lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 2\n",
    "\n",
    "- Statistical summary of data\n",
    "- Correlation of data\n",
    "- Deal with categorical data\n",
    "- Deal with missing data\n",
    "- Normalise data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda env create -f env.yaml \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data setup\n",
    "\n",
    "Link to data: https://www.kaggle.com/datasets/uciml/student-alcohol-consumption\n",
    "\n",
    "## Data Attributes\n",
    "\n",
    "- school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)\n",
    "- sex - student's sex (binary: 'F' - female or 'M' - male)\n",
    "- age - student's age (numeric: from 15 to 22)\n",
    "- address - student's home address type (binary: 'U' - urban or 'R' - rural)\n",
    "- famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)\n",
    "- Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)\n",
    "- Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)\n",
    "- Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)\n",
    "- Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n",
    "- Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n",
    "- reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')\n",
    "- guardian - student's guardian (nominal: 'mother', 'father' or 'other')\n",
    "- traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)\n",
    "- studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n",
    "- failures - number of past class failures (numeric: n if 1<=n<3, else 4)\n",
    "- schoolsup - extra educational support (binary: yes or no)\n",
    "- famsup - family educational support (binary: yes or no)\n",
    "- paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n",
    "- activities - extra-curricular activities (binary: yes or no)\n",
    "- nursery - attended nursery school (binary: yes or no)\n",
    "- higher - wants to take higher education (binary: yes or no)\n",
    "- internet - Internet access at home (binary: yes or no)\n",
    "- romantic - with a romantic relationship (binary: yes or no)\n",
    "- famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n",
    "- freetime - free time after school (numeric: from 1 - very low to 5 - very high)\n",
    "- goout - going out with friends (numeric: from 1 - very low to 5 - very high)\n",
    "- Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n",
    "- Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n",
    "- health - current health status (numeric: from 1 - very bad to 5 - very good)\n",
    "- absences - number of school absences (numeric: from 0 to 93)\n",
    "\n",
    "These grades are related with the course subject, Math or Portuguese:\n",
    "- G1 - first period grade (numeric: from 0 to 20)\n",
    "- G2 - second period grade (numeric: from 0 to 20)\n",
    "- G3 - final grade (numeric: from 0 to 20, output target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_math = pd.read_csv(r\"./Data/student-mat.csv\")\n",
    "data_port = pd.read_csv(r\"./Data/student-por.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_math.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        4   0  11  11  \n",
       "1      5        3      3     1     1      3        2   9  11  11  \n",
       "2      4        3      2     2     3      3        6  12  13  12  \n",
       "3      3        2      2     1     1      5        0  14  14  14  \n",
       "4      4        3      2     1     2      5        0  11  13  13  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_port.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Maybe add class attribute if combining\n",
    "data_math[\"class\"] = \"math\"\n",
    "data_port[\"class\"] = \"portuguese\"\n",
    "\n",
    "df = pd.concat([data_math, data_port], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>math</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  freetime goout  Dalc  Walc  health absences  G1  G2  G3 class  \n",
       "0        3     4     1     1       3        6   5   6   6  math  \n",
       "1        3     3     1     1       3        4   5   5   6  math  \n",
       "2        3     2     2     3       3       10   7   8  10  math  \n",
       "3        2     2     1     1       5        2  15  14  15  math  \n",
       "4        3     2     1     2       5        4   6  10  10  math  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To be decided by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine the G scores?\n",
    "\n",
    "# G3 seems like it is already the combined value\n",
    "# G1 - first period grade (numeric: from 0 to 20)\n",
    "# G2 - second period grade (numeric: from 0 to 20)\n",
    "# G3 - final grade (numeric: from 0 to 20, output target)\n",
    "\n",
    "# df[\"score\"] = (df[\"G1\"] + df[\"G2\"] + df[\"G3\"]) / 3\n",
    "# df.drop([\"G1\",\"G2\",\"G3\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Summary and Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1044 entries, 0 to 1043\n",
      "Data columns (total 34 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   school      1044 non-null   object\n",
      " 1   sex         1044 non-null   object\n",
      " 2   age         1044 non-null   int64 \n",
      " 3   address     1044 non-null   object\n",
      " 4   famsize     1044 non-null   object\n",
      " 5   Pstatus     1044 non-null   object\n",
      " 6   Medu        1044 non-null   int64 \n",
      " 7   Fedu        1044 non-null   int64 \n",
      " 8   Mjob        1044 non-null   object\n",
      " 9   Fjob        1044 non-null   object\n",
      " 10  reason      1044 non-null   object\n",
      " 11  guardian    1044 non-null   object\n",
      " 12  traveltime  1044 non-null   int64 \n",
      " 13  studytime   1044 non-null   int64 \n",
      " 14  failures    1044 non-null   int64 \n",
      " 15  schoolsup   1044 non-null   object\n",
      " 16  famsup      1044 non-null   object\n",
      " 17  paid        1044 non-null   object\n",
      " 18  activities  1044 non-null   object\n",
      " 19  nursery     1044 non-null   object\n",
      " 20  higher      1044 non-null   object\n",
      " 21  internet    1044 non-null   object\n",
      " 22  romantic    1044 non-null   object\n",
      " 23  famrel      1044 non-null   int64 \n",
      " 24  freetime    1044 non-null   int64 \n",
      " 25  goout       1044 non-null   int64 \n",
      " 26  Dalc        1044 non-null   int64 \n",
      " 27  Walc        1044 non-null   int64 \n",
      " 28  health      1044 non-null   int64 \n",
      " 29  absences    1044 non-null   int64 \n",
      " 30  G1          1044 non-null   int64 \n",
      " 31  G2          1044 non-null   int64 \n",
      " 32  G3          1044 non-null   int64 \n",
      " 33  class       1044 non-null   object\n",
      "dtypes: int64(16), object(18)\n",
      "memory usage: 277.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.726054</td>\n",
       "      <td>2.603448</td>\n",
       "      <td>2.387931</td>\n",
       "      <td>1.522989</td>\n",
       "      <td>1.970307</td>\n",
       "      <td>0.264368</td>\n",
       "      <td>3.935824</td>\n",
       "      <td>3.201149</td>\n",
       "      <td>3.156130</td>\n",
       "      <td>1.494253</td>\n",
       "      <td>2.284483</td>\n",
       "      <td>3.543103</td>\n",
       "      <td>4.434866</td>\n",
       "      <td>11.213602</td>\n",
       "      <td>11.246169</td>\n",
       "      <td>11.341954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.239975</td>\n",
       "      <td>1.124907</td>\n",
       "      <td>1.099938</td>\n",
       "      <td>0.731727</td>\n",
       "      <td>0.834353</td>\n",
       "      <td>0.656142</td>\n",
       "      <td>0.933401</td>\n",
       "      <td>1.031507</td>\n",
       "      <td>1.152575</td>\n",
       "      <td>0.911714</td>\n",
       "      <td>1.285105</td>\n",
       "      <td>1.424703</td>\n",
       "      <td>6.210017</td>\n",
       "      <td>2.983394</td>\n",
       "      <td>3.285071</td>\n",
       "      <td>3.864796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age         Medu         Fedu   traveltime    studytime  \\\n",
       "count  1044.000000  1044.000000  1044.000000  1044.000000  1044.000000   \n",
       "mean     16.726054     2.603448     2.387931     1.522989     1.970307   \n",
       "std       1.239975     1.124907     1.099938     0.731727     0.834353   \n",
       "min      15.000000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      16.000000     2.000000     1.000000     1.000000     1.000000   \n",
       "50%      17.000000     3.000000     2.000000     1.000000     2.000000   \n",
       "75%      18.000000     4.000000     3.000000     2.000000     2.000000   \n",
       "max      22.000000     4.000000     4.000000     4.000000     4.000000   \n",
       "\n",
       "          failures       famrel     freetime        goout         Dalc  \\\n",
       "count  1044.000000  1044.000000  1044.000000  1044.000000  1044.000000   \n",
       "mean      0.264368     3.935824     3.201149     3.156130     1.494253   \n",
       "std       0.656142     0.933401     1.031507     1.152575     0.911714   \n",
       "min       0.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%       0.000000     4.000000     3.000000     2.000000     1.000000   \n",
       "50%       0.000000     4.000000     3.000000     3.000000     1.000000   \n",
       "75%       0.000000     5.000000     4.000000     4.000000     2.000000   \n",
       "max       3.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "              Walc       health     absences           G1           G2  \\\n",
       "count  1044.000000  1044.000000  1044.000000  1044.000000  1044.000000   \n",
       "mean      2.284483     3.543103     4.434866    11.213602    11.246169   \n",
       "std       1.285105     1.424703     6.210017     2.983394     3.285071   \n",
       "min       1.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     3.000000     0.000000     9.000000     9.000000   \n",
       "50%       2.000000     4.000000     2.000000    11.000000    11.000000   \n",
       "75%       3.000000     5.000000     6.000000    13.000000    13.000000   \n",
       "max       5.000000     5.000000    75.000000    19.000000    19.000000   \n",
       "\n",
       "                G3  \n",
       "count  1044.000000  \n",
       "mean     11.341954  \n",
       "std       3.864796  \n",
       "min       0.000000  \n",
       "25%      10.000000  \n",
       "50%      11.000000  \n",
       "75%      14.000000  \n",
       "max      20.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tp0+XWh4yrVarLbWtUz7qJ4/FDoGISklGZrbYIYjCQv1U7BBIQL907iF2CKJoOHu62CGIQh4YJHYIRKXOxMZG7BBEIdXrFpIWewdbsUMQRXnpdzCtYPVa9evVq4fw8HAsXrxYVxYYGIguXbpg1qxZ+epPmDABP//8M+Li4nRlQ4YMwaVLl3Dq1CkAQK9evZCWloY9e/bo6rRt2xb29vZYv37966b0SjgijoiIiIiIiIiIyqysrCycO3cOrVu31itv3bo1fvvttwLXOXXqVL76bdq0wdmzZ6FWq4usU9g2SwKfmkpERERERERERIJSqVRQqVR6Zebm5jA3N89XNzk5GTk5OahUqZJeeaVKlZCQkFDg9hMSEgqsn52djeTkZLi6uhZap7BtlgR2xAksOz1d7BBEIdUh/0REhkaqUzR/m/iF2CGIQqrtnZmcJHYIoqjSvqPYIYhCqtfnMLUUOwIiKjUysQN4JbNmzcK0adP0yqZMmYKpU6cWuo5Mpp+bVqvNV/ay+i+Wv+42i4sdcUREREREREREJKjIyEiMHj1ar6yg0XAA4OjoCGNj43wj1R4+fJhvRFseFxeXAuubmJhALpcXWaewbZYE3iOOiIiIiIiIiIgEZW5ujooVK+othXXEmZmZoVatWjhw4IBe+YEDB9CwYcMC12nQoEG++vv370ft2rVhampaZJ3CtlkSOCKOiIiIiIiIiIjKtNGjR6N///6oXbs2GjRogKVLl+LevXsYMmQIgNwRdv/++y9WrVoFIPcJqQsXLsTo0aMxePBgnDp1CsuWLdN7Guonn3yCJk2a4Ouvv0bnzp2xY8cOHDx4ECdOnCi1PNgRR0REREREREREZVqvXr2gUCgwffp0xMfHo3r16ti9ezeqVKkCAIiPj8e9e/d09b29vbF79258+umn+OGHH+Dm5ob58+eje/fuujoNGzZETEwMJk+ejM8//xy+vr7YsGED6tWrV2p5yLR5d6ojQTxNLL0nb5RlfFgDSUFGZrbYIYjCQv1U7BBIQIq4q2KHIAo+rEFa+LAGaZHqwxoy+bAGkgB7B1uxQxCF+skTsUN4JaYVKogdgih4jzgiIiIiIiIiIiIBcGoqEREREREREZGB4LTHso0j4oiIiIiIiIiIiATAjjgiIiIiIiIiIiIBsCOOiIiIiIiIiIhIAOyIIyIiIiIiIiIiEgA74oiIiIiIiIiIiATAp6YSERERERERERkILR+bWqZxRBwREREREREREZEA2BFHREREREREREQkAE5NJSIiIiIiIiIyEFpwbmpZxhFxREREREREREREAmBHHBERERERERERkQDYEUdERERERERERCQA3iPuDfz111/w9vbGhQsXULNmTbHDAQBs2LYN0etjkJySAl8vL4wbMRzhoaEF1k1KVmDeoh8Qd/0G7t2/j/e6d8f4kSMEjrj0xWzciBXRq5CUnAw/Xx9MGDsWtcLDxQ6r1DFvw8xbq9Xip2VR2LFjO9LT0hEUHIxxY8fBx8e30HXu3LmNpVFLce3aNSQkxGPUJ5+id+/3BIy6eKR6XpNq3juOHsPG/QegSE2Fl5srhvZ8FyFVqxZY9/KtW4jaug33EhKhyspCJQcHdGzSGD3ebilw1KXHsWYoqvXrA/uAAFg6OeLE+Eg8+PW42GGVGKm2987Y37H1xAmkpGfA09kJH3Voh+peXi9d7+rff2PCTytQxdkZC0cMLf1ABaTVarFoyRJs3rIVaenpqFG9OiZHToSfb+Gfb+WFVM/ngDSvWwDmLbW8yzItbxFXpnFEnAHYd+gw5ixYiA8H9EfMT1EICwnBsPETEJ+YWGD9LHUW7G3t8GH/fvD3K/8XOQXZs28fZs+Zi8GDBmHT+nUIDwvDkOEjEB8fL3ZopYp5G27eq9eswvr16zFmzDgsX74ScrkcIz8ZgcePHxe6TmamCu5u7hg2dBjkcrmA0RafVM9rUs37yJmzWLRxE/q0b4slkyehhp8fIhf8gMSUlALrW5iZo0uzZvhu7GismDoFfdu3w4odP2OnAXVUmVha4tHNWzg/71uxQylxUm3vX/+4jKjde9CraVPMH/YxqntVwZToNXj46FGR6z3OzMS8zVtR08dbmEAFtnxlNFatWYtJEycgZs1qOMrlGDzk4yI/38oDqZ7P80jtuiUP85ZW3kRvqtx1xKWnp6Nv376wsrKCq6sr/ve//6FZs2YYNWoUAECpVGLAgAGwt7dHhQoV0K5dO9y8eVNvG1u2bEFwcDDMzc3h5eWFefPm6b0vk8mwfft2vTI7OzusXLkSAODtnXshFBYWBplMhmbNmpVGqq9s9caN6NqhPbp17AgfLy+MHzkCLk5O2LR9R4H13V1dMeGTkejUti2srawFjlYYq9asRbcuXdCjW1f4+vhg4rhxcHGphJhNm8UOrVQxb8PMW6vVYsOGGERERKB5s+bw9fXFF59PQWZmJvbv31foekFBQRgxYiRatWoNU1MzASMuPqme16Sa9+aDh9CuUUN0eOstVHF1xbBePeFsb49fjv1aYP2qnh5oUbcOvNzc4OIoR6v69VA7KAiXb90SOPLSk3AqFleWROHfowX/Dsozqbb3tpO/oXWtcLSpU+vZaLj2cLStiN2nzxS53sLtP6NZSAiqeXoIFKlwtFotVq9bh48GDUKrli1R1c8PM2dMR2ZmJnbt2SN2eMUi1fM5IM3rFoB5Sy1vouIodx1xo0ePxsmTJ/Hzzz/jwIEDOH78OM6fP697PyIiAmfPnsXPP/+MU6dOQavVon379lCr1QCAc+fOoWfPnujduzcuX76MqVOn4vPPP9d1sr2K33//HQBw8OBBxMfHY+vWrSWa4+tQq9WIu3EDDerU0SuvX6cOLl25IlJU4lKr1bgaF4eGDerrlTes3wCXLl0SKarSx7wNN+8HDx5AoVCgXt3/cjQzM0NYWDguX/5DxMhKh1TPa5LNOzsbN+7dQ+2gIL3yWkGB+PP2nVfaxs17/+DPO3cQ4l/w1EYqO6Ta3ursbNx6EI+wF0Y6hfv5Ie7evULXO3DuPOJTUtCnRbNSjlAc9//9F8nJyXqf4WZmZqhdqxYuXiq/n29SPZ/nkdp1Sx7mLa28yzqttnwsUlWu7hGXnp6O6OhorFu3Di1b5t4XZMWKFXBzcwMA3Lx5Ez///DNOnjyJhg0bAgDWrl0LDw8PbN++He+++y6+/fZbtGzZEp9//jkAwN/fH1evXsWcOXMQERHxSnE4OTkBAORyOVxcXEo4y9ejTE1FTk4OHOwd9MrlDvZILmSKh6FTKh8hJycHcgf9Ic5yuQOSFQqRoip9zNtw81Y8y8PBQf84d3BwQEKC4Uy/zSPV85pU807NyIBGo4F9RRu9cnsbG6SkpRa5bq8JkUjNyEBOTg4GdOqIDm+9VZqhUgmQanunPXkCjUYDO2v9kU521lZQZmQUuM6/yQqs3HcA33w0CMbGxkKEKbjk5NzPt4I+wx+U49tLSPV8nkdq1y15mLe08iYqjnLVEXfnzh2o1WrUrVtXV2Zra4uAgAAAQFxcHExMTFCvXj3d+3K5HAEBAYiLi9PV6dy5s952GzVqhO+++w45OTkleqGjUqmgUqn0yjQqFczNzUvsZ+SRyfRfa7W5U2ylLP/vRCuJ3wnzzlWe8967by++/nqW7vW8uf8DkP+Y1mq1kKF85vgqpHpek2reKGBfftn+/d24MXiqUiHuzl1EbdsOdycntKhbp8h1qKyQZnsXeHwXkHeORoM5Gzehb8sWcHd0FCi60rdz925M+/Ir3etF8+cDMNzznqHm9SKpXrcw71xSyZuoJJWrjjjts7GLBR3kz/9b0Hp56xT0x/mL68lksnxleVNbX8esWbMwbdo0vbJJY8Zg8rixr72twtjb2sLY2BiKF75dS1EqIbe3L7GfU57Y29vB2Ng432iolBQl5C98U2NImLfh5N34rcYIDgrWvVarswDkfuPo+NwfZEqlMt+3j4ZAquc1qeZta20NIyMjKNPS9MqV6emwr1ixyHVdnx0PPu7uUKalIXrnznLXMSM1Um3vihUq5Oadrj/6LfXxY9hZW+Wr/1Slws1/H+B2fAIW79wFIPd6VavVotPnU/FlxACE+voIEntJat60KUKqV9e9znp2fZ2sUOhmnABASkpKvlFy5YnUzudSvW5h3rmkknd5o5HyvM9yoFzdI87X1xempqa6e7QBQFpamu5hDEFBQcjOzsbp06d17ysUCty4cQOBgYG6OidOnNDb7m+//QZ/f3/daDgnJye9py3evHkTT5480b02M8u9mWROTk6R8UZGRiI1NVVvGVfCjyE3NTVFoL8/Tp09q1d++uxZhD53oSMlpqamCAoMxKnY03rlp2JjEVrII+MNAfM2nLytrKzg4eGhW7y9fSCXy/H7mf9yVKvVuHDhPGrUCBEx0tIh1fOaZPM2MYG/pyfOPRu5nudcXByCX6OjQYvc+3BR2SbV9jY1MYGfmysu3LqtV37h1m0Eenrmq1/B3Bw/jByGBcM/1i3t6tRGZUdHLBj+MQI8KgsVeomysrKCp6enbvH18YGjoyNOxcbq6qjVapw9dw41Q8vv55vUzudSvW5h3tLKm6gklasRcTY2Nhg4cCDGjRsHBwcHODs7Y8qUKTAyMoJMJkPVqlXRuXNnDB48GEuWLIGNjQ0mTpwId3d33XTUMWPGoE6dOpgxYwZ69eqFU6dOYeHChVi0aJHu57Ro0QILFy5E/fr1odFoMGHCBJiamured3Z2hqWlJfbu3YvKlSvDwsICtra2+eI1NzfPNw316dMn+eoVV/+ePfHZV18hOCAAIcHB2PLLTsQ/fIgend8BAMxfshQPk5Pw5Wef6da59qzz8unTp1A+eoRrN2/C1NQUvl5eJR6fGAb064vIyZ8jOCgQoSEh2Lx1K+ITEtCrR3exQytVzNsw85bJZOjVqzeio1fCo7IHPDw8ER29AhYWFmjduo2u3rRpU+Dk5IyhQ4cByL0Iunv3LgAgO1uNpKQk3LhxA5aWlvDwKNtP35PqeU2qefd4uyVmr1gJ/ypVEOTjjV3HT+BhihKdmjQGAPy0bTuSHz3CxPcjAADbjxyFs4MDPJ/dp/XKrVvYtP8AujRvJk4CpcDE0hLWld11r63dXGFX1Q9Zael4kpgoYmTFJ9X27tqoIeZt3oqq7u6o5umBvWfOIik1Fe2fjepbue8AFGlpGPNudxgZGcGrUiW99e2srWBqYpKvvDyTyWTo36cPopYth6enJ6p4eiJq2XJYWFigQ7t2YodXLFI9nwPSvG4BmLfU8iYqjnLVEQcA3377LYYMGYKOHTuiYsWKGD9+PP755x9YWFgAyH14wyeffIKOHTsiKysLTZo0we7du3UdaeHh4di4cSO++OILzJgxA66urpg+fbregxrmzZuH999/H02aNIGbmxu+//57nDt3Tve+iYkJ5s+fj+nTp+OLL75A48aNcfToUSF/DXratGyBR2mpWBK9CskKBfy8vbHw66/h9uyCNUmhQHziQ711eg/6UPf/q9evY8/Bg3B1ccGejRsEjb20tGvTBqmpqfhxaRSSkpNR1c8XixfM1z3Yw1Axb8PNu3+/AVCpVJgz9xukp6cjOCgY33+3AFZW/01pSkhMhMzov4HOSclJGDCwn+712nVrsHbdGoSFhWPxoh8Fjf91SfW8JtW8m9epjbTHj7F61y6kpKbBy80Vs4YPQyV57tQ0RWoqHj43xUur1WLZ9u1ISFbA2MgIrk5O+LBbF3Rs3FisFEqcfWA1NF+0QPe65qiRAIC7u3bjzIyZYoVVIqTa3k1CaiDtyVOsP3IUKenpqFLJGdMG9IOzvR0AICU9HUmpRT+wwhB9EDEQmapMfDlrNtLS0hBSvTqWLl6k9/lWHkn1fJ5HatcteZi3tPImelMybWE3VisnHj9+DHd3d8ybNw+DBg0SO5yXepqYIHYIojCxsXl5JaJyLiOz/EyTKkkW6qdih0ACUsRdFTsEUfw28QuxQxBFw9nTxQ5BFJnJSWKHIIoq7TuKHYIostPTxQ5BFJmmlmKHQFTq7B3yz1yTgvTU8nFes7GVZj9BuRsRd+HCBVy7dg1169ZFamoqpk/PvUB88UmoREREREREREREZUm564gDgLlz5+L69eswMzNDrVq1cPz4cb0ntBAREREREREREZU15a4jLiwsTO9+bURERERERERElKt834DM8Bm9vAoREREREREREREVFzviiIiIiIiIiIiIBFDupqYSEREREREREVHBNJybWqZxRBwREREREREREZEA2BFHREREREREREQkAHbEERERERERERERCYD3iCMiIiIiIiIiMhBa3iOuTOOIOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAnJpKRERERERERGQgNBpOTS3LOCKOiIiIiIiIiIhIAOyIIyIiIiIiIiIiEgCnphIRERERERERGQg+NbVs44g4IiIiIiIiIiIiAbAjjoiIiIiIiIiISADsiCMiIiIiIiIiIhIA7xFHRERERERERGQgNLxHXJnGEXFEREREREREREQCYEccERERERERERGRADg1lYhKXHZ6utghiCIpQyN2CKLwsJbmdzqZppZihyAKeWCQ2CGIouHs6WKHIIrfJn4hdgii8H2njdghiCK7sTQ/v6UqSflY7BBE4WRvJXYIRKVOo+HU1LJMmn89ERERERERERERCYwdcURERERERERERALg1FQiIiIiIiIiIgPBh6aWbRwRR0REREREREREJAB2xBEREREREREREQmAU1OJiIiIiIiIiAyEhnNTyzSOiCMiIiIiIiIiIhIAO+KIiIiIiIiIiIgEwI44IiIiIiIiIiIiAfAecUREREREREREBkKj4T3iyjKOiCMiIiIiIiIiIhIAO+KIiIiIiIiIiIgEwKmpREREREREREQGQqvl1NSyjCPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgFwaioRERERERERkYHQcGpqmcYRcURERERERERERAJgRxwREREREREREZEA2BFXiJUrV8LOzk7sMIiIiIiIiIiIyEAY3D3iIiIiEB0dDQAwMTGBg4MDQkJC8N577yEiIgJGRobZ97hh2zZEr49BckoKfL28MG7EcISHhhZYNylZgXmLfkDc9Ru4d/8+3uveHeNHjhA44tIXs3EjVkSvQlJyMvx8fTBh7FjUCg8XO6xSp9VqsWjJEmzeshVp6emoUb06JkdOhJ+vr9ihFZuU93OtVov1a6Oxb+9OZGSkwz8gEEOGfoIqVbwLXWff3p04fGg//v77LgDAz88fAwZ+CP+AQKHCLhapt/dPy6KwY8d2pKelIyg4GOPGjoOPT+HH8Z07t7E0aimuXbuGhIR4jPrkU/Tu/Z6AURePVNt7x9Fj2Lj/ABSpqfByc8XQnu8ipGrVAutevnULUVu34V5CIlRZWajk4ICOTRqjx9stBY669DjWDEW1fn1gHxAASydHnBgfiQe/Hhc7rBLjFF4Lleo3gKm1DZ4mJeH+wX3I+OefQuvLjI3h+lYTOFSvDlMra6jT0xB/8gQUf1wSMOrik+rxLdW8AWletwDS/PwGpJt3WcZ7xJVtBtkr1bZtW8THx+Ovv/7Cnj170Lx5c3zyySfo2LEjsrOzxQ6vxO07dBhzFizEhwP6I+anKISFhGDY+AmIT0wssH6WOgv2tnb4sH8/+PuV/86ZguzZtw+z58zF4EGDsGn9OoSHhWHI8BGIj48XO7RSt3xlNFatWYtJEycgZs1qOMrlGDzkYzx+/Fjs0IpF6vv5ls0x2L5tE/7v45H49rsfYW/vgC8+G4cnT54Uus7lPy6iSdMWmDnrf5gz7wc4OTnji8njoEhOEjDyNyP19l69ZhXWr1+PMWPGYfnylZDL5Rj5yYgij+PMTBXc3dwxbOgwyOVyAaMtPqm295EzZ7Fo4yb0ad8WSyZPQg0/P0Qu+AGJKSkF1rcwM0eXZs3w3djRWDF1Cvq2b4cVO37GTgPqqDKxtMSjm7dwft63YodS4uwDg1C5VRvEnzyBuGVRyPjnHvx69YFpxYqFruPTtTtsvLzw966d+HPJItzZvg2ZCoWAURefVI9vqeadR2rXLXmk9vmdR6p5E70pg+yIMzc3h4uLC9zd3REeHo5JkyZhx44d2LNnD1auXAkA+Pbbb1GjRg1YWVnBw8MDQ4cORUZGRpHb/fnnn1G7dm1YWFjA0dER3bp1EyCbl1u9cSO6dmiPbh07wsfLC+NHjoCLkxM2bd9RYH13V1dM+GQkOrVtC2sra4GjFcaqNWvRrUsX9OjWFb4+Ppg4bhxcXCohZtNmsUMrVVqtFqvXrcNHgwahVcuWqOrnh5kzpiMzMxO79uwRO7xikfJ+rtVq8fP2zejZux8aNmqCKl7e+HTMRKhUmTh29GCh640dPxkdOnaBj68fPDw8MXzkWGg0Wly6dF7A6N+M1Nt7w4YYREREoHmz5vD19cUXn09BZmYm9u/fV+h6QUFBGDFiJFq1ag1TUzMBIy4+qbb35oOH0K5RQ3R46y1UcXXFsF494Wxvj1+O/Vpg/aqeHmhRtw683Nzg4ihHq/r1UDsoCJdv3RI48tKTcCoWV5ZE4d+jBf8OyrNKdetDcekCFJcuIlORjPsH9yMrLQ1O4bULrF/RxxfWnlVwa8N6pP91F1mpqXgS/wCP/70vcOTFI9XjW6p5A9K8bgGk+fkNSDdvouIwyI64grRo0QKhoaHYunUrAMDIyAjz58/HlStXEB0djcOHD2P8+PGFrr9r1y5069YNHTp0wIULF3Do0CHUrl3whZOQ1Go14m7cQIM6dfTK69epg0tXrogUlbjUajWuxsWhYYP6euUN6zfApUvlayrH67r/779ITk7Wy93MzAy1a9XCxUt/iBhZ8Uh9P09MiIdSmYKw5/5YMzU1Q/UaobgW9+crb0elUiEnJxvW1oWPvigLpN7eDx48gEKhQL26+sdxWFg4Ll8uv8dxYaTa3ursbNy4dw+1g4L0ymsFBeLP23deaRs37/2DP+/cQYh/wVNZqeyQGRmhgqsr0u7ot23a3duwrly5wHVsq/rjSfwDuNRviBojPkHw/w2Fe4u3ITMpP3eWkezxLdG880jtuiWP1D6/80g177JOq9GWi0Wqys8neQmoVq0a/vgj92QwatQoXbm3tzdmzJiBjz/+GIsWLSpw3a+++gq9e/fGtGnTdGWhhdzjQUjK1FTk5OTAwd5Br1zuYI/kQqa2GDql8hFycnIgd9Af4iyXOyC5nE3neF3Jybn5FZT7g3I8LVfq+7lSmZujnZ29XrmdnT0ePix4iktBolcshVzuiJphtUo0vpIm9fZWPDtPOTjo5+/g4ICEhPJ7HBdGqu2dmpEBjUYD+4o2euX2NjZISUstct1eEyKRmpGBnJwcDOjUER3eeqs0Q6USYFKhAmRGRlC/ME0r+/FjmBYy+snczg7WHp7QZGfj9uZNMKlQAZ5t2sHE0hJ/7/pFiLCLTarHt1TzziO165Y8Uvv8ziPVvImKQzIj4oDcYbMymQwAcOTIEbRq1Qru7u6wsbHBgAEDoFAoCp3HfvHiRbRs+Xo3Q1apVEhLS9NbVCpVsfMoyLO0dLRa6HKVqvy/E63B/U527t6NOg0b6Za8eyAa6v5gqHm96OiRA3i3Wzvdkp2T1676ub5O/ls2rcevxw4jcvJ0mJmVj+H/Umnvvfv2onmLprrlv+P4xfbWQgbDyz+PVNo7v/w5vqydvxs3BosmTcSovn2w5dBhHP79TGkFRyXuxW//ZQWUPXtHJgO0Wtz9eTuexD9A2u1buH/oAOQhoeVqVBwg3eNbKnlL9bpFqp/fUs2bqCSVr0/xYoqLi4O3tzf+/vtvtG/fHkOGDMGMGTPg4OCAEydOYNCgQVCr1QWua2lp+do/b9asWXoj6ABg0pgxmDxu7BvFXxB7W1sYGxtD8cK3aylKJeT29oWsZdjs7e1gbGycb/RbSooS8he+qSnvmjdtipDq1XWvs57tv8kKBZycnHTlKSkp+UbJlSdS28/r1msE/4D/pqup1VkAcr9hdniuHVNTlfm+bS7I1i0bsGnjWsz4ah68vcv+DaCl1t6N32qM4KBg3eu89lYoFHB0dNSVK5XKfN82GwKptXceW2trGBkZQZmWpleuTE+HfRE37wcA12f7hY+7O5RpaYjeuRMt6tYpch0SV/aTJ9BqNPlGv5lYVcg3Si6POiMDWenp0Dz3JW5mcjJkMhnMbCpCpSz7I6ukenxLLW+pXrdI9fNbqnmXN3xqatkmmRFxhw8fxuXLl9G9e3ecPXsW2dnZmDdvHurXrw9/f388ePCgyPVDQkJw6NCh1/qZkZGRSE1N1VvGlfBjyE1NTRHo749TZ8/qlZ8+exahz3XQSImpqSmCAgNxKva0Xvmp2NgyMZ24JFlZWcHT01O3+Pr4wNHREadiY3V11Go1zp47h5qhISJGWjxS288rVKgANzd33eLp6QV7ewdcPP9f/mq1GlcuX0K1wOAitgRs3RyDDetXY+qMb1DVP6C0Qy8RUmvvvIcG5S3e3j6Qy+X4/cx/5zC1Wo0LF86jRo3yexwXRmrtncfUxAT+np44FxenV34uLg7Bvj6vvB0tcu83R2WbVqPBk/h42Hjrt21Fbx9k3C/44QsZ9/+BmY0NjExNdWXmDg7QajTISk8rcJ2yRrLHt8Tylup1i1Q/v6WaN1FJMsgRcSqVCgkJCcjJyUFiYiL27t2LWbNmoWPHjhgwYAAuX76M7OxsLFiwAJ06dcLJkyfx448/FrnNKVOmoGXLlvD19UXv3r2RnZ2NPXv2FPmAB3Nzc5ibm+uVPX1a+CO731T/nj3x2VdfITggACHBwdjyy07EP3yIHp3fAQDMX7IUD5OT8OVnn+nWuXbz5rN4nkL56BGu3bwJU1NT+Hp5lXh8YhjQry8iJ3+O4KBAhIaEYPPWrYhPSECvHt3FDq1UyWQy9O/TB1HLlsPT0xNVPD0RtWw5LCws0KFdO7HDKxYp7+cymQzvdOmBTRvXws29MtzcKmPjhjUwN7dA02Zv6+p9O3cm5HInDHx/MIDcaR1rVq/A2PGfoZKzC5TPvpm3sLR8o1G+QpJ6e/fq1RvR0SvhUdkDHh6eiI5eAQsLC7Ru3UZXb9q0KXBycsbQocMA5F703r17FwCQna1GUlISbty4AUtLS3h4eIiSy6uSanv3eLslZq9YCf8qVRDk441dx0/gYYoSnZo0BgD8tG07kh89wsT3IwAA248chbODAzxdXAAAV27dwqb9B9CleTNxEigFJpaWsK7srntt7eYKu6p+yEpLx5PEV7+3VFmU+HssvN7p8uzJp//CMSwMZhVtkXz+HADArVkLmNnY4K9fcp+qmfLnFbi+1RheHd/Bg+PHYGJZAZVbvI3kSxehLUedr1I9vqWaNyDN6xZAmp/fgHTzJioOg+yI27t3L1xdXWFiYgJ7e3uEhoZi/vz5GDhwIIyMjFCzZk18++23+PrrrxEZGYkmTZpg1qxZGDBgQKHbbNasGTZt2oQZM2Zg9uzZqFixIpo0aSJgVoVr07IFHqWlYkn0KiQrFPDz9sbCr7+G27ML9SSFAvGJD/XW6T3oQ93/r16/jj0HD8LVxQV7Nm4QNPbS0q5NG6SmpuLHpVFISk5GVT9fLF4wH25ubmKHVuo+iBiITFUmvpw1G2lpaQipXh1LFy+ClZWV2KEVi9T38+49eiNLpcLiH75DRkY6/AMCMf3LOahQoYKuTlLSQ8iM/hvovHvXDmRnqzF75lS9bb3XZyD69IsQKPI3I/X27t9vAFQqFebM/Qbp6ekIDgrG998t0DuOExIT9do7KTkJAwb2071eu24N1q5bg7CwcCxeVPSXTWKTans3r1MbaY8fY/WuXUhJTYOXmytmDR+GSvLcqVyK1FQ8fG5qm1arxbLt25GQrICxkRFcnZzwYbcu6Ni4sVgplDj7wGpovmiB7nXNUSMBAHd37caZGTPFCqtEKOOuwsTSEq5vNYGptTWeJiXh1ob1yHr2cA5Ta2uYPTctWaNW48b6tfBs3RaB73+I7KdPoIy7in+PHRUpgzcj1eNbqnnnkdp1Sx6pfX7nkWreRG9KptVy8rCQniYmiB2CKExsbF5eiQxGdnq62CGI4p8MjdghiMLDWjJ3OdCTaVr2v50vDRbqp2KHIApF3FWxQxDFbxO/EDsEUfi+0+bllQxQ0KDBYodAApLqdYuTffn+cppej72DrdghiOLG7aJvvVVW+Psa/kCZgkjzryciIiIiIiIiIiKBsSOOiIiIiIiIiIhIAAZ5jzgiIiIiIiIiIinSaHgHsrKMI+KIiIiIiIiIiIgEwI44IiIiIiIiIiIiAXBqKhERERERERGRgdBoOTW1LOOIOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgA7IgjIiIiIiIiIiISAO8RR0RERERERERkILS8R1yZxhFxREREREREREREAmBHHBERERERERERkQA4NZWIiIiIiIiIyEBoODW1TOOIOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAnJpKRERERERERGQgNBpOTS3LOCKOiIiIiIiIiIhIAOyIIyIiIiIiIiIig6BUKtG/f3/Y2trC1tYW/fv3x6NHjwqtr1arMWHCBNSoUQNWVlZwc3PDgAED8ODBA716zZo1g0wm01t69+792vGxI46IiIiIiIiIyEBoteVjKS19+vTBxYsXsXfvXuzduxcXL15E//79C63/5MkTnD9/Hp9//jnOnz+PrVu34saNG3jnnXfy1R08eDDi4+N1y5IlS147Pt4jjoiIiIiIiIiIyr24uDjs3bsXsbGxqFevHgAgKioKDRo0wPXr1xEQEJBvHVtbWxw4cECvbMGCBahbty7u3bsHT09PXXmFChXg4uJSrBg5Io6IiIiIiIiIiMq9U6dOwdbWVtcJBwD169eHra0tfvvtt1feTmpqKmQyGezs7PTK165dC0dHRwQHB2Ps2LFIT09/7Rg5Io6IiIiIiIiIiASlUqmgUqn0yszNzWFubv7G20xISICzs3O+cmdnZyQkJLzSNjIzMzFx4kT06dMHFStW1JX37dsX3t7ecHFxwZUrVxAZGYlLly7lG033MuyIIyIiInqJzOQksUMQhe87bcQOQRS3f94ndgiiCBo0WOwQSEBO9lZih0BEpURTmjdgK0GzZs3CtGnT9MqmTJmCqVOn5qs7derUfHVfdObMGQCATCbL955Wqy2w/EVqtRq9e/eGRqPBokWL9N4bPPi/z8nq1aujatWqqF27Ns6fP4/w8PCXbjsPO+KIiIiIiIiIiEhQkZGRGD16tF5ZYaPhhg8f/tInlHp5eeGPP/5AYmJivveSkpJQqVKlItdXq9Xo2bMn7t69i8OHD+uNhitIeHg4TE1NcfPmTXbEERERERERERFR2fU601AdHR3h6Oj40noNGjRAamoqfv/9d9StWxcAcPr0aaSmpqJhw4aFrpfXCXfz5k0cOXIEcrn8pT/rzz//hFqthqur6yvlkIcdcUREREREREREBkKjKR9TU0tDYGAg2rZti8GDB2PJkiUAgI8++ggdO3bUe2JqtWrVMGvWLHTt2hXZ2dno0aMHzp8/j507dyInJ0d3PzkHBweYmZnh9u3bWLt2Ldq3bw9HR0dcvXoVY8aMQVhYGBo1avRaMfKpqUREREREREREZBDWrl2LGjVqoHXr1mjdujVCQkKwevVqvTrXr19HamoqAOD+/fv4+eefcf/+fdSsWROurq66Je9Jq2ZmZjh06BDatGmDgIAAjBw5Eq1bt8bBgwdhbGz8WvFxRBwRERERERERERkEBwcHrFmzpsg62uceaOHl5aX3uiAeHh44duxYicTHjjgiIiIiIiIiIgPxsk4lEhenphIREREREREREQmAHXFEREREREREREQCYEccERERERERERGRAHiPOCIiIiIiIiIiA6HhLeLKNI6IIyIiIiIiIiIiEgA74oiIiIiIiIiIiATAqalERERERERERAZCq+Xc1LKMI+KIiIiIiIiIiIgEwI44IiIiIiIiIiIiAXBqKhERERERERGRgdBwamqZxhFxREREREREREREAmBHHBERERERERERkQDYEVeIo0ePQiaT4dGjR2KHQkREREREREREBsAg7hH3448/Yty4cVAqlTAxyU0pIyMD9vb2qF+/Po4fP66re/z4cTRp0gTXr1+Hv7+/WCGXuA3btiF6fQySU1Lg6+WFcSOGIzw0tMC6SckKzFv0A+Ku38C9+/fxXvfuGD9yhMARlz6tVotFS5Zg85atSEtPR43q1TE5ciL8fH3FDq1UxWzciBXRq5CUnAw/Xx9MGDsWtcLDxQ6rREh5P9dqtVi/Nhr79u5ERkY6/AMCMWToJ6hSxbvQdfbt3YnDh/bj77/vAgD8/PwxYOCH8A8IFCrsYpF6e/+0LAo7dmxHelo6goKDMW7sOPj4FH7+unPnNpZGLcW1a9eQkBCPUZ98it693xMw6uKRanvvjP0dW0+cQEp6BjydnfBRh3ao7uX10vWu/v03Jvy0AlWcnbFwxNDSD7SEOYXXQqX6DWBqbYOnSUm4f3AfMv75p9D6MmNjuL7VBA7Vq8PUyhrq9DTEnzwBxR+XBIy69DjWDEW1fn1gHxAASydHnBgfiQe/Hn/5iuWEVI9vqeYNSPNzDGDeUsu7LNNoeI+4sswgRsQ1b94cGRkZOHv2rK7s+PHjcHFxwZkzZ/DkyRNd+dGjR+Hm5mZQnXD7Dh3GnAUL8eGA/oj5KQphISEYNn4C4hMTC6yfpc6Cva0dPuzfD/5+htsptXxlNFatWYtJEycgZs1qOMrlGDzkYzx+/Fjs0ErNnn37MHvOXAweNAib1q9DeFgYhgwfgfj4eLFDKzap7+dbNsdg+7ZN+L+PR+Lb736Evb0DvvhsnN757UWX/7iIJk1bYOas/2HOvB/g5OSMLyaPgyI5ScDI34zU23v1mlVYv349xowZh+XLV0Iul2PkJyOKPH9lZqrg7uaOYUOHQS6XCxht8Um1vX/94zKidu9Br6ZNMX/Yx6juVQVTotfg4UtG4z/OzMS8zVtR06fwjviyzD4wCJVbtUH8yROIWxaFjH/uwa9XH5hWrFjoOj5du8PGywt/79qJP5cswp3t25CpUAgYdekysbTEo5u3cH7et2KHUuKkenxLNe88Uvscy8O8pZU30ZsyiI64gIAAuLm54ejRo7qyo0ePonPnzvD19cVvv/2mV968eXOsWbMGtWvXho2NDVxcXNCnTx88fPiwyJ9z8uRJNG3aFBUqVIC9vT3atGkDpVJZWmm9stUbN6Jrh/bo1rEjfLy8MH7kCLg4OWHT9h0F1nd3dcWET0aiU9u2sLayFjhaYWi1Wqxetw4fDRqEVi1boqqfH2bOmI7MzEzs2rNH7PBKzao1a9GtSxf06NYVvj4+mDhuHFxcKiFm02axQys2Ke/nWq0WP2/fjJ69+6Fhoyao4uWNT8dMhEqViWNHDxa63tjxk9GhYxf4+PrBw8MTw0eOhUajxaVL5wWM/s1Ivb03bIhBREQEmjdrDl9fX3zx+RRkZmZi//59ha4XFBSEESNGolWr1jA1NRMw4uKTantvO/kbWtcKR5s6tZ6NhmsPR9uK2H36TJHrLdz+M5qFhKCap4dAkZasSnXrQ3HpAhSXLiJTkYz7B/cjKy0NTuG1C6xf0ccX1p5VcGvDeqT/dRdZqal4Ev8Aj/+9L3DkpSfhVCyuLInCv0d/FTuUEifV41uqeQPS/BwDmLfU8iYqDoPoiAOAZs2a4ciRI7rXR44cQbNmzdC0aVNdeVZWFk6dOoXmzZsjKysLM2bMwKVLl7B9+3bcvXsXERERhW7/4sWLaNmyJYKDg3Hq1CmcOHECnTp1Qk5OTmmnViS1Wo24GzfQoE4dvfL6derg0pUrIkUlvvv//ovk5GQ0bFBfV2ZmZobatWrh4qU/RIys9KjValyNi9PLGQAa1m+AS5fK99Qdqe/niQnxUCpTEPbcH6mmpmaoXiMU1+L+fOXtqFQq5ORkw9q68FEnZYHU2/vBgwdQKBSoV1f//BUWFo7Llw3v/CXV9lZnZ+PWg3iEvTDyJdzPD3H37hW63oFz5xGfkoI+LZqVcoSlQ2ZkhAqurki7c0evPO3ubVhXrlzgOrZV/fEk/gFc6jdEjRGfIPj/hsK9xduQmRjEHVYMmmSPb4nmnUdqn2N5mLe08i7rtNrysUiVwVzBNGvWDJ9++imys7Px9OlTXLhwAU2aNEFOTg7mz58PAIiNjcXTp0/RvHlz+Pj46Nb18fHB/PnzUbduXWRkZMDaOv+3UN988w1q166NRYsW6cqCg4NLP7GXUKamIicnBw72Dnrlcgd7JKekiBSV+JKTc6eryB30hznL5Q54YADTNAuiVD5CTk5OgTknl/PpO1Lfz5XK3Bzt7Oz1yu3s7PHwYcFTXAoSvWIp5HJH1AyrVaLxlTSpt7fi2fHq4KCfv4ODAxISDO/8JdX2TnvyBBqNBnYvXHPYWVtBmZFR4Dr/Jiuwct8BfPPRIBgbGwsRZokzqVABMiMjqF+YrpT9+DFMCxkFZG5nB2sPT2iys3F78yaYVKgAzzbtYGJpib93/SJE2PSGpHp8SzXvPFL7HMvDvKWVN1FxGMyIuObNm+Px48c4c+YMjh8/Dn9/fzg7O6Np06Y4c+YMHj9+jKNHj8LT0xM+Pj64cOECOnfujCpVqsDGxgbNmjUDANwr5FvovBFxr0OlUiEtLU1vUalUxU21QDKZ/mutFpC9WGjAdu7ejToNG+mW7OxsANL8veTPWWswOUulPY8eOYB3u7XTLdk5efuzfq6vk/+WTevx67HDiJw8HWZm5WP4v1Tae+++vWjeoqlu+e/89WJ7ayGD4eWfRyrt/aIC8y6gnXM0GszZuAl9W7aAu6OjQNGVphe/BpcVUPbsHZkM0Gpx9+fteBL/AGm3b+H+oQOQh4RyVFw5weM7l6HmLdXPMeYtrbyJSpLBXL34+fmhcuXKOHLkCJRKJZo2bQoAcHFxgbe3N06ePIkjR46gRYsWePz4MVq3bo3WrVtjzZo1cHJywr1799CmTRtkZWUVuH1LS8vXjmnWrFmYNm2aXtmkMWMwedzY10+wEPa2tjA2NobihW/XUpRKyO3tC1nL8DRv2hQh1avrXmep1QCAZIUCTk5OuvKUlJR8I8YMhb29HYyNjfONfktJUUL+wjdU5Y3U9vO69RrBPyBI91qtzj0vKZUpcHhu/01NVeYbJVeQrVs2YNPGtZjx1Tx4e5f9G0BLrb0bv9UYwUH/jbDOa2+FQgHH5zpclEplvm+bDYHU2jtPxQoVYGRkBGW6/ui31MePYWdtla/+U5UKN/99gNvxCVi8cxeA3D9ytFotOn0+FV9GDECor0++9cqa7CdPoNVo8o1+M7GqkG+UXB51Rgay0tOhee7LzMzkZMhkMpjZVIRKafgjjMorqR7fUstbqp9jzDuXVPIubzRSnvdZDhjMiDggd1Tc0aNHcfToUd0INwBo2rQp9u3bh9jYWDRv3hzXrl1DcnIyZs+ejcaNG6NatWovfVBDSEgIDh069FrxREZGIjU1VW8ZV8KPITc1NUWgvz9OPffEWAA4ffYsQp/rmDJ0VlZW8PT01C2+Pj5wdHTEqdhYXR21Wo2z586hZmiIiJGWHlNTUwQFBuJU7Gm98lOxsQgNDRUpqpIhtf28QoUKcHNz1y2enl6wt3fAxfP/5a9Wq3Hl8iVUCyx6ivzWzTHYsH41ps74BlX9A0o79BIhtfa2srKCh4eHbvH29oFcLsfvZ/47ltVqNS5cOI8aNQzv/CW19s5jamICPzdXXLh1W6/8wq3bCPT0zFe/grk5fhg5DAuGf6xb2tWpjcqOjlgw/GMEeBR8f7WyRqvR4El8PGy89TsNK3r7ION+wQ9fyLj/D8xsbGBkaqorM3dwgFajQVZ6WqnGS8Uj2eNbYnlL9XOMeUsrb6KSZDAj4oDcjrhhw4ZBrVbrRsQBuR1xH3/8MTIzM9G8eXNYWFjAzMwMCxYswJAhQ3DlyhXMmDGjyG1HRkaiRo0aGDp0KIYMGQIzMzMcOXIE7777rl7P//PMzc1hbm6uV/b06ZPiJ/qC/j174rOvvkJwQABCgoOx5ZediH/4ED06vwMAmL9kKR4mJ+HLzz7TrXPt5s1n8TyF8tEjXLt5E6ampvD18irx+MQgk8nQv08fRC1bDk9PT1Tx9ETUsuWwsLBAh3btxA6v1Azo1xeRkz9HcFAgQkNCsHnrVsQnJKBXj+5ih1ZsUt7PZTIZ3unSA5s2roWbe2W4uVXGxg1rYG5ugabN3tbV+3buTMjlThj4/mAAudNR16xegbHjP0MlZxcon30zb2Fp+UajfIUk9fbu1as3oqNXwqOyBzw8PBEdvQIWFhZo3bqNrt60aVPg5OSMoUOHAci96L179y4AIDtbjaSkJNy4cQOWlpbw8CjbT9eUant3bdQQ8zZvRVV3d1Tz9MDeM2eRlJqK9nVzb/C+ct8BKNLSMObd7jAyMoJXpUp669tZW8HUxCRfeVmX+HssvN7p8uzJp//CMSwMZhVtkXz+HADArVkLmNnY4K9fcp8umfLnFbi+1RheHd/Bg+PHYGJZAZVbvI3kSxehfTYlqrwzsbSEdWV33WtrN1fYVfVDVlo6niS++r1AyyKpHt9SzRuQ5ucYwLylljdRcRhcR9zTp09RrVo1VHruorRp06ZIT0+Hr6+v7qBeuXIlJk2ahPnz5yM8PBxz587FO++8U+i2/f39sX//fkyaNAl169aFpaUl6tWrh/fee6/U83qZNi1b4FFaKpZEr0KyQgE/b28s/PpruLm4AACSFArEJ+qP+Os96EPd/69ev449Bw/C1cUFezZuEDT20vRBxEBkqjLx5azZSEtLQ0j16li6eBGsrPJP+TEU7dq0QWpqKn5cGoWk5GRU9fPF4gXz4ebmJnZoxSb1/bx7j97IUqmw+IfvkJGRDv+AQEz/cg4qVKigq5OU9BAyo/8GOu/etQPZ2WrMnjlVb1vv9RmIPv0iBIr8zUi9vfv3GwCVSoU5c79Beno6goOC8f13C/TOXwmJiXrtnZSchAED++ler123BmvXrUFYWDgWL/pR0Phfl1Tbu0lIDaQ9eYr1R44iJT0dVSo5Y9qAfnC2twMApKSnIyk1VdwgS4Ey7ipMLC3h+lYTmFpb42lSEm5tWI+stNxcTa2tYVbxv6c7a9Rq3Fi/Fp6t2yLw/Q+R/fQJlHFX8e+xoyJlUPLsA6uh+aIFutc1R40EANzdtRtnZswUK6wSIdXjW6p555Ha51ge5i2tvInelEyr5eRhIT1NTBA7BFGY2NiIHQIJKDs9XewQRPFPhkbsEEThYW1Qdzl4ZZmmZXtUYWmxUD8VOwRR/Hv8mNghiCL1xg2xQxDF7Z/3iR2CKDrt2Cx2CCQgqX6OkbTYO9iKHYIojp6+LnYIr6RZvfJx65ySJs2/noiIiIiIiIiIiATGjjgiIiIiIiIiIiIBGNQ94oiIiIiIiIiIpEyj4R3IyjKOiCMiIiIiIiIiIhIAO+KIiIiIiIiIiIgEwKmpREREREREREQGgjNTyzaOiCMiIiIiIiIiIhIAO+KIiIiIiIiIiIgEwKmpREREREREREQGQqvl3NSyjCPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAvEccEREREREREZGB0PAecWUaR8QREREREREREREJgB1xREREREREREREAuDUVCIiIiIiIiIiA8GZqWUbR8QREREREREREREJgB1xREREREREREREAuDUVCIiIiIiIiIiA6HRcG5qWcYRcURERERERERERAJgRxwREREREREREZEA2BFHREREREREREQkAJlWywfbCkn95LHYIRBRKcnIzBY7BFFYW0jzdqPZ6elih0ACMrGxETsEUXA/l5ZfOvcQOwRRdD28T+wQRCHV6xaSFnsHW7FDEMXOo1fEDuGVdGxWXewQRMERcURERERERERERAJgRxwREREREREREZEApDmfiIiIiIiIiIjIAPEGZGUbR8QREREREREREREJgB1xREREREREREREAuDUVCIiIiIiIiIiA6Hh3NQyjSPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAvEccEREREREREZGB0PIecWUaR8QREREREREREREJgB1xREREREREREREAuDUVCIiIiIiIiIiA6HhzNQyjSPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgFwaioRERERERERkYHQcG5qmcYRcURERERERERERAJgRxwREREREREREZEAynRHXLNmzTBq1KhS/RleXl747rvviqwzdepU1KxZs1TjICIiIiIiIiIiw1amO+LEIJPJsH37drHDKFFarRY//PgjmrdqjVr1GyDiw8G4dfu22GGVOqnmHbNxI9p06IjwevXRs08fnDt/XuyQBGHo7a3VahH101J07NQeTZs2xsdDh+DOnaLzu3PnNiZGTkCXrp1Rv0FdxMSsFyja0mfI+/mGbdvQvmcv1H27Fd77cDDOX7pUaN2kZAUmTp+Ozn37IaxpM3wzf4GAkZYsqeZdFEM+r0m1vaWad2Eca4birblfo9Mv29Ez9gTcmjQWOyTBGPLxDUj3uoV5SyvvskyrLR+LVLEjTgKWr4zGqjVrMWniBMSsWQ1HuRyDh3yMx48fix1aqZJi3nv27cPsOXMxeNAgbFq/DuFhYRgyfATi4+PFDq3UGXp7r16zCuvXr8eYMeOwfPlKyOVyjPxkRJH5ZWaq4O7mjmFDh0EulwsYbeky5P1836HDmLNgIT4c0B8xP0UhLCQEw8ZPQHxiYoH1s9RZsLe1w4f9+8Hfz1fgaEuOVPN+GUM9r0m1vaWad1FMLC3x6OYtnJ/3rdihCM5Qj+88Ur1uYd7SypvoTZX5jjiNRoPx48fDwcEBLi4umDp1qu691NRUfPTRR3B2dkbFihXRokULXHrum8Xbt2+jc+fOqFSpEqytrVGnTh0cPHiw0J/l5eUFAOjatStkMpnudZ7Vq1fDy8sLtra26N27N9LT00sy1VKh1Wqxet06fDRoEFq1bImqfn6YOWM6MjMzsWvPHrHDKzVSzXvVmrXo1qULenTrCl8fH0wcNw4uLpUQs2mz2KGVKkNvb61Wiw0bYhAREYHmzZrD19cXX3w+BZmZmdi/f1+h6wUFBWHEiJFo1ao1TE3NBIy4dBnyfr5640Z07dAe3Tp2hI+XF8aPHAEXJyds2r6jwPrurq6Y8MlIdGrbFtZW1gJHW3KkmndRDPm8JtX2lmreRUk4FYsrS6Lw79FfxQ5FUIZ8fAPSvW5h3tLKm6g4ynxHXHR0NKysrHD69Gl88803mD59Og4cOACtVosOHTogISEBu3fvxrlz5xAeHo6WLVsiJSUFAJCRkYH27dvj4MGDuHDhAtq0aYNOnTrh3r17Bf6sM2fOAABWrFiB+Ph43Wsgt1Nv+/bt2LlzJ3bu3Iljx45h9uzZpf8LKKb7//6L5ORkNGxQX1dmZmaG2rVq4eKlP0SMrHRJMW+1Wo2rcXF6OQNAw/oN9DqoDZGht/eDBw+gUChQr65+fmFh4bh8ufzn9zoMeT9Xq9WIu3EDDerU0SuvX6cOLl25IlJUpU+qeb+MoZ7XpNreUs2bCmaox3ceqV63MG9p5V3WabTacrFIlYnYAbxMSEgIpkyZAgCoWrUqFi5ciEOHDsHY2BiXL1/Gw4cPYW5uDgCYO3cutm/fjs2bN+Ojjz5CaGgoQkNDddv68ssvsW3bNvz8888YPnx4vp/l5OQEALCzs4OLi4veexqNBitXroSNjQ0AoH///jh06BC++uqrUsm7pCQnKwAAcgf94b5yuQMeGMA0rsJIMW+l8hFycnIKzDlZoRApKmEYensrnrWfg4ODXrmDgwMSEsp/fq/DkPdzZWoqcnJy4GCv385yB3skP/uCyRBJNe+XMdTzmlTbW6p5U8EM9fjOI9XrFuYtrbyJiqPMj4gLCQnRe+3q6oqHDx/i3LlzyMjIgFwuh7W1tW65e/cubj+70enjx48xfvx4BAUFwc7ODtbW1rh27VqhI+KK4uXlpeuEez6OoqhUKqSlpektKpXqtX/269i5ezfqNGykW7KzswEAMpl+Pa0298EUhkKqeRckf85ag8vZ0Nt77769aN6iqW75Lz/9XLRaLWQof/mVBEPezw1lP35dUs07j6Gf115kqHm9jFTzljpDP76let3CvKWVN1FJKvMj4kxNTfVey2QyaDQaaDQauLq64ujRo/nWsbOzAwCMGzcO+/btw9y5c+Hn5wdLS0v06NEDWVlZJRZHUWbNmoVp06bplU2eFIkvPvvstX/+q2retClCqlfXvc5SqwEAyQqFbsQfAKSkpOT7Fq48k2rez7O3t4OxsXG+UUEpKUrIX/iGqrwz9PZu/FZjBAcF616r1bnnLIVCAUdHR125UqnM9+2joTPk/dze1hbGxsZQvDA6JkWphNzeXqSoSp9U836RoZ/X8ki1vaWaN+Uy9ONbqtctzDuXVPIub7QSnvZZHpT5EXGFCQ8PR0JCAkxMTODn56e35J0Ajh8/joiICHTt2hU1atSAi4sL/vrrryK3a2pqipycnBKJMTIyEqmpqXrLhLFjS2TbhbGysoKnp6du8fXxgaOjI07FxurqqNVqnD13DjVDQ4rYUvki1byfZ2pqiqDAQJyKPa1Xfio2Vm+KtiEw9Pa2srKCh4eHbvH29oFcLsfvZ/5rW7VajQsXzqNGjfKXX3EY8n5uamqKQH9/nDp7Vq/89NmzCH3uDzhDI9W8X2To57U8Um1vqeZNuQz9+JbqdQvzllbeRCWpzI+IK8zbb7+NBg0aoEuXLvj6668REBCABw8eYPfu3ejSpQtq164NPz8/bN26FZ06dYJMJsPnn3/+0lFsXl5eOHToEBo1agRzc3PYF+NbSnNzc9396/Konwj7SHKZTIb+ffogatlyeHp6ooqnJ6KWLYeFhQU6tGsnaCxCkmreA/r1ReTkzxEcFIjQkBBs3roV8QkJ6NWju9ihlSpDb2+ZTIZevXojOnolPCp7wMPDE9HRK2BhYYHWrdvo6k2bNgVOTs4YOnQYgNyLoLt37wIAsrPVSEpKwo0bN2BpaQkPDw9RcikJhryf9+/ZE5999RWCAwIQEhyMLb/sRPzDh+jR+R0AwPwlS/EwOQlfPjey+trNmwCAp0+fQvnoEa7dvAlTU1P4vvDk77JMqnkXxZDPa1Jtb6nmXRQTS0tYV3bXvbZ2c4VdVT9kpaXjSWKiiJGVLkM+vgHpXrcwb2nlTVQc5bYjTiaTYffu3fjss8/wwQcfICkpCS4uLmjSpAkqVaoEAPjf//6HDz74AA0bNoSjoyMmTJiAtLS0Irc7b948jB49GlFRUXB3d3/pCLry4IOIgchUZeLLWbORlpaGkOrVsXTxIlhZWYkdWqmSYt7t2rRBamoqflwahaTkZFT188XiBfPh5uYmdmilztDbu3+/AVCpVJgz9xukp6cjOCgY33+3QC+/hMREyIz+G+iclJyEAQP76V6vXbcGa9etQVhYOBYv+lHQ+EuSIe/nbVq2wKO0VCyJXoVkhQJ+3t5Y+PXXcHv2AKEkhQLxifr3J+096EPd/69ev449Bw/C1cUFezZuEDT24pBq3i9jqOc1qba3VPMuin1gNTRftED3uuaokQCAu7t248yMmWKFJQhDPb7zSPW6hXlLK++yTMOZqWWaTMvJw4ISekQcEQknIzNb7BBEYW1Rbr/TKZbs9HSxQyABmTz3wCYp4X4uLb907iF2CKLoenif2CGIQqrXLSQt9g62YocginW7L4gdwivp0z5M7BBEUW7vEUdERERERERERFSesCOOiIiIiIiIiIhIANKcT0REREREREREZIA0vANZmcYRcURERERERERERAJgRxwREREREREREZEAODWViIiIiIiIiMhAaDViR0BF4Yg4IiIiIiIiIiIiAbAjjoiIiIiIiIiISACcmkpEREREREREZCD41NSyjSPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAvEccEREREREREZGB4C3iyjaOiCMiIiIiIiIiIhIAO+KIiIiIiIiIiIgEwKmpREREREREREQGQsO5qWUaR8QREREREREREREJgB1xRERERERERERkEJRKJfr37w9bW1vY2tqif//+ePToUZHrREREQCaT6S3169fXq6NSqTBixAg4OjrCysoK77zzDu7fv//a8bEjjoiIiIiIiIjIQGi05WMpLX369MHFixexd+9e7N27FxcvXkT//v1ful7btm0RHx+vW3bv3q33/qhRo7Bt2zbExMTgxIkTyMjIQMeOHZGTk/Na8fEecUREREREREREVO7FxcVh7969iI2NRb169QAAUVFRaNCgAa5fv46AgIBC1zU3N4eLi0uB76WmpmLZsmVYvXo13n77bQDAmjVr4OHhgYMHD6JNmzavHCNHxBERERERERERUbl36tQp2Nra6jrhAKB+/fqwtbXFb7/9VuS6R48ehbOzM/z9/TF48GA8fPhQ9965c+egVqvRunVrXZmbmxuqV6/+0u2+iCPiiIiIiIiIiIhIUCqVCiqVSq/M3Nwc5ubmb7zNhIQEODs75yt3dnZGQkJCoeu1a9cO7777LqpUqYK7d+/i888/R4sWLXDu3DmYm5sjISEBZmZmsLe311uvUqVKRW63IOyIE1janTtihyCKCk5OYocgChMbG7FDICp12enpYocgikxTS7FDEIW1hTQvHaS6n5O0dD28T+wQRLGtxatPJzIkrXbvEjsEEpBUP7+lSqstxRuwlaBZs2Zh2rRpemVTpkzB1KlT89WdOnVqvrovOnPmDABAJpPle0+r1RZYnqdXr166/1evXh21a9dGlSpVsGvXLnTr1q3Q9V623YLwaCQiIiIiIiIiIkFFRkZi9OjRemWFjYYbPnw4evfuXeT2vLy88McffyAxMTHfe0lJSahUqdIrx+bq6ooqVarg5s2bAAAXFxdkZWVBqVTqjYp7+PAhGjZs+MrbBdgRR0REREREREREAnudaaiOjo5wdHR8ab0GDRogNTUVv//+O+rWrQsAOH36NFJTU1+rw0yhUOCff/6Bq6srAKBWrVowNTXFgQMH0LNnTwBAfHw8rly5gm+++eaVtwvwYQ1ERERERERERAZDo9WWi6U0BAYGom3bthg8eDBiY2MRGxuLwYMHo2PHjnpPTK1WrRq2bdsGAMjIyMDYsWNx6tQp/PXXXzh69Cg6deoER0dHdO3aFQBga2uLQYMGYcyYMTh06BAuXLiAfv36oUaNGrqnqL4qjogjIiIiIiIiIiKDsHbtWowcOVL3hNN33nkHCxcu1Ktz/fp1pKamAgCMjY1x+fJlrFq1Co8ePYKrqyuaN2+ODRs2wOa5+77/73//g4mJCXr27ImnT5+iZcuWWLlyJYyNjV8rPnbEERERERERERGRQXBwcMCaNWuKrPP8Ay0sLS2xb9/LH1hkYWGBBQsWYMGCBcWKjx1xREREREREREQGopw8NFWyeI84IiIiIiIiIiIiAbAjjoiIiIiIiIiISADsiCMiIiIiIiIiIhIA7xFHRERERERERGQgNBreJK4s44g4IiIiIiIiIiIiAbAjjoiIiIiIiIiISACcmkpEREREREREZCA4M7Vs44g4IiIiIiIiIiIiAbAjjoiIiIiIiIiISACcmkpEREREREREZCC0nJpapnFEHBERERERERERkQDYEUdERERERERERCQAwTri/vrrL8hkMly8eFGoH0lERERERERERFRmcEScgdiydy+6fzwUzXq/h/fHjcfFq1cLrXs0NhafTJuO9u9/gLf79cfgyEmIvXBRuGBL0IZt29C+Zy/UfbsV3vtwMM5fulRo3aRkBSZOn47OffshrGkzfDN/gYCRCidm40a06dAR4fXqo2efPjh3/rzYIQlCq9Xihx9/RPNWrVGrfgNEfDgYt27fFjusEqPVahH101J07NQeTZs2xsdDh+DOnaLzu3PnNiZGTkCXrp1Rv0FdxMSsFyjakiHl41uK7V0UQz6+pbqfM29p5V0UQz6+C+NYMxRvzf0anX7Zjp6xJ+DWpLHYIZU4qX6OSTXvwkjx+C4rNFptuVikih1xBuDgyZP4fsVKDOzeDSvnzkFoYCDGfDUTCUlJBda/eDUOdUJDMPezSVjxzTeoVT0Y42fPxvU7dwSOvHj2HTqMOQsW4sMB/RHzUxTCQkIwbPwExCcmFlg/S50Fe1s7fNi/H/z9fAWOVhh79u3D7DlzMXjQIGxavw7hYWEYMnwE4uPjxQ6t1C1fGY1Va9Zi0sQJiFmzGo5yOQYP+RiPHz8WO7QSsXrNKqxfvx5jxozD8uUrIZfLMfKTEUXml5mpgrubO4YNHQa5XC5gtMUn9eNbau39MoZ6fEt1P2fe0sr7ZQz1+C6KiaUlHt28hfPzvhU7lFIj1c8xqeZdGCke30SvokQ74vbu3Yu33noLdnZ2kMvl6NixI26/0ON97do1NGzYEBYWFggODsbRo0d17ymVSvTt2xdOTk6wtLRE1apVsWLFCt37//77L3r16gV7e3vI5XJ07twZf/31l+79iIgIdOnSBXPnzoWrqyvkcjmGDRsGtVqtq6NSqTB+/Hh4eHjA3NwcVatWxbJly3TvX716Fe3bt4e1tTUqVaqE/v37Izk5Wff+5s2bUaNGDVhaWkIul+Ptt98W/UQS88sv6NSiBd55+214Va6MUR+8D2e5HNv27S+w/qgP3ke/Ll0Q5OcHDzdXDOnbFx4uLjh59pzAkRfP6o0b0bVDe3Tr2BE+Xl4YP3IEXJycsGn7jgLru7u6YsInI9GpbVtYW1kLHK0wVq1Zi25duqBHt67w9fHBxHHj4OJSCTGbNosdWqnSarVYvW4dPho0CK1atkRVPz/MnDEdmZmZ2LVnj9jhFZtWq8WGDTGIiIhA82bN4evriy8+n4LMzEzs37+v0PWCgoIwYsRItGrVGqamZgJGXHxSPr6l2N5FMeTjW6r7OfOWVt5FMeTjuygJp2JxZUkU/j36q9ihlAqpfo5JNe/CSPX4JnoVJdoR9/jxY4wePRpnzpzBoUOHYGRkhK5du0Kj0ejqjBs3DmPGjMGFCxfQsGFDvPPOO1AoFACAzz//HFevXsWePXsQFxeHxYsXw9HREQDw5MkTNG/eHNbW1vj1119x4sQJWFtbo23btsjKytJt/8iRI7h9+zaOHDmC6OhorFy5EitXrtS9P2DAAMTExGD+/PmIi4vDjz/+CGvr3Iub+Ph4NG3aFDVr1sTZs2exd+9eJCYmomfPnrr333vvPXzwwQeIi4vD0aNH0a1bN2hFHFKpVqtx/fYd1K0ZqldeNzQUl69ff6VtaDQaPMnMREXr8nORp1arEXfjBhrUqaNXXr9OHVy6ckWkqMSlVqtxNS4ODRvU1ytvWL8BLhUx9cUQ3P/3XyQnJ+vlbmZmhtq1auHipT9EjKxkPHjwAAqFAvXq6ucXFhaOy5fLf34vkvrxLbX2fhlDPb6lup8zb2nl/TKGenxLnVQ/x6Sad2F4fItLqy0fi1SZlOTGunfvrvd62bJlcHZ2xtWrV3WdXcOHD9fVW7x4Mfbu3Ytly5Zh/PjxuHfvHsLCwlC7dm0AgJeXl25bMTExMDIywk8//QSZTAYAWLFiBezs7HD06FG0bt0aAGBvb4+FCxfC2NgY1apVQ4cOHXDo0CEMHjwYN27cwMaNG3HgwAG8/fbbAAAfHx/dz1i8eDHCw8Mxc+ZMXdny5cvh4eGBGzduICMjA9nZ2ejWrRuqVKkCAKhRo0ZJ/gpf26P0dORoNHCwtdUrd7CzRcqjR6+0jfU//4KnmZlo0ahhKURYOpSpqcjJyYGDvYNeudzBHskpKSJFJS6l8hFycnIgd9Af0i6XOyD5WWe3oUpOzs2voNwfGMC03LwvKxwc9Pd3BwcHJCSU//xeJPXjW2rt/TKGenxLdT9n3tLK+2UM9fiWOql+jkk178Lw+CYqXImOiLt9+zb69OkDHx8fVKxYEd7e3gCAe/fu6eo0aNBA938TExPUrl0bcXFxAICPP/4YMTExqFmzJsaPH4/ffvtNV/fcuXO4desWbGxsYG1tDWtrazg4OCAzM1Nv+mtwcDCMjY11r11dXfHw4UMAwMWLF2FsbIymTZsWGP+5c+dw5MgR3fatra1RrVo1XW6hoaFo2bIlatSogXfffRdRUVFQKpWF/j5UKhXS0tL0FtVzo/dK1LPOyTyv2ru8//gJLNu4ETNGj87XmVcevJA2tFroOmqlKv/vRGtwv5Odu3ejTsNGuiU7OxuA4ewPe/ftRfMWTXXLf/m9eJxrIUP5y+9VGUp7vgzbW5+hH98vMtS8XoZ555JK3nmkdnxLhVQ/x6Sad2F4fBO9uhIdEdepUyd4eHggKioKbm5u0Gg0qF69ut7U0YLkHYjt2rXD33//jV27duHgwYNo2bIlhg0bhrlz50Kj0aBWrVpYu3ZtvvWdnJx0/zc1Nc237bypsZaWlkXGodFo0KlTJ3z99df53nN1dYWxsTEOHDiA3377Dfv378eCBQvw2Wef4fTp07pOx+fNmjUL06ZN0ysb9/EQTBg6tMg4XoedjQ2MjYzyjX5TpqbCwc6uyHUPnjyJWYsW4cuxY1AnNKTEYhKCva0tjI2NoXjhW+QUpRJye3uRohKXvb0djI2N841+S0lRQv7CN3PlXfOmTRFSvbruddaz+0AmKxR654OUlJR838KVB43faozgoGDda7U69xyqUCh00/WB3PtqvvitqyGQ2vEt9fZ+kaEf33mktp/nYd7SyvtFUjm+pUaqn2NSzbswPL7LFik/kbQ8KLERcQqFAnFxcZg8eTJatmyJwMDAAkeLxcbG6v6fnZ2Nc+fO6UadAbmdahEREVizZg2+++47LF26FAAQHh6OmzdvwtnZGX5+fnqL7SuO5KpRowY0Gg2OHTtW4Pvh4eH4888/4eXlle9nWFlZAcjt2GvUqBGmTZuGCxcuwMzMDNu2bStwe5GRkUhNTdVbRn344SvF+qpMTU0R4OuD31+YZ3/mjz9QIyCg0PX2Hz+BLxf+gKmjRqFRrVolGpMQTE1NEejvj1Nnz+qVnz57FqHPfQBIiampKYICA3Eq9rRe+anYWISGhhayVvlkZWUFT09P3eLr4wNHR0eceu78olarcfbcOdQsZ53MQG5+Hh4eusXb2wdyuRy/n/mvbdVqNS5cOI8aNcpffi8jteNb6u39IkM/vvNIbT/Pw7yllfeLpHJ8S41UP8ekmndheHwTvboSGxGX9yTTpUuXwtXVFffu3cPEiRPz1fvhhx9QtWpVBAYG4n//+x+USiU++OADAMAXX3yBWrVqITg4GCqVCjt37kRgYCAAoG/fvpgzZw46d+6M6dOno3Llyrh37x62bt2KcePGoXLlyi+N0cvLCwMHDsQHH3yA+fPnIzQ0FH///TcePnyInj17YtiwYYiKisJ7772HcePGwdHREbdu3UJMTAyioqJw9uxZHDp0CK1bt4azszNOnz6NpKQkXYwvMjc3h7m5uV6Z2qzkn4TTu1MnTJ+/AIG+PqgeEIAdBw4gMTkZXZ7dN2/xmrVISlHgi5EjAeR2ws1YsACjPngf1f2rQvGsw9TczAzWzzocy4P+PXvis6++QnBAAEKCg7Hll52If/gQPTq/AwCYv2QpHiYn4cvPPtOtc+3mTQDA06dPoXz0CNdu3oSpqSl8n7sfYXk2oF9fRE7+HMFBgQgNCcHmrVsRn5CAXj26v3zlckwmk6F/nz6IWrYcnp6eqOLpiahly2FhYYEO7dqJHV6xyWQy9OrVG9HRK+FR2QMeHp6Ijl4BCwsLtG7dRldv2rQpcHJyxtChwwDkXuzcvXsXAJCdrUZSUhJu3LgBS0tLeHh4iJLLq5Ly8S3F9i6KIR/fUt3Pmbe08i6KIR/fRTGxtIR1ZXfda2s3V9hV9UNWWjqeJCaKGFnJkOrnmFTzLoxUj2+iV1FiHXFGRkaIiYnByJEjUb16dQQEBGD+/Plo1qyZXr3Zs2fj66+/xoULF+Dr64sdO3bohu6amZkhMjISf/31FywtLdG4cWPExMQAACpUqIBff/0VEyZMQLdu3ZCeng53d3e0bNkSFStWfOU4Fy9ejEmTJmHo0KFQKBTw9PTEpEmTAABubm44efIkJkyYgDZt2kClUqFKlSpo27YtjIyMULFiRfz666/47rvvkJaWhipVqmDevHloJ/KJ5O1GjZCano7lmzZDoVTCx9MTcydNgqtz7hBghVKJxORkXf0dB/YjJycH86J+wryon3Tl7Zs1w+QRwwWP/021adkCj9JSsSR6FZIVCvh5e2Ph11/DzcUFAJCkUCA+8aHeOr0H/Tci8er169hz8CBcXVywZ+MGQWMvLe3atEFqaip+XBqFpORkVPXzxeIF8+Hm5iZ2aKXug4iByFRl4stZs5GWloaQ6tWxdPEi3WjW8q5/vwFQqVSYM/cbpKenIzgoGN9/t0Avv4TERMiM/hvonJSchAED++ler123BmvXrUFYWDgWL/pR0Phfl9SPb6m198sY6vEt1f2ceUsr75cx1OO7KPaB1dB80QLd65qjcr8sv7trN87MmFnYauWKVD/HpJp3YaR4fJcVGs5MLdNkWi0nDwtJceWy2CGIosJz9wWQEhMbG7FDIAFlZGaLHYIoLNRPxQ5BFJmmRd931FBZW5To7WXLjez0dLFDICp1Ur1u2daizcsrGaBWu3eJHQIJSKqf36YVpNnpN3fNKbFDeCVj+zV4eSUDVKJPTSUiIiIiIiIiIqKCsSOOiIiIiIiIiIhIANIcn0pEREREREREZIB4B7KyjSPiiIiIiIiIiIiIBMCOOCIiIiIiIiIiIgFwaioRERERERERkYHQcGZqmcYRcURERERERERERAJgRxwREREREREREZEAODWViIiIiIiIiMhA8KmpZRtHxBEREREREREREQmAHXFEREREREREREQCYEccERERERERERGRAHiPOCIiIiIiIiIiA6HRiB0BFYUj4oiIiIiIiIiIiATAjjgiIiIiIiIiIiIBcGoqEREREREREZGB0GjFjoCKwhFxREREREREREREAmBHHBERERERERERkQA4NZWIiIiIiIiIyEBotZybWpZxRBwREREREREREZEA2BFHREREREREREQkAHbEERERERERERERCYD3iBOYkZun2CGI4smDe2KHII6kJLEjEIVU93ONRPfzfYnSvAdFhwa+YocgiozMbLFDEIeppdgRiCJJ+VjsEEThZG8ldgjikOjx3Wr3LrFDEMWB9h3EDkEUlnI7sUMQhaWjg9ghiOLt6FVihyAKjTQvz8sNjogjIiIiIiIiIiISADviiIiIiIiIiIiIBMCpqUREREREREREBkKj5dzUsowj4oiIiIiIiIiIiATAjjgiIiIiIiIiIiIBcGoqEREREREREZGB4MzUso0j4oiIiIiIiIiIiATAjjgiIiIiIiIiIiIBsCOOiIiIiIiIiIhIALxHHBERERERERGRgdDwHnFlGkfEERERERERERERCYAdcURERERERERERALg1FQiIiIiIiIiIgOh0XJualnGEXFEREREREREREQCYEccERERERERERGRADg1lYiIiIiIiIjIQHBmatnGEXFEREREREREREQCYEccERERERERERGRADg1lYiIiIiIiIjIQGg4NbVMY0ccgISEBMyaNQu7du3C/fv3YWtri6pVq6Jfv34YMGAAKlSogKVLl2LdunU4f/480tPToVQqYWdnJ3boerRaLX5aFoUdO7YjPS0dQcHBGDd2HHx8fAtd586d21gatRTXrl1DQkI8Rn3yKXr3fk/AqItny969WLfjZyiUSnh7eOCT9yNQMyiowLpHY2Oxbd9+3PzrL2Sp1fD28MCgnj1RP6ymsEGXAKnmDXA/l2J7H9y1CadPHsTTJxnw9KqKzr0+hIubxyutf/HsSaxf/h2CQupg4JDxpRxt6YvZuBErolchKTkZfr4+mDB2LGqFh4sdVomR4vENSDvv9WujsW/vTmRkpMM/IBBDhn6CKlW8C11n396dOHxoP/7++y4AwM/PHwMGfgj/gEChwi42Kbc385ZO3gVxrBmKav36wD4gAJZOjjgxPhIPfj0udlglpkqH9vDr1g3mDg5Iv3cPfy6NQsqffxZa36tDB3h16ogKzs54mpSEmxs24v7hwwJGXDIqt2iJKu3bw8zWFo8f/Isba9fi0Y0bhdZ3adAAVdp3QIVKlZD99CkUl//AzfUxUD/OEDBqInFJfmrqnTt3EBYWhv3792PmzJm4cOECDh48iE8//RS//PILDh48CAB48uQJ2rZti0mTJokcceFWr1mF9evXY8yYcVi+fCXkcjlGfjICjx8/LnSdzEwV3N3cMWzoMMjlcgGjLb6DJ0/i+xUrMbB7N6ycOwehgYEY89VMJCQlFVj/4tU41AkNwdzPJmHFN9+gVvVgjJ89G9fv3BE48uKRat55uJ9Lq72PHdiB44d3okvPQRgxYTasK9rhpwUzoMp8+tJ1lYok7Nq6Ct5+5ecP9KLs2bcPs+fMxeBBg7Bp/TqEh4VhyPARiI+PFzu0EiO14zuPVPPesjkG27dtwv99PBLffvcj7O0d8MVn4/DkyZNC17n8x0U0adoCM2f9D3Pm/QAnJ2d8MXkcFMkFnxPLIqm2N/OWVt4FMbG0xKObt3B+3rdih1Li3Bo3RvXBg3Fzw0b8OnIkUq78iXrTpsLSyanA+lXat0O1iIG4sXYdjg4diutr16HGx0NQqW5dgSMvnkp168G/b1/c/eVnnP7iCzy6fgM1x4yFuUPB+61tVX8Ef/R/ePDrMZyaFIk/Fi5ERW8fBA76QODIicQl+Y64oUOHwsTEBGfPnkXPnj0RGBiIGjVqoHv37ti1axc6deoEABg1ahQmTpyI+vXrixxxwbRaLTZsiEFERASaN2sOX19ffPH5FGRmZmL//n2FrhcUFIQRI0aiVavWMDU1EzDi4ov55Rd0atEC77z9NrwqV8aoD96Hs1yObfv2F1h/1Afvo1+XLgjy84OHmyuG9O0LDxcXnDx7TuDIi0eqeQPcz6XY3icO70KLtt1QPaweXNw80WvAcKizVLhw5kSR62o0OYhZ+T1adegJB0dngSIuXavWrEW3Ll3Qo1tX+Pr4YOK4cXBxqYSYTZvFDq1ESPH4BqSd98/bN6Nn735o2KgJqnh549MxE6FSZeLY0YOFrjd2/GR06NgFPr5+8PDwxPCRY6HRaHHp0nkBo39zUm5v5i2dvAuTcCoWV5ZE4d+jv4odSonz6doF9/YfwL39+5Hxz338GRWFp8nJqNK+fYH1K7dogb/37MGD48fxJCERD379Fff2H4Bfj+4CR148nm3b4sGvx/Dg2DE8iX+AG+vWQpWSgsotWxRY39bPF0+Tk/DPgQPITE5G6s0b+PfIEVT0KnwkNJEhknRHnEKhwP79+zFs2DBYWVkVWEcmkwkc1Zt58OABFAoF6tX9r6PQzMwMYWHhuHz5DxEjKx1qtRrXb99B3ZqheuV1Q0Nx+fr1V9qGRqPBk8xMVLS2Lo0QS4VU887D/TyXVNo7RfEQ6WmPUDXwv/xNTE3hUzUIf98pOv+DuzfDyroi6jZqWdphCkKtVuNqXBwaNtD/Mqhh/Qa4dOmSSFGVLKkd33mkmndiQjyUyhSEhdfWlZmamqF6jVBciyt8KteLVCoVcnKyYW1dsTTCLHFSbW/mLa28pUZmYgJbPz8kXbigV550/gIcAqsVuI6RqSk0WWq9spysLNj5+0NmbFxqsZYkmbExbLy8oLhyRa9cceUy7PyqFrhO6s2bsLB3gDwkBABgVrEinOvUQbKBXMuUJVqttlwsUiXpjrhbt25Bq9UiICBAr9zR0RHW1tawtrbGhAkTRIru9SgUCgCAg4ODXrmDgwMUKQoxQipVj9LTkaPRwMHWVq/cwc4WKY8evdI21v/8C55mZqJFo4alEGHpkGreebif55JKe6enPgIA2Njo529tY4v0tEeFrvfX7Ws489thdO87pBSjE5ZS+Qg5OTmQvzDVQy53QLLCMPZ9qR3feaSat1KZAgCws7PXK7ezs9e99yqiVyyFXO6ImmG1SjS+0iLV9mbe0spbaswqVoSRsTFUj5R65apHSpjb2xe4TtK58/Bs0xq2frn3CrT184Nnq7dhZGoKs4rl44sFUxsbGBkbIys1Va88KzUNZi9cu+ZJvXULV378ETWGDkOLZcvRZMFCZD95gutrVgsRMlGZwYc1IP+ot99//x0ajQZ9+/aFSqV64+2qVKp866tUKpibm7/xNvPs3bcXX389S/d63tz/Acifi1arhQzlY1TfG8mX76uttv/4CSzbuBFfT5iQr5OjXJBI3tzPn5FIe1/4/Ti2rl+ie/3+x5G5/3kx//xFOqrMp4hZOR/d+w6BVTkZIfM6Xsxbq9WWm5HbL5Lq8S3VvI8eOYAfFvx3X6gvpuX+DvLn/eqzEbZsWo9fjx3GzK//BzOzsjl9T6rtzbxzSSVveuaF6zOZTFboRduNmBiY29vjrXnzAJkMKuUj/HPoEPx69IBWoxEg2BL0YooyFJq3lZsbAvr1w90dO6C4chlmtnao2rsXqg2MQNzyZaUeKlFZIemOOD8/P8hkMly7dk2v3MfHBwBgaWlZrO3PmjUL06ZN0ysbP34CJk6ILNZ2AaDxW40RHBSse61WZwHI/ebN0dFRV65UKvN9C2cI7GxsYGxklG9UkDI1FQ4veZrtwZMnMWvRInw5dgzqhIaUXpClQGp5cz+XVnsHhdSGh5ef7nV2djYAID3tESra/veN8uP0VFjb2BW4DUVSApSKJEQvnq0ryxv2Hjm8F8ZO+R5yJ5dSiL502dvbwdjYON/ot5QUJeTldN+X6vEt1bzr1msE/4D/nvacl7dSmQKH50Z6pqYq842SK8jWLRuwaeNazPhqHry9C3/6pNik2t7MO5dU8pa6rLQ0aHJy8o1+M7O1g6qQGQyarCxc+v57/LFwIczt7JCpVKJK2zZQP3mCrLQ0AaIuPnV6OjQ5OTCz0/+y16xixUJz8OrYCY9u3sTfe3bnFvzzD65Fq1Bn8mTc3rI53+g6enMa6c76LBckPTVVLpejVatWWLhwYZFPLnpTkZGRSE1N1Vs+HTW6RLZtZWUFDw8P3eLt7QO5XI7fz5zW1VGr1bhw4Txq1Cgff4S/DlNTUwT4+uD3S/r31zjzxx+o8cJU4+ftP34CXy78AVNHjUKjWuVjGsvzpJY393Nptbe5hSUcnV11SyXXyrCpaIebcf/ln52txp2bV1HFp+D8nVzc8enkefhk0hzdElijNnz8g/HJpDmwtS+fT58zNTVFUGAgTsWe1is/FRuL0NDQQtYq26R6fEs17woVKsDNzV23eHp6wd7eARfPn9XVUavVuHL5EqoFBhexJWDr5hhsWL8aU2d8g6r+hZ8LywKptjfzllbeUqfNzkbqrVtwCqupV+4UVhMpcdcKXilv3ZwcZCoUgEYD9yZNkPj7768+9UFk2pwcpP/1FxyCq+uVOwRXx6NbNwtcx9jMLP99wfJGAJbTEf5Eb0LSI+IAYNGiRWjUqBFq166NqVOnIiQkBEZGRjhz5gyuXbuGWs/+iE1ISEBCQgJu3boFALh8+TJsbGzg6elZ6Dda5ubm+aah5mSXzolVJpOhV6/eiI5eCY/KHvDw8ER09ApYWFigdes2unrTpk2Bk5Mzhg4dBiD3YuDu3bsAcv/ATUpKwo0bN2BpaQkPD49SibWk9O7UCdPnL0Cgrw+qBwRgx4EDSExORpfWrQEAi9esRVKKAl+MHAkgt3NixoIFGPXB+6juXxUKZe59HMzNzGBdyMM6yiKp5g1wP5die7/VogOO7NsKR2cXODq74sjerTA1M0dYnbd09TasXICKdg5o16UvTE3N4OLmqbcdywoVACBfeXkzoF9fRE7+HMFBgQgNCcHmrVsRn5CAXuXsCWuFkeLxDUg773e69MCmjWvh5l4Zbm6VsXHDGpibW6Bps7d19b6dOxNyuRMGvj8YQO501DWrV2Ds+M9QydkFypTc+8lZWFoWeyaDEKTc3sxbOnkXxsTSEtaV3XWvrd1cYVfVD1lp6XiSmChiZMV3Z9t2hI0ZjUc3b0F5LQ5V2raFpZMT/t6dO/Kr2sCBsJDLcfHb3Cn6Vm5usAvwx6PrN2BqbQ2fLl1gU6UKLnz7PzHTeG339u5F8P/9H9Lv3sWjW7dQuXkzWMjl+PfwYQCA77vvwsLeHn8uXQoASLp4AYHvfwD3Fi2QcvkyzOzs4N+nL1Jv30bWK97/mMgQSL4jztfXFxcuXMDMmTMRGRmJ+/fvw9zcHEFBQRg7diyGDh0KAPjxxx/1ppk2adIEALBixQpERESIEXo+/fsNgEqlwpy53yA9PR3BQcH4/rsFek+ETUhMhMzov4GQSclJGDCwn+712nVrsHbdGoSFhWPxoh8Fjf91vd2oEVLT07F802YolEr4eHpi7qRJcHV2AgAolEokJifr6u84sB85OTmYF/UT5kX9pCtv36wZJo8YLnj8b0qqeefhfi6t9m7aqjPUWVnYHvMTnj55DA8vP3w4YjLMLf77g/uRMhkyI8P/FrVdmzZITU3Fj0ujkJScjKp+vli8YD7c3NzEDq3ESO34ziPVvLv36I0slQqLf/gOGRnp8A8IxPQv56DCs85zAEhKeqiX9+5dO5CdrcbsmVP1tvVen4Ho0y9CoMiLR6rtzbyllXdB7AOrofmiBbrXNUflfol4d9dunJkxU6ywSsSD48dhWtEG/u/1hrmDA9L//hunp0zF06QkAICFgz0snZx09WXGRvDt2hXW7u7Q5ORA8ccfODF2HJ4+fChWCm8k8ffTMLW2hnfnzjC3s0PGv/dx8dt5uaP8AJjb2sHiudsPxJ84ARMLS3i8/Tb8e7+H7CdPkBJ3Fbc2bhQrBYPFqallm0wr5WfGikCZIs1575oH98QOgQRkVM5HHr0pqe7nxxOl+THSoUHZvS9VacrIzBY7BBJQkrLkb91RHjjZl68RxERv4kD7DmKHIApLuZ3YIYjC0lGa9yV8O3qV2CGIYvSCY2KH8Eq+HdFU7BBEIel7xBEREREREREREQmFHXFEREREREREREQCkPw94oiIiIiIiIiIDIWGdyAr0zgijoiIiIiIiIiISADsiCMiIiIiIiIiIhIAp6YSERERERERERkIzkwt2zgijoiIiIiIiIiISADsiCMiIiIiIiIiIhIAp6YSERERERERERkIjUbsCKgoHBFHREREREREREQkAHbEERERERERERERCYAdcURERERERERERALgPeKIiIiIiIiIiAyERit2BFQUjogjIiIiIiIiIiISADviiIiIiIiIiIiIBMCpqUREREREREREBkKr5dzUsowj4oiIiIiIiIiIiATAjjgihPP99AAAXH9JREFUIiIiIiIiIjIISqUS/fv3h62tLWxtbdG/f388evSoyHVkMlmBy5w5c3R1mjVrlu/93r17v3Z8nJpKRERERERERGQgpP7U1D59+uD+/fvYu3cvAOCjjz5C//798csvvxS6Tnx8vN7rPXv2YNCgQejevbte+eDBgzF9+nTda0tLy9eOjx1xRERERERERERU7sXFxWHv3r2IjY1FvXr1AABRUVFo0KABrl+/joCAgALXc3Fx0Xu9Y8cONG/eHD4+PnrlFSpUyFf3dXFqKhERERERERERlXunTp2Cra2trhMOAOrXrw9bW1v89ttvr7SNxMRE7Nq1C4MGDcr33tq1a+Ho6Ijg4GCMHTsW6enprx0jR8QREREREREREZGgVCoVVCqVXpm5uTnMzc3feJsJCQlwdnbOV+7s7IyEhIRX2kZ0dDRsbGzQrVs3vfK+ffvC29sbLi4uuHLlCiIjI3Hp0iUcOHDgtWJkRxwRERERFcjJ3krsEIiolFjK7cQOQRRPFY/EDoGo1JWXe8TNmjUL06ZN0yubMmUKpk6dmq/u1KlT89V90ZkzZwDkPnjhRVqttsDygixfvhx9+/aFhYWFXvngwYN1/69evTqqVq2K2rVr4/z58wgPD3+lbQPsiCMiIiIiIiIiIoFFRkZi9OjRemWFjYYbPnz4S59Q6uXlhT/++AOJiYn53ktKSkKlSpVeGtPx48dx/fp1bNiw4aV1w8PDYWpqips3b7IjjoiIiIiIiIiIyq7XmYbq6OgIR0fHl9Zr0KABUlNT8fvvv6Nu3boAgNOnTyM1NRUNGzZ86frLli1DrVq1EBoa+tK6f/75J9RqNVxdXV+ewHP4sAYiIiIiIiIiIgOh1ZaPpTQEBgaibdu2GDx4MGJjYxEbG4vBgwejY8eOek9MrVatGrZt26a3blpaGjZt2oQPP/ww33Zv376N6dOn4+zZs/jrr7+we/duvPvuuwgLC0OjRo1eK0Z2xBERERERERERkUFYu3YtatSogdatW6N169YICQnB6tWr9epcv34dqampemUxMTHQarV477338m3TzMwMhw4dQps2bRAQEICRI0eidevWOHjwIIyNjV8rPk5NJSIiIiIiIiIig+Dg4IA1a9YUWUdbwJC8jz76CB999FGB9T08PHDs2LESiY8dcUREREREREREBkJTWvM+qURwaioREREREREREZEA2BFHREREREREREQkAE5NJSIiIiIiIiIyEBrOTC3TOCKOiIiIiIiIiIhIAOyIIyIiIiIiIiIiEgA74oiIiIiIiIiIiATAe8QRERERERERERkI3iOubOOIOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAnJpKRERERERERGQgODW1bOOIOCIiIiIiIiIiIgGwI46IiIiIiIiIiEgAnJpKRERERERERGQgtJyaWqaxIw5AQkICZs2ahV27duH+/fuwtbVF1apV0a9fPwwYMACZmZmYMmUK9u/fj3/++QeOjo7o0qULZsyYAVtbW7HD19FqtfhpWRR27NiO9LR0BAUHY9zYcfDx8S10nTt3bmNp1FJcu3YNCQnxGPXJp+jd+z0Boy6eLXv3Yt2On6FQKuHt4YFP3o9AzaCgAusejY3Ftn37cfOvv5ClVsPbwwODevZE/bCawgZdAqSaN8D9XIrtfXDXJpw+eRBPn2TA06sqOvf6EC5uHq+0/sWzJ7F++XcICqmDgUPGl3K0pS9m40asiF6FpORk+Pn6YMLYsagVHi52WCVGisc3wLyZN/MuDPMun3lX6dAeft26wdzBAen37uHPpVFI+fPPQut7degAr04dUcHZGU+TknBzw0bcP3xYwIhLl2PNUFTr1wf2AQGwdHLEifGRePDrcbHDKjFsb6LXJ/mpqXfu3EFYWBj279+PmTNn4sKFCzh48CA+/fRT/PLLLzh48CAePHiABw8eYO7cubh8+TJWrlyJvXv3YtCgQWKHr2f1mlVYv349xowZh+XLV0Iul2PkJyPw+PHjQtfJzFTB3c0dw4YOg1wuFzDa4jt48iS+X7ESA7t3w8q5cxAaGIgxX81EQlJSgfUvXo1DndAQzP1sElZ88w1qVQ/G+Nmzcf3OHYEjLx6p5p2H+7m02vvYgR04fngnuvQchBETZsO6oh1+WjADqsynL11XqUjCrq2r4O0XKECkpW/Pvn2YPWcuBg8ahE3r1yE8LAxDho9AfHy82KGVGKkd33mYN/Nm3gVj3uUvb7fGjVF98GDc3LARv44ciZQrf6LetKmwdHIqsH6V9u1QLWIgbqxdh6NDh+L62nWo8fEQVKpbV+DIS4+JpSUe3byF8/O+FTuUEsf2Jnozku+IGzp0KExMTHD27Fn07NkTgYGBqFGjBrp3745du3ahU6dOqF69OrZs2YJOnTrB19cXLVq0wFdffYVffvkF2dnZYqcAIPfbtg0bYhAREYHmzZrD19cXX3w+BZmZmdi/f1+h6wUFBWHEiJFo1ao1TE3NBIy4+GJ++f/27ju+pvv/A/jrJrJkTxKyIyGLqFWKJCXUakJLv2aDamntVa1NaalVau89anRQYldQMSIxEkJWNUKmhEy59/dHfrl1JVEl9x455/V8PO7j4X7uufF6u+fGvZ/zGb+ia2AgurVrB6e6dTFqYChsLC2x/0hYhcePGhiKvsHB8HRzg72dLT7r0wf2tWvj7KXLGk7+eqRaN8DzXIqvd/iJgwjs2B3efs1R284Bvfp/geKiQkReDH/hc+XyEuzcuATtO/eEhZWNhhKr1+at29A9OBgfdA+Bq4sLvhw/HrVr18LOPT8JHa1KSPH9DbBu1s26WXd51blul5BgJIcdRXJYGB7/dQ831qxBfno6HDt1qvD4uoGBSPr9d6ScOYO81AdI+eMPJIcdhdsHPTScXH1Sz/+J66vW4O9Tfwgdpcrx9SZ6NZLuiMvIyEBYWBg+//xzGBoaVniMTCarsP3Ro0cwMTFBjRpvxuzelJQUZGRkoHmzFso2XV1d+Pk1xrVr0QImU4/i4mLcuhuPZo0aqrQ3a9gQ127deqmfIZfLkVdQABMjI3VEVAup1l2G53kpqbzemRkPkZuTjXoN/qm/ho4OXOp5Iin+xfUfO/QTDI1M0KzVu+qOqRHFxcW4GRODlm+3UGlv2eJtREVFCZSqaknt/V2GdbNu1s26xUJWowZM3dyQFhmp0p52JRIWDepX+BwtHR3Ii4pV2kqKimDm7g6ZtrbastLr4+v9ZpMrqsdNqiTdEXfnzh0oFAp4eHiotFtZWcHIyAhGRkaYOHFiuedlZGRg1qxZ+PTTTzUV9V9lZGQAACwsLFTaLSwskJGZIUQktcrOzUWJXA6L59boszAzRWZ29kv9jB2//Ir8ggIEtmqphoTqIdW6y/A8LyWV1zv3UTYAwNhYtX4jY1Pk5mRX+rzEu7G4eO4EevT5TI3pNCsrKxslJSWwtFCdomRpaYH0DHGc+1J7f5dh3ay77D7rFh+p1a1rYgItbW0UZmeptBdmZ0HP3LzC56RdvgKHDkEwdStdM8/UzQ0O7dtBS0cHuiYmas9Mr46vN9GrezOGcwns+VFvERERkMvl6NOnDwoLC1Uey8nJQefOneHp6Ylp06a98OcWFhaWe35hYSH09PReO/PhI4fx3XdzlfcXfL8IQPlaFAoFZKh4VJ8olKv35Z4WdiYc63bvxncTJ5br5KgWJFI3z/P/J5HXOzLiDPbtWKW8Hzp0Uukfnq+/fJNSYUE+dm78AT36fAZDI/F9oHu+boVCUenI7TedVN/frLsU62bdAOsWrec+p8hksko/vNzeuRN65uZ4Z8ECQCZDYVY2/jp+HG4ffACFXK6BsPTa+HoT/WeS7ohzc3ODTCZDbGysSruLiwsAwMDAQKU9NzcXHTt2hJGREfbv3w8dHZ0X/vy5c+dixowZKm0TJkzElxMnvXb21u+0hpenl/J+cXERgNIrb1ZWVsr2rKysclfhxMDM2BjaWlrlRgVlPXoECzOzFz732NmzmLt8OWaPG4umDX3VF1INpFY3z3Npvd6evk1g7+SmvF+2BmduTjZMTP+5svok9xGMjM0q/BkZaanIykjDphXfKtsU//9hcNIXvTBu2hJYWtdWQ3r1Mjc3g7a2drnRb5mZWbCspue+VN/frLsU62bdAOsWm6KcHMhLSsqNhtI1NUNhJSP55UVFiFqyBNHLlkHPzAwFWVlw7NgBxXl5KMrJ0UBqelV8vd9s8pe9ck+CkPTUVEtLS7Rv3x7Lli174c5FQOlIuKCgIOjq6uKXX36Bvr7+v/78SZMm4dGjRyq30aPGVEl2Q0ND2NvbK2/Ozi6wtLRExMULymOKi4sRGXkFPj7V40v4f6GjowMPVxdERKmur3ExOho+z001flbYmXDMXvYjpo8ahVZvvaXumFVOanXzPJfW662nbwArG1vlrZZtXRibmCEu5p/6nz4tRnzcTTi6VFy/de06GD15AUZ+NV95a+DTBC7uXhj51XyYmlev3efK6OjowLNBA5z/84JK+/k//0TDhg0redabTarvb9bNull3KdYtnrrLKJ4+xaM7d2Dt10il3dqvETJjYit+UtlzS0pQkJEByOWo06YNHkREvPwUABIEX2+iVyfpEXEAsHz5crRq1QpNmjTB9OnT4evrCy0tLVy8eBGxsbF46623kJubi6CgIOTl5WHr1q3IyclBzv/32FtbW0O7koUl9fT0yk1DLXmqnl8wMpkMvXp9hE2bNsK+rj3s7R2wadMG6OvrIyiog/K4GTOmwdraBsOGfQ6g9MNAQkICgNIvuGlpabh9+zYMDAxgb2+vlqxV5aOuXTHzh6Vo4OoCbw8P/Hz0KB6kpyM4KAgAsGLrNqRlZmDqiBEASjsnZi1dilEDQ+HtXg8ZWaXrGejp6sKoks063kRSrRvgeS7F1/udwM44eWQfrGxqw8rGFicP74OOrh78mr6jPG7XxqUwMbPAe8F9oKOji9p2Dio/x6BmTQAo117d9O/bB5MmT4GXZwM09PXFT/v24X5qKnqJZKcxKb6/AdbNulk36xZX3fH7D8Bv7Bhkx91BVmwMHDt2hIG1NZIOHQIA1B8wAPqWlri6cCEAwNDODmYe7si+dRs6RkZwCQ6GsaMjIhcuErKMKlXDwABGdeso7xvZ2cKsnhuKcnKR9+CBgMleH19volcj+Y44V1dXREZGYs6cOZg0aRLu3bsHPT09eHp6Yty4cRg2bBgiIiJw4ULplSw3NzeV5yckJMDJyUmA5OX169sfhYWFmP/9POTm5sLL0wtLFi9V2RE29cEDyLT+GQiZlp6G/gP6Ku9v274V27ZvhZ9fY6xYvlKj+f+rdq1a4VFuLtbv+QkZWVlwcXDA9199BVsbawBARlYWHqSnK4//+WgYSkpKsGDNWixYs1bZ3snfH5OHf6Hx/K9KqnWX4Xkurde7bfv3UVxUhAM71yI/7wnsndwwePhk6On/s3RAdlY6ZFoiXmvn/73XoQMePXqElavXIC09HfXcXLFi6Q+ws7MTOlqVkdr7uwzrZt2suxTrLlWd6045cwY6JsZw/99H0LOwQG5SEi5Mm478tDQAgL6FOQysrZXHy7S14BoSAqM6dSAvKUFGdDTCx41H/sOHQpVQ5cwb1EfA8qXK+41GlV48TTh4CBdnzREqVpXg6/3mkvKOpNWBTKHgGFBNysp8JHQEQchTkoWOQBqkVc1HHr0qqZ7nZx5I87+Rzm+7Ch1BEI8LngodgYiIqkB4vz5CRxBEfka20BEEYWBpJnQEQXQ9+JvQEQTRc+ZxoSO8lN1T3xU6giAkvUYcERERERERERGRprAjjoiIiIiIiIiISAMkv0YcEREREREREZFYcI24NxtHxBEREREREREREWkAO+KIiIiIiIiIiIg0gFNTiYiIiIiIiIhEglNT32wcEUdERERERERERKQB7IgjIiIiIiIiIiLSAE5NJSIiIiIiIiISCQWnpr7ROCKOiIiIiIiIiIhIA9gRR0REREREREREpAHsiCMiIiIiIiIiItIArhFHRERERERERCQScq4R90bjiDgiIiIiIiIiIiINYEccERERERERERGRBnBqKhERERERERGRSHBq6puNI+KIiIiIiIiIiIg0gB1xREREREREREREGsCpqUREREREREREIsGpqW82jogjIiIiIiIiIiLSAHbEERERERERERERaQCnphIREb2Cp7m5QkcQho6B0AmIiKqUkb40vxIZWFkIHYE0KD8jW+gIpEFyudAJ6EU4Io6IiIiIiIiIiEgD2BFHRERERERERESkAeyIIyIiIiIiIiIi0gBpLohARERERERERCRCcoXQCehFOCKOiIiIiIiIiIhIA9gRR0REREREREREpAGcmkpEREREREREJBKcmvpm44g4IiIiIiIiIiIiDWBHHBERERERERERkQZwaioRERERERERkUhwauqbjSPiiIiIiIiIiIiINIAdcURERERERERERBrAjjgiIiIiIiIiIiIN4BpxREREREREREQiUcI14t5oHBFHRERERERERESkAeyIIyIiIiIiIiIi0gBOTSUiIiIiIiIiEgk5p6a+0TgijoiIiIiIiIiISAPYEUdERERERERERKQBnJpKRERERERERCQSnJr6ZmNHHIDU1FTMnTsXBw8exL1792Bqaop69eqhb9++6N+/P2rWrIlPP/0Ux44dQ0pKCoyMjNCyZUt89913qF+/vtDxlRQKBdauW4Offz6A3JxceHp5Yfy48XBxca30OfHxd7F6zWrExsYiNfU+Ro0cjY8++p8GU7+evYcPY/vPvyAjKwvO9vYYGfoxGnl6VnjsqT//xP4jYYhLTERRcTGc7e0xqGdPtPBrpNnQVUCqdQM8z6X4eh87uAcXzh5Dft5jODjVw/u9BqO2nf1LPf/qpbPYsX4xPH2bYsBnE9Sctmrs2r8fm3bsRHpmJlydnDB++Bdo3LBhhcempWdgwfIfEXPrNpLv3cP/evTAhBHDNZy46kjx/Q2wbtbNuisjhroro1AosHzVKvy0dx9ycnPh4+2NyZO+hJtr5f8e1UXdwHfh2KkTdE1N8STlb9zetg3Zt29Xenztt9+GY6fOqFmrFp7m5yPjWjTiduxE8ZPHGkz9+hw7d4Jb9+7Qs7BAbnIybqxeg8wbNyo93qlzZzh17YKaNjbIT0tD3K7duHfihAYTq5dVo4ao37c3zD08YGBthfAJk5DyxxmhYxG9ESQ/NTU+Ph5+fn4ICwvDnDlzEBkZiWPHjmH06NH49ddfcezYMQDAW2+9hQ0bNiAmJgZHjhyBQqFAUFAQSkpKBK7gH1u2bsaOHTswdux4rF+/EZaWlhgxcjiePHlS6XMKCgpRx64OPh/2OSwtLTWY9vUdO3sWSzZsxIAe3bHx+/lo2KABxn4zB6lpaRUef/VmDJo29MX3X3+FDfPm4S1vL0z49lvcio/XcPLXI9W6y/A8l9brffrozzhz4jcE9xyE4RO/hZGJGdYunYXCgvx/fW5WRhoO7tsMZ7cGGkhaNY4cP4H5S5dhcP9+2Ll2Dfx8ffH5hIm4/+BBhccXFRfB3NQMg/v1hbtb9f/yJrX3dxnWzbpZd8XEUHdl1m/chM1bt+GrLydi59YtsLK0xCefDX3hv0d1UKtZc7j36YOEX3/BhalTkX3rNhqNHQc9i4pfP9N67vAa8ilS/jiN819NQvSyZTBxdkGDQQM1nPz12LVuDe9PPkHcrt34Y8QIZF6/geYzpsPA2rrC4x07vYf6Hw/A7W3bcWrYMNzath0+Qz9DrWbNNJxcfWoYGCA77g6uLFgodBSiN47kO+KGDRuGGjVq4NKlS+jZsycaNGgAHx8f9OjRAwcPHkTXrl0BAEOGDEGbNm3g5OSExo0bY/bs2fjrr7+QmJgobAH/T6FQYNeunfj4448R4B8AV1dXTJ0yDQUFBQgLO1Lp8zw9PTF8+Ai0bx8EHR1dDSZ+fTt//RVdAwPRrV07ONWti1EDQ2FjaYn9R8IqPH7UwFD0DQ6Gp5sb7O1s8VmfPrCvXRtnL13WcPLXI9W6AZ7nUny9w08cRGDH7vD2a47adg7o1f8LFBcVIvJi+AufK5eXYOfGJWjfuScsrGw0lPj1bdm9GyGdO6F7ly5wcXLChBHDUdvaGnsO/Fzh8XVsbTFx5Ah07dgRRoZGGk5btaT4/gZYN+tm3WKuuzIKhQJbtm/HkEGD0P7dd1HPzQ1zZs1EQUEBDv7+u9DxXotDx45I+eM0Uk6fRt79FNzevg2FmZmo+25ghceburkiPz0Nfx09ioL0dDyKu42/T56EiZOzhpO/HpeQYCSHHUVyWBge/3UPN9asQX56Ohw7darw+LqBgUj6/XeknDmDvNQHSPnjDySHHYXbBz00nFx9Us//ieur1uDvU38IHYXojSPpjriMjAyEhYXh888/h6GhYYXHyGSycm1PnjzBhg0b4OzsDHv7l5sepW4pKSnIyMhA82YtlG26urrw82uMa9eiBUymHsXFxbh1Nx7NGqlO12rWsCGu3br1Uj9DLpcjr6AAJkbV58urVOsuw/O8lFRe78yMh8jNyUa9Bv/UX0NHBy71PJEU/+L6jx36CYZGJmjW6l11x6wyxcXFiLl9G283barS3qJpU0Rdvy5QKs2R2vu7DOtm3axbvHVX5t7ffyM9PR0t31b992jy1lu4GlV9/z1k2towdnJCxnP/Z2VcvwYzt3oVPudRXBz0zS1g6esLANA1MYFN06ZIj4pSe96qIqtRA6ZubkiLjFRpT7sSCYsGFS9jpKWjA3lRsUpbSVERzNzdIdPWVltWkg65onrcpErSHXF37tyBQqGAh4eHSruVlRWMjIxgZGSEiRMnKtuXL1+ubD98+DCOHj0KXd034+pcRkYGAMDCwkKl3cLCAhmZGUJEUqvs3FyUyOWwMDVVabcwM0VmdvZL/Ywdv/yK/IICBLZqqYaE6iHVusvwPC8lldc791E2AMDYWLV+I2NT5OZkV/q8xLuxuHjuBHr0+UyN6ape1qNHKCkpgYW56vltaWGO9MxMgVJpjtTe32VYN+suu8+6pSM9vbRmy+ema1paWiA9I12ISFVCx9gYWtraKHr0SKW96FEOdJ/7LFPm0Z07uL5yJXyGfY7AdevRZukyPM3Lw62tWzQRuUromphAS1sbhdlZKu2F2VnQMzev8Dlpl6/AoUMQTP9/WQlTNzc4tG8HLR0d6JqYqD0zEQlL0h1xZZ4f9RYREYGrV6/Cy8sLhYWFyvY+ffogMjISp0+fRr169dCzZ08UFBRU+nMLCwuRk5Ojcnv2572Ow0cOIyCwrfL29OnTCmtRKBSQofyoPtEoV+/LPS3sTDjW7d6NWWPGlOvkqBYkUjfP8/8nkdc7MuIMpozuq7zJS0pf73L1l29SKizIx86NP6BHn89gaFQ9P8g+X5tCUfHo7OpOqu9v1s26AdZdRmx1V+a3Q4fQtGUr5e2ffw/V40Tz+/75zykyVPrhxdDODh59+yLh558RMX0arsyfD31rK9Qf8LG6U1a950qUyWSV1n175048vHQZ7yxYgM6//IymU6bgr+PHS3+MXK7upEQkMEnvmurm5gaZTIbY2FiVdhcXFwCAgYGBSrupqalyR9UWLVrA3Nwc+/fvx//+V/HuTXPnzsWMGTNU2iZMmIgvJ0567eyt32kNL08v5f3i4iIApVccrayslO1ZWVnlrj6KgZmxMbS1tMqNCsp69AgWZmYvfO6xs2cxd/lyzB43Fk0b+qovpBpIrW6e59J6vT19m8DeyU15v+yLSm5ONkxM/7mi/CT3EYyMzSr8GRlpqcjKSMOmFd8q2xT//yF40he9MG7aElha11ZD+tdnbmoKbW1tZDw3+i0zKwuWlVxRr86k+v5m3aVYN+sGxFd3ZQLatoWvt7fyflFx6ZTE9IwMWD+zmH9mZma5UXLVSXFuLuQlJdA1U734p2tigqKcnAqf49SlK7Lj4pD0+6HShr/+QuymQjSdPBl39/5UbnTdm6goJwfykpJyo990Tc1QWMkMBnlREaKWLEH0smXQMzNDQVYWHDt2QHFeXqX/VkT/RYmEp31WB5IeEWdpaYn27dtj2bJlr7RDkUKheOEIt0mTJuHRo0cqt9GjxrxOZCVDQ0PY29srb87OLrC0tETExQvKY4qLixEZeQU+PtXjS/h/oaOjAw9XF0Q8t47Gxeho+Dw31fhZYWfCMXvZj5g+ahRavfWWumNWOanVzfNcWq+3nr4BrGxslbdatnVhbGKGuJh/6n/6tBjxcTfh6FJx/da162D05AUY+dV85a2BTxO4uHth5FfzYWr+5n7B0dHRQQN3d5y/dEml/cKlS2j4zBc4sZDq+5t1s27WXUqMdVfG0NAQDg4OypuriwusrKxw/s8/lccUFxfj0uXLaFRNLp5VRFFSgtzERFh4qf6fZeHljew7cRU+R1tXV3nBTKlsRFg1GR2oePoUj+7cgbVfI5V2a79GyIyJrfhJZc8tKUFBRgYgl6NOmzZ4EBHx8lMfiKjakvSIOKB03bdWrVqhSZMmmD59Onx9faGlpYWLFy8iNjYWb731FuLj47Fr1y4EBQXB2toaf//9N7777jsYGBigUyU74QCAnp4e9PT0VNpKnqrnF6tMJkOvXh9h06aNsK9rD3t7B2zatAH6+voICuqgPG7GjGmwtrbBsGGfAyj9Tz8hIQFA6RfctLQ03L59GwYGBm/MRhSV+ahrV8z8YSkauLrA28MDPx89igfp6QgOCgIArNi6DWmZGZg6YgSA0s6JWUuXYtTAUHi710NGVuk6Dnq6ujCqZLOON5FU6wZ4nkvx9X4nsDNOHtkHK5vasLKxxcnD+6Cjqwe/pu8oj9u1cSlMzCzwXnAf6Ojooradg8rPMahZEwDKtb+J+vXsia+/+QZeHh7w9fLC3l9/w/2HD/HB+90AAD+sWo2H6WmY/fXXyufExpV+ucnPz0dWdjZi4+Kgo6MDVycnIUp4ZVJ8fwOsm3WzbjHXXRmZTIZ+vXtjzbr1cHBwgKODA9asWw99fX10fu89oeO9luTDh+H16afITUhA9p07qBvgD31LS/x94gQAwPXDD6Fvbo4bq1cDANKuRqJB6EDUCQxE5rVr0DUzg3vvPnh09y6KXnI93DdB/P4D8Bs7Btlxd5AVGwPHjh1hYG2NpEOlI/3qDxgAfUtLXF24EEDplFwzD3dk37oNHSMjuAQHw9jREZELFwlZRpWqYWAAo7p1lPeN7GxhVs8NRTm5yHvwQMBkRMKTfEecq6srIiMjMWfOHEyaNAn37t2Dnp4ePD09MW7cOAwbNgzZ2dk4c+YMFi9ejKysLNSqVQtt2rTBuXPnYGNjI3QJSv369kdhYSHmfz8Pubm58PL0wpLFS1V2hE198AAyrX8GQqalp6H/gL7K+9u2b8W27Vvh59cYK5av1Gj+/6pdq1Z4lJuL9Xt+QkZWFlwcHPD9V1/B1qZ0iH9GVhYepP+z4O3PR8NQUlKCBWvWYsGatcr2Tv7+mDz8C43nf1VSrbsMz3Npvd5t27+P4qIiHNi5Fvl5T2Dv5IbBwydDT/+fpQOys9Ih06oeV83/TYd3A5Gd8wirNm1GekYG3Jydsey772BXu3Q6bVpGBu4/eKjynI8GDVb++eatW/j92DHY1q6N33fv0mj2qiC193cZ1s26WXcpMdZdmYEfD0BBYQFmz/0WOTk58PX2xuoVy1X+PaqjBxEXoGNkBOf334eemRke/30PVxcuKB31BUDP1Az6z0y/vR8ejhr6BrBv1w7uH/0PT/PykBlzE3d27xaqhFeScuYMdEyM4f6/j6BnYYHcpCRcmDYd+WlpAAB9C3MYPDMNWaatBdeQEBjVqQN5SQkyoqMRPm488h8+rOyvqHbMG9RHwPKlyvuNRpVeNE44eAgXZ80RKpZkSHlH0upApig3FpjUKSvzzV/nQB3kKclCRyAN0qoGI4/UQarn+ZkH0vxvpIP3m3MhRpMKdAz+/SAiomrESF+aYxNODx0qdARB5KeLfyfyiuRnZAsdQRA9/wwXOoIgmo4+LnSEl3Jx0btCRxCEpNeIIyIiIiIiIiIi0hR2xBEREREREREREWmANMdhExERERERERGJUIk0V46pNjgijoiIiIiIiIiISAPYEUdERERERERERKQBnJpKRERERERERCQSck5NfaNxRBwREREREREREZEGsCOOiIiIiIiIiIhIAzg1lYiIiIiIiIhIJLhr6puNI+KIiIiIiIiIiIg0gB1xREREREREREREGsCpqUREREREREREIsGpqW82jogjIiIiIiIiIiLSAHbEERERERERERERaQA74oiIiIiIiIiIiDSAa8QREREREREREYlEiVwmdAR6AY6IIyIiIiIiIiIi0gB2xBERERERERERkSh88803aNmyJWrWrAkzM7OXeo5CocD06dNhZ2cHAwMD+Pv748aNGyrHFBYWYvjw4bCysoKhoSG6deuGe/fu/ed87IgjIiIiIiIiIhKJEkX1uKlLUVERPvzwQwwdOvSlnzNv3jwsXLgQy5Ytw8WLF1G7dm20b98eubm5ymNGjRqF/fv3Y+fOnQgPD8fjx4/RpUsXlJSU/Kd8XCOOiIiIiIiIiIhEYcaMGQCAjRs3vtTxCoUCixcvxtdff43u3bsDADZt2oRatWph+/bt+PTTT/Ho0SOsW7cOW7ZsQbt27QAAW7duhb29PY4dO4YOHTq8dD6OiCMiIiIiIiIiIo0qLCxETk6Oyq2wsFDjORISEpCamoqgoCBlm56eHtq2bYtz584BAC5fvozi4mKVY+zs7ODt7a085qUpSBIKCgoU06ZNUxQUFAgdRaNYN+uWAtbNuqWAdbNuKWDdrFsKWDfrJiozbdo0BQCV27Rp06rs52/YsEFhamr6r8edPXtWAUDx999/q7R/8skniqCgIIVCoVBs27ZNoaurW+657du3VwwZMuQ/5ZIpFAo1zsylN0VOTg5MTU3x6NEjmJiYCB1HY1g365YC1s26pYB1s24pYN2sWwpYN+smKlNYWFhuBJyenh709PTKHTt9+nTllNPKXLx4EU2aNFHe37hxI0aNGoXs7OwXPu/cuXNo1aoVUlJSYGtrq2z/5JNP8Ndff+Hw4cPYvn07QkNDy+Vt3749XF1dsXLlyhf+Hc/iGnFERERERERERKRRlXW6VeSLL77ARx999MJjnJycXilH7dq1AQCpqakqHXEPHz5ErVq1lMcUFRUhKysL5ubmKse0bNnyP/197IgjIiIiIiIiIqI3lpWVFaysrNTys52dnVG7dm0cPXoUfn5+AEp3Xj19+jS+++47AMBbb70FHR0dHD16FD179gQA3L9/H9evX8e8efP+09/HjjgiIiIiIiIiIhKF5ORkZGZmIjk5GSUlJbh69SoAwM3NDUZGRgCA+vXrY+7cuQgJCYFMJsOoUaMwZ84c1KtXD/Xq1cOcOXNQs2ZN9O7dGwBgamqKQYMGYezYsbC0tISFhQXGjRsHHx8f5S6qL4sdcRKhp6eHadOmvfSwT7Fg3axbClg365YC1s26pYB1s24pYN2sm0jdpk6dik2bNinvl41yO3nyJPz9/QEAt27dwqNHj5THTJgwAfn5+Rg2bBiysrLQvHlzhIWFwdjYWHnMokWLUKNGDfTs2RP5+fl49913sXHjRmhra/+nfNysgYiIiIiIiIiISAO0hA5AREREREREREQkBeyIIyIiIiIiIiIi0gB2xBEREREREREREWkAO+KIiIiIiIiIiIg0gB1xREREREREREREGsCOOJG7c+cOjhw5gvz8fAAAN8klMSoqKsKtW7fw9OlToaNolNTq/uuvv3Dv3j3l/YiICIwaNQqrV68WMBWpm9TO88DAQGRnZ5drz8nJQWBgoOYDkVo9evQImZmZ5dozMzORk5MjQCJSp/z8fOTl5SnvJyUlYfHixQgLCxMwFRERaZpMwZ4ZUcrIyECvXr1w4sQJyGQyxMXFwcXFBYMGDYKZmRkWLFggdESi15aXl4fhw4dj06ZNAIDbt2/DxcUFI0aMgJ2dHb788kuBE6qHVOtu3bo1hgwZgn79+iE1NRUeHh7w8vLC7du3MWLECEydOlXoiFWme/fuL33svn371JhEOFI9z7W0tJCamgobGxuV9ocPH6JOnTooLi4WKBmpw3vvvYeuXbti2LBhKu0rV67EL7/8gkOHDgmUjNQhKCgI3bt3x2effYbs7GzUr18fOjo6SE9Px8KFCzF06FChI2pUVlYWfv31V/Tv31/oKGohl8uhpVV+3ItcLse9e/fg4OAgQCr1UigUSExMhL29PWrUqIGioiLs378fhYWF6NSpE6ysrISOSPRG4Ig4kRo9ejRq1KiB5ORk1KxZU9neq1cvHD58WMBk6ufs7AwXF5dKb2KVnJz8wpsYTZo0CVFRUTh16hT09fWV7e3atcOuXbsETKZeUq37+vXraNasGQBg9+7d8Pb2xrlz57B9+3Zs3LhR2HBVzNTU9KVvYiW18zw6OhrR0dEAgJs3byrvR0dHIzIyEuvWrUOdOnUETqleH3zwAb799tty7fPnz8eHH34oQCL1u3DhAgICAsq1+/v748KFCwIk0pwnT55gypQpaNmyJdzc3CTxee3KlSto3bo1AOCnn35CrVq1kJSUhM2bN+OHH34QOJ3mJScnIzQ0VOgYVS4nJwc9e/aEoaEhatWqhWnTpqGkpET5eFpaGpydnQVMqB63bt2Cs7Mz3Nzc0KBBAyQkJKBly5YYNGgQhg4digYNGiAuLk7omERvhBpCByD1CAsLw5EjR1C3bl2V9nr16iEpKUmgVJoxatQolfvFxcWIjIzE4cOHMX78eGFCaYCTkxNkMlmljz/7AUAsDhw4gF27dqFFixYqtXt6euLu3bsCJlMvqdZdXFwMPT09AMCxY8fQrVs3AED9+vVx//59IaNVuQ0bNggdQXBSO88bNWoEmUwGmUxW4RRUAwMDLF26VIBkmnP69GlMmzatXHvHjh3x/fffC5BI/QoLCyucdl1cXKxcVkSsBg8ejNOnT6Nfv36wtbV94WcYscjLy4OxsTGA0s/q3bt3h5aWFlq0aCHKz+f/Nr06NzdXQ0k0a8qUKYiKisKWLVuQnZ2N2bNn4/Lly9i3bx90dXUBiHO5oIkTJ6Jhw4b49ddfsX79enTp0gX16tXD+fPnoVAo0LNnT8ycORNbtmwROiqR4NgRJ1JPnjxRGQlXJj09XflFVqxGjhxZYfuPP/6IS5cuaTiN5kRGRqrcL+uAXLhwIb755huBUqlXWlpauelbQOn5L+YP9FKt28vLCytXrkTnzp1x9OhRzJo1CwCQkpICS0tLgdOp19OnT3Hq1CncvXsXvXv3hrGxMVJSUmBiYgIjIyOh46mF1M7zhIQEKBQKuLi4ICIiAtbW1srHdHV1YWNjA21tbQETqt/jx4+VX1KfpaOjI9r10po2bYrVq1eX62RduXIl3nrrLYFSacbvv/+OgwcPolWrVkJH0Rg3NzccOHAAISEhOHLkCEaPHg2gdOq5iYmJwOmqnpmZ2Qt/XysUClH+Pj9w4AA2bdoEf39/AEBISAg6d+6Mrl274pdffgEAUdZ97tw5hIWFwcfHB7Nnz8aSJUuwatUq6OjoACjtqPvoo48ETkn0ZmBHnEi1adMGmzdvVn5RlclkkMvlmD9/foVTIKTgvffew6RJk0Q70qRhw4bl2po0aQI7OzvMnz//P605VV00bdoUBw8exPDhwwH886FmzZo1ePvtt4WMplZSrfu7775DSEgI5s+fjwEDBijP+V9++UU5ZVWMkpKS0LFjRyQnJ6OwsBDt27eHsbEx5s2bh4KCAqxcuVLoiGohtfPc0dERQOnaQVLl7e2NXbt2lVvvcefOnfD09BQolXp98803aNeuHaKiovDuu+8CAI4fP46LFy+KfgF/c3NzWFhYCB1Do6ZOnYrevXtj9OjRCAwMVP4uCwsLg5+fn8Dpqp6xsTG+/vprNG/evMLH4+Li8Omnn2o4lfqlp6crf6cDgKWlJY4ePYoOHTqgU6dOWLt2rYDp1Ofx48fK97ShoSEMDQ1ha2urfLxu3bp48OCBUPGI3ijsiBOp+fPnw9/fH5cuXUJRUREmTJiAGzduIDMzE2fPnhU6niB++uknyX3gAwB3d3dcvHhR6BhqMXfuXHTs2BE3b97E06dPsWTJEty4cQPnz5/H6dOnhY6nNlKt29/fH+np6cjJyYG5ubmyfciQIRWOABaLkSNHokmTJoiKilIZ+RcSEoLBgwcLmEy9pHqeb968+YWPi3VRc6B0OlePHj1w9+5d5fTc48ePY8eOHdizZ4/A6dSjVatWOH/+PObPn4/du3fDwMAAvr6+WLduHerVqyd0PLWaNWsWpk6dik2bNon6d/izPvjgA7zzzju4f/++ygXUd999FyEhIQImU4/GjRsDANq2bVvh42ZmZqKcomlvb4+YmBiVdeCMjY0RFhaGoKAgUb7WAGBnZ4fk5GTlJhTz5s1TGdmelpam8vmNSMq4a6qIpaamYsWKFbh8+TLkcjkaN26Mzz//XOXKhBj5+fmpDPdWKBRITU1FWloali9fjiFDhgiYTn2en7ajUChw//59TJ8+HbGxsbh69aowwdTs2rVr+P7771XO84kTJ8LHx0foaGol1bqlOEXTysoKZ8+ehYeHB4yNjREVFQUXFxckJibC09MTeXl5QkdUGyme589/SSkuLkZeXh50dXVRs2ZNZGZmCpRMMw4ePIg5c+bg6tWryk6padOmVfpFnqqX5z+j3blzBwqFAk5OTsrpa2WuXLmi6Xgac+fOHdy9exdt2rSBgYGBaKdorlmzBnl5eZUuG/PgwQOsXLmywrUhq7MRI0bg/v37FV5AyM3NRfv27XHx4kXRrd/82WefoUmTJpVeJPz2229x5swZHDx4UMPJiN487Igj0ZkxY4bKfS0tLVhbW8Pf3x/169cXKJX6aWlplfsQp1AoYG9vj507d4pyKhdJy/NTNG/fvg0XFxeMGjVK1FM0LSwsEB4eDk9PT5WOuPDwcPTo0YPTPCQgLi4OQ4cOxfjx49GhQweh49Br+i/r3Ylt3bDnP6O9iNg6ZwAgIyMDPXv2xMmTJyGTyRAXFwcXFxcMGjQIZmZmWLBggdARqQpkZWUhJSUFXl5eFT7++PFjXL58WXIXGBISEqCvry/6QSFEL4MdcSIVHR1dYbtMJoO+vj4cHBxEv2mD1Dw/VausA9LNzQ01aoh7FvrDhw/x8OHDcmsr+fr6CpRIM6RWd3BwMIyNjbFu3TpYWloqO6ROnz6NwYMHIy4uTuiIatGrVy+Ymppi9erVMDY2RnR0NKytrfH+++/DwcFBtOtelpHaeV6ZS5cuoW/fvoiNjRU6Cr2mii6cPa9shJTYRsxIXf/+/fHw4UOsXbsWDRo0UP4/FhYWhtGjR+PGjRtCR6xSBQUFOHbsGLp06QIAmDRpEgoLC5WPa2trY9asWdDX1xcqolrk5+fj+PHjldZdo0YNzJw5U3R1S/X1JnoV4v52LmGNGjVSfsgr62t99kOfjo4OevXqhVWrVonil6GUry6XkdpVNQC4fPkyBgwYgJiYmHJrjIj5C4xU6w4PD8fZs2fL7aro6OiIv//+W6BU6rdw4UIEBgbC09MTBQUF6N27N+Li4mBlZYUdO3YIHU9tpHqeV0ZbWxspKSlCx6hy5ubmLz0lTyzTck+ePCl0hDeCi4sLLl68WG7X6+zsbDRu3Bjx8fECJVOfsLAwHDlyBHXr1lVpr1evHpKSkgRKpT6bNm3Cb7/9puyYWbZsGby8vGBgYAAAiI2NhZ2dnXL3WLHYvHnzv9Zta2srurql+noTvQp2xInU/v37MXHiRIwfPx7NmjWDQqHAxYsXsWDBAkybNg1Pnz7Fl19+icmTJ+P7778XOu5r+7ft0Z8lpi9vZVugv4xu3bqpMYkwQkND4e7ujnXr1qFWrVqiXF+lIlKtWy6XV/j+vXfvHoyNjQVIpBl16tTB1atXsXPnTuVaaYMGDUKfPn2UH27FSKrn+fO/18vW+1y2bBlatWolUCr1Wbx4sdARNE6KF84qkpiYWOHv9MLCQty7d0+AROr35MmTCjemSE9PF+VMlW3btpXrdNm+fTtcXFwAAFu3bsWPP/4ouo4Z1v0PKdRN9CrYESdS33zzDZYsWaKyloyvry/q1q2LKVOmICIiAoaGhhg7dqwoOuKevbqcmJiIL7/8Eh9//LFyXbTz589j06ZNmDt3rlAR1SI4OFjlvkwmUxk58uwXVzF1QJZJSEjAvn374ObmJnQUjZJq3e3bt8fixYuxevVqAKXn9+PHjzFt2jR06tRJ4HTqUVxcDA8PD/z2228IDQ1FaGio0JE0RqrneUW/162trREYGCjK9aMGDBggdIQ3Ql5eHpKTk1FUVKTSLsYp2M92Nh85cgSmpqbK+yUlJTh+/LjKbpNi0qZNG2zevBmzZs0CUPr+lsvlmD9/PgICAgROV/Vu374Nd3d35X19fX1oaWkp7zdr1gyff/65ENHUinWXkkrdRK+CHXEide3aNTg6OpZrd3R0xLVr1wCUTl+9f/++pqOpxbNXl2fOnImFCxfif//7n7KtW7du8PHxwerVq0X1of/ZNZOOHTuGiRMnYs6cOXj77bchk8lw7tw5TJ48GXPmzBEwpfq8++67iIqKktwXdanWLcUpmjo6OigsLJTMaLBnSfU8f34tPKnKz89HcXGxSpsYl5ZIS0tDaGgofv/99wofF+NFtLLOZplMVu4zmY6ODpycnETZ6QwA8+fPh7+/Py5duoSioiJMmDABN27cQGZmJs6ePSt0vCr36NEjlXWK09LSVB6Xy+Uqa4iJBesuJZW6iV4FO+JEqn79+vj222+xevVq5XpKxcXF+Pbbb5U7h/7999+oVauWkDHV4vz58xXunvii7bTFYNSoUVi5ciXeeecdZVuHDh1Qs2ZNDBkyBDExMQKmU4+1a9diwIABuH79Ory9vaGjo6PyuBin4wLSrVuqUzSHDx+O7777DmvXrhX9xivPkup5/qyK1ngVsydPnmDixInYvXs3MjIyyj0uxk6pUaNGISsrC3/++ScCAgKwf/9+PHjwALNnzxZtZ1RZZ7OzszMuXrwIKysrgRNpjqenJ6Kjo7FixQpoa2vjyZMn6N69Oz7//HNR7iRZt25dXL9+HR4eHhU+Hh0dXW69PDFg3dKqm+iVKEiUzp49q7C0tFRYW1sr3n33XUW7du0UNjY2CktLS8X58+cVCoVCsXnzZsW8efMETlr13N3dFWPGjCnXPmbMGIW7u7sAiTRDX19fER0dXa49KipKoa+vL0Ai9fv5558VJiYmCplMVu6mpaUldDy1kWLdRUVFCmdnZ8WNGzeEjqJxwcHBCmNjY4Wtra0iKChIERISonITKyme52U2bdqk8Pb2Vujp6Sn09PQUPj4+is2bNwsdS+2GDRumaNCggWLPnj0KAwMDxfr16xWzZs1S1K1bV7F161ah46lF7dq1FRcuXFAoFAqFsbGx4tatWwqFovT8b9WqlZDRiF7biBEjFJ6enor8/Pxyj+Xl5Sk8PT0VI0aMECCZerFuadVN9CpkCsVzW5GRaDx+/Bhbt27F7du3oVAoUL9+ffTu3VvUi5oDwKFDh9CjRw+4urqiRYsWAIA///wTd+/exd69e0W7llSbNm2go6ODrVu3Kq+qpqamol+/figqKsLp06cFTlj1nJyc0KVLF0yZMkWUozsrI9W669Spg2PHjqFBgwZCR9Gof1sXbsOGDRpKollSPc8XLlyIKVOm4IsvvkCrVq2gUChw9uxZ/Pjjj5g9e7aoF7l2cHDA5s2b4e/vDxMTE1y5cgVubm7YsmULduzYgUOHDgkdscqZmJggOjoaTk5OcHJywrZt29CqVSskJCTAy8sLeXl5QkesUj/88MNLHztixAg1JhHGhg0bYGRkhA8//FClfc+ePcjLyxPV8ikA8ODBAzRq1Ai6urr44osv4O7uDplMhtjYWCxbtgxPnz5FZGSk6H7Hs25p1U30KtgRJ3I3b96scPFfsU/p+euvv7BixQrExsZCoVDA09MTn332Gezt7YWOpjZ37txBSEgIbt26BQcHBwBAcnIy3N3dceDAAVGus2RsbIyrV6/C1dVV6CgaJdW6v/32W8TGxkpuiqZUSfU8d3Z2xowZM9C/f3+V9k2bNmH69OlISEgQKJn6GRkZ4caNG3B0dETdunWxb98+NGvWDAkJCfDx8cHjx4+FjljlmjZtitmzZ6NDhw4IDg6GiYkJ5s6dix9++AE//fQT7t69K3TEKvWymzDIZDLEx8erOY3meXh4YOXKleU2Zjh9+jSGDBmCW7duCZRMfRISEjB06FAcPXpUZbp9+/btsXz5cuWOmmLDuqVVN9F/xY44kYqPj0dISAiuXbum3ElT7DtoUul6QkePHlXpgGzXrp1o1xcaMGAAWrduLeq1/yoi1bpDQkJw/PhxGBkZwcfHB4aGhiqP79u3T6BkpA5SPc/19fVx/fr1chdP4uLi4OPjg4KCAoGSqZ+vry+WLl2Ktm3bIigoCL6+vvj+++/xww8/YN68ebh3757QEavMnTt34Obmhm3btqG4uBgff/wxIiMj0aFDB2RkZEBXVxcbN25Er169hI5KVUhfXx+xsbFwcnJSaU9MTESDBg2Qn58vTDANyMzMxJ07dwAAbm5usLCwEDiRZrBuadVN9LI4pECkRo4cCWdnZxw7dgwuLi64cOECMjMzMXbsWHz//fdCx1O7M2fOYNWqVYiPj8eePXtQp04dbNmyBc7OziqbGYiNTCZDUFAQ2rRpAz09PdF2wJVxd3fHpEmTEB4eDh8fn3KLuYtxWgsg3brNzMzQo0cPoWNoXEZGBqZOnYqTJ0/i4cOH5XbVzMzMFCiZekn1PHdzc8Pu3bvx1VdfqbTv2rUL9erVEyiVZoSGhiIqKgpt27bFpEmT0LlzZyxduhRPnz7FwoULhY5Xpdzd3VGnTh0EBAQgICAAiYmJ8PPzQ2JiImJjY+Hg4CCpTQykwsbGRjkV+VlRUVGwtLQUJpSGWFhYoFmzZkLH0DjWTUQV4Yg4kbKyssKJEyfg6+sLU1NTREREwMPDAydOnMDYsWMRGRkpdES12bt3L/r164c+ffpgy5YtuHnzJlxcXLB8+XL89ttvolxjBijdheybb77BypUr8eDBA9y+fRsuLi6YMmUKnJycMGjQIKEjVrkXTXER67QWQLp1S9V7772Hu3fvYtCgQahVq1a5DnaxrSlURqrn+d69e9GrVy+0a9cOrVq1gkwmQ3h4OI4fP47du3cjJCRE6Igak5ycjEuXLsHV1RUNGzYUOk6VOnPmDE6fPo1Tp07h/PnzKCgogIODAwIDA5Wdc3Xq1BE6ptrdu3cPv/zyS4XLqIit8xUAJkyYgN27d2PDhg1o06YNgNJpqQMHDsQHH3wgiYvlRETEjjjRMjc3x+XLl+Hi4gJXV1esXbsWAQEBuHv3Lnx8fES3+O+z/Pz8MHr0aPTv3x/GxsaIioqCi4sLrl69io4dOyI1NVXoiGoxc+ZMbNq0CTNnzsQnn3yC69evw8XFBbt378aiRYtw/vx5oSMS0SswNjZGeHi46DoiqHKXL1/GokWLEBMTo1xmYOzYsfDz8xM6mtrI5XJs3LgR+/btQ2JiImQyGZydnfHBBx+gX79+oh7hXVxcjPPnz+PUqVM4deoU/vzzTxQWFsLNzU2Ua4aVOX78OLp16wZnZ2fcunUL3t7eSExMhEKhQOPGjXHixAmhI1a5oqIi9OvXD3v27FGudSqXy9G/f3+sXLkSurq6AickIiJNYEecSLVu3Rpjx45FcHAwevfujaysLEyePBmrV6/G5cuXcf36daEjqk3NmjVx8+ZNODk5qXTExcfHw9PTU7Tr67i5uWHVqlV49913VeqOjY3F22+/jaysLKEjEr0WZ2fnF34ZF+sIqaZNm2Lp0qXKXaCJxEahUKBr1644dOgQGjZsiPr160OhUCAmJgbXrl1Dt27dcODAAaFjql1+fj7Cw8Nx5MgRrFmzBo8fPxb1mr7NmjVDx44dMXPmTOXnFhsbG/Tp0wcdO3bE0KFDhY6oNrdv30ZUVBQMDAzg4+MDR0dHoSMREZEGcY04kZo8eTKePHkCAJg9eza6dOmC1q1bw9LSErt27RI4nXrZ2trizp075dbfCA8PF/VOPX///XeFO6PK5XIUFxcLkEg9xowZg1mzZsHQ0BBjxox54bFimtYi1bqfNWrUKJX7xcXFiIyMxOHDhzF+/HhhQmnA8uXL8eWXX2Lq1Knw9vYut1aaiYmJQMmqHs/zUiUlJThw4ABiYmIgk8ng6emJbt26QVtbW+hoarFx40b88ccfOH78eLndJE+cOIHg4GBs3ry53E6y1V1BQQHOnTuHkydP4tSpU7h48SKcnZ3Rtm1brFixAm3bthU6olrFxMRgx44dAIAaNWogPz8fRkZGmDlzJt5//31Rd8S5u7vD3d1d6BhERCQQdsSJVIcOHZR/dnFxwc2bN5GZmQlzc3NRT+8AgE8//RQjR47E+vXrIZPJkJKSgvPnz2PcuHGYOnWq0PHUxsvLC2fOnCl3VXXPnj2ims4UGRmp7FgU81qHz5Nq3c8aOXJkhe0//vgjLl26pOE0mmNmZoZHjx4hMDBQpb1sN2wxjZjheV66m2bnzp1x7949eHh4QKFQ4Pbt27C3t8fBgwfh6uoqdMQqt2PHDnz11VflOuEAIDAwEF9++SW2bdsmqo64tm3b4uLFi3B1dUWbNm0wfPhwtG3bFrVq1RI6msYYGhqisLAQAGBnZ4e7d+/Cy8sLAJCeni5kNLUpKSnBxo0bcfz48Qo33xHjdFwiIiqPU1NJlL7++mssWrRIOQ1VT08P48aNw6xZswROpj6//vor+vXrh0mTJmHmzJmYMWMGbt26hc2bN+O3335D+/bthY5IpBbx8fFo1KgRcnJyhI6iFs2aNUONGjUwcuTICjdrEPuoGanp1KkTFAoFtm3bBgsLCwClO+f27dsXWlpaOHjwoMAJq17t2rVx+PBhNGrUqMLHIyMj8d5774lqjVcdHR3Y2toiODgY/v7+aNOmjeR2SQ0ODkbnzp3xySefYMKECdi/fz8+/vhj7Nu3D+bm5jh27JjQEavcF198gY0bN6Jz586wtbUt9/t80aJFAiUjIiJNYkcciVZeXh5u3rwJuVwOT09PGBkZCR1JLeLj45VrZx05cgRz5szB5cuXIZfL0bhxY0ydOhVBQUFCx1SLgQMHYsmSJTA2NlZpf/LkCYYPH47169cLlEy9pFp3ZebNm4fly5cjMTFR6ChqUbNmTURGRsLDw0PoKBol1fPc0NAQf/75J3x8fFTao6Ki0KpVKzx+/FigZOqjq6uLpKQk2NraVvh4SkoKnJ2dlaOnxODJkyc4c+YMTp06hZMnT+Lq1atwd3dH27Zt4e/vj7Zt28La2lromGoVHx+Px48fw9fXF3l5eRg3bhzCw8Ph5uaGRYsWiXLdNCsrK2zevBmdOnUSOgoREQmIHXEkGgMHDnyp48T25U1bWxv379+HjY0NAKBXr15YsmQJateuLXAy9Xu+9jLp6emoXbs2nj59KlAy9ZJq3X5+fiqjBxQKBVJTU5GWlobly5djyJAhAqZTnzZt2mDq1Klo166d0FE0SqrnuYWFBX777Te0bNlSpf3s2bPo2rUrMjMzBUqmPtra2khNTa204+nBgwews7MT1TTs5+Xm5iI8PFy5XlxUVBTq1asn6s21pMjOzg6nTp3i+nBERBLHNeJINDZu3AhHR0f4+flBSv3Lz9f6+++/Y+7cuQKl0YycnBwoFAooFArk5uZCX19f+VhJSQkOHTpU7su7GEi17jLBwcEq97W0tGBtbQ1/f3/Ur19fmFAaMHz4cIwcORLjx4+Hj49Puc0afH19BUqmHlI/z7t06YIhQ4Zg3bp1aNasGQDgwoUL+Oyzz9CtWzeB06mHQqHAxx9/DD09vQofF9NIuMoYGhrCwsICFhYWMDc3R40aNRATEyN0LLXLzs7GTz/9hLt372L8+PGwsLDAlStXUKtWLdSpU0foeFVu7NixWLJkCZYtWyb6NZuJiKhyHBFHojFs2DDs3LkTDg4OGDhwIPr27atcX0fMtLS0kJqaqvxiamxsjKioKFHvEKulpfXCD7AymQwzZszA119/rcFU6ifVuqVOS0urXJtMJhPlZg0Az/Ps7GwMGDAAv/76q7LTtbi4GO+//z42bNgAMzMzYQOqQWho6Esdt2HDBjUn0Ry5XI5Lly4pp6aePXsWT548QZ06dRAQEKC8iXF6Zpno6Gi0a9cOpqamSExMxK1bt+Di4oIpU6YgKSkJmzdvFjpilQsJCcHJkydhYWEBLy+vchdW9u3bJ1AyIiLSJHbEkagUFhZi3759WL9+Pc6dO4fOnTtj0KBBCAoKEu2Vx+en9BgbGyM6OhrOzs4CJ1Of06dPQ6FQIDAwEHv37lXpcNXV1YWjoyPs7OwETKgeUqz7v2zAYGJiosYkwklKSnrh42L7oi7F87wid+7cQUxMDBQKBTw9PeHm5iZ0JKpCJiYmePLkCWxtbeHv7w9/f38EBASIclfcyrRr1w6NGzfGvHnzVC4injt3Dr179xblup//1uksps5mIiKqHDviSLSSkpKwceNGbN68GcXFxbh586YoN2zQ0tLCe++9p5zS8+uvvyIwMBCGhoYqx4nxKmtSUhIcHBxE28lamcTERDg6Okqi7n8bHQVAtCPDpE6q7+8xY8ZU2C6TyaCvrw83Nze8//77khjxLWarVq1CQECApNcKMzU1xZUrV+Dq6qrSEZeUlAQPDw/lzvdERERiwzXiSLRkMply+pZcLhc6jtoMGDBA5X7fvn0FSqIZ0dHRKvevXbtW6bFiWzurTEBAAEJDQ/Hxxx/DwcFB6DhqdfLkSaEjvDFu3ryJ5ORkFBUVqbSLdd0wR0dHnDlzBqtWrUJ8fDz27NmDOnXqYMuWLXB2dsY777wjdES1iIyMxJUrV1BSUgIPDw8oFArExcVBW1sb9evXx/LlyzF27FiEh4fD09NT6Lj0ij799FOhIwhOX1+/wlHPt27dEvWOsU+fPsWpU6dw9+5d9O7dG8bGxkhJSYGJiYkoLxgTEVF5HBFHovLs1NTw8HB06dIFoaGh6NixY4XrLFH1UzZC6t9+dYl5hNTSpUuxceNGREVFISAgAIMGDUJISEilC51T9RYfH4+QkBBcu3ZN5dwvGykm1vN879696NevH/r06YMtW7bg5s2bcHFxwfLly/Hbb7/h0KFDQkdUi8WLF+PMmTPYsGGDcrp1Tk4OBg0ahHfeeQeffPIJevfujfz8fBw5ckTgtESvbsiQIUhLS8Pu3bthYWGB6OhoaGtrIzg4GG3atMHixYuFjljlkpKS0LFjRyQnJ6OwsBC3b9+Gi4sLRo0ahYKCAqxcuVLoiEREpAHsiCPReHazhtDQUPTt2xeWlpZCx6Iq9m/rZT1LbGtnPS8qKgrr16/Hjh078PTpU/Tu3RsDBw5E48aNhY5WZaKjo+Ht7Q0tLa1yoyGfJ9YRkF27doW2tjbWrFkDFxcXREREICMjA2PHjsX333+P1q1bCx1RLfz8/DB69Gj0799fZdra1atX0bFjR6SmpgodUS3q1KmDo0ePlhvtduPGDQQFBeHvv//GlStXEBQUhPT0dIFSEr2+nJwcdOrUCTdu3EBubi7s7OyQmpqKFi1a4Pfffy+3xIYYBAcHw9jYGOvWrYOlpaXy99rp06cxePBgxMXFCR2RiIg0gB1xJBpaWlpwcHCAn5/fC9cUEuNaaSRtxcXFWL58OSZOnIji4mJ4e3tj5MiRCA0Nrfbraz27K/CLRkOKeQSklZUVTpw4AV9fX5iamiIiIgIeHh44ceIExo4di8jISKEjqkXNmjVx8+ZNODk5qXTExcfHw9PTU7TrRxkZGeG3336Dv7+/SvupU6fQtWtX5ObmIj4+Ho0aNfpPm5kQvalOnjyJy5cvQy6Xo3HjxmjXrp3QkdTGysoKZ8+ehYeHh8rvtcTERHh6eiIvL0/oiEREpAFcI45Eo3///tW+04H+uy1btmDlypVISEjA+fPn4ejoiMWLF8PZ2Rnvv/++0PHUqri4GPv378eGDRtw9OhRtGjRAoMGDUJKSgq+/vprHDt2DNu3bxc65mtJSEhQrhWUkJAgcBphlJSUKNcNsrKyQkpKCjw8PODo6Ihbt24JnE59bG1tcefOHTg5Oam0h4eHw8XFRZhQGvD+++9j4MCBWLBgAZo2bQqZTIaIiAiMGzcOwcHBAICIiAhJL/JP4nH8+HEcP34cDx8+hFwuR2xsrPL/rfXr1wucrurJ5fIKLxrdu3cPxsbGAiQiIiIhsCOORGPjxo1CRyANW7FiBaZOnYpRo0bhm2++UX64NTMzw+LFi0XbEXflyhVs2LABO3bsgLa2Nvr164dFixahfv36ymOCgoLQpk0bAVNWjWenF4t9qnFlvL29ER0dDRcXFzRv3hzz5s2Drq4uVq9eLeoOqU8//RQjR47E+vXrIZPJkJKSgvPnz2PcuHGYOnWq0PHUZtWqVRg9ejQ++ugjPH36FABQo0YNDBgwAIsWLQIA1K9fH2vXrhUyJtFrmzFjBmbOnIkmTZrA1tZWEhdT27dvj8WLF2P16tUASkdzP378GNOmTUOnTp0ETkdERJrCqalEVG15enpizpw5yjVXyqZ4XL9+Hf7+/qJdP0lbWxvt27fHoEGDEBwcDB0dnXLHPHnyBF988QU2bNggQEL1ksLuoc+ujXfkyBHk5eUhJCQE8fHx6NKlC2JjY2FpaYldu3YhMDBQ6Lhq8/XXX2PRokXKaah6enoYN24cZs2aJXAy9Xv8+DHi4+OhUCjg6urK3RRJdGxtbTFv3jz069dP6Cgak5KSgoCAAGhrayMuLg5NmjRBXFwcrKys8Mcff8DGxkboiEREpAHsiCOiasvAwACxsbFwdHRU6YiLi4uDr68v8vPzhY6oFklJSZIcHSal3UO1tbVx//592NjYwMXFBRcvXlTZfCYzMxPm5uaSGEGSl5eHmzdvQi6Xw9PTkx1SRCJhaWmJiIgIuLq6Ch1Fo/Lz87Fz506VdfH69OkDAwMDoaMREZGGaAkdgIjoVTk7O+Pq1avl2n///fdyOw6KiRQ74QBg5MiRcHZ2xoMHD1CzZk3cuHEDf/zxB5o0aYJTp04JHa9KmZmZKdfES0xMhFwuV3ncwsJCEp1wQOkIkoyMDPj4+MDIyKjCzTqIqPoZPHhwtV/H9FUYGBggNDQUy5Ytw/LlyzF48GB2whERSQzXiCOiamv8+PH4/PPPUVBQAIVCgYiICOzYsQNz584V3fpJ/2X0U2ZmpprTCOP8+fM4ceIErK2toaWlBS0tLbzzzjuYO3cuRowYIardQ3v06IG2bdsq101q0qQJtLW1Kzw2Pj5ew+k0IyMjAz179sTJkychk8kQFxcHFxcXDB48GGZmZliwYIHQEYnoPxozZozyz3K5HKtXr8axY8fg6+tbbpmFhQsXajqe2m3atAlWVlbo3LkzAGDChAlYvXo1PD09sWPHDsleaCMikhp2xBFRtRUaGoqnT59iwoQJyMvLQ+/evVGnTh0sWbIEH330kdDxqtTixYuVf87IyMDs2bPRoUMHvP322wBKO6mOHDmCKVOmCJRQ/aS0e+jq1avRvXt33LlzByNGjMAnn3wiuR31Ro8eDR0dHSQnJ6NBgwbK9l69emH06NHsiCOqhp6/YNKoUSMAwPXr11XaxTrid86cOVixYgWA0v+3ly1bhsWLF+O3337D6NGjsW/fPoETEhGRJnCNOCIShfT0dMjlckksdNyjRw8EBATgiy++UGlftmwZjh07hgMHDggTTM1at26NsWPHIjg4GL1790ZWVhYmT56M1atX4/Lly+W+yIlFaGgofvjhB8l1xNWuXRtHjhxBw4YNVdaATEhIgI+PDx4/fix0RCKi/6RmzZqIjY2Fg4MDJk6ciPv372Pz5s24ceMG/P39kZaWJnREIiLSAK4RR0TV1owZM3D37l0ApSOkpNAJBwBHjhxBx44dy7V36NABx44dEyCR+kRHRyvXR5s8ebJyfbDZs2cjKSkJrVu3xqFDh/DDDz8IGVOtNmzYILlOOKB059+aNWuWa09PT4eenp4AiYiIXo+RkREyMjIAAGFhYWjXrh0AQF9fX7QbTBERUXnsiCOiamvv3r1wd3dHixYtsGzZMslcSba0tMT+/fvLtR84cEBlZ00x8PPzQ3p6OgBg6NChaNOmDQDAxcUFN2/eRHp6Oh4+fIjAwEAhY5IatGnTBps3b1bel8lkkMvlmD9/PgICAgRMRkT0atq3b4/Bgwdj8ODBuH37tnKtuBs3bsDJyUnYcEREpDFcI46Iqq3o6GjcuHED27Ztw8KFCzFmzBi0a9cOffv2RXBwcIWjacRgxowZGDRoEE6dOqVcI+7PP//E4cOHRbdJRdnuoTY2NpXuHkriNH/+fPj7++PSpUsoKirChAkTcOPGDWRmZuLs2bNCxyMi+s9+/PFHTJ48GX/99Rf27t2rvHh2+fJl/O9//xM4HRERaQrXiCMi0Th79iy2b9+OPXv2oKCgADk5OUJHUpsLFy7ghx9+QExMDBQKBTw9PTFixAg0b95c6GhVasiQIdi8eTNsbW2RnJyMunXrSm73UClLTU3FihUrcPnyZcjlcjRu3Biff/45bG1thY5GRERERPRKOCKOiETD0NAQBgYG0NXVRW5urtBx1Kp58+bYtm2b0DHUjruHSlNxcTGCgoKwatUqzJgxQ+g4RERVJisrC+vWrUNMTAxkMhnq16+PgQMHcoQ3EZGEcEQcEVVrCQkJ2L59O7Zt24bbt2+jTZs26N27Nz788EOYmpoKHU8tkpOTX/i4g4ODhpJollR3D5Uqa2trnDt3DvXq1RM6ChFRlTh9+jS6desGU1NTNGnSBEDptNTs7Gz88ssvaNu2rcAJiYhIE9gRR0TV1ttvv42IiAj4+PigT58+6N27N+rUqSN0LLXT0tKCTCar9PGSkhINpiFSj7Fjx0JHRwfffvut0FGIiKqEt7c3WrZsiRUrViiXWSgpKcGwYcNw9uxZXL9+XeCERESkCZyaSkTVVkBAANauXQsvLy+ho2hUZGSkyv3i4mJERkZi4cKF+OabbwRKRVS1ioqKsHbtWhw9ehRNmjSBoaGhyuMLFy4UKBkR0au5e/cu9u7dq7LWqba2NsaMGaOySzQREYkbO+KIqNqaM2eO0BEE0bBhw3JtTZo0gZ2dHebPn4/u3bsLkIro9UVHR8Pb2xtaWlq4fv06GjduDAC4ffu2ynEvGhFKRPSmaty4MWJiYuDh4aHSHhMTg0aNGgkTioiINI4dcURUrd27dw+//PILkpOTUVRUpPKY1EbMuLu74+LFi0LHIHplfn5+uH//PmxsbJCUlISLFy/C0tJS6FhERK8sOjpa+ecRI0Zg5MiRuHPnDlq0aAEA+PPPP/Hjjz9yGj4RkYRwjTgiqraOHz+Obt26wdnZGbdu3YK3tzcSExOhUCjQuHFjnDhxQuiIapGTk6NyX6FQ4P79+5g+fTpiY2Nx9epVYYIRvSZLS0scOnQIzZs3h5aWFh48eABra2uhYxERvbKydV3/7SuXTCbjGq9ERBLBEXFEVG1NmjQJY8eOxcyZM2FsbIy9e/fCxsYGffr0QceOHYWOpzZmZmblpuYpFArY29tj586dAqUien09evRA27ZtYWtrC5lMhiZNmqispfSs+Ph4DacjIvrvEhIShI5ARERvGI6II6Jqy9jYGFevXoWrqyvMzc0RHh4OLy8vREVF4f3330diYqLQEdXi9OnTKve1tLRgbW0NNzc31KjB6ytUvR0+fBh37tzBiBEjlJ3sFRk5cqSGkxERVY2bN2+WW1JDJpOha9euAqYiIiJN4Tc2Iqq2DA0NUVhYCACws7PD3bt3lTuopqenCxlNrWQyGVq2bFmu0+3p06f4448/0KZNG4GSEb2+stGsly9fxsiRIyvtiCMiqm7i4+MREhKCa9euqUxXLRvlzqmpRETSoCV0ACKiV9WiRQucPXsWANC5c2eMHTsW33zzDQYOHKhcBFmMAgICkJmZWa790aNHCAgIECARUdXbsGEDO+GISFRGjhwJZ2dnPHjwADVr1sT169fxxx9/oEmTJjh16pTQ8YiISEM4Io6Iqq2FCxfi8ePHAIDp06fj8ePH2LVrF9zc3LBo0SKB06mPQqEot0YcAGRkZMDQ0FCARERERPRvzp8/jxMnTsDa2hpaWlrQ1tbGO++8g7lz52LEiBGIjIwUOiIREWkAO+KIqFoqKSnBX3/9BV9fXwBAzZo1sXz5coFTqVf37t0BlE5h+fjjj6Gnp6d8rKSkBNHR0WjZsqVQ8YiIiOgFSkpKYGRkBACwsrJCSkoKPDw84OjoiFu3bgmcjoiINIUdcURULWlra6NDhw6IiYmBubm50HE0wtTUFEDpiDhjY2MYGBgoH9PV1UWLFi3wySefCBWPiIiIXsDb2xvR0dFwcXFB8+bNMW/ePOjq6mL16tVwcXEROh4REWkIO+KIqNry8fFBfHw8nJ2dhY6iERs2bAAAWFtbY/r06ahZsyYAIDExEQcOHECDBg1gZWUlZEQiIiKqxOTJk/HkyRMAwOzZs9GlSxe0bt0alpaW2LVrl8DpiIhIU2SKsu16iIiqmbCwMEycOBGzZs3CW2+9VW59NBMTE4GSqVf79u3Ro0cPfPbZZ8jOzkb9+vWho6OD9PR0LFy4EEOHDhU6IhEREb2EzMxMmJubV7j2KxERiRM74oio2tLS+mfj52c/wJZtZlBSUiJELLWzsrLC6dOn4eXlhbVr12Lp0qWIjIzE3r17MXXqVMTExAgdkYiIiIiIiCrAqalEVG1t2LAB9vb20NbWVmmXy+VITk4WKJX65eXlwdjYGEDpqMDu3btDS0sLLVq0QFJSksDpiIiIiIiIqDIcEUdE1Za2tjbu378PGxsblfaMjAzY2NiIdkScr68vBg8ejJCQEHh7e+Pw4cN4++23cfnyZXTu3BmpqalCRyQiIiIiIqIKaP37IUREb6ayKajPe/z4MfT19QVIpBlTp07FuHHj4OTkhObNm+Ptt98GUDo6zs/PT+B0REREREREVBmOiCOiamfMmDEAgCVLluCTTz5R7h4KACUlJbhw4QK0tbVx9uxZoSKqXWpqKu7fv4+GDRsq18qLiIiAiYkJ6tevL3A6IiIiIiIiqgjXiCOiaicyMhJA6Yi4a9euQVdXV/mYrq4uGjZsiHHjxgkVTyNq166N2rVrq7Q1a9ZMoDRERERERET0MjgijoiqrdDQUCxZsgQmJiZCRyEiIiIiIiL6V+yIIyIiIiIiIiIi0gBu1kBERERERERERKQB7IgjIiIiIiIiIiLSAHbEERERERERERERaQA74oiIiIiIiIiIiDSAHXFEREREREREREQawI44IiIiIiIiIiIiDWBHHBERERERERERkQawI46IiIiIiIiIiEgD/g+a+n8spjsmOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr().round(1)\n",
    "\n",
    "# Resizing the heatmap\n",
    "fig, ax = plt.subplots(figsize=(16,11))\n",
    "\n",
    "sns.heatmap(corr_matrix, annot=True, vmax=1, vmin=-1, center=0, cmap='vlag', ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G3            1.000000\n",
      "G2            0.910743\n",
      "G1            0.809142\n",
      "Medu          0.201472\n",
      "studytime     0.161629\n",
      "Fedu          0.159796\n",
      "famrel        0.054461\n",
      "absences     -0.045671\n",
      "freetime     -0.064890\n",
      "health       -0.080079\n",
      "goout        -0.097877\n",
      "traveltime   -0.102627\n",
      "Walc         -0.115740\n",
      "age          -0.125282\n",
      "Dalc         -0.129642\n",
      "failures     -0.383145\n",
      "Name: G3, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.corr()[\"G3\"].sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5vepVr9Lw8HDl6z/+x/+oxx9/XJL0K7/yK/rUpz6l5z//+Xrve99bU935N3/zNzp37pxGRkYqPzs5OalcLlf5+W6JZykScAhxDsWCKM4VlbR8AwAAAAissbFim3U/r79LfN+vCTQzmUxLg3FM06z8fJlt200ve+/25cvZe1715XhesaDlS1/6kk6cOFGzXTqdliTddtttunDhgr70pS/pq1/9qv6H/+F/0Nvf/nb9/u//vjzP04/92I/pT//0T/fdhpmZmQNv51HE75M9uiLKVV0Ek8EW16CSkBIAAABAIBlGy2tA9tt/+2//bd/pM2fO6DnPeY4cx9G3v/3tSov3ysqKHnvsMT372c9uepmpVEqu69acVw73Ll26pInS+pwPP/xwzTbPetaz9MADD9Sc99BDD+27nL/7u7+rOe/hhx+urIlZHtZz8eJFvfzlL2+4jzMzM3rLW96it7zlLfrJn/xJ/W//2/+m3//939cLX/hCffrTn9bs7KxGR0eb3s5O49MtjoxwEkEQx8dqI+dF+vUHAAAAAN305JNP6t3vfrceffRRffKTn9THPvYxveMd79CZM2f02te+Vm9961v1jW98Q9/5znf0T//pP9WJEyf02te+tullXn311frud7+rRx99VMvLy7JtW9dff71OnTql3/zN39Rjjz2mL33pS/qDP/iDmp/79V//df3lX/6lPvzhD+vs2bP6N//m3+jLX/5yTVXlP/gH/0APPfSQ/uN//I86e/as/s//8/+sCSxHRkb0nve8R+9617t0zz336PHHH9ff/u3f6uMf/7juueceSdIHP/hBff7zn9e5c+f093//9/riF79YCV1/8Rd/UdPT03rta1+rv/7rv9b58+f19a9/Xe94xzv01FNPdepur4uAEmggjoFX2MV1fUpCSgAAAABo3+23366dnR3dcsstevvb365f//Vf19ve9jZJxeEwP/ZjP6af/umf1kte8hL5vq+//Mu/rFQrNvLWt75VN954o170ohdpZmZG3/zmN5VMJvXJT35SjzzyiG666Sbddddd+u3f/u2an/vxH/9x/cmf/Ik+/OEP66abbtL/+//+v3rXu96lgYGByjavec1rdMcdd+i9732vbr75Zm1sbOj222+vuZzf+q3f0gc/+EHdeeedevazn63XvOY1+ou/+Atdc801kooVnu9///v1vOc9Ty972ctkWZY+9alPSZIGBwd133336aqrrtLP/uzP6tnPfrb+l//lf9HOzk7XKyoNf2/zOpTNZjU2Nqb19fWel7SGTRSDkTgGXFEVt7ZvWr4BAAAA9FIul9P58+d1zTXX1ARpYfCKV7xCz3/+8/WRj3yk37vS0Fvf+lY98sgj+uu//ut+70pDzZ4D7eRr8fr0DhyAcDJaNvNOrEJK1qUEAAAAgPD6/d//fb3qVa/S0NCQvvzlL+uee+7RH//xH/d7t3oiPp/c0XFRq54knIymuA3RKb8uCSoBAAAAIFweeOAB/e7v/q42NjZ07bXX6qMf/aj+1//1f+33bvVEPD6xo+MIJxE2VFMCAAAAAMq+9rWv9XsX9vnMZz7T713oGz69IvYIJ+Mjbo911P6RAAAAAACIJgJKtC1KoUfcAivEb9L3Rs6L1GsWAAAAQPAwfzm+OvXYE1AituIUUmG/uD3+hJQAAAAAOi2ZTEqStre3+7wn6JdCoSBJsizrSJcTnwXZ0BFRCTniFk6hvjiuSykxQAcAAABAZ1iWpfHxcS0uLkqSBgcHZRhGn/cKveJ5npaWljQ4OKhE4mifrePzyRwoIZxEtbhN+ZYYoAMAAACgc+bn5yWpElIiXkzT1FVXXXXkYDo+n8hxZFGonoxDOJnd6fxtHM1E/1cF1ZQAAAAA0D7DMHTs2DHNzs7Ktu1+7w56LJVKyTSP/rkyPp/GEXtRCye7EUS2c11RDC3jFlJKVFMCAAAA6AzLso68DiHiK16fxHFoYa+ejEo42ctQ8iB79yUqgWVcQ0qJakoAAAAAQH/E61M4Yins4WSQQslmohRYxjGklAgqAQAAAAD9Eb9P4GhbmKsnwxpOhiWUbKb6NoQxrIxrSCkRVAIAAAAAeiuen74RC2EMJ6MQTNZTvl1hCyrjHFJKBJUAAAAAgN6I7ydvtCTM1ZNhEdVQsp4wBpVxDymlaAeV3fwdF8X7CwAAAAC6Id6fuhFZYaiejFMwuVfY2r8JKYuqw7ygh29B+OfKQfsQ9PsQAAAAAHqFT9yIHMLJcAlLVSUhZa294Vsvw7YghI+d0Oh2EFwCAAAAiBs+baOhMIYAQQ8nCSYby+44hJQh1qmwLYy/dzqtn+EvAAAAAPQDn7SBHiCYbE0YqikJKdtD4Hh0BJYAAAAAoo5POagrjKFCUKsnCSfbl91xAn2/BfW5hnjYyHmVLwAAAACIAgJKREJQA6Mgh2xhEOT7L6jPOcQLYSUAAACAKKBPEeiCIAdrYRPktm/avREkYZqyDgAAAADV+ASDfcJWiRO0SjbCye4I6v0atOcfIImqSgAAAAChQkCJUAtaOBTUEC0qgro2ZdCeh0AZLeAAAAAAwoCAEjX4EHt4QQzOoiqI9zUhJYKOoBIAAABAUBFQIrSCFAgFMTCLuiBWUwbpOQk0QlAJAAAAIGgIKIEjClpIFjfc/8DhEFQCAAAACAoCSlSE6YNqUCrVCMeCIUiPQ1Cem0CrCCoBAAAA9BsBJUInKAFQkEIxBOvxCMpzFGgHQSUAAACAfiGgBA4hSGEYdgXpcSGkRFgRVAIAAADoNQJKSApPe3cQQp8ghWDYL0iPTxCer8BhheXvAgAAAIDwI6AE2hCk8AuN8TgBnUE1JQAAAIBeIKBEaPS7Go3QK1yC8nj1+3kLdAIhJQAAAIBuIqAEHzxbEJSwC+0JyuNGSIkooJoSAAAAQLcQUCIU+hnwBCXkwuEE5fEjpERUEFICAAAA6LREv3cAQO+sb9ttbT82mOzSnvRWdsfRaIZfd0CnlEPKkQH+zwkAAADg6PjEHnNhqISherJ97QaR7VxOWEPLIISUm3lHw2l+7SI6NnIeISUAAACAI+OTMtBA2MLJToWS7V5PmAJLQkqg8wgpAQAAABwVn5IRaP2qngxLONmrULLVfQhDWElICXQeLd8AAAAAjoJPEjEWhvZu1Le+bQcinNwrqPu1V1gCaCBs+LsCAAAA4DAIKIE9ghxehSUALO9nkPe1348zU70RVYSUAAAAANpFQInA6keA0+/QqpGgh33NBHm/+/14E1IiqggpAQAAALSDRdCAAAtyuNeO8u0IwxqVADqD4TkAAAAAWsUnh5gKenVL3Ksnw1wx2UwQb1e/H3eqKBFlQf9bAwAAACAYCCgB9T+kqha0AK8bghZUBunxB6KGkBIAAADAQQgoEThxrigLUmjXC0G6vf0MKeP8nEc8EFICAAAAaIaAErEXhOq5oFUU9lJcb/dehJSIuo2cR1AJAAAAoC4CyhjiA2KwENAFJ6ANQlgNRB1/gwAAAADsRUCJQOl1FVm/A6kghHJBEoT7g1ZvAAAAAAB6i4AS6JMghHFBFPf7hZAScUAVJQAAAIBqBJQxw4fCXf2slIt7CHeQft8//a6sBeKAv0cAAAAAyggoERi9rBwjnAy+fq9LSas30H2ElAAAAAAkAkqgpwgn2xfXkBKIC0JKAAAAAASUCIQ4VE8STh5eHO87qigRJ4SUAAAAQLwRUMYIHwD7J44BW6f16z6kihLoDf5GAQAAAPFFQIlY6UfYRDjZOXG7L6miRNwQUgIAAADx1NeA8r777tPP/MzP6Pjx4zIMQ5/73Odqvm8YRt2v3/u932t4mXfffXfdn8nlcl2+NTisKIcwcQvUeqEf9ykDc4DeIaQEAAAA4qevAeXW1pZuuukm/dEf/VHd71+6dKnm6z/8h/8gwzD0cz/3c00vd3R0dN/PDgwMdOMmIER6HTIRTkYLrd4AAAAAAHRHop9Xftttt+m2225r+P35+fma05///Of1yle+Utdee23TyzUMY9/Pxh0VKYiS9W1bY4PJfu9Gz2zmHQ2n+/rrGuipjZynkQFWoQEAAADiIjTv/i9fvqwvfelL+uVf/uUDt93c3NTp06d18uRJ/fRP/7T+9m//tun2+Xxe2Wy25gu90av2Vaonoydurd5A3PCPNQAAACA+QhNQ3nPPPRoZGdHP/uzPNt3uWc96lu6++2594Qtf0Cc/+UkNDAzox3/8x3X27NmGP3PnnXdqbGys8nXq1KlO7z5ihHCyd+J0X7MWJeKIkBIAAACIB8P3fb/fOyEV27I/+9nP6nWve13d7z/rWc/Sq171Kn3sYx9r63I9z9MLX/hCvexlL9NHP/rRutvk83nl8/nK6Ww2q1OnTml9fV2jo6NtXV9QBfFDXhSrJ+MUmAVJr9u9RzP9abemzRtxRKs3AAAAEE7ZbFZjY2Mt5Wuh+LT713/913r00Uf16U9/uu2fNU1TN998c9MKynQ6rXQ6fZRdDLQghpNRRDjZP3FZk5K1KBFHrEcJAAAARF8o3vH/X//X/6Uf+7Ef00033dT2z/q+r4cffljHjh3rwp4h6FgzMD56GRDzvAJ6i3+0AQAAANHW11Kczc1NnTt3rnL6/PnzevjhhzU5OamrrrpKUrEc9P/+v/9v/cEf/EHdy7j99tt14sQJ3XnnnZKkD33oQ3rxi1+sM2fOKJvN6qMf/agefvhhffzjH+/+DULLoraeXtiqJy9kt1ra7vToUJf3JLyyO05fWr2pokRcUUkJAAAARFdfP+U+9NBDeuUrX1k5/e53v1uS9OY3v1l33323JOlTn/qUfN/XP/kn/6TuZVy8eFGmufuB5cqVK3rb296mhYUFjY2N6QUveIHuu+8+3XLLLd27IQikXlW5BT2cbDWMbPVngxxaxqXVGwAAAACAKAnMkJwgaWcRz6ALaltcLyoo4xxQHiWUbEdQw8pehpQMzAF6iypKAAAAIBwiNyQH0RKl9u4ghZO9CiUbXWeQwkoqKYHootUbAAAAiB4CSkRSL6ongxJO9iOYrKe8H0EKKnuBtSgBAAAAADgaShCAQwhCOHkhuxWYcLJaUPYrCI9Rt0WpGhloR1CXLwEAAABwOASUERbED3BRWnuyX4ISAB4kCPvZq5Ay6s85IIiC+DcOAAAAwOEQUAJt6mdlXr8Dv8MI4z6HCVWUAAAAAICwI6BEpHS7kq1f4WQQqhGPop/7TxUlEF1UUQIAAADRQECJnqHS63DCHEzuFfWQsl94bQEAAAAAwoyAMqLiWFUSxerJKIWTZWGvBm2GKkqg9+L49w4AAACIGgJKIICiHOKV9fr2UUUJAAAAAEAwEVCiJ7odnkSpejLqwWS1KIaUVFECvUcVJQAAABBuBJQRxAe1ziKc7K443uZuoYoSccbfPgAAACC8CCiBgIhzUNfL204VJQAAAAAAwUJAia4Lc3t3r6on4xxOlkUtpOwXqigRZ1RRAgAAAOFEQAk0QDjZe1EaDkQVJQAAAAAArSGgjJigVY+EuXqyF6ISxnVaL+4XqiiBaAra30EAAAAAByOgBOroRXhFONlcFO6fsAfoAAAAAAD0AgElQivM4U8Uwrde6Pb9RBUlEE1UUQIAAADhQkCJrglrQNLt0IpwMli6/XiHOUgHAAAAAKAXCCgjJE4VI90KfQgng4f77PDC+k8CoBPi9DcRAAAACDsCSqBHCNoOj1ZvAAAAAACii4ASXRHGyq1uhlSEk0cX5vuwn23eYXwtAp1CFSUAAAAQDgSUCJ2wrekX5mAtaLp5X0a5ipKQEgAAAAAQZASUEUGVyNF0K5winOy8sIaUYQvWgajg7yMAAAAQfASU6LhuVmuFKeQhnOwe7tv2UUUJAAAAAAgqAkrEXjeq5gjQuq9b9zFVlED0UEUJAAAABBsBJUKjG+FOlNcdjAOC4PZQRQkAAAAACCICyggIUmUIAQihWRRQRQkAAAAAQO8QUCK2aO2OBu7z9vBPBMRVkP6ZBwAAAKAWASVCIQxVZwRl/dON+z7KVZSElAAAAACAICGgRMeEKfTodPhEONl/YQspAfQeVZQAAABAMBFQhlwcPmz1u9oM6IZ+P6/D9A8FAAAAAEC0EVAidqiejC6qKAEAAAAACJ9Ev3cA0dCtaqx+V5kdhHAyeC5kt3R6dKjfuxEKm3lHw2n+DHRCs9+B3MfBspHzNDLA/2cBAACAIOFTU4jFob270zpZDUc4GVydDinXt22NDSY7dnll2R1Ho5n+/hompGzPYf4Z0+hnuN8BAAAAoIhPR4gNWnUBHEa3KsSrL5ewsreoogQAAACChU9EOLI4tneHvXrygac2WtrulpMjXd6T7qGKsnVUUe7X6yFC5evjcQAAAAAQR3wSQizEtbW71SCynZ8PU2jJepStI6Qs6vd0c4JKAAAAAHHEJ6CQivr6k0GtngxDOHnUULKdyw9DWNnJkDLKVZRSvEPKfgeTexFUdh9t3gAAAEBw8MkHRxK0D/X1xGHtyW6Hkq1cbxjCSmCvoP8OI6gEAAAAEAeUDiBwqJ5s3QNPbfQtnNwrSPuyVycfu24F3kF53gc9sOuUzbwTqtsapn0FAAAAgHYRUCLSOhUmBS2cDHIYGNR9C0NIGRRRDsPCFkxWC+t+B1nUl0sBAAAAwoKesRAKygcqPiz3XhCDv0bK+0rrd+uCshZlVEXhdxYt3wAAAACiiApKBEon21yjVj0ZpnCyWpAqKqmibF0UwryyMFdNNhK12wMAAAAg3ggogSaCEE4GKeA7iqDchiA8ps0EZS1KKfwhWBSDyWpRvm29FJSuBAAAACDOCChxKEH/YByV6raghHqdEpWwtSwqz7Nmgv5abySs+92uuNxOAAAAANFGQInACFLlmNTfSruoBXl79fu2UUXZnjCFYFGvmqwnbrcXAAAAQPQQUIYMrWgH60RVW7/DyTjo9+3s1GMchypKKRwhWBj2sVvifNs7gb+tAAAAQH8RUKJt3fggHLSKsX7pd2jXa1GvFI2aoIZgcayarIf7AAAAAEBYEVAiUsJcPRnnoK5ftz3IVZRBDe2DFoIFbX/6jfsDAAAAQBgRUAJ9RhVhUdhDyjgJQghG1WRj3C+HQ5s3AAAA0D8ElCEShA9PQW7vDmP1JMFkrTDfH3GqopT6G4IRwB2M+wgAAABAmBBQAiWEk8HQj/uFKsrD6XUVI1WT7eG+AgAAABAWBJToqyBVT/YS4WRzYQ0p41ZFWdbtIIxgEr0ShE4FAAAAII4IKAH1toKOcLI1rM0ZLt0IEAkmj477DwAAAEAYEFCGRBCqOvige3QEbu3r5X1GFeXRlAPFo/6uIJjsLO5LAAAAAEFHQIm+CUp7d6+qJwknD4/7LnzaCSurtyVM6w7uVwAAAABBRkAJ9AAB29H16j6kirLz9gaQBJIIsiB0LAAAAABxQ0CJUAtD9SThZPgw1RtRRBgMAAAAIKgIKNGSTn+wDXM1GPonTGEvVZQIIkJKAAAAAEFEQBkCtJt1B9WT4RSmVm8AAAAAAHAwAkqEVjcq1DqJcLJ7wnLfUkWJIKKK8mD8YxAAAADoLQJKHCiK7d3dro4LS4AWZr24j6miRFQRUgIAAAAIEgJKhNJRKtMIJ6MjDPd10Ct9AQAAAADoNwJKAKHW7ZAyiFWUQahCRvhRRdkcbd4AAABA7xBQBhwfkMIlDBV96D2qKBFUhJQAAAAAgoCAEk0Fcf3JoLZ3E072D1WUAAAAAACEFwElgEgIekhJFSWCiirKxuhiAAAAAHqDgBKhQvUkmonb40AVJQAAAAAgChL93gHER1TDlLCEYg+eWznyZdx8/VQH9iS8LmS3dHp06NA/v75ta2ww2cE9AjpjM+9oOM1bAgAAAAD9waeRAOt3a1mU2v6CuIZgt3UikGx2mUENKx94akO3nBzp9270THbH0WiGX+U4OkLK+jZynkYGaDgBAAAAuolPIgiNIK7hF8TqyW4EkwddT9DCym6GlFRRAgAAAADQWQSU6IkotncHKZzsVSh50PUHKaiMUyUlVZToFKooAQAAAPQDPUuIvKi3d/c7nKz24LmVQO1Pt0T9OQUAAAAAQC8RUCIUgtbeHYTqySCHgUHZtyA8TvV04/kcxSpl9EeU1h/ulH6vCQ0AAABEHQEl6orKB9QoVroFJfxrRRD2s1shZRSfWwAAAAAA9AMBJbouapVd/azKC0Lg164wBaq9FLSqYKBaVP5JBQAAACAcCCgDinayXQQ5RWEP+fq5/3GpoozaPwOAIOHvMgAAANA9BJSIrG6ER/2qngx7OFkWldvRKYTvCDKqKAEAAAD0CgElEHBRC/X61fJNFSUAAAAAAMFEQIl9Olk1E6WwpNfVk1FfuzFKISUQVVRR1qLNGwAAAOgOAkoE2mFbYINW1dauKAeT1aJyO4/yfOtGm3eU/jEAAAAAAIg+AkqgBb2svItKaNeqXt9eqiiB9lBFWYsqSgAAAKDzCCgB9F3cQtm9qKIEAAAAAMQZAWUAUZ1xNJ1u76Z6sjd6edu78ZiGfVkBoBmqKGvxdxoAAADoLAJKdM1RK7i6UVUWZHEOJ8vifB9QRQkAAAAAiCsCStSgSqZWr6on4xzM7dWr+4IqSqA9/H0AAAAA0C0ElIiUMAZEhJP7cZ8ACDravAEAAIDOIaAEEFtBm+hNmzeCjipKAAAAAN1AQIlACsL6k70Ir6gUbCys900Yq3gBHA5VlAAAAEBnEFCiK/pRtRW2YCisAVwv9eI+CloVZTdQRYlOoooSAAAAQKcRUAJ1xCG0CoswBrlHCcuDUD0MoHVUUQIAAABHR0CJCqpieieMoVuUEUgD7eHvBQAAAIBOIqAMGCoxqCDDfnELdBmWA4QLf7sBAACAoyGgRCR0cv3JblfTxS1s65Ru32+dftzDtiYqAAAAAAD9QkAJ9BDhJPqJKkp0Em3etaiiBAAAAA6vrwHlfffdp5/5mZ/R8ePHZRiGPve5z9V8/y1veYsMw6j5evGLX3zg5f7Zn/2ZnvOc5yidTus5z3mOPvvZz3bpFqAeQhB0S5yqKFnqAAgfQkoAAADgcPoaUG5tbemmm27SH/3RHzXc5qd+6qd06dKlytdf/uVfNr3M+++/X2984xv1pje9Sd/5znf0pje9SW94wxv07W9/u9O7jwjqZns31ZOdwf14NPwDAZ1EFSU6ZSPnHeoLAAAA0ZDo55Xfdtttuu2225puk06nNT8/3/JlfuQjH9GrXvUqvf/975ckvf/979fXv/51feQjH9EnP/nJuj+Tz+eVz+crp7PZbMvXh86iagwAEGYbOU8jA6yg00wng8V6l8X9DwAAED6Bfwf3ta99TbOzs7rhhhv01re+VYuLi023v//++/XqV7+65rzXvOY1+ta3vtXwZ+68806NjY1Vvk6dOtWRfQ+TMFfBhGEYCVV/ndXN+5M2b6A9Yf770S1U9tXqddUjFZYAAADh09cKyoPcdttt+vmf/3mdPn1a58+f1x133KF/8A/+gf7mb/5G6XS67s8sLCxobm6u5ry5uTktLCw0vJ73v//9eve73105nc1mYxlSIl4eP7vU8rbXnZnp4p6g17I7jkYzgf71DyDEghQMVu8LlZUAAADBFehPqG984xsrx3/0R39UL3rRi3T69Gl96Utf0s/+7M82/DnDMGpO+76/77xq6XS6YeCJ+OjW+pNBqp5sJ5Rs9HNBCSsfPLeim6+f6splP/DUhm45OdKVywYQD3Fr9Q5SKNlIeR/j9LgAAACERaADyr2OHTum06dP6+zZsw23mZ+f31ctubi4uK+qMojC8Ob+IAzgCKbDBpPNLisoQWUYXMhu6fTo0KF+dn3b1thgssN7RBUlOmsz72g4zfNpr6iHlGF930JVJQAAQPCE6l3ZysqKnnzySR07dqzhNi95yUv0la98pea8e++9Vy996Uu7vXtA4Dx+dqmj4WSvLrtVQapOBYB6whriNRK19R2jcjsAAADCrq8B5ebmph5++GE9/PDDkqTz58/r4Ycf1sWLF7W5uan3vOc9uv/++/XEE0/oa1/7mn7mZ35G09PTev3rX1+5jNtvv70ysVuS3vGOd+jee+/VXXfdpUceeUR33XWXvvrVr+qd73xnj28d2nWYgSCdGpATxfbuXoWH/Q4qu3Ufd+s5AUQVw3IaC3sIFrVQcq8o3zYAAICw6GtA+dBDD+kFL3iBXvCCF0iS3v3ud+sFL3iBPvjBD8qyLH3ve9/Ta1/7Wt1www1685vfrBtuuEH333+/RkZ214a7ePGiLl26VDn90pe+VJ/61Kf0iU98Qs973vN0991369Of/rRuvfXWnt8+oB/6FRj2u5oyyro1zZslGYDeCVsAFvVQsp643V4AAIAgMXzf9/u9E0GTzWY1Njam9fV1jY6O9ux6+/mmuFOVL0cJPKJWQdmP6skghIT9WJuyW8NyOj0o57DrUErqyjqUkliHEh3FOpQHC/Kah4RztYL8WAEAAIRBO/ka77yAiAhCOCn1p4KTNu/Do4oSnUSb98GCGAJSOVgf9wkAAEDvEFCCD5QREJRwsloQ9wkAgiAIgWAcW7gPg/sIAACgNwgoEVq0dxcFOQjs5b6FYaL3UZ6z3VqHEug0/unVul6GX9WBJIFb+7jfAAAAuouAEh1Dq2jvBTmcLAvDPjZDmzeAbutW+EUg2XnclwAAAN3BavYIBKrD0AkPnlvp2sAcAOi2veFXO0NaCM56ZyPnMUAHAACgwwgogQ7rVatxmCoTHz+71Jfp3lGzvm13bZp3dsdhojc6ZjPvMNG7Awgdg6v82BBUAgAAdAbvqhBrYW3fDVM4WRbGfS7r5POkU2unAgD6jxAZAACgMwgogZAJc9DXi30Pw7AcIA4YloO4YJ1PAACAoyOgDAje2ALoN4blAMDh8V4OAADg8AgoEUpBbZPtdvVemKsny6JwG/qJgVIAEFyElAAAAIdDQAmg57odUnYjKI7LOpRUUaKTaPNGHNHyDQAA0D4Cypjjw2N4UHkIAEB4EFICAAC0joASHXGUqqt+tax2eoI3w1naQ+AKxAP/CEOcEVICAAC0hoASCAHCvPYRGB8ebd4A0DmElAAAAAcjoATQN2ELXoOyDiWDcgAgXAgpAQAAmiOgBAIubCFeu6J++wDQ5g1IDM8BAABohoASoRPECci0EwcTj8vh0eYNAN1BSAkAALAfASUQYHGpLozL7QTijCpKYBchJQAAQC0CSsRSpyd4Iz6C8tzpxTqUVFECQPcQUgIAAOwioAQQaUFu8w7icgUAgN4hpAQAACgioAQCKm5tz3G7vWFBFSU6iTZvYD9CSgAAAAJK9Fkv2lS7LcgVegAAIPgIKQEAQNwl+r0DAPaLazXh42eXdN2ZmX7vBvbI7jgazfDnotphK0u5HwE0spHzNDJA7QAAAIgn3gUBiLxOV7nGaVAOirI7Ts1XJy4nrmjzBhqjkhIAAMQVASWOLM4ftNF5caseDcugnLi+zrsZJhJWAqiHkBIAAMQRvWYBwBvR1nUizAlK9VsjcQvogCDqdWhYvj5awAFItHsDAID44Z1PjNFmd3QMyAGiIwgVjXGppuTvD3Aw/oENAADihIASQOB0o4o0qmFyL9ehjHJ4FqTb1u+QFEBwEFICAIC4IKAEgEMI+lIBaE2Qw8Ag71snUEUJtIaQEgAAxAEBJRAgrD+JoItKYBam8C8s+wmgewgpAQBA1BFQAgikOIW1YZnkHQVhCiarhXGfAXQWISUAAIgyAkoAQFvCGpaFdb/Lwr7/9dDmDbSHkBIAAEQVASVwSFEduhJlUX3MejkoJ4zCWjVZT1RuB4DDI6QEAABRRECJvulHqBLkwSZxamluFfdJcIUlKAvLfrYjircJQHsIKQEAQNQQUALAIQU58O6FIAdlUaqarCdKt482bwAAAAAElAgNBokAaEVUgjsAaIYqSgAAECUElEAA0MqMsAbwQQoDo1RV2Kq43V4AtQgpAQBAVBBQAgi0Toe3UR2U009BCMmCsA/9EoXbTps3cHiElAAAIAoIKAEgAuI6yTuOVZP1cB8A8UZICQAAwo6AEjgEqvCAWv0IyAjlanF/AAAAAAgrAkocCR+Io2f57GNaPvtYv3cjNOI+ybtar34fUDXZWJjvF9q8gaOhihIAAIRZot87AMRdEAbk1Askq8+bPnNDL3dnn8fPLum6MzN93Qe0JrvjaDTTnT8tYQ7feqmbjwGAYNvIeRoZoP4AAACED+9ggBhrtVqSqsreCOsk7706HSRSMRkfVFECR0clJQAACCMCSsQGrbi1DhM4RiWkZA3R7utEqEgweXjcbwAAAADChIASiKGjBI1RCSmjKIiTvA8TlBFMdgb3IRBfVFECAICwIaAEYqYTAWM/QsogrNWJwykHjo2Cx4O+j8ML4/1JmzfQGYSUAAAgTAgogT7qdejWyWCRSkocFoEkAAAAAKAaASXQprCuX9iNQJGQsoj1TRFkhMBAfFFFCQAAwiLR7x1Af9BCBwDxkd1xNJoJz5/8zbyj4XR49hcIso2cp5EBahLiohuhNM8fAEAv8NcGfRHEYR5R1s1Kx7BWUQa1EvZCdqvfu4CIopISAKJnI+fVfPXiOqjMBQB0AwElQoHQ5vB6ESD2KqRkUM7BCP8BAHsRKEVHUILCIOwDACBaCCgBAIiJMFVRshQJ0FkESeEW5DCQsBIA0AkElECf9KIasJft12Ft9QYAAAiiMAZ/YdxnAEAwEFACABAjYaqiBNBZhEbhEJWALyq3AwDQGwSUADqGKkogHMISUtLmDSBOohroRfV2AQA6i4ASiKiohoVBHZTzwFMb/d4FAAAORFAUPHEJ8OJyOwEAh0NACSC2Hjy30u9d6AomeaMVYamiBICoimtgF8fbDAA4GAFln/EHujc6Vd0W1UCrk6JaudlLF7Jb/d4FxEQYQkravIHO4/1n/8X9MYhrOAsAaIyAEoggQkIAANAM4VB/EMzV4v4AAJQRUALoOAJSIDzCUEUJAFFAENcY9w0AgIAS6IOgDnoBgCCizRvoDkKh3uG+Phj3EQDEGwElEDFxqF4k4D0Yg3LQDqooAaA7aGFuD/cXAMQXASWArohDUApECSElEE+EQd3DfXt43HcAED8ElAAAIPBo8wYQJgRsR8d9CADxQkAJAAAkUUUJxBVBUGdxf3YOLd8AEB8ElECEBK2tOmj7U8+D51Y6dlkPPLXRscsCAADhQpjWPdyvABB9BJQAEEAXslv93gXEVJCrKGnzBrqHAOhouP+6j/sYAKKNgBKHFuQPsQCY5A0AQC8QnPUO9zUARBcBJQAAqME/oIB4IvxpH/dZ73GfA0A0EVACERHU9R67tV+Pn13qyuUCCDbavAEEBUFZ/3DfA0D0EFACPUawBiAMqKIE4ongpzXcT/3HYwAA0UJACQAA6iKkBID9CMaCg8cCAKKDgBIAAIQKbd5AdxH6NMZ9Ezw8JgAQDYl+7wAAoHvWt22NDSb7vRsIseyOo9EMbxcAgCAsuDZynkYGqL1B7xz0+4DnI9A+PnEALXrw3Eq/d6GhoA7IAYBu2cw7Gk7zNgboFgKfWoSTwcdzFp12lNc9ASbQPt7ZA+i65bOPafrMDf3eDQCHRBUlgDgjnAwPQkocVq9f5/Wuj+cu4o5PGwAAAAAAIFaC9s+HvftDYIm44RmPwLuQ3er3LiDiOtm+/8BTGx27LJ77CJIgTvRmWA7QXUH78N4P3Afhw2OGZjZyXuUr6MKyn0CnUEEJAAAAAHsQDIQXrd7YK8yv5+p953mNKOPZDSC0Hj+71O9dCIX1bbvfu4CICGIVJYDuCvOH+qOI6+2OEh5DSNGrQoza7QGqEVACAIDQos0bQKfx4T86eCzjKUxt3IcV9duHeCKgBAAALaOKEoifOH0IjtNtBaIobq9hgkpECQElIq+TQ0tweMtnH+v3LgAAADTEh/xo4nGNh7gHdXG//YgGAkr0HOvhdRbBH4BeC1oVJW3eQPfxwRdhxvM3ugjmanF/IMwIKAEAAADEGh/oo4/HOHp4TBvjvkEYEVACQAxQuYxOC1oVJYDui+oH3qjeLiCqqBJsDfcTwoaAEuihx88u9XsXACCSaPMGcBh8eI8XHu/w4zFsH/cZwoKAEgAAHApVlED8ROmDbpRuC1rH4x5ePHaHx32HMCCgBIAAu5Dd6vcuAKFBFSUAoBWENeHDY3Z0tHwj6BL93gEAwZNxC5q0t2UblmzTUqF0aBumfIP/awDYld1xNJrh7QQQJxs5TyMD4X4/wId0IBx4rXZeFH6HI5r4RAFgH9P3lXFtZbR/sIpjmHJMS7ZhqVAKLctBpndAeLl89jFNn7mhW7uNA6xv2xobTPZ7NyJt7zAi7m8ACB4CD0iENGHAa7V7eP4jiAgoAbQl4XtKuJ4G6oSXnmHIqQSXxfCyYCZkG6Zc0+rD3gKd185E9EbbRi24DFIV5Wbe0XA6GPsCRFlYP9wSeKBaWJ/HccBrtft4/iNoeAcPhNjy2cf6vQs1TN9XyneU8vavA+dLsk1LI1urxXZxMyHbTMgxLTkWv4oQbO2Eku1eXtTCSgAIKgIPIBx4rfYOISWChFQghhgigH4wJKU8V4P2zr7v+TKKbeNWsia8tK3EgZWXj59d0nVnZrq014izToeSrVxPmMNKqiiB+OGDLaKA53GwEE72Hq8BBAXv3gH0nSFfSc9Rsm7lZTG8dMyE7PKhtXu6U0N7Hjy3opuvn+rIZSHcehVMNrvuMAeVAOIlLB9sCT3QTFiex1HH67R/eA0gCPr6DLzvvvv0Mz/zMzp+/LgMw9DnPve5yvds29b73vc+Pfe5z9XQ0JCOHz+u22+/Xc8880zTy7z77rtlGMa+r1wu1+VbA6AbyuFlxslptLClydy65rZWdWJjUVevX9Lp9UuaXVvQZHZFo1vryuS2lHQKMrz+vcF54KmNvl33QfoZvgXd+rYdmPsnKPsBAFFA6AEEH6/T/uMxQL/1tYJya2tLN910k37pl35JP/dzP1fzve3tbf33//7fdccdd+imm27S2tqa3vnOd+of/aN/pIceeqjp5Y6OjurRRx+tOW9gYKDj+4/4ePDcSr93AQ2YvqeUXVDKLuz7nmtachJJOaVWcc8sDutxDUuuZRXbxw2jD3uNIAlqGBjGakravIF4CnLlDR+40aogP4+jjtdpcPA6QD/19Z37bbfdpttuu63u98bGxvSVr3yl5ryPfexjuuWWW3Tx4kVdddVVDS/XMAzNz8+3vB/5fF75fL5yOpvNtvyzQNTc9ehf6KqdVa0nMrqUHtVCekQLqREtpEe0mhySH6JAz/JcWQVX6Sbb+IZZCStH1jy5VkKelSgdWnITSULMCAtqOFktjEElgPgJ4odaQg+0K4jP46jjdRo8vA7QL6EqLVhfX5dhGBofH2+63ebmpk6fPi3XdfX85z9fv/Vbv6UXvOAFDbe/88479aEPfajDewuE06tWHtWztxbrfs82TF0uhZXF0HK0El4WzxvVemKgaaC39sQTmrj66i7tffsM31PC8ZSQrfT2Zt1tPCshz7TkJnbDy/Jxz0rIN/kDHjZhCCb3Wt+2QxFSUkUJAABaQTgZXISU6IfQvGvP5XL63//3/12/8Au/oNHR0YbbPetZz9Ldd9+t5z73ucpms/pX/+pf6cd//Mf1ne98R2fOnKn7M+9///v17ne/u3I6m83q1KlTHb8NQOD5vk7mrjT8dtL3dDK/rpP59YbbbJtJXU4N63IpsKwOL8vBZtiYriPTdZSw83W/7xtmTXjpJRJKbBnyEgl5iaRkNZ9EfpAL2S2dHh060mVUC0vQ1S1hDCfL4v7YAQi2IH2gJfjAYQXpeRxlvEaDj9cCei0UAaVt2/rH//gfy/M8/fEf/3HTbV/84hfrxS9+ceX0j//4j+uFL3yhPvaxj+mjH/1o3Z9Jp9NKp5s1gQJH9/jZpY5e3vLZxzp6eVJxIM27nvU6Xbu9ojPbS5rPb2i+sKG5/IZG3frh3F6Dnq1rcmu6JrfWcJvNvx3Q4uBE8WtoYvd45WtchUSqUzer6wzfU8IuSFXrYA5kdu8v3zDlJ4rBpZ9IyrOs4mGHAky0JszBZLUwhJRBqqIE0FtB+EBL8AEEG6/R8AjC73TER+A/Pdi2rTe84Q06f/68/uqv/qpp9WQ9pmnq5ptv1tmzZ7u0h0B0+IapPz3+Ig05eR3L167FOuTkNV/YKIWW2eJh6fhcKcjMeE5L1zNs5zS8fknXrl9quM2V9HAlrCwHmZcHJ4unhya0nBkvDrkJAcP3ZNgFmXUG+ZS2kJdM1gaYpUpM30rITwT+V3XgRSWcLGNdytbR5g3EC8EHOoFQBtjF6wG9Euh37OVw8uzZs/qv//W/ampqqu3L8H1fDz/8sJ773Od2YQ/jK7vTWhCF6NhKpPV4Iq3HB6frb+D7GnNyOpbPloLMbFWgWazCnCtsKOm39sFhPL+p8fymblh7su73PRlayYxqcXBCmzNzWh2Z0spo8Wt1ZEorY1NaHxqTb4Thj6kvs2mAKaXHRuUnrFJgmZSfSMi3rGJ4WQ4xGeZTV9TCyWpBrqakihKIr359mCWcRCcRynQHr1MAjfT1k8Pm5qbOnTtXOX3+/Hk9/PDDmpyc1PHjx/U//8//s/77f//v+uIXvyjXdbWwsCBJmpycVCpVbP+8/fbbdeLECd15552SpA996EN68YtfrDNnziibzeqjH/2oHn74YX384x/v/Q0E4sQwtJ7MaD2Z0SOaq7+J72vK3tINk8Oa3V6rfM1sr2l264pmt9c0lVuX5fsHXp0pXzM765rZWZdWnqi7jWNaWh2Z1OrolFZGpoqHo1NaHZ3U8ui0VkemtJkZDkWwZ7iODNeR1LjV3jetqhCzHFyWTlfOK1adBjnY6pQoB5PV4vBYHhVVlEDv9TrcIfQAgo/XaXgR2KMX+vpu/aGHHtIrX/nKyunyoJo3v/nN+s3f/E194QtfkCQ9//nPr/m5//pf/6te8YpXSJIuXrwos2qC7pUrV/S2t71NCwsLGhsb0wte8ALdd999uuWWW7p7YxBIDzy10e9dQBXfMLScGpY7fbV+oKvrbmN5rqZ21ovh5daa5qqCzNmt4uFEvv607b0SnqvZ9SXNrjde/zOfSFWCS//YMWUnprUxMaPs5EzxcGJahUznBtR0k+G5MgqupMaVmL4M+QlLshKyRgakcjVmMlkMNJNJKQJTyeMSTpYFNaSkihKIt159oCX0QLcQynQOr9Pw4/WAbjN8v4VSpZjJZrMaGxvT+vp622tetqNfv6Q380dvzz5Ki3c7wcGF7Nahr0fqXED54LmVI19GJ4fkdGNATrV6a1B22sTVVx/6Z1OuXay63L6iua1VnUnlNJVd1WR2RVMbK5rMrmgov92xfc0PDNYElsXD3RBzY2JaTqp20Nbz5jsbap4YHuzo5UnS8ECD4Mg05SeSUmK3ArN4vKqdPMCDfeIWTlYLYkgZpICSKkqgP7r5gZbQA91GIHN0vE6jhdcE2tFOvsY7dQChU7CSenpkVk+PzEqSvn96ct82A/kdTW6saCpb/KoOL6eyq5rcWNGA3dpk8nRuWzPPXNDMMxcabrM9PKrsxEwlvBw+fVw7U7PKTc9qZ2ZO+cnp8Ay78TwZhbxUyKth87thFENLs7QOZjnI3LsuZo+DzDiHk1IwKympogTQraobQg/0AlVjR8PrFECr+MQAIJJy6YyeSZ/UM9Mn62/g+xrKbdWEl8/SpkbXljSytqTRtWWNrC3LclurFh7czGpwM6v5Jx+vf3WGofzEVDGwnC4Gl+Wv8un82ESgKxNr+L5k2zJky2iW85aDTCshP5moqsxMFtvJOzjcJ+7hZFkQQ0oA6HTIQ+gBAP1BaI9uIaAEsI9rmCqYlizfl+l7javoAuLihVVdVaeKsinD0FZmWFuZYV2cOy1JWjw9XruN52lo48puYLm6VAowlytB5vCVVZktTCY3fF8Dq8saWF3W+GPfr7uNZ1nKTc5UhZczlfAyc9VpFWbm5I6NdyzQ28w5jdu8O6UcZNq2jFyDbRJVQ3yqW8rL5yWTTW8zweR+hJT1MSwH6K9yqHjUD7aEk+g1ApnD4bUaXbwm0A28S0dPxTVI6OT6k72Qs5K6mNkN/Azfk+X7snxPlkqHpfMSleO7X0EPNFtmmtoam9TW2KQWrr6x7iaG62p4fXW38nK1eHhye02Z5UUNLC8qvb7W2tW5rgaXFjS4tNBwGy+Vlj0zK3tmToXZedkzc8Wv2TkVZuZkz87LGxo+1M3tG8eR4TjNKzFNsxhWliaVy0rItyytF3yZlrVvSjmChTZvANUOG1QSdqCfCGTaw+s1+nhNoNP4tAAcoBMDcjqp2wNy6vENU44hOWot/DF8rxRc+kr6rhKl0wnPrQSaUeFbljYmZ7QxOVNzfvWQHLOQ18DKsgZKgeXA8qIyK6XjS5eVWV5Ucqu1yeRmIa/0008q/fSTDbdxB4dKgeV8JbwsB5mF0nE/PXC4G9wvniejUJxOXg7AN3OOUns282WU1sEshZal4NK3rKpW8+YVmVFBFWV9VFECwdFqUEnQAYQLr1kAh8E7dAAd5xumbMOULSmn+gHJE4ubmr/uulLVpSvL2z1M+J7McpjpuTLk9/YGdMB3F7YqIaWXSmv72AltHzvRcHtrZ7sYXFaFmHtPJ/KNeqT3XNb2lqwnfqiBJ37YcBtndEz2zJy8+WNyZufkzM7LnSseOrPzcmZmi63VAbWZq782qCFfch0ZriOpcUmmb1aFmIlkMcQsTyo3rciEmEEKKamiBNAIYQbCgooxoBavCXQSnxQA7OMbrmTakm+WviwZnW7cNgw5VkKtjKAxfK8UXLoyS5WYxUCzeNz0PbmmVVwv0z98mPm9C1f03L3rUPaImxnU1qmrtXXq6vob+L6u8h0lly4XvxYvK7V0WcmlBSUXS+ctL8q0W1tGIZFdVyK7Lj1evyLXNwy5k1PFsHJuXu5sObwsHc7NyZ2c7ktLdaNwsh2G58oouJIKDbfxDbMSYu6uiWlVVWUWzw+6IIWUQUEVJQAA3cE/HAAcFu/OAexnOPKTGzVn+TJlVALLYmi593jHQ8zydRumHMuU0+RXVma62GJteMWqS9PzZHlOsULTLYaYxUO3cvooYWbPGYbckTG5o2PKXXdD/W08T4n1td3AcumyUot7QsyVJRlea0N9EivLSqwsSz/4u7rb+FZCzvTMbng5tzfEnJc3PtHRSsROhJOtMnxPhu1JB4S+NcGllaipxvQTyWKI2+dqzKCElFRRAgDCjoqxxggn44nXBDqFTwkxs5nv3Yd7dF4/1p/c5ck3PDXLIH2ZMjxTUim0LB/3ygFm96vtfNOUY5b/QKabbmt6blWY6cr0XO0Mj8n0XBmeJ9N1ZHrFdnOFIcw0TTkTU3ImprRz43Pqb+M6Sq4sV6owk0vFSszMyqISlxdkLS4osbba0tUZrqPk5UtKXr7UcBsvlZI7Mydnriq4rAoz3dk5ecMjLQV4vQwn22G00lJeDjETyZp1MYuDfUqt5RFoKQ8TqigBAAA6g5ASncA7cwAd5Mk3q/5zuieP9KViu3ip6tI1ssXQ0rdK4WVvQszK3pqWPLP2+rbGJutua7jFANNybJmuK8t1ZLqOLMeR5ToyPLcXu3x0VkL27Lzs2XnpR3bPHh6o+nNQKCixdFmJywtKLF5WYrF4aC0uKHG5eNrayLZ0dWahIPPpJ5VsMtTHywzWBpil427V8Y0Ga5mGRSXEzDcJMfe2lJvW7oCf6knl5uHe/AWlihIAgLAjjNmP6kkAR0VACaC3DLe4xqVseeZGvQ1k+JYkqyq8TMjwE5ISMtSfN4O+Zcm1LLnJvXOjyxv4teGl48geGpDp2DKdcoVdSKRSck6cknPiVMNNjJ2dUnBZDC2txaow83LxfHNnp6WrM3e2lXrih0o1G+ozMlaZRl4oTSSvnk5uz8wVB9uEWMst5TLkJ0qhpbl7WAwyTfmGuee83RbzIISUQWrzpooSAICjI5yERHCPo+NdOSLrgafqhV+99/jZpX7vQmCtPfGEJq6+es+5vnzDkeQUKy73dL0aMiU/URta+gn5vifD6OMfRMOQm0zJTUrleCk/PbL7fd+XUQoqTdctDmlx3GJlnefKrDreD5s5p7aK8gB+JiP79DWyT1/TYANf5sZGsdqyHF7uqchMLC7IaHWoz8a6EhvryjQY6iNJ9sTUbog5O78bXs4WTzsTU30Z6tNphorPJTmth96+jFIVpqlt09LIUFqyzOLEcrMUaFpW8bRlFas0A7B2JuKn3aVoCJgB9AthDAB0Fu/qEFgXslv93gUEkC9PMgryVagJLx89v6jTp6dlGAkZKlZdmkYpyCyd7neA6SeT8pNJHfQ/ZsO2i0Gm48h0bBmlQ99KhKcS0zDkjY6qMDoqXd9gqI/vy7yypsTlS5XW8erw0lxYUHJlueXQNrm2ouTaivTo9+tfnWXJnprZDS0rAeZ8pTLT7fBQn6Aw5EuuI6N0V5pqLRguB5WVqkzLrKnclGnWBpvlcPMAVFGiU2ti17scHk8A6B2qJ1GN4B5HwTs4tC27E5KApAMePLfS712o6O+AnHDw5cn3C9Vn1DBk1gSYhlFsIzeVqJwOgkZB5mOSTo8MFisxHVtyiiFm+bjh2MWqzL03vEXtVlEemWHIm5hUYWJShWf9SM23KgNxXEfJ1ZWaoT7F6eQLlePJ1dZep4brKrW4oNTigvT39bfxkinZM7OV6svC7HxVK/m8CrNz8oaGQx9itvxYe57keZX/BbR0qw2jTnBZqtasrKmZkKy0dIQ1NREuvRzSt/e6CCwBdEvcwxjCSQCdxDs2ALFxUIApGVrP78g0Evu+rNJh31VVYkqqX41ZaiUvVmK6ldbySoAZ8CrMmmndVqISFqrBYHLDtnfDyj3TycunExvrLV23aReUfuYppZ95quE2bmawtn28Zk3MeRVm5uRnMu3c5L7oWiDt+5LrynCL5ZqNQs0dScMDpTZyK7EbatZUZhaDzZrvJRIdb9WnirI7ehlKNlO9HzzOAAB0V9yDexwe79IA9FX9dSj7xZfr23L9xu2v1YGlUT6UVQowkzKCUFmXKE579tMD9b9fDpBK7ePVIaafKE6clusWt+uxmnCyRX4yqcLxkyocP9lwG2NnR6nlqirMxYVigFlVlWntbLd0fdbOtqyL5zVw8XzDbZyR0ZoBPrWVmHOyp2flpxoMXIob35ec4muu+tVz4CupXKFZDiyr1tGsHK/+CsJrMwaCEko2Ut4/gkoAnRLXMIbqSTQT19cFjoZ3Z+iZ9e0W1zwDAszzHXm+o0Yfwc1S27hlJLSez8syLFlGUpZZPgzAr13D2A0x93xrRdqd8uw4xWEsrlNqKy8fdyvnyevcm9PDhJOt8jMZ5U9drfypqxts4Mvc2lRqsRheJpcWSq3kVZWZS4sy7UL9n98jsZFVYiOrzA/PNtzGnpiqmUa+bzr51HSxsrCLet7W30muWwzaqx6TphFkqRqzOrishJumpc1EQsOD6d0qTbQs6KFkPVRVAsDhEU4C6AbekQFd1KkJ3qw/2Tvfu3BFzz09fuif93xXkivXL+j+J7f1vPmhfdtYRkKWWay+rD5uGpYSZlJmQNbCLIeYUp1u+DLPq1r/0qlZC1OlNvNWKjG7GU62xDDkDY8oNzyi3LXX19/G92VdWatqHV/YbSUvB5nLS+0P9XmswVAf05I9NV3bPl5dhTk7J2ds4shrOPYzpNzMucU2714or6fp7P6zbG+guSNpMFW6LyprZe62lfuJxG6LeXXLeUyFMZish6pKAEdFtRiwH68LtIt3YgDQY67vyG2yDqQhoxhYmlYlxDSN4vGcYyphllvMu9Oyur5t71ZRHsQ0pVRKfirVQohZbCNX5bDYWr65mWtt+Eq/GYbciUntTExq54Zn19/GdZVYWykO8TnqUB/PVaoUgKp+hikvmZQ9PbtvDczqdnJ3ZJT25sMoVWhWa3gvVgLLPe3m9cLMkA8FikooWQ9BJQAcjOpJAN3COzAAfdepdSgvXljVVacnj75DfebLl+Pbkrt/WYTB7Y3KcdOwZBqmLCOhhLkbYhbbya1KdaZp9DkQaRJirm/bUvkhqwzzKR/urpNZbi0/7ITynrEsOdOzcqZnmw71SSwv1gzxKVZi7oaaiWyrQ31spS89rfSlpxtu4w5kate/nJkrVmLOzlcCzU1l4lFF2YLtgrNbRdmqSpjZQru5YVTayrV3/czqQLN8PCDhcpSDyb0YmgTgMOJQLUY4iXbF4XWBzuHdFxBwtHejEc935fmuHNnKN+0qNpQwrEpFZrGVPCHLsEohp1UJOMthZltVlIe0b13aynCfJj9UPZW8ej1M2y5NLncCH2L6yaTsYydkHzvRcBsjl6sJLMtBZqpqWrm1vdXS9Vm5HVkXn9DAxScabuOMjMqdnZczN1c8nJ2TUz6cm5czMycx1KczfF9yHEmOVMhXzm4YQ5pme4Fmh8UpmKxGNSUAAEBv8a6rT/jvU3c98NTGwRsd4MFzrbVhRo07dk4y8/J9Q4Y9LLkDMsLRgBsLT29u68TwYJs/5cvxi1WIUv6AbXfDzJw/UKzENBNKlFrOE5U1M60jtZgfemhWKZjx1STFrJ5QXvmyi18FO/ABpiT5AwPNh/pIMjc3lVzaP428eriPWTjo8S4qD/VJP974HyLOxGQpxKwKMKuOu1PTkVmP8VBVlN1SWjtTsmtevt1uN49rMLkXQSWAdkS5WozPrzisKL8u0Fm82wJQw7n6S/KHq9pFPUuyh2TYQ6XD4dLx4WKAWf6eMyhD/OEJv90wc3Ej37TttxJeltrIK+FlKdBsFGQeOpxsVTnEbJBhGnYprLQLxcDSLsgsVWCGiTc8rPzw9cpf02Soz/qVUoC5UFuJWT7dxlCfxNqqEmurSj/aaKiPKXd6pqr6crcC0y2HmBOTdUOxoLV5SwELKdtxxHbzLdeXb1kySgGnH8B2836g7RsAAKC7eKcFdEmnJnj3mp/K1p5hulI6Kz9dPL9h7ZlvSPbgbmjplELMwlBVkDksOUMy/P1BRKfWoURnNZvw7PquXNdVwW1epWeVppNv5/2qqeXF87o98KceP5mUn0xKmT2VqJ5XDC/LgaVdKH05MvwQVg0YhtzxCe2MT2jnzLPqb1MZ6lOuvlzQ0OqSrMXLSlxeUGLxsqzVZRktTGI3PE+JxctKLF6W9N262/jJZHGNzrk91Zez80qcOi53bl7+KEN9eqaq3Xx7s7hkgKED2s3NUmhZrs4sh5dmVbVmOdSM2ONINSWAVkSxWozqSQC9wDssBNKFbGtrq0Vdr9ef9OXL2JmS71lSaqMYTrbK8KXUlvzUVumymnAyNRWZ5cNcakWmNybTG5Xljclo1saLnmkWUrbC9V2tbzUPMQ2ZpeE+9Qb+JCrVml0d+GOa8tNp+em09r0NdxyZdqFUeVnVMm47LVcgBlL1UB89V5K0LNU+3ratxNKiEosLSiwuyLp8uXR899Bav9LS1Rm2reSlp5VsMtTHH8jInZuTNzcvd25e3nzpcG730B8aOvxtbkNoqyjbtF1osXq41G5uOLtV0E0jyFKFpm/Vtpf7VVWb1WFnWCacU00JAEB7ohjco/N4dxUjrCeFgxgylP67X5Vv5uWl1iUrJz+5JSU39xxuya86T1abLbuJHfmJHSmzXBNk7p1bbHgDMv3dwNL0RmsCzPL3DD9TWSezE5O8v3fhip57evxIlxE1RwkpN3MH/+7x5cnxvAMH/hgyZRlmqQqzPKXcqrSSmzJlGIYMw5QhoxRoFg+PFG4mEvIara/o+7tt49UBZrn6MgRrXjaVTMo5fkLO8WZDfXaUWFyUVQoxE1UVmOXT5tZmS1dn5HaUuPCEdOGJhtt4wyPy5uZKAeax3QBzvnQ4OyelO/MPjiiHlC0Hk4dVqtA0nNrraaXl3N87EGhvhWb5q08IKQHEBdWTAHqFd1YA6jJkSG5GhpuRctNNt/XNwr7Qsl6gqUSurX3wzZxc5eRal9U0AvWTsqrCSz8/q6QxoaQxrpQxXjmeMEZkdLMCL+IOE1K2Ek62w5cnx/fkHGq9SKM0udystJmXqzQrFZuHqdQ0DPmplPxGU673Vl9Wt44HNLxs97H2BzKyrzot+6rTDbcxNjeqgss9FZilMLPVoT7m5obMzQ0lHj/XcBtvYrJYiVkdYFYHmdMzLQ/1iVpI2fVg8rCqWs6NViacVwea9So06w0I6iBavgE0QrUYsB+vCxyEd1RAQPW6vfsoDC8l5VMy8hNNt/MNZzesrAk0d88zBnbkGZvFlvGWd8CWa63ItYqT1xcbpJmGLCWMsX3BZdIcLx4aE0oZ40oYY61f9wG+u7Cl5833ph21F1oJrjodSnaOL9d35PqS7RUO2HZ3mrllFKs0y8OAdtfRLLajH6hJ9aVRWeuytPZloXQ8zG3jDfjDI7KHR2Rf23ioj7l+RaNXlmReXpB1eWHP4WWZly+3PMzIXFuVubYqPfKD+ldnmvKmp0uB5bHaALN06E3WH+oTVoENJg+rOtCsOrtp23mjQLM0+XxfhWYL62hSTQkgqqieBNBLvJtCW7I7Eftw08CD51b6vQuRZPgJqTAmo9A4AJy4+mr5cuUZG/LMrDxzXW7psPiVlWusl76XlYzWgxxfrmx/Vba/qu3me6q/f2RUmcSkBpNTGkxMFI8nJjWYnKwczyQmlTAbVM1F2N4AshxYBjeYPIzdaeZSs6o+ozTwJ1ETXFpmcQjQQSFmZWDPXlUDewyn3DJu93Ta+FHXHm2bYcgbn5AzPy0969n1t3FdmasrxcByoV6AuSBzeanloT7W4qKsxUUlv9d4qI83O1sJLM3jx4trYM4fkz9fXB9TY+OBHwYTuWDyKEpTzo2qf2S1so5mTahpmrvHDVOyLG1umxoeTJcGCZl9bT8HEAxUiwH78bpAMwSU6In17TbXKAy5sE7wDgpDlix/XJY7LjXJH3158o1tueZ6Jcz0jKwGx/Oy/TXZ3hXZ/hXZ/po8HVQxV3vJOXddOXdda/nzTbdMmcMaTE7UhJaDiUllkqVAMzGpnHNSaWuwY5Oqn97c1onhwYM37JFoBZPtKlVlNg0NjVLruKWEkayEmKZh7hsIVFE1sGf/Vfq14WXEKi83c66GBxqEO5Ylb2ZW3sysnB99Xv1tHFvm0lIxuFxoUIm5ttrSvhi2Levpp2U93Xyojz83J29+Xv6x4/JLg338+WOVQ/VoqM9eBJMd0EaV5o5UuxRAOagshZiV0+WA09x//u7PBDv0BhB9VE8C6DUCSgCBsvbEE5q4+uqWtjVkyvCHZbrDkrs7wOOqdO2QHN/35WlHhT2hZcHfPV4+3z2gtnKvgrepQn5TV/JPNtzmz85JKXNAo+kpjaamSoeTxcOa86Y0lBzrWJCJoNgNMQstVGOW18gsDvwxSwOAisOBKqctU6aVkmlkZBrm7nPG84rBZaFQrLY84rTxnldRdkIiKe/YcXnHjjfeJpeTtXh5N7hcWJB1+VKpMrNUidnGUB/jwhMymwz18UdGSpWXVcHl3G4Vpj9/rGNDfcoIJ/ujfL8PphKVqef1HPhbvlK5adYGnKUvf2+gGcJp6AAAANVC9qkDONgDT230exeOLEzrTwbR3knehmHI0qAyxqAyZuNJxJLk+nnZ/hVdNedo217VjrOqbWetdLhaOS/n7p053lzBy2l552kt7zSuxJIky0hopBxeloLLsfT0vmBzfuiELIMWwmhpZ43MvYzKJPNygGklLBmyZBpJmYYl0/dluZ4sx5Plekq4rkzbleW4MnvUNh4YAwNyrzot96rTDQdwGZubDdbCXJC5cFnW5Usy8q0N9TE2NmRtbEjnzjbcxp+YkDd/rCrArK7CnJc/MyvVWw5gD4LJYDjyYKVK5WZ9rbal14SWliW/XtVmddjJP8iAjgprOyvVk+imsL4u0H0ElABQxTLSsow5zQ2ON93O9WztuFe046xqx14rhpfOaiXI3LHLp9fkq/U3ea7v6Ep+UVfyi023M2RqLD2hiYFpjQ9MayI9rYmB4td4zfEpJa34rZMZP8VwU77kNJ95X0w2EqWvctGe78t0PCUcV5bjyKo6XNvxNZJJVdrSTbNY5WkaliwVT3da0zbvHvGHh+UOXy/3usZDfQa3N2UuXJKxsCDzcvHQWFionGcsLspwWlvixFhbk7W2Jv3g+/WvzjDkz8zsCy69uWPyjx3T9tSMvMkpqucCpG/T36va0vdqu3KzukrTNIsBp7n//H1fAAAAbSKgBIA6vnfhip57erzh9y0zqWFzRsPJGSnT+HJ839O1U46y+RVlCyvK5le0nl+uHN89XJXTRtWcL09X8iu6kl+R1h9tuu1wckwTA1P7g8yq0+MD08okgrOuJXrMMOQlLRWSlqQ9gbbnKZeQTNuWaTsynOLal8UW8mLbeLn9vNiebsmQUQwxSwGmIUNGqRW92LpeOl1cqKHyvVAxDG0PjWjw2RPSs59Tf7lcz5OxslwbXF7eE2IuLbY21Mf3ZSwuSouLUp2hPoOS/ERC3uyc3Lm5htPJ/bExquR6qG8h5WEdpXKzWiWsrBNglis56629yRqcQN9RPQmgX0L0jglAXLSzDmXQGYapkdSERlITOqEGlVgqrpO542zWDy9rgswV5d2dtvZh017Xpr2uJzd+2HS7AWtwN7gcmKoJLycHZiqVmUPJkfCFSTg809SGJw0PDe0P4Xxfhu3ILE8ad0rHbVum40jewcHbXoYhGTI0VEhUjhsypFKoaZZDTpkyzWIoaqgcjpoyVRxIZHWhsrOepgGUacqfmS22Zz/3efVDTNsuhpQLxbUwjVKIWTm+sND6UB/HkfXM07KeaTLUJz1QDCzn52qCy+pDf3i4petDa0IXUnZCZf3N+mFnW5Wc5p429b3rbVaf5m8TAIQCbd6oJ2bvloDuO+oEb9afjJ7vLmzpefPNp/gahqHB5IgGkyM6pmuabpt3tpUtrChlbmktv6wruWWtlb8qp1e0abe3TmbO3dalrYu6tHWx6XZJM1VVfdm4MnM0PSHTiMcbj6c32xuuFKQp7EdiGPJTSbmppOqWErtuKbx0StWXzm6I6RRb0vfyfcmXr42crcH00Z4/5enplmlVgkuzVLmpUpVnJQQtnV+u/rRMq+Ug/kgBVDIp//gJ+cdPNF4MIp+vhJb2U0/XXRvT3Ght/WUjn1Pi4hPSxScabuMNDcub3xNcVp+enZMGBtq9pbEWy5DyKJpUcjZ9Ve6dnF76ahpq0pKOLiOIAYDW8E4JAEImnRjUTGLwwJDLdgu6kl+phJdX8vWCzGWt59tbJ9P2ClrafkZL28803c40LI2lJ2sqMScGpjWRnqo5PZ6eVsIM35+jdkPJej8bmaCyEcuSZ1lSOl23etCw7ZrA0rSLE8dN264bXrbL9V25rqv6pYutKU5WN2QYVqlCs7j+pllqaTdL5+cdS5ZhamQgJaPDwfy2YUnzJ4pfz/+xutsYW1t1hvlUn74sI9da9bW5tSnz8XNKPH6u4Tbe+EQpsKyqxJyvCjRnZ6XEwUN94oSQsgcaTE5vbahQVbVmOdisF2ZSrYkIo70bvUR4j714l4TAuZDd6uv1P3hupa/Xj87YO8k7jpJWSjODxzQzeKzpdq7nKFu4shte5pa0Vgozr9QEmivFQSwt8ny3eFm5JemAYs6R1HgpvKwOMvcGm9NKJ/pftXWUYLLZZQU9rNzMORoe6OzbBj+ZlJ9M1o3HDdtWIuEXQ0y7HF6W1r1sYc3GTnF9V64vSU5rQedm8WAoldptOy8FmpZKa3WaZmnq+u55Vum86tCjnYnc/tCQ3Guvk3vtdQ028GVks7IWLjWeTn75crGytQXmlTWZV9akR39Q/+oMQ97UtLz5Y7trYu4JMr2p6dhVrxFSBtBhqzX3BpvVw4QaTUqvXnMTAADU4B0SgP28hAx7WDJ8SV7x0PAk+ZLhF6vtjO7+hzVK61AGnWUmKu3ZzXi+p81Ctklb+e7xvJtrax82Cle0Ubiii2pcsSVJg4nhfaFlvYE/g4mhjq+T2clgstHlBz2k7CU/mVRW0vDontbx0rqXhlMMLM1CsQrTqBraEwRbheLQq0yyvbUwc7ZXqdQ0TKu05mZ1xWY58DRkGuUBREblew2f94Yhf2xMztiYdOOz6m/jeTJXV/ZUXu5pJV9ellGnQm3f1fm+rOUlWctLSv5d/W18KyFvdnZ/JWZVS7k/PhG5SjVCyog4bLApFZ/T1e3oe0JMv1m4afVmfV0AAHqNd0cA9jFkSW7j0dTlN95+TXhZCi2N8nmuJE++0f0wE71hGqZG0+MaTY/r9OhBA3+26rSVr9QEm1fyy9qyW1s3r2zb2dT25qae3nyi6XZpa0Dj6amm1ZjjA9MaSY0duE5mt4PJetcV1KCyG1WUbSute+mniu3DNZGk5xWrLQuFYnhZrrq0nZ5WXVbbsdsPTR3PleRKnn2o6zSrhgmpPD29arhQcYq6URkwZBil1TcNyRgbkjF+vfSsM5JUmrOu3XU5XUfm8rKshfoBpnV5QeZqi0N9XEfWpWdkXWq8XISfTsudndvfQl4VZPrDI4e6n/qJkDLmfF9y3eJXHQcGnHvX2qxqPd/Xll7dnh6xsB+dRXs3gH7jnREQIGEbkGPILK4T5zf+b76h4sCL3fDS3XPo9aQi8zC+d+GKnnt6vN+7ETrFgT/DGkwO68TI1U23zbs5Xcmt1AaZpfBytarFPFtYa2sf8m5Ol7ef1uXtxtOMJckyEqUgc6pSRVrdZm57wxpLT2s4OS6rh+tkUk25azPnanigxYoh05SfTslNp2rPL1ddlkPLQqFYeWnbfQsuu8nzvdIA9c5XlBoyZA6asq6bla6fr0xOL1dvWoYls2ArubSk5NKSEpeXlFi8rMTlRSUWLx9iqE9eiScvSk82Ht7lDQ3tCS7nSq3lu0FmEIf6EFLi0BqstSm1MEQokSgGm4lE4yFC1V9UbHYEa+0B9fHaQDXeFQHoOkNGMcT0LUn7hybsq8gsHVrehCS3GHCqHGYWKzMlv1id2YlJGiEVhRArbQ1obuiE5oZONN3O8RytNx34s1I6f0We33oo4/qOVnKXtZK73HQ7Q4aGUxMaTU1pND1ZOpzaPUxPaSw1rdH0pBJmqulltSoKj29gVFVd7v1Ib1SqLEvt4uUhPQ0qm+LOl19al7N0/zS6m6YGpKlT0rNO7fuWaZhK5HJKL64oubSs9NKKkouLSi0uF0PNxUUlF5dk7rQ61GdL5g8fV+KHjzfcxhsb3z+NPABDfQgp0VOeJ5WWn1B+9+yWKjbLX9VVm6Uvv/o0IScA4JB4RwR00ONnl/q9C6FWqciUJfnS6mNPafrMDbsb1Mkii9OnS4FlpRLTleTrqfMFXX3thHyVqjSLH60l329rajX6L2EmNJWZ01Rmrul2nu9po2rgz5VSgFkbbBbDTNvLN72sar58bRRWtVFY1dObzbcdTIxWQsu9geZYVbCZThwcPgax5TsQbd4dVB7UI+3J2ly3MlU8KO3iUeH5ngrplAqnjkmnGgzx8n1ZG5tKLS0rtbhUOlxWamn3eHJ5Wabd4lCf9Ssy169Ijz1S/+rKQ30qlZhz+wJNb2q6K0ELISUCr0nFptRCwCkdLuSsruwEAEQe74YAhJohU5JZOq59IWbKnGr4s75fDDeLAaYvX04pwCxWavryZBkp+XLbqsqr57sLW3re/NCRLgMHMw1TY+lJjaUndfXYDQ23831f285m3SDzmc3LyuZXlC2saD2/rLzb3hqU205W205WC1vnm26XtjJ1KzH3njeYCN/6ep3WVpt3p1iWPMuS9nYGV7WLGw5Vl11jGHJHR7QzOqKd666pv43nKXFlvRhY7gsyi9WYqeXV9of6/P336m7jW5bcmRk55fBytliF6c8dkzd/rLge5iGH+hBSIvKOGnJWWtBr19b06623WQ440TLWn0Q/0eaNMt4JAYgtwyiGm7tvitOlb+xuM5beDTg935XvF1vOPb8YYPqlQ68cavp+KdAshp9HDTbRHYZhaCg5oqHkiE6O7IYf9Qbi5N2dYmBZCi3rH65qy15vax/y7o6Wdp7S0s5TTbdLmCmNpCY1nZmpGvgztW/gz2h6QpbRmxAvalWUbWk2pIeqy94yTTmTE3ImJ7R945n627iukiurtRWYpQCzfDy5dqWlqzNcV4mFBSUWFiR9p+42Xiole2Za9uyM7NlZObMzcmaLgaYzNytvbl7+8KjM0oR2wzCLy6DIUMEpaCidrAwsMsvfY7AJ0HCoUMNXh2G0F2haFq81AOizmH66QFQ98FR7E4GDJGwDcuLINCzpEAGQ73vy5Go6M1gaXuGWvjx5vlN1vHQ+7ed90Whad9rKaGbwpGYGTzb9eccraKOwpvXq4LJOmLlRWGtriQHHK2gtt6C13ELT7UzD0lhqQhMDM3smlk/VnB5LTylp9n6tvVg4qOrSYa3LnrOsUlg4o60fqb+JUbCVWl6urIFZCTArFZlLSmxutXR1ZqGg9NPPKP1048nkbiajwsy0CnMzxcOZaRVmi8fXZqdlHZ+Tv2eoj2mYkozKMCKzMnm9GGLuPW0YZlXIuTvV3TDM0kR2s/RPOiCifF9yHEm1y0AcOESo3VATANAxBJQA0GWGYcqSqZTV2hTZcnDpeq7cSoBZPO76jjyPILPTGoWT7UiYKU0MzGli4KB1Ml1tFq7sr8KstJWvVs5zfbvl6/d8t9imnl+WDijmHEmN75tYXqzMrD2dTgRv8nEoUXUZaH4qqfzxY8ofb7AepiRzZ2dfBWZyz5qYVi7X0vVZOzvKXHxSmYtPNtzGGR2RPTsjZ7ZcjTldCVrt2WKoWV479SiKgacpqxRcVo6Xw0yzeL5pWDIrAWkp8DQIORExlTb02r+9TUPNdlrPU0lazwGgCQJKAIG2fPax2kE5MVD8MJhS4oD3sL7v68RwphReulVBpld1vHS+Vzyf4UD9ZxpWZb1JNVlesrhOZrYSXq5XBZq2u14zyTzvtjbtuGyjcEUbhSu6qHNNt8skhoph5cC0xksBZnWweWJsTpMD0xpKjsjocmtcX9ah7AWqLkPBy2SUO31KudP7p5JLKg712dzarbosVV4my63li0tKtTHUJ5HdUCK7IZ37Yf2rMww5E+N7QsyqIHNuRs7kxIEVXp7vSb7XcBh7K3ZDS7Olys7qcLN0bs3Pm4ZJqy3Co53W87S523rezhevB8QA61BCIqAEgKYePLeim69vPGinnwzD0FObOZ0eHVKrdTS+71dCS9crVmQ6dQ7rjkyPqE5UT3ZDcZ3MMQ0lx3Rs+Nqa7+2d6r1jb1WG/OybXJ5bqZzesrNt7cOOs6WdzS09s3mh6XYpK63JgWlNDkxrIjNTPJ6ZLh3OaKq0fuZoerzUqooDNau69Lyq0NKW4TiV41Rd9olhyB0Z1s7IcM+G+iRX15RcXZMeqb9EjG9ZsqcnSyFmdRXmbqDpjo8dOfzoRMhZz26reinQ1G4YWg4/TRUDn5rgsxKIqrRdsT3eqISlrO+JPiq3njut/bNC0v6J5q18AUAIEVACiLTHzy7pujMz/d6NwDAMQwkjISkhWemG2+22l3sHVmY6vqs4BZpB8PTmdk1ImUkOKZMc0vHh001/ruDmdSW/shtk5pa1mlsqVWOWzs8vK5tfk9/GY1pw81rYeloLW0833c4yEpoYmCqFlzOVEHNi7+mBKVkmb1EaMk356bTcdJ3XsOPUbxl3XMLLfmt1qM/qWqWdfGhlVcnFJSUXl5RYXC4eX11r6eoM11Xq8pJSl5cabrM71Gd6T4i5e9wbHupLkOfLl++7kq+Oh5/VWl7fs3SeqrapDT9VtV111ahoh8fRlFvP7daXfakJNesdN83a47axexoA+oR3/wCAfSzTkqXW36R6vleqyCytlentrptZrsx0PTdwLeZBrZ5sxd6QshUpK63ZweOaHTzedDvHc7SeX62pxqxXmXklvyy3jUn1ru9oeeeylncuN93OkKGx9EQlyJyoqsY8PjqrqcyMpjLTmhqcUbpJ0B5LiYS8RELK7Dm/Xsu44+yGlwgGyyoGhjPT2tKztCYpk6z9XWwUbCWWi2tglsPL8vFyiJnItjY0sNWhPs0CTHt2Rn4mvOvVer5XOuz+66C6nX03DN2tEjXrBKGVak/tD0bNmmFIVISiSpuhppGven9WDi/N/YGmvzfkrBd2AsAhEVACAI7MNEyZVurAVvNii3ltaOl4thzfLh72sL08zOFktyXMhKYys5rKzDbdzvM9ecZmsRJzp1iNWTxc1urOUumwvE5mawNEpGLl1JX8qq7kV/VD1W9fLRtOjWgqM6PpwZlScLkbXk5lpivfG0wOtXz9kXRgy7hTVW1Z1TLeQrsxestPJWUfPya7yVAfYyen5NKeAPPyopKLy0qUTls7ra1da+3syLrwpHShyVCfkWHZc7MNh/o409OV516clatCuxWG7q3eNI1ydWdtq3yrE98t06T6M44qw4L2t6G3FIGXg0pjf5C5L+DcG3YSsgOxRkAJVHnw3Eq/dwF1xHFQTlQVW8yTSpiNP6gWg8ril105LMRubcxWHKaKspNMw9TowKTGByZ1zXjj16jv+9q2NyuBZSXI3FkqHq+cv6xte7OtfdgsbGizsKEL6/WHiZRlEpk94eX+IHMqM6PR9FjXB/4EjmnKT6fkplP7v1eeMl4KLE27XHnJlPFe2bHdfVWUB/EzAypcdVKFq0422MCXublVFWAuVVVklkLMpWWZhdaqrxIbm0psbDYc6iNJ9uREMcCcqb8epjM1KSVoLz2KYgDqS37n/7HQaBhSpfJzT+BZCUvLkWhVG3x1a3zxso3a7eP2OzhqGgwOkloIOI2qVvNmAWe9Kk7a00OPQTkgoASAHnngqQ3dcrLJ2GZIkhJm4wDT8WzZbkGOb8t2i8Gl69ly/DYWm0fPGYahodSIhlIjOjXaYIBISc7Zqaq83F+ReSW/rJXtZa3nW1uHr2zH2dFTGxf11MbFptslzaQmK4HlngCz6vjEwKQsMwYfhipTxve08JZbxm1bhsOU8W47TEjZlGHIGxlWfmRY+UZDfXxf1pX1xgHm5SUlV1ZkuK2FYeWhPplHzta/OsuUMzkpe26mThv59O5QH1pI+6Jbw5Aa2b+W526Le82aoS2sD1q9jbF3mx6FocNpnrct8f2jBZyN2tMtq7Z6s95xAH1HQAkACI1G4WX1dPLyEJ/ddTB318MsDv9x9fTmVh/2vjv6XUW5mXM0PNC5txMDiYyOD5/S8eFTDbcZHrBku7ZWcyta2V7Sys6SVnaWi4fbxePL24ta2SkO//HaqCayPVuXty7p8talptuZhqnxgcmaIHM6M11sjR8stZZnioN/klYEW1ubtYzXq7osDe7B4XU8pDyIYcidGJc7Ma5ck6E+iZW1OiHmYYb6eMW29KVlST+ou42XTMiZqd9GXp5W7o4MU4EXAd2sBt2r0cT4ZkFoZZJ8q0EoeuOw7enVlZtmbXjp1ws0y1/8rgE6ioASABB6NdPJW+D62cpQH893qgb7OHJ8R65ntzXFGr2XtJKaG5rX3NB80+1cz9WV/FptkLkv1Cwe2l7rE1I93yu1qS+rfj3YrrH0eN0qzHKQWQ45M8n+Bc0d1ULVZTm0LA/roeoypCxLzuy0nNlp7ejZdTcxbFuJpZX6AeZSsRKz5aE+tqPUMwtKPbPQcBs3MyCnQRt5uTozzEN90Hm9mBg/uGNWhhkZlbVBS8ORSgGmURWIGlVBqWHshpxmzXqiu5dVfRqH0KRy88Bgsxxq7gkya4LNRGL/30QA+xBQAgGwfLb5EAigmQvZLZ0ejfkAkDZcyG7JNCyZltV0qI9bGeTjyPXt0mAfu6ZSMyj6XUUZZJZplQLBaalBgCIVq3A3CtlKeLm8pyKzOtzccdobsLSev6L1/BX98Mq5ptsNJod2KzJrAszddTOnM9MaTo2G80NoVdXlvpoo160ZzlM57jgM6qnS8yrKDvCTSdnH52Ufb/zPBCOXazqVPLm4JGu71aE+OVkXn1T6YvOhPs2mkjszDPVB51Wq+bv8/8/qoFPS/kCzUum5e/52wa87MGn3sszdqfJUhO7yfclxdGDFZjIp/+rmS9wAIKAEatx8/RSDcgBIkiwzIUsJqUEWUJ5I7lVax51KK3mltdxz5O2PYhBAhmFoND2m0fSYrpm4vum22/Z2nfBytxKzfF42v97WPmzbW9q2t/Rk9omm26WsdP2KzMx01TTzaY0NTISntbBUbeKm0/u/5zgyHafYMu44tS3jMRzUE8aQ8iD+wAFDfaQ9Q332B5jJxWWZhUJL11ce6jPw+PmG2zDUB2Hlq7jsTTtB6HahvfcqrU6MN1VqiW+wXXmbupcVlr9fADqGgBIIgOkzN1BF2UWPn13SdWdmDv3zD55b0c3XT3VwjxAF5YnkUrJhiCkVKybqhZiuZ5fWyrQ7EmL2s4qy0+tQHnx9roYH+hcMDCYHNZg8rVOjp5tul3fzWq20lDeuyFzLrba1pEDBzevS5tO6tPl00+0sI6HJzORuG3l1RWZVuDmZmWo4mCoQEgl59drjyi3jTlW7OIN6IssbHlJ+eEj5a6+uv4Hvy1rPFof3VIeYS1Wt5UsrMlp8brQ81KfOMJ/iepjTcibGGb4BDaai9xzo1Rqhh1kftHYq/P5KUELQYGOSd7wRUAIAYuNCtvfDcYrrS6WUaPJey/NdOZ5dOXR9R7aXr5yHcEpbaR0bPqFjwyeabud4jq7kVout5duNKzJXd5bleK0PmnF9R0vbi1raXmy6nSFDYwMTNZWYe6eXT5eOpxMBWkOr2aAez6tZ47JmvcsItIxHsYryyAxD7viY3PEx5W5sUAXtukqsXaldD/PyUnEqeXlC+eqajBYqc2uG+vx9/W0Y6oOwGEyZbVdR9kIv1gc9KAQ1a9ria4ckVVrmqypGi6315UC0dFkxrPYHDoOAEpFyy8kRPfBUawutA0BQmIallFU/bHA9R45nVwJLxyvI8Qq0jkdIwkxoenBW04OzurFJsbbne8rm1yvh5fLO4v6hP6UKzbyba/n6ffm6klvVldyqHl9rXs0/nBwpVWLO1lRkTu9pNx9MDvV3nUzTlJ9ON28ZLw3sKbaNF4+HKbwkpDwEy5IzPSVneko7z6m/iWHbSiyvKnl5sf5Qn8VlJdazLV1dK0N9vIF00wDTnp2WN8gaw0C39CIEtdIZndC1Xbp0IDoIKAGEwvLZxzR95oZ+7wbQc5aZkGUmlFam5vxycOn4hUpwubBV0PxQqk97im4zDVPjAxMaH5jQdRONfx/6vq8te3N/FWad6eWbdnv/1Nu0N7S5vqEL643X7pOkgUSmYUXm7jqZMxpNj/U+yKy0jNf5XvWwnghWXuJgfjIp+9ic7GNzDbcxcrli9eTeALPSXt76UB8zl1f64lNKX3yq4Tbu8HD9qeSz08UQc2Zafprf/UEUxfZuAOgWAso+GRkwtZHjjS4A4HAaBZcnRzKlisuCnHKI6dlyPVuO33p7cND1ex3KIDMMQ8OpEQ2nRnR6rHnFRt7J7U4tr1OJWT59Jbfa1j7knB09vfGknt5oPElZkpJmUpN1gszhxJQmM9OaHJjRZGZaY+lJWWbt492Vx/+gYT2l4TyGbRePlyox+9W+RxVlf/gDAyqcOqnCqd4M9bE2N2Vtbmrgh0803MYZHyuGlnN7p5KXQsypKYb6AAACjYASAIAIMQ1TKSutlLU/YClPHi+Hlo7nlE47pQDTVVtjP6v0elAOOiOdGNDxkVM6PnKq6XaOZ2t1Z6VhJWZ53czVnZXi9NgW2Z6ty1uXdHnrUtPtTMPUWHpCEwPTNcHl5MC0JjMzpcNpTQzMaHIo0/SyDq1cebn34usN63HK7eOsIRtXLQ/12VuFubhbhdnOUJ/ElXUlrqwr89i5+ldnmnKmGOrTS2GrngzqOpQA4oNPEgic06NDfRlkAQBRV5483mxis+u5cnxbbk146ZQmjztHCjERXgkzqdmhec0OzTfdzvM9refWKuHlcrkSc3tx3/Tygtda9Vj5ctdyK1rLreiHVx5tuu1walSTA8WqzNmh6rUyq6s0ZzSY7NC6fgcO6ylXXtq77eMdXO+SKsqQqh7qc8N19bepM9QnUT3gZ3FZiZXV1ob6eC0O9Zmebhhg2nOzDPUBAHQNAWWMDKcT2sxHp70PALDfheyWTo8OHfrnLdOSJUtqkne4nlsJLIsTx4st5UnTl+3Zh75uhJ9pmJrITGkiM6XrdeO+72/mivGd7/vatDe0urOk1dyy1naWtZorTipfzRWDzLXS93ac7bb2YbOQ1WYhq4vZHzbdLpMYbLA25m6IOZ2Z1nBq9PDrZJqm/HRKbr31AV23GFyWAkuzXHV5iJZxQsqIamGojxxHyeWVYlhZ3VJetR5mW0N9Li0odYmhPgCA3iOgRFtGMwlldwg5AYQPldmdsxti1raRjw0m5ft+JbC03cKetTD5+xFX5WCyzDAMjaRGNZIa1emxBtVjJTv2dk142SjU3Cist7VPO862nspe0FPZC023S5mpmiCzOsCsXjdzfGBSptFGS6dlybMsaWD/tB6jXHFp25UJ47SMo65EQvb8nOz5JkN98vk662AuK7m4O6nc2mrtHwGtDfUZkj1TL8DcPS/qQ33C1t5dRps3gH4ioERPjA0mtb5NVQ0ARJ1hGEpZKaWslLSnk9z3/ap1L+2a4LI42Ie/E1G0N5xsVyY5qBPJ0zoxcrrpdgW3oLXcciWwLFdgru4JMq/kVuS3sUxBwSvo0ubTurT5dNPtLMPSxMBUVUv5niAzM1M6PdV0mQWpOEnaT7bXMk4VJRrx02kVTp1Q4dSJhtuYm1ulyeRLtdWYlw8z1GdL1uaWBs43Dv8rQ332tJOXqzDt6SkpEc6PqmENJwGg38L5Wx8AAISOYRhKWkklldT+SSOqqb50PLtUgWk3rL5kknewHTWYbFfKSmlu6Ljmho433c71HK3n17S6s6wtd6Uy4Gd32M9yaeDPcltVv67vanlnUcs7iwduO5aeqNNSvhtiTpeOpxN7qisPaBm35O8LLg3b7tuUcYRHZajPNQ3+EXDgUJ9lJZZXZDqtvWZaGuozObF/GnklzJyVMznOUB8AiBACSqCDrjszo8fPLh3qZ6fP3KDls491eI+iY/rMDf3eBQBdVlN9uYfne5UqS9stVELLpOlReRlAvQ4n22GZieLk78xM5bx6Qbfne8rm1/dNKt+dZL4baOacnbb2YT2/pvX8mh5fa/53fzg5UluRWanCLB8vHg4lh2VYlrYkDWaKoWbNI7AvtNxtGwda0spQH89TYvXK/mE+S+VKzDaH+iyvKLm8In2//mAsL5GQMz21f5hPVZDpjo32dKhPFKonafMG0C8ElMAeN18/pQfPrfR7NwB02OnRIdahDDHTMJWy0kpZ6ZrW8dFMolJ5Way6LK1/6dly3GKg2U47L44myMFkM/WqcU3D1PjAhMYHJnTdRON/kvm+r217qya8XK6qxKwONzcLG+3tl72hzfUNXVg/33S7tDVQqcScHZrV9ODM7ldmRjOl42Oj47XrZPr+7vqWhWKIWR7aY7jhfCzRR6YpZ3pSzvSk9Jz9Q7IkNR7qUz2p/Epr68majqPUwmWlFi433MZLpxu3kZeCTG+IoT4AEAQElIicW06O6IGn2vsAAAAIr2brXkoqhZa7lZcFr0B42QVhDSfLDrtkgGEYGkoNayg1rKvGrmm6bd7JlYLLcnhZXYm5G2au5Vbb2oe8m9Mzm0/pmc3Gg0skKWEmNZWZqgkva8LMwRnNjM9qIjOjhMzdsLIysKcYZNIyjkNraahPQcml5doqzMp08kUll5Zlbbb2D0czn1f6yaeVfrLxGrIHD/WZlp9ON/z5sihUTwJAPxFQAgCAjljftjU22Hz4Rz8kraSSVlJK1lbJlIf22F5BhXJ4WTV5HPFTDlm7tbZpOjGg4yMndXzkZNPtHM/W6s5KVTt5/YrM1Z0VuX7rwbDj2bq8taDLWwtNtzNkaLISZE7vDzIHJjWTnNCsNaa0XxtkAkflp1MqnDyuwsnG68ma29sNqjB3j5u5fEvX19JQn7HRmgBzbxWmPTMlpaIzmZw2bwD9QEDZRyMDpjZy/OKvh1ZMAEC3VYb2WEkNJodqvuf5XqVdvBxa2m7xOFWX+4W9enKvfg9gSphJzQ7Na3Zovul2nu9pPbdWCS+XSxWZ2fyKlkuny195t7WwRpJ8+ZVKz0cPWPVmND2m6cz0bniZntJMekIzyUnNJMc0Y45p1hrVsHFwBRrQKm9wUPmrr1L+6qvqb+D7sjY2iwFmZRJ5bTt5Ymm59aE+61kl1rPKnH28/tUZhtzpSTmzM3LnigGmM1f8ckvH3akJhvoAQBMElDEznE5oM09VSFAxKAcAgsE0TKUTaaW1P1ShZbxW1MLJsn6HlK0wDVMTmSlNZKZ0vWrX/BtM7b7N931fm4UNLW8vaam0Ruby1mIxvNwTZG7Z7f2DOJtfVza/rh9eqR/clGUSmWKQOTCt2fSkZlKTmkmOayYxppnkRPF0elKjiWEZPRxqgogyDLmjI3JHR5S//tr62zQb6lNeD3NlTYZ3cEGJ4ftKLK0osbQi/X39bXzLkjMzJbcUXJZDzMrpuRl5PR7q0wxVlAB6jYASbRvNJJTdaT/kHBtMan07+q0/R5nkDQAIvkYt45J2h/RUDespBplOJMPLqIaTZWEIKVthGIZG0qMaSY/qmokGE5hLduztSli5VCfALH+t56+0tQ87zo6e3HhST2482XS7lJnSdHpC06lJzaQmSl+Tmi6Hmuni+ROpMVlG+B8b9FGrQ31WVvesh1laC7PNoT6G6yq5sKjkwmLDbbx0qlJx6cxO7waYVRWZ/vBQw58HgDAjoATqYJJ3tFx3ZqbfuwAgJnbDy9rzqyeNR2W9y6iHk2G3XXBqqihblUkO6tTYaZ0aO910u4Jb0MrOct3wsroyc3VnRZ7fehVWwSvomZ3Leman8WRmSbIMU5PJcc2kJzWd2q3AnE5NFlvMK6cnlDSDtzYuQiKRkD03K3tuVjt1vj2YMmXkC7JK62EmLi/JurxUOZ64vCRrcVnWxmZLV2fmCzKffFrJJkN9vMFBOXPTNe3j1V/u7Iz8gc4sqUAVJYBeIqBEJIV5kjdt3vtNn7mh37uAiGB9W/RL9aTxvetd+r6/u85lqeKyfLqdYAfdEZUqyk5LWSkdGz6uY8ONB5lIkuu5WsutNgwwq79sr/VOG9f3tFRY1VLh4Inn48lRzaRKVZmlSszpqhBzJlVcNzNjDbR8/UB5arefTsk5dVzOqcavBWN7pxhYLpYCzHKIubAkqxRmtjrUx9zeVur8RaXOX2y4jTs2WqnCdOdn96yJOS1ndlpKEAUACBZ+KwHAAW6+fqrfuwAgwgzDaLjepeu5lTUuC16x8tJ2i0FmP8PLuFVPElIenmValQE6zfi+r2x+vdRavtgkyFzWjrPd1j5csbO6Ymd1dqvxlGZJGrYG91VhVlrLUxOaTk9qNj2lYWuQdTLRFn8wI/uaq2Rf03ioj5nd3B9gVioyl5VYXJZhtxbiW+tZWetZpR9rMtRnaqI01KdOS/ncrNzJccmyqKIE0DMElAisMFc6sQ4lAKATLNNSxsxIicy+7zmeU6m4LLePByG8jKowhpSHbfPuB8MwNDYwrrGBcV03eabptluFLS1vLzZcH7MYbC4rm29tbcCyTXdbm9vbOr/9VNPtBsx0sa28Uo1ZWi+z1FJertScSI7KNJjaHEXl6smOMQx5YyMqjI1IZxoP9THX1ve0jxerMCth5vJq60N9lleVWF6Vvv9o3W2qh/rkZqblzMzInp2RPTtdPJybkRugoT6IhpEBfmfGWTjesQB90M91KGnzBronzP/8AKolzIQSZkKZOuGl67n7wsvy2peuf7Tqx7hVTyJ4hlJDGkpdo9Pj1zTdLu/kS+tkLjZoMS9+b3Vnta0hVjkvr6dyC3oqt9B0u4RhaSo1XtNaXn1YDjmnkhNKmOEKv+Os4+Fkq0xT3tSEClMTKjy7wfJHjitrZbU2wKyqyLQuLyuxutbS1VUP9Wm0+IGXSsmemZY9Oy2nOrwsBZj27Iw8hvoAaBEBZZ+NDJjayPW2ymE4ndBm/mgL8jPJG73C+pMHOz3KGz8AtSzTkmVaGkjs/1jp+d6+KeOdCi+jLoxVlHGWTqR1fOSEjo+caLqd4zla21ndbS/fWdLy1qKWNy8XD0sVmSv5VTltvEYc39Xl/Iou51ekJkujGzI0kRyrTCjfO7F8t818Umkr1fL1I4YSltxSm3bDFS0LhWLLeHVoWVkTc7G9oT6FgtJPP6P008803MYdzFRCS6cSYFYFmbPT8gdY/xUAASUirN+Dco7a5k0VZWcwwRvonbFBJuWGgWmYB655abvFr8rxNoaXIFjC1ObdLwkzoZmhWc0MzerZ+pGG23m+p/WdNS1nL2llY0HLm5e1srVYajdf1mJ+Rcv5NS0VVpX3Ci1fvy9fq/YVrdpXVL/ZdtdoYrhSeTldFWJWr5c5m57UUGKw5etH6/pWPdlJqZSck8flnGx/qI9xaUnJxWUllpZk7eRaujpre0fWExc18ETjoT7O6EiTAHNGzsyU/CTvMYCo490KACB2otzmTUUtjqK85uXetnHf92V7tla2tpXKFKsui2tgFg/jhCrK+DINUxODU5oYnNKZ+R/dv4Fty7RtKV/Q5vaaVjYXtLK5qKXcipYKq1rOF6eOL+XXtFxY1VJ+VZtuewN/ss6mss6mfrj9ZNPtMtZAMcgsDfyZTlVVZVZVaI4lRhj406JIhJMtajTUpzIsx/dlbm4qubis5OJS6WtZycuLlfMSS8sy7db+PiSyG0pkN6RzP6y/P4YhZ2J8T4hZG2Q6UxOSxe9mIMwIKBFo/Q4R+rkOpUQVZdTau285OdLvXQCAQzEMQykrpaHk/g/onu/VrHFpewXZrk3LOOInmZSXTEqDgxqcGNegrtEpSXIcGbYts2DLsG0Z5UPH0Y6b2w0sS6FlMcxcq5xeLqxqzc62tSs7bk5P7lzSkzuXmu+ykahbkTm9Z83MydSYLCO+4U+cwslmKhO9DUPeyIjyIyPKX9dgLVjPk3VlfTe8rDksTiZPrqzIcFsb6pNcXVNydU16pP5nI98yZU9N7Q8w53YrMd3xMYb6AAFGQAl0EdO8w+/m66f6vQvokn7/AwSICtMwlbLSSln1W8YroaVXW3kZ5knjYauipM27zxIJ+YmE3MyegVaeJ6tg65ht63h1gGnbkl87tMf2bC0XrlQqL/dWYpZDzRV7TW4bry3bd3Qpv6RL+ebvV02ZmkyNVVrJZyuDfooVmtWDf5ImrbiQZJpyJyfkTk4o96zGQ30Sq2u1VZiLS0peXlJyqRRitjzUx1NqcUmpxSVJP6i7jZdKlob6zOwf6lM67g0PEWL2CRO8wTsVHFoYBuX0ex3KTohrFWXUqicRTFELKWnvRtCUh/Wk68yALU4aL1VdugUVStPGba8g3299ojIQWqYpfyAtf6AY7tfUG5faxcvVlpbtaD6V0TGn+draru9qrbCupcJaVXBZCjQLa1Vt5quy/dbfx3vytFxY03JhTdp8vOm248nRYhVmVQXm9J4QcyY1qcE9S0kEFdWTtSpVlJ2QsOTMTsuZndaOnl13E6NgK7G8vL+dvFyFubhUbA9vgVmwlX76ktJPN64sdjOZ+lPJZ6dlz80y1AfoIgLKmOrEJO+4OGqbN1WU/ROHATkEUgDCqhheZiTtDykczylVW5arL51KmBmU8DJsVZQImUq7+J7zPW+3Rbz0VQ4y5fuyDEvT6WKV47NHrmt48b7vK+tsVlVjrpbazNd2j+eLp7fdnbZ2/Yqd1RU7q7NbF5puN2Rldqswq9fKrGovn05NaDQx3Ld1Mgkn6+toSHkAP5WUffyY7OPHGm5j7OSUXNofYCZLw32Si8uydlp7Hls7O7IuPCldaLzOa/OhPtNypqflp6gkBtpFQBkAIwOmNnLhbXPqtqhVOB1GXKsogV6Iyu8YwmpEScJMKGHuf5taHtZjl6aLF9x85XRQgkugq/ZUXdaorrp0nJq1LvcyDENjyRGNJUd0vU43vcptZ6d2fcyq6szqw6yz2dZN2XJ3tLX9tJ7YfrrpdmkzpelKRWbtxPLi6SnNpCc1kRyVaXQuUCScDA8/M6DCVSdVuOpkgw18mZtbVeHlYs26mInFJSWXlmUWWuvyO2iojyTZkxPFAHNmRs6xeSWf/6Dcn/hJeTffcpibCMQCASXQA52oooxTSEl7NwCgnvKwnpSVkvYUp9huMagsuHmCS8TTQVWXjiOjUNhXdXmQwURGpxMndHrwRNPt8m6hGF6WQszlmhBz9/RqYV2eWi/OyHsFPZ27rKdzl5tulzAsTaXGa1vLSwOAqkPNqdS4knX+AYL29LKK8sgMQ97IsPIjw42H+vh+1VCf8lTypUMN9ZFUGeqTeeRs8YzP/Lnyv/FBFQgogYb4zYwjics6lP2e5h0nnQonO9He3ckBOUzwDrawV1EGoXpybJBWJvRX0koqaSU1mNx9PZQrLgtuXrabV6Gq+jJuGJQTY+WqS6Ul7fl70WTCeLvSVkonMnM6kZlrup3juVq1r+yfWF69Xma+uN6l08Y6mY7v6nJ+RZfzK1KTt/6GDE0kR3erMPdMLZ9OTeiq4WnNWpMaqDP8CxFlGHInxuVOjCt345n627iuEit1hvpUrYfZbKiPd+pUl3Y+/BiQA4mAEugZqiiBYAt7SBlHoxnexqC5mopL7f6jqBhcFlRwCzWH7a5xyTqUCL1GE8Z9fzesrK66tB3JO1rVXMK0NJue0my6+T+CPd/Tur1Rf2J56bxyqLnj5Vu+fl++Vu11rdrrekznm247khjSbHqy5mtm7+mBSQ1bg31bJ7OfQlVF2QlWC0N9bFuJpZWaADO1sqaRHUfes57T4x0GwqXtd/a+7+uJJ57QqVOnlEgkVCgU9NnPflb5fF7/8B/+Q01PT3djP9EFYRqUE4TgIChVlFEOKWntbk8QquaiJgi/a9rF8wBoXzG4TCu1pzqqpuKyVGlJqzhiyTDkp1Py0ykdWHVpO5WBPZ1kGqYmUmOaSI3phuEGbbkqvm633J1SeLlSaSnfO+xnubCqDae9v/EbzpY2nC09vtV4YIokZaz0vvCy3vGJ5GjkgszYhZQH8JNJ2cfnZR+fr5xnpTNKPO9lfdwrIBzaCigfffRRveY1r9GTTz6pa6+9Vvfee69+/ud/Xo888oh839fg4KC+9a1v6cyZBiXRaIhBOd3ViTbvTujURO8ohpSdDCfjML0b3RPGkBJAZ9RWXNayXVsFL18KLQuVNS+B2Gml6rJ6ynjBPnLVZTOGYWg4MajhxKCuGWowJKVkx81VhvxUD/tZzq9q1VnTYr4YaK4U1tvahx03rwvbl3Rh+1LT7ZJGQtPpiaYh5my6uE5mwqQ6G/FAezfK2goo3/e+9+mmm27SX/zFX+g//If/oJ/+6Z/WmTNndP/998v3fb3hDW/Qv/gX/0L/6T/9p5Yu77777tPv/d7v6W/+5m906dIlffazn9XrXve6yvd939eHPvQh/dt/+2+1tramW2+9VR//+Mf1Iz/yI00v98/+7M90xx136PHHH9d1112nf/kv/6Ve//rXt3NT0QO9XIcyiqIYUgZJJ9efRPiEJaSkehLonfIal3uH8wymVRrMU5osXqq8dH23PzsK9EtN1eUerlsJL83qtS47XHV5kIw1oFOZYzqVObbve9VTu23P0XJhN7BczK3uHs+XjxdbzF2/9fDV9h1dyi3pUq55wYIpU5OpsargcqImxJwbmNJselLT6QmlzTr3d49RRQmgEwy/jX6V2dlZ3XvvvXr+85+vra0tjYyM6L777tNP/MRPSJLuv/9+/eN//I914cKFli7vy1/+sr75zW/qhS98oX7u535uX0B511136V/+y3+pu+++WzfccIN++7d/W/fdd58effRRjYzUHzhx//336yd/8if1W7/1W3r961+vz372s/rgBz+ob3zjG7r11ltb2q9sNquxsTGtr69rdHS0pZ/phH5UUHaqxfswg3IktR1QHiUw6FQFZSfavDtRRVkWhZAyiNWTQR+QQzDVG0EOKYP0HOjXgBzWoOy9w/69j7JGz0PXc5V3dysud9e57N99yJAcBI7v71Zb7m0Zd3sX8leHk+3wfE+rhawW8ytVweVq3eMFrzth7HhypCa83LdGZulwKJE5+MKOgICyMSud0QlavBuigjLa2snX2gooBwcH9cgjj+iqq66SJI2MjOjhhx/WddddJ0l68skndebMGeVyubZ32jCMmoDS930dP35c73znO/W+971PkpTP5zU3N6e77rpL/+yf/bO6l/PGN75R2WxWX/7ylyvn/dRP/ZQmJib0yU9+sqV9iVNAKXUmpAxDQCl1JqQMWkAphTuk7PS6kwSU6LQghpRBe/wJKOODgHK/dp+HrudWqizLFZcFN9+T4JKAEqFSVXVpOE7Vmpe21ME1YQ8bTrbD931lnc39weWeysyl/Jo23e2u7MOwNaiZPZWYswP7Q83RxPCh18kkpKyPgLIxwsnoaydfa+tdyvHjx3Xx4sVKQPm7v/u7mp2drXx/aWlJExMTh9jl/c6fP6+FhQW9+tWvrpyXTqf18pe/XN/61rcaBpT333+/3vWud9Wc95rXvEYf+chHGl5XPp9XPr87+S2bzR5t5xFpnRiW06m1KMvC2u4dh3AS4Re0du+ghZP9QjjZH6OZBCHlEVmmpYyZUWZPNZPnezVt4oVK9SXL4SCmLEt+xpKfGZAkVeopfV9ynN1WccfZPWyzZbwX4aRULMYZS45oLDmiM8Onm2677eQOrMZcyq9qzW7vM+umu63N7W2d33666XYpM3ngGpkz6UlNpcZkGrX3H63eAI6irXf3/+P/+D/qkUceqbR0/8qv/ErN9++991698IUv7MiOLSwsSJLm5uZqzp+bm2vaQr6wsFD3Z8qXV8+dd96pD33oQ0fY286I46CcdtehPGpQEJRhORIhJRO7o6G6ci7Ka8qWQ8F+B5VBDCf7VT0JRI1pmBpIDGggMVBzvu/7lVbxgpvvacUlEEiGISWT8pJJaXDP96pbxivTxZ1DhZf9MpgY0NWJ47p66HjT7fJeQcv5K3VDzMv5lcrxlfy6PLX+GbPg2Xpq57Ke2rncdDvLMDWdmtgXXI4nxjWTKh6fTk1qOjWhpMk/FQEcrK3fFH/yJ3/S9PtvfOMb9eY3v/lIO7TX3vJy3/cPLDlv92fe//73693vfnfldDab1alTpw6xt/EVt4qKTlRRdkM59At6UNmNcDKok7uj1t7dLIyKQ1jZz2rKIIaTALrPMIy6waXnezXBZd7Nq+Dm5bUxsAOIHMOQn0rJT9UZHFMOL0sVl8OmJ99xpEJBhuN2tG28F9JmSicyszqRmW26neu7WslfqVOJubYv3LT91j/Pub6ny/kVXc4f/JloIjmq6dSkZtNTmklNaDo9WRViFkPOmdSkBqx0y9eP8KO9G3u1FVDmcjl99atf1U//9E9LKgZ71a3RlmXpt37rtzqyY/Pz85KKFZHHju1OWVtcXNxXIbn35/ZWSx70M+l0Wul0fH8ZDqcTHRuWg/Z0uoqyLKjVlGGomqS9u752q+SiHFb2upqSYBJBE7d/SgaVaZjKJPa3ijueUxNYFgPMgnyFK3wBOq4qvByeMFUzgqfUNr7bKl6quCxVYIYtvKxmGZZmB6Y0O9D8Pa7v+7pib7TUXr7ttjdzYs3Oas3O6uzWE023G0kMaTo1qZlSaDldCjH3nh62Bg+9TiaA4GoroLznnnv0xS9+sRJQ/tEf/ZF+5Ed+RJlM8Y3RI488ouPHj+9bA/IwrrnmGs3Pz+srX/mKXvCCF0iSCoWCvv71r+uuu+5q+HMveclL9JWvfKVmH+6991699KUvPfI+oTvC2ubdqSrKuISU3Qwng1o9GRVHbeFt9zUeFt0OKsMQTDIcBwiehJlQwkxoMLn7O8T3fRXcglIJrxJe5mkTB3aV2sb9ZLJ+lF/dKl4oB5fhDy+rGYahidSoJlKjunHk6qbbbjrbB4aYl3Oryjqbbe3DhrOlDWdL57efbLrdgJmuqbycrgoxd09Pajw5sm+dTADB1dY7/D/90z/dFz7+l//yX3TttddKkv7zf/7P+vjHP95yQLm5ualz585VTp8/f14PP/ywJicnddVVV+md73ynfud3fkdnzpzRmTNn9Du/8zsaHBzUL/zCL1R+5vbbb9eJEyd05513SpLe8Y536GUve5nuuusuvfa1r9XnP/95ffWrX9U3vvGNdm5q34R5HUoqKoInKC3fYaiclDpfPRn29u5Ohk9RDSml2sekE2FlGIJJgL/54WIYhtKJYrfQ1ODu3ybP95R3iutaOq4t2yt9ubZc3210cUCoDacPEVg1Cy/LlZdV615GofKymeHEoIYTg7p26GTT7VZ3drRcWNNSYVXL+VUtFYpt5XtPr9nrbVV557y8nsot6Klc4zkTkpQwLE2lJmrayYtt5pOaTu+eP5kcV8K0Wr5+HB3t3ainrYDyscce0w037AYNAwMDMs3dJ9Ytt9yit7/97S1f3kMPPaRXvvKVldPldSDf/OY36+6779Z73/te7ezs6Fd/9Ve1tramW2+9Vffee69GRnbfWF28eLFmH1760pfqU5/6lD7wgQ/ojjvu0HXXXadPf/rTuvXWW9u5qYi4uFRRlvUrqOxFMEn1ZHd0ozKufJlRDSql/eFiK4ElgSQQXmGr5h1O1+6vaZjKJDPKKLNvW8/3ZFeFlo5rq1CaME7lJcLqUOHkQRIJ+YmE/L2vo+q2cduWkS8UDwu25IWzIKVdk5mMBqy0Tmbmm25ne45W7eLAn+WqEHPv6ZXCmpw2/nni+K4u55d1Ob8sNfnoZ8rURGqstpV8X5t5MeBMW3XWNwXQEYbvt/5vnUwmo4cfflg33nhj3e8/8sgjev7zn69crr01KYImm81qbGxM6+vrGh0d7fn196uCshPrUB6lmqLd0OKolUqdmubdyWE53Qwpq3UzqOxltWSnw0kqKIt60bYb5ZAyLmjvhnS0v/tRELbn496A8rA83yuGlW6hssZlwSvIdm3WukRgdSWcPKxSUBmX4HK70Jnb5fmertgbpfBypRRcrmm5FGYWKzOLFZt5r9CR69xrNDFcE1jubSufKVVmDiV2R8xb6YxOPO9lXdmfMKJ6Ml7aydfaepdy8uRJ/d3f/V3DgPK73/2uTp5sXuYNBEXQqiil7ldSlu0NETsRWPa6jZtwsjt6FTrFoZoSiANavePJNMy6k8V935ft2ZXwsuDmK8cJLoEqjVrGYxZctss0TE2mxjSZGtONw9c03M73fW04W7Wt5JXjq1rKF0PNxfyKtv7/9u49VrO6Pvz9Z19m9myGmeGAzDDoCCPWS7l4ASsY9aciKCCpkTbF9BSoSRMUqUpIFZtUbTVjk9ZYqkK1ghJqbJpBoUGoqFzqBY8QrGiQ4JEySmcg4HE2UNjX5/wx7D37Pvuy1vP9ftd6vZL9x+zLzNrPs5691/c9n7XW+NPL2oahsSdjaOzJ+H+f2rXo5w32rdsXLNf+X3H44OGx/dET4q0vPCveePSbl/XvQZssK1CeeeaZ8Vd/9Vdx1llnxbp1Mw9Inn766fjYxz4WZ511VqUbSFnaulApMVJOt1BcnC9clnI9ScrQ5GtTNlmq6UnISWnTk93Q09MTa/vWxtp5ToEcffYU8X3XuByZCpljE2PiJbXLanpyMQuFy7Gx/cGywHB50NreyqYol6Knpyc2rjk4Nq45OI5Z//xFP/d/x5+ZFi5nTmHuO8V8X+D87ejQsrbh6fFnYtfT/xO7nv6fiL0Rsee2OHz95tYHStOTLGZZp3g/8sgj8fKXvzzWrl0b733ve+NFL3pR9PT0xM9//vP4zGc+E2NjY3HPPffEli1b6tzm2qU+xTsizWneVZziHdHO07wjqj3VO6J7p3uXqI3TkxH1T1CmjE4iZVmc3s1sbfzPyRL3x6pO8a7S5NTl5PUuJ6cuh8eHY6JTRnwhb8XEyZUoLFx2M1JWbXRidNb1MWedWv7sxx4f+W1MxPzf5+fO+Of4v0/40y5veV4Eyvap7RTvLVu2xPe///1497vfHR/60Idism329PTEaaedFp/73OeKj5NtdvBAf2WRshRVneYdUe0UZUSaScoStPWmOE2Ok5P/vkhZhtT7Cnlq6xkUrN5iU5djE2MxPDYcw+PDMfpstHS6OEwzdYOeWRa6Oc/40m8ww0xretfE1nWbY+u6zYt+3nhnPP6/kb0z7lj++PgT8b8HD8TxW17enY3NlDjJgSz7v1G3b98eN998c/zmN7+JX/ziFxER8cIXvjAOPfTQyjeOMnVzkXLUxvWrnqLMmUg5Ux1xsurpyRLlEpxESihbmyJlidOTJerv7Y/+tf2xPmb+J93+a1y6ziWLa/T05GIWurP4+Pi+cDk2FjEysj9ijo7tu+t4zbp9qncKfT198ZyBfTfNeemGY/a9z01yYElWfHR16KGHxu/93u9VuS2QJFDkPEUZIVJOKmVysrSb4+QSJyeJlHlLub8IQmVoQ6S0L6a30MTl9HA5+uydxUfGR5wqDpP6+qIz2Dc35Xc6+27QMzo2NW1Z19RlGyIlc5meZCkcYWVqw7reJNehbONp3lUTKatXV5xs+/RkbnFykjt8Q9naECnJ02Knik+/Qc9UxJzwe6bJWjs9uRI9PRFr10Zn7dq58XJ8fCpcVjV1KVIC8xEoqcVqFifLnaCq4jTvKqco69LWSFlSnCxtejJ3pinzkmvQJk9NjZQlT0/meIOcbunv7Y/+3rnff6fT2T91OTESY+Ojpi4bQpysUF9fdPr6orNuYO7HZk1butYl8zE9yVK190gFalTHFGVE+yJlKad1l6iU2CRS5iH1/lJyFGqzpkVK+2Hz9PT0xED/QAz0zw0vk1OXU6eLT0bMiebs07Bqa9ZEZ82auVOXExNTwXJq6vLZG/dMTl2aogRmc6RFI+Q4RSlSrk6dcbLt05OpY9NyiZRQrqZESnGyfSanLg9ac9CM98+euhyddrq4qcs8mJ7MQG9vdNYNHHDqct3IaPzvU8/eZXys/N8VzGV6kuVwtJWx0q9D2c3TvKtSUqSMiMaGytLiZElKi5OTRMp0Uu8zwlD5So+U9kGmM3UJqzRr6nIgIp4cnpgxdTn51jt5yngX7jBO9cRJlssRF41RxRRlaZo4TVniad11TE8yl0jZfanjJM0xGflKC5XiJMux0NTlRGciRsdH50xdjoyPRGfuybGsgunJgh1g6rJ3vruMm7qERnHUBbOUMkU5qSmRshthsqTpyTpO725CbHKH73YRh5qnpGnKJu1/bb5BTg56e3oXnLqcHS5Hn71Rj6lL2uTggd59U5QLWbMmJtbMcxw7MTEtWo7sn7ocHYuYcMmFlExPshKOVphXqad55zpF2Y1IGVHuKd8lx8lSpiebECenM01Zv6btM+SjhGnKJsVJ8ramb02s6VsT62Pmf0xOdCZmnC4+OjFq6vIATE+W7YCRcj69vdEZWBudgbURs15DMbYvXPZOv8v4s++jXuIkK+XoK3OprkPZdlVPUUbUHykjyguVJZ7O3Q1VT082NTSJlPXJYZ8RiJovx1BpvyMXvT29sa5/XazrXzfnY6YuaaoVRcqF9PdHp78/xgcHZ76/05lzrcvJkGnqEtJyFEbWmhYguhEpI/IPld0Ok22fnmyypv2MyEEOcZJ2ySFUNjlMOr27eZY6dTkybfKy6VOXpiebo9JIOZ+enmlTl7M8O2HZMzo2dYOeyYDJ0pieZDUcsbCgHE7zXomqTvOuY4qy23ILlSkmJku67mSE6cmVECmbp8mxiIWlCpX2N5piOVOXkyFzvDOeYEshU89OXXYGI2Yk0smpy7GZ0dLU5UziJKvliIzspYwPpZ7qPVvqUJnqVO4646Tpyby4eU412hC0yd/0YFhHrBQkaaPlTF3ui5ijjZ+6JF+1T1Eu1/Spy9mzBOPjU9Gyd3Q0eoZH3GEcVsgRWgGacB3KUqco65IiUkbMDIV1x8rU15csbXIywvRkFUxTrlwu+4t4xHTz7Q/LPZ5o8z7l9G4OpAlTl07vbqbsIuVC+vqiM9gXncF1M6cuJyamwmV0mr+Pmp6kCo5a4ADqOtU7VaScNF9AXE20TB0kp6s7TtYxPSlOVkekXL427y+Up83BEbrpQFOXkzfnmX6X8YlOAUGJYhQTKefT2xuddQPRWTcQE73NPs4SJ6mKIzwWVdV1KFdrJcEh9ynKiPSRcracImOuSji1W2wSKZcjp/1FeILqmJ6kLotNXY5PjE/dnGd6uBydGK0lXpqebL6iIyWwLI5c6Jpun+ZdpTpvmJNbpCydU7uZ5LqUB5ZTnASgfH29fTHYOxiDMTjnY2MTY1NTlyNjwzEyPhLD48PZnTJOfkTKfJmepEoCZSGacB3K1Uo9RSlS5q/EU7urJjjNZZpyfrntK02bnlxoei2HsxJoPtOT5Ki/tz/6e/v3xcuB/e8fnxiP4fHhZ69xObzka12anoS0xEmq5uiFA8rlNO+mEylXp9Q4WeX0ZG7BKSci5Uy57StNiJNLDUKzP8/vV6Dt+nr74qDeg+KgNQfNeP/k6eKzb9IzOuH3eRuZosyLOEkdyl8RUJQUp3mXMkUZIVKuVImnddN9TvneJ7c4WbrVTqpN/3qxkiqYnqQpFjpdvNPpxMj4SAysGYvh8eEYfvZ0ceGy+UTKPIiT1MURDEXJYQpKpMxLN+Kk6clmyeHnSAq57iOlTk/WEYGcsQBwYD09PfGcg/dFyw2x/xhtojMxFSunh0vXuGwWkRKaq8xVQUulvA5l6YumEu7oPZ1IeWDdmpp03clmats0pX2kOnVPp03+/SX/ziUd05PVcf338vT29MbgmsEYXDNz4nJsYmzecNmJTqItZbVEynRMT1InRzF03WpP885h+qnuKcoIkXIxTTil252789CGUJlznCxterKb8UeoZLnEydWbvfBeaCEuXKa13EDS39sf/Wv7Y33MPPYaGR+ZES4nr3FJGUTK7hMnqZsjGVqj6inKbkXKiBAqp+lmnHRqd3vk8B8fVct93xAnl/fvCpVQj5UsuCe/Rqgs29q+tbG2b+2M901e33L2tS1Hx0dNXGZIpOwecZJuKGt1QFJVnubdhCnKbjJNuU8T4mSVcg9QpWnSNKV9o1o5TKYJlSwmh320RKtdcAuV3Vd3JOnp6YmB/oEY6B+IGNj//k6nMxUqp99NfGR8JMYm/FxOSaSsnzhJtziaKYzr4axOiVOUk9oeKZtwWneEU7tLUHKoLCVMljQ9mVv4Kf2a0JCDqhfbQmXz9fT0TE1czj5VfHLqcnq0HBkfidHxUTfo6RKRsj7iJN2U11E3FEikrFeKMGl6koiyQmVJ+0MpcTK3MDmdaUqmy3lfzU3dC22DBPXKNZTMmLqcZXxifEa0nAyXbtJTPZGyerm+5mguRzQsSxNO8y7tjt6ztem6lE2Lk1VNT5YUo5og51BpX6hHKcHHNCWl7Ks56NZC2zQl0/X19kVfb1+s618352NjE2MzwuXkTXtMXa6cSFkdcZIUHNVABbo5RTmpydOUqU7nLiFOks70GJg6VpYaJkuYniwt+JimbK/S9tWUUiy0TVNWq4mxpL+3P/p7++OgNQfNeP/4xPj+YPns3cWFy6UTKVevia83yuDIhqLlNEWZKlJGNGuasinXmqxLqWGqaVLEytKfe3GyXqYpYX4pF9qmKVmJvt6+GOwdjME1gzPeL1wunUi5cuIkKZV7JN5iTfof2dWe5p2bFJEyohmhMnWYND3JStUVK0sPknSfacr2KDmmd1MuC+0mHbunkMvzmJpwuTwi5fJ5rZGaoxuWLbcpjZymKFMr8bTv1GEyooyb4kQIViU40HM0/WdVm55P05PdldvvaarVpH21LjkuskVK6rKccDkyPhJjE+35/SBSLl2OPzdpH0c4JGeKslqlTFPmECYj6o+TbozDdG18HsXJNETKZmrivlq1nBfZTvlevpyfz9wJl/uIlAfmdUYuHOXQCLlNUaaOlBH5hspcwmREOXESqE+Tg49TvpulyftqVUpZZJumXJpSns/StDFcipQL8zojJ450CpX6wKZJkxlNjpQRM4NgqliZU5QsURun7miG3Kcn2xJ8mvQ7u63asq+uRmmLbNOUiyvt+WyCpodLkXIurzNy42iHLDTtNO9cdTNW5h4lS5meFCcplTiZF9OU5WrbvroSJS+yUw8dwIE0KVyKlPuV/HOT5nLEQ2Os9DTviOZPUc5ndkBcbbDMPUhOV0qchFKJk/kyTVmWNu+rS9WERbZpypma8Jy2wVLC5fR4mUu4bHuk9PoiZ456WDGLnKXJOVJOV1JgXI1S7tgdYXoSqIdpyjKIkwfWtIW2UNm857SNFgqXE52JGB4bnhMvU4TLgwf27WdtC5VeX+TOkU/BmnZKSBWneec4RRlRTqRsum7ESad203amJ8vhPxrzZT89sCYvtJt2jL9UTX5Oiejt6Y3BNfOHy9Hx0RmniY+Mj8To+Gh0olPrNrVpmtLrixI4+oEuESnTKilO5mq54cl1ZdtHnCyPacr82E8PrA0L7bZNU7bhOWV+vT29MdA/EAP9A3M+Njo+OiNaTr6Nd8Yr+/fbECm9viiFIyBWJcfpi1ynKEmnpNO6I/KbnlxpdJr+dWJl84mTZcvx93kb2U8PrG0L7aaHyrY9nyzPmr41saZvTayPmUMA4xPjMToxN16udOqyqad8e31RGkdBZKXpd/M2RdlcTZuerDo2Tf59TX59Q+lMU6YlTh5YmxfbTQyVbX4+WZ2+3r7o6+2Ldf3rZry/0+nMGy5HxkdionPg106Tpim9viiRI6HCtfUaNQeS8xSlSNldpieXr85JOKGymUxPNotpyu6yfy6NxfY+TQmVnk/q0NPTE2v71sbavrVzPjY2MTbjJj2T17ycHS5Ln6b02qJkjojITtOnKCNEym7pVpxsyo1xuhmZhMrmECebyTRld9g/l8aCe65SQ6XnklT6e/ujf23/nNPFxybG9gXLWfHy4IHyIqXXF6VzVMSqNXHSohvXohQp61VanEwtVWQSKsuWe5xk9YTK+oiTS2PBvbjpj0/OsdLzSK76e/ujv7c/Dlpz0Iz3T4bLkfGRePypp2u5QU9VvL5oCkdGNNZqTvOOEClLVtpp3RFppydziExtmJxumhz2mwMRgKrTxP+MTMV+uXQW3cuT41Sl55BSTQ+Xh6w7JCL2vbbGJ8anTg8fnXaqeIpw6fVF0zhCaoAmXoeyTbFCpKxWN+NkE07tzikymaakSiJQ9UxTrp79cuksvFdu9mPX7XWC546m2rdv98YTz/TNmbgcnxifOj18dNqp4nWES68xmspREpXIdbKihCnKCJGyKiXGyVRyCpOztek/KEqV8/5D/YTK5RMml8fiu1rdCJaeM9pkvmnlvt6+GOwdjME1gzM+d3xiPMYmVv/70muMNnC0BJkQKVenxNO6I9JMT5YQl0xT5quE/UcM6g6hcmnsj8tjEV6/xR7jxeKl5wZmWso1YPt6+6Kvt6+SfwOazhET2apqiqqUKUpWrttxsuTpyRLi0nSmKfNSwv4jBnWfUDk/++LyWYin5zmAlZnvtbPcaWWvP9rOkVND5HAdylxP8y6JKcrlKzlOdnt6soS4NB/TlHkodf+he4TKfYTJlbEwB5rGzzVYHq8YslbVgni1IaibE3OlnqqcQsmPlTi5fE34HkpVymMvDOXh4IH+Vj4Xbf2+q2ARDwA4GoAl6nakLDm+dUOKx6fkU7ubopRQ1iSlPObCUH4mg13Tn5s2fI8AAHUTKKlUzgfoKW5Gsloi5Vyp4q1Tu/OxcbC/cd9TrjzOVKVpEa8t8bUbTE8CABECZaM09QAvpwVyigk6kXI/j8Xy5fT6qVqTv7cclPT4ikTlKD3slbztOWrqsSsAsHyOCmiVEqcoI4S5iLSPQanTkyUFppVqw/eYQkmPq1hUrlJiZSnbWRpxEgCYzpEBlavjAD6nxXKq6xC2OVKKk8uX02umbk75rpbHkhSmR8DUITCnbWkqcRIAmM1RF6zAURvXx0NDT3X93/29522I/+fXT3T9302pzWF2pdoamDYO9sfQ02OpN6Nope074lFzLfTcPjlc7WvcPtR94iQAMB9HZQ2zYV1vPPHMROrNyNqmg9bE3v8dTb0ZKzYZ7JoeKnMIk6VOT7aZSLly4iQl8LyXTZwEABbiKIFaNP0074h0p3pPyiHg1SWH7y3187tSub1OUnDK9/J5vIC6iZMAwGIcKdBKVU2zpY5Yv/e8DVnEvKo07fuZ1K3pSZFpJo/H0pT4OJmig7KIkwDAgThaoCglLqS7oQlRL6fvIXV4pjqmKRfnsQHqJk4CAEvhiKGBcjkQzH3CpSlTlJNKnT7Mbburfj5NT+bB4zNXqY9J7r9bAACA5XOUT6tVdcOcVHf1nk8pN9HJKUpOEiebbfJxavtNdEreX8RJKEsu/2kOAOTPUQPFKXlx3U05BsCI/CYmS+f1sHxtfsza/L0D3SVOAgDLYaXSUBvW9cYTz0yk3ow4eKA/nhzOe1qpiVOUk6aHwNQTlblHyVKnJ1mZNk5Tlh4nTU9COcRJAGC5HO1TpI2D/VmGhRwj5aRUsTL3MBmRz3VEl6v04JSDXH+WVKkJ+4k4CeUQJwGAlXDED1HdFGUp6oyVJQTJunVjerIJ0SkXTZ6mtJ8A3SROAgArZeXSYE0/zTvXyaecpyjnM19QXEq0bEqIdGo3k5oUKpsUJk1PQhnESQBgNRz1w7OqnKIsLVLO1pT4eCBO7WY+JYdK+waQgjgJAKyWowmoSanxqy3qeH6c2t0sGwf7i3m8S9rW5TA9CfkTJwGAKjiioCvqWmRWvSCvOjCJlFC+yfiXYwDMdbuqIE5C/sRJAKAqjv4bLpfrUJakbTfMaSPTk6zU9Ocg1Sng9gMgB+IkAFAlRxZ0TSlTlFUzRZmXUuMk+enmZGXOU5x1MD0JAADtYgUA86h6irL0m+Y0RcmxuC1hqlTzPT8rnbBs+3MtTkL+TE8CAFWzCmiBnE7zPnigP54crv60yI2D/ZWfbilSNktdcdKp3SzE8wY0kTgJANTBEQZ0UckTfCUrOU5Cm5iehLyJkwBAXRxl0Bh1TCvVEaBEyu4q/fE2hUdbiJOQN3ESAKiTI42WyOmgsrRFqEjJfJzaDUBb5HQcCQA0k6MNGqWkoCNS1s+p3VCG0v7jCtpEnAQAusERB0nUuRgt5VTvCJGyTqU/tiXFdlgNcRLyJU4CAN3iqKNFHGSujkhZjjofU6d2Q3XESciX40YAoJsceZBMaVOUdRIpq3HUxvXFx0kASE2cBAC6zdEHLEOdgUqkXJ2mPH6lxXVYKdOTkCdxEgBIwRFIy7TpoLOu0CNS5qcbj5vpSaiOOAl5atNxIgCQF0chJFXqIlWkzEeT4qTpSdqg1J/70HTiJACQkiORFmrTAWipwafuayk2hTgJAKvXpmNDACBPjkZIru5pmhJP9Z4kUi7MYwPlMT0J+REnAYAcWCnAKmw6aE3s/d/RWv+NyRD30NBTtf47pehmmDQ9CdURJyE/4iQAkAtHJS2V2wFpqVOUEd2LWCYGxUkolTgJ+cntWBAAaLfsj0yOPvro6OnpmfN20UUXzfv5t91227yf//Of/7zLW06bdDNStjFUdvv7dsduqI44CfkRJwGA3GS/avjRj34U4+PjU3/+6U9/Gqeddlr84R/+4aJfd//998fGjRun/nz44YfXto1U4+CB/nhyeKy2v3/jYH8MPV3f39+N070nHbVxfWtO+e52kO1mnDQ9SdOJk5AfcRIAyFH2K4fZYfGTn/xkHHPMMfF//s//WfTrNm/eHIccckiNW1a+Det644lnJlJvRlc1LVJGNPfalE2fFBUnAeg2cRIAyFVRRykjIyNx7bXXxrve9a7o6elZ9HNf8YpXxNatW+PUU0+NW2+9ddHPHR4ejqGhoRlvpGHaZvmadtp3yu/Hqd1Ld/BA/4JvEOHnOeRGnAQAclbUkcrXv/71+O1vfxsXXHDBgp+zdevW+PznPx87d+6M6667Ll784hfHqaeeGnfccceCX7Njx47YtGnT1Nu2bdtq2HpyUffkWqrIVXqoTL39Tu1e3HIipGiJ5xzyIk4CALnr6XQ6ndQbsVRvectbYu3atfHv//7vy/q6s88+O3p6euKGG26Y9+PDw8MxPDw89eehoaHYtm1b7N27d8Z1LJsqx9O867wW5aQ6T/WOiK6d6r2YEk7/ziGqipPzqysydeP1TTriJORFnAQAUhkaGopNmzYtqa8Vs4p46KGH4lvf+lZcd911y/7ak08+Oa699toFPz4wMBADAwOr2TyYo5vXo1xIrtepzCFKThIn56o7ME3/+8XKZhEnIS/iJABQimJWEldffXVs3rw5zjrrrGV/7T333BNbt26tYauaIceb5dR9R++I+m+YE5FHpIyYGQRTxcqcouQk15ycKUVcmvw3hcryiZOQF3ESAChJEauJiYmJuPrqq+P888+P/v6Zm3zZZZfFww8/HNdcc01ERHz605+Oo48+Oo499tipm+rs3Lkzdu7cmWLTyVybIuWk+UJhHdEyxyA5XbfjZO7Tk6njkqnKsqXef4D9hEkAoERFrCi+9a1vxa5du+Jd73rXnI/t3r07du3aNfXnkZGRuPTSS+Phhx+OwcHBOPbYY+PGG2+MM888s5ubTAW6MUUZ0b1IGZHHdSnns1hMXChe5h4gFyNO7pdjWDJVWY4c9x9oM3ESAChVUTfJ6ZblXMSzSXI7zXtSE26YM12ukbItxMn9SolLQmWeStl/oC3ESQAgN8vpa45kILobkVz3MB2P/X4lxaWDB/qL2t428HxAXsRJAKB0VhhMyfFmORHNOtV7Um7XpWyDFHEyx+nJksOSU7/zUPI+lLsDRaYcf0eTljAJADSFVQYkIlJ2R6qpSXGyPkJlOk3Zh3KwkrC00NcIl+0kTgIATWKlwQymKLs3RRkhUtZNnNyviWFJqOyeJu4/KdQVlGb/vTn+Hqc6wiQA0ERWHDBLikgZ4eY5VRMn92t6XBIq69X0/acbuh2Upv97YmWziJMAQFM5yqEY3Vwkp4hMbuBSHY/lfm2KS26mUy2P5+ptWNebPChNbkPq7WD1PIcAQJNZeTBHrqd5d1u3JykjnPK9WqnDZG7Tk22NSyYqV6+t+05Vcg1JJivLlOv+BABQJSsQitKta1Gm5JTvlREnZxKYhMqVsN+sXikxSazMXyn7EgBAFaxEmFfOU5TdjJQppignmaZcmtRhMkKczN30x0OsnJ99ZvVKjkliZX5K3p8AAFbCigQOIHWkjDBNuRBxci6haXGmKmeyv1SjSTFJrEyrSfsSAMByWJlQpG6f6p0yUkYIlbPlECYjxMmStT1U2leq0fSYJFZ2T9P3JQCAA7FCYUE5n+adQupIGeG071zCZIQ42RRtO/3bflKdtgWlye/XcUG12rYfAQAsxEqFYqW4YU4ukTKifdOUOcXJ3IhO1WhyrLSPVKvNUclU5eq1ef8BAFiIFQuLyn2Ksq2RMqI9oTLHMJnT9KTwVI8mxEr7Rj3Epf1mPxY5Hy/kwL4DALAwqxcoXBNDZY5RcpI42T4lxUr7RL0EpsWZrpyf/QYA4MCsZDggU5Rz5TJFOd30qFdqrMw5TEaIk8x93FMHS/tBdwhMy9fm6Ur7CwDA8lnZwArlGCknlTRVmXuUnCROMp+Fnos6wqXnPQ2xqRpNn660nwAArI7VDo2QYooyIu9IGZH3VGUpYTJCnGT5PE/NIDrVY77HtbRoad8AAKiWFRRLkvtp3hEi5YHMDoLdDpYlBcnpxEloJwGqu3KOlvYFAID6We1CBUqJlNPNFwyripalxsjZcoqTQPcIUnlYyvNQVcT0nAMApGX1zZKZolxciZFytqaExSrkFidNT0J3CFVl8XwBADSDozoaJ2XIyS1qsTK5PY/iJHSH2AUAAGk4EoeK5Ra3WJ7cnj9xErpDnAQAgHQcjbMspSzgUked3CIXS5Pb85Z6P4a2KOV3GwAANJUjcqhJbrGLhW0c7M/u+RInoTvESQAASM9ROctWymIuh8CTW/Rirhyfoxz2XWiDUn6fAQBA0zkyp9FyCD05BjD28dxAe4mTAACQD0fn0AVCWH5yfU5yiOrQdOIkAADkxRE6K1LS4i6X4JNrEGujXJ+LXPZVaLKSfn8BAEBbOEqnFXIJPznejKVtcn38c9lHocnESQAAyJMjdVbMQm/lco1kTZZzHBYnoX5+ZwEAQL4crdMauUWgXGNZE+X8WOe2X0ITiZMAAJA3R+ysSmmLvtxiUM7hrClyfoxz2x+hiUr7PQUAAG3kqB0SyzmglSznU7qB7hAnAQCgDI7caZ0cp9bEtGqV8FjmuB8CAABACgIlq1bihEqucaiEsJa7Eh7DXPc/aJISfzcBAEBbOXqHzJQQ2HJUyhSqOAn1EycBAKAsjuCpRImLwZxDUSmxLRelPFY573PQFCX+PgIAgLZzFE+r5R6MSglvqZQUcnPf16AJxEkAACiTI3kqY2FYj5IiXDeV9JiIk1A/v4MAAKBcjuZpvVLiUUlBrk6CLTCbOAkAAGWzyofYFymfHB5LvRkHNBnmhp7Of1urVmqULCWAQ6nESQAAKJ+jeipV8kKxpJDUpinCkr/XkvYpKFHJv3MAAID9rJ6hYE2eqCw1Sk4SJ6Fe4iQAADSHo3sqV/KisdSoVPKU4WxN+F5K3Y+gFCX/ngEAAOayioZZSrke5XxKnqgsPUpOEiehXuIkAAA0j5U0tdiwrjeeeGYi9WasWMmRMmJm7Ms5VjYlSgLdIU4CAEAzqQPQcLnFyiZHSdOTUB9xEgAAmstqmtqYosxPqljZ5Cg5SZyE+oiTAADQbFbUsIgmRspJ80XDKqJlG2LkbOIk1EecBACA5rOqplalT1FGNDtSztbGuLha4iTUR5wEAIB2cOQPsELiJNRHnAQAgPZw9E/tmrDIFKKYzT4B9WnC7w0AAGDprABgiQQpgPqJkwAA0D5WAXRFUxacIiUR9gOoS1N+VwAAAMtjJQDLJE61m+cf6iFOAgBAe1kN0DVNWnyKVO3keYd6NOn3AwAAsHxWBLBCYlW7eL6hHuIkAABgVUBXNW0hKlq1g+cZ6tG03wkAAMDKWBnAKolXzeb5hXqIkwAAwCSrA7quiYtSEauZPK9Qjyb+HgAAAFbOCgEqImY1i+cT6iFOAgAAs1klkERTF6iiVjN4HqEeTf3ZDwAArI6VAsk0daEqbpXN8wf1aOrPfAAAYPWsFqAGIleZPG9QD3ESAABYjBUDSTV50Sp2lcXzBfVo8s95AACgGlYNUCPRqwyeJ6iHOAkAACyFlQPJNX0BK37lzfMD9Wj6z3YAAKA6Vg9koekL2YMH+oWwDHlOoB5N/5kOAABUywoCukgQy4NgDPURJwEAgOWyiiAbbVnUCmNpefyhPm35OQ4AAFTLSoKstGVxa4IvDY851GPDut7W/PwGAACqZ7UOCR080B9PDo+l3ozGEybrsZIg9cQzEzVsCSkJkwAAwGpZtZOdDet6WxUxJuOZUFkPcbJaq41Rs7++Ta/1JhInAQCAKli5k6W2RcoIobIO4mQ16oxQ0//utr3mSydOAgAAVbF6J1ttjJQRTvuugjBZjW4HKLGyHOIkAABQJat4yJBpypURJquRQ3ya3AahMj857B8AAECzWGWQtbYvhN3te+k8TtXI7TXn7tD58FwAAAB1saIne2091Xs6E5ULEyarkXt4MlGZVu77BwAAUDYrDopgcbyPicr9PBbVKen1ZYqv+zzeAABA3azuKYZJyv2mh7m2TVWKktUpOTy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      "text/plain": [
       "<Figure size 1600x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maybe remove score outliers? When they are removed the regression gradients are more similar but math has lower average score. Outliers could be due to people dropping the maths course or missing exam.\n",
    "dfo=df[df[\"G3\"]>1]\n",
    "# dfo = df\n",
    "dfm = dfo[dfo[\"class\"] == \"math\"]\n",
    "dfp = dfo[dfo[\"class\"] == \"portuguese\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,11))\n",
    "sns.kdeplot(x=dfo[\"Dalc\"], y=dfo[\"G3\"], cmap=\"Blues\",shade=True)\n",
    "\n",
    "sns.regplot(x=dfm[\"Dalc\"], y=dfm[\"G3\"], scatter=False, color=\"Green\", label=\"math\")\n",
    "sns.regplot(x=dfp[\"Dalc\"], y=dfp[\"G3\"], scatter=False, color=\"Red\", label=\"portuguese\")\n",
    "plt.legend()\n",
    "plt.ylim(0,20)\n",
    "plt.xlim(0.1, 5.9)\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,11))\n",
    "sns.kdeplot(x=dfo[\"Walc\"], y=dfo[\"G3\"], cmap=\"Blues\",shade=True)\n",
    "sns.regplot(x=dfm[\"Walc\"], y=dfm[\"G3\"], scatter=False, color=\"Green\", label=\"math\")\n",
    "sns.regplot(x=dfp[\"Walc\"], y=dfp[\"G3\"], scatter=False, color=\"Red\", label=\"portuguese\")\n",
    "plt.legend()\n",
    "plt.ylim(0,20)\n",
    "plt.xlim(0.1, 5.9)\n",
    "plt.show()\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# sns.kdeplot(x=df[\"Walc\"], y=df[\"G3\"], cmap=\"Blues\", shade=True)\n",
    "# sns.regplot(x=df[\"Walc\"], y=df[\"G3\"], marker=\" \", color=\"Red\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# No missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"school\"] = df[\"school\"].apply(lambda x: 0 if x==\"GP\" else 1)\n",
    "df[\"sex\"] = df[\"sex\"].apply(lambda x: 0 if x==\"F\" else 1)\n",
    "df[\"address\"] = df[\"address\"].apply(lambda x: 0 if x==\"U\" else 1)\n",
    "df[\"famsize\"] = df[\"famsize\"].apply(lambda x: 0 if x==\"LE3\" else 1)\n",
    "df[\"Pstatus\"] = df[\"Pstatus\"].apply(lambda x: 0 if x==\"A\" else 1)\n",
    "df[\"class\"] = df[\"class\"].apply(lambda x: 0 if x==\"math\" else 1)\n",
    "df[\"schoolsup\"] = df[\"schoolsup\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"famsup\"] = df[\"famsup\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"paid\"] = df[\"paid\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"activities\"] = df[\"activities\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"nursery\"] = df[\"nursery\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"higher\"] = df[\"higher\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"internet\"] = df[\"internet\"].apply(lambda x: 0 if x==\"no\" else 1)\n",
    "df[\"romantic\"] = df[\"romantic\"].apply(lambda x: 0 if x==\"no\" else 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One Hot Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "OHE_mjob = pd.get_dummies(df[\"Mjob\"], prefix=\"Mjob\", drop_first=True)\n",
    "OHE_fjob = pd.get_dummies(df[\"Fjob\"], prefix=\"Fjob\", drop_first=True)\n",
    "OHE_reason = pd.get_dummies(df[\"reason\"], prefix=\"reason\", drop_first=True)\n",
    "OHE_guardian = pd.get_dummies(df[\"guardian\"], prefix=\"guardian\", drop_first=True)\n",
    "\n",
    "df = pd.concat([df, OHE_mjob, OHE_fjob, OHE_reason, OHE_guardian], axis = 1)\n",
    "\n",
    "df.drop([\"Mjob\", \"Fjob\", \"reason\", \"guardian\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>...</th>\n",
       "      <th>Mjob_teacher</th>\n",
       "      <th>Fjob_health</th>\n",
       "      <th>Fjob_other</th>\n",
       "      <th>Fjob_services</th>\n",
       "      <th>Fjob_teacher</th>\n",
       "      <th>reason_home</th>\n",
       "      <th>reason_other</th>\n",
       "      <th>reason_reputation</th>\n",
       "      <th>guardian_mother</th>\n",
       "      <th>guardian_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>1039</th>\n",
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       "      <th>1040</th>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      school  sex  age  address  famsize  Pstatus  Medu  Fedu  traveltime  \\\n",
       "0          0    0   18        0        1        0     4     4           2   \n",
       "1          0    0   17        0        1        1     1     1           1   \n",
       "2          0    0   15        0        0        1     1     1           1   \n",
       "3          0    0   15        0        1        1     4     2           1   \n",
       "4          0    0   16        0        1        1     3     3           1   \n",
       "...      ...  ...  ...      ...      ...      ...   ...   ...         ...   \n",
       "1039       1    0   19        1        1        1     2     3           1   \n",
       "1040       1    0   18        0        0        1     3     1           1   \n",
       "1041       1    0   18        0        1        1     1     1           2   \n",
       "1042       1    1   17        0        0        1     3     1           2   \n",
       "1043       1    1   18        1        0        1     3     2           3   \n",
       "\n",
       "      studytime  ...  Mjob_teacher  Fjob_health  Fjob_other  Fjob_services  \\\n",
       "0             2  ...             0            0           0              0   \n",
       "1             2  ...             0            0           1              0   \n",
       "2             2  ...             0            0           1              0   \n",
       "3             3  ...             0            0           0              1   \n",
       "4             2  ...             0            0           1              0   \n",
       "...         ...  ...           ...          ...         ...            ...   \n",
       "1039          3  ...             0            0           1              0   \n",
       "1040          2  ...             1            0           0              1   \n",
       "1041          2  ...             0            0           1              0   \n",
       "1042          1  ...             0            0           0              1   \n",
       "1043          1  ...             0            0           1              0   \n",
       "\n",
       "      Fjob_teacher  reason_home  reason_other  reason_reputation  \\\n",
       "0                1            0             0                  0   \n",
       "1                0            0             0                  0   \n",
       "2                0            0             1                  0   \n",
       "3                0            1             0                  0   \n",
       "4                0            1             0                  0   \n",
       "...            ...          ...           ...                ...   \n",
       "1039             0            0             0                  0   \n",
       "1040             0            0             0                  0   \n",
       "1041             0            0             0                  0   \n",
       "1042             0            0             0                  0   \n",
       "1043             0            0             0                  0   \n",
       "\n",
       "      guardian_mother  guardian_other  \n",
       "0                   1               0  \n",
       "1                   0               0  \n",
       "2                   1               0  \n",
       "3                   1               0  \n",
       "4                   0               0  \n",
       "...               ...             ...  \n",
       "1039                1               0  \n",
       "1040                1               0  \n",
       "1041                1               0  \n",
       "1042                1               0  \n",
       "1043                1               0  \n",
       "\n",
       "[1044 rows x 43 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school                0\n",
       "sex                   0\n",
       "age                  18\n",
       "address               0\n",
       "famsize               1\n",
       "Pstatus               0\n",
       "Medu                  4\n",
       "Fedu                  4\n",
       "traveltime            2\n",
       "studytime             2\n",
       "failures              0\n",
       "schoolsup             1\n",
       "famsup                0\n",
       "paid                  0\n",
       "activities            0\n",
       "nursery               1\n",
       "higher                1\n",
       "internet              0\n",
       "romantic              0\n",
       "famrel                4\n",
       "freetime              3\n",
       "goout                 4\n",
       "Dalc                  1\n",
       "Walc                  1\n",
       "health                3\n",
       "absences              6\n",
       "G1                    5\n",
       "G2                    6\n",
       "G3                    6\n",
       "class                 0\n",
       "Mjob_health           0\n",
       "Mjob_other            0\n",
       "Mjob_services         0\n",
       "Mjob_teacher          0\n",
       "Fjob_health           0\n",
       "Fjob_other            0\n",
       "Fjob_services         0\n",
       "Fjob_teacher          1\n",
       "reason_home           0\n",
       "reason_other          0\n",
       "reason_reputation     0\n",
       "guardian_mother       1\n",
       "guardian_other        0\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving/loading clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Data/processed_data.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Min-Max Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(df.pop(\"G3\"))\n",
    "X = np.array(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.24      , 0.        , 0.01333333,\n",
       "       0.        , 0.05333333, 0.05333333, 0.02666667, 0.02666667,\n",
       "       0.        , 0.01333333, 0.        , 0.        , 0.        ,\n",
       "       0.01333333, 0.01333333, 0.        , 0.        , 0.05333333,\n",
       "       0.04      , 0.05333333, 0.01333333, 0.01333333, 0.04      ,\n",
       "       0.08      , 0.06666667, 0.08      , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.01333333, 0.        , 0.        , 0.        ,\n",
       "       0.01333333, 0.        ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled = (X - np.amin(X)) / (np.amax(X) - np.amin(X))\n",
    "X_scaled[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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