covid19-data-analysis-notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.1: Importing Covid-19 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv = pd.read_csv(\"covid19_Confirmed_dataset.csv\")\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 104)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv.drop([\"Lat\",\"Long\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3144</td>\n",
       "      <td>3435</td>\n",
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       "      <td>3892</td>\n",
       "      <td>4003</td>\n",
       "      <td>4127</td>\n",
       "      <td>4285</td>\n",
       "      <td>4428</td>\n",
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       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1401</td>\n",
       "      <td>1473</td>\n",
       "      <td>1523</td>\n",
       "      <td>1596</td>\n",
       "      <td>1677</td>\n",
       "      <td>1746</td>\n",
       "      <td>1808</td>\n",
       "      <td>1867</td>\n",
       "      <td>1932</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2969</td>\n",
       "      <td>2971</td>\n",
       "      <td>2976</td>\n",
       "      <td>2982</td>\n",
       "      <td>2994</td>\n",
       "      <td>3002</td>\n",
       "      <td>3004</td>\n",
       "      <td>3016</td>\n",
       "      <td>3016</td>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Province/State       Country/Region  1/22/20  1/23/20  \\\n",
       "0                           NaN          Afghanistan        0        0   \n",
       "1                           NaN              Albania        0        0   \n",
       "2                           NaN              Algeria        0        0   \n",
       "3                           NaN              Andorra        0        0   \n",
       "4                           NaN               Angola        0        0   \n",
       "5                           NaN  Antigua and Barbuda        0        0   \n",
       "6                           NaN            Argentina        0        0   \n",
       "7                           NaN              Armenia        0        0   \n",
       "8  Australian Capital Territory            Australia        0        0   \n",
       "9               New South Wales            Australia        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  1/29/20  ...  4/21/20  \\\n",
       "0        0        0        0        0        0        0  ...     1092   \n",
       "1        0        0        0        0        0        0  ...      609   \n",
       "2        0        0        0        0        0        0  ...     2811   \n",
       "3        0        0        0        0        0        0  ...      717   \n",
       "4        0        0        0        0        0        0  ...       24   \n",
       "5        0        0        0        0        0        0  ...       23   \n",
       "6        0        0        0        0        0        0  ...     3031   \n",
       "7        0        0        0        0        0        0  ...     1401   \n",
       "8        0        0        0        0        0        0  ...      104   \n",
       "9        0        0        3        4        4        4  ...     2969   \n",
       "\n",
       "   4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  \\\n",
       "0     1176     1279     1351     1463     1531     1703     1828     1939   \n",
       "1      634      663      678      712      726      736      750      766   \n",
       "2     2910     3007     3127     3256     3382     3517     3649     3848   \n",
       "3      723      723      731      738      738      743      743      743   \n",
       "4       25       25       25       25       26       27       27       27   \n",
       "5       24       24       24       24       24       24       24       24   \n",
       "6     3144     3435     3607     3780     3892     4003     4127     4285   \n",
       "7     1473     1523     1596     1677     1746     1808     1867     1932   \n",
       "8      104      104      105      106      106      106      106      106   \n",
       "9     2971     2976     2982     2994     3002     3004     3016     3016   \n",
       "\n",
       "   4/30/20  \n",
       "0     2171  \n",
       "1      773  \n",
       "2     4006  \n",
       "3      745  \n",
       "4       27  \n",
       "5       24  \n",
       "6     4428  \n",
       "7     2066  \n",
       "8      106  \n",
       "9     3025  \n",
       "\n",
       "[10 rows x 102 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Deepak Jha\\AppData\\Local\\Temp\\ipykernel_6504\\160133868.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
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       "      <th>4/21/20</th>\n",
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       "      <th>4/27/20</th>\n",
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       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
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       "    <tr>\n",
       "      <th>Country/Region</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/21/20  4/22/20  4/23/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1092     1176     1279   \n",
       "Albania               0        0        0  ...      609      634      663   \n",
       "Algeria               0        0        0  ...     2811     2910     3007   \n",
       "Andorra               0        0        0  ...      717      723      723   \n",
       "Angola                0        0        0  ...       24       25       25   \n",
       "\n",
       "                4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                                                 \n",
       "Afghanistan        1351     1463     1531     1703     1828     1939     2171  \n",
       "Albania             678      712      726      736      750      766      773  \n",
       "Algeria            3127     3256     3382     3517     3649     3848     4006  \n",
       "Andorra             731      738      738      743      743      743      745  \n",
       "Angola               25       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()\n",
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.4: Visualizing the data related to a country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d87c5ada90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"India\"].plot()\n",
    "corona_dataset_aggregated.loc[\"China\"].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the good measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"India\"][:50].plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3.1: Calculating and plotting the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"India\"].diff().plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3.2: Find the maximum infection rate for India and China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1893.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"India\"].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].diff().max()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3.3: Maximum number of infection rate for all the countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = list(corona_dataset_aggregated.index)\n",
    "max_infection_rates = []\n",
    "for i in countries:\n",
    "    max_infection_rates.append(corona_dataset_aggregated.loc[i].diff().max())\n",
    "corona_dataset_aggregated[\"max_infection_rate\"] = max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "      <th>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/22/20  4/23/20  4/24/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1176     1279     1351   \n",
       "Albania               0        0        0  ...      634      663      678   \n",
       "Algeria               0        0        0  ...     2910     3007     3127   \n",
       "Andorra               0        0        0  ...      723      723      731   \n",
       "Angola                0        0        0  ...       25       25       25   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \\\n",
       "Country/Region                                                         \n",
       "Afghanistan        1463     1531     1703     1828     1939     2171   \n",
       "Albania             712      726      736      750      766      773   \n",
       "Algeria            3256     3382     3517     3649     3848     4006   \n",
       "Andorra             738      738      743      743      743      745   \n",
       "Angola               25       26       27       27       27       27   \n",
       "\n",
       "                max_infection_rate  \n",
       "Country/Region                      \n",
       "Afghanistan                  232.0  \n",
       "Albania                       34.0  \n",
       "Algeria                      199.0  \n",
       "Andorra                       43.0  \n",
       "Angola                         5.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3.4: Create a new dataframe with only needed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data = pd.DataFrame(corona_dataset_aggregated[\"max_infection_rate\"])\n",
    "corona_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.769           1.340           1.587   \n",
       "1             2           Denmark  7.600           1.383           1.573   \n",
       "2             3            Norway  7.554           1.488           1.582   \n",
       "3             4           Iceland  7.494           1.380           1.624   \n",
       "4             5       Netherlands  7.488           1.396           1.522   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.986                         0.596       0.153   \n",
       "1                    0.996                         0.592       0.252   \n",
       "2                    1.028                         0.603       0.271   \n",
       "3                    1.026                         0.591       0.354   \n",
       "4                    0.999                         0.557       0.322   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  \n",
       "3                      0.118  \n",
       "4                      0.298  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv = pd.read_csv(\"worldwide_happiness_report.csv\")\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4.1: Drop the unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.drop([\"Overall rank\", \"Score\", \"Generosity\", \"Perceptions of corruption\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Country or region</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country or region  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0           Finland           1.340           1.587                    0.986   \n",
       "1           Denmark           1.383           1.573                    0.996   \n",
       "2            Norway           1.488           1.582                    1.028   \n",
       "3           Iceland           1.380           1.624                    1.026   \n",
       "4       Netherlands           1.396           1.522                    0.999   \n",
       "\n",
       "   Freedom to make life choices  \n",
       "0                         0.596  \n",
       "1                         0.592  \n",
       "2                         0.603  \n",
       "3                         0.591  \n",
       "4                         0.557  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4.3: Changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4.5: Joining the two datasets"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corona datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>max_infection_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### World Happiness Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = corona_data.join(happiness_report_csv, how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4.5: Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_infection_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.078196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.250118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.394603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.078196</td>\n",
       "      <td>0.394603</td>\n",
       "      <td>0.456246</td>\n",
       "      <td>0.427892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection_rate  GDP per capita  \\\n",
       "max_infection_rate                      1.000000        0.250118   \n",
       "GDP per capita                          0.250118        1.000000   \n",
       "Social support                          0.191958        0.759468   \n",
       "Healthy life expectancy                 0.289263        0.863062   \n",
       "Freedom to make life choices            0.078196        0.394603   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_infection_rate                  0.191958                 0.289263   \n",
       "GDP per capita                      0.759468                 0.863062   \n",
       "Social support                      1.000000                 0.765286   \n",
       "Healthy life expectancy             0.765286                 1.000000   \n",
       "Freedom to make life choices        0.456246                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "max_infection_rate                                0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5: Visualization of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>max_infection_rate</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>232.0</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>291.0</td>\n",
       "      <td>1.092</td>\n",
       "      <td>1.432</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.055</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_infection_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5.1: Plotting the GDP vs Maximum Infection Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GDP per capita', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fZqHCnhL2shgLgx4i4he3yemZ16X0ubXIp1F6vnONOeNQHPRUVVVh/fr1KCgowO23346EhAQUFRUhMTER8fF1uxSJSG784jYvPfO6lD63LPk0ZCyK6vT88ssv6Nq1K4YMGYKJEyfi+PHjAIA5c+Zg6tSpqjaQiIi0pWdhQaXPbYS6QOHOXuFAwbFy2A6dQsHxckPU9FKUyDx06FAkJCTgjTfeQFJSEnbt2oV27dph/fr1GD9+PA4cOKBFWxVhIjMRUcP0TMgN5rldU905LBtaoZjtp8X1W1HQk5SUhC1btqBjx45ISEhwBz0///wzOnXqhIqKClUapwYGPURE/tEzgGDwYhyhmu0nzewtp9OJ6urqOtt//fVXJCQkBN0oIiIKPT3zuphTZhxGruKuKKfn6quvxrx589x/WywWlJeXY8aMGbjuuuvUahsRERFJxshV3BX19Lz44osYPHgwOnXqhLNnz+L222/HgQMHkJycjNzcXLXbSEREpAk1lt4wGyNXcVcU9LRq1Qq7du3Ce++9h127dqG8vBx333037rjjDsTGsgIlERHJT8+lN4zMyOUCFCUyb9y4EX369EGjRp4xU1VVFbZs2YKcnBzVGhgsJjITEVFtei69EQ5CMdtPmkTmAQMG4MiRI0hJSfHYbrfbMWDAAK9JzkRERLIwcjKuDIxaxV1R0COEgMViqbP95MmTaNKkSdCNIiIi0pKRk3FlYcQZdwEFPTfffDOA87O1xowZg+joaPdt1dXV2L17N/r06aNuC4mIiFRm5GRcUi6goMdqtQI439OTkJDgkbQcFRWFyy+/HOPHj1e3hURERCozcjIuKRdQ0LN06VIAQNu2bTF16lQOZRERkSG51u7ylYxrtGEb8o+i2VtGwtlbZCSsGUIUWlz+Ql7SzN4CgPfffx8rVqzAoUOH4HB4rqy6Y8eOoBtGZDasGUIUekZMxiXlFC1DMX/+fIwdOxapqamw2Wzo2bMnkpKScPDgQVx77bVqt5Eo7NkrHHUCHuD81NlpK3fDXuHwcU8iIvKXoqDnlVdewWuvvYYFCxYgKioKjz76KNauXYtJkybBbrer3UaisOdPzRCzslc4UHCsHLZDp1BwvJwBIBEppmh469ChQ+6p6bGxsSgrKwMAjBo1CpdffjkWLlyoXguJTIA1Q7zjkB8RqUlRT0+LFi3w+++/AwBat26Nb775BgBQWFiIMM+LJtIEa4bUxSE/Y2BPnHI8dqGnqKfnz3/+Mz766CNkZ2dj7Nix+Mtf/oL3338f33//vbuAIRH5jzVDPNkrHDhiP8tlAiTHnjjleOz0oWjKutPphNPpdC84+u6772LLli3IzMzEfffdh6goeb6IOGWdjCIUC/gZgetiMLJna0x42/dM0A8n9EH31s1C2DJz8LdsAhfsVI7Hzj9STFmvqqrCzJkzMW7cOLRq1QoAMGLECIwYMUKVBhGZlVEX8FNTzSGtMX3a1ruvGYf8tBZI74NRF+yUoRaWUY9dOAg46GnUqBGef/553HXXXVq0h8jUzF4zpObFwHa4BH07JGFz/sk6+5lxyE9rDeVQ1e59MGLyvSxDSkY8duFCUSLzwIEDsWHDBrXbQkQmV/NisGRTIcb2zUDfDkke+3CZAG0EWjbBaMn3MiXGG+3YhRNFiczXXnstpk2bhj179uCyyy6rswbXjTfeqErjiMhcal4MKhzVmJRrw7h+GRjXNwOVVU60S26CltYYBjwaCLT3wWjJ9zINKRnt2IUTRUHPhAkTAABz586tc5vFYkF1dXVwrSIiU6p9MahwVGPhunwATPDUWqC9D0ZbsFOmISWjHbtwoijocTqdareDiEiqi4EMCa+hpKT3wUjJ97INKRnp2IUTxQuO+qNr16749NNPkZ6eruXTEFEYkeFiIEvCaygpDTiNknwv45CSUY5dOFFUp8dfCQkJ2LVrF9q1a6fVUzSIdXoo3JmtR0JrZq+h4jqfwrH3gbWwjEWKOj2h9ttvv+Gxxx7DZ599hoqKCnTo0AFLly5Fjx499G4ake7M2COhNZkSXvUQzr0PNXsRT1eegzU2Co5qJ4pLz6LiXDV/MJiA1EHPqVOn0LdvXwwYMACfffYZLrroIhw4cADNmrEKK1GgdVXIPzIlvJL6XEGdHj8Y2CurP6mDnjlz5iA9PR1Lly51b8vIyKj3PpWVlaisrHT/XVpaqln7iPRk9h4JrWid8MoLn/70+MHAXlk5KCpOGCofffQRevTogVtvvRUpKSnIzs7G66+/Xu99Zs2aBavV6v7HJGoKV+yR0IYr4dWbYBNei0rO4MFcGwbO3YCbXtmCgS9uwEO5NhSVnFH8mMEy40rfgRZiDJZMhRHNTuqg5+DBg3j11VeRmZmJzz//HA888AAmTZqE5cuX+7zP9OnTYbfb3f8OHz4cwhYThY5sU3DDhWsWU+3Ap/YsJl/BQn3bZbvwyRiEhUKofzCEOsgi3zQd3vrXv/6F1NRUxfd3Op3o0aMHZs6cCQDIzs7GDz/8gMWLF2P06NFe7xMdHY3o6GjFz0lkFDJOwQ0XDU2b9zVU8ezQLnj64x/x5d5jHttnD8vCGUe1VMORZs4JC/UPBvbKykNx0JOXl4e8vDwcO3asTrHCJUuWAABuv/32oBrXsmVLdOrUyWPbpZdeipUrVwb1uEThQKZCfuHI1yym+oKFv63ag+6tm3kEPa4g4uFBmfU+X6gvfGbOCQv1Dwb2yspDUdDz1FNP4emnn0aPHj3QsmVLWCwWtdsFAOjbty/27dvnsW3//v1o06aNJs9HZDQyFPIzm/qChU35JzG2b93JFhsPnMDfrru03scN9YXPzL0Pof7BwF5ZeSgKehYvXoxly5Zh1KhRarfHw1/+8hf06dMHM2fOxPDhw7Ft2za89tpreO211zR9XiIjCee6KjJqKFiorPK+TE9khEWqC5/Zex9C+YOBvbLyUBT0OBwO9OnTR+221PGnP/0Jq1atwvTp0/H0008jIyMD8+bNwx133KH5cxMRedNQsBDdyPv8kMgIi1QXPjP1PvgqExDKHwy1g6wm0Y0QFRmBY2UsjBhKipaheOyxxxAfH4/HH39cizapistQEJGatXHsFQ48lGvzGiz065CE7q2buVeGd6m5fIUWyzwofX1mWJZBxvo4MrZJRlpcvxUFPZMnT8abb76JrKwsZGVloXFjz18+c+fOVaVxamDQQ2QcWhTu0+IC4ytYeHZoFzzz8Y9YW2v2lpZBRLCvL5zX2pJxHTUZ2yQraYKeAQMG+H5AiwXr1q0LqlFqYtBDZAxaBCdaXmB8BQuhDCJ4Aa1fwbFyDJy7wefteVP6o31KfAhbJGebZCXNgqNfffWVKk9ORARoVzPG32nZSnqYfOWDhDJPROtp50ZbMqN2e6uFQFxUJCoc1V7312OGmplnzckg6OKEv/76KwCgVatWQTeGiMxJq4u3PxcYI+dXaHkBNdpx8dbeKzKTMX9kNibl2rwGPnrMUDP7rDm9KVqGwul04umnn4bVakWbNm3Qpk0bNG3aFM8880ydQoVERA3R6uLd0AWmSXQj6ZaGCIRWF1AZl8yoj6/2fn3gBJZtLsS4fnVrJ+k1Q03Ltd2oYYqCnr///e9YuHAhZs+eDZvNBpvNhpkzZ2LBggWGmNFFRHLR6uLd0AUmKjLC0GsiaXUBNdpaUQ0VjOzTLsljm571cfxd2420oWh4a/ny5fj3v/+NG2+80b0tKysLf/jDHzBhwgQ899xzqjWQyMVo+QXkP61qxjRUFO5Y2dl67y97foVWRe+C6XnT43PaUHtjGkcib0p/aWao6VVJnd+hCoOe33//HZdcckmd7Zdccgl+//33oBtFVJvR8gsoMFpWrK3vAuMrwdXFCPkVWlxAlfa86fU5bai91tjG0s2ICnUldX6Hnqco6OnWrRsWLlyI+fPne2xfuHAhunXrpkrDiFzMvBq0HvT6Najlr19fF5hwqUqs9gVUyXHR83MaLu+jVvgdeoGioOf555/H9ddfjy+//BK9e/cGAGzduhWHDx/Gp59+qmoDicy8GnSo6f1rMNS/frkmkndKjouen1O+j/Xjd+gFioKe/v37Y//+/Vi0aBF++uknAMDNN9+MCRMmIC0tTdUGErGuRWjU/jUYFxWJcf0ykJ3eFHuPlOJ0ZRVSEqLD7suRK9V719Bxqd0jaD9Tf3Kz1p9Tvo++8Tv0AsV1etLS0piwTCER7nUtZEkurPlrMC4qEvNHZmPp5kKPdaTCNQeAK9V75+u4eOsRfOeeXvU+Vig+p3wfvQv379BA+B307N69G126dEFERAR2795d775ZWVlBN4zIJZzH6/UeTqqp5q/Bcf0ysHRzITbnn/TYx4w5AGqQJbBVg6/8kC0HT6JfhyRsqnXOAMb/nBpdOH+HBsrvoKd79+4oLi5GSkoKunfvDovFAm/LdlksFlRX1z8jgigQ4TpeL1tyYc1fg9npTeusFO5ithyAYCkNbGUNlHzlhyzZVIj5I7NhsVjqvFYjf07DQbh+hyrhd9BTWFiIiy66yP3/RKEUjuP1siUX1vw1WFlVf2V1M+UABENpYCtTD2BtvvJDKhzVmJRrw+qJfRFhsYTN5zRchON3qBJ+Bz1t2rRx//8vv/yCPn36oFEjz7tXVVVhy5YtHvsSqUWW8Xq1foHLllxY89dgdKP6i7WbKQcgGEoCW9l6AGurLz+kwlGNCItFupo4dJ4s36F6UpTIPGDAABw5cgQpKSke2+12OwYMGMDhLQpbav4ClzG50PVrsKTiHK7ITPZ6wTZbDkAwlAS2svUA1sb8EDIyRWtvCSFgsVjqbD958iSaNGkSdKOIlLBXOFBwrBy2Q6dQcLxc9UUR1V6EUdaFB61xUWiT3ARzFK4PpPX7YCRKAlvZegBr49pRZGQB9fTcfPPNAM4nK48ZMwbR0dHu26qrq7F792706dNH3RYS+SEUORBq/wKXPblQSQ6AzLkoelDSKyJjD2BtzA8howoo6LFarQDO9/QkJCQgNvbCl1hUVBQuv/xyjB8/Xt0WEjUgVDkQWvwCl/3iEUgOgOy5KHpQEtgaZfiI+SFkRAEFPUuXLgUAtG3bFo888gji4uI0aRRRIEKVA6HVL/BwuXjInouil0ADWxl7AGWdPk8UKEWJzHfddRd+++03ZGZmemw/cOAAGjdujLZt26rRNiK/hCoHwii/wPUiey6KngINbGXqAeSQJYUTRYnMY8aMwZYtW+ps//bbbzFmzJhg20QUkFDlQDCBs35GyEUxEmtcFNqnxKN762ZonxKvWw+Pmsn7RHpT1NNjs9nQt2/fOtsvv/xyPPjgg0E3iigQoeyBkekXuGzYExZ+OGRJ4UZRT4/FYkFZWVmd7Xa7nTV6KORC3QMjwy9wGbEnTD9alQngkCWFG0U9PTk5OZg1axZyc3MRGRkJ4PyU9VmzZqFfv36qNpDIH+yBkYMe74PZk2y1zLlRY8jS7O8PyUVR0DNnzhzk5OSgY8eOuOKKKwAAX3/9NUpLS7Fu3TpVG0jkr3CZBWV0oXwfzJ5kq3WZgGCHLM3+/pB8FA1vderUCbt378bw4cNx7NgxlJWV4a677sJPP/2ELl26qN1GIqI6mGTrX85NMIIZsuT7QzJS1NMDAGlpaZg5c6aabSEi8huTbEOTc6N0yJLvD8lIUU8PcH44684770SfPn3w22+/AQD+85//YNOmTao1jojIFybZhrZcQ6DJ+3x/SEaKgp6VK1di8ODBiI2NxY4dO1BZWQng/Owt9v4QUSiwLpC8i9YCnu9PXFQkHvxzB7wxugdeueOPWDLmT2jGXh7SgaKg59lnn8XixYvx+uuvo3HjCyd23759sWPHDtUaR0Tki8wX/FCRuUyA6/2Ji4rE/JHZsB06hbuXf48Jb+/AuGXf4fHVP6Co5Ixu7SNzsgghRKB3iouLw48//oi2bdsiISEBu3btQrt27XDw4EF06tQJZ8+e1aKtipSWlsJqtcJutyMxMVHv5hCRiopKzvhco6qlTrOD9Jii7XpO2co1FJWcwYb9x/Hx7iJszj9Z5/aczGRTLkRL/tHi+q0okblFixbIz8+vs8bWpk2b0K5dOzXaRURhSs2gQLb6TGpM0VZyfGQt15DWNBY92jTD9A/2eL2dCc0UaoqCnvHjx2Py5MlYsmQJLBYLioqKsHXrVkydOhWPP/642m0kojChRd0WWS74atTMCce6NuWVVfXezoRmCiVFQc+0adPgdDoxcOBAVFRUICcnB9HR0Zg6dSoeeughtdtIRGFA60J6egt2ina4Hh9ZE85ZKdqc/A56du/ejS5duiAiIgIWiwV///vf8cgjjyA/Px/l5eXo1KkT4uPjtWwrERlYuNdtCXaKtt7HR6sgQMaFaMOxR43843fQk52djSNHjiAlJQXt2rXDd999h6SkJHTq1EnL9hFRmDB63ZaGgoJgezRKz55DXFQkxvXLQHZ6U1RWORHTOBI7Dp3Ckk2Fmh4fLYMA1wwzXwnnoQ50w7VHjfzjd9DTtGlTFBYWIiUlBT///DOcTqeW7SKiMCPrMIc//AkKgu3RsMY2xvyR2Vi6uRAL1+W7t/ftkIT5I7ORGKvN8dE6CLBXOHD2XDX+8f86wSkEKiqrYY0NLOFczV4ovXvUSF9+Bz3Dhg1D//790bJlS1gsFvTo0cO9wnptBw8eVK2BRHrgeL/6ZBzm8Ie/QUGwPRpNohth6ebCOlO7N+efhAXAi8O7q/WSPGgZBNQXLPr7mGr3Qhm9x5GC43fQ89prr+Hmm29Gfn4+Jk2ahPHjxyMhIUHLthHpguP92pBtmMNfgQQFwUyhLz9b5bWWDQBsyj+J8rNVSNWg1JhWQYAaPUha9EIZuceRghfQ7K1rrrkGALB9+3ZMnjyZQY/G2NsQehzv15ZsdXVq8/aZK68MLChQOoVerx4IrYIANXqQtOiFMmqPI6lD0ZT1pUuXqt0OqoW9DfrgeL/2ZKmrU5uvz9zTQ7ogLioSFY5qr/dTq2dArx4IrYIANYI4LQJBo/Y4kjoUBT2nT5/G7NmzkZeXh2PHjtVJamZOT3DY26AfjvebU32fuSdW/4DH/18nr1WF1ewZ0KsHQqsgQI0gTqtAUPYeR9KOoqDnnnvuwYYNGzBq1Ch3YjOph70N+uF4vzk19Jn7+/WX1glI1O4Z0LMHQosgQI0gTstAUNYeR9KWoqDns88+wyeffIK+ffuq3R7D0DLfhr0N+gnH8X7mhjWsoc/cGUd1SHoG9OyBUDsIUCOI41AUqU1R0NOsWTM0b95c7bYYhtb5Nkp7G3hxC54eX7Javm/MDfOPP5+5UPUMhFMPhK8gDgAKjpX7dc5zKIrUZBFCiEDv9NZbb2H16tVYvnw54uLitGiXatRemt5e4cCDuTavXeE5mcmq5NvYKxx4KNfms7fB23Pw4qYuVyCi9Zeslu9bKM5VtekVuCv5zJEy/K4if6l9/QYUBj3Z2dkoKCiAEAJt27ZF48aev5J27NihSuPUoPZBKzhWjoFzN/i8PW9Kf7RPCX4NsqKSMz57G1rW+mIw4sWNtH/fQnWuqkXvi6E/nzn2pgaH31UUCC2CHkXDW0OHDlXlyY0oVPk2gXTpMvHZmLR+34yUGybDjMWGPnN6B2XhgN9VpDdFQc+MGTPUbodhhHJ2j79j+0a6uNEFWr9vRpqJJsvF0NdnLtCgjD1C3vG7ivSmKOgxMxln9xjp4kYXaP2+yXiu+iL7xTCQoIw9Qr7xu4r0FuHvjs2bN8eJE+c/xK7ZW77+hTPX7J6czGSP7XpOoXRd3LyR7eIWCvYKBwqOlcN26BQKjpfDXuHQu0leaf2+yXiu+iL7xdDfoKyhHiFZz8VQqe+cvyIzGfEx/B1O2vL7DHvppZfca23NmzdPq/YYgmxTKFnL4gIj/coOxfvm77mq93CM7L1S/gZlsgzTycrXOd+3QxJG92mLf6zag6eGdJHus0rhQ9HsLX/Nnj0b999/P5o2barVUzRIi+xvWYVqmrWsjDozRO/3TZZAMZAZi6Hm75R226FTuOmVLT4f58MJfdC9dTMtm2oIR0vPouBYOUrOnEN0owjYDpdgyaZCVDiqpf6sUmhJM3vLXzNnzsTw4cNVC3pmz56N6dOnY/LkyabvbfImnIqaKWHUX9l6vm8yzJpyka0HtSZ/e+VkH6aTRfnZKtz+72+93ibzZ5WMT9OgR81OpO+++w7/+te/kJWVpdpjUniRPRlWRrIFig0FgHoOw/kTlMk+TCeLQD+reg+/UvgwRNZYeXk57rjjDrz++ut49tln6923srISlZWV7r9LS0u1bh5Jgr+yA2ekQFGGYbiGgjLm1/knkM+qDO87hQ+/Z2/paeLEibj++usxaNCgBvedNWsWrFar+196enoIWkgy4Cy2wBklUDTSrChXj1DelP74cEIf5E3pjwUjs6XISwpmVqOasyL9/awa6X0nY5C+p+fdd9/Fjh078N133/m1//Tp0zFlyhT336WlpQx8TIK/sgNnlOEY2YbhGiJbfl2wvSVq97b4+1k12vtO8pM66Dl8+DAmT56MtWvXIiYmxq/7REdHIzo6WuOWkaxkToaVkVECRSMNw8km2GR1rZLd/fms8n0ntWka9FxxxRWIjVXepbt9+3YcO3YMf/zjH93bqqursXHjRixcuBCVlZWIjIxUo6kURmT7lS07IwSKRhmGk1GwvSVa9rY09Fnl+05qU5TTs2zZMq/bq6qqMH36dPffn376KVq2bKmoYQAwcOBA7NmzBzt37nT/69GjB+644w7s3LmTAQ+RSqxxUWifEo/urZuhfUq8VAEPwHytYATbW6Jnbwvfd1KboqBn0qRJuPXWW3Hq1Cn3tn379qFXr17Izc1VrXEJCQno0qWLx78mTZogKSkJXbp0Ue15iEhuaiypYZTlSdQWbG+Jnr0tRlpKhYxB0fCWzWbDnXfeia5du2Lp0qXYv38/Hn30UQwdOhSvvPKK2m0kE2JdDqrNNQx38rQD1U6BaqdAhaMKFeeqYa+of4jFzNOe42Ma4YrMZJ+VyhvqLdE72d0Iw69kHIqXoXA6nXj44YexaNEiREZGYvny5Rg5cqTa7QuamZahCBdmvkBRwwI9P4y6PIkaikrO4InVP2BEz9ZYurkQm/NPum8LZIkPmZcIofClxfVbcdDz3//+F3fffTcuvvhi7N+/H1lZWXjzzTeRlpamSsPUwqDHWMx8gaKGKTk/Co6VY+DcDT4fM29Kf7RPiVe9rWpR2utZ81jFRUViXL8MZKc3RWWVE01jG6N9SjxSE/2bFVuzHextoVCRZu2t++67D8uXL8dzzz2HKVOm4OjRoxg3bhy6du2KV199FcOHD1elcWQ+rMtB9VFyfhhh2rOvwCaYXs+ax6rCUY2F6/I9bs+b0h+pAVxHOCuSwoGioGfz5s349ttv0a1bNwBAixYt8Omnn2LRokUYN24cgx5SzAgXKNKPkvND9mnPvgKbWTd3xbQP9iiuj8PPElFdimZvbd++3R3w1DRx4kRs37496EaRecl+gSJ9KTk/ZJ72XF/hv19OVjTYq1UffpaI6lIU9NRX8bhjx46KG0Mk8wWK9Kfk/FBr2rMWU97rG64rORNcTw0/S0R1Ka7I/P7772PFihU4dOgQHA7PD/+OHTuCbhiZk1GWRSB9KD0/gp32rNWMwvqGoKIb1f+btKGeGn6WiOpSFPTMnz8ff//73zFmzBisXr0aY8eORUFBAb777jtMnDhR7TaSybAuh/kEMkNJ6fmhNBFXq7WngPqHoGyHS4KqrwPws0RUm6Kg55VXXsFrr72GkSNHYtmyZXj00UfRrl07PPHEE/j999/VbqPhsLBe8DhTxDznkZJelFCeH1rOKKyv8N++I6WYdVNX/G3VnqB6avhZIrpAUZ2euLg47N27F23atEFKSgrWrl2Lbt264cCBA7j88stx8uTJhh8kREJdp4eF9UgNZjmPjFCXyXboFG56ZYvP2z+c0AfdWzdT/PjeCv9ddWkKnryxM86ec8J+xoG46EaItFgQGWFBUpgGv0S1SVOnp0WLFvj999/Rpk0btG7dGt988w26deuGwsJCKKx1GBa07AYn4wmmqJxZziMj1GXyNgRVs9jfOadAwfFyxT1xtYegEmMbIyoyos50dVfQq/fxIDIyRUHPn//8Z3z00UfIzs7G2LFj8Ze//AXvv/8+vv/+e9x8881qt9EwjPAFLptwHcJRq6hcbeF2HhmhlkztIai4qEjMH5mNpZsLPQr+BdMTV3MIylfvVzgGvUShpijoee211+B0OgGcr82TnJyMzZs348Ybb8T999+vagONxAhf4DIJ1yGcYHtqzHQeqVlLRqsAuvYsqHH9MuqsYwWoF5SYKeglCjVFQU9ERAQcDgd27NiBY8eOITY2FoMGDQIArFmzBjfccIOqjTQKFgPzXzgP4QR70TLTeaTWCt5aB9A1h6Aqq+ou6eCiRlBipqCXKNQUBT1r1qzBqFGjvCYsWywWVFdXB90wI1LrC9wMwvnXbLAXLTOdR2rUkglVAO0agrIdOlXvfsEGJWYKeolCTVFF5oceegjDhw/HkSNH4HQ6Pf6ZNeAB1Kv8agbh/Gs22IuW2c4jVy9K3pT++HBCH+RN6Y8FI7PR0s8eGn8CaDVpHZSwkjKRdhT19Bw9ehRTpkxBamqq2u0xPBYD8084/5pVo6fGbOdRMLVkQh1Aa90Tx0rKRNpRFPTccsstWL9+Pdq3b692e8ICi4E1LJyHcNS6aPE88k+oA+hQBCVmC3qJQkVRccKKigrceuutuOiii9C1a1c0buz5pTJp0iTVGhisUBcnJP95K8rmunD4O7QhM9dsIl60tGWvcOChXJvPAFqrpHi+v0Ta0uL6rSjoeeONN3D//fcjJiYGSUlJsFgsFx7QYsHBgwdVaZwaGPTITemFI1zr+5Ay4R5AE5mRNEFPixYtMGnSJEybNg0REYpyoUOGQU/4Cdf6PhQc9rwQhRdplqFwOBy47bbbpA94KLzYKxwoqTiHf3y4B19rVBiOjIs5UETUEEVRy+jRo/Hee++p3RYKE/YKBwqOlcN26BQKjpfDXhH8lOGikjN4MNeG/OPldQIeFy2mJxMRUfhQ1NNTXV2N559/Hp9//jmysrLqJDLPnTtXlcaR8Wgx9FSz+NzInq3r3dfI9X2IiEhbioKePXv2IDs7GwDwww8/eNxWM6mZjCeYBGGtKuPWLD4X3aj+zsma05OZ7Bx6POZEJDNFQc9XX32ldjtIAsH20mi1tETN4nO2wyXo2yGpzmKPrra66vsw2Tn0eMyJSHbMRCYADffS+JOXo1Vl3JrF55ZsKsTYvhno2yHJY5+aheHUeC0UGB5zIjICRT09FH7U6KXRqjJuzerNFY5qTMq1YVy/DIzrmwEAaN08DikJ0e72hfNiprLiMSciI2BPjwS0mO0UKDV6abRaKLH2ApwVjmosXJeP5Vt+RqeWichMTfC4oIbzYqay4jEnIiNgT4/OZMmDUKOXRss1iQJZiyicFzOVFY85ERkBgx4daTXbSQm1FgDVcqFEf4vPhfNiprLiMSciI+Dwlo78yYMIldpDSC5KemmscVFonxKP7q2boX1KfMhzOdR8LeQfHnMiMgL29OhItjwILXtpQi3Ur4X1acLr/CGi8MSgR0cy5kGE0/pFoXotsuRlySCczh8iCj8c3tKRVrOdZHG09Cx+OlKKbYW/46fiUhwtPat3k/wSyGw61qchIjIO9vToSMvZTno7dPI0pq/a41E5uV+HJMy8qStaJzXRsWX1C7TXhvVpiIiMg0GPzsIxD+Jo6dk6AQ8AbMo/ib+t2oMXh3dHamKMTq3zTclsOtnyskg7ocrbYn4YkXYY9Egg3PIgTp12eF0bCzgf+Jw67ZAy6FHSayNjXhapL1R5W8wPI9IWc3pIdaVnq4K6XS9Kem3CPS+LQpe3xfwwIu0x6CHVJcbU34HY0O16UdJrw/o04S9U9bRkqttFFK7kvPqQoTVrEoV+HZKwycsQV78OSWjWRM5AQGlV4XDMy6ILQpW3xfwwIu2xp4dUl5oYg5k3dUW/Dkke212zt2TM5wGC67XRuwo1aSdUeVvMDyPSHnt6SBOtk5rgxeHdceq0A6Vnq5AY0wjNmkT5HfDoNYOFvTZUW6jWFeP6ZUTaswghhN6N0FJpaSmsVivsdjsSExP1bg75gTNYSDZFJWd81tNqqfLsrVA8D5ERaHH9ZtBDUrFXOPBgrs1rQmdOZnJIV54nqsnV+6h1D2ConodIdlpcvzm8RVIVQztWVskKxySlUNXTCre6XUQyYdBjcjINJRWVnMGh3yvq3YczWIiISCnO3jIxmYqhudrSEM5gISIipRj0mJhMxdBcbbEdLkHfWlPdXTiDhYiIgsHhLROTqRiaqy1LNhVi/shsAPBYv+uKzGQ8PaRLyNojC5nyrYiIjI5Bj4nJVAzN1ZYKRzUm5dowrl8GxvXNQGWVE9GNInBRQjSum/81erRpZpqp6zLlWxERhQMOb5mYTItl1mxLhaMaC9fl4+7l32PC2zuwZHMhvvjxKCoc1aZZfFGmfCsionDBoMfEZFos01db+nZIwti+GViyqdC9rWa+kb3CgYJj5bAdOoWC4+VhEwzIlG9FRBQuOLxlcjItu1CzLSdPn2+P7XAJJuXaUOGo9tj3dOW5sB7+kSnfiogoXDDoIamKobnbcqwcw/+11fd+sVH1Dv8YvXKzTPlWREThQvrhrVmzZuFPf/oTEhISkJKSgqFDh2Lfvn16N4s01lC+kaPaGdbDPzLlWxERhQvpg54NGzZg4sSJ+Oabb7B27VqcO3cOV199NU6fPq1300hDDeUblVdW1Xt/ow//yJRvRUQULgy34Ojx48eRkpKCDRs2ICcnp8H9ueCosflafLHgWDkGzt3g8355U/qjfUp8CFuqDS4+aS6sy0R0ARccBWC32wEAzZs393p7ZWUlKisr3X+XlpaGpF2kDV/5Rq7hn40+VmMPl+EfmfKtSFvhnJhPJAvph7dqcjqdePjhh9G3b1906eK9Ou+sWbNgtVrd/9LT00PcSgoFDv9QOGFdJqLQMNTw1gMPPIDPPvsMmzZtQqtWrbzu462nJz09ncNbYYrDPxQOzDJcSxQIUw9vPfjgg/j444+xceNGnwEPAERHRyM6OjqELSM9cfiHwgHrMhGFhvRBjxACDz30EFatWoX169cjIyND7yYpwgRF0hrPMeNiXSai0JA+6Jk4cSLeeecdrF69GgkJCSguLgYAWK1WxMYaI7mPCYqkNZ5jxmaWxHwivUmf02OxWLxuX7p0KcaMGdPg/fWesm6vcODBXJvXQno5mcmGrxxM+uM5Fh6KSs5g2srdHoGPKzG/JQNXMiFT5vRIHpM1yJ+FI3lBomDwHAsPMq2DRxSupA96jI4JiqQ1nmPhg4n5RNoyVJ0eI2KCImmN5xgRkX8Y9GiMC0eeZ69woOBYOWyHTqHgeLm72Jqv7eQ/nmNERP6RPpE5WHonMgNMUPQ1s+jZoV3w9Mc/4su9xzy2c8ZR4Mx+jhFR+NHi+s2gJ0TMWjm4vplF/TokoXvrZli4Lt9jO2ccKWPWc4yIwpMpZ2+FC7MmKNY3s2hT/kmM7Vu32CRnHClj1nOMiMhfzOkhTTU0s6iyyul1O2ccERGR2hj0kKYamlkU3cj7KcgZR0REpDYGPaSp+mYW9euQBNvhkjrbOeOIiIi0wKCHNGWNi8LsYVl1Ap+czGTMvKkr9h0prbN9zrAs5qYQEZHqOHuLQsLXzCLOOCIiIm84e4sMy9fMIs44IiKiUOHwFhEREZkCgx4iIiIyBQY9REREZAoMeoiIiMgUGPQQERGRKTDoISIiIlNg0ENERESmwDo9RKQ5VxHK0rPnkBjbGMlNQlefSc/nJiK5MOghIk0VlZzBYyt34+sDJ9zbcjKTMXtYFtKaxobtcxORfDi8RUSasVc46gQdALDxwAlMW7kb9gpHWD43EcmJQQ8RaeZEuaNO0OGy8cAJnCjXLvDQ87mJSE4MeohIM6Vnz9V7e1kDtxv1uYlITgx6iEgziTGN6709oYHbjfrcRCQnBj1EpJnk+CjkZCZ7vS0nMxnJ8drNotLzuYlITgx6iEgz1rgozB6WVSf4yMlMxpxhWZpOHdfzuYlIThYhhNC7EVoqLS2F1WqF3W5HYmKi3s0hMiVXrZyys+eQENMYyfGhr9Ojx3MTkXJaXL9Zp4eINGeN0y/Q0PO5iUguHN4iIiIiU2DQQ0RERKbAoIeIiIhMgTk9RBKSYZFMGdpARKQmBj1EkpFhkUwZ2kBEpDYObxFJRIZFMmVoAxGRFhj0EElEhkUyZWgDEZEWGPQQSUSGRTJlaAMRkRYY9BBJRIZFMmVoAxGRFhj0EElEhkUyZWgDEZEWGPQQSUSGRTJlaAMRkRa44CiRhGRYJFOGNhCReXHBUSKTkGGRTBnaQESkJg5vERERkSkw6CEiIiJTYNBDREREpsCgh4iIiEyBQQ8RERGZAoMeIiIiMgUGPURERGQKDHqIiIjIFBj0EBERkSkw6CEiIiJTYNBDREREpsCgh4iIiEyBQQ8RERGZgiGCnkWLFqFt27aIiYlBr169sG3bNr2bRH6wVzhQcKwctkOnUHC8HPYKh95NIgPh+UNEamukdwMa8t5772HKlClYvHgxevXqhXnz5mHw4MHYt28fUlJS9G4e+VBUcgaPrdyNrw+ccG/LyUzG7GFZSGsaq2PLyAh4/hCRFqTv6Zk7dy7Gjx+PsWPHolOnTli8eDHi4uKwZMkSvZtGPtgrHHUuWACw8cAJTFu5m7/YqV48f4hIK1IHPQ6HA9u3b8egQYPc2yIiIjBo0CBs3brV630qKytRWlrq8Y9C60S5o84Fy2XjgRM4Uc6LFvnG84eItCJ10HPixAlUV1cjNTXVY3tqaiqKi4u93mfWrFmwWq3uf+np6aFoKtVQevZcvbeXNXA7mRvPHyLSitRBjxLTp0+H3W53/zt8+LDeTTKdxJjG9d6e0MDtZG48f4hIK1IHPcnJyYiMjMTRo0c9th89ehQtWrTwep/o6GgkJiZ6/KPQSo6PQk5mstfbcjKTkRwfFeIWkZHw/CEirUgd9ERFReGyyy5DXl6ee5vT6UReXh569+6tY8uoPta4KMwellXnwpWTmYw5w7JgjeNFi3zj+UNEWpF+yvqUKVMwevRo9OjRAz179sS8efNw+vRpjB07Vu+mUT3SmsZiwchsnCh3oOzsOSTENEZyfBQvWOQXnj9EpAXpg57bbrsNx48fxxNPPIHi4mJ0794da9asqZPcTPKxxvEiRcrx/CEitVmEEELvRmiptLQUVqsVdrud+T1EREQGocX1W+qcHiIiIiK1MOghIiIiU2DQQ0RERKbAoIeIiIhMgUEPERERmQKDHiIiIjIFBj1ERERkCgx6iIiIyBQY9BAREZEpSL8MRbBcBadLS0t1bgkRERH5y3XdVnPhiLAPesrKygAA6enpOreEiIiIAlVWVgar1arKY4X92ltOpxNFRUVISEiAxWJR9bFLS0uRnp6Ow4cPm35dLx6L83gcLuCxuIDH4gIeiwt4LC7wdiyEECgrK0NaWhoiItTJxgn7np6IiAi0atVK0+dITEw0/QnrwmNxHo/DBTwWF/BYXMBjcQGPxQW1j4VaPTwuTGQmIiIiU2DQQ0RERKbAoCcI0dHRmDFjBqKjo/Vuiu54LM7jcbiAx+ICHosLeCwu4LG4IFTHIuwTmYmIiIgA9vQQERGRSTDoISIiIlNg0ENERESmwKCHiIiITIFBTw2LFi1C27ZtERMTg169emHbtm317v9///d/uOSSSxATE4OuXbvi008/9bhdCIEnnngCLVu2RGxsLAYNGoQDBw5o+RJUE8ixeP3113HFFVegWbNmaNasGQYNGlRn/zFjxsBisXj8u+aaa7R+GaoI5FgsW7aszuuMiYnx2Mcs58WVV15Z51hYLBZcf/317n2Mel5s3LgRN9xwA9LS0mCxWPDhhx82eJ/169fjj3/8I6Kjo9GhQwcsW7aszj6BfgfpLdDj8MEHH+Cqq67CRRddhMTERPTu3Ruff/65xz5PPvlknXPikksu0fBVqCPQY7F+/Xqvn4/i4mKP/Yx2TgCBHwtv3wMWiwWdO3d276PWecGg5//33nvvYcqUKZgxYwZ27NiBbt26YfDgwTh27JjX/bds2YKRI0fi7rvvhs1mw9ChQzF06FD88MMP7n2ef/55zJ8/H4sXL8a3336LJk2aYPDgwTh79myoXpYigR6L9evXY+TIkfjqq6+wdetWpKen4+qrr8Zvv/3msd8111yDI0eOuP/l5uaG4uUEJdBjAZyvKFrzdf7yyy8et5vlvPjggw88jsMPP/yAyMhI3HrrrR77GfG8OH36NLp164ZFixb5tX9hYSGuv/56DBgwADt37sTDDz+Me+65x+OCr+Rc01ugx2Hjxo246qqr8Omnn2L79u0YMGAAbrjhBthsNo/9Onfu7HFObNq0SYvmqyrQY+Gyb98+j9eakpLivs2I5wQQ+LF4+eWXPY7B4cOH0bx58zrfFaqcF4KEEEL07NlTTJw40f13dXW1SEtLE7NmzfK6//Dhw8X111/vsa1Xr17ivvvuE0II4XQ6RYsWLcQ///lP9+0lJSUiOjpa5ObmavAK1BPosaitqqpKJCQkiOXLl7u3jR49WgwZMkTtpmou0GOxdOlSYbVafT6emc+Ll156SSQkJIjy8nL3NqOeFzUBEKtWrap3n0cffVR07tzZY9ttt90mBg8e7P472OOrN3+OgzedOnUSTz31lPvvGTNmiG7duqnXMB34cyy++uorAUCcOnXK5z5GPyeEUHZerFq1SlgsFvHzzz+7t6l1XrCnB4DD4cD27dsxaNAg97aIiAgMGjQIW7du9XqfrVu3euwPAIMHD3bvX1hYiOLiYo99rFYrevXq5fMxZaDkWNRWUVGBc+fOoXnz5h7b169fj5SUFHTs2BEPPPAATp48qWrb1ab0WJSXl6NNmzZIT0/HkCFD8L///c99m5nPizfeeAMjRoxAkyZNPLYb7bxQoqHvCzWOrxE5nU6UlZXV+a44cOAA0tLS0K5dO9xxxx04dOiQTi3UXvfu3dGyZUtcddVV2Lx5s3u7Wc8J4Px3xaBBg9CmTRuP7WqcFwx6AJw4cQLV1dVITU312J6amlpnfNWluLi43v1d/w3kMWWg5FjU9thjjyEtLc3jw3rNNdfgzTffRF5eHubMmYMNGzbg2muvRXV1tartV5OSY9GxY0csWbIEq1evxltvvQWn04k+ffrg119/BWDe82Lbtm344YcfcM8993hsN+J5oYSv74vS0lKcOXNGlc+dEb3wwgsoLy/H8OHD3dt69eqFZcuWYc2aNXj11VdRWFiIK664AmVlZTq2VH0tW7bE4sWLsXLlSqxcuRLp6em48sorsWPHDgDqfBcbUVFRET777LM63xVqnRdhv8o6hdbs2bPx7rvvYv369R4JvCNGjHD/f9euXZGVlYX27dtj/fr1GDhwoB5N1UTv3r3Ru3dv9999+vTBpZdein/961945plndGyZvt544w107doVPXv29NhulvOC6nrnnXfw1FNPYfXq1R55LNdee637/7OystCrVy+0adMGK1aswN13361HUzXRsWNHdOzY0f13nz59UFBQgJdeegn/+c9/dGyZvpYvX46mTZti6NChHtvVOi/Y0wMgOTkZkZGROHr0qMf2o0ePokWLFl7v06JFi3r3d/03kMeUgZJj4fLCCy9g9uzZ+OKLL5CVlVXvvu3atUNycjLy8/ODbrNWgjkWLo0bN0Z2drb7dZrxvDh9+jTeffddv76YjHBeKOHr+yIxMRGxsbGqnGtG8u677+Kee+7BihUr6gz71da0aVNcfPHFYXdOeNOzZ0/36zTbOQGcn9m6ZMkSjBo1ClFRUfXuq/S8YNADICoqCpdddhny8vLc25xOJ/Ly8jx+tdfUu3dvj/0BYO3ate79MzIy0KJFC499SktL8e233/p8TBkoORbA+RlJzzzzDNasWYMePXo0+Dy//vorTp48iZYtW6rSbi0oPRY1VVdXY8+ePe7XabbzAjhf2qGyshJ33nlng89jhPNCiYa+L9Q414wiNzcXY8eORW5urkf5Al/Ky8tRUFAQdueENzt37nS/TjOdEy4bNmxAfn6+Xz+QFJ8XQadCh4l3331XREdHi2XLlokff/xR3HvvvaJp06aiuLhYCCHEqFGjxLRp09z7b968WTRq1Ei88MILYu/evWLGjBmicePGYs+ePe59Zs+eLZo2bSpWr14tdu/eLYYMGSIyMjLEmTNnQv76AhHosZg9e7aIiooS77//vjhy5Ij7X1lZmRBCiLKyMjF16lSxdetWUVhYKL788kvxxz/+UWRmZoqzZ8/q8hr9FeixeOqpp8Tnn38uCgoKxPbt28WIESNETEyM+N///ufexyznhUu/fv3EbbfdVme7kc+LsrIyYbPZhM1mEwDE3Llzhc1mE7/88osQQohp06aJUaNGufc/ePCgiIuLE4888ojYu3evWLRokYiMjBRr1qxx79PQ8ZVRoMfh7bffFo0aNRKLFi3y+K4oKSlx7/PXv/5VrF+/XhQWForNmzeLQYMGieTkZHHs2LGQv75ABHosXnrpJfHhhx+KAwcOiD179ojJkyeLiIgI8eWXX7r3MeI5IUTgx8LlzjvvFL169fL6mGqdFwx6aliwYIFo3bq1iIqKEj179hTffPON+7b+/fuL0aNHe+y/YsUKcfHFF4uoqCjRuXNn8cknn3jc7nQ6xeOPPy5SU1NFdHS0GDhwoNi3b18oXkrQAjkWbdq0EQDq/JsxY4YQQoiKigpx9dVXi4suukg0btxYtGnTRowfP176D65LIMfi4Ycfdu+bmpoqrrvuOrFjxw6PxzPLeSGEED/99JMAIL744os6j2Xk88I13bj2P9frHz16tOjfv3+d+3Tv3l1ERUWJdu3aiaVLl9Z53PqOr4wCPQ79+/evd38hzk/lb9mypYiKihJ/+MMfxG233Sby8/ND+8IUCPRYzJkzR7Rv317ExMSI5s2biyuvvFKsW7euzuMa7ZwQQtnno6SkRMTGxorXXnvN62OqdV5YhBAisL4hIiIiIuNhTg8RERGZAoMeIiIiMgUGPURERGQKDHqIiIjIFBj0EBERkSkw6CEiIiJTYNBDREREpsCgh4iIiEyBQQ8RkYratm2LefPm6d0MIvKCQQ+RSRUXF2Py5Mno0KEDYmJikJqair59++LVV19FRUWFe7+2bdvCYrHAYrEgNjYWbdu2xfDhw7Fu3TqPx/v555/d+1ksFiQlJeHqq6+GzWYL9UvT1XfffYd7773X/bfFYsGHH36oX4OIyI1BD5EJHTx4ENnZ2fjiiy8wc+ZM2Gw2bN26FY8++ig+/vhjfPnllx77P/300zhy5Aj27duHN998E02bNsWgQYPw3HPP1XnsL7/8EkeOHMHnn3+O8vJyXHvttSgpKQnRKzvv3LlzIX2+mi666CLExcXp9vxEVI+AV+siIsMbPHiwaNWqlSgvL/d6u9PpdP9/mzZtxEsvvVRnnyeeeEJERESIn376SQghRGFhoQAgbDabe5/NmzcLAB6ridc0Y8YM0a1bN7F48WLRqlUrERsbK2699VaPVbeFEOL1118Xl1xyiYiOjhYdO3YUixYtct/met53331X5OTkiOjoaK+LeQohxKlTp8S9994rUlJSRHR0tOjcubP473//K4QQ4sSJE2LEiBEiLS1NxMbGii5duoh33nnH4/79+/cXEydOFBMnThSJiYkiKSlJ/OMf//B5vGovxtumTRshhBD5+fnixhtvFCkpKaJJkyaiR48eYu3atV7bTETqYU8PkcmcPHkSX3zxBSZOnIgmTZp43cdisTT4OJMnT4YQAqtXr/a5T2xsLADA4XD43Cc/Px8rVqzAf//7X6xZswY2mw0TJkxw3/7222/jiSeewHPPPYe9e/di5syZePzxx7F8+XKPx5k2bRomT56MvXv3YvDgwXWex+l04tprr8XmzZvx1ltv4ccff8Ts2bMRGRkJADh79iwuu+wyfPLJJ/jhhx9w7733YtSoUdi2bZvH4yxfvhyNGjXCtm3b8PLLL2Pu3Ln497//7fW1fffddwCApUuX4siRI+6/y8vLcd111yEvLw82mw3XXHMNbrjhBhw6dMjncSIiFegddRFRaH3zzTcCgPjggw88ticlJYkmTZqIJk2aiEcffdS93VdPjxBCpKamigceeEAIUben59SpU+Kmm24S8fHxori42Ov9Z8yYISIjI8Wvv/7q3vbZZ5+JiIgIceTIESGEEO3bt6/T4/LMM8+I3r17ezzvvHnz6n3dn3/+uYiIiBD79u2rd7+arr/+evHXv/7V/Xf//v3FpZde6tGz89hjj4lLL73U/Xft4wVArFq1qsHn6ty5s1iwYIHfbSOiwDXSN+QiIlls27YNTqcTd9xxByorK/26jxCiTq9Qnz59EBERgdOnT6Ndu3Z47733kJqa6vMxWrdujT/84Q/uv3v37g2n04l9+/YhISEBBQUFuPvuuzF+/Hj3PlVVVbBarR6P06NHj3rbunPnTrRq1QoXX3yx19urq6sxc+ZMrFixAr/99hscDgcqKyvr5OdcfvnlHq+5d+/eePHFF1FdXe3uNWpIeXk5nnzySXzyySc4cuQIqqqqcObMGfb0EGmMQQ+RyXTo0AEWiwX79u3z2N6uXTsAF4akGnLy5EkcP34cGRkZHtvfe+89dOrUCUlJSWjatGlQbS0vLwcAvP766+jVq5fHbbUDDF9DdS4Nva5//vOfePnllzFv3jx07doVTZo0wcMPP1zv0JxSU6dOxdq1a/HCCy+gQ4cOiI2NxS233KLJcxHRBQx6iEwmKSkJV111FRYuXIiHHnqowWDBl5dffhkREREYOnSox/b09HS0b9/e78c5dOgQioqKkJaWBgD45ptvEBERgY4dOyI1NRVpaWk4ePAg7rjjDkXtdMnKysKvv/6K/fv3e+3t2bx5M4YMGYI777wTwPkcoP3796NTp04e+3377bcef3/zzTfIzMz02cvTuHFjVFdX13muMWPG4KabbgJwPrj7+eeflb40IvITE5mJTOiVV15BVVUVevTogffeew979+7Fvn378NZbb+Gnn36qcwEvKytDcXExDh8+jI0bN+Lee+/Fs88+i+eeew4dOnQIqi0xMTEYPXo0du3aha+//hqTJk3C8OHD0aJFCwDAU089hVmzZmH+/PnYv38/9uzZg6VLl2Lu3LkBPU///v2Rk5ODYcOGYe3atSgsLMRnn32GNWvWAAAyMzOxdu1abNmyBXv37sV9992Ho0eP1nmcQ4cOYcqUKdi3bx9yc3OxYMECTJ482efztm3bFnl5eSguLsapU6fcz/XBBx9g586d2LVrF26//XY4nc6AXg8RKaB3UhER6aOoqEg8+OCDIiMjQzRu3FjEx8eLnj17in/+85/i9OnT7v1qTruOiooSrVu3FsOHDxfr1q3zeDxvU9Yb4pqy/sorr4i0tDQRExMjbrnlFvH777977Pf222+L7t27i6ioKNGsWTORk5PjTsQO5HlPnjwpxo4dK5KSkkRMTIzo0qWL+Pjjj923DRkyRMTHx4uUlBTxj3/8Q9x1111iyJAh7vv3799fTJgwQdx///0iMTFRNGvWTPztb3+rd4r/Rx99JDp06CAaNWrknrJeWFgoBgwYIGJjY0V6erpYuHCh6N+/v5g8ebLfx46IAmcRQgh9wy4iMqsnn3wSH374IXbu3Kl3U/xy5ZVXonv37lxmgsigOLxFREREpsCgh4iIiEyBw1tERERkCuzpISIiIlNg0ENERESmwKCHiIiITIFBDxEREZkCgx4iIiIyBQY9REREZAoMeoiIiMgUGPQQERGRKfx/46P+EC7+ax0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[\"GDP per capita\"]\n",
    "y = data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x=x,y=np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GDP per capita', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x,y=np.log(y))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5.2: Plotting Social Support vs Maximum Infection Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Social support', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[\"Social support\"]\n",
    "y = data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x=x,y=np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Social support', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x,y=np.log(y))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5.3: Plotting Healthy Life Expectancy vs Maximum Infection Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Healthy life expectancy', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[\"Healthy life expectancy\"]\n",
    "y = data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x=x,y=np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Healthy life expectancy', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x,y=np.log(y))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5.4: Plotting Freedom to Make Life Choices vs Maximum Infection Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Freedom to make life choices', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t6tXn2CR4EJg293nkej0j8OT8zZ4ZItKFS4dD6vkqubm5OTtGye1pODxTUHLGPrTkjJFzOeTKQWvu8xg1501pDGaISDfUPNA3N2dHK7k9ctLCEJ+a5MpBa+7zGDHnTWmSg5mamhqsW7cORUVFeOCBBxAREYGjR48iMjIS4eHG7G4kIvWpdaBv7gndiCd+5nKQ1kmqM3P48GH06NEDd955J8aOHYsTJ04AAObPn4/JkyfL2kAiIi1obkFCIxYuNHL9Il/gIpPKk5QAPHToUERERODtt99GdHQ0du3ahfbt22PdunUYM2YMCgsLlWirJEwAJiK5+HsSZ8OkYOZyNI/eZrZpiSfnb0nBTHR0NDZt2oTOnTsjIiLCHsz88ssv6Nq1K6qqqiQ3Xm4MZohITs09ofPET0aZ2aYWxWcz1dXVoba28fS8X3/9FREREVKekohIF5jESc1llJlteiApZ+bGG2/EokWL7P83mUyorKzEzJkzccstt8jVNiIiIt0y4sw2rZLUM7NgwQIMGTIEXbt2xYULF/DAAw+gsLAQMTExyM3NlbuNREREuuPtzDa9LImhBZKCmTZt2mDXrl147733sGvXLlRWVuKRRx7Bgw8+iNBQJjQRERF5M6WdicOekZQAvGHDBmRkZKBFC8dYqKamBps2bUJWVpZsDfQWE4CJiEgtUma2MXH4d4onAA8cOBClpaWIjY112G61WjFw4ECnycFERET+RkrVaiYOe05SMCOEgMlkarT91KlTaNmypdeNIiIiMgpPZ7YxcdhzHgUzd999N4DfZy+NHDkSZrPZflttbS12796NjIwMeVtIRETkR4y4JIbSPApmLBYLgN97ZiIiIhySfYODg3HttddizJgx8raQiIjIj3AtLM95FMzk5OQAAFJSUjB58mQOKREREcmsfi0sV4nDzJdpTNJsJj3hbCYiIvKEVuq7+PuSGIrPZgKADz74AO+//z5KSkpgszmuALpjxw6pT0tERKQaLdV34ZIYzSdpOYPFixdj1KhRiIuLQ0FBAa655hpER0fj559/xs033yx3G4mIiBRnrbI1CmSA36dDT12zG9Yqm4tHktokBTOvvfYa3nzzTSxZsgTBwcF49tln8c0332DChAmwWq1yt5FIU6xVNhSVVaKg5AyKTlTyAEdkEM2p70LaJGmYqaSkxD4FOzQ0FGfPngUADB8+HNdeey2WLl0qXwuJNERLXdBEJC/Wd9EvST0z8fHxOH36NACgbdu2+OGHHwAAxcXFMHg+MfkxdkET+Y4aPaBGrO/iLz3Jknpmrr/+enz66adIT0/HqFGj8PTTT+ODDz7Atm3b7IX1iIyGJcaJfEOtHlCl67v4epaUP/UkS5qaXVdXh7q6OvtCk6tXr8amTZuQmpqKxx57DMHB2jmgc2o2yaWg5Azuem2Ty9s/fjIDPdu28mGLiLRNyslb7UUWpSwM2dzn9WVgofZ+lIOiU7Nramowd+5cjB49Gm3atAEA3H///bj//vultZZIJ4zYBU2kFKknb7V7QKUsDNmUpoaolQgs1N6PvuZxzkyLFi3wyiuvoKamRon2EGlWfRe0MywxTvQHb/LLtJCEawkLRofYcPRs2wodYsO9PumrMUtKC/vRlyQlAA8aNAjr16+Xuy1EmlZfYvzSgIYlxokceXPyNmIPqPW8+2DFel7+wMKI+9EdSQnAN998M6ZOnYo9e/agV69ejdZouuOOO2RpHJHWKNEFTWQ03vQKGHGRxbBg96fasOBA2V/TiPvRHUnBzJNPPgkAWLhwYaPbTCYTamtrvWsVkYaxxDiRe970ChhxkcWAABMyO0Yj/9CpRrdldoxGYIBJ9tc04n50R1IwU1dXJ3c7iIjIILztFXDVAwoARWWVqi8A6akWASaMymwHAA4BTWbHaIzKbKdIMAP4V0+yoqtm9+jRA2vXrkVSUpJSL9EkTs0mIvI9uac467lmirXKhsn/swudEyKRnhSF6po6mFsEoOBIOQ6UVuDVe680ZIDhLU/O34oGMxEREdi1axfat2+v1Es0icEMEWmNr4unqaX+fXrbK2CEmilK1a8xMkXrzPjab7/9hueeew5ffPEFqqqq0LFjR+Tk5KB3795qN42IyGN67mHwlFz5ZUaomeKrIR9/CZQvpelg5syZM8jMzMTAgQPxxRdf4LLLLkNhYSFatWKVVSLSHzWKpxmBUWqmKD15oKlA2ciBjqaDmfnz5yMpKQk5OTn2be3atXP7mOrqalRXV9v/X1FRoVj7iIg8YYQeBjU0NTsqJCgQBSVnDHeC9oS7QHnmJ3sx8/ZumPbRHsP2CEoqmucrn376KXr37o17770XsbGxSE9Px1tvveX2MdnZ2bBYLPY/NZOPiYgaMkoPg6+5q77dr2M0PttTirte24RBC9ZjfG4Bjpafd/t8RlxJ2l2g3DkhEtM+lFaRWS80Hcz8/PPPWL58OVJTU/HVV1/hiSeewIQJE7Bq1SqXj5k2bRqsVqv978iRIz5sMRGRa+56GMKCA9EqLNhwJ9lLSQkkXFXf7tcxGiMz22HFxmL7tqZO0EfLz2NcbgEGLVzvUQCkde4C5fSkKHzvpMYNoNxyCr6m6DDTG2+8gbi4OMmPr6urQ+/evTF37lwAQHp6Ovbu3YvXX38dI0aMcPoYs9kMs9ks+TWJiJTiqv5KWHAgVoy8Gs9/vBffHzLmMADgXfLzpQm0IUGB+GxPKSbkFqDK5lio1dWQnZFzltwFytU17mvDGaFHUHIwk5eXh7y8PJSVlTUqordixQoAwAMPPOBV4xISEtC1a1eHbV26dMGaNWu8el4iIjW4qso647auWPbtIYdABjDGSbaeHIFEwwTagpIzWPrtIZf3dXaCNnLOkrtChVGhxl+nSVIw89JLL2HWrFno3bs3EhISYDIpU70wMzMTBw4ccNh28OBBJCcnK/J6RERKczZFt04ITPtwj9P76/0kW0/uQELKkglGzllyt3xBcnSY4ddpkhTMvP7661i5ciWGDx8ud3scPP3008jIyMDcuXNx3333YcuWLXjzzTfx5ptvKvq6RERKunSKbkHJGbf31/NJtp7cgYSUJROMvpK0u1o2Rl+nSVIwY7PZkJGRIXdbGrn66qvx0UcfYdq0aZg1axbatWuHRYsW4cEHH1T8tYmIfMXoJ1lA3vdYXy9lwqBUPHFdB+QXncKKjcWostW6PUHLsZK01mu1uKplY/R1miQFM48++ijeffddzJgxQ+72NHLbbbfhtttuU/x1iIjUIsdJVuu8fY/WKhtOnbNBAHjxk70Os3P6p8bgf8f3gwlAtJvgwtseCr1Xb1a6aJ+aJK3N9NRTT+Ff//oX0tLSkJaWhqAgx4h64cKFsjXQW1ybiYj0QIm1e7TWi+AsGMjsGI3x16ciuXWYy/dZ/7grk6JQUHLGYeXpep6s0SRlzSgjrA+lN4qvzbR792707NkTALB3716H25RKBiYiMjK5hwG02IvQMjgQt/RIwMiMFIeVo0ev3Ireya2cBgQNZ0GNzEhxOYPJkyRiKT0URp4JZQSSgpnvvvtO7nYQkYK0doVOznkzDNDwM27dMhjPf7RXc1O9T1baPJ611TCIULNeit5mQvnbb97ronm//vorAKBNmzZeN4aI5KfFK3SS16Wf8dsjejcKZOqp2YsgJSBo+BhzC/dF65VMlNZTkrY//uYlLWdQV1eHWbNmwWKxIDk5GcnJyYiKisLs2bMbFdAjIvU0VajMiOXy/Y2zz1irFV+lBAQNH1NwpByZHaOdPlbpRGl360NpKUnbX3/zkoKZ6dOnY+nSpZg3bx4KCgpQUFCAuXPnYsmSJT6Z4UREzdOccX7SN2efsZo9GO5ICQgaPmbFxmKMymzXKKDxRb0UV+tDaa1Wi7/+5iUNM61atQr//Oc/cccdd9i3paWl4fLLL8eTTz6Jl19+WbYGapW/jUeSPultnJ885+wzru/BcDXrR61eBClToy99zITcAozu1w5jr+sIc1AAokKD3SZKe3qsdnd/PdRq8dffvKRg5vTp07jiiisabb/iiitw+vRprxuldf44Hkn6pKdxfpLG2We8YmMxFg9LBwCHgEYLvQhSAgKpQYSnx+rm3F/rtVr89Tcvqc5Mnz590KdPHyxevNhh+/jx47F161b88MMPsjXQW3LXmWGtAdITa5UN43ML7FfBYcGBGN2vHdKTogAAbVuHITbCzO+sjl36GdcLCw7EjNu6ondyK5yrrtFkL4KSPD1WG+XY7ur7AOjrfQA+qDPzyiuv4NZbb8V//vMf9O3bFwCwefNmHDlyBGvXrpXylLrBWgOkJw276LcdPoPFw9KRk1/sUKuDvYr65mropndyK1zX6TLJBff0ztNjtVGO7f6wDpMzkoKZAQMG4ODBg1i2bBl++uknAMDdd9+NJ598EomJibI2UGv8dTyS5KFGrlV9F3151UU8//GeRnkUatceIe95OgzjDzl/nh6rjXRs10Nuj9wk15lJTEz0i0TfS/nreCR5T81cK0tY8O9Xnk4SQgF9XXkagRLBRHNzOfwl58/TY7XRju1az+2RW7ODmd27d6N79+4ICAjA7t273d43LS3N64ZplT8sCOeKP1zNKaWp2g++6BUx0pWnnqkZTGjhe+grnh6r/fnYbgTNDmZ69uyJY8eOITY2Fj179oTJZIKz3GGTyYTa2lpZG6kl/joe6S9Xc0rRwni80a489UjtYEIL30Nf8fRY7YtjOy8IldPsYKa4uBiXXXaZ/d/+zN/GI9U+ABuBFnpFeOWpPrWDCS18D33J02O1ksd2XhAqq9nBTHJysv3fhw8fRkZGBlq0cHx4TU0NNm3a5HBfo/Kn8Ui1D8BGoIVeEX/tVdQSXwcTl/YEtA4LRlhwIKpsznvPjdg75+mxWoljOy8IlScpAXjgwIEoLS1FbGysw3ar1YqBAwcaepjJH/nb1ZwStNIr4m+9ilrjy6DWVU/AipFXY/TKrY0CGvbOKYcXhMqTFMwIIWAymRptP3XqFFq2bOl1o0hbtNCroHda6hXxp17FhrSQr+CroNZdT4AAMOO2rpj24R6H12bvnHKMekGohd9UPY+CmbvvvhvA70m+I0eOhNlstt9WW1uL3bt3IyMjQ94Wkuq00qugd+wVUY9W8hV8FdS66wn4vvAkXritK/ImDeD30EeMeEGold9UPY+CGYvFAuD3npmIiAiEhv7R4ODgYFx77bUYM2aMvC0k1WmpV0Hv/LVXRE1ay1fwRVDbVE/Aueoa9GzbSrbXI/eMdkGotd8U4GEwk5OTAwBISUnBlClTEBYWpkijSFlSugbZq0B6pcV8BaWDWiP2BOiZ0S4ItfibkpQz8/DDD+O3335Damqqw/bCwkIEBQUhJSVFjraRArzpGmSvAumRUfMV3DFaT4ARGOmCUIu/qQApDxo5ciQ2bdrUaPt///tfjBw50ts2kUKa6hq0VtlUahmRcvyxl6K+JyArNcZhu157AozCEhaMDrHh6Nm2FTrEhuv2c9Dib0pSz0xBQQEyMzMbbb/22msxbtw4rxtFytBi1yCR0vy1l8JIPQGkLVr8TUnqmTGZTDh79myj7VarlTVmNEyLXYNEnrBW2VBUVomCkjMoOlHZrN5Ef+6lMEpPAGmLFn9TknpmsrKykJ2djdzcXAQGBgL4fWp2dnY2+vXrJ2sDST5a7BokY5OzDoU3+V7spTAeLdU48Uda+01JCmbmz5+PrKwsdO7cGf379wcAfP/996ioqMC3334rawNJPlrsGiTjkrMOhRxTQZnAbhxaq3Hir7T0m5I0zNS1a1fs3r0b9913H8rKynD27Fk8/PDD+Omnn9C9e3e520gy0WLXIBmT3Mnmzcn3Iv/AiQzkjKSeGQBITEzE3Llz5WwL+YDWugbJmORONme+F9XjRAZyRlLPDPD7sNJDDz2EjIwM/PbbbwCAd955Bxs3bpStcaQMJgWS0uQOPpjvRfUY2JIzkoKZNWvWYMiQIQgNDcWOHTtQXV0N4PfZTOytISK5g4/6fC9nPMn3kjIbirSFgS05IymYmTNnDl5//XW89dZbCAr644uTmZmJHTt2yNY4ItInuYKPenLkex0tP49xuQUYtHA97nptEwYtWI/xuQU4Wn7eo7aQuuT+bpExmIQQwtMHhYWFYd++fUhJSUFERAR27dqF9u3b4+eff0bXrl1x4cIFJdoqSUVFBSwWC6xWKyIjI9VuDpHfOFp+3uVaNAkSZ5zUT8f1NN/LWmXDuNwCp7kWWakx9tlQnO6rD0p8t0h7PDl/S0oAjo+Px6FDhxqtwbRx40a0b99eylMSkcEokWwudSpoc5JGz9lqnU73nXVnd1jP2xAewuBGK6R8txioGpukYGbMmDF46qmnsGLFCphMJhw9ehSbN2/G5MmTMWPGDLnbSEQ6pZU6FE0ljVrPX8SL//uj0+m+0z/eg/S2rbD020OsZaIhnny3WJfG+CQFM1OnTkVdXR0GDRqEqqoqZGVlwWw2Y/LkyRg/frzcbSQi8kpTSaNhwYEue27yD53C6Mx2ADwr0kfaIEfBRb3w596nZgczu3fvRvfu3REQEACTyYTp06djypQpOHToECorK9G1a1eEh4cr2VYiMiBfHICbqn4dEGBy+/jqmjr7v1nLRF/8pS6Nv/c+NTuYSU9PR2lpKWJjY9G+fXts3boV0dHR6Nq1q5LtI/I7SpzctXrF5qsDcP1sKFdJo+cvul8g19zCceIna5nohz/UpfGn3idXmh3MREVFobi4GLGxsfjll19QV1fX9IOIyCNKnNy1eMVmrbKh7Gw1Sk5XYVRmO1yZFIUVG4tRZatV7ADsLmnUWmVz2XOT2TEaBUfKHbaxlol+GLkuTf1FyukqW6PfUT0j9T650+w6M/fccw8GDBiAdu3awWQyoXfv3mjfvr3TPyLynBJrzmhxHZvS8vNYu/cYSk5X2YdvEi0hWPbAVQgLDrS3T4n1llxVv3ZVxyazYzRGZbbDio3F9m2sZaIvRq1L07Bu0r2vb8bolVtRUHIGi4el239H9YzQ+9SUZvfMvPnmm7j77rtx6NAhTJgwAWPGjEFERISSbSPyK0qM7WstX8BaZcPh01X4bPdR5B86Zd+e2TEa4wZ2xGMD2uPv3xQC8P0B+NKem9DgQOwoKceE3AL7lS4XZdWfpoYY9fhZurpIqf9Nje7XDku/PWTfrufep+byaDbTTTfdBADYvn07nnrqKQYzZEhq5ZcoMbavVL6A1H1UXnURS74tdAhkgD8Ows/ddIU9mFHjAHzpdN/4yBBck9Kai7LqnNEW2HV3kdJw9h2g794nT0iamp2TkyN3O4g0Qc38EiXG9pV4Tm/20TlbTaNApl7D7Vo5AGulTg55z0ifZVMXKfXDt3ruffKUpLWZzp07hxkzZiAjIwMdO3ZkzgwZgtr5JUqM7cv9nN7uo3M297OGqmy1fnUAJpKiqYuUlOgw5E0agCXD0v1meQdJPTOPPvoo1q9fj+HDhyMhIQEmk/saDUR6oHZ+iRJj+3I/p7f7KCrU/UE4KizIL6aREnmjqbpJl0eF+t1vSFIw88UXX+Dzzz9HZmam3O0hUo0W6lEoMbYv53N6u49iI8zonxrjNCDqnxqDhMgQvzsIk3K0WF9JjjYZManZW5KCmVatWqF169Zyt4VIVVqpR6HE2L5cz+ntPrKEBWO+zAdhLZ6wSH1Hy8/jhU/24oqESKQnRaHUegFlYUFo2zoMl7cKU61Nci1marSkZm9JCmZmz56NF154AatWrUJYmDpfCiK5NdV1q4WEVLXJsY/kPAhrsSAgqc9aZcMLn+zF/de0RU5+scM05X4dozHv7jS0ae35ucubwNldvpnUxUyNlNTsLZMQQnj6oPT0dBQVFUEIgZSUFAQFOV6N7dixQ7YGequiogIWiwVWqxWRkZFqN4c07mj5eZe9Bv6SSNcUrewja5UN43ILnA5ZZaXGMPdGo3zRk1ZUVomPdv6GgpIzTmfP9U+NwVIPvx/eBs5FZZUYtHC9y9vfHtEbj6zaZn9efn89O39L6pkZOnSolIcRaR67bpumlX2kdsI2ec5XPWkVFy4iPSnKoUemoe89/H7IsfZRc6dT1z8vv7+ekRTMzJw5U+52EGmGlrputZoPooV9pIWEbWo+Xy6GGBkShFLrBbf38eT7IUfg3FS+GRcz9Y6kYIaIlMd8EPe0krBNzePLnrSY8GAcr5Dv+yFH4Owu34yLmXqv2UXzWrdujZMnf/8Q6mczufojIu+oXcBPD5oqCBge0gJFZZUoKDmDohOVhtxn1iqbbt6jL3vSLGHBSI4OQ7+O0U5v9zShX47AmYuZKqvZPTN///vf7WsxLVq0SKn2EBGYD9Ic7mptzBnaHdM/2oP/7C9z2O7LXi2lhwj11nPn6560y1uFYd7daZj20Z5G+8jTMgByzXTkYqbKkTSbqbnmzZuHxx9/HFFRUUq9RJM4m4n0qKDkDO56bZPL2z9+MgM927byYYu0qz5oqE9GDg9p0SiQqeerWSJKBxp6nMllrbJhfG6By4BAqTZf+v3wpgyAErP45GqfEXly/pa0NlNzzZ07F6dPn5bt+ebNmweTyYSJEyfK9pxEWsR8kOazhAWjQ2w4erZthQ6x4ai8UOM0kAH+6NVSki+GCJvTc6c1roZZlO6JuPT7IfV16ntV8iYNwMdPZsi29pFc7fN3iiYAy9nps3XrVrzxxhtIS0uT7TmJtIoF/KRTe5aTL4YI1X6PUnk7rV/t2X1KzOJT+z0ZhS5mM1VWVuLBBx/EW2+9hTlz5ri9b3V1Naqrq+3/r6ioULp5RLLj2ivSqd2r5YtAQ+336A2pAYHecoSaw4jvSS2KDjPJZezYsbj11lsxePDgJu+bnZ0Ni8Vi/0tKSvJBC4nkp1S3thR6mjXT1CwnpXu1fBFoqP0efc0XQ3e+/o5zxqK8NN8zs3r1auzYsQNbt25t1v2nTZuGSZMm2f9fUVHBgIZ0SwvF6fR29ah2r5YvhgjVfo++pvTQnRrfcc5YlJemg5kjR47gqaeewjfffIOQkJBmPcZsNsNsNivcMiL/4MuqrXJSc8kFXwUaWllWwheUHLpT6zuu17wnrVI0mOnfvz9CQ6VHtdu3b0dZWRmuuuoq+7ba2lps2LABS5cuRXV1NQIDA+VoKhE5oeerRzV7tXwVaGih584XlBy6U+s7rue8Jy2SlDOzcuVKp9tramowbdo0+//Xrl2LhIQESQ0DgEGDBmHPnj3YuXOn/a9379548MEHsXPnTgYyRArj1aN0nHIrHyVzhNT6jvtb3pPSJAUzEyZMwL333oszZ87Ytx04cAB9+vRBbm6ubI2LiIhA9+7dHf5atmyJ6OhodO/eXbbXISLn/PHqUU/Jzv5CyRo1an3H1aq7Y1SShpkKCgrw0EMPoUePHsjJycHBgwfx7LPPYujQoXjttdfkbiMR+cilNS/CQ1r4Vb0bXyaCsr6IZ1wN3QFAUVml5P2oZk0nf8p7Uprk5Qzq6uowceJELFu2DIGBgVi1ahWGDRsmd/u8xuUMiJrH2Yn8hi6xmHFbVzz/8V7Zy7hrjS+XCNDbDDGtkms/KrVUAXnHk/O35GDmf//3f/HII4+gU6dOOHjwINLS0vCvf/0LiYmJkhqtFAYzRE1zdyK/oUss5tzVA5UXamS/etRS70RRWSUGLVzv8va8SQPQITbc69fR47pKWiT3fuQaSdrjyflb0jDTY489hlWrVuHll1/GpEmTcPz4cYwePRo9evTA8uXLcd9990lqOBGpw92Mjm/2l2HqzTWynMgb0lrvhK8SQfU8Q0xL5N6PvpgZpqXg3WgkBTP5+fn473//iyuvvBIAEB8fj7Vr12LZsmUYPXo0gxkinfH1jA4t1q/xVSIoZ4jJQ2/7UWvBu9FIms20fft2eyDT0NixY7F9+3avG0VEvuXrGR1aXPXZV1Nl/XGGmBL0tB+5dIHyJAUz7irsdu7cWXJjiEgdvq55ocWral9NlWV9EXnoaT9qMXg3GskVgD/44AO8//77KCkpgc3m+EHs2LHD64YRke/4eq0frV5V+2KqrCUsGHOGdsdfP9qDjYdO2bf36xiNOUO7M4eimfS0PpUWg3ejkRTMLF68GNOnT8fIkSPxySefYNSoUSgqKsLWrVsxduxYudtIRD7gy5oXatb2aIrSiaDWKhtmfbYPPdu2wqjMdqiuqYO5RQAKjpRj9mf78Oq9V/r0RKznpFS91GnRavBuJJKCmddeew1vvvkmhg0bhpUrV+LZZ59F+/bt8cILL+D06dNyt5GILqHUCchXa/3o6apabicrbfjP/jL8Z3+Zy9t99f6NkJSqh/WptBy86zmYbUhSMFNSUoKMjAwAQGhoKM6ePQsAGD58OK699losXbpUvhYSGYCcBwwjnIAA/VxVy00rQw5anFFmVFoN3o1yLAEkBjPx8fE4ffo0kpOT0bZtW/zwww+48sorUVxcDIk1+Ih8Qo2rEDkPGEY7AenhqlpuWhlyqE9KDQsOxOh+7ZCeFIXqmjqEBAViR8kZnDrHejdy0lrwbrRjiaRg5vrrr8enn36K9PR0jBo1Ck8//TQ++OADbNu2DXfffbfcbdQdo3TbGY0aVyFyHzBYcE3/tDLkUHHhIsKCA7F4WDpy8oux9NtD9tsyO0bjrvTLfdIOf6Kl4N1oxxJJwcybb76Juro6AL/XlomJiUF+fj7uuOMOPP7447I2UG+M1G1nJGpdhch9wNDKEAVJp5Uhh8iQIIzu1w45+cXIbzCrCgDyD53Ci5/+iKU6uzpvDl5s/s5oxxJJwUxAQABsNht27NiBsrIyhIaGYvDgwQCAL7/8ErfffrusjdQLo3XbGYlaVyFyHzC0MkRB3tHCkENMeDAy2kc79Mg09L0Or86bwovNPxjtWCIpmPnyyy8xfPhwnDp1qtFtJpMJtbW1XjdMj4zWbWckal2FyH3A0MoQBXlP7SEHS1gwglu4r5uqt6tzd3ix6choxxJJFYDHjx+P++67D6Wlpairq3P489dABjBet52RqHUVIneVUl9VqSX/0KqJ74vers7dYRVeR0Y7lkjqmTl+/DgmTZqEuLg4uduja0brtjMSta5ClMiP0MIQBRmD0a7O3eHFZmNGOpZICmb+9Kc/Yd26dejQoYPc7dE1fzow6I2aSZdKHDDUHqIgY9BKMrIv8GLTOaMcS0xCQmGYqqoq3HvvvbjsssvQo0cPBAU5fgkmTJggWwO9VVFRAYvFAqvVisjISMVf72j5eZcHhgQ/SzDTovqZDHq/CiGSkz/8LqxVNozPLXB5selvOTN64Mn5W1Iw8/bbb+Pxxx9HSEgIoqOjYTKZ/nhCkwk///yz561WiK+DGcA/DgxERHqj1sUmp4NLo3gwEx8fjwkTJmDq1KkICJCUQ+wzagQzRESkTb6+2OR0cOk8OX9Lypmx2Wz485//rPlAhkguvLIiMgZf5ohwOrjvSApmRowYgffeew9//etf5W4Pkebwykp9DCZJj1h7zHckBTO1tbV45ZVX8NVXXyEtLa1RAvDChQtlaRyR2nhlpT4Gk6RXnA7uO5KCmT179iA9PR0AsHfvXofbGiYDE+kdr6zUpWYwyd4gbdPS5+OqLZwO7juSgpnvvvtO7nYQaRKvrNSlVjDJ3iBt09Ln464trD3mO8zgJXKDV1bqUiOYbKo3yFrlX2XvtUZLn09TbQFgqCUDtExSzwyRv+CVlbrUCCY5tKhtWvp8mtOWDrHhhlkyQMvYM0PkhtEWY9MbuRfqbA45eoOsVTYUlVWioOQMik5UsjdHRloa+m1uWyxhwegQG46ebVuhQ2w4jxsKYM8MUROMtBib3qixdpC3vUFayucwIi0N/WqpLf6OwQxRMxhlMTY98nUw6c3QIqfyK09LQ79aaou/4zATEWmeL7vpvRlabE4OBXlHS0O/WmqLv2PPDBHRJaT2Bnmbz6Gl2ilapqWhXy21xZ8xmCEickLK0KI3ORTMtfGMloZ+tdQWf8VhJjIszijRNiN+PlJnX2mpdkrDNhnt8yHjYs8MGRKvcrXNqJ+P1NlXWqqdAhj38yHjMgkhhNqNUFJFRQUsFgusVisiIyPVbg75gLXKhnG5BU5PDlmpMZxRojI5Px+t5pjUt6u5ORQFJWdw12ubXN7+8ZMZ6Nm2laQ2eLpv+PshrfDk/M2eGTIcrV3lkiO5Ph8t9x54mkMhd70Sb/YNfz+kR8yZIcPRUoVQakyuCrtayzHxhpyVjr3dN/z9kB4xmCHDYVVObZPj8zFaPRc565V4u2/4+yE94jATGQ6rcmqbHJ+PEXsP5KpX4u2+4e+H9Ig9M2Q4rMqpbXJ8PkbtPZCj0rG3+4a/H9Ij9syQIbEqp7Z5+/mo1Xug1dlTDcmxb/j7Ib3h1Gwi0qWj5edd1nNJUGA2k5ZnT13K1/uGSAmenL8ZzBCRbnlaz8Wb19Fb7RVf7RsipbDOjI7ooduaSKt8tSaOHmuvyLlveJwirWMwoyI9dVsT+TMjzp5qLh6nSA84m0klRiv6RWRkRp091RQep0gvGMyoxGhFv4iMTM4KvXrC4xTpBYMZlfhztzWRFlmrbCgqq0RByRkUnah06HUweu0VV++dxynSC+bMqMRfu62JtKg5eSFGrb3i7r1bQnmcIn1gz4xK/LXbmkhrPMkLkaNCr5Y09d5bmlvwOEW6wGBGJUbvtibSC2/yQtwNTelBU++98kINj1OkCxxmUpFRu62J9ERqXogRpiw35713iA3ncYo0j8GMynxV9IuInJOSv9bU8IwWKwI709z3zuMUaZ3mh5mys7Nx9dVXIyIiArGxsRg6dCgOHDigdrOIyCCk5K8ZZcoyc/fIKDQfzKxfvx5jx47FDz/8gG+++QYXL17EjTfeiHPnzqndNCIyACn5a0aZsszcPTIK3S00eeLECcTGxmL9+vXIyspq8v5caJKImsOThRmLyioxaOF6l8+VN2kAOsSGK9VU2cmxKCXXbyK5GXqhSavVCgBo3bq109urq6tRXV1t/39FRYVP2kVE+uZJXkj98MwGF6to6214xtucGCMkQ5O+aX6YqaG6ujpMnDgRmZmZ6N69u9P7ZGdnw2Kx2P+SkpJ83EoiMjoOz/yB6zeRFuhqmOmJJ57AF198gY0bN6JNmzZO7+OsZyYpKYnDTEQkOzmGZ/TOaENupB2GHGYaN24cPvvsM2zYsMFlIAMAZrMZZrPZhy0jIn/FKcvGSYYmfdN8MCOEwPjx4/HRRx9h3bp1aNeundpNajYmxJGc+H0iLeI6c6QFmg9mxo4di3fffReffPIJIiIicOzYMQCAxWJBaKh2E8uYEEdy4veJtMpoydCkT5rPmTGZTE635+TkYOTIkU0+Xo2p2dYqG8blFjgtqpWVGqOb6qCkDfw+kdYdLT+PqWt2OwQ09cnQCQy2SSJD5cxoPNZyqjnVQXnyoebi94m0juvMkdo0H8zoERPiSE78PpEeMBma1KSrOjN6wYQ4khO/T0RE7jGYUQAXb1OPtcqGorJKFJScQdGJSkMU7OL3yZiM+F0lUovmE4C9pdbaTEyI8z0jz/jh98lYjPxdJZKLJ+dvBjMKYnVQ3/GHGT/8PhmDP3xXieRgqNlMesaEON/xhxk//D4Zgz98V4l8jTkzZAic8UN6we8qkfwYzJAhcMYP6QW/q0TyYzBDhsAZP6QX/K4SyY/BDBmCJSwY8+5Ja3SSqJ/xwxwE0gp+V4nkx9lMZCic8UN6we8qkXuczUR+izN+SC/4XSWSD4eZiIiISNcYzBAREZGuMZghIiIiXWMwQ0RERLrGYIaIiIh0jcEMERER6RqDGSIiItI11pkhItKJ+kJ7FRcuIjI0CDEtWatGS/j5qIfBDBGRDhwtP4/n1uzG94Un7duyUmMw7540JEaFqtgyAvj5qI3DTEREGmetsjU6UQLAhsKTmLpmN6xVNpVaRgA/Hy1gMENEpHEnK22NTpT1NhSexMlKnizVxM9HfQxmiIg0ruLCRbe3n23idlIWPx/1MZghItK4yJAgt7dHNHE7KYufj/oYzBARaVxMeDCyUmOc3paVGoOYcM6YURM/H/UxmCEi0jhLWDDm3ZPW6ISZlRqD+fekcfqvyvj5qM8khBBqN0JJFRUVsFgssFqtiIyMVLs5RESS1dcxOXvhIiJCghATzjomWsLPR16enL9ZZ4aISCcsYTw5ahk/H/VwmImIiIh0jcEMERER6RqDGSIiItI15sz4IS6GRkTO8NhAesVgxs9wMTQicobHBtIzDjP5ES6GRkTO8NhAesdgxo9wMTQicobHBtI7BjN+hIuhEZEzPDaQ3jGY8SNcDI2InOGxgfSOwYwf4WJoROQMjw2kdwxm/AgXQyMiZ3hsIL3jQpN+iIuhEZEzPDaQlnChSXKLi6ERkTM8NpBecZiJiIiIdI3BDBEREekagxkiIiLSNQYzREREpGsMZoiIiEjXGMwQERGRrjGYISIiIl1jMENERES6xmCGiIiIdI3BDBEREekagxkiIiLSNQYzREREpGsMZoiIiEjXdBHMLFu2DCkpKQgJCUGfPn2wZcsWtZtEGmStsqGorBIFJWdQdKIS1iqb2k0i8iv8DZJaWqjdgKa89957mDRpEl5//XX06dMHixYtwpAhQ3DgwAHExsaq3TzSiKPl5/Hcmt34vvCkfVtWagzm3ZOGxKhQFVtG5B/4GyQ1ab5nZuHChRgzZgxGjRqFrl274vXXX0dYWBhWrFihdtNII6xVtkYHUQDYUHgSU9fs5tUhkcL4GyS1aTqYsdls2L59OwYPHmzfFhAQgMGDB2Pz5s1OH1NdXY2KigqHPzK2k5W2RgfRehsKT+JkJQ+kRErib5DUpulg5uTJk6itrUVcXJzD9ri4OBw7dszpY7Kzs2GxWOx/SUlJvmgqqajiwkW3t59t4nYi8g5/g6Q2TQczUkybNg1Wq9X+d+TIEbWbRAqLDAlye3tEE7cTkXf4GyS1aTqYiYmJQWBgII4fP+6w/fjx44iPj3f6GLPZjMjISIc/MraY8GBkpcY4vS0rNQYx4cE+bhGRf+FvkNSm6WAmODgYvXr1Ql5enn1bXV0d8vLy0LdvXxVbRlpiCQvGvHvSGh1Ms1JjMP+eNFjCeCAlUhJ/g6Q2zU/NnjRpEkaMGIHevXvjmmuuwaJFi3Du3DmMGjVK7aaRhiRGhWLJsHScrLTh7IWLiAgJQkx4MA+iRD7C3yCpSfPBzJ///GecOHECL7zwAo4dO4aePXviyy+/bJQUTGQJ44GTSE38DZJaTEIIoXYjlFRRUQGLxQKr1cr8GSIiIp3w5Pyt6ZwZIiIioqYwmCEiIiJdYzBDREREusZghoiIiHSNwQwRERHpGoMZIiIi0jUGM0RERKRrDGaIiIhI1xjMEBERka5pfjkDb9UXOK6oqFC5JURERNRc9eft5ixUYPhg5uzZswCApKQklVtCREREnjp79iwsFovb+xh+baa6ujocPXoUERERMJlMsj53RUUFkpKScOTIEa77dAnuG/e4f1zjvnGN+8Y97h/X9LhvhBA4e/YsEhMTERDgPivG8D0zAQEBaNOmjaKvERkZqZsvh69x37jH/eMa941r3Dfucf+4prd901SPTD0mABMREZGuMZghIiIiXWMw4wWz2YyZM2fCbDar3RTN4b5xj/vHNe4b17hv3OP+cc3o+8bwCcBERERkbOyZISIiIl1jMENERES6xmCGiIiIdI3BDBEREekag5kmLFu2DCkpKQgJCUGfPn2wZcsWt/f/n//5H1xxxRUICQlBjx49sHbtWh+11Pc82Tc//vgj7rnnHqSkpMBkMmHRokW+a6gKPNk3b731Fvr3749WrVqhVatWGDx4cJPfM73zZP98+OGH6N27N6KiotCyZUv07NkT77zzjg9b61ueHnPqrV69GiaTCUOHDlW2gSrzZP+sXLkSJpPJ4S8kJMSHrfUtT7875eXlGDt2LBISEmA2m9GpUyf9nrMEubR69WoRHBwsVqxYIX788UcxZswYERUVJY4fP+70/vn5+SIwMFC88sorYt++feL5558XQUFBYs+ePT5uufI83TdbtmwRkydPFrm5uSI+Pl78/e9/922DfcjTffPAAw+IZcuWiYKCArF//34xcuRIYbFYxK+//urjlvuGp/vnu+++Ex9++KHYt2+fOHTokFi0aJEIDAwUX375pY9brjxP90294uJicfnll4v+/fuLO++80zeNVYGn+ycnJ0dERkaK0tJS+9+xY8d83Grf8HTfVFdXi969e4tbbrlFbNy4URQXF4t169aJnTt3+rjl8mAw48Y111wjxo4da/9/bW2tSExMFNnZ2U7vf99994lbb73VYVufPn3EY489pmg71eDpvmkoOTnZ0MGMN/tGCCFqampERESEWLVqlVJNVJW3+0cIIdLT08Xzzz+vRPNUJWXf1NTUiIyMDPHPf/5TjBgxwtDBjKf7JycnR1gsFh+1Tl2e7pvly5eL9u3bC5vN5qsmKorDTC7YbDZs374dgwcPtm8LCAjA4MGDsXnzZqeP2bx5s8P9AWDIkCEu769XUvaNv5Bj31RVVeHixYto3bq1Us1Ujbf7RwiBvLw8HDhwAFlZWUo21eek7ptZs2YhNjYWjzzyiC+aqRqp+6eyshLJyclISkrCnXfeiR9//NEXzfUpKfvm008/Rd++fTF27FjExcWhe/fumDt3Lmpra33VbFkxmHHh5MmTqK2tRVxcnMP2uLg4HDt2zOljjh075tH99UrKvvEXcuyb5557DomJiY0CYyOQun+sVivCw8MRHByMW2+9FUuWLMENN9ygdHN9Ssq+2bhxI95++2289dZbvmiiqqTsn86dO2PFihX45JNP8O9//xt1dXXIyMjAr7/+6osm+4yUffPzzz/jgw8+QG1tLdauXYsZM2ZgwYIFmDNnji+aLDvDr5pNpCfz5s3D6tWrsW7dOkMnKnoqIiICO3fuRGVlJfLy8jBp0iS0b98e1113ndpNU83Zs2cxfPhwvPXWW4iJiVG7OZrUt29f9O3b1/7/jIwMdOnSBW+88QZmz56tYsvUV1dXh9jYWLz55psIDAxEr1698Ntvv+Fvf/sbZs6cqXbzPMZgxoWYmBgEBgbi+PHjDtuPHz+O+Ph4p4+Jj4/36P56JWXf+Atv9s2rr76KefPm4T//+Q/S0tKUbKZqpO6fgIAAdOzYEQDQs2dP7N+/H9nZ2YYKZjzdN0VFRfjll19w++2327fV1dUBAFq0aIEDBw6gQ4cOyjbah+Q47gQFBSE9PR2HDh1SoomqkbJvEhISEBQUhMDAQPu2Ll264NixY7DZbAgODla0zXLjMJMLwcHB6NWrF/Ly8uzb6urqkJeX5xDpN9S3b1+H+wPAN9984/L+eiVl3/gLqfvmlVdewezZs/Hll1+id+/evmiqKuT67tTV1aG6ulqJJqrG031zxRVXYM+ePdi5c6f974477sDAgQOxc+dOJCUl+bL5ipPju1NbW4s9e/YgISFBqWaqQsq+yczMxKFDh+wBMAAcPHgQCQkJugtkAHBqtjurV68WZrNZrFy5Uuzbt0/85S9/EVFRUfapfcOHDxdTp0613z8/P1+0aNFCvPrqq2L//v1i5syZhp6a7cm+qa6uFgUFBaKgoEAkJCSIyZMni4KCAlFYWKjWW1CMp/tm3rx5Ijg4WHzwwQcOU0jPnj2r1ltQlKf7Z+7cueLrr78WRUVFYt++feLVV18VLVq0EG+99ZZab0Exnu6bSxl9NpOn++ell14SX331lSgqKhLbt28X999/vwgJCRE//vijWm9BMZ7um5KSEhERESHGjRsnDhw4ID777DMRGxsr5syZo9Zb8AqDmSYsWbJEtG3bVgQHB4trrrlG/PDDD/bbBgwYIEaMGOFw//fff1906tRJBAcHi27duonPP//cxy32HU/2TXFxsQDQ6G/AgAG+b7gPeLJvkpOTne6bmTNn+r7hPuLJ/pk+fbro2LGjCAkJEa1atRJ9+/YVq1evVqHVvuHpMachowczQni2fyZOnGi/b1xcnLjlllvEjh07VGi1b3j63dm0aZPo06ePMJvNon379uLll18WNTU1Pm61PExCCKFWrxARERGRt5gzQ0RERLrGYIaIiIh0jcEMERER6RqDGSIiItI1BjNERESkawxmiIiISNcYzBAREZGuMZghIiIiXWMwQ6RD69atg8lkQnl5udpN0ZwXX3wRPXv2lPU5TSYTPv74YwDAL7/8ApPJhJ07d9pvz8/PR48ePRAUFIShQ4fK+tr1rrvuOkycONGr51Bi3xBpAYMZIg+MHDkSJpOp0Z/RVuF1p+GJ3R8lJSWhtLQU3bt3t2+bNGkSevbsieLiYqxcuVK9xjVh8uTJjRbDJTICBjNEHrrppptQWlrq8NeuXbtG97PZbCq0jpQWGBiI+Ph4tGjRwr6tqKgI119/Pdq0aYOoqCj1GteE8PBwREdHq90MItkxmCHykNlsRnx8vMNfYGAgrrvuOowbNw4TJ05ETEwMhgwZAgDYu3cvbr75ZoSHhyMuLg7Dhw/HyZMn7c9XV1eH7OxstGvXDqGhobjyyivxwQcfOLzm2rVr0alTJ4SGhmLgwIH45ZdfGrVrzZo16NatG8xmM1JSUrBgwQKH21NSUjBnzhw8/PDDCA8PR3JyMj799FOcOHECd955J8LDw5GWloZt27a5fO8pKSkAgLvuugsmk8n+fwBYvnw5OnTogODgYHTu3BnvvPOO2/04cuRIDB06FHPnzkVcXByioqIwa9Ys1NTUYMqUKWjdujXatGmDnJwch8c999xz6NSpE8LCwtC+fXvMmDEDFy9edPk6RUVFaN++PcaNGwchBKqrqzF58mRcfvnlaNmyJfr06YN169a5bWtDDYeZ6v996tQpjB49GiaTyd4z09Tn7kx+fj6uu+46hIWFoVWrVhgyZAjOnDljv72urg7PPvssWrdujfj4eLz44osOjy8pKbF/lpGRkbjvvvtw/Phx++3OhplWrFhh/94kJCRg3Lhx9tvKy8vx6KOP4rLLLkNkZCSuv/567Nq1y377rl27MHDgQERERCAyMhK9evVy+/0hUozKC10S6Yq7VYkHDBggwsPDxZQpU8RPP/0kfvrpJ3HmzBlx2WWXiWnTpon9+/eLHTt2iBtuuEEMHDjQ/rg5c+aIK664Qnz55ZeiqKhI5OTkCLPZLNatWyeEEKKkpESYzWYxadIk8dNPP4l///vfIi4uTgAQZ86cEUIIsW3bNhEQECBmzZolDhw4IHJyckRoaKjIycmxv05ycrJo3bq1eP3118XBgwfFE088ISIjI8VNN90k3n//fXHgwAExdOhQ0aVLF1FXV+f0PZaVlQkAIicnR5SWloqysjIhhBAffvihCAoKEsuWLRMHDhwQCxYsEIGBgeLbb791uy8jIiLE2LFjxU8//STefvttAUAMGTJEvPzyy+LgwYNi9uzZIigoSBw5csT+uNmzZ4v8/HxRXFwsPv30UxEXFyfmz59vv33mzJniyiuvFEIIsWvXLhEfHy+mT59uv/3RRx8VGRkZYsOGDeLQoUPib3/7mzCbzeLgwYMu2wpAfPTRR0KIP1aALygoEDU1NaK0tFRERkaKRYsWidLSUlFVVdWsz/1SBQUFwmw2iyeeeELs3LlT7N27VyxZskScOHFCCPH79ysyMlK8+OKL4uDBg2LVqlXCZDKJr7/+WgghRG1trejZs6fo16+f2LZtm/jhhx9Er169HFamb7hvhBDitddeEyEhIWLRokXiwIEDYsuWLeLvf/+7/fbBgweL22+/XWzdulUcPHhQPPPMMyI6OlqcOnVKCCFEt27dxEMPPST2798vDh48KN5//32xc+dOl++RSCkMZog8MGLECBEYGChatmxp//vTn/4khPj9ZJOenu5w/9mzZ4sbb7zRYduRI0cEAHHgwAFx4cIFERYWJjZt2uRwn0ceeUQMGzZMCCHEtGnTRNeuXR1uf+655xyCmQceeEDccMMNDveZMmWKw+OSk5PFQw89ZP9/aWmpACBmzJhh37Z582YBQJSWlrrcBw1P7PUyMjLEmDFjHLbde++94pZbbnH5PCNGjBDJycmitrbWvq1z586if//+9v/X1NSIli1bitzcXJfP87e//U306tXL/v/6E3Z+fr5o1aqVePXVV+23HT58WAQGBorffvvN4TkGDRokpk2b5vI1XAUz9SwWi0Pg2NTn7sywYcNEZmamyzYMGDBA9OvXz2Hb1VdfLZ577jkhhBBff/21CAwMFCUlJfbbf/zxRwFAbNmyRQjROJhJTEx0CPQa+v7770VkZKS4cOGCw/YOHTqIN954QwghREREhFi5cqXLNhP5SgtnvTVE5NrAgQOxfPly+/9btmxp/3evXr0c7rtr1y589913CA8Pb/Q8RUVFuHjxIqqqqnDDDTc43Gaz2ZCeng4A2L9/P/r06eNwe9++fR3+v3//ftx5550O2zIzM7Fo0SLU1tYiMDAQAJCWlma/PS4uDgDQo0ePRtvKysoQHx/v7O07tX//fvzlL39p9Pr/+Mc/3D6uW7duCAj4Y7Q7Li7OIbE2MDAQ0dHRKCsrs2977733sHjxYhQVFaGyshI1NTWIjIx0eN6SkhLccMMNePnllx1mAO3Zswe1tbXo1KmTw/2rq6tlzSVp6nO/9PUBYOfOnbj33nvdPm/Dzw8AEhIS7Ptm//79SEpKQlJSkv32rl27IioqCvv378fVV1/t8NiysjIcPXoUgwYNcvkeKisrG+2X8+fPo6ioCMDvic+PPvoo3nnnHQwePBj33nsvOnTo4PY9ECmBwQyRh1q2bImOHTu6vK2hyspK3H777Zg/f36j+yYkJGDv3r0AgM8//xyXX365w+1ms1mmFv8hKCjI/m+TyeRyW11dneyv3VR76l/f2bb69mzevBkPPvggXnrpJQwZMgQWiwWrV69ulB902WWXITExEbm5uRg9erQ92KmsrERgYCC2b99uD/DqOQs8pGrqc3cmNDS0yed1t2881dTrVVZWIiEhwWk+UX2S84svvogHHngAn3/+Ob744gvMnDkTq1evxl133SWpTURSMZghUtBVV12FNWvWICUlxWH2S72uXbvCbDajpKQEAwYMcPocXbp0waeffuqw7Ycffmh0n/z8fIdt+fn56NSpU6OTtreCgoJQW1vr9PVHjBjh8Ppdu3aV9bU3bdqE5ORkTJ8+3b7t8OHDje4XGhqKzz77DLfccguGDBmCr7/+GhEREUhPT0dtbS3KysrQv39/WdvWUFOfuzNpaWnIy8vDSy+9JOk1u3TpgiNHjuDIkSP23pl9+/ahvLzc6ecQERGBlJQU5OXlYeDAgU7fw7Fjx9CiRQuHRO9LderUCZ06dcLTTz+NYcOGIScnh8EM+RxnMxEpaOzYsTh9+jSGDRuGrVu3oqioCF999RVGjRqF2tpaREREYPLkyXj66aexatUqFBUVYceOHViyZAlWrVoFAHj88cdRWFiIKVOm4MCBA3j33Xcb1TJ55plnkJeXh9mzZ+PgwYNYtWoVli5dismTJ8v+nupPgMeOHbPPtJkyZQpWrlyJ5cuXo7CwEAsXLsSHH34o++unpqaipKQEq1evRlFRERYvXoyPPvrI6X1btmyJzz//HC1atMDNN9+MyspKdOrUCQ8++CAefvhhfPjhhyguLsaWLVuQnZ2Nzz//XLZ2NvW5OzNt2jRs3boVTz75JHbv3o2ffvoJy5cvb3IGVL3BgwejR48eePDBB7Fjxw5s2bIFDz/8MAYMGIDevXs7fcyLL76IBQsWYPHixSgsLLR/9+qfr2/fvhg6dCi+/vpr/PLLL9i0aROmT5+Obdu24fz58xg3bhzWrVuHw4cPIz8/H1u3bkWXLl2k7TQiLzCYIVJQYmIi8vPzUVtbixtvvBE9evTAxIkTERUVZc8VmT17NmbMmIHs7Gx06dIFN910Ez7//HN77Zq2bdtizZo1+Pjjj3HllVfi9ddfx9y5cx1e56qrrsL777+P1atXo3v37njhhRcwa9YsjBw5Uvb3tGDBAnzzzTdISkqy5/UMHToU//jHP/Dqq6+iW7dueOONN5CTk4PrrrtO1te+44478PTTT2PcuHHo2bMnNm3ahBkzZri8f3h4OL744gsIIXDrrbfi3LlzyMnJwcMPP4xnnnkGnTt3xtChQ7F161a0bdtWtnY253O/VKdOnfD1119j165duOaaa9C3b1988sknze7ZMZlM+OSTT9CqVStkZWVh8ODBaN++Pd577z2XjxkxYgQWLVqE1157Dd26dcNtt92GwsJC+/OtXbsWWVlZGDVqFDp16oT7778fhw8fRlxcHAIDA3Hq1Ck8/PDD6NSpE+677z7cfPPNknuWiLxhEkIItRtBREREJBV7ZoiIiEjXGMwQERGRrjGYISIiIl1jMENERES6xmCGiIiIdI3BDBEREekagxkiIiLSNQYzREREpGsMZoiIiEjXGMwQERGRrjGYISIiIl37/1cbV9lPQ4ZqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[\"Freedom to make life choices\"]\n",
    "y = data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x=x,y=np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Freedom to make life choices', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ljy7EcuSzAZxFid9DGaICkQqtTe6C6D2Bk/0+kk2g+ELhLGvJwhNCEAAYVWD027DpuYeXP/sRlMsVSZ7L4Q1DUIAkgxAAEJEbgMi3d2He3+MZlZl4PEttlvQ0h1AIKwiktwh0BsIY9lLm/Dc4uPir2HO4mSGwQxRgRhYe8MhK0O+Xwi1N3ii19fgGQwhBPb19EMVAlZZhgAitVyEhFqP3TRLlcneCxZZhiQBsSxRSYrcFpPs/TFcDlqqgV3s+NotCvZ09UMTAoosQZIBISSEVQ2fNPfgsXW78L1jJo76741l3JpNVCAKvfZG7ES/qdkLl92CarcdLrslfqJfu60t30M0ncEzGP0hDYGwBossQZKk+MU/rGlDlmKMLNl7wWGTYbcoCKsawpoGu0WGwxp53MneH4NnrRxWBbIsRXNs7PAFVCxf0zjitu7Y8e3yR2a6LIoEWZIgIfJfiyJBEwKr1u1COKyN+u+NZQxmiApEOrU3zEaPCwsNNbiSbnwpJvp9IRJnMGyKhL6QijVbWg1dnyXZe0FoQEmRFQKRx+UpskIIDPv+SCcHbTixWZ5AWI3MyGDwe0+CIklo8fbj/33YPOq/N5YxmCEqILHaG9Pr3PAHwmj1BeAPhDG9zm3qKWo9Lix6SbXgmhkKsw2ewYgvxQAQEFA1AbtFgcMmwxcIY3ubH96+EP78+nZc8ui7WLhinWFnxJK9F4QQmFnvicy+CIz4/tCj/9PMeg+qPXZEYsWhs6VhTcBuidz/511+9psaBebMEBWYeQdV4pgpFQWVJGuU5OZUc3bMktsTm8G4/tmNaPEGUFJkgU2R0R9SAQCKLKPKbUdvQMXuDj/CmkCRVUF9iQMhTRg+l2O49wKAA74/9MhBk2UJ5x41ATc//zHCqoBFEZAQCWvCmoAiSShxWgEBjCt1FnzOWzZxZoaoAMVqb8yfVoVZ40tMHcgAxmgsmGrOjtlyewbOYPQFo1uXJQmyLKHKbUeRVUZLdz/CWiQoqylxQFFk0yzxJXsvpPL+0CsH7byjJmBGnTvSWiE6G6MJgSKrjPpSB4JhganVxTjjsLqCznnLNgYzRGR4+U5uTjVnx6xJnPMOqsTKRUfhgQuOxN3nzsGvzpyJoydXQAiB5u5+BMMqiqwKxpU5UWzfP6FfyLkceuWgybKEJadNR32JAy6bBVVuOxrKnKjxOOALqPH7sVjkgs15ywUGM0RkePlObk41Z8fMSZwDZyu+d8xE/J/vR4KbHxw3GW6HFZMqEgOZmELO5dArB23eQZVY9o3DMGt8CSQAPYEw+oLqkPsp1Jy3XGDODBGZQiqNBbMl1ZydVJI4zVK4cGCbgMfe3oWQJqAMTeUo+FwOvXLQUr2fQsx5y4WMg5lwOIzXXnsNjY2NOO+88+B2u7Fnzx54PB4UFxfrOUYiIgD5O9GnmgxaiEmcsSW+Tc09qPXICTNOsSW+6XXugs7lSNb/KR2Dq1Yff1DliK/Z0f69sSijYGbnzp346le/il27diEQCODkk0+G2+3Gb3/7WwQCAdx///16j5OICEB+TvSpXtDPOKwOz6zfXVAX/sE7nkqdVtgVGQFVQ1e02zRzOYZnlp1tZpdRzszPfvYzHHnkkejs7ERRUVH89rPPPhurV6/WbXBEREaQas5OoSZxMpcjM2bb2WZmkhi8NSAFFRUVWLt2LQ4++GC43W5s2LABU6ZMwY4dOzBjxgz4/f5sjDUjXq8XJSUl6O7uhsdjnk9DRGQ8CZ+yozk7B6wzM8LPmQ2bfKZO0wQWrliHTc3eeBPPGCEEWrwBTK9zY+Wio3gMh5HO9TujZSZN06Cq6pDbd+/eDbfbncldEhEZ3lhP4mQuR+rSqVrNYzp6GQUzp5xyCu666y48+OCDACJPjM/nw80334yvfe1rug6QiMhIUr2g88I/tulRtZozYanLKJi54447cOqpp2LGjBno7+/Heeedh61bt6KyshKPP/643mMkIiIyldG2Q2DicHoyCmbGjx+PDRs24IknnsCGDRvg8/nwgx/8AOeff35CQjAREdFYNJot7bHEYV8gjDKnDTZFRlDVDN8LK58ySgD+97//jXnz5sFiSYyFwuEw1q5dixNOOEG3AY4WE4CJiCgf9gclatIt7cmCEiYO75fO9TujrdknnXQSOjo6htze3d2Nk046KZO7JCIiKiiZbGlPJ3GY9stomUkIMeQgA0B7eztcLteoB0VERFQI0t3Zpkfi8FiUVjDzjW98A0AkOrzoootgt9vj31NVFR9++CHmzZun7wiJiIhMLJ2dbaNNHB6r0gpmSkoiT4YQAm63OyHZ12az4ZhjjsHFF1+s7wiJiIjGCPbCykxawcyKFSsAAJMmTcI111zDJSUiIio4+azvwl5YmcloN5OZcDcTERGlyij1XQq1JUY60rl+ZxzMPPXUU3jyySexa9cuBIOJiUjvv/9+JneZFQxmiIgoFcPVd+kcYSt1No31CsBZ35p99913Y9GiRaipqcH69etx1FFHoaKiAp999hlOO+20jAZNZBaaJrBxdzfWbNmHjbu7oWkFPblJNCZomsDyNY3wBcKo9TjgsCqQZQkOq4Jajx2+gIrlaxpz+n6PJQ7Pn1aFWeNLxlQgk66Mtmb/6U9/woMPPohzzz0XjzzyCH7+859jypQpuOmmm5LWnyEqFEaZgiYifbExpLllNDOza9eu+BbsoqIi9PT0AAAuuOAC9maighWbgt7U7IXLbkG12w6X3RIvMb52W1u+h0hUMHI9A5pKfZeQCeu7ZPs4BsMaAmFV1/vMREYzM7W1tejo6MDEiRMxYcIEvPXWW5g9eza2b9+OAs8npjFq8BR07JObQ1ZQ65HR4g1g+ZpGHDOlglPBRKOUjxnQXNR3yXUOTDaOoxACfSEVvQEV/SEVIVVDmdMGu2XoMculjIKZL3/5y3j++ecxZ84cLFq0CFdeeSWeeuopvPvuu/HCekSFhFPQROnL5OKdryaL2a7vkusATc/jGAir6A9q8IfC6A9phpy0yCiYefDBB6FpGgBg8eLFqKiowNq1a3HmmWfikksu0XWAREbAEuNE6cnk4p3PGdBs1ncZGFgUWRVYFAlCAJuavVkJ0EZ7HMOqhr6Qir5QJIgJR6/3RpZ2zkw4HMatt96KlpaW+G3f/e53cffdd+OnP/0pbDaWWKbCM3AKOhmWGCfaL9P8snw3WcykMeSBxAKLTn8QfUEVLd5+7OnqR4u3H31BFZ3+oO67pNI9jmFVgy8QRpsvgKYOP3Z1+LGvJwBff9gUgQyQwcyMxWLB7bffjgsvvDAb4yEyJJYYJ0rNaGYFjDADmm5jyAP5eI8Xn+zxojcQSZJVZAmSDAgBBMIaAmHgkz1eXZeoD3QcbbKETlXD9vZelDqtCA3zIc1MMtrN9JWvfAVr1qzReyxEhhWbgi62K2jxBtAXUqFpkUS4Fm+AJcaJokYzu2KUGVA967u0+wLw9ocghIBFkSBLEiRE/htZbhLw9ofQ7gvoNv7Bx1EIAVUTCKkagmENvqAKBYBNlgsikAEyzJk57bTT8Itf/AIbN27EEUccMaRH05lnnqnL4IiMJDYFHcsD6I6WGJ9e52adGaKo0cyuFOIMaKc/BE0TkOVIEDOQBAmyLEHTBDr9Id3+5sx6DyZXurCppQeVrsTAT0Cgpz+EKVXFOKimcPorZhTM/OQnPwEA3HnnnUO+J0kSVDX/e86JskHvKWiiQjOaLc6pJOGeOrMWr29rM817r9RljQQsQkAIMSRA00Qk0Cl1WUf1d2Izxf6gir6gim98cRzufHUL9vkCcDussCkSgmokkHHaFJx3VANkydjHLh0ZBTOaSRKCiLIhNgVNREONdnZluBnQuhI7AOBP/9pmqurblS47PA4LvP1hhDQBiwxIAASAsCYgSxI8DgsqXfa07zsQjgQufSF1yJbpORPKcNXJ0/DYuiY0tfeiRwhYJQlTqopx3lENmDOhTL8HaQAZBTOpmjVrFl588UU0NDRk888QEZlKITcQ1GOL8+AZ0KYOPx76dyN6g2pOa8/oYWa9BzPqS7ChqRuqpiGoahACkCTAYZGhyDJm1JektHQ2cMt0X1CFeoAdUHMmlGF2Qym27e1Fd38QJQ4bDqpxFdSMTExWg5kdO3YgFNJvHZCIyOzGQn8vPfLLYjOgmiawcMU69AZVU1bfHhjc9fSHUeayQZEkqEKgL6jC7bAMG9yFVQ39YQ19wf3Vdg9EE2JI8DKttjgbD81QshrM6OHzzz/Hddddh5deegl+vx8HHXQQVqxYgSOPPDLfQyMiSku+qtvmg175ZYVQfXtwcBfQInlDM+o9CcGdGs17SSd4GWj9rs74slIoGkA2VLjiy0rJAp1CmaUxdDDT2dmJY489FieddBJeeuklVFVVYevWrSgrK6y1PiIqfGOxv5ce+WUHrJmiSGgPqVizpRUADLtklyy4O6TWjYCqoc0XQF8w/eBloPW7OnHnq1vgD6rwOKzwKBJCqsBn+3y489UtOOOweryzs3PYQMfsDB3M/Pa3v0VDQwNWrFgRv23y5Mkj/k4gEEAgsH+/vtebnUqRRETpKIQZhnwYaXeULxBGS3c/gmEVf359Ox57e1dKS3b5ylkSACZXuVAXcqAvqKKp06/L/WpC4LF1TfAHVVQW2+JbwO0WCZXFNuzpCuDhtdvhslmSBjpXnTzN9AFNRkXzcuX555/HkUceiW9/+9uorq7GnDlz8NBDD434O8uWLUNJSUn8i8nHRGQEqdRfCaoa3tvViTVb9mHj7m5dS9wbhaYJbNzdnfJjjO2O6vSHEnbr+AJh7O7woz+kwm5RUF/iSKllwtptbVi4Yh0uefRdXPPkBlzy6LtYuGLdsD8/Gqom0BttE7C704+d7b1o9fbD2xfStVjdtr29aGrvhcdhHVLLBgDCmoZQWKDYboHdIkOWJNgtMiqLbfAHVTy2rgmaAZtHpsPQMzOfffYZli9fjquuugrXX3893nnnHVx++eWw2WxYuHBh0t9ZsmQJrrrqqvi/vV4vAxoiyrsD1V/p7AvC2xfGff+7DQAKMjE4k+TnZLujbIqElu5+hLXIfdSUOKAoMhQFIy7ZZTtnSYgBOS9hDYFQbmqudfcHEdIEPMrQQCYQElBVDRIwJGCRIMHtsKKpvRfb9vaaOlE4qzMzDzzwAGpqajL+fU3T8MUvfhFLly7FnDlz8KMf/QgXX3wx7r///mF/x263w+PxJHwREeXbcDMMANDTH0JLdz8EBEqcqTdmNJNMm08CQxtANkeXloqsCsaVOVFs3/+5fLiWCYNzlhxWBbIswWFVUOuxwxdQ0274KKI7kjp7g9jT1Ycd7X60dPejuy+Us0AGAEocNljlyNLRYKrQoCGyFVyRhl7ybYqEkBDo7s9ev6tcyHhmZvXq1Vi9ejVaW1uHFNF7+OGHAQDnnXfeqAZXV1eHGTNmJNw2ffp0PP3006O6XyKiXBuu/kp/WMXnXX0AgHGlRSiyRk7LhZQYrEfy88AE2jVbWvHn17ejPjojM1iylgl65CypmkB/KLLTqD8c6XM0ODDNh4NqXGiocOGzfb6EnBkAkCUJQgBWiwy7deixDaqRYnoljuz2u8q2jGZmfvWrX+GUU07B6tWr0dbWhs7OzoQvvRx77LHYvHlzwm1btmzBxIkTdfsbRES5MniGodUXQHdfCLIkoa6kCG5HYkn7AzVmNIvRNJ8caH8DyGo4bQpCw8yiJGuZkErOUmhQABRSNfT0h7CvJ4CmjkjOy17v/pkXIwQyQCRgOe+oBjhtCtp8QfSHNWhCoD+soac/DKtFgjXJElSsT1NDhcv0fZoympm5//778cgjj+CCCy7QezwJrrzySsybNw9Lly7FOeecg3Xr1uHBBx/Egw8+mNW/S0SULYO36O7Y14t7/7UNpUXJe/OM1JjRLEbTfDKZ6bVuVHsc+GxfL6qKbSiyKfEgabiWCan0jLJEq/K29vQjENKSJukatVbLcO0LplYX40sTy/D/PtyDNl9Qtz5NYVVDU2cfdrT1orm7H5oAbjpjxoF/MUsyCmaCwSDmzZun91iG+NKXvoRnn30WS5Yswa9//WtMnjwZd911F84///ys/20iomwZWH9lNI0ZzWLwYxQQ6A9qCGsaLLIMSOKAjzG2nfo/29rwj49b0NThR28gBF8gBLtFQbXbDqtFHrZlwuCeUQCgiUhwomkC7b4AplQVo6LYBl9/OOkYDlSULt9Gal/whZrijPo0qZpAS3c/trf1Ynt7L3a09WJ7Wy92d/YhPGBmzGaRcf3XDoFlmIA12ySRwTzZddddh+LiYtx4443ZGJOuvF4vSkpK0N3dzWRgIjKcWLn+yEXWPqQxY4s3gOl1bqxcdFTaOTNG6QE18DEW2yNLIYGwisjVJ9JJ+pBaN55bfFzS8cV2QX2ypxud/kiLHLtFiTdwDIQjybZlTitm1Jck3R2laQKvbW7Fzc9/DF8g0kZg8AzFSPVWBhels0ZrtXhT+F2jGGlWSQiBNl8wErS09WJHe+S/O9v9CIRT20a++ur5mFql346odK7fGc3M9Pf348EHH8Q///lPHHbYYbBaE6dH77zzzkzulohozNGjMWMyRuoBFXuMVz75AXZ1+CEBUGQJkgyoauQi29oTwFuftQ8ZW2wXVE9/CH1BLborR0JI1dDpD2FcmQMyJLT5Amgod2HFwi/BYonMDvSH9neVDoQ1TKp04YoFX0h7huJARenafEE8tq4JsxtKDbHkNBxZkjCtthjd/hC2t/fiuQ/2xGdatrf3ojeQ3g4sRZYwvqwIB9e4cei4Erhs+av2ktFf/vDDD3H44YcDAD766KOE7w1O7iKi/DPKJ3RKTo/GjAOf46YOPx56/TP0GqgH1DFTKlDttqOjN5IXowlAAlBkU1BZvH9r9MAdTQN3QZUW2eDt98MiR4q+yRAIqwL7eoKYVOlEpWRHS3cf3treganVLgRCWtJCcJl0kh6pKJ1Ra7VoQmDjbi+2tvbA2xeGPxiOz7bEZrdSJQGoK3VgcoULkypdmFThxORKF8aXOWGzyChz2lDmyu8yaEbBzL/+9S+9x0FEWWKkT+g0vNE0Zhz4HAfDGrz9YQgIjCstgsMaycPJ91bvj/d40e4LYmK5E4AUz5dxWGVIkgSLIg/ZGj1wF1RI1SBEpF4KAEAAsgT0h8Lo6QvDqkgIqBo+7/KjvtQx4lhiMxSpGqkoHRCp1dKTx1otgZCKXR1+bG/3Y0dbLzY0daFxXy+CGVQZrii2YUqlC5MqXJhc6cKkSicmVrhQZB2az2Uko54T2r17NwBg/Pjxox4MEelrLHVpLgSZNGYc/Bw7rAJdfZHCfHu6+jGuTIoXlctnD6jYjia7RYkGUYkXx2Q7mgbugooUsxPxGR0AgAQILVKuX0DOWr2UgUXp7Jb81WoJqxp2d/VhR1skaNkenWnZ09WHdDtfeBwWTK6MBSyu6KyLc0h5ALPIKJjRNA233nor7rjjDvh8PgCA2+3G1VdfjRtuuAGybOiWT0Rjwljs0jzWJHuOe/ojSwgWRYKqCezrCcBlV/bneeRpq3c6u7Y0TSAQ1mCTZcgS0BuMzLzYFBn9YQ0WORKYxWZq5OjjnlJVnJV6KSMVpYvVatHzb2ti/w6iyNJQJHjZ1eFP2EGUCkmKPOc2i4yQqmF8mRNLzz4U5a6hNX/MLKNg5oYbbsBf/vIX3HbbbTj22GMBAP/5z3/wy1/+Ev39/fjNb36j6yCNiDkIZHTs0lz4kj3HFlmOLsVIUGQgEFbRH9RQZIsEEPna6j14a/TAujCaJtDRG8RB1S54HBbsaO8FAFR5bBhf7owHEeXFduyN9mRSZAFVjVS29QXCw9ZLSbcuzHA/f95RDbjz1S261mqJ7SDaEd/y7Mf29l7sbOtFf4o7iGKsioRajwP7egJwWBU4bQrsFhkWWYof6/6whm5/EO2+ECqK7Wndv9FlFMysXLkSf/7zn3HmmWfGbzvssMMwbtw4/OQnPyn4YIY5CGQGehcqI+NJ9hw7rDLsFhl9ocgMhhCRZRhAGbagXC7IsoRLTpiC65/diD3d/fA4LLDKkV1bsYDgO0c2QB2QtJssiKhy29HeG0QwHGmeWGRVht2NlG5dmAP9fLKidKnUagGA7r5Qws6hWPDiCySvaTPscZSA8WXO6BKRM5qQ68K40iK8v6sTt730KSpctqSBVb5ze7Ipo2Cmo6MDhxxyyJDbDznkEHR0dIx6UEbGHAQyi+Gm9YUQ6A9p6A2GIYQYtvIsGV+y51iSJFS5Hfi8sw9hVYsvw/SF1FFt9U6HEAJBNdK7KKSK6H811JcW4Wdf2b81OpRCQDA4iAgJAY9dQUWlC8d/oRJHTChPOtsyuC6MJ1oX5rN9Ptz56pYhdWFS/fkD7YTyB8PY2e7fX6+lrRfb2/3xXVzpqCtxYGKFE1MG5LY0RHcQJWOU3J58yCiYmT17Nu69917cfffdCbffe++9mD17ti4DMyLmIJCZJJvW9wXC2NfTj0BYQ1iLfKL/3T824ycnclbRjIZbuim2W1Bf6sDnXX3RfJJIzkk6W71TFQtcAmENgZAWD2KGq8eaydbodH8n3bow6f78tNpiBMMamjr8+N9PWwcELn60ePvTPoYVLlskCbfSuX8XUYUrvjSYqlzn9hhJRsHM7bffjtNPPx3//Oc/MXfuXADAm2++iaamJrz44ou6DtBImINAo5XLXKvBxdhsFgltPQGo0QRCiyShstiOT1s4q2hWIxXc8wVU1Jc4cPEJU9FQ7kx4vY3mdRhWtXgRukCGnaPT3Rqd7u+kWxdmpJ+HABxWBVv39uCuV7fCGwhhR5sfuzv9ae8gcsd2EMXqtUSDlxKdZkezldtjBhkFM/Pnz8eWLVtw33334dNPPwUAfOMb38BPfvIT1NfX6zpAI2EOAo1GPnKtYsXY/vTaNqzb3omQKqDIgMNqQZXbjmK7JV4yn7OKuaNnUJtuwb10X4dhVUN/WENfUEV/SE3afNFo0q0L090fRFDVUAQBXyCMYCxQi80yRX/v7xubU/r7DquMiRWuSL2WShcmR4vM5WIH0Whze8wqo95MZqJnb6aNu7txyaPvwmW3xAtRDdQXUuEPhPHABUcW5MwMd3Blbrhcq85oDkO2Z0U2NHXhhyvfhVWR4LRZ4LDJCZ9AC/21ayTZCmpTeX+O9Dp02WTc8vVDceTkcgRC+2ddIsnD5rKlxYebntuIIpsF9kH5JUII+EMaevtDWDCjFv5gGJuae7CjvRfpXgytioQJ5fuXhmJf1R573mc/ctndO1sVgLPSm+nDDz/EoYceClmW8eGHH474s4cddliqd2sqw61PA8O3nS8U3MGVOSPkWnX1RWqPlDltSf8GZxVzI5sbCA5UcC/2OuzpD6HG4wAQyRVRZAnlTiv2+QL44/9uw2+/OSvvF+LRiuWObGvtgdtuQTAsEFA1BMOR5bHY8tBT7+9O+T7tFhlHTS6P57NMqXRhXFkRlDTes7kMMDJZyjOzlIOZww8/HC0tLaiursbhhx8eLVg0NI6VJAmqml6zKrPIVkM4o+MOrtExQq5VOgXLKDtyHdTGEnNDqkBY1fBhUzc2t/Sg2G5FWI1U0x3IiP2FUtUXVPfXammPJOJu3dsDb38Y3X3pb322WWQ4LAoUOfKecdsVXH3KwaNaokl3mzilJ+VgZvv27aiqqor//1ilR0M4MzHCrILZGSHXaizPKhpFNoPa4ID8jtCA/w6cCWjq7EMorMHjSH7aN0MNkmBYQ1Onf3+9ljY/drT3ork7/R1E5S5bvGFi7KvLH8Qz6/fEt4ArEjCtxj3qgCPdbeKUvpSDmYkTJ8b/f+fOnZg3bx4slsRfD4fDWLt2bcLPFqLRNIQzGyPMKpidEWZFxuqsopHoEdSG1ciW+ljAEstrSdYdevBMAAD4AmFYLRJKi4a+1oxUg0TVBD7v6ksoMrezzY+mDHYQFdstqPU4UOm2YVK5C0dNKcPkyuJhdxAdPaVC16WgdLd9U2Yy2s100kknobm5GdXV1Qm3d3d346STTirYZaaBMmkIZ0ZGmFUwO6PMioy1WcXB8p3AnmpQ67Fb0BdUEVQ1hKPLRKFoEJPqfo1kMwHBaKXdfT1BWBUZLtv+03++apAIIbC3JzBgpiWyRLSzoxchNb2oxWGJ7CCaVOmM57VMrnRFAog0ggS9c03S3SZuVIosQZElWGQZshxpmyFLwNa9Puxo96Oq2J7XD/UZBTNCiKQvjvb2drhchVeMZywzwqyC2RlpVmQszSoOZIQE9lhQ+8keL2o9dgASBKK9iYRAuy+IqVUuuIssaO7uy/jvDDcT4LAoqPE4sKerH3u9/agrKYLdIuekBokQAp3+UEKn5x3RJSJ/ML0PvxZZQkP5gHL+0aCltsRhyJmNdLeJ55IsRQIUWZagRP9/f9CS+N/B13wjvKcGSiuY+cY3vgEgsrxw0UUXwW7f36hKVVV8+OGHmDdvnr4jpLwyyqyC2RlpVmSszCrG5COBXQgRSbzVNITCAiEtksPyzS+Ow2f7fNjT3Z+0oNm5OgQTI80EuGyR+kKd/iB8gTB8Qeheg8TXH452eu5N6PrcHd1RlypZAupLizApOtsSq9kyvrQIlmFmio0oHy0GJCkShFiU/bMpFiVymywNH6CkyoibQtIKZkpKIidAIQTcbjeKiori37PZbDjmmGNw8cUX6ztC0l060+1GmlUwu7E6K5JP2U5gjyXahlQtuiwkIl/D1GY5bHxp1guaHWgmwOOwIKRq+N7REzG+vCjjvJC+kIpdA3sQRQOYNl/6MwzVbns8CTeWlDuh3Al7knpeZpOtFgOyFAlWrEqkM7bVIsMaDVqsWQz2jLopJK1gZsWKFQCASZMm4dprr4XT6czKoCh7MpkaNNKsgtmNtVmRfBttArsQAmFNQNWi25yjPa2C4fRyWAbKpDdROlKaCZAlzKwvSSlHI6Rq2N3ZN6hxYi+au/rTLjJX5rRGK+JGy/lXRLo+F9szyngwhUxaDEhS4jKPRZETlnys0X/ng1E3hWT0Crrwwgvx+eef4wtf+ELC7Vu3boXVasWkSZP0GBvpbDRTg5xVIDMaKYFdCAGbLKFL1fB5dx8mlDsR0jSoWiSACatavI+V3rJZ0CzTmQBVE2ju7otsdx6wi2h3Z1/ax8FlV+KF5SYNmHEpHaO5dcO1GDiouhgL507C3KkVsFlkWBU5r4FKKoy6KSSjYOaiiy7C97///SHBzNtvv40///nPeO211/QYG+lIj6lBziqQWUQCEg1OqwJFjiyJ2KJl7YWIXNQhgP6wBhkANKC9N5DXMevlQDMBRVYZpx1ag3d2dCQELjs7/AiG02tdYLfImBhdFor1IspkB1EhUqIzKlZFglWWceqhdTjt0DpsbulBV38Ila787v7JlFE3hWQUzKxfvx7HHnvskNuPOeYYXHbZZaMeFOnPqFODROmI5Xu1+vrhsVsxraYYmsD+GRVVJCz/lLmsGFfm1D1fwehiMwEr39yJXW29CGgahAYoioS+oIpbX/g0rftT5FgPogHbnqtcqPU4DD2LkG0DE20tsgybRYZNifx3uOMyZ6K5i+MZdVNIRsGMJEno6ekZcnt3d/eYqDFjRkadGqTCNpraLmq0OFwoWmvlzW1teOTNHdjZlno5+EzyFczIFwhjx4CdQ7Hclq7BO4gOUNlfAlBX6sDkaLASy20ZX1aU1aTSTGS7z9HgvJXYriAluisolssy1hh1U0hGXbPPOOMMFBUV4fHHH4eiRKaZVFXFd77zHfT29uKll17SfaCZ0rNrtpmN9Y7flHsHSjYfuH05HJtVGVDhdmCexuAicNZoOXhvNCA5UDn4hGq40XwFM/bF6Q+p2NXhT0zGbfNjny/9JbKqYjsmVzojy0NVrvgOomTnB6PRo89RrMZKbPdPbCdQbJZlLM84pSLh/R19DvSuM5PO9TujYOaTTz7BCSecgNLSUhx//PEAgNdffx1erxf/+7//i0MPPTSzkWcBg5kITRNYuGJddGrQPmRqsMUbwPQ6N1YuOsp0a7hkPLFk857+EEqdNljlSLJ5lz8Ep03G1accjNkNpSndlyYErnt647BLRW2+IKZUFR+w23MuOxaPVljV0NTZh53t+3sQbW/rxZ6uvrR3EJUUWePF5SZVRpNyK1woHqZHk9GlEtgeMbE8YdtyLHdFjxortF+2q2qnc/3O6NU8Y8YMfPjhh7j33nuxYcMGFBUV4cILL8Rll12G8vLyjAZN2WXUqUEyv9gMS2z7ciCk4q5/bkF3XygefGhCwCJLqCi2os0XxF/f3oVZ40tSCib0KgefzR1EmdKEQHN3f2I5/3Y/mjr8CKe5g8hpU+LVcOOVcatcKCugHUQDqxtXFdvj5yurEnn8rT0BPLv+c5w9ZzzPZTlgpE0hGYfm9fX1WLp0qZ5joSxjvRjKRGzpZ/CW5fjS0KACcVtafNjRpl8vGiOXg0+VEJEZpITKuG1+7GzvRX+aO4hsFhkTYuX8o3VaJle6UO22F8xsQyxfxapEi8BFl4A2t/RgT6cflcX2pAX1ylw2fLavlxsZxqCMg5nXX38dDzzwAD777DP8z//8D8aNG4dHH30UkydPxnHHHafnGElHrBdDMeqA3JSwJqDF/hsrFDdCJduR6B186FkOPhdLTd3+0ID+Q/vrtfQG0tscIUuI9CCKN08sxuRKJ+pKigoin0OSJFgVCTZFhmVQ0DJcsnFPIIywBm5koCEyCmaefvppXHDBBTj//PPx/vvvIxCIJJ91d3dj6dKlePHFF3UdJOnLSFODlB2xOiuDZ1CSbV/Wm969aPQqB69H0uhAvYFID6IdbX5sb98fuHT60+tBFNtBFFsiim1/Hl/mjNfGMbOBfYGsigzrgO3L6TJqjRPKv4yCmVtvvRX3338/LrzwQqxatSp++7HHHotbb71Vt8ERUXIDd/2E1Ugjw7C6fykoW4FKKvTuRaPH9urBSaOeaNLoZ/t8uPPVLfHdUMlmbkJhLbKDqN2fkNvS2pP+DqKKYls8ATfWi2hChRNFJthBNJyRZlgsOifaGrXGCeVfRsHM5s2bccIJJwy5vaSkBF1dXaMdE9GYNXjpRx3wFZtZyVaJfb1ko7bLcOXgU2nQODBpdGBwZbdIqCy2oc0XxGPrmhBSNfyfN3dhd0cvgmo0IJQkBMNa2juIPA5LQnG52FKR22FN856MYXBxOGtsO3OGMyyZGs1GhmzvvKH8yiiYqa2txbZt24b0YPrPf/6DKVOm6DEuooIxsFlhLB9FiwUpQkDTIs388j2joqfRBB8j3WcmDRoH74YSQiAUbRYZiH6t39WJ93Z2JvntkZ+PIqsS2TkUDVxivYjKnFZTJuNaZBlWy4BAJbql2UjF4TLZyJBJg10yl4yCmYsvvhg/+9nP8PDDD0OSJOzZswdvvvkmrrnmGtx44416j5HIcGIzJZoGqCKSLKuK5AGLViABSrqy0R06ne3VQgi09wbxzs529ATC6AtrCEa/MnlGrEpkTsdhVfCtI8ZjwfQaVHvshq1VM5xY1+XYrIp1wGyLWQKwdDYyjKbBrtmM5dmnjIKZX/ziF9A0DV/5ylfg9/txwgknwG6345prrsFPf/pTvcdIlFOaJuK9fmL1UwZvTy6UGZRsSyX40GOHUXdfKJqMO6Ccf3svevoH1O8PpbYzy6pI0DQBTQCxCQkhgDpPEexWCW2+IDbs7sZ5R08wbCAzcGuzVZFgtUQq3BptlmU0UtnIoEeDXbMY67NPKVcA/vDDD3HooYdClve/EYLBILZt2wafz4cZM2aguNhYBakAVgCm/YQQibkoIrb9WCTkquR7JiUb24eNWv023R1G/mAYO9v3Byuxrs/tvelvxbXKEmwWGcGwBlUI1Jc44LAqCIYF9nT7IUmRirGx101tiQMumwX9YQ39wTB+/fVZeS/CZ1Vk2C2xGRY5PutSCFu39TBW2rgMN/vUGc0jMuvsU1YqAM+ZMwfNzc2orq7GlClT8M4776CiogIzZswY9YCJMjWwNspw9VLMtNyj9/bhbN3naGlC4O8fNuPRN3cgGNZQ5rTBY5HjO4zueGUzzj1qAuxWJaHIXIu3P+2/VeGyoaLYhs+7+iAE4HZY4LIpCGtAT38ILpuCoKpBkWXIkgRVqBACiH1uEwAkCVCkyA25LtIXm2WJBSyxpSGbIptmWShfCr3BrqYJbPy8G0tf3IQufxDjSoviEw6FOPs0kpSDmdLSUmzfvh3V1dXYsWMHtAyKaRGlYmDC7MAZFDVJwFJIyz2pbh/O932O1vpdnfi/b+3Ch593IawBMoBAuA8OqwWaEAiENXT6Be54dWta9+uO7iCK7RyKJeWWFFnjfzcW1HX2heIJyd/90nisemd3fCu5IsmQpMjSEhB5zTksMuzWyIUg3To56VBkCXaLApuFQYseCrkuTWxZ6dPmHrT3BiBLEnZ2+FHldqDYHrm0S5KEUqcVja2+gq+KnHIw881vfhPz589HXV0dJEnCkUceGe+YPdhnn32m2wCpcGixGZSByz3RIGVgUTejbz3OhlS3D89uKE15eSgb95kpTQi0egNYvWkvHl+3C32h/Um4GoCgCgTV8Eh3EeewypGmifFtz5Eic+Uu24gX/ZESkmVJGrCV3AKrIiMQ0gAJUCQJ5cX2yE6oDOrkDCe2JGS3yLBbFdijMy+kn0KtSzNwWcmmyJAQmUnsC2n4vLMP48qK4gGN2WefUpVyMPPggw/iG9/4BrZt24bLL78cF198MdxudzbHRgY3uPbJwJmU2C6fWACjCZhmFiUf+SV6NVPM9n3GDHeMhBDo6A3GGybuiJby39HmR18ovXL+AFBX4sDMek+043MkaKnxODJ+PoZLSB68lVyRZUDSIEtAmcsKh0VGf1jLqE6ORd4/yxKrfsu8ltwoxAa7g5Oa+0MaZFmCBAlWGQhpAvt6+uGyuSBJkqlnn9KR1m6mr371qwCA9957Dz/72c8YzBQYTUvcXqxqkaAkrGnxAGXgdmSzBCfpyFd+STaaKWarQWPsGO1s88WbJBZZLXA7LGjzBeDtT22GZTgVLhtsFhnhsIqb/2tmzpJsB8/cNHf34/Ut+9DU4Ue7P3jAOjmxGi22gSX7FdlUF8pCVGgNdj/e40Vjqw9lzshMpMMmw25R0B9S41WXA2EN/SENDqts2tmndGW0NXvFihV6j4OyYODunVhwkpCHou0PWAo1OElHPvNL9O5npOd99gXVyLbndj/WbW/HW591IKhqGPhy8QVU7POlVt5fQiShdrjVRAlAf0jVZSknXYNnbv7rsLohM1BKdKYl1l/IbmHQYnSF1GB3cFKzBAlVbjs+7+xDWBWQo/levcEwuvqEKWefMpFRMNPb24vbbrsNq1evRmtr65BkYObMZEc8+BADl3MG7NgRDE4yle/8Er37GWVyn8GwhqbOgf2H/NjR3ovm7vR3EJU5rfvL+Ud7EG3c3Y0HX/8MFhmQZRmaiNTxGaw3GEa5y5Z2y4NsUGQZsxpKYFNk2K37Axcm5JpPoTTYTZbUXGy3YFxZEfb1BNAfCkMTkari0+s8ppx9ykRGwcwPf/hDrFmzBhdccEE8IZjSM3DHTrzUfSxASZJzMhaTYnMpm/klqchGP6Ph7jMQ1tDdF4JFjgRqt/x9E3a09aKp0z/sbMnwfwPxOieyJEFoAjecPgNHTBo6gyVHe/uoqoAkC8iSBKsChFWRUJF3YrkTF58wJS/bxq2KDIdVgd0aCVrsFgYuZmbEirijHdNwSc3FdgucNhmfd/VjXGkRlp49C7PGleT98eZKRsHMSy+9hBdeeAHHHnus3uMxtSEl7gfVPBk8s0LGka38knTo3c9ICIH60iKcdmgtXvpoL1q9/dHKxvt/5h8f703pvuwWGVVuO1q9/XA7IgmxNouc0BVZEwLt/iCGaxYwraYYkyuc2Nrai3BYQFEiy00WRYoHNA1lRbjn3C9CGeZ5SCbThG1JitRucVgiAYzDqjApt4DEti5v29uDvlAkmXtChQvXnDINx32hKq9jGlild0pVMb56aC0ayp0pBTcHSmouLbLihq9Nx+yG0tw9MAPIKJgpKytDeXm53mMxnd5AGB29QQYnBSAbOSuZyKSfkRACnf5Qws6hWIVcfzC9HUQWWcKEcicmVjgxpcoV3UXkQl2JA9v29uKm5zaiyGaBPUmn5AMdI1mScMn8qVj64iZ094WgaQLRxtRQZKC0yIorFnwhrUAmnYTtSH6LwuWiMSC2dbmjN4iwGqnurQH4cHcXfvh/3sXVJ0/DxSdMTft+RzOrkqxKb1dfCG9vb8ebn7Wj2GaBy66k1IKg0JKa9ZByO4OB/vrXv+K5557DypUr4XQ6szEu3WSznUF3XwjtKSY9krFpQuC6pzcOm1/S5gtiSlUxfvvNWXnN4/D1h6Nl/HsTSvp394XSuh8JQH1pUSSvpdIZ7/Y8vrRo2N49eh2j9bs68djbTfisbcCn08pinHd0erNPgxO2rdGEbW90We7606bj2C9URpaNuFxkGNle+tE0gYUr1mFDUxf6QypUEQnSJQAaBEJhAbtVxp8vPDKtGZrR9D6KjWlTszfeI8oXCOPzzj6o0ZxTh1VBXYkDXX3hlFsQGHEZTU9ZaWcw0B133IHGxkbU1NRg0qRJsFqtCd9///33M7lborzJRs7KaPSFVOyK9iDa3taLndGgJdUdQwNVu+3xJNxJlZEicxPKnbAn6VUzEr2OkR7dtAcnbMuSDFkCbBYJxXYFe3uCWPVuE06bVVdQJ3ezy0UzxI/3eLFtbw/CqoAqIj24YoGsAglQIp3Tf//KFsybWpnS62O0nbcHb6cWENjXE4AmBKwWGUIAQVUDIKHWY0+5BUGhJDXrIaNg5qyzztJ5GET5p3fOSipCqoamDj92xJonRpeKmrv6h8k8GV6Z0xoNVqJBS6UTEytc8UqgetDrGKXSTXs4FlnGZ/t8+LzTj3KXDXaLMmjWZeyUcDeT0QYEqerwB9EX0hBStYScrhhZliBpAk0d/pReH3p03h68nbo/qCEQVqFEi90BkSXXsKZBkhS+fjOQ0Vnu5ptv1nscRIagx6xBMqom0NzdF+/yvD0atOzu7Es738phlXFQVXE0YInOuFQ4UZqjCp/ZOkbJSJIEqyLFE3RjJf+3t/dC1QDHkEAmYqyUcDcLPQKCVJU7bZClSJuMZHOPsTwtTaT2+hg8qzJQqr2PBm+nDmuROk3SoGamlmiTSL5+06ffRzaiAjGaWQMhItPH26PLQrHAZWeHH8Fw+s1ZFVmK77ZRJAmBsAqXXcGiYyflreM1MLpjNJJYo0WHNZqsa0lejC7VBoIdviDWbNlXkPkEgHlyJvQICFI1s96DCRUufLi7KxrQJOZ2qZqAVVFQZFVSKvGvR+ftwdupLXJiM9OwJlBkleGwRv7GWGlBoKeUg5ny8nJs2bIFlZWVKCsrGzGZrqOjQ5fBERlZlz+YUFwutkzUm+YOIkWW0FBWFM9pmVThxJPv7sbnnX5Uue2DEm0tOW0QmW22aKBmj+40siXZJZXMgRoI7uvphyRJuP3lTQhryEpuxoFkO9DIRf6JXvQICFIlyxKuOWUafvh/3o00C1UivYuEiMyQSojkVk2tLk6pxL8enbeHbqe2wKbI6A+p0WamMqrckRkrMzfAzKeUg5k//OEP8V5Md911V7bGQ2Q4vYFwfNfQ/u3Pvej0p7+DqK7UEa+KG99BVFaU0C15S4sP+7z9KCmy5aWAX7ZIkgR7vKaLDIdFyfjiPlKtjX09/egNqHDaFBQ7rFnLzRhJtgONXOWf6EWPgCAdx32hClefPA13vLoFwbAGSROQJMCqKLBZJJQ5bSmX+Ner8/bg7dQWRQbCkfo3lW4bnFYFfSHVtA0w8y2jrdmpuu222/DjH/8YpaWl2foTB8St2ZSqQEjFzo6BOS2R/2/tSf85riq2xxNwp1RFgpcJ5U44UthB9M6ODtz20qeocNmSzrzEitP94quH4EuTjFvvSZKkeNBSZMvO9uiEoCFaa8MfUqFpAhPKnUMuPC3eAKbXubFy0VFZu1AMF2h0Ri9Sow00km3zjcnVY0zX/jH3oNZjz9mY/7N1H37/yhY0dfihCYEia2p1XAbb/5yqSTtvp/OcDpyxa+rw4+WPmvHZvv11kow6u5YPWd+anaqlS5finHPO0S2Yue2227BkyRL87Gc/4+wQZSysamjq7Itud96/TPR5Z1/aO4hKiqyYXOmMF5ebEu1HVOzI/K1llAJ+6ZIlaf+sS45quwxuINjhC+L2lzeh2GHNem5GMrlIdM1l/oleDlS1NtWZiHSX7o77QhXmTa0c9XKfnkXqBm+n/u6RDfh/Hzbj8y4/xpU6ccZhdbCkuNxK+2U1mNFz0uedd97BAw88gMMOO0y3+6TCpgmB5u7+AY0TI52fmzr8CKe5g8hpUwY0TXTGdxKVZSFBLxtNJ7NBkaPBS7SqbiqzTtkw8OKwZss+hDXkJDcjmVwEGrnMP9HTaAOCTJfu9KrFko3O28ke0zPrd3NmJgOm2M3k8/lw/vnn46GHHsKtt9464s8GAgEEAvuXBbxeb7aHR3kmRKT6bEJl3DY/drb3oj/NHURWRcLEiv3bnWNbn6vd9qzPMsQYrYAfEAkMt+/zwx8Ko9rtwOENJSiyGe/0kevcjMFyEWjk+zGORqYBQS5yhFKZ9dGzSJ3Z8p6MznhnoyQWL16M008/HQsWLDhgMLNs2TL86le/ytHIKNe6/aHotufehHotvYH0dhDJEtBQ7owWmHNicmUxJlc6UVdSZIhmg/ko4DeYIksosir4cHcXVqzdge37eg2/a0avZM1M5SLQyPdjHK10A4JcLN3lemdYLuvujBWGD2ZWrVqF999/H++8805KP79kyRJcddVV8X97vV40NDRka3iUJb2BMHbGyvm37w9cRruDaFI0eGkoc6a8DThfclmcDogU7HJYZdit++u8rN3Whltf2GSaT4965WZkKheBRr4fY65le+kuHzMkZsx7MjpDBzNNTU342c9+hldffRUOhyOl37Hb7bDb7VkeGeklEFKxq8Mf3zkUWybKZAdRRbENkyr2b3mO7SYqylMuhx6yVZwOAKyKnJCwax20NGLWT4/57Cicq0BjLHVNzubSXb5e42bNezKyrAYzxx9/PIqKijL+/ffeew+tra344he/GL9NVVX8+9//xr333otAIABFMe+FaixRNYHdndEeRPt640tFe7r6kGYuLjwOy4BZFld8N5GnyHrgXx7DBgYvRVZl2O7YMWb+9JiNZM10/nYuAo18PsZcyubSXb5e42bOezKqjIKZRx55BBdddNGQ28PhMG688UYsW7YMAPDiiy+OanBf+cpXsHHjxoTbFi1ahEMOOQTXXXcdAxkD0oRAS3c/drRHknBjy0RNHX6E1PSiliKrEslnqXBhYnTbc2QH0dBtt5Qo1bYAIzH7p8dMkzX1qNybq0BjLHRNzubSXb5e42bPezKijIKZyy+/HC+88AIefPBBlJVFEhE3b96M8847D+3t7fFgZrTcbjcOPfTQhNtcLhcqKiqG3E65JYRAe29wQCKuH9vbe7GzLcMdROWRXJZ4bkulEzUeh+nL9eeKLV5Zd38zxkwMvJB3+IJj7tOjnomgBwo0zNJXKd8OtHTnssk4dWYtXt/WlvZxzNcMyVjLe8qFjIKZ9evX43vf+x5mzZqFFStWYMuWLfj5z3+Os846C3/605/0HiPlWXdfKDrTEg1a2nqxo70XPf3htO5HloDxZc74bEusF9G4UmPsIDILPdsCDDT4Qm6Rgb6wht5gGA1lQ6vpFtqnx1wmgpqpr5IRDLd0V1cSyY/807+2ZXQc8zlDMpbynnIh43YGmqbhiiuuwH333QdFUbBy5Uqce+65eo9v1NjOIHX+YGQHUaz/UKwXUXtv+lOsdSUOTIzWaYkl5TaUG38HkVHZLJE8lyKbolvwMtBwF/K93gD8wTBcdgVVbseoyrgnY5TZiVy2CMh2u4NCNrgVwEOvf4beUR5HPVsVjPYxcYYuUU7aGbzwwgtYtWoV5s6diy1btuAvf/kL5s+fj/r6+kzvknIkGNbQ1OGPJ+HGisy1ePvTvq8Kly2ehDu5Itb12YUiG/OZRsMiy3DYZDhtFhRZlazOXI20o2NCeRF2dfRBkgB/IKzrp0cjzU7kKhHUrDvEjCK2dBcLPnt1OI65nCEZLnAp9LynXMgomLnkkkuwcuVK/OY3v8FVV12FvXv34vvf/z5mzZqF5cuX45xzztF7nJQBVRP4vKsvobjcjjY/dnf6095B5HZYBmx7dsaDlhLuIBo1SZIiOS+WSI2X0eS8ZOJAF/Jqjx29/SFce+ohKC+26VbG3UjVT3OVCGrmHWJGovdxzEXCtpGC90KUUTDzxhtv4O2338bs2bMBALW1tXjxxRdx33334fvf/z6DmRzThECrNxDPZYmX8+/oTXsHkcMqR7Y8V7gwucqFydGlonLX0JMGZUaWJNij26Nz1ZBxJCldyAVQXmzD/GlVo/57RpydyFUiqNl3iBlFNo5jNmdIjBa8F6KMgpn33nsvaWG6xYsXY8GCBaMeFCUnhECnPzRgaWj/bEtfKL1y/hZZwoSKgeX8Iwm53EGkv3x0k05Hrnd0GHF2IleJoAOPtV2W0R/UENa0+LJiIe4QywYz1WkxYvBeiDIKZkaqsHvwwQdnPBjar6c/FKnTMqgPkTeDHUT1pUWR5aFYTkulE+NLiw5YNI0yEwteiqwKHLZInRcjy/WODiPOTuRqq2zsWH+4uwthVSCoahACkKRIp2+LIuGw8aUFs0MsW8xUp8WIwXshyjgB+KmnnsKTTz6JXbt2IRhMPOm8//77ox7YWNEXUrEr1oNowDJRmy/9E3mNxx6v0xL7msAdRFkXK1BXZFVgj86+mEmua14Y9VN1LhJBZVnCCV+oxJuN7VA1AYsiQZEBTQD+oAol+v1cfkI3424aM9VpMWLwXogyCmbuvvtu3HDDDbjooovw3HPPYdGiRWhsbMQ777yDxYsX6z3GghBSozuI2vzxgGV7Wy9auvuR7t74Mqc1HqxMqnBhSpULEyuccNoM3WqrYMS6SQ9syJhrel+Acrmjw8ifqrOdCKppAv/e2ganTYGqRWZmVC0yM+O0KVBkGf/e2oYfHDclJxdiMyelmqVOi1GD9xgzBrPJZFRn5pBDDsHNN9+Mc889F263Gxs2bMCUKVNw0003oaOjA/fee282xpqRXNeZUTWBPV192DGwcWJ7L3Z39kFNcwuRy64kLg9Fk3FLDbAOPJYoshSvrptp8KLnCSObF6BcndjyXdsjXzbu7sYlj74Ll90Cu0VGf2hAzoxVRn9Ygz8QxgMXHJn1JYdCqXdj9Ivx/hpGPaj12LNawyhdRg9ms15nZteuXZg3bx4AoKioCD09PQCACy64AMccc4yhgplsEUKgubsP7+/sjCbiRpaKdnX4EUyznL/dIscLzA2ccaks5g4ivWlCYNveXnT3B1HisOGgGteQhGc9gpeB9DxhZHtXRK5qXpjlU7XeBi45SJIUrce0//WVqyWHwUmpAOKBVYnDgq6+kGmSUo1ep8WoS2KFtsMqo2CmtrYWHR0dmDhxIiZMmIC33noLs2fPxvbt25FhQWFTaerw47Q/vg5fIL1kXIssYUK5ExMrnJhS5Yrnt9SWcAdRLqzf1YnH1jWhqb0XoejFs6HChe8dPQHHHlSZlWUjPU8YhbYrYqx0fR7IKEsOA5NSe4Mq9vX0IxDen4xskWV8sqebSak6MVrwXmjnEiDDYObLX/4ynn/+ecyZMweLFi3ClVdeiaeeegrvvvsuvvGNb+g9RsOpLXEgEB55K7QiRSJyqyyhpqQI3zpiHE6eXsMdRHmyflcn7nx1C/xBFSVFkU9GIU1gR1sv/rh6K2o8Dt1PKHqfMApxV4TRP1XrzSj5QrEZomBYQ3N3P1QhYJElSBIgAARVFQG/iv9sayu45ydfy1JGCt4L8VySUTDz4IMPQtMiSymLFy9GZWUl3njjDZx55pn48Y9/rOsAjciqyJhaVYxPW3pQ7bbHl4YkCfjnJ3sRUjWUFEU+gYVUgS5/EI+9vQu1HgfmTCjL9/DHFEmKBJT/895u9IdU1Jc4IMuRgNKGSNJltj6F6H3C4K4I8zPKkkO50waLDOzrCUAVkVmC2GtUAqBIEsJC4B8ft+CSE3KTjJwL+c4RMUrwXojnkoyCGVmWEQwG8f7776O1tRVFRUXxYnkvv/wyzjjjDF0HaUTLv3dEZLo4mh+jCYHrnt6IsCZQ5bZDQuTNb7dIqCy2oc0XxGPrmjC7oZRLSllmj9Z4KYouG330uRe72v0od9njgUxMNj+F6H3CMMoSBY2OEZYcZtZ7UFNShL09XbAoUuIMEQQ0AdgtClq9/ab6dD6SQssRGY1CPJdkFMy8/PLLuOCCC9De3j7ke5IkQVXTq0ZrRpMrXQm7mbbt7UVTey88Dms8kImRIMHtsKKpvRfb9vZiWm1xPoZcsKyKjCLb/gBm8KfIfH0K0fuEYZQlChq9fC85yLKEU2fWYOPuLqiqgKSIyBKTiOzIlKM9ufxB1VSfzodTiDkio1GI55KMEjh++tOf4pxzzkFzczM0TUv4GguBTDLd/cFIUqmS/I1gUySEhEB3v/lPDPlmkWUUOyyodNsxodyJhnInKovtcNktSU9EA4OKZLL1KSR2wuj0h4YkxsdOGFOri1M+YcSWKIrtkaWxvpAKTRPoC6lo8QYMVSiMDiy25DB/WhVmjS/J+fN23EFVKHXaYLMo0IRAWBXQhIDDqmBcWRGsimy6T+fDSWfJdywoxHNJRsHM3r17cdVVV6Gmpkbv8ZhWicMGqywN29gxqApYJQklDvOfGHLNqshwO6yoctvRUO7EhAonqt0OeBzWlBKq9Q4qUpWNE0ZsiWJ6nRv+QBitvgD8gTCm17nH1DQ5jd7Meg9m1HvgsiuYUObE+LIiTCyPtDtx2ZSsvS/yIZXZ2ZDJckRGq9DOJRktM33rW9/Ca6+9hqlTp+o9HtM6qMaFhgoXPtvni9SHQeIadE9/CFOqinFQjSuPozQHqxJpCVBkU+CwyKPeAZbPpMts5Efke4mCCsPA90V3fzj+vugPGa8lwGgVYo6IHgrpXJJRBWC/349vf/vbqKqqwqxZs2C1WhO+f/nll+s2wNHKZQXggdt/3Q4rbIqEoBoJZJw2BVedPI27mZKIdQyO5bxka/t6wk6GaFCRq50MRq9SSmNXPt8XuZLvKrx8/2cmnet3RsHMX/7yF/z4xz+Gw+FARUVFwgtDkiR89tln6Y86S3LdziChMJuILC01VLhw3lENDGSirIoMu1WO7jZSYM1h7R2eVIiGGgvvi3y10Mj3dnAzy3owU1tbi8svvxy/+MUvhmx1NZpcBzNAaiXzx5J8Bi96GQsne6JCl+tZqELpf5UvWe/NFAwG8Z3vfMfwgUy+yJI0prdfF0LwMhA/WeUfg0nSQy5zRLgdPLcyCmYWLlyIJ554Atdff73e4yETiiXsOqIBTCG1bGChrfxjMEl6ylUV3kJsGWBkGQUzqqri9ttvxz/+8Q8cdthhQxKA77zzTl0GR8ak924jo+Inq/zLZzDJ2SBjM9Lzk2wshdgywMgyCmY2btyIOXPmAAA++uijhO8NjkDJ/MZK8DIYP1nlVz6DSc4GGZuRnp/hxnLqzFpuB8+hjIKZf/3rX3qPgwwkV1uljY6frPIrX8EklxaNzUjPz0hj2dXhR0WxDc3dgYJpGWBkY/MqRQlkSYLLbkFFsR3jy/ZX2HWnWGG3UOWrDQJF5KNq6+DZIEe015fDqqDWY4cvoGL5mkZoWtqbQEkHRnp+DjSW3kCktY/LJhdMywAjG7tXqjFMkiJvuDKnDfWlRZhU6UKNx4GSIitsFr4kYvLVBoEi8hFM6tXDR9MENu7uxpot+7BxdzeDH50YqcdSKmNp9wVx8QlTC6ZlgJFltMxE5hPLe3HakneWpqHy2QaB8tPZV4+lRSPlcxQaIy39pjqWhnInVi46yjDJyoWKH8MLVGzpqDLanLGh3Ikq9/CdpSm5QmvGZib56Ow72tmgWA7FpmYvXHYLqqPvuVg+x9ptbbqNdSwy0tJvOmPJd4f0sYAzMwVCkiTYLdGkXVukWB3po5CasZlNNhp1jmQ0s0Hcyp99+ZitM8NYiMGMaSnRRDOHRYHdKsNukbktPotyVWiLhsplMDmapUU9dl8ZqXaKERlp6ddIYyEGM6YxsMpuIbQIIEpHLoPJTGeDRpvPwVyb1OR6ts4sYxnrGMwYlBzdcVRkiyTtMnhJHz/lGpuRn59MZoMG5lCkWyTNSLVTYgrt+RkLYxnLGMwYiC2a8+K0WeCwctloNPgp19jM8PykOxuUaQ6FEXNtCvH5ySYjjWWs4sf9PBq442hCuRPjy5yoKLajyKYwkBkF7igxNj2fHyPVc8l091W2aqdkemz4/iEz4sxMDkmSNGD2RWHSbhYY8VMu7afn82PE2YNMciiyUTsl02PD9w+ZFYOZLLNH+xs5rDIcFharyzY2hzQ2vZ4fI+aYxKSbQzGaXJtkRnNs+P4hs2IwozPbwFovDF5yzkgVQmkoPZ4fM8wepJNDoWe9ktEeG75/yKyYMzNKw+W9OG2stJsPRqoQSkPp8fwYqT+PHvSsdDzaY8P3D5kVg5lRKLZbMLHCiRqPA54x3mHaKNgc0tj0eH7y0U072/RqmzHaY8P3D5kVl5lGQeHMi+GwKqex6fH86J1jko5s1l7Ro17JaI8N3z9kVgxmqOCwKqexjfb5yVdPnFzsnhptvRI9jg3fP2RGkhg8l1hgvF4vSkpK0N3dDY+HU6NjiZErmNLonp/9O3bUpLMHeu9mGm6HUGeW/t5o6HVs+P6hfEvn+s1gJs94wiDKTMJMSXT2IBt1ZjRNYOGKddjU7E3YIQREZjtavAFMr3Nj5aKjDPPe1fvY8DxF+ZDO9ZvLTHlkxKJfRGaRq544Zqy9ouex4XmKzIDBTJ4YuegXkVnkoieOWWuv6HFseJ4is+Be4jwYXNjKYY0U13NYFdR67PAFVCxf05jXPjNEFDFWa6/wPEVmwmAmDwqt6BdRIRiuMeNYqL2S7LHzPEVmwmWmPDDrtDVRoTpQXkgh114Z7rHPm1rJ8xSZBoOZPMhn0S8iSpRqXkgh1l4Z6bFva/VBExrPU2QKDGbyIF9Fv4goUTqNGZPtEJpe68amlh6s2bLPdFuWD/zY+6EKoKM3iLqSoVvSeZ4iI2EwkwcsGU5kDOluux64Q2jttjYsWvmOabcsH/ix29DZG4TNIvM8RYbHBOA80auxHBFlLtPGjLHlmU3NXrjsFlS77XDZLfGlqbXb2nIx/FFJ5bHLsoQL507keYoMjzMzeZSrol9ElFwm+WvpLE0Z+b2c6mM/7qAqXHLCVJ6nyNAMPzOzbNkyfOlLX4Lb7UZ1dTXOOussbN68Od/D0k1s2nr+tKr4NDYR5UYm264LZctyOo+d5ykyOsMHM2vWrMHixYvx1ltv4dVXX0UoFMIpp5yC3t7efA+NiEwulr9WbFfQ4g2gL6RC0wT6QipavIGkeSGZLk0ZTSaPfSTD1ekhygXDLzO9/PLLCf9+5JFHUF1djffeew8nnHBCnkZFRIUi3W3XhVRaQa8t5+zfRPlm+GBmsO7ubgBAeXl50u8HAgEEAoH4v71eY0/1ElH+pZO/VmilFUabu8f+TWQEhl9mGkjTNFxxxRU49thjceihhyb9mWXLlqGkpCT+1dDQkONREpEZpZoXovfyjBFkmhPD/k1kFKYKZhYvXoyPPvoIq1atGvZnlixZgu7u7vhXU1NTDkdIRGMBSytEFEoyNJmfaZaZLrvsMvz973/Hv//9b4wfP37Yn7Pb7bDb7Tkc2fBizdq4nZH0wNeTsbC0AvvMkXEYPpgRQuCnP/0pnn32Wbz22muYPHlyvoeUEibEkZ74ejKmgRWBx6JCSoYmczP8MtPixYvx17/+FY899hjcbjdaWlrQ0tKCvr6+fA9tWIVQHZSMg68nMqpM6vQQZYPhg5nly5eju7sbJ554Iurq6uJfTzzxRL6HlhQT4khPfD2RkRViMjSZkymWmcwk3cZ1RCPh64mMTq9aNUSjYfhgxmyYEEd64uuJzIDJ0JRvDGZ0xoS4/Cq0HT98PRWuQnutjvVkaMovBjM6K7TqoGZSiDt++HoqTIX4WiXKJ8MnAJsNE+Lyo1B3/PD1VHgK9bVKlE8MZrKA1UFzq9B3/PD1VDgK/bVKlC9cZsoSJsTlzljY8cPXU2EYC69VonxgMJNFTIjLjbGy44evJ/MbK69VolzjMhOZ3sAdP8lwxw8ZBV+rRNnBYIZMjyXVySz4WiXKDgYzZHrc8UNmwdcqUXZIwmz9AtLk9XpRUlKC7u5ueDz8tFPIEmp3REuqs3YHGRFfq0QHls71m8EMFZRCq6pKhYuvVaKRpXP95m4mKijc8UNmwdcqkX6YM0NERESmxmCGiIiITI3BDBEREZkagxkiIiIyNQYzREREZGrczUREZBLczm1sfH7yh8EMEZEJJBTaUwWsCgvtGQmfn/ziMhMRkcGt3daG65/diE3NXrjsFlS77XDZLdjU3IPrn92Itdva8j3EMY3PT/4xmCEiMjBNE1i+phG+QBi1HgccVgWyLMFhVVDrscMXULF8TSM0raCLuRsWnx9jYDBDRGRgH+/xorHVhzKnDZKUmH8hSRJKnVY0tvrw8R5vnkY4tvH5MQYGM0REBtbhDyKkCtiU5KdruyIjpAl0+IM5HhkBfH6MgsEMEZGBlTttsCoSgqqW9PsBVYNVllDutOV4ZATw+TEKBjNERAY2s96DqdXF6PSHIERi3oUQAl3+EKZWF2Nm/chdhSk7+PwYA4MZIiIDk2UJl86fimK7ghZvAH0hFZom0BdS0eINoNiu4NL5U1nPJE/4/BiDJAaHkgXG6/WipKQE3d3d8HgYGROROSXUMdEErDLrmBgJnx/9pXP9ZjBDRGQSrDBrbHx+9JXO9ZsVgImITEKWJcwaX5LvYdAw+PzkD3NmiIiIyNQ4MzMGcSqUiJLhuYHMisHMGMNmaESUDM8NZGZcZhpD2AyNiJLhuYHMjsHMGMFmaESUDM8NVAgYzIwRbIZGRMnw3ECFgMHMGMFmaESUDM8NVAgYzIwRbIZGRMnw3ECFgMHMGMFmaESUDM8NVAgYzIwRbIZGRMnw3ECFgL2Zxhg2QyOiZHhuIKNho8kBGMwMxSqfRJQMzw1kJGw0SSNiMzQiSobnBjIr5swQERGRqTGYISIiIlNjMENERESmxmCGiIiITI3BDBEREZkagxkiIiIyNQYzREREZGoMZoiIiMjUGMwQERGRqTGYISIiIlNjMENERESmxt5MVDDYJI8ov/gepHwxRTBz33334Xe/+x1aWlowe/Zs3HPPPTjqqKPyPSwykLXb2rB8TSMaW30IqQJWRcLU6mJcOn8q5h1Ume/hERU8vgcpnwy/zPTEE0/gqquuws0334z3338fs2fPxqmnnorW1tZ8D40MYu22Nlz/7EZsavbCZbeg2m2Hy27BpuYeXP/sRqzd1pbvIRIVNL4HKd8MH8zceeeduPjii7Fo0SLMmDED999/P5xOJx5++OF8D40MQNMElq9phC8QRq3HAYdVgSxLcFgV1Hrs8AVULF/TCE0T+R4qUUHie5CMwNDBTDAYxHvvvYcFCxbEb5NlGQsWLMCbb76Z9HcCgQC8Xm/CFxWuj/d40djqQ5nTBklKXJuXJAmlTisaW334eA9fB0TZwPcgGYGhg5m2tjaoqoqampqE22tqatDS0pL0d5YtW4aSkpL4V0NDQy6GSnnS4Q8ipArYlOQvZbsiI6QJdPiDOR4Z0djA9yAZgaGDmUwsWbIE3d3d8a+mpqZ8D4myqNxpg1WREFS1pN8PqBqssoRypy3HIyMaG/geJCMwdDBTWVkJRVGwd+/ehNv37t2L2trapL9jt9vh8XgSvqhwzaz3YGp1MTr9IQiRuCYvhECXP4Sp1cWYWc/XAVE28D1IRmDoYMZms+GII47A6tWr47dpmobVq1dj7ty5eRwZGYUsS7h0/lQU2xW0eAPoC6nQNIG+kIoWbwDFdgWXzp/KWhdEWcL3IBmBoYMZALjqqqvw0EMPYeXKldi0aRMuvfRS9Pb2YtGiRfkeGhnEvIMqsfTsWZhe54Y/EEarLwB/IIzpdW4sPXsWa1wQZRnfg5Rvhi+a953vfAf79u3DTTfdhJaWFhx++OF4+eWXhyQF09g276BKHDOlgtVHifKE70HKJ0kMXuQsMF6vFyUlJeju7mb+DBERkUmkc/02/DITERER0UgYzBAREZGpMZghIiIiU2MwQ0RERKbGYIaIiIhMjcEMERERmRqDGSIiIjI1BjNERERkagxmiIiIyNQM385gtGIFjr1eb55HQkRERKmKXbdTaVRQ8MFMT08PAKChoSHPIyEiIqJ09fT0oKSkZMSfKfjeTJqmYc+ePXC73ZAkfRueeb1eNDQ0oKmpiX2fBuGxGRmPz/B4bIbHYzMyHp/hmfHYCCHQ09OD+vp6yPLIWTEFPzMjyzLGjx+f1b/h8XhM8+LINR6bkfH4DI/HZng8NiPj8Rme2Y7NgWZkYpgATERERKbGYIaIiIhMjcHMKNjtdtx8882w2+35Horh8NiMjMdneDw2w+OxGRmPz/AK/dgUfAIwERERFTbOzBAREZGpMZghIiIiU2MwQ0RERKbGYIaIiIhMjcHMAdx3332YNGkSHA4Hjj76aKxbt27En/+f//kfHHLIIXA4HJg1axZefPHFHI0099I5Nh9//DG++c1vYtKkSZAkCXfddVfuBpoH6Rybhx56CMcffzzKyspQVlaGBQsWHPB1ZnbpHJ9nnnkGRx55JEpLS+FyuXD44Yfj0UcfzeFocyvdc07MqlWrIEkSzjrrrOwOMM/SOT6PPPIIJElK+HI4HDkcbW6l+9rp6urC4sWLUVdXB7vdjmnTppn3miVoWKtWrRI2m008/PDD4uOPPxYXX3yxKC0tFXv37k3682+88YZQFEXcfvvt4pNPPhH//d//LaxWq9i4cWOOR5596R6bdevWiWuuuUY8/vjjora2VvzhD3/I7YBzKN1jc95554n77rtPrF+/XmzatElcdNFFoqSkROzevTvHI8+NdI/Pv/71L/HMM8+ITz75RGzbtk3cddddQlEU8fLLL+d45NmX7rGJ2b59uxg3bpw4/vjjxde//vXcDDYP0j0+K1asEB6PRzQ3N8e/Wlpacjzq3Ej32AQCAXHkkUeKr33ta+I///mP2L59u3jttdfEBx98kOOR64PBzAiOOuoosXjx4vi/VVUV9fX1YtmyZUl//pxzzhGnn356wm1HH320uOSSS7I6znxI99gMNHHixIIOZkZzbIQQIhwOC7fbLVauXJmtIebVaI+PEELMmTNH/Pd//3c2hpdXmRybcDgs5s2bJ/785z+LhQsXFnQwk+7xWbFihSgpKcnR6PIr3WOzfPlyMWXKFBEMBnM1xKziMtMwgsEg3nvvPSxYsCB+myzLWLBgAd58882kv/Pmm28m/DwAnHrqqcP+vFllcmzGCj2Ojd/vRygUQnl5ebaGmTejPT5CCKxevRqbN2/GCSeckM2h5lymx+bXv/41qqur8YMf/CAXw8ybTI+Pz+fDxIkT0dDQgK9//ev4+OOPczHcnMrk2Dz//POYO3cuFi9ejJqaGhx66KFYunQpVFXN1bB1xWBmGG1tbVBVFTU1NQm319TUoKWlJenvtLS0pPXzZpXJsRkr9Dg21113Herr64cExoUg0+PT3d2N4uJi2Gw2nH766bjnnntw8sknZ3u4OZXJsfnPf/6Dv/zlL3jooYdyMcS8yuT4HHzwwXj44Yfx3HPP4a9//Ss0TcO8efOwe/fuXAw5ZzI5Np999hmeeuopqKqKF198ETfeeCPuuOMO3HrrrbkYsu4Kvms2kZncdtttWLVqFV577bWCTlRMl9vtxgcffACfz4fVq1fjqquuwpQpU3DiiSfme2h509PTgwsuuAAPPfQQKisr8z0cQ5o7dy7mzp0b//e8efMwffp0PPDAA7jlllvyOLL80zQN1dXVePDBB6EoCo444gh8/vnn+N3vfoebb74538NLG4OZYVRWVkJRFOzduzfh9r1796K2tjbp79TW1qb182aVybEZK0ZzbH7/+9/jtttuwz//+U8cdthh2Rxm3mR6fGRZxkEHHQQAOPzww7Fp0yYsW7asoIKZdI9NY2MjduzYgTPOOCN+m6ZpAACLxYLNmzdj6tSp2R10Dulx3rFarZgzZw62bduWjSHmTSbHpq6uDlarFYqixG+bPn06WlpaEAwGYbPZsjpmvXGZaRg2mw1HHHEEVq9eHb9N0zSsXr06IdIfaO7cuQk/DwCvvvrqsD9vVpkcm7Ei02Nz++2345ZbbsHLL7+MI488MhdDzQu9XjuapiEQCGRjiHmT7rE55JBDsHHjRnzwwQfxrzPPPBMnnXQSPvjgAzQ0NORy+Fmnx2tHVVVs3LgRdXV12RpmXmRybI499lhs27YtHgADwJYtW1BXV2e6QAYAt2aPZNWqVcJut4tHHnlEfPLJJ+JHP/qRKC0tjW/tu+CCC8QvfvGL+M+/8cYbwmKxiN///vdi06ZN4uabby7ordnpHJtAICDWr18v1q9fL+rq6sQ111wj1q9fL7Zu3Zqvh5A16R6b2267TdhsNvHUU08lbCHt6enJ10PIqnSPz9KlS8Urr7wiGhsbxSeffCJ+//vfC4vFIh566KF8PYSsSffYDFbou5nSPT6/+tWvxD/+8Q/R2Ngo3nvvPfHd735XOBwO8fHHH+frIWRNusdm165dwu12i8suu0xs3rxZ/P3vfxfV1dXi1ltvzddDGBUGMwdwzz33iAkTJgibzSaOOuoo8dZbb8W/N3/+fLFw4cKEn3/yySfFtGnThM1mEzNnzhQvvPBCjkecO+kcm+3btwsAQ77mz5+f+4HnQDrHZuLEiUmPzc0335z7gedIOsfnhhtuEAcddJBwOByirKxMzJ07V6xatSoPo86NdM85AxV6MCNEesfniiuuiP9sTU2N+NrXvibef//9PIw6N9J97axdu1YcffTRwm63iylTpojf/OY3IhwO53jU+pCEECJfs0JEREREo8WcGSIiIjI1BjNERERkagxmiIiIyNQYzBAREZGpMZghIiIiU2MwQ0RERKbGYIaIiIhMjcEMERERmRqDGSITeu211yBJErq6uvI9FMP55S9/icMPP1zX+5QkCX/7298AADt27IAkSfjggw/i33/jjTcwa9YsWK1WnHXWWbr+7ZgTTzwRV1xxxajuIxvHhsgIGMwQpeGiiy6CJElDvgqtC+9IBl7Yx6KGhgY0Nzfj0EMPjd921VVX4fDDD8f27dvxyCOP5G9wB3DNNdcMaYZLVAgYzBCl6atf/Sqam5sTviZPnjzk54LBYB5GR9mmKApqa2thsVjitzU2NuLLX/4yxo8fj9LS0vwN7gCKi4tRUVGR72EQ6Y7BDFGa7HY7amtrE74URcGJJ56Iyy67DFdccQUqKytx6qmnAgA++ugjnHbaaSguLkZNTQ0uuOACtLW1xe9P0zQsW7YMkydPRlFREWbPno2nnnoq4W+++OKLmDZtGoqKinDSSSdhx44dQ8b19NNPY+bMmbDb7Zg0aRLuuOOOhO9PmjQJt956Ky688EIUFxdj4sSJeP7557Fv3z58/etfR3FxMQ477DC8++67wz72SZMmAQDOPvtsSJIU/zcALF++HFOnToXNZsPBBx+MRx99dMTjeNFFF+Gss87C0qVLUVNTg9LSUvz6179GOBzGtddei/LycowfPx4rVqxI+L3rrrsO06ZNg9PpxJQpU3DjjTciFAoN+3caGxsxZcoUXHbZZRBCIBAI4JprrsG4cePgcrlw9NFH47XXXhtxrAMNXGaK/X97ezu+//3vQ5Kk+MzMgZ73ZN544w2ceOKJcDqdKCsrw6mnnorOzs749zVNw89//nOUl5ejtrYWv/zlLxN+f9euXfHn0uPx4JxzzsHevXvj30+2zPTwww/HXzd1dXW47LLL4t/r6urCD3/4Q1RVVcHj8eDLX/4yNmzYEP/+hg0bcNJJJ8HtdsPj8eCII44Y8fVDlDV5bnRJZCojdSWeP3++KC4uFtdee6349NNPxaeffio6OztFVVWVWLJkidi0aZN4//33xcknnyxOOumk+O/deuut4pBDDhEvv/yyaGxsFCtWrBB2u1289tprQgghdu3aJex2u7jqqqvEp59+Kv7617+KmpoaAUB0dnYKIYR49913hSzL4te//rXYvHmzWLFihSgqKhIrVqyI/52JEyeK8vJycf/994stW7aISy+9VHg8HvHVr35VPPnkk2Lz5s3irLPOEtOnTxeapiV9jK2trQKAWLFihWhubhatra1CCCGeeeYZYbVaxX333Sc2b94s7rjjDqEoivjf//3fEY+l2+0WixcvFp9++qn4y1/+IgCIU089VfzmN78RW7ZsEbfccouwWq2iqakp/nu33HKLeOONN8T27dvF888/L2pqasRvf/vb+PdvvvlmMXv2bCGEEBs2bBC1tbXihhtuiH//hz/8oZg3b57497//LbZt2yZ+97vfCbvdLrZs2TLsWAGIZ599VgixvwP8+vXrRTgcFs3NzcLj8Yi77rpLNDc3C7/fn9LzPtj69euF3W4Xl156qfjggw/ERx99JO655x6xb98+IUTk9eXxeMQvf/lLsWXLFrFy5UohSZJ45ZVXhBBCqKoqDj/8cHHccceJd999V7z11lviiCOOSOhMP/DYCCHEn/70J+FwOMRdd90lNm/eLNatWyf+8Ic/xL+/YMECccYZZ4h33nlHbNmyRVx99dWioqJCtLe3CyGEmDlzpvje974nNm3aJLZs2SKefPJJ8cEHHwz7GImyhcEMURoWLlwoFEURLpcr/vWtb31LCBG52MyZMyfh52+55RZxyimnJNzW1NQkAIjNmzeL/v5+4XQ6xdq1axN+5gc/+IE499xzhRBCLFmyRMyYMSPh+9ddd11CMHPeeeeJk08+OeFnrr322oTfmzhxovje974X/3dzc7MAIG688cb4bW+++aYAIJqbm4c9BgMv7DHz5s0TF198ccJt3/72t8XXvva1Ye9n4cKFYuLEiUJV1fhtBx98sDj++OPj/w6Hw8LlconHH3982Pv53e9+J4444oj4v2MX7DfeeEOUlZWJ3//+9/Hv7dy5UyiKIj7//POE+/jKV74ilixZMuzfGC6YiSkpKUkIHA/0vCdz7rnnimOPPXbYMcyfP18cd9xxCbd96UtfEtddd50QQohXXnlFKIoidu3aFf/+xx9/LACIdevWCSGGBjP19fUJgd5Ar7/+uvB4PKK/vz/h9qlTp4oHHnhACCGE2+0WjzzyyLBjJsoVS7LZGiIa3kknnYTly5fH/+1yueL/f8QRRyT87IYNG/Cvf/0LxcXFQ+6nsbERoVAIfr8fJ598csL3gsEg5syZAwDYtGkTjj766ITvz507N+HfmzZtwte//vWE24499ljcddddUFUViqIAAA477LD492tqagAAs2bNGnJba2sramtrkz38pDZt2oQf/ehHQ/7+H//4xxF/b+bMmZDl/avdNTU1CYm1iqKgoqICra2t8dueeOIJ3H333WhsbITP50M4HIbH40m43127duHkk0/Gb37zm4QdQBs3boSqqpg2bVrCzwcCAV1zSQ70vA/++wDwwQcf4Nvf/vaI9zvw+QOAurq6+LHZtGkTGhoa0NDQEP/+jBkzUFpaik2bNuFLX/pSwu+2trZiz549+MpXvjLsY/D5fEOOS19fHxobGwFEEp9/+MMf4tFHH8WCBQvw7W9/G1OnTh3xMRBlA4MZojS5XC4cdNBBw35vIJ/PhzPOOAO//e1vh/xsXV0dPvroIwDACy+8gHHjxiV832636zTi/axWa/z/JUka9jZN03T/2wcaT+zvJ7stNp4333wT559/Pn71q1/h1FNPRUlJCVatWjUkP6iqqgr19fV4/PHH8f3vfz8e7Ph8PiiKgvfeey8e4MUkCzwydaDnPZmioqID3u9IxyZdB/p7Pp8PdXV1SfOJYknOv/zlL3HeeefhhRdewEsvvYSbb74Zq1atwtlnn53RmIgyxWCGKIu++MUv4umnn8akSZMSdr/EzJgxA3a7Hbt27cL8+fOT3sf06dPx/PPPJ9z21ltvDfmZN954I+G2N954A9OmTRty0R4tq9UKVVWT/v2FCxcm/P0ZM2bo+rfXrl2LiRMn4oYbbojftnPnziE/V1RUhL///e/42te+hlNPPRWvvPIK3G435syZA1VV0draiuOPP17XsQ10oOc9mcMOOwyrV6/Gr371q4z+5vTp09HU1ISmpqb47Mwnn3yCrq6upM+D2+3GpEmTsHr1apx00klJH0NLSwssFktCovdg06ZNw7Rp03DllVfi3HPPxYoVKxjMUM5xNxNRFi1evBgdHR0499xz8c4776CxsRH/+Mc/sGjRIqiqCrfbjWuuuQZXXnklVq5cicbGRrz//vu45557sHLlSgDAj3/8Y2zduhXXXnstNm/ejMcee2xILZOrr74aq1evxi233IItW7Zg5cqVuPfee3HNNdfo/phiF8CWlpb4Tptrr70WjzzyCJYvX46tW7fizjvvxDPPPKP73//CF76AXbt2YdWqVWhsbMTdd9+NZ599NunPulwuvPDCC7BYLDjttNPg8/kwbdo0nH/++bjwwgvxzDPPYPv27Vi3bh2WLVuGF154QbdxHuh5T2bJkiV455138JOf/AQffvghPv30UyxfvvyAO6BiFixYgFmzZuH888/H+++/j3Xr1uHCCy/E/PnzceSRRyb9nV/+8pe44447cPfdd2Pr1q3x117s/ubOnYuzzjoLr7zyCnbs2IG1a9fihhtuwLvvvou+vj5cdtlleO2117Bz50688cYbeOeddzB9+vTMDhrRKDCYIcqi+vp6vPHGG1BVFaeccgpmzZqFK664AqWlpfFckVtuuQU33ngjli1bhunTp+OrX/0qXnjhhXjtmgkTJuDpp5/G3/72N8yePRv3338/li5dmvB3vvjFL+LJJ5/EqlWrcOihh+Kmm27Cr3/9a1x00UW6P6Y77rgDr776KhoaGuJ5PWeddRb++Mc/4ve//z1mzpyJBx54ACtWrMCJJ56o698+88wzceWVV+Kyyy7D4YcfjrVr1+LGG28c9ueLi4vx0ksvQQiB008/Hb29vVixYgUuvPBCXH311Tj44INx1lln4Z133sGECRN0G2cqz/tg06ZNwyuvvIINGzbgqKOOwty5c/Hcc8+lPLMjSRKee+45lJWV4YQTTsCCBQswZcoUPPHEE8P+zsKFC3HXXXfhT3/6E2bOnIn/+q//wtatW+P39+KLL+KEE07AokWLMG3aNHz3u9/Fzp07UVNTA0VR0N7ejgsvvBDTpk3DOeecg9NOOy3jmSWi0ZCEECLfgyAiIiLKFGdmiIiIyNQYzBAREZGpMZghIiIiU2MwQ0RERKbGYIaIiIhMjcEMERERmRqDGSIiIjI1BjNERERkagxmiIiIyNQYzBAREZGpMZghIiIiU/v/XKx5ViYymToAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x,y=np.log(y))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}