Analyzing Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1466964</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1993</td>\n",
       "      <td>136.83</td>\n",
       "      <td>135.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1346989</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2539</td>\n",
       "      <td>134.86</td>\n",
       "      <td>135.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338232</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2139</td>\n",
       "      <td>339.50</td>\n",
       "      <td>437.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1448490</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2631</td>\n",
       "      <td>243.50</td>\n",
       "      <td>242.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1466964     1         55     1993          136.83      135.83   \n",
       "2  1346989     1         55     2539          134.86      135.86   \n",
       "3  1338232     1         55     2139          339.50      437.53   \n",
       "4  1448490     1         55     2631          243.50      242.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders  \n",
       "0                      0                  0         177  \n",
       "1                      0                  0         270  \n",
       "2                      0                  0         189  \n",
       "3                      0                  0          54  \n",
       "4                      0                  0          40  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "meal_info=pd.read_csv('meal_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>center_id</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>679</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>590</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_B</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>590</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>648</td>\n",
       "      <td>34</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>632</td>\n",
       "      <td>34</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>53</td>\n",
       "      <td>590</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>30</td>\n",
       "      <td>604</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>76</td>\n",
       "      <td>614</td>\n",
       "      <td>85</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>68</td>\n",
       "      <td>676</td>\n",
       "      <td>34</td>\n",
       "      <td>TYPE_B</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>51</td>\n",
       "      <td>638</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    center_id  city_code  region_code center_type  op_area\n",
       "0          11        679           56      TYPE_A      3.7\n",
       "1          13        590           56      TYPE_B      6.7\n",
       "2         124        590           56      TYPE_C      4.0\n",
       "3          66        648           34      TYPE_A      4.1\n",
       "4          94        632           34      TYPE_C      3.6\n",
       "..        ...        ...          ...         ...      ...\n",
       "72         53        590           56      TYPE_A      3.8\n",
       "73         30        604           56      TYPE_A      3.5\n",
       "74         76        614           85      TYPE_A      3.0\n",
       "75         68        676           34      TYPE_B      4.1\n",
       "76         51        638           56      TYPE_A      7.0\n",
       "\n",
       "[77 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_center=pd.read_csv('fulfilment_center_info.csv')\n",
    "f_center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the Data Sets to have complete data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.merge(train,meal_info,left_on='meal_id',right_on='meal_id')\n",
    "train=pd.merge(train,f_center,left_on='center_id',right_on='center_id')\n",
    "test=pd.merge(test,meal_info,left_on='meal_id',right_on='meal_id')\n",
    "test=pd.merge(test,f_center,left_on='center_id',right_on='center_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456548"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1018704</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1196273</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1116527</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1343872</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1493612</td>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.53</td>\n",
       "      <td>146.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1110832</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>145.53</td>\n",
       "      <td>146.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1461167</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.53</td>\n",
       "      <td>145.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1102364</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>134.83</td>\n",
       "      <td>134.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1018130</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>144.56</td>\n",
       "      <td>143.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1047565</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.53</td>\n",
       "      <td>144.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1282497</td>\n",
       "      <td>12</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>145.53</td>\n",
       "      <td>146.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1136216</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>145.50</td>\n",
       "      <td>146.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1196793</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>142.62</td>\n",
       "      <td>141.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1324644</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>117.37</td>\n",
       "      <td>143.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1239157</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>117.37</td>\n",
       "      <td>141.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1056819</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>122.28</td>\n",
       "      <td>143.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1278127</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>120.28</td>\n",
       "      <td>142.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1027349</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>120.28</td>\n",
       "      <td>143.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1466073</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>121.28</td>\n",
       "      <td>142.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0   1379560     1         55     1885          136.83      152.29   \n",
       "1   1018704     2         55     1885          135.83      152.29   \n",
       "2   1196273     3         55     1885          132.92      133.92   \n",
       "3   1116527     4         55     1885          135.86      134.86   \n",
       "4   1343872     5         55     1885          146.50      147.50   \n",
       "5   1493612     6         55     1885          146.53      146.53   \n",
       "6   1110832     7         55     1885          145.53      146.53   \n",
       "7   1461167     8         55     1885          146.53      145.53   \n",
       "8   1102364     9         55     1885          134.83      134.83   \n",
       "9   1018130    10         55     1885          144.56      143.56   \n",
       "10  1047565    11         55     1885          146.53      144.53   \n",
       "11  1282497    12         55     1885          145.53      146.53   \n",
       "12  1136216    13         55     1885          145.50      146.50   \n",
       "13  1196793    14         55     1885          142.62      141.62   \n",
       "14  1324644    15         55     1885          117.37      143.62   \n",
       "15  1239157    16         55     1885          117.37      141.62   \n",
       "16  1056819    17         55     1885          122.28      143.62   \n",
       "17  1278127    18         55     1885          120.28      142.62   \n",
       "18  1027349    19         55     1885          120.28      143.62   \n",
       "19  1466073    20         55     1885          121.28      142.62   \n",
       "\n",
       "    emailer_for_promotion  homepage_featured  num_orders   category cuisine  \\\n",
       "0                       0                  0         177  Beverages    Thai   \n",
       "1                       0                  0         323  Beverages    Thai   \n",
       "2                       0                  0          96  Beverages    Thai   \n",
       "3                       0                  0         163  Beverages    Thai   \n",
       "4                       0                  0         215  Beverages    Thai   \n",
       "5                       0                  0         285  Beverages    Thai   \n",
       "6                       0                  0         148  Beverages    Thai   \n",
       "7                       0                  0         135  Beverages    Thai   \n",
       "8                       0                  0         175  Beverages    Thai   \n",
       "9                       0                  0         175  Beverages    Thai   \n",
       "10                      0                  0         244  Beverages    Thai   \n",
       "11                      0                  0         190  Beverages    Thai   \n",
       "12                      0                  0          68  Beverages    Thai   \n",
       "13                      0                  0         135  Beverages    Thai   \n",
       "14                      0                  0         366  Beverages    Thai   \n",
       "15                      0                  0          67  Beverages    Thai   \n",
       "16                      0                  0         270  Beverages    Thai   \n",
       "17                      0                  0         350  Beverages    Thai   \n",
       "18                      0                  0         244  Beverages    Thai   \n",
       "19                      0                  0         188  Beverages    Thai   \n",
       "\n",
       "    city_code  region_code center_type  op_area  \n",
       "0         647           56      TYPE_C      2.0  \n",
       "1         647           56      TYPE_C      2.0  \n",
       "2         647           56      TYPE_C      2.0  \n",
       "3         647           56      TYPE_C      2.0  \n",
       "4         647           56      TYPE_C      2.0  \n",
       "5         647           56      TYPE_C      2.0  \n",
       "6         647           56      TYPE_C      2.0  \n",
       "7         647           56      TYPE_C      2.0  \n",
       "8         647           56      TYPE_C      2.0  \n",
       "9         647           56      TYPE_C      2.0  \n",
       "10        647           56      TYPE_C      2.0  \n",
       "11        647           56      TYPE_C      2.0  \n",
       "12        647           56      TYPE_C      2.0  \n",
       "13        647           56      TYPE_C      2.0  \n",
       "14        647           56      TYPE_C      2.0  \n",
       "15        647           56      TYPE_C      2.0  \n",
       "16        647           56      TYPE_C      2.0  \n",
       "17        647           56      TYPE_C      2.0  \n",
       "18        647           56      TYPE_C      2.0  \n",
       "19        647           56      TYPE_C      2.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1018704</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1196273</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1116527</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1343872</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1018704     2         55     1885          135.83      152.29   \n",
       "2  1196273     3         55     1885          132.92      133.92   \n",
       "3  1116527     4         55     1885          135.86      134.86   \n",
       "4  1343872     5         55     1885          146.50      147.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders   category cuisine  \\\n",
       "0                      0                  0         177  Beverages    Thai   \n",
       "1                      0                  0         323  Beverages    Thai   \n",
       "2                      0                  0          96  Beverages    Thai   \n",
       "3                      0                  0         163  Beverages    Thai   \n",
       "4                      0                  0         215  Beverages    Thai   \n",
       "\n",
       "   city_code  region_code center_type  op_area  \n",
       "0        647           56      TYPE_C      2.0  \n",
       "1        647           56      TYPE_C      2.0  \n",
       "2        647           56      TYPE_C      2.0  \n",
       "3        647           56      TYPE_C      2.0  \n",
       "4        647           56      TYPE_C      2.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_copy=train.copy()\n",
    "train_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 55,  24,  11,  83,  32,  13, 109,  52,  93, 186, 146,  57, 149,\n",
       "        89, 124, 152,  97,  74, 108,  99,  66,  94,  91,  20,  34, 137,\n",
       "        92, 126,  36, 162,  75, 177,  27, 157, 106,  64, 129,  14,  17,\n",
       "       153, 139, 161,  81,  26,  73,  50, 104,  42, 113, 145,  53,  72,\n",
       "        67, 174,  29,  77,  41,  30,  76,  59,  88, 143,  58,  10, 101,\n",
       "        80,  43,  65,  39, 102, 110, 132,  23,  86,  68,  51,  61],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.center_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1885, 1993, 2539, 2139, 2631, 1248, 1778, 1062, 2707, 1207, 1230,\n",
       "       2322, 2290, 1727, 1109, 2640, 2306, 2126, 2826, 1754, 1971, 1902,\n",
       "       1311, 1803, 1558, 2581, 1962, 1445, 2444, 2867, 1525, 2704, 2304,\n",
       "       2577, 1878, 1216, 1247, 1770, 1198, 2760, 2492, 1543, 2664, 2569,\n",
       "       2490, 1571, 2956, 1438, 2494, 1847, 2104], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.meal_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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></th>\n",
       "      <th>id</th>\n",
       "      <th>week</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal_id</th>\n",
       "      <th>center_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1062</th>\n",
       "      <th>10</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2956</th>\n",
       "      <th>152</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3597 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id  week  checkout_price  base_price  \\\n",
       "meal_id center_id                                          \n",
       "1062    10         145   145             145         145   \n",
       "        11         145   145             145         145   \n",
       "        13         145   145             145         145   \n",
       "        14         145   145             145         145   \n",
       "        17         145   145             145         145   \n",
       "...                ...   ...             ...         ...   \n",
       "2956    152          8     8               8           8   \n",
       "        153         81    81              81          81   \n",
       "        157         83    83              83          83   \n",
       "        174         80    80              80          80   \n",
       "        186         45    45              45          45   \n",
       "\n",
       "                   emailer_for_promotion  homepage_featured  num_orders  \\\n",
       "meal_id center_id                                                         \n",
       "1062    10                           145                145         145   \n",
       "        11                           145                145         145   \n",
       "        13                           145                145         145   \n",
       "        14                           145                145         145   \n",
       "        17                           145                145         145   \n",
       "...                                  ...                ...         ...   \n",
       "2956    152                            8                  8           8   \n",
       "        153                           81                 81          81   \n",
       "        157                           83                 83          83   \n",
       "        174                           80                 80          80   \n",
       "        186                           45                 45          45   \n",
       "\n",
       "                   category  cuisine  city_code  region_code  center_type  \\\n",
       "meal_id center_id                                                           \n",
       "1062    10              145      145        145          145          145   \n",
       "        11              145      145        145          145          145   \n",
       "        13              145      145        145          145          145   \n",
       "        14              145      145        145          145          145   \n",
       "        17              145      145        145          145          145   \n",
       "...                     ...      ...        ...          ...          ...   \n",
       "2956    152               8        8          8            8            8   \n",
       "        153              81       81         81           81           81   \n",
       "        157              83       83         83           83           83   \n",
       "        174              80       80         80           80           80   \n",
       "        186              45       45         45           45           45   \n",
       "\n",
       "                   op_area  \n",
       "meal_id center_id           \n",
       "1062    10             145  \n",
       "        11             145  \n",
       "        13             145  \n",
       "        14             145  \n",
       "        17             145  \n",
       "...                    ...  \n",
       "2956    152              8  \n",
       "        153             81  \n",
       "        157             83  \n",
       "        174             80  \n",
       "        186             45  \n",
       "\n",
       "[3597 rows x 13 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(by=['meal_id','center_id']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.565480e+05</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.00000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548</td>\n",
       "      <td>456548</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548.000000</td>\n",
       "      <td>456548</td>\n",
       "      <td>456548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Italian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TYPE_A</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127890</td>\n",
       "      <td>122925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>262881</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.250096e+06</td>\n",
       "      <td>74.768771</td>\n",
       "      <td>82.105796</td>\n",
       "      <td>2024.337458</td>\n",
       "      <td>332.238933</td>\n",
       "      <td>354.156627</td>\n",
       "      <td>0.081152</td>\n",
       "      <td>0.10920</td>\n",
       "      <td>261.872760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>601.553399</td>\n",
       "      <td>56.614566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.083590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.443548e+05</td>\n",
       "      <td>41.524956</td>\n",
       "      <td>45.975046</td>\n",
       "      <td>547.420920</td>\n",
       "      <td>152.939723</td>\n",
       "      <td>160.715914</td>\n",
       "      <td>0.273069</td>\n",
       "      <td>0.31189</td>\n",
       "      <td>395.922798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.195914</td>\n",
       "      <td>17.641306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.091686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1062.000000</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>55.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.124999e+06</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>228.950000</td>\n",
       "      <td>243.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>553.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.250184e+06</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>296.820000</td>\n",
       "      <td>310.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.375140e+06</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2539.000000</td>\n",
       "      <td>445.230000</td>\n",
       "      <td>458.870000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.499999e+06</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>2956.000000</td>\n",
       "      <td>866.270000</td>\n",
       "      <td>866.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>24299.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id           week      center_id        meal_id  \\\n",
       "count   4.565480e+05  456548.000000  456548.000000  456548.000000   \n",
       "unique           NaN            NaN            NaN            NaN   \n",
       "top              NaN            NaN            NaN            NaN   \n",
       "freq             NaN            NaN            NaN            NaN   \n",
       "mean    1.250096e+06      74.768771      82.105796    2024.337458   \n",
       "std     1.443548e+05      41.524956      45.975046     547.420920   \n",
       "min     1.000000e+06       1.000000      10.000000    1062.000000   \n",
       "25%     1.124999e+06      39.000000      43.000000    1558.000000   \n",
       "50%     1.250184e+06      76.000000      76.000000    1993.000000   \n",
       "75%     1.375140e+06     111.000000     110.000000    2539.000000   \n",
       "max     1.499999e+06     145.000000     186.000000    2956.000000   \n",
       "\n",
       "        checkout_price     base_price  emailer_for_promotion  \\\n",
       "count    456548.000000  456548.000000          456548.000000   \n",
       "unique             NaN            NaN                    NaN   \n",
       "top                NaN            NaN                    NaN   \n",
       "freq               NaN            NaN                    NaN   \n",
       "mean        332.238933     354.156627               0.081152   \n",
       "std         152.939723     160.715914               0.273069   \n",
       "min           2.970000      55.350000               0.000000   \n",
       "25%         228.950000     243.500000               0.000000   \n",
       "50%         296.820000     310.460000               0.000000   \n",
       "75%         445.230000     458.870000               0.000000   \n",
       "max         866.270000     866.270000               1.000000   \n",
       "\n",
       "        homepage_featured     num_orders   category  cuisine      city_code  \\\n",
       "count        456548.00000  456548.000000     456548   456548  456548.000000   \n",
       "unique                NaN            NaN         14        4            NaN   \n",
       "top                   NaN            NaN  Beverages  Italian            NaN   \n",
       "freq                  NaN            NaN     127890   122925            NaN   \n",
       "mean              0.10920     261.872760        NaN      NaN     601.553399   \n",
       "std               0.31189     395.922798        NaN      NaN      66.195914   \n",
       "min               0.00000      13.000000        NaN      NaN     456.000000   \n",
       "25%               0.00000      54.000000        NaN      NaN     553.000000   \n",
       "50%               0.00000     136.000000        NaN      NaN     596.000000   \n",
       "75%               0.00000     324.000000        NaN      NaN     651.000000   \n",
       "max               1.00000   24299.000000        NaN      NaN     713.000000   \n",
       "\n",
       "          region_code center_type        op_area  \n",
       "count   456548.000000      456548  456548.000000  \n",
       "unique            NaN           3            NaN  \n",
       "top               NaN      TYPE_A            NaN  \n",
       "freq              NaN      262881            NaN  \n",
       "mean        56.614566         NaN       4.083590  \n",
       "std         17.641306         NaN       1.091686  \n",
       "min         23.000000         NaN       0.900000  \n",
       "25%         34.000000         NaN       3.600000  \n",
       "50%         56.000000         NaN       4.000000  \n",
       "75%         77.000000         NaN       4.500000  \n",
       "max         93.000000         NaN       7.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['diff']=train.base_price-train.checkout_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0     1         55     1885          136.83      152.29   \n",
       "1     2         55     1885          135.83      152.29   \n",
       "2     3         55     1885          132.92      133.92   \n",
       "3     4         55     1885          135.86      134.86   \n",
       "4     5         55     1885          146.50      147.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders   category cuisine  \\\n",
       "0                      0                  0         177  Beverages    Thai   \n",
       "1                      0                  0         323  Beverages    Thai   \n",
       "2                      0                  0          96  Beverages    Thai   \n",
       "3                      0                  0         163  Beverages    Thai   \n",
       "4                      0                  0         215  Beverages    Thai   \n",
       "\n",
       "   city_code  region_code center_type  op_area   diff  \n",
       "0        647           56      TYPE_C      2.0  15.46  \n",
       "1        647           56      TYPE_C      2.0  16.46  \n",
       "2        647           56      TYPE_C      2.0   1.00  \n",
       "3        647           56      TYPE_C      2.0  -1.00  \n",
       "4        647           56      TYPE_C      2.0   1.00  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train.head()\n",
    "train.drop(columns=['id'],inplace=True)\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(456548, 15)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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></th>\n",
       "      <th>week</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal_id</th>\n",
       "      <th>center_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1885</th>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>96</td>\n",
       "      <td>136.83</td>\n",
       "      <td>135.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>97</td>\n",
       "      <td>133.86</td>\n",
       "      <td>135.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>98</td>\n",
       "      <td>120.28</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>99</td>\n",
       "      <td>146.47</td>\n",
       "      <td>148.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>100</td>\n",
       "      <td>144.53</td>\n",
       "      <td>146.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   week  checkout_price  base_price  emailer_for_promotion  \\\n",
       "meal_id center_id                                                            \n",
       "1885    55            1          136.83      152.29                      0   \n",
       "        55            2          135.83      152.29                      0   \n",
       "        55            3          132.92      133.92                      0   \n",
       "        55            4          135.86      134.86                      0   \n",
       "        55            5          146.50      147.50                      0   \n",
       "...                 ...             ...         ...                    ...   \n",
       "        55           96          136.83      135.83                      0   \n",
       "        55           97          133.86      135.86                      0   \n",
       "        55           98          120.28      134.86                      0   \n",
       "        55           99          146.47      148.47                      0   \n",
       "        55          100          144.53      146.53                      0   \n",
       "\n",
       "                   homepage_featured  num_orders   category cuisine  \\\n",
       "meal_id center_id                                                     \n",
       "1885    55                         0         177  Beverages    Thai   \n",
       "        55                         0         323  Beverages    Thai   \n",
       "        55                         0          96  Beverages    Thai   \n",
       "        55                         0         163  Beverages    Thai   \n",
       "        55                         0         215  Beverages    Thai   \n",
       "...                              ...         ...        ...     ...   \n",
       "        55                         0         271  Beverages    Thai   \n",
       "        55                         0         203  Beverages    Thai   \n",
       "        55                         0         134  Beverages    Thai   \n",
       "        55                         0         109  Beverages    Thai   \n",
       "        55                         0         163  Beverages    Thai   \n",
       "\n",
       "                   city_code  region_code center_type  op_area   diff  \n",
       "meal_id center_id                                                      \n",
       "1885    55               647           56      TYPE_C      2.0  15.46  \n",
       "        55               647           56      TYPE_C      2.0  16.46  \n",
       "        55               647           56      TYPE_C      2.0   1.00  \n",
       "        55               647           56      TYPE_C      2.0  -1.00  \n",
       "        55               647           56      TYPE_C      2.0   1.00  \n",
       "...                      ...          ...         ...      ...    ...  \n",
       "        55               647           56      TYPE_C      2.0  -1.00  \n",
       "        55               647           56      TYPE_C      2.0   2.00  \n",
       "        55               647           56      TYPE_C      2.0  14.58  \n",
       "        55               647           56      TYPE_C      2.0   2.00  \n",
       "        55               647           56      TYPE_C      2.0   2.00  \n",
       "\n",
       "[100 rows x 13 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.set_index(['meal_id', 'center_id'],inplace=True)\n",
    "train.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the qualitative amount of workers needed in the centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>id</th>\n",
       "      <th>week</th>\n",
       "      <th>center_id</th>\n",
       "      <th>meal_id</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>num_orders_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1379560</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>136.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1018704</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.83</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1196273</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>132.92</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1116527</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>135.86</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1343872</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1885</td>\n",
       "      <td>146.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
       "0  1379560     1         55     1885          136.83      152.29   \n",
       "1  1018704     2         55     1885          135.83      152.29   \n",
       "2  1196273     3         55     1885          132.92      133.92   \n",
       "3  1116527     4         55     1885          135.86      134.86   \n",
       "4  1343872     5         55     1885          146.50      147.50   \n",
       "\n",
       "   emailer_for_promotion  homepage_featured  num_orders   category cuisine  \\\n",
       "0                      0                  0         177  Beverages    Thai   \n",
       "1                      0                  0         323  Beverages    Thai   \n",
       "2                      0                  0          96  Beverages    Thai   \n",
       "3                      0                  0         163  Beverages    Thai   \n",
       "4                      0                  0         215  Beverages    Thai   \n",
       "\n",
       "   city_code  region_code center_type  op_area num_orders_category  \n",
       "0        647           56      TYPE_C      2.0              Medium  \n",
       "1        647           56      TYPE_C      2.0              Medium  \n",
       "2        647           56      TYPE_C      2.0                 Low  \n",
       "3        647           56      TYPE_C      2.0              Medium  \n",
       "4        647           56      TYPE_C      2.0              Medium  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def no_category(num):\n",
    "    if num < 136.0:\n",
    "        return 'Low'\n",
    "    elif num < 324.0:\n",
    "        return 'Medium'\n",
    "    elif num < 609.0:\n",
    "        return 'High'\n",
    "    else:\n",
    "        return 'Very High'\n",
    "    \n",
    "train_copy['num_orders_category'] = train_copy['num_orders'].apply(no_category)\n",
    "train_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data =train_copy.groupby(['num_orders_category'])['num_orders'].sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_orders_category', ylabel='num_orders'>"
      ]
     },
     "execution_count": 47,
     "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.barplot(x='num_orders_category', y='num_orders', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x24402dd5248>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#paiplot\n",
    "sns.pairplot(train_copy[['base_price','num_orders']][:15000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2440af8cb88>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#regplot\n",
    "sns.jointplot(x='base_price',y='num_orders',data=train_copy[:5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5UAAAO5CAYAAACJxgGeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3wUxf/H8ddc6D0JJaEoCFgRQq8CoYuC+uX7s1JFQBQQEKQKSLGhiEpHEEHs+kVQBEE60jtY6D0hkIQeQpKb3x93pCfgkYa8n48HD+5uZ3ZnJnu7+9mZnTPWWkREREREREQ84cjsAoiIiIiIiMitS0GliIiIiIiIeExBpYiIiIiIiHhMQaWIiIiIiIh4TEGliIiIiIiIeExBpYiIiIiIiHhMQaWIiIiIiMi/gDFmpjEmxBizO4XlxhjzkTFmvzFmpzGmSlpsV0GliIiIiIjIv8MsoEUqyx8Gyrv/dQUmp8VGFVSKiIiIiIj8C1hrVwFhqSR5DJhtXdYDhYwx/je7XQWVIiIiIiIit4cSwLF474+7P7sp2W52BXLrijpz0GZ2GbKC56v2y+wiZBkOYzK7CFnGZRud2UXIMmLQoeKaGOvM7CJkGTmMV2YXIctwoGPnNU4dL2Jl034R68sj826JxrgVro1zFCnbDdew1WumWWun/YNVJPe3uOl6K6gUERERERG5BbgDyH8SRCZ2HCgV731J4ORNFQoNfxUREREREbldzAfau2eBrQWcs9YG3exK1VMpIiIiIiLyL2CM+RJoCBQ2xhwHhgPZAay1U4CFQEtgP3AZ6JQW21VQKSIiIiIi4ozJ7BLcNGvtM9dZboGX03q7Gv4qIiIiIiIiHlNQKSIiIiIiIh7T8FcRERERERH9bJTH1FMpIiIiIiIiHlNQKSIiIiIiIh7T8FcRERERERGnhr96Sj2VIiIiIiIi4jEFlSIiIiIiIuIxDX8VEREREZHbntXsrx5TT6WIiIiIiIh4TEGliIiIiIiIeEzDX0VERERERDT7q8fUUykiIiIiIiIeU1ApIiIiIiIiHlNQKSIiIiIiIh7TM5UiIiIiIiL6SRGPqadSREREREREPKagUkRERERERDym4a8iIiIiIiLOmMwuwS1LPZUiIiIiIiLiMQWVIiIiIiIi4jENfxUREREREdHsrx5TT6WIiIiIiIh4TEGliIiIiIiIeEzDX0VERERERJwa/uop9VSKiIiIiIiIxxRUioiIiIiIiMc0/FVERERERG57VrO/ekw9lSIiIiIiIuIxBZUiIiIiIiLiMQ1/FRERERER0eyvHlNQKZli6JvjWLV2Iz7ehZj3+ZTMLk6ae7BBZdoNfx6Hl4MVXy3lp8n/S5Km3YjOVAqsQmREJNP6TeDI7oP4+PvS7YNeFCzijXU6Wf7FEn799GcAXp7wKv53FQcgT4G8XD5/iaEtX83QenniwQYBPDfM1RYrv/6Nn5Npi+eGP0+lwCpcjbjK9H4fc2TPIXz8fek6rhcFixTCOi3Lv1zCEndblLrvTjqO6UbOPLk4c/w0U3qP58rFiIyuWqo6jehClcCqREZEMrHfhxzafTBJmqKlitL74/7kK5SPQ7sP8nGfD4iOik41f0CDynQa3gWHl4PfvlrCvMnfA1D6/jJ0GdOdHDmzExPj5JOhU9i/Y1/stgoXL8wHSyfwzfivWDBtXvo3QAo6j+hClcBqREZEMqHfeA4m2y7F6PtxP/IVys+h3Qf4MF67pJT/0c6tafJ0M7CWI38dYUL/D4mKjOKZV5+jetOaWKeTc6Hn+PjVDwkPCcvQOt+ILm90paq7Xh++Op6Duw8kSVO0VDH6T3iNfIXyc3D3fj7oPY7oqGhKlC1Jr/d6U7ZCWT4fO5t501zfsRJ3laDfxAGx+f3u8OOLcZ+zYMb8DKvXjUhpn47vn34f8hXMR5+J/SlSsiinj4cw7qV3uXT+Uuz6ssr3IbFKDSrTcfgLOLwcLPtqCT9O/iFJmo4jXqCyuy0m9/soti2ul/fRro/RbkgnXghox4XwCwDcce+ddHmrO7nz5cE6LYNb9yMqMir9K3oD0mO/qNWyDk/2eYYS5UoyqHV/Du7aD0CRkkUZ/9sETh44AcDebXuZPmRyBtX0n6nUoDLt3X/n5V8tYX4y+0iHES8QEFiVq+595LC7XbqN7UHlRtU4H3qO15q9Epu+Zss6/LfP0xQvV5LXW/fn4K6kxx+R69Hw138JY8wKY0y1zC7HjXq8ZVOmjBud2cVIF8bhoMOoLoztMJoBTV6hduuHKF6+ZII0lQKrUKyMP/0avMzMQVPoNLorADExTr4Y/RkDG/fijccH0qT9w7F5J/Z4n6EtX2Voy1fZtGg9mxetz/C6/VPG4aD9yC6833EMg5r2plbrehQvl7AtKjasgl8Zf15r2INPB0+mwxh3W0TH8OXoWQxq8gojnxhIk3YtYvM+//ZLfPPO5wxt0ZctizfQsutjGV631FQOrIp/GX96NniRqYMm0mV092TTPTewAz/NmE+vht25eO4ijZ5qkmp+h8NB51HdGNPhDfo06UHd1g9RsnwpANoO6sC3H35F/5Z9+HrcF7Qd1CHBtjoM68y2FVvTsdbXVyWwKv5livNyg25MGTSRrim0S7uBHVgwYz49Gr7IxXMXafxU01Tz+xTz4ZFOrXjt0b70btYTh5eDeq0eAmDe1B/o26IXr7bszebfNvHkK09lTGX/gaqB1fAvXZwX63dl4sAJdB/zUrLpOgzqyPxPfqR7g65cPHeJJu52uXj2AtOHT2XetIQXlycOnqDPw73o83AvXn2kN5ERkaxftC7d6/NPpLZPX+PJ9+Hxl9qwa+1OejXszq61O3n8pTYJ1pkVvg+JGYeD50d1460OI+nbpCd1Wz9EiUTnjoDAqviV8eeVBt2ZPmgSnUe/eEN5ff0LU7FeAKePh8R+5vBy0GN8Hz4ZPIV+TXvxxlNDiY6KyZjKXkd67RfH9h7lvW5v8+eGPUm2GXwkmP4t+9C/ZZ8sG1Aah4NOo7rxToeR9GvSkzqp7CN9Eu0jACu/XcbbHUYmWe+xvUcZ1+1t/trwR7rXQf69FFRKpqgW8CAFC+TP7GKki7IB5Th1OIjTx04RExXN+gVrqNq0RoI0VZrWYM33KwA4sG0veQrkpWBRb86FhHPEfUfxyqUrnNx/HJ9ivkm2UfOROqybvybd63Kz7goox6kjwbFtsWHBGqo0q54gTZVm1Vn7w0oADmzbR578eSlYpBDnTp/lyJ5DgLstDhzH288HAP+7ivO3++S3Z80Oqj1cKwNrdX3Vm9Zg5ffLAdi3bS95C+SlUFHvJOkq1KnI+oVrAVj5/TKqN6uVav5yAeUJPhxMyLFTREdFs3bBaqq59y1rIU++PADkyZ8nQW9c9WY1CTl6imN7j6ZfpW9AjaY1WeGu195tf5O3QF68k2mXB+tUZJ27XZZ/v4wazWpeN7+Xl4McuXLg8HKQM3dOwk656h8Rrwc7V55cWJt+9fNUjWY1Wf79MiD1dqlYpyJrF7q+98u++41azWsDcC70HPt37iM6OuWAoGLdSgQfDeL0idPpUAPPpbZPX+PJ96F605qscLfpiu+XUaNZ3DEiq3wfEisXUJ5Th4MIcR8vf1+whupNayZIU71pDVa5zx2J2yK1vO2HPc/ctz5LsP9XrF+Zo38d5sifhwHXzQmbRYb+pdd+cWL/cU4ePJHh9UkrrrrF/Z3XLVhDtUT7SNWmNVjt3kf2u68vrp1//tr4BxfPXkyy3pP7jxN08GS6l1/+3RRUZhJjzGvGmF7u1x8YY5a5Xzc2xnxujGlmjFlnjNlqjPnWGJPPvbyqMWalMWaLMWaxMcY/0XodxpjPjDH/zm7AW4C3ny9hQaGx78OCQmODobg0PoSdPBOXJjgUn2IJ0xQuWYQ7HyjD/u17E3x+T437OXfmLKcOB6VD6dOWd7FE9QwKwztRkOxdzIfQRG3h7ZcwTeGSRbjz/jIc2O4aznl871EqN3UFp9Vb1sHHv3B6VcEjPn6+CeoUGnwmyc2B/N75uXz+Es4Y10VcaFAoPu79JKX8Pn6+hAbFb89QfN1tNWvkJ7Qb3JHJ62bQfkgn5r4zB4CcuXPyePf/8O34r9Knsv+Aj58vZ07GBTWhwaHJtsulRO1yrY4p5Q87FcaP0+Yxdd0MZmz6jMsXLrFj9fbYdM/2b8u0dTOo/3gDvho3Nx1r6BlfP1/OxPu7ngmOq/M1+b0LJGqXM/j4Jb3hlJKHWtdn1Y+r0qbAaSi1fTpBmn/4fShYuCBnQ8IBOBsSToHCBYGs9X1IzMfPJ0F9QlM4dyRsC9e5I7W8VZtUJyw4NDZ4vKZ4meJYC4NnD+ftn9+ndbcn0qFWnkmv/SI1RUsV492FH/DG12O4t/r9aVCLtOd9A/uIj1/Sc2ri6wtJhXVm/X9ZlILKzLMKeMj9uhqQzxiTHagH7AKGAk2stVWAzUBf9/KPgf9aa6sCM4Ex8daZDZgL7LXWDs2YakhiJpnPEveOGJM0lY2XKGeeXPSa8hpzR85M8qxg7db1WH8L9FLC9evpTpQ0Y6K26Dm5P3NHfhrbFjNem0STdi14Y8G75M6Xixj383ZZRXL1TrwTpJbmRvLHfez6vFnbh5k1agbda3dm1sgZdH+3JwBP9n2Gnz6Zz5XLV/5BDdJH8tW6frvY2HZJPn/eAnmp0awm3et14YUaHcmZOxf1n2gYm+aLsZ/TtXZnVs1bycMdHrmZKqQLk8xRI2m7JM2X5LuUgmzZs1GjaQ3W/nxrHDduZJ+43vchJVnp+5BYcvsBN3TuSDlvjlw5eKLH//HNuC+TLHZkc3Bv9fv4+JVxDGsziOotalKhbkVPi5/u0nO/CA8Jo3vtF3itZR8+GzWTVz56ldz5cntc1vRyM/uISHrTRD2ZZwtQ1RiTH4gEtuIKLh8C5gP3A2vdB4ccwDrgHqACsMT9uRcQv7tqKvCNtTZ+oJmAMaYr0BVg0vujeaH9M2lbK3HdFfSPuyvq4+/L2VMJJwYJCwrFp3hc75qPny/h7rvqXtm86DWlP7/PW8XmRRsS5HN4OajWohavP9o/HWuQdsKCE9XT34eziSZJCQ8Oxbd4Ya5NKePj50u4u728snnRc0p/fp+3mi2L49oi6MAJxrYfBUCxMv5UCqyavhW5Ac3bt6TJ065n3Pbv3I9vvHr7+hUmLFG9z4edJ0+BvDi8HDhjnPj6+xJ2yrUPhAadSTZ/thzZ8PWP356+scM8G7YJ5NMR0wFY9/NaXnynBwDlA+6m1sN1aDuoA3kL5MVaS1TkVRZ9tjAdWiGpFu1b0vTpZgDs37mPwsWLAH+66+WbZNKc82HnyZukXVxpQoNCk81fsV4Ap46d4nzYeQA2LFrHvVXvZdX/ViRY9+ofVzLk02F8/UHSC+yM1rL9IzR9pjngbpd4f9fCfnF1viZpuxSO/Z5cT5WGVTmw+wDnzpxNs/KnlbDg0BT36Ws8+T6cO3OOQkW9ORsSTqGi3pw/cw7I/O9DakITtYWvv2+Sv3FYUGiitnB9BxK3xbW8xe70p2iporz7y/jYz9/+eRyDH+tPWFAof6zfEztpz7blWylT4S52r92ZjrW8Mem1X6Qk+mo0F6+62uHg7gOcOhKEf5kSsRP5ZBWJ2yW5fSQ00T7ik8xxViQ9qKcyk1hro4DDQCfgd2A1EAiUBQ4BS6y1Ae5/91trO+PqBNsT7/MHrbXN4q32dyDQGJMrle1Os9ZWs9ZWU0CZPg7u2I9fGX+KlCqKV/Zs1GpVj61LNiVIs3XpJuq1aQhA2cp3c/nCZc65g8oX3n2Zk/tPsOiTBUnW/UC9SgQdOEF4cGiSZVnRoR37KVban8IlXW1Rs1U9ti3ZnCDNtiWbqPufBgCUrVyeiAuXOXf6LACd33mJk/uPs3hGwrbI71sAcN2RfazHf1k299f0r8x1LJ69MHaSh02/rqdBm0AAyle+m8sXLsUOxYtvz7pd1GpZF4AGbRqxaYkrcN68dGOy+ffv2Id/GddFYrbs2ajb6iE2L9kIQFhIGPfXqgBAhboVCT7sej5m2P8N5uV6XXm5Xld+nrmAHyZ+l6EX0ItmL+TVlr15tWVvNv66gYbuet1d+R4uX7gcezMlvt3rdlHb3S6B8dpl09KNyeY/c/I0d1e+hxy5cgDwYN1KHN9/DAD/0nFPCFRvWoMTB46nX2X/gYWzf46dRGf94nUEtmkEuOp1KYV22bVuF3Vb1gOg0X8bs+HXG5usq/5jDVidBYe+Aqnu09d48n3YvHQjDd1t2jDePpTZ34fUHNixL8G5o06resm2RX33uSN+W6SU99jfR+hatSM963WlZ72uhAaFMvCRvpw7fZYdK7dx5313xj6LfH/NBzi+71gm1Dyp9NovUlLApwAOh+uSuGipYviXKU7I0eD0qdxNSPx3rt2qHlsS1W3r0o085N5HyqVy/pEUOGOy/r8sSj2VmWsV0A94HteQ13G4ejDXAxONMeWstfuNMXmAksDfQBFjTG1r7Tr3cNi7rbXXpjGbAdQHvjXGPGGtzVpjAuPpP/xtNm3bydmz52n8eFte6tyONq2aZ3ax0oQzxsnsYZ/Qf/YwHF4OVn3zGyf2HaPRc674f9ncX9mxbAsBgVV4b9UkrkZEMr3fBADurnYv9do05Oifhxm98H0Avh07lx3LXbMU1m5Vl3XzV2dOxTzgjHEyZ9gn9J/9urstlnFi3zEC3W2xfO6v7Fi+lYqBVRi7ciKREZF80n8iAOWr3UvdNg059ucRRi58D4Dv3v2CnSu2Uqv1QzRp1wKAzYs3sPrbZZlTwRRsXbaFyoHV+HjVFK5GRDKx38exywbNep0pr00kPCSMz9/6jD4T+vFMv+c4tOcgy75ekmp+Z4yTGcOmMWT2CNd08t/8FnsROHXARDqNeAGHlxdRkVFMHTgpw+t9PVuWbaZKYFUmrZrq/kmQj2KXDZk1jEmvTSA8JIw5b82i74T+PNuvLYf2HGSpu11Syr9v+17WLVzLez+PxxkTw8E9B/n1i8UAtB3YgRJ3lcDptJw+EcLUwVmzXaoFVmPK6ulERkTycb/xsctenzWCiQM+IuxUGJ+99Sn9Jgzguf5tObjnIEu+dt1MKVSkEO//NJ48+fLgdDpp1fkxejTuTsTFCHLkykmlhwKYNGhCJtUudSnt002fc32/l8xd5NH34X+TvqfvpP40eqoJZ06eZlz3dzOphjfOGeNk5rDpDJ49HIeXFyu+Wcrxfcdo8pzr3Lh07mK2LdtC5cCqfOhui8nu70BKeVNz6fwlfvpkPm8ueA+sZdvyrWxbtiXd63kj0mu/qNG8Fs+/0YUCPgUZ9OnrHP7jEGPaj+C+mg/wVN9niYmOwel0Mm3wZC6eSzqhTWZzxjiZNWw6g66zjwQEVmX8qilERkQyNd5xtudHfbmvdgXyexdgwvpP+O6Dr1jx9VKqNa9JR3e7vOZul7fbv5FZ1ZRblLnRZzIk7RljGgOLgELW2kvGmL3AFGvtOGNMI+AdIKc7+VBr7XxjTADwEVAQ102B8dba6caYFUA/a+1mY8wbwN3Ac9am/ERv1JmD+uMDz1ftl9lFyDIcyT2jcpu6nHXvyWS4mMQP7dzGYrLwJAkZLYfxyuwiZBmOZJ+mvz05dbyIlU37Rawvj8y7JRoj8q+VWX4HznlvgyzZluqpzETW2t+A7PHe3x3v9TKgejJ5tuPqjUz8ecN4r4encVFFRERERP7ddOPQY3qmUkRERERERDymoFJEREREREQ8puGvIiIiIiIiTg1/9ZR6KkVERERERMRjCipFRERERETEYxr+KiIiIiIiotlfPaaeShEREREREfGYgkoRERERERHxmIJKERERERER8ZieqRQREREREdFPinhMPZUiIiIiIiLiMQWVIiIiIiIi4jENfxURERERkduetTGZXYRblnoqRURERERExGMKKkVERERERMRjGv4qIiIiIiJiNfurp9RTKSIiIiIiIh5TUCkiIiIiIiIe0/BXERERERERp4a/eko9lSIiIiIiIuIxBZUiIiIiIiLiMQ1/FRERERER0eyvHlNPpYiIiIiIiHhMQaWIiIiIiIh4TMNfRUREREREnDGZXYJblnoqRURERERExGMKKkVERERERMRjCipFRERERETEY3qmUkRERERERD8p4jH1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VURERERExKnhr55ST6WIiIiIiIh4TEGliIiIiIiIeEzDX0VERERERDT7q8cUVN7Gnq/aL7OLkCXM3PJeZhchy7Dnz2R2EbKMdoFvZnYRsoywmIjMLkKWsTJkT2YXIct43L9qZhchy4jBZnYRsoxcurSMFYUCFLl9aPiriIiIiIiIeEy3k0RERERERDT7q8fUUykiIiIiIiIeU1ApIiIiIiIiHtPwVxEREREREQ1/9Zh6KkVERERERMRjCipFRERERETEYwoqRURERERExGN6plJERERERG571sZkdhFuWeqpFBEREREREY8pqBQRERERERGPafiriIiIiIiIflLEY+qpFBEREREREY8pqBQRERERERGPafiriIiIiIiI1fBXT6mnUkRERERERDymoFJEREREREQ8puGvIiIiIiIimv3VY+qpFBEREREREY8pqBQRERERERGPafiriIiIiIiIZn/1mHoqRURERERExGMKKkVERERERMRjCipFRERERETEY3qmUkRERERERD8p4jH1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VURERERERD8p4jH1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VURERERERLO/ekw9lSIiIiIiIuIxBZUiIiIiIiLiMQ1/FRERERER0fBXj6mnUkRERERERDymoFJEREREREQ8puGvkmYebFCZdsOfx+HlYMVXS/lp8v+SpGk3ojOVAqsQGRHJtH4TOLL7ID7+vnT7oBcFi3hjnU6Wf7GEXz/9GYCXJ7yK/13FAchTIC+Xz19iaMtXM7Re6W3om+NYtXYjPt6FmPf5lMwuTrpbs3kn70yZQ4zTyX9aNOSFJ1slWH7uwiWGfTCdY0Eh5MyRnZF9XqB86VIANO/Qhzx5cuHlcODl5cXXH43MjCr8I5UaVKbj8BdweDlY9tUSfpz8Q5I0HUe8QOXAqkRGRDK530cc2n3whvI+2vUx2g3pxAsB7bgQfgGvbF50e+dlylQoi1c2B6u+X8G8Sd9nSD098fLIl6jZqDqREZG82+c99u3enySNXyk/hk4aTP5C+dm3ax9vv/Iu0VHRVKpdkZEz3iD4WDAAa35Zw5zxcyl5V0lenzwkNr//HX7Mem82P8xIejzKqj4YN5KHWzTickQEnTv3Ydv23UnSzP7sY6pWrURUVBSbNm2n+0sDiI6OpkCB/Mz+7GNKlSpBtmxejBs3hc9mf5MJtfhnnh/RhcqB1bgaEcmEfuNjvwPxFS1VjD4f9yNfofwc3H2Aj/t8QHRU9HXzOxwO3vlpHGHBobz1/CgASt9fhq5jXiJ7zuw4Y2KYPnQK+3fsy5jKXkd6tEX2nNkZ+c1bZM+RHa9sXqxbuJZvPvgydn0Pd3yEFu0fwRnjZMuyzXz+1qyMqu4NqZjo+mJBCtcXAfGuLw67ry9eTHR9sdh9ffHM4PZUblyN6KhoQo6cYlr/j7l8/nJGV+2GpMd55L+9n6bxM005H3oegC/Hfs725Vvwyp6Nrm92566K5bBOJ7PemMEf65Meg/61rIa/eupf01NpjJlljPlvFlpPb2NMnptdj3tdLxpj2qfFutKLcTjoMKoLYzuMZkCTV6jd+iGKly+ZIE2lwCoUK+NPvwYvM3PQFDqN7gpATIyTL0Z/xsDGvXjj8YE0af9wbN6JPd5naMtXGdryVTYtWs/mReszvG7p7fGWTZkybnRmFyNDxMQ4GTPxMyaN6s+PU9/hlxXrOHDkRII0n3w9n3vL3sEPk99kTL9uvDPl8wTLZ749mO8mjrklAkrjcPD8qG681WEkfZv0pG7rhyiR6HsREFgVvzL+vNKgO9MHTaLz6BdvKK+vf2Eq1gvg9PGQ2M9qPVKX7Dmy07/5Kwx85FUaP9ucIiWLZkxl/6EajapTskwJ2tfrxLgB43nlrV7JpusyuDPfT/+BDg914uK5izz8dIvYZbs37qJb8+50a96dOePnAnD84PHYz7o//DKREZGsWbQ2Q+qUFh5u0Yjy5cpw7/316N59ABMnvJVsui+//B8PVKhPQOXG5M6di87PPwvAS9078uefe6larSmNm/yXse8OI3v27BlZhX+scmBV/MsUp2eDbkwZNJGuo7snm67twA78NGM+PRu+yKVzF2n0VNMbyt/y+VYc338swWftBnXk2w+/pH/L3nw17gvaDeqYLnX7p9KrLaIio3jjmaH0e/gV+j38CpUbVKF85XsAeKD2g1RvWpNXW/SiT9MezJ+WtW7AXLu+eLfDaF5r8gq1Uri+8Cvjz6sNXmbGoCl0dF9fON3XFwMa92JEouuLXat3MLBZbwa36EvQoZO0eqlNhtftRqTneeTnGfMZ0LIPA1r2YfvyLQA0fsa1L/Vv/gqj246g3dBOGGMyqLZyK/vXBJVZUG/gpoNKY0w2a+0Ua+3smy9S+ikbUI5Th4M4fewUMVHRrF+whqpNayRIU6VpDdZ8vwKAA9v2kqdAXgoW9eZcSDhH3HfUrly6wsn9x/Ep5ptkGzUfqcO6+WvSvS4ZrVrAgxQskD+zi5Ehdu09wB3Fi1HKvyjZs2fj4Qa1WL5+S4I0B46eoGalBwC4q1RxTpw6w5nwc5lR3JtWLqA8pw4HEeL+Xvy+YA3Vm9ZMkKZ60xqscn8v9m3bS94CeSlU1Pu6edsPe565b32GtXHrstaSM08uHF4OcuTKSXRUFJcvZM0773Wb1eHX75YA8OfWv8hXIC8+RX2SpKtcN4CVP68C4Ndvl1C3eZ0b3kblepU5eSSIkBMh10+cRbRq1Zw5c78DYMPGrRQsVBA/v6Q3Bn5ZtCz29aZN2ylZ0h9w7QP58uUDIF++vISFnSU6OjoDSu656k1rsuL75QDs2/Y3edzfgcQq1KnIuoWuGwQrvl9GjWY1r5vfx8+Xqo2q8dtXSxKsy1pL7nyuU3Se/HkJCwlLn8r9Q+nZFlcuXwHAK5sXXtmzce3g0bztw/xv0vdEX3XtJ+dDs9bx9kauL6omur64dhw9GxLO4RSuL3av3oEzxhmbx8c/6XVHVpCe55HklCxfil2/7wRc+8Kl85e4q2K5dKmb/LvcskGlMaa9MWanMWaHMWaO++P6xpjfjTEH4/c2GmP6G2M2udO/cZ11xN/GKHfPpZcxZqwxZrcxZpcx5in38obGmJ/ipZ9gjOlojOkFFAeWG2OWp1KHi8aY940xW40xvxljirg/X2GMedMYsxJ4xRgzwhjTz72snDFmqbvMW40xZVOrY0bx9vMlLCg09n1YUCjefj6J0vgQdvJMXJrgUHyKJUxTuGQR7nygDPu3703w+T017ufcmbOcOhyUDqWXjBJyJhy/InF/82KFfTgVGp4gzT133cHS3zcDsOvvAwSFnOHUGdcFnzHQbcg7PNnzdb5duIyszsfPh9CguH0+NIXvRWi870Wo+3uRWt6qTaoTFhzKkT8PJ1jXhoW/E3n5ClM3fcrEddP5adqPXDp3MR1qdvMK+/ly+uTp2Peng85Q2C/hRV0B7wJcPH8x9sLPlaZw7PL7q97PtF8n89acMdx5951JthHYugHLfkzxEJwllSjux/FjJ2PfnzgeRInifimmz5YtG88914bFi131nDjpU+67tzzHjmxl+9bf6PvqcGz8Ow9ZkK+fL6Hx9oWw4FB8E91YzO+dn0vnL8XuC6FBofi495fU8nca/gJz3pyFTTSj46cjP6Hd4E5MWTeD9kM6MfedrHHfNj3bwuFwMHbheGZsncPO1dvZ5z7P+pcpzn017ueteWN54+s3KZvFAogbvb4ITXR94Z3C9cWBRNcXAPWfbMTOFVvTuORpI73OIwDN2z/Cu4vG8+LYHuQtkBeAI38cpnrTGji8HBQpVZS7KpTFt3jccfdfz+nM+v+yqFsyqDTGPAAMARpZaysBr7gX+QP1gEeBt91pmwHlgRpAAFDVGFM/lXVc28a7QFGgE/C4O28loAkw1hjjn1L5rLUfASeBQGttYCpVyQtstdZWAVYCw+MtK2StbWCtfT9RnrnARHeZ6wBBKdUxuQ0aY7oaYzYbYzbvu3golaL9M8kNjEh8HZPc8In4Fzs58+Si15TXmDtyJlcuRiRIV7t1Pdb/C3spbzeWpBe3JtHe0/n/WnH+4iX++/IQvpi/hHvL3kk2L9ehavb7w/hmwmgmj+rHVz8tZfOuvzKk3J5KXDeAxE2Q/Pci5bw5cuXgiR7/xzfjvkyyuFxAeZxOJy/WeJ6e9brxaJfHKFqqmKfFT18p1DthkmTSuBtw3679PFOzLV2bded/n85j5IwRCdJly56NOs1qs+qnVWlW5IxwveNkYhM+fpPVqzewZu1GAJo1a8iOHXsodWcVqlZvxofjR5M/f750K2+aSG5XT1TnVNslhfxVG1XjXOg5Du4+kGR587YPM2vUJ7xYuzOzRn7CS+/29KjoaS6d2gLA6XTSv2VvutV6nnIB5Sl19x2Aq+cyX8F8DHq8P3Pe/JS+kwbcXB3SWLIDLz24vnhlymt8PnImEYmuL1r3aIMz2sna/2XNY0V6nEcAlnz+C73qv8iAh/sQHhJOu9c7AbD8m6WEBoXy1oL36TCsM3u3/oUzOuZmqyG3gVsyqAQaAd9Za88AWGuvjVuZZ611Wmv/AK5dSTVz/9sGbAXuxRWApbQOgNdxBXXdrOuoVA/40lobY609hSsArJ4G9XACX7tff+7ezjVfJ05sjMkPlLDW/s9d5ivW2sup1DEJa+00a201a2218vnKpEEVXMKCQxMMHfHx9+XsqYTDicKCQvGJd7fLx8+X8BBXL5VXNi96TenP7/NWsXnRhgT5HF4OqrWoxfoFt85zUZK8YoV9CD4dt1+cOhNGUd9CCdLky5ub0X278t3EMbzZrxvh5y5Qophr+F9RX9cwLt9CBWlcpxq7/056sZiVhAaH4usft8/7+vsSnsz3Iv5dYF8/X8JDwlLMW+xOf4qWKsq7v4zn4zXT8PX35e2fx1GwSCHqPlaf7Su2ERMdw/nQc/y95c8sNWzpsQ6tmLp4MlMXTyb0VChFiheJXVbEvzChp0ITpD8Xdo58BfLhcN9UKOJfmNBgV5rLFy/HDufbuGwT2bJ5UcC7QGzeGoHV2bdrP+FnzqZzrW5e9xc7sHnTr2ze9Csng4IpWap47LISJf05GXQq2XyvD+1DkSK+9Os/Ivazju2f4n/zFgJw4MBhDh8+xr33ZJ194JoW7VsyduF4xi4cT/ipMHzj7Qs+fr5JhqOeDztP3gJ5Y/eF+N+l0KDQZPPfU+1+qjepwaQ10+n9cX8q1KlIr/F9AWjQphEbflkHwLqf11Ku0t3pWt/UZERbxHf5/CX2rNtN5YZVYvNsWORqi/079mGdTgr4FCCrSO764nrHUR8/X87Gu754JYXri4faNKRy42pMeuWDdKzBzUmP8wjAuTPnsE4n1lqWfbmEcpVcl43OGCezR81kQMs+vNflLfIUyEvQ4ZPIrcUY08IY87cxZr8xZmAyywsaYxa4Rz7uMcZ0utlt3qpBpSHJfRoAIhOlufb/W9baAPe/ctbaGamsA2ATrt4+n3jrSE40Cdsw1w2VPmXxy3MpmeUplSOlOmaYgzv241fGnyKliuKVPRu1WtVj65JNCdJsXbqJem0aAlC28t1cvnCZc+6D/gvvvszJ/SdY9MmCJOt+oF4lgg6cIDw4NMkyubVUuPsujpwM5nhwCFFR0fyycj0Na1VJkOb8xUtEuWcx/H7RCqo+eA/58ubm8pUrXLrsusN8+coVft+6i3LuWWGzqgM79iX4XtRpVY/NSzYmSLN56Ubqu78X5SvfzeULlzgbEp5i3mN/H6Fr1Y70rNeVnvW6EhoUysBH+nLu9FnOnDhNhToPApAzd07KV76HkweOZ3S1U/TjZwtiJ9FZu+h3mv3XNSHEfVXu5dKFS8k+17b99x00eMQ18KLZ/zXl919dF7/eReKeM7sn4B6Mw8H58POxnzV6LPCWGfo6ecpnVKvejGrVmzF//mLaPed6eqNmjSqcP3ee4OCkz4Q+3+kZmjVtyHNtX07QI3P02AkaNXLdnyxatDB3330XBw8dyZiK/AOLZi+kf8ve9G/Zm42/bqBhG9egnvKV7+HyhcuxAUF8e9btonbLugA0bNOITUtcAcLmpRuTzf/Fu7PpVut5XqrXhfE9x7L795181HscAOEhYTxQqwIAD9atmKkXzRnRFgV8CpDHPbwxR84cVKxXiRP7XceGTb+up0KdioBrKGy27Nk4H3Y+8SYzjafXF2cTXV/8kuj6omKDyjza/QnGdX6Lq1euZkhdPJEe5xEgwbO61ZvX5NjfRwHXaJicuXMC8GC9SjijYzixL+ucR+T6jDFewETgYeB+4BljzP2Jkr0M/OEe+dgQeN8Yk+Nmtnur/qTIb8D/jDEfWGtD4wV/yVkMjDLGzLXWXjTGlACikltHvN7KRe58P7uHlq4CuhljPgN8gPpAfyA7cL8xJieugLIxcG2M5gUgPxA3mD0pB/Bf4Cvg2Xh5k2WtPW+MOW6MedxaO8+9Xa+U6mitzbDZKZwxTmYP+4T+s4fh8HKw6pvfOLHvGI2eawbAsrm/smPZFgICq/DeqklcjYhker8JANxd7V7qtWnI0T8PM3qha7Tvt2PnsmO56/mG2q3qsm7+6oyqSobrP/xtNm3bydmz52n8eFte6tyONq2aZ3ax0kU2Ly8Gd2/Pi0PHEhPj5Ilm9Sl3Z0m++fk3AJ58pDEHj51kyHtTcTgclL2jBG/0fgGA0PDz9B41HnDNItuyYW3qVauYWVW5Ic4YJzOHTWfw7OE4vLxY8c1Sju87RpPnXH/fpXMXs23ZFioHVuXDVVO46p4KPrW8qVk8+xdeeq8n7y35CGMMK779jaN/Zb2AAmDDso3UbFSDOWtmceVKJGP7vhe77M3Zo3m//zhCT4Ux/c1PGDppMJ1e68D+3Qf45atFANR/5CFat3uUmJgYIq9cZfRLb8bmz5krJ1XrV+GDgeMzulo3beEvv9GiRSP+/nMtlyMieOGFvrHLFvw4m64v9ico6BSTJr7NkSPHWbN6PgDz5i1k9JjxjHlzPDM/+YBtW5dijGHQkDcJDU0alGQlW5dtpkpgVSasmkpkRCST3N8BgMGzhjH5tQmEh4Qx561Z9JnQn6f7teXwnoP89vWS6+ZPyZQBE+g0ogteXl5ERV5l6sCJ6Va/fyK92sK7qA89xvXG4XBgHIbff1rDlmWuZ9eXfbOUl8b2YtyvHxMdFc2EVz/M+Iqnwhnj5LNhn/Ca+/piZTLXF9uXbaFSYBXed19fTIt3ffGQ+/pijPv64hv39UWHkS+QLUd2Bn7uevJo/7a9fDpkauZUMhXpdR55blAHSt9fBmstp4+HMH3wZAAKFi7E4NnDsdZJWHAYE/qMz5R6Z5p/x0+K1AD2W2sPAhhjvgIeA/6Il8YC+Y1r7HQ+IAxXZ5nHTFZ/gD8lxpgOuAK7GFzDPgF+stZ+515+0Vqbz/36FeAFd5qLQFtr7YHE67DWdjTGzLq2HmPM80A7oCUwElfEb4HR1tqv3et+F9cfah9wFZhvrZ1ljOmJ6y5AUErPVRpjLgIfuNd/DnjKWnvaGLMC6Get3exONwK4aK19zxhTHpgKFMYVHP+ftfZgSnVMrQ3b3fmfW/OPn8Zmbnnv+oluE/Z8avdAbi/tAt+8fqLbxJmYrDmDbGZYGbIns4uQZTzuXzWziyBZUK5btr8i7UXxrwhQ0sTXR+bdEr9LEvHju1n+2jj3Y6+l2pbuyUpbWGtfcL9vB9S01vaIlyY/MB/XI3P5ccUgP99MuW7ZoPLfIH7gmxkUVLooqIyjoDKOgso4CirjKKiMo6BSkqOgMo6CyjgKKtNOnscHdAO6xvtomrV22rU3xpj/A5onCiprWGt7xkvzX6Au0BcoCywBKllrPR77rm++iIiIiIhIFv7JjmvcAeS0VJIcB+JPOlES169SxNcJeNs9Iel+Y8whXL2WG/GQgsoMYIzZAORM9HG7zOylFBERERGRf51NQHljTBngBPA0rrlb4juKay6Y1caYYsA9wMGb2aiCygxgra2Z2WUQEREREZF/N2tttDGmB66JPL2AmdbaPcaYF93LpwCjgFnGmF24fkViwLWfWfSUgkoREREREZF/x+yvWGsXAgsTfTYl3uuTuH7jPs3cqr9TKSIiIiIiIlmAgkoRERERERHxmIa/ioiIiIiI3AKzv2ZV6qkUERERERERjymoFBEREREREY9p+KuIiIiIiIiGv3pMPZUiIiIiIiLiMQWVIiIiIiIi4jEFlSIiIiIiIuIxPVMpIiIiIiJibWaX4JalnkoRERERERHxmIJKERERERER8ZiGv4qIiIiIiOgnRTymnkoRERERERHxmIJKERERERER8ZiGv4qIiIiIiGj4q8fUUykiIiIiIiIeU1ApIiIiIiIiHtPwVxEREREREavhr55ST6WIiIiIiIh4TEGliIiIiIiIeEzDX0VERERERDT7q8fUUykiIiIiIiIeU1ApIiIiIiIiHtPwVxEREREREWszuwS3LPVUioiIiIiIiMcUVIqIiIiIiIjHFFSKiIiIiIiIx/RMpYiIiIiIiH5SxGPqqRQRERERERGPqafyNuYwJrOLkCXY82cyuwhZhilQOLOLkGXUsPkyuwhZxt5s2TO7CFnGtpx5MrsIIlmaE82eec0LV3JmdhFEMoyCShEREREREQ1/9ZiGv4qIiIiIiIjHFFSKiIiIiIiIxzT8VURERERExGr4q6fUUykiIiIiIiIeU1ApIiIiIiIiHtPwVxERERERue1Zp34Sx1PqqRQRERERERGPKagUERERERERj2n4q4iIiIiIiFOzv3pKPZUiIiIiIiLiMQWVIiIiIiIi4jEFlSIiIiIiIuIxPVMpIiIiIiJi9Uylp9RTKSIiIiIiIh5TUCkiIiIiIiIe0/BXERERERERp83sEtyy1FMpIiIiIiIiHlNQKSIiIiIiIh7T8FcRERERERGnZn/1lHoqRURERERExGMKKkVERERERMRjGv4qIiIiIiKi4a8eU0+liIiIiIiIeExBpYiIiIiIiHhMw19FRERERESszewS3LLUUykiIiIiIiIeU1ApIiIiIiIiHlNQKSIiIiIiIh7TM5UiIiIiIiL6SRGPqadSREREREREPKagUkRERERERDym4a8iIiIiIiJO/aSIp9RTKSIiIiIiIh5TUCkiIiIiIiIe0/BXERERERERq9lfPaWgUtLMgw0CeG7Y8zi8HKz8+jd+nvy/JGmeG/48lQKrcDXiKtP7fcyRPYfw8fel67heFCxSCOu0LP9yCUs+/RmAUvfdSccx3ciZJxdnjp9mSu/xXLkYkdFVu2lrNu/knSlziHE6+U+LhrzwZKsEy89duMSwD6ZzLCiEnDmyM7LPC5QvXQqA5h36kCdPLrwcDry8vPj6o5GZUYUMMfTNcaxauxEf70LM+3xKZhcnXZVuUJHAEe0wXg52f7WCjZMWJFjuU9af5u91pWiF0qwd+y2bpy0EwPsufx6d2CM2XcE7ivL7uO/YOmNxhpb/Zj3QIIAnh3XC4eVgzde/sXjyvCRpnhreiQqBVbgaEcmsfhM5tucQAGPWTCTy4hWcTifO6BjebD0QgC4T+lDsruIA5C6Qh4jzlxndsn+G1SmtvPXu6zRt1oCIiAhefnEAO3f8kSTN1E/eJ6BKBaKjotm6ZSd9er1OdHQ0/32yNa/06QLApUuXebX3cPbs/iujq/CPPT+iC5UDq3E1IpIJ/cZzaPfBJGmKlipGn4/7ka9Qfg7uPsDHfT4gOio6xfy+/oXp+UFvChXxxjotS75YzMJPXd+z2i3r8mSfZyhRriSDWvfjwK79GVrf1KRHWwC8NLYXVRtV41zoOfo26xm7rjvvK03XN18iV55cnD4ewoevvE9EFjvPVmxQmfbDO+PwcrD8q6UsmPxDkjTtR3QmILAqVyMimdLvYw676911bA8qN6rG+dBzDGj2Smz6O+8vzfNjXiR7zhw4Y2L4dOg0DuzYl2F1Sgu+gZW4Z3RHjJeDE3OXcfjjHxMsL9KiGmUHPAlOi42O4e/XP+Psxr8BuH/8ixRpWoWrZ86zrkG/zCi+/Mto+KukCeNw0H5kF97vOIZBTXtTq3U9ipcrmSBNxYZV8Cvjz2sNe/Dp4Ml0GNMVgJjoGL4cPYtBTV5h5BMDadKuRWze599+iW/e+ZyhLfqyZfEGWnZ9LMPrdrNiYpyMmfgZk0b158ep7/DLinUcOHIiQZpPvp7PvWXv4IfJbzKmXzfemfJ5guUz3x7MdxPH/KsDSoDHWzZlyrjRmV2MdGcchsajO/BDh3eZ1fg17mldC5/yxROkiTh7iWXD58QGk9eEHwxizsNDmPPwED5/ZCjREZHsW7Q5I4t/04zDwTMjO/NxxzGMaNqH6q3r4p/oeFGhYWWKlvHn9YY9+XzwVJ4b0yXB8vefGcHolv1jA0qA6T0+YHTL/oxu2Z9tv2xg26INGVKftNSkWQPKlr2TagFN6NPrdd7/IPnv/LffzKdmlebUrfkIuXLlol2HJwE4euQYjz78HA/VbsV770xk/EdZ//tUObAq/mWK07NBN6YMmkjX0d2TTdd2YAd+mjGfng1f5NK5izR6qmmq+WNiYvhs9Ex6N36ZQY/3p0X7lpQs77pZd3TvEcZ2e4s/N+zJmEreoPRqC4Dl3/7G6A4jkqyr+zs9mfv2Z7zavBcbF6/nsW7/SZe6eco4HHQa1ZV3O4yif5Ne1GldjxLlEx4vAgKr4FemOH0bvMQngybz/OhusctWfbuMdzok/R49M6gDP3z4DYNb9uW7cV/yzKD26V6XNOUw3Pv282x79i1+f6gvfk/UJe/dJRIkCVu1i/WBr7G+8QD29JnC/ePi2uXkVyvZ+vRbGV1q+RdTUBmPMaa0MWZ3ZpcjOcaYhcaYQpldjpTcFVCOU0eCOX3sFDFR0WxYsIYqzaonSFOlWXXW/rASgAPb9pEnf14KFinEudNnOeLugbhy6QonDxzH288HAP+7ivP3Btdd+j1rdlDt4VoZWKu0sWvvAe4oXoxS/kXJnj0bDzeoxfL1WxKkOXD0BDUrPQDAXaWKc+LUGc6En8uM4maqagEPUrBA/swuRrrzCyjL2cOnOHf0NM6oGP5esJ5yzaomSBMRep5TOw/ijI5JcT131H2As0dDuHAiNL2LnKbKBJQj5EgwZ46FEBMVzeYFa6nUrFqCNJWaVWe9+3hxaNs+cufPS4EihW54G1Ufqc2m+WvSstgZouUjTfjqy3kAbN60nQKF8lOsWJEk6Zb+ujL29dYtOyheohgAGzds49zZ8wBs2rQdf/fnWVn1pjVZ8f1yAPZt+5s8BfJSqKh3knQV6lRk3cK1AKz4fhk1mtVMNf/ZkPDYXrorlyI4sf84PsV8ATix/zgnD55Iso3Mll5tAfDnxj1cPHsxybqK31WCP9zB9Y7V26n5cO20r9hNKBdQnlOHgwhxX1+sW7CGqk1rJEhTtWkNVrvrvX/b3gT1/mvjH1w8eyHpiq0ld77cAOTOn4fwkLD0rUgaK1ilHJcPnSLiSAg2Kobgeb9TpEXC666Yy5Gxr73y5MTGm9j07Po/iUpmf7jtOW3W/5dFKajM4oyLw1rb0lp7NrPLkxLvYj6EnTwT+z4sKAxv98k7fprQ+GmCQ/H2S5imcMki3Hl/GQ5sdw1BOb73KJWbug6S1VvWwce/cHpVId2EnAnHr4hP7PtihX04FRqeIM09d93B0t9dvU27/j5AUMgZTp1xneCMgW5D3uHJnq/z7cJlGVdwSTf5/Ly5cDLuAuZCUBj5iiW9cLyee1vX5q8f16Vl0TJEoWI+hJ+MC4TDg8IolOh4UaiYD2Hx0pwNDo292YSF3nOGMnjBOzz0TJMk6y9f4z4unDlHyOHg9KlAOvIvXowTJ4Ji3588EYx/8ZQDw2zZsvHk04/z29LVSZa1a/9//LZkVbqUMy35+vkSevJ07Puw4FB8E+0P+b3zc+n8JZwxruedQoNC8XGfP24kf5GSRSn9wF3s2/53elUjTWREWyR2bO8Rqjd1BaW1H6lL4Sx2nvX28yE0KP71RVx949L4JjhehAWH4l3Mh9TMHjmTZwd34ON103luSEe+fufzVNNnNTn9fIiMV+fIk6Hk9Et6HinycHXqrBlH5c8H8kefyRlZRLnNKKhMKpsx5jNjzE5jzHfGmDzGmGHGmE3GmN3GmGnGGANgjOlljPnDnfYr92d5jTEz3em3GWNSHK9pjOlojPnRGLPIGPO3MWa4+/PSxpg/jTGTgK1AKWPMYWNMYffy9u5t7jDGzHF/VsQY8717u5uMMXXTu6ES1SXJZ9baxImSZoyXJmeeXPSc3J+5Iz+NfW5yxmuTaNKuBW8seJfc+XIR435m5FZiSXpXyZCwLTr/XyvOX7zEf18ewhfzl3Bv2TvJ5uX6es5+fxjfTBjN5FH9+OqnpWzelfWfj5LUJfd9SWY3SZUjuxdlm1Zh78+33hBPkqk+iY4XyR9TXP+/22YoYx4dwMcdx9CgfXPK17gvQbrqreux8RbspYQbPJbG894HI1i3dhPrf084BLreQzVp2/7/GDFsbJqXMc0le2q4kf3B3lD+XHly0W/KQGaN/CTLPSuYRDq3RXIm9v+IFu1b8s5P48idN3fss5lZReLzJSTXJslkvE69m7RtzpxRM+lZuwtzRs6k67sv30wxM16ylU7q9C+b+L1eX7Z3fI+yA55K50LJ7UwT9SR1D9DZWrvWGDMTeAmYYK0dCeAO4h4FFgADgTLW2sh4Q1OHAMustc+7P9tojFlqrb2UwvZqABWAy8AmY8zPwBl3OTpZa19ybxf3/w+4t1HXWnvGGHPtVtyHwAfW2jXGmDuAxUDCKy1X/q5AV4BaPpW5O38ZjxopsbDgUHyKx93d9PH34WyioSThwaH4Fi/Mtcfgffx8CT/lSuOVzYueU/rz+7zVbFkcd5EcdOAEY9uPAqBYGX8qBSYcIngrKFbYh+DTcW1x6kwYRX0LJUiTL29uRvd1PWNqraVFx76UKFYUgKK+rjuPvoUK0rhONXb/fYBqD96bMYWXdHEhKIz8xePuouf39+FiSHgqOZIq07ASp3Yf5vKZ82ldvHR3NjgM7+JxPQ3eKRwvfIr7csD9vpCfL2fdx4tz7ra6EHqe7Ys3UrpSOfZt/BMAh5eDys1rMKbVgPSvSBrp3OU52nd0Xext27qTEiX8Y5cVL+FHcFBIsvleG9gD38I+9OmV8GL4/gfu4cMJb/Jkm86Eh51Nt3LfjBbtW9L46WYAHNi5D9/iRQDX39DHz5ewRPvD+bDz5C2QF4eXA2eME1//uPNHaFBoivm9snnRb8pAVs9byYZFWbNXP6PaIiUnD5xgVLvhAPiXKU6VRtVSTZ/RwoJD8fWPf30RV9/YNEGu40VsGj9fwq9zTK3fJpDZI2YAsOHn3+nyzq0VVEYGhZIzXp1zFvclMjjlOp9d/yd5Shcju09+osKSGQ4sAFinZn/1lHoqkzpmrV3rfv05UA8INMZsMMbsAhoBD7iX7wTmGmPaAtdu7TUDBhpjtgMrgFzAHalsb4m1NtRaGwH84N4ewBFr7fpk0jcCvrPWngGw1l47sjYBJri3Ox8oYIxJ8nCatXaatbaatbZaWgWUAId27KdYaX8KlyyKV/Zs1GxVj21LEt4537ZkE3X/0wCAspXLE3HhMudOnwWg8zsvcXL/cRbPSDgDZn7fAoArqH6sx39ZNvfXNCtzRqlw910cORnM8eAQoqKi+WXlehrWqpIgzfmLl4hy3x3+ftEKqj54D/ny5ubylStcuuy6s375yhV+37qLcu5ZYeXWFbzjIIXK+FGgVBEc2b24p1UtDizZ+o/Wce9jt+bQV4DDO/ZTtLQ/vu7jRbVWddmR6HixY8lmarmPF2Xcx4vzp8+SI3dOcubNBUCO3Dm5/6FKnNx7LDbfffUqEnzwJGeDb53no2ZMn0uDuq1pULc1P/+0lKefeRyAatUDOH/uAqdOnU6Sp12H/6NRk4fo0qlPgl6bEiX9mT13It279uPA/sMZVIN/btHshfRv2Zv+LXuz8dcNNGwTCED5yvdw+cJlziYTEOxZt4vaLV2DcBq2acSmJa4bkJuXbkwx/0vv9uT4/uP89MmPSdaXVWRUW6SkgG9BwHWe/W/PJ1kyd1Ga1S0tHNixD78y/hQp5Tpe1G5Vjy1LNiVIs2XpJh5y17tc5buJuIF6h4eEc18t1+XcA3Uf5NThoFTTZzXntx0gz11+5LqjCCa7F36P1+H04oTH0dyl44bO53+wDCZ7NgWUkm7UU5lU4vESFpgEVLPWHjPGjMAVKAI8AtQHWgOvu3sRDdDGWnujD24ktz2AlHo2TTJ5wHWDoLY7OM1wzhgnc4Z9Qv/Zr+PwcrDqm2Wc2HeMwOdcd1+Xz/2VHcu3UjGwCmNXTiQyIpJP+k8EoHy1e6nbpiHH/jzCyIXvAfDdu1+wc8VWarV+iCbtWgCwefEGVn976z1TmM3Li8Hd2/Pi0LHExDh5oll9yt1Zkm9+/g2AJx9pzMFjJxny3lQcDgdl7yjBG71fACA0/Dy9R40HXLPItmxYm3rVKmZWVdJd/+Fvs2nbTs6ePU/jx9vyUud2tGnVPLOLleZsjJNlr39Gmzmv4fBysPvrlYTuPUHFto0A2Pn5MvIUKUjbn0aRI19urNNJlc4tmNV4AFcvRpAtVw7ufKgCSwbNzOSaeMYZ4+SrYTN4ZfYQHF4O1n6znKB9x6n/nGsGy1Vzl7B7+VYeDKzM6JUfczXiKp+5jxcFChfkxWmunwnx8vJi449r2LNye+y6q7Wqe0tO0HPNksUraNqsAVt2/EZERAQ9usfNbvv1d9N5pccQgoNDeH/8SI4dPcni374F4Kf5vzL2nQm8NrAHPj6FGDvuDQCio6Np3CBrzeaZ2NZlm6kSWJUJq6YSGRHJpH4fxS4bPGsYk1+bQHhIGHPemkWfCf15ul9bDu85yG9fL0k1/73V7qNBm0Yc+fMwYxeOB+CLsXPYtnwLNZrXovMbXSngU5BBnw7j8B8HGd1+REZXPYn0aguA3h/144HaFcjvXYCp62fy9QdfsuzrJdRrXZ8W7VsCsGHROpZ9szRjK30dzhgns4ZNZ+Ds4Ti8HKz45jdO7DtG4+dc54bf5i5m+7ItBARW5YNVk4mMiGRqv49j8/f4qC/31X6A/N4F+Hj9dL7/4CtWfP0bnwyYRPsRrp8piYqM4pOBkzKrih6xMU7+HjSTKl8Nxng5OPnlCi79fZyS7V3PmR+fvZRij9bE///qY6NjiLlylV1dx8fmf3BKL7zr3E92n/w8tG0SB8Z+y8kvlmdSbeTfwFxvrP3txBhTGjgE1LHWrjPGTAf+Al4DSgNewHrgO2AkcIe19rAxJjtwHNeQ1deAAkBPa601xlS21m5LYXsdgTdxDX+NADYAz+Ma/vqTtbZCvLSHgWpAMeB/uALIUGOMj7U2zBjzBbDNWjvWnT7AWrs9tfp2KN1Gf3xg+rJbZ5hcejMFstYEDZnpoyrDMrsIWcZeR+T1E90mvg3dntlFyDIa+SR5wkKEHHhldhGyjE5XcmR2EbKMpqe+vrGHQDPZpTHts/y1cd4hs7NkW2r4a1J/Ah2MMTsBH2AyMB3YBcwDro258AI+dw+J3YbrecazwCggO7DT/fMko66zvTXAHGA78L21NtUfnLPW7gHGACuNMTuAce5FvYBq7gl8/gBevNEKi4iIiIiIeErDX+Ox1h4G7k9m0VD3v8TqJf7APfy0WzJpUxJire2RTDkqJPqsdLzXnwGfJVp+BtC0XiIiIiIikqHUUykiIiIiIiIeU09lBjDGNAfeSfTxIWvtE8CsjC+RiIiIiIgkYPWTIp5SUJkBrLWLcf1upIiIiIiIyL+Khr+KiIiIiIiIx9RTKSIiIiIi4szyvyiSZamnUkRERERERDymoFJEREREREQ8puGvIiIiIiIiTs3+6in1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VURERERERLO/ekw9lSIiIiIiIuIxBZUiIiIiIiLiMQ1/FRERERERsZr91VPqqRQRERERERGPKagUERERERERjymoFBEREREREY/pmUoRERERERH9pIjH1FMpIiIiIiIiHlNQKSIiIiIiIh7T8FcREREREbntWad+UsRT6qkUERERERERjymoFBEREREREY9p+KuIiIiIiIhmf/WYeipFRERERETEYwoqRURERERExGMa/ioiIiIiIqLhrx5TT6WIiIiIiIh4TEGliIiIiIiIeEzDX0VERERERKwzs0twy1JPpYiIiIiIiHhMQaWIiIiIiIh4TMNfRURERERENPurx9RTKSIiIiIiIh5TT+Vt7LKNzuwiZAntAt/M7CJkGTVsvswuQpbRa+vIzC5ClvF3jV6ZXYQso2Xe6pldhCxjDhczuwhZhqb2iHPJRmV2EbKM6o3OZnYRRDKMeipFRERERETEY+qpFBERERGR257VM5UeU0+liIiIiIiIeExBpYiIiIiIiHhMw19FREREREQ0/NVj6qkUERERERERjymoFBERERER+ZcwxrQwxvxtjNlvjBmYQpqGxpjtxpg9xpiVN7tNDX8VERERERFx3vq/OmuM8QImAk2B48AmY8x8a+0f8dIUAiYBLay1R40xRW92u+qpFBERERER+XeoAey31h601l4FvgIeS5TmWeAHa+1RAGttyM1uVEGliIiIiIjILcAY09UYsznev66JkpQAjsV7f9z9WXx3A97GmBXGmC3GmPY3Wy4NfxUREREREbkFZn+11k4DpqWSxCSXLdH7bEBVoDGQG1hnjFlvrd3rabkUVIqIiIiIiPw7HAdKxXtfEjiZTJoz1tpLwCVjzCqgEuBxUKnhryIiIiIiIv8Om4DyxpgyxpgcwNPA/ERpfgQeMsZkM8bkAWoCf97MRtVTKSIiIiIicgsMf70ea220MaYHsBjwAmZaa/cYY150L59irf3TGLMI2Ak4gU+stbtvZrsKKkVERERERP4lrLULgYWJPpuS6P1YYGxabVPDX0VERERERMRjCipFRERERETEYxr+KiIiIiIitz1rb/1nKjOLeipFRERERETEYwoqRURERERExGMa/ioiIiIiIvIv+EmRzKKeShEREREREfGYgkoRERERERHxmIa/ioiIiIiIaPirx9RTKSIiIiIiIh5TUCkiIiIiIiIe0/BXERERERG57VkNf/WYeipFRERERETEYwoqRURERERExGMa/ioiIiIiIqLhrx5TT6WIiIiIiIh4TEGliIiIiIiIeExBpYiIiIiIiHhMz1SKiIiIiIg4M7sAty71VIqIiIiIiIjHFFSKiIiIiIiIxzT8VTzSaUQXqgRWJTIikon9PuTQ7oNJ0hQtVZTeH/cnX6F8HNp9kI/7fEB0VHSq+QMaVKbT8C44vBz89tUS5k3+HoDS95ehy5ju5MiZnZgYJ58MncL+Hftit1W4eGE+WDqBb8Z/xYJp89K/AZJRqUFlOg5/AYeXg2VfLeHHyT8kSdNxxAtUdtd7cr+PYut9vbyPdn2MdkM68UJAOy6EX8Armxfd3nmZMhXK4pXNwarvVzBv0vcZUs+bVbpBRQJHtMN4Odj91Qo2TlqQYLlPWX+av9eVohVKs3bst2yethAA77v8eXRij9h0Be8oyu/jvmPrjMUZWv6MMvTNcaxauxEf70LM+3xKZhcnXeWrXwX/YV3B4SD8m185M+W7BMvzN6lJsb5tsU4LMTEEjZrO5c1/AODbsTXeTzUHA+FfLyb00/mZUYU0UzSwIg+Oag9eDo7OXc6+CQm/HyX/U5dyPVoBEHPpCjsGzOT8H0cBuKvrw9z5XCBYy/k/j7Gt91SckVEZXgdPPD+iC5UDq3E1IpIJ/cancE4pRp+P+5GvUH4O7j6Q4JySUv5Ja6YTcSkCZ4wTZ0wMA1q9CkC7wR2p1rgG0VHRBB8JYmL/j7h8/lLGVTgVnUd0oUpgNSLddTmYQlv0dbfFod0H+DBeWySXv/hdJXh1Qv/Y/MXu8OOrcV/w08z5PNX7GZo804zzoecAmDt2DluXb8mYyv4DXd7oSlV3vT58dTwHdx9IkqZoqWL0n/Caex/Zzwe9xxEdFU2JsiXp9V5vylYoy+djZzNv2v9i8zz6fGuaPdMcY+DXLxezYMatcwzJVqk6udv3AIcXV5f/TOT8LxMuv68SefuNxhkSDMDVTauJ/GE2AAU++hIbcRmcTqwzhotDXszw8mdFVj8p4rFM6ak0xhQ3xnznft3QGPNTGq13rDFmjzFmbFqsL7MYYzoaY4rHe/+JMeb+zCxTfJUDq+Jfxp+eDV5k6qCJdBndPdl0zw3swE8z5tOrYXcunrtIo6eapJrf4XDQeVQ3xnR4gz5NelC39UOULF8KgLaDOvDth1/Rv2Ufvh73BW0HdUiwrQ7DOrNtxdZ0rHXqjMPB86O68VaHkfRt0pO6rR+iRPmSCdIEBFbFr4w/rzTozvRBk+g8+sUbyuvrX5iK9QI4fTwk9rNaj9Qle47s9G/+CgMfeZXGzzanSMmiGVPZm2AchsajO/BDh3eZ1fg17mldC5/yxROkiTh7iWXD58QGk9eEHwxizsNDmPPwED5/ZCjREZHsW7Q5I4ufoR5v2ZQp40ZndjHSn8NB8Te6c7jTcPY3f4mCrRqQs1ypBEku/b6D/S17cuDRXhwf8CEl3uoJQM6778T7qeYceKIv+x/pSf5GNchRunhyW7k1OAwV3+rEumffZVn9/pR4og757y6RIMmloyGsfWIUKxoN5O8P/kfAey8AkMvPm7teaM7K5kNY3nAAxstBicdrZ0Yt/jHXOaE4PRt0Y8qgiXRN4ZzS1n1O6dnwRS6du0ijp5reUP4RTw+hf8vesQElwM7V2+nTrAevtuhF0KGT/Oel/6ZfBf+BKu66vHydtmg3sAMLZsynR8MXuXjuIo3dbZFS/pMHT/Bqy9682rI3/R/tS2REJBsWr4td308zfoxdnhUDyqqB1fAvXZwX63dl4sAJdB/zUrLpOgzqyPxPfqR7g65cPHeJJu52uXj2AtOHT2XetIQ3bO+4+06aPdOcfq368krznlRvXAP/W+UYYhzk7vQKl94ZyIV+HclRpzGOEncmSRb91y4uDOrChUFdYgPKay6O7sOFQV0UUEqayJSg0lp70lrr0RHcGJNa72o3oIq1tn8qaW50XdfL6+Vp3hvQEYg9qllrX7DW/pGO2/tHqjetwcrvlwOwb9te8hbIS6Gi3knSVahTkfUL1wKw8vtlVG9WK9X85QLKE3w4mJBjp4iOimbtgtVUa1oDAGshT748AOTJn4fwkLC48jSrScjRUxzbezT9Kn0d5QLKc+pwECHHThETFc3vC9ZQvWnNBGmqN63Bqu9XAEnrnVre9sOeZ+5bn2Hj3Tyz1pIzTy4cXg5y5MpJdFQUly9czoiq3hS/gLKcPXyKc0dP44yK4e8F6ynXrGqCNBGh5zm18yDO6JgU13NH3Qc4ezSECydC07vImaZawIMULJA/s4uR7nJXupvII0FEHTuFjYrm3E+ryN+0VoI0zstXYl87cueK/S7kLFuSy9v/wl6JhBgnlzbspkCzWyOQSo535XJcOnSKy0dDsFExnJi3Dr/mCb8f4Zv3EXXO1aMWvmU/ufx9Ypc5vLzwypUD4+XAK3cOrgSHZ2j5PVW9aU1WxJ4T/iZPKueUde5zyorvl1GjWc1/lD++Hau344xxzcixd9vf+Pr7pll9bkaNeHXZu+1v8hbIi3cydXkwXlssj9cWN5L/wboVOXU0mNMnTqdnVdJUjWY1Wf79MiD1dqlYpyJrF64BYNl3v1Gruet4cC70HPt37iM60XmlZPmS7N36F1evROKMcbJ7/W5qtbg1jiFe5e7FGXwSZ0gQxERzdd0yslerm9nFktvYDQWVxpi2xpiNxpjtxpipxhgvY8xFY8w7xpgtxpilxpgaxpgVxpiDxpjW7nyljTGrjTFb3f/qxPt8dzLbyWuMmWmM2WSM2WaMecz9eUdjzLfGmAXArymUcT6QF9hgjHnKGHOnMeY3Y8xO9/93uNPNMsaMM8YsB95JYV0jjDFzjDHLjDH7jDFd3J83NMYsN8Z8AewyxuQyxnxqjNnlLm9gvPLOM8YsMMYcMsb0MMb0dadZb4zxcacLcL/faYz5nzHG2xjzX6AaMNfd3rnd7VrNnecZ9/Z2G2PeiVfmi8aYMcaYHe51FruRv60nfPx8CT15JvZ9aPAZfIolPCHn987P5fOXYk/aoUGh+Pj5pJrfx8+X0KC4z8OCQvH1c6131shPaDe4I5PXzaD9kE7MfWcOADlz5+Tx7v/h2/FfpU9lb5CPn0+CsocGheLt55MgjbefT6J6h+JTzCfVvFWbVCcsOJQjfx5OsK4NC38n8vIVpm76lInrpvPTtB+5dO5iOtQsbeXz8+bCybgbAheCwshXLPWLv+Tc27o2f/247voJJcvL7udLVFDcxW100BmyF0t6gZ+/WW3KL5nMnTOGc2LAhwBE7j1C3hoV8CqUH5MrJ/kbViO7f+EMK3tay+XvTcTJuBslEUFhCYLGxO54tiEhy3YAcCU4nP2Tf6bZlo9pvnMSUecjOL1yV7qXOS34+vkSejJuHwgLDsU3mXPKpSTnFN/r5rfA65+P5J2fxtHkmebJbr/Rk03YmokjXeLz8fPlTLy6uM4T12+La+fKG8lfr3V9Vs9fleCzh9s/wrhFH/Hy2F7kLZA3TeuUFnz9fDkT7zx5Jjiuztfk9y6QqF3OxO4jKTn69xHur1mB/IXykyNXTqoGVqPwLXIMcXgXxhkaN4LJGXoah3fSsnuVv5/8b39C3gFv4yhZOvZzay15B40l35ip5Gj0aEYU+dbgtFn/XxZ13aDSGHMf8BRQ11obAMQAz+EK4FZYa6sCF4DRQFPgCWCkO3sI0NRaW8W9jo+us7khwDJrbXUgEBhrjLl2dKsNdLDWNkouo7W2NRBhrQ2w1n4NTABmW2srAnMTbftuoIm19tVkVnVNReAR93aHxRuOWgMYYq29H3jZve0HgWeAz4wxudzpKgDPutOPAS5baysD64D27jSzgQHuMu4ChltrvwM2A8+56xJxrUDuMrwDNAICgOrGmMfdi/MC6621lYBVQJfkKmWM6WqM2WyM2Xzw4uFUqp8yY0zSD6294TQ3kj/uY9fnzdo+zKxRM+heuzOzRs6g+7uu4W9P9n2Gnz6Zz5V4PRmZwZBcnRKlSabe1qacN0euHDzR4//4ZtyXSRaXCyiP0+nkxRrP07NeNx7t8hhFS6XbfYQ0k/zf/p+tw5Hdi7JNq7D35w1pUyjJepI5Hlz4dR37mnbnaLfRFOvbFoDIA8c5M/U7Ss8eRelZb3Dlr0PYmJR7uLO6f3JsLFz3fu58piF7RruOD9kL5sWvRVWW1HiFxZVeJluenJRsc4v0WiRb7eufU2LTpJJ/6H8G8NojfRjT4Q1atG/JfTUeSJDuPz3+j5joGFb/b4VHRU9rye8CN94W18ufLXs2qjepwe8/r439bNHnv/BS/W68+vArhIeE0fH1zh6WPv0kd55M2i5J8yVOk9jx/cf5YfJ3vDF3FCPmvMHhPw/hvFWOIclVONEJNfrwPs73fJoLA18gcvH/yNt3VOyyiyN6cnFwNy69M4CczR7H696K6Vxg+be7keGfjYGqwCb3gSw3rmDxKrDInWYXEGmtjTLG7AJKuz/PDkwwxgTgCkbvvs62mgGtjTH93O9zAXe4Xy+x1oYlny1ZtYH/uF/PAd6Nt+xba+31jho/ugO6CHevZg3gLLDRWnvInaYe8DGAtfYvY8wR4uq43Fp7AbhgjDkHXJttYRdQ0RhTEChkrV3p/vwz4NvrlKk6rkD+NIAxZi5QH5iH6+9x7dnULbgC/CSstdOAaQD/d+djN3w537x9S5o87Vrl/p378S0edzfM168wYSEJ/zTnw86Tp0BeHF4OnDFOfP19CTvlGooVGnQm2fzZcmTDN94dQh9/X8JOudbbsE0gn46YDsC6n9fy4juuCVvKB9xNrYfr0HZQB/IWyIu1lqjIqyz6LOHzeOktNDg0Qdl9/X0JP5WwTcKCQhPV25fwZOp9LW+xO/0pWqoo7/4yPvbzt38ex+DH+lP3sfpsX7GNmOgYzoee4+8tf3JXxXKEHDuVvhW9SReCwshfPK7nJb+/DxdD/tkQvTINK3Fq92Eunzmf1sWTTBAVHEp2/yKx77P5FyYqJOVD/eVNe8hxhx9e3gWICT9P+DdLCP9mCQDF+rUnKvhMinmzuoiTYeQuHtezktvfJ9khrAXuK0XA+11Y9+w7RIW7RigUqV+By0dDuBp6AYCghZvwqX43x79fmyR/VtCifUsaP90MgAM79+FbvAjwJ+DqbUvunJI30Tnl2jE2NCg0xfzXHpU4H3qOjYvXUz6gPH9u3ANAgzaNqNq4Om88MzS9q5uqFu1b0tTdFvt37qNwvLpcO0/El1xbhMVri9TyV25YlYO7D3DuzNnYz+K/XvLlrwyZ+XraV9IDLds/QlN37/L+nfsS9CAW9our8zVJ26VwkvNwcpZ+vYSlX7uOIW1fa59g5FBW5gw7jcM3bi4Fh28RnOGJHgmJiHssJnr7BszzvTH5C2AvnMe609rzZ4natJpsZe8l5q+dGVJ2+Xe6keGvBvjM3WsWYK29x1o7AoiycbeAnEAkgLXWSVyw2gc4BVTCNaQzxw1sq028bd1hrf3Tvexmp2WLH0DdyLoSB1zX3sfPm9xtomsi4712xnsfv33+qdS2F//vEXMT20jW4tkL6d+yD/1b9mHTr+tp0CYQgPKV7+byhUucTSYw2LNuF7Vauu6UN2jTiE1LXD1Lm5duTDb//h378C/jCqSyZc9G3VYPsXnJRgDCQsK4v1YFACrUrUjw4ZMADPu/wbxcrysv1+vKzzMX8MPE7zI8oAQ4sGMffmX8KVKqKF7Zs1GnVb3Ysl+zeelG6rdpCCSsd0p5j/19hK5VO9KzXld61utKaFAoAx/py7nTZzlz4jQV6jwIuIYAl698DycPHM/oav9jwTsOUqiMHwVKFcGR3Yt7WtXiwJJ/Nuzs3sc09PXfJGLnXnKWLk72ksUw2bNR8NH6XFiasBc6x53+sa9zPVAWkz07MeGumwpevgUByF68CAWa1+bs/JXcqs5uP0Deu/zIc0cRTHYvSjxem+BfE06akruEL9Vn9mFLj0lcOhgc+3nE8TN4Vy2PV27XabbwQw9wYd+JDC3/P7Fo9kL6t+xN/5a92fjrBhrGnhPu4fKFyymeU2q7zykNE51TksufM3dOcuXNDbiOk5XqB3D0b9ez9wENqvB49//wTufRXL1yNd3rm5pFsxfGTpITvy3udtclPJm22B2vLQLjtcWmeG2RXP6HWj/EmkRDX+M/m1izeS2O/n0kbSvooYWzf6bPw73o83Av1i9eR2Ab10C1uyvfw6UU2mXXul3UbVkPgEb/bcyGX9dfdzsF3ceQwsWLULtFbVbdIseQmAN/4fArgaOIH3hlI0ftRkRt+T1BGlMw7m/rVfZeMAZ74TzkzAW5XN8NcuYiW8VqxBw/hOC6Ss/q/7KoGwk8fgN+NMZ8YK0NcT8PeKOzRxQEjltrncaYDsD1JrdZDPQ0xvS01lpjTGVr7bYb3FZivwNP4+qlfA5Y8w/zP2aMeQvXsNKGwECS9rSucq97mTHmbly9qn8DVa63cmvtOWNMuDHmIWvtaqAdcO1IdoHk23gD8KExpjAQjmvI7cf/sF43beuyLVQOrMbHq6ZwNSKSif3iijBo1utMeW0i4SFhfP7WZ/SZ0I9n+j3HoT0HWea+E5hSfmeMkxnDpjFk9ggcXg6Wf/Mbx/cdA2DqgIl0GvECDi8voiKjmDpwUkZXO1XOGCczh01n8OzhOLy8WPHNUo7vO0aT51x3WZfOXcy2ZVuoHFiVD931ntzvo1Tzpmbx7F946b2evLfkI4wxrPj2N47+lTUuBFJjY5wse/0z2sx5DYeXg91fryR07wkqtnVdLOz8fBl5ihSk7U+jyJEvN9bppErnFsxqPICrFyPIlisHdz5UgSWDZmZyTdJf/+Fvs2nbTs6ePU/jx9vyUud2tGmV/DNht7QYJydHTKH0ZyMxDgfh3y4hct9RvJ99GIDwL36hQIs6FHqiETY6BnvlKsd6xT0Of8ekwXgVyo+NjuHk8Ck4s8jPQnjCxjjZOXgWtb8ciPFycPTLFVz4+wSl2zcG4PDs37in73/I4Z2fSm93is2zsvlQwrcd4ORPG2jw65vYmBjO7TrMkTnLMrM6N2zrss1UCazKhFVTiYyIZFK/uKdVBs8axuTXJhAeEsact2bRZ0J/nu7XlsN7DvJb7Dkl+fwFCxfitWmDAfDK5sXqH1eyfaXrJlbnkd3IniMbr3/uelpn37a/mTZkckZWO1lb3HWZ5K7LhHhtMWTWMCbFa4u+E/rzbL+2HNpzMLanLbX8OXLloNJDAUwZnPD82W5QR8rcXwZr4fTxU0mWZwVblm2mWmA1pqyeTmREJB/3Gx+77PVZI5g44CPCToXx2Vuf0m/CAJ7r35aDew6y5GvXNByFihTi/Z/GkydfHpxOJ606P0aPxt2JuBjBgKmDKeCdn+ioGKa+PoVL526RY4jTScSsj8g76F1wOLi64hecxw+To4nrJ4euLl1A9poNyNn0MYiJwV6N5NJHruGvjoLecUNhvby4unYp0Ts2ZVZN5F/CXG+8OYAx5ilgEK6ezShczxIutdbmcy8fAVy01r7nfn/RWpvPGFMe+B64DCwHero/Lw38ZK2tYIxpCPSz1j5qjMkNjAfq4OqVO+z+vCNQzVob9yN1yZfzYrwylQZmAoWB00Ana+1RY8ws97a/S2U9I3DNvloWV6D4rrV2evyyutPlAqbgGh4cDfS11i5PXF5jzGH3+zPxl7mHBU8B8gAH3WUMN8a0Ad4EInAN4/3Fvd3Nxphn3X8LAyy01r6WTN3/Czxqre2YWnv9k+Gv/2aOVDuAby81XLuQAL22jrx+otvE3zV6ZXYRsowDlwtkdhGyjDk5s/7kYBklC3ceZLgYq9a45rO6t0iAmgEKfbn8lrjYOvtUYJa/Ni70ddZsyxsKKm83iYPkfysFlS4KKuMoqIyjoDKOgso4CirjKKiMozAqjoLKOAoq49wqQWX4/zXM8tfG3t+uyJJtmSm/UykiIiIiIiL/Dmk6mUtGMMY8iOs5yfgirbU1k0t/nXV1Al5J9PFaa+3LnpZPRERERETkdnLLBZXW2l24fqMxLdb1KfBpWqxLRERERERuYRq97TENfxURERERERGPKagUERERERERjymoFBEREREREY/dcs9UioiIiIiIpDXrzPK/KJJlqadSREREREREPKagUkRERERERDym4a8iIiIiIiL6SRGPqadSREREREREPKagUkRERERERDym4a8iIiIiInLbsxr+6jH1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VURERERERMNfPaaeShEREREREfGYgkoRERERERHxmIa/ioiIiIjIbU+zv3pOPZUiIiIiIiLiMQWVIiIiIiIi4jEFlSIiIiIiIuIxPVMpIiIiIiKiZyo9pp5KERERERER8ZiCShEREREREfGYhr+KiIiIiMhtTz8p4jn1VIqIiIiIiIjHFFSKiIiIiIiIxzT8VUREREREbnsa/uo59VSKiIiIiIiIxxRUioiIiIiIiMc0/FVERERERG57Gv7qOfVUioiIiIiIiMcUVIqIiIiIiIjHNPxVRERERETEmswuwS1LQeVtLAab2UXIEsJiIjK7CFnG3mzZM7sIWcbfNXpldhGyjHs2fpTZRcgyjj8wOLOLkGVk12CnWHoMK04hR47MLkKWcWitV2YXIcuonNkFkHSnM4KIiIiIiIh4TD2VIiIiIiJy29Psr55TT6WIiIiIiIh4TEGliIiIiIiIeExBpYiIiIiIiHhMz1SKiIiIiMhtzzr1kyKeUk+liIiIiIiIeExBpYiIiIiIiHhMw19FREREROS2p58U8Zx6KkVERERERMRjCipFRERERETEYxr+KiIiIiIitz1rNfurp9RTKSIiIiIiIh5TUCkiIiIiIiIe0/BXERERERG57Wn2V8+pp1JEREREREQ8pqBSREREREREPKbhryIiIiIictuzTs3+6in1VIqIiIiIiIjHFFSKiIiIiIiIxxRUioiIiIiIiMf0TKWIiIiIiNz2rM3sEty61FMpIiIiIiIiHlNQKSIiIiIiIh7T8FcREREREbnt6SdFPKeeShEREREREfGYgkoRERERERHxmIa/ioiIiIjIbU/DXz2nnkoRERERERHxmIJKERERERER8ZiGv4qIiIiIyG3P2swuwa1LPZUiIiIiIiLiMQWVIiIiIiIi4jENfxURERERkdueZn/1nHoqRURERERExGPqqZSb1nlEF6oEViMyIpIJ/cZzcPfBJGmKlipG34/7ka9Qfg7tPsCHfT4gOio61fyPdm5Nk6ebgbUc+esIE/p/SFRkFM+8+hzVm9bEOp2cCz3Hx69+SHhIWIbW+Ua8PPIlajaqTmREJO/2eY99u/cnSeNXyo+hkwaTv1B+9u3ax9uvvEt0VDSValdk5Iw3CD4WDMCaX9YwZ/xcSt5VktcnD4nN73+HH7Pem80PM/6XYfX6px5oEMCTwzrh8HKw5uvfWDx5XpI0Tw3vRIXAKlyNiGRWv4kc23MIgDFrJhJ58QpOpxNndAxvth4IQJcJfSh2V3EAchfIQ8T5y4xu2T/D6pQW8tWvgv+wruBwEP7Nr5yZ8l2C5fmb1KRY37ZYp4WYGIJGTefy5j8A8O3YGu+nmoOB8K8XE/rp/MyoQoYZ+uY4Vq3diI93IeZ9PiWzi5OuCgdW4r7RHcDLwfG5yzj0ccK/rX+butzVozUA0Zci+eO1T7jwx1EA7uzyMCXbNgLg+NxlHJn2S8YW3kOVGlSm/fAXcHg5WP7VEuZP/iFJmg4jXiAgsCpXIyKZ3O8jDrvPEynlvfP+MnQe8yLZc+bAGRPDzKFTObBjHwB33Hsnnd/qTp58eXA6LUNb9yMqMirjKpyKSg0q09Fdn2VfLeHHZNqi44gXqBxYlUh3WxyK1xbJ5X3y1Wep1rQG1mk5F3qOya9+SHhIOOBqiy5vdSd3vjxYp2VwFmqL+Co0CODZYZ0wXg5Wf/0bC5M5jzw7/HkeDKzM1YirzOg3gaPu8wiAcTgYtuAdzgaH8WHntwCo1rI2j/V+Ev9yJRj92CAO7zqQUdVJM/kbVKbkiC4YLwehXy3h1KTvEywv2LQG/v2ewzqdEOPk+BufcGnTn+S8qwSlJ/aLTZfzDj+Cxn3B6RkLMroK8i+ioFJuSpXAqviXKc7LDbpxd+V76Dq6OwMfT3px325gBxbMmM/aBavpNqY7jZ9qyuLPf0kxv08xHx7p1IpXGr/M1cirvDrxNeq1eojl3y1j3tQf+PL9uQC07PgoT77yFFOHTM7oqqeqRqPqlCxTgvb1OnFflXt55a1e9GjVK0m6LoM78/30H1g+fwW93+rFw0+3YMGcnwDYvXEXQzoOS5D++MHjdGveHQCHw8HXm79gzaK16V8hDxmHg2dGdmZ821GEB4cxaP5b7FyymaD9x2PTVGhYmaJl/Hm9YU/KVC7Pc2O68Pbjg2OXv//MCC6FX0iw3uk9Poh9/d8h7Ym4cDn9K5OWHA6Kv9GdQ+2HEh0cyl3zPuDC0g1E7j8Wm+TS7zvYv3QDADnvLc0dHw9gX9Pu5Lz7Tryfas6BJ/pio6IoPWskF5Zv5urhk5lVm3T3eMumPNumNYNHvZfZRUlfDsP9bz/PpifHcOVkKLUXv0nI4i1c2nsiNknEkdNseHwk0ecuUbhRAA+835X1Dw8l370lKdm2EetaDMFejabqV4M4vWQblw8FZ2KFrs84HHQa1Y03nxtOaHAoY+aPZcvSjZzYF3eMCAisil8Zf/o06E65ynfTefSLvP74a6nmfXZQB77/8Gt2rNhKQGBVnh3UgVFPD8Xh5eDl8X2Y2Gc8R/88TL5C+YmOisnEFohjHA6eH9WNMe76vDV/LJtTaItXGnSnvLsthrrbIqW8C6b+j2/e/wKAFh0foc0rT/HJkCk4vBz0cLfFkSzWFvEZh4O2I1/g/bYjCQsOY9j8t9m+ZDMn451HHmxYmWJl/BnUsCd3VS5P+zFdGf34oNjlTTu1JGj/cXLnyxP72Ym/jzLxxbG0f7NbhtYnzTgclBrdjf3PDScqKJR7FrzHuSUbubIv7jxyYe1Ozi3ZCECue++kzKTX+LPRy0QePMHfD/eJXU+FjTM5u2h9ZtRC/kXSfPirMaa0MWZ3Wq83qzHG9DLG/GmMmetB3tLGmGfTo1zu9Xc0xkxIr/XHV6NpTVZ8vxyAvdv+Jm+BvHgX9U6S7sE6FVm30BX8LP9+GTWa1bxufi8vBzly5cDh5SBn7pyEnXL1RkZcjIhdb648ubLk9M91m9Xh1++WAPDn1r/IVyAvPkV9kqSrXDeAlT+vAuDXb5dQt3mdG95G5XqVOXkkiJATIWlT6HRQJqAcIUeCOXMshJioaDYvWEulZtUSpKnUrDrrf1gJwKFt+8idPy8FihS64W1UfaQ2m+avSctip7vcle4m8kgQUcdOYaOiOffTKvI3rZUgjfPyldjXjtxx+3nOsiW5vP0v7JVIiHFyacNuCjSrnZHFz3DVAh6kYIH8mV2MdFeoSjkuHwom4kgINiqG4Hm/U6xFwu/L2c17iT53yfV6yz5y+buOK3nLl+Dsln04I65iY5yE//4nxVpWz/A6/FPlAsoTfDiIkGOniImKZt2CNVRrWjNBmqpNa7D6+xUA7N+2lzwF8lKoqHeqea215M6XG4A8+fPEjmapWL8yR/86zNE/DwNw8ewFVy9OFlAuoDyn4tXn9wVrqJ6oLao3rcEqd1vs27aXvPHaIqW8Sc+ZroPJtbY4kgXbIr673OeR0+7zyIYFawlolnDfrtysOr//sAKAg9v2kSd/Hgq6zyPefj5UbFSVVV/9liBP0IETBB+8dW/G5QkoT+ThYK4edZ1HwhespmCzGgnSJDiP5MmV7O9l5K9bkcijwUSdOJ3uZb4VWGuy/L+sSj2VnnsJeNhae+i6KZMqDTwLfPFPMhljvKy1Weo2oo+fL2dOxh2IQoND8SnmGzu0BiC/d34unb+EM8Z1sgoNCsXXzzfV/Ad27efHafOYum4GV69cZcfqbexYvT023bP929LwP4FcvnCZYU/HDQfNKgr7+XI6Xr1OB52hsJ8vYfGG6RbwLsDF8xdj28WVpnDs8vur3s+0XycTeiqMKaOmcWTvkQTbCGzdgGU/Lk/nmtycQsV8CD8ZGvs+PCiMMgHlk6QJi5fmbHAo3n4+nD99Fiz0njMUa2H1F0tY/eXSBHnL17iPC2fOEXI4a/fGJJbdz5eooLj9IzroDLkD7kmSLn+z2vj1b4+XbyGOdH4DgMi9RyjWrz1ehfLjvHKV/A2rEbFrX4aVXdJPTj8fIuJ9F66cDKNglXIppi/5bCCnl20H4OJfx7h70NNk985HzJWrFGkSwLkdSR9FyGq8/XwIDToT+z40KJRylRMeI3z8fAg9GZcmLDgUn2I+qeadPXIGg2YPp+2QThiHYfh/XEPn/csUx1oYOHs4BXwLsG7+GhZMzRqPD/jcQFt4J2qLUHdbXC/vU/2fo/5/Aom4cIk3nn4dgOLuthjsbovf569hfhZpi/hc54i4uoUHhXJXovOIdzHfBOeRsOAwvP18OXf6LM8M68S3b80hl/smw79FDj9frsZrl6tBoeQNuDtJuoLNa1F8QDuyFS7IgY6jkiz3bv0Q4T+uSteyyu0hvSbq8TLGTDfG7DHG/GqMyW2MCTDGrDfG7DTG/M8Y4w1gjFlhjPnAGLPK3fNX3RjzgzFmnzFm9LUVGmPaGmM2GmO2G2OmGmO83J9fNMa8b4zZaoz5zRhTxP15F2PMJmPMDmPM98aYPO7Py7rLsckYM9IYczHeNvq7P99pjHkjpcoZY6YAdwHzjTF9jDF5jTEz3Xm3GWMec6crbYxZ7S7bVmPMtW6ot4GH3HXpk7hn0RjzkzGmYbz6jTTGbABqp9IOnYwxe40xK4G6N/sHvFEmmRsmNtGdMJNMomtpUsqft0BeajSrSfd6XXihRkdy5s5F/Scaxqb5YuzndK3dmVXzVvJwh0dupgrpI9k6J06STBpcifbt2s8zNdvStVl3/vfpPEbOGJEgXbbs2ajTrDarfsriJ4Lkbqjd0P7h+v/dNkMZ8+gAPu44hgbtm1O+xn0J0lVvXY+Nt1gvZYqSuYN84dd17GvanaPdRlOsb1sAIg8c58zU7yg9exSlZ73Blb8OYWOy1L0m8VSyN6CTH4rhU/d+Sj4byN5RrnuTl/ad5OCE+VT7ZgjVvhzE+T1HsNFZr9cpMZNcpW/kWGlTz9u0bQvmjJpJj9ovMGfkTLq+2wMARzYH91S/j4mvjGNEm0FUa1GTB+pWvNlqpIn0aguAr8fO5eXaL7Bm3ipadGgJuNri3ur38fEr4xjWZhDVW9SkQhZpi/hSu4aIS5Q0n7WWSo2qcj70HEeSmevhlncD51eAc4vX82ejlzn4wpsU7/dcwlVkz0bBpjU4+3PWfYxGbh3pFVSWByZaax8AzgJtgNnAAGttRWAXMDxe+qvW2vrAFOBH4GWgAtDRGONrjLkPeAqoa60NAGKAa9+MvMBWa20VYGW89f5gra1ura0E/Al0dn/+IfChtbY6EDvuwRjTzF3uGkAAUNUYUz+5yllrX3TnDbTWfgAMAZa51xkIjDXG5AVCgKbusj0FfORexUBgtbU2wJ0/NXmB3dbamkBocu1gjPEH3sAVTDYF7k9pZcaYrsaYzcaYzYcuHkkpWapatG/J+wvH8/7C8YSdCqNw8SKxy3z9fJNMmnM+7Dx5C+TF4eXa3Xz9fWOHsoYGhSabv2K9AE4dO8X5sPPERMewYdE67q16b5KyrP5xJbUfvvEho+npsQ6tmLp4MlMXTyb0VChF4tWriH9hQk+FJkh/Luwc+Qrki22XIv6FCQ12pbl88TJX3MNWNi7bRLZsXhTwLhCbt0Zgdfbt2k/4mbPpXKubczY4DO/ivrHvvf19OJto/wgPDsUnXppCfr6cde8f59w93hdCz7N98UZKV4rrtXF4OajcvAabf/o9PauQLqKCQ8nuH7d/ZPMvTFQqk01d3rSHHHf44eXeB8K/WcKB1r059PRAYs5e+Fc/T3k7iQwKI3e870Ku4j5EBocnSZfv/juoMK4bWzu8R1R47H1RTnyxnHVNB7Hx8TeIOnuJSweDMqTcNyMsOBRf/7gRGr7+voSfSvhdCA0Kxbd4XBof93kitbz12wSy8Zd1AKz/eS1lK7l6tsKCQvlz/R4uhF/g6pWrbF++lTIV7kq3+v0ToTfQFmGJ2uLaOfNG8gKs+XEVNR+uHbuuP+K1xbYs1Bbxuc4RcXXz9vflbEh4Mmnivjs+fj6cPRVGuWr3ENCkOu+umcSLH/fm3joV6PJB0vkNbkVXg0LJEa9dcvj7pnoeubTxD/d5JO5RggINq3B59wGiz5xL17LeSqwz6//LqtIrqDxkrd3ufr0FKAsUstaudH/2GRA/YLs2vd0uYI+1NshaGwkcBEoBjYGqwCZjzHb3+2tHPifwtfv150A99+sK7l7CXbgC0Afcn9cGvnW/jj/8tJn73zZgK3AvriDzRjQDBrrLtgLIBdwBZAemu8vwLakEe6mIAa5N55VSO9QEVlhrT1trrxLXHklYa6dZa6tZa6uVyXenB8WBRbMX8mrL3rzasjcbf91AwzaBANxd+R4uX7icYOjrNbvX7aJ2S1cHamCbRmxa4pqAZNPSjcnmP3PyNHdXvoccuXIA8GDdShx3T2LiX9o/dr3Vm9bgxIHjZAU/fraAbs270615d9Yu+p1m/20KwH1V7uXShUsJhr5es/33HTR4xPVVaPZ/Tfn9V9dFkHeRuOdS7wm4B+NwcD78fOxnjR4LzPJDXwEO79hP0dL++JYsilf2bFRrVZcdSzYnSLNjyWZq/acBAGUqlyfiwmXOnz5Ljtw5yZk3FwA5cufk/ocqcXJv3AQE99WrSPDBk5wNznoz/15PxM695CxdnOwli7nuFD9anwvuSXmuyXFn3H6e64GymOzZiXHvA16+BQHIXrwIBZrX5uz8lcit79y2A+S5y4/cdxTBZPfC7/E6hCzekiBNrhK+VJ7Zl50vT+RyoqAxR+ECsWmKtaxO0P+y/g2XAzv24VfGnyKlXMeI2q3qscU9scg1W5du5KE2DQEoV/luLl+4xNmQ8FTzhoeEcV+tCgA8ULciwYddbbVz5TbuuO/O2Of176v5ACfiTWySmRLXp06remxO1Babl26kvrstyqfSFvHz+sU7Z1ZrWoMTB1wTP+1YuY0747XF/TUf4HgWaYv4Du3YT7HS/hR2n0dqtqrL9iWbEqTZvmQzdf7TEIC7Kpfn8oXLnDt9lu/f/YJ+tbvxWr2XmNJzPH/9vpvpfT5KZiu3nss79pGzjD85ShXFZM+Gd6uHYifluSbHnX6xr3NXuAuTIxsx8Sa+836sPuE/rs6wMsu/W3o9UxkZ73UMUOgG0zsT5XXiKqMBPrPWDkqcMRnX+v5nAY9ba3cYYzoCDa+TzwBvWWun3sA2ksvbxlr7d4IPjRkBnAIq4QrgryTNCkA0CQP8XPFeX4n3HGWy7WCMeZyUxkilsy3LNlMlsCqTVk11/yRI3MF6yKxhTHptAuEhYcx5axZ9J/Tn2X5tObTnIEu/XpJq/n3b97Ju4Vre+3k8zpgYDu45yK9fLAag7cAOlLirBE6n5fSJEKYOnpTxFb+ODcs2UrNRDeasmcWVK5GM7Rs3a+Wbs0fzfv9xhJ4KY/qbnzB00mA6vdaB/bsP8MtXiwCo/8hDtG73KDExMUReucrol96MzZ8zV06q1q/CBwPHZ3S1/jFnjJOvhs3gldlDcHg5WPvNcoL2Haf+c66Ae9XcJexevpUHAyszeuXHXI24ymf9JwJQoHBBXpzmmknYy8uLjT+uYc/K7bHrrtaq7i03QU+sGCcnR0yh9GcjMQ4H4d8uIXLfUbyffRiA8C9+oUCLOhR6ohE2OgZ75SrHer0Tm/2OSYPxKpQfGx3DyeFTcJ6/lFk1yRD9h7/Npm07OXv2PI0fb8tLndvRplXzzC5WmrMxTv4Y9CnVvhqM8XJw/MvlXPz7OKXaNwHg2OyllH21DTm883H/O8+78kTHsK6567nygBl9yeGdD2d0DH8M+jR2Qp+szBnjZNaw6QyaPRyHlxcrvlnK8X3HaPKc6++7dO5iti3bQkBgVcavmkJkRCRT3eeJlPICTB8wifYjXsDLy0FUZBSfDHSdJy6dv8TCT+YzZsF7WGvZvnwr25ZtSb5wGcwZ42TmsOkMvk5bVA6syoerpsT+vEpqeQGeHdie4ncVx+m0nDlxmumDXbOlXzp/iZ8+mc+bC94Da9mWhdoiPmeMk8+HfULf2a7Ze9d8s4yT+47T8LlmAKyY+ys7l2+lYmAV3l45gasRkczsf/3rgirNa/DsiM7k9ynAKzMHcezPw4xrP/q6+bKMGCfHX59G2TkjXD8p8vVvXNl7DN+2LQAI/XwRhVrWwadNIDYqGnvlKodfHhub3eTKQf6HKnF0UNa7hpJbk0kyLv1mV2hMaeAna20F9/t+QD7gCaCHtXa1O9gqaK3tY4xZAfSz1m52P0fYz1r7qDvvCqAfcBnXsNi61toQY4wPkN9ae8QYY4FnrLVfGWOGAsWstT2NMWdw9QyGAwuBE9bajsaYn4HZ1tqvjTFdgXHW2nzu4a+jgMbW2ovGmBJAlLU22ak1jTGHgWrW2jPGmDeBAkBPa601xlS21m4zxnwAHLfWvm+M6QTMdC02Vd3bbeBeVz3gXVy9rCWAPUBra+0KY8xFa20+d7r7k2sH4CqwHqgCnAeWATustT1S+1v9587WWXDe1Ix3LialWP/2Uy5bwcwuQpbR0xFx/US3iXs2/jvu7KeF3x4YfP1Et4nPcunYeU0WHpGW4fIazQF5TU9n1vvNz8xS+eiPWXfa0nj23tciy18b3/3noizZlhn5ze8ATHFPmHMQ6HSjGa21f7gDxl+NMQ4gCtdzl0eAS8ADxpgtwDlczxwCvA5scKfZhSv4AugNfG6MeRX42Z0Ha+2v7mc317kfCr8ItMX1XOT1jALGAzuNK/Nh4FFgEvC9Meb/gOXusgLsBKKNMTtw9aiOBw65y7kb1/DbG24Ha+16d6C+Dghy5/e6gXKLiIiIiIjclDTvqcxo8XvybjB9HiDC3aP4NK5ezsfSr4RZl3oqXdRTGUc9lXHUUxlHPZVx1FMZRz2VcdRTGUc9lXHUUxlHPZVpRz2VWUdVYIK7R/Es8HzmFkdERERERDKbtVkyXrsl3PJB5T/ppXSnX41r4pzrMsb4Ar8ls6ixtTY0mc9FRERERERuK7d8UJme3IFjQGaXQ0REREREJKtSUCkiIiIiIrc969TwV085rp9EREREREREJHkKKkVERERERP4ljDEtjDF/G2P2G2MGppKuujEmxhjz35vdpoa/ioiIiIjIbe8W/6VFAIwxXsBEoClwHNhkjJlvrf0jmXTvAIvTYrvqqRQREREREfl3qAHst9YetNZeBb4CHksmXU/geyAkLTaqoFJEREREROQWYIzpaozZHO9f10RJSgDH4r0/7v4s/jpKAE8AU9KqXBr+KiIiIiIicguw1k4DpqWSJLkpbBMP7B0PDPh/9u47PKqi7eP4dzb0noSSUJRqRUjoCEqRIKAoj7zPY0GaCGKlg6ACithQRCkCVlAs2BAURXpHOgg2upSEkgChhJBk5/1jl3Taks0m7O/DlYvs2ZndeyZnz+69M2eOtTbJmKxZ8VZJpYiIiIiI+L2r5JIi+4AKqW6XBw6kK1MH+NKdUJYE2hhjEq21Mzx9UiWVIiIiIiIiV4c1QDVjTCVgP/AA8FDqAtbaSud+N8Z8Avx4JQklKKkUERERERG5KlhrE40xT+Fa1TUA+Mhau9UY09N9f5adR5makkoREREREfF7TntVTH/FWjsbmJ1uW6bJpLW2S1Y8p1Z/FREREREREY8pqRQRERERERGPafqriIiIiIj4PXuVTH/1BY1UioiIiIiIiMeUVIqIiIiIiIjHNP1VRERERET8nrW+jiD30kiliIiIiIiIeExJpYiIiIiIiHhM019FRERERMTvObX6q8c0UikiIiIiIiIeU1IpIiIiIiIiHlNSKSIiIiIiIh7TOZUiIiIiIuL3rM6p9JhGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifs9aX0eQe2mkUkRERERERDympFJEREREREQ8pumvIiIiIiLi95xa/dVjGqkUERERERERj2mk0o8lWaevQ8gRFh/a6usQcowN+Qv5OoQco03hur4OIcfYd/MQX4eQY9yx9RVfh5BjTK8z0Nch5BhJaHWPc25xFvB1CDnGL0Z9cU64rwMQr1NSKSIiIiIifs9q+qvHNP1VREREREREPKakUkRERERERDym6a8iIiIiIuL3tPqr5zRSKSIiIiIiIh5TUikiIiIiIiIe0/RXERERERHxe7o4kOc0UikiIiIiIiIeU1IpIiIiIiIiHlNSKSIiIiIiIh7TOZUiIiIiIuL3dEkRz2mkUkRERERERDympFJEREREREQ8pumvIiIiIiLi96ymv3pMI5UiIiIiIiLiMSWVIiIiIiIi4jFNfxUREREREb/n9HUAuZhGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifs+i1V89pZFKERERERER8ZiSShEREREREfGYpr+KiIiIiIjfc1pfR5B7aaRSREREREREPKakUkRERERERDympFJEREREREQ8pnMqRURERETE7zl1SRGPaaRSREREREREPKakUkRERERERDym6a8iIiIiIuL3rKa/ekwjlSIiIiIiIuIxJZUiIiIiIiLiMU1/FRERERERv+f0dQC5mEYqRURERERExGNKKkVERERERMRjmv4qIiIiIiJ+T6u/ek5JpWSp7i/2oHazOsTHxfNOvzHs3LIjQ5nSFcowYNxAipQoys4t23m792gSExIpV6U8z7zZmyrVq/DZqKnMmPw9AOUql6P/+EHJ9UOuCeHz0Z8x68OZ2daurPD26Jdo3ao5p+Pi6NatDxs2bslQZuqUsdSuXZOEhATWrNnI408MIjExkWLFijJ1ylgqVChHnjwBjB49kSlTp/ugFVnj1TdeIKJlE+Li4niy5yA2b/ojQ5lJH7xFWK3qJCYksn7dZvo88wKJiYn83//uoVef7gCcOnWafr2HsXXLX9ndhCtWulkNbhnRCQIc/DttIdvGzUpzf/n7GlH1qbYAJJ06w6ZBHxH7x78AVO7Rmms7NANrif1zLxt6T8IZn5DtbcgqJZvV5MaXO0OAg33TFrBrbNrXdmj7RlR+6h4AEk/F88fADzjh7otru7em/MPNAdg3bQF7Jv+cvcFns+dfGc2S5asJCizBjM8m+jqcLFe9SRgPDe2KCXCw9Kv5zH5vRoYyDw17hFuahXM27iwf9h/Hv1t3Jd9nHA6GznqdY1ExvNPtVQD+O7gjYS3qkHg2kcP/RvHhgPHExZ7OriZliVuahNFh6CM4Ahws/mo+P733fYYyHYY9Qs1mtTgbd5b3+49lz9Zd5M2flyFfjSBP/rwEBASw5ueVfP/2Vz5oQda5tkkNmg7viCPAwZYvF7FmQtpjZ2CVUFq+2YPS1SuyYtTXrJs8O/m+8G6tuOXBplhrOfLXPn7tP5mkXHzsrNykBi2Gufpi45eLWPVe2r4IqhLK3W/2oMzNFVn85tesTtUXjy97m7OnzmCTnDiTkvik7dDsDl+uMpr+KlmmdrM6hFYsS8/bezD+2XE8PvKJTMt1HtyFmR/8wONNenDy+Cla3B8BwMljJ3h/2CRmTP4uTfn9O/fTp/Uz9Gn9DP3u6k18XDyrflnp9fZkpdatmlOtaiVuuKkxjz8+iPHjXs203BdffM/N1W8nLPwOChYsQLdHHgLgice78Oef/1C7TgR3tPg/Rr0xlLx582ZnE7JMi5ZNqFLlWuqEtaDPMy/w1tsvZVru6+kzqV/rThrVv4sCBQrQsfP/APh3z17ubt2B2xq25c3XxzPm3ZezM/ys4TDUeLUrKx96gwW3D6Dcf26l6HXl0hQ59e8hlv9nBIuaP8vfb39P2JuPAlAgJJDKj97J4jufY2HTQZgAB+XaNfRFK7KGw3DTa4+w9qHXWHZbP0L/04jC6foibs9hfmv3EsubDWLH6O+4+a0eABS5oTzlH27OylbPsaL5IEpF1KJQpRBftCLbtGsTwcTRuXCfvwTG4eDhlx7l7S4jeT6iD/XvaUzZquXTlLmlaThlKoUyuOnTTBkykU4je6S5P6JrGyK370uz7Y9lm3mhZR+Gte5H1K5I7nriPq+3JSsZh4NOL3XnrS4jGRzRmwaZ9EuNprUIqRTKwKZP8fGQ9+js7peE+ARee2g4L7Tuxwtt+nFLkzCqhFfzRTOyhHEYmr/cmRmd32DKHQO5/p4GBFUrm6bMmWOnWDTs0zTJJEDhMoGEd23JtLte4NOIwTgCHFzftkF2hp+ljMPQckRnpnd+g8ktBnLTPQ0IzqQv5g77lN/en53pY3z+wEg+avOcEkrJEkoqcyBjTEVjTMZhrByuXsv6LPx2AQD/bPibwsUKE1g6MEO5GrfWYPnsZQAs+GY+De50fSA+Hn2c7Zu3kZiYdN7nqNGoJlH/RnJ4/2EvtMB72ra9k0+nfQPAb6vXU7xEcUJCSmco9/MvC5J/X7NmI+XLhwJgraVIkSIAFClSmJiYYyQmJmZD5FmvzV0t+PKLGQCsXbORYiWKUqZMqQzl5v26OPn39es2UbZcGQBW/7aB48diAVcfhbq35yaB4VU5tesgp/89hE1IYv+MlYTcWTtNmaNrt5Fw/JTr93XbKRAalHyfIyCAgAL5MAEOAgrm40zU0WyNPyuVqFWV07uiiNvj6ouoGSso06pOmjLH1v5Dorsvjq3bltwXhauV49i6bTjjzmKTnBxd8Sdl2tTN9jZkpzpht1C8WFFfh+EVlcOqcmhPFIf3HiIpIZHfZi0nrGXav2d4y7qs+G4RADs3bKNQ0UIUL1UCgMCQIGo0r82SL+enqbN16SacSU53nX8IDAn2eluyUuWwqhzcE8XhvQfd/bKMWun6pVbLuiz/znXM3LFhG4WKFk7ul/jTZwAIyBNAQJ48WJut4WepkLAqHNt9kOP/HsaZkMTfs1ZRpWXaY2dcdCwHN+/EmclnCUeeAPK4j515Cubj5MHce+wsG1aFo7sPcmyvqy/+nLWK6yLS9sXp6FgiN+/EmXD+z1WSljMX/ORUSipzAGPMFU1DNsYEZFUsVyI4JJgjkUeSbx+JiiY43Zt30cBinIo9lfwGHx15hKDLeIO/7Z7bWfLDkqwJOBuVKxvCvr0Hkm/v3xdJubLnH1HJkycPHTq0Z86chQCMn/AxN95Qjb171rNx/Xz69huGzaWfDELLlmH//sjk2wf2RxFa9vyJYZ48efjfA+2YP29phvs6dvov8+fmvv2hQGggcQeik2/HRcakSRrTu+ahphxasAmAM1FH2f7eT7RcN5Y7N08gITaOw4t/93rM3pI/JChNX5w5EEP+kPP3RfmHmnF4wUYATv61l6AGN5I3sAiOgvko1SKMAuVyV8IgKUqUCSLmQMp7yNHIaALLpN0XAssEE5Nqf4mJiklOEh8c2pWvX/30gsfGxv9tzu+L1mdx5N4VmK5fYiJjCCwTnKFMdOoyUdHJ/WIcDl6a/SZj133E1mWb2LlxW/YE7gVFQgI5cSAm+fbJyBiKlMn45XVmTh08yrrJs3l01Tv0WDuO+NjT/Ls0131/n6xISCCxkSl9cSIyhqIhl9YXLpYHPnuWLj+OIOzBZlkfoPgdv0wq3SOBfxpj3jfGbDXG/GqMKWiMWWSMqeMuU9IYs9v9exdjzAxjzCxjzC5jzFPGmL7GmA3GmFXGmPN+AjLGhLnLbDbGfG+MCXRvX2SMecUYsxjoZYypbYzZZIxZCTyZqn6AMWaUMWaN+zEec29vaoxZaIz5HPjdGFPYGPOT+zG2GGPuP088PYwxa40xa3ef/DerutT12Jmc3Jz+zd1kcv7zpSZHefLmoV5EPZb/tMyj+HzJZNLwC7V73NhXWLr0N5YtXw1Ay5ZN2bRpKxWurUXtui15Z8zLFC1axGvxetPl9sWbbw9n5fI1rFqxNs32xrfV5+FO/2X40FFZHqO3ZdYH5xs+KNnoJq59sClbX/4CgLzFCxPSqjZz6/ViTs0nyVMoP+XbN/JmuN6V6ZoImfdFUKObKP9QM/4Z8TkAp7YdYOe4mdSZ/hx1vhhM7NY92MSc/D2uXMglHRvO8x5Ss3ltYqOPs2fLzvM+/t1P3oczKYlVMzJ+QZWTXVq/nP+YYp1OhrbpT5+GPahcsxrlrqvgjTCzR6Z9cWlV8xcvROWIWnzUqA/v132avIXyc8N/cu+xM/PPXJde/9P7XuLju55neudR1OrUggr1rs/C6MQf+WVS6VYNGG+tvRk4BrS/SPnqwENAPWAkcNpaGw6sBDpdoN5UYJC1tgbwOzAs1X0lrLVNrLVvAR8Dz1hr058c1Q04bq2tC9QFuhtjKrnvqwc8Z629CWgFHLDW1rTWVgd+ySwYa+1ka20da22dikWuuUiTL65Np7t4++d3efvnd4k5FEPJ0JLJ95UMCSbmYEya8rExsRQuVhhHgGvXCw4tydF0Zc6nVtPa7Niyg+NHjl1x3Nnh8Z6dWbvmV9au+ZUDkVGUr5ByrkO58qEciDyYab0Xnu9DqVLB9B8wPHlbl0738/0M1zkRO3bsZvfuvdxwfVWvxp+VunXvwOLlM1m8fCZRkQcpVy40+b6y5UKIijyUab2Bzz5FcMkgnhv8SprtN918Pe+Me4UOD/TkaMwxb4buFXEHYihYNmWkoWBoUKZTWIvdWIGwt7rzW5e3SDh6EoBSt1fn9L+HOBt9ApuYROTsNQTVvS7bYs9q8ZFp+6JA2SDiM+mLIjddQ/XRj7G+85vJfQGw//OFrIwYzOp2L5Jw7BSndkZmqCu5w9GoaILKpryHBIYGc+zQ0UzKpOwvQSFBHDsYQ9U61xPWoi5vLJtAz7G9ueHW6nR/+5nkcre2b0KNO2ozudc73m9IFotJ1y9BoUEcO5T2ffNoVDTBqcuEBGd4bz0de5q/Vm2hRpNw7wbsRScjYyhaNuV7/CKhQZw6dGlTWK9pXJ3YvYeJizmBMzGJ7b+spWzt3Ht+6YmoGIqlmuFSNDTosqbznjx0DHBNkf1nzjpCw6pkdYjiZ/w5qdxlrd3o/n0dUPEi5Rdaa09Yaw8Dx4FzS2z9fr66xpjiuBLHcyeHTQFuT1Xkq/OU+zRVmZZAJ2PMRuA3IBhXQgyw2lp7btm734EWxpjXjTG3WWuPX6Q9WWL21J+SF9FZNWclzdq7VmG8Lvx6Tp04zdFMDva/r/ydRm0aA9D8/+7gt19XXdJz3X5vE5bmoqmv702cQp26LalTtyUzZ86hY4f/A6B+vVrEHo8lKipjIvVI1wdpGdGUDg8/meab6H/37qd5c1eflS5dkuuuq8zOXXuypyFZ4MP3p9Gk0T00aXQPP/04jwcebAdAnbphxB4/wcGDGc+R7dj5vzRvcRvdu/ZJ0xflyocyddp4Hu/Rnx3bd2dTC7LWsY07KFw5hELXlMLkDaBcu4ZE/bouTZmC5YKp+1Ef1j01gVM7o5K3x+07QmDtagQUzAdAydtu5sS2/dkaf1Y6vmEHhSqHUNDdFyHtbuXQnLR9UaBcMOEf9WXzk+M5nS5pzFeyWHKZMm3qEvn9imyLXbLWrk3bKVMxlJLlSxOQNw/12zZi49w1acpsnLuWW+9rCkDl8GqcPnGa44eP8e0bn9O/4WMMbPwEE58ew18rtvB+n3cB14qybXq2Y+yjr3P2zNnsbtYVy9gvjdkwN+3MjQ1z19DoviYAVAmvRpy7X4oGFaNQsUIA5M2fj5sa1eDAjtx7vIjatJPASiEUq1AKR94Arm/bgJ1zL20684n90YTWqkqeAq5j5zWNbiZme+7tiwPuviju7osb2zZg2yX2Rd6C+clXuEDy75Vur86Rv/ddpJZ/8PX5krn5nEp/vqRIfKrfk4CCQCIpiXaBC5R3prrtxPN+POX+33C++V6u+5621s5Js9GYpqnqY639xxhTG2gDvGqM+dVam/myml6ybsFa6jSrw8Sl7xMfF8/Y/mOS73vhk+GMH/QuMQdjmPLqx/QfN4gOAx5m59adzP3qVwBKlCrBWz+OoVCRQjidTtp2u5en7nicuJNx5CuQn5q3hTFh8LjsbFKWmf3zfFq1as7ffy7ndFwcjz7aN/m+WT9MpUfPAURGHmTC+NfYs2cfy5a6LqkwY8ZsXh45hpGvjOGjD95mw/p5GGMY/NwrREfnzgUG5s5ZRETLJqzbNJ+4uDieevzZ5Pu++uZ9ej31HFFRh3hrzEvs/fcAc+Z/DcCPM39l1OvjGPjsUwQFlWDU6BcBSExM5I4muWs1R5vkZPOQT2j4xbOYAAf/frGIE3/vp2KnOwDYPXU+1/e9j3yBRan5WtfkOovvfJ6jG3Zw4MffaPLrK9ikJI7/vps9ny640NPlaDbJyR+DP6bOl0MwAQ72fbGQk3/vo0KnFgDsnTqPKv3aky+wCDe9/oirTmISK+98DoCwD/uSL7AIzsQk/hj8cfKCPlerAcNeY82GzRw7Fssd7R7miW4dad/2Tl+HlSWcSU4+G/oBfac+jyPAwbLpCziwbR9NO7QEYNG0X9m8cD01mtXitcXjOBsXz0cDJlz0cTu82I28+fLS77MXANdCNp8+N9mrbclKziQnnw79gAFTX8AR4GDJ9AXs37aXZu5+WTjtVza5+2XU4vHEx8XzwYDxAJQoHUj3t57C4QjAOAyrf1rBpgXrLvR0OZpNcrLghSnc9+lATICDrV8tJvqf/dRwX1Zo82cLKFSqOA/9OIJ8RQpinU7Cu7Vi6h2DiNq4g22zV9Nh9ss4k5I4vHUPv3++0Mct8pxNcjJ36BQemOrqi83TF3Nk237CO7j6YsO0BRQuVZwus0aQ390XdR9pxfstBlEosAj3Te4NuBYv+uOHFexcvNmHrZGrgcmti31cCWNMReBH9zRRjDH9gSJAeWCdtfY9Y0xvoLe1tqIxpgtQx1r7lLv8bvftI+nvy+S5NgFPWWuXGmOGA8WttX2MMYuA/tbate5ym4EnrLXLjDGvA3dZa6sbY3rgShT/a61NMMZcB+zHNRW2v7X2bnf9skCMtfaMMaYd0MVa2+5C/XDvNXf73x8/Ez9FbfB1CDlGsfyFfB1CjvFx4at7FdHLkd/m5O9Gs9cdW1+5eCE/8Vidgb4OIcdIOu/3wv4nzFnQ1yHkGGcyPWfcPw3e81mu6I3ZZR7I8S/mNge/zJF96c8jlZl5E5hujOkIZNVX/52BicaYQsBOoOt5ynUFPjLGnAZSj0p+gGt67XrjOlv/MNAuk/q3AKOMMU4gAXg8S6IXEREREfEDNvPV4+QS+GVSaa3djWvhnXO330x1d41Uvz/vvv8T4JNU5Sum+j3NfZk810Ygw9V1rbVN091eB9RMtWm4e7sTGOL+SW2R++dc/TmkTUZFRERERES8zp8X6hEREREREZEr5Jcjld5gjBkPpL/g0TvW2o99EY+IiIiIiFw6p2a/ekxJZRax1j7p6xhERERERESym6a/ioiIiIiIiMc0UikiIiIiIn7PqdVfPaaRShEREREREfGYkkoRERERERHxmKa/ioiIiIiI37O+DiAX00iliIiIiIiIeExJpYiIiIiIiHhM019FRERERMTvOX0dQC6mkUoRERERERHxmJJKERERERER8ZiSShEREREREfGYzqkUERERERG/5zTG1yHkWhqpFBEREREREY8pqRQRERERERGPafqriIiIiIj4PevrAHIxjVSKiIiIiIiIx5RUioiIiIiIiMc0/VVERERERPye09cB5GIaqRQRERERERGPKakUERERERERj2n6q4iIiIiI+D2n8XUEuZdGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifs+J5r96SiOVIiIiIiIi4jEllSIiIiIiIuIxJZUiIiIiIiLiMZ1TKSIiIiIifs/6OoBcTCOVIiIiIiIi4jEllSIiIiIiIuIxTX8VERERERG/59QVRTympNKP5TMBvg4hR2gXWtvXIUgO9CknfR1CjpFXk1qSTa8z0Nch5BiT1r7h6xByjMe0XyQ7bnRW2jl/csrXIYhkG31SEBEREREREY9ppFJERERERPye09cB5GIaqRQRERERERGPKakUERERERERj2n6q4iIiIiI+D0tM+U5jVSKiIiIiIiIx5RUioiIiIiIiMc0/VVERERERPye0/g6gtxLI5UiIiIiIiLiMSWVIiIiIiIi4jFNfxUREREREb/n9HUAuZhGKkVERERERMRjSipFRERERETEY0oqRURERERExGM6p1JERERERPyezqn0nEYqRURERERErhLGmFbGmL+NMduNMc9mcn8HY8xm988KY0zNK31OJZUiIiIiIiJXAWNMADAeaA3cBDxojLkpXbFdQBNrbQ1gBDD5Sp9X019FRERERMTvWePrCLJEPWC7tXYngDHmS+Be4I9zBay1K1KVXwWUv9In1UiliIiIiIjI1aEcsDfV7X3ubefTDfj5Sp9UI5UiIiIiIiK5gDGmB9Aj1abJ1trU01czG2+153msZriSysZXGpeSShERERER8Xu5YfVXdwJ5oXMg9wEVUt0uDxxIX8gYUwP4AGhtrY2+0rg0/VVEREREROTqsAaoZoypZIzJBzwAzExdwBhzDfAd0NFa+09WPKlGKkVERERERK4C1tpEY8xTwBwgAPjIWrvVGNPTff9EYCgQDEwwxgAkWmvrXMnzKqkUERERERG/lxumv14Ka+1sYHa6bRNT/f4o8GhWPqemv4qIiIiIiIjHlFSKiIiIiIiIxzT9VURERERE/F6m192QS6KRShEREREREfGYkkoRERERERHxmJJKERERERER8ZjOqRQREREREb/nNL6OIPfSSKWIiIiIiIh4TEmliIiIiIiIeEzTX0VERERExO85fR1ALqaRShEREREREfGYRirlioQ1CafrsO44AhzM/3IuM977NkOZrsO7U6tZbeLj4hnf/x12bdl5wbpFihehz/gBlCpfmsP7DjH6iTc4FXsq+fFKli3J2/PGMX3Ml8yaPCNb2nmpHhnenfBmdTgbF8+4/mOS25pa6Qpl6DO2P0VKFGXnlh2M7fM2iQmJF63vcDh4/cfRxERF8+ojIwCoeFMleox8grz58+JMSuL95yeyfdO27GnsRXijL4JDS/L0270pUSoQ67TM/XwOsz+eBUDDNo34X58HKVe1PIPv6c+O37dna3svxFv7xYRl7xN3Kg5nkhNnUhKD2vYDoOOQLtS5ox6JCYlE7Ylk/IB3OZ3qNeQrNZuE02nYozgCHCz8ci4z3/suQ5nOwx8lrFltzsbF817/d9ntbuv56l57UyW6jexJ3vz5cCYl8dHzk9jhfg1cc8O1dHv1cQoVKYTTaXn+nv4kxCdkX4MvUfUmYTw0tCsmwMHSr+Yz+70ZGco8NOwRbmkWztm4s3zYfxz/bt2VfJ9xOBg663WORcXwTrdXAfjv4I6EtahD4tlEDv8bxYcDxhMXezq7mpQtnn9lNEuWryYosAQzPpvo63CynDf2i//0fYCwiLpY6yT2SCwf9R/HsUNHs6tJWaJqkxq0GtYRR4CD9V8uYtl7s9LcX7JKKPe++RihN1dkwZvTWTF5NgB58uel6/QXCMiXB0eeAP6YvZpFb2f8zJIT1WwSThf38W/Bl3P5IZNjZ5fhjxLu/qz1Xv93k98nzlf32psq0d197ExKSuJD97GzSs1q9Hj1CQCMga/HfMmaOb9lX2Ml19JIpXjM4XDQbcRjjOz8In1aPEWje26jfLUKacqEN6tNaKVQnm7Sk0mDx9P95ccvWrfdE+35fflmnmn6OL8v30y7J9qneczOQ7uxYdH67GnkZXC1tSxPN3mMiYPH08Pd1vQefrYzP344k6eb9uTU8ZM0vz/ikuq3eaQt+7bvTbOt4+AufP3OFwxo05svR39Ox8FdvNK2y+WtvkhKSmLKyx/R+44nGdxuAK06tUneb/79Zw+jHnuVP3/bmj2NvETe3i+GP/AcA9r0Tk4oATYv3Uiflk/Rr9UzRO46wH1P/J/3GniJjMNB1xGP8Xrnl+jf4mluvec2ylUrn6ZMWLPahFQKpU+Tx3l/8AS6vdzzonUfGtyZb9/5isFt+vD16C94aHBnABwBDp4c04cPh0xkQMQzjLj/eRITkrK30ZfAOBw8/NKjvN1lJM9H9KH+PY0pWzVtv9zSNJwylUIZ3PRppgyZSKeRPdLcH9G1DZHb96XZ9seyzbzQsg/DWvcjalckdz1xn9fbkt3atYlg4uiXfR2GV3hrv/h58g8Ma92P4W0GsHnBOtr2+q/X25KVjMPQZkQXpnV+g/EtBlL9noaUqlYuTZm4Y6f4edhUVrz/U5rtifEJTHlwJBNbD2Fi6yFUbVKD8uFVszN8jxiHg0dGPMarnV+ib4unaXSBY2evTI6d56vbYXBnvnnnKwa16cP00V/QwX3s3Pv3Hga37cegNn14pfNLdH/lcRwB/pMuOHPBT07lP3tJFjPG9DTGdHL/3sUYU9YHMew2xpTM7uc9p2pYNaJ2R3Fo70ESExJZPmspdSLqpSlTN6Iei79dCMC2Df9QuFhhSpQOvGDduhH1WfTtAgAWfbuAei0bpDxey/oc+vcge//5N5taeelccZ9r698Ucrc1veq31mDl7OXAufbVv2j9oJBgajevw/wv56Z5LGstBYsUAqBQ0cLEHIrxTuMuk7f64tiho8nfvp45Fcf+7fsIKhMMwP7t+ziwc7/X23a5vLlfnM+mpRtxJrneev7Z8DfBocFZ1h5PuV7zkRzae5CkhERWzlpGnYj6acrUjqjH0m8XAbB9wz/Jbb1QXddroCAAhYoW4qj7NVDj9nD+/Ws3//65G4CTx05gnTnv7bhyWFUO7Yni8N5DJCUk8tus5YS1rJumTHjLuqz4bhEAOzdso1DRQhQvVQKAwJAgajSvzZIv56eps3XppuR9YOeGfwgM8f0+kNXqhN1C8WJFfR2GV3hrvzhzMi7593yF8oO1Xm1HVisXVoWY3Qc5uvcwSQlJbJm1iusjaqcpcyo6lgObd+LM5Euks6fjAQjIE0BA3gBsLmh/1bBqHEx1/Fsxaxl10x0760bUY4n72Jn+s9Z5657n2Hn2zNnkY0fe/Hlz2y4iPqSk0kPW2onW2qnum12AbE8qfS0oJJjoyCPJt2MiowlO98ElKCSY6AMpZaKjjhBUJviCdYuXLJ48HefYoaMUK1kcgPwF89Pu8fv4esyXXmvTlQgOCSb6wOHk2zFR0QSXSdsfRQOLcir2VPIBOzoymiB3uy9Uv+uwR/n0lU8yfCj++KUP6DikKxNXfkin57oy7fWp5ATe7ItzSpUvTcWbK7Nt49/eakaW8GZfWOCFz17i9R9H0+LBOzN9/ub/a8H6HDCyHxgSlOY1Hx0ZTWBIUJoyQSFBaY4XMVHRBJUJumDdqS99SIchXRi38gM6PNeFL1//FIDQSmWxFp6dOoxXfnqLto/9x5vN81iJMkHEpGrz0choAsuk7ZfAMsHEHIhOvh0TFZOcJD44tCtfv/rpBT8cN/5vc37PAfuAXDpv7hf39X+QN1dMpMG9tzFj9FdeaoF3FAsJIjYypc2xkTEUC7nwl2ypGYeh5+xXGLD+PXYs3cL+jTu8EWaWCrqEY2dgumNntPvYeaG6U176kIeHdGH8yg/o+FwXvnAfO8GVyL45913enPMOHzz3XvJ7k8iFKKm8RMaYTsaYzcaYTcaYT40xw40x/Y0x/wfUAaYZYzYaY+4yxnyfql6EMSbj5PeU+1sZY9a7H3e+e1uQMWaG+/lWGWNquLcHG2N+NcZsMMZMAkyqx3nYGLPaHcMkY0yA1zrjAtK/gRmTyVVkz/Ph52LfGP6v74P8+MFMzpw+43F8XpVpUy/eH8llzlO/dvM6HI8+zs4tGd/87ny4NZ+M+ICeDbvxyUsf8MQbT3sUepbzUl+cU6BQAfpPfJZPXvqAuFTfvOdIXuyL5+8bxMC7+jCy84u06tSGG+vdnKbcfU/9l6TEJJZ+v8ij0LOSybQh6cpk2g8XrhvxcCs+HfERTzV8lE9f+ogebzwFgCOPg+vr3sj4XqMZ3n4wdVrV5+ZGNa60GVnugn/75EIZ61lrqdm8NrHRx9mTyTm659z95H04k5JYNWPplYYq2cib+8V3b35B/1t7suqHpTTv3CorwvWpyxlttE7LxDZDGN3gacqFVaH0deUvXsnHvHnsnDLiI55s+ChTXvqInu5jJ8D2jdvoH/EMQ+4ZQLsn2pM3f94raUKuYnPBT06lpPISGGNuBp4DmltrawK9zt1nrf0GWAt0sNaGAbOBG40xpdxFugIfn+dxSwHvA+3dj3vu5IYXgQ3W2hrAEODc8NMwYJm1NhyYCVzjfpwbgfuBRu4YkoAO53nOHsaYtcaYtTtP7r7MnkgrJiqa4NCU2bdBocHEHEw7/TI68gjBZVPKBIeUJOZQzAXrHj9yPHl6X4nSgcQeOQ5AtbDreHhwZ8Yvm8xdj7Tlvif/j1ad21xRG65Uq05tGDV7DKNmj+HowRiCy5ZKvi8oJDjDdNTYmFgKFyucfH5CcGgwR93tjo6MzrT+9XVuom6LekxY9j69xw6g+q01eGZMXwCatG/Obz+vBGDlT8upWvM6r7b3QrKjL8A1ban/xGdZOmMxv/2y0tvN8kh29cW56Uqx0cdZPWcV1cKqJZdr0r45te+oyzu93vJOIy9T+td86jae42prquNCSDBHMzlepK57e/tmrHa/Blb9tJwqNV19EBMZzZ+rtnLi6AnOnjnLxoXrqVS9stfa56mjUdEEpWpzYGhwhoVTXGVSRreDQoI4djCGqnWuJ6xFXd5YNoGeY3tzw63V6f72M8nlbm3fhBp31GZyr3e83xDJUt7cL8757Yel1G7VIMP2nCw2KoZiqabzFwsN4sTBY5f9OGdiT7N75Z9UbZrzvmhKL/oSjp0x6Y6dwe5j54XqNjnPsTO1/dv3ER8XT4XrrsnSNsnVSUnlpWkOfGOtPQJgrT3viWvW9ZXZp8DDxpgSQEPg5/MUbwAssdbuSve4jd2PgbV2ARBsjCkO3A585t7+E3DuHeYOoDawxhiz0X07009P1trJ1to61to6lYtUvGjDL2T7pm2EVgqldIXS5Mmbh0Ztb2Pt3NVpyqydt5om7ZsBUC38Ok6fOMWxQ0cvWHftvNU0bd8cgKbtm7NmrmvVsaH/HcKTjXvwZOMe/PTRLL4b/w2/TJl9RW24Ur9Mnc2ANr0Z0KY3q3/9jabJbb2e0ydOZ7qq3taVv9OwTSMgbftc7c5Y//M3pvJYg0d4onF3xjw9ii0rNvNu79GAK6m4uUF1AG5pVIPI3Qe83ubzyY6+AHjijafZt30fP37wQ3Y0yyPZ0Rf5C+anQGHX+TD5C+an5u1h/Pu361zjsCa1aPf4fbze7WXOnjnr9fZeih2bthFSKZRSFUoTkDcPDds2Zl2648X6eau5rX1TAKqmOl5cqO7RQzHc6H4N3NyoBlG7IwHYvHgD19x4LfkK5MMR4ODG+jezf1vaha5ygl2btlOmYigly7vaVr9tIzbOXZOmzMa5a7n1vqYAVA6vxukTpzl++BjfvvE5/Rs+xsDGTzDx6TH8tWIL7/d5F3CtHNqmZzvGPvp6jtkH5NJ5a78oXTEkuX5Yi7pE7ch556FfyIFNOwmuFEKJCqUIyBtA9bYN+HvuukuqWyioKAWKudYgyJM/L5Ub38yR7ZHeDDdLpD/+3dq2caaftW53HzurXeDYmbru0UMx3OQ+dlZPdewsVaF08hecJcuVIrRyOQ7vO5RNrZXcTJcUuTSGyxtx/hiYBZwBvrbWJl7m42YyXyG53PnKT7HWDr6MGK+YM8nJh0Mn89zU4a5l/qfPZ9+2vUR0cE2nmTvtF9YvWEd4szqMXTKRs3HxjO8/9oJ1Ab6f8C19Jwyg+f0tOHLgMKMffyM7m+Wx9QvWUqtZbcYtmUR8XDwT+r+bfN+QT4by3sBxHD0Uw6evfkKfcQN4oP/D7N66k/lfzb1o/fOZOGgcXYd3JyAggIT4s0x6drzX2nc5vNUXN9S5kSbtm7Pnz92Mmj0GgM9HfcqGheuod2cDur3Yg2JBxRn88VB2/7GTlzsNz+6mZ+CtvihesgQDJw8BXKO3S39YzMbFrvPmur30GHnz5eGFz14CXAv8TH7uvexsdgbOJCefDH2fwVOH4QgIYNH0eezbtpcWHVzngs6bNocNC9YR1qw2Y5ZMJD4unknutp6vLsD7gybQafijBAQ4SIhP4INnJwBwKvYUsz+YychZb2KtZePC9WxYcGkfPrOTM8nJZ0M/oO/U53EEOFg2fQEHtu2jaYeWACya9iubF66nRrNavLZ4HGfj4vlowISLPm6HF7uRN19e+n32AgA7Nmzj0+cme7Ut2W3AsNdYs2Ezx47Fcke7h3miW0fat8383OLcxlv7xf8NepiQymWxTkv0/sNMzWX7hDPJyeyhn9Bx6iBMgIMN0xdzeNt+6nS4A4C10+ZTpFRxesx6mfxFCmKdTho80prxLQZStHQJ2o3uicPhwDgMW3/8jX8WbPBxiy7OmeTko6HvM+Qix87wZrV5x/1Z671Ux87M6gJMGjSBLu5j59n4BCa7j5031LmJe5+4j6SEJKx18uHzkzhx9IRvGu8Dzsw+gcslMblh5Stfc09//R5oaK2NNsYEAc8AJ621bxpjZgGjrbULU9WZBdQCIqy1f5zncUsB64HbrbW7jDFB1toYY8y7wGFr7QhjTFPgbWttuHv7IWvty8aY1rim2pYCSgM/4Jr+esgdX1Fr7Z4Lteu/196rPz5gc/QMdRHfy6tJLckKGn0Xe86ktbnjC7/s8Fidgb4OIce4xub3dQg5xp/4/vrAOcVXe2bkinTtjWsfzvEfCgfu+SxH9qXeHS+BtXarMWYksNgYkwRsAHanKvIJMNEYE4cr8YwDpgGlzpdQuh/3sDGmB/CdMcYBHAIigOHAx8aYzcBpoLO7yovAF8aY9cBi4F/34/xhjHke+NX9OAnAk8AFk0oREREREZErpaTyEllrpwBTznPft8C36TY3xrUIz8Ue92fSnXPpPrfy3kzKRgMtU23qk+q+r4DctTa4iIiIiIjkekoqvcAYsw44BfTzdSwiIiIiInJxuiKn55RUeoG1tnb6bcaY34D0Jxp0tNb+nj1RiYiIiIiIZD0lldnEWlvf1zGIiIiIiIhkNSWVIiIiIiLi93L80q85mNaJFxEREREREY8pqRQRERERERGPafqriIiIiIj4PacmwHpMI5UiIiIiIiLiMSWVIiIiIiIi4jFNfxUREREREb/n9HUAuZhGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifk9rv3pOI5UiIiIiIiLiMSWVIiIiIiIi4jFNfxUREREREb+n1V89p5FKERERERER8ZiSShEREREREfGYkkoRERERERHxmM6pFBERERERv+c0vo4g99JIpYiIiIiIiHhMSaWIiIiIiIh4TNNfRURERETE7zmxvg4h19JIpYiIiIiIiHhMSaWIiIiIiIh4TNNfRURERETE72nyq+c0UikiIiIiIiIeU1IpIiIiIiIiHtP0VxERERER8XtOXweQi2mkUkRERERERDympFJEREREREQ8pumvIiIiIiLi95xa/9VjGqkUERERERERjympFBEREREREY9p+qsfc2B8HUKOkKSpDpIJrQCXQn2RQseLFI/VGejrEHKMSWvf8HUIOUbX2v19HYKI+ICSShERERER8Xv62tBzmv4qIiIiIiIiHlNSKSIiIiIiIh7T9FcREREREfF7WkPAcxqpFBEREREREY8pqRQRERERERGPafqriIiIiIj4PafWf/WYRipFRERERETEY0oqRURERERExGOa/ioiIiIiIn5Pk189p5FKERERERER8ZiSShEREREREfGYpr+KiIiIiIjfc/o6gFxMI5UiIiIiIiLiMSWVIiIiIiIi4jFNfxUREREREb9ntf6rxzRSKSIiIiIiIh5TUikiIiIiIiIeU1IpIiIiIiIiHtM5lSIiIiIi4vd0SRHPaaRSREREREREPKakUkRERERERDym6a8iIiIiIuL3nLqkiMc0UikiIiIiIiIeU1IpIiIiIiIiHtP0VxERERER8Xua/Oo5jVSKiIiIiIiIx5RUioiIiIiIiMc0/VVERERERPyeVn/1nEYqRURERERExGNKKkVERERERMRjmv4qIiIiIiJ+z+nrAHIxjVSKiIiIiIiIx5RUioiIiIiIiMeUVIqIiIiIiIjHdE6liIiIiIj4PatLinhMSaVckZpNwuky7FEcAQ4WfDmXH977LkOZLsMfJbxZbeLj4nmv/7vs2rLzkure3eNeOj7XlUfDOnLi6AkArrnhWrq/+jgFixTCOi1D7ulPQnyC9xt6iR4Z3p3wZnU4GxfPuP5jktuaWukKZegztj9FShRl55YdjO3zNokJieetnzd/Xl6a/ip58+UlIE8AK2cvZ/rbXyQ/Xusud9Gq0104k5ysW7CWz179JLuae0He6AuAJ0Y9Q+3mdTgefZy+LZ9Ofqxrb6xIj1eeoEChAhzed4h3er1F3Mm47GnsRXQb3p1azeoQ727LzvP0RV93X+zasoN3UvVFZvXLVi5Hv3EDkuuXuSaEL0d/zo8fzeT+3g/S4sGWxEYfB2DaqE9Zv3Bd9jT2ArxxvPhfv4eoE1EP67Qcjz7Oe/3e4eiho0DOP15k5pYmYXQY+giOAAeLv5rPT+99n6FMh2GPULNZLc7GneX9/mPZs3UXefPnZchXI8iTPy8BAQGs+Xkl37/9lQ9acGWqNwnjoaFdMQEOln41n9nvzchQ5qFhj3BLs3DOxp3lw/7j+HfrruT7jMPB0Fmvcywqhne6vQrAf/o+QFhEXax1Ensklo/6j+OYex+5Wjz/ymiWLF9NUGAJZnw20dfhZLkaTcLpOMz1ulj05TxmZfK66Di8G2HNahEfF8/k/uPYvWUnQaHB9Hz7GYqXCsQ6nSz8fC5zPv4JgP/r9yC1IupinZbY6ONM6jc2x+4X3jh2/l/vB7jjwQhio2MB+GLUZ2xcuI4iJYrSd+JAqtSoyqJvFvDx0Pezr6GSq2n6q3jMOBw8MuIxXu38En1bPE2je26jXLXyacqENatNSKVQejV5nPcHT6Dbyz0vqW5waElqNA7j8L5DydscAQ6eGtOHD4ZMpH/EM7x4//MkJiRlT2MvQXiz2oRWKsvTTR5j4uDx9Hj58UzLPfxsZ378cCZPN+3JqeMnaX5/xAXrJ8Qn8OKDz9O/dS/6t+5FeJNaVAu/HoCbG95C3Yj69Gv1DH0inmLm5IxvtL7grb4AWPj1fF7uPDzDYz3++tNMe20K/e58htVzVnHvY/d5pW2Xq5a7LU9epC86PtuZWR/O5KmmPTl5/CR3uPvifPUP7NxPvza96demNwPu7kt8XDy/zVmZ/Hg/fvhD8v05IaH01vFi1qTvGdiqN4Pa9GH9/DW073U/kPOPF5kxDgedXurOW11GMjiiNw3uaUzZqmn7qEbTWoRUCmVg06f4eMh7dB7ZA3AdJ157aDgvtO7HC236cUuTMKqEV/NFMzxmHA4efulR3u4ykucj+lA/k/bf0jScMpVCGdz0aaYMmUgnd/vPiejahsjt+9Js+3nyDwxr3Y/hbQawecE62vb6r9fbkt3atYlg4uiXfR2GVxiHg84juvNG55cZ2KIXDe65jbLpjh01m7leF/2aPMmHgyfS5WXXfuFMcvL5y1MYdMczDG/3LC06tU6u+9OkGQxp1Zfn2vRjw/y1/KfX/7K9bZfCm5+1fvpwJoPa9GFQmz5sdL9PJMSf5as3P+fTkZ9kWxvl6qCk8jIZY14yxrTwdRwAxpguxphxvnr+qmHVOLg7kkN7D5KUkMiKWcuoG1E/TZm6EfVY8u0iALZt+IfCxQpTonTgRet2GvoI016dgk01C6HG7eH8+9du9vy5G4CTx05gnTln8ee6EfVZ9O1CALZt+JtC7ramV/3WGqycvRyARd8uoF7L+hetf+b0GQAC8gQQkDcP5zrmzodb8/2Eb0k86xrROjcy5Wve7Is/V2/l5LGTGR6rbOVy/PHbVgA2Ld1I/dYNs75hHqiXqi3/bPibwsUKE5hJX9ySqi8WpuqLS6l/S6MaHPw3isP7D3uzKVfEW8eL1KPRBQoVwLpfGzn9eJGZymFVObgnisPudv42axm1WtZNU6ZWy7os/24xADs2bKNQ0cIUL1UCgPjUx4k8edIcP3ODymFVObQnisN7D7nbv5ywdO0Pb1mXFd8tAmDnhm0UKloouf2BIUHUaF6bJV/OT1PnTKp9JF+h/OS6jrkEdcJuoXixor4OwyuqhFXl4O7I5NfFqlnLqB1RL02Z2hH1WOY+duxIdew4dugou90jdmdOneHA9n0ElQkG0h478qc6duQ03vyslZn4uHj+Xvtnjp/V4S3OXPCTU/l1UmlcLqsPrLVDrbXzvBVTbhIUEkR05JHk29GR0QSGBKUpExgSRPSBVGWiogkqE3TBurVb1CUmKjr5w+A5ZSuVxVoYMnUYr/30Fvc89h8vtMpzwSHBRB9I+VAfExVNsPvN65yigUU5FXsKZ5LrsBAdGU1QSPBF6zscDkbNHsOH6z9l89KNbNv4DwChlcpyY72beHXGKF786hWq1Kjq1TZeKm/2xfns/WdP8ptlw7saUTK0ZJa05UoFhQRzJFVbXK+Bi/dFsLsvLqV+43tuZ+nMJWm2te50F6N/eZcnRz1D4WKFs7RNnvDW8QLg/gEdGL/yAxq3u53po11Tw3P68SIzgWWCiEnV/pjIGALT/a0Dy6Tto5ioaALd+4pxOHhp9puMXfcRW5dtYufGbdkTeBYpka79RyOjCSyTbh8pE0zMgejk2zFRMcntf3BoV75+9dNMk4P7+j/Imysm0uDe25gxOvdNC/ZngSHBxESm+ptfwrEjJirjvlOyfCmuvbkSO9zvnwD/HfAQ76yczK3tbufb0V96qQVXxpvHzjs73cUbv4yh56incsT7hORufpdUGmMqGmP+NMZMANYDLxhj1hhjNhtjXkxV7gVjzF/GmLnGmC+MMf3d2z8xxvyf+/c7jDEbjDG/G2M+Msbkd2/fbYx50Riz3n3fDReIp4gx5mN3uc3GmPbu7Q+6t20xxryeqnxXY8w/xpjFQKNU20sZY751t2WNMaZRJk+HMaaHMWatMWbtjpO7r6QrMZiMG9O9lxuTsYy156+br0A+/vPUf5M/GKbmyOPghro3MrbXaIa2H0zdVvWp3qiGp+FnvcyalO7DTeb9YS9a3+l0MqBNbx5r8AhVw6pR4bprANeIRJHiRRjcbgCfvvIxfScMurI2ZBUv9sX5jB/wLq06teH1H0dTsHDB5PMRfS2TZl5WX1ysfp68eajboh4rflqevO2Xz37midsfo1/rXhw9FEOXF7p5GH3W8cbx4pyvRk3jyYaPsmzGElp1bgPkguNFJi74mkgplLGiu4x1Ohnapj99Gvagcs1qlLuugjfC9JpLa3/GetZaajavTWz0cfZkcr4ywHdvfkH/W3uy6oelNO/cKivClWySyZ/8Eo8dKYXyFypAr4kD+eylj9KMUH496nN6NezBihlLiOjcOosizlreOnbO/exnnrm9J4Na9+HooaN0fKFrFkQr/szvkkq364GpwCCgHFAPCANqG2NuN8bUAdoD4cB9QJ30D2CMKQB8Atxvrb0F16JHqU+WOmKtrQW8B/S/QCwvAMettbdYa2sAC4wxZYHXgebuuOoaY9oZY0KBF3ElkxHATake5x3gbWttXXfsH2T2ZNbaydbaOtbaOlWKVLxAWBcXHRVNcKrRoODQYI4ejElTJiYymuCyqcqEBHP0UMx565a5NpTSFUrzxs9jGLtsMsGhwbz202iKlypBTGQ0f6zayomjJzh75iwbFq6nUvXKV9SGK9WqUxtGzR7DqNljOHowhuCypZLvCwoJJuZQ2v6IjYmlcLHCOAJcL73UfRYdGX3R+qdjT7F15RbCm9ZKrvPbL67z6LZv2oZ1OikWVCzrG3oJsrsv0juwYz8jOg5j0N19WTZzCVF7orKqaZetVac2vDV7DG/NHkPMwRhKpmrLuddAapn1RUyqvrhQ/fCmtdm5ZQfHjxxL3nb8yDGcTifWWuZ+8SvVavr+3DpvHC/SW/bDkuRpzznxeHExMVHRBKVqf1BoEMfS7StHo9L2UVBIxr44HXuav1ZtoUaTcO8GnMWOpmt/YGhwhoVTXGVSRm+DQoI4djCGqnWuJ6xFXd5YNoGeY3tzw63V6f72Mxme47cfllK7VQPvNUKyXExUNEGhqf7ml3DsCApJ2XcC8gTQa+IAVsxYwtpffsv0OVb8sJS6OeSUifS8dew8fuQ41v0+seCLuVTNAe8TOYHNBf9yKn9NKvdYa1cBLd0/G3CNWt4AVAMaAz9Ya+OstSeAWZk8xvXALmvtuXkUU4DbU91/bmmudUDFC8TSAhh/7oa19ihQF1hkrT1srU0Eprkfu36q7WeBr9I9zjhjzEZgJlDMGOPVEyx2bNpGSKVQSlUoTUDePNzatjFr565OU2btvNXc3r4pANXCr+P0iVMcO3T0vHX3/r2HHrW78HTjHjzduAfRkdE8e1dfjh8+xqbFG7j2xmvJVyAfjgAHN9W/mX3b9nqziRf1y9TZDGjTmwFterP6199o2r6Zu63Xc/rE6UxXktu68ncatnENJDdt35w1c11vcmvnrc60frGgYhRyT0vJlz8fNRrXZL97IYo1v66i+q2u0ZfQSmXJkzcPsTGx3m30eWRHX1xIseDigOsb2/97+n/MnfZLlrXtcv0ydXbyIjmp++I6d1uOZtKWLan6olmqvliTqi8yq3/bPbexLN3U19TnXNa/swH//r0naxvoAW8cLwBCKoYm168TUY/9O/YD5MjjxcXs2rSdMhVDKVne1c76bRuzYe7aNGU2zF1Do/uaAFAlvBpxJ05z/PAxigYVo1CxQgDkzZ+PmxrV4IC7L3KLjO1vxMa5a9KU2Th3Lbfe1xSAyuHVOO1u/7dvfE7/ho8xsPETTHx6DH+t2ML7fd4FoHTFkOT6YS3qEpXL+sXf7dy0Pc3rv0HbxqxPt1+sn7eGxu5jR5Xw69K8Zzz6xpMc2L6fnz9I+1GuTKpjR62IukTm0P3CW8fO1Osc1L2zPnv//jfb2iRXJ3+9pMgp9/8GeNVaOyn1ncaYPpfwGJnOyEgl3v1/EhfuZ0OGiQwXfOzzfUXhABpaa7PtGgrOJCcfDX2fIVOH4QgIYNH0eezbtpcWHe4EYN60OWxYsI7wZrV5Z8lEzrqXub5Q3Qs5FXuKHz+YySuz3gRr2bBwPRsW+H5Vy3PWL1hLrWa1GbdkEvFx8UxwtxVgyCdDeW/gOI4eiuHTVz+hz7gBPND/YXZv3cn8r+ZesH5g6SCeGt0bh8OBcRhW/LiMdQtcHzQXTJ/HE6OeYfSvY0lMSGRcv3eyv+GZ8FZfAPR+tz83N6xO0cBiTFr1EV+9/QULvppL43tup1Un19TH335ZyYLpOePU53Xutkxwt2VcqrY898lQJqTqi77jBvBQ/4fZtXUn89x9caH6+Qrko+ZtYUwcMiHNc3Yc3IVKN1XCWji872CG+33BW8eLh57tRNnKZXE6LUf2H+b9Ie8BOf94kRlnkpNPh37AgKkv4AhwsGT6AvZv20uzDi0BWDjtVzYtXE+NZrUYtXg88XHxfDDA9Z1kidKBdH/rKRyOAIzDsPqnFWzK4e1Nz5nk5LOhH9B36vM4Ahwsm76AA9v20dTd/kXTfmWzu/2vLR7H2bh4Phpw8X37/wY9TEjlslinJXr/YaY+N9nbTcl2A4a9xpoNmzl2LJY72j3ME9060r7tnb4OK0s4k5xMGfoBA6cOdV1qZ/p89m/bS3P3frFg2q9sXLCOms1q8daSCZx1X1IE4Lo6N3Bb+6b8++duRs5+C4Dpo6axaeF67n/2YUIrl8M6nRzZf5iPh0w6bwy+5K1jZ4fBnal4UyWstRzedyj52AkwdtlkChUt6Dq9omV9RnYczv5t+zIGJ5KKyamrXXmLMaYi8KO1troxpiUwArjDWnvSGFMOSACuBSYBt+JKCNcB71tr3zTGfAL86P75B2hurd3u3r7BWvuOMWY3UMdae8Q9lfZNa23T88TzGlDAWtvbfTsQKACsAmoDR4E5wFhgtXt7LSAWWABsstY+ZYz53P38o9yPE2at3Xihvrj/2nb+9cc/j6QcvZaW+Ir2ihR5/XZSS0YFTICvQ8gxAi763ar/mLT2DV+HkGN0rX2hM378S4LeSZJ9tWdGrjhgdK7YPsd/Np6y+9sc2Zd+/UnBWvsr8Dmw0hjzO/ANUNRauwbXFNJNuKaxrgWOp6t7BugKfO2u6wQ8ueLwy0Cge0GeTUAza20kMBhY6I5hvbX2B/f24cBKYB6uKbvnPAPUcS/28wfQ04NYRERERERELovfjVReKmNMEffoZSFgCdDDWrv+YvVyE41UumikUjKjvSKFRipTaKQyhUYqU2ikMoVGKlNopDKFRiqzTk4dqfTXcyovxWRjzE24pqJOudoSShERERERSeHUYJvHlFSeh7X2oax8PGNMV6BXus3LrbVPZuXziIiIiIiIZCclldnEWvsx8LGv4xAREREREclKOlFGREREREREPKaRShERERER8Xs6o9JzGqkUERERERERjympFBEREREREY9p+quIiIiIiPg9pybAekwjlSIiIiIiIuIxJZUiIiIiIiLiMU1/FRERERERv2c1/dVjGqkUERERERERjympFBEREREREY9p+quIiIiIiPg9p68DyMU0UikiIiIiInKVMMa0Msb8bYzZbox5NpP7jTHmXff9m40xta70OZVUioiIiIiIXAWMMQHAeKA1cBPwoDHmpnTFWgPV3D89gPeu9Hk1/VVERERERPye8+pY/bUesN1auxPAGPMlcC/wR6oy9wJTrbUWWGWMKWGMCbXWRnr6pBqpFBERERERuTqUA/amur3Pve1yy1wWJZUiIiIiIiK5gDGmhzFmbaqfHumLZFIt/RDspZS5LJr+KiIiIiIifs/mgumv1trJwOQLFNkHVEh1uzxwwIMyl0UjlSIiIiIiIleHNUA1Y0wlY0w+4AFgZroyM4FO7lVgGwDHr+R8StBIpYiIiIiIyFXBWptojHkKmAMEAB9Za7caY3q6758IzAbaANuB00DXK31eJZUiIiIiIiJXCWvtbFyJY+ptE1P9boEns/I5lVSKiIiIiIjfc/o6gFxM51SKiIiIiIiIx5RUioiIiIiIiMc0/VVERERERPye61RD8YRGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifs+Jpr96SiOVIiIiIiIi4jEllSIiIiIiIuIxTX8VERERERG/5/R1ALmYRipFRERERETEYxqp9GM6GdmlgF4GybRPpDhlE3wdQo5RwpHP1yHkGLc4C/g6hBzjuNHx4pyutfv7OoQc4+N1b/o6hBzj/tq9fR2CSLbRp2kREREREfF7Vl+ue0zTX0VERERERMRjSipFRERERETEY0oqRURERERExGM6p1JERERERPyeFiz0nEYqRURERERExGNKKkVERERERMRjmv4qIiIiIiJ+z1pNf/WURipFRERERETEY0oqRURERERExGOa/ioiIiIiIn7P6esAcjGNVIqIiIiIiIjHlFSKiIiIiIiIxzT9VURERERE/J5Fq796SiOVIiIiIiIi4jEllSIiIiIiIuIxTX8VERERERG/59T0V49ppFJEREREREQ8pqRSREREREREPKbpryIiIiIi4ves1fRXT2mkUkRERERERDympFJEREREREQ8pqRSREREREREPKZzKkVERERExO/pkiKe00iliIiIiIiIeExJpYiIiIiIiHhM019FRERERMTvWU1/9ZhGKkVERERERMRjSipFRERERETEY5r+KiIiIiIifs9pNf3VUxqpFBEREREREY8pqRQRERERERGPafqriIiIiIj4PU1+9ZxGKkVERERERMRjSipFRERERETEY5r+KlckrEk4XYd1xxHgYP6Xc5nx3rcZynQd3p1azWoTHxfP+P7vsGvLzgvWbdDmVv7X50HKVS3P4HsGsPP37QCUKl+aMfPHcWDHfgD+2fAP7z/3Xja19PLUaBJOx2GP4AhwsOjLecx67/sMZToO70ZYs1rEx8Uzuf84dm/ZSVBoMD3ffobipQKxTicLP5/LnI9/AuDBIZ0Iv6MOiQmJHNpzkMkDxnI69nR2N+2y1WgSTqdh3XAEOFj45TxmvfddhjKdhncjrFltzsbFM7H/WHa795Eeo54ivHkdYqOPM6hlr+Ty195UkUdG9iRv/nw4k5L4+PnJ7Ni0LdvadCW6v9iD2s3qEB8Xzzv9xrBzy44MZUpXKMOAcQMpUqIoO7ds5+3eo0lMSKRclfI882ZvqlSvwmejpjJjcsp+dfcj99DywTsxBn79Yg6zPpyZnc26bNWbhPHQ0K6YAAdLv5rP7PdmZCjz0LBHuKVZOGfjzvJh/3H8u3VX8n3G4WDorNc5FhXDO91eBaBOm4bc2/t/hFYtx8v3Dmb37xn7Nqe7tkkNmg7viCPAwZYvF7Fmwqw09wdWCaXlmz0oXb0iK0Z9zbrJs5PvC+/WilsebIq1liN/7ePX/pNJik/I7iZkmapNatBqmKsv1n+5iGXvpe2LklVCuffNxwi9uSIL3pzOCndf5Mmfl67TXyAgXx4ceQL4Y/ZqFr2d8b0pp/PG+8j/9XuQWhF1sU5LbPRxJvUby7FDR7O7aV71/CujWbJ8NUGBJZjx2URfh5Nlug3vTi33e8e4/mPY6X6fTK10hTL0HdufIiWKsmvLDt7p8zaJCYkXrH93t3to8UBLsJY9f+1h3IB3SIhP4MF+HagbUR/rdHI8+jhj+73D0UMx2dpmX3BqAqzHNFIpHnM4HHQb8RgjO79InxZP0eie2yhfrUKaMuHNahNaKZSnm/Rk0uDxdH/58YvW3fvPv7z52Gv8+dvWDM8ZtSeKAW36MKBNnxybUBqHg84juvNG55cZ2KIXDe65jbLVyqcpU7NZLUIqhdKvyZN8OHgiXV7uAYAzycnnL09h0B3PMLzds7To1Dq57u9LN/Fsy94MadWXyF0HaPtE+2xv2+UyDgddR/Tgjc4jGNDiGW69pzHl0vVFWLNahFQqS98mT/DB4Pd45OXHku9b8vUCXu/8UobHfXBwZ757ZzpD2vTlm9Ff8ODgTl5vS1ao3awOoRXL0vP2Hox/dhyPj3wi03KdB3dh5gc/8HiTHpw8fooW90cAcPLYCd4fNokZk9Mm5tdcdy0tH7yT/m370uvOp6l7Rz1CK5b1ens8ZRwOHn7pUd7uMpLnI/pQ/57GlK2adr+4pWk4ZSqFMrjp00wZMpFOI3ukuT+iaxsit+9Ls23/3/8yvuco/ln9p9fb4A3GYWj+cmdmdH6DKXcM5Pp7GhBULe3f8cyxUywa9mmaZBKgcJlAwru2ZNpdL/BpxGAcAQ6ub9sgO8PPUsZhaDOiC9M6v8H4FgOpfk9DSlUrl6ZM3LFT/DxsKive/ynN9sT4BKY8OJKJrYcwsfUQqjapQfnwqtkZ/hXz1vvIT5NmMKRVX55r048N89fyn17/y/a2eVu7NhFMHP2yr8PIUrWa1Sa0UlmebPIYEwePp4f7s1R6HZ/tzKwPZ/JU056cPH6SO9zvHeerH1QmiLu6tmXg3X3p3fJpHAEOGre9DYAZk76jb6tn6NemN2vnr+F/ve7PnsZKrqWkMhcxxuSokeWqYdWI2h3Fob0HSUxIZPmspdSJqJemTN2Ieiz+diEA2zb8Q+FihSlROvCCdfdv38eBnfuzvT1ZpUpYVQ7ujuTw3oMkJSSyatYyaqfrl9oR9Vj27SIAdqTql2OHjiaP0p05dYYD2/cRVCYYgC1LN+FMcibXCQoNzr5GeahqWDUO7o7kkLsvVp6nL5a695HtG/6hkLsvAP5a/Qcnj53I+MDWUrBIQQAKFi2Ua749rdeyPgu/XQDAPxv+pnCxwgS625pajVtrsHz2MgAWfDOfBnc2BOB49HG2b95GYmJSmvLlq5Xnn/V/cfZMPM4kJ1tWbaFBq4Zebo3nKodV5dCeKA7vPURSQiK/zVpOWMu6acqEt6zLiu8WAbBzwzYKFS1E8VIlAAgMCaJG89os+XJ+mjqRO/YTtfNAdjTBK0LCqnBs90GO/3sYZ0ISf89aRZWWtdOUiYuO5eDmnTjT7QMAjjwB5CmQDxPgIE/BfJw8mHtHoMqFVSFm90GO7j1MUkISW2at4vqItH1xKjqWA5t34kzI2BdnT8cDEJAngIC8Adhcdu05b72PxJ2MS66fv1CBXNcvl6JO2C0UL1bU12FkqXoR9Vnkfp+80HvHLbfWYOXs5QAs/HYB9VrWv2j9gAAH+QrkwxHgIH/B/MQcdL2fpt5XChQqwFW4q0gWU1KZRYwxfY0xW9w/vY0xFY0xfxljphhjNhtjvjHGFLpA/aHGmDXu+pONMca9fZEx5hVjzGKglzGmtjFmsTFmnTFmjjEm1F2uu7v+JmPMtxd6rqwSFBJMdOSR5NsxkdEEhwRnLHMgpUx01BGCygRfUt3MlK5Qhjdmv82LX43khro3ZUErsl5gSDAxkdHJt2MiowkMCUpXJihNv8RERRNYJm2ZkuVLce3Nldix8Z8Mz3H7/5qzedH6LI486wWGBGX4Owel+zsHhgQTcyBVf2XSF+lNfekjHhrSmbEr36fDc1346vXPsjZwLwkOCeZIqv44EpVxvy8aWIxTsaeSv0CIjjySoc/S+/fvPdxUvzpFSxQlX4H81G5Wh5KhJbO+AVmkRJkgYlLt/0cjM/7NA8uk3y9iCHT3w4NDu/L1q59edR+Ii4QEcuJAyhckJyNjKFIm4wfHzJw6eJR1k2fz6Kp36LF2HPGxp/l36RZvhep1xUKCiE11HI2NjKFYyKX1BbhGOnvOfoUB699jx9It7N+Yu6ZCe/N95L8DHuKdlZO5td3tfDv6Sy+1QLJSUEgwRw4cTr4dHRWd/EXBOUUDi6Z770h5fzlf/ZiDMfwweQaTVn7Ih2umcPrEKTYt3Zhc7qEBDzN55Yfc3q4JX46e5sUWytVASWUWMMbUBroC9YEGQHcgELgemGytrQHEApnPdXMZZ62ta62tDhQE7k51XwlrbRPgXWAs8H/W2trAR8BId5nv3PVrAn8C3bKsgZch/Yc8d26cvtAl1U3v6KEYHm/4KAPb9GHKiI/o9W6/5NGqnCSTFmdYozqzfknd/vyFCtBr4kA+e+mjNN8WAtzzVHuciU6Wf78kC6L1LpNJb2TcRzKpeJF9ocXDd/LpiI94umF3Pn3pI3q88eSVhJltPO2Pi7029m3fx3fvfcOL00Yw/NMX2f3nLpxJGUdvcoqL7f+uQhnrWWup2bw2sdHH2ZPJ+US5Xqb9cmlV8xcvROWIWnzUqA/v132avIXyc8N/GmVxgL51OV8iWKdlYpshjG7wNOXCqlD6uvIXr5SDePN95OtRn9OrYQ9WzFhCROfWWRSxeNOlvC9caH84X/3CxQpTr2V9Hm/cnUfrdSF/wQLc/p+myWU+H/UZPRp2Y8mMxbTufNeVNCHXcGJz/E9OpaQyazQGvrfWnrLWngS+A24D9lprl7vLfOYudz7NjDG/GWN+B5oDN6e67yv3/9cD1YG5xpiNwPPAuXfK6saYpe76HdLVT2aM6WGMWWuMWbvz5O7LbWcaMVHRBKcaDQkKDU6eNnFOdOQRgsumlAkOKUnMoZhLqpte4tnE5KmQO7fs4OCeSEIrlbtgHV+IiYpOMzU1KDSYo+naFhMZnaZfgkKCkxdLCMgTQK+JA1gxYwlrf/ktTb3b2jcl/I46TOj1thdbkHUy+ztn1hdBZVP1V0gwRy+ycMTt7Zux5udVAPz20woq16yWhVFnrTad7uLtn9/l7Z/fJeZQTJoRxJIhGff72JhYChcrjCPAdXgODi2Zoc8yM++rufS9qzdD/vssJ46d4MCunDsN9GhUNEGp9v/A0OAMi4W4yqTeL4I4djCGqnWuJ6xFXd5YNoGeY3tzw63V6f72M9kWuzedjIyhaNmUkaYioUGcusRFVK5pXJ3YvYeJizmBMzGJ7b+spWztnPu6uJjYqBiKpTqOFgsN4sTBY5f9OGdiT7N75Z9UbVojC6PzPm++j5yz4oel1G2dc6fJ+7tWndrw1uwxvDV7DDEHYyhZtlTyfcEhwRlO+8j43pHy/hIdGZ1p/RqNwzi49yCxMbEkJSbx2y8ruaH2DRliWfrDYhq2vtUbzZSriJLKrJHpl4pkvIZqpl8vGGMKABNwjUDeArwPFEhV5FSq59lqrQ1z/9xirW3pvu8T4Cl3/RfT1U8JwNrJ1to61to6lYtUvEizLmz7pm2EVgqldIXS5Mmbh0Ztb2Pt3NVpyqydt5om7ZsBUC38Ok6fOMWxQ0cvqW56xYKK4XC4dtnSFcoQWqksh/6NuqI2eMPOTdsJqRRKqQqlCcibhwZtG7N+7po0ZdbPW0Pj9k0BqBJ+HadPnE7+MPDoG09yYPt+fv4g7UqHNZqEc/fj/2F0t1c5e+ZstrTlSu3YtC1NXzRs25h16fpi3bw13ObeR6qGX0dcqr44n6OHjnJjA9f3Jjc3uoWDuyO904AsMHvqT/Rp/Qx9Wj/Dqjkrada+OQDXhV/PqROnM02gf1/5O43auL6Dav5/d/Dbr6su+jzFg4sDULJsKRq2asiSmYuzsBVZa9em7ZSpGErJ8q79on7bRmxMt19snLuWW+9rCkDl8GqcPnGa44eP8e0bn9O/4WMMbPwEE58ew18rtvB+n3d90IqsF7VpJ4GVQihWoRSOvAFc37YBO+de2jT3E/ujCa1VlTwF8gFwTaObidmee89NP7BpJ8GVQihRoRQBeQOo3rYBf89dd0l1CwUVpUAx1xkgefLnpXLjmzmyPeceIzLjrfeRMhVDk3+vFVGXyB25dx+52v0ydTb92vSmX5verP71N5q63yevC7+e0+d579iy8ncatnHNUGjWvjlr5rq+UFgzb3Wm9Y8cOMx14deTz33cuKVRTfZt3wtAaKp9pW5EPfbvSLswmkh65mo7J8UXjDG1cCV1DXAlfr8BHYH1wK3W2pXGmPeBv6y1b2VSvwTwN1ARCABWAd9Ya4cbYxYB/a21a40x+YA/gI7ux8wLXGet3WqMOQLcBBwFZgP7rbVdLhT3f6+994r/+OHNatNlqPtyEdPn8924r4no0AqAudN+AaDbiMcIaxLO2bh4xvcfm3yJkMzqAtS7swGPvNidYkHFORV7it1/7GJkp+HUb92Q+/s+RFJiEk6nk+mjv2Dd/DWZB3YZ8hNwxY+RXs1mtXh4qGsp+MXT5zNz3Lc07+DK/xdM+xWAziO6U8PdL5P7j2PX7zu4rs4NDP32Ff79czfW6frzTB81jU0L1/PW4vHkyZeXk0ddo7XbN/zDx89NytK4vTGtIqxZLTq6/86Lps/nh3HfcEeHOwGYP20OAF1G9KBmk3Di4+KZ1H8su9yXgnjq3b7c2PBmigYW4/iRY3z79pcs+mo+19e5kU7DXY+ZEJ/Ax89PSr5UTVY5Zb1zKYbHRvQkvKnrEjtj+49h+2bX6+GFT4YzftC7xByMocw1Zeg/bhBFSxRh59adjO71JolnEylRqgRv/TiGQkUK4XQ6OXP6DE/d8ThxJ+N45ZvXKRZYlMSEJD4a8QGbl2/KspiDHZl+R3VFbmkazoNDu+IIcLBs+gJ+HP8dTd2vkUXu18jDLz1K9SZhnI2L56MBEzJcIuT6BjfTqvs9yZcUqXVnPR4a3o2iQcU4HXuKvX/uZnSnrF0F8hZn1vdFahWb1aTpsIcxAQ62frWY1eNmUuNh1xcRmz9bQKFSxXnoxxHkK1IQ63SScDqeqXcM4uzJOBr2vY/r7m6AMymJw1v3MHfgBySdTfRarMeNdz8/VGtWk1ZDO2ICHGyYvpil436gToc7AFg7bT5FShWnx6yXye/ui7On4xnfYiAlypek3eieOBwOjMOw9cffWPxuxstxZKXtxF280GXyxvvIMxMHEFq5HNbp5Mj+w3w8ZNIlzYS4HB+vezNLH+9yDRj2Gms2bObYsViCg0rwRLeOtG97p09iub927yx7rO4jHiO8SS33JUHeZYf7s9RznwxlwsBxHD0UQ5kKZeg7boDrkiJbdzKm91skuo8B56t/f58HaXT3bTiTkti5dScTBo0l8WwiAyY+S7nK5XA6LYf3H2LSkAkXnVF2Id/tmXm+AZgcpUHZpjk+MVp1YFGO7EsllVnEGNMXeMR98wNgBq7kbglwK7ANVzKY6YUFjTEvAw8Au4G9wJ70SaW7XBiucyuL47rO6Bhr7fvGmMeBgcAe4HegaHYklVcDbySVuVVOnquf3byVVOZG3kgqcytvJ5W5ibeTytzEG0llbuXrpDInycqkMrdTUpl1cmpSmaMuUZGbWWtHA6PP3TbGVASc1tqel1j/eVznSKbf3jTd7Y3A7ZmUew/ImRduFBERERGRq5aSShERERER8XuaseU5JZVeYq3djWul1jSMMd8DldJtHmStnZMdcYmIiIiIiGQlJZXZzFr7H1/HICIiIiIiklWUVIqIiIiIiN+zmv7qMV2nUkRERERERDympFJEREREREQ8pumvIiIiIiLi96zV9FdPaaRSREREREREPKakUkRERERERDympFJEREREREQ8pnMqRURERETE7zl1SRGPaaRSREREREREPKakUkRERERERDym6a8iIiIiIuL3dEkRz2mkUkRERERERDympFJEREREREQ8pumvIiIiIiLi97T6q+c0UikiIiIiIiIeU1IpIiIiIiIiHtP0VxERERER8XtW0189ppFKERERERER8ZiSShEREREREfGYpr+KiIiIiIjfc1pNf/WURipFRERERETEY0oqRURERERExGOa/ioiIiIiIn5Pq796TiOVIiIiIiIi4jEllSIiIiIiIuIxJZUiIiIiIiLiMZ1TKSIiIiIifk+XFPGcRipFRERERETEY0oqRURERERExGOa/ioiIiIiIn5PlxTxnEYqRURERERExGNKKkVERERERMRjmv7qx/JgfB1CjpCA09ch5BiPnsnv6xByjLrNj/k6hBxj1/IAX4eQY/xiCvg6hBzjT075OgTJge6v3dvXIeQYX60b4+sQ5DJp9VfPaaRSREREREREPKakUkRERERERDym6a8iIiIiIuL3tPqr5zRSKSIiIiIiIh5TUikiIiIiIiIe0/RXERERERHxe1r91XMaqRQRERERERGPKakUERERERERjympFBEREREREY/pnEoREREREfF7uqSI5zRSKSIiIiIiIh5TUikiIiIiIiIe0/RXERERERHxe9Y6fR1CrqWRShEREREREfGYkkoRERERERHxmKa/ioiIiIiI33Nq9VePaaRSREREREREPKakUkRERERERDym6a8iIiIiIuL3rNX0V09ppFJEREREREQ8pqRSREREREREPKbpryIiIiIi4ve0+qvnNFIpIiIiIiIiHlNSKSIiIiIiIh7T9FcREREREfF7Wv3VcxqpFBERERERucoZY4KMMXONMdvc/wdmUqaCMWahMeZPY8xWY0yvS3lsJZUiIiIiIiJXv2eB+dbaasB89+30EoF+1tobgQbAk8aYmy72wEoqRURERERErn73AlPcv08B2qUvYK2NtNaud/9+AvgTKHexB9Y5lSIiIiIi4vecV/85lWWstZHgSh6NMaUvVNgYUxEIB3672AMrqRQREREREckFjDE9gB6pNk221k5Odf88ICSTqs9d5vMUAb4FeltrYy9WXkmliIiIiIhILuBOICdf4P4W57vPGHPQGBPqHqUMBQ6dp1xeXAnlNGvtd5cSl86pFBERERERv2dzwb8rNBPo7P69M/BD+gLGGAN8CPxprR19qQ+spFJEREREROTq9xoQYYzZBkS4b2OMKWuMme0u0wjoCDQ3xmx0/7S52ANr+quIiIiIiMhVzlobDdyRyfYDQBv378sAc7mPraRSRERERET8nr36V3/1Gk1/FREREREREY9ppFKyTM0m4XQa9iiOAAcLv5zLzPcyLhbVefijhDWrzdm4eN7r/y67t+wE4LFRTxHevA6x0ccZ2LJXcvn6bW7l//o8QNmq5XnhngHs/H1HtrXnctVsEk4Xd/sXfDmXHzJpf5fhjxLerDbx7vbvcrf/fHX/r/cD3PFgBLHRrpWcvxj1GRsXriMgbx56vPI4lWtUxTqdfPLih/yxakv2NfYKBDeryfUvd8EEONg/bQG7x6Y9R7xUqzpUGfQ/cFpsYhJ/vzCFY6v/BuCmMT0pFVGLs0diWdmkvy/CzzJ5atalYKenwBHA2YU/ET/zi7T331iTwv1fxnkoCoCza5YS/91UAIq9+wU27jQ4nVhnEief65nt8Welok3CKT+8OybAQfSXczk44ds09xePqEdo/w5YpxOSnOx78QNOrfmT/JXLUXF8yn6Q/5oQIkd/zuEPZ2V3E7JM5SY1aDGsI44ABxu/XMSq99K2JahKKHe/2YMyN1dk8Ztfs3ry7OT7Hl/2NmdPncEmOXEmJfFJ26HZHb5HvHHsvPamSnQf2ZO8+fORlJTEh89PYsembVSpWY0erz4BgDHw9ZgvWTPnopdfyzbZ+T5SpERR+k4cSJUaVVn0zQI+Hvp+9jX0EnUb3p1azeoQHxfPuP5j2Olua2qlK5Sh79j+FClRlF1bdvBOn7dJTEi8YP27u91DiwdagrXs+WsP4wa8Q0J8Ag/260DdiPpYp5Pj0ccZ2+8djh6KydY2Z6XnXxnNkuWrCQoswYzPJvo6HLnKKanMZYwxw4GTQDFgibV2njHmNmAikAA0BF7CNS96trV2QLbE5XDQdcRjvNJhGNFR0YycOYp181azf9u+5DJhzWoTUimUPk0ep2r4dXR7uScvtBsIwOKvFzBnymyeGN0rzePu/edfRj/2Go++8kR2NMNjxuHgkRGPMdLd/ldnjmLtedrfq8njVHO3//l2Ay9a96cPZ/Lj5LSJ1x0PRgAw4M5eFAsuzuApQxnStn/On7bhMNzw2iOs/99IzhyIpv6cVzk8Zy2n/tmfXCRmye8c/mUtAEVuuoYak3uzonFfAA58uZi9H86h+rgnfRJ+ljEOCnbtxalXBuCMPkzRkRNJWLcC5/49aYol/vU7p0YNyfQhTr7cB3viopeNyvkcDiq8/BjbOwwjITKa62e9yfG5qzmzbW9ykRPLN3N87moACtxwLZUmDOTP5k8Sv3M/f7fuk/w41Vd/xLFfVvmiFVnCOAwtR3Tmyw6vERsVQ5eZL7Ft3jqitx1ILnPm2CnmDvuUanfWzvQxPn9gJHFHT2ZXyFfMW8fODoM78807X7Fx0XrCmtWmw+DOvPTA8+z9ew+D2/bDmeSkROlA3vj5bdbNW4MzyenDXnDJ7veRhPizfPXm51S4/hoqXH9Ntrb1UtRqVpvQSmV5ssljXBd+PT1efpxn22X8SNPx2c7M+nAmy2ct5bGRj3PH/RHM+ezn89YPKhPEXV3b0uuOJzkbf5Z+4wfSuO1tLPxmATMmfccXb00DoE2Xu/lfr/uZ9Nx72d30LNOuTQQPtb+HISPe9HUouYbzyldX9Vua/ppLWWuHWmvnuW92AN601oZZa+OAx4Ba2ZVQAlQNq0bU7kgO7T1IUkIiK2cto05E/TRlakfUY+m3iwDYvuEfChUrTInSgQD8tfoPTh7L+EHowPZ9RO48kGF7TlM1rBoHU7V/xaxl1E3X/roR9Vjibv+2Df9Q2N3+S6mbXvlqFfh9xWYAYqOPcyr2FJVrVPVK27JS8VpVOb3rIHF7DmETkoiasYJSreqmKZN0Oj7594BC+UmdJx9b9ScJmewnuU1A1RtwRh3AeSgSkhI5u3IBees08nVYPlEorBrxu6M4++9BbEIiR2ctpXjLemnKOE+fSf7dUagAZPLlSdFGNYj/N4qE/Ye9HrO3lA2rwtHdBzm29zDOhCT+nLWK6yLSJo+no2OJ3LwTZ0KSj6LMWl47dlpLwSIFAShUtFDyaNPZM2eTE8i8+fNmtiv5THa/j8THxfP32j9JiE/wVpOuSL2I+iz6diEA/2z4m8LFChPo/syQ2i231mDl7OUALPx2AfVa1r9o/YAAB/kK5MMR4CB/wfzEHHTtH3En45Ift0ChAjlq//BEnbBbKF6sqK/DED+hkcpcwBjzHNAJ2AscBtYZYz4BfgRKAP8D7jTGtACKAoWB34wxr1prv8qOGANDgoiOPJJ8Ozoymqrh1dKUCQoJIvpASpmYqGiCygRx7NDR7AjRq4Iuof2B6dof7W7/xere2ekubr+vGTt/386nIz7mVOwp9vyxm7oR9VgxcynBZUtSuXoVgsuWZMembV5s5ZXLHxJE/IHo5NvxB6IpVitjMlyqdV2qPfcg+UoWZ8PDr2VniNnCEVgSZ3TK9Yad0YfJU/XGDOUCqt1E0dc+wHn0CHHTJuLctxtwLSRQePAosHB2/izOLvgxu0LPcvlCgjmb6nVxNjKawmHXZShX/M4GlB3UkTwli7Ojy4gM9wfecxtHf1ji1Vi9rUhIILGRKVPtTkTGUDa8ymU8guWBz57FWsvGaQvY+MXCrA8yi3nr2DnlpQ8ZMnUYDz/XFYfD8MJ9zyaXqxpWjZ6jnqZUuVKM6zMmR4xSQva/j+R0QSHBHDmQ8iWRq63BHE31maFoYFFOxZ5K/htGR0YTHBJ8wfo7ft/OD5NnMGnlh5w9c5ZNSzewaenG5HIPDXiYpvc14/SJ0wx94Dkvt1Lk6qGRyhzOGFMbeAAIB+4D0gzrWGs/wHUh0wHW2g7W2nuAOPeoZbYklAAms5WH033D57qWaroiufxbwHOupP0Xqjv3s5955vaeDGrdh6OHjtLxha4ALJw+j+jIaF6d9Radh3bjn/V/4UzMBSMXmfRBZg7/vIYVjfuyscubVBl0v5eD8oFM+yHtDpO4exuxTz/AiWcfJX7O9xTum5JInRz+NCeHPMap1weRv2U7Am6o4eWAvSjTrsh4YDg+ZxV/Nn+SnY++Qtn+HdI+RN48FI+ox7GflnspyOyR2bHgco6Rn973Eh/f9TzTO4+iVqcWVKh3fRZG5x3eOnZGPNyKKSM+4smGjzLlpY/o+cZTyUW2b9xG/4hnGHLPANo90Z68+fNeSROyTHa/j+R0mR0m05/ikXl/2AvWL1ysMPVa1ufxxt15tF4X8hcswO3/aZpc5vNRn9GjYTeWzFhM6853XUkTJBey1ub4n5xKSWXOdxvwvbX2tLU2FlcC6TFjTA9jzFpjzNrtJ3dnSYDgGnUMDi2ZfDs4NJijB9Oe3B4dGU1w2ZQyQSHBufoE+NSiL6H9MenaH+xu/4XqHj9yHOt0Yq1lwRdzqVrT9c2zM8nJ1BEfMahNH97s/iqFihUmcnfOnyYcHxlN/rLBybfzlw0mPur8I9XHVv1JoYplyBt0dU3fccYcxhFcOvm2I7gUzqPRaQvFnYZ417TPxI2/YfLkwRQtBoB1l7Wxx0hYs5Q8VW7InsC94GxkNPlSvS7yhQaTcIHjwqnVf5DvmhACAlP2iWJNa3F6yw4Sjxz3aqzediIqhmKhQcm3i4YGcfLgpc/kOHnoGOCaIvvPnHWEhl3OKKdveOvY2aR9M1b/vBKAVT8tp0rNtCN+APu37yM+Lp4K1+WM8wmz+30kJ2rVqQ1vzR7DW7PHEHMwhpJlSyXfF5zJZ4bYmFgKFyuMI8D1cTY4NDh5Kmt0ZHSm9Ws0DuPg3oPExsSSlJjEb7+s5IbaGY+hS39YTMPWt3qjmSJXJSWVuUOWfS1hrZ1sra1jra1TtUjFrHpYdmzaRkilUEpVKE1A3jw0bNuYde6FNc5ZP281t7VvCkDV8Os4feLUVTH1FTK2/9a2jVmbrv1r563mdnf7q6Vq/4Xqlkh1/kjdO+uz9+9/AchXIB/5C+YH4JbGNXEmJqVZzCGnit2wg0KVQyhwTSlM3gBC2t3K4Tlr05QpWLFM8u9Fb6mEyZuHhJgT2R2qVyXt+AtHSDkcpUIgIA/5GjYnYd2KNGVM8ZS/fUCVG8AY18I8+QtAAde5YuQvQJ4adUjatys7w89SpzdtI3+lUPJVKI3Jm4fAtrclL8pzTr5rQ5J/L1i9MiZfHpKOpuwTgffeztEflmZbzN5yYNNOAiuFULxCKRx5A7ixbQO2zV1/SXXzFsxPvsIFkn+vdHt1jvyd848J3jp2Hj0Uw00NqgNQvVENonZHAlCqQunkBKRkuVKEVi7H4X2HyAmy+30kJ/pl6mz6telNvza9Wf3rbzRt3wyA68Kv5/SJ02mmvp6zZeXvNGzjOie9WfvmrJnrWs13zbzVmdY/cuAw14VfT74C+QC4pVFN9m13LQwWWjE0+XHrRtRj/46c/xoSySl0TmXOtwT4xBjzGq6/V1tgkm9DysiZ5OSToe8zeOowHAEBLJo+j33b9tKiw50AzJs2hw0L1hHWrDZjlkwkPi6eSf3fTa7/9Lt9ubFhdYoGFmPcqg/45u0vWfTVPOrcWZ8uL3anWFBxBn78Arv/2MVrnV70VTPPy5nk5KOh7zPkIu0Pb1abd5ZMTL6kyoXqAnQY3JmKN1XCWsvhfYd4f4hrFbriJUswZOowrHUSExXDuD5jfNLuy2WTnPw9+CNqfTkEE+DgwBeLOPX3Psp3agHAvqnzKHN3fUL/ezs2MYmkM2f5vceY5Pq3THyGwFtvIm9QUW7bMIEdo77mwOc5/7yxDJxO4j55l8KD3wCHg7OLfsa5bzf5WrQF4Oy8WeSt34T8EfdCUhL2bDyn3nVNf3UUD0yZChsQwNnl80jctMZXLblySU72vTCZKp8Od11S5Kv5nPlnL8EPtwIg+rNfKNHmVoLaN8MmJGLPnGX3k6OSq5sC+Sh6W03+HTzBVy3IMjbJydyhU3hg6kBMgIPN0xdzZNt+wjs0B2DDtAUULlWcLrNGkL9IQazTSd1HWvF+i0EUCizCfZN7A+DIE8AfP6xg5+LNPmzNpfHWsXPSoAl0Gf4oAQEOzsYnMPlZ1/5xQ52buPeJ+0hKSMJaJx8+P4kTR3PGl1bZ/T4CMHbZZAoVLUievHmo27I+IzsOzzFfUK5bsJZazWozYckk9yVBUj4zPPfJUCYMHMfRQzF8+uon9B03gIf6P8yurTuZ99XcC9bftvEfVs5ezps/jcGZlMTOrTv59fM5ADz8bGfKVS6H02k5vP8Qk4bk7uPKgGGvsWbDZo4di+WOdg/zRLeOtG97p6/DkquUyclzc8Ul1UI9e4B9wB9AdeBHa+035xbtsdZ+4y5/0lpb5GKP++C17fTHB3LGEg05w6Nn8vs6hByjbvOcMXqRE+xaXszXIeQYv5irayr2ldhI7l+JWbJegt5Vk321boyvQ8gx8pasfGmLKvhYUNFqOf6zccyJbTmyLzVSmQtYa0cCIy9wf5d0ty+aUIqIiIiIiGQFnVMpIiIiIiIiHtNIpYiIiIiI+D2dFug5jVSKiIiIiIiIx5RUioiIiIiIiMc0/VVERERERPyeM+suDe93NFIpIiIiIiIiHlNSKSIiIiIiIh7T9FcREREREfF7Wv3VcxqpFBEREREREY8pqRQRERERERGPafqriIiIiIj4Paemv3pMI5UiIiIiIiLiMSWVIiIiIiIi4jEllSIiIiIiIuIxnVMpIiIiIiJ+z6JzKj2lkUoRERERERHxmJJKERERERER8Zimv4qIiIiIiN/TJUU8p5FKERERERER8ZiSShEREREREfGYpr+KiIiIiIjfs5r+6jGNVIqIiIiIiIjHlFSKiIiIiIiIxzT9VURERERE/J5F0189pZFKERERERER8ZiSShEREREREfGYpr+KiIiIiIjf0+qvntNIpYiIiIiIiHhMSaWIiIiIiIh4TNNfRURERETE72n6q+c0UikiIiIiIiIeU1IpIiIiIiIiHlNSKSIiIiIiIh7TOZUiIiIiIuL3dEal5zRSKSIiIiIiIh4zWuVIfMkY08NaO9nXceQE6osU6osU6osU6osU6osU6osU6osU6osU6gvJDhqpFF/r4esAchD1RQr1RQr1RQr1RQr1RQr1RQr1RQr1RQr1hXidkkoRERERERHxmJJKERERERER8ZiSSvE1zfFPob5Iob5Iob5Iob5Iob5Iob5Iob5Iob5Iob4Qr9NCPSIiIiIiIuIxjVSKiIiIiIiIx5RUioiIiIiIiMeUVIqIiIiIiIjHlFSKSI5ijCns6xhERHIbHTvFGPO6+///+joW8T9aqEeynTHmJWvt0FS3A4Cp1toOPgzLZ4wxjYFq1tqPjTGlgCLW2l2+jiu7GWNuBT7A1f5rjDE1gcestU/4OLRs594PugMVgTzntltrH/FVTNnNGDMWOO8blLX2mWwMJ0dwf1D8xVp7whjzPFALeNlau97HoWU7Y4wBOgCVrbUvGWOuAUKstat9HFq207ETjDEFgG7AzUCBc9v96ZgJYIz5Hddx4TdrbS1fxyP+RSOV4gvXGGMGAxhj8gPfA9t8G5JvGGOGAYOAwe5NeYHPfBeRT70N3AlEA1hrNwG3+zQi3/kBKA7MA35K9eNP1gLrcH1ArIXrGLENCAOSfBeWT73gTigb43qtTAHe83FMvjIBaAg86L59Ahjvu3B8SsdO+BQIwdUPi4HyuPYJf/MLcASoYYyJTfVzwhgT6+vg5OqW5+JFRLJcV2CaO7FsBvxsrX3bxzH5yn+AcGA9gLX2gDGmqG9D8h1r7V7XAEQyf00eCllrB/k6CF+y1k4BMMZ0AZpZaxPctycCv/owNF8693q4C3jPWvuDMWa4D+PxpfrW2lrGmA0A1tqjxph8vg7KV3TspKq19r/GmHuttVOMMZ8Dc3wdlA88b60dYIz5wVp7r6+DEf+ikUrJNsaYWsaYWriSqHeA+3GNPCx2b/dHZ61rDroFvz8nZq97Gpc1xuQzxvQH/vR1UD7yozGmja+DyCHKAqm/aCni3uaP9htjJgH/A2a7Z3r46/t4gvvUiXPHzlKA07ch+YyOnZDg/v+YMaY6rpkeFX0Xjs+sdP+vUUnJdhqplOz0VrrbR4Gb3Nst0DzbI/K96e4PiSWMMd2BR4D3fRyTr/TE9WVDOWAfrtGoJ30ake/0AoYYY86S8mHJWmuL+TAmX3kN2GCMWei+3QQY7rtwfOp/QCvgTWvtMWNMKDDAxzH5yru4Tp0obYwZCfwf8LxvQ/IZHTthsjEmEHgBmInry6ehF65yVcpnjOkM3GqMuS/9ndba73wQk/gJLdQj4mPGmAigJWCAOdbauT4OSSRHMcaEAPXdN3+z1kb5Mh5fMMY4gM3W2uq+jiWnMMbcANyB69g531rrb6NzImm4z7fugOsLqJnp7rb+tnCRZC8llZLtjDFlgFeAstba1saYm4CG1toPfRxatjPGVAIirbVn3LcLAmWstbt9GpgPGGOmAL2stcfctwOBt/z1TdAYcw8pi20sstb+6Mt4fEWrfKYwxkwDBltr//V1LL5ijAm60P3W2pjsisXXtEJyCn2uSMsY081f2y6+46/nYohvfYLrBPpz50X9A/T2VTA+9jVpzwNKcm/zRzXOJZTgWngD1/m3fscY8xquKbB/uH96ubf5I63ymSIU2GqMmW+MmXnux9dBZbN1pKwMfBjX+8c29+/rfBiXL2iF5BSfoM8VGGPOnUZ01BhzX/ofnwYnVz2dUym+UNJaO/3cZUWstYnGGH97Azwnj7X27Lkb1tqzfryCocMYE+hOJs+NSPjrMaoNEGatdULyKO4G4FmfRuUbWuUzxYu+DsDXrLWVIHkV4JnW2tnu262BFr6MLbtpheQ09LnC5XZgAdAW1yi2Sfe/zqkUr/HXD2ziW6eMMcGkrNrXADju25B85rAx5h5r7UwAY8y9uK4x5Y/eAlYYY75x3/4vMNKH8fhaCeDcVL7iPozD17TKp5u1drEx5lqgmrV2njGmEBDg67h8pK61tue5G9ban40xI3wZkA+dWyH53PHCH1dI1ucKlxPGmL7AFlKSSbjANGmRrKKkUnyhL64TyKsYY5YDpXCt3OePeuK6Zuc4XAf/vUAn34bkG9baqcaYtbhWATbAfdbaP3wclq+8SsqKpwbXt8+DfRuSz2iVTzf3CtE9gCCgCq7VPifiWqzG3xwxxjwPfIbrA/PDQLRvQ/IZrZCszxXnFHH/fz1QF/gB13tIW2CJr4IS/6CFesQnjDF5cB30DPD3uWk7/soYUwTX6/GEr2PJbsaYYtba2PMtwOFPC2+k5r5cRF1crxF/XvG0Aa4RGL9f5dMYsxGoh2t/CHdv+91ae4tPA/MB9/FiGCmLWS0BXvTj48W5FZItsNqfjhfumQzPAGPR5woAjDG/Au3PfaYwxhQFvrbWtvJtZHI100ilZDv3lK2+wLXW2u7GmGrGmOv9aXVLY8zD1trP3NNUUm8HwFo72ieB+cbnwN24FpxI/S3XuXNAKvsiKF8wxtxgrf3LGFPLvWmf+/+yxpiy1tr1vorNF6y1TmPMW9bahsBfvo4nB4h3n3cNJH8555ffDLuTx17GmGKA01p70tcx+Vg94Db37xaY5cNYspW1NskYc6+19m1gq6/jySGuAc6mun0WqOibUMRfKKkUX/gYVwLR0H17H64VT/0mqQQKu/8v6tMocgBr7d3uy0Y08edLJbj1xTW98a1M7rO4pgb7m1+NMe2B76ym1iw2xgwBCrqvb/sEfpQ8pGaMuQWYimsqMMaYI0Bna+0WnwbmA+6VoesC09ybnjHG3Gqt9acp88vdp5F8BZw6t9HfvohL5VNgtTHme1zvHf8Bpvg2JLnaafqrZDtjzFprbR1jzIZUU7g2WWtr+jq27HRuyo7721W/Z4xZZ62t7es4cgJjTIFz1y690DZ/YIw5getLmETgDO4RbGttMZ8G5gPu6cDdgJa4+mEO8IE/JtvGmBXAc9bahe7bTYFXrLW3+jIuXzDGbCbtatEBwAZrbQ3fRpZ9Up1Pmpq11vrjF3EAuGe8nBu9XmKt3eDLeOTqp5FK8YWzxpiCpKzSVgWI921I2c89ZeceQEmlyypjTF1r7RpfB5IDrMB13bmLbbvqWWv9fjT/HHfS8L77x98VPpdQAlhrFxljCl+owlWuBH68WrS1tpmvY8hp3KO0/jpSKz6gpFJ8YRjwC1DBGDMNaAR08WlEvrNCU3aSNQMeM8bswdUX50ak/Onb9hBcK3oWNMaEk7IcfDGgkM8C8yFjzO2ZbbfW+s1KhsaY37nAuZP+9BpJZacx5gVc0/zAtfrrLh/G40taLRowxtwF3AwUOLfNWvuS7yIS8S+a/irZzhjzKfA7EAfsxLWSoV9em1FTdlK4r7+XgbV2T3bH4ivGmM64vmCpA6xNddcJ4BNrrd9duNoYk/qcwQK4FiRZ50+vkVSvjSfd/59LpDoAp/3xg7MxJhB4EWjs3nRu9dejvovKd/x9tWhjzERcX7w1Az7AdTmR1dbabj4NTMSPKKmUbGeMaY7rg8BtuFb23Ihrvv87voxLfM99DkhjXKMyy/10xBZjTHtr7be+jiMnMsZUAN6w1j7o61iymzFmubW20cW2iX8xxvwHWGCtPe6+XQJoaq2d4cu4spMxZrO1tkaq/4vgWtyrpa9jE/EXDl8HIP7HWrsAGAm8gOsbxTrA4z4NykeMMcHGmHeNMeuNMeuMMe8YY4J9HZcvGGOG4lqdLhgoCXzsvri5P5pvjBltjFnr/nnLGON350mdxz6guq+D8JHCxphzI3MYY24lZSVpv2KMmetOns7dDjTGzPFhSL407FxCCWCtPYbrNBN/Euf+/7QxpiyQAFTyYTwifkfnVEq2M8bMx/VBaCWwFKhrrT3k26h85ktc07bau293wHV+ZQufReQ7DwLh51Y4dS+Tvx542adR+caHwBbgf+7bHXFdiuc+n0XkI8aYsaScT+gAwoBNPgvItx7B9WVLcVx9cty9zR+VdCdPAFhrjxpjSvswHl/KbIDA3z7f/ej+kmEUrvcNixa0EslW/nbQkZxhM1Ab12jDceCYMWaltTbuwtWuSkHW2hGpbr9sjGnnq2B8bDeuc+bOXTYjP7DDZ9H4VhVrbftUt180xmz0VTA+lvrc0kTgC2vtcl8F4yvuy0Q0sdbWNMYUw3X6yvGL1buKOY0x15y7tq37vFN/PZ9nrTFmNDAeVx88jeta0H4j1fvot8aYH4ECqV8fxpgIa+1c30Qn4h80/VWynbW2j7X2dlwX443GNQJzzKdB+c5CY8wDxhiH++d/wE++DspH4oGtxphPjDEf4xqpO+meHvyuj2PLbnHppjk2ImV6l78pYa2d4v6ZZq1dbozp5eugspu1Ngm41/17rJ8nlADPAcuMMZ+6F39bgh+ueOr2NHAW1yyX6biOFU9esMZVzFobn8nr43WfBCPiR7RQj2Q7Y8xTuBbpqQ3swfVhYKn7XEu/kurC7knuTQGkXFrEry7w7l759LystVOyKxZfM8aE4Tq/tDiu1RxjgC7WWr+b9mmMWW+trZVu2wZrbbivYvIVY8xIXPuELkEE/H979x5sV12ecfz7JAYDqBAoVlAJIIp4QeRSoOItVGpVlIJSEAGhOt4GLHQYxqHKGEpb0TCFKBEvhICXSrgKRUExgjQgkAuEi0zxgkpRRxsgXOQSnv6x1uHsczhJKDPudyXr+cxkctbaycyTM9n77N/+vb/3lfRnwO40z5FrB7uIS3ql7VvLwnWIpNm2j6zOUamvrxkRw5RFZQydpGNpFpKLbD9enafL8sZolKTzx5WErvPaMkds31+dZdgkHQS8l6Yb8I8GHnousNJ2784dZwTR0zfRhxF9le9FvgcRw5AzlTF0tj9bnWEtcg6QH4SNbaoDDEvbcOJQYCvgWZIAsH1UXaqhWwjcQ9MJeNbA/RU057J7x/abqzOsRVQdICKiT7KojOi2vDEa1aeyisuA64BlwBPFWUrYvoumPH6P6ixd0XZ9PQF4Q3vrKmBmzldOqE+vF7Fmv6gOELGuy6Iyotvyxqifpto+pjpEF0jaHZgNbA+sR3vuuE/njQecSUbNxP/fOv/hpKSpwEdpyuUNXAPMGRlRZTvPkYg/sSwqI2Jtsc6/MRpwjqQPApfSdMUFwPb/1kUq83ngQGA+sAtNWfC2pYnqZNTM0/dodYAOObU6wBCcTVMaP7u9Pojm+Mh7yhJF9EwWlRHd1qs3RpLWB7a0fccEDx837DyFHqUZ4n08o7vVpkfnSgfZvlPS5HasxlxJC6szFXlY0p62r4F+j5qRdD7Nzu13bD+lRNz27sNPVUPSy4BjgekMvK8baeBk+6yaZEO1ne3XDFwvkNS7btkRlbKojCgk6Urbe63qXs/eGO0DfI6mxHHrdqzGTNvvBLB9RWG8YTsG2HZwREKPPSRpPWCppJNpmvdsWJypyoeBs9uzlQDLgdWO4lmHzQEOB06TNB84y/ZPijNVmQ98Efgyo+Op+maJpN1tXwcgaTfgv4ozRfRKRopEFGjPf2wALADexGhp5/NoPnnfvihaGUmLgBnAD0fmiUm62fYOtcmGT9K3gQNtP1SdpZqk6cBvaT5sOJpmTuPptu8sDVZoVaNmJB3Wp3mu8GTzooNodvV/RbOw+prtx0qDDZGkRbZ3rs5RSdLtwHbAL2mqOqYDt9M0OnMff45EDFt2KiNqfAj4B2ALYHBw+f3AFyoCdcDjtu8bGZ/RcytpduYWMPZMZZ9GigBNF9i2LHpz25+uztMFq5lb+nGgN4tKSZsC76NpWLQE+DpNo5bDaD6s64tLJH0UuJD+nsF+KzANeH17fTVwb1maiB7KojKigO1TgVMlHWl79hr/Qj/cIum9wGRJLwWOoplV2EcXtb96b01l0TFGbz6RkXQB8HKaZiz72L6nfehbkm6sS1ZipAT62IF7fTuDvS/wAeACmufBOcCX8/M1YnhS/hpRSNKhE923ffaw3A7wZAAAD1RJREFUs1STtAFNCdveNG8KLgdOHGkJ3zftOcKXtZd39Kmcb1DKop8+SYtt71SdYxgkvc32ZePuPdv2I6v6O7HuknQzsIftB9vrDYFr8zoRMTzZqYyotevA11OBvWjKYXu3qGzPDx4PHC9pMrBhjxeUb6IpY/wFzQL7xe15uasLY1VJWfTT16dv0j8Dl427dy3Qi0X1IElTgI8Ab2hv/RA4o2cfRImxTYpW0q/nQ0S5LCojCtk+cvC6bTpxTlGcUpK+QdPdciWwCNhI0im2P1ubrMQsYO+R0SrtyIBvAn1sxpGy6Kdvne92KekFwAuB9SW9lrFNzjYoC1ZrDjAFOL29PqS994GyRMM3F/ixpAvb632Br9bFieiflL9GdEj7ifPNPe3+utT2jpIOplk8HQcs6mP50kTlnX0t+RxXFg1NWfQ/93EXW9LGwKHAVoydR9ibBk6SDgPeD+wCDJ6dXEEzVuSCilyVJN00bkbjhPfWdZJ2omnUJOBq20uKI0X0SnYqIwpJuoTR4faTge2Bc+sSlZrSLqr3BT5v+zFJff3Ua5GkrzK6a30wze5tb0g6x/YhwAdtH0+zsOy7y4DrgGU0oxJ6px2ZMk/S/rbPr87TESslvcT2TwEkbUMP51XaXszYbuoRMURZVEbU+tzA148Dd9n+dVWYYmfQnCG8Cbi6nU+4qtEJ67oPAx+jKfUUTXv801f7N9Y9O7f/B46QdDbjzkf1bFzCiKm2j6kOUUnS+2x/DdhK0lO+F7ZPKYhV7VhggaSf0TxPpgOH10aKiL5J+WtEMUl/zmjDnutt/64yT5dIepbtx6tzDJOkSTQl0K+qzlJJ0lE0zUe2Ae5m7KLStvs0LgEASUcDDwCX0tN5hJI+ZPsMSSdM8LBtzxx6qA6Q9GxgO5rnyU/SBTcihi2LyohCkg4APkvTrU80g5uPtX1eZa4qkt4OvJKmEy4AfXyTKOnrwCds/7I6SzVJc2x/ZDWPT7O9fJiZqkj6GHASzVD3kR/efV1gzwM+bvve9noaMMv2EaXBhkjSDNs/kLTfRI/38XxpRNRJ+WtEreOBXUd2JyVtBnwf6N2iUtIXabo3vhn4CvBu4PrSUHU2B26VdD3w4MhN2++si1RjdQvK1pX0Z4zEMcC2tn9fHaQDdhhZUALYXt52g+2TNwI/APaZ4DEDWVRGxNBkURlRa9K4ctc/AJOqwhT7S9s7tF1OPy1pFv19U/Tp6gBrkT7NorsVeKg6REdMGtyllrQJPXtPY/uE9vecn4yIcr16AY7ooO9KupxmBiHA3wHfKcxT6eH294ckbUGzwN66ME8Z21e18/j+gmbH4QbbvymO1VV9OsOxElgqaQFjz1T2ZqTIgFnAQknn0fwfOICmNLh3JmpYBNxHM5Jp6ZDjRERPZVEZUes4mhEaI7O1vkQzNqCPLm3n8J3M6PiMr9TFqSPpA8CnaErbBMyWNNP2mbXJothF7a/es322pBuBGTTPkf1s31Ycq8ou7a9L2uu3AzcAH5Y03/bJZckiojfSqCeikKQzBxtLSHoOcLHtvQpjlZC0Pk23z9fT7Dz8CJjT0yH3d9CUA/+hvd4UWGh7u9pk3SNpie2+naWLeFJb7bK/7Qfa6+fQnMv/W5rdyldU5ouIfujr2a2Irrhb0hx4snvhFcDXaiOVmUfT+fU0YDawPXB2aaI6vwZWDFyvAH5VlKWcpD0lHd5+vZmkwbLo3nwAI+nnkn42/ld1rii3JfDowPVjwHTbDzNQJh0R8aeU8teIQrY/KekzbefTnYF/s31+da4i29l+zcD1Akk3laWpdTfwY0kX0+zavgu4fuTsVJ8GvLfzCHehmcE3F5hC88HL66BfMxppvg8jpgLvATYpyhLd8Q3guvb1AppusN+UtCHQ15LgiBiylL9GFBg3V0zAJ2nGZ3wX+jlfTNJZwBdtX9de7wYcZvujpcEKrGKw+5Ns96Y7rKSlwGuBxSNlrm2H4B1Kg3WEpGts71mdI2pJ2pnRs/nX2L6xOFJE9Ex2KiNqjJ8rtoRmB2YfejZfTNIymn/zFOBQSb9sr6fT00/Z17RolDTb9pHDylPsUduWZIB296WXJA3O45xEs3P53KI40S3rA/fbnjtSIm7759WhIqI/sqiMKJC5YmO8ozrAWuh11QGG6FxJZwAbS/ogcATw5eJMVWYxOkLlceAXNCWw0WNrKhGPiBiGlL9GFJI0D/i47Xvb62nArMGOsBHjSVpse6c1/8l1g6S3AHvTlPZdbvt7xZFKSJoK7A9sxeiHwrY9syxUlEuJeER0QXYqI2rtMLKgBLC9XFLGI0QMaBeRvVxIjnMRcC+wGOjdqJ1YpZSIR0S5LCojak2SNM32cgBJm5DnZayZqgMMi6QVjJZ8jrgPuBH4R9t9GqnxIttvrQ4R3SFJwKUpEY+IannzGlFrFrBQ0nk0b5wPAE6qjRSVJE2mGS1z7Gr+2KnDytMBpwD/QzM2QcCBwAuAO4AzgTeVJRu+hZJebXtZdZDohnaHcl/gOOB+mnOVn+priXhE1MmZyohikl4BzKB5w3yl7V52PI1Rkn4A7OW8QCPpx7Z3G3fvOtu7S7pp3GzTdZqk24BtgZ/TDLUXzboiZ+d6TNIXgLNs31CdJSL6KzuVEfU2AR5MK/gYsAS4WNJ84MGRm32cXwo8IekA4Lz2+t0Dj/Vt0f031QGik94MfEjSXYx9vciHDRExNNmpjCg02Are9sskbQHMt51W8D0mae4Et93HrsCStqEp992DZhF5HXA0cDews+1rCuNFlJM0faL7tu8adpaI6K8sKiMKpRV8RERERKztUv4aUSut4OMpJL0ImE0zvNzANTTzTH9dGqxAO5vx74FXAlNH7vdx1zYiIqKrJlUHiOi5c8e1gv8+aQUfMBf4NrAF8ELgkvZeH51D0+31r4GrgBcBK0oTRURExBgpf40oJuktwN40nRwvTyv4kLTU9o5rutcHkpbYfu1IWbikKTTPkxnV2SIiIqKR8teIYu0iMgvJGPR7Se8DvtleHwT8oTBPpcfa3++V9CrgN8BWdXEiIiJivJS/RhSStJ+k/5Z0n6T7Ja2QdH91rih3BHAAzQLqHpoxGn09Q/glSdOAT9KUBN8GfKY2UkRERAxK+WtEIUl3AvvYvr06S9ST9Bnbx0l6j+351XkiIiIino7sVEbU+m0WlDHgbe2ZwU9UB+kKSZtKmi1psaRFkv5d0qbVuSIiImJUzlRGFJC0X/vljZK+BVwEPDLyuO0LKnJFue8Cvwc2bMugRTNSRIBtP68yXJH/AK4G9m+vDwa+BfxVWaKIiIgYI+WvEQUkjYyHGFkwDHJm8PWbpIttv6s6RxdIWmR753H3brS9S1WmiIiIGCs7lREFbB8OIGkezVD7e9vracCswmjRAWtaUEq61vYew8pTbIGkA4Fz2+t3A/9ZmCciIiLGyU5lRKGRGXxruhcxqE//RyStADYEnqDZ2Z8MPNg+3NeS4IiIiE7JTmVErUmSptleDiBpE/K8jDXrzaeBtp9bnSEiIiJWL29eI2rNAhZKOo9moXAAcFJtpIjukCSa5jxb2z5R0ouBzW1fXxwtIiIiWil/jSgm6RXADJqGPVfavq04UnRcz8pf59CUvs6wvX177vgK27sWR4uIiIhWdiojirWLyCwkAwBJk4HLba9uZMYhw8rTAbvZ3knSEgDbyyWtVx0qIiIiRk2qDhAREaNsrwQekrTRav7MLUOMVO2xdqFtAEmb0excRkREREdkpzIionv+CCyT9D1GO51i+6i6SGVOAy4Eni/pJJqRIv9UGykiIiIG5UxlRETHSDpsovu25w07SxdIejmwF6Pnjm8vjhQREREDsqiMiOggSesDW9q+ozpLtbY5z4sZqK6xvbguUURERAxK+WtERMdI2gf4HLAesLWkHYGZtt9ZGqyApBOB9wM/ZXQ+p2k6JkdEREQHZKcyIqJjJC2iWTT9cGR0iKRltl9dm2z4JN0BvNr2o9VZIiIiYmLp/hoR0T2P275v3L2+fgJ4C7BxdYiIiIhYtZS/RkR0zy2S3gtMlvRS4ChgYXGmKv8KLJF0C/DIyM0+lgJHRER0VcpfIyI6RtIGwPHA3jQdTy8HTrT9x9JgBSTdCpwBLGNgPqXtq8pCRURExBhZVEZERGdJusr2G6tzRERExKplURkR0RGSLmE1Zyf7WPIp6RSastdvM7b8NSNFIiIiOiKLyoiIjpC02h25PpZ8SlowwW3bzkiRiIiIjsiiMiIiIiIiIp6xdH+NiOgISefaPkDSMiYog7W9Q0GsUpI2Ak4A3tDeugqYOcHIlYiIiCiSncqIiI6QtLnteyRNn+hx23cNO1M1SefTzKqc1946BHiN7f3qUkVERMSgLCojIqKzJC21veOa7kVERESdSdUBIiJiLEm7S7pB0gOSHpW0UtL91bmKPCxpz5ELSa8DHi7MExEREePkTGVERPd8HjgQmA/sAhwKbFuaqM5HgHnt2UqA5cBhhXkiIiJinCwqIyI6yPadkibbXgnMlbSwOlOR24GTgZcAGwP3AfsCN9dFioiIiEFZVEZEdM9DktYDlko6GbgH2LA4U5WLgXuBxcDdtVEiIiJiImnUExHRMW33198BU4CjgY2A023fWRqsgKRbbL+qOkdERESsWhaVERHRWZK+BMy2vaw6S0REREwsi8qIiI6R9A7gRGA6zTEFAbb9vNJgQyRpGWCaf/9LgZ8BjzD6vdihMF5EREQMyKIyIqJjJN0J7Acsc09fpNsS4FWyfdewskRERMTqpVFPRET3/Aq4pa8LSsiiMSIiYm2SncqIiI6RtCtN+etVNCWfANg+pSxURERExCpkpzIiontOAh4ApgLrFWeJiIiIWK0sKiMiumcT23tXh4iIiIh4OiZVB4iIiKf4vqQsKiMiImKtkDOVEREdI2kFsAHwKPAYPRwpEhEREWuPlL9GRHTPRsDBwNa2Z0raEti8OFNERETEhLJTGRHRMZLmAE8AM2xvL2kacIXtXYujRURERDxFdiojIrpnN9s7SVoCYHu5pHSBjYiIiE5Ko56IiO55TNJkwACSNqPZuYyIiIjonCwqIyK65zTgQuD5kk4CrgH+pTZSRERExMRypjIiooMkvRzYi6bz65W2by+OFBERETGhLCojIiIiIiLiGUv5a0RERERERDxjWVRGRERERETEM5ZFZURERERERDxjWVRGRERERETEM5ZFZURERERERDxj/wcLprJdTd7MVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(train.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='emailer_for_promotion', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(x='emailer_for_promotion',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decrease the size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.emailer_for_promotion, train.homepage_featured= train.emailer_for_promotion.astype(np.int8), train.homepage_featured.astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.checkout_price, train.checkout_price= train.checkout_price.astype(np.float32),train.checkout_price.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.num_orders= train.num_orders.astype(np.int16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data according to meal_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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></th>\n",
       "      <th>week</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>category</th>\n",
       "      <th>cuisine</th>\n",
       "      <th>city_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>center_type</th>\n",
       "      <th>op_area</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal_id</th>\n",
       "      <th>center_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1885</th>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>136.830002</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2</td>\n",
       "      <td>135.830002</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3</td>\n",
       "      <td>132.919998</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>135.860001</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5</td>\n",
       "      <td>146.500000</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>Thai</td>\n",
       "      <td>647</td>\n",
       "      <td>56</td>\n",
       "      <td>TYPE_C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   week  checkout_price  base_price  emailer_for_promotion  \\\n",
       "meal_id center_id                                                            \n",
       "1885    55            1      136.830002      152.29                      0   \n",
       "        55            2      135.830002      152.29                      0   \n",
       "        55            3      132.919998      133.92                      0   \n",
       "        55            4      135.860001      134.86                      0   \n",
       "        55            5      146.500000      147.50                      0   \n",
       "\n",
       "                   homepage_featured  num_orders   category cuisine  \\\n",
       "meal_id center_id                                                     \n",
       "1885    55                         0         177  Beverages    Thai   \n",
       "        55                         0         323  Beverages    Thai   \n",
       "        55                         0          96  Beverages    Thai   \n",
       "        55                         0         163  Beverages    Thai   \n",
       "        55                         0         215  Beverages    Thai   \n",
       "\n",
       "                   city_code  region_code center_type  op_area   diff  \n",
       "meal_id center_id                                                      \n",
       "1885    55               647           56      TYPE_C      2.0  15.46  \n",
       "        55               647           56      TYPE_C      2.0  16.46  \n",
       "        55               647           56      TYPE_C      2.0   1.00  \n",
       "        55               647           56      TYPE_C      2.0  -1.00  \n",
       "        55               647           56      TYPE_C      2.0   1.00  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(columns=['category', 'cuisine', 'city_code', 'region_code', 'center_type', 'op_area'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([55, 55, 55, 55, 55, 55, 55, 55, 55, 55,\n",
       "            ...\n",
       "            61, 61, 61, 61, 61, 61, 61, 61, 61, 61],\n",
       "           dtype='int64', name='center_id', length=11092)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[1885].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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></th>\n",
       "      <th>week</th>\n",
       "      <th>checkout_price</th>\n",
       "      <th>base_price</th>\n",
       "      <th>emailer_for_promotion</th>\n",
       "      <th>homepage_featured</th>\n",
       "      <th>num_orders</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal_id</th>\n",
       "      <th>center_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1885</th>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>136.830002</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>15.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2</td>\n",
       "      <td>135.830002</td>\n",
       "      <td>152.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3</td>\n",
       "      <td>132.919998</td>\n",
       "      <td>133.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>135.860001</td>\n",
       "      <td>134.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5</td>\n",
       "      <td>146.500000</td>\n",
       "      <td>147.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   week  checkout_price  base_price  emailer_for_promotion  \\\n",
       "meal_id center_id                                                            \n",
       "1885    55            1      136.830002      152.29                      0   \n",
       "        55            2      135.830002      152.29                      0   \n",
       "        55            3      132.919998      133.92                      0   \n",
       "        55            4      135.860001      134.86                      0   \n",
       "        55            5      146.500000      147.50                      0   \n",
       "\n",
       "                   homepage_featured  num_orders   diff  \n",
       "meal_id center_id                                        \n",
       "1885    55                         0         177  15.46  \n",
       "        55                         0         323  16.46  \n",
       "        55                         0          96   1.00  \n",
       "        55                         0         163  -1.00  \n",
       "        55                         0         215   1.00  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Data\\anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\ipykernel_launcher.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for i in meal_info.meal_id.unique():\n",
    "    for j in train.loc[i].index.unique():\n",
    "        temp=train.loc[i,j]\n",
    "        filename=f'{j}_{i}.csv'\n",
    "        temp.to_csv(filename)\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.num_orders.dtype"
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}