiCpFKxeDZWVoSMREREREck+JcLywLZt27jtttsO/71+/XquuOKKdt9PRUUFzz77bKvus2/fPi666CLKysp44IEHmtzuxhtv5MknnwQglUqxcOHCY4pVRETaJor8sqIiaBgiIiIiIkEoEZYHGibCTjrpJB566KF2309bEmFLlizhwIEDLF26lA9/+MNNbve9732Piy66qE1xHTx4sE33kxyQmdqGInIMRo+GXr00PFJEREREkkmJsDzwjW98gzVr1lBWVsZ1113H2rVrGTlyJAB33303l19+OZdeeilDhgzhF7/4BT/+8Y8ZM2YMEyZMoLq6GoA1a9ZwySWXcM455zB58mRWrlxZbx9r167l9ttv59Zbb6WsrIw5c+bwxhtvcOGFFzJq1CguvPBC3nzzzXr32bRpE1deeSVLly6lrKyMNWvW8L3vfY9x48YxcuRIrr76atKTPHzyk59sNHnXrVu3w78/9NBDfPKTnzy8/TXXXEMURVx//fXs3r2bT3/604wbN44xY8bw6KOPttvzKxmgem0iOatDB5g6VYkwEREREUkmzRrZSl/9Kixd2r6PWVYGP/lJ07ffcsstLFu2jKXxjteuXVvv9mXLlrFkyRJqamo4/fTT+eEPf8iSJUv42te+xr333stXv/pVrr76am6//XaGDh3KggUL+MIXvsDMmTMPP0ZpaSmf//zn6datG9deey0Al156KVdddRXTpk1jxowZfPnLX+aRRx45fJ9+/frxm9/8hunTp/PYY48B8O///u/ceOONAHziE5/gscce49JLL23T8/Lqq6/y5JNPUlRUxLe+9S3Ky8uZMWMG27ZtY/z48Vx00UV07dq1TY8tIpJkqRQ88gi88QacckroaEREREREskc9wgpAFEV0796dE044gR49ehxOPJ199tmsXbuWXbt28eyzz/KhD32IsrIyPve5z7Fhw4ajPu68efP42Mc+Bvik1ty5c496n6effprzzjuPs88+m5kzZ7J8+fI2H9eHPvQhioqKAHj88ce55ZZbKCsrI5VKUVNTc0QPNRERaZnycr9UnTARERERSRr1CGul5npuhdKxY8fDv3fo0OHw3x06dODgwYPU1tbSs2fPwz3K2sqOMtytpqaGL3zhCyxcuJDBgwdz8803U1NT0+LHbLht3d5ezjn+8Ic/MHz48DZELiIidY0YAX36+OGR06aFjkZEREREJHvUIywPdO/enZ07d7b5/scffzxDhgzhf//3fwGfVHrhhReOup+JEydy//33A3DfffcxadKkZveTTmT17duXXbt2taig/4knnsiKFSuora3l4YcfbnK7d7/73fz85z8/XHNsyZIlR31sERFpXIcOfnik6oSJiIiISNIoEZYH+vTpwwUXXMDIkSO57rrr2vQY9913H3feeSejR49mxIgRjRabv/TSS3n44YcPF8v/2c9+xl133cWoUaP47W9/y09/+tNm99GzZ08++9nPcvbZZ3P55Zczbty4o8Z1yy238P73v5/y8nIGDBjQ5HY33HADBw4cYNSoUYwcOZIbbrjh6ActIiJNiiJ480147bXQkYiIiIiIZI+le9gkTdeuXd3u3btbtO2KFSs488wzMxyRtAf9r3JEjx6wYwds2+Z/F5Fw4iHoA/o7LrsMfvUrv3r5chg5Eu68Ez796YDxiYiIiEhBMbM9zrmcndlOPcJEJHMSmmgXyQdnnQX9+ml4pIiIiIgkixJhItL+jjKxgoiEZ/ZOnTDlrEVEREQkKZQIa6GkDiHNJ/ofiYi0TioFb70Fq1eHjkREREREJDuUCGuBTp06UVVVpURLDnPOUVVVRadOnUKHIiKSN6LILysqgoYhIiIiIpI1xaF2bGadgNlAxziOh5xzN5nZEOB+oDewGPiEc26/mXUE7gXOAaqADzvn1saP9U3gM8Ah4MvOuX+0Z6yDBg2isrKSzZs3t+fDSjvr1KkTgwYNCh2GiEjeGD4c+vf3wyM/+9nQ0YiIiIiIZF6wRBiwDyh3zu0ysxJgrpn9DbgGuNU5d7+Z3Y5PcP0yXm51zp1uZh8Bfgh82MzOAj4CjABOAp40s2HOuUPtFWhJSQlDhgxpr4cTERHJCek6YRUVvk6YyvuJiIiISKELNjTSebviP0viHweUAw/F6+8BLo9//0D8N/HtF5qZxevvd87tc869DqwGxmfhEERERPJeeTls2ACvvBI6EhERERGRzAtaI8zMisxsKbAJeAJYA2xzzh2MN6kEBsa/DwTWAcS3bwf61F3fyH1EJCTV1RPJeamUXz79dNAwRERERESyImgizDl3yDlXBgzC9+I6s7HN4mVjAzZcM+uPYGZXm9lCM1t48ODBxjYRkfag8VUieeP002HgQBXMFxEREZFkyIlZI51z24AKYALQ08zStcsGAevj3yuBwQDx7T2A6rrrG7lPw/3c4Zw71zl3bnFxyPJoIiIiucHMzx6ZrhMmIiIiIlLIgiXCzOwEM+sZ/94ZuAhYATwNXBFvNg14NP79T/HfxLfPdM65eP1HzKxjPOPkUOC57ByFiIhI/osi2LQJXn45dCQiIiIiIpkVslvUAOAeMyvCJ+QedM49ZmYvA/eb2f8FlgB3xtvfCfzWzFbje4J9BMA5t9zMHgReBg4CX2zPGSNFREQKXbpOWEUFjBgRMhIRERERkcwyl9BxEF27dnW7d+8OHYZIYerVC7Ztg+pq/7uIhBPX7BvQ33HZZfCrXx25iXNQWgrjxsFDDx15u4iIiIhIS5nZHudc19BxNCUnaoSJiIhIOHXrhNXWho5GREQke/7wB/j2t0NHISLZpESYiGROQnuciuSjVAqqqmD58tCRiIiIZM8VV8D3vw87doSORESyRYkwEWl/8VAsEckfUeSXTz8dNg4REZFsGjjQL+fODRuHiGSPEmEiIiLCKafAkCFKhImISLJMnOiXuv6JJIcSYSIiIgL44ZGzZqlOmIiIJEfnzn6pRJhIcigRJiIiIgCUl8PWrfDCC6EjERERya4lS/yk5yJS+JQIExEREcD3CAM/e6SIiEiS1NbCnDmhoxCRbFAiTESkNd58E/70p9BRiGTEoEFw+ukaHiIiIsnSvz907Kjrn0hSFIcOQEQKmHOhI2h/Y8ZAdXVhHpsIfvbIBx+EQ4egqCh0NCIiIpnXqROcf74SYSJJoR5hItL+zEJHkDnV1aEjEMmoVAq2b/e1UkRERJKivNzXyNRHPZHCp0SYiIiIHKY6YSIikkSplO/wP3t26EhEJNOUCBMREZHDTjoJhg/X8BAREUmW8eOhc2dd/0SSQIkwERERqSeV8jNnHTwYOhIREZHs6NgRLrhAiTCRJFAiTEREROqJIti5ExYvDh2JiIhI9qRS8NJLsGVL6EhEJJOUCJNEe+opuO660FGIiOSWdJ0wtYqLiEiSRJFfzpoVNg4RySwlwiTR/uVfYPp0eOut0JEUKOdCRyAibXDiiXDWWUqEiYhIsowbB1276vonUuiUCJNEO/tsv9TFrp2ZhY5ARI5RFMHcuXDgQOhIREREsqOkxNcJ08zJIpllZjPMbJOZLWvktmvNzJlZ30ztX4kwSbTRo/1SiTARkfpSKdi9G55/PnQkIiIi2RNFsHw5bNwYOhKRgnY3cEnDlWY2GHgX8GYmd65EmCRaUZFfKhEmIlLf1Kl+qVZxERFJEtUJE8k859xsoLqRm24F/gPIaI2d4kw+eC7r3bs3Ffp0n3jr1p0GDOb11+GBB+Zx4on7QodUGG64AQ4ehKVLobjATjPTp/ulzh+SL+LX7P7v72P9+ioqKl5t8V1PPfVc/vjH/Uyc+GKmohMREQnq7bfPoKamBxUVCwA4dMjo0uUC7rtvI/36rQocnUjeKjazhXX+vsM5d0dzdzCzy4C3nHMvWIZL7ZhLaDHrrl27ut27d4cOQwK75hq49Vb/+913w7RpQcMpHH37QlUVbN7sfy8k6ZNyQs+dkofi1+yA/o7LLoNf/arld/3KV+DXv4Zt2+C44zIUn4iISEDTpsHs2fD66++se9/7YM0aWLkyXFwi+czM9jjnuh5lm1LgMefcSDPrAjwNXOyc225ma4FznXNbMhGfhkZK4nXtCn36aHikiEhDqRTs3QvPPRc6EhERkexJpeCVV2DDhtCRiCTGacAQ4IU4CTYIWGxm/TOxMyXCJPE6dPC1cDTSTUSkvqlTfYeymTNDRyIiIpI96Tph+n4gkh3OuZecc/2cc6XOuVKgEhjrnHs7E/tTIkwEf7F74436XaKlHWj4oEhe693bz66rLwIiIpIkY8ZAjx4aMSKSKWb2e2AeMNzMKs3sM9ncf4FVsRZpm3Srz9NPw5AhYWMpCBkubigi2ZNKwe23Q00NdOoUOhoREZHMKyqCKVOUCBPJFOfcR49ye2km968eYSLAWWfBCSeo14OISEPl5T4JtmBB6EhERESyJ5WC1avhrbdCRyIi7U2JMBF8B6ZUyrf6aDSfiMg7Jk/250i1iouISJLUHTEiIoVFiTCRWBRBZaWfKllERLyePWHsWH0REBGRZBk1yl8Ddf0TKTxKhInE1OojItK4KIL582Hv3tCRiIiIZEdRkR8xotIpIoVHiTCR2PDh0L+/EmEiIg2lUrB/Pzz7bOhIREREsieVgtde87PLi0jhUCJMJJauE1ZRoTph7UZPpEhBmDzZt4yrVVxERJIkPWJE1z+RwqJEmEgdqRRs2ACvvho6kjxnFjoCEWlHxx8P55yjHrMiIpIsI0dCnz66/okUGiXCROooL/dLXexEROpLpeC552D37tCRiIiIZEeHDppZXqQQKREmUsfpp8NJJykRJiLSUBTBgQOqEyYiIsmSSsGbb8LataEjkax68km45BKorQ0diWSAEmEidZj5L3uzZqnVR0SkrkmToLhYDQUiIpIsmlk+of75n+Ef/4CdO0NHIhmgRJhIA1EEGzfCihWhIxERyR3dusG4cfoiICIiyXLWWdCvn65/IoVEiTCRBlIpv9TFTkSkviiChQvVOCoiIslhBlOnqk6YSCFRIkykgVNPhcGDlQhrF/q0IFJQoggOHoS5c0NHIiIikj3l5fDWW7BmTehIRKQ9KBEm0kDdOmGqjdhGZqEjEJEMmDgRSkqgoiJ0JCIiItmjESMihUWJMJFGRBFs2QLLl4eOREQkd3TpAuedpy8CIiKSLMOHQ//+uv6JFAolwkQaoVYVbLnhAAAgAElEQVQfEZHGRREsWgTbt4eOREREJDvSI0YqKlT5Q6QQKBEm0ojSUv+jRJiISH2plB82rjphIiKS1zZvbtXwj1QKNmyAV17JXEiSg5T5LEhKhIk0IYpg9mzVCRMRqev886FjRzUUiIhInjvrLBg5ssWbR5Ffqk5mQqjmcUFTIkykCVEE1dXw4ouhIxERyR2dO8OECUqEJdqrr8KqVaGjEBE5Nlu2tGrz00+HgQN1/RMpBEqEiTRBdcLagboSixSkVAqWLIFt20JHIkEMHw7DhoWOQkQkq1QnTKRwKBEm0oTBg+G009T9uU3UlVikoJWX+y8Bs2aFjiQP/fjH8I9/hI5CRETaIJWCTZtgxYrQkYjIsVAiTKQZUeS/6B06FDoSEZHccd550KmTesy2yde/DpdcEjoKERFpg3SdsJkzw8YhIsdGiTCRZkQRbN8OS5eGjkREJHd07AgTJ6rHrIiIJMuQIXDyybr+ieQ7JcJEmqE6YSIijYsiP5lIVVXoSERERLLDzH8/mDVLM8uL5LNgiTAzG2xmT5vZCjNbbmZfidf3NrMnzGxVvOwVrzcz+5mZrTazF81sbJ3HmhZvv8rMpoU6Jik8J53k6wErESYiUl8q5euEzZ4dOhIREZHsiSI/4eSyZaEjkazQzAgFKWSPsIPA151zZwITgC+a2VnAN4CnnHNDgafivwHeAwyNf64Gfgk+cQbcBJwHjAduSifPRNpDFMGcOXDwYOhIJJfs3g1r14aOQiSc8eOhSxc1FEgCPPggvP566ChEJEek64RpeGSB0+RfBS1YIsw5t8E5tzj+fSewAhgIfAC4J97sHuDy+PcPAPc6bz7Q08wGAO8GnnDOVTvntgJPAKpCK+0mimDnTli8OHQkeaiAW1A++UlfJ6KmJnQkImEcdxxccIESYZIAH/4wjBkTOgoRyRGnnOI/A+r6J5K/cqJGmJmVAmOABcCJzrkN4JNlQL94s4HAujp3q4zXNbVepF2oTlgbJKAF5aWX/HL+/LBxiIQURX5oyObNoSMRybDt20NHICI5JD2zvOqEieSn4IkwM+sG/AH4qnNuR3ObNrLONbO+sX1dbWYLzWzhQY1zkxY68UQ480x1f5b6Jk/2SyVIJcnSDQWzZgUNQ0REJKtSKdi61U8aIyL5J2gizMxK8Emw+5xzf4xXb4yHPBIvN8XrK4HBde4+CFjfzPojOOfucM6d65w7t7i4uP0ORApeuk7YgQOhI5Fc0bOnXypBKkl27rnQtasSwiIikiwaMSKS30LOGmnAncAK59yP69z0JyA98+M04NE666+KZ4+cAGyPh07+A7jYzHrFRfIvjteJtJso8sXRFy4MHYnkmvnzYc+e0FGIhFFS4ntH6ouAiIgkyeDBcPrpuv6J5KuQPcIuAD4BlJvZ0vjnvcAtwLvMbBXwrvhvgL8CrwGrgV8DXwBwzlUD/wd4Pv75XrxOpN1MneqXuthJQ/v3w7x5oaMQCSeVghUrYOPG0JGIiIhkTyoFs2fDoUOhIxGR1go5a+Rc55w550Y558rin78656qccxc654bGy+p4e+ec+6Jz7jTn3NnOuYV1HmuGc+70+OeuUMckheuEE+DsszUMTuqIZ8QsKtLrQpKtvNwv1VAgIiJJEkV+Ho2lS0NHIhnlGi0/LnkueLF8kXyRSsHcub4HkAhAly5wzjkwc2boSETCGTMGundXQjgxNNmQiAjwTp0wfQ4sUNbYnHxSKJQIE2mhKIK9e+G550JHkkcS0IISRf41sXt36EhEwiguhilT1CMsMX7zm9ARiIjkhJNOguHD1RAkko+UCBNpoSlTfMOAvuy1QIJaUFIp30HimWdCRyISThTBq6/C+kbnbJaCsmNH6AhERHJGKuVnlldnWZH8okSYSAv16QOjRysRJvVNmuR7xOh1IUmmaeRFRCSJogh27oTFi0NHIiKtoUSYJNOCBfDNb7b6bqmUnyFw3772D0nyU7duMG6cusVLspWVQc+eeh+IiEiyqCFIJD8pESbJNGEC3HJLq+8WRVBTA/PnZyAmyVtRBM8/71sEpRnLl6vIXoEqKlKdMBERSZ4TT4SzztL1TyTfKBEm0gqqEyaNiSI4dMjPKirNGDkSzjsvdBSSIakUrFkDlZWhIxEREcme9MzyBw6EjkREWkqJMJFW6NkTxozR8B+pb+JEKClRglSSrbzcLzWNvIiIJEkU+dnDFy4MHYlkhHOhIyhIZjbDzDaZ2bI66/7LzFaa2Ytm9rCZ9czU/pUIE2mlKPJ1wvbuDR1JHkjIhaNLF9/RSQlSSbKzz4bevfU+EBGRZFGdsAJlFjqCQnc3cEmDdU8AI51zo4BXgdYX9W4hJcJEWimKYP9+nwyTJiTwwhFFsGgRbN8eOhKRMDp0gKlT9UWg4CXw/C4i0py+fX1jkK5/Ii3nnJsNVDdY97hz7mD853xgUKb2r0SYSCtNnuy/8Olil3ANerulUlBbC3PmhAlHJBdEEaxdC2+8EToSERGR7Eml4JlnfGO5iLSLTwN/y9SDF2fqgXNd7969qdD4jeSaPh2AdfPXcejQACoqWlflfNiwsTzyiOPCC5dkIrr8d/31vmLoyy/DqlWho2lf6dfO85XU1g6kosJnvg4c6EBJySTuvfctunVbEzLC3BU/dxo7l2Xx877/+/tYv76KiopXM7arrl27AuO47baVvOc9b2dsP3kt398HJ5+c/8fQWkk7XpGkiN/bbz/+NjU1PaioWNDmhzrhhL7s3TuS229fwqhRGh5QEG68EQ4ehKVL/fTY0lrFZla3ct4dzrk7WnJHM/s2cBC4LyORAeYSUsOnoa5du7rdu3eHDkNCiYd2XPM1x29+Azt2tO7u118Pt94KW7dC164ZiC/fDRwI69f76eMGDgwdTfuKXzvXXXOI227vQN3TSCrlX0uLF4cJLeelh1Ql9LoTTPy8D+jvuOwy+NWvMrer2lo/lfx73wv33JO5/eS1fH8f/Nd/wX/8h/89X4+htfL9fybZ89GPwv3367WSL+L39rSrHLNnw+uvt/2hqqv9EMnvfhduuKGd4pOw+vTx/9gtW/zv0ipmtsc51+w3ZTMrBR5zzo2ss24a8HngQufcnkzFp6GRIm0QRb7D07PPho5EckkU+UajrVtDRyISRocOPiH89NP6HigiCXT//aEjkEB694bRo1U6ReRYmNklwPXAZZlMgoESYSJtMmkSFBfrYif1RZH/8q86YZJkUQTr1sFrr4WOREREJHuiyDeS19SEjkQk95nZ74F5wHAzqzSzzwC/ALoDT5jZUjO7PVP7VyJMpA26dYNzz1Ui7KgS1iXkvPOgUyeYOTN0JCLhpKeRVzklERFJklQK9u2DBW0vNSa5KGHfZ7LFOfdR59wA51yJc26Qc+5O59zpzrnBzrmy+Ofzmdq/EmEibRRFsHAh7NoVOpIclK6nkjAdO8LEiUoASLKdeaavE6aEcIFK6PldRORopkzxp0g1lBcIXe8KmhJhIm0URX4ikbmtm3BSClwUwQsvQFVV6EhEwjDzreIVFWpEFRGR5OjZE8aOVSJMJB8oESbSRhMnQkmJLnZSXxT55axZYeMQCSmV8hPHrl4dOhIREZHsSaVg/nzYuzd0JCLSHCXCRNqoa1cYP17D4KS+ceOgSxe9LiTZ0glhNRSIiEiSRBHs3w/z5oWOREJ79VUoK/MNg5J7lAgTOQZRBIsWwY4doSORXHHccXDBBaqPJMk2bBgMGKBEmIiIJMvkyVBUpM+BAr//vS+XcuutoSORxigRVuDefhuKi2H27NCRFKYogkOHYM6c0JFI9jVd/CiKYPly2Lw5i+GI5BAz/z5QnTAREUmS44/3dcI0MkDOOssv1SiYm5QIK3Avv+wTNV/5SuhICtP55/seQLrYNSGh34DTw8L0upAkiyLfGLNyZehIREREsqe8HJ57DnbvDh2JBOEcrFpFhzjTsmhR2HCkcUqEFbjjj/fLpUvDxlGoOnf2yTBl+htI+HTD55wD3brpdSHJlkr5pRLCBSbB5/cdO3zjoohIc1IpOHAAnnkmdCTSLlrbsD9jhq8RsWzZ4VVr17ZvSHLslAhLELVKZEYqBUuWwLZtoSORXFFSApMmKQEgyXbaaTBokBLCUjh69ICPfzx0FCKS6yZN8qVp9Dkwz7W14ef55/3yrbcOr9JnodyjRFiCPPts6AgKUxRBba3qsEl9qRSsWOGHhokkkeqESSF64IHQEYhIruvWzc8iruSHpOm1kHuUCEsQvQEzY8IE6NRJz6/UV17ul2oNlCRLpfykEcuXh45EpP3s2RM6AhHJdVHkOwbt3Bk6Eglt+HD/PVGNgrlFibAE0RfyzOjY0dcJ0/ObMEe5mI0ZA927K0EqyZZOCLf5fWAG06a1Wzwi7WHu3NARiASwYYO6ubdCKuVrCqpOmEQRVFbCmjWhI5G6lAhLiFGj1CqRSVEEL7wA1dWhI5FcUVwMU6YoQVqQnFOzXguVlsIppxzj++Dee9spGpH2ofO6JNJJJ8GAAaGjyBsXXOBrxuZEg+i3vgWXXRY6isR+ftJs8rlJibCESKXg4EG1SmRKFPnz+qxZoSPJMQm82NUVRfDqq/VqZUoh6N8fTj45dBR5I4r8ubG2NnQkIu0jJ77YikhO69IFzjsvR84XP/gB/PnPYWPYvx86dIDvfCdsHAGccYb/6KhEWG5RIiwhJk7MoVaJAjR+PHTurBPcYW2dZaXApFJ+qddFgdm0yfdxlxaJIqiqgpdeCh2JSPtYuFA97EXk6FIpWLQIduwIHUkO2LvXL3/xi7BxBGAGU6fCzJmJ7yOQU5QIS4iuXXOoVaIAHXec7wI9c2boSCSXlJVBz55630mypRPCeh8UiIQ3dJx/vu9hrzphInI0mlm+QLQ2e9XIdTKKfJm9VavaKSY5ZkqEJUhOt0ps3uy7reXxGLIogmXL/KFIEhz9olhUpDphIiefDKeeqveBFIZ03R+9nkXkaM4/3zeW63yRp9qx4SddJ0yNgrlDibAESbdKzJkTOpJG3HUXzJsHP/1p6EjaLN3rQXXCpK4o8rPErFsXOhKRcKLIt4gfOhQ6EpFjk1N1f3LIyy/DgQOhoxDJLZ07+2SYzhckfkzg0KF+vgm9FnKHEmEJkm6V0BswM8aN80NQ9fxKXRoWJuLfB1u3+tl1RfJdFOVwD/sA9uyBESPgQx8KHYlI7okiWLLEXwOFxA6vN/OfhSoqEp8TzBlKhCWIWiUyq6QEJk3S8yv1jRoFvXvrdSHJpqnDpZCkUjncwz6AdE+wRx8NG4dILkqlfOJD5wuJIti4EVauDB2JgBJhiZNKwdKlsG1b6EgKUxTBihX+JCcCfqboqVOVAJBkGzjQDwtQQlgKgXrYN23fvtARiOSWCROgUyedLxLZDarBMatOWG5RIixh0nXCVMcqM9TroYEkXvQaEUWwdq3/EUkq1QmTQqEe9k1bsCB0BCK5pWNHPx+YzhexJAyNbOIYTz0VBg/WayFXKBGWMOlWCSVqMmPsWOjeXSe4RFzkWpHjUwuQiO+RvGOHr5UieSwJ5/cWSNf9UQ/7+nSdEzlSKuVrZFZVhY5E2qSdGvbN/LWjosJ3TJGwlAhLGLVKZFZxMUyerESj1DdiBPTtq9eFJJsSwlJI0nV/1MO+Pr2/RY6Uvv7Nnh02jqDycZRIBhp+UinYssXPtCthKRGWQFEEL76oVolMiSJ45RVYvz50JJIr0jPFzJyZn58DRNpD//5wxhn6oiyFQT3sj2QG8+dDTU3oSERyy/jx0KWLrn9A4nsVq1EwdygRlkDpVsxEt0pkkOqESWOiCCor4bXXQkciEk4q5WfOOngwdCQix0Y97I8URb5Y/rx5oSMRyS3HHQcXXKDzhUBpqf/RayE8JcISSK0SmVVWBj16KBEm9aVSfqn3nSRZeTns2gULF4aOROTYRZHq/tQ1ebKfKVnXOZEjRREsWwabN4eOREJLpfywetUJC0uJsEJWVdXoOCy1SmRWURFMmeKHwUkha90YxzPPhBNPVIJUkm3qVL/U+0AKQbqBQz3svR49/KRBen+LHCl9vkjs+yOJtUGaOOYogupqeOmlLMcj9SgRVqiWLfPVuR95pNGbUym1SmRSFMGaNX4oXKIl8aLXhHSdsKef1tMiydWvn588Qg0xUgjUw/5IUQQLFsCePaEjEckt554LXbsmOBGWloQaYUc5RtUJyw1KhBWqlSv9cv78Rm9OvwE121FmJP4El4SLXBukUn4ShVWrQkciEk4Uwdy5sH9/6EikTXR+P0w97I8URf69/eyzoSMRyS0lJTBpks4XAoMHw6mn6rUQmhJhCZVuldAbMDNGjYLevfX8Sn3l5X6p14UkWSrle4s8/3zoSESOnXrY1zdpki8RkfheLyKNKC+HFSvg7bdDRxJAPg+HyEDsUeSH1R861O4PLS2kRFhCqVUiszp08HXC9EFQ6ho6FAYM0OtCkk11wqSQpBs41MPe697dN7bq86XIkdJ1whJ9vsinXsUZjDWKYNs2WLo0Y7uQo1AiLMHSrRIbN4aOpDBFEbz+OrzxRuhIJFeY+ddFRUV+N4yJHIu+fX2vWX1RlkJwzjnQrZtez3VFETz3nJ8hVkTeMXYsHH+8zheiyRNyQdBEmJnNMLNNZraszrreZvaEma2Kl73i9WZmPzOz1Wb2opmNrXOfafH2q8xsWohjyUd6A2ZW4uuEFbo2JrKiyHeJT5fxE0miVAqeeQb27QsdicixUQ/7I6VScPCgf48nytatoSOQHFdcDJMnJ/R8oRbgegYOhGHD9D08pNA9wu4GLmmw7hvAU865ocBT8d8A7wGGxj9XA78EnzgDbgLOA8YDN6WTZ0KzJ52xY30X9kSejLNgxAjf80HPb4E5xm7S6QS0XhfSZgcOQE1N6CiOSXm5P4QFC0JHInLsoijBdX8aMWmS/8KfuC943/1u6AgkD6RS8OqrfvKkRMqnoZEZlkr5YbIHD4aOJJmCJsKcc7OB6garPwDcE/9+D3B5nfX3Om8+0NPMBgDvBp5wzlU757YCT3Bkci15WnCSKS72daz0hTwzOnTwJ7hED4NL7IE37bTT/GwxifuCIO3n3HOhc+fQURyTKVP8ZUrvAykEqvtTX9euMH48zJwZOpIsq60NHYHkgXRdQV3/ClgLk31RBDt3wpIlGY5HGhW6R1hjTnTObQCIl/3i9QOBdXW2q4zXNbX+CGZ2tZktNLOFB5V6BdQqkWmpFLz5pq8Vlihq7WmSmRKkcoxefDF0BMesVy8oK1NDTF7S+f0Iia77s3x5oz1UowgWLYIdOwLEJJLDRo+GHj0Ser6QejRKJKxcTIQ1pbFPXq6Z9UeudO4O59y5zrlzi4uL2zW4fJWuY6VWiczQCU4aE0WwebP//iCSVKkUzJuX96M8RZJb92fbNhg5Er7whSNuiiI4dAjmzg0QVyhKEksLFBUldESOWn+P0L8/nHlmAl8LOSIXE2Eb4yGPxMtN8fpKYHCd7QYB65tZLy1QVqZWiUw66yzo10/Pb2Fq+wVdCVIR/0V53z6fDBPJd4nsYb9nj18+8sgRN02cCMcdp+ucSGOiCNasgXXrjr5twcnHhHEGk3iplG8wOHAgY7uQJuRiIuxPQHrmx2nAo3XWXxXPHjkB2B4PnfwHcLGZ9YqL5F8crxM46hu3qAimTtUHlUzRMLgC1A4X8CFDoLRU7ztJtilTfC1F9UiWQpDIHvYbN/pl7aEjburcGc47L2HPRz5+wZcgEnm+yEdZeE9HEezaBQsXZnxX0kDQRJiZ/R6YBww3s0oz+wxwC/AuM1sFvCv+G+CvwGvAauDXwBcAnHPVwP8Bno9/vhevS7ZWvHFTKd8qUVmZuXCSLIrgrbdg9erQkUguSc8Uo9q6klQ9evjaSkoISyEoK4OePRP2ej5KF4YogsWLYfv2LMUjkidGjYLevRN2vlCPgEalR4kkMSlqZjPMbJOZLauzrreZPWFmq+Jlr0ztP/SskR91zg1wzpU45wY55+50zlU55y50zg2Nl9Xxts4590Xn3GnOubOdcwvrPM4M59zp8c9d4Y4oP6VbJRI3u0+WpJ/fRF3s5KiiCKqrYdmyo28rUqiiCObPf2eElUi+Smzdn2ZEkW/smT07dCRZoh5h0kIdOiR4RE6S3ictSP6dcAKMGJHMRBhwN3BJg3XfAJ5yzg0Fnor/zohcHBopWTZqlJ/BK6FvwIwbNgwGDEjoxU6tP01KtwApAS1Jlkr5TiWqEyaFQD3s65swATp21OdLkcakUrB2rf+RAtPKZF95ua8Ttn9/huLJUc652UDDkXwfAO6Jf78HuDxT+1ciTOjQ4Z06VtL+ElknLEmtPW108slw6ql630myTZ7se9IoISyFQD3A6+vUCc4/P0Hvb332kVZIXJ2wxHwJar1UyveMf+650JHkhBPjOvDEy36Z2lFxph441/Xu3ZuKQj7zdOgA06fzyrbToQJeeuklunWranLzgQMH8vDDQ7n//vn07x9gLvtTToHp0/08stn4v0yfDsC6+es4dGgAFRWZnd/7pJMG8Pbbw7n33uc45ZQEjAG65ho/Hdzq1fDmm6GjaV8/+hEA657bQG3tICoq5rT5oc44YzhPPdWXp556hqKi9gowh8Xvu7z/1Bf6OFq7/3j7/d/fx/r1VVRUvJqZuNpo2LCxPPqo413vWnLkjaGf60zI92MaMCD/j6G10sd7Laxdu5aKirWNblZbC8cffwG/+90WBg9+JXvxhbJnD0yfzq69x8ENsHr1aioq6neHKy09hXvuKeXPf36G7t0PhokzW8aOLez3RiEeW3xMbz/+NjU1PaioWJC1XdfWQo8eE/n976spLV2Z2Z3lwv/u4EEfR0lJ/ryGvvlN32192TJ4pRXn9PHjYfp0lq3vAcDzzz9PVdXuJjcvKirG7ALuumstBw++caxR55JiM6s7DcAdzrk7gkXTkHMukT9dunRxBe2Pf3QO3PNTv+7AuT//ufnNX3zROXDurruyEt2RfvhDH8B112Vnf75dwn3ta85175753a1a5Xd5222Z31dOGDLEH/CaNaEjaX9FRc6Bu/bL+9yxnkb+53/807RoUfuElvPi913eC30crd1/vH3//s5dfXXmwmqr6693rrjYuV27Grkx9HOdCfl+TD/9af4fQ2vFxwvO3Xhj85v+0z85V1qanbCCW7DAOXDbON6Bcz/+8ZGbzJ7tn76HH85+eFl3zTWF/d4oxGOLj+mqq8K8b6+4wrnBg52rrc3wjnLhf7dhg4/hxBPDxtEa/fv7mNevb939/v3fnQP30Kf/4sB/zz6a0aOdKy9vW5i5CtjtjpKTAUqBZXX+fgUYEP8+AHjlaI/R1h8NjRTAF+nr21fd+TPltNNg4MAMNIB897vw3//dzg8q2ZKuE6b3nSRZeblvKJ6b2Y65IlkRRar7U9f48dC5s65zIo1JpWDdOnjttdCRSLNc5od1plLw7LN+QE3C/QmYFv8+DXg0UztSIkyAd+qEPf20hnBngpn/cNzuz+/NN8O117bjA0o2DRwIQ4fqC4Ik2wUXQHFx/oyUEGlOuoFDr2evY0eYODEhz4dqhEkrlZf75TF/Dqys9K+/Bdkb2tlq+fgF81jf06045iiCmprc/he2NzP7PTAPGG5mlWb2GeAW4F1mtgp4V/x3RigRJoelWyVefz10JIUpimDzZnj55dCRSC6JIpgzx/eIEUmirl19rxElhKUQqIf9kaIIXnwRtmwJHYlIbjnjDOjXrx0SxU8+6Ze//OWxhpR5SUgYt+EYp0zxd0vStcM591Hn3ADnXIlzbpBz7k7nXJVz7kLn3NB42XBWyXajRJgcptmOMiuRz28+tv5kWRTBjh2wpJE64SJJEUWwcCHs3Bk6EpFjox72R0p//pk1K2wcGZeEL/jSrjI2YkTyTq9eMGZMQnrP5gglwgpdK86qZ54JJ56YsERNFpWW+skx9fzmuXb+oKs6YSL+i8ChQ6oTJoVBPezrGzcOunTRdS5v1Nb6GUElK1IpWL8eVq1qhwfL5WxaLseWI26/HWbMCB1FcigRVqja8GXdDKZOVatEppj5i92sWf4zRkFTq2iL9e/vu8brC4Ik2fnn+xnV9T7IA3XO75s3+x6tUl+71f0pECUlMGlSAno6FMpnn+uv92PWlQzLinSPyWN6f+TTay+fYs2yceNgyJDQUSSHEmFST3l5O7ZKyBGiCKqqYNmy0JFILoki3xPmwIHQkYiE0aULTJigxEG+6dcP+vQJHUXuOeMM9bBvKIpg+XLYtCl0JHJUd9/tl7t3Bw0jKYYNgwED2ul8oZ4MIi2mRJjUo9mOMiuRdcLkqKIIdu2CRYtCRyIFYeHCvOx2mkrB4sWwfXvoSKQ1Dh7UZB8NqYf9kdql10uu27YtdASSh9qlTph6WYm0mhJhUk+7tkrIEU4+2Xd51fMrdU2d6pd6Xcgxe/ZZ37f+Rz8KHUmrlZf7/N2cOaEjkdZavDh0BLkn3cN+9erQkeSGsWOhW7cCT4S9+GLoCNqHsrdZl0rBxo3wyivH+EC5/L/L5diOJp9jlyYpESb1FPzsJb/+NfTsGTSEKILZs31haMlj7fgG6dcPRowo8C8Ikh1vvumXS5eGjaMNJkyAjh2VEM4n6WGROncdSROh1FdSApMnF/jzUWi9cgrteHLYMY8Yyaf/VRJizadjTDAlwgrVMbwB060SK1e2Xzgtluns2xe/GHzcTRTB1q2F03CYOBm6uKXrhO3fn5GHbz+rV/tCd61VkJl1aU+dOvmi+QX9RbnA9O/vl/qfHSkRPex37WrV5lHkP1tu2JCheKR96HqddaedBoMGFfj5InbIdSCVgkcfDR2JJJ0SYXKEIHUc/vhHv0wX6CxgiWolzqcPU7ffDr/9bbDdR5GfoOn554OF0DJDh8Lpp7f+fiNGtH8sUnCiyHdm20JeLL0AACAASURBVLo1dCTSGnPmaLKPhtI97Csq8utS2CqtrImViDphhUS9WrKm3c4XuXyyiWPb7bowaxZcfnngeKQgmNHZjOFtua8SYXKEIK0S6Wkqt2zJ7H5y4KI+aJDPJRR0IiwHnudW+7d/g6uuCrb7dJ2wvPiC0JaCwCtWtH8cUnCiyH9WnjUrdCTSpEbO77t3+zkapL5UCt5+O1AP+xw0Zgz06FHgn39E2iiKYPNmePnlNtw5jz531w01l/N2wDszweR8oMlkxqXAUuDv8d9lZvyppfdvWSLMrBdm4zGbcvhHCpaZ//CW1VbMbO0oRy4UqZTqhEl9ffrA6NH6giDJNn68HyKZFwlhAWBK/IlQ564jqQdUfUVFvk6Yno8cpy/9QbTLiJE8+9+l+0HkrE2b/PIvfwkbhzTlZmA8sA3AOZYCpS2989ETYWb/CswG/gF8N17e3NooJb+kWyWWLw8dSWGKItixA5YsCR2J5JJUyk/6t29f6EhEwujYES64QEmVfHLCCTBypJIbjUlS3Z+WiiL/5beyMnQkclRr18Kdd4aOIjGGDIFTTmnj+SJHGvqb1UiSLm/OjemEmOSag87R5uLfLekR9hVgHPAGzkXAGGBzW3coWdbGloGst2ImsEcY5NEFQN6RwddQFMHevbBgQcZ2IZLzoshPJpLpkfLSfqIInnkmDyb7yLIgPexzXEH3ksuRz5jHLP1inTQJ/vVfVQAwi9J1wmpr2/gA+XCiqfM+KfjvQfnw/8hvy8z4GFBkxlAzfg4829I7tyQRVoNzNQCYdcS5ldC2gmSSRcd4MS4tPYZWCTmqAQPgjDP0/Ep9U6b4t65eF5Jk6YaC2bODhiGtkEr5yT6eey50JLlHPezrGzUKevUq0ERYoampCR1B4qRSUF0NL73UyjvmaRI2bxoJWhtknv4/8tCXgBHAPuB3wHbgqy29c0sSYZWY9QQeAZ7A7FFgfRsClTySnr1k1qxjaJVojYT1CAN/sZsz5506jCK9ekFZmb4gSLKNGwdduyohnE/Sk33of3akdA8oPTdeUZFv9NHzIXKkY+4xmRdZJe+MM2DjRk0mIm1jRhHwXef4tnOMi3++4xwtzuAfPRHm3D/h3Dacuxm4AbgT+EBbg5b8kUpBVVUbWiVyWQ4lwqIIdu2CRYua2ejAAR/zzTdnK6z2lUcX5FwRRTBvXh40xD7+eOgIpEAdd5zqhOWb9GQfSuIfKV33R8/NO6IIXnsN3nwzdCQtsHgx/O53oaPILn12C+bkk+HUU9tw/cuh7zdNavC6yrlGgj174NJL/clJcp5zHALOOZbHaEmx/N/W2eMsnPsTMONYdir5Iat1HBJ40W1RnbC9e/3yxz/OdDjtKx8uyDkqinyx/HnzQkdyFO/+/+xdd5jUVPd+w+6y9A5SRHqRJr2XCYiKIGIXURRsKAqCoIKiWFCxIMpPBBWsH9+nolRRRNyV3jvSpINIX9oubDu/P87cTTKTzGRmMpPM7LzPc5+ZJDc3597c3PKec8+90W4J4ohhuFy8lOwEytstShyeMGjfZTm+2YcRXK4IWthHEiH6oXXMBNgXWrQA+vUzFzdWxz6xmi+HQrQXMbuzvLs+1azpsM1Efv0VmD8fGDHCbkniMI+NkoS5koQHJAm3i2D2ZjNLIxtqjiQpASGyb3FEECEQTEFrJYJBPlwaWaEC0KBBDGqJc3Pzh5frMNXZTp2AAgVisF7EEUcA6NqVf1PhslWOOMzD5WJL1vhmH97o2jUGLexDQKNGbEXomAlwHFrkQ+W0kyDLQFoasHlzEDdH0buTJG4bQ9ocIByIojKMA2UAnAbQFcAt7tDL7M3GRJgkjYIkXQDQBJJ03h0uADgBYE5IIscRflhE+Ag/YTGjlXAQEQZw+S5dGkM7baWlsQOQtDS7JQkfwlyHSpYEmjcH/vgjrI+JIw5Ho3lzoFixOBEWTejShZvHeNvlDWEBHnMKjiAnjAUKcH2JufKIIw4LEJTFpMPmN7rQaS9cLtad//VX5MXxgihDK4mwOKkWVhBhgE4YaPZ+YyKM6C0QFQfwLohKuENxEJUF0SgrhI/D+XC5mNPYsiXMD8qnDYUs85L0devslsQixD1eWgJZZquK9HS7JYkjDnuQlMTWkSmQ/UfOygKGDOGt+eKwDaVKAc2aRTG58cILrPkLAyJqYR8lkGXg4EFg/367JYnDC/l0TO4UVKkC1KkTZFsaDe9ORdo5apm0lURYNBCTMQBJwheShOmewez9Zpzlj4IklYYktYYkdc4LceQLOKqBsgIOa5hibqcth5VvtMLl4rn9ihUhJrRlC/Dii9ExMIojDg/IMrAT1+IYKvqOOHs2MGkSMHRoZASLwxAuF7BqleLeMqowfrxiuhUGxJyFPRBS3xJz48tYRnxsF3G4XMCSJQG0F1H6jqpX5+CIdiAcFmFxhBvzAfzsDosBlABw0ezNZpzlPwJgCYCFAF51/44NXM44ohFCKxH2BiqfNjrlygGNGzukA7ACOh1xTA36I4ROnXiFaciWFR06AG++GZxp2U8/hc06Io44zCBvOZm/5ZGikYk3NrZDbPaxapXdkjgPEbOwB3htcZs2EXhQ8GjQAChfPobGP3HEYSG6dgXOn+dNSwOCk+dTBrI5ZjMRX0SYk8s1H4MIP6rCfwDcDaCR2fvNOMsfCqAVgIMgkgE0AxBff5CPELBWwslwoMYklnfaGvtRGSQmsnWTI7FsGfDtt3ZL4YXixYGWLS3wtSNGFcF04HfcEVbriDji8IdmzYASOOefCBP124Hte8xh715g2zbDy507s/+nuJ8wbwgLqIiUzcaNwJo14X9OCJNDSeIuJjXVgXPMefOAn3/WnvM89oN//gE2bbJQpkjCcS8k/0GsGDGtEI2m/s9DVlkGzpxxwGYicYuwWEAdANeYjWyGCLsMossAAElKBtFOAPWCky2OiMOCj1mWWSuxcaMF8hghH+4aKSDLvIwkEmPWsMOjfGfMLw4AWL/eDmFMoFMn4IEHArsnQnVIloG1a4GLpg18dRDv1OOIYiQmAp2xxL+fMKuIsPnz2ZlTNGslnniCdxkIF2rXBqZMMbxcogTQokUU+wkLI0Ly+xOjkGXgyBHmVx2F3r2BXh4bj/XqFVCH7HIxmR8TSuQ4Io5KlYD69fOHxaRjlknHx8xRB0nCBUnCefELYB6A583eb4YIOwJJKgVgNoBFkKQ5AP4JTtw4IgYLJ+uOaaBiFJ078+uKifL1qHddWrGjmJiyDrh8OSKPkWUgOxtYvjyEROKdehxRDhkp2IO6OHrURySriLAhQ4DDh3lmHq2YMgW4dMlWEcRmHzaL4UhErYX9998Ds2Z5nw+xb4m68WUAL+7wYf4NqxI5XIiPGRwBsbN8dnYAN6nf3VNPsfLCKuzbF5ZNsapWBWrVckA74EBjiTh8gwjFiVBC9VuXCD+avd+Ms/zbQJQGorEAxgCYBqBP0BLHEXWoWDECWol8bBFWpgxw3XUxoiX2KN/yZbj3jom8RRgdOrBFTEjfXZwIiyPK4UIqAD9tiFVEWPx7sQSybNFmHzEI4fcn6siRe+4Bbr/d8mTr1eMxpu0TYKugaoOEZ4GYyVscEYfLxUaIplZV6PV/H39srbllrVrAtdcGf7+PvtVRSoL4GMDxkCQ09xXMpmNMhElSGa8AbAWwDEAY7e7jcCKC0ko4EQ4kwgDFT1iEjI2sBxFw4YJh+S5fDmRmRlimKEfRokDr1iGSiAXcTXy8U48jSnEdNqMUzvqeTFpFhKm/l2PHQksrFlGypKloHTpYtNlHDCLmdooOsW9xtJ8wPQQgZKVK/Bv/DuIIFoJMDWhVRTR8SDp9tSzzZiKbN9sgj4CVyjCHzjdjCO/7CO+ZTcSXRdh6AOvcvycB7Aawx/3fqR5/4ggThFZi3bowPSAaGu4wIup32vriC3YOs3u37uX09Cjygeag9TyyzN/c+fNBJiA6Ytu34okjjuCQgFz2E2Y1EbZwITBtmvacuP/774HKlWOIrbAIJhui4sWZxI8Xnzfyk98fs5Bl5p0Nhg/OxL//Ar//bipqVCqR8/mY3CmoUAFo2NAkmepE4mXRIqBJE1OacEdYUMatwqMGRJB9hK5m0zEmwohqgKgmgIUAbgFRORCVBdALwE8h5yCOyMCijzlvG/tUS5KzD07sKMA+2wsUiOLB8fz5/Nu3r2GUqMnbV1+ZjBj+jlKW2Ux82bIgE4hEp753Lz9ny5bwPSOOfI2u+AP79gGHDhlECIYIu+km4JFHgF9/5e3dAMUiTDjmC3jf+hjF/v0Br1dxuSzY7CNGISzsHbubciCwaEMmIMrGlx06AN27m4p64YKDNwyKw/GQZR4Dml5V4SQS57HHeCvIIUP42IdsVaoAdeva3A7EibCogSQx2SVJuF0vmE3HjLP8ViBakHdE9AuALgFLHEdkYTHhI7QSYSMz8rGPMAAoVYp3F4qqgaAZEJCUFGU+0BzUAbZrBxQsGELZRaJTnz2bf00TiA5GVlaM7ewQG5DBHY9h/xPK0sgePYBWrfTvD+W7SU3l9MJmRh0h7N0L1KwJvPZaQLe5XBZs9hFOLFgAPP64LY8OyO9PPkDt2jwJdmTTm5Ghf37fPlO3FyzIv1GjCBTw0/b16wf07x8hWfI5ZJlXVfjtSqyc37zzDqdnlSZj6lS2KL77bj42kFX4CbPNgtLXmNlBc4M4AChc1C06oZfRTZ4wQ4SdgiS9BEmqDkmqBkl6EcDpAIWNIwbgcgWolYgjIMgysHIld3h5EEvaLlywRaY87N4dkgMz4QPtyhULZQoXNmwAtm2zWwoAQJEiQJs2IUwQIuEjTAwc/vMf4Pjx8D0nEpBloFs332uUBw2KnDxxAAAaYRvKlvVBCIfqI0xYhIn7rVhSvMCtP3Tk7D4AiO06A8xHhw6sBHEsAdCzJ/Dpp7Y8OmYs7AFL+paI+AlLTzcmtXxh8uSQHnvVVUCDBg7+DmbP1h9f+imrGTOAb76JNW7AmZnp3Jl/I1qHRL0/dUo598EHvu85epQ/5sWLjeP4Yf9l2ebNRKxQHu/YwePhOMIKIrzi/h2gEwaaTccMEdYXQHkAs9yhvPtcHPkMprUSZpGdDfz5J//P5xZhgLLT1sqVqpNOWFdy6BBv7dSvX9BJuFzMo61ebZ1YYcP06UDjxnZLkQdZ5kHBuXNB3Gylj7ClS4Fdu7zPC7Lt+HGeXArk5gY38bATwnzl5EnjOFOnRkYWsxgyBHj4YbulCCsKgNClS5gswjQP8hgSxcIs7+uvQyP2g5wYiM0+Ijp5276d5fV0SJmVBbzxhoeWyQMRfNdht7CPFI4eZV9ZFkCWgRMneA4ZFhQtqnivF9i/n/0B+oJn3/nDD8B//6sce9YbAyfgy5Y5cCnsrl3AbbcBDz0UUhJRg/R0ZvCiDOXKsZst0+2FlW2ZOq3hw33HFZMXI/LYs3/VgW1+wtLTOa9WEGENGgD332+NXHGYgiShpyThOUnCyyKYvdd/rSQ6A6KhIGoGouYgegZEZ0KSOI6ohOUN1NixnKjZtRMXLwZmL7tkCa9NjxJ07Mg7bTlqcJydDVSrxv8XLdKPs3+/Yv3gBe5MOnfm/sU244jWrYHmpnfTZeTk8CA5PZ2F/+gjW7b1dLl4LL5kSRA3B9upb9rkfa5zZ/by7An14ObwYeX/4MFs0vbJJ44moHWhJy8R8NtvkZdFjexs4NlntecmTWLyVg9r1kRH2R8+7NcRniwDBw9yc+MFq4gwz/tjYZOJBx8MjdgPYWIgy2wAEPRmH4FC9EM//KA9P306MGYMk2FGiDDp6dfvT0ZGYCbU587xmConh9caRgJXX+3dHgUJ4ScsrOMfT21SixbAPff4vsdz8v7YY8B99ynHJtoIoUReu9aknJGC2BhIt1H1jaJF+TeqrBqHDmWFbtBOV+2DLPNUyWeTYGVfL9LKzORvfNw48/cQsaDPPQccOOBbvtWr+by7k6hYEbj22gjPg44d4wo9YULwZXjmDLsPULcH4vuKBYWagyFJmALgHgBPA5AA3AWgmtn7zViExZGfMXly3iSrbNkAtRL+sH07/544Ya6hKF5cOwDxhy5dWGA1HDwpLFECaNnSo3wTEmyTBytX8toWAaN31KiRce+8/S8AQOnSEfCBdu4cd0Z6WLuWzaoMvW3rYPJkHiS/+y4fv/MOcP31yvUI9W3t2gHJyUF+d2Ymsbt2eS9pbNbMXPrz5mknF+rniGVHovyiCXqay++/B268MbzPTUtTfK6pMXYsv5NffuHBmlnMmWOZaCHBkMFyo3Zt3jHEB1yvdwNg0IaEiwgz+m6I/E8e9e599lngySeDk80uhLC8Wmz2sXSpxTIZwUhG0T/pWVj/+69iDRBBuFx+LOyLFAmM0HruOeDVV4GZM9mvW7jw++/AqFGWJ1ujBlC1agBjBCuUUmfP+o8zYoTv6ybqTRe3J5uA+/BixYBbbuH/ofqVeOMN480/zNT9xESN9Z+omlG18nvzZv4Nh6uR995jsj1MCGhVBRFQuDDQu3fwDxR9Yf36POZ46SXz9xCxAt1z7KceV4m4YlWQSokq3PBEzE+YePb//qecC7Q/eOop4JVXePMdAZuW3udDtCdCfwBnifAqgHYAqpq9OU6ExeEbgwdrlt24XBb6ehLMeSCTF09NrxqZmf4dmPla8uQAuFxsxCEUCXlqN384cSL0h//7r3YJjeeozWiZpq/lJqqBpsvF3FrYVsuVLs1srS/06WM+vWPH+Fe9o5wNnp8LFWIyLCgS0QwRVr8+a/YBrkdffmk+/d69gZf9WCA7mHw2hJ7Mamu3cKFfP16qotaiAjzB3bQp8IGtiaUIYcXhw9yHVK/ODtcBJsVKlWK/gwImHE82PPEHyhe+4Hsy6auurVyp+LvSw333eVsQE3FjrNbyjhnDk9OaNY0/ys8/V9gftUwTJrCFZKB47jlg1qzA7wsE8+frL6EMYXl1yJt9BAojQlR8B3p5qFSJ+1kxSQ4n9u/Ps6w2ZWF/5Ij5tEX/7Ln+7r33mK04d857yagRLlwAXn9df6fQ7t2Bt982L5dJSBITp6mpJqranDk80XfCrq4mBsPlyrFRZsDfwaVL/F0uXswDgVAY5TFj2AJODaP2sm5d/fM//uh1q6V+3b74AnjzTYsS80BqqmKSFw5HxyNHMtl48KDveNu2BaUcFKsqDOtQnz7AHXcox5cvs6IyGEyaZM5K8OhR7bxAPd7U8xerV990xqiyzMkG5IZnyxYfK1P8QC1DsBbQwuw5xt1UOBRCK5IuSagMIAtADbM3x4mwWEF6OnD6NA9cPv004K3OvXDgAPD++16nZZmJDF2tRLNmgfkaEAO2AgUCb3TOnePRknrQV7w4O98wQrCNZAQhywY7bRUvzr8LFzIT6YkePfQTJGIrEzOoUUO7hMZiDXnXrjz+0PhAsxJm5BUeOD1JDT21pugQhVoqktZ5GRnAE0+wDNOmQZaZB9EosIkU2bKz2WLJswzMWnNkZ3P7cdVVwIABwct98iRPlA4dCo7oDhUrV3rPom6/HZgyxfd96rIEtDJfucLHRssPrUBuLjvoFzuRuVz8skuXDq3dspuEfOghb38h333H7fdnn/Gxr93Xvvgi768EwFXlb6Sk6FRn9QkifWc87duzr8OcHP2Zttrvj0BGBltlqJd/vfEG8PPP/N+oXj36qNJJPvecfpxA8O67XI89sX+/SmsSIm65RX8JZQhLIwsXBtq2DcAS5vDh4L6zESO0BP6772o182bIPDVJ0KZN4DL4wo4dTGbXqQPccAOAMFjYG72fkSN5A5BevThfZhxV9ejBCo7vvrNIOHOQZfbNLRYLGEK822Ccjlq93LlrV9/KQDdMLW0zghifmPWPkJmpVTT4g2fd2bNHP57OvOLkSeCvv8w/yicGDgRefJH/Hzhg3kfuwoXeG9xcvKhdIaBel/rYY/rpEICcbNM7guqienXf11u14j4hwPa0TBmgafkj+GOiAWGvtv7WsyoPBEOG+I/TqRMrUDt2VM6JPspM3kS/JYYoqu8yKDc8112n9VGrxuefK5aVejBLhH3zjXHdEPLr+Uy0bQvMfIN5koRSAN4FsAHAAQA6Azp9+CfCJOkrSFIp1XFpSFIYZwRxBISnn2Yb0ubNWe30+ee8JbjY1zhYR6Y9euiahPvUSmzaBHz1lfbclStA377eWpJLl3iZD+A9WevrZy+GM2fYoqBRI1Y3i144M1NZpqVn+mzUSPrDsmXWeXlPS/M5EO1QfjcSE8m4A7jpJt6OyxNGmtEPP+TJtKeFiR7EUoNJk/idqM2ELUCnTszLBD3w/+orttj4+WfudCZPDm55xJ9/Atdco/S2AE8UPOE5eTJbhmbN7s+eZbJLz0RuyhRlkv3SS5BzF4MI+DMlVxlJDx6sLF0dP56XcXpaTIo8NGkC3Hyz70mA0XLI3NzARu+//674lQOss0rKzvatbV20iMmOiRO152fN4nLeto3LQ5K8y2nQIO0yYHWbdNq9SbLVXpz/+IPb2dxcttpo1w7YuZOvHTwI3Hsvtxe+liGp645eu6JX9kScZri8Uh8/rrDdegoZ9UBzyhTjAWqbNjwxUsF14AscOeIxFp0zR5nYXLnCypCCBfUdU126xEt8xDZc/iA6uq++Aj7+GJg2TXv9u+84H2XLKlvf6ZEoI0caTy4BbjfEuw8ENWsysSLKWe3bzzO9lBRvpcjWrSy3r6V04v0EqRgRm32Y0sd07coadbNt6J49/N28/z4T+GoZe/TgvFWpwstWAL7+7rv+CeI1a7j+iHbPLCF04QLw99/8X/RRGRlcL4T/LhUstbAXeTea5BtpoDIzmXl6+mkuJyJFE/fuu9yuzp0bunzbt2stcTZt4r5CBdN+wkS75pcx08H+/d7vc/t239+nL6xZY0wcZCttslAimzXK08WmTcp7Pn3a2NXD008z6e9vpYCYH2zebE5p4jF+ELx5WCw+a9RgBbCZduemm7gdUKNCBe0KAbUi88QJdpovSd6V7fBhoFat4OX2BzFmNUPIHjigJYhOfI9VZ+tZ46r2+HHgzju929qFC83dL/ysbd7M34963GXmnZ0UddNd7y5ezCOUy5fnzUQ09YrI23dtdjbw7bf+y/LRR9my0hN9+vC4V7Qnly97E2FqS+F9+7huPPigti1PS/NtnTojvoNkOCBJuMv991sipBHhR7BvsPpE5p3lg4h8B2CjqXNRFooUKUJRj9xcIv5clfDWW5rjtWhBANG8eSbTzMwk2riRqHRpbboqNGtGJMs69+rEpdmz+dwtt2jPHz2qxB8xwjsfRES7dhFNmeKd/jffaON+/bX2ul5aJ07oXhs2jKh4cT9lopcvNTZt4uu33kp06JD/tG69VXtu1y6iHj2ItmwhAqgdllPbJpf4Xaxdqzx/0CCtLJ9+qs3P/v18fs0aon37+L8s87Xff/ctl15d0guzZxMNHUr0n/9w+PVXn/FHJb1LSUm5RN9+S3TNNdSqzB7q2CHXtyy+yk6EJ57g3+ee875ORLR1K9Fnn3G+PO81G0aO5N8HHtC9PmJwOnk1I9Wr+64roqxzcojuvpvjTprkHefddzXPuoyCVDjxCg2p+4uSvrj+/vtETz7pndbx495ynz9PtHs30eTJHOf0af/lcP/9wZWfCHXr6n+TgeLZZ/neo0f1r3/2GV8fOJCoRQui9u215fTmm8r/ggWJkpP5f79+3vItXMjf5bBhRHPn+m5bzOLYMX7vAqKNPXUq+LLt21f5v2QJpzt2LB+3a0dUqpQmfsWKRI89cImPK1VSZBF15bPPAsuTHipV4rTS04k6d/YuL1G3hw/3zk9WFtGlSxxPJ787UI8AbvryINo4z/D330Rly3L9Ndu+ieApt6+wZk3w70/grruU71MPnvEzMogeflg5362bfpl5nitalOjDD5V0xDelbm/69+ffTZu097dsqZuHhg2J7rjDuDqkpHDUOXP8VRwiKlbMuBxyc4l27tSe8/x2PcY/loQFCzTHANHLLxvIL+KtW0f09tv8/9lnvb5DATE8+vNPH2kREaWlEV28qFy7fJnD+PFE//5LtH07UYcOvvMhSfybnc1prF9P9Mwz3vH89Ol5YfNmzXEaShBANGGCTl7EeO/xx/Xzp0KNGkR9emVqT4pvPDOT6/5TTyn3b9zIvytXKvEzM7n90StPX99tbq7SPwdaTz78kOiHH4g6dco7N6DncapalR9/+jS/gldf1SkfPaxbp6Q9erT2WR06EBUqpFt+RERUpw5fU38v6vJ+9lmievW0bQhAdO6cflnphKZNiW6RL9A1V+fQ7bebzBMR0cyZRMuW6V9Tyyj+634cxGlkZGjj9u7N/fYvvyjnRFvywQf6ebnxxrz//R/IperYZ1yuvuD5fV+5QrR3r3e8hASlLvuCkLdv37xTc9GLAKKUBencz6enc13du9f/O1PLeP48f4sA0cSJyjPFXCbUcMst+uczMvL+n0cxAojeg2ocUKxYnihPPUVUpAgXIxERffklx1F3JKK9//pr7/bk//6Pj7OylGszZ+rPCTZsUP6LDqt9e6KpU33nc/du7XdqEGbidgJ4ike5uUSrV/t+9zEGAJeIrOdxANqg/g06Hb+RgM0ElFYdlyFgazgyFckQE0SYXmdtRIT1/IQHLv4wZIj+x6zC8OE8hxR9EBFxoy7itmrFk/wlS5TG6OabuVGpWZOP16/33XgQ8aAdIDpwQBkxAkStW2vjfvklxzdK66eftAMnVbCECBOTGH/x9NJSN+DuMBpvUAKy6DyKGefp5Envczt28EBG/Yzrr+f/3buznJoZJBFddZXv9xBiGIVxlFRA6Yiew9uUlJSrGdMTEdGRIwp5p8bffxMdPMgDfnXaN9zAv489xr2LOt+HDmnjDhgQlrzpEmH+6sDy5cogVR2+rDx6yAAAIABJREFU/14bz4MII4Cux2/UBO6BypQp2us9e/Jvnz5En3zCA2A9uVWDdFuDWeTmEv3vf9y+NGzI9959N08MiXhSt2ABxxMDR/X7Vr+Txo3Ny/fbb+biCdJm3TotKeuJI0c4vnoGLYgw9aA9lLB4MdFXX/mMU7Ei0WP90/m4TBlFlqVL+Vy9etyOpKURTZ/unY/Vq4kuXPA+P3MmP19d3rfd5l3fiBSibNgwbxnFAFqdjirkAlSxfBbddx8xud65M5HLpZ/f3bsjU5f9DZZ9hZkzWUkh6sLp00qZLlzIBKW6LMTkSUwK1EGPBFq8WP+5giQQRNg773jHSUjg9lccN2+um5Y/IuzyZZ6zDxtmHCcPRYpwup4dRE6OUrcXLFDOexJhQ4eG/X0DJogwdeje3fucG2fOeJAj4v2qyXE1MXXxondf2KxZYHkQZLzR9R9/NJdO27aa4zwi7OWz3uUiSLNGjbzL6osvFMImO5sGlJhJpXGacjZs8o7bqBH/CiWSZ/jrL67b4nj6dFbY6eVXr/1Rf5cW1JUBrr1UtYq7vHNzqWnNNJLlXB6LVayo5G/KFKJrruFJu6gYagWMJxHmWZf27yeaNo3f7SOPKNd27PAuQxUZ4aXkW7nS/9jcHZo2zqLemE0PFp9JZUvnaHQ8RMRjsnfe4TydPasQsB7fQB7Uygp1PNGvnDnDfce6ddpvwlddFmHNGiYq/cQLighr0oTo6qu938mDD/L/N99U8k5ElJSkvAciHkO0a8f95aFD3Bjk5mrTvPtuovffpzSUoALIppcxVrmmft++Qo0a2uP77lP+CyWaUCaGGoxIedW3qUuEqcpdNEPLUrN4blC7Np947z2lLEWe1EYCR47wGFgcjxmjTf/bb73rjCDTRX2zogxUQUOEibH7oEH6RGkMIoxE2CKAUgA6C9Bcz2A6Hb+RgP4E7CDgdXfYScAD4chUJENMEGFqptsg5BFh6MmdyNSpWs0ZETe6e/fqkjKejRN98UWeVuIPuMw3BuXLB9Z4CGscgK03LG6YRBg2NIeKF8tRtARERCtWEFWpoky2PctAYM8eJmn0ymr+fO6sZ8xQBhvlyilxvv/e8P0tQjcCiBbgpsDyU7Qos3riWFiG6IUqVbR5C1MYhXGUJCkk6S+4kQCi3z7Ypi1LozI2+yw9q6MwhxGDLzERtm0ba6nUk5dTp/j95ubyQOfECSZ0fKWZkcEWgA88QPTCC17X38BoAohOoqz3vWEmNC0Ps2Yp73jUKCap1EhLY1JPkAyVKxPVr69NQ10/1JM39eD+7Nng5Fu0yFw8MSkXxxcv8u/bb+dZeBKgEDxq8qlMGT73008RK/eKFYkee/AyH5csqcgitKAi3Hsv/06cSLRqFf9XW4/8+qtyr5rI82d5pUfg64Xbbze8di9mUKUKWZQrznXpoh93+fKIlWvIQbTbGzdymQolV7Vq2vo1bhy3NyVKhPa8uXN5HCCIsBBCw+oX6Y7uaaxkaduW5T19mhVi7oG+LBM1bar6vmfPVr7TzEz+//TTSrpijJKZyebsjz6qXLvzTqKuXfm/kTVgGANA9PLtW5V3RcT1Xk0a+guCyDx0iJqV+JtcFbazVThA9PnnRBUqhC8P99xjbB0DEL3xRlDp5hFheIa8WBFhcaHWOnqmcfw40ciR9DXuJ4Bo4yuzmEDYty8wWfS+jWPHIl5PCKABmEZVcZC/g88/p2F4n5ITsygDbmtkIu9xoGhP1RaY9eoZP2fCBN9yhEkR2BQbqDdm05foT4CHnl29kkGEIUO0752IWfK77vKef6jfYefOTGSpLdP1VpH4ClOmsLW8n3j9+2UrRNjJk0p+Ll1S5ghHjrAVplE9BrRkLMDjGL13/fjj+oqwpUt5zKOTdkusoU740/p32qMH0U0BzjlCCD6JsJwcOjVmIgFEr7eeq1g/Atx2+Sp7f6FlS29FpzDQCFPQEGGDByvXEhMpPyCMRFhBgNoCtAegLp7BdDqmIgINCHiKgKcJaBCODEU6xAQRduWK3w9QQ4Spr02ezNr9efN4aZW/j3n6dB6U1q6tr5WI0jAM71NxnGN2vlkzxdII4MkwkXLsa/CoDmKCG2S4hMKUhCs0EuNDSscJYRTGURKUenoexSgBWTQab/DEgYg1tuKenBw2265cOSyaGSvDiCa/UpHCPib+ekvuQgjL0J4AopkwJgmiKnz+uVaTp7aGERYqamK3YkXt/T1VbdrNN1srW8eO5uOqJ8BqE3ujsG0bazVtKPOKFXPpsat/Vs4REb30krJcw2zo3p2tL3wQVrqhQIGQ8zAVjxJAtBORJ78jEjZt0mq009Lsl8lHaIitdAd+UM4RaeXPzKTXXswgCTl0+o5HeVKpTsPI+lJYVDgsAKSMfQRMTLKNwnC8R8nIUMiRKA0aIkycHzSIy0e9DJFI36Jr+3YigA6jCqdTf0pY5Y1EyCPC3Md5S9vQxXbZQg2CCDuIqgQQTbx/Db/bXbuM73v1Ve2xmoDo08f2PPXHlwoRBngTQ6+9pvyfO1c7Pwg26M2/Zs70Hu+4wwi8QwVxmS6hsO3lFUrwSYS5rZuvw0bqit+110W5/Pxz2GW0KuQRYe/pLDs/coRZ5NWr2bAiBuGPCAMwDMB2ANvATu4L+YrvfT+VDyS+1/2GF4ES7t8yuiGEh4YjALgJwC4AfwN4wV/8mCDCLlzw+wEaEmEhhrBpJSIc8ogwveujRhHVqmWLXB2xhFoiBN8zDgmeRBgB1BYrqB2WswawRQvtPY89ZrvMZsMIvENFcDFiz7uCJCqKCzQYwU+6nBL+Qn3aiobe1yZMUHx3xYPloSL+occQ3RPMXahDANEneNx2WeJBhwjTsSpYig4EEP0E+ye7oQZARYQ9/jhbfwhrriBCrJAjukSYXlD7ajQItbCHbsEc2/MUavAkws6iZMwokQURRgDVwF7qg5/8Ls13evAiwhwYFuAmAogWoZvtsoQSDImwK1fyLMCG4gMqhHS6jPCtCopEyCPC0Mh//Kuu0q5QigH4IsIAVAGwH0Bh9/H3AB4yih+O4Gs7rxnu3/UA1qmCOHYMJElKAPAxgB4AGgDoK0lSA3uligDuu8+2R7uQilVoi3QUtk2GsOOtt3zvphVGyEjBBjTHOZSw5fnhhIwUrEUrXOz/BLB+vfbip5/aI1QUoCCy0AHLkQqX3aKEjAbYgcbY5n1h+HCLtlCLI1ZRB3tQGUdj4juISfz6q9ep1liDwkhHCmQbBAojpk4FihRRdsAOAp2wFAWQgz/Q1ULBHIzRo/1GkZGCJeiMHBMb20cTSuEcmmNDzH0HMlLwJ7og58EBdosS8+iIZUhAduz2f8nJeTtrykjBZRTGaujsxByrOH6cd7FcutRuSSKJRACFJUlKBFAEwD+RfLhxL0PUy/1bA0Q1VYGPnYXWAP4mon1ElAngfwButVmm8GPePNseLSMFWSiIlWjnP3IcAUNGCnKRgKXoZLcolsOFVGQjCcvQ0W5Rog4yUrAdjXAC5e0WxRIcQ0W7RYgjyiCBv4MUyCC7hYnDFGKJxLcaghyJl40CGSk4h1LYhKZ2i2I5XEjFarRBBgrZLYplkJGCsyiDLWhitygxj+K4iJZYF3Nkqh46Ywkk5OaLvHqhc2e7JYgIiOgogPcAHAJwDMA5IvotkjIk+o0hSYtB1M3vOXtRBcBh1fERwDeFXKZMGaSmpoZTpvDjvff8Rtl1+CrgQ2DrwIdRrIGFjcnlJBR4ORdfyuOQ0GO5delGGIfndkbO6mSkjvNflpFEVlYCksZk4+v2Y1Gsd/R2AgcXdAT9mYDU8Ur55lxJROLLOfiq02so1Ku7jdKFhsPzOiN3RUGkvhW5ulPiYEVgEvDx/dMgN90dsedajaIvXcGly8n4pN9n6Npsl93i5Btkvloc/zRsi9Q7ndXeBYpKq8vjxA9X4asR01G94hm7xcnXuPRuRZysUBCpD/quU9UWF8LvvzTBnFc/RsmiGRGSLgwYARzofgNSbyxmWZK15gM/LW2PX1+fiEIFsy1LN5K4mFEQGAP83bs3UjtfHVJaBc8VBV4HPu81ARdcjlqAEhCOfdcaV/aUQupLyrdRdkcNZE5LxuTHv0aLOodslC40XJxQBadKF0fqgPeQnFYMeAP47JYPcHeX9f5vdij+/W9LXN5XBqkvOrt/rLVAwnepbfHL6x+icHKW3eIEhfTLScBLwN5etyDVVdkwXu0PTmJW8iPo8mT0ro7ZtqUO8DWw9tkROF3plPkbo52jUJAoSZK6If+UiD4FAEmSSoMNl2oASAPwgyRJ9xPRt2YTlyRcDWASgI4AcgEsAzCUCEdMJWC4bhIoROwPbDMBpUnxD1adgB2RXL/pLwC4C8DnquMHAEzSifcY3Es8CxYsGMAKV4fCxNrkcPkII4BaYxW1xzLL041k8OkjzObQBSnUDOa2snZq0PMRRgB1wFJqjVW2yxdKiLSPMAIoCwlUDOdpECbbnv9QQhekEED0KKbaLkt+CrHgI4wA+hs1CSD6GE/YLkso4TW8FPjuwA4LXj7CDMJytCOA6AfcYbvMoQSALPfx9DN6EED0O7ranr9gg2kfYSZDXeykmzHf9nyFEjx9hBFA51CcEpBFL+J12+ULJah9hBFAtbGbemGu7XKFEqLBRxgB9CtuIIBoIbrbLkuwwdBHmEcQm4mko1BE5ApHCMhHmDrECODbR9hdAKapjvsDmGwUXz8NWgTQAIAS3eEhgBaZvd/XAvzHwf7A6rt/RZgD9sflJBwBUFV1fDV01pgS0adE1JKIWiYm+jeGczxs9BEGAF3xB9agNS6iqK1yxCpkpGATmuIsStktiuWQkYL1aIHzKG63KFGFROSgE5ZG/TKaRLDVQ7TnIw57UBP7UBWHot6v0st4HTcjeP9S0YRWWIuiuBj/5nXQCUuRgOyor89WQkYKlqITspFgtyiWogQuoAXWx9x3IN5XrPl1cyI6YDkSkZUv2gsZKbiCQnE3PLGLQwDaSpJURJIkCUA3ADsCTKM8Eb4gQrY7fAmY9x/jy0fYhyCqAWAEFN9gNUB0HYj+L0Ahw421AOpIklRDkqSCAO4FMNdmmWIewtfTcnSwW5TQkJgIXHMNcM89dkuigYwUEApgCWJvrbiMFOQgMSZ9oIUbMlKwE9fGhH+tPaiLozA2i48jDj0IP2F/ogtyIdktTsiIJX9BRkhCNjpiWWz5e/k/a4bCxXExJsmRUCAjBRdQAhvQ3G5RrME77+T9lZGCNWiNSyhio0DWQvh124hmdotiHerVs1sCXRTDJbTGmnzRXojNRPJDXjXo3dtuCSICIloNYCaADQC2gnmpQHdNOyVJuF+SkOAO9wM4bfZmM9T9v5AkNtuQpJcgST9BkhzVMxFRNoCnACwEM4nfE9F2e6VyIJKT9c+3bg2MGRNwckIr4dVAsXmjNahUybq0jFC4CHDwINCqFR8PHw688oo2zm238S6SEyYAxazzDeILbbAahZAR/RMHSQLq1tWcavdEMxTEFa47779vj1yholNn3jFMDxkZwOLFxvc2N2hCj/hf0i4jBUD0W1MVSeCdgTT5aOdD62dlu2IlKlQAXnyR/7tc/uM3acL145NPwiqWKeTmep9btQoYPJj/L1yoGOobwab3IiMFp1AefyH6N4hehbb85+abvS82bQr8/LNy/OabkRHKLBo2BKqofEJ9843+IH7HDsjNz+EvNMRxVPC+fnUQfqWqVQMa2PD+O3cBDh3i76RgQe/r+/Zpj4mAo0d9JqmxsO/b10JhI4ybenifK1gQGDECyM4GJk0Cvv7abzIupAKI/n4OADB0KOffDbHZVFQrkUuW1Hx7rmn9AUA7Xn3zTeCDD7T3PfhgJKQLDeXKsewzZwJXXQUMHGi3RF6QkYJ1aIkLiMx8JGJISuJf93ysJM57Kwn02txowEtjeMw1e7b+9bp1gUGD+H///pGTy2YQ0StEVJ+IGhHRA0QU6NbxAwHcDeBfsMP9OwGY38LW7/pJYIv7tyMBSwm4lYDVgazfdGIoUqSIn1WtUYD77vO7xjjPR9iDPxClpRG1b0+0d68S5+RJTmu92xdVs2bKtRUriPbsITp/nmj/fqI33vBKv33Ly9SmySWiQYOI/v2XKD2d07NibfWPPxL98ovvOFeuEI0YEfQzhj1+iYoXd5fnjh18ft06ouxsouXLiebNIxo6lCgnR1v2Z86Ye8bo0UQFCijHdwTmH6UrfqfrsNFc/PHjtcc//0yUkBB8+btcRB07hvQOR2EcJUmZRPfey+eee45o0yai1aupM1KpRcJGotxca+qLXlgVPj9kI0YQFSmiqutERBs2EM2fr60revf/oPKpk1cBfcS/5568/1lIoBJIi2pfT92wiNq2yaVSRS7Tww9mEbVqxdcuXyZavZqoVCnv+4i4DZo5UzlXvz7R++8TZWQo566+WntPoPLVq6c9fvll47iffkp08CC3e926EW3dSvTss/ztGd0jsGYNH996K9GSJUTXXsvfRhjLvaJ0jB4r9CVRz55ES5eyHNdco4134YJ+f1OhgnF+Xn3VOjn/7/9MxduPagQQfZQ4zLZ6HGpITsomgGgM3OX3++/c92RlKfFuvNH7Xdx/vzat7dvNPbNRI66jZuL27Ml9gJ94DRsS3VHDPX6YOpXl++wzbbwzZ4hIaY6/u/N7ooULefwBEJUsSfTAA/rPWO/hJ/OJJ7iMxowhOnuWKCXF+57SpcP2zgBuEvKQnk506RK3+yIeEdFff/H/mjW92/YTJ7zS1fj9+d//ApNr8WJz8WrX9v7eLQp5PsImEFGZMkQ1ahC99BJfHzPGuw57pnH+PNFtt2nOXYvtdCNUY0ATY17d8PLLRDNmmG5bAg7Hj+uez/MRNnQo57lyZSKALsxZTImJufRC4/lESUn6aRYubPy8vXuJli3TfvfHjxO98AL/r1+ff0ePDk9+AWralKh3I/dc4umniYioXp1surniOqKLF73f9+LFXB+IuH3799/gn//hh/rnp4bmc7T/1YupetUsb9lFOQ4cSJSYyG2XmTTNzk0WLiSSpIBkXYRuBFDU+pjM8xFWWuUrr2xZLu8ffiBauTLv/EiMpyRcoUsoTPT22zzWckAezIY8H2GfrtBv/269lfv0jAzvuhcDgA8fYVYEgDqYOWd4v99IwEb371sE3Kc5F8UhJogwMUAcZjwRyCPC5nncK+IIZGTwZHTFCu9rAp5EC0CjRzPXcv68R9zRo4lGjSLq0EF7z/79RA8/TLR7t0KOfPstUSEPR4hqgT3zddddWhlzcoiGqxwuTppEVK2aYZmog4YICxSLFhGtXeudbrlyRH/8YVzeq1YR/fQT0axZxrK5JwWv40UCiE6irPa6Z7mULKn/LL3yA4iqV/dfPunpRO+8Y6ocjcKohPGUlJTLEwUx8Xbjlds2U4ECuXT2rIGMANG5c0Rz5ijHEycStWvnHc+IQCDSkio33KCdsJgJt9+ue16XCNMDQNSmjfZdqL+zEiX064qaePB4j70wl2pjN9fzUaO85fvmG6IGDfznTV22RqF8+ZDqAH33nde5blhEHTpw/1+zJhFlZiokOhGTMY0bM9GtV7733EM0ZIj23JAhHO+pp/TLLTtbIYbvvNNYXjGBFWH/fuO4Rjh1yrguCeTmEn38MdHp0/rvX4Tatc2V88SJfuNULJ9Fjz3mIeulS9p4ly/r5+nwYSWOmHhXrqwve48e3D52767kYfly7XMaNjQuT6M8pKdze+8+rl6d6LarPNLdvZuodevQ6mwgwWPyHkioUCGXAKKOxd3Kjm3blDJ4/30+16+f97vIyOB+tF8/JoeJlPZiwgTtc06fJjp2jOjIEeX+jRuVSbOvuj19ut88NGxIdMetWTw5zc3l+9RE2Acf5D02K4t5/0GD3CfEd1KmDJNar72mnWSKgcXDDyvnFi7UloVe2z94sDXvVof8BTyIMIHsbG3Z7dzJ/+vU8a7XAjVq8PGXX9IFFKVEZNILeFNfAZiaavxtEBENGMD/Bw3y/U5FGjfeqL1WtGhIZaUhwnJzObz2Gl9/8UXv8hL3/vqr/vmlS+nJJ3KoaOFsymzYlM9t3kz0/PPccYh4jTwcUHfpQlSlivacuk1Tn+/d27t/u/nmwPNPxH2ux/k8Ikz0VTt3Er3+OhHxMEYMC3TTVBNhY8fy78CB2u+YiOjuu/nauXMKYfPWW3ztrbes+Q5E2LYt73/TpkS9e2axTO4+bNAg/r6zdLgkXcyerU1/9WrvcjUq78OHeZwgztWsyePuEPLXv9V2ql5dR05RrqKtJeKxv1BmeZbR6dM8vidSZCxalOiRR4y/ydxcrh9vvMFjEL0xiqoOX0JhSsIVGonxbMAgy4Hld906ZewoiFMRKlYMvPwCbHPziLBybynvXk2gCqJ0zBha8N80AogWoRsTwKdO8bjyGR8bc0yfzn2DWhlOFPj4318wIrJVYea1LxFAtOXXo5p3SADP3y9dMvnBRCciQIRtMHPO8H6/kYD5BEwlYC8BpQhIJmBzODMViRATRJiA2qLGQ9NoSIS5XERTpuin59noChw6xA3knj15cRYt4r8LFhjI5kn2qCGIsBkztIOZWrW08X76ibXT4vrOnTwQ8rS8mTWLaPJk/m9S6xkSESbgqWXUg9E1cf6GG4j+/ptHRqJTOXmSlqE9AUQ/4jbvZ6gHDV99paT51Vc8CfV8hjrUrKk9bt+erVLU5y5e9J5UBRhGPXWekpL0i0Qo8ufMUcl4661aCzqBDz8kqlSJ/wsi7MUXmRARWhRxz0cfae8fOFA5zs42LhPPIAgVA+2jaSLsn3+Uju7WW5mMIeIJAMADbzXmzOHJ9d9/K++KiLXABw4QnTlD72MYAURHDmRpvse8NoBIsbLyfM+e9chfOYiBdrDhnPeurIIIE9zNwYPGxUfz5vHE2h8yM3mSpH6eOn9ECmHuy8rL07rmwAHjuL7w9tveFgxm4PmMTz5hiwp/5bxhA9Ett2jP1aqlOa5YkbyJMPUzPUk5TwjFxpYtbFkkLIo90xEQRNjChURHj2pl+/Zb7XHdusZlABB17qy9ftVVNGAAUZmkc5QDtza9QQO+npPDsokJpKeFXpMm2mNP6wtf1hieQVg1qcNHH2nJOCKuxx7xBBGWlJRLl1Zt0ZalKJ++fX2/E4Fz57jNIOJJYkaGllzWg6c17rvvamWeNk051iPc4SbC7vBINzVViTNzpubSzTez0SURKZY05cpp78/KYhLcEyJ/nvCUS5DiIvTo4R3Hsw/UCzrfvmg+dJGezvWcSGlH6tf3llNgxw5us3JzierUofZYRm1K/qV9L5068e+ff2rTuOoqJoYERD/naY0nwpdfKnHPnOH2Un3dhPWfr6AhwgTEKoLRo73LSlje7tih/y5zcvIMp1fc7q6XR3UmkhkZ3B6J4y5dmHwUz/3oI/30b7+dyddJk7R52bw58Pwb1MM8IuyLL7yyr1EiHzzI1qDq+wsV4oF1aqpiWffqq97lmJHBxAkRtwGPP64QCkJ5rR5T+QvvvWdMKKny2LQp84hqCJ3XqlXeYupi7lz9chREprpMly1jImnJEv33KfqujRtZ/r17uf7VqaNV4gA8Ntq0idtu1Zyp/52XzBNhns8vV06bBzUyMhQlgVHd8YSYH335JY/BfvrJ65md8Ce1xBq2Qs/K4jGx3nubN4/nRr6eq+4j1RZXT5jcmVkvb55B1ed7EWGe7QARv5+cHDp/nr+V0SMztdfT04nGjSP6/HPlGZs2Ee3bp403cqS2YTKSr21bov/8x/y3cv/9THwKQw51f/nPP3lzlZlDlxDAzZRGhuRk4/cfQwgXEQZQO4CeBegwQMNVYSxApnkq/5GAIgTcTkAd93ElAm4IR6YiGWKKCCMybIzW9n41rx00jTFjmNzx97z+/enSJSbER440iKfu6MaP114Ty71mzFAmLL56UX8dhxpVqyrx+/QxbMgsIcJycnjAUras/oBPyC4IEDVOn2ZtTloaHwvtxezZRER05ePPqAgu0lP4SLvcQ52uvzLRy7vayqRePWXppzrOhQtaEqhMGWXZ2dtvc8fsp6MY9exlQyIsI4P7gWHDdPLRpg3XDz389hurHc+d088nEdGTTyr/1QN+X2XiGYS2SVhmAPy+3P9NE2G+sGmT/jICIqJdu5R35YENaEoAK6HzljrXqKGN1KWLd55GjtSYnPsti7FjjYmwnTuVSZpREBYuHucFESaMOdRzNEtw9qzyTX30kWKNKN7phAl5y1R0ZVYfHzpknD9/yM7mScpdd7FG2Aw8nyHyQcQTJs9lcSJs2sQDQ7E8pGpVrzR9EmGPP+5ftqNHid58UxnUe2LcOLZMERAD6kWLlG9HlplE8yRh1FYjevn791/l+unTRBcu0Ndf86WNuI7/NGyolSc3l+sCEU8URN1PT1csqlesUJaDivosJjUiJCdzu2NUB/TOCaZf3fkePKixHKlQITfPeFmtuyAi7hcBnhCFEw8+yM+ZPl2bFyJlgtGundbiSRV0iTAizv/QoV7mIYJr++cf4roNsPVDKPCU6/nnmRgW7c+FC2xlfeoU11+A5TIg96htW273iNiaSLWMC/BBhKkhCFK3BRARsQWvLOvHP3GCXmy9kBIScpkcAdjSW0xu9+zR5vW117T3C4uwzz/XWkFffz3//vab9zPFBwQQde3qXQ6+XDl4WJ7pEmHColxvaeScOTym8SRrVfVPcBTjXsvm/sYgnsZqt0sXheQUZWZ0HxHXA7XiNCtLSwibCZ5pu8OAMrOYCNMZ0+oqkRcuJHrlFb7QqZNy/sABVmocOuSdH18QlruibvgKVaooZKtOXjzbOj0iTHwmb79tUj5Polng4kX+VomUJYNGZmabN2sJYSOon+Pp6sR9vn9/0ifCDhzgOnX4sPe1hQuZxDGjePCUQ53aR+OPAAAgAElEQVRnT/Trx9e//tr7mts9xMsYSwWkHEo75S4bPRI8000effKJ/+fqtQWChDXzDXz5pXKsZyGmekYeEVb+bT4nyFwDtG3L3ZAh1HXGHzZsUGT6+GPlf4cORN9/b5xHtRWt+lsU1nueyhs3mTnzGR0ibN48HrvnA4SRCOsC0CsAHXP/ijAcoDqm0/EbCbhGN4QhU5EMcSLMArgnQ506EbVsaRAnO5uoeXN9kzExwf7vf9mKScht1Jj56zjUUBNhWVlswqyzXMkSIswM9u9XLHX8Qe2jZ98+6o6F1BBbtX4oBMyUiWdj3qmT1g+S2hKjRQvl/PnzWnP/0qUVTZ3oBPx0jr6IMCKeEzRtStyxm7H6MZNPM9f0lmV5hvfe41/18iBVWhoizGhyEwqEz7o88wkF2dt2UOliV2jgQFImAdWqaSMdOeKdpxEj+JpYkqPKjxcBJJZgCMJ6+HBluY+4V+8Z6iAGBh7nu8k51KEDj0fLlCF66CGrC88AwlJk4kRduQjw9qHkqU32HNhZDWHNIIIgcgTEANkzbN/O18UMq2tX5R53HEMizIjYChUPPcTPXrOGj3/7TZufSZMUK1jhP0YlLzVt6rOsBUf5QTu3KUKjRqHJK9593brask1O1pDgXnXg+ecVv3/XXKOkZ1Su7nsrVMilfv3c2m5PHcp//8vxBCETLojJ0/LlGtk01wYO1F5TBUMizADr1vGt//mP+8TWreYmkL4g5Fm2jNspIz93eli2zNx37b4GmCTCiHhyFsC3pSFHzp3j+7OztYSOmGB++KH2ZvGtTZumJcJEfPENGuSLHn2Uf9UuMDyVXcWK8bsSZay6pkuEqeOahVhq5EajRmxYaig3kdbvrculKK+8fHaQ/jseN045L6zGn3/eu17oLR/VS9sdBjRew0TY/v1eYvhUIq9erVWAhIJ//lFka91aOw4WYwy9D9iorXP/1yPCiHhopefW0BDi+zD6ToQ7g8xM/etmIfLw/POG1wyJMCshrI0PH/ZN/ghC9Jdf9K+fP09/3PkxAWxvQET6RJiA2uLQ06pOoHFj1kwL69Devb0JNBE8fbwRaYkwPUsyoryx5/miFQkgem/YER5j+llP+8IL7J4tkKbEJwSptWABK0oBXjGh9t/rGcaN47nq779r08rI0P3G84gwPYuwfIQILI2sFsr9ZnaN/BnAfPfvYgD7APxi2ht/HJHHoEFAx47hf47E29bLMrBhA3DunE6chARg/Xqgh85OQup0iPj/Aw8AZcvqx1uwAOjTx5xsIj0ASEzkHUjKlgUKmKnyYUD16kD58ubiqnellCTISMF2NMJJSWenrWCwaJGyyx0A3Hmn8n/JEmVHw8RE4L77eLdMQCu/+91j0SJt2hMnBiSKLAObNwNn5DuARx4J6F4v7NkDbNtmLu7mzUBmpu84zzwDfPst73L03HPGecvM9C4HKyB29NOpswkN66Nzt4JITVVd99wBsEoVoFAh7TmxHfju3d75r1uXd4H6+Wfe1bBKFT7/wAP8++ST3tuJizh6GD3aeHcft8wFCvBGiykpxslYisGDWea77jKOo2479u4FKlcGHn0UeOKJ8MsH8G5cANDWvZOg5zvU2+nx9deBa6/VnhPfqBkEEjcQ/N//Ad9/r+zI2707UKqUcv2pp5SdEvVk8CNX1apArVpASpGefKJOndDkFTtWAcC4cUDPnspxmTLARx9p419/Pf++/Ta3o8ePA9tVG1abKNfixYGWLcHfshrdu3MGR40KKAsB4+GHuZ63b28cR+RjgPmNmIzQtClX8bz8NmoEFC4ccroAgA4deBfiQHZ27hDGnfuKFg3o22rfnpvMP/4AUKIE35+QANSu7R3ZM13RbkmS9tr77wO//KJ8g0Z46y1uFx9/nHfOLlyYx1tbtmjjFS5svowDiSuwYQPw4Yd5h7IMLFvmp7v2bBOHD+fyKF7c3DPFLrn+8Nhj5uIJNGvG/Uf16l6XihQB2rQx6Ptat1b6gVAhdl7fuBH47TceB0+YANxyC1C/Pu+MOH26Nc8C9+fLlgFZWSZvEHXV6DsR59X9cjRj3ToeS159tXefrcZLLwHz5wM33aR/vXhxtPvmSSQnq9pSX21Nt27K/06d9ONs2cJ1Q+yG/txzxumVK2d8DdAfpwDAihXa4ypVgP/9j+cbPiDLvOns8uW+H2sa6nplto5JEu/mqC5LgMdoOt942MZVcWhAhIOh3O+fFSBqDKIm7t86AFoDWBbKQ+MIMz75BHjooYg9Tpa5zVuyJMAb9RodXw1Hjx7ArFnBpx2NcBNhgLuzW7qUBzSBYNYs3rJcIDmZyQ0iZi9ff125VqQIv8jNm3kQW6AA8MILwLRpwO+/AwsX8lbgggARE0GBunUDEs3lYjECrjt6qF0baNjQXNyEBO2k1yhOv35cBuPHc771kJTEca1GTg7/GpC3sgzs2wccyqrEk5xp03ynt3IlT3oBHnR45j8piSfxN9+sJV969uSXVKtWYPKPHWsqmssFHDwI7N8fWPJBoW5d4MgRnpx4olcvnnyo246aNbn8P/0UaNIkAgJCaQMnTADOn/dNhE2cCPz9Nw+anThhKFrUN+kI+JbXxARNloE/1xVFzqy5wFdfBSigD5lGjwZ++IGPPSdrgwfzbO/HH7X3VagQ2KTfDVkG1qwBLl5UnSxbFjh0iJmjcEKSuJ7r4a67uI1/+WU+tmDCnJAAdO4cQfI7iiDIES9SVI2WLfnXs69TE2FqJCcbT6bVKFuWSeuSJZm8Sk/ntBo3Vtr+xo219wRR1/2iUSNgyJC8Q1lmvcyaNR7x5s1TOg11mxjM5LNkSe9+Vm8MPX48cN11xumcOKE9lgoACcYTfJ9KZKshGGgAGDYMmDuX/99xB5OuFkGWgUuXgLVrLUqwXTv+tYpUsLt/bNDAeCypRmKiVhGjg0KFuHjy2lKrymj6dOCNN5iZN1NeJ096P9/ovqJFgxKpQwcuEsv6jU8/ZeV79+76Y6fevZmgU8MuQ4o4worA3yrRBgB+VEtx5Ce0bQutVsIsjAZuVsBf4y0GcE5n7CUJLbAexaSL3AF07Bj4xKhPH7a80EOJEt6Ne9Gi3pP+gQPZOqFhQ558G5Vbbi4TZibRpg3zbX/8YfqW/AMxuDcg2WSZf1OWJfEsoXt33+m1bWv83gKxIB0wAPjsM+XYl9bQBEQ+Am4/rMa8eUBamtJ2eE76IjWAHj+eieYmTfQtGtQWLEOHehOU4WxXwwE9ec+c4cG1ibZOlnkiuanqLeYtQIxgtsyI+D2EPIFUrKot1XZbhZIl2dr1mmssTVaWmb89csTSZGMCLpcfcuTBB9mit2tX7fnkZP4tWND6b//uu/m3Vy/t+ZUrlf/lTFq8B4jOnTk7Xv1Dr16KFUaNGsp5P5YlAPQtEOfOBW64QRkP1a+vzR/AyqJNmxQrOc8+wqzVvxtCibx0aUC3ORpduvCvZf35/Pk8vvGnuMynkGWukmfPWpjoVVfxqhF1O3LddcDWrcqx+pqedZhojwTMkPE+ULQoG0paVq+qVuVxbFKSvnVYYiJwzz3c3gpEy5gqjoDgnwiTpOGqMAKSNAPAyfCLFke0wEsrESjUSyOtwuOPW5ueXZAkJCEbHQuujQ4NOhF3MCZRsCDPJ8NOgvz7L3DsmLm4L7wAfPFFeOUxA2F1Z7AcqVEjVuKHXC8yMwN7AdOna5exqichlSoFrDVr2JDnD46p30ZEUqSIMFlmhsBIc2pEagvEAhFWurT/pRduuFz8G5b64/nOw1Smlmu7HY6wvrNg8N//ml9WH2b4tbCXJP0lwO+8w5NXQVpFAmprVTMEVBAoW5bn4D6VZUlJzCQ/84z/vvvwYX0GtmdPtng3Y9XSuDFw+bL3stEA0a4d8wWO+Q4sQLlyXDyWKTdLlPC/rDcQeLp3iHJoVlWIutu3r3UPEN9Au3Y86DSLN95Q/mdmssuNECHLvLL0woWQk9JCtF16ZGuZMsr/aBlT5TNIEupKEhZLEra5j5tIEl4ye7+ZGUtxVUgG+wq7NRhh47ABEZy7BayVCHRpZCAYMybwZzsRwg9b8grs3Ml8TtB49FHF31O4kJvrvZTLD2SZFU2nToVJJoA1XBUrGl9X14datSK6tNgQ5crx4N7AjL5AAda+hkwihrq0s29foEUL/l+jRsDfsCQpfsIc8VkaEUlGPi8iDX/lG21EWIioXJlXvFpKphsRYP782QQJy7XdDkeTJsx1Oia/995rfll9mCHIkYDLplQpnnyGiZDSRYTaGJeLjbMuX/YRKSEB+OAD334rAfbNpJ7c+oK6zff0Jedp8RIEChViQ23HfAcWQZbZFdSVK3ZLooJYUmyBn0MnQayqSEkB+6m84Qb2WWk1PL91Xz49776bCczDh4E5c3iMacGyQllmjyGWW1C2aMEK8BkzvK+pCb1g2ztHDGxjGp8BGAUgCwCIsAXAvWZvNuMj7FUQvQpgAoAPQfQfEPnqjuKwA4sWaRyMRnoSFJKvp3DI6s/xZpRBLshrZkIaMH36KfD115bIY4jc3ICX0QjrAMcMBp3UaSUk+KyzsmzSv5Z6cwSrUbIkq+lmzeJBj56/hePHfSbhcrGSfu/e8Impi/Hjja/ZZRFmFaK0rQsGssyD4+zsEBPy984HDGCH2Wq/ihZBaLvPn7c8acchIYFJ/FiyhLEKIVvYA5H79iPkM0eWmQRbvToij1Mgvv/27dknYBggy+z21dKlbVYjQCtDQ79udmLpUi7kGOsXk5O5eqakgF0DLFzIY/CUFF5/bjVSU9m6y1c5ik2Srr6afW1ZhHbtOGnL+40CBXizkKpV2c8noGxQIjYOEPECQYzVNQejCBE8WxvTo0EzSyMbQZI2AtgGYDskaT0kKQD7yDgiguuv1zgYjTQ0WgmzUE8yIj3JjJZJrbshbZa4FSVKRMHEQfjeCMCJbqtWbBERJ8ICh+klRuEkwgT69GErNj0irILvHU+Fu5uI12/h32zgQOWc0ft3ikWYP0RR/QVgiQWbLDOBtGGDRTJ5QshWuDAwdSqbM1kMoe0O03zbPEqWjEh7IctM4B8Mab+n2ISwsD9zJgIPC2UpZYQmep078xw04v1Dy5Y8tp48OWyPkGULNwwKF777LqB+xdCvm50oVEi7Y3EMweXiVbqaVRUuV+AbHOnB87136aLs9BxhFCkSAQvKTp2AX3/VWoIJxIktp+KUJKEW3GvgJAl3AjDpC8fc0shPAQwHUTUQVQPwrPtcHHHkQaOVMAu9CVC8odHCXR6JyHb+TlsffcRm2QEiKYl9tTs6bw6F4/xrAYrWLICBc716vHrVloEzkXbHTbt9hIWKaFsaaYG8wkGzZd+BDe+6ffswabsDRVqa/iTAYuRt9mF3fh2IkHdTDuRbmjGDt/kL93NCQKlSvG9GxOtKoUK82sLXTpG+YKJ42rThx8TSd1CmDBdZLOXJyRCKxD//9BPx22/9+xg1QiDfehj7T7+biViBG2/U9xcWLWOq/IfBAKYCqC9JOArgGQBPmL3ZDBFWFERKc0aUCiC4/U/jiGkIrcTp0wHeGA5n+bEClXWNywXs2QMcPWqrRN5YsYJ9Ejz9dNAdhSwDf/3ldwWd9Rg4kM231Yiiuug4/1qA1nm+SUgS1wFH5MPpPsL8IdqIMIEQ5K1YEbj2WguIVE8ZxGD49ttDTNg/ChfmSXF+mTw2bMgGpI6yGgGUnQhthLCwD7psAtlZOiFBu/wnEESwjZFlYNUqXnLnaDz5ZEDRg1IiRwGEnzCfft3isAQtW/In7Le96NcPmDTJugeXK6e1loxAe+B3M5FwIuj82T2ojW0QYR8RrgdQHkB9InQkwgGz95shwvZBksZAkqq7w0sA/HmkiSMfQmh4/WolBEaOZFVYp07KuWibvIUbqvIQ5eu4iUO7dsDzz4eUhG1+wqZNY4eeatjOxAQGWWZyNOL+tYyweDHwww9BbZpw7Biwe3eY5DKL+vXZ0eu4cdrz0VIvoo0Is6hcZZmXFWZlWZIco2BB4J9/eKfUCED4CwqrttshEJt9WEIArFxpjT+c1asd4dgoZHLkxx8tlccQRjvbhgGyzJvPrVwZsUcGhyA2npHlIJXIDoYss7P8VavsliT2UbBgGFdV+OqfT54EnjBteGMJ2ra1cafVuI8wR0KSUEqSMATA6wDGSRI+kiR8ZPZ+M291IJhl+8kdygGIrW034rAErVqxVsJ0A9WuHav3ypcPq1wxASJcdx0vEYg1zSHAm7YUL+5Aki8K4LglRpUr8+5FAcIxmyYUK8ZMxE03ac+XLGmPPLEOi4g7lwu4eJEdzocM9eC/UiX9ZRJhgNB2m1YmRTlkGTh0yMRmH/7Qtq01/nBat3bMeCRoC3uAiXwrIQgvT+KrbFlWekQAnTrZ5CcsAghYiRwJ+PHr6Q/ifdnen+cTyDKwfTtw4kSYHuCQpZGWbCYSLOLEllOxAEB1AFsBrFcFUzCza+RZEA0BUXN3eAZETt7fJA41ImjFEJJWIhxy9u0LvPqq9elGEqqlkQkJcL6fsCCRmMgDJ0fkLVosf9yoV4+Xhjmi7EJAnTrMof3xh92SGGDAAODjj4EHH7RbEt+INouwxETtb5CwhEi1ucyEtju/TB7FO3PsN28jHGUBPmIE76qmt+yvXbuIiFCiBCvMHFEepmGuPQlYiRwJhDgOKlUKaNbMYXmKYYi21FYyNUL9pywDmzfbsNNqtIyp8h8KEWE4Eb4gwlcimL3ZzK6RiyBJpVTHpSFJC4OTNY6IwaYPNmSthJVyz5gBvPyy/rVoITs8duCTZWDfPu/VfLEAlwvYtYuXx9mKVq1sFiAwCD9hqanRU631IPyEOTYfCQk8Efzyy7xTgwcHtT9E6Jg3D/jgA/1r0UaEjRzJOx6HuOtx+fJAo0YhTrwEGWfT7mJC2x1dk/3g0aABG57kl/wGgtatHUSOJCcDL7wQMctII8gyr14N1re/U1GwINChQ+x9B1Hj1y0G0KIFG7Nb3l4MHAg89JCjjArETqsRJ/2iZUyV//CNJOFRSUIlSUIZEczebGZpZDkQpeUdsTVYaDazccQsgtbKR2rm60EsOb5h0yHCAIcMji2G2PnGtry99x6TCy1b2iRA8HCMf60QIctMou/YYbck5jB5Mm8qduVKhB/cqxfwzDP616KlbRMoVgz48MPgHXarIMvA8uXsSygoVKsGTJwIzJ0bsizBQpaBTZuAM2dsEyFicORmHw6B2E051siRUCDL7ANwxQq7JbEesgxs28ZulxyBqVNDTkL4dYvF9+U0JCWFaVVFkSLAF1/wMmizCHNj3ro1byYS8bnCPfcEd1+8bws3MgG8C2AllGWRpp1kmCHCciFJ1+QdSVI1xF9rHAYQWomgB2/RMnmLFDzKo3Fj3po6Fomwpk3ZDZNtA/9nnzUmFxyOWCFIHeMnLEA4wL+2N/JhWyrLQHo6sHZtCIkMHQpUqWKZTIFCaLtt2RXLBojNPqzwdR9rCLvfnyhDhw5stBnt/ZweHLUUFgBuu40d+IWwbrlTJzakjsX35US4XMDOncC//9okQITGHBHfaVVYwhYrFth9+XAMZhOGA6hNhOpEqOEONc3ebIYIexHAMkjSN5CkbwAsATAqSGHjiHGETSuR3+HWsFi605bDEMs+0MKN2rXZv5ZjBtFBomZNoGrV6KkDzZvzr6PKPR+b1nTuzL/R7HPKNm23TYhW8jsSiJeNFsWLs8F2LH4bYVvaFgqqVlUYuiBQvHg0+nWLXsSKQtQMunYFtm4FTp2yW5I4HIDtANKDvdmMs/xfATQH8B2A7wG0AFHcR1gchpDlILQS+Xjy5hOeSznB5XvwoHunrb//Bg4csEW0cECWOUtHjtgtSXRB+NeK9iVGaj9hubl2S+MfZdxeCBw18Iy2pZEWomxZ4LrronvilZzMli+OqlNhRL16vDFnfslvIGjZMkQL+xiELPPOsBcv2i2JtRBK5Fh717LMFtOx5tfNiWjenDeViLU6pAdHbA4Qh1OQA2CTJGGqJOEjEczebMYiTDzkBIBzABpAkjoHIWgc+QQhaTEjNXmLFrbAgAgD3OVbqxb7tYkRiLoTnxQFjmjzr2UEWWYt3/btdktiHitWAJcv2y2FG/mYCAO4/qxYYYPfNgvhcuUfbXfcT5gxHLWbskMgy0B2NrBsmd2SWA+Xi/tv25a2hQHCr9vy5XZLEvtwzKqKCDTkrVoBRYtGKK+hjqXiHVu4MRvAOAAroPgIW2/2ZjO7Rj4CXg65EMCr7t+xgcsZR35Bs2aslQiogYo3FPrQIcIaNuQd0mJR69OkCVC6dGzmLdzQNYsP1KeBAxCNZOiVK7w7liMQJ8Jw+bKD3kcQEN9yftF2yzJP/nftslsS5yEoC3uAzcnatQuLTHaifXu2noqm/sEsYnEpbCz7dXMiZBnYs4f9LkYcERxziM1EHF2v8ukYLNIgwld6wez9ZizChgJoBeAgiGQAzQA4ZV+TOPzBBoIpJC1muBuOTz4BSpVi1Uk0QKc8JIn9hP3xR+zxhwkJsesDLdyoUQO45hqPslu2DHj3XXY6ZDe2bjUVrXp1DtEyGWjQgH33OU7efDoI69SJs+649xEAIqrtdgDyk2+bQBE0ObJ2bUxu11e0KPvRi8W6EotL24oV4/YslvLkZESjIjFYuFzAX3/FNxPJr5AkfO/+3SpJ2OIZzKZjhgi7DKLL7qcmg2gngHpBSR1H5GDzJMhWrYQvPPIIcPYsz1yjCR6MlyyzH619+2ySJ4yQZfZ/dvCg3ZJEF8QSoz//VPnXqlcPGDHCTrEUNGpkOmo0+QkrVYqtYB0z8Iw1djxAlC7tsPcRBJKS8pefsFq1eKPO+GTZG8LCPpo3gLAasgxs2ACcP2+3JH4Q4DhcKJFj7V3LMvOyFy7YLUns47rreExia1saoTGI43ZajSPSGOr+7QXgFp1gCmbYgCOQpFLgNZiLIElzAPwTmKxx5DcE3EDl88mbIYoW5d+xYzWnY1mDHst5Czei0b+WHmSZ+eotpnU69kKWeSleRobdkiDfL40E+H2sXOmQ9xEkZDn/aLvVm2TEhwJaJCay35/4ZE+BLAM5OcDSpXZLYj2EEvmfGJplifcVi37dnAZbV1VEeMzRogXvTBqfK+RPEOGY+++TRDioDgCeNJuOmV0jbwNRGojGAhgDYBqAPkHIHEc+gtBKBNxA5ePJmy6SknhmMGyY5nT9+sBVV8VmB9CwIVCuXGzmLdyIFRIx2nylyDKQmemQlUhxIgwuF7+PWPATFi3fQKgQm3389ZfdkjgPLpdDLextQrt2QMGCsfltxEofrkYs+3VzImSZV4scOmS3JOFFYiL7CXN8OxDX7oQb3XXO9TB7c2Drw4j+BNFcEGUGdF8c+Q4B714S6YYiyhsmsQwuFjXoBQqwRsvxnZsDUa1adPnXMkLVqrxcKloGzh07cpvnqHLPx0RY587cjkRL/dFDixbsXyea8xAI8hvxFwi6duXf/FIX/KFwYSbDYrE8glYiOxhFigBt2sS/7Ugh2hSJoUBsJnLsmP+4EUc+HoNFApKEJyQJWwHU8/APth+w1kdYHHEEha5dg9BKRLrhiOJ2SpbZfH7PHrslsR6yzPUmFn2ghRuy7OEnLEoh8pGTY7ck/lGiBBMXjpi8xBozHgTE+4hmXzvCX1B+mMwATOB7bfYRBwDeTdl2vz8Og8sFbNwIpKXZLYm1EErkWHvXsgysXw+cO2e3JLGPxo2BsmVt7P8iOAaJqAIlPrZyGmaAfYHNhdY3WAsi3G82kTgRFkfYkJ+0EnYgFk3oBWI5b+GGLANnzkSPfy0juFw8aN60yW5JzEGWgTVrgEuXbBYkvjQSANefNWuA9HS7JQkejtZ2Wwy1n7BoJ/GtRsAW9vkAssz1ZMkSuyWxHrIM7N0bW0vbxPuKRb9uToNtqypsGHOIzUTC2jbm87GUU0GEc0Q4QIS+Hj7CzgSSTpwIi3XYyGA3bgyUKWOygYoz7QGjTh2gcuXYHBxfey1QoUKcRA0GsbJ9drSRoS4XkJUFLF9usyDly/NvzZr2ymEzunZ1yPsIAfltuaDLBZw+DWzbZrckzkNQFvYxjDZtgEKFoqd/CASx+N23bQskJ8fm+3IiXC7eef3AAbslCS/E5gCx9K3EEVnEibBYhQMY7AIFuDEOqONzgNzRgljeaUv4QEtJib28hRvCv1a0DwwqVwbq1o2efHTsyMvZbJe3Wzdg7lzgtddsFsRedOjAg+Ronng1awaULOmAOqXGO++ELWnhC8tR+XUIYkXBYRUK/T97bx5dV1bd+X+PJMuSLE/ybHmeR3meJNm+W1Qz/H5QQIBepDshq35Js8hQKyGh05CskJCE5pfGDb9kNSQUUyAJU0PSQFIEKKqeq1xll+d5tssuz/M8ycP5/bF1y3JZw3t6975zzz77s5aWqp6f3tvnnnvPvWcP310lVycsDiJLug6qq9kZJmlMWSZ2pjopjyzxQ3vcTOT48ZJ+rSIEdYQpqRJHJV57rZs3qlh+j4gi4MwZLp+RBhF3yTp40LUl/uGTvlZXEHEpxb17ri3pntpaYNGijGzM3vUubtMVMH378nz4vPEqL2edsExpnf3X/wr85V+m8tFjxgDjx2fkGsoYse6Pz+dz0hCxBMDFggphsk9c2ibtOiBiXbdLl1xbIp+ZMzk5vKTrhaNEhsxmUGpihxeoI0xJlYIXqJIvHH4vVL6VjxWCasz1HCK/9LU6gwi4ehXYvNm1JflBBGzYAFy75toSBXg4H9evu7ak5xBxMCCUaLeUZh9JI9U5UgxEHNNcvdq1JclDxGVtkkrb4vmSqOuWNYx5uF4Iift3SkMDMHCgro1Kz1BHmJIqcVRCF6h0mDCBS+EkHt+pU4ERIzKWDeEJUspoVq7k3744Q4k4C2/NGteWKABfB/fu+T0fmYx2p7vVbnkAACAASURBVBiwIuKMkW3bUvsKb8k7wz4QFi/mkjvf73MdITHIuXgxl7Rmai0TTEsLB1AOHSrxF5fY86Y6YUoxqCNMSZW8oxLSQxYpEeuESYygxzphEjXQ0mbkSHYk+v4QPXw4N07wZRyNjVyR6Iu90mlq4vnw+QE5tGi3FCd+GmTSKeqQykq+xiUeD4lB5KoqvkdqcLM0lHwtdVgKGEUZbiaiG5hMo44wJXXiqMThw3m8WWuqCyaKgHPngN27XVuSPFEEnD4N7Nvn2hL/iCJ/9LW6ItYJu3vXtSXdU1PD3cwkbsx8pE8fzkLI3Gayrg546qm83lpeDqxYEc45NWoUMGlSOOMtBInOkWIhAnbs4GcgScRBZGmBwHi+LlxwbYl8pk0Dhg0LYy3NZAal7me9QB1hSurEUYkuo0CS7vQlJpM3gISIu4hJHFvaELFWlS/6Wp0RRcCNG8DGja4tyQ8iYNMm1jZT3JPJ+bhwAfja1/J+O1GGo90pQMQ6Qr43+0ga7ab8OPHzj1SdsGPH8gwie4LqhJWOuGIkhPVi1ixuJpLqXkH6QQwUdYRJJUOe6ExGJQQtaOPGAWPHynQWTZwI1Ndn7NzxBCklRr6Ng4jLlPVBPxtEkf+6bZkLdqT8fBE3+9iyJdWv8ZIoKiDDPgAWLuTMz8xcGwmSues+ARYt4sxpLY8sDVEEnDoFHDhQgi9rauLfv/VbJfiyR4mbiaSyV8jQflpJHnWEKakTRyXySvEu9YIjZH2TrBOW97mjPMKwYayv5bsTccgQjvb5shlYupS1a3w/7lJobOT58HnjVZJod4bQjsGdEztHfD6fk6RXL2D5cpnXRhxEljQ2ybpuWaSk68XIkfygHn9piSHSZiI+YowZYIz5vjFmrzFmjzFmWSm/Xx1hSkmIIuDkSWD//k7eoF6OoiACLl5k7QVpEAFnz8rUQEsbn/S1uoIIeOUVoLXVtSXdU10NLFsma/PiM9XV7Jz0eeMVR7tDOadGjJDR7CMNMplh75goAvbsAc6ccW1JskgNBBIBO3fK03XLIpMns38qhPUisxmUki7edPhrAP9urZ0GYA6APaX8cnWEKSUh725HmoLaIzJ7A0gAyWNLGyK/9LU6gwi4eRNYv961JflBxGVdly65tkQBHs7H5cuuLek5RKwRFkq0O3bi+97sI2lC0v3JF8ndNOMgcklK20qE5PnKGlKdqR0xYwYwdGiG9gq6n+0WY0w/ACsAfBUArLWt1tqSPqmpI0wpCXFUIjMLlDBGjwYmTJB5fMeNA8aM0YemnrByJf/2/bxYsYJ/+zKOKOKHzpdecm2JAsjQbQstICCl2UcaELHuT6cZ9oExfz7Qt6/Ma0Pidb9ggVxdtywSRZwtuaekeTalJ+60qkECr5gA4ByArxtjthhjvmKM6VNKAypK+WVZoq6uDjnJO+s+fYBVq7Dv6hTgWWDHjh2orXXbr3jGjOn42c8G4oUXXnncUT5sGLBqFbvzSzEvn/408OABjq09g/v3RyKX81hJuY1p06bi+ecH4xe/eBnl5Y6N+fM/Bx48wNFNp2DtGORyxe1Ap0+fhp//fBCef/5llLl2369aBQA4tuF1PHhQj1wu296OCRMW4p//uRWNjdtdm/LGsbv0rUtobS1DLpe/GvbEiQvxL/9yF8uXb0vLuvyJx/Hti7h9u/yxcbS2GlRWNuMf/uEk+vU75MLCR2mzt/W/38HJkxeQy3m0g26zvZj7QmtrGXr1asY//MOJbMxHG3fvNuLkyXPI5bpP97AWGDiwEd/5zkVMmLC3BNZ1wfjxb8zLjf91A+fO3UQutyvRr+jVqxeAJnz1q4dw8+axRD+7R8Tn4ceAI0eOIJc74syU6upqAEvwpS/tw5NPnnJmBwCuu1+1Ctfv9QE+Dhw8eBC53PGSmzFjxmw8+2w1cjnHacOLFr1xrpx67hTu3BmIXG5djz/OWmDw4GX47nevYOrUDOhDtI3t+t9dw/nzd5DL7ezRx8ycORvPPluFXG5Dktb1jLYxnf7Zady+3R+53KuODUqW6uoqAEvxpS/tx3vfe9KtMQ8eAKtW4WZrb+CPgEOHkl0v6utH4sSJKfjWt15Fff2tZD7005/mC3HdusKyvFauBGbOxM7TNQCADRs24MKFG8nY5BcVxpj2dSnPWGufif8NwHwAT1trXzXG/DWAjwP4k5JZZ60N8qempsaK5hvfsBawG97xJxaw9sc/dm2QtV/+srWAtbt3d/CPf/3X/I9PP10aY6qqrAXsR3/7tu3btzRfmTb/+I98CDdtcm2Jtba21lrAfuIPbttevYr/uK9/nce2bVvxn1U0fEu0H/uYtT4sI08/bW11tbV37ri2xL5x7N7yFmubmgr709/7Pb5sb91Kx7SCaBvHE09Y29jY8VtaWqydM6e0ZnVKm73Dh1v74Q+7NqZA2mwvFiJr585NwJ4EGTrU2o98JP/3f+AD1o4aZe2DB+nZlBef+cwb8zJzprXve186XzNjhrVve1s6n10wbeMFrP3kJ92a8uCBtSNHWvsf/6NbO6y11h4/bi1gLw+fagFrP/c5N2Z89rM8RSdPuvn+N3j66TfOlaeesnb06OI/8j/9J2uHDcvAdW/tG2ObO9faJ5/s+cf81V/xR506lZxpPaZtTB/6kLXjxrk2JnkePODz8P3vd22JtfbqVWsBe7XPcAtYu2pVsh+/Zw9P5zPPJPihbftFe/NmYX/31FPWAvb7v/mcBazdvj1BmzwCwA3biS8GwHAAR9r9/3IA/9bZ+9P4cZ1boaRFBmuTu0zxtq7yWLN3nHqKxBT6GNWU6DlEwK1b/uhrdQYRcPs28KonwVoiYPt24ILbRFylDSJg2zZuKuIrRMDx48Ch7CS1pQoRsGaN/80+kibW/Vm9WkuAYqQ//5w5A+x1nAiaJNoZtnS01wmT1ln+zUydCgwfnpF1IIP78KxhrT0N4JgxZmrbS28BUNLUV3WEKSVjwgTWsupygdKFo8eMHAlMmZKRG0DCjB3L1TjaMr5wVq7ky8r382L5ch6HLw/ORLxJ9VmXShKxbtvq1a4t6TmSN/sdETf72JCB6qmsEev+SHKOFMPcuUD//jKvDYnX/fz5QL9+ssaUZaIIOH8e2JVsBXvmMIbHmkpzAI06pMXTAP7JGLMdwFwA/72UX66OMKVkhBSVcEUUye20RcROBT13CqOuDmho8P+Bc+BAYN48f5yhixYBNTX+H3cpLFkCVFf740jtiMxEu0sUsIqbffg8Z2kh0TlSDOXl3FRF4vGYMAEYNUrW2CoqOLil13ZpCKmqoqUl4WYimqCRKtbardbahdbaBmvte6y1Je23ro4wpaQQdRKVUE97IhABV68CW/LXIPcGIuDSJS5vUgqDCFi7lksLfYaI9UpvJaSBmiaVlUBTk6zNi89ImI842h1KV6zBg4HZs/2es7TIK8M+MIi4bPh46bX6U0VqKSwROytOOtZvD4Fx4/gnhPUiLrsNYaxK8ThxhBljPmCM2WWMeWCMWfimf/uEMeagMWafMeZt7V5/e9trB40xH2/3+nhjzKvGmAPGmO8aYypLORalMLrVBSiV513S00Q7JN8ANALec3zT1+qMKAJaW9kZ5gNEwM6dwNmzri3xmPp64OMf7/59eRBFwI4dHIzxFSLg9Glg3z7XlpQGIuDll4E7d1xbki1SLQHyFMnPCETAuXOyStskz1cWiZ2p0qsqJk3ix4bMZL/pAp1pXGWE7QTwSwAeUU8xxswA8EEAMwG8HcAXjTHlxphyAF8A8A4AMwD8ctt7AeCvAHzeWjsZwCUAv16aISg9odOohC4UiTB8ODBtWoZuAAlSX883OIljS5sVK2TohK1YAZSV+VMeGTumfdalcs7x48BnPpPIR7W08G+f5yMTJS4DBpTsq6Q0+0iDlhZ26u7c6dqSbNDQwCX0vt/nOkKi02jOHF5KJI0py0QRN4vZscO1JemSmcxpLan0AieOMGvtHmttR/HMdwP4jrX2jrX2NQAHASxu+zlorT1srW0F8B0A7zbGGAAtAL7f9vffAPCe9EegFEOXUQldOIqmpYV1wiR22op1wu7fd22JXwwYwPpavjsR+/UDFizwZxwLFwK1tf7YK52FC4E+ffxxpHZEJqLdv/EbJfuq2Imv19DjaOe9RykrY105iY6VceO4aZCkuY513SSNKctIdKZ2BhFn4u/Z49oSJetkTSOsHsCxdv9/vO21zl4fBOCytfbem17vEGPMh40xG40xG+9JVBPviAxmWsVRie3bXVsikygCrl8HNm1ybUnyEAFXrsjUQEubWCfMB32trogiLvG8ccO1Jd3TqxfQ3BzGg6cPxPPh88YrE9Hu8vKSfVVdHWeO+Oy8TIuQdH/yhQg4cgQ4etSRAW99a2ofLbHZVKzrduxY9+9VimP0aGDixDDWi5CcfkpxpOYIM8Y8Z4zZ2cHPu7v6sw5esz14vUOstc+0dSZYWFFR0fUAfCfDmVUdlnZk0GHnK3GnLYk3AI2A9xwi1tdau9a1JcXR0sLZjq+84tqS/CDiqOTp064tUQCej927gTNnXFvSc0KLdktp9pEGEp0jxeB8A/zOd6b20UTyStucz1dghFJVMX48MGaM7hWU7knNEWatfcJaO6uDnx928WfHAYxu9/+jAJzs4vXzAAYYYyre9LqSYbqMSmTYgecLQ4cCs2bJfLAYMQKYOlXm2NJm+XIuHfH92DU3c0KKL+PIhKaT8gYS5iO0zWMUsVi+L00ySkkUcTdlzbBnZs7kbqMSrw2J1/3s2Zz16fN67BNEwOXLwNatri1Jl/aZ086DBJrokWmyVhr5IwAfNMb0NsaMBzAZwHoAGwBMbusQWQkW1P+RtdYCeAHA+9v+/tcAdOVoUzJCrBP2RlSi1AuF8IUpirjTVmura0uSJ45ohVLdnBSxvpbvD9G1tcCiRf48OM+bx8feF3ulM38+0Lev3/MRR7t9v5bzJW6SEcp4C0GCYzdJYp0wid00JZa2lZU9dFgo6RNSVQURcOFCgp1WC11QNLHDC5w4wowx7zXGHAewDMC/GWN+CgDW2l0AvgdgN4B/B/Db1tr7bRpgvwPgpwD2APhe23sB4L8B+H1jzEGwZthXSzsapSdEEWs9bdv2pn8o9cIhdKEiAm7eBDZscG1J8hDJ1UBLGyLuvuaDvlZXEPG5ff26a0u6p6KCs/H0QT8bSJiPONrdadMZYUhp9pEGEp0jxUIEvP46cPiwa0uSJ4rklbZFEeu6HTni2JAAGDkSmDIljPUisQxKoftEhXHVNfJfrLWjrLW9rbXDrLVva/dvn7bWTrTWTrXW/qTd689aa6e0/dun271+2Fq72Fo7yVr7AWvtnVKPRykciSneWUJ1wpSOIPJLX6szoogzAtescW1JfhAB+/cDJ7VwPxMQAfv2+T0fRMD588DOna4tKQ1EXBrpe7OPNAhF9ydfJGfJxaVtjwWRPUb3A6UllKqKsWM5e1rPK6UrslYaqQTCY1EJaTnsjhk0CGhokHkDGDoUmDFD5tjSprmZM2J8P3ZNTdwB0Jdx6IN+toid6atXOzWjKCRv9jsibvbhuxM/DaIoDN2ffJk+nZ8TJK638dolaWwzZwJDhsgaU5aJIuDaNWDzZteWpE9ImdNKz1BHmHQy7GAiAl566U1RCU1BTQwi3jTcEZgjScTZQHfvurbEL2prgYUL/X/g7NMHWLzYHyfAnDlA//7+2CudefN4Pp5/3rUlPWfsWGDcOP+v5XyJm2ToNfQ46mh/lLh0WKJOWH09MHmyrOvAGLm6blkkpCAKkcNmIrqf9QJ1hEnFgwswioCrV9uiEiqWnzhE3G5eYqctIta5kqiBljZEwMaNfuhrdQUR68Rdveraku4pL+cHfd2oZoPychZg930jEDedCSHaLaXZRxrEGfa+n89JQgScOAEcPOjakuSRWApLBBw7JlPXLWsMG8ZZkyGspRokULpDHWGKMzrUevLAgecLK1bw4ZT4cCxZAy1tiPzS1+qMKOKNwEsvubYkP4iAQ4f4YV9xDxFvko8fd21Jz3Ea7XYAEfDqq/43+0iDDjPsA0byBpioXRBZCJLnK4tEURhVFaNGAZMm6XmldI46whRnDB8eTlTCBQMHAnPnyjy+gwcDs2fLdPKlTWOjX/pandHYCFRW+jMObfKQLSRo7YS2eYybZKhO2OM8kmGvYMoUYMQImdeGhLXrzUybxplKen8sDXH39Y0bXVuSPhIzKJXkUEeY4pQ4inn3nmaCpUFLi9xOW0TAyy/L1EBLkz59gCVL/H+Irq4Gli7158G5oQGoq/Nbl0oSc+ZwsMCX86cjQot2S2n2kQbqaH8UyTphEoPI8Xy98IK8+coiIa0XUQRcueKwmYie0JlGHWGKU6KIyxw2HR/GL2hpZKJEETuKpOqE3boFrF/v2hL/iCLOHPBBX6sriIAtW7hjWtYpK3soCKy4J54P3zeTURROtLu2Fli0yP85SwOJzpFiIQJOnwb27XNtSfJILG0jAk6eBA4ccG2JfIYMAWbNCmO9SCyDslCHlu5nvUAdYYpT3ligDo9xaodUli/nDZ/Em51kDbS0IfJLX6szooiFwl980bUl+RFFwJEj/KO4hwh47TXg6FHXlvQcIsfR7hJDxE1Srl1zbUn2eCPDXpBzpBgklw7HpW2bNrm2JDkklnxmmSjiqorWVteWpMvIkcDUqUWcV+rQEo06wqST8ZTMN6ISrU3Ar/868Ed/VJovzvhxSYr+/eV22qqr4/ImLTUrnGXL/NLX6oylS4Hevf1xhra08G9f7JWOhI2X5M1+R8ROfN+bfaRBnGEfgu5PPkycCNTXy7w2JKxdb2bKFHZa6P2xNBABN2+GUVURRdpMROkYdYRJxSMPNhHw8roKtH7xK8CAAaX9co+OU0+JIu60dfOma0uShwhYuxa4fdu1JX4R62v5/hBdVcWi+b44Q2fM4EYPvtgrnVmzeD583niNGMEbSN+v5XyR0uwjDSQ6R4rBGH5GWL1aXuxzyBBg5kxZc606YaVl5cpwqiqIOIu4pM1Ehgzh3zU1JfxSpVDUEaY4J45KbNjg2hKZEHGphMROW0RyNdDShojLqXzQ1+oKImD7duDiRdeWdE9ZmVwBZx+RohMWl8SVNNr9ta8B3/xmCb+QqanhZh8hbN4KJc6w12PzECLg7Flg927XliRP3DBIUmkbEXDmDLB3r2tL5DNoEDfx8f3+lw9OggR/8ifAM89w9EbJLOoIU5yzYgX/1iyJdGhuBsrLZd7sJGugpQ2RX/panRFF7FTyZRxEwLFjwOHDri1RAJ6P11/3ez6cRLufegr41V8t4Rc+hIi1ka5ccfL1mSYU3Z98kZwlJzGIHFqpt2uIOEguvfv6sGEOmon07g38l//CmxQls+jsKM4ZNIi1njSKmQ59+wILF8o8vgMGAPPmyRxb2ixZwqWFvj9wLlnCpZ6+jCOktuU+EG+8fJ4PyZv9joid+L43+0iDlpZwdH/yYfx4YOxYmdfGypX8W9LYJkwARo+WNaYsE0UsLfLqq64tSR8ieZ1WleJRR5iSCUoelQisLomIH4yvX3dtSfIQcWnkrVuuLfGLqioWzff9gbOyEmhq8mcc06dzdNIXe6UzfTowdKjfGclOot0O8a1JRimJM+xDORe6I9adWr2anaeSiEvbJF0H7ecrsMd0J8Td132+/+ULkTYTUR5HHWFKJggpKuECItaPefll15YkDxGXgUjUQEubWF/rwgXXlhQHEbBjB3D+vGtLukcFgbNFPB++67aFFO2Om32EsHkrFM2wfxwivsft3OnakuRpaeHnOkmlbUTAuXPArl2uLZHPwIHhVFUUnUHp8wOC0inqCFMyQRyV0ChmOjQ1ARUVMo+vZA20tCHyS1+rM3wrbyMCTp4EDhxwbYkC8HycOAEcPOjakp4TWrQ7bvZx6ZJrS7JHKLo/+SJZd0piEFnyfGWRKOLu69KrKnrcTMSYNMxRMoI6wqTi2YUbRyX0xpcOffrI7bTVrx+wYIGeOz1h8WLuwub7sVu4kM9xX85v1QnLFhI0tiTqBXVF7MRXnbDHiZ0j2k2ZGTOGtackXhsSg8hjx/KP3h9LQ0sLV1WsXevakvSJM6e1mYgSo44wJTOEEpVwRRRxtsDVq64tSZ5YA+3GDdeW+EVlJXd29v0hulcvzgz0ZRxTpgAjR2ppV1aYOhUYPtzvjVcc7fblGiiWuNmHXkOPEztHfD6fkyaKOPP5/n3XliSLxCCyMfxMl8vJ03XLIs3N3NgwhPWCiPeY2kxEiVFHmJIZYq0njWKmAxE/BK5Z49qS5IkiuRpoaUPE2innzrm2pDiIgN27gTNnXFvSPSoInC3ijZfvum1EvAaGEO3u3Zud+CFs3gpFonOkWIi4jHb7dteWJE8UyWsYRARcvMjan0q69O9fwqoKxzfYlSvlZVAqxaGOMCUzrFjBUYmSRHh93u30kMZGzgCSeANobpargZY2sR7H6tVu7SgW38oNiYDTp4G9e11bogAP52PfPteW9JyWlrCi3VKafaQBkWbYt0ey7hQR68FJCiL7dj/3HSLWmbt5s0Rf6Ei+p66Om4lIXAeUnqGOMCUzxFpPJb3x+SWlVhRxpy2JN4DaWmDRIpljS5tYX8v3Y7dgAdC3rz8PzpI3Zj4iQSdMol5QV8Q6Yb478dMgijTDvj319cCkSf7cHwph+XIOIku67seMASZO1NLnUhFF3HE4hO7rsQyPNhNRAHWEKRmj5FGJwIgiYMsW4MoV15YkDxFroF275toSv4j1tXzfIFRU8IbAl83AhAnAqFH+H3cpTJrk/3zU1QENDf5cA8WyaBE3+/B5ztIizrAP5VzIByKZOmH9+wPz58u7DqTqumWRkLqvE2kzEeUh6giTjmclgEQclVCtp3QgYvHRF190bUnySNZASxuf9LW6Ioq4tO3UKdeWdE97QWDPlmmRxLptvs9HSwtH9W/fdm1J+lRWAk1NYWzeCkW7KT8OEQcBt2xxbUnyEPHGXlIQmQi4fBnYts21JfLp2zecqorQMqeVrlFHmFQc1V8XS1MTRyWkRbaywtKlLDIs8QbQ2MjZTXruFE5cpuf7sfOt3JCImxTs2uXaEgXg+Th7lp3CvhJFXPLx6quuLSkNUpp9pEGcYa/dlBkJ5c+dEQeRJZW2+XY/950oAjZsAK5fd21JugwYUGAzkRUr+HdFRWo2Ke5QR5iSKUoWlfA55F8EVVXAsmX+Ozw6oqYGWLJEH5p6wvz5fO35fuzmzeMyEV/GIXlj5iMSNl6hlcSpqHbnSHSOFMOIEcC0aTKvDYmlbSNHApMnl2BM//t/A08/nfKXZJ+WlnC6r7e0FNBp9Xvf464s1dWp26WUHnWEKZmDqJRRCT8z54qBCNi6lVtTS4MI2LRJpgZamlRU8Aba981keblf4xg/Hhg71h97pTNuHIs0+zwfBUe7PWfhQm6WEsp4C0Ez7B8nioCXXuINvyTiILK0uSYqwXy9//3A3/xNil/gB3FVRQgNCuJmImvX5vHmPn2A2bPTNklxhDrClMwRReFEJVwQd9qSqhP24AE/OCmFEetrnTzp2pLiiCLg4EHg+HHXluRHrBP24IFrS5T2um0+z0cUFRDt9hwpzT7SICTdn3wh4iDrpk2uLUmeKALWr5dV2kYEXL0qU9cta/TpAyxeHMZaKrHTqtIz1BGmZI6mJn641QUqHRYv5gxficd36VIWUA7hRp40qhPmhiji7MwdO1xbogB8/ly4wLpTvkJUQLRbAETAnj3+N/tIA4nOkWKQXI5OJC+ILHm+skhcVXH1qmtL0kWbiSgx6ghTMkccldAFKh1695bbaau6mjXQJI4tbebO9UtfqzPmzAEGDvTHoeeb4046EjZey5fL0wvqCr2GOke7KT/K0KHAjBkyz5WmJpY5kDS24cNZ182X+7nvxOtFCFUVRBwk0GYiYaOOMCWTRFEYUQlXRBFnoJw/79qS5CHiNPpLl1xb4hexvpbvD9FlZcDKlf6MY8wYYMIEf+yVztixrN3m88YrtGj3vHkymn2kgWbYPw4RZ03dvevakmSRGkSOdcKkzVcWWbYsnKoKbSaiAOoIk4+n3RFbWlKOSnh6XJIijqCvXu3WjjSINdBCiGglDRFw6JA/+lqdQQS89hpw9KhrS/KDiDX77t93bYkC8HysXu2/Tlgo0W4pzT7SICTdn3wh4uti/XrXliRPXNp27ZprS5JDsq5b1qiuZokRac7UjmhulpdB6SvGmHJjzBZjzL+W+rvVESYV43c3xDgqkfoC5fdh6jGLFvEDssQbwJIlQFVVGJ1vkkZKiVFc3pba5u9TnwL+8i8T+zgi4PJlYNu2xD7yUbZuBQ4fTunD5RFFnFGa2nyUgNCi3UTA/v3AiROuLckemmH/KCtX8m+JzkGJpW0SytV9ggjYvJmfSSRTW8tdh/W8ygS/C2CPiy9WR5iSSeKohMQHlSzQq5dcnbDevbkNtJ47hdPQwPpavp8Xs2YBgwen6Az95CeBP/7jxD4u9Qf9OXO43k/JCwkO4ebmsHTC4mtIYpZzsaSeYe8ZgwcDs2fLvDYaG0sURC4hQ4YAM2fKGlOWiaJwqipaWoCNG7WZiEuMMaMA/N8AvuLi+9URpmSWWOtJelTCFUTA7t0yO20RAdu3c/c3JX9ifS3fnYjtx+FDFXR9PTB5sv/HXQqjRgGTJvm98aqtlakX1Blz5wIDBoQz3kIoWYa9RxBxtuSdO64tSZbqas6Kl3YviXXdWltdWyKfpUu5qiKE9SKKuNOqNhNxyv8H4A8BOBGjqHDxpVmgrq4OOWl3ivZUVgKrVmHflUnAc8COHTtQW+uXV2DAgAF48GAuvvCFHWhqStj2VasAAMfWncT9+yOQy4W3Cvbr1xfAAnzxi7tAdC7ZD//zPwcePMDRTadg7Rjkci8m0ccBMgAAIABJREFU+/nd0L9/P1g7H1/4wk6sWJFwR4D43NnwOh48qEcuJytsNmpUPf7P/5mM73xnHYYPv53sh7cdu0vfuoTW1jLkcluS/fx21NfX4wc/mIxvf3sdRo5MeBxtXLrUgNu3yxMZx9SpU/DCC0Pxi1+8jPLydLx3ra3LcPLkBeRy+1P5fEk8nI81KC9P5zvu3m3EyZPnkMsdSOXzJ0wYj29/ewyefXYNamrSEaC7cWMRzp27iVxuVyqfXwgzZszCs8/2QS73avIf3rZ24WPAkSNHkMsdSf47UmTatLn48Y/L8c53Jiy0dPcusGoVrt/rA3wcOHjwIHK57ItMDhkyGLduzcKXvrQFDQ1Xkv3wtnPl1HOncOfOQORy65L9/G4YP34c/vEfx+Jf//Vl1NbeS/bD28Z2/e+u4fz5O8jldib7+Z0wZMhg3Lw5C1/60mbMnp1Oje/p09Nw+3b/dNYPz5g+fQ5+/OMKPPlkwuvFgwfAqlW42dob+CPg0CG368W9e2WoqGjGN75xHFVV6chH7Nw5GMAsbNiwARcuBCDa+TgVxpiN7f7/GWvtMwBgjHkngLPW2k3GmMiJddbaIH9qamqsaL7zHWsBu+GJj1vA2h//2LVBhXPrlrW9e1v70Y+m8OGcKGI/+rv3bN++KXy+B9y9a21trbUf+UgKH15bay1gP/EHt22vXil8fjfcuWNtTY21Tz+dwoe3nTsf+xh/hzS2b+chfv3rKXx427F7y1usbWpK4fPbsWsXf91XvpLedzzxhLWNjcl81re/zfauX5/M53XE8OHWfvjD6X2+JL71LZ6PDRvS+46hQ1Naf9v42c94DD/5SXrfMXOmte97X3qfXwif/zyP9/XXU/jwtrULsPaTn0zh81PmT//U2rIyay9dSviDjx+3FrCXh0+1gLWf+1zCn58SFy5Ya4y1n/pUCh/edq489ZS1o0en8Pnd8MILbMKPfpTCh7eNbe5ca598MoXP74Tz5/mr/+Iv0vuOD33I2nHj0vt8n/jUp/j6uHAh4Q++csVawF6tHWEBa1etSvjze0Bzs7WLFqX3+d//Pp+727en9x1ZBsAN24kvBsBnABwHcATAaQA3AfxjZ+9P40dLI5XMUlXFegfppucGqpYP7payfLm8FHqAEyKlaqClzcyZrKHi+7GbPh0YOtSf8zt1gX+lICQINDc2sh6kz2MoBL2GOoeIkzFeLG1ydmapq2PpRInXxtKlrJUqaWyDBrGGqV7bpaGlhT2eIWguxp1WtZlI6bHWfsJaO8paOw7ABwE8b639lVLaoI4w6fggkNMFRNy56+JF15bIhAjYuxc4dcq1JclDBOzcCZw969oSv4j1tV54we/lwxjeGPsyjuHDgWnTZG1efGbECGDqVL83Xn36hKUT1tDADg7tGPw4S5awc8Tn8zlpiIC1a4Hb6VTOO6M0QeTSE+uESdN1yyKLF7PeXAjrRRRxkCCE5gDK46gjTCpGRqZT3L1Eo5jpEHdHk3iz0y5iPYcIOHYMeO0115YUBxFw4gRw8KBrS/KDiB/G7t51bYkCPJyPewnL7JSSkKLdUpp9pIFU50gxELFTZV1pJbxKQhTJCyITsdPyVZXwSp2Qqiq0mUg2sNbmrLXvLPX3qiNMyTRLlnBUQheodJg3D+jfX+bxXbiQO6dJHFvaxA5S34+db6VSRNzGe1PC+rRKzyACrl3zez7ikrhQot1EwJEj/KM8imbYP8qKFew89f0+1xFE8oLIK1ZwjN+X+7nvEAE7dgDnEu6llTWqq7mcWOI6oHSPOsKUTBNSVMIF5eX8cCHxwaJXL6C5WebY0ibW1/L9ups6lUvcfBnHypX82xd7pRPPh89rSGjRbilO/DTQDPtH6d+fg4ESzxWJQeSBA3m+tPS5NMRraQhVFUTA1q3A5cuuLVFKjTrClMwTRRyVOH/etSUyiSLgwAEuIUuMJ5/k32XlCX5o4RABe/YAp087NcM7Yn2tXM4Pfa3O8E0nbOhQblbgs+NFEsOGATNm+L2ZDC3aHTf70GvocWLniDoSHkLEpXY3b7q2JFkqK2WWwkYRl7JK03XLIgsXss5kCGupNhMJF3WEKZlHso5VFkglgv71r7PIVEVFgh9aOHru9Bzf9LU6I4rYEbpvn2tL8oMIWLMGaG11bYkCPJwPn3Xb4mj3pUuuLUkf35zfpSTOsNf74UOIeK1du9a1JckTl7ZJCiLHum4S5ytrxFUV0pypHbFkCesohjBW5VHUEaZknkWLwolKuKChgVPOE70BVFYCo0Yl+IE9Y948oG9fvbn1BCklRi0t/NuX9YOIsxM2bHBtiQKwU+XGDb/nI0SdsGPHgMOHXVuSPeIMe+m6P/nS3MwSEb7f5zpCYmnb8uVydd2yCBGwezdw5oxrS9KlqoplBHx5TlSSQx1h0hEQEg0pKuECyTphFRVyx5Y2U6awvpbvx27iRPbJ+rJ+rFjBv32xVzq+NVzoiNCi3fGchTLeQogDA5KcI8XQrx+XgEk8VxYtAmpqZI2tf39g/nxZY8oyEp2pndHSws1ELlxwbYlSStQRJhVjXFuQKKFEJVxBxNHzo0ddW5I8RMD+/cDJk64t8Qtj+Nj5XmLkm97Z4MGcpemz40USgwcDs2f7vfEKLdo9fTrru4Uy3kIISfcnX6IIWL+eMz8lIbVhkFRdtywyf344VRXaTCRM1BGmeIGEqHyWkaylJaXEzwW+6Wt1BhFw9iw7032ACHj5ZdZCUdwjYT6Iwol2q05Y52iG/eMQAffu8TUuDSJg1y5ZQWQi1mx85RXXlsinooLLUUNYLxYvlpdBqXSPOsIUL1iwgKMSiTtqhGXO9ZRZs4BBg2TeAObMAQYMkOnkSxspTkTfxhFF3BXr1VddW6IAfP7cusVZI75CFFa0O4o4C/jAAdeWZA/NsH+Upibe8PtyfygEiaVtknXdsggRB0OlV1XEzUT0vAoLJ44wY8xnjTF7jTHbjTH/YowZ0O7fPmGMOWiM2WeMeVu719/e9tpBY8zH270+3hjzqjHmgDHmu8aYylKPR0mfkKISLigrexhBl0asgaYt4wvHN32tzhg3Dhgzxh9n6MqV7KP3/bhLYcUK/+cjtGh3rIUVyngLQTPsH6W2lvW0JJ4r8+fz+CSNrW9fLvHV87c0SK4YeTNRBOzcqc1EQsJVRtjPAcyy1jYA2A/gEwBgjJkB4IMAZgJ4O4AvGmPKjTHlAL4A4B0AZgD45bb3AsBfAfi8tXYygEsAfr2kI1FKRhRxVOLUKdeWyCSKgNdfB157zbUlyRNroB075toSv/BNX6szYr2z1au5e17WGTgQmDs3jAdPH6ir48xSn+ejshJobJS1Ie6KyZNlNPtIg9Qy7D2GCNi4Ebh2zbUlydKrFweRpc01EWfoXr/u2hL5zJ3LTQqknUMdITGDUukaJ44wa+3PrLX32v53HYBRbf/9bgDfsdbesda+BuAggMVtPwettYetta0AvgPg3cYYA6AFwPfb/v4bAN5TqnEopUUjvOniW/lYIWgEvOcQcXTMF32tziACzp/naJ8PEAFr13KJpOIeItak8Xk+iMKJdsfOb9+d+GmgGfaPQwTcvw+sWePakuQhAvbulRVElqzrljXiqooQ1ou4mUgIY1WYLGiE/T8AftL23/UA2udsHG97rbPXBwG43M6pFr+u9O/PvwcPdmtHgoQUlXDBjBnA0KEybwANDZzVoeWRhSPFQeqbM5SIxdnXrnVtiQI8nI9161xb0nNCi3YTcbOPvXtdW5I9Yt0fSc6RYmhs5OypRO5zTU0JfEhySCxti3XdJI0pyxABBw8Cx4+7tiRdtJlIeKTmCDPGPGeM2dnBz7vbveePAdwD8E/xSx18lO3B653Z9GFjzEZjzMZ79+519jYZ/If/AHzzm8DTT7u2JDFCikq4wBjWJpLYaausjMemD02FM24cMHas/9fd2LHA+PH+OEOXL+fz1vfjLoV4PnxeQ0KLdsfO71DGWwh6bB6lpgZYujSh6/u55zLVnnXePA4iS5rrPn1Y99CX+7nv+BZILAYiYM8ebSYSCqk5wqy1T1hrZ3Xw80MAMMb8GoB3AvjP1r6x7T4OYHS7jxkF4GQXr58HMMAYU/Gm1zuz6Rlr7UJr7cKKiorO3iYDY4Bf/VWgqsq1JYkSSlTCFUTAiRPAoUOuLUkeIuDIEf5R8ifWCfNFX6sriLhrng/j6N+fhY5DePD0gQEDeEPp82ZSql5QZ8TNPkIZbyHEzhE9Ng+JImDTJuDKlSI/qKqKU9AzQnm5zOueiOdLmq5bFpkzh7VLfb7/5YvEDEqlc1x1jXw7gP8G4Elr7c12//QjAB80xvQ2xowHMBnAegAbAExu6xBZCRbU/1GbA+0FAO9v+/tfA/DDUo1DKT0axUwXKWVwHSF5bGlDxAHuHTtcW1IcUQRcugRs2+bakvwg4lK8mze7f6+SPvF83Lrl2pKeE0Ws93f6tGtL0kd1wjondo7o/fAhRBwkeekl15YkDxFw4ICsIHKs6yZxvrJGXFURwnoxfz43EwlhrIo7jbD/BaAvgJ8bY7YaY/4OAKy1uwB8D8BuAP8O4LettffbNMB+B8BPAewB8L229wLsUPt9Y8xBsGbYV0s7FKWUxFEJ9dSnw9SpwPDhMm8AM2eyZJ7EsaWNlAiZb85QIuDuXRZpV9xDBLS2+q3bFqJO2LlzwK5d3b83NFpaNMO+PUuXAr17+3N/KAQp9/D2NDZyN1yJ85VFooi7yh896tqSdNFmImHhqmvkJGvtaGvt3Lafj7T7t09baydaa6daa3/S7vVnrbVT2v7t0+1eP2ytXdz2mR+w1t4p9XiU0hFSVMIFcRmcxAi65LGlzZgxrK/l+3U3ahQwaZI/m4HmZs7c8MVe6cTz4bMuTWjR7pC0bQpFM+wfpboaWLZM5vFoaODybkljq64GlizRa7tU+BZILAYiYP9+4GSnYkuKFLLQNVJRCoIojKiEK4i4k9T+/a4tSR4i4Ngx4PDhIj5k2LDE7PGJlhbOIrl/37UlxRHrhPkwjr59gUWL/Ha8SKJfP2DBAr83XhUVYTWdGT+eG37oNfQ4DQ2aYf9mogjYupVL6CVRXi4ziEwEbN4MXL7s2hL5zJoFDBoUxnohMYNS6Rh1hCnekWgU03TUeDRsJEd9Ejl3Nm4EfvKT7t8njCjih83t211bUhxELIa8ZYtrS/IjioANG4Dr111bogB8/qxfD9y44dqSnhNFYUW7pTT7SBqpzpFiIOKM8RdfdG1J8sRB5Ndfd21JckjWdcsaZWW8loawXsydK6/TqtIx6ghTvGPWLNZ6Uk99OkyaBNTXy7wBTJ/OCV1FnTujRgFvf3tSJnmDlDIa38YRRcC9e8DLL7u2RAEe6rb5PB8h6oRdvAjs3OnakuyhGfaPsmQJN3305f5QCBKDnJJ13bIIETtSi6qq8AANEoSDOsIU71CdsHSRrKUVj+2FF+SNLW1GjQImT/b/uhsxgptC+OJIb27mcjbfj7sUmpp4Pnw5fzoijnaHUi4YO79DGW8hqIbao/TuzSLsEo9HXNom6V5SVcXzJWlMWca3QGIxRBFw6BDLqShyUUeY4iVRFEZUwhVEwNmzwJ49ri1JHiIuCTpwwLUl/hFF/uhrdUWsE3bvnmtLuqdPH2DxYpkbMx+prfVft628PCydsDFjgAkT9BrqiDjD3ufzOWmIgG3bgAsXXFuSLFKDyFHE83XxomtL5DNjBjB0aBhrqeqEhYE6whQvkZjinSUkH1/JY0sbIuDqVX/0tTqDiDW3Nm1ybUl+ELE03bVrri1RABnz0dLC0e7jx11bUhqIZDT7SJrYOaKbvYdILh2OS9tee821JckhWdcta4RUVdHQANTV6V5BOuoIU7wkpKiEC8aP5yi6xBvA5MnAyJEyx5Y2UtLiV67k376sH0S8gVdB4GwQz8eaNa4t6TlSruV8IeJmH9u2ubYkexSVYV9Zyb/Hj0/SJKcsWgTU1Mi8NiQGAhcvBqqrZY0pyxABJ04ABw+6tiRdyso4c9qX50SlZ6gjTPGSkKISLoiPr8ROW5I10NLGN32tzhg2jJ3pvjw4L1sG9Orl/3GXQmOj//PR0AAMHOjPNVAsoTn+CqGoEqAhQ4Af/hD41reSNMkplZWszSjxXJkxg6dM0tgk67plkZB0BeNmIkeOuLZESQt1hCneEkpUwhVEwPnzwK5dri1JHiLgzBlg717XlviHT/paXUHEGT1377q2pHtqarg7lqTNi8/U1HB3OZ/no6zsYTApBOrrORs4hM1bocQZ9j0+F558kr2qgogifvY5e9a1JckiNRBIBGzfzs+sSrpMncpB0RDuHaoTJh91hCneElJUwgUSU+hjNDug5/imr9UZRMCNG8CGDa4tyQ8iYPNm4MoV15YoAK8hmzb5PR9RxJHuUKLdUpz4SaMZ9o8jXSfs+HHWCJSC5PnKGsY8bLogfb2YOZObieheQS7qCFO8JaSohAvGjmXZD4mOxokTgVGjZI4tbaQ4EWOdMF/GEUVcpqyCwNmgpYXnw2fdttCi3VEko9lHGmiG/aMsWMAdYn25PxSCxCDnwoVydd2ySEsLcPo0sH+/a0vSpX3mtHSnX6ioI0zxFo1ipg8Rb5Ik6oQR8bkjbWxpM3QoR8l83zwPHgzMnu3POJYtYy0UX+yVztKlPB8+b7xCi3aH5vgrBCkBjqTo1UuuTtjUqcDw4bKug1jXTdKYsky8Xjz/vFMzSkIUAceO9bCZiJJ51BGmeE0UcVRi3z7XlsgkioBLl1h7QRqxBtru3a4t8Y8o4kwYH/S1uoIIePll4M4d15Z0T1UVO8Mkbsx8pLqanWE+b7zKysIpcQF48z9tml5DHSHROVIsRKwjevq0a0uSJQ4iP/+8rOueiHXdzpxxbYl8Jk1i3cUQ1gsNoMhGHWGK1+gClS6So8R67vQcIuDmTX/0tTqDCLh1C1i/3rUl+UEEbN3KzmnFPURcZufzfBBxtPu111xbUhqI2ImvOmGP0j5LWpJzpBgkPyMQySttU52w0hGvF9KaLnTE9OncaVziPkhRR5jiOXFUQheodBg9mo+xxOM7bhzroIWQ2p00vulrdcaKFfxA58tGh4gfOvVBPxtEEc+Hz7ptEvWCuiJu9rFxo2tLsodm2D/KvHlAv34yrw2J1/38+UDfvrLGlGWIuKuq9KqKuDlACE6/EFFHmOI1IUUlXBFFvNG7f9+1JclDxE4F1QkrjMGDgYYG/x846+qAOXP8GcfixVyS54u90lm6lEtWfXGkdsT06az7F8o5JcWJnwYtLfzb5/M5SSoqgOXLZR4PiUFkybpuWSSuGJF4fbwZbSYiF3WEKd4TSlTCFUTAlStckiUNIuDiRWDnTteW+EcUAa+8ArS2urakOIh4HLdvu7ake3r3Bhobw3jw9IF4PnzeeMV6QaEEk6Q0+0iDiRPZOaJZ0g8h4vLBEydcW5IsUq97Is5oPHXKtSXyGT8eGDPG7/tfvsQZlLo2ykMdYYr3SEzxzhKSj6/e3HpOrK/16quuLSmOKGKxfF/GQcTNKy5ccG2JAvB8bNvm93yEFu0mAtas6YETv74+FXuyQpxhv3q1LOdIMUjXCTt7Ftizx7UlySF5vrJG+4oc6VUVU6YAI0boeSURdYQp3jNuHEcldIFKhxEj+CYg0RE2ejQwYYKeOz0h1tfy/bxYsYK75/niDA2pHMEHJAg0Sw52dESPmn389Kf+dNUoAs2wf5Q5c4ABA2ReGxKv+3nzgP79ZY0py0QRB4GkV1WoDI9c1BGmeE9IUQlXSO60FUfAJWqgpUmsr+W7Q2bAAH549mUcixYBNTX+2CudeD583njF0W6fx1AIK1bw74Kuobe+FRg5Mg1zMoXkTtE9obycdeUkHo/x4zkYKOleUl4uV9cti4SkK6jNRGSijjBFBKFEJVxBBFy7Bmze7NqS5CECLl/m8ialMHzS1+oKImDdOi71zDqVlSoInCUqK4GmJr83AqFFu6U0+0iDWPfH5/M5aaIIOHwYOHbMtSXJYgw7MqQFkYmAAweA48ddWyKfMWN4zQhhLZWYQamoI0wRguoCpIvkKLHksaUNEetrrVvn2pLiIGK9oFdecW1JfhABu3ZxCZPiHiIOwpw759qSnhNatDt24t+549qSbNFeJ0ySc6QYJG+Aowg4f57vJ1LQ/UBpiaIw1ouJE4FRo2SuAyGjjjBFBGPHhhOVcMGwYcD06TKPb309MHmyPjT1hOXLWV/L9/OiuZlLKnwZh+qEZQsJGy/Jm/2OkNLsIw2I2DmiGfbM7NksBSDx2pAYCGxoAAYO9Hs99gki4NIl+VUVoWVOlwJjzGhjzAvGmD3GmF3GmN8ttQ3qCFPEoFHMdIk7bd2969qS5CECXnxRpgZamgwYAMyf7/9DdL9+wIIF/jw4L1wI1Nb6f9ylsGAB0KePP+dPR4QW7ZbS7CMN1NH+KGVlcnXCxo2TF0QuL+frW9KYsoyEQFC+RBFnfmszkcS4B+APrLXTASwF8NvGmBmlNEAdYYoYooijEtu3u7ZEJkTAjRvAxo2uLUmeKAKuXgW2bHFtiX9EEWdV+KCv1RVEPI4bN1xb0j0VFSoInCV69eL58HnjZQxfy6FEuwcOBObO1WuoI+IMe1866ZYCIuDoUeDIEdeWJI/E0jYi1nU7etS1JfIZNQqYNMnv+1++hJY5nTbW2lPW2s1t/30NwB4A9aW0QR1hihji7iX68JYOElPoY/Tm1nN809fqDCLOCHz5ZdeW5AcRsHcv6zop7iEC9uzxez6Iwop2EwFr1/rf7CMN4ixp7abMSH5GiEvbJAWRNauxtISyXowbx4ECieuAa4wx4wDMA1BSwYKKUn5Zlqirq0MugBVy376+ABZgx44dqK294Nqc1KmvX4wf/OAm5s/vRtxi1SoAwLF1x3D//gjkcmtKYJ3/TJiwEP/8z61obMzvieno0fGwdjRyuRdTtqx4xo5dhB/84DYWL96R1/uPHZuABw/qkcu9lLJlWaccZWXN+Pu/fx3l5a91/da26+7Sty6htbUMuVx2UvAePChHeXkTvvGNY6is7GYcbVy61IDbt8udjKNfP17bv/CF3XjLW/JTzW9tXYaTJy8gl9ufrnEB0rcvz8cXv7gbLS35zcfdu404efIccrkD6RqXJ9XVVQCW4plnDuC97z2R19/cuLEI587dRC7nn9r24MGDcOfObPzt327FvHmX8/yrCEeOHEEudyRN05wzbNgwXLo0HV/72kZMnny92/dfv14OYDkOHjyIXE5eu74HD4D+/Rvx7W9fxPjxe/P6m1OnpuLOnYHI5bLdTaaqqjeAZfjylw/iAx/oZu7a7uHX/+4azp+/g1wum0JyDx4A/fo14dvfvoCxY/Obr9Onp+H27f7I5VQ4sFCGDh2KK1dm4Ctf2YSpU691/sYHD4BVq3CztTfwR8ChQ/6tF9OmTcNzzw3C88+/jLI80ol27hwMYBY2bNiACxc8KDlIngpjTPtaomestc+0f4MxphbADwD8nrX2akmts9YG+VNTU2NDYMMGawFrf/xj15aUht/4DWv797f23r1u3sjVH/ajH7W2b9+SmCaCp5+2trra2jt38nv/Jz5hba9e6dqUFL/5m9bW1lp7925+7//Yx6wNZBnplsWLrW1qyuONbdfdW96S5/tLzLJl1i5dmv/7n3jC2sbG9Ozpinv3rO3Xz9oPfzj/vxk+vLD3K/lz927h8zF0qLUf+Uh6NhXKgwfWjhlj7S/9Uv5/M3Omte97X3o2pcnly9aWlVn7yU/m/zdAYe/3lWPHeKz/83/m9/7Ll/n9n/tcuna55AMfsHbUKL5O8uGpp6wdPTpdm5Ji4kRr3/WuPN7Ydg+fO9faJ59M3ayi+KVf4vUsXz70IWvHjUvPHsmcOMGnxmc/280br1yxFrBXa0dYwNpVq0piXqL8/d/zWLduze/93/8+v3/79nTtyioAbtgu/DEAegH4KYDf7+p9af1oaaQiipYW4MqVPLSeVq4siT3SkNxpiwi4fl2mBlra+KSv1RVRBGzYAFzrIqCZFVQQOFvEum0+z4cxfA+VphfUGf37y2j2kQax7k8AhRN5E0XA8eOsPSUNiaVtRMDrrwOv5ZfgrRTByJHA1KlhSNOE1BwgbYwxBsBXAeyx1n7OhQ3qCFNEkbcuwL/9G3AgG+UoPrFyJW+WJN4AYt+obooKxzd9rc5oaeGNgC/jIOJl7ER+VWxKykQRz8fJk64t6TlRBFy4AOzMZsVT4hAB69YBN2+6tiR7SHSOFIN0nbArV4CtW11bkhyS5yuLxJ3lpXdfHzMGmDBBz6uEaALwqwBajDFb237+r1IaoI4wRRQjRnBUotsFqk8fDncqBVFXBzQ0yLwBDB0KzJwp08mXNk1NnBHj+3nR2MgdAH2JakpuYOEjccMWn+cjtM1jFAF37/rf7CMNYueIdlNmpk0Dhg2TeW1IvJfMmAEMGSJrTFkmijibfvNm15akDxFnTmuQoDistWustcZa22Ctndv282wpbVBHmCKOOIopPSrhCsmdtuKIVmura0v8orYWWLTIfydiTQ2wZIk/45gzBxg40B97pTNnDjBggN8br9Ci3cuXc5mxXkOPo533HsUYPiYvvMBCWZIYORKYMkXWXMfzlcvJm68sEq8XvgQSiyGKgMuXZXVaDRV1hCniiCLWetq0ybUlMiFiJ5hUnbCbN1knSikMIn/0tbqCiNeOK1dcW9I9qhOWLeL58H0zGUXhRLv79gUWLgxj81YoeWfYBwQRcOqUTGUNiUFkItZ1O3TItSXyGTaMs/B8v//lQ2iZ05JRR5giDo1ipsuKFUBZmcwbQKwTpudO4RDxxnnNGteWFAcRC4W/9JJrS/KDiMWbX3/dtSUKwPNx6BBw7JhrS3oOUVjR7tiJf/26a0uyh0TnSDFI3gATySs0wyPRAAAdcElEQVRtkzxfWSSuqrh717Ul6VJfD0yerOeVBNQRpogjjkroApUOAwYA8+bJPL6DBrEGmmYHFE6sr+X7ebF0KVBZ6Y8zVB/0s4WE+ZAwhkKIIhnNPtJAM+wfZfJkLiOUeG1I1AmbOhUYPtyf+7nvEHH38BCqKqJIm4lIQB1hikiiKIyohCuiiDtt3brl2pLkiTXQ7txxbYlf1NSwE8n3B87qamDZMn82A7NmsQPXF3ulM3s2NxXx+ToILdrd3Cyj2UcaaIb9o0jWnZIYRJas65ZFQqqqIAKuXtVmIr6jjjBFJC0t4UQlXEDEgvJr17q2JHmiiB18EjXQ0iaK/NHX6goifri5dMm1Jd1TVsYPnyE8ePpAPB++byajiMuDQ4h29+kDLF6s11BHxM4RzZJ+SEsLcOYMsGePa0uSR2IQOdZ127/ftSXyGTyYg0G+3//yQWIGZYioI0wRSRyV0AUqHeJOWxKP78qVHEWUOLa08U1fqzOiiKPHL77o2pL8IAKOHgVee821JQrA83HkCP/4ChE7tEOJdhMBGzdyhF95FCIuG5XkHCkGyVlycWnbxo2uLUmO0Eq9XROvF9KrKkaMAKZN0/PKd9QR5jHPPcee9/PnXVuSPeKohMQHlSzQrx8wf77MG8DAgcDcuXru9IRly4Devf0/L5YuBaqq/BmHPuhnCwmRYgljKAQpzT7SICTdn3yYMAEYPVrmM4LEIPKkSVzuLWlMWSauqli/3rUl6RNnTmszEX9RR5jH9O0L7NwJ/OIXri3JJqFEJVxBxDe6GzdcW5I8sU6YRA20NKmqkqET1rs3i//7Mo4ZM4AhQ/yxVzqzZvF8+LzxGjGChaZDOaeWLeNmH6GMtxAkOkeKob3u1IMHrq1JliFDeP2SNNeSdd2ySFxVEcJaSqTNRHxHHWEes2ABO8Mk3bCSJKSohAuIuFRCYqctInagrlvn2hL/8ElfqyuiCNi+HbhwwbUl3aOCwNnCmIe6bT7PBxGXB4cQ7Y6bfejz1ONohv3jEHE1xu7dri1JnjiI3Nrq2pLkIALOnpWp65Y16uqAOXPC0BUMLXNaIuoI85iKCtZq0oeTjgkpKuGC5mbWCZN4fJcvZ9FrvbkVDpFf+lqdEY9j9WrXluQHEXD8OHDokGtLFIDn49gx4PBh15b0nNCi3UTA5s3+N/tIA82wfxTJ5ehE8oLIkucri8RVFbdvu7YkXYYOBWbO1PPKZ9QR5jlEwL59wMmTri3JHnFUQheodKitBRYtknl8+/dnDTSJTr60WbLEL32tzli8mLNEfDkHJAs4+4iEjVdo0e4oktHsIw00w/5Rxo3jH4nXxooV8oLI48fL1XXLIlEUTlUFEWtLSsqgDAl1hHlO/LCti3vHEAGvvCI/KuGKlhYW0L12zbUlyUPEN/GbN11b4he9ewNNTf5vECor/RrHtGnA8OFhlCP4wLRpwLBh/pw/HRFatDtu9qHX0ONohv3jRBFnDEvTCRs0SF5pmzH8vJrLyZuvLLJiBVdVhLBeRBHvEyR1Wg0JdYR5zty5nL0SwmLTE0KKSrggirjTlkSdsChiDbRXXnFtiX/4pK/VFUTckOTsWdeWdI8KAmcLKfMRRRztvnvXtSXpU1XFzjB9nnoczbB/HCLg4kVgxw7XliRPFMkrbYsi1nXbtcu1JfIZMACYN6+D9aJ3b/79vveV3Ka00GYifqOOMM8pL2fPu16AHRNHJfT4pENjI3faknh8ly+Xq4GWNnGmqi/6Wp0Rl4b5Mg4i4NQpYP9+15YoAM/HyZPAgQOuLek5RBztDqUkjgjYutX/Zh9poBn2jyKh/LkziHieX33VtSXJEVqpt2s6rKro3ZsjpJ//vDO7kmbwYKChQc8rX1FHmACIgIMHWShZeZQ4KqHOjHTo04e1lCTeAPr2BRYulFUeUCoWLWJ9Ld/Pi4UL+Rz3Zf2QvDHzEQm6bRLGUAi+NckoJZph/yijRwMTJshcbyUGkceNY60wSWPKMlHEullr177pH+rquNubIKKIgwTaTMQ/1BEmAI1ydE0clbh1y7UlMiHirmJXr7q2JHmIWAPt+nXXlviFb/pandGrF2cG+uIMnTQJGDnS/+MuhSlTeD58OX86YtAgYPbscM6pxYuB6upwxlsIEp0jxdLSwh2S7993bUmyDBjA0ivS5lqqrlsWCamqQmKn1VBQR5gA5swBBg6Ud8NKCiKOSqjWUzoQye20RQTcuydTAy1tiFiL49w515YUBxGwdy9w+rRrS7rHGLZ39Wq/damkIEUnrKUlnGh3795c8h/C5q1QNMP+caIIuHwZ2LbNtSXJIzGITMRlz9u3u7ZEPv36AQsWhLE3jZuJhDBWaThxhBlj/sIYs90Ys9UY8zNjzMi2140x5m+MMQfb/n1+u7/5NWPMgbafX2v3+gJjzI62v/kbY4xxMSaXlJXxRagPJx3T3BxOVMIFy5ZxBpDEG0BTE2dwSxxb2nTY0fbkSeDwYRfm9BgfdcLOnAH27HFtiQI8nI+9e11b0nOiiDfDkvSCuoKIN8rnz7u2JHtIdI4Ug+SKjDiI/Fhpm8eofEBpIeIsqRs3XFuSLgMHajMRX3GVEfZZa22DtXYugH8F8Mm2198BYHLbz4cB/C0AGGPqAPwpgCUAFgP4U2PMwLa/+du298Z/9/ZSDSJLEAGvvQYcPerakuwRUlTCBdXVwNKlMo9vrIGmTtTCWbAAqK1903kxYgSLdHjE/Pm8hvhS3qYP+tmiQ4ewZ6xYwdFun8dQCL45v0uJZtg/Sn09l0BLXG+XL5dXCjtqFEsISBpTlgmp+zqRvE6rIeDEEWatba8m1AdAXDTwbgDftMw6AAOMMSMAvA3Az621F621lwD8HMDb2/6tn7V2rbXWAvgmgPeUbiTZQTc/XUPE0WzpUQlXEAFbtsjstEUEbNwIXLvm2hK/6NWLszF9X5MqKnhD4IsTYPx4FnH2xV7pTJjAmy9fHKkdUVcnUy+oMxYt4iBIKOMthOZmec6RYokiloa4d8+1JckiNYgcRTJ13bJIczM/Q/l8/8sXIpYPkJRBGQLONMKMMZ82xhwD8J/xMCOsHsCxdm873vZaV68f7+D14Jg5k1u4SrthJYVqPaVL3GlLok5YFPEDk8SxpY1P+lpdQQTs38+VnVkn1gnL5VQQOAtI0W2LonCi3XGzD3UmP06/ftxNV4/NQ4i4WdCWLa4tSZ6WFnmlbUTAlSvA1q2uLZFPbS0HFkJYL+JmIiGMVRKpOcKMMc8ZY3Z28PNuALDW/rG1djSAfwLwO/GfdfBRtgevd2bTh40xG40xG+8JC9201wnz+WE7LVTrKV2WLAGqqmQe38ZG3hjpza1wJJSFAf5l3BKxvtGuXa4tUQCej3Pn/J6PONq9bp1rS0pD3Ozj7FnXlmSPUHR/8kWyTpjE0jbJ85VF4u7r0qsq+vdnKQ09r/wiNUeYtfYJa+2sDn5++Ka3fgvA+9r++ziA0e3+bRSAk928PqqD1zuz6Rlr7UJr7cKKioqeDSzDEAGvv85aYcqjxFEJXaDSoaqKHUYSj29NDTv6JI4tbebN4wwC34/dnDncMc0Xh54UB6QUfHOkdkQc7fZ5DIUQz5nqhD0OETtHNMOeGT4cmDZN5rURB5El3UtGjpSr65ZFiLiqYs0a15akTxRxsOjmTdeWKPniqmvk5Hb/+ySAuJ/SjwB8qK175FIAV6y1pwD8FMBbjTED20Ty3wrgp23/ds0Ys7StW+SHALzZ0RYMGuXomihSrac0iSLutHXhgmtLkocI2LyZ0+mV/In1tXxfk8rL2RHgyzjGjgXGjQtDl8MHxo3jOfF5MxlatDtu9qHX0ONIdI4UCxFv9O/edW1JsvTty6Ww0q6DlhaZum5ZZNky1owN4d4RBwlUJ8wfXGmE/b9tZZLbwU6t3217/VkAhwEcBPBlAL8FANbaiwD+AsCGtp8/b3sNAH4TwFfa/uYQgJ+UahBZY8YMYOjQMBabnhBHJTSKmQ6xTtiLL7q2JHmIWG9J4tjShgg4cAA4ccK1JcVBBBw6BBw71v17swARn6+qE5YNWlr8122LIm46c+uWa0vSx7cmGaVEM+wfhwi4fh3YtMm1JckTNwy6ft21JckRRRwU37zZtSXy6dOHqypCWEuXL+fAqa6N/uCqa+T72sokG6y177LWnmh73Vprf9taO9FaO9tau7Hd33zNWjup7efr7V7f2PZZE621v9PWPTJI2oskh3sUOqepKZyohAsWL+YyQonHd+lSoHdvmWNLGyller5l3BIBFy9ylqbiniji+dixw7UlPYcIaG0NJ5gUN/s4dcq1JdkjisLQ/ckX3+4PhRA3m5JU2iZ5vrIIETuJpVdVxBmUel75g7OukUo6RBFnXhw86NqS7KFaT+lSWSlXJ6yqitO7fXfmuCDW1/L9vGhoAOrq/DkHJOhSSSLeePly/nREHO32eQyFIMWJnwYtLeHo/uTDkCHcvV3ietvYKC+IPGwYV9FIGlOWiSLOhg5hvYgibSbiE+oIE4ZufromlKiEK4iAnTu5Q5o0iLjd9qVLri3xi1hfy/fNZNyZ1xetlFGjgIkT/T/uUhgzBpgwwe97c9++rJ3l8xgKYe5cbvah19DjSHSOFAsRZ0u2trq2JFn69OGMf2lzHUUydd2yyLJlHCyXdg51hMQMSsmoI0wYU6YAI0aEsdj0hFjr6aWXXFsiE8mdtmINNIljS5tYX+v1111bUhxEwNGjwJEjri3Jj1gn7P5915YoAM/H6tV+zwdRONHuigq/mmSUkjjDXp2EDyHibnEbNri2JHneCCKjn2tTEoOI17GNG7t/r1Ic1dXsDAthLdVmIn6hjjBhGMNRDtUJ65ilSzkqoQtUOixcyNFDiTe7xYv5Zi5xbGkjpcTIN10RIuDyZc5kVNwTz8e2ba4t6TlxtDsknTAJzT7SQDPsH2XlSv7ty/2hEOIg8ho0uzYlMSTPVxaJImDLFvlVFbW1MjMopaKOMIEQAadPA/v2ubYke4QUlXBBr15yO2317s3lIBLHljazZ/ulr9UZs2YBgwf7Mw7fHHfSkTAfcbTb5zEUgoQ5SwvNsH+UQYNYE1PiufJGaRvItSmJMWQI39MlzlcWaWmR21n+zcSdVrWZSPZRR5hAVCesa4jCiEq4IoqA3buBM2dcW5I8RNyF7/x515b4RVkZnxe+6Gt1Rpxx+8ILfmTcjhzJ5fK+OO6kU18PTJ7s93yEFu2eMwcYODCc8RaCZtg/ThQBr7wC3Lnj2pJkeSOILMgRBvAz3SuvyNN1yyJLlnDjqRDW0ijSZiK+oI4wgUycyA/c+nDSMVHEm1iNYqaDlDK4jpCsgZY2UeSXvlZnEAHHjgGHD7u2JD9inbB791xbogAydNuiKJxot5RmH2mgGfaPQwTcvg28+qprS5InioAtmIdLGODalMSIdd3Wr3dtiXxCqqrQZiL+oI4wgRjDi7vqhHXM0qUclfA9OyWrzJ/P3cUk3gAWLmSR4BBu5EkjpcTIt3EQscNi82bXligAz8fVq37PB1FY0W4idnz73uwjDTTD/lFWrOBncF/uD4VABFiU4SUsd21KYqhOWGkhYo3MCxdcW5IuNTW819TzKvuoI0woRMDZs1yipjxKSFEJF0jutFVZCTQ3yxxb2sycyZocvh+76dOBYcP8WT98c9xJR8J8hBbtVrmJzokz7EPQ/cmHgQOBuXNlnitLlwJVuCWqPLKujufLl/u574RUVRFFHPDSZiLZRh1hQpHwsJ0msdaT9KiEK6II2L8fOHnStSXJQwTs2sWOZiV/yso4+uqLvlZn+KYTNmwYO+/0QT8bDB8OTJvm93zU1LDeSyjPF7NmsRB6KOMthDjDXo/NQ1pagHXrgFu3XFuSLL17A8uwVpQjDHio63b7tmtL5LNoUThVFdpMxA/UESaUCROAsWPDWGx6AhFvYkOISrggBJ0wiWNLGyLg+HF/9LU6g4idvAcOuLYkP4j4YezuXdeWKICM+SAKJ9odO/F1zX8czbB/nChisfx161xbkjyEF7AdDbh40bUlyRFFcnXdskZlJXceDsFxvmwZr48hjNVn1BEmmFicWnmcOCqhxycd5s4F+veXeXwXLODOaaoXUzixE9F3wfw449aXc4AIuHFDM2CzAhFw/brfukpxtPv4cdeWlAYimfezJCDyf01PkuXL2Xkq8ZgQXoBFmTf3vnxYuZIzvfX6Lg2hrKVVVZwxG8JYfUYdYYIhWdnLiVJZyVFMJR3iTlsSqajgB12lcKZN41I935kyBRg50rUV+RMLAivZIHak+kwc7Q4FfZ7qHAnnc5L0788BM4ksxnpU46ZrMxJlwABg3jzXVoRDSOuF3jeyjzrCBKMXYNe0tPDvEFrAuyA+/3wu/+mMeGw3ZT0Ppk6srwX4oa/VGb6NY8gQ1jlSsoGE+aiqYmdYKMyYwfOmPM7ixa4tyB5SnxEqcRdNeNm1GYkTz5c0XbcssnChawtKh+7Du8cY83ZjzD5jzEFjzMdL/f3qCBPMmDGuLcg2IUUlXCD5BiB5bGkTHztf9LU6Ix7H3r1u7ciX2N7WVrd2KEx8//F5PuIx+FzimS++Ob9LSWWlOsPeTHyuSCxHJ7Do0blzjg1JkPj+eOaMWztCoFcv1gkLgSVLXFuQbYwx5QC+AOAdAGYA+GVjzIxS2qCOMOE0NPDvBw/c2pFFQopKuCA+9ySiafQ9J37g9P0hOh6HL06A2N71693aoTDxfGzY4NaOYojH8OKLbu0oFfF4Dx1ya0cWkbKuJ0Vzs2sL0iN2hK1d69iQBFG5i9ISO4qlOx5Dkg/oIYsBHLTWHrbWtgL4DoB3l9IAdYQJJ344OXjQrR1ZpFcv1xbIpkzw6lJe7toCf5k82bUFyTBhgmsLCiPW7Nu9260dChPrtu3Y4daOYoij3aGsh/Hz1Natbu3IIvHGdssWp2Zkhr59XVuQHgux0bUJidOvn2sLwiKWpglhvYgD5yrD0yH1AI61+//jba+VjIpSflmWqKurQy6Afs9PPFGBnTsnY9y4g8jlBIo1Fcn/+B8DsXXrAORyr7k2RSSf+9wAvPLKIORy8kLon/vcAGzcOFDPnR7w+78/AtYCudwp16YUxcc+NhzWGm/G8Su/Mh4zZ15BLnfRtSkKeD5mz/Z7Pj70oXGor7+FXE54aB9cEvme90zGW95yBrncVdfmZIqKCoN3vGMK3vveE8jlrrs2JxP82Z8NwZEjNcjlBLWN++53AWPw4ecOYfDgVlHX/Z/92WCcOFGDXE5QS8yMYq3BO985GU8+eVL8evGHf9gbX/7yBFy7tg+5XJDlWRXGmPbe82estc+0/bfp4P0lFR8wNlCxgz59+tgbN264NkNRFEVRFEVRFEVRFEUMxpib1to+nfzbMgB/Zq19W9v/fwIArLWfKZV9gouXFEVRFEVRFEVRFEVRlAyxAcBkY8x4Y0wlgA8C+FEpDQi2NFJRFEVRFEVRFEVRFEUpHdbae8aY3wHwUwDlAL5mrd1VShu0NFJRFEVRFEVRFEVRFEVJhK5KI7OAlkYqiqIoiqIoiqIoiqIoQaCOMEVRFEVRFEVRFEVRFCUI1BGmKIqiKIqiKIqiKIqiBIE6whRFURRFURRFURRFUZQgUEeYoiiKoiiKoiiKoiiKEgTqCFMURVEURVEURVEURVGCQB1hiqIoiqIoiqIoiqIoShCoI0xRFEVRFEVRFEVRFEUJAnWEKYqiKIqiKIqiKIqiKEGgjjBFURRFURRFURRFURQlCNQRpiiKoiiKoiiKoiiKogSBOsIURVEURVEURVEURVGUIFBHmKIoiqIoiqIoiqIoihIE6ghTFEVRFEVRFEVRFEVRgkAdYYqiKIqiKIqi/P/t3VuIXVcdx/Hvj9xaejFJvVCaQhMoYh6kjUFSKkVa6U0xPvQhIDR4QVAfFB8kpSD4qA8iRbFIq1hQW423UCg12IovmprapE2NaSY10NBolNpaRay1fx/2mnoYJqNzMnP2nHO+H1jsvf97n7M2zA/WsM6+SJI0FVJVfZ9DL5K8Bvyj7/NYAquBV/s+CU09c6i+mUH1zQyqb2ZQK4E5VN/M4MpwflWt2AuvpnYibFIkOVhV2/s+D003c6i+mUH1zQyqb2ZQK4E5VN/MoP4fK3aGTpIkSZIkSVpKToRJkiRJkiRpKjgRNv6+3vcJSJhD9c8Mqm9mUH0zg1oJzKH6Zgb1P/mMMEmSJEmSJE0FrwiTJEmSJEnSVHAibIwluTnJsSQzSfb0fT4aD0m+keRMkiMDtY1J9ic53pYbWj1J7moZezLJtoHP7G7HH0+ye6D+jiRPtc/clSTD9qHJlOTyJI8mOZrk6SSfanVzqJFIcl6Sx5Icbhn8fKtvTnKg5eOBJGtbfV3bnmn7rxj4rjta/ViSmwbq847Rw/ShyZVkVZInkjzYts2gRirJyTZeHkpysNUcjzUySdYn2Zvkd+n+N7zGDGrZVZVtDBuwCjgBbAHWAoeBrX2fl23lN+A6YBtwZKD2RWBPW98DfKGt3wo8BATYARxo9Y3As225oa1vaPseA65pn3kIuGWYPmyT24BLgW1t/SLgGWCrObSNMIMBLmzra4AD7e/+PWBXq98NfLytfwK4u63vAh5o61vb+LsO2NzG5VULjdGL7cM22Q34DPAd4MFh8mEGbefagJPAG+fUHI9to8zgt4CPtvW1wHozaFvu5hVh4+udwExVPVtVrwD3Azt7PieNgar6BfDCnPJOukGItvzAQP2+6vwKWJ/kUuAmYH9VvVBVfwH2Aze3fRdX1S+rqoD75nzXYvrQhKqq01X1m7b+MnAUuAxzqBFpf+e/tc01rRVwPbC31efmYzY3e4Eb2i/KO4H7q+qfVfV7YIZufJ53jG6fWWwfmlBJNgHvBe5p28PkwwxqOTgeaySSXEz3I/29AFX1SlW9iBnUMnMibHxdBjw3sH2q1aRhvKWqTkM3SQG8udXPlrOF6qfmqQ/Th6ZAu/XmarorcsyhRibdLWmHgDN0/zCfAF6sqlfbIYMZeD0fbf9LwCUsPpuXDNGHJteXgc8Cr7XtYfJhBnWuCvhpkseTfKzVHI81KluAPwHfTHeb+D1JLsAMapk5ETa+5vuFzleAaqmdLWeLrQ/ThyZckguBHwCfrqq/LnToPDVzqHNSVf+uqquATXRXz7xtvsPacqkyuFDOzOAUSfI+4ExVPT5YnudQM6jldm1VbQNuAT6Z5LoFjnU81lJbTffIlq9V1dXA3+luUzwbM6gl4UTY+DoFXD6wvQl4vqdz0fj74+wlv215ptXPlrOF6pvmqQ/ThyZYkjV0k2DfrqoftrI51Mi1WzB+TvcckPVJVrddgxl4PR9t/xvobjFfbDb/PEQfmkzXAu9PcpLutsXr6a4QM4Maqap6vi3PAD+i+2HA8Vijcgo4VVUH2vZeuokxM6hl5UTY+Po1cGW6N/+spXuo6b6ez0njax8w+3aV3cBPBuq3t7en7ABeapcOPwzcmGRDe8PKjcDDbd/LSXa054rcPue7FtOHJlTLxr3A0ar60sAuc6iRSPKmJOvb+vnAe+ieVfcocFs7bG4+ZnNzG/BIe9bIPmBXurftbQaupHso77xjdPvMYvvQBKqqO6pqU1VdQZePR6rqg5hBjVCSC5JcNLtON44ewfFYI1JVfwCeS/LWVroB+C1mUMutVsAT+23DNbo3WjxD91yTO/s+H9t4NOC7wGngX3S/eHyE7hkgPwOOt+XGdmyAr7aMPQVsH/ieD9M9lHcG+NBAfTvdP1EngK8AafVF92GbzAa8i+4S8yeBQ63dag5tI8zg24EnWgaPAJ9r9S10kwgzwPeBda1+Xtueafu3DHzXnS03x2hvomr1ecfoYfqwTXYD3s1/3xppBm2jzN4WujeKHgaens2J47FtxDm8CjjYxuQf07310QzalrXNhkCSJEmSJEmaaN4aKUmSJEmSpKngRJgkSZIkSZKmghNhkiRJkiRJmgpOhEmSJEmSJGkqOBEmSZIkSZKkqeBEmCRJkiRJkqaCE2GSJEmSJEmaCk6ESZIkSZIkaSr8B3zGfXCKZOCvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('Acoustic data and time to failure:')\n",
    "plt.plot(train_ad_sample_df, color='r')\n",
    "ax1.set_ylabel('acoustic data', color='r')\n",
    "plt.legend(['acoustic data'], loc=(0.01, 0.95))\n",
    "ax2 = ax1.twinx()\n",
    "plt.plot(train_ttf_sample_df, color='b')\n",
    "ax2.set_ylabel('time to failure', color='b')\n",
    "plt.legend(['time to failure'], loc=(0.01, 0.9))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last step of this section, we will look at both dates in relation to each other. Here it is noticeable that the `time_to_failure` data does not end at 0 as in the diagram above (i.e. an earthquake is triggered), but at a negative value. However, this is due to the plotting of the diagram. The data has not changed! Nevertheless, this can lead to misunderstandings. We find that when an earthquake occurs (time_to_failure = 0), the seismic activity (accustic_data) has large swings at the same time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in how the training and test earthquakes occur, you can find out more in the following video:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/m_dBwwDJ4uo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x2488035fb70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('m_dBwwDJ4uo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build model<a id=\"build_model\"></a>\n",
    "\n",
    "In this section, we will perform all required preprocessing of the data and generate our model to analyze the data.\n",
    "\n",
    "The training file was read in the **Load Data** section. However, this one training file is too large and contains too much information to be used for training. Therefore, the information is first separated from each other and then divided into blocks of equal size.\n",
    "- Separate the columns of the training file from each other (acoustic_data && time_to_failure)\n",
    "- Now the training data will be divided into blocks of equal size \n",
    "    - X_train consists of three outputs:  &lt;Number of Sequence (acoustic_data) && Sequence length && Number of channels&gt;\n",
    "    - Y_train consists of three outputs:   &lt;Number of Sequence (time_to_failure) && Sequence length && Number of channels&gt; <br> Here only the \"time_to_failure\" data is needed to label our X_train data, so that Y_train can be formatted to a one dimensional array\n",
    "    \n",
    "One of the most important things is what the model should learn. In our case, we want the model to predict when an earthquake will occur. We have some test data to prove our model. At the end of this notebook, the model should load the test data and make a prediction for each test file when an earthquake will occur, starting from the beginning of the file.\n",
    "\n",
    "Our model should therefore recognize when an earthquake occurs by using the seismic acoustic data read in. This has the following consequences for us: First, we divide our training sequence into blocks of different sizes of the length `sequence_length`. Each of the blocks contains a sequence of acoustic data, which means a coherent structure. We also know for each `aucustic_data` its `time_to_failure`. If we have a sequence of aucustic_datas and a sequence of time_to_failure we can tell how long a certain structure or sequence of swings is distant from an earthquake. \n",
    "\n",
    "That's what we want to make use of. However, since each sequence block in X_train can only have one label, we have decided on the following procedure: If an earthquake occurs in a sequence block, it gets the label 0. 0 means that an earthquake has occurred. We can calculate this by using the following formula: Take the first and last element from the sequence block and compare both values (Attention: Float values --> compare epsilon!).\n",
    "The first element describes the time at the start of the sequence, the last the time at the end of the sequence until an earthquake. If the value is greater than 0, there is no earthquake in this sequence, otherwise there was an earthquake. In case there is no earthquake, we want to calculate an average time for the sequence block that this sequence block has until an earthquake. Since it would be too complicated to calculate the median, we use the following method: We add the first and the last element of the block together and divide them by 2. This gives us a relatively useful mean value.\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = load_data[:num_sequences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(629000000,) (629000000,)\n",
      "(12580, 50000, 1) (12580,)\n"
     ]
    }
   ],
   "source": [
    "#Training data for 1D-CNN\n",
    "X_train = train['acoustic_data'].values\n",
    "Y_train = train['time_to_failure'].values\n",
    "print(X_train.shape,Y_train.shape)\n",
    "\n",
    "#Read values and reshape\n",
    "X_train = X_train.reshape(-1,sequence_length,num_features)\n",
    "Y_train = Y_train.reshape(-1,sequence_length,num_features)\n",
    "\n",
    "#\n",
    "mean = np.zeros(len(Y_train), dtype = float) \n",
    "for i in range(len(Y_train)):\n",
    "    if(abs(Y_train[i][0]-Y_train[i][-1]) < 0.0000001):\n",
    "        mean[i] = 0\n",
    "    else:\n",
    "        mean[i] = (float(Y_train[i][0])+float(Y_train[i][-1]))/2\n",
    "        \n",
    "Y_train = np.float32(mean) \n",
    "print(X_train.shape,Y_train.shape)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the data has been formatted, the model can now be created. Since the data set consists only of numerical values which do not have or need a spatial relationship to each other, an embedding layer can be dropped and the data can be transferred directly into a convolution layer. Please note that the first convolution layer needs the `input_shape` argument. The `input_shape` argument needs the **sequence length** and the **number of channels**. At this point there are 100 ways to build the model. The following is a simple model:"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is designed in such a way that the input data passes through a convolution layer twice and then a pooling process selects the best results. `MaxPooling1D` checks all channels of the previously created convolution for a value that is larger and takes this value. `GlobalAveragePooling1D` determines an average value from all channels and then checks the channels and takes the value closer to this average value. Then a dropout is executed and then everything is pressed into a dense layer. [[2]](#r2),[[3]](#r3)\n",
    "\n",
    "- Larger filter -> More learnable features and therefore better results, but the better hardware is needed\n",
    "-  All convolution pairs should have the same number of filters (since pooling is performed on the convolution pair and the better one is selected), but the filters of the next convolution pair must be at least 1 larger than the convolution pair before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv1D(filters=100, kernel_size=10, activation='relu',strides=5, input_shape=(sequence_length, num_features)))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(filters=200, kernel_size=10, activation='relu',strides=5))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(filters=400, kernel_size=10, activation='relu',strides=5))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(loss='mean_absolute_error', optimizer= adam(lr=1e-4))\n",
    "#print(model.summary())"
   ]
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   "cell_type": "code",
   "execution_count": 15,
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      "Train on 8806 samples, validate on 3774 samples\n",
      "Epoch 1/100\n",
      "8806/8806 [==============================] - 22s 3ms/step - loss: 3.1571 - val_loss: 2.6873\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 2.68730, saving model to 1d_cnn_model\n",
      "Epoch 2/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.5749 - val_loss: 2.5887\n",
      "\n",
      "Epoch 00002: val_loss improved from 2.68730 to 2.58870, saving model to 1d_cnn_model\n",
      "Epoch 3/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.4403 - val_loss: 2.5343\n",
      "\n",
      "Epoch 00003: val_loss improved from 2.58870 to 2.53427, saving model to 1d_cnn_model\n",
      "Epoch 4/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.4166 - val_loss: 2.5038\n",
      "\n",
      "Epoch 00004: val_loss improved from 2.53427 to 2.50380, saving model to 1d_cnn_model\n",
      "Epoch 5/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.4169 - val_loss: 2.4763\n",
      "\n",
      "Epoch 00005: val_loss improved from 2.50380 to 2.47632, saving model to 1d_cnn_model\n",
      "Epoch 6/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3859 - val_loss: 2.5310\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 2.47632\n",
      "Epoch 7/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3819 - val_loss: 2.4428\n",
      "\n",
      "Epoch 00007: val_loss improved from 2.47632 to 2.44284, saving model to 1d_cnn_model\n",
      "Epoch 8/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3581 - val_loss: 2.4400\n",
      "\n",
      "Epoch 00008: val_loss improved from 2.44284 to 2.44001, saving model to 1d_cnn_model\n",
      "Epoch 9/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3441 - val_loss: 2.4476\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 2.44001\n",
      "Epoch 10/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3379 - val_loss: 2.3951\n",
      "\n",
      "Epoch 00010: val_loss improved from 2.44001 to 2.39514, saving model to 1d_cnn_model\n",
      "Epoch 11/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3288 - val_loss: 2.3988\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 2.39514\n",
      "Epoch 12/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3436 - val_loss: 2.3511\n",
      "\n",
      "Epoch 00012: val_loss improved from 2.39514 to 2.35113, saving model to 1d_cnn_model\n",
      "Epoch 13/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3165 - val_loss: 2.3631\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 2.35113\n",
      "Epoch 14/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.3095 - val_loss: 2.3668\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 2.35113\n",
      "Epoch 15/100\n",
      "8806/8806 [==============================] - 17s 2ms/step - loss: 2.2943 - val_loss: 2.4374\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 2.35113\n",
      "Epoch 16/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.3019 - val_loss: 2.3158\n",
      "\n",
      "Epoch 00016: val_loss improved from 2.35113 to 2.31580, saving model to 1d_cnn_model\n",
      "Epoch 17/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.3074 - val_loss: 2.4057\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 2.31580\n",
      "Epoch 18/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2942 - val_loss: 2.3665\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 2.31580\n",
      "Epoch 19/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2820 - val_loss: 2.2914\n",
      "\n",
      "Epoch 00019: val_loss improved from 2.31580 to 2.29141, saving model to 1d_cnn_model\n",
      "Epoch 20/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2813 - val_loss: 2.2512\n",
      "\n",
      "Epoch 00020: val_loss improved from 2.29141 to 2.25123, saving model to 1d_cnn_model\n",
      "Epoch 21/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2761 - val_loss: 2.2545\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 2.25123\n",
      "Epoch 22/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2614 - val_loss: 2.3331\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 2.25123\n",
      "Epoch 23/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2701 - val_loss: 2.3560\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 2.25123\n",
      "Epoch 24/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2541 - val_loss: 2.3457\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 2.25123\n",
      "Epoch 25/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2442 - val_loss: 2.2640\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 2.25123\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
      "Epoch 26/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2300 - val_loss: 2.2237\n",
      "\n",
      "Epoch 00026: val_loss improved from 2.25123 to 2.22372, saving model to 1d_cnn_model\n",
      "Epoch 27/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2299 - val_loss: 2.2415\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 2.22372\n",
      "Epoch 28/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2251 - val_loss: 2.2688\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 2.22372\n",
      "Epoch 29/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2282 - val_loss: 2.2355\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 2.22372\n",
      "Epoch 30/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2344 - val_loss: 2.2512\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 2.22372\n",
      "Epoch 31/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2235 - val_loss: 2.2545\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 2.22372\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n",
      "Epoch 32/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2175 - val_loss: 2.2321\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 2.22372\n",
      "Epoch 33/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2306 - val_loss: 2.2301\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 2.22372\n",
      "Epoch 34/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2204 - val_loss: 2.2378\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 2.22372\n",
      "Epoch 35/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2222 - val_loss: 2.2276\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 2.22372\n",
      "Epoch 36/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2287 - val_loss: 2.2278\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 2.22372\n",
      "\n",
      "Epoch 00036: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n",
      "Epoch 37/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2235 - val_loss: 2.2296\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 2.22372\n",
      "Epoch 38/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2236 - val_loss: 2.2299\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 2.22372\n",
      "Epoch 39/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2191 - val_loss: 2.2300\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 2.22372\n",
      "Epoch 40/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2272 - val_loss: 2.2298\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 2.22372\n",
      "Epoch 41/100\n",
      "8806/8806 [==============================] - 16s 2ms/step - loss: 2.2184 - val_loss: 2.2297\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 2.22372\n",
      "\n",
      "Epoch 00041: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.\n",
      "Epoch 00041: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train.reshape(-1,1), batch_size=batch_size, epochs=epochs, validation_split=0.3, verbose = 1,\n",
    "                    callbacks = [\n",
    "                        EarlyStopping(monitor='val_loss',patience=15, verbose=1, mode='min'),\n",
    "                        ModelCheckpoint(filepath='1d_cnn_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1),\n",
    "                        ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=5,verbose=1,min_delta=1e-4,mode='min')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weights of the network are stored automatically. In this model, however, the weight of the model is stored only when the validation loss has improved from the last stored validation loss. This allows you to train the model over a longer period of time without losing optimal weighting.  On the other hand, an overadaptation of the prediction model is prevented, since the validation_lost is not improved and therefore not saved if the training net is overadapted. At this point it is not worth saving the entire model or loading it later. For more information see section [Prediction](#Prediction)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arguments:\n",
    "- **verbose:** Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.\n",
    "- **callbacks:** List of keras.callbacks.Callback instances. List of callbacks to apply during training and validation (if ).\n",
    "    - **ModelCheckpoint:** Save the model after every epoch.\n",
    "    - **EarlyStopping:** Stop training when a monitored quantity has stopped improving. (After 7 iterations in which the model has not improved)\n",
    "    - **ReduceLROnPlateau:** Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.\n",
    "- **validation_split:** Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation data<a id=\"evaluation_data\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'loss', 'lr'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lost of the model falls continuously and an over or underfitting is not visible. After that the reduction of the lost stagnates a bit, but continues to decrease steadily.\n",
    "The model has also independently recognized that it comes to an overfitting and broke off the model. Info: The predictive value of new or data applied to our model is often based on the validation loss. This is therefore an indicator of how accurate our prediction values are. [[6]](#r6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction<a id=\"prediction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some test files exist in the **LANL-Earthquake-Prediction/test/** folder to predict when an earthquake will occur. Each test file has exactly 150.000 lines of data and represents a separate and independent sequence of randomly generated acoustic data. In the prediction, a separate prediction is created for each file. For this purpose, all files, including their contents, are first read in.\n",
    "\n",
    "Reading in all test sequences can take some time (about five minutes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84fe9d6384124436b4e8265debf7f2a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=2624), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_sequence_length = 150000\n",
    "\n",
    "test_files = [f for f in listdir(path_testData) if isfile(join(path_testData, f))]\n",
    "\n",
    "test_data = np.empty([len(test_files), test_sequence_length])\n",
    "test_ids = []\n",
    "    \n",
    "i = 0\n",
    "for f in tqdm_notebook(test_files):\n",
    "    test_ids.append(splitext(f)[0])\n",
    "    t_df = pd.read_csv(path_testData + f, dtype={\"acoustic_data\": np.int16})\n",
    "    test_data[i, :] = t_df['acoustic_data'].values\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2624, 150000, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = np.expand_dims(test_data, 2)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating CNN model for Prediction\n",
    "\n",
    "The 1D CNN model for prediction must correspond exactly to the 1D-CNN model in training except for one value. The only difference or change to the training model is in the argument `input_shape` of the first convolution layer. The argument `input_shape` must contain the length of the sequences and the number of channels of the input data. For the prediction, however, test sequences with a length of 150_000 are given. \n",
    "\n",
    "Remember: The training models had a sequence length of `sequence_length` (50_000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prediction = Sequential()\n",
    "model_prediction.add(Conv1D(filters=100, kernel_size=10, activation='relu',strides=5, input_shape=(test_sequence_length, num_features)))\n",
    "model_prediction.add(MaxPooling1D(2))\n",
    "model_prediction.add(Conv1D(filters=200, kernel_size=10, activation='relu',strides=5))\n",
    "model_prediction.add(MaxPooling1D(2))\n",
    "model_prediction.add(Conv1D(filters=400, kernel_size=10, activation='relu',strides=5))\n",
    "model_prediction.add(GlobalAveragePooling1D())\n",
    "model_prediction.add(Dropout(0.4))\n",
    "model_prediction.add(Dense(1, activation='linear'))\n",
    "model_prediction.compile(loss='mean_absolute_error', optimizer= adam(lr=1e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prediction.load_weights('1d_cnn_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the input sequences of the prediction model have a different sequence length, the training model cannot be used for the prediction. So a new model is created. In order to be able to use the results of the training model for the prediction, we have to be able to load them somehow into the prediction model. Loading the entire training model using the load_model method at this point would be very wasteful, since the entire training model would be loaded. As we have already learned, the possibility of learning a neural network is determined by adjusting the weights between the individual neurons in the network. This means that neurons connections that are more heavily loaded, e.g. because they often recognize patterns in training data and are therefore called more often, receive a greater weighting in this network than other neuron connections that are rarely used. In Python, these neuron connections are represented by simple matrices. The wih(weights between input and hidden layer) matrix contains the weights for the connections between the input and hidden layers and the who(weights between hidden and output layer) matrix contains the weights for the connections between the hidden layer and the output layer. When saving and also later when loading the weights into a model, these matrices are simply saved or loaded. The only important thing is that the parameters of the model (e.g. the number of layers or the filter sizes) are identical. Otherwise the saved matrices would no longer be compatible with the matrices of the new model. The model does not care if the input data are the same, because it only takes care that the matrices are compatible. You can also enter all data into a model, but if something comes out of it depends on whether the data is available in a form that can be used for the model. At this point you could have created a method that returns a model with different `input_shape` arguments. The advantage of this approach is that the training model can be calculated on a very strong machine (workstation/cloud), for example, and the prediction can take place on a weak machine such as a smartphone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_prediction.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction now generates a probability value (number) for each test file, from when (after starting the sequence) an earthquake will occur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot diagram provides an overview of the probability value of the test files. To do this, the lowest and highest values of the prediction value are taken and divided into equally wide ranges.\n",
    "<br>**Index[0]:** Number of sequences in this area\n",
    "<br>**Index[1]:** Median value of the time_to_failure of all sequences in this range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15., 128., 670., 581., 329., 265., 213., 200., 154.,  69.]),\n",
       " array([-0.33714852,  0.7824451 ,  1.9020387 ,  3.0216322 ,  4.141226  ,\n",
       "         5.2608194 ,  6.380413  ,  7.5000067 ,  8.6196    ,  9.739194  ,\n",
       "        10.858788  ], dtype=float32),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A negative value indicates that this test sequence did not cause an earthquake.\n",
    "\n",
    "After the prediction is created for each test file, the prediction values are still saved in a CSV file. First, each prediction value (`time_to_failure`) is assigned to the corresponding test sequence (`seg_id`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df = pd.DataFrame({'seg_id': test_ids, 'time_to_failure': y_pred[:, 0]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file is then saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df.to_csv(\"my_prediction.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compromise<a id=\"compromise\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the time limit for submitting the model for this challenge had expired at the time of creation, the model could not compete directly with other models. However, since some of the developers have made their models freely available, we can compare our model this way. We were looking for a kernel that also uses CNN and has the best public score. The best model that was found was \"Transformer Network With 1D-CNN Feature Extraction\" by Siddhant Sarkar (https://www.kaggle.com/buchan/transformer-network-with-1d-cnn-feature-extraction) with a public score of 1.569. \n",
    "\n",
    "To compare the winner of the challenge had a public score of 1.08022. More information at: https://www.kaggle.com/c/LANL-Earthquake-Prediction/leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "myModel = pd.read_csv('my_prediction.csv', dtype={'seg_id': object, 'time_to_failure': np.float64})\n",
    "compromiseModel = pd.read_csv('compromise_predicition.csv', dtype={'seg_id': object, 'time_to_failure': np.float64})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_step = 30\n",
    "myTimeToFailure = myModel['time_to_failure'].values[::n_step]\n",
    "compromiseTimeToFailure = compromiseModel['time_to_failure'].values[::n_step]\n",
    "\n",
    "fig = plt.subplots(figsize=(20, 8))\n",
    "plt.title('myModel vs. Siddhant Sarkar:')\n",
    "plt.plot(myTimeToFailure, color='b')\n",
    "plt.plot(compromiseTimeToFailure, color='r')\n",
    "plt.ylabel('time to earthquake')\n",
    "plt.xlabel('Epoch (per 30)')\n",
    "plt.legend(['my time to failure', 'compromise time to failure'], loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diagram shows that our model (blue) is quite close to the model of Siddhant Sarkar (red). If you look at the structure of the two models, you will notice that our model has a quite simple structure compared to the comparison model. For a better comparison: Under the link of Siddhant Sarka you can find the structure of the model. \n",
    "\n",
    "*Conclusion:* Although our model is very simple and doesn't contain any extra features, it provides predictive values that show similarities in the course. However, the difference is mainly visible in the peaks and swings (up and down). At these peaks, the models often show different values or a slightly different course for a short time. Whether our model or Siddhant Sarkar's model provides the \"more accurate\" values cannot be said. Our model would have had to take part in the challenge so that the scores could be compared.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary<a id=\"summary\"></a>\n",
    "Let's recap what we have learned in this tutorial:\n",
    "\n",
    "- What is kaggle\n",
    "- Explore Dataset\n",
    "- Load and Prepare Data\n",
    "- Build a simple 1D-CNN sequential model\n",
    "- Prediction Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References<a id=\"references\"></a>\n",
    "\n",
    "### Code\n",
    "[1] https://www.youtube.com/watch?v=TffGdSsWKlA<a id=\"r1\"></a><br>\n",
    "[5] https://www.kaggle.com/buchan/transformer-network-with-1d-cnn-feature-extraction<a id=\"r5\"></a><br>\n",
    "[6] https://www.kaggle.com/adubitskiy/rnn-with-cnn-feature-extraction/notebook<a id=\"r6\"></a><br>\n",
    "[7] https://github.com/llSourcell/Kaggle_Earthquake_challenge/blob/master/Earthquake_Challenge.ipynb<a id=\"r7\"></a><br>\n",
    "[8] https://www.kaggle.com/fanconic/earthquake-cnn/output<a id=\"r8\"></a><br>\n",
    "\n",
    "### Explanations\n",
    "[2] https://keras.io/layers/convolutional/<a id=\"r2\"></a><br>\n",
    "[3] https://keras.io/layers/pooling/<a id=\"r3\"></a><br>\n",
    "[4] https://medium.com/@abhigoku10/activation-functions-and-its-types-in-artifical-neural-network-14511f3080a8<a id=\"r4\"></a><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## All-in-one solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Load packages\n",
    "#\n",
    "from __future__ import print_function\n",
    "#data preprocessing\n",
    "import pandas as pd\n",
    "#math operations\n",
    "import numpy as np\n",
    "#data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "#host-os operations\n",
    "import os\n",
    "from os import listdir, makedirs, path\n",
    "from os.path import isfile, join, basename, splitext, isfile, exists\n",
    "from pathlib import Path\n",
    "#import keras.utils as utils\n",
    "from tqdm import tqdm_notebook\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Conv1D, MaxPooling1D, Embedding, GlobalAveragePooling1D\n",
    "from keras.optimizers import adam\n",
    "from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from keras.utils import plot_model\n",
    "from keras import optimizers\n",
    "# Graphic output\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "#\n",
    "# Prepare data\n",
    "#\n",
    "Earthquake_Prediction = Path('LANL-Earthquake-Prediction')\n",
    "Test = Path('LANL-Earthquake-Prediction/test/')\n",
    "\n",
    "if Earthquake_Prediction.is_dir() and Test.is_dir():\n",
    "    print('Data already available')\n",
    "else:\n",
    "    import zipfile\n",
    "    zip_ref = zipfile.ZipFile('LANL-Earthquake-Prediction.zip', 'r')\n",
    "    zip_ref.extractall('LANL-Earthquake-Prediction')\n",
    "    zip_ref.close()\n",
    "print(os.listdir('LANL-Earthquake-Prediction'))\n",
    "print(\"There are {} files in test folder\".format(len(os.listdir(os.path.join('LANL-Earthquake-Prediction', 'test' )))))\n",
    "\n",
    "num_sequences = 629000000\n",
    "sequence_length= 50000\n",
    "num_features = 1\n",
    "batch_size = 128\n",
    "epochs = 100\n",
    "path_trainData = 'LANL-Earthquake-Prediction/'\n",
    "path_testData = 'LANL-Earthquake-Prediction/test/'\n",
    "#\n",
    "# Load Data\n",
    "#\n",
    "load_data = pd.read_csv(path_trainData+'train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})\n",
    "print(load_data.head(10))\n",
    "print('\\n')\n",
    "print(load_data.tail(10))\n",
    "#\n",
    "# Data exploration\n",
    "#\n",
    "n_step = 100\n",
    "train_ad_sample_df = load_data['acoustic_data'].values[::n_step]\n",
    "train_ttf_sample_df = load_data['time_to_failure'].values[::n_step]\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('Acoustic data:')\n",
    "plt.plot(train_ad_sample_df, color='r')\n",
    "plt.legend(['acoustic data'], loc=(0.01, 0.95))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('time to failure:')\n",
    "plt.plot(train_ttf_sample_df, color='b')\n",
    "plt.legend(['time to failure'], loc=(0.01, 0.9))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.title('Acoustic data and time to failure:')\n",
    "plt.plot(train_ad_sample_df, color='r')\n",
    "ax1.set_ylabel('acoustic data', color='r')\n",
    "plt.legend(['acoustic data'], loc=(0.01, 0.95))\n",
    "ax2 = ax1.twinx()\n",
    "plt.plot(train_ttf_sample_df, color='b')\n",
    "ax2.set_ylabel('time to failure', color='b')\n",
    "plt.legend(['time to failure'], loc=(0.01, 0.9))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "#\n",
    "# Informtion\n",
    "#\n",
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('m_dBwwDJ4uo')\n",
    "#\n",
    "# Prepare Data\n",
    "#\n",
    "train = load_data[:num_sequences]\n",
    "#Training data for 1D-CNN\n",
    "X_train = train['acoustic_data'].values\n",
    "Y_train = train['time_to_failure'].values\n",
    "print(X_train.shape,Y_train.shape)\n",
    "\n",
    "#Read values and reshape\n",
    "X_train = X_train.reshape(-1,sequence_length,num_features)\n",
    "Y_train = Y_train.reshape(-1,sequence_length,num_features)\n",
    "\n",
    "#\n",
    "mean = np.zeros(len(Y_train), dtype = float) \n",
    "for i in range(len(Y_train)):\n",
    "    if(abs(Y_train[i][0]-Y_train[i][-1]) < 0.00000001):\n",
    "        mean[i] = 0\n",
    "    else:\n",
    "        mean[i] = (float(Y_train[i][0])+float(Y_train[i][-1]))/2\n",
    "        \n",
    "Y_train = np.float32(mean) \n",
    "print(X_train.shape,Y_train.shape)\n",
    "#\n",
    "# Build model\n",
    "#\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=100, kernel_size=10, activation='relu',strides=5, input_shape=(sequence_length, num_features)))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(filters=200, kernel_size=10, activation='relu',strides=5))\n",
    "model.add(MaxPooling1D(2))\n",
    "model.add(Conv1D(filters=400, kernel_size=10, activation='relu',strides=5))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "#model.compile(optimizer = 'adam', loss = 'mean_absolute_error')\n",
    "model.compile(loss='mean_absolute_error', optimizer= adam(lr=1e-4))\n",
    "#print(model.summary())\n",
    "SVG(model_to_dot(model,show_shapes = True).create(prog='dot', format='svg'))\n",
    "#\n",
    "# train model\n",
    "#\n",
    "history = model.fit(X_train, Y_train.reshape(-1,1), batch_size=batch_size, epochs=epochs, validation_split=0.3, verbose = 1,\n",
    "                    callbacks = [\n",
    "                        EarlyStopping(monitor='val_loss',patience=15, verbose=1, mode='min'),\n",
    "                        ModelCheckpoint(filepath='1d_cnn_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1),\n",
    "                        ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=5,verbose=1,min_delta=1e-4,mode='min')])\n",
    "#\n",
    "# Plot \n",
    "#\n",
    "history_dict = history.history\n",
    "history_dict.keys()\n",
    "fig, ax1 = plt.subplots(figsize=(20, 8))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "_= plt.legend(['Train', 'Validation'], loc='lower left')\n",
    "#\n",
    "# Prediction\n",
    "#\n",
    "test_sequence_length = 150000\n",
    "test_files = [f for f in listdir(path_testData) if isfile(join(path_testData, f))]\n",
    "test_data = np.empty([len(test_files), test_sequence_length])\n",
    "test_ids = []\n",
    "i = 0\n",
    "for f in tqdm_notebook(test_files):\n",
    "    test_ids.append(splitext(f)[0])\n",
    "    t_df = pd.read_csv(path_testData + f, dtype={\"acoustic_data\": np.int16})\n",
    "    test_data[i, :] = t_df['acoustic_data'].values\n",
    "    i = i + 1\n",
    "\n",
    "X_test = np.expand_dims(test_data, 2)\n",
    "X_test.shape\n",
    "#\n",
    "# build prediction model\n",
    "#\n",
    "model_prediction = Sequential()\n",
    "model_prediction.add(Conv1D(filters=100, kernel_size=10, activation='relu',strides=5, input_shape=(test_sequence_length, num_features)))\n",
    "model_prediction.add(MaxPooling1D(2))\n",
    "model_prediction.add(Conv1D(filters=200, kernel_size=10, activation='relu',strides=5))\n",
    "model_prediction.add(MaxPooling1D(2))\n",
    "model_prediction.add(Conv1D(filters=400, kernel_size=10, activation='relu',strides=5))\n",
    "model_prediction.add(GlobalAveragePooling1D())\n",
    "model_prediction.add(Dropout(0.4))\n",
    "model_prediction.add(Dense(1, activation='linear'))\n",
    "model_prediction.compile(loss='mean_absolute_error', optimizer= adam(lr=1e-4))\n",
    "model_prediction.load_weights(path_model+'1d_cnn_model.h5')\n",
    "#\n",
    "# prediction\n",
    "#\n",
    "y_pred = model_prediction.predict(X_test)\n",
    "plt.hist(y_pred)\n",
    "submission_df = pd.DataFrame({'seg_id': test_ids, 'time_to_failure': y_pred[:, 0]})\n",
    "submission_df.to_csv(\"my_prediction.csv\", index=False)\n",
    "#\n",
    "# Compromise\n",
    "#\n",
    "myModel = pd.read_csv('my_prediction.csv', dtype={'seg_id': object, 'time_to_failure': np.float64})\n",
    "compromiseModel = pd.read_csv('compromise_predicition.csv', dtype={'seg_id': object, 'time_to_failure': np.float64})\n",
    "n_step = 30\n",
    "myTimeToFailure = myModel['time_to_failure'].values[::n_step]\n",
    "compromiseTimeToFailure = compromiseModel['time_to_failure'].values[::n_step]\n",
    "\n",
    "fig = plt.subplots(figsize=(20, 8))\n",
    "plt.title('myModel vs. Siddhant Sarkar:')\n",
    "plt.plot(myTimeToFailure, color='b')\n",
    "plt.plot(compromiseTimeToFailure, color='r')\n",
    "plt.ylabel('time to earthquake')\n",
    "plt.xlabel('Epoch (per 30)')\n",
    "plt.legend(['my time to failure', 'compromise time to failure'], loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  }
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