Data science with python overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents:\n",
    "    1. Install Software requirements for the labs\n",
    "    2. Loading and exploring the dataset\n",
    "    3. Ploting the data\n",
    "    4. Cleaning the data \n",
    "    5. Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software Requirements \n",
    "\n",
    "Here is a list of what you'll need:\n",
    "- Python3: [https://www.python.org/downloads/](https://www.python.org/downloads/)\n",
    "- Pandas Library: []()\n",
    "- Scipy and Numpy Libraries: []()\n",
    "- Sklearn Library: []()\n",
    "- Matplotlib module for plotting: []()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported all required components\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from IPython.display import Image\n",
    "from sklearn import preprocessing, neighbors\n",
    "print(\"Imported all required components\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once all the software requirements have been installed, you should see the message \"Imported all required components\" printed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading the UCI Adultdataset**\n",
    "\n",
    "In this tutorial, we will be interacting with the UCI Adult Dataset. We will be using in a local version of the dataset that we download from [here](https://archive.ics.uci.edu/ml/datasets/adult)\n",
    "\n",
    "The dataset is currently stored into the \"data/adult\" local directory.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define dataset path in variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ADULT_DIRECTORY = os.path.join(os.getcwd(), 'data','adult')\n",
    "ADULT_DATA_PATH = os.path.join(ADULT_DIRECTORY, 'adult.data')\n",
    "ADULT_TEST_PATH = os.path.join(ADULT_DIRECTORY, 'adult.test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLUMN_NAMES = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\", \"occupation\", \n",
    "                \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\", \"hours-per-week\", \"native-country\", \"salary\"]\n",
    "CONTINUOUS_FEATURES = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "CATEGORICAL_FEATURES = [\"workclass\", \"education\", \"marital-status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"native-country\", \"salary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Load the datasets ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_data = pd.read_csv(ADULT_DATA_PATH, names=COLUMN_NAMES)\n",
    "adult_test = pd.read_csv(ADULT_TEST_PATH, names=COLUMN_NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
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       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
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       "      <td>2174</td>\n",
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       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  salary  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
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       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>|1x3 Cross validator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                    age   workclass    fnlwgt      education  education-num  \\\n",
       "0  |1x3 Cross validator         NaN       NaN            NaN            NaN   \n",
       "1                    25     Private  226802.0           11th            7.0   \n",
       "2                    38     Private   89814.0        HS-grad            9.0   \n",
       "3                    28   Local-gov  336951.0     Assoc-acdm           12.0   \n",
       "4                    44     Private  160323.0   Some-college           10.0   \n",
       "\n",
       "        marital-status          occupation relationship    race    sex  \\\n",
       "0                  NaN                 NaN          NaN     NaN    NaN   \n",
       "1        Never-married   Machine-op-inspct    Own-child   Black   Male   \n",
       "2   Married-civ-spouse     Farming-fishing      Husband   White   Male   \n",
       "3   Married-civ-spouse     Protective-serv      Husband   White   Male   \n",
       "4   Married-civ-spouse   Machine-op-inspct      Husband   Black   Male   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country   salary  \n",
       "0           NaN           NaN             NaN             NaN      NaN  \n",
       "1           0.0           0.0            40.0   United-States   <=50K.  \n",
       "2           0.0           0.0            50.0   United-States   <=50K.  \n",
       "3           0.0           0.0            40.0   United-States    >50K.  \n",
       "4        7688.0           0.0            40.0   United-States    >50K.  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16282, 15)"
      ]
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting and Filtering Data\n",
    "* Select columns you are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  hours-per-week\n",
       "0   39              40\n",
       "1   50              13\n",
       "2   38              40\n",
       "3   53              40\n",
       "4   28              40"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_you_want = ['age', 'hours-per-week'] # specify columns you're interested in\n",
    "chosen_columns = adult_data[columns_you_want] # select the columns\n",
    "chosen_columns.head() # show first couple of lines of this new variable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the data - let's get the data of all people under age 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt   education  education-num       marital-status  \\\n",
       "0   39   State-gov   77516   Bachelors             13        Never-married   \n",
       "2   38     Private  215646     HS-grad              9             Divorced   \n",
       "4   28     Private  338409   Bachelors             13   Married-civ-spouse   \n",
       "5   37     Private  284582     Masters             14   Married-civ-spouse   \n",
       "8   31     Private   45781     Masters             14        Never-married   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "5     Exec-managerial            Wife   White   Female             0   \n",
       "8      Prof-specialty   Not-in-family   White   Female         14084   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  salary  \n",
       "0             0              40   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  \n",
       "5             0              40   United-States   <=50K  \n",
       "8             0              50   United-States    >50K  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under_40 = adult_data[adult_data.age < 40] # select the age_colum column and only take entries that are less than 40\n",
    "under_40.head() # display first couple of lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data by Groups\n",
    "* [Split](https://pandas.pydata.org/pandas-docs/stable/groupby.html) adult by sex (male vs. female)\n",
    "* Are there more  males in the workplace? Are males in the workplace relatively older than females?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fnlwgt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">education-num</th>\n",
       "      <th colspan=\"2\" halign=\"left\">capital-gain</th>\n",
       "      <th colspan=\"2\" halign=\"left\">capital-loss</th>\n",
       "      <th colspan=\"2\" halign=\"left\">hours-per-week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>36.858230</td>\n",
       "      <td>10771</td>\n",
       "      <td>185746.311206</td>\n",
       "      <td>10771</td>\n",
       "      <td>10.035744</td>\n",
       "      <td>10771</td>\n",
       "      <td>568.410547</td>\n",
       "      <td>10771</td>\n",
       "      <td>61.187633</td>\n",
       "      <td>10771</td>\n",
       "      <td>36.410361</td>\n",
       "      <td>10771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>39.433547</td>\n",
       "      <td>21790</td>\n",
       "      <td>191771.449013</td>\n",
       "      <td>21790</td>\n",
       "      <td>10.102891</td>\n",
       "      <td>21790</td>\n",
       "      <td>1329.370078</td>\n",
       "      <td>21790</td>\n",
       "      <td>100.213309</td>\n",
       "      <td>21790</td>\n",
       "      <td>42.428086</td>\n",
       "      <td>21790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age                fnlwgt        education-num         \\\n",
       "              mean  count           mean  count          mean  count   \n",
       "sex                                                                    \n",
       " Female  36.858230  10771  185746.311206  10771     10.035744  10771   \n",
       " Male    39.433547  21790  191771.449013  21790     10.102891  21790   \n",
       "\n",
       "        capital-gain        capital-loss        hours-per-week         \n",
       "                mean  count         mean  count           mean  count  \n",
       "sex                                                                    \n",
       " Female   568.410547  10771    61.187633  10771      36.410361  10771  \n",
       " Male    1329.370078  21790   100.213309  21790      42.428086  21790  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = adult_data.groupby('sex') # split data\n",
    "grouped_data.agg(['mean', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying a Function\n",
    "* Apply a function to a column of a DataFrame\n",
    "* Let's transform the age of a person from the current time to get the year in which the person was born"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_birth_year(age, current_year):\n",
    "    \"\"\"\n",
    "    Calculate the birth date of an individual\n",
    "    Params:\n",
    "        age : an int corresponding to the age of the individual\n",
    "        current_year: The current year from which to subtract the age\n",
    "    \"\"\"\n",
    "    return current_year - age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1955\n",
       "1    1944\n",
       "2    1956\n",
       "3    1941\n",
       "4    1966\n",
       "Name: birth_year, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume the data was collected at the end of the year 1994\n",
    "adult_data['birth_year'] = adult_data[['age']].apply(lambda entry: get_birth_year(entry, 1994), axis=1)\n",
    "adult_data['birth_year'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALIZATION\n",
    "* identify hidden patterns and trends\n",
    "* formulate hypothesis\n",
    "* determine best steps for modeling\n",
    "* communicate results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore individuals that only have a full time job(more than 20 hours per week). We will also remove individuals where some information about hours per week is missing [dropna() function](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html) to make plotting easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt   education  education-num       marital-status  \\\n",
       "0   39   State-gov   77516   Bachelors             13        Never-married   \n",
       "2   38     Private  215646     HS-grad              9             Divorced   \n",
       "3   53     Private  234721        11th              7   Married-civ-spouse   \n",
       "4   28     Private  338409   Bachelors             13   Married-civ-spouse   \n",
       "5   37     Private  284582     Masters             14   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "5     Exec-managerial            Wife   White   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  salary  birth_year  \n",
       "0             0              40   United-States   <=50K        1955  \n",
       "2             0              40   United-States   <=50K        1956  \n",
       "3             0              40   United-States   <=50K        1941  \n",
       "4             0              40            Cuba   <=50K        1966  \n",
       "5             0              40   United-States   <=50K        1957  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_time_employees = adult_data[adult_data[\"hours-per-week\"] > 20].dropna()\n",
    "full_time_employees.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Scatter plots - numerical data - useful for exploring correlations in data\n",
    "* Age vs. work hours per week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Work hours per week (in hours)')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(full_time_employees['age'], full_time_employees['hours-per-week'])\n",
    "plt.title('Scatter plot of Hours per week by individual\\'s age')\n",
    "plt.xlabel('Age in years')\n",
    "plt.ylabel('Work hours per week (in hours)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Histograms - distribution of the variable\n",
    "* Useful for identifying outliers, multi-modality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of Individual working hours')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(full_time_employees['hours-per-week'], bins = 10)\n",
    "plt.axvline(full_time_employees['hours-per-week'].mean(), color='green', label='Average')\n",
    "plt.legend()\n",
    "plt.xlabel('Hours per week')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Individual working hours')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Bar plot - useful for categorical data\n",
    "* Let's [obtain counts](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) of number of users by gender and plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Male      20412\n",
       " Female     9221\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_counts = full_time_employees['sex'].value_counts()\n",
    "sex_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Individuals by sex')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(2), sex_counts, align='center', color=['gray', 'teal'])\n",
    "plt.xticks([0, 1], ['Male', 'Female'])\n",
    "plt.title('Individuals by sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA CLEANING\n",
    "* Wrong values\n",
    "* Messy format\n",
    "* Too many observations - do preliminary analysis on a subset of data\n",
    "* Missing data\n",
    "* Drop samples with problematic values\n",
    "* Use mean, median or most common value of the feature\n",
    "* Use a model to estimate the value\n",
    "* Data might not be missing at random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping missing values TBD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "      <th>birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age          workclass  fnlwgt   education  education-num  \\\n",
       "0  39.0          State-gov   77516   Bachelors             13   \n",
       "1  50.0   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2  38.0            Private  215646     HS-grad              9   \n",
       "3  53.0            Private  234721        11th              7   \n",
       "4  28.0            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  salary  \\\n",
       "0          2174             0            40.0   United-States   <=50K   \n",
       "1             0             0            13.0   United-States   <=50K   \n",
       "2             0             0            40.0   United-States   <=50K   \n",
       "3             0             0            40.0   United-States   <=50K   \n",
       "4             0             0            40.0            Cuba   <=50K   \n",
       "\n",
       "   birth_year  \n",
       "0        1955  \n",
       "1        1944  \n",
       "2        1956  \n",
       "3        1941  \n",
       "4        1966  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data_dropped = adult_data.dropna()\n",
    "adult_data_dropped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping wrong values - TBD\n",
    "* Explore outliers in the dataset\n",
    "* Age, hours-per-week should be positive\n",
    "* It's unreasonable for an individual of age **over 80 years to be working full time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32476, 16)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data_cleaned = adult_data[~((adult_data['age'] >= 80) & (adult_data['hours-per-week'] >= 20))]# & adult_data['hours-per-week'] >= 20]\n",
    "adult_data_cleaned.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot original vs cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Work hours per week (in hours)')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(1)\n",
    "plt.scatter(adult_data['age'], adult_data['hours-per-week'])\n",
    "plt.title('Original data')\n",
    "plt.xlabel('Age in years')\n",
    "plt.ylabel('Work hours per week (in hours)')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.scatter(adult_data_cleaned['age'], adult_data_cleaned['hours-per-week'])\n",
    "plt.title('Cleaned data')\n",
    "plt.xlabel('Age in years')\n",
    "plt.ylabel('Work hours per week (in hours)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling in missing data with summary statistics\n",
    "* Impute missing data by replacing it with mean, median or the most frequent value\n",
    "* Most frequent value could be a good choice for categorical data\n",
    "* Imputation reduces variability within the dataset, which will impact your model's performance\n",
    "* Evaluate which imputation technique gives the best perfomance\n",
    "* Let's impute user's age and hours per week using the most frequent value\n",
    "* We will specify the parameters for the [imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html)\n",
    "* Fit the imputer (find most frequent value in this case) and transform the data accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp = preprocessing.Imputer(missing_values='NaN', strategy='most_frequent') # specify the imputer\n",
    "adult_data[['age', 'hours-per-week']] = imp.fit_transform(adult_data[['age', 'hours-per-week']]) # fit the imputer and transform the input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling in missing data with k-nearest neighbors (k-NN)\n",
    "* Fit a model to the data that is not missing\n",
    "* Use the model to predict the values for missing data\n",
    "* k-NN finds $k$ samples closest in distance to the missing point and predicts the label from these closest points\n",
    "* k-NN classification: output is a category decided by majority vote of its $k$ neighbors\n",
    "* k-NN regression: output is the average of the values of its $k$ nearest neighbors\n",
    "* Weigh contribution of each point can be weighted by its distance from the point of interest\n",
    "* Distance metric matters, number of neighbors matters\n",
    "* Pick parameters that give you best performance on the final task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/knn.png', width= 500, height= 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the age column to impute missing hours-per-week values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "          weights='distance')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = neighbors.KNeighborsRegressor(n_neighbors=10, weights = 'distance')\n",
    "model.fit(adult_data_dropped[['age']], adult_data_dropped[['hours-per-week']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-6e9fd2f74a8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# TODO(need to add missing values for age)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmissing_ages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madult_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madult_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hours-per-week'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mimputed_ages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_ages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/regression.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0mTarget\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \"\"\"\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    460\u001b[0m                              \u001b[0;34m\" minimum of %d is required%s.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m                              % (n_samples, shape_repr, ensure_min_samples,\n\u001b[0;32m--> 462\u001b[0;31m                                 context))\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required."
     ]
    }
   ],
   "source": [
    "# TODO(need to add missing values for age)\n",
    "missing_ages = adult_data[pd.isnull(adult_data['age'])]['hours-per-week']\n",
    "imputed_ages = model.predict(missing_ages.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: hours-per-week, dtype: float64)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-976de98982c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmissing_fnlwgt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madult_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madult_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fnlwgt'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hours-per-week'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_fnlwgt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mimputed_fnlwgt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_ages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/regression.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0mTarget\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \"\"\"\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    460\u001b[0m                              \u001b[0;34m\" minimum of %d is required%s.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m                              % (n_samples, shape_repr, ensure_min_samples,\n\u001b[0;32m--> 462\u001b[0;31m                                 context))\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_features\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required."
     ]
    }
   ],
   "source": [
    "# TODO(need to add missing values for fnlwgt)\n",
    "missing_fnlwgt = adult_data[pd.isnull(adult_data['fnlwgt'])]['hours-per-week']\n",
    "print(missing_fnlwgt[:5])\n",
    "imputed_fnlwgt = model.predict(missing_ages.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE ENGINEERING\n",
    "* What is the question you are trying to answer\n",
    "* Creating new features that might correlate well with your prediction target\n",
    "* How to transform categorical data (male vs. female) to numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert categorical features to one-hot encoding\n",
    "* One-hot encoding is a representation of categorical variables as binary vectors\n",
    "* Could use integers to denote different categories\n",
    "* However categories will then be ordered, which is often not desired\n",
    "* Solution: one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+eZABNMj4cBKWTAPMBKPWSAB5AkbfEeT5wvHBPpAKaoEgA4nnJ3Lw6cHvtLZBA/GY+Zut3BvafK4fVPzutgy4b0WNBPA70GpoMLdPcJ3eewf7vpL7RsL+3N5lrxOj1SRg0IlW8L4SNACsgT77BeGdaga/Pk/++zX45dqh14vjjB+C33jM6lSSD2iWxHM8LYL/YQeLjYXkk/gKH0nayj7o5JDwyjLBn8wVLYL/YQeLjXluJC3vXkUZQUqd4F/3N1cpeg79Lr+NhPlCyuhR6oQWUEAmXEqG5AHcN2xOmTL/lDqhBRSwEBrJM888I+suhx1Ll3z+Lyw8XbKWGEv8hB1Lh7yrwkzxsGPp8oADDjCHHXZY6LFESp1ItoDgbbfdFhqeCvnO4kUXXRR6LNNk6aGw8DAWiibJdCPh9mHh6XL8+PHmkEMOCT2WDs8991z59Z/6yAR5lWKbbbYJPZZIqROpFBAfGg0LT4W8jbj77rub448/Xt4r4aUulh3C5GGfOLzqy/sllX2Zh3XAwsLDWAiNhDcVX3nlldBj6ZLbZ7IC7rTTTrJAYdixdMgbp6+//nrosXTJV5//97//hR5LpNSJZAsIIsUqK8lY5ZHvm7zzzjtmjz32kEbiGwONheP+g0CVfcf78ccfT/plLvVJlGGUOqEFFFAbiTKMUie0gALmu5EoogstoAS4eptTuFsrIgotoAS4eptTuFtXiNatW0vHRyZBF3AmwWo1Rx55pNvLDDK9YDeva1944YVur2Kk1Ei+++47WQy7srjxxhvdVnaRQv2LIai2uYW7dVL4448/3FZmkOLtKwSf42BV+0zh8svdTPAMg4+yJovM5lASqFmzpqy5xfpPgN6nbOHpp592W8kjqLa5hbt1heDbLIMHD3Z7mUEKt08Ke+21V+yzG5nCyJEj3VZmwBDEu+++6/YqRko5NGzYMBkMrCxuvfVW+cgP3zRJbCTegc4EHn74YbeVPIJqm1u4WysiCi2gBLh6m1O4WysiCi2gBLh6m1Nw37BuaWX+KXVCCyigDiYqwyh1QgsoYCE0kubNm8ti42HH0mX79u1Dw9MlX//104oyQcqlQ4cOocfSJT1bN910U+ixREqdSLaAIKu/88WosGPJkF6t2rVrx/b9uxyJ26zFGr+fDpm0l8rcr0JoJNtuu62ZM2dO6LF0+N5778lvJr+DQgPJZCPJxixg5ghuvfXWoccSKXUi2QKClX1PgPpAjxbz+RlwYr9t27amQYMG8m0RuofZv+CCC6RCDBw4MPQ6yXLIkCGh4WEshEbSo0cPs99++4UeS4dUPKa2hx1Ll5UVbolEKGSygcADDzxQBmXDjiVS6kSyBQQvvvjiSmUA9YFfuoCZKs8+39/gc8q8rIM5cdlll5nTTz/dbLTRRqZv375lrpEsmXZ/ySWXJD3dPsONZKj7TQqplIEyt5Q6EYUCQmN069Yt9FiumOFGwnWSbihRKANlOKVO8I8BvWIn78lIhmQGoiUtk2oocmNFZKEFlABXb5OBbwgVscKG4m6tiCi0gBLg6m0ySGwMK+NKG4q7dVLgjc5M4s0333RbmQHzoh544AG3V3kwoTbFLKoQdAG3bNnS7VWMlO7+8ssvu630QBcw3W8eP//8c+is4kzMNE4XQbVNCvGNoCKuFO7WFYLexVQm5lWEr776ym1lDrx+nclZwCCFLEoaTOlPFpm/ewqgkMhU8OuvwdquvP8OWPJl3XXXle100alTJ7eVPIJqmxTiG8HKWCHcrZNCixYt3FY0wafI6Z3MJFLMogpx5plnmptvvtntVYycNpJJkyaZrbbaSrbjX/bx26y0AciUpk2bVjpzqld3izCnACptBiDPHmxWDHdrRUShBZQAV28ri5Su426tiCikgBYuXFj09O/JuHqbU3DfsD56Zf4pdUILKGAhTEtR5p5SJ7SAAhZCI6lfv7659957Q4+ly9NOOy00PF0yt+zFF18MPZYOmSyJox12LF1+++23oeFhlDqRbAHByswATmTjxo3lt2PHjvLrZ45mcnIcXc5h4WEshEbC+MM111wTeiwd0lPGbyZnAY8ZMyajawGfc8458pvJSY6prEIqdSLZAvL87bffQsNTIQ2B6fD0dtFI+F1rrbVMmzZt5DjLvSSekyqZ4AiTbXSF0Eh4Rnr9wo6lQwRTpldsP+6448z+++8feixdsqRQWHi6ZBwOPzTsWCKlTiRbQJApxmHhqdK/x3DiiSeaPn36mIkTJ0rGem3y3HPPlYqfC6pPogyj1AktoIDaSJRhlDrBv6VLlxY9/SqBrt7mFHJjRWShBZQAV29zCndrRUShBZQAV29zCnfrCjFhwgSz6aabur3MgNemMwn8TD8PL1NIIYuSAhNF6S1LFindnYUgKgNezfV46623zFVXXRXbpseBlTs8yOh8TOYLqm1u4W5dIegO5bXnTOHLL790W5kD3clHH32028sMUsiipJGKcEj57tddd53bSh8sqgyYBcz368BPP/0kv77hMAv45JNPlu1cIqi2uYW7dYVgLeDVVlvN7UUTLHC96667ur3KIxvvkzzxxBOi8ZJFSnfv37+/20ofw4cPl98BAwbIL/DbDEQB9seNG1fp+8XfI1kE1Ta3cLdWRBRaQAlw9TancLdWRBRSQEw3KXbyvXDJkDyA+4b10SvzT6kTWkABdTBRGUapE/kuIOZXZXqFvnRYCI1k9dVX5/lCj6VLrhkWni7PPvtseW067Fg6ZO5dpp9x9OjRoeFhlDqRbAF5YpqEhadKPrDDLxMc6f+n54bG0qpVK6mwifHTIb0sYeFhLBRNwsePwsLTIYtK8JvJWcBDhw7N6CxgGh2/BTML+JFHHgkNT5XY/ySaHiw/Czi+YWRiFjBryNKgWXgu7HgiC6GRfP7556Hh6ZLK4hcnDzueDq+//nqzySabhB5Lh3z+LpOLhENmPpPusGOJlDqRbAHBTD2sbxA8LOYW2/FTl5NdvzeTLBRNoswtpU5oAQXMdyNBsimjR6kT/OO71sVOP5PA1ducgvvilymjR6kT8t+C0W2lEvqZD8C14yoPl1yFQpEmShpRZ7upVMJBJcv5Ollb5eGSa5rssIppsqNSqUyGB+1ey7WceGXSwW4qlbB/8SqT0PxQKpXh7JSuMvndEqxYYsyyOeFxkuWI64xZOCr8WGX5h6V/TvBbwvE++xuzYETpsFyTZwKL/w72QY9ty8ZLh76cOq4WftxzwUhjxj1cNlyVycr5q2PYsVQ4+09jeu8ZfqwypP7756OeJdb/fJNna+9+O1QrezwdzukRpDvsWDqkDflyzuR1s06bnzyzL3N+Z3UKfuf0TIibAaatTHjQya8F8b0Q7NbQCuf9grCeOwa/VBTQub4x8wfZ401s/CnGDL3Ihq1jhfzSEmXyb2tj/n7XmO7BN0ykEMvcNwVS8MsX2OtfWzqsxzbGjLnLmKUzjRlwnH2uIcEx0GNrY/77xZjxzYN9//z+l3hTPjZm+tf2/Bn22tcE11w00V7reJueZUFcf79kSOGCbpsZ8897wTbKBKAExtxtK0EHY6a1sff+pERByO8KY352+5wzr5/9dfnnGwDgPoBnJYxze+1my2KyMf0OVWUSB5fcsnmRSMqa8vL1dMFod6LFL3Z/+cKgbYD5w4LfZbY+dkn4OFXXjYPfQScZM+SCYBt0XrvsPVPloFOCukkdm97W1iFbb7tsELQL6gJGlq8/y90aFgtdOub2LWmLYIltt732sBvLg31+KzJSVka5v70G7WXm71ahdrN1/V53bQvqJ4rB5yF1lPM4xwNjcPE/QTjwAo1rg+XB1yLkuvH3TpZT/mfb1IBg27e7P+taGVGyHkcA93W+YZcHv2DGj/Z5atp8+9cFWJBWZB6/yI4ZPxkz9v7gGOE8d+IzpMtBJweyCvDsIsNcfgPutSR4jy5273j0Pzz8uuUxbWXSZT0X2WLqZyVCy+Mv91k6H965XlBJh10WKJOFbmB30GnGDL8y2OdhlgdfiDP//Rp+31RJI0JgAjKs+xbBNs8AfMYummDMxBeDbdB1Q/vPVRDO61TbVvYuwT5KiHRRUYddXHIfMPTCss9QEf25bPc7JNjutqkxk14Ktuf1t/fuaivet1bhfllS6P6XNPz7ebAN/lwz+OW4vzZ5O9MqJIB1gmUCqOg8MwJn7H1ln02VSfkkf/mdZb2KqVbJg547BcJt9G22fljhPMzm7cibSuo36Lh68EsdWjiuRIAOdIvh/LWDjVPDXrt66fulQ5TJ4knB9ozvS5QJ4Pm9MkFA99zVtrufg7ok9d+izz7Bs2NkAdo17cELVgygxHsmS7m/E27syzV5jl1s+le1tOlHmUx6uUTh9j3I1llrxKGoOb//kYFR558nXpnwnNR/jCTabuL9k+GkV6wBMC/Y9rKMZwPcA6L0/f6yuYGB6p8HZQs4RlsbdmkgNyiDic8HyoRjvs1idCc+Qzr07d6DvMDAiVcmxMEgiq9rgHTO+M6m/dXS16yIaSuTynDAMUHmhR2LEvseaBvjqYE3FXY8nlQeLMuwY4VMVSaZp/dMaMxhx5Xlc3q7QCn23jvIQ9pdWLyoEM8EhB2rasyLMlEWDlWZKJXKZKjKRLlSFrEyadiwoVKpTJLbbuvGeS1ijahevXpKpTB+DXMna6s8XHJDl0ZQKpXhZBEmD21EyjL068oAJ2urPFxyQ/OjMkz2G8D5ZNSfkQXIeMYorANbHln+kWfM5Ap3mebcuXPlGZNdqTAZqjJRrpSqTDLDvn37kn8prbuaSyIIWD4nys/49ttvyzOy3bhx49h37qNEvho1duxY2eYTa1FUeqwnvHjxYtlmzenE4+kyJ8rk8ccfNyNGjDAvv/xy6PFc8YYbbjCLFi2S7auvvlosCL5PyKfqWIb1zjvvlK9msbZwzZo1RZDSuD788EM557333jO1a9c2nTt3lop83nnnyfHbbrut1H1yzaZNm5oHHnjAjBw5MvR4ZajKJHPk0lEV1BDBF+VnfP/9902PHj1ke7vttostnRwlPvvss7KYPNubb755JL0T1mDnU/1sZ/JLDjnzTPhcRBSVCR+69Wt6swpqv3795NMbuNObbbaZxK1fv74oD5TGqquuKgXA/o477ijHzz///Ix+2SAd3n///apMMgSX3ND8SJc0NM+w41FgITwjLITnK8Zn5HoeWVMmfK0EIQ7zafWgIHwfod+GPBfKgTCsMxQL4SgZ4vpfPBji8rV79v0viiSTfY+p0j8XzPRzqDJRKpXJMCfKRFm4LGZlolAo0oM2IsVK4WRtlYdLrkKhSBPaiBQrhZO1VR4uuQpFlceKFW79wQxDG5FipXCytsrDJTfjWL58uVlzTbegXwQxcOBAytjtRQ/t2rWTmZZg9913l9+ogRlnjGECpgZHEddff73bMjI7NRvIei267777zJIl7rsiecA999zjtozMzAJHHVWyXAiZPGnSJNOqVSuzxRZbyDThrbfeWqYMA2ZM7bLLLqZ169ayz1z37bffvtQ18gHS9fTTT5s5c+a4kOwgELVVHy65GcWwYcPMxRdf7PaiiywlPyP4/PPPY98Ypx1GEa+99ppMyAHxy4tECZdeeqnbMvIKRDaQ1Vq00UYbyVuX+USYMolfLgSlwWyoIUOGyP6WW24pv+uss478Hn744TK9eK211pJ9lAm44oor5DefUGWSObjkKhSKNFEUjShbfYRRQTbT52RtlYdLrkKhSBPaiBQrhZO1VR4uuaFTpZVKZTj1PRPlSqkvLSqVymSoykS5UqoyyQxZYYGVIMKORYWs/sAzZnqZjUwz6vnIChrF+Iw5USbchCVJGCAOO54Lcv9MPsOrr75qJk6cGHos10TgkzYEVtjxylCVSWbJ7L+w8HyTtfO+/fZb2d53333NzJkzy8TJNxF+rJtHEWWjrmeCrGzMorZsb7XVVpFUzEwi8gs9rrHGGmWOp8ucKBPPs846S6behh3LJtHC1apVk1+mF44ZM8acdtpp5quvvjLbbLONCEwqKAtRVq9eXSrt3XffLVOECWdJaV+Bv/76a9OlSxdZbZhpiggHZonx69fqyhdr1aqV8UamyiRz9KvyRtFq7dixo/n+++9le//994+kMoHUb/Iwqsrk0UcfFcXMNlODo6hMTj/9dHlFg+2CVSbfffedvKMRdiyb9MrEV0D2eTeExENfQQlv06aNGTx4sNlggw1kheAvv/xShIBfKZgG17VrV3PBBReIMqHCcE1WG27btm2p++aKpIHnz0bFVWWSGTItHmajjDJFyjrqz+jzEYYdjwJZMJbny+firxWRHppMPyP1xiPryqRQSKPaeeedRYm88sorWVnavVAYYWXC8wwNNjMLl9zQ/FAqleFUZaJcKSOuTDwzqlRccqVfWalUJk+PWCNSKMLgZG0mEa8QMsGMKBWXXIVCkSa0ESlWCidrM4lEZZApVkqpuOQqFIo0oY1IsVI4WZtJJCqBTHIjy7TgkptR8O1y1nx79913XUi0wDI8zOgCBxxwgPxGDUx8YSYm2GmnneQ3iujfv7/UwaiiefPmbsvIbK5sIKupP+WUU0yDBg3cXm7x8ccfu60APgN9gR933HHmhx9+iPX5+WXCmWoL1l9/fZmF9vrrr5uFCxeaF198UcLBYYcdJp/LZTbXBx984ELzg2xXYARthiHPnGGmrUQ8XHIzBuoOU9KZVn7TTTe50GgBAfjXX3/J9qGHHiq/UcOHH34o43hgjz32kN+oIgvVKGM4++yz3ZYxderUcVuZRdZSf+yxx4q1w/TaU0891YXmDjSUL774wu0ZmSYMLrzwQvkFNHi+T8/8a7+SMNYk04Tr168v+wxGY1myzLTHkUce6baM2Xvvvd1WfhC/AnI2EIjayCBegcBKKxEPl1yFImUU2kKy+nGsHMMvQV/scLI2Ksi4EvFwyVUoFGlCG5FipXCyNirIuBLxcMmVt8CVSmXy9Ig1osR3DZTFywi/Z5I1uOSG5odSqQynvrSoXClVmSiVymSYE2XSrVs3WWWXhRSjtl7NZ599Ju4Z26zL9fvvv5szzjjD9OnTp0zcKHP8+PGyjljYscpQlUlmeO6554aGR4UsPskkFbaZKUW5J8bJN3/77TfTuXNn2d56661lLb3EOPkmE3mY8MP2JptsEsl1zpiROnDgQNpz6PF0mRNl8sADD8hMo3vvvTf0eL74yy+/mJ49e8YytV27djLX/qGHHhLhfNFFF8ky8yzg2KtXL3PttdeaFi1ayBRKZnUlXi9fRAnyDkOjRo1Cj1eGqkwyRxrbeuutJ7MGw47nk7SFn3/+WbabNm0aM7CixOeff16EINu77bZbJFcORsaNGzdOtnkXJorKBFIHqephx9JlTpQJ39rgl3c5hg4dWuZ4PsgijgceeKD58ccfZf7/CSecUEaZ0PB5djKJ+FdccYWcy1LzTCtOvGY+iHWGICAdW2yxhSiVsHjpUpVJZsi7SFiEUV5Jdvbs2fKMURWAkJVuecawY1Ghf8aoljXPxfNBL5szwZwoE2XhUpWJUqlMhqpMlCtlMSsTvvmgVCqTIx6ZR6wRKRRhcLK2ysMlV6FQpAltRIqVwsnaKg+XXIVCkSay2oiYbrvxxhubxYsXu5DcY8MNN5RB6jC0b9++lJtWSGAhS/KWwfdswsnaKg+X3IwifnG9qILPaoM999xTfqMGvnjKtFtQXjuOAljwNUvVKCN488033VYBrhpMxm6++eZ5y+CXXnrJbQXwK2Wuu+668rt8+XLz1Vdfybebwfbbby+/zzzzjJk8ebJZa621RNEMHz7ctGrVqsyqwYQz2yvfi7zxXfpsAkFbDHDJzRioF5988olsH3LIIfIbNXTv3t307t1btuMXL40SEILMXgRNmjSR36giC9UoY2Dmqsfaa6/ttjKLrKX+mmuuMfPmzZNvEfDORj4Q34j96r61a9eWX+aFY5XxCxo2bCi/t9xyi0yfQ5mgLHgnpW/fvpIG3j9hKmWzZs3kF7Aycj4UCvPtAXPvs4lA1FZ9uOQqFIo0oY2oHNSsWdNtFTecrK3ycMlVKBRpQhuRYqVwsrbKwyVXxveUSmXy9Ig1orD3DZTFSX1pUalUJkN9aVG5UqoyUSqVyTDryoSB67p165oNNthAZg7cddddofHyQRLPszGAzr5fm+uee+4xgwYNKhM/ymRtru222y70WGWoyiRznDRpktl5551Dj+WbzGT0i5fuuOOOsopwYpx8k2nB33//vWwjT5AtiXHyTV4x6NSpk2zzue/E41Egk4reeOMN2nPo8XSZM8+E1+232Wab0GP5Igs68o13n6nxCz1OmDAhNoWY6cHM9rrqqqtkijArql533XVlrpcv8izM6NJVgzMDl9zQ/EiXrNJavXr1SK50C5mhyIKhbDPzEeWSGCfffOyxx6S9ss2MzCjm5c033yyyg+3dd99dBGxinCiwYFcNxoLgwaNkSfTo0cM88cQTsTVlmP777bffllo1mCXn+/XrJxnP0tdRXDWYFUD92jhYlJluYKpMMsPRo0dHVrB4MnV/2LBhkV7ZmHzkGcOORYWjRo2SZ4yi5wSphzwf78fBsDjpMGeeibIwqcpEqVQmw1BlwkdolErIygAeTtZWebjkijetVCqTI924HrFGpFCEwVaYooBLrkKhSBPaiBTlwsnZooBLskKhSAPSgBg4YvqdUgk9nIwtCrgkKxSKNCANaP/99zPVq62iVAo9nIwtCpDe1q1bmyeffFKpVCZJj0Bq9N/PmM52U6mEDk7GFgVI7+3XnWia7LiKUqlMkh7BVh+rSDrYTaUSOjgZWxSQBA88MTw/lEplOB2CLVUkyng6OBlbFJAEqyJRKlOjQ7CViiL5zTHsWLL8w3LUrcZ0XDX8eCaYiefMJn+3jD1ftcw+a6/djenbNPyYZ6daxoy8PiiLxGMOTsYWBSTBqkiUytToEGwlq0hG32Ujr7B/y4zpd0h4nGSIEAV/rhl+vLKc+IJ7zqXGTG9X+hiCE3RrWDo815zxbfAcKJBfLRdPCY+XDkffacykl8KPeXZ338ymLBKPOTgZWxSQBCejSCiv9pZh+ZYKuc7M38KPVZY8H8YJlO0IkXzjmajzlc1Dz64bGzP10/Bj6ZLn4zkzaeDlgj5//f6M74wZcKyVB69ZmX1Y6biZoEOwlawiWTDSCua2QSYjkKe1kdMFHF8+35i5fYL9aV8GvzN+NKbHNsYsDT5ja5YvCAoHoEj+fifYRjllotDIROAtbTJ23sAgjHsMOSvYBtxvafCNd/P3WyVKxqNL8D14AenpuavbsfjX7vPsifdLltOtIhn/hDFzepUokk6r2zweVfIcP7tfj/lDgt9BpwVxViwJ9kfeYJ91PZvOAcH+v58H5dSxRrAPECoe079WRZIASXBFiqRT8ElnM7ODzftFQRj5B+Prm9+HPn/jj7NNmVMf/bH4a1SWgLrt2xneZ/xzJD6f/02M4/fj4/h7pMuhFwTPNLdv8Jv4HMTx946/n99PfA62u25kzOS3g/3Ea6XDCU8Fz0Y5Ay8f/TOwvWhScA/P+Hv6bR83fjssLJOc8b2VsbZudts02F9m5S69P+NtmvoeGIRx3/jn8M/Cr79OsnQItpJVJAjNJVPlFPPXTsHNezW2mTrBar1j7LHpwe9kq/0QkHRdAS+0aDyg957Bb0crOEFPe63Z3YwZc3f4fVNhn/1tA11eOlO4D0pg+WJb6RoE98QjGXaRfc6/rKLbOggbfVtg2Yy4xphxjwZKj24irgW623h4OaT7rx2CsGGXB4oq/hmSIYrEKzWuhyLhOecPLbmfVyTkGxWi+5b2fpcFCn3aV4Fy+2v7II5Xelz731aBAuda/Q4tyYtum9n9g4N43TYJfn2FiqeDk7FFAUlwRYoEZQ1QyuRpz12CfbBwtKt7tn6A5QsDRQE4d1anEiNrwfCgLXGc6xAXLJ1Z+n7pElB3fP1BkYjSqh4Icu5D78Iy+wuWlqyzJufPH+x2LH6x+8SnjYOJz5e+V6oURbKixABDOfs8WzQxqI/xoO577x388779Z89HZmCIkpc9tgsMwr4H2Hb0TxAPIyusbldEf3+MMPZH3mTzaZ6911pcNDjWaY3gF/S17Qk5AcjjzvXtvhXk3kD9r70xw22bRW7Gru3kHhh+VdlnqAwBMnmeLUP2vSJZONbKjkutvGgUxAHIaspjga27gGf2siJZOgRbySoS7zFM/8YKq9Zyqhl4QvCQI64LFMmgkwPrHsXQcbUgDtoReEVCQoHP0C7rW25gC6tu2XumSn8PGg/7vvDIQArY35tnIoNndw2USue1baWxaVhmvSricS4VPF6RoHCooOxDKg4VJP7+yVIUybmBQhr/ZIkiwdLxz+wFAfvL5gSCbISteBT4NFsGU/8XpKPzOoEiIQ7X9ooE76b/kcGz9j/aXnt8iQLEigNcO/HZHJyMLQpIgpPp2vLWKnlLe6Axjn0gqAu99wqUQqwOWiGJd4jniiLBSEEwA345h/qFoB5uDRIElW9jlSGY/EZAIIrE1tuYIplhjTarSBb/XfKsnLdwTGC18tzDrwjq9ug7gvgjrrT10ypQ6mfi/VKh90gA1jOClOtL+i14RkC9nNfftpFzAkHNcyLQfb713NkpkmGBIkFgosiB98TSyUt/rlck/CIP6CKnHL2gxXDAUKbOcJzw6TY9WP70zHS18qzXbvZCtkxp57Rv3659TwF5nIny9uy6oS2fycE2kDzzisSWLYoEjL0/6P4GlAfHfD0Ikwcro0OwlawiQeiCFdayR/D7fSrkcFvRKHAsXjKcYzFFYgW1xxJbafxDUxGmfBxsg4xkqq3sdPV4jLaZSOX3oOFiJQAKf9ncYBsPasS1wTYgDZ3ruR0LvCUqL/CVCXc6fj8VzvjBVrDzgnOpiAvH2fwqWQhNEK9IjG08KFwUNsoOqwjPC0z9LFAyXgH++0VQ8eO7tnwl9qD8QFjFcXAytiggCU5GkUDv/fU/KhBkWMeUBZ42ZeCFURer4On+RWCiSNj2lnhMkVhhgxUtgsvW3bD7pUrAM/jniCkSu4+3HVMkVrj5OGLE2DpI/QK9bFoQluObB/FH3hjUFbyGxPulQu+R+H08eoQfdd97eoBfPLjB1mvHQKL+YxwRTr7hCWAkhSkSX9fTkSf+XGQX+3Qnz+5i25T1/jHcfDzyBqU34LigzOV5BwTKAUWCtyrPY9M69PwgD31e491wDbrOZv5Rcs3KkmeKBzI5UZGIwbOPq2/OsKDc/bOFyYOV0SHYSlaRpEvfdZTqQ+aauMnjH7cC5aTgecPixHPii1YB9Qw/Vsh0cDK2KCAJrkiR4Pl5IFi7bx5s02UKfFeXb5R0c+A5Tvs6UCR0SYIJT5ceW1swIthebAUq+2H3ToWAZ/DPgdHhFcSMn+zzWq8Jwwgh4+MgOJf8awX3GcEkAMDx8Y9Z4WM98xFXB+2XbrCweybLYZfYC8cpEq5JjwZYaD1mL8g5hhAfeHLQ0+GB1zfq5mAb4S5e9vZBmvCmgL8GBqu/TyqkO9hj3qCgTFC0GNCAcsWTAn0PCoxQQDrwUlAk3sgedEpwfjx8lz/ovXf4M6RKb5yjHNhneIGyouxQJBgrdIt33yqIB1A0lAeGdk49knSJqzzo1PBjUSKNCWEyyCoSb5GsjHTjMWMk7Fgh08HJ2KKAJDhZjyQdStdWnEWrTI5/Bl8xlW368ulaTIwTNaJIMDrCjlU1OgRb2VYkysKig5OxRQFJcDYVCW2M2Ythx5QrJ93RWPbxE0eizIHNkjNEqwIdgi1VJMp4OjgZWxSQBGdTkSiVVZEOwZYqEmU8HZyMLQqQ3l8/PNG0vHsVpVKZJD2CLVUkyng6OBlbFCC9l156qWnYsKFSqUySHtKABrfZz3R/cxWlUujhZGxRgPTOnDnTTJkyRalUJkkPaUBHHHGEqVevnlIp9HAytihAev/77z/zzz//KJXKJOkhDWjGjBmh2kZZnPRwMrYoQHpVkSiVqdFDGtD06dNDIymLkx5OxhYFSG+mFcm0adMir5wwImHYsahw4cKFZv78+aHHosJifUYPVSTKMvRwMrYoQHozKfT//vtvs+aaa5rHH3889HgUOHnyZFOnTh3z0ksvhR6PAm+++WYzZswYc9FFF5mRI0eGxsk3L774YjN+/Hhz8sknmxEjRoTGyTcbN25sZs+ebTbddFMzceLE0Djp0EMVibIMPZyMLQqQ3kwqkm7duplFixZFWpHwjAjpqCoSygNFN3XqVDN06FBz6623hsbLJ5mgUbNmTekSHjJkiHn44YdD4+WTc+bMMZtssols84wtWrQoEyddemRNkUyYMAFBlFe3edasWfIMbJNGv10V2Lt3b7PZZpuFHqssPRCwxQLSm+luqHnz5kVakUAs6KgqEizo+vXri5AeO3asueKKK0Lj5ZMIaZQd26NGjTK33357mTj5Jt1ZhxxyiGxT3i+//HKZOOnSIyuKhOudd955Irjz6e1gLVSrVk22eY611lpLKuc+++wjYc2aNZMuiJ133tnUqFHDHH/88ea3334za6yxhpxLH/eqq65qtt12W6nMVJIePXqY1VdfXYRE/L1yydGjR5uOHTuarbfeOvR4ZekRiNjiAOlVRRItYoTWqlVLPJJBgwaZV155JTRePomcQB74Z3z33XdD4+WTyDxkGtt4JN9++22ZOOnSI2seCRlL+8y3IuEZbrrpJnPLLbfI1FYsCBQDxw888EBRJMTheenjRHsvWLDAPP3006JIiPfjjz9K/+wNNwTL09Nlccwxx5S6V65J2rbZZpvQY5WlBwK2WEB6M6lIunfvbt555x1z0kknmd9//z00Tr7ZtWtXqednnXWW6dKlS2icfJOuIpQxbZa2GhYn32Qc56mnnjLrrruuGJxhcfJN5NXbb79tqlevntFeIo8qr0gSPZIwRUIfJ/vnnnuuKBK6xB555JFY9xzXwG297rrr5JpoeM7198kHVZFkFqQ30x4JQiWqgsVTnzEzLJRnDAuvDD2ypkiWLl0qQhiEHc8FwxQJ2tg/F78rUyQXXHCBXINuuigpEioE2G677czixYtD41SGHgjYYgHpzbQiUSqrOj2ypkgQcHQBwbDjuSBKIv7+zKPmF2XA86EQ4sPZ5xyIUEFgc77fRsH443Pnzo1dN9f0z5Wt/PVwMrYoQHpVkSiVqdEja4pEWbj0cDK2KEB6VZEolanRQxWJsgw9nIwtCrgkKxSKNKCKRFmGHk7GFgVckhUKRRrQBqQoF07GFgVckhUKRRrQBqQoF07GFgVckhUKRRrQBqQoF07GFgVckhUKRRrQBqQoF07GFgVckjMGVklYsmQJeWiWL1/uQqOH2rVrm5YtW7q96OHVV18148aNM2effbYsORNF8M4ZK+oeddRRkX3GY489Vt6Rq1u3rlmxYoULzRxUkSjKRSBiiwMuyRnHgw8+KC/nRhGdOnWS96mirEhYEBEwIYhlSKII1rECLKn0xhtvyHbUsMEGG8gv+chaW5lG1hrQXXfdZb788kvTqlUrF5IfxMsI3mCvKrjzzjvNjjvu6Payg0DEFgdckjOOLF46I+Al3CgrEtavAqx/x5pWUQRWPuAlZdYGiyJYkBbwUvWHH34o25lEVms5LtTmm2/u9vIDlkjx8N8j32ijjeSXVYDpdmDl3y222MI0atTIvPbaa1J5eXYsDJYh8fsskfLmm2+aDTfcUN4ozxd4Ex9sv/328pstBCK2OOCSnFGst9560p0QZURdkcR7JKysG0XEeySfffaZbEcNa6+9tvyyRFTnzp1lO5PIqiJhyfVsuFGpABmBJwLXWWcdCUNhgMMPP1wUBMtAg0suuUR+AUtre/Tp00cWe8QL8DjyyCPdVv6giiRzcEnOGFifrV+/frJsNwImimCpoJ49e5rHHntMtqMIVu7mA1x77LGHWbZsmQuNFs455xzJx4YNG7qQ6AFDma84ZqGqC7KmSP79919z6qmnur38Icwj8V1CDEChSLxFcdlll8kveOGFF0QQnHHGGTLQl6hIUEL5hiqSzMElWaFQpIGsNSC6j1i7COYT8TKC1X+BHytByaBI/P6ll14qvwBFgqXGR6QOPvhgUSR33HGHO2rMYYcd5rZyD56ZPmOWkc9m/gYitjjgkqxQKNKANqAEIKQVAZyMLQq4JCsUijSgDUhRLpyMLQq4JCsUijSgDUhRLpyMLQqQXl1GXqlMjR6qSBTlwsnYogDpVUWiVKZGD2lAuoy8Mp4eTsYWBUivKhKlMjV6qCJRlqGHk7FFAdKrikSpTI0eqkiUZejhZGxRgPRmUpHwXX3aFe9ThR2PCnlGXpgMOxYVTp06VfIz7FhUWAj5mI1n9FBFoixDDydjiwKkN5OKhJdxf//9d/LQ/P3336Fx8s0ffvjBfPLJJ/IWPqvXhsXJN1kbive9vvvuu9DjUeAzzzxjvvjiC7P77rubsWPHhsbJN88//3zzyy+/mFVXXTX0eLr0yJoiYTllXpoLO5YLYsVwf5iJhoxlyZLWYcfyQdLFOk5hxypLj0DEFgdIbza6tljiY9KkSaHH8kksfJJNO2HpDJb5CIuXbz700EPm119/Nd9//33o8XyTOsMSS+TjoEGDzMsvvxwaL59EGbOwJNuDBw8WpZcYJ116ZEWR8OAsXoYbxUKHYXGyTRYm+/HHH+VZWEOLMBpPooscv++PlxeH9YgSw/JBGhYV97bbbjNDhw4NjVMZegQitjhAejOpSFhnjsVAr7322tDj+SaL97FkEPWYNrLXXnuFxosCUSJRVSSseLHmmmvK9qhRo8ztt99eJk6+icF5xBFHyPbw4cPNK6+8UiZOuvTImkcCUSTNmjULPZZtoki+/vpr2UZO4HKyZtY999xj/vrrL/O///1PVv9t27atefrpp83kyZPNMcccY+6//37z3nvviRsISQMf/+H3hBNOkMrSoEEDWYzyuOOOK3PfXLJjx47mnXfeCT1WGXoEIrY4QHqz4ZG0a9fOtGjRIvRYPomHjaLDIJkwYYK58MILQ+NFgVFWJBiXLLGEQmaBztdffz00Xj6Jslt//fVlG8MTmZcYJ116ZE2RsKjh3nvvbbp37x56PNtEkdx9993m8ccfF88EyxAFgQLZZJNNxGPq1auXVADWrELRtG/f3gwbNkz6jVEi7KNAWIKZ31NOOUXW3nrrrbekAdLfmK/xJQTBZpttlpVuEw8nY4sCpDeTimSnnXYS44R12vgNi5Nv0j769u0r/ecok7A4+SZ5h9EGozrW1KRJEzNixAiz7bbbRvYZqeJ4nvzijYbFSYceWfVIIA+OEA47lk16j8R3Qd1yyy3yyU72IYqE7gcUAhWAY1gWfHuE+CgSupDCFAlWB+dVr149b4rkwAMPLNXVlkl6IGCLBaQ3k4qEesGHjmi8YcejQp6Rb5KEHYsCEXo8I4zyDLio5yPkGTPtdXtkRZGQoSeddJK54IIL5GM0YXGyTT4j2rp169g+igKFcfHFF4tr9/HHH8t3DlAIrFSMJUFX1Y033ihjD6QB+YLyYJl58ujoo4+Wri2+VcJ5HM+HIiFddMtddNFF5txzzw2NUxl6iIQtEpDebHRtKZVVmR5Z90gKhSwRj5LA86DLCkURFq8Y6OFkbJSQtWcivapIlMrU6KGKxBHFgaUPo/4SWbbp4WRslCBeYLCZWZBeVSRKZWr0UEWiLEMPJ2OjBK9IMv5spJfv9/M5V6VSmRw9YoqEMQKlEno4GRslxCuSjD6fS7JCoUgD2oAU5cLJ2EwjURlUlhmBS7JCoUgD2oAU5cLJ2EwjURFkgpWGS7JCoUgD2oAU5cLJ2EwjUQlkipWCS7JCoUgD2oAU5cLJ2EwjUQFkkmnDJTnj4NIrVqxwe9ECb2M/8sgj8v4UEw2iiD/++ENWp+C9qcWLF7vQ6IGXllkVI6pgGShepM5WVVdFoigXImEzDy/0s8G04ZKcUbAq7K677hpZReKTzUrSrFIcRdSqVUt+mZ7PEkdRBGv4dejQQRRzVMFL1YBXG/7880/ZziSyqkg23HBD06ZNG7eXW7AKZ5cuXWS7UaNG8puIhg0buq0S0OhTafj5EBJ+ZtWXX35p+vXrJ9vZQCBiM454wZ9JVgouyRlDnz59ZDYka25FVZFgRXvsv//+bitaWGeddeR34cKFssxRVPHTTz9FWpEcddRR8suSPR9++KFsZxJZUyQPPvig/CLs8oF42YDbftVVV8naWmhjL4g33XRT+WVFzCeffFJWycTNB7zdDvx1/O9dd90llZoluAELO+YLfJiI775kCwjYLEDyMsOsNFySM4bVVltNhMvmm28u5RRF4DGBpUuXmssuu0y2owa+owFYc+u1116T7Sgi6oqEBV4B+cjyUZlG1hQJgnvJkiV5UyRrrbWW2wpw7LHHmvXWW8/tBWAVYHDyySfLL/AyhfW0QOPGjeXXh3tFwsrGJ554ooTlA7jSN998s6z+my2IhM08vPDPFDMCl+SMA48kqqA7i7rM8kDZNEgqg0MPPVQ8OtbuW7RokQuNHvikQ/w7WFGDr+LZqupZuSoLIjZt2lTc5e22286F5hZ8a8GDvtUxY8aU6craeOON5ZfFJT349gh44IEH5BeLEvgCoELTp+xx7733uq3cgwbm05ANIGAjBimHOGYMLskKhSINZLUB4ZHwWcd8gO4s5ANLvQ8YMMCFBgqBbgeAAvEyhN94ecI3RziXz2cC0sFxPBWsOBQl35LOB/zKxDxfNvvf7T2ihlg5yV4G4ZKsUCjSgDagcvDCCy+4reKFk7FRQlaUCHBJVigUaUAbUAiiOssm13AyNkrI2jO5JCsUijSgDUhRLpyMLQqQXgac+eqkUqlMjh7SgHhjlH5/pRJ6OBlbFCC9+j0SpTI1ekgD0u+RKOPp4WRsUYD0qiJRKlOjhyoSZRl6OBlbFCC9qkiUytTooYpEWYYeTsYWBUivKhKlMjV6qCJRlqGHk7FFAdKbSUVy9913m1NPPdWccsopZvLkyaFx8s2BAweaI444wuyyyy7yVnZYnHyT1X9ZJ2rrrbeWVRzC4uSbP/74ozn66KPNRhttFNlnZI0tnm/+/Pmhx9OlR1YUyZQpU2TJ4ldeecV8/fXXoXHyxXHjxpn3339ftlkJExnCm+r8ssJoYvwo84033siKkPIQCVskIL2ZVCT333+/mThxYuixqJBkU+dHjx4ty6WExcknp02bJuuB8Yy8VMxLwmHx8klkJy84+2dkQcSwePkmZf3UU0/JzMSw4+nSIyuKhOux+GHYsXyT9anIVIQGioS3w/ml0nIcJcg2v+xTQdiPmpLp27evufDCC0Uxhh2vDD0QsMUC0ptJRfLbb7/J8jksERS1ugNZvI8FEXk2VkrYa6+9QuPlkzwXKxTTFqnnV155ZWi8fJKFXuvUqSPbKGRWHU+MExXyXZeCUySsf49bH3Y8X6RCspZW27Ztzc8//xxTJFQGLB8yecsttxQPZc011xQ3lVWA/XIrNL6w6+aapIPVPFmuRRVJZkB6szFGwqcMXn755dBj+STGUc2aNUWRUM/PPPPM0Hj5JOVBO+QZ+WhU8+bNQ+Plk3QZIetok6wizooYYfGiwIJTJPGkjSKww47lmiiChx9+WAp9m222KaNI+IYEioaVg7HW6DZikUbO3XHHHcVCSrxmPsi6XzQsFMmECRNC41SGHoGILQ6Q3kwqElaGZrVaVp0OOx4FUrcR0nTDUZ/C4uSbO+ywg5QL6+KNHTs2NE4+iSxp0KCByBBWEY/iM0KM4xdffFHadSblsUdWFAmZ63+x6P1+vrnFFlvIWvwMjjF4R+YmeiR0FxGXMCy1KCoSvCnSwId+vvnmm4znr4eTsUUB0ptJRUJd4eXOqNSZ8sgzZtpKzSSp2/qMlSeD7DwjLBhFQoPcd999zUEHHZTxWQLpksw78sgjY/soFLq4UBS86u+74ejbPvDAA+VjRHgkjz76qIRfe+21GRU0mSDfelGPJDMgvVErX6Uy6vTIiiJRFjY9nIwtCpBeVSRKZWr0UEWiLEMPJ2OLAqRXFYlSmRo9VJEoy9DDydiiAOllzIyxMaVSmRw9pAEtW7bMLF26VKkUejgZWxRwSVYoFGlAG5CiXDgZWxRwSVYoFGlAG5CiXDgZWxRwSVYoFGlAG5CiXDgZWxRwSVYoFGlAG5CiXDgZWxRwSc4Y+O5/48aN5c3sMWPGuNBogXe8GjZsaDbffHMXEj3wLhfLAfGMLFUUVRx66KGyREpUwYKSrObBC+LZQFYVycYbb2xq1arl9nKP008/XZ6hd+/eLqQ0KPxCxXrrrSeCKptwMrYo4JKcMTzwwANmyZIlbi+aqFatmvwy+4Z1mKIIL/hY5r5du3ayHTXw4vX3339vRowY4UKiB1/FUczlycPKIGuKZNttt3Vb+QFrZXm0aNEi9qF6lrdn//fff5eVRV999VUJJ+zNN9+UbcCidixbzbIH4KOPPpJfVt1llhuLtXE8nxg1apRp06aN28s8AhFbHHBJzhgwoE477TRZWodVFaKI+Day//77u61ooX79+vLL0h4sCRRV/PTTT5FWJE2aNJFf5BZL3WcaWVMkfrn2eOGcS6yzzjpuK8AJJ5xgLrvsMrcXYJNNNpFf1un38GEstgfWXXdd+fWy5q677jKLFy8WdxvQhZEvsF5Y/HTdTEMkbJHAJTljYNVaj8MPP9xtRQu+t4A6fO6558p21MACqgBD8LnnnpPtKCLqiqRRo0byyzuDLAOVaWRNkXz88cfye8ABB8hvrpEoG1BsicrFKw1cUw9/HstBg913311+fTiKhBfXunbtKmGsnpov9OzZ09xxxx1uL/Ow6SsauCRnDNQflDxlRF2JInzXLh96Y+HBKIIxHMCad753IIr4888/I5uHwFfxQw45RAzhTCPjDchjv/32k998DeTtueeeMW+hadOm8ktXA6BrCtC1BV577TVxncEee+whv94VrFevnvz6guCXuGh2wCdVcw3cU//LYpPZAgK2WOCSrFAo0kDWGhBCjpkC+Zxp0a9fP3mG+O6n/v37y7eqARYEYx6AMOJ7oFzinx8vhLgsC871+O4A18oX4tORLTgZWxRwSVYoFGlAG1A5ePrpp91W8cLJ2KKAS7JCoUgD2oAU5cLJ2KKAS7JCoUgD2oAU5cLJ2KIA6WVwnIFIpVKZHD2kAfHOBC+qKJXQw8nYogDp1e+RKJWp0UMakH6PRBlPDydjiwKkVxWJUpkaPVSRKMvQw8nYogDpVUWiVKZGD1UkyjL0cDK2KEB6VZEolanRo+gVyaJFi2RBOF4yDDtejPRwMrYoQHozqUi45Oqrr25q1Khhzj777NA4USDPuf7664ceiwJZT45nZBWKsONR4MiRI+UZN9hgg9DjUSDvzLGCB6sDhB1Plx5ZUSTDhw+XTIVkMOvkhMXLB/3SJmyzmF716tXN3LlzZXE9ljtJjB9VMkGCdPBiZNjxytDDXr9oQHqz4ZFcd911oeFR4GqrrSb1iDWiHnnkkdA4+STtkaLhGXlZuHXr1qHx8knfDvnlBWEWUQ2Ll2/yjPfff39hKRJPZgAdf/zxocfyRdYX2nXXXUVDe0XC2+rbbbedhLFmDgs1DhkyRJ7/scceM2ussYasmBl2vXzxqquukrfvx40bF3q8MvRAwBYLSG+mFQnCZaONNgo9lm/OmDHD1KlTR4Q19X+vvfYKjZdP8lwsUUS7pJ5feeWVofHySZbgr127tjzj6NGjze233x4aLwrkUwEFqUhuuOEGM2zYsNBj+SDp5OMuWA4sJ+8VCZWBbgiWdbn33ntlH28KRUIDo+uLfY6HXTcfvOSSS2RhQFUkmQHpzbQiwfj45ptvQo/lmyg5DCTvhTdr1qxMnHyTngyWukdI4zXRNsPi5ZMoO1ZR5hmRdU8++WRovCiwIBUJjZKP0kSpu6hVq1ay0ikKAtmRqEhYS6tly5ZSKeCkSZPMSSedJOfS9UWlSbxmPrjPPvtImT3//PPy9b2wOJWhRyBiiwOkN5OKxHd5ZMtIywTxwnk+vrXDKsVhcfJJ2iDL8aNQXnrpJVEmYfHySeQbChnZgGFHT0ZYvHyTPHzmmWdk7cBMymSPrCkSPhQ1ePDg0GP5IJUSy4Gv1qE4TjzxREl3vCJhrITvHzAA//DDD0dWkfC80HtVYXEqQ49AxBYHSG8mFQleLAPFYceiRKxoBovDjkWFPGPU8zLq+YgS5hkhci0sTjr0yJoiiYrQjWf8zCwsRtLNqr40en4J92E+T3w6cAmJ58+PAunnDguvLD2cjC0KkN5Md20plVWdHllTJMrCpYeTsUUB0quKRKlMjR6qSJRl6OFkbFGA9OJ10pesVCqTo4c0oM8++0wGbpVK6OFkbFGA9PKdfjaVSmVy9JCtiy++WD5Nq1RCD1tRigakl3GzCRMmKJXKJOlRolIUigQ4GVsUcElWKBRpQBuQQqFIC04HK6oQXNEqFAqFQpFzlFJCrJTQoEEDpVKpLEUWMU6Es2MVVQiuaBUKhUKhyDpWrFjhtgKUUkIdWu5n2jRfRalUKkux8/+OclKiBM6OVVQhuKIVHHX0MZSxUqlUKpVZYDWnbUpQumesz37WM7FBSqVSGc/+6pQUA1zRCmb3ONFMa7uKUqlUKpUZ5/TvajltU4JSSkidEqVSGUp1SooCrmgDDDwxvC4olUqlUllZdlKnJOAfIWFKZSEwX3VXnZKigCvaAOqUKJVKpTJbzK1TUs2YX+3v8MuNmf6tMXP+MmbGj8aMui0kbo74u+WIa4K0LhhhzJ9rhseLKjFIh5xj87OdMbO7GzOzgzGT37TpqBseP56c296SMilmp4w6MOpWY+b2sexpTL/DS/LjN0vq6b+fG9Nlg7Ln5pM829RPgro743tb5rXD4yXD7psbs3BMcK0x9wZ5EhYvnuqUFAVc0Qaosk6J0020qWKWhcro0dfLsGNKZRlaWUZ9ickyt18odl5OnRIMnflD7EVXGDPplcAg7rmbMRNfsL+7BnF4oD4HWOXXLGD/I4zpvHZwDEMbQ6jfIcZ0bWDMgGMCJdl7T2M6rmp/97L7J9jwY60BuZ67njXUuEa/w+z5a5Wc09feg2PilFwXpHXhqBKnpNMa9jmaBNcbeHwQXww1W8Aczzd5lqHnB8+9Yrkx4x835hcbRj6Mvc8alpbE6dYwSDtpHnCcTcdBwfkY2OMeDc5fNMGYYZfaeIe6Slvd5aVNN2VAfvv7cpy8JU+5puSP5aCTS/Kuy7rBOXLMnt/3QPtcqwXHpDxsGfY/0hrCWwXHKe8++9trnFpS1tynly1X7vHXDiX3zwZpsDjJK5bavJgU5MnQC4P8oyGDxVOM6bGtjW/zZtApQZp8XehBOmxaqS+c02uPIF09trdh+wbHSO+fdYLy6H+0i2/TLHXKsaO9NvVM8t0e772PDVvdHbO/5E+P7YLzBp5k83n94Jn6NrV5uUXJ85CHPv+p7902CcK5P3F5NtjfOl++zDh/wcggreMeLv1c5VGdkqKAK9oAtMewupAKu21mnfzWxixfEFxz6TRjRt6UX4Xp2/mKZbad2bYVFidqpCzAvIElRiu/k98Kwv/5wMrbmmXPK3ZSz+jIo6xjsDYJ8m/0HcnJvlwReQ+Wzc5v+yiPPNOwy20d7BfoT7DEtudZfwb6PelntroP/Rd6TFmK1E/kpce0rwPd7o9jV1NfjLUL6Wxl33c4YufFXyuKzKlTgsD85313YYul/wU90AhOKi+ZvXiyMX+/Yw2r+vbe1iiTzLXgeC/rwCyZHuzTq91zZ9sYBgf7CJixDwVhHhhyPbYODEqw5N/gGlM+DvYpqG6bJjglznADS2fahoURaQ3KZXODe3S1Bl4UhANOxbI59iFtxRtxbfgzYTgvmmjM8KtsXlinYHzzIA2L/w4qKkYumD80MG4ph792tHGW2MsusgatNVx77mLjTw3CMHIZ5UKAk/9UntlW+IDRdwXlO+OHYH/BsOD44NPtc84PwkZcb69vDfWlM4L9hWOD8hGn0RrIYKktb56NMiB9y+YFx7OZ594pARjxjDYhYAefWdYpoU4B8ow85bnG3B2EzbLn/Wz3p7UJ9lcstsrPXmOIcx4B5YETM61tsM8oDM6kXMPmK/W/V2Nb961zgrNJfe+6ceDoeczpaeupdbBx0v9tFYQx4sjzjL0/2Jf7bGPTcEawjcOy+B9jJr0anNvPOqfUaUD61SlRlANXtAEq65TQoeGBg07dQ2ZjJNKWqHfU/9F3Wj3wtjWwXw86T2h7HO+6ka3DL9l6/kDQWTHxuWBkmNFu5BfyiQ4viEyh3tPBhIz/p4VrIw/aa1vDffyTQecL9wxzSrgfnTgTnw3uwb3ocEhMU75I5whYPCnIR8JEBn8fhGOsIIPpeCDPeH7yg3T12j3Io/FPBGnve7BN4xs2n6yuoPMKOcE+nRfoYmYzUB5cg3LjXlznr0a2rG4vKavxjwU6hGN0mkx4xtLmMyOxE1w+Ej+fMxJ4tqEXBPIVmfuT3aczb27fIN8wqEkzeYk+mPi8qy9P2XxyHUlcA8Nw0stBnaAjELuF/Ols5au/D52nyHbJH5t2zyFn2/zZ0OXZ4/acdYJzqMNTWtpz7gnqKu2FcsK45Hj3Le05tlwwLqWc7P25BvUbvek7pnLBeFtryLnB85JnvXYNHBMw9TMbbnWmrw+kg/RTvwafFYRPsc6z2DIW5DOd1LRtroUNQ/6QZpztkTcEecV5HWvYuC8GjiQdeL5+ob+8bu69d5B/2D5cjzA6wrknzpR/tqEX2XivBfcnLyn/sDRHgdQ37BHsWd/WkX+kg+OJTgnpps2RP9hzxJF8sO10nM0XKQ+bR9h3/h7IxSkf2XLYyeb5jUHeIEOoc1zPx8sGc+qUCG2joVGjZEZaIxVDCfxtM4ZwMgIDOBFkDAatd0romUeYUonA7K620lvDjcqIwQho2PQSe6cEI5N7YAQChBKZH++UUNHn9gr2w4Ay5BqhacshUbQY7IB89BUynigCDNhEYIyilBhRAoxeoWi4xt/vBmFhQFjQ809lp9zYX2IdFuCFIsCgZ9SAZyCvqPgAw7+7dUJwSijj4ZeVPDe/U/8XxMNgn2gbAEDoZrsRcH3vlCBceeZ/3gv2UVagjFNi88wLvvKcEhxurkUcL3Spo+yj/Kl/hKMUZ/4RHA8DzoV3Sojv84znjndKCPPtg/KNTyPPQVzfmxUPnFaMPXVKFCFwRRugsk4JCmfaV+5iFtRH2jgGC8fpNJrVMXBKkNN0SEiHkI1HfcP4WO46OWb+HrSF2d2CfYBRzHW8UYQOYFrjrE7BPh0kpAGHhY4XrkunTkx2OaeE+h+TrzcHumaCNUrB9O+Sax/ZpndKyoN3SujAQt6SHnqkyTOM8uULA1mMzBpzl41j5RH5gaHHeUtc5xEyhw4sjBgP7o9cogzQiZQVBiAdjYBnw2j1HVBzbBmhszCAAB1SdAgmpikX9On3Tgl5grHrZSNGcCcr28kf8g1ZTfqmfhocx7jjGnN6BPvoQGYjxHf09LfODAY1xiH5R2fn8Cvs9Wz9mtsvyHM6O9mnk5C84tlw1oDv1EscKWGU3D8n+hInZsbPwT76J9u60pP7eDuLETn0ZPxxdCMgD0k7zh2gPvS0hi5GPx1y1E1sLq97aFdcm/MX2TpC+Uz5JAing9PY/ALot05WF3tZwNR7RmDRZeQpoH4Os87G8sWB/qYNcx3aO5jxU/Csch+LYZcEcei0pqzI7/g0RYHMNMFpBtjm2Hhe59PZyTMnOiXsx4+UkL90KgMcadJMpxBgpgjHaR8eyGLks8wiseWBc5JN+ZdTpwSFs2hyYPDh8WPgeoeBCuEzhgykB4YMFMFhgdFLWLlOiRV69CrEOyV40Ag+f4+l9lymKM1sH+zPHx4YY/TgAxkpsYXuRxAQJniGODYoO4QBBRyWtlyTSuGfE/xn04ThimJAAbM/4engGEqUSkWvBZVKnBJbFkzxAVRglP9w623TkAENeZRVxggUjG7ykN77ydZjBhgSCCJ66+kZQuFQmRllAPMGBb1ElCHKH2AI0IsWc0ouL9vwpTE5zO0fXDP+eDbIPeKdEsIQlAhbr1RjTokTtoD6OuQsmxYnBJNySmwdZd87JYwMcY533FDqMv2Q6XW2zi2wdZJ8jDkltux8nvHcpUZK7DNPej3YR1EyrZFpaBgAMsJlQZ2n7cl7VK5tcayUU/JIckJHnZKigCvaAJmYvgWpw3RQMAWVEWlAjy+dI3PoFLJyKh60FXr7mNKJIYJMGWbrNm3AT2VAb9CLKo6Pa4PSLqzx4p0S3sHiHHTH3N5BGKMFsY4q25YHHG2v7dpLGLh/LuRSRfROCSOhPD9tFv3E+4WgjFNiZUfMKbH5HuaUIOeYSk36/nPG7lQrY8jDrhvae40LwugkZH9WF7vj5Eg8ZMTE6muR9VZvEp/ng4BnxogMS1e2KemPc0pwdLEL+GUUmbQzmpZYBz2Qp0w18k4JupBrMqLtjT0ZCbF1cYGVt9Sp2TafsC/Af78E5YTOFafE2kT0SPNsOBkgGaeE5yQ/6UQC3CNX9ZL7+PSjO9E9/hjP6W0T2jY6EwcEMEISfx3IdOOFo4Pj2BRSPrZt08aZ4ondQRikrQKuwz29U0K5cS2mTC8cbwNs2TFrAH0uTsk/QZ6TX8OvDs7BKcEJRL/Gz+DBGadtcb/EZ80npaytbgbk63+/BnbeItJrQUcMTgvpXJlTguxbGShb75TQ3olPmLeRyHueJewZM8GcOiUUMhWAISSMZ0YpMFKpGL4CYHzhBXOc3gPmJeK84Flj+DK0j/DAS+YcnA72MaZl6NKSa/LcGMzx07eGXxl46dyXxu6fi2tzD3l3wc1rpHC5jn9OnsVX/KiRqTc+zzBEUQY0bsgxnh/6eAz/kZec27me3bfnSB66KRIc4xpcS8rBlheKjWOMZIF/vwzyK35urs9TuSbl5PLOX5djCF0cIq7t3x/xJA7T5QDCCAM+/ni2KPe19ax3k6Au+HAUj9QXmw7S5nuDJA0ufeQByoj0ECd2LXuM/PbXkjTbPPbCm7RJvbJ5G8sbm+/Us1i+U+fcNAfOY/Spp72vv6bcy96DcHpM/NA9aSAe1+C+tCnCcUwpe7m2TRdtRtqWLS/SJve2x3yvdUVUp6Qo4Io2QGWdEuQ/wAmnrtFD7Tst5F0re32AokUGTbYKEPninRLaTMwpuShQloxkABQvhmApp+QHW7fjnBKAwek7V5AzGFAobsC9fIcX01bB9LbBs/JsvH/HCLFvs/lkbPqWNWq9Mcqvn0LrnRIcNZ4b0NtPOmLv8/xXsVMiU6wTnZLrA7kDMPiQg4wkxUYbbPkSFnmnxIKyTzyObvIdSThl2BPMtFho8xGHg3TEnJJXgnPEKXFTwJia5A1zZnGQd8hb7BFkNucjp3EMAfUc59o/U0pOiTNUc+mUoGvQSUwhAtRB0szoj+/JXzrLthur83huP1KCszDCOgW8N0nayTM6NXkvCjBNa5Ctn+hA37lM2yX/mUYH6JjG7kMPe6cE0JlAXQWMCmL/yTRwu02+TngucFQ8/EgJxxkdQRdj1wDKnPwvk+48kTykPQFG1rpZfY+Mou7CeQOCY7RvOiik7to0hzkl1BkcGkDHL7KN90sZ9eQ6HI85JVYOe6dkxndBWJVySvJBBIEfUmZ+bDYzsxjI/GA/rcGD3n7mHMa/bJUOaTxeETDKFRZHGR2qU1IUcEUboNIjJdaY6bqBNcy2LDHScFTi4+BE49hzHOOD0RO2xXleLTgG/WpzOO4YbzIdiE4lew86UQjDwcb4i42UfBZcn44SjGIMG66B0udZIB1SsWex55d6Vmtc+mP5JvI2lu64cJwQSbvNA99RQb75fKQDgw420oWBTNrJW3/Mv3DM6Klcx15PrrFqkGeExTq1bN7Gyspen2eRsrLhGK2SdzbffOcKJO/lPq6DJh+UOuPSS/rD4hBOefv08Ut9iOWPNaiZUu47KyV/SL9NH4YVDjdguhZlgRO3wI0IYHyLLWLLh2fweULdI8/ISzH8bbmR3xzjHuS7r6f+OXGgJP/dgia5ptQB0mCfQfLJ1qmwji2eXeoP8ezzS6ejr5/W6fXHJB9su5R6aetemfbnzol3SrinlJONQxnElyn35bi/NueTp9RvtqkLseP2XlFq456kp5utbzxjWGet1AHqkH1+kZGkx+YFDp+cS97a4zhqsXNsm/fplnyLqz/SPmx8qYcuTORBOffPJKu8U6JUKrNDdUqKAq5oA1TaKckDMZroRQbMoc5Zb7KyqMk7EH4kwYOROHqkGQWMN5yVqROnxE8dpI2HxVEWHtUpUSqVaVGdkqKAK9oAheiUKJVKpbIwqE6JUqlMi+qUFAVc0QYYap2SrjZIqVQqlcpMs3utMktM2NA4qFOiVCrDqE5JUcAVraBZsxMpY6VSqVQqM87Va1Y0UtLPOiV/2iClUqmM50B1SooBrmgFgwcPNp06dVIqlUqlMuPs3Lmz0zYlKKWEmh6wn6mx6ipKpVJZiscco05JMcAVreC///4z//zzj1KpVCqVGeeUKe4THnEopYQ6dOhg2rRpo1QqlaUY2qOhqHJwRStQp0SpVCqV2WKFTsn06dNDT1QqlcVNDNREODtWUYXgilagTolSqVQqs0V1SpRKZVpUp6Q44IpWEGWnZNasWeann34yG264oXnppZdUd6XJOXPmmM8++8w0aNDAvPPOO5qPaXDq1Knm9ttvN7Vq1TJ169Y1e+21lxk2bJgYXGHxleH8999/zU033WTWWGMNycfdd9/djB8/XvI3LL6yLP/++2+R2yeddJLk45prrml22203kedh8fNNdUqUSmVaVKekOOCKVhBVp+Tll1829erVk+nGPPLjjz9uZsyYERpXGU6MgaeeesrUr1/ftGvXTvIR507zMXmShxjN5B2Onc+7vn37mtq1a5tffvlFHZMkiDPSvXt3ycdvv/02lo8DBw6UfPzxxx/LnKMsS/Ltvffek3wcMWKE5Cvh5C352LFjxzLn5JtVyikhMdOmTSv6Rk8vAvmQbLgyGkRgeKFRCFSnpDjgilYQ5ZESOG/ePFHA6pSkT/QEBgz5qE5Japw9e7b07K+11lpm0KBBsR79sWPHmoMPPthceeWV2tGbBBmtO++882SkCSfP23SjRo0yBx54oIxC6WhJxZw/f77ZeeedzTbbbGMWLFgQCx8+fLjZeuutzSuvvFIqfhRY8E4JCejdu7fZcccdzaGHHmqqVasmvWbF2vBnzpxpbrjhBsmHRYsWxcLJj6uvvlqEJYKTIT0aPpWWbR9v7ty5Mg2CbRo9x8ljrrVw4UI5zjGcGx+WqLTY98doCPGGNtfmvoQRBxIW/wzFxEmTJknv5GqrrWaOPvpos/3225uRI0eGxo0a1SkpDriiFahTUvWpTkn6RJfdd999ZZwSjOn99tvP3HrrrdoxmASxUbBXEp0S6uU+++xjHnroIXVKkiDykHqX6JQwlXCjjTYyLVu2LBU/Cix4pwQiNBEGNHwEqTolgVOydOnSWO87xsQ111wTc0pwDI4//niz7bbbxior8eiFuO6668RJ6N+/v1yHPKWnB2N5s802M7vuuqtp0qSJWbx4sQyjcpxpE+R5q1atTLNmzcR5oUFccMEFMg903LhxIozpRSI+jQR069ZN9t98882iLDMaIHUX3n///ZIv6pQoogRXtIIoOyW0JXVKKk/0ADKIfESXRt0RjRIxlIcOHRqrg+g06mXbtm1lugzTj8LOU5YmddDbBi+++GIsH7E3yMc+ffqEnqcsTWTgBx98IPn4zTffSD5ih9HZsN1220WyPlYJpwQiDEaPHh0TpMVo4MJ4p6RLly7y4if89ddfzamnnpqSU9KvXz95MYoP2nAcw/ncc88Vx8SfQxhzkB955BE5nzCUGKMksEePHvLiKUqOxsC1d9ppJ3lO4vIsOC5NmzaVbcKKkeSHOiWKKMIVrSCqBipyBuOF6QgtWrSQTg72f//995hcUlZMDMEXXnjBvPrqq5KPb7zxhuyjS7RnOnmSVzgi1Ef4xRdfFK1NUhliM3z11VexfPz888/FdgmLqwwnthz2Be+WYBtD7EFst7D4+aY6JVWM8U4JU6N8OPkRP30L4qRsvPHGZsmSJRKHPNxyyy3lfO+U1KxZM9QpYVqXD/NOCddkJIRrMC2J+/OCX6JTwlS7eKfk/PPPF2dInRJ1ShTRgytagfaaK5VKpTJbLHinhF6w999/X5wRP9XIE8+62IbwMW6vv/56SX+iU3LVVVeZOnXqxIx/DAxeGPP5RW/OWWedJc6Ld0pq1KgRW6EBB+Tss882m2yySamREhydhx9+WJwOHMPGjRvL9SiPBx54IGZoc5wpZI0aNSrllODoHHDAAUXplDCtbc8994yVQTzj54BGkeqUFAdc0QrUKVEqlUpltlhlRkqUSmVuqU5JccAVrUCdEqVSqVRmi+qUKJXKtKhOSXHAFa2AMmcUValUKpXKTBPbIhHqlCiVygqpTklxwBWtQqFQKBQ5RyklxNKuzP9XKpXKeLL8aiKcHauoQnBFq1AoFApFzqFKSKFQpAVnxyqqEFzRKhQKhUKRc6gSUigUacHZsYoqBFe0CoVCoVDkHKqEFApFWnB2rKIKwRVtpMFy4ixXvvfee5t99tnHPProo+6IIh307NnTHHPMMeaXX35xIYpU8e6778py79THiy66SD4mrEgeK1askF8+4LnHHntIPvJNMx+uSA4+v+6++27JR2QkH6wuJKhTolAo0oKzYxVVCK5oCwbLly+XD5Gut956ZunSpS5UkQw+/vhj2rBp166d/LZs2dIdUaQC8o4vkXuMHz/erLbaambIkCEuRFERJk+eLPn4888/uxAjqzPxQWe+oaZIDj/88IPk48SJE12IkXpYt25dM2zYMBcSbRSkU/LZZ5+JR/3WW2+ZDh06uNDiBF9R/+STT9xeCXr37m0+//xzt6eIGtq2bWu+++47+cJ+ocIKP0UVgyvagsHixYvNqaeeKj39y5Ytc6GKVMDIE0WvTknqePDBB029evXM1KlTXYgx8+fPN0cccYS5+eabXYiiIvAR59q1a8tHoD3mzJljDjvsMPlYsyI57LXXXma77bZzewFYqIaPWn/44YcuJNooOCUUP5xHLxlfLm/SpEnR9pLdc8898jX1RNx2220iLBXRAQbUCSecIL0WTz/9tNl+++1F8BYqAjNWUZXgirYgQM8fj3zLLbfoNI9KQJ2S9NG8efMyTgkrFWJMo5sVyeGGG24IdUoOPvhg89RTT7kQRUXYb7/9Qp2SLbbYwnzxxRcuJNooyJGSeGDknXzyyUXbS4bgq169utsrwX333VfKKaE3sVGjRm4vwOGHH27uvPNOUegMlaKYzjrrLHfUyLzOtdZaK6asWDKaOH7u8ZgxY2To34PRq7XXXlt6igDPQHymCQDuU6tWLfPII4/IfjFDnRJFFOGKNtLge1rM3z/uuONciKIyUKckfXi9Gd8LPWDAALP66qubCRMmuBBFRSDPyMcvv/zShRgzYsQImQY3evRoF6KoCNhadFIPGjTIhRjz7bffilMSP6UryihYpwSPmkpcaC/xZBrljZTce++9KTsla6yxRql5sJdddpnZcsst3V6AddZZx7zwwgtuz5hZs2bJObBNmzZm/fXXjxnaXLtx48ay7XHFFVfIfYsd6pQooghXtJHGHXfcYW6//XYZJb/yyiuFvFz8/fffuxiKZNC5c2dz4YUXSv4hq6+//nrZ79Spk4uhSBbvvfeeufzyyyUvX375ZReqSBZ+pPP111+XfMROeP755yVMkTx8Pt51112Sj9hw33zzjYQVCgrOKWFEhCE95hDrChfJT9+65pprZHjUY8GCBeLUMZqRrlNy6aWXykiVx6hRo0yDBg3UKUkC6pQooghXtAqFQqFQ5BwFp4QwghkdOemkk2Qb8lLZb7/95mIUF1q3bm0uvvhit1cCFgPg5bF4tG/f3hx//PHm0EMPNZMmTTL/+9//TKtWrcQpYd4hnjWrYHjQ+4NzE49rr73W/PTTT27PyHKcXJNyoAweeOCBmLPIi/asjBOP999/XwzyYgTvlJCfTDs544wzZMlD8u2oo45yMQoLzo5VVCG4olUoFAqFIudQJaRQKNKCs2MVVQiuaBUKhUKhyDlUCSkUirTg7FhFFYIrWoVCoVAocg5VQgqFIi04O1ZRheCKVvDff/+Zf/75R6lUKpXKjHPKlClO25SglBLivYJp06YplUplKbLKWiKcHauoQnBFK1CnRKlUKpXZYoVOCeu/h52oVCqLmxioiXB2rKIKwRWtQJ0SpVKpVGaL6pQolcq0qE5JccAVrUCdEqVSqVRmi+qUKJXKtKhOSXHAFa0gqk7JuHHj5OOwfCeJbym9+eab5t9//w2NqyyflC9fe/b5ePfdd8u3pjAUwuIryyffm+Ijdbfeeqvp3bu3mTp1amg8ZfnE/vzkk0/MJZdcIvXxwQcflLqo9TF58r05plq/9NJLko+07YceesgsWbIkNH6+qU6JMmfESFBDoepQnZLigCtaQVSdEvSUN/r43hLfTtp4441lOzGuMpy8J7bFFlvIB2693u/Tp4/ZZJNNzAcffCDHE89RliX5xLe4aDqPP/64/H7//feafymQtsxHm8m7d99918yYMUPC//rrL7P66qubTp06lTlHWZa04zZt2kg+/v7771IHMfp/+OEHs/baa5tu3bqFnpdPFrxTgheI4tl1111N/fr1zbrrrmvOO++8onKmMPR79OghX07fYIMN5AvrkA/xLVq0KPScXJPyaNKkiXDMmDGhcYqRCF96MOrWrStltu2225o//vijIHqC1CkpDriiFUTVKaEdLViwQHr/OnbsaOrUqWOef/550Q9h8ZWliXymR7pWrVrm559/jjl4GIPHHHOM2WOPPczs2bPLnKcMJ/nGIkE4IzQhdUpSI6NM9OhTH8ePHx/ThyNHjjT77befjD75Oqosn/PmzTN77rmn2XrrrUU++vBhw4aZhg0biu0RHz8KrBJOydy5c2PPyf7NN99sdtxxRzNx4sQy8asicUo6d+5sqlWrZr7++uvYaETLli3NaqutJk4ABU0jfvHFF80jjzwiw3f9+/eXuByjR+y5554TAfDKK6+Yhx9+2Lzxxhulyp8vu7/zzjty/mOPPSZKn+FUKvjQoUPl2vRucB84YMAA8+qrr5oRI0bIftu2bc2XX34p16FsuBbTLuiF4zpcF2FeTIYE+Y/g8EKXnovatWub119/PfJtT52S4oArWkGUnRL0wPz586UtoYS32WYbM3r06ND4ytJE7iKHE50SDOvjjjvO7LvvvuqUpEicEHVK0iNOyYUXXij1ERvB60emEjZt2tTcddddsTqqLJ/YFo0bNy7jlAwfPlxGRbEz4uNHgZR1IgrKKfHE0KXRYxivuuqqMjwVFq8qMswpwbBnGH6XXXYxEyZMEAdl8803FweCc6isjC4dccQRMpry2WefifBs0aKFKCjyEidhn332kd4JBAOV+KuvvpLzcTg222wzyWfu98svv5iaNWua9u3by7kQJ4Qhwq5du8r+BRdcYE455RR5HoyF7bff3txwww0iXDj+xRdfyDNgmHPN+DQWCz///HPJAxy5sONRojolxQFXtIKoOiXIM2+k8OuNwY8//lhkS2J8ZVmiM8gzOpzQAYTxLgQyvFWrVkUrk9Ml9U6dkvSI3YltQd5R93x9pPOUjlZGQxPPUZYl8vrpp58W25C8ow1j9GObMauGGTZh5+WTVcIpIRG8yIN3Tc/ORRddJI5Jv379Yh52VSYVDaeEBnzNNdfIiAekEtKYOX7OOeeY3Xff3Tz11FPmmWeeEeLAffTRR5J33imhkhIfxc5cRKYTMfrByMaJJ54oc2QXLlwojgZOCPcgbjpOCdf2njrXoLyqV68u5xWKM5wJknbylykSG264oRk7dmxB1Ft1SooDrmgFUXVKkGPbbbed2WmnnYSXX355UY24ZorIfkbRmWlAPqI3kNfFoEczRXQdMw0YqaOXmp59funUQ09qXiZH2i/yhhfcd9hhh1h9XL58eWh8ZTjJR0aQDz74YMnHRo0amebNm5vFixeHxs83aR+JKCinBIOOF3hICM+KQHjttddiBjbHw86rSvROSeL0LU/ypnXr1majjTYyXbp0kXzCWcEJGDhwoGyvzClhahYvlvG+Diuz8LIZ8XA2iMf1GTlZa621ZEoXvZYY2TQAXjZVp2TlvO2226TscEZ8HS6E9KtTUhxwRSuIqlOiVCqVysJnwTslkHmGGOUY3LBv376SsLC4VZUY+t27dxfDNuw4+cEUN5wJ8oj8GjRoUMxp470Tzo9/D4drMXzPue3atZPeHuYi4rRgnHz66afiyJD/3jnBAeHaOBh46DgwXJNjgwcPFieIcMi1OdffjwULeIb4OaRVmaSRaVpMVyPffP315ePLJqpUpyRlkD9Dg83CgStagTolSqVSqcwWsYsSUXBOiTK7xDjGUOb9FFb04qV0Vr9gOtjLL7+shkqRUp2SlCFOvGPBOCeuaAXa1pVKpVKZLapTolQq06I6JSkj3inxjLxz4opWwFRPRlyVSqVSqcw0mUWTCHVKlEplhVSnJGUkOiTxjKxz4opWoVAoFIqcQ5WQQqFIC86OjToSHYIoMXLOiStahUKhUChyDlVCCoUiLTg7NupIdASiyMg4J65oFQqFQqHIOVQJKRSKtODs2Kgj0QGIMvPunLiiVSgUCoUi51AlpFAo0oKzY6OORMO/EJg358QVbcGgf//+5ssvv5Rl0hWpg29I8RXtzz//XL6qvWjRIndEkQpYAt/nIx8V5qN/ivTAB5z5fhnfQFOkBz47wPfoqJPUx0KCOiUKhSItODs26kg0+AuJG1nmFK5oCwLXXnut2WyzzUyTJk3Mu+++a1asWOGOKJLBXnvtZW655Ra3Z+S7VHwAt9CMmHyDj+G++OKLbs/IN7lq1KghqwspkseTTz4pcu+aa66RX77rpUgNPXv2lLrHR8Y9vv/+e9OgQQP5JlwhoKCdEr4STuX94osvXEhxYNmyZebPP/80O++8s2nYsKEoZr7A/tRTT5kFCxa4WCsHXnSdOnXMzJkzXYgi26Dn54YbbjAbbLCBlFndunXlK/eFCozYAoDIiAJjzp0RD1e0kcaSJUvMCSecYO644w6RhTvttJM6JSni559/NrVr1xYDOh7k6z777OP2FBUBQ7pevXrykWGPuXPnmkMOOURkvSI5xLfdH3/8UeSgOiWp48ADDzSNGjVyewFYOXOTTTYxLVu2dCHRRkE6JXyRHIOuV69eYpQzfF8sYGgTZfLRRx+5kBIwZEdjplF7MBxfvXp1s9Zaa5n11ltP1oW+8MIL5Ro+nN94rL322hJeq1Yt88ADD0iYFxp77LGHOETnnXeelMFGG20kPWweLVq0kLB4ILjPPvtss3DhQtlnygXPwvkXX3yxhBUj+ML9OuusI0PVhQixYqMPb+gXAvPmjHi4oo0skEMHHHCAfMgVYACqU5I62rRpE+qUnH766ZK/iuRw7733ir6Md0roGDzqqKNKjUIpksdPP/0k8lCdktTBR64TnZJZs2aZ7bff3nz44YcuJNooOKeEIdHNN99chqLoMWNYqpickieeeMLUr1/f7ZUFox/HH3+8bKNcDj30UNlOxFdffSXeMxXW4+abb47F9woeBwcHZejQobJPr9D1118v24AvwOMY4ngAhrETbZu77rrLHHbYYWbx4sUy93b11VcvmKHETII87datm3n00UfN448/Lo4ZxkGhQqzY6MMb/FFm3p0RD1e0kQXTE3BInn32WfPMM8+Y5s2bmw033NCcdtppMRmkqBjIYor7vffecyGBLGf6VuvWrV2IoiIMGjRI8pGRJw90m+80VaQOdUrSx/PPPy/TCefMmeNCjOnbt690fg4bNsyFRBsF5ZRQWddYYw35mBsOCdNh6JXnZR72iwGvvfaaNNgwMBKBwY9jQN7gvNx///3uaGkkOiX0OO63337mwQcflP14cL9ffvlFtqncL7zwgmwDXujbbbfdZH43WJlTEj+1jBcCGYlh2hmNphjB1Dnmw++7774upLBgy7kQIPUxooyMM+LhirZggJw76KCDzCeffOJCFKngpptuEp3KqAlTjuKNGUVyYAohMwHIRzoFjzzySHdEkQouu+wys+qqq0onKB0Na665pshJHD9FcvCdyXQUUx9r1qxpbrvtNgkrFBScEkoEPTvfffedbBfL8D2jJbzM1L17dxcSrKJCA8Zp8XjppZckjOluAAXOS2QARwCPmvM8cPpWW201mZ7lwajU1Vdf7faCkRKu6eO8/fbb0ivkv/jN1CyO+zJ56KGHZJ+5ykuXLpWXKD/99FM5BlCGhfxeRSrghbMPPvjA7QXTt8ib+N7KQoJ9dkX5kLIth5FzRjxc0SoUCoVCkXOoElIIknXoGClhiDCTKBZnsqrB2bGKcCQ6IjCyzoiHK1qFQqFQKHIOVUKKlMBICqt8KRTOjlWEo6CcEQ9XtAqFQqFQ5ByqhBQKRVpwdqwiHAXljHi4olUoFAqFIudQJaRQKNKCs2MVVQiuaAW8J8aiIkqlUqlUZppTpkxx2qYEpZTQ9OnT5TsWSqVSGc8ZM2Y4KVECZ8cqqhBc0QrUKVEqlUpltpiUUxJ2olKpLG761dXi4exYRRWCK1qBOiVKpVKpzBbVKVEqlWlRnZLigCtagTolSqVSqcwW1SlRKpVpUZ2S4oArWkFUnZKRI0eaHj16yHeaYLdu3Uy/fv1C4yrLJwYBX832+di7d28zadKk0LjK8kk+8rVsn499+vSRKa9hcZXlk3wcOnRoqXwMi6esmOTl4MGDRU5OnTo1NE4UqE6JsgwnT55sxo4dGxOiEyZMKLWvVEJ1SooDrmgFUXVK7r//fnPBBReIsuVdJ6i6KzX++++/5rDDDjOnnnqqbJOHfDyX71Dx4dsoGzJRIh8kXnfddc2NN94odZB8/Pzzz+Vr2gMHDgw9R1maGKbjx4+XfLzrrrskD5E9rVq1kq+79+rVK/Q8ZVnOmjXLXHXVVWajjTYy+++/Pzpa6mhY3Ciw4J0Snu/hhx8222+/vXnzzTfNW2+9Zd544w3Trl07NaLLIYX+7bffyvdFqKAISq9wUEbkXfXq1U3Hjh1FEPzxxx/mo48+MuPGjZNzE6+nrDxnz55tHn/8cSmPnXbaSZzAsHhRojolxQFXtIKoOiX33XefOeCAA8z//vc/MVx+/vlnlf8pED361VdfmVq1aplffvklpg+Q90cccYTZbbfdREYlnqcszZkzZ4o9UrduXemV9vqSkbwmTZqYSy+9VJ3lJEhdu/7666U+0inq83H48OFmzz33NLfccos6yUkSObho0SKpm97GUKcki+T5mjdvbpo1a2amTZumPWRJcPTo0aZRo0bmww8/lF6xXXfdVRwOjiU6JXPnzjV33323VGR6LhAExH3iiSdMnTp1zFZbbWUaNGggxgCVn6H+888/3xxzzDHm3nvvNZtuuqk4P1deeaUcV6emLKmvTZs2Needd5557733pGx8eUSZ6pQUB1zRCqLqlCBXkE2etCMenR5+ZFrYOcoSojdbtGhhateubdq3bx8z+DAOTzjhBDEE1SmpmOTRDTfcYOrVq2eGDBkS03fI80MOOcRcfvnlap8kwTlz5phzzjlH6iM2hc9HbJeDDz7Y3HbbbbE6qkyOtHF1SnLEMWPGmL59+woZ1qOn+aijjpJpSGHxi5kYFeeee670KpJvCMv69etLTyNOQ6JTgnB46KGHzOqrry49FgiIVVdd1bRs2dIsXrxYnBbyeeeddzaffvqpXAMH5LjjjosJDRoAI1kXX3yxKrYEkp9HHnmkeeedd2T/hRdeUKdEESm4ohVE1SlBrnjDBbnToUMH6Qxp27atOiVJkLyjN5/iZsYBHXyEM59/iy22MM8995wa00mQfPvxxx8lH6l7Ph95F4KpSG3atInVU2X5xID+5JNPJB9///33WD4OGDBApm+Rt4nnKFdOdUryyD///FOGTzGSfWVWBnMLGVWiUh5//PHm1ltvNTfffLP0PBCGE0J5l+eUTJw4UXp/iMsoy/Lly82CBQuECxculLzGyL7iiivMSSedFLvv/PnzzY477mguvPBCdUriSL7g4DE14s4775SywEFZb731zLXXXmt+++23SBtU6pQUB1zRCqLqlDDiy6NWq1ZNfs844ww1otMg00ZPOeUUyUO4ww47SGdfWFxl+WSa0YEHHhirk3SU0qkXFldZPkeNGmV23333WD5us8020hEaFlcZTuyMhg0bxtq0J++K0XkTdk4+WdBOCT1irHBRo0YNWTGEnn+EwYYbbhjr+Q87rxhJXuFskFetW7cuVa7MNcSRoKIiBJjykOiU0OvI9C2Mkg8++CA2WoISY5Ubps8xSuWdkhNPPDF2fe+U8CKqOiUrp46UKKIGV7SCqDolSqVSqSx8VomREoalGAXAuIbaQxZORjLIp7BjOC3kHT3zftsPM3Me+/HxiRef5/E9+uQ/ZeL3cQ4xZrRcKib5WCiGnzolxQFXtAJ1SpRKpVKZLVbJ6VtKpTL7VKekOOCKVqBOiVKpVCqzRXVKlEplWlSnpDjgilagTolSqVQqs8lElFJCTN1h+o1SqVTGkyl6iXB2rKIKwRWtQqFQKBQ5hyohhUKRFpwdq6hCcEWrUCgUCkXOoUpIoVCkBWfHKqoQXNEqFAqFQpFzqBJSKBRpwdmxiioEV7QKhUKhUOQcqoQUCkVacHasogrBFa1CoVAoFDlHQSqhFStWuC2FQpEvODtWUYXgijbSGDlypNlggw3kw7CK9HHppZeaQw891O0Z0759e1OvXj0zcOBAF6JIBnyF/JJLLnF7xnzyySemfv36spqpInn8+OOPZvvttzd77bUXukU+kq1IDaxmte6665onnnjChRhz2223yUeaC6U+FpxTwhfDjznmGHP11Ve7kOJD7969pdHedNNNLqQkLFlF3aZNG7PJJpvIV9cVuUHfvn3NgQce6PaMuf/++6XM+CBlIcI+u6KKwRVtZEGHVJMmTcxLL70kSxbzAVK4cOFCF0ORDLp06WJq1apl+vTp40KCvEW3kr+K5PDaa6+ZtdZaS1Yu9eBDw/vvv7+58cYbXYiiIsR3NOOcIIrUKUkdxx13nNlhhx3cXgBWzsRR+eCDD1xItFFwTgmZfuedd7q94sM333wjDTZMCSMMa9asKcYuQFk3bdrUXHfddeaHH34wr7zyijn33HPN0KFDzd133y0V9dNPPzVfffWVxOf8U045xRx00EHm119/NW+++aYI3BdeeEGOA3rSdtllF7lH27Ztzemnn2723ntvd9SYZ599Vp4vHnjqPMeCBQtk/7DDDjPNmzc33377rbn11lvNX3/9JeHFhuHDh0tejR071oUUFuyzK6oYXNFGFhgvderUMQ8//LALCeTW5ptvbh588EEXoqgIX375palbt64ZMGCACwlwxhlnmH333dftKSoCepRRkfiOJXTz0UcfrU5Jmvjpp5/UKUkT2GZ77rmn2wuAfMRRadmypQuJNgrOKaGyHnvssW7PmCFDhkhYhw4dXEjVxr333ivKpDygsI8//njZxul47LHHZDsROCKMlFBhweLFi81ZZ51VRpDSG0n+/vnnn7LPNV999VXZBjgaO++8s7niiitk/8UXX5T48bjrrrvEEVm0aJGZMGGCqV69uhgVPXr0cDGKAxhU7733ntl2223NTjvtJM4fTib5Uoiw5ayoYnBFG1nQhmg7dHQsX77chRoZgWzWrJnbU1QEpnJQ3MgfD3QBRs1zzz3nQhQVoWPHjqZGjRoyU8Fj0qRJpmHDhub77793IYpUoE5J+qCzuFq1am4vAJ2e2IXxo6JRRsE5JRjkN998s9sLlBQK6eKLL3YhVRvfffedNFh62RPBkDzHGOUA66yzjnnkkUdkOxHeKfHTt3AucPYSp8Xx4TyciK5du8o+13z55ZdlG8ybN0/mK3rnh1GVRNvmgQceEKfEj5QAnCDuvfXWW4sjU4zAqDrhhBPMRhtt5EIKC7acFVUMrmgjDdrNhRdeaNZbbz2z4YYbmm222UbkoiI1MP+c6Vprr7225OOOO+5YMIZLlMA7Tkx5Y8SEfKTTKexL1YqV4/rrr5c8XHPNNcWGwNaoXbu2dDwrkgfvhJF/2BW8e3fIIYcUVMdnwTklTEliutCZZ54pU38YFWAItZgwZcoUmZJFjyFT2eg1PP/882UqFU6CBw7FSSedJE4b8VDk9913nxzj3RycCY4jDACjIoyU8OLePffcY8477zxxJuJffERo8HIkw/zk/z777BO7JliyZImMuKDgGNUh3uGHHy5zbDnGVC2Gtu+44w55JgyK8ePHu7OrNhjNa9y4cazMTjzxRJnW5kerCg1ixSqqFFzRKhQKhUKRc6gSUqQEei/i3zFRFC+cHauoQnBFq1AoFApFzqFKSJES6O0vlpENxcrh7FhFFYIrWoVCoVAocg5VQgqFIi04O1ZRheCKVqFQKBSKnEOVkEKhSAvOjlVUIbiiFbC+Pe+lKZVKpVKZaYYtCFFKCbFMIJGUSqUyniyCkAhnxyqqEFzRCijzsLqgVCqVSmVlyaJNiVCnRKlUVkh1SooDrmgF6pQolUqlMltUp0SpVKZFdUqKA65oBeqUKJVKpTJbVKdEqVSmRXVKigOuaAVRdUr4CCvfWUrkzJkzY/OUlcmRMvb5N3fuXPkOWFg85cpJ3YvPx6lTp4bGU66cifmI0RoWT1kx+f4Z+RhlmVjwTgkJIKPDOG3atNBzlEG+0djJp/gKiuD84Ycf5Iu+nTt3FiHAhxTXXXddM2DAABWsWSRlwhftC6U3Wp2S4oArWkFU6+bixYvlC8WQ7W7dupkaNWqYb7/9Vo3qJIlsv+WWW+TjtewvXLjQvP322/IFbT5wq8ZgckSn8jFgPjKMDUKdfPDBB02DBg3MsGHDQs9RliZ1DR500EHm2GOPlQU2li5dam6//XazxRZbmEGDBoWepyxL6mDr1q3NfvvtJ9+UQ6TzQe2wuFEg5Z6IgnNK4nvJ2P7jjz8k4xGkakSXJXn29NNPmw033FDy6Z133pFGzzEUeLt27Uz16tVNx44dZZ88BfF56cMhFTz+GM4OpO74OP76yrKkPPjFGOCr93zhfdy4cWXiRY3qlBQHXNEKCsFhHjFihHSiPPPMMzpKkiQxXH7++WdTs2ZN+fXyfPLkyebggw82++yzj3SYJJ6nLE303nPPPWfWXHNNMZx9PuKM7LHHHubqq6/W2SdJEMfunnvuMWussYZ8A83ryKFDh5pddtnF3HnnnWrbJUnyDnuCevf444+rU5Jrjho1SjL9448/Dj2u/Mf079/frL766qZLly7SE4YCp2eRY4lOyZw5c8xDDz0k8REOOBefffaZWXXVVc2ff/4pjuATTzxhtt56azNkyBAzadIkc91110kZHHPMMVKHPvnkE9nHWdTRq7Ls2bOnOIi//PKLeeWVV0yjRo3UKVFEBq5oBVF2Srzh0rBhQ3PqqafG9pUVE7neokULU7duXdOpU6eYwYcjcsIJJ5g999xTnZIkSB7dcMMNMtMAW8TXQeT5IYccYi6//HJ1SpIgdsfZZ59t6tSpIzLH5+Po0aPFSb7tttvUKUmRtHF1SnJIKujw4cNlqPmKK67QnvlyyNQGeh/oscEBoSfx+OOPN0ceeaQ4FCtzSiZOnGgGDhxoqlWrZjbbbDOzww47iAG97bbbSkX/4IMP5Hrk/8knnxy7JyMlO+20k7ngggtUscWRxtenTx+z8cYbm65du0rYCy+8oE6JIlJwRSuIslNCB8n6668v02SQc2FxlOFEf/br10/kOJ1O6AHCMax32203MQJVp1ZM32nHiBP60xvOdARuueWW5t1331VjOgkiZ1577TWz2mqriY6Mz0faN53O2umQGtUpyTERnhh3Rx11lBjBYXGKnQyJ0ltTv35906RJE7PrrrsKGVam8TMkihNSnlMyYcIEEQ5s33333aUcDOoJlQnHBqfkpJNOih3DWGBa0oUXXqhOSRzJ27322svsu+++sbLYdNNNxWnE4fv0008j3f7UKSkOuKIVRNUpocPkiCOOkJ5+DGo1WFInedahQwcZFUEuHXjggdKZ9OGHH4bGV4aT+vfVV1+ZnXfeWaa90bO/+eaby0h4WHxlODGi0YFMa0ZHYrvwTgQdeNq+kyc212mnnSb1kFFkRPrhhx9umjVrJq84hJ2TT1YJp4RpRbwMhTD1PTzK0mTa1AMPPCAVknmv8ccoYxQPx95//3150Z1tFBQvut93332y7+d24ngwb5YwXiZldIoefn/skksukfLw18dJZHrXOeeco05JBXz22WelR23s2LGhx6NEdUqKA65oBVEeKVEqlUplYbNKOCVKpTL3VKekOOCKVqBOiVKpVCqzRXVKlEplWlSnpDjgilagTolSqVQqs8UKnRKm2zC3T6lUKuPJOzGJcHasogrBFa3g888/N4899phSqVQqlRnnU0895bRNCUopIV6+RS8plUplPJs2beqkRAlsuKKKwRWtgOVOeYGXbwUolUqlUplJsshGIkopoSVLlsS+lKtUKpWeyIZEODtWUYXgilahUCgUipxDlZBCoUgLzo5VVCG4olUoFAqFIudQJaRQKNKCs2MVVQiuaBUKhUKhyClUASkUirTgbFhFFYIrWoVCoVAocoqYAlq+fLl82p9P+iuVSqUncmHFihVOUpTA2bCKKgRXtAqFQqFQ5BQxBTRv3jxTq1Yt06BBA6VSqYxxjTXWMEuXLnWSogTOhlVUIbiiVSgUCoUip4gpIL47gD6qXk2pVCpLiFzQ1baKA65oZUTswQcfNE8++aRSqVQqlRnlI488YsaNG+c0ToCYAlq6eI5pde8qpk1zpVKpLCFyYflydUiKAa5ozbJly8QRVSqVSqUyG/ylfXuncQKUDNEvm2NMB7vbWalUKuOIXFihDkkxwBWtdUCXmUYbr2Ka7KhUKpVKZWa5y+armJ7df3MaJ0BMAYlD0t4ZH0qlUumJXFCHpCjgitaW9zIztfUqZlpbpVKpVCozy3+/WsUsmaYOiVKpTIXqkBQNXNGKQ6L6QKlUKpVZ4W+WM9UhUSqVqVAdkqKBK1p1SJRKpVKZPapDolQqU6Y6JEUDV7TqkCiVSqUye1SHxPGPkDClspiYShtQh6Ro4IpWHRKlUqlUZo85d0gwerhpPPPpDHRaw5jBpxsz8gZjemxXeI5JWH7+bhkWVxnOxDyMP0Ze/up+O1QrfSzf/LOOMUPOtnX3OmM61wuPkwxJf49tjRl1mzEDjrNh1cvGSaQ6JEUDV7TqkCiVSqUye8T+yqlD0m1TYya9bMzs7sbM+tOYf1sb02Wd8Li5YNeNjZk/JEjv+OaFZczzrF03MmbyWzYvOxozp4cx074yZui54fETSeFTvlE0tnPJ7lsZM72drZPdjJnykTXu6wfhGOrk5dy+xgy/Onp146/tjVkyLai7g05O//k4b+RNwXXmDzam4+rh8eKpDknRwBVt1XZIaOt0PEiHRBHLQmWEaOsh9THWIaZUJsFEWRbf4Rp12cYz5swh+bOuNe76BNdesTQg+O8Xl1l5YLdNjJk3MHiOCc/k7zlSJZWsz77GLHUGKcYCBuKK5TbsP2N67RF+nmfHGsb8815w7sibi1vgST7ODPLC2Pwbck4QTh6PuScInvq/6NUN6u7cfrbMFwfOCc8bFq8iUvajbg3SuWCYOiSKUnBFG8iYTOgD6hsKk2t5xZlu3c0UBxwTdDxMeq1AZKE1LMg7GJ93bPu8zXeeRpYu78gjT18PQ+PniTzP328bM7uLMYPPjm558pw+P/mF6kBll/H1N7Fe9NzZ2rQDrH33QVAOvfa0Rv7vxkz/Lnp1PJE8X84cku5bBtddOsuYzusEGUnP7LiHSirwX42M6X+UMQNPDJREnyZBPNhrNxveLLhOn/2COP2PNqZTLWO6bujOO8ke2z+4Fuf03MWGnWDP2cKY3nu76x5r9zcP4iQ6JDwH5B7+OfjtvlnZgs8nyT8EFVgwyubbjsb8YsN772PMZKtUe2xtDcvqQZqZhkMe9D8ySDfpIE2zuwbn//1OsE8ecqzLenb/iOAcyoBr+ft2XNWWQ2N7zJYDxz37H1aSP733Cs7z5YOx7M8nXwedGuR7P3sO8WgwXKPvQcEzE4+pdAPsuVwj2/mOQ7JstjELx1p/ZL51kG2D6FQzuG+8Q4IQkDpkn5VRFY5D8pU8ph7i6LHd29Zb8k3ywcbnPOJSPuQdaevaoCRt/HJNX+fij8O+TW3Y8UEc6i/5yr3Ia9oC+cV1qLvcw+c/bcE7GH/tFHd9e9w/E+eoQ6IoB65oM+OQUN+GXmjb2hh3TetMz+pk6+YOwbGwc3LBYZcEz4MMoJ2HxYkSecblC4I2yJRjn3e9djdm8d9BWvoemN88jSoxehaODvLIA1uHUXJmHISdkw9SxovG24dbbsz4J0tspCgRe2Da1yUdo2ZF0IaGX2mPJ9sbTzz0fsR776NC8nzOXy6/LbA/4tt53wOCcGZPUNexiZHdIOqyLacOCQYawCEZdFpgQPMANDQylGkniyYGcTwwFIeeFzzH9G+CMLy/eCBI5vR0OxbLrFE57OLgnKmfBmEcX74w2AbzhxrzZ20rgDYu7ZBQYBiUXmF6cH63huHpygd7WuPSA2fLCyvykW3yFcdkuVX48WCECmN7+TwXYIWdx6BTbBlZ52FuLxfgsGCkTbvNJ67f72BbfjPcgTigHCnPsQ/EjTY4IKAwjHmucY8FYbOcMwX+bhH8LrVljXPCfTDGAWWWbUHsHZIFI6zgfyq4LyNM5GW8Q0L6Zv4R7HsnGuLEAMqh89rB9iJrFJBuD67PiBQjWB7Tvw3iyzNYB2bBcHfAYV7/wPGkTvpphXNtmAeOI3kGcFKo78OvCNqtBwbLyOut0WLb2+J/XKAD5UiZU1fUIVGUA1e0gVKrjD6grQw9313L1h2m7SKPli+y7emRknbuZZhnvLL1x/z1EuPEhyWew6/f9vs+TswhsXI/Xmn7uJ4+PN+kHADyBAfPp4VOHS9/6fjx6fS//nwf5vd9+ny4Z3xcf9yfk3gs8bg/trI4+SDybvHkII/mDQp6j70Tt+RfGyfOMI5/bujD49MWn77EtMUfi2fiNRLj88tzTnzOmBnfB7ZR/LXit6E/P1fk/tggXvdht83sYNtzb7vjdNy/X5R+tsRnZp+2ht0FMESp14Sv7JzEY/zGxwuL4/fDwuLPTTw/iqTjGHj9O+PH0jIr0SHpuWvgNCbO8khMtw+n/sfnRWicLJHny5lDwtx8P+ed6VrzBltD6qjgGAkfe1/gKODRYTRPbxvEnfpZYAxOaxPsUxAjrJGFceyBIqFXmsIB/7UPzpn6SbAPJr5ojbh1SwQ2DYFnijkkTweZLoah9fQnvhA8mzdCx9xt9yPixdMDARCg8ZXMU4xYK2xn/GCdjG2sQ7GpFbqTgnMYhWL6nM9PXmYmXVzHj5pM/dzGWdMa6I8H+9NsWXBdf5wyYlSGslg2Nyg/RlUwvMm7MfcGL13P7hHE57xO1tAd/0SwDya+bMtjg6Csl0wPwpguRRlMfjPYn/Jx9gWEn7K1whpGOJ302jIVijxMdEiYXgjGPljSSOVca6wx+oSDgYEFcCLIe+8sU+dH27xmxET2bd4xvNpxtcD5ABNfCkY7prYK9qn7P9t78DwABw2BQ68IDv6SqUE4vaTd7f7iKfa6ViFMeDbo7fvv52DYHwcK5YvjwvV9Oia/HqRTHRJFOXBFG9TxyugD5Av1ESB72O9U27aH44KefR9n6EWBIca7cZOsDB5yblBnOU5nETKhi5Xbo608nvyGjft8MBKObKcn+W973tj77b4b8UVO/fO+NepODNreJNvGqPfDLrPXdPI80SHhPGTk6DuDuHD0XdmXRclSHBIrZymToReUPBejIjGHxBqxpIVOCuTpYOugEA+yiMuUD6y828yeZ/OAdJKPjKKOe9jm4bv2t7mVBTWsfGxqj9lyYCR9xDXuXk5f4GCiR8nzSVa/0hHYycp94jACTlnRmYWOIQ8nvWrLeq+S580Hee5FThciD8kjRo69IT3w+CAOnZXIReQnU/lG3x6Ecw1GpknLqFtsOg+x21aXkcekPz5tyGmpb/aY5z8tgmM4jOQPo9+cgy6hXIjDeTzX6DuCTjLicO8xtg5SFoxwo5uIO8EepwPP3zMntOXv7SJ669FzPB+6HDuKugnIX9LGMUb40UvUIzog6Pwk77z9tXCcTY9tzyPc+5qd17Lb1wZ5S/3iuv0ODa7FNbEVSD8j/jjlnEsd5D4ch8zAoZ5Tljw3NgnlSBmwiBHXwmDHzqGcqcvMHCiT3oiQ551snxOgw7E1sAGxW0gvcRIdEkafqSNjrEwkX/21yGfJW1sedCT7a3DOpFdsPln5ik2HTCWfkRHknz8/G+TeOXNISCyKY04vm5HOswbjHg2O+UrIFB+MZpQIYGoSQ3regOZFeBpEl/WDSgxGXBc8qzcgaSRc0zskMzsGjYUpQdwPzLcNgQz2DYKMp4eJZ8PIHnyGFZ6N7TO70QjiIaDD0pZLki4EJMAgpRDLxHMKA6OUPCc/mSMNEHoM+5GPsn+jy/t6QdoxeolDQ2X6jzgd1tHgujhr7OOsEJ8X6YEovsuDbcoE453nxJAAXPNPW7beIZlvDV/O4Zo8J8IETPsmOG+BG6HCkSqVrixQHJL/gvvxTFNcnWHlKRohWKlDYs/FMEh0SLgWaUMYIKCpP8SnnvrRCgwxHDVAHuGgUF44dOTzwlHG/GTje4cE5cY1ySPK1DskjDgOPis4hxFI7kMc4kIpX/tsnEP7wtEBGIjUaXVIFOXAFW1QxyujD6iHdJAADEI6lHxd9se9XCsF23Zoo8RBUQJGM+NBDzdTVz14VhwanhdjBeCs+xFFD5wf7p/okHTdJNAh3rASWIOVXl8vt/JJ0uUdEjojeCbYe88SWYZDQrwZ3wX7jJh7mTW7WxCG8YUuoLOC9PmRAg86Vfz1gOgGa+RxL4zLMrDXwKAnT9GnYEHi9ChbBnSm+HLPNaln3iHBKSAMPUlHFBhsDV32sVO8k+IxxeoB8pT6QgcTaZFOOAfyhxkdpI0Rc8onDBz39g3GIPs8Fwv9AGwUOqIWTQj20ZvoHzoZKXffsesxh5kPtcqmNRvkWfv6mRL2WeiopU7540zL9m0RvUl+0YGaOFsDXYpzkAg6k6mTHE/MP65Be+aa/7QMwhJns/Bc2C08p5/ahC3Ds+GYeF2KfccUaGyReHC9+PREiaSbzlvKnzQtnmjzyOrhWEeBZaJDMugku+PkmJddLH6UqL/JKzphuAdg1suShNkwOC/YlvHPlEnyvDlzSCCZhsFDT9XUj0sqHEY/PSqJczsBhhzGrHdIMFpxLhi68g0WAUFm07gBlYrj3ANQucls7j/s0iCMiljKIbGNHuHuBVMiuGYUHBJIYwIIQCmjhEoiCsMawjhWiaAXHgcv3iGhItBbwfVCYSs01/UvwjN9zm+j6Dnfr9TElDpf8WnYfgQBZ9M7JN5h9M9L7ybg/vRoAinDHMzpjXdIEPo4pTRW6gy9i6CMQ2LrmVfuCEDSl+iQkD6O09tL/vlRF/LKOxIITupyeUCoxBwSe42J1gjwwrKUQ2IV6/DL7IZVoOR3fLslPkJJ5iMngNEb2pY6JIpy4Io2qOOV0QfUQ+SWbx+AaY30eiKPaBf0ojNFBWOO9uZHvOkk4Br0SgNkNCMcvaxS9p1b6A56/OnpBBhF9Pp5hwQwTZL7MAIMaBPsxzskpBEjC3ANep/pZUSWMh04F++1VUSe0RsZ5SHRISHvKANYxiFx06DRp72tPPQ6Esz4ycpn3lnsHuz/2yq4Lj31yEPkNcZzvH7mmn76q7H1Bh2DjPcyiHIm38PSlm1yX++QkE6mxXrDlbpJ/tB7DHB8OQdHz4/i01HFqJTUYytveXkY24J3oYDMJrD6eLqtxwDZityncwlgsJM/MYfETf/lufw1GJ0ShyTuHRLag7dV0LnUSab6ehuKZ0hMazZI3Wd0QhwM+2z0ose3B9LqjUrqCscWMX2ZvGoRpJOOY9o2um2Cc1ypP6SR47xDCTCK/SwCP7qKg0s8n3/oIUYv6dhb5KbiYftwX99h6h0SWQjGdcwii5jKzHOhZznOyNPszkF5+PREhVIvrwuenRknpO/fL4N9Ot69bbqyd0hot9Rd5Cd5K++Y2ryVDh4rT2in5K0HU2q5DyOcYNksew/e97Fh2SDPmzOHxDc6Egh77xEYc4BMYp4boBKjBOjRAQhLGttKHRIrWGgI3iGh0jKC4kdIaOi8M4Kg8EYxApae/niHhMZFQZF2hk15DrxqhBYMS1c+iAPnK9nwq4J8JW3kK/nAcKT04Myzlez6QBl4ocvQfLxDwrAo5zMaISMkNv3DLgrSThjKSNJuz5lj8yzeoOD9Ev/9Fj+NjFEUKjVhnW25AXqRKMOYQ2INYY7Hp2mWFQRAeussxj9W+ni2GO+QUN/JC+oLhj0GDIg5JO2DfXoHiUd8pkiR12k7JH6ExOa75Lk1omJ1rl5wTswhsdfywjJxhAQlgXCmF9jnLXFxMBghBFM+tNfeoMSZ9L0i6pAoyoErWlvelXRIkE/UfaZU/GeN3Hg5glFGveP6vL9FLzNOAj15AAOa871DgtFAu6CtePlNRwttlGldgN5+jOXYCIltK8TnWTBQPHimeIeENsNICOA+fOsH44VjtMFRN5WVXbkm+SQOiSVTZ+i5ZJoV8sr3RKfjkGAokh84iYy0Agwa8pXpHYD70YnBtXs2DqbUMVXJj7pixFCW3iFB1nNN8gyDFCB/CAtLW7bJfb1DEo+F1vjHjiA/vC761zqujKINPrOkJ53pgNQJ6i9OKqPc1EXfIed7+L1z4afO+fwhPjM1KuOQYCCKbLdGqB8tpEzC0ptpUo4yQoT9gUOS8FI1afVGJXqz3+GBHYJ+Y2SFuDw78fjFHgG8C0weEMY2oCPCx+d9R7HNbJ6j+6jvgPrFtTh3ArrWgpE95MVKHRJbpuh+bD2cZto8HeWSFnuuT09UKPniXmPgXVycOe984KBihxFvZQ4JdYj6C+gMogOe9utH5nAOiQM4x0+5ww6hrLEPq4RDQmbSqGj09NZicPn3E7gPzgMvRQF6T+ghkxekLMQhSRwhWcNeI94huSyolOU5JIBC7G8FrR8FYNpN4jskNHAvrHghnueggDGSESxhacsHMXx9jwENFAFBGIIKAcXcVioUSgUDgFWZfE+iOCS2wTF/Fcz8Nej1YTTCKyZe9KOCM6SJN07j5xzyjvzhhWqcOYxZDGcqLZWXHkRAOeDM+HcjEMZU5JU5JDK06CBDs+eXjZMNJjok3JN0Az/CRF2gocaUrhVqTK3iux0A4zxdh0R6KFzvGU4ieYpBQHzqb+wdkpU4JAgWFJ0Msdp4zPmkB40pMpSvd0gQ2Mz39c+tDomiAriiteVt5Ukm9AHtgpFpej79PHTkOPIHHYHxkgimqsQ7JHOsQU07oM35usz8cdoXchrQodDHtq+YQ4KD4pQ2Pd6+/tIe4x0SdIA3SMPANJNcyKWVkXKgnVMmGMekgfygx7y8KVtMdyMO9Cs0JjokfnGB/hiRbioS+oN8RS8D4tLBhSEdP13JA/1Dh6F3SHBQeGaui1wCvqc8MV25IPf1Op73ZUgr79741SSRx7zLWh7QbTGHxNZVepm5ph9VYdSI6VM4MgDDz4+scw5TZks5JNZApD6Rx3xPDFTkkPC+LedgB/lnzaVDwog8zgF1kLygbP1xbAP/nNgNjEJgNxC/j7Wl4tsO+RbmkHhHgvbu70lHBZ1t4gTuFueQWB1G3nEeIyWATlHpQHXXoWOC68iULVsfAVOcuS6dDfFTFZFJXC9qTgn2hl+MgTLH/os33nEsiFeRQ+Lt7jAw6hnvkFB2XJN3fZGX1Pcq4ZBIJbYebqIRQ2Old5cHGXJeYEB7+FWHEHAoMO8d4sliNOGQkOnA90LgZAAUHIaWd0iW/Bv8ejDliHtSQX0PAwVFeqmg3tHxYLgWYR+WtnyQ/MRhEEFnBVY8cCDILz+c5+EFF737nM9ysfFzqulZJCxRGCMAmLrEfWe7UZZ4kLdMcyI/x9wZVNp40OuDEYzAQNACBAXPEJ8mXsr2+Y7CyMV0LYhD4uulr++kxb9LAnhu6heKK361KvLKrwwXv8oWIH2k2fcsIqS5Btf2ipyFGBB89DLFr8oFMCwQ/DyTLxOEtxf+OCTeYWJkj3g4otLj44ACxFkfenFJXOCvh2OFY8k7Q4A2pw6JIg6uaAMF5dtHuqRN+HZPPaZnHdCmMGQwWphOgBzHUMZABrSdCh2Sl4L2Fe+Q4PB7hwQZTpvh3rQ7QJpoj4kjJLFFJT4N9nFSZElyjJQIGCqUg3dIVvZSO/G8Q8LUItJKerzOS8YhYZly8jXmkPS3hnBtex+rexkZ5kVg8ifeGCzPIeHlZBAVh8S/QxJP0kKHHMDpIm/jy5996ksZh+Sa4Bx0Mnka32EImFKIbSHXwyFx70D882GQN9grsXKIsEMCqTeMOIL4suSZqHd+2juzL5iKTx7glGBjEMfH5bwwh8TP3mCExcclf9E56EXyyo+2UR+Jw3neyaMDjjA/K4SRL/bpeJWRTgu+7cI5XJvyop77Ok9nKMc4JwrkGZm1EqJzY2CUg/pZkUPi37Omk5PjUrdt+r1so62DKu2QeNI7QO8UFZoeXXrRfQXFEJLj9hhxcBZQKPT0klHy/RGrSGTY3e6TkXiNGJT0SHAdnBTOxyMmcd4hIZGsjCH3td61TN/invYaxCWcTCeM66DoeD6Jbz1z9qXQ7LGokOfEAKan0aeNoUnygOMIKF7KJw9JM/mJw+ENfdKOUyNptGmlkUse2uPEl2taSs+RzW+m+iBocBqp9AhiP7TKECkVmetSXuSZXNfen940X8Y8G+WFg8J+PMlfL4i4rr9etkldIP3QPydkmJI0UOe8sOd4j62CfCGNTLEiLewz9Yo0+LT76zBMzD51m/Mh5STTtVw9lHi2Xku+k2/2Gv7FT0iecg/K0MdH6ZO/PB/bPjzWxmx8DDDKmeNsc09pW/a5pW3ZuAgYRrkoFzHYEEzuWuVRHZKigSvaQEFVRh/QnjGwMPYwBJFFfvoEU42o51KnrAHGSC9Otp8THnNI3DRepo5iMIhD4pbErsghASybTfvyPY3//RpcJ+aQjA3S6B10ZB3zqml/GDtMjcCZ4VnD0pgr8ozikNi8KtchOSWI5504nh2ji2loHuQFMssburxMTX6Q9zGH5ITgOn7hkZhDwn3sM+DE9DvI5p1bYMaPkMhqSxaUMc/GdQvBIaGeMQ0aYANhnCJj/btJGHWlHBKrC7lmvEPCNajrONj0xDPijjEMvfHn41PHWAjAL/gAxic4JH4qjZ/O7EdV8uWQ0M58faAOMFpBW8Mp9R1ifqVT9Kt3gOmcJC9xjnEMOB7rRLZpxXFguiYzBAT22ow8sQqaTyfT6Mgb73yAf+z96dD2y+rTfskfFrYALMNPXfcjJgDnkHJEFjA7g3fDaCMA3ZnvNh5PbGM/zRpHlulalAHtEvvDgxk/2GWgvHdIYqNbFsxYwf4dbZ0UOoWYdk+ZgDIOyVJ7XlVzSHJJEucdEhoHBndYPGXFRJmMujnIS3rAEH70aPppdLyPU1kHAqHtey/8HNmweMr8E7mgDklRwBVtoKAqow+QxyhTFFs8/PRQjntHwMOPPvKuGkYcS4UDplQhH+jQ8iOLGMDIIKaOAqZ94th7h4SeVT+iDui5R5ljePjFKxih5RrItikf2Wd1Pb0evNOC0g9LXy5JOXgwVcMbTxhdfmou+eDziCmyHoxoM+0ZYBxiIPvZCOQ/5+CQYAwC4ogB6qZb4bwwAj/88qBOePiy4heHxH9zitWqeDau641Ifinv+DTliqQlNq3NOm1hcejoo/z99G4POsw4H8PZA2eVtPgw6jN11Y+yxQOZidPHPegU8nP3AY51bLaGzTuMbj+zg5e0MRT9i/FMG/MOia//LBqTmI5skk4sZpV4B9gDRxbnjY4w4lHuvDviHVaBrVt+YQOZNu9GOQGLWpA2uXbcjBmAPUeHqcgSV5e4X3y7ZiaNvPNq4zBrwTsyYK6ti6yuCjDUcaJ854QHIzb5qpvlkcU6/EwYtuOdJdLqp24jH+lwBOQd6aCOe1B3cSgYYYufSQGYWUMnaLxs8Q4JHcke6pCkSRLnP4zIig4I0bB4yuTIy+1eYMZjvlVmKLD4RpIO6d3zqOy1lNmlOiRFA1e0gfFZWX1ApxAjgSg+nAFGODD+fHtnKgthcmyrwPjgF6Oa4yhf3gOJjfJaY1qut21wHcIw0tjHGGHfz+Vnnjgju1yb+3ujBfJcnOPvI9epHSh/eVaO2e2odGqRX4zU8mzxeo2eVMk/+7zkg49LWiUdlhjCpIPRUT+ySrrZ99dixgEzEsgrb1gy0i35wCitNbih5E98Wdlz/AwGyoNrcj/Ol2tYR49r8OvD8kFfx+JHqBMp5U/dor5YklYfn2mu5CXplhEPwmze4VhjvJGvOD04xfTcE4/ptB6+vmOM+3IhfzmXexFOnO4uf/0+5UT++bouZcAz2vtm01Asl/aezACQNJBP9pd9X688eXbSRj5IHbLP7POSY1Iv3DXIB8J5l0euzXUtac++Tsc7JLxrLHnv4pQaKbL5E7u2vTftgHeLpX3YOk7ZySwHnovzbb3w9T1KlNksLn/KHKcMXL2gDTJTg/pAfeW45I09l7z39U7kpj1HysPlm69jUO5lGZtJZOOTR7Tv2H2zwCrtkJCxTIGRF37tb14abBUjAoKl8Rj2g2wT5it6ZYjg4AU55pxm4nrK7FEdkqKBK9rMOCS5Jr2zMYfkn8BADounVGaK1DneiwU4JegzjEI+TAeY5qX6rXKMd0j8S+1h8ZSFxSrtkCiVyuxQHZKigSvawnVI6KEGsjRmWA+jUplhMtLnV8yKB6N0fEsi7Bxl8sRwZUodUIek6lAdEqVSmTLVISkauKItTIcE8kImo+S8KOunfCiV2SaLorB6Ii9MM4+fF9f98rPKytHPfiFfecdJR5yqBtUhUSqVKVMdkqKBK9rCdUiUSqVSGX2qQ6JUKlOmOiRFA1e06pAolUqlMntUh0SpVKZMdUiKBq5oA4ekk93tqlQqlUplhvmn5Sx1SJRKZSpUh6Ro4IrWrFi+zLx14yqm5d1KpVKpVGaW79yyivl7lDokSqUyFapDUjRwRWuWLVtG+SqVSqVSmRX++ps6JEqlMhWqQ1I0cEUrDsmaa65pGjZsqFQqlUplRlm/fn3TpUsXp3ECxBSQOCQsp8a8LqVSqfTk+w7qkBQFXNGaFStWmN9//9106tRJqVQqlcqM8o8//jAzZ850GidATAEtWzLHdH5pFdP9TaVSqSxh5xdXMSuWq0NSDHBFK/j777/NlClTlEqlUqnMKNEvixcvdtomQEwBzZ0zR+Z01VhVqVQqS4hcWLJEHZJigCtaweTJk80///yjVCqVSmVGuXKHZO5cMTzq1aunVCqVMSIX1CEpDriiFahDolQqlcpscKUOydKlS02rVq1MmzZtlEqlMkbkwvLly52kKIGzYRVVCK5oBeqQKJVKpTIbXKlDwkuMRAqb66VUKouXyIUwOBtWUYXgilagDolSqVQqs8EKHRJVQEqlMpHIhTA4G1ZRheCKVqD6QKlUKpXZoDokSqUyZapDUjxwRSuIsj5g5G7OnDlm3rx5oceVyZF85P1RGHZcWTGnTp1qFi5cKFy0aJGZMWNGaDzlyjlt2rRS+Th9+vTQeMqVk/oXn4///vtvaLx8Ux0SpVKZMtUhKR64ohVEWR+gzM477zxzwAEHRFbhFgJxSE4//XTTrFkzydOwOMryidE8YMAA+YgoXH311c37779vZs2aFRpfGc7//vtPvk2x9tprSz6uttpq5quvvlLnLkVS71q2bCkfHSQfa9SoYfr37x+beh0lqkOiVCpTpjokxQNXtIIo6gN6o4cPH272339/6p/ZYYcdpGc1LK5y5Rw9erTZZpttJB8PPPBAdUhSJHXxt99+MzVr1jSvv/662FA//vijqVatmnniiSfko29h5ylLE2P5+++/N7Vq1TKfffaZWbZsmfnyyy+lXr799tvirCSeoyxL8vGjjz6SfOvTp4+Mjrz88suy37Vr18h13KhDolQqU6Y6JMUDV7SCqOkDFC5GII95ww03mCuvvNJsu+226pCkSPJw4MCBko+XX3655GWTJk3UIUmR1MezzjrLHH300TLljfxjmswVV1xh1llnHZ1ylCQnTpxozjjjDHPOOeeI80E+ssz8iSeeaDbddFMdbUqS48aNk46Fhx56KDYiMnv2bOm0Oe644yI3vVUdEqVSmTLVISkeuKIVRNEhGTt2rPSiYvjdfvvt0sOvDklqJB9HjhxpvvnmGzH2brnlFnVI0uCwYcPM7rvvbp577jlx8ghjihG9+rVr1za///57LFxZPhnx3HHHHc1rr70WC2N0iVEmRp/++usvzcckSD4yVYvRJR+Go3zmmWeatdZaS/a9oxIFVimHhOEnnTsc5ENYJUNJayOOJikvyidKwmFlVIekeOCKVhBlfUD7ue2229QhqSQxCm6++WZ1SNJg3759TYMGDWRajNe11MWff/5Z3iVh+pbq4IrJOzh169Y1b731ViwMx+7TTz+VfOzYsaPaeklw8ODBMurJt8N8GB0O9957r7yTM378eHVIMk08Z+Ztbr311maXXXYJjVMsZIiOitaiRYtS4f6r+1REVdbRIUJ10qRJ5r777pPyoae3EMpHHZLigStagTokVZ/qkKRP5ulvtNFG5uOPP44ZetTFX375RQzpH374QR2SJIhDUqdOHRmx82E4JOhH7Bt1SJKjd0h++umnWBgOyf333y/5iL2oDkmGedddd5lVV13V7LHHHvJiY1icYiEe73rrrWc+/PDDUuEsh0nFfPTRR0VAUgkXLFhQSnEzv5VpDxyjYuDEcB6CgJehOMY2dYJz2SdOfIXmPMIh5zBf0Ss1f4z7MHeR7fnz5xetgEagdujQwRx55JEyfMoKGK1bty4IY0odkuKBK1qBOiRVn8hpdUjSI6sXbbHFFqVGSNB39FDzgrZO2UqOgwYNMhtssEGZEZJ3331Xp2ylwCFDhojdFz9Cgk124403ympb5Kk6JBkiGdmjRw8ZImWu3J133mn22Wef0LjFwmQdElZTadiwoQwhc5ywDz74wKy77roylxiHgZeeLrnkElmlAYeP5fcYeQHrr7++WWONNWRpyAkTJsg1KA9ATxDCl9/rrrsu9uIUz8D16fXYbbfdZE7tTjvtZHr37h17zmIiPRUXXHCBCAdWvNhwww3VIVFEDq5oBeqQVH2qQ5I+sUP22msv88gjj8QMZl7KfvHFF029evWk5z9KBmBUOWLECLPrrruaZ599NhaGvmQmAXYH741pPlZM8pHOzlatWsXC6ARm0YXNNttMOoXj4+ebBe2QQBo9hi4JYaREHZLAIWHImB54TyohtoV3SEaNGiUNu127dnIeYfRGEIdKzAjIQQcdJPus7b906VLz/PPPyz6OBEPT9Pyw5j/Ki23O22qrrcTZIX779u0lPuchlP20MdirVy/x1C+77LKiXV6SNDPdkF4KenzoEVKHRBE1uKIVRFUfYJwgg+JfaleDJXWSZ8gl75D4sMR4ynCSd3QyMX0c24S8wz6hTh511FGih8POU5Ymq2yRj9hz5KnPR4xr7BFsibDzlKXJlCxmYJx99tkiu6mTjOLxoru3y8LOyxcp6yrxUjsJUYckcEgYMWLk4qabbooRRY1tkYpDgqOAF+2FKAqf46wYQsVm/9ZbbzWbbLKJOBcIjfgpWDglOCgIYoSJd0gee+wxaQjE//bbb2XosFDqWbaoDokiqnBFK4hiO+WZ+OYDK/IcccQR0hPN9nvvvSejvWHnKEsTWYzx8uabb8p0o8MOO0zkOtvkI51MYecpSxPd9+eff5qdd95ZDOd33nlHdPG+++4retXrRuXKST798ccfMv3t2muvFdvk8MMPl05S3rmkvoadpyxN8pFZMIhxHBDk4qGHHmqOOeYYscewm8POyxfVIalixCHBsH344YflRSZP3lWgUqbikDRt2lSmVnmHhLrAi2affPJJ7ByGUFlfHYeEMASFf4cEMO2Lyh/vkLzyyiuxF9J+/fVXebmqUOpZtqgOiSKqcEUriGI75ZneeOMNkSsY1MwzZ5uvY6tDkhy9Q4IOePXVVyUfMabZJh/VIUmedNQxNQvjj3qIU4deVmckNTJzgJkUPh9feuklqafqjKRGOn9x7mjL5CNT88PiRYHqkFQxJvsOSZhDwsgHceIdksaNG5dySJiuxXQwfw4OCe+F+BESXkbjGrxzAtk+4YQTSjkkCGh1SEpTHRJFVOGKVhDVduoNlUSGxVWWz7A8hGFxlSun5l9mqPmYGRZCPqpDUsWYikPCi+feIaFXhxfxiJOOQ8L1WYqP8+lR82C4OnGERB2SslSHRBFVuKIVFHs7VSqVSmV2WPAOCd4exjNThJimxLK/LDcb/y5DMTFZh4R4++23n6yCRX516dJFjsN0HRKmhnF+586dpf60bdtWXkLDKVGHpCxpfAynkv+szsL69eTh8uXLI/eyWSLVISkeuKIVqEOiVCqVymywoB0SnJFhw4aZTTfd1FSrVi1mULO93XbbyWoCYedVZeJo8E5H4jxB75DgtPkeeBwHn3c4HoxsEMc7JDh3fGgy3iFhDXCWAWaf69xzzz2yHDDXx4h+6KGHYuVw5plnmgsvvFBeNI13SJgL6h0SVuKqXr16URo65Ffz5s1j+RVPXvyPskOtDknxwBWtQB0SpVKpVGaDVWaEhF7meBbrCAkkP5iCFR/mCzq+55388XnHt0I4h23ylPg4EISz7c9hJCr+2ixbyzWIA32PP+S9Esh1/HHCeVnNn49jErW1sHPFxPyKZ3w5RZHqkBQPXNEK1CFRKpVKZTaITVQl3iFRKpW5ozokxQNXtALVB0qlUqnMBtUhUSqVKVMdkuKBK1qB6gOlUqlUZoPqkCiVypSpDknxwBWtgI+SoTSUSqVSqcwksSvUIVEqlSlRHZLigStahUKhUChyCnVIlErlSqkOSfHAFa1CoVAoFDmFOiRKpXKlVIekeOCKVqFQKBSKnKKUQ8LyoyztqlQqlZ7IhTA4G1ZRheCKVqFQKBSKnEIVkEKhSAvOhlVUIbiiVSgUCoUip1AFpFAo0oKzYRVVCK5oFQqFQqHIKVQBKRSKtOBsWEUVgitahUKhUChyClVACoUiLTgbVlGF4IpWoVAoFIqcQhWQQqFIC86GVVQhuKKNLFh8RaFQKBRVD+qQKBSKtOBsWEUVgivaSOP99983u+22m9l7773NvvvuK0tTK1KHd+4eeughc+edd6qzlybmzJlj9ttvP6mPu+++u/nzzz8lXPMzNUyaNMkccMABsXzs37+/O6JIBZ07dy6Vj3PnznVHog91SBQKRVpwNqyiCsEVbWTRokULc8ghh7g9Y4455hhTq1Yt069fPxeiSAVNmzalHZvDDz9cDeg0MHbsWLPeeuuZ559/Xva//fZbU6NGDfP555/LviI5DB482Ky77rrmo48+kv1PP/1U8vHXX3+VfUVy6NSpk7RnLw+ffvpp2Z8yZYrsRx3qkCgUirSAAauoWnBFG3l44/nff/8VhUsPvyJ50KtfrVo1c9BBB5k77rhDftUhSR1XXnml5B3w+Xf55ZebDTfcULYVFWPZsmXmiiuuMKeddprs+3w89thjzfbbby/bioqxaNEi06xZMxnxjMd2221nLrzwQrcXbahDolAo0kJgwiqqElzRFgymTZsmDslNN93kQhTJgB7TN954Q7YfeOABGSlRhyQ1zJ49W6bGvPXWWy4kwP/+9z9Tt25dM2zYMBeiWBnIR6YXffjhhy4kwEsvvSSjn3///bcLUawM5ONGG21kfvvtNxcS4JxzzjHrr7++24s2Ck4B/fXXXyJI33zzTfP222+b5cuXuyPFB9LeunVrM3HiRBdSAuZZDx061O0pooRZs2aZli1bFvzc98CEVVQluKItGPz000/ikLRq1cqFKFIBOuT+++83Bx54oDokKWLy5Mlms802E1kejx49epiaNWvKr6JiTJ061dSvX9989tlnLiTA999/b1ZffXWZzqWoGNOnTxdZ2KFDBxcS4LHHHjOrrbaajKBEHQWlgH7++Wdz3HHHmR9++MG0b9/eNGjQwOy///5m5syZLkZxgQpWr1498/XXX7uQElAxn332WbeniAroxUCJUT6//PKLCy1M2DQoqhhc0RYEFi5caLbcckuz9tpruxBFqsAJUYckPfAS9sYbb2x+/PFHFxKgd+/e4pDQeaqoGIzWrbnmmqZLly4uJAD2Hg7JkCFDXIhiZfCjxb169XIhAZ566inJR3VIsoB4oUmDpwASh0yLBVQw5qq2a9fOhZSAfHnxxRfdniIK+PLLL6VccKoxpAr9hT2bFkUVgyvagsBll10mzgg9rIr0oA5J+mAqEXI8cYQEw5qpRn379nUhipWB9svCAIkjJG3bthXHbuTIkS5EsTKUN0LCOyWMkBQCCs4hiQcrXFAADz/8sAspLiTrkNArn1hRWc2CMKYPgT333FPmcT755JNmhx12kB6LW2+91QwcONDsuOOOplGjRmaPPfaICYelS5dKTxACuXHjxmadddYxBx98sFmwYIEcB1z/7rvvlhVcdt55Z7PpppuW6U0qJjzzzDMyDM0Lpcz1VIdEETW4oo0svNHcvHlzGSFnVRlF+lCHJH0wM6NJkybmtddecyEBPvnkE5m5wJQuRcXABsGGYJp5PF544QWzxhprmHnz5rkQxcpAPvLuUqJdcfrpp4usLAQUtEOCIY4OZam9YgQOyQYbbGD++OMPF1IC8sU7JBjA9NjED4ky55o4OCvgyCOPlFVXmA4HmLfJ8RNOOEGmRoBzzz3XnHrqqbLNvd955x3ZBosXL5b4ODEe7O+zzz5yDCC4cWCKXfHhSKtDoogiXNFGGu+99570+LVp08aFKNKFOiTpg9WhLrjgAunMiwcde+SnIjlgH5CPrKoVDzowvb2hqBh0BrMk+u233x57t9p3frL8byGgYB0SjGQ+RlQonl82gFOAgV+9enUZ2ownzkUqDslhhx0mH9HxoELTOxE/+vLEE0+IA1Qejj76aHPyySe7vcAhie/16NOnjxgSxbwQAVCHRBFVuKKNLHh3kMc8//zzpQea+eVw1KhRsY4PRcVghHv48OHS8cTStXxokm3yccmSJS6WoiJ0795ddCK9+RMmTJAVy7BJCuW7D1EBI53MymClLfQjHZu8n+M7QxXJgQ5l5CPfIWE2yyWXXCIzXkAhdDgUrEPCkD0Z/99//7mQ4oMfIUlc5g2QN6k6JHvttZdsA3p/6tSpY7777jsXYmQ6F3M948HShhgECGO881NOOcUdCZ7hgw8+cHtG5tSqQ6IOiSK6cEUbWfDBubvuukuW+eXbBRjT/N533336LkkKQCfcfPPNkn833nijGIBs33vvvWbGjBkuliIZ4NhdffXVUg8vvfRSnWKUJuiwvOaaa2L5qEgPTAunLfM9HBzkQhr5LCiHxGfsu+++K6sGFPP7CCDZd0hQPox2xDskTHcgTroOCUKXYcBNNtnE3HbbbcItttgi9nEjwPXVISkLdUgUUYUrWoVCoVAocoqCU0B8dIgpSvwWO5J1SHAeeNmpY8eOsg/wnomTrkPCUDXnz58/X/YB8z1POukkt6cOSXlQh0QRVbiiVSgUCoUipygoBdStWzcZGWF4mTmwTNeCGNXFaOT6KVssj5cIbAvmtQLyiiHlE088UfLLLwYAvUNy6KGHlno5D4ekdu3aZRySddddV7aZqsW9eUeEOcd8qLJGjRqyooMH1090SIhTrA4J5UX+9+/fX/Lum2++kdErwgsRtnwVVQyuaBUKhUKhyCkKSgHxHYfzzjvPnHnmmaZZs2ayAhRk3mExvkuCo3HDDTfI3MtEkEd8WMgDx4M5w+TXnXfeKS8vXnTRRbFlevl4DutVe+A0MA+Rl6M8cE6uv/56txe8iMY7I8ccc4w4JF988UVsVAawKlfnzp3dnjHjx4+XuaHFupoLjuPxxx8vo0isKuLzrlBXiXM2rKIKwRWtQqFQKBQ5hSoghS73qEgLzoZVVCG4olUoFAqFIqdQBaRQKNKCs2EVVQiuaBUKhUKhyClUASkUirTgbFhFFYIrWoVCoVAocgpVQAqFIi04G1ZRheCKVqFQKBSKnEIVkEKhSAvOhlVUIbiiFfAl9H/++UepVCqVyozy77//NosXL3baJoA6JAqFIi04G1ZRheCKVqAOiVKpVCqzQXVIFApFxuBsWEUVgitagTokSqVSqcwGV+qQsPTr1KlTzbRp05RKpTJG5EIYnA2rqEJwRStQh0SpVCqV2WCFDokqIKVSmUjkQhicDauoQnBFK1B9oFQqlcpsUB0SpVKZMtUhKR64ohWoPlAqlUplNqgOiVKpTJnqkBQPXNEKVB8olUqlMhtUh0SpVKZMdUiKB65oBaoPlEqlUpkNqkOiVCpTpjokxQNXtIKo6oN///3XTJ8+PcYpU6aExlNWzPh8JF/D4iiTIwuAaB6mT9pxfH3Udp0eWYSmEPJRHRKlUpky1SEpHriiFURRH8yZM8d8/vnn5qKLLjKXXnqpufLKK82IESNE8YbFV5bPpUuXmiuuuELy8ZJLLjGfffaZmT9/vhgKYfGV4SS/Zs+ebR5++GHz4Ycfqh2VBmm/Q4cONVdddVWsPvbs2dPMmDEjNL4ynP/995/5888/JR8vu+wyycfx48dH0ilRh0SpVKZMdUiKB65oBVHTBxgtb775ptl7771N165dTffu3c3OO+9stthiC9nX3unkiHFCL+o+++wjefnXX3+Zjz/+2Ky22mrm8ccfN7NmzQo9T1mWfkn0hg0bIg/NBRdcYCZOnBgaVxlO6iPOx7bbbmvuvPNOact33XWXWX/99c3PP/8sI09h5ylLk3z87bffTN26dU2LFi3EMbn44ovNuuuuK502UXNK1CFR5oRUfIRI1BqAMj2qQ1I8cEUriKI+8NO1/HavXr3EELztttvMzJkzy8RXliW9zo8++qjk26JFiyQMJ6R58+Zm4403lp5qld0VE2ekc+fOpn79+sIbbrjBnHPOOeqQpEjkDL35Bx98cKxTYeHChaZJkyZml112kVG7xHOUZclIyIknniijnvGjnJtuuqmMOjGKFx8/3yx4h4QEIDgXLFggpNIiFMLiVmXG54HPh7B4+SDl0alTJ3PzzTebbt26aa9lHFHy8eXG9JN4wRFVqkNSPHBFK4i6PkC2DB48WAxrpsswXSEsnrI0ybdjjz1WevP9lBjC6F1dffXVzbfffluUejVVkkft2rUzTzzxhOjgs88+Wx2SNDhq1ChxPl544YVYGDbOgw8+aGrVqiUOstbHijly5EizySabyJRWHzZv3jxz/PHHmwYNGkRu5LPgHRI8ZXrC1l57bbPOOuuYevXqmeHDhxfVkB7C7tZbbzUbbbSR5AGsU6eOGLdRaLSUxTvvvCNGwv/+9z+dA+pI2aD0fZnRo3bmmWfKPO6oOyXqkBQPXNEKoqgPfKcUiowRkZNOOknk3+jRo9VoSZLozD322MNcc801MflMZ8nYsWNFbr///vual0kSmU4eopdxRtQhSZ0DBw4Unch0TB9GnrZs2VIc5A4dOkg+x5+jLMshQ4ZI+23Tpk0sDFmJzVyjRg0zbty4SI18FrRDgoB84403pMHTu7xs2TLp5aEAimn+MMLurLPOkt4tvN8lS5aY6667TvKBkYn4fKDyecZfIz487HhF8eK3E+OyzTPMnTs39izxx/y23y8W0ra22WYbKSOmSTD/HWF76KGHRm4oNZHqkBQPXNEKoqgPaCvXXnutyLvq1aub2rVriyzRl9qTJz3OTIVhilZ8Zx46lnz94IMP1CFJkeqQpM8BAwZIpwKjTT4Mh4RFFnivqWPHjuqQJEE/Wsx7Nz4Mh+S+++6TfIzay+0FP0JCJWUkgG0qKC/jUQAI1mIZrvcOCYLPN1KM/1VXXdWce+65UgHJpz59+pgHHnhApjI8//zzkj++MrZu3dp06dLF/PDDDzIsSpxhw4bF7oFy53x/7LnnnjNPPvmkDAVyje+//958+umnZtKkSbFnoletffv2ouAQMNyTa3Kt33//3bz00kvSA8d1HnnkETk/akOI2SYjfJQZDRGnmiF+hlLJryg7aOqQFA9c0QqiqA9oO4yM0H7A/fffLzoApVtMI+WVIXJ59913LzVCAilv8vKjjz5ShyRFqkOSPhkhYbZL4gjJJ598Ip12vKej9bFiljdCcvfdd4tDgr2mDkmWSIXFoKYAWrVqVTQ9ZGEOCaNFOCTMFaT3HYdj6623Ns8884xp27atDM9ffvnlMUHJi0/0SPAi2ddff21OP/10efEJg5mGT+/95ptvLg4J3jZ5zDVYCYN85qWpAw44QIYAuR7TJbbffntz++23i8P4zTffyDn0bOAsYSywf8wxx5j33nvPfPHFF7L/3XffRdoQzybJp9NOO03yfdCgQeqQKCIBV7SCqOsDFBqyD1m01VZbSQ9hWDxlaU6YMEFeIOZFV++QoEsYvcUApMNJDcDUqA5J+sRBbtSokXnllVdiYXQ6PPbYY6ZmzZqRXCEqimQqJnbdl19+GQtjFs2pp54qU8Qx/uPj55tVwiEhETwnPfgYzYcddphkeljcqshEhwTFwUoz1apVk9EH9pnGgFfsRyB42Qlb49dff5X9k08+WaYKoZho6DggVGSWfsQpoecMBU8vJIKBpSAxnPGwUWAcP+SQQ0o5JCy/yVJ9TKlg6JXpFCg4HBIcG+Yw/vHHH/LM3JNGwhrZfpSl2MhLkCh/HL2o1191SIoHrmgFUdMHyH7kS3yvPh0wRx99tHTAxI/yKssnnUp0RiF//DtsyCB0xoYbbigvGasBmBrVIUmf2BHNmjWT98Gom9RH7FGcFDobim0mRbocM2aM2W233cQOIw9pw9hXLEnNNFddZSvDxDimt535rww5YwSzIghDVcXSo+MFX9OmTeUjTKw3TcM98MADpQx/+eUXGZ5jmhXHeTHsrbfekpfGGA3hGoyQXH311bHypmKw5vdTTz0lFQTBgJLHOUH5Mx2L4zg2TP1KxyFB+cUPGTJtCyGEU8R+MZG6etNNN4njiJMY9akm6pAUD1zRCqKmDzBMWAgC2UYbot28/vrr0hnDlFBdZSs50inENwrohEIOMVqLHiUf0RfxDp9y5UR3oteYn88UXMh21G2pKJG2jK2C+GF2BfWPTlD2dYWt5Ilt9dprr0m+4ZyQb9dff71Zc801IykbC94hgVRWBCjGMlOCyHyEQLH06OCQ8K7IXnvtJYr56aeflnc7cASogMwfZDSCDwxxnGlbzz77rFRUP6UBh4NpV95B4He99daTuPSUMYcYB4SeMoADiANCpSb/03VI4l+qIuyEE04oOoeEMuKlUeotUw1xssPiRYnqkBQPXNEKoqYPkB0o2n333dfstNNOwh122EHeXfPvFiqTI3Lcv9y+4447mu22206mQEetFzXKRJazAtR+++0n+o8PTUK2jzjiiFIdcMqVEzuBl9gbN24s7ZqPJPpe/rD4ynBiF2MTUgdp13QsR9XGqhIOSTwxng866CAZIcB4DotT1Rg/ZYseQhqsb7ReQOKQ4JigdPzx+Ia9MoeECnLGGWfIetbYJ5ARFT/VimsyusKUL++QYCSg2NQhWTkpH97DIU/j58tGneqQFA9c0QqiqA+QHzgfyBVPfZk9PSKPNB8rR/IwsT5CdexSJ9O1fP5h28XbLMrkScexz0fqZlTzsaAdEjK1X79+8oITFRfDmATRk3/44YeHnlMVGe+Q4CTEH/MVj6/tXnjhhXKcvEJo9ujRI2b8r8whoTLT63jjjTea/v37iwHNKhesBIXCgi+//LIsYeu/AfPuu++Kka0OSfmkLHCayaeLL75YRkZ8flI+YedEheqQFA9c0QoKrYNKqVQqlYXBgnZIMNqYdsS7Exh0rBCCUYxx3rdv3zLGeVUlDgkjFryYHpZmHBCG3hlGZiUV8grn5IYbbpB3QIhz5JFHyndM4h0SHAZWtaCC3HHHHWI480EdvnGCc4KTw+gL8RkZwWlhOPCiiy6SKXPMU+Tr7HjkvKvC+bzEjkOCo8J+vENCGI5ksTgkjCLxchn5cP7550u5kHdsf/XVV7F8iSLVIUkLG7nfgoIrWoE6JEqlUqnMBgt+yhaGM98e4RsakJ57DNqo9zBnkhQiy8TCsOOQ/GAkifwhn/hwJE6EN3pZ95uFAOLPYSQEh4V4zOHkJTNeIsXpYdSEJYWZqsX9ic/oiC8Hev4ZTWGVG++wsHKXLxviMkLjz/XnM+oSH1aViSPJamjki883SBlFfXUgdUjSAvlTcE6JK1qBOiRKpVKpzAYL3iFRZpc4ExjIdevWlW+ZMC0Oh4KKw4gLoyXF4kAoS6gOSVqQ0TDLgnJKXNEKVB8olUqlMhtUh0RZIRnVYOrVuuuua5544gnz6KOPyugI06t69+4d6alFyuxQHZK04B2SgnJKXNEKVB8olUqlMhtUh0RZIXE4mKbFeyCMkkC+/Dl27NiieU9HWZrqkKSFeIekYJwSV7QC1QdKpVKpzAbVIVEqlSlTHZK0kOiQwMg7Ja5oBbyzR9krlUqlUplJol8WLVrktE0AdUiUSuVKiVwIg7NhFeFIdEY8I+2UuKIVLFu2TKlUKpXKrBC/Ix7qkCiVypVSHZK0kOiIxDOyTokrWoVCoVAocopSDglDKMzrUiqVSk/kQhicDasIR6ITkshIOiWuaBUKhUKhyClUASkUirTgbNgoI9EJiBoj55S4olUoFAqFIqdQBaRQKNKCs2GjjEQHIIqMlFPiilahUCgUipxCFZBCoUgLzoaNMhKN/6gyMk6JK1qFQqFQKHIKVUAKhSItOBs2ykg0/KPMSDglrmgVCoVCocgpVAEpFIq04GzYKCPR6I868+6UuKJVKBQKhSKnUAWkUCjSgrNho4xEg78QmFenxBWtQqFQKBQ5hSoghUKRFpwNG2UkGvuFwrw5Ja5oCwL//fef+emnn8x3331nli9f7kIVqeCLL74wrVq1Mp999pkZPHiwC1WkgqVLl5bKR77dpEgftOeBAweW+WieIjlQ/1q3bh2rj9TPQoE6JAqFIi04GzbKSDT0C4l5cUpc0RYEXnjhhVh+LVy40IUqksX1119vNttsM/PJJ5+Yhx56yKy33nrmm2++cUcVyWD27NnmtNNOM8ccc4z56KOPzCWXXGJ23XVX069fPxdDkQrOPPNMac/XXHNNQRnSUcG4cePMjjvuaB544AHz3nvvmcMOO8wcfPDBBSMf1SFRKBRpAQM24ogZrAXIoZY5hyvayGPKlCmmUaNG5txzzzU1atRQhyRFvP/++1LP4oFTsu2225o5c+a4EEVFuP/++812221Xqjf/uOOOMzvttJPbUySDoUOHmm222cZsuOGG4tSpQ5I6lixZYi6//HJz0UUXxfJu3rx5ZoMNNhAHpRBQJRwSHdpTKHKPwISNNOIN/EJiXpwR4Io28rjpppvMlVdeaZ566il1SNLAWWedJYZLPHr27Glq165tevXq5UIUK8P8+fPN0UcfbZ5//nkXEuDdd98166yzjpk8ebILUVSEdu3amccff1y2qZvqkKSOuXPnilPHNNZ4nHDCCWbrrbd2e9FGwTokbdq0iSnwYsOMGTPMpZdeaho2bChD7ptvvrmpW7eu+fvvv5NyzqZPny7nfv311y5EkQt069bN1KtXz2yxxRbyu/3225tp06a5o4UHsWCjjZiMKCDmzRkBrmgjjZEjR5patWqZYcOGmRdffFEdkhTBNKP99tvP3HnnnS4kwKxZs6QOolsVFYO5+hh6H3zwgQsJ8Mcff5iaNWuKvFekBuyXs88+Wx2SNIBdSPvt0KGDCwlwzz33FIyMLDhr3hvchxxyiAiDAtGhGQO9Mg0aNDAXX3yxCwnQsmVLyYvOnTu7kJWPHDVu3LhMxU0XqYxQpRK3KoF0M+fdT4fgJVyG+plvXKgv5Nr6FnVImygg5tUZAa5oIw3kPtOLwHPPPacOSYrAcNljjz1EHiWCKtC2bVu3p1gZJk2aZDbeeGN5CTsevXv3Fofkr7/+ciGKZIGeVIckPdC5SftlpDMeTz/9tFl99dXNokWLXEh0UZDW/EsvvWSOOOII6eEpEB2aMTz66KNmt912k96sRDCFoVmzZqUqHo7bWmutJQKS6Q2APFt11VVleL5OnTrmmWeekXDw5ptvShg9+LvssouZMGGCO2LMq6++ak488UTZ5gXINddc0xx55JGy73HggQeaFi1auD1jhgwZIvOSBwwY4EKMadq0qdyb+aKLFy8uWifl5ptvltEtVgsqRNh6FHVIXS8Q5t0ZAa5oIwvf8eKhDknqwCHZa6+9zB133OFCSkDeqkOSHHBINt100zIjJEx5U4ckPahDkj68Q9KxY0cXEgC7Tx2SLIJMp3f/kUceKaWcigE777yzufXWW91eabDUG1O3cFaofLxcd84557ijxjRp0kR+aej0zP/222+y70F+ImC9gYwDQv6iwAAVm6le66+/vrxABajoF1xwgWwD5jAyjcKjf//+Mr3Cz0s+5ZRThGD06NHmxhtvlO1ixJZbbimrYBQqbN2IOqT+FgAj4YwAV7SRBAqXKar33nuvad++vfn999/NFVdcIZ0rTD9lyqqiYqAb6NCjQyQew4cPF3netWtXF6JYGfxIE++MxAM9TCcgKx4pUoM6JOlj5syZ0kmMXIwHs2moj4WAgrPmTzrppFgvPStcRFyHZhz0BvqRjkR06dJF8gNBOXHiRBmFCFPSOBw4NvFDzYxUcG78eyW8JIXj4qdHMPRXrVq1Uuusf/zxxzLSMXbsWNmvyCFh9ZHEnrliGSHBiWvevLmMSKHIWCpywYIF7mjhAQM24pA6HXFGxhkBrmgjCTpaPv30U5EvyCJ+jz/+eFO9enXpTNHvaCQPVuNh6i/w8veNN96Qke9Cfq/t/+ydBbhU1dfGP8XAwEaxW0RssLu7sTv+dncnit0YGGB3dzctoBggGIRKiHRIuL/zW7P3eO7MmZszw9yZ9314n+HOydln7b1X7XWKCQw7dBHK/oLQjjgBWR8o1B0ySOoPKmqh1911111VdCqqEbLmuDGgUWnzhKJIJwre+Uo0SPi98RSrOIJBgsHBWpJll13WjRw50m/9D0kGCftj7MRTqwAGIN400KFDB4uOxMH+PJPvvvvO/q7JIOnYsaPdI9GaENKuFINk5syZJsO0NV400rUocdhYET3HUofJWgmzpIwR4B9towEVjhi3wpwg1A7kmfO4iYKDwYMHm2HXqVMn+1uoHXghIu34xRdf2N/vvPOO/d29e3f7W6g7jjjiiCqZE5WiH+QDIaslAF2RvxuLcddoJiAWc5PewvqE0LhUDwiNXylv6qWcICUvk0C0gpfikE/Ny65Ir6JefyaSDBJe6kS4PtPLSHoVL9YBGCR40OJgjUhdDJIA0sl4dueff77/pvJArjZtQMnDxojo3ksd1r4lypIzRoB/tI0GePVxvDSG/OhSw5AhQ2ytA5F0It9vvfWW3yLUBcyjtCGkCylVq+7ASYe+gi6x+OKLm57D/0lrFuoGUlpJ3ULvatWqVeJ641JFo5mAKFV42mmnue22285tscUWtngaAWYA4LvPP//c71neuPjiiy3dJ57qEzwIbdq0MWMF46xHjx4mlEkGCSldpE7FyzuSP0xb4uEJYJJn8eNll11mf5MqxiARn/w5B3ndITUMg+iWW26x/wPW+nDeTIMEBO9SpYI24/djPDZGRPcu5IY92xwsSWME+EfbKCDPqTC7IRksPNTGtUdjb6tGrQ1W4qL2UaNGmdWbmRPIy2+aNm1qlT8AVjHeBazlgJNPPtle1kR6w6abbpq1loOKWTvssIP/y7lnnnnGvI9hzUgI/2200Ub2N4YPxkj8Xs4777x0RIXOQVoSx2CQsOiqffv2tg1ceeWVrkWLFv6v8gaG4emnn+7/SoG2ZrFZY32BFgqskBMm9wksWWME+EcrCIIgCEVFo56A4ilblQYiHFSXIe+XNrjiiiv8lv+AEoxBwX7sgzEQLOj33nvPzT333OnvAaleRKH4jvNyDcL6AaRsEU5lXQr7QIycOIi+UNaXFIAll1wyHc4OtbFJseB+2M69VdICyjPOOMN+N23LJ8UAGjOi5y/kRrqPxFjSxgjwj1YQBEEQiopGOwHFQ1OVFtLL9XsL3Q5Ja0hAba9bac8pjtn1zAoJr8MKyWh0xgjwj1YQBEEQigpNQEKtkcsgESoTXocVktHojBHgH60gCIIgFBWagIRagzU7VGURBOB1WCEZjc4YAf7RCoIgCEJRoQlIqDMqOfVK+A9ehxWS0eiMEeAfrSAIgiAUFZqABEGoF7wOKySj0RkjwD9aQRAEQSgqNAEJglAveB1WKCP4R2ugYh6lukVRFEUxn+QF3ZlvkJdBIghCveB1WKGM4B+tgffj8A4iURRFUcwneTH0P//842ebFKpMQLyHQhRFMZNJ8DqsUEbwj9Ygg0QURVEsBKs1SFiozFu+2UkURTEwvP0/E16HFcoI/tEaZJCIoiiKhSC6RbUGiSYgURQzybiQBK/DCmUE/2gNmg9EURTFQlAGiSiKdaYMksqBf7QGzQeiKIpiISiDRBTFOlMGSeXAP1qD5gNRFEWxEJRBIopinSmDpHLgH61B84EoiqJYCMogEUWxzpRBUjnwj9ag+UAURVEsBGWQiKJYZ8ogqRz4R2soxfmAim+9e/d2PXr0cN27d09z0KBBbsSIEYnHiMkcPXp0uv26devmfvzxRzdq1KjEfcVkInP0k3g7/vLLL27kyJGJ+4vJpB2HDRuW7te042+//aY+XQ/SZij7tOPAgQMT9ykFyiARRbHOlEFSOfCP1lBq8wFKXp8+fZA7t9NOO7kDDzzQtWvXzh100EHu5ZdfNgU76Tgxmxged999t5trrrnc4Ycf7rbffnu3+uqruw8++MD99ddficeIVYnih9J87rnnupVWWskddthhbt1113U777yz+/rrr2Xc1ZK0488//+xOPPFEt84667hDDz3UtWzZ0u23336uf//+Mu7qQPrur7/+6s4++2wbJ/ks1XFRBomYxaFDhxrD3wyweCHj+4iVTRkklQP/aA2laJD07dvXzTnnnK5Xr15u/PjxbsyYMUYpLbUnCkrnzp1NYfn222/T7Yhh0rp1a5sDko4TqxLl7+qrr3ZLLbWUeaLHjRtnyvWGG27oNt10U3uhbNJxYjYvv/xyM0J++ukna0eU1RVXXNHttddebvLkyYnHiFWJ7H300UfmWNhss83cqquu6s4//3wZJIUiVvSTTz7p7rnnHvPuBOKN0ISUTISU9unYsaP74YcfqmxDUPEw7rjjjmaUMKgySV144YV2XHxfMX+kbV9//XX30EMPua5du5a87MogqRz4R2soZYPkq6++kge6nvz7778turT33nvb//mOueCNN95wCy64oPv44481/teCGG5EQy6++OK0LI4dO9bdcMMNbrHFFrP5Vu1YM/Hoo4Ncd9116e8wkok8NWvWzHQTtWPNpI1uvvlmazfkcbXVVnPnnXeeDJJCkMYmJ26eeeZxN910k3vwwQfdAw88YGSSkkGSzAkTJljoE10Db058G4J61FFHuX333dc6PTmcTz/9tLVzfD8xv/zmm2/c0ksvbc/kqquuskksab9SoQySyoF/tIZSNEhCytYdd9zhXnjhBffss8+al19pRrXn4MGD3cYbb+xOO+00i4zwHW1Legxt++ijj2o+rQWRO7zQOEVDeyGHyCR6yptvvql2rAW///57mw/R6cJ3GMo4UWnHTz75RM6HWpJ2I8KEbieDpIDEIGHB0/zzz28DgTp6zaSNmHyYZAiHMnjGPQ2ZBkn4LrNt+S4wvo1z8R2fDBhhn/ixYlXSCQmjEpmaY4453LXXXpv2UpYqZZBUDvyjNZTafMA4Q5Qch9Rll13mrrzySlv7sPbaa9sakqBci9WTxevk6tOGcUOOsRsR6NKli+bXWhDjmHQt0t/CvEobsg4HRfqdd95RO9aC6HMLLLCAe/7559Pf0Zcx7Oaee2732WefySCpI5FDGSQFJB0eg2S++eazARWvsnKHqyeWMlERwp5M2KQ6vPvuu+nOjaDGDRKMlxYtWrjbb7/dtiMw/fr1c/POO68J9yKLLGL7k+fJREYlDCYwrrHVVlu5VVZZxS2xxBLurbfeqnIfYorI8HfffecWXXRR9+GHH9qCUhkkQinBP1pDqc4HjPmQcQyut956rm3btlbdKGl/sSqZP1l8fcYZZyQaJCjYmldrJgYJ8+W9996bbq+4QfL222+rHWvBYJA8/PDD6e/iBsmnn34qg6SOlEFSYKLMoQA3adLE7brrrm7//fc3RfqLL76wfMOkY8Q/bYETC8OYeNZYYw2rTjNlyhTblmmQ4H1kHwZYtn/55ZeWU/zcc8+Z4LA/3ki8ktOmTTMDkQlsu+22s2NZfHbOOee4jTbayPJC4/ch/ummT5/uNtlkE3fsscdaeyPLMkiEUoJ/tIZSng8C6TuXXHKJLYDFeZK0j1iVjM1bbrmlO/nkk6ukbDG/4iR57bXXpEjXgqwRWWuttSx9MLQX7fnII4+YE5DUZ7VjzcRAxpl53333pb/D4cx6CBzQpHSpHetGGSRFINWfyHNlzQhCSuoLCjPrSMLAKqZIB2bhNEovE8zEiRNtAiJKEkrp1WSQ3HLLLW6bbbaxwYHjZ8yYYfmytHmIWKHDUGggeNree+89t9xyy7kBAwZUuZ9KJ/JJSJr2woAeMmSIDBKh5OAfraEU5wPGori3FOfKcccdZ44XRUhqR8Z+UkZbtWpllaBQDIim33///RYFpwhKSEEScxPDDucc5X6RS9oReTz++OPd8ssvb2sy1Y41k36LnnHqqaem+zaKKo7UlVde2b5TO9aNMkhmA1GQGVQpVUjnT9qnUsmkc+aZZ1q1j0mTJplCTDQJnYMSewygNRkkVA9h/4UWWijNxRdf3PKPEaZgkDzzzDNpoSdcLYMkm7Qt4X2MZwwS5BWDpH379qYMJB1TKpRBUjnwj9ZQavMBY9YJJ5xgFbYY+wHRW26bBbClbtiXChmrg3OE9CyAgw+vPhUW42lcYm4yx956663Wjp9//rm14/vvv29/UxhA8lg7Ymx06NDBnKU4mwHOVNqRTzmba0/6LtkrM2fOtHXD6HqzZs0yhzI6W9Ixs4tlZ5CgyJ100kkW7mNdQ9I+lUraA8MB4wArmVQqDBQ6OfnDPOuaDBKEGc8FCjSen0BSs/BayCCpHZm4iDbRVpdeeqm9rIj8bQbgrbfe2pQp2j/p2FKgDJLKgX+0hlKbD5hsicaSVkRBCMgts63UJttSJ+M3YzXtF9rxrrvuSo/jYu2IsnznnXdWaUeyBOTRrxtpRypOxtuxd+/eifuKyUTmcDDg6AxtGLjbbruZUZJ03OxiozdIUIyD94Yfwz2jXO+www6yomNEMKnwwcI6JnD+D/He4NFhoRgLz2nL6gwSjsVrxiQVJnzamVLCKNkySGpPFrPT/jwHCguEdDoM6p49e5Z035NBUjnwj9ZQijLJOEOaUSDewKT9xJqJUUL7hXbUHFo/0m5xeQxpR2LtiX5BRCnejugYSfuKuYlOF9owTnS2UnPaNGqDhJs/5JBDXKdOnSx3k3tlTUTTpk3tvQ4S3v+IUB555JEW3cjMq6adMFQIzTNwYpDss88+aYOEXOxgkLDYrE2bNm6DDTaw42jz66+/3vYnSiKDpH7EYNQaEqEU4R+toZTnA1EURbHxslEbJCjE5A7vscceVmJ2iy22cHvuuacs6QTisSHqgfGWFIKnjj8FAXjeREx4QyoFA3jzLB77V1991fajXYlKcS6qsmy22WbuxBNPNCFCqWZxPAvPPvroo7RXCG//McccY8ZN/Jrif6TtWENCu8YLApQqZZBUDvyjNcggEUVRFAvBRm2QQBRkFjZC1o/wmbSfmFoAmmSMQAyWEJ5HGY6H6vHWZ4ac420e9+ajWPNd3CDk/+zDtvCdmMzqnlEpUQZJ5cA/WoMMElEURbEQbPQGiSiKxacMksqBf7QGzQeiKIpiISiDRBTFOlMGSeXAP1qD5gNRFEWxEJRBIopinSmDpHLgH61B84EoiqJYCMogEUWxzpRBUjnwj9ag+UAURVEsBJlfZJCIolgnyiCpHPhHa6B2vSiKoigWgrxRPo4qBgmlXwmjiKIoBjIuJMHrsEIZwT9aQRAEQSgqqkxA06dPdzNmzBBFUUyTcSEJXocVygj+0QqCIAhCUaEJSBCEesHrsEIZwT9aQRAEQSgqNAEJglAveB1WKCP4RysIgiAIRYUmIEEQ6gWvwwplBP9oBUEQBKGo0AQkCEK94HVYoYzgH60gCIIgFBWagARBqBe8DiuUEfyjFQRBEISiQhOQIAj1gtdhhTKCf7SCIAiCUFRoAhIEoV7wOqxQRvCPVhAEQRCKCk1AgiDUC16HFcoI/tGWNKZNm+aefPJJt8wyy7hWrVoZjzrqKDdr1iy/h1Ab9O/fnz7s1llnHbfsssu6bbfd1o0aNcpvFWoDZO7pp5928847r7Xjggsu6M4880x7C7VQd3z66acmkyeccELO918JufHPP/+4M844w6288squdevW1paPPPJI1hvRSxVlY5DwpvlKQa7fWkltUC5ozM8MBVYoL/hHW9I47LDDzAiR0ld/fPnll6asdO/e3X/j3M477+y22morGXZ1wGOPPeYWXnhh9/PPP9vfkyZNcquuuqo78MAD7W+hdhg5cqQ7++yzTSY322wzd/rpp9tLeYXag357/fXXu3XXXdecNuD777+3Nn3mmWfs71JHozVIFlpoIfOQwYMOOsh/Wzl44YUX3FJLLWW/f8kll3Q77bST31Izxo0b5/bZZx/32Wef+W+EYmHppZc2tmjRwv3vf/+z7xqrUYICK5QX/KMtWfz00082wfbs2dN/I9QHjD277bab/yuFjz/+2JTrH3/80X8jVAc8+AcccIC75JJL/Dcp3HLLLa558+YymOuAZ5991h166KHujz/+cIcffrg75ZRTZJDUEZMnT3Ybbrihe+655/w3KRD53GijjfxfpY1GZ5C89dZbFT0hEZKjw2bqDm3atHErrLCC++uvv/w3ufH333+7tdde29pSKA7wXqy22mqWJgFQrOaee263xx572N+NESiwQnnBP9qSRadOndJjH15VUowY8xQdrj3w4m+99dbuwgsv9N+kMGzYMGvbV155xX8jVAdkb6211nJdunTx36Tw5ptvuqZNm1aJPgm1A/34kEMOkUFSD6DXoVOQ9hYHKVx8P3XqVP9N6aJRGSRjxoyxXM2nnnrKf1N5+OCDD9ycc85pUY4wCYfPNddc05133nlVQu6vv/66e+CBB1zHjh3dN998Y9916NDBIiuERR988EH39ddf2/dgwIAB7v7777fv8VqE0B9AmX733Xft/w899JCdN9Ooee2116p42MaOHWvhQp5dwKuvvuruu+8+y22sRITnddxxx7nll1++Sts0JpgGK5QV/KMtWZx44olu8cUXd9dcc415+Pfff38bD1EKZZTUDow3eFJpw0wgAozhQs0YPny4zaPPP/+8/yaFvn37unnmmUdRvHpABkn9MXr0aOu/Xbt29d+kcPPNN5s8xnW5UkWjMkjwPCyyyCL+r8rE9ttv74499lj/V1XgPSRKQqiYRUz33HOPW3/99c1oeOmll1zLli1tv86dO9t+V1xxhRksAwcOtO8J2a+xxhpmvGA0EEJt27atbQPXXnutefm5h8cff9wMDTrAo48+6vdwpmATsg5gcJ5jjjnSgzP3RI7jhx9+aPtdffXV9n0lIShO2223neUbN1agwArlBf9oSxannnqqW2yxxdyff/7pv0mlr5JqhDNFqBnBILnyyiv9N/8BEVCEpHYgooRBkpmfHwwSRUjqDhkk9UcwSFgfFkcwSKZMmeK/KV00KoME5ZXUJBbu0PAQ71hjsPzyBXJT+f1J+OKLL6xNCN2NGDHCzT///OkUIRAiJyFlCwMvDo699957/V/ODRkyxAyMYHAg2Ozzzjvv2N8ALxuRmVCdBYPljjvusP8DojLzzTef6927t/29wQYbVIlwVapX86abbrK2zBw8GhOi+xfKDP7RlixOPvlkt9xyy/m/UiAVgVv/5JNP/DdCdSDXnLzyzJQtxvsmTZpobWEtgQLIPJqZsoWTr1mzZm7QoEH+G6G2kEFSf6DXkSqYmbJ12mmn2feNAY3KILnuuuts4qHDB5CDv9JKK9kgWwng96PMJiFUTkEwv/vuO/PeJJVxTDJI+vTpY8ZdZpiZtAgW7gFSvVhAH0e3bt2qGD41GSQUIKBzkCZG2lklAQ8FUSrK8a2yyioWcWrMC0gjWRPKDP7RlizuvPNOG+NYPxIwfvx4+w6HjFA7MA4ToY2DSDoZCFKkawfW4lBM5vzzz/ffpHDVVVdZGWW2C3WDDJL6g8wYnDWZEbstttjCbbPNNv6v0kajMkhIMSLUHAeDJwpvv379/DflDSbeyy+/3P9VFW+//balM0ycONHyWhFOIiWZSDJI2J+wXmbaA8bI5ptvbv/HIFliiSXs/wGke9XFIOF+WH9CigULAsOalErEMcccY2uivv32W/9N4wIKrFBe8I+2ZBHSEigRGrDXXnu5jTfeuGKcUvkAaW60Y69evexvokykj6IICrUHaceM4b///rv9PXToUEtRvvvuu+1voe448sgjq/TvSs2iqCtI0z/33HOtgmdAKAJF+d/GgEZlkLD+IHPO/O2336yCQBhYyx277767LeRMemkQCz733Xdf24aiT7pV3JMYEAyS+IJ09idc/9VXX/lvUsADxFoSgEFCylgchPeJmvzyyy/2N5MaXswAIjUYLMEgCWACvOGGG2zwrlSQIoE8P/HEE/6bxoXo3oUyg3+0JY1ff/3Vxg0mXhwk++23n0qs1hEoeawT5JETSWfsb9++vV5GV0egBNJuOPNoR9rzxRdf1Ltc6ggyLXi3EK8yoA0h/0dPEWoPokrobLRdYGNKZW1UBgmLxBg444o06xoQ5MZaqaiuIBKEgv/555/7b1LgxUx04vfee8/+ZtEnf8fzCZnImXBIlWKxe9xwAKRSxSuvYEzwzgwGCxDWkFCBK4BFprxMK6zj2Xvvve27gPCyo2CQBE8SQBHneVaCMkG7YzzHgZcSxaqx5r5Hz1UoM/hHKwiCUBJQhKRy0OgmIErNMm+SvsUCRxZJB4W5EkDnfOONNyxSwaJEyvxefPHFti6B6lkBeG74e8UVV7T9qEW94447WsoDoEAAa2/YRilhgPFCviHlaDkvb+6NV8xi7coCCyxg244++mh3xBFHWIpWvGww61j4joVUZ511lnkveV49evQwo+WEE06w47nuOuusY+V/KwGk0fEG2gsuuMB+P2l3tEvmgsjGBBRYobzgH60gCIIgFBWNcgLCc8/aBKIFpB9VIkiRYu0BrK4deAFf0j6kTP3www/WjvG0LhbB8x3HDB48uIp3Ir6GhNKG7Je5aJ79SUXieM7PdVhnwicgOsM2juXeKgnxtuWzsS8e9TqsUEbwj1YQBEEQigpNQEKtkbSoXahceB1WKCP4RysIgiAIRYUmIKHWoEwtFbMEAXgdVigj+EcrCIIgCEWFJiCh1mBRvF6aJQR4HVYoI/hHKwiCIAhFhSYgQRDqBa/DCmUE/2gFQRAEoajQBCQIQr3gdVihjOAfrSAIgiAUFZqABEGoF7wOK5QR/KM1UKb7n3/+EUVRFMW8kvkl8wWiMkgEQagXvA4rlBH8ozUMHz7c/fHHH6IoiqKYV/KSbAyTONITEO+PYAJiJ1EUxUDGhSR4HVYoI/hHa+DZ//nnn6IoiqKYV2KUVGuQaAISRTGTjAtJ8DqsUEbwj9ag+UAURVEsBGWQiKJYZ8ogqRz4R2vQfCCKoigWgjJIRFGsM2WQVA78ozVoPhBFURQLQRkkoijWmTJIKgf+0Ro0H4iiKIqFoAwSURTrTBkklQP/aA2aD0RRFMVCUAaJKIp1pgySyoF/tAbNB6IoimIhKINEFMU6UwZJ5cA/WkOpzQdMYOPGjXOTJ0/O4sSJE92oUaMSjxOTSXvG23Ds2LH2XdK+Ym6OHDmySjv+9ddfifuJ1ZP+G2/H0aNHJ+4n1kxkcsqUKe7vv/9O3F4KlEEiZnH8+PFuwoQJ9v8RI0aUvBCLxacMksqBf7SGUpsPGKv22msvN/fcc7t55pknTW57oYUWcu+9956NYUnHitnk/UK03bzzzmuf++67r7Wf2rD2RPF7++23q7TjVVddlbivmJu041NPPVWlHW+//XYZyHVk6Lsff/yxteG5555bsoZdozdI8DzgIcsknh0NormJQDKZ0+kzvz/xxBPdoYce6oYOHeoGDRrkVlxxRXfjjTeqPQtMjD5ktzF4gWSQVA78ozWU2nzABIbzZNq0aWmCo48+2q255pru119/TTxOrEo80V988YUpLB07drQ2ZLxfffXV3Y477qhIUy3J2P3kk0+aUfzWW29ZO37zzTdu4YUXdieccIJF7ZKOE6sS2XvkkUes3T777DNrxx49epjj4ZJLLjHdJek4MZs//PCDO/vss60t4YUXXiiDpBDk5rH2Fl98cbfkkksaW7Ro4eacc063yCKLuK5duyYeV+lE8aVTM/k8/PDDVbYhqEcddZR5xjBIfv75Z9e2bVt39913V9lPzC9/++03t/baa9szufzyy82gTtqvVCiDpHLgH62h1B1UKM49e/Z0c801l7vuuutKvh+VCmmn4447zm2wwQYWEWduZZ54+umnTYnp1q2bHFK1IP1jn332MYM4ZBVghFxwwQWmpzCnqh1r5pAhQ9zee+/tzjzzzLTyTMoW7brEEkvYd2rHmkkb3XLLLW777bc3h0OrVq3ceeedJ4OkUGQgjecYgo022shtttlmEtgE0iYII4PjfPPNZ4Iaj5JkGiTsD0IKF0RoaGsmLjhmzJj0tpCnyHkYiPn/pEmTsiIxYlU+9NBDbv311zeD5Nprry35FDkZJJUD/2gNpT4fME6ddNJJ5pT65ZdfNO7UkkSSttxyS3fKKaekx3Parnv37jYmPfbYY2rLWvD77793a621lrvzzjvT7UV7du7c2TVt2tS98847asda8Mcff3Qrr7yyu//++9Pfoevdeuutlr71+eefK2pXS5KGCdHJVlttNRkkxSKN/MYbb9gAimdHC8mySZuQlznHHHO49u3b2ych5TBIZhokTFQYLV26dLHtwUAhlL/YYou5BRdc0AYNUo04x9dff+1WWWUV98EHH9hxGD6bb765GzBggAzEasjgy6TVpEkTGSRCScE/WkMpzweML3379rXouNI66kbSOtZdd1131llnVZk3mQ8QAcZ/KdI1s0+fPpalQdpbfE5lPiSNSwZJ7fjtt9+6BRZYwD366KPp75DL5557ztK2SOOSQVI3IocySIpIlDjCpXj+p0+fnrhPpRMPIgYCoXmsZtaHkPaGQcH2TIOElK011ljD3Xvvvbb9p59+ci1btrR82JkzZ9rAQXtfffXVdm7yPJnAWFD6ySefWHSEHOR27dq5YcOGVbkXMSWzpJa0adPGDRw4UAaJUHLwj9ZQ6gZJhw4dbPxBwZbiV3vSXuuss467+OKLEw0SnCVqz5qJQbLUUkvZ+ofggIsbJCx2VzvWzGCQPPPMM+nviDQFg+TTTz+VQVJHyiApIun85MjNP//8Fl5WdCSbtBG5wLQReYUYELvuuqtr1qyZDZJsr8kgYc0JRgwpWxwP8EYSjiZFKxgk99xzT9pD+dJLL7nlllvOFO74/VQ6GVApGkB7EYKmr8kgEUoN/tEaSnk+GDx4sKUdHXnkkVJW6kjG+U033dSddtppVVK2+vXrZ1H0F198UYp0Ldi/f3/LHmDNZWgvdBEyNlCw0VHUjjUTAxmd4cEHH0x/x5yIXoEDlEio2rFulEFSRPJDWAyMZx5FOmmfSidCeMMNN5jSS3SEiSeU1SM0ysBZk0FCG5PDSZSEKjbky7LIjJxtZCUYJM8++2xa6D/88EMbXEjbit9PpZP23mqrrdzhhx9ua21Y2C6DRCg1+EdrKOX5gIgst4v3NGm7mJvMn7vttpvbfffd04UAGL9feeUVc1jRpsHjL+YmDiYyEFjEHoxi2vOaa66xeVKpy7Uj7chaYDIvQnvh4DzjjDMsJRP9Re1YN9KfZZAUiax1oILAFVdckbhd/NMWee6www5VyjhicDCJb7HFFhbxqMkgYaBlbUgYEOLknMEgIdQahJ5wtQySqqS9iOQxSdFudERkGIOEySt4KUuVMkgqB/7RGkp1PqC/7Lfffm7rrbe2SEnSPmJu0n5EzXncREWIgPP9JptsYlF0RZxqR8Z1SqwuuuiiJoekLDPv4ig99dRTVfa3lmScocLWCiusYPMi7RayCXini9aH1Z70XVLykUWid6Rl8v9S1DHKwiChwfHII6wsdkraR/zTKqbg7dpwww1tASOkstN6661nC/Eok4zXvjqDhJDpSiutZIITzkv7cxyfMkhqRyYuolJUgwvPghxu0iN4FpTgJGKSdGwpUAZJ5cA/WkMpzgeMO6Ea1JVXXml/J+0n5ibjEeMN7UdKL06rVVdd1R1yyCFW8UjpMbUj7Uhq8mGHHWbzJA5ACrvw7gfmU7YnHSdWJe2E3O2xxx6WhYETFN2FQjxJ+4vJpB0p9LTtttsaGSNZ40Rmxvnnn59eO1wqLAuDhB+AJc0gqg6fTCZpPO9YyHgciIZAPA94chBU3oJaU4SENSiETC+66CKTkVmzZpnAs46E88kgqT1pn/hz4G8iJDfddJOlcCUdUyqUQVI58I/WUKrzARFbFEHGrKTtYu3I82WchiiEFCLRnFo30l60W7wdG4MeVWqkHXkfSbwd445QsXZkTAxtyBgZ/o9uV2p9u9EbJHjmX331VVOCWUjG30n7VTqxhFkMxlvYg6EQ5y677GJWM8+bt7STSxwMEhax33XXXel9GRhYM0K1C9odzz7fc97gqWRtSrjO+++/75o3b27HhXOIVcnAgIeStiMkHfK4S5XISRKi+xfKDP7RGqRYiaIoioVgWURIUHzxKMsYyU0eNHnBuRRdcjLx1PN/jJeQo4miTL5hZtvS3nDq1Knm3Q+eC0L7fB83eojOcI5Ss8ZLkbRdqS9ohzJIKgf+0RpkkIiiKIqFYFkYJKIoFpcySCoH/tEaNB+IoiiKhaAMElEU60wZJJUD/2gNmg9EURTFQlAGiSiKdaYMksqBf7QGzQeiKIpiISiDRBTFOlMGSeXAP1oD65tYhyaKoiiK+STzy4wZM/xsk0IVg4TFzLxARRRFMZBxIQlehxXKCP7R2nxw2WWX2bsARFEURTGf5D1IvJYijvQEROjk+uuvd7fddpsoimKajAu8gyYTXocVygj+0bqZM2fyfEVRFEWxIOS9dXGkJyDKwCYdIIqiOH36dD9S/Ifoe6HM4B+tGaDLL7+8vXdIFEVRFPPJVVZZxV6uHUd6AuK9Fauttppr06aNKIpimowLmbmewOuwQhnBP1oDL+/khamiKIqimE8yv0ybNs3PNilUmYB48R07iKIoBjIuJMHrsEIZwT9aQRAEQSgqNAEJglAveB1WKCP4RysIgiAIRYUmIEEQ6gWvwwplBP9oBUEQBKGo0AQkCIIgCELR4O1fQRBKGL67CoIgCIIgCIIgVARkBAmCIAiCUDR4H6wgCCUM310FQRAEQRAEQRAqAolGEAU/7733Xnfaaae5Cy64QBRFURRFMSfRFx566CF7139N8D5YQRBKGL67CoIgCIIgCIIgVAQSjaAJEya4ddbdEEeGKIqiKIpijdxiy23c9OnTvSaRG9G+giCUOHx3zcLo0aPtletJr2IXRVEURVEURVEsNWK/DBs2zBaFVIdEI2jK5Anu4lM2czut/39u/61FURRFURRzE33hmvN2djNnKEgiCOUA312rYNasWe6MM85wyy+/vFt77bXdOuusI4qiKIqiKIqiWNJcZZVVXNu2bV2PHj28ZZOM5EyxmRPcv19v5ma9/3/u309EURRFURRz0/SFfjs796+CJIJQDvDdtQoop7fr7nulV4+JoiiKoiiKoig2Bs7RpKn74MOPvGWTjJxBEtdnM+c+iDZ/KoqiKIqiWA3RF75RkEQQygW+u1bBv7Nmujc67e1uPv7/3N1niqIoiqIoiqIolj5vPen/XMfz5nPDflKQRBRFURTFQlJBEkEoK/juWhX/znRuwN7OfRFt7iaKoiiKoiiKotgI+GXEHvM5N/4j9683bZIQ7ZUABUlEURRFUawtFSQRhLKC765VQZCk/96yD0RRFEVRFEVRbDz8KOLn8zk3VitJRFEURVEsJBUkEYSygu+uVaEgiSiKoiiKoiiKjY0KkoiiKIqiWBQqSCIIZQXfXatCQRJRFEVRFEVRFBsbFSQRjZ9ERBg+9OT/H0dM2lcURVFsAOdIfTLGxsdd/mYsztq/iEyaC/J5TwqSCEJZwXfXqlCQRBRFURRFURTFxkb8H2UfJPlszuhHzhtxfue+WDDiQp7NPKPvPm8a7esdV5VEnHJfLeHc94c6N+lb/2AjjP/KuYEnpoRDwZICE/mcJ5LDBVKyaDIZl1G+i2SXfSpRRsXi0MbJaBysMkbCSC4/mzvaJ4fsmexG+33Jvoyl0ViLTCftK6bImNpteed+u9q5qb/4QTfCHw8512udqL2jNi12sIR76trCuR+Pdm7KIH9DEcZ+nPrus7nyMxcoSCIIZQXfXatCQRJRFEVRFEVRFBsbyzZIgoPpsybOdV/RuZ9OjX7gp87984e/8QSM7+rcj0c413XpygoKmLNuOecGneHcpO98Y0TAcTfsNmdOeoQk6Vix/kQ+cT733sC5ny9wbsw7zk0f7Rs/ATPGOvfXq8593y4V1GqQjM4ZnWNx577eJJL5I50bcJxz/fdyruea0T3NX3znrDj7SR//uq1zo553btY/Xug8Zo537vf7Illdr+pYgJyw0uDXK/2OEZDTPx6Oxt1VNG5UR9qmxxrOjegStdkY33gR/nrTuW/3iCblqB8Wex7iet1Xdm7weVWDJPx/yHWpYFg+7klBEkEoK/juWhUKkjROEqD/cuGIi6Q+SXwgQJ60ryiKYsUysqUZH9NjZTOfJJa0ryiKYj2JP5tE1PhYg66W3h7paDYWed2NxFVLbo2dQ6w78dWUZZAE592Xizk35u3oRv9N3S8Y3yPlBPp2t5TDeeitzk3sm3JQT4i2fbtrtiOQv0PpEX5rYPiO7VUcy3NUPS7si4MJhuPi5wnngOwTPzZ+rSpOqmquk/l95jnYzjn4f7dlnfvpNOcm9feNFGHq4Khtbk51Os4TrhnuL5wrnDvz/OH+wnFiirQfgx3BuCHXZwfupvyUavd+2zrXd+tU4G743dHz+Dnqb+OcG/dZ1Oc2TbVzeBaZzzn+LDKfB/vCPls6N/Ipf9EIXHfgSamB971oezh//BnWdK30dRKY69gk1nTtzHOE79hux8UY2igcwyd/Z34Xjofx82aeL6nPQc7XmOWd34NcjXnTuRmRnAHGxFmTU/8HyGX3lVLySxtA2uCXS/0OEf4ZEcnV0871WD11znDueJtaW8HQXtFn/HlAjrG29ytS4ufg/zAcEz+O/6ePjcg+/J25D0zfRwI5Pun88WuwPVzH6H9PfP+k++ST71BwCHj2bOXc1xtFbJMKWJuRFZel6P9cJ/yO+PnD35n3kmv/+DFsjx/DOXi+g8/NCJIMSK144X7ZJ+xfX3IPCpIIQtnAd9eqKJUgCWMcBiQ619cbRzr+7ind/4dDUvz+QOe+2yelczH+EaBOOk8lkGc18H/+AXpMHhg9xz1Tc0bSMWLdyBzabZmUDKLfI3/f7pKa++PzMeRvHB69I92AvkRS0w+HpuQY3YHVv/H9RTEXkaXuK0RytEckcwel5CiMgemx8OBoLNw/5R/Bt0OyE8fmQ+8rNzIekug09Vc/UEaYMT7Sla9NtbXarGbSRjYeLpuyP7/ZKTUXm3wik5GMmkzuG8lkJLfM0ewbt0GTzlsfhmeGXRTsNtsWt8VEsR40OY3kqMeqkQzvEMl1JNM/HJaS7x8OT421JC4j10nHo5f13SoaYCKdOgBfC8ntyCrb0dHw48Ux4FjpbQ0l7VuWQZLPIoHE8RSEBoNt8oCUA6jKBMYACFE2w//9NkrP4Lgafpdz47unhDI4djgfzsRpw1LlSFByQwYBjdqrtXN/dvbHcO3vnfvlslTW/rgvUhnXAdP/cu6v16NrtU11oh+PSjkrp4+MNvoAD21NCSwUGLL9uU+ug1H3e8foPoam9iW48fNF0WSzYypANOPv1PGAjPAJ0e/4+cLo/tb7715rGyShzTiO38Fvnj7K7xyB7HN+64SeqTYO+/Mp/kfasmfL1MoRFKqAKVF7D7s9Ve4mtHeamTIafYfRj1E1+OzoXO9Fz+uXmEM7koOZ0f8JwPBMfz4/MrQWTZ33+wNSfeLfjNUCBi9rs6al5BVlmgGW/sJ9YcwhU/8Mj/aZyo7RIZFsE7yZEhnSv10T7bdUynALygsygDH3XXRdnOf0l39npK7D8VXor8/90UY2rkS/98tm0bUjg5I+MflHL9PsH4H+SP+Z2Cdqv1tTxxE95/oQJX9Q1Eb/0Jci/PN7JOunOPfrFak2s34cnW/cl86NeMK5v99PfQcm9kspbUTkw++x/hK1+6gX/+tb/0b38tt1qd8a9mts5HehpP71Rmqc4BnhKCdwPCmSBZ4Nv5cxDaOd/fmtyMcvl6TaAViQ5KlUkOT9aBuO9b9e8xujdprQKzU+8IxCe/HJdzwb2nLWlJScUY6Kc5AZMfLJ6PtIprmvvz+IxqZbUvJG0JBxmOM4//To+n+/m5qXmNjoHzxTAj4md/yOMamVgwOOyX5m/B9FBWOSFTL8fn5TfHUN5+KcOJOQr3A8bfHVYqlzA/rIkBtT5xnfLTWGg+nR9VGUUIp+f8CP3SCSOwKirOgKMhyeDcH7v6N+Tj+zucP3FcYQZPenk6N7WTnaP+ovzEN9N0+dm/ZmnA59jk+Onzok1cYoXlbGL7oGbaEgiSAIdYTvrlXBPDo7gyQ2lkd6EmPaoDOj+bx3NBZGukIuMF4zXmJYZs4LlULmMH5/AM9w6m8pJ5WM7fyQ/oA+HMc/f6YCIEGvCvvyd98tUjoUOn3A6FdTThGVQxZrQ2QKWRpwdEofrBGRfokdQHWJ4XdG+uo2qXNU4piYi4yH2DnxcrnYIgqS1J7YnFQpIFEZuyTYKbkwc6Jzk/tHdujDqTH0qyUb3s4cT2IEQRl8ffjixn6S8nPhc9FzFBvKMK//fn+kZ07zwhwDPq/fro/keanUvpnHozOgBwQfgvk6Il0WW5392U4QkYSWOPBxSG9rGGnfsl1JgnMWp30cBB4oaUIUj9rvZPEGJ2hQAMIEh2PoXy/QDOA4jglwULpr4tfRd7EyKeCPqANwLI693utH13nlv33SwZXok8yDCZHBxsQQz9ROI+oA04annLTcfzzQgTMQZy5ONK7TY7WUU9ICKhGYRIIDje+4T+45UzH6+8PUs8OhTcesLkjC88Vh+ecj0XliJaFwwuMQRHh4n8nMSX5DBDorGXuaYKqS9kDhnBFrR543RjzP02TQB0JyEdnmHNMi49UOj2R0ys/Rs+iemtx5JtOGRGIQUzgmfeNc12UiuVzXuVHPpGQPZ20ABpg5xSN5wwE84vFUeaXPF4ju7azU8w3gOJRnHNQ4FZDVAPoJmR+hPAPl7uLlmHCAp4/tERv0I2CQc66RkXwTlMF5+91+qf0DbJ/ot9EHOQd9knPG8etVkdw2S7UlQT0c1Zl9lTYjcMLvJeA35q3ISI0MCFZFpB3KUV8lsIqjhza3ZxP1F7JZCPaEfQjQEPzk2do+jZBMBPEgCSAYRkYPgaIwfvBJ0I1xh99Lu9QUJCGoEWAO9+j5ZAZJOIc5/yMwxsQNgTUAAKyTSURBVPFskR1WNRGUGf1y9L0PVPDsQiArBHJ4Tjb2BURjYNgHmbJgTyRHcZnntyDnBKZNkYieLfdN8CWuyODAQM5t3I+eddzRRqCBUnWUzmOcJPBBXwtAlvg9fNp47u8DhYfM5lHPRefzYzcgEIKyHspt9Yr667jP/UYPG3ejvj7+y9TvtuBRJIeMz4wdrFALMsy9Mq5zjnHcvzdG0ojaaWiH/56lgiSCINQRvrtWBePv7AySMKcRtGY+iuvQjG3M82QBo98SKB5yg9ebhkb6w2X/zU3hXPw/kG1JDNvj9xCOje8XP1/8e5h5jqR9wn5hn/i+8f3DPtWdI+wTyByQFST5JdLD9vFzZAbDOWp7fjHVH9Brg51GG6Nr5QySRPKJfhqft0dHtiWBq3iQJPM5JLV9eCbx/Wybtzkyt4e/M4+Jb6/u3Jn7ZDIck3RcYNiedGx8v/j54t/DXOeoFPLbkSVkDH3YkopAJIPom2xDv+u3XSpRJ66TgpGRzdh1yf/OFT8vzGzv6to885jwd/y7OPk+8xxxVndsJuPnyjwm13Xi+/H/rP2ivoMfxVZ1wQz7Pen4wPB9fHv82HIkskaiL3Y5yaEB2CzMNSQcUoYcG575GbsU2yb4OwKw937ivbmRLZnkM8nVxqGdQ1szrzGO4u+K3w/lzdmH8TocU5drwLA987hwbOZ+gfFzxPdJOkfm/rYtQQbj+9X2XPH9k44RaybtRjCPJGN8AAH4kCxh2fspSADusUqyvsx3FiQJidXR+I0vFls9PB/bN4xFTfx4lNAvYHiemc85MNezjh8TzhEYPz6+Pek8jYmMV2UZJOHh4IgjWDGkQ0oJzUKkIOAowyGHEwiHKqtHwgPGQcXKDbJ+cQoyAeK0ezsi1yBzHYdtAKsDcOzRqARgcOzFHWoERn69PHUursGS69FkpMeUX4w0JgTeF8E94ChmhQGOugAcXgSAaHc6FVnW8e1kfvECYJ5NEFR+B4p2AM8OZzgrXpiMEoMk0SRGe5A5/8tF/90nDvWRz6acxThC34nIb+4T3TO/McAU+ei6YWlk5jOqJIbnEOQxOPZRVnEss7qBNqxNO7EPKzZwQLDao/eGqWeIPPAskNEfDooG5F7/OXsJErCEOgxgyDQrOwJw3FJuixJ1nId7gQRjgsKMkxoHcMi0fDdiCNTFBxBKgzF4c698MvgH0CdwJHMcChDOingQhFVObOMeCGQiy0wIgCwd3t9Cvwn3h+LOfjixA2jPPzpF518gdW84QNJBkqjPk31vL8iOZJ+25LfgiEdZoowBgcW4kxynOffMvkx0OMvDdvodBi/tn/mcGhNpyxAkiY9HyBjjHvKZnuCjNiRQQrvRZjUGSaJxJgBZIGiWFSS5OLpuWJkTKQ049DODJPFnQpCATEq2cQ6UkBGPpfpTcD4QTEBekAH2IfDAteOBacY8goHhXpGbeLAFRZkVGsgj8k478Y4rVnQE5YYMZYIL3CtyEleEQFhpSOCQ4wnK85tZ9TQqGkfjBilzCBlSFozbPvU8wljBJwENDAieGef4qnm0X2TYIpd9Nk99zxhj81b0va0uib4L8xarTAjms2ongLE8vIur2woKkgiCUCf47loVjI+zPUgSjY/x+Yf5Ab2HJIywH2Ma+0Lms/gYh+6K/svKU1YDxsfNOGZNSo3zrFrtumzqXBzPWM2qxwD0EMZqVnszV2aCuYNrfRPNFzgv4/p0AMbxn4+m9DmuwbVYbYkewqrCAOYW5gDeMxZPRgE4pCZEeg4OKX4f8yPk99cYJIkMb9qVzMU/Hkw5XZMwLWorAva91k61I+cP7VrJpD9kBkloQ/RYtiF/8eeBHTcGvSwW6IsHSTgGe4AVtGlE5yZrFT0inI9PSnugOwUgt+i86Ac8I/SluL6ObkNGNU5y9o0DfQTd4ZvIlkCmh99T1R4OCDYDFQv4/dyLJedFOtvgc1L2bHo1bQZY3UXwEh2TZBZkneNJbkSXCzoEn1wDPTXpHvhuyE0pHdcc2dE5Kom0GW2XFCRBVwxjtMlSNDZiz8WrDZAchI+AbcgR/gdKxTGWxstNBXB+nim+CxIs43L44zFV7TWrQhCNQ4wlmclswCpoRP2Fa3J8+D34JtCVSXSK+z+qA/tyLL4eVloHYFtShj3YClyDvoeNSvWNAGxb7EH6HX0GHX567Nok3jF+cw5sir5bpn5fALKOnUK/wfdUBVGbYQvTR63kYw4HZ2Mm7cI4QF81296DBNs/oueBjCCD4RmEY6wtt4rGuA+ifYOjOAJBjaFRv8bWtzk3ajP+j7ww7yXJJtciSZjV95wfn1wWQv+Iwey4XVP3yPPh/4yxPLNclTkYS3lHJ7Yq98X9cZ/YYchiAL+DvoSuEJ/DA5i/2Y48IZNhHCQ5M64jMEdgz4b+bH0VH0nU7+N+ljHRXEESM7/Dngk+vktTY61VCcnA5OhYVjr0bpvaP/58xOrJOII+R4IiehfzJsmSzF+MFUGe+Z55vX+ka2W2Mc8zV5AEWWA7+kBcP2UMpipR0Ns4H2MmidIkAk2KdM3gW4iDMXj0S9GxR1cdc7kGKxEDmEd+vjili5q/wOszAWxHZvFLWMDGn6ex0fprOQZJqjAamHCmoSDhjGOiig+0ATgHGThx4IVje60VKX93RA30aWrAw6HFQIQgIWDBUQZQBjmeQYySKZlBktABGCBpMxRbHFZxBRFDbMDxKeFEuLkXOlJccUUgye7HIcfgZkGSmMHFBECtOxy3DJL8Dh40zr0A7nvyD6kSXwzeSUESnMtcn86HopJ2Ukadgd+OYsHvM0Ydls+444vzMwHhHAz3UakMgx6Bt9/aR/LnHbUMZJQIqkuQJPCrRSM5OD2a8N5MKbEMSsgmzwFHr2WT+4GLvkqmBME7FFYM7FEvpLYBnhWrWVBow30QqBsWyX56II3OxfPlXOF582l9ISgJ0T5cG+OYe8SZbkqivw8UDcYMroH8c+9xpzQTNXJP/+C3BZi8fh8dG91TGPQhcsW98tuC85t7ZMkszujukWEVD5KwDcNv4An/HRvOxf95MRYOXOQ9YMovqXOwD5NSyNLnGRJ4xLEcv6fGSJ5HOkgSM8Yx3FFQaRueTShbFh8fGIsCihUkYQVGODach6C1OYO8rOFwQkHk2bDdxsCTIpmNfkMwErkmZRm5DuNyfNUL5+Ga8fGtSt/ywADHgCeIUiVIQl+IFPJht/13r/H2Zo5IDJJERiv7sFQW4y99r9F4i0OjJllDIfl6g9QyduY6VvVhkNEH+L0oweGcAKcJRgvzFsESBUkEQagDfHetCsbx2R0kYe6ghEZY+QkYu0maGh3pP+in6ALowWH1XhjjOB7DlHKmtlov+j3o+BiFOI45liQnM0zDeBqN+ZR4pLwp58BA/P3e1PFxYGvgkEBvGdEl9XcmGB+ZLylxScISqwbD3AY43gLj0XhPkIT5gvE+E5ybeY0yMOhF6IkBzAUYsv12TP1unlVikGTf1DbaBOcK5Z7SKzujT+wWEgy4xojIHok7ZLBvcM7iYGUejD+jSiTtGA+SmJ4Q2aM4JgieIVfYfJC/KS2DDh13XmUGSdBz0KWDXoK8/X5PdL2Yc5pPnCYTomcVzhXXfwiSkAxndmBMngH6wKjo/ii9xGrXuP4QgH6BrofjkD6CzhQH33Mf6IWWRBj9Bis5HF0D+wO54TejZ2LTolfF9axQzpdzkAiCczTo9QHoZjgNh0X3Sb/DqR13YFJVASc9/TP080ogbUbfTQqSkMBDch1yRLUBbNG4/geQTXR6zoOjlv7O2AA4F4mmjGXILs7peAAAm4GACsdCxhechGl93t8LYzTjE8E2W2GdIT880zCGYKdiH8YDYjiXWQVDJQEL8sTsBRKm/nwoFTSmPfhkXA2/ge2WKBvJZXz8RydGZgLoh0OuS/U7+gyrEdP3QD/Gzo7Gau4ROe8TzR/mRA9t5XVe9kPGsa8JmMTHZIC9E5zh8efYmEmb8HsY+5CfdPnAqN2wO/FZ5fIVcSzb8J8gOwH0bfwYIUkUO4y2i1c1IQBBQIK5Cdma6Ff6I+PYa9hhw25JnTc+D4fgHfLEmMT9EeBB9ugP6fuPQDCGCi9D2kfniuxQxkH8OgHYbjxnjg+2JvIXfCvMoUE2kBcCKwQYTY5jYyDzBH2Y9oADj4/uu2fqeID9TRsFG5G2tHL6UV9J26URGMOt4kc05tMX4kFR7gm9xuag6Ldjk9IXmTNoB54h1858RmJVmsxG7YtM4isJY4D5b89M6Zs8m4mxIBfzNjKEzzrexsh2jUGSfVJyGK4D8CWzDTkgKZjgCGNdAPvTX+gbyBtyG3QD5lIC1wQnOZ7zIHuZQG7QGZiPmeN573fcV8O2n89L/aakvl3qpH3LOkgSBpNAHhI/ms9vd44GmO4pwQywWq8EMqJG4aHHwe9F4WfwRNBR0OKOHkoSEWxgwE4MkkTnNsU2GiAZxCxIEq9JH4HJg+hwtUGSSPH7+fxon+ZRJ8sVJDksNckGoeQ3V3E6RwMv0WHeuVLXIAlKEWVryASwtoA/pT4x5Pg/Si/vbUAhZaBojJ2jEKSvMInFHaMgtHWQ08zjMmUY5wMTbwADGzKAQsAzYCC0CdgbOQx4OGvJMsgZJDkjpQCESRCHbJUgSfTc6SsodMhj+rnzvL0ccB6M98Fnpe6bAALGTZJRFYc5159xVtqJ30c7ZQVJkNfIoIs7idmXe+W3hcGdPkkN51xBErKYcIyE9gznip+PicoUfd9+vJeDTCMyMAKsnx2Suk5jl2+eea4gCc+CNrEMnQP+G2t47igAZB8G2HOsJkiCjJjD3QdJINcmSBIUAJ513oIkfhxlO9fJCpL4wEM6SBK7V87D9vi4ZuRv/x1joAXKonGYc2QFSaL74b7Cvcbbuz5BkswgIeTc4fxkiqB8p8E9RO2KIWf9NRofMATi8xZKNv2BeQsjVEESQRDqAN9dq4JxfHYGSYyRYcaYiL6DkyIxGBHpwjhrGbcpAULAhPGZsY45D52IlYKMjZyLbYz1kHdSYWCGOZHfTAlGHBBcG30Fx0ywMcyg/CYaY89Jjbeci3MzvjI2B2AAow+RMc8+OIgwUOPzk5Umia7N8RjcOMDjQRJ+E0kpvJ8QHTzMEzyTuKHMHIHxzHWYs5OCJBzDvMMciU4YgI6EUxRnIccG8rtDiVbOwbzJO7zCXJ34rCqE9IcqQZJ6IGeQxM+pOAmRu6wgSSRD2JjpIEmkF2cFSaLnFnQO7Nfh9zqrGsB2ZIQMahzHcUckDmpL3ItsUuSEvoIeEpczdDgSu7gXdAl0JutXS6fkl/tDdkg2wTYmQITsBVBKGKcPfaFn61RfTev1yFhkk1DKFR2Mc3G/X2+Y0mkDcMQTdMIu4bckPZ9yJG3O780KkkRAh2X1POMf2+J6NsA5hN7POagsUCW4EOmnVEZgRUXo+8gjzr/4iiWSTBlXGBNxNseDJNiXOJgpW4sccJ/YD2TVE+QKwPZixRLPlfuwVQVeBtmP4C3X5tnjl+JdrcGmQA4JgAQ5x84kkJf+HdF2HMKseuN42oz7sCBJbEw1XThXkCRqU65He9BWtAVBbPwqcUc3QXv6CffBtSyQH/XVabG+gr8Fu7KcZDTIIHaNyaBve9qN1TXYBPRt9ks6nmOZf5nTAhjHCPLzPHgW8TGBwAG2GStz0rZLJH+MLdherBrieoxXzPMESvB7BeBD4F7YzrGQlaE837g/gbFvIMnV/hqQ8+G3o2+F38kx+Nmo1oH9yzgU9z0yR/LOWMbO4FBm7rZgckxv4feT5U+bEHy0vuQDHOghiUGSU6P+3Te1Dwh9iX2Yi9A9wn2GAB7PI/x27GhsfT7D8xCrZxg/SCyI+3gJQJHUYvNkNPcxnth7Xz0Yi9EPCVjT9pyLca2mIAl+a+bLdL+KgC+ZbezD/BwPPBOMY3xiO2OV6bPRfGw+idh+zKkczz5VgiTRPTBums87GrfpK9wvfSu+GipTF8lsp1KnjTvlFiThYTEQ9t06eoCnpBQromEIJj84dHyUPSajuLLHQIlCOPDE6EHHs8kHRoPjDalBGmEiE4WSPcE4AqUSJEFwcVSyEoD2QIDt93zmd4jAoMskwH0yGScFSSjtwu+1ySf6f2gnlBqOZXClHWmPUIoG5ZNl+LQ5gz2/IfP5VDJpI5y/ZHnHs0dwTKP04ZTm2dOnQr9iMqetUS5+iIxvBiqy9gI4FqcxZbcYyHgmOIORmbTRFAuSkGnOgIo8BjCZkwnEoG33uHJKxlE8g9McA58giBn40eDPtSjhw/NHlll2T9AAZzsyxaBIdDooFGQU8T4gMi0JgPCJ4cxL2ckMQ1aCvPBJgG16zGFPYO6Xy1P3FvY1BQDZjb0zhRUnrFZIy24dgiThbxRqZD4+4VhbesOWsW9QdF2ekR0TKTXx8zQ28syrC5KEfZAJJuYwVmAIxR3qSUESnEYBGEbIaijvxPmQGQJOAaZ4zoYgCfdKNmP8mdMnaReORdYZ57iejXO7pZRaDDaMO9opL0GS6JzIE0ajLX8OxmRkEDBukMnHOTgfY6yNt1FfYYwnY4gyKgE8J7L72N/6aaR4YVTE5yUFSQRBaAB8d60KxtHZHiTxZPyFGDsYhiRAMXcx/scdWACnA6VH0a8Zp9HXGVtxuFhmejXjFvMKyRzo54zhvdukDMTgDGEsJ5N/wFGpsZTzM89hwKK3BGDssoIdhwrzF/eN7cA8GIAOz9zK/MXYnBkkwYGJbYADk+vQDpyLIBArj+O/m5W/7MOcnRQkCeUi0cmSVqtUi2DQR/oe1+A5ZD6fSiL9IR4koY2xBXAesw25CPoCfyfpZcUKkqDPkeSE4y3IK3o+mchph00EApDoIcghMoY9gJM6/h4BzmuJWJHNavIY9Q/0S34L9nW4p1zAiWhZ/FF/RHeqEiSJZBmZJkOXNuZeA+NllRQkSclaPEiCzND2jE2MKWQXMwYOjfRWVpih2/JMzRcR6ZI2TtUxwEdCEcdie1oZwViQxGT1rtS4HO7TVrScnHqmAQSIQ/a7lV6KdGXT9yNgG/4a6ajm+EaOL/Z6tb9P7FZsOvoK94DzeXYFSUJAOvRJ7FT067iPp5yDJDj28S+l59Go3QiSUZKopiAJNgrB0gB8UcxxjDuMqfGxjW2MQcFequ682Jj4R+JBOVY1IUvxZ8W9V7lG9Jl5jUDmZfpQGCfNBxPN6Yyl/E78h/EgCXYY9jB9jXNxPcY5Vm/EVyshJ3wXxs+kIAlyHs6RM0gS9SVkD5sV3QHnvCHet5m7I1sRXxG+XuQ+VzuK/5E24hkzt8bblWeMfU0Qjf1of/MD3+/3iWA6YvRM0dOCLPA86xskYRziPCT+1gfoylyD88SDJKa3RPM7PpBwn+yHLoKfL5cuEtqosZDfhNyXVZCEB2G1gG+OJv3ICAJkVeEIZoBhwiRyatEyPyAw2KGIElixRokUMQa/ACY5Bgsm+5CpX2UwicBkiBLI5Mbgxovb484oBl0cYHQM2iwdJImVAsBww3FcJUhyZeqaAXQEsmwQRgZAXnQdD5IE2GqPaF8G/nggCKDUcg3aCoWE+64SJImOIWCEksC9ogzYUiw/GAM6LApVaFOOif/eeK1TsSqRgcw2ByzhRIlFESXSi5xaiaNI1uhvYz9MyTZOxvhyT7bxrDkOOY9PvsAm6NtTgzYDOM89Xn7Nzh/JSOgjKKf0BcpPoTzEZRTlAwOcbHruH9kkozCArBBTGqJJHIXSgkHR+RlU6XMcgyMZo4n/I/M4IHgXDwFKZIb7I6jZf/fonqI2CGDgRflF3qAp/EHZisB94dAIikp4B0w8SMKkRR8L+yQ9H8YA2gqHc1iGGkBb46RH0eY+c52jMZHfizGOoR3PWokHSWBoM1aP8MwzHUx8R2Zpz2i8sCBJNH4RJIiPT4y1tiJvUPR9vMa7N9w4Z41Bkuga3AftH54Bcoqch3GZjN2Q2ch2fiOGVzxIwrNEmbV7jSZxVtYxocdhfSsa5+iP1r+ifhbkifGOoBBBH+4VhZPrGuhTPjgZ7jXe3hYkieQoHiRJenF7+nweOEtoOxwLtB/tSd/iPrh+PIBq/SV6JvRp2z9hnrAgSdQfLEgS/Q5WTFUJkkTHkbGsIIkgCAnw3bUqGJNma5Ak0j/4jM8RYRxm/GW8ZszDeGR8DAhzGAFjyqXEV3ig++CsJeEIfR7DCT0kOE2YV9BpQnmBMJ/UK0hyZzSe1xQkifSmnEGSaKxnTmC1SzBieRboRKZ3+nkSML5zL8yDiUGSXVPHo9PHS1KGuZzvyJDEhsJApm1wfnO/zHGUbSCpIDyH9DOqQPIMMoMkDXlxO+fLKrcVyRn2K+cK5+STa5AkFHSpfAVJeMbU3a82SBLpMZZEuEAkg+ulrhOAXYmcsUIFGcLWREbjdqslKtYjSIKNHaAgibeZ/PNFBllVQQIQcsSzCwxyw/G0I7o874zEdgu6OuMh4xZ2KTJJv7cxIPT/iIylOItJ+uFcPx6ecgIGGcwZJOE9iBlBEtONI5lHz2WcYiwG8aBPHOjfHMe5QtDbgiSR/vlnl+i4qO+BsCKGsZHfyn3wyRxAolKAgiT1Z/i9tD0ykh4/IhlEprAp2S/zN3Mc8ojfA9smbsNMjdoePx8OaXQN5mVkEliQ5P7U+Mk5YPw+YLheYpDktf+2h2NIUq1yjWj+IwiLryRcI+xLgPHPx/4bw5BFdA0SCBODJNG4jFzHr0eJpLQPBURtxbhpbRXpF9aXIvst+OWwJZG5+G+jL1liY2y8JUhCX8K/E/o6YzdthfyOeialp4TxNQBdBr8hDm/ukWuIVUm70J7Mh7baDZ9EHcF4gd8E/ZFzMpY0JEjCPszjaURyhD7BqiTG57TexuerKb2NknL4sJET5Ilr1DZIUl3CRrytGgP5TWUXJIHhh/FiGRRMoqgMINwzwoGABaWMQQHnbBiYOJ7/U6qHFREcz0TIsZBBmgGN4AVLT4Nj7u+oERmoGWwwTMj6xYGMwDMYkq1FFnyITjPYILjUULXBMhJ8MpnJeEARZB8GU5zZRM+DkswgzWDGfWJgZa4koSYcNQ3ZD6c3AaIZ0e/F8UVEmM6GosDx/F6y1bhvE4Lod5hiEXVulhDShuxDe9AReN4sv8aJybnptHb+v6N7iAZyfgvKMVlFX0YDvgbS3DQZjRQkDE8m84lR2yFnM/6KOM7LWqS8MbnxUkYGRF5UTpsSQMDoxPhESaX9GYxw2vKcOR+ZNTh3AYYsLxXt7l9MxzNFaewfGVrIBNdBvpj8uQcGTiZbnjn78qI7AnPcB4opfYn9kRWuyUugKA+BkcIgyXHIOc7XsZEcIleAyZzfxrHhN8aDEMgS94Nch4EX5Zw2ModEpEjyQlC7dnQs/RgnBaXDmBBCu/JpfXil6B5iNXbpJyOeitpu99T2XPJp7RP1TyaF+LJxQJCF31iK4159SZthuKKEslINAwYFFMMmPIdAk79FU9vIquGZ2XOMJkWeHcohzqfQthzfc62o3R9PPT9klWdPoJlJmqwYVgghP8gVzxSHCxmHTPAYC9QTZnwxmYlkDqOK8/Ocwvhky5uje+f8yDETPmMbv43tfJIJRH9C+UPBZV7AARB+I/sx5rEqC6WZcd4C6qOi6/qxDoWV78l+op4rx4bz0y5jcSJFCgT3QWAFwzLca2hD9mWOoM4yS23pA7Q5gcywDJxjUIQZI74/KJqH3kntiwxbH4r6HQFEFF4cZAQ4ODdKOHMGYzhOAdqLe6ePY3zR3gTJaSOMS9rpm11T18FY+K5dtJ83DLnOX9H8x0oV+mH8N9SX9BsFSQShbOC7a1VgRM3OIAljFXoSejfvJ2CMtCxO78iFlJBgLokHQtDZ0csJVHP/AYzPrBLhO+Ylju+3fTRXRcYk8wIopSAJ94L+R4Y0v5PnQHYiuqHZLLZTNLdF8xSrg7kXfle15bai6zBHBZCog35EMgXtheMQhyu/jaQLVgyT1U2Qhfnc5rQKJ88h70GSSJfnu3jiFHM9jppwPl66SnDDAnr+2rMjSIKe0TeyLYLejx4wNdLP0TE4NsgptjC6YICCJPUnMsDvTQqS1CaZkePxGWD/x5NHkQH0UqoBsB9yBNmfsY+VzTix0f+5Bs8DO62+QZJQxtDq7/sxO4xBPHv6CnYxgR8C3KwMQV64NueGOOuwZ7mvtF2KLRDZdTjB2YfrM55iz6DHByhI0jDym2k7kijTGfYe/H6C9TzDsHqJ/Zm7sNt4J5KNXR7IMHYjYw3n7tEy+jtq2zBO0uY2l0fnZJ8g48gGz4hxDf2A77HbeIdT+llGwAbqu03KlsUeQyaY0+z9mZFdBBi7SRYN1+CeeaY2fkX2bnqejYCOwe8O8ygrmeJBEuSMcRSdIrTTQCqCkITh+wrARmfO57fQHyhlF7Zj01HmHruU34WNyf0y9savZStJIpsSm44SzqxwYaUXx3FtK3kY2ZP4EdMBmghci7keO5HfGZ6rWJW0IXNpOpgczXXIIroY4y3ybDw8as+IjDXMbekk8+g4fA5UQeFcjDUNLbfFSl4S5wPwteALoJws5w+Bcp47+gmBOMZUZI17YJuCJDkR7ZUAHGOlGiRhIuWTh8KP5CFC7hWGvyHbEYLMc/Adx+c6Npw7/n04D5/humF7levE7q+u+4TrsC8OtcwgCcqElWyJtjNRx4+rcn7or8F3mdfguvF9+AzXZXt8/3AMZHv6WDE3Y8+3pjaF7Bd/dvFnkfQM4tuyjk+4dvwcfGf7xuSDfdkWGPaPH8M+EOUDZWRi7KVUGOwog+z/ZkQ+LZgYGTFxJYCBHiOIc4bfmuva4e9w7fhvy7zv+H2m2yEH2U6ZOltdERkBARhZOBlCxkfSsY2SGX07tJW1U6w944y3a2Bi2yacO5wfch4YtsP0eXwwN/NY/g7nDveXeT9V9vGfmfvw/6TfGL/fwPgxMH2PsfPzmbkv14xvD/9PapP0OTOY2T7x84drhOP4DL8z176cK3yfec1wbHw739n2jHaqDzmngiSCUDbw3bUqMKJmZ5CEMYxMYIxTu58Zkc0yOeXgsEB9RJwYcaOSQDLJIBxv41SkwxDoTju5cIRFx2Bs4oyJ6y3AgiQExn25LZwlJBVVCZL0SDmswzhMkAQDFmdIALoSjsMqQZLIcM0KkkS6SZ8cQZI0/k1dn99r9+ENd4BTiKAIDiDmAH5zVpAkuhcc8mzjnnGSY3OEbFqAE4B24RoY+rRxaBv+j0MSpynXiM81lUjasUqQJHoeJIzw/DLnYv4mS5nnnBUkic6BXCAfrM744YiU7ZdGdF6cirS/OVd47lwz9vx5XllBEpy6fh8LklyXcioHeWUFNdepEiR5PurrvFMikkN+nwVJKCkcD5J861KVFpqlnNf0g7QOEN2XJXmFfhVzhgZUKbfVKpKp2Ivb6Z+T+kb96KxUu3GvgVWCJMOdrViv5CAJAbl0kCRCbYIkgZwDxz815+NjH/8PyZI2rkb/j2/HIYsvgueRGCS5JzrvFv/dpwVJMkoEIXNkNYeVIOPJoPdjEHLE9XEc2vjDODQyGlN/jO71i+j8d6dkkr4SfgfO6JBEaAjjJP0luqfQT0xG/RyRDtRF/S4rSIIMR/dQ5cXtBEmYP2JzDM5ors8+tIcFSa75b54COOjLMUgC+d0450mmI9GsCngGfswyWYrGApujY0EmQGIXSX2MBZyP89JWOHxJmOT4AJ4fvko7X/R9kDueJQ5hnhPPgLl+5HOpbYA+gnxxP6zYZwymtDIBFZKjcWIHVLkG41fGPMux+OR4pjafR/edGSQJIHhsvz+jD3Ef47ulAjdBLkwviHSs6ZHeEsB9m54T3YslwsVkL2DMe6k+yTko82jB6Og42tmuzThMW/lANuA8yC4yG9pcrEqeCwkL9Gfm0QBW+/wQjb1so/2SjkNXRa7jcx/BX4If7MM8jYwZomfFOBcPkiS+uN0HSXhejKn2PqDY6x3YN8iKjZv0N8Y+tkXPnkBm0FXpJ1lBkmjsyxkk8X0WGScAqSCJWHTy8CxI8lRKmQ2wFztFCjMDcmMUSrFxkwGZAAOR73iQhMGfQRz5ZEUKWQkTv0kNzgEYMWQ1oLwg30nnLwYZ9Fnea++EiE1aLJHFQEPJk6IginUn+oKCJIJQNvDdtSowomb3ShJzIkRGHKsCqWFPZjKrStFDrOTjwFRgAacHq15DJnz8PAQqyMZEX2ElCceSyIHTF2c1zmDKy5CtyUpGviM7kCAJzliy8VgBiCHK5/CO/2Vsco+suP52z5SBjJOY+yKDeYB/TxR6EPfF+01wDLMdpwYGNA5sEk3IvM0MkrDSm3OyapBVhfabI/0KZxwORzIFe60fnbvJf3YCvx3nOw5pAkY4GSk7EsoQs499RgZvvx2clQ9hVQEriePtynF8jy5Hljn60uzU50qJtDGBJlYOmSOXldBk/0bfZdpr/E0mNc4Qni26Mu1L0Al5QC6CHkr7IgusCsIpF54Hz9tk/OFILg9L9QdWwuLQHd8tFbCgj+J8tuz5l6JjWZE7NnUczkPKsnAdroGzBhm26gzR/SBn3B+lnzkH+7CClxW0vGuOxCKcbgTWKGcUHJvINitL2Ie+RJ+yfhX1MUp/0K8ICuKo4Xfw/p3wguSvlor6x/HR74jsC/oV8krWM/2Itgh9i09ePIssI/v04ZB9ntnW5czw7Ahkjfskas/BUXsMif7/ZapvZ455ORmNabQb5yJYxkpoZA2HIOdDFhgDJ0d/81ytSsf2KcdyeB7cA6uEGCOQMcZNMuJJPOMa7EemOjoiDm9kjOfHChaS1hh7CD7YynMfJMOZzD5c28ag6F5wYsed62yn+keQDa6FDBK4o1KHlbb+PdVvkHtWJeDIJ4DDvMF5WVVNdj8yjNOQSgWUGmM8ps8QkCG4wzXoU8g75WuYZ+gHrDrAcUn7hfsgsMjcw1jNNVghjk+HwCf7VWn/MiK/H/Kseb/n6OhZ037IhcmSf448F+xwZIH5rjerQZOStaLvQrtbckL03JjnbGzhXLQ/89K7KSc2cklwhGM4nrbGtqcCB/Mlzyrcg8nDpdFYuU7qmdFfWLE/+PzUfQX5sPuOxrAp0XVYDWTBv81S/pAgc1wnKUjCcSRLMO6hJ3A+ZBEHO2MyYzfH2nn87+feORdjMr+JdqK96IfoKlSTYWxm/GZFLfMNAXkSN76O+hIrZKySTNQnWaHCKlfmI7t2dB7GeJ4J/ZwqOOm2FhPJ82EeZBURq86oxsGzoMoFzy3IQCb5nrGIOQ39kTme8Ypy7zwr9EhKmBNcZlxDLqhcRICLY5FHdErknW3Mdcg9+ncYQ4Kcc38kwqBjjo+ed5A1rsmx6JXoI1SeievDfCInjGX4m9Er0Hu5brgGn+jLVkXJyxEyFN6hk+v3lzL5TQqSNFLy8JikLWMnEvAABjtqGcYVaFEsJk3uokGRiZX3QKAwMHCHKLXh3+hfpNyivAQDCMWjFGSWMQ3jIWQqkVGB8Ub2Bf1O/UoU60f6loIkglA28N21KmZ3kCQ4EjDMmLMx8nKR7WkDzh8XyFwfDNGk4wLj34VzxY+lHcL2tP4Qc3bwfXyfzPsJ+2SeC+Jg/PHolDMnACOb9xVS5oqsZ/aPk/On7yMwulbmb+X8tl9Cu4T7yUW2Z/6OimfUDrRdZjsltXHYt1oZCszYN5PhWWSey84zZ+ocbAvPtMr2QH+NzH04Lr2f/w3x+2C/pHuO75NJ9g/XCX+zf+axOdsk4T5g+hx+e8XQP7t4W0Brs3q0BcfFn08SqzwTf434cXH5SZ87Y7+wD0ROSV4j4BdAoIVydAQTqVDwDvs1SQUDsdniwDfCeZPkKJN8n/kb+X/62Ohe2M738XvM3B4/Pr09tLf/5Lt4m/D/+PZyZngeme2USbZD9k86T5pRmyW1e5yJ56nhOO7RjuGZ+H1ruu+k6/BdUpAEB3goWZl0HpObHKzNfce3Z95XfX6HmMAcMpR+dshO+H+c/vuk5xDaHvJ31lgTk8fwfaA9s4xr8l1tnnfWPUeffJe5X5Vr+M/Mc2edqxGR36IgSWNlJHAIaBBInkOYYE1wRXE2MwzI8QEzyGkg3yGz7Fcycuv7Vvx++b8N9o1woBfFUiF9SUESQSgb+O5aFbM9SFIhRCcJLzIm6zOArNFRz6ZKl6JfJR0riqLYGIg9RuY7iaGUIkzj39RqIlazMP6RkDexXypxNK5j8m5MysVwnpKxM8WKozldCZJ0/i8JE7AqjlUd+BmSjhNFsfi0/qogiSiKoiiKhaaCJIJQVvDdtSoUJCkOCZJQquGbXVPlYAIoc0DJI2pRW3KHKIpiIycBDpzJ9g6Jp5yVBaLcVfr9CVQo4N0KBE6+SZU8+nb3aIxU+XGxBGhO13lSFWCQUeQVUtaSd7kqoUEUS4cKkoiiKIqiWBQqSCIIZQXfXatCQZLiEuchBh3tDdMrX6FWv4qiWEYM4x3jXHzFf3z8g+yjlSNiydDPxUF2g7zyd3y7KIqzn/RLBUlEURRFUSw40RcUJBGEsoHvrlWhIIkoiqIoiqIoio2NCpKIoiiKolgUKkgiCGUF312rQkESURRFURRFURQbGxUkEUVRFEWxKFSQRBDKCr67VsW/M920Pnu7SW/8n5v8riiKoiiKoiiKYulz0lvR5wfzuZljFCQRRVEURbGQVJBEEMoKvrtWwaxZM90V5+7tNln9/9yObf7P7dRWFEVRFEVRFEWxtLlFq/9ze2wxn+vbS0ESURRFURQLSQVJBKGs4LtrFcycOdPttPOu9GFRFEVRFEVRFMVGxDnc++9/4C2bZCQaQQqSiKIoiqJYaypIIghlBd9dq2DWrFnuoYcecieccII766yzRFEURVEURVEUS54nn3yyO//8891PP/3kLZtkJBpBCpKIoiiKolhrKkgiCGUF312zMGnSJPf333+7sWPHiqIoiqIoiqIoljyxX8aNG+dmzJjhrZpkJBpB06ZMcLdfspk7fuf/c2ceIIqiKIqimJvoCx2v3dnNnKkgiSCUA3x3zQIGxu+//+7+/PNPURRFURRFURTFkucff/zhRowY4f755x9v1SQj0QiaMGGCW3f9tr5mlyiKoiiKYvXccqvt3PTpCpIIQjnAd9csKEgiiqIoiqIoimJjYoOCJFOmTLFaXdtuu63ba6+9RFEURVEUc3KbbbZxV1xxhb3YuSZ4H6wgCCUM312zoCCJKIqiKIqiKIqNiQ0Kkvz7779Wc5i6XdTsEkVRFEVRzEX0BfSG2sD7YAVBKGH47poFBUlEURRFURRFUWxMbHCQ5K+//pIRJIqiKIpijURfwHlaG3gfrCAIJQzfXbOgIIkoiqIoiqIoio2JCpKIoiiKolgUKkgiCOUF312zoCCJKIqiKIqiKIqNiQqSiKIoiqJYFCpIIgjlBd9ds6AgSf04cuRIM8x+++0317dvX/f555+7Hj16uAEDBtj3MOk4USwER40aZTL3888/uz59+rgvvvjC9e7d2w0cONC2Sx7FQjOMe8wn/fv3d59++qn7+OOP3SeffGLk/926dXODBg2yfRhDk84jivlgkMehQ4e6b7/9Nqc8/vLLL7a/5FEsJJEvyPjH3JxLHhkb8dsnnUPMpoIkoiiKoigWhegLCpIIQvnAd9csKEhSe2KMjRkzxk2dOtWcLvfff7/bZ5993MILL8w46JZddll35ZVXutGjR9t+SecQxXwSOZs2bZoF6m699Va30047ufnnn9/kcf3113c333xzWm6TjhfFhjI4o3/99Vf3wQcfuFNOOcVtvPHGrmXLlm6//fZzJ510kjvmmGPcJpts4tZYYw237bbbuhtvvNGCysOGDbMAX9J5RbE+RBaZg3FGv/766+6oo45ybdu2dRtttJHbf//93f/+9z933HHH2fi4zDLLuN13393dddddrl+/fm748OEKloh5JfI4fvx4991335nOuNVWW7lVV13V7bXXXu6II45wxx9/vMnkyiuvbPP24Ycf7l544QUL7oWxNem8YooKkoiiKIqiWBQqSCII5QXfXbOgIEntiTP6o48+MmcLTbrooou6008/3W299db2Nw7A66+/XkESsaAMjhOcA2+88YbbYostTP6aNGliDunddtvN/t5ss83cnXfeqSCJWFDiY5o0aZK7/PLLLWCM4/m8885zgwcPtrGQOYZ9cD6/9NJLFkCZd955zXFNsJmgc9J5RbGuDA5lVnhecsklbpFFFnFrrbWWySar7BgHkUc+2Rd5XGWVVVyzZs1s3OzVq5ebPHly1nlFsT5k/CMQ/Nlnn1kgpGnTpm7nnXd2nTp1siDI2LFjTR75JIhyxx13uAUWWMASbq666ipLfmBb0rnFFBUkEUVRFEWxKFSQRBDKC767ZkFBkpoZMksxWK+44go3zzzzuOuuu87sKwIn559/vjmlV1ttNQVJxIKTzHtkknIdl112WTo4h6Ma5+DZZ59t8kjmvoIkYiHJWIfMsYKEVUwEPy699FL31VdfpeU07Iuzj6BIhw4dbKxcbLHF3H333WfOwVAuLn5uUawrGecon/Xcc8+57bff3s0999w2NiJ3bI/L47hx4+x7AnrLLbecW3DBBV3nzp3dkCFDTBYlj2JDyZhHcI4VxszTc801l3v88cfNoZ+5L/M3ZQqRW4Ip7P/WW28paFcDFSQpUTKpMyDTCfjUktHKZsiYQR6YiOm4SfsFsj/7coxkRxRrT/pX6GuMvXHFV2w4FSQRhPKC765ZUJCk9mSeYc4hMMInetvEiRPNyUITK0giFpPIH3I4ZcoUK+dBP/7pp58UJBGLxuBoJus5ZOTffffd7vvvv7fxMu5oxhdFCSQc2JtuuqkFmyl79Oabb6ZXmsTPLYp1JeMgsnfaaae5Fi1auPnmm8898MADlrXP9rg8ovvwziZKILGqCXk855xz7P0QyKL8MmJDSeDjxx9/dFtuuaXNyauvvrp7+eWXbc7O3BfZZV/KFTZv3twCKszflDFku4J2yVSQpITIw0ApgCyfuvbaa63G4YUXXuheffVV20cDa2WR/sVyugcffNAdffTR7oADDnDvv/++mzBhQuL+yAeTc8eOHa0WJvUHn376aTl7RbEG0nfA119/bYrEHnvs4c444wzLqESRUP/JDxUkEYTygu+uWVCQpP4kGIIRrCCJOLuJ7oN9qiCJWEzi2OvTp4/p4UsttZSVN+rSpYtl8+O4ijv2GBfDqhPeS4JT+sQTT3TvvPOOgiRiXojfhaDdoYceamWLIO93QE4z92VMJMv/2WeftZKZrDphVSi+PQVJxHyQVSAE7Vq1amVzMqUx3377bdMbM/fFr8z8TWk4dEnkkYAzK5vYriBJMhUkKREyYPIQqGG43XbbWcYEQj/nnHNalJAXP7Ef7Z15rFi+JJOLQQwlcYkllnBzzDGHe+yxxyzbMGl/5IOJ/OSTTzb5QY6uueYam8RRIpOOEcVKJn2GvkG92G222cb6TeDaa69tSjAZauo/+aGCJIJQXvDdNQsKktSfzDcKkoilQAVJxNnBeJCEzH3eSXLPPfckriRhXMwMklCnH6ehgiRiPhgPklA+a/7553cPPfSQJbKyPS6PSUES5nIFScR8MQRJeC8Oc3LLli3dK6+8kriSJClIgl+ZMZPtCpIkU0GS2UgansGSLGWy/VEAEPR11lnHXrxz1llnpZVRBUkqkw0Nkiy00EK2IikESZA3+ivZ8p9//rnr2rWrZeXEFUjkEjKg9uzZ0zLpUQxCP2dflAK2URuWWtp8hyMZBRV++OGH7uOPP7Z9OA8KAb+FAZnvWQ3DPpB9ODacO9xHuBccLZwjfu7AL774wn3zzTe23DWudIS+xX1z/yja9DPu+8svv3Tvvfde+hz8Ta1GtklxqRwiV8jjzTffbGMsSkPr1q1tiX779u3diiuu6FZddVUFSfJMBUkEobzgu2sWFCSpPxUkEUuF6NIKkojFJvPHDz/84G6//XbTzXFM8/Jh7DVkMm4vIoMDBgxwjzzyiFtvvfUsQZB3O/Xo0SNt+8bPLYp1JY5mShbxriZsQ97twHtvkhzN+DvY96abbjInNqW5kF3kOfgnwr6iWB/i62PM23PPPS1gt9JKK1mi/dSpUxP3RfYOO+yw9CooAio4/zP3Ff+jgiSzkWGgRHDJdrjllltsaSgCTptefPHFpoxuvPHGCpJUKPMdJGEbDl9WK7F96aWXtoEyPqhyDoIF9957r2vTpo3td8IJJ1h9TQYMotQERjbccENb6bTMMstYGTCcywzA1Os8+OCDTaldfvnl3c4772zZP8gz5b/IwjjppJPsnGT8kK2/6667Wkk5Mi/oE1yfoAYBDpaotmvXzo5j+TTBQ67BsSuvvLLVquWFpwRb6Dccy2+lnjfX4P7JQtp8883tPrk2hh4llXbbbTdbqkgtR174RzCFNpACU/5EzgmuPfHEExaUJniHUYZiS7YFdTtxTClIkl8qSCII5QXfXbOgIEn9yXyjIIlYCkQfVpBELDYZ67ADSWrbYYcdTO6wLbFZsY3ZjvyxLzYsyX+UpV500UXdCius4F577TU3fPhw87XEHdiiWB8yzpFUSlJz8KHgTyB5E1mEYd/p06dbkiY+BoJ7zN9vvPFGeh/Jo9hQErQbPHiwu+SSS8wXhjzeeuutWSub+ER2SSrGL4dfkMAK/jX8ZPFzilWpIEmJkIeAcxfyUHAWX3TRRSb0CpJULpOCJDhtAUvtMsnEDMIqpKQgCYMq71sgC4LgwOuvv15leR77IYe8kGyrrbay8xCUwEBCNtmX1RssIWUb5yFjp3fv3ibHyCj3TYQ7vFCKYMrpp59uq1cYwNmHQRvj68gjj7R9eNkeWUAcy/UZV+gH3bt3t6AM1w4BED6ZIB5++GE7lnZBeea3hd/K4B/aAR500EGmtIQJg3NwLV6sRtsutthitjyb6/B9/DmI5UtkECIXPHeCbQRJllxySQVJCkAFSQShvOC7axYUJKkb0Utw9pEEM3PmTGtD5iKamFIKZFTzPbYX+h7zVXBgJ51PFOvLoCcjjzNmzDC9H505JO9R/5zkErYD5JH+jjwmnU8U68Ogn5OFT5UNZG+XXXaxpCbkEzBekmh6yCGH2HZs0yeffNIy/JHJpPOKYl3JmBhIBYJll13W5A1/Cv4PfA+AOZqMft4Ly3b8KCRAI4/y4Yn5InLIJ4FgAiXIGjzqqKPM1xdkERA8WXzxxW07Ccf45zhW83X1RLemnRUkKREGg0dBEhFiBIcgCZkxyEOTJk0sMFEdCRpAXnRXiCBJv379LADCOTbYYAOLUMfPQQCDcYGVI2RRULroqaeeMoMqDOxch/1wAhCgIPvn6quvtnvl+uFcMCgmcWKUoSizCoWX+vGJM5vfGIIk3Df3j3LNahbaM35urvXRRx/ZpEK5JQw/7pvv49cXK4MKkhSeCpIIQnnBd9csKEhSM9Fl+ETPY/UuOhVNWhMprYDDmpKntHM4jyg2hNigyBIljQjKYUMkyV8m0fGphoCzEDtV8ijmk4xx2K6MkVQRSJLBdddd1+w55hw5/8RCEluRVU6UZm7btm2iPJLYgK8k7hsRxUIQXxoJyEcccUSiDok/48ADD7TVdlo9UnsqSFJiVJBEjDMeJAkrSV588UXrfwQcMhmixmFJfKFWkoQgybzzzmv1X1khQtAinCMESSi/Ra1EAjyUNYrvE4Ik1PckQEKUO7xknuvTF9iHbCFedsZqlSuvvNJ41VVXuRtuuMHKb/Eb+J38Jn5bUpCEmqD0oaQgCe8lYWIhSMLv5b4VJKlMKkhSeNK/FCQRhPKB765ZUJCkbgyZ+xhkZEdXR60kEQtJHAO1lUe2YxfQ3+WcFgvB4KxCJrHtkmSQ8oSZCXaiWAgGeWTMQ+6S5JE5WvOzWAwGGcN3xZydKY/Mz/jGJI91Y+jn9OfqoCBJkRgEWEESESYFSfLxThLqZBIkIdOBOpnxIAnngI8++mi6DmwxgyTcI4oH7+phyTTvNOElaVzvtttus/t9/vnn3ZtvvmlLXnk3CseztFVBErEhVJCk8FSQRBDKC767ZkFBElEURVEURVEUGxMVJCkxKkgixpnvIAlBCV48duqpp1pQYq655rKXkBF55ngCCPTnXr16Wd1XzgFZmVKsIAnBDcgKF160zvX32WcfyyDC6RKO53e+//77bscdd7SXbBP4yVVuS0ESsTZUkKTwpH8pSCII5QPfXbOgIIkoiqIoiqIoio2JCpLMZtL4Q4cOtRdT46zmRXi8jPq+++5ze+21lzl4cTAfc8wx9lJr+Nxzz1ndV14CpaXN5U2CCDznU045xTVr1szkgRUedNik/UOQ5IQTTrB955lnHitPFYIkIQiCI5gXPSFbbdq0sbJVQbauuOIKK2G1/PLL27tCOA9BlXiQBHkleMe2Vq1auU8//bTKapQQJKEGIi9t550hBHfi+4QgCcHA+eabzy2wwAJ2r9w/zmquQWCjdevW5qw+9thj7eV9rC7hXs8//3y33XbbpYM9rDjJDJLwu7hHjqe+clKQhEALL31nv0022cTuW0GS8idjJ8+6W7du9pI9xt3HH3/c3XjjjW7fffe1lVYE784991zXsWNH63eMz+xPP2PsTjqvWD1pcwVJBKF84LtrFhQkEUVRFEVRFEWxMVFBktlMGp+VAjit77//ftehQwd36623WlkhHMR8x4vK7rzzTvseB95DDz1kL2tkRYCCJOXNENRgtQNOWuSB4AGrKpL2Rx4IEHzwwQfu7rvvNln58ssv7bu4U5d+S/AFOercubO9cBH54j0fBDMIiFDuigAC1+R8BPPCsbywjAz7Bx980D3zzDO2P9+H83PfDC5vv/22OZY5J/cd34f7YT/eN4KDmsAHL4AP98q+/J4ePXq4Z5991gIk3CMltvhkpUnXrl2tbQgwvvLKK/biqnA8n2yj//DSeM7D9eLtwN8DBgyw++QdLLzvhfvm+7CPWJ7kGQ8fPtx98sknrkuXLtYHIME0xl4CIxC5YzxG7m666SYLCNL/4nIk1p4KkghCecF31ywoSCKKoiiKoiiKYmOigiQlRB5EbZl0vFjerOuzr+3+Yb9c+1d3juq2BeY6b5x12Sdp3+qOrW5bnLXdTyxPBrmqiUnHirWngiSCUF7w3TULCpKIoiiKoiiKotiYqCCJKIqiKIpFoYIkglBe8N01CwqSiKIoiqIoiqLYmKggiSiKoiiKRaGCJIJQXvDdNQshSIKhIYqiKIqiKIqiWOoM8Q0FSURRFEVRLCgVJBGE8oLvrlmYOnWqmzhxops0aZIoiqIoiqIoimLJM9gvM2fO9FZNMhQkEUVRFEWxQVSQRBDKC767CoIgCIIgCIIgVAQUJBFFURRFsUFUkEQQygu+uwqCIAiCIAiCIFQEcgZJRo8e7YYNG2aOD1EURVEUxVxEXyC5ojbwPlhBEEoYvrsKgiAIgiAIgiBUBGQECYIgCIJQNHgfrCAIJQzfXQVBEARBEARBECoCMoIEQRAEQSgavA9WEIQShu+ugiAIgiAIgiAIFQEZQYIgCIIgFA3eBysIQgnDd1dBEARBEARBEISKgIwgQRAEQRCKBu+DFQShhOG7qyAIgiAIgiAIQkVARpAgCIIgCEWD98EKglDC8N1VEARBEARBEAShIiAjSBAEQRCEosH7YAVBKGH47io0EP/++6+bOXOmmzFjhps+fXoW2cY+gjA7MGvWrLRs8snfglBMhDEyc2yUTAqzAzXJo+ZroZiIz9GSx+JBRpAgCIIgCEWD98EKglDC8N1VaABGjhzpXn75ZdeuXTu36aabug033NC1bdvW/r/OOuswFrpVVlnF3X777a5///5u6tSp/khBKCwmTpzoPv30U3fWWWe55ZZbzmQR+XzggQfkdBGKAhzRQ4YMcY888ojbY489XLNmzdyaa65p4yPj5BJLLOFWWmkld9JJJ7m33nrLjR07VgEToWDA6Txo0CB32223uV122cW1aNHC5HGTTTZxG220kVt44YXdkksu6S644AIbOydMmCB5FAqGKVOmuF69ern//e9/rnXr1q5Vq1ZuvfXWcxtvvLHJ5FxzzeXmmGMOd88999g4yngq5A8yggRBEARBKBrMAysIQknDd1ehAfjuu+/c22+/bQ6+ESNG+G//w7PPPuu23XZbc1Bvvvnm5iyUoSsUEt9//707++yzzfk899xzu/33399tttlm5mxBFjt37qwgiVAUPPzww26ttdZyiy66qAVChg4d6rekQND4ueeeM0c1Dup9993XjR492m8VhPyBeff+++93a6yxho2NjJF//vmn35oCgeUnn3zSAiUQmR0+fLjfKgj5AbKIXF1zzTVukUUWsYAxgbtp06b5PVIYOHCgu/zyy01/RG4ff/xxjY95hIwgQRAEQRCKBvPACoJQ0vDdVSgQcETjdGnfvr0ZuXPOOac78cQTrXyCIBQCBO2uu+46c/BddNFFbvLkyZadf9lll5kMbrXVVgqSCAXHP//84wYMGGABOrKhcfSxki4Jf/zxhzkIWXHXvHlz98orr7jx48f7rYLQcBCM+/LLLy0I16RJE3fjjTdaZn4SCJwwdi6zzDIW3HvnnXc0Xgp5BfNyx44d3frrr2/JCy+++KLfko3ff//dbbfddq5p06auTZs2rnfv3n6L0FDICCogNGgKuZBLNqqTmaRtkjFBqBnqJ6UF88AKglDS8N1VKBAIhvzwww/utNNOS2cC4pzRShKhWKBUDA7nkI2qIIlQDIwaNcrdfffdVj5moYUWcl26dMmZAT1u3DhbjbfFFluYI/DMM8903bt391sFoeH466+/3BVXXOFWXHFFN//889vqJUpvJYGg8mOPPWYlj+add1535ZVXWvBZEPKFv//+2+26664WQGaMpLRbLowZM8bGREoTsjKUsVRlW/MDGUEFBooAmWHLLrusKaCQbDEig/x/m222cT/++GPOwVgoX7Bs7sILL3SLL764ycKrr77qt+QGyz/Zd4EFFrDMGkEQqkcw9jGyVlhhBes/1Pb84IMPrJ6sUHxEz0AQhBKH765CgYBj5pxzzjHjlua++uqrLXtVDmqhWEDWFCQRig1KyVx88cXpbPwXXnghpz6Ow69v3772joh55pnHxsyePXv6rYLQcFAK87jjjnMLLrigEXsxFyZNmmT240477WTyyLxNsoMg5AsEjMM76yiF2bVrV78lGwSRb775ZgumII8E8DLLcgn1g4ygAuLdd991Z5xxhrvppptcv379/Lf/gYmeqB+d4Pjjj3c9evTQC6AqCAxil1xyiVtqqaUsaPb666/7Lblx3nnnmbxQm/WOO+7w3+ZGXQyd+hpFxT5OEGoC4ygB6m7durlbb701HYhs2bKlfa677rruww8/VJBkNiF6BoIglDh8dxXyhLjO895771mSFM1MGZn77rvPXvIuCMUEMqkgiVBsZAZJnn76acueTkJmkEQrSYR8Ix4kIQm1uqTVECTZeeedTR6vuuoqrSQR8op4kGTDDTe0UnC5kBkkYf7mhe9CwyEjaDYBBZSMMZbp0QlY4kc0WitKKgf5DpIwKDJQHnrooaZEUu81CV999ZXJ2n777eeeeOIJq30YDCKUVGSS1U/UQwRk7Gy55Zb2UlGM+t13392OxwEdwMv29thjD1t+yj7UR2SfXr16VWtsER3feuut0+fmpZF8HnXUUfYC08yXpgWwnJD7pw14cRX3TaCReoycg3Oi8PCCNZbGCpUFZJpVV5tuuqnbe++9TWl4//33TV6WX355C5YoSDL7EI1hgiCUOHx3FfKIYcOGuaOPPtocg5Tq+N///uc++eQTv1UQigsFSYTZAZyADz74oDkCKbeFzJHYlARsuNdee81tsskmrlmzZlZFYdCgQX6rIDQclCziXU2rrrqqm2+++dyzzz5r781JAv6Ghx9+2JzXlObq1KmTBVkEIV9AxvbZZx+TRfwV1emIrEg+5ZRTzC9IOcKPP/7YbxEaChlBswlkOvNyHQQbxbRt27aWSaYXNlYO8h0kweHLS/AYJNdcc82ckWcm/x122MHOQ7kvDKRgEPGCvB133DG9wgm2a9fODR482Opks6SUIApli8jOZxDH2Cczn2AKSgXKLy+Zon7sYost5u655x7366+/2vkBUe+33nrLMogIpvz000+WmUGfINDDS/n4Hbw8jfPTLpnKyqWXXpq+P0i5irD8mnZgkjjiiCPsHIcddphlfcjoqywQcEZuwpj622+/2aq+Fi1aKEgymxH1WUEQShy+uwp5AEbvLbfcYroSjpUDDjjAffTRR9L5hdkKBUmE2QFsvi+++MKy8SlBTlITSXVJYNUJ8rnccssZeVE2iVCCkC9MnDjRvfzyy+aTQB5ZHUICZhJI3jzhhBNc8+bNTR6ZxwUhn0AeeUcOSZ34B5966im/JRu//PKLleRiFRS+PVbdCfmBjKDZBDrANddc41ZbbTVz5OL0xRGtcluVgxAkWXrppW0QZBUIQQiWEWeSgNo333xjq0QwZBZZZJGcQRIizwRJWDGShNoESdhG8CbXObgO+0BWdSSBpdRs52WkrAqpCXGjjOuGEkmsBMh0ZseDJB06dPDfpoKPAQRK1l9/fQv4HHzwwepbFQ4CdQqSlAaifisIQonDd1ehASALmqQRVuMy9+CEqe3KETmqhWKhffv2pk8jn88//7z/VhAKB2yy22+/3a200kpmpx1zzDFWlSAOHIAhgEfmPqtPFCARCgF8MiQy4HvA4UxFDbL04+Adwueee67J4wYbbGBlufDnCUI+wdhI+dULLrjAZI2EY6qzZAL5Y4Ud+1BdhaRjVSTKH2QEFQlxY6dPnz5uzz33NKEmAx7nOJkSQmUhBEnIREAWcOTjxCWDIZPXXnutu/766618EPuyeqNQQZLtt9/ezkHprKSSXawoIYuCZc8sTc31gjN+C/LN4F7Xl8zj0D7wwANttQmrSTKd2dw39095LUqGJYGgEiu1UL4x/BQkqWwoSFI6iPquIAglDt9dhXoCvYrSp6zupTkDSXLBEcNnnJTfwvHCe7RImhKEfIOSRujMK6+8sunGyB0r0+PyGb6nvjmJRiRCUSZOEPINxkhWeeMHoewyCYO8EwK7ERlEHtHX33zzTatSoMCxUEgEeSTJk1JwzN1BHvG7II9k91NuHJ+C5FEoJJAvSgtSzp6xMeiKYd5ecsklbTVekEchv5ARVASEQZRPDCYi1Ay0Rx55pGW7S7ArE5nltlACa8L5559vslNdua18BElQDCgBx8qWTBAk4f0fKA4YWrnumxUeKBYESsgWSgJ1PVE8uGeCLtSm5bdRjmKuueYypwHlvnIFSdZbb72cK1lYcki9bww+fq/6WWVDQZLSQdR3BUEocfjuKgiCIAiCIHgoQCKUEiSP+YeMoCLhsccec61bt7bSWjvttJMtt+f9C0LlIt/vJGHJJ6svCDhQxi3XO0kIKpCxw3kuuuiiogdJWD7NyhKOp6bnDTfckLWklSxKlg5Siqy6lSQKkgi1hYIkpQMcsIIglDZ8dxUEQRAEQRAEQagIyAgqIHgx2eeff27LpCipRN04ahNPnTrV7yFUMvIdJEGuePk/LwZln7vuuqtKII76hgRFcBKzHfJiqGIHSbp27eqOOuoouz59I7Oe55AhQ2x/3mXCqitWxyhIIjQUBOLoE5Rwa9WqlS1PzYQyMYqDqO8KglDi8N1VEARBEARBEAShIiAjqIAgaxk7M5AMf15QRhYz73KIE8c1deVefvllqxkrlD8Ikpx11llW9xf5IIBWE3jHRpCn+AvLAwhgfPrpp+7oo482ZzAyh3wRpOO9HARWrrvuunS5rdNPP92NGzcuHUDg3TgbbbSRbVt99dVd//797fs4uMYhhxxi+1Aq65VXXvFbqoJ3qcw555xWNovVIgE4on/++Wd3xhlnWF+gxBb3R8CFe+XlfbwYbbfddrO63QRtCOTEwX1zffpTp06d/LdVwcvuDzvsMNuP96soSFI5oG917tzZHXDAASZXq6yyiskYZdyQB8gqJuRnhRVWsL7Caj+hOIjaXxCEEofvroIgCIIgCIIgCBUBGUGCIAiCIBQN3gcrCEIJw3dXQRAEQRAEQRCEioCMIEEQBEEQigbvgxUEoYThu6sgCIIgCIIgCEJFQEaQIAiCIAhFg/fBCoJQwvDdVRAEQRAEQRAEoSIgI0gQBEEQhKLB+2AFQShh+O4qCIIgCIIgCIJQEZARJAiCIAhC0eB9sIIglDB8d83C9OnT3dSpU0VRFEVRFEVRFBsNp02b5mbNmuWtmmQoSCIIgiAIQtHgfbCCIJQwfHfNwt9//+2GDx/u/vjjD1EURVEURVEUxZLn77//7v7880/3zz//eKsmGQqSCIIgCIJQNHgfrCAIJQzfXbNAkCQYGaIoiqIoiqIoiqVOAiUjRoyof5CEJSgzZ84URVEURVGskTUtXQ3wPlhBEEoYvrtmQUESURRFURRFURQbExsUJPn333/d6NGjbTk9hpAoiqIoimIuDhs2zP31119ei6ge3gcrCEIJw3fXLChIIoqiKIqiKIpiY2KDgyQ4O2QEiaIoiqJYE9EXcJ7WBt4HKwhCCcN31ywoSCKKoiiKoiiKYmOigiSiKIqiKBaFCpIIQnnBd9csKEgiiqIoiqIoimJjooIkoiiKoigWhQqSCEJ5wXfXLChIIoqiKIqiKIpiY6KCJKIoiqIoFoUKkghCecF31ywoSCKKoiiKoiiKYmOigiSiKIqiKBaFCpIIQnnBd9csKEhSe2KMjRw50o0ZM8aNHz/eTZw4MYtjx461fZKOF8VCELlEJpPkETmlj0smxdnB+FiJHI4aNSpxP1EsFJE5ZC9zbITI5ujRo83JmnSsKOabyNu4cePchAkTEuWRuZo5PelYMZsKkoiiKIqiWBQqSCII5QXfXbOgIEnNDA4UjLF3333XHX744W6VVVZx8847r1tggQVcs2bNXNOmTRkL3QYbbOAee+wx9+2332adRxTzTfru4MGD3bHHHmvyN9dcc5k8IpdzzjmnW3LJJd0ee+zhnn32WTd8+HA7Rg5BsVBEtnAC8v9u3bq5Qw45xM0333wmmwcddJB7/fXX3bBhw8wvlXmsKOaLyCEcMGCA69y5s9tnn31sPAzj44ILLmgy2bx5c3fjjTearGp8FAtFAh/IVf/+/d0ll1zi1l9/fTf//PObDoksIpPII/P2888/b8coUFI7KkgiiqIoimJRqCCJIJQXfHfNgoIkNTM4/TBer776anfVVVe5rl27WvtNmzbNPslWZduqq65qxu62227rOnXqZG1LNnXmOUWxvkTWcLp88skn7rTTTjN5a9WqlXv44YfNWQCmTJli+zzyyCOudevW5pDZbrvtXPfu3dMOm6Rzi2JdiSxBgnWfffaZe+CBB9zGG29scrn66qu7NdZYw/5/5JFHurfeesuCJGFMFcV8MsgifPDBB92mm27qFl10Ubf33nu7L7/80uZi/KLTp093X3/9tbv00kvNUb388su7888/35zYZPMnnVsU68qwKmTQoEHu+uuvdy1atDCecMIJ7pdffjH9cebMmSaTb7zxhgXzGCuZs5955hn3008/KaBcAxUkEUWxrIiCTFkKlhfyqSXYolg6VJBEEMoLvrtmQUGS2hM9BXsKYvyG74Mh/PPPP7uLL77YjFwy+M8++2zLTkXHiZ9HFBtC5IlyHcccc4xlRuOQJhgyadKkKs5nHII4Z1hFgtNl4YUXTgf42IZjIX5eUawPGReRpyeeeMIdcMABrk2bNu6kk05yr732mnv55ZfTq5wOPfRQBUnEghK5GjJkiHvvvffcXnvtZXJ3zjnnWIAEOY3L3eTJk23F53HHHecWX3xx44svvpj2R2h8FBtK9Otff/3V3XHHHW7DDTc0ebznnntMhwzBvLAv/rA+ffq4jTbayFYmb7LJJu7DDz+0hIf4OcWqVJCkBBgMIpbuIcQ9e/bM4vfff59+WBpcy5coeD/++KPr3bu369WrlzFTFnr06GHygNwoAPAfUVAYj8iw3GyzzWzC4POpp56yqLoyLsUkMp6SpRb6VryvkQ303XffuaFDh6qv5YkKkghCecF31ywoSNJwotMw/+AU3H///U2v2XPPPc1pSNuyPek4Uawr0aFxulD2bYsttjBnyqmnnmpOQfTnzOAd8ofOtM0225hc4oDp0qVLuvZ5/Nyi2BDi/whETgkQExQhOILsKUgiFpoEj/HPkKyw0kor2fh4//33W+CE7XHfHMFm9r3tttvcuuuuaytKLrjgAvf555/LdyPmhQQ+8BvvuuuuVnZwtdVWcy+99FJi4IN3lPzwww/u6KOPtoQG5JGASpLsiv9RQZLZSBqe9vviiy9sqdTOO+/stt9+e3fYYYcZjzrqKKuziQIw99xzu9NPP92988477rfffksfn3lOsXESpQ7ljgk0GMJkC1JWIcgDy4mpwcrEvNhii7nLLrvMIsEYNaEjJ527UhgUj08//dTdfvvt7uSTTzYF5auvvpKTW6zC0Ff69etnS6JRMrbeemu33377WT/D4MLwpzY8fY2/X3jhBZvrFGxrGGlDBUkEoXzgu2sWFCSpPdFf0O0pWfT2229bljSBEZzO5557brrWObogyTMzZsyw49D9Ms8livUhug2OlIceesgce9QwZ8XSRx99ZLZqZuAjvJg4ZFW3bdtWQRKx4Az2soIkYjGJo5nVIfhhGBspM8g7ScL2uA+G8ZLVn88995zZkvjwKLlFyTgFScR8kNVKJExTDpMxcO211zadke8z9yXAR3mtK664wsoUIo933323giQ1UEGS2UwGS5RSAiVkLpPRjKIKg6KJ0rnjjjtaJ9hyyy1dx44dLYtCzrryIRMmyh3LNg8++GA3xxxz2As8qSNIRkIgyh9G9JlnnmnyQOT4yiuvtIEOh0Q4H50a+WCwpC7h1KlTjfyfKDP9Nn79QM6PIsC+nI/7YnDlPPFz8Hd1imi4Pkv049dPYtygCtfjHrmPJEWCc3Ntzs1+tAvHB2cBv41t1GIM98kxYbDjd4VjOT8kIs/f4Z64Z74Lx2Xeg9j4yXNlhREOAMbfEEhDbpEhPl955ZUq9Y9fffVVC0omyaVYOypIIgjlBd9ds6AgSe3JnENWILWi27dvb87p8847z4L4Z511lpVTQP8nYHLXXXe5Dz74wPSeXLqcKNaV6DwhSLLOOutYUO6iiy4yxx5yFvT0wMwgCTKK01BBErGQROYUJBGLzcwgCXz66adt3GQ7/oKwL+NlZpCE+ZwkTsZG2ZBiQ4l/Kx4kIUnhzTfftO8z900Kktx5553mz2B7XHbF/6ggSQmTQZQHRNYYBlNQQm+55RZTBMLALDZ+8qwzgyQofThp2RafUOmsTMxNmjQxmeBlTDh7mcCDU5+ybaymoA4m+7Bv2J+VKCyzo55wGACCUklpqt133932wzjCED/xxBPTLwydc8457XORRRZxjz32mA2wTPjxAZZzUabo1ltvtcE4HBeun0mW/7Hag/PgsP7f//5n3+MQeP/99y27MtwfnygetEvLli0tk4PsDI7n97MdRzbZlpwD4w0DDwc4QQ+CkKzAWWGFFayNX3/9dbsGpQUWWmghOyb8xvXXX98MRiYXTSCVRwx96msjj6wmWWqppWzsRbaZ9yQT9aOCJIJQXvDdNQsKktSdwYESiE7Dd+iHN954o70oliZHZ8EpiO4X1w9Fsb4M+jV6MS8lxlbg5e3oyNibyGHYN/y/b9++tuIdfRp9m3If6MzxfUUxn0ROFSQRi03GNXwCJCost9xyVuKIF7jjX2B73CZE9yHpgWx9fAns26FDB6tewH4aH8WGEp8WJd2oQETQA38bK5BJ+M3cF38G+/JSd3RI9ud9YkkBFfE/KkhSIuQhoIQyCCPMCD9tiwJK9HnNNdc0Jy4OXr7jwWmQLR9i5NYUJEFGIDLy8MMPW9ABI4byQAQrGBgJFnAcpbooFUTdalYdsaoCMMFT2o0XMrIPgZT+/ftbgIH7IIuRl+OFgAaR6fvuu88CKgGcM7wkigDNwIED7VhWdKAsoDQQ4GA7UWsCE6zOAKyC2WCDDez6ZFdg4DP4IOvIP8bYKaecYvUSMbwI0iQFSVhSyNJCgjUEc3Bmh6w2DDzahGAH5ZMoYRaCJBzLyhuy5Lg/gizt2rWz63AsmD59ugUl2cY5rr32Wmt3rh8+xfIncolcrLfeeiYrK6+8stXqpp9q7K0/FSQRhPKC765ZUJCk9gz6Xa5t6DfMPySAoJswH5Hs8s033yhhSswL0WuwLZGpkCy1yy67WPIU+n08GIfOjl1AUAQdiXJwF154oSUlIY/SlcVCEVtNQRKx2ESfYczDJ0KFARzN+BN4hyzjXdwuxKfCOMo7nVq0aGGOacZR/DHVzfWiWFuiEyKPyFjz5s3NX/Xoo49apZTMfZFHfMf45gjYEbhDn1SQpHoqSFICRPlE0HEoo5DigMY5zRKqpZde2i2zzDLm9OUFebR3cGjz8DLPJTZOxoMkvIeGIMkxxxxj7xyh9vSsWbOsBBRBBSLB88wzjymGZDQgO3RiBsCrr77ajsXA+eSTTywwADgWAuSH990wqK611lpW0ovAAPdBkITAB+dG5lgayrmRO7ajBHAPTz75pGXXE7y74447LELN+QnW7LvvvnbuzTff3N6hE6LaGE5kVjCII9sEaTgP98/5UUCKFSQh4k4b8s4Sgjgcx2+D/F6COaxQoS05BzXCOYeU7/IncgpRIOgn9IXll1/eHACUtSNImXScWDsqSCII5QXfXbOgIEnNDI5nMgBZncsqYP7G0EVX4xMbgVIJlOEig5UyH3vssYfpNgrai/lk0H/Q61u3bm36z957720VDdDTQ4lc+jW6PPYqujyrSFhli44sB6CYbzLGIZfIHjYlcwuVB7D1kFE++ZvvkVH09HhQTxQbSmSQcQ8/xjnnnGPOZvxzlMPEt4Bs4nQOGf6sHEE2sR9JTuU79kk6tyjWlcy16H/46Y444giTNeZsKl4wVuLYR3/EN4efjwRsEpQJlOBDQ2aDb09MpoIkJUQizAg8zlhI2yLEZPbj8CVqjcMWxzJtLuEuH8aDJMExC3HSB4a/yWCg7EIIHnAspMwUpauYuDOPzWQ4P6tNCCokBUkoM0RgDrnk/AwUKAn8jUG/xBJLWLDh5ptvthUqKKbcE8cxUO+www7maA5RbQx96nmiOFDuatlll7XgA+fjdzCoFztIwvt94i+uCs4GlsSyH+fA+ON6oU+yXSxPIoOhJndwEGyyySZWVxb5kvHfcDJ3KUgiCOUD312zoCBJzQw6BXo92dAEQXjXHHreRhttZPMPq2/JRCUpBf0KPe27776z+UiOQDGfDDoO+i72yA033JDOmkYn2myzzVybNm1MfyfZCZsDeQwryhWwE/NJ5Am7DjsORzOlZVi5hAxix7GqjimIT/7me+R0zz33tCRDZe6L+SRyxJxN0gKr6PAxMF/jS1l33XWtTCHjJT4Myp2TiMlKk+BsTTqnKNaXyBS+NQJ3jzzyiCU+EwhhHER3RIdEHgnUUaKe95CE1cfSHWumgiQlzPBwAE7phRde2JQBFFOcySixEvLyIM8xaSUJQQJWksCwGgRnP86H+LGQSDEGC8fyYjGyushEpNRWODZOghpkPaCABjmqb5CEl0cRDGEbS0rJcuQc2223nSm2KBNk+ZAJybJ8ss9CoIcBnnOHIMnJJ59sAQyORcnNDJKwkoWXU1EyS0ESMR9Ebuh/zz//vD3vYHRRj5u+Rj/Ryr38UEESQSgv+O6aBQVJak/mF/QnVuOi4/AZJ9+hq6g9xWISuUT3zZRJ/kY3R2alG4mFJuMechhkLy6LmQyyqbFSLDQZ/5iXc8kg++BbyDxOFAvBMEZmEllkW/ArJx0rZjO0l4Iks4k8AJaF4rDGSZwkvDi5KVtEZBB7FAcvtTcR+uDcFhs3eY4hSFLTi9szGZz7BCpYZodjnwAC9aoZFEN5Nhg6PDLH9zgxQqYO2+sbJAnLSNmfbAoyKI477jgLkNx11112X2SkUcuTMmCcj8AP5wv3xFjCskFKiLEahiWBBENQNsL90U8oR8F7eljiT1kvBUnE+jDIHfMYMhkCe7znB/ki24KJMeybebxYP6IvKEgiCOUD312zoCCJKIqiKIqiKIqNicFPpCDJbCCOYhy3OOR4vwMlXnDYkpEPeShk2Xfq1Cn9IuwtttjC3XvvvbafHLblQ2ShvkGSQAIf1A1mBQpBClZs8F4NAgisHEGmQmDi9NNPtzrD1113na04QQ45R0PKbXEN3i/C+1AIQJCJD6mfHYgDGrIk8IILLrABKJ6lT+CG94HwojPugfea8JsAfYEgy1ZbbWXbIGW7FCQR60PkgWdPeTvklGfNe0foD8gScok8BrI/RAYUNKk/FSQRioAW/lMoAnx3zYKCJKIoiqIoiqIoNiYqSDIbScPjfO7fv7974IEHzHFMHUPKagVSToiseupr4uzlOJzFmecSGzdDkIQAAe/kwPlPqR/e2VHbIAmdmf1w6FKv+u6777YahMhPkCf+zwoMzv3ee++ZLNGHw/kJyrAChLrYOIyppUn/DtuRWYIkrGSiLiwBCwIX1EPkugRJLrnkknRdTsplARzOkCANK6MoqYVvhTrHBGpwShOg4Pzht/DukhDswYlNIJGXwnfp0sWu165dOys9x2qVnj17WpCIIAm/ixep0Ya85L5bt27WtlyDVSmsauG+qSHauXNnC36E3xaCJPRJ9ltxxRXdsccea+9W4RxhRYvYeMlzRuZZkUQfadu2rckZNTvpG9R+Z9wN428gMsj4jFwgy0FWxLpRQRKhCEB2foyoYEkR4LtrFhQkEUVRFEVRFEWxMVFBkhIgzjZKbeF4o5zQlClTqpDvyGDGQRucyGJ5ElnA2c9zD07/pP1qIufBQZEpU+H/uc7NdwQTwj65HMHsx7lZncJ1IAEV3jOy4IIL2otGWRVDYCEe4OE4AjOs9uC9KfhXWPXBShTGkXA95Jz9uN9A7olADH2F/fg/98C1+Ttk93O90IYh+ML3YbBjf+6b7Yxf4d7i5Bj2C+eo73MQS5c8Y2QJWYj3kVwMcohcBFkT604FSYQiwOYWTwVLCgzfXbNAP5d9IIqiKIqiKIpiY6GCJKIoNpiMA7wT5cEHH7QVH5QLO+KII6x0EcC5zAoSgg2syuCl7E2aNLEVJ4888ogdrwCgKJY/FSQRioB4kCRQwZICwXfXLChIIoqiKIqiKIpiY6KCJKIo5oVk5jOYUI6L95Twzg/8JwRDAvmbAAqrRwiWcBzZ/JnnEkWxPKkgiVAEhMBIEhUsyTN8d82CgiSiKIqiKIqiKDYmKkgiimJeyYCCc4QXuecipbJUwkoUK48KkghFQGZgJIkKluQJvrtmQUESURRFURRFURQbExUkEUVRFEWxKFSQRCgCMgMi1VHBkgbCd9cshHeC8Z4wURRFURRFURTFUif2Cwnd06dP91ZNMhQkEUVRFEWxQVSQRCgCMgMhtaGCJfWE766CIAiCIAiCIAgVgRqDJCxJEUVRFEVRzEX0Bd5DVBt4H6wg1BWZAZC6UMGSOsJ3V0EQBEEQBEEQhIpATiNo6tSpbvLkyaIoiqIoijWS9xLVBt4HKzQMmUEAsXZUsKSW8N1VEARBEARBEAShIiAjSBAEQRCEosH7YIWGIdP5L9aNCpbUAN9dBUEQBEEQBEEQKgIyggRBEARBKBq8D1ZoGDKd/mL9qGBJDvjuKgiCIAiCIAiCUBGQESQIgiAIQtHgfbBCw5Dp7BcbRgVLMuC7qyAIgiAIgiAIQkVARpAgCIIgCEWD98EKDUOmk1/MDxUs8fDdVRAEQRAEQRAEoSIgI0gQBEEQhKLB+2CFhiHTuS/mlxUfLPHdVRAEQRAEQRAEoSIgI0gQBEEQhKLB+2CFhiHTqS8WhhUbLPHdVcgz/v33X/8/QZg9qK0MSlYFQahEaOwTZjfqIoOS1/xDRpAgCIIgCEWD98EKDUOmM18sLCsuWOK7q1AAfPzxx+6II45wiy++uMnXfPPN5zp37uymTp0qY1coCq655prMMc643HLLuVNPPdV9++23fk9BKBxmzpzpBg0a5M4666xEeWzZsqWNjb///rs/QhCKhzPOOMPNO++8Jotnn322++mnn9yMGTP8VkEoLNAHGf923HFH16RJk6zxcYUVVnADBw70ewv5hIwgQRAEQRCKhkixExqOLGVZLAorJljiu6uQR0yfPt317dvXHXrooSZPCy+8sJt77rmNjzzyiIIkQkHxxx9/uLvuustkb+mll3Y333yz++eff/xWZ/9/6KGHXPPmzd0CCyzgDjnkEPfnn3/6rYKQX3z55ZfuxBNPtPFv8803d++8806V8Q/n36WXXurmmWcet8Yaa7hrr73WjRs3zm8VhPxi2rRpbvjw4e6LL75wxx13nI2TTZs2dYsuuqj9n0CegiRCsfD0009bkJgg3QEHHODGjBnjt6Tw2muvuY022shkc6uttnJff/216ZBCfiAjqIQgw0gQBEEod0QKndBwmGIsFpUVtZrEd1chT5gyZYp788033SqrrOI23XRTd8UVV5gDcLPNNnNzzTWX69Spk4IkQkFx3nnn2aqlNddc0z355JP+26r2J05onC9rrbWWW2yxxdx9993nfvvtN79VEPKDsWPHusMPP9zmVlbVffbZZ35LVXkksHfJJZeYo3qJJZZwX331ld8iCPnFG2+84XbbbTe3/vrrW4CYxIUnnnjC/e9//zM5PfnkkxUkEQoOdMVnnnnGbb311iZ3zMEk2CRhyJAhrk2bNjav77rrrq5///5+i9BQyAiaDRg8eLBl8rCE77DDDnMnnHCC69Onj98qlDtQ8Fjmvs8++xj3339/I/9nUr7ooovcr7/+6vfOHyZNmmRR5quuusqdf/757ptvvrGBWBDKFR999JFlX+y9995uv/32S/c1xtw777yzyvJ9OaaKBxywQoNhyrNYFFbke0l8dxUagPi8ctttt7nVV1/dMqLJ1icgcscdd7jWrVtbkCR8p7lIyDdYIfLdd9+5nXbaybLyzz33XNerVy+/tSpwAOIIpLwHw8AOO+zg3nrrLb9VEBoOMqIff/xxt+GGG7pmzZqZM3rEiBF+a1X8/fff7sUXX3Qbb7yxm3/++d3FF18sn4lQEEycONFWkvz1119u1qxZ9t17773nDj74YBsLTznlFAVJhIKDADKrjRdaaCFLqqE8ay4gq8cff7zty+pPgiuSz/xARlARwYDLsmWc1Ay2cb7yyit+L6HcQL3VYcOGWSZMqGtJMGT8+PHGUaNGGYkSIwfLLrus7bPlllu6rl275i2QgVL60ksvmUG+zDLLWEajli0L5QbG2X79+pkjCgMM454syAkTJlg/++CDD2x5P06pBRdc0AImChYWF9H4JjQcaf1BLBgr9qXtwHdXoYFA/7v++uvdyiuvbAZvcE6TuHLrrbcqSCIUHEH/J+MURwrJWDU5mtu1a2fjIGWQWFkiCPkCjmhsYuzdRRZZxD322GNu9OjRfmtVMCYiqzvvvLPZ0Oecc47r2bOn3yoIhQNz8bvvvms+G8ZCBUmEYoCxcL311jOZY0VnfJVdJvDj3XTTTbY6lAQIxlLKxgkNh4ygIgJDCcf3Siut5I499liLEiLUdAKyJITyBC8/ZIkwz5llnLmyZeKGcYcOHdI1MDGuqwMO4B9++MH9+OOPRv4/YMAAiy4ToAE4hwm43H333ZbJuNRSS7kHHnjAjHUmfI5h/yQQ0eZ84fyBP//8sw3kuZQFsn9++eUXk/vghEYxjp+LerNDhw7NuYyQa/P7uEf+nwQmA+6dFVqcn78znQx8x3m4XvzaehFgZYJnj9KLQrHiiiuaAwB5FYqDaFwTGg6bH8SCsKKDIwG+uwoNAAYsq0VozoMOOsiy+YNeRuBeQRKhGAhBEhJHeA8O7yJBD6oOIUhCObhXX33VfysIDUcIkpCwR0k3dPBcyUohSLLLLruYzs57IXr06OG3CkLhoCCJMDuAb22dddYxmdt+++2rHe8ygyRdunSxMVNoOGQEFRBxQ4esfV6uw4vyLrvsMhNgSi7xN51AQZLyBC+ho9QPz5hJltqqYQlndRg5cqS75557bMAjI/6GG26wZaAAucK4ZvkdZbNWW201k60zzzzTSO1MloZi2CBjgNrDBOUI0qCQkkm255572r6nnnqqZdNzrwHI5/fff2/X3WabbUyRxcCnnvFpp52WPv+qq65qWfksR83ElVdeaTUSyZw88MADTe5ZEgg5B59hEuD6SXVmb7zxRrfkkkvaPvw/CSjPKM3sw3JsJpO4sk3JJRQbsjj32msvy0I6/fTTzQA86qijrN6o6i1XFgioUXYLmSFoTek5OaaKh6jdhYbD5FfMKxUcicF3V6EewImCExr9D93jwgsvtMSSTFBret1117UgCS/pjENzkpAvYD988sknlqhHySLksbqVJDhpdt99dxsXKQXMClxByBdIFqT8IPYtQTsce7lWkmDvfvjhhya72K68vB37VBAKDQVJhNkBkhooc4nMtWrVyn366ad+SzZI8Lzgggss+Xnuuec2f7OQH8gIKjBwNmP4sMQZYQ9ltRhgeWmjgiTljY4dO1qAgVqBKHZ1AQYNxgzywfsUQqY7iiQZOIsvvrgNiCiPmSBTkYk8bpSjaJINRmBiueWWs6AI5R6ScP/999sS6OWXX96CG0mrOHg5FEEZgi4ES5BtBvYAMtVQfrn/TTbZxN17770WJIqDd6TwIjTKHm2wwQbu/fffr1ICjAxMgkCcg/8nAQc3pQMIyBBx7927t9VfBrzbpW3btvZ7H330UQs+BbB6hTYlo4m2ESoD1OvE2EKmUEKef/75nCuZhMIganuh4TAZFvNCBUcS4LurUA+ge7EimAAJTQnnnHNO16RJkyqfc8wxR3p7/P/U6xeEfIHkLFZtkyiEnPE+zFx1zidPnmzJRiRCoZvzDghKmApCvkCpaV6SvdVWW5ntRiY0NmsSCKhQ+YBMaezNp556yqojCEKhoSCJMDvA+Bj8a/j5qnsnGH4tEqApRcgY2b17d79FaChkBBUQBEjIvsGZvemmm5pCGlYD4JS7/PLLFSQpcxBg4PliaORaCZELX3zxhWXNcDyGTQiSkJ3I6gqCL1dffXUVx3914HiW26+99tpWBzbpnSTIJeUgjjjiCDOkTjrpJPfll1/6rdmgXBUvd8TgP/zww123bt38llTJMAb45s2bu7vuust/WxX0ByLk1FykjbgewZeAhgZJBg0a5Lbbbjs7nr723HPP2fcERaTklC9QbAkA3nLLLWnHE4oG5P9k7vKiSFaUIAe1Wd0l5A/RMxAaDpNlsUFUcKQa+O4q1BF1WQFCIk1YSYLzLw6tJBHyjdtvvz393kNWkSfVLv/8888tMQv9feutt87pvBaEhgA77Oyzz7axj4Q8Khck2WUEU0h0w5dy9NFHV0nGE4RCAz8elTgYM6mCQRlxQSgk8GGRKE3VFuSOFcmvv/663/of8AcybuI/IyEYX7LGx/xBRlABgGGD85kXtK+wwgq2VPntt9/2W/8DKwtwdNMBMqOEMo7KA9ddd509XzIGQ+mr2oIVIjj+mzZt6o477rj0agdWg6AsojQSaMv1jpNM1CZIQgYZtWEJNlDqCxnlHSLV4YADDrDfyMsdybgICEES3seCYZYE5JxBnlUkrJphVQlBmoD6Bkni5ba4/86dO1vgJ5wLovTQ71i2rZUklQXGY0rUIQes8mLlFO/OEYqDqN2FhiM9lol1poIjtYDvrkKBgN7BO+ewE2huVtuSXCX9XygUkDkSsHD28WJYVjphX1x66aXmsKYMLytIdtppJ9epUyc3ZMgQf6Qg5B+s5KfaBqXdkEdsOEo4Uy3h3HPPtSoc2G3HHHOM6e3YdhofhUKBFaAkK+BbYTykPDcOaoJ4zNGUhyNQxzbGUPx8mRUyBCFfINGXsRBfGtVdWF3Cyk5K7eP3IjCCP5BS+Pi+AjRG5gcyggoAonjt27e3WvcMqgRCmPxxBEMm/fXXX9+1aNEindnMy4N5nwJLpogeCuWBF154wYIHPGNWWhDQqE3WOqspGAA5buedd7bABZFlymgRTWawxLBGzmq77DgeJCHAgsLJkr44CJIQhKEMEdk9vOujZ8+efms2KMOFzHKf++67r2WgBRAkYfBm1QvvV0kCDgFWn7CShH1RTJgUAqhZyztNOH+u1Si8dJ4AFKtuWDWSGSSJgxe8E4Shjx177LG2uoQ6jg8//LDfQ6gE0AdZRbLHHnuYbDFG894eoTiI2lwQ6gPrrw2ggiN1gO+uQoHAPIT+RqLIt99+a7aDjFuhGCBjn8QQSt6iMwf26tXL5JFgimRRKBaQNzL0k+RR5Y2EYoHqFpTpprwgvg9kEJns27evJWTyGWSUkoR8l8vfIAj5AoE4dMQwJoZP/FkEmjVXFwYyggoIhDaJAId3UrmtsF0oDxDU+Oyzz8wJy8oM6lrWZqlmWGKHfPAOhTioK8zL1NlOAKK2L7ALQRJKO1AC68EHH8yZPc/yPc5PfUPKQSSB+yCIwaoUstFYRRIPuvBOErL0OQ9ZQrzcPfMdKCi/rLZZdNFFLYBIhlt8H347mUWcg+h5kuHGddgOyYJj8qiN0kKfIwDFcWQssapG/a8ygHygAIdSbAQy9bKz4iFqc0GoD9JjfR2p4Eg94LurIAiCIAiCIAhCRUBG0GwC737A6csSKmxRXh4slCfIFiQYwMqKDTfc0FYQ8dJEymlBgguQd39QeoHAAsGR008/3V7Snglkh9UTLMFr2bKlrVJiSSjn+uijj6x+Ky+5IxjC6o54IISVH7z3gyDJFltsYffEu3IoO0VmfQDH8OJQ6hHzondKVfE3AR9e+M7KC5Y/swSVTwIgmeBFfAQ/eJkUqzV4Qd+FF15oGfucg6X83B+1FPl8+eWX/ZH/gawOXgjPyiteDt+uXTsL9PBiKgIovA+F5a9htQkrYIiuk3VE0APHN8cQnOKatE8gwReuzZJZAitCeYB39DCetm7d2oJg1JPleYd+1rVrVysjh7w0a9bM6nJruXRxgQNWEOoB67d1oIIjDYDvroIgCIIgCIIgCBUBGUGzCTjOWbJHLU5eIMzyPqEyQDkr3tFx5513ViEvmX7iiSfcjz/+WOsVDZTvIljA+zrCeVjdQW1rzpNU2ouSUwQZCHSwP/fCtVk6mgmCDQQQKJcVrkHZK66BI5plfrkQf3E7Mg4oP8bKD87D+aj9ST+obik1bUFJChzcd999t12f+33ooYfsfSOskKH/cC6CPQSW4ucj+EPgiOP4rVw3XLtPnz5+L63iKjcgVzzjIGt8BnlndRQv8a9tqTohv/A+WEGoKzKDILmo4Ege4LurIAiCIAiCIAhCRUBGkCAIBUFtXtwuCELlwftgBaGuyAyGZFLBkTzCd1dBEARBEARBEISKgIwgQRAKAt5rsvDCC9tL5ll5IgiCALwPVhDqisygSKCCIwWA766CIAiCIAiCIAgVARlBgiAUBLyE/cYbb7R3k/AeCEEQBOB9sIJQVyg4UkT47ioIgiAIgiAIglARkBEkCIIgCELR4H2wglBXKDhSRPjuKgiCIAiCIAiCUBGQESQIgiAIQtHgfbCCUFcoOFJE+O4qCIIgCIIgCIJQEZARJAiCIAhC0eB9sIIglDB8d83CmDFj3LBhw9zvv/8uiqIoiqIoiqJY8hw+fLj7448/3LRp07xVkwwFSQRBEARBKBq8D1YQhBKG765Z+Pvvv83Q+PPPP0VRFEVRFEVRFEueBEhGjBjh/vnnH2/VJCPRCPr333/dX3/9JSNIFEVRFMUaib6A87Q28D5YQRBKGL67ZkFBElEURVEURVEUGxMVJBFFURRFsShUkEQQygu+u2ZBQRJRFEVRFEVRFBsTFSQRRVEURbEoVJBEEMoLvrtmQUESURRFURRFURQbExUkEUVRFEWxKFSQRBDKC767ZkFBElEURVEURVEUGxMVJBFFURRFsShUkEQQygu+u2ZBQRJRFEVRFEVRFBsTFSQRRVEURbEoVJBEEMoLvrtmQUESURRFURRFURQbExUkEUVRFEWxKFSQRBDKC767ZkFBktpz5MiR1l5TpkxxU6dOtc9cnDBhghszZkzieUQxn8RJMHbs2CyZ5O/Jkye7cePGuVGjRtl+SceLYj7JOInMIX9xmeT/kyZNsnGRfZKOFcV8k7EPeQwymCmP+EhxsiYdK4qFImPg+PHj0+MkuqXm6LpTQRJRFEVRFItCBUkEobzgu2sWFCSpmcGh179/f3fXXXe5Nddc0y2++OKuefPm9hnnwgsvbN/vttturmPHjta2CpaIhSLOP3DzzTe7RRdd1LjYYouZLC600EJu/fXXdxdddJHr3bu3mzZtmjkT5IgRC0F8TTikv//+e3fJJZe4JZZYwi2yyCJpeWzWrJnbYost3BNPPOF++ukncxDKOS0Wiug2o0ePdl999ZU7//zz3VJLLWXzc5DHBRdc0G266abutddec8OGDbP9FbwTC03GSXTCn3/+2Z100kk2ZyObHTp0sOQaxtCk48RkKkgiimKNZGBlkmeQjU/2YQBhUEYpZbsyJ0RRzEUFSQShvOC7axbo57IPqmfQpfr27esuv/xyN+ecc7qjjjrKHM84YSZOnGiZ/BCnNZ/oWzJ2xUIQ3R3+8ssv7oEHHnBzzz23W2CBBdwxxxxjDmrsfvo1/0de55prLnNWsx3nNDKadF5RrA8ZHxkHP/30U3fBBRe4pk2bupYtW7o777zTDRo0yM2YMcPGxDfeeMMdfvjh6Ixugw02cLfccottZ1vSeUWxPgz+jnfffdf973//c/PPP79be+21LTiHYxpnKnP2M8884/bcc0+Tx0022cQ98sgjNj4qqUHMN8Ocjfy9/PLLpj/OMcccJnuBJDOwsomxNOkcYjIVJCkBogSgdLJEjwdBRk4uBid00nlEsSYmyRqDZq7sL/bHGP/kk0/c1VdfbZk6KKq9evWy7AgCIxhT119/vdtll13c1ltv7R566CH3ww8/2PEMLpnnFMVSJ3KPohv6CMoF/QZ5VqZkw6ggiSCUF3x3zQL9XPZB9WSu4TMeJDnssMMsQxUdS8EQsZikz8Kzzz7bVouQFY3DedasWebgC/oPwZAff/zRAimrrLKKZVDjuEaOsSmk+4sNJTLE+Mg4eMghh5izb8cdd3RPPfWU6edxeaSkDLJ3/PHHWyAFuf3www9tH8mimA8yFyOLPXv2dAcddJDJI5+sFmHMjMvj9OnTbXXoHnvs4eabbz63/PLLW2AFn0nmeUWxvmRsw5/HPMyqkRYtWrh99tnH3XPPPe6MM85w6667rsnpeeedpyBJPUh/po0VJJkNpOFpvx49erj27du7JZdc0oS5Ou61117usccec8OHD1ewRKw1g6x9/fXXZvAwYSNPZIE999xz5ghOOg6lgEH19ddft+g0Bvy+++7rPvvsMzd06FAbdMnWOeGEE0wpnWeeedy1117r+vXrZ9cMDgBRbAxEXpnPunfv7rbaaqv0uEtmGnL922+/mTKcdKxYOypIIgjlBd9ds0A/l31QPTODJGQAHnfcce67774z/WrmzJmmn2Gk8Q4I2pRjFKwX8030fWxLnIDbb7+9a9Kkien277zzjjkA4/p8cBh+/vnnljzFMLDTTju5Z5991gIo0v3FhhKZGzhwoCXebbjhhrai6dZbb3XffPONyVdcxtiX5DychTgG2ZcySCT4YcNKHsWGklVJyOONN97oWrdu7eadd1537733WqIo2/F5xPdFHkkgpYQm+15zzTWWYJopu6LYEOLbw2fRqVMn17VrV/ubORg5XW211WxuVpCkflSQZDYyHiS57rrrLNqMA3rIkCEWGSQSjXEUp1aSiPUhsoZxzYS+0kor2fJ4Bk64//7729LQII/x42oKkrCd4xh8yTQDZEokKQAMNnzPfaBAxMl34VyZx/Ed98B+QdllX5Ti2p5DFGtD5P+FF15wW265pSkXZEfSR6i9rSBJfqggiSCUF3x3zQL9XPZB9Qy6Up8+fdyVV16Z1svQtXBSB/IdjkLeD0GpI2yBzHOJYkOITs3qkEcffdR0HmzSU045xb333ntZQRKI3o2+TbY08tm2bVvXpUuXnDaAKNaFyBHB47POOssts8wytrKJ1UoEkJGvuIxhG/76668mq9tss40l7J144onu7bffNr1e8ig2lMy53377rZV1QxYZHwnK4bNje9z3wHg5ePBg9/TTT1vCHfJI0A7fCbKIryLsK4r5YJA/ZI/xkACdgiQNo4Iks5E0PO0XgiTUNtx9990tS4I21aQu5oMMikzib775ZtqYOf30092ZZ55pARNqDrM8jwyJzCADf3N8riAJ0WrO/eSTT7rTTjvNHXzwwbYvGWbhHJyPcwBku127dlaWi0w1yP8PPfRQ98orr1hGRshC4zj6AQoxSwc5DgOMLLf333/fnXzyyW6zzTazc2y77bZWH5SMNxRlJon47xDF6hhkjpJyrBpZbrnl3O23325ZazioWrVqpSBJnqggiSCUF3x3zYKCJLUn8w/tRYIUpWP4hKwk4XtKehxwwAGmv/GSYlabsIoXG0K6jpgPhiAJ9fPXWWcdcwSi93Tr1s10+EybNOhNofa+giRiPokcETzGVqWMDOVksDWxPRnz4uMe8onth22IPYhTGptQQRIxXwxBEsphslIJvvrqqzZPZ+4bgiQkoOLjwM+iIIlYDCpIkj8qSDIbScPHgyREpSmnhbMao4gBmUwdFFH+Vpa8WB8yMGL48M4QZIyX3rEEGQMbJzAZOhAZpBwXshaOrSlIgozysqirrrrKrb766qYIsFolZFYg3ziWqQ1Lds+CCy5oMk6JL8YNgiLU6Tz33HPN8N94443d/fffby84Q0Fm6T/949RTT7WBHpLdj9JBxj/3x/UpIcbL0bg+ijGBFI6VIiLWRGSUzFyW9NMP1lhjDZPPEJwjSMJyaQVJ8kMFSQShvOC7axbo57IPGk6cKjhiPvroI8tipXQH74HAmc3cxRyWdJwo1oXIEbo3JbMIeGAvoHvjeMbxEnc0Y4tiG2BH7Lzzzqabb7fddpY5HYIn8XOLYl2JHJFYd+mll1pCH0G7u+66K3ElCbKLU5pku80339xk95xzzrExEzmVPIoNJT4JZI8VSs2bN69xJQlj6cMPP2y+CeZsVoriz8iUXVHMJxUkyR8VJJmNDEommRIdO3a0iZ1sCbjUUkvZZ3hPyYorruguvvhiW0pKtkQ4PvOcohgY5Iv3g5AVT4ktMuQvu+wyy4Zgwqc+JqWFkLEllljCde7c2RTTcI66BElwLpO9Ew+SMCiT+cP7SsgC4qV6jBmUjgvnZvDB0GJlCIESVlRhlJFNydiCUnHSSSfZPeKsps4i1+X+4/fJdVCiV1hhBQu6oKDEf4soxkn/CH2EWvDI12677eaef/55+47JkX6jIEl+qSCJIJQXfHfNgoIk+SHzFG1JvWnmKhw0vFfutttuM/sBozjpOFGsC9GjSS6ivjkrtHk/Dqu8yZZG/uKOPfYNCVA4AXEY4jzERs3cVxTrQ8Y1kkYJBhO0wzZEDycJLtPRjMzhwKYcF4mAoTQXQRZkVf4SsaEkaRm/AiUvWWlH4INE0wEDBtj2uDzie0Aer7jiCvPfNW3a1BLx8N8F2zPsK4r5pIIk+aOCJCVABlaEGgEmW4yXM0IeysSJE82RTU1OnHUMypQ06t+/vyZ+sVoiHwQayLxZdtllzeA58sgjbZJnOxM+AwArN0KghPeTvPjii9anGUw5B591DZKEclu8uCzU2SZLn9UsRx99tAVLjj32WCMvhiQ7kiAh54cEazDWuD8UYlaHhPvDic2544M9v/Pjjz92a6+9tgWCjjjiCFNc4oEUUYyTMRf5ZDxdeeWVbXXSp59+6mbMmGHbcQAoSJJ/KkgiCOUF312zQD+XfVA7ok+RXJLUXuj5OF3I0t90001NF8KJjY6EDpa5vyjWl+hF2PWUhqGcDLKG/Yn8oXMHm5O/ydJH1ya5CR2Jv5FfBUjEfBA5wgZktRw2IrKInUmiHLIYl0d8JW+99ZYFSAjY7bDDDpYMiF8l87yiWB/iD8G38dVXX6XlkZLfd999t21n3Az74sMj2ZPkUOQRO7N3794mp2EfUSwEFSTJHxUkaQRkUMUBzCBLkAQl4eWXXzbntAReTCJygcFNVhflrRgkqdPKagtqXNPhCSwwkTMAUK6KfSCBDAYG5A4llXPVNUiC8c59UL7rggsusAANxj3KLceyOuTLL7+sQhQPSFCEMlxcm7GFfUOQhHenUOMz/Mbwe/kdH3zwgVtrrbUsSMK9KkgiJhF5Ry5eeukl6xus1qO+O4oFK5xCv8DBRwBxrrnmcq1bt7Y+wnf0Hea9YJyJdSN9WkESQSgf+O6aBQVJaibOZvQt3uvAXMT74siA5t0OZFA/8cQTVvZxp512Mkc0JY3QsXihMcfJIS3mk8gTug2JeJSKIYGK0m58ogNRiouMaFZrI4skmPDOCFaUII9xR6EoNpTII7o5CUyU3cLOJBmORDvGSQLHjJMHHnigBUjWX399y/RnBYoCdmK+iTzh92C8O+WUUywBlXkZJzTzMj6Wxx57zOZzqnNstNFGJqf4RBhXZTeK+Sa+MCrGMBY++uijpjuSaIzPmPL0qOi87zpsR6fkHcUkTMuHXD0VJCkB1jRwMiDzDgleFsVyUwZk6m4qSCLmIk5gyjAQVKDUFdnw4UVjBNriZBko35MNxmBK7dcOHTpY1gMGPH28rkEStnEfZF2El18jt9dcc41tYxVL5j2HfhD6AlkbKBYKkoj5JEou8oLBj/wH2c/sF5AACXJHkA8i/zirqENLIA/jLekaYm4qSCII5QXfXbOgIEnNDPoOpTkwXJmX2rdv72644YYq5Du2kw2IXibnn1hIon9Dkq3eeOMNS3BCDslODfKIIwbnDI4E6UJiIYl8IY8Eh3lnIPKILMbl8fHHH7fV3uyr8VEsJNFt8I1QmpDASJDH+HzN+IgcMmdX5+MTxYaQ8Q4/GSua8N0FOUSXvOOOO6yaDP/ne+SSMq1PPfWUvatYSQ3VU0GS2UQancanPiEvoEaAydzBqUsGP6T0Fn9Th5iX5+Fopg4iws85khzNYmUTuUKRRJbIXiCotvTSS7sLL7zQjB1Apnyc06dPt0mcY3AA4/MgOwJHcFhij5FUlyBJeCcJzmgCfByDM5qlfygOHEPWfpBzxpFu3bpZAARFlwg38q0giVgIMvai5CKDYfVIYFhlhezQB8JKEoJ99AfKcaGUSOmtHxUkEYTygu+uWVCQpG5kTqmJSceJYiGZJIeBSfuLYiGZJIeBSfuLYqGYJINxJh0jioVgkvxVx6RziFWpIMlsYhBSosxE+gh+8HInsvhbtmxpDufwyQvIqD/M6hFqc4ZSRqKYSQIHOHJ5f8LCCy9sgQVWINW01DNk3lDWoU2bNnYc5bkISDBIkMHYrl07+55ACuXfCJIQ5Bg8eLC76KKLXIsWLWw773EIQRLOyfhAwOLtt992p512mu1D5n6rVq1MvldffXVbZcKyVJZTU3KLc3NP3DdBQuoec9yOO+5oQRTOHQ+SEGh59913LUDCublXouQEccI+olgbhj5CRhrZQcgd79O57LLLLKit4HTDqCCJIJQXfHfNgoIkoiiKoiiKoig2JipIUgIMzmscyrmYy7ktikmMy1NdZCcui+H/8W25zhe21XS9+H5JzHVsOI7P6s5f03lEsS4McieZyh8VJBGE8oLvrllQkEQURVEURVEUxcZEBUlEURRFUSwKFSQRhPKC765ZUJBEFEVRFEVRFMXGRAVJRFEURVEsChUkEYTygu+uWVCQRBRFURRFURTFxkQFSURRFEVRLAoVJBGE8oLvrllQkEQURVEURVEUxcZEBUlEURRFUSwKFSQRhPKC765ZUJBEFEVRFEVRFMXGRAVJRFEURVEsChUkEYTygu+uWRgzZowbPny4GRqiKIqiKIqiKIqlTuwX/BbTpk3zVk0yFCQRRVEURbFBVJBEEMoLvrsKgiAIgiAIgiBUBHIGSXB24PgYOXKkKIqiKIpiTqIvjB071msR1cP7YAVBKGH47ioIgiAIgiAIglARkBEkCIIgCELR4H2wgiCUMHx3FQRBEARBEARBqAjICBIEQRAEoWjwPlhBEEoYvrsKgiAIgiAIgiBUBGQECYIgCIJQNHgfrCAIJQzfXQVBEARBEARBECoCMoIEQRAEQSgavA9WEIQShu+ugiAIgiAIgiAIFQEZQYIgCIIgFA3eBysIQgnDd1dBEARBEARBEISKgIwgQRAEQRCKBu+DFQShhOG7qyAIgiAIgiAIQkVARpAgCIIgCEWD98EKglDC8N1VyBP+/fdf/z9BmL2QLAqCIAhCeUBzev4hI0gQBEEQhKLB+2AFQShh+O4q5BHTpk1zl112mdttt93cNtts47bddlu33XbbGTfaaCN39tlnu6FDh7pZs2b5IwShMHjttdfcDjvsYDIY5HCrrbZyRx11lOvSpYsbM2aM31MOGKGwmDRpknvsscfcjjvuWGVcRB5PPvlk1717dxs7AySPQrHwxBNPuIMPPtjtt99+7tFHH3V//PGHmzlzpt8qCIVBfIxj/Lv66qvdzjvvbGNimLP5/0knnWTjp5B/yAgSBEEQBKFo8D5YQRBKGL67Cg0Ezr3vv//eHXDAAW6++eZze+21l3vxxRfdP//84yZPnuwmTJhgHD9+vP2tAIlQKOB4+eCDD9wKK6zgFlxwQXP8fffdd7Zt3Lhxrn///u7CCy90c8wxh1t55ZXdVVdd5aZOnWrbBSHfGDJkiHvggQdcixYtXMuWLV2HDh3cTz/9ZNsYExknDzzwQDfPPPNYYPmll16SQ1AoOAiCPPjgg27++efHXkmTJAbkc8aMGX5PQSgcunbt6vbee2+bjzfYYAPXs2dPN3bsWNMpkdHrrrvOLbDAAiabxx9/vBs9erQCyHmEjKAi46+//nLDhw+3SHScfMc2CXf5Y8qUKfasf//9d2OQgfD/UaNGuenTp/u98wcMb4wdrs01GGQlb0I5A4dTvG8Fjhgxwv39998F6WdCzYgUOkEQShy+uwoNxKuvvmpGLI5AsgHjGdGCUEzghF5mmWXcXHPN5W699Vb/bdWs1ZEjR7qHH37YrbTSSm6ppZZyzz33nNkMgpBvsEqEsXGLLbZwzz//vP+2qjz+9ttv7thjjzWZZQwdMGCA3yII+UW3bt1MJps3b+4WWWQRt8suu7gbb7zRnX766SanbFOQRCg00BHfe+89C5Agd6w+/vXXX/3WqiCxYbXVVnMLLbSQjZMDBw70W4SGQkZQEYAz7osvvnA33HCD23DDDd26667rDjvsMOMhhxxi3HPPPV379u018JYhUPYY8Hr37m0GCiUVkINMGWCJO0vn5p13Xvv79ddfz2vgjHM988wzbvnll7fI8yuvvGIRaUEoN/z555/ujjvusMzdLbfc0u2zzz7uiCOOsH619dZbu1VWWcUtueSS7pRTTrGxWcHC4iJS+gRBKHH47io0AO+++6476KCDzNAlQCIIswtkmbKKCVk87bTTrIRHEljhhOOFskdNmjRxhx56qPv000/9VkFoOFg19+abb5rNiz16//33W7JoErBdn3zySbf++uubI/Dmm2+WI1AoCPr27Wsrm5i3GS/BJ598kp7DsRkVJBEKDcbHs846yy299NJuiSWWcB999JHfkg3GR3wbrHrCr/HWW2/5LUJDISOowPjhhx+sbhyCTkTwq6++8luESgFL2PfYYw8355xzWlYWGVph8s0Eqz2+/fZb258JmUwushCTUB/HLpHp9dZbzwIln332mf/2P9TXWZx5XH3OU5tj6nOdfO0jNA7wLCdOnOh69Ojhvvnmm8SsXZTezTbbzPpYq1at3Oeff24rvITiIGp3QRBKHL67CvUEc9G5555reh/Z+yRKkZxCkgzGLPrdiiuu6JZbbjn7jkxqVj8KQr5BQhSJVxtvvLE5U84//3xL3MoFnICU4mIYQFfKZYcIQn3A6m5KuTH+Lbzwwq5z5862gikJjIkE9HhfCUmEOA/R7wWh0GAOJ2CCE5qxUEESoRgg8EFCNTK3xhprVJukwNxO2a1VV13VzT333PY+Ma1Wzg9kBBUQX3/9tRlICC01NVmuLGdsZYH6gSh0KHZkZTHZ4sCtCdQIvuSSSyyLi8z3p556qooTN8jRsGHDrI4rAyk1CyH/Z1kytTOpgw2QP76PM74/ZJCNg3qHrIJCkU06BsP/uOOOy2locT4i4Bj/1JXlXLvvvrv1h/j5Vl99dcvmT8Jtt91mdZHZl/8noV+/fu6CCy6w89LGvXr1ynJ4f/jhh7Z6gEBVuDb7E7hkMhIqC4MGDbJ+ueiii5qj6u2337ZgplAcRP1PEIQSh++uQj2BnkbGPvofzYkuRBZ0Zk19DOCddtrJ9llnnXXsnRFkEgpCvsBL2F9++WWTQcrI3H333e6XX37xW5PRrl07k8nNN9/cXvIuCPkCq0YuvfRSCx4vvvji7p133snpeMaew5/CS4sZS8855xyzrQWh0FCQRJgdIJGaqkPIHJWGqktowHdBSTh8gby76bHHHlOQJE+QEVRA8NInHLxE91hmzwvKGGxxeFPj9dlnnzWifJJVIZQfHn30UYsCM9BdfPHF/tvagawvXvKJUnj00UenS2NhYLMtRJkJEAwePNi2BRCIIYBx5pln+m9SeP/996tdSRLAiifKf3H9Nm3aWJmuTKUA2Q0BDIyon3/+2W9JgUEb5ReynJql0h9//LHfmgLnIJMoTASUSYqDkknUWmQ7/08CqwUuuugiu9ftt9/eJpPwokkcDTvssIMt0Ua5iRuFBEfojzgtqEMqVAZ47vfcc48FSMioRM6lUBQXUX8WBKHE4burUE/gYDnppJPsBdnoObx0OCAzYQqjmLr8JHLgnOaFnYKQL2A/UIZjk002qdVKEkpu7bvvvqZ7o1erhIeQT/BuwGuuucaSlGqzkoTxEDls2rSp2dJ9+vTxWwWhcFCQRJgdwE+x6aabmszVZiUJYyn+OJJ/8S8L+YGMoAICoUXAEVpKbvF3p06d3OOPP27LoV544QV36qmnWjY9BhQTv16OV1649tprTQZQAgka1AUEKggucDwBBDLBAME2sg3JBuOdJpmBhVxgJQdG+tprr+2WXXZZqwebmT1PuS8GZxQBrstKC7LsA+KGPYEIggth+f55551npcIC+L04oglekDGUVM6I38Q9scqDYAr7oYAE1DdIgoEHWEnD7+V43k/x448/2vdCeQM5RT4JwrG6iLrHZOoiH82aNbOXP+IkQHaE4iPqj4IglDh8dxXqCeYh9HpeAotzL9dqWMC+JHTQ7LxHi9WvgpAvTJ8+3d7jQDY+K9TR8XMF4tCfWc3OKnbkltVQytwX8gkce+jn2I8EkTt27GiVEZKATUpyKZnVJLw99NBDOd9fIgj5hIIkwuwAvjlewo6/Aj9ade8kwbfHO3NIqKbCy5dffum3CA2FjKACIpQpouY9LyVLwoQJE8wYwumNk5gXe2euChAaLy6//HKTAQa6m266yX9bOzDQxYMkYSUJqzEwugl0cP4RI0bY9zWhNkESHMu8r4HrEdy74oor0iW7ciHULcYZzUqVAGR5scUWs5JbuQIclPTi/Kwy4beigGCcBTR0JQnnp8QAL+7mHJDrUOLrjTfesH2E8gWOAbLQWH2FTPB/FNzrr78+XQKFzMqaZFzIL6J2FwShxOG7q9AA8C6HkJF/5JFHWjnQkMQRwEpGvscOIKGGUquak4RCgJXnoexsPHErngCFDYpOjUOaBD4StrTaVsgnsM0IdBx++OEmiwRLeDl7QFwesQl5Lw6lZKha8Ntvv/ktglBYKEgizA6QVEzZLBIVSKQn2WbAgAGWyBwHPg1KFVK2cIUVVnDt27d3Q4cO9VuFhkJGUAHBOxkYVFlOSv3XJDAAM+GT6YxTmrJKGEtCeYCVQ5S2CpNrbUFAg8AaMkG5K0q3YaQwID799NMWWea8V155Zc4lypnIDJKwfD4zSEKZLhRVFFEU0ssuu8z179/fb80GA3YIQBCgiGc/hiAJAZ0777zTf1sVKBqsPqEEGIEkyoP98MMPfmvtgiQczwoUVrOEIEnSqhVAiTGuQZuSTUdgBeWnutJjQvmBgAnvwSGwh2wRyGZln1AcRG0uCEKJw3dXoYGgNEx4dxy6FytwSXrBmEUHQ1diGy9zJ/FFEAoF7E2y9rFLKc9xwgkn2CoR7ANK5vL+wEMPPdSy+ylFSlIWDm1BKARIBsQ2xlakhPR9991n75lk9QiyesMNN9j3bGdspDS55FEoFPCzUPqSVU0E8X799VdzVu+xxx42RxPUI5GUQDLzN/uRjCcIhQCl9al8gewxDhKgY2xkHEROTzzxRNtGNRbGTiG/kBFUQFAXboMNNkgPrEkv7A4ZZGTSo5Sy+oQl0UJ5ACWPAAHBDjKy7rrrLgt0JCGeOUOGFwMjGV+8zwZDOmxn5UQIHFBCqLblthhYkcnWrVu7pZde2v7PIBtHZrktyj+88sorfmvVeyQ7/7333nNrrbWWZZzdfvvtVd75wcoZSoLxEnlekp0UzOH6BGW4Hww23n0SLzn3wAMPWACFe8GRkARWhIQXXO2yyy7VBkni4HcRpec4Si9lRuiF8gVyjJJBOTmeP32T4KNQHERtLghCicN3V6EBiOtMJGPw0mHsAcojkKCx//77W3Y/ekw8Wz9+nCDkG9ijlDBCFpHDgw8+2D55Hw5lgklWkjNaKBZYOXfvvfeaPCKLcXnE9kvynwhCvkFJboLI//vf/6xENzLIClCCyZTHP/74401GCSRjP5588snK3BcKgrgOSEUUyl4id+iOjI8HHnigO/fcc82XJ/9VYSAjqIDAGc77HHAUY29SCzZzMOX9JJT/YR8G4MzMfqHxg5Jq1F4NDnlWDeXKWu/Ro4ctm2M/VopQvopgRBwoi88//7yVCWI/JvDM2sIETqhrvd1221kmBGAQHTRoUDp7nheK5lolwvl48TvBHQIQ3H/cgEe2WR1FySICNrxvh98ZBy9ED7IPCfpQeiIAWWe1FStWKMl14YUXZpWhIOjC8kGOJ1jyyCOP+C2pIA2TBDWTwzUoE0aQBOOOYAvtRF3HpFJnp59+uh1Dhmd8mbfQuMEYy7iK7BNgTMLXX39tGb08/+WWW85WQGX2M6FwiNpdEIQSh++ugiAIgiAIQgKU1CAI5QcZQUUCGTuUE8KhS1CEVSOU+8HR/fDDD6dfyi2UNygvRbAM/wMyECd1B1l5xOoJVnPUBIIWLPsMpaPCefg/S5N5uV1S0I2VS9S8xjmMPIZ3MyS9WJ5AA4EMzhuXXVa4EPQhCJjLuRwvt4X8AwI34bdz3Q033NCCINWVDAulkYieh3ugrVh50qVLFwuKEHRiRQtZmay0Ce8kAUTZKQlG/+OalOWCtDXXDhF4KTnlA5Y/82yRL541ZdVC/+D/YWm/yqzNHuCAFQShtOG7qyAIgiAIgiAIQkVARlARUFvnq5y05Y26Pt/ZIQ/5vGYIkrBK5LbbbvPf5ka+f29dzjc72looDOr6LPXsiw/vgxUEoYThu6sgCIIgCIIgCEJFQEaQIAgFQTxIwvtKBEEQgPfBCoJQwvDdVRAEQRAEQRAEoSIgI0gQhIKAd5LwHhLescIL6wVBEID3wQqCUMLw3VUQBEEQBEEQBKEiICNIEIS8I6mEkcoaCYIAvA9WEIQShu+ugiAIgiAIgiAIFQEZQYIgCIIgFA3eBysIQgnDd1dBEARBEARBEISKgIwgQRAEQRCKBu+DFQShhOG7qyAIgiAIgiAIQkVARpAgCIIgCEWD98EKglDC8N01C2PHjnV//PGHGzFihCiKoiiKoiiKYskz2C///POPt2qSkWgE8e6AyZMnu/Hjx7sJEyaIoiiKoijmJPoCekNt4H2wgiCUMHx3zcLff//tfv/9d/fnn3+KoiiKoiiKoiiWPBscJPnrr79kBImiKIqiWCPRF8aMGeO1iOrhfbCCIJQwfHfNgoIkoiiKoiiKoig2JipIIoqiKIpiUYi+gPO0NvA+WEEQShi+u2ZBQRJRFEVRFEVRFBsTFSQRRVEURbEoVJBEEMoLvrtmQUESURRFURRFURQbExUkEUVRFEWxKFSQRBDKC767ZkFBElEURVEURVEUGxMVJBFFURRFsShUkEQQygu+u2ZBQRJRFEVRFEVRFBsTFSQRRVEURbEoVJBEEMoLvrtmQUESURRFURRFURQbExUkEUVRFEWxKFSQRBDKC767ZkFBktpz5MiRbvTo0W7UqFG1JsdgwCWdTxTzxepkUzIoFpvIXJIsQsmiWEwib0nyGMZLyaM4uxjmbcj/k/YRq6eCJKIoiqIoFoUKkghCecF31ywoSFIzsaH47NixozvppJPcWWedlZPnnHOOu/DCC93FF1/sOnTo4J544gn3zTffpM8hivnkmDFj3PTp0927777rzj33XJPBM8880z7POOMMd+2117qXXnrJ/fzzz27ixInmTMCpkHQuUWwIg7Nv6NCh7uWXXzZ5DLIY5PGmm25yXbt2dcOHD7e5Rw5qsVBkzoUDBw50zz//vDvvvPNMBuPyeOONN7o+ffqYHLKv5FEsNBkjGfv4/5NPPmlyedFFF7k333zT5nMFS+pGBUlKgESbEd7akjZXhFqsD5EZBklkKMhSdXLENjh48GDXq1cvM5a6d+/ufv31Vxs8kEMU0t69e7sPPvjAtn/77bemyCadTxQbA5F5lI0w5ioTI39UkEQQygu+u2ZBQZKaGeaVL774wpx/r7/+eiLfeecd9+CDD7qjjz7azT///G7JJZd0V155peljcgiK+Sa6/fvvv++23XZb17x5c7feeuuZ/H388cfu1VdfdQ888IA76KCDXNOmTd0mm2zibrnlFjds2DA3bty4xPOJYn3IuMYY+f3337tHH33UbbDBBq5ly5buiCOOsL8//PBDGx8JHG+11VZuqaWWcocffrh75ZVX3G+//WY2btJ5RbE+RB6Zbwl+3HHHHa5169ZuzTXXtIBdly5dbMx866237O911lnHLbvssu744483OUUesSWTziuK9SW+OGRywoQJrl+/fpZAwxiJah54wQUXuEmTJkn+6kgFSWYTaXQanwyce++91+25555uv/32cwcccEAW999/f1NGUQogkUEyyHBGSwEQa0uMnkGDBlkgg2xEZOrEE080I3vy5Mk5j2FQReE8+OCDbbDdZZdd3CeffGKBkClTplgmRbt27aoMxiF7Qo5lsTERmYWMy2SlHXLIIe64445zV111lWVM4gSQktEwKkgiCOUF312zoCBJfsichIHbqVMnt/TSS5uetf3221uCCoZx0jGiWB9iU6LjXH/99W7VVVc1WcPmxEaI25tjx4615Kirr77aLbPMMuYMfPzxx92AAQOUxCfmjcjc+PHjbZUdstiqVSv3/+2d+4uV1ReHsZt01y5YkpmXEiIkLcNbRZFWTpAGFQWFSXf6waIfMjIyRUsruqD+EBVFXsogxQg0BAslGpIi00Aoqj8hgsALO5/Fu+f7Oued25kz8z1zej7wMM6c933neGbtvddea+29X3/99RhbyvZI/0iimVjKSSedlMaMGROFfcxRy88TqRfiGfgzv/zyS3rkkUfCHknMkTDmNQrq8rXY3bfffpumTZuWhg8fnq6++uqwz65iLSL1wDjLKs4tW7akGTNmpFNPPTVddNFFaeHChRGzmzx5cscYbpKk75gk+T/DZ0eQed++fRFYroIOmQDdfffdF1U7l112WYczqsFLb8gdKctCb7rpprAjOk6cSTrPvXv3xnVMbsr35SQJlToPPvhgXD9//vz09ddfR5KE9k8ncujQoUjasfUDAWb7BBmKEHAiaUgVEI4GbQSoXmNrCSqBmJxV3Su9g77BJIlSraOiudbIJEn/4TNk3KGKf9asWTEeUZnKyl18L+cA0iiYJ2BPrBRva2sLW6MqH/+fAGC56Im5AXZJhTQrSU477bQo4mNbDxIoFkhJf2F+yXyS+AfB6NNPPz2tWLEiknPYV9nG6Cd//vnn9Oabb0Yi5dxzz42KahIl2Kr2KP0FG2NXjXfffTdNnz49+jwSdsQ/eL1sY6yowx6XLl2axo0bF7b7zjvvRNyOfhbytSL1kmN7rGIiSYKfyM9JiKxevfqEQgeTJH3HJEkTkztROtu33347DJ2MNBMkPnOqKzrfI9IZBm7shSRGrn644YYbokNlkKcykT00qUosV0JAT0kSXqMTwXHg+ST1fvvtt/hZ+TnYMtdSic817e3t4bwC//7hhx86kiudnQfeF44F9+GM8HuZxJGQKT+H7/ndVc8Q6Y5sx7t27YqVIzkQRbIEm2dJtUmSxkD75DPsjY7/HZRSTa6iudaIdk57r+oHpGfwY/j82MLj2muvTWeeeWb4bWx35DgkjYb5PEV7GzZsSFOnTo3A3pNPPpl27NhRkyTBNrken/u2224Ln4l72AKJualBaekvxD7YPoY99S+99NJ0zjnnpLfeeiuCz9hX2cZyQmXbtm1p9uzZMbfFh2frI17THqW/UESH7WFX559/fvSP69evT3/88Ue8Xo470F+ycwf9YY61cI7O7t27wxY7F6SK1AuxC/o4kiXYHb4hMQ1Wg06cODHGZpMk9WGSpInJe7uSiR47dmwYOnsQ0/Hyh3PQl97AwI7zyEFiLEG+4IIL0meffRZ7Cz/88MOxqoQ9NT/55JNIQtCm8709JUnolHk2+8HmbSDYpzM7DdgotkqSgy3iqDRjL20qz3je/fffH3ses98xXwlEU8nPs+nsSZCwZJV9uHn2VVddFSuqHnrooXgW/wYqh/j9c+fOTe+9917HmSkmS6Qnso1y4CNLU2kfzz77bNqzZ09UDJ1yyinRPkySNAaTJEq1lormWiOTJPXDuERFPr4XfhLjEIkSqqopSin7aSKNAJ+bnQvwodlPn2r85cuXh0/OPKCzP80cFTtliyO6AeyTilaTJNIIsCOK4/LK7pEjR0YCj/khtli2R+yTeV8+R4egNNtJc5aTSRJpBMRSKAZlPKZgARif//nnn5pr6UsZpzdt2hRFqWyDxLyS2Am2aJJEBgpszyRJYzBJ0qRgyFT0bNy4MQJ0OKsEiulg6air7hEpQ8NmMCawy8Q6V+IwwLNcmY6UCrErr7wyOlGqwLZv337CgN9TkiQnYDiz4YorrgjHlDN2cpKEfTn5HTfeeGNUXZAcIRjNcwmg8B74+s0338Q2YFxDwoPEyOHDhyNJ8t1336Unnngi3uOIESPifbA9GJ0XTjTPOHjwYAwIw4YNi0QKh0jyvgxoS3fQRo4ePZo++OCDmIRhw1StMdnC9kj4nXzyySZJGohJEqVaS0VzrRHt3PlB38E/YvxhhS3bHfERU7m6ZMmS+Ewdg2QgwJemoIlEB3vo44/je1etJAGSePjh8+bNCxvFv//www9NkkhDwI7oA59++uk484b5K9tp7d+/P+yrbGM5SUJShKA0u248/vjjcQanSRJpBMQ7sL0HHnggYnJ5JQnzQl4vJ+3oL0mSfPzxxx0rmzgLlnNJsEWTJDJQYHsmSRqDSZImhD8InTGV/dddd10YOJ0sWwrxB3Owl96AnTCJwY5uv/32SCDMmTMnDl0nicEqECbiHLR+ySWXhJ2x7RZJCeyMQbzeJAnbavEecB5WrlwZz6a6h4NHWQnF78WhBfoPqjNIbLCt0dlnnx2rTvg574P3k7cJ43dv3rw5nl3u7EnGsDcy9/N/wYlhsoeTna8RKYMjgX2uXbs29pdlRRWHn5E04XVee+ONN0ySNBiTJEq1lormWiOTJPXB4a65YpVADAE/gjH4WswPysEYkUaBv8/cAP+eQDOrl9ha5ssvv6xJknAtfj5Bv5kzZ4bvj9/N9kaeSSKNgPGDlU3MKdltgMp95olswYV9lW0M+6RYjkO0KZRjHvnqq69GQSC2qj1Kf6FfI66wbNmyNGnSpJgzcs4IWw7yenlc5tpcvHn55ZfHtdgxu3U4hstAYpKkcZgkaTL4PAkgE5ResGBBGDcJEg7jYZDH+KvuEymTB2CCumyphR1x4D+V8UeOHImkAkkSBnIqI6hw4BqqFZ966qnoGHI1WD1JEmyY379v376ozOe+s846K1aJMKHCpjmEFPL3rCQhkUJggENJuZ++pZwkufPOO2P5Kq+VO3uCCtzDqhiSJLxXkyRSBW2DfpYKNdrDhRdemKZNmxYVaNg9K6mwGyZW2DJJEuzqlVdeifZA2+G1qmdLz5gkUaq1VDTXGpkk6RuML0BwhXMIzzjjjPDJqIjmzDV8tqr7RBpB9o1os2xpS9PGBil0Yt7A/BMfiWsJuOBDvfDCC+m8884LP4qtkCiCwj8yCCj9BTtinsnOAm1tbWGP7ErAym/mnmV7xD7ZFu6WW26JgDQ+O/NUrtEWpRHQNxJT+eKLLzpWz911111RXMdrZXskPkr/SMKOrbZYZUeBat5GX2SgwA5NkjQG2jPjh0mSJoA/BE4BlQ8333xzGDYTJJbrMdmsukekChxIkgR0jBMmTAhbohqRRAmrkwgKA3sIX3PNNWn06NFxDbAa4/PPP4/kR+5s611JQtXESy+9FM+lWp9qSJ5HoBlnoYpchUZbINlikkQaSU50rFixosPm2euYZB2OLG0itwvaCyuwqOZluT/VbI899lisZsJZZvyr+h3SNSZJlGotFc21RiZJ+gb+Fp8ZRStUTfPRcvYah8XyOTJ2Vd0n0ijwu7EzfHz20GfewFa9zCWwQ3wnAgdsY5TPCqTAifPbWIWCDVc9V6QesEdsijgIOyIwB2UO+9FHH8X8ksAf9sqKcIrusEe2KGSFE/NQg4LSSLA14gok6pgjYm/MG1lBx84YOXm3evXq2IEg26NjuAwkxI8B+8KHJDZBcUNOkjCWEyfDNrnGxHHvMEnSJPBHwLA5mwFjZu9NAr6sIKGqTMdTekPuKGmTnEPCIelMckguUGWDjZHcKEMnQEKB6wkC06Fef/318T0TItp4vWeSUJXPIfEEmwk0szoq39f5vdNx4xAD35skkYGENlJuBxlsjq/sJ8vS/rzdFsk+qiSxO8e8+uGzox/qjY63eaVUk6torjWindtX9g78HRLvVJuyxdHkyZMjuEKVKoUj2S8SGQyYc2JzBw4cSM8991waP358GjduXMC/8bPx55kb4O/jaxt4kYGCOSNzxG3btsV8kOI/5pXZHiliuueeeyIYTX+Jn649ykBAzIRVnfSRbA3OOM2WWp3tkf6R+eSxY8e0RRkQsCts8dNPP41CT5Ii2CE2yM4tnC1GMo/YGLZJwcOUKVPSwoUL43zgqlic/A+TJE1A7jz5HDkTgjknPProo+F4MuB3vkekCiY1eeDm/BHs6NZbb027du2KiUxXVTXZqXzmmWficHTuwxFluywcU5aX5gNE77jjjrR79+4IJNM5kyRhyT0dMK+zTUROktA/EGzmQPi77747XXzxxTG5olKSSjRWrPBsqoTY5osEDHbPnrNUCOFgkNxZtGhRPJsl1yzp59nl/wvJnJ07d4ajMmrUqNiyyySJ1EPujwlYvfbaa2F3TMhIklApZH/cP0ySKNVaKpprjUyS9B18OFbU4v/gv+BDVV0nMhjg/+PzUIiETWb4ngAL9mkAUAYD7Ax7y/1jZ3ukv8Req+4VGQiIt2B3VfZI/4jNEmitulekEWBfxMOwt852WAVxO/pQ+kpts3tMkjQBGCrBYA5mJ7jNEimCxu3t7fHHcdCX3oKt0FmSgFi1alUEdjl0kQ6xu0rEbGMkJ1j5gQ2+//77ae/evdFJUJ3DyhKeyUoODtOj3dP+WdLMKo5169bFuQ179uw5oU/AhnlPBExIuvAMKtOWLFmSnn/++TizZOnSpbFcmlUjdOC81+xcsKR669at8Z743WxHV37PgKNCxRuH9pGk4b12lxQS6Qrsjq/0yazsw17ZJo5EIz/rrh1Jz5gkUaq1VDTXGpkkERERERGRoYRJkiYjJ0X4moN1In0l21G2paprqsh2V2WD5Z+X7ym/1tPvK1/XGV7r6t58X3fXQPm6qtdF+kJv7U56j0kSpVpLRXOtkUkSEREREREZSpgkERERkUHBJIlSraWiudbIJImIiIiIiAwlTJKIiIjIoGCSRKnWUtFca2SSREREREREhhImSURERGRQMEmiVGupaK41MkkiIiIiIiJDCZMkIiIiMiiYJFGqtVQ01xr9+eef6aeffkoHDhwQERERERFpevbv358OHjyY/v7772JWU63KSdCxY8fSoUOH0vfff59+/PFHERERkS7BX/j111+jyKInFTFYpVQTq2iuJ4j5weLFi9OECRPSlClT0tSpU0VERERERJqaSZMmpRkzZqT29vZiZlOtykkQmZV77703jRo1Kh4kIiIi0hX4C4sWLUpHjhwpPImuVcRglVJNrKK5nqCjR4+muXPn0oZFRERERESGFF999VUxs6lW5STor7/+StOnT698oIiIiEhn5syZkw4fPlx4El3r+LVKqSZX0VxPECvFtmzZkl588cW0atUqERERERGRpmfZsmVpzZo16ffffy9mNtWqnARxkMmGDRvSyy+/HA8RERER6Qr8BYKnbMfTk4oYrFKqiVU0V6WUUkoppZRqGXW3RbiTIKWUUkoNmooYrFKqiVU0V6WUUkoppZT6T8hJkFJKKaUGTUUMVinVxCqaq1JKKaWUUkr9J+QkSCmllFKDpiIGq5RqYhXNVSmllFJKKaX+A0rpX+HlXkAbu3NwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/onehot.png', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how one-hot encoding works on the gender variable. First we will transform the categories \"Male\", \"Female\" to values 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "      <th>birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
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       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age          workclass  fnlwgt   education  education-num  \\\n",
       "5  37.0            Private  284582     Masters             14   \n",
       "6  49.0            Private  160187         9th              5   \n",
       "7  52.0   Self-emp-not-inc  209642     HS-grad              9   \n",
       "8  31.0            Private   45781     Masters             14   \n",
       "9  42.0            Private  159449   Bachelors             13   \n",
       "\n",
       "           marital-status        occupation    relationship    race  sex  \\\n",
       "5      Married-civ-spouse   Exec-managerial            Wife   White    0   \n",
       "6   Married-spouse-absent     Other-service   Not-in-family   Black    0   \n",
       "7      Married-civ-spouse   Exec-managerial         Husband   White    1   \n",
       "8           Never-married    Prof-specialty   Not-in-family   White    0   \n",
       "9      Married-civ-spouse   Exec-managerial         Husband   White    1   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  salary  \\\n",
       "5             0             0            40.0   United-States   <=50K   \n",
       "6             0             0            16.0         Jamaica   <=50K   \n",
       "7             0             0            45.0   United-States    >50K   \n",
       "8         14084             0            50.0   United-States    >50K   \n",
       "9          5178             0            40.0   United-States    >50K   \n",
       "\n",
       "   birth_year  \n",
       "5        1957  \n",
       "6        1945  \n",
       "7        1942  \n",
       "8        1963  \n",
       "9        1952  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data_chunk = adult_data[:10].copy().dropna()\n",
    "adult_data_chunk['sex'] = adult_data_chunk['sex'].fillna('None') # replace nans by another name so that python doesn't recognize it as a nan\n",
    "le = preprocessing.LabelEncoder() # specify paramets for encoder\n",
    "adult_data_chunk['sex'] = le.fit_transform(adult_data_chunk['sex']) # fit the model and transform the sex variable\n",
    "adult_data_chunk.tail(5) # number in paranthesis specifies the numbers of lines to show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the method get_dummies to directly encode each category into a one-hot vector. Let's try this for the salary category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
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       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>salary_ &lt;=50K</th>\n",
       "      <th>salary_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1944</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1956</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1941</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age          workclass  fnlwgt   education  education-num  \\\n",
       "0  39.0          State-gov   77516   Bachelors             13   \n",
       "1  50.0   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2  38.0            Private  215646     HS-grad              9   \n",
       "3  53.0            Private  234721        11th              7   \n",
       "4  28.0            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race  sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White    1   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White    1   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White    1   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black    1   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black    0   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  birth_year  \\\n",
       "0          2174             0            40.0   United-States        1955   \n",
       "1             0             0            13.0   United-States        1944   \n",
       "2             0             0            40.0   United-States        1956   \n",
       "3             0             0            40.0   United-States        1941   \n",
       "4             0             0            40.0            Cuba        1966   \n",
       "\n",
       "   salary_ <=50K  salary_ >50K  \n",
       "0              1             0  \n",
       "1              1             0  \n",
       "2              1             0  \n",
       "3              1             0  \n",
       "4              1             0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data_chunk = pd.get_dummies(adult_data_chunk, columns=['salary'])\n",
    "adult_data_chunk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Think**: Notice the effect of converting the salary category into one_hot vectors, and how we're able to preserve the meaning of each one-hot label. This is going to be helpful in understand what each column represents\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting all categorical features into one-hot encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did for salary, we are going to convert all categorical features into their one-hot representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>13</td>\n",
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       "      <td>40.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0  39.0   77516             13          2174             0            40.0   \n",
       "1  50.0   83311             13             0             0            13.0   \n",
       "2  38.0  215646              9             0             0            40.0   \n",
       "3  53.0  234721              7             0             0            40.0   \n",
       "4  28.0  338409             13             0             0            40.0   \n",
       "\n",
       "   birth_year  workclass_ ?  workclass_ Federal-gov  workclass_ Local-gov  \\\n",
       "0        1955             0                       0                     0   \n",
       "1        1944             0                       0                     0   \n",
       "2        1956             0                       0                     0   \n",
       "3        1941             0                       0                     0   \n",
       "4        1966             0                       0                     0   \n",
       "\n",
       "       ...       native-country_ Scotland  native-country_ South  \\\n",
       "0      ...                              0                      0   \n",
       "1      ...                              0                      0   \n",
       "2      ...                              0                      0   \n",
       "3      ...                              0                      0   \n",
       "4      ...                              0                      0   \n",
       "\n",
       "   native-country_ Taiwan  native-country_ Thailand  \\\n",
       "0                       0                         0   \n",
       "1                       0                         0   \n",
       "2                       0                         0   \n",
       "3                       0                         0   \n",
       "4                       0                         0   \n",
       "\n",
       "   native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "0                                0                              1   \n",
       "1                                0                              1   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              0   \n",
       "\n",
       "   native-country_ Vietnam  native-country_ Yugoslavia  salary_ <=50K  \\\n",
       "0                        0                           0              1   \n",
       "1                        0                           0              1   \n",
       "2                        0                           0              1   \n",
       "3                        0                           0              1   \n",
       "4                        0                           0              1   \n",
       "\n",
       "   salary_ >50K  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data = pd.get_dummies(adult_data, columns=CATEGORICAL_FEATURES)\n",
    "adult_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 111)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE NORMALIZATION\n",
    "* Range of values for each feature might vary dramatically\n",
    "* Often in machine learning it is critical to rescale or standardize features before fitting a model\n",
    "* Example: if a classifier is computing a distance between two points\n",
    "    * If one of the features has a broad range of values, the distance will be dominated by this particular feature\n",
    "    * Normalize each feature to be within the same range so that each feature contributes approximately proportionately to the final distance\n",
    "* Some algorithms such as gradient descent converge much faster with feature normalization\n",
    "* Standardization $$x_{new} = \\frac{x - \\bar{x}}{\\sigma}$$\n",
    "* Rescaling $$x_{new} = \\frac{x - \\min(x)}{\\max(x) - \\min(x)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO(create our own image)\n",
    "Image(filename='images/rescaling.png', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's standardize the duration, birth date and age variables\n",
    "Let's standardize the age, fnlwgt, education-num, capital-gain, capital-loss, and hours-per-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>workclass_ ?</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
       "      <th>workclass_ Local-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_ Scotland</th>\n",
       "      <th>native-country_ South</th>\n",
       "      <th>native-country_ Taiwan</th>\n",
       "      <th>native-country_ Thailand</th>\n",
       "      <th>native-country_ Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_ United-States</th>\n",
       "      <th>native-country_ Vietnam</th>\n",
       "      <th>native-country_ Yugoslavia</th>\n",
       "      <th>salary_ &lt;=50K</th>\n",
       "      <th>salary_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030671</td>\n",
       "      <td>-1.063611</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>0.148453</td>\n",
       "      <td>-0.21666</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.837109</td>\n",
       "      <td>-1.008707</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.21666</td>\n",
       "      <td>-2.222153</td>\n",
       "      <td>1944</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.042642</td>\n",
       "      <td>0.245079</td>\n",
       "      <td>-0.420060</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.21666</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.057047</td>\n",
       "      <td>0.425801</td>\n",
       "      <td>-1.197459</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.21666</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.775768</td>\n",
       "      <td>1.408176</td>\n",
       "      <td>1.134739</td>\n",
       "      <td>-0.145920</td>\n",
       "      <td>-0.21666</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age    fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "0  0.030671 -1.063611       1.134739      0.148453      -0.21666   \n",
       "1  0.837109 -1.008707       1.134739     -0.145920      -0.21666   \n",
       "2 -0.042642  0.245079      -0.420060     -0.145920      -0.21666   \n",
       "3  1.057047  0.425801      -1.197459     -0.145920      -0.21666   \n",
       "4 -0.775768  1.408176       1.134739     -0.145920      -0.21666   \n",
       "\n",
       "   hours-per-week  birth_year  workclass_ ?  workclass_ Federal-gov  \\\n",
       "0       -0.035429        1955             0                       0   \n",
       "1       -2.222153        1944             0                       0   \n",
       "2       -0.035429        1956             0                       0   \n",
       "3       -0.035429        1941             0                       0   \n",
       "4       -0.035429        1966             0                       0   \n",
       "\n",
       "   workclass_ Local-gov      ...       native-country_ Scotland  \\\n",
       "0                     0      ...                              0   \n",
       "1                     0      ...                              0   \n",
       "2                     0      ...                              0   \n",
       "3                     0      ...                              0   \n",
       "4                     0      ...                              0   \n",
       "\n",
       "   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \\\n",
       "0                      0                       0                         0   \n",
       "1                      0                       0                         0   \n",
       "2                      0                       0                         0   \n",
       "3                      0                       0                         0   \n",
       "4                      0                       0                         0   \n",
       "\n",
       "   native-country_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "0                                0                              1   \n",
       "1                                0                              1   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              0   \n",
       "\n",
       "   native-country_ Vietnam  native-country_ Yugoslavia  salary_ <=50K  \\\n",
       "0                        0                           0              1   \n",
       "1                        0                           0              1   \n",
       "2                        0                           0              1   \n",
       "3                        0                           0              1   \n",
       "4                        0                           0              1   \n",
       "\n",
       "   salary_ >50K  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = adult_data[continuous_features]\n",
    "adult_data[continuous_features] = (X - np.mean(X))/ np.std(X)\n",
    "adult_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Persisting cleaned data to permanent storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now store cleaned adult data to permanent storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_data.to_csv(os.path.join(ADULT_DIRECTORY, 'adult_data_cleaned.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXERCISE\n",
    "Clean test set adult_test, by converting all categorical features into one-hot encoding and normalizing continuous features. Once that is done, persist the cleaned test-set to permanent storage, by storing it as \"adult_test_cleaned.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "Congratulations on completing this tutorial. You now know the basics of data manipulation in pandas. Next, you are going to apply a machine learning to predict whether a person makes more or less than 50k USD"
   ]
  }
 ],
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