Living Planet Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7bf334a7",
   "metadata": {},
   "source": [
    "# Import Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3ad4655",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import shape\n",
    "import pygwalker as pyg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d122813",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('living-planet-index.csv', sep = ';') #data comes from https://www.kaggle.com/datasets/elmoallistair/living-planet-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd6ff4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Average Index</th>\n",
       "      <th>Upper Index</th>\n",
       "      <th>Lower Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1970</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.461969</td>\n",
       "      <td>104.910803</td>\n",
       "      <td>87.790567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>94.683413</td>\n",
       "      <td>110.867839</td>\n",
       "      <td>81.479839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1973</td>\n",
       "      <td>99.181174</td>\n",
       "      <td>120.870721</td>\n",
       "      <td>81.908753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1974</td>\n",
       "      <td>99.384934</td>\n",
       "      <td>127.877780</td>\n",
       "      <td>77.144610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Year  Average Index  Upper Index  Lower Index\n",
       "0  Africa  1970     100.000000   100.000000   100.000000\n",
       "1  Africa  1971      95.461969   104.910803    87.790567\n",
       "2  Africa  1972      94.683413   110.867839    81.479839\n",
       "3  Africa  1973      99.181174   120.870721    81.908753\n",
       "4  Africa  1974      99.384934   127.877780    77.144610"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da67e7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf1a8c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Average Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</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>Africa</th>\n",
       "      <td>48.0</td>\n",
       "      <td>59.116522</td>\n",
       "      <td>20.819728</td>\n",
       "      <td>31.293573</td>\n",
       "      <td>42.313076</td>\n",
       "      <td>53.044350</td>\n",
       "      <td>76.532881</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia and Pacific</th>\n",
       "      <td>49.0</td>\n",
       "      <td>76.622352</td>\n",
       "      <td>22.063351</td>\n",
       "      <td>43.996630</td>\n",
       "      <td>61.274500</td>\n",
       "      <td>75.636391</td>\n",
       "      <td>96.717307</td>\n",
       "      <td>106.015460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe and Central Asia</th>\n",
       "      <td>49.0</td>\n",
       "      <td>108.348277</td>\n",
       "      <td>11.167906</td>\n",
       "      <td>82.291534</td>\n",
       "      <td>104.428163</td>\n",
       "      <td>107.576949</td>\n",
       "      <td>113.817977</td>\n",
       "      <td>130.741073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freshwater</th>\n",
       "      <td>49.0</td>\n",
       "      <td>47.680265</td>\n",
       "      <td>27.219049</td>\n",
       "      <td>17.144189</td>\n",
       "      <td>23.917999</td>\n",
       "      <td>41.450490</td>\n",
       "      <td>69.969215</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>49.0</td>\n",
       "      <td>37.971016</td>\n",
       "      <td>29.185001</td>\n",
       "      <td>6.413632</td>\n",
       "      <td>15.012580</td>\n",
       "      <td>28.683863</td>\n",
       "      <td>58.292376</td>\n",
       "      <td>101.007164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>49.0</td>\n",
       "      <td>82.195526</td>\n",
       "      <td>8.814125</td>\n",
       "      <td>70.264173</td>\n",
       "      <td>73.567398</td>\n",
       "      <td>84.330566</td>\n",
       "      <td>89.530625</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>49.0</td>\n",
       "      <td>59.545904</td>\n",
       "      <td>22.209139</td>\n",
       "      <td>30.885270</td>\n",
       "      <td>41.295152</td>\n",
       "      <td>56.200898</td>\n",
       "      <td>77.281385</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Average Index                         \\\n",
       "                                        count        mean        std   \n",
       "Region                                                                 \n",
       "Africa                                   48.0   59.116522  20.819728   \n",
       "Asia and Pacific                         49.0   76.622352  22.063351   \n",
       "Europe and Central Asia                  49.0  108.348277  11.167906   \n",
       "Freshwater                               49.0   47.680265  27.219049   \n",
       "Latin America and the Caribbean          49.0   37.971016  29.185001   \n",
       "North America                            49.0   82.195526   8.814125   \n",
       "World                                    49.0   59.545904  22.209139   \n",
       "\n",
       "                                                                    \\\n",
       "                                       min         25%         50%   \n",
       "Region                                                               \n",
       "Africa                           31.293573   42.313076   53.044350   \n",
       "Asia and Pacific                 43.996630   61.274500   75.636391   \n",
       "Europe and Central Asia          82.291534  104.428163  107.576949   \n",
       "Freshwater                       17.144189   23.917999   41.450490   \n",
       "Latin America and the Caribbean   6.413632   15.012580   28.683863   \n",
       "North America                    70.264173   73.567398   84.330566   \n",
       "World                            30.885270   41.295152   56.200898   \n",
       "\n",
       "                                                         \n",
       "                                        75%         max  \n",
       "Region                                                   \n",
       "Africa                            76.532881  100.000000  \n",
       "Asia and Pacific                  96.717307  106.015460  \n",
       "Europe and Central Asia          113.817977  130.741073  \n",
       "Freshwater                        69.969215  100.000000  \n",
       "Latin America and the Caribbean   58.292376  101.007164  \n",
       "North America                     89.530625  100.000000  \n",
       "World                             77.281385  100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Region').describe()[['Average Index']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cd71771",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Average Index': 'Average_Index', 'Upper Index': 'Upper_Index', 'Lower Index': 'Lower_Index'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9cf1888",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df.loc[df.Region != 'Freshwater']\n",
    "df_plot = df_plot.loc[df.Region != 'World']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e98b21",
   "metadata": {},
   "source": [
    "# Country Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63e375f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Africa' 'Asia and Pacific' 'Europe and Central Asia'\n",
      " 'Latin America and the Caribbean' 'North America']\n"
     ]
    }
   ],
   "source": [
    "print(df_plot['Region'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db966844",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df_plot.sort_values(by = 'Average_Index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c864b1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary to map regions to colors\n",
    "color_map = {\n",
    "    'Latin America and the Caribbean': 'firebrick',\n",
    "    'Africa': 'firebrick',\n",
    "    'Asia and Pacific': 'firebrick',\n",
    "    'North America': 'firebrick',\n",
    "    'Europe and Central Asia': 'lightgreen'\n",
    "}\n",
    "\n",
    "# Create a custom color palette based on the color_map\n",
    "custom_palette = [color_map.get(region, 'gray') for region in df_plot['Region'].unique()]\n",
    "\n",
    "# Create the bar plot using the custom palette\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "sns.barplot(data=df_plot, x='Region', y='Average_Index', ci=None, palette=custom_palette)\n",
    "\n",
    "# Customize the plot (add labels, title, etc.)\n",
    "ax.set_xlabel('Region', size=15)\n",
    "ax.set_ylabel('Average Species Index', size=15)\n",
    "ax.tick_params(axis='x', labelsize=13)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.axhline(y = 100, color = 'grey', linestyle = 'dashed', label = '1970', linewidth = 3)\n",
    "plt.legend()\n",
    "plt.suptitle('All World Regions, Barring Europe, Have Experienced On Average Lower Species Populations across the Last 50 Years', size = 15)\n",
    "\n",
    "# Save the plot with appropriate settings\n",
    "plt.savefig('Figure1.pdf', bbox_inches='tight', dpi=300)  # Adjust the file format and DPI as needed\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2980c05",
   "metadata": {},
   "source": [
    "# Species Population Change Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4a9d36e",
   "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>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Average_Index</th>\n",
       "      <th>Upper_Index</th>\n",
       "      <th>Lower_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1970</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.461969</td>\n",
       "      <td>104.910803</td>\n",
       "      <td>87.790567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1972</td>\n",
       "      <td>94.683413</td>\n",
       "      <td>110.867839</td>\n",
       "      <td>81.479839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1973</td>\n",
       "      <td>99.181174</td>\n",
       "      <td>120.870721</td>\n",
       "      <td>81.908753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1974</td>\n",
       "      <td>99.384934</td>\n",
       "      <td>127.877780</td>\n",
       "      <td>77.144610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Year  Average_Index  Upper_Index  Lower_Index\n",
       "0  Africa  1970     100.000000   100.000000   100.000000\n",
       "1  Africa  1971      95.461969   104.910803    87.790567\n",
       "2  Africa  1972      94.683413   110.867839    81.479839\n",
       "3  Africa  1973      99.181174   120.870721    81.908753\n",
       "4  Africa  1974      99.384934   127.877780    77.144610"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c38858d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8940f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS.Year = pd.to_datetime(TS.Year, format = '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87f11d5f",
   "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>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Average_Index</th>\n",
       "      <th>Upper_Index</th>\n",
       "      <th>Lower_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1971-01-01</td>\n",
       "      <td>95.461969</td>\n",
       "      <td>104.910803</td>\n",
       "      <td>87.790567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1972-01-01</td>\n",
       "      <td>94.683413</td>\n",
       "      <td>110.867839</td>\n",
       "      <td>81.479839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>99.181174</td>\n",
       "      <td>120.870721</td>\n",
       "      <td>81.908753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1974-01-01</td>\n",
       "      <td>99.384934</td>\n",
       "      <td>127.877780</td>\n",
       "      <td>77.144610</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>337</th>\n",
       "      <td>World</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>31.669021</td>\n",
       "      <td>38.071988</td>\n",
       "      <td>26.383458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>World</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>31.627531</td>\n",
       "      <td>38.068720</td>\n",
       "      <td>26.281212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>World</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>31.145408</td>\n",
       "      <td>37.562137</td>\n",
       "      <td>25.770213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>World</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>30.885270</td>\n",
       "      <td>37.343040</td>\n",
       "      <td>25.465452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>World</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>30.897567</td>\n",
       "      <td>37.437807</td>\n",
       "      <td>25.400315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region       Year  Average_Index  Upper_Index  Lower_Index\n",
       "0    Africa 1970-01-01     100.000000   100.000000   100.000000\n",
       "1    Africa 1971-01-01      95.461969   104.910803    87.790567\n",
       "2    Africa 1972-01-01      94.683413   110.867839    81.479839\n",
       "3    Africa 1973-01-01      99.181174   120.870721    81.908753\n",
       "4    Africa 1974-01-01      99.384934   127.877780    77.144610\n",
       "..      ...        ...            ...          ...          ...\n",
       "337   World 2014-01-01      31.669021    38.071988    26.383458\n",
       "338   World 2015-01-01      31.627531    38.068720    26.281212\n",
       "339   World 2016-01-01      31.145408    37.562137    25.770213\n",
       "340   World 2017-01-01      30.885270    37.343040    25.465452\n",
       "341   World 2018-01-01      30.897567    37.437807    25.400315\n",
       "\n",
       "[342 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0cfb985",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(30, 20), ncols=2, nrows=3)\n",
    "\n",
    "regions = ['Latin America and the Caribbean', 'Africa', 'Asia and Pacific', 'North America', 'Europe and Central Asia', 'World']\n",
    "\n",
    "for i in range(6):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "\n",
    "    region_data = TS[TS.Region == regions[i]]\n",
    "    for j in range(len(region_data) - 1):\n",
    "        color = 'lightgreen' if region_data['Average_Index'].iloc[j] > 100 else 'firebrick'\n",
    "        ax[row, col].plot(region_data['Year'].iloc[j:j+2], region_data['Average_Index'].iloc[j:j+2], color=color, linewidth=5)\n",
    "    \n",
    "    \n",
    "\n",
    "    ax[row, col].set_ylabel('Average Index', size=20)\n",
    "    ax[row, col].set_title(f'{regions[i]} Species Population', size=20)\n",
    "    ax[row, col].set_ylim(0, 135)\n",
    "    ax[row, col].axhline(y=100, color='grey', linestyle='dashed', linewidth=4)\n",
    "    ax[row, col].tick_params(axis='x', labelsize=15)\n",
    "    ax[row, col].tick_params(axis='y', labelsize=15)\n",
    "\n",
    "plt.suptitle(\"However, as of Today, All World Regions Have Smaller Species Populations than in 1970\", size=30)\n",
    "\n",
    "# Save the plot with appropriate settings\n",
    "plt.savefig('Figure2.pdf', bbox_inches='tight', dpi=300)  # Adjust the file format and DPI as needed\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ecc28c",
   "metadata": {},
   "source": [
    "# Time Series Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd2e51a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS = TS[TS.Region == 'World'].set_index('Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b413ef68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bring in global CO2 levels\n",
    "\n",
    "co2 = pd.read_csv('2100-projections_climate-scoreboard_2015-1214.csv') # data is sourced from here https://www.co2.earth/23-co2-future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37386592",
   "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>Year</th>\n",
       "      <th>CO2_emission</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year)</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2096</td>\n",
       "      <td>138.624</td>\n",
       "      <td>80.9863</td>\n",
       "      <td>67.5537</td>\n",
       "      <td>52.4193</td>\n",
       "      <td>33.4344</td>\n",
       "      <td>6.91386</td>\n",
       "      <td>3.45631</td>\n",
       "      <td>1.120440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2097</td>\n",
       "      <td>138.803</td>\n",
       "      <td>81.0791</td>\n",
       "      <td>67.5724</td>\n",
       "      <td>52.3261</td>\n",
       "      <td>33.1017</td>\n",
       "      <td>6.67606</td>\n",
       "      <td>3.31673</td>\n",
       "      <td>1.065390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2098</td>\n",
       "      <td>138.983</td>\n",
       "      <td>81.1672</td>\n",
       "      <td>67.5875</td>\n",
       "      <td>52.2318</td>\n",
       "      <td>32.7686</td>\n",
       "      <td>6.44644</td>\n",
       "      <td>3.18283</td>\n",
       "      <td>1.013070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2099</td>\n",
       "      <td>139.162</td>\n",
       "      <td>81.2505</td>\n",
       "      <td>67.5990</td>\n",
       "      <td>52.1360</td>\n",
       "      <td>32.4351</td>\n",
       "      <td>6.22471</td>\n",
       "      <td>3.05436</td>\n",
       "      <td>0.963331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2100</td>\n",
       "      <td>139.342</td>\n",
       "      <td>81.3288</td>\n",
       "      <td>67.6067</td>\n",
       "      <td>52.0385</td>\n",
       "      <td>32.1011</td>\n",
       "      <td>6.01062</td>\n",
       "      <td>2.93112</td>\n",
       "      <td>0.916054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  CO2_emission  Global CO2 Equivalent Emissions\\n(GtonsCO2/year)  \\\n",
       "196  2096       138.624                                           80.9863   \n",
       "197  2097       138.803                                           81.0791   \n",
       "198  2098       138.983                                           81.1672   \n",
       "199  2099       139.162                                           81.2505   \n",
       "200  2100       139.342                                           81.3288   \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1  \\\n",
       "196                                            67.5537    \n",
       "197                                            67.5724    \n",
       "198                                            67.5875    \n",
       "199                                            67.5990    \n",
       "200                                            67.6067    \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2  \\\n",
       "196                                            52.4193    \n",
       "197                                            52.3261    \n",
       "198                                            52.2318    \n",
       "199                                            52.1360    \n",
       "200                                            52.0385    \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3  \\\n",
       "196                                            33.4344    \n",
       "197                                            33.1017    \n",
       "198                                            32.7686    \n",
       "199                                            32.4351    \n",
       "200                                            32.1011    \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4  \\\n",
       "196                                            6.91386    \n",
       "197                                            6.67606    \n",
       "198                                            6.44644    \n",
       "199                                            6.22471    \n",
       "200                                            6.01062    \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5  \\\n",
       "196                                            3.45631    \n",
       "197                                            3.31673    \n",
       "198                                            3.18283    \n",
       "199                                            3.05436    \n",
       "200                                            2.93112    \n",
       "\n",
       "     Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6  \n",
       "196                                           1.120440   \n",
       "197                                           1.065390   \n",
       "198                                           1.013070   \n",
       "199                                           0.963331   \n",
       "200                                           0.916054   "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a3f781e",
   "metadata": {},
   "outputs": [],
   "source": [
    "co2['Year'] = pd.to_datetime(co2['Year'], format = '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "845c554a",
   "metadata": {},
   "outputs": [],
   "source": [
    "co2.set_index('Year', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2314d58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "future_exog = co2[co2.index > '2017-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52dd8050",
   "metadata": {},
   "outputs": [],
   "source": [
    "future_exog = future_exog[future_exog.index < '2035-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9fa06e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS = pd.merge(TS, co2, how = \"left\", on = 'Year' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be2f96c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Average_Index</th>\n",
       "      <th>Upper_Index</th>\n",
       "      <th>Lower_Index</th>\n",
       "      <th>CO2_emission</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year)</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1970-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>99.100276</td>\n",
       "      <td>100.953212</td>\n",
       "      <td>97.314895</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>97.929884</td>\n",
       "      <td>101.193083</td>\n",
       "      <td>94.765395</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>96.782841</td>\n",
       "      <td>101.005601</td>\n",
       "      <td>92.663605</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>95.345434</td>\n",
       "      <td>100.618550</td>\n",
       "      <td>90.268275</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>93.555801</td>\n",
       "      <td>99.797105</td>\n",
       "      <td>87.630311</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "      <td>31.0271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>91.489219</td>\n",
       "      <td>98.407939</td>\n",
       "      <td>84.933780</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "      <td>32.4536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>88.535241</td>\n",
       "      <td>95.610893</td>\n",
       "      <td>81.853089</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "      <td>33.3080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>85.684047</td>\n",
       "      <td>92.787161</td>\n",
       "      <td>78.986261</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "      <td>34.0199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>83.347480</td>\n",
       "      <td>90.670679</td>\n",
       "      <td>76.508506</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "      <td>34.8079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>81.508093</td>\n",
       "      <td>89.210509</td>\n",
       "      <td>74.388272</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "      <td>34.6377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>79.836875</td>\n",
       "      <td>87.825113</td>\n",
       "      <td>72.519106</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "      <td>33.9303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>77.281385</td>\n",
       "      <td>85.332295</td>\n",
       "      <td>69.951930</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "      <td>35.2381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>74.518185</td>\n",
       "      <td>82.545935</td>\n",
       "      <td>67.245496</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "      <td>35.3898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>71.832024</td>\n",
       "      <td>79.880610</td>\n",
       "      <td>64.543403</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "      <td>35.8773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>69.753114</td>\n",
       "      <td>77.904704</td>\n",
       "      <td>62.438582</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "      <td>36.7489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>68.125666</td>\n",
       "      <td>76.379340</td>\n",
       "      <td>60.787447</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "      <td>37.3720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>66.322487</td>\n",
       "      <td>74.570414</td>\n",
       "      <td>59.047279</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "      <td>38.6859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>64.546701</td>\n",
       "      <td>72.782719</td>\n",
       "      <td>57.360105</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "      <td>39.2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>63.574344</td>\n",
       "      <td>71.838379</td>\n",
       "      <td>56.403678</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "      <td>39.9081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>62.681123</td>\n",
       "      <td>71.030615</td>\n",
       "      <td>55.489490</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "      <td>39.9539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>61.609140</td>\n",
       "      <td>69.999808</td>\n",
       "      <td>54.354237</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>40.7695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>60.119851</td>\n",
       "      <td>68.492014</td>\n",
       "      <td>52.881281</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "      <td>40.3535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>58.109296</td>\n",
       "      <td>66.366006</td>\n",
       "      <td>50.978496</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "      <td>39.8946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>56.200898</td>\n",
       "      <td>64.327545</td>\n",
       "      <td>49.202333</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "      <td>40.2212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>54.036096</td>\n",
       "      <td>62.032497</td>\n",
       "      <td>47.202937</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "      <td>40.5536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>53.042136</td>\n",
       "      <td>61.012035</td>\n",
       "      <td>46.222431</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "      <td>41.1754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>51.750076</td>\n",
       "      <td>59.614370</td>\n",
       "      <td>44.999585</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "      <td>42.1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>50.466451</td>\n",
       "      <td>58.177723</td>\n",
       "      <td>43.809250</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "      <td>41.1657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>48.664379</td>\n",
       "      <td>56.162402</td>\n",
       "      <td>42.201586</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "      <td>41.1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>47.221537</td>\n",
       "      <td>54.568173</td>\n",
       "      <td>40.889674</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "      <td>41.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>45.671369</td>\n",
       "      <td>52.914044</td>\n",
       "      <td>39.431147</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "      <td>41.8078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>44.498394</td>\n",
       "      <td>51.682968</td>\n",
       "      <td>38.306534</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "      <td>43.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>43.515657</td>\n",
       "      <td>50.666522</td>\n",
       "      <td>37.361896</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "      <td>44.5460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>42.927831</td>\n",
       "      <td>50.121294</td>\n",
       "      <td>36.773179</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "      <td>46.4957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>42.203151</td>\n",
       "      <td>49.447080</td>\n",
       "      <td>36.074307</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "      <td>47.4133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>41.295152</td>\n",
       "      <td>48.593138</td>\n",
       "      <td>35.203315</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "      <td>47.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>40.142804</td>\n",
       "      <td>47.357877</td>\n",
       "      <td>34.139635</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "      <td>48.9497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>38.719391</td>\n",
       "      <td>45.781640</td>\n",
       "      <td>32.835359</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "      <td>49.1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>36.877125</td>\n",
       "      <td>43.730484</td>\n",
       "      <td>31.167858</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "      <td>49.1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>35.153151</td>\n",
       "      <td>41.861284</td>\n",
       "      <td>29.596213</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "      <td>50.7118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>33.441041</td>\n",
       "      <td>40.005718</td>\n",
       "      <td>28.050019</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "      <td>52.1069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>32.371090</td>\n",
       "      <td>38.842243</td>\n",
       "      <td>27.085878</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "      <td>53.2092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>31.738264</td>\n",
       "      <td>38.135354</td>\n",
       "      <td>26.500441</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "      <td>54.1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>31.669021</td>\n",
       "      <td>38.071988</td>\n",
       "      <td>26.383458</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "      <td>55.6712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>31.627531</td>\n",
       "      <td>38.068720</td>\n",
       "      <td>26.281212</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "      <td>57.1566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>31.145408</td>\n",
       "      <td>37.562137</td>\n",
       "      <td>25.770213</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "      <td>58.3418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>30.885270</td>\n",
       "      <td>37.343040</td>\n",
       "      <td>25.465452</td>\n",
       "      <td>59.5269</td>\n",
       "      <td>58.3401</td>\n",
       "      <td>58.3401</td>\n",
       "      <td>58.3064</td>\n",
       "      <td>58.2087</td>\n",
       "      <td>58.1517</td>\n",
       "      <td>58.3240</td>\n",
       "      <td>58.3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>30.897567</td>\n",
       "      <td>37.437807</td>\n",
       "      <td>25.400315</td>\n",
       "      <td>60.7120</td>\n",
       "      <td>58.3267</td>\n",
       "      <td>58.3267</td>\n",
       "      <td>58.2586</td>\n",
       "      <td>58.0676</td>\n",
       "      <td>57.9501</td>\n",
       "      <td>58.3045</td>\n",
       "      <td>58.3045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Region  Average_Index  Upper_Index  Lower_Index  CO2_emission  \\\n",
       "Year                                                                       \n",
       "1970-01-01  World     100.000000   100.000000   100.000000       29.2509   \n",
       "1971-01-01  World      99.100276   100.953212    97.314895       28.9959   \n",
       "1972-01-01  World      97.929884   101.193083    94.765395       30.0417   \n",
       "1973-01-01  World      96.782841   101.005601    92.663605       30.9854   \n",
       "1974-01-01  World      95.345434   100.618550    90.268275       30.8474   \n",
       "1975-01-01  World      93.555801    99.797105    87.630311       31.0271   \n",
       "1976-01-01  World      91.489219    98.407939    84.933780       32.4536   \n",
       "1977-01-01  World      88.535241    95.610893    81.853089       33.3080   \n",
       "1978-01-01  World      85.684047    92.787161    78.986261       34.0199   \n",
       "1979-01-01  World      83.347480    90.670679    76.508506       34.8079   \n",
       "1980-01-01  World      81.508093    89.210509    74.388272       34.6377   \n",
       "1981-01-01  World      79.836875    87.825113    72.519106       33.9303   \n",
       "1982-01-01  World      77.281385    85.332295    69.951930       35.2381   \n",
       "1983-01-01  World      74.518185    82.545935    67.245496       35.3898   \n",
       "1984-01-01  World      71.832024    79.880610    64.543403       35.8773   \n",
       "1985-01-01  World      69.753114    77.904704    62.438582       36.7489   \n",
       "1986-01-01  World      68.125666    76.379340    60.787447       37.3720   \n",
       "1987-01-01  World      66.322487    74.570414    59.047279       38.6859   \n",
       "1988-01-01  World      64.546701    72.782719    57.360105       39.2844   \n",
       "1989-01-01  World      63.574344    71.838379    56.403678       39.9081   \n",
       "1990-01-01  World      62.681123    71.030615    55.489490       39.9539   \n",
       "1991-01-01  World      61.609140    69.999808    54.354237       40.7695   \n",
       "1992-01-01  World      60.119851    68.492014    52.881281       40.3535   \n",
       "1993-01-01  World      58.109296    66.366006    50.978496       39.8946   \n",
       "1994-01-01  World      56.200898    64.327545    49.202333       40.2212   \n",
       "1995-01-01  World      54.036096    62.032497    47.202937       40.5536   \n",
       "1996-01-01  World      53.042136    61.012035    46.222431       41.1754   \n",
       "1997-01-01  World      51.750076    59.614370    44.999585       42.1954   \n",
       "1998-01-01  World      50.466451    58.177723    43.809250       41.1657   \n",
       "1999-01-01  World      48.664379    56.162402    42.201586       41.1582   \n",
       "2000-01-01  World      47.221537    54.568173    40.889674       41.5400   \n",
       "2001-01-01  World      45.671369    52.914044    39.431147       41.8078   \n",
       "2002-01-01  World      44.498394    51.682968    38.306534       43.0830   \n",
       "2003-01-01  World      43.515657    50.666522    37.361896       44.5460   \n",
       "2004-01-01  World      42.927831    50.121294    36.773179       46.4957   \n",
       "2005-01-01  World      42.203151    49.447080    36.074307       47.4133   \n",
       "2006-01-01  World      41.295152    48.593138    35.203315       47.9600   \n",
       "2007-01-01  World      40.142804    47.357877    34.139635       48.9497   \n",
       "2008-01-01  World      38.719391    45.781640    32.835359       49.1355   \n",
       "2009-01-01  World      36.877125    43.730484    31.167858       49.1382   \n",
       "2010-01-01  World      35.153151    41.861284    29.596213       50.7118   \n",
       "2011-01-01  World      33.441041    40.005718    28.050019       52.1069   \n",
       "2012-01-01  World      32.371090    38.842243    27.085878       53.2092   \n",
       "2013-01-01  World      31.738264    38.135354    26.500441       54.1858   \n",
       "2014-01-01  World      31.669021    38.071988    26.383458       55.6712   \n",
       "2015-01-01  World      31.627531    38.068720    26.281212       57.1566   \n",
       "2016-01-01  World      31.145408    37.562137    25.770213       58.3418   \n",
       "2017-01-01  World      30.885270    37.343040    25.465452       59.5269   \n",
       "2018-01-01  World      30.897567    37.437807    25.400315       60.7120   \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year)  \\\n",
       "Year                                                           \n",
       "1970-01-01                                           29.2509   \n",
       "1971-01-01                                           28.9959   \n",
       "1972-01-01                                           30.0417   \n",
       "1973-01-01                                           30.9854   \n",
       "1974-01-01                                           30.8474   \n",
       "1975-01-01                                           31.0271   \n",
       "1976-01-01                                           32.4536   \n",
       "1977-01-01                                           33.3080   \n",
       "1978-01-01                                           34.0199   \n",
       "1979-01-01                                           34.8079   \n",
       "1980-01-01                                           34.6377   \n",
       "1981-01-01                                           33.9303   \n",
       "1982-01-01                                           35.2381   \n",
       "1983-01-01                                           35.3898   \n",
       "1984-01-01                                           35.8773   \n",
       "1985-01-01                                           36.7489   \n",
       "1986-01-01                                           37.3720   \n",
       "1987-01-01                                           38.6859   \n",
       "1988-01-01                                           39.2844   \n",
       "1989-01-01                                           39.9081   \n",
       "1990-01-01                                           39.9539   \n",
       "1991-01-01                                           40.7695   \n",
       "1992-01-01                                           40.3535   \n",
       "1993-01-01                                           39.8946   \n",
       "1994-01-01                                           40.2212   \n",
       "1995-01-01                                           40.5536   \n",
       "1996-01-01                                           41.1754   \n",
       "1997-01-01                                           42.1954   \n",
       "1998-01-01                                           41.1657   \n",
       "1999-01-01                                           41.1582   \n",
       "2000-01-01                                           41.5400   \n",
       "2001-01-01                                           41.8078   \n",
       "2002-01-01                                           43.0830   \n",
       "2003-01-01                                           44.5460   \n",
       "2004-01-01                                           46.4957   \n",
       "2005-01-01                                           47.4133   \n",
       "2006-01-01                                           47.9600   \n",
       "2007-01-01                                           48.9497   \n",
       "2008-01-01                                           49.1355   \n",
       "2009-01-01                                           49.1382   \n",
       "2010-01-01                                           50.7118   \n",
       "2011-01-01                                           52.1069   \n",
       "2012-01-01                                           53.2092   \n",
       "2013-01-01                                           54.1858   \n",
       "2014-01-01                                           55.6712   \n",
       "2015-01-01                                           57.1566   \n",
       "2016-01-01                                           58.3418   \n",
       "2017-01-01                                           58.3401   \n",
       "2018-01-01                                           58.3267   \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "1975-01-01                                            31.0271    \n",
       "1976-01-01                                            32.4536    \n",
       "1977-01-01                                            33.3080    \n",
       "1978-01-01                                            34.0199    \n",
       "1979-01-01                                            34.8079    \n",
       "1980-01-01                                            34.6377    \n",
       "1981-01-01                                            33.9303    \n",
       "1982-01-01                                            35.2381    \n",
       "1983-01-01                                            35.3898    \n",
       "1984-01-01                                            35.8773    \n",
       "1985-01-01                                            36.7489    \n",
       "1986-01-01                                            37.3720    \n",
       "1987-01-01                                            38.6859    \n",
       "1988-01-01                                            39.2844    \n",
       "1989-01-01                                            39.9081    \n",
       "1990-01-01                                            39.9539    \n",
       "1991-01-01                                            40.7695    \n",
       "1992-01-01                                            40.3535    \n",
       "1993-01-01                                            39.8946    \n",
       "1994-01-01                                            40.2212    \n",
       "1995-01-01                                            40.5536    \n",
       "1996-01-01                                            41.1754    \n",
       "1997-01-01                                            42.1954    \n",
       "1998-01-01                                            41.1657    \n",
       "1999-01-01                                            41.1582    \n",
       "2000-01-01                                            41.5400    \n",
       "2001-01-01                                            41.8078    \n",
       "2002-01-01                                            43.0830    \n",
       "2003-01-01                                            44.5460    \n",
       "2004-01-01                                            46.4957    \n",
       "2005-01-01                                            47.4133    \n",
       "2006-01-01                                            47.9600    \n",
       "2007-01-01                                            48.9497    \n",
       "2008-01-01                                            49.1355    \n",
       "2009-01-01                                            49.1382    \n",
       "2010-01-01                                            50.7118    \n",
       "2011-01-01                                            52.1069    \n",
       "2012-01-01                                            53.2092    \n",
       "2013-01-01                                            54.1858    \n",
       "2014-01-01                                            55.6712    \n",
       "2015-01-01                                            57.1566    \n",
       "2016-01-01                                            58.3418    \n",
       "2017-01-01                                            58.3401    \n",
       "2018-01-01                                            58.3267    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "1975-01-01                                            31.0271    \n",
       "1976-01-01                                            32.4536    \n",
       "1977-01-01                                            33.3080    \n",
       "1978-01-01                                            34.0199    \n",
       "1979-01-01                                            34.8079    \n",
       "1980-01-01                                            34.6377    \n",
       "1981-01-01                                            33.9303    \n",
       "1982-01-01                                            35.2381    \n",
       "1983-01-01                                            35.3898    \n",
       "1984-01-01                                            35.8773    \n",
       "1985-01-01                                            36.7489    \n",
       "1986-01-01                                            37.3720    \n",
       "1987-01-01                                            38.6859    \n",
       "1988-01-01                                            39.2844    \n",
       "1989-01-01                                            39.9081    \n",
       "1990-01-01                                            39.9539    \n",
       "1991-01-01                                            40.7695    \n",
       "1992-01-01                                            40.3535    \n",
       "1993-01-01                                            39.8946    \n",
       "1994-01-01                                            40.2212    \n",
       "1995-01-01                                            40.5536    \n",
       "1996-01-01                                            41.1754    \n",
       "1997-01-01                                            42.1954    \n",
       "1998-01-01                                            41.1657    \n",
       "1999-01-01                                            41.1582    \n",
       "2000-01-01                                            41.5400    \n",
       "2001-01-01                                            41.8078    \n",
       "2002-01-01                                            43.0830    \n",
       "2003-01-01                                            44.5460    \n",
       "2004-01-01                                            46.4957    \n",
       "2005-01-01                                            47.4133    \n",
       "2006-01-01                                            47.9600    \n",
       "2007-01-01                                            48.9497    \n",
       "2008-01-01                                            49.1355    \n",
       "2009-01-01                                            49.1382    \n",
       "2010-01-01                                            50.7118    \n",
       "2011-01-01                                            52.1069    \n",
       "2012-01-01                                            53.2092    \n",
       "2013-01-01                                            54.1858    \n",
       "2014-01-01                                            55.6712    \n",
       "2015-01-01                                            57.1566    \n",
       "2016-01-01                                            58.3418    \n",
       "2017-01-01                                            58.3064    \n",
       "2018-01-01                                            58.2586    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "1975-01-01                                            31.0271    \n",
       "1976-01-01                                            32.4536    \n",
       "1977-01-01                                            33.3080    \n",
       "1978-01-01                                            34.0199    \n",
       "1979-01-01                                            34.8079    \n",
       "1980-01-01                                            34.6377    \n",
       "1981-01-01                                            33.9303    \n",
       "1982-01-01                                            35.2381    \n",
       "1983-01-01                                            35.3898    \n",
       "1984-01-01                                            35.8773    \n",
       "1985-01-01                                            36.7489    \n",
       "1986-01-01                                            37.3720    \n",
       "1987-01-01                                            38.6859    \n",
       "1988-01-01                                            39.2844    \n",
       "1989-01-01                                            39.9081    \n",
       "1990-01-01                                            39.9539    \n",
       "1991-01-01                                            40.7695    \n",
       "1992-01-01                                            40.3535    \n",
       "1993-01-01                                            39.8946    \n",
       "1994-01-01                                            40.2212    \n",
       "1995-01-01                                            40.5536    \n",
       "1996-01-01                                            41.1754    \n",
       "1997-01-01                                            42.1954    \n",
       "1998-01-01                                            41.1657    \n",
       "1999-01-01                                            41.1582    \n",
       "2000-01-01                                            41.5400    \n",
       "2001-01-01                                            41.8078    \n",
       "2002-01-01                                            43.0830    \n",
       "2003-01-01                                            44.5460    \n",
       "2004-01-01                                            46.4957    \n",
       "2005-01-01                                            47.4133    \n",
       "2006-01-01                                            47.9600    \n",
       "2007-01-01                                            48.9497    \n",
       "2008-01-01                                            49.1355    \n",
       "2009-01-01                                            49.1382    \n",
       "2010-01-01                                            50.7118    \n",
       "2011-01-01                                            52.1069    \n",
       "2012-01-01                                            53.2092    \n",
       "2013-01-01                                            54.1858    \n",
       "2014-01-01                                            55.6712    \n",
       "2015-01-01                                            57.1566    \n",
       "2016-01-01                                            58.3418    \n",
       "2017-01-01                                            58.2087    \n",
       "2018-01-01                                            58.0676    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "1975-01-01                                            31.0271    \n",
       "1976-01-01                                            32.4536    \n",
       "1977-01-01                                            33.3080    \n",
       "1978-01-01                                            34.0199    \n",
       "1979-01-01                                            34.8079    \n",
       "1980-01-01                                            34.6377    \n",
       "1981-01-01                                            33.9303    \n",
       "1982-01-01                                            35.2381    \n",
       "1983-01-01                                            35.3898    \n",
       "1984-01-01                                            35.8773    \n",
       "1985-01-01                                            36.7489    \n",
       "1986-01-01                                            37.3720    \n",
       "1987-01-01                                            38.6859    \n",
       "1988-01-01                                            39.2844    \n",
       "1989-01-01                                            39.9081    \n",
       "1990-01-01                                            39.9539    \n",
       "1991-01-01                                            40.7695    \n",
       "1992-01-01                                            40.3535    \n",
       "1993-01-01                                            39.8946    \n",
       "1994-01-01                                            40.2212    \n",
       "1995-01-01                                            40.5536    \n",
       "1996-01-01                                            41.1754    \n",
       "1997-01-01                                            42.1954    \n",
       "1998-01-01                                            41.1657    \n",
       "1999-01-01                                            41.1582    \n",
       "2000-01-01                                            41.5400    \n",
       "2001-01-01                                            41.8078    \n",
       "2002-01-01                                            43.0830    \n",
       "2003-01-01                                            44.5460    \n",
       "2004-01-01                                            46.4957    \n",
       "2005-01-01                                            47.4133    \n",
       "2006-01-01                                            47.9600    \n",
       "2007-01-01                                            48.9497    \n",
       "2008-01-01                                            49.1355    \n",
       "2009-01-01                                            49.1382    \n",
       "2010-01-01                                            50.7118    \n",
       "2011-01-01                                            52.1069    \n",
       "2012-01-01                                            53.2092    \n",
       "2013-01-01                                            54.1858    \n",
       "2014-01-01                                            55.6712    \n",
       "2015-01-01                                            57.1566    \n",
       "2016-01-01                                            58.3418    \n",
       "2017-01-01                                            58.1517    \n",
       "2018-01-01                                            57.9501    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "1975-01-01                                            31.0271    \n",
       "1976-01-01                                            32.4536    \n",
       "1977-01-01                                            33.3080    \n",
       "1978-01-01                                            34.0199    \n",
       "1979-01-01                                            34.8079    \n",
       "1980-01-01                                            34.6377    \n",
       "1981-01-01                                            33.9303    \n",
       "1982-01-01                                            35.2381    \n",
       "1983-01-01                                            35.3898    \n",
       "1984-01-01                                            35.8773    \n",
       "1985-01-01                                            36.7489    \n",
       "1986-01-01                                            37.3720    \n",
       "1987-01-01                                            38.6859    \n",
       "1988-01-01                                            39.2844    \n",
       "1989-01-01                                            39.9081    \n",
       "1990-01-01                                            39.9539    \n",
       "1991-01-01                                            40.7695    \n",
       "1992-01-01                                            40.3535    \n",
       "1993-01-01                                            39.8946    \n",
       "1994-01-01                                            40.2212    \n",
       "1995-01-01                                            40.5536    \n",
       "1996-01-01                                            41.1754    \n",
       "1997-01-01                                            42.1954    \n",
       "1998-01-01                                            41.1657    \n",
       "1999-01-01                                            41.1582    \n",
       "2000-01-01                                            41.5400    \n",
       "2001-01-01                                            41.8078    \n",
       "2002-01-01                                            43.0830    \n",
       "2003-01-01                                            44.5460    \n",
       "2004-01-01                                            46.4957    \n",
       "2005-01-01                                            47.4133    \n",
       "2006-01-01                                            47.9600    \n",
       "2007-01-01                                            48.9497    \n",
       "2008-01-01                                            49.1355    \n",
       "2009-01-01                                            49.1382    \n",
       "2010-01-01                                            50.7118    \n",
       "2011-01-01                                            52.1069    \n",
       "2012-01-01                                            53.2092    \n",
       "2013-01-01                                            54.1858    \n",
       "2014-01-01                                            55.6712    \n",
       "2015-01-01                                            57.1566    \n",
       "2016-01-01                                            58.3418    \n",
       "2017-01-01                                            58.3240    \n",
       "2018-01-01                                            58.3045    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6  \n",
       "Year                                                            \n",
       "1970-01-01                                            29.2509   \n",
       "1971-01-01                                            28.9959   \n",
       "1972-01-01                                            30.0417   \n",
       "1973-01-01                                            30.9854   \n",
       "1974-01-01                                            30.8474   \n",
       "1975-01-01                                            31.0271   \n",
       "1976-01-01                                            32.4536   \n",
       "1977-01-01                                            33.3080   \n",
       "1978-01-01                                            34.0199   \n",
       "1979-01-01                                            34.8079   \n",
       "1980-01-01                                            34.6377   \n",
       "1981-01-01                                            33.9303   \n",
       "1982-01-01                                            35.2381   \n",
       "1983-01-01                                            35.3898   \n",
       "1984-01-01                                            35.8773   \n",
       "1985-01-01                                            36.7489   \n",
       "1986-01-01                                            37.3720   \n",
       "1987-01-01                                            38.6859   \n",
       "1988-01-01                                            39.2844   \n",
       "1989-01-01                                            39.9081   \n",
       "1990-01-01                                            39.9539   \n",
       "1991-01-01                                            40.7695   \n",
       "1992-01-01                                            40.3535   \n",
       "1993-01-01                                            39.8946   \n",
       "1994-01-01                                            40.2212   \n",
       "1995-01-01                                            40.5536   \n",
       "1996-01-01                                            41.1754   \n",
       "1997-01-01                                            42.1954   \n",
       "1998-01-01                                            41.1657   \n",
       "1999-01-01                                            41.1582   \n",
       "2000-01-01                                            41.5400   \n",
       "2001-01-01                                            41.8078   \n",
       "2002-01-01                                            43.0830   \n",
       "2003-01-01                                            44.5460   \n",
       "2004-01-01                                            46.4957   \n",
       "2005-01-01                                            47.4133   \n",
       "2006-01-01                                            47.9600   \n",
       "2007-01-01                                            48.9497   \n",
       "2008-01-01                                            49.1355   \n",
       "2009-01-01                                            49.1382   \n",
       "2010-01-01                                            50.7118   \n",
       "2011-01-01                                            52.1069   \n",
       "2012-01-01                                            53.2092   \n",
       "2013-01-01                                            54.1858   \n",
       "2014-01-01                                            55.6712   \n",
       "2015-01-01                                            57.1566   \n",
       "2016-01-01                                            58.3418   \n",
       "2017-01-01                                            58.3240   \n",
       "2018-01-01                                            58.3045   "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27984eec",
   "metadata": {},
   "source": [
    "## Test train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1aca8fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "total_points = len(TS)\n",
    "train_size = int(0.7 * total_points) #this means 70% of our data points get used to train our data set\n",
    "\n",
    "train = TS.iloc[0:train_size]\n",
    "test = TS.iloc[train_size:]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 8))\n",
    "train['Average_Index'].plot(ax=ax, label='train')\n",
    "test['Average_Index'].plot(ax=ax, label='test')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d802566b",
   "metadata": {},
   "source": [
    "#### Look at autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd094c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(TS['Average_Index'], lags=40) \n",
    "plt.show()\n",
    "\n",
    "#strong non constant autocorrelation but no sign of seasonality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c259b0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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I+KebuP9O4E6Am266aSuqKHWFiGCot8xQb5nnjvZf9tqUEtONBU5PtiQJphrL2yfPz/Ho+DR//tA4jeZKgqBUCI7s61mVDGhNEPRUilv9NSVJkqQ9p+MDf+A7gMdTSuMAEfEh4O8BpyLicP5r/2FgbK2bU0r3APdA9oz/NtVZ6moRQX+1RH+1xNEDfetet7iYODU5x9fOzPDkmRm+dnaaJ/Ltzz31DBNzzVXXL40UuGk5IdDLTft7ueVAH8O9la3+WpIkSVJX2g2B/5PAyyOil2yo/7cDx4Fp4A7g7nz94R2roaQ1FQrB4aEeDg/18PJbRy45f36mwdfOzPC1szM8eWYlKfCXj4zzu5+pr7p2X2+ZWw70ccuBfm492Jdv93F0pM+RApIkSdJldHzgn1L6ZET8DvAZoAn8Ddkv+P3AByLizWTJgdfsXC0lXY3h3grDvRW+/sbhS87NNhZ46twMXzszwxOnp3ns9DRPnJ7mrx45ze9+5sSqa68fqnHLcjKgn1vzpMAN+3qcU0CSJEl7Xse/zq+dfJ2f1B2m602eODPN46eneXw8Wz92eprHxqdWPT5QKgQ3jfQuJwJuOdC/PFLg0GCVCF9TKEmSpO7QDa/zk6RlfdUSf+f6If7O9avfUpBS4tzMPI+fnuKxPCGwtPzlI6dXvYmgp1zk5pFs/oCjB/o4OtLL0ZEsKXBwwKSAJEmSuoeBv6SuERHs76uwv28/L715/6pzi4uJZyfmsoTAmeyxgSdOT/PQqUn+9MunmF9YGf3UVylyc54EOHpgJSFw80gfB/orJgUkSZK0qxj4S9oTCoXg+uEerh/u4Vued2DVuebCIs+cn1tJCOTrL52c4I+++CzNxZWkwEC1xM0tyYCjI9lcAtcP9zA6WKVacqJBSZIkdRYDf0l7XqlY4KaRXm4a6eVbn39w1bn5hUWePje7apTA42dm+PyJC3zkCydZvGialAP9VQ4P1VaW4Z58O1sfGqxRKTnhoCRJkraPgb8kXUa5WMjmADjQBy9Yfa7RXOSpczM8c36Wk+fnOHlhjpMXZjl5YY4nzkzz8cfOMNky2eCSA/1Vrh+ucd1gjeuHe7huOVGQJQcODlSplR05IEmSpPYw8Jekq1QpFXjOwX6ec7B/3Wum6k2evTDLM+fnePbCHM9cmOXZC3NXTA4MVEuM9FcY6a9yYHmdb/e1Hqsw1FN23gFJkiSty8BfkrZQf7XEc0cHeO7owLrXtCYHTl6Y5fRUg/HJOmemG5yZqvP46WmOP3GOszMN1noDa6kQWZKgr8qBgSoH+iqM9Fc40F9lpL/KwYEqo/myr7dCoWCSQJIkaS8x8JekHbaR5ABkkxCem5nnzHSd05MNzkzXVyUITk9l60fHphifqtNoLl5SRqkQHOivMjqYJQIODtRWJQZGB2uMDmSjC5yLQJIkqTsY+EvSLlEqFjg4kP2Cz3WXvzalxHRjgdOTdcan6oxN1BmbnGN8ss5Yvpw4N8vfPHl+3ZEE+3rLjA7UGB2sLn/u6EAtTxhUl9f91ZKPGkiSJHUwA39J6kIRQX+1RH+1lE1MeBnzC4ucmWowNjnH2MTqRMHYZDaq4LHxacYn6zQWLh1F0FMutiQG8iRBPqpgKVlwcKDKSF+FUtFRBJIkSdvNwF+S9rhyscB1QzWuG6pd9rqUEudn5hmfqucjB7IRBEujCMYn6zw8NsVfP3qGC7Pzl9wfASN92dwDSwmBAwMVBmtl+qsl+vJERbZdZKC2cqyvUnJuAkmSpKtk4C9J2pCIYF9fhX19FZ5/6PLzEdSbC5ckBVZtT9V5dOw041N15hfWeM5gDb2VYktioHVdpH8pSVDJ1n3VIr2VbN2XH+utFPNzJXrLRRMJkiRpzzDwlyS1XbVU5IZ9vdywr/ey16WUqDcXma43mcqX6foC0/Umk/Um0/kyOZdvN1q26ws8fX525Zp6c80JDdfTUy5miYFqKUsSLCcGiqv2+2slBmplBmtZsmGgVmagVsqWapn+WomiSQRJktTBdkXgHxHDwK8DLwQS8CbgIeD9wFHgCeC1KaVzO1NDSdLViAhq5SK1cpGR/uo1lze/sMhMfYHpxlKiYIGZfL2UOJipLzBVbzLTaDleX2Cm0eT8TIOnz6/cM1VvsrB45REJfZUiA7VyniRoSQ5UV+8P1srsz0dN7O+tsL+/Ql+l6OSIkiRpS+2KwB94B/CHKaV/EhEVoBf434GPppTujoi7gLuAt+1kJSVJO6tcLDDUW2Cot9yW8lJKzM0vMjk3z2Q+8mBybp7JuSZTc00mlrbrK8cn55pcmJ3nxLmZ5evn5tcfiVApFdjfmyUDRvpa1nliYH9vhf19K8u+3rKTJEqSpE3p+MA/IgaBVwA/DJBSagCNiLgdeGV+2b3A/Rj4S5LaKCLoqRTpqRQZvYZy5hcWmcoTAudmGpydzpZzMw3OTDc4N71y7MS5Gc5ON5iYa65b3lBPNnJgpK/CoaEahwdryxM0Xpdvjw7UqJRMEEiSpF0Q+AO3AuPAeyLi64EHgLcCh1JKJwFSSicjYs1/k0XEncCdADfddNP21FiSpBblYmF5YsSjXP71ikvmFxZXJQnOLicI5jk7XefszDynJ+t8+ZkJ/uzLY8zOL1xSxoH+KtcNVblusIfDFyUGlrb7qrvhnwKSJOlaREobm015p0TEMeATwDenlD4ZEe8AJoAfSykNt1x3LqW073JlHTt2LB0/fnxL6ytJ0nZLKTEx2+TZiTlOXpjl1MQcJy/MLa+fvTDHsxNznJ+59DWLA7XSSjJgsMahwRqHhmocGqhm24M1DvRXfLxAkqQOFxEPpJSOrXVuN6T5TwAnUkqfzPd/h+x5/lMRcTj/tf8wMLZjNZQkaQdFBEO9ZYZ6y7zguvVftTjbWLgkKXAqTxY8e2GOr56aZHyyzsXzGRYiGz2QJQKqjA4uJQmy7UMD2fb+vooTFUqS1IE6PvBPKT0bEU9FxAtSSg8B3w58KV/uAO7O1x/ewWpKktTxeipFjh7o4+iB9R83WFhMnJmqc2qizqmJbKTA2MRctj85x4lzs3zmyfOcnW5ccm+lWODgQJVDgyujBY4M93D9cA/XD9e4friHg/1VCr7+UJKkbdXxgX/ux4Dfymf0fwx4I1AAPhARbwaeBF6zg/WTJKkrFAvB6GCN0cEaL2Jo3evqzQXGJ1cSBKfy5MDYxBynJrPRAx/76jjTjdVzD5SLwXVDNa4f6uHIcA+H84TA9cM9XD+UJQgGau15K4MkScrsisA/pfRZYK1nFb59m6siSZKAaqnIDft6uWFf77rXpJSYmGvyzPnZleXC3PL2Jx8/y7MTcyxc9GzBQK20PFLg8FCWGDgy3MOB/ir9tRL91XyplegtFx1BIEnSFeyKwF+SJO0+EcFQT5mhnjJ/+/DgmtcsLCbGJrNkwNPn5ziZJwWePp8d+5snz3FujUkJVz4D+isl+vJEwHJS4OL9WnbNQL7fVy0x2FNiuLfCUE+ZvkrR+QkkSV3LwF+SJO2YYiE4PNTD4aEeXnrz2tfMNLJRA2emGkw3mkzONZmuLzBVn2dqrsnU0nY9356bZ2xyjun6ApNz2fGLJyy8WKkQDPeWGewpM9xTXk4IDPWUGe5dWQ/3VLJrerPrBnvKlH3jgSSpwxn4S5KkjtZbKfHc0QGeO3p196eUmJtfZDJPFCwlBCbmmkzMznN+tsH5mXnOz85zYXaeCzNZ4uDhsUnOz8wzOde8bPn91dKqJMFSomCop7KSNOjJ3rowlCcVhnvK9DrKQJK0TQz8JUlSV4sIeipFeipFRtd/2+G6mguLTM41OT87z/mZRpYcmJ3n/MzK+vxsI0sizMzz1VNTywmExsLiuuUujTJYSRpUlkcRDPeWGemvcrC/yuhgldGBKgcHqlRLxWv4k5Ak7VUG/pIkSZdRKhbY11dhX18FWP9ViBdLKTE7v7CSHMgTBRfyEQYXZjc/ymC4t9ySDKgtJwQODuT7g9n2QLXkaAJJ0jIDf0mSpC0QEfRWSvRWShwe6tnUvc2FRc5ONxibrDM+WWdsco6xiTrjU3XGJrL9Tz9xlrHJOo3mpaMKauXCqsTA6ECVkf5qlsDoLbO/N0tk7O/LHkdwJIEkdTcDf0mSpA5TKhYYHawxOli77HVLr0wcXyMxMDaZbT88NsVfPXKaicvMVdBXKS4nAvb1ZsmBfX2VSxIE+/Njw70VKiUnNZSk3cLAX5IkaZdqfWXic68wgcH8wiLnZ+Y5N9Pg7HSDc9MNzs5k63Mz86v2Hzs9xbnp7I0I6xmolhjpz5IC+/uqjPRVlvez9epjjiqQpJ1j4C9JkrQHlIuF5fkANqrRXOT8TJYQyJIF88vJgbPTDc5MNzg7XefEuRk+d+I856YbNNd5d2J/S6JgpG8pQVBd3l5KYAz2lBmslRnsKdFT9s0HktQOBv6SJElaU6W0sUcOlqSUmJhtcma6zpnpBmemsgTB2ek6p5e3Gzx9fo4vPH2BM1PrJwoge/NBlggoXZIUyNbZucGWc0P5uf6aiQNJWmLgL0mSpLaICIZ6ywz1lrn14JWvX5qj4Ox09jrEibnsLQcTs00m5uaXj7Xun7wwl18zT32NiQ1bFQL6qiX686WvWmKgVqKvUqK/tvp4tl+kv1qmr1pkIF8vXVctFSkWTCJI2p0M/CVJkrQjWucouBpz8wtMzrUmCZpM5K9InK43mVpa5ppMN5pMzjWZrjc5NTHHdH2Bybl5phsLLFxm1EGrcjGolopUS4VsKbdsl4pUyxcfz9flwiX39SwtlQK15e2V47V8u1x0EkVJ127XBP4RUQSOA0+nlL43IvYD7weOAk8Ar00pndu5GkqSJGk71cpFauXipuYtuFhKibn5xeUkwXR9JUHQeqzeXKTeXKA+v0i9ucjc/MLKseYi9flFputNzk4vXnJtvbnA3PzlRyesp1SI5e/ZUymsJAYuShT0Vov0VbPRDNk6368W6a1koxZ6K8VsXS3RWy5ScASDtGfsmsAfeCvwZWAw378L+GhK6e6IuCvff9tOVU6SJEm7T0RkAXTl2hIIV5JSorHQkjSYX2R2foHZxkK2nl9gLt+ey8/NrXN+6b5z0w2emV9gppEtSwmKjepdSg5UVpIGvdUsOTDYU2agtsZcCi3zKAzUytTKBedRkHaBXRH4R8QNwD8Efgb4V/nh24FX5tv3Avdj4C9JkqQOFLH0mECRwdrVPdqwEfMLi8tJgJlGk6n6AjP5yIWZxkK+Xjk+3WgyXc+un240OTPV4GtnZpjM51uYX7j8YxDlYqyZHBhY3s4mZhzuzd7esK+3wr6+Mvt6K9TKvuJR2i67IvAHfgn434DWF9QeSimdBEgpnYyI0bVujIg7gTsBbrrppi2upiRJkrRzysUCQz2Fq543oVVKiXpzcWX+hIvmUpiYm8/mWLjo2MkLc8vbl3vEoadczJIBeSIgW8rsW04QVNjfW2G4t7ycNOipmCyQrkbHB/4R8b3AWErpgYh45WbvTyndA9wDcOzYsY3N3CJJkiTtcREr8wuMDl75+rXUmwtcmJ3n/Mw856YbnJtpcHZ6nnMzjXw/355p8NTZGc7NZCMN1lMrFxjpqzI6WGV0oMroQI3RgSqHBmscbDk20ldxDgOpRccH/sA3A98XEa8GasBgRPw34FREHM5/7T8MjO1oLSVJkiStUi0VGR0oMjpQ2/A9zYVFzs/Oc36dJMHpyTpjk3UePz3NJx8/y/mZSxMFxUJwsH8lQXBwoMahwZVEwehgliwY6atQ8s0J2gM6PvBPKb0deDtA/ov/v0kp/dOI+L+BO4C78/WHd6qOkiRJktqjVCxwoL/Kgf6NTbY4N7/AeJ4MGJ+cY2yyzqmJOcYmsmNPn5/jb548z5npxiX3RsBIX5UD/RUODlSzpb+6vH2gf2U93FN2FIF2rY4P/C/jbuADEfFm4EngNTtcH0mSJEnbrFYucuP+Xm7c33vZ6+YXFjk9VWdsIk8MLCcL8mWqzmPj04xP1Wms8XaEUiGyhMRAZTk5cGCNJMHBgSoD1ZJvO1BH2VWBf0rpfrLZ+0kpnQG+fSfrI0mSJGl3KBcLHB7q4fBQz2WvSykxMdfk9FRLUmCyvrI/lS1fOjnB6akGC4uXTiNWKRU42F/lwPIIgsqq/dZ1X6VokkBbblcF/pIkSZK0lSKCoZ4yQz1lnnOw/7LXLi4mzs/Ot4wamOP0ZIPxqTqn8yTBiXMzfPapc5yZbpDWmGq8p1xcHkVwoP/SRwz29Zbpr5WWX5PYXy05L4E2zcBfkiRJkq5CoRDs76uwv6/CC64buOy1zYVFzs40LkkMLK+n6jxxZprjXzvH2TXmI2jVUy4yUCvlS3llu5olCVYdr66+pqdSpFbK3tZQLRWct2CPMPCXJEmSpC1WKhbytwpc+Q0H8wuLnJ1uMD5Z58LsPJNz80zONVuWeabq2fZEvn3ywtzydTONhQ3Xq1IsUC0XqJaK1MqF5YRA9irHluOlYst1K9cUCxAES08rRARLqYQIiKVj+Tb5+Ww/Wq7J9pduXikjlvdXPmPpmli1v9b1pUKBaqlApVSgXFxaR3asWKRcCirFlvPF7kyGGPhLkiRJUgcpFwscGqxxaHDjr0FstbCYmGpJCiwlCybnmszNL2RLc5G5+QXq+XpufpF6c4H6fL6fb1+YnV8+N5efq88v0li4dALEblEuRkuSIEsGVPPtYiGWl1IhKOTr5eMRG7ymwHpPbKz1SMjyuav8Tgb+kiRJktRFioVgqLfMUG95yz5jcTFRby6ykBIpJRJ5wJpHpolESuTHV84nsoOt+63XwaWB7/J9LefS8rm0vL9yX7axmLLRE43mIvMLiUZzkcbCAo1morF8fGVdb64+1lhYOZ7dv8DCIiwsLtJcTCymRHMhMb+wyOx8YnEx0VxMLLQu+TWLKTu3dM3Ser15HS835uBqJoM08JckSZIkbUqhEPRUijtdDbWI/7D+OaeDlCRJkiSpixn4S5IkSZLUxSJdbuaALhMRk8BDO10PLTsAnN7pSgiwLTqN7dE5bIvOYnt0Ftujc9gWncX26Cx7qT1uTikdXOvEXnvG/6GU0rGdroQyEXHc9ugMtkVnsT06h23RWWyPzmJ7dA7borPYHp3F9sg41F+SJEmSpC5m4C9JkiRJUhfba4H/PTtdAa1ie3QO26Kz2B6dw7boLLZHZ7E9Oodt0Vlsj85ie7DHJveTJEmSJGmv2Wu/+EuSJEmStKfs6sA/It4dEWMR8WDLsa+PiI9HxBci4vcjYjA//vqI+GzLshgRL87PvTS//pGI+OWIiB36SrtaG9vj/oh4qOXc6A59pV1tk+1Rjoh78+Nfjoi3t9xj/7hGbWwL+0YbbLI9KhHxnvz45yLilS332DeuURvbwr7RBhFxY0T8ef7fni9GxFvz4/sj4k8i4uF8va/lnrfnfeChiPjuluP2j2vQ5rawf1yjzbZHRIzk109FxDsvKsu+cY3a3B57p3+klHbtArwCeAnwYMuxTwPfmm+/CfiPa9z3IuCxlv1PAd8EBPAHwKt2+rvtxqWN7XE/cGynv89uXzbTHsAPAe/Lt3uBJ4Cj+b79o3Pawr6x/e3xFuA9+fYo8ABQyPftG53TFvaN9rTHYeAl+fYA8FXgNuDngLvy43cBP5tv3wZ8DqgCtwCPAsX8nP2jc9rC/rH97dEHfAvwI8A7LyrLvtFZ7bFn+seu/sU/pfQx4OxFh18AfCzf/hPgH69x6w8Cvw0QEYeBwZTSx1PW+r8JfP+WVLjLtaM91D6bbI8E9EVECegBGsCE/aM92tEW21HPvWKT7XEb8NH8vjHgPHDMvtEe7WiLra/l3pFSOplS+ky+PQl8GTgC3A7cm192Lyt/128nS1TWU0qPA48AL7N/XLt2tcW2VrqLbbY9UkrTKaW/BOZay7FvtEe72mOv2dWB/zoeBL4v334NcOMa1/wAK4HmEeBEy7kT+TG1x2bbY8l78uE2P+0QqLZarz1+B5gGTgJPAj+fUjqL/WMrbbYtltg3tsZ67fE54PaIKEXELcBL83P2ja2z2bZYYt9oo4g4CnwD8EngUErpJGT/4CYbcQHZ3/mnWm5b6gf2jza6xrZYYv9okw22x3rsG212je2xZE/0j24M/N8EvCUiHiAb+tFoPRkR3wjMpJSWnidcq3F91UH7bLY9AF6fUnoR8Pfz5Z9tV2X3gPXa42XAAnA92RDBfx0Rt2L/2EqbbQuwb2yl9drj3WT/MDsO/BLw10AT+8ZW2mxbgH2jrSKiH/hd4MdTSpcbcbReP7B/tEkb2gLsH22zifZYt4g1jtk3rlIb2gP2UP/ousA/pfSVlNJ3pZReSvYr8qMXXfI6Vv+6fAK4oWX/BuCZra3l3nEV7UFK6el8PQm8F4eqtc1l2uOHgD9MKc3nQ2j/imwIrf1ji1xFW9g3ttB67ZFSaqaUfiKl9OKU0u3AMPAw9o0tcxVtYd9oo4gok/1D+rdSSh/KD5/KhygvDVUey4+fYPWoi6V+YP9ogza1hf2jTTbZHuuxb7RJm9pjT/WPrgv8l2ZijIgC8G+Bd7WcK5ANG3zf0rF8GMhkRLw8H9rxBuDD21rpLrbZ9siHcB7It8vA95IN+1QbXKY9ngS+LTJ9wMuBr9g/ts5m28K+sbXWa4+I6M3bgYj4TqCZUvqSfWPrbLYt7Bvtk/9d/g3gyymlX2w5dR9wR759Byt/1+8DXhcR1fzxi+cBn7J/XLt2tYX9oz2uoj3WZN9oj3a1x57rHxuZAbBTF7JfAk4C82QZtDcDbyWb2fGrwN1AtFz/SuATa5RzjKyRHwXe2XqPy/a2B9msmw8Anwe+CLyDfFZal61rD6Af+GD+Z/4l4CdbyrF/dEBb2Dd2rD2OAg+RTRz0p8DNLeXYNzqgLewbbW2PbyEbdvx54LP58mpghGxixYfz9f6We34q7wMP0TI7uf2jM9rC/rGj7fEE2eSlU/l/327Lj9s3OqQ99lr/WPqfqSRJkiRJ6kJdN9RfkiRJkiStMPCXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFSjtdgSUR8W7ge4GxlNIL82P7gfcDR4EngNemlM7l594OvBlYAP5lSumPrvQZBw4cSEePHt2K6kuSJEmStGMeeOCB0ymlg2udi5TSdtdnTRHxCmAK+M2WwP/ngLMppbsj4i5gX0rpbRFxG/DbwMuA64E/BZ6fUlq43GccO3YsHT9+fEu/hyRJkiRJ2y0iHkgpHVvrXMf84p9S+lhEHL3o8O3AK/Pte4H7gbflx9+XUqoDj0fEI2RJgI9f7jPOTDf43QdO0Fct0lct0VspZduVUr5fpFoqEBFt/GaSJEmSJO2cjgn813EopXQSIKV0MiJG8+NHgE+0XHciP3aJiLgTuBOgct1z+dcf/NxlP7BUCHorWWKgr1qir3JRkiA/1lspMVArMdhTZrBWZrCnxGCtzFBPmcGeMgPVEoWCCQRJkiRJ0s7q9MB/PWtF1Gs+s5BSuge4B+DFL3lpuu8n/wFT9SYzjSbTjQWm602m601mGgsrx+sLlxx75vws0/m5mUZ27rIVDOivlvKkQJnBNZIElzveXy1RNHEgSZIkSbpGnR74n4qIw/mv/YeBsfz4CeDGlutuAJ65UmGlQnDTSG9bKrawmJhuNLkwM8/E3DwTs818Pc/EXDNfrz5+4twsE7MTTMzOM1lvXvEzBqpZUmCgVlqVGGgdaXDxqIOBWpZMGKiVqZR8aYMkSZIk7XWdHvjfB9wB3J2vP9xy/L0R8Ytkk/s9D/jUdlasWIgs2K6Vr+r+hcXE1FyWFLgw25IwmJtnsiVx0Lr9zPk5HqpPMjHbZHJunsUrzMvYVymyr6/C/r4K+3or7OstZ/u9FYbz9b6+8vL54d4y1VLxqr6PJEmSJKkzdUzgHxG/TTaR34GIOAH8O7KA/wMR8WbgSeA1ACmlL0bEB4AvAU3gLVea0b/TFAvBUG+Zod7yqqELG7WYjziYnGsujyyYnFsZZXBhdp7zM/Ocn2lwdqbBuekGj52e4vz05Ucb9FdL7Osr54mCLGkw3Ftmf2+FgwNVDg5UGR2ocXCgyoH+CqWiowokSZIkqZN1zOv8toOv88s0mostCYF5zs00ODudJQfOzbTsz+TL9DxTayQLImCkr8KB/iqjgzUO9lcZHawyelGCYHSgSl+1Y3JMkiRJktR1dsXr/LR9KqUCo4M1RgdrG75nbn6BM9MNxibmGJ+sM5Yv45N1xiezYw+fmmR8sk5zjWcQeitFRluSAQcHqhweqnHdUI3rh3u4brDGocGa8xJIkiRJUpsZ+GtDauUiR4Z7ODLcc9nrFhcT52fnGcuTAWMTdcanWtdzfPnkBH/x1folowgi4EB/lhDIlh6ua9k+PGRyQJIkSZI2y8BfbVUoBPvzCQX/1nWXv3Zybp5nL8xx8sIcJy/MZuvzc5ycmOOx8Wn++pEza85HcKC/yvXDNa4bzJMCw1lS4DkH+7n1YB+9Ff9aS5IkSdISIyTtmIFamYFamecdGlj3mrWSA89emOOZC3M8cWaajz96aXLgyHAPtx7s47mj/TznYP/y+kB/hYjY6q8lSZIkSR3FwF8dbaPJgWfOz/HY+BSPjE3x6PgUj4xP8b5PPcXs/MrLHoZ6yjxnjYTAjft7KRZMCEiSJEnqTgb+2vUGamVecF2ZF1y3OjmwuJg4OTHHo2MtCYGxKf7sK+N84PiJ5esqxQK3HFhKCPTxnDwh4GMDkiRJkrqBUY26VqEQyxMSvuL5B1edOz/T4NHxKR4dm+aR8SkeHZviwWcu8AcPnqT1pQStcwfceqCPW/Pt64d6KDhKQJIkSdIuYOCvPWm4t8JLb97PS2/ev+r43PwCXzszwyNjUzw2PsVjp6d5bHyKD33m6VVvIaiVC9xyIEsCPCdPCDznYD+3HOyjv2q3kiRJktQ5jFCkFrVykRdcN3DJYwMpJcYn6zw6Ps1jp6d4bDxLCDz49AX+4AurRwkcGqxy61JSYHm0QD+Hh2uUi76KUJIkSdL2MvCXNiAiGB2sMTpY45ueM7LqXL2ZjRJ4bHwqSwzkyYHf/9wzTMytjBIoBBwe6uHIvh5u2NfDDft6uWFfDzfm68NDNUomBiRJkiS1mYG/dI2qpSLPPzTA8w9dOkrgzHSDx8aneeL0NCfOzXDi3Cwnzs3yiUfPcHLiaVLLSIFiIbhusLYqKdC6bWJAkiRJ0tUw8Je2SERwoL/Kgf4qL7tl/yXnG81Fnr0wx1PnZlYlBU6cm+GvHz3NsxNz6yYGjuzrYXSgxqHBKqMDNUYHqxzK17VycRu/pSRJkqROtysC/4j4CeCfAwn4AvBGoBd4P3AUeAJ4bUrp3A5VUdq0SqnATSO93DTSu+b5RnORkxdWkgFPnZ1dThB84tEzjE/VmV9Il9w3UCsxOlDl0GCN0YFq9ohC6zo/1+ckhJIkSdKe0PH/8o+II8C/BG5LKc1GxAeA1wG3AR9NKd0dEXcBdwFv28GqSm1VKRW4eaSPm0f61jy/uJg4N9NgbLKeLRNzq9anJuY4/rVzjE3WaTQXL7m/r1JkdLDGwTwZcKC/ysGBKgf6K8sjFUbybUcRSJIkSbtXxwf+uRLQExHzZL/0PwO8HXhlfv5e4H4M/LWHFArBSH+Vkf4qf/vw+tellLgwO58nBeqMTc5xKl8vJQoefPoCp6caq15Z2GqgWuLARUmBA/1VDgys7B/M93sru+U/K5IkSdLe0PH/Qk8pPR0RPw88CcwCf5xS+uOIOJRSOplfczIiRne0olKHigiGeysM91YumYDwYnPzC4xP1jk9Vef0VIPTU3XO5NvjU3VOT9b56qlJ/vrRM1yYnV+zjJ5ykeuGaty0v5ebR3rzdd/ytqMHJEmSpO3V8YF/ROwDbgduAc4DH4yIf7qJ++8E7gS46aabtqKKUteolYvcuL+XG/evPe9Aq0ZzkTPTdU5PNvJEwUqy4OSFWb52ZobPfO0ckxeNIrhusMZNI73cvJQYGOlb3h7urWzVV5MkSZL2rI4P/IHvAB5PKY0DRMSHgL8HnIqIw/mv/YeBsbVuTindA9wDcOzYsUtnQpN0VSqlAoeHejg81LPuNSklzs3M87Uz0zx5doavncmWJ89O8xdfHWdssr7q+sFaiaMH+pZHC9y8v4+bRnq55UAfowNVImKrv5YkSZLUdXZD4P8k8PKI6CUb6v/twHFgGrgDuDtff3jHaihpTRHB/r4K+/sqfMNN+y45P9NoLicEnjwzw9fOTvO1MzN84ekL/MGDz7KwuJKr660UueVA3yXLrQf6Geotb+fXkiRJknaVtgX+EfGPLnc+pfShqyk3pfTJiPgd4DNAE/gbsl/w+4EPRMSbyZIDr7ma8iXtnN5Kib913SB/67rBS841FxZ55vwcXzs7zROnp3ns9DSPn57mC09f4CNfOElLToD9fZWLkgF93HKwj6Mjfc4pIEmSpD0vUmrP6PeIeE++OUo2FP/P8v1/ANyfUrpsYmA7HDt2LB0/fnynqyHpGjWaizx5dobHT0/z+OkpHj89k6+nOTWx+vGBI8M9q0cJHOzjlpE+btjXQ6lY2KFvIEmSJLVXRDyQUjq21rm2/eKfUnpj/mH/A7htacb9/Pn7X2nX50hSpVTguaP9PHe0Hzi06txUvckT+eiApeWx09P8988+zeTcykSDpUJw4/5ejo70cvRANjrg6IEsKXBkXw/FgvMJSJIkqTtsxTP+R5eC/twp4Plb8DmSdIn+aokXHhnihUeGVh1PKXF2urH8yMATp6d54sw0j5+e4ZOPn2WmsbB8bbmYJQVuyZMBWWKgl6MjfVw/bFJAkiRJu8tWBP73R8QfAb8NJOB1wJ9vwedI0oZFBCP9VUb6q/zdo/tXnUspMT5ZzxICeTJgKTHwV4+eZm5+cfnaSqnATfuzJMAtB7LRAjfs6+X6oRqHh3vor+6GOVMlSZK0l7T9X6gppR/NJ/r7+/mhe1JKv9fuz5GkdokIRgdrjA7W+MZbR1adW1xMnJqc44nTMzxxZnr5MYInzkzzsYfHaTQXV10/UC1xeLjGdUM9XD9U47qhGtcP9WTr/LjJAUmSJG2nLfnXZz6D/1XN4i9JnaRQCA4P9XB4qIdves6lSYGTE3M8c36WZ87P8uyFOU5emOPkhVlOXpjjyycnGJ+sX1LmQK3E4aFaXm7LerjG4aEaBwdqDNZKRPhIgSRJkq5d2wP//Nf+nyWb3T/yJaWULn1flyTtYoVCcGS4hyPDPete02gucmpidULg5Pl8fWGOLz4zwempS5MDlWKBkf4KB/qrjPRXGOmrcmCgwoG+6qrjB/qr7O+rUPYNBZIkSVrHVvzi/3PA/5RS+vIWlC1Ju0qlVODG/b3cuL933WvqzQXGJuo8kycETk/VOT3V4PRUnTNTdc5MN/jqs5OcnmrQWFhcs4zh3jIjfVkioDUpMNJf4WB/ldHBGgcHqhzsr1IpmSSQJEnaS7Yi8D9l0C9JG1ctFa+YHIBsEsLJepMzUw3OTNWXEwRnlpIE09n+l5+d4MxUgwuz82uWs6+3zOhAjdHBLBFwcLCa7Q9UOThQZXQgSxQ4F4EkSVJ32Ip/1R2PiPcD/x1YHr+aP/cvSbpKEcFgrcxgrcwtB/queH2jucjZ6Qbjk3XGJucYm6yvbE/UGZus89j4NOOT9TVHEvRWiiuJgIF8xEC+f7Dl2P6+iq84lCRJ6mBbEfgPAjPAd7UcSzjZnyRtq0qpwHX5mwVgaN3rUkpcmJ1nbLLO2ESd8amVxECWLJjjy89O8LGv1pmsNy+5v1gIRvoqq5ICrYmB1mO9FUcRSJIkbbeteJ3fG9tdpiRp60QEw70VhnsrPP/QwGWvnW0sMD65khwYn8qTBflIgvGpOl86OcHpqQYLi+mS+/urpeW5Bg7mjxoM1kr0VbNloFair9KyXS3Rny+1csE3HUiSJF2FrZjV//nArwKHUkovjIivA74vpfSf2v1ZkqTt1VMpctNILzeNXH4+goXFxLmZRktyYG4lSTCVJQq+9Ez2usOpNUYRrKVYCPoqRfrzJEF/LUsI9FVatqvFLIlQKdGbX9tbLdFXaTlezY5XSyYSJEnS3rAVYy7/C/CTwK8BpJQ+HxHvBQz8JWmPKBZi+Q0DV7K4mJiZX2BqrslUvcl0PVsvbU/Xm0wuby8wOZdvN5pMzjV59sLcqmvWGGiwpkKwnAi4XLKgPx99kC3l5XV/tcRgvu1oBEmS1Mm2IvDvTSl96qJ/AG3s55x1RMQw8OvAC8nmC3gT8BDwfuAo8ATw2pTSuWv5HEnS9isUYnk4/7VKKVFvLjJdbzLTWGCq3mSmkSUMZhpNpvL1yn6TmfoC042V609NzjFzOjs2NddkurFwxc8tFYL+PDnQX82SA4MtCYLWpMH+vsqqZbinTKnoKxYlSdLW2YrA/3REPIcsQCci/glw8hrLfAfwhymlfxIRFaAX+N+Bj6aU7o6Iu4C7gLdd4+dIknaxiKBWLlIrFxlpU5kLi2l5BMLk3DyTc63rbJmqt+5n28+cn2OyPrl8fK05D7I6w1BPmf29WSJgX1+FkdZ1b4X9/ZXl8/v7KvRWio4wkCRJGxYpbXBM5EYLjLgVuAf4e8A54HHg9Smlr11leYPA54BbU0tlI+Ih4JUppZMRcRi4P6X0gsuVdezYsXT8+PGrqYYkSVctpcTc/CIXZuc5N9Pg7HS2nJtpcGYqX083ODe9cu7sdIPmOsmCaqnA/r4KI/0VrhvM3tyQrXtW9odqbRlFIUmSdoeIeCCldGytc239F0FEFIH/NaX0HRHRBxRSSpPXWOytwDjwnoj4euAB4K1kkweeBMiD/9F16nQncCfATTfddI1VkSRp8yKCnkqRnkoxf73ilaWUmKw3OTvV4OxMY3ndmhw4PVXn6fNzPPC1c5ybmb+kjIFqaTkJ0JoQaE0W7O+rOHpAkqQu17bAPyJKKaVmRLwUIKU03aaiS8BLgB9LKX0yIt5BNqx/Q1JK95CNQODYsWPtHd4gSdIWiQgGa2UGa2WO0nfF6+fmFzg1McfJC3PL62fz5eTEHA+fOs3Y5Nwlkx9WSgUODVY5PNjDoaEahwaqXDdUY3SwZXugRk+luEXfVJIkbbV2/uL/KbIA/W8i4j7gg8By8J9S+tBVlnsCOJFS+mS+/ztkgf+piDjcMtR/7OqrLknS7lYrF7l5pI+bR9ZPEjQXFjk91eDkhdmV5MBEnhy4MMfnT5zn1MQcc/OLl9w7WCtxKB8pMDpQ49BglUODtXzJtg8OVCk7UaEkSR1nKx7+2w+cAb6NbIK/yNdXFfinlJ6NiKci4gUppYeAbwe+lC93AHfn6w+3oe6SJHWtUrGwPNx/PSklJuaajE3McWqizrMT2QiCpf1Tk3M8Onaascn6JXMQRMBIX3VVUuDIcI3rh3uyZaiH64ZqVEomByRJ2k7tDPxHI+JfAQ+yEvAvudYh9j8G/FY+o/9jwBuBAvCBiHgz8CTwmmv8DEmS9ryIYKinzFBPmecdGlj3usXFxNmZBqfyxMCpifryemwiG0nwuafOc2a6cVH5cLC/yuHhniwpMJQnBVoSBCPOOyBJUlu1M/AvAv2sDviXXFPgn1L6LLDW7ITffi3lSpKkq1MoBAf6qxzor/J3rh9a97q5+QVOXpjjmfOz+ZJvX5jlK89O8udfGWd2fmHVPZVSgeuHskTA4aGe5VEDB/qr9NdK9FdLDNRK9FWz7WqpYKJAkqTLaGfgfzKl9B/aWJ4kSdrlauUitxzo45YDa889kFLi/Mw8T+eJgaUkwdL+Xz96mlMTl05K2KpcjOUkwPJSu8x+njQYrJUZ7i0vj3BwfgJJUrdqZ+Bvql2SJG1KRLCvr8K+vgovPLL2yIH5hUWevTDH2ekG0/Umk/Um0/UmU/Umk3Mr21Nz+bre5Ox0gyfPziwfm2ksrFl2q/5qaTkJMNzbmhSoZPv5uaHeMsM9lXxdprdSdMSBJKmjtTPwd9i9JElqu3KxwI37e7lxf+9Vl7GwmJhuZMmB6XqTibkmk3PzXJjNlvMz+TLbYCLf/+qpqfxcg/mF9YcclIvBUE+FoZ4Sw72VVQmCoZ4sOTDcW2lJGqyMMig5ykCStA3aFvinlM62qyxJkqR2KhaCwVqZwVp50/emlJidX+D8zEqS4MJsY2U/PzYxmyUOnp2Y46FTk1yYmWey3rxs2UujDJZGF7SOMjjQX+HgQJWDA1VGB2qMDlYZqJYcXSBJ2rSteJ2fJElS14gIeisleislrh/u2dS9zYVFJuaanJ9pcD4fXXBhJhtFcGG2yfnZxsqx2WyUwVJiYa1RBrVyYSURMFBltCUxcHBwZX+kr0qxYIJAkpQx8JckSdoipWKB/X0V9vdVNnVfSomJ2SbjU3OMTdQZm6wzNjnH+GS+PVHnq6cm+atHTjMxd+mogmIhGOmrMDpY5WB/lhgY6c/qMdxbYX9fmX292f6+voojCSSpyxn4S5IkdZiIyOYI6C3z3NGBy147N7+QJwRWJwbGJufyhEGdB5+Z4Ox0g4V1Xo9QKuSTLPauTgjs762sHM/3l871OamhJO0aBv6SJEm7WK1c3NDkhyklJutNzk03ODvd4NxMg3PT85ybWdnP1vM8MjaVnZ+ZXzdZUCkVGMlHM+zvqzDSV2Gkv7q8vT/fH+mrsL/fUQWStJMM/CVJkvaAiJUJDm8e6dvQPYuLicm5ZpYUmGmsShqcmW5wdirbPz3d4Ikz05ydajC9zqsTKy2PPSw9djDSV13eXnrTwWCtzGBPicFamYFayTcfSFIbGPhLkiRpTYXCyiMHR9lYsmBufmE5KXBmus6ZPDlwZrrB2Xz/zHSDr52Z4ex0g6krvPmgr1Jk8KKEQLZfYnCNZMHStf21En3VItVSsR1/FJK0qxn4S5IkqW1q5SJHhns4ssE3IMzNL3B2usHE3DwTs00uzGavRlzaz9Yr+0uvS5yYzV6XmNZ+EmFZpVigr1rMEgGVEgO1En3VEv0tS191jeMXXV8rF6iWir4tQdKuZOAvSZKkHVMrF7l+uIfr2dyrEiF7FGGq0cwSAy1Jgguz80zXm0zVm0zVF5iqzzNdX2Byrsl0vcmZqQZPnplhsp7tz6zzeMJaSoWgWipQLRezdSlLCFTLK9tLSYLsupbtlvtq5SI95SI9lWxda9nuKRepVQrLx8s+7iDpGu2awD8iisBx4OmU0vdGxH7g/cBR4AngtSmlcztXQ0mSJG2nQmFl3gL2XX05C4uJ6UaTqTwxsJQQmJrLkgfT9Sb15mK+LFCfX2QuXy8fay5Sn19kptHk3Mzqa+vNRebms2uuRqkQeTKgNTFQpKdcWE4e9FaykQq9lSJ91RJ9S+ulZfl49ghEX7VEtVRwwkVpj9g1gT/wVuDLwGC+fxfw0ZTS3RFxV77/tp2qnCRJknanYmsCYQullGgsLDI3v0h9foHZpaWRrefmF5htZEmClf2V61bvLzLXWOD0VIOZRjZqYbreZLqxsO6bGNb63r2V4nLCIFtnjzkM1C6dTyH7MyqtmnOhv+oEjNJusCsC/4i4AfiHwM8A/yo/fDvwynz7XuB+DPwlSZLUoSIiH/ZfhJ6tSTKklKg3F5cfYZhuZCMWpusriYFsvfp467VPnZ1hci57dGJy7vKTL8LaEzAOtCQIhnrKDPeW2d9XYV9fhX29Ffb3VhiolSg4Z4K0LXZF4A/8EvC/AQMtxw6llE4CpJRORsToWjdGxJ3AnQA33XTTFldTkiRJ2jkRQS2fG2CkDeUtLCam6k0mL5lsMZtbYSlB0DoB46nJOR4eWzm+3gCEQsC+3qVkQDnbbt3vyxIE+/pWzg31lE0WSFeh4wP/iPheYCyl9EBEvHKz96eU7gHuATh27NjGxj1JkiRJolgIhvLXJl7NPAopJSbrTc5Pz3NupsHZmQbnphucm5nP1/kyPc+TZ2f43InznJuep7Gw9nwIS8mC0cEaowPVbBmsMjpQ49BglYMDteVjvspRWtHxgT/wzcD3RcSrgRowGBH/DTgVEYfzX/sPA2M7WktJkiRJq0SszJ9w00jvhu5JKTHdWGhJDLQkCaYbjE81GJ+cY2yyzkPPTjI+VV9zXoPh3nKeHFhKBtQuSRSMDtToqZggUPfr+MA/pfR24O0A+S/+/yal9E8j4v8G7gDuztcf3qk6SpIkSWqPiKC/mk0ceOP+KycLFhcTZ6YbjOXJgPGJOqcmsu2lY598fJrxyfqaIwkGqiUODFQ52F/l4ECVA/0VDg5Ul5cD+fGRviqVkhMZanfq+MD/Mu4GPhARbwaeBF6zw/WRJEmStM0KhVgO0v/OZa5LKXF+Zp6xydWJgfHJ+vLylWcnGJ+sM7HOpIbDveXlBEFrUuBgf3VV8mB/X4WicxGog+yqwD+ldD/Z7P2klM4A376T9ZEkSZK0O0RENnFgX4UXXDdw2Wvn5hc4M91YlRQ4PbV6+7NPnWd8ss5MY+GS+wsB+/taRg/0r04UHGgZXbCvt+KEhdpyuyrwlyRJkqStVisXOTLcw5HhniteO11vXpIUGJ+s53MRZPuPjU8zPlWn0bz0UYNiIRjpq1w2MdBfKzFYKzFQK9NfLdFbKRJhskAbZ+AvSZIkSVepr1qir1ri5pG+y1639IaD8ck6pyfrjE+1rhvZeqrOV09NcnqqzvzC+i8kKxZW5kEYqJUYrJUZqJXor2X7SwmC1mTB0vGeSpFauUCtVKSarx1x0P0M/CVJkiRpi7W+4eA5B/sve21KiQuz84xP1rkwO8/kXJPJepPJuXx7bp6puSaTc00m5ppM1ed5dmKOybEmU/l1l0scXKxSLFAtFaiWs6RAtVSgVi5SKxdbtgtUSyvrpaRBsRAEEMHyKISlwQhBZMeXzuf7S38eK8fz/Xy7tZBo2Q0uLp+L9lcKWCqzXAwqxQKVUoFyy7paWn2sUiwsX1cpFbpujgYDf0mSJEnqIBHBcG+F4d7KVd2fUqLeXGSiJUGwlDCYay4wN7/I3PwC9Wa2nptfpJ4fr7ceby5Qn8/Kab1m6d61Hl3oFoVgdZKgWKCcJwRKhaAQQakYFCPyYwUKBfJ1dk2xkJ/Pr1s+1rJcLK2Rr0lcenCt6y7HwF+SJEmSukhELP9iP3r5eQyvyeJiYjFlYWlKWYDaGpC2HsuuWbmWdc4t3QctAW9+zVrnVvazMlYdSzC/mCUo5heydaO5SGNhZb18fCEtn59fWOO6fL2wmFhYTDQXE4tL65RoLmTHZxcWVp1bWLzMPYuJtcYVrDV/w9rXXaGBWhj4S5IkSZI2rVAICmuGpNoJ8dPrnytsXzUkSZIkSdJ2M/CXJEmSJKmLRdrsrAC7WERMAg/tdD207ABweqcrIcC26DS2R+ewLTqL7dFZbI/OYVt0Ftujs+yl9rg5pXRwrRN77Rn/h1JKx3a6EspExHHbozPYFp3F9ugctkVnsT06i+3ROWyLzmJ7dBbbI+NQf0mSJEmSupiBvyRJkiRJXWyvBf737HQFtIrt0Tlsi85ie3QO26Kz2B6dxfboHLZFZ7E9OovtwR6b3E+SJEmSpL1mr/3iL0mSJEnSnrKrA/+IeHdEjEXEgy3Hvj4iPh4RX4iI34+Iwfz46yPisy3LYkS8OD/30vz6RyLilyMidugr7WptbI/7I+KhlnOjO/SVdrVNtkc5Iu7Nj385It7eco/94xq1sS3sG22wyfaoRMR78uOfi4hXttxj37hGbWwL+0YbRMSNEfHn+X97vhgRb82P74+IP4mIh/P1vpZ73p73gYci4rtbjts/rkGb28L+cY022x4RMZJfPxUR77yoLPvGNWpze+yd/pFS2rUL8ArgJcCDLcc+DXxrvv0m4D+ucd+LgMda9j8FfBMQwB8Ar9rp77Yblza2x/3AsZ3+Prt92Ux7AD8EvC/f7gWeAI7m+/aPzmkL+8b2t8dbgPfk26PAA0Ah37dvdE5b2Dfa0x6HgZfk2wPAV4HbgJ8D7sqP3wX8bL59G/A5oArcAjwKFPNz9o/OaQv7x/a3Rx/wLcCPAO+8qCz7Rme1x57pH7v6F/+U0seAsxcdfgHwsXz7T4B/vMatPwj8NkBEHAYGU0ofT1nr/ybw/VtS4S7XjvZQ+2yyPRLQFxEloAdoABP2j/ZoR1tsRz33ik22x23AR/P7xoDzwDH7Rnu0oy22vpZ7R0rpZErpM/n2JPBl4AhwO3Bvftm9rPxdv50sUVlPKT0OPAK8zP5x7drVFtta6S622fZIKU2nlP4SmGstx77RHu1qj71mVwf+63gQ+L58+zXAjWtc8wOsBJpHgBMt507kx9Qem22PJe/Jh9v8tEOg2mq99vgdYBo4CTwJ/HxK6Sz2j6202bZYYt/YGuu1x+eA2yOiFBG3AC/Nz9k3ts5m22KJfaONIuIo8A3AJ4FDKaWTkP2Dm2zEBWR/559quW2pH9g/2uga22KJ/aNNNtge67FvtNk1tseSPdE/ujHwfxPwloh4gGzoR6P1ZER8IzCTUlp6nnCtxvVVB+2z2fYAeH1K6UXA38+Xf7Zdld0D1muPlwELwPVkQwT/dUTciv1jK222LcC+sZXWa493k/3D7DjwS8BfA03sG1tps20B9o22ioh+4HeBH08pXW7E0Xr9wP7RJm1oC7B/tM0m2mPdItY4Zt+4Sm1oD9hD/aPrAv+U0ldSSt+VUnop2a/Ij150yetY/evyCeCGlv0bgGe2tpZ7x1W0Bymlp/P1JPBeHKrWNpdpjx8C/jClNJ8Pof0rsiG09o8tchVtYd/YQuu1R0qpmVL6iZTSi1NKtwPDwMPYN7bMVbSFfaONIqJM9g/p30opfSg/fCoforw0VHksP36C1aMulvqB/aMN2tQW9o822WR7rMe+0SZtao891T+6LvBfmokxIgrAvwXe1XKuQDZs8H1Lx/JhIJMR8fJ8aMcbgA9va6W72GbbIx/CeSDfLgPfSzbsU21wmfZ4Evi2yPQBLwe+Yv/YOpttC/vG1lqvPSKiN28HIuI7gWZK6Uv2ja2z2bawb7RP/nf5N4Avp5R+seXUfcAd+fYdrPxdvw94XURU88cvngd8yv5x7drVFvaP9riK9liTfaM92tUee65/bGQGwE5dyH4JOAnMk2XQ3gy8lWxmx68CdwPRcv0rgU+sUc4xskZ+FHhn6z0u29seZLNuPgB8Hvgi8A7yWWldtq49gH7gg/mf+ZeAn2wpx/7RAW1h39ix9jgKPEQ2cdCfAje3lGPf6IC2sG+0tT2+hWzY8eeBz+bLq4ERsokVH87X+1vu+am8DzxEy+zk9o/OaAv7x462xxNkk5dO5f99uy0/bt/okPbYa/1j6X+mkiRJkiSpC3XdUH9JkiRJkrTCwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcr7eSHR8T3AO8AisCvp5Tuvuh85OdfDcwAP5xS+kx+7glgElgAmimlY1f6vAMHDqSjR4+28ytIkiRJkrTjHnjggdMppYNrnduxwD8iisCvAN8JnAA+HRH3pZS+1HLZq4Dn5cs3Ar+ar5f8g5TS6Y1+5tGjRzl+/Pg1112SJEmSpE4SEV9b79xODvV/GfBISumxlFIDeB9w+0XX3A78Zsp8AhiOiMPbXVFJkiRJknarnQz8jwBPteyfyI9t9JoE/HFEPBARd673IRFxZ0Qcj4jj4+Pjbai2JEmSJEm7x04G/rHGsbSJa745pfQSsscB3hIRr1jrQ1JK96SUjqWUjh08uObjDpIkSZIkda2dDPxPADe27N8APLPRa1JKS+sx4PfIHh2QJEmSJEktdjLw/zTwvIi4JSIqwOuA+y665j7gDZF5OXAhpXQyIvoiYgAgIvqA7wIe3M7KS5IkSZK0G+zYrP4ppWZE/CjwR2Sv83t3SumLEfEj+fl3AR8he5XfI2Sv83tjfvsh4Peyt/1RAt6bUvrDbf4KkiRJkiR1vEjp4sfqu9exY8eSr/OTJEmSJHWbiHggpXRsrXM7OdRfkiRJkiRtMQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV2sdLmTEfGSy51PKX2mvdWRJEmSJEntdNnAH/iFy5xLwLe1sS6SJEmSJKnNLhv4p5T+wXZVRJIkSZIktd+VfvFfFhEvBG4DakvHUkq/uRWVkiRJkiRJ7bGhwD8i/h3wSrLA/yPAq4C/BAz8JUmSJEnqYBud1f+fAN8OPJtSeiPw9UB1y2olSZIkSZLaYqOB/2xKaRFoRsQgMAbcunXVkiRJkiRJ7bDRZ/yPR8Qw8F+AB4Ap4FNbVSlJkiRJktQeGwr8U0r/n3zzXRHxh8BgSunzW1ctSZIkSZLUDpuZ1f8IcPPSPRHxipTSx7aqYpIkSZIk6dptdFb/nwV+APgSsJAfToCBvyRJkiRJHWyjv/h/P/CClFJ9C+siSZIkSZLabKOz+j8GlLeyIpIkSZIkqf02+ov/DPDZiPgosPyrf0rpX25JrSRJkiRJUltsNPC/L18kSZIkSdIustHX+d0bERXg+fmhh1JK81tXLUmSJEmS1A4bndX/lcC9wBNAADdGxB2+zk+SJEmSpM620cn9fgH4rpTSt6aUXgF8N/Cfr/XDI+J7IuKhiHgkIu5a43xExC/n5z8fES/Z6L2SJEmSJGnjgX85pfTQ0k5K6atc4yz/EVEEfgV4FXAb8IMRcdtFl70KeF6+3An86ibulSRJkiRpz9vo5H7HI+I3gP+a7/9T4IFr/OyXAY+klB4DiIj3AbcDX2q55nbgN1NKCfhERAxHxGHg6AbuvcRj49P8wK99/BqrLUmSJEnS7rHRX/z/V+CLwL8E3ppv/8g1fvYR4KmW/RP5sY1cs5F7AYiIOyPieEQcn593PkJJkiRJ0t6y0Vn968AvAr8YEfuBG/Jj1yLW+qgNXrORe7ODKd0D3ANw7Nix9P5/8U2bqaMkSZIkSR3vA5f5aX5Dv/hHxP0RMZgH/Z8F3hMRv3iN9ToB3NiyfwPwzAav2ci9kiRJkiTteRsd6j+UUpoA/hHwnpTSS4HvuMbP/jTwvIi4JSIqwOuA+y665j7gDfns/i8HLqSUTm7wXkmSJEmS9ryNTu5XyifVey3wU+344JRSMyJ+FPgjoAi8O6X0xYj4kfz8u4CPAK8GHgFmgDde7t521EuSJEmSpG6y0cD/P5AF2X+ZUvp0RNwKPHytH55S+ghZcN967F0t2wl4y0bvlSRJkiRJq210cr8PAh9s2X8M+MdbVSlJkiRJktQeGwr8I6IGvBn4O0Bt6XhK6U1bVC9JkiRJktQGG53c778C1wHfDfwF2Sz6k1tVKUmSJEmS1B4bDfyfm1L6aWA6pXQv8A+BF21dtSRJkiRJUjtsNPCfz9fnI+KFwBBwdEtqJEmSJEmS2majs/rfExH7gJ8G7gP6gf9jy2olSZIkSZLaYqOz+v96vvkXwK1bVx1JkiRJktROGxrqHxGHIuI3IuIP8v3bIuLNW1s1SZIkSZJ0rTb6jP//A/wRcH2+/1Xgx7egPpIkSZIkqY02GvgfSCl9AFgESCk1gYUtq5UkSZIkSWqLjQb+0xExAiSAiHg5cGHLaiVJkiRJktpio7P6/yuy2fyfExF/BRwE/smW1UqSJEmSJLXFZX/xj4i/GxHXpZQ+A3wr8L8DdeCPgRPbUD9JkiRJknQNrjTU/9eARr7994CfAn4FOAfcs4X1kiRJkiRJbXClof7FlNLZfPsHgHtSSr8L/G5EfHZLayZJkiRJkq7ZlX7xL0bEUnLg24E/azm30fkBJEmSJEnSDrlS8P7bwF9ExGlgFvh/ASLiuTirvyRJkiRJHe+ygX9K6Wci4qPAYeCPU0opP1UAfmyrKydJkiRJkq7NFYfrp5Q+scaxr25NdSRJkiRJUjtd6Rl/SZIkSZK0ixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmL7UjgHxH7I+JPIuLhfL1vneu+JyIeiohHIuKuluP/PiKejojP5surt6/2kiRJkiTtHjv1i/9dwEdTSs8DPprvrxIRReBXgFcBtwE/GBG3tVzyn1NKL86Xj2xHpSVJkiRJ2m12KvC/Hbg3374X+P41rnkZ8EhK6bGUUgN4X36fJEmSJEnaoJ0K/A+llE4C5OvRNa45AjzVsn8iP7bkRyPi8xHx7vUeFQCIiDsj4nhEHB8fH29H3SVJkiRJ2jW2LPCPiD+NiAfXWDb6q32scSzl618FngO8GDgJ/MJ6haSU7kkpHUspHTt48OBmvoIkSZIkSbteaasKTil9x3rnIuJURBxOKZ2MiMPA2BqXnQBubNm/AXgmL/tUS1n/Bfgf7am1JEmSJEndZaeG+t8H3JFv3wF8eI1rPg08LyJuiYgK8Lr8PvJkwZL/GXhwC+sqSZIkSdKuFSmlK1/V7g+NGAE+ANwEPAm8JqV0NiKuB349pfTq/LpXA78EFIF3p5R+Jj/+X8mG+SfgCeBfLM0ZcIXPnQQeavf30VU7AJze6UoIsC06je3ROWyLzmJ7dBbbo3PYFp3F9ugse6k9bk4prfl8+44E/jslIo6nlI7tdD2UsT06h23RWWyPzmFbdBbbo7PYHp3DtugstkdnsT0yOzXUX5IkSZIkbQMDf0mSJEmSutheC/zv2ekKaBXbo3PYFp3F9ugctkVnsT06i+3ROWyLzmJ7dBbbgz32jL8kSZIkSXvNXvvFX5IkSZKkPWVXB/4R8e6IGIuIB1uOfX1EfDwivhARvx8Rg/nx10fEZ1uWxYh4cX7upfn1j0TEL0dE7NBX2tXa2B73R8RDLedGd+gr7WqbbI9yRNybH/9yRLy95R77xzVqY1vYN9pgk+1RiYj35Mc/FxGvbLnHvnGN2tgW9o02iIgbI+LP8//2fDEi3pof3x8RfxIRD+frfS33vD3vAw9FxHe3HLd/XIM2t4X94xpttj0iYiS/fioi3nlRWfaNa9Tm9tg7/SOltGsX4BXAS4AHW459GvjWfPtNwH9c474XAY+17H8K+CYggD8AXrXT3203Lm1sj/uBYzv9fXb7spn2AH4IeF++3Qs8ARzN9+0fndMW9o3tb4+3AO/Jt0eBB4BCvm/f6Jy2sG+0pz0OAy/JtweArwK3AT8H3JUfvwv42Xz7NuBzQBW4BXgUKObn7B+d0xb2j+1vjz7gW4AfAd55UVn2jc5qjz3TP3b1L/4ppY8BZy86/ALgY/n2nwD/eI1bfxD4bYCIOAwMppQ+nrLW/03g+7ekwl2uHe2h9tlkeySgLyJKQA/QACbsH+3RjrbYjnruFZtsj9uAj+b3jQHngWP2jfZoR1tsfS33jpTSyZTSZ/LtSeDLwBHgduDe/LJ7Wfm7fjtZorKeUnoceAR4mf3j2rWrLba10l1ss+2RUppOKf0lMNdajn2jPdrVHnvNrg781/Eg8H359muAG9e45gdYCTSPACdazp3Ij6k9NtseS96TD7f5aYdAtdV67fE7wDRwEngS+PmU0lnsH1tps22xxL6xNdZrj88Bt0dEKSJuAV6an7NvbJ3NtsUS+0YbRcRR4BuATwKHUkonIfsHN9mIC8j+zj/VcttSP7B/tNE1tsUS+0ebbLA91mPfaLNrbI8le6J/dGPg/ybgLRHxANnQj0bryYj4RmAmpbT0POFajeurDtpns+0B8PqU0ouAv58v/2y7KrsHrNceLwMWgOvJhgj+64i4FfvHVtpsW4B9Yyut1x7vJvuH2XHgl4C/BprYN7bSZtsC7BttFRH9wO8CP55SutyIo/X6gf2jTdrQFmD/aJtNtMe6RaxxzL5xldrQHrCH+kfXBf4ppa+klL4rpfRSsl+RH73oktex+tflE8ANLfs3AM9sbS33jqtoD1JKT+frSeC9OFStbS7THj8E/GFKaT4fQvtXZENo7R9b5Crawr6xhdZrj5RSM6X0EymlF6eUbgeGgYexb2yZq2gL+0YbRUSZ7B/Sv5VS+lB++FQ+RHlpqPJYfvwEq0ddLPUD+0cbtKkt7B9tssn2WI99o03a1B57qn90XeC/NBNjRBSAfwu8q+VcgWzY4PuWjuXDQCYj4uX50I43AB/e1kp3sc22Rz6E80C+XQa+l2zYp9rgMu3xJPBtkekDXg58xf6xdTbbFvaNrbVee0REb94ORMR3As2U0pfsG1tns21h32if/O/ybwBfTin9Ysup+4A78u07WPm7fh/wuoio5o9fPA/4lP3j2rWrLewf7XEV7bEm+0Z7tKs99lz/2MgMgJ26kP0ScBKYJ8ugvRl4K9nMjl8F7gai5fpXAp9Yo5xjZI38KPDO1ntctrc9yGbdfAD4PPBF4B3ks9K6bF17AP3AB/M/8y8BP9lSjv2jA9rCvrFj7XEUeIhs4qA/BW5uKce+0QFtYd9oa3t8C9mw488Dn82XVwMjZBMrPpyv97fc81N5H3iIltnJ7R+d0Rb2jx1tjyfIJi+dyv/7dlt+3L7RIe2x1/rH0v9MJUmSJElSF+q6of6SJEmSJGmFgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5W2skPj4jvAd4BFIFfTyndfdH5yM+/GpgBfjil9Jn83BPAJLAANFNKx670eQcOHEhHjx5t51eQJEmSJGnHPfDAA6dTSgfXOrdjgX9EFIFfAb4TOAF8OiLuSyl9qeWyVwHPy5dvBH41Xy/5Byml0xv9zKNHj3L8+PFrrrskSZIkSZ0kIr623rmdHOr/MuCRlNJjKaUG8D7g9ouuuR34zZT5BDAcEYe3u6KSJEmSJO1WOxn4HwGeatk/kR/b6DUJ+OOIeCAi7lzvQyLizog4HhHHx8fH21BtSZIkSZJ2j50M/GONY2kT13xzSuklZI8DvCUiXrHWh6SU7kkpHUspHTt4cM3HHSRJkiRJ6lo7GfifAG5s2b8BeGaj16SUltZjwO+RPTogSZIkSZJa7GTg/2ngeRFxS0RUgNcB9110zX3AGyLzcuBCSulkRPRFxABARPQB3wU8uJ2VlyRJkiRpN9ixWf1TSs2I+FHgj8he5/fulNIXI+JH8vPvAj5C9iq/R8he5/fG/PZDwO9lb/ujBLw3pfSH2/wVJEmSJEnqeJHSxY/Vd69jx44lX+cnSZIkSeo2EfFASunYWud2cqi/JEmSJEnaYgb+kiRJkiR1MQN/SZIkSZK6mIG/JEmSJEldzMBfkiRJkqQuZuAvSZIkSVIXM/CXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXax0uZMRsf9y51NKZ9tbHUmSJEmS1E6XDfyBB4AEBHATcC7fHgaeBG7ZyspJkiRJkqRrc9mh/imlW1JKtwJ/BPxPKaUDKaUR4HuBD21HBSVJkiRJ0tXb6DP+fzel9JGlnZTSHwDfujVVkiRJkiRJ7XKlof5LTkfEvwX+G9nQ/38KnNmyWkmSJEmSpLbY6C/+PwgcBH4P+O/AaH5MkiRJkiR1sA394p/P3v/WLa6LJEmSJElqsyu9zu+XUko/HhG/TzbEf5WU0vdtWc0kSZIkSdI1u9Iv/v81X//8Vnx4RHwP8A6gCPx6Sunui85Hfv7VwAzwwymlz2zk3rV84ekLfPPdf8ZPfvcL+P5vOHLV9f7vf/M0//cfPcQz52e5frjH8nawvE6um+VZnuXt3vI6uW6WZ3mWt33ldXLdLM/yLG/7yuvkurWWV7nuuS9d75pI6ZIf8i8rIvYBN6aUPn/VNcvKKQJfBb4TOAF8GvjBlNKXWq55NfBjZIH/NwLvSCl940buXUv18PPS4Tt+iZ5ykf/rH73oqv5w//vfPM3bP/QFZucXlo9Z3s6U18l1szzLs7zdW14n183yLM/ytq+8Tq6b5Vme5W1feZ1ct4vLO3nvj1M/+XCsdd2GAv+IuB/4PrIRAp8FxoG/SCn9q03XbKXMbwL+fUrpu/P9twOklP6vlmt+Dbg/pfTb+f5DwCuBo1e6dy1LgT9AsX6BG//mnk3X+6lvuJOF6tAlxy1v+8vr5LpZnuVZ3u4tr5PrZnmWZ3nbV14n183yLM/ytq+8Tq7bxeVdLvDf6Kz+QymlCeAfAe9JKb0U+I5N12q1I8BTLfsn8mMbuWYj9wIQEXdGxPGION56fKEyeFWVXu8+y9v+8jq5bpZneZa3e8vr5LpZnuVZ3vaV18l1szzLs7ztK6+T67aZ+zY0qz9QiojDwGuBn7qqGl1qrUzExcMP1rtmI/dmB1O6B7gHsl/8l44f2dfL/fffv6GKtvrmu/+Mp8/PXnLc8ra/vE6um+VZnuXt3vI6uW6WZ3mWt33ldXLdLM/yLG/7yuvkul2uvItt9Bf//wD8EfBoSunTEXEr8PCma7XaCeDGlv0bgGc2eM1G7l1XT7nIT373CzZV2SU/+d0voKdctLwOKK+T62Z5lmd5u7e8Tq6b5Vme5W1feZ1cN8uzPMvbvvI6uW7rlbeWDf3in1L6IPDBlv3HgH98VTVb8WngeRFxC/A08Drghy665j7gRyPifWST+11IKZ2MiPEN3LumI9c4a+LSfe2ahdHyrr68Tq6b5Vme5e3e8jq5bpZneZa3feV1ct0sz/Isb/vK6+S6XVzeyctct9HJ/Z4P/CpwKKX0woj4OuD7Ukr/6apqt1Luq4FfInsl37tTSj8TET8CkFJ6V/46v3cC30P2Or83ppSOr3fvlT7v2LFj6fjx41e6TJIkSZKkXSUiHkgpHVvz3AYD/78AfhL4tZTSN+THHkwpvbCtNd1iBv6SJEmSpG50ucB/o8/496aUPnXRsea1VUuSJEmSJG21jQb+pyPiOeQz50fEP4HLPkIgSZIkSZI6wEZf5/cWslfi/a2IeBp4HHj9ltVKkiRJkiS1xUZn9X8M+I6I6CMbJTAL/ADwtS2smyRJkiRJukaXHeofEYMR8faIeGdEfCfZzPp3AI8Ar92OCkqSJEmSpKt3pV/8/ytwDvg48L8A/xtQAb4/pfTZra2aJEmSJEm6VlcK/G9NKb0IICJ+HTgN3JRSmtzymkmSJEmSpGt2pVn955c2UkoLwOMG/ZIkSZIk7R5X+sX/6yNiIt8OoCffDyCllAa3tHaSJEmSJOmaXDbwTykVt6sikiRJkiSp/a401F+SJEmSJO1iBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC5m4C9JkiRJUhcz8JckSZIkqYsZ+EuSJEmS1MUM/CVJkiRJ6mIG/pIkSZIkdTEDf0mSJEmSupiBvyRJkiRJXczAX5IkSZKkLmbgL0mSJElSFzPwlyRJkiSpixn4S5IkSZLUxQz8JUmSJEnqYgb+kiRJkiR1sR0J/CNif0T8SUQ8nK/3rXPd90TEQxHxSETc1XL830fE0xHx2Xx59fbVXpIkSZKk3WOnfvG/C/hoSul5wEfz/VUiogj8CvAq4DbgByPitpZL/nNK6cX58pHtqLQkSZIkSbvNTgX+twP35tv3At+/xjUvAx5JKT2WUmoA78vvkyRJkiRJG7RTgf+hlNJJgHw9usY1R4CnWvZP5MeW/GhEfD4i3r3eowIAEXFnRByPiOPj4+PtqLskSZIkSbvGlgX+EfGnEfHgGstGf7WPNY6lfP2rwHOAFwMngV9Yr5CU0j0ppWMppWMHDx7czFeQJEmSJGnXK21VwSml71jvXESciojDKaWTEXEYGFvjshPAjS37NwDP5GWfainrvwD/oz21liRJkiSpu+zUUP/7gDvy7TuAD69xzaeB50XELRFRAV6X30eeLFjyPwMPbmFdJUmSJEnatSKldOWr2v2hESPAB4CbgCeB16SUzkbE9cCvp5RenV/3auCXgCLw7pTSz+TH/yvZMP8EPAH8i6U5A67wuZPAQ+3+PrpqB4DTO10JAbZFp7E9Oodt0Vlsj85ie3QO26Kz2B6dZS+1x80ppTWfb9+RwH+nRMTxlNKxna6HMrZH57AtOovt0Tlsi85ie3QW26Nz2BadxfboLLZHZqeG+kuSJEmSpG1g4C9JkiRJUhfba4H/PTtdAa1ie3QO26Kz2B6dw7boLLZHZ7E9Oodt0Vlsj85ie7DHnvGXJEmSJGmv2Wu/+EuSJEmStKfs6sA/It4dEWMR8WDLsa+PiI9HxBci4vcjYjA//vqI+GzLshgRL87PvTS//pGI+OWIiB36SrtaG9vj/oh4qOXc6A59pV1tk+1Rjoh78+Nfjoi3t9xj/7hGbWwL+0YbbLI9KhHxnvz45yLilS332DeuURvbwr7RBhFxY0T8ef7fni9GxFvz4/sj4k8i4uF8va/lnrfnfeChiPjuluP2j2vQ5rawf1yjzbZHRIzk109FxDsvKsu+cY3a3B57p3+klHbtArwCeAnwYMuxTwPfmm+/CfiPa9z3IuCxlv1PAd8EBPAHwKt2+rvtxqWN7XE/cGynv89uXzbTHsAPAe/Lt3uBJ4Cj+b79o3Pawr6x/e3xFuA9+fYo8ABQyPftG53TFvaN9rTHYeAl+fYA8FXgNuDngLvy43cBP5tv3wZ8DqgCtwCPAsX8nP2jc9rC/rH97dEHfAvwI8A7LyrLvtFZ7bFn+seu/sU/pfQx4OxFh18AfCzf/hPgH69x6w8Cvw0QEYeBwZTSx1PW+r8JfP+WVLjLtaM91D6bbI8E9EVECegBGsCE/aM92tEW21HPvWKT7XEb8NH8vjHgPHDMvtEe7WiLra/l3pFSOplS+ky+PQl8GTgC3A7cm192Lyt/128nS1TWU0qPA48AL7N/XLt2tcW2VrqLbbY9UkrTKaW/BOZay7FvtEe72mOv2dWB/zoeBL4v334NcOMa1/wAK4HmEeBEy7kT+TG1x2bbY8l78uE2P+0QqLZarz1+B5gGTgJPAj+fUjqL/WMrbbYtltg3tsZ67fE54PaIKEXELcBL83P2ja2z2bZYYt9oo4g4CnwD8EngUErpJGT/4CYbcQHZ3/mnWm5b6gf2jza6xrZYYv9okw22x3rsG212je2xZE/0j24M/N8EvCUiHiAb+tFoPRkR3wjMpJSWnidcq3F91UH7bLY9AF6fUnoR8Pfz5Z9tV2X3gPXa42XAAnA92RDBfx0Rt2L/2EqbbQuwb2yl9drj3WT/MDsO/BLw10AT+8ZW2mxbgH2jrSKiH/hd4MdTSpcbcbReP7B/tEkb2gLsH22zifZYt4g1jtk3rlIb2gP2UP/ousA/pfSVlNJ3pZReSvYr8qMXXfI6Vv+6fAK4oWX/BuCZra3l3nEV7UFK6el8PQm8F4eqtc1l2uOHgD9MKc3nQ2j/imwIrf1ji1xFW9g3ttB67ZFSaqaUfiKl9OKU0u3AMPAw9o0tcxVtYd9oo4gok/1D+rdSSh/KD5/KhygvDVUey4+fYPWoi6V+YP9ogza1hf2jTTbZHuuxb7RJm9pjT/WPrgv8l2ZijIgC8G+Bd7WcK5ANG3zf0rF8GMhkRLw8H9rxBuDD21rpLrbZ9siHcB7It8vA95IN+1QbXKY9ngS+LTJ9wMuBr9g/ts5m28K+sbXWa4+I6M3bgYj4TqCZUvqSfWPrbLYt7Bvtk/9d/g3gyymlX2w5dR9wR759Byt/1+8DXhcR1fzxi+cBn7J/XLt2tYX9oz2uoj3WZN9oj3a1x57rHxuZAbBTF7JfAk4C82QZtDcDbyWb2fGrwN1AtFz/SuATa5RzjKyRHwXe2XqPy/a2B9msmw8Anwe+CLyDfFZal61rD6Af+GD+Z/4l4CdbyrF/dEBb2Dd2rD2OAg+RTRz0p8DNLeXYNzqgLewbbW2PbyEbdvx54LP58mpghGxixYfz9f6We34q7wMP0TI7uf2jM9rC/rGj7fEE2eSlU/l/327Lj9s3OqQ99lr/WPqfqSRJkiRJ6kJdN9RfkiRJkiStMPCXJEmSJKmLGfhLkiRJktTFDPwlSZIkSepiBv6SJEmSJHUxA39JkiRJkrqYgb8kSZIkSV3MwF+SJEmSpC72/wfQRNm7uX6aJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seasonal decomposition\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "decompose = seasonal_decompose(TS['Average_Index'], period = 1) #no seasonality\n",
    "\n",
    "fig = plt.figure()  \n",
    "fig = decompose.plot() \n",
    "fig.set_size_inches((16, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1cc9330f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.9687451690523868\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "result = adfuller(TS['Average_Index'], regression = 'ct') #Tests for stationarity assuming a constant mean and a linear trend.\n",
    "print('p-value: ' +str(result[1]))\n",
    "\n",
    "#null hypothesis = data is not stationary\n",
    "#alternative = data is stationary\n",
    "\n",
    "#fail to reject null, data is not stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa5aa22",
   "metadata": {},
   "source": [
    "## Model 1 = Holts Linear Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1bcec97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "data": {
      "image/png": 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hhIUhhEH1qUOSJDV9BkmSJKmxXRBj7ACMAkqAbx2y/jXgioNvQggZwIeBZXW2+RYwBhgHdATOBGYf7jx1Hp+rZ32VwF3AVfXYdjdwJdAFyAV+BDyYrLm+lgMfO/gmhHAy0O7QjZJhzWXAVup8PnVMPeR6O8QY19azhqXA14GH6rHttcBFwEhgBHA+8Kk6dV5N4rN7H9AhuX5zPeuQJElNnEGSJElKiRjjeuBREoFSXQ8CE0IIucn35wJzgfV1thkL3BtjXBsTymOMtzdQXYtjjH8C5tdj2/3J7WuAAFSTCJTyjuKUdwCX13l/BXC4a5kEdAe+CHw0hNDmKM5xRDHG22KM/wZ21WPzK4CfxRhXxxjXAD8DpgCEENKA7wJfjjEuSH43y2KMWxuqVkmSlFoGSZIkKSVCCD2B80iMhqlrP/AA8NHk+8t5a7AyDfhKCOGzIYSTD51a9Q7n7Z2chtb7GEs/3DHn8kbdf4wxbjyK3acBnUIIQ0II6cAlwJ8Ps90VJEK2vyffn38U9f06hPDro6jpSIYBc+q8n5NcBtAz+RgeQliVnN72/WTAJEmSWgD/py5JkhrbfSGEXcAqYCOJESyHuh24PITQGTgduO+Q9T8gMY3sUmAGsCaEcOh0r/uSgdHBxzUAMcaVMcacGOPKhrqgGOMIoBPwceCFYzjEwVFJZwOLgDV1V4YQsklM77szxlgJ3M1bp7edesj11k4FjDF+Nsb42WOo63A6ADvqvN8BdEiGeT2Ty84BTiYx5fBj1G+aoCRJagYMkiRJUmO7KMbYETgDOAkoOHSDGOMLJPoO/RfwrxjjvkPWV8cYb4oxTgBygP8Dbg4hDDnkPDl1Hn84MZdTW9P+GONfgW+GEEYe5e53kAihpnD4aW0fAKqAh5Pv/wKcF0LoUmebaYdcb/+jrKG+dpMIzQ7qBOyOMUbg4Pf04xjj9hhjOfA74L0nqBZJktTIDJIkSVJKxBifBW4Ffvo2m/wZ+A8OH6zUPc6+GONNwDZgaEPWeIwygX5Hs0OMcQWJptvvBf55mE2uIDESaGUIYT3wj+R5PnaYbU+0+SQabR80kjf6SS0GDgBHvNudJElqvgySJElSKl0PnB1CGHWYdTeSmOr13KErQghfCiGcEUJoF0LISE5r68hb79x21JK3t88C2iTfZ4UQ2r7NtqeGECaGENoka/kGUAS8nFx/RgihvqHKVcC7Yox7DjlHD+AsEj2RRiUfI0lM7Tvc3duOWgghM3nNaUBG8prT32bz20n0p+oRQuhOIuy7FSDGuJdED6evhxA6JvtgXQP8qyHqlCRJqWeQJEmSUibGuIlEMPGdw6zbGmN8Mjll6lD7SNwtbD2JW8tfB1wcY3y9zjYPhhB213ncC7XNtncfodl2n+TxD46y2UdipA3J/f8dQvh28m1b4CZgC4m+Ru8F3hdjXJtc3wuYeuRPofZ6l8UYZxxm1WVAWYzxsRjj+oMPEkHbiBDC8OR2px1yvbtDCGOTNf82hPDbI5z+D8nr/Bjwn8nXlyX3nRRC2F1n29+RaPr9KjAPeCi57KDPkZj+tjZ57XcCN9fnM5AkSU1fOPzfzSRJknS8Qgh/BP4RY3w01bVIkiQ1BIMkSZIkSZIk1YtT2yRJkiRJklQvBkmSJEmSJEmqF4MkSZIkSZIk1UtGqgs4HgUFBbG4uDjVZUiSJEmSJLUYM2fO3Bxj7HK4dc06SCouLmbGjMPdJVeSJEmSJEnHIoSw4u3WObVNkiRJkiRJ9WKQJEmSJEmSpHoxSJIkSZIkSVK9NOseSZIkSZIk6dhUVlayevVq9u/fn+pSlCJZWVn07NmTzMzMeu9jkCRJkiRJUiu0evVqOnbsSHFxMSGEVJejRhZjZMuWLaxevZq+ffvWez+ntkmSJEmS1Art37+f/Px8Q6RWKoRAfn7+UY9IM0iSJEmSJKmVMkRq3Y7l+zdIkiRJkiRJUr0YJEmSJEmSpEa3fft2fv3rXx/1fu9973vZvn17wxekejFIkiRJkiRJje7tgqTq6uoj7vfwww+Tk5NzgqrSO/GubZIkSZIktXLff3A+C9bubNBjDu3eie9eMOxt13/zm99k2bJljBo1iszMTDp06EC3bt0oKytjwYIFXHTRRaxatYr9+/fzxS9+kWuvvRaA4uJiZsyYwe7duznvvPOYOHEiL730Ej169OD++++nXbt2DXodejNHJEmSJEmSpEb3wx/+kP79+1NWVsZPfvITXnnlFf7v//6PBQsWAHDzzTczc+ZMZsyYwY033siWLVvecowlS5Zw3XXXMX/+fHJycrjnnnsa+zJaHUckSZIkSZLUyh1p5FBjGTduHH379q19f+ONN3LvvfcCsGrVKpYsWUJ+fv6b9unbty+jRo0CYPTo0ZSXlzdWua2WQZIkSZIkSUq59u3b175+5plneOKJJ5g6dSrZ2dmcccYZ7N+//y37tG3btvZ1eno6+/bta5RaWzOntkmSJEmSpEbXsWNHdu3addh1O3bsIDc3l+zsbBYtWsS0adMauTq9nRMWJIUQbg4hbAwhzKuzLC+E8HgIYUnyObfOum+FEJaGEBaHEN5zouqSJEmSJEmpl5+fz4QJExg+fDhf+9rX3rTu3HPPpaqqihEjRvCd73yHU089NUVV6lAhxnhiDhzCZGA3cHuMcXhy2Y+BrTHGH4YQvgnkxhi/EUIYCvwVGAd0B54ABsUYj3jPv9LRo+OsmTNPSP2SJEmSJLVkCxcuZMiQIakuQyl2uJ+DEMLMGOOYw21/wnokxRifCyEUH7L4/cAZyde3Ac8A30gu/1uMsQJYHkJYSiJUmnqkc8xfu5NzfvEso3rlMKpXLqN65TCoqAMZ6c7YkyRJkiRJamiN3Wy7KMa4DiDGuC6EUJhc3gOoO+FxdXLZW4QQrgWuBcjr0ZceOe14fMEG7pqxGoB2memc3LMzJb1yEgFT7xy6dW53oq5HkiRJkiSp1Wgqd20Lh1l22Dl3McbfA78HGDNmTLzlk+OIMbJy617KVm1n9srtlK3azi0vlnOgugaAok5tGdkzESqN6pXDiJ45dGjbVC5dkiRJkiSpeWjsNGVDCKFbcjRSN2BjcvlqoFed7XoCa+t70BACffLb0ye/Pe8flRjIVFFVzcJ1uyhbuY2yVYlw6bEFGwBICzCwsGPtiKXElLiOpKcdLs+SJEmSJEkSNH6Q9ABwBfDD5PP9dZbfGUL4OYlm2wOBV47nRG0z0pO9k3Jql23bc4Cy1dspS45aenTBev4+YxUA2W3SOblHZ0b1zqGkVw4lvXMp6pR1PCVIkiRJkiS1KCcsSAoh/JVEY+2CEMJq4LskAqS7QghXASuBDwPEGOeHEO4CFgBVwHXvdMe2Y5Hbvg1nDi7kzMGJ1kwxRsq37KVs1bbacOnmF5ZTWZ2YVde/S3smD+rC6YO6cGq/fLIy0xu6JEmSJEmSpGYjxHjYVkTNwpgxY+KMGTMa9Jj7K6tZsG4ns1Zs47klm3n59S1UVNXQJiONU/rmcXoyWBpQ2IEQnAonSZIkSWqeDnfb98a0fft27rzzTj772c8e9b7XX3891157LdnZ2SegstblcD8HIYSZMcYxh9veIOkd7K+s5uXlW3l28SaeW7KJpRt3A9CtcxanD+rC5EFdmDCggM7tMk9oHZIkSZIkNaRUB0nl5eWcf/75zJs376j3LS4uZsaMGRQUFJyAylqXow2SvHXZO8jKTK8dhQSwZvs+nnttE88u3sRDc9fxt+mrSE8LjOqVUxssndyjs427JUmSJEnNx7+/Cetfbdhjdj0Zzvvh267+5je/ybJlyxg1ahRnn302hYWF3HXXXVRUVPCBD3yA73//++zZs4ePfOQjrF69murqar7zne+wYcMG1q5dy5lnnklBQQFPP/10w9atIzJIOko9ctrxsXG9+di43lRW11C2ansiWHptE7944jV+/vhr5GZnMnFgInyaPLCAQpt2S5IkSZL0Jj/84Q+ZN28eZWVlPPbYY9x999288sorxBi58MILee6559i0aRPdu3fnoYceAmDHjh107tyZn//85zz99NOOSEoBg6TjkJmextjiPMYW5/Ef5wxmy+4KXli6OTkNbjMPzlkLwJBunZg8qIDTB3VhTJ882mSkpbhySZIkSZLqOMLIocbw2GOP8dhjj1FSUgLA7t27WbJkCZMmTeKrX/0q3/jGNzj//POZNGlSSuuUQVKDyu/QlveP6sH7R/WgpiayYN1OnluSmAb3p+eX87tnXye7TTrj++czeVAXJg3sQnF+tk27JUmSJEmtWoyRb33rW3zqU596y7qZM2fy8MMP861vfYtzzjmH//7v/05BhTrIIOkESUsLDO/RmeE9OvPZMwawa38lU5dtSQRLr23iiYUbAeiZ245JAxNT4MbbtFuSJEmS1Ep07NiRXbt2AfCe97yH73znO1x66aV06NCBNWvWkJmZSVVVFXl5eXziE5+gQ4cO3HrrrW/a16ltjc8gqZF0zMrknGFdOWdYV2KMrNiyl+eXvDEF7q+vrCQtwKheOYlgaVABI3vmkJHuNDhJkiRJUsuTn5/PhAkTGD58OOeddx4f//jHOe200wDo0KEDf/7zn1m6dClf+9rXSEtLIzMzk9/85jcAXHvttZx33nl069bNZtuNLMQYU13DMRszZkycMWNGqss4bgebdj//2iaeXbKZuau3EyN0bJvB+AH5TEo27u6Vl53qUiVJkiRJLcThbvuu1udwPwchhJkxxjGH294RSU1A3abdXzlnMNv3HuDFpVsSI5Ze28Sj8zcAUJyfzaSBXZg0sIDT+ufTMctpcJIkSZIkqfEYJDVBOdlteN+IbrxvRDdijLy+eQ/Pv7aJ55ds5p5Zq7lj2grS0wKlvXNqg6URPXNIT7NptyRJkiRJOnEMkpq4EAL9u3Sgf5cOTJnQlwNVNcxauY3nlySCpV888Ro/f/w1OrfLZMKAfCYP7MLpg7vQrXO7VJcuSZIkSZJaGIOkZqZNRhqn9svn1H75fO09sHXPAV5cupnnkiOWHn51PQBji3O5YGR3zhvejS4d26a4akmSJEmS1BIYJDVzee3bcMHI7lwwsjsxRpZs3M1j89fz4Jx1/Pf98/neA/MZ37+AC0Z24z3DupKT3SbVJUuSJEmSpGbKIKkFCSEwqKgjg4o68rl3DWTx+l38a+5aHpyzlm/c8yr/dd88Jg/swvkju3H20K50aOvXL0mSJEmS6i8t1QXoxBnctSP/cc5gnv7qGTz4uYl8ckJfFq7byZf/PofR//M4n/nzTB5+dR37K6tTXaokSZIkqZUpLy9n+PDhR7XPlClTuPvuuwG4/vrr2bt372G3O+OMM5gxYwYA733ve9m+fftx1Xq0vva1rzFs2DC+9rWvNep5386tt97K2rVrG+RYDklpBUIInNyzMyf37Mw3zz2J2au28eCcdfxr7jr+PW897dukc/bQIi4Y2Z1JA7vQJsN8UZIkSZLUtF1//fV84hOfIDs7+4jbPfzwwye0jqqqKjIy3hyv/O53v2PTpk20bVu/nsWHO0ZDuvXWWxk+fDjdu3c/7mMZJLUyaWmB0X3yGN0nj++cP5SXX9/Cg3PX8u9567mvbC2dsjI4d3hXLhjZndP65ZORbqgkSZIkSS3dj175EYu2LmrQY56UdxLfGPeNI25TXV3NNddcw0svvUSPHj24//77adeuHWVlZXz6059m79699O/fn5tvvpnc3Nza/W688UbWrl3LmWeeSUFBAU8//fTbnqO4uJgZM2awe/duzjvvPCZOnPiW8y1btozrrruOTZs2kZ2dzR/+8AdOOukkHnzwQf73f/+XAwcOkJ+fz1/+8heKior43ve+x9q1aykvL6egoIA777yz9nwXXnghe/bs4ZRTTuFb3/oWp556KldeeSWbNm2iS5cu3HLLLfTu3ZspU6aQl5fH7NmzKS0t5bOf/exha9iwYQOf/vSnef311wH4zW9+w/jx47noootYtWoV+/fv54tf/CLXXnst1dXVXHXVVcyYMYMQAldeeSW9evVixowZXHrppbRr146pU6fSrt2x3+ndlKAVS08LjB9QwA8+OILp//lubvnkWN49tIiHX13PZX96hVP+vyf5zn3zeGX5VmpqYqrLlSRJkiS1MEuWLOG6665j/vz55OTkcM899wBw+eWX86Mf/Yi5c+dy8skn8/3vf/9N+33hC1+ge/fuPP3000cMkep7vmuvvZZf/vKXzJw5k5/+9Kd89rOfBWDixIlMmzaN2bNn89GPfpQf//jHtceaOXMm999//5tCJIAHHnigNgy75JJL+NznPsfll1/O3LlzufTSS/nCF75Qu+1rr73GE088wc9+9rO3reELX/gCp59+OnPmzGHWrFkMGzYMgJtvvpmZM2cyY8YMbrzxRrZs2UJZWRlr1qxh3rx5vPrqq3zyk5/kQx/6EGPGjOEvf/kLZWVlxxUigSOSlJSZnsaZgws5c3Ah+yureWbxJh6cu5Z/zFzFHdNW0LVTFu8b0Y33j+rOyT06E0JIdcmSJEmSpAbyTiOHTpS+ffsyatQoAEaPHk15eTk7duxg+/btnH766QBcccUVfPjDHz5h59u9ezcvvfTSm85RUVEBwOrVq7nkkktYt24dBw4coG/fvrXbXHjhhfUKZaZOnco///lPAC677DK+/vWv16778Ic/THp6+hFreOqpp7j99tsBSE9Pp3PnzkBiVNa9994LwKpVq1iyZAmDBw/m9ddf5/Of/zzve9/7OOecc476M3onBkl6i6zMdM4d3pVzh3dlT0UVTy7ayINz1nLH1BX86YXljOjZmSnji3nfiG60zUhPdbmSJEmSpGaqbg+h9PR09u3b1+jnq6mpIScnh7Kysrds//nPf56vfOUrXHjhhTzzzDN873vfq13Xvn37Y6qh7sCMg8c4Ug2H88wzz/DEE08wdepUsrOzOeOMM9i/fz+5ubnMmTOHRx99lJtuuom77rqLm2+++ZjqfDtObdMRtW+bwYUju/OHy8cw/b/ezf+8fxh7Kqr4yl1zmPDDp/jZY4tZv2N/qsuUJEmSJLUQnTt3Jjc3l+effx6AO+64o3Z0Ul0dO3Zk165dx32+Tp060bdvX/7xj38AEGNkzpw5AOzYsYMePXoAcNtttx3T8cePH8/f/vY3AP7yl78wceLEo6rhrLPO4je/+Q2Q6Cm1c+dOduzYQW5uLtnZ2SxatIhp06YBsHnzZmpqarj44ov5n//5H2bNmgU03GcFBkk6Cp3bZXLZacU88ZXTueOqcYzqlcOvnl7KxB89xefunMXMFVuJ0V5KkiRJkqTjc9ttt/G1r32NESNGUFZWxn//93+/ZZtrr72W8847jzPPPPO4z/eXv/yFP/3pT4wcOZJhw4Zx//33A/C9732PD3/4w0yaNImCgoJjOvaNN97ILbfcwogRI7jjjju44YYbjqqGG264gaeffpqTTz6Z0aNHM3/+fM4991yqqqoYMWIE3/nOdzj11FMBWLNmDWeccQajRo1iypQp/OAHPwBgypQpfPrTn2bUqFHHPeorNOdf/MeMGRNnzJiR6jJatZVb9nLHtHL+Nn0Vu/ZXMbxHJ644rZgLRnYnK9Npb5IkSZLUVC1cuJAhQ4akugyl2OF+DkIIM2OMYw63vSOSdFx652fzn+8bysvfPov/+8BwKipr+Nrdcxn/w6f4yaOLWLfjxM5vlSRJkiRJjcdm22oQ2W0yuPSUPnx8XG+mLtvCrS+V85tnlvHbZ1/n3GFduWJ8MWOLc73bmyRJkiRJzZhBkhpUCIHxAwoYP6CAVVv38udpK/jb9FU89Oo6hnTrxCfHF3PhKKe9SZIkSVJTEGP0H/xbsWNpd2SPJJ1w+w5Uc1/ZGm57qZxF63eRm53JJWN7c9lpfeiR0y7V5UmSJElSq7R8+XI6duxIfn6+YVIrFGNky5Yt7Nq1i759+75p3ZF6JBkkqdHEGHl5+VZufbGcxxasB+CcoYlpb6f2y/M/XJIkSZLUiCorK1m9ejX79+9PdSlKkaysLHr27ElmZuablhskqclZvW0vf562kr9NX8n2vZWc1LUjV4wv5gMlPZz2JkmSJElSChkkqcnaX1nNA2VrueWlchau20lhx7Z8+vT+fPyU3gZKkiRJkiSlgEGSmrwYI1OXbeGGJ5fw8vKtFHRoy6dP78fHT+lNdht7wkuSJEmS1FgMktSsTHt9C798agkvLt1Cfvs2XDO5H5ed2of2bQ2UJEmSJEk60QyS1CzNKN/KDU8u4fklm8nNzuTqSf24/LQ+dMzKfOedJUmSJEnSMTFIUrM2a+U2fvnkEp5evInO7TK5emJfrphQTCcDJUmSJEmSGpxBklqEuau3c+OTS3hi4UY6ZWVw5cS+fHJ8XzpnGyhJkiRJktRQDJLUosxbs4Mbn1zCYws20LFtBlMmFHPlhL7ktm+T6tIkSZIkSWr2DJLUIi1Yu5NfPb2Eh19dT/s26VwxvpirJ/Ujz0BJkiRJkqRjZpCkFm3x+l388qklPPTqOtplpnPZqX24ZnI/Cjq0TXVpkiRJkiQ1OwZJahWWbtzFr55aygNz1tImI41PnNKHa0/vR2HHrFSXJkmSJElSs2GQpFZl2abd3PT0Uu4vW0tGWuBj43rzmTP6U9TJQEmSJEmSpHdikKRWqXzzHn79zFLumbWG9LTApyb34wtnDSQzPS3VpUmSJEmS1GQdKUjyN2q1WMUF7fnxh0byzFfP4H0nd+OXTy3l4t+8xOubdqe6NEmSJEmSmiWDJLV4vfKy+cUlo/jNpaWs3LqX9934Ane+vJLmPBpPkiRJkqRUMEhSq3Heyd145IuTGd0nl2/f+yrX3D6TLbsrUl2WJEmSJEnNhkGSWpWunbO4/cpxfOf8oTy3ZBPvuf55nl60MdVlSZIkSZLULBgkqdVJSwtcNbEvD3xuAgUd2vDJW6fz3/fPY9+B6lSXJkmSJElSk5aSICmE8MUQwrwQwvwQwpeSy/JCCI+HEJYkn3NTUZtaj5O6duK+6yZw1cS+3D51BRf86gXmrdmR6rIkSZIkSWqyGj1ICiEMB64BxgEjgfNDCAOBbwJPxhgHAk8m30snVFZmOt85fyh/vuoUdu2v5AO/fpHfPLOM6hobcUuSJEmSdKhUjEgaAkyLMe6NMVYBzwIfAN4P3Jbc5jbgohTUplZq4sACHvniZN49pIgfPbKIj/9hGmu270t1WZIkSZIkNSmpCJLmAZNDCPkhhGzgvUAvoCjGuA4g+Vx4uJ1DCNeGEGaEEGZs2rSp0YpWy5fbvg2/vrSUn3xoBPPW7ODc65/j/rI1qS5LkiRJkqQmo9GDpBjjQuBHwOPAI8AcoOoo9v99jHFMjHFMly5dTlCVaq1CCHx4TC/+/cXJDCzswBf/VsaX/jabHfsqU12aJEmSJEkpl5Jm2zHGP8UYS2OMk4GtwBJgQwihG0Dy2XuyK2V652dz16dO4ytnD+LBuet47w3PM+31LakuS5IkSZKklErVXdsKk8+9gQ8CfwUeAK5IbnIFcH8qapMOykhP4wtnDeTuT59GZnrgY3+Yxo8eWcSBqppUlyZJkiRJUkqkJEgC7gkhLAAeBK6LMW4DfgicHUJYApydfC+lXEnvXB76wiQuGdOL3zyzjA/+5kWWbtyd6rIkSZIkSWp0Icbme5vzMWPGxBkzZqS6DLUij85fzzfvmcu+ymr+871D+MSpfQghpLosSZIkSZIaTAhhZoxxzOHWpWpEktQsvWdYVx790mTG9c3nO/fP58pbp7NpV0Wqy5IkSZIkqVEYJElHqbBTFrdOGcv3LhjKi8u2cO71z/HEgg2pLkuSJEmSpBPOIEk6BmlpgSkT+vKvz0+ksFMWV98+g6/9Yw4791emujRJkiRJkk4YgyTpOAwq6sh9143nujP7c8+s1Zx3/fO8uHRzqsuSJEmSJOmEMEiSjlPbjHS+9p6TuOcz42mbkcalf3yZ794/j70HqlJdmiRJkiRJDcogSWogJb1zeegLk/jkhGJum7qC997wPDNXbE11WZIkSZIkNRiDJKkBtWuTzncvGMad15xCZXXkw7+dyg//vYiKqupUlyZJkiRJ0nEzSJJOgPH9C3jkS5P4yJhe/PbZZVz4yxeZt2ZHqsuSJEmSJOm4GCRJJ0jHrEx+ePEIbpkylm17D3DRTS9y45NLqKyuSXVpkiRJkiQdE4Mk6QQ786RCHvvyZN57cjd+/vhrXPybl1iyYVeqy5IkSZIk6agZJEmNICe7DTd+rISbPl7Kqq17ed8vX+CPz79OdU1MdWmSJEmSJNWbQZLUiN43ohuPffl0Jg/swv8+tJCP/X4aK7fsTXVZkiRJkiTVi0GS1Mi6dGzLHy4fzU8/PJKF63Zy7g3P8ZeXVxCjo5MkSZIkSU2bQZKUAiEEPjS6J49+eTKlvXP5z3vncfnNr7Bux75UlyZJkiRJ0tsySJJSqHtOO+64ahz/c9FwZpRv45xfPMc/Z612dJIkSZIkqUkySJJSLITAZaf24d9fnMTgoo585a45fPrPM9m8uyLVpUmSJEmS9CYGSVITUVzQnr9/6jS+/d6TeHrRJs75xXM8Mm9dqsuSJEmSJKmWQZLUhKSnBa6d3J9/fWEi3XOy+PSfZ/HFv81mw879qS5NkiRJkiSDJKkpGlTUkXs/O4EvvXsgD7+6jjN+8gzXP/Eaew9Upbo0SZIkSVIrZpAkNVGZ6Wl86d2DePIrZ/Cukwq5/oklnPGTZ7hr+iqqa2zGLUmSJElqfAZJUhPXOz+bmy4t5Z7PnEaP3HZ8/Z65vO/G53lhyeZUlyZJkiRJamUMkqRmYnSfPP75mfH86uMl7DlQxSf+9DJTbnmF1zbsSnVpkiRJkqRWwiBJakZCCJw/ojtPfOV0vv3ek5i5YhvnXv8c3773VTbtqkh1eZIkSZKkFs4gSWqG2makc+3k/jz7tTO5/LRi7pq+ijN+8jQ3Pb2U/ZXVqS5PkiRJktRCGSRJzVhe+zZ878JhPPblyUwYUMBPHl3MmT99hn/OWk2NDbklSZIkSQ3MIElqAfp16cDvLx/D3649lYIObfnKXXN4/00vMu31LakuTZIkSZLUghgkSS3Iqf3yuf+6CVx/ySi27K7go7+fxtW3zWDZpt2pLk2SJEmS1AIYJEktTFpa4KKSHjz11TP42nsGM+31LbznF8/x3fvnsXXPgVSXJ0mSJElqxgySpBYqKzOd684cwDNfO4OPjuvFn19eyek/eZrfPbvMhtySJEmSpGNikCS1cAUd2vK/F53MI1+cxNjiPH7w70W8++fP8sCctcRoQ25JkiRJUv0ZJEmtxMCijtw8ZSx/ufoUOmZl8oW/zuYDv36JJxdu8A5vkiRJkqR6Cc15RMKYMWPijBkzUl2G1OxU10T+OWs1P3/8Ndbt2E/fgvZ8ckIxF5f2pH3bjFSXJ0mSJElKoRDCzBjjmMOuM0iSWq/K6hr+PW89f3phOXNWbadTVgYfG9eby8cX0yOnXarLkyRJkiSlgEGSpHc0c8U2bn5xOY/MWw/AucO7cuWEvozuk5viyiRJkiRJjelIQZJzWCQBMLpPLqP75LJm+z5uf6mcO19ZyUNz1zGqVw5XTuzLecO7kpluWzVJkiRJas0ckSTpsPZUVHHPrNXc8mI5yzfvoVvnLC4/rZiPjetFTnabVJcnSZIkSTpBnNom6ZjV1ESeXryRm19czotLt9AuM52LR/dgyvi+DCjskOryJEmSJEkNzCBJUoNYtH4nN7+wnPvK1nKgqoYzBnfhqol9mTiggBBCqsuTJEmSJDUAgyRJDWrz7gr+Mm0ld0xbwebdFQwq6sCVE/pyUUkPsjLTU12eJEmSJOk4GCRJOiEqqqp5cM46bn5hOQvW7SSvfRsuPaU3l53ah8JOWakuT5IkSZJ0DAySJJ1QMUZeXr6VP72wnCcWbiAjLXDBiO5cPr6YkT07O+1NkiRJkpqRIwVJGY1djKSWJ4TAqf3yObVfPiu27OGWF8v5x4xV/HP2GoZ068THx/Xi/SU96JSVmepSJUmSJEnHwRFJkk6IXfsreWDOWu58eSXz1+6kXWY6F4zsxsfG9WZUrxxHKUmSJElSE+XUNkkpNXf1dv76ykruL1vL3gPVnNS1Ix8/pTfvH9WDzu0cpSRJkiRJTYlBkqQmYXdFFQ+UreWvr6zk1TU7yMpM44IR3fnYKb0pcZSSJEmSJDUJBkmSmpxXV+/gzldW8kDZGvYkRyl9bFxvLipxlJIkSZIkpZJBkqQma3dFFQ/OSYxSmrs6MUrpfSd35+On9KK0d66jlCRJkiSpkTW5ICmE8GXgaiACrwKfBLKBvwPFQDnwkRjjtiMdxyBJalnmrdlR20tpd0UVg4s68rFxvfhASU86ZztKSZIkSZIaQ5MKkkIIPYAXgKExxn0hhLuAh4GhwNYY4w9DCN8EcmOM3zjSsQySpJZpT0UV/5qbuOPbnNU7aJuRxvtGdOPj43ozuo+jlCRJkiTpRDpSkJTR2MXUOW+7EEIliZFIa4FvAWck198GPAMcMUiS1DK1b5vBJWN7c8nY3sxbs4O/TV/JfbPX8s9ZaxhY2IGPjevNxaWOUpIkSZKkxpaqqW1fBP4P2Ac8FmO8NISwPcaYU2ebbTHG3MPsey1wLUDv3r1Hr1ixopGqlpRKew9U8a8567jzlZWUrdpOp6wMvn7uSXxsXG/S0xyhJEmSJEkNpalNbcsF7gEuAbYD/wDuBn5VnyCpLqe2Sa3TvDU7+L+HFjL19S2M7JXD/100nOE9Oqe6LEmSJElqEY4UJKU1djHAu4HlMcZNMcZK4J/AeGBDCKEbQPJ5Ywpqk9QMDO/RmTuvOYUbPjqKNdv2ceGvXuC7989jx77KVJcmSZIkSS1aKoKklcCpIYTskOiYexawEHgAuCK5zRXA/SmoTVIzEULg/aN68OR/nM7lpxVzx7QVnPWzZ7lv9hpSMWVXkiRJklqDRg+SYowvk5jKNgt4NVnD74EfAmeHEJYAZyffS9IRdW6XyfcuHMYDn5tIj9x2fOnvZXz8Dy+zdOOuVJcmSZIkSS1OSpptNxR7JEmqq7om8rfpK/nRvxexr7Kaayb14/PvGki7NumpLk2SJEmSmo2m1iNJkk6I9LTApaf04amvnsH7R/Xg188s490/f5bHF2xIdWmSJEmS1CIYJElqcQo6tOWnHx7JXZ86jfZt07nm9hlcfdt0Vm3dm+rSJEmSJKlZM0iS1GKN65vHQ1+YxLffexIvLdvC2b94lpueXsqBqppUlyZJkiRJzZJBkqQWLTM9jWsn9+eJr5zOGYMK+cmjiznvhud4aenmVJcmSZIkSc2OQZKkVqF7Tjt+e9lobpkylsrqyMf/+DJf+ttsNu7an+rSJEmSJKnZMEiS1KqceVIhj315Ml84ayAPv7qes376LLe9VE51TfO9g6UkSZIkNRaDJEmtTlZmOl85exCPfnkyo3rn8N0H5nPhr16gbNX2VJcmSZIkSU2aQZKkVqtvQXtuv3IcN328lM27K/jAr1/k2/e+yva9B1JdmiRJkiQ1SQZJklq1EALvG9GNJ75yOldO6Mvfp6/irJ89y7/mriVGp7tJkiRJUl0GSZIEdMzK5DvnD+XBz02kR247PnfnbD7z51ls2lWR6tIkSZIkqckwSJKkOoZ278Q/PzOeb5x7Ek8t3sjZv3iW+2avcXSSJEmSJGGQJElvkZGexmfO6M/DX5hE34L2fOnvZVxz+ww27Nyf6tIkSZIkKaUMkiTpbQwo7MDdnx7Pf71vCC8s3cy7f/4sd81Y5egkSZIkSa2WQZIkHUF6WuDqSf349xcnM6RrJ75+91ym3DKdtdv3pbo0SZIkSWp0BkmSVA99C9rzt2tP5fsXDmN6+VbO+cVz3PnySkcnSZIkSWpVDJIkqZ7S0gJXjC/m0S9NZkTPznz73lf5xJ9eZtXWvakuTZIkSZIahUGSJB2lXnnZ/OXqU/i/DwxnzqodvOf657h9ajk1NY5OkiRJktSyGSRJ0jEIIXDpKX149MuTGd0nl/++fz4f+8M0yjfvSXVpkiRJknTCGCRJ0nHokdOO268cx48vHsGCdTs594bn+NMLy6l2dJIkSZKkFsggSZKOUwiBj4ztxeNfPp3x/Qv4n38t4CO/m8qyTbtTXZokSZIkNSiDJElqIF07Z/GnK8bwi0tGsnTjbs674Xl+++wyqqprUl2aJEmSJDUIgyRJakAhBD5Q0pPHvzKZMwd34Yf/XsTFv3mJ1zbsSnVpkiRJknTcDJIk6QQo7JjFbz8xml9+rIRV2/Zx/o0v8KunllDp6CRJkiRJzZhBkiSdICEELhjZnce/PJmzhxXx08de46KbXmT+2h2pLk2SJEmSjolBkiSdYPkd2nLTx0v57SdK2bCzggt/9SI/emQR+yurU12aJEmSJB0VgyRJaiTnDu/GE1+ZzMWlPfjNM8s49/rneGnZ5lSXJUmSJEn1ZpAkSY0oJ7sNP/7QSP5y9SlE4ON/eJlv3jOXHXsrU12aJEmSJL0jgyRJSoEJAwp45IuT+dTp/fjHzNW8+xfP8u9X16W6LEmSJEk6IoMkSUqRdm3S+dZ5Q7j/ugkUdmzLZ/4yi0/dMYMNO/enujRJkiRJOiyDJElKseE9OnP/dRP45nkn8cziTbz7Z89y58srqamJqS5NkiRJkt7EIEmSmoCM9DQ+fXp/Hv3SZIb36My3732Vj/5hGss27U51aZIkSZJUyyBJkpqQ4oL23HnNKfz44hEsWreT8254npueXkpldU2qS5MkSZIkgyRJampCCHxkbC+e+I/TOXtIET95dDEX/PIF5qzanurSJEmSJLVyBkmS1EQVdszipktL+f1lo9m29wAf+PWL/O+/FrD3QFWqS5MkSZLUShkkSVITd86wrjz+ldP5+Cm9+eMLyznnF8/x3GubUl2WJEmSpFbIIEmSmoFOWZn870Unc9enTqNNRhqX3/wKX7mrjG17DqS6NEmSJEmtiEGSJDUj4/rm8fAXJvH5dw3ggbK1vPvnz3J/2RpijKkuTZIkSVIrYJAkSc1MVmY6/3HOYP71hYn0zMvmi38r46rbZrBm+75UlyZJkiSphTNIkqRm6qSunfjnZ8bz3+cPZeqyLZzz82e56eml7K6wGbckSZKkE8MgSZKasfS0wJUT+/LYlydzWv98fvLoYib+6Cluenopu/ZXpro8SZIkSS1MaM59NcaMGRNnzJiR6jIkqckoW7WdG59cwlOLNtK5XSZXT+zLlAnFdMzKTHVpkiRJkpqJEMLMGOOYw64zSJKklmdOMlB6ctFGOmVlcNXEfnxyYjGdDJQkSZIkvQODJElqpV5dvYMbnlzCEws30DErgysn9OXKiX3p3M5ASZIkSdLhGSRJUis3b80ObnxyCY8tSARKn5zQl6sm9KVztoGSJEmSpDczSJIkATB/7Q5++eRSHpm/no5tM5gyoZirJvYlJ7tNqkuTJEmS1EQYJEmS3mThup388qklPPzqejq0zeCK8X24emI/ctsbKEmSJEmt3ZGCpLR67Jx1mGUFx1HM4BBCWZ3HzhDCl0IIeSGEx0MIS5LPucd6DknSkQ3p1olfXzqaR740idMHdeHXzyxj4o+e4kePLGLrngOpLk+SJElSE/WOI5JCCK8C18QYpyXfXwz8IMY46LhPHkI6sAY4BbgO2Bpj/GEI4ZtAbozxG0fa3xFJktQwXtuwixufXMJDr66jXWY6l53Wh2sn9SO/Q9tUlyZJkiSpkR3X1LYQwsnAzcAzQHcgH7g6xri6AQo7B/hujHFCCGExcEaMcV0IoRvwTIxx8JH2N0iSpIa1ZMMufvnUUh6cu5asjHQuP60P10zuR4GBkiRJktRqHHePpBDCRcAdwC5gcoxxaQMVdjMwK8b4qxDC9hhjTp1122KMb5neFkK4FrgWoHfv3qNXrFjREKVIkupYunE3v3pqCQ/MWUvbjHQ+cWpvrpncj8KOb5ntLEmSJKmFOd4RSX8C+gOfBAYB1wO/ijHedJxFtQHWAsNijBvqGyTV5YgkSTqxlm3azU1PLeW+sjWkhcC7hxRxydheTB7UhfS0kOryJEmSJJ0ARwqSMuqx/zwSU9kisDyEcCrw8wao6zwSo5E2JN9vCCF0qzO1bWMDnEOSdBz6d+nAzy8ZxefPGsidL6/gn7PW8Mj89XTrnMWHRvfkI2N60SsvO9VlSpIkSWok9Z3a1gcYGGN8IoTQDsiIMe46rhOH8Dfg0RjjLcn3PwG21Gm2nRdj/PqRjuGIJElqXAeqanhy4Qb+PmMVz762iRhhwoB8PjKmF+8Z1pWszPRUlyhJkiTpOB3v1LZrSPQkyosx9g8hDAR+G2M86zgKygZWAf1ijDuSy/KBu4DewErgwzHGrUc6jkGSJKXO2u37uHvmav4+fRVrtu+jc7tMPlDSg0vG9mJIt06pLk+SJEnSMTreIKkMGAe8HGMsSS57NcZ4ckMXerQMkiQp9WpqIi8u28zfp6/isfkbOFBdw4ienblkbC8uGNmdTlmZqS5RkiRJ0lE43h5JFTHGAyGEgwfLAN55PpwkqVVISwtMGtiFSQO7sG3PAe6dvYa/T1/Ff947j//51wLed3J3Lhnbi7HFuRz8f4kkSZKk5qk+QdKzIYRvA+1CCGcDnwUePLFlSZKao9z2bbhyYl8+OaGYOat38Pfpq3hwzlrumbWafgXt+cjYXnywtAeFHbNSXaokSZKkY1CfqW1pwFXAOUAAHgX+GOvTpfsEc2qbJDV9ew9U8dDcdfx9+ipmrNhGelrgrJMKuWRsL04f1IWM9LRUlyhJkiSpjuPqkdSUGSRJUvOydONu/jFjFffMWs3m3Qco6tSWD43uyUfG9KJPfvtUlydJkiSJYwySQgivcoReSDHGEQ1T3rEzSJKk5qmyuoYnF27k79NX8uxrm4jAmYMLmTK+mEkDC+ylJEmSJKXQsTbbPj/5fF3y+Y7k86XA3gaqTZLUCmWmp3Hu8K6cO7wr63bs46+vrOLOl1dw+c2v0L9Le6aML+aDpT1p37Y+rfwkSZIkNZb69Eh6McY44Z2WpYIjkiSp5aioquahueu45cVyXl2zg45ZGVwypheXn1ZM7/zsVJcnSZIktRrHOiLpoPYhhIkxxheSBxsP2MhCktSg2mak88HSnnygpAezVm7n1pfKufWlcv704nLOOqmIKycUc1r/fKe9SZIkSSlUnyDpKuDmEELn5PvtwJUnrCJJUqsWQmB0n1xG98ll/XuH8OdpK7jzlZU8sXADg4o6MGV8Xz5Q0oN2bdJTXaokSZLU6tT7rm0hhE7J7Xec2JLqz6ltktQ67K+s5sE5a7nlxXIWrNtJ53aZfHRcLy47tQ89c532JkmSJDWkY7prW52d2wIXA8XUGcEUY/x/DVjjMTFIkqTWJcbI9PJt3PrSch6dv4EYI+cM7cqUCcWc0jfPaW+SJElSAzjeHkn3AzuAmUBFQxYmSdLRCCEwrm8e4/rmsWb7Pv48bQV/fWUlj8xfz5Bunfjk+GIuHNWdrEynvUmSJEknQn1GJM2LMQ5vpHqOiiOSJEn7K6u5b/Yabn2pnEXrd5GbncnHxvXmE6f2oXtOu1SXJ0mSJDU7xzu17ffAL2OMr56I4o6HQZIk6aAYI9Ne38qtLy3n8QUbCCFw7rCufHJCMaP75DrtTZIkSaqn453aNhGYEkJYTmJqWwBijHFEA9YoSdJxCSFwWv98Tuufz6qte7lj2gr+9spKHnp1HcO6d+KK05z2JkmSJB2v+oxI6nO45THGFSekoqPgiCRJ0pHsPVDFvbPXcPtLK1i8YRc52ZlcMta7vUmSJElHckxT20IIeUc6aIxxawPUdlwMkiRJ9XFw2tvtU8t5bEHibm9nDSliyvhixvfPd9qbJEmSVMexTm2bCUQSU9kOFYF+DVCbJEknXN1pb2u37+MvL6/gr6+s4vEFGxhQ2IErTuvDB0p70qFtfWZ8S5IkSa3XO05ta8ockSRJOlb7K6t5aO46bptaztzVO+jYNoOLR/fk8tP60K9Lh1SXJ0mSJKXMcd21rSkzSJIkHa8YI2WrtnPbS+U89Oo6Kqsjkwd14YrT+nDG4ELS05z2JkmSpNbFIEmSpHrYtKuCv76ykr+8vIINOyvonZfNZaf24SNjetE5OzPV5UmSJEmNwiBJkqSjUFldw6Pz13P7Syt4pXwrWZlpfKCkB5efVsyQbp1SXZ4kSZJ0Qh13kBRCmAgMjDHeEkLoAnSIMS5v4DqPmkGSJOlEm792B3dMXcF9ZWvYX1nDuL55TBlfzNlDi8hMT0t1eZIkSVKDO64gKYTwXWAMMDjGOCiE0B34R4xxQsOXenQMkiRJjWX73gP8ffoq7pi2gtXb9tG1UxafOLU354/oTp/8bEKwl5IkSZJahuMNksqAEmBWjLEkuWxujHFEQxd6tAySJEmNrbom8tSijdw+tZznl2wGoKBDG8b0yWNMcS6j++QyrHtn2mQ4WkmSJEnN05GCpIx67H8gxhhDCDF5sPYNWp0kSc1Ielrg7KFFnD20iPLNe3hx2WZmlm9j+oqtPDJ/PQBZmWmM7JnD2OI8RhfnUto7l87tbNYtSZKk5q8+QdJdIYTfATkhhGuAK4E/nNiyJElq+ooL2lNc0J5LT+kDwIad+5lRvo0ZK7Yyo3wbv3l2GdVPR0KAwUUdGVOcy5g+eYzuk0vP3HZOh5MkSVKzU99m22cD5wABeDTG+PiJLqw+nNomSWrK9lRUMWfVdqYnw6XZK7ezu6IKgK6dshhdnMvYPrmMKc7jpK4dybB5tyRJkpqA475rW1NlkCRJak6qayKL1u9MjlraxozyrazbsR+A9m3SKemdWztqqaR3Du3b1mfgsCRJktSwjrfZ9i7g0I12ADOA/4gxvt4gVR4DgyRJUnO3Zvs+ZpRvrQ2XFq3fSYyQFuCkrp0Y1TuHUb0SjwFdOpCW5nQ4SZIknVjHGyR9H1gL3EliattHga7AYuAzMcYzGrTao2CQJElqaXbur2T2yu3MKE9MhZuzaju7ktPhOrTNYETPzozs9Ua4VNQpK8UVS5IkqaU53iDp5RjjKYcsmxZjPDWEMCfGOLIBaz0qBkmSpJaupiby+uY9lK3aTtmqbcxZtYOF63ZSVZP4/3e3zlmM6pVTGy6d3KOzU+IkSZJ0XI4UJNXnb5o1IYSPAHcn33+ozrrm22BJkqRmIC0tMKCwAwMKO/Ch0T0B2F9Zzfy1OylblRixVLZqO/+etz6xfYBBRR3fFC4NKupIulPiJEmS1ADqMyKpH3ADcBqJ4Gga8GVgDTA6xvjCiS7y7TgiSZKkhC27K5i7egez64RLO/ZVApDdJp3hPTpTUidc6tY5ixAMlyRJkvRW3rVNkqRWJsZI+Za9taHS7FXbWbh2JweqawAo6tSWU/vlM75/Pqf1K6BXXjuDJUmSJAHHObUthJAFXAUMA2o7esYYr2ywCiVJUoMKIdC3oD19C9pzUUkPACqqqlm4bhdzVm1nxoptvLh0C/eXrQWgR047TuufDJb659Otc7tUli9JkqQmqj49ku4AFgHvAf4fcCmw8EQWJUmSGl7bjPTau71dMb6YGCNLN+5m6utbeGnpFp5YuIG7Z64GoG9B+9oRS6f2y6dLx7Yprl6SJElNQX16JM2OMZaEEObGGEeEEDKBR2OM72qcEt+eU9skSWo4NTWRhet3MnXZFqYu28Iry7eyq6IKgEFFHTitXz6n9S/g1H555GS3SXG1kiRJOlGO965tlcnn7SGE4cB6oLiBapMkSU1EWlpgWPfODOvemasn9aOquoZ5axPB0kvLNnPXjNXcNnUFIcDQbp04rV8+4wfkM7Y4j45ZmakuX5IkSY2gPiOSrgbuAU4GbgU6AN+JMf7uhFf3DhyRJElS4zlQVcOc1dtrg6VZK7dzoKqG9LTAyT061/ZYGtMnj3Zt0lNdriRJko7RMd+1LYSQBnwoxnjXiSrueBgkSZKUOvsrq5m1Yluix9KyLcxZtZ2qmkib9DQmD+rCRSXdefeQIrIyDZUkSZKak2MOkpI7PxdjnHxCKjtOBkmSJDUdeyqqmF6+lede28y/5q5l464KOrTN4D3DunJRSXfG9y8gPS2kukxJkiS9g+MNkr4D7AP+Duw5uDzGuLUhizwWBkmSJDVN1TWRqcu2cF/ZGh6Zt57dFVV06diWC0d256JRPRjeoxMhGCpJkiQ1RccbJC0/zOIYY+zXEMUdD4MkSZKavv2V1Ty5cCP3la3hmcUbqayO9OvSnotG9eD9o7rTJ799qkuUJElSHccVJDVlBkmSJDUv2/ce4OFX13Nf2RpeWZ4Y3FzSO4f3j+zO+SO7U9ChbYorlCRJ0vGOSMoGvgL0jjFeG0IYCAyOMf6r4Us9OgZJkiQ1X2u27+OBsrXcX7aGRet3kZ4WmDiggItKunPO0K60b5uR6hIlSZJapeMNkv4OzAQujzEODyG0A6bGGEc1eKVHySBJkqSWYdH6ndw3ey0PlK1h7Y79tMtM5+yhRVxU0p1JA7uQmZ6W6hIlSZJajeMNkmbEGMeEEGbHGEuSy+bEGEceR0E5wB+B4UAErgQWk2joXQyUAx+JMW470nEMkiRJallqaiLTy7dyX9laHn51HTv2VZLXvg3vO7kbF5V0p7R3rk26JUmSTrDjDZJeAs4CXowxloYQ+gN/jTGOO46CbgOejzH+MYTQBsgGvg1sjTH+MITwTSA3xviNIx3HIEmSpJaroqqaZxdv4v6ytTyxcAMVVTX0ymvHpaf04dJTetMxKzPVJUqSJLVIxxsknQP8JzAUeAyYAEyJMT5zjMV0AuYA/WKdk4cQFgNnxBjXhRC6Ac/EGAcf6VgGSZIktQ679lfyyLz13DNrNdNe30qnrAymTOjLJ8cXk9u+TarLkyRJalGO+65tIYR84FQgANNijJuPo5hRwO+BBcBIEv2XvgisiTHm1NluW4wx9zD7XwtcC9C7d+/RK1asONZSJElSMzRn1XZ+9fRSHl+wgew26Xzi1D5cPbEvhZ2yUl2aJElSi3C8I5IeAP4KPBBj3NMAxYwBpgETYowvhxBuAHYCn69PkFSXI5IkSWq9Fq3fyW+eWcaDc9aSkZ7GJWN68anT+9EzNzvVpUmSJDVrRwqS6nMLlJ8Bk4AFIYR/hBA+FEI4nn/yWw2sjjG+nHx/N1AKbEhOaSP5vPE4ziFJklq4k7p24oaPlvDUf5zBB0t68LfpKznjJ8/wH3fNYdmm3akuT5IkqUWq19Q2gBBCOvAu4Brg3Bhjp2M+aQjPA1fHGBeHEL4HtE+u2lKn2XZejPHrRzqOI5IkSdJBa7fv4/fPvc7fpq+koqqG9w7vxmfP7M+w7p1TXZokSVKz0hA9ktoBFwCXkBg99K8Y4+ePo6BRwB+BNsDrwCdJjI66C+gNrAQ+HGPceqTjGCRJkqRDbd5dwc0vLOf2qSvYXVHFu04q5LozBzC6zxFnzEuSJCnpeHsk/R04BXiERNDzTIyxpsGrPAYGSZIk6e3s2FfJ7S+Vc/OLy9m2t5JT++XxuTMHMmFAPiGEVJcnSZLUZB1vkHQu8HiMsTr5fgLw8RjjdQ1e6VEySJIkSe9kT0UVf31lJb9/7nU27qpgZK8cPnfmAN49pNBASZIk6TAaYmrbKOBjJKa2LQf+GWP8ZUMWeSwMkiRJUn3tr6zmnlmr+e2zy1i1dR8nde3IZ88cwPtO7kZ6moGSJEnSQccUJIUQBgEfJREgbQH+Dnw1xtjnRBV6tAySJEnS0aqqruGBOWv59TPLWLpxN30L2vOZ0/tzUUkP2mTU54a2kiRJLduxBkk1wPPAVTHGpcllr8cY+52wSo+SQZIkSTpWNTWRR+ev51dPL2X+2p1075zFNZP7ccnYXmS3yUh1eZIkSSlzpCDpSP/sdjGwHng6hPCHEMJZgOO+JUlSi5CWFjjv5G786/MTueWTY+me047vP7iA037wFD99dDGbdlWkukRJkqQmpz7NttsDF5GY4vYu4Dbg3hjjYye8unfgiCRJktSQZq7Yyu+fe53HFmwgMz2ND5b04OpJ/RhQ2CHVpUmSJDWa4262XedAecCHgUtijO9qoPqOmUGSJEk6EV7ftJs/vbCcu2eupqKqhncPKeTayf0ZW5zrnd4kSVKL12BBUlNjkCRJkk6kzbsruH3qCu6YWs62vZWM6pXDtZP78Z5hXb3TmyRJarEMkiRJko7DvgPV3D1zFX98YTkrtuylT342V0/sy4dG96Jdm/RUlydJktSgDJIkSZIaQHVN5LH56/ndc69Ttmo7udmZXHZaMVec1of8Dm1TXZ4kSVKDMEiSJElqQDFGZqzYxu+efZ0nFm6gbUYaF4/uyTWT+tG3oH2qy5MkSTouRwqSMhq7GEmSpOYuhMDY4jzGFuexdONu/vTC69w9czV/fWUl5wwt4trJ/RjdJy/VZUqSJDU4RyRJkiQ1gE27Krh9ajm3T13Bjn2VjO6Ty7WT+3H2kCLSbMwtSZKaEae2SZIkNZK9B6q4a3qiMffqbfvoW9Ceqyf15eLSnmRl2phbkiQ1fQZJkiRJjayquoZH5q/n98+9ztzVOyjo0IbPnDGAS0/pbaAkSZKaNIMkSZKkFIkxMu31rdz09FJeWLqZrp2y+Ny7BvCRMb1ok5GW6vIkSZLewiBJkiSpCZi6bAs/fWwxM1dso1deO7541iAuGtWdjHQDJUmS1HQcKUjyby2SJEmN5LT++dz96dO45ZNj6dwuk6/+Yw7nXP8cD85ZS01N8/3HPUmS1HoYJEmSJDWiEAJnDi7kwc9N5LefGE1GWuDzf53Ne298nscXbKA5jxaXJEktn0GSJElSCoQQOHd4V/79xcnc8NFR7K+s5prbZ3DRr1/i+SWbDJQkSVKTZJAkSZKUQulpgfeP6sETXzmdH188gs27KrjsT69wye+n8cryrakuT5Ik6U1sti1JktSEVFRV8/fpq/jlU0vZtKuCyYO68B9nD2Jkr5xUlyZJkloJ79omSZLUzOw7UM0d08r5zTPL2La3krOHFvGVswcxpFunVJcmSZJaOIMkSZKkZmp3RRW3vLCc3z//Orsrqjh/RHe+9O6B9O/SIdWlSZKkFsogSZIkqZnbvvcAf3j+dW55sZz9ldVcXNqTL5w1kF552akuTZIktTAGSZIkSS3E5t0V/PaZZdw+bQUxRi4Z24vPnTmQrp2zUl2aJElqIQySJEmSWpj1O/bzq6eX8PfpqwgERvfJZWzfPMYV51HSO4f2bTNSXaIkSWqmDJIkSZJaqFVb93LrS+VMXbaFhet3EiOkpwWGd+/EmOI8xhbnMbY4l/wObVNdqiRJaiYMkiRJklqBnfsrmbViG9PLtzJ9+TbKVm/nQFUNAP27tE+GSnmM65tHz9x2hBBSXLEkSWqKDJIkSZJaoYqqal5dvYNXyrcyfflWZqzYxq79VQB07ZTF2L6J0Upji/MYXNSRtDSDJUmSdOQgycnzkiRJLVTbjHTGFOcxpjgPzoCamsjiDbuYXr6VV5Zv5ZXlW3hwzloAOmVlJLfNZVxxHif37EzbjPTUXoAkSWpyDJIkSZJaibS0wJBunRjSrROXn1ZMjJHV2/bxyvKtiXCpfCtPLdoIQNuMNEb2ymFscS6TBnZhTJ9cMtLTUnwFkiQp1ZzaJkmSpFpbdlcwvTzRZ2lG+Vbmrd1JdU0kJzuTd51UyDlDi5g0sIt3hZMkqQWzR5IkSZKOye6KKp57bROPL9jAU4s2smNfJW0y0pg4oICzhxZx1pBCCjtmpbpMSZLUgOyRJEmSpGPSoW0G7z25G+89uRuV1TVML9/K4ws21AZLIcCoXjmcPbSIc4YW0b9LB+8GJ0lSC+aIJEmSJB21GCOL1u+qDZVeXbMDgL4F7Tl7aBHvHlLE6D65pHsnOEmSmh2ntkmSJOmEWrdjH08s3MjjCzYwddlmKqsjee3b8K6TCjl7aBGTB3ahXRvvAidJUnNgkCRJkqRGs2t/Jc/W6au0a38VbTPSmDTwYF+lIgo6tE11mZIk6W0YJEmSJCklKqtreGX5G32V1mzfRwhQ2juXs4cWcXayr5IkSWo6DJIkSZKUcjFGFqzbWRsqzV+7E4B+Xdpz9pBEqFTS275KkiSlmkGSJEmSmpw12/fx5MJEqDTt9S1UVkfyk32V3j20iEkDC8hu402GJUlqbAZJkiRJatJ27q/k2cWbeGLhm/sqTRyQ6Kv0riGFFHbMSnWZkiS1CkcKkvwnHkmSJKVcp6xMLhjZnQtGdqeyuobpy7fy2IINPLFwA08u2kgIMKpXDu9OToEbWNiBEJwCJ0lSY3NEkiRJkpqsGCOLN+zi8fmJUGnO6h0A9MnPrg2VxvTJJSM9LcWVSpLUcji1TZIkSS3C+h37eXJRoq/SS0u3cKC6hpzsTN41ONFXafKgLnRo66B7SZKOh0GSJEmSWpzdFVU8/9omHk/2Vdq+t5I26Wmc1j+fs4cW8e4hRXTtbF8lSZKOVpMLkkII5cAuoBqoijGOCSHkAX8HioFy4CMxxm1HOo5BkiRJkgCqqmuYuWIbTyTvAle+ZS9pAc4eWsSU8X05tV+ePZUkSaqnphokjYkxbq6z7MfA1hjjD0MI3wRyY4zfONJxDJIkSZJ0qBgjyzbt5p+z1vDXV1aybW8lJ3XtyJTxxbx/VA/atUlPdYmSJDVpzSVIWgycEWNcF0LoBjwTYxx8pOMYJEmSJOlI9ldW80DZWm55qZyF63aSk53JR8f25rLT+tAjp12qy5MkqUlqikHScmAbEIHfxRh/H0LYHmPMqbPNthhj7mH2vRa4FqB3796jV6xY0UhVS5IkqbmKMfLK8q3c+lI5j85fD8B7hnVlyvhixvV12pskSXU1xSCpe4xxbQihEHgc+DzwQH2CpLockSRJkqSjtXrbXv48bSV/fWUlO/ZVMrRbJ6ZMKObCkd3JynTamyRJTS5IelMBIXwP2A1cg1PbJEmS1Ej2HajmvrI13PpiOYs37CKvfRs+Nq4Xnzi1D906O+1NktR6NakgKYTQHkiLMe5Kvn4c+H/AWcCWOs2282KMXz/SsQySJEmSdLxijEx9fQu3vljO4ws3kBYC5w7vyifHFzO6T67T3iRJrc6RgqSMxi4GKALuTf4POQO4M8b4SAhhOnBXCOEqYCXw4RTUJkmSpFYmhMD4/gWM71/Aqq17uWPaCv72ykoemruOk3t0Zsr4Ys4f2Y22GU57kyQp5VPbjocjkiRJknQi7D1Qxb2zE9PelmzcTUGHNnx8XG8uPbUPRZ2yUl2eJEknVJOa2taQDJIkSZJ0IsUYeXHpFm59aTlPLtpIegi89+RuTJlQTEmvHKe9SZJapKY2tU2SJElqFkIITBxYwMSBBazYsofbp67grumreGDOWvp1ac8FI7pzwcjuDCjskOpSJUlqFI5IkiRJko7Cnooq7i9by4Nz1jJt+RZihKHdOnHByO6cP6IbvfKyU12iJEnHxaltkiRJ0gmwYed+Hpq7jgfnrmX2yu0AlPTO4YIR3XnfiG72U5IkNUsGSZIkSdIJtmrrXv41dx0PzlnLgnU7CQFO7ZvPBSO7c97wruS2b5PqEiVJqheDJEmSJKkRLd24iwfnJEKl1zfvISMt0WvpghHdOWdYER2zMlNdoiRJb8sgSZIkSUqBGCML1u2sDZXWbN9Hm4w0zhzchQtH9uBdJxXSrk16qsuUJOlNDJIkSZKkFIsxMnvVdh4oW8tDr65j064Kstukc/bQIi4Y0Z3Jg7rQJiMt1WVKkmSQJEmSJDUl1TWRl5dv4cE56/j3vHVs31tJp6wMzhvejQtGdufUfnlkpBsqSZJSwyBJkiRJaqIqq2t4YclmHpyzlscWbGB3RRWFHdtyUUkPLi7tyeCuHVNdoiSplTFIkiRJkpqB/ZXVPL1oI/fMWsMzizdSVRMZ3qMTHyzpyftHdSe/Q9tUlyhJagUMkiRJkqRmZsvuCh6Ys5Z7Zq1m3pqdZKQFzhhcyMWlPXjXkELaZtikW5J0YhgkSZIkSc3Y4vW7uGfWau6dvYZNuyrIyc7kghHduXh0T0b27EwIIdUlSpJaEIMkSZIkqQWoqq7hhaWbuWfWGh6bv56Kqhr6d2nPB0t78sHSHnTr3C7VJUqSWgCDJEmSJKmF2bm/kofnruOeWauZXr6NEGBC/wI+WNqDc4d3JbtNRqpLlCQ1UwZJkiRJUgu2Ysse7pm1hn/OWs3qbfto3yad807uxsWlPTmlbx5paU59kyTVn0GSJEmS1ArU1ESml2/lnlmrefjV9eyuqKJHTjs+WNqDD5b2pG9B+1SXKElqBgySJEmSpFZm34FqHluwnrtnruaFpZuJEUb3yeXCkd05Y3AX+uQbKkmSDs8gSZIkSWrF1u/Yz72z13DPrNUs3bgbgOL8bCYP6sLpg7pwar982re1p5IkKcEgSZIkSRIxRsq37OXZxRt5bslmpi7bwr7KajLTA2OL8zh9UBdOH9yFwUUdCcG+SpLUWhkkSZIkSXqLiqpqZpRv49nXNvHs4k0s3rALgKJObZk8MBEqTRxQQE52mxRXKklqTAZJkiRJkt7Ruh37eP61zTz72iaeX7KJnfurSAswslcOpw/qwuRBXRjZM4d07wInSS2aQZIkSZKko1JVXcOc1Tt49rVNPPfaJuas3k6M0LldJpMGFtT2VyrqlJXqUiVJDcwgSZIkSdJx2bbnAM8v3cxzr23i2dc2sWlXBQAnde2Y6K00qAuji3Npm5Ge4kolScfLIEmSJElSg4kxsnDdLp5bkuitNGPFViqrI1mZaZT0ymVscS5jivMo6Z1Dx6zMVJcrSTpKBkmSJEmSTpg9FVVMXbaFF5ZuZsaKrSxYu5OaCGkBhnTrxNjiPMYW5zGmONepcJLUDBgkSZIkSWo0uyuqmL1yG9PLtzGjfCuzV25nX2U1AL3zshlTnJsMl3Lp36UDIdi8W5KakiMFSRmNXYwkSZKklq1D2wwmDezCpIFdAKisrmHB2p1ML9/KjPJtPLt4E/+ctQaA3OxMRvfJq50Od3KPzrTJSEtl+ZKkIzBIkiRJknRCZaanMbJXDiN75XD1pESPpeWb9zCjfFsiXFqxjScWbgCgbUYao3rl1E6FK+2TSyf7LElSk+HUNkmSJEkpt2lXBTNXbK2dDjdv7U6qayIhwEldOzG2OJePjevNkG6dUl2qJLV4Tm2TJEmS1KR16diWc4d349zh3YBEA+85q7YzPTlq6e6ZqzlrSBFDuqW4UElq5QySJEmSJDU57dtmMH5AAeMHFABQVV2T4ookSQB2sZMkSZLU5GWkp5GRfoJ+fVn6JGyYD1UHTszxJakFcUSSJEmSpNYrRvjHFKjYCWkZkD8QCodA0VAoHJp4nVMMaf4bvCSBQZIkSZKkVm7dR++gcOcG0jctgo0LYM0MmP/PNzbIzIYuJ70RLB0MmToUQQipK1ySUsAgSZIkSVKrFYGPvvJdKqsrGVk4ktLh76ak8GsM71hM1rbyxJS3jQsTAdOSx6Dsz2/s3C43GS4lA6aDz+1yUnQ1knTihRhjqms4ZmPGjIkzZsxIdRmSJEmSmqmqmioeKX+E2RtmM2vjLJZuXwpARloGw/KHUVpYSklhCSWFJeRk5cCezYlQ6WC4tCH5+sCuNw7aqUcyWBoChcOg63DoMgTS/Xd8Sc1DCGFmjHHMYdcZJEmSJElSwo6KHZRtLGPWxlnM3jibeZvnUVlTCUC/zv0oKSyhtCgRLvXs0JMQQqLP0o7VyXCpzgimTa9BdUXiwJntoXsJ9ByTePQYA526pfBKJentGSRJkiRJ0jGoqK5g/ub5tcHS7I2z2ZUcfdSlXZc3BUuDcgeRkVZn1FF1FWx9HdbNgdXTE72X1s2FZDBFp55vBEs9x0K3kZDZLgVXKUlvZpAkSZIkSQ2gJtawbPsyZm2YVRsurduzDoD2me0Z2WVkIlwqLGV4wXCyM7PffIDK/bB+Lqye8Ua4tH1lYl1aBhQNfyNY6jEG8vvb0FtSozNIkiRJkqQTZN3udczeOLs2WFqybQmRSEbIYEj+kNpgaVThKPLb5b/1ALs3vjlYWjMLDuxOrGuXCz1GJ4KlnmMSr9vlNu4FSmp1DJIkSZIkqZHsPLCTORvnMHvjbGZumMm8zfM4UHMAgOJOxbXNu0uLSundsXeiz1JdNdWwafEbwdLqGYm+SyR/d8sfWGdK3LjEKKa0tMa9SEktmkGSJEmSJKXIgeoDLNiyIDFiacNsZm+azY6KHQDkZeXV3hmutKiUwXmDyUzLfOtB9u+EtbPfCJZWT4c9mxLrsgug/5nQ/12JR8eujXh1kloigyRJkiRJaiJqYg3LdyyvDZZmbZzFmt1rAGiX0Y4RBSMoKUqMWhrZZSTtM9u/9SAxJnorrXgJXn8alj31RrBUOAwGJEOl3uMhM6sRr05SS2CQJEmSJElN2IY9G5i9aXZixNLG2SzetpiaWEN6SGdw3uDaUUslhSV0ye7y1gPU1MCGeYlAadmTsHIaVB+AjCzoMyERKg04C7qcZPNuSe/IIEmSJEmSmpHdB3Yzd9Pc2gbeczfNZX/1fgB6dexV28C7pKiEvp36vrXP0oE9UP5iMlh6CjYvTizv2O2NKXD9zoT2h2n+LanVM0iSJEmSpGassqaSRVsW1QZLszfOZuv+rQDktM15U7A0NG8omemH9FnavioxBW7pk/D6M7B/OxCg+6g3gqWe4yCjTSNfmaSmqEkGSSGEdGAGsCbGeH4IIQ/4O1AMlAMfiTFuO9IxDJIkSZIktUYxRlbsXMGsjbOYtSERLq3ctRKAtultObngZEoKSxhdNJqRXUbSoU2HN3auqYa1ZYkpcMueglWvQKyGNh2geFJiClz/d0FeP6fBSa1UUw2SvgKMATolg6QfA1tjjD8MIXwTyI0xfuNIxzBIkiRJkqSEzfs2M3vj7NpgadHWRVTHatJCGoNyB70xaqmwhKL2RW/suH8HLH/+jf5K28oTy3P6wJALYNgHoUepoZLUijS5ICmE0BO4Dfg/4CvJIGkxcEaMcV0IoRvwTIxx8JGOY5AkSZIkSYe3t3IvczfPrb0z3JxNc9hXtQ+AHh161DbvLi0spV9OP9JCWmLHra8nQqXXHks811QmQqVhH4DhH4SuIwyVpBauKQZJdwM/ADoCX00GSdtjjDl1ttkWY8w9zL7XAtcC9O7de/SKFSsaqWpJkiRJar4qayp5betrtX2WZm2YxZb9WwDo1KZTIlQqKqW0sJSh+UNpk94G9m2DRQ/BvH8meivFasgfkAiVhn0Qioam9qIknRBNKkgKIZwPvDfG+NkQwhkcZZBUlyOSJEmSJOnYxBhZtWvVm4Kl8p3lALRJa8PwguGUFiWmwo0qHEWnykpY+ADM/yeUvwCxBrqclAiUhn8QCgam9oIkNZimFiT9ALgMqAKygE7AP4GxOLVNkiRJklJmy74tlG0qq50Ot3DLQqpiFYHAgNwBtT2WSjv0plv5NJh/L6x4CYhQdDIMT45Uyuub6kuRdByaVJD0ppO/eUTST4AtdZpt58UYv36k/Q2SJEmSJOnE2Ve1j1c3vVo7aqlsYxl7q/YC0LV910So1Kk/Jds3MmDpM6SvTv5+1r0kESgN+wDk9ErhFUg6Fs0lSMoH7gJ6AyuBD8cYtx5pf4MkSZIkSWo8VTVVLNm25E3T4Tbt2wRAx8yOjMwdTGkVlKxbzMnr5tM2Aj3HJaa+Db0IOnVLaf2S6qfJBknHyyBJkiRJklInxsia3WsSodLGWczeMJtlO5YBkJmWwbDMXEp276B0yxpGVRwgp9epiVFKQy+CDl1SW7ykt2WQJEmSJElqFNv3b6dsU1ltsDRvyzyqaqoA6F8dKNmzk9KKSkq6n0qPU79A6DsZQkhx1ZLqMkiSJEmSJKXE/qr9zN8yv3YqXNmGmexK9lkqrKqihHaU9D6D0pFXMKhgOOlp6SmuWJJBkiRJkiSpSaiJNSzZtoTZ615h1pIHmbVtERvSEr+Xtg+ZjOwykpLupzC6aDTDC4bTLqNdiiuWWh+DJEmSJElS0xQj6xb8k1mzfsfs7a8xKyuLpW0yiUBGyGBo/lBKCksoKSqhpLCEvKy8VFcstXgGSZIkSZKkpm/Ta/Dyb9kx56/MyahhdteBzOqYx7w9azhQcwCA4k7FlBaVUlJYQmlhKb069iLYY0lqUAZJkiRJkqTmY+9WmHUbvPx72LWWA/n9WXDyRczKKWT2lvnM2jiLnQd2ApCflU9pUSmlhaWUFJUwOHcwGWkZKb4AqXkzSJIkSZIkNT/VlbDgfph6E6ydBVmdYfQUasZezXIqa+8MN2vjLNbsXgNAu4x2jOwysjZYGlEwguzM7BRfiNS8GCRJkiRJkpqvGGHVKzDtJlj4IBBg6PvhtOugZ+J33Q17NjB7U/LOcBvLWLxtMTWxhvSQzkl5JyWmwiWnxBW0K0jt9UhNnEGSJEmSJKll2LYCXvk9zLodKnZCz7Fw6mdhyIWQ/saUtl0HdjF309zEqKWNs3l106vsr94PQO+Ovd8ULBV3KrbPklSHQZIkSZIkqWWp2AVld8K038C25dCpJ5xyLZReAe1y3rJ5ZXUlC7cuZPbGxKil2Rtns61iGwB5WXmM6jKqNlgakj+EzLTMRr4gqekwSJIkSZIktUw11fDaI4lAqfx5yGwPoz4GY66CoqFvu1uMkfKd5czeOJuZG2Yye+NsVu1aBUBWehYjuoyovTPciC4j6NCmQ2NdkZRyBkmSJEmSpJZv3ZxEoDTvHqg+AL3Hw9irEtPeMtq84+6b9m5i9sbZiVFLG2exaOsiamINaSGNwbmDKSksoaQoES4VZhc2wgVJqWGQJEmSJElqPfZsgbI/w4ybYVs5tO8CJZfBmE9CTu/6H6ZyD3M3za0NluZumsu+qn0A9OjQo/bOcKWFpfTt3Je0kHaCLkhqXAZJkiRJkqTWp6YGlj0FM/6UmP4WIwx6T2La24CzIC39qA5XWVPJ4q2La0ctzdwwk637twLQuW1nSrq8MWJpaP5Q2qS/8ygoqSkySJIkSZIktW7bV8HMWxN3e9uzEXL6JEYolVwG7QuO6ZAxRlbuWlnbvHv2xtmU7ywHoE1aG4YXDK9t4D2qcBSd2nRquOuRTiCDJEmSJEmSAKoOwKIHYfrNsOIFSG8DQy9K9FLqdQqEcFyH37JvC2Uby5i1MREuLdyykKpYRSAwIHcApYWliUdRKV3bd22Ya5IamEGSJEmSJEmH2rgo0Udpzl+hYicUDYcxV8KIj0Dbjg1yir2Ve5m3eV5tsFS2sYy9VXsB6Na+W+2d4UqKShiQM8A+S2oSDJIkSZIkSXo7Fbth3t0w/Y+w/lVo0xFGXpLopVQ0tEFPVVVTxZJtS2qDpVkbZrFp3yYAOrbpyKguo2qnww0vGE7b9LYNen6pPgySJEmSJEl6JzHC6hmJ5tzz/gnVFdD7NBh7NQy5ADIaPtSJMbJm95ra5t2zN87m9R2vA5CZlsmw/GG1DbxLCkvo3LZzg9cgHcogSZIkSZKko7F3K8z+c2Lq27blkF0ApZfB6E9Cbp8Teupt+7dRtrEsMWJp4yzmb5lPVU0VAANyBlBSWJKYEldUSvf23QnH2ddJOpRBkiRJkiRJx6KmBl5/KtGc+7V/J0YtDTwn0Utp4NmQln7CS9hftZ95m+fVBktlG8vYXbkbgMLswtrRSqVFpQzMGUh6I9Skls0gSZIkSZKk47VjNcy8DWbdBrs3QOfeMPoKKLkMOhY1WhnVNdUs3b400Wdpw2xmbpzJxr0bAeiQ2YGRXUa+qc9Su4x2jVabWgaDJEmSJEmSGkp1JSx+GKb/CZY/C2kZiR5KY66E4knQyFPNYoys27OuNliatXEWS7cvBSAjLYOh+UNrRy2VFJaQm5XbqPWp+TFIkiRJkiTpRNi8FGbekuintH87FAxKBEojPwrtUhfY7KjYwZxNc5i1IXF3uFc3v0plTSUAfTv3fWM6XGEpPTv2tM+S3sQgSZIkSZKkE6lyH8y/N9Gce/V0yGgHwy+GsVdC99JGH6V0qIrqChZsWcCsDbMSI5c2zmbXgV0AFLQrqA2VSopKGJw7mIy0jJTWq9QySJIkSZIkqbGsmwMzboG5d0HlHug2EsZcBSd/CNq0T3V1ANTEGpZtX1bbwHv2htms3bMWgHYZ7RJ9lpLB0oiCEWRnZqe4YjUmgyRJkiRJkhrb/p0w9++JUUobF0DbTokpb2OuhMIhqa7uLdbvWZ8IlpLT4V7b9hqRSHpI56S8kygtKqW0sJRRhaMoaFeQ6nJ1AhkkSZIkSZKUKjHCqpcTzbkX3AfVB6DPhESgNOQCyGib6goPa9eBXW/ps1RRXQFAn0593pgOV1hCn0597LPUghgkSZIkSZLUFOzZAmV/Tkx927Ycsgug9DIYPQVyi1Nd3RFVVleyYOuC2jvDzd44m+0V2wHIy8qrvStcaWEpJ+WfRGZaZmoL1jEzSJIkSZIkqSmpqYHXn05Me1v8cGLU0oCzYPB50Pd0yB+Q8gbd7yTGyPKdy2tHLM3aMIvVu1cDiT5LJxecXBssjSwcSfvMptEfSu/MIEmSJEmSpKZqxxqYdTuU/QV2rEos69AV+k6CvpOheFJitFITD5YANu7dyOyNs2uDpcXbFlMTa0gLaQzOHUxpUWntyKXC7MJUl6u3YZAkSZIkSVJTFyNsfR2WPwflzyee92xKrOvc641Qqe8k6NwztbXW057KPczZNCcRLm2YzdzNc9lXtQ+Anh161gZLpYWl9O3c1z5LTYRBkiRJkiRJzU2MsGlxMlR6FspfgH3bEuvy+iVDpWS41LEotbXWU2VNJYu2LKrtsTR742y27t8KQE7bHEYVjmJ04WhKikoYmjeUzHT7LKWCQZIkSZIkSc1dTQ1smJcMlp6HFS9Cxc7EuoLBiVCp76REsJSdl9pa6ynGyIqdKxJT4ZLh0oqdKwBom972jT5LRaWM7DKSjm06prji1sEgSZIkSZKklqa6CtbPSYRKy5+DldOgck9iXdHJb/RY6jMesjqnttajsHnfZso2liWCpQ2zWbh1IdWxmkBgUO6g2mCppLCEru27prrcFskgSZIkSZKklq7qAKydlQiWyp+DlS9DdQWENOg2ErqXJO4Glz8QCgZA596QnpHqqt/R3sq9zN08l9kbEqOW5myaU9tnqXv77pQUJXoslRSW0D+nP2khLcUVN38GSZIkSZIktTaV+2H19Demwm2cD/t3vLE+vQ3k9oWCgZDfPxkwDUyETdn5TfYucVU1VSzetrg2WJq9cTab920GoGObjrV3hSstLGVYwTDaprdNccXNj0GSJEmSJEmtXYywdwtsXgJblsKWJbB5aeL11tehpvKNbbNy3giV8ge88TqvP2RmpewSDifGyOpdq2tDpVkbZ7F8x3IAMtMyGV4wnNLC0to+S53bNp9pfqlikCRJkiRJkt5edRVsXwFbliUDpoNh01LYta7OhgFyeiVGL9UNmDp2hbRMSEuH9ExIy3ibR3qjjHTaun8rZRvLaoOlBZsXUBWrABiQMyAxFS45Ja5b+26EJjr6KlUMkiRJkiRJ0rGp2JUMmJa+eTTTlmVwYPfRH+9tQ6aMRM+m2vfJYCotHcLB57Q3HrXv0+u8D4e8T6zfR2RezR5mV+9kVtUO5lTtYHcyWCrKaE9px76UdBlJaY8JDOh+CukZbRr4Q2xeDJIkSZIkSVLDihF2rU+ESns2QU011FS98aiuevP7murE9Lk3va+C6so3v6+pfOv6WPPmR0118nV1ndeHWRdroKbmLdtWx2qWpsPMjMDszMCsrLZszEg0Hu9QU8PIqkBpRmdKs7szPHcwWTl9oFMP6Nwz8WiX22R7SDUEgyRJkiRJkqTD2b+DuH01azfNY9aGGcze/hqz961jaU3iznAZMTKs4gCl+ysoqahg1P4KctOz6gRLPaBzr+T7Oq/bZKf4wo6dQZIkSZIkSdJR2FGxg7KNZczaMJPZ619h3tbFVCanw/VL70AJbSmtOEDJji303Lmet4xPapcH7Qsgq/PhH207JV/nvHVdihuaHylIymjsYiRJkiRJkpq6zm07c3qv0zm91+kAVFRXMH/z/Nq7wz22cTb3ZOyC/DYU9hxNSe5gSrK7U5rWiUEHKkjftQ72boX9OxLPW5cnXu/f8eY75B1Oets6wVKnwwdRGe2AmJhiCHVeJ98ffP2263mb9UdmkCRJkiRJkvQO2qa3pbSolNKiUgBqYg3Lti+rvTPc7A2zeXTtCwC0z2zPyC4jKel1GqWFpQwvGE52ZnKqW4xQue//b+/eY+us7zuOv7+2Q64EkjSOspA0oEJEUogdojZCk4omtWWqOqpWMLa2Q9o0qoqibmq3wtaKqv2n3dZqmrpuqrpy6SpaelOBClhVteK6oWKTcAkRvYQ0kNqB3AMmF3/3x/Pk5BBs93E4x+f4+P2SLB//not/QfooJx9+z+8UhdKrB06WSyP7YWTfKT/vh5HynH2/PXnO8SOt+Y+Aj7ZJkiRJkiQ1xO8O/64oloYGGBge4Nm9z5IkPdHDhUsupL+3nw29G+jr7WPJ3CWn/4uOjsCxV4AoN/0uH6w78bq2EfhpHI9orz2SImIOcD8wm2JF1Pcy86aIWAx8B1gNbAeuysy9E93LIkmSJEmSJLWrA0cOsHl4c23V0hO7n+DIaLGaaPXC1fT39hfl0rINrDpzFdEmnwTXbkVSAPMz81BEzAIeBD4OvB/Yk5lfiIgbgEWZ+amJ7mWRJEmSJEmSposjx4/w9EtPn3wcbniQ/a/uB2DxnMVs6N1QK5bWLF7DrK5ZLZlnWxVJr/nlEfMoiqSPArcBl2XmrohYDvw8M9dMdL1FkiRJkiRJmq5Gc5Tt+7fXSqXHhh7j+UPPAzC3Zy4XL724Vi6tX7r+5D5LTdZ2RVJEdAOPAW8B/j0zPxUR+zLz7Lpz9mbmojGuvRa4FmDVqlWXPPfcc1M0a0mSJEmSpOYaOjzE4O5BBocGGRweZNvebYzmKN3RzZrFa2rFUn9vP0vnLW3KHNquSKr98oizgR8C1wMPVimS6rkiSZIkSZIkdbJDRw6xZfeW2qqlLbu3MHJ8BICVZ66sbeDdv6yfcxee25B9liYqknre8N3fgMzcFxE/By4HhiJied2jbcOtnJskSZIkSVKrLThjAZeuuJRLV1wKwNHRo2x9aWvt0+Ee2PkAd/7qTgAWzV5EX29frVhau3gts7obu89SKzbbXgocLUukucD/AF8E3gG8VLfZ9uLM/PuJ7uWKJEmSJEmSNJNlJtsPbK8VS4PDg+w4uAOAOd1zuGjpRbVVS+uXrmfBGQt+7z3b6tG2iLgYuBXoBrqAOzLzcxGxBLgDWAXsAK7MzD0T3csiSZIkSZIk6bVefOXF1xRLz+x5huN5nK7o4oJFF5x8HK63n2Xzl73u+rYqkhrJIkmSJEmSJGliLx99mS0vbmFwaJCB4QE2797MK8deAWDFghW1zbs39G7gvLPPo7uruz33SJIkSZIkSVJzzZs1j03LN7Fp+SYAjo0eY9vebbVi6ZEXHuHuX98NwFmzz5rwXhZJkiRJkiRJM0hPVw/rlqxj3ZJ1fGjth8hMdh7cycDwAAPDAzzEQ+Ne66NtkiRJkiRJqploj6SuqZ6MJEmSJEmSpieLJEmSJEmSJFVikSRJkiRJkqRKLJIkSZIkSZJUiUWSJEmSJEmSKrFIkiRJkiRJUiUWSZIkSZIkSarEIkmSJEmSJEmVWCRJkiRJkiSpEoskSZIkSZIkVWKRJEmSJEmSpEoskiRJkiRJklSJRZIkSZIkSZIqsUiSJEmSJElSJRZJkiRJkiRJqsQiSZIkSZIkSZVYJEmSJEmSJKkSiyRJkiRJkiRVEpnZ6jmctog4CGxr9TzUUGcB+1s9CTXMm4AXWz0JNYz57Czms/OY0c5iRjuL+ews5rPzmNHXe3NmLh3rQM9Uz6TBtmXmxlZPQo0TEV/LzGtbPQ81RkT8wox2DvPZWcxn5zGjncWMdhbz2VnMZ+cxo5Pjo21qN3e1egKSxmU+pfZmRqX2ZT6l9mZGJ8EiSW0lMw2w1KbMp9TezKjUvsyn1N7M6ORM9yLpa62egKQJmVGpfZlPqb2ZUal9mU/NaNN6s21JkiRJkiRNnem+IkmSJEmSJElTxCJJkiRJkiRJlbRdkRQR34iI4Yh4sm5sfUQ8EhFPRMRdEbGwHP9gRDxe9zUaEX3lsUvK838ZEf8WEdGiP5LUMSaZz1kRcWs5vjUibqy7xnxKTTDJjJ4RETeX45sj4rK6a8yo1GARsTIiflb+nfhURHy8HF8cET+JiGfL74vqrrmxzOG2iHh33bgZlRposvmMiCXl+Yci4iun3Mt8quO1XZEE3AJcfsrY14EbMvMi4IfA3wFk5rcysy8z+4APA9sz8/Hymv8ArgXOL79OvaekybuFivkErgRml+OXAB+JiNXlMfMpNcctVM/oXwOU4+8EvhQRJ94XmFGp8Y4Bn8jMC4FNwHURsRa4AfhpZp4P/LT8mfLY1cA6igx+NSK6y3uZUamxJpVPYAT4DPDJMe5lPtXx2q5Iysz7gT2nDK8B7i9f/wT4wBiX/hlwO0BELAcWZuYjWewmfhvwvqZMWJpBJpnPBOZHRA8wFzgCHDCfUvNMMqNrKd4Uk5nDwD5goxmVmiMzd2XmQPn6ILAVWAFcAdxannYrJ/N2BfDtzHw1M38D/BJ4mxmVGm+y+czMw5n5IEWhVGM+NVO0XZE0jieBPylfXwmsHOOcP6UskihCv7Pu2M5yTFLjjZfP7wGHgV3ADuBfMnMP5lOaauNldDNwRUT0RMS5FCsHV2JGpaYrV+j2A/8HLMvMXVD8YxboLU9bAfy27rITWTSjUhNVzOd4zKdmhOlSJP0lxfLCx4AzKVY21ETE24GXM/PEnhBjPYeazZ2iNGONl8+3AceBPwDOBT4REedhPqWpNl5Gv0HxBvcXwL8CD1Ms7TejUhNFxALg+8DfZOaBiU4dYywnGJf0Bk0in+PeYowx86mO09PqCVSRmc8A7wKIiAuA95xyytWcXI0ExRvjc+p+Pgd4oZlzlGaqCfL558C9mXkUGI6Ih4CNwAOYT2nKjJfRzDwG/O2J8yLiYeBZYC9mVGqKiJhF8Y/Ub2XmD8rhoYhYnpm7ysdihsvxnbx2Ff6JLPo+V2qCSeZzPOZTM8K0WJEUEb3l9y7g08B/1h3roliq/+0TY+Wyw4MRsancJf8vgB9N6aSlGWKCfO4A/igK8yk2LnzGfEpTa7yMRsS8MptExDuBY5n5tBmVmqPM038BWzPzy3WH7gSuKV9fw8m83QlcHRGzy8dPzwceNaNS451GPsdkPjVTRLEHWPuIiNuBy4A3AUPATcAC4LrylB8AN5abl1F+XPEXMnPTKffZSPHpNXOBe4Drs93+sNI0M5l8lkuDb6bY0DeAmzPzn8v7mE+pCSaZ0dXAfcAo8DzwV5n5XHkfMyo1WET8IcWq3CcocgfwDxT7sNwBrKL4nzBXlnsKEhH/SPF46jGKR23uKcfNqNRAp5nP7cBC4AyKD6x4V2Y+bT41E7RdkSRJkiRJkqT2NC0ebZMkSZIkSVLrWSRJkiRJkiSpEoskSZIkSZIkVWKRJEmSJEmSpEoskiRJkiRJklSJRZIkSdIbEIUHI+KP68auioh7WzkvSZKkZojMbPUcJEmSprWIeCvwXaAf6AYeBy7PzF+dxr26M/N4Y2coSZLUGBZJkiRJDRAR/wQcBuaX398MXAT0AJ/NzB9FxGrgm+U5AB/LzIcj4jLgJmAX0JeZa6d29pIkSdVYJEmSJDVARMwHBoAjwN3AU5n53xFxNvAoxWqlBEYzcyQizgduz8yNZZH0Y+CtmfmbVsxfkiSpip5WT0CSJKkTZObhiPgOcAi4CnhvRHyyPDwHWAW8AHwlIvqA48AFdbd41BJJkiS1O4skSZKkxhktvwL4QGZuqz8YEZ8FhoD1FB96MlJ3+PAUzVGSJOm0+altkiRJjXcfcH1EBEBE9JfjZwG7MnMU+DDFxtySJEnThkWSJElS430emAVsiYgny58BvgpcExH/S/FYm6uQJEnStOJm25IkSZIkSarEFUmSJEmSJEmqxCJJkiRJkiRJlVgkSZIkSZIkqRKLJEmSJEmSJFVikSRJkiRJkqRKLJIkSZIkSZJUiUWSJEmSJEmSKvl/wAJqX5jWm6MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.api import Holt\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# create Holt model\n",
    "# If your data is showing an exponential trend (not linear), set exponential=True\n",
    "holt = Holt(train['Average_Index'],exponential=False).fit(optimized = True) \n",
    "\n",
    "\n",
    "\n",
    "test_holt=test.copy() # create test set for this example\n",
    "test_holt['predicted']=holt.forecast(len(test_holt)) # for each observation in test set, assign holt forecast\n",
    "\n",
    "# calculate errors and round to 2 d.p\n",
    "rmse_holt=round(np.sqrt(mean_squared_error(test_holt['Average_Index'],test_holt.predicted)),2)\n",
    "mae_holt=round(mean_absolute_error(test_holt['Average_Index'],test_holt.predicted),2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "train['Average_Index'].plot(ax=ax,label='train')\n",
    "test_holt['Average_Index'].plot(ax=ax,label='test')\n",
    "test_holt.predicted.plot(ax=ax,label='holt linear forecast')\n",
    "plt.legend()\n",
    "plt.title('RMSE: '+str(rmse_holt)+', MAE: '+str(mae_holt))\n",
    "plt.suptitle('Holt Linear Trend Forecasting',fontsize=18)\n",
    "plt.ylabel('Average Index')\n",
    "plt.show()\n",
    "\n",
    "# Sometimes you might get a convergence warning, this is not an error, but an indication the model struggled to get going.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "69902bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-01    30.625132\n",
      "2019-01-01    30.909864\n",
      "2020-01-01    30.922161\n",
      "2021-01-01    30.934459\n",
      "2022-01-01    30.946756\n",
      "2023-01-01    30.959053\n",
      "2024-01-01    30.971350\n",
      "2025-01-01    30.983647\n",
      "2026-01-01    30.995944\n",
      "2027-01-01    31.008241\n",
      "2028-01-01    31.020538\n",
      "2029-01-01    31.032835\n",
      "2030-01-01    31.045132\n",
      "2031-01-01    31.057430\n",
      "2032-01-01    31.069727\n",
      "2033-01-01    31.082024\n",
      "2034-01-01    31.094321\n",
      "2035-01-01    31.106618\n",
      "Freq: AS-JAN, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "holt = Holt(TS['Average_Index'],exponential=False).fit(optimized = True) \n",
    "forward_forecast=holt.predict(start=TS.index[-1],end='2035-01-01')# the start date is the last observation in the data, you choose the end date\n",
    "print(forward_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "111f2e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Year')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "TS['Average_Index'].plot(ax=ax,label='Current', linewidth = 3)\n",
    "forward_forecast.plot(ax= ax, label = 'Prediction', color = 'red', linewidth = 3)\n",
    "\n",
    "plt.legend()\n",
    "plt.suptitle(\"Using a Holts Linear Forecast, It appears that World Species Population Decline will Plateau Over the Next 10 Years\",fontsize=18)\n",
    "plt.ylabel('World Average Index', size = 15)\n",
    "plt.xlabel('Year', size = 15)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8978fe2",
   "metadata": {},
   "source": [
    "## Model 2 - ARIMAX"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "626ce2d4",
   "metadata": {},
   "source": [
    "### Integrative part of model - D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8d622af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:0.44171130108322176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\AppData\\Local\\Temp\\ipykernel_26072\\3920404441.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['difference'] = train['Average_Index'].diff(periods = 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "#How many times do I need to difference data to make it stationary?\n",
    "\n",
    "train['difference'] = train['Average_Index'].diff(periods = 1)\n",
    "train.head()\n",
    "result_diff = adfuller(train.difference.dropna())\n",
    "print('p-value:' +str(result_diff[1]))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "train['difference'].plot()\n",
    "\n",
    "#Once is still not stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ac70c31a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:6.67699954524058e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\AppData\\Local\\Temp\\ipykernel_26072\\848809148.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['difference_2'] = train['difference'].diff(periods = 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "#How many times do I need to difference data to make it stationary?\n",
    "\n",
    "train['difference_2'] = train['difference'].diff(periods = 1)\n",
    "train.head()\n",
    "result_diff_2 = adfuller(train.difference_2.dropna())\n",
    "print('p-value:' +str(result_diff_2[1]))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "train['difference_2'].plot()\n",
    "\n",
    "#twice and data is stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "86eb1411",
   "metadata": {},
   "outputs": [],
   "source": [
    "# D = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321fb00b",
   "metadata": {},
   "source": [
    "#### The order of autoregression - P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f75ba4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0R5Lzqup5VTWR5Kokt8w8oKqeW1U1fP7i4Xl3HU5fAAAAAPo17ylsrbXJqro2yceTLEtyU2vtnqp63XD/O5P8iyQ/VVWTSZ5IclVrrSWZs+98awIAAABgdEaxBtL0tLRbZ7W9c8bztyd5++H2BQAAAGDxGMUUNgAAAACexQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQKfxvgugX3/5d7v6LgEAAACOWi97/qq+S1gQRiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQaSYBUVZdV1b1Vtamqrptj/49V1ReHj/9ZVS+asW9zVX2pqjZU1Z2jqAcAAACA0Rmf7wtU1bIk70hyaZKtSe6oqltaa1+ecdjfJ/m+1tojVXV5khuTvGTG/le01h6eby0AAAAAjN4oRiC9OMmm1tr9rbX9Sd6f5MqZB7TW/mdr7ZHh5l8lOWsE5wUAAABgAYwiQDozyZYZ21uHbQfz2iS3zdhuST5RVZ+vqmtGUA8AAAAAIzTvKWxJao62NueBVa/IIED67hnNL2+tbauq1Uk+WVV/01r77Bx9r0lyTZKcffbZ868aAAAAgMMyihFIW5OsmbF9VpJtsw+qqu9M8vtJrmyt7Zpub61tG/7ckeTDGUyJe5rW2o2ttfWttfWnnnrqCMoGAAAA4HCMIkC6I8l5VfW8qppIclWSW2YeUFVnJ/lQkte01v52Rvvyqjpx+nmSH0hy9whqAgAAAGBE5j2FrbU2WVXXJvl4kmVJbmqt3VNVrxvuf2eSNyVZleT/q6okmWytrU9yWpIPD9vGk7y3tfax+dYEAAAAwOiMYg2ktNZuTXLrrLZ3znj+k0l+co5+9yd50ShqAAAAAOCZMYopbAAAAAA8iwmQAAAAAOgkQAIAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBO430XAADTpqZaNmzZnc279mbtquVZt2Zlxsaq77IAAGDJEyABsChMTbXccNvGbNqxJ/snpzIxPpZzV6/I9ZdfIEQCAICemcIGwKKwYcvubNqxJ/smp9KS7JucyqYde7Jhy+6+SwMAgCVPgATAorB5197sn5w6oG3/5FQ279rbU0UAAMA0ARIAi8LaVcszMX7gH0sT42NZu2p5TxUBAADTBEgALArr1qzMuatXpIbLHR07XANp3ZqVvdYFAAAIkABYJMbGKtdffkHOXHl8Tl0xkZ995XkW0AYAgEXCXdgAWDTGxionHjeeE48bz0XnnNx3OQCLwtRUy4Ytu7N5196sXbU869asFK4DsOAESAAAsEhNTbXccNvGbNqxJ/snpzIxnN5rhCYAC80UNgAAWKQ2bNmdTTv2ZN/kVFqSfZNT2bRjTzZs2d13aQAsMQIkAABYpDbv2pv9k1MHtO2fnMrmXXt7qgiApUqABAAAi9TaVcszMX7gV/aJ8bGsXbW8p4oAWKoESAAAsEitW7My565ekRoud3TscA2kdWtW9loXAEuPAAkAABapsbHK9ZdfkDNXHp9TV0zkZ195ngW0AeiFu7ABAMAiNjZWOfG48Zx43HguOufkvssBYIkyAgkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBO7sJGb6amWjZs2Z3Nu/Zm7arlWbdmpVvSAgAwJ98dAfolQKIXU1MtN9y2MZt27Mn+yalMjI/l3NUrcv3lF/giAADAAXx3BOifKWz0YsOW3dm0Y0/2TU6lJdk3OZVNO/Zkw5bdfZcGAMAi47sjQP8ESPRi86692T85dUDb/smpbN61t6eKAABYrHx3BOifAIlerF21PBPjB15+E+NjWbtqeU8VAQCwWPnuCNA/ARK9WLdmZc5dvSI1nLJ+7HAe+7o1K3utCwCAxcd3R4D+CZDoxdhY5frLL8iZK4/PqSsm8rOvPM8iiAAAzMl3R4D+uQsbvRkbq5x43HhOPG48F51zct/lAACwiPnuCNAvI5AAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADo5C5sAACL0NRUy4Ytu7N5196sXbU869asdMtyAKA3IwmQquqyJL+TZFmS32+tvWXW/hruvyLJ40l+orV21+H0BQBYaqamWm64bWM27diT/ZNTmRgfy7mrV+T6yy8QIgEAvZj3FLaqWpbkHUkuT3JhkldX1YWzDrs8yXnDxzVJfvcI+gIALCkbtuzOph17sm9yKi3JvsmpbNqxJxu27O67NABgiarW2vxeoOplSd7cWvtnw+1fTJLW2n+cccx/TvKZ1tr7htv3Jrk4ydpD9Z3LKedc0C69/qZ51b0YfHn7o0mSC08/qbcaHn3y672dO0ke2PV4kuScVSf0WgewePhcgGTnY/vy8J79T2s/dcVEnnPisT1U1J/WWvbseypPfv2pHHfMsqw4dlkGg9uXFp+NA94HEp8LLD4nHXdM3yWMzAde908/31pbP9e+UUxhOzPJlhnbW5O85DCOOfMw+yZJquqaDEYvZcXpz59fxYtEn8HRYuEP/wFfhrwHifdg2lL//RPXwrSl/D4cd8yyVCUz/52vKjn2mGX9FdWD1lq+8rUn8vj+p5IM3oPjj1mWs085fsn9ZXEp/n8wF+/D0v5sTHwuzLTUr4XEe7DQRhEgzfV/6exhTQc75nD6DhpbuzHJjUmyfv369sf/58uOpEYO4i//blffJZDkVz96T5LkTT/0gp4r6Y/3wHvAN7kWBpby+2ANpIG7Hngkb/v0fd/Ybi15aqrlh7/zzFx0zsk9Vgb9WcqfjYnPhZmW+rWQLJ734GXPX9Xr+UfpA687+L5RBEhbk6yZsX1Wkm2HeczEYfQFAFhSxsYq119+wZK/C9vmXXuzf3LqgLb9k1PZvGvvkvuLIjDgcwH6M4oA6Y4k51XV85I8mOSqJP9m1jG3JLm2qt6fwRS1f2itba+qnYfRFwBgyRkbq1x0zslL+i9Ea1ctz8T4WPbN+MvixPhY1q5a3mNVQJ98LkB/5n0XttbaZJJrk3w8ycYkH2it3VNVr6uq6cFPtya5P8mmJL+X5N919Z1vTQAAHP3WrVmZc1evyLHjY6kkxw6n8q1bs7Lv0oCe+FyA/oxiBFJaa7dmEBLNbHvnjOctyU8fbl+ApWZqquWxJyfz5Nefyl0PPLIkp6oAzGYqHzCbzwXoz0gCJOBbJzhgerHcB3c/kdaSt336viW5WC7AXEzlA2bzuQD9mPcUNuBbNzM4eHjP/rzt0/flhts2ZmpqzpsR8iy1YcvubNqx5xu36943OZVNO/Zkw5bdvdYFAAAwTYAEPRIckHTfTQQAAGAxECBBjwQHJN+8m8hM7ibCUjc9vXfnY/ty1wOPGJkJANAzARL0SHBA4m4iMJvpvQAAi49FtKFH08HBph17sn9yKhOCgyXJ3UTgQF3Tey2YCgDQDwES9EhwwDR3E4Fv6pre6/8RAIB+CJCgZ4IDYKbptX+e/PpTueuBR5ZkqDw9vXffjBDJ9F4AgH5ZAwkAFglr/wxYFwwAYPExAgkAFglr/wyY3gsAsPgIkABgkbD2zzeZ3gsAsLiYwgYAi8T02j8zWfsHAIDFQIAEAIuEtX8AAFisTGEDgEXC2j8AACxWAiQAWESs/QMAwGJkChsAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAR5GpqZbHnpzMzsf25a4HHsnUVOu7JJYAARIAAAAcJaamWm64bWMe3P1EHt6zP2/79H254baNQiSecQIkAAAAOEps2LI7m3bsSRvmRfsmp7Jpx55s2LK717p49hMgAQAAwFFi86692T85dUDb/smpbN61t6eKWCoESAAAAHCUWLtqeSbGD/yr/MT4WNauWt5TRSwVAiQAAAA4SqxbszLnrl6RY8fHUkmOHR/LuatXZN2alX2XxrPceN8FAAAAAIdnbKxy/eUXZMOW3dm8a2/WrlqedWtWZmys+i6NZzkBEgAAABxFxsYqF51zci465+S+S2EJMYUNAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAAA4qkxNtTz25GR2PrYvdz3wSKamWt8lPesJkAAAAICjxtRUyw23bcyDu5/Iw3v2522fvi833LZRiPQMEyABAAAAR40NW3Zn0449acO8aN/kVDbt2JMNW3b3WteznQAJAAAAOGps3rU3+yenDmjbPzmVzbv29lTR0iBAAgAAAI4aa1ctz8T4gXHGxPhY1q5a3lNFS4MACQAAADhqrFuzMueuXpFjx8dSSY4dH8u5q1dk3ZqVfZf2rDbedwEAAAAAh2tsrHL95Rdkw5bd2bxrb9auWp51a1ZmbKz6Lu1ZTYAEAAAAHFXGxioXnXNyLjrn5L5LWTJMYQMAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADrNK0CqqlOq6pNVdd/w58lzHLOmqv5rVW2sqnuq6udm7HtzVT1YVRuGjyvmUw8AAAAAozffEUjXJbm9tXZektuH27NNJvn51toFSV6a5Ker6sIZ+9/aWls3fNw6z3oAAAAAGLH5BkhXJrl5+PzmJK+afUBrbXtr7a7h88eSbExy5jzPCwAAAMACmW+AdFprbXsyCIqSrO46uKrWJvmuJJ+b0XxtVX2xqm6aawocAAAAAP06ZIBUVZ+qqrvneFx5JCeqqhVJPpjk9a21R4fNv5vk+UnWJdme5Dc7+l9TVXdW1Z07d+48klMDAAAAMA/jhzqgtfb9B9tXVQ9V1emtte1VdXqSHQc57pgMwqP3tNY+NOO1H5pxzO8l+WhHHTcmuTFJ1q9f3w5VNwAAAACjMd8pbLckuXr4/OokH5l9QFVVkncl2dha+61Z+06fsfmjSe6eZz0AAAAAjNh8A6S3JLm0qu5LculwO1V1RlVN31Ht5Ulek+SVVbVh+LhiuO/Xq+pLVfXFJK9I8oZ51gMAAADAiB1yCluX1tquJJfM0b4tyRXD53+RpA7S/zXzOT8AAAAAz7z5jkACAAAA4FlOgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAHAWmploee3IyOx/bl7seeCRTU63vkoAlRIAEAACwyE1Ntdxw28Y8uPuJPLxnf9726ftyw20bhUjAghEgAQAALHIbtuzOph170oZ50b7JqWzasScbtuzutS5g6RAgAQAALHKbd+3N/smpA9r2T05l8669PVUELDXjfRdAv172/FV9lwBJkpOOOyaJaxIAYC6P75/Mn39pex7f/9Q32o6fWJbLXvhc35+ABWEEEgAAwCJ38fmrs27NypwwsSyV5ISJZVm3ZmUuPn9136UBS4QRSAAAAIvcsrHKH772JfnMvTvy5W2P5sIzTsrF56/OsrHquzRgiRAgAQAAHAWWjVUuueC0XHLBaX2XAixBprABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARLQu6emWh55fH8efOSJ3L7xoTw11fouCQAAgBkESECvnppqec27PpdNO/Zk6+4n8jPv++u85l2fEyIBAAAsIgIkoFefuXdHNmzZnem86PH9T2XDlt35zL07+i0MAACAbxAgAb26Z9ujeWL/Uwe0PbH/qXx526M9VQQAAMBsAiSgVy8446QcP7HsgLbjJ5blwjNO6qkiAAAAZhMgAb26+PzVWbdmZU6YWJZKcsLEsqxbszIXn7+679IAAAAYGu+7AGBpWzZW+cPXviSfuXdHvrzt0Vx4xkm5+PzVWTZWfZcGAADAkAAJ6N2yscolF5yWSy44re9SAAAAmIMpbAAAAAB0EiABAAAA0EmABAAAAECneQVIVXVKVX2yqu4b/jz5IMdtrqovVdWGqrrzSPsDAAAA0J/5jkC6LsntrbXzktw+3D6YV7TW1rXW1n+L/QEAAADowXwDpCuT3Dx8fnOSVy1wfwAAAACeYfMNkE5rrW1PkuHP1Qc5riX5RFV9vqqu+Rb6AwAAANCT8UMdUFWfSvLcOXa98QjO8/LW2raqWp3kk1X1N621zx5B/wyDp2uS5Oyzzz6SrgAAAADMwyEDpNba9x9sX1U9VFWnt9a2V9XpSXYc5DW2DX/uqKoPJ3lxks8mOaz+w743JrkxSdavX98OVTcAAAAAozHfKWy3JLl6+PzqJB+ZfUBVLa+qE6efJ/mBJHcfbn8AAAAA+jXfAOktSS6tqvuSXDrcTlWdUVW3Do85LclfVNUXkvyvJH/eWvtYV38AAAAAFo9DTmHr0lrbleSSOdq3Jbli+Pz+JC86kv4AAAAALB7zHYEEAAAAwLOcAAkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBOAiQAAAAAOgmQAAAAAOgkQAIAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBOAiQAAAAAOgmQAAAAAOgkQAIAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBOAiQAAAAAOgmQAAAAAOgkQAIAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADoJEACAAAAoJMACQAAAIBOAiQAAAAAOgmQAAAAAOgkQAIAAACgkwAJAAAAgE7zCpCq6pSq+mRV3Tf8efIcx5xfVRtmPB6tqtcP9725qh6cse+K+dQDAAAAwOjNdwTSdUlub62dl+T24fYBWmv3ttbWtdbWJfnHSR5P8uEZh7x1en9r7dZ51gMAAADAiM03QLoyyc3D5zcnedUhjr8kyd+11h6Y53kBAAAAWCDzDZBOa61tT5Lhz9WHOP6qJO+b1XZtVX2xqm6aawrctKq6pqrurKo7d+7cOb+qAQAAADhshwyQqupTVXX3HI8rj+REVTWR5EeS/MmM5t9N8vwk65JsT/KbB+vfWruxtba+tbb+1FNPPZJTAwAAADAP44c6oLX2/QfbV1UPVdXprbXtVXV6kh0dL3V5krtaaw/NeO1vPK+q30vy0cMrGwAAAICFMt8pbLckuXr4/OokH+k49tWZNX1tGDpN+9Ekd8+zHgAAAABGbL4B0luSXFpV9yW5dLidqjqjqr5xR7WqOmG4/0Oz+v96VX2pqr6Y5BVJ3jDPegAAAAAYsUNOYevSWtuVwZ3VZrdvS3LFjO3Hk6ya47jXzOf8AAAAADzz5jsCCQAAAIBnOQESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQCcBEgAAAACdBEgAAAAAdBIgAQAAANBJgAQAAABAJwESAAAAAJ0ESAAAAAB0EiABAAAA0EmABAAAAEAnARIAAAAAnQRIAAAAAHQSIAEAAADQSYAEAAAAQKd5BUhV9S+r6p6qmqqq9R3HXVZV91bVpqq6bkb7KVX1yaq6b/jz5PnUAwAAAMDozXcE0t1J/nmSzx7sgKpaluQdSS5PcmGSV1fVhcPd1yW5vbV2XpLbh9sAAAAALCLzCpBaaxtba/ce4rAXJ9nUWru/tbY/yfuTXDncd2WSm4fPb07yqvnUAwAAAMDoLcQaSGcm2TJje+uwLUlOa61tT5Lhz9ULUA8AAAAAR2D8UAdU1aeSPHeOXW9srX3kMM5Rc7S1w+g3u45rklwz3NxTVYca+XS0eE6Sh/sugkXBtUDiOuCbXAtMcy0wzbXANNcC01wLTBvVtXDOwXYcMkBqrX3/PE++NcmaGdtnJdk2fP5QVZ3eWtteVacn2dFRx41JbpxnLYtOVd3ZWjvoAuQsHa4FEtcB3+RaYJprgWmuBaa5FpjmWmDaQlwLCzGF7Y4k51XV86pqIslVSW4Z7rslydXD51cnOZwRTQAAAAAsoHkFSFX1o1W1NcnLkvx5VX182H5GVd2aJK21ySTXJvl4ko1JPtBau2f4Em9JcmlV3Zfk0uE2AAAAAIvIIaewdWmtfTjJh+do35bkihnbtya5dY7jdiW5ZD41PAs866bl8S1zLZC4Dvgm1wLTXAtMcy0wzbXANNcC057xa6FaO+L1rAEAAABYQhZiDSQAAAAAjmICpJ5U1WVVdW9Vbaqq6/quh35U1Zqq+q9VtbGq7qmqn+u7JvpVVcuq6q+r6qN910J/qmplVf1pVf3N8PPhZX3XRD+q6g3DPx/urqr3VdVxfdfEwqiqm6pqR1XdPaPtlKr6ZFXdN/x5cp81sjAOci38xvDPiC9W1YeramWPJbJA5roWZuz7v6qqVdVz+qiNhXWwa6GqfmaYM9xTVb8+6vMKkHpQVcuSvCPJ5UkuTPLqqrqw36royWSSn2+tXZDkpUl+2rWw5P1cBjccYGn7nSQfa619R5IXxTWxJFXVmUl+Nsn61toLkyzL4G62LA3vTnLZrLbrktzeWjsvye3DbZ793p2nXwufTPLC1tp3JvnbJL+40EXRi3fn6ddCqmpNBjel+spCF0Rv3p1Z10JVvSLJlUm+s7X2giT/adQnFSD148VJNrXW7m+t7U/y/gz+Q7PEtNa2t9buGj5/LIO/JJ7Zb1X0parOSvKDSX6/71roT1WdlOR7k7wrSVpr+1tru3stij6NJzm+qsaTnJBkW8/1sEBaa59N8rVZzVcmuXn4/OYkr1rImujHXNdCa+0Tw7tdJ8lfJTlrwQtjwR3kcyFJ3prk/05igeMl4iDXwk8leUtrbd/wmB2jPq8AqR9nJtkyY3trhAZLXlWtTfJdST7Xcyn057cz+MN/quc66Ne3J9mZ5A+G0xl/v6qW910UC6+19mAG/3r4lSTbk/xDa+0T/VZFz05rrW1PBv8IlWR1z/WwOPzvSW7ruwj6UVU/kuTB1toX+q6F3v2jJN9TVZ+rqv9WVf9k1CcQIPWj5miTFi9hVbUiyQeTvL619mjf9bDwquqHkuxorX2+71ro3XiSi5L8bmvtu5LsjWkqS9JwfZsrkzwvyRlJllfV/9ZvVcBiUlVvzGBJhPf0XQsLr6pOSPLGJG/quxYWhfEkJ2ewNMq/T/KBqpore/iWCZD6sTXJmhnbZ8WQ9CWrqo7JIDx6T2vtQ33XQ29enuRHqmpzBtNaX1lVf9RvSfRka5KtrbXp0Yh/mkGgxNLz/Un+vrW2s7X29SQfSvJPe66Jfj1UVacnyfDnyKcncPSoqquT/FCSH2ut+cfopen5GfwjwxeG3yHPSnJXVT2316roy9YkH2oD/yuDWQ0jXVRdgNSPO5KcV1XPq6qJDBbEvKXnmujBMBF+V5KNrbXf6rse+tNa+8XW2lmttbUZfCZ8urVmpMES1Fr7apItVXX+sOmSJF/usST685UkL62qE4Z/XlwSC6ovdbckuXr4/OokH+mxFnpUVZcl+YUkP9Jae7zveuhHa+1LrbXVrbW1w++QW5NcNPwuwdLzZ0lemSRV9Y+STCR5eJQnECD1YLjg3bVJPp7BF8EPtNbu6bcqevLyJK/JYLTJhuHjir6LAnr3M0neU1VfTLIuyQ39lkMfhqPQ/jTJXUm+lMH3tht7LYoFU1XvS/KXSc6vqq1V9dokb0lyaVXdl8Edl97SZ40sjINcC29PcmKSTw6/P76z1yJZEAe5FliCDnIt3JTk26vq7gxmNFw96tGJZbQjAAAAAF2MQAIAAACgkwAJAAAAgE4CJAAAAAA6CZAAAAAA6CRAAgAAAKCTAAkAAACATgIkAAAAADoJkAAAAADo9P8Dd2cSGFM4ZZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "fig, ax = plt.subplots( figsize=(20,8))\n",
    "\n",
    "plot_pacf(train.difference_2.dropna(),lags=15,ax=ax,method='ywm') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4f4715c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# P = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a763c1",
   "metadata": {},
   "source": [
    "#### Moving Averages- q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2ea8b6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots( figsize=(20,8))\n",
    "\n",
    "plot_acf(train.difference_2.dropna(),lags=20,ax=ax) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37ae6c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "23a212a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "data": {
      "image/png": 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no03qnAf3FXqGEM4i+Tt+OlAA9Aa+W+2QJ4HTQgidD+UNSZJUXYP5P39Jkg6HGOMa4DmSUKm6p4BxIYR2qednA7OBNdWOGQ08FmNcFROFMca/1VFd78YY/wK8U4tjS1PHVwEBqCQJlXJTh5wH/DLGuCHGWAL8kv2HF3vbBTwOXAGQ6qK5DLinhmOvBp4AnqFaGHeoYoxrY4y/Bd6u5fF3xhj/AWw91GsdpKtIgplyks+/RjHGRcCr/PvPX3WH/fOPMa4E/gEMCiG0CyH8PYRQkupk+3sIodvuY1OdUz8IIbwK7CAJrq4NIcxPddIsCSHcUO34U0PSvfeNEEJxCGF1COHCEMKHQwjvhRA2hBD+s9rxGSGEm0MIi0PSbfdgCGH3z/Lk1NdNqY6qsalzPpV6/Y0h6ejrWe16MYTwuRDCQmBhKgD7eaqWzSGE2SGEQbX9rGrpHGBStecXA+/EGB+KMZYC/w0MDSEcs/eJMcbtMcb/Tv3vSVWM8e/AUmDkPl7rauAvMcZ3Yowbge+ThFm7r1cKTAPOrIP3JUkSYJgkSWrkUr8kn0PSyVJdKcm/6F+Ren4VsHdQ9AbwlRDCjSGEwSGEcACv2yMkS9J6HGTpNV1zNv+q+88xxuLdu1IPqj3vFkJocwCX/xvJZwBwFknItWqv128BXEoSctxD0pWTfQD1bwohnHQANdVLIYSTgW7A/STdZVft59hjgJP595+/vR3Wzz+E0B34MElnXQbwV6An0APYCey9PPOTwPUkHXnLgGLgXKA1cC3w8xDCiGrHdwKaAV2B7wB/Aj5BEpCcDHwnhNA7dewXgQtJOnO6kHR4/Sa1b3zqa9tUZ9vrIYQLgf8kCWzygVeA+/aq90LgeGAgSagynqRLry1JZ9j6fXwuv039XNb0mF3TOSmDgXerPT8OmLX7SYxxO0mX4wfOMgohdEzVuq9g+X3XTn3fca9OtvnA0A96LUmSasswSZLUWD0eQthKspykGPivGo75G3BVKnQ5haQ7pLofkSwp+zjJspWVIYS9u0Ee3+sX0M8AxBiXxxjbxhiX19UbijEOIfll/kpgSrVd/wBuCiHkhxA6kfyyDtDiAK79GpAbQhhAzcEaJL/MlwHPA38nWa7zkb2O+eVen8f3q71G2xjjFBq+q4F/pLpE7gXOCSF02OuY6SGE7SS/5E8E/m22UnV1+Pnv7fEQwiaSn5dJwA9jjOtjjI/EGHfEGLcCPyD5+a/ujlQnTEWMsTzG+HSMcXGqQ29SqoaTqx1fDvwgxlhOErLlAb+IMW6NMb5DEpQMSR17A/D/YoxFMcYyki6eS/ezRO0G4Ecxxvkxxgrgh8Cw8P6ZRD9KdebtTNWSAxwDhNR5q2u6cIzxxtTPZU2PITWdk9KW93fFtQI273XM5lQd+xSS2Wb3AHfGGBfs47C9r737++rX3pqqSZKkOmGYJElqrC6MMeYAp5L8Upm39wGpYCOfZLnS33fPP6m2vzLG+JsY4ziSX9R+ANweQjh2r9ep/gvonw7P29lTU2mM8T7g5hDC7k6EH5B0nMwEXiMJxcpJQrQDcRfweZLZUI/VsP9q4MFUwFBGMiNm73Dti3t9Ht8+wBrqtRBCc5LZWvcAxBhfB5aTBHzVjSAJAS4n6ZhpWYvL18Xnv7fdP589U8HJzhBCixDCH0IyAHoLydKytqHakHCSEHaPEMI5IYQ3UkvWNpF0OVX/O7U+xliZ+n7336O11fbvJPk8IOmIemx34EgSuFUCHffxHnoCv6h2/AaS7ruuNdUbY/wnSafVb4C1IYQ/hhBa7+PaB2sj7w9ztpEEvdW1poZlmLuFZBbVXSTLHPc3a23va+/+vvq1c4BN+61YkqQDYJgkSWrUUl0Ud5Dc8agmdwNfpeZOkOrX2Rlj/A3JL5ED67LGg5RFMoh3d22fjzF2jTH2JlnSM63aL/e1dRdwI8lg4R3Vd6SWC34I+ERI7iq1hmTJ1YdDMny4sbiI5Jf531b7HLpSw1K3VBfPg8DrJEu/PsiR+vy/CgwAjo8xtuZfS8uqL5Xcc4fBkAwYf4Tk71DHGGNbkplNtV72uZcVwDl7hY7NUnOdarqz4Qrghr2Ob57q5vq3egFijL+MMY4kWSLWH/h6TYWEEH4f3n/3weqP/c0zm5267m7vUG2ZWQihJdCHfSxdSy2Z/QtJgHZJqqNrX9537dT3a2OM1ZfuHcv7l8JJknRIDJMkSYLbgAkhhGE17PslMIF/Df7dI4TwpZAMF24eQmiSWuKWw7/f0e2ApYYENwOyU8+bhZrvCkYI4YQQwkkhhOxULd8k+SX0zdT+riGELqlrnkByd6j/qnb+HSGEOz6ophjjUpLlTv+vht2fJLm72ACSYdLDSH6ZLgI+Vrt3vX+pz2P3Z9A09Xxfx2al9mcATVKfX2a1/bW+1gG6GridZGbOsNRjHMmyq8H7OOdW4PrUEsR9OoKffw5Jp9CmkAy+rmkJaHXZJJ9lCVARktvYH8qw598DP9i9TC21PPOC1L4Skrvx9d7r+FtCCMeljm8TQvjovi4eQhgdQjg+tYRsO8mcsRqD1RjjZ+P77z5Y/bG/eUfP8P6lgY+RDDe/JPWz9h1g9u6layGEa0IIhdWO/x1JAHTe3h2RqeNjCOHU1NO/AdeFEAaG5IYB3yIJyHcf25RkNtUL+6lXkqQDYpgkSWr0YnKHs7+RhCx779sQY3wpxlhTR8RO4Kckd3hbB3yOpItgSbVjntqrm+Ex2DOAe1vY9wDunqnr7+5c2Em1gb4hhH+Ef90BqynJkp31wEqSJUYfiTHuHtDch2R523bgTuDmGOPz1V6rO8kdxT5QjHFKtetWdzXw2xjjmuoPkl/0qy+1+vVen8e0au9pW0iGV+/LTpIlPQAL+Ndyqd0dJL+vduyfUvs/RhK+7CQJXD7wWgcrhNCV5Pbst+31OUwDnmUfS85ijHNI5hXV2B2z17GH+vnXxm1Ac5Kf6TdSte+vpq0kc7geJOnMu5JkCPzB+kXq/OdDMtfsDZKlgKQ6sn4AvJpa1nZCjPExktll96eW5c0lGaq/L61Jfj42kgwPX8++OxMP1t9IusKap+ouAS5J1b4x9X6uqHb8nr+DqRDtBpJAcE21vysfT+3vRvKzOyd17WeBHwMvp97PMt4fAJ4PTNzHz40kSQcl1PzfxpIkqTEIyd2+ZgFDPmApjaQDEEL4IVAcY7ytFsc+D9wUY5xfi2M/ARwXY7yllnW8CVwXY5xbm+MlSaoNwyRJkiRJkiTVmsvcJEmSJEmSVGuGSZIkSZIkSao1wyRJkiRJkiTVWpN0F3Co8vLyYkFBQbrLkCRJkiRJOmpMmzZtXYwxv6Z9DT5MKigoYOrUqekuQ5IkSZIk6agRQli2r30uc5MkSZIkSVKtGSZJkiRJkiSp1gyTJEmSJEmSVGsNfmaSJEmSJElKv/LycoqKiigtLU13KToAzZo1o1u3bmRlZdX6HMMkSZIkSZJ0yIqKisjJyaGgoIAQQrrLUS3EGFm/fj1FRUX06tWr1ue5zE2SJEmSJB2y0tJS2rdvb5DUgIQQaN++/QF3kxkmSZIkSZKkOmGQ1PAczJ+ZYZIkSZIkSZJqzTBJkiRJkiQ1eJs2beK3v/3tAZ/34Q9/mE2bNtV9QUcxwyRJkiRJktTg7StMqqys3O95zzzzDG3btj1MVR2dvJubJEmSJEmqU9996h3mrdpSp9cc2KU1/3Xecfvcf/PNN7N48WKGDRtGVlYWrVq1onPnzsycOZN58+Zx4YUXsmLFCkpLS7npppu4/vrrASgoKGDq1Kls27aNc845h5NOOonXXnuNrl278sQTT9C8efM6fR9HAzuTJEmSJElSg3frrbfSp08fZs6cyf/93//x1ltv8YMf/IB58+YBcPvttzNt2jSmTp3KL3/5S9avX/9v11i4cCGf+9zneOedd2jbti2PPPLIkX4bDYKdSZIkSZIkqU7tr4PoSBkzZgy9evXa8/yXv/wljz32GAArVqxg4cKFtG/f/n3n9OrVi2HDhgEwcuRICgsLj1S5DYphkiRJkiRJOuq0bNlyz/cTJ07kxRdf5PXXX6dFixaceuqplJaW/ts5TZs23fN9ZmYmO3fuPCK1NjQuc5MkSZIkSQ1eTk4OW7durXHf5s2badeuHS1atGDBggW88cYbR7i6o8thDZNCCLeHEIpDCHOrbcsNIbwQQliY+tqu2r5bQgiLQgjvhhDOOpy1SZIkSZKko0f79u0ZN24cgwYN4utf//r79p199tlUVFQwZMgQvv3tb3PCCSekqcqjQ4gxHr6LhzAe2Ab8LcY4KLXtx8CGGOOtIYSbgXYxxm+GEAYC9wFjgC7Ai0D/GON+7+E3YuSoOH3a1MP2HiRJkiRJ0gebP38+xx57bLrL0EGo6c8uhDAtxjiqpuMPa2dSjHEysGGvzRcAd6a+vxO4sNr2+2OMZTHGpcAikmBpv95ZtZkP/+IV/vOxOTw0dQWLirdSVXX4AjJJkiRJkqTGLB0DuDvGGFcDxBhXhxA6pLZ3BaovWixKbfs3IYTrgesBcrv2IrdlNk/NXMW9by4HIKdZE4Z2a8vwHm0Z1j15tG/VtKZLSZIkSZIk6QDUp7u5hRq21dhiFGP8I/BHgFGjRsW7P308VVWRJeu2MX35Jmau2MTM5Zv47cTFVKa6lLrnNmdY93YM796WYT3aclyX1jRtknnY3owkSZIkSdLRKB1h0toQQudUV1JnoDi1vQjoXu24bsCq2l40IyPQt0MOfTvkcNmo5DI7dlUwd+UWZizfyMwVm5hauIGnZiWXzMoMDOzcmuE92u3pXurZvgUh1JRpSZIkSZIkCdITJj0JXA3cmvr6RLXt94YQfkYygLsf8NahvFCL7CaM6ZXLmF65e7at2VzKzBUbmZHqXnpw6grueK0QgHYtslLBUjuG9WjLsG5tadMi61BKkCRJkiRJOqoc1jAphHAfcCqQF0IoAv6LJER6MIRwHbAc+ChAjPGdEMKDwDygAvjcB93J7WB0atOMs9t05uxBnQGoqKxiYfE2ZizfxMwVSQfTxPdKiBFCgCFd23BK/3xOGZDP0G5taZJ5WGeWS5IkSZIk1WshxoZ957NRo0bFqVOn1uk1t5aWM7toM1MLNzJ5YQkzlm+kKkLrZk04qV8ep/TPZ3z/fDq3aV6nrytJkiRJUkNV0+3lj6RNmzZx7733cuONNx7wubfddhvXX389LVq0OAyV1X81/dmFEKbFGEfVdLxhUi1s3lHOlEXrmPReMZPeK2HtljIABnTM4ZQB+ZzSP59RBe0c6C1JkiRJarTSHSYVFhZy7rnnMnfu3AM+t6CggKlTp5KXl3cYKqv/DjRMqk93c6u32rTI4iNDOvORIZ2JMfLe2m17gqU7Xi3kj5OX0DwrkxP7tGd8/yRcKshrme6yJUmSJElKj3/cDGvm1O01Ow2Gc27d5+6bb76ZxYsXM2zYMCZMmECHDh148MEHKSsr46KLLuK73/0u27dv57LLLqOoqIjKykq+/e1vs3btWlatWsVpp51GXl4eL7/8ct3WfRQyTDpAIQQGdMphQKccrh/fh+1lFbyxZD2T3ith0nslvLQguTldz/YtkllL/fMZ26c9LbL9qCVJkiRJOlxuvfVW5s6dy8yZM3n++ed5+OGHeeutt4gxcv755zN58mRKSkro0qULTz/9NACbN2+mTZs2/OxnP+Pll19utJ1JB8qE4xC1bNqE04/tyOnHdgSgcN12Ji8sYdK7JTw0tYi/vb6M7MwMRvdqlwqXOtC/YytCCGmuXJIkSZKkw2Q/HURHwvPPP8/zzz/P8OHDAdi2bRsLFy7k5JNP5mtf+xrf/OY3Offcczn55JPTWmdDZZhUxwryWlKQ15KrxhZQVlHJ1MKNSdfSuyX88JkF/PCZBXRq3Yzx/fMY3z+fk/rm0bZFdrrLliRJkiTpqBFj5JZbbuGGG274t33Tpk3jmWee4ZZbbuHMM8/kO9/5ThoqbNgMkw6jpk0yGdc3j3F98/jPDx/L6s07mfxeCZPfW8ezc9fw4NQiQoAh3dpySr88Tu6fz7DubcnKzEh36ZIkSZIkNSg5OTls3boVgLPOOotvf/vbfPzjH6dVq1asXLmSrKwsKioqyM3N5ROf+AStWrXijjvueN+5LnOrHcOkI6hzm+ZcProHl4/uQUVlFbOKNjP5vRJeWVjCr19exC//uYicpk0YmxrkPb5fPj3aN87bEkqSJEmSdCDat2/PuHHjGDRoEOeccw5XXnklY8eOBaBVq1bcfffdLFq0iK9//etkZGSQlZXF7373OwCuv/56zjnnHDp37uwA7loIMcZ013BIRo0aFadOnZruMg7Z5h3lvLZ4HZMXJp1LKzftBKCgfQtO7pfP+NQg71ZNzf8kSZIkSfVPTbeXV8NQ059dCGFajHFUTcebTNQTbVpkcc7gzpwzuDMxRpas284r75XwysJ1PDK9iLveWEaTjMCInu0Y3y+ZtzSoSxsyMhzkLUmSJEmSjhzDpHoohECf/Fb0yW/FNeN6UVZRyfRlm5i8MFkS95Pn3+Mnz79HuxZZjOubt2dJXKc2zdJduiRJkiRJOsoZJjUATZtkMrZPe8b2ac83zz6GddvKeHXROialOpf+Pns1AP07ttqzJO74Xrk0y8pMc+WSJEmSJOloY5jUAOW1asoFw7pywbCuxBhZsGYrr6RmLd31xjL+MmUpLbMzOfO4Tpw3tDMn9c0nu4l3iJMkSZIkSYfOMKmBCyFwbOfWHNu5NdeP78POXZW8sXQ9z81dwz/mruGxGStp2yKLcwZ14rwhXTi+d3synbMkSZIkSZIOkmHSUaZ5dianDejAaQM68L0LBvHKwhKemrWKJ2au4r63VpCf05SPDO7MeUO7MKJHW0IwWJIkSZIkSbXn2qejWHaTDE4/tiO3XTGcad+awG+uHMHIHu24963lXPK71zjpf1/m1n8s4J1Vm4kxprtcSZIkSZIO2WOPPUYIgQULFuzZVlhYSPPmzRk2bBgDBw7kqquuory8HICJEydy7rnnAnDHHXcQQuCll176t+s9/PDDe7aVlJSQlZXFH/7wh33W8corr3DccccxbNgwdu7cWddv84DNnDmTZ555pk6uZZjUSDTPzuQjQzrz+0+OZNq3zuCnHx1Kv46t+PMrS/jIL6dwxs8mcduL77G4ZFu6S5UkSZIk6aDdd999nHTSSdx///3v296nTx9mzpzJnDlzKCoq4sEHH6zx/MGDB3PffffteX7//fczdOjQ9x3z0EMPccIJJ7zvuL3dc889fO1rX2PmzJk0b978A+uurKz8wGMORV2GSS5za4RymmVxychuXDKyGxu27+LZuWt4atYqfvHSQm57cSEDO7fmvKFdOG9oZ7q1a5HuciVJkiRJDcz/vvW/LNiw4IMPPADH5B7DN8d8c7/HbNu2jVdffZWXX36Z888/n//+7//+t2MyMzMZM2YMK1eurPEaJ598Mq+88grl5eWUlZWxaNEihg0b9r5j7rvvPn76059y5ZVXsnLlSrp27fq+/X/+85958MEHee6553jxxRe5++67+cY3vsE//vEPQgh861vf4vLLL2fixIl897vfpXPnznuCrptvvpmJEydSVlbG5z73OW644QYAfvzjH3PXXXeRkZHBOeecw6233sqf/vQn/vjHP7Jr1y769u3LXXfdRYsWLXjooYf47ne/S2ZmJm3atOHFF1/kO9/5Djt37mTKlCnccsstXH755bX/8PdimNTI5bbM5srje3Dl8T1Yu6WUp2ev5qnZq/jfZxfwv88uYESPtpw3tAsfGdKZDjnN0l2uJEmSJEn79Pjjj3P22WfTv39/cnNzmT59OiNGjHjfMaWlpbz55pv84he/qPEaIQTOOOMMnnvuOTZv3sz555/P0qVL9+xfsWIFa9asYcyYMVx22WU88MADfOUrX3nfNT796U8zZcoUzj33XC699FIeeeQRZs6cyaxZs1i3bh2jR49m/PjxALz11lvMnTuXXr168cc//pE2bdrw9ttvU1ZWxrhx4zjzzDNZsGABjz/+OG+++SYtWrRgw4YNAFx88cV85jOfAeBb3/oWf/nLX/jCF77A9773PZ577jm6du3Kpk2byM7O5nvf+x5Tp07l17/+9SF/zoZJ2qNj62Z86qRefOqkXqzYsIOnZq/iqVmr+e5T8/j+3+dxQu/2nDe0C+cM6kTbFtnpLleSJEmSVE99UAfR4XLffffxpS99CYArrriC++67b0+YtHjxYoYNG8bChQu59NJLGTJkyD6vc8UVV/DLX/6SzZs389Of/pQf/vCHe/bdf//9XHbZZXuOu+666/4tTNrblClT+NjHPkZmZiYdO3bklFNO4e2336Z169aMGTOGXr16AfD8888ze/bsPfOZNm/ezMKFC3nxxRe59tpradEiWT2Um5sLwNy5c/nWt77Fpk2b2LZtG2eddRYA48aN45prruGyyy7j4osvPtCP8QMZJqlG3XNbcOOpfbnx1L4sKt7Kk7NW89SsVdzy6Bz+68l3uGBoF64+sYBBXduku1RJkiRJkli/fj3//Oc/mTt3LiEEKisrCSHw4x//GPjXzKTVq1dz6qmn8uSTT3L++efXeK0xY8Ywd+5cmjdvTv/+/d+377777mPt2rXcc889AKxatYqFCxfSr1+/fda2v5tetWzZ8n3H/epXv9oTCu327LPP1ng39muuuYbHH3+coUOHcscddzBx4kQAfv/73/Pmm2/y9NNPM2zYMGbOnLnP1z8YDuDWB+rbIYevTOjPP796Cn//wklcPqo7T89Zzbm/msJHf/8aT89eTUVlVbrLlCRJkiQ1Yg8//DBXXXUVy5Yto7CwkBUrVtCrVy+mTJnyvuM6d+7Mrbfeyo9+9KP9Xu9HP/rR+zqSAN599122b9/OypUrKSwspLCwkFtuueXfhn3vbfz48TzwwANUVlZSUlLC5MmTGTNmzL8dd9ZZZ/G73/1uz53m3nvvPbZv386ZZ57J7bffzo4dOwD2LHPbunUrnTt3pry8fE+4BUkX1vHHH8/3vvc98vLyWLFiBTk5OWzdunW/ddaWYZJqLYTAoK5t+P6Fg3j9ltP51keOZe2WMj5373RO/vHL/OblRWzYvivdZUqSJEmSGqH77ruPiy666H3bLrnkEu69995/O/bCCy9kx44dvPLKK/u83jnnnMNpp51Wq9fY313dAC666CKGDBnC0KFD+dCHPsSPf/xjOnXq9G/HffrTn2bgwIGMGDGCQYMGccMNN1BRUcHZZ5/N+eefz6hRoxg2bBg/+clPAPj+97/P8ccfz4QJEzjmmGP2XOfrX/86gwcPZtCgQYwfP56hQ4dy2mmnMW/ePIYNG8YDDzyw33o/SNhfq1VDMGrUqDh16tR0l9FoVVZFXl5QzB2vFTJl0Tqym2Rw4bBkCdxxXVwCJ0mSJEmNxfz58zn22GPTXYYOQk1/diGEaTHGUTUd78wkHZLMjMAZAztyxsCOLFy7lTteK+TR6St5cGoRY3rlcu2JBUwY2JEmmTbBSZIkSZJ0NDBMUp3p1zGHH1w0mG+cdQwPTVvBna8X8h/3TKdLm2Z8YmxPrhjdg9yW3gVOkiRJkqSGzHYR1bk2LbL49Mm9mfi10/jTVaPold+SHz/7LmN/9BLffHg281ZtSXeJkiRJkqTDoKGP0mmMDubPzM4kHTaZGYEJAzsyYWBH3l2zlTtfL+TR6UU8MHUFx/fK5RqXwEmSJEnSUaNZs2asX7+e9u3b13gbe9U/MUbWr19Ps2bNDug8B3DriNq0YxcPTl3Bna8tY+WmnXRp04xPji3gitHdaecSOEmSJElqsMrLyykqKqK0tDTdpegANGvWjG7dupGVlfW+7fsbwG2YpLSorIq8OH8td7xayOtL1tO0SQYXDe/K1ScWcGzn1ukuT5IkSZKkRs0wSfXagjVbuPO1ZTw2o4jS8io+dEwHbjq9H0O7t013aZIkSZIkNUqGSWoQNu3YxV2vL+Mvry5l045yTh2Qz02n92N4j3bpLk2SJEmSpEbFMEkNytbScv72+jL+/MoSNu4oZ3z/JFQa2dNQSZIkSZKkI8EwSQ3StrIK7np9GX96ZQkbtu/i5H553HR6P0YV5Ka7NEmSJEmSjmqGSWrQtpdVcPcby/jj5CWs376LcX3bc9Pp/RnTy1BJkiRJkqTDwTBJR4Uduyq4543l/GHyYtZt28XY3u256Yx+nNC7fbpLkyRJkiTpqGKYpKPKzl2V3PPmMv4weQklW8s4vlcuN53Rj7G92xNCSHd5kiRJkiQ1eIZJOiqVlldy75vL+f2kxRRvLWNMQRIqndjHUEmSJEmSpENhmKSjWml5Jfe/tZzfTVrM2i1ljOrZjpvO6MdJffMMlSRJkiRJOgiGSWoUSssreWjqCn47cTGrN5cyokdbbjqjP+P7GSpJkiRJknQgDJPUqJRVVPLQ1CJ++/IiVm0uZVj3ttx0Rj9O7Z9vqCRJkiRJUi0YJqlR2lVRxcPTivjNy4tYuWknQ7u35TvnHsvInrnpLk2SJEmSpHptf2FSxpEuRjpSsptkcOXxPXj5a6dy68WDWbe1jI/+/nV+9sJ7VFRWpbs8SZIkSZIaJMMkHfWym2RwxZgePPulk7lweFd++dJCLv396yxbvz3dpUmSJEmS1OAYJqnRyGmWxc8uG8avPjacJSXb+PAvXuHBqSto6Es9JUmSJEk6kgyT1OicN7QLz35pPIO6tuEbD8/mxnums2nHrnSXJUmSJElSg2CYpEapS9vm3PuZE/jm2cfw4vy1nH3bK7y2aF26y5IkSZIkqd5LW5gUQrgphDA3hPBOCOFLqW25IYQXQggLU1/bpas+Hf0yMwL/cWofHrtxHC2aZnLln9/kh8/Mp6yiMt2lSZIkSZJUb6UlTAohDAI+A4wBhgLnhhD6ATcDL8UY+wEvpZ5Lh9Wgrm14+gsn8/Hje/DHyUu46DevsXDt1nSXJUmSJElSvZSuzqRjgTdijDtijBXAJOAi4ALgztQxdwIXpqc8NTbNszP5wUWD+fNVo1izpZRzfzWFv71e6HBuSZIkSZL2kq4waS4wPoTQPoTQAvgw0B3oGGNcDZD62qGmk0MI14cQpoYQppaUlByxonX0O2NgR5790smc0Ls933niHT51x9uUbC1Ld1mSJEmSJNUbaQmTYozzgf8FXgCeBWYBFQdw/h9jjKNijKPy8/MPU5VqrDrkNOOOa0fz3fOP49XF6zn7tsn8c8HadJclSZIkSVK9kLYB3DHGv8QYR8QYxwMbgIXA2hBCZ4DU1+J01afGLYTA1ScW8PcvnER+TlM+dcdUvv34XHbucji3JEmSJKlxS+fd3DqkvvYALgbuA54Erk4dcjXwRHqqkxL9O+bwxOfH8emTenHXG8s491evMHfl5nSXJUmSJElS2qQtTAIeCSHMA54CPhdj3AjcCkwIISwEJqSeS2nVtEkm3zp3IHdfdzzbyiq46Lev8odJi6mqcji3JEmSJKnxCQ39blWjRo2KU6dOTXcZaiQ2bt/FLY/O4dl31jC2d3t+etlQurRtnu6yJEmSJEmqUyGEaTHGUTXtS2dnktTgtGuZze8+MYIfXzKEWUWbOPu2yTw9e3W6y5IkSZIk6YgxTJIOUAiBy0Z35+kvnkyv/FZ87t7pfPXBWWwrq/UNCSVJkiRJarAMk6SD1CuvJQ9/dixf/FBfHptRxDm/mMybS9anuyxJkiRJkg4rwyTpEGRlZvCVMwfw4A1jyQiBK/70Bv/z93mUllemuzRJkiRJkg4LwySpDowqyOWZL57Mx4/vwZ+nLOW8X01hTtHmdJclSZIkSVKdM0yS6kjLpk34nwsHc+enxrCltJyLfvsqt734HuWVVekuTZIkSZKkOmOYJNWxU/rn8/yXTuHcIZ257cWFXPzb11i4dmu6y5IkSZIkqU4YJkmHQZsWWdx2xXB++/ERFG3cwUd+NYU/v7KEqqqY7tIkSZIkSTokhknSYfThwZ15/sunML5fPv/z9Hyu+NMbrNiwI91lSZIkSZJ00AyTpMMsP6cpf7pqJP936RDmrdrC2bdN5r63lhOjXUqSJEmSpIbHMEk6AkIIfHRUd5790skM6daWWx6dw6fueJviLaXpLk2SJEmSpANimCQdQd3ateCeTx/Pf503kNcWr+fM2ybz1KxV6S5LkiRJkqRaM0ySjrCMjMC143rxzE0n07N9S75w3ww+f+90Nm7fle7SJEmSJEn6QIZJUpr0yW/FI58dy9fO7M+zc9dw5m2TeXlBcbrLkiRJkiRpvwyTpDRqkpnB5z/Uj8c/N47cFtlce8fb3PzIbLaVVaS7NEmSJEmSamSYJNUDg7q24ckvjOOGU3rzwNQVnH3bZN5Ysj7dZUmSJEmS9G8Mk6R6ommTTG4551geumEsmRmBj/3pDf7n7/MoLa9Md2mSJEmSJO1hmCTVM6MKcnnmiyfz8eN78OcpSzn3V1OYXbQp3WVJkiRJkgQYJkn1UsumTfifCwfzt0+NYVtpBRf+5lVufmQ2xVtL012aJEmSJKmRM0yS6rHx/fN57svjuXZcLx6ZXsSp/zeRX760kJ27XPomSZIkSUoPwySpnmvTPItvnzuQF758CuP75fOzF97jtJ9M5NHpRVRVxXSXJ0mSJElqZAyTpAaiIK8lv//kSB64/gTyc5rylQdnccFvXvWub5IkSZKkI8owSWpgju/dnic+N46fXz6UddvKuOKPb3D936aydN32dJcmSZIkSWoEDJOkBigjI3DR8G7886un8rUz+/PqonVM+NkkvvvUO2zasSvd5UmSJEmSjmKGSVID1jw7k89/qB8vf/1UPjqqG3e+Vsgp/zeRP7+yhF0VVekuT5IkSZJ0FDJMko4CHXKa8aOLh/DMTSczpFsb/ufp+Zz580k8O3cNMTqkW5IkSZJUdwyTpKPIMZ1a87dPjeGv144mKzODz949jcv/8AazizaluzRJkiRJ0lHCMEk6yoQQOG1AB/5x08n8z4WDWFyyjfN//SpffmAmqzbtTHd5kiRJkqQGLjT0JTCjRo2KU6dOTXcZUr21tbSc305czF+mLCUAnzm5N589tQ+tmjZJd2mSJEmSpHoqhDAtxjiqpn12JklHuZxmWXzz7GP451dP4azjOvHrlxdx6v9N5L63llNZ1bDDZEmSJEnSkWeYJDUS3dq14JcfG85jN55Iz/YtuOXROXz4F68w6b0Sh3RLkiRJkmrNMElqZIb3aMfDnx3Lbz8+gh3lFVx9+1tc8JtXeXzGSsorq9JdniRJkiSpnnNmktSIlVVU8tDUIm5/dSlLSrbTsXVTrhpbwMeP70HbFtnpLk+SJEmSlCb7m5lkmCSJqqrIpPdK+MuUpUxZtI5mWRlcMqIbnzqpF33yW6W7PEmSJEnSEWaYJKnWFqzZwu1TlvL4zFXsqqjitAH5XHdSb8b1bU8IId3lSZIkSZKOAMMkSQds3bYy7n5jGXe/sYx123ZxTKccPjWuF+cP60KzrMx0lydJkiRJOowMkyQdtNLySp6ctYrbpyxlwZqt5LXK5uPH9+QTJ/QkP6dpusuTJEmSJB0GhkmSDlmMkdcWr+cvU5byzwXFZGdmcMGwLlx3ci+O6dQ63eVJkiRJkurQ/sKkJke6GEkNUwiBcX3zGNc3j8Ul2/jrq0t5eFoRD00rYlzf9lx3Ui9O7d+BjAznKkmSJEnS0czOJEkHbdOOXdz71nL+9toy1mwppXd+S64d14tLRnSlRbZZtSRJkiQ1VC5zk3RYlVdW8cyc1fxlylJmF22mTfMsrjy+B1ePLaBTm2bpLk+SJEmSdIAMkyQdETFGpi7byF9eWcrz89aQEQJnHdeJK4/vwdje7V0CJ0mSJEkNhDOTJB0RIQRGF+QyuiCXFRt2cOdrhTw8vYin56ymoH0LrhjTg0tHdiOvlXeBkyRJkqSGys4kSYdVaXklz85dw71vLuetwg1kZQbOPK4THx/Tg7F92hOC3UqSJEmSVN+4zE1SvbBw7Vbue2sFj0wvYvPOcnrlteSK0d25dGQ32tutJEmSJEn1hmGSpHqltLySZ+as5r63lvN24UayMzM4a1AnPjamO2N7260kSZIkSelmmCSp3npv7VbufXM5j04vYktpBb3zWvKxMT24ZGQ3cltmp7s8SZIkSWqU6mWYFEL4MvBpIAJzgGuBFsADQAFQCFwWY9y4v+sYJklHh9LySp6enXQrTV2WdCudPagTHxvTgxN659qtJEmSJElHUL0Lk0IIXYEpwMAY484QwoPAM8BAYEOM8dYQws1AuxjjN/d3LcMk6ejz7pqt3PfWch6ZXsTW0gp657fkyjE9uHiE3UqSJEmSdCTU1zDpDWAosAV4HPgl8Cvg1Bjj6hBCZ2BijHHA/q5lmCQdvXbuquTpOau5981lTF++iezMDM4Z3Ikrx/RgTC+7lSRJkiTpcKl3YRJACOEm4AfATuD5GOPHQwibYoxtqx2zMcbYroZzrweuB+jRo8fIZcuWHaGqJaXLgjVbuO/N5Tw6YyVbSyvok9+Sz5zcm8tGdScjw1BJkiRJkupSvQuTQgjtgEeAy4FNwEPAw8CvaxMmVWdnktS47NxVyd9nr+LuN5Yxq2gzw7q35X8uHMSgrm3SXZokSZIkHTX2FyZlHOliUs4AlsYYS2KM5cCjwInA2tTyNlJfi9NUn6R6qnl2Jh8d1Z3HPzeOn18+lKKNOzj/11P47lPvsLW0PN3lSZIkSdJRL11h0nLghBBCi5AMPTkdmA88CVydOuZq4Ik01SepngshcNHwbrz0lVO58vge3PFaIWf8bBJ/n72KdC3flSRJkqTGIC1hUozxTZJlbdOBOak6/gjcCkwIISwEJqSeS9I+tWmRxf9cOJjHbhxHfk5TPn/vDK66/S0K121Pd2mSJEmSdFRK2wDuuuLMJEm7VVZF7nq9kJ88/x67Kqu48dQ+fPaUPjTLykx3aZIkSZLUoNTHmUmSVOcyMwLXjOvFP796Cmcd14nbXlzI2bdN5pWFJekuTZIkSZKOGoZJko46HVo341cfG85d140hhMAn//IWn793Omu3lKa7NEmSJElq8AyTJB21Tu6Xzz9uOpkvn9Gf5+et5fSfTuKvry6lorIq3aVJkiRJUoNlmCTpqNYsK5ObzujH818az4ie7fjuU/O44DevMnPFpnSXJkmSJEkNkmGSpEahIK8ld147mt9cOYJ128q46Lev8v8em8PmHeXpLk2SJEmSGhTDJEmNRgiBjwzpzItfOYVrT+zFfW8t5/SfTeTR6UU09DtbSpIkSdKRYpgkqdHJaZbFd84byFNfOInuuS34yoOz+Nif3mBR8dZ0lyZJkiRJ9Z5hkqRG67gubXjksyfyw4sGM3/1Vs75xSv8+NkF7NxVme7SJEmSJKneMkyS1KhlZASuPL4HL331FM4f2pXfTlzMGT+bxEvz16a7NEmSJEmqlwyTJAnIa9WUn142lAeuP4EW2Zlcd+dUvnT/DDZu35Xu0iRJkiSpXjFMkqRqju/dnqe/eDJfOqMff5+9mgk/n8yzc9ekuyxJkiRJqjcMkyRpL9lNMvjSGf158vMn0SGnKZ+9expfuG8GG+xSkiRJkiTDJEnal4FdWvPE58fx1Qn9eXbuaib8bBLPzFmd7rIkSZIkKa0MkyRpP7IyM/jC6f146gsn0aVtc268Zzqfu2c667aVpbs0SZIkSUoLwyRJqoVjOrXmsRtP5OtnDeCFeWs58+eTeWrWKmKM6S5NkiRJko4owyRJqqUmmRl87rS+/P2LJ9G9XXO+cN8M/uPu6ZRstUtJkiRJUuNhmCRJB6h/xxwe+Y8TufmcY/jnu8VM+Pkknpi50i4lSZIkSY2CYZIkHYQmmRl89pQ+PPPFk+iV15Kb7p/J9XdNo3hLabpLkyRJkqTDyjBJkg5B3w45PPzZE/l/Hz6Wye+VMOHnk3lsRpFdSpIkSZKOWoZJknSIMjMCnxnfm2duOpm+HVrx5Qdm8Zm/TWWtXUqSJEmSjkKGSZJUR/rkt+LBG8by7XMHMmXROib8bBIPT7NLSZIkSdLRxTBJkupQZkbgupN68Y+bxjOgUw5fe2gW197xNqs370x3aZIkSZJUJwyTJOkw6JXXkgeuH8t/nTeQN5ds4MyfTebBt1fYpSRJkiSpwTNMkqTDJCMjcO24Xjz7pZMZ2KU133hkNlf/9W1WbbJLSZIkSVLDZZgkSYdZz/Ytue8zJ/D9C45jauEGzvz5ZO5+YxlVVXYpSZIkSWp4DJMk6QjIyAh8cmwBz31pPEO7t+Fbj8/lij+9wZKSbekuTZIkSZIOiGGSJB1B3XNbcPd1x/PjS4ewYPUWzv7FK/zm5UWUV1aluzRJkiRJqhXDJEk6wkIIXDaqOy9+9RTOOLYD//fcu5z/61eZU7Q53aVJkiRJ0gcyTJKkNOmQ04zffnwkf/jkSNZvK+OC30zhR8/MZ+euynSXJkmSJEn7ZJgkSWl21nGdeOErp3D56O78YfISzv7FZF5bvC7dZUmSJElSjQyTJKkeaNM8ix9dPIR7P3M8AbjyT29y8yOz2byzPN2lSZIkSdL7GCZJUj1yYp88nv3SeG44pTcPTSvijJ9N4tm5q9NdliRJkiTtYZgkSfVMs6xMbjnnWJ743DjyWzXls3dP5z/unkbx1tJ0lyZJkiRJhkmSVF8N6tqGJz4/jm+cPYCXFhRzxk8n8cDby4kxprs0SZIkSY2YYZIk1WNZmRnceGpfnr3pZI7p3JpvPjKHj//5TZat357u0iRJkiQ1UoZJktQA9M5vxf2fOYEfXDSIOUWbOeu2yfxx8mIqKqvSXZokSZKkRsYwSZIaiIyMwMeP78kLXzmFk/rm88NnFnDx715j3qot6S5NkiRJUiNimCRJDUynNs3401Uj+fWVw1m1aSfn/3oK//fcAkrLK9NdmiRJkqRGwDBJkhqgEALnDunCC18+hfOHdeE3Ly/mw798hbeWbkh3aZIkSZKOcoZJktSAtWuZzc8uG8bfPjWGXRVVXPaH17np/hksKt6W7tIkSZIkHaUMkyTpKDC+fz7PfWk8/3FqH55/Zy0Tfj7JUEmSJEnSYRFijOmu4ZCMGjUqTp06Nd1lSFK9sX5bGX96ZSl/e72QneWVnDekC188vS99O+SkuzRJkiRJDUQIYVqMcVSN+wyTJOnoZKgkSZIk6WAZJklSI7Zh+y7+9MoS7nwtCZXOHdKFL36oL/06GipJkiRJqplhkiSJDdt38edUqLTDUEmSJEnSfhgmSZL22DtU+sjgznzx9H70N1SSJEmSlHJYwqQQQnaMcdchVVYHDJMk6eBs3L6LP09Zwh2vGipJkiRJer/9hUkZtbzAxBBCQbXnY4C3D6GgASGEmdUeW0IIXwoh5IYQXgghLEx9bXewryFJ2r92LbP5+lnHMOWbH+LGU/vw8oJizrptMp+7dzrvrd2a7vIkSZIk1VO16kwKIZwF/AL4JdAVOAf4dIxx+iEXEEImsBI4HvgcsCHGeGsI4WagXYzxm/s7384kSaobG7fv4i9TlnLHa4Vs31XBhwclnUoDOtmpJEmSJDU2dbLMLYRwKvACsA4YHmNcU0fFnQn8V4xxXAjhXeDUGOPqEEJnYGKMccD+zjdMkqS6tWlHEir99dVCtpVV7Fn+ZqgkSZIkNR6HHCaFEL4NXAZcDwwBvgx8Ncb4dB0UdzswPcb46xDCphhj22r7NsYY/22pWwjh+lQt9OjRY+SyZcsOtQxJ0l72DpU+PLgTN57al0Fd26S7NEmSJEmHWV2ESb8Abo4x7kw97wn8OcY44RALywZWAcfFGNfWNkyqzs4kSTq8Nu3Yxe1TlnJ7KlQa1LU1l4/uwQXDutC6WVa6y5MkSZJ0GNTVMrfmQI8Y47t1WNgFwOdijGemnrvMTZLqqc07y3li5krue2sF81dvoVlWBh8e3JkrRvdgdEE7QgjpLlGSJElSHdlfmNSklhc4D/gJkA30CiEMA74XYzz/EGv7GHBftedPAlcDt6a+PnGI15ck1ZE2zbO4amwBnzyhJ3NXbuH+t5fz5MxVPDp9Jb3zWnL56O5cPKIb+TlN012qJEmSpMOotsvcpgEfIukUGp7aNifGOPigXziEFsAKoHeMcXNqW3vgQaAHsBz4aIxxw/6uY2eSJKXPjl0VPDNnDQ+8vZy3CzfSJCNwxrEduXxMd8b3yyczw24lSZIkqSE65M4koCLGuHmvJQy1Wx+3DzHGHUD7vbatB04/lOtKko6cFtlNuHRkNy4d2Y1Fxdt4cOoKHplWxLPvrKFzm2Z8dFR3PjqyG91zW6S7VEmSJEl1pLadSX8BXgJuBi4BvghkxRg/e3jL+2B2JklS/bKrooqX5q/l/rdXMHlhCQAn9c3j8tHdmTCwI02bZKa5QkmSJEkfpC7u5tYC+H/AmUAAngO+H2MsrctCD4ZhkiTVXys37eShqSt4aGoRKzftpF2LLC4e0Y0rRnenX8ecdJcnSZIkaR/q5G5u9ZVhkiTVf5VVkSmL1vHg2yt4ft4ayisjI3q05YrRPfjIkM60bFrbVdeSJEmSjoSDDpNCCE+xn9lIdXA3t0NmmCRJDcv6bWU8NmMl97+9gkXF22iZncn5w7pwxegeDO3eNt3lSZIkSeLQwqRTUt9eDHQC7k49/xhQGGP8z7os9GAYJklSwxRjZPryjdz/1gr+Pns1O8srGda9LdeOK+DDgzuTlZmR7hIlSZKkRqsuZiZNjjGO/6Bt6WCYJEkN39bSch6dvpI7Xitk6brtdGzdlE8c35Mrj+9B+1ZN012eJEmS1OjURZg0H/hIjHFJ6nkv4JkY47F1WulBMEySpKNHVVVk0nsl3P7qUl5ZuI7sJhlcMLQL147rxcAurdNdniRJktRo7C9Mqu3E0y8DE0MIS1LPC4Ab6qA2SZL2yMgInHZMB047pgOLirdyx2uFPDJtJQ9NK+L4XrlcO66ACQM7kZkR0l2qJEmS1GjV+m5uIYSmwDGppwtijGWHraoDYGeSJB3dNu8o54Gpy7nztWWs3LSTrm2bc/WJPbl8VA/atMhKd3mSJEnSUemQl7mlLnIiSUfSnm6mGOPf6qLAQ2GYJEmNQ2VV5IV5a/nrq0t5c+kGmmdlcvGIrlw7roC+HXLSXZ4kSZJ0VKmLmUl3AX2AmUBlanOMMX6xroo8WIZJktT4vLNqM3e8WsgTs1axq6KKk/vlce24Ak7t34EMl8BJkiRJh6yuBnAPjLVtYzqCDJMkqfFav62M+95azl1vLGPtljJ65bXk6rE9uXRUd1o1re1YQEmSJEl7q4sw6SHgizHG1XVd3KEyTJIklVdW8cyc1fz11UJmrthEq6ZN+OioblxzYgE927dMd3mSJElSg1MXYdLLwDDgLWDP4O0Y4/l1VONBM0ySJFU3Y/lG7nitkKdnr6YyRk4/pgPXnNiLcX3bE4JL4CRJkqTaqIsw6ZSatscYJx1ibYfMMEmSVJO1W0q5541l3PPmctZv30W/Dq246sQCLh7elZYugZMkSZL2q07u5lZfGSZJkvantLySp2at4s7XC5m7cgs5zZrw0ZHduWpsTwryXAInSZIk1eSgw6QQwlagpgMCyd3cWtdNiQfPMEmSVBsxRqYv38gdry3jH3OSJXCn9s/n6hMLGN8v37vASZIkSdUc9s6kEEK7GOPGQ77QQTBMkiQdqLVbSrnnzeXc++Zy1m0ro3deSz45tieXjuxGTrOsdJcnSZIkpd2RCJOmxxhHHPKFDoJhkiTpYJVVVPKPOWu447XkLnAtszO5dGQ3rjqxgD75rdJdniRJkpQ2+wuT6moCqWsDJEkNTtMmmVw4vCsXDu/KrBWbuPO1Qu57awV3vr6Mk/vlcc2JBZw2oINL4CRJkqRq7EySJKmakq1l3P/Wcu5+cxlrt5TRI7cFV43tyUdHdadNc5fASZIkqXFwmZskSQeovLKK595Zw52vFfJ24UaaZ2Vy0YiuXHNiAf075qS7PEmSJOmwcpmbJEkHKCszg3OHdOHcIV2Yu3Izd75WyMPTirj3zeWM7d2eq08sYMLAjmS6BE6SJEmNTK07k0IIJwH9Yox/DSHkA61ijEtT+3JjjBsOY537ZGeSJOlI2bB9F/e/vZy7X1/Gqs2ldG3bnE+c0JNzBnWiZ/sWhGCwJEmSpKPDIS9zCyH8FzAKGBBj7B9C6AI8FGMcV7elHjjDJEnSkVZRWcWL89dyx2uFvLEk+beUvFZNGV3QjlEFuYzq2Y6BXVqTlZmR5kolSZKkg1MXy9wuAoYD0wFijKtCCA6MkCQ1Sk0yMzh7UGfOHtSZJSXbeH3JeqYVbuTtZRv4x9w1ADTPymR4j7aM6pkETMN7tCWnmQO8JUmS1PDVNkzaFWOMIYQIEEJoeRhrkiSpweid34re+a34+PE9AVizuZSpyzYwtXAjU5dt4NcvL6IqQkaAYzq1/lf3UkE7OrdpnubqJUmSpANX22VuXwP6AROAHwGfAu6NMf7q8Jb3wVzmJkmqz7aVVTBj+cY94dKM5ZvYsasSgK5tm78vXOrfIYcMB3pLkiSpHjjkmUmpi0wAziS5c9tzMcYX6q7Eg2eYJElqSCoqq5i/eitvF25g6rINvF24kZKtZQC0btaEkT3/NXdpaPe2NMvKTHPFkiRJaozqJEyqrwyTJEkNWYyRFRt2vi9cWlS8DYCszMBxXdowrHvbPQ/vGidJkqQjoS7u5rYV2PvAzcBU4KsxxiWHXOVBMkySJB1tNm7fxbRlyUDvGcs2MWflZnaWJ0vj2rXIYmi1cGlY97a0bZGd5oolSZJ0tKmLu7n9DFgF3EuyzO0KoBPwLnA7cOqhlylJkgDatczmjIEdOWNgRyBZGvfe2m3MXLGJmSs2MnPFJia9V8Lufw8qaN/iX+FSj3Yc2zmHpk1cHidJkqTDo7adSW/GGI/fa9sbMcYTQgizYoxDD1uFH8DOJElSY7S1tJw5KzcnAdPyTcxcsYni1Oyl7MwMBnZpzbDubRneoy1Du7k8TpIkSQemLjqTqkIIlwEPp55fWm1fwx66JElSA5TTLIsT++RxYp88IJm9tHpzaap7KQmYHnh7BXe8Vgi4PE6SJEl1p7Zh0seBXwC/JQmP3gA+EUJoDnz+MNUmSZJqKYRAl7bN6dK2OR8e3Bn44OVxvfNbMrZ3e07sk8cJvXNp36ppGt+BJEmSGgrv5iZJUiNSfXnc20uTu8dtK6sA4JhOOYztk4RLY3rl0qZ5VpqrlSRJUrrUxd3cmgHXAccBzXZvjzF+qq6KPFiGSZIkHbyKyirmrNzMa4vX8/ri9bxduIGyiioyAgzu2oaxffIY26c9owva0SK7tg3NkiRJaujqIkx6CFgAXAl8j2TZ2/wY4011WejBMEySJKnulFVUMmP5plS4tI6ZKzZRXhnJygwM696WsX3yOLFPe4b3aOsd4yRJko5idREmzYgxDg8hzI4xDgkhZAHPxRg/VNfFHijDJEmSDp8duyqYWrhxT7g0Z+VmqiI0bZLB6IJcxvZpz9g+7RnStQ1NMjPSXa4kSZLqSF3cza089XVTCGEQsAYoqIPaJElSPdYiuwnj++czvn8+AJt3lvPW0g28tngdry9ez/899y4ArZo2YUyvXE5MhUvHdmpNRkZIZ+mSJEk6TGobJv0xhNAO+BbwJNAK+PZhq0qSJNVLbZpnMWFgRyYM7AjA+m1lvLHkX+HSPxcUA9CuRRZnD+rMRcO7MqpnO4MlSZKko8gHLnMLIWQAl8YYHzwyJR0Yl7lJklR/rNlcyutL1vHyghJemLeWneWVdG3bnAuGdeHC4V3p3zEn3SVKkiSpFupiZtLkGOP4Oq+sDhgmSZJUP20vq+D5eWt4fMYqXllYQlWEgZ1bc+HwLpw/tCud2jT74ItIkiQpLeoiTPo2sBN4ANi+e3uMcUNdFXmwDJMkSar/SraW8ffZq3h8xkpmFW0mBBjbuz0XDu/K2YM60bpZVrpLlCRJUjV1ESYtrWFzjDH2PtTiDpVhkiRJDcuSkm08MXMVj89cybL1O8huksGEYztywbAunDqgA9lNvCucJElSuh1ymFSfGSZJktQwxRiZuWITj89Yyd9nr2b99l20bZHFhwcng7tH9nBwtyRJUrrURWdSC+ArQI8Y4/UhhH7AgBjj3+u21ANnmCRJUsNXXlnFlIXreHzmSp5/51+Duy8c3oULh3Wln4O7JUmSjqi6CJMeAKYBV8UYB4UQmgOvxxiHHUJRbYE/A4OACHwKeJdkLlMBUAhcFmPcuL/rGCZJknR02T24+7EZq5iSGtx9XJfWXDisK+cP60LH1g7uliRJOtzqIkyaGmMcFUKYEWMcnto2K8Y49BCKuhN4Jcb45xBCNtAC+E9gQ4zx1hDCzUC7GOM393cdwyRJko5eNQ3uPrFPez55QgFnDuzoMjhJkqTDpC7CpNeA04FXY4wjQgh9gPtijGMOsqDWwCygd6xWQAjhXeDUGOPqEEJnYGKMccD+rmWYJElS47CkZBuPz1zFo9OLKNq4k/4dW/G50/rykcGdaZLp0G5JkqS6VBdh0pnA/wMGAs8D44BrYowTD7KgYcAfgXnAUJIldDcBK2OMbasdtzHG2K6G868Hrgfo0aPHyGXLlh1MGZIkqQGqqKzi6Tmr+c3Li3hv7TZ6tm/Bjaf24aLh3bwTnCRJUh2pk7u5hRDaAycAAXgjxrjuEAoaBbwBjIsxvhlC+AWwBfhCbcKk6uxMkiSpcaqqijw/by2/eXkRc1ZupkubZtxwSh8uH92dZlmZ6S5PkiSpQdtfmFSrf74LITwJnEmy7OzvhxIkpRQBRTHGN1PPHwZGAGtTy9tIfS0+xNeRJElHqYyMwNmDOvHk58dxx7Wj6dK2Of/15Duc9L8v84dJi9lWVpHuEiVJko5Kte0F/ylwMjAvhPBQCOHSEMJB30olxrgGWBFC2D0P6XSSJW9PAlentl0NPHGwryFJkhqHEAKnDujAQ58dy/3Xn8CxnXP40T8WMO7Wf/KLFxeyeUd5ukuUJEk6qtR6mRtACCET+BDwGeDsGGPrg37hZG7Sn4FsYAlwLUm49SDQA1gOfDTGuGF/13GZmyRJ2tvMFZv49T8X8eL8tbRq2oRPju3JdSf1Iq9V03SXJkmS1CDU1cyk5sB5wOUkS9KeijF+sc6qPEiGSZIkaV/mr97Cb15exNNzVtO0SQYfG9OD68f3pnOb5ukuTZIkqV6ri7u5PQAcDzwLPABUApfHGD9Xl4UeDMMkSZL0QRaXbON3Exfz2IyVZAS4dGR3/uOUPvRo3yLdpUmSJNVLdREmnQ2sBz4KXAYsBR6JMf66Lgs9GIZJkiSptlZs2MEfJi/mwbeLqIyRC4Z24cbT+tC3Q066S5MkSapXDjpMCiH0B64APkYSJj0AfC3G2PNwFHowDJMkSdKBWrullD9NXsI9by6ntKKScwZ14sZT+zKoa5t0lyZJklQvHEqYVAW8AlwXY1yU2rYkxtj7sFR6EAyTJEnSwVq/rYy/vlrIna8VsrWsgtMG5PP5D/VlZM/cdJcmSZKUVvsLkzI+4NxLgDXAyyGEP4UQTgdCXRcoSZKUDu1bNeVrZw1gys0f4mtn9mfmik1c8rvXueR3r/HcO2uoqqr9XW8lSZIai9rOTGoJXEiy3O1DwJ3AYzHG5w9rdbVgZ5IkSaorO3ZV8ODbK/jzlKUUbdxJ77yWfPrk3lw8oivNsjLTXZ4kSdIRc8gDuPe6WC7JIO7LY4wfqoP6DolhkiRJqmsVlVX8Y+4a/jB5MXNXbiGvVVOuObEnnzihJ21bZKe7PEmSpMOuTsOk+sYwSZIkHS4xRl5fvJ4/TF7CpPdKaJGdyWWjunPdSb3ontsi3eVJkiQdNoZJkiRJh2j+6i38afISnpy1igh8ZHBnrh/f2zvASZKko5JhkiRJUh1ZtWknf311Kfe9tYJtZRWM69ueG8b34eR+eYTgfUokSdLRwTBJkiSpjm3eWc59by3n9ilLKd5axrGdW3P9+F6cO6QLWZkfdMNcSZKk+s0wSZIk6TApq6jkiZmr+NPkJSws3kaXNs341Em9uGJMD1o1bZLu8iRJkg6KYZIkSdJhVlUVmfheMb+ftIS3lm4gp1kTPn58Tz41roAOrZuluzxJkqQDYpgkSZJ0BM1csYk/Tl7Ms3PX0CQjgwuHd+H68b3p2yEn3aVJkiTVimGSJElSGhSu286fpyzhoalFlFVUccaxHfnyhH4c18U7wEmSpPrNMEmSJCmN1m8r42+vL+Ovry5lS2kFHxncmS9P6E/fDq3SXZokSVKNDJMkSZLqgc07y/nT5CXc/upSSssruWh4N750Rj+657ZId2mSJEnvY5gkSZJUj6zbVsbvJi7mrjeWEWPk8tHd+cKH+tHRQd2SJKmeMEySJEmqh1Zv3smv/rmIB99eQWZG4KqxPfmPU/uS2zI73aVJkqRGzjBJkiSpHlu+fge3vfQej89YSfOsTK47qRefHt+b1s2y0l2aJElqpAyTJEmSGoCFa7fy8xff45k5a2jTPIsbTunNNScW0CK7SbpLkyRJjYxhkiRJUgMyd+Vmfvr8u7z8bgl5rZryudP6cOXxPWjaJDPdpUmSpEbCMEmSJKkBmlq4gZ88/y5vLNlAlzbN+OLp/bhkZDeyMjPSXZokSTrK7S9M8r9EJEmS6qlRBbnc95kTuPu648lv3YybH53DhJ9N4omZK6mqatj/IChJkhouwyRJkqR6LITASf3yePzGE/nTVaNolpXJTffP5JxfvMJz76yhoXeZS5KkhscwSZIkqQEIITBhYEee+eLJ/PJjwymvrOKGu6ZxwW9eZdJ7JYZKkiTpiHFmkiRJUgNUUVnFo9NX8ouXFrJy005G9WzHWcd1YnSvXI7r0tq5SpIk6ZA4gFuSJOkoVVZRyQNvr+CvrxaydN12AFpkZzK8R1vGFLRndK92DO/ejubZ3glOkiTVnmGSJElSI1C8pZS3Cjfw9tINvFW4kQVrthAjZGUGBnVtw5iCXEYX5DKqoB1tW2Snu1xJklSPGSZJkiQ1Qpt3ljN92cY9AdPsos3sqqwCYEDHHEb3asfoglzG9Mqlc5vmaa5WkiTVJ4ZJkiRJorS8klkrNvF2YdK5NK1wA9t3VQLQrV1zxqSCpdG9cumd15IQQporliRJ6bK/MKnJkS5GkiRJ6dEsK5Pje7fn+N7tgWSI9/zVW/d0Lk16r4RHZ6wEIK9VNqN6JsHSmIJcBnZpTWaG4ZIkSbIzSZIkSSkxRpas256aubSBtws3sGLDTiAJl04/piMTBnbkpH55NMtyoLckSUczl7lJkiTpoKzZXMqbS9fz4vxiJi4oZmtZBc2zMhnfP48JAztx+jEdaNfSYd6SJB1tDJMkSZJ0yHZVVPHGkvW8MG8tL8xby5otpWQEGF2Qy5nHdeLMgR3pntsi3WVKkqQ6YJgkSZKkOhVjZM7KzXuCpQVrtgJwTKcczhzYkQkDOzGoa2uHeEuS1EAZJkmSJOmwWr5+B8/PW8ML89byduEGqiJ0btOMCQOTOUvH92pPdpOMdJcpSZJqyTBJkiRJR8yG7bv454JiXpi3hsnvrWNneSU5zZpw2oAOTBjYkVMH5JPTLCvdZUqSpP0wTJIkSVJalJZXMmXhOl6Yt5YX569l/fZdZGUGxvbJS7qWju1IpzbN0l2mJEnai2GSJEmS0q6yKjJj+UZemLeW5+etZem67QAM6daGCcd2ZMJxHRnQMcc5S5Ik1QOGSZIkSapXYowsLtnG86kB3jOWbwKge25zJhzbiQkDOzK6oB1NMp2zJElSOhgmSZIkqV4r3lrKS/OLeWHeWqYsWseuiiratsjiQwM6cMbAjozvn0+rpk3SXaYkSY2GYZIkSZIajO1lFbyysIQX5hXzzwVr2bijnOzMDE7s254JAztyxrEd6djaOUuSJB1OhkmSJElqkCoqq5i2LJmz9ML8tSxbvwOAod3bMuHYDkwY2In+HVs5Z0mSpDpmmCRJkqQGL8bIouJ/zVmauWITAD1yWyR3hhvYkVE9nbMkSVJdMEySJEnSUad4Sykvzi/mhXlreHXx+n/NWTqmAxOOTeYstXTOkiRJB6VehkkhhEJgK1AJVMQYR4UQcoEHgAKgELgsxrhxf9cxTJIkSdLuOUvPz1vLPxcUs2lHOdlNMhjfL5+rxvbk5H55LoWTJOkA1OcwaVSMcV21bT8GNsQYbw0h3Ay0izF+c3/XMUySJElSdRWVVUxNzVl6YuYq1m0ro2+HVlx9YgGXjOhKi2y7lSRJ+iANKUx6Fzg1xrg6hNAZmBhjHLC/6xgmSZIkaV92VVTx9JxV/PXVQmYXbaZ1syZcMaYHnzyhJ91zW6S7PEmS6q36GiYtBTYCEfhDjPGPIYRNMca21Y7ZGGNst7/rGCZJkiTpg8QYmb58E399dSn/mLuGGCMTBnbkmhN7cULvXJfASZK0l/2FSens8R0XY1wVQugAvBBCWFDbE0MI1wPXA/To0eNw1SdJkqSjRAiBkT3bMbJnO1Zv3sndbyzj3jeX89w7azm2c2uuPbGA84d1oVlWZrpLlSSp3qsXd3MLIfw3sA34DC5zkyRJ0hFQWl7JEzNX8tdXC1mwZiu5LbP52JjufPKEAjq1aZbu8iRJSqt6t8wthNASyIgxbk19/wLwPeB0YH21Ady5McZv7O9ahkmSJEk6FDFG3liygb++upQX5q8lMwTOGdyZa04sYESPti6BkyQ1SvVxmVtH4LHU/zE3Ae6NMT4bQngbeDCEcB2wHPhomuqTJElSIxFCYGyf9ozt054VG3bwt9cLuf/tFTw1axVDu7XhmnEFfGRwF7KbZKS7VEmS6oV6scztUNiZJEmSpLq2vayCR6cX8dfXCllSsp38nKZ84vieXHl8D/Jzmqa7PEmSDrt6t8ytLhkmSZIk6XCpqoq8smgdf311KRPfLSE7M4Nzh3bmU+N6Mahrm3SXJ0nSYVMfl7lJkiRJ9V5GRuCU/vmc0j+fJSXbuPO1Qh6eVsSj01cysmc7LhnRjXMGdaJdy+x0lypJ0hFjZ5IkSZJ0ALaUlvPQ1CLueWMZS9Ztp0lGYHz/fM4b2pkJAzvRqqn/XitJavhc5iZJkiTVsRgj76zawlOzVvHUrFWs2lxKs6wMTj+mI+cN7cypAzrQLCsz3WVKknRQDJMkSZKkw6iqKjJ9+UaenLWKp2evZv32XbRq2oQzj+vI+UO7MK5vHlmZ3g1OktRwGCZJkiRJR0hFZRWvL1nPkzNX8ew7a9haWkFuy2w+PLgT5w3pwuiCXDIyQrrLlCRpvwyTJEmSpDQoq6hk0rslPDlrFS/OX0tpeRWdWjfj3CGdOX9YFwZ3bUMIBkuSpPrHMEmSJElKs+1lFbw4fy1PzVrFpPdKKK+MFLRvwXlDu3D+0C7065iT7hIlSdrDMEmSJEmqRzbt2MVz76zhyVmreH3xeqoiHNMpZ0+w1D23RbpLlCQ1coZJkiRJUj1VvLWUZ2av5slZq5i+fBMAw7q35aLhXTl/aBfatcxOb4GSpEbJMEmSJElqAFZs2MHfZ6/miZkrWbBmK1mZgdOP6cglI7tx6oB87wgnSTpiDJMkSZKkBuadVZt5ZNpKnpi5kvXbd9G+ZTYXDOvKJSO7clyXNukuT5J0lDNMkiRJkhqo8soqJr1bwiPTi3hpfjG7Kqs4plMOl47sxgXDupKf0zTdJUqSjkKGSZIkSdJRYOP2XTw1exWPTCtiVtFmMjMCp/bP55KR3Tj92A40bZKZ7hIlSUcJwyRJkiTpKLOoeCsPT1vJYzOKWLuljDbNszhvaGcuHdmdod3aEEJId4mSpAbMMEmSJEk6SlVWRaYsWscj04p47p01lFVU0Se/JZeM7MbFw7vRqU2zdJcoSWqADJMkSZKkRmBLaTnPzF7NI9OLeLtwIyHASX3zuHRkN84c2Inm2S6DkyTVjmGSJEmS1MgsW7+dR6av5JFpRazctJNWTZvwkcGduWRkN0YXtHMZnCRpvwyTJEmSpEaqqiry5tINPDK9iGfmrGbHrko6tW7GKf3zOWVAPuP65tGmeVa6y5Qk1TOGSZIkSZLYsauCZ+eu4cX5a3ll4Tq2llaQmREY3r3tnnBpUJc2ZGTYtSRJjZ1hkiRJkqT3qaisYuaKTUx6r4RJ75Uwu2gzAO1bZjO+fz6n9M/n5H55tG/VNM2VSpLSwTBJkiRJ0n6t21bGlIXrmPReCZPfK2H99l2EAIO7tkm6lvrnM6x7W5pkZqS7VEnSEWCYJEmSJKnWqqoi76zawqT3ipn0XgnTl2+isiqS06wJJ/fL45T++Yzvn0/nNs3TXaok6TAxTJIkSZJ00DbvLOe1RUnX0sR3S1izpRSAAR1zOGVA0rU0qqAdTZtkprlSSVJdMUySJEmSVCdijCws3sakd5NZS28t3cCuyiqaZ2VyYp/2nNwvjzG92jOgUw6ZDvKWpAZrf2FSkyNdjCRJkqSGK4RA/4459O+Yw2fG92bHrgreWLKeSe+WMPG9El5aUAxATtMmjOjZjjG9chnVsx1Du7elWZadS5J0NLAzSZIkSVKdKdq4g6mFG3m7cANvF27gvbXbAMjOzGBwtzaMKmjH6J65jCpoR9sW2WmuVpK0Ly5zkyRJkpQWm3bsYtqyjbxVuIGphRuZXbSJ8srkd5D+HVsxqiCXMQVJuNStXYs0VytJ2s0wSZIkSVK9UFpeyawVm5i6bCNvLd3A9GUb2VpWAUCXNs0YVZDL6IJ2jO6VS/8OOWQ4d0mS0sIwSZIkSVK9VFkVeXfN1j3L4t4u3MDaLWUAtG7WhJE9k2BpdEEug7u2ce6SJB0hhkmSJEmSGoQYI0Ubd1YLlzayqPhfc5f+dt0YTujdPs1VStLRz7u5SZIkSWoQQgh0z21B99wWXDyiGwAbtu9iauEGpi7bSL8OrdJcoSTJMEmSJElSvZbbMpszj+vEmcd1SncpkiQMkyRJkiQ1djHCPZdCuwLocCx0OC752rxtuiuTpHrJMEmSJElS41a2NXnMfhDKtvxre+uuqXBpYPLoOBDyBkBWs/TVKkn1gGGSJEmSpMatWWuePu0m+rbtS9/QlMySd6F4HqydB8XzYelkqNyVHBsyILdPEix1qPbI7QUZ3mlOUuNgmCRJkiSpUdtctpmbX7kZgFZZrRiSP4Rh+cMY2vtahuQNoVVmU9iwBNa+k4RLxfNgzRyY9ySQujt2k2aQP+BfS+R2h005nSGE9L05SToMQowx3TUcklGjRsWpU6emuwxJkiRJDVSMkaJtRcwsnsmsklnMKJ7Bwo0LiUQyQgb92vZjWIdhDM0fyvAOw+naqishBNi1HXZ3MRXP/1fYtG3Nvy7erG0SKnUeAt1GQ7dR0LanAZOkei+EMC3GOKrGfYZJkiRJkvR+23ZtY/a62cwsnsnM4pnMXjeb7eXbAchrnsew/GEM65A8BuYOJCsz618nb18PJfNTy+RSj9WzoWJnsr9lPnQdlQRL3UZD1xHQNCcN71KS9s0wSZIkSZIOQWVVJYs2LUrCpZKZzCiewcptKwHIzshmUN4ghnYYuidkym2Wu9cFKpJQqehtKJqafF2/MLUzJEvj9oRLo5Ilc85gkpRGhkmSJEmSVMdKdpTsWRY3s2Qm89bPo6KqAoCerXvuWRY3LH8Yvdv2JiNkvP8COzfCymn/CpeKpkLppmRfdk7SsdRt9L+Wx7XMO7JvUFKjZpgkSZIkSYdZWWUZ76x7h5klM/csj9tYthGAnKwcBucPZmj+UIbmD2Vw/mBaZ7d+/wVihPWLU8HS27ByKqyZC7Ey2d+u4F/hUtdR0GkwNMk+sm9SUqNhmCRJkiRJR1iMkeVblzOjeAazSmYxq2QWizYuIhIJBHq36c3QDkP3BEy92vT69+6lXTtg9cz3L4/bujrZl9kUOg+F7mOgz2nQcxxkNT/i71PS0ckwSZIkSZLqgW27tjFn3Zw94dLsktls2bUFgJzsHIbkDXlf91JOdg2DuTevTLqWdgdMK6dB5S5o0gx6ngh9Toe+p0P+Md41TtJBM0ySJEmSpHooxkjhlsI94dLe3Ut92vbZEy4NzR9KQZuCmruXlr0Ki/8Ji16Cde8m23O6QJ8PQd8PQe/ToEXuvxcgSftgmCRJkiRJDcQhdy9tWpEES4v/CUsmpoZ6B+gyPOlY6nN6Mncps8mRfmuSGhDDJEmSJElqoKpiFYVbCpldMnuf3UvDOgzbc+e47jndCbuXt1VVwsrpsPilpGtp5VSIVdC0NfQanwqXPpQM95akauptmBRCyASmAitjjOeGEHKBB4ACoBC4LMa4cX/XMEySJEmS1NhU716aWTKT2cWz2Vq+FYDcZrkMy0+FSx2GMbD9QLIzU3d927kJlk5KgqXF/4TNK5LtuX3+1bVUcBI0bZWeNyap3qjPYdJXgFFA61SY9GNgQ4zx1hDCzUC7GOM393cNwyRJkiRJjV1VrGLxpsXMLJnJzOLksXzrcgCyM7I5Lu84huUPY1iH5JHbLBdihHULU0viXoLCKVC+AzKyoMcJqXlLZ0CnwQ7ylhqhehkmhRC6AXcCPwC+kgqT3gVOjTGuDiF0BibGGAfs7zqGSZIkSZL079btXMes4qRzaUbxDOatn0d5VTkAPVv33BMuDe8wnF5tepFRWQ7LX091Lb0Ma+ckF2pXAMddDIMugY7HGSxJjUR9DZMeBn4E5ABfS4VJm2KMbasdszHG2K6Gc68Hrgfo0aPHyGXLlh2hqiVJkiSpYSqrLGPe+nnMKJ6xp3tpY1kyVaR1dmuG5g/dszRuUN4gmu/cDAufh3cegyWTIFZCXv8kVDruYsjvn+Z3JOlwqndhUgjhXODDMcYbQwincoBhUnV2JkmSJEnSgYsxsmzLsj1L42YUz2DJ5iUANAlNGJA7gOEdhjMkfwhDWnanS+EbhHmPJ8vhiNBxMAy6KAmWcnul9b1Iqnv1MUz6EfBJoAJoBrQGHgVG4zI3SZIkSUqLzWWbk6HeqXBp7rq5lFaWAslg7yH5QxiS04shWzYwaOnrtCxK/S7WZQQMuhiOuwjadEvjO5BUV+pdmPS+At7fmfR/wPpqA7hzY4zf2N/5hkmSJEmSdHiUV5WzcONC5pTMYfa62cwumU3hlkIAAoE+OT0YGpoxeP0KhqxdTO/ycjK7n5AshRt4AeR0TO8bkHTQGlKY1B54EOgBLAc+GmPcsL/zDZMkSZIk6cjZXLaZuevmMrtk9p6AacuuLQC0CE0YXBEZvHUDQ8rKGdxhGHmDLoNjz4eW7dNcuaQDUa/DpENlmCRJkiRJ6RNjZPnW5cwumc2sklnMWTeH9za8S0WsBKBreQVDynYxOKcnQ3qfzbHDriG7VYc0Vy3pgxgmSZIkSZKOmNKKUuZvmM/s4lnMLprC7HWzWVO5E4AmMXJsRguG5A1mcN9zGdxpJN1zuhNCSHPVkqozTJIkSZIkpVXx9rXMefcJZi1+hjmbF/FOZmRnRgYAbbNbMyh/CEPyhjA4fzCD8wbTpmmbNFcsNW6GSZIkSZKk+qOqiorCSSx+41fMXv0mc5o1Y07r9iyuKiWS/I7as3VPBuUNYnDeYIbkDWFA7gCyM7PTXLjUeBgmSZIkSZLqpw1L4M0/wPS72F6xg3d6jGR2zxHMoYw56+ZSsrMEgKyMLI7NPXZP59KQvCF0y+nm8jjpMDFMkiRJkiTVbzs3wfS/wVt/hM0roF0v4vH/wdr+ZzB7y2LmrJvD7JLZzN8wn50Vyfyltk3bMihvkMvjpMPAMEmSJEmS1DBUVsD8J+GN30LR29CsDYy4Go6/Adp0o6KqgkWbFjFn3RzmlMxhzro5LN60+H3L4wbnDWZo/lCGdxhOv3b9yAgZaX5TUsNjmCRJkiRJanhWvA1v/AbmPZk8H3gBjP0cdHv/77fbdm3jnfXv7OlemrNuDut2rgMgJzuH4R2GM6LDCEZ2HMnA9gOdvSTVgmGSJEmSJKnh2rQ8NVfpb1C2BbqNSUKlY86FzCb/dniMkaJtRcwonsH0tdOZtnYahVsKAWia2ZRBeYP2hEtD84fSKrvVEX5DUv1nmCRJkiRJavjKtsKMe+DN38HGQmjTI1n+NuKTyXK4/Vi/c30SLhVPZ/ra6SzYsIDKWElGyGBAuwGM6DiCER1GMKLjCPKa5x2Z9yPVY4ZJkiRJkqSjR1UlvPuPZK7SslchuxUM/2QSLOX2qtUltpdvZ1bJLKavnc6M4hnMLplNaWUpkMxdGtFhBMM7DGdkx5F0z+nuXePU6BgmSZIkSZKOTqtmwBu/g7mPQKyCAR9OlsD1GAsHEACVV5Yzb8M8pq9NOpemF09ny64tAOQ1z9vTtTSiwwj6t+tPZkbm4XpHUr1gmCRJkiRJOrptWQVv/Qmm/RV2boTOQ2H0p2HQpZDd4oAvVxWrWLJpCdOLk5lL04uns2b7GgBaZbViaP5QRnRMupcG5w2mWZNmdf2OpLQyTJIkSZIkNQ67dsCs++DtP0PxvGSW0rCPw6jrIK/vIV169bbVTCuetmdp3KJNiwBoktGEge0H7lkaN7zDcNo1a1cX70ZKG8MkSZIkSVLjEiMsfz0JleY9CVXl0PvUpFup/zk13gXuQG0u2/y+uUtz1s2hvKocgF5teu0Jl0Z0GEG3nG7OXVKDYpgkSZIkSWq8thXD9L/B1L/CliLI6QKjroURV0FOpzp7mbLKMt5Z9w7Ti5NwaUbxDLbu2gpAfvP8JFhKLY3r364/TTIOPdCSDhfDJEmSJEmSKitg4fNJt9LilyCjCRxzbtKtVHDSAQ3sro2qWMXiTYuZUTwjCZjWzmDV9lUAtGjSgqH5QxneMelcGpw3mBZZBz7bSTpcDJMkSZIkSapu/WKYejvMuBtKN0HegCRUGnp5MmfpMFmzfQ0zimcwbe00ZhTPYOHGhUQimSGTY3OPZXjH4XvmLuU1zztsdUgfxDBJkiRJkqSalO+EuY8m3UqrpkNWSxhyGYy+DjoNPuwvv2XXFmYVz9rTvTSnZA67qnYB0K1VN4Z3GM6wDsMY3mE4fdr2ISNkHPaaJDBMkiRJkiTpg62cBm/fDnMfhopS6H5C0q008Hxo0vSIlFBeWc68DfOYWTxzz9ylDaUbAMjJzkmWxqU6lwblDaJ5k+ZHpC41PoZJkiRJkiTV1o4NMOu+pFtpwxJokQcjPgkjr4V2PY9oKTFGVmxdsSdYmlk8k8WbFwPQJDThmNxj9nQuDe8wnPwW+Ue0Ph29DJMkSZIkSTpQVVWwdCK8/Rd49xmIEfqflXQr9TkdMtKz5Gxz2WZmlczaEzDNXTeXssoyALq26ronWBrWYRh92/Z1aZwOimGSJEmSJEmHYnMRTLsDpt0J24uhbU8YdS0M/yS0TO+g7PLKcuZvmL+nc2lG8QzWl64HICcrhyEdhjA8/19L47xrnGrDMEmSJEmSpLpQsQsW/D25E1zhK5CZDQMvgFHXQY8TIIR0V0iMkaKtRcwo+dfSuEWbFgHJ0rgBuQMY3mE4QzsMZXj+cDq27JjmilUfGSZJkiRJklTXihckodKs+6BsC3Q4DkZ/CoZcDk1z0l3d++xeGjezeCYzS2Yyp2QOpZWlAHRu2ZlhHYYxLD+ZvdSvXT+aZDRJc8VKN8MkSZIkSZIOl13bYc7DycDuNbMhuxUMuSzpVuo0KN3V1ai8qpz3Nrz3vsHexTuLAWjRpAWD8wfvCZeG5A8hJ7t+hWM6/AyTJEmSJEk63GKEldOSgd3vPAoVpdD9BBh9XbIUrknTdFe4TzFGVm9fvWfm0qySWby78V2qYhWBQN92ffeES8M6DKNbq26EerCkT4ePYZIkSZIkSUfSjg0w895kGdyGxdCiPQz/BIy8FnJ7pbu6Wtlevp056+Yk4VLxLGaVzGJb+TYA2jdrvydYGtZhGMfmHkt2ZnaaK1ZdMkySJEmSJCkdqqpg6cSkW+ndf0Csgr6nJ0vg+p8FGZnprrDWKqsqWbx5cTJ3KdXBVLStCIDsjGwG5Q1iRMcRe0Km1tmt01yxDoVhkiRJkiRJ6bZlFUy7E6bfCVtXQ+tuMOoaGH4V5DTMO6qV7ChhVsmsPXOX5q2fR0WsIBDo164fIzqMYETHEYzoMMK7xjUwhkmSJEmSJNUXleVJl9LUv8CSiZDRBI49L+lWKjgJGvAsoh3lO5i7bi7TiqcxY20ye2lHxQ4AurbqyogOIxjecTgjO4ykV5tezl2qxwyTJEmSJEmqj9YtSuYqzbwHSjdB+37J8rde46HHWGjWsJeKVVRV8O7Gd5mxdgbTi6czbe00NpRuAKBt07YM7zB8T8A0MHcgWZlZaa5YuxkmSZIkSZJUn5XvhLmPwqz7YMWbULkLQiZ0HZEESwUnQ/fjIbtFuis9JDFGlm9dzvS105lePJ0ZxTNYtmUZAM0ymzE4f3CyNK7DCIZ2GErLrJZprrjxMkySJEmSJKmhKN+ZBEpLJ8PSV2DlNIiVkJkN3cZAr5OTgKnrKGjS8O+gtm7nOmYUz9gTMC3YsICqWEVGyGBAuwGM7Dgy6WDqOIK85nnpLrfRMEySJEmSJKmhKtsKy16Hwsn/v717j63zru84/v4eO7FzcxIncZtekrRrWhrSxqeEy8YE1SoYE9qYhtqxTQxpaJ0mhtgEG7CLQNo/7KppYgyhDSgbAzoK4iZuQyulwEDEdkOaNjQhaZs22Lk6iZuLY3/3x/PEOYkc9zi1fY7t90s6Os95nuc8/rnSR3U//T2/pyiYDmwHEhYsLmYr3fAquOHVsHYLtLQ2erQv2NDw0Nii3j39PWw/uJ3TI6cBWLds3diC3tWuKus71rvu0jSxTJIkSZIkaa547gg8+d1i1tLeh+DgY8X+tg5Y/wtlufQq6HoxVCqNHesUGB4d5rHDj42VS70DvRw9cxSAzvbOi54Yd0vnLbRWZn+h1gwskyRJkiRJmqtODsC+slja+xAc+Wmxf1Fn8XS48zOXVm+c1U+KOy8z2Xt871ixtK1/G8+cfAaARa2L2LJmy1jBdNvq21i8YHavM9UolkmSJEmSJM0Xg/svzFra+xAc31/sX3o1XH1bUSqtuql83wjLrp71JVP/UH8xc2mgh57+Hn5y9CckSWu0cuuqW8fWXKp2Vels72z0cGcFyyRJkiRJkuajzGKm0r7vwL6H4eDjcHgPDD934ZyFy2DVz10omcaKpptg4ex8mtqJsyfoG+gbm7m049AOzo6eBWBDx4aLFvW+bul1rrs0DsskSZIkSZJUGB2FE8/CoSfg8O7yvdw+9jRQ0xN0XHvxLKbVZdm0/HqotDTsV5issyNnefTwo2O3xvUM9HDi7AkA1ixaQ3dX99ii3q67VLBMkiRJkiRJz2/4VDGT6XzBdGj3hfczgxfOa2krZjPVFk1L10BlAVRaa14t0LLg4s+VSz+3XjgnKjNyy91ojrLn2B56+nvYNrCNvoE+DgwdAIp1l25ffTvdXd1Uu6psWbOFpQuXTvuYmo1lkiRJkiRJunKZMHTw4plM54umo/tg9NzU/axKa03h1HKheIpK8TkCoqXcrhTbUSmeXHfR59rjUX6+9FgFWhbCsrX8bPEK+uIsvcOH6T2xj12DP2U0R6lEhY0rNl40e2nt0rVT9/s2KcskSZIkSZI0PUaGi0Lp1NGiVBo9V+wbHbnwefSSzxMdHxku943UHDsHOVrcopejkCPl5/L9/Gvs88jEx2qvde40nDhw8TpSwFBrG9tXrqV3yTJ6W2H76BDPZVGaXdW+iju6qnRf/VKqXVVuXnkzLbPotr96TFQmeROgJEmSJEm6ci0LilvdZrPMogwb3A/Hn4HB/SwZ3M/Ply+OPsO548/ykwUVetvb6GsbYtvJfr761P8AsIQKty9YQXXpOro7X8yWtS9lcedN0HFN8c9njnFmkiRJkiRJ0vMZOQcn+4vCafBpcnA/B47upndwD71nBujNUzzRWiEjaMnk5rPDVE+foTraQjUWc1XbCmhfPrnXwmXF7XsN4MwkSZIkSZKkF6KlFZZfW7x4OQFcU75eX55y4uQA259+kJ4DP6DvyON8/rln+K8cAeAahqiOnqV6/Gd095/ipqFBWsonyl1eQHtHTcG0onhvK/e1tgFZzKyCS7Ypt8c7ns9zfGKWSZIkSZIkSVNg2dIuXnnrPbzy1nsAGB4dZteRXfQO9NI70MsPBnr5yqlD0FZh2Zp13L7mNqorb6XacQObF13F4uEzcHqweJ05fmG79nV0X/F+6hiMnC1+cAQQF2+PPRUvLjnO8xyv2b4Mb3OTJEmSJEmaAZnJ/pP76Rvoo2egh76BPnYf2w1Aa7Tyos4XFU+Nu6p4atzqRasbNtame5pbRLQDDwFtFLOjPpuZ74uITuAzwAZgH3BPZh6d6FqWSZIkSZIkabYaPDPIIwcfGZu9tOPQDs6MnAHguqXXccdVd9Dd1U11TZUbV9xIJWZmDaVmLJMCWJKZJyNiAfAw8A7gN4AjmfmBiHgPsDIz3z3RtSyTJEmSJEnSXDE8MszOIzvpG+gbK5iOnD4CQMfCjqJY6qrSvaabzas3097aPi3jaLoy6aIBRCymKJP+EPgEcGdmHoiItcCDmXnLRN+3TJIkSZIkSXNVZvLUiafGiqXegV72Du4FoLXSyqZVm7ij646xkqmzvXNKfm5TlkkR0QJsA24C/iUz3x0RxzJzRc05RzNz5TjfvRe4F2DdunUvefLJJ2do1JIkSZIkSY119PTRYubSwV76BvrYcWgHw6PDAGzo2DBWLFW7qmzo2EDExAtqj6cpy6SxAUSsAD4PvB14uJ4yqZYzkyRJkiRJ0nx2ZuQMOw/vHJu51DfQx7EzxwBY2baSLV1buKOrWNR706pNLGxZ+LzXnKhMap3S0V+BzDwWEQ8CrwP6I2JtzW1uA40dnSRJkiRJUnNra2kbm4kExa1xe4/vpbe/LJcO9vHg0w8CsLCykM2rNxdPjStvj1vetnxSP69RC3CvAYbLImkR8A3gb4BXA4drFuDuzMw/m+hazkySJEmSJEma2KFTh3hk4MJT43Ye2cm50XMA3Lj8xrEyqtpV5fpl11OpVJpuZtJa4L5y3aQKcH9mfjkivg/cHxFvBZ4C7m7Q+CRJkiRJkuaM1YtWc9f6u7hr/V0AnD53mh2HdoyVS9948hs88MQDAKxqXzXhtRpSJmXmdqA6zv7DwF0zPyJJkiRJkqT5o721na1Xb2Xr1cXko9EcZc+xPWNrLn2bb1/2uw1fgPuF8jY3SZIkSZKkqTXRAtyVmR6MJEmSJEmSZi/LJEmSJEmSJNXNMkmSJEmSJEl1s0ySJEmSJElS3SyTJEmSJEmSVDfLJEmSJEmSJNXNMkmSJEmSJEl1s0ySJEmSJElS3SyTJEmSJEmSVDfLJEmSJEmSJNXNMkmSJEmSJEl1s0ySJEmSJElS3SyTJEmSJEmSVDfLJEmSJEmSJNXNMkmSJEmSJEl1s0ySJEmSJElS3SyTJEmSJEmSVDfLJEmSJEmSJNUtMrPRY3hBIuIEsKvR49CUWQ4MNnoQmlKrgUONHoSmjBmdW8zn3GI+5x4zOreY0bnFfM4t5nN86zNzzXgHWmd6JNNgV2ZubfQgNDUi4iOZeW+jx6GpExE/MqNzhxmdW8zn3GI+5x4zOreY0bnFfM4t5nPyvM1NzeZLjR6ApAmZUal5mU+puZlRqXmZz0myTFJTyUxDLDUxMyo1L/MpNTczKjUv8zl5c6FM+kijByBpQmZUal7mU2puZlRqXuZT89qsX4BbkiRJkiRJM2cuzEySJEmSJEnSDLFMkiRJkiRJUt2askyKiI9GxEBE7KjZtyUivh8RP46IL0VER7n/dyKir+Y1GhHd5bGXlOfvjoh/joho0K8kzRmTzOeCiLiv3P9YRLy35jvmU5oGk8zowoj4WLn/kYi4s+Y7ZlSaYhFxfUT8b/nvxEcj4h3l/s6I+GZEPFG+r6z5znvLHO6KiF+u2W9GpSk02XxGxKry/JMR8cFLrmU+Nec1ZZkEfBx43SX7/g14T2beBnwe+FOAzPxkZnZnZjfwZmBfZvaV3/lX4F5gY/m69JqSJu/j1JlP4G6grdz/EuAPImJDecx8StPj49Sf0d8HKPe/BviHiDj/t4EZlabeOeCdmXkr8ArgbRGxCXgP8K3M3Ah8q/xMeexNwIspMvihiGgpr2VGpak1qXwCp4G/At41zrXMp+a8piyTMvMh4Mglu28BHiq3vwm8cZyv/hbwKYCIWAt0ZOb3s1hl/BPAr0/LgKV5ZJL5TGBJRLQCi4CzwHHzKU2fSWZ0E8UfxmTmAHAM2GpGpemRmQcys6fcPgE8BlwLvAG4rzztPi7k7Q3ApzPzTGbuBXYDLzOj0tSbbD4zcygzH6YolcaYT80XTVkmXcYO4NfK7buB68c55zcpyySK4O+vOba/3Cdp6l0un58FhoADwFPA32fmEcynNNMul9FHgDdERGtE3EAxg/B6zKg07cqZulXgB8BVmXkAiv+gBbrK064Fnq752vksmlFpGtWZz8sxn5oXZlOZ9HsUUw23AcsoZjiMiYiXA89l5vk1Isa7LzWnd4jSvHW5fL4MGAGuAW4A3hkRN2I+pZl2uYx+lOKP3B8B/wR8j2KavxmVplFELAUeAP44M49PdOo4+3KC/ZJeoEnk87KXGGef+dSc09roAdQrMx8HXgsQETcDr7/klDdxYVYSFH8cX1fz+Trg2ekcozRfTZDP3wa+lpnDwEBEfBfYCnwH8ynNmMtlNDPPAX9y/ryI+B7wBHAUMypNi4hYQPEfqp/MzM+Vu/sjYm1mHihvkRko9+/n4tn457Po37nSNJhkPi/HfGpemDUzkyKiq3yvAH8JfLjmWIVi2v6nz+8rpyCeiIhXlKvn/y7whRkdtDRPTJDPp4BfisISisUMHzef0sy6XEYjYnGZTSLiNcC5zNxpRqXpUebp34HHMvMfaw59EXhLuf0WLuTti8CbIqKtvBV1I/BDMypNvSvI57jMp+aLKNYEay4R8SngTmA10A+8D1gKvK085XPAe8sFzSgfZfyBzHzFJdfZSvFUm0XAV4G3ZzP+wtIsMpl8ltOEP0axyG8AH8vMvyuvYz6laTDJjG4Avg6MAs8Ab83MJ8vrmFFpikXEL1LMzv0xRe4A/pxiXZb7gXUU/yPm7nKNQSLiLyhuVT1HcdvNV8v9ZlSaQleYz31AB7CQ4iEWr83MneZT80FTlkmSJEmSJElqTrPmNjdJkiRJkiQ1nmWSJEmSJEmS6maZJEmSJEmSpLpZJkmSJEmSJKlulkmSJEmSJEmqm2WSJEnSCxSFhyPiV2r23RMRX2vkuCRJkqZDZGajxyBJkjTrRcRm4L+BKtAC9AGvy8w9V3CtlswcmdoRSpIkTQ3LJEmSpCkSEX8LDAFLyvf1wG1AK/D+zPxCRGwA/qM8B+CPMvN7EXEn8D7gANCdmZtmdvSSJEn1sUySJEmaIhGxBOgBzgJfBh7NzP+MiBXADylmLSUwmpmnI2Ij8KnM3FqWSV8BNmfm3kaMX5IkqR6tjR6AJEnSXJGZQxHxGeAkcA/wqxHxrvJwO7AOeBb4YER0AyPAzTWX+KFFkiRJanaWSZIkSVNrtHwF8MbM3FV7MCLeD/QDWygehnK65vDQDI1RkiTpivk0N0mSpOnxdeDtEREAEVEt9y8HDmTmKPBmisW6JUmSZg3LJEmSpOnx18ACYHtE7Cg/A3wIeEtE/B/FLW7ORpIkSbOKC3BLkiRJkiSpbs5MkiRJkiRJUt0skyRJkiRJklQ3yyRJkiRJkiTVzTJJkiRJkiRJdbNMkiRJkiRJUt0skyRJkiRJklQ3yyRJkiRJkiTV7f8B50NtrtXWzLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "# create ARIMAX model with best order\n",
    "arima = ARIMA(train['Average_Index'],order=(0,2,0), exog = train['CO2_emission']).fit() # exogenous variable is mean annual Co2\n",
    "\n",
    "test_arima=test.copy() # create test set for this example\n",
    "\n",
    "# To use this function for prediction you need to provide the first and last date\n",
    "test_arima['predicted']=arima.predict(start=test_arima.index[0],end=test_arima.index[-1], exog = test['CO2_emission']) \n",
    "\n",
    "# calculate errors and round to 2 d.p\n",
    "rmse_arima=round(np.sqrt(mean_squared_error(test_arima['Average_Index'],test_arima.predicted)),2)\n",
    "mae_arima=round(mean_absolute_error(test_arima['Average_Index'],test_arima.predicted),2)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "train['Average_Index'].plot(ax=ax,label='train')\n",
    "test_arima['Average_Index'].plot(ax=ax,label='test')\n",
    "test_arima.predicted.plot(ax=ax,label='ARIMA forecast')\n",
    "plt.legend()\n",
    "plt.title('RMSE: '+str(rmse_arima)+', MAE: '+str(mae_arima)+ '    ARIMA Parameters = (0,2,0)')\n",
    "plt.suptitle(\"ARIMAX Forecasting\",fontsize=18)\n",
    "plt.ylabel('Average_Index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "236f8bff",
   "metadata": {},
   "source": [
    "#### forward facing prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ddee8267",
   "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>Region</th>\n",
       "      <th>Average_Index</th>\n",
       "      <th>Upper_Index</th>\n",
       "      <th>Lower_Index</th>\n",
       "      <th>CO2_emission</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year)</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5</th>\n",
       "      <th>Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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>1970-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "      <td>29.2509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>99.100276</td>\n",
       "      <td>100.953212</td>\n",
       "      <td>97.314895</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "      <td>28.9959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>97.929884</td>\n",
       "      <td>101.193083</td>\n",
       "      <td>94.765395</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "      <td>30.0417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>96.782841</td>\n",
       "      <td>101.005601</td>\n",
       "      <td>92.663605</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "      <td>30.9854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>World</td>\n",
       "      <td>95.345434</td>\n",
       "      <td>100.618550</td>\n",
       "      <td>90.268275</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "      <td>30.8474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Region  Average_Index  Upper_Index  Lower_Index  CO2_emission  \\\n",
       "Year                                                                       \n",
       "1970-01-01  World     100.000000   100.000000   100.000000       29.2509   \n",
       "1971-01-01  World      99.100276   100.953212    97.314895       28.9959   \n",
       "1972-01-01  World      97.929884   101.193083    94.765395       30.0417   \n",
       "1973-01-01  World      96.782841   101.005601    92.663605       30.9854   \n",
       "1974-01-01  World      95.345434   100.618550    90.268275       30.8474   \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year)  \\\n",
       "Year                                                           \n",
       "1970-01-01                                           29.2509   \n",
       "1971-01-01                                           28.9959   \n",
       "1972-01-01                                           30.0417   \n",
       "1973-01-01                                           30.9854   \n",
       "1974-01-01                                           30.8474   \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).1  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).2  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).3  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).4  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).5  \\\n",
       "Year                                                             \n",
       "1970-01-01                                            29.2509    \n",
       "1971-01-01                                            28.9959    \n",
       "1972-01-01                                            30.0417    \n",
       "1973-01-01                                            30.9854    \n",
       "1974-01-01                                            30.8474    \n",
       "\n",
       "            Global CO2 Equivalent Emissions\\n(GtonsCO2/year).6  \n",
       "Year                                                            \n",
       "1970-01-01                                            29.2509   \n",
       "1971-01-01                                            28.9959   \n",
       "1972-01-01                                            30.0417   \n",
       "1973-01-01                                            30.9854   \n",
       "1974-01-01                                            30.8474   "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5490bdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Chris Butler\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "arimax= ARIMA(TS['Average_Index'],order=(0,2,0), exog = TS['CO2_emission']).fit()\n",
    "future_forecast=arimax.predict(start='2017-01-01',end='2035-01-01', exog = future_exog['CO2_emission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "abb8ab8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the forecast\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "TS['Average_Index'].plot(ax=ax,label='Year to Date')\n",
    "future_forecast.plot(ax= ax, label = 'Forecast', color = 'red')\n",
    "\n",
    "plt.legend()\n",
    "plt.suptitle('Forecasting Shows Worldwide Species Population Declines may be Stabalising',fontsize=18)\n",
    "plt.title('ARIMA Parameters = (0,2,0) with an exogenous variable of Annual CO2 Emissions ')\n",
    "plt.ylabel('API Value')\n",
    "plt.axhline(y = 100, color = 'grey', linestyle = 'dashed', linewidth = 3)\n",
    "# Save the plot with appropriate settings\n",
    "plt.savefig('Figure3.pdf', bbox_inches='tight', dpi=300)  # Adjust the file format and DPI as needed\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3620634c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    30.663286\n",
       "2018-01-01    30.625132\n",
       "2019-01-01    30.928089\n",
       "2020-01-01    30.940385\n",
       "2021-01-01    30.952682\n",
       "2022-01-01    30.967586\n",
       "2023-01-01    30.982491\n",
       "2024-01-01    30.997395\n",
       "2025-01-01    31.012300\n",
       "2026-01-01    31.027205\n",
       "2027-01-01    31.042110\n",
       "2028-01-01    31.057016\n",
       "2029-01-01    31.071919\n",
       "2030-01-01    31.086825\n",
       "2031-01-01    31.101731\n",
       "2032-01-01    31.111136\n",
       "2033-01-01    31.120542\n",
       "2034-01-01    31.129948\n",
       "2035-01-01    31.139354\n",
       "Freq: AS-JAN, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f47358cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:          Average_Index   No. Observations:                   49\n",
      "Model:                 ARIMA(0, 2, 0)   Log Likelihood                 -25.514\n",
      "Date:                Sun, 29 Oct 2023   AIC                             55.029\n",
      "Time:                        12:30:25   BIC                             58.729\n",
      "Sample:                    01-01-1970   HQIC                            56.421\n",
      "                         - 01-01-2018                                         \n",
      "Covariance Type:                  opg                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "CO2_emission    -0.0154      0.077     -0.201      0.841      -0.166       0.135\n",
      "sigma2           0.1734      0.034      5.054      0.000       0.106       0.241\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.98   Jarque-Bera (JB):                 0.60\n",
      "Prob(Q):                              0.32   Prob(JB):                         0.74\n",
      "Heteroskedasticity (H):               0.60   Skew:                             0.27\n",
      "Prob(H) (two-sided):                  0.31   Kurtosis:                         3.13\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "print(arimax.summary())"
   ]
  }
 ],
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