NoteBook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>city</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.6</td>\n",
       "      <td>33988</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>brio</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.9</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>city</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.35</td>\n",
       "      <td>11.0</td>\n",
       "      <td>87934</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>city</td>\n",
       "      <td>2017</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.5</td>\n",
       "      <td>9000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>brio</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5464</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "296     city  2016           9.50           11.6       33988    Diesel   \n",
       "297     brio  2015           4.00            5.9       60000    Petrol   \n",
       "298     city  2009           3.35           11.0       87934    Petrol   \n",
       "299     city  2017          11.50           12.5        9000    Diesel   \n",
       "300     brio  2016           5.30            5.9        5464    Petrol   \n",
       "\n",
       "    Seller_Type Transmission  Owner  \n",
       "296      Dealer       Manual      0  \n",
       "297      Dealer       Manual      0  \n",
       "298      Dealer       Manual      0  \n",
       "299      Dealer       Manual      0  \n",
       "300      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fuel_Type\n",
    "#Seller_Type\n",
    "#Transmission\n",
    "#Owner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ---------------Unique Values--------------- \n",
      "['Dealer' 'Individual']\n",
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(\" ---------------Unique Values--------------- \")\n",
    "print(df['Seller_Type'].unique())\n",
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      "Car_Name         301 non-null object\n",
      "Year             301 non-null int64\n",
      "Selling_Price    301 non-null float64\n",
      "Present_Price    301 non-null float64\n",
      "Kms_Driven       301 non-null int64\n",
      "Fuel_Type        301 non-null object\n",
      "Seller_Type      301 non-null object\n",
      "Transmission     301 non-null object\n",
      "Owner            301 non-null int64\n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Car_Name</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>city</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>26</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Car_Name Fuel_Type Seller_Type Transmission\n",
       "count       301       301         301          301\n",
       "unique       98         3           2            2\n",
       "top        city    Petrol      Dealer       Manual\n",
       "freq         26       239         195          261"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include= 'object' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Current Year'] = 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['noOfYears'] = df['Current Year'] - df['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Car_Name','Year','Current Year'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>noOfYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  noOfYears  \n",
       "0       Manual      0          6  \n",
       "1       Manual      0          7  \n",
       "2       Manual      0          3  \n",
       "3       Manual      0          9  \n",
       "4       Manual      0          6  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfFinal=pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>noOfYears</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  noOfYears  \\\n",
       "0           3.35           5.59       27000      0          6   \n",
       "1           4.75           9.54       43000      0          7   \n",
       "2           7.25           9.85        6900      0          3   \n",
       "3           2.85           4.15        5200      0          9   \n",
       "4           4.60           6.87       42450      0          6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFinal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFinal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "var = dfFinal.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>noOfYears</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
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       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>noOfYears</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
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       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "noOfYears                   -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                        noOfYears  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price           -0.236141          0.552339         -0.540571   \n",
       "Present_Price            0.047584          0.473306         -0.465244   \n",
       "Kms_Driven               0.524342          0.172515         -0.172874   \n",
       "Owner                    0.182104         -0.053469          0.055687   \n",
       "noOfYears                1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel        -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol         0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual   0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual     -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "noOfYears                             0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7a60a5a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kBoMvG6p+Fw2UJK3/ba2SmjaSVNp1NDEl6ZDn6odPv1PbLu313SffaE/ZvV6S5rw7S5fceXngDyBITh41QN3L7lHbft2g+Kb77ttxKYnKTc082EtV4inWqjm/qOOAk7R+3gpJksvt0nGDuuu5YZMCmzhQDTVp6ckzxiSp7PtsY8wpkrL/+iWorY5re5Q279ilbam7VVxcoi/n/aI+3TtViElp2EA//1Y6oHXDth0qKipRYnyMmjRM1MLlq2WtVX6hR7+t2ahWzZJDcRgBkf/rWkW2aqKIFo1lwsPUYNhpyp69sEJM9uyFSjq/9EN/wtBeyl1QOrDcHVdfbV67R9sfeVN5i1dVeE1YUmlR6Y6vr4ajhij93bo728+Wf83Sgn53akG/O7Vr5mI1HXm6JCm+W1sV5+bLs6vih3fPrix59xQqvltbSVLTkacr9cvSbjSenZlK7NlRkpR42vHK21BaKBb+ma6k046XJEU0ilf9Nk2Vv3lXUI4vUFa+PkcfDZ6kjwZP0savflH7Eb0lSY27tlFRbr7yd1UuerpPOF8RsfU0f/Jb5c8V5Rbo9c436O2et+ntnrdp19L1jit4JOm45Fhtyc7XnzkFKvb69NXaVPVpVfHLlf27u323MU2tGpR2tc0pLFaRt/SLhcyCIi3bkaXWic7phitJX70xQxOG3qYJQ2/Tolk/6YwRpR9W23Vtr/zcPGXtqvgB1eV2KbasG647zK1u/bpry+otys/N19VdR2lM72s1pve1Wrt0taMKHkn68o0ZmjD0Vk0YeqsWzvpZfcrPVQfl5+ZX61xtXb1ZkiqM/zlpQA/9uW5bkI4i8H5+c7aeHTpRzw6dqD9mLVbX806TJLXo2lae3ALlplW8R0VER5aP83G5XWrft4vS1m8v39+m9/FK27BdOTsrF5VAsNWkped2SZ9JamOMmS+pkaTzA5JVAE247+9atPQ3ZWXlqN/wS3Xj1aM0YtigQ7/QYcLcbk0cfaFueOBZeX0+De93qtq2bKrn3v1cHdscpb49Omn8FSN0//Nv683P58oYowdvHiVjjC4acrruefZNnXfrFFlrde6Zp6r90c1DfUj+4/Vp2z3T1ebNyaVTVr//tQrXbFXK7X9T/vJ1ypm9UOnvz9ZRT92mjt+/oJKsXG26qXQ2wIaXD1XE0U2UcssFSinrL7/+0skqSc9W88mjFdWxlSRp51Pvy7Nx+0FTqEvS5ixVw35ddPrPT8tb4NHysfumgu/59d+1oN+dkqSVd7yiE565Qe6oCKV9vUy7y8airBg3XcdOuVwmzC2fp1grx78kSVr/5Cc64Zkb1OvbRyVjtPrBd1SckVs5gTpqy9xlanlmZ1087wmVFBTp23HTy/ed/+VUfTR4kuqnJKrbLcOVufZPnT+z9Fv4Fa/N1qr3vg1R1sEV5nLpjtM76MZPl8pnpXM7NlGbpBg9//N6dWwcpz6tGundX7fqu0275TZG8VFhur9/aQG9ITNPU79ZJWOMrLW6stvRlWZ9c5Ilc39R174n6Z/fv6CiAo+eG79v5qzHZvxDE4bepvCIcN395mS5w8Lkcru0fN6v+vrdWSHMOjSWzF2sE/t207PfvyhPgUfPj3+mfN9jM57ShKG3lp2r+xVWdq5+m7dMc8rO1dArhqn7gB7ylni1JztXz45/KlSHElCrv1mm9n276Pbv/qHiAo8+mbBvtrqbZjykZ4dOVHh0pC59eZzCIsJl3C5tWLBSC9+eUx7Xadip+o2ubaglzMH6kFcZbEyYpA4q7RG72lpbrSmD6N5WPb5UZ31LGyi/D3nm0EGQJKV6HDQGJoA2RRxxs/f/zy77v7hQp1AnXP74n6FOoU6w4uNBdXUwzi3a/W3qpneqGkla63hWfu3IP4DI4/rVyvNfk9nbxkiKsdautNaukBRjjLkxcKkBAAAAwOGryZiea6y15Z05rbWZkq7xf0oAAAAA4D81KXpcZr/5C40xbkkR/k8JAAAAAPynJh3Zv5L0gTHmBZXO4Ha9pC8DkhUAAADgZLV4TRsnqknRc4ek6yTdoNKJDGZJejkQSQEAAACAv1S76LHW+iRNK/sBAAAAgDrhkEWPMeYDa+0FxpjlUuW5Ja21nap4GQAAAADUCtVp6Rlb9t+zA5kIAAAAAATCIYsea+2OspnaXrHW9g9CTgAAAICz+ZjIIJiqNWW1tdYrKd8YEx/gfAAAAADAr2oye1uhpOXGmNmS8vY+aa29xe9ZAQAAAICf1KTo+aLsBwAAAADqjGoVPcaYript3Vlprf0jsCkBAAAAzlY6egTBcsgxPcaYeyW9L2mEpC+MMdcEPCsAAAAA8JPqtPRcKKmLtTbfGJMk6UtJLwU2LQAAAADwj+rM3lZorc2XJGttejVfAwAAAAC1QnVaetoYYz4re2wO2Ja19pyAZAYAAAA4lWWdnmCqTtFz7gHbjwciEQAAAAAIhEMWPdba76rzRsaYj621Iw4/JQAAAADwH3+Oz2ntx/cCAAAAAL+oyeKkh2L9+F4AAACAc/kY0xNMzMQGAAAAwNH8WfQYP74XAAAAAPiFP4ueO/z4XgAAAADgF9Ue02OMWa7K43ayJS2WNMVaO8ufiQEAAACAP9RkIoOZkryS3inbvqjsvzmSXpM0zH9pAQAAAA7G4qRBVZOip5e1ttd+28uNMfOttb2MMZf6OzEAAAAA8IeajOmJMcacvHfDGNNDUkzZZolfswIAAAAAP6lJS89oSa8aY2JUOlNbjqTRxpj6kh4ORHIAAAAAcLiqXfRYaxdJOsEYEy/JWGuz9tv9gd8zAwAAAJzK5w11BkeUmszeFilphKSjJYUZU7osj7X2gYBkBgAAAAB+UJPubZ+qdIrqXyR5ApMOAAAAAPhXTYqe5tbawQHLBAAAAAACoCZFzwJjzAnW2uUBywYAAAA4ErBOT1DVpOjpLekKY8xGlXZvM5KstbZTQDIDAAAAAD+oSdEzJGBZAAAAAECAHLLoMcbEWWtzJOUGIR8AAAAA8KvqtPS8I+lslc7aZlXarW0vK6l1APICAAAAnMvHmJ5gOmTRY609u+y/rQKfDgAAAAD4V3W6t534V/uttUv8lw4AAAAA+Fd1urc98Rf7rKQz/ZQLAAAAAPhddbq39Q1GIgAAAACczxgzWNLTktySXrbW/v2A/S0lvS4poSzmTmvtjMP5ndXp3nbeX+231n5yOAkAAAAAR5wjdHFSY4xb0nOSBkjaJmmRMeYza+3v+4XdLekDa+00Y0xHSTMkHX04v7c63duG/cU+K4miBwAAAEB19JC0zlq7QZKMMe9JOlfS/kWPlRRX9jhe0vbD/aXV6d525eH+EgAAAACQ1EzS1v22t0k6+YCYyZJmGWNullRfUv/D/aWu6gYaY5KNMa8YY2aWbXc0xlx9uAkAAAAAcAZjzLXGmMX7/Vx7YEgVL7MHbF8s6TVrbXNJQyW9aYypdt1Slep0b9vrNUn/kjSpbHuNpPclvXI4CQAAAABHHIcuTmqtnS5p+l+EbJPUYr/t5qrcfe1qSYPL3u9HY0yUpIaSdv2vedWkYmporf1Akq8sgRJJ3v/1FwMAAAA44iyS1M4Y08oYEyHpIkmfHRCzRVI/STLGHCspSlLa4fzSmhQ9ecaYJJU1PxljTpGUfTi/HAAAAMCRo6zh5CZJX0n6Q6WztK00xjxgjDmnLGycpGuMMb9KelfSFdbaA7vA1UhNurfdrtIqrI0xZr6kRpLOP5xfDgAAAODIUrbmzowDnrt3v8e/S+rlz99ZnXV6ukvaaq1dYow5Q9J1kkZImqXSPnkAAAAAasKhY3pqq+p0b3tRUlHZ454qncjgOUmZ+utBSgAAAAAQctXp3ua21maUPb5Q0nRr7ceSPjbGLAtcagAAAABw+KrT0uM2xuwtjvpJmrvfvpqMCQIAAACAoKtO0fKupO+MMbslFUj6QZKMMW3F7G0AAABAjVnLyi/BdMiix1o71RjztaQmkmbtN12cS9LN1fklvtSN/3uGRxBXcqtQp1AnNG1NrV1dJWsPa/HiI0ZTd16oU6gzbDr/SFdHgokIdQp1QgEf+qrNXeUi9gCqq1rd06y1P1Xx3Br/pwMAAAAA/sXXwAAAAAAcjaIHAAAAgKMx+xoAAAAQbCxOGlS09AAAAABwNIoeAAAAAI5G0QMAAADA0RjTAwAAAASbZUxPMNHSAwAAAMDRKHoAAAAAOBpFDwAAAABHY0wPAAAAEGys0xNUtPQAAAAAcDSKHgAAAACORtEDAAAAwNEY0wMAAAAEG+v0BBUtPQAAAAAcjaIHAAAAgKNR9AAAAABwNIoeAAAAAI7GRAYAAABAsLE4aVDR0gMAAADA0Sh6AAAAADgaRQ8AAAAAR2NMDwAAABBsLE4aVLT0AAAAAHA0ih4AAAAAjkbRAwAAAMDRGNMDAAAABBvr9AQVLT0AAAAAHI2iBwAAAICjUfQAAAAAcDTG9AAAAADBxpieoKKlBwAAAICjUfQAAAAAcDSKHgAAAACORtEDAAAAwNGYyAAAAAAINstEBsFESw8AAAAAR6PoAQAAAOBoFD0AAAAAHI0xPQAAAECwsThpUNHSAwAAAMDRKHoAAAAAOBpFDwAAAABHY0wPAAAAEGys0xNUtPQAAAAAcDSKHgAAAACORtEDAAAAwNEY0wMAAAAEG+v0BBUtPQAAAAAcjaIHAAAAgKNR9AAAAABwNIoeAAAAAI7GRAYAAABAsLE4aVA5suiZt2SlHnn1Q/l8Vuf176mrzxtUYf+OtAzd/c/XlZtXIK/Pp1svHa7Tuh2v4hKvJj//lv7YsFVer1fD+pys0SMGh+goQu/uh57U9/MXKrFBgv7z1guhTqfWiOjRQ7E33SS53Sr44gvlv/NOhf3RI0eq3llnyXq98mVlKefRR+VLTQ1RtoER16erWj5wtYzLpbR352jnc59U2G8iwtT66bGKPqGNSjJztf6Gx1W0LU2S1OSm89Twov6yPp+23POycr5bJklKvmaYGl3cX9ZKBas2a+Pt/5T1FKvxFUOUPHqYolo10dLjL1NJZm7Qj9cfYs44Uc3uvUZyu5Tx/mylTfuown4TEaYWT96uese3kTcrV5tvelTF23aV7w9v2kjtZz+n1Kfe1e6X/i1Janj1uUq8cKBkrQpXb9LWCU/LeoqDelyB5m7bWRGDL5NcLpUs+UbF8z6rOq5jD0VdcJsKpk+Sb/sGuU/opfBeZ5fvdyW3VOGLE+XbuTlYqQfdRfddqRP6nqiiAo/+Nf45bVm58aCxY166Q41aNtbkQeMkSdc+e5tSWjeVJNWLi1ZBTr4eGDohKHmHwqjJV6tL3xPlKfBo+vhntWnFhkoxk957QAmNG6iosEiS9MioB5STnq0OPTpq1H1XqcUxR+nZm5/Uohk/Bjv9oBl632Vq17eziguK9O/xL2rHyk2VYka9/n+KbZwgl9utzYtW67/3/EvWZ9X31vPU7aK+yssovWfPefR9rf321yAfAbCP44oer9enh156X9Pvu0XJSQm6+P8eUZ/undSmRZPymOkfzdTAnt104eDTtX7rDo2Z8py+fHGKZi1YouLiEn3y1N0q8BTp/93ygIac1l3NGieF8IhCZ/jQAfrbiHM08cHHQ51K7eFyKXbsWGWNHy9vWpoSX3hBnvnz5d2874NU8dq1yr/uOsnjUb1zzlHsddcp+4EHQpi0n7lcOmrqtVpz8WQV7UhXxxmPKmvWQhWu3VYe0vDi/irJztPy3jcq8ZzeajHpMq2/4QlFtWuuxHN7a8WZtyg8OVEd3rtfy08bo/DGCUq+6iwt73uLbGGR2rwwXonn9lb6B99oz6JVypqzWMd8NCWEB32YXC41e+B6bbz0HhXvTFfbz55Uzuyf5Vm3tTwk8YKB8mbv0eo+1yl+2GlqcucV2nLTo+X7m94zWrnf/lK+HZacqIZXDNPq/jfKeorU8tk7lDDsdGV+9HVQDy2gjFHE0CtV+OZDsjnpirpmqkpW/yKb9mfFuIgohZ88WN5ta8uf8i6fL+/y+aVv07iFoi4e5+iC5/g+XdW4VRNN6nOzWndtp0umXqOHh0+sMrbroB7y5BdWeG76Tf8ofzxy0mUqyM0PaL6h1LnviUpp1UTjzhijNl3b66wwOJ4AACAASURBVIop12ry8DurjH1+7FPauHx9hefSt6fpxXH/1NBrzw1GuiHTrk9nJbVK0dN9xql517YaNvVKTR9+X6W4D8b8U549BZKkC6eN1XFnnawVn/8kSfrxlZma/9KMoOYNHIzjxvSsWLdJLZs0UvOUhgoPD9Pg3t30zcKK3ywYGeWV3fD35BeoUWJ86fNGyvd4VOL1ylNUpPCwMMXUiwr6MdQWJ3U5QfFxsaFOo1YJP+YYef/8U94dO6SSEhXOnavIXr0qxBQvWyZ5PKWPf/9drkaNQpFqwNTv2k6eTTvk2ZIqW1yijE/nqcGgHhViGgzsod0ffiNJyvhigWJ7dyp9flAPZXw6T7aoREVbd8mzaYfqd20nSTJhbrmiIiS3S656kSremSFJyl+5sbyVqK6K7tJORZt3qGhr6TnL+vx7xQ08uUJM3MCTlflxacGSPWO+Ynp23m/fKSraslOetVsqvrHbVfGcpWYE/FiCydWsrXwZO2Uzd0ler7wrflRYh5MqxUWceYGK538ulVTdyhV2Qk+VLF8Q6HRDqsvA7vrpk+8kSRuWrlV0bH3FN0qoFBcZHaUBo4fpi39+fND3OumsU7Xws3kByzXUug3ooXkffytJWr90jerH1VdC4wbVfv3ubWnaumqzrMPXWDlmYDct++QHSdK2pesUFRutmCquqb0FjyvMrbDwMMkGNU2g2hxX9KSmZyk5ad/NKzmpgXZlZFeIueHCs/Tf7xeq/+iJunHKc7pr9IWSpAGnnqjoyEj1u/ouDbz2bl1+bn/Fx9YPav6o3VyNGsmXtu8DuC8tTe6/KGrqnXWWihYuDEZqQRORkqii7bvLt4t2pCs8pWJraHhK0r4Yr0/enHyFNYgtez69wmsjUhJVvDNDO1/4VJ0XTleXpa/Km5OnnO+d0w0iPDlJxfuds+Id6QpPTjp4jNcnb26e3A3iZOpFqtH1I5T69LsV4ktSM5T20r91zIJX1XHhG/Lm5mnPD0sDfizBZOIayObsu15sTrpMXMUPp66Uo2XiEuVdc/BjDzvuVJWscHbR0yA5URn7/W1l7kxXQkpipbhzx12o2S9/rqJCT5Xv067HscrZna1dm3YGLNdQa5CSqPT9/h4zdqarQXLlcyVJ1z5+k6bOeELDbxkZrPRqjbjkRGXvd03l7MxQXErVxeFlb9yhO36ZJk9eoVbO+Ln8+R6XD9SNMx/W8EevUVRcdMBzrnN8Pmf+1FLVLnqMMe2NMV8bY1aUbXcyxtwduNT8xxywPXPeYp3b9xTNefkhPX/3GE18+jX5fD6tWLtJLpdLc15+WDOnPajXP5ujbTt3V/meQDlb9ddaUQMGKKxDB+W9916QEwowc+BflCqdgypD/uK17vj6ShjUQ7+dcr1+PfFquaKjlHTeGX5Jt1aoxjk7WEzKbZdo9yufyndAdyR3XH3FDzhZq04brd9Pvlyu6CglDO/jv5xrhYNdSHt3G0UMHqWiWW8d9B1czdpIxR7ZXdsOGuMIVVw/B15iLToercZHpWjpVwf/IqbHOb0d3cojSabKc1X5Pv782Kd016Db9ODISerQ/Vj1Pq9PELKrPaq+JVX9790blz2ix3qMkTsiTK17HidJWvjWHD11+m2aNnSicndlafDdlwQyXeCQatLS85KkuyQVS5K19jdJFx0s2BhzrTFmsTFm8csf/vfwsqyB5KQEpaZnlm+npmeWd1/b699fL9CgXidKkjp3aC1PcbEyc/I044dF6tW1o8LD3EpKiFXXY9po5Xrn9gFHzfnS0ip0V3M1aiTv7sqFcUS3bqp/6aXKmjhRKnbWwPKiHemKaNqwfDuiSVKlblUVYtwuueOi5c3MVfGO3YpomlThtUWpmYo7rbM8W1JVkpEjW+JV5syfFHNSh6AcTzAU79yt8P3OWXiTJBXvyjh4jNsld2x9ebNyFd2lvZrcdYWOmfeyGl51jhqPGamky85STO8uKtqaKm9GjlTiVfaXCxTd7dhgHlbA2ZwMmbh914uJS5LN3Xd/V0SUXI1bKOqKe1Xv1mfkat5WkRePl6tp6/KQsON7OraVp8+oQbp3xmO6d8ZjykrNUOJ+f1sNUpKUfcDfZesT2+uoE1rr4XnP6Y4PH1Ryq6Ya/97k8v0ut0snDuqhxf913vnqf9lgTZ3xhKbOeEKZqRlK2u/vMTElSVm7Miu9JrPs/BXmFWrBpz+odZe2Qcs3VHqMGqAbZjykG2Y8pJzULMXvd03FpSQqNzXroK8t8RRr9ZwlOmZAN0lS3u4cWZ+VtVa/vPeNmnVuE/D8gb9Sk6In2lp74NdDJQcLttZOt9aeZK09afTIsw8W5nfHtT1Km3fs0rbU3SouLtGX835Rn+6dKsSkNGygn39bLUnasG2HiopKlBgfoyYNE7Vw+WpZa5Vf6NFvazaqVbPkoOWO2q949Wq5mzeXKyVFCgtT1JlnyrOg4geEsLZtFXv77cqaOFE26+D/QNRVecvWKrJVE0W0aCwTHqbEc3src9aiCjFZsxap4ci+kqTEs3oqd/5ySVLmrEVKPLe3TESYIlo0VmSrJspbulZFf6Yp5sT2peNTJMX17qSCtc75Zj7/17WKOLqpwpsny4SHKWHY6cqZXfF2mjP7ZzUY0U+SFD+0l/Ys+E2StP6CO7Wq92it6j1au1/9TLue+1Dpb3yhou1piu56jExUpCQpplfnChMjOIFv+3q5klJkEhpJbrfcx5+qktX7JnOQp0D5j16rgqduUcFTt8i3bZ087z4u3/aymbiMkfu4k1Wywpmza3375ld6YOgEPTB0gpbNWqRTylpHW3dtp4LcfGWnVbz/fPfWLE04+Trd1XuMHhl5j1I3btfjF00u339s707asWG7Mnc6a2yYJM1540tNGjpOk4aO0y+zFqr3iD6SpDZd2ys/N79S0eNyuxTToHRMqzvMra79TtK21VsOfFvHWfjmbE0bOlHThk7UqlmL1eW80yRJzbu2VWFugfYccE1FREeWj/NxuV1q17eL0tZvl6QK43+OHXSSdq1xzj0ddVNNZm/bbYxpo/JeKuZ8STsCktVhCHO7NXH0hbrhgWfl9fk0vN+patuyqZ5793N1bHOU+vbopPFXjND9z7+tNz+fK2OMHrx5lIwxumjI6brn2Td13q1TZK3VuWeeqvZHNw/1IYXMhPv+rkVLf1NWVo76Db9UN149SiOGDTr0C53M61Xu00+rwWOPSS6XCmfOlHfTJtW/8kqVrF4tz4IFirnhBpl69RR///2SJF9qqrImTQpx4n7k9WnL3S+pwzv3SS6Xdr//tQrXbFXT8Rcr/9d1ypq9SGnvzVHrZ27VCfOeV0nWHm248QlJUuGarcr4fIGO/+afsl6vtkx6SfL5lLd0rTK++FEdv3pCtsSn/JUblPb2LElS46vOUpMbhyu8UQMdN+cpZc/9RZsmPB/KM1BzXp+23/uCWr9xv+R2KfODOfKs3aLk2y5RwfK1ypmzUBkfzFaLJ29Xh29flDdrj7bc/OhfvmXBsjXKnjlf7b54SirxqmDlBmW8+2WQDihIfD4VzXhNUaPukoxLJUu/lU3bpvC+58u3faO8+xdAVXAddYxsTkbpRAgOt/ybJTqhb1dN/e6fKioo0msTnivfd++Mx6o1/XSPYb20yOFd2yRp2dxf1LnviXri++dVVDZl9V5TZzyhSUPHKTwiXHe8ea/cYW653C6tnPebvnl3jiSpdae2unX6HYqOr6+u/btrxG0X6s4Bt4bqcAJmzTfL1K5vF9363ZOlU1ZPeLF83w0zHtK0oRMVHh2pS16+Xe6IcLncLm1YsFKL3y6dkGXgXRerScejZK1V1rY0fTbx1VAdSu1Vi8e/OJE5WP/MSoHGtJY0XVJPSZmSNkq61Fq76VCv9az8mrk8qsGV3CrUKdQJGedfFeoU6owta6s/I9GRLDL8oI3WOECbK2JCnUKdcOtrXFPVUWC9oU6hzmht6oU6hTrjgU1vVzEiqfYp+OABR34+rnfBvbXy/Fe7pcdau0FSf2NMfUkua23dXCEQAAAAwBGlJrO3PWSMSbDW5llrc40xDYwxdXi1QAAAAABHgppMZDDEWls+gs1amylpqP9TAgAAABzOWmf+1FI1KXrcxpjIvRvGmHqSIv8iHgAAAABCriazt70l6WtjzL9UOoPbVZJeD0hWAAAAAOAnNZnI4FFjzHJJ/VS6TPaD1tqvApYZAAAAAPhBTVp6ZK2dKWlmgHIBAAAAAL87ZNFjjJlnre1tjMlV2cKke3dJstbauIBlBwAAADgRi5MG1SGLHmtt77L/xgY+HQAAAADwr2rN3maMcRljVgQ6GQAAAADwt2oVPdZan6RfjTEtA5wPAAAAAPhVTSYyaCJppTFmoaS8vU9aa8/xe1YAAACAkzGmJ6hqUvTcH7AsAAAAACBAqlX0GGOGS2oraTlr8wAAAACoSw45pscY87yk2yQlSXrQGHNPwLMCAAAAAD+pTkvP6ZI6W2u9xphoST9IejCwaQEAAAAOZhnTE0zVmb2tyFrrlSRrbb5KFyUFAAAAgDqhOi09xxhjfit7bCS1Kds2kqy1tlPAsgMAAACAw1SdoufYgGcBAAAAAAFyyKLHWru5Om9kjPnRWnvq4acEAAAAOBzr9ARVdcb0VFeUH98LAAAAAPzCn0WP9eN7AQAAAIBf+LPoAQAAAIBax59FD1NZAwAAAKh1ql30GGMeOcRzo/ySEQAAAOB01jrzp5aqSUvPgCqeG7L3gbV2xeGnAwAAAAD+dcgpq40xN0i6UVLr/RYplaRYSfMDlRgAAAAA+EN1Fid9R9JMSQ9LunO/53OttRkByQoAAAAA/KQ6i5NmS8qWdLExxi0puex1McaYGGvtlgDnCAAAADgLi5MGVXVaeiRJxpibJE2WlCpp7/8lK6mT/9MCAAAAAP+odtEj6VZJHay16YFKBgAAAAD8rSazt21VaTc3AAAAAKgzatLSs0HSt8aYLyR59j5prX3S71kBAAAATsaYnqCqSdGzpewnouwHAAAAAGq9ahc91tr7JckYU99amxe4lAAAAADAf6o9pscYc6ox5ndJf5RtdzbGPB+wzAAAAADAD2rSve0pSYMkfSZJ1tpfjTGnByQrAAAAwMksY3qCqSazt8lau/WAp7x+zAUAAAAA/K4mLT1bjTE9JVljTISkW1TW1Q0AAAAAaquatPRcL2mMpGaStknqUrYNAAAAALVWTWZv2y3pkgDmAgAAAAB+V5PZ2x41xsQZY8KNMV8bY3YbYy4NZHIAAACAE1mfdeRPbVWT7m0DrbU5ks5Wafe29pImBCQrAAAAAPCTmhQ94WX/HSrpXWttRgDyAQAAAAC/qsnsbZ8bY1ZJKpB0ozGmkaTCwKQFAAAAAP5Rk4kM7jTGPCIpx1rrNcbkSzo3cKkBAAAADuVjcdJgqslEBtEqnaJ6WtlTTSWdFIikAAAAAMBfajKm51+SiiT1LNveJmmK3zMCAAAAAD+qSdHTxlr7qKRiSbLWFkgyAckKAAAAAPykJhMZFBlj6kmykmSMaSPJE5CsAAAAACezjOkJppoUPfdJ+lJSC2PM25J6SboiEEkBAAAAgL9Uq+gxxhhJqySdJ+kUlXZrG2ut3R3A3AAAAADgsFWr6LHWWmPMf6y13SR9EeCcAAAAAMBvatK97SdjTHdr7aKAZQMAAAAcCXw21BkcUWpS9PSVdL0xZpOkPJV2cbPW2k6BSAwAAAAA/KEmRc+QgGUBAAAAAAFyyKLHGBMl6XpJbSUtl/SKtbakJr/k9yHP/G/ZHWGats4OdQp1QuJHr4Y6hTojq/eYUKdQJ7QY3SzUKdQZGR9uCHUKdUKfoqahTqFOSPB6Q51CnZHjDnUGQN1WncVJX5d0kkoLniGSnghoRgAAAADgR9Xp3tbRWnuCJBljXpG0MLApAQAAAA7nY3HSYKpOS0/x3gc17dYGAAAAAKFWnZaezsaYnLLHRlK9su29s7fFBSw7AAAAADhMhyx6rLUMnQMAAABQZ9VkymoAAAAA/sCYnqCqzpgeAAAAAKizKHoAAAAAOBpFDwAAAABHY0wPAAAAEGzWhjqDIwotPQAAAAAcjaIHAAAAgKNR9AAAAABwNMb0AAAAAMHGOj1BRUsPAAAAAEej6AEAAADgaBQ9AAAAAByNogcAAACAozGRAQAAABBsPhYnDSZaegAAAAA4GkUPAAAAAEej6AEAAADgaIzpAQAAAILNsjhpMNHSAwAAAMDRKHoAAAAAOBpFDwAAAABHY0wPAAAAEGys0xNUtPQAAAAAcDSKHgAAAACORtEDAAAAwNEY0wMAAAAEmfWxTk8w0dIDAAAAwNEoegAAAAA4GkUPAAAAAEej6AEAAADgaExkAAAAAAQbi5MGFS09AAAAAByNogcAAACAo1H0AAAAAHA0xvQAAAAAwWZZnDSYaOkBAAAA4GgUPQAAAAAcjaIHAAAAgKMxpgcAAAAINtbpCSpaegAAAAA4GkUPAAAAAEej6AEAAADgaIzpAQAAAILNxzo9wURLDwAAAABHo+gBAAAA4GgUPQAAAAAcjaIHAAAAgKMxkQEAAAAQbCxOGlS09AAAAABwNIoeAAAAAI7mmO5tsWd0VfPJ18i4XUp/b7ZSn/+4wn4TEaaj/nGbok9oo5LMXG0a85iKtu1S7Gmd1fTOy2TCw2SLS/Tn1Ne0Z8FySVLCsN5KuWmk5HYpZ+5ibX/o9RAcWXBE9Oih2JtuktxuFXzxhfLfeafC/uiRI1XvrLNkvV75srKU8+ij8qWmhijb2uXuh57U9/MXKrFBgv7z1guhTifoont3U/Kk6yWXS9kffamMlz6ssN+EhyvlkXGKOq6dvFk52n77wyr5c5cU5lbKlFsV1bGN5HYr59OvlTH9A0lSwqhzlTBysGSMsj/8Uplv/CcUhxYwrqOPU8SZF0vGpZLlP6hk4cwK+93H9VTEGSNl92RKkoqXfiPv8h9kGrVQxIBLZSKiJGtV/NMX8q5eFIpDCJrIU7or4fabZFwu5X02Q7lvvFthf0SXTkq4bYzC27ZWxj0PqmDu96Wv69ZF8bfeWB4XflRLpd/9oAq/nx/U/IOp24Oj1OzMLiop8OjH26Yrc/mmSjGd7xipViN7KyK+vj5oN7r8+RMnX6LkXh0lSWFREYpqGKcPj70uWKmHVMepl6txvy7yFhTp11umKaeK89bhrgvUbOTpCk+or69aXxn8JEPkxAcvU9MzO8tbUKSfbnuxymuq0x0jdfTI0xQRX18ftbu6wr4Ww07WCeNGSNYq8/ct+nHMc0HKHKjMGUWPy6UWU67TukvuU/GOdHX4/HFlz16owrVby0OSLhwgb/Ye/X769UoYdpqa3nW5No15TCUZOVp/1VSVpGYoqn1LtXlrslb2uEruhFg1m3iFVp91u0oyctTyybGK6dVJe+b/FrrjDBSXS7Fjxypr/Hh509KU+MIL8syfL+/mzeUhxWvXKv+66ySPR/XOOUex112n7AceCGHStcfwoQP0txHnaOKDj4c6leBzuZR87xhtu2qiilN366gPn9aeuT+raP2W8pD48wfKl7NHGwddrdihZ6jRuKu04/a/K3bwaTLh4dp0zo0yUZFq9cWLyvniW7mi6ylh5GBtvuBW2eJiNX9pivZ8t1DFm7eH8ED9yBhF9L9Eng+flM3NVNSld8u7fpls+o4KYSWrF6n464pfPqikSEUzXpHN2iVTP15Ro+5RwaYVkqcgiAcQRC6XGkwYq7SbJ8i7K02NX5umgh8WqGTjvnuTNzVVmQ8+ophLLqjwUs8vy7Rr1LWSJBMXqyYfvSnPz4uDmn4wNT2zs+JapeizXuOUdGIb9Xj4Cn119uRKcdtmL9Hqf83WOfMr3q+WTH67/HH7qwYo8fijA5xx7dCoXxfVb5Wib0+5TQnd2ur4R6/WgiH3VIpLnbVEm16ZpT4//SMEWYZGkzM7K7ZViv7ba5ySTmyrkx6+UrPPvq9S3J+zl2rNv2br7PlPVHg+plWyjrv5HM0+d7KKs/MVmRQXrNTrDsvipMHkiO5t0V3aybNpp4q2pMoWlyjz8x8UP7BHhZj4gScr/aO5kqSsGfMV26uTJKlg5UaVpGZIkgrXbJErMlwmIkyRLZPl2bhdJRk5kqTceb8qYcipQTyq4Ak/5hh5//xT3h07pJISFc6dq8hevSrEFC9bJnk8pY9//12uRo1CkWqtdFKXExQfFxvqNEIiqlN7FW/ZruJtO6XiEuXO+E4x/U6pEBPT71Rl/2eOJCn3qx8UfWqX0h3WyhUdJbldMlERssXF8u3JV0TrFir4dZVsoUfy+lSwaLli+/cM9qEFjCullWzmLtns3ZLPq5JVC+Vu06Var7WZqbJZu0of52XL5ufK1HPutRfR8RiVbPtT3u2l96aC2XNV7/SK14J3R6qK1234y5XNo888XYU/LpQtu4c5UfNB3bTho3mSpPQl6xURX19RjRMqxaUvWa/CXVl/+V5HDz9Vm/7zY0DyrG2SB3fTnx/+IEnK+mWdwuOiFVnFecv6ZZ08hzhvTtN8UDdt+qj03KQvWaeI+OiDXFPrqrym2l5ypta8NlvF2fmSJE96TmATBg7BEUVPREqSirbvLt8u2pGu8OSkCjHhKYkq3hvj9cmbmyd3g4ofFhKG9lTByo2yRSXybN6hyDbNFNG8seR2KWHgyYpo2jDgxxIKrkaN5EtLK9/2paXJ/RdFTb2zzlLRwoXBSA21XFhyQxXv2HftlOzcrbAD/vbCGiepZMe+vz1fbr7cCXHK/WqefPmFavPDO2oz9w1lvPqJfNl7VLR2s6K7Hy9XQqxMVKTqn9FdYU2cU2Sb2AayuZnl23ZPpkxsg0pxYe1OVNTlkxVxzvVV7neltJLcYbJZaZX2OYW7cUN5U3eVb3t37f7Le9PB1BtwpvJnzfVnarVOdEoD5W9PL9/O356h6JTK182h1G+WpJgWjZU6b6U/06u1opokquDPfeetcEeGopokhjCj2qNeSqLyDuOaim2dorjWTdT/0/s04PP71aRPp0CkCVRbtbu3GWMaSbpG0tH7v85ae5X/06ohU8Vz9oBpAE0VQfuFRLVvoaZ3XaZ1l06WJHmz87R10gs6+rkJks+nvF9WKaJlit9SrvUOPH9logYMUFiHDsocOzbICaHOOPDSqeJvz8qq3gkdJJ9P60+/RO64GLV8+3HlL1iqog1blfHSh2rxykPy5RfIs2qDbIk3OLmHygF/b971v6pg1ULJW6KwzmcoYshV8nywX9eR+vGKGHq1ima+qson3Emqum/X7HhdSYkKb9NKhT85e+xT1f/G1fzaOGr4qdryxULZI2QqXVPFNWb/h/PmSFVeUtU/N8btVkyrZH09YoqimySq/7/v1Ywz71BxTr4fkwSqryZjej6V9IOkOZIO+QnEGHOtpGslaVKDThoRc/T/kl+1FO1Ir9AKE9EkScW7MirEFO9IV3jThiremS65XXLH1pc3K1eSFJ6SpFbT79Lm255S0ead5a/JmbNIOXNK/6FM+ttA2b/oPlGX+dLSKnRXczVqJO/u3ZXiIrp1U/1LL1XG2LFScXEwU0QtVZK6W+H7tcKEpTRUya70SjFhTRqqJHW35HbJFRstX1auYs/uo7wfFkslXnkzslWw5HdFHd9Oxdt2KvvjWcr+eJYk6f+zd99xUlXn48c/Z2cbZWlL2aWoiNgVxRrFBmJLLImabxJNrDGxd2NijSYmscWYGNtPjWkmUZOoUVTELiqg2EWaCAgsS9lC2TZ7fn/MCiyg7urOLDt+3q/XvGTmnjvz3HHvmXnmnPPc3uceR8OCdf8eO6pY3XxkJ3TtSVy21tSQmuWr/tnw1vPk7X3k6m35hRR+6yzqX/wPjfNnpjvcdpVcWE6iX99V9xN9e6+3b/osnfffl5XPvQjJ7EucNz9+f4Ycsx8AS96YSef+q0dZO/fvxYqy1k/H2vjw3Zn4s+wt2gOw8QmjGXTsSAAq35hJpwHFfDL2Wljai9oFSz995yw39PjRq/6mFr8xky79i/nkjOvcvxcrW/E3tWL+Eha/Pp3YkGT5nHKqZsyjaHAJS97M7n6rVb4iPy5sKFozva1zjPEnMcZ/xRgf/OT2aY1jjHfEGHeOMe6czoQHYMWb0ygYXEr+oL6EvFx6HroXlWObT7+qHDuB4qNSnVyPQ/akenyqIEGiWxeG/Oky5v3mLyyfNKXZPrnF3VNtuneh9/cPZvF9Y9N6HO2l/oMPSAwcSE5JCeTmUjhyJLXjxzdrk7vZZhSddx4VP/sZseKrNa9Zn67m7ankbdyfvAH9IC+XokP2YdnTrzRrs+zpV+h+xP4AFB24FyteeROAhvnldN59GAChUwGFw7akbmaq+EiiV+rcyy3tQ9fRe1L16HOZOqS0a1wwi9CzH6F7b8hJkLvlriRnvNm8UZfuq/6ZGLIDjZ8UOchJUHD46TS8+zLJqa9lMOr2Uff+FHIHDSBRmuqbOo0eycrnW7fWpNMBI1mZpVPbpv7pKcaMvoQxoy9hzuOvselRIwAoHj6EuqoVn7t2Z21FQ0rJ796FRZOmpSPcDcZH94zlxVE/5cVRP6VszCQGHL0XAD122oyG6hVfubU7a5r2p7E8PvpnPD76Z3z8+CQ2OSr13hQP34z6qpWt+pv6+PFJ9NtjKwDye3WlaEgpy2Yv/Jy9pPRpzUjP/0IIh8QYH0tbNF9UspG5l93BkL9cmSpZ/c9x1EydQ8l532PF29OpGjuBxf8cy8Y3ncvWz99GQ0U1s85IVa7pfdwh5G9SSslZ36bkrFT1nxnHXknD4koGXnkyhVsPBmDBTf+k9sMsqR61tmSS6t/9jp7XXQc5OdSMGUNy1iy6nHACDR98QO348XQ99VRCp050//nPAWgsK6PikkvaOfANw4VX/JqJk9+ioqKKUUccy2knfZ8jDz2wvcPKjGQjC6++ixJQvwAAIABJREFUlYF3/QJyElQ++CR102dTfOb3qXlnKsufeZXKB56g9NoLGfzEXSQrq5l/3q8BWPr3Ryi95jw2eeS2VGnqfz9J7dRZAPS/+VISPboRGxpYeNUfaaxa1o4H2cZiI3Xj/k7BkedATg4Nb79EXDyPvD0Pp3HBLJIz3iRv+CgSQ4ZBYyOxZjl1j98DQGKLXcgZOJTQqQu526YW9NeOuYdYPuezXrHjSjZScf3v6X3zbwg5CZY/MoaGD2fR7ZTjqXt/KjUvjCdvqy0ovvYqcoq6UrjX1+j2w+Mp+25q1nWitB+5fftS+/qbn/NCHd+8cW8wYNQwDht/A8mVdbx87h2rth089peMGZ3qr3e89DtscsQe5HbK55uTbmb6fc/y9g3/BlIFDD566JX1Pn+2WvjUZPqM2oF9X72J5Mpa3jr79lXbRoz7FS+O+ikAW172Pfp/aw8SnfIZOfkPzPnbM0y7/lN/980K88a9QemoHfjG+BtJrqzj1XNXvzcHjb2Gx0f/DIAdLv0uGzf9TR0+6ffMuO8Z3rnh38x/9i1K9tmOQ569lphs5I2r/07d0izqy9XhhJbOzwwhVANdgLqmWwBijPFzaxBO3uhwx+9aoP+mle0dQofQ64G72zuEDmPmiNPbO4QOYdDJA9o7hA5jyf1OTWmJ5+b0b+8QOoQeWTjtMF2qEllReyojvjvvb+tb7b3BWX7J0Vn5/bjLL+/fIN//Fo/0xBizty6qJEmSlEHZulZ8Q9Xinw1CyrEhhMua7g8KIez6eftJkiRJUntqzVjpH4GvAd9rur8MuKXNI5IkSZKkNtSaQga7xRiHhxAmA8QYl4YQ8tMUlyRJkiS1idaM9NSHEBI0XQmv6WKlTkaUJEmStEFrzUjPzcB/gL4hhF8CRwGXpiUqSZIkKZt5cdKMak31tr+FEF4DRpEqV31EjPH9tEUmSZIkSW2gxUlPCOF3wD9jjBYvkCRJktRhtGZNz+vApSGE6SGE60IIO6crKEmSJElqK62Z3nYvcG8IoRdwJPCbEMJGMcahaYtOkiRJykau6cmo1oz0fGIzYEtgE2BKm0YjSZIkSW2sxUlPCOE3IYRpwFXAu8BOMcZD0xaZJEmSJLWB1pSs/hD4WoxxUbqCkSRJkqS29rlJTwhhyxjjFGACsFEIYaM1t8cYX09XcJIkSVJWio3tHcFXSktGes4DTgFuWM+2CIxs04gkSZIkqQ19btITYzwlhJADXBpjfCkDMUmSJElSm2lRIYMYYyNwfZpjkSRJkqQ215pCBk+GEI4E/h1jtLC4JEmS9EV5nZ6Mak3Scx7QBUiGEFYCAYgxxm5piUySJEmS2kCLk54YY1E6A5EkSZKkdGjRmp4QQm4I4dAQwoVNt6+HEFozSiRJkiRJhBAOCiF8EEKYHkK4+DPaHRVCiCGEnb/sa7bkOj39gWeA+cBkUtPavgHcGELYL8Y478sGIUmSJH2VxK/omp4QQgK4BRgNzAUmhhAejjG+t1a7IuAs4NW2eN2WjNZcA9waY7xprUDOAn4FHNcWgUiSJEnKersC02OMMwFCCP8ADgfeW6vd1cC1wAVt8aItmd62+9oJD0CM8WZg97YIQpIkSdJXwgBgzhr35zY9tkoIYUdgUIzxf231oi1JelZ+xrYVbRWIJEmSpI4thHBKCGHSGrdT1m6ynt1WzfULIeQAvwXOb8u4WjK9rXsI4VvreTwAlquWJEmSBECM8Q7gjs9oMhcYtMb9gcCaNQKKgG2BZ0MIACXAwyGEw2KMk75oXC1Jep4DDv2Ubc9/0ReWJEmSvrK+ooUMgInA0BDCYOBj4DvA9z7ZGGOsBHp/cj+E8CxwwZdJeKAFSU+M8YSWPFEI4bgY471fJhhJkiRJ2SvG2BBCOAN4AkgAd8cY3w0hXAVMijE+nI7Xbctr7ZwNmPRIkiRJ+lQxxseAx9Z67PJPabtvW7xmiy5O2kLrW5QkSZIkSe2qLUd6vrITEyVJkqRWaWxs7wi+UhzpkSRJkpTV2jLpeakNn0uSJEmS2kSLk54QwtkhhG4h5a4QwushhAM+2R5jPCM9IUqSJEnSF9eaNT0nxhh/F0I4EOgDnADcAzyZlsgkSZKkbPXVvU5Pu2jN9LZP1uwcAtwTY3wT1/FIkiRJ2sC1Jul5LYTwJKmk54kQQhFg2QlJkiRJG7TWTG87CdgBmBljXBFCKCY1xU2SJEmSNlgtTnpijI0hhAZg7xDCmvu91fZhSZIkSVnMNT0Z1eKkJ4RwN7A98C6rp7VF4N9piEuSJEmS2kRrprftHmPcOm2RSJIkSVIatKaQwcshBJMeSZIkSR1Ka0Z67iWV+CwAakmVq44xxu3TEpkkSZIktYHWJD13A98H3sZS1ZIkSdIXFqOFDDKpNUnP7Bjjw2mLRJIkSZLSoDVJz5QQwt+BR0hNbwMgxmj1NkmSJEkbrNYkPZ1IJTsHrPGYJaslSZIkbdA+N+kJIQyMMc6NMZ6wnm2HpicsSZIkKYt5cdKMaknJ6nEhhE3WfjCEcAJwU1sHJEmSJEltqSVJz7nA2BDC0E8eCCH8FDgP2CddgUmSJElSW/jc6W0xxsdCCLXAmBDCEcDJwC7A3jHGpekOUJIkSZK+jBYVMogxjgshHA88C4wHRsUYa9IYlyRJkpS9XNOTUS0pZFBNqkpbAAqAUcDCEEIAYoyxW3pDlCRJkqQvriXT24oyEYgkSZIkpUNLChlIkiRJUofVmouTSpIkSWoD0TU9GZWRpKestlMmXqbDa5jmwFtLVIw4vb1D6DA2ffGW9g6hQyg/7KT2DqHDmDqrT3uH0CFs37myvUPoELp0rW3vEDqM2Qu7t3cIUofmt2xJkiRJWc2kR5IkSVJWM+mRJEmSlNUsZCBJkiRlmoUMMsqRHkmSJElZzaRHkiRJUlYz6ZEkSZKU1VzTI0mSJGVaY3sH8NXiSI8kSZKkrGbSI0mSJCmrmfRIkiRJymqu6ZEkSZIyLHqdnoxypEeSJElSVjPpkSRJkpTVTHokSZIkZTXX9EiSJEmZ5pqejHKkR5IkSVJWM+mRJEmSlNVMeiRJkiRlNZMeSZIkSVnNQgaSJElSpjW2dwBfLY70SJIkScpqJj2SJEmSsppJjyRJkqSs5poeSZIkKcOiFyfNKEd6JEmSJGU1kx5JkiRJWc2kR5IkSVJWc02PJEmSlGlepyejHOmRJEmSlNVMeiRJkiRlNZMeSZIkSVnNNT2SJElShnmdnsxypEeSJElSVjPpkSRJkpTVTHokSZIkZTWTHkmSJElZzUIGkiRJUqZ5cdKMcqRHkiRJUlYz6ZEkSZKU1Ux6JEmSJGU11/RIkiRJGRZd05NRjvRIkiRJymomPZIkSZKymkmPJEmSpKzmmh5JkiQp01zTk1GO9EiSJEnKaiY9kiRJkrKaSY8kSZKkrOaaHkmSJCnDvE5PZjnSI0mSJCmrmfRIkiRJymomPZIkSZKymkmPJEmSpKxmIQNJkiQp0yxkkFFZlfRs9cvj6D1qRxpX1vL2WbdS9fasddp0234w2918KjmF+SwaN5n3L7kXgKJtNmab604mpyCP2JDkvYvvpnLyDHKLOrH9H8+gcEBvQiKHWbf+j4//8VyGj+zL6bbvjmx01UmEnBzK73uKBbf8u9n2kJ/Lpr87m87bDaFhaTUzTr2eurnlAJSe8S16f2d/YmMjsy/7f1Q99wYA/X54KH2+uz8xwsopH/Hheb8n1tbT9/iD6XfyoRQOLmXytj+gYWl1xo+3rXQesRP9Lvkx5ORQ+cDjLLnz/mbbQ14eJb85n8JthpKsqGLeeb+i4eOFkJug5BfnULj1EEgkqHpoHEvu+BcAPb5/OD2OPghCoPL+x1n65/+2x6G1i0uvuZHnX5pAr549+O9fb2vvcNpVwW670P2cMyCRYMUjj7LsL/c1256/w/Z0O/t08oYMYekVV1HzzPOrtnU77UcU7LE75ARqJ75G1W9/n+nwM2LoL0+guKk/f++sP7Ls7Q/XaVO0/WC2uvl0cgrzWTxuMtMuuQeAwRccTf9jR1G3uAqAmdfcx+JxkynacQhbXv+j1M4BPrzufhaNmZixY2prXfceTv8rfgg5OSz951jKb3ug2faQn8vAG86j07ZDSFZUM/uMa6n/eCF5A/qy+VN/pHbmxwCsmPwB8y79Y2qfvFz6//xHdNl9O2JjpOz6v1D1+PiMH1s6ddpzZ4p/ciohkUPVvx+n8q5/NtteuNN2FF/0Y/I335SFF13D8rEvAJC/xab0vuwscrp0JjY2UnHHfSx/omN9H2itzX55AsWjhpNcWcuUs25Z73nYdftN2fLm00kU5rN43OtMbzoPN7ngaEqP3Z/6Vefh31kybnJG45fWlDXT23qP2oHOg0t5YfdzeOeCO9n62pPX227ra0/i3Qvu5IXdz6Hz4FJ6j9wBgC0uP4bp1z/I+FEXM+3a+9nismMA2OjEA1n2wceMH/kTJnzrKra48vuEvETGjutLy8lh41+ewrRjr+ad/c6i+IgRFA4d2KxJ7+/uT0Plct4ecRpldz7CoEt+AEDh0IH0OnwE74w8i6nHXMXG1/wIcnLIK+lFvxO/zruHXMi7o84mJHLodfgIAJZNnMIH37mC2jkLM36obSonh36Xn87cH17Gh9/4EUVf35f8IRs1a9L9qANorFrGhweexNJ7/0uf808EoOigvQh5ecw67DQ+OvIsevzfIeQO6Ev+0I3pcfRBfPTtc5h1xGl02XdX8jbu3x5H1y6OOGQ0t934i/YOo/3l5ND9grNZfP7FLPze8XTafxS5m2zcrElyQRkVv/gNK8eOa/Z43rbbkL/9tpT/4CTKjz2R/K22IH/HYZmMPiOKR+1I58ElvLL7WUy54A62+JT+fItrf8iUC27nld3PovPgEno19ecAs29/lImjLmLiqItY3PRFa/mUOUw64GImjrqIN79zDVtefwoh0UE/BnNy6H/Vj/nw+CuZdsDpdD9sbwo2G9SsSc9vH0CychlT9/sRi+56iJKLj1+1re6jBUz/+tlM//rZqxIegD6nf5uGxZVMHfljpo0+jeWvvpOpI8qMnBx6X3IGC067hDmH/5CuB+9L3qbN+/aG+Qspv+x6lj32dLPHY00tC392LXO/eQoLfvwzin/yY3KKumQy+ozqNWpHOg0u5dXdz2TqBbez+bU/XG+7za/9IVMvuJ1Xdz+TToNLm52Hc2//H5NGXcikURea8Kjdtai3DyEkQgjnpjuYL6PfQTsz7/7Ur6GVr00nr1tnCvr2aNamoG8Pcrt2omLSNADm3f88/Q7eObUxRnKLOgGQ160zNWVLVz/etRCA3C6F1FcsIzZ0nPHILjsOpXbWfGpnlxHrG1jy0Iv0PHDXZm16HrAri+5/BoAlj46naMT2qccP3JUlD71IrGugbs5CamfNp8uOQwEIuQlyCvMhkUNOpwLqFywBYMW7H64aJerICrffnPrZ86ifuwDqG6h+7Dm6jtq9WZuuo75G5X+fAqD6iRfo/LWmjj5GcjoXQiKHUJhPrK+ncdkK8jcdxMo3pxBraiHZyMqJb1O0/x6ZPrR2s/MO29G9W1F7h9Hu8rbekoa580jOmw8NDax86mkK99qzWZvkgjIaZsyExrX7mgj5+ZCbS8jLg0QujUuWZi74DOl90M4saOrPq16bRm63LuSv1Z/n9+1Bomsnqpr68wX3P0+fg3f5zOdtXFlHTKbe05zCPIgxDdFnRudhQ6n7aD71c1J9e+Ujz9Nt9G7N2nQbvRsVD6YS58oxL9F1j89PkHsevT8L/9g0qh0jyaVVbR57eyrYbgvqZ8+jYe4CaGhg+Zjn6LJf8364YV4ZdVM/XOfvo/6jj2mYPQ+AZPkSkksqyOnZPWOxZ1rvg3ah7P7USNZnnYe5XTtRNWkqAGX3P0fvg3dd57mkDUGLkp4YYxI4PM2xfCkFpb1Y+fHiVfdr5i+hoLTXOm1q5i9Z3Wbe6jbvX3YvW1x+DPu8fgtbXHEsU3+Zmm7y0V1P0GXzAez71q3s+ex1TLn03g71QZlf0ou6eYtW3a+bv5i8kuJmbfJKile3STaSrFpBbs+ipscXN9s3v6QX9QuWsOC2hxg24Q52mHw3yarlVD3/ZkaOJ1Ny+/Wmfv7q5K1hwSJy+zV/33L7FtMwf/X71li9gkSPblQ/8SKNK2oY8sLfGfL0n1ly979prFxG3bSP6LzLtuT0KCIUFtBln13ILe2TycPSBiDRpzfJstUjocnychJ9erdo3/p33qPu9cmUPPIg/R55gNoJE2n4aHa6Qm03BaW9qPl4db9VO3/xevvz2vlr9PnzmrcZeOKB7PrMdWx506nkdl/9a3y34Zux63M3sOuzNzDlwjtXJUEdTW5JMfXzV79H9QvW07f3K6ZujT4qWb2cRM9uAOQP6sdm/7uJwf/4FZ132Rpg1ahFyXnHstkjN7HRLT8ht3fzL7kdXW7f3jQsWKNvLysnsVbf3hIF225ByMujYc78tgxvg1JQ2ovaNb5XteQ8rF3rPBxw4kHs/Mz1bLHWeaiU2Jidtw1Va8b1Xwoh/CGEsFcIYfgnt7RF1hbWTk7Cp7fZ6PjRTLn8zzw3/HSmXP5ntv1tat537/2GUf3ORzy7/amMH/kTtvrVCSS6dkpz4G0orOeg13pf1tvkM/ZNdO9CjwN35a3df8ybw08ip3Mhxd/ap03C3aCtneuu5/2JRDpttwU0NjJj72OYuf/x9DrhW+QNLKFu5hyW3Hk/g+66hoF3Xk3tlJnEhmRmYtcGZD1/Ny38ISUxoD+5m2xM2RFHU3b40RTstCP5O2zf1gFuAD6/3/r0jgvm3vskL+92JhNGXkRd2VI2+/kPVjWpen06E/Y5n0kH/pRNzv4mOQV5bRh3Bq2v/2nRexRpKF/ClD1PZPo3zmH+L/4fg266gJyunQi5CfL792H5a+8z/dBzWPH6FEp+dmKaDqCdfMb3gJZK9O5Fn2suovyy6zvUj6Ctt76/sbWbfPrf4cf3Pskru53JpJEXUldWwZA1zkOpPbQm6dkD2Aa4Crih6Xb9pzUOIZwSQpgUQpj02MoZXy7KT7HRCQewx7hfs8e4X1NbtpROA1b/WlNY2ovaBc2nfdTOW0LhGr9AFPZf3ab/t/eh7NEJACx4+BV67DgEgAHfWf34illlrJy9kK5DO846jLr5i8nvv/pX5PzSYurLlnx6m0QOiW6dSS6tpn7+IvL7Fzfbt65sKd32Gkbt7DIallQRG5IsHfMKXXfeIiPHkykNZYvIW2MUJrekNw0LF6/TJrd09fuWU9SZxopqir6xL8tfmAQNSZJLKln5+nsUbpuaFlj54JN8dOSZzPn+RSQrq6n/6OOMHZM2DMnychL9+q66n+jTh8ZFiz9jj9UK99mLunfeI66sIa6soeblCeRvs3W6Qs2oASccyC7jrmWXcddSV7aUwgGr+62C0uL19OeLKShdo8/vX0xt0zTb+vJKaIwQI/P+Oo5uTf35mlZM+5jkihq6bDlonW0dQcP8ReSVrn6P8kqKaVirb69fsIj8NfqoRFEXkhXVxLoGkhWpIjM178ygbvYCCgYPILm0isYVNVQ98TIAlY+9RKdt1n3vOrKGskXklqzRt/frQ3Lhks/Yo7nQpTMlt1zN0j/8idq3pqQjxHbV/4QD2Xncdew87jpqy5ZQsMb3qoLSYuoWNH+v1j4PC/oXU9d0rqbOw0aIkfl/fYpuO26WmYOQPkWLk54Y437ruY38jPZ3xBh3jjHufEin9HSas+95kvGjLmb8qItZOGYS/Y/eG4DuO21GffUKahdWNGtfu7CC5LIauu+UOvH6H703ZY9PSm1bsJRee6S+PPTaa1uWz1wAQM3Hiynea1sA8vt0p8uQ/qz4qOMs0l/+xjQKBpeSP6gvIS+XXoePYOmTzasVVTw5kd5H7wdAr6/vQfVLbwOw9MmJ9Dp8BCE/l/xBfSkYXMryydOo+7icrsM3T63pAbqN2J6V0+Zm9sDSrObtqeRt3J+8Af0gL5eiQ/Zh2dOvNGuz7OlX6H7E/gAUHbgXK15JTfFrmF9O591Tc+dDpwIKh21J3cw5ACR6peZ/55b2oevoPal6NLsr/2hd9e9PIXfgABKlJZCbS6f9R1LzYsuqYyXLFlKw4zBI5EAiQcGOw6if9VGaI86Mj+95YlXhgfIxEyhp6s+77TSUZPUK6tbqz+sWVpBctpJuO6V+UCg5em8WNfXna6476HPIriyfkjr/Cjfqs6pwQeHA3nQe0p+aOR1zDeKKt6ZRsEl/8gb2I+Tl0v3Qval6akKzNlVPvUqPI0cB0P3gPVn28lsAJHp1g5zU+5A3qB8Fm/SnbnbqM69q3AS67L4dAF32GEbt9OyaPln7zgfkbTyA3AGp86/Lwfuw/NmXW7Zzbi4lN11B9SNPsfzJF9IbaDuZd88TqwoPLBozkX5Hp2ZxdNtpKA2fch42rHEe9jt6HxY9nvqOseZ52HuN81BqL6Gl0ypCCP2Aa4D+McaDQwhbA1+LMd71efs+3u87GRn/3epXJ9Bn5A4kV9by9tm3UfXmTAD2GPdrxo+6GIBuwzZlu5tPJVGYT/m4N3j/Z6nSij123YKtfnEcITdBY2097/3kLqre+pCCfj3Z7uZTKejXA0Jg5s0PMf/BF9MSf3FubVqet/vI4Wz085MgJ4dF/xzH/JsfoP8F32XFm9OpGDuRUJDHpjefQ+dtBtNQsYyZp91A7ewyAErPOore/zeKmEwy54q7qXzmdQD6n/8deh22J7GhkRXvzmTWBbcQ6xroe+LXKT3tCPL69KR+USWVT7/GrAv/+FnhtVq3opo2fb5P02XvXej7s1MgJ0Hlg0+y5PZ/UHzm96l5ZyrLn3mVkJ9H6bUXUrDVEJKV1cw/79fUz11A6FxI6TXnpaq9hUDlv59k6d0PAjDor9eR6NGN2NBA+a/vZMUrb6T1GDZ98Za0Pn9rXHjFr5k4+S0qKqoo7tWD0076PkceemB7hwVA+WEnZfT1Cr62G93PPh0SOaz43xiW3fs3ik4+gbopH1D74njyttqCXr+6mlDUFerqSC5eSvmxJzRVfjsnNaUtRmpfnUjVzW17fn2eKTMysw5t81+dRPHIYSRX1vH+2X+kuqk/32XctUwcdREARcM2ZaubT2sqlfsGU392NwBb/+EMum67CcTIyjnlfHDBHdQtrKDkqL3Y6MwjUtNKGxv58MYH01ayuk/nlWl53jUV7bsTpZc3lay+/ynKb/kXfc89hpVvT6P6qQmE/DwG/fY8CrfelGTlMmafeS31c8rodtAe9Dv3GGIyCclGym76G9XjUu9D3oA+DLrxPHK6dSG5uIq5F/2O+nnpSwy7dE3P595n6bTXLhRflCpZXf2fJ6i48z56nv4Dat+dyopnX6Fgm83p97sryCkqItbVkVy0hLnfPIWu3xhFn6vOp27G6h8ayi+9jroPZmYk7tkLM180YeivTqLXyB1Irqzjg7NvWXUe7jzuOiaNuhBInYdbNpWOXzLuDab9LPW1cMs/nLnqPKyZU87UC25fJ2lKl33L7l/fRMYNzsJR+2Tl/Mi+457bIN//1iQ9Y4B7gEtijMNCCLnA5Bjjdp+3b6aSno4uXUlPtslU0pMNNqSkZ0OW6aSnI8tU0tPRZSLpyQbtkfR0VO2R9HRUJj3ta0NNelqzpqd3jPFfNF0/NsbYALgKW5IkSdIGrTVJz/IQQjGrCnuF3YHKtEQlSZIkSW0ktxVtzwMeBoaEEF4C+gBHpSUqSZIkKYttyNe0yUYtTnpijK+HEPYBtiBVvP2DGGN92iKTJEmSpDbQmpEegF2BTZr2Gx5CIMb45zaPSpIkSZLaSIuTnhDCX4AhwBusLmAQAZMeSZIkSRus1oz07AxsHVta41qSJEmSNgCtSXreAUqA+WmKRZIkSfpqiBvk5WyyVmuSnt7AeyGECcCqq4nFGA9r86gkSZIkqY20Jum5Ml1BSJIkSVK6tCbpGQK8EGOclq5gJEmSJKmttSbp2QQ4NoSwMfAa8AKpJOiNdAQmSZIkZSsvTppZOS1tGGO8PMY4EtgWeBG4kFTyI0mSJEkbrNZcp+dSYE+gKzAZuIDUaI8kSZIkbbBaM73tW0AD8CjwHPBKjLEmLVFJkiRJUhtpcdITYxweQigCRgCjgTtDCGUxxhFpi06SJEnKQrHR6/RkUmumt20L7AXsA+wMzMHpbZIkSZI2cK2Z3nYT8CxwCzA5xrgsLRFJkiRJUhv63OptIYTcEMK1wA6k1vXcDHwYQrg2hJCX7gAlSZIk6ctoyUjPdUARMDjGWA0QQugGXN90Ozt94UmSJEnZx+v0ZFZLrtPzDeCHnyQ8ADHGKuBU4JB0BSZJkiRJbaElSU+MMcb1PJgE1nlckiRJkjYkLUl63gsh/GDtB0MIxwJT2j4kSZIkSWo7LVnTczrw7xDCicBrpEZ3dgE6Ad9MY2ySJEmS9KV9btITY/wY2C2EMBLYBgjAmBjjuHQHJ0mSJGWjGL04aSa1+Do9McangafTGIskSZIktbmWrOmRJEmSpA7LpEeSJElSVmvx9DZJkiRJbcOLk2aWIz2SJEmSsppJjyRJkqSsZtIjSZIkKau5pkeSJEnKsNjodXoyyZEeSZIkSVnNpEeSJElSVjPpkSRJkpTVXNMjSZIkZViM7R3BV4sjPZIkSZKymkmPJEmSpKxm0iNJkiQpq5n0SJIkScpqFjKQJEmSMsyLk2aWIz2SJEmSsppJjyRJkqSsZtIjSZIkKau5pkeSJEnKMNf0ZJYjPZIkSZKymkmPJEmSpKxm0iNJkiQpq7mmR5IkScqwGNs7gq8WR3okSZIkZTWTHkmSJElZzaRHkiRJUlZzTY8kSZKUYV6nJ7Mc6ZEkSZKU1Ux6JEmSJGW1jExvm5XvLLqW6J9Y3t4hdAiDTh7Q3iF0GOWHndTeIXQIfR6+q70u5Py+AAAgAElEQVRD6DDe3+an7R1ChxCCtWhboqqqsL1D6DA6JZLtHYLUoTnSI0mSJCmrOQQjSZIkZViMFjLIJEd6JEmSJGU1kx5JkiRJWc2kR5IkSVJWc02PJEmSlGGxsb0j+GpxpEeSJElSVjPpkSRJkpTVTHokSZIkZTXX9EiSJEkZ1uh1ejLKkR5JkiRJWc2kR5IkSVJWM+mRJEmSlNVc0yNJkiRlWHRNT0Y50iNJkiQpq5n0SJIkScpqJj2SJEmSsppJjyRJkqSsZiEDSZIkKcNio4UMMsmRHkmSJElZzaRHkiRJUlYz6ZEkSZKU1VzTI0mSJGVYjO0dwVeLIz2SJEmSsppJjyRJkqSsZtIjSZIkKau5pkeSJEnKMK/Tk1mO9EiSJEnKaiY9kiRJkrKaSY8kSZKkrOaaHkmSJCnDGqNrejLJkR5JkiRJWc2kR5IkSVJWM+mRJEmSlNVMeiRJkiRlNQsZSJIkSRkWLWSQUY70SJIkScpqJj2SJEmSsppJjyRJkqSs5poeSZIkKcNibO8Ivloc6ZEkSZKU1Ux6JEmSJGU1kx5JkiRJWc01PZIkSVKGNXqdnoxypEeSJElSVjPpkSRJkpTVTHokSZIkZTXX9EiSJEkZFl3Tk1GO9EiSJEnKaiY9kiRJkrKaSY8kSZKkrGbSI0mSJCmrWchAkiRJyrAY2zuCrxZHeiRJkiRlNZMeSZIkSVkta6e37fnz77PRyB1oWFnLM+fdwaJ3ZjXbnluYz+jbzqLbxn2JyUY+emoyr/76n83abHrILhxw+9k8+PXLKH/rwwxGnz5d9xnOgMt/CIkclvxzLOW3PtBse8jPZdCN59Fp2yEkK6r56IxrqZ+7cNX2vP592HzsLZTddB+L7vwPAL1POpxe/3cAxEjNB7OYc+HviLX1GT2udMvZZBvyR34XQg4Nb79Aw4QxzbYnttmD/H2OJi5bCkD95GdIvv0Coc8g8kcfS8gvhBipf+VRkh9MbI9DyIiC3Xah+zlnQCLBikceZdlf7mu2PX+H7el29unkDRnC0iuuouaZ51dt63bajyjYY3fICdROfI2q3/4+0+FvMC695kaef2kCvXr24L9/va29w2kXm//yeIpH7UhyZS3vn3Ur1W+v2wcXbT+YrW8+jZzCfBaPm8zUS/60atvAkw5i4IkHEhuSLH5qMtOv/tuqbQUDitn9hRv58Lr7mX3r/zJxOGnXde/hlF5+CuTksPRfT7LotnX79oHXn0fhtpuRrKhmzpm/of7jVN9esOUmDPjFGeR07QQxMuPwc4l12dWHr6lon+EMuOJkQiLB4n88ycJbH2y2PeTnstGN59J5u81oWFrFR2dcR12zz8HebPnULSy46T7K7/hvpsNvMxtffRI9Rg6ncWUtM879AyvenrlOm87bbcqQm84kpzCfiqdf56PL7gIg0aMrQ287n4KBfaidW860H11PsnL5pz5v/oA+bH7XRZDIIeQmKLv7MRb+5UkAeh22JwPOOhISOVSMe405v/hL5t4EfaVk5UjPRvsNo/vgEu7b63ye+8ld7HXN8ett9+btj/LP/S7igYMvoWSXzRm07/artuV1KWTbEw+k7PXpGYo6A3JyGHDVj/nw+CuZOvp0ehy2NwWbDWrWpNe3DyBZuYwP9v0R5Xc9ROnFxzfb3v+yk6l+9rVV93P79aL38Ycy7dBzmXrgGZCToMehe2fiaDInBPL3P4baB2+i5p7LyN1yV0Jx6TrNGj6YSM2fr6Lmz1eRfPuFpgfrqHvsLmr+dAW1D/yW/P3+Dwo6ZfgAMiQnh+4XnM3i8y9m4feOp9P+o8jdZONmTZILyqj4xW9YOXZcs8fztt2G/O23pfwHJ1F+7Inkb7UF+TsOy2T0G5QjDhnNbTf+or3DaDfFo3ag0+ASXt79bKZccCdbXHvSetttce3JvH/BHby8+9l0GlxC8cgdAOi55zb0OWhnXt3vQl7d5wI+uvWRZvttftVxLB73RtqPI2Nycuj/81OZdcIVTD/wNLofus86fXvPbx9Asmo500aewuK7H6LkJ8enNiRyGHTj+cy79BamH3Q6H373p8SGZOaPIVNychh49Y+YedzPmbL/6fQ8bG8Khq71Ofh/o0lWLuP9fX5E+V0PU3rxcc22D7j8ZKqffT2TUbe57iOHUzi4lDf3PJ0PL7qNwb86Zb3tBv/6R3x40a28uefpFA4upft+OwLQ/4xvUvniW7w54gwqX3yL/md86zOft37hUt497Ke8M/p83v36xfQ/41vk9etJbs+ubHTZD3j/21fy9n7nkNe7B91GbJeZN2ED0BhDVt42VFmZ9GxywE5MffBFABZOnkFBty507tujWZuGmjrmvfw+AI31SRa9PYuupb1Wbd/lgqN449b/kcyiEYvOOwyl7qP51M0pI9Y3UPHI83Q7YLdmbbodsBtLH0x9Ia187CW67jFsjW27Uzd7AbXTZjd/4kQOOYX5qf92KqC+bEnajyWTckoGE5cuJFYugsYkDVMmkBiyQ4v2jUvLiBWpXwjj8kriimpCp6J0httu8rbekoa580jOmw8NDax86mkK99qzWZvkgjIaZsyExsa19o6Qnw+5uYS8PEjk0rhkaeaC38DsvMN2dO+WnX8nLdHnoF1YcH9qFLDqtWnkdutC/lp9eH7fHuR27UTVpGkALLj/efocvAsAA44bzazfP0SsawCgflHVqv16H7wzKz8qY/kHczJxKBnRadjm1H40n/qmvr3yf89TNHr3Zm2K9t99dd8+5kW6NPXtXfcaTs2UWdRMSY2kJSuq13N+Zo/OOwyldtbqz8Glj7xA99HNPwe7j96NJQ8+DUDFYy9RtOfqz8HuB+xG3ewF1Exd63Owg+l54K4seuBZAJa9PpVE9y7k9e3ZrE1e354kijqx7LWpACx64Fl6HrTb6v3/ldp/0b+epedBu37m88b6hlXnY05BLuSkvhgXbFRCzcx5NCxJnaNVL7xFr0O+lrbj1ldbi5OeEMIZIYRuTf++PYQwIYQwKn2hfXFdSnqybN7iVfeXzV9Cl5Ken9o+v1tnNt5/R+a+9C4AxdtsTNf+vZidTb8EAnn9iqmft2jV/fr5i8nrV/zpbZKNJKuXk+jZjdCpgD4/PpKy3zWfrtRQtoTyO//DluPvZusJfyZZvZxlL0xO+7FkUijqSaxe/QU8LltKKFr37yl36HAKj7uS/MN+vN7tOSWDIZFLrChPa7ztJdGnN8my1VNAkuXlJPr0btG+9e+8R93rkyl55EH6PfIAtRMm0vBRx/5SoS+uoLQnNR+v7sNr5y+mYI0fpVJtelE7f/UPLLXzllBQmjrvOg8ppcduW7LzmF8w/D9XULTDEAByOhewyRmH8+H1zad+dXR5JcXUz1/drzTMX7T+vv2TNslGGqtXkOjZjYLB/SFGNv7TVQx5+CZ6n3JkJkPPuNR7tebn4CLySorXbbPO52AROZ0K6HvqkSy46R+ZDDkt8kt6UbvG94G6eYvJL+m1Tpu6+YvX2yavdw/qFzZN5164lLzi7p/7vPn9i9nuqRvZYdKdzL/lP9SXLaVm1nw6DRlI/sA+kMih50G7kj+g+f8Pqa20ZqTnlBhjVQjhAGAAcCpwbXrC+pLCukNr8VPqAoZEDvv/4XTevucJqmeXQwjsecWxvHz139MdZeat531Zp17ip7QpOfcYFt31EI0rapptSnTrQvfRuzFlr5N5b7fjyOlcSI8j9m27mDdUa71vyRlvsvLOi6m590oaP3qf/INPbN6+S3fyDzmJusfvAbK1RmXLz7u1JQb0J3eTjSk74mjKDj+agp12JH+H7T9/R2WpL9ZXfdIk5CbI69GFSQdfyvSr/sp2d54DwKYXHs3s2x8luaK2rQPe8Kzzfn1Km0SCzjtvzdxzr2fmt39CtwO+tmoUKDt90c9BKDnve5T/v3U/BzuklnxPWt/78HmfX5/xvHXzFvP2/ufx5h6n0fvo/cjt3Z1k5XI+/OntDL3tfLb+zy+pnVNObMjekUa1r9YUMvjkL/1g4J4Y42shhE9NmkIIpwCnAHyvx67s1XXoF4+yBbY5bn+2+u5+AJS/OZOu/Vf/UtC1tBcryirWu98+vzmJyg8X8PZdTwCQ37WQnlsM5LB/XQJApz7dOeju83j8xBs7fDGD+gWLyOu/+pf3vNJi6hcuWW+b+gWLIZFDoqgLyYpqOu+wOd0P2YPSnx5PolsXYmMk1tbRsKiCujllJJuGpisfH0/nnbai4r/PZvLQ0ipWNx/ZCV17Epet9fdUs3zVPxveep68vdf4tTS/kMJvnUX9i/+hcf66C0WzRbK8nES/vqvuJ/r0oXHR4s/YY7XCffai7p33iCtTXyZqXp5A/jZbU/fGW2mJVRuegSccQP9jU5MHqt6YQeGAYiqbthWUFlO7oPl0x9p5zUd/Cvr3oq6pTe28xSx8dELquSbPIDY2kldcRPfhm9H3G7ux2WXHkNu9CzRGGmvrmXv3E+k/wDSqX7CYvNI+q+7nlvZeT9+eatPQ1LfnFHUmWVFNw4LFLH/1HZJLU3149bOT6LTNEJaPfzOjx5Ap9QsWkVe65udg73WmZNfP//TPwR4H70H/Tz4HYyTW1rPo3kczfRhfSL/jD6LPMaMBWP7GdAr692ZZ07b8/sXUlzU/x+rmLya/dPV3qfz+xavOsfpFFeT17Zka5enbk/rFlav2+bznrS9bysqpc+i229YsefRlKsZOomLsJAD6HDOa2JjFa8rWEjfg9S/ZqDUjPW+GEB4DDgXGhBC68hkpf4zxjhjjzjHGndOd8AC8e+9TPHDQJTxw0CV8+MRrbH7kCAD67jiEuuoVrFi4btKzy4VHkV/UiZeu/Ouqx+qqV3LvsFP52x7n8rc9zmXh5BlZkfAArHhzGvmb9CdvYD9CXi49Dt2bqrETmrWpGvsqPY9MffHofsieLBuf+tI549sXM2XEyUwZcTKL7n6Yhbfcz+I/P0rdvHI677globAAgK57DqN2evbMlQdoXDCL0LMfoXtvyEmQu+WuJGes9YWgS/dV/0wM2YHGxfNTd3ISFBx+Og3vvkxy6mtks/r3p5A7cACJ0hLIzaXT/iOpeXF8i/ZNli2kYMdhkMiBRIKCHYdRP+ujNEesDcnce55kwqifMGHUTygfM5GSo1MFUbrtNJSG6hXUrdWH1y2sILmshm47pT5fSo7em/LHU5URy8dMpNeIbQDotGkpOXm51C+u5rXDr2T8LmcyfpczmXPHY8z63X86fMIDsPKtqRSs0bd3/8beVD/1arM21ePW6NsPHsHyl1N9e/Xzr1G45SapPjyRQ5fdtqVmevZOLV3x5jQKBvcnf1Dqvep56F5UjW3+XlU9NYFeR44EoMche1Ld9Dk4/eif8t6IH/LeiB9SfvcjlN1yf4dJeADK/vQ474w+n3dGn8/SxyfQ+6h9Aeg6fHOSVStWTVf7RP3CpSSX1dB1+OYA9D5qX5Y+kfrOsPTJifT+dmr/3t9e/XjFkxPX+7z5pcWEwnwAEt270HXnLVk542MAcpumxiW6d6Hf8QdR/ven0vYeaMMRQjgohPBBCGF6COHi9WwvCCH8s2n7qyGETb7sa7ZopCeEEIBLgRJgeoxxRQihN7D+kjrtbPbTb7DRyGF898UbaFhZx7Pn37Fq21GP/5IHDrqELiW92OmsI1g67WOOGpOqkvTOn8Yy5R/PtlPUGZBsZN7lt7Hpn38OiRyW/uspaqfNpt+5x7Dy7WlUPTWBJf8ay6Abz2OLZ28nWbGM2Wd+9gzGlW9MpXLMSwx99CZoSLLy3Zksue/xDB1QhsRG6sb9nYIjz4GcHBrefom4eB55ex5O44JZJGe8Sd7wUSSGDIPGRmLN8qZpbJDYYhdyBg4ldOpC7rZ7AFA75h5ieXYlhgAkG6m88WaKf3stJHJY8b8xNHw4i6KTT6BuygfUvjievK22oNevriYUdaVwxNdInnQC5ceeQM0zz1Gw0470+cvdECO1r06k9qWX2/uI2s2FV/yaiZPfoqKiilFHHMtpJ32fIw89sL3DypjFT02m96gd+dqrv6NxZR3vnX3rqm27jvsNE0b9BIApP/l/TSWr81g87o1VFdnm3fcMW910Krs9dz2NdQ28d9Yf2+U4MibZyLwrb2OTe68i5OSw9P6x1E6bTd9zUn179bgJLP3nkwy88XyGPn0HycplzDnrNwA0Vi1n0V3/Zch/b4SYGulZ9sykdj6gNEo2Mvfy29n0z1cSEjks+ddT1EybQ8l532PFW9OpemoCi/85lo1/ex5bPXc7DRXVfHTGde0ddZurGPcaPUYNZ9j4P9K4spaZ5/5h1bZtx97AO6PPB2DWxbez6Sclq595ncqnU1Xr5v/h32x22wX0/c4oaj9exLQfXf+Zz1s4dCBbXH4cMaZmwM2/7SFWTkkl1xtffSJdtt4EgLm//Rc1M+dn6m1QOwkhJIBbgNHAXGBiCOHhGON7azQ7CVgaY9wshPAd4DfA/32p123pnPsQwmsxxp2+yIvcNujYbF3E0Kb2SFR+fiOx2Rn92juEDqPi3zPaO4QOoc/Dd7V3CB3G89v8tL1D6BD6dVn++Y1EQzIri8imRW191l5asc3tNu/fHWLe2MQB38zK78e7fPyfz3z/QwhfA66MMR7YdP+nADHGX63R5ommNi+HEHKBBUCf2NLEZT1acwZNCCEMjzF27OL0kiRJUjvbkK9pk2YDgDWnvMwFdvu0NjHGhhBCJVAMLOILak3SMwL4YQhhBrCcVAmUGGMc/kVfXJIkSVL2WLOYWZM7Yox3rNlkPbutPYLzBcoHfrbWJD1HfJkXkiRJkpTdmhKcOz6jyVxg0Br3BwLzPqXN3Kbpbd2BJXwJLZ5MG2OcEWOcASwFVq5xkyRJkqSWmAgMDSEMDiHkA98BHl6rzcPAcU3/Pgp4+sus54FWJD0hhK+HEKaSyrxeJTXP7ukv8+KSJEmSvjpijA3AGcATwPvAv2KM74YQrgohHNbU7C6gOIQwHTgPWKesdWu1ZnrbL4E9gSdjjDuGEEYDR37OPpIkSZLWkpWl21ooxvgY8Nhaj12+xr9rgKPb8jVbUyuyIcZYDuSEEEKMcSxgEQNJkiRJG7TWjPRUhhC6AC8Bfw4hLAQa0xOWJEmSJLWN1oz0HAHUAGcDzwIfA4emISZJkiRJajMtHumJMVaHEAYCQ2OMd4UQCoFE+kKTJEmSstNX+OKk7aI11dtOJFU+7v81PbQR8FA6gpIkSZKkttKa6W1nAbsDVQAxxqlAv3QEJUmSJEltpTVJT02Mse6TOyEEp7ZJkiRJ2uC1pnrbSyGEi4DCEMJ+wOnA/9ITliRJkpS9omt6Mqo1Iz0XAdXAFFIV3MYBl6QjKEmSJElqK5870tM0unNDjDEJ3Np0kyRJkqQOoSUjPRsDr4UQ9kx3MJIkSZLU1j53pCfGeHoIYTjw+xDCFFIjPY1rbH89jfFJkiRJWafx85uoDbWokEGM8fUQwiXAg8AQIH6yCRiZptgkSZIk6UtryZqevsANwKbAyBjjm2mPSpIkSZLaSEvW9LwMvACMMOGRJEmS1NG0JOmZFWO8A/h1uoORJEmSpLbWkjU9pSGEfYDDQgj/AJpdSclCBpIkSVLrRLw4aSa1JOm5HLgYGAjcuNY2CxlIkiRJ2qC1pGT1A8ADIYTLYoxXZyAmSZIkSWozLSpZHULIBz4OIVxPanTnPeDvMcbadAYnSZIkSV/W5xYyCCFsTSrJ2QeYDcwF9gXeDSFsk9boJEmSpCzUGLPztqFqyUjP74FTY4xj13wwhLA/8Adgv3QEJkmSJEltoSUlqwesnfAAxBifAkraPiRJkiRJajstSXpyQggFaz8YQiikhWuCJEmSJKm9tCRp+TPwYAjhjBjjLIAQwibAzcBf0haZJEmSlKUavU5PRrWkZPUvQghnAM+HEDqTujjpMuD6GOPv0x2gJEmSJH0ZLZqeFmP8A/CHEEJR0/3qtEYlSZIkSW2kJWt6AAghdAd+DjwTQpgUQrih6TFJkiRJ2mC1OOkB7gaqgW833aqAe9IRlCRJkpTNIiErbxuq1lRfGxJjPHKN+z8PIbzR1gFJkiRJUltqzUjPyhDCiE/uhBD2BFa2fUiSJEmS1HZaM9JzKnDvGut4lgLHtX1IkiRJktR2WpP0vA9cCwwBegCVwBHAW2mIS5IkSZLaRGuSnoeACuB14OP0hCNJkiRlv8b2DuArpjVJz8AY40Fpi0SSJEmS0qA1hQzGhxC2S1skkiRJkpQGrRnpGQEcH0L4EKgFAhBjjNunJTJJkiRJagOtSXoOTlsUkiRJ0lfIhnwhz2z0/9m77/CoyryN4/czk0ogCQklEHoRRKU3FZEiRVaU1cXVV7GhWJFFRQTsigUVFddFcV37qqvuuhZQBAUFlSYgNnoPAZJAEkLqzPP+MTEQE2SyTAkn349XruTMeWa4z7kyx/zmKcfvosdauzWYQQAAAAAgGKoypwcAAAAAjjsUPQAAAAAcrSpzegAAAAAEAPfpCS16egAAAAA4GkUPAAAAAEej6AEAAADgaMzpAQAAAEKMOT2hRU8PAAAAAEej6AEAAADgaBQ9AAAAABwtJHN6Lrs9PhT/jAPEy2buC3eIas8eyNO+ObvDHeO4sG5L/XBHOC78ctIdsjLhjnFc6Pvjw+GOcNyIbXxGuCMcFyY37hfuCMeFXh5mgADHgoUMqhEKHv9Q8CDQKHgQaBQ8/qHgQU3G/3tCi+FtAAAAAByNogcAAACAo1H0AAAAAHA05vQAAAAAIeZlSk9I0dMDAAAAwNEoegAAAAA4GkUPAAAAAEdjTg8AAAAQYl7u0xNS9PQAAAAAcDSKHgAAAACORtEDAAAAwNGY0wMAAACEmA13gBqGnh4AAAAAjkbRAwAAAMDRKHoAAAAAOBpFDwAAAABHYyEDAAAAIMS84Q5Qw9DTAwAAAMDRKHoAAAAAOBpFDwAAAABHY04PAAAAEGJeY8IdoUahpwcAAACAo1H0AAAAAHA0ih4AAAAAjsacHgAAACDEbLgD1DD09AAAAABwNIoeAAAAAI5G0QMAAADA0ZjTAwAAAISYN9wBahh6egAAAAA4GkUPAAAAAEej6AEAAADgaBQ9AAAAAByNhQwAAACAEPOacCeoWejpAQAAAOBoFD0AAAAAHI2iBwAAAICjMacHAAAACDGvmNQTSvT0AAAAAHA0ih4AAAAAjkbRAwAAAMDRmNMDAAAAhJgNd4Aahp4eAAAAAI5G0QMAAADA0Sh6AAAAADgac3oAAACAEPNym56QoqcHAAAAgKNR9AAAAABwNIoeAAAAAI5G0QMAAADA0VjIAAAAAAgxb7gD1DD09AAAAABwNIoeAAAAAI5G0QMAAADA0ZjTAwAAAISYDXeAGsaRRc/irZl67Kt18lqrER0a66puLcrt/+DnND25eIMa1I6WJP35lCY6/6RUSVK3Z+erTXJtSVJK7Rg9fU6nkGYPJXebTooaepnkcqnkuy9UvOiDytt16KmYC8crf9YUedM2yX3K6Yo8/Zyy/a6GzVTw/GR507eGKnrIRffuocRbbpJxuZT3wWzlvvpmuf1RnTsqcfyNimzTSll3PaD8z7/0Pa9bZyX85YaydpHNmynzzgdU8OXikOYPtrZTr1TywC7y5hfqp5v/pgNrNldoU6djS50440a5YqKUOX+l1k95SZLU8raRanzpQBVl5kiSNj30pjLnr1SdLq3V/vFrfU820ubH3lHGnGUhO6ZgOGHqFUoe2EWe/EL9fPNM5R7hPHWYcUPZeVo35eWyfU1GD1WTq4bIlniUOW+lNjzwRtm+6NRk9f5qujY/9o62zfwoFIcTdnc+NF1fLl6qpLqJev/158IdJ+yenH6/zh46QAfz8zV69HitXPVDhTYjR56rSXeMldvt1pw583XHpKmSpKZNG+ulF59WQmK83G6Xpkx5WHM++TzUhxAyw+65TG37d1JxfpH+c9vz2vXjlgptRr1yu+o0SJTL7dbWZWv10V0vyXqt+v/lfHW7qL/ysnIlSfOmva31C1aH+AiCp8PUy9VgYGd58ou0+uaZylmzpUKb+I4t1WnGdXLHRGnP/FX6acorkqQ6HZrplMdGyx0Xo/zte7Xq+mdVciBfJtKtUx67WgmdW0leqx/vfEVZX/8c4iNDTee4osfjtXpk4VrNPK+LGtaO1iX/WqYzW9ZT66Ta5doNadtQd5zZrsLzoyPcevuiXqGKGz7GKGrYlSp47SHZnEzFXDNVJWtXyO7dWb5dVIwiew2VZ8f6soc8axbLs8b3R7tp0FQxF9/q6IJHLpfqThinvWMnyLNnrxq8PFP5X32tks2Hjtmze7f2PfCoal9yYbmnFq5YpT2jxkiSTHwdNXr3NRUuWR7S+MGWPLCLarVM0be9b1Z8t7ZqN+1qrTh7SoV27aZdo19ue145y9er0z8nKWlAZ2V9vkqStO35j7V95ofl2uf9sl3LB98h6/EqqkGien7xmBbPXSHrOT7Xu0ke2FmxLVP0Te9xpedptJaffWeFdu2mXa2fb5tVep7uUPKAzsr8fJXqnn6S6g/triX9J8gWlSiyXny5551w/+XKnL8qVIdTLYwYNkj/d8G5mvzA4+GOEnZnDx2gtm1aqn2HPurVs6ue/evDOq3P8HJtkpLq6tGH71TP3kOVkZGlf7z4lAb076PPv1ikyZPG6Z13P9Tzs17ViSe21Yf/fU1tTugdpqMJrrb9Oim5ZYqe7nermnRpo+FTr9SsEfdUaPevG59R4YF8SdKfZ47TSX/opR8+/FaS9M2Lc7T4hdkhzR0K9Qd2VlzLFC3oPV6J3dro5Gmj9fXZd1Vod8q0q7Tmtr9r//L16vHPiao/oJP2fr5aHaeP0c/3vaGsb35Wk4v7qdWN52jdo++o2aUDJElf9ZuoqHrx6vnPiVo05E7J0teB0HHcnIkMIDgAACAASURBVJ4fdueoaUKsmiTEKtLt0pC2DbVgU0a4Y1U7rtQ28maly+7bI3k88vzwjSLada/QLmrAhSpe/KFUUlzp60SccppK1nwd7LhhFdWhvUp27JQnbZdUUqL8zz5XbN/TyrXx7Nqt4g2bJO+R/yCvNaCvCr5ZKltYGOzIIVVvaHelv+Pr2cpZsV4R8XGKapBYrk1Ug0S5a8cqZ7mveE5/50vVP7vH776uN7+orMBxxUQe9/9zrD+0h1/nKeII5yn18kHa8sx/ZYtKJEnFGTllz6t3dnflb92tvLXbQ3Eo1Ub3zqcoIb5OuGNUC8OHD9Frb7wrSVqy9DslJCYoJaVBuTatWjbT+vWblJGRJUma//lX+uMfh0nyvb3i430fDibEx2vXrt0hTB9a7Qd306p/fyVJ2rFyg2Lq1FLt+okV2v1a8Lgi3IqIjKgRY5EaDu2mne/4zs3+FRsUGV9L0b+5TkWXXqf2l16ndr7zlRqe7fv7Ia5NI2V94+vByVj4vVL+0FOSVPuEJsr86kdJUlFGjopzDvp6fYAQOmpPjzHm5t/bb62dEbg4x25PXoEa1okp225YO1o/7M6p0G7+xj36Lm2/miXG6rY+Jyil9DlFJV7939tLFeEyurJbC/VvVT9k2UPJxNeVzcks27Y5mXI1aVOujSulhUx8kjzrVirytHN++xKSpIiTTlXBW87+lNXdoJ48u/eUbXv2ZCjqpBOr/DqxgwbowD/fCWS0aiG6UZIKdh76YKFwV6aiGyWpaM/+cm0Kdx36fStI87X5VZOrhqjRhX2Vs3qTNtzzqkqy8yRJ8V3bqP2T1yumaX39dOMzx20vjyRFN6qrgp2HzsGRz1PWoTZpWYpuVFeSVKt1IyX2aq/Wk/4sb0Gx1t/3unJXbZSrVrRa3HSeVo58UM1uKP/JPmqO1MYp2rE9rWx7545dSm2covT0Q9euDRu3qF27NmrevIl27Nil884doqioKEnS/Q88oTmz/6kbb7hKcXGxGjL0opAfQ6jEN0xSdtqh92JOepbiU+rqwN79Fdpe9upEpXZqrfULVuvH2UvKHu95+WB1Ov8Mpa3ZpE8efEMFOQdDkj3YYholKf+w61TBrizFNEpS4WHXqZhGSSo47DqVn5apmNLr+YFfdqjh0G7a/ckKNRreW7GpyZKknJ+2quHQbkp7/2vFpCYroWNLxTZOVvbKjSE6surJa8KdoGbxp6en/lG+KmWMGWOMWW6MWf6PxT8FImvA9G1RXx9ffrr+dXEv9WqapLvnHco3+/LT9c8/99RDg0/WY1+t0/ZsZ1zIKqrknXb4p1jGKGroKBXNff2Ir+BKbS0VF8ru2RH4eNVKZeeqah/5uZKTFNm6pQq+Pb7npFTOj/Njjvz7tuOVufqm11gtHXC7inbvU5v7LitrkvPdBi0981YtHzJJLcb9Ua7oyADmDrX/7Tz92sREuBWZGKflZ9+pDfe/rlNe+IskqdWEkdr2/MfyHHRWDyKqxlT6u1P+92v//mzdNHaS3nxjphZ+8R9t3bJDJSW+nsOL/jxCr776jlq06q7h516ml1+eUelrOkGll6MjXNNfvexRPdbzRrmjItTqtJMkSUtfn6en+o7XzGGTlbtnv4beeUkw44aUqeQ6VeHcVPZrUdpm9V+eV/MrB6vP3KmKqB0rb2nP9I5/LlD+riydPneqOjxwmfYtWyfr8QQ4PfD7jtrTY62tOJjTD9baWZJmSdLBZ24IWadwg7gY7c4tKNvefaBQ9eOiy7VJjD30h9P5HVI14+sNh55furhBk4RYdU+tq1/25qppQq0gpw49m5MlE59ctm3ik2Vz9x1qEBUjV4Omirnibt/+2gmKvvg2Fb75uLxpmyRJESefppIfnD20TZI8e/bK3fDQMBF3g3ryZFRtyGSts/opf+EiySEX+dQrh6jxpQMlSbmrNiomtZ6ytVaSFN0oWYXp+8q1L0zLVHSjQ79vMY2TVZju+6SweG922eNpr89Xx9cnVvj3Dq7fKc/BAsW1b6rc1ZsCfjzB0uTKwWXnKWfVRsWkJuvXoz3yeTrUAxbdOElFpW0K0zK15+OlvtdauVHW61Vkch0ldG2jBuf0Upu7LlFEQpzktfIWFmvHPz4N/gEirK6/7nKNHu37g3v58lVq0rRx2b7UJo2UVskQtY8+/kwfffyZJOnq0ZfI4/Vdk6688iL94ZxLJUnfLlmhmOho1auXpL17Myu8xvGo56hB6nZxf0nSztWblND40PUoPiVJubsr9vL8qqSwWGvnfaf2g7pp46IflHfY0NIVb32hS168LXjBQ6D5lYPUtHTOTfaqTYpNTdavV6aYRkkVrlMFaVllPTuSFNs4WQWlbfI2pGnpnx+WJMW1SlGDQZ0lSdbj1c93v1b2nNM+uk95m9KDdUhApfye02OMaWOM+dQYs7p0u6MxZlLwov1vTmpYR9uyD2pnTr6KPV59un63+rWsV67N3rxDn4gu3LxXLevGSZJyCopVVDp8Zl9+kVbt2q9WSXGhCx9C3rSNciWnyCTWl9xuuU8+VSVrVxxqUJivg9PGKP+pm5X/1M3y7thQruCRMXKf1EslP3wTngMIoaKff1FE01S5G6VIERGKHTRA+V9W7bhjBw9Q/lznrIS086VPtWzg7Vo28HbtnbNUKSP7SpLiu7WVJ/dguSFbklS0Z788B/IV362tJCllZF9lfOJb0OHweS31h/VU3i++eSkxzerLuH2XqJgm9VSrdWMVbN8b9GMLpB0vzdXSgRO1dOBE7Z2zrNx5KjnieSood572fuLrHdw7Z5mS+vg+aY5t1UiuyAgVZ+ZqxXn36useY/V1j7HaPmu2tjz9HwqeGmLmc6+oe4/B6t5jsD744FONuuRPkqRePbsqJzun3NC2X9Wv7/tjPzExQdddd7le/IdvJcrt23ZqQP8+kqT27dsoJibaMQWPJC197TPNHDZZM4dN1i9zl6vz+WdIkpp0aaOC3PwKQ9uiakWXzfNxuV1q27+z9m70DR88fP7PiUO6a8+643u0w9aXPtOigZO0aOAk7Z6zXKkjfecmsVsbleQeLDe0TZIK9+xXyYECJXbzDYlPHXmGdn/i+/sh6tcFVoxRm/F/1NZX5kuSXLFRctfyfahcr+8p8pZ4dGDdbxZOAoKsKqu3/V3SZEnPlm6vkfSmpIcDHepYRLhcmti3nW7470p5rXReh0ZqnVxbf1uyUR0axKtfy/p6c/V2LdySIbcxSoiJ0H1ndZAkbdqXp6lf/CJjjKy1urJbiwqrvjmG16ui2S8rZtQkybhUsnKB7N4diuz/J3nTNstzeAFUCVfz9rI5Wb6FEJzO49X+x59RvRmPyrjcyvtwjko2b1H8mCtU9PM6FXz1tSJPbKfkaffLVae2Ys44VfHXXKHdF18lSXI3aqiIBg1U+J1zljQ9XOa8lUoe2FWnLpkhT36Rfh73t7J9PeZP07KBt0uS1k78u06ccYPcMVHKnL9KmfNXSpLa3H2pap/cQrJW+dv3au1tsyRJiT3bq9nYEbIlHsnr1do7XlRx6RKxx6PMeStVb2AXnbrkaXnzi/TTuJll+3rOf1RLB/p6uH6Z+PfSJasjS8+Tb0W2tDe/0IlPXa9eCx+Xt6hEP938t0r/nZpkwj2PaNnK77V/f44GjrhUN4wepQuGDwl3rLCYPWe+hg4doLU/L9bB/HxdffUtZfuWL5ur7j0GS/Ita92xo+//eQ9OfVLr1/s+yJow8X49P/MxjRt3jay1Gn31+NAfRIis+2KV2vbvrL8snO5bsnrC82X7rp/9kGYOm6zIWtG65O+3yB0VKZfbpU1f/6jlb/j+gB886WI16tBc1lrt37FXH0z+R7gOJeD2zFup+gM7q9+Sp+TJL9T34w6dmz7zH9aigb7Pun+Y+A91mnGdXDFR2jt/lfaWXqca//E0Nb/S97uWPnupdry5QJIUXS9ePd+aJHmtCtKztPomrl+SdPzOUj0+mSONY63Q0Jhl1toexpiV1toupY+tstZ2PtpzQzm87XhmM/cdvRG0b45zVxUKtHVb6h29EWQrHaSOyvT9sVp9zlVtxTY+I9wRjguTG/cLd4TjRq98/kT21x92v3lcXNRfaHKpI/8+vmbH69Xy/FdlyepMY0xLlU4/NsaMkMSATAAAAADVWlWGt90k6UVJ7Y0xWyXtknRxUFIBAAAAQID4XfRYazdIGmCMSZBvWNyRlzoBAAAAcEQMWAytqqzedpMxJt5amy3pEWPMUmPMwCBmAwAAAIBjVpU5PWOstTnGmMGSmki6XtK04MQCAAAAgMCoStHz6woTZ0t6yVq7oorPBwAAAICQq0rRstoYM1vScElzjDG1dagQAgAAAIBqqSqrt10pqZukDdbag8aYepJGBycWAAAA4Fy2Wt7Nxrn87umx1noktZJvLo8kxVbl+QAAAAAQDlVZve2vkvpLurT0oTxJzwUjFAAAAAAESlWGt51mre1qjFkpSdbaLGNMVJByAQAAAEBAVKXoKTbGuFS6eIExJlncVwkAAACoMv6IDq2qzMl5VtJ7kuobY+6TtEjSo0FJBQAAAAAB4ndPj7X2VWPMCklnSTKSRlprfwhaMgAAAAAIgKMWPcaYOGttnjEmXtJ2SS8dti/eWpsTzIAAAAAAcCz86el5V9LZkn6Ubz6P+c33ZkFLBwAAADgQc3pC66hFj7X27NLvTYMfBwAAAAACy685PcYYt6TBktqXPvSTpHmlNywFAAAAgGrLnzk9jSR9LilT0kr5hrVdIOlJY8wAa216cCMCAAAAwP/On56ehyT93Vr7xOEPGmPGS3pY0pXBCAYAAAA4lQ13gBrGn6LnVGtthcLGWvukMeaXIGQCAAAAgIDx5+ak+f/jPgAAAAAIO396ehKMMedW8riRFB/gPAAAAAAQUP4UPYsljTzCvq8DmAUAAAAAAs6f+/SM8ueFjDGXWmtfP/ZIAAAAgLN5TbgT1Cz+zOnx1y0BfC0AAAAACIhAFj3UqwAAAACqnUAWPSw3DgAAAKDa8WchA3/R0wMAAAD4wRvuADVMIHt6vg3gawEAAABAQPhd9Bhj6htjnjfGfFS63cEYc8Wv+6211wchHwAAAAAck6r09LwsaaGkpqXb6yXdGuhAAAAAABBIVSl6Glhr/6nSIYjW2mJJnqCkAgAAABzM69Cv6qoqRU+eMSZJpau0GWN6SMoNSioAAAAACJCqrN52m6QPJbUyxiyUlCrpT0FJBQAAAAAB4nfRY61dbozpL+lE+Zan/slaWxS0ZAAAAAAQAH4XPcaYaEnXSuoj3xC3r4wxL1hrC4MVDgAAAHAiG+4ANUxVhre9IqlQ0gul2xeXPnZRoEMBAAAAQKBUpejpYK3teNj2Z8aY1YEOBAAAAACBVJXV21aVrtgmSTLGdJP0TeAjAQAAAEDgVKWnp6ukJcaYTaXbLSX9aIxZKclaa7sGPB0AAAAAHKOqFD3nBS0FAAAAUIN4TbgT1CxVKXqukfSStXZtsMIAAAAAQKBVZU7PFkmvGmMWG2OuNsbUCVImAAAAAAgYv4sea+1z1tpe8vX4tJe0xhjzqjHmjKClAwAAAIBjVJXhbTLGuORbwKCFpH2S1kqabIzJtNZeGvh4AAAAgPN4wx2ghvG76DHGTJN0vqSFkqZba78+bB/zfAAAAABUS0cteowxzay12yRtkNTZWnugkmanBjwZAAAAAASAP3N63pcka+2sIxQ8stZmBTQVAAAAAASIP8PbWEUcAAAACCAb7gA1jD9FT6oxZsaRdlprbw5gHgAAAAAIKH+KnnxJK4IdBAAAAACCwZ+iJ9Na+0rQkwAAAABAEPhT9BT580LGmJOstT8eYx4AAADA8bzM6gmpo67eZq3t7edrvXaMWQAAAAAg4Py+OakfjrjK2+WP7wzgP+NciSYq3BGOC/2KGoc7wnGjY63scEc4LhjDp23+im18RrgjHBfy074Kd4TjQv6U68Md4bixa6E/dxkBcCSBfAfxVwMAAACAaoePDQAAAAA4WiCHt/m14AEAAABQ03nDHaCGOWrRY4zp+nv7rbXflX73d8EDAAAAAAgZf3p6nvidfVbSgABlAQAAAICAO2rRY63tH4ogAAAAABAMfi9kYIypZYy50xgzq3S7rTHmnOBFAwAAAJzJOvSruqrK6m0vybdYwWml2zskPRjwRAAAAAAQQFUpelpba6dJKpYka22+fueGpAAAAABQHVSl6CkyxsSqtOfKGNNaUmFQUgEAAABAgFTlPj33SPpEUlNjzBuSTpd0RTBCAQAAAE7GfXpCy++ix1r7mTHmO0m95RvWNs5amxG0ZAAAAAAQAH4XPcaYvqU/5pZ+72CMkbX2y8DHAgAAAIDAqMrwtgmH/RwjqaekFeLmpAAAAACqsaoMbxt++LYxpqmkaQFPBAAAADiclzWQQ6oqq7f91g5JJwcqCAAAAAAEQ1Xm9DyjQzdadUnqLGl1MEIBAAAAQKBUZU7P8sN+LpH0prV2cYDzAAAAAEBAHbXoMcY0s9Zus9a+EopAAAAAABBI/vT0vC+pqyQZY96z1l4Q3EgAAACAs3nLZo0gFPxZyODwtSVaBSsIAAAAAASDP0WPPcLPAAAAAFDt+TO8rZMxJke+Hp/Y0p9Vum2ttfFBSwcAAAAAx+ioRY+11h2KIAAAAEBNwfCp0DqWm5MCAAAAQLVH0QMAAADA0Sh6AAAAADiaPwsZAAAAAAggb7gD1DD09AAAAABwNIoeAAAAAI5G0QMAAADA0ZjTAwAAAISYlzv1hBQ9PQAAAAAcjaIHAAAAgKNR9AAAAABwNIoeAAAAAI7GQgYAAABAiLGMQWjR0wMAAADA0Sh6AAAAADgaRQ8AAAAAR6PoAQAAAELM69CvY2GMSTLGfGaMWV/6ve7vtI03xuw0xvzVn9em6AEAAABQHdwhab61tq2k+aXbR/KApIX+vjBFDwAAAIDq4DxJr5T+/IqkEZU1MsZ0k9RQ0lx/X5iiBwAAAEB10NBau0uSSr83+G0DY4xL0hOSJlTlhblPDwAAABBiXofeqccYM0bSmMMemmWtnXXY/nmSUip56hQ//4kbJM221m43xvidi6IHAAAAQECUFjizfmf/WUfaZ4zZbYxpZK3dZYxpJGlPJc1OlXSGMeYGSbUlRRljDlhrf2/+D0UPAAAAgGrhA0mXS3qk9Pt/f9vAWnvJrz8bY66Q1P1oBY/EnB4AAAAA1cMjkgYZY9ZLGlS6LWNMd2PM34/lhenpAQAAAELMmTN6jo21NlPSwEoeXy7p6koef1nSy/68Nj09AAAAAByNogcAAACAo1H0AAAAAHA0ih4AAAAAjsZCBgAAAECIecMdoIahpwcAAACAozm2p+fKe69R1/7dVJhfqGdve1qbf9hUoc29bz2oug2SVFRQKEl6YNS9ysnMLtvfe9hpunXmRE0851ZtWrMhZNlD6aJ7rtQp/buqKL9QL932rLb9uPmIbW98YaLqN2uge4fcKkka89fxSmnVWJIUG19L+TkHdf+wCSHJHQ7dHhil1AGdVZJfqG/Gz9K+NVsqtOk0caRajuyjqIQ4/avtoZUVu957iRqe3kGSFBETpZh68XrnxGtDFT2oavftqsb3XCO5XNr39mfa+9y75fabqAg1eeIWxZ7cWp79udp20zQV79yjyNQGOmHe31S4aack6eDKtUq782++50RGqPF91yqu9ymyXqvdj7+mnE++DvmxBUvtvl3V6O4xvnP2r7nKqOycPX6LYk5uI8/+XG0f+6iKd/puSh3dvoVSH7xJrtqxkrXaeN542aLicBxGyDw5/X6dPXSADubna/To8Vq56ocKbUaOPFeT7hgrt9utOXPm645JUyVJTZs21ksvPq2ExHi53S5NmfKw5nzyeagPIezufGi6vly8VEl1E/X+68+FO05YuU/qrpgLr5NxuVW0aI6KPv1Xuf2Rff+gqH7DJa9XtjBfBa8/Le+ubZIkV2pLxVx6s0xMnGS9yntorFTinPdfrT7d1HDKdZLLpex3P1HWC++U228iI5Xy6K2KOamtPPtzlHbLwyrZuUeKjFDKfWMVc3JbWa/VnoeeU/7SNZKken+5XPHnDZQ7vrbWdzs/HIcFSHJo0dOlfzc1atlIY8+8Tm27nKBrHrxek0dU/sf40+OmV1rQxMTF6uwrztG679YGO27YnNyvixq0bKQp/caqVZe2umTqNXp4xORK23YZ0lOFBwvKPTbrpifLfh455TLl5x4Mat5wajygk+JbpuiD029VctfW6vnwFfr0nHsrtNvx2Xda+9JnOnfx4+Ue/+7eN8p+PuGqQUo6uUWQE4eIy6XG91+nzaPuUkl6plr/d7py5i1R4YbtZU3qXjhYnuwDWtf/WiWcc4ZS7rhC28dOkyQVbU3Xhj+Mq/Cy9W+8UCWZ2Vo34DrJGLkT64TskILO5VLj+67X5svuVEl6plq9/6RyKztnOXlaP2CMEs7pq5SJV2j7zdMkt0tNp9+qHbdMV8Evm+VOrCNb4gnjwQTf2UMHqG2blmrfoY969eyqZ//6sE7rM7xcm6Skunr04TvVs/dQZWRk6R8vPqUB/fvo8y8WafKkcXrn3Q/1/KxXdeKJbfXhf19TmxN6h+lowmfEsEH6vwvO1eQHHj96YyczLsVefKPynpokuy9DcZOeUcn335YVNZJUvPQLFX/5sSQpomNvxYy8VgdnTJFcLsVedbvyX3pM3h2bZOLqSB4Hvf9cLjW8+0btuGqyindnqPk7T+vA50tUtPHQuUn402B5cw5o85DRqjPsTNW/9SrtuuURJY4cKknacu4NciclqMkLD2jrn8ZJ1urAF0u0740P1OqTF8N1ZIAkhw5v6zGopxa+94Ukaf3KdYqLj1Nig7pVeo2Lbv0//fe5f6u4sCgYEauFzoN76Nt/L5QkbVq5XrXqxCmhfmKFdtG1YjTo6uH6+Jn3jvha3f9wqpZ+sChoWcOtyZBu2vSu7/gyv9uoqIQ4xTSoeK4yv9uogj37f/e1Wow4VVve/yYoOUOtVqe2Ktq6S8Xbd8sWlyj7wy8VP6hXuTbxg3pp/3vzJUnZcxar9mmdjvq6dUeepT1/K/2E0Vp59uUEPHu4xHY6QYWHn7OPvlSdQeX/CK9zVm/tKztnixRXes5qn9FVBb9sUcEvvh5Zz/5cyevsUeHDhw/Ra2/4esKWLP1OCYkJSklpUK5Nq5bNtH79JmVkZEmS5n/+lf74x2GSJGul+PjakqSE+Hjt2rU7hOmrj+6dT1FCvIM+PPgfuVu2k3dPmmxGuuQpUfHyBYrodGr5RgWHfYAXHeP7JZIU0aGbPDs3y7vDN3LE5uVK1jnvv5iOJ6h4W5qKd6RLxSXKnb1QtQeWvzbVHniqst+fJ0nK/fQr1Tq1syQpqnUzHfxmlSTJk5UtT06eYk5uK0kqWP2LPHv3hfBIjh/Wof9VV44sepJSkpWZllG2nZmeoaSGyZW2vfHxsXps9pO64OYLyx5rcVJLJTeup+8+Xx70rOFUt2GSstIyy7b3pWcqMSWpQrvzbv2zPvv7h2XDAH+rbc8TlZORrT1b0oOWNdxqpdTVwcPO1cG0LNVKqVohLUlxqcmq3bSBdi/6MZDxwiYiJVnFuw6914rTMxWZUv69FtkwWUW/tvF45cnNk7tuvCQpqmlDtfnoKbV862HV6uEb/ueqEydJSrnlUrX58Ck1e3aiIupVLDCPV5EpySretbdsu2RXhiIbVjxnZW08XnlzD8pdN17RLRtL1qr5y/er9QdPqd6YC0IZPSxSG6dox/a0su2dO3YptXFKuTYbNm5Ru3Zt1Lx5E7ndbp137hA1beobenv/A0/o//7vfG3ZtFwffvCqxv3lzpDmR/ViEpPl3Xfo/Wf3ZciVWK9Cu8h+w1X7wZcUc/7VKnjbN+zW1bCJZK1q3TxVcVP+qqjBI0OWOxQiGtYrf21Kz1DEb65NEQ2SVXLY9dybe1DuxHgVrt2s2gNPldwuRaY2VMxJbRTRqH4o4wNHddThbcaYm39vv7V2RuDiBIYxpuKDtmLlOWPcdGXtzlJMXKxue26i+p7fX1/9Z4GuuGu0nr2t2h1W4FVynn57mpp2aKEGzVP0rwdeUXKTyi9gPc/t4+heHkmVnqvKfqeOpvmIU7Xt46Wy3ur7SUiVVPo7ZI/aRtaqZG+Wfjn9Knn25yrm5NZq/vwUrR9yo0yEW1GN6ytvxc/aNfVF1Rt9nlImX6Udt0wP0kFUAxXO2RHauN2q1b2DNo64Rd78QrV8faryf9igvK9XhyRmOFR2Pf/t79j+/dm6aewkvfnGTHm9Vt98s1wtWzWTJF305xF69dV39ORTz6t3r256+eUZ6tR5QMXfU9QQlb65KjxSvOBDFS/4UBE9+itq2P+p4OXHJZdbEW1OVt5DY2WLClXrlkfk2bZenl9WBT92uPz21FT2fpRV9nufKqpVUzV/d4aK0/Yof+XPksOH3uL448+cnv+pVDfGjJE0RpK6JnVUq9ot/peX8duQy4bprIsGSZI2fL9ByY0PfXKTnFJPWXuyKjwna7fvsYK8fC3675dq27mtln+2RE3bNde9bz0oSUqsX1cTX5yiR0dPdcRiBv1GDVHfi8+SJG1evUFJjQ99ilM3JVnZu8ufp1ZdT1DzU1rp4UXPyu12q05ygm576149ftG9kiSX26WuQ3rqweETQ3YMoXLCFWep9SX9JUlZqzap1mHnqlbjJB3c/fvD2CrT/LzeWjb5lYBlDLeSXRmKbHTovRaZkqyS3/wOFadnKKpRPZWkZ0pul9x14nzDsiR5inzfC37YqKJt6Ypumar8NRvkPVignE99QwCzZy9W3QsHh+iIgq84PVORh30CGtGonor3/Pac+dr8es5cdWrJsz9XJemZylvyQ9lwv9wFyxV7UmvHFT3XX3e5Ro++RJK0fPkqNSnttZGk1CaNc1jiEgAAIABJREFUlFbJELWPPv5MH338mSTp6tGXyOP1/cF15ZUX6Q/nXCpJ+nbJCsVER6tevSTt3ZtZ4TXgfHZ/hlx1D73/TN168u4/8u9CyfIFir1krAokefftVcm672XzfO+/kjXL5G7WxjFFT8nujPLXppR6KtmTWaFNRKN6KtmdUXZt8pZez/c+MqusXbM3n1DR1jQB1clRh7dZa+/6va/fed4sa213a233YBc8kvTpq7M1Ydh4TRg2XsvmfqszL/D9sdq2ywk6mJun/XvKjyd1uV2qU9c3vtkd4Va3gT20be02Hcw9qNFdRunGPmN0Y58xWr9yrWMKHkla8Nqnun/YBN0/bIJWzV2m3uefKUlq1aWt8nMPKntv+T/kF74+VxN6XatJfW7UoyPv0u7NaWUFjySd2Kejdm1K0770ikXl8W7dy/M0Z9AUzRk0Rds/WaFWf+ojSUru2lpFOQePOnfnt+q0bqSohDhlLF8fjLhhcfD79Ypu0ViRTRrKREYoYXhf5cxbWq5NzrwlSrxgoCQp4ezTdeCb7yVJ7qR4yeW7BEU2bajoFo1VtM03RDJn/lLF9T5FkhR3WicVbtgmp8j/fl35c3ZOX+XOW1KuTe78Japbds76KK/0nOV+uUIx7VvIxERLbpfiep2sAgedm1/NfO4Vde8xWN17DNYHH3yqUZf8SZLUq2dX5WTnKD19T4Xn1K/v+1AiMTFB1113uV78x5uSpO3bdmpAf997t337NoqJiabgqcE8W9bK1SBVJrmh5I5QZPd+Kln9bbk2rgaHiuyIU3rKu8e3wmTJTyvkbtJSioyWXC5FnNBR3jTnvP8K1qxTZPPGikxtKEVGqM6wM3Xg8/Ln5sDn3yphhO+D0zpDztDBb30fuJiYaJnYaElSrdO6yJZ4yi2AgMp5HfpVXfm9epsxprGkpyX1KX3oS0njrbXVrpT/7vMV6tK/u5758jkV5Rfq2dueKdv32OwnNWHYeEVGRerO1+6VOyJCLrdLaxat1vw354Yxdeit+eI7ndK/i6YufEZF+UV6ecKzZfvunv2YX8tP9xx+upY5fWibpLT5q5Q6sJPO/foJefKL9M34Q59onf3ZVM0ZNEWS1OXOi9RixGmKiI3SH5fP0IY3F2jNE/+W5FvAYOt/v6309Y9bHq/S7nlOLV+9z7f88jvzVLh+mxqMv0T5a9Yrd95S7Xv7MzV98had8MXz8mQf0LbSldviep6shuMvkfV4JI9XO+98Vp7sA5Kk9EdfVtPpt8h199XyZOZox+1Ph/MoA8vjVdq9z6nFK/fLuFza985nvnP2l9JzNn+p9r09V02m36q2n8+SJ/uAtt/8qCTJm5OnjBffV+v3p0vW19Nz4Atnzz2cPWe+hg4doLU/L9bB/HxdffUtZfuWL5ur7j18vYBPTr9fHTv65oU9OPVJrV/vm2w+YeL9en7mYxo37hpZazX66vGhP4hqYMI9j2jZyu+1f3+OBo64VDeMHqULhg8Jd6zQ83pV8NazqjXuIRmXS0WL58q7a6uih18mz9Z1Kvn+W0X2O1cRJ3aVPCWyBw8o/6XSFe8OHlDRvH8rbvIzviG6PyxVyQ9Lf//fO554vNrzwEw1efFByeVW9ntzVbRhm5LHjlLBD+uU98USZb/7qRpNm6CWn74oT3audt3yiCTJnZygpn+fKuv1qmR3pnZNPLRKYP3brlKdc/rLxEar1YLXlP3uJ8r86xtHSgEEjfF3XLMx5lNJ70p6tfShUZJGWmuPetUc2fw8Bk/7IdFEhTvCcaFfUXS4Ixw3OkZnH70RZAyXKH913rEy3BGOC/lpX4U7wnEhf8r14Y5w3Ni10JFrTwVFu1/mVDZ5q9q5qcWfHfk/n79uebtanv+qvIMaWmtfsNYWln79XVLDYAUDAAAAgECoys1Js4wxF0l6u3T7QknOm8gBAAAABJm3Gt/Txomq0tNzlaTLJGVI2ivf8LbRwQgFAAAAAIHiV0+PMcYt6Vxr7bAg5wEAAACAgPKrp8da65Hk/Ft/AwAAAHCcqszp+coY87SktyTl/fqgtfb7gKcCAAAAgACpStFzZun3roc9ZiX1DVwcAAAAwPlYxiC0qlL0XGqt3Xr4A8aY5gHOAwAAAAABVZXV2/7j52MAAAAAUG0ctafHGHOCpBMlJRhjzj1sV7ykmGAFAwAAAIBA8Gd420mSzpeUKGnkYY/nSro2GKEAAAAAJ+PmpKF11KLHWvsfSf8xxvSx1i4KQSYAAAAACJiqzOlJN8Z8aoxZLUnGmI7GmElBygUAAAAAAVGVoufvku6T5C3dXiPp0oAnAgAAAIAAqsqS1XHW2q+NMZIka601xhQHJxYAAADgXN6jN0EAVaWnJ9MY01Kl91IyxoyQlB6UVAAAAAAQIFXp6blJ0ouS2htjtkraJemioKQCAAAAgADxu+ix1m6QNMAYkyDJWGv3By8WAAAAAATGUYe3GWN6GGNWGGP2G2O+kpRCwQMAAAD876xD/6uu/JnT8zdJd0pKLf356aAmAgAAAIAA8qfocVtr51hr86y1b0pqEOxQAAAAABAo/szpSTTGnHukbWvtB4GPBQAAAACB4U/Rs1jSyCNsW0kUPQAAAACqraMWPdbaUf68kDHmUmvt68ceCQAAAHA2bk4aWlW5OenR3BLA1wIAAACAgAhk0WMC+FoAAAAAEBCBLHqq78LcAAAAAGosfxYy8Bc9PQAAAIAfqvONPJ0okD093wbwtQAAAAAgIPwueowx9Y0xzxtjPird7mCMueLX/dba64OQDwAAAACOSVV6el6WtFBS09Lt9ZJuDXQgAAAAAAikqhQ9Day1/1TpsuLW2mJJnqCkAgAAABzM69Cv6qoqRU+eMSZJpau0GWN6SMoNSioAAAAACJCqrN52m6QPJbUyxiyUlCrpT0FJBQAAAAAB4nfRY61dbozpL+lE+Zan/slaWxS0ZAAAAAAQAH4XPcaYaEnXSuoj3xC3r4wxL1hrC4MVDgAAAHAir+U+PaFUleFtr0gqlPRC6fbFpY9dFOhQAAAAABAoVSl6OlhrOx62/ZkxZnWgAwEAAABAIFVl9bZVpSu2SZKMMd0kfRP4SAAAAAAQOFXp6ekqaYkxZlPpdktJPxpjVkqy1tquAU8HAAAAAMeoKkXPeUFLAQAAANQgLGMQWlUpeq6R9JK1dm2wwgAAAABAoFVlTs8WSa8aYxYbY642xtQJUiYAAAAACBi/ix5r7XPW2l7y9fi0l7TGGPOqMeaMoKUDAAAAgGNUleFtMsa45FvAoIWkfZLWSppsjMm01l4a+HgAAACA83iZ1RNSfhc9xphpks6XtFDSdGvt14ftY54PAAAAgGrpqEWPMaaZtXabpA2SOltrD1TS7NSAJwMAAACAAPBnTs/7kmStnXWEgkfW2qyApgIAAACAAPFneJsJegoAAACgBrHM6Qkpf4qeVGPMjCPttNbeHMA8AAAAABBQ/hQ9+ZJWBDsIAAAAAASDP0VPprX2laAnAQAAAIAg8KfoKfLnhYwxJ1lrfzzGPAAAAIDjecMdoIY56upt1trefr7Wa8eYBQAAAAACzp8lq/3FKm8AAAAAqp1AFj2suwcAAACg2vFnTs8xYx1y/+RbT7gjHBcSPZwnf8XVLgx3hONCTk5MuCMcNyY37hfuCMeF/CnXhzvCcSF26sxwRzhuFPfkDiHAsQhk0ePXggcAAABATeelUyCkjlr0GGO6/t5+a+13pd/9XfAAAAAAAELGn56eJ35nn5U0IEBZAAAAACDgjlr0WGv7hyIIAAAAAASD33N6jDG1JN0iqZm1dowxpq2kdtbaj4KWDgAAAHAgFvoKraosWf2SfIsVnFa6vUPSgwFPBAAAAAABVJWip7W1dpqkYkmy1uaLG5ICAAAAqOaqUvQUGWNiVXoTUmNMa0ncBAQAAABAtVaV+/TcI+kTSU2NMW9IOl3SFcEIBQAAADiZN9wBahi/ix5r7WfGmO8k9ZZvWNs4a21G0JIBAAAAQABUZfW2vqU/5pZ+72CMkbX2y8DHAgAAAIDAqMrwtgmH/RwjqaekFeLmpAAAAACqsaoMbxt++LYxpqmkaQFPBAAAADictdynJ5Sqsnrbb+2QdHKgggAAAABAMFRlTs8zUtmtY12SOktaHYxQAAAAABAoVZnTs/ywn0skvWmtXRzgPAAAAAAQUEcteowxzay126y1r4QiEAAAAAAEkj89Pe9L6ipJxpj3rLUXBDcSAAAA4GxesZBBKPmzkIE57OdWwQoCAAAAAMHgT9Fjj/AzAAAAAFR7/gxv62SMyZGvxye29GeVbltrbXzQ0gEAAADAMTpq0WOtdYciCAAAAFBTeMMdoIY5lpuTAgAAAEC1R9EDAAAAwNEoegAAAAA4mj8LGQAAAAAIIMuiyCFFTw8AAAAAR6PoAQAAAOBoFD0AAAAAHI05PQAAAECIeZnTE1L09AAAAABwNIoeAAAAAI5G0QMAAADA0Sh6AAAAADgaCxkAAAAAIWYtCxmEEj09AAAAAByNogcAAACAo1H0AAAAAHA05vQAAAAAIeYNd4Aahp4eAAAAAI5G0QMAAADA0Sh6AAAAADgac3oAAACAELPiPj2hRE8PAAAAAEej6AEAAADgaBQ9AAAAAByNOT0AAABAiHmZ0xNS9PQAAAAAcDSKHgAAAACORtEDAAAAwNEoegAAAAA4GgsZAAAAACFmLQsZhBI9PQAAAAAcjaIHAAAAgKNR9AAAAABwNMfO6bnq3mvUpX93FeUX6q+3PaXNP2yq0Oa+t6YqsUFdFRUUSZIeGHWPcjKz1e9PAzRq8pXKSs+UJH3y6sea/9ZnIc0fKqPuHa3O/buqML9Qs277q7ZUcp6mvHV/ufP06Kj7lZOZrXY9O2jUPVepafvm+uvY6Vo2+5tQxw+bDlMvV4OBneXJL9Lqm2cqZ82WCm3aTbpQqSP7KjIxTp+2ujL0IcMg9vTuSp54vYzbpZx/f6LsF98utz+m2ylKvv06RZ3QSntuf0h5n30lSYpq10r17rpZrrhasl6v9s96U3mfLgzHIYRMnTO7KvWeq2XcbmW+NVd7Zr5Xbr+JilCz6eNV65Q2KtmXo603PaaiHXvK9kc2rqf2855V+lNvau+s90MdP6SG3XOZ2vbvpOL8Iv3ntue168ctFdqMeuV21WmQKJfbra3L1uqju16S9Vr1/8v56nZRf+Vl5UqS5k17W+sXrA7xEQSf+6TuirnwOhmXW0WL5qjo03+V2x/Z9w+K6jdc8nplC/NV8PrT8u7aJklypbZUzKU3y8TESdarvIfGSiXF4TiMsLvzoen6cvFSJdVN1PuvPxfuOCFXu29XNbp7jORyad+/5irjuXfL7TdREWry+C2KObmNPPtztX3soyre6bsuRbdvodQHb5KrdqxkrTaeN162qFjNX7pPEQ2SZNwuHVz+k9Lunil5veE4vGqHm5OGliOLni79u6lRy8Yae+a1atulncY8eL0mjZhQadsZ46Zr45oNFR7/+qNFevHu54MdNaw69e+qlJaNdOuZN6p1lxN0xYNjdO+IOypt+7dxT2nzmo3lHstM26vnb31Gw8acF4q41Ub9gZ0V1zJFC3qPV2K3Njp52mh9ffZdFdrtnvudtrw4V/2+fTIMKcPA5VK9KTdp15g7VJKeodS3ntHBL75R8aZtZU1Kdu3R3rseV8Llfyr3VFtQqD2Tp6lkW5rc9ZOU+vazyv96uby5eaE+itBwudTkgWu18ZK7VZyeqRM+eELZ85aqcP32siZJfx4kT/YB/XzmtUocfoYa3XG5tt70WNn+1LuvVu6C78KRPqTa9uuk5JYperrfrWrSpY2GT71Ss0bcU6Hdv258RoUH8iVJf545Tif9oZd++PBbSdI3L87R4hdmhzR3SBmXYi++UXlPTZLdl6G4Sc+o5Ptvy4oaSSpe+oWKv/xYkhTRsbdiRl6rgzOmSC6XYq+6XfkvPSbvjk0ycXUkjydcRxJ2I4YN0v9dcK4mP/B4uKOEnsulxvddr82X3amS9Ey1ev9J5c5bosINh65LdS8cLE9OntYPGKOEc/oqZeIV2n7zNMntUtPpt2rHLdNV8MtmuRPryJb4fo+2j31E3tL3ZtO/TVLCsD7K/ujLsBwiajZHDm/rMaiXFrz3hSRp/cq1qhUfp8QGdcOcqvrpNqinFr23QJK0ceU6xVXxPGXs2Kvtv2yVrWGf2DQc2k073/H1UOxfsUGR8bUU3SCxQrv9KzaocM/+UMcLm+hT2ql4W5pKdqRLJSXKm7NQcf1PK9emJG23itZtln6zYk3x1p0q2ZYmSfLszZIna79cdRNClj3UanVuq8Itu1S0fbdscYn2ffiVEgb1KtcmYVAvZb33uSRp/+zFqnN6p0P7BvdS0bZ0FazbJqdrP7ibVv3b937bsXKDYurUUu36Fd9vvxY8rgi3IiIjVJM+QHW3bCfvnjTZjHTJU6Li5QsU0enU8o0KDh76OTqm7D0Y0aGbPDs3y7vD18tv83IlW7Ou6Yfr3vkUJcTXCXeMsIjtdIIKt+5Scel1KfujL1VnUO9ybeqc1Vv73psvScqes0hxp/muS7XP6KqCX7ao4JfNkiTP/tyy3pxfCx5FuGUiI1mxDGHjyJ6e5JRkZabtLdvOSs9UcsNk7d+zr0LbGx6/WV6PV0s++Ubvzjg0FKf32aeqQ8+TlLZ5p16+/0Vl7soISfZQqpuSpMy0Q8eVlZ6pug2TKj1PYx6/SV6PV8s++Vbvz3gnlDGrnZhGScrfmVm2XbArSzGNkmpUgVOZiAb1VJJ+6H1Xsnuvoju2r/LrRJ/cTiYyUiXbdwUyXrUSmZKs4sOuKcW7MlSrS7uKbX59f3q88uTmyV23jmxBkRpcf4E2XnK3Goz5Yyhjh0V8wyRlpx16v+WkZyk+pa4O7K34frvs1YlK7dRa6xes1o+zl5Q93vPywep0/hlKW7NJnzz4hgpyDlZ47vHMJCbLu+/Qe8/uy5C7ZcX3XmS/4Yo+63zJHamDT94uSXI1bCJZq1o3T5Wpk6DiZQtVNLdmX+NrKt916bBr+K4MxXb+zXWp4WFtPF55cw/KXTde0S0bS9aq+cv3KyIpXtkffaWMWYeG7DZ/+X7V6nSCchcuV86cxSE5HuC3jlr0GGP+o9/5zMxae35AEwWCqfhQZZ8sPD3uCWXtzlJMXKwmPHeHzjy/vxb++wstn7dMiz74UiVFJRp8yVDdNP0vuu/iO0MQPLSMqXiiKjtPfxv3lPbtzlJMXIzGPXe7+pzfT4v+veD/2bvvOKmq84/jn2cbvfci0lVUREE0xIaIBaMxokZj158mdkWNXWNijxqjGHtsib3FGAtFASsISLVQrPQOCyzLluf3x70Lu7Ds3pWZubO733de+1rmzp3xOyc7u/fMOec5KUiYnqycHzB9ckW577stR3Qqk9myOa1u+yNLr/9rlR9bvZT7S2qLU8o7B9oO+x1LH/8Pxes3JCdamim3Gbbxs/HMaXeSVSebofedT9cBuzL3oxlM+Ncoxtz/OjgcfPlxHH79ybzxx8eSnDrVyn3zbXWkYMx/KRjzX7L2HkjOkN+x4am7ISOTrO67se62i/CN+dQfdgdFP86m6OspyY8t6W+r30vbOCczk/r9ejH3mGEU5+XT5V+3kjdjDus+CdbP/XDGjVhONh3vu4IGA3qz7iP9fAF4bRqSTgNRRnqG/5wnNrNzgXMB9mzem64Nd/w5TxPZ4acNYdCJhwIwd9psWrRvBXwFQPO2LVixZMVWj1mxODi2YV0eH/5nLN379GTsax+wdlXupnNGPT+Ck68+PanZU+mQ0w5n4ImDAfh22hxatG+56b7mbcsfDVu5qZ028Ml/PqRrn+61rtOz45mD2eGUgwFYPeVb6nVoQUlL1W3XnPxFW7dbbVO4eBlZbVttup3VphVF5bzvtsUa1Kftg39h5fCnyJ/2dTIipo2CRcvIbrf5vZfdriUFi8u2VcHCZWS3b0nBouWQmUFmowYUrcqlfp+eND1iAO2vOYPMxg1wdzy/gGVP/y/VLyNp+p86mL4nDQRg/tRvadK+xab7GrdtTu7ibY+qFuYX8M2oyew8uC9zP5rBumVrNt036YUPOPmJK5IXPCa+ahkZzTa/96xZS4pXLd/m+YUTx1Dv5IvYABSvXErhrGn4uqCdCqd/Tman7ur01EIFi5aT3a7U7/B2LSnY4nd4yTmF4e+ljEb1KVqVS+Gi5awbP4OilcHPUe6YidTbtdumTg+Abywgd9R4Gh+yrzo9EotK1/S4++iKvip43KPu3s/d+yW7wwPw7jNvc+WQS7lyyKVMGDGeg4YGfzB77LkT63PXb3Uxn5GZQaNmwbzdzKxM+g7am5+++QGgzLqWfoP7M3/OvKTnT5VRz7zLdUMu57ohlzNpxAT2G3oQAN327LnNdmpYqp32HNSPed/U/HUEW/rhyZF8NOgaPhp0DYvfmUiH4/cHoGnf7hTmrq/1U9sA8md8Q/aOHcjq0BaysmhwxIGsGxOxol9WFm3vu4nc/45i3YgPkxs0DayfOps6XdqTs0MbLDuLZkftz5qR48ucs2bUBJoPDTraTYf8ktxPpgEw5/hr+HK/c/hyv3NY+s//svjBl2tUhwdgwrMjeWjItTw05Fq+HjGRPscG77eOe3ZnQ27eVlPbcurX2bTOJyMzgx4D+7B0brBGrPT6n10O68eSWTXn93mJou+/IaN1B6xFG8jMIrvfQRRO/azMORmt22/6d9bu/SleMh+Awi8nkdmxC2TXgYwMsnr2pnhB7fsdL5A3bRZ1Orcnu2Pwe6nJrw4gd1TZ30u5o8fTbOggAJocsR/rPg1+L+WOm0TdnTtjdetAZgYN9tmNDXN+JKN+XbJahddUmRk0Oqgf+XNr3ntQqofIa3rMrBtwK9ALqFty3N17JiHXdpn8/kT2GtiX4eMeIT8vn39ccf+m+/769n1cOeRSsnOyuf7Zm8nKyiIjM4NpH01h1PMjABhyxlHsPbg/RYVFrF2dy/Ar7ovrpSTVlPcnscfAvbhn3D/YGJasLnHr2/dw3ZDLyc7J5qpnbyQzK5OMzAxmfjSND54fBUDX3t259NGrqN+kAXsesjdDL/stVw++NK6XkzJLRn1Bq0F9OGj8fRTl5TPtks1V/vYbfTsfDboGgJ1v+B3tjx1AZr0cDv5iOD/9+wNm3/3qtp62+isqZtltw2n78G1YZga5r79HwdwfaHbBaeTPnMX6MZ9RZ9eetPn7TWQ0akT9A/el2fmnMu8359Lw8AOp23d3Mpo2ptGvgxHbpdf/lY3fbF1CvUYoKmbejY/Q9Zk/YZkZrHhpFBtm/0TbYb9j/bQ5rBk1geUvjmTHvw1jl7GPULgqt0zlttpk1gdT6DGwD5eOvTcoWX3l5vfbeW/fxkNDriW7fh1OfnwYmTnZZGRm8O0nM5n47+AzuUOvOYl2vXbE3Vk1bylvXvvPuF5K8hQXs+GFB6l/yW1YRgYbPx5B8cIfqHPUaRT9MIvCaZ+RfdDRZO2yFxQV4uvXkvdkWJ1s/Vo2jnqNBtc+AO4UzphA4YwJ8b6eGF150x18/sU0Vq1aw6BjTuH8s09l6FGHxR0rNYqKWfCnh+n89J+xjAxWvjyS/Nk/0vrSk8mbPpvc0RNY+eIIOt57OT3ef5Si1Wv56eI7AShes45lT7xBtzfuBQ9GetZ+MJHMlk3p9NgNZORkQ0YG6z6dxornanAlRUlrFnUtgpl9CNwC3A0cA5wJFLv7jZU99rgdj9akxQjq1sy6Egl30oY6cUeoNnZpve0pLrLZmjV1Kz9JAHjNamdlq6q64rCaV/wmGerd+lDcEaqNb/pfHHeEamO3b98qb/VR2jmgw6AaeX08bv7otGz/qpSsru/u7wG4+1x3vx4YmJxYIiIiIiIiiVGVoYV8C8p9zTWzPwDzgdbJiSUiIiIiIpIYVen0XAY0BC4mWNvTBDgrGaFEREREREQSJXKnx91LSnjkAqcmJ46IiIiIiEhiVaV620jK2e3M3Q9NaCIRERERkRquRlYxSGNVmd52fal/1wWGAvmJjSMiIiIiIpJYP2d6W4mxZjY2wXlEREREREQSqirT2xqXupkB9AXaJTyRiIiIiIhIAlVlettMgumHBhQC3wHnJCOUiIiIiEhNVqxVPSlVleltOyQziIiIiIiISDJU2ukxs6Mrut/d30xcHBERERERkcSKMtJzfPi9JTAAGBPePhAYC6jTIyIiIiIiaavSTo+7nwpgZm8Cvdx9fni7A3B/cuOJiIiIiNQ8WtOTWhlVOLdrSYcntADYKcF5REREREREEqoq1dvGmdn/gOcJqridCIxLSioREREREZEEqUqn5wKC9T37h7efAV5JeCIREREREZEEqkrJagdeCr9ERERERORnCi6tJVWilKwe6+4HmtlKKLPiygj6Qs2Tlk5ERERERGQ7RRnpGRh+b5nMICIiIiIiIslQafU2dy8O/3kXsJe7F5X+Sm48ERERERGR7VOVktVfArea2Swzu93M+iQrlIiIiIiISKJUpZDBE8ATZtYKOA64z8zauvvOSUsnIiIiIlIDaXPS1KrKSE+JHYDOQAfgu4SmERERERERSbDIIz1mdivBCM9PwIvAPu6+IlnBREREREREEqEqm5MuBA5w98XJCiMiIiIiIpJoUfbp6eHus4FxQBsza1P6fneflqxwIiIiIiI1kWtNT0pFGem5GjgbeLCc+xw4IKGJREREREREEqjSTo+7nx1+3z/5cURERERERBIryvS2oyu6393fTFwcERERERGRxIoyve348HtLYAAwJrx9IDAWUKdHRERERKQK3LWmJ5WiTG87FcDM3gR6ufv88HYH4P7kxhMREREREdk+VdmctGtJhye0ANgpwXlEREREREQSqir79Iwzs/8BzxNUbTuRoIy1iIiIiIhI2qpKp+cCgvU9JVUx2p2qAAAgAElEQVTcngFeSXgiEREREZEarlj79KRU5E6PB6utXgq/REREREREqoXIa3rM7Ndm9pWZrTazNWaWa2ZrkhlORERERERke1Vlets9wG/cfXqywoiIiIiIiCRaVaq3LVaHR0REREREqpuqjPR8bmb/Bt4A8ksOurs2JxURERERqQJtTppaVen0tACKgaNLHXNAnR4REREREUlbVanedmoyg4iIiIiIiCRDpZ0eM/sbbLuQuLsPS2giERERERGRBIoy0jMj6SlERERERGoRbU6aWpV2etz9iShPZGb3uful2x9JREREREQkcapSsroyByTwuURERERERBIikZ0eERERERGRtFOVktUiIiIiIpIArjU9KZXIkR5L4HOJiIiIiIgkRJU7PWZWZxt3Dd/OLCIiIiIiIgkXudNjZv3NbDowO7y9h5k9UHJ/1CpvIiIiIiIiqVSVNT33A78C3gBw96lmNjApqUREREREarBi15qeVKrK9LYMd/9hi2NFiQwjIiIiIiKSaFUZ6fnJzPoDbmaZwEXArOTEEhERERERSYyqjPScBwwDOgFLgH3DYyIiIiIiImkr8kiPuy8BTkxiFhERERERkYSrSvW2zmb2upktCr9eNbPOyYsmIiIiIlIzeQ39X7qqypqe54FHgd+Gt38XHvtFZQ/cyRpWPVktlKn9XSNZkxl3gurjxyVN4o5QLdTLVE2WqPYpKo47QrWwcGwi9/6uuQr6Xxx3hGpjpwn3xx1BpFqravW2J919Y/j1VBUfLyIiIiIiknJVGel538yuAF4AnGDE579m1hjA3dckIZ+IiIiIiMh2qUqn55Tw+yVbHP89QSeoU0ISiYiIiIjUcNqcNLWqUr1th2QGERERERERSYaqVG/7zMzONbNGyQwkIiIiIiKSSFUpRHAG0A2Yamb/MrNByYkkIiIiIiKSOJE7Pe7+tbtfBfQAXgWeMbPvzOwGM2uatIQiIiIiIjVM3Pvp1LZ9eqpUctrMegF3ALcD/yEobrAReD/x0URERERERLZf5EIGZjYeyAP+Cdzo7nnhXR+b2S+TEU5ERERERGR7VdrpMbNj3f014FR3n1XeOe5+dMKTiYiIiIiIJECUkZ7rgde21eEREREREZGq0T49qVWlNT0iIiIiIiLVTZSRnp3NbFo5xw1wd++d4EwiIiIiIiIJE6XT8x1wVLKDiIiIiIiIJEOUTs9Gd/8h6UlERERERESSIEqn5+MoT2Rmp7v709uZR0RERESkxkvnjTxrokoLGbj7hRGf65LtzCIiIiIiIpJwiazeZgl8LhERERERkYRIZKdHY3QiIiIiIvKzmFlzMxtpZrPD7822cd5dZjbTzL4ys/vNrNLBF430iIiIiIikWLF7jfzaTlcDo929BzA6vF2GmQ0Afgn0BnYD9gYOrOyJI3V6zCzDzE6o5LRIBQ9ERERERETK8WugpDDa08Ax5ZzjQF0gB6gDZAOLK3viSJ0edy8GKixoUIWCByIiIiIiIltq4+4LAcLvrbc8wd0/BT4AFoZf77n7V5U9cZSS1SVGmtkVwIvAulL/4RVVeA4REREREamhzOxc4NxShx5190dL3T8KaFvOQ6+L+PzdgV2AjuGhkWZ2gLuPq+hxVen0nBV+v6DUMQe6VuE5RERERERqvZq6T0/YwXm0gvsP2dZ9ZrbYzNq5+0IzawcsKee03wCfufva8DHvAPsCFXZ6IhcycPcu5XypwyMiIiIiIonwJnB6+O/Tgf+Uc86PwIFmlmVm2QRFDCqd3ha502Nm9c3sejN7NLzdw8x+FfXxIiIiIiIiFbgDGGxms4HB4W3MrJ+ZPR6e8wowF5gOTAWmuvt/K3viqkxvexKYBAwIb88DXgbeqsJziIiIiIiIbMXdlwODyjk+Efi/8N9FwO+r+txV6fR0c/ffmtlJ4X8wL8pGQCIiIiIiUlZQHFlSpSqbk240s3oExQsws25AflJSiYiIiIiIJEhVRnpuAt4FdjCzfxPshHpGMkKJiIiIiIgkSuROj7uPNLPJBCXhDLjE3ZclLZmIiIiIiEgCVNrpMbO9tji0MPzeycw6ufvkxMcSERERERFJjCgjPfdUcJ8DBycoi4iIiIhIrVBcQzcnTVeVdnrcfWAqgoiIiIiIiCRDlOltx1Z0v7u/lrg4IiIiIiIiiRVlettRFdzngDo9IiIiIiKStqJMbzszFUFERERERGoLd63pSaXIm5OaWRsze8LM3glv9zKzs5MXTUREREREZPtF7vQATwHvAe3D27OASxMdSEREREREJJGq0ulp6e4vAcUA7l4IFCUllYiIiIiISIJEKWRQYp2ZtSAoXoCZ7QusTkoqEREREZEaTPv0pFZVOj3DgDeBbmb2MdAKOC4pqURERERERBKk0ultZra3mbV198nAgcC1QD4wApiX5HwiIiIiIiLbJcqankeAjeG/BwDXAQ8CK4FHk5RLREREREQkIaJMb8t09xXhv38LPOrurwKvmtmU5EUTEREREamZtE9PakUZ6ck0s5LO0SDg/VL3VWVNkIiIiIiISMpF6bQ8D4w1s2VAHvAhgJl1R9XbREREREQkzVXa6XH3W81sNNAOGOGbx+IygIuSGU5ERERERGR7RZqe5u6flXNsVuLjiIiIiIjUfMVa05NSUdb0iIiIiIiIVFvq9IiIiIiISI2mTo+IiIiIiNRo6vSIiIiIiEiNpn12RERERERSzFEhg1TSSI+IiIiIiNRoNXak58ibTmOngX0oyNvIq1c8zIKZ3291zulPX0Wj1k3JyMzkh8+/5s0bnsSLnd8Ov4hWXdsBULdxAzasWcfwIdem+BWkxpCbTqPHwD0oyNvI61c8wsJy2unUp/9Yqp2+4a2wnQZeeix9TxzIuhW5AIy660Vmj5ma4leQOnv95TTaH7wHRXkb+eyyR1g5/futzul91fF0Pn5/cpo04JUeZ5e5b4ej9mH3y4eCOyu//JFPL3gwRclTq/utZ9Ji0F4U5eXz9cUPsnb6d1ud07B3V3a+/wIy6+awfPRk5lz3JACdrziedqccQsHyNQB8e9tzrBj9RUrzJ8KOfzmbpgfvRXFePnMvG8766d9udU793bvS7b6LyKibw6r3J/PDDU8AkNm0IT0evpw6HVuRP28ps39/N0Wr123zeXM6tKLnE3+EzAwsK5PF/3ybJc+OAKD50b+kw8VDITODVaMn8dMtz6auEbZTr1tPp/WgPhTlbWTqxQ+xppz3W+PeXdjj/j+QWTeHJaOn8OV1TwPQqFcndv/r2WQ2qEveT0uZct6DFK7Nw7Iz2f2v/0eTPl2h2Jl5/dOs+OSrFL+yxKm/X1/aXPcHyMhg9SvvsuKxl8vcb9nZtL3zcuru2oOiVWtYMOx2Cucvgews2t58EXV364EXO0tue5i8CdMBaHnp6TT+9SAyGzdkdt9j43hZSdHwgL1od+O5kJHBypdGsOzhV8rcbzlZdLx7GHV3607Rqlx+uuhOCuYvAaDOzp3pcMuFZDSsB+7M/fVl+MYCdnzyZrJaN8cyM1g/8UsW3PgQFBfH8fJS7vrb7mXcxxNo3qwpb/zr4bjjiERSI0d6eh7Uh5Zd2nLvQcN449rHOfrWs8o974UL7mf4Eddw/6F/pH7zxux25L4AvHjhAwwfci3Dh1zLzHcmMPPdz1MZP2V6HLQHLbq05e8HXc6b1z7BUbeeWe55L13wAP844lqGH3oV9Zs3Ytcj99l036dPvMNDQ67loSHX1ugOT7uD96BRl7a89cvLmfDHJ+h3e/ltNX/kF4wYcuNWxxt2acOuFx3NyF//ibcHXsXkG6vPxWdVNB+0J/W6tGP8vhcx64pH6HnXOeWe1/Ouc5h1xSOM3/ci6nVpR/OD+2y6b94jbzFx0JVMHHRltezwNDl4L+p2acfUX17Ad398mC63n1vueV3u+D3f/fEhpv7yAup2aUeTgXsC0P7C37D6o2lM3e9CVn80jfYXHlvh8xYsWcnMo69hxuDLmXnk1bS/8Fiy2zQjq1lDOt1wGl+d8CemD7yU7JZNabzf7qlphO3UalAfGnRpy5h9L2P6FY+x211nl3ve7nedxfQrHmfMvpfRoEtbWh28BwC97z2Xr295gQ8PuopFb0+k6wW/AqDTKQcD8OFBVzH+hNvo9adTwCw1LyrRMjJoc+MFzDvnBr771e9pdORB5HTrVOaUJscdSvGatXx32NmsfPoNWl0e/C1sevzhAHx/9PnMO+taWl91zqZ2WPvBeH444ZLUvpZky8ig/c3n8f2ZNzHnsPNpctSB1Om+Q5lTmp1wKEVr1jH74HNZ/s//0PaqM4I7MjPY4d7LWXD9g8w5/AK+O+kavLAIgJ8uuoO5R17EnMMvILN5Y5oM2S/FLyw+xwwZzMP33hJ3DJEqqZGdnl0O7csXr30IwE9fzKFuo/o0atV0q/Py1+YBkJGVSVZ2FpSzSdRuR+7LtDc/TW7gmOx8aF+mhO00L2ynhpHaKaUx00LHw/ry/StBWy2fPIecJvWp23rrtlo+eQ4blqza6nj3kw9m1lMjKVi9HoD8cCSjpml5+N4sfnksAGsmzSarcQNytminnNZNyWpYjzUTg/2NF788lpZH9E951mRpdlh/lr0yBoC1k2eR2aQB2a2blTknu3UzMhvVY+2koA2WvTKGZofvs/nxLwWPX/bSGJod3r/C5/WCQnxjIQAZdbIgI7h4rdOpLRu+XUDhiuBnbc2H02g+5BdJe92J1Obwvsx/OXi/rZo0h+zG9amzxc9RnfDnaNXE2QDMf/lD2hzRD4AG3dux4tNgBGfZ2Gm0PTJow4Y9O7L8w5kAbFy2hoI164NRn2qobu+eFPy4gIJ5i6CgkNy3x9Jw0L5lzmk46BesfmMUALnvfUj9XwQfLuR068T6T6cAULRiNUVr1lF3tx4AbJj6NUVLV6bwlSRfvT16kv/DQgp+WowXFLL6rXE0Gly2rRodsi8rXx0NwOp3PqLBgKAD3XD/vdjw9fds+DoYsS5albtpNKc4/NtIViaWnY3Xoo0m+/XZnSaNG8Udo9pz9xr5la4q7PSY2etm9tq2vlIVsqoat2nG6gUrNt1es2gFjds2K/fcM565mmsnPUz+ujxmvD2+zH2d++/MumWrWf79oqTmjUvjNs1ZvWD5ptsVtdNpz1zFVZMeIn/dBmaWaqf+px/K+e/czjF3nUPdxvWTnjku9do2Z12ptlq/YAX1t9FW5WnUtS2Nu7bjkP/cxOD/3ky7g3onI2bs6rRrTv78ze2Uv3A5ddo13/qchaXOWVD2nA5nHU6/D+5mp/vOI6tJg+SHTrCcts3JX7Bs0+2NC5aT07b5VudsLNUGpc/JbtmUgiXBRWfBkpVkt2hS6fPmtG/B7qPupc/Ex1j44OsULF7Jhu8XUq9bR3I6toLMDJod3p+cDi2S86ITrG675uSV+jnasHAFdbf4OarbrjkbFm7+PZ+3YPmmc9Z+PY82h/cFoN1R+1IvfN1rvvyBNof3xTIzqNepFU16d6Fe++rRJlvKatOSgoVLN90uXLSMrDZlX0tW6xYULgx/ZoqKKc5dT2bTxuR/8x0NB/0CMjPI7tCGurt2J6tdq1TGT6nsti3KttXCZWRv0VbZbUqdU9JWzRpTp0t7cGfHp/5Mtzfvo+W5Q8s8bsen/swun/+b4nXrWfPOx0l/LSLy81W2pmd4SlIkmJUzXWFbHc+nTruDrDrZHH/fBXQdsCtzP5qx6b7eRw9g6pufJCtm7Mqb1bGtHvozp91JVp1sht53/qZ2mvCvUYy5/3VwOPjy4zj8+pN544+PJTl1TKrQVuU+PDOThl3aMHroLdRv15xDXr+Rtw++ioI16xMYMh1EeO+V+/4MTpr/9Ai+v/dVcKfL1SfS7ebT+ObSh5IRNHkqeH0VnVPpEGoFz7txwXKmHzKM7DbN6PnPq1n+1qcULlvNd9c8Qo+HL8eLnbUTv6HOjm2ivopYWbk/R1u2YTkPDM+Zeukj7Hrr6fQYdiyL35tMcTgSNu+5MTTs0YFfjriVvHnLWPn5LLyoKMHpYxTlvYaz+tX3yOm6Azu+cj8FC5aQ98VXUFiD2iGKqD9PmZnU79eLuccMozgvny7/upW8GXNY90kwnfuHM27EcrLpeN8VNBjQm3UfTUl+dhH5WSrs9Lj76J/7xGZ2LnAuwBHN92bPRt1/7lNFss+pg9n7pIEAzJv6LU3ab/5UsHHb5uQu3vZwfWF+AV+PmkSvwf02dXoyMjPY9bC9efCo65KaO9X6nzqYvmE7zZ/6LU1KfcoZtNPWU7NKFOYX8M2oyew8uC9zP5rBumWbp2hNeuEDTn7iiuQFj0GPMwbT7eSgrZZP+ZYG7VtQ8jl7/fbNyaugrba0fuEKlk+egxcWse6npayZu4BGXdqyYurWC9yrm/ZnHkb7Uw4BYM2UOdQpNZpQp10LNi5aUeb8YGSn1DntW7BxUTiysXT1puML/zWK3f91dTKjJ0ybMw6n1cmDAVg3ZQ512rdkbXhfTvsWFGzx+2fjwuXklGqDnNJtsGwV2a2bBaM8rZtRsHz1psdU9rwFi1eSN+snGu/TixX/+5RVIyeyauREAFqdPBgvTt8L2x3PHMwO4Zqb1VO+pV6HFpS8urrtmpO/qOxr3bCg7OhPvfYt2BCes27OAib89nYAGnRtS+vBwbQuLyrmq1Lr6Qa8dTPrvq2eI/mFi5eRXWp0JqttSwqXLN/qnKx2LSlcvAwyM8hoVJ/iVUHhmaV3PLrpvE7P38PGHxakJngMChYtL9tW7VpSsGRFuecULlq+qa2KVuVSuGg568bPoGhl8Pcud8xE6u3abVOnB8A3FpA7ajyND9lXnR6RNBZpTY+ZdTOzF8xsmpnNKvmq6DHu/qi793P3fsnu8ACMf3bkpuIDX42YyJ7H7g/ADnt2Jz83j9ylZS9Qc+rX2bTOJyMzg54D+7B07uZf+t32242l3y5gzRYXbNXdhGdHbio88PWIifQJ26njnt3ZkJvH2nLaqWGpdupRqp1Kr//Z5bB+LJk1L0WvIjVmPzWSdwdfy7uDr2X+uxPpfFzQVi326k7Bmrxy1+5sy/x3J9JmwC4A5DRvSKNu7Vj745Kk5E61BU++t6nwwLJ3PqfN8QcC0LhvDwpz17Nxi3bauGQVhWvzaNw3WEPQ5vgDWRYWCym9/qflkP6s+/qnFL2K7bP4qXeZMfhyZgy+nJXvTqDlcQcB0HCvnhStWb9pulqJgiUrKVq7gYZ79QSg5XEHsfK9CQCsHPE5LU8IHt/yhM3HV434vNznzWnXAqubA0BmkwY07LczeXPnA5AVTo3LbNKANmccztLnRiWtDbbXD0+O5KNB1/DRoGtY/M5EOhwfvN+a9u1OYe568rf4OcpfsorCtRto2jf4+9Lh+P1Z/O4kAHJaNg5OMqP7Zb/hh6eDz+8y6uWQWb8OAC0P2J3iwiLWzpqfipeXcBumzyJ7x/Zkd2gD2Vk0GnIga9//rMw5a9//jCbHBB9INDpsf9Z/FlyoW906WL2gHeoP2BMvLGLj3B9T+wJSKG/aLOp0bk92xzZYdhZNfnUAuaPKTmfPHT2eZkMHAdDkiP1Y9+m04Pi4SdTduTNWtw5kZtBgn93YMOdHMurXJatVOMU5M4NGB/Ujf27N+hsoyVeM18ivdBW1ZPVTwC3A3cARwJlA2tZl/OaDKfQc2IdhY/9GQV4+r135yKb7Lnz7NoYPuZbs+nU45fHLycrJxjIz+PaTmUz49+YLgt5H/YJpNXhqG8CsD6bQY2AfLh17b1CyulQ7nff2bTwUttPJjw8jMyebjLCdJv47uIA49JqTaNdrR9ydVfOW8ua1/4zrpSTdgtFTaDeoD7/65F6K8jYy/rLNbXX4yNt4d3BQ0rzP9Sex4zEDyKqXw68nPsDc5z9gxj2vsXDMNNoeuDtDxtyFFxUz5S/PsXHl2m3956qtFaMm02LQnuwz/gGK8jbyzSWby3L3G/1XJg66EoDZVz3GzvdfQEbdHFaMnrKpSlvXG0+l4W6dwZ0NPy1l1hWPlPefSWurRk+i6aC92OOTf1Ccl8+3l22eJbzbyHuYMfhyAL6/+hG6lpSs/mAyq9+fDMDC4a/R/eEraH3iIPLnL2P27++u8Hnr9ujITjeejnswm2nhw/8h7+vgAnbHv5xFg16dAZj3t5fY8O3CVDXDdlky6gtaDerDQePvoygvn2mXbP452G/07Xw06BoAZlz1T/a4/w9k1M1h6egpLB0dfMre/jcD2PHMQwFY9PYE5j0/BoA6LRvT/4VroNjZsGgFUy/8R2pfWCIVFbPkLw/R8YlbICOT1a+OYOOcH2lx0alsmDGLdR+MZ/Ur79Hurivp8t4TFK3OZeGwOwDIbNGEHR6/FS8upnDxchZedfemp211xVk0+tVArF4duo55ltWvvMvy4f+O61UmRlExC/70MJ2f/jOWkcHKl0eSP/tHWl96MnnTZ5M7egIrXxxBx3svp8f7j1K0ei0/XXwnAMVr1rHsiTfo9sa94MFIz9oPJpLZsimdHruBjJxsyMhg3afTWPHc2zG/0NS58qY7+PyLaaxatYZBx5zC+WefytCjDos7lkiFLMq6BDOb5O59zWy6u+8eHvvQ3feP8h+5rvPv0rfbl0Yyy51ULFvaZWPcCaqPdkVqrCjqZabvtK90s6yoTtwRqoXuzaKPBNdmBRsz445Qbew04f64I1Qb2S27VosLqlZNdqqR18dLV3+Tlu0fdaQn34LqAHPN7A/AfKB18mKJiIiIiIgkRtROz2VAQ+Bi4FagCVD+jp8iIiIiIlKhdN7TpiaK1Olx95IVf7nAqcmLIyIiIiIikliROj1mNpJyNpFw90MTnkhERERERCSBok5vu77Uv+sCQ4H8xMcRERERERFJrKpObysx1szGJiGPiIiIiIhIQkWd3ta41M0MoC/QLimJRERERERquGIVMkipqNPbZhKs6TGgEPgOOCdZoURERERERBIl6vS2HZIdREREREREJBmijvRgZv2BzqUf4+7PJSGTiIiIiIhIwkRd0/MU0AuYAhSFhx1Qp0dEREREpIq0OWlqRR3p2Rfo5e7FyQwjIiIiIiKSaBkRz5sJtExmEBERERERkWSIOtLTBPjKzD6j1Kak7n5sUlKJiIiIiIgkSNROz+1JTSEiIiIiUosUozU9qRS1ZPXoZAcRERERERFJhkhresxsbzP7zMxWm9kGM8s3szXJDiciIiIiIrK9ok5v+wdwCvAC0B84A9CGpSIiIiIikvaidnoy3P0bM8ty9wLgMTP7BLgxidlERERERGok7dOTWlE7PevMLAeYama3AQuBhsmLJSIiIiIikhhR9+k5Izz3QqAI6AEcl6RMIiIiIiIiCRO1etu34T83ADckL46IiIiIiEhiVdjpMbPJFd3v7nslNo6IiIiIiEhiVTbSkwMUAM8B/wPyk55IRERERKSGK1Yhg5SqcE2Pu+8GnAo0Bf5NMLWtG/Cdu89NfjwREREREZHtU2khA3ef4e7XufuewDsEoz6XJz2ZiIiIiIhIAlRayMDM2gK/JajWtha4Eng1yblEREREREQSorJCBqMJpra9TFC2emmp+xq7+5qkphMRERERqYEcrelJpcpGenYCHLgAOL/UcQuPd0pSLhERERERkYSosNPj7h2jPImZ7ezuXycmkoiIiIiISOJUWsggoucS9DwiIiIiIiIJVWkhg4gsQc8jIiIiIlLjaZ+e1ErUSI/+XxMRERERkbSUqE6PiIiIiIhIWkpUp6coQc8jIiIiIiKSUJHX9ISblHYq/Rh3/yT8vnfio4mIiIiI1EyuNT0pFanTY2a3AacAX7N5VMeBIUnKJSIiIiIikhBRR3qGAj3dfUMyw4iIiIiIiCRa1DU931XhXBERERERkbQRdaQnF/jCzEYB+SUH3X1YUlKJiIiIiIgkSNROz7vhl4iIiIiIbCfXNpcpFanT4+5PmFkW0D08NMfdC5MXS0REREREJDGiVm/bH3gWmA8Y0NbMTnX3j5MZTkREREREZHtFnd72N2CIu38JYGa7EHSC+iUrmIiIiIiISCJE7fTklHR4ANz9KzPLSVImEREREZEaTZuTplbUTs9kM3uEYHQH4GTgi+REEhERERERSZyonZ4/ABcDfyRY0zMOeCBZoURERERERBIlavW2DcBd4ZeIiIiIiEi1UWGnx8yed/eTzOwL2LqYuLvvlbRkIiIiIiI1lNb0pFZlIz1Xht+PS3YQERERERGRZMio6E53nxf+cwHwrbvPDW/vBPyQzGAiIiIiIiKJUGGnp5QPgXpm1g4YC5wH/DNpqURERERERBIkaqcnw93XA0OB4e5+FNA7ebFERERERGour6Ff6Spyp8fM9gZ+B7wVHstMTiQREREREZHEidrpGQbcDPzP3WeYWVeCKW8iIiIiIiJpLeo+Pe8D7wOYmQGL3f38ZAYTERERERFJBItSI9zMngEuBAqBiUBL4A53vze58ZLHzM5190fjzlEdqK2iUTtFp7aKRu0UjdopOrVVNGqn6NRWUl1End62u7uvAY4BRgAdgTOSFSpFzo07QDWitopG7RSd2ioatVM0aqfo1FbRqJ2iU1tJtRC105NjZlnAr4E33H0jUJy8WCIiIiIiIokRtdPzOPAj0AwYa2adgLVJSyUiIiIiIpIgkTo97v43d2/v7od6sAjoJ+Dg5EZLOs0/jU5tFY3aKTq1VTRqp2jUTtGpraJRO0WntpJqocJCBmZ2krs/b2YXl3e/u9+ftGQiIiIiIiIJUFnJ6mbh91bJDiIiIiIiIpIMkUpWi4iIiIiIVFeRNicNCxdcCHQu/Rh3PzY5sURERERERBIjUqcHeBN4BhiJSlWLSBWYWSZwsbv/Le4sIiLy85jZ68A2pwfpg3BJd5Gmt5nZBHfvn4I8SWVmbYDbgPbufoSZ9QJ+4e5PxBwt7da0GsYAACAASURBVJhZT+AhoI2772ZmvYGj3f2WmKOlHTNrBZzD1iOhZ8WVKd2Y2Rh3PyjuHNWBmV0IPOPua8zsEWBP4Bp3Hx1ztLSxreI6JVRkJ6B2ikYX89GY2aCK7tfvKEl3UUd6HjCz64H3gPySg+4+LSmpkucp4EnguvD2LOBFQJ2erT0GXAk8AsH/12b2HKBOz9b+A3wIjAKKYs6Srj42s+EE77d1JQfdfXJ8kdLWue4+3MwOBToA5xGUhO0bb6y0ouI60aidohked4DqQJ0aqe6idnp6Av8HHMHm6W0OHJCMUEnU0t1fMrNrANy90Mx0kVq++u4+wcxKHyuMK0yaq+/uV8UdIs0NCL//udQxp/rv95UMJZ84HwE86e6TzCzqRtK1grvfEHeG6kDtFI0u5qvGzLoBtwK9gLolx929Z2yhRCKI2uk5Aejs7vmVnpne1plZC8KLCjPbF1gdb6S0tSz8xVbSVscBC+ONlLbeMrMh7v523EHSlbsPjDtDNTLVzN4m+LDpOjNrSAVTb2ozM+sOPAi0dfc9wmm4R7r77TFHSytm1h74O7BfeGgccJm7L4gvVfrRxXxkTxHM+rib4MOZM9F6b6kGon56OA1olMwgKTKMoChDNzP7mKA4w0XxRkpbFxBMbdvZzOYDlxJMs5GtXULQ8dlgZmvMLNfM1sQdKp2YWRsze8LM3glv9zKzs+POlW4sGFq9HvgT0N/d1xNcfKmtyvc4cDObL7imA6fEFydtPQmMIFh32JmgKNGTMeZJV08RtIsRXMy/BLwQZ6A0Vd/d3wNw97nufj2gD7Yk7UUtZPA+0BsYT9k1PdVucZ+ZZQE7EfxS+8bdC2KOlNbMrAGQ4e65cWeR6ivs7DwJXBd+Ip8FfOHuu8ccLe2Y2SR31/qdCMzsc3ff28y+cPc9w2NT3L1P3NnSSXltonbaWsl7z8yml/xuMrMP3X3/uLOlEzP7BPgl8DrwLjAfuNvdd4o1mEgloo703Ar8FriXYCpByVe1YmYXAA3dfaa7zwAamtn5cedKR2Z2m5k1dfd17p5rZs3MTEUMymGBU8zshvD2DmZW7asdJlhLd3+J8BN5dy9ERR+2ZYKZ7RV3iGpiuZl1YfM03GOARfFGSksrzOxE2+y3wIq4Q6Wh/HC0da6Z/cHMjgJaxx0qDV0GNAQuJuj8nAOoWqmkvagjPfWADe7u4ZzXnYAR4YVLtbGNT7s2fUIom5XXLmY22d11MbYFM3uI4GL+YHffxcyaEbw/9o45WtowszHAUGCku+8Vrqe7090PjDdZ+jGz6cAuwFyCSncGuN57WwvX9DwK7AssJVh3eJK7fxdrsDRjZp2BfwD7EHQQPwMuUjuVZWb7AF8CzQg+7G1C8Hvq41iDiUhCRC1k8CFwgJk1AcYCXwAnAqclK1iSZJiZedjTCzdNzIk5U7rKNLM6JcUrwo5vnZgzpat9wgv5LwDcfaWZ6eeqrC3X07UCjos3Uto6Ju4A1YW7zwEODv82mbuvijtTugn/zh3t7kPizpLu3H18+M9c4NQ4s6QzMxtJOcVV3P3QGOKIRBa105Ph7uvN7CxguLvfYWZTkhksSd4DXjKzhwnesH8gmI8qW/sXMNrMniRoq7OAp+ONlLYKwguLks50K1TJpgx3n2xmB6L1dJVy97kAZtacUhWkZGulNnJdbWYPh9MCtZFrKe5eZGZDAW1EWgldzEd2fal/1yUYxa/u1X2lFog6vW0KwZzNvxNsnDej9EK/6iLc6+L3wCCCC68RwOPurrUF5TCzIyjVViXVWqQsMzuZYM3bXgQdw+OA69395ViDpRkzG0BQOWrThy3u/kxsgdKUmR0J/A3oCCwH2gOz3X3nWIOlITOb5u69w41cLwZuAh5VIYiywvWYjQgqkZXeHLi6bTCeVOH0thKbLubd/cqYIlUbZjZW05Ul3UUd6RlGUBb0f2GHpyvBlLdqxd2LgYfCL6mEu78DvBN3jnTn7v82s0ls7iAe4+5fxRwrrZjZs0A3YAqbCxg4Qdl4KetWgsXBI9x9TzMbTHDxJVvTRq7RlFyMll4XVh03GE+qUtPbSow1s7GxhEljZta41M0MoC/QLqY4IpFFGump7szsJXc/IVwgXN7Qde8YYqUlM/vI3fczs1zKtlXJYurG23horWVmfwdedPdP4s6SrszsK6CX14ZfONvJzCa6ez8zmwr0CQvITHB3VQTcgpk9A7Qk2Mi1N8EF2DgVfSjLzHZ09x8qO1bbbeNi/iFtTlqWmf1EcH1gQCHwHXCzu6uDKGkt0khPWCFnGFtPTaku81wvCb//KtYU1YC77xd+rwmb0abKZOB6M+tJsG/Bi+4+MeZM6WYG0JagupZUbHW4P9bHwDNmtgStEduWMwkuTOeE605boo1cy/M6ZUd5tnWstpvJ1hfz58SaKA25+w5xZxD5OaqypucJYBKl9tYoZyg4bYULzd9z90PizpLuwukh09x9t7izVCfhwvOhBJUNO7l7j5gjpQ0z+wDoA0yg7AbHR8cWKk2ZWSNgPcEnzacRlM191t2XxhosTZnZiUA3d7/VzHYAWrv7pLhzpYPwg5hdCPbYu6zUXY2Ba929VyzBpNoL96LrTNkPwp+LLZBIBFHX9BS7+wNJTZJkYQWb9WbWxN1Xx50nnbl7sZlNNbNO7v5j3Hmqke7AzgR/CL6MN0ra+VPcAaqLcDPgjkAPd3/CzOoCmXHnSkdmNhzIJlibcivBIv2HAe2RFdgVOBZoChxf6nguQVEfAcyswg9f3P3NVGWpDszsKaAXW6/RVKdH0lrUTs9/zOxcguHw0p/SrklKquTZAEwPy1KWrmBzcXyR0lY7YKaZTaBsW+mT+S2Y2Z0EFxZzgZeAv2i/kK10Az5099lxB0l34dYAFxKM8HQDOhFsLKlR6q0N2GKPrBXaI2szd38deN3M9nP3j+LOk8ZKOoQtgQHAmPD2gQR7E6rTU9a+BGs0Ne1WqpWonZ7/C7/fUOqYE/wxrk7+F35J5W6OO0A18h3wC3dfFneQNNYZOMXMdiSYJvshQSeoOu73lWwXA/2B8QDuPsvM2sQbKW0VhNNxS/bIaoHWP5VnkZm9B7R19z3MrDdwpLvfHnewdODupwKY2ZsEF/Pzw9sd0P5G5ZlJ0EFcEncQkaqI1OmpCYvWzGxPghGLmSonXDEzO4ZgqtZ07c2zbWa2s7t/TbBOpZOZlfkQwN0nx5Ms/bj7jQBmVo9gYfCVwH1o2lZ5Nrj7RjMDNq1HlPI9CLwKtDKzm4ET0Ac25XkcuJagvQCmA88D6vSU1bWkwxNaQLChspTVBPjKzD6j7OyfY+OLJFK5qCM9mNnOBHM4N+0QXl0WrZnZjcApBJ8w32Vmt7v7YzHHSktm9g+CeeCfAH8xs/7u/peYY6WrYcC5wD3l3OfAwamNk77M7HqCvWcaAl8AV1AN9/pKkY/N7I9AXTMbCFwAvBVzprTk7s+Ee2QdQlBx63h3nxFzrHTUwN0/KelIh2XQC2LOlI7Gmdn/CDqETlCUZly8kdKSOstSLUWt3nY9cCjBIu33gMOAj6pLr97MZgJ7hyVNWwDvursWupbDzGYAe4SFH+oTTEHS7ubbEE6t+YW7fxx3lnRmZpMJSsD+j2CO/GfuviHeVOkpHNk5l+B3rhH8zn1E8+c3M7MG7r5ui31VNqmG602TyszeBc4DXg3XQB0D/MHdD485WlqxoFd4PLB/eGgc8Ir2FxOpGaJ2eqYTlJudHM4HbkfwR7haLGo3s0mlL9y3vC2bmdnk0hv7bXlbtmZmn7r7L+LOke7CUsz7hV8nAItL9oUSCEd37nH3okpPruXM7B13P2KLTRI3fXf36rbeNKnCvfYeJViAvpRgv6wT3f37OHNJ9WRmewMPEJRDr0PwvsvX5uWS7qJOb8sLP/kvDC9cFgFdk5gr0bqFCxQheHOWvq2KZGXtbGbTwn+XtNU0Nl9M9I4vWtoaYWZDgdf0iWD5zGw3gk9PDwT6AT+h6W1b2hGYZGYXaOSwYu5+RPi92q83TQV3nwMcbGZNCD7sVHXJUsxsrLsfaGYrCYtilNxF8HeveUzR0tU/CJYMvEBQdOUMQO9FSXtRR3oeAa4CTiaoLLQG+MrdT0tuvMQwswMrut/dx6YqS7oLq2ttk7v/kKos1YWZ5QINCPYryGPzH0p96hUys1EEZWDHAl+4+9p4E6UnM9uL4BPUr4GHKFWJTIUxygqnAZZMu4Zgb6xRGinbLPxE/mGC0ufTgf9z92/iTZV+zCwj3J+u3KIh+pkqq2S2jJlNd/fdw2OfuPuAuLOJVKTSTk84x7Wtuy8Mb3cHGtfEP8Bm9qq7D407R3WgKV0ShZllAbcBZwE/EnQIOwJPAte5uxZTb8HMDiKoSDadzZ86u7urMEYonGL9PrCcoDCGEUzBbg4c7O6LYoyXNszsc+BGgrUpRwOnax3PtpnZPcAL7v553FnSmZmNIyge8k+C3+sLgXM0E0TSXdSRnlqxBsbMvnD3PePOUR2orQLhRf0RlP20+T13L4wvVfows78BjYDL3D03PNYYuJtg2uwlceZLJ2bWmqASYFfgfHefGnOktGVmTwIz3P2eLY5fBvR29zPjSZZetEazaszsbOC3BPuKvQq8qL3EtmZmXQnKedcFLicoYT3c3WfFGkykElE7PQ8Bj9XE0Z3S9AchOrUVmFl74AOCT7lKPm3eE2gLDHT3BTHGSwtmNhvoueVap3Aaydfu3iOeZOnHzOYCdxL8rtXasAqY2dfuvnNV76ttzOxb4NJSh+4rfdvd39zqQYKZtQKOI+gAtdXPk0jNUGEhAzPLCj+x3g84J/yjvI7NaxZq9UWv1Hq3AQ+5+32lD5rZxQT7GJweS6r04uVdwIeFUXRhX9b37v6omd1JsIZSti3vZ95X23xMUIK5vNsOqNNTvh0IRns6AHPijZI+wq0HtknXhJLuKqveNgHYCzgmBVnSgcUdoBpRW8G+7n7Glgfd/X4z02LhwJdmdpq7P1P6oJmdQrBYXzZrFxZdOdrMXmCL91hNH2mvoiZmVl7VTQNUQCTk7qdGOc/MTnH3fyU7T7ozs1sJRnh+Al4E9nH3FfGmSis5QAHwHMGea/nxxhGpmgqnt9W2dRtmdqi7j4g7Rzowszvd/aptHTOz3Wr7zucVvT9q23tnW8ysA/Aawafvkwg+Xd4bqAf8xt3nxxgvrZjZccDZBCPrE7e4W4UMSjGzZyu6P+rFvgQ0XTlgZhcCL7v74rizpKtw+4GTgCHAVIIO0ChtnizVQWWdnnnAvdu63923eV86CjdZ3fIFrya4wLjF3ZenPlV6Ku+PoJlNU3WWzcL58leUdxdwl7t3S3GktGVmBwO7ErTNTHcfHXOktGVmN7j7X+LOURNoBCOa2v4hjZn1cPfZZlbu3zd3n1be8drOzH4LPAjc6e5/jTuPSGUqm96WCTSk5kxleodgL5Xnwtsnht/XAE8BR8WQKa2Y2XnA+UDXUpuUQlCBSxsmljWWbf/MjEtlkHTn7u8TlBiWCphZDjDfzO4m+IDmS+A5d9c0kp9nGKBOT+Vq+/q6qwlGWR8s5z4HDkhtnPRlZm0JCjwcB6wFriSodCeS9iob6alRQ95m9rG7/7K8Y6U32arNwh27mxEsxL+61F25mtv885jZ6e7+dNw5JL2ZWS+CheUfE0wFNII1lb8Efu3uM2OMVy3V9hGMqNROEoWZjQaaAi+HX0tL3+/ua+LIJRJVZSM9NWWEp0RDM9vH3ccDmFl/gpEsAO2rArj7aoIpfyeFZYXbEPycNDSzhu7+Y6wBq6dLAHV6pDIPAOe5+8jSB83sEGA4MDCWVNVbbR/BiOqzuAPEaRtFMTZRae9NdiJ4T11AMCOkhIXHO8URSiSqykZ6mtekT/fNbG+CHYRLpuytAf4PmAkc6e4vxRgvrYQLOv8ELAZKFii61vRUnT5FlSgq2XvmK3ffJdWZqju99wLhvjO3AB3c/VfhqGJ/d38q3mTpoVRhjJbAAGBMePtAYKy7/zqOXNWVme3s7qrOKWmnwpGemtThAXD3z4Hdwylc5u6rSt2tDk9ZlwI7qbhDQujTZokiw8zqbLl+x8zqUvmovJSvVo9glPIU8G827/80m6Ak81Mx5UkrJdX+zOxNoFdJVcmw+uT9cWarpp4jmJorklYy4g6QSmZWx8x+RzA0e7GZ3WhmN8adK039RDDNTbZfTZsmKsnxDPCqmXUuORD++yWgwhLNtZWZtTKzR8zsrfB2LzM7o+R+dz8vtnDppbW7P0c4au/uBQRFfaSsrluU0V9AMKVLqkZ/8yQt1bZPD/9DcCE/CW2qVZlvgTFmVmYDsupWpjxNqOqdVMrdbwmnlY4zs/oEFw5rgbvd/YF406Wtp9AIRhTrzKw54ahzONU7N95IaWlc+DfveYK2OhFV4vw5NLtB0lKFa3pqGjOb4e67xZ2jOjCzm8o77u43pzpLujOzS4AnCS4iHgf2BK7WRrfyc5lZIwB314VpBczsc3ffu/TaHTOb4u594s6WTsysH/B3gr2ypgIdgOPcfUqswdKMmRlwPLB/eGgc8IrXpgulBKhplX+l5qhtIz2fmNnu7j497iDprqRzY2YN3H1d3HnS3Fnu/nczOwxoBZxJ0AlSp0eqLFxzeBNwQHANxljgz2FlRSlLIxgRuPtEMxsI7EIwgvilu2+MOVbaCTs3L6E1vttLUyclLdW2Ts9+wBlm9h3BlC1DFcnKZWa/AJ4gqHTXycz2AH7v7udX/MhaqWT+8hDgSXefGn5iKPJz/BOYAZwQ3j6VoBN9bGyJ0tcVwH8JNlMeSziCEW+k9GNmdYDfE/wNdOBDM3vs/9u7/yC7yvqO4+9PQi2hEnUCo1JZkaBoBBJ+xImE4iiWFkZQ0RGDgIiOP9oKNFZnFKpjWx3GGdQB2o6xIUSkiE4oEikIFQQFIj8SwKBiqfy0UiaKDYZATPz0j3O2uZtsdu+ucJ9z9nxeM3fuvefczHzmTnb3POf7PN8nm96OJOnNwNnAHlS/14evEWYWDdZA9SalQ/RcR9q+uX6eXypXxFi6Nr3tpaMdt/3goLM0naQfUF08XNEzbSTTA0chaRnVxdbLgLnAdOC7tg8uGixaabTpWZmytWOSnkMqGGOS9DWqG31frQ8tAnax/c5yqZpH0n3AWzMbZGySPgucCPyErVUd2z66XKqI8XWi0iNpZr1TcKY9TIDth7cpWKRkPbr3AvOAn9l+UtIsqiluEZOxUdJhtr8PIGkhsLFwpkZKBaNvc7aZ0XCtpLuKpWmu/8mApy9vA15h+6nSQSImohODHqqe8W+i6tpmRrZTNLB3iVAN97CkQwHXd1JPA35cOFMj2f6dpM1UazB6f6buLpUpWu1DwPJ6bQ/A48C7C+ZpsuVUFYwv1+8X1cdSwRjpTknz673qkHQwcEvhTE10m6SLgcsZ2bX0inKRGul+OrblSUwNnZreFv2TtBtVt583Ug0SrwFOz2al25N0AXAAcA/1PhhUpf5Ty6WKtqqrF28HZgPPp2qzb9t/VzRYA0m6e9s1mZLusj23VKYmkrQWmEO1FQFUU3HvoareO522KpJG2w/Ltk8eeJgGk/QNqr95/8HIweHiYqEi+tCJSo+kMX+h2149qCxtYXsd8K7SOVpige05pUPElPFN4NfAauDn43y261LB6M+bSwdoA9snlc7QElfXj4hW6USlR9L1Y5y27TcMLExLSPoc8A9Uawmuplqgf4btr475DztI0lLgHNs/Kp0l2i8NQ/qXCkZ/JJ1N1Vny3tJZmkjSFxhjQ81UMLZXT+Xep357n+3NJfNE9KMTlR7bry+doYWOtP0xSW8FHqHasO16tnb/ia2WA7dIepS0Qo/fX/YT618qGP15APhKvfZwGXBpNr4dYW3pAG0i6U+Ai6gq0QJeJOkk2zeVTRYxtq5Uesbc38L2ZYPK0haS7rH9aklfBlbYvjpz5UdXtzldDPyQrWt60go9JkXSj6juoGY/sXGkgjExkuYAp1KtGbsR+LLt75VN1R6Svmj7jNI5SpN0O3Dy8OwGSa8CLrJ9SNlkEWPrRKUHOGaMcwYy6NneSkk/oZre9heSdgfSnnJ0D6W7TzyDjiodoEUeIBWMvkiaRjX9by+qjoD3Ap+Q9EvbJ5bM1iKHlw7QEM/pnc5t+8d1l9eIRutEpScmR9ILgPW2t0j6I2BX24+WztU0kv6JqsvWSkZ2sslgOmIAUsEYW71G8zjgBmCp7Zt7zt1re99i4VpE0uqsEwNJF1L9rRvudvcuqs1u01o/Gq0rlR4AJL0Q+Cywh+2j6j+Ur7W9tHC0xpG0C/CXwBDwfmAPYF/gWyVzNdQMqj8AR/YcSwUxYgBSwdgxSUO2HwLuA+bZ/s0oH3vtgGNF+32Qau++j1FNv70ROK9ooog+dKrSI+kqqikQZ9qeW3cfWWN7/8LRGkfSpVSbuZ5sez9JM4BbbM8rHK0xJL3E9iM7OHeM7ZWDzhTRJalgjC2ViWeWpDW2DyydIyImp2s76u5m++vUi83rFotbykZqrNm2Pwf8FsD2Rqo7OrHVdyTtte1BSe8BvjjwNBEdIWmofjlcwXhv74CnlgpGfmdPSr1B8GjOH2iQhpF0Sf28RtLqbR+l80WMp1PT24ANkmZR9+OXtIBqt/PY3qa6ujP8Xc2mZ71KAPDXwLWSjrb9nwCSPg6cALyuaLKIqe1y4CDbS3b0Adu/GmCepvpjSefu6KTt0wYZpukkvQZYCjwPGJI0F3if7Q8DZCo8H62f3140RcQkdW3Qsxi4Apgt6SZgd/LDuyOfotqUdE9JFwMLgVOKJmoY2/8u6WngKklvAd4HzAcOt/142XQRU1oqGP3ZSDVNOfpzLvAmqkE1tu+SlH3+aj3Tuf8beMq26xui+wLXlEsW0Z9OrOmRNB942Paj9TqeDwBvA34EfDJ3BEeSJOAlwJPAAqoLjFW21xUN1lCSDqP6I3kz8A7bae0d8SyS9BjwtR2dTwWjkjU9EyPpVtuv6V27k/3ptlfv03M4VUXsNmAN8Ljtk4sGixhHVyo9XwLeWL8+FDgT+DAwD1hCqj0j1HdvLrd9MHBl6TxNJekJqul/Av4QOAJ4rB402vbMkvkiprBUMPqzqZ8PSXq17Xue7TAt8HA9xc2SplNdJ/y0cKYmmmb7SUmnAufbPlvSnaVDRYynK4Oe6T3VnOOBJbZXACvyg7pDqyTNt31b6SBNZXvX0hkiOuqXtpeXDtF0thf0+dGLgFSE4ENUU9yGgMeAa+tjMdK0egbNCVRbWgBML5gnoi+dGfRI2qnu1nYEW39IoTvfwUS9HvigpAeADVTVDNs+oGiqiIhUMJ5pWSMF2H4MeGfpHC2wGPg0cKXttZL2BrIZcDReV9b0nAkcDayjuoNzUD2Fax9gue2FRQM2kKSXjnbc9oODzhIRMRlZ09KffE+VeguCL7C13flNwEdsP1AoUuPV07l3sb2hdJaI8XRinx7bnwE+AlwIHOatI71pVHN2oyZpZ0lnULWm/HPg57YfHH4UjhcRMRGpYMREXELV4XWofqysj0UPSV+RNFPSLsA9wP2SFpfOFTGeTgx6AGyvsv1vvXcjbP/UdjbUGmk5cAjwQ+Ao4JyycSIiJm3qT2V4ZvQ1XbADptleZntT/biQDl0nTcD+ttcDb6FqVf0SsqVFtEDWs8S25tjeH0DSUuDWwnkiImISJI05ZW34pt8EGh5MdddJ+huqduimany0UtJMgPpCP+A59fYfbwb+2fYmSb8rHSpiPBn0xLZ+O/zC9uZqum5ERCt1vYIxVqXewBsGFaQlTqyfT9/m+Aeovq+hwcZprH8BHgLWAjdIGgJ+UzZSxPg60cgg+idpC1W3Nqjmw8+g2qQ0e89ERCP0W8GIiGdf3czgD2x3/SZDNFwGPRER0SqSrh/jtG2ngtGjXnC+GBiy/X5JLwf2tf2twtEaRdIq4ALgEttPlM7TNJIW2b5E0mmjnbd97qAzRUxEprdFRESr2H596Qwtswy4Azi0fv8I8A0gg56RTgHeA9wl6WZgme3vlI3UKC+on3cvmiJiklLpiYiIVkoFoz+Sbrd9iKQ1tg+sj91le27pbE0kaTpwLHA+1bqwC4DzbP+6aLCI+L2k0hMREW2VCkZ/NkmaQd3CW9Js4OmykZpJ0hyqas8xwDeBi4HDgOuAzm/gClA3LvgrYC96riNtH1cqU0Q/MuiJiIi2mm37eEmLAGxvVFpOjuZTwNXAnpIuBhaSfVW2I+kHwEaqys4nbW+sT90kaWG5ZI1zBfAV4FograqjNTLoiYiItkoFow+2r5W0GlhA1YnzdNvrCsdqDEnH2b4MOMn2T0f7jO1jBxyryTbZ/nzpEBETlTU9ERHRSpL+FDgLmEO1M/xC4BTb3y2Zq2kkHT7acds3DjpLE0labTtT1/ok6STgpcC36bnJYPvuYqEi+pBBT0REtJakWWytYKxKBWN7klb2vN0ZeA1wR1p7VzLomRhJfw+8D/gZW6e32faog+uIpsigJyIiWikVjMmRtCfwOduLSmdpAklPAveNdorqYv6AAUdqNEn3AgfYzlTSaJWs6YmIiLb6aM/r/69gAKlgjO0RYL/SIRrkfqpubdGfu4Fdyfq5aJkMeiIiopVsj7hQHa5gFIrTWJLOo272AEwD5gF3lUvUOJtsP1g6RIvMAn5Sd7vrXdOTltXRaBn0RETEVJEKxuhu73m9GbjE9k2lwjRQX9+FpHfbXv5sh2mBz5QOEDEZWdMTERGttIMKxgO2TyyXqjkkDdl+qHSOqSINDyp1m/inbLtuE78vcI3tzYWjRYwpg56IiGglSe/uebuZasCTCkat9yJd0grbbyudqc0krbF9YOkcpUm6HTgceB5wG7AGeNz2yUWDRYwj09siIqJVhisYmWo0LvW83rtYiqkjd4kr02w/KelU4HzbyHfXxwAABBhJREFUZ0u6s3SoiPFMKx0gIiJigi4ffiFpRckgDecdvI7J0fgf6YRpkuYDJwDfqo9NL5gnoi8Z9ERERNukgtGfuZLWS3oCOKB+vV7SE5LWlw7XJJKmSXrHOB/L1MnKYuDTwJW210raG/he4UwR48qanoiIaJVt1qpkcXk8IyTdaHvUDW8jov0y6ImIiFaRtAXYQFXxmQE8OXwKsO2ZpbJFe0n6W2AjcCnV/y8AbP+qWKgGkrQPVbVnL3rWhts+slSmiH5k0BMRERGdJ+n+UQ7bdqZQ9qibFiwF7gC2DB+3/YNioSL6kEFPRERERPQlU0qjrdLIICIiIjpP0i6SzpK0pH7/cklvKp2rgb4p6f2Sdpc0c/hROlTEeFLpiYiIiM6TdCnVlK2Tbe8naQZwi+15haM1iqSHRzls20MDDxMxAdmcNCIiIgJm2z5e0iIA2xslZW+ebdjes3SGiMnIoCciIiICNtXVHQNImg08XTZSM0l6JTAH2Hn4mO1/LZcoYnwZ9ERERETAp4CrgT0lXQwsBE4pmqiBJJ0FHAm8Evg28GfA94EMeqLRsqYnIiIiApA0C1hAtefTKtvrCkdqHEk/BOYBq23PlfRi4Eu2jy0cLWJMqfREREREZ0natv3yL+rnIUlDtlcPOlPDbbS9RdJmSbsCjwLZyygaL4OeiIiI6LJzxjhn4A2DCtISayQ9H7gAuB1YD2RgGI2X6W0RERERMa66m92LbP+ifr8PMDPVsGiDDHoiIiKisyQdN9Z525cNKksbSLrD9sGlc0RMVKa3RURERJcdM8Y5Axn0jHSrpINS3Ym2SaUnIiIiIsYkaSfbm+vuba8C/gvYQNXpzra3bQgR0Sip9ERERETnSXoh8FlgD9tHSZoDvNb20sLRmuJW4CDgLaWDRExGKj0RERHReZKuApYBZ9b7z+wErLG9f+FojSBpje0DS+eImKxUeiIiIiJgN9tfl/RxgHoq15bSoRpkd0mLd3TS9ucHGSZiojLoiYiIiIANkmZRNS9A0gLgf8tGapTpwHOp1vBEtE6mt0VERETnSToIOA/YD1gL7A683fbdRYM1hKTVaVYQbTatdICIiIiIUiTNl/SiugXz64BPAE8D1wCPFA3XLKnwRKtl0BMRERFd9iVgU/36UOBM4B+Bx4ElpUI10BGlA0T8PrKmJyIiIrpsuu1f1a+PB5bYXgGskHRnwVyN0vMdRbRSKj0RERHRZdPr9tRQVTOu6zmXm8MRU0R+mCMiIqLLLgFukLQO2Ah8D0DSPqR7W8SUke5tERER0Wl1e+oXA9fY3lAfewXw3LrBQUS0XAY9ERERERExpWVNT0RERERETGkZ9ERERERExJSWQU9ERERERExpGfRERERERMSUlkFPRERERERMaf8HKNOZqgAy9mAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "plt.figure(figsize=(15,20))\n",
    "sns.heatmap(var, annot = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>noOfYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  noOfYears  \n",
       "0       Manual      0          6  \n",
       "1       Manual      0          7  \n",
       "2       Manual      0          3  \n",
       "3       Manual      0          9  \n",
       "4       Manual      0          6  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfFinal.drop('Selling_Price',axis=1)\n",
    "Y = dfFinal['Selling_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>noOfYears</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  noOfYears  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0          6                 0   \n",
       "1           9.54       43000      0          7                 1   \n",
       "2           9.85        6900      0          3                 0   \n",
       "3           4.15        5200      0          9                 0   \n",
       "4           6.87       42450      0          6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rishi/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "                    max_features='auto', max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                    oob_score=False, random_state=None, verbose=0,\n",
       "                    warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Feature Importance\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.34845861 0.03429569 0.0004538  0.08052349 0.22645247 0.00051533\n",
      " 0.16474032 0.14456029]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.0s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.1s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.1s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.0s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.3s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.4s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.2s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.3s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.3s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.4s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.7s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.9s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   44.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                   iid='warn', n_iter=10, n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.6219805515396706"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe79dbc9dd8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Y_test-predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe79d5e24e0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.scatter(Y_test,predictions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.870164515089828\n",
      "MSE: 3.812885879402094\n",
      "RMSE: 1.9526612300658028\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(Y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(Y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(Y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}