9. Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4092d8bf",
   "metadata": {},
   "source": [
    "# 9. Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c344ba",
   "metadata": {},
   "source": [
    "![](https://www.datacatchup.com/wp-content/uploads/2019/05/image.png)\n",
    "![](https://static.javatpoint.com/tutorial/machine-learning/images/difference-between-supervised-and-unsupervised-learning.jpg)\n",
    "![](https://miro.medium.com/max/1400/1*6iDmbHflsN6NULBLpVHA6A.png)\n",
    "![](https://scikit-learn.org/stable/_static/ml_map.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60ad76b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83a8d17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the model used below is classification, a subset of supervised learning "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d33f8bc",
   "metadata": {},
   "source": [
    "## 9.1 Support Vector Classifier (SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e843a49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "covidvisual = pd.read_excel('covidvisualApril5th2023.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6216f4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 43)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c99fcf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HDI rank</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>Life expectancy at birth</th>\n",
       "      <th>Expected years of schooling</th>\n",
       "      <th>Mean years of schooling</th>\n",
       "      <th>Gross national income (GNI) per capita</th>\n",
       "      <th>...</th>\n",
       "      <th>NewRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>TotalTests</th>\n",
       "      <th>Population</th>\n",
       "      <th>Continent</th>\n",
       "      <th>New Cases/1M pop</th>\n",
       "      <th>New Deaths/1M pop</th>\n",
       "      <th>Active Cases/1M pop</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.796</td>\n",
       "      <td>76.4626</td>\n",
       "      <td>14.448000</td>\n",
       "      <td>11.286455</td>\n",
       "      <td>14131.110390</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,941,032</td>\n",
       "      <td>2866374</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>125.663992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.745</td>\n",
       "      <td>76.3767</td>\n",
       "      <td>14.626896</td>\n",
       "      <td>8.069284</td>\n",
       "      <td>10800.225460</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81,825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230,861</td>\n",
       "      <td>45350148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,804</td>\n",
       "      <td>15.173049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>148</td>\n",
       "      <td>0.586</td>\n",
       "      <td>61.6434</td>\n",
       "      <td>12.172100</td>\n",
       "      <td>5.417391</td>\n",
       "      <td>5465.617791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,499,795</td>\n",
       "      <td>35027343</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.518546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Country Code  Latitude  Longitude  HDI rank  \\\n",
       "0  Albania          ALB      41.0       20.0        67   \n",
       "1  Algeria          DZA      28.0        3.0        91   \n",
       "2   Angola          AGO     -12.5       18.5       148   \n",
       "\n",
       "   Human Development Index 2021 (HDI)  Life expectancy at birth  \\\n",
       "0                               0.796                   76.4626   \n",
       "1                               0.745                   76.3767   \n",
       "2                               0.586                   61.6434   \n",
       "\n",
       "   Expected years of schooling  Mean years of schooling  \\\n",
       "0                    14.448000                11.286455   \n",
       "1                    14.626896                 8.069284   \n",
       "2                    12.172100                 5.417391   \n",
       "\n",
       "   Gross national income (GNI) per capita  ...  NewRecovered  ActiveCases  \\\n",
       "0                            14131.110390  ...           NaN        1,709   \n",
       "1                            10800.225460  ...           NaN       81,825   \n",
       "2                             5465.617791  ...           NaN           41   \n",
       "\n",
       "   Serious,Critical  TotalTests  Population Continent New Cases/1M pop  \\\n",
       "0               NaN   1,941,032     2866374    Europe              NaN   \n",
       "1               NaN     230,861    45350148    Africa              0.1   \n",
       "2               NaN   1,499,795    35027343    Africa              NaN   \n",
       "\n",
       "   New Deaths/1M pop Active Cases/1M pop  covid_mortality_rate (per 100000)  \n",
       "0                NaN                 596                         125.663992  \n",
       "1                NaN               1,804                          15.173049  \n",
       "2                NaN                   1                           5.518546  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02366724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                     0\n",
       "Country Code                                0\n",
       "Latitude                                    0\n",
       "Longitude                                   0\n",
       "HDI rank                                    0\n",
       "Human Development Index 2021 (HDI)          0\n",
       "Life expectancy at birth                    0\n",
       "Expected years of schooling                 0\n",
       "Mean years of schooling                     0\n",
       "Gross national income (GNI) per capita      0\n",
       "GDP 2021 (US$)                              0\n",
       "GDP per capita 2021 (US$)                   0\n",
       "GDP growth 2021 (annual %)                  0\n",
       "Inflation Rate 2021 (%)                     0\n",
       "Unemployment Rate 2021 (%)                  0\n",
       "Region                                      0\n",
       "IncomeGroup                                 0\n",
       "total_vaccinations                          0\n",
       "date                                        0\n",
       "people_vaccinated                           0\n",
       "people_fully_vaccinated                     0\n",
       "daily_vaccinations_raw                     21\n",
       "daily_vaccinations                          0\n",
       "total_vaccinations_per_hundred              0\n",
       "people_vaccinated_per_hundred               0\n",
       "people_fully_vaccinated_per_hundred         0\n",
       "daily_vaccinations_per_million              0\n",
       "Country,Other                               0\n",
       "TotalCases                                  0\n",
       "NewCases                                  118\n",
       "TotalDeaths                                 0\n",
       "NewDeaths                                 131\n",
       "TotalRecovered                             10\n",
       "NewRecovered                              117\n",
       "ActiveCases                                10\n",
       "Serious,Critical                           55\n",
       "TotalTests                                  3\n",
       "Population                                  0\n",
       "Continent                                   0\n",
       "New Cases/1M pop                          118\n",
       "New Deaths/1M pop                         131\n",
       "Active Cases/1M pop                         0\n",
       "covid_mortality_rate (per 100000)           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a27de73c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Country Code', 'Latitude', 'Longitude', 'HDI rank',\n",
       "       'Human Development Index 2021 (HDI)', 'Life expectancy at birth',\n",
       "       'Expected years of schooling', 'Mean years of schooling',\n",
       "       'Gross national income (GNI) per capita', 'GDP 2021 (US$)',\n",
       "       'GDP per capita 2021 (US$)', 'GDP growth 2021 (annual %)',\n",
       "       'Inflation Rate 2021 (%)', 'Unemployment Rate 2021 (%)', 'Region',\n",
       "       'IncomeGroup', 'total_vaccinations', 'date', 'people_vaccinated',\n",
       "       'people_fully_vaccinated', 'daily_vaccinations_raw',\n",
       "       'daily_vaccinations', 'total_vaccinations_per_hundred',\n",
       "       'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred',\n",
       "       'daily_vaccinations_per_million', 'Country,Other', 'TotalCases',\n",
       "       'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered',\n",
       "       'NewRecovered', 'ActiveCases', 'Serious,Critical', 'TotalTests',\n",
       "       'Population', 'Continent', 'New Cases/1M pop', 'New Deaths/1M pop',\n",
       "       'Active Cases/1M pop', 'covid_mortality_rate (per 100000)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06f3ac26",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = covidvisual.loc[:, ['total_vaccinations_per_hundred',\n",
    "                              'daily_vaccinations_per_million','covid_mortality_rate (per 100000)',\n",
    "                              'Human Development Index 2021 (HDI)','GDP per capita 2021 (US$)',\n",
    "                              'IncomeGroup']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1acc21bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.03</td>\n",
       "      <td>6180</td>\n",
       "      <td>125.663992</td>\n",
       "      <td>0.796</td>\n",
       "      <td>6492.872012</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.00</td>\n",
       "      <td>5722</td>\n",
       "      <td>15.173049</td>\n",
       "      <td>0.745</td>\n",
       "      <td>3690.627878</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.88</td>\n",
       "      <td>5147</td>\n",
       "      <td>5.518546</td>\n",
       "      <td>0.586</td>\n",
       "      <td>1953.533757</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                          108.03                            6180   \n",
       "1                           34.00                            5722   \n",
       "2                           69.88                            5147   \n",
       "\n",
       "   covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "0                         125.663992                               0.796   \n",
       "1                          15.173049                               0.745   \n",
       "2                           5.518546                               0.586   \n",
       "\n",
       "   GDP per capita 2021 (US$)          IncomeGroup  \n",
       "0                6492.872012  Upper middle income  \n",
       "1                3690.627878  Lower middle income  \n",
       "2                1953.533757  Lower middle income  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d18a9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6fa04f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.03</td>\n",
       "      <td>6180</td>\n",
       "      <td>125.663992</td>\n",
       "      <td>0.796</td>\n",
       "      <td>6492.872012</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.00</td>\n",
       "      <td>5722</td>\n",
       "      <td>15.173049</td>\n",
       "      <td>0.745</td>\n",
       "      <td>3690.627878</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.88</td>\n",
       "      <td>5147</td>\n",
       "      <td>5.518546</td>\n",
       "      <td>0.586</td>\n",
       "      <td>1953.533757</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.45</td>\n",
       "      <td>6441</td>\n",
       "      <td>294.047778</td>\n",
       "      <td>0.759</td>\n",
       "      <td>4966.513471</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>243.27</td>\n",
       "      <td>11302</td>\n",
       "      <td>76.167703</td>\n",
       "      <td>0.951</td>\n",
       "      <td>60443.109165</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>218.04</td>\n",
       "      <td>12311</td>\n",
       "      <td>4.761201</td>\n",
       "      <td>0.727</td>\n",
       "      <td>1983.064723</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>112.21</td>\n",
       "      <td>27064</td>\n",
       "      <td>4.350096</td>\n",
       "      <td>0.607</td>\n",
       "      <td>2996.621062</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>271.40</td>\n",
       "      <td>17064</td>\n",
       "      <td>43.642703</td>\n",
       "      <td>0.703</td>\n",
       "      <td>3756.489121</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>63.90</td>\n",
       "      <td>9006</td>\n",
       "      <td>20.842071</td>\n",
       "      <td>0.565</td>\n",
       "      <td>1137.343633</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>74.89</td>\n",
       "      <td>7435</td>\n",
       "      <td>37.041559</td>\n",
       "      <td>0.593</td>\n",
       "      <td>1773.920411</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                            108.03                            6180   \n",
       "1                             34.00                            5722   \n",
       "2                             69.88                            5147   \n",
       "3                             77.45                            6441   \n",
       "4                            243.27                           11302   \n",
       "..                              ...                             ...   \n",
       "144                          218.04                           12311   \n",
       "145                          112.21                           27064   \n",
       "146                          271.40                           17064   \n",
       "147                           63.90                            9006   \n",
       "148                           74.89                            7435   \n",
       "\n",
       "     covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "0                           125.663992                               0.796   \n",
       "1                            15.173049                               0.745   \n",
       "2                             5.518546                               0.586   \n",
       "3                           294.047778                               0.759   \n",
       "4                            76.167703                               0.951   \n",
       "..                                 ...                                 ...   \n",
       "144                           4.761201                               0.727   \n",
       "145                           4.350096                               0.607   \n",
       "146                          43.642703                               0.703   \n",
       "147                          20.842071                               0.565   \n",
       "148                          37.041559                               0.593   \n",
       "\n",
       "     GDP per capita 2021 (US$)          IncomeGroup  \n",
       "0                  6492.872012  Upper middle income  \n",
       "1                  3690.627878  Lower middle income  \n",
       "2                  1953.533757  Lower middle income  \n",
       "3                  4966.513471  Upper middle income  \n",
       "4                 60443.109165          High income  \n",
       "..                         ...                  ...  \n",
       "144                1983.064723  Lower middle income  \n",
       "145                2996.621062  Lower middle income  \n",
       "146                3756.489121  Lower middle income  \n",
       "147                1137.343633           Low income  \n",
       "148                1773.920411  Lower middle income  \n",
       "\n",
       "[149 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb90e489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "46aa8dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "943e60f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.465973</td>\n",
       "      <td>12556.147651</td>\n",
       "      <td>140.651667</td>\n",
       "      <td>0.737940</td>\n",
       "      <td>17043.642330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>79.618981</td>\n",
       "      <td>11346.083328</td>\n",
       "      <td>140.407090</td>\n",
       "      <td>0.148244</td>\n",
       "      <td>23472.569989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.280000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.300994</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>221.477676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>77.450000</td>\n",
       "      <td>7465.000000</td>\n",
       "      <td>17.799897</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>2535.623432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>147.860000</td>\n",
       "      <td>11268.000000</td>\n",
       "      <td>98.920361</td>\n",
       "      <td>0.751000</td>\n",
       "      <td>6104.136709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>213.920000</td>\n",
       "      <td>15163.000000</td>\n",
       "      <td>224.750550</td>\n",
       "      <td>0.858000</td>\n",
       "      <td>21391.925334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>319.780000</td>\n",
       "      <td>117113.000000</td>\n",
       "      <td>652.641143</td>\n",
       "      <td>0.962000</td>\n",
       "      <td>133590.146976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "count                      149.000000                      149.000000   \n",
       "mean                       147.465973                    12556.147651   \n",
       "std                         79.618981                    11346.083328   \n",
       "min                          0.280000                       32.000000   \n",
       "25%                         77.450000                     7465.000000   \n",
       "50%                        147.860000                    11268.000000   \n",
       "75%                        213.920000                    15163.000000   \n",
       "max                        319.780000                   117113.000000   \n",
       "\n",
       "       covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "count                         149.000000                          149.000000   \n",
       "mean                          140.651667                            0.737940   \n",
       "std                           140.407090                            0.148244   \n",
       "min                             0.300994                            0.400000   \n",
       "25%                            17.799897                            0.627000   \n",
       "50%                            98.920361                            0.751000   \n",
       "75%                           224.750550                            0.858000   \n",
       "max                           652.641143                            0.962000   \n",
       "\n",
       "       GDP per capita 2021 (US$)  \n",
       "count                 149.000000  \n",
       "mean                17043.642330  \n",
       "std                 23472.569989  \n",
       "min                   221.477676  \n",
       "25%                  2535.623432  \n",
       "50%                  6104.136709  \n",
       "75%                 21391.925334  \n",
       "max                133590.146976  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d352abf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IncomeGroup\n",
      "High income            49\n",
      "Low income             16\n",
      "Lower middle income    44\n",
      "Upper middle income    40\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('IncomeGroup').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06af77dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(dataset.loc[dataset['IncomeGroup']=='Low income'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8f4efd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IncomeGroup\n",
      "High income            49\n",
      "Lower middle income    44\n",
      "Upper middle income    40\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('IncomeGroup').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0bb3be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.groupby('IncomeGroup', as_index=False, group_keys=False).apply(lambda x: x.sample(n=39))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ac5210d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IncomeGroup\n",
      "High income            39\n",
      "Lower middle income    39\n",
      "Upper middle income    39\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('IncomeGroup').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99b9950d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a26bc16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>167.56</td>\n",
       "      <td>12632</td>\n",
       "      <td>508.116635</td>\n",
       "      <td>0.846</td>\n",
       "      <td>18728.121895</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>230.55</td>\n",
       "      <td>13774</td>\n",
       "      <td>204.055391</td>\n",
       "      <td>0.942</td>\n",
       "      <td>51203.554473</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>309.24</td>\n",
       "      <td>13653</td>\n",
       "      <td>58.925903</td>\n",
       "      <td>0.925</td>\n",
       "      <td>39312.660373</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>258.91</td>\n",
       "      <td>15359</td>\n",
       "      <td>187.373461</td>\n",
       "      <td>0.918</td>\n",
       "      <td>33486.671984</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>202.85</td>\n",
       "      <td>10561</td>\n",
       "      <td>345.176169</td>\n",
       "      <td>0.921</td>\n",
       "      <td>70248.629000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>175.02</td>\n",
       "      <td>12927</td>\n",
       "      <td>253.532968</td>\n",
       "      <td>0.758</td>\n",
       "      <td>10045.680500</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>68.37</td>\n",
       "      <td>10630</td>\n",
       "      <td>220.946125</td>\n",
       "      <td>0.715</td>\n",
       "      <td>9414.226231</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>67.99</td>\n",
       "      <td>3987</td>\n",
       "      <td>559.112538</td>\n",
       "      <td>0.795</td>\n",
       "      <td>12221.496606</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>190.06</td>\n",
       "      <td>18958</td>\n",
       "      <td>12.065077</td>\n",
       "      <td>0.745</td>\n",
       "      <td>4426.000637</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>118.72</td>\n",
       "      <td>11503</td>\n",
       "      <td>163.466806</td>\n",
       "      <td>0.714</td>\n",
       "      <td>9998.544311</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "59                           167.56                           12632   \n",
       "49                           230.55                           13774   \n",
       "69                           309.24                           13653   \n",
       "85                           258.91                           15359   \n",
       "142                          202.85                           10561   \n",
       "..                              ...                             ...   \n",
       "88                           175.02                           12927   \n",
       "116                           68.37                           10630   \n",
       "20                            67.99                            3987   \n",
       "135                          190.06                           18958   \n",
       "55                           118.72                           11503   \n",
       "\n",
       "     covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "59                          508.116635                               0.846   \n",
       "49                          204.055391                               0.942   \n",
       "69                           58.925903                               0.925   \n",
       "85                          187.373461                               0.918   \n",
       "142                         345.176169                               0.921   \n",
       "..                                 ...                                 ...   \n",
       "88                          253.532968                               0.758   \n",
       "116                         220.946125                               0.715   \n",
       "20                          559.112538                               0.795   \n",
       "135                          12.065077                               0.745   \n",
       "55                          163.466806                               0.714   \n",
       "\n",
       "     GDP per capita 2021 (US$)          IncomeGroup  \n",
       "59                18728.121895          High income  \n",
       "49                51203.554473          High income  \n",
       "69                39312.660373          High income  \n",
       "85                33486.671984          High income  \n",
       "142               70248.629000          High income  \n",
       "..                         ...                  ...  \n",
       "88                10045.680500  Upper middle income  \n",
       "116                9414.226231  Upper middle income  \n",
       "20                12221.496606  Upper middle income  \n",
       "135                4426.000637  Upper middle income  \n",
       "55                 9998.544311  Upper middle income  \n",
       "\n",
       "[117 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02c1c3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#datasetrand = dataset.sample(frac=1)\n",
    "#The frac keyword argument specifies the fraction of rows to return in the random sample, so frac=1 means return all rows (in random order)\n",
    "#but in order to work in my case it needs to be in order like it is and not shuffled, otherwise it won't run later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95afea21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b312dc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummie_class = pd.get_dummies(dataset['IncomeGroup'],prefix='IncomeGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4b6a405",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>IncomeGroup_Upper middle income</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IncomeGroup_High income  IncomeGroup_Lower middle income  \\\n",
       "59                         1                                0   \n",
       "49                         1                                0   \n",
       "69                         1                                0   \n",
       "85                         1                                0   \n",
       "142                        1                                0   \n",
       "..                       ...                              ...   \n",
       "88                         0                                0   \n",
       "116                        0                                0   \n",
       "20                         0                                0   \n",
       "135                        0                                0   \n",
       "55                         0                                0   \n",
       "\n",
       "     IncomeGroup_Upper middle income  \n",
       "59                                 0  \n",
       "49                                 0  \n",
       "69                                 0  \n",
       "85                                 0  \n",
       "142                                0  \n",
       "..                               ...  \n",
       "88                                 1  \n",
       "116                                1  \n",
       "20                                 1  \n",
       "135                                1  \n",
       "55                                 1  \n",
       "\n",
       "[117 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummie_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed1e2bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = dummie_class['IncomeGroup_High income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dff46628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61     0\n",
       "49     1\n",
       "145    0\n",
       "78     1\n",
       "30     0\n",
       "123    1\n",
       "88     0\n",
       "2      0\n",
       "117    0\n",
       "56     0\n",
       "Name: IncomeGroup_High income, dtype: uint8"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.sample(n=10) # 1 is high income, 0 it's not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44cf8256",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset[['total_vaccinations_per_hundred',\n",
    "            'daily_vaccinations_per_million',\n",
    "             'covid_mortality_rate (per 100000)',\n",
    "            'Human Development Index 2021 (HDI)','GDP per capita 2021 (US$)']] \n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6298480e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>const</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.0</td>\n",
       "      <td>167.56</td>\n",
       "      <td>12632</td>\n",
       "      <td>508.116635</td>\n",
       "      <td>0.846</td>\n",
       "      <td>18728.121895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.0</td>\n",
       "      <td>230.55</td>\n",
       "      <td>13774</td>\n",
       "      <td>204.055391</td>\n",
       "      <td>0.942</td>\n",
       "      <td>51203.554473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1.0</td>\n",
       "      <td>309.24</td>\n",
       "      <td>13653</td>\n",
       "      <td>58.925903</td>\n",
       "      <td>0.925</td>\n",
       "      <td>39312.660373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1.0</td>\n",
       "      <td>258.91</td>\n",
       "      <td>15359</td>\n",
       "      <td>187.373461</td>\n",
       "      <td>0.918</td>\n",
       "      <td>33486.671984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.0</td>\n",
       "      <td>202.85</td>\n",
       "      <td>10561</td>\n",
       "      <td>345.176169</td>\n",
       "      <td>0.921</td>\n",
       "      <td>70248.629000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1.0</td>\n",
       "      <td>175.02</td>\n",
       "      <td>12927</td>\n",
       "      <td>253.532968</td>\n",
       "      <td>0.758</td>\n",
       "      <td>10045.680500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1.0</td>\n",
       "      <td>68.37</td>\n",
       "      <td>10630</td>\n",
       "      <td>220.946125</td>\n",
       "      <td>0.715</td>\n",
       "      <td>9414.226231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.99</td>\n",
       "      <td>3987</td>\n",
       "      <td>559.112538</td>\n",
       "      <td>0.795</td>\n",
       "      <td>12221.496606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1.0</td>\n",
       "      <td>190.06</td>\n",
       "      <td>18958</td>\n",
       "      <td>12.065077</td>\n",
       "      <td>0.745</td>\n",
       "      <td>4426.000637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.0</td>\n",
       "      <td>118.72</td>\n",
       "      <td>11503</td>\n",
       "      <td>163.466806</td>\n",
       "      <td>0.714</td>\n",
       "      <td>9998.544311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     const  total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "59     1.0                          167.56                           12632   \n",
       "49     1.0                          230.55                           13774   \n",
       "69     1.0                          309.24                           13653   \n",
       "85     1.0                          258.91                           15359   \n",
       "142    1.0                          202.85                           10561   \n",
       "..     ...                             ...                             ...   \n",
       "88     1.0                          175.02                           12927   \n",
       "116    1.0                           68.37                           10630   \n",
       "20     1.0                           67.99                            3987   \n",
       "135    1.0                          190.06                           18958   \n",
       "55     1.0                          118.72                           11503   \n",
       "\n",
       "     covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "59                          508.116635                               0.846   \n",
       "49                          204.055391                               0.942   \n",
       "69                           58.925903                               0.925   \n",
       "85                          187.373461                               0.918   \n",
       "142                         345.176169                               0.921   \n",
       "..                                 ...                                 ...   \n",
       "88                          253.532968                               0.758   \n",
       "116                         220.946125                               0.715   \n",
       "20                          559.112538                               0.795   \n",
       "135                          12.065077                               0.745   \n",
       "55                          163.466806                               0.714   \n",
       "\n",
       "     GDP per capita 2021 (US$)  \n",
       "59                18728.121895  \n",
       "49                51203.554473  \n",
       "69                39312.660373  \n",
       "85                33486.671984  \n",
       "142               70248.629000  \n",
       "..                         ...  \n",
       "88                10045.680500  \n",
       "116                9414.226231  \n",
       "20                12221.496606  \n",
       "135                4426.000637  \n",
       "55                 9998.544311  \n",
       "\n",
       "[117 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb38ab0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "probit_mod = sm.Probit(Y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a545cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andre\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "probit_res = probit_mod.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b82b61b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            Results: Probit\n",
      "=======================================================================================================\n",
      "Model:                        Probit                            Pseudo R-squared:           inf        \n",
      "Dependent Variable:           IncomeGroup_High income           AIC:                        12.0000    \n",
      "Date:                         2023-04-16 20:16                  BIC:                        28.5730    \n",
      "No. Observations:             117                               Log-Likelihood:             -1.4877e-14\n",
      "Df Model:                     5                                 LL-Null:                    0.0000     \n",
      "Df Residuals:                 111                               LLR p-value:                1.0000     \n",
      "Converged:                    0.0000                            Scale:                      1.0000     \n",
      "No. Iterations:               35.0000                                                                  \n",
      "-------------------------------------------------------------------------------------------------------\n",
      "                                    Coef.     Std.Err.       z    P>|z|       [0.025         0.975]    \n",
      "-------------------------------------------------------------------------------------------------------\n",
      "const                               4.3302 139591930.3062  0.0000 1.0000 -273595151.6024 273595160.2627\n",
      "total_vaccinations_per_hundred      0.0016    159077.7160  0.0000 1.0000    -311786.5924    311786.5957\n",
      "daily_vaccinations_per_million      0.0002      4241.1104  0.0000 1.0000      -8312.4234      8312.4239\n",
      "covid_mortality_rate (per 100000)   0.0046     66624.8141  0.0000 1.0000    -130582.2314    130582.2407\n",
      "Human Development Index 2021 (HDI)  0.5075 160776640.5602  0.0000 1.0000 -315116424.5458 315116425.5608\n",
      "GDP per capita 2021 (US$)          -0.0000       184.2464 -0.0000 1.0000       -361.1164       361.1164\n",
      "=======================================================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andre\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:3511: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return 1 - self.llf/self.llnull\n"
     ]
    }
   ],
   "source": [
    "print(probit_res.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17102672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Probit Marginal Effects          \n",
      "==========================================\n",
      "Dep. Variable:     IncomeGroup_High income\n",
      "Method:                               dydx\n",
      "At:                                overall\n",
      "======================================================================================================\n",
      "                                        dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "total_vaccinations_per_hundred       1.58e-10      0.038   4.18e-09      1.000      -0.074       0.074\n",
      "daily_vaccinations_per_million      2.227e-11      0.007   3.27e-09      1.000      -0.013       0.013\n",
      "covid_mortality_rate (per 100000)   4.479e-10      0.134   3.35e-09      1.000      -0.262       0.262\n",
      "Human Development Index 2021 (HDI)  4.908e-08     27.723   1.77e-09      1.000     -54.335      54.335\n",
      "GDP per capita 2021 (US$)          -1.794e-13   6.76e-05  -2.65e-09      1.000      -0.000       0.000\n",
      "======================================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(probit_res.get_margeff(at='overall', method='dydx').summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4b1dff6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.0</td>\n",
       "      <td>167.56</td>\n",
       "      <td>12632</td>\n",
       "      <td>508.116635</td>\n",
       "      <td>0.846</td>\n",
       "      <td>18728.121895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.0</td>\n",
       "      <td>230.55</td>\n",
       "      <td>13774</td>\n",
       "      <td>204.055391</td>\n",
       "      <td>0.942</td>\n",
       "      <td>51203.554473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1.0</td>\n",
       "      <td>309.24</td>\n",
       "      <td>13653</td>\n",
       "      <td>58.925903</td>\n",
       "      <td>0.925</td>\n",
       "      <td>39312.660373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    const  total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "59    1.0                          167.56                           12632   \n",
       "49    1.0                          230.55                           13774   \n",
       "69    1.0                          309.24                           13653   \n",
       "\n",
       "    covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "59                         508.116635                               0.846   \n",
       "49                         204.055391                               0.942   \n",
       "69                          58.925903                               0.925   \n",
       "\n",
       "    GDP per capita 2021 (US$)  \n",
       "59               18728.121895  \n",
       "49               51203.554473  \n",
       "69               39312.660373  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d07b96a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143    1.0\n",
      "25     1.0\n",
      "112    1.0\n",
      "119    1.0\n",
      "105    1.0\n",
      "67     1.0\n",
      "60     1.0\n",
      "34     1.0\n",
      "11     1.0\n",
      "45     1.0\n",
      "42     1.0\n",
      "19     1.0\n",
      "79     1.0\n",
      "51     1.0\n",
      "111    1.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictions = probit_res.predict(X)\n",
    "print(predictions[15:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "65a2adbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143    1\n",
       "25     1\n",
       "112    1\n",
       "119    1\n",
       "105    1\n",
       "67     1\n",
       "60     1\n",
       "34     1\n",
       "11     1\n",
       "45     1\n",
       "42     1\n",
       "19     1\n",
       "79     1\n",
       "51     1\n",
       "111    1\n",
       "Name: IncomeGroup_High income, dtype: uint8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[15:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd92a0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='box', subplots=True, layout=(3,2), sharex=False, sharey=False, figsize=(7, 7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3381d4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZcHzX4AOpwc8JL5d2LudgrqGfIulPuUred7yalUBEnBYR20dEJ3As8OuIOAGYDUzJg00BrmhRiGbWi4E+nOU7wGeByO9nAO+uNWAjVXDtWoXRjGo8WFOVN5hy3v7w8qbEMmni1k2ZTl/LpmOzxpZdO/7m/dGu6+0QmQlcIulk4EHgmBbHY2ZVBpTQI2Jx5bOk7wNX9jFs3Sq4dq3CaEY1HqypyhtMOZsdy2D1Fc/0Sas44/b6q9ZQVXEOl3Zdb5slIrqB7vx5KXBAK+Mxs74NKKFXrqXlr28G7uhreDOzdufHJdtIVzehS7oQ6ALGS1oIfBrokrQ7qcp9AfDeoQvRzMzM6mnkLvfjanQ+ewhiMTMzswFyS3FmZmYl4IRuZmZWAk7oZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl4IRuZmZWAk7oZmZmJTDQp61ZP1TaiJ4+aVXTHrJSBm4728yseXyGbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl4IRuZmZWAk7oZraapE0l3SzpNkl3Svqv3H2cpGsl3ZPfx7Y6VjNbm/+2tp5p1l/FrLT+AbwhInokbQTcIOkXwFuAORExU9IMYAbwsVYGamZr8xm6ma0WSU/+ulF+BXAUMCt3nwUcPfzRmVlffIZuZmuRNAqYB7wE+HZE3CSpIyIWAUTEIknb9jLuNGAaQEdHB93d3b3Op6enp8/+/TF90qqmTKcZqsvUzHK2M5ez9ZzQzWwtEfEcsLukMcDlknbtx7hnAWcBTJ48Obq6unodtru7m77690c7tcC44Piutb43s5ztzOVsPVe5m1lNEbEM6AYOARZLmgCQ35e0LjIzq8UJ3cxWk7RNPjNH0mbAgcBdwGxgSh5sCnBFSwI0s165yt1GPD/kpakmALPydfQNgEsi4kpJvwcukXQy8CBwTCuDNLN1OaGb2WoR8SdgjxrdlwIHDH9EZtYoV7mbmZmVQN2ELukcSUsk3VHo5lajzMzM2kgjZ+jnku5yLZpBajVqJ2BO/m5mZmYtUjehR8T1wBNVnd1qlJmZWRsZ6DX0tVqNAmq2GmVmZmbDY8jvcm+kKch2bUqv2c1JdmzWXk1UDpWRWs7+roPtut6a2fppoAl9saQJuU3nPluNaqQpyHZtSq/ZzUlOn7SKM24v/z8FR2o5q5vsrKdd11szWz8NtMrdrUaZmZm1kUb+tnYh8HtgZ0kLc0tRM4GDJN0DHJS/m5mZWYvUrReNiON66eVWo8zMzNqEW4ozMzMrASd0MzOzEhh5tyI3oFlP3zIzMxspfIZuZmZWAk7oZmZmJVDKKnczGxl8ecyseXyGbmZmVgJO6Ga2mqQdJF0nab6kOyWdmruPk3StpHvy+9hWx2pma3NCN7OiVcD0iHg5sA/wb5J2AWYAcyJiJ2BO/m5mbcQJ3cxWi4hFEXFL/rwCmA9MBI4CZuXBZgFHtyRAM+uVb4ozs5okdQJ7ADcBHRGxCFLSl7RtL+PUfVxyRU9PD9MnPdfkqFuvuszry2N2Xc7Wc0I3s3VIGg38FPhQRDwlqaHxGnlcckV3dzdn3LBy8MG2merH8K4vj9l1OVvPVe5mthZJG5GS+fkRcVnuvFjShNx/ArCkVfGZWW1O6Ga2mtKp+NnA/Ig4s9BrNjAlf54CXDHcsZlZ31zlbmZF+wEnArdLujV3+zgwE7hE0snAg8AxrQnPzHrjhG5mq0XEDUBvF8wPGM5YzKx/XOVuZmZWAk7oZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl0HZ/W+uccVWrQzAzMxtxfIZuZmZWAk7oZmZmJTCoKndJC4AVwHPAqoiY3IygzMzMrH+acQ19/4h4vAnTMTMzswFylbuZmVkJDDahB3CNpHmSpjUjIDMzM+u/wVa57xcRj0jaFrhW0l0RcX1xgJzopwF0dHTQ3d29zkR6enpWd58+adUgQ2pfHZuVu3wVI7WctdbNvhTXWzOzVhtUQo+IR/L7EkmXA3sD11cNcxZwFsDkyZOjq6trnel0d3dT6T61xP9Dnz5pFWfc3nZ//W+6kVrOBcd39Wv44nprZtZqA65yl7SFpC0rn4GDgTuaFZiZmZk1bjCnUR3A5ZIq07kgIn7ZlKjMzMysXwac0CPifmC3JsZiZm1A0jnAEcCSiNg1dxsHXAx0AguAt0fEk62K0czW5b+tmVm1c4FDqrrNAOZExE7AnPzdzNqIE7qZrSX/U+WJqs5HAbPy51nA0cMZk5nVN/JuRTYbIv190t/0SavW+VfGgpmHNzOkdtIREYsAImJR/quqmbURJ3Qza5pG2p2o6OnpYfqk54YpsuFTXeb1pb0Cl7P1nNDNrBGLJU3IZ+cTgCW1Bmqk3YmK7u5uzrhh5VDE2lLV7RmsL+0VuJyt54RuZo2YDUwBZub3K1obTvuqvnRT69JMI0p8+caGiG+KM7O1SLoQ+D2ws6SFkk4mJfKDJN0DHJS/m1kb8Rm6ma0lIo7rpdcBwxqImfWLz9DNzMxKwAndzMysBJzQzczMSsAJ3czMrASc0M3MzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSsAJ3czMrASc0M3MzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSsDPQzczK6nOGVc1ZToLZh7elOnY0PIZupmZWQkMKqFLOkTS3ZLulTSjWUGZWXvyNm/WvgZc5S5pFPBt4CBgIfAHSbMj4s/NCs7M2oe3+eHVrOpyq22gy3f6pFVMLYzbTpcjBnOGvjdwb0TcHxHPABcBRzUnLDNrQ97mzdrYYBL6ROChwveFuZuZlZO3ebM2Npi73FWjW6wzkDQNmJa/9ki6u8Z444HHBxHLiPBBl7NUapVTX2po1B2HIp5hUHebb3B7r1hv15ORpsH1esSXsxHVv2cDy2bYtvfBJPSFwA6F79sDj1QPFBFnAWf1NSFJcyNi8iBiGRFcznJZX8pZUHebb2R7r1hflp/LWS7tXM7BVLn/AdhJ0gslbQwcC8xuTlhm1oa8zZu1sQGfoUfEKkmnAL8CRgHnRMSdTYvMzNqKt3mz9jaoluIi4mrg6ibE0VAVXQm4nOWyvpRztSZu87D+LD+Xs1zatpyKWOc+NjMzMxth3PSrmZlZCbQ8oZepKUlJ50haIumOQrdxkq6VdE9+H1vod1ou992S3tiaqPtH0g6SrpM0X9Kdkk7N3ctWzk0l3SzptlzO/8rdS1XOVhiJ23wz13tJe0m6Pff7hiTl7ptIujh3v0lS57AXNMUxStIfJV2Zv5eujDmWMZIulXRX/l33HfFljYiWvUg31twHvAjYGLgN2KWVMQ2yPK8D9gTuKHT7MjAjf54BfCl/3iWXdxPghXk5jGp1GRoo4wRgz/x5S+AvuSxlK6eA0fnzRsBNwD5lK2cLluuI3Oabud4DNwP75nXsF8Chufv7ge/mz8cCF7eorB8GLgCuzN9LV8Y8/1nAe/LnjYExI72srd5I9gV+Vfh+GnBaK2NqQpk6WTuh3w1MyJ8nAHfXKivpzuF9Wx3/AMp7Balt79KWE9gcuAV4VZnLOUzLshTb/EDX+zzMXYXuxwHfq15nSDcsP06+z2kYy7U9MAd4A2sSeqnKmOe9FfDX6nmP9LK2usp9fWhKsiMiFgHk921z9xFf9lyFtAfp7LV05cxVj7cCS4BrI6KU5RxmI345DXK9n5g/V3dfa5yIWAUsB543JIXo3deAjwL/LHQrWxkh1RA9BvwwX174gaQtGOFlbXVCb6j52JIa0WWXNBr4KfChiHiqr0FrdBsR5YyI5yJid9JZy96Sdu1j8BFbzmE2opdTE9b7vsrf0mUj6QhgSUTMa3SUGt3auowFG5Iuj34nIvYAVpKq2HszIsra6oTeUPOxI9xiSRMA8vuS3H3Ell3SRqSd2vkRcVnuXLpyVkTEMqAbOIQSl3OYjNjl1KT1fmH+XN19rXEkbQhsDTzR/JL0aj/gTZIWkJ6k9wZJ51GuMlYsBBbmWjeAS0kJfkSXtdUJfX1oSnI2MCV/nkK69lbpfmy+E/KFwE6kmyvaWr6D82xgfkScWehVtnJuI2lM/rwZcCBwFyUrZwuMyG2+Wet9rsZdIWmfPM13VY1TmdbbgF9HvgA7HCLitIjYPiI6Sb/LryPiBEpUxoqIeBR4SNLOudMBwJ8Z6WUd7psRatyccBjpjtH7gE+0Op5BluVCYBHwLOno7GTSNZM5wD35fVxh+E/kct9NvjOy3V/Aa0jVRn8Cbs2vw0pYzlcCf8zlvAP4VO5eqnK2aNmOuG2+mes9MDmvU/cB32JNA1+bAj8B7iUdDL6oheXtYs1NcWUt4+7A3Pyb/gwYO9LL6pbizMzMSqDVVe5mZmbWBE7oZmZmJeCEbmZmVgJO6GZmZiUwohK6pB5JL+ql31RJNwx3TIMlqVvSe/Ln4yVd0+qYmkHSwZJ+1uo42pmkN0m6qNVx2OCN1P3PUFoflomkXSTNHeJ5dOSHx2xSb9gRldAjYnRE3N/qOPoiKSS9ZCDjRsT5EXFwM6Y1GJLOlfS5QU7mC8DMZsTTCEln5acg/VPS1Br9/13So5KWKz0Vb5NCv3GSLpe0UtIDkt5ZNe4BSk9k+pvSE7d2LPSTpC9JWppfX87/R63078zj/C1P48BKv4iYDewq6ZXNXh4DJWlBMcbcbUTsmCWdLulZSSvy6y+SvlVpKMTW1sjvWjzhaAf5f+Bn5+10hVKzrYdWDdPX9voRSXfkcf8q6SNV435W6clpqySd3kBInwW+Uhi/7vaTh3k6x7BM0u8kvU/SBoVhVu+DI2IxcB0wrV4wIyqhtzOlloDa3nDEKelfgK0j4sYhmHZv8d9GerrRLTXGeSOpWccDSA/PeRHwX4VBvg08A3QAxwPfkfSKPO544DLgP4FxpP+tXlwYdxpwNLAb6b/rRwDvLfS/kPR/9ueR/sd6qaRtqvrX3VCtYRdHxJak3+rNwPOBeU7qpbEhqX3015NaXvtP4BLlR5M2sL1WGn8ZS2r58RRJxxb630tqy/6qeoHkdWp/0n/Y++vIvJ7uSDrx+Rip4aLenM/a+5XahvBP+zuQFuxjwFLgW7n7BsAngQdIzer9iLTzB/glcErVdG4D3pI/B/CSQmMHs4GnSH/a/yxwQwNxBWnHfw+wIo/3YuD3eVqXABsXhv9/pB/5iTy/7aqm9W95Wn8Frs/dVgI9wDtIK86VeTk8mT9vX5hGN2se4Te1UoZepnUHaUWojLsR6Qk+u/dR3s48nZOBB4Hrc/efAI+SHhhwPfCK3H0aqWGcZ/J8f567b0dq9vKxXNYP9jHPTwE/qLHcPwjcn2P+b2CDQv93A/PzMvoVsGNvy7nO73sDMLWq2wXAFwrfDwAezZ+3yGV9aaH/j4GZheXxu0K/LYCngZfl778DphX6nwzcmD+/FPgHsGWh//8B7yt8369emYbzBSwADqzqtnq9rN4O8/dzgc/lz12kRpU+Stq+F5EOeCqNyTwBfLww7t6kbW9ZHvZbrL39BfC+/Ns/STr4qvnEKuB04LyqbqNI+5CvFLodQWoYZln+/V6Zu88ALq0a/+vAN/LnrUk73UXAw8DnWPMIzepl9GpSq3jL8/urq7b5L5L2W8tJLYuNq9peTyIlridz+f+F1ADKMvK+tB/bzjrLD3g58HfgOdJ2vqyXZdrNmv1T5bedXvhtTyoM2+c+GXgZcG1eB+4G3p67vzh3qzyedjvSPqKrwXX2T8BbG9lea4z7DeCbNbqfB5xeZ77vAv53ANtPrWH2Jj0QZ9fqbSp/3xD4W/G3rfUakjN0SaNIiesB0go6kdQ2cKVwU0lHNi8CRpM2Ykg73uMK09mFdART62jp26QVcgJphX53P0I8BNiL9HzrjwJnkc7MdgB2rcQg6Q2kDe/teT4PFMpRcTTpsZq7RMTrcrfdIl0euJh0APPDXI4XkFaub1FHL9P6EXBCYbDDgEURcWsDZX49aSN+Y/7+C1LzhduSzmrPz/M9K3/+cp7vkbkq6OekHeNEUkL8UD7zrWUSaYOt9mZSq0p7AkeRfzNJRwMfB94CbENKehdWjXs0eTk3UNZqr8ixV9wGdEh6HinpPhcRf6nq/4pa40bESlKLUDX71xj3/ohY0Ut/SDviTklbDaBc7er5pFayJpIO7r5PWm/3Al4LfEpr7oV5Dvh3YDzpcZQHkA64i44gJbTdSNtib+vdOiLiOVLCfC2ApD2Bc0hnO88DvgfMzpdgLgQOq/wWeT/2dtJ+CdLzs1cBLyE9be1gYJ3qaEnjSPusb+R5nAlclde3ineR1v/t8jS/UTWZV5G2z3eQnoD2CVLzw68A3i7p9XleR1N/21ln+UXEfFKi/33ezsf0tgyrPJ90YDORdPD6bUljc79e98lKTzK7lrQstyXtY/9H0isi4j7SGer5kjYn7S/PjYjuesFI6iBtw3fmTvW21+K4Iq0Xd1b3a1Bv+7l+i4ibSQdLr+2l/yrSieVufU1nqKrc9yatqB+JiJUR8feIqFxDOB44MyLuj4ge0nNmj81VqZcDuxeueRwPXBYR/yhOPG9obyU1x7kyIu4gbWyN+lJEPBURd5LOeq/J8SwnJbo9CvM/JyJuyTGcBuxbqd7JvhgRT0TE07VmFBFLI+KnEfG3vGP/PCm5DsR5FHY4wImks8lGnJ6X1dM5rnMiYkUu1+nAbpK27mXcfwG2iYjPRMQzke5j+D6pvedaxpBqP6p9KS+rB0k7qcrB23tJy3F+XnG/wNrrAdRZznWMJp0JVVQ+b1mjX6X/lr2MW6//cmB03lnUGxfWLKcx9QoxjH6Wr+0tk7QM+J9+jv8s8PmIeJZ0ADwe+Hpe3+4k7UBfCRAR8yLixohYFRELSAm2evuYGRHL8npzHanJzv54hFT9CqnG7XsRcVOkp+nNItWi7BMRD5AObo/Ow74B+FtE3JgTx6Gkp6ytjIglwFepvQ0cDtwTET/O5bqQ9ByAIwvD/Dgi7sgJ5z9JSXpUof9n837zGlIt3YURsSQiHiYl7co+qpFtZ7DLr+hZ4DMR8WxEXE06u9+5gX3yEcCCiPhhXia3kGr83gYQEd8n1SLcRDog+ES9QJQelnM+MCsi7sqdG9nmKk5nzQnXQIyh9n5uoNtPcT2tZQV19hNDldB3AB7IK1i17UhnuhUPkKoTOnLCu4o1G8mx5DPHKtuw5lpKcTqNWlz4/HSN76NrxZoPQJay9vObizGsQ9Lmkr6Xb+J4ilS9PaZq421IRDwC/BZ4q9KDQw6l9vKpZXWcSs/5ninpvhzTgtxrfC/j7ghsV7WSfpx0zbmWJ6m9AVX/XtsVpv/1wrSfIFULNryc6+gBimfAlc8ravSr9K9sqP3tvxXQE6merN64sGY5LatXiGF0dESMqbxY94y5nqX5zBjS9gS9bGOSXirpSqUbFp8iJaTq9fDRwue/sWb7bNRE1jzlakdgetW6vANr1sViLeE7WXN2viPpEteiwnjfY83zsouq93Hk772tzw/kaRfL3eg+qpFtZ7DLr2hp1X69Mr16++QdgVdVLffjSWf8Fd8n1ZB+s/okrlquNfwx6XLZKYVejWxzSDqFVEtyeL159aG3/dxAt5/ielrLltTZTwxVQn8IeEEvNzA9QvpxK15AqnKqrLAXAsdJ2hfYjHREWe2xPE7xcXYvGGzQNawVa642eh7p+llF1JnGdGBn4FURsRVQqUqv9azcRswiVV8eQ6oue7jO8BXFON9JqvI+kFR91lkVU3WZHiJd5x1TeG0ZEYf1Mq8/karBqlX/XpXHDD4EvLdq+ptFxO96ib+/7mTtqqrdgMURsZR0XXdDSTtV9b+z1rh5HXhxb/1rjPsiSVv20h/SZZAF0feztdvN34DNC9+f39uADfgO6ex1p7x9fJyBbxvryDv+I0lntZDWtc9XrWub57NoSPeWdEnannSJ6ILCeP8AxhfG2yoi1qnKZd19HKT1vbitVm8Lz5KuG/dXI9tObwazTVWrt09+CPhNVZyjI+JfYfVz5r9Gukfh9HzZoqZc+3U26YTirbkmqKLe9oqkd5Nvko2IhQMpbNbbfq7f8o3EE0n3ANXqvyHpUs9ttfpXDFVCv5l0w8RMSVtI2lTSfrnfhcC/Kz0+cTTpiPziwlHf1aSN4TO5+z+rJ56P/i8j/fCb52vtU4agHBcAJ0naPV9j+wJwU64a7M1i0r0BFVuSjqiX5ZX00/2Yf/W0IN1RuSdwKuma+kBsSdo5LSXtmL9QZ743A09J+pikzfIZ/q55JazlampfVviIpLGSdsjxV+4+/S5wWuHO8q0lHdOfAknaWNKmpGSwUV7nKuv3j4CTlf4zOpZ0U+a5sPoa22XAZ/K6uh/pYKdyKeNy0l/L3pqn/yngT4Uqvh8BH5Y0UdJ2pAO4yrT/Qrr56tM5njeTqpp/Wgj99aTLPCPJrcA783pwCAO/hARpXXwK6JH0MuBfmxAfkjaS9HLS/ub5pOvYkM4C3yfpVUq2kHR45aArIh4j3Qj2Q9JB7PzcfRFwDXCGpK0kbSDpxcrXsqtcDbxU0jslbSjpHaR7P64sDHNCXh83J+3rLi3UavTHYLadxcD2So+xHZQG9slXkpbJifm32UjSv+TfCNLNh/Mi4j2kWtrv9jG775AOhI+scQmuz+1V0vGk/d1BUeMv0DmuTUm5ccO83fZWm3otsGcefkDyunQE6dLUeRFxey+D7k068O+zJnpIEnr+cY8kHVE8SLrY/47c+xzSzvJ60t3Sfwc+UBj3H6QV40DWHB3XcgqpqudR0g50oNdBehURc0jXt35KOkB5Mb1fN644HZiVq5XeTjrq3Ix09H0j6U7+RlVPi7wC/xR4IWk5DcSPSNVhD5OeAVz997KzgV3yfH9W+D13J/1mjwM/IJ3dryNfH1su6VVVva4A5pESwlV5PkTE5cCXgItytesdpMsJ/XEN6cDp1aSbHJ8m14ZExC+BL5Nqex7Ir+KB1ftJv9ESUgL413ytt7KDfyvp3ocnSTcrFdeB75FuGLw9x31V7lZxLOlGwCdJf095W55mxXFVw48Ep5LWh2WkatOfDWJa/0GqMVpBSrYX9z14Xe+Q1JNjm006aN0rX64iIuaSrqN/i/Sb3Eu6SbfoAmrvf94FbEzaZp4ELiVd711Lrvk5gnRwt5R04+0REVE8A/8xab/1KOkGwg8OoKyD3XZ+TTpzfVTSQGoHqvW6T86XUw8mbQ+P5GG+BGwi6SjSjcrvy4N/mJQoj6+egdK9Ae8l7YseVWpsrKcybAPb6+dItax/KIxbPHj4PmnfcRzpOv7TpHuV1hHp/+G/Jp0A9NfPJa0g1Vx8gnTAeVIfwx9P3wc5AH586kgk6VOkv1mdUHfgFpF0MPD+iDg6fw9Steq9LQ2sjUg6EjgxIt7e6lhs+EjqJp2N/aDVsdjg5JqIWcDeMUTJVNK2wG+APSLi730O64Q+suRq+z+SEsH1rY6nUU7oZokTug2V0rUUJ+m1haqUtV6tjm2wJP0/UhXNL4rJXKkN+FplHuj/K83MbITxGbqZmVkJlO4M3czMbH3khG5mZlYCw/qEsPHjx0dnZ2efw6xcuZIttthieAIaRi7XyDKU5Zo3b97jEbFN/SFHturtvZ3XFcc2cO0cXzvENqzbewzx05uKr7322ivque666+oOMxK5XCPLUJYLmBvDuN216lW9vbfzuuLYBq6d42uH2IZze3eVu5mtJmkHSddJmi/pTkmn5u7jJF0r6Z78PrbetMxseDmhm1nRKmB6RLyc9Hjhf8uNZ8wA5kTETsCc/N3M2ogTupmtFhGLIjXdW2mucz7poRFHseZxmLNY84hRM2sTTuhmVpOkTtJzt28iPd54Eax+UEmtx4aaWQsN613ujbj94eVMnXHVoKaxYObhTYrGbP2k9CTEnwIfioinpMaeaCppGjANoKOjg+7u7tX9enp61vreTsoa2+0PL29KDJMm1nwOE1DeZTcSNZzQ8yPk5gIPR8QRuU3xi0nP0l4AvD0inhyKIM1s+EjaiJTMz4+IyhP9FkuaEBGLJE0gPZluHRFxFulpd0yePDm6urpW9+vu7qb4vZ2UNbbBnhxVLDi+9/mXddmNRP2pcj+VdD2twjfJmJWM0qn42cD8iDiz0Gs2a55vPYX0KFwzayMNJXRJ2wOHk56BXeGbZMzKZz/S85/fIOnW/DqM9Cz3gyTdAxyUv5tZG2m0yv1rwEeBLQvd1rpJJj+z1cxGsIi4AejtgvkBwxmLmfVP3YQu6QhgSUTMk9TV3xn0dZNMLR2bwfRJq/o7m7W0400QZb05w+UyM2sPjZyh7we8KVe7bQpsJek8mnCTTC3fPP8Kzrh9cDff93UDR6uU9eYMl8vMrD3UvYYeEadFxPYR0QkcC/w6Ik7AN8mYmZm1jcE0LOObZMzMzNpEv+q2I6Ib6M6fl+KbZMzMzNqCm341MzMrASd0MzOzEnBCNzMzKwEndDMzsxJwQjczMysBJ3QzM7MScEI3MzMrgcG1sWpmZjYCdTbrWfEzD2/KdJrBZ+hmZmYl4IRuZmZWAk7oZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl4IRuZmuRdI6kJZLuKHQ7XdLDkm7Nr8NaGaOZrcsJ3cyqnQscUqP7VyNi9/y6ephjMrM6nNDNbC0RcT3wRKvjMLP+cdOvZtaoUyS9C5gLTI+IJ6sHkDQNmAbQ0dFBd3f36n49PT1rfW8n7Rjb7Q8vB6BjM/jm+VcMaBrTJzUnlr7m35/4Jk3cujkBNaiv33X6pFVNmUc7rTd1E7qkTYHrgU3y8JdGxKcljQMuBjqBBcDba23gZlYK3wE+C0R+PwN4d/VAEXEWcBbA5MmTo6ura3W/7u5uit/bSTvGNjW3NT590irOuL19z736E9+C47uGNpgqff2uU5vVlvswl6kvjVS5/wN4Q0TsBuwOHCJpH2AGMCcidgLm5O9mVkIRsTginouIfwLfB/ZudUxmtra6CT2Snvx1o/wK4ChgVu4+Czh6KAI0s9aTNKHw9c3AHb0Na2at0VA9iaRRwDzgJcC3I+ImSR0RsQggIhZJ2nYI4zSzYSLpQqALGC9pIfBpoEvS7qSD+QXAe1sVn5nV1lBCj4jngN0ljQEul7RrozPo6yaZWjo2G/zNCu10k0JFO9500wwuV/lExHE1Op897IGYWb/0606LiFgmqZv0H9XFkibks/MJwJJexun1Jplavnn+FYO+AaSdblKoaMebbprB5TIzaw91r6FL2iafmSNpM+BA4C5gNjAlDzYFGNj/KszMzGzQGjkVngDMytfRNwAuiYgrJf0euETSycCDwDFDGKeZmZn1oW5Cj4g/AXvU6L4UOGAogjIzM7P+cdOvZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl4IRuZmZWAk7oZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmZmYl4IRuZmZWAk7oZmZmJeCEbmZmVgJO6GZmZiXghG5mZlYCTuhmthZJ50haIumOQrdxkq6VdE9+H9vKGM1sXU7oZlbtXOCQqm4zgDkRsRMwJ383szZSN6FL2kHSdZLmS7pT0qm5u4/YzUooIq4HnqjqfBQwK3+eBRw9nDGZWX0bNjDMKmB6RNwiaUtgnqRrgamkI/aZkmaQjtg/NnShNq5zxlVNmc6CmYc3ZTpmJdAREYsAImKRpG1rDSRpGjANoKOjg+7u7tX9enp61vreTtoxtumTVgHQsdmaz+2oP/E1Yxnf/vDyhoft2Ay+ef4VNftNnzToUIDmlKlZ6ib0vBFXNuQVkuYDE0lH7F15sFlAN22S0M2sNSLiLOAsgMmTJ0dXV9fqft3d3RS/t5N2jG1qPjGZPmkVZ9zeyLlXa/QnvgXHdw16flP7ccI2HMuuGWVqln5dQ5fUCewB3ETVETtQ84jdzEphsaQJAPl9SYvjMbMqDR+6SBoN/BT4UEQ8JanR8XqtgqulnaqXmlmV0o5Ves3gcq03ZgNTgJn5vXY9ppm1TEMJXdJGpGR+fkRcljsvljQhX0/r9Yi9ryq4Wr55/hVtU73UzKqUdqzSawaXq3wkXUi6nDZe0kLg06REfomkk4EHgWNaF6GZ1VI3cyqdip8NzI+IMwu9fMRuVkIRcVwvvQ4Y1kDMrF8aORXeDzgRuF3Srbnbx/ERu5kNkv+Rsn5p1u9ttTVyl/sNQG8XzH3EbmZm1gbcUpyZmVkJOKGbmZmVgBO6mZlZCTihm5mZlUB7/OHbzKwkfCe3tYrP0M3MzErAZ+h98H9kzcxspPAZupmZWQk4oZuZmZWAq9zNbMQb7OWx6ZNW9es522btyGfoZmZmJeAz9GHQOeOqQZ8B+MY6MzPri8/QzczMSsAJ3czMrASc0M3MzErACd3MzKwEnNDNzMxKwHe5m1nDJC0AVgDPAasiYnJrIzKzCid0M+uv/SPi8VYHYWZrq1vlLukcSUsk3VHoNk7StZLuye9jhzZMMzMz60sj19DPBQ6p6jYDmBMROwFz8nczK78ArpE0T9K0VgdjZmvUrXKPiOsldVZ1Pgroyp9nAd3Ax5oZmJm1pf0i4hFJ2wLXSrorIq6v9MxJfhpAR0cH3d3dq0fs6elZ6zukNtTbQcdm7RNLtXaODdo7vuGIrXqdbiVFRP2BUkK/MiJ2zd+XRcSYQv8nI6JmtXvVBr7XRRdd1Oe8ljyxnMVPNxr+yNGxGW1RrkkTt27q9Hp6ehg9enRTp9kOhrJc+++//7wy3Ewm6XSgJyK+Uqv/5MmTY+7cuau/d3d309XVtdYwg32oSrNMn7SKM25vz1uK2jk2aO/4hiO2es1ySxq27X3If4WIOAs4C9IGXr1BV/vm+Ve07coxGO2y0i84vqup06u1ky6DspZrMCRtAWwQESvy54OBz7Q4LDPLBpphFkuaEBGLJE0AljQzKDNrSx3A5ZIg7TsuiIhftjYkM6sYaEKfDUwBZub3K5oWkZm1pYi4H9it1XGYWW2N/G3tQuD3wM6SFko6mZTID5J0D3BQ/m5mZmYt0shd7sf10uuAJsdiZmZmA+S23M3MzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSqD1TZfZsGpWU5v1mjs0M7Ph5TN0MzOzEnBCNzMzKwEndDMzsxJwQjczMysBJ3QzM7MScEI3MzMrASd0MzOzEnBCNzMzKwE3LGMDUmmgZvqkVUxtUmM1A+VGbszMfIZuZmZWCk7oZmZmJeCEbmZmVgKDSuiSDpF0t6R7Jc1oVlBm1p68zZu1rwHfFCdpFPBt4CBgIfAHSbMj4s/NCs6sEc16glzRQG/2K/MNet7mzdrbYM7Q9wbujYj7I+IZ4CLgqOaEZWZtyNu8WRsbTEKfCDxU+L4wdzOzcvI2b9bGBvM/dNXoFusMJE0DpuWvPZLurjPd8cDjg4irLX3Q5RpRBloufamhwXbs73TbRN1tvs723rbrSjuvx+0cG7R3fMMRWwPb/LBt74NJ6AuBHQrftwceqR4oIs4Czmp0opLmRsTkQcTVllyukaWs5Rqkutt8X9t7Oy9TxzZw7RxfO8c2FAZT5f4HYCdJL5S0MXAsMLs5YZlZG/I2b9bGBnyGHhGrJJ0C/AoYBZwTEXc2LTIzayve5s3a26Daco+Iq4GrmxRLRcPV8yOMyzWylLVcgzLIbb6dl6ljG7h2jq+dY2s6RaxzH5uZmZmNMG761czMrATaJqGP5CYlJe0g6TpJ8yXdKenU3H2cpGsl3ZPfxxbGOS2X9W5Jb2xd9PVJGiXpj5KuzN9HfLkkjZF0qaS78u+2bxnK1Wr1tmNJXZKWS7o1vz7VTvEVYrw1b8u/aZfYJH2ksNzukPScpHFtEtvWkn4u6ba83E4ajrj6Ed9YSZdL+pOkmyXtOpzxDZuIaPmLdIPNfcCLgI2B24BdWh1XP+KfAOyZP28J/AXYBfgyMCN3nwF8KX/eJZdxE+CFueyjWl2OPsr3YeAC4Mr8fcSXC5gFvCd/3hgYU4ZytXiZ1t2Oga7KetSm8Y0B/gy8IH/ftl1iqxr+SODX7RIb8PHC9rIN8ASwcRvF99/Ap/PnlwFzWrEODvWrXc7QR3STkhGxKCJuyZ9XAPNJLWgdRUoc5Pej8+ejgIsi4h8R8VfgXtIyaDuStgcOB35Q6DyiyyVpK+B1wNkAEfFMRCxjhJerDbT7dtxIfO8ELouIBwEiYkkbxVZ0HHDhsETWWGwBbClJwGhSQl/VRvHtAswBiIi7gE5JHcMU37Bpl4RemiYlJXUCewA3AR0RsQhS0ge2zYONpPJ+Dfgo8M9Ct5FerhcBjwE/zJcSfiBpC0Z+uVqt0eW0b66a/YWkVwxPaEBj8b0UGCupW9I8Se9qo9gAkLQ5cAjw02GICxqL7VvAy0kNDd0OnBoR/2R4NBLfbcBbACTtTWq9bfthiW4YtUtCb6gZ2XYnaTRpI/tQRDzV16A1urVdeSUdASyJiHmNjlKjW9uVi/R3zT2B70TEHsBKUhV7b0ZKuVqtkeV0C7BjROwGfBP42VAHVdBIfBsCe5Fqpd4I/Keklw51YPRvHTsS+G1EPDGE8RQ1EtsbgVuB7YDdgW/lmrDh0Eh8M0kHarcCHwD+yPDVIAybdknoDTUj284kbURK5udHxGW582JJE3L/CUCl+m6klHc/4E2SFpCqsd4g6TxGfrkWAgsj4qb8/VJSgh/p5Wq1RpqGfSoievLnq4GNJI1vl/jyML+MiJUR8ThwPbBbm8RWcSzDV90OjcV2EulSRUTEvcBfSdeq2yK+vN6dFBG7A+8iXef/6zDFN2zaJaGP6CYl83Wjs4H5EXFmoddsYEr+PAW4otD9WEmbSHohsBNw83DF26iIOC0ito+ITtJv8uuIOIGRX65HgYck7Zw7HUC6EWpEl6sN1N2OJT0/by+Vqs8NgKXtEh/pN3+tpA1z1farSPfEtENsSNoaeD1r1s3h0EhsD5K2I/K16Z2B+9slPqV/tWycv74HuL5OLerI1Oq78iov4DDS3eH3AZ9odTz9jP01pCqeP5GqnW7N5Xke6UaMe/L7uMI4n8hlvRs4tNVlaKCMXay5y33El4tULTg3/2Y/A8aWoVytftXajoH3Ae/Ln08B7iRd07wReHU7xZe/f4R0gHcH6fJZO8U2lXSDZrv9rtsB15Cun98BnNBm8e2bt+u7gMuAscO9DIfj5ZbizMzMSqBdqtzNzMxsEJzQzczMSsAJ3czMrASc0M3MzErACb3E8kMSulodx1CQdKGko4dguufW6HampPc1e15mZs00IhO6pGMl3SRppaQl+fP7C/9vPVfSM5JW5Ncdkr6Y/8NZmcbU/LSiHklP5ScYHdG6UjVfRLwiIroBJJ2eG4UZEEmHS7pB0jJJj0r6vqQtC/03kXROXpaPSvpwod9LJV0h6TFJT0j6VeE/4EjaNXd7XFLdv11IeiWpsY8r+iqbpJD0kvz5FZKukfRkLsM8SYc1WPz/Bj5R+B+rmVnbGXEJXdJ04OuknezzgQ7S/w33Iz1pp+LLEbElqUWgk4B9gN8qtdld8fuIGE16wtLZwCUa4scRStpwKKc/hLYGPkf6v+nLSa0x/Xeh/+mkBld2BPYHPirpkNxvDKmhh51Jv9fNrN0wxrPAJcDJDcbyXlKLfP35z+XPgWvz/LcFPgisblhC6fGoDwDHSVoo6dOVfpHadb8LeFM/5mdmNrxa/Uf4fjYesDWp3e231hnuXOBzVd22BBYBp+TvU4EbCv23IDUOM7nG9KYCvyW1Pb2ctHM/oCqus/P0HyYlvlFV436V9ASiz9WY/ijS4wfvA1YA84Adcr+vkx488FTu/trCeKeTmi29OI93C7Bbof8C4EDSgxyeISXOHuC23P8kUitYK0itOr23H7/FW4DbC98fBg4ufP8svTSAAYzLy/p5Vd1fklbJuvO+H3hN1XI4r8Zwkac5Pn8e08v09s2/zcvyurMN0FU1zCeAH7Z6G/DLL7/86u010s7Q9yU9k7rfzR5GeqzptcBrq/vls+b3kJLdPb1M4lWkRDIe+DRwWeFsfhapof+XkJ60dnCeXvW42wKfrzHtD5Meh3gYsBXwbuBvud8fSK2ajSM9k/wnkjYtjHsU8JNC/5/lduWLZf8l8AXg4ogYHenBGJDaKj8iz/Mk4KuS9uyl/NVeR2rxC0ljSWfutxX63wb09iSt1wGPRkS/m/zMNSwvJLXY1qilpEeenifpaK372MQO4PHKNCPisciXKgrmMzxtepuZDchIS+jjgccjYvVTciT9Ll8TfVrS6+qM/wgp8VXsI2kZ8Cgpob45Ipb3Mu4S4GsR8WxEXEza+R+ek8OhpCYiV0Z6fvJXSe0Jr55vRHwzIlZFxNM1pv0e4JMRcXckt1WSXUScFxFL87hnkA5odi6MOy8iLo2IZ4EzgU1JlxfqioirIuK+PM/fkJpuXOeAp5qkg0htnX8qdxqd34vLbjmpVqR63O2Bb5MOYgZiTH5f0egIERGkywALgDOARZKul7RTHuRa1tRw7JHvFxhVNZkVhXmbmbWdkZbQlwLji9ehI+LVETEm96tXnomkqtWKGyNiTESMj4h9IuJ/+xj34ZwYKh4gnZXuCGxEShLL8gHC91jzLG1Y+1m9texAqm5fh6TpkuZLWp6nvTXpwGadaUd6/vDCHFddkg6VdGO+UW0ZqYagzydfSdqHVBPwtoj4S+7ck9+Lj0vciqqkK2kb0kHD/0TEQJ8WtSy/Fw8WVpF+g+K8Kt+fBYiIhRFxSkS8mPSbrQR+lPutBPYGppMexfgloLvqfoctC/M2M2s7Iy2h/x74B6mauV+UnlV+IPB/A5z3xMpd9NkLSGf8D+WYxueDgzERsVVEFKub69289RDw4hoxvxb4GPB20sMExpDOfItx7FAYfgN6f+ziWjFI2oT0uNevAB152ldXTbs6nj1IN7e9OyLmrJ5wxJOk+weKVdK7kavk87hjScl8dkTUuuzQkJx87wOKz6h+EOisGvSFwHOka/vV03iIVEuwa6HbcxHxa9JZ+mTglaRLHRUvZ+1LCmZmbWVEJfSIWAb8F/A/kt4mabSkDSTtTrqpbR3571R7kZ6o9STwwwHOflvgg5I2knQMaQd/daQ7oK8BzpC0VY7nxZJe349p/wD4rKSdlLxS0vNIZ4WrgMeADSV9irXPggH2kvSWfDb5IdLBxY015rEY6MxJH9I/AjbJ014l6VDStf+aJO0K/BL4QET8vMYgPwI+KWmspJcB/490gxmStgJ+Bfw2ImbUmLbyfQEb5++b5gOO3lxNeoRkxS+BnSWdmH+fcaR7Bi6NiFU5pv+S9JL8+4wn3adwY57fZEmvKkzvxYVlU/F64Bd9xGRm1lIjKqEDRMSXSddfP0q6rr2YVMX9MeB3hUE/KmkFqYr9R6Q7xF+dz/AG4ibS37IeJ93Y9rbCTV3vIiWjP5MOGi4FJvRj2meS/rZ1Delu9rOBzUhJ8BekxwI+APyddavvrwDeked7IvCWfD292k/y+1JJt+SbBD+Y5/sk8E76fgb9dNLd32cr/Xe/R9Kdhf6fJp05PwD8BvjvfDMewJuBfwFOKozbI+kFuf+OwNOsOaN/mr5vejsLOL5SY5LvWziM9He2JaTHNy4H/jUP/wzpDP5/Scv3DtKBz9Tc/zlgpqSHgLeRlvm/R8QDAJImALuQDgrNzNqSH5/aAElTgfdExGtaHUuRpNOBl0TECa2OZbhJugC4JCJ+1uTpnhsRU6u6nQHcFxH/08x5mZk100ht5MTWcxHxzmGc1/ThmpeZ2UCNuCp3s6FUfXZuZjZSuMrdzMysBHyGbmZmVgINJXRJ/670KM47lB5buamkcZKulXRPfh871MGamZlZbXWr3CVNBG4AdomIpyVdQvof8C7AExExU9IMUsMnH+trWuPHj4/Ozs6a/VauXMkWW9T8K/mwaoc4HEP7xDAUccybN+/xiNimaRM0M6Pxu9w3BDaT9CywOaklstOArtx/FtBN+i94rzo7O5k7d27Nft3d3XR1ddXsN5zaIQ7H0D4xDEUcSo9pNTNrqrpV7hHxMKl50AdJzXsuj4hrSM2FLsrDLGLttsvNzMxsGDVS5T6W1Ob3O0gPp/gJqSW0b+X2vyvDPRkR61xHlzQNmAbQ0dGx10UXXVRzPj09PYwePbpmv+HUDnE4hvaJYSji2H///edFxOSmTdDMjMaq3A8E/hoRjwFIugx4NbBY0oSIWJSbxlxSa+SIOIvUVCeTJ0+O3qouy1q96hhGdgztFIeZWV8aucv9QdJzwzfPbWcfAMwntfs9JQ8zhdSmuJmZmbVA3TP0iLhJ0qWkx0quAv5IOuMeDVwi6WRS0j+mGQF1zrhq0NNYMPPwJkRiZmY2cjR0l3tEfJr0NK2if5DO1s3MzKzF3FKcmZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk0lNAljZF0qaS7JM2XtK+kcZKulXRPfh871MGamZlZbY2eoX8d+GVEvAzYDZgPzADmRMROwJz83czMzFqgbkKXtBXwOuBsgIh4JiKWAUcBs/Jgs4CjhyZEMzMzq0cR0fcA0u7AWcCfSWfn84BTgYcjYkxhuCcjYp1qd0nTgGkAHR0de1100UU159PT08Po0aO5/eHlAypI0aSJWw943EocreQY2ieGoYhj//33nxcRk5s2QTMzGkvok4Ebgf0i4iZJXweeAj7QSEIvmjx5csydO7dmv+7ubrq6uuiccVU/i7CuBTMPH/C4lThayTG0TwxDEYckJ3Qza7pGrqEvBBZGxE35+6XAnsBiSRMA8vuSoQnRzMzM6qmb0CPiUeAhSTvnTgeQqt9nA1NytynAFUMSoZmZmdW1YYPDfQA4X9LGwP3ASaSDgUsknQw8CBwzNCGamZlZPQ0l9Ii4Fah1ze+ApkZjZmZmA+KW4szMzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSsAJ3czMrASc0M3MzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSsAJ3czMrASc0M3MzErACd3MzKwEnNDNzMxKwAndzMysBJzQzczMSsAJ3czMrASc0M3MzErACd3MzKwEGk7okkZJ+qOkK/P3cZKulXRPfh87dGGamZlZX/pzhn4qML/wfQYwJyJ2Aubk72ZmZtYCDSV0SdsDhwM/KHQ+CpiVP88Cjm5qZGZmZtawRs/QvwZ8FPhnoVtHRCwCyO/bNjc0MzMza5Qiou8BpCOAwyLi/ZK6gP+IiCMkLYuIMYXhnoyIda6jS5oGTAPo6OjY66KLLqo5n56eHkaPHs3tDy8faFlWmzRx6wGPW4mjlRxD+8QwFHHsv//+8yJictMmaGZGYwn9i8CJwCpgU2Ar4DLgX4CuiFgkaQLQHRE79zWtyZMnx9y5c2v26+7upquri84ZV/W/FFUWzDx8wONW4mglx9A+MQxFHJKc0M2s6epWuUfEaRGxfUR0AscCv46IE4DZwJQ82BTgiiGL0szMzPo0mP+hzwQOknQPcFD+bmZmZi2wYX8GjohuoDt/Xgoc0PyQzMzMrL/cUpyZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCTihm5mZlYATupmZWQk4oZuZmZWAE7qZmVkJOKGbmZmVgBO6mZlZCdRN6JJ2kHSdpPmS7pR0au4+TtK1ku7J72OHPlwzMzOrpZEz9FXA9Ih4ObAP8G+SdgFmAHMiYidgTv5uZmZmLVA3oUfEooi4JX9eAcwHJgJHAbPyYLOAo4coRjMzM6tDEdH4wFIncD2wK/BgRIwp9HsyItapdpc0DZgG0NHRsddFF11Uc9o9PT2MHj2a2x9e3p/4a5o0cesBj1uJo5UcQ/vEMBRx7L///vMiYnLTJmhmRj8SuqTRwG+Az0fEZZKWNZLQiyZPnhxz586t2a+7u5uuri46Z1zVcPC9WTDz8AGPW4mjlRxD+8QwFHFIckI3s6Zr6C53SRsBPwXOj4jLcufFkibk/hOAJUMTopmZmdXTyF3uAs4G5kfEmYVes4Ep+fMU4Irmh2dmZmaN2LCBYfYDTgRul3Rr7vZxYCZwiaSTgQeBY4YkQjMzM6urbkKPiBsA9dL7gOaGY2ZmZgPhluLMzMxKwAndzMysBBq5hj7iDOavb9MnrWJqHn8wf38zMzMbTj5DNzMzKwEndDMzsxJwQjczMysBJ3QzM7MScEI3MzMrASd0MzOzEnBCNzMzKwEndDMzsxIoZcMyzdKMZ7ODG6gxM7Oh5zN0MzOzEnBCNzMzKwEndDMzsxJwQjczMysB3xQ3DPp7c13xiW8VvrHOzMz64jN0MzOzEvAZ+nqm0dqCWrUERa4xMDNrL07oI0Sz/hNvZmblNKgqd0mHSLpb0r2SZjQrKDMzM+ufAZ+hSxoFfBs4CFgI/EHS7Ij4c7OCs/bVjBoDV9ubmTXPYKrc9wbujYj7ASRdBBwFOKHbiNTbQUq9+wmKfJBiZq0ymCr3icBDhe8LczczMzMbZoqIgY0oHQO8MSLek7+fCOwdER+oGm4aMC1/3Rm4u5dJjgceH1AwzdUOcTiG9okBmh/HjhGxTROnZ2Y2qCr3hcAOhe/bA49UDxQRZwFn1ZuYpLkRMXkQ8TRFO8ThGNonhnaKw8ysL4Opcv8DsJOkF0raGDgWmN2csMzMzKw/BnyGHhGrJJ0C/AoYBZwTEXc2LTIzMzNr2KAalomIq4GrmxRL3Wr5YdIOcTiGpB1igPaJw8ysVwO+Kc7MzMzahx/OYmZmVgJtkdCb2YSspB0kXSdpvqQ7JZ2au58u6WFJt+bXYYVxTsvzvlvSGwvd95J0e+73DUnK3TeRdHHufpOkzl5iWZDHv1XS3NxtnKRrJd2T38cOVRySdi6U91ZJT0n60FAvC0nnSFoi6Y7C+MNSbklT8jzukXR9jTj+W9Jdkv4k6XJJY3L3TklPF5bJd5sYx5Ra64eZWVNFREtfpBvq7gNeBGwM3AbsMojpTQD2zJ+3BP4C7AKcDvxHjeF3yfPcBHhhjmVU7nczsC8g4BfAobn7+4Hv5s/HAhf3EssCYHxVty8DM/LnGcCXhjqOwnJ+FNhxqJcF8DpgT+CO4Sw3MA64P7+PJf2N8vVVcRwMbJg/f6kQR2dxuKrlMtg47gfGtnpb88svv8r9aocz9NVNyEbEM0ClCdkBiYhFEXFL/rwCmE/fLdgdBVwUEf+IiL8C9wJ7S5oAbBURv4+IAH4EHF0YZ1b+fClwQOWsrQHFcWdVTXMo4zgAuC8iHqgT26BjAP4PeKIF5X4jcG1EPBERTwI/B15ZDCIiromIVfnrjaT2E3rVpDiuBQ7paz5mZoPVDgl9yJqQzVWgewA35U6n5KrWcwpVvr3Nf2L+XCuu1ePk5LAceF6NEAK4RtI8pRbzADoiYlEedxGw7TDEAekM8sLC9+FeFsNR7lrTen6NWCreTTrjrnihpD9K+o2k1xbm1Yw43CyymQ2pdkjotc4oB33rvaTRwE+BD0XEU8B3gBcDuwOLgDPqzL+vuBqNeb+I2BM4FPg3Sa/rK+ShikOp4Z83AT/JnVqxLHrTzHk2HIukTwCrgPNzp0XACyJiD+DDwAWSthrqOMzMmqUdEnpDTcj2h6SNSMn8/Ii4DCAiFkfEcxHxT+D7pKr+vua/kLWrY4txrR5H0obA1qxbxUxEPJLflwCX53kuztW4lercJUMdB+mA4paIWNyqZTFM5a41rcXVgeSb1I4Ajs/V6OQq/6X58zzStfyXNjGOQa3TZmb1tENCb2oTsvka5tnA/Ig4s9B9QmGwNwOVO59nA8fmO5ZfCOwE3JyrhVdI2idP813AFYVxKncuvw34dSUxFOa3haQtK59JN2PdUTXulKppNj2O7DgK1e3DvSxqDDdU5f4VcLCksfkywsHA9cUgJB0CfAx4U0T8rdB9G0mj8ucX5Tjub2Icv6qxTMzMmqfVd+Xlff9hpLvR7wM+MchpvYZUvfkn4Nb8Ogz4MXB77j4bmFAY5xN53neT72DO3SeTkt19wLdY0xDPpqTq63tJd0C/qEYcLyLdvX0bcGelXKRrrHOAe/L7uCGOY3NgKbB1oduQLgvSwcMi4FnS2erJw1Vu0nXxe/Prxhpx3Eu6vl1ZNyp3qb81/063AbcARzYxjpNavY355Zdf5X+5pTgzM7MSaIcqdzMzMxskJ3QzM7MScEI3MzMrASd0MzOzEnBCNzMzKwEndDMzsxJwQjczMysBJ3QzM7MS+P8X+mEstr9pjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(figsize=(8, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "73655468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eZdcSToSi/Nkt5qIWhr80GCKZNhgLJ+mbiM05V9BkUrxrTxNXhkIFTTgTiKX48el+FIr37GuSmx955rCa+fiR9lI3Y117x661O1rkUGcViXQGp9XMllsCr8tDIZ6/PEJ7tYsDbZWc7QtgaM3FwRAP72i4baj6Utgt5rwOU12Od+1u4lx/IK83yYZDcX52ZhCXzcx79jWveLjqzbEIoXgagKvD4ang0GI2sW8FNyvE7IoRHEaBk0qpp5kWIGqt/8VcKyil7gT+C5ABjmmt/5VSKgC8kXvJB7XW40qpTwJfJFsm43GtdbBQH0KI9eTCQJDRUALQfP21buxWM2/ZWseuZt+yg7odTV6uDoexW810rqKEJaK8DIfiPHV+mIzWOK0mvE7bghlzN9d7Cp6s4NJgiOFg9hR3eSi0rBsoQojScFjNPLyjYdbffe3VLrrHo/RUu7hzQw0H2it59fo42xoqVhQYlov2GteKsqfO5mxfgO7xCNdHIgRjaT53byemFcyP3FDnpqbXRiqj2dooScMKrRjB4Q9zP0vRBbxVax1XSn1NKbUHOKO1fsvkC5RSVuA3gQeADwFfAP4sP00WYn3rrHXzRo+fRCrDcCiB02rm6I3x2y7CLw8FefbiCFsaKnhoe/2879ngdfDr928sZLNFmQvGU/zszABKKR7b07SsGl8nuvwMBeMAvG9/84JzdKLJNE6rueAZLjtrXBzrMqFQtFfLzQ8hZpPKGGQMXdaJT168MsrV4RB3bqyh0esgmswQTWaIJNN4HBbu6Kgu2s2fjKFJZYyi/L2euThE91iU+7bUrfhm2qY6D9853kfa0IyE4wzkEoItl9dh5Vfu7lxRm8TiFaOUxd8vY53ppS7SZHsQdyilXgBeAn4f2Eo2YEwrpZ4CvpKP9gohoLnSyW8+uIlU2uCfjvcwFk6yoXZmYJjKGPzFM1cZDia4OhLmYEcVPqcMpRNzO98fpN+fDewuDYU4uIxhqJ21Li4OBnHbLFNDi+by5PkhzvYF2FDr5v0HWpbV5sWq9zr4wgObACSTrxCzCMRSfOO1bhJpg3fvbVpU8pVii6cyvH5zHIBXr4/xqbs62NtWSYPXwf1baov63Y6nMnz9tW4CsRSP7GgoaBmpQDTFqZ4AAK/dGF9xcNhR4+Y37t/ILy8N4XPaqHHb8tFMUSTFyFZ6g2x20hm01gt2ISil9gK1WuvzSqktwATw18B7gDFgchhpAJj1KkMp9Xng8wDt7TJfQYjFMpsUZpuZT97ZQTyVmerlSWcMYqkM8ZSBLZeO3NCsioLnYuXCiTQOi2lZqeg7alwc75oAoLVqeXeRtzd66ah2YzGrBYtoXx/JZle8MRrBMPSKhjUthgSFYj3QWhNKpPHYLEv6Tg0EYkSTGQC6xqJlGRzaLSZaq5z0TsTYVOfBajbx+JH2GefAYhkNJ5iIJEllDK6PRgoaHHocFhq8DoaCcTbV5Wfkw/72SrY2erCZl3e+EKVTjD390LTHDuAjwIL98UqpauC/AR8F0FqP55Z/HzgA/ACYnD3rBfyzvY/W+ivkehUPHTp0W5AqxHo3Hk7y9KUh9rX62Npw+4R0s0lNnRRTGYOvv9bNWDjJ3RureXR3E11jER7eXi8XxuvA8a4Jnr88QqXLyuN3tmO3LG2oU5PPyW/cvxGlIJJIc20kzIYa95KDNqdtcdu9Z1Mtx7vG2dHkLXhgOJee8Si9EzH2tPrkBopYE352dpBLgyE21rl53/7F98hvqHWzodZNJJlmbwGTQ80mlsxwqtdPo9cxb8ZUpRQfvqOVeMqYOs5MPwdOSmcMbo5FqffaC5aduc5jZziUYDAYZ0eBM/SaTYqPH27jVK+fjKFJZ4y8BHRzZZG+Vc94FKvZRKNPMo6Wg2IMKx27ZdGfK6VeBP79XOsopSzAPwC/q7UeVEq5gbjWOgPcC5wBLgO7lVJm4BHg1YJ8ACHWuP/85CWuDof50cl+/vKTB/HMc6ILxdOM5WqNdY1H+dhh6Y1fT7rGIkA25XkgmqLeu/R5MDaLiXAizdeOdpNMG+xvr+ShbfPPV12uPa2+gmYoXUg0meZ7b/SRMTT9/hgfuqXGohCr0c3cceDmaBSt9aLn89ot5oIP757L0xeHuDIUxqQUn723c94pEEqpBW9A/fzcEJeHQrhsZj5374aCJKaJpwwavA4avA4iiUze3/9WPRNRnr00AkAybXDP5tqCbxOyCfCeODuIUvDBA615T44jlq4Yw0oPTntqItuTuFCqoY8Ah4Ev5w46vw/8d6VUhGxpjD/QWmeUUn8DvEB2uOnj+W67EOtBKmMAkNFvPp5LtdvGgfZK+vwx7t5YnBOHKB93bqwhlsrQ6HVQV2Ff9vskUhmS6ey+NpmefC0yKYXZpMgYGotZetbF2vDAljpO9vjZ1ewteKKnfLHkRg4oBfkYRBCKZ2sCxlIZ0oaBjfwHhz6XlTs6qugej3L3xpq8v/+tpo/+KeZIoMlzgNYQSpRvrcX1pBhjXP7ztMdp4Ca5oaJz0Vp/Hfj6LYsPzvK6rwJfXWH7hFjXfufhLfz49AD7Wn1Uue1kDM3Pzw3ij6Z4ZGf9bYVl31KgXh5R/loqnXzyzo6FX7iAGo+dt+1sYDAQ5/CG/GX9e6N7grN9Afa1VbK3tTJv77tcDquZj9zRSn8gznZJvy7WiN0tvoLOfyuEh7bX0+RzUu+1L6sGaW+uV63R6+DhHfW8bWcDJ7r9tFe7Fj10cjke2FpXsPe+VWuVi/cfaCGSSLMzjzUPBwIxnrk4TI3bztt3Ntw2xH9/WyXxVAaLWbGjsbDDZ8XiFGNY6UOF3oYQYvlaqlx84cFNU897xqNcGgwBcKJrgkd3NwHZDGbDoTj3bKqlWjKPiRWavMBMpg2eOj8EZC+EVjI864Uro2QMzQtXRssiOIRsFtOFsqoKIVaudyLKiW4/m+rct9XjtVvMKyqW/tqNcUZCCUZCCfa2+qj3OnjbztnrIpYTrTWvXBvDH0tx35baBedHbphnPuZyHbs5wXAwwXAwwe4WL61VM4eN2iymogbBYmHFGFZqJ1uHsHP69rTW/59Cb1sIsXR1FXY8dguRZHqqXttwMM5LV0eBbN2lpSQhEGI+Z/sDnOnLplCvcltXVD+so8bF9ZFIQS5whBDl7ekLw4xHklwfCbO53rPkhFnz6ax10zUWpcplxedaPSWbesZjHL2RLc1hNinesaux6G3orHFzbSRMhcNKjXv50xFE8RRjWOkPyJaaOA4kirA9IcQKuO0WPntvJ6mMMTVcxuOw4LCaiacy1Hrk4C7yp8ZtY3La0kovHN67r5lwIi1ZQYVYh2o9dsYjSSqdVqym/M4BPNhexbaGCuzLLONTKj6nFZvFRDJtUOspzYifPa0+Nta5V93fbj0rxhm0VWv9aBG2I4TIE6vZhAJ+cnoAfyzJ23Y08Om7OwjGUjRJqmmRRx01bj59V3YeY80KbzwopRY1n+hUj59TvX52Nfu4o2PWErlCiFXm0d2N7GvzUeuxF6R0zfmBIBcGghxsr1o1cy59LiufvruDaCJT1DIRw6E4T54fwue08uiuxqLXiBQrU4z/rZeVUnu01meKsC0hxAo9c3GIq8Nh2qtdXB7KzT3szs49lB4ZUQgrDQqX6qVroyRSBi9eGZ03OHzq/BDXR8Pcs6l21VwMCrFemU3qtvlsi5HKGPzoVD/jkSTv2NVIW/Xt75ExNC9dHUVrePna6Ko6Hngd1oLVYpzLiS7/1DxDn2OMS0Mh6irsvHtvs9REXgUKdqWnlDoD6Nw2PqeUuk52WKkCtNZ6b6G2vV50fuknpW5CSa3k89/8k8fy2JLVIZpMc64/SJPPMecJNJ7KcKonO/+rayxKhcNCOJGet2iwEKvNxlo3FwZCbKibe7+OJtNTcyGPd03k5WLw6nCIYDzNnhYfVhleJVYBfzTJ5aEwG2rdKypfU876/TG6xqIAnO4NzBocmk2KjhoXN0ejbKj15L0NXWMRRkIJdrf4cFjzN1eyVDbUurk4GMRts9AfiBGKpwnF0wwF4zRXOkvdPLGAQnYDvHsxL1JKVWmtJwrYDiEE8OT5Ia6PRLCYFL92/4ZZ02/bLSbaqp2c6wtypLOaOzdWk8roBQsCC5EP/f4YXqe14D3U79jVyP1b6nDNs187rWY6a7MXg/koQ9Hvj/GjUwMARBJp7t8i2flE+fvByWyP2onuCb7wwMZVU9dwKRq8DqrdNgKxFNsa5w783r+/hWgyM+sQyVA8RTiRpsm39MDHH03y/Tf6MbRmNJzk0d3FTxqTb9saK2ivdmE1K64MhxkMDFHjsZV9zoJk2mA4FKfB61jXN/AKdgbWWnct8qVPM0sNQyFEaUQSGSocFsaiSQBeu5nNdHbPppp1fbAUhfXytVGOXh/HbjXxK3d3FjRAVEotOAdGKcUHDrSSMTRmk2IklOB41zgdNW52LKMG2PRrasXau8AWa9PkfrvW9tjTvX4GAnGOdFZT5bbxK3d3YOj5i7/PddwIxlN89ZUukmmDezfXcmQFtVuLEXtfGwlzZSjEntZKWgrYizd5U3lHk5etDRWrYjjp997opd8fp6XSyUcPt5W6OSVTDhOIyn9vEWINePvORs4PBGjyOecs2ps2NP5oCqUUY+EEZ/oCnOjKdux77JYlJe/IGJprI2Gq3eV/t1BkxZIZ3uiZoM5jZ0vD7L1lg4E410bCbGusyOv/61g4ezMikTIIxwuXcXQ0nGA8kmRTnWdRFyuTr3nqwhCDgTgXB0O0V7uWnGChyefkffubCcbT7G6WQs9i8SYiSc4PBOmsdRf0Yj6eynBzLEJLpXMqsdP79rdwdThEZ417zfQajkeSPH1hGMge895/oAWlFOZlfrxQPE0ybQAwFp47KX8onuJMb4DmSufUVI1ALMVgMM5jexsJxtPsKvCxIWNofnp6gLSh6fPH+bX7NhR0e5NWQ2AIMJo7D41G1ndxhXIIDnWpGyDEeuC0mResITcaTtBa5UQDd26oJpUxpn5XucTaTs9fGeFktx+LSfGZezuLPiFeLN1zl4e5MBBCKfjULEG91ppvHe+hdzxGXYWNf/2O7Xnb9n2ba1EK6jz2gmXVC8ZTfOO1blIZzf62Sh7aXr/odSudVgYDcVw287J70DfW5X+uklj7fnxmgNFQgpM9fr7wwMa8lAMwDM25/iA2i4ltuWHTPzzZT58/RoXDwq/dtwGlFD7nymqPliOH1YTdaiKRMvA5V35eaql0ctfGGsYjSe7ZVDvn6358up8TXX4qXTZ+55EtOK1mvvl6N5FEho4aFx882LritizEpKDCYWEimsrLZ19r3rGrkfMDwYIH6eWuHIJDIUQZiCUzfPtYL2lDs6HWPTUp//E729GaJV+wRxMZAMYiSU71+LlnU+2quXu4Xk0GPSalsMzxf9U7HuPmWISRcILhUJz6isXvFxlDc2EgSIXDQkfNzGQwVW4b797bvPzGL0IybZDKZO9HRpOZJa379l2NbG/yUldhx2aR4dWieGy5Li2LSeWt9+6NHj/PXx4Bsr06m+s9RJJpINuDaGiW3ZNW7lw2C5+8s4OJSJL2WZLPLFUglsJpM3Nfc+28AdeZ3iDd41EGg3EMQ5MxNPFU9gbsUo9Hy6WU4mOH2xkMxgvaC10IWmsuDIRwWE0Fu9G2ud7D5nq5iVcOweEaPfwIsTY0eG+/+E+kM1wfidDsc+Kbo0fxwW11JNMGx7rGOXZzArNS3LN57ruqovQe3Fo3lZyh0nV7wWSlFA9syfbwVbttSx73cfTGGEevZ+ewfvxI27zJG7TWXBuJ4LSZ83YRU+ux845djQyH4hzqXFpviNmk2CBZe0UJvGdfM1eHw7RVuQp6g+2xPU2c6w+yuX5xQ66X4uZoBJNStNesPBjLB5/Tmrees+8c7yUQS/GGy8rn7p17mObOZi9aa6rcNpy5EQjv3dfMjdEIe1pnZkOOpzLcGI3QXOnMew+f02ZelceyE91v3tD4wIEWyaJeQAUNDpVSJuC01nr3PC97uJBtEELM7kxvgKM3xqZ6cfa1VvLBO1rp98cWHFLx0zMD3ByN4rKZ+bX7Nsw6zMljt3B4QxU3xyJAdj6jKG8Ws2nBkg1v391IY6WTKpeV+lluHMwnndGzPp7N5IWAUvDRQ21LTn8eT2U42eOnrsLOpml3mXc2e9lJeQ0ZSqYNfny6n2AsxaO7m4parFqUP5fNwt7Wyry+54G2SqxmhdVsmuopqfc6lvydXoyLg0F+dmYQyAa6xeqZGQ7F+dmZQdx2C+/e27TkEhGXBkMEYin2tfmwW+ZeN5M7t2UWOMe9b3/ztCye2XNmZ6171iDnR6f66Z2I4bFnh/ia5gnWj90c52SPn90tPu7aWLOYj7YqTf/7yvXE4hmG5mRvdorPnhbfokYfFDQ41FobSqlTSql2rXX3HK8ZL2QbhBCze+3mOCOhBE+eH+JQZxVj4SSP7W1aVC9NLJkdCpNIG2S0nvNA0lrl4p17GgnG0uxvq8xf40XJWM2mJSUmmu7uTTU4rGYqHJZZa4lNF8sNs9IaYqmlD7l69tKb8yd/5e7ObE9nmeqZiE7VWTvV66fRt/pT2YvyZjKpvAecc5k+ZDJWpOGTkL0BOh5JMh5J0jUWnZpbuRgDgRg/PZMrPZNM89C2uecnv/9AC1eGQnMm8ZpU4bByeJEjFuK5Y148lSGjNaZ5BtkdvTFOMm3w2o3xNR0cHmyvxGwCu8UsQz+X4FSvn+cuZXtcrWbTorJtF2NYaRNwTin1GhCZXKi1fm8Rti0KaCVF6EXpbWuoYCQUp9ptw2IyYVnCBJN37GrgdF+ADTXuee+oAmxvnHkgujIU4vkro7RXu3hkR/2ayYC3Ho1HkvzkzAB2s4l372uaMwvuJKvZtOg070c2VKNUdgjUpmXMLzGbsnfmFQpzme9jTT4HXqeVSCItFz1iystXRzk/EORQZ/Wqvrm2r7WSZNrApFRRE31sqvNwvj+I02amuXJpPaJmpVAqe3NqrvnXk+oq7NRVLJy5OZUx+MnpAfzRJO/Y3TjvsPp37mniTF+ATbWeBRNgbW2o4GxfgK0Na/vYYTGb1lxypGKwmEzTHi/uXFiM4PAPi7ANIcQS3bclW49pLJJgJJS4LYibTXYCfYYaj33eO6nzef3mBMFYirN9AY50Vs85Z1GUv/P9QUZD2ZTf14ZvnzezEjaLiXtnmaM6uQ8uVEriLdvqaPDaqfHYy34fc9ks/Oq9nWQMnZdMlGL1MwzN0RvZgVVHr4+t6uDQbFIl6dHqrHXzW2/ZhEmpeYdlzqbe6+BDB1sJxFJTPS3JtIGh9ZKHp07qnYhxYzTbR3Kqxz9vcFi7hHPs23Y28ODWulWXKCuaTGO3mCVRXYHtbvFitWSTzG2uX1zvecGDQ631c0qpDmCL1voppZQLWN43SwiRVzaLiSafc96T1KR0xuDrr/cwGkpwz6Ya7lzCyT6VMUimDdx2C1sbPAwF4zRXOvA4yiEnlliujmoXR2+M4bFbaK0qfOa7VMbgG691MxpOct+W2nmHaFnNpqINm8sHpdSSeu/F2mbKZRC9Ohxm6wLDFecTTaaxmk3LLr+y2q3kZktbtYvJMugTkSTfeL2HVMbgvfual5UMpcFrx+e0Eoqnaa50kkwbeQvoVltgeLxrgucvj1DttvHxI20LjkASy6eUWtTN/+kKfmWmlPoN4PNANbAJaAH+mnkS0Sil7gT+C5ABjmmt/5VS6neB9wFdwGe11iml1CeBLwLjwONa62BBP4wQq1DvRJTxSJIdTd4ZFwgjoQQvXxulwetY1F3dUDw91Ut0YzSy6OAwlszwtaNdhBNpHt7ewKHOava2ZpMhyJDS1UtrzcvXR0Fn039XLXFO37WRMPFUhh2N3kXf1Q/GUlNFim+MRhY9f6dcJdIZfnlxBNC8ZVv9snskxNr0nn3NJNKZZV84n+8P8ovzg7htFh6/s33B3vZCS2UMLgwEqfHYy7KMQjyV4dlLwyileGhb/YyAqz8Qm5oH2DUeXVZw6LJZ+Ow9nVwaDPKL88O8cm2Mjx9px+e0cnkohNYsaV7kanYz14M6HkkSiKao9y5+Hx8NJ3jp6ih1FfZ560qK5SvGkeKLwBHgKIDW+opSaqG+8i7grVrruFLqa0qp+4GHtNb3KaV+D3i/Uur7wG8CDwAfAr4A/FmhPoQQq5E/muQ7x/swtGY4mOCRnQ1Tv3vp6ig3RiNcH4mwsc49a7260XCCYzfHaa1ysbvFx/72So5eG6NnPMoLV0a4f0vdjNdnDM3FwSBVLttUdsnRcIJQPFs/q2s8O/Rwtd3lFLdLpA36/XFMJkXPeHTG78bCCfr9cbY0eGYNeLrGIvzwZD+j4QQWk+KBrXU8vKNhxmsGA3FO9kywsc4z1XNS7baxr81Hnz/OnYucu1jOzvUHuTCQvadZ67EvubyGKB+BaIru8Sgb69x5DcJW0qPSNRZBawgn0oyEEiULDs/3B3n52ujUecCkFJ+5p2PWcjmFYhiaV66PkUhnuGdT7azHpdO9AS4MhIBsCafpQ3kne3HjqQz7ljF8PpHOcGUoTIPXQZ8/jqE10WSGkVCcfn+MJ84OYmjNU+fNuO3mBeck5sPrN8eZiCS5Z3MtniLvG0c2VBNNZWjyOhY1X3O6l66Ocn0ke+2yuc5TkAy7610x9oaE1jo52UOglLKwQHUsrfXgtKdpYC/wbO75U8DjwHngjNY6rZR6CvhKntstxKqXMTQ693W7NfVzfYWdG6MR3HYzNpOJJ85mM7NN78F45uIwfRMxLg6GaK9x8dC2eq4Nh+n3x/jbl24QTqR5dFfjVA/gC1dGeKPbj0kpPn13B9VuGy2VTnY0eRmPJFd9T494k8Nq5q6NNVwdCXPXtEAtmTb45rEeEimD568M0+RzsqfFNyOT3+S+2O+PUeO2c7o3wJEN1VQ43pwb+Ivzg4yFk1waDNNZ48ZmMaGU4q3bZwaRixVOpHni7CAKeOeexgWT5xRDnceOKffdWeoFkigfWmu+dbyHUDxNfZ+dT97ZUeomAXBHZxX+WIpKp3XB7MCFEkmk+duXbjAWSTAUiNNZ62ZLQ8WCZR/y7dJQiNdyczgdVvOsPU51FXaUyiaxqvXMDFztFjPv29+y7O3/4twQV4fD2Cwm3r+/mdFwAk+ujNQr10a5OJi9SVTlstHgddw2J3E4GOepC8NUuay8fVfjiufp9YxHefHK6NTzt+8qbobktmoXn75red+T+goH10ciuGzmGecMkT/FODs+p5T6PwCnUuptwG8DP1rMikqpvUAt4Cc7xBQgAFQBlUDwlmWzvcfnyQ5rpb29fVkfQIjVqsZj5z37mhkNJdh3S0KDezbXsrneQ4XDyvmBN++Y1lU4pkoVVDqt9E3EcFrN2HJDUrc0VPD6zXGsZhMXB0IcaKuaqsuWTGdLXBhak85kH5tMikd3S2r+tejuTTXcvWnm8GJDazK5GoanegIk05rBYHxGcLipzsPbdzVQ67ExHErQUuXEfUuw5nNaGQsn8Tgsi86wNp9zfYGpHs4LA6Fll+PIp7ZqF5+9pxONLmovisi/ZO54l8odA8tBfYWDTxwp7XXPie4JDK25MRqhzmPHYTHzwJY6ajzFvRnic1qnso/OVVR+Q62bz9zdiUmpvCexmjw3pjPZ7/rHp/2/jIQSVLpspDMGzT4HKHVb1uJjXRMMBeMMBePsbPbSUbOyAvAee/a4mjZW37Hn7k01bKpzU+Gw4rTJUPxCKEZw+CXg14AzZId+/hT4nwutpJSqBv4b8FHgDrJzFQG8ZINFf+7x9GW30Vp/hVyv4qFDh6Rqplh3NtV55iwF4HVasZgUdR7HVA9G/bQejId3NLC1oYIaj22qN/HBrXU4rSZeujpGpctK5bST6ANb6/A4LNS47TLUY51yWLN32LvHo1S5bExEkzTOsi/savaxq9lHMm3MOv/0XXua6J2I0eC1LynTYDyVwWxStyXgaKt2Yb05jlKqKMlzFqvcM6mKhSml+MCBFq4NR9jetD7mjC1Wg9dBW7ULBVS6bHTUuPKa1XixmiudfPLODlIZY2rKw2yWOnd6ukgijctmnnUu/dt3NXC6N0BLpfO24b2NPieN3mzB+0/e2Y7VcnsCoY4aF5eHQnjsFmrzEFhXuW186q4Owol0yXqVV6JY1xdznU/WumJkKzWUUn9Pds6hBi5precN0nJDT/8B+F2t9aBS6nWyPY5/CjwCvApcBnYrpczTlgkhFuniYJAnzg7isVv4xJF2PntPJzDzYtVsUrNOvD+yoYZdzT7sFtOMbHBzDdcR60t7jYv2Ghd3baxmLJKkZp4Lrrnmn1rNJjYsMenDzdEIPzzVj81i4mOH2mZc6DVXOvn1+zcCSOIXkXeLzfq83mxtqKDWY8diVpiUwmktXemCQg7dfvrCEKd7A3TUuPjgwdbbfl/hsM5amgfg3s21bKn34HVa5zw27Wr2saHWndfMs1Vu24qC4bVuvvPJWleMbKWPkc1Oeg1QwAal1Be01j+bZ7WPAIeBL+fuwPw+8LxS6kWgG/jzXLbSvwFeACbIzkMUYlE6v/STFa1/808ey1NLSufmaDZZQSieZjiUWPKF+HzJDbLJD8xlMa9LFIZhaEbCCapctjkDPIvZREMRe5C7xqNkDE0smWEgEL/tZC5BoRALi6cyBGOp3By8lQdy1evgovr6SDb7ZtdYlHTGWLCERsbQjIYTVLttWM2mRfWEyfm0uKafT/oDMQkO8+w/k800ehVAKbUJ+AkwZ3Cotf468PVbFr8CfPmW130V+GpeWyvEOnGwvYqxSBKH1USlM3+Hgsn6RQ6rmU/d1S4Txteon58b5OJgiFpPdnhSIm2QSBklHSa5t8XHgD+Gw2pmU/3K5uQIsR6lMgb/eLSbQCzFvjbfshNAlYLWmoloCq/DsqL6hstx96YaTnRPsK2hYlHb/vHpfq6PRGj05X9eaCyZIWUYeOXcuyIzzidzTM1Zq4oRHA5PBoY514HhImxXCDGPeq+Dt+1s4J9e7+F/vdLNe/Y1sTEPB8ChYBzI3n32R1MSHK5Rg7n/57FIEn80xbeO9xBJZHjLtjoOtJcm2UuVe2aiByHE0sRTGQKxFABDwUSJW7M0T10Y5mxfgHqvnU8cbl/SXOWV2t3iY3fL4udSTp4nh4MJDEPnra0TkST/+Fo3qYzBu/Y0TZUBEku3ns8nxQgOzymlfgr8E9k5hx8BXldKfRBAa/3dIrRBCDGL4WCCVC6z5EAgnpfg8K6NNaQyBtVuW1kl/hD59dbt9RzvmmBzvYdIMk0kkU0o3eePlSw4FEKsTIXDygNba+kai3LnxpqFVygjfRPZbMTDwQTJjIHDVL7DyB/e0cCpHj/bG715DWKHQ4mpzKh9EzEJDsWyFCM4dABDwIO55yNANfAessGiBIdClMjWhgq6x6Mk0wZ785RBrtptm7MeVDJt8J0TvYyFEzy6u5HN9XLiWq06atxT6dS11uxr8zEWTnLnhsVdUJ7vD/L0hSEafQ4+cKCl6MPAhBCzu6Ojmjs6llaT9tlLw5zqCbC31cdD2+sL1LL53b+1jmM3x9lc7ynr+cWBaIpfXhwmY2ju25zfeWyb6tzsaKogmsxwUG7SiWUqRrbSz833e6XU72ut/7jQ7RBC3M5mMfGuPU0Lvk5rzc/ODtI9HuXBrXXsaPIuuM5shkNxBgPZ4TTnB0ISHK4RCxWnvzQY4peXhmmpdPLYniZMJsW5/gBpQ9M7EWMimpIi8EKUiVA8xfdP9pPOGLx3X/OiahKe7QtgaM2ZvkDJgsP5yjaVkxtjEULxNABXh8O3JaOZPF62Vjl51+6mJfUsWswmHt298DldiPmUw63aj5S6AUKI2aUyBhORJBPRJC9cGaFvIsbJHv+y36/B66C1yonTZmZ38/ICTLH6nOrxE0tmuDocZiKaBGBvayV2a7ZcxXzZDKPJNCe6JxjOzdFZjFA8RSyZWXG7hViPro9EGA0l8EdTXBoMzfvaiUiSVMbgQHsVNouJA+2VeWnDWDjBie4JQvFUXt6vnGysc+N1WrBZTGxtnHmDNGNofny6jwF/jCtDYfyxtff5Rfkrh7y4pSl4I4SYVzpj8I3XuhkNJ3HbzYyFkwRjKe7ZtPx5KFaziY8castjK8VqsKPJy0AgTlOlg0pXNhDc1ljBtsaFe45/dibbY22zmPj1+zdgt8w/XOzqcJgfn+7Hajbx8cNti+r1EEK8qaPGRYXDQiqj2VQ/d0/c85dHON41QY3HxuNH2ues47dUhqH51vFeYskMFwaCfPLOjry8b7nQBiTTmlTGYCSUmFHU/tXrY4yEktwcjfDwjnp8TknoJoqvHIJDXeoGCLGWZYzsV2yxhYczhuZE9wTJdIaRUAKlFCOhRG4oqb7tTqcQC9nT6mN3i3dZNdMMnd1/tdboRZwt+v0xtM7Obx0KJvISHPqjSc70BeiscdNW7ZparrUmbei8FaUWohxUumz8+v0b0VqTMTSptMGpXj8mk+JAW+XU97h3IgbAWDhJNJXBm8fvweT33jDW3iXiSDhOPJVL4DURY1OdZ6pWbMbQ2CwmmiodPLitftHnbSHyqRyCQ9nzhSiQ4WCcb5/oRaH4yKHWGXco53KyZ4IXr4wC0JLLNnq4sxp/LIXdYloVczpE+VluMe1Hdzdyrj9IW7VrUUkm9rdXMhpO4LCa2dKQn331Z2cHGQzEOdnt5/MPbsRuMWMYmm+f6KVvIsZ9W2o53Lm0BB5ClLtrIxF+emYAfzSJw2rGajZht5jY1ZxNXnbf5lpeuT5Ke7U7rzX1TCbFhw62cmM0wvY1eDNyQ62Hnc1eQvE0yXSG//7Lq2yq9/Defc3sbvHyk9P9uO0WzvcHJduoKIlyCA6/VeoGCLFW3RiNkEhl01p3jUUXFRzazG9egB/urM5LeQshlqvCYeWuJaTU9zqsfPBga17bYMv1iFjMJky5IDeUSNOX6zm5NBiS4FCsOVeHw2QMTSyVIW1oqly2GcO622tctNcUpg5cg9dBwy2JWtYKs0nxjl2NAHzl+WsAXBsOk84YOKxm2qpdpDJ6qjdRiGIreHColPpT4D8CMeAJYB/wL7XW/wCgtf7/FroNQqxX2xu9XBkOY1KKrYvsRdnT6sNhNWExZ5OFCLHePba3iavDYZornVNDSL0OC7tbfHSNRTjUKSnjxdqzr83HYCBGR42L7Y1enDaznBPy7MiGGo7dHGd7oxeLOXve/djhdoaCcek1FCVTjJ7Dt2ut/41S6gNAL9nspL8E/qEI2xZiXfO5rHzqrsVP5jcMzXNXRgjGUjy4ta6ALRNi9XBYzexumVkHVCnF23bOXb5jLQhEUzx7eRivw8qDW+vyWqxblL8mn5PP3ruh1M3glWtjDARi3Lu5ds31Ju5vq2R/W+WMZXUV9kWV9hkJJXjx6gj1FY68JQMSAopTymJyIPq7gK9rrceLsE0hxDyuj4T55aVhxiPJGcu7x6Oc7PZzfSTC0Rvzf1VjyQxPnB3kmYtDpDNGIZsr1qGXr43y49P9+KPJhV+8hpzpDfD85ZGyKMVx9MYY10cinOzx0zUeLXVzVq31ui/nw2g4wavXx+gai07NhZ/LWDjBLy8Nc3M0UqTWLd3N0Qg/ONnHxcHgit/r5Wuj3ByN8tqNcYamlfqJJNI8d3mE8/0r34ZYn4rRc/gjpdRFssNKf1spVQcsvmCVECKv4qkMPz49QMbQDAbifOLIm3NGqtw27FYTiZRB4wJ3aN/omeDCQPbkU1/huK1nRYjl6vPHOHo9e3NCoXhs7/oo6tzvj/HUhSEgm231kRL3TDb5nJzrD2KzmKh2zV2LUsxt+r5sUop37Vkf+3K+eOwWKhwWQvE0jb75z0k/OzvISCjB2d4AX3hwU1nO2XvqwhCheJqbo1G21FesKBtpo9fB9ZEIbruZCsebl/PPXx7hYq4+ZW2FjfqKtdXbKgqv4MGh1vpLSqkvA0GtdUYpFQHeV+jtCiHg6nCI4WCC/e2VuGzZr7vZpLBZTMSSGVy2mdkffU4rn72nk1gys2AJgLrc701KUeNZGxeO8VSGZy+NYFLwlm31ZXlxsRpFk2lOdvsJxFIk0gYH26tor3HN+XqvwzJ1k2Ixw6vWCrslm/DG0BqnbeHMrIW2p9VHc6UDh9WM214O+etWn+n78mISguXTmd4A0WSagx1VC5Zb0Vrz0tUxxqNJ7t9cS5W7PI7pDquZT93VQTCeWjDImTyf2a0mynUEdK3HTiieJpFO859/cYmD7VXLvgl058YaNtZ58NgtM44Xjtxji0ktWBdWiNkU62i/A+hUSk3f3v8q0raFWJcmIkn+/uUuEukMo+EE793fAmQL0X/icDsDwdisyQVcNstUIDmfLQ0VfOouG1azmipsvtqd6QtM9YbWVdg50C6JRvLh2UvZIU7HusbZ21LJRDTJ5+aZy1ThsPIrd3cSXkRvQb4EYikuDgTZUOumvkTzmmo8dj5+pI1gLFU2JWPyUSdyPSvFvgzZqQOTvdAZrbln0/xz0vr8MV6/me3htJjKq4fTYTUvqozNu/Y0cXMsQpPPiSUXDBuG5kxfAItZTZXgKKV3721iIBDnj358nkAsxfn+IAfaK5f9PZvt5tkDW+poqXRS5bLhc2ZndqUyBqd7A/icFjbXS6IbMb9iZCv9KrAJOAlMTqLQSHAoREENBOJcGQ6hNbRVZ+cLpTMGaUPjc1nxuVZel2qt9erUeewolR3KWOy7/GuZzWzCbMre2TcpFjXMyWO34Clib9WPT/czHExwvHuCLzywqSjFp+OpDFazaca21nIK//Wq2PsyMKOn0LaI4vQ+p3Wqh7N+lRzXDUOTSBtTvWYOq5ntjd4Zr3mjx8/zl0eA7N9hS4kzgFrMJloqnVR7bARiKVy2/PXKx5IZ7Jbs8eTWTKevXBvjeNcEAB87bKG50pmXbYq1qRhHq0PATq21LsK2hBBAz3iUo9fHaPQ6cNrMHGivJJJI8/XXuokkMjy6u5FtCxQXnogkSWaMdXWh2lnr5jN3d6IUa6Y3tNjCiTQvXB7BZbdw/+ZaTCbFg9vqaPQ5+NAdrVjNJhpuCQ6HgnFsZlNJh7Ip1Ix/C+18f5BfnB+kwmHl8SPtZTGMVBTH1eEQ5/qD7GnxFayObFu1iw8dbCWaSrNtEQFRhcPKZ+7uJJJMF2WOWsbQ9Ptj1FXYF9UrONv633y9h6FgnLs31cxZC1Wp6Y9LO9Y0lszw3OUR7BYT//ptWzmb2weW8/knaa3pnYhxbSTMG91+GrwOPna47babW6Zpn73EfwaxChQjODwLNAIDRdhWUXV+6SelboIQs3rm4jD+WAqv08r79jezv62KrrEIoXgagBujkXmDw+FgnG+83kPG0LxtZ8O6SjZTLnNtVqvXb4xPJUNoqXSwub4Cq9k05z50ti/Ak+eHMJsUHz/cVrIhne/Z18TloRDt1e6i9BreGI2gNQRjKUZCiXnnYIq15Ymzg6Qymn5/nN96S+GGDy91n3LbLUWbW/rE2UEuD4WodGWH3S71OxeOp6cydF4ficwZHB5oq8RmNmE1m9hcX9qh2m90T8yYtvCWbfUrfs9nL41wssfP5aEQG+vcDAXjhOPp20YG3bWxmgpHNrlPk096DcX8inEUqAXOK6VeAxKTC7XW751rBaVUM/BjYCfg0VqnlVIB4I3cSz6otR5XSn0S+CIwDjyutZa8vUIANR4b45EkG2vdHGirwmRStFe72FzvIRBLcbC9ct71/bEUGSPb2X9ruQsh5jM5HNdiWtxc1Mn9K2No/LFUyYLDCoeVOzqqi7a9OzqqGI8mqXbZaKmSi7X1pMZjZzAQp3aNJPJajrFI9nIwEEuRyhiYTUvrPfO5rOxvr6R7LMqdG+f+3iqlyubmZm1F/pO4jYazf8c6j50Ku4VtTd5Zp4xYzCb23VJPUYi5FCM4/A/LWGcceBj43rRlZ7TWb5l8opSyAr8JPAB8CPgC8GfLbqUQa8g7dzexvy1Grcc+VbjaYjbxnn3Ni1p/c52HOzqqiKUyHOqUpCxi8fa0+mjw2rFbzVPJEOZzqDO7nzmtZjaXSRKWYmj0Ofj0XR2lboYogQ8dbGU4FF9XQ/Zv9fCOBt7onmBjrWfZwyofykPPWzFtbaigKs9J3N6yrZ6jN8ZorXKxX4I/kSfFKGXxnFKqATicW/Sa1np4gXXiQPyW8eE7lFIvAC8Bvw9sJRswppVSTwFfyX/rhVidzCZFa9XMIUWpjMH33+hjNJzkHbsa6Kxx8+LVUcKJNPdvqaXC8eaFvMmkeGBrHRlD8+r1MTKG5u5NNQumQ59kGJoT3RNo4GB7VVGG6Yni+uXFIZ66MMyWeg8fOdQ2YzhaXYWdl6+NMRFNcv/mOiocFl69MUYybXD3ppoZ6dVdNgvv2NVYio8g1qmusQg/OztIlcvK+w+05DXd/8XBIKOhJAc7KufM+myzmG47Pi/H6zfHGQjEuXtjzVRysFTG4JVrY5hNirs21pTtsbel0knLOkqKEoil+N6JXoaCcVw2M2ORFEc2VPGuPYu7YTuXugo77967svcQ4lbFyFb6UbI9es8CCvgLpdTvaq2/vcS32gJMAH8NvAcYAyaHkQaAWbs3lFKfBz4P0N7ePttLhFjzJiJJjt4Y4+pwGIfVzJm+AIYml71ME0mkeffe5tuSYpzrD/DajWx6c7fdPGPYXTiRJp0xZr0Deq4/yAtXRoFs1jy5o7m23BiN8IOT/QwE4kSTGQ5vqJ6RJv76SIRfXhzGZTNjMSk6atxThcDtFjMHOyoZDSdp9DrK9uI1X7TWnOsPYjYpdjR5F15BFNy5/iCxZIZYMsOAP07nLCV9lmMklOBnZwYBCCdSPLp75eUgJoOJ6TfvIDuc8MXcMTadMfjgwVYATvX4p7JSeh1W9rTmf0hlNJkmnjKoLrP52ZPfNYtZ3Za1tNSujYQZCMQ50TVBnz+GzWKiayzCvraqqSA5lTGYiCapdb854mcuGUNzrj+Ay2Yp+VxKsfYUY1jp/wkcnuwtVErVAU8BSwoOtdbjufW/DxwAfgBMfvu9gH+O9b5Crlfx0KFDkjFVrEvfOdGLP5qi3x9jW2MFu5q9VLmsWM2Ky0NhhoIJArEUv3J354zC795pFyTTL05GQgm++Xo3aUPz2J6m29KDO6xvvoddCsmvOXaLiRqPjZFQgkqXlfbqN3tBUhmDn58f5PxAkLoKOw9uy/YcKgVaQ4XDzD+93sNoOMmmeg/vXeRQ59XqTF+Apy9kB8uYlFowS7AovB1NXm6MRqh0WfNae9BqVphNioyh89IbeezmOC9cGcVmMfGpuzpmDNP22C24bGaiycyMkkJe5/Rjdv4v8QKxFF872kUiZfDIjoaCBJ/Ldbo3wDMX3/yu3VrOoZQ21rrxOq2YTdljZDyVIZkx8Ob+j7TW/NOxHoaDCbY3VvDOBepMvnZjnFevjwHw4TtaaauWhFYif4oRHJpuGUY6BizpalEp5QbiWusMcC9wBrgM7FZKmYFHgFfz1F4h1qxGn5OPH2mfShry6bs7+eZr3USSGULxNPF0BpvFhGFoXr42RjyV4f0HmrFZzDOGAI2EEqQy2XstA4H4bcHhloYK3n/AhKF12RTzFvnTXOnkc/duIJrMsKXeMyNFfCyVIZEy2Nfqw+eyTfU2f+JIO8m0QZPPwdMXsnXHRkKJWd9/NTjeNc5IKMndm2rmnVs5vYiTZub9yT5/jFA8xdb6igV7CkT+bKh188WHNuf9fStdNj52uI3xSDIvgclkspFk2iAYS83YzxxWM5+6q4NALEXTtAB3a0MF7sMWTIrbslJeGAhyYzTCHR1Vy57vOBFJkkgZAPQHYmUVHE7/di21eJrWempkTSECrUqXjS8+tJl7NtXwj0e7yRia+7bUcmM0Qt9EjAPtlVPHw/5AfOH2Tvu0S/mswXiK7rEoG2rdRctMK1afYuwZTyilfg58Pff8Y8BP51shl2zmZ8A+4OfA/wH8lVIqAlwH/kBrnVFK/Q3wAtnhpo8XqP1CrHofONDC1452k8wYfPP1Hn713g04bdmEIe/d38LRG2O0V7umegovD4d4/WZ2GKDTZubezbUz3m9Lg4eeCS/xVIaDHbMnrNmQp6FaojzNNWfK67By35Zausai3DUti+D0i9G37WzgynCIA22rM9nRYCDO85ezQ/oyhuaxvXPf5d/T4kOpXK/htIBhOBjnW8d60BomNqa4e9PsqfjF6tLgdeQt0czdG2tJG5oql43WWTLazlV6Yra5fPFUhp+fG0TrbIbgTy0zGVJ7tYt9bT6CsTR3bihedt/F2Nviw6Syc+6X2kP/Ro+f5y5lb1oVsiduf1slkUQGfyzJvtZKvn28F8hO03hoWz2Xh0LcMcc5dbojndU4rWbcdsuSSpb80+s9hOJp6irsy94HxNpXjIQ0v6uU+hDZHj8FfEVr/b0F1kmR7Q2c7uAsr/sq8NV8tVWItarGY6et2snN0SjpjCZtGEB22FOjz8H79rfMeL3PacWkFIbWVM6SFttqNkkSETGnw53VHO6c+8JxZ7OXnc3lNSdoKdx2MzaLiWTaoGqW78d0JpNib2vlbcsTaWPqjn8inSlAK8Vq53NZ85ZsxGJSeOwWQvE0VSvIlGkyKd66vSEvbcq3ub5rizHZGwrZ72ahKKW4b0v2Zms8lZkaGlzttrGvrXLR5SYsZhMH2pd2c01rTTKT/WyF/Ixi9StKn7LW+jvAd4qxLSHE7B7Z0cDJHj/Nlc7bkhvcqsnn5FN3tZPMGFIwV4hbVDisfOquDoKx1Kw9OovRVu3ibTsbCMZTi+opEGIlLGYTj9/ZzkgokZdMqWvNoc4qlMoO1y1WgheH1cwn7+pgPJxc9nFkKZRSfOBAC1eGwmxvKp/5mKL8FCw4VEq9qLW+TykVYuZQcAVorfXqvW0sxCoST2U42ePHbTezv61ywcBwUo3HvvCLhMijRDrDG91+Kl3Wsss2eCuf07qoOo7zKUZx7oyhCcfTeJ0WbikPJdYZl81CR83Cl33dY1F6J6LsbvXNSEpWLrTWBGNpPA5L3rIdW80m7tpY3KHdkUSa0z1+miqdS55zbBiaYDyF12Fd0rpNPqfc8BULKlhwqLW+L/ev3J4QooRevDLKsa5xzvQF2NHk5UMHWyWlvihLL18d42SPH8jOXWxeR3XQCuW7J3rpnYixo6kiL6UVxNoWTab5/sk+MoamPxDnw3e0lrpJt/n5uUEuDIRorXLykUNtpW7Osj11YYjrIxFMSvG5+zqXFIj/6HQ/10cibKxz3zYtRIiVKniOeaXUbXMCZ1smhMiveCqD1hqrxUQ0mSGT0SigZzxa6qYJMSurOXtKUgos5vLu5TIMTTxV3nMFM4amzx8DoFu+92IRTEpN9cZZi/QdjKcyGMbiU25O7st9/hiZJaxXbiaPd2YTmOfo1U+mDdKZ2+cHTp7H5XwuCqEYcw53TX+ilLIAdxRhu0KsW0+cHeTCQJBtjRW8Y1cjVS4rZ3r9WC1mmd8kytbdm2qoclvxOqzUV+Sv/ly+JdMG33y9m7FIkoe21S86iUSxmU2KB7bWcXEgxMGOylI3R6wCDquZjx5qYyAQK0qdwNO9fp65OEy1O1sGZDH1IR/YWseJLj/bmyryNqy0FB7Z0UBblYsGr33WrLM3RyP88FQ/douJjx9pnzGM/cGt9ZzpC7C3jEqJiLWjkHMOf59sCQqnUio4uRhIkitKL4QojGsj4ey/w2HMe7IZ3JabxU2IYjGbFLuay/9ixx9NMhpOAtnvWrkGhwAH26s4uMSshmJ9q6uwU1dRnDnnV4fDaA1j4ST+aIoG78LB4fZGb9nPSV4Mm8U0b53IG2MRMoYmmszQ74/NCA73tPrKqsakWFsKNqxUa/3HufmGf6a19uZ+KrTWNVrr3y/UdoUQcNfGGipd1ttqpw0H4zxxdoCLg8GpZYFoipevjU4NPxNiNRsKxnn52igTkeS8r7s6HOaJswPL2u9rPXZ2NHmpdts41FFetd7E+nFjNMIr18aIJtOlbsq8ZjvvTDrUUU2128aOpgrq1mEStGgyzSvXxrg5GgHgXH+AJ84OMhpOsLfFR12FnY4aFxvrpG6wKJ5i1Dn8faVUFbAFcExb/nyhty3EenVHR9Wsw0efvDDEcDDBxcEQHdVunDYzPz07wGAgzomuCT7/wCZsloJPRRaiIAxD850TvSRSBleHw/zK3Z2zvi5jaH56ZiCbdMMf51fv27Ck7ZhMikd3S51PUTqBaIofnuzH0JqxSCJv9RAL4RfnhxgJJbg0GKazxo3D+mbvYHuNi8/c01m6xpXY0xeGuTocxqQUH7qjhSfPD6E1BOMpPnqoTQrVi5IoeHColPp14HeAVuAkcBfwCvDWQm9bCPGmE90T9PvjgKbKZZtKNjA5Z8NkUhQ60333WJSXro3SVuWaKgQsxFx6xqO8eHWUlkonD2ytW/D1SmWLfSdgxlyks30BRkIJDnVWUeGwYlJQ4bDgj6ZWXI5CiFIwmbL7Ozq7z5/u9fPdE31sa6jgo4fbymouXqXLykgogdtuxlJG7SqV6ccji0nR748xEU1xz+ZqnFYz0WRGjkuipIqRkOZ3gMPAq1rrh5RS24E/LMJ2hRA53WNRnrs0gglornLx3n3NWHKZ0t65u5H/9UoXChgNJwpaA+mla6MMBuIMBuLsafHhc8kJUMzt5Vv2lyq3bd7XXxwMkc5oqt023rsv25MyGk7w5PkhACLJNO/e24xSio8fbmcwGKdFymWIVajCYeUjh1oZDibY0eTl975zin5/nGvDYe7cWM3GujcLuWuteebiML0TMR7YWseG2uIOUXx0VyO7mmPUV9inzjvr1UjozeNRNJnhga21PHt5hI5qF2d6g3z8cBs/PNXPgD9Gz3iUtmpXiVss1qNifEvjWus4gFLKrrW+CGwrwnaFEDkOmwmTUphMik11nhmZ0fzRFMm0QTxlcLxroqDtaM+d6Go8Ntz2hRMPiPVt8sKo2m3D41j4XuYr18ZIpA3GI8mpNPF2i2mql3z6fu+0mdlQ65Zh1GLVavI52ddWic1iotGbvcnhtpupvSWZzEQ0xeneAOORJK/dGCt6Oy1mExtq3bNm5Fxv7NY3j0cuuxmP3cKBtko8Dgvt1S6iqQyj4SQT0VTBz8dCzKUY39RepVQl8H3gSaXUBNBfhO0KsW4MB+NMRFNsrvfMOpyovsLBJ460EUqk2XjLXeNajx2f00ownir4HeV7N9eyu8WH22Ze93eQxfyGgnFq3HY+d08nbodlKtibz6Z6Dye6JmitcmLPBX0VDiufONLORDTJxlrPAu8gROFMRJIMBOJsrvfk/abEP394M5cGQ2ys9dxWTN3rsFBXYWcklJjRoyiKzzvteFRht3JpKMQHDrQQTxl4nRaSGYMqlxV/rPDnYyHmUoyENB/IPfwPSqlfAj7giUJvV4j1IhBN8Y3Xe8gYmv3tlTy0rX7W19V7Hcz2G6fNzGfu6SSVMWYkCiiU5c6lON3rJ54yONheKYFlCUUSaU72+GnyOQp2oTkeSfKN13owtOZwZ/Wi56c+uLWOw51VOK1m1LQJtDUeOzXrMBOiKL4rQyFGQgkOtFfhtL15PE2kM3zj9R7iqQxXRzxTw57zxWWzcGCOkiUWs4nHj7STSBsz2iSKL5HOcHkojNWseOLsIKmMZk+Lj0d2NgBgt5j59N3FOx8LMZui9PErpcxAA3Ajt6gR6C7GtoVY6xKZDBlDZx+nMst6D7NJYTaV74no6nCYpy8MA2BozV0baxZYQxTKUxeGuD4SwaQUn723syCJExLpDIbO7tOxJe7TLpsMXROlMRJK8OPTAwAE4+kZGW0zhiaZNgCIJ5d3nF4Jk0lJYFgGXr46xskeP7FkhozWeOyW245x5X4+FmtfMbKV/nPgD4AhwMgt1sDeQm9biPWgvsLBO3Y1MhpO0Frl5GxfgO2NFUXvXbs0GGI0nODgLXfM88E+bQjWYoYXisKZ/L8wmyhYRsQmn5O37WxgIpqcqiM4Fk7Q74+zpcEjd9SX6PJQiOFggoMdlRI8F5DVrDCbFBlDY7PM/G64bBbes6+J7vEo+9sqS9PAIgvGU3SNRumsdVHhWNxNpGsjYfr9Mfa3VS56ndVk8vzlspu5c0M1qYymym3jylCILQ0VTESSnOkLsKHWLcloRMkUK1vpNq118WdBC7FO7Gz2MhFJ8r9e6cLQmpFQgoe2zz68dLFevT7G0evjbGv08OjupnlfOxJK8NMz2TvmoXhqwdcvVVu1iw8ezM7L2Nogc2ZK6a3bG2itclFfYcdTwAQTu1t8U48T6QzfPNZDImVwbSTM+w+0FGy7k5Jpg++c6GUsnODR3Y1srq8o+DYLYTyS5KdnBtAaArEUj+3N73dTvKnSZeMjh1oZCyfZ3nj7/rKxzlNWc/601vzwVD9dY1Hu21LLwTmGpS7Xd4734o+mqHbbFlXLMBhP8aNT/WgNY+FkUb7nxXb3phqiyTTHuia4PhrhQFslPz+XzV76tp0Gp3sDDAXjnOrx84UHpe6wKI1i7HU9QKAI2xFiXUsbemooXiJtLPDqhZ3tC2BozYWB0NRwqLnYzKapXqRCncw6atxsa6yYMZdMFJ/NYmJ3i496r6No29QaMpnsvr3Qvpgvw6FsCY1URnN+IFSUbRaC2aQw574zdrnQLLgmn5PdLb5VMS86nEhzfSRCxtCc68v/ZdrkdzWVWdx31mJSU3UQ1+q+ajYpUhmN02pmOJhgwB+f+l0yY0ydP60WE1ISUpRKMXoOrwPPKqV+AiQmF2qt/++5VlBKNQM/BnYCHq11Win1u8D7gC7gs1rrlFLqk8AXgXHgca11sICfQ4iyVldh59HdjZzvD3CgvXJJ614YCPL0hSGafE7ef6AFs0mxv62SozfG2dZQsWDA53NZ+eihNsYjSenZE3nnsJp53/4Wusej7Gn1LbxCHjR4HbRWORkNJ9nd7F3WexzvmuDlq6NsrPPwrj2NK7qxMRpOEEmk6ahZWgZDn9PKRw+3MRpOsK1hdfZ+isLw2C1sa6zgxmiEfQUY6vqBAy1cHgqztfHNc8IvLw5zti/Agfaq2xJNuWwWPna4naFgnK237Kv9/hhKUdA6vMWyq9lLz0SUapeN+7bUUu2xYWjNvtZKdjZ5uTocpqXSOesNhucvj3Cqx8+eVh9vuSX5XJ8/hlkpGn3Fu3En1qZiBIfduR9b7mcxxoGHge8BKKXqgIe01vcppX4PeL9S6vvAbwIPAB8CvgD82VIa1vmlnyzl5UKUrXgqQ/d4lGvDYbrHY3znRC+fu2fDouf+ne0LkMpousejjEUS1Fc4ONRZzaHO6kW3odHnkJPSOuWPJhkJJdhQ6y5Yj0l7jYv2muLNwbGaTXzkUNuK3uNMr5+0obk8FOKh7XXLnu83Fk7wj0e7yRia+7bUcngJ30vIBroNRezpFauDUop7N9eypd5DZwHKJtR7HTNGGGitOdXrR2s43eefNQtxXYWdulvqNF4dDvOjU9kKaO/d38ymMhqauxytVU4e3l5PfYUDu9U8I8us2WSeMaT+Vmf6AqQNzenewIzg8OJgkJ+dGUSpbFC+1JtIQkxXjFIWf7iMdeJAfNpd1iPAs7nHTwGPA+eBM7lexaeAr6y8tUKsTj882U+fP8bNsQgd1S4SKYPkEtKW72n1MRxK0ORzUOOWlP9i8eKpDP/4WjeJlMGOJu+MDI3r3d62Sl6+OsqmOg/OFSTRiSTezEgcjKXy1TyxziXTBl9/rZtYMsPmeg/vyXN5jVsppdjXVsm5vgD7WysXvV4w/uY+vxb2/yfPD3FxMITDauZz93YuKcHW3lZfruewcsbyUDwNZIfgTz4WYrkKFhwqpf5ca/0vlVI/IpuddAat9XuX8HaVwOSQ0QBQNcey2drxeeDzAO3t7UvYpBCrRziRPRm0VjnZ1eKjpdKJz7X4TG/bG71sb1ze0DmxviUzxtTcokhCLkqmO9helZckH+01Lu7fUkswnpIyLiJvMoYmkSrud/ehbfVz1uKdy94WH5FEGoVizzy9aqtFKPe3TqQzpI3bLo/ndf+WOu7fUnfb8n2tlUSTGawmxY4mOZeLlSlkz+FXc//+X3l4Lz8wmbbKm3vuzz2evuw2WuuvkOtVPHTo0NK+hUKsEo/tbeJsX4DN9R4ZTiKKyuuw8s7dTfT5o3nPdijetJQh3kIshtNm5rG9jXSNlXd5DYvZNGtAtFq9bUcDJ7onaKt25S3js81i4sGta+dvJEqrYMGh1vp47uExIKa1NgCUUmZgqePWXgd+G/hT4BHgVeAysDv3fpPLhFiXZE6RKKVtjRVsmyV1vxCivG2ur1i1ZVpWqyq3jYd3NJS6GULMqRi5gp8GpmcRcJKdNzgnpZQ1N49wH/BzYAPwvFLqRWA/8H2tdQr4G+AF4DPA/8h/04UoX4ahSaQzpW6GWGe01sRTst+JtSGVMUgvstSCECsl522xGhQjW6lDax2efKK1Diul5k05lwv8Hrll8VHgy7e87qu8OXxViHUjlTH45us9jIQSPLitbtbhfL0TUb57og/D0HTWubGaTNyzqYYq92KTBgsxk9aabx/vpXcixpEN1dy7+fZsg4WWMTTPXBwmFE/x1u31VLqWtz9fHQ5zYSDI7hYfGwqQqbFcaa156eoYwXiK+7fUUuFY/NzktabPH+N7J3oxm0x87HAb1evg2Hi618/FgRAH2ivZsoLSJkevjzESTnDvptolnVO01rxyfYxANMW9W2rxrqP9b/K8PRSM47FbqKuw89C2eq6NhBkIxLl7Uw21nsIkhDveNcFgIM5dG6upKdA2xNpRjJ7DiFLq4OQTpdQdQKwI2xVizfJHU4yEsmVDrw6HZ33Nz88N8dLVUZ6/MsK3X+/h8lCIF66Ormi74US6aEXIRfmJpTL0TmQP39P3u0AsNZVNs9BujkU42xegayzK6zcnlv0+T5wd4OpwmJ+fG8xj68rfzbEor98c59JgiKPXx0vdnJLqGo2QymR7wq8Nh4km13ZCpckbK33+GM9eGln2+wwG4rx8bYwrQ2FeXOI5pXcixtHr41wcDPHKtbFlt2E18kdT9E3EGI8kOXZznK6xKM9cHOaFK6NcHQ7zwpXl/5/MZyyc4PnLI1weCvHc5cJsQ6wtxQgO/yXwLaXUC0qpF4BvAv+sCNsVYs2q9djY2eyl0mWds+ZZfYUdt92Cy2ahtiJ7Z7dmBXfGz/cH+Z8vXOfvXr4xlR1VrC8um4WDHVX4nFaObMjud7+8NMz//8Ub/NOxHowiBIi1Hjt2qwmloLly+fNsq3MlW9ZDb9F0lU4rVnO2TFSNZ3199lvtbPbS4HXgc1l56doof/P8DW6MRkrdrIIxmxRNuVq0zZXLLybvcViwW7OXj0vdh7wOKzZLbt119t0bDsXpmYjS54/SXJX9+3fWuqZKThWqjJTbbnlzG9JrKBahGHUOX1dKbQe2AQq4mBs2KoRYJqUU79g1fz25d+1pYm+LD4tZUeGwEEpkaJl2QZBMG1Mn6cXoHo+idbbm2kgokbcsa2J1eXBr3YyseN1jUSDbm5BYQm3N5fI5rfzqvRtIpIwllWu51YfvaGUoGKfRd3uAaRgaQ2ss5mLcPy2uKreNT9/dSSyZmfWzryeVLhuP39nO2b4AT54fQqPpGY+u6WHGHzrYSiCWoio3HHuyx99sUvOtNoPHbuFX7u4kGEstOcj0uax86q4OIon0igLU1ahnPMamOg8A79zdSIPXQYXDwvZGL/5YiuYCfR8dVjOfvquDiWhyxjWAEHMp+NWdUuqLwNe01mdzz6uUUp/QWv9lobctxHqktUYphdmk6Jh2keOddk74xblBzvUH2dnsXTDInHRHRxWBWBKf00p79bzThsU6cu/mWo7eGGNjrafggeEkh9W8pMLRs7FZTLTNsh+HE2m+8Vo30WSGd+9tYmPuYg7e/G6tdj6nFZ9z/cz1WsiWBg/XRyMkUhn2lXFJh3ywmE1TvUfDoTjfPt4LZG+W1FcsPjjx2C147JZlfSfW6/43/RxaV2HnW8d7SKQNPnCghdaqwp5T3XYLbrmhKxapGHvKb2it//vkE631hFLqNwAJDoXIs57xKD881Y/bZuajh9tw2SwMBuK47eYZiScuD4Wy/w6GFh0c1lXY+djh9oK0W6xem+s9bK73LPzCVWIwECMUzw6bvjYSmQoOrwyFeOLsINUeGx+5o21Jve7FlEwbjIQTNFTY12TPZyHYLWbeu6+51M0ouu6xKIlUdg5511h0ScFhxtB890Qv/f44b91ez57WuYvTG4ZmIBinxm1b8U2d1Wz6OfSVa6M8e3GEZMagvdpZ8OBQiKUoRnBoUkoprbWGqTqH62uguRBFcqJrgje6J7CYTBzurCaezvD85VFsFhOfurNjahjenRtrONXjZ29rZWkbLESZaat20V7tIhRPsXfaBe+FwRBpQzMcTDAcis96MXemN8DJXj+7m70cmCWDcDF863gPw8EEHTUuPniwtSRtEKvD1sYKLg+F0egF65Q+d3mEnvEo922upbPWjT+anEpOdX4gMG9w+Ivzg1wYCOFzWvnMPZ1LGsK6VsWTaa6PhkllNGd6g7xvf6lbJMSbihEc/hz4J6XUXwMa+E3giSJsV4h1x0CTSBtoc3bI0OneAJFEGrAQiKWmgsPDndVzJrIRYiUiiTSnev00+5x0rsK5W3aLmQ/dcXtQtafFx2AgRo3bToN39h6W56+MkEwbvHBltCTBodaasXASgNFwoujbX2+uDocZCSXY31ZZtCHV+eR1WHn8zoVHg/ijSU50ZTMDv3J9jM5aN1UuG5vrPfT5YwsOxR3J7ZPBeIpkEeYll6ue8Sg9E1F2t/iw5YbG2y1aEryJslOM4PD3gC8Av0U2Ic0vgP9ZhO0Kse7sb6uiZzyGxaw42x8klswwGk7w0PZ62qplIroovKcuDHF9JIJJKT53X+eaqWO2odbN5x/YNO9rOmvcXB4K0VFTmiFiSike3d3IhYEg+2RUQEGNR5L8+HQ/WmeDp3fuaSp1kwrGY7dQW2FnNJSgsyZ7w8dkUrxnkUNx37q9nmM3x4s6L7ncxFMZvv9GH2lD0zcR4117mnhgax0joQTv2bd29x2xOhUjW6kB/FXuRwhRQBtq3fzG/RsxmeCbr/cQS2YwmRRj4QQXBoJsrPOs6zkfovAspuw8N5MC8zKStxzvmuDmaIQjG6pIpDVep2VJc6FK6V17Grl/ay0VJUz8sLWhgq0rKG4uFsesFCalyKySrLZ9/hivXhujo8bFoUWOGokk0vT5Y7RVuXj8SDvRZHrG3PXFaql00rK/ZcnrrTUmkwJDE4yleOLsIO/e28SuZt9UophUxuDGaCRbXmUdJuwR5aMY2Uq3AH8M7ASmzvBa642F3rYQa5nWGn80xZ8/dZk+f4wP3dHKO3c3Td2Zff+BFr5/oo+Mofn28V5aqpzsa63k40feHEbkjyaxmE1SlkIs2ngkyffe6APggwdaqLqlVtkjO7O91A1ex5Kz44UTaZ7PFWk+PxDAY7diNik+eWd7Qetzaa0ZCSfwOa3YLcu/eaKUwm2z8MNT/QwHEzy8o35GttP1Yq1kdZ3N1eEQPz83RH2Fnffvb8YfS7GjyVvqZi3ouUvDXB+JcH00zNbGikX16H/rWA8T0RQNXgeP39lOhcNKIp3hhyf7CcRSvHNP04KlEdbyvrAUDquZLfUenrs8wmAgRqPPycvXRumsHeeO9iru2VzLE2cHuTwUQmv47bdswrXMbLBCrFQxbnf9LdlewzTwEPC/gK8WYbtCrFm/ODfI//P0Ff7peA9XhsOE4mmeuzQy4zVeh5V7NtfisJowtMZtsxBJZqZ+f2UoxN+9fJO/e+kGw6H4jHUT6Qwne/wMBmYuF+LiQJArQyH6JqJcGwnf9nu7xcze1so55+XNx2ExUZWbF+uyZgPLjKGJpTLzrbZiv7w0zNde7ebrR7tJZ4wVvddIKMH1kQjh3NzL9SRjaL51rIf/+vRVzvQGSt2cgjjbFySZNuidiGExm9jbWol1iT2HN0YjnO0LYORqDBbDSCjBmb4AV4fDWBaREEZrPXW+iEybE9c3EaN3IpvRd77/42gyzd+9dIO/fPYaPePRlX+ANaBrLIpJKQYCcTKGgT+aIpU2OJabzxlNprkyFObYzXG+faKXgUCMv37uOv+/F28QiEl5cFE8xegucGqtn85lLO0C/oNS6gXgD4qwbSEKovNLP1nR+jf/5LFlr6u15lx/kFA8RTyVoa3KyUAgzj2ba2577eZ6D5+7dwPv2NXIQCDOruY3M8oNBuNoDamMZiSUmDF076nzw1weCmExKX71vg1SH0lMGQjE6fPHMClFbZ578yxmE5+4s51ANIXbbub1mxNUumxLTvM+Gk5gNZmmEjAtZCB3E2QimiKeNvCsYJhgjcdGg9fBSCjBtoby71HKp6VksFytdjV76fPHqPPYqatY+v7fOxHl+7me93AizV0bbz9uF4LLZmZrgwef00p6EUGpUor37mvm8lBoRs9ok89JlctKKJ5ma8PcveK9EzEmotmA5tJgaNaaoutNjcfGMxeHqXZbOdhRzZ5WH+f7Q2xvrGAgEOO+zbUcuzlBXYWNiUiKy4Mh4qkM8VSGnvEovpa1930S5akYV3xxpZQJuKKU+mdAH1BfhO0KsSYppUhmDC4OZk8qf/zBPSil5hx6UumyUemysbN55onlQHsV/mgKu8V02xyltJHtPTE0GLp4d7dF+XPZzBxsr0Ip8BZgXozdYqbemx3a+ZZtSz9VXBoM8dMzA5hNio8dbltUD+aDW+s4en2czlrXiodYW80mHr+zHcPQ2TlG68hkBsveiYUzWK5WWxoq2LKCOZ3GtI7pTJF6Dq8OhxkIxhkOJrl7U+2ik0S1VbtuC+qcNjOfvXfDgvt3e7WLRp+DSCLNzub1dZNkLrtbfHSNZXtRvQ4LhzrreduORl64Oso3XuvBbTfzuXs7OT+QPbfXe+1cHYlgt5hWZeZnsXoVIzj8l4AL+BfAH5EdWvqZImxXiDWr1mPncGcVJmXC0CxqmNCtPHbLnNnm3razgdO9AZp8jmUlIBBr11u21VHltlFfYafaXX4laydLOGSMbFmHxQSHrVUuWu/Ib8/GegsMYWkZLMtFsed0tde4eHR3I5FEumgB9Gg4QYXdyvYmK215Kra+0P7tsJr5xJGFy2SsJ1vqPbxtZwOJtMH+3P+9yaQYCWWPWZFEhkafc8aN3F+7b0MpmirWuWIEh2mtdRgIA58rwvaEWPOqXVZevznGHe1VGBqeOj8EwANb67BZVj6V2GWzFG24k1g9hkNxvneiD5NSfHiWWoDl4EB7JaF4CrvFPO+wNzG/E90T9E3EuGtjzbKGT64Gr14f49XrY2ypr+CxvcUrJ1DsBDb72yrxR1NYzYrtTfnPZBuIpnjx6ijVbht3b5LzxmxeuDLC8a4JdjZ5efuuxhm/O9Beybn+AO3Vrqk510KUUjGCw/9bKdUEfAv4htb6XBG2KcSaNhpOsq3BSziR4VTPBGf6sokBqtw27ujIFt8+2xfg+SsjdFS7edeeRsl4Jlbs+kiEaC5JxY2xyG2ZSq+PhPnF+SFqPXbet795yYk68sFls/DobqkbthL+aHIqwVU8leEjh9pK3KKFxVMZvnuij0gizWN7m2heIIsmwLn+IFrD5aEQj6TrV5Sptpw5rGYe3d248AuX6eVro1weCgHQVu1c8hzh9eBnZwa5ORbh5miER3Y0zOh5vTocxuuw4o+m6J2IyfxMUXIFP3NrrR8C3gKMAF9RSp1RSv3bQm9XiLXsQHsldquJfW0+6r0OlAKloGbaxfrJHj+JlMHloRDBeHqedxNicbY2VFDpslLjsbFplhINZ/oCxJLZ5AnDuaFSYvVxWM247dlAqXaV9Br2jEcZCsYJJ9Kc7w8uap3J4+ieFt+aDQyLYbLMjM1iKsg85LXhzfmlt96nrfVkz9s2i2nR80GFKKSipCDUWg8C/1Up9Uvg3wD/HviPxdi2EKtdKmNwvGsCp9U8NUflUGc1hzqrGQ0neO7SCJvq3LnhX2/Or9rd4uO5SyN01LhKWpRbrB2VTit7W31ozaz71M4mL91jUWor7NTlKZPplaEQb/T42dnkZbdk6ysKh9XMp+7qYCKaotm39JIkpdBS5aTabSOcSLOt8fahkxORJGf7A3TWuKd6Zg62V3GwvarYTS2qS4MhTvUW9vtzZEM1bdVOPHaLzFGfwzv3NPHqtXFsFhPXRyNsqvNwti/A+YEgB9oq+fiRNtx2iwSHoiwU/IpRKbUD+BjwYWAM+Abwvy/jfTqBo8AFIKm1frtS6neB9wFdwGe11lIIRqw5x25O8Or1MQDcdgub69/ssTl6fZzuXA2pA7dc5Oxvq2Rfq0+Gk4q8Odsf4PnLo0D2Lvfe1soZv9/SUMHmek9e97lnLg4TTWYY8MfZ2eRdl4leSsFls+CyrZ6bSi6bhc/c0zlngpmfnR1kKBjnZLefLzy4KS9zs1eDX14aJpbMMBiIs6vZW7DzQZNv4WG869n9W+oYCye4MRrlR6f6+cw9nTx9YRhDayYiSb7w4KZSN1GIKcU4Ov4tMAG8XWv9oNb6r7TWw8t8rye11m/JBYZ1wENa6/uA08D789ReIcrK9IsY+y0XNE2V2bv6LpuZKtftmSMlMBT5NH3o3VwX1/ne5ybnjjX5HBIYigXNtf9N7q9Wi4n1tBs15Xp+m3wOOR+UmMOaPX5aTAqb2TT1f7OY+bFCFFPBbwtqre+a7/dKqe9orT+0yLd7SCn1AvBd4DLwbG75U8DjZJPeCLGmHGyvpMJhwWEx3zZR/WB7FZ01blw289SJR4hC2dZYgdWs0DDrnMNCeGxPE+PRJJUyl0mswLv3NnF1OExLpRNLCRIllcp79jYzHk3OevNQFNfDOxpoq3ZRX+HAbbfwwYMt+GMpquX/RpSZchgzsnGRrxsAtgIJ4AeAFxjK/S4AzDpxQCn1eeDzAO3tUnNHrD5KqduK1E9XjrXmxNq1sUhB4SSTSVGbp/mLYv1yWM3rcs6qfH/Kh9VsYte0GoYWs0n+b0RZKofbZ3rhl4DWOqG1jmit08CPgatkA0Ry//rnWO8rWutDWutDdXV1+WivEGVBa83V4RBdY5Elrdfvj/Ht4728cm2sQC0T60HPeJTLQyG0XtQhnGM3x/n28V56cnNkhVgNDENzcTBInz9W6qbM6nX5Xq0aqYzBuf4Aw6E4sWSGH5/u52dnBkikM6VumhAzlEPP4aIopSq01qHc03uBvyA7lPRPgUeAV0vVNiFK4XRvgGcuZqfvfvBgCx017kWt9+LVUfomYvSMR9neWHFbrTohFtKXu8EAcP+WWg51Vs/7+nAizQtXsols4qkMn7qro+BtFCIfjt4Y59XrYygFjx9pp95bPtlbQ/EUL+a+V8m0weN3yuiocvbMxWHO9wexmBS7W31cGQoD0FTpZH8uE7kQ5aAceg4XO0P6fqXUcaXUy0C/1voo8LxS6kVgP/D9ArVPiLKUSBuzPl7I5CR4r9OKyy7zFMXSJZe47zkspqnhz82V5XNxLcRCJnt1tF7acbYYnFYzVa7sXNymVVJyZD2b3H8yWlPjtmFSCrNJUb9KaomK9aMceg5/bzEv0lr/FPjpLcu+DHy5EI0SotwdbK9Ea43NYmJLrrzF5aEQT18YprnSwbv3NmOeJS3f/Vvq2N7oxeu0SOFnsSwbat08vKOeSCLDHR0L14mzmE184kg7gVhqquBzPp3ozpZ72Vzn4e27GvP+/mL9untTzVRx8lsTgs0nnEjzvTf6SKQyvHd/M/UV+Q/eLGYTn7iznWAsXZDvlcivh7fXU+2y0eiz0+hz8ur1cVLpDBazZJEV5aVgwaFS6gyzzydUgNZa7yX74BeFaoMQa5nFbOLOjTUzlp3q8RNPZbg+EmEskpjzgqRO7lSKFbq1xuFCbBZTwfa7k91+EimDc/1BHthaJ5l7Rd7YLWbu2VS75PVujkYYDSUAuDgQKkhwCNn21VXI/r4auO0W7tuS3ZfO9gWIJNIAXBos3P4hxHIUsufw3QV8byHENKmMwQtXRgjH02g0rVUuSY8tCu5Mb4Cu8QiHOqppLOGwtt0tPl65Nsbmeo8EhqIstNe48DqtJNMGWxqKm+F3pU73+ukej3K4s5qGMppjuVYMBeNcGAgSTqSp9djZUj93NnIhSqFgwaHWuqtQ7y2EyAaEJ3v8VDgsxFMGp3oCANy3uZYjG2oWWFuIpTMMzclePxaTYmOtm6cvDqE1BGPpkibDOLKhmsOdVVLkW0zRWnO2L0jKMNjfWolpliH2heR1WPm1+zagtV5V+2U4kebpC9lEZ+F4mo8fkSQ3+ZBIZzjVE6DKZeV41wQDgTgeu4VP3tmG1yk3ckV5KficQ6XUXWQzi+4AbIAZiGitvfOuKISY1yvXxjjeNQHAPZtrmLz+qJG6SaJATvb6ee7SCACmnfV47BZC8XRZ1NpcTRfgovAuD4V56kK2FLLWLGpubCGstv3SZjZR4Sif7/Va8dLV0akbuC1VTgAqHBYc1nJI/SHETMXYK/8b8HHgW8Ah4FeAzUXYrhBrmmXanfD2aheb6jxoLfMJReFM3+ccVjOP39nOWDhJS6WzhK0S4nbTk3FZitxruJrZLCb5XheA2ZQtDqAUPLi1jmTaoMZjw2Yph6IBQsxUlFsWWuurSimz1joD/G2uHIUQYgXu3FiD12mlwmGhyScncVF4e1p8WM0mrGbF5tw8GVe13PkW5WdzvYf37GsildFsb5Q5XUvhslnke51n926qodplo9JllXmcouwV49sfVUrZgJNKqT8FBoDFVesWQszJbFLsbvGVuhliHVFKsaNJZgSI1WGzJPoQZcJiNrGnVc7XYnUoRn/2p3Pb+WdABGgDPliE7QohhBBCCCGEWKRiBIfv11rHtdZBrfUfaq3/N6TMhRBCCCGEEEKUlWIEh5+ZZdlni7BdIYQQQgghhBCLVLA5h0qpTwCPAxuUUj+c9isvMFao7QohbheIprBbTVIgXORFKmMQiqepcllXXap+IZYqnsoQT2WodElpB7F8sh+J1aKQCWleJpt8phb4z9OWh4DTBdyuEGvClaEQ0WSG3S2+GWnZl+pcf4BfnBuaKj3gc1rz2Eqx3mQMzV89e43eiShv3V7Po7ubSt0kIQpiOBTn4kCIN3omMAx46/Z69rVVlrpZYhWKJtN89ZUubo5FONxRzQfvaC11k4SYU8GGlWqtu7TWz2qt7wYuAhW5n16tdbpQ2xViLegai/Dj0wM8c3GYYzfHV/Re/f44kL1rORpO5KN5Yh0bDsV5+eooXWNRfnlppNTNEaIgMobm28d7eebi8FTx8j5/rMStEqvVRDTFjdEI10ciPHlhiIuDwVI3SYg5FXzOoVLqI8BrwEeAjwJHlVIfLvR2hVgr9ArXP9RRRXu1i90tPjprpIqMWBmXzUJ7jZsKh4WdUtZCrHFeh4XWKidt1S6ObKgudXPEKtXsc7CtsQKvw0pLpdQlFuWtGHUO/y1wWGs9DKCUqgOeAr5dhG0LsSq1VDp5bG8Tsdyw0pWoctv4kAxhEXnic1r552/dTL8/yr62qlI3R4i8SWcMzCaFUgqzSfHhO1rpGotOXdQLsVxKKT57TyeHO6sxDM22BqnBKcpXMYJD02RgmDNGcbKkCrHqZAzNd0700jcR44GttdzRIXeqRfk50T3B9ZEIaQPu3Vxb6uYIsWKXh0L87MwglS4rHzvchsNqpr7CQX2Fo9RNE2uEUooboxGuDofxx1I8sLWu1E0SYlbFCNJ+ppT6uVLqs0qpzwI/AX5ahO0KseqE42muDYe5PhLmmYvDC68gRJEl0hlOdE1wbSTMazdWNh9WiGIZDsb5xblBrg6HZv395aEQhtaMR5KMhGRutsi/y0Mhfn5ukHAizaXB2fdDIcpBMYJDDfwPYC+wD/hKPt9cKfVflFIvKKX+n3y+rxCl4HVaSGUM/LEU/miKiUiy1E0SYgaryUQoniYQTRFOSG4xsTr8/PwQ5/qD/OT0IIl05rbf72utxOe0sqHWTZNPegtFfqUzBk+cHcRiUvSMRznUKUPyRfkqxrDSt2mtfw/47uQCpdQfAr+30jdWSh0E3Frr+5VSf6WUOqy1fn2l7ytEqSiluHdzLRUOK3arCbtVRmCL8qIU7Gn14Y+maKmSxApidfA6LIyGErjtZiym24+rbdUufvW+DSVomVgPTErhtlvoqHHTUePiQLsEh6J8FSw4VEr9FvDbwEal1PS6hhXAS3nazN1kk9uQ+/cuQIJDsao9tK2ezlo3tW47Llsx7t8IsXhKKT52uI1+f5y2agkOxerwrj1NdI1FafQ5VlQ3VojlMJkUHz/cxkAgTnu1q9TNEWJehbzy/EfgZ8AfA1+atjyktc7XRJVK4FrucQDYdesLlFKfBz4P0N7enqfNClE4JpNiU52n1M0QYk4um4XN9bKPitXDajbJPitKym2X46ZYHQoWHGqtA2QDtk8UahuAH5gstOXNPb+1HV8hN8/x0KFDKy0ZJ4QQQgghhBBrktJ69cZLuTmHX9Baf0Ep9ZfA32mtX5vr9bW1tbqzs7No7RNisW7evInsm6Jcyf4pypXsm6Kcyf4pytXx48e11nrWxBarekKT1vqEUiqulHoBODVfYAjQ2dnJsWPHitQ6IRbv0KFDsm+KsiX7pyhXsm+Kcib7pyhXSqkTc/1uVQeHAFrr3yl1G0Tx9YxH+eWlYeor7Lx9ZyMmSTAgRFEcvT7GhYEgB9qr2NdWWermCLHmnesP8PqNcbY2VHDP5tpSN0cs07WRMC9eGaW1yslbt9ejlFy3iPIkefLFqnS8a4KxcJILAyGGQvFSN0eIdcEwNK9cH2MimuKV62Olbo4Q68Ir17LfuaM3xkmmjVI3RyzTazfGGY8kOd0bIBBLlbo5Qsyp6MGhUupXlFJPK6WeVUq1KKV+Vyn1olLqa0opa+41n1RKvayU+rFSyptb9lal1CtKqV8qpVpzy3bn1n1JKbW32J9FlM7GOjdKQbXbRrXbVurmCLEumEyKDbVuAMmoK0SRbMpluOyocWGzyD391WoyU2m9147HvuoH7ok1rKh7p1KqBXhQa/1w7nkd8JDW+j6l1O8B71dKfR/4TeAB4EPAF4A/A/4d8HZgJ/D7wBeBPyKbDdUA/hJ4XzE/jyidva2VbG2owGo2Sc0qIYrovfuaiacMnDZzqZsixLrw0LZ67tpQg8MqgeFqdrizmj0tPmxmk0yFEWWt2EeadwDmXM/hXwBHgGdzv5ssYr8VOKO1Tk8uU0q5gJjWOqS1Pko2QASo1lr3aK37AF8xP4goPYfVLIGhEEWmlJLAUIgic9rMMkdtDXBYzRIYirJX7H7tBsCmtX5YKfVlskXsg7nfBYCqOZZVTVsGMHllMj24nTXQVUp9Hvg8QHt7+4o/gBCrQeeXfrKi9W/+yWN5aokQQgghhFgtit1zGACeyz1+Bujk9iL2/lmWTUxbBtlhpNP/vfXxFK31V7TWh7TWh+rq6lbUeCGEEEIIIYRYq5YdHCqlPqiUuqKUCiilgkqpkFIquMBqLwOTiWP2Az3Ag7nnjwCvApeB3Uop8+QyrXUUcCqlPEqpI8D53DrjSqlWpVQz2cBTCCGEEEIIIcQyrGRY6Z8C79FaX1jsClrrk0qpmFLqWWAUeBxoUkq9CHQDf661Timl/gZ4gWyP4eO51f8T8CQQBz6TW/YHwDcARTZBjRBCCCGEEEKIZVhJcDi0lMBwktb6X9+y6Mu5n+mv+Srw1VuWPUU2Qc30ZaeB+5baBiGEEEIIIYQQM60kODymlPom8H0gMblQa/3dlTZKCCGEEEIIIURxrSQ49AJRsrUHJ2lAgkMhhBBCCCGEWGWWHRxqrT+Xz4YIIYQQQgghhCidlWQrbVVKfU8pNayUGlJKfUcp1ZrPxgkhhBBCCCGEKI6V1Dn8W+CHQDPQAvwot0wIIYQQQgghxCqzkuCwTmv9t1rrdO7n7wCpMi+EEEIIIYQQq9BKgsNRpdSnlFLm3M+ngLF8NUwIIYQQQgghRPGsJDj8VeCjwCAwAHw4t0wIIYQQQgghxCqzkmyl3cB789gWIYQQQgghhBAlsuTgUCn1b7TWf6qU+guydQ1n0Fr/i7y0TAghhBBCCCFE0Syn5/BC7t9j+WyIEEIIIYQQQojSWXJwqLX+Ue7fv89/c4QQQgghhBBClMJyhpX+iFmGk07SWss8RCGEEEIIIYRYZZYzrPT/ynsrhBBCCCGEEEKU1HKGlT5XiIYIIYQQQgghhCid5QwrPcP8w0r3rqhFQgghhBBCCCGKbjnDSt+d91YIIYQQQgghhCip5Qwr7VruxpRSncBRsuUwklrrtyulfhd4H9AFfFZrnVJKfRL4IjAOPK61Diql3gr8JyAOfFpr3auU2g38NaCA39Jan15u24QQb+r80k9WtP7NP3ksTy0RQgghhBDFYlrqCkqpF3P/hpRSwWk/IaVUcBFv8aTW+i25wLAOeEhrfR9wGni/UsoK/CbwAPBV4Au59f4d8HbgS8Dv55b9EfAJ4KO5x0IIIYQQQgghlmHJwWEukENrXaG19k77qdBaexfxFg8ppV5QSv0r4AjwbG75U8BdwFbgjNY6PblMKeUCYlrrkNb6KLAzt0611rpHa90H+Jb6WYQQQgghhBBCZC1nzuEUpVQV0Db9fbTWJ+ZZZYBs8JcAfgB4gaHc7wJAFVAJBG9ZVjVtGYA59+/04HbWQFcp9Xng8wDt7e0LfCIhhBBCCCGEWJ+WHRwqpf4I+CxwHTByizXw1rnW0VonyAaGKKV+TDbga8n92gv4cz/eW5ZNTFvGtO0Zsyy7dZtfAb4CcOjQoTmzrAohhBBCCCHEeraSnsOPApu01snFrqCUqtBah3JP7wX+Angc+FPgEeBV4DKwWyllnlymtY4qpZxKKQ/ZIaXnc+8xrpRqJRsYBlbwWYQQQgghhBBiXVtJcHiW7BDQ4SWsc3+uxzEBvKi1PqqUej6X5KYb+PNcttK/AV4g22P4eG7d/wQ8STZb6Wdyy/4A+AbZbKVfXMFnEUIIIYQQQoh1bSXB4R8DbyilzpIbKgqgtX7vXCtorX8K/PSWZV8GvnzLsq+SzVQ6fdlTZBPUTF92Grhvme0XQgghhBBCCJGzkuDw78kGdWeYY76fEEIIIYQQQojVYSXB4ajW+r/mrSVCCCGEEEIIIUpmJcHhcaXUHwM/ZOaw0vlKWQghhBBCCCGEKEMrCQ4P5P69a9qyeUtZCCGEEEIIIYQoT8sODrXWD833e6XUZ7TWf7/c9xdCCCGEEEIIUTymAr737xTwvYUQQgghhBBC5FEhg0NVwPcWQgghhBBCCJFHhQwOdQHfWwghhBBCCCFEHknPoRBCCCGEEEKI5QWHSimTUuqjC7zspeW8txBCCCGEEEKI4ltWcKi1NoB/tsBr5v29EEIIIYQQQojysZJhpU8qpf61UqpNKVU9+ZO3lgkhhBBCCCGEKJpl1zkEfjX37xenLdPAxhW8pxBCCCGEEEKIElh2cKi13pDPhgghhBBCCCGEKJ1lDytVSrmUUv9WKfWV3PMtSql3569pQgghhBBCCCGKZSVzDv8WSAL35J73Av9xxS0SQgghhBBCCFF0KwkON2mt/xRIAWitY0htQyGEEEIIIYRYlVYSHCaVUk6ySWhQSm0CEnlplRBCCCGEEEKIolpJcPgHwBNAm1Lqa8DTwL9ZzIpKqf9NKfVi7vHvKqVeVEp9TSllzS37pFLqZaXUj5VS3tyytyqlXlFK/VIp1Zpbtju37ktKqb0r+CxCCCGEEEIIsa4tOzjUWj8JfBD4LPB14JDW+tmF1lNK2YF9ucd1wENa6/uA08D7cwHibwIPAF8FvpBb9d8Bbwe+BPx+btkfAZ8APpp7LIQQQgghhBBiGVbScwjwIPAw8BBw/yLX+XXg73OPjwDP5h4/BdwFbAXOaK3Tk8uUUi4gprUOaa2PAjtz61RrrXu01n2Ab4WfRQghhBBCCCHWrZWUsvhLsj18Z4CzwBeUUv99gXWswINa62dyiyqBYO5xAKiaY1nVtGUA5lnaP+tnUUp9Xil1TCl1bGRkZOEPJoQQQgghhBDrkGUF6z4I7NZaTyak+XuygeJ8Pg3847TnfqAl99ibe+7PPZ6+bGLaMgDjln9vfTxFa/0V4CsAhw4d0gu0TwghhBBCCCHWpZUMK70EtE973kZ23uB8tgG/pZR6AtgFHCIbZAI8ArwKXAZ2K6XMk8u01lHAqZTyKKWOAOdz64wrpVqVUs1kexmFEEIIIYQQQizDSnoOa4ALSqnXcs8PA68opX4IoLV+760raK1/b/KxUupFrfUfKqV+L5e5tBv4c611Sin1N8ALZHsMH8+t8p+AJ4E48Jncsj8AvkG2vuIXV/BZhBBCCCGEEGJdW0lw+O9XsuFchlK01l8GvnzL775KNlPp9GVPkU1QM33ZaeC+lbRDCCGEEEIIIcQKgkOt9XPz/V4p9YrW+u7lvr8QQgghhBBCiOJZaSmL+TgK+N6iDBiG5rnLI/zoVD/BeKrUzRFClLmzfQG+/0Yf3WPRUjdFCDGN1poXr4zyw1P9TESSpW7OmjccivODk328dmO81E0R4jYrGVa6EMkMusbdHItwomsCAKfVzCM7G0rcIiFEuUqmDZ66MITWMBFN8rl7N5S6SUKInD5/jNdvZgMVi0nxrj1NJW7R2vbC5VG6x6NcH4mwqc5Njcde6iYJMaWQPYdijaty2bBZsrtQvVcObEKIuVnNihq3DYD6ChlYIkQ58Tmt2K2583mFnM8LbfKayWO34LYXsp9GiKUr5B6pCvjeogxUuW38yt0dxFMGdXIyEULMQynFRw+3MR5J0iDBoRBlpcJh5TN3dxJJpuXmTRHcv6WOrQ0VeB1WHFZzqZsjxAzLDg6VUm4gprU2lFJbge3Az7TWk5PPPp2PBoryVuGwst7OI8e7Jrg4GORAWxU7m72lbo4QJRNNpvn5uUEA3rGrEZdt/lOK3WKmyecsRtOEKCitNc9cHGY4lODBrXU0V67+/dotvVgFkzE0T54fJBBL8dbtDdRV2GnwrrOLJ7FqrGRY6fOAQynVAjwNfA74u8lfaq3PrqxpQpSfjKF54coIw8EEL14dKXVzhCip8/1Bbo5GuTka5Xx/sNTNEaJohoIJTvcGGAzEJamIWFD3eJQLAyH6/XGOd8n+IsrbSoJDpbWOAh/8f9n77/DIrvPA8/+eWzki59BodI7sZnczBzEoUYmiMmUly5bk0c74tzPrn62d8Xo8Hv92ZD/PrneSZ+SZ9XhkW5JtRSsziKRIimQ3u8nOuRs5o3Kuuuf3xy2AQCM0QhVSv5/nwYPCLVTdg0LVvfec8573Bf6D1vqDwO7SNEuItclmKFqrvAC0V/tWuTVCrK7mSg8Om8JhU7RUrf+ZEyEWqtLrIOhxANBe413l1oi1ri7gwueyoZRcO4i1bznxA0opdTfwSeDzJXg+IdaFJw62EMvkCbrl7S5ubc2VHj5/XydKIetmxC3F7bAV19wXCLgdq90cscb5XXY+e89msgUTv4TuijVuOe/Q3wa+AnxXa31GKdUJ/KI0zRJi7TIMRYVHLgaEAPA4pVMobk0Om4HDJknfxcI47cZkhnch1rIldw611i9grTuc+Pkq8M9K0SghhBBCCCGEECtrOdlKtwP/G9Ax9Xm01g8vv1lCCCGEEEIIIVbScsJK/x74L8B/AwqlaY4QQgghhBBCiNWwnM5hXmv95yVriRBCCCGEEEKIVbOclbH/qJT6J0qpJqVU9cRXyVomhBBCCCGEEGLFLGfm8DPF778zZZsGOpfxnEIIIYQQQgghVsFyspVuLmVDhBBCCCGEEEKsnuVkK3UAvwU8UNz0HPBftda5ErRLCCGEEEIIIcQKWs6awz8HDgH/ufh1qLhtTkqpvUqpl5VSv1RK/aWy/I5S6kWl1N8UO5wopT5Z/L0fKqWCxW0PK6V+pZT6hVKqdcrzvaiUekkptX8Zf4sQQgghhBBC3NKW0zk8orX+jNb62eLX54AjN3nMBa31PVrr+4s/HwYe0lrfB5wEHi92EL+ENSP5deCLxd/9feAdwO8BXylu+yPgE8BHi7eFEEIIIYQQQizBcjqHBaXUlokflFKd3KTe4Q0hpxlgO1Y4KsDTwF3Fbae01vmJbUopL5DSWse01q8Cu4uPqdZa92it+4CK2faplPqCUuqYUurYyMjIov9IIYQQQgghhLgVLKdz+DvAL5RSzymlngeeBf7FzR6klHq/Uuo0UI+15jFavCsCVAGVs2yrmrINwDZL+2f9W7TWX9NaH9ZaH66rq1vI3yWEEEIIIYQQt5zlZCt9Rim1DdgBKOC81jqzgMf9APiBUuo/AHkgWLwrCISLXzduC03ZBmDe8P3G20Ksax2/96PVboIQQgghhLjFLLpzqJR6WGv9rFLqiRvu2qKUQmv9nXke65rSgYxizQA+CPwJ8CjwCnAR2KuUsk1s01onlVIepZQfK6T0bPE5xovJaUysWUYhhBBCCCGEEEuwlJnDB7FCSN83y30amLNzCLxLKfXPi7cvYSWZaVJKvQh0A3+mtc4ppf4C+CXWjOGTxd//Y+ApIA18prjtD4BvYs1cfnkJf4sQQgghhBBCCJbQOdRa/0Hx5r/RWl+bep9SavNNHvt94Ps3bP5q8Wvq730dK1Pp1G1PYyWombrtJHDfghsvhBBCCCGEEGJWy0lI8+1Ztv3DMp5PlFD3WJJXr46RzOZXuylCiA0kmc3z6tUxusYSq90UIdacqyNxXrs2Tjo3b/J2cYvK5k2OXR/n0lBstZsixJyWsuZwJ7AHqLhh3WEQcJeqYWLpYukc3z3Rh6k1g9E0HzjQstpNEkJsEM+cG+bycBxDKT53XwdBt2O1myTEmjAWz/CDN/vRGkLJLO/c07jaTRJrzEtXRnmjOwzAx++w01ThWd0GCTGLpaw53AG8F6vkxNR1hzHgN0vQJrFMhlIYCkwNNkOtdnOEEBvIxDHFUNZibyGERSmFQqHR2JR8OsRME+8LpZD3iFizlrLm8PvA95VSd2utf1WGNoll8rnsfPhwK4ORNLuagjd/gBBCLNAju+ppqnDTWOEmILOGQkyq9jl54vYWxhNZdjfLuVfMdM+WGiq9Dio8DuqDEmwn1qYl1zkETiilvowVYjr5Dtda//qyWyWWranCI+EKQoiSc9ltHGyvWu1mCLEmtVV7aav2rnYzxBpltxnsb61c7WYIMa/lJKT5OtAIvBN4HmjFCi0VQgghhBBCCLHOLKdzuFVr/ftAQmv9V8B7gH2laZYQQgghhBBCiJW0nM5hrvg9rJTaC1QAHctukRBCCCGEEEKIFbecNYdfU0pVAb8P/ADwA/9HSVolhBBCCCGEEGJFLblzqLX+b8WbzwOdpWmOEEIIIYQQQojVsOTOoVLKBXwIK5R08nm01v9m+c0SQgghhBBCCLGSlhNW+n0gArwOZErTHCGEEEIIIYQQq2E5ncNWrfW7StYSIYQQQgghhBCrZjnZSl9WSknpClFWWmvOD0a5PCwlNIVYa7TWnBuIcmUkvtpNEWLDCSWyvNETJpbO3fyXxbrQM57kdF+EgqlXuylCzGk5M4f3AZ9VSl3DCitVgNZa7y9Jy0RJpXMFzvRHaa5001ThWZU2nBuI8vTZIRor3HzwYAt2283HJk71RXjm3DAA77sNttYHyt1MIcQCvdET5rkLIwB84EAznXX+BT/2bH+UZ87NfzyYOG41Vbhprlyd45YQq0Frzd+/3kMiU+B0n4tfu2vTvL+fL5ic6Y8S9DjYXOtboVa+5fmLI7zRHWZ/WwUP7ahf8f2vB8PRNN8+3ks6W+Abr3XTXu3lg7e3oFB0jyfZ0RjA71rOZbkQpbGcd+G7S9YKUXY/OzPI1ZEEdkPx+fs343Wu/AHoTH+UvKnpDaUYT2apD7hv+pj8lNG1fJlG2oZjaX51ZYzWKg+HNlWXZR9CbERTP5OLHQk/0x+ZPB6EkjnqAq4Zv/PU2SEuD8exG4pfv28zvikXTrF0jhcujuJ327l/ay2GoZb+hwixxmj91udrIZ+tX10d49j1EACfuKOdxoqbn19L6VRvGFNrTvVGNnznsHssyYmeEFvr/exprljw4/KmRmsIpXKkswWS2QIXBmKc6o+QyZlcGorx8Tvay9jy9StXMHnh4gi5guZtO+pwO2yr3aQNbdE9BKVUUGsdBSTObx3RxXOLnnJ7pe1vrWA4lqapwk2Nb+aF4FSmqSlozW2tlWgNDptiR0N5Zg1fuDhKz3iSqyMJOmv9VPmcZdmPEBvNwbZK8qaJ3+lga/3CZw0B9rdWMhLP0FzhoXqOz5xZPFjp4tdUR6+Pc3HIOg21VHoWvX8h1jLDUDxxsJWrI3F2NgVv+vtT+4/mlJN8rmBiU6rsgycH26t4oyfM/taFd5bWq6fPDRFJ5bg2mmB7QwDHAqKgAJorPTy2r4m+cJLLQ3EMQ7Gt0c/Jvggw/X8opjs/EONkr/U6VXgc3L2lZpVbtLEtZfrob4H3YmUp1VjhpBM089Q8VErdCfzfQAE4prX+X5VSvwN8AOgCPqu1zimlPgl8GRgHntRaR5VSDwN/DKSBT2mte5VSe4H/UmzDb2mtTy7h77klvGNPA6f7rPAs3yqFLWxvCLB9AR28aDrHt17rIZ0r8P4DzRzaVDXt/oKpsZXwRFcXcNEznsTvsuNxymiUEAuhteYHb/bTNZbkjs3VKLWwz6RpapSCHY0BdjTOfzx4++63jls3hlvV+d1ABIdNUeV1LPXPEGLNaqxwL3gG8J4tNfhddio89skQ7EtDMX58apCA284n7mgv6/nt3q213Lu1tmzPv5bUBVxEUjmqfU7sxWuRhV6X7GgM4LApTvdF8Tpt+F0OPnyoleujiQUNAtyqavxODKUwtUmtX4735bboXoLW+r3F75uXsL8u4GGtdVop9TdKqfuBh7TW9ymlfhd4XCn1PeBLwANYdRS/CPwp8PvAO4DdwFewOo9/BHwCMIH/jNXJFLPwOu3csXl9hEz2h1PEM3kArozE2VTz1vqJl6+M8urVcbbU+3n/bc0l2d8D22rZ3uCn0uOUUAUhFiiTN+kaSwJwcSi2oAvD66MJfniyH5/LzseOtN00vH2+49a+1goaKly4HTaCbrlYELc2h82YMZB6eTiOqTWRVI6haJqOVViLuBE9tq+JoWiaGr8TpRRPnx3iVF+E/a0VPLKr4aaPvzKSoGBqYuk8A+EU2xoCNARXNgx4vWmu9PCOPfX88OQAz18cpT7oocIjx/1yWXK2UqXUB5VSFVN+rlRKPT7fY7TWg1rrdPHHPLAfeK7489PAXcB24JTWOj+xTSnlBVJa65jW+lWsDiJAtda6R2vdB2z8WIZbREeNj7ZqLzV+J/taKqfdd27ACiM70R3i+2/0cWlo+dHNSimaKjwyayjEIrgdNg62VxL0ODjSsbCBp4tDMXIFTTiZoz+cWnYb6gPuRXcMX+8K8dPTA4ST2WXvX4i17La2Sqq8VoKalqqbJ3TqGU/y41MDkh38JmyGornSg8tuXTOcG4hO+34zt7VWUON30l7tJZUr8ONTAwxEln883OhGYlnshkEsnadnPLnazdnQlhNf+Ada6+9O/KC1Diul/gD43s0eqJTaD9QCYawQU4AIUAVUAtEbtlVN2QYwcRU/tXM7a0dXKfUF4AsA7e2y0HexusYS/OzMIFVeJx840ILTvpzqJwvjdtj48KHWWe87tKmKV6+OMRxLc3UkwfXRJF+u9S0o86kQojRS2QLfPdFHMpvnPfubFpwBeV9rBT2hFAG3ndYqb5lbOdNwNM0LF63sqtmCLln0gRBgre/7wRv9jMYzvH13w6Ky95ZDc6WHz9678CCvn58dIprKcXk4zpcf8pd0+cZGdrijmpO9Yfa3Vs77ewVT849v9jMYTfPIznraa7z8l+euYmrNaDzDp+/uWJH2rle7moJcHYnjctjorJNZ8HJaTudwtqvxmz6fUqoa+I/AR4FDQEvxriBWZzFcvD11W2jKNrDCSKd+v/H2JK3114CvARw+fFiW+y7Sqb4IiUyBRCbFYCRNe01pLugy+QLPnBvG1JpHdjYseNbuQFslB9oq+d6JPq6NJqjw2OUEJsQK6x5Pcn4wykA4DWh+4/4tC3pcU4WHz9+3lBUJpeF12XE5DDI5k2qvJJ4SpTUcy9BdnNE41RdZ9c7hYlV5HURTOcLJLN893stdW2pWZRBnvbl7S82CEqSMxTNcG00AVibmGr+LeCaH12mnUo5HN1UXcC1qsEMs3XI6h8eUUv8X8J+wEtH8U6wkNXNSStmBvwZ+R2s9qJQ6CvwT4E+AR4FXgIvAXqWUbWKb1jqplPIopfxYIaVni085rpRqxeoYRpbxt4g57GoKcn00QaXXSUPF/BlGF+Nsf5QLg1boSn3APbmu6LVr41wfTXBXZ828HdH37G+iP5yiIehecCIMIURptFZ5GI6mSWTz9IfTZPKFyRArsDKJXhtJcGdn9bQ1w6vN77Lza3dtIpLM0bqAMDshFupkb5jTfRGUAptS7FqHyUXed1szF4di/PjUAD2hFNmLozx5p0RclULPeJKXLo+SzhXwueyMxrNk8iYuu43HDzbTJp1wsYYsp3P4T7GSxHyr+PPPgX91k8d8BDgCfLV4Qf8V4AWl1ItAN/BnxWylfwH8EmvG8MniY/8YeAorW+lnitv+APgmVrbSLy/jb9kw4pk84WSWlkpPSTpNW+r8fPmhrSXvgNUH3dgMhdbQELQ6nfFMnpcujwLwwqURfq1m7qK/Dpuxpi46hbiV+Fx2Hj/YyrmBCHUBN84pYd2JTJ4XL018jkf51Br7nAbdjlsugU0snSOSypXsvCCmM03NL86PYGqNz2XjN+/vXJevs8NmsKMhwNFr44SSOZoqVz5JSiKTJ1TCa5i14qXLowxE0rgdNj5z9yaevzTKleE4bdUeNte+NcNcMDX94RR1AZckyBOrZsmdQ611Avi9RT7mG8A3btj8K+CrN/ze14Gv37DtaawENVO3nQTuW0wbNrJkNs/Xf9VFOlfgcEcV92+rK8nzluMA3VLp4XP3dmBqJjNOeRw2avxOxuLWiWE9GY1nME1NvWQcE7eId+5p4NCmKiq9jmnHCLfDRq3fyWg8S0ulm+FYGoWatci9KL9EJs9fv9JNOlfgSEc19227NcoNrCTDUDRVuOkLp2he550au83gyTs3EUnlqPWvbKjjSCzNX750HYdNcaCtiod21q/o/supudLDQCRNldeB12XnvfuaGEtkZ5Th+enpQS4Oxaj0Ovj03R1lXTYzFs+QN7VkShUzLLlzqJR6CviI1jpc/LkK+KbW+p0laptYpESmQDpn5fcZT6z9THyBG0bvbYbiE3e0Ey3WD1ovesaTfPt4L1rD+25rYmv9zWs5CrHeKTV7h89mKD5e/ByHk1n+9tVuAB4/0CKp9FdBIpufPC+MJTKr3JqN60OHWgkns1RtgLVjTrux4oM5sXSOv3q5i9e7QrRUemir3ljHige217G3pQK/y46jGGkx22s88RmNpHLkCiY2ozyzh33hFP9wrBdTax7b13TTmrPi1rKcsNLaiY4hgNY6pJTaOMM861BdwMUD22sZjGQWtDi6lLTWFEy97KyhDptBjb+8J6VcweRnZwYZiWW4Z0vtTQ+KJ7pDHLseYntjgAe3z5yNDSWz6GKqo/FErhxNFmJdsRuKo9fH+dXVcQoFkxq/i1AyS0PWzbPnhzEUPLyrfto6xRuV4piSzOa5OpKgrdp7y9bEqg+4uX9bLUPRlT8v3Epshprz3FWq8+Ni/OL8MJeH49zVWcO+1rVT6atnPEkqV2BbvX/aDGsyW8BmKDpqfPhctlnPtSvlRHeIKyMJDm+qKumA1sSg96WhGM9fHKEh6OaxfU3TZgcf2dXAie4QnbV+3A4bQ9E044ks2xsCJZ1FDCezmMULl4nJhNeujdM9nuTuLTXrLnpLlNZyOoemUqpda90NoJTahJWYRqyiQ5tKV+g+ky8QSuSoD7gw5jkopbIFvnm0m1g6z7v3NrKtYW2PQD19doi/eaULlCKSyt20c3jseoh4Js/xrhD3bKmZHPWbsLspyHgiS8HU3Na2dk7CQqyWwWiao9dC+J02Elkry/DelgpOdIe5WKxN2ljh5mB71ayPT+cKfOtoD+FkjnfubWBn49KSe3zvRD9D0TQBt53P37d5XYf7LcfhBdahFKVXqvfyYvf5Rk8YgGNd4yXvHMYzeVLZwqJnF/vCKf7h9V4A7t9WO+192RC0BjEmBm0rvKszmJPOFXjuglXuJp7O8dna0mbH/MWFYb7xajc2Q7G9IcBoPDMZ1pnNmxgK3r3X6jCGk1m+dbSHgqkZjKZ5aEfp5l92NgYZjWfJ5U0OtlcSSeUmcz7kCiafuEMSEd3KltM5/JfAi0qp54s/P0CxnqBY/0xT862jPYzFs+xsDPDufU1z/u5QNE04ac2YXRqOL7lz2DOe5OlzQ9T6XTNG0wDOD0Y52RthX0vFsjLBZfImfreVsrt+ASe3nU0Bjl0PsbXeP6NjCNYajbeV8KAtxHqWK5j86OQAXeMJfC47n72nY7IT2BB0YSiFUsy7PncklmEgkuLaaIJ4JsfvvmvnkmZdMvlC8btJOJXjx6cGMJTivfubZoS1C1EOI7HM5MzMpaH4inQOXXaDzbU+ro0mSh4uGEnm+OtXu8jmTR7aWc+BtsqbPiaVLfA3r3bx2rXxYqfITyY/s/rYWhjEcNoMagMuRmMZmsowe3ZxMEaN38m1kQS1ftfkbKLWmm8d62E0lmFbg5/dTUF+cWGYvnCKxqCbgXCa//HSNap8Th7b1zTrtchi2Aw1bXbWZigqvQ7CyRxNFbIG8Va3nIQ0P1VK3Q7chZUt9H/VWo+WrGViVWUL5uQJbTg2/zqVlioPm2t9hJJZblvAiWIux7tDDEXSvHJ1jFSuwIdub53WQXzm3DDZvMlwNL2kzmEym+dMf5RdTQEqvQ68TtuCZlrv31bH3Z01KxoSJMRalM4VON0XoS7gmjNbcDpXIJbOs6e5grqAa9rs4KYaH5+9twOlrKyhPeNJXrs2Tketd9pnsanCjdbWRWW+oLkysrSL3Pfub+bcQJQt9X4uDsYYjlrHsotDcQ5tmn3WUohSaqpw01nnYyyeXVBHqhSUUjx+sIV8wSz5eSuUzBJN5RhLZLk8HFvQ3/T0uSG+c9yaMWyu9LCnuYIja6AjOBvDUHz8SBuhZJa6MixxuWNzNTZD8e69TTwwpXOWNzWjsQzXxxJ0jyfpGk2SLZhkcgW8Ths2A0LJHKFkjv5wquTZ2h02gyfvbCeaykvyMLGsmUOAAjAMuIHdSim01i8sv1liqlzBXPYo0WK5HTYe2dnA5ZEYh2/SgXLYDB4/2LLsfW6rD/DMuSEyeZPusQRPnx2i2u/kQFslDptBc6Wb66NJmiqWNpr31Nkhro4ksBuKX79vMz7XW29/rTUnesIUTM3t7VUzZi2lYyiEtY7p/GAMQyk+e0/HZOjX1GNUwO3gge119IwnubNz5rFj6tq/5y+OMFIsHL6jMYi/+Jm02wyeuL2Vn5wewGEzpmVNvD6aoD+S4rbWymmf4dnUBVzUBeroD6cYiqbJmyZ+l4P2aqkpJqYzTY2GkmeHtNsMPnBg+efHpe671DbVeIln8iQyefrDqQU95upIHKUUkWSOd+6p4OGd9WvunJormBzvCuF22LitrZL6wMzZs9N9EdK5AgfaKpfc/n0tFRxoq5wR4u6wGextDnJ+IEpjhZtoOofNUJOvtak1hlIEPfZZ27Ycw9E0F4ZibG8IrFjm0mQ2z5s9ERqCLjrr/Dd/gFhRy8lW+hvAbwOtwBtYM4i/Ah4uScsEYMWnv9EdZkdjgMfmCe0sh32tFSVZq5DOFRiNZ7g6kmBTjXfOEa/dzUF+/b7N/OL8MKmsyfFu60CdyZnct62W99/Wwngiu+BMpheHYsQzefa3VMw4kN+49OjcQIzni+sMDKVmnVVIZa0QNY9Tag+JW9O0z03x9q+ujPHK1TE21Xj54MEWVPHzM/UzFEvneKMnTFOFh631b10INFW4GYmlSedMrgzH2d9aMXnRtKMxQGPQjcOuyORM8gWTZK7A99/ox9SakVhmQRfdpqn57ok+snmTaq+Tz967eckdgFzBJJkt3LLJbTaqSDLHt451kytoPnCgmdZbvCC51prj3WHyBZNDm6qmnT+VUmyr9xNK5vA4FnYJubu5glSuQIXbwW8+0LmgjlXB1ERTuRmlcmb7vXg6T4XXQTSd4/xAjPZqL42LDI08dj3EK1fHAKuO69TjFMCVkThPnR2a3OednYtP7nR5OM6PTw0QcNv5+JH2GdcSd3TWcGkkzkgsQ63fya7mIE6bgcaacX1sXxM2pebNATFVLJ3DZbfRPZ4kksqxv7Vi1omG77/RTzyT52x/lC8+uGXRf9dSPHt+mEtDcZSCz97TQeUGyPK7kSxn5vC3sQrav6K1fkgptRP4w9I0S0w4P2Alb7g4FOPdexvXXUKF8USWb7zWzYnuEE0VHuoCLr7wQOecxV0PtFXRXu1jLJHhh28OAGC3WX+zzVh4rbSe8SQ/Omk9Pp0tcM/WWt65p5Ez/VGaK914ndPf+k77WwdMrTW9oSTNFZ7Jg/BAJMW3X7fKVTxxqFUyeYlb0kM766kPuqnzuyY7SBcGowB0jVlZCG/8bIEVEn5tNIFSIX79vs2TRegf3lmP027wytUxnj0/jM1Q7G15a0CqwuvguQvDnOgOUxtw8YHbmjEUmJoFR1MoZa0jyuZNPE77rB3D4Vgah2FQNc/AUzZv8o3XuhlPZLl7Sw13LeHiUKxNPaEkiYw1+Hd1JHHLdw7PD8Z44aI1WGoz1Iy1gB882Mrlkdi04u3zeXRXPbe1VVDpcRLP5MnkzZvOUH379V76wil2NQV4197ZB8a11vz9sR4GImn2t1YU1yqnOWo3+MIDnYuKuJp6DeCyz3ycw3hr28Q1yWJdHo5RMDXhZI7BaJrNN2RCrfA4uKuzmv/wzGUuDsYYT+T44O0txNJ5djcFp/0944ksedOccxbxzZ4wz54fBjS5gsZhM4ilc7PmR3A5DOKZ6a9BudmLr6exiM6uWDnL6RymtdZppRRKKZfW+rxSakfJWiYAuGNzFce7wuxuDq5ax7B7LInHaVtSHPpwLE02b2IzFNFUjuZKD8ZN/o5qn5Nqn5MnbjeIZ/LsmmcBf28oWSy6Pb1ts81wuB22abMZA5EUFwZj7GoKsrXez+MHW0hl87x0ecyacWyt4JFdDQD0h1PkCnrytnQOxa3IZbdx+w0ZRg93VPPK1TE663yzdgytxxkkMnmUsspcTFBKUeNzTV4ozKYnZIWujcYyGIbiAwebef7CCA3BhR2PlFJ89HAbPaHkjIsxgAuDsclENR890jpn2Ho4meXKSByf0073eFI6hxtIZ52P5ko3mbzJ7ubyJ4wpt75wCkOx5CUYUwdvZys3U+F1LCozulKK+oCb4Viab75mZd98++6GaQNBU+ULJv0R63PfMz536GombzIQSQPQPZ7EW5yJW8ilUiSVI5TI0l7txTAUt7dX4nfZ8ThstN0Qdn55OE5fOMWjuxuwG4qdS0zys7+1kv5wGrfDIJ0rzLpkaDSWxeu0kcwWyBZMWqs8M46rU2sUvnd/06xJALvHk4BV/9rUVudw4hoylS0wGE3TUunBaTf44MEWusaStNeUdlAkmc3zeleIWr9rRp6Ih3fW01zppj7gnhwsFGvHcjqHvUqpSuB7wFNKqRDQX4pGibcc2lRd0vIUi3W8O8TzF0YwlOITd7TNm2FwNvUBF+lcgY4aHw/trGdHQ2De0anXu8Z5+fIYW+r9N50pPdUb4elzQxhK8bEjbTRWuLkyEieUyLK/tZL33dZMIpOf8wT0/Tf6SWULXByK8YUHtrC51kcklSOesUJHxuLZyd/d3VRBz3gKjWZP8eLh2PVxBiJp7t5SM6NzOp9cwSSSylHjc667mWAhbrS3pYJM3qQ/nGIklpl1EGlnY4BfnB/G47RxdSQx7TO5qykw6+1j18f51ZUxgh47TRVuOmp9+F12jl4bZzSe5YWLozQE3TNmedK5Ai9dHsXtsHF3Zw2GoajwOqjwvrXP8USWS0MxttT7GYtbSWpMrRlPZOe8oH69K0Q0lWMkluEjh1uX9mKJNcnrtPOxIxsjdf/Fodhk1MwHD7YsqU7f5lofT9zeQq6gZ4RXTjUSy3Ds+jht1d45z7NTRZI5CqY1yDoanzvRnd1m8OD2Oi4Mxrj9hiUel4djnOmPsq+lgs46P/dureXScIw7N1fTWOHhwmCUtmrvjE5XLJ3j26/3ksmbvHNPIz8+PUAmZ3KgrZKHdtajlJpMejX1HB3P5PnhyX60hiqfg12NQSKp3JLCIJsrPXz67k38vy9d47/98ipVPif//O3bp3X+DndUM57IEs/ked9tzbMOuIUSb9UoHEtk2TbLvu7YXE0ik6fG76Kz1kcsk2dvc9DKinq0m5F4hqFIhs56H4/tbZrz/2eamvFkliqvc1Hh+LmCyV++dI3BSJpqn4sav3PaLKfTbrC/tXJBzzUQSXH0eoiOGu+CHyOWZznZSj9YvPmvlVK/ACqAn5akVbeIbN4s6TT+xSErFKS1ysM79yw9BLVgas4NRKnwWOUewLpwimXyeDN5Lg7FaKvyLmgm8ej10OQoZEulZ96wLYCTvRHypubCYIyHdtTPu74vnMpOti2azmEY8I9vWgfxaDrHwzsb5t2X224QT+dJFms2eZw2esaT+Fw2Kr0O3jYlk5jHaZuWdGc0nuGXl96qCfTE7Qu7WCyYmm+81s1YPMttbRU3baMQa1GuYGI3FEopQoksL1wcIZ0rcLw7xIdub51xodEXSdETSqK1VQB66v1aW59hj9M27Zg1cSwYT+T4rbe1Tx5HXA7rmKnU7GFQx66HONkbAaDW75o1y+n3TvQRSeV4szfMp+/uIJ7J47Qb08oM5AsmPzo1wEgswzt2NxJO5dhU40MpqyZbXzjFeDzLzqbA5IXoUDSNYv4yHUKYpsbUuixJWSbO2QCxdH7afaPxDL2hFDsaAnicNrTWPH1umOujCe7dWjtt1nQh2TCfPT9Ef9hKZjIxeDOfLXV+Dm2qIpktsKc5yPHu0JzXEgfbq2atg/qzM0Nk8yb94TS/9TY/d2yu5o7Nbw2gzzWY3jWWJFQsuXWmP0ImZ5XSiEx5veCtMl4jsQx7moPcv60Om6G4PBTjxcspesdTXByK8am7O+b9W+eSy5ucH4hxrCuEz2ljR4Of7Q1BdjYFcNltVPucfPwmNQZ3NgYYiWfI5U12NQY40R2iscI9bWCrIeie9XkKpiaazhNP5xmOpWmp8nBuIDrnIMI/nuzn6kiCtmovHz608EGxl6+M8UZPmLF4lkObqnAu473+7PlhhqMZrgzH2VLnn5aILJHJ88rVMSq9TslAXULLSUjz/wDf0lq/rLV+/qYPENO8eGmUo9fH6azzlSyT2fGuELF0nnMDMe7YXLPgxC03+tWVMY5eH0cpeOL2FrS2Fmh31vr4u2M99IfTuBwGX7h//oXlI7EMmZy1jsNhUwTcN3+77W+t5FdXRtlS579p4pcjHdXWOiKHja11fsYSb830qWIs6ZWROAPhNAeKISNTffhwG3/50jVS2QLfPNrN+29rnlxw3hB0z3uB53PaJ0M/FjNrmM4VJmck+8LpBT9OiLXidJ81Y1/jd/Gxw214nDZ8LhvnB6O4HTaeOjtEc6Vn2vHH77LTVOGhoDXV/unHpWNdocniyx+6vXUytOm2tgp+dWWMrfX+aWFud22uocbnmjVr31A0zbXROIlsnoDLMWfimIkBcEMp3A4b79jTOON3BqNpro4kAHi9O8TWOj8eh8H2hiDZvDkZ1jUST/PwzgaujMT5wRv9KAUfONAyawirELF0jm8d7SGVLfC+25qXNLM3n9vaKklmC9gMNa2zl82b/N2xHjLF5E8fOtRKPJPndJ81kPJ61/i8IbW9oSQ2Q03rgFR4nPSH03idtgVd/BuGmizf8HdHe+gLp3A5DH7z/oWvD6zxORmIpKdlMF6ITTVeqn1OMvkChzuqaav20h9Oc+fm6Z3JbMFkpFi+qz+cwuO0Uet38tNTcUbjWQyl2LTE/1kokeXvjvUwGE1jN6x107+8NEpvKE1PKMl79zcDVod1NJ6ho8Y362yd3WbwUHHt4D++2c/l4fisWdhnYzMU79nfxNn+SDFpj5r3/95XzEjbH06htV7wpMO1kTiRZI58QXNXZw1n+6PUzDFYdzO1fhfD0QxBj2PGgOBLl0c502+te2+scMuSnxJZTljpceBfKaW2A9/F6igeK02zNr7zxSQOV0cSZPKFWeP6F2tHY4DBaJqmCveysunlTGtETWtr0fBDO99awFyMCEFr0Dc87sJgjKFomkObqkjlCnzjtW4KpmZ7gxX6sZAwjBuzHM7H7bDREHRzsjeCz2UjnMrjshtsbwhw37ZaoukcP3xzAFNrxhIzMxv6XXb8Ljv5giaWzmM3FE67lbjiZjHwHqeNX7trE5HU4grG+lx27t9Wy9XRBHdtljVLYv25NBxDa2sN4FgiQ1OFh1+7axPVPic940mcdmNGQoddTUEe3d1A3tQzSuNMhJnFM3n+8WQ/OxsDPLyzfs6QesNQc15gfPdEnxUF4LDxybva5xy4WUhCjVq/i0QmT9d4glQ2z/XRBH6Xnffu9xNO5dDFI+DEWuRwcVZCa2t9Iqz/zmE6V+D1rhBBt6MkmasFDETSkzN6V0biJe8cOmzGtPp5EzQas/hZyxfP8T6nnbZqLz3jSXbOUzt4Yl0uTA9VffvuBnY2BqgNuBYdBTURFqm19TWfS0MxBiJpDrZX8sTtrQzH0osuuRBwO/jMPR2TPzcE3ewvToSZpuYXF4YJJXO8bUcdD+6o4/Jw/K1ajFpR0G8NKj2wrXb632Jqnjk/zHgiYyXtmiNJTE8oSTJboLPWR43PSVu1B1txvfXEcTCdK/C3r3aTzlmzq7MNXE1IZPKcG4gSSeVoCLonX9Ob2VLnZ8sCy0c8vLOek70RdjctLu9Fe42Xzjo/LrvBuYEob/aE8brs/MvHdi06suLtu6z1qTU+54xBhEDxWs1uKLxzJDpcjFO9EU71RdjfWrGgUOmNajlhpX8F/JVSqhr4EPBVpVS71nq28Gdxgzs2V/PatXG2NQRK0jEEKwxj3yxlGxbrni01eB02Kr1Omm8YhXlsXxPnBqJsqpke0z8Wz/CT0wNobYWy7G0JTh7sPE5bSdMU94VTvHLFSp3/6rVxsnmT66OJyZnGWDqPo5id0GaAWWDOUc137GnkZE+YbQ1+KrxOPnlnO+OJLB0LCKnxuew3HaWbzeGO6hnZ34RYL7bU+Xn+wijNle7JItFep50nDrZyfSxBtc8543PhstvmLMVzpKMKp93gtWtjpHMmJ3sj7GoKzjj2LITTZpCiQF3ANe+M/kISamgNXqeNnQ1Bro8l6Kzzk8wWyJuaWr+L9+5vYnRKYfP9rRXE0jmUUhvmouJXV8d4ozsMQKXXMSNRh1i89movbdVeEpk8+1bwfeKyW0sjuseT7GmuIJnN8+z5YQJuO196sBPPHMmkwJrtfOv2W6GqNkMtuXP72P4mzvZb1xLzdSwjyRw/OmVdW4SSWT5woKXk2WR7Q6nJUPTXro3z2L6maYm3Ht3dwKVha/B7W31gxvKYvnBqcgb26LUQ79k/+7HOaTcYiKSo8jr47Ue3U+t30jWWnKzbClaSnUzeiri6MSz4Rs8XM8pmciYPbq+b7CiV0s7G4LRw+4W6u7MW07SOoc9dHCaazhNL5xlPZBfdOTQMNeeM4F2d1TRWuAm67TddtrQQz18cJlfQPH8xu2GO40uxnJnDCVuBnUAHcLYEz3dL2N9aWbKFtbqYSCHocSwqdfNcXHbbnDV8rFTLM+9z2A1sSpHXGrfDYFONj/uLs3cLzeqXzhU4NxCludIz76jg8xdGGIqm6R5P0lzpoT+cYluDn0gqRyydp6XKOoj4XHY+eqSN4WiG7bNk8wJrHeTUg06l1yn1doSYR384xaZi6OdAJD3ZYTAMNaOYcTiZ5epogi11/jmjGew2g0ObqlDK+mwH3HaqlvgZ/PDhVrpGk3TULv/i0WU3qAu6GY1ZswF+l53OOt9kiOvW+gBbp2SFd9iMGWniS31sXmlu+1vZHyfWeorlcTtsi1q7VUqtVd7JjtXLl0e5NBQHrPPgfBfCE6GqdmP+EMTZmKbm7IAVcr613l8s5WAlOFnItYHdprAbilxBz1kCa7mq/c7JZSKzdULqAi6+8tguLgzGqAu4ZkQW1fid+F124pk8bdVzD2q9cmWMpgoPSkHAbUcpq3PdVOnm3ECM+oCL5koP79jdSF84xeFZoqgmXr9KrxO3w8BhM9hU4y35DPRyeZw2Ht1t5VSw2xTxdIEan3NBa1kXQylV0hD+tmpvsZzNrR2eupw1h18FngCuAN8C/khrHS5Ru8QiPHNumFN9EWoDLj55R/uMmjFj8QyvXB2nudLNgbbKOWuR3ahgamLpHBWe+YvQAgTdDj52Rxtj8SzbitnNFjs79rMzg1wdSeCwKT5/X+ecaw6bKtwMRdNUeh08fqCZVK5A0O0gZ5okM4Vpo0f1AfdkiEcokcFuM+YcXUtm8xzvClMXWFpcvBC3goagdSHjdtio8M4/Uv0Pr/cSS+c52RPms/duntxeMDWj8TROm0GVz5rhu729ylpfaLctOVFXPJ3n+liCvGnOmsxiMQxD8fEjbcTSeapuUoh7LtaxOUyl18ln7u5Yd/W87txcPXnhO1eo3Eo63Rchmspx+6aqsnUU1ppoOofLbswaYVQwdbF+581fizd7wvSGUtyxuZq6gIv6oBulwKZuXj94IlS1eyzJi5dH2ddSseCcBid6Qrxw0VpT/MGDLRy9Pk5vKMWWej/vv635po/3uaxMsqPxzOS1xVKlcwWcNmPG59DvsvOZezrI5Mw5j2kOmzGjAx1KZPG77Xid1uPT+cK8S1IaKzyEkjmCHgfpXGHyPfzfXrjG5eEYbdVefvOBTlK5PLmCOetz/OOb/VwbTbCpxssHDrTQVOGhxu9c1lKiqQqmpmDqkiZLvGNzDduLSZDmipSLZ/IYigVdm5bT+/Y3E03nbvnyGsv5L1wD7tZaj852p1Jqj9b6zDKeXyzQRD2g0ViGzCwniucujNA9nuTiUIwz/RFGYtlpNfzm8u3jvfSF5i9CO9XUjth88gWT17tC2G2K29urJi+6JsJQTc3kep4J3WNJXrs+zuZaH2/bUceeliAVHgcuuw3XRBZDY+4DzytXRvkPv7iCoeB/f8+uWWsnPn9hhPODMcAaCVxMohkhbhUH26tor/bicdqmncjP9kcZT1iZ6SaOQRNrYAo3LIX55mvd/PTMIBUeax3QxNqe5Z6Qnzo7xMtXRokkreLRdsOgPuji/m0z12AthMNmLDmxF1hrjE73RYln8myp8/HA9pkFqNcyw1BzRl2stN5QcjJhWKZgTibk2MgmyjX5XDaevHPTtKRq2bzJN49ama8f3FE3o/7oVJFkrlgQ3RoE/cjhNrbW+/nsPR3YDLWgcMRMvsD33+gjb2r6QimevHNhpT+m9nHy5lt1CftCc9cvvFFdwLWg7OjjiSyn+iJsrvHNqNn3Rk+Y5y4MU+N38fEjbTNm8t0O27QBB9PUPH9xhHAqy9u2188IWXzuwjAnusPU+p184o52nHbjph2qd+xuYHuDn5+eHuR/vHydR3c10BdO8d0TveRNTV5rhqKZyc50rmDOyJUwkSCmL5TCZqgZ9QPBGghIZPMc3lS9qE5eNJ3jW6/1kMpZyZJKOSM3X0TW9dEE33+jH5sBHz28+JJppWQYasNFjyWzeZ45N4zdUDy8q35BS9mWs+bwv9zkV74O3L7U578VDMfS9IVS7GgMLGu05IFtdbx2zcp8OtsIYrXPSfd4EpfdYCCcxm4zuDqS4JFdcz9nwdT0h1PkTZNnzw/jddq5d2vtourczOWNnjAvXxkDrFEir9OGzVC8Y08jp/sitFS6GYtnMX1MngyfvzTCYDjFtZE4u5oCix7FPtUXJV88S53ujczaOZwIm7IZal2GgK0lHb/3o2U9/vq/e0+JWiLKoeaGgZPhaJqfnRkEIJHN8849jbx2bZwqr5OtdU5uK67LA2tw6NpogmzeqiXWG0q+lfhhmSq9DgYjadwOGy9dHmVzrZ/u8STb6gPFzHyzS2ULnB+M0lLpKemFyaFNVbx0eZT2ai89c1wMj8YzaG2F7OcK5pLWMd8KnHYDpay1oDcmPNqoekJvFTIfj2endQ7DqSxn+qPE0zmqfc55O4cuh9Vx6R5L0D5l3ehiLoJtSuGwG+SzhUW9/oc2VWEzFG6Hwdb6AA/vNDnbH2V/W+nXc/3k9ADD0Qwne8J88cEtOO0GyWzeKkUxHJ9MpBVKZue8hhiOpTGUIp7O80ZPGIBXHeO8a+/0xDC9xc/zaDxLKlfAYWq6xpK0VHnmLOlhGApDKaLpHL2hFHZD4XNaYfTxTJ7b26vwu2yMJTJ4nXZqfDM7xA/tqOd0X4Q9LW9dw+hiqbGAy8610cTkQIBpwn03JM+Zz0A4TTxjrXO8OhJfsYzL/ZEUptaYBStL9M2OwVpr+sIpKjyOsqyznOraaAK3w5iz/u16cLI3wuVhK4S8qdIzuU5+PuU8C83oRSilmoEfArsBv9Y6r5T6HeADQBfwWa11Tin1SeDLwDjwpNY6qpR6GPhjIA18Smvdq5TaC/yX4r5+S2t9sox/T0ll8gX+/lgv2bzJ1ZEEH7phDYLWmkzeXFDoTEetb95487ftqGNrvZ8qn5PTfRHOD0RvGvJpK6ac/smpARw2g9e7QlT7nCVZoDv1b+oLpThVXMj9+MEW7uqsmRyR8zhtfPaeDtwOGx67wfHitrF4Fm/14t6679nfyKXhGE6bwSNz1BZ8YFudFaLhK12IhhC3AofNwGYoCqa1LqhnPDlZniLocUzrTNptBu/e20hBa6q8Tu7uXPjFy820VXlprHCTzBTY1VRBOlfA57Ld9PP80zMDXB+1Mq1+/r7NOGxGSUKr9rdW8uSd7XSNJbljlmNu11iC757oI5MvYJrWOp137W1cUgKIja4+4Oajh60w3+WGF64XRzqqiafzVPmcM9ZAGVjZKhOZwpwhiBPcDqscw0gsQ384OS2kcWLQ9GaJ7Ow2g48faaMvnFpwpkuwriWmZiDf21K+LJATa2SddgNDweXhGD86OYjLYXDvlhri6RyNFZ7JRFo3ujIS5x/f7AesWT6XwyCTM2munNlZeWBbHb+6OsqmGh8Bt4O/O9ZDXyhFwG3n8/dtnjMMva3ai9ZWiGsomeXwpiri2Tx+l51P37WJvz3ag99lx1Bwz5aZx4zdzcEZ6z5/fGqQi0MxOut8HOmonhxEcS9ynXBHrZdNNV4S2cKKFpvf31rJcDSD3TZ3JuqpXrpslVtzO2x8+u5NZRtQe7MnzLPnh1EKPnyoteSJkFZKY9CNoVSxRu/CIuLK2TmcLafuOPAIVukLlFJ1wENa6/uUUr8LPK6U+h7wJeABrCyoXwT+FPh94B1YHcuvYHUe/wj4BGAC/xmrk7kq8gWrKGtdwLWg+H8rfXMx5MrUN9yn+e6JPrrGkhzaVDVrWurFUEpNJo24q7NmwQlibm+vwue08+NTAyjFnBdY8Uye757oI5c3ef+B5puGY+5tqcDjtGE31GQ9IbBOdGCFhoA1mp/MWiexzbU+9rYEcdlt05JgLFRzpZc/+fBt8/6O3WbMGqIhhJhflc/Jx460EUpm2VYfIJbO4bBZSSQqi8eNZDbPaCxLS5WHQx3VHCpDxt50rjDZsXp0V/1k5tSbDbJNXFubpiaeyfO9E30kswXes79pURfCs3l4jsEosI51WkMiXSCdL+CwufnGq920Vnl5195GyQ56g6VksF3P6gIuPnqkbdb7vC47u5uDxFJ5Ah47pqnnXdPqsBnFc7iaDPcejqb5+9d7AfjIodabztis9YRtrdUeK6y01ofdZtA9nsTUmlS2gMthm7bueTYTn0ewStR89p4O0jlz1tDy9hov7TVvhdZOXL+kcwVMDbY5/hU2Q/HgDuua7s3eMFrD5+7dPPlZN02Ny26FuC50nfP1Masea9dYkg8caOHOzTU8e36IrrEEB9oqF5zB3mW38cTtK58sye+y8/jBhdf7HktY143pXIFENl+2zuHE/1Rr63p0veqo9fHZezswFAueaV3R+BWtdRpIT3nD3wE8V7z9NPAkVsbTU8VZxaeBrymlvEBKax0DXlVK/bviY6q11j0ASqlVzTn749ODXBmOU+Fx8Nl7bp54wO2w8cHbW+kZT84YBcrkTbrGrHCSS8PxZXcOpyqYmsGoVUB2IXHHOxoDBD12bIaaMwzj6kic0WIn7/xAjPu23XxkYuKCq6XSUyw5odhd7Jg9uL2OV66O01Tpnjwo72oO0h1KYpqwZ5HZ0oQQ5dcQdE9mGa70Ovm1uzYRS+dpq/aSK5j87avdxNL5Ba9hXorDHdWYxRHzrfX+eS+uMvkCo/EsDQEX79prhbS3VnkIJ7OTKeSvjiSW3Tmcz96WCsLJHLmCSTpXoHs8SSZvEM/kOdUXkc6hmJPXaedjh9v47y9eo288xc/PDs0IfZzqrWUbnsllLN3jSbJ5a2SkazxZtrVepqkZiKap8TnLmkjobH+UCo+Da6MJ0rkCB9qqGIll8LnsCwqR3N9aQTSVw1BWVlaHzWChfeH37GvidH+ErXWBmy6/ubuzhstDcY5dH+dMf5TXro1PftbfvruB410hjnRULbhz+MC2Ot7sDU/OyA7H0niddrrHUwzFMhuuMPwD2+pw2AzqA66yJsq68Xyyni02Gq6cncPsAn6nEogWb0eAqjm2VU3ZBjBxdJk6HDLr0IhS6gvAFwDa2xe2gHopQsXZrlg6T840cRk3PwDeWEZhgtth49CmKi4Nx7lzc2lH1390aoArw3Fq/dbF22wHn0y+QM94iuZKNy67jRPdYcYSWR7dVU9ThYc3esKcG4hysL2SnY1BNlX7CLjtZAvmoj9AdpvBPVunh5XV+F0z6gR5nXY+eHD2ES2trVj/Co+jJHVuhBA3F0pk+emZQdwOg3fvbZpx0Td1liGbNyfXsowncjOeq1ScdmNBa2y01vzd0R5G41k663x84EDLZERFNm/SUeslls5zW4kLv4/FMyQyBdprvIzEMvzgzT7cdhufKGaZTucK/P2xHsLJnGRMLqOhaJqnzg5R5XXyzj0Ny64NvFpcDiuzr9ZW2Zj5+F32GVFDOxoD1lq84u3ZZPIFekMpmis8C4qKms3Pzw5ybiBGpdfBp+/uKEnuAoCXr4zSPZbkni21tNd42d0U5FdXx9hSLL7udtj42JGFX/cNRzPsa6lYUie5Pujm4Xkel8zm+fGpQUxT8659jWyu9VHtc5I3NQ1BF+FkluFohucvDhPPFPC6bDQscJ3bvtYKKjwOXr4ySjKbZ2djkK6xJLV+14wQ2mQ2T384TWuVZ91m/K3yOeesm1tKCz2fbETLKWXxbeD/BX6itZ4R8K61vmsBTxMGJuaSg8Wfw8XbU7eFpmwDK4x06vcbb09tx9eArwEcPnx4tlDXknj77gZOdIfZUu9bVFH7iYXEfqd92mzjA9vrSjpj+GZPmDP9Ua6NxvE67YwnchRMjaHg6XNDhJM5HtpZT13Axfff6KcvlKLS6+COjmrODUQxlOL1rhCP7XXz3IVhtLaye+5sDFLhdfAb93eitV5SuvflevnKGK9dG8dhU3zq7g5ZLyjECjjZF2GwmHnwykicPc1zd6R8Ljtv391A11hy1tpdc0lk8jjtxrISRGmt+cWFYYajGR7cYa0rLph6spM6NbQdrAuCuQailiqRyTOeyPDdE/0UTM3922q5MhLnqbNDaG0l0nnP/mbcDhufurujpMfS490hzg/EuH1TpaxlLHq9K8RILMNILMPelmDJa6+tFL/LzqO7GugeT+Jx2PjbV7u5ra1i3s/iVAG3g4/fMXfnyTQ133yth/FEhmqfi8/c07Gkdo7ErY5rJJVbcOmN+ZwfjPLSpTEuDEVpqvDw0pVR2mvaubOzhj3NQQxDLfrzc7I3zDPnrPVlHz3cVvIQ5vODMXrGk2it+etXugi67LxrbxM1fiuXw9+82k0snaNnPElnnX/GcelGp3ojHL0+zm1tFRzaVM1LV0YZjKQZiKT59fs2808f3jrra/D3x3oZT2RprnQvquMsbi3LmTn8c+BzwL9XSv098D+01ucX+RxHgX8C/AnwKPAKcBHYq5SyTWzTWieVUh6llB9rzeHZ4uPHlVKtWB3DyDL+FsA6cJ0phvIsfk2bZ0kHk5+dGeLcQJRNNd45Y73TuQI/eKOfeCbPY/ua5s26NxutNc9dGMHUGkMpOut8bKsPYLcZdI0lON4VYjSexdSaj9/RTjRlXTSd7Y8yHE1zbTTJ9gY/m2t9GIaipdJDbyg14zVajY4hMNneXMFaWyCdQyHKq2c8yVg8Q65gEnA7aF7ACPee5ptftOYKJid7I1R4HGTzJj8/O4jPaefJO9uXvK5kKJrhzR7r9PDq1XEeP9iC3WbQEHDxq6ujPLJr7lC8UugZT/LdE31EU1k0Cr/LTjSdo9LrQGtw2GZeyJbqWFowNS9cHEFreOHiiHQOizbX+rg4FMPvsi+oRMKFwRihZJYDbZVrbrZlIsHLv3/mEgVT8/zF7II7h3PpD6foK2YHf/rcEAG3nd1Nxk3XNc7l4Z31HLs+TmetH5uh+PbrvYSSWd61t3FJST5euDhCLJVnJJahMeieTNbTPZbke2/0YSj4yOG2yTD3hZgIJdeaySiHUmqt8hBN5xiNpanyOsn4XLRW2fC57HSNWeG9Q9EM44ksPleaT8zTaQer1Ni5gSi/uDDMv//4QVqrPAxG0tT4nfic09crmqbmx6cH6A+nGIykCbgdk3+vELNZTimLp4Gni2v9PgE8pZTqAf4C+Gut9YzYIaWUA/gJcBvwM+B/B15QSr0IdAN/VsxW+hfAL7FmDJ8sPvyPgaewspV+prjtD4BvYmUr/fJS/5YJPz09QH84zetdIb7wYOeiZgCX6upInOtjCXpCycnimxcGY3TW+Sc7gT3jSbrHkxRMzem+yKI7h1ZCGg9dY0n2t1ZOC9ms8bvoGk8WD0g28gWTx/Y1cbovMplQYlu9nyfv3DS53ydubyWasi5u5jIUTXP0+jibqn3sW2Ro1rPnh7gwGOfOzup503NPuG9bLXabQY3fuejXRgixOOlcge+dsOqdNVd6+NgsNcNuJpm1klilcybvu61pct3IS5dHeersEJFUjv2tFZMXahPrhiZk8+aCyz5Ueh0E3PbJ9Y8ThmIZOmr9k+UCluJUb4Su8QRHOqrnvBDtC6comBqfy0F90EVj0M1dnTV4nXZcdhuZfIEHtpWuZt9QNM2V4Tg7GgPU+F2Tg3ntsn5x0q6moJW0xFA3DSkdiqb58akBAOLpPI/unr8+8Gppq/ZwfTRJ2xydrbF4hmNdIVqrPLRWeQm67bMOQqSyBb79ei950+R0XxSHoYimcjy2r2lJHUOwZjjr/C6i6Rz/11MXuDaSYHtDgFO9kSV1DturvZwbiPHOPY28a2/jZPj6xGetAAxE0ovqHB7aVEWuYGWIL0c2XIVVtsJV5aU3lGI0nuXKSJxnzg2RK2j8bjt9oRR7m4NUeJ0E3PMf2ybKificdkbiGe7fVse+lgp8LvuM9/RoIsOlIauUQX3QRWetnz1lyhgrNoZlrTlUStUAvwZ8CjgB/A1wH1bn7W03/n6xw/joDZtfBb56w+99HatO4tRtT2MlrZm67WRxfyVhN4p17mwKNbMSx7JcHo5jKOi8IblBrd/F0evj1Ppd/PLyCBeH4qSyBd7oDfNbD25BKUWV18mp3nDxgmlpI7+PH2ghms7NmFXzu+zcs6WG/nCKgMeBUmpyFnRXU3CyRtfUTpfNUGjgay9c5exAlN3NQT5+uJ2KKZ3FZ84NMxRNc3k4Tmedb8Gj/pl8YXKU/3hXaEGdw4DbwdvX6AlbiI3GUAqbTZE3NV6nbVrHMF8wuTAYI5M32dEYmPzcXxyK8fMzg9QH3XzwYAvXRhMMR62wqXMDscnOYTZvTtZjiqVzNFd6qfI6pnXqoukc33i1m1SusKCyD1a68w5SuelRBbuaApzpj7KryVprdbY/ytHr42xvCHD3lptndI5n8jx9bqjY1vycI/37WioYjKQxDMU7djdMm3l6W4kLuWut+c7xPtK5AheGYnzu3s08cXsr8XSeoOetY/Bw1KpntrnWt2oRH+XQM54kmS2wvWH+ZETAgmcAbYaaLA3guKG0SSydI5LK0VLpWfXX8QO3Wef4YDEbYSKT5x9e7yWVK/D+25p58dIofeEU3369ly31fm5rrZw1gY1S1hcovE4b8Uye1irvjHWCY/EMoWSOzmJE0XwmBt4vDcVor/GSyZuk8wW2NSxtXe079zRyd2ctAff05Tj7WysYiqaxGYqdxTWUkWSOn54ZwG4YvGf/zLXRE9wOW8k/j1Nl8wVGYunJmo8nusL0R1JWqS6HjWqfVQ/Wbjc40lF906ySX3ywkx+eHKDW76SzmGxnrkyy1V4nPpeNV66Oc3hTFQ/trJdazmJey1lz+B1gJ1Yn7n1a64HiXd9SSh0rReNW2mP7mrg0HKO50rPs+lZTne2PThaIfu/+pmkHxP1tFfSFU/SGkrxw0Tp4b63zTSui2h9JkcmbOO0G54fivHvf4ttgGGrGgcMKC9M8frCFi0Mx2qt9004AbdXeWdcj9IwnefHyKBeHYgxG0vhddq6Oxjk4pSNX43cyFLXuW8xr6bLb2N4Q4NJwbEYWVyHE6nPaDT56uI3+cIrtN1zc/fLSKN890WutLeyo5nfeuQO3w8aZ/gi5gqYvlGIsnqWt2kvQ4yCdK0xLYnXf1lpevjKKqWFnY8WMxFRgrRFMFtOKd48lFxQq6bQbM45D79jTyCO7GiaPeS9fGSWWzvPK1TEOd1Td9OIpmy+QN01sSlEzTyIs3yLTtC+Xvfj3TMwe2Aw1beBuJJbhG6/1YGrNPVtquHOBpY3WuoFIim8f7y0mZynd31Xrd/HhQ62Ek7nJDgdYna+vv9JFJmdyx+Zq7t268MQV8UyenvEkm2q8k5lDl+vGc3x3MSII4MJQjKDHTk9Ik8wWsCnoHk/M+jxuh40PH2qjL5zkjs3Vk+v5g1M6K9F0jm+81k2uoDnYXnnTTtXEwHtDhZXk7h17GvngwRY8Thtaa3pDKSq8jmn7mI9S09/TE2b7rJ3uj9AfttZGXxyKrWj9vqmePjfM1dEE8XSe9moPyWweh2FNQzjsBslcgUyuwP/69u3TyoENRFK47LYZ5TSqfS4+fXfHgvZttxlsqvERS+fJ5E26x5NlzcIs1r/lHJX+o9b62dnu0FofXsbzrhqP01aWA0d2SoHaTH563pydjUECbgfPXxhmKJrB47BxqKOKw5veSmM8sS4imS2wqUShQf3hFH9/rBdTa969r5FDmxaWFfX6qFW0OZTMoopta6n00Fk7/UDz9l0N7G4KUuN3LnqE6j37m9C6cdVHYoUQs6v1u2atZ5rJFxiIpBmNZzjRHaJrLMmOxgB7myvoD1thXrV+J3abVWz+xsQrXpedf/GOHYzEMnOGQW6q9rKj0aqleGgRyW1mM3UwbEudnzd6wrRXe296zEpm83zrqNURaa/18uiutRG5oJTiI4db6RpL0lk3e5KVVLYwWecukd04646yeXOyRt2N59nlaq3y0nrDWy2RyZPJWfsZjc+fPORG/3Csh1AyR13Axa/dtalUzZymvdpLrd9JKldgZ2OA+oCb7Q0B7uiopms8OW9UTmOFezJaqLPOh9dhn9YZS+cK5ArF91Dm5vXfHtvXxMWhGC1VHmp8zmmf+Rcvj3LsegiXw+Azd3eUvGZde7WX410hbDa1qiUdktkCqWyBWCZPKm9yaFMVoWSOwx3VhJJZ/C5rYHzqcfV0X4Snzg5hMxQfP9K2rFIjm2t9nB+I4XXaqF/AOltxa1v0p1Ap9cRstydorb+z3EatF4lMnjd7wjRWuGeEi061r6WCXMHEUEzW8puqpdLDe/Y18+KlEXrCKS4NxYkkc9y/vZb6gPXcn713M7F0bkYq6oKpeaMnjNNmsLcliNZgan3TdRThZG7yAmFidHEhJkbsrRTgjdyxuXrWCynDUMuq0SUdQyHWnwe313O6L8IbPVY9tYnZum0NgVlDyGb7nAfcjjlDqnIFE4fNmJHGPJzMcqbfSuy1lDVMAA/trOeOzdV458ikGEnmePnKKLUBF5trfaRzBRw2K5PqRGjb5eE4p/si7G4OzphVXa7zg1GiqTwH2irnjca4WaHy9hovD+6oI5qaeT5ZzzbV+Hh0VwPxTH7ZgwYLUR90c/+2WoZjGe6e43U82Rsmkzc5eEMh8lSxU1nOwto+l51P3TCz1Fnnp7POz/2LeJ6mYrKp17vG6Q2luHNzDY0Vbt6+u4FXr46RzhUYjWdmHSwCJrOT3tZWOev9E9cfmZxJMluYtXNompo3e8MopbittWJR1wdt1V5+84FODKVKGhG2WB861MJILM3VUat26uMHmvi7Y71cG43z+IEWTA1HbihdNvHaFExNOJWbt3Nompo3esPYDcW+lpmv0faGAO3V3gWtsxViKUM075vnPg3cMp3DZ84Pc2U4jlLwuXs3z5kl02YojnTMPzNX4XWwvTHA6f4Ix7vDNAbd5E1zMtXwXCe7N3pCvHBxFIC8afJ6V4hUtsD7DzTPm557R2OA0XiGbN5c0Lq+CTsbAySyeXIFkyMds3cMhRC3Jo/Txj99ZBtHr4XwuWwLKjy9EKap+fbxXnpDqVlDIX90aoDhqDVb+cUHtyz5uDTfrMVEKD2DMVqrPDy4o46hSHpaB+ups0OkcwV6Q8mSdg77wil+cspampDM5pe9Nmoxx/z1ZLHJz5br8Dzn9cvDMZ45NwxY6xXvmHLh//4DzVwcjLGzaX3UsoykcpPXGelcgY8daae1ysNT6TzRdJ7nLozw4UMzs63/9PQg5wai7G2pmDMvwAPb6nDaDOqD7jkzx57pj/LchRHACpveu8hkKmshw2x9wM1XHtvF0WvjOO0Gr3eHefb8MAVTc200wbv2NrG3pWLadeSRjmoyeROPw8bWm4SBvtEb5vnia+S0G7OG26+F10GsD4vuHGqtP1eOhqwnz18c4fJwnIJpjf7ZlCpJUdcqnxO3w4bTZuBz2amaZ/QXrOQDPaEU44ks1T4no/HMZHriqyMJmis95At61ppCNkMtqY6isYCOrhDi1vDs+SGujybZWUw+s6MxgNthK0nh4HzBJFc8fiVzViFusNYN3dg5nOgM2m1GiVOJvaXKZ120Oe0Gfpd91g5WfcBF93hyMsFOqdinJkWRAbl1Yer/yW6z3pWmqUnlCrRUelY1xHGqkVhmcv3wxBrAZ88P0zWW5L5ttdZ2h20y4+/Ee9vtsOF12khmC1T7Zh8YvzgUm/w+V+ewyufk3TcpaD71+qoU11qrxWEzuGdrLaf7Irx0aZRUtoBS4HXayOZNro0mpiX/8zhtC062Z5/yutjX8Wsk1oalhJX+mtb6r5VS/3y2+7XW/9fym7V2pXMFjneFAAi4rQK09UHXtAQys+kaS/DMuWEagm7evbdxMgypZzxJLJ1nZ6MVa/65+zbzwYMtKHXz+Pg3eyNcG4ljas2Btkru3lJDOJkjlSvQUePlL1+6Ripr8q69jexoXB8jlEKIte/ycIxExsosnCuY/PeXRtnbXEFPKMl79zcv+/mT2Tx/+2o38Uyet+9uYE9zBftbK7g2mph1tuZ9+5u5NByjtcpbtpCpe7bU0l5MpDNX2OsHDjQzMk+I3VI1BN08cbCVaDrHrlmWJpSa1pqfnRmiL5ziwe1105IG3eryBZPzgzFq/a55SydtqvHx+MEWsnmT7Q1+CqbmW0d7GIqmuXNzNfcsIoFNuWTyBf7uWA/ZvMnV0TgfPNhKNJ3nZK+VMXwie6/TbvBrd20inMzRELTe226HjU/etYlQIgsa/vKla1T7nLxnX9PkZ/DOzdWc6ovMGVK6ULubg9htVvKWpWY4XQsSmTxXRxL8txevEk7lsNsUu5oCtFV5uTwco7HCtaBkWLPZ11KB025gNwz5vIplW0pY6USc0Pr9hC6Dy27QXu2lezzJrqbgvGEsY/EML18ZoyHopi+cJJKy0l4f2lRFY4Wb7vEE//aH58gWTD52qI13728i6F54xi5r5lJR63fRUuXB7bDxkcNtAFwZiU8uFL8+lpDOoRCiJM4NRPnp6UG01lZZG81kJr18MUnFco3GspNREK9dG+fycJxdTUEemSPxS7mSid3oZusZ7TaDhoCbn54ZZCia5uGd9fOG9y9Ge83K1SkcT2Q5NxAFrJJCcrH5lucvjnCyN4LNUHzm7o5Zs2ZOmBpWncjkGIpaWTOvjiZWrHN4bTTByd4wu5uCMzpWWluzmcBkghm/y05zpZv+cJpt9W/9vttho7HCRq5g8qOT/URSOd65p5G2ai8/PNlPOJkjnMzRH05Pvlfv7Cxd1thSr+FdDf/4Zj8DkTQjsQy5YgKl/nAG01TcvqmKwUiGvlCKjiWE4yulFpS5WYiFWEpY6X8tfv/D0jdnbTNNzTPnh4trAdtoLs7sFUzNT08PMhrP8Miu+skLiF9eGuXaaILLw3EOb6qiayxJjc85eSF1bTRBJJUD4OJwjHczd2hFvmAyFMtQ53dNLqo+0FYFKJw2Y0bR1k3VXrY1+Imm8ht2fYkQYmUlMnl+cmqAN3rCOGwG2xv9fOruTVaZinCK20rUQWup8rCzMcB4MstgJE04maNrLMnWOv+SC3GvlOFYhguDVjjd8e5QyTqHK6nC46Ah6GY4lmZrw/rsGGbyBX5+Zoi8afL23Y1zRveMxjPYlKJqjnIkw9E0PzszSNDj4LF9TWSLmVBNrcmbC8+KGnQ7OLSpiq6xxILqaJbKz88MkswW6B5LsrV+ev1Ht8PG4wdb6AklJ9fx2QzFRw+3kS2YuOwzl6T0jCe5NmqVwXijJ8y7KhrZWu/nynCCCo+d+qBkwpzNaDzDM+es2fiGoJvP3r2Zr/7sErmCSSiZxVCKgNtOwzIykgpRKsupc+gGPg/sASbfzVrrXy9Bu9ak3lCK031WuMWbPeHJzuFAJDUZW3+8OzzZOaz1u7g2msDjtHH7pkrcxXj+nvEEA5EMbpuNpgo3dpuakX3vRt9/o5/u8SQNQTdP3mklqbEZas5ENXabUZLwLiGEmHC8K8TzF4cZiWWp8TvZ0xzk/GCMh3c2lLQTZDPU5Dqk7xy36ibW+J0L6hjmCybHu8O47Ab7F5nZsBSqfU5q/U7GEtlpMy/rid1m8Ik72sgV9KIzPA5G0gxG0+wsrj9dLRcGY1wejgNW1tB7tsycqXu9a5y/eaWbKq+Tz9+/efKcPtWJnjCj8Syj8Szd40netqOeCo+DuoCLmkWGD1vr/Be/1n85av0uusYSxDIFTvSEOdhWOe0z0VbtnZFZXCk1a8cQrDIXQY+DRCbP1nrrM7+zMciWOj82paZ9Rq+NJkhk8uxqCq7rtYKl8My5IQYiKYaiabSGVB7u6qzh8nCcrfV+bmuroLXSMy1HhNaaswNRXHabzN6LFbWcgjJfB84D7wT+DfBJ4FwpGrVWVfudkwuwp4YX1fpdVHkdhFM5OqeEA9y3rZYt9T6Cbgd94RQvXholXzAZTWTxu+xWaGpjgIObqm4aRz9RR2ksnplRG0wIIVZCKJllLG6lV3faDOw2g8215b1oef9tzZNREwvxeleIl6+MAVaih5VeozSxPmspHau1RCmF076480wik+fvj/WQNzW9JVp/ulSNQTcOm6JgMuf6/R+dHKQvnGIgkmY4mp61c7ilzqoP53PZaAi68Thta2K94EK9/0Azz5wb4nRflOcvjOBx2Ja1btXrtPO5ezrIm9Pf3zeuk+sLp/jeiT4AYun8is6WrkWXhxPkCpq8qan1u0jnCvzB+/bQF05yojvMie4wb/SE+VzQPZmx9Hh3mBcuWhlIHz/YUrLsz0LczHI6h1u11h9RSn1Aa/1XSqm/BX5WqoatRX6Xnc/c00Emb05LN+x22Pj03R1kC+aMkdKJGkGTB05lXVRprbEp6wS8kNPvO/Y0crI3zK6moHQMhRCrorPOz13FxFefvruDOzur55xhKBW7zVhUZsepF6zlbttcltKx2ghMrSkuYaNglmb96VLVB938+n2bMTVzhpRuqfcxFE3jdRpzdpi21gf40tu82A1jXc5+OWwGLZVezg1Y0U2lGLAwDIXzJq+FOeX/P1FT+Va2qcbLQzvriSRz3Lmlhnu31OK0W4NrE2HodkNNyzQ69XUrLCKEWYjlWk7nMFf8HlZK7QUGgY5lt2iNcztss4bKGIbCbcx9IbK51spcliuYVHsdDEatGcB03lzQOp3NtT4ZNRJCrKq9LRV86cEtKKXYcpO6W6vlQFslPpfdSh62gklcBATcDj54sIX+SIr9K1xzcDZe5/yXOE8cbGV/SyWtVR5c84TArtYgQ6nsa63A7ZiY6V+Z64i2ai/v3tdIPJ1fdrbSjeDxgy1cG0nQXu2dkcTo4Z0NtFZ5qQ+6ptVavb29Cpth5ZXYuk5D1MX6tJzO4deUUlXAvwJ+APiB3y9JqzaQqSGgUw/KtSWugyWEECthrV+kKKU2RGbD9aq9xrtmO+U3LsnwOG3zZhzfSFajBIRkz3yL32VnX2sFepZZVKfdmEwINJXNUJJQUKyKpdQ5nFrf8HPF7/+p+F2mtqZ4/uIIb3SH2dsydwp2IYRYL8biGb5zvA+N5kO3ty46IYcQq+Xo9XFevjzGlnof79nXJMszxIo73Rfh2fPDNFW4eeL21nUZpixuDUsJPg8Uvw4DvwW0AM3AF4HdpWva+ne6L4KpNaf7oqvdFCGEWLZrownimTyJTIGrxXT2QqwHE+fjS0Nx0jlZvyVW3tn+KAVT0xtKEUpmV7s5Qsxp0Z1DrfUfFmsc1gK3a63/hdb6XwCHgNZSN3AlDUbSjCdK94E92F6J025wsL2yZM8phBCrZWu9nyqvA4/DwOuwzRoiJcRadKCtEpfDYHdzcFq5gPmkc1Z9wHxBOpNi+W5rqyRvmtQFXFR7Z6+rKcRasJw1h+3A1J5UlnWckOZMf4SfnxnCUIqPHWmjsWL5awLv2VI7a20lIUR5dfzej5b1+Ov/7j0lasnGUul18tEjbfzVy138/OwQo4ksD25f2bptQizFwfYqDi5i/ZbWmm++1k0omaOzzscHDrSUsXXiVmAzFHbDYDSeoTeUWrNrc4VYbp3D15RS3wU08EHgr0rSqkVQSv3fWCGux7XWv72Qx4zFM/zw5ACxdJ4djX6OdFQTSljJV02tCaeyJekcCiHERpPKFnijZ5zeUIpUNi+dQ7EunOwN88rVMbbU+WfNAZDOFTjeFaLa72RnY5C8qYmk8gAljSgSt65fnB/ipcsj+Jx2bm+vks6hWLOW3DnUWv+xUuonwP3FTZ/TWp8oTbMWRil1O+DTWt+vlPpzpdQRrfXRmz3u3ECMkViG17tC9IWSjMSyfPBgC6lcAbfDYPsaz8YnhBCrxeey0xdKk8mZXBqOr3ZzhFiQ17tCJDIFTvZGuGdL7YzQ0hcujnCm38oPUOV10hB08669jVwejnNb262R0VSUTzZvMp7MEs8USOVMrozEeUAG1sQatZyZQ7TWx4HjJWrLUtwNPF28/TRwF3DTzmFnnY8T3SHcDoOgx4HTbuBx2nj7bskoKsRasNyw0FtdOcNqHTaDbQ0B+sMpOtdorUMhbrSzMcgrV8fYXOvD7ZiZbmGizqGhFA6bdf+OxgA7GmWwWCyf026wszHIqd4o9QEXnnnqagqx2pbVOVwMpZQPSGutCyV82krgSvF2BNgzy36/AHwBoL29HYDmSg9ffmgr4WSW/kiarfVygSOEEAthMxT/x3t30z2elHqCYt24e0sNd2yunrN8wH1ba6nzu6jyOaj2SbIQUXofONDC/dvqigNrUvlNrF1l6xwqpQzg48AngSNABnAppUaAHwNf01pfWuZuwsBEldVg8edptNZfA74GcPjw4cnUeoahqPa7qJY6XUIIsShVPidVcgEt1pn56srZDMXuZinaLsqr2ueUwQex5i2lzuFC/QLYAnwFaNRat2mt67HWKL4C/Dul1K8tcx+/Ah4p3n60+LxCCCGEEEIIIRZJlatOlVLKobXOLfd3FrCf/we4HXhTa/2/zPe7tbW1uqOjYzm7E6Isrl+/jrw3xVol70+xVsl7U6xl8v4Ua9Xrr7+utdazThKWrXM4uQOlGoAWrHIX/VrrobLucB6HDx/Wx44dW63dCzGnw4cPI+9NsVbJ+1OsVfLeFGuZvD/FWqWUel1rfXi2+8q55vAg8OdABdBX3NyqlAoD/6SY6VSINWM4mua5iyPUB1w8uL0OpeZen7KR5AomT58dIp0v8MiuBoJux2o3SQghxBrzetc4l4fjHOmolkzFJWKammfPDxNO5XhoRx01kgdDrAHlXHP4l8Bva613aa0fLX7tBP4/xfuEWFN+dXWMvlCKE91hBqPp1W7Oirk0FOf8YIzro0ne6A6vdnOEEEKsMelcgRcujtIfTvPCxZHVbs6G0RNKcqovQs94kteuja92c4QAyts59GmtX71xo9b6FUBy+Io1p7XKA0DAbafSc+tkE6sPunDaDZSyyrwIIYQQUzltBvVBa1artcq7yq3ZOKp9TjxOq+ahvK5irShnncOfKKV+BPxPoKe4rQ34NPDTMu5XiCU5tKmaLXV+PE4bLvutU6C21u/i1+/dTM40JaRUCCHEDIah+NjhNqLpPFVeOU+USsDt4LP3dJDJmVTI6yrWiLJ1DrXW/0wp9W7gA1gJaRTQC/wnrfWPy7VfIZaj0nvrzBhO5XHa8HDrdIiFEEIsjt1mSI2+MnA7bLgdcv4Va0c5Zw7RWv8E+Ek59yGEEEIIIYQQYvnKma3UDnweeJwppSyA7wP/fbn1DYUQQqyOjt/70bIef/3fvadELRFCCCFEKZVz5vDrQBj4Q6xwUoBW4DPAXwMfK+O+hRBCCCGEEEIsQjk7h7drrXfcsK0XeEUpdbGM+xVCCCGEEEIIsUjlLGURUkp9RCk1uQ+llKGU+hgQKuN+hRBCCCGEEEIsUjk7hx8HPgwMKaUuKqUuAYPAE8X7hBBCCCGEEEKsEeUsZXGd4rpCpVQNoLTWowt9vFLq01jrE23AJ4EnscpidAGf1VrnlFKfBL4MjANPaq2jJf0jhBBCCCGEEOIWUc6ZQ5RSFcUw0s8An1JKfUwpVbmAx7UAD2qtH9Favw3IAg9pre8DTgKPK6UcwJeAB7CS33yxTH+GEEIIIYQQQmx4ZescFmf+jgNvA7yAD3gIeL1433zeCdiUUs8opf4DcAfwXPG+p4G7gO3AKa11fso2IYQQQgghhBBLUM5spf8SOKS1Dk/dqJSqAl4F/uc8j20AnFrrR5RSXwUqgYmQ0QhQNce2GZRSXwC+ANDe3r6EP0MIIYQQQgghNr5yhpUqrML3NzKL980nAjxfvP0s0AEEiz8HseonhmfZNoPW+mta68Na68N1dXULa7kQQgghhBBC3GLKOXP4x8BxpdTPgZ7itnbg7cAf3eSxLwO/Wbx9oPj4jwF/AjwKvAJcBPYqpWxTtgkhhBBCCCGEWIJyZiv9K6XUD7DWD7ZgzRY+B3xFaz1vnUOt9RtKqZRS6jlgFCtTaZNS6kWgG/izYrbSvwB+iVU38cly/S1CCCGEEEIIsdGVc+aQYifwm0qpauvH+TuFNzz2f7th01eLX1N/5+tYmUqFEEIIIYQQQixDObOVtiulvqmUGsZKQHNUKTVc3NZRrv0KIYQQQgghhFi8ciak+RbwXaBJa71Na70VaAK+B3yzjPsVQgghhBBCCLFI5ewc1mqtv6W1Lkxs0FoXtNbfBGrKuF8hhBBCCCGEEItUzs7h60qp/6yUulMp1Vz8ulMp9Z+BE2Xc77pxcSjG0evjZPPmajdFrDPD0TS/ujLGeCK72k0RQgghZjg3EOXY9XFyBbnGWYqCqXm9K8TpvshqN0XcYsqZkObTwOeBP+StbKW9wA+A/17G/a4L/eEUPzo5AEAik+dtO+pXuUVivdBa850TfaSyBc4PRvncvZtXu0lCCCHEpJ7xJD89PQhAJm9y79baVW7R+nO8O8SLl0YBcDsMttYHVrlF4lZRzlIWWeDPi1/iBnZDoRRoDTZDrXZzxDqilMKmrPeMXd47Qggh1hhjyrnJUHKeWoqp14Y2o5yBfkJMV9ZSFkqpdwKPY80caqAf+L7W+qfl3O96UB9088TBViKpHLubg6vdHLHOfORwK9dGE2yp9692U4QQQohpWio9fPBgC/FMnt1Nco2zFAfbKnHbbTjtBptrfavdHHELKVvnUCn1Z8B24H9ihZMCtAL/TCn1bq31b5dr3+tFe413tZsg1qlKr5OD7c7VboYQQggxqw7p0CyLUkomD8SqKOfM4WNa6+03blRKfQu4CNzynUMhhBBCCCGEWCvKGcScVkrdMcv2I0C6jPsVQgghhBBCCLFI5Zw5/Czw50qpAG+FlbYB0eJ9QgghhBBCCCHWiHJmKz0O3KmUamRKKQut9WC59imEEEIIIYQQYmnKmq0UoNgZnNYhVErt1FqfL/e+hRBCCCGEEEIszGoVTvn5Ku1XCCGEEEIIIcQsylnK4t/PdRdQWa79CiGEEEIIIYRYvHLOHH4OOA28fsPXMSBbxv2KVTISy3BhMEbB1KvdFLEMQ9E0l4ZimPJ/FEIIscpG4xnOD0bJF8zVbsqaUjA1FwZjjMQyq90UscGUc83hUeC01vrlG+9QSv3r+R6olOoAXgXOAVmt9TuUUr8DfADoAj6rtc4ppT4JfBkYB57UWkdL+yeIhYqmc3zztW7ypua2tgoe3tmw2k0SSzAaz/DN13owtebOzmru2VK72k0SQghxi0pk8nzraA/ZvMme5iDv2NO42k1aM164OMIbPWHshuIz93YQdDtWu0ligyjnzOGHgTdmu0NrvXkBj39Ka/22YsewDnhIa30fcBJ4XCnlAL4EPAB8HfhiaZpdHj3jSf7HS9f4yamBDTkjk8ub5It/VzJbWOXW3Fpi6RzfOtrNN1/rJpbOLeu50rkCprb+jyn5PwohhLiJ/nCK//mr6/zjm/0ln93LFUxyxedM5TbmOWkgYr1+P1jk6zdxrZU3Ndm8zKqK0ilnKYvxZT7FQ0qpXwLfAS4CzxW3Pw08CZwFTmmt80qpp4GvLXN/ZXWsa5xQMkcomeNgexWNFe7VblJJ1fhdvGtvI8OxDIc2Va12c24pFwZj9IfTAJwfjHGko3rJz9Va5eWRXfVEUrllPY8QQohbw/HuEGPxLGPxLP3hNO013pI9d6XXybv3NjEQSW3Ya4s3usOTr19vKEVHrW9Bj3vbjjr8bjt1fhe1fleZWyluJWUvZbFEA8B2IAN8HwgCQ8X7IkAVVlKb6A3bZlBKfQH4AkB7e3vZGnwzW+r8dI0lqfY5qfJtzKn/XU1BdjWtdituPe3VXpx2Y/L2cu1vrVz2cwghhLg1bKnzc2U4QcBtpy5Q+k7KjsYAOxoDJX/etaKzzs/FoTh+t5364MJfP5/LzoPb68rYMnGrWpOdQ611BqtjiFLqh1idwJbi3UEgXPwK3rBttuf6GsVZxcOHD69aPOf+1kp2NAZwGAaGoVarGWIDqg+6+cIDnQA4bKtVnUYIIcStaFdTkM46n1zfLNGOxgAdtV7shoFNXj+xBpT1SlIpZVNK/ekSHjd1iOhe4DLwYPHnR4FXsEJN9yqlbFO2rWkuu00OnKIsHDZDOoZCCCFWhVzfLI/LbpOOoVgzyjpzqLUuKKUOKaWU1noxs3b3K6X+CGv28EWt9atKqReUUi8C3cCfFbOV/gXwSyCEtQ5RCCGEEEIIIcQSrERY6Qng+0qpvwcSExu11t+Z6wFa6x8DP75h21eBr96w7etYmUqFEEIIIYQQQizDSnQOq4Ex4OEp2zRWFlIhhBBCCCGEEGtA2TuHWuvPlXsfQgghhBBCCCGWp+ydQ6XUduDPgQat9V6l1H7g/Vrrf1vufW8U6VyBbx/vJZ7O8579TbRWla6GkNi4Xu8K8crVMbbU+XnX3sbVbo4QQogNIFcw+d6JPkbiGd6xu5Gt9f7VbtK6VTA133+jj4FImkd3NWzokh1i/ViJ9IZ/AXwFyAForU8CH1+B/W4YvaEkw9EMyWyBcwOx1W6OWCfe7AmTzZucG4iSzhVWuzlCCCE2gJFYht5QikzO5Ex/ZLWbs66NxTN0jSXJ5k1O9clrKdaGlegcerXWr92wLb8C+90wWiq91AZcuBwGO2VUSSzQ/tYKbIZiZ2MAt8O22s0RQgixAdQFXLRUenDaDXY3BW/+ADGnap+TtmovDptib4u8lmJtWImENKNKqS1YSWhQSn0YGFiB/W4YHqeNT921aUG/m82bpLIFKryOMrdKrHWHO6o53FG9YvvL5Auks6a894QQYgNz2Aw+eqStLM+dzZsks3kqvc6yPP9aY7cZfPhQ6+TPWmvCyRwBtx271C4Wq2QlOodfBr4G7FRK9QHXgE+uwH5vOelcgb9+pYtYOs9922o5ssCOwVA0TSSVY2udX4rYbmADkRTxdJ6t9X6UKu3/OZUt8DevWu+9B7bXcWhTVUmfXwghxMaWzZv8zatdhJM57uys5p4ttbP+Xipb4PpYgtYqDwH3xhqMfObcMKf6ItQHXXziSPuc12SJTJ6usSSbarz4XCtxKS9uJSuRrfQq8KhSygcYWmtZNFcm0VSOWNqK2O0NJRfUORyLZ/jmaz2YWnPH5mru3Tr7wVisb8OxNN862oPWcPeWGu7qrCnp84eS2WnvPekcCiGEWIx4Jk84mQOgN5Sa8/cmErgE3HY+f9/mkg92rqbeUBKA4WiGbMHEbcy+JOTbx3sZi2ep8Tv59N0dK9hCcSso+5y1UqpGKfXvgV8Czyml/h+lVGmvTAVgrQO4fVMVLZUe7u5cWCcvkzcxtQas0TixMWVyJsV/M6kyJKdpqnBzoK2SlioPd5e44ymEEGLjq/Y5OdJRTXOlm3u2zH0emTiHZfJvndc2ivu319FU4eb+bbXz5gqYuF6T6zZRDisxF/1N4AXgQ8WfPwl8C3h0BfZ9S1FK8eD2ukU9prnSw9t3NxBKZjm8aeXWp4mV1Vbt5eGd9cTSeQ53lH5WTynFQzvrS/68Qgghbh33bbv5wPZ79jdxpj+6IZfCbKnzs6Xu5qVB3n+gmfODMUlSKMpiJTqH1VrrP5ry879VSj2+Avtdl8LJLBcGY3TW+akLuFZkn3tbKlZkP2LlWemxw1R4nNzWVrnazRFCCHGLiaVznBuI0VbtoanCs+znqw+4qd/hLkHLVlfXWILhWIZ9LRWLzijeVFGa11KI2axEKqRfKKU+rpQyil8fBX60Avtdl37wZj8vXxnj28d70RstXkKsuJeujPLCxVH+8c1+hqLp1W6OEEKIW8xPTg/y0uVRvnO8j0xewiABIskc3zvRz4uXRvnF+eHVbo4Q06xE5/CLwN8CmeLXN4F/rpSKKaWiK7D/dUXd8F2I5Zh8Pyl5TwkhhFh5atptORPdaCMl1BEbw0pkK5WA6EV4/4EWLg/H6KjxyQFDLNt9W2up9Dqp9DioD67/MBwhhBDry2P7mjg/GKOtyoPTLrX7ACq8Dp64vYWReIY9zcHVbo4Q05Stc6iU6tBaX5/nfgW0aK17y9WG9ajC4+CQJIYRJWK3GRyQtYZCCCFWic9ll/JGs2ir9tJW7V3tZggxQzlnDv9UKWUA3wdeB0YAN7AVeAh4BPgDQDqHQgghhBBCCLHKyja/r7X+CPD7wA7gP2HVOfw+8BvABeBhrfVT8z2HUuqfK6VeLN7+HaXUi0qpv1FKOYrbPqmUelkp9UOllMzLCyGEEEIIIcQSlXXNodb6LPAvl/JYpZQLuK14uw54SGt9n1Lqd4HHlVLfA74EPIBVQ/GLwJ+Wot1CCCGEEEIIcatZyyuDfwP4q+LtO4DnirefBu4CtgOntNb5KduEEEIIIYQQQizBmuwcFsNGH9RaP1vcVAlMlL2IAFVzbJvtub6glDqmlDo2MjJStjYLIYQQQgghxHq2JjuHwKewaiNOCAMTawqDxZ9n2zaD1vprWuvDWuvDdXV1ZWjq6jJNzem+CBcGY6vdFLEGpHMFXu8K0RdOrXZThBBCiGXTWnO2P8rZ/iha69VuzpqktXUteH5QyoeL5St7ncNiyYpPAp1a63+jlGoHGrXWr83zsB3AAaXUl4A9wGGs0NI/AR4FXgEuAnuVUrYp2245b/SGef6CNSNqMxRb6/2r3CKxmp4+N8SloTg2Q/Hr923G7yr7R1wIIYQomzP9UZ46OzT5826pCzjDyd4Iz54fBsBQiu0NUmJcLN1KXDn+Z8AEHgb+DRADvg0cmesBWuvfnbitlHpRa/2HSqnfLWYu7Qb+TGudU0r9BVYW1BDwZBn/hjVr6iCajKgJeT8IIYTYqEw5r81q6qsiL5FYrpXoHN6ptb5dKXUCQGsdUko5F/pgrfV9xe9fBb56w31fB75eysauNwfbKrEbCofNYJuMFN3yHt3VQGOFm8agm4DbsdrNEUIIIZZlT3MQrUEp67aYaX9LBYayIsh2NMq1oFielegc5oqhnxomy1KYK7DfW4JhKG5rq1ztZog1wuO0caSjerWbIYQQQpSEUop9rRWr3Yw1zTAU+1srV7sZYoNYiYQ0/x74LlCvlPpj4EXg/1yB/QohhBBCCCGEWKCyzxxqrf9GKfU68AiggMe11ufKvV+xckxT88rVMbIFk7u31OCy21a7SeImLgzGuD6W4GB7JfUB92o3RwghhFiWoWiaN3rCbKnzsbX+1g2tjKZzvHp1nLqAiwMSWSaWYCWylX5da/0p4Pws28QGcH4wxqvXxgFwO2zc1Vmzyi0S80nnCvz09CCm1ozFszx5Z/tqN0kIIYRYlp+fGWQ0nuX8QIwvvc17yw5U//LiKBeHrPJmzRVu6oMyACwWZyXCSvdM/aG4/vDQCuxXrJCA245S1u1gmZKg5Aomz10Y5hfnh8nmZcnqctgMhddpI5bOcWUkNnkSEUIIIdaroMe6/vC5bNgNg2Q2z1Nnh3j58uiGzt7dNZbgx6cGuDaaACDoseZ9nHYDt/PW7CCL5SnbzKFS6ivA/w54lFJRrJBSgCzwtXLtV6y8tmovT97RTrZg0lrlLcs+zvRHOdEdBqwD36FNknRlqRw2g0/c2c5/ff4KAD85NUhHjQ+nfSXGioQQQojSe2xfE93jSRqDbmyG4rVr45zuiwBQH3Rv2DrQPzk9SCpb4Npogi8/tJX7ttbSVuWl0uso24C92NjKdjWotf4/tdYB4E+11kGtdaD4VaO1/kq59itWR33QXbKOYSyd4/mLI1yaMqNV5XVMzk5WeBZcCUXMwe+y01btpWc8SSpXwG6omz9ICCGEWKMcNoMtdX58Lmveo8prXSvYDDU5mzafgql59eoYR6+PY5rrZ6axymt1ACuL35VSdNT6qPRuzGulRCbPCxdHOD8YXe2mbFgrkZDmK0qpKmAb4J6y/YVy71sszZWROG/2hNnRGGBP88z00dF0ju6xJJ11PrzO0r+Fnj0/zNWRBCcUfC7gpsLrYFONjyfvbEdraJD4+SUZjKT58akBgh47TxxsxWkzqPA4sBkQTuWo9m3ME4kQQoiNZyyeYTCaZlt9YNbIl9vaKqkNuPA4bDc9v8Uzef7ypWtcHIzRUePD47Cxt2V9lM94/GALXaNJEtk8Y/EMNX7XnL97tj/K+cEo+1sr1+1M6nMXRiaXw9T5XfP+vWJpViIhzW8Avw20Am8AdwG/Ah4u977F0jx7bph4Jk/PeIpdjUGMKbNKWmv+7mgPsXSehqC7LMlM3A4rRt5uKOy2t/YtWTWX54cn+3nq7BAAQZcDv8tOpdeJw6Zw2GTmUAghxPqQzhX45tEesnmTqyMJ3ndb86y/11LpWdDzHe8KMRBOMxzLEHA7Jq9D1gOX3caFoRiXh+M47Qafv2/zrO3XWvP0uSEKpmYkllm3nUO3wxoIsBsKhyyHKYuydw6xOoZHgFe01g8ppXYCf7gC+xVLVB90ER/JUxdwTesYAmgNmWJCmEy+UJb9P7KznvZqL3UB12R4iFi+iZATp91AKXjbjjpaqz3U+FwEZF2CEEKIdaJgavIFK/QzU4IkdQ1BN/VBF26HwXv3N627jtPEa5AvaApzhMQqpagPuBiIpNd1BNaD2+toqfJQ7XPKmsoyWYkr77TWOq2UQinl0lqfV0rtWIH9iiV67/5mRuOZWcMwDEPx+MEWLg/H2dVUnjpCdpvBrqZgWZ77Vvah21upD7gxTc1dW2qw2wx2NsrrLIQQYn3xuey877YmekMpbitBLb8djQHqAi4cNrUuB0vfvruBN3vCtFZ55h1U/9ChVsYTWWrXcSimXLuU30p0DnuVUpXA94CnlFIhoH8F9iuWyGaoeUeVWio9Cw7VWAkDkRTPnh+mxufkHbsbZ8x2Csv1sSRd40naqjzrKmRGCCHE6jjdF+FEd4hdTUEOd6ytLOGddX4660o3w7da6+611jx1dojhWMaK6FlCcr8Kj4MHttfd9PccNmNdzxqKlVH2YF2t9Qe11mGt9b8Gfh/478Dj5d6vuHUcux5iOJrh3ECM/khqtZuzZr1ydYzRWIYT3WHCyexqN0cIIcQa9+LlUUbjWV68PLquMniuJ0PRDGf6o4zEMhy9Pr7azRGivJ1DpZShlDo98bPW+nmt9Q+01nJlug4MRdN8/40+TnSHVrsp89pc60MpqwBujW/uUIls3izbOsm17vJwjP5wilAyS13Ahf+GsJNkNr+hiwQLIYRYvM21PgA6anxrPionnSvwszODPHNuiFxh5jrEVLYw53q81VTpdUzmBNhcu7CZ0Hgmz49PDfD8xZGSdtrzBZN07ta8ThJvKWtYqdbaVEq9qZRq11p3l3Nft7J0rsDl4TgtlR6qShgW8dyFYfrDaa6OJOis81PhWZtx+HtbKuis8+G0Gdhts493jMYz/N2xHkxT8/jBlpLVZFwvfnZmCEMpqn1OPnFHO7YpJ/nnLgxz7HoIp93g40faqJeQEyGEEMA7djdw79ZafM7SLEWYKIW1udZX8oRzb/aEOdtv1b6rC7jY31o5ed/x7hDPXxih1u/kY0faZy19sVrcDhufumsT2YJ50/Jg8Uye66MJuseTXBi0yjk0V7jZ1rD8HBCRVI5vvtZNJm/ynv1NbClhyK5YX1ZizWETcEYp9RqQmNiotX7/Cuz7lvDjUwN0jSVxO2z8xv2bcczRQVqsuoCL/nCagNuOZ42vUbvZAbUvlCKTs0YSu8eSt1znsNbvpD+cpqXSM61jCHBlJMH1sQQjsQym1vzG/Z0zZhaFEELcepRSJT0f/P2xXqKpHLUBF5+6a1PJnhegxu9CKVCoGbXvro5Yl5+j8SzhVHbNlcayzzO4PdV3jvcyFs+SzhVwO2zYDVWySYHBSJpk1po17BpLSOfwFrYSV4CLLluhlNoLfA0oAJeBXwf+N+ADQBfwWa11Tin1SeDLwDjwpNY6WrJWr5B8waSgNS770jtfb6UwNimYmlL14x7aUc/OxiBVXueaGmVbiu0NAS4Nx8kXTPask8K2C5Ut/v/n+x89cXsrw7EM9YGZYbd3d9ZwbiA6mWSoUFh7YTdiY+n4vR8t6/HX/917StQSIcRi5QsmeVMvOrGZ1nryfJUpQ+ji1no/n7prE4aa2WG6o6OaZDZPU4WHujWUqTOZzeNx2FBqYSG7E4PcVT4nT9zegstuK1lU1+ZaH511PuKZ/LRZV3HrKXvnUGv9/Hz3K6V+pbW++4bNF7TW9xTv/0vgMPCQ1vo+pdTvAo8rpb4HfAl4APgQ8EXgT0vd/nKaOoX/vtuaJ2P7F+tdexo51Reho8ZX0iyUSima11BW0uXwOG18+FDrajej5AYiKb5zvA+AjxxunXM01GEz5swwu7s5yL9+/x5OdIdoqnBT4V2b4cNCCCFWVyKT5xuvdZPIFHj3vka2LyKcUSnF4webuTQUZ2djeUph3ThjOKG9xsun7+4oyz6X6qenBzg3EGN7Q4D37G9a0GPef6CZC4PWY0o9++m0G3zgQEtJn1OsT2shdmzGu1trnZvyYwbYDjxX/Plp4EngLHBKa51XSj2NNdO4rgxEUpNT+NdHE2yu9dEzniSZLbC9wb/gkaQqn3NBKYzFxtM1lpwcie0Npajzu7g0HMdtt9Fes/DQWb/Lzv3b5D0khBBiptF4hsFIGqfdIJbOA1ao5mI6hwBNFR6aKjbGoPNynegOE0vnsS71FtY5bAi6pRSFKLu10DmcNYZNKfV+4P8HXMRq50TIaASoAipn2Tbb83wB+AJAe3t7qdpcEptrfWyu9ZHI5tnfWkFfOMU/vN4LQDRdy5E1VlNoIzrZG+bo9RA7GgLct612tZuzaLubg3SNJTCUYkdDgBM9YZ6/MALAhw+10la9tLWV4WSWH54cwGkzeO9tTTdd0ymEEGJjSmULfOtoD9m8SWedjy31fqKpHLe3V65201bEpaEYL1waZVO1l0d21S944H4+o/EMkVSOgUiKLfVLixpby0ZiGX56egCP08579zdJbeV1Zs1e8WmtfwD8QCn1H4A8ECzeFQTCxa8bt832PF+jOKt4+PDhNbWYymW38fjBt6bwr41O5uuZjCsX5fXatXFi6TxHr49zZ2d1yZL5rJSg28HHjrw16DExiwgsq2zH2WLNJYBLQ3Fua6tc8nMJIYRYvwpaky+uRc8XNB86dGuFHh69HiKaynGqL8KRjuqSLL3IF/TkLOCm6o3XOTzVF2Y0ngWyXB9LsLMxeNPHiLVjLXQOZwzBKKVcWutM8ccoYAMeBP4EeBR4BWtGca9SyjZl27q2udbHI7vqSWQKHNo060SoKLGt9X5OdIfprPOtu47hbCbeN26Hja31S1/TsanWx4meMDZDLXn2UQghxPrnd9l5321N9IVTt+RA4bYGP0PRNM2Vbvzu0lw2N1a4eeeeRsLJLLdvwOu9zlo/Z/qiuB22DZO74lZS9s6hUup/Af5Gaz1XJfVPzbLtXUqpf168fQn4faBJKfUi0A38WTFb6V8AvwRCWOsQ172NkiEqXzC5PpagPugm6F67CU7etqOeu7fULCtb7FrisBnc1VkzbVtvKIlSas6ENLNpqfTwxQc6UUrNKH0hhBBi49Nac30sid9lp7POT+ctWtrgSEc1+1srcNqMkoSUTtjdHCSTL9AznqSpwlPyuo+rqaPWx5fetgVDriHWpZV4JzYCR5VSx4H/F/iZ1noyvFNrffrGB2itvw98/4bNXy1+Tf29rwNfL3mLbxGj8Qz5gqaxYubi5qFoGo/TtuSO3c/ODHFxKIbXaeNz925e06UwNkrHcKpMvsBgJE0sneeps0MAfOBA86JO7vFMnlxBUzdL+QshhBDrSzpXYCyRpSnoxljABftr18Z5+coYNkPx5J3t1JahBITWmt5QigqvY00PJJfrOuEfjvVyZSROc6WHz9+3eUbnsz+cwu2wUV2iWoYraSNEY92qVqKUxb9SSv0+8A7gc8B/VEr9HfDftdZXyr1/Mbu+cIp/ONaLqTXv2tvIrqa34sGPd4d4/sIITrvBJ+9sp9K7+INSPGMlnE3nTPKmiRM5SKyk7x7vYyCSJp0v4C6e1OKZ/IIfPxRN862jPRRMzbv3Ncp6ASGEWMcKpuYbr3UTTubY1RTgXXtvnh1z4pxRMDWpbOnrEgL88tIor3eFcDkMPnN3x4aaPbuZTL7AcxdHiKZyhJM5TA22KX3D030Rnjo7hKEUn7ijjXrJUipWyIp8CrXWWik1CAxiJZepAv5BKfWU1vr/uxJtENNFkjnM4gRuKJmddt9oMRFJNm8SSeWW1Dl8dFcDJ7rDtNd4JdPlKgglrc6532XnYFsVhoI9zRULfvx4IkvBtN4fo7GsNf8vhBBiXZo4nwOMxLM3+W3L3VtqMJQi6HGUbe35xPVHJmeSzBZuqc5hKlugvdrLcDTNtnr/jPDL8YT12phaE0nlpHMoVsxKrDn8Z8BngFHgvwG/U1wvaGCtJ5TO4SrY0RhgLJEhmze5vX36Yui7ttSQK2gqPA7al3hCqPG7eHR3QymaumZcG02gtV4X6y7evbeRswNRdjUF2Vy7+Exo2xsCDEbTZHIFbt9Uidaay8Nx3A6bJKgRQoh1xuO0clyU7gABAABJREFU8cjOBq6Oxjm8wDJZXqedh3bWl7VdD26vw2U3qA+6l7SEIZzM0htKsbXev+7KJVR6nTy2z0r0c1fnzP/JkY5qMnkTr9PGlkVedxRMzcWhGFVe56xLh4SYz0oM0dQCT2itu6Zu1FqbSqn3rsD+1xWtNc+cG6Z7PMn922rZtsgCswtlM9ScRc+Dbgfv2b+wgqyrKZbO8ez5YTwOGw/vrMdexvj2i0MxfnRyAGBNh1kOx9L87PQgPped9+xvWvI6CZuheGhHPelcgWfPD3NlJE46W8BuM/jI4VZaq6SDKIQQ68m+1gr2tS48gmQ+Wmt+fnaIvlCKB3fULbrzMqHS61xQiOts8gWTbx3tIZktcH4wxocPtS74sS9fHmUgkua+bbWrUlQ+kcnzw5P95E3Ne/c1z1oew+O08fYlDrL/8tIIJ7qtjOOfumsTVVPWLJ7qjXB+MMrB9iq21q/9wW6x8lZiIdjmGzuGSqmvA2itz63A/teVaCrPqb4IkVSOo9fnSvC69pmm5tnzQ3z/jT4ixRDHUjveHebqSIIz/VEuj8TLso8JU+tOruUalCd7IozGs3SNJekeSy7pOZJZ66T109ODvNET5sJgjGsjCYaL4caZ/Nr9+4UQQpRWKlvgRycH+Onpgcn6ueOJLGf7o0RSOY5dH1+Vdmnequ2bzi18TeRoPMOr18bpHk/y4qXRMrVufheHYvSH0wxHM5wdiE6779WrY3z79V4GIqklP3+6eJ1SMDW5wlvn7IKpeeb8EL2hFL84P7zk5xcb20rMHO6Z+kOxLuGhFdjvuuR322mqcDMQSbOtYf2O6FwfS/BmTwQAr3N8yaNf82mqcKMU2A1VlixqU+0pppzWwN6W0oy8lkNnnY+zA1G8TtuSQ0ne7IlwacjqbO9rCU7WOuys9bGp1rfkEWIhhBDrz8neMBeHYgD/f/b+O0qy67rTRL8T3puM9D6zsry3qCp4ECRIQgSNCFL0FCmRknr0ut/r12vUmjdvunverJZ6Wm2mjXrU6tWaoURKFElJJCgagAQIEKYKVYXy3qT3Gd5H3HveHxGZlVnpsyIi3fnWqlWZ4e7NiBvnnH323r8f9V47B1p8eO1m6jw2RqLph/LUfRjMRgMfO9jE3fEEe5cxL7usJtw2E7F0ngbf6pRctlQ5sJmN6FLSXn2/EieUyPLWnQkAtFuSTx1pWdHrP7mtBpfVRMBlmdGraDQI6jw2hiPpVfvbFWufsgWHQoh/Cvw+YBdCTG6LCCAL/Em5jrveMRoEnz7aQlbTl10SmMlr3BiOUe+xrXrjcsBpxWIykM3rNJSp3n1bnZtatxWz0YDTauL6cJTRaIbDbf6SN7UbDGLJfRqrSWeNi996cgtGw8q9hTRdp2ciQbXbikEIPnawEZ/DsqZlxhUKhUKxfMZiGYYiKbbVueft2avz2DAIgRBQW+wLNBkNfObYytYqpaSlyrHsPnib2cjnj7cRz+SnNpYvD0QIJ3McafeXvXcxr+kMR9J8aG8dTT7HDMsHp9WEx24mmso91NrJbjHy2NbqOe978XAzoWSOQIXsMRKZPOd6Q9S4rWu2JUcxk7IFh1LKfwn8SyHEv5RS/tNyHWcjIoRY0WD78tURbo3EMRsFX3msY1VVQr0OM7/+aDvpnF5Wf55JJdWJeIYfXRoGIJbOr4ueyXLxMJ6S2bzOe71h6jw2BsJJLvRHuDUa5zce7yzhGSoUCoVitcnkNb59po9sXufuWIKPHWya83Ht1U6+fLIdYWDGJuFK1yprAZvZOBUEDkVSU37AqZxWlkqn6bx5Z4JzPSEMQvC54zP9IyctxGLpfNk8hk1GQ0X9i1+/Ocb14ULmudplLXull+LhKWfmcIeU8jrw10KIQw/eL6U8V65jbwQmFbi21LiwW5Y2+Oa1gvWApkPRhWBVcVhMrMAFY0WYTQZMBkFel9gtylNxMeKZPN3jCdoCDtzTJnuDKExONt04NenndYmUkkLiX6FQKBQbASkL1lWalDRp9gUfO5dgykbBYjRgEAJdSuwVUDzNFfskQ8kst0ZiBJwWhLg/v04PXDcCk3+LySBmZEkVa5dyppb+MfCbwB/NcZ8Eninjsdc1mi6nFLiu+aO8uMSa82d31XGxL0yjz45rE3kFQWE389PHWggmsmxdpf6H9cT3zvUzEc/id5j58qMdU7ebjAZ+7WgrA+EUfqeZu2MJOqqdZVWCVSgUCkXl6SmKlsXTeTpXYHm0UQi4rHz6aAuRVI6tFVDvfGxrNfFsnjP3grxzN4jdYuJAi6/sx10tnthWQ4PPRpXTgte+cTcZNhLlLCv9zeL/T5frGBsVXd5Xl1qOMqTLauJk19w15puBWreNWrdqsF4Kk4qrc11fXod5ape4wbvwbrJCoVAo1ieZvIbHbsZjN2/6jE6911YxP0Cb2cjuBg/3xhIAZJahtLoeMRqE6jVcZ5SzrPQTC90vpfxeuY693jEbDXz0QBP3xhNrVhlzNJbmR5eGcViMfGR/44YqgdgopHMaP7gwSDKr8aG99TMC5xcONHJjOMb2epVlVaw/2n/vhw/1/O4/eL5EZ6JQrF/2NHrJaTpSsiy1z/VEKJHlpUtDmA2Cj+xvLLlY3UrpqnXx1PYasnmdw23+sh9vOJLmx5eH8NjN/Mq+xofSJlBsfMr5LfnIAvdJYFMHh1JKXro4xL3xBI92Vc8aHFaiwFVJrgxECSayBBMF24qNtCsUz+SZiGdo8TswrFDxcy3QM5GkP1TwSbo8EOGZHfeDwzqPDV1K/ua9AawmA5883Dyj9/BBRqJpBKy6Cq5CoVAols+FvjC/uDlGa5WDF/Y3YjAIDAbB4bbKqnBPxDOk8zpNvspUpVwdijJe9Oi9MxZnX7OvIsddDCEEB1tnB4Vv3h7nTHeIXY2eBYVxxmIZNF0uOdt5oT9MKJkjlMzRG0zSVYHyWcX6pZxlpb9ertfeCCSzGrdHC15yVwYjFdk5KiWdNU4uD0SwmY00VmiQrwSZvMZfvNNDMquxu9HDB3bXr/YprZhGnw23zUQqq83pTXhrJE4qq5HKavRMJOfNUt8Zi/P984MIAR890ETHJu5NUSgUivXI5cEImi65N54gls6visDMaDTNX77bh6ZL3reztiKBWke1k/N9YUwGQYt/7W64T3JpIIIuJZcHIrxvR+2cG9R9wSTfPdePlPDhvQ1LqgDqqnVxYziG02oqm72YYuNQkfy6EOJ5YDcwdUVKKf9FJY69VnFYjOyod3N3PMH+NbKTtRzaAk5++6ktGIRY19m1B8nkdZLZQv1/OJlb5bN5ONw2M195tANdyjkFZXbUu7k5EsNqMtC+QMA3+T5IWVDRBRUcKhQKxXpif7NvKnPosa9OaWU0nUMrSqmHKjS/NvrsfP2JznWzVjnQ4uNsT4hdDZ55zzeSyiGLivShZHZJr7ulxsXvbMA1m6I8lH2EEEL8F8ABPA38KfBJ4HS5j7vWEULwob1L8+LL5DW+f36QRCbPh/Y2UPcQpX2XByKc6Q6yrd7NyS0PJ16zERUsPTYz799VR18wydGOtW96vxgGg0Dq8P0Lg4xG03TVuuiZSNJR7eSJbTVL8i/c1+wlms4hYM32wCoUCoVifvY0eVdt/L4xHOPtO+N0VDs53ObjFzfHuT4UpavWVZHy0vW0Vklm87isJloD82c5dzZ4CCay5HWdg62+RV9T1yU/vjLMYDjF0ztq56wkUiimU4lvzEkp5ReBkJTynwMngAW9GYQQjwgh3hJCvCGE+LfF2/6JEOKXQoi/EEKYi7d9rvi4l4QQG6fp7QF6i71joWSOS/2Rh3qtd+5OEErmOHU3OKWIulHI5nX+6t1e/vNrt7k3nljx6+xp8vKhvQ0bxqh1NJbmzmicWDrPDy4MEkxkOdsTIpHJL/i8dE7jW6d7+dM37rGl2sVT22s3vaKdQqFQKJbH5LrjXG+YGpcNl9VEMqtxsS+82qe2LH52bYT/+PNbvHN3oiyvH05mudAXIZjIcvpecN7HGQ2CJ7bV8MyOuik/4oUYT2S4MRwjls5zridUylOel96JJH/82h2+eaqX9AZXY92IVGKllyr+nxRCNAI5oGOBxwP0AM9IKR8HaoUQjwNPSykfAy4CHysGiL8FPAF8A/h6Wc5+DdDgs+O2mTAZxEM3EXfWFEoC2wKOdb3Q7x5P8Dfv9XN54H6wPBxJMxhOk8npXBl8uCB6IxFwWql2WzEIwaFib2uTz76o2e9QJM1wJE06p3F1aPnv53g8w9+dH+CtO+MrOm+FQqFQVI57xXm11PPnZKaqyWentcqBz2HGaBBsrVs/GSxNl1zsj5DTJBdKGNRe6o/wN+/10xdM4rKapirDSpnd8zss1HlsCEHFFMqvDkVJ5zRGomkGw6nFn6BYU1Si8PwlIYQP+N+BcxSUSv90oSdIKYen/ZoH9gGvFX9/BfgscBW4JKXMCyFeAf5krtcSQnwN+BpAa2vriv+I1cRlNfHVxzrQZWHH6GF4ZkcdxzsDiwYGq0Emr3GxP0K1y7qo6MnPro8STeXomUiyo96NyWigzmul3msjmMiyq2HDJpKXjcVk4POPtKLphd7DVFbDajIs2nfQ4LVR67ESSeXY+cD7mc5pXBqIUOOyztuv+Obtce6OJbg7lmBLjeuhyqEVilKirDAUitn87NoIsXSe3okUO+o9D73emOSxrdXsbHBzazROMJnlyyfbp+aj9YLRINjb5OXaUJS9zaUpz03nNH52fQQpIZrK86WT7fza0RYyeR27ZeYaLZvXudgfxuewLDtJYDYa+OwjreQ1vWLv+c4GN3fH43jt5g0lWrhZqERw+K+klBngu0KIlyiI0qSX8kQhxD6gGggDk3npCOAHfED0gdtmIaX8E4qB45EjR+SK/oI1gBACY4l6iB2WteHz8yC/uDHGlcEoQsAXjrcRWKCss9ZtJZrKEXBZpyYwq8nIZ46tzw2AciOEwFS8gB6cdObDZjbyuUfaZt0upeS1G6NcG4ohBHzpRDt+p2XW4+o8Nu6OJbBbjLhta/OaUygUCkWBWo+NWDpOwGUpWWA4yS+Lm4VGg+Arj3XgWiN+g8vh2V11PLuAvcRysRgNVDktTMSz1LoL6x2DQcw5R795e5zzxYzl5x5pXZGt1EKBoZQSIUr3mbcFnPzOU10lez1FZanEt/Nt4BBAMUjMCCHOTd42H0KIKuA/Ap8CDgNNxbs8FILFcPHn6bcp5uF8X5irg1H2t3jZ3bg2RUUMxYFJIKZ+no8P721gLJahymkpyYAWTmb5ztl+NF3y8UNNMwzjNwJ5TeeVayNEU3me3VVH1RzB3GLouuT7FwbpDSanDHQX+qyOdwborHbispnW7IaEQqFQbAQu9IW5MhhlX/PKhWeenzavlpr783vh31pjMJzi784PYjMv7vv7MEgpefXGKKPRDE9tr+XTR1sIJXJTweF8TE6zogxv4C9ujnG+N8y+Zi9P76gt7Ysr1iVlW7EJIeopBHR2IcRB7l/OHgrqpQs91wT8OfBPpJTDQoh3gd8B/hXwLPAOcBPYI4QwTrtNMQdSSn5xYwxdSl6/mVuzweET22oIuCxUu6xzZqKmYzSIJZu/zkUkmcNhNU71XU56PwHcGU1suOCwJ5jk2lAMgLM9oQXNdecjlslPCf3YzQYe6ajCZ194Al3J7qZCoVAolscvbo6h6ZLXb2VXHBw+7Ly6EO/fVUeT347fbkaXa6+I6+ZIjHROI53T6A0my7ZOGo6mudBX6Ok8dW+Cjx5oot47fzVPJq+Rzuk82lWNz2HB7zCXfH1yueiteGkgooJDBVDezOFzwJeBZuCPuB8cRoHfX+S5LwJHgT8sZoX+KfC6EOKXQC/w76SUOSHEfwXeAEIU+hAVcyCEoKXKTs9EktaqtWsCazEZONg6Z3UwmbzGndEEjT4bPsfD7Wq+fWeCd+5O4HeY+ewjbVhMBjprXFzsL5gEb1tHTfJLpcZtxW4xks5pNPtn1v+PxTJMJDJ01bgWLDvx2Exsq3PTE0xwuK2KrXUuvnmql3Ayx8ktAR7pDJT7z1AoFArFHLRU2ekeX7tzvM1spCPg5Fvv9pLN6zy/t4GtdZURR1kKOxs83B6NYzMbaQ8U+uiHI2mi6RxdNa6SeQP6HRbcNhOxdJ6WRT6rZDbPN0/1EkvneWp7zbzro4flYIuP9/oKmUPFxuTuWByTwbCgRcp0yhYcSin/LyHEN4DPSCn/YpnP/RbwrQdufhv4wwce9w0KSqWKRfjYgSZi6fyqmd8+LD+6NMy98QQ2s5GvPtYxVda4EvqCSaBgwhvP5KkyWfDazXzpZHuJznbt4bGZ+fVH28lpckavRyyd4y9P95LXJXubvAv2UwgheH7ffW/O8XiGcNHIuC+U4pHynb5CoVAoFuCj+wtz/Fru7x6NZcjkChZa/aHUmgoO6zy2Gb6/Y7EMf/VuH7qUPNJRxcmuh/OFnsRmNvLFE+2k8xqeRUpXQ8ncVEVTXyhVtuDwZFd1yf4+xdrjymCEn14ZAeCFA41LUsItq2yRlFJnA1tMrJQ7Y3FevT5KKJGt2DENBoHXYS6oYqVzyAqVdaRzGm/cGpthObHS1wHIafpDl6Sc7ArQ4LVxpN1flt6KSjAaS/Pq9VF6J5JLfo7VZJwlApDTJJqUZPLanL6Hd8bivHZjlHBy9rVa7bJyuM1Po8/GiS0qa6hQKBSVJK/pxIvj9uQcP1eGK6/pvHVnnNP3guh66eb+SDLHazdGuTMWX9LjO2uc7Kh301Ll4FCZAp1SkZ221kjnS+vTZzEZuDuW4Je3xknnNAbDKV65NsKN4diMxzV6bexv8dLks3O8o6qk56DYPEz3mVyq52QltpheFkL8v4G/AqacyaWU8zt8bmBSWY2XLgyhS8loLM2nj5ZfXVPXJT3BJFVOCy9fHaEvmGR3o4cP7K4v+7F/eWucS8XAsMppmSVpnNN0Tt8LYjQIjrVXzVu68dzuei70h2kPOLEtw4Yjk9eIpfMzDO2b/Q5+bZ2rmv7o0jDBRJYrgxF++6muFSnLpbIaF/vDpLIafcEkFpMRTdMxFu0ugons/Ws1muFTR1tmvcYT22pK8ecoFAqFYhlk8hrfOtVLKJnj8a3VHGmfP3h4ry/M6zfHSGY1bGYD+5p9JTmHn14dpj+U4kJfhN94vAPnIgqkZqOBD+1tWPAxa4Umn53376ojnMxxpL0QyE7EMzitpjnXICPRNJcHInTVumgLLGzF1TOR4NXrowBcG4pypidIJqezt9lLvaeQvQwls7QFHDyzo3TqqKvJQu/dWiWT1zh9L4jNbORIm7+kaq6VZH+zj5wmMRkEO+uXZvNWieDwK8X//8G02yTQOcdjNzxGg8BqLiy+7RVScHz1xigX+yNYTIJERsNsNNCzjIzTwzApyWwQAuscpaAX+sKcvlfYJ3BZTfM20vudFp7avrxG6Uxe48/f6SWaynGso4pHN1DZhN1ihEShRGWlrRBv3i4E7pcGIqRzGhOJLG+0+jjU5ucv3uklnimUtPgdFmxLtL9QKBQKRfmJpHKEimX93RPJBYNDowEuDUTIa5JL/ZGSBYeT87vZJEpufbEWmL4eeefuBG/fmcBlNfGFE22zgpy/vzREOJnj2lB00Q1bm9mIECBlIQg0Gw2Ekzk0XZLVNL59pp9sXudQm58nN8AG7NmeIK/fHMdhMfL5422LbiKsFc52hzjTHQLAZzevqTLo5WAyGji+TE2Isn9CUsqOch9jtchrOnfHE1S7rEsuT7SYDHzmaCtD0RSd1ZURPomkChNINi852u5nIJzmcFtlSjpOdAaocVvx2s1z+hZOHyRKPWAkMhrR4t8+GE6V9LVXmxf2N9I9kaDJZ5+xm6Xpkjtj8SVdk5Pvd6PXxnA0TZ3HRjqnEUpkSec0TIbCDvPuRs+SatQVCoVCcZ+BcIpsXqejeuFM0kqocVnZ3+JlKJLmkUVKDrfUuAuiKkJgLWHm5gO76tlSE6feY1tXGaGVMBQprCHimTzRdG7W3+u0mggnc9gtpkU3bOs8Nj59tIW7Y3EEcGfMBgg+vLceTUqy+UJf5uT6Zb0zFClYmyezGuFUbt0Eh5PnKQQ41sk5l4qy/7VCCDPw28ATxZteA/5PKeW6v+pfvVHopbOYDHz5ZPuSL3ivw4zXUR4Pnbl4enstp7uDNHrt7K2wGpXBINi2wG7LzgYPTosJo1HQ9EDJ6cNS5bRwvDPAQDjFo10bqyfOZjayY47ygJ9fH13yNXm8s4pajxW3zURfMEUwkeV4ZxVOi4kDLT7CqSxPbatd1FZEoVAoFDPpCyb5ztl+AN63s7Zk2bpJhBBLLjn02s28eKSFgXCKowtkGJeLxWRgZ8PSytTWO49uqUbXx6n32ua0knhhfyO9wSSND2zYzkdek5y+V8hKPbuzbsba7OkdtYxG0xtGAfx4Z4BsXifgstJYJquUcrC/xYfXbsZqNtDgLe36dK1TiVD4jwEz8J+Lv3+heNtvVODYZSWZLTSC5zSdnKav8tnMj99p4bkK9BeulKVK666EzSaUspxrUggxlRF8cLJTXkcKhUKxcpLZ+8IPiUxpBU1Wwp4m74r9DxUFz95fPdw87/02s3HBjfAHmXF9ZGeKwR1o8S37/NYy1S4rnzg0/3u3lmkvQ9Z/PVCJ4PColHL/tN9/LoS4UIHjlp2nd9TisYdo8D68955i9RiPZ3jn7gSNPvuaV1BbjLV8TaayGq/fGsNmNvJYV/WG7FFRKBQKgG11LmLpajJ5vWJtHIrK0BdMcr4vzNY615wVPEthW52LaLqarLo+lkRe03nj9jh5TfL41uoNX8a82lQiONSEEFuklHcAhBCdwOpvo5UAj83M08sUSXkYdF2WzIhVcZ/XbozRF0xyayROR8C5rsso57om18p1c6YnyNXBKAB1HuuKJ1WFQqFY6wghFhSJWU3WypywXnnl2gjhZI67Ywm21LgwG5fvCieE4HCrX30OS+T6cIzzvWEA3DbTsgVWFMujEsHhPwFeFULcBQTQBvx6BY67YcjkC+pVoUSW53bXs71+fSomLZdkNk82r5ctA3Z7NMaVwSjZooeRw2KcUl/bKPzgwiC3R+M80lnFyS0rU2t9tzvIUCTNic4AHrtpljXIUgk4C88xGgT+NZbVVCgUis3Ajy4NcX04xpF2P49vXf9KmJXgbE+I/lCS/c0+/A4LVU4L4WQOn8OMaQXBXTKb59vv9hFL5/mV/Y1lESxaC4QSWX55e5xql3XOFp/xeAbXEu0t/E4LBiHQpSSwjjfw1wuVUCv9mRBiK7CdQnB4XUqZKfdxNxJjsQzjscJbdmMktimCw1AiyzdP95LTdJ7bXV+WpvefXBkhm9exmgy8eKS5YNmwgUoVMnmN26MFc+JrQ7EVBYcT8Qy/vDUOQDqrEc/kiaRyHG7zL9vjcFejh4DLgsVoWNfZWYWi/fd+uOLndv/B8yU8E4Vi6Wi65HrRaP3qYFQFh0sgkswVPSLz/P2lIXY2eHhudx2HWv3UuK0r8r4bDKenbEhujsQ2bHD45p1xbo/GuT0ap73aMUPU5e07E7xzdwK3zcTnj8+2BnmQJp+dL5xoI6/rcwoCKUrL8nPhy0QIYQSeA54C3gf8AyHE/6vcx91I1HtstFc7cFlN7F9EbXQ8nqEvWBkPw3IykciQzetIeV9CutRUuwoBSo3bSrPfsW7klRcinMzSM5FASonVZORAiw+HxcihVt+KXs9pNeG0FgZtl800ZYuy0s+kzmNTgaFCoVCsAkaD4FCbH4fFOGXsXk7SOY27Y3HSufXbSWSzGHBZTSQyGjaTESlhLJalpcqx4s3klio7TX47bpuJvU1eRqLpsq1zVpOaYoWRzWzEbZup0D/598bSBWuQpVDltKjAsEJUYjX8AyANXALWrqTnGsZkNPDxg4srPY1E0/zl6T50KXlmRy3717HiVUe1iz1NXhKZPIfbytO38YlDzYzGMtS6l18iuRaJpnP8xalesnmdYx1VPNpVzdM7ah9KedRmLpjWRlI56j02qpwW+kOpTacCq1AoFBuBJ7fVVMxY/Ttn+xmLZaj32vjMsdaKHLPUWE2FOXAslubqUJRMXufgCjdbp7/mp460ANAzkeDbZwaQEp7f17AsxdO1ziOdAdoCTlw2E64HNt8f7apGyvmtQRSrSyWCw2Yp5b4KHGfNkczmOd8XptZto6u2/CbisXQOXUrgvvH9aiOl5PS9IMmcxonOwJJ32owGwft3Lc3DaaWYjYaSeytWmnRO473eMAGXBY/NPGWeG06W7vN3WEwYDYJf3CwojX7iYJNqolcoFIp1wHg8w43hGF21Luo8lV2ET65DVrIeuTee4NZIjL3N3lX3mLNbjLQGnLQGSlv+ORxJ8/rNMRKZPA5LoTInldWmyi3XqqDRcqifx9ewbhFrEMXqUong8EdCiA9IKX9agWOtKV69PsbNkRhCwJdOtJe9nG5LjYvjnQGS2TzHOmYOKqPRNO/cC9Lit3OwgnYNd8bivHVnAgCTQagehxLzxq1xLg9EAPjc8Vae3F7DeCzD8RJn9s52h3ivqBTmd1hW3Pc6Ek1zahWuQ4VCodjIRNM5fnlrHI/NzKNdgaleuO+fHySSynFpIMLXn+hcUY/cSvmVfQ1cG4qyq2F5/oqaLnnpwiB5XdIfSvGVxzrKdIarh5SS773XTyqrMRhO0VLlIOC08PbdcS70Feb0GreVthIHpArFUqhEcPgO8DdCCAOQoyBKI6WUG17H3mwsDMIGITAa5x6QL/VHONcbYmeDZ1ZAt1yEEDPK/aLpwi5UncfGazfGGAinuDMap7PGhdduXuCVlk8klSORydP4QCbOZTUjBEhZsFlYjNFomvf6wnRWO9n6QHnFO3cnuNAXZl+zT5U1FjEVr6u8rjMRz8zr0/hud8FG4lCrn72L9K1OZyKe4UxPaKpnRIhC7+Gl/ghv3x0n4LTw9I46qpa48fHajVEGw+myXYcKhUKx1oimc8TSeQJOCxOJLA0eW8mrL07dDXKjKDbT7LdPmXdPrkNMBrHswPBsT5DLA1EOtPhW1KbSFnDSFnASTmb5v9/uRgjBRw80LroWMIhCv3sklcNtK80yVdcl79ybIJPXObklgNW0OuJzOU1nOJKm2mVB6oWKr1RWw2oy8t2z/aTzGhPxLHaLke+e6+dgi/+hWkMW4mxPkFAix4ktgQ2huaAoHZW4Gv4IOAFckrJY87gIQohG4CVgF+CSUuaFEP8E+CjQA3xZSpkTQnwO+AdAEPislDJalr9ghTy9o5YGr50at3XewfDNO+Okshpv3RnnSFvpPG9CiSx/caqHnCZ5ansN1W4LA+EUbpsJm7m0OkSRZI4/P9VDNq/z2NZqjk4rhaj32vjssVbSOZ3WgGPR1/rJ1RHGYxmuD8X4rYBjxgB+pjtITpOc6Q6q4LDI413VuKwmfnZthB9fHiGSys/y/9F1yZu3x5GycL0tJzj8+fVR+kMphIDn9tQRcFqp89h4+cowvRNJfnZtlHvjST77SOusjYG5qHZZGQyncdtM2DeQMqxCoVDMRTSd4xtv95DKakTTOfwOC7sbPXxgd31JjzMpsGY2ihmbbh872MS98QRtVcvLQEkp+eWtCXQpefPO+ENpGNwYjjERzwJwayS+qOm7EIJPH21hKJKmpao0JaU3R2OcuhsEwGoyrNja6WH5wYVBeiaSeGwmcrpe2MAvll6OJzLUe+zkNYnTakIgON8X5mRX6YPZvmCS128WlMh1KUt+PSrWN2VXKwVuAZeXGhgWCVJQNn0HQAhRAzwtpXwMuAh8TAhhBn4LeAL4BvD1kp51CTAbDext9s5bcw3QUe1kLJbGajKUdCcxnMqR0wpv+Xg8y9Pba/nU0RY++0hryQeZaDo31es2OQFMp9ZjW1JgCOAp7hI6rUZMhpmX56SdxY4y2FqsV0xGA81++9RnOhab6RKj65KrQ9GpQKxzmZLZnuIiw2Y20ll9v2dlZ4OHZE7D5zBjEBBMzP7c5+KZHYXr8HOPtGExVWL4USgUitUjni749Wq6ZDxeGJ/H55gnH5aDrX4++0grXzw5s4XFbTOzr9mHxWTgXG+I4Uh6Sa8nhKCjpjBfLHfeeJCOGic2sxGHxUj7EtcCTquJrlpXydYrbluhigmWVsVULoKJLDlN59JghFg6R73XzqFWPy8eaeZX9jUChTXO5Ab4lhK+B9NxWU1THo0eVcGjeIBKZA6HgNeEED8CplauUsp/M98TpJRpID2tBOIY8Frx51eAzwJXKWQj80KIV4A/Kf2pl596jw2v3UIqq3FrJDarlHKltAccHG2vIprO8UhnFUKIecVXzvWGOHU3yPZ6F8/sWL4ITLPfzvHOAOFklpNdD5fR+/DeBvqCSeo8NowPBMvv21nHU9trZ92+2Wnw2jmxJcB4PMOjD+yGnu8P8/3zg9wejXG0vYqnti+vPOXZnXVsrXVR7bbOEBN6pDPAniYvb94ex2IysGOJPYgLXYcKhUKx0Wj02Tm5JcBEIstjXdVMJLMcK5PQyEKCMz+5Msy98QRmo+Crj3Vit8wdcEgp+fHlYXqCSR7vquZ9O2pxzPPYpVLrthX7Haloz+N0mnx2Pnuslaym0+xfWoBaDj6wq54/eeMOLouJoXAagyFDvcdKo9dOs9/B4TY/NpMRg0Fwckt12dY7fqeFzx1vI5bO0Vq1eu+HYm1SieDwXvGfpfhvJfiAyZLRCOCf57ZZCCG+BnwNoLV17Ukp61JOZVD05eRWF0EIwWNbl1Y2ca7YU3ahL8KjXdXL3qV6sNfxYTAbDXTWzK/s+uBA+W53kPO9YfY0eTd1qemDpaSTSCkZjabJaYVd6+FIetEs7i9ujnFzOMaxjir2t/jm/TycVpMqRVEoFIpFeGSe8bmSTCqZL1bDFU3nuV7sXTzfH2Z30/LEZOZjLShc15ZJrTWv6fzw0hAT8Szv31VHywLBVmvAwb5mHwOhFGPxDJ0BJ8PRDMFklmqXFYfl/rK83BvhVU7LkvUCNgun7k5wsT/C/hbfQ+uArGfKHhxKKf/5QvcLIf6DlPJ3F3mZMNBU/NlT/D1c/Hn6bXMd/08oZhWPHDlSwvCrNOxv9hUEawxixQqQD8uuRg+n7wXZWuteUmCYzmlYTYZV2wGczul7QbJ5nXfn6ENMZgvlPD7H5h38Drb4+dDeFKfvBdlW76HWs7CnYzavc64nBMCpexPsbPBMbV6kcxqJTJ6Aa2P4QioUCsVm4bnd9VweiNDkt8+bNQRwW020BRz0BpNTrRzlotRrCSkl4/EsPocZs7FybQtDkTR3xxIAXOgPLxgcAnxoTz1XB6Psb/FyqT9Kg8+G/yHWKemchsU4f2tSKquRzmllV8zfCJy+FySvS07fm1DB4Srz6BIe8y7wO8C/Ap6l0It4E9gjhDBOu23dYTCIVTerP7mlmhOdgSUN0K/fHONsT4jWKgefONS06gHi9jo3lwYis4xjQ4ks3zzdS07T+cCuenY1bs4+RYNB8JH9TfzKvsYlfVYWk4HOGifXhqKMRtP8n7+4w0f2N1LnsfHn7/QQz+Q5uSWwJnbCFQqFQrE0nFbTksZtg0HwiUPNSCnLOr+/cWuMM90hWqoc/GqJ1hI/vTrC1cEo1W4rnzvWWrFsZY3bSpXTQjiZY2vt4pv8bpt56rN4envtQ/3tZ3uCvH5znFqPlU8facH0QFAcTef4i3d6Sec03rezln3NvhUfazOwrd7N1cEo2+s355pxkrUQHM6iKDbzI2A/8BPg94HXhRC/BHqBf1dUK/2vwBtAiEIfomKFLHVwuj0aB6A3mCST15dsar9URmNpfnplBLfNxIf2NCwqWvLsrjqe3F4za5dwIpGZEskZjqY2bXA4yXImn48eaKKz2skr10bJ65J7EwnsFiPxTB4o2mIMRdle7141xTeFQqFQlI9SBoY3hmO8fWeczhoXT2wreB1PriX6SriWGAqnABiPZchqOjZDZRSxbWYjXzzRhqbLWcHZYkx/n/Oazt9fHiaSzPKB3fUL9pBOMvk+jkYzRNP5WWWioUR2yopqMJxSweEiPLe7nmd21FY087wWWZPBoZQyRyEbOJ1TwB8+8LhvUFAqVVSI450BTt2boKvWtezB/OZIjFeujdDgtfHC/qY56+kv9EXoDyWJpfNsrXWxq3Hxfoe5vsQd1S72NHlJZPIcbqsip+mc7wvjsprKXiqz1hiPZwgns3RWu+bcSY1n8lzqj9Dks0/1I26tc3NrNE4yq7GvyUvAZeVIu5/RaIbeYJJwMsepu0GOtlctaxDNaTp/+94A4/Esz+2uW7C/VKFQKBTrgzdvj3O+L8y+Zi+Pb62Zcd87dycIJXOc6Q7id5pp8Np5pGPla4n5eGp7Le92B0v6mvMRS+e4NBChxe+gpcqBEGLKd3gxoukcf3NugJym87GDTVS7rPQFk/QFk9wpBnvv9Yb44J6GRV/raHsVr98co8nvwO+YrTra4ndwoMVHOJXlkY61WfGj6ZLzfWHMRsHeJu+qV6Rt9sAQ1kZwuPqNa4o5uT4c5cZwjAMtPtoCBSnrXY2eFWfhLvZHyOR0useTTCQy1Lpn74q1BRz8+Ts96Lrkvd7wkoLDuTAaBO/fdV95dbKEBQoSzov1BGwUIqkc3zrVS16XHGz1zalW+t2z/bx9ZwK/08z/9PwuXFYTNrORTxxqnvG4yQn/1RujnO8N0xZwLHsQHYmm6Q8VdncvD0ZVcKhQKBTzEEnmeOP2GFUOCye2LK31Y7V4rzdETivM2w8Gh121Lk7fC5LIarx8ZQSLycAXT7azq7GjpOfQXu2k/SFtN5bKT66M0BdMctYQ4jef6FxyMCql5G/PDXDq7gStAQc3h2OEPVl+cGGInFawPHFYTHRUL21u7KxxLTiPGgyCp3csT6W80pzvC/P6zTEArCbjqulvKO6zFoLDf7/aJ6CYjaZLfnJ5BF1KxmIZfuPxzod+zd2NHobCKeq9Nqrmab7uqHZyoMWHLiXaHLJqUkpCyRwem2lZ5RvTs5QmoyCd0xiOpGnw2criIbRWyOQ18kUZ3GRWm/MxN4ZjhJJZIqksiUwOl3XhYeHp7bUc7whgMxvQdElfMEm12zrv89I5jUxex2s3U+u2Ue+1MRHPsKtBTQCKzUf77/3woZ7f/QfPl+hMFGudt++Oc2ukkElqqXKs6U3Nvc0+LvSF2ds8e0P30a5qDrf5eeXaCLdG4uQ0STqnrarf4EIks3lGoxma/fZ51xmTHoEGg0DXJcFEFp/dvGifY18wxUA4RTyTZzSWoavOxVC44D1pNhp4384adjd6yp75nIt4Js94LENLlaOilmGmacda7LjRdA6TQcxQdVWUnrK9u0KIHwDzqoNKKV8o/v9n5ToHxcoxGgRVLgvjsQzV86hT6rrkFzfHiKZzPLW9Fu8iRqo7GzyLlnSajQY+frCJO2PxOSeZl6+OcGUwSp3HxmeOtSx5J/WRjgAemxm3zUSD185fnOphNJqh0Wfj00fXnsVJqah123j/rjrG4hmOzuOt9fi2anQpqfPY8DuWpkQ6qXb38pVhrg1FcVqNfPlkx6we0UgqxzdP9ZLJa1PCQJ85tnbf73RO4+fXRzEIeHpH7YbeOFAoFGubwtwbw2IylN2oXErJG7fGCSayPLGtZtkWB09uq+HJbTXz3m8zG3lqey1Oq4kal3XOyqG1gKZLvnW6j2gqx5ZaFy/sb5zzcR/cU8/14RiNPhs/uTpM93iSjmonHzvYNOfjJ/HazXjsZrbWuXFaTdwdS3C0vYpkVkMiOdDiWxUv50xe45unekhkNHY1eniugjZV+5q9WEwGzEYDXbXzZ0Fvj8Z56eIgZqOBTx1pocatlNPLRTlD739d/P8TQD3w58XfPwN0l/G4q0JfMMm53hBbagq9bhuBTx1pZjSWIRjP0D2emFWu0T2R4Ex3EACnxcSz08o45yKTL8gtLxbQLVQaMlmSOBJNk9X0JS/ejQYx43MJJ3MAhIr/V5KJeIbBcJqtdeXvi8hrOh3Vzhl/+2g0zdt3J2j22zncVsUHd9ezv9mH32GZEdzpuuT6cIzxeIZaj5VttS5yupzxnoeTWaCQlcxq+qzgcDyemWqGHwivfWGgSwMRbhQ9vuo8Ng62zmmfqlAoFGXnSHsVzX4HTqsR9zxZtvN9YXomEhzrqKLBa0dKyY2RGEYhaAs40XS5oHXFJAPhFGeLNkYWk4EP7KpDUtr+K5fVxNNztDasJfK6zlgsTTiZw22bf4lsMxs5UFSaHyiuSwaKgjgL4XWYeX5vPf/9zXtcHUxwbyxOe8Axy4prqeulUpHN61PVRZFp66JYOse98QRtAeeiCYCVIoRYkhbEUCSFlIVzHY2lVXBYRsoWHEopfwEghPhfpZRPTLvrB0KI18t13NXiZ9dGCCULX6Ktda4NkXGwmowMhFK8fWcCgE8dbaHJZ5+6P53TuNAfQZdy0Ubnt+9M8M7dCZr8dj55qHnFEtNPbq/hTHeQriV6Ms7Hh/c2cH0oyu4V9jSulExe46/O9JHJ6dwei/Hxg82LP+khjvXNU72Ekzme3F7DoWKg84ubY/SHUtwdS7ClxoXPYaFx2uc6yenuIC9dGOTWaJxdjW6cFjNGg+DxrdUcKWYh37ezjjPdQVoDjjnLStsDTnY3eoil8xxpW/uBVq3bikEIhEBNPAqFYtWp986fYYtn8rx6fRQobNB95lgrVwajvHx1hHSu0FLgsZn5yP6GRfu7vXYzNrORdE7DbjbyX9+4hy4lHzvYNGPe3+gYhSCnSaLppW8cP72jlssDkSUlBnJFRdK37gQZixWql/qCKeq999/jsz0hXr85Rp3HxqeONC9bAXUluG1mnt1ZR38oyeG2+1VGkwJyHruZrz5W2h7R5XKgxcd4PIPNZFySZYhi5VSiaLdGCNEppbwLIIToAOavPVin1LhthJI5/A4LZsPGUTrS9PuVwZo2s0o4ksqzr9lLXtNx2xe+lG6PFrIxA6EUyZy2aF/bfGypcbGlBCImHdVOOkrYuH5nLI6uS7bWLTxgSXn/fczl5626LgnhZG4qQ9ozkZgKDqvdVvpDKVxW04I7ytm8zuTHn87qJLMZ6j02bo3Gp4LDGreVD+1tYDCc4spghO117hkTmdEg+EAFy1MelraAky+fbAdB2XZJFQqFohRYi+Wm0VSOmmL7x2SPeSxdsB5yWU30BJOLBodum5kvnWwjmdUYCKWmKj56xhMVCw6HIikm4ll21LsrEhDNhS7B7zDjspqW3Ne2u9G75I1mXUryeR272YjHbsLvtNDgm7kBMLleGomm57SnKBX3xhPkNJ2ttS6EKFRXPRjgZoqWYDlNL8s5LAe3zVzWDXXFfSoRHP4/gdeEEHeLv7cDX6/AcSvKB/fUc7DVR8BlqZjxaiU41lGFxWTAaTFN2RxMsqfJQ38oicko2LGIYejRjireuj1BR41z3sDw+nCUN26O01Ll4LnddWtamW06t0dj/ODCEADv36UvuHtoMxv52MEmeoPJspcf17qt7G3yMhJLz+g3fGpbDTvq3fjslqnsaySV4/sXBgF4YX8jXruZ450BzEbBUCRNe8DBRDxLXyjF0faZGcBQIstfn+lHl5LRWGbNlw0thncOOXCFQqFYa5iNBj73SCvBRJaGYoZxX5MXKSWaLhkMp0jlNA4s0dvOYTEV/xm5MRwjp+sP3QowFsvww4uD2MxGXjjQOG/AFU4W5hFNl4zG0jyzY+E2lXJhMRn42MEmuseT7GkqfRuE1WTkV4+04LKbiSZznNgSIJ3T+dM37lLvtfHhPQ0caa/iFzfGaPbb57SnKAX3xhP87XsDADyzo5b9xRLZB/nogSZuDMfYWqeUxTcTZQ8OpZQ/FkJsBXYUb7oupcyU+7iVxmgQc5bmrXd0KYmn86RzGpouZzRKu21mXjzSsqTX2VHvWTSAPNcTJp7Jc20oyonOwJpZpN8ejXOuJ8S2evdUj8F0stMygNkl7K5VSnVOCDFnH6gQYqo35e07E8QzeWxmA+Oxwtfy1kiMI+2FTYETi5jcBxNZ/u78ALfHYnQEnGTzq7+7qFAoFJuBC31hhiJpHumomtpMNRgEB1v9nO0Jkc7pHOsI4F9m5slhMfGpo0ub2xfj6lC02Nuf4+5YYt5N0bwupyqVVnseafY7aPaXb45OZfP0TCRorXLwSGeA75ztJ5bOE0vHGe/IlKxCaiGmv8cLZQVr3FbVYrEJKada6TNSyp8LIT7xwF1bhBBIKb9XrmOvV8bjGZyWhUv9Ksk7dyd4pdi7EHBZ8Tss7Gny0hdMktdlScsyAbbXuzndHQQpGYqk1kxw+NqNUWLpPIORFHsaPbPKXXY2uMnkC8Hz/iXu0JabdE7j9mgcm9lIe8AxZ4nOvfEE79wt9JN21DinxHEmPS0XIpPX+MmVEc50B7GbjXjtZjpqnGyvc/ONd3qodlr4wO76VVFdUygUikqQzmnE0nlq3FZO3wtybSjKoVb/nErb04ln8vQFk7QFHCuW5A8msvy82G+YzmkzVDLTOW3KNy55Y5SO6tL1iuU1nZevjhBJ5XjfzrpFA4ctNU4uD0SwGA0LbopWu6w8v6+B8VhmQwqB3R2L89qNUfKa5MZwlP5wmutDMY51VLG9zk1/KEkio/GjS8M80lm16Gb6w7KtzkUqV0tO0zfk+614OMqZOXwS+DnwkeLvAaAOcAB5IYQJeEVKGSzjOawZhiNpDAJqPXM3l5/tCfL6zXHsFiNfON6Gc4U9eaUindN4+84E6ZxGXyhFtduK126mezzB3xRLEd6/q66kpZHb6ly0+OwYDIJ3e0LsWIJ6VSVo8tm5PhyjzmObM8gSQqy5wfWHF4f4yZVhJhIZXtjfxJdOts96jNtWEJjRdElblYMX9hUku5dSFn17NM6d0Ti5vE40lWNHg4f37azjFzfGGI9lGI9l2NPkXdO+XAqFYmMxEc+QymllzfpMkspqfOOdbhIZjeOdVZy6F0RKeOvO+KLB4XfO9BFK5qh2W/nC8bYVHd9mNmA1G8jk9FkbqRajgRq3lbFYhqYSvxd9oRTXi4rOZ3tCfHDPwj3lzX4Hv/XkFgyCRVtFttW52bZI3/565Y2bY3z37AChZBaP3YTVZMTvsFDrtlHnsdFV6+K//OIOwUSWX94aL3twKISYsxJKoYDyqpX+L8UfXwd+F7gH/B0wCtiAx4D/UQhxGfifpZS95TqX1eb2aJwfXBhECPjYgaY5bRqGIgUT1FRWI5zKrXpwaDUZaCw2SR9s9fH4thpq3TauDkanHpPKzW2qvlKcFhNNfjtDkTSdJc5KLpVYuiDi0uy3T01kH9xTz7GOqnUlUJLMafQGk8TTOV6+OsyvHW3B+oBtRo3byuceaZ13MRVL5wglCu/FgwFjg9eO1WygwWfnyW3VbK8vmPZ2VDu5MxbHbTPP64+pUCgUpWYsluFbp3vRdMlT22vKvmEXTedIZApz4GgsQ3vAyb3xxKLCLwCpXKGML51d+RzqsJj4/PE2QoksLQ+M3waD4NNHW4imciUXM6l2WXBajSSzGm2BpQWeG6GCJJnNMxbL0OxfmUF8TzDJSDSNjmSbx8UnDjVzvDMwlTCwmQ00+e0MhFK0L6F6R3Gf/lASt9W8ZqrNNgKViED+CPgb4JvAz6WUMyQahRAHgK3Ahg0OI6mCF5yUEE7NLY98ojNATtMJOK00LiBdXSmEEHzycAuJbB7PNH+lHfVuktk8WU3n4Ap3nRKZPK9cG8FkMPDsrvtG4waD4FNHWkjntRWX2jwMyWyeP3+nl3RO41Cbf8rQVwhBYJ0FOh/aU8+ZniATsQxtAWfBn3COx7ltZk7dC3KmO8Szu+qmxILSOY2/ONVLKqtxoMXH0ztmisxUOS189bEONF3O+Kz2NHnZUuPCbBSrpjanUCg2H7F0bqpnbb55tpTUeWwc66hiJJrm5JZqql0WklltSRu7Lxxo5OZIjJ0PmR3y2Mwz5ufpmI2GssxbbpuZL5/sIKfpq76J/SB3x+K82x2kq9Y1w47hYclrOt881UssnWdbnZvn9zUs+zUavHYe2xpgIJTixSPNfHhv44xMqhCCTx5qJp7N436I9zWv6bxybZRkNs/7dtatq03tlXD6XpA3b49jNgq+cLxdBYglohLf7CYKpaX/APhvQoiXgL+UUv4SQEp5vgLnsKrsbfIRTecxCMGeeZS/Ai7rnBK9eU1nLJ6h2mWdZUYbTGT54cVBzEYDH9nfWPKB2mgQsyYeg0FM2RislAv9Ye6OJQBoqbKzb1qfnsEgViUwhELWdlK+O5TILvhYXZf85MowQ5E0T++oLXn/5XJ5rzfE2Z4QO+o9PLa1mmqXlf/Ph3dxtidIS9XcHoQAN0diU6bvF/rCPNpVEKBJZTVSWY2eiQTdEwlqPVZ2N3oZjaVxWEy4rKZ5fSbXSs+sQrERaf+9H67q8bv/4PlVPf58dFQ7Od4ZIJHJ80jH8ueoSCpHXtOXFVBNjpeTLHUObvLZ14x34CtXR+ieSHByS/WSlUktJgMW0+ps/p2+F+TSQIQDLd5ZAeDrN8cIJXMMhtPsbvRO9dE/LHldEs8UrEGCyYXXBvPxwT31NPvtVLutmAyC//bLe7RUOfjArroZYkLzBftL5c5YgmtDhQqvcz2hWRu765Hrw1HevD1BR7VjloptsLhWm/SmVMFhaajEKvwPi//3A2PA+4DfEUL8RwAp5f+jAuewqlhMhhXL+790cYh74wnqvTY+c6x1xn3XhqKMxwtfjDtj8RlB1lqm0WvHIARGQ2H3dS6klEgJkkJfg9EgONTqK6u9RcBl5antNQxH0hzvDMz7uO7xBGd6glwdjOK2mTnbE1r14PDd7iCJjMa73UFObAlgNAhq3FY+uGfhHc5ajxWzUaDpTEmhA/idFh7tCnBrNEaTz87pe0FymuTV66NYTAY+/0jbsgfhSbXbSCrHhb4wzX77kkqwyklO03nn7gRGIXikM7Ahyp8Uis2IEIITW+YftxdiNJrmr97tQ5OSD+9tWHLf26X+CKOxNEc7qh56UT8XA+EUd0bj7GzwrFgxUtflvH3ksXSOSwMRoKB78LC2FZXgnbsTaLrknbvBWcFhk99BKBmh1mPFUsLKFZvZyHO767k3ft8veLm0VDl48/Y4f3m6j2g6x6E2H7F0juMdpVVmr3FbsZgM5DR9wyjov9sdIprKcaEvwtH2KtzTvmuPdhW+836HWWkclJBKBIdnge3AI8A+4C7wn4q3rzuklPz9pWFuj8Y52RWY4R9XDkZjhV7EsVgGKeWM4Kij2sn5vjAmg5jVc1BKrg5GCSayHG7zlyQr1F7t5CuPtRNO5uZcjMfSOf7q3T7SOY3OGtdUZstuNpZ98lqsT0XXJS9dHCST1xmOpPHYzWytXX3/n0avnb8600fAZSGUzM7b73d9OMp4LMuhNh8Oi4lat42vPNZBXpezFjdH26voD6XomUiyrc7NSLRwLWbzOuFUdsEJbSiSwmU14baZSec0/roowPDBPfVc7I/QF0zyXm+Y33i8Y1VLky70hTnTHQIK5VKLCUkoFIr1TzqncbYnhM9hZnejl/F4dso8fjSaWVJwOBbL8Mq1EQCSWY2P7G8s6TnquuRv3xsgm9e5Mxbn1x9dvuLo7dE4f39pCK/dzKePtszKpDktJlqqHPQFk2wvlrjquuS75/q5MRzjw/saVhwMlYttdS6uDcXmnHef3VnLoVYfXru55H7TOxs87HwIkbzeiQTfOdNHdzCJLiV3x2I0Vzn53LE2oHTBYZXTwlce7SCr6RumpHRbrYvxWIYmvx3nA5Vlbpt5UVEkxfKpxKrsnwHngT8Fvi+lTFTgmIuSzOYLaSnBjDLGTF7DYjTMm6HK5HVujhSClcsDhV0MKSWv3hhlPJblqe018yqSroT37azjYn+YHfWeWefU6LPzW09uQbA0hcmVcHUwyr95+QZ2s5FEJs9zJfoShpM5vnuuHyiYrk/PIA2EU8TShRKOkWhq6narefV72ETxeslpOZ7aXsPHDjaXvLxG1yWj8TQB5+xS4unE0jleuTaCxWik2m1lf7MPo0FwezQ+KzjMaTrBRJYfXRoGIJ7JTWUV5yvjFULw8YNNZDUdq8lIJJkjk9fx2c20LrBDd/pekF/cHKVnIsljXdUcavNNZbhvDMewFt8vk1GseqbOZTPN+bNCodh4RFI5XFYTv7w1PpUx8zssbKtzMRTxEkvn2dc8MwAIJrK8cnUEl83Ec9PseWzmQmllNq/jLo4dUkpeuzHGWCzz0GsBIQrCcNm8Pm8J/2LcGomh6ZJgIstwJD1LDM9gEHzycDM5TZ+aa4LJLN+/MEg2r5POa2suOPzgngae3lE753uylvUB+kIpYpkc0VQOIQr9ouFklv/25j3+6Yd3lvRYdosROxunveORzgAHWn0Lrs0VpaUSq6H9wMeBfwL8afGDvQb8H1LK//thX1wI8W+BI8A5KeU/XMpzeiYS/O17A1wbjtHit/P41hq217v5P352k4FwmvfvquOzx1rnvAhtxezV7dE4+4uCLIORNBf6ChPNO/eCvLCEHcRYOseN4Rgj0QwGAY9trZ6RKp9kuhlqMpvHbjbOOK/5Ftej0TQj0Qzb6l0rnligUDYTT+eJpfNTNfelYCKRZVKaaCKRpbPm/n3tASeNPhvJrMbzexuIpvOYDGJqYoulc1weiOK2mtj6kH/fchGioAI3FEnR7HeUPDDUdMkf/ug653oLpbRPbqvh44eaaPDOLg95rzfM7ZE4sUwen92MlBKv3TJr1/vOWJwfXhzCIO6Xdj6oXDpJXzBJfyjF1joXvcEk9R7bVGmK12Fe0rU9Ec8wFiv8uzEcoy3gpL3awUQ8y/5mH/VeG501Meo9tmX1hHSPJ0jnNbbXuUs2Qeyo9+C0mDAaxIYpwXlYwsksdouxot8rhaLcvHp9lPN9YRq898c0gxCARJdQ77VxaWCEvz6b5bPHWrFbjPz0yjDfvzBAfyiFzWTEYjRwuM3Pm3fGqXUXWj1CySwdRXXJoUiaN26NkcpqmE0GPj7Ne3A6d8biJDJ5djd6553DhRC8eKSFvmCSjmoHE/EMXrt5WUJfe5u9DIRT+B2WWeObpkvO94WxmgwzLKksRoHLaiKi5/A7Sqt0WirW49jkt5sZjGTIaoWFTyKTp97rwmoyLFj6qyiwHj/z9UwlgsNfB/45hTxdAngH+HMKNhY8TIAohDgEOKWUjwsh/lgIcVRK+e5iz+sPpcjkdUKJLH67mbtjcSQwEEqTymlcGYjMqTomZaHOXdclXzjRNlWG53eYcVlNxDN5Wvz3B+DbozEuDUTY1eBlS42TN26Nk9V0ntxWw0sXh7g+FOX2aJzDbX7MRgPP7prZaDudn1wZ5upglC21rkUX6IlMnm+f6SOnSfpCST68d/nKWpPsbHRzc8SLEJL3L3B+y2V3o4eJeAZdwr4HSvlsZiOfPnq/v7L2gUqOn14Z4Z27E/SHUjyxrZovn+xYUrlrKqtxdShCg9f+UIGA02qiq7Y8XkzxdJ5743HSOY1EJk8omeV8b5hzMozNbOCp7bVT2cFvvN3NjZE4ZqPg6e21+J0WvvLY7NKjO6NxNF2iAY9vrcZjN89ZNpXM5vmb9wbQdMmPLg/hd1gwGQS//ljHDEGbgXCK4Uhq3ob/k1uqmUhkQRZ6F1uq7LN6gXY3Lq98s3ciOeWvmchoHG4r3W626lO4z6TfqstakMlX4kKK9Upe03nj9jh5TfL41mrO94W4PhRjJJrmhf2N1Hqs3BuL87/98Bo1bivb6z1ICdFUjvF4hjqPjSuDUQzCQDCRo8VvIpTM8sbtce6Mxrk1Eqct4JjavJ3k9licTE6no2buPvS+YJLvnx8ECmPZQn2SXrsZb5OXH18e5tpQlHqvjV872rLkzbFmv4PfeLxzzvve6w3xxq1xoJCh3FqcEzx2C7/91BZ6JpIcm0fYJ5zMcns0TmeNq+RWGRuV/nCSePr+Bru5aDdyrCOwYGA4GE4xFEmxq8GrxmNFxahEcPjPgf8f8O+Kv38e+E3gV4G/BB4me3gCeKX48yvAcWDR4HBvs3fKVzDgsnBiSzUeu4mOGieD4RSPba2Zsw+qP5TinbsTQGFXb7LO2WEx8cWTbaSzM81oX746Sjqn0R9M8fSOWs73hQFw20zoUmItZgFl8TwWYlLd8+5YfFbv4YNoUqIVbJTITf6wQvY1+2gLOAuGuyXcuTEbDbxv58qCTZNREEvnQBSCqUgqt6RB86dXh7k7lsBkEHz18Y5VU0VdCK/DzAd21/Pz66OYDIIqp4V0XqN7PAlAk9/OjnoPP7w0RE8wSU7TsZtNRRuUua+h/S0+hiJp3DYTB1v982Y7CyJBAk2XTF5dksKmyCSJTJ7vnu1H0yUD4fScGxVeh5nPH28jeagJKZeu4LcQ2WnX8cNe04r56Q8VyrjjmTzhVBa7RWVTFeuT68MxzveGgcKcq+mSnK6TL2ZpdtR7+NapXsbjWcbjWU5uCaDpkipnIctmNAh2NngwAA1eK9UuKye7qukZT3CHwibmgyrQFpOB/c0+cppOa9XiImUPOHvNy0C48L0ciabJaRKL6eGzTNMzlg9mL/c1+xYUuPvb9wYIJXO81xvmN5+YO/hUzORHl4dn/O5zmnlmR92CG9XJbGG+zeuS/lCKjx6YOxOtUJSaSqyObVLKfz3t9z8TQvwjKWW3EOJh1UV8wJ3izxFg94MPEEJ8DfgaQGtrIRvlsZn55OHZthH/y0dmPX0GHpt5qsfgwWDOappdhlXrttIbTFLnsRFwWTAIgS4l1a6CLcD1oSifOtKMy2qmfhFvw0e7ArzXG2Z34+zew7nO84UDjQyFU+xboRfhdNZaU/Nzu+vx2EzcHUvQVeemzrM2ewxWyotHWnjxSMvU79eHo3SPJ6eCRYC2Kju1bitSwonOKj55pGVeEYU6j40vnWxf9Lg2s5EXDzczGEnTVuXg1micBq9tRrnz0pYyBUoZfHfVunh2Zx3pvLZif03F4pzYEiCb16lxW6kvYe+0YuOwXqw0fA7z1JwbcFrY0+TFaDBQ5bRMKVnuavTSH0rhsZt5dEsNzgf6jj+4p36W2EWTz057tROP3Txr46vaZeWF/Y2MxTPz9uq1VDn48N4GEtk8+5qWVkHx5LYazvWE2FrnKlkrw4EWH1aTEYvJsGzVaDn1/3JmhM1Nk99Bq9/OcDSF32nl9z68c1kVTEvcR1AoSoJY6s7Vig8gRBT4HeBbxZs+A/y6lPJ9QoizUsrDD/Ha/wAYk1J+WwjxCaBZSvl/zPf4I0eOyDNnzqz0cEChoT2RyS/pS53XdMbjWQIuC2ajgVCioIi2UklqxcORzOa5MhilcQ15TE1y5MgRFro2J+IZzCbDVClzXtMZjqbRdInPbqmot09/KMlQJM2eRlXmsll48Ppc7QBBsXl5MDhcaOyciGfQpKTWbSsIfcUy+J3mqY1cTZcMRVJUu6wl88TbDIQSWW6Nxumscc6rjK0oMHl95jWd830hHBYznTXOJV9vA+EUg+EUuxs9a7LaSbF+KcZgR+a6rxJXmpmCdcWfFX9PAINCiEvAw9YjvA18Hfg28Oy0Y5QNr9285EyayWiYkRH0q9r8VcVhMZXdeqRcPKjAZjIaaC6jfclCNPsdq3ZshUKhWCrTx02DQcyq0DEahBrLVoDfaZm3H1ExNyajgSPty/fhbFqDm9mKjU8lgsMd5XphKeU5IURaCPEGcEFKebpcx1IoFAqFQqFQKBSKjUwlykr/L+AfSinDxd/9wB9JKb9S1gPPQXV1tWxvb6/0YRWKRenu7kZdm4q1iro+FWsVdW0q1jLq+lSsVc6ePSullHM2MVciONQolJJOxwkkASmlfFhRmiVTip5DhaIcLNZzqFCsJur6VKxV1LWpWMuo61OxVlmo57C0Dt5zcxloKwaBd4B24KqU0l3JwFCxNknnNH5yZZifXRtZkUVBMpvnxnCMRCa/+IMVM4ikcvzw4hBv3h6fIakeS+e4MRwjndNW8ewUmw1dl9waiTEez6z4NZLZPD++PMSrN0bRdCXvp1AoFIqlM7mmTGbzm3o+qUTP4R8BbwkhvgPUA28B/1sFjqtYB1zoC3N1MAoUZMD3L2JTMBhOcb4vTFeti211br53boCxWIYqp2VJdg2K+7xzd4KbIzEAWvwOWgMOdF3yV+/2EUvnaalyzGn5shyklLxzN0gsnePRruqSeB4q1i+JTJ43b4/jtpk53lk1w5bn9VtjvNcbxmQQfPFk+4osdM72hLg2VLimG7w2dtSr/UeFQqHYCKRzGm/eHsdqMnJySwCD4eH9Ph/kO2f7mYhnqXZbaQ84puaTeo+NnQ2bZz4p+0pNSvl/CyHOAM8AeeATUsqr5T6uYn0QcFkRAgRilnfkXLx8dYRgIsutkTjtASfJbCFjmMiqzOFymZQgt5gMUwtxXUpS2ULGMFmC97RnIsk7dyeAgjLg+3bWPfRrKtYvp+5NcKW4GVTvtdFRfd8oPFm87vK6JJPXKAhdL4/Ja9poEFQ5lDq0QqFQbBTO9oS42B8BoNptKcvm3+Q8lMzkZ84nm8xtoOzBYdF/EGAQyAE7hBBTCqZSyu+V+xwUa5euWhefP96GUYglWX34HGaCiSxumwmTQfCR/Y1cG4rOawCvmJ/DbX6afHYcVuOUf6LJaOCFA43cGYuzZ4kGzQsx+TnldYlPLdY3PZPXgNEgcD9gOP7EthocFiPVLiu1bttcT1+UnQ0eAq6Cybm63hQKhWLj4Cv6ORuEWFFlyVL4yP5GbgxH2VHvodFn37TzSSVqvD4y7edfPPC7BFRwuMlZjonu83sb6A+lqPPYMBgEDV47Dd7FPYBGo2l+dHkYt83E8/sapkyQNzv1Xhu3R2P89Zl+Wvx23r+rjraAk7aAc/EnL4GAy8rnj7eRyOaVn5iCQ61+at1W7GYjp+4FGY6keWZHLe3VTlxWE09tr33oY6w0sFQoFApF5Qkns7x0cQizUfAr+xrnbT/Z3eilymnBbDQsa924HB70ldys80klykp/vdzHUGweTEYD7dXLD1wu9EcIJrIEE1n6gkm6au9nGnsmErx+c4xGn51ndtTO6IPaDJzpDhFN5biSynG0vWpJGdzl4Hda8DsthBJZfnxlGLvZyIf21qsAfZPS7HcwEk1zY7jQy3GuN7Si7/R4PMNPr4zgspn40J56zMZK6KspFAqFopRcHYwyFisIkd0ejc+pPZHKavz9pSHyus4H9zRU+Aw3H5UoK/088E0p5ZxSlEKILUCDlPKX5T4XxeZlS42Ta0NRHBYj9Q9kGk/dCzIezzIez3KgxUegTDtSa5WtdS6GImkavLZZpX6l5OJAhOFIGoA7owl2NW6e5m7FTPwOC9VuKxPxDFtrV1YSfr43zEg0zUgUuscTbFWl5QrFQ9P+ez98qOd3/8HzJToTxWahvdrJe31hjAZBS9XcFUY3R2L0BpMAXBmIcLKrupKnuOmoRFlpAHhPCHEWOAuMATagC3gSGAd+rwLnoVhn9EwkSGQ0dtS7l6RKJaXktRtjBBNZntxeQ7XLiq5LsppOZ42L335qC0YhZr1WZ7WTgVCKapcFT5nq2Ncyh9uq2Nvkw2wUU1lTTZdk8xpv3Zkgksrx1PZaqpwWRqNphqNptte7l535a6tycKEvjMVkoNG3OUs1FAUsJgOff6SVnCaxmJaX8SsEhGkafTauDEaxWwzUee9fT+mchs288LV5fTjKxf4Iuxo8JemtVSgUCsV9Qoksr94YxWs38/T2WnK6jtlgmFp/ZfM6N4Zj1LitNPrsfP2JToQQGOdZ6zX57VjNBjRNzhtAKkpHJcpK/70Q4j9SUCt9FNgHpIBrwBeklL3lPgfF+mMwnOJ75waAgu/eI52BRZ/THyrYXACcvhfkA7vq+KszfYxGMzy5vYZDrf45n3ew1c+uRg9Wk5F3uwt9UCe7Apuq1nz6Aj2ZzfOt032c6Z4gksyxq9GLw2LiiW3VfPtMHzlN0htM8iv7Gpd1jPZqJ197ohOjQagSwA3IaCzNW7cnqPfaOL6E76sQAotpeSXciUyeb7/bR16XbK938/UnZ15Pf39piBvDMXY1enhud/28r/Pz66NkcjrDkXRJg0O96IVVDol1hUKhWC+c7g7SPZ4AQJdwZTCCz27m1461YjMb+fn1Ua4NRTEZBF96tH1KFG8+ql1WfuOxTiRyxsZ0OJnlFzfH8DssPL61etO1BZWLipiOSSk14OXiP4ViUfLafcPRnLY081G/04LDYiSZ1Wjw2oikcoxGi3XsI/FZweGdsTjfO9dPPJ3nSHsVxzsDvH2nYLsgkXz84MN5/K1XRqIZ7o7FuNAfIZfXSeU1qpwWdjV60IrF4TltzirxRVkso6NYv/zy1jg9E0nujSfYUuOixl368mxNSlI5je6JBHld50N76mcsBm6Pxqf+f273/K/T5LNzdyxBo29xMaulMhpL892zAwgBnzzcXDbBBIVCoVjrGIXg7bsTpLIasXQej91MKJljLJahpcpBXi+sITQppzbVFmOuKpN37k5wdywBJOiodqqsYolQjtSKNUlrwMEHdteRyGgcbPUt6Tkuq4kvnWwnldXwOy1IKdnd6GEwnOJI++ys4Y8vD3OxP4JWtFl4ansNTquRREbbVFnDB2n223FbTdhNRvx2Mw6zkUxe50x3kI8dbGQglGLfHA3jis1NncdGz0QSp9VYtt5Vj81Mo8/OcDSNlNAbTM5Q1j2xJcCl/sicggbT+ZV9jYSSWfwllCfvHk+SzhU8snomEio4VCgUmxaDgYLdhIRgMovfaaGzxjm1IffMjkKrSr3H9lA2EbUeG9eGYljNBryOzdcWVC5UcKhYs+xuvF/uNR7PcKEvTFvAMUNp9EFsZuNUdkoIwQcWKC0LOC00eu3EM3kOtvpw28x84Xg7sXSOWs/mCQ4vD0QYjaU50l6Fx2bGbDTwtSe7qHbbSOU0dCnR9YItRSltLhQbi0e7qtla68JtM5c1Q7yzwcNYLIPZKGaVIh1tr+JoexVQKEd/tztIrds2q3TUaBAlD96217m5MRJDgBLHUSgUm5o9jd4pS4iOaie//mg77mnjtcNi4uSWhxeVOdTqp9lvx2kxzWuBsdnJaTqn7gYxGgSPdFQtqe1BvZOKdcHLV0cYjqS5PBDl6086SrL4/MShZh7tqqbOY5sqV7BbjNgtm6f0cSKe4eWrIwDEMxov7C/0EVY5LfzG451Aoc8rlMzO8P5RKOaiEpsqh9v8NPpsOCymBY2QX785zs2RGBChzmMrS5nrdLwOM1843lbWYygUCsV6oNZj459/dA8D4RQem2lGYFjyY23iSq+lcKEvzLvdQQDcNtOS+uxXNTgUQvy6lPK/r+Y5KNYHkztCNrNhXjWr5fBud3Cq/KzSNeqv3hilezzByS3VbK9f3QyD1WzEYjKQzeu4rDOD4tFYmp9cHsZlM/H83sY5G71vjcT45e1x2gIOntlRV6nTVqwzktk8L10cIpbKIwQ4rUae39eIa4U7vQ3exTcqnMXr2WwUWM1KAEmhUCjmQ0rJz6+P0htM8vjW6qkKrXO9Ic73htnT5OVYR9WyX1dtKq8+0zOqS82urnbm8J8DKjhUzCCd07g0EKHeY5sK3D60p57u8QR1XltJlC7fvjOBpkveuTvB4ba5VUzLQTyT53xvGIDT9yZWNThM5zSuD0V5rCuA02qio9o14/4LfZEp/8eeibl95E53Bwknc4STEQ63VS2YyVFsXm6NxBkIpegPJZESWqoc3BiOlfW798TWGpr9DvwO86JKeKuFlJIrg1GkhD1NHqW0p1AoVoVoKs/F/ggAp++FpoLDt+9MkM3rvH1ngqPt/jUxRqVzGpcHItS4rarNZQnsbPDgspowGMSSg/WyB4dCiIvz3QWoVMMaJJrOcXs0TnvASZWzdIINS+WVayPcGoljEIJff6x9qg+ulH08XbUubgzH6Kq9HxDdHImR1yQ7G9xlGwAdZiNNfjsDodSCvZPlIJ3TuDYUpd5ro8Fr59Xro1wfjmEQgi+fbJ+Vke2scXJtKIrDYqS+6CM3Ek0zEE6xs96D3WKkq8bFaDRDo8+24iyQYuPT7LdjMxupcVvRJVjNBlrLnLE3GMTU93sinqEnmKSr1rWmAsWfXRvllWsjUyWve5uV56JCoSgfwUSW7omCovTkZm4kmePmaAyv3UQklWdr3f110dZaF1cGo3TVutZEYAjw2o1Rrg0V1i5fOtn2UII2m4XlVshVYjVXBzwHhB64XQBvVeD4imXyd+cHGY9leNcS5GtFY9JKIigcTwhYzpF/eWuc0Viax7ZWL1qD/uG9DTyzo3aqd/HmSIwfXhwCIK/r7Gv2reTUF8VgELx4uJlMXq+4rcNPr45wZzSOySD4ymMdzPhYp/38Xm+IO2MJjrT5+a0nt2A0FIxp0zmNv570OZxI8rGDTTzSGWB/iw+rybBmJg7F2iPgsvKbj3cwKVguAFOFvC6llHznbD/JrMbVwSifX0Zf4LneEHfHEhxrr6I1UNpgdiiS4tUbo9wbT5DJ66ivj0KhKCdSSr57tp94Js/lgQhfPNEOwPfe6yeczOGyGvntp7bMWJt8YHc9T2yreaj1ylgswxu3xqhxW3ms6+G9CKc/XyxrlahYKpUIDl8CXFLK8w/eIYR4rQLHVyyTSc+ZJVrPlJz37ayl3muj3mtbchPzSDQ91XBrNEzw0QNNiz5n+mCny/t/rFbmP1wIsSp+f7L4N8riv6d31FLrsVHjsk7tIKZzGq/dGAMgns7x5Uc7pj2/8A9mvl/Ku1CxFCoVDD6IlPfHsunX7WKkshq/KH4XUtk8XygupEqFLqHaZQFc7Kz3sLvRU9LXVygUigfRimPgdG/B+2ueudcmDzvHv3Wn4IHbM5Fka617qhJppTy1vYYat7WwdlH2FWWh7MGhlPKrC9z32XIfX7F8XtjfyI2RGJ3VzlXJBtnMxmX3Inls5imPwvoVKCZur3OT1yR5XbJvCUpO65H376rjUn+ERp99qgT0UOvM99liNFDttjIey9DwQG263WLk44ea6A+llqR2pVCsBQwGwa8eauLueIIdy+jxtZgMBFwWJuJZ6pcggLNcmnx2nt/XSDSVY3+LT2XeFQpFWRFC8IlDTdwdS7BtWpvOxw82cWs0PqPNppQ0+uzcHUvgtBrx2B8+7LCajLPWLorSopqEFLPwOy0c7wys9mksC7vFyBdPtBPP5FfkXyaE2PABj8Ni4pFFPleDQfBrR1sIJ3PFrMZMmv0Omv2VVXdVKB6WWo9t2TYbRoPg1462EknN/V0oBduUH6JCoaggtW7brLabgMtKoMS+r9M52l5FZ7UTp9WkKo3WCWWv8xFC7BNCvCOE6BNC/IkQwj/tvtNLfI1/K4R4Qwjx7x+4/YAQ4s3ifY+X+twVa5uRaJo/feMuf3Gqh2Q2j81sLLmx9WbEbDRQ47YihCCSzPF/vdXNn715j3Ayu9qnplBUFIvp/nfh8kCEP37tDj+6NDRVoq1QKBTrjVdvjPLHr92ZasWpBAGXVQWG64hKNIH8Z+CfAXuBm8AvhRBbivctWiwshDgEOKWUjwMWIcTRaXf/C+DTFARv/qdSnrRi7XN1KEosnWc0mqF7PLnap7MhuTUaI5jIEkrmuDkSX+3TUShWjff6wgX7l+EYsUx+tU9HoVAolo2mS873Fsay93of1IlUKApUIjh0SSl/LKUMSyn/NfA/AD8WQhwHlrL9egJ4pfjzK8Dxaff5pZT9Usok4BRCKLfNTcS2OjdWswGv3UxLlfroy0FHtROn1YjDYqSzRvkJKTYvuxs9GISgo9qJy6I6MhQKxfrDaBDsbPAgBOxu3NitNIqVU4kZTgghvFLKCICU8lUhxK8C3wWqlvB8H3Cn+HME2D3tvjEhxB5gBNhTfGzqgYN/DfgaQGtr68r/CkVFuD0a4527QbpqXYv2PTb57Pz2k1uWLORwbShKXpOFRZ5BiT9MEk3nuDEco63KMasvK57J47SaaPbZVcmuoqLcHIlx+l6QrbWuRXtlK8GhVj8HVyAcMxhOMRhOsavRg0MFlQqFYpX54J56nt1ZyyvXRvjL0708s7N2qg/x1kiMRFZjb5N3lvexYvNQiczhHwI7p98gpbwIvA/43hKeHwYmNb49xd8n+T3g3wD/BbgIjD/4ZCnln0gpj0gpj9TU1Cz33BUV5pe3xhmLZXj7zgTpnLbo45e6ULs1EuPHl4d55doIF/rDD3mWG4sfXhzil7fG+c65fvKaPuO+t+9MMBrNcK43rHoOFRVlcix4a4ljQSVYbmCYymp892w/b9wa5ydXhst0VgqFQrE8+kMprg3FGIqkOdtdKC/tCyZ56eIQr14f5dS9iVU+Q8VqUvbgUEr5TSnlO3Pc3iul/M0lvMTbFAJJgGeBqdeSUt6UUn4A+DrQK6XMleKcFYsTS+f4/oVBXrk6UlJfwLbqQulig9eGZZV80TYKmi752bURvn9hkGh6ZV+NSePvapdlyv5CoagE7dWFa6/RZ8NqKv9YcG88wXfP9nOpP1L2YykUCkUliSRz/N35AV69MYquS6rdVlxWE0Lcn+cViknKvtoTQpiArwIfBxop9BkOAn8H/LfFAjop5TkhRFoI8QZwQUp5WgjxH6SUvyuE+CrweSBJoZdRUSHO9oS4M1oQKGkNOEomyf709loOt/lxWkwzSj+zeZ3Xb46hS8mT22uwmpanerW1zs0H98ipstLNwN2xOBeLC123zcTT22tn3N8XTHKuN0RXrYuuWhdtVY5ZRuUnt1Szt8mL3WxcNRNzxebkmR11HGmvwmkxVcQD8OfXR4mmcvSFkuxocGMuwfVutxj51cPNU2Wl8yGl5M3bE0TTOR7fWo3bpoydFQrF0rg7FufSQITdjR66audei53uDnJ3LAFAW5WDzhoXXzrZTk7TcRY3fluqHPzKvoapslLF5qUSqYBvUCgF/WdAf/G2ZuBLwJ9TUBtdECnlP3zg998t/v/fgP9WulNVLJW6Ym+a2SiocpbWA8wzx8LoymCESwOFQKfKaeFI+1LaVWeys2FzBIWTVDktWEwGsnmdOvdsj7efXRshlMxxbzzB7zzVhWWe7IxaqCpWi7nGgnJR77ERTeWodlkxlbDXptFnp9G3sGBWz0RySlbebDTw/l11JTu+QqHY2PzkygjpnEZfMMn/8MzcwWG9x8blgQgWk2FqzWYxGWbN+1uV96qCygSHh6SU2x+4rR94RwhxswLHV5SBnQ0e6jw2LCZDRcoNq11WJpMH5TRr3UgEXFa+dLKdbF6fM4APuKyEkjn8DktJF8MKxXrkQ3vqOdLux++wVCRTOR2v3YzZKMhpkmpXaTfbFArFxqbaZaE/lFpQNG5vs5dGnw2b2TiVKVQo5qMSV0hICPEi8F0ppQ4ghDAALwLKZGUdU+qM4UK0VDn44ol2pJQqOFwGLqsJ5nm7Pry3gaFIYUJR6q2KzY7BIKYqIiqN32nhiyfbSWW1VTsHhUIB7b/3w4d6fvcfPF+iM1k6HzvYxEg0PaU4Oh9q7aRYKpUIDn+NgmLpfxZCTAaDPuDV4n2KNcqtkRiv3xqntcrBsztr591Nl1JyoT9CTtM51OpfVP5Y0yXv9YYwGgQHliENv9xg9PJAhHgmz6FW/7wlk5uF0/eCXOwPc6DFx5H2KvqCSXomkuxp9HBjOEZW0znY4luwr/D2aIzRaIYDrT4lya+YxY3hGBOJDIda/djMS+8J1nXJe31hAA62+DbcRkUkmeOlS4MYheBX9jfOW2nhsZnx2MxcG4oSSmaX/T4qFIrNw2gszY8vD+O0mHh+XwPN/vuiMme6g1zoC3NyS4CdjV6uDUUJJ3McbPWpMUWxJMq+wpNSdlPsKxRCBAAhpZxlOaFYe5zpCRFN5bg8EOFoux+fY+7g7OZInFevj079fnSRfsDzfSHeuFW4BOwWIzvqS98L2DOR4OWrIwDkNJ3Ht25uG5N37k6g6ZJT94LsafLyt+8NkNclZ3uD6NPcK+b77IKJLC9dHEJKCKdyfHhvQ4XOXLEeGI2m+ftLQwDE03k+sLt+yc+9Mhjl9ZtjQKGHeV+zrxynuGpcHYoyGs0ABe/GQ63+eR87FEnx48sFy4tUVuN9O1XvoUKhmM3lgQgT8SwTZOmZSLK9vtAreG88wX9/8x7j8SxXhqJ89bEOfnqlsBZK5fI8s0ONKYrFqUg6RQjhEUJskVJOTA8MhRD7KnF8xcrYWusCCrYSC4mSTM/KLcV+wmK8v3NVCkXAuZj+usoSA7bVFT7LrbUuDEJgNBayM3bz0j4Lo0FgKGZ4y/WZKdYvJqPh/vWxzCz99PFjI15bHdVOLCYDNrOR1qqFJeNNBsNUb/Vmr3ZQKBTz01ntwmQQuG0mGnz3y0ktJsNU9ZbZYMBmmjamGFXWULE0KmFl8Sng3wGjQggz8GUp5bvFu/8MOFTuc1CsjCPtVexr9mE2igVLPzuqnXzsYBM5TZ8KKBdiT5MHm9mAyWigo+hrWGoafXY+ebiZeCbP9hKpb90ejSHl+lTz+uCeBp7eUTtlAfLpIy0MhtNsrXMxFEkv+tl5bCYOtvpIZzWe2r65s7CK2VQ5Lbx4pJlQMrvsSoDt9e6pxUzXEsaP1WQkmmYkmmZ7vXvJdjr1Xhtff6ITIcSiJfc1bisvHmkhvIL3UaFQbB7aq5381lNbMAoxoxS/yWfnHz27jUv9ETx2EzaLiRePtBBJ5qayiwrFYlSicej3gcNSyiEhxDHgG0KI35dSfg/YWM0lG5C5dq+j6Rx9wSQd1c6p3rPlBHlCiIoEWC2L7NIvh+vDUX50qVDu9dxuuaBn2VrFajIyFEkRTeXZWuuaak5fymd3tifEme5Cy/COBk9J31vFxmAplg3zsdaDQoBEJs+33+0jr0v6Q6lllVbP1cur65Jbo3G8djP13vs7/00+O00rfB8VCsXmYXqlRTavc3s0Tr3XRmeNi56JJOf7wlzsj/KFE200NaoxRbF0KhEcGqWUQwBFA/ungZeEEM2ArMDxFSVESsm33+0jls5T77XxmWOtq31KFSGv3b9U89Ob9NYRY7EM3363H11KHums4uSW6iU/N6vd/5tz2vr8+xWKh0GTEr04DJTiO/DmnXHOdIcwCMEXTrRVVP1ZoVBsLH56dZhbI3GsZgNfebRjaozSpVy3axbF6lGJ4DBW7De8A1DMID4F/C2wuwLHVyzCme4goWSOE1sCi3oWSgmZfGGgSWW1SpzemmBXg4ecpiOBPY3e1T6dZSOl5NTdCW6PxmipcpDOLe+zO9ZehdlowG420lmz9rM8CkWp8djMvHCgkaFIiv0lEM1J5+4v3rL58i3e7ozFuTUSY2+zT2UkFYoNQs9EgmtDUXY1eGkNOKbGk1xeoumSJ7bV4LaZqXZZFrW4UCgepBLB4W/zQPmolDImhPgg8KkKHF+xAH3B5JRyqC4lzy2iMmgwCD52sIlbI7F1WVq5UgwGwcEHVAavDkYZjqY43FaF1z6/YM9aoGciya3ROE6rCbvFuOSs4Z2xOPfGEuxv8S2qQqvYvFwbijIYTnGkrQqvY21/Fx6GjmpnyfqkH+uqxm424nPMLCstJbou+fuLQ+R1yUA4zVcf6yjLcRQKRWX54aUhMjmdu+MJfuepLt6/s47z/WGa/fYpk/sTWwKrfJalYSKe4XxfmLaAg65a1TdZCSphZXFhnttzwF+U+/iKhXFZTZgMgrwu8S0xwFE9MRBKZPnJlUIPYiyd56MHmlb5jBbGZTNhLJp8n9wSWJLXUSav8cOLQ2i6ZDia5vPH2ypwpor1RiSZ4ydXhpGy0I/88YPNq31K6wK7xchjW5de2r0ShAC3zUQomVvzG1gKhWLpeO1mRnMZfPZCObrXYebJbRtTLO7lqyMMRdJcHojym0/Ylc9yBVDv8AYgms5hFGJqt2g5+J0WPn+8jVg6T0vV5g74loPFZMBiMpDN6zjX2EA1Ec/gsplmqClWu6x87pFWklltyWIyRiGwm43EM/lFy40VmxeLyYDZuDa/CxuJVFYjk9fm9ZudCyEEnz7aylAkNcMkW6FQrG9+9VAzg+HUDBGwaDqHQYgNN19Prm2tZgMmg7L4qQQb6wrahNwdi/ODC0MYDfCpIy3UepZfnuR3WvCXQAyhZyLBWCzDnibvkjJT6xmn1cRnj7UykcjQUb12evDeuj3OqXtBPHYznz/eOiNADLisLKXI5PZonEgqx75mL58+1sJIJE1boDyWI4r1j91i5HOPtDIeX1vfhY1EJJXjm6d6Sec03r+rjj1NS+97tltW3iec03Qu9kfw2k2qnEuhWEPYzEYafXYuD0SoLiqP/935QQwCXjzSUrZS9dXgg3vq6R5PUOuxKf/XClGx4FAIUSulHH3gtu1SyhuVOoeNyHAkjS4lugYj0cyKgsNSEE5m+dv3BtGlZDye4YN7li7zvl4pVVBdSgbCKQCiqRyJjLZkL7ZJhiNpfnBhEIBYOsdT22vx2FQ5mmJhfA7LsjJaiuURTGSnRKQGw6llBYcPw9t3JjjbU7Cw+fRR04qtShQKRel57cYY14aiCAG7GzyFtaAs+LFupODQbDSsS3/p9UwlM4dvCCH+ZynltwGEEP8Y+Cqwq4LnsOHY3+JjLJ7BYjSsGYNTIZR95WrxaFc1b94ep8lnX5E0vhCFf1KCQX2OCsWaoK3Kwb5mL5FUrqLCUNOHADUcKBRri8nvpECwp8lLKq9jMgh2NKyNtaBi/VLJ4PAp4E+EEC8CdcA14FgFj78hcVpNa0IMxeew8IlDTYzFM+yepmKq6ZLXb42Rymo8ua1mRX2RiqXT6LPz4pGWFT+/zmPjoweaiKRy7Jn2OSazeX5xYwyr2cATW2vmNPVWKMrBYDjFu91B2gNO9rf4Vvt0VgWDQfC+nXUVP+6JzgBumxmPzUSDV2UNFYpyE03neP3mGB6bmce6qjEY5t+VeWp7DTVuKzUuKw0+Oy+ozL6iRFRspV70N/wx8E8BHfinUsp4pY6vKD8tVY5ZYid3xuKc7w0DhUB2o6ppbSTmkuo/1xPm+nAMgAavnZ0Nm8fGRLG6vHpjlNFohrtjCbpqXWqDqYKYjAYObNKAfL3S/ns/XNfH7/6D50t0JuuT03eD3BopLI1bqhwLWudYTUYOPWCxpVCUgopt/wshXgYeAfYAHwb+rRDiX1fq+JuFS/0RXr46QiSZW+1TAcDvsGAq7nzVFJumFeuPanehRNVoEPOWq57vC/Py1RGi6bVx7Sk2BpNiC167GasSI1gRF/sL381ISn03FYq1jNEguDMWJ5rKLtleTKEoNZXcgv1PUsq/Lf4cFkKcAH6/gsff8IzHM7xybQQolAGuhXLTGreVL55sJ5vXqXFvzOBQ1yXvdgfRpORYe9WGLLncUe+h2mXFbDDMaXL++s0xvnuunyafnWxe5/l9G1+QSLE4mi45fS+IEHC0vQrjAiVS8/H+nXXsbfJS5bRsyO9WuRmLZfjZtYIWXCqn8cL+xlU+I4VCMR/94RT1RVVOh7V8qu+6LjnTEyKn6RzrqMKsxlbFNCpZVvq3QojHgK1Syv8O+IE/r9TxNwM2s3HKe8+zhnacNrr58tWhKG/dmQDAajJwuK1yghGVpHqezO9INM1bd8YZjWXIazontizFMEOxGbjYH+adu4Xvht1sXFHPoMEglErmQ2C3TJsXbKokV6FYy3hsJpxWE26bqayefjdGYrx5exwoqIEe69iY6xbFyqiklcX/AhwBtgP/HbBQCA4frdQ5bHRcVhOfe6SVUDJH2xKMzpPZPJoucSurgofCYTFO+3ltLL50XRJO5fDazSvK1iwHm8mI02Jib5OXrhoXj3VVl/V4ivXD9P5AZxl3wVeDTF4jlV2eKf1qsNx5QaFQrB4f3ttAbzBJnceG0SCIpnOYDQbsltKOn85paxVHiV9bsf6p5Er248BB4ByAlHJQCKH0dkvMUv3GRmNpvv1uH5oOH9nfsGKTZAV01rj45OFmdCnXjFn8Dy4OcncsQXu1g48fbC7rsbwOM792rJVwMsuWGpeyMlFMsa3Oje2QESGYJVa1nknnNP78nR5i6TyPdlWv+V135UOpUKwPzEYDW4rrsVsjMX54aQiz0cBnjrWuyJ5qPloDDl480kxek7QvIHqj2JxUssg4K6WUgAQQQqircRUZjWbIaRJdSoYi6WU9N5XVKHyUiklaqhxrJjAE6A+lCv8HUyt+jbymk8lrS3psjdvK1jr3grLbis1Ja2C2ivGDZPIaeU2v0Bk9PNFUjlg6D0B/KLnKZ6NQKDYiA+EUUkI2rzMam3+dls5p6Pry12TNfocKDBVzUsnM4beFEP8n4BNC/CbwFeC/VvD4imlsq3PTG0ySzevsa/Yu+Xmv3xzjbE+IlioHv3qoSWWJ1ihPb6/l0kCY3Y1L/2ynE0nm+Mt3e8nkdV7Y36gmEEXZ6B5P8P0Lg1hNBn7taOucgkdrjRq3lcNtfoajaU5uUWXUCoWi9Bxs9RNMZHFYjFPZxAc50x3kjVvj1Lit/NrRFiXapSgJlRSk+ddCiPcDUQp9h/9fKeXLlTr+ekbXJb+8PU4ik+fxbTW45vD5klIuK1CzmAx8eO/yFSVvjxb8d/qCSTJ5HZtZ1aqvRXY1etg1zcT+9L0go7HCQna+0pTp19BgJEUyW8ga3ptIqOBQUTbuTSTQdEkyqzEYSVUsOLw9GuPqUIw9jZ5ll9ULIXhiiZ6tyx2bFQqFAgpifp84NLstZPqYcmessCYbi2WIpvPzzu9ne4IMhtOc2BKYV1xOoZikouoZxWBQBYTL5O54grM9IaCgSPr0jtoZ97/bHeTN2+N01rj4yL6Gsi5EjncGOH1vgq5ad9kCw0Qmz3A0TYvfgaUMvmYT8Qzvdgdp8jnYO0fW9PJAhL5gkqMdVRtiEL05EuOHFwfx2s3okjml7K8MRvjZtVHqPTY+caiJzhon7dUOklmNnfVuXr0xigAe7aqeIXl9oS/MUCTFsY5ASfshFBuf0ViaTE5nX5OXwXAKh8VIZ01pNyH6gkkuD0TYWuemq3ZmAPjjy8PkNEnvRIKdDR6sJiMntgRKKuBUybFZoVCsX4YihRaQBu9MZeazPUHGYllObAngtZt59cYo53vDGA2CPU0e9jf7SOeCNPns+OfZWJuIZ3j9ZkGZNKfpcwacCsV0yh4cCiFiFPsM50JK6ZnvPkUBv8OMySDI65KAa/YC/OpgFCnhzmicVE4rq2LmgxmpUiKlpC+U5AcXhsjm9bKJqbx6Y4y+YJJrQzFaqxwzMhXRdI6Xrxa8ImOZPJ860lLy41eSWDrHDy8OcXs0To3byvHOuW0mrg5G0XTJQDhFMJml1m2beu/P9oQ43xsGCjuZB1v9SCm5Mhjlx5eHsJiMpHJa2YVvFBuHwXCKb5/pQ0p4dmcdn3ukrSzH+cmVYWLpPLdH4/yDp7sYT2QQCGrcVgIuK8ORNLFMnov9EQCqnJaSjm9XBiIVG5sVCsX65M5YnO+fHwTgI/sbpzayhiPpqaBO0yUHWn2c6w4xnshwb7xQceG0mPjSyfYFX99pNeGwGElmtQ2x4a0oP2WfqaSUbgAhxL8AhoFvAAL4HLAktVIhxL+lYINxTkr5D6fd/iTwrygEn38mpfwvpT37ypPXdEZjGapd1qmsWcBVMJJP5zTqPLZZzznQ4uOtOxN01jgXXXxoumQkmibgsmA1ra2S0LfvTPDWnQlO3Ztgb5OXaCo/4/6hSIrrwzG217kfyvfM7zDTFyz4f1nNMzOTVpNhahD1bQB/xlRWI5HJs7PBQ3u1k+OdAYYiKfwOy4zM74EWHxOJLA1eGwHnzMnDVwyehSioHmq65C9P9/LL22OMx7Mcba/CZ1dZQ8XSiaXzSFnYELo3EWdrnWtWJUI2r/NudxC7xcihVv+KjuNzWIil83jsZu5NJPjBhcIC7OMHm/jIvgZujMQwIHjt5hgGIaau9VJxsNW/5LG5VGTzOuPxDLVuq+o/UijWMMlsnmgqTzSVm7qtL5gklctT77FxtjdIMpvHYTExEi0ozI/E0jgsRhqLGcalqBDbzEY+f7yNSCpHg3f2GlKheJBKbmM+J6V8ZNrvfyyEOEUhuJsXIcQhwCmlfFwI8cdCiKNSyneLd/9j4EWgH3gbWPfB4UsXh7g3nqDWY52xm+61m+c1k9/f4luyufQPLw1xZzROtcvC54+3rakyp4FwisuDEaKpHP2h1Kys3ffPD5LMatwYjvFbT25Z8XGe3l5LV62LKqdl1oLUajLyueNtTMQztPjXv/R+XyhFLJ0nq+l8+dF2Xr42wtXBKD6HmS+eaJ8qodtS4yJaVF988IrYUuPis4+0IoBaj40fXBjkm6d6iGc1alwWntxWw9H2tS3lr1gdRqJpbo3E2VbvotZ9f1GytdbF8c4Ab90Z58ZQjPFYli+eaJsRzLzbHeT0vSBQGP/mE2RYiBf2NzIYTlHvtXG5mMUDCCayvH5rnPFYhs4aJ58+2oLZaKDGXdpd9eWMzaXiO2f7GYmmK2Jjo1AoVkYqq/GNt3tIZjV2N3pwmI0YjYI/e+se6ZyOz2FmR70Hi9HACwcauTkcKwZ3dj55uBmb2Uhe12eVoc6H02qa4TurUCxEJa8UTQjxOeAvKWT6PgMsRSf/BPBK8edXgOPAZHB4BfACY0CipGe7SozFMgBMxLPouly2NUA6p5HT9HmN7adeP5FF0yUm49oJDjM5nXg6j6no82N+oN9wMqP3sIatBoNY0HbCZTXNKfqzHsjkNdI5fWojYSyWob64U6jrMFr8/COpHNm8PmWse3kwwus3xwCwGA2zejGnZ6xHYxlqPTayoRQ76j0cbvMrCwvFnPzNewOkshrXh6P8xuOdU7cbDIITWwLcHY8zGs0QSeXI5PUZweHkxo0QYDcbyWs68Ux+WX59FpNhSkxpX7OPSCqHQQh21LunyrXGYpmHqkRYS0gpGY8XvuOTY71CoVh7xDK5KdG3126M4XOYCSdzU2ugWDGb6HNY6Ag4qXFbp6p3NpJnrGJtUskV8GeBf1/8J4E3i7cthg+4U/w5Auyedt/fAi8Vf/4Xcz1ZCPE14GsAra2tyzzl8qHpkpevjhBN5XhmZ+1UHfizu+q40Bdme/3yPeNCiSzfereXbF7n+b0NbK2bXbX77M5a3usNs7XOteZKjjwOM0fb/fSHUxxq89PxQAD3iUPN9AaTtKqBkfN9Ya4ORjnQ4pvqkUpm83zzVC+xdJ6nttdwsNXPic4AOU3H77DQUmXnmR21nOkO0h5wTgWGwAyRGbNp4evu2Z21eG0mbBYjj3fVrLnrSLF2sBgNpNDmFZZ6enst73YHaQs4Z+1qH2r14bWbsVuM1HlsfPN0L+OxDAdbfTy1vXbO11vwXEwG3rezbur39++q4+ZIjAMVzuyVEyEEH9hdx/Wh2LIsikrJ6zfHGAineKyrWi1iFYp5qHXbOLklwI3hGNeHo9wdj7O70cOzO+sYjqV58VAL6bzG9eEYP7w0xAd21/HBPctXmFcoVkIlrSy6gY+u4KlhYFIhwFP8fZL/HXgMGAFeFkJ8S0o5w5FYSvknwJ8AHDlyZM04t/cGk1wbigJwpjvEB/fUA9BR7aRjhbYBo7EMmVzBSLo/lJozOGwLONeUWft0nttdR0fASb3XNmd5l9NqYmeD0i+SUvKLG2PoUvL6rbGp4DCUvG/M3RdKcbDVj9dh5iPT1EmbfHaaDjTNes2dDR7MRgGIWaqOD7KWryHF2uJXDzfTO5GkvXruIKHRZ+ejc1yPUAh0Jq/FRCbPeDET1hdKleTcyimutZrsqPewo351/q5gIjulrP32nQkVHCoUC/BIZwCLycBoLMNEPMOR9io+NC0AfOPWGGOxDGOxDB3VTvY0rc6Gj2LzUbHgUAhRA/wm0D79uFLKryzy1LeBrwPfBp4F/mzafRoQllJmhRA6sG4URKpdFhyWgspjS1VpSpo6a5zsqHeTyGorFnBYTawm45zWEoqZCCFoqbLTM5Gc0RfZ6LWxv8XLeCzL8Y7l9wB21S5JH0qhWDJeu7kk32mn1cSJLQHujSc4MY/irmL1cVlN+B1mQsmcCgwViiWws8FDXyiFrrt4YutM79Rmv4NzPWFMRjHVHqJQVIJKlpX+HfAGhb7BpfQaAiClPCeESAsh3gAuSClPCyH+g5Tyd4E/BF4pBoY/klJGynLmZcBtM/PlR9vJ5ufvD1wuZqOBD63A2F6x/vjYgaaiCuP9r7AQgmd21C3wLIVi/XK8MzCvFYtibWAxGfjc8TaSWW1eATWFQnEfm9k4p/cwFCrJfuPxDowGUTZfaYViLioZHDqklP/jSp443b6i+PvvFv//MfDjEpzbqmA1GdecnUQ50XVJbzCJ32lRC4eHxGAQM/wZ1xPhZJZIKkdrlWNNqeUqKsvkeFDlsuAp0QaZYvUxGw147aoPWaFYiNFoGk3KRdVGlcKoYjWo5FX3khDiw1LKv6/gMdcMsXSOM90hatzWTVs3/oubY5zvC2M1G/jyyXZlCF0iNF1y6t4EAMfaq9a0QEw0neMvThVEk451VPFoV/Vqn5JilZgcD2xmI18+2T5DIEmx+lwfjjIYTnG4tWrdbkQpFGuRvmCS757rR0r48N4GWqrsnLoXxO+wbCiBLMX6pZKr838I/L4QIgPkKNipSSnlxlMEmIPXboxxezQOQL3XNqVOupmIFKWZMzmddE5nGYr0igW4NBDh1N2CH5zdbOTgGu43TWY0svmCaFJkmvGvYvMRTmWBgv1OKqep4HANEUnl+PHlYaSEcDLHJw4pv0SFolREUrkpz9VwMktvMMnlgUJXVI3bStMGsdZRrF8qqVa6qdUuJn3zzEaBdR5Z90myeZ1YOkdgAwSQmbzGe71h/I6CWbrNbKTBa6PKqSLDUuGyGqf9bCKSzGExGUqy2O4LJukLJtnT7C1J6V+918YT22qYiGc4vkX1j21mntpWyylzkEZfaceDvKZPZSRLXaURSmSxW4wbvv/HYjRgMRnI5HRV1qZQlJidDR7O94UJJjK0BuzcGy+I7BsNAvsGH1sU64Oyj/pCiEML3S+lPFfuc1gLPLmthpYqO1VO64ICNDlN55unegglcxxq8/PktoJ6VSavkdPkujNnf/P2OBf6CjtinznWOmXZoSgdXbVuPnm4MKHEM3n++1v3MBsNfO6RVgQCl82EcQUm9amsxt+8N4CmSwbCKV480lKS8z3ctnYzm4rK4XdayjIenOkJ8fadQpm13WJkS83C1ixL5WxPiNdvjuG0Gvn88bZ1XRa/2Hxitxj57LHWKQl9hUJROgbDKe6NJbg6FKV7IsnvPNnJMztrafTa1ca5Yk1Qidntjxa4TwLPVOAcVh2DQcxpFZDTdDRdTu1EJ7MaoWSh3G4wXPDziqRyfOt0L+mcxof2NLC9vjJJWF2XpPPaQy2CTIZCllQIVhSglJJ0TuPaUJQGr31DyUJn8hp1HhsWk4GfXRtBykL2+aWLg4zFsjR4bXz6aMuyxV8mPzNNl5in9TEORVIMR9J01bowGw0bPouiuE8mr3FtKLZo6VMpxo6VUvDrLP5sKF3/7eR4nMgUxuiF/jZNl2Tz+poslY2mc3zrVC+pnMZzu+vn9Y71OSz4VO2/QlFyTEZBPJNH03WyOY1vvNOLz2Hh0a4ADotxRrZ+JJpmMJxiZ4NHzbWKilH2mVtK+XS5j7FeiSRz/OW7vWTyOr+yr4HOGhdeu5kTWwL0BpNTfl5jsQypbMH9ozeYrEhwKKXkO+f6GQilZmQwl8ujXdVUFdVJ5zK2ryQ/vTrCndE4ZqPgq491rsmF23IZDKf43rl+hBC8eKSZI21VRNM5nBbTlFn4UCRNJq8ve2KxmY186kgLg+HU1DWXyOT5zpl+Ypk8I9E0XTUuXjjQSFtAZRc2A69eH+PaUBSjQfClk+1zqg5ruuSv3u1jJJrmeGeAExUuHz7U6sdhMWEzG2kNlM5r73hngExep9ploXGBzaVMXuNbp3oJJXM8vaN2zQlMjMUyJKfNJ/MFhwqFojw0eO385uOd/KfXbhFOZMlqEp/Dwt+cG+DN2xMcaPHx9I5aUlmNvz7TR04rKDt/9EDTap+6YpOwdmUNNwFD0RTJrIamS3omklO3H+8M8KkjLVMmwu0BBzvq3TT57BUrycvkdQaKwcXdsfiKX8doEOxp8q4JQ2RdL3SA6xL0yW7waWi65OZIjLFYptKntmJ6g0lyWiFL0R9K4XWY+fjBZj6wu54nt1VT67Fycktg0cBwIJzizhyfc43byv4W39TzZfFfPJ0nk9PI65LuadeuYmMz+b2RsrCBNBfx4sYBwN3xlY8dK0UIwc4GT8nLIWvcVj55uJmnttcumIUPJXJT1R/3VuHvX4y2Kgc7GwrzyZF1VuI9HElzezQ277WnUKwXuupcdNW62dvswyAEUt6vIJuciyWS4rJlzjWLQlEu1m/TxAago9pJZ42TREZjX3NBOGE0liaV1WZkYkyrYG5vMxs51lHF7dE4j3RWVfTY5eL9u+q4NBChyWefU2Th9VtjnO8NYzIIvjhPVmStsavRQ+9EEoNBsKOY3esLJrGYDHTVuucsZX6QvmCS75ztB1g00+GymvjYgSZ6JhIMRFJICfs2qTXLZuSZHbUEnBbqPLZ5Sw69djMHW330BpObzrQ+ndOIpnJ01boIJ7McaVt7Y6fJaOCDeyo7n5SCsViGv3q3D13KVclIKxSlZk+TlzdujuGxmxBC0OSzYzYZONpeGDccFhMfP9jEQDjFXjXPKiqICg5XEavJOKNMYCSa5i9PFya/p7bXrLolwaNd1Sv2oUvnNL5ztp9oOsdH9jWuicyh02pacLE6WbqbL/YLrQc8NjOfOnpfKOZSf4RXro0gBHzqSAuNS5DETue0qZ+T2TyjsTR/994gRoPgVw83zwqSWwOOkpbrKdYPNrORR5YQ8D21vbYCZ1M5zvaEeOv2OFtqXXxoT/28mcPvnRtgJJqm2mXhCyfaK3uSG5x0TpvKnkwfsxSK9UgokeViXxhdSvJa4bpur3by9I5avn9hgJ9fH+HpHbXsblwblVeKzUXFykqFED9bym2bmXgmPzX5xdL5VT6bh6M/lGIsliGT07k6FC3rsRKZPK/dGJ3yCVopT26r4VCbn+d21696f+RKiaUL5WxSFq6n6USSOV69PsqtkdiM27tqXTyxrZpjHVUcaavi9kiceCZPJJXj3niiYueuUKxVLvWHyeuSG8Mx0rn5N44mv3/Recbv831hXr85poKbFdBS5eCZHbUcaferrKFi3ZMstmW4bWZ2NHiocVnRdMlgOEX3eKFd5GHXNArFSqmElYUNcADVQgg/MLnl6gEay338tc5YLMO1oShdtS46q5082lVNIpvnWMfaK0daDolsjpsjMRwWI796qLxN1G/cGuPaUCHgqXFbqfOsTInUaTWtWHhnrXC43U9WK4jPbK11EUnm+MHFQaKpHKPRNGaTAafVxFe9tilLFSEEh6eVv22tc3O1KDqiZOwVq0U6p3GuJ4TPYWFX4+qKpuxr8RUyhzUubObCnqqUkleujdIzkeCxrdXsqPfw/L4Grg5G2VE/+3x7J5K8en0UKPQ3P71jY2VXK8H+NSbuo1CslCafnSe313CuJ0QokeVaJE2d28rF/hB+p5VMXi+5T6tCsVQqUVb6deAfUQgEz3I/OIwC/6kCx1/TvHRxkHAyx6WBCL/z1JZ1HxROcrE/yra6Qr+br+jbE0xkcdtMM2wRSsGkpLzRILCaNrfGktVknFHSd304ymg0zZmeEFCwpHikM7DgZ1DjtvIbj3fOuj2n6cTTefzKh0mxAkKJLE6rCcsSv6Nv3h7nYn9h59znMC+pRLpcHGr1c+iBMv9oOj+1s3+mO8SOeg/NfgfN/rlLwGwWAwYh0KXEsQGUkhUKxcNxqNXPme4gmpQMR9JEUzmcVhNWs4lPHm6m2rU+K5gU659KWFn8e+DfCyF+V0r5H8p9vEowGk1PWUosZGi/FCYXSqsZ1ETTOW4Ox2gNOKh1l8b/b0e9m1/GMjT//9m77/hIz+uw978zfQZT0Hvb3rnL5XLZqyiSEtULZUmWZMu2JFtxHOfGsZ3E1lV8fa/tJI7vlaMkkmPHkVVsSbakqJMSe9nlktzG7RW9Y3qfee4fMwCxuwAWwAIzKOf7+eCDmXdm8B4AU97zPs9zTpUbr8PGU6eGONwdpNbn5CP727EInOyPkDeGHc3+effgm+rujbU0BVxrui+XMWbav+e62goOXBwlmcmzvtZDc5WbD9/aPu+2Fplcnq8d6GIslubWzmru3jTzWtR0Ns/xvhDVHgedOvK4akWSGV44P4IV4c6NtdMWeZrw4rkRDlwco9Jj56O3dcyYIGZyeY73hgi47ThtheeoRWTOCWUp+Zw2Wqrc9I4n5tReqN7n4hf2txFJZtlQp68LpdaqeDrLwYtjhJMZEukcqUyed+5uIpzMEEvl6A3G+dqBy9R4nbO+Xyq1VEpWkMYY8wURuRPonLpfY8z/KlUMiyGTy/PNV3tIZ/OcH47yoVvbb+jnvWdPCxdHYrRVe24oQboR3z/Sz2A4ifOShU/fu2FRmtXf2lnNnrbKyRGqnmID6ZFIimQmR9dYnJ+8MQAU1sftal349AmLRdjUsPS9H5ezUwORaf+e9X4X25sDxFI5Isks77u5dUEjf/FUjrFYGoCe8dlbVzx/bpgj3SFE4KO3dazY9Ztqdt95vZcfHutHRBiPZ64ojHS1idd/MJ4hmspSbZv+Ofji+VFeuzwOwC/c2kaNt9AjdTmeQbdYhMf3tZHJ5ec8G6LB76JB2woqtab95I0Bvvt6H73BBLVeB7d2VvOuPS3YrcLZwSgHL44SSmQJxjPEUlkcM7xfKrVUSpYcishXgA3AYWBiNb4BVlRyuNgqnLZlNa+80D9q7slhbzBBNJllU70Xy1VJ5dQDpns21nLg4ijrar3XjDAY5t+/5+JIjNe7xtnc4FtWf7/l4Oq/p1UEl92K22HF41zYdLaAx85t66vpGUvMuRhEPJ3jx8f72VDn5c4FVr0th/PDUSyi6y2vZz6v2rs21vLC2REq3Xb6ggk8Dut1R68n+hUuthfPjTAUSXHXxtpFOXGx2NPklVKrn8GQyuYIxjOcHojQPRpnW7OfnS0BKj12Xjw3SkuVW5dxqLIoZSuLfcB2s8K719qtFj5wSytdY/HJvnIr3Tt2N3F6IEJHtQfbPA50BsNJvnmoG2O4bt+pztqKK6YYbm30kTcGY2DHAopN/PzUEOFEZnJ671o/QJvt73nXxlr8bjtVHvsNTRu+c0Nt4fTOddy9sY6A28FrXeOMRNOMRMdYX+elMbA4U5aX0om+8OQI7DtualrzI9Kzee/NLdT7nFhErtvypqXSzftvaeWvnrvAG/1hTvSHeXzftSONd26oweeyEXDbl+T5MhhOcuDiGAAiXNFKSCmlSuGRHY2cH4phtwo9Ywnq/U4OXBxlW/Gzu7XKw+O3avsKVT6lTA6PA41Afwn3uSQKU4OW/4HuXPld9smmq/ORzuaZSPVT2fmVZhcRdjQvfMSvwe8knMhQ63ViW4RpsCvdbH9Ph83CLR2l65k5sb9sLs+L50fxOKz43SujperU53FqhfS6LBefyz6vZurGGDK5wt90pr+t3Wq5pvDLYvK5bHgcVuLp3Kp6D1dKrRweh41NDV4sArk8VDhsK+LkqVo7SnnEVgucEJGDQGpiozHmXSWMYcVIZnIMhVM0V7pmHM1LZ/O8fGG0UIFyXTWhRIZXL4/TUuVekulYV2ur9vDW7Q2Ek5mSJh8Ab9/ZxHBniiqPo2xrNZeTgVCSY70hNtV751QEJp3NMxBK0hBwThb+uNpgOMnRnhAb6ipYX+edd0y3ra9hXV0FPqcd9wqpznhTayXZvMEisL0Er6HV4mR/mN7xBLd0VM04DcpmtfDu4hrrhcwWWAweh42P39FJJJWZdRQ9GE8TT+cYjqQYjaXYv64G7ywFd5RSaq56xuKc7A+TzOT4lbs72dRQ6HOo1HJRyk+7/7OE+1rR8nnD1w92EYxn2FDv5V27p28H+XrXOK8WizcE3HZO9IfpHotzvC9ES5Ub/w1WUp2Lcqz3M8YwGElS5XFoFa+iHx7rJ5TIcKo/zG88sPG6RYX+6fUe+oJJGgMuPrz/zaJKyUyO8XiaRr+LHx8fYCyW5lR/mF+/f8O8phxPWKzqt6VitciCRtHXmqFwEo/ThtdpI5zM8JM3BjAGgokMH7ildcbHtVV7aKsu73Qpt8M668mKkWiKrx/oIpTIEElmaQy4SGXyvG3X3EdJlVLqavl84djlvz1znuN9YYYjKXJ5Q413dc1GUytfKauVPiMiHcAmY8yTIuIBVsZwQoll84ZwIgvAWDQ14/28rsK/T6QwXWrizLbTZsWxitfg/fzUEEd7Qvjddj5+R8eaX28IhedCqNgjaS6zbEeLlUcnKpBCYTTxqwe6CCcy7G4L4HXaGIulcTusWHR0VhW9enmcZ88M47BZ+MXbO3DaLDhtVpKZHD7Xyh9dCycyZPMGm1VIF6cZe1fB76WUKq+fvDHAqYEIPeNxsrkcVkvheG3q57BSy0Epq5X+GvApoJpCWYsW4L8BbylVDCuFw2bhkZ0NnBuKcvMs6292NAfwu+zYrEJTwE1TwMXGei91Xue8+9itJEORQsIcTmRIZnKaHALv2t1Mz3icpoB7TtNs37aziTf6QldMnUxmc4QTGQCGwineu7eF7rE4jQH3NZVo1do1HEkChZMJ4USGtmoPH9nfznA0tSoqvK6rreC2ddWEk1l23OYjkzOr4vdSai3o/L0flDuEGU0cu+xqCbCjOUAomaG6wlnyZTlKXU8pT4d+FtgPHAAwxpwVkfoS7n9F2droZ2tj4cA9nzf8/NQQo7EU92+pv2L6wdQpWjarhY31818bttLcv6WOgxfHaK/24CvB1NmVwGW3srH+2sqahy6NcWYwyq2dVVdU3lxXW3HNAa/fZee+LXV0j8W5bV0NTtv0P1OtbXesryWTM1R5HLRWuYFCq5OApzSvxWwuz5MnBwknszy0rYHqRS71LiIrqvWKUmpleHBrPa91jdNRXcFAOIGIsLe9clWfzFcrUymTw5QxJj0xqiEiNubXKmvVMcaQyuaxWgSBGdd09YcLxUYADlwcm3EN4mIZj6V56vQQlR4792+uX3ajRk0Bt5agn4NUNsdzZ0fIG8MzZ7LTtmXI5PJYRCbXKO5tr2JvexWZXJ4fHx8glc3x4NZ6TcLVpIDHzjuX8D1o4n1xpgOmy2NxTvZHgMLJj4d3NC7KfpOZHE6bZU4j78YYnj07wkgkxb2b6xalX6JSanWbWHN9fjjKEydD2CzC1w500RhwcdfGWporCyfbkpmcJoyqrEqZHD4jIv8GcIvIW4HfAP53Cfe/bIzH0vQGExztCXJuKEoslaOztoLH97VSM03FqmqPA5/LRiSZpX0BxRyGwklePD9Kc6Wb/euuX2zj4KUxLo/GuTwK62vnVv1SlVYinePiSIyWKjcB9/SJm8NqwW238MyZYZor3YzH0ldUkuwei/Od13uxW4XO2gpS2Tz3bqqjqsLB2cEoJ/vDAFRXBLlnU11Jfi+1tuXzhm+92kNvMMEdG2q4ff21vVPrfE7cjsIax6uL23SPxTl0eQynzcqulsCci98cuDDKi+dHaal084FbWq97Qqw/lOS1YjGwly6MLvkJO6XU6nFxJMrh7iB2i4XmShfpXJ7eQ9384u0dHOkO8sNj/VQ4bfzzt2yiVquYqjIoZXL4e8CvAMeATwM/BP6qhPtfFtLZPN94pZtwIsPFkSgep42xaJrGgIvu8cS0yaHbYeXjd3SSzOYWVIH02bMjdI/FuTgSY2O997rTsJoDbk70hXHZrTOWpVfl9b+P9NEbTOB12viVu9dNezArImxvCjAez+C0WegZT1zx/7w4EiObN4zF0lwei1Pvc2GzWHjspibqfE7sViGbNzRp/yVVIrF0lt5gAoCzQ9Fpk0O/y84v39VJOpu/ZkT7ZycHOTkQ4dJIjH2dVfzCre1zShDPDkUB6A0miKaz132frfTYqXBaiaVytFTq60MpNXd9wSR72irJZA3VFQ6O9YZw2Cz8/SvdhOIZesYL74HPnRnmvXtnrv6s1FIpZbXSPPDl4teacGogzBNvDNLgd/G+vS3YrBZy+UIjaIfNQnOlm1qvk2RlnpZKN5sbZl4v6LBZFty2od7npHssjtdZaAB9PbtaA7RWuXHZZy/5vpRGoimSmRytVeUte79cJTKFKop9oQR/+dQ56nxO3r+39ZrnyK7WAN3BOI5p1qPubAnQNRan1utgPJ4hlzc0+AsnJ+p8Tn75rnVkc2ZR1pIF42nCiSxt1XMrmKNWhng6y2A4RVuVe0GtTq7mc9nZ01bJxZEY+4stRV44N8KhS+PsaPbz0PYGoFDhb7r+nPV+F8d6Q7jtVqwik6+T69m/rprnz47QWeuZ0wm4iX6J8XRu0dc8KqVWt/3rqvnh0X7S5Imls9T5HATcDjK5PPvXVXNhJErAbacx4C53qGqNKmW10ncAfwR0FPcrgDHGrNpO0yf6wmTzht5ggtFYmga/C7fDyjt3N3N5NMaetkoqPUt/YHHPplq2NPrwu+xznsc+lxHDTC7PwYtjWC3C/s7qRVubOBRO8vWD3eSN4cGt9exuq1yUn7uavH1XodropZEY4/EMA6Ekg+HkNaMkfaEEzQE3t3RWXZPoV1c4+MXbOwCIpbLEUlnqpxQ7qlikpt+hRIa/e/kymZzhtvXV3LlBi32sBrm84WsHuogks7P2YwWIJDMcujROnc953d6oD2yt54Ep14/1hsgbw/G+EA9unX0N9KM7GtnZ4ufiSAyv086mORbo2tzgY/M0a3Jn47JbdV2QUmre4ukcwXiaAxfHuKWjiga/iz3tVTQHXGxq8HHnxhoiyaz2PlRlU8pppX8BvA84ZoyZVyEaEfnPwD7gNWPMb03Z/hfAnuLV3caYZVUPeFdLgIFwkka/i5opydZ0lSKXkogsyZvMke4gBy+OAeB12q570DdX4WSGfPEpEiy2Viin17vGeeXSGFsa/dy3eXmsvavzObl/Sz0Xa2L8+PgANV7HNf/j0WiKn74xCEA0lZ21iEiF07agZLA3mODHxweodBeKlEw3uh1LZcnkCv/PULz8/0+1OLL5PLFUYWQuFL+yT5cxhp+8MUD3WIL7ttRxeiDCueLUzcaAa17raHa3VvJa1zjbm/3XPQFlsQjt1RW0V+s6aaXU8nSyP8wzZ4ZJZ/OcH47yrj0tV0yh9zhseBzaW1WVTymffd3A8QUkhnuBCmPMPSLyX0XkVmPMKwDGmH9RvM/NwP+x2AHfqE0NvmkrRK4kmVyew91BPA4rO5qvTP6mJhOLNcoEsKHOy23rq0mkc5NTy8rp0KVxYqkcr10e584NNcuqr+K62gp+/f4N0942MRU5nc3jXeD/xxjDkZ7CyM2e1sprDs6PdgcJJzKEExl6g4lpT3o0V7q5d3Mto9E0t2+4dg2ZWpmcNitv29XIheEoe9quPC83Hs9MVhR97fI4DX4XQ+EkCNjmOcPgjg013KHPGzVHN9rn7tKfPLZIkaiFWM59ChfLxMk0n9vOQ9sapl1brVQ5lTI5/NfAD0XkGSA1sdEY8+fXedwdwJPFy08CtwOvXHWf9wL/uEhxqikOXhybHB30Oe2017w5bXFbk58Khw2rVWipXLy58SKyrKYebm3ycejSOBvrvcsqMbwen8vOh/e3MxZLsb52Yf0vT/ZHeOrUEAAWEfZcNcV3U4OPs0NR/C4bjbOMTt/SUf4kXy2+maZjBtx2Wird9IUSbGn04XFYsVktuOwWzg5FuXUZnPRRSqlSS2Vz9IeS1Ptc+N12fmF/e7lDUuoapUwO/xiIAi5gPgvtKoHzxcshYMc093kU+JPpHiwinwI+BdDevnZfhIl0lsPdQTprKmiaRyJnnXKW32q99oz/1GRxtbpnUx13rK9ZlIIbpVZd4ZgsmJHN5cnmzbzWSdmm/M+nG/HZWO/lsw9sxCLMqdBMMpPjZH+Y5kq3rqdYxawW4fFb28jm8tisFi6OxCafh/MZOZx4vjQGXDTdQHGGXN6QzubLVmBLKaWgcJK1MeDG67Sxod6Hz2VnJJqiayzO5gbfgmf5KLWYSvksrDbGPLyAxwWBiaI1/uL1SSKyCeg1xsSne7Ax5kvAlwD27ds3rymtq8mf/Pg0p4oHWf/P+3bNeT77/s5qfC4bXqdtUUcHV5qVmBhOFUlm+MbBbuLpHG/f1Tjn6c6bG3xYdkMuz4zVdK3zONj/yRsDXBiOYbcKv3L3ej1YX+UmXjfrait4955m0rk8W+Yx1f6JE4OcG4piswifvHvdgqavJzM5vnGwi2AiwwNbtMCVUqp8cnmDMYbxeIaOajfZXJ5vHuohmclxdjDCh25du4MYavko5RHvkyKykOTwJeAtxcsPAS9fdft7gX+6kcAWSz6/PHNPYwyDoSRQKFIyn1WfFouwozlAR40WeFjJBsMpoqkseWO4OBKb13N1Y72PLY2+RWlBMVFoKG/evKzWhvV1XjbXz+95NPEcMcWvhRiPpxmPZzCm0NtTKaXKZTyeJpMzNPhd9IwnMMZMvs/l8mUOTqmiUo4cfhb41yKSBiZKFl63lYUx5jURSYrIc8ARY8xBEfmCMeY3i3d5B/DupQt7bl46P8qBi6NsqPPOWhWyHESEj97ezs9PDnHHhppFLR6jVoaOGg8b6r1EkhkcNgtf+Pk5mipdvH9v67xG/m7Uw9sbOdYboqXSrc/DNSSfN3z3SC+XR+Pcs6l2zmtQ37q9gaM9IZqL07AWosHnYluTn+FIkn2dy6qgtVJqjZl4P7owHOH0YIThF1O8ZWs94/EM25tWbWc3tcKU7OjMGLPgsp1T21cUr//mlMv33khci+Vkfxhj4NxQlGQmt+z6X92zqY57NtXxxIlBvvCzs9y6rnpOFbKGIkm++3ofNqvwvr2tBNw33hBdlZ7dapnsQ/f3r3SRN4bLIzG+/NwFusfiuB1Wbmmv4tGdjUvapL7CadPKbGtAKpvjH1/rZSyW5u27mqj1Org0Upj5f6I/Mufk0OOY//PlyRODnOwPs6+zmjs21GCxCI/ubJz376CUUovNYhE2NXh56vQQo9EUtV4nl0di3NJZzf51WqhLLQ8lXUglIu8TkT8Xkf8kIu8p5b6X2t6OQpPxm1oDyy4xnJDN5TneGyKbNxztCc7pMecGo0RTWYLxDJd0StaqsKetCo/DSpXHQTyVpWc8wWAoyamBCMmMzmtRN24glGQglCSdzXOyP4zXaWNbkx+3w8rNS7jmL5c3HJvne5xSSpXSib4wAZedTM4wHkvjc9u5PBpnLJa+/oOVKoGSjRyKyBeBjcDXi5s+IyJvNcZ8tlQxLKU9bZXXlPlfbmxWC7taApwaCHNTa+WcHrOpwceJ/jBWi9A5TQ87tfJsaSysIUykc3z7tR7aqzO47Ba2Nvq0QIxaFI0BF40BF+PxNNub/IiUZvTOapHJ9zgtPKNWorXQ52+t29Hsp3s8ztt3NbKtyc9zZ0doCrgmKzorVW6lXPRzH7DTmMLKWxH5W+BYCfevgIe2N/DQ9oY537/O5+RX71m/hBGpcnE7rPzi7R3lDkOtQk6blQ+XqX/XfN/jlFKqlNbXefmN+zdOXp/ryXqlSqWU00pPA1OPFtqAoyXcv1JKKaWUUkqpGZQyOawBTorI0yLyNHACqBOR74nI90oYh1oFMrk8R3uC9AUT5Q5FqVVj4nXVO8vrKhhPc7g7SCSZmfE+SimlrpTM5DjSHWQonCx3KErNqpTTSv+whPtSq9zTp4c53hvCahE+cUcnAY9WUVXqRj1zephjvSEsInzizg4qPVeugTHG8M1DPURTWY71OvmYTktWSqk5eeLEIOeGotitwq/cvV7X+Ktlq2Qjh8aYZ4BTgK/4ddIY88zEV6niUHN3eiDC94700TUaL2scl0dj9IxfGUMuX6iqmTeGnDZTvyGD4STfO9LH4e5gSfaXyeU5MxghGNfKbMtNNj/RdN6Qy0//upq4T67YsfnSSGzWkcbpnBoI870jfXSPlfe9RSmlSiWbzxNLZTjaG+KHx/oI6+wLtUyVslrp48B/AJ4GBPiCiPyOMeZbpYpBzV0ub/jJGwPk8oahcLJsRWlO9IX5yRsDALz35pbJiqn3b6mn0uOgzufUCl836OnTQ/QFk5wfirK+rgK/a2lHYZ84McjpgQguu5Vfvqtz2bZ+WYvu31JHpcdOrddJjdd5ze0iwvv2tnB+OMqWBh/He0M8cWIQgPfvbaW9xnPdfWRzeX5yfJC8MQxHUvzK3esW/fdQSqnlZm97FT99Y5DBUJIXz4/iddl5ZIf2YFXLTymnlf5b4FZjzBCAiNQBTwKaHC5DVotQVeFgJFJo0lousXR28nI09eZll92qzdQXSU2Fk75gEp/Lhsu29IlarPh/TGVzk6NQanmYy+uqwe+iwe8C4OxQdHL71NfqbArvLXZGo2lqvXpiRym1NlhE6Kj2EEpkSGfz+v6nlq1SJoeWicSwaJTSFsRR8/T4vlaGIykaiweC5bCnrZJ0No/NImxv8pctjtXswa31bGv2U+1x4LAt/UvyrdsbeK1rnLYqD15nKd+C1GLb215FNmdw2CxsafDN6TEiwuP72hiJlve9RanpaJ9BtVTaqj08uquJm9oq2d7k197Ratkq5ZHZj0XkJ8DXi9c/BPywhPtX8+S0WWmtuv40saVkt1q4a2NtWWNY7SwWoaXSXbL9VXocPLhV+9CtBg6bhbs3zf/16bKX/71FKaVKbU9bJXvaKssdhlKzKllyaIz5HRF5P3AXhTWHXzLG/FOp9q+UUkoppZRSamYlndNljPk28O1S7lMppZRSSiml1PUteXIoIhFguqoTAhhjjC4kU0oppZRSSqkyW/Lk0BgztyoFSimllFJKKaXKpqTVQkXkbhH55eLlWhHRBldKKaWUUkoptQyULDkUkc8Bvwv8fnGTA/i7Uu1/ucnk8lwciRGfY2+w5SKXNzx/doSnTg+RyubKHY6awUAoyY+PD3B2MLKgx+fzhksjMcLJzCJHplY6YwyXR2OEEtM/NxLpHD87OcjLF0YxRvtYKqUUFHr7fuf1Hr53uJe89vhVy1gpC9K8F7gZeA3AGNMnImt2yukPjvZzcSRGwG3nl+7sxGKRcoc0Jyf7w7xyaQwAj93KbdqIfln66YkBRqNpTg9E+PWainn3L3zq9BBHe0K4HVZ+6c5OXHbrEkWqVprnzo7w6uVxHDYLn7iz85pelQcujnK0JwRAnc/JhjpvOcJUSqll5SdvDPD1g90AWC3CYzc1lzkipaZXymmlaVM4jWwARGRNd/8MxtMARJJZckt0dv14b4gXzo2QzCzeCF/AbUeKeWzAY1+0n6tuzKmBMM+fHZkciQ64C/+bCqcV6wJOPATjhVGhRDpHKpNfvEDVihcsjhims/lpZz5MPPcsIvhcCz//+EZfiOfPLu77l1JKlcvUT+JcHs4MRnju7DDR1MqaQaZWv1KOHP6DiPx3oFJEfg34JPDlEu5/WXlkZyNHuoNsqPNity5+jt49FueJE4MApHN5HthSvyg/t63aw0f2t5PJm5I2TlczG46k+NGxAQCiqQyP7mzi7bua6B6L0+B3LSg5fGBrPQcvjtFS6daTAOoK922qw2mzUO9zUu9zXXP7ze1V1HqduB1War3OBe1jIJTkp28U3r8SmRxv3d5wQzErpVS5vX1XM7l8YWr+/vVVfONgN8YUTsa+c7eOIqrlo2TJoTHmP4rIW4EwsAX4Q2PME6Xa/3LTFHDTFFi65MppsyACxoDLtrhTAuv91x4Qnh+OEoxn2NUSmPcURnVjHFYLVouQyxucxemfdquF9Tcwna+6wsGjOxtnvU8+bzjWG8JutbC9WTvSrBUBj51Hdsz+3Gir9tzQPhw2C6FEhng6y562yhv6WUoptRxYLcIDW+o5PRghkc5jFSFrjC7bUMtOyZJDEflt4JtrOSEspXq/i8f3tRFJZtlUv7RrfgbDSb53uA+AcDKzaKOUam4CHjsfurWNsViazQ2lW8b7eneQZ88MA+CwCRvr1+wSYrXI8sZgswh2q4VMTqc1K6VWh+8d6WUkmsbtsPL4vjbG4uklP0ZTar5KOa3UD/xERMaAbwDfMsYMlnD/iy6Ty3N6IFKYXjXNaFq5NZdo2qfA5Cjlyiirs/o0+F00+F1kcnlO9oZL8pyUK/7Z+p9Xi8vrslHhtM1pWnQineP8cJSWSjdVFY4SRKeUUgsw5YMzns5itciCln4otZRKOa3088DnReQm4EPAMyLSY4x5qFQxLLafnRziZH8Ym0X45bvXXVO1b62o97t4954WgvE0O1sC5Q5nTSvlc3JPayV2iwWHzcJGPfOpFlGt18l7b25hNJZmZ/P131O+f7SPnvEEboeVX717HbYlWMetlFI36l27mzk7GMFqEb5TnHH1wNZ6nT6vlpVyZDNDwAAwCqzo+YcTff5yxpDLre2eNetqK4A1XYB2WUgXp+CV4jlpsQi7WvVkgFoaHTUVdNTM7T1l4nmfzeXR9mFKqeUq4Lazr7OaM1N6EKezOnVeLS+lXHP46xRGDOuAbwG/Zow5Uar9L4WHtjVw2Bukwe9adhUd3+gL0RdMsq+j6oamWUVTWQ5cGKWqwsHe9qpFjFAthbdsraemwlH252Q2l+flC2MYDLevr7nhirzGGA5cHCORyXHH+hpdwF9GwXiagxfHGI9nqPc5uWND+f8fb9/ZxPG+EJ0L6OmplFKlcujSGM+cGeaW9ioe2FpPOptnb3tlucNS6gqlHDnsAP6FMebwfB8oIv8Z2Ae8Zoz5rSnbXcB/AdYBbxhjfnORYp2TCqeNuzbWlnKXcxKKZ3jixCDGQCSZ4X17Wxf8s54/O8LJ/jAAjX5XydYxqoVZLs/JN/rCvHJpDACPw8YtHTd2YuHsUJSXzo8C4LRauHMZ/I5r1VOnh3i9K8jpgQh72iqxWIT7NteVNaaqCgf3bCpvDEopNZvRaIq/ffESQ5EUZwcj/NvHtt9wZWellkLJTrEaY34P8IrILwOISJ2IrLve40RkL1BhjLkHcIjIrVNu/ufA14wxD5Y6MVzOnHYLzmL7Cp/rxkaP/O7C+QObRfA4ln504GcnB/mr5y5MJqRqZekei/PXz1/k4MUx8sX5ff4baIQ+weu0Ta7jv9HntLoxPqcdh9WCrdhCZbr/78Tr+ESfvo6VUursYIS/f6Wb8XgGYwxuR6HgllLLUSmnlX6OwujfFuBvADvwd8Bd13noHcCTxctPArcDrxSv3w+0iMgfAH9ujPneIoe9IrnsVj5yWzsj0RSdc1yzM5M71tfQHHDjd9up9CxtFcBIMsPRnhBQmHqxrUl75600h7uDhBIZAB7YWkdDwE3LIow2N1e6+cj+dlLZvJ5pLbMHttazvq6CvDE4rFbaa678f0RT2Tdfx5fHtAemUmrNe+XSOKlsnh3NPva0VbGvo4pqrayslqlSLs54L/AuIAZgjOkD5tIYrRKYOP0cAqbOT9sA/AB4DPgDEbkm2RWRT4nIIRE5NDw8vPDoV5iA286GOu8Nl0gWETprK0ryJlbhsNFaVUgkStmvTy2eTQ1eLCJUVzjY3hxYlMRwQr3fpYnhMmC1COvrvGys912TGAJ47NbJ/5O+jpVSCrY0Fip6r6/z8vCORuqWYfszpSaUckw7bYwxImIARGSuQ1pBCj0SKX4PTrktBDxjjEmJyDmgAeid+mBjzJeALwHs27dv2daxG46keObMMDUVDu7fUofI2ut7Y7EIH9zXRiaXv+ECJkvpaE+QU/0Rbm6vZNMyPvg9NRDmaHeI7c3+krUY2droZ0OdF5tFVsRzOJ7O8sSJQawW4aFtDWUvrLLSJNI5njxZaFf71u2Fv99AOEneGPZ2VHL7+poyR6iUUuV3c1sVL54bnZwVdVNrZblDUmpGpTwC/wcR+e9ApYj8GoUpol+ew+NeAt5SvPwQ8PKU214EbhIRK9AJrNihwZcvjNI9Fudwd5DeYKLc4ZTVck4Mc3nDz08N0RtM8NTpoXKHM6uJOH9+aghjSndexG61rIjEEOBoT4gLwzHODkZ1nesCvNEX4txQlHNDUd7oK0wlff7cCL3jCV67HGQsli5zhEopVX6nBsI8f26ErrEE3zjYXe5wlJpVKQvS/EcKLSy+TWHd4R8aY74wh8e9BiRF5Dkgb4w5KCITj/tT4I+BF4C/MsasuCORoXCS88NRmisLUwwqnFaqlnhtn1o4q0VoChT+Vy2Vy2OKYyKd4/RAhHg6e8X2iSmdzZXuFZOslVpzwI1FBJtFaAzoNJ+56h6L0z0WpzHgwmoRrBahoThNqrX4vAu47Xi14IJSStEYcFHhsBFPZ2mv1qrvankrZUGaSgpTQv8BOGOMCc31sVPbVxSv/2bxez/w8OJFWVrDkRRfP9hN3hju3FDDL9/Victu1alty9z797YSSmSWTRL/7dd6GI6kqPE6+PgdnZPb33lTM+Px9JIXElrJ2ms8fPLuTiwiWjlujs4ORvj+0X4A3rm7iU/eXSg6PZEI3rmxlq1NfrxOm/YcVEopoLrCyf1b6zg/FMVmtWCM0ZO2atla8qMhEXFQWPP3HuAChdHKDhH5J+AzK3G0b7Ek0jnyxel+sXRWD+JXCJvVQo3XWe4wJsVS2eL33BXbLRZZVnEuV9oaY36iqTdHqGOp3LSjg1qFTymlrpTPGyo9juKxH1g1N1TLVClOlf87Cm0r2owxEQAR8VFoXv8Hxa81qb3Gw/1b6ggns9y2rrrc4agV6p27mznZH2ZL4/ItjqNWj10tARKZHIKUrNCRUkqtdG/f1cSx3hAb62+8krxSS6kUyeH7gP3GmPjEBmNMRER+g0JxmWWTHJ7sD/PS+VHW1VXwwJb6eT327GCE4WiKve1V85oWenN71fXvpNQ0srk8r3cHcVgtPLi1fsYpKoPhJD95YwCfy8Zju5p1qp+6ITarhVQ2z4vnRugej/Pem1uWdREppZQqt1ze8OzZYY72hHDZrHTcYA9qpZZSKZLD/NTEcIIxJjrR1mK5eP7sCMFEmlAiw/7O6jmvQRqOpCbX4ESSWR7Z0biUYao1IpnJkc2bGYt6vN4d5PmzIwB4HNYZ22oc6Q4yGk0zGk3TNRZnY713yWJWq18sleXnJwc50R/m7FCUlko3d22sLXdYSim1bPWHEvzs5BDDkRQDoSQ7WwIEPLqkQS1PpUgOjYhUAdMNa+RLsP85OT8c5fRAhL5Qgrdub8A9j9E/m0WwiJA3BoeeQVeLYDyW5uuvdJHJGh67qZGN9dcmflNHa2YbuVlf5+XUQASPwzpZaVWphbII9IeSDISSeJ02HTVUSqnrqPU6sVqEgVCSbD7PSDSlyaFatkqRHAaAV5k+OVw2I4e94wlaqtw0BJzs76zGMo/54FUVDj64r5WxWJqtuu5LLYLBSJJUpnDupHs8MW1yuLs1gNtuxWGz0Fk78xSVjfVePnPfhsJJDF3noG5QJJVle5OfxoCLBp+LfR06NV4ppWbjslv50K2teBxWfC47oWSm3CEpNaMlTw6NMZ1LvY/FsLutkuFICpd95ul5s2mudNNcqb1r1OLYUOdlS6OPeDrH3rbpD75FZM5FaHSdoVosdV4nezuqGAgluXdznZ5wUEqpOdjbXs1oNIMIbG/ylzscpWakjb2KAm4777+ltdxhKAUUpom+fVdTucNQ6hoiwv3zLNillFJrXYXTxntubil3GEpd15oeTjgzGOHHxwcYDCfLHYpSk070hfnx8QFGoqlyh6LUtM4NFd47B0L63qmUUnN1fjjKj48P0BdMlDsUpWa0ZkcOk5kcPzo2QN4YhqMpPnZ7R7lDmtaBC6O82jXO9ia/nq1fA6KpLD89MYAxEE5kePzWtnKHNKl7LM6PjvcTcNt5z80tOG1zL9qkVo90Ns8PjhbeOwfDSXa3VfLi+RHW13p5dKdWalZKqelcGonx+e+9gd1moWs0xqfu21DukJSaVklHDkXEKiLNItI+8VXK/U9lt1qocBYObv2u5Zsjv9YVJJXJc7g7iDHLpn6PWiJ2q0xWyvW7l9fz8o2+ELFUjr5gkr6gjhitVTaL4C2+Zwbcdg53jZPK5DnZHyaezpY5OqWUWp5O9ocRgWgyS14P59QyVrKjTxH5TeBzwCBvtrAwwE2limEqq0X48P52BsNJ2qo9S74/YwzHe8Okczn2tFVhnWMRh10tAV69PI7bYeF/vniJHc0B9q+rXuJoVbk4bVY+cls7w5HUrE1yx6Ip/u7lyxjg3XtaZq1Wuli2Nvo5PxzD77JpS4w1Kp83/M8XL9E1GuPuTTXcv6WBI91BXjg3yvq6inm1AFJKqbViNJoins5it1jADndvqil3SErNqJRDE78FbDHGjJZwn7OqcNpYX1eahuBnh6I8eXIQAGNgX+fcEry7N9Vy18Ya/tszF4ilMrx4foRbO6sQ0QqBq5XPZcfnmr3/0Tde6eaF86NYLUJjwFWS5LCztoLfuH+DPvfWsIOXRvnJGwMAeBw23rq9iX2d1dzSoe9JSik1k+8d6aMvmCCeybGvo4qT/RF2tlSWOyylplXKaaXdQKiE+1tWbFNGCuc6ajhBRNhYX0hiN9R5S3oQls8bfnZykH841M1wRAukLBc+lw2Pw4pFhA31czvBYYzhqVND/MOhboYWWIRJE4C1rcJhw1ecUtoQcPKPr/Xww2P9ZHI6R0oppWbTNRYnmsqSyeXn/LmtVDmUcuTwAvC0iPwAmMwyjDF/XsIYymZ9nZd37Wkmnc2zdY696aZ66/YG7tlUi7PE/ep6gwmO9hRy+oMXx3jsJm2vsBy8d28rO5sDNAZcNM2xv2Z/KMnh7iAAL18c4127m5cwQrUa7WwJ8K8e3kIubxiNpXn18jgAnTUVbG/Wvl1KKTWdzQ1eTvVH6Kj2cOfGWva2T9+/WKnloJTJYVfxy1H8WnM23OAUVlcZ1vNUVziocFqJpXK0Vs0tCVFLz+u0cXPH/D5cqjwOvE4b0VRW/5dqQUSErcXmzeeGorzeFcRmFRr8zjJHppRSy9eGOh+tVUEMhZNpSi1nJUsOjTGfL9W+rieZyfHCuRHsVgt3bayd9zTPubo8GuONvjBbG30lW9u42CqcNj5+RyepTJ6AZ/Z1cGpxjMXSHLgwSlOlmz1tlSQzOZ4/O4LLbuXODTVYFvh8dTusfPzODpJp/V+qhcnk8jx/boT+YAKfy84jOxroqKnA7dBCNEopNZPGgIvd7ZX87OQgpwcjJSmEqNRClbJaaR3wr4EdwGSpQ2PMg6WKYcLrXcHJqZI1Xgc7mgNLsp8fHR8gkc5xcSTGZx/YuCT7KAWX3VqWUcu16unTQ1wejXNqIEJ7tYcTfWGO9Raer3U+J1sWMC15gtNm1f6EasGO9YZ4/fI4By+N0V7tYTCcnBxJVEopNb1MLs9XX+oimEhzaSTGvo4qKj1rchKdWgFKuYDtq8ApYB3weeAS8EoJ9z+pqqIwamIRWdIXZ3XxZ1fqKI2ah6qKwvPG7bDitlsnnz8WEQJufS6p8qnyOLBYCr04XXYrVXpwo5RS12WzCNXewud3wG3XE+5qWSvlmsMaY8z/EJHfMsY8AzwjIs+UcP+Ttjb6qXQ7sFmFWu/81srE01kS6Rw1c3jce25uoT+UoMGvPeHU3N2/uY4NtV6qKuy4HVZ2tgSo8TpwWC3XPO/GYmmcNgsVzlK+lNVata62gnfe1Mwj2xvwOG00z7EYklJKrWUiwmcf2MjZwQi72yo1OVTLWimPKDPF7/0i8hjQB7SWcP9XaFxAE+9wMsNXX+4imcnxwNZ69rRVznp/h80yayNzpaYjIrTXXLkeoSlw7UH48d4QT5wYxGGz8NHb2nWKilpyF0di/O+jfVhE+OC+VuzW0lZPVkqplej1rnGePj2My26dc59rpcqllJ/s/5eIBID/A/hXwF8Bv13C/d+wYCxDMpMDYCCUKHM0aq0bCBV6FaazeUZj6TJHo9aCwXASYyCXNwyFte+pUkrNxcTndTKTIxjPXOfeSpVXKauVfr94MQQ8UKr9Lqa2ajc3t1cSjGe4bV1NucNRa9ytndXE0ll8LhvrdIRalcDu1kpGoilsFgvbtBCNUkrNyW3ra0hl81R67NpKSi17paxWug74TaBz6n6NMe8qVQw3SkS4f0v9gh57YTiKRYTOWj2IV4sj4LHz7j0tM96eyuY4NxSlKeCmukKnnKob53ZYecdNzTPenssbzgxGqK5w6FprpZQqctosbKz30l7jQWRp2qcptVhKuebwO8D/AP43kC/hfsvuZH+YHx8fAOCdu5vYWL/wVgRKzdVP3hjk/FAUp93CJ+9apwvg1ZJ79swwh7uDWC3CJ+7o1H6aSikFfOdwL0PhFH63nV+5e125w1FqVqVMDpPGmP+vhPtbNibWKRYur6m8WJVRqvi8y2QNeWPKHI1aC1LZwnMulzekc/pep5RS8OaxXyqbwxijo4dqWStlcvj/isjngJ8Ck5UMjDGvlTCGsriptZJMzmAR2K7rdFSJPLy9kSM9QdqqPXgc2upCLb17N9fhddqp9Tmo882vTZBSSq1W79zdxMn+CJvqvZoYqmWvlEeMu4CPAQ/y5rRSU7y+qlktwv51WrpYlVbAY+fezXXlDkOtIR6Hjbs31ZY7DKWUWlbqfS7qfboOW60MpUwO3wusN8ZozX2llFJKKaWUWmbElGgtkoj8PfCbxpihkuxwGrW1taazs7Ncu1dqRpcuXUKfm2q50uenWq70uamWM31+quXq1VdfNcaYafvdl3LksAE4JSKvcOWaw5K1sujs7OTQoUOl2p1Sc7Zv3z59bqplS5+farnS56ZazvT5qZYrEZmx5kspk8PPlXBfapUZjqQ43B2ks8bDpgZtBbKYjveGGAgluXVdNQG3th5Qq0M4meGVi2PU+Zzc1FpZ7nCUUivMq5fHCSXS3L6+Rou6qTWlZM92Y8wzpdqXWn2eODHIYDjJib4wn672aM++RTIaTfHEiUEAYuks797TUuaIlFocz54Z5uxgFIDGgBaDUErNXfdYnGfPDAOQz8ND2xvKHJFSpTPtXNOlICIREQkXv5IikhORcKn2r1Y2n6twHsPtsGC1aBnoxeK0W3HYCm8DfpeOGqrVw1d8PtutgltPJiml5sHjsE4ea3hdOmqo1pZSjhxeMRdQRN4D7C/V/tXK9ujORi6PxmnwO7FbS3ZOY9XzOm18ZH87Y/E062oqyh2OUovmno21tFW5qfI4JhNFpZSaixqvk4/c1k4kmaWzxlPucJQqqbKdDjHGfEdEfq9c+1cri91qYWO9t9xhrEpVFQ6qKhzlDkOpRWWxCOvr9D1DKbUwtV4ntV5nucNQquRKlhyKyPumXLUA+4DS9NFQSimllFJKKTWrUo4cvnPK5SxwCXh3CfevlFJKKaVUWXT+3g8W/NhLf/LYIkai1MxKuebwl0u1L6WUUkoppZRS87PkyaGIfIFZpo8aY/75UseglFJKKaWUUmp2pRg5PDTl8ueBz5Vgn0oppZRSSiml5mHJk0NjzN9OXBaRfzH1ulJKKaWUUkqp5aHUDeO0OqlSSimllFJKLUPaTVwppZRSSimlVEkK0kR4c8TQIyLhiZsAY4zxL3UMSimllFJKKaVmN2tyKCKtwC8A9wDNQAI4DvwA+JExJn+9HRhjfIsQp1JKKaWUUkqpJTTjtFIR+Rvgr4E08KfAh4HfAJ4EHgWeF5F7Z3l8s4i8JiJJEbGJyDoReU5EnhWRr4mItXi/j4rIiyLyfRHxF7c9KCIvichTxQQVEdkpIs+LyAsictOUffy8+PiHFudPopRSSimllFJrz2wjh//JGHN8mu3HgX8UEQfQPsvjx4C3AP9UvB4E3mmMCYrIHwNvF5EfA58B7gXeD3wa+A/AHwAPA9uB3wc+C/wRhQQ1D3wReDfwe8C/A44C36eQuCqllFJKKaWUmqcZRw5nSAyn3p42xpyb5fakMWZ8yvVxY0yweDUL5IDNwDFjTJZCYne7iHiAhDEmYow5QCFBBKg2xnQbY3qBQHHbTcBLxpgoEBERncKqlFJKKaWUUgswa7VSEbl/yrTODhF5UkRenm066fWISDPwEPBToBKYKFATAqqKX+EpD7FOE+vEZasxZqLYzcTjr97fp0TkkIgcGh4eXmjYSimllFJKKbWqXa+VxZ9QSLoA/m/gW8BvAX+xkJ2JiBP4W+DXiqOFQWCiWqm/eH18yjYoTCOd+n3q5dyUbROPv4Ix5kvGmH3GmH11dXULCVsppZRSSimlVr0Z1xyKyOcorCn8bRER4BHgAtAA1IrIHwJPG2Oencf+vgR80Rhzonj9DLCzWJzmIeBlY0xcRNwi4qUwpXTivmPFUcw8byasR0XkDgprDv3GmKkjjkoppZRSSiml5mjG5NAY83kReQx4HmgCXjTG/AGAiDxsjPn3s/1gEbEDPwJ2Az8RkX8PvA/oEJHfAv5fY8w/iciXgecojBh+pPjwPwaeAJLAJ4rbPgd8g0J/xM8Wt/0Z8L8Ad/F2pZRSSimllFILMGufQ+C3gT8HUsCnAERkB3D4ej/YGJOhMBo41TUFY4wxXwG+ctW2J7mq8qgx5ihw91XbeoAHrxeLUkoppZRSSqnZzZocGmNeAG67atsbvDlyp5RSSimllFJqFZixII2I3CoijVOuf1xEvisi/5+IVJcmPKWUUkoppZRSpTBbtdL/DqQBiq0r/oTC+r4QhcIySimllFJKKaVWidmmlVqNMWPFyx8CvmSM+TbwbRE5vOSRKaWUUkoppZQqmdlGDq0iMpE8vgX4+ZTbrlfIRimllFJKKaXUCjJbkvd14BkRGQESFNpNICIbebPPoFJKKaWUUkqpVWC2Pod/LCI/o9Dj8KfGGFO8yQL8ZimCU0oppZRSSilVGjMmh8WKpGeKX04RcQBBY8yZUgWnlFJKKaWUUqo0ZptW+iowMVooxe9eETkC/Kox5tJSBqaUUkoppZRSqnRmm1a6brrtIvI+4L8Bjy5VUEoppZRSSimlSmu2aqXTMsb8I1C/BLEopZRSSimllCqTeSeHIuJdyOOUUkoppZRSSi1fsxWk+ZfTbK4C3gX85ZJFpJRSSimllFKq5GYrSOO76roBBoBfNMYcW7qQlFJKKaWUUkqV2mwFaT5fykCUUkoppZRSSpXPjGsHReRLIrJzhtsqROSTIvLRWR7fLCKviUhSRGzFbb8jIs+LyFdFxF7c9lEReVFEvi8i/uK2B0XkJRF5SkRai9t2Fh/7gojcNGUfPy8+/qGF/xmUUkoppZRSam2brbDMF4E/FJGTIvJNEfmiiPy1iDwHvEhh2um3Znn8GPAW4GUAEakDHjDG3A0cBd5TTBA/A9wLfAX4dPGxfwA8DPwe8PvFbX8EfBh4vHiZ4u3/rnjffzfn31oppZRSSiml1BVmm1Z6GHi8WJ10H9AEJICTxpjT1/vBxpgkkBSRiU37gaeLl58EPgKcAI4ZY7Ii8iTwJRHxAAljTAQ4ICJ/UnxMtTGmG0BEAsVtNwG/ZYwxIhIREV/xcUoppZRSSiml5mG2gjQAGGOivJnU3YhKIFy8HKJQ+XS6bVVTtgFYi9+njnJOXLYaY8xVj78iORSRTwGfAmhvb7/BX0EppZRSSimlVqdS9isMAv7iZX/x+nTbxqdsA8hf9X3q5dyUbROPv4Ix5kvGmH3GmH11dXULjV0ppZRSSimlVrVSJoevAPcVLz9EYS3iGWCniFgnthlj4oBbRLwisp/C1FOAMRFpFZFmCqOEAEdF5A4RqQD8xpipI45KKaWUUkoppeboutNKF6pYbOZHwG7gJ8C/AZ4VkeeBLuAvjDEZEfky8ByFEcOPFB/+x8ATQBL4RHHb54BvAAJ8trjtz4D/BbiLtyullFJKKaWUWoAFJYci8iVjzKdmu48xJkNhNHCqA8CfXnW/r1CoVDp125MUitZM3XYUuPuqbT3Ag/MKXimllFJKKaXUNWZMDkWkeqabgLcvTThKKaWUUkoppcphtpHDYeAyhWRwgiler1/KoJRSSimllFJKldZsyeEF4C3GmK6rbxCR7qULSSmllFJKKaVUqc2WHP4Fhb6B1ySHFArBKHXDQokMPzrWj9UiPHZTEx7HktVIUlcZDCf56YlBKt123razEZu1lMWLlVp8Y7E0Pz4+gMtu4e27mnDZrdd/kFJK3aDjvSEOXRpja5Of29fXlDscpW7IjEeDxpj/Yow5MsNtX1i6kNRacqIvTH8oSc94gtMDkXKHs6a83jXOSCTFuaEo3eOJcoej1A071htiMJzk8micC8OxcoejlFojXjo/yng8w0vnR8nm8td/gFLL2KxDBSLiF5EN02y/aelCUmtJR40Hu1Vw2i20VnnKHc6asq7Wi0UEn8tGvc9Z7nCUumGdNR5sFsHtsNJc6Sp3OEqpNWJDfQUAnbUenYWjVrzZqpU+TmFq6VCxZ+EvGWNeKd78P4G9Sx6dWvWaK9186t4NiIB9AW+oQ5EkZwejbKr3Uu/Xg8H52NLoKybnFqyWN+tO9QYTXBqJsb3JT1WFo4wRqrUkkc7xevc4dV4nmxp8C/oZHTUVfOq+9VhF9ABNKVUyrVUeLCLctm6mQv9KrRyzLfD6N8Atxph+EdkPfEVE/o0x5h+5soKpUjfEYVv4Qdz3DvcRSWY53hvi0/ddM8itruPqNVnZXJ7vvN5LOpvnwkiMj93eUabI1FrzzJkhTvZHEIFfrHBQ613YaLbTpusMlVKlMxRO8oOj/QBkcoa3bm8oc0RK3ZjZkkOrMaYfwBhzUEQeAL4vIq0UWlooVXYTo406SrA4RASbRUgDDqueA1KlM/FathSfg0optRJYLYJFhLwx2PVzU60CsyWHERHZYIw5D1AcQbwf+A6wY+lDU+r63ru3hUsjMTpqKsodyqpgtQiP72ujezzOxnpvucNRa8h9m+to8LuornBQ6dHpzEqplaHG6+QD+1oZj6XZ2riwKfFKLSezJYe/zlUFa4wxERF5FHh8SaNSao78Ljs3tVaWO4xVparCoWsNVcnZrBZ2tgTKHYZSSs1bS6Wblkp3ucNQalHMmBxObWMhItWFTWbcGJMBvlqK4JRSSimllFJKlcaMC7VEpF1EviEiw8AB4BURGSpu6yxZhEoppZRSSimlltxsVTz+HvgnoNEYs8kYsxFoorDm8BsliE0ppZRSSimlVInMlhzWGmP+3hiTm9hgjMkZY74B1Cx9aEoppZRSSimlSmW2gjSvisgXgb8Fuovb2oBPAK8vdWBKKaWUUkoppUpntpHDjwPHgM8DPwF+CvyfwHHgYwvZmYh4ROQHIvK0iHxXRJwi8jsi8ryIfFVE7MX7fVREXhSR74uIv7jtQRF5SUSeKvZaRER2Fh/7gojctJCYlFJKKaWUUkrNkhwaY9LGmP9qjHnUGLPLGLPTGPM2Y8wXjTGpBe7vUeCAMeZ+4CDwC8ADxpi7gaPAe4oJ4meAe4GvAJ8uPvYPgIeB3wN+v7jtj4APU2it8UcLjEkppZRSSiml1rzZRg4RkUdE5FdEpOOq7Z9c4P7OA87i5UqgE3i6eP1J4HZgM3DMGJOd2CYiHiBhjIkYYw4A24uPqTbGdBtjegFtkKWUUkoppZRSCzRbK4v/G/i3wC7g5yLym1Nu/mcL3N9Z4DYReQPYB5wDwsXbQkAVhaTx6m1VU7YBWKeJf9rfRUQ+JSKHROTQ8PDwAsNWSimllFJKqdVttpHDdwIPGmP+BXAL8DYR+c/F22SB+/sE8BNjzA7gBxQK4viLt/mBYPHr6m3jU7YB5K/6fvXlScaYLxlj9hlj9tXV1S0wbKWUUkoppZRa3WZLDm3FqZ0YY4IUkkW/iHwTcCxwfwKMFS+PUJhWel/x+kPAy8AZYKeIWCe2GWPigFtEvCKyHzhRfMyYiLSKSDOFUUallFJKKaWUUgswWyuL8yJynzHmGSj0OAR+RUT+L+D9C9zf14C/F5GPARngQ8CvicjzQBfwF8aYjIh8GXiOwojhR4qP/WPgCSBJYQQS4HPANygknZ9dYExKKaWUUkoptebNlhx+cLqNxph/JyL/dSE7K45APnLV5j8tfk2931coVCqduu1JCgVqpm47Cty9kFiUUkoppZRSSr1pxuTQGJOY5bbepQlHKaWUUkoppVQ5zNrKQqm1ZjSa4sxghFzelDuUshuLpTkzGCGbm7bWk1JXyOUNZwYjjEYX2gZXKaWWp1Aiw+mBCOmsfh6q1W+2aaVKrSnRVJZvvNJNOptnV0uAh7Y3lDuksomns3z9YBfpbJ4dzX4e3tFY7pDUMvfUqSGO9YZw2Cx84s5OvE79eFFKrXyZXJ5vHOwins6xvq6Cd+9pKXdISi2pOX96i0g94Jq4bozpWpKIlCqTTDY/eVYwnsmVOZryymQNmeKIYTy9tv8Wam5i6SxQOJDKZPPgLHNASim1CHJ5Q6p4bBBL6eehWv2umxyKyLuA/wQ0A0NAB3AS2LG0oam1KpvLc244Sp3XSY23dEeYVRUOHt3ZyEA4yb6OqpLtt1wm/s61Xie1V/2dAx47j+5spD+YZO8a+FuoG/fA1nr87nEa/S6qKhba7WjtuTwawxjorK0odyhKrXkXR2JYRWiv8Uxuc9mtvOOmJi6NxtjdWlm+4JQqkbmMHP4RcDvwpDHmZhF5APjw0oal1rKfnRriRF8Yh83CL93ZSUUJp6dta/Kzrclfsv2V09OnhznWG8JuFT5xZyc+l/2K27c2+tnauDb+FurG+V12HthSX+4wVpRzQxH+95F+AN62q1Ffb0qV0cn+MD8+PgDAu/Y0s6HOO3nb+jov66dcV2o1m0tBmowxZhSwiIjFGPMUsGdpw1JrWbI4pTOTy09ObZwwHEnx1KkhLo3EyhHaqpIo/p2zeUMmt3QFeA53B3n2zPDk/1WtXclMjufODvN613i5Q1kWEun8lMv6+lCqnBJTPqPm83rsCyZ46tQQfcEZi/wrtaLMZUgmKCJe4FngqyIyBGSXNiy1lj24tZ5KT5CmgItKz5XT0358vJ+RaJrjvSE+c/8G7NYbL7g7EEry4vkRmivd3L6+5oZ/3kpRmAZop8HvpHqJpgF2jcZ56tQQUFiTls0ZXHYrD2ypw7YI/zu1srx0YZTDXUEAqiscdNQsfCplKJHh6dND+Fw27ttcj9UiixRl6exo9pPI5DDGcJNOV1OqrHa3VpLO5rFahO2zzCC6OBLjtcvjbGrwclNrJd870kcinePMYIRP37ehhBErtTTmkhy+G0gAvw18FAgAn1/KoNTa5nPZuW9z3bS3eRw2II3LbsUii3Mw+NzZYXrGE1wejbO5wTfnRGkkmmIkmmJjnXdFJjpep23Gv/OEUDxDbzDB+roKXHbrvPfhcliwiJA3hu6x+ORi/tYq95qZvrsWjEZTDEVSbKz3znrCpsJR+MgRAfcCnk9THbo0xoXhwgyC9uoKNtavvClfFouwf111ucNQasWIprJ0jcbpqPEs+pITq0XmdIL456eGCCcydI/H2dbkx+Owkkjn8Dhu7D1NqeViLq+sPzTG/C6QB/4WQET+FPjdpQxMqek8VlwU3lzpXrSRgqaAm57xBD6XjQrn3N7co6ks3zjYRSZnVm2rh2wuzzdeKZTv7qjx8L69rfP+GfU+Fx+6tY1oKksun+dHxwewWeSaAjhq5Yqn32wBs63Jz6M7Z34t3NpZRXWFA6/TRr3fNeP95qIx4OJoT6F1Ro0WwFFqTfjWoW7G4xlqfU4+dntHWWJoCrgIJzLUep3YLML797bSPR6nvdpz/QcrtQLMJTl8K9cmgm+bZptSS85lty560Ya7N9WyudGL32XHaZtbcpjN5cnmC+v0Eqt0LV3OmMnWHjfyOzYG3kwC6n0ubFa5pviNWrkyuTfbnlxvXamILNoI347mAE0BN06bpaRFq5RS5ZPIFN9ryrhG99EdjezrqKKqwoGIUOG0aTEptarM+IkqIr8O/AawXkSOTrnJB7yw1IEpVUr1vvmNYlR6HLxtZxMD4SR72yuXJqgyc9qsvGtPMxdHYuxqCSzKz9QWB6tPwG3nsV1N9IWS3Fzi18JSrZVVSi1P797TzOnBCNvKmIxZLHLDMx+UWs5mO936NeBHwP8D/N6U7RFjzNiSRqXUCrCl0ceWRl+5w1hSHTUVN1Q0RK0Nmxp8bGpY3a8FpVT5NVe6aa50lzsMpVa12ZJDY4y5JCKfvfoGEanWBFEppZRSSimlVo/rjRy+A3gVMMDU6h8GWL+EcSmllFJKKaWUKqEZa44bY95R/L7OGLO++H3ia8GJoYh8XER+JiJPi0iLiPyOiDwvIl8VEXvxPh8VkRdF5Psi4i9ue1BEXhKRp0SktbhtZ/GxL4jITQuNSSmllFJKKaXWujk1ZxOR94nIn4vIfxKR9yx0ZyLSAtxnjHmLMeZ+IA08YIy5GzgKvKeYIH4GuBf4CvDp4sP/AHiYwvrH3y9u+yPgw8DjxctKTWsonOQrL13in17vmazAqQrODUX5mxcu8sSJQYwx5Q5HrQH5vOFHx/r5mxcucnk0Vu5wlFJqXs4MRvjr5y/y81OD5Q5FqUV33eRQRL5IIVk7BhwHPiMi/2WB+3sEsBZHDr8A7AeeLt72JHA7sBk4ZozJTmwTEQ+QMMZEjDEHgO3Fx1QbY7qNMb3A4pRTVKvSkZ4QI9E0l0bidI3pwehUhy6NEYxnON4bIhjPlDsctQYMR1OcGogQjGd49fJ4ucNRSql5OXhxjFAiw5HuEOGkfm6q1WUuI4f3AY8YY/7GGPM3wNuB+xe4vwbAYYx5CxAHKoFw8bYQUDXDtqop2wAmmtFNjX/a30VEPiUih0Tk0PDw8ALDVivd+roKrBbB57LRGNBKZ1NN9J1rDLjwubRfnFp6VR4HtV4HIixa30OllCqVTcX3rZZKN16Hfm6q1WUuz+jTQDtwuXi9jcIU0IUIAc8UL/8c2AdMnHLxA8Hil/+qbeNTtgHkr/p+9eVJxpgvAV8C2Ldvn86ZWyFyecMzZ4aIpnLcv6UO/w02Td9Q5+Uz923AahGsFrn+A9aQfZ3V7GwJ4LRZEJn9b/PKpTG6x+LcsaGGJk2y1QI5bBY+elsHmXwep816/QdMceDCKL3BBHduqKUxoL3GlFJLL5vL89TpYZKZHA9sree29TXsbquc0+emUivNXEYOa4CTxQIyTwMngDoR+Z6IfG+e+3sRmCgcswfopjAyCfAQ8DJwBtgpItaJbcaYOOAWEa+I7C/GADAmIq0i0kwh8VSrxMWRKEe6Q5wfinLo0uJ0TXHYLJoYzsBlt173Ay4YT/P82REuj8Z57sxIiSJTq5XFIvNODMdiaV48P1p4Dp7VmSBKqdI4OxTleG+Ic0NRXu8qTIWfy+emUivRXEYO/3CxdmaMOSwiiWKSOQJ8BGgSkeeBLuAvjDEZEfky8ByFEcOPFB/+x8ATQBL4RHHb54BvUGizcU0/RrVyVVc4cdgspLN5Gvw6OrAceBw2/G474USGBh2xUWVQ4bTic9mIJLM6aqiUKplarxObRcgZo8ckatW7bnJojHnmeveZD2PMv7pq058Wv6be5ysUKpVO3fYkhQI1U7cdBe5ezPjU8lBd4eATd3aSzuaprnCUOxzFxFTAdsKJDHU+Z7nDUWuQ02blF2/vIJzMUO/TAzSlVGnU+Zz80l2dZHOGKj0mUavcdZNDEbkd+AKwDXBQKAYTM8b4Z32gUjfI67SB5iDListuxWWf31RApRaTPgeVUuXgu8HaB0qtFHOZVvqXwC8A36RQQObjwKalDEqp+crlDT881s9QJMVbttbTWVtR6KV2fIDeYJwHttSzqcFX7jBXhCdODPC9I300+Fz80l2dtFZ5yh2SUnPy4rkRjvWG2NNWyW3ra2745/381CAXhmPcsaGGHc3aLUmp1SiTy/ODo/2MxdI8vKNh2s+888NRfn5yiIaAi8d2NWn9ArWqzaUgDcaYc4DVGJMrtrO4f0mjUmqehiJJzg1FCScyvN5dWCw+Hk9zZjBCLJXj9a5geQNcIZKZHM+dHWEonOLsUIRjPVrnSa0cr1waJ57OcWgReifG01mOdIeIJLMcuqS9GJVarfqCCS6OxCb7Fk7ncFeQaCrL+aEoI9FUiSNUqrTmkhzGRcQBHBaRPxOR3wYqljgupealpsJJrc+JAOtrC0/PgNtOc6ULEdjadO2o4bNnhvnLn5/lyRODGKNdTgCcNgs7mv247VZqKpy0Vbn51qvd/JenznFqIHz9H6DUEusajfNfnz7P1w50kczkrrhtW/F1vm2a1/vVMrlpux9NctuttFcXRhA2z3HWQTqbJ52d/ecqpZaXBr+LKo8diwibGq7su5rPG757uJdDl8cYjiap9Tq0DoJa9eYyrfRjFJLIfwb8NoU+h+9fyqCUmi+HzcIH9rbw9YPdPH16BLfDxuYGHx+6tZ1c3kw7BeRwd5BzQ1FeODdCKJHh/be0liHy5UVE+NCt7exfV80/vtrLn/z4NIlMlu1Nfo71hNjaqEuNVXmd6A+TzORIZnL0BROsr3vzYO7hHY28ZVvDdad8/ezkIEd7Qmxr8vHozqZp7yMivP+WVjK5PHbr9c+jDoSSfPu1HgA+cEurVjRUaoVw2a184s5OcnmD7arXejCR4dkzw/SMJ8jn89RUOHnh3Aj3b6kvU7RKLb25jByOAGljTNgY83ngd4C+pQ1LqfkbjqQJJTLkjeHMYGRy+0wHirtaA4QSGRr8LrrG4tcdSVhL+oNJwskM6WwOm8VCKJFhZ4uuuVLlt63Jh9Nuoc7npLnSfc3tc1kLdGqg8P5weiB63fvOJTEE6BqLT44c9ozH5/QYpdTyICLXJIYAlW47FosgQCJTeH2fHohc+wOUWkXmMnL4MwrN6Cc+Rd3AT4E7lyoopRaiudLFutoKxmJpdrdWXvf+D2ypp97n5NClcTY3+OZ8ELgW7GgJcGk0RjqXZ11NBe/Y3TztgbhSpdZRU8Fv3L/xhn7GbeuqOdwdXNQiM9uafFwajSGgI+xKrRIWi/CZ+zbwwrkRIsksTpuFvR1V5Q5LqSU1l+TQZYyZPL1qjImKiJYvVMuOzWrhPTe3zOsxO5oDWoVwGgG3nY/c1lHuMJRaEvs6q9nXWb2oP9PnsvP4vrZF/ZlKqfLbUOdlQ533+ndUapWYy1BJTET2TlwRkVuAxNKFpJRSSimllFKq1OYycvgvgG+KyMQ6wybgQ0sWkVJKKaWUUkqpkrtucmiMeUVEtgJbAAFOGWMySx6ZUkoppZRSSqmSmcvIIcVk8PgSx6KUUkoppZRSqky0PKNSSimllFJKqdmTQynQ8mtKKaWUUkoptcrNmhwaYwzwndKEopRSSimllFKqXOYyrfRlEbl1ySNRSimllFJKKVU2c0kOH6CQIJ4XkaMickxEjt7ITkXkX4rI88XLvyMiz4vIV0XEXtz2URF5UUS+LyL+4rYHReQlEXlKRFqL23YWH/uCiNx0IzEppZRSSiml1Fo2l2qlb1vMHYqIE9hdvFwHPGCMuVtEfhd4j4h8B/gMcC/wfuDTwH8A/gB4GNgO/D7wWeCPgA8DeeCLwLsXM1allkI+b0hkclQ451QsuKwS6Rw2q2C3au0qdWNS2RzGgMtuLXcoSik1o3zeEM/k8K6Az2illsJ1j/iMMZeBNuDB4uX4XB43i18F/rZ4eT/wdPHyk8DtwGbgmDEmO7FNRDxAwhgTMcYcoJAgAlQbY7qNMb1A4AZiUqok8nnD3x/q5kvPXuD5syPlDmdWZwcjfOnZC/zNCxcJJ7W1qVq44UiKv3ruIl9+9gLdY/Fyh6OUUtMyxvCtV3v48rMXePr0ULnDUaosrpvkicjngN+lMFoHYAf+biE7K04bvc8Y8/PipkogXLwcAqpm2FY1ZRvAxKnnqfFP+7uIyKdE5JCIHBoeHl5I2Eotmngmx0AoCcDFkWiZo5nd5dE4eWOIpXIMhZPlDketYH3BBOlsnmzeaHKolFq2Utk8vcEEABdHYmWORqnymMsI4HuBdwExAGNMH+Bb4P4+BnxtyvUg4C9e9hevT7dtfMo2KEwjnfr96suTjDFfMsbsM8bsq6urW2DYSi0Or9PGvs4qar0O7thQU+5wZrWnvZIGv4uN9V46airKHY5awTY3+Oio8dBS6WZnq07yUEotTy67ldvWV1PjdXDnhtpyh6NUWcxlQnXaGGNExACIyI0cJW4B9ojIZ4AdwD4KU0v/DHgIeBk4A+wUEevENmNMXETcIuKlMKX0RPHnjRWL0+QpjDIqtezds6mOezYt/xMVtV4nH7mtvdxhqFXA7bDyvr2t5Q5DKaWu684NtZoYqjVtLsnhP4jIfwcqReTXgE8CX17IzowxvztxWUSeN8Z8XkR+t1i5tAv4C2NMRkS+DDxHYcTwI8WH/DHwBJAEPlHc9jngG4BQKFCj1LSSmRw/Pj5AJpfnkZ2N+F12Mrm8FloBTg9EeOXSGJvqvdy2fv6jmfp3VPORyxsArBYpcySl8dzZYS6PxrlrYy3ranUEXqly6B6L8+zZYZoDbu7fUofI7O8/+rmm1rLrJofGmP8oIm+lsOZvM/CHxpgnbnTHxpi7i9//FPjTq277CvCVq7Y9SaFAzdRtR4G7bzQWtfqdHohMrh843hMinMxysj/MzpYAb93eUOboyuuFcyOEEhmGIyn2tFfitM29muShS2M8d3aElio379/bumYO+NXCDIaTfPu1HgThg/taqfU6yx3SkgolMhy6NA7Ai+dHNDlUqkwOXBxjKJxiKJxiZ0uAOt/07z3GGL5zuJdLI3FuW1fNnRt1BFGtPXM9LXKMwkjes8XLSq0ozZVuHDYLNovQVu3hzGAEYPI7FCoqDq7BwiudtR4AWirdOK5zpjSWytI1Gp8c/Tld/Pv1jieIprJLG6ha8S6OxEhl8iQzOS6Plq4wTWF/MTK5aZemL5kKh3XyILRT1+0qVTadNYXPuSqPnYDbPu19+kMJ+oIJLo0U3ptOTzk+UGotue7IoYj8KvCHwM8pTN/8goj8e2PMXy91cEotljqfk1+5ex3GFNY/7V9XzfHeELvbKoHClJNvv9aDMfCOm5rY1LDQmksrz4NbG9jXWY3XYZt1qk06m+drB7qIprJsb/bzyI5G9nVU89zZYTpqKvC7tCeUmt22Rj/nhqJYRNjc4C3Zfv/hUDej0TQdNZ6Srn20WS18eH87sXQWv2v6A1Kl1NLb11nNlkYfbrsV2zQnQY/3hnjixCAWETprKhiNpbi1s7oMkSpVfnM5mvsd4GZjzCiAiNQALwKaHKolYYy57nqAhZjafPv29TXcPmV9XTCewRQGwxiPr72efrMduE78P9K5PLF0YXRwPJYGYEujjy2NayeRVjcm4LHzi7d3zPn+i/FekMsbgsXX9FjxeVtKVotoYqjUMuBz2TETH/RXmXiPyBvD9ma/fq6pNW0uyWEPMHVsPQJ0L004aq07eHGMF8+PsL7OyztvalqSJHE625p8jMZS5I1hd9v8Su1HU1m+f6SPnDG8Y1czAc/qORA80h3k6dPDtFS5ee/NLTy0rYGusTj7Oqsm75PLG35wrJ/RaIqHtjXQVu0pY8RqOQknM3z/SD8i8NhNTfNKkl6+MMrLF0bZUOflnbubFxyD1SK8fVcjpwei3KRtNJRalnJ5ww+P9TOyRJ8joUSGbx7qJp3L896bW2gKuK+4/ZaOKhKZHG67lU31pZvVoNRyNJfksBc4ICLfBQzwbuCgiPxLAGPMny9hfGqNeaMvhDFwfihKMpPH7Zi9OEr3WByn3UK9z3VD+7VZLdy/pX5Bjz07GKG/2Nj+RH942fcvnI8T/WFCiTShRJq3bK1nZ0uAnS1XHmD3BROcH4oC8Hp3UJNDNelUf2RyHe+ZgQj75jFN60RfGGPg3FCUZCZ3xcj/fG2s97Gx/sZGArK5PJfH4tT7nPh0JFCpRTUQTnKu+DnyWtf4on+OdI/FiSQLM1/ODUXx2G2MxdN0VHuwWAS3w7rmi9MpNWEuBWnOA9+hkBgCfBfoB3zFL7XKJNI5Tg2EiSRLP71yb3sVLruVnS2B6yaGR3uCfOvVHr5+oJv+UKJEEV6rvdqDy27FYbMs62qEmVye0wOReU2tq/U6OD0YYSCUZCSSmvY+dT4n1RUOLCJ6xlVdobPGg9NuwWm30F4zv4O9m9srcdmt7GoJ3FBiuFh+emKQ7x3u4+sHu0hnF6ewzUAoybmhyIxT3ZRaK2q9Dmq8DkRg4xJ8jqyrraDO58TvttNRXcFXD17mO6/38vSZocn7XBiO0hcs37GEUsvFXFpZfL4Ugajl47uHe+kPJfG77Xzyrs6STe0E2N1WOVkk5nrCicJZwLwxRJNZKNOMsRqvk0/dux5jzLQL3ZeLn50c5GR/BIfNwifvWnfd5BugzudiX0dhtCeSnr4aqctu5eN3dJDNG+0Lpa5Q73fx6Xs3APPva3hzexU3t1dd/44lEk4UTpbF0zmy+TyOORf7nt5QJMnfv9JN3hju3FCzoB6jSq0WTpuVj92+dJ8jFU7b5HrnsViaVKZwgmfiOOJwd5CnTg0hAh+4pZXWKp0Bo9YuLS+4QoSTGZKZ3A1Pn5yLeDoHFMq/GwPT5YahRIafnRzE47Dy0LaGsiRF+zqryOTyuOzWJTnTOB+FA9/l1eMvm8vz5Mkh4uksd6yvoS9YmN6XyeXJ5PO4uX5yuLPZTyRZKNazq2Xm7FtEsFuX1++vlofl2vsyk8szHElR53Nit1o4NxTl1ctjbG7wTZuUPrS9gVcvj9NR48HjuPGPzmQ6T744YjjxnqvUWhBJZoinczT4rzyeWYrPkWgqy89ODmKzWHhoez1Om5XqCgcPbWugP5Rg/7rCyc94sRWTMYXZU0qtZZocrgCj0RRfP9hFJmd4aFsDu5a4qMJjNzVxoi/MxnovlhkO7F7rGp/sU7au1luWyl4uu5UHts68TjCZyZHO5ddspcALIzFO9odJZ/O8dH6UmgoHAY+dt2xrmPPfxGa1cM+mOgBS2RzBeJpKj2Mpw1ZrUKx4YFbhLN1H0rdf7aE/lKSt2sMHbmnl6dNDRJJZ+kNJdrYErhm9qPU6eWRH46Ltv73GwwNb6wknMpMHqEqtdqFEhr97+TLpbJ57N9dxS8fizQ4wxjAez+B32SZPWB/tDnJhOAYUloBMHD/tag1ccSy1r7MaA7jslrKfbFaq3ObS57DWGDNSimDU9MbjGTK5whnm4WiSpZ4/2eB3XXNG72otlW6OdAexWy2TTZ6Xk2A8zdeKa4Me3dnI1kZ/uUMquTqvE4fNQiydxWmz4LRbaa50s3kBPRyTmRx/9/JlIsks92yqnVdhEaVm0xtM8I+v9mCA9+1tKdl0ruHiGtqJ761VHk72h2nwu7CVaLRzzxyn0Cu1WoTimck1u8MzrGNfqCdPDnG8N0SD38Uv3NqGxSI0VbqxiGC1QIN/5mMVh83CXRtrFzUepVaqGZNDEXknhV6GWRHJAY8bY14sWWRq0vraCvZ2VBFNZpdNU9bNDT4aAy7sFsuc1q6V2nAkNbmmoHc8sSaTw6oKB5+8ax3pXJ6T/WGGIinuWmAl1VAiM1nprWc8wb7ORQxUrWkDoQTZvCleTpYsOXxkZyMn+8OT1Xcf2dHA/nXV+F22kq6zVmotaat2c2tnNcFEmjsWeZ1t73hhNtNgOEk6l8dlsbKutoJP3t2J1SKLMh1cqbVgtlfKHwP3GGNOichtwJ8B95UmLDWVxSLct7mu3GFcYzlP11xXW8G2Jj+xVHZRpq3E01l+fHwAY+DRnY0lnf52I9wOK26s3H6DH8IOqxBNZolncnzgltZFik4p2N4UoDeYxBjDjubSVZXa3OC7YhRdRKiumH3K9FOnh+gLJrh3U522bFFqAUSEuzct3gjdyxdGOTcUZf+6au7ZXMehS2NsrPdeUeFYW88oNT+zHeFmjTGnAIwxB0RE21aoaSUzOX50vJ9M1vDIzkYC7uu/EQfjaSwWWbIE02a18OjOxVsfdLI/PLnG8kR/eNmM4C7U82dHuDga44711TT4XeTyZta1hId7QnhdNrwuG9HU9FVL1dqWSOeIpbPUeuc3zdztsPKuG2hyXyqj0RSHu4IAvHRhdEmSw1ze8JM3BgjGMzy0rZ7660zvV2otS2ZyvHR+FIAXzo1w+/oaRqMpmgL6ulHqRsyWHNZPNLqf7rox5s+XLiy1kpwdjHJppJA4He8NXXfe/oXhKN870odFhMf3tdG4At7IWyo9k1XUWirdZY7mxsRSWV65NAYwORqaM4Z33NQ840L89moPR7tDOGwWGvWAVV0lmsrydy9fJpHOce/mWm7pWNknT6bjd9up8ToYjabprFmafqY943FOD0QAePXyOG/b1bQk+1FqNXDaLDRXuugLJumsqeBvXrjI5dE4By+Ns6+jGs8KmeGj1HIz2yvny1zZ5P7q62qZSGZyWERw2MrTY66p0oXDZiGfN7TNYb3QYDg1mZAMRZIrIjlsDLj41XvWAyyLhtyziaezOG3WGVsIuO1WGgMuBoq9LEejaQCGwskZk8MNdV5+7d51WC2C07a8f39VesF4erL8+0BocYtMLBd2q4WP7G8nkckt2TS1Wq8Tr9NGLJ2l46oENJvLk82bZf/+o9RSSGUL7y9TP39EhA/e0kY0ncXvsvPkyUEA7BYLy7SDzg3p/L0f3NDjL/3JY4sUiVrtZkwOjTGfX+ydFdcu/mcgBxwyxvy2iPwO8G7gMvBLxpiMiHwU+CwwBnzEGBMWkQcprINMAh8zxvSIyE7gv1FoMPfrxpijix3zctc9Fuc7r/ditQof2tdGzTyndC3Ea13jnOwPc3NbFdub/dR6nfzqPeswZm6J0+62AGOxNFaLrKhCMSvhoOzQpTGeOztCrdfBL+xvv6Icfy5veOLEAKFEhrdsrcfjtOG0WXjq1BDpXJ7d16mcqIv51UxaKt3c0lHFWCzN7etX36jhBJvVgm+ReroOhZP8/NQQVRUO3rqtAYtFqHDa+KW7Osnk8le83qKpLF8/0EU8nePRnY1laR2kVLkMhJJ8+7UeoNCgfqKa+vNnR7g8FuOuDbX4XXY+de96nj87wvZmPy79vFJqwWZ99YjIA8A/A7YWN50E/tIY8/QC93cZeNAYkxSRr4rIPcADxpi7ReR3gfeIyHeAzwD3Au8HPg38B+APgIeB7cDvU0ge/wj4MJAHvkghyVxTusbiZPOGbN7QG0wseXKYzxuePTOMMfDc2WG2NxeSu/mMJnkcNh67aX7TpdLZPM+dHSZv4N7NtTp6NYMLI4V+TiPRNOFE5ornQ9dYnJP9hSlrr3UF2dHs5/XuIBvrvJP/R6UWQkS4dxkWzVrOXrk0Tn8oSX8oydZGHz3jCcKJDHdvqr1mZHIonJxc63tpNKbJoVpTusfjk+0vusfiNPhdhBKZyeURL54fpbO2go6aiitG3GOpLM+dHcbjsHH3xtoZ+zYrpa404ylQEXmMQiuL7wMfAT4K/BD4axF5+0J2ZowZMMYki1ezwE3A08XrTwK3A5uBY8aY7MQ2EfEACWNMxBhzgEKCCFBtjOk2xvSy1M3/lqmdzQGaK1101HgW1L9uvk70h7kwHOXSaIyOmtJV6zvRH+ZoT4jjvSGO9YRKtt+V5rZ11dR6HdzUGrim8mKt14HHYUWksIbwZycHOT8U5YkTg5MfvBOSmRzfPNTN/3rp0qL3olJqquFIiv/10iW+eaibZCZX7nBKpqPGgwiFaaSpLAcvjnFqIMKBC2PX3Le92sOmBi/1fic3t1eWPlilFtnx3hB/9dwFnj49dN37bmvy01LlprXKzbamwonMCoeV2mKP5ZmORQ5eGuNkf4RXL49zYSS6eMErtcrNNnL4O8B7jDFHpmw7LCKHgC9QSBQXRERuAmqBIIUppgAhoAqoBMJXbauasg1gYthoanI7baIrIp8CPgXQ3t6+0JCXrYDHzoduXbrfK5LM4HHYJtevHbw4RmdNBels/or2Gtlcnn96vZehSIq3bm9Y9ES12uNgovXY9crNr2UdNRV87I5ri2UYYzDAL93ZSSZv8DptnB+OMh7PUOmxTzb9TmZy/ONrvZwZCoOBgNvB8b4QD2ypL/FvotaK432hyXWvF0dikwd/q8lE0jt1avrOlgCdtRU4rBZiqSx2q5DJGWq8176/2awW3nFT6Su6do3G+f6xPvwuOx+4pXVFTK1XK8Mrl8aIJLO83hXk9vU1Mz63cnnDT44PMBRO8pZtDZNtpGxWCx++tY14JndF1fOpr7XaikLyaLPIrNW4lVJXmi05bLwqMQTAGHNURBoWukMRqQb+EngcuAVoKd7kp5AsBouXp24bn7INCtNIp36/+vLUeL8EfAlg3759ZqFxr1b5vOGZs8ME42nu21x/ReL14vkRDlwYo9bn5MO3tmGzWtjS6OPgxTG2NPmueDMfiabpGU8A8EZfaNGTw/YaDx+7vQMD8y6VP1+hRIaRaIrOmooZi7qsNN890sfPTg5S7XHw22/dDMDbdzXRH0pQ63VOTrfpDyUZDCdx2awMR1LUep1sqPXSFyz8b5tXeKVWtfxsqPXyRm8Ip81KS1V5nl/dY3EcNsvkWqbF1DMe559e68ViET54S+sV7Sm8xQNdh83Bx+7oJJHOLXqBrmM9IU4NhLm5vWrGglMzOTkQJpXJM5xJ0RtMsKFufo9X6mpDkSTJdJ4tDT4OXByjo8aDc5ZiemOxNF1jb1ZD39bk59xQhNe7gmxr8rOz5c1JYxM1GCwW4YP7WtnVGqDe78RlsxLwaK9DpeZqtuQwtsDbZiQiNuDvgN8xxgyIyCvAbwB/BjwEvAycAXaKiHVimzEmLiJuEfFSmFJ6ovgjx0SklUJiqHMNF6BnPDHZu8ttH+XRnW+uBZzo6zcSSRFL5Qh4LNy1sZZbO6uvqYxa63XQVu1hMJxk5xI1si5FsZ1kJsfXD3aRSOfY1uRf1F6J5XSkK8hQOMVoNM0rl8Z56/YGrBah9arqsk0B1+R6jg/d2kZblYdLozG+/Vo/AO/a06wHiGpRtdd4+Mx9G7CIlGVN0PHeEE+cGESkUOzi6tfEjeoZT5DNGyiuC5+pd2HAbZ9Tj9j5yOUNPzs1iDEQjGfmnRxub/JzYTiG321b8S18VPkNhpN842A3eWN4YGs9n31g43WrrFdXOOio8dAfSrKrtXBs8dSpYaKpLL3BBNua/JMncae+1vqCSep9riU54aPUajdbcrhBRL43zXYB1i9wfx8EbgX+VApzBH8feFZEnge6gL8oViv9MvAchRHDjxQf+8fAExSqlX6iuO1zwDeKMX12gTGteOeGIrx8YYwNdV7u2FAzr8dWVthx2a0kMzkaA1d++N+xvobnz43QXu254qzbdG/mNquFD9zSurBfYBnJ5PKT01IiyUyZo1k8b9nWQE8wQU2FY9oGwT3jcZ49M0JTwMWH97ch8uZBejj5ZtP7yJTLSi0W2yJVAF2Iiee0MYv//D43FOGNvjCpbJ4tjd6SV2e2WoQGf6FtTVPl/A+S26o9/Pr9G5YgMrUWRZJZ+oIJhiJJarwO9lynQjYUnsPv23vlsUVjwMW5oSiNftcVs3t2tvjpDSawWYStWrRJqQWbLTmcrfLnf1zIzowxXwe+ftXml4A/vep+XwG+ctW2JykUqJm67Shw90JiWU1eODfKWCzNcCTF7rbAvFoO+F12funOThKZ3DVr+TprK+isXZpmzzMJxTO4HJayVSP1uew8sqOR7rE4+zpXT0n+uzfVsrejklQmT9U0azZfvjDGYLgwpXRnS4A635ujtDe1BIilsgjCzilVTUPxDG6HtWz9NZWar1gqi8iVbVn2dlSSyuZw2qxsWeTp8C+cGyWcyOC0WXh0RxNuR+nf1z54Sytj8fTk+iulyqXaYyObz+N32wnGF37y9bFdTYzEUlRftY7QV1wbq5S6MbP1OXymlIGoheuo8TAWS9MYcOFaQFLldljLctBytde6xnnm9DBep41fvL2jbDFta/KvyqIYHoeNmdbkd9Z46B6LU+Wx43df+bZgs1q4Z9OVbQom+in6XIX/lRaqUMtd91icf3q9FwE+sK+VpuJMCafNyv1LVHCpfcp782zrqpaSzWqh3qdT61R5vXhuhAMXxzAG2qo8857iPJXFIvqcVmoJzZgcisi7gVZjzH8pXj8ATBwh/mtjzLdKEJ+ag/u31LO3o4oKh21F9/GZKGgTTWUZj6dxO3SNS6ns66xmS6MPt906pyl+E/+rSDJLKJHR5FAte/2hJLm8mbzcFFj695cHttRzyyp4b1bqRk18ZrRWufnALa20VZeuFZZSan5mm3/4r4FfmHLdSWG9YAXwN4AmhzdoKJwknMyyoa7iijVeC+F3zb+QwWA4STqbXzZv0revqyaZyVHndU67Lu5G9QUTvHxhlIDbTjZvaKl0X1HpbCWJpbL0jCfoqPFMm5glMzl6xhO0VrnnnLhd3Xh7JgcujDIeS+N12rBbhcNd4+xfVzPtdFW1ugxFkrxwboQGn4s7N9aWO5zr6g0mONYTZHODj50tfvpDCUSE7YswMyAYTzMWS9NZUzFr4reQ9+aVbDyWZjiaYn1tRVnXkqqlc3YwwtGeENubZ59lMxhOEktlWVdbwZ0ba3j+7DCRZI6zQ1Hq/S5cdiv5vOHSaIzqCoe2m1BqmZgtOXQYY7qnXH/eGDMKjIpIaReirUIj0RRfL1btun19zbwLydyo7rE4336tB2Pg4R0N7FiiCqPzUe938fi+tlnvk8nlsS/wgOO5s8P0BZOc7A+zvraCE31h2qo8K7LE9T8c6iYYz9AYcPHh/df2ufzH13oZDCep9Tn52O0di7bf8ViaF8+PAuBz2RiPZxiPZ4hncrz3Zl3rsdq9cG6ESyNxLo3E2djgXfKpXbm8QWDBo24/OtZPJJnl7GCUzz6wkXfvabn+g+Yglsry1QNdpLN59rRV8sBW7QMKkEjn+NrBwt9lNVV7Vld68uQQyUyO3mCCrY2+aU9uD4aTfP1gF8bAPZtqubm9ir3t1fzgWD9He0LYrRbu3VzHM2eHOdwVxGGz8Ik7Oyfbu6jF1/l7P7ihx1/6k8cWKRK13M32KqyaesUY88+mXK1D3ZBEOkfeFKY4xdOlrwAZTWUp7p5wYun3b4xhLJbG77YvOLl74sQgx3sLZysf2TH/g44Gv4u+YJKqCgc2qwWX3YrTvvLObBtjiKcLFVVjqen/d+FipdXFrLiazhYqufpcNk72h0llcxgjbKyvWPQS/Gp5avS7uTQSx+u0zXtEbKw42jzXAkZdo3G+d6QXl93K47e2LWgELuC2E0lm8bkWd1pnMpMjnS201g2voqrGNyqdy5PJFf4u5fhcU4snlsqSM2ba111TwMXFkRiNfteMs57i6dzkMUZvMMHBS2NEk1lyeYPLbp38zJioEDzx+aLJoVLlN9ur8ICI/Jox5stTN4rIp4GDSxvW6tdW7eH+LXWMxlLcuWHxpmdlc3mS2fwVb7CD4cJUsOZKN7evL4xQbmnwMR5Pk87m2dtRuWj7n5DLG544MUg4mWFXi5/D3UEGQilqvQ4+clvHgprLnx4IA3BmILKg5PC+zXVsb/bjddgYjKSo8TpW5Fo5EeFdu5s5OxRhe9P0I76P7WriZH/4uoV1ssUDuRfPFyre3ru5DofNwk/fGMBmtfDw9gZcdiuZXJ4v/OwMsXSOOzfUksjkyOYMqUyOt+1sYnODj0wuTzqbp0I/3FetOzbUsLHei89lm9dr54VzIxy8OEbAbecXb++YU4J4fjhKJmdIZjK8dH6U29fXXPckxOXRGIcujbOx3svutkretaeZ3vHEnNYXprN57FaZ9mD3/HCUgxcL7YL2r6umxuvkoW0NDIST7F+3/KsaT/e5sBQCbjtv29lEXzDB3vaq6z9AlcyBC6NcHo1z96YamitnX0oyGE7yD690kzeF3rb1Pic/PTGARYRHdjTyzt3NjEZT11Q4n2pdbQX3bq4rVrqGC8Mx7FYL+zoCbGrwTS5nuW9zHW67lUa/i1qvc/K5CtA7y9IJpdTSme2T4reB74jIR4DXittuobD28D1LHNeql88bzg1F6R6Lk80ZHtrWcMPrM1LZHF870EUwnuHezXXc0lH4cH72zDA94wkuj8bJm8J+Nzf4FjUpvdql0Rgn+8NEkhmeO1NoWDsxBS2Zyc2YQESSGZ4/O4LXZeOuDbVXnO3fv66Goz1Bdi1wnaDImxXO1q3wBKat2jPrWtGrb8/m8ozHM4zFUhy4OMa62go6ayr47uHeyUTPZbdit1qoqrBzeTQOwOlqD7vbKjl4YYyDl8YBsFst+N024uksd26oZWuTn2Qmx1cPdBFOZHhwaz2759C/Sq1MU9uczFVvsFCMIpTIEE1lqbYVDir7QwleuTROZ42Hm1orr3jMjhY/pwbCXByJc7Q7yMWRGJ+8a92sieVTp4YYj2foHo+zpdGHy25lfd30VREPXBjl9GCEWzqqiCazvHh+lJbKQrGMq0cZnz87wlgszUAoyU2tAVx2K7taA+yi/NPxryeZyfH1g4XPhfu21C150ral0ccW7TG3rIzH0nzr1R66xuIcvDTKn75/96wnaAfDSbJ5gzGGU/1h+oN2Lo0UPhNO9oe5ub2K+jk0l584BvnpG/08d3aY1ioPv3rPuivWtwfcdt66vQEoLBv5+sEuRqMpxuMZqjwOWirdPH7r7MtNlFKLa7ZWFkPAnSLyILCjuPkHxpiflySyVS5RLBhydijK0Z4QoUSGD9167dqx+QjGM5O9gy6PxibfmJsCbnrGE/hcNo52B0lk8rwUHeWWjqpZp3j++PgA54ej3LGhZt4HFHU+J26HlfF4Gp/LTo3Xgd1q4e5NtbOOLB28OMapgQgAzZVuNkw5sNu/rnpFnKVfjv7xtV56gwn6QgmaA25Go2kS6RyZnCGfh1Q2j8tupTHgos7r5JBlHKtFJgsDiaVQZS6UyBBNZrCIcH4oRo3XyfZmP7m8IZwoPPcujcY0OVzDjDH84Fg/l0fj3Le5jp0tAe7eWMvz50ZorXRfMdrw81NDDIVTnB+KsqHOe8V7Q38wSTKTJ5zIUF1hJ5XJT1YbnUljwMV4PEON14ljlve2bC4/uXb2xXOjuIptc3qDCaLp7DVT6dqq3YzF0jT4XbP+3OXo6s8FHdFbezxOK7HiNN9szhBNZmdda7+l0UfPeILnzw5ztCdEnc+B1SJYBKorHHz9YBehRIbHdjXNqaDd9470Y7daJovgzSScyDAaTWNMIUGt8jgm41ZKlc51h0+KyaAmhDcom8tzejBCrddJhdPGSCTFrtYAb/SFaK50MxBKkc+bG1oXU+9zclNr4JqpTndvqmVzoxe/08YL50c52hOirdoza2KYzOQ42V+YxnmkOzjvAwq/y84v3dlJOpvjzGCURCbH/nXVsza3z+UNqWyeoUiSVCZH/joHghMGQkl+fmqIGq+Dt25rWBMl4zO5PD3jCRr9LtwOK+eHowBXJNMTjveEeOnCKG67hYvDMTLZPLevr+HmtkoGIymcNgsPbW2gP5ygpsJJY8DFr9y9DovIZK/JfR3V5PPgtFvoHY9z8OI4eQyJdJZTAxHu2VjLjmY/I9H0dRN4Y8wNV+dVy1e4WAAG4NDlMTwOK82V7iuKTRljeK1rnOFIilzeUFXhuGZEsD9UGG1sr3bTWVPBvs7q6/Y+fXh7I3vbq0hkcozG0jOOctqsFjpqPFwejbOutoK2ag/Pnxuhs8Yz7RqrB7c2sLe9Cq9z4WsXs7k8Pz0xSDiR4aHtDdR6S9OUvsHvZFdLgMFIkv3r5l74bDiSYjCcZHODb87rRFV5zfTe6rRZ+eAtrbx0YZRbO2uu6WU73f3ftrORc0NRBkJJzg9H+fidndzSXkV/KMFAKAnAG31h2qo95PKG0wMRqirs007hrvU66B1P0Oh3cqQ7SGPAzfbma5c81Hid7GmvpHc8wR0baoincwueKaSUWriVPbduBXn69DDHekMAWC1CLm/Y2ujjdx/dyuvdQbY1+mc86Ehn8/zwWD+RZIZHdjbOWiGwwmmjOeC+5sBjIJTk66eGqfM5eGBLHVubZp/247Jb2dLo49xQdEFvzsYYoqnCGfh9nXMb7XvixCAn+sJcGI5xU2uAly+Msqlh5jgnkulXLo0xGE4yGE6yo9lPa9XCWnMMhpM8c2aYOp+T+zfXLesE5n8f6ePyaJxKj5071tfwo+MDAKyr9VDhtHPb+mp6xhI8eXKQM4MRXHYr54djbGvy4XPZeXh7A9XeQiXTaDLLF58+y6mBKJsbvHxwX9s1SabDVhj1Bbi5rZJtTX6eOztCNm/Y3ODFYhEenmYd6NUnPI50B3n69DBt1W7es6dlTSTya43PaWN9XQVdo3H6Q0m+e7jvmqq654aiPHtmBGMMmxt9PLy94ZqTVfvX1XB6IMpQJMXGBh/pbJ6fvDHA3vaqGZM+i0XoGovz3cN9BDw2PrK/g+bKaw9W83nDe29uIZ5+c4r79aZCTldmP53NE0kWRiqv5/JYnNPFWRGvXR6f9vWyFESEh4rT9qBwYumHx/oJJzI8sqNx2umB8XSWv3+li0zO0D0W5227mkoS62xyecPPTg4STmZ5cGv9rOvd1opgPM1oLM26mgq6xuL84Fg/PpeND97SdsWJlNMDEQ53h/A4bDNWF50w8Z4tIjy4tZ6/ePIMtV4nhy6N4bAKpwciGGO4VFx6sKPZz4WRGK9dHsciwsfu6Jj833SNxvnJG4XPpl0tATprPRzpCXGkJ0Sdzznt6/iBLVr5V6ly0+RwiSXSORw2C6niVIpsPk8yU1jfFUxkaKv20BhwzdpjrmssxsWRGABHukO8dfv0yeH54RgvFadKWS3CvZvfLCr7Rl+YXD7Pj48PcHk0ztmhKB8snslPZXNcHo3TVIyjZzzOj48P4Hfb+fR962cd7ZvJ02cK5amrKxx89Lb2Oa2nDCcyWKSQiNgsgtsx89PzxXMjHLw0xqZ6Hx01Hs4NRfG5bNRULPxs/MsXRukdT9A7XijPXYom2QsVSkxUI83SH06QyuTI5A0vXxijwe8ik8szEEoyEEpwuGucqgoHe9urqPQ4qPU58U8p7PG/XrrE06dHiKYy+Fw2wokM8XSW77zeRzKT4x27m644IWGzWtjS6GdL4+zFbk4NhPnpG4PUep184JZWHDYLJ/rD5I3h8miccDKjfa1WIYtFJltG/JenzpHO5ienHE+YOHAVEdqr3yw4YYzhwkBEGnUAAEMYSURBVEgMn8tGvc9FY8BJ3hjODkQ4NxjB7bAxFktP275l4vHfO9zHmcEIPpeNUCJzRXKYzub51qs9jERTPLyjga3XeQ7PJJvL80+v9/LEiUGaAi4e2FrP/VMOatPZPIl07oqpe/U+Jx6HlUQmR3tN+XrLdo3FuTBc+Dx5vTs4bXGvbN5QrFU1+dlVbpdHY7zRV5jN8sqlsQUVJVtNprZT2d0WIJ01pLN5RqNp+kKJyRN8xhgGQgmMMZwfjvE/nr/I+/e2sqv12pO+z58d4dDlMVor3exuqySby+O0WekeK6zhfeFc4fjCAM2Vhc+E17rG8RQ/q/PGTFarBfj+sT7ODkYZiiTZ11GNw2oFstgsoqPRSi1jmhzegKFwkhfPj9IUcLGutoLD3UE21Hsn35Rf7xrn6dPDVFc4ePeeZio9dup9TjI5Q9dYjPV1Xv76hYuks3ke29VER00FX3zqLAcvjVNbYefmjmoe3FpPY8CNz2Ujns6xrnbmFpM+lw2LCHljcDssfPNQN+FklrftbGR3ayVjscL6P6fNMlk+GuD7R/rpGovjc9n45F3rON4bIpLMEklm6R1PzFjQAeDiSJR4Osf2Jv8VZyP7igUoxmLpQpW8aZLDUDyDwUwmCG/ZVs+hy+Pcs6kOr8s26+96oj+MMXBmMMLDOxrYUOfFYbMsuE0GFNbUXRguHJhWupd30vLwjkaOdgcZjab4b09fYDCcZGeLH0H4x9d6aK1y88m71vHKxTGyecN4LE0sneVX71mH22ErFI9JFkYOxCL43DaMMdzaWcWulgCnBiIMhgtTh072R+bdz244kuJId5Bc3jAYTjIaS9EUcLO7tZJgvDByuFybg18ciWER6KjRdq6zyecNw9EUVZ5rp4ROeMdNTZzsj7Cj2c+RniAXhqI0+F3s7aji8VvbSGZyV4xSH7g4NnmCq6bCQfd4nK6xwgiF02ZhXa132udN12icV7vGWFdTQVWFgwZ/4QTIlqtmHozGUpPP61P9kWmTw3ze8MyZYSKpLPdvqZvcXzCe5oVzo9R6HbRWe7gwHCWUKKy/7QsWfubh7iCvXh6jeyxOwO3gnk21kzMnfC47v3zXOrL5/OTB9FJ4/uwIJ/pD3NJRxS0d187aaPC78LlsxFI5NtRN/xz3u+y8Y3cT/cEku9uWx7S+2uI69mQmR2vV8j1xNyGby/P06WFS2Tz3b6nD47Dy0xODXB6NcffGummnVc5HKluoDj0STfHi+VHetbuZrrEYAbedluIJkUyucEL49ECYRDqH3Sr4XTZe6xpnV2uAZCbH06eHALh/Sz0HLo4yEklx4Pwob/SHsVmEDXUVpHNu7FYLF0aiVDisJDN5gokMDpsFp83CO3Y3UeGwFl97b35W+F12mgOFk5XD0STbGn3sX19Nlceh7Y9WIO2TuHZocrgA4WSG/mCSw93j9AWTXByJ8VrXOMlMnlMDET5z3wYcNsvkaN9YLE0qm+eujW9WB93e7Of0QIRUJk84meFYb4i8gZcvjtE1Guf0QJ5YsZfdJ+7opDngZjiWpMJ55SjexZEYdqvQWuXB57KxvdmH32WnusLJ82cLB1nHekM8sqOR7c1+Lo7EODsYueKs4cSC70Q6R84YNjf4ODsYxe+20+Bz8drlcRw2Czuvml56pHucP/nRaUTg43d08OjON6ce3bupjpcujBJw2/j+kT4qPXb2tFXx8oVRGgMuWqvcfPvVXgDevaeZztr/v73zDI+ruhb2u2ZGXbIkS7Is927ABRvbgOkEU3JDD+V+SQiEJFzSCNyQnpuQdkPKhRRCEkISAyGB0EJL6Da9uOCKe++yem8z6/ux90hH4xkVS/ZoRvt9Hj06c8o+a505s89Ze6+SRUF2Wvto8Ord1azYVRUzac7sMfks2V7B1OIcUvx9MwrDzBk7lElFOaSn+jrNltY1t7G5tI4xQzMHjCvTyLwMRuZl8MsXN9LcZmquldY0U1bXTEtbiB3lDZTVN1Hd2EJtUysZqQHSA378Ph+vbjjI82v3EwyZEeVzji1mZG46Ywuy2hPJjCkwManLdlbQ2NJGyZA0pkR5kQ6GlHX7asjNSGlPTLBsRwWvbSyjoaWNIekBRg/NoqK+hW0H65k9Jo/KhlzK6pqpamwdMNczzLp9NTxnXXQvOr6EScNc1sVYPL92P69vKqOuuY3Pnz0RQZhQlNX+W3xvWznbyur5yIwS1uyt4Y4XNnCwrplxBVn8R1UJZ04pYsnuaspqmznJltipa2qjpS3I/pomqhpayMtMJSPFz9iCLFqDIS48voTReRk8s2ovtU1tLDi2mKKcNF5Zf4DKhlZ2lDdwxuRCqhpbmFCUTWsoRJqv47dclJ3G+MIsSmtN1tFobCuvZ8WuKitPK1OKc5gxKpc3N5ez8UAtGw/AR/PSGZmfSXl9K6PyMzjduly/ubmMyvoW1u+v5aTxBeyubGTuuI62UwM+UjlyMyahkLJ0RwWqsGR7ZVTjMDst0G6kduUVMrEoO2oMc7wIx7E3t4USwrDYcKC2PZQkNyOFmaNz+cDOfC7fWdln43BoVipTirNZtqOCY4YPYd2+Gm44YyJgjMIN+2u4/+3tvL+ziolF2YwvzGTYELNfQ0uQtXuraWoN8fqmMkptopjSmma2lZs4w7qWNo4rGcKxJUPw+4QtpXWMK8giGFICPmFYa5D1+2pZuqOCqoZWrpw7ijV7q/H7hCl2UOaKOaPYU9XIovWl1Da1sW5/LadPKXLljhyOAc6g/oW2BUPUNrWRl5nS4/iytmCIh97bSX1zkOa2IGkBP9lpAXIzUthT1Uhmqr89RfS8cUOpb26jeEg6w6L41k8oymovKJ4W8DG1OIfR+ZnsrWokMzWFzNQAxUPS2VfTxIYDtYCyZHslFx9vRgXX7KnmxQ8OAHD5CSNZsauKrQfr8fuEUycWUFHfQl5mCpOHZfPaxoOs2VvNCWPyD4l1uWDacFbtrmbisGxS/D4mFGXzhbMn8c7Wcr771Bo27K8lLzOVW8+bwixPYprtZaY0BmpiiPZUNbaPWIZLKfx79T72VTexr7qJnRUN1DcH2VZmjISQrZBbWtvMOM8s4bayel5aZ/QKhrSTUR3GjIr3f9a9aBncnl65l/3VTWSm+vns6ROOeJxcazBEXVMb+T0wnD4ys4RdlQ2U17Wwq6IBzNdBWsBH8ZB0DtY1IwgtbSGCqjyzai856QGy0gJU1Dezr6qZRetLUYwbbzihwZD0FDJSfVTWtfBc6QG2HKzjR5fNOMTV9plVe1m7p5rMtAAfO2kMw3LSOVDTDEBmaoBLZ48iLeDjwXd3AsalbZ9NZuD3CRfOHNHj67JqdxVNrSFOGJPX57IvsWhsDbYvN7UODHe6gUZ9cxvLtlfwztZy3th8kLagcus/6jjn2GKOLcnhgukl7Cir55cvbSIYMrFJw3LSOFDTTGVDC8Nz0lmzp4bXN5eRmeKneEg6k4ZlU5CdxrxxQ3nR1lOrbmwlxe/j3OOKKa9v4diSIUwelsOWg3XtCW/e32li94qHmCylQ7NSqWluIy8jlYq6FraU1jOlOJunVu6ltLaZc48r5tLZI7vUb6idCa1qaGHNniYrd8dARlqKj/zMVD5x8thDYmonFmWxri3I7NF5jPTUlT1a+HzC1OIc1u+v5ZguYij9PsHvS7zacekp/n6veaf2OST2nktP8R1WKEWYTQdqOVjbzMi8DAI+IahKRqqfqoYWRg/NZHdlQ5ffjZc9VY28uHY/+VmpfGRGSad+r7qxlQ376wBhb3UjGal+7l68mdH5GWwra2DxxlKqG1ppagmyvbyBktwMlDYO1jUzKj+DNzeXmSzCNnZcZB/HlgxhSEaAgzUHaG4L0RpULj9hlHnvWbKLg7XNnDK5kNqmVsrqmtl8sJ765jb21zTx9Mq9iAh7KhvbjcP0FD8Ti7IpqzWzm+MKM8nsJqmUwxELN3N59Bi0xmFVQwtPvL+HqoZWZo7KZf7EAl7dcJD0FD9nTCmKWQMoqNr+0jgqP5NzjytmSHoKe6saSU/xcdrkjmOHpKcwbWQuEwqzDjE+N5fWsmR7Ja3BECW56aQFTCzKDy6ZRm1TK/UtQdL8fnx+WL27hu3l9RyoaepUxLihJdhpOXyOltYgr2woJT8zhfGFWYwvzOKplXtRNSOWkS8sw4aksyAijlExLl7by+rZVdFAXXMbe6sbmR7M5c0t5bQFQ5wyqYDNB+uobWqlsSXIP5bs4rTJhczzJKAZU5DJhgO1ZKWaJBWrdleTnRbghDH5NLWGUNVDRvC9LmrNbUEeeHs7fp+Pi2eNIDstQGswxKYDtVQ3tDGuKPOIxwaGC8W3hZSe5U/t27n+/t5OdlU0kJ0WYHJxDqdPKmRHRQPpKX4mDctu3+/RZbtZtbua0yYX4hNh28E6UgJ+Lpw5nKnDc9hb2cjQrDRa2kKU5GbgE6G+OciY/ExuPW8KpTXNvLetgrX7asjLSGHNnmqmFufQ1BYk1S+s3l1DXUsbqkpOegqbDtSx9aBxW5o+Mpf1+2t4eV0ppTVN7eUsAOZPKKCyvoU1e6r51+q9nHNscbu7c15mCptK69h4oJbUgK/HmUs3l9bx8jrj/hRSPWIv3cePyqO1LYTPJxxX0reR/USkNRhi/b5aCrJToyZyUVU+99elvL2lnFAI/AEhK8XP3tYQZXVN1Ddn0tDSxsvrDlBZ30JOeoBgSBmTn8mYoRkMz03npPFDqW8N4rcvkt4SFikBoSQvg6bWINsO1pOR6mdoVhof8QwiDMtJIyvNT2NLqH1Q6fxpw5k9Jp/8rBT2VDayalc1KQFheK4ZIAnX7Vy9u7rb2bD8rFSuPWUc+6oaeXb1PlTBJzB/YgFjC4yHRjhGPHKg6Pxpwzl9snEhjFdCqw/PKOHc4/peNzfZqGtqZen2So4pyWG4fWYcqGni8eV7CPhM/dbVe6qZNCybT84fR1ZagA/21rB+fw2ZqQGmFGczoSibUEjZUdFAfmYKOekpNLcF292E91Y1cvu/19PQ0sZ504bzyVPGUdXQwjOr9tHSFmL+xAIumlnC39/byZMr9nLV3FHMiKjv6eWh93ayYmcVhTlppAV8nDi+oJO3hd8nTB85hGE56VQ3tNLcGrKuz2mk+IxHTUqGj1MmFjAsJ42apjay0wIEFcbZ2fi8zBQCPiE3I4XzphWzt6qRivoWapva2hPXBfw+PnbiGJo8uoJxUX5jUxkTirJIC/jYU9UUNcHRSRMKmDM2392TDkeCkPDGoYjcCcwFlqvql3tyzLayeh5ftpulOyqYNjKXXRUNpAZ8LNpQyq6KRjaV1vLZ0ydEfbhvOlBnE7cEmDtuKIXZaZTXNfP0KmN8ZaYG2jPDPfH+biobWlm+o5KPzCyhzMbnjMrP5NWNZeyvbmTV7mqKh6SRGvDx+qYyVu6u5mMnjmHYEDO69qc3tlHT2EpFXQuzRuVR3xJsf6GePcYEjKfYWcfxhVmszcsgNyPAc2v20xpU0lLMS8r0Ebms3VvD9BE9ix/x+4SJw7JZubuSzDQzG1qUnc76/bUs32GKoWelBfjKeVPZVdHAo8t2A8aF1su0EbmMKzCuZqkBHzNG5pKTnkJGqp8Lph+aUKC0tonSmiYunFlCSKG8rpmyOtPm5tI6Zo3O44W1B3huzT4q6luYMy6fT586ocuaTX3lwpkjWLevhvFFWfh9wqsbD7JuXw1zx+b3OBNrT6lvCbJyVxUbD9TS1Bri/GkhU3vNxohefsJIxhZkUVrbzMvrDrCjvIG3tpQxZ2w+Q7NT+Y8ZJVw4cwQPvruTd7aWU99sBj/SUszIdX1zG+kpPk4Ym8/U4UMoyUtnRF46e6uaKMpJ5ZlVe2luC1Fe38zw3DTQXEYNzWBUfiY1jS1sKjWu0vlZqVTWtzJ6aAapfh9zxuVTkptBazDErsoGhmalMGxIOg0tJkHCVfNGUVnfytThOeyvaUIEm6ik63pbYby15frDhThMRX0LT63Yg9/v45JZIxiSntLu4jgYeW2jqWvmE+HaU8YekjBoybZy3txcTtCOkgTbFDRIXoaPHeWN3HROAb9+eRNvbS6jNajMGp3H58+aSFrAT2VjKy1tIT48fTivbTrI1oP1nDmlkAuml7TPBmWmBvjw9BLe31lJMKT4RKhs6NynhGP3WoMdsXs+nzEEASYUZfPZM8bj9wlpAX/7ANzB2maO7aHBHx6YuTzgp7y+mWm234xmMHsRkQHhMjdYX8LL6pppbAkyemgmqsqyHZW0Bk0s9S9e2NjuAn/bRcfx5haTgKy8rpmWYIhdlY3tA1wV9S2kBny88MF+th6sY92+Wopy0vjyOZNpbguxzD4Dtxyso6rBJEiaP7GQd7eVU93Ugl987KtuJDcjhfrmtvbafhX1LeysaOD5Dw4QCin3v72dn185K6ouQRsr3hoMsX5/DSNy09le3sBnT5+A3xpzl58wktLaJqaNyOXNzWWs2VPDGVOKqGlsJSdjGBMLs8lJDzBqaCbpAR/LdlRy/vRiRudnsr2snpfWlTJpWDbDctK5Zv4YRuRlMm1ELuMLTfbT2aM7vHN8PjkkVvacY4uZN34oWdbrpKyuhcLs6B4vg/WedHTQ15k/x9Ej/k+xPiAiJwBZqnq6iPxOROap6pLujttb1YjPJ4zMzyQjxc/pU4poDRp/e0UprWnulOLce1zYjXPGyNyodaq8M0vhF6jS2ibueXUrH+yrYerwHP5z3hhG52ews7ye7DSTeCU8G1nT2Ep5fTOjUk38VnqKj5pGGF+YRW5mKtNGdCR+SfH7OMXjcpnu87e7Wl41N4UDNc3t6dkXHFfMOccO69Vo9sXHjyA/M8AfXt1KeoqfvdWNzBmbjwio0j6COXpoJvMnFlDV0ML8iYe+WHuvY7RRxTDNbUEeWbqblrYQYwsyufyEUeyvTuH9XVUEfCarIRi3tpAqwZDSFlSCemTn8/KzUtuvczCk7cbxsh2V/W4c5makUGRdMwO+NprbQgxJT2k3DsMvL29tLmNnRSOltU2kp/gJqXL86HwunT2qXeatB019yc2l9Zxz7DAaWkxsYlNriHe2VXDx8SOYNCyHScNy2uMW//TGNgCyUgOkBfzMGpPHx04aS2rAx1uby4B6fCKk+n3MHpNHfXMbKeN9nGavzxubylixq4qWthDZaX4y0wJMKc4hPyu1fYZ3xsg8qhpMFsns9J51QWMKMrls9kia2oKHJBnpCxv211JpC4RvLq0b9AXCW22npWjUgvPvbCvH+3MTIMUv+HxCSW4a6/bVsnR7BdvLG6y7fWq7gXnFnFHtx10ya2S7W34kU4fnMHV4Dku2V1Be18z8iYe6lXcXZ+x9iU3x+/jPE8ccVn3NMQWZcc0s6ug5pbVN/P3dXYRUOXNqEekBP69vKgPMPVpeZ9zda5vbeHtrObsrG1m1u4r65jYyUgNMKspib3UTxwzPYWReBiImKdIHe42HSzCkbDpQR16WGcw6UNPEnsrG9r5xT1UjtU1tFGWnUzwknSvnmGzgI/IyOHVSIRX15l5WVXLSTCbd0UNjJ73y+4T5EwvJy0ylprGFgN9HMKTWDdbcx+HwDTCG2oeO6foZ7w0pCXsejcrP5ILpwxmR13Gfh58LPaEjQVTHAI3D4UhsEto4BOYDL9nll4CTgW6Nw+NH53Gw1hhOC44tbndjvPaUcSzbYQLFo/nFpwV8nmygHdsLstO4dNZIyuubOyVtuXTWCDaV1lHf3MrL6w4CJsNYdWMr5x5XzLxxQ9l4oJa65jbGF2bx1haTCW+Ex03ystkj2VZWz+ih0Yszx2LYkPRDDLHDcXM6YcxQzphSS31zkHEFWYzKz+QTJ48lGNJOWcn6w81PlfbC9+EX0+G56dx45kSEDheuc48rJic9QF1zG7PH5B/VpCZ+n3BsiYnp6WtCgVh8cv5Y3thcRm56CrPH5lGQlcbK3VWkB/ztmWNrmtqYNiKHVL+P8UWZzJ9Q2Ok7+I/pw1myrZwd5SYLbWrAT2F2Gg0tQZrbQodk+0sL+EkL+PnIDBPDOHt0Pj4RMtP87S/hJ08ooCA7jSEZgfb6VN76adDxvaWl+Lhq3piodazmjM3n+FG5vR5JHtdF9trDZWKRyTLs98F4l52Us6YW2QGK1Ki1+4pzMhhXkMmeqkZSA8Lk4hwyAn7GFGTymTMmtmcAPVDTzMRh2RTlxP5tdhfXNa+fB14Gct1SR9+pa2prj2Ovbmwlv6Dj3ktPMfHiz67ex5yxeZTkZrKzopGA38eccUPJSPFz0cwSgqpMLMpuv1eumjeakycU8NjyPbQGg5w6uZCC7FTSAn5Om1TIkyv2sK+6iQXHFbN+fx0+EU6eUMC1p4zrJNuJ4zvfy7+48nh2VzZ0a4B9ZGYJ508rNslc9tUwrjCry36zN/f4CWPzaQ0qqdbzyOFIdlzMYs8RPcKzLkcSEfk2sExVnxORBcApqvqDiH1uAG4AGDNmzJwdO3b06Zz7q5uobmxl8rDsHicmaQ2GeHNTGRv21zKpOJvTJhf2KeD9aNPcFqSp9ehkiNtd2cCuikamjxzSZe3HeBOZiKKvzJ07l6VLl/Z4/33VjazaVUWK38cxJUOiuruV1TWzdHslYwsy293pmtuCNLWEjpgbbnNbkJW7qhmaldoeHznQ8SakcEQnfH+2BkM8vnw3KT4fl8weSUNLG34RMmyMXU1TK+/YUhQ56SkD/nfsSHzC96aq8t62Cuqa2zhlYiEZqX52ljfQGgpFjTOtbmylqsFklp1YlH3Y/VX4WVBa08SWg/UcMzynR8nEHIODyGe7c61MXJLNOBSRZao6N9q2RJ85rALC0zdD7OdOqOo9wD0Ac+fO7bMlPDw3vdeuEyl+H2cdM4yzjhnW/c4DkPCs0tFgVH4mo/IHvhvXkc5Y2h0luRndJuIpzE47JK7zSH+XaQH/IaPkAx1nFPacFL+Pq+d1FKCPNPyGpKcckg3Z4TgaiMgh8cJduQTnZqSQm5HS53qm4WdBNG8dh8Ph6C+O5sxnokcIvw2cY5cXAO/EURaHw+FwOBwOh8PhSFgS2q0UQER+BZwArFTVL3az70FgB1AIlB0F8Y4WTp+BT3c6nQAs7+G+iUiy6ZRs+kDXOnnvz+72TRQSXYdElx/6R4fIezOeDKTvZCDJAoNXnvD9OdD0P9IMNn0h8XQeq6pF0TYkvHF4OIjI0lh+tomI02fg0xudBrv+iUCy6QOD7x5NdB0SXX5IDh28DCR9BpIs4OQZaPofaQabvpBcOie6W6nD4XA4HA6Hw+FwOPoBZxw6HA6Hw+FwOBwOh2PQGof3xFuAfsbpM/DpjU6DXf9EINn0gcF3jya6DokuPySHDl4Gkj4DSRZw8gw0/Y80g01fSCKdB2XMocPhcDgcDofD4XA4OjNYZw4dDofD4XA4HA6Hw+HBGYcOh8PhcDgcDofD4XDGocPhcDgcDofD4XA4IBBvAY4GIjIHOBnIB6qAd1R1aVyFcjgs7v50DHTcPepwxEZEfEAxcFBV2+Itz0BDRNKBFFWtjbMcaUAeUKaqwSN8LtdnDgJEJAv7HatqXbzl6S+SPiGNiNwJpAEvAdXAEGABEFTVm+Ip2+EgItnAjZhOJw/b6QB/iHfHezgkmz7QO52S7f4EEJHpwI8wuvgAxVyD76nqqjiKdlgkmz7QO52S4R5N9H4m0eWH5NDBi4j8SlW/LCIXAf8DbAbGAveq6l+OsizlwLPA48Bzqtp0NM8fRZ4bgOuBOuBB4FogCCxS1R/FQZ5Tge8AWcA0YBWwE/iuqu44AudL+D6ztyTjc7IrRORDmN99jf0bAuQA/6uqL8VTtn5BVZP6D3itN+sH+h/wFHAlMBTwY0YsrgSejrdsTp/e65Rs96eV/XWgJGLdCOD1eMvm9Om9TslwjyZ6P5Po8ieLDhH6vGT/LwIy7bIfeCsOsiwCZgHfB94FHgM+AeTG6dq8Zf9nANuAgP38ZpzkeRXIsMtFwCNAwZG695KhzzwMnZPuOdmNvm+Ef/eedVnxusf7+28wuJUuFZHfY0Zwwtb9OcDyuEp1+BQAj6lqyH6uFJHHgJvjJ1KfSDZ9oHc6Jdv9GUa6+ZxoJJs+0HOdkuEeTfR+JtHlh+TQwct2ETkbWAnMF5HXgOOBeMyCqqquAFYA3xORScBlwJPAWXGQxyciIzEDAAIME5FqjPEcD/yY2epGjHGYo6rl1iXwSJAMfebhkIzPyVg0AzMwgzFhZgBxnbXvL5LerRRARGYD8+lwZXlbVd+Pp0yHi4h8DOOaswrT6eRi3CT+qKoPxlO2wyHZ9IHe65RM9yeAiEwDfoiZIfABIaAcuE1VV8dTtsMh2fSB3uuU6PdoovcziS4/JIcOXmz82k0Y42si5nfxFvAzVd1/lGW5U1VvOZrn7AoROQP4CrAGY6D+HGjDXJvn4yDP8cAPMMZqKfANVd0sIter6p+P0DkTus/sLcn4nOwKESkBvgHMxAw+BDEDRT9X1T3xlK0/GBTGYbIhIgFgCubhWgVs0gQOgk82fSA5dXI4EplE/00muvyQHDo4HA5HsuOMwwRDRPzApURkwQL+mYgP2WTTB5JTp94gIiOAbwHHYkbUQsAHwO2qujuesh0OyaYPJKdOXZHov8lElx+SQ4eeICK/1gGSdGQgyQIDUp5fqeqX4y1HMjAInymRCXhCmORD39MkSMDjjMMEQ0QeAFZzaBas41X1E/GU7XBINn0gOXXqDSLyMsZtZ4ln3YnAT1T1nPhJdngkmz6QnDp1RaL/JhNdfkgOHSIZSOUKBpIsTp7BxyB8prwOXK2qez3rRgAPq+rp8ZOsfxgMCWmSjXGqek3EuvftjZqIJJs+kJw69YYMYG3EurV2fSKSbPpAcurUFYn+m0x0+SE5dGgnolzBeoyx+ykRueZoz0YNJFmcPIOWwfZMiUbSJOBxxmHi8aSIPAMspiML1pmYNOGJyFNJpg/E1unpeAp1FPk28IyINGAy9w0B0jE1gRKRZNMHklOnrkj0fiZS/lzgDBKrT4n17EokHbzMUdUzItY9YbOWDmZZwMkzGBlsz5QbgbtEJDIBz+fjKlU/4dxKExARKQROpCML1hLMqOySLg4bsIjIaZgUwFUYd6MlwARVfber4wYyIjIXk3ghnMXKp6p/ja9URxcRycDeo6raGGdx+kyy6QPJqVMsEr2f8fT74WQuc1X1h3EVqhfY7H6tdDy7xmEKkT+UiDGHInIHkMmh5QqaVfXmwSqLk2dwM5ieKcmMMw4TDBHxxdj0vKqee1SF6QdE5P+AYRgDqgC4XlUPisgrqvqh+Ep3eIjIn+xiC6am0l7MA2mYqt4QN8GOEiKSDfwXhyae+IOqxqMGWJ9INn0gOXXqikTvZ6zrZfhhHXZdOg5YG2VGZEASvtYi8mugno7C7XNV9aq4CneYDKRyBQNJFifP4GMQPlOSOgGPcytNPOowPzgvgqm1kojMVdUzAURkJvCIiHw1zjL1lUkenVar6hV2eVF8xTpq/A34K/AXOiee+BtwURzlOlySTR9ITp26ItH7mScwffxCVV0MICL/VtUPx1Wq3hGy/49T1QV2+YVE7hetcTEgDIyBJAs4eQYhg+2Z8gDRE/Dch5mVTmiccZh4rAMuU9Vq70oReTFO8vSVgIikqmqLqq4SkcswHcy0eAvWB7y/q295lpMmWLkbCoBHVTX8MlgpIo8BN8dPpD6RbPpAcurUFQndz6jqHSKSCnxGRG7EvHAlGveJyL3ALhH5K/AqxuB1GSMdjsRnsD1TkjoBjzMOE48LgWh+3Ik0guzlFoybRymAqlaKyMXAlfEUqo/cICJ+VQ2q6tMA9sXujjjLdbT4LbBYRFbRkTxjGnB3XKU6fJJNH0hOnboi4fsZVW0B7haRe4BrgJVxFqlXqOoDNt39+UAx5v3jXlVNKD0cDkdUBtszJakT8LiYQ4fD0e+ISACTkCecPGNTIiadCJNs+kBy6uRIfESkTlWzPZ+vw7gFfzF+UrXLsh3zIggmzuhx4Ieq2tzP5+l0DQYCInIzcI+qNkTZthi4tTd1A/tDRxGZBfwO82IeBH6sqg/bbeOBh4ChwHLgGlVtEZGPA1+3TdQBnwsPUIjInzED8KWqOr2L894MVKjq/SKyEHhGVR+N1E1ExmG8vdZjDIda4Leqep/d7zrsvS0iXwTqVfUvfbkm8WQwPlOSNQGPmzl0OBz9ioj4gUuICEwXkX8m4oMi2fSB5NTJ4ThKnK2qZTYBxz3279o4y3Q0uBnjin2IcRhHGoBPquommyBkmYg8r6pVwE+BO1X1IRH5PfBpjCG5DTjTeg98GPP9nWTbWwjcBdwf64TWALoeOKGHMm5R1dn22AnA4yLii2IE/hl4ExOzl3AMtmdKtAQ8IpI0CXhiZb50OByOw2UhMBH4O/AT4EFgvF2fiCwkufSB5NTJkeSIyEIRucLzuc7+P0tEXhWRf4jIRhG5XUQ+LiLvichqEZlo97tIRN4VkfdF5CURKbbrbxORP4vIYhHZKiI3dSeLqtZhap1dKqbWGSLyVRFZIiKrROT7dt1PRaS99pk911di7R+hr4jIz0VkjdXjao++r4nIEyLygYj8XmwmcxGps+dcZnU80aPXxXYfv203fO7/8rS7WEQeFZH1IvKgleEmYASwSLpJIGTP/2MRWSki73iu8XgRedue84cRx0S7bpdZ+UVESuz3OjziO9ioqpvs8l6M23iRiAjwISA8m3cfcKnd7y1VrbTr3wFGedp7DajoSj/b7vLDMXhUdSvw38Ah95edkd0uJqlJIrKQwfVM+RuwC2Mgng98FthBYsaDH4KbOXQ4HP3NOFW9JmLd+2LS8SciyaYPJKdOjuQgQ0RWeD4PBZ7qwXHHY9LKVwBbMfGMJ4rIl4EvYWa+3gBOVlUVkc8AXwO+Yo8/BjgbyAE2iMjvVLW1qxOqao2IbAMmi0guMBlTx1GAp0TkDIxr4y/piL26CrhARM6Ltr81UMJcjin3cTxQCCyRjsLtJ2LKmewAnrP7PgpkAYtV9esi8gTwI+Bcu+999lp+GqhW1Xkikga8KSIv2HZnY2LF9mJmsk5V1V+LyH9jZ027uib2/O+o6rdF5GeYl+YfAb8CfmddMb8Q3rmL6/CEiHwU+AJwAfA9Vd0f66TWqEoFtmCSo1R5DLjdwMgoh30a+Hc3+kRyKrAsYt3PReQ7PTx+OeZei8ZS4HTgvV7KNBAYbM+UpE7AM6hnDkUkzzuiF2OfcSLysR60NU5E1vSfdD1HROaKqR11uMd/K+LzW32X6uhzJL8DEblORO7qYxt1/SXPAOcpEXlGRG4VkRvs/6fp2QveQCRSn69afZ6Ot2B94MkY39ER0UlEfiAiC6KsP0tEnjkS54whx3Vi3M+62699hkpE7hWR4+zyt7o+sl9kPKxz2NmeCUdAngIRWWRnhO6K2DZHzIzWZhH5tZ2xQUTSRORhu/5dMbFX4WOuFZFN9u9az/rxIvIuxkDYAJyoqrOA7wKni8hm4GLMbEQ4yVeGGDc/gCWqus/G/20BwsbOaiB8/lHA8yKyGvgqnbPVPquqzdb4KcUkzenRJbL/z7N/79NhAEy2JRSGicgIETkeqFTVnbH2j2j7NODvNrnZAUyG13l223uqulVVg5jZmtPs+haMsRjW/VVr5Hqvw3nAJ60R/i7mRXeyp93d9qV3heeYntIChH/TyzzHn2rlBFMGIExX1+FLwDcxxer/TgxEpMS2+Skrd7TM4BpxzNkY4/DrUfbtihLgYMS6r6rqrPBfN8d3lbW8FDNDm4jEeu4n8nOyK8IJeO4Skf8Vkd8Cr5AkCXgGtXGICSLt0jjEdGzdGofxRFWXqmq3bjBd0OllRFVP6aNIRxQxvu0DAs+LicOiqj8HbscUuq7FFIa9Hnitq+MGKlaf6zB61AJrMC9QP4unXH3kQTp0qsOMuD/MEcqoq6rfVdWXjkTbPcX2G9fRy5cvVf2Mqn5gP/bZOOxB/9Xrc4jINMBv3db6RJQ+rQmTge/WKLv/DrgB8zI/GTPDA+alu1JVJwF3YuK/EON++T1MjNeJwPdEJN8e81O7bwNQadsAU+4iz7b/uj0fQCvmhf9q+9mbFCbk+Ryiw0vqN8BdqjoD4w6W7jnGe3yQHnhWiUgO5h1hI+al/yceI2GSqv7J7voocIWV9aHw4V3sj2efWERmEwx/btWOTIPt18EaTWGdBPiS59zjVTVsTPf6OkTgPX/k8dEyIHZ1HUZaHYrFus0ecrDIEOBZ4DuqGq4BXQbkee7lUZiZ0PAxM4F7gUtUtbyX+jXS+b7pLbMxSWqikU70bPQDnojnZDGwB/hBgj8nY6Kqf8O4GN+NMYDvAhao6oNxFayfGOzG4e3ARBFZIcb//hDffrvP6XafW+zs1Osistz+9ciQsqOn0zyfF9tR1xNF5C0xMRBvichUu90vIr+wsqwSkS/Z9fPsfivFxFPkeEffpYvYCRH5p5g4hLUicoNddzvWjUdEHrTrwnEcEu2aSIy4hHB7YmIgVonIL7q4HgvFxEm8LiaW4EKP3rFiIRaJyN8wI6Cx8IvIH62OL4jJJBW+3nPtcqGYrHPhmYTHReQ5O5Ld3pGJyKesbK9iRj29st8hJvbipyIy0R6/zOpzjN1vvMSIsUhmROT/MC9eJ2EGVpao6kFMHELCIcYt5nHgG5jBpG8BN0uHe1ci8qCdIbkAmIFxZxpBlHgJEfmk/S2uFJEH7LqxIvKyXf+yiIwRkVwR2S4dsU+ZIrJLRFKk80zcBbbfeAPjChcT25/dZ3/L20XkchH5me2PnhORFLvfObYPXW37vzS7fruIfNee6/8Bc4EHbX+XYbctsX3cPeF+LEKGxWK8Mzr1lSLyQzEui+H9fiwxYtWi9V/S8/74E2L6+hUi8geJblx+HHjSc746Efk/Mc+ol0WkyK6P1Vd16tO8Datqvaq+gTESvTqVAENU9W1rDNyPjevCJKa4zy4/Cpxjr+35wIuqWmHjvl7EuFjGjBHDvEyvt+dYAgy1574EkzH049GueQxyMS+t0MckMmISUtwN/NPq8jxwvV2PiIwUkWF294eA/8QYiGEdu9o/zGvA1WKei0XAGXS4HJ5onzE+jNH5Ri/Efx74nOf3M0VEsro5phbjcnu4vIm5BtD5O4t6HcQYdn/BPEPWYWL1OiFm9vgJ4H5VfSS83t4rizDXG8x3/aQ9ZgymP79GVTcehh7rgEmHcRxiZtB/gRmkiMYUzOBjwiEiz9lnyhTMsz8XuMn2aUmHdCTg+RRmIOvTmPjj5JgwUNVB+4cZ8Vtjlz+KeVD5MaMeOzHuA2dh0hSHj8kE0u3yZGBpZFsxznUL8H27XAJstMtDgIBdXgA8Zpc/Bzzm2TYUM7q/FZjnPdYrI3Ab8BaQholRKAdSwm3Y/xmYDqjAfq6LkLWuB9ekGjMa5wPexri0DMW4A4VLpOR1cT0WYlxffPY67saMmt2AGQHE6rAU40Z0FmYmanw332cbMMt+/gfwCbu8GJMyGntdttvl6+w1zbXn3wGMtnruBIrsdX8TM+Iclv0ZzEg9wMsY9yEwneIrdvkpTCY1MHETdbFkT6Y/jBtTeHmmvfbzwtcl0f4wLyULgbM86/4db7n6qNNL3v+e9YsiPk+zv+lC+znchzwNXGuXr8e8IIN5ATvbLl+NifsK/2ausL+xXfY3L/Y3+kwXct6GeelNwcRdNQAfttuewBgQ4Tan2PX3Azfb5e3A1zzttfcDXn3s8gPARV55I4/x/oYx/c1yu+zDxjrF0OMsIvovetAfY2LonqajD7873KdEtP8qMMPzWYGP2+Xv0tF3xeqrFuLp02LocF24Hft5rvf+wcRKhZ9Da4BRnm1bMP3urdj+3a4Pz0gWApvD+mP64PCzeQUdz8VizLNnDWawqQ7j4ncWnZ/T3u+sfRvmZW4rZgby55jYPDD32a2e49dgYqgir8F2jHG/BjND8mPs+4Dd/mW7fTXmuTjRs201h/6+ou5PxzNYrJxr7D5Xe3R6BTPb/wHwe8AX5f6J1Cvcrg/4X48uizDPwMjreBdwnV3+EqYkw6Io18V7vb3nvwJYaJfHWx2XYAba6rq6Dpj79g67Pcee+9iI834CM4O8wvM3y26bgDGkNwOPAGl2/b2Ymenw/ks97f0d2Gfb3A18OoquY4HXPJ8XYvuKKNd5HGYm8H2MUfkexvU11m9qObavTbQ/OvqSV8P3ov38RrxlO0L6PoCJWT7B3q+zMa7qf423bP3xlxwWbv/Q7tsPHBAzWzQPU8zTSwpwl5j6OkHMKElP+AfG0PoeJiA9PMqVC9wnIpMxD/QUu34B8Hu1AdWqWiEiM4B9qrrErqsBiDLY/ayauItmEQnHTuzGjOJcZvcZjXlB68qloqtr8p6q7rbnX4HpBN/BjC7fKyLP0hF3EPOaqHF12SQiWzGxBucBM6UjI12ulbPFnnNbN21uU9UVdtkb79AVL6tqtdXlA0znX4h5cTho1z9M5+/6EVUN2tHOU4BHPN9Dmv1/KsbABtORdBqRT2ICIpKqqi2qusrec3+lc3xPwqCqd9gR6s+IyI0kRzay+0TkXmCXiPwV80CfiRmM8fIhTNB9GZh+yK6fT8es3wNAeMb9YYxRuAgzSxAZf3EM5je6CcCe+wa65t+q2iomTsxP53iqccBU22Z4FuA+zGDMLz0yxeJsEfkaZtBvKLCWHsbIqOp2ESkXkdmYPvZ97dpFLbL/6kl/fA4wB5OIBIwhWRql7cg4qBAdev8Vkz6/q74KbJ/WhfyRdBXXFWtbt+vV1Icb7WlrD8bdFFU9ICJLMQb/MuCbIrIHWKaqF3raOMuzvBhjvKCqT+KZYfXsc1vE56g17lR1XLT1nu2/wiReibZtRk/3V1v/T81b6FftXyQNqnp15Er11A6Mole43RDGAyLShXmx/Qvv/0XP8m+IMeMVcb29538UO1Nq7/35nsNu9+wX7Tr8wLO9lihJXFT1r5j7O5pMWzGuy5HrPwN8JsYx/y/a+oh9dtjf/WRV3aSq10XZJ3ydt2N+s7HaWojN5mn7kbXafcKfgcpxInI/xlBKo8M9ti8uuAOZcZrECXiccdhBV779Xm4BDmBGsX1EuNrEQlX32A5lJubl6b/sph9iRuIusy4Hiz3yaEQz0dZF45CYARE5C2NwzlfVBjFFa7v70XZ1TQ45h6q2ickYdg7mxfCLmJfLWETqEn5J+JKqPt9JECN/fTfyRpMr3DG30eFGHal3rBiLrq51WBYfJivarBj79eT7SjZuwcQIlQKoqSd1MXBlPIXqC6raAtwtIvcA1wAr4yxSn1DVB0TkZYybXzHmnr9XbTFoDz3tc8L7PAX8RExs2RzM7EasfXtKe8yUiETGUwXovu+O2m+ISDrGeJ2rqrtE5DZ6/yJzL2b0fzimTlmP5OhFfyzAfar6zW7a7i4OSum+r+pJ/+plN54yAHSO69qNMXh3WzerXEwW0d2Y2SnvMYvxxIjZAdFobUU7D5gX0R49hx2OfuIbmAGZTf3YZiFmJj1RCdeK/B/M+1bY9TqRdeqKJ8WEcy3GTJgMAc4kcRPvdWKwxxx6fehj+fZH+tnnYmbvQpiXxN4kR3kIMw2dq6rhuLlcOmIgrvPs+wJwY9h/2b5srQdGiMg8uy6nF/7NuZgEAQ02zuRkz7bWcOxBBF3FOxyC7QhyVfVfmHS+s7qR6UoR8YmpQTUB4752OLEQPWE75mUVOuIQuuJd4CwxmfpSiGHY2NnbbSJypZVXxGSkg9gxFkmNqr6nqqUR64Kq+lCsYxIFVW1T1b+o6jfiLUtfUdW9VpfbVfV3UQxDMG6IV4lIAbT3Q2Bc17339hu2zTpMH/ErjHta5EzUemC8/c2DiQPsK+uBcSISjgO6BjMTGg1vfx42psIFzXvSL0T2lU9g4jbnYfquntLT/vhl4IpwLJqIDBWRsVHai4yD8tGhz8cwrl1d9VW9RlX3AbUicrKYqchP0jEr9xQdMX1XYFzOFHONzhORfDGJaM4DnrfbFhElRsy29Ukr78mYEgz7rA4FwEHtpuREsqGqi70zpY6ji6pu0M4lR/qjzRftTGNCoqo7PH+tdl2dqva2VEhCoKq/oHNSt3DivcXxk6r/GNTGoXUBelNM+YP5wCrMjMArGLeV/XZdm5hkDLdgRpqvFZF3MG6GvRltfRTzQvUPz7qfYUba36SzoXkvJuZtlYisBD5mZy+uBn5j171Iz0e6n8PMIK7CzFa+49l2jz1PZJalJ4h+TWKRAzxjz/EqZgapKzbY/f4N3KiqTRi9PwCW2+/lD/TPDPcvMEbnW5gRui6xLx+3YeIfXsLEAsTi48Cn7XeyFhPXAiaO4gsisgTzMuhwJBSquhYTV/Wqvb/D2UxvAj5lf+vXYO71MA9jYoEOcee0v/EbgGfFJInZ0Q8yNmGSAjxiXU9DmBisaCwEfi/GFb4Z+CPGPfWfmFio7ujUV9o+eRHGRb43Lpk96o/VZEn9DvCC3fdFzIxFJM/SeUauHpgmIssw3hthF71YfVWXiEngdQdwnYjsFlvaAxMbfy8mrmsLHTXj/gQUiCk/8d+YmZawW/IPMdd6CSabYdhV+evAf9tjCmwbAP/CxAluxnxf3gzjZ9vtDofDcdQQkwiqAtOXP2T/l2NieROecOIQh+OoIiILMTMLj3a3r8PhcAxE7AvCcuBKtXGUcZIjA2OknqomFrrOG/uVrIjI48A3VXVDvGVxOByDBxFpoPOgHpgwgJmqWhAHkfoVF3PocDgcDkcvsbNnzwBPxNMwBFDVRhH5HqYu3M54ynK0EJMk6p/OMHQ4HHFgHXCZ2mSGYUTkxTjJ06+4mcN+RkTO59CslNtU9bJo+yc7IvJtDo3Xe0RVf9yHNgswsTiRnKO9L2ibsNgEGnXW9z3a9hsxGe3uH4gztSLyL4y7dNVhHHspphzMB/bzDzDpxeNaaP1IIiJvqeopNnHVM6o63SY2uVVVL7RJf45T1YSrKyUin6KzayrAm6r6hXjIc7iIySj9QMTqZlU9Kdr+DofD4Ug8xNRaLbehBd714aRaCY0zDh2OBKU74zBi34UMMOOwLySiPiLi72VcWqx2xhHFOOxruw6Hw+FwOByDOiGNw5FoiMi3RWSDiLyEqe+GiHxWRJbYpEmPiUimXX+biNwacfw5IvKE5/O5Nm4n2rk+JyI/83y+TkR+Y5f/KSLLRGStiNzg2ecCEVluZXnZrssWkb+IyGoRWSUiH7Xrt4tIoYiME5F1IvJH294LNoYqqm4icgpwMfBzEVkhIhNFZKHY2phWx/ft+f4sImme833fyrdaTJZIRORM284Ke5w3O7H3epwlIq+JyBMi8oGI/N7GnCEi54nI27btR8Rkvwyf87tikq9EzXgrIotF5E7b9joRmScij4vIJhH5kWe/umjHR3w/d9nlsSLysr3eL4vIGLt+oYj8WkTeEpGt0lFP1OFwOBwOh8MZhw5HoiAiczDZbmdjCpDPs5seV9V5qno8xg/+01008wpwrJjSJGCyPP4lxr6P0lHoHEym3HAGyutVdQ4wF1PMu8C2+Ufgo1aWsDH0P5j08zNUdSbRa99NBn6rqtOAKuCjsXRT1bcw6e2/qqqzVHWL5xqlYzJSXq2m6HQAk1ExTJmqngD8DggbzrcCX7D1306no3hvNE4EvgLMwBT7vVxECjEZJRfYtpdiMjSGaVLV07op5dGiqmdgsmw+iSniPh2THfJwgtvvAu631/tB4NeebSXAacCFeApROxwOh8PhcDjj0OFIHE7HJL9osDXLwsVWp4vI62LS+H8cmBarAVtP7AHgEyKShynhErUOkaoeBLaKqWNWgJmpfNNuvklMOvx3MAWqJ2Nqtb2mqtvs8eEU9QuA33rarYxyum2qusIuLwPG9VY3y1Tb1kb7+T5Mfc4w4VlS7zneBO4QkZuAvG7iBd5T1a3WPfTvGCPrZOA4TFmcFZgabd5adIeUdIhC+LtcDaxV1X2q2oxJ4T869mExmQ/8zS4/YOUM809VDdl4zeLDaNvhcDgcRwkRKRaRv1lvj2XWS+Uyu+0sEam2Xi8brAfKhZ5jbxORPdYzZo2Y2PSEQET+JSJ59u/z3R/R6dhMEXlWRNZbj6TbPdvSRORhEdksIu+KCdVARGbZa7vWet1c7Tnmi3Z/tQPCsc47W0TutcvRvLe2h48X4wkWPtcKETkpYt/bIj7PEBNSc8RxxqHDkVhECxJeCHzRzpR9n+5rX/4FU4fu/2GSA3VlDD0MXIWZyXtCVVVMnNsCYL6d0XvfnlNiyBdrvZdmz3KQjkzKC+mdbtLD87SfwyZw+QyQAbwTdjeNQaQeas/5op3FnKWqx6mqd/a2J7VQw3KF6HwtQvRPVmmv3N72u7teDofD4YgTIiKYOqyvqeoE67Hzn8Aoz26vq+psVZ2KqUF7l4ic49l+p/WMuRL4czgcor/k68/2vKjqf9iEdXl0rm/aU36hqsdgvK1OFZEP2/WfBipVdRJwJx1JJBuAT1oPpguAX9pBdDCDyAvovjbvt4DfdCeYiMzHeO+cYD18FgC77LbTxNTHvlFE3hORDwGo6mpgVDhM5EjijEOHI3F4DbhMRDLExMVdZNfnAPtEJAUzu9YlqroX2ItxhVzYze6PA5diDMnwDFgupmNtsIbUyXb928CZIjIeQESG2vUvAF8MNygi+d3J6CGWbrV2WyTrgXEiMsl+vgZ4tasTiMhEVV2tqj/FuIR2ZRyeKCLj7cPwauANzOzpqeFz2hHLKT3Q7UjyFuYFAsx1eyOOsjgcDofj8PgQJuzg9+EVqrpDVaMaINYD5wd4nrmebeuANqDTzJed4XpARF6xse6f9Wz7qpi4/1Ui8n27Lpwn4G5MndfREe3Ns3HtK61xk2OPeV1MXP5yMbkDuovlD8+y3Q5MtLNrPxeTx+Bl6cgfcEkUXRtUdZFdbrFyhg3qSzBeRWDCZ84REVHVjeGyRPY9qRQosp/fV9Xt0a65R+8cTJ3DlV3tZynBhLk02/bL7DkB7gC+iQkzORfY7DnuaTqe7UcMZxw6HAmCqi7HGGgrgMeA1+2m/wHeBV7EGEc94UFgV7gURBfnrAQ+AMaq6nt29XNAQERWAT/EFoK1bqg3AI+LcTkNG5M/AvKtS8tK4OweygixdXsI+KoYV5qJHnmbMHGUj4hxRQ1hOtiuuNkjWyMx3Gwtb2MeVGuAbZjZ1IPAdcDf7TV5h64NzKPBTcCnrDzXcGiZCIfD4XAMfKZhDJvesJwozyDrthgCDkY5ZibwEUxIwndFZISInIcJGTkRmAXMEZFwmMZUTFz7bFVtn00TU3/0YeDL1rNoAea5Wgqca+Pyr6ZzHPwhsfwRsn0D2GI9c74KNGFqDJ6AeZ/4PzvDGhU7+3cRHSXQRmJn6aznVDVQEHHMiUAqsIWeMxfzbtATXgBGi8hGEblbRM70bGsBhlv5qlXVW7t2KSbE6IjiSlk4HIMQMVkt31fVP8VblkRBXNkIh8PhcBxFxMTCj1fVW+zn32JiyFtUdV6055KIzAb+pqrH2ri1z2IMwlrgW6r6esQ5bgN8qvpd+/l+jNfQacAVmCRxANnATzBG1iJVHR9F3hnA71X11Ij1uZhEabMwYR1TVDXTyv8Dm5ANEbkeM/t2s4hsxxhc2djyTXafFIw76BkYY3eqvUb7o8gTwMy2Pa+qv7Tr1gLnq+pu+3kLcKLaOtliahguBq5V1Xci2tsOzFXVsijn+hhwhqreaD9/D6hXT7kxe/wcVS0XET/G0Dsb+C/gG6q6UESOw7i6ngo8D3wzPGspIpOBh6x78RGjP2JZHA5HAiEiyzBxcF+JtywOh8PhcDhispaO7N2o6hesq+XSLo6ZjcnuHeZO7b4ecqx4+p+o6h+8G8QkcIkVSx8rx8AtwAHgeIzXYlM35+6Kj2PcPeeoaqs1uGLlI7gH2BQ2DC27Ma6wu63xmAtUAIjIEOBZ4DuRhmEPaIyQoxzjPuolB2ts28R2i4HF1tPpWmCh9ei6SER+DOwB/gSEY0jT6Tqjer/g3EodjkGGqs5R1TPCvu4AYjJ2rYj4mxFPOeOFmIxgkdfiXVVd3JdZQxH5bZR2P9WfsjscDocjqXgFSBcRb0mmzFg7i8hMTDjGb2PtE4NLRCRdTGbys4AlmFmr66Wjbu9IERnWTTvrgREiMs8ek+MxwPapaggT6uD3HBMtlt9LZI6BXKDUGoZn0zk7eDti6gTnAjdHbHoKY4iBmRl9xSbbSwWewLjLPtKNntFYB0zyfH4NuNjGIiIilwMrVTUoIlPtLGCYWdhkNyIy3a5rxbgIe3WfQs9dVw8bN3PocDhQ1ZO632twYDOCzToC7X6hv9t0OBwOR/JijZZLgTtF5GsY99B64Oue3U4XkfcxRmMpcJOqvnxIY13zHmbGbAzww3DiOhE5FnjbhvTVYTKdB7uQt0VMCYjfiEgGZpZrAXA38JiIXAksovPMYziWfwbGoHoios1yEXlTRNZgcgL8FHhaRJZicjAckmtBREYB37bbllv571LVezEzcQ+IyGbMjGE4wctVGFfVAhG5zq67TlVXWPfer2FiAVeJyL9U9TMRcq4XkVwRyVHVWlVdZUN43hARxXw34WOy7TXKwyQJ2ozJ2QDweRGZhZndvJTOxu3ZmO/piOJiDh0Oh8PhcDgcjkGIjTms64Hr6ZE491kkUSy/iNwC1FojtK9t3aaqt3k+p2Gyr5+mXZcg6zPOrdThcDgcDofD4XA4+sbv6FxLuC8sjvg8BpO05ogahuBmDh0Oh8PhcDgcDofDgZs5dDgcDofD4XA4HA4Hzjh0OBwOh8PhcDgcDgfOOHQ4HA6Hw+FwOBwOB844dDgcDofD4XA4HA4Hzjh0OBwOh8PhcDgcDgfOOHQ4HA6Hw+FwOBwOB/D/Acg9VNMXZXvUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(dataset, figsize=(15,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3bf34c43",
   "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>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>167.56</td>\n",
       "      <td>12632</td>\n",
       "      <td>508.116635</td>\n",
       "      <td>0.846</td>\n",
       "      <td>18728.121895</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>230.55</td>\n",
       "      <td>13774</td>\n",
       "      <td>204.055391</td>\n",
       "      <td>0.942</td>\n",
       "      <td>51203.554473</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>309.24</td>\n",
       "      <td>13653</td>\n",
       "      <td>58.925903</td>\n",
       "      <td>0.925</td>\n",
       "      <td>39312.660373</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>258.91</td>\n",
       "      <td>15359</td>\n",
       "      <td>187.373461</td>\n",
       "      <td>0.918</td>\n",
       "      <td>33486.671984</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>202.85</td>\n",
       "      <td>10561</td>\n",
       "      <td>345.176169</td>\n",
       "      <td>0.921</td>\n",
       "      <td>70248.629000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>175.02</td>\n",
       "      <td>12927</td>\n",
       "      <td>253.532968</td>\n",
       "      <td>0.758</td>\n",
       "      <td>10045.680500</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>68.37</td>\n",
       "      <td>10630</td>\n",
       "      <td>220.946125</td>\n",
       "      <td>0.715</td>\n",
       "      <td>9414.226231</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>67.99</td>\n",
       "      <td>3987</td>\n",
       "      <td>559.112538</td>\n",
       "      <td>0.795</td>\n",
       "      <td>12221.496606</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>190.06</td>\n",
       "      <td>18958</td>\n",
       "      <td>12.065077</td>\n",
       "      <td>0.745</td>\n",
       "      <td>4426.000637</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>118.72</td>\n",
       "      <td>11503</td>\n",
       "      <td>163.466806</td>\n",
       "      <td>0.714</td>\n",
       "      <td>9998.544311</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "59                           167.56                           12632   \n",
       "49                           230.55                           13774   \n",
       "69                           309.24                           13653   \n",
       "85                           258.91                           15359   \n",
       "142                          202.85                           10561   \n",
       "..                              ...                             ...   \n",
       "88                           175.02                           12927   \n",
       "116                           68.37                           10630   \n",
       "20                            67.99                            3987   \n",
       "135                          190.06                           18958   \n",
       "55                           118.72                           11503   \n",
       "\n",
       "     covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "59                          508.116635                               0.846   \n",
       "49                          204.055391                               0.942   \n",
       "69                           58.925903                               0.925   \n",
       "85                          187.373461                               0.918   \n",
       "142                         345.176169                               0.921   \n",
       "..                                 ...                                 ...   \n",
       "88                          253.532968                               0.758   \n",
       "116                         220.946125                               0.715   \n",
       "20                          559.112538                               0.795   \n",
       "135                          12.065077                               0.745   \n",
       "55                          163.466806                               0.714   \n",
       "\n",
       "     GDP per capita 2021 (US$)          IncomeGroup  \n",
       "59                18728.121895          High income  \n",
       "49                51203.554473          High income  \n",
       "69                39312.660373          High income  \n",
       "85                33486.671984          High income  \n",
       "142               70248.629000          High income  \n",
       "..                         ...                  ...  \n",
       "88                10045.680500  Upper middle income  \n",
       "116                9414.226231  Upper middle income  \n",
       "20                12221.496606  Upper middle income  \n",
       "135                4426.000637  Upper middle income  \n",
       "55                 9998.544311  Upper middle income  \n",
       "\n",
       "[117 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "163795d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        'High income'],\n",
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       "        'High income'],\n",
       "       [309.24, 13653, 58.92590314126932, 0.925, 39312.66037306932,\n",
       "        'High income'],\n",
       "       [258.91, 15359, 187.3734609815036, 0.918, 33486.67198385498,\n",
       "        'High income'],\n",
       "       [202.85, 10561, 345.1761686582059, 0.921, 70248.62900022416,\n",
       "        'High income'],\n",
       "       [154.84, 13367, 86.9272367563218, 0.816, 19509.46646338666,\n",
       "        'High income'],\n",
       "       [165.28, 10041, 362.2486012740691, 0.875, 23723.34025103447,\n",
       "        'High income'],\n",
       "       [101.96, 6362, 386.7079423749307, 0.848, 21391.92533360423,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
       "       [263.28, 14465, 217.9909931762326, 0.809, 17313.1883484186,\n",
       "        'High income'],\n",
       "       [252.13, 14195, 135.7727183294524, 0.936, 51987.93905297284,\n",
       "        'High income'],\n",
       "       [282.31, 13856, 23.12146487399808, 0.855, 66838.35743301403,\n",
       "        'High income'],\n",
       "       [188.01, 11068, 26.87131943897528, 0.875, 23185.87079177973,\n",
       "        'High income'],\n",
       "       [199.13, 16275, 193.6602140246694, 0.805, 14617.60448163719,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
       "       [216.0, 20236, 66.30128578170374, 0.959, 68727.63666470899,\n",
       "        'High income'],\n",
       "       [133.02, 8818, 445.1275421342571, 0.858, 17685.32528345529,\n",
       "        'High income'],\n",
       "       [254.2, 13306, 291.8939709869786, 0.937, 51247.01435316263,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
       "       [261.27, 13437, 28.9726032237321, 0.939, 72794.00302267385,\n",
       "        'High income'],\n",
       "       [156.47, 9204, 340.2138749927657, 0.863, 21148.16294054151,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
       "       [88.95, 8471, 207.981703602353, 0.812, 27478.38962898315,\n",
       "        'High income'],\n",
       "       [103.26, 11697, 310.8237326574647, 0.81, 16032.50276809923,\n",
       "        'High income'],\n",
       "       [243.27, 11302, 76.1677027458733, 0.951, 60443.10916499681,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'Lower middle income'],\n",
       "       [4.45, 656, 7.362835301165219, 0.535, 1829.593044172109,\n",
       "        'Lower middle income'],\n",
       "       [89.71, 4301, 3.006239785903821, 0.55, 2549.041297247338,\n",
       "        'Lower middle income'],\n",
       "       [148.35, 9226, 13.35576032025309, 0.544, 1505.010192794294,\n",
       "        'Lower middle income'],\n",
       "       [144.98, 11297, 72.7525111066485, 0.662, 3293.233054306135,\n",
       "        'Lower middle income'],\n",
       "       [13.96, 1187, 6.65769200585394, 0.571, 2290.382891332437,\n",
       "        'Lower middle income'],\n",
       "       [178.19, 13840, 168.8156367128418, 0.774, 4091.209186705343,\n",
       "        'Lower middle income'],\n",
       "       [66.87, 13212, 4.512979446187659, 0.632, 2363.299296214216,\n",
       "        'Lower middle income'],\n",
       "       [278.81, 35391, 17.79989666041671, 0.593, 1625.235019554537,\n",
       "        'Lower middle income'],\n",
       "       [31.7, 4514, 1.274958884531432, 0.525, 1319.154994517401,\n",
       "        'Lower middle income'],\n",
       "       [147.86, 10714, 43.14220545622882, 0.683, 3795.38012695313,\n",
       "        'Lower middle income'],\n",
       "       [7.23, 512, 7.21037251905341, 0.558, 2672.945790355177,\n",
       "        'Lower middle income'],\n",
       "       [85.9, 15163, 20.33871612313471, 0.556, 2166.046798582141,\n",
       "        'Lower middle income'],\n",
       "       [71.4, 7971, 101.0936030609437, 0.715, 3663.969054688752,\n",
       "        'Lower middle income'],\n",
       "       [220.13, 61197, 3.319024649289729, 0.667, 2045.535401890141,\n",
       "        'Lower middle income'],\n",
       "       [161.63, 36918, 64.50413519166817, 0.739, 4566.140154330138,\n",
       "        'Lower middle income'],\n",
       "       [155.7, 7083, 37.74377979073893, 0.633, 2256.590408705059,\n",
       "        'Lower middle income'],\n",
       "       [60.14, 4026, 1.336523506462628, 0.549, 1099.28759765625,\n",
       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
       "       [120.18, 8817, 186.5725157129497, 0.692, 3345.196588738308,\n",
       "        'Lower middle income'],\n",
       "       [147.71, 14098, 59.01394869818141, 0.699, 3460.53096344408,\n",
       "        'Lower middle income'],\n",
       "       [58.07, 7486, 33.23069965100871, 0.514, 1094.09818489303,\n",
       "        'Lower middle income'],\n",
       "       [161.27, 7715, 57.69225843290137, 0.705, 4332.709280893962,\n",
       "        'Lower middle income'],\n",
       "       [183.75, 21785, 78.02244751328824, 0.782, 4013.687656836036,\n",
       "        'Lower middle income'],\n",
       "       [107.21, 13547, 243.7439900334167, 0.731, 3807.13915027254,\n",
       "        'Lower middle income'],\n",
       "       [74.89, 7435, 37.04155933811253, 0.593, 1773.920410880781,\n",
       "        'Lower middle income'],\n",
       "       [79.77, 6423, 258.283211924619, 0.773, 4835.57177734375,\n",
       "        'Lower middle income'],\n",
       "       [34.0, 5722, 15.17304860835294, 0.745, 3690.627877975985,\n",
       "        'Lower middle income'],\n",
       "       [15.33, 8319, 7.050845048078308, 0.576, 1666.932734431219,\n",
       "        'Lower middle income'],\n",
       "       [203.62, 20639, 15.32839857792018, 0.707, 3857.318464018038,\n",
       "        'Lower middle income'],\n",
       "       [100.05, 5545, 23.1855378462622, 0.731, 3698.834981058614,\n",
       "        'Lower middle income'],\n",
       "       [112.21, 27064, 4.350095702105446, 0.607, 2996.621061544835,\n",
       "        'Lower middle income'],\n",
       "       [53.36, 2251, 1.455614592453705, 0.535, 2065.749067509225,\n",
       "        'Lower middle income'],\n",
       "       [209.05, 21955, 17.53931509909698, 0.661, 2457.924879863609,\n",
       "        'Lower middle income'],\n",
       "       [43.24, 4545, 10.11825605026084, 0.575, 2081.799854584381,\n",
       "        'Lower middle income'],\n",
       "       [165.52, 16728, 108.7049359045917, 0.621, 2771.717460509461,\n",
       "        'Lower middle income'],\n",
       "       [69.88, 5147, 5.518545897129566, 0.586, 1953.533757215079,\n",
       "        'Lower middle income'],\n",
       "       [200.26, 18574, 39.76763789031424, 0.602, 1208.218533837672,\n",
       "        'Lower middle income'],\n",
       "       [271.4, 17064, 43.64270299331683, 0.703, 3756.489121173255,\n",
       "        'Lower middle income'],\n",
       "       [59.53, 5334, 501.8285381204727, 0.78, 7143.310548432097,\n",
       "        'Upper middle income'],\n",
       "       [219.79, 19704, 198.8421696006611, 0.74, 5965.132870544159,\n",
       "        'Upper middle income'],\n",
       "       [64.52, 4028, 168.8636052968149, 0.713, 7055.044775987833,\n",
       "        'Upper middle income'],\n",
       "       [69.79, 4942, 301.1583608074512, 0.767, 5230.661732894971,\n",
       "        'Upper middle income'],\n",
       "       [113.85, 5420, 108.6308525288824, 0.627, 5025.542290777896,\n",
       "        'Upper middle income'],\n",
       "       [108.03, 6180, 125.6639922075766, 0.796, 6492.872012246336,\n",
       "        'Upper middle income'],\n",
       "       [124.2, 9379, 207.7888218396915, 0.802, 9230.178316026007,\n",
       "        'Upper middle income'],\n",
       "       [120.58, 21576, 114.7404391842901, 0.693, 6805.221273988972,\n",
       "        'Upper middle income'],\n",
       "       [174.05, 7990, 276.9760239219943, 0.752, 6104.136709303895,\n",
       "        'Upper middle income'],\n",
       "       [70.62, 8551, 111.1599178850929, 0.751, 8666.38704104884,\n",
       "        'Upper middle income'],\n",
       "       [201.21, 15829, 81.82144098304971, 0.802, 9106.237202306995,\n",
       "        'Upper middle income'],\n",
       "       [181.67, 14973, 57.48773071342089, 0.747, 10366.29335840659,\n",
       "        'Upper middle income'],\n",
       "       [53.78, 4871, 118.2207327474445, 0.709, 5183.581304586759,\n",
       "        'Upper middle income'],\n",
       "       [78.27, 7927, 428.8264934596338, 0.802, 5023.274379619787,\n",
       "        'Upper middle income'],\n",
       "       [81.82, 10093, 235.2424723246614, 0.73, 4869.134226212424,\n",
       "        'Upper middle income'],\n",
       "       [123.94, 11274, 166.9133166743492, 0.683, 6228.26730928353,\n",
       "        'Upper middle income'],\n",
       "       [141.89, 13359, 272.4120953598373, 0.717, 5891.499965046411,\n",
       "        'Upper middle income'],\n",
       "       [260.29, 11457, 652.6411426980857, 0.762, 6621.574336018813,\n",
       "        'Upper middle income'],\n",
       "       [198.94, 11802, 48.42875323158614, 0.8, 7066.190545953232,\n",
       "        'Upper middle income'],\n",
       "       [178.75, 14816, 118.6181113909758, 0.838, 9661.23597511355,\n",
       "        'Upper middle income'],\n",
       "       [38.44, 6368, 155.2845732180051, 0.615, 4865.557764708638,\n",
       "        'Upper middle income'],\n",
       "       [225.92, 9016, 325.3049973491736, 0.754, 7507.160970802335,\n",
       "        'Upper middle income'],\n",
       "       [244.84, 15727, 0.3639699064821024, 0.768, 12556.33312000579,\n",
       "        'Upper middle income'],\n",
       "       [89.12, 7773, 137.0952295384011, 0.72, 4103.258965995832,\n",
       "        'Upper middle income'],\n",
       "       [88.97, 7465, 464.4203826062891, 0.77, 6694.641125817087,\n",
       "        'Upper middle income'],\n",
       "       [43.95, 9784, 60.18029423242732, 0.686, 4775.377453901298,\n",
       "        'Upper middle income'],\n",
       "       [77.45, 6441, 294.0477784739126, 0.759, 4966.51347122366,\n",
       "        'Upper middle income'],\n",
       "       [133.78, 8302, 98.92036129378008, 0.745, 5387.997974953834,\n",
       "        'Upper middle income'],\n",
       "       [108.31, 17983, 448.4433474002707, 0.832, 9465.703998795567,\n",
       "        'Upper middle income'],\n",
       "       [213.92, 16225, 111.4551091055768, 0.803, 11109.26183877448,\n",
       "        'Upper middle income'],\n",
       "       [144.89, 16405, 39.65135030756026, 0.767, 8476.75215730978,\n",
       "        'Upper middle income'],\n",
       "       [128.97, 7049, 272.5677574728828, 0.822, 12194.77734375,\n",
       "        'Upper middle income'],\n",
       "       [202.28, 8087, 75.46009668391146, 0.808, 7302.257794187743,\n",
       "        'Upper middle income'],\n",
       "       [248.7, 16861, 179.6287941734586, 0.809, 12472.44372949542,\n",
       "        'Upper middle income'],\n",
       "       [175.02, 12927, 253.5329675175892, 0.758, 10045.68050049632,\n",
       "        'Upper middle income'],\n",
       "       [68.37, 10630, 220.9461247994468, 0.715, 9414.226230859833,\n",
       "        'Upper middle income'],\n",
       "       [67.99, 3987, 559.1125379624249, 0.795, 12221.49660641709,\n",
       "        'Upper middle income'],\n",
       "       [190.06, 18958, 12.06507717008975, 0.745, 4426.000637062813,\n",
       "        'Upper middle income'],\n",
       "       [118.72, 11503, 163.4668060374412, 0.714, 9998.544311361555,\n",
       "        'Upper middle income']], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3eecdac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "array = dataset.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ae6baa1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
       "       [133.02, 8818, 445.1275421342571, 0.858, 17685.32528345529,\n",
       "        'High income'],\n",
       "       [254.2, 13306, 291.8939709869786, 0.937, 51247.01435316263,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
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       "        'High income'],\n",
       "       [212.99, 10506, 353.962245642645, 0.887, 20192.59630390607,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
       "       [236.15, 14945, 87.38872511677522, 0.875, 26562.96913540261,\n",
       "        'High income'],\n",
       "       [250.21, 17003, 66.81485969804851, 0.925, 34997.78164278481,\n",
       "        'High income'],\n",
       "       [261.27, 13437, 28.9726032237321, 0.939, 72794.00302267385,\n",
       "        'High income'],\n",
       "       [156.47, 9204, 340.2138749927657, 0.863, 21148.16294054151,\n",
       "        'High income'],\n",
       "       [141.36, 9020, 341.669096848271, 0.918, 29291.40062344306,\n",
       "        'High income'],\n",
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       "        'High income'],\n",
       "       [103.26, 11697, 310.8237326574647, 0.81, 16032.50276809923,\n",
       "        'High income'],\n",
       "       [243.27, 11302, 76.1677027458733, 0.951, 60443.10916499681,\n",
       "        'High income'],\n",
       "       [222.32, 14090, 257.7658639364567, 0.905, 30103.51373319099,\n",
       "        'High income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
       "       [89.71, 4301, 3.006239785903821, 0.55, 2549.041297247338,\n",
       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
       "       [144.98, 11297, 72.7525111066485, 0.662, 3293.233054306135,\n",
       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
       "       [71.4, 7971, 101.0936030609437, 0.715, 3663.969054688752,\n",
       "        'Lower middle income'],\n",
       "       [220.13, 61197, 3.319024649289729, 0.667, 2045.535401890141,\n",
       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
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       "        'Lower middle income'],\n",
       "       [218.04, 12311, 4.761200629955996, 0.727, 1983.064722889918,\n",
       "        'Lower middle income'],\n",
       "       [120.18, 8817, 186.5725157129497, 0.692, 3345.196588738308,\n",
       "        'Lower middle income'],\n",
       "       [147.71, 14098, 59.01394869818141, 0.699, 3460.53096344408,\n",
       "        'Lower middle income'],\n",
       "       [58.07, 7486, 33.23069965100871, 0.514, 1094.09818489303,\n",
       "        'Lower middle income'],\n",
       "       [161.27, 7715, 57.69225843290137, 0.705, 4332.709280893962,\n",
       "        'Lower middle income'],\n",
       "       [183.75, 21785, 78.02244751328824, 0.782, 4013.687656836036,\n",
       "        'Lower middle income'],\n",
       "       [107.21, 13547, 243.7439900334167, 0.731, 3807.13915027254,\n",
       "        'Lower middle income'],\n",
       "       [74.89, 7435, 37.04155933811253, 0.593, 1773.920410880781,\n",
       "        'Lower middle income'],\n",
       "       [79.77, 6423, 258.283211924619, 0.773, 4835.57177734375,\n",
       "        'Lower middle income'],\n",
       "       [34.0, 5722, 15.17304860835294, 0.745, 3690.627877975985,\n",
       "        'Lower middle income'],\n",
       "       [15.33, 8319, 7.050845048078308, 0.576, 1666.932734431219,\n",
       "        'Lower middle income'],\n",
       "       [203.62, 20639, 15.32839857792018, 0.707, 3857.318464018038,\n",
       "        'Lower middle income'],\n",
       "       [100.05, 5545, 23.1855378462622, 0.731, 3698.834981058614,\n",
       "        'Lower middle income'],\n",
       "       [112.21, 27064, 4.350095702105446, 0.607, 2996.621061544835,\n",
       "        'Lower middle income'],\n",
       "       [53.36, 2251, 1.455614592453705, 0.535, 2065.749067509225,\n",
       "        'Lower middle income'],\n",
       "       [209.05, 21955, 17.53931509909698, 0.661, 2457.924879863609,\n",
       "        'Lower middle income'],\n",
       "       [43.24, 4545, 10.11825605026084, 0.575, 2081.799854584381,\n",
       "        'Lower middle income'],\n",
       "       [165.52, 16728, 108.7049359045917, 0.621, 2771.717460509461,\n",
       "        'Lower middle income'],\n",
       "       [69.88, 5147, 5.518545897129566, 0.586, 1953.533757215079,\n",
       "        'Lower middle income'],\n",
       "       [200.26, 18574, 39.76763789031424, 0.602, 1208.218533837672,\n",
       "        'Lower middle income'],\n",
       "       [271.4, 17064, 43.64270299331683, 0.703, 3756.489121173255,\n",
       "        'Lower middle income'],\n",
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       "        'Upper middle income'],\n",
       "       [219.79, 19704, 198.8421696006611, 0.74, 5965.132870544159,\n",
       "        'Upper middle income'],\n",
       "       [64.52, 4028, 168.8636052968149, 0.713, 7055.044775987833,\n",
       "        'Upper middle income'],\n",
       "       [69.79, 4942, 301.1583608074512, 0.767, 5230.661732894971,\n",
       "        'Upper middle income'],\n",
       "       [113.85, 5420, 108.6308525288824, 0.627, 5025.542290777896,\n",
       "        'Upper middle income'],\n",
       "       [108.03, 6180, 125.6639922075766, 0.796, 6492.872012246336,\n",
       "        'Upper middle income'],\n",
       "       [124.2, 9379, 207.7888218396915, 0.802, 9230.178316026007,\n",
       "        'Upper middle income'],\n",
       "       [120.58, 21576, 114.7404391842901, 0.693, 6805.221273988972,\n",
       "        'Upper middle income'],\n",
       "       [174.05, 7990, 276.9760239219943, 0.752, 6104.136709303895,\n",
       "        'Upper middle income'],\n",
       "       [70.62, 8551, 111.1599178850929, 0.751, 8666.38704104884,\n",
       "        'Upper middle income'],\n",
       "       [201.21, 15829, 81.82144098304971, 0.802, 9106.237202306995,\n",
       "        'Upper middle income'],\n",
       "       [181.67, 14973, 57.48773071342089, 0.747, 10366.29335840659,\n",
       "        'Upper middle income'],\n",
       "       [53.78, 4871, 118.2207327474445, 0.709, 5183.581304586759,\n",
       "        'Upper middle income'],\n",
       "       [78.27, 7927, 428.8264934596338, 0.802, 5023.274379619787,\n",
       "        'Upper middle income'],\n",
       "       [81.82, 10093, 235.2424723246614, 0.73, 4869.134226212424,\n",
       "        'Upper middle income'],\n",
       "       [123.94, 11274, 166.9133166743492, 0.683, 6228.26730928353,\n",
       "        'Upper middle income'],\n",
       "       [141.89, 13359, 272.4120953598373, 0.717, 5891.499965046411,\n",
       "        'Upper middle income'],\n",
       "       [260.29, 11457, 652.6411426980857, 0.762, 6621.574336018813,\n",
       "        'Upper middle income'],\n",
       "       [198.94, 11802, 48.42875323158614, 0.8, 7066.190545953232,\n",
       "        'Upper middle income'],\n",
       "       [178.75, 14816, 118.6181113909758, 0.838, 9661.23597511355,\n",
       "        'Upper middle income'],\n",
       "       [38.44, 6368, 155.2845732180051, 0.615, 4865.557764708638,\n",
       "        'Upper middle income'],\n",
       "       [225.92, 9016, 325.3049973491736, 0.754, 7507.160970802335,\n",
       "        'Upper middle income'],\n",
       "       [244.84, 15727, 0.3639699064821024, 0.768, 12556.33312000579,\n",
       "        'Upper middle income'],\n",
       "       [89.12, 7773, 137.0952295384011, 0.72, 4103.258965995832,\n",
       "        'Upper middle income'],\n",
       "       [88.97, 7465, 464.4203826062891, 0.77, 6694.641125817087,\n",
       "        'Upper middle income'],\n",
       "       [43.95, 9784, 60.18029423242732, 0.686, 4775.377453901298,\n",
       "        'Upper middle income'],\n",
       "       [77.45, 6441, 294.0477784739126, 0.759, 4966.51347122366,\n",
       "        'Upper middle income'],\n",
       "       [133.78, 8302, 98.92036129378008, 0.745, 5387.997974953834,\n",
       "        'Upper middle income'],\n",
       "       [108.31, 17983, 448.4433474002707, 0.832, 9465.703998795567,\n",
       "        'Upper middle income'],\n",
       "       [213.92, 16225, 111.4551091055768, 0.803, 11109.26183877448,\n",
       "        'Upper middle income'],\n",
       "       [144.89, 16405, 39.65135030756026, 0.767, 8476.75215730978,\n",
       "        'Upper middle income'],\n",
       "       [128.97, 7049, 272.5677574728828, 0.822, 12194.77734375,\n",
       "        'Upper middle income'],\n",
       "       [202.28, 8087, 75.46009668391146, 0.808, 7302.257794187743,\n",
       "        'Upper middle income'],\n",
       "       [248.7, 16861, 179.6287941734586, 0.809, 12472.44372949542,\n",
       "        'Upper middle income'],\n",
       "       [175.02, 12927, 253.5329675175892, 0.758, 10045.68050049632,\n",
       "        'Upper middle income'],\n",
       "       [68.37, 10630, 220.9461247994468, 0.715, 9414.226230859833,\n",
       "        'Upper middle income'],\n",
       "       [67.99, 3987, 559.1125379624249, 0.795, 12221.49660641709,\n",
       "        'Upper middle income'],\n",
       "       [190.06, 18958, 12.06507717008975, 0.745, 4426.000637062813,\n",
       "        'Upper middle income'],\n",
       "       [118.72, 11503, 163.4668060374412, 0.714, 9998.544311361555,\n",
       "        'Upper middle income']], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "53d19cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = array[:,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9bcfc68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [260.29, 11457, 652.6411426980857, 0.762, 6621.574336018813],\n",
       "       [198.94, 11802, 48.42875323158614, 0.8, 7066.190545953232],\n",
       "       [178.75, 14816, 118.6181113909758, 0.838, 9661.23597511355],\n",
       "       [38.44, 6368, 155.2845732180051, 0.615, 4865.557764708638],\n",
       "       [225.92, 9016, 325.3049973491736, 0.754, 7507.160970802335],\n",
       "       [244.84, 15727, 0.3639699064821024, 0.768, 12556.33312000579],\n",
       "       [89.12, 7773, 137.0952295384011, 0.72, 4103.258965995832],\n",
       "       [88.97, 7465, 464.4203826062891, 0.77, 6694.641125817087],\n",
       "       [43.95, 9784, 60.18029423242732, 0.686, 4775.377453901298],\n",
       "       [77.45, 6441, 294.0477784739126, 0.759, 4966.51347122366],\n",
       "       [133.78, 8302, 98.92036129378008, 0.745, 5387.997974953834],\n",
       "       [108.31, 17983, 448.4433474002707, 0.832, 9465.703998795567],\n",
       "       [213.92, 16225, 111.4551091055768, 0.803, 11109.26183877448],\n",
       "       [144.89, 16405, 39.65135030756026, 0.767, 8476.75215730978],\n",
       "       [128.97, 7049, 272.5677574728828, 0.822, 12194.77734375],\n",
       "       [202.28, 8087, 75.46009668391146, 0.808, 7302.257794187743],\n",
       "       [248.7, 16861, 179.6287941734586, 0.809, 12472.44372949542],\n",
       "       [175.02, 12927, 253.5329675175892, 0.758, 10045.68050049632],\n",
       "       [68.37, 10630, 220.9461247994468, 0.715, 9414.226230859833],\n",
       "       [67.99, 3987, 559.1125379624249, 0.795, 12221.49660641709],\n",
       "       [190.06, 18958, 12.06507717008975, 0.745, 4426.000637062813],\n",
       "       [118.72, 11503, 163.4668060374412, 0.714, 9998.544311361555]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80c42667",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = array[:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ca4d62cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'High income',\n",
       "       'High income', 'High income', 'High income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Lower middle income', 'Lower middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income', 'Upper middle income',\n",
       "       'Upper middle income'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b83e67d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_size = 0.2 #it sets 80% to train 20% to test, the default is 75% train 25% test\n",
    "seed = 8\n",
    "X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9f13c4fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[159.21, 7663, 224.7505503400383, 0.89, 27943.70121988203],\n",
       "       [223.5, 12724, 94.58628253955006, 0.961, 89154.2760934922],\n",
       "       [258.91, 15359, 187.3734609815036, 0.918, 33486.67198385498],\n",
       "       [248.7, 16861, 179.6287941734586, 0.809, 12472.44372949542],\n",
       "       [120.18, 8817, 186.5725157129497, 0.692, 3345.196588738308]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "24fd598e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High income', 'High income', 'High income', 'Upper middle income',\n",
       "       'Lower middle income'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bd97d6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "85948aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "942ba920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9f996d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c88c7c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[167.56, 12632, 508.1166352062754, 0.846, 18728.12189486889],\n",
       "       [230.55, 13774, 204.0553912352542, 0.942, 51203.55447310433],\n",
       "       [309.24, 13653, 58.92590314126932, 0.925, 39312.66037306932],\n",
       "       [258.91, 15359, 187.3734609815036, 0.918, 33486.67198385498],\n",
       "       [202.85, 10561, 345.1761686582059, 0.921, 70248.62900022416]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0d460d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High income', 'High income', 'High income', 'High income',\n",
       "       'High income'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cfe1ee05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "    X_training size: 93\n",
      "    Y_training size: 93\n",
      "Validation\n",
      "    X_validation size: 24\n",
      "    Y_validation size: 24\n"
     ]
    }
   ],
   "source": [
    "print('Training')\n",
    "print('    X_training size:', len(X_train))\n",
    "print('    Y_training size:', len(Y_train))\n",
    "print('Validation')\n",
    "print('    X_validation size:', len(X_validation))\n",
    "print('    Y_validation size:', len(Y_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "df94ec56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(probability=True)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(probability=True) #training the model\n",
    "#svm = SVC()\n",
    "svm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a464f07d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(probability=True)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7152b9c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Lower middle income' 'High income' 'High income' 'Lower middle income'\n",
      " 'Upper middle income' 'Upper middle income' 'Lower middle income'\n",
      " 'Upper middle income' 'High income' 'High income' 'Lower middle income'\n",
      " 'Lower middle income' 'High income' 'Upper middle income' 'High income'\n",
      " 'Lower middle income' 'High income' 'Upper middle income'\n",
      " 'Lower middle income' 'Upper middle income' 'Upper middle income'\n",
      " 'High income' 'Upper middle income' 'Lower middle income']\n"
     ]
    }
   ],
   "source": [
    "predictions = svm.predict(X_validation) #making the predictions\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d9c8d4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Upper middle income' 'High income' 'High income' 'Lower middle income'\n",
      " 'Upper middle income' 'Upper middle income' 'Lower middle income'\n",
      " 'Upper middle income' 'High income' 'High income' 'Lower middle income'\n",
      " 'Lower middle income' 'High income' 'Upper middle income' 'High income'\n",
      " 'Lower middle income' 'High income' 'Upper middle income'\n",
      " 'Lower middle income' 'Upper middle income' 'Upper middle income'\n",
      " 'High income' 'Upper middle income' 'Upper middle income']\n"
     ]
    }
   ],
   "source": [
    "print(Y_validation) #what the true outcome is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4f2dd00c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "#comparing the true outcome (validation) with the predictions\n",
    "print(accuracy_score(Y_validation, predictions)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1cd25665",
   "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>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>IncomeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>167.56</td>\n",
       "      <td>12632</td>\n",
       "      <td>508.116635</td>\n",
       "      <td>0.846</td>\n",
       "      <td>18728.121895</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>230.55</td>\n",
       "      <td>13774</td>\n",
       "      <td>204.055391</td>\n",
       "      <td>0.942</td>\n",
       "      <td>51203.554473</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>309.24</td>\n",
       "      <td>13653</td>\n",
       "      <td>58.925903</td>\n",
       "      <td>0.925</td>\n",
       "      <td>39312.660373</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "59                          167.56                           12632   \n",
       "49                          230.55                           13774   \n",
       "69                          309.24                           13653   \n",
       "\n",
       "    covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "59                         508.116635                               0.846   \n",
       "49                         204.055391                               0.942   \n",
       "69                          58.925903                               0.925   \n",
       "\n",
       "    GDP per capita 2021 (US$)  IncomeGroup  \n",
       "59               18728.121895  High income  \n",
       "49               51203.554473  High income  \n",
       "69               39312.660373  High income  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4baf0010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_vaccinations_per_hundred                     309.24\n",
       "daily_vaccinations_per_million                      61197\n",
       "covid_mortality_rate (per 100000)              652.641143\n",
       "Human Development Index 2021 (HDI)                  0.962\n",
       "GDP per capita 2021 (US$)                   133590.146976\n",
       "IncomeGroup                           Upper middle income\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce715419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_vaccinations_per_hundred               4.45\n",
       "daily_vaccinations_per_million                512\n",
       "covid_mortality_rate (per 100000)         0.36397\n",
       "Human Development Index 2021 (HDI)          0.514\n",
       "GDP per capita 2021 (US$)             1094.098185\n",
       "IncomeGroup                           High income\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b4b03e1d",
   "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>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.217265</td>\n",
       "      <td>12214.777778</td>\n",
       "      <td>153.945255</td>\n",
       "      <td>0.762513</td>\n",
       "      <td>17499.696475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>74.862689</td>\n",
       "      <td>7535.319356</td>\n",
       "      <td>142.430879</td>\n",
       "      <td>0.120768</td>\n",
       "      <td>23173.542050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.450000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>0.363970</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>1094.098185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>88.970000</td>\n",
       "      <td>7773.000000</td>\n",
       "      <td>37.743780</td>\n",
       "      <td>0.692000</td>\n",
       "      <td>3698.834981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>155.700000</td>\n",
       "      <td>11297.000000</td>\n",
       "      <td>111.455109</td>\n",
       "      <td>0.767000</td>\n",
       "      <td>7055.044776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>213.920000</td>\n",
       "      <td>14945.000000</td>\n",
       "      <td>243.743990</td>\n",
       "      <td>0.858000</td>\n",
       "      <td>21391.925334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>309.240000</td>\n",
       "      <td>61197.000000</td>\n",
       "      <td>652.641143</td>\n",
       "      <td>0.962000</td>\n",
       "      <td>133590.146976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "count                      117.000000                      117.000000   \n",
       "mean                       154.217265                    12214.777778   \n",
       "std                         74.862689                     7535.319356   \n",
       "min                          4.450000                      512.000000   \n",
       "25%                         88.970000                     7773.000000   \n",
       "50%                        155.700000                    11297.000000   \n",
       "75%                        213.920000                    14945.000000   \n",
       "max                        309.240000                    61197.000000   \n",
       "\n",
       "       covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "count                         117.000000                          117.000000   \n",
       "mean                          153.945255                            0.762513   \n",
       "std                           142.430879                            0.120768   \n",
       "min                             0.363970                            0.514000   \n",
       "25%                            37.743780                            0.692000   \n",
       "50%                           111.455109                            0.767000   \n",
       "75%                           243.743990                            0.858000   \n",
       "max                           652.641143                            0.962000   \n",
       "\n",
       "       GDP per capita 2021 (US$)  \n",
       "count                 117.000000  \n",
       "mean                17499.696475  \n",
       "std                 23173.542050  \n",
       "min                  1094.098185  \n",
       "25%                  3698.834981  \n",
       "50%                  7055.044776  \n",
       "75%                 21391.925334  \n",
       "max                133590.146976  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c1d780cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Lower middle income'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.predict([[6.0, 600 , 60, 0.6, 6000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e4e38dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05209198, 0.40758963, 0.54031839]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.predict_proba([[6.0, 600 , 60, 0.6, 6000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "57427c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59             High income\n",
       "49             High income\n",
       "69             High income\n",
       "85             High income\n",
       "142            High income\n",
       "              ...         \n",
       "88     Upper middle income\n",
       "116    Upper middle income\n",
       "20     Upper middle income\n",
       "135    Upper middle income\n",
       "55     Upper middle income\n",
       "Name: IncomeGroup, Length: 117, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.IncomeGroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "53c798ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8 0 0]\n",
      " [0 6 0]\n",
      " [0 2 8]]\n"
     ]
    }
   ],
   "source": [
    "labels = ['High income','Lower Middle income','Upper middle income']\n",
    "cm = confusion_matrix(Y_validation, predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a94267cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-71-4eb4716b0a1e>:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] +labels)\n",
      "<ipython-input-71-4eb4716b0a1e>:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + labels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm)\n",
    "plt.title('Confusion Matrix do Classifier')\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] +labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "64dc277a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02538851 0.51043988 0.46417161]\n",
      " [0.98668233 0.00243655 0.01088112]\n",
      " [0.96809934 0.02394082 0.00795985]\n",
      " [0.01165998 0.95789536 0.03044466]\n",
      " [0.0244778  0.16276879 0.81275341]\n",
      " [0.21772722 0.03474832 0.74752446]\n",
      " [0.0186639  0.95867404 0.02266207]\n",
      " [0.05220473 0.0164749  0.93132037]\n",
      " [0.99315657 0.00156341 0.00528003]\n",
      " [0.99366975 0.0041341  0.00219615]\n",
      " [0.0134359  0.9644229  0.0221412 ]\n",
      " [0.01661269 0.97040099 0.01298632]\n",
      " [0.87455414 0.01297444 0.11247142]\n",
      " [0.2834738  0.05118213 0.66534407]\n",
      " [0.962913   0.02898923 0.00809778]\n",
      " [0.02269131 0.83874111 0.13856758]\n",
      " [0.96119164 0.0066433  0.03216507]\n",
      " [0.05374301 0.01909737 0.92715962]\n",
      " [0.01365898 0.95008367 0.03625735]\n",
      " [0.04620222 0.09926521 0.85453257]\n",
      " [0.08425281 0.02018753 0.89555966]\n",
      " [0.99601426 0.00133787 0.00264787]\n",
      " [0.03227891 0.04600624 0.92171485]\n",
      " [0.02207919 0.69314615 0.28477466]]\n"
     ]
    }
   ],
   "source": [
    "probs = svm.predict_proba(X_validation)\n",
    "print(probs)\n",
    "#probability associated to each observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "197b25af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#it's possible to make one prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "13c36d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Lower middle income']\n"
     ]
    }
   ],
   "source": [
    "Data = [[7.0, 578 , 55, 0.6, 5000]]\n",
    "pred_data = svm.predict(Data)\n",
    "print(pred_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "23e19109",
   "metadata": {},
   "outputs": [],
   "source": [
    "#or many at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7ab67199",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['High income' 'Upper middle income']\n"
     ]
    }
   ],
   "source": [
    "Data = [[5.5, 550, 80, 0.59, 15000], [6.4, 400, 60, 0.57, 9500]]\n",
    "pred_data = svm.predict(Data)\n",
    "print(pred_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6a83f84d",
   "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>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.217265</td>\n",
       "      <td>12214.777778</td>\n",
       "      <td>153.945255</td>\n",
       "      <td>0.762513</td>\n",
       "      <td>17499.696475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>74.862689</td>\n",
       "      <td>7535.319356</td>\n",
       "      <td>142.430879</td>\n",
       "      <td>0.120768</td>\n",
       "      <td>23173.542050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.450000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>0.363970</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>1094.098185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>88.970000</td>\n",
       "      <td>7773.000000</td>\n",
       "      <td>37.743780</td>\n",
       "      <td>0.692000</td>\n",
       "      <td>3698.834981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>155.700000</td>\n",
       "      <td>11297.000000</td>\n",
       "      <td>111.455109</td>\n",
       "      <td>0.767000</td>\n",
       "      <td>7055.044776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>213.920000</td>\n",
       "      <td>14945.000000</td>\n",
       "      <td>243.743990</td>\n",
       "      <td>0.858000</td>\n",
       "      <td>21391.925334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>309.240000</td>\n",
       "      <td>61197.000000</td>\n",
       "      <td>652.641143</td>\n",
       "      <td>0.962000</td>\n",
       "      <td>133590.146976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "count                      117.000000                      117.000000   \n",
       "mean                       154.217265                    12214.777778   \n",
       "std                         74.862689                     7535.319356   \n",
       "min                          4.450000                      512.000000   \n",
       "25%                         88.970000                     7773.000000   \n",
       "50%                        155.700000                    11297.000000   \n",
       "75%                        213.920000                    14945.000000   \n",
       "max                        309.240000                    61197.000000   \n",
       "\n",
       "       covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "count                         117.000000                          117.000000   \n",
       "mean                          153.945255                            0.762513   \n",
       "std                           142.430879                            0.120768   \n",
       "min                             0.363970                            0.514000   \n",
       "25%                            37.743780                            0.692000   \n",
       "50%                           111.455109                            0.767000   \n",
       "75%                           243.743990                            0.858000   \n",
       "max                           652.641143                            0.962000   \n",
       "\n",
       "       GDP per capita 2021 (US$)  \n",
       "count                 117.000000  \n",
       "mean                17499.696475  \n",
       "std                 23173.542050  \n",
       "min                  1094.098185  \n",
       "25%                  3698.834981  \n",
       "50%                  7055.044776  \n",
       "75%                 21391.925334  \n",
       "max                133590.146976  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae13bf2",
   "metadata": {},
   "source": [
    "## 9.2 Logistic Regression, Decision Tree and Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9e11b5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making predictions in a different ways, this will also be classification, but not SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "70ae7857",
   "metadata": {},
   "outputs": [],
   "source": [
    "covidvisual = pd.read_excel('covidvisualApril5th2023.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e09960e1",
   "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</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HDI rank</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>Life expectancy at birth</th>\n",
       "      <th>Expected years of schooling</th>\n",
       "      <th>Mean years of schooling</th>\n",
       "      <th>Gross national income (GNI) per capita</th>\n",
       "      <th>...</th>\n",
       "      <th>NewRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>TotalTests</th>\n",
       "      <th>Population</th>\n",
       "      <th>Continent</th>\n",
       "      <th>New Cases/1M pop</th>\n",
       "      <th>New Deaths/1M pop</th>\n",
       "      <th>Active Cases/1M pop</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.796</td>\n",
       "      <td>76.4626</td>\n",
       "      <td>14.448000</td>\n",
       "      <td>11.286455</td>\n",
       "      <td>14131.110390</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,941,032</td>\n",
       "      <td>2866374</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>125.663992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.745</td>\n",
       "      <td>76.3767</td>\n",
       "      <td>14.626896</td>\n",
       "      <td>8.069284</td>\n",
       "      <td>10800.225460</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81,825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230,861</td>\n",
       "      <td>45350148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,804</td>\n",
       "      <td>15.173049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>148</td>\n",
       "      <td>0.586</td>\n",
       "      <td>61.6434</td>\n",
       "      <td>12.172100</td>\n",
       "      <td>5.417391</td>\n",
       "      <td>5465.617791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,499,795</td>\n",
       "      <td>35027343</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.518546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>85</td>\n",
       "      <td>0.759</td>\n",
       "      <td>72.0431</td>\n",
       "      <td>13.116760</td>\n",
       "      <td>11.330300</td>\n",
       "      <td>13157.993900</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3,242,901</td>\n",
       "      <td>2971966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,430</td>\n",
       "      <td>294.047778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.951</td>\n",
       "      <td>84.5265</td>\n",
       "      <td>21.054590</td>\n",
       "      <td>12.726820</td>\n",
       "      <td>49238.433350</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43,652</td>\n",
       "      <td>33</td>\n",
       "      <td>78,835,048</td>\n",
       "      <td>26068792</td>\n",
       "      <td>Australia/Oceania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,674</td>\n",
       "      <td>76.167703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>UZB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>101</td>\n",
       "      <td>0.727</td>\n",
       "      <td>70.8616</td>\n",
       "      <td>12.477140</td>\n",
       "      <td>11.895590</td>\n",
       "      <td>7916.785725</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,716</td>\n",
       "      <td>23</td>\n",
       "      <td>1,377,915</td>\n",
       "      <td>34382084</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254</td>\n",
       "      <td>4.761201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>VUT</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0.607</td>\n",
       "      <td>70.4490</td>\n",
       "      <td>11.535315</td>\n",
       "      <td>7.064846</td>\n",
       "      <td>3085.411633</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24,976</td>\n",
       "      <td>321832</td>\n",
       "      <td>Australia/Oceania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>4.350096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>VNM</td>\n",
       "      <td>16.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>115</td>\n",
       "      <td>0.703</td>\n",
       "      <td>73.6181</td>\n",
       "      <td>12.951932</td>\n",
       "      <td>8.370280</td>\n",
       "      <td>7867.371345</td>\n",
       "      <td>...</td>\n",
       "      <td>+23</td>\n",
       "      <td>869,254</td>\n",
       "      <td>7</td>\n",
       "      <td>85,826,548</td>\n",
       "      <td>98953541</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8,784</td>\n",
       "      <td>43.642703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>154</td>\n",
       "      <td>0.565</td>\n",
       "      <td>61.2234</td>\n",
       "      <td>10.928760</td>\n",
       "      <td>7.187091</td>\n",
       "      <td>3217.767739</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3,994,061</td>\n",
       "      <td>19470234</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>20.842071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>146</td>\n",
       "      <td>0.593</td>\n",
       "      <td>59.2531</td>\n",
       "      <td>12.110969</td>\n",
       "      <td>8.710909</td>\n",
       "      <td>3809.887158</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348</td>\n",
       "      <td>12</td>\n",
       "      <td>2,525,756</td>\n",
       "      <td>15331428</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>37.041559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country Country Code  Latitude  Longitude  HDI rank  \\\n",
       "0       Albania          ALB      41.0       20.0        67   \n",
       "1       Algeria          DZA      28.0        3.0        91   \n",
       "2        Angola          AGO     -12.5       18.5       148   \n",
       "3       Armenia          ARM      40.0       45.0        85   \n",
       "4     Australia          AUS     -27.0      133.0         5   \n",
       "..          ...          ...       ...        ...       ...   \n",
       "144  Uzbekistan          UZB      41.0       64.0       101   \n",
       "145     Vanuatu          VUT     -16.0      167.0       140   \n",
       "146    Viet Nam          VNM      16.0      106.0       115   \n",
       "147      Zambia          ZMB     -15.0       30.0       154   \n",
       "148    Zimbabwe          ZWE     -20.0       30.0       146   \n",
       "\n",
       "     Human Development Index 2021 (HDI)  Life expectancy at birth  \\\n",
       "0                                 0.796                   76.4626   \n",
       "1                                 0.745                   76.3767   \n",
       "2                                 0.586                   61.6434   \n",
       "3                                 0.759                   72.0431   \n",
       "4                                 0.951                   84.5265   \n",
       "..                                  ...                       ...   \n",
       "144                               0.727                   70.8616   \n",
       "145                               0.607                   70.4490   \n",
       "146                               0.703                   73.6181   \n",
       "147                               0.565                   61.2234   \n",
       "148                               0.593                   59.2531   \n",
       "\n",
       "     Expected years of schooling  Mean years of schooling  \\\n",
       "0                      14.448000                11.286455   \n",
       "1                      14.626896                 8.069284   \n",
       "2                      12.172100                 5.417391   \n",
       "3                      13.116760                11.330300   \n",
       "4                      21.054590                12.726820   \n",
       "..                           ...                      ...   \n",
       "144                    12.477140                11.895590   \n",
       "145                    11.535315                 7.064846   \n",
       "146                    12.951932                 8.370280   \n",
       "147                    10.928760                 7.187091   \n",
       "148                    12.110969                 8.710909   \n",
       "\n",
       "     Gross national income (GNI) per capita  ...  NewRecovered  ActiveCases  \\\n",
       "0                              14131.110390  ...           NaN        1,709   \n",
       "1                              10800.225460  ...           NaN       81,825   \n",
       "2                               5465.617791  ...           NaN           41   \n",
       "3                              13157.993900  ...           NaN        4,250   \n",
       "4                              49238.433350  ...           NaN       43,652   \n",
       "..                                      ...  ...           ...          ...   \n",
       "144                             7916.785725  ...           NaN        8,716   \n",
       "145                             3085.411633  ...           NaN           24   \n",
       "146                             7867.371345  ...           +23      869,254   \n",
       "147                             3217.767739  ...           NaN          122   \n",
       "148                             3809.887158  ...           NaN          348   \n",
       "\n",
       "     Serious,Critical  TotalTests  Population          Continent  \\\n",
       "0                 NaN   1,941,032     2866374             Europe   \n",
       "1                 NaN     230,861    45350148             Africa   \n",
       "2                 NaN   1,499,795    35027343             Africa   \n",
       "3                 NaN   3,242,901     2971966               Asia   \n",
       "4                  33  78,835,048    26068792  Australia/Oceania   \n",
       "..                ...         ...         ...                ...   \n",
       "144                23   1,377,915    34382084               Asia   \n",
       "145               NaN      24,976      321832  Australia/Oceania   \n",
       "146                 7  85,826,548    98953541               Asia   \n",
       "147               NaN   3,994,061    19470234             Africa   \n",
       "148                12   2,525,756    15331428             Africa   \n",
       "\n",
       "    New Cases/1M pop  New Deaths/1M pop Active Cases/1M pop  \\\n",
       "0                NaN                NaN                 596   \n",
       "1                0.1                NaN               1,804   \n",
       "2                NaN                NaN                   1   \n",
       "3                NaN                NaN               1,430   \n",
       "4                NaN                NaN               1,674   \n",
       "..               ...                ...                 ...   \n",
       "144              NaN                NaN                 254   \n",
       "145              NaN                NaN                  75   \n",
       "146              0.5                NaN               8,784   \n",
       "147              NaN                NaN                   6   \n",
       "148              NaN                NaN                  23   \n",
       "\n",
       "     covid_mortality_rate (per 100000)  \n",
       "0                           125.663992  \n",
       "1                            15.173049  \n",
       "2                             5.518546  \n",
       "3                           294.047778  \n",
       "4                            76.167703  \n",
       "..                                 ...  \n",
       "144                           4.761201  \n",
       "145                           4.350096  \n",
       "146                          43.642703  \n",
       "147                          20.842071  \n",
       "148                          37.041559  \n",
       "\n",
       "[149 rows x 43 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c63f75a1",
   "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</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HDI rank</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>Life expectancy at birth</th>\n",
       "      <th>Expected years of schooling</th>\n",
       "      <th>Mean years of schooling</th>\n",
       "      <th>Gross national income (GNI) per capita</th>\n",
       "      <th>...</th>\n",
       "      <th>NewRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>TotalTests</th>\n",
       "      <th>Population</th>\n",
       "      <th>Continent</th>\n",
       "      <th>New Cases/1M pop</th>\n",
       "      <th>New Deaths/1M pop</th>\n",
       "      <th>Active Cases/1M pop</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.796</td>\n",
       "      <td>76.4626</td>\n",
       "      <td>14.448000</td>\n",
       "      <td>11.286455</td>\n",
       "      <td>14131.110390</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,941,032</td>\n",
       "      <td>2866374</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>125.663992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.745</td>\n",
       "      <td>76.3767</td>\n",
       "      <td>14.626896</td>\n",
       "      <td>8.069284</td>\n",
       "      <td>10800.225460</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81,825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230,861</td>\n",
       "      <td>45350148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,804</td>\n",
       "      <td>15.173049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>148</td>\n",
       "      <td>0.586</td>\n",
       "      <td>61.6434</td>\n",
       "      <td>12.172100</td>\n",
       "      <td>5.417391</td>\n",
       "      <td>5465.617791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,499,795</td>\n",
       "      <td>35027343</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.518546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Country Code  Latitude  Longitude  HDI rank  \\\n",
       "0  Albania          ALB      41.0       20.0        67   \n",
       "1  Algeria          DZA      28.0        3.0        91   \n",
       "2   Angola          AGO     -12.5       18.5       148   \n",
       "\n",
       "   Human Development Index 2021 (HDI)  Life expectancy at birth  \\\n",
       "0                               0.796                   76.4626   \n",
       "1                               0.745                   76.3767   \n",
       "2                               0.586                   61.6434   \n",
       "\n",
       "   Expected years of schooling  Mean years of schooling  \\\n",
       "0                    14.448000                11.286455   \n",
       "1                    14.626896                 8.069284   \n",
       "2                    12.172100                 5.417391   \n",
       "\n",
       "   Gross national income (GNI) per capita  ...  NewRecovered  ActiveCases  \\\n",
       "0                            14131.110390  ...           NaN        1,709   \n",
       "1                            10800.225460  ...           NaN       81,825   \n",
       "2                             5465.617791  ...           NaN           41   \n",
       "\n",
       "   Serious,Critical  TotalTests  Population Continent New Cases/1M pop  \\\n",
       "0               NaN   1,941,032     2866374    Europe              NaN   \n",
       "1               NaN     230,861    45350148    Africa              0.1   \n",
       "2               NaN   1,499,795    35027343    Africa              NaN   \n",
       "\n",
       "   New Deaths/1M pop Active Cases/1M pop  covid_mortality_rate (per 100000)  \n",
       "0                NaN                 596                         125.663992  \n",
       "1                NaN               1,804                          15.173049  \n",
       "2                NaN                   1                           5.518546  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d79a45c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 43)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2068657f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                     0\n",
       "Country Code                                0\n",
       "Latitude                                    0\n",
       "Longitude                                   0\n",
       "HDI rank                                    0\n",
       "Human Development Index 2021 (HDI)          0\n",
       "Life expectancy at birth                    0\n",
       "Expected years of schooling                 0\n",
       "Mean years of schooling                     0\n",
       "Gross national income (GNI) per capita      0\n",
       "GDP 2021 (US$)                              0\n",
       "GDP per capita 2021 (US$)                   0\n",
       "GDP growth 2021 (annual %)                  0\n",
       "Inflation Rate 2021 (%)                     0\n",
       "Unemployment Rate 2021 (%)                  0\n",
       "Region                                      0\n",
       "IncomeGroup                                 0\n",
       "total_vaccinations                          0\n",
       "date                                        0\n",
       "people_vaccinated                           0\n",
       "people_fully_vaccinated                     0\n",
       "daily_vaccinations_raw                     21\n",
       "daily_vaccinations                          0\n",
       "total_vaccinations_per_hundred              0\n",
       "people_vaccinated_per_hundred               0\n",
       "people_fully_vaccinated_per_hundred         0\n",
       "daily_vaccinations_per_million              0\n",
       "Country,Other                               0\n",
       "TotalCases                                  0\n",
       "NewCases                                  118\n",
       "TotalDeaths                                 0\n",
       "NewDeaths                                 131\n",
       "TotalRecovered                             10\n",
       "NewRecovered                              117\n",
       "ActiveCases                                10\n",
       "Serious,Critical                           55\n",
       "TotalTests                                  3\n",
       "Population                                  0\n",
       "Continent                                   0\n",
       "New Cases/1M pop                          118\n",
       "New Deaths/1M pop                         131\n",
       "Active Cases/1M pop                         0\n",
       "covid_mortality_rate (per 100000)           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "05050354",
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0083de78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(covidvisual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f4b66ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                   149\n",
       "Country Code                              149\n",
       "Latitude                                  107\n",
       "Longitude                                 111\n",
       "HDI rank                                  128\n",
       "Human Development Index 2021 (HDI)        128\n",
       "Life expectancy at birth                  149\n",
       "Expected years of schooling               149\n",
       "Mean years of schooling                   149\n",
       "Gross national income (GNI) per capita    149\n",
       "GDP 2021 (US$)                            149\n",
       "GDP per capita 2021 (US$)                 149\n",
       "GDP growth 2021 (annual %)                149\n",
       "Inflation Rate 2021 (%)                   149\n",
       "Unemployment Rate 2021 (%)                145\n",
       "Region                                      7\n",
       "IncomeGroup                                 4\n",
       "total_vaccinations                        149\n",
       "date                                       75\n",
       "people_vaccinated                         149\n",
       "people_fully_vaccinated                   149\n",
       "daily_vaccinations_raw                    128\n",
       "daily_vaccinations                        149\n",
       "total_vaccinations_per_hundred            149\n",
       "people_vaccinated_per_hundred             146\n",
       "people_fully_vaccinated_per_hundred       148\n",
       "daily_vaccinations_per_million            149\n",
       "Country,Other                             149\n",
       "TotalCases                                149\n",
       "NewCases                                   31\n",
       "TotalDeaths                               148\n",
       "NewDeaths                                  12\n",
       "TotalRecovered                            139\n",
       "NewRecovered                               32\n",
       "ActiveCases                               134\n",
       "Serious,Critical                           56\n",
       "TotalTests                                146\n",
       "Population                                149\n",
       "Continent                                   6\n",
       "New Cases/1M pop                           25\n",
       "New Deaths/1M pop                          13\n",
       "Active Cases/1M pop                       133\n",
       "covid_mortality_rate (per 100000)         149\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "cc327308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                    object\n",
       "Country Code                               object\n",
       "Latitude                                  float64\n",
       "Longitude                                 float64\n",
       "HDI rank                                    int64\n",
       "Human Development Index 2021 (HDI)        float64\n",
       "Life expectancy at birth                  float64\n",
       "Expected years of schooling               float64\n",
       "Mean years of schooling                   float64\n",
       "Gross national income (GNI) per capita    float64\n",
       "GDP 2021 (US$)                            float64\n",
       "GDP per capita 2021 (US$)                 float64\n",
       "GDP growth 2021 (annual %)                float64\n",
       "Inflation Rate 2021 (%)                   float64\n",
       "Unemployment Rate 2021 (%)                float64\n",
       "Region                                     object\n",
       "IncomeGroup                                object\n",
       "total_vaccinations                          int64\n",
       "date                                       object\n",
       "people_vaccinated                           int64\n",
       "people_fully_vaccinated                     int64\n",
       "daily_vaccinations_raw                    float64\n",
       "daily_vaccinations                          int64\n",
       "total_vaccinations_per_hundred            float64\n",
       "people_vaccinated_per_hundred             float64\n",
       "people_fully_vaccinated_per_hundred       float64\n",
       "daily_vaccinations_per_million              int64\n",
       "Country,Other                              object\n",
       "TotalCases                                 object\n",
       "NewCases                                   object\n",
       "TotalDeaths                                 int64\n",
       "NewDeaths                                 float64\n",
       "TotalRecovered                             object\n",
       "NewRecovered                               object\n",
       "ActiveCases                                object\n",
       "Serious,Critical                           object\n",
       "TotalTests                                 object\n",
       "Population                                  int64\n",
       "Continent                                  object\n",
       "New Cases/1M pop                          float64\n",
       "New Deaths/1M pop                         float64\n",
       "Active Cases/1M pop                        object\n",
       "covid_mortality_rate (per 100000)         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d5620e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covidvisual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4864b158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(covidvisual['IncomeGroup'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0ac1c1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High income            49\n",
       "Lower middle income    44\n",
       "Upper middle income    40\n",
       "Low income             16\n",
       "Name: IncomeGroup, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual['IncomeGroup'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9262e08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High income            0.328859\n",
       "Lower middle income    0.295302\n",
       "Upper middle income    0.268456\n",
       "Low income             0.107383\n",
       "Name: IncomeGroup, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual['IncomeGroup'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "51291a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4de6ef02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='IncomeGroup'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=covidvisual, y = 'IncomeGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a342c955",
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HDI rank</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>Life expectancy at birth</th>\n",
       "      <th>Expected years of schooling</th>\n",
       "      <th>Mean years of schooling</th>\n",
       "      <th>Gross national income (GNI) per capita</th>\n",
       "      <th>GDP 2021 (US$)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "      <th>...</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>Population</th>\n",
       "      <th>New Cases/1M pop</th>\n",
       "      <th>New Deaths/1M pop</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>1.490000e+02</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>1.490000e+02</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.490000e+02</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.568903</td>\n",
       "      <td>18.110738</td>\n",
       "      <td>89.590604</td>\n",
       "      <td>0.737940</td>\n",
       "      <td>72.099044</td>\n",
       "      <td>13.872340</td>\n",
       "      <td>9.282668</td>\n",
       "      <td>21483.297314</td>\n",
       "      <td>6.267643e+11</td>\n",
       "      <td>17043.642330</td>\n",
       "      <td>...</td>\n",
       "      <td>147.465973</td>\n",
       "      <td>61.090268</td>\n",
       "      <td>56.137584</td>\n",
       "      <td>12556.147651</td>\n",
       "      <td>4.424775e+04</td>\n",
       "      <td>10.222222</td>\n",
       "      <td>4.966243e+07</td>\n",
       "      <td>50.808065</td>\n",
       "      <td>0.331667</td>\n",
       "      <td>140.651667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.937936</td>\n",
       "      <td>65.261616</td>\n",
       "      <td>55.154576</td>\n",
       "      <td>0.148244</td>\n",
       "      <td>7.600011</td>\n",
       "      <td>2.858387</td>\n",
       "      <td>3.145162</td>\n",
       "      <td>20484.572503</td>\n",
       "      <td>2.471488e+12</td>\n",
       "      <td>23472.569989</td>\n",
       "      <td>...</td>\n",
       "      <td>79.618981</td>\n",
       "      <td>23.674900</td>\n",
       "      <td>24.003123</td>\n",
       "      <td>11346.083328</td>\n",
       "      <td>1.286413e+05</td>\n",
       "      <td>14.901858</td>\n",
       "      <td>1.698311e+08</td>\n",
       "      <td>65.684372</td>\n",
       "      <td>0.317143</td>\n",
       "      <td>140.407090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-41.000000</td>\n",
       "      <td>-175.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>52.676000</td>\n",
       "      <td>6.957112</td>\n",
       "      <td>2.114962</td>\n",
       "      <td>731.786709</td>\n",
       "      <td>4.692313e+08</td>\n",
       "      <td>221.477676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.077490e+05</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.300994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>67.239800</td>\n",
       "      <td>12.087473</td>\n",
       "      <td>7.088480</td>\n",
       "      <td>5472.104781</td>\n",
       "      <td>1.609182e+10</td>\n",
       "      <td>2535.623432</td>\n",
       "      <td>...</td>\n",
       "      <td>77.450000</td>\n",
       "      <td>42.720000</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>7465.000000</td>\n",
       "      <td>1.298000e+03</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.249317e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>17.799897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>0.751000</td>\n",
       "      <td>72.614600</td>\n",
       "      <td>13.951780</td>\n",
       "      <td>9.827750</td>\n",
       "      <td>13157.993900</td>\n",
       "      <td>6.360510e+10</td>\n",
       "      <td>6104.136709</td>\n",
       "      <td>...</td>\n",
       "      <td>147.860000</td>\n",
       "      <td>65.550000</td>\n",
       "      <td>62.310000</td>\n",
       "      <td>11268.000000</td>\n",
       "      <td>7.100000e+03</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.021897e+07</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>98.920361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>0.858000</td>\n",
       "      <td>77.198200</td>\n",
       "      <td>15.928960</td>\n",
       "      <td>11.895590</td>\n",
       "      <td>32803.234230</td>\n",
       "      <td>3.661376e+11</td>\n",
       "      <td>21391.925334</td>\n",
       "      <td>...</td>\n",
       "      <td>213.920000</td>\n",
       "      <td>80.610000</td>\n",
       "      <td>75.190000</td>\n",
       "      <td>15163.000000</td>\n",
       "      <td>2.537500e+04</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>3.502734e+07</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>224.750550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>0.962000</td>\n",
       "      <td>85.473400</td>\n",
       "      <td>21.054590</td>\n",
       "      <td>14.090967</td>\n",
       "      <td>90918.644710</td>\n",
       "      <td>2.331508e+13</td>\n",
       "      <td>133590.146976</td>\n",
       "      <td>...</td>\n",
       "      <td>319.780000</td>\n",
       "      <td>105.820000</td>\n",
       "      <td>105.820000</td>\n",
       "      <td>117113.000000</td>\n",
       "      <td>1.155668e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.448471e+09</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>652.641143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Latitude   Longitude    HDI rank  Human Development Index 2021 (HDI)  \\\n",
       "count  149.000000  149.000000  149.000000                          149.000000   \n",
       "mean    20.568903   18.110738   89.590604                            0.737940   \n",
       "std     24.937936   65.261616   55.154576                            0.148244   \n",
       "min    -41.000000 -175.000000    1.000000                            0.400000   \n",
       "25%      4.500000   -8.000000   40.000000                            0.627000   \n",
       "50%     20.000000   20.000000   89.000000                            0.751000   \n",
       "75%     41.000000   45.000000  135.000000                            0.858000   \n",
       "max     65.000000  175.000000  189.000000                            0.962000   \n",
       "\n",
       "       Life expectancy at birth  Expected years of schooling  \\\n",
       "count                149.000000                   149.000000   \n",
       "mean                  72.099044                    13.872340   \n",
       "std                    7.600011                     2.858387   \n",
       "min                   52.676000                     6.957112   \n",
       "25%                   67.239800                    12.087473   \n",
       "50%                   72.614600                    13.951780   \n",
       "75%                   77.198200                    15.928960   \n",
       "max                   85.473400                    21.054590   \n",
       "\n",
       "       Mean years of schooling  Gross national income (GNI) per capita  \\\n",
       "count               149.000000                              149.000000   \n",
       "mean                  9.282668                            21483.297314   \n",
       "std                   3.145162                            20484.572503   \n",
       "min                   2.114962                              731.786709   \n",
       "25%                   7.088480                             5472.104781   \n",
       "50%                   9.827750                            13157.993900   \n",
       "75%                  11.895590                            32803.234230   \n",
       "max                  14.090967                            90918.644710   \n",
       "\n",
       "       GDP 2021 (US$)  GDP per capita 2021 (US$)  ...  \\\n",
       "count    1.490000e+02                 149.000000  ...   \n",
       "mean     6.267643e+11               17043.642330  ...   \n",
       "std      2.471488e+12               23472.569989  ...   \n",
       "min      4.692313e+08                 221.477676  ...   \n",
       "25%      1.609182e+10                2535.623432  ...   \n",
       "50%      6.360510e+10                6104.136709  ...   \n",
       "75%      3.661376e+11               21391.925334  ...   \n",
       "max      2.331508e+13              133590.146976  ...   \n",
       "\n",
       "       total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "count                      149.000000                     149.000000   \n",
       "mean                       147.465973                      61.090268   \n",
       "std                         79.618981                      23.674900   \n",
       "min                          0.280000                       0.260000   \n",
       "25%                         77.450000                      42.720000   \n",
       "50%                        147.860000                      65.550000   \n",
       "75%                        213.920000                      80.610000   \n",
       "max                        319.780000                     105.820000   \n",
       "\n",
       "       people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "count                           149.000000                      149.000000   \n",
       "mean                             56.137584                    12556.147651   \n",
       "std                              24.003123                    11346.083328   \n",
       "min                               0.260000                       32.000000   \n",
       "25%                              37.800000                     7465.000000   \n",
       "50%                              62.310000                    11268.000000   \n",
       "75%                              75.190000                    15163.000000   \n",
       "max                             105.820000                   117113.000000   \n",
       "\n",
       "        TotalDeaths  NewDeaths    Population  New Cases/1M pop  \\\n",
       "count  1.490000e+02  18.000000  1.490000e+02         31.000000   \n",
       "mean   4.424775e+04  10.222222  4.966243e+07         50.808065   \n",
       "std    1.286413e+05  14.901858  1.698311e+08         65.684372   \n",
       "min    1.300000e+01   1.000000  1.077490e+05          0.050000   \n",
       "25%    1.298000e+03   1.250000  3.249317e+06          3.000000   \n",
       "50%    7.100000e+03   4.500000  1.021897e+07         22.000000   \n",
       "75%    2.537500e+04  11.750000  3.502734e+07         78.500000   \n",
       "max    1.155668e+06  58.000000  1.448471e+09        282.000000   \n",
       "\n",
       "       New Deaths/1M pop  covid_mortality_rate (per 100000)  \n",
       "count          18.000000                         149.000000  \n",
       "mean            0.331667                         140.651667  \n",
       "std             0.317143                         140.407090  \n",
       "min             0.010000                           0.300994  \n",
       "25%             0.100000                          17.799897  \n",
       "50%             0.200000                          98.920361  \n",
       "75%             0.475000                         224.750550  \n",
       "max             1.000000                         652.641143  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "46a07077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "covidvisual['covid_mortality_rate (per 100000)'].hist(grid=False, bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "843456c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=covidvisual, x = 'covid_mortality_rate (per 100000)', hue='IncomeGroup');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "76956649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=covidvisual,x='covid_mortality_rate (per 100000)', hue='IncomeGroup');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5d7170bd",
   "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>IncomeGroup</th>\n",
       "      <th>High income</th>\n",
       "      <th>Low income</th>\n",
       "      <th>Lower middle income</th>\n",
       "      <th>Upper middle income</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia/Oceania</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IncomeGroup        High income  Low income  Lower middle income  \\\n",
       "Continent                                                         \n",
       "Africa                       0          16                   17   \n",
       "Asia                        11           0                   17   \n",
       "Australia/Oceania            2           0                    4   \n",
       "Europe                      29           0                    1   \n",
       "North America                5           0                    4   \n",
       "South America                2           0                    1   \n",
       "All                         49          16                   44   \n",
       "\n",
       "IncomeGroup        Upper middle income  All  \n",
       "Continent                                    \n",
       "Africa                               4   37  \n",
       "Asia                                10   38  \n",
       "Australia/Oceania                    2    8  \n",
       "Europe                               9   39  \n",
       "North America                        8   17  \n",
       "South America                        7   10  \n",
       "All                                 40  149  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(covidvisual['Continent'], covidvisual['IncomeGroup'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "72b01f9a",
   "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>IncomeGroup</th>\n",
       "      <th>High income</th>\n",
       "      <th>Low income</th>\n",
       "      <th>Lower middle income</th>\n",
       "      <th>Upper middle income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia/Oceania</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0.743590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IncomeGroup        High income  Low income  Lower middle income  \\\n",
       "Continent                                                         \n",
       "Africa                0.000000    0.432432             0.459459   \n",
       "Asia                  0.289474    0.000000             0.447368   \n",
       "Australia/Oceania     0.250000    0.000000             0.500000   \n",
       "Europe                0.743590    0.000000             0.025641   \n",
       "North America         0.294118    0.000000             0.235294   \n",
       "South America         0.200000    0.000000             0.100000   \n",
       "\n",
       "IncomeGroup        Upper middle income  \n",
       "Continent                               \n",
       "Africa                        0.108108  \n",
       "Asia                          0.263158  \n",
       "Australia/Oceania             0.250000  \n",
       "Europe                        0.230769  \n",
       "North America                 0.470588  \n",
       "South America                 0.700000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(covidvisual['Continent'], covidvisual['IncomeGroup'], normalize='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b89004b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.crosstab(covidvisual['Continent'], covidvisual['IncomeGroup']), annot=True, fmt='.5g', cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cb2d4913",
   "metadata": {},
   "outputs": [],
   "source": [
    "covidvisual = covidvisual.dropna(subset=['daily_vaccinations_per_million','covid_mortality_rate (per 100000)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d20818ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "incomegroupdummies = pd.get_dummies(covidvisual['IncomeGroup'], prefix='IncomeGroup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c86c83c4",
   "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>IncomeGroup_High income</th>\n",
       "      <th>IncomeGroup_Low income</th>\n",
       "      <th>IncomeGroup_Lower middle income</th>\n",
       "      <th>IncomeGroup_Upper middle income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IncomeGroup_High income  IncomeGroup_Low income  \\\n",
       "0                          0                       0   \n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          1                       0   \n",
       "..                       ...                     ...   \n",
       "144                        0                       0   \n",
       "145                        0                       0   \n",
       "146                        0                       0   \n",
       "147                        0                       1   \n",
       "148                        0                       0   \n",
       "\n",
       "     IncomeGroup_Lower middle income  IncomeGroup_Upper middle income  \n",
       "0                                  0                                1  \n",
       "1                                  1                                0  \n",
       "2                                  1                                0  \n",
       "3                                  0                                1  \n",
       "4                                  0                                0  \n",
       "..                               ...                              ...  \n",
       "144                                1                                0  \n",
       "145                                1                                0  \n",
       "146                                1                                0  \n",
       "147                                0                                0  \n",
       "148                                1                                0  \n",
       "\n",
       "[149 rows x 4 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomegroupdummies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6cd9bb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 43)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "780f7c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                     0\n",
       "Country Code                                0\n",
       "Latitude                                    0\n",
       "Longitude                                   0\n",
       "HDI rank                                    0\n",
       "Human Development Index 2021 (HDI)          0\n",
       "Life expectancy at birth                    0\n",
       "Expected years of schooling                 0\n",
       "Mean years of schooling                     0\n",
       "Gross national income (GNI) per capita      0\n",
       "GDP 2021 (US$)                              0\n",
       "GDP per capita 2021 (US$)                   0\n",
       "GDP growth 2021 (annual %)                  0\n",
       "Inflation Rate 2021 (%)                     0\n",
       "Unemployment Rate 2021 (%)                  0\n",
       "Region                                      0\n",
       "IncomeGroup                                 0\n",
       "total_vaccinations                          0\n",
       "date                                        0\n",
       "people_vaccinated                           0\n",
       "people_fully_vaccinated                     0\n",
       "daily_vaccinations_raw                     21\n",
       "daily_vaccinations                          0\n",
       "total_vaccinations_per_hundred              0\n",
       "people_vaccinated_per_hundred               0\n",
       "people_fully_vaccinated_per_hundred         0\n",
       "daily_vaccinations_per_million              0\n",
       "Country,Other                               0\n",
       "TotalCases                                  0\n",
       "NewCases                                  118\n",
       "TotalDeaths                                 0\n",
       "NewDeaths                                 131\n",
       "TotalRecovered                             10\n",
       "NewRecovered                              117\n",
       "ActiveCases                                10\n",
       "Serious,Critical                           55\n",
       "TotalTests                                  3\n",
       "Population                                  0\n",
       "Continent                                   0\n",
       "New Cases/1M pop                          118\n",
       "New Deaths/1M pop                         131\n",
       "Active Cases/1M pop                         0\n",
       "covid_mortality_rate (per 100000)           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b6d6cae0",
   "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</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HDI rank</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>Life expectancy at birth</th>\n",
       "      <th>Expected years of schooling</th>\n",
       "      <th>Mean years of schooling</th>\n",
       "      <th>Gross national income (GNI) per capita</th>\n",
       "      <th>...</th>\n",
       "      <th>NewRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>TotalTests</th>\n",
       "      <th>Population</th>\n",
       "      <th>Continent</th>\n",
       "      <th>New Cases/1M pop</th>\n",
       "      <th>New Deaths/1M pop</th>\n",
       "      <th>Active Cases/1M pop</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.796</td>\n",
       "      <td>76.4626</td>\n",
       "      <td>14.448000</td>\n",
       "      <td>11.286455</td>\n",
       "      <td>14131.110390</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,941,032</td>\n",
       "      <td>2866374</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>596</td>\n",
       "      <td>125.663992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.745</td>\n",
       "      <td>76.3767</td>\n",
       "      <td>14.626896</td>\n",
       "      <td>8.069284</td>\n",
       "      <td>10800.225460</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81,825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230,861</td>\n",
       "      <td>45350148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,804</td>\n",
       "      <td>15.173049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>148</td>\n",
       "      <td>0.586</td>\n",
       "      <td>61.6434</td>\n",
       "      <td>12.172100</td>\n",
       "      <td>5.417391</td>\n",
       "      <td>5465.617791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,499,795</td>\n",
       "      <td>35027343</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.518546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Country Code  Latitude  Longitude  HDI rank  \\\n",
       "0  Albania          ALB      41.0       20.0        67   \n",
       "1  Algeria          DZA      28.0        3.0        91   \n",
       "2   Angola          AGO     -12.5       18.5       148   \n",
       "\n",
       "   Human Development Index 2021 (HDI)  Life expectancy at birth  \\\n",
       "0                               0.796                   76.4626   \n",
       "1                               0.745                   76.3767   \n",
       "2                               0.586                   61.6434   \n",
       "\n",
       "   Expected years of schooling  Mean years of schooling  \\\n",
       "0                    14.448000                11.286455   \n",
       "1                    14.626896                 8.069284   \n",
       "2                    12.172100                 5.417391   \n",
       "\n",
       "   Gross national income (GNI) per capita  ...  NewRecovered  ActiveCases  \\\n",
       "0                            14131.110390  ...           NaN        1,709   \n",
       "1                            10800.225460  ...           NaN       81,825   \n",
       "2                             5465.617791  ...           NaN           41   \n",
       "\n",
       "   Serious,Critical  TotalTests  Population Continent New Cases/1M pop  \\\n",
       "0               NaN   1,941,032     2866374    Europe              NaN   \n",
       "1               NaN     230,861    45350148    Africa              0.1   \n",
       "2               NaN   1,499,795    35027343    Africa              NaN   \n",
       "\n",
       "   New Deaths/1M pop Active Cases/1M pop  covid_mortality_rate (per 100000)  \n",
       "0                NaN                 596                         125.663992  \n",
       "1                NaN               1,804                          15.173049  \n",
       "2                NaN                   1                           5.518546  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidvisual.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bcee9e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b913355f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = covidvisual[['total_vaccinations_per_hundred',\n",
    "            'daily_vaccinations_per_million',\n",
    "             'covid_mortality_rate (per 100000)',\n",
    "            'Human Development Index 2021 (HDI)','GDP per capita 2021 (US$)']]\n",
    "y = incomegroupdummies['IncomeGroup_High income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "39f07eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>covid_mortality_rate (per 100000)</th>\n",
       "      <th>Human Development Index 2021 (HDI)</th>\n",
       "      <th>GDP per capita 2021 (US$)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108.03</td>\n",
       "      <td>6180</td>\n",
       "      <td>125.663992</td>\n",
       "      <td>0.796</td>\n",
       "      <td>6492.872012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.00</td>\n",
       "      <td>5722</td>\n",
       "      <td>15.173049</td>\n",
       "      <td>0.745</td>\n",
       "      <td>3690.627878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.88</td>\n",
       "      <td>5147</td>\n",
       "      <td>5.518546</td>\n",
       "      <td>0.586</td>\n",
       "      <td>1953.533757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_vaccinations_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                          108.03                            6180   \n",
       "1                           34.00                            5722   \n",
       "2                           69.88                            5147   \n",
       "\n",
       "   covid_mortality_rate (per 100000)  Human Development Index 2021 (HDI)  \\\n",
       "0                         125.663992                               0.796   \n",
       "1                          15.173049                               0.745   \n",
       "2                           5.518546                               0.586   \n",
       "\n",
       "   GDP per capita 2021 (US$)  \n",
       "0                6492.872012  \n",
       "1                3690.627878  \n",
       "2                1953.533757  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "63dc3e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_vaccinations_per_hundred        0\n",
       "daily_vaccinations_per_million        0\n",
       "covid_mortality_rate (per 100000)     0\n",
       "Human Development Index 2021 (HDI)    0\n",
       "GDP per capita 2021 (US$)             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "313a4a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "5    1\n",
       "6    0\n",
       "7    1\n",
       "8    1\n",
       "9    0\n",
       "Name: IncomeGroup_High income, dtype: uint8"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(10) #1 it's in the high income group, 0 it's not in the high income group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0c28ca0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=8) #by default from the library train is 75% test is 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fa858835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((111, 5), (38, 5))"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e001f824",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ce0982d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "12fd0c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e1426391",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "def classification_results(classifier, x_test, y_test, cmap='Blues'):\n",
    "    print('Model Results:', classifier.__class__.__name__)\n",
    "    \n",
    "    y_pred = classifier.predict(x_test)\n",
    "    \n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    cmd = ConfusionMatrixDisplay(cm, display_labels=classifier.classes_)\n",
    "    cmd.plot(cmap=cmap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b15ae7bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Results: LogisticRegression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.89      0.94        27\n",
      "           1       0.79      1.00      0.88        11\n",
      "\n",
      "    accuracy                           0.92        38\n",
      "   macro avg       0.89      0.94      0.91        38\n",
      "weighted avg       0.94      0.92      0.92        38\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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IJF0MTANGS1oKnBYR5+V1viI6aL9dOGi/XVpdhtXw7pn9X1jfYZftm1xJuRQrxnIMsoiYmdexzazFCpZk7lqaWSaV4a/GJJmkxcDzQA/QHRFTB3IcB5mZZdP49cgOiIhVG3IAB5mZZVawnqVvvzCzrISUbqNysW9u1TZrnYMFcIOkef18lppbZGaWWYau5ao64177RcRySWOBGyU9mExvzMQtMjPLJO29sGmyLiKWJz+fAq4E9hxITQ4yM8uuAUkmaVNJI/peAwcBA1r2y11LM8usQbdfjAOuTMbSuoCLIuK6gRzIQWZmmTXi9ouIeBSYsuFHcpCZWVZ+rqWZtYOirdnvIDOzTIRbZGbWBgqWYw4yMxuAgiWZg8zMMhs0CyuaWfsqVow5yMxsIAqWZA4yM8ukkQsrNoqDzMyy8Q2xZtYOCpZjDjIzy+qVRRMLw0FmZpkVLMccZGaWTZMfIp6Kg8zMsitYkjnIzCwz335hZqXnMTIzKzdBh4PMzMqvWEnmIDOzTLywopm1hYLlmIPMzLJzi8zMSs9TlMys9IoVYw4yM8tIXsbHzNqB7+w3s/IrVo45yMwsu4LlmIPMzLKSHwdnZuVWxDv7O1pdgJnZhnKLzMwyK1qLzEFmZpn59gszKzffEGtmZVfEwX4HmZll5q6lmZVe0Vpkvv3CzDJTyq3ucaTpkh6S9Iikkwdaj4PMzLJrQJJJ6gTOBg4BJgMzJU0eSDkOMjPLRECHlGqrY0/gkYh4NCJeAi4BZgykpkKNkc2fP2/VsI30t1bXkYPRwKpWF2GZtOu/2TYbeoD58+ddP2wjjU759aGS5la9nx0Rs5PXE4DHqz5bCuw1kJoKFWQRMabVNeRB0tyImNrqOiw9/5utX0RMb9Ch+muyxUAO5K6lmbXKUmCrqvcTgeUDOZCDzMxa5W5gB0mTJG0MHAlcPZADFapr2cZm1/+KFYz/zXIWEd2SjgeuBzqB8yNi4UCOpYgBdUnNzArDXUszKz0HmZmVnoMsR42afmHNI+l8SU9JWtDqWiw9B1lOGjn9wprqAqBR90lZkzjI8tOw6RfWPBExB1jd6josGwdZfvqbfjGhRbWYtTUHWX4aNv3CzGpzkOWnYdMvzKw2B1l+Gjb9wsxqc5DlJCK6gb7pFw8Alw50+oU1j6SLgTuBHSUtlXRsq2uy+jxFycxKzy0yMys9B5mZlZ6DzMxKz0FmZqXnIDOz0nOQlYikHkn3SFog6TJJm2zAsS6QdFjy+qe1JrRLmiZp3wGcY7H0r0/bWd/+db7zQsZznS7pC1lrtPbgICuXtRGxe0TsCrwEfKL6w2TFjcwi4uMRsajGV6YBmYPMrFkcZOV1O/BvSWvpVkkXAfdL6pT0bUl3S7pP0nEAqvihpEWSrgHG9h1I0m2Spiavp0uaL+leSTdL2pZKYJ6YtAbfImmMpMuTc9wtab/kd7eQdIOkP0s6l7rPmgZJv5Y0T9JCSbPW+eyspJabJY1J9m0v6brkd26XtFND/jat3CLCW0k24IXkZxdwFfBJKq2lNcCk5LNZwKnJ6yHAXGAS8D7gRioPedgSeBY4LPnebcBUYAyVFTv6jjUq+Xk68IWqOi4C/iN5vTXwQPL6+8CXk9fvojJJfnQ/f47FffurzjEMWABskbwP4Kjk9ZeBHyavbwZ2SF7vBdzSX43eBtfmpyiVyzBJ9ySvbwfOo9Ll+1NEPJbsPwjYrW/8C3gdsAOwP3BxRPQAyyXd0s/x9wbm9B0rIta3LtfbgcnSKw2ukZJGJOd4X/K710h6JsWf6QRJhyavt0pqfRroBX6Z7P85cIWk4cmf97Kqcw9JcQ5rcw6yclkbEbtX70j+Q6+p3gX8V0Rcv8733kn9ZYSU4jtQGZLYJyLW9lNL6jlvkqZRCcV9IuJFSbcBQ9fz9UjO++y6fwdmHiNrP9cDn5S0EYCkN0jaFJgDHJmMoY0HDujnd+8E3ippUvK7o5L9zwMjqr53A5UJ8STf2z15OQc4Ktl3CLB5nVpfBzyThNhOVFqEfTqAvlblB4E7IuI54DFJhyfnkKQpdc5hg4CDrP38FFgEzE8eoHEulZb3lcDDwP3AOcDv1v3FiFhJZYztCkn38mrX7jfAoX2D/cAJwNTkYsIiXr16+hVgf0nzqXRxl9Sp9TqgS9J9wBnAH6s+WwPsImke8Dbgq8n+o4Bjk/oW4uXDDa9+YWZtwC0yMys9B5mZlZ6DzMxKz0FmZqXnIDOz0nOQmVnpOcjMrPT+H681akz3p0ywAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classification_results(lr, x_test, y_test)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fdd4f752",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "85f3535f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "263ff714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "73d631cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "def classification_results(classifier, x_test, y_test, cmap='Blues'):\n",
    "    print('Model Results:', classifier.__class__.__name__)\n",
    "    \n",
    "    y_pred = classifier.predict(x_test)\n",
    "    \n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    cmd = ConfusionMatrixDisplay(cm, display_labels=classifier.classes_)\n",
    "    cmd.plot(cmap=cmap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c4a7794a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Results: DecisionTreeClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        27\n",
      "           1       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        38\n",
      "   macro avg       1.00      1.00      1.00        38\n",
      "weighted avg       1.00      1.00      1.00        38\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classification_results(dt, x_test, y_test)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "480b84ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "95f5f34f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Results: RandomForestClassifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        27\n",
      "           1       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        38\n",
      "   macro avg       1.00      1.00      1.00        38\n",
      "weighted avg       1.00      1.00      1.00        38\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train, y_train)\n",
    "classification_results(rf, x_test, y_test, cmap='Oranges')\n",
    "plt.grid(False)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}