01_data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Downloading the files**\n",
    "\n",
    "- GCI: http://www3.weforum.org/docs/GCR2017-2018/GCI_Dataset_2007-2017.xlsx\n",
    "- Worldbank: https://databank.worldbank.org/source/world-development-indicators\n",
    "    - select countries\n",
    "    - select series \n",
    "    - select time\n",
    "    - click on download options\n",
    "    - advanced options\n",
    "    - Excel, Metadata yes\n",
    "\n",
    "- UNDP: https://hdr.undp.org/en/indicators/137506"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import klib\n",
    "import miceforest as mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary for matching different namings of countries to one \n",
    "countries_dict = {'Bahamas': 'Bahamas', \n",
    "                'Bahamas, The':'Bahamas',\n",
    "                'The Bahamas':'Bahamas',\n",
    "                'Brunei Darussalam':'Brunei',\n",
    "                'Bolivia':'Bolivia',\n",
    "                'Bolivia (Plurinational State of)':'Bolivia',\n",
    "                'Congo':'Congo',\n",
    "                'Congo, Rep': 'Congo',\n",
    "                'Congo (Democratic Republic of the)':'DR Congo', \n",
    "                'Congo, Dem. Rep.':'DR Congo',\n",
    "                'Congo, Democratic Rep.':'DR Congo',\n",
    "                'Côte d\\'Ivoire':'Cote d\\'Ivoire',\n",
    "                'Cote d\\'Ivoire':'Cote d\\'Ivoire',\n",
    "                'Czechia':'Czech',\n",
    "                'Czech Republic':'Czech',\n",
    "                'Curaçao':'Curacao',\n",
    "                'Egypt':'Egypt',\n",
    "                'Egypt, Arab Rep.':'Egypt',\n",
    "                'Eswatini':'Eswatini',\n",
    "                'Eswatini (Kingdom of)':'Eswatini',\n",
    "                'Gambia':'Gambia',\n",
    "                'The Gambia':'Gambia',\n",
    "                'Gambia, The':'Gambia',\n",
    "                'Hong Kong SAR':'Hong Kong',\n",
    "                'Hong Kong SAR, China':'Hong Kong',\n",
    "                'Hong Kong, China (SAR)':'Hong Kong',\n",
    "                'Hong':'Hong Kong',\n",
    "                'Kyrgyz Republic':'Kyrgyzstan',\n",
    "                'Iran (Islamic Republic of)':'Iran',\n",
    "                'Iran, Islamic Rep.':'Iran',\n",
    "                'Korea':'Korea, Rep.',\n",
    "                'Korea (Republic of)':'Korea, Rep.',\n",
    "                'Dem. People\\'s Rep. Korea':'\"Korea, Dem. People\\'s Rep.',\n",
    "                'Lao People\\'s Democratic Republic':'Lao PDR',\n",
    "                'Saint Lucia':'St. Lucia',\n",
    "                'Moldova (Republic of)':'Moldova',\n",
    "                'Micronesia (Federated States of)':'Micronesia',\n",
    "                'Micronesia, Fed. Sts.':'Micronesia',\n",
    "                'Saint Kitts and Nevis':'St. Kitts and Nevis',\n",
    "                'Slovak Republic':'Slovakia',\n",
    "                'Syrian Arab Republic':'Syria',\n",
    "                'Tanzania (United Republic of)':'Tanzania',\n",
    "                'Venezuela (Bolivarian Republic of)':'Venezuela',\n",
    "                'Venezuela, RB':'Venezuela',\n",
    "                'Viet Nam':'Vietnam',\n",
    "                'Saint Vincent':'St. Vincent',\n",
    "                'Yemen, Rep.':'Yemen',\n",
    "                'Swaziland': 'Eswatini',\n",
    "                'São Tomé and Principe':'Sao Tome and Principe',\n",
    "                'Russian Federation':'Russia',\n",
    "                'Macedonia, FYR':'North Macedonia' \n",
    "                }\n",
    "\n",
    "#Datasets contain regions as well as countries\n",
    "regions=['Arab States', 'Eurasia', 'Europe and Central Asia', 'Europe and North America','Latin America and the Caribbean','Middle East and North Africa', \n",
    "'Organization for Economic Co-operation and Development', 'Sub-Saharan Africa']\n",
    "\n",
    "#Datasets label and group countries into buckets\n",
    "designations =['Very high human development','South Asia','Small Island Developing States','Least Developed Countries','Low human development',\n",
    "'Medium human development','High human development','Human Development']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to unify the names of the countries. \n",
    "def unify_countries(original, replacement_dict):\n",
    "    ### original: original text of country name\n",
    "    ### replacement_dict: the dictionary of different variations of country names and the unified name\n",
    "    if original.strip() in replacement_dict:\n",
    "        return replacement_dict[original.strip()]\n",
    "    else:\n",
    "        return original.strip()\n",
    "\n",
    "#fucntion to rename the series codes to more readable names\n",
    "def rename_indicators(x,y,val):\n",
    "    if val == np.nan:\n",
    "        return np.nan\n",
    "    return x[y.index(val)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>Code</th>\n",
       "      <th>Long Name</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>Region</th>\n",
       "      <th>Lending category</th>\n",
       "      <th>Other groups</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>Latest population census</th>\n",
       "      <th>Latest household survey</th>\n",
       "      <th>Special Notes</th>\n",
       "      <th>...</th>\n",
       "      <th>IMF data dissemination standard</th>\n",
       "      <th>Source of most recent Income and expenditure data</th>\n",
       "      <th>Vital registration complete</th>\n",
       "      <th>Latest agricultural census</th>\n",
       "      <th>Latest industrial data</th>\n",
       "      <th>Latest trade data</th>\n",
       "      <th>2-alpha code</th>\n",
       "      <th>WB-2 code</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Short Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Islamic State of Afghanistan</td>\n",
       "      <td>Low income</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>IDA</td>\n",
       "      <td>HIPC</td>\n",
       "      <td>Afghan afghani</td>\n",
       "      <td>1979</td>\n",
       "      <td>Demographic and Health Survey, 2015</td>\n",
       "      <td>Fiscal year end: March 20; reporting period fo...</td>\n",
       "      <td>...</td>\n",
       "      <td>Enhanced General Data Dissemination System (e-...</td>\n",
       "      <td>Integrated household survey (IHS), 2016/17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>AF</td>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Republic of Albania</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albanian lek</td>\n",
       "      <td>2020 (expected)</td>\n",
       "      <td>Demographic and Health Survey, 2017/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Enhanced General Data Dissemination System (e-...</td>\n",
       "      <td>Living Standards Measurement Study Survey (LSM...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DZA</td>\n",
       "      <td>People's Democratic Republic of Algeria</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algerian dinar</td>\n",
       "      <td>2020 (expected)</td>\n",
       "      <td>Multiple Indicator Cluster Survey, 2018/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Enhanced General Data Dissemination System (e-...</td>\n",
       "      <td>Integrated household survey (IHS), 2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZ</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>U.S. dollar</td>\n",
       "      <td>2020 (expected)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AS</td>\n",
       "      <td>AS</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>American Samoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AND</td>\n",
       "      <td>Principality of Andorra</td>\n",
       "      <td>High income</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Euro</td>\n",
       "      <td>2011. Population figures compiled from adminis...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>AD</td>\n",
       "      <td>AD</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code                                Long Name         Income Group  \\\n",
       "0  AFG             Islamic State of Afghanistan           Low income   \n",
       "1  ALB                      Republic of Albania  Upper middle income   \n",
       "2  DZA  People's Democratic Republic of Algeria  Lower middle income   \n",
       "3  ASM                           American Samoa  Upper middle income   \n",
       "4  AND                  Principality of Andorra          High income   \n",
       "\n",
       "                       Region Lending category Other groups   Currency Unit  \\\n",
       "0                  South Asia              IDA         HIPC  Afghan afghani   \n",
       "1       Europe & Central Asia             IBRD          NaN    Albanian lek   \n",
       "2  Middle East & North Africa             IBRD          NaN  Algerian dinar   \n",
       "3         East Asia & Pacific              NaN          NaN     U.S. dollar   \n",
       "4       Europe & Central Asia              NaN          NaN            Euro   \n",
       "\n",
       "                            Latest population census  \\\n",
       "0                                               1979   \n",
       "1                                    2020 (expected)   \n",
       "2                                    2020 (expected)   \n",
       "3                                    2020 (expected)   \n",
       "4  2011. Population figures compiled from adminis...   \n",
       "\n",
       "                      Latest household survey  \\\n",
       "0         Demographic and Health Survey, 2015   \n",
       "1      Demographic and Health Survey, 2017/18   \n",
       "2  Multiple Indicator Cluster Survey, 2018/19   \n",
       "3                                         NaN   \n",
       "4                                         NaN   \n",
       "\n",
       "                                       Special Notes  ...  \\\n",
       "0  Fiscal year end: March 20; reporting period fo...  ...   \n",
       "1                                                NaN  ...   \n",
       "2                                                NaN  ...   \n",
       "3                                                NaN  ...   \n",
       "4                                                NaN  ...   \n",
       "\n",
       "                     IMF data dissemination standard  \\\n",
       "0  Enhanced General Data Dissemination System (e-...   \n",
       "1  Enhanced General Data Dissemination System (e-...   \n",
       "2  Enhanced General Data Dissemination System (e-...   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "   Source of most recent Income and expenditure data  \\\n",
       "0         Integrated household survey (IHS), 2016/17   \n",
       "1  Living Standards Measurement Study Survey (LSM...   \n",
       "2            Integrated household survey (IHS), 2011   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "  Vital registration complete Latest agricultural census  \\\n",
       "0                         NaN                        NaN   \n",
       "1                         Yes                       2012   \n",
       "2                         NaN                        NaN   \n",
       "3                         Yes                       2008   \n",
       "4                         Yes                        NaN   \n",
       "\n",
       "  Latest industrial data Latest trade data 2-alpha code WB-2 code  \\\n",
       "0                    NaN            2018.0           AF        AF   \n",
       "1                 2013.0            2018.0           AL        AL   \n",
       "2                 2010.0            2017.0           DZ        DZ   \n",
       "3                    NaN               NaN           AS        AS   \n",
       "4                    NaN            2018.0           AD        AD   \n",
       "\n",
       "       Table Name      Short Name  \n",
       "0     Afghanistan     Afghanistan  \n",
       "1         Albania         Albania  \n",
       "2         Algeria         Algeria  \n",
       "3  American Samoa  American Samoa  \n",
       "4         Andorra         Andorra  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The world bank file has multiple sheets. \n",
    "# Loading the sheet with the countries and their meta data.\n",
    "df_countries = pd.read_excel('data/World_Development_Indicators.xlsx',sheet_name='Country - Metadata')\n",
    "df_countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are only interested in the name, income group, and region\n",
    "df_countries = df_countries[['Short Name','Income Group', 'Region']]\n",
    "\n",
    "#renaming columns \n",
    "df_countries.columns = ['country','income_group', 'region']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing any empty rows read by pandas\n",
    "df_countries.dropna(inplace=True)\n",
    "\n",
    "#unifying country names\n",
    "df_countries['country'] = df_countries['country'].apply(lambda x: unify_countries(x,countries_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### World Bank Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lists of original series name and clean, readable series name \n",
    "series_id = ['SE.XPD.TOTL.GD.ZS','HD.HCI.OVRL','NY.ADJ.AEDU.CD','SE.XPD.PRIM.PC.ZS','SE.XPD.SECO.PC.ZS','SE.XPD.TERT.PC.ZS','NY.GDP.PCAP.CD','NY.GDP.MKTP.CD','SP.POP.TOTL']\n",
    "series_name = ['edu_exp_pct_gdp','hci_index','edu_exp_usd','edu_prim_exp_pct_gdp','edu_sec_exp_pct_gdp','edu_tert_exp_pct_gdp','gdppc','gdp','population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we are using the same datafile to get values of the indicators that we are interested \n",
    "df_wb_data = pd.read_excel('data/World_Development_Indicators.xlsx',sheet_name='Data')\n",
    "\n",
    "#replacing .. with nan\n",
    "df_wb_data.replace(to_replace='..',value=np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing any empty rows read by pandas\n",
    "df_wb_data  = df_wb_data.iloc[0:-5,:]\n",
    "\n",
    "#dropping the columns that we are not interseted in\n",
    "df_wb_data.drop(columns=['Series Name','Country Code'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE #######\n",
    "df_wb_data = df_wb_data.melt(id_vars=['Country Name','Series Code'],var_name='year')\n",
    "\n",
    "#Renaming the columns\n",
    "df_wb_data.columns = ['country','series_code','year','value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the unreadable series codes to comprehendable namings in the indicators column\n",
    "df_wb_data['indicator'] = df_wb_data['series_code'].apply(lambda val: rename_indicators(series_name,series_id,val))\n",
    "\n",
    "#changing the year from a fiscal year or range to a specific calendar year\n",
    "df_wb_data['year'] = df_wb_data['year'].apply(lambda val: val[0:4])\n",
    "\n",
    "#unifying the names of the countries\n",
    "df_wb_data['country'] = df_wb_data['country'].apply(lambda x: unify_countries(x,countries_dict))\n",
    "\n",
    "#removing series_code after processing it\n",
    "df_wb_data.drop(columns='series_code',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE #######\n",
    "df_wb_data = df_wb_data.pivot(index=['country','year'],columns='indicator',values='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 4340 entries, ('Afghanistan', '2001') to ('Zimbabwe', '2020')\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   edu_exp_pct_gdp       2448 non-null   float64\n",
      " 1   edu_exp_usd           3491 non-null   float64\n",
      " 2   edu_prim_exp_pct_gdp  1412 non-null   float64\n",
      " 3   edu_sec_exp_pct_gdp   1274 non-null   float64\n",
      " 4   edu_tert_exp_pct_gdp  1285 non-null   float64\n",
      " 5   gdp                   4125 non-null   float64\n",
      " 6   gdppc                 4125 non-null   float64\n",
      " 7   hci_index             601 non-null    float64\n",
      " 8   population            4331 non-null   float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 328.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_wb_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### UNDP - HDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the Human development index from the csv\n",
    "df_hdi = pd.read_csv('data/Human Development Index (HDI).csv', skiprows=5)\n",
    "\n",
    "#cleaning the dataframe from empty rows and columns read by pandas\n",
    "df_hdi.dropna(axis=1,how='all',inplace=True)\n",
    "df_hdi.dropna(axis=0,how='all',inplace=True)\n",
    "\n",
    "#replacing .. with nan\n",
    "df_hdi.replace('..',np.nan,inplace=True)\n",
    "\n",
    "#droping the rank column as we are not interested in the rank\n",
    "df_hdi.drop(columns='HDI Rank',inplace=True)\n",
    "\n",
    "#clean column names\n",
    "klib.clean_column_names(df_hdi)\n",
    "\n",
    "#### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE #######\n",
    "df_hdi = df_hdi.melt(id_vars=['country'],var_name='year',value_name='hdi_index')\n",
    "\n",
    "#Unifying country names\n",
    "df_hdi['country'] = df_hdi['country'].apply(lambda x: unify_countries(x,countries_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6180 entries, 0 to 6179\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   country    6180 non-null   object\n",
      " 1   year       6180 non-null   object\n",
      " 2   hdi_index  5602 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 145.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_hdi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GCI - Quality of Education Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the GCI \n",
    "df_gci = pd.read_excel('data/GCI_Dataset_2007-2017.xlsx',sheet_name='Data',skiprows=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lists of original series name and clean, readable series name \n",
    "series_global_id = ['GCI.B.05.02']\n",
    "series_rename = ['edu_quality_index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting the indicators that are of concern\n",
    "df_gci = df_gci[df_gci['GLOBAL ID'].isin(series_global_id)]\n",
    "\n",
    "#removing unneeded rows\n",
    "df_gci = df_gci[df_gci['Attribute']=='Value']\n",
    "\n",
    "#getting the year information\n",
    "df_gci['year']= df_gci['Edition'].apply(lambda x: x[-4:])\n",
    "\n",
    "#dropping unneeded columns\n",
    "df_gci.drop(labels=['Placement','Dataset','Series','Code GCR','Series unindented','Edition','Attribute'],axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE #######\n",
    "df_gci = df_gci.melt(id_vars=['GLOBAL ID','year'], var_name=['country'])\n",
    "\n",
    "#Unifying country names\n",
    "df_gci['country'] = df_gci['country'].apply(lambda x: unify_countries(x,countries_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remaning the indicators to meaningful names\n",
    "df_gci['indicator'] = df_gci['GLOBAL ID'].apply(lambda val: rename_indicators(series_rename,series_global_id,val))\n",
    "\n",
    "#Dropping unneeded columns\n",
    "df_gci.drop(columns='GLOBAL ID',inplace=True)\n",
    "\n",
    "#### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE ########### DEFINE WHAT WE ARE DOING HERE #######\n",
    "df_gci = df_gci.pivot(index=['year','country'],columns='indicator',values='value')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1760 entries, ('2008', 'Albania') to ('2018', 'Zimbabwe')\n",
      "Data columns (total 1 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   edu_quality_index  1538 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 24.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_gci.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Joining the indicators together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the data frames together for analysis\n",
    "df_indicators =df_wb_data.merge(df_gci, on=['country','year'],how='outer')\n",
    "df_indicators = df_indicators.merge(df_hdi, on=['country','year'],how='outer')\n",
    "df_indicators = df_indicators.merge(df_countries, on=['country'],how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the groupings from the dataframe\n",
    "df_indicators = df_indicators[~df_indicators.country.isin(designations)]\n",
    "df_indicators = df_indicators[~df_indicators.country.isin(regions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6294 entries, 0 to 6293\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   country               6294 non-null   object \n",
      " 1   year                  6294 non-null   object \n",
      " 2   edu_exp_pct_gdp       2425 non-null   float64\n",
      " 3   edu_exp_usd           3439 non-null   float64\n",
      " 4   edu_prim_exp_pct_gdp  1403 non-null   float64\n",
      " 5   edu_sec_exp_pct_gdp   1267 non-null   float64\n",
      " 6   edu_tert_exp_pct_gdp  1281 non-null   float64\n",
      " 7   gdp                   4053 non-null   float64\n",
      " 8   gdppc                 4053 non-null   float64\n",
      " 9   hci_index             594 non-null    float64\n",
      " 10  population            4231 non-null   float64\n",
      " 11  edu_quality_index     1492 non-null   object \n",
      " 12  hdi_index             5056 non-null   object \n",
      " 13  income_group          6294 non-null   object \n",
      " 14  region                6294 non-null   object \n",
      "dtypes: float64(9), object(6)\n",
      "memory usage: 786.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the columns data types and non-null value counts\n",
    "df_indicators.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating new metric\n",
    "df_indicators['edu_exp_per_capita'] = df_indicators.apply(lambda row: row['edu_exp_usd']/row['population'] if row['edu_exp_usd'] != np.nan and row['population'] != np.nan else np.nan,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adjusting the data types\n",
    "df_indicators['hdi_index'] = df_indicators['hdi_index'].astype(float)\n",
    "df_indicators['edu_quality_index'] = df_indicators['edu_quality_index'].astype(float)\n",
    "df_indicators['year'] = df_indicators['year'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The years of the observations are not consistent across the datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>edu_exp_pct_gdp</th>\n",
       "      <th>edu_exp_usd</th>\n",
       "      <th>edu_prim_exp_pct_gdp</th>\n",
       "      <th>edu_sec_exp_pct_gdp</th>\n",
       "      <th>edu_tert_exp_pct_gdp</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gdppc</th>\n",
       "      <th>hci_index</th>\n",
       "      <th>population</th>\n",
       "      <th>edu_quality_index</th>\n",
       "      <th>hdi_index</th>\n",
       "      <th>income_group</th>\n",
       "      <th>region</th>\n",
       "      <th>edu_exp_per_capita</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>213</td>\n",
       "      <td>113</td>\n",
       "      <td>177</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>213</td>\n",
       "      <td>120</td>\n",
       "      <td>179</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>213</td>\n",
       "      <td>107</td>\n",
       "      <td>179</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>213</td>\n",
       "      <td>119</td>\n",
       "      <td>180</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>77</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>213</td>\n",
       "      <td>109</td>\n",
       "      <td>180</td>\n",
       "      <td>78</td>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "      <td>201</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>213</td>\n",
       "      <td>107</td>\n",
       "      <td>181</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>213</td>\n",
       "      <td>107</td>\n",
       "      <td>182</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>213</td>\n",
       "      <td>118</td>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>126</td>\n",
       "      <td>182</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>213</td>\n",
       "      <td>117</td>\n",
       "      <td>182</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>87</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>130</td>\n",
       "      <td>182</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>213</td>\n",
       "      <td>136</td>\n",
       "      <td>182</td>\n",
       "      <td>99</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>102</td>\n",
       "      <td>212</td>\n",
       "      <td>130</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>213</td>\n",
       "      <td>136</td>\n",
       "      <td>184</td>\n",
       "      <td>104</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>136</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>213</td>\n",
       "      <td>124</td>\n",
       "      <td>183</td>\n",
       "      <td>95</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>139</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>213</td>\n",
       "      <td>138</td>\n",
       "      <td>183</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>141</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>213</td>\n",
       "      <td>135</td>\n",
       "      <td>184</td>\n",
       "      <td>93</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>145</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>213</td>\n",
       "      <td>150</td>\n",
       "      <td>184</td>\n",
       "      <td>88</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>141</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>213</td>\n",
       "      <td>147</td>\n",
       "      <td>183</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>137</td>\n",
       "      <td>184</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>213</td>\n",
       "      <td>157</td>\n",
       "      <td>182</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>155</td>\n",
       "      <td>211</td>\n",
       "      <td>134</td>\n",
       "      <td>185</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>213</td>\n",
       "      <td>147</td>\n",
       "      <td>180</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>165</td>\n",
       "      <td>211</td>\n",
       "      <td>133</td>\n",
       "      <td>185</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>213</td>\n",
       "      <td>103</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>212</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>172</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  edu_exp_pct_gdp  edu_exp_usd  edu_prim_exp_pct_gdp  \\\n",
       "year                                                                \n",
       "1990      185                0            0                     0   \n",
       "1991      185                0            0                     0   \n",
       "1992      185                0            0                     0   \n",
       "1993      185                0            0                     0   \n",
       "1994      185                0            0                     0   \n",
       "1995      185                0            0                     0   \n",
       "1996      185                0            0                     0   \n",
       "1997      185                0            0                     0   \n",
       "1998      185                0            0                     0   \n",
       "1999      185                0            0                     0   \n",
       "2000      185                0            0                     0   \n",
       "2001      213              113          177                    65   \n",
       "2002      213              120          179                    71   \n",
       "2003      213              107          179                    64   \n",
       "2004      213              119          180                    77   \n",
       "2005      213              109          180                    78   \n",
       "2006      213              107          181                    69   \n",
       "2007      213              107          182                    76   \n",
       "2008      213              118          181                    88   \n",
       "2009      213              117          182                    91   \n",
       "2010      213              136          182                    99   \n",
       "2011      213              136          184                   104   \n",
       "2012      213              124          183                    95   \n",
       "2013      213              138          183                   100   \n",
       "2014      213              135          184                    93   \n",
       "2015      213              150          184                    88   \n",
       "2016      213              147          183                    82   \n",
       "2017      213              157          182                    39   \n",
       "2018      213              147          180                    24   \n",
       "2019      213              103          173                     0   \n",
       "2020      212               35            0                     0   \n",
       "\n",
       "      edu_sec_exp_pct_gdp  edu_tert_exp_pct_gdp  gdp  gdppc  hci_index  \\\n",
       "year                                                                     \n",
       "1990                    0                     0    0      0          0   \n",
       "1991                    0                     0    0      0          0   \n",
       "1992                    0                     0    0      0          0   \n",
       "1993                    0                     0    0      0          0   \n",
       "1994                    0                     0    0      0          0   \n",
       "1995                    0                     0    0      0          0   \n",
       "1996                    0                     0    0      0          0   \n",
       "1997                    0                     0    0      0          0   \n",
       "1998                    0                     0    0      0          0   \n",
       "1999                    0                     0    0      0          0   \n",
       "2000                    0                     0    0      0          0   \n",
       "2001                   55                    59  197    197          0   \n",
       "2002                   64                    64  201    201          0   \n",
       "2003                   59                    54  201    201          0   \n",
       "2004                   73                    77  201    201          0   \n",
       "2005                   69                    67  201    201          0   \n",
       "2006                   62                    67  202    202          0   \n",
       "2007                   70                    67  202    202          0   \n",
       "2008                   77                    73  203    203          0   \n",
       "2009                   78                    87  203    203          0   \n",
       "2010                   89                    94  204    204        102   \n",
       "2011                   95                    91  206    206          0   \n",
       "2012                   81                    89  205    205          0   \n",
       "2013                   93                    95  206    206          0   \n",
       "2014                   84                    85  206    206          0   \n",
       "2015                   82                    82  206    206          0   \n",
       "2016                   77                    77  205    205          0   \n",
       "2017                   36                    37  205    205        155   \n",
       "2018                   23                    16  205    205        165   \n",
       "2019                    0                     0  202    202          0   \n",
       "2020                    0                     0  192    192        172   \n",
       "\n",
       "      population  edu_quality_index  hdi_index  income_group  region  \\\n",
       "year                                                                   \n",
       "1990           0                  0        142           185     185   \n",
       "1991           0                  0        142           185     185   \n",
       "1992           0                  0        142           185     185   \n",
       "1993           0                  0        142           185     185   \n",
       "1994           0                  0        142           185     185   \n",
       "1995           0                  0        146           185     185   \n",
       "1996           0                  0        146           185     185   \n",
       "1997           0                  0        146           185     185   \n",
       "1998           0                  0        146           185     185   \n",
       "1999           0                  0        149           185     185   \n",
       "2000           0                  0        171           185     185   \n",
       "2001         212                  0        171           213     213   \n",
       "2002         212                  0        172           213     213   \n",
       "2003         212                  0        173           213     213   \n",
       "2004         212                  0        174           213     213   \n",
       "2005         212                  0        181           213     213   \n",
       "2006         212                  0        182           213     213   \n",
       "2007         212                  0        182           213     213   \n",
       "2008         212                126        182           213     213   \n",
       "2009         212                130        182           213     213   \n",
       "2010         212                130        184           213     213   \n",
       "2011         212                136        184           213     213   \n",
       "2012         211                139        184           213     213   \n",
       "2013         211                141        184           213     213   \n",
       "2014         211                145        184           213     213   \n",
       "2015         211                141        184           213     213   \n",
       "2016         211                137        184           213     213   \n",
       "2017         211                134        185           213     213   \n",
       "2018         211                133        185           213     213   \n",
       "2019         211                  0        185           213     213   \n",
       "2020         211                  0          0           212     212   \n",
       "\n",
       "      edu_exp_per_capita  \n",
       "year                      \n",
       "1990                   0  \n",
       "1991                   0  \n",
       "1992                   0  \n",
       "1993                   0  \n",
       "1994                   0  \n",
       "1995                   0  \n",
       "1996                   0  \n",
       "1997                   0  \n",
       "1998                   0  \n",
       "1999                   0  \n",
       "2000                   0  \n",
       "2001                 177  \n",
       "2002                 179  \n",
       "2003                 179  \n",
       "2004                 180  \n",
       "2005                 180  \n",
       "2006                 181  \n",
       "2007                 182  \n",
       "2008                 181  \n",
       "2009                 182  \n",
       "2010                 182  \n",
       "2011                 184  \n",
       "2012                 183  \n",
       "2013                 183  \n",
       "2014                 184  \n",
       "2015                 184  \n",
       "2016                 183  \n",
       "2017                 182  \n",
       "2018                 180  \n",
       "2019                 173  \n",
       "2020                   0  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping by year and checking the number of values within each year ()\n",
    "df_indicators.groupby('year').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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>hdi_index</th>\n",
       "      <th>hci_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hdi_index</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.93989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hci_index</th>\n",
       "      <td>0.93989</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hdi_index  hci_index\n",
       "hdi_index    1.00000    0.93989\n",
       "hci_index    0.93989    1.00000"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicators[['hdi_index','hci_index']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "high correlation keep it, consistent years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSpec(6, 6)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dropping years with lots of missing data\n",
    "df_indicators = df_indicators[(df_indicators.year >= 2008) & (df_indicators.year <= 2018)]\n",
    "\n",
    "#Visualizing missing data\n",
    "klib.missingval_plot(df_indicators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indicators.drop(columns=['edu_prim_exp_pct_gdp','edu_sec_exp_pct_gdp','edu_tert_exp_pct_gdp'],inplace = True)\n",
    "df_indicators_2018 = df_indicators[df_indicators.year == 2018]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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>year</th>\n",
       "      <th>edu_exp_pct_gdp</th>\n",
       "      <th>edu_exp_usd</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gdppc</th>\n",
       "      <th>hci_index</th>\n",
       "      <th>population</th>\n",
       "      <th>edu_quality_index</th>\n",
       "      <th>hdi_index</th>\n",
       "      <th>income_group</th>\n",
       "      <th>region</th>\n",
       "      <th>edu_exp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.76083</td>\n",
       "      <td>7.242098e+09</td>\n",
       "      <td>1.628182e+11</td>\n",
       "      <td>2044.527804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79636081.0</td>\n",
       "      <td>3.032582</td>\n",
       "      <td>0.660</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>90.939907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.350305e+09</td>\n",
       "      <td>1.891470e+11</td>\n",
       "      <td>2331.268840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81134789.0</td>\n",
       "      <td>2.685391</td>\n",
       "      <td>0.662</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>102.918920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.54265</td>\n",
       "      <td>9.467114e+09</td>\n",
       "      <td>2.189837e+11</td>\n",
       "      <td>2645.968759</td>\n",
       "      <td>0.478853</td>\n",
       "      <td>82761244.0</td>\n",
       "      <td>2.869702</td>\n",
       "      <td>0.668</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>114.390664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.34430</td>\n",
       "      <td>1.014294e+10</td>\n",
       "      <td>2.359897e+11</td>\n",
       "      <td>2791.810766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84529251.0</td>\n",
       "      <td>2.924906</td>\n",
       "      <td>0.671</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>119.993281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.36830</td>\n",
       "      <td>1.202636e+10</td>\n",
       "      <td>2.791167e+11</td>\n",
       "      <td>3229.685630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86422240.0</td>\n",
       "      <td>2.740733</td>\n",
       "      <td>0.677</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>139.158167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.12253</td>\n",
       "      <td>1.239650e+10</td>\n",
       "      <td>2.884341e+11</td>\n",
       "      <td>3262.657586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88404652.0</td>\n",
       "      <td>2.573859</td>\n",
       "      <td>0.683</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>140.224499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.25139</td>\n",
       "      <td>1.315984e+10</td>\n",
       "      <td>3.055954e+11</td>\n",
       "      <td>3379.557986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90424668.0</td>\n",
       "      <td>2.345346</td>\n",
       "      <td>0.685</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>145.533776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.93085</td>\n",
       "      <td>1.427731e+10</td>\n",
       "      <td>3.293666e+11</td>\n",
       "      <td>3562.932658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92442549.0</td>\n",
       "      <td>2.267891</td>\n",
       "      <td>0.691</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>154.445263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.446717e+10</td>\n",
       "      <td>3.324417e+11</td>\n",
       "      <td>3519.873240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94447071.0</td>\n",
       "      <td>2.458142</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>153.177526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019698e+10</td>\n",
       "      <td>2.357337e+11</td>\n",
       "      <td>2444.290387</td>\n",
       "      <td>0.486000</td>\n",
       "      <td>96442590.0</td>\n",
       "      <td>2.458142</td>\n",
       "      <td>0.698</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>105.731073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.073814e+10</td>\n",
       "      <td>2.497130e+11</td>\n",
       "      <td>2537.125185</td>\n",
       "      <td>0.492512</td>\n",
       "      <td>98423602.0</td>\n",
       "      <td>2.905224</td>\n",
       "      <td>0.701</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>109.101223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4087</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2008</td>\n",
       "      <td>5.51145</td>\n",
       "      <td>6.935643e+09</td>\n",
       "      <td>1.331314e+11</td>\n",
       "      <td>31252.962564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4259800.0</td>\n",
       "      <td>5.084727</td>\n",
       "      <td>0.902</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>1628.161651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4088</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.28817</td>\n",
       "      <td>7.495065e+09</td>\n",
       "      <td>1.213736e+11</td>\n",
       "      <td>28209.362327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4302600.0</td>\n",
       "      <td>5.043750</td>\n",
       "      <td>0.905</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>1741.985125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4089</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2010</td>\n",
       "      <td>7.00039</td>\n",
       "      <td>9.711719e+09</td>\n",
       "      <td>1.465175e+11</td>\n",
       "      <td>33676.774124</td>\n",
       "      <td>0.778691</td>\n",
       "      <td>4350700.0</td>\n",
       "      <td>5.411847</td>\n",
       "      <td>0.906</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2232.219919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4090</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.93969</td>\n",
       "      <td>1.130993e+10</td>\n",
       "      <td>1.682914e+11</td>\n",
       "      <td>38387.627078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4384000.0</td>\n",
       "      <td>5.403280</td>\n",
       "      <td>0.909</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2579.820780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4091</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.15993</td>\n",
       "      <td>1.244896e+10</td>\n",
       "      <td>1.762067e+11</td>\n",
       "      <td>39973.380759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4408100.0</td>\n",
       "      <td>5.422700</td>\n",
       "      <td>0.911</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2824.110569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4092</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2013</td>\n",
       "      <td>6.70155</td>\n",
       "      <td>1.351076e+10</td>\n",
       "      <td>1.909066e+11</td>\n",
       "      <td>42976.649588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4442100.0</td>\n",
       "      <td>5.413982</td>\n",
       "      <td>0.914</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3041.524887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4093</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2014</td>\n",
       "      <td>6.34243</td>\n",
       "      <td>1.421032e+10</td>\n",
       "      <td>2.013135e+11</td>\n",
       "      <td>44572.898754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4516500.0</td>\n",
       "      <td>5.348252</td>\n",
       "      <td>0.916</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3146.311989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4094</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.32506</td>\n",
       "      <td>1.264397e+10</td>\n",
       "      <td>1.780645e+11</td>\n",
       "      <td>38630.726589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4609400.0</td>\n",
       "      <td>5.456278</td>\n",
       "      <td>0.921</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2743.084132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4095</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.41179</td>\n",
       "      <td>1.342379e+10</td>\n",
       "      <td>1.889435e+11</td>\n",
       "      <td>40080.494114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4714100.0</td>\n",
       "      <td>5.485907</td>\n",
       "      <td>0.924</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2847.583144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.25974</td>\n",
       "      <td>1.456577e+10</td>\n",
       "      <td>2.069506e+11</td>\n",
       "      <td>42992.895274</td>\n",
       "      <td>0.767000</td>\n",
       "      <td>4813600.0</td>\n",
       "      <td>5.466286</td>\n",
       "      <td>0.926</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3025.962523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4097</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.04850</td>\n",
       "      <td>1.493508e+10</td>\n",
       "      <td>2.122257e+11</td>\n",
       "      <td>43306.070003</td>\n",
       "      <td>0.771214</td>\n",
       "      <td>4900600.0</td>\n",
       "      <td>5.518816</td>\n",
       "      <td>0.928</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3047.603130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  edu_exp_pct_gdp   edu_exp_usd           gdp  \\\n",
       "1666        Egypt  2008          3.76083  7.242098e+09  1.628182e+11   \n",
       "1667        Egypt  2009              NaN  8.350305e+09  1.891470e+11   \n",
       "1668        Egypt  2010          3.54265  9.467114e+09  2.189837e+11   \n",
       "1669        Egypt  2011          3.34430  1.014294e+10  2.359897e+11   \n",
       "1670        Egypt  2012          3.36830  1.202636e+10  2.791167e+11   \n",
       "1671        Egypt  2013          4.12253  1.239650e+10  2.884341e+11   \n",
       "1672        Egypt  2014          4.25139  1.315984e+10  3.055954e+11   \n",
       "1673        Egypt  2015          3.93085  1.427731e+10  3.293666e+11   \n",
       "1674        Egypt  2016              NaN  1.446717e+10  3.324417e+11   \n",
       "1675        Egypt  2017              NaN  1.019698e+10  2.357337e+11   \n",
       "1676        Egypt  2018              NaN  1.073814e+10  2.497130e+11   \n",
       "4087  New Zealand  2008          5.51145  6.935643e+09  1.331314e+11   \n",
       "4088  New Zealand  2009          6.28817  7.495065e+09  1.213736e+11   \n",
       "4089  New Zealand  2010          7.00039  9.711719e+09  1.465175e+11   \n",
       "4090  New Zealand  2011          6.93969  1.130993e+10  1.682914e+11   \n",
       "4091  New Zealand  2012          7.15993  1.244896e+10  1.762067e+11   \n",
       "4092  New Zealand  2013          6.70155  1.351076e+10  1.909066e+11   \n",
       "4093  New Zealand  2014          6.34243  1.421032e+10  2.013135e+11   \n",
       "4094  New Zealand  2015          6.32506  1.264397e+10  1.780645e+11   \n",
       "4095  New Zealand  2016          6.41179  1.342379e+10  1.889435e+11   \n",
       "4096  New Zealand  2017          6.25974  1.456577e+10  2.069506e+11   \n",
       "4097  New Zealand  2018          6.04850  1.493508e+10  2.122257e+11   \n",
       "\n",
       "             gdppc  hci_index  population  edu_quality_index  hdi_index  \\\n",
       "1666   2044.527804        NaN  79636081.0           3.032582      0.660   \n",
       "1667   2331.268840        NaN  81134789.0           2.685391      0.662   \n",
       "1668   2645.968759   0.478853  82761244.0           2.869702      0.668   \n",
       "1669   2791.810766        NaN  84529251.0           2.924906      0.671   \n",
       "1670   3229.685630        NaN  86422240.0           2.740733      0.677   \n",
       "1671   3262.657586        NaN  88404652.0           2.573859      0.683   \n",
       "1672   3379.557986        NaN  90424668.0           2.345346      0.685   \n",
       "1673   3562.932658        NaN  92442549.0           2.267891      0.691   \n",
       "1674   3519.873240        NaN  94447071.0           2.458142      0.696   \n",
       "1675   2444.290387   0.486000  96442590.0           2.458142      0.698   \n",
       "1676   2537.125185   0.492512  98423602.0           2.905224      0.701   \n",
       "4087  31252.962564        NaN   4259800.0           5.084727      0.902   \n",
       "4088  28209.362327        NaN   4302600.0           5.043750      0.905   \n",
       "4089  33676.774124   0.778691   4350700.0           5.411847      0.906   \n",
       "4090  38387.627078        NaN   4384000.0           5.403280      0.909   \n",
       "4091  39973.380759        NaN   4408100.0           5.422700      0.911   \n",
       "4092  42976.649588        NaN   4442100.0           5.413982      0.914   \n",
       "4093  44572.898754        NaN   4516500.0           5.348252      0.916   \n",
       "4094  38630.726589        NaN   4609400.0           5.456278      0.921   \n",
       "4095  40080.494114        NaN   4714100.0           5.485907      0.924   \n",
       "4096  42992.895274   0.767000   4813600.0           5.466286      0.926   \n",
       "4097  43306.070003   0.771214   4900600.0           5.518816      0.928   \n",
       "\n",
       "             income_group                      region  edu_exp_per_capita  \n",
       "1666  Lower middle income  Middle East & North Africa           90.939907  \n",
       "1667  Lower middle income  Middle East & North Africa          102.918920  \n",
       "1668  Lower middle income  Middle East & North Africa          114.390664  \n",
       "1669  Lower middle income  Middle East & North Africa          119.993281  \n",
       "1670  Lower middle income  Middle East & North Africa          139.158167  \n",
       "1671  Lower middle income  Middle East & North Africa          140.224499  \n",
       "1672  Lower middle income  Middle East & North Africa          145.533776  \n",
       "1673  Lower middle income  Middle East & North Africa          154.445263  \n",
       "1674  Lower middle income  Middle East & North Africa          153.177526  \n",
       "1675  Lower middle income  Middle East & North Africa          105.731073  \n",
       "1676  Lower middle income  Middle East & North Africa          109.101223  \n",
       "4087          High income         East Asia & Pacific         1628.161651  \n",
       "4088          High income         East Asia & Pacific         1741.985125  \n",
       "4089          High income         East Asia & Pacific         2232.219919  \n",
       "4090          High income         East Asia & Pacific         2579.820780  \n",
       "4091          High income         East Asia & Pacific         2824.110569  \n",
       "4092          High income         East Asia & Pacific         3041.524887  \n",
       "4093          High income         East Asia & Pacific         3146.311989  \n",
       "4094          High income         East Asia & Pacific         2743.084132  \n",
       "4095          High income         East Asia & Pacific         2847.583144  \n",
       "4096          High income         East Asia & Pacific         3025.962523  \n",
       "4097          High income         East Asia & Pacific         3047.603130  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicators[(df_indicators.country == 'Egypt') | (df_indicators.country == 'New Zealand')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indicators_encoded = df_indicators.copy()\n",
    "df_indicators_encoded['country'] = df_indicators_encoded['country'].astype('category').cat.codes\n",
    "df_indicators_encoded['income_group'] = df_indicators_encoded['income_group'].astype('category').cat.codes\n",
    "df_indicators_encoded['region'] = df_indicators_encoded['region'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = mf.ImputationKernel(df_indicators_encoded, datasets=6, random_state=1)\n",
    "\n",
    "kernel.mice(iterations=1, boosting='gbdt', min_sum_hessian_in_leaf=0.01)\n",
    "df_indicators_imputed = kernel.complete_data(dataset=0, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indicators_imputed.country = df_indicators.country\n",
    "df_indicators_imputed.region = df_indicators.region\n",
    "df_indicators_imputed.income_group = df_indicators.income_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>year</th>\n",
       "      <th>edu_exp_pct_gdp</th>\n",
       "      <th>edu_exp_usd</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gdppc</th>\n",
       "      <th>hci_index</th>\n",
       "      <th>population</th>\n",
       "      <th>edu_quality_index</th>\n",
       "      <th>hdi_index</th>\n",
       "      <th>income_group</th>\n",
       "      <th>region</th>\n",
       "      <th>edu_exp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.76083</td>\n",
       "      <td>7.242098e+09</td>\n",
       "      <td>1.628182e+11</td>\n",
       "      <td>2044.527804</td>\n",
       "      <td>0.479000</td>\n",
       "      <td>79636081.0</td>\n",
       "      <td>3.032582</td>\n",
       "      <td>0.660</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>90.939907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.96523</td>\n",
       "      <td>8.350305e+09</td>\n",
       "      <td>1.891470e+11</td>\n",
       "      <td>2331.268840</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>81134789.0</td>\n",
       "      <td>2.685391</td>\n",
       "      <td>0.662</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>102.918920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2010</td>\n",
       "      <td>3.54265</td>\n",
       "      <td>9.467114e+09</td>\n",
       "      <td>2.189837e+11</td>\n",
       "      <td>2645.968759</td>\n",
       "      <td>0.478853</td>\n",
       "      <td>82761244.0</td>\n",
       "      <td>2.869702</td>\n",
       "      <td>0.668</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>114.390664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.34430</td>\n",
       "      <td>1.014294e+10</td>\n",
       "      <td>2.359897e+11</td>\n",
       "      <td>2791.810766</td>\n",
       "      <td>0.484796</td>\n",
       "      <td>84529251.0</td>\n",
       "      <td>2.924906</td>\n",
       "      <td>0.671</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>119.993281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.36830</td>\n",
       "      <td>1.202636e+10</td>\n",
       "      <td>2.791167e+11</td>\n",
       "      <td>3229.685630</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>86422240.0</td>\n",
       "      <td>2.740733</td>\n",
       "      <td>0.677</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>139.158167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.12253</td>\n",
       "      <td>1.239650e+10</td>\n",
       "      <td>2.884341e+11</td>\n",
       "      <td>3262.657586</td>\n",
       "      <td>0.492512</td>\n",
       "      <td>88404652.0</td>\n",
       "      <td>2.573859</td>\n",
       "      <td>0.683</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>140.224499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.25139</td>\n",
       "      <td>1.315984e+10</td>\n",
       "      <td>3.055954e+11</td>\n",
       "      <td>3379.557986</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>90424668.0</td>\n",
       "      <td>2.345346</td>\n",
       "      <td>0.685</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>145.533776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.93085</td>\n",
       "      <td>1.427731e+10</td>\n",
       "      <td>3.293666e+11</td>\n",
       "      <td>3562.932658</td>\n",
       "      <td>0.508000</td>\n",
       "      <td>92442549.0</td>\n",
       "      <td>2.267891</td>\n",
       "      <td>0.691</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>154.445263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.58728</td>\n",
       "      <td>1.446717e+10</td>\n",
       "      <td>3.324417e+11</td>\n",
       "      <td>3519.873240</td>\n",
       "      <td>0.508000</td>\n",
       "      <td>94447071.0</td>\n",
       "      <td>2.458142</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>153.177526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.91249</td>\n",
       "      <td>1.019698e+10</td>\n",
       "      <td>2.357337e+11</td>\n",
       "      <td>2444.290387</td>\n",
       "      <td>0.486000</td>\n",
       "      <td>96442590.0</td>\n",
       "      <td>2.458142</td>\n",
       "      <td>0.698</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>105.731073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.63752</td>\n",
       "      <td>1.073814e+10</td>\n",
       "      <td>2.497130e+11</td>\n",
       "      <td>2537.125185</td>\n",
       "      <td>0.492512</td>\n",
       "      <td>98423602.0</td>\n",
       "      <td>2.905224</td>\n",
       "      <td>0.701</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>109.101223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4087</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2008</td>\n",
       "      <td>5.51145</td>\n",
       "      <td>6.935643e+09</td>\n",
       "      <td>1.331314e+11</td>\n",
       "      <td>31252.962564</td>\n",
       "      <td>0.783440</td>\n",
       "      <td>4259800.0</td>\n",
       "      <td>5.084727</td>\n",
       "      <td>0.902</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>1628.161651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4088</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.28817</td>\n",
       "      <td>7.495065e+09</td>\n",
       "      <td>1.213736e+11</td>\n",
       "      <td>28209.362327</td>\n",
       "      <td>0.788688</td>\n",
       "      <td>4302600.0</td>\n",
       "      <td>5.043750</td>\n",
       "      <td>0.905</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>1741.985125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4089</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2010</td>\n",
       "      <td>7.00039</td>\n",
       "      <td>9.711719e+09</td>\n",
       "      <td>1.465175e+11</td>\n",
       "      <td>33676.774124</td>\n",
       "      <td>0.778691</td>\n",
       "      <td>4350700.0</td>\n",
       "      <td>5.411847</td>\n",
       "      <td>0.906</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2232.219919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4090</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.93969</td>\n",
       "      <td>1.130993e+10</td>\n",
       "      <td>1.682914e+11</td>\n",
       "      <td>38387.627078</td>\n",
       "      <td>0.780834</td>\n",
       "      <td>4384000.0</td>\n",
       "      <td>5.403280</td>\n",
       "      <td>0.909</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2579.820780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4091</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2012</td>\n",
       "      <td>7.15993</td>\n",
       "      <td>1.244896e+10</td>\n",
       "      <td>1.762067e+11</td>\n",
       "      <td>39973.380759</td>\n",
       "      <td>0.776982</td>\n",
       "      <td>4408100.0</td>\n",
       "      <td>5.422700</td>\n",
       "      <td>0.911</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2824.110569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4092</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2013</td>\n",
       "      <td>6.70155</td>\n",
       "      <td>1.351076e+10</td>\n",
       "      <td>1.909066e+11</td>\n",
       "      <td>42976.649588</td>\n",
       "      <td>0.770567</td>\n",
       "      <td>4442100.0</td>\n",
       "      <td>5.413982</td>\n",
       "      <td>0.914</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3041.524887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4093</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2014</td>\n",
       "      <td>6.34243</td>\n",
       "      <td>1.421032e+10</td>\n",
       "      <td>2.013135e+11</td>\n",
       "      <td>44572.898754</td>\n",
       "      <td>0.771000</td>\n",
       "      <td>4516500.0</td>\n",
       "      <td>5.348252</td>\n",
       "      <td>0.916</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3146.311989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4094</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.32506</td>\n",
       "      <td>1.264397e+10</td>\n",
       "      <td>1.780645e+11</td>\n",
       "      <td>38630.726589</td>\n",
       "      <td>0.781000</td>\n",
       "      <td>4609400.0</td>\n",
       "      <td>5.456278</td>\n",
       "      <td>0.921</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2743.084132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4095</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.41179</td>\n",
       "      <td>1.342379e+10</td>\n",
       "      <td>1.889435e+11</td>\n",
       "      <td>40080.494114</td>\n",
       "      <td>0.788688</td>\n",
       "      <td>4714100.0</td>\n",
       "      <td>5.485907</td>\n",
       "      <td>0.924</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>2847.583144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.25974</td>\n",
       "      <td>1.456577e+10</td>\n",
       "      <td>2.069506e+11</td>\n",
       "      <td>42992.895274</td>\n",
       "      <td>0.767000</td>\n",
       "      <td>4813600.0</td>\n",
       "      <td>5.466286</td>\n",
       "      <td>0.926</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3025.962523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4097</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.04850</td>\n",
       "      <td>1.493508e+10</td>\n",
       "      <td>2.122257e+11</td>\n",
       "      <td>43306.070003</td>\n",
       "      <td>0.771214</td>\n",
       "      <td>4900600.0</td>\n",
       "      <td>5.518816</td>\n",
       "      <td>0.928</td>\n",
       "      <td>High income</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>3047.603130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  edu_exp_pct_gdp   edu_exp_usd           gdp  \\\n",
       "1666        Egypt  2008          3.76083  7.242098e+09  1.628182e+11   \n",
       "1667        Egypt  2009          3.96523  8.350305e+09  1.891470e+11   \n",
       "1668        Egypt  2010          3.54265  9.467114e+09  2.189837e+11   \n",
       "1669        Egypt  2011          3.34430  1.014294e+10  2.359897e+11   \n",
       "1670        Egypt  2012          3.36830  1.202636e+10  2.791167e+11   \n",
       "1671        Egypt  2013          4.12253  1.239650e+10  2.884341e+11   \n",
       "1672        Egypt  2014          4.25139  1.315984e+10  3.055954e+11   \n",
       "1673        Egypt  2015          3.93085  1.427731e+10  3.293666e+11   \n",
       "1674        Egypt  2016          3.58728  1.446717e+10  3.324417e+11   \n",
       "1675        Egypt  2017          3.91249  1.019698e+10  2.357337e+11   \n",
       "1676        Egypt  2018          3.63752  1.073814e+10  2.497130e+11   \n",
       "4087  New Zealand  2008          5.51145  6.935643e+09  1.331314e+11   \n",
       "4088  New Zealand  2009          6.28817  7.495065e+09  1.213736e+11   \n",
       "4089  New Zealand  2010          7.00039  9.711719e+09  1.465175e+11   \n",
       "4090  New Zealand  2011          6.93969  1.130993e+10  1.682914e+11   \n",
       "4091  New Zealand  2012          7.15993  1.244896e+10  1.762067e+11   \n",
       "4092  New Zealand  2013          6.70155  1.351076e+10  1.909066e+11   \n",
       "4093  New Zealand  2014          6.34243  1.421032e+10  2.013135e+11   \n",
       "4094  New Zealand  2015          6.32506  1.264397e+10  1.780645e+11   \n",
       "4095  New Zealand  2016          6.41179  1.342379e+10  1.889435e+11   \n",
       "4096  New Zealand  2017          6.25974  1.456577e+10  2.069506e+11   \n",
       "4097  New Zealand  2018          6.04850  1.493508e+10  2.122257e+11   \n",
       "\n",
       "             gdppc  hci_index  population  edu_quality_index  hdi_index  \\\n",
       "1666   2044.527804   0.479000  79636081.0           3.032582      0.660   \n",
       "1667   2331.268840   0.490000  81134789.0           2.685391      0.662   \n",
       "1668   2645.968759   0.478853  82761244.0           2.869702      0.668   \n",
       "1669   2791.810766   0.484796  84529251.0           2.924906      0.671   \n",
       "1670   3229.685630   0.490000  86422240.0           2.740733      0.677   \n",
       "1671   3262.657586   0.492512  88404652.0           2.573859      0.683   \n",
       "1672   3379.557986   0.500000  90424668.0           2.345346      0.685   \n",
       "1673   3562.932658   0.508000  92442549.0           2.267891      0.691   \n",
       "1674   3519.873240   0.508000  94447071.0           2.458142      0.696   \n",
       "1675   2444.290387   0.486000  96442590.0           2.458142      0.698   \n",
       "1676   2537.125185   0.492512  98423602.0           2.905224      0.701   \n",
       "4087  31252.962564   0.783440   4259800.0           5.084727      0.902   \n",
       "4088  28209.362327   0.788688   4302600.0           5.043750      0.905   \n",
       "4089  33676.774124   0.778691   4350700.0           5.411847      0.906   \n",
       "4090  38387.627078   0.780834   4384000.0           5.403280      0.909   \n",
       "4091  39973.380759   0.776982   4408100.0           5.422700      0.911   \n",
       "4092  42976.649588   0.770567   4442100.0           5.413982      0.914   \n",
       "4093  44572.898754   0.771000   4516500.0           5.348252      0.916   \n",
       "4094  38630.726589   0.781000   4609400.0           5.456278      0.921   \n",
       "4095  40080.494114   0.788688   4714100.0           5.485907      0.924   \n",
       "4096  42992.895274   0.767000   4813600.0           5.466286      0.926   \n",
       "4097  43306.070003   0.771214   4900600.0           5.518816      0.928   \n",
       "\n",
       "             income_group                      region  edu_exp_per_capita  \n",
       "1666  Lower middle income  Middle East & North Africa           90.939907  \n",
       "1667  Lower middle income  Middle East & North Africa          102.918920  \n",
       "1668  Lower middle income  Middle East & North Africa          114.390664  \n",
       "1669  Lower middle income  Middle East & North Africa          119.993281  \n",
       "1670  Lower middle income  Middle East & North Africa          139.158167  \n",
       "1671  Lower middle income  Middle East & North Africa          140.224499  \n",
       "1672  Lower middle income  Middle East & North Africa          145.533776  \n",
       "1673  Lower middle income  Middle East & North Africa          154.445263  \n",
       "1674  Lower middle income  Middle East & North Africa          153.177526  \n",
       "1675  Lower middle income  Middle East & North Africa          105.731073  \n",
       "1676  Lower middle income  Middle East & North Africa          109.101223  \n",
       "4087          High income         East Asia & Pacific         1628.161651  \n",
       "4088          High income         East Asia & Pacific         1741.985125  \n",
       "4089          High income         East Asia & Pacific         2232.219919  \n",
       "4090          High income         East Asia & Pacific         2579.820780  \n",
       "4091          High income         East Asia & Pacific         2824.110569  \n",
       "4092          High income         East Asia & Pacific         3041.524887  \n",
       "4093          High income         East Asia & Pacific         3146.311989  \n",
       "4094          High income         East Asia & Pacific         2743.084132  \n",
       "4095          High income         East Asia & Pacific         2847.583144  \n",
       "4096          High income         East Asia & Pacific         3025.962523  \n",
       "4097          High income         East Asia & Pacific         3047.603130  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicators_imputed[(df_indicators_imputed.country == 'New Zealand') | (df_indicators_imputed.country == 'Egypt')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSpec(6, 6)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLIAAAUgCAYAAABUxX4FAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzde5xdVX338c/JJJP7ZX4QrgECggREiBIQrQgIKIgV6qNVtFxqrVULj1K1WlurFq1UsU9RsdUiFRUULeViRFDQYNMaK9gIQaJEjBIhClmTTG6Tmcmc5499okOcZPbAnuxZk8/79TqvmbPP2ut895lzzpz5zVprN5rNJpIkSZIkSdJoN67uAJIkSZIkSVIZFrIkSZIkSZKUBQtZkiRJkiRJyoKFLEmSJEmSJGXBQpYkSZIkSZKyYCFLkiRJkiRJWbCQJUlSzSJiUUSsHMH+V0bEopHqv04R8b6IaEbE3LqzlBURn42IZt05JEmScmQhS5KkpygiTm4VU5oR8YkdtNkrInpabRbt4ogaQyLinIh4X905JEmS6mAhS5Kk6nQDr4mIiYPcdh7QAPoGue1FwOEjmOvw1n1obDgHeG/dISRJkupgIUuSpOrcCHQAZw9y2x8DtwJbtr8hpdSTUvqd7VVJKW1JKfWMVP+SJEnSrjK+7gCSJI0hPwCOpChafXnbxog4HngG8DfAqdvv1JpqODelNHfAtmcA7wOeB+wJdAIPAJenlL7WajMJeBdwLnAA0AM8DNyWUnrHgL5WAitTSidvvw14I/BR4AVAP/BN4KKU0urtMh4NXA78HsXIs4XA24DHgGtSShfu6EGJiDMpinhvSSl9bJDbvwscCuyXUuptPV5vbh37HGArcG/r2G/c0f0M6O+zwAUppcYgtzUHyxsRrwIuBo4B2oD7gI+klP69xP2dDHyb4uc+vdXPgcAvgI+nlD4+VB+tfo4G3k/xs5gKPAR8FvhoSmlrq80i4KQBx7LNH6eUPlvmfiRJknJmIUuSpGr9G/CPETEnpbSqte11wK8pij9Diog9gG+1rv4L8HOKYtYC4DnA11q3Xdnq+3PA/6MowBwGvLBk1v2BRRQjyd5BUcT5M2AGA6YiRsRhwH9SjOT+GPBL4CXA10vezzeAR4HzW/v/RqvvE4CPpZR6W5v/AJhHUQz8ObAHcAHwHxHx2pTSdSXvt5SI+ADw18BtwHsoCnp/AHwlIi5KKV1ZsquLgX2ATwHrKQqMH4uISCm9f4gMC4C7gF6Kn+tq4PeBf6D4uby21fSDFD+HEymmq27z3yUzSpIkZc1CliRJ1foC8GGKos3fR8Rk4NXAVSmlvogo08fvAXsBr0opfXkn7f4A+HpK6YInmfXQ7e8jIvqBN0fEvJTS8tbmD1IUt56fUvqv1rZPRMT1FMW1nUopbY2Ia4G3R8SRKaUfDbj5/NbXawZs+0BK6a8G9hERHwP+l2JUW2WFrIh4NkUR60MppXcPuOljEXET8KGI+FxKaX2J7p4OHLGtgBkRVwKLgb+JiM8MKGwO5gpgIvDclNK9rf0/AVxPse7a1SmlO1NK34yI1wInppS+MMzDlSRJyp5rZEmSVKGU0hrgFuDC1qaXAzOBq4fRzbrW1zMjYsYQ7Z4REUcNN2fLI4MUyraNBDsUICLaKEZf/c+AItY2Hx3GfW0rVG0rXBERDeCPgGUppR9s255S2jigzZTWCLUprWxHDPGYDNdrgSZwTUTsOfBC8XOcDjy3ZF/XDixWtdYl+38U/zj8/R3tFBF7UUyjvGVbEau1fxP4+9bVPxjGMUmSJI1ZFrIkSarevwGHRcTzKab+/c92o5B2KqV0F8V0wQuBxyPivyLi/RFx5HZN30qxuPx9EfHTiLgqIs6OiLK/3x8aZNua1tc9Wl9nU6zX9ONB2g62bVAppWUUI6peOyDfC4C5PHE0FhGxV0R8OiJ+BWwEHqdYi+uNrSazyt5vCUdQnE1yees+Bl4+02qzd8m+Hhhk27af+yE72e/g1tf7d7B//xD7S5Ik7TYsZEmSVL3bKdaRei9wCsMbjQVAa7rgMymm0q2hWFj93oi4aECbmykKQedRjFY6FbgJWBQR7SXuZutObmts93UwzZ3cNphrKBZv37aG1/mtDNdua9AapfUNijWxPge8CjgDOJ3fTikc6vPLoLkiYrAlFRqt9tvuY7DLHUPc387ud2eP33DaSJIkCdfIkiSpcq01oT4H/BWwGfjSk+xnGbAM+HBEzAK+B1wWEVe2pp2RUkoU63J9oVUEugz4S+Bs4CtP9VgoFqnfCBw+yG3zhtnXdcBHgPMj4r+AVwDfTCk9OqDN0RSLm/9dSum9A3eOiNeXvJ/Uah+tx2ebwUY1PUhRxPpFSmmwEVXDsf2IOShGfMHgo9/Y7rZnDHLbPIrC3cD9h1tAlCRJGjMckSVJ0sj4F+D9wBtTSuuGajxQFJ7wOzqltBb4GcVaUZMioq1V3BrYpkkxfQ+g1KryQ0kpbaU4O+HxEfF72938tmH29Virr5dTrE01g+2mFfLbUWJPGKXUWges7DpRP2l9PW277YPl/Xzr69+31gN7gtb6VWW9NiLmDNi3HbiE4ph2eMbKlNKvKc46+PsD1ztrFSa3LXp/44BdNrRur+RnLEmSlBNHZEmSNAJSSr8A3vckdz8fuCQibgRWAL3AScCLgS+nlDa3iliPRsQtFMWrX1OstfQmoBP46lM6gCf6m9Z939Y6k94q4CyK9bNgeCOErgFeRrFQ/Drg5u1uf4Birai/jIgpFOtwPR34M4rRac8ucR9fpFgk/dMRMY9iauaZwJ7bN0wpfT8i3ktRdFwaEV8BHgH2BY6lWOi+zDRNKApo34uIfwHWA68BjgMuTSk9PMS+bwHuAv6zdbbD1cBLKR7361JKdw5ouwS4CPhkRHyN4vnxvZTSz0rmlCRJypaFLEmSRp9FwLMoChn7Uozo+RnwduATrTabgH+iWBfrNGAa8CjFmfY+lFJ6pKowKaUfR8QLgMspCi7dFCOM/pxiytvmYXS3kGLqXwBXpZSesG9rWuZZrfu6gGKh+WWt74+hRCErpdQVES8B/hF4N8UIpv+gOENi5yDt/y4i7gH+L8UC+lMpCoPLWsdb1scpRpldDBwI/AJ4a0rpihKZ746I51EU1N7cyvAQ8E5+9+yQX6R4frwaeCXFCPs/pniOSJIkjWmNZtNlFiRJ0vBFxLHA3cBfpZQuqztPXSLiZODbwB+nlD5bbxpJkqSxzTWyJEnSkCJi8nbXGxSLygN8c9cnkiRJ0u7IqYWSJKmMpRHxLeA+imlvvw+cCFyfUrqn1mSSJEnabVjIkiRJZdxMUbw6j+Lzw8+A9wD/UGcoSZIk7V5cI0uSJEmSJElZcI0sSZIkSZIkZcFCliRJkiRJkrJgIUuSJEmSJElZsJAlSZIkSZKkLFjIkiRJkiRJUhYsZEmSJEmSJCkLFrIkSZIkSZKUBQtZkiRJkiRJyoKFLEmSJEmSJGXBQpYkSZIkSZKyYCFLkiRJkiRJWbCQJUmSJEmSpCxYyJIkSZIkSVIWLGRJkiRJkiQpCxayJEmSJEmSlAULWZIkSZIkScqChSxJkiRJkiRlwUKWJEmSJEmSsmAhS5IkSZIkSVmwkCVJkiRJkqQsWMiSJEmSJElSFixkSZIkSZIkKQsWsiRJkiRJkpQFC1mSJEmSJEnKgoUsSZIkSZIkZcFCliRJkiRJkrJgIUuSJEmSJElZsJAlSZIkSZKkLFjIkiRJkiRJUhYsZEmSJEmSJCkLFrIkSZIkSZKUBQtZkiRJkiRJyoKFLEmSJEmSJGXBQpYkSZIkSZKyYCFLkiRJkiRJWbCQJUmSJEmSpCxYyJIkSZIkSVIWLGRJkiRJkiQpCxayJEmSJEmSlAULWZIkSZIkScqChSxJkiRJkiRlwUKWJEmSJEmSsmAhS5IkSZIkSVmwkCVJkiRJkqQsWMiSJEmSJElSFixkSZIkSZIkKQsWsiRJkiRJkpSF8XUHkCRJ0tjVaDSmADNaV7uazeamOvNIkqS8NZrNZt0ZJEmSNMY0Go0Afh/YF1jf2jwdeBRY2Gw219SVTZIk5ctCliRJkirXaDT+BPg+cF+z9YGz0Wg0gGcCxzebzavqzCdJkvLkGlmSJEkaCZObzea9zQH/NW0W7gUm1ZhLkiRlzEKWJEmSRsLmRqPxzNYoLKAYkdVoNI4GumvMJUmSMubUQkmSJFWu0WjsAbyUYo2srtbm6cBq4GvNZvPxurJJkqR8WciSJEnSiGk0GlMpzlrYDvQBa5rNpiOyJEnSkzK+7gCSJEkaexqNxhHAORRnLLwJeDnQA0xrNBo3NZvNH9eXTpIk5cpCliRJkkbCScDVFAu7vwb4YrPZfLjRaMwG/g9gIUuSJA2bhSxJkiSNiGaz+SuARqPR02w2H25te2zA+u+SJEnD4lkLJUmSNBKajUZjdqPROABobzQac+A3i8BbyZIkSU+KI7IkSZI0Er4NvA5oAl8BXthoNKZRLPz+tTqDSZKkfHnWQkmSJI24RqMxDtgH6Go2mxvqziNJkvJkIUuSJEmSJElZcI0sSZIkSZIkZcFCliRJkiRJkrJgIUuSJEmSJElZsJAlSZIkSZKkLFjIkiRJkiRJUhYsZEmSJEmSJCkL4+sOMBzr169vTp8+ve4YkiRJ2Rv34ufUHWGX6b/9e3VHkKRh6ezsvL2jo+OMunNIo1FWI7KazWbdESRJkiRJGml71h1AGq2yKmRJkiRJkiRp92UhS5IkSZIkSVmwkCVJkiRJkqQsWMiSJEmSJElSFixkSZIkSZIkKQsWsiRJkiRJkpQFC1nSKNHT08Ojjz7Kli1b6o4iSZIkSdKoNL7uANLuauHChZxyyilMnTqVX/ziF1x//fVMmTKFTZs28fKXv5ynPe1pdUeUJEmSpCFFxCJgWUrporqzaOyzkCXV5OGHH2bq1KkAfPvb3+Y1r3kN+++/P2vWrOGGG26wkCVJkiRpWCKiOUSTa1JKFw6x/ytTSv9eabDB7+tPgfOBZ1DMFvtf4D0ppcVD7Hcc8CHgWKAB3AP8VUrpf1q3nwx8G5idUnq8tW02cFur/RkppV+PxDFp13BqoVSTvr6+33y/ZcsW9t9/fwD22GMPtm7dWlcsSZIkSfnad8DlTwfZ9paacg3mZOB64FTgOcCPgdsj4rAd7RAR0ygKUo8AzwOeCzza2m/6DvY5CFgMrAdOtoiVPwtZUk0OOeQQbr/9dnp7ezn44INZtmwZAD/96U+ZPHlyzekkSZIk5SaltHrbBVg7yLZXR8SKiOhpfd1W7CIiVra+/UpENLddj4inRcTNEbE6IjZGxA8i4qUVZH1tSukTKaX/TSn9GHgTRbHpjJ3sNg8I4L0ppQdSSg8A7wFmAYdv3zgijgT+C/gRxUisrqeaW/WzkCXV5MUvfjH9/f189KMf5YEHHuCGG27g0ksv5bvf/S5nn3123fEkSZIkjSER8QfAJ4B/Ao4CrgA+GRG/32pyXOvrn1KM3tp2fRrwdeB04BjgBuA/ImLeTu7rfSWmOW6vHZgEdO6kzY+Bx4A/iYiJETGxlfcXwP3btX0O8J/AN4BXpJS6h5lHo5RrZEk1GT9+PGeeeSannnoqnZ2ddHd3M2HCBPbYYw8mTpxYdzxJkiRJY8vbgc+nlD7Ruv6TiDgWeCfw1ZTSYxEBsLY1eguAlNIPgR8O6OeDreLXK4AP7OC+HqcoOg3HB4ANwC07apBSWt9aA+tm4K9am1cCp6eUNm/X/D+Am1NKrxtmDo1yjsiSarJw4UI2btxIe3s7W7Zs4ctf/jI33XQTH/vYx/jpT39adzxJkiRJY8sRFNPsBloMHLmznSJiakR8OCJ+FBGdEbEBWAAcuKN9WlMGdzhia5D7eAvwZ8DLdzb9LyImA1cD3wVOAH6PYpH4myNi6nbNbwJeGhGnlc2hPDgiS6qJZy2UJEmStIsNNt1vqCmAl1OsW/V24EFgE/A5iqmAT1mriPUB4MxtZx7cidcATwN+L6W0tbX/ayimI/4B8IUBbS+imIb41Yg4O6X0jSryqn6OyJJq4lkLJUmSJO1CDwDP327b8ykWQt+mF2gbpM3nUko3pJTuBVZRFJOesoj4C+CDwFkppcUldplCUXjrH7Ctv7Vt+/pGM6V0EfAvwC0RcWYFkTUKWMiSauJZCyVJkiTtQh8BzouIP4+IwyLiYuC1wIcHtFkJnBoR+0RER2vbT4A/iIhnR8QzKUY9TdrZHUXERRGxfIg27wAuA15HsV7XPq3LzJ30801gBsUi9UdExDOAfwO2At8a7H5SSpcAHwduquJsi6qfhSypJp61UJIkSdKuklK6CbgYuIRiFNZbgDenlL46oNnbgFOAhynWngL4C+DXFGcA/DqwpPX9zuwJHD5Emz8HJgDXA48OuFyxo35SSsuB3weeSbFO1mJgDsW0xFU7uqOU0juAjwI3RMQ5Q+TSKNdoNod7Rsz6dHV1NWfMmFF3DKlSPT09dHZ20t/fz8yZM5kyZUrdkSRJu4FxL35O3RF2mf7bv1d3BEkals7Ozns6OjoW1J1DGo1c7F2qWXt7O3vvvXfdMSRJkiRJGvWcWihJkiRJkqQsWMiSJEmSJElSFixkSZIkSZIkKQsWsiRJkiRJkpQFC1mSJEmSJEnKgoUsSZIkSZIkZWH8UA0iYhLwHWBiq/2/p5TeGxEBXA/MBVYCf5hS6hxk/zOAK4A24KqU0mWt7f8AnAksTSmd39p2HhAppSue+qFJT964d1xcd4Rdpv8jH687giRJkiRJpZQZkbUFeGFK6RhgPnBGRJwAvAu4M6V0GHBn6/oTREQbcCVFwepI4NyIODIiZgLPSykdDbRFxDMjYjJwIfDJp35YkiRJkiRJGmuGLGSllJoppQ2tqxNalyZwNnBNa/s1wDmD7H48sCKl9FBKqQf4Umu/fqA9IhrAZKAXeAfwsZRS75M/HEmSJEmSJI1VpdbIioi2iFgK/Br4Zkrpe8DeKaVHAVpf9xpk1/2BhwdcXwXsn1JaD9wA/C/wM2AdcFxK6eYneyCSJEmSJEka20oVslJKW1NK84E5wPERcVTJ/huDbGu2+vxwSml+SultwKXA30bE6yPiyxHxN9vvFBFvWL16NcuXLwfgjjvuYMOGDaxdu5ZFixYBsGzZMlasWAHAbbfdxubNm3n88cdZvHgxAEuXLmXlypUALFy4kN7eXlavXs2SJUsAuPvuu1m1ahUAN99c1NRWrVrF3XffDcCSJUtYvXo1vb29LFy4EICVK1eydOlSABYvXszjjz/O5s2bue222wBYsWIFy5YtA2DRokWsXbuWDRs2cMcddwCwfPlyj2kUHtMhU6ez36QpXHnscwF41YGH8KoDDwHgymOfy36TpnDI1OlcPv94AC48+DBetv+BAHzmuBPpaG/nGTM7uPSZxwLwpkPncfre+wNw3QknM6mtjQWxJ+8+8hgALnn6UZw4e28Abnz+aQCcOHtvLnl68VJ795HHsCD2ZFJbG9edcDIAp++9P286dB4Alz7zWJ4xs4OO9nY+c9yJALxs/wO58ODDALh8/vE7PaZcf05j8bnnMXlMHpPHtCuP6U0nncnpR84H4LrXv41JE9pZcNChvPslrwTgktPO5sTDjgTgxje/G4ATDzuSS047G4B3v+SVLDjoUCZNaOe6178NgNOPnM+bTjoTgEvPfi3P2O9AOqZM4zMXFOtPvuyY47nweacCcPkr/phDZu/DfjODK1/zZwC86rgTeVXrd9mVr/kz9psZHDJ7Hy5/xR8DcOHzTuVlxxS/fz9zwcV0TJnGM/Y7kEvPfu0Ojyn3n9NYfO55TB6Tx7TzY5K0Y41mszmsHSLivcBG4E+Bk1NKj0bEvsCilNLh27V9LvC+lNKLW9f/CiCl9KEBbZ4F/DnwFuDrKaUXRMSXgPeklB4c2F9XV1dzxowZwz1Gadhc7F2SNNaNe/Fz6o6wy/Tf/r26I0jSsHR2dt7T0dGxoO4c0mg05IisiJgdEbNa308GTgOWA7cAF7SaXQAMNi3w+8BhEXFwRLQDr27tN9ClwN9SrL3V1trWD0wZ1pFIkiRJkiRpTCsztXBf4NsRcS9FYeqbKaWFwGXA6RHxIHB66zoRsV9E3AqQUuoDLgJuBx4AvpxSun9bxxFxDvD9lNIjKaW1wHcj4j6gmVL6YUXHKEmSJEmSpDFg2FML6+TUQu0qTi2UJI11Ti2UpNHLqYXSjpVa7F2SJEmSJEmqm4UsSZIkSZIkZcFCliRJkiRJkrJgIUuSJEmSpN1URLwvIpZV1NeiiPhEFX1JO2IhS5IkSZKkMSIiPhsRzYi4apDbPty6beGAzZcDJ1V09y8H/qqivoYlIhqtotwjEbG5VVR7xhD7nBQR/x0Ra1r7LI+It2/X5ncKfRFxfEQ83nqsx4/E8WjHLGRJkiRJkjS2PAy8KiKmbtvQKricB/xiYMOU0oaU0poq7jQV1lfR15Pwl8DbgIuB44BfA9+MiOk72WcD8DHgBcCRwAeA90fEm3e0Q0ScBtwJXAP8cUqpr5r4KsvKoSRJkiRJY8u9wH7AHwL/1tp2FtANfAfYY1vDiHgf8IqU0lGt688E/omiGNQAHgLemlL6dkRMAD4KvKLVx6+Ba1NK72rtuwhYllK6qHV9JXAVcABwLtAFXJFS+siA+3868K/Ac4CfA5cAXwYuSil9tszBRkQDeCtwWUrphta2C1r5XgN8arD9Ukr3APcM2PSziHg5cCLwyUHu55XA54H3p5Q+VCabqueILEmSNKje3l4eeeQRuru7644iSZKG7zPA6wZcfx1FUas5xH7XAY8CxwPPAt5HUQAD+L/AHwCvBg4DXgX8eIj+LgHuA54N/APw4Yh4LkBEjANuBPqAE4ALgfcCEwd20JomuGgn93EwsA/wjW0bUkqbKYp2zxsi38D7eVar/V2D3PZnwLUUBTaLWDVyRJYkSQLggQce4KabbmL69Omcc845fOUrX6G9vZ0NGzZwzjnncPjhh9cdUZIklXcdcHlEHAasB86gmHb3d0PsdxBweUppeev6iu1u+wnwnymlJsU0xf8eor9vpJS2LQD/8Yj4v8CpwHeB04HDgRellH4JEBGXAP+1XR+/YOf2aX391XbbfwXsP8S+RMQqYDZFjeT9KaV/2a7JYcC/UBSxfmftMe1aFrIkSRIAd911F6973evo7u7muuuu49xzz+WAAw7gscce44YbbrCQJUlSRlJKnRFxI8VIrLXAopTSLyJiqF3/EbiqNTXvTuCGAUWtzwLfBH4SEd8AbgW+nlLq30l/9253/RFgr9b384BHthWxWr4PPKG/lNL5Q4Vu2X60WWOQbYM5EZhGMSrsHyLiZymlzw+4/VGKaYqXRMRXU0pDFdY0gpxaKEmSfmPvvffmoIMOor29nQMOOACA2bNn15xKkiQ9SVcD51MUs64us0NK6X0UC5/fRDHN7t6IeF3rth8Ac4F3U9QTrqFYUH1ntYXe7a43+W0tomyhaSirW1/32W77XvzuKK3fkVL6WUrpvpTSv1IU8t63XZMNwGkUxaxFEXHQU4urp8JCliRJAqDRaPDYY4/x8MMP09PTw6pVqwBYs2YNzWYVnzElSdIudifQA+xJUZgqJaX0YErpYymlsyjW2nr9gNvWp5S+klJ6E8UC8i8EDn2S+R4A9o+I/QZsW8DwaxU/oyhmnb5tQ0RMohhpNdTUx+2NY7s1ugBSSl3Ai4FfAndFxMHD7FcVcWqhJEkC4JRTTuHqq6+m0Wjwyle+km9961ts2LCBrq4uzjrrrLrjSZKkYUopNSPiaKCRUtoyVPuImAxcDnwFWAnsDTwf+F7r9r+gmGa3lGKk1WsozkS46klG/CbFYvHXRMTbgckUI6L6GDBSKyI+1zqeQacYto7zn4C/jojlFOt4/Q3FSKrrdtRPRFxMUQTbtmD9C4C3M8gZC1v7rY+IM4CvURSzTkkp/fRJHbmeNAtZkiQJgKc//em8853v/M31gw46iO9+97scc8wxTJs2rcZkkiTpyUoprR9G861AB8WUwX2ANcBCiuIOFIvGv4Ni8fMm8L/AmSmlTU8yW39E/AFwFfA/FMWztwH/wW/PlAhwYInuPkxRCLuydQzfo1hEfuDxb99PG8WZFOdSFM9+CryLYmH3HWXeGBEvAW7ht8WsB0vkU0UaOU0V6Orqas6YMaPuGNoNjHvHxXVH2GX6P/LxuiNIGiUee+yx39n2+c9/nvPOOw9wrayxZtyLn1N3hF2m//bv1R1Bkoals7Pzno6OjgV156hDRBxDMeJrQUrpnprjaBRyRJYkSQLgk5/8JDNnznzCtg0bNnDttdfSaDR4y1veUlMySZI0VrVGZG0EHqQYGfWPwA+BH9QYS6OYhSxJkgTASSedxC9/+UvOOussZs2aBcA//dM/8da3vrXWXJIkaUybTjG97wCgE1gEXJJSymf6mHYpC1mSJAmAk08+mUcffZQbbriBY445hgULFtBoNOqOJUmSxrCU0ueAz9WdQ/kY7iktJUnSGLbvvvty4YUXsnbtWq655hq2bt1adyRJkiTpNxyRJUmSnqCtrY3TTjuNVatWsXLlyrrjSJIkSb9hIUuSJA1qzpw5zJkzp+4YkiRJ0m84tVCSJEmSJElZsJAlSZIkSZKkLFjIkiRJkiRJUhYsZEmSJEmSJCkLLvYuSVImTvnk4roj7DLffvPz644gSZKkUcgRWZIkSZIkScqChSxJkiRJkiRlwUKWJEmSJEmSsmAhS5IkSZIkSVmwkCVJkiRJkqQsWMiSJEmSJElSFsYP1SAiDgA+B+wD9AOfTildEREBXA/MBVYCf5hS6hxk/zOAK4A24KqU0mWt7f8AnAksTSmd39p2HhAppSue+qFJkiRJkiRpLCkzIqsPeFtK6QjgBODPI+JI4F3AnSmlw4A7W9efICLagCspClZHAudGxJERMRN4XkrpaKAtIp4ZEZOBC4FPVnBckiRJkiRJGmOGLGSllB5NKf2g9f164AFgf+Bs4JpWs2uAcwbZ/XhgRUrpoZRSD/Cl1n79QHtENIDJQC/wDuBjKaXep3REkiRJkiTpKYmIlRHx9gr6WRQRn6gikwTDXCMrIuYCzwK+B+ydUnoUimIXsNcgu+wPPDzg+ipg/1ZB7Abgf4GfAeuA41JKNw/3ACRJkiRJUiEiPhsRzdalLyJ+ERH/HBEdI3y/F0bEhkFuejnwVyN53zvI84KIuCUiftl6LC4cpM3LI+L2iHis1ebkEv3uGxHXRcTyiNgaEZ8dpM3pEfGTiOiKiM9HRPuA26ZFxIMR8YyneIi7rdKFrIiYRlF8emtKqavkbo1BtjUBUkofTinNTym9DbgU+NuIeH1EfDki/maQ+3/D6tWrWb58OQB33HEHGzZsYO3atSxatAiAZcuWsWLFCgBuu+02Nm/ezOOPP87ixYsBWLp0KStXrgRg4cKF9Pb2snr1apYsWQLA3XffzapVqwC4+eaiprZq1SruvvtuAJYsWcLq1avp7e1l4cKFAKxcuZKlS5cCsHjxYh5//HE2b97MbbfdBsCKFStYtmwZAIsWLWLt2rVs2LCBO+64A4Dly5d7TKPwmA6ZOp39Jk3hymOfC8CrDjyEVx14CABXHvtc9ps0hUOmTufy+ccDcOHBh/Gy/Q8E4DPHnUhHezvPmNnBpc88FoA3HTqP0/feH4DrTjiZSW1tLIg9efeRxwBwydOP4sTZewNw4/NPA+DE2XtzydOPAuDdRx7DgtiTSW1tXHfCyQCcvvf+vOnQeQBc+sxjecbMDjra2/nMcScC8LL9D+TCgw8D4PL5x+/0mHL9OY3F557H5DENdUxv3X8NAIdP3sIZHesBeFl0cfCkHiY0mrx53+L2o6Z0c+qs4rPkK/Zcx5z2XqaO6+f1+yQAnj1tMyfO2AjAubPXsteEPmaN38oFexfLXZ4wfRMnTN8EwAV7dzJr/Fb2mtDHubPXAnDijI08e9pmAF6/T2LquH7mtPfyij3XAXDqrA0cNaUbgDfvu4YJjSYHT+rhZVF8hDijYz2HT96yw2PK/eeUy3PvTSedyelHzgfgute/jUkT2llw0KG8+yWvBOCS087mxMOOBODGN7+7+NkfdiSXnHY2AO9+yStZcNChTJrQznWvfxsApx85nzeddCYAl579Wp6x34F0TJnGZy64GICXHXM8Fz7vVAAuf8Ufc8jsfdhvZnDla/4MgFcddyKvav0uu/I1f8Z+M4NDZu/D5a/4YwAufN6pvOyY4vfvZy64mI4p03jGfgdy6dmv3eEx5f5zGovPPY/JY/KYdn5MFbgD2JdiTevXA79PTcv4pML6Gu56GrAMeAuweQdtpgL/DfzFMPqdCDwOXEYxyOcJImIccC3wL8BzgQXAGwY0+QDwpZTS/cO4Tw3QaDabQzaKiAnAQuD2lNI/trb9GDg5pfRoROwLLEopHb7dfs8F3pdSenHr+l8BpJQ+NKDNs4A/p3hyfT2l9IKI+BLwnpTSgwP76+rqas6YMePJH61U0rh3XFx3hF2m/yMfrzuCpJJO+eTiuiPsMt9+8/PrjjDmjXvxc+qOsMv03/47f2dI0qjW2dl5T0dHx4Ins29rhNCeKaWXDtj2UeDClNIeA7b9McUSP4cAvwD+GbgipdTfun0l8ImU0uWt639Bsa7104C1wNeBt6eU1rZGMn17uyjvTym9LyIWActSShdFxIeAF6WUjt0u838D308pvaVMtifxmGwALkopfXYHt+8JPAacklJaNIx+FwKPp5QuHLBtL+BXwOSUUnfrRHfTUkp/HhHHA58FnpVS2vJkjkUlRmS11rH6DPDAtiJWyy3ABa3vLwAGmxb4feCwiDi4NZTu1a39BroU+FtgAsWZDaFYQ2tK2YOQtPvYvHlH/0yRJEmShm+sf76MiEOAMyjWpt627U+Bv6f4W/wI4G3AO4E376SrfuCtwDOA11Csib3tv+L/3bptE8VIsH2Bywfp4/PAsyNi3oAsB1OMXPpC2Wyt6ZMrhzz4ejwGPAq8qHVSuxOBeyNiPPAp4E0WsZ6a8SXa/B5wHnBfRCxtbXs3xTC6L0fEn1BUSF8JEBH7AVellF6SUuqLiIuA2ymKVFcPHD4XEedQVF0faV3/bkTcB9ybUvphFQcoKV+rV6/m5ptvZty4cZxzzjl84xvfYOXKlUyePJnXvOY17LPPPnVHlCRJUkZ2o8+XZ7RGIbUBk1rbBk6few/wlymlf29d/1lEXEZRLBp0YfaU0j8NuLoyIv4SuDkiLkgp9UTEOqCZUlq9o1AppR+16gqvbWWg9f1PUkrfH0a2R4Gf7vjw65NSakbEHwL/D7gCuBW4mmKE2feBX0XEdyiKfdemlN5XV9ZcDVnISiktZvC1rgBOHaT9I8BLBly/leIHN1jfNwE3Dbj+duApnxVB0tjw9a9/nZNPPpnu7m6+8IUvcOqpp/La176WH//4x3zjG9/g/PPPrzuiJEmSMrIbfb78DsW6TJOBP6WYDvgxgIiYDRwAfCoi/nnAPuPZ8d/+RMQLKRZtPwKYSVEkawf2AR4ZRrYvUBSlBhayto3GKpUtpbTLF48fjlYd5bht1yPiUIqfx7Mo1i/7Z+DLwPcj4vsppa/VEjRTwzproSTtSj09PRx++OEcc0yxKP7RRx8NwOGHH86mTZvqjCZJkqQM7UafLzellFaklO5LKf1fiqV7thWOttUB3gjMH3A5imLa4O+IiIOArwEPUMzGOhZ4Xevm9sH22YnrgLkR8dyIeDYwj2Jx9CeVLROfAv6SYnrmsRSLva8Hvgq8sM5gOSoztVCSajHwZBRPe9rTdnibJEmSVMZu/Pny/cDXI+LTKaVHIuKXwNNSSp8ruf8CioLVJSmlrQAR8dLt2vTw23Wvd6h1wrhvUYzE2gL8d0rpodZtv3oS2Ua11sL1G1NKX4mIWa3NE1pf24Ex/cQbCRayJI1as2bNYsuWLUycOJGXvexlv9ne1dXFhAkTdrKnJEmS9Lt218+XKaVFEXE/8DcU0/reB3w8ItZSLAU0AXg2sH9K6UODdPEgxWipt0bEfwAnUCzuPtBKYFJEnA78L8WosB0Nc/sCxWLwPcAHtrttyGytsx8en1L6neWOtomIacChravjgAMjYn7xcKRftNoEcCAwq9Xu0Nb9rt621ldEfI5ip/MH9D2/9e0MoL91vSel9KPtMuwFvJdiwXdaZ3i8H3hb63F8BfCWHR2DBufUQkmj1qtf/WomTpz4hG2bN29m0qRJnHvuuTWlkiRJUq5288+X/wj8SUQclFK6imJq4HnAD4H/pFjD6WeD7ZhSupei4PIXwI+A17Pd+tYppf8G/gX4IsWZ+/5yJ1luoJjuOJtiraiB/ZTJti/Ful87s4CioPa/FGuFvb/1/d8NaPOy1rZvt67/a+v6Gwe0ObB1GWhbvycCv9/6frC1wa8APppSenjAtguAc1r3eUPromFo5DR8squrqzljxoy6Y2g3MO4dF9cdYZfp/8jHh25Uk1/96lfcdNNNu8NZZaRSTvnk4roj7DLffvPz644w5o178XPqjrDL9N/+vbojSBolcvl82dnZeU9HR8eCunNIo5EjsiSNWrfeeisnn3wyxx9/PF/4whd45jOfyV//9V9z1lln8Y1vfKPueJIkScqMny+l/FnIkjRq7UZnlZEkSdIu4OdLKX8WsiSNWrvxWWUkSbup3t5eHnnkEbq7u+uOIo1Jfr6U8udZCyWNWrvrWWUkSbuPBx54gJtuuonp06dzzjnn8JWvfIX29nY2bNjAOeecw+GHH153RGlM8fOllD8LWZJGrVe/+tWDbt9NziojSdoN3HXXXbzuda+ju7ub6667jnPPPZcDDjiAxx57jBtuuMFCllQxP19K+bOQJSk77e3ttLe31x1DkqRK7L333kDx++2AAw4AYPbs2XVGknY7fr6U8uEaWZIkSVJNGo0Gjz32GA8//DA9PT2sWrUKgDVr1rhejyRJg3BEliRJklSTU045hauvvppGo8ErX/lKvvWtb7Fhwwa6uro466yz6o4nSdKoYyFLkiRJqsnTn/503vnOd/7m+kEHHcTq1auZMWMG06ZNqzGZJEmjk4UsSU/aJ25bXneEXeaiM+bVHUGSNAZt2rSJO+64g3Xr1nH44Ydz/PHHs99++wFw/fXX86pXvarmhNKu5edLSUNxjSxJkiSpJgsXLmTy5MksWLCA5cuXc/3119Pf3w9AZ2dnzekkSRp9LGRJkiRJNUkpcfrpp3PEEUdw3nnnMW3aNK677jr6+vrqjiZJ0qhkIUuSJEmqycCCVaPR4KyzzmKvvfbi2muvtZglSdIgLGRJkiRJNeno6ODnP//5E7a96EUvYs6cOaxZs6amVJIkjV4u9i5JkiTV5OUvf/mg20899VSOPvroXZxGkqTRz0KWJEmSVJPJkyfv8LbZs2fvwiSSJOXBqYWSJEmSJEnKgoUsSZIkSZIkZcGphZIkSdIgbvrOQ3VH2GXOecEhdUeQJKkUR2RJkiRJkqRRJSI+GxEL686h0cdCliRJkiRJY0SrANSMiKsGue3DrdvGZIEoIk6KiP+OiDURsTkilkfE2wdp95bWbZsjYlVEXBkR04bo+8CI+GpEbIyIxyPiYxHRPuD2uRHxndbt34mIudvt/x8R8YbKDnY3ZiFLkiRJkqSx5WHgVRExdduGiBgPnAf8orZUI28D8DHgBcCRwAeA90fEm7c1iIjXAB8GPggcAZwPvAS4YkedRkQb8DVgOnAicC7wCuCjA5p9FPglMB94FLh8wP7nAHsC//rUDk9gIUuSJEmSpLHmXuBB4A8HbDsL6AYWDWwYEcdFxDdao4y6ImJxRDx3wO0nRURvRJw8YNsbW213uMBeRLRHxN9HxM8jYktEPBQR/3fA7S+IiO9FRHdE/Coi/t/AEU5PRkrpnpTSl1JK96eUfpZS+gJwO0XxaZvnAUtSSp9PKa1MKX0L+BzwnJ10/SLgGcB5KaUfpJS+Cfwl8KcRMaPV5gjgmpTSg8BnW9dp3f5R4A0ppeZTOT4VLGRJkiRJkjT2fAZ43YDrrwP+Ddi+mDId+DxFsed4YClwa0TsCZBSugv4CPD5KMyjKMxcnFLa2VkxrqEY7fQXFEWdPwHWAkTE/sDXgf8FntW67VzgQzvqLCIubE2LnDvEcQ/c51kUhau7BmxeDMyPiBNabQ4EXgbcupOungs8kFJ6eMC224GJwLGt6z8ETouIcRSFr3tb2y8DPptSWl42t3bOsxZKkiRJkjT2XAdcHhGHAeuBM4CLgb8b2Kg1Iuk3IuJi4P+02n+htfm9wOnAVcBcYGFK6Zod3XHrPl8NnJlSuq21eWDR680U0+/enFLqBx6IiHcBn4qI96SUNg3S7Trgx0DvEMdNRKwCZlPUPN6fUvqXAcf7pYjYA/hORDRabT4PvHMnXe4D/Gq7bY8DW1u3Abwd+BSwkqKI9WcR8TyKAuEpEXEtRUFsCfDGlFLXUMehwVnIkiRJkiRpjEkpdUbEjRQjsdYCi1JKv4iIJ7SLiL2AS4FTgL2BNmAycOCAvnpba0vdD/waeOGA/V9LUcDZ5kxgX6Af+PYO4h0BfLdVxNpmMdAOHMpvRzMNPJ4bgRuHOu6WE4FpwAnAP0TEz1JKn2/lPQl4D0Ux7Xut+7sCeD/wtzvpc0fTAputfL8EXrptY2ua5G3AnwHvAvqAp1NMO3wP8I6Sx6LtWMiSJEmSJGlsuppiit8GdlykuYaigHUJxWiiLcCdFEWlgU6gWJ5oFsVop7Wt7bdQFIS2+SXFVL2daTBEYeipSCn9rPXtfRGxN/A+ilFXUCwA/8WU0lUD2kwFroqIv0sp9Q3S5Wrg97bbtidF0W/7kVrbvAv4TkrpvyPiE8B7Ukp9EfFFthsVp+FxjSxJkiRJksamO4EeiqLLTTto83zg4ymlr6WU7qeYhrjvwAatdak+Afw58E3g2tZZEEkprU8prRhw2Qz8gKLecMoO7vNHwHNb60kNzNED/HTYR7lz4yjWstpmCsWUwIG2UhTXduS7wBERMWfAttMpin73bN+4tY7YBcBfDcgwofV9O0UBTE/SkCOyIuJqiuFxv04pHdXaFsD1FHNjVwJ/mFLqHGTfMyiG6LUBV6WULmtt/weK4YZLU0rnt7adB0RKaYenvJQkSZIkSeWklJoRcTTQSClt2UGznwB/FBHfA6YCH6YoKAEQEW0Ua2XdlVL6VET8O3AfxbpZ79nB/T4YEV+mGOX0ForC1hxgbmuK3yeBtwKfjIgrgEMoFkX/xA7WxyIi/oBiMfhTW9P4BmtzMfAzirW0AF5AsXbVJwc0+yrwFxFxN7+dWngpxbpffa1+LgIuSinNa+3zDYpplZ+LiLcBe1AsgP+v26911Vp369PA2wbcthh4Y0Q8ALypdV1PUpkRWZ+lWORtoHcBd6aUDqOo8L5r+51aT/YrKQpWRwLnRsSRETETeF5K6WigLSKeGRGTgQt54pNLkiRJkiQ9Ba0RUztbWPx1FOtJ3QN8iWI64soBt7+botjzJ63+1lCMNnpXRDx/J/2eT7Hg/MeA5RS1hZmtPn5JUSt4FsVZEq8Gvti6rx2ZCRzOb0c2DaYN+IdWn3dTjCB713b9foDirIuXUowMu5qiUPX6AW32bN0XrbxbgbOATcB/UQzs+Q+KItn23gA8llK6acC291FMmbybYu2w9+3kGDSERrM59PTT1jDChQNGZP0YODml9GhE7EuxaNzh2+3zXOB9KaUXt65vG1L3CeAOivm1N1A8of4Q+GFK6ead5ejq6mrOmDFjGIcnPTnj3nFx3RF2mf6PfPxJ7/uJ23afM8hedMa8oRtJI+yUT+4+/7z79pt39rlYVRj34ufUHWGX6b/9e0M3GsRN39nZWeXHlnNecEjdESTAz5fbdHZ23tPR0bFgF8aRsvFk18jaO6X0KEDr616DtNkfeHjA9VXA/iml9RQFrP+lGPK3DjhuqCJWRLxh9erVLF9evLHdcccdbNiwgbVr17Jo0SIAli1bxooVKwC47bbb2Lx5M48//jiLFxcf/JcuXcrKlSsBWLhwIb29vaxevZolS5YAcPfdd7Nq1SoAbr65iLNq1SruvvtuAJYsWcLq1avp7e1l4cKFAKxcuZKlS5cCsHjxYh5//HE2b97MbbcVZxhdsWIFy5YtA2DRokWsXbuWDRs2cMcddwCwfPlyj2kUHtMhU6ez36QpXHnscwF41YGH8KoDiw94Vx77XPabNIVDpk7n8vnHA3DhwYfxsv2Lk3p85rgT6Whv5xkzO7j0mccC8KZD53H63vsDcN0JJzOprY0FsSfvPvIYAC55+lGcOHtvAG58/mkAnDh7by55+lEAvPvIY1gQezKprY3rTjgZgNP33p83HVr88rv0mcfyjJkddLS385njTgTgZfsfyIUHHwbA5fOP3+kxPdmf09StawHYf8tPaDT7mbR1A3v2Fj+b6H2EKVuLf/wcsKUY2TtlaxfR+wgAe/auYtLWDTSa/ey/5ScATN26lo7e1QDM7vkFE/s3Ma7Zx35bihzT+xKz+n4NwN49K5nQ3834/h726Sn+0JjR9zgz+h4HYJ+ehxjf38OE/m727imeT7P6fs30vgTAfltWMK7Zx8T+Tczu+QUAHb2rd3pMvp48ptFwTG/dfw0Ah0/ewhkd6wF4WXRx8KQeJjSavHnf4vajpnRz6qwNALxiz3XMae9l6rh+Xr9P8Rp49rTNnDhjIwDnzl7LXhP6mDV+KxfsXawUcML0TZwwvRjZf8Hencwav5W9JvRx7uy1AJw4YyPPnrYZgNfvk5g6rp857b28Ys91AJw6awNHTekG4M37rmFCo8nBk3p4WRTvC2d0rOfwyVt2eEy5/5xyee696aQzOf3I+QBc9/q3MWlCOwsOOpR3v+SVAFxy2tmceNiRANz45uKf2ScediSXnHY2AO9+yStZcNChTJrQznWvfxsApx85nzeddCYAl579Wp6x34F0TJnGZy4o/kn0smOO58LnnQrA5a/4Yw6ZvQ/7zQyufM2fAfCq407kVa3fZVe+5s/Yb2ZwyOx9uPwVfwzAhc87lZcdU/z+/cwFF9MxZRrP2O9ALj37tTs8pif7c6LzfgDYkmBjsQ/rH4LeDdDfC2sfKLZ1PwabHi2+73oQ+jbD1i2wrjWzZfOvigsU27ZuKdp0PVhs2/Ro0QcUffb3FvexvlVI27iqyABFpuZW6OmCDSuLbRt+AT1rAWh03lds61lbbIeiXU9Xsd8OjsnXk8c0Wo6p7s97u/Iz7M5+TpJ27MmOyFqbUpo14PbOlFLHdvu8EnhxSun1revnAcenlC7ert1VFFMQjwVeBNybUvrAYDkckaVdxRFZ5fgfM2nXckSWquSIrKE5Ikva9fx8WXBElrRjT3ZE1q9aUwppff31IG1WAQcMuD4HeGRgg4h4VuvbnwDnp5T+EDgqIg57krkkSZIkSZI0Rj3ZQtYtFIu70fo62LTA7wOHRcTBEdEOvLq130CXAn9LsVjbttNP9lOcDlOSJEmSJEn6jSELWRHxReC7wOERsSoi/oTitJinR8SDwOmt60TEfhFxK0DrtJUXAbcDDwBfTindP6Dfc4Dvp5QeSSmtBb4bEfcBzZTSDys8RkmSJEmSJI0B44dqkFI6dwc3nTpI20eAlwy4fitw6w76vQm4acD1tzP4qSslSZIkSZKkJz21UJIkSZIkSdqlLGRJkiRJkiQpCxayJEmSJEmSlAULWZIkSZIkScqChSxJkiRJkiRlwUKWJEmSJEmSsmAhS5IkSZIkSVmwkCVJkiRJkqQsWMiSJEmSJElSFixkSZIkSZIkKQsWsiRJkiRJkpQFC1mSJEmSJEnKgoUsSRpjent7eeSRR+ju7q47yqjm4yRJkiTlZ3zdASRJT80DDzzATTfdxPTp0znnnHP4yle+Qnt7Oxs2bOCcc87h8MMPrzviqODjJEmSJOXPQpYkZe6uu+7ida97Hd3d3Vx33XWce+65HHDAATz22GPccMMNFmhafJwkSdLuICI+C1wAfCal9Prtbvsw8A7gaymll9YQrxYR8XTgMuCFQDuwHHhtSumBiAjg/cDpwEHA48BC4G9SSmuG6Pf/AJcCTwN+Cvx1SunGAbe/tnW/U4HPppT+YsBt+wP/BTwnpfSrqo51d+DUQkkaA/bee28OOugg2tvbOeCAAwCYPXt2zalGHx8nSZK0m3gYeFVETN22ISLGA+cBv6gjUES013S/B1MUjH5GUcg6CvgbYEOryX7A/sBfAs8E/gh4AfDFIfp9LnA9cC0wv/X1KxHxnNbtewJXAW8HXgz8UUQMLB5eCVxqEWv4HJElSZlrNBo89thjdHd309PTw6pVq5gzZw5r1qyh2WzWHW/U8HGSJEm7kXspCjR/CPxba9tZQDfwHWCPbQ0j4jjgg8CzKUYr3Qu8I6X03dbtJwF3AKenlBa1tr0R+DAwP6X00GABImIl8FngQODlwDeBV0bEyylGQD0d+DXwL8Dfp5SaEfEm4P+mlI5o9XE68A3gXSmlf2htuxbYlFL605KPxQeBb6SU3jZg228yp5SWtfJtsyIi3gEsjIgZKaWuHfT7VuDbKaUPbrufiDiltf1c4BBgXUrp+lbubwNHtPr9P8BM4OqSx6ABLGRJUuZOOeUUrr76ahqNBq985Sv51re+xYYNG+jq6uKss86qO96o4eMkSZJ2M58BXsdvC1nbvj9ku3bTgc8DbwGawEXArRFxWErp8ZTSXRHxEeDzEXEMsBfwUeDNOypiDfAXwAeABUAjIo4FvtLadi1wHPApoAv4OLAI+GRE7JtSehQ4mWKq3ynAP7T6PAl4J0BELAJIKZ082J1HxDjg94HLIuI24FhgJXD5tgLTDswAtgCbdtLmua3MA91O8fgBPAhMiYhnAT9vHevVETET+AhwRkrJ/6Y+CRayJClzT3/603nnO9/5m+sHHXQQq1evZsaMGUybNq3GZKOLj5MkSdrNXAdcHhGHAeuBM4CLgb8b2Cil9K2B1yPiYuD/tNp/obX5vRRrSF0FzAUWppSuKZHhrpTShwf0fW1r23tbm37SyvdO4OOtNat+RVHA+mLr6+XAe1pTIw+mmAa4qLX/UNMk9wKmAe8G3gO8i2J64bURsTGltHD7HSJiFsW6V/+aUurbSd/7ANtPC/xVazsppc6IuAD4HDAZ+FxK6faI+BTF47hnRFxHsX7WFSmlfxniWNRiIUuSxphx48ax33771R1j1PNxkiRJY1mrkHIjxUistcCilNIvirXNfysi9qIo3JwC7A20URReDhzQV29EvAa4n2I64AsH7P9ailFV25yZUvrP1vd3bxfrCOBr221bDLx3wDS+u4CTI+JmipFcLwfeRDGi6ShgRUrpl61c5w/xMGxbF/zmlNI/tr5fGhELgD+nWNR94GMxFfgq8EuKNbOGsv2IqsbAba2F3wcu/v584ATgbcCPKRblvx+4NyL+K6V0X4n73O1ZyJKkEbZu9fq6I+wyM/eZ/qT28zGSJEkaEVcD11AsbP63O2hzDUUB6xKKaXdbgDsp1ssa6ASKwtAsYDZFcQzgFuB7A9r9csD3G7fr4wmFnu1s276oleX3KIpWv4qIuygKbc/gt6Oxyngc6AN+tN32B4BXD9wQEdOAW1tXX5pS6h6i79W0Rl8NsBe/O0prW//tFOuBvZ5iemd7SumO1m2LKEafWcgqwbMWSpIkSZI0Nt0J9AB7AjftoM3zKab1fS2ldD/FNMR9BzaIiLnAJyhGMX2TYmreeICU0vqU0ooBl807yfOj1v1tf/+rUkrb/rO5CDgMeC2/LVotoihkncQwClkppR7g+8Dh2930dIp1q7Yd33TgNorRaC9JKW1gaN+lmG450OnAf++g/V8D30opLaGoxQwcWNTeum+V4IgsSZIkSZLGoNaZAI8GGimlLTto9hPgjyLiexTrNX2YovgFQES0UayVdVdK6VMR8e8UI4feS7Hu1HB8FPh+RLyPYg2v4yim2b17QOZt62T9Eb8dNfVt4F8pij2LBmT7XGufnU0x/DDw5Yj4T+BbFAWxVwPntPqYTnFmxBmtbVNbUwxbXaeeVrs7gf9JKf1V67YrgO9ExF9RTB/8g1bf2xfqiIgjKQpzz2pt+jHQ1zr74/3AqRTTO1WCI7IkSZIkSRqjWiOmunbS5HUUC6LfA3yJYjriygG3vxs4FPiTVn9rKNZ2eldrzafhZPkB8EqKxeSXAZe1Lp/YrukiinrFXa39VgKrGLA+VsuBDFjLawf3eRPwBuDtFAW4i4HzU0rb1uo6lmLa5JEURb1HB1yeN6CrpzFgpFpK6b8pCmIXAPcC5wOvSikNnGZJRDSATwOXbBt11hq1dh7wDooi2AdTStuvJ6YdcESWJEmSJEljRErpwuHcnlL6IfCc7Zp9fsDtl7LdaKGU0jeBCUPcz9wdbP8P4D+G2PfVbLeG1WD9pZRO3lk/A9p9FvjsDm5bRLF211B9DHb//w78+xD7NRlklFZK6TaK4piGyRFZkiRJkiRJyoKFLEmSpGHYtGkTt9xyC5///Of5n//5nyfcdv3119eUSpIkafdgIUuSJGkYFi5cyOTJk1mwYAHLly/n+uuvp7+/H4DOzs6a00mSJI1tFrIkSZKGIaXE6aefzhFHHMF5553HtGnTuO666+jr66s7miRJ0phnIUuSJGkYBhasGo0GZ511FnvttRfXXnutxSxJkqQRZiHrSdi0aROrV69m9erVbNq0qe44o5aPkyRpLOro6ODnP//5E7a96EUvYs6cOaxZs6amVNLY52fLcnycJI114+sOkJOUEl/96ld59NFHmT59OgDr169n33335aUvfSl77LFHzQlHBx8nSdJY9vKXv3zQ7aeeeipHH330Lk4jjX1+tizHx0nS7uIpFbIi4gzgCqANuCqldNl2tzdat78E2ARcmFL6QUTMBm4EZgF/k1K6qdX+ZuBNKaVHnkqukXLjjTdy3HHHcf7559NoNABoNpvcd9993Hjjjbz+9a+vOeHo4OMkSapT34aeEe1/Am07vJ+OyTNH/P4HGj+tfZfdl1QXP1uW4+MkaXfxpKcWRkQbcCVwJnAkcG5EHLldszOBw1qXNwD/3Np+LnAN8FzgHa3+fh/4wWgtYgFs3ryZo48++je/GKBYG+Poo4+mu7u7xmSji4+TJEmSquJny3J8nCTtLp7KGlnHAytSSg+llHqALwFnb9fmbOBzKaVmSmkJMCsi9gV6gcnARKA/IsYDbwU+8hTyjLjJkydz33330Ww2f7Ot2Wxy7733MmnSpBqTjS4+TpIkSaqKny3L8XGStLt4KlML9wceHnB9FfCcEm32B65rXc4H3gm8maLgNapXIzznnHNYuHAht9566xPmne+zzz6cc8459YYbRXycJEmSVBU/W5bj4yRpd9EYWLEfjoh4JfDilNLrW9fPA45PKV08oM3XgA+llBa3rt8J/GVK6Z4BbTqA64GXA/8P6AA+mlL67nb394b777//o5MmTfrxkwpcofXr149bu3ZtG8CsWbO2Tp8+vb/uTKORj5MkSZKq4mfLcnycxoyDOjo6ZtcdQhqNnsqIrFXAAQOuzwG2X9+qTJu/BT5IsW7WPRQjtW4GThnYKKX0aeDTTyFvZTo6OjjwwAPrjjHq+ThJkiSpKn62LMfHSdJY91TWyPo+cFhEHBwR7cCrgVu2a3MLcH5ENCLiBGBdSunRbTdGxGHAfimlu4ApQD/QBJzELUmSJEmSpCd40oWslFIfcBFwO/AA8OWU0v0R8caIeGOr2a3AQ8AK4F8p1sIa6IPA37S+/yJwIbAEuPzJ5pIkSZIkSdLY9KTXyJIkSZIkSZJ2pacytVCSJEmSJEnaZSxkSZIkSZIkKQsWsiRJkiRJkpQFC1mSJEmSJEnKgoUsSZIkSZIkZcFCliRJkiRJkrJgIUuSJEmSJElZsJAlSZIkSZKkLFjIkiRJkiRJUhYsZEmSJEmSJCkLFrIkSZIkSZKUBQtZkiRJkiRJyoKFLEmSJEmSJGXBQpYkSZIkSZKyYCFLkiRJkiRJWbCQJUmSJEmSpCxYyJIkSZIkSVIWLGRJkiRJkiQpCxayJEmSJEmSlAULWZIkSZIkScqChSxJkiRJkiRlYfyuuJOIOAO4AmgDrkopXbbd7Y3W7S8BNgEXppR+sCuySZIkSZIkKQ8jPiIrItqAK4EzgSOBcyPiyO2anQkc1rq8Afjnkc4lSZIkSZKkvOyKqYXHAytSSg+llHqALwFnb9fmbOBzKaVmSmkJMCsi9t0F2SRJkiRJkpSJXVHI2h94eMD1Va1tw20jSZIkSZKk3diuKGQ1BtnWfBJtJEmSJEmStBvbFYu9rwIOGHB9DvDIcNtExBt+9KMffWrKlCkjElKSJGksufvuu+uOkIUFCxbUHUGSfsfWrVsf7+jomL2zNmvXrm2OG1fx2JQmgw8zkXaxnb0GGs3myA58iojxwE+AU4FfAt8HXpNSun9Am7OAiyjOWvgc4GMppeO376urq6s5Y8aMEc0rSZI0FjQa/iVSxkh/FpakJ6Ozs/Oejo6OnVba/ftYY9nOXgMjPrUwpdRHUaS6HXgA+HJK6f6IeGNEvLHV7FbgIWAF8K/Am0c6lyRJkiRJuerq6qo7Qml33HFH3RGGJae8OWWFavKO+IisKllxliRJKscRWeXk9FlY0u6jzIistWvXNmfNmlXtHfduhUYDxlc75mXDhg1Mmzat0j5HUk55c8oK5fPu7DWwK9bIkkYlP+CX4wd8ScqT79+SNLaN2Pv8CPyZ1NfXV32nIyinvDllhWry7oqzFkqSJEmSpApt2rSp+k4ntEFb9WWCpUuXVt7nSMopb05ZoZq8Ti3UbssRWeXk9B4hSZIkjQUu9q7dXa2LvUuSJEmSpGpt3ry57gilLVu2rO4Iw5JT3pyyQjV5XSNLuy1HGkmSJEnK1bhx+YxLmTRpUt0RhiWnvDllhWry5vPMlyRJkiRJAEycOLHuCKUdeuihdUcYlpzy5pQVqslrIUuSJEmSpMysW7eu7gil3XbbbXVHGJac8uaUFarJayFLkiRJkqTMTJ8+ve4IpZ100kl1RxiWXPJ+8d6fcMvmNtZ1b6k7SmlVPLaukaXdlmctVFVcb01V8r2pHF93Q/O5pCr5mlNVfG8qJ6U0ZJv+/v5dkKQaGzduZPLkyXXHKC2XvB/6zj1M7dvCf/3iUV7y9Ll1xymlisfWEVmSJEmSJGWmu7u77gilLV++vO4Iw5JL3k+ffQpvffo+nPa0A+qOUloVj20jp/+udHV1NWfMmFF3DI0R/jdIVcnpfVSjn+9N5fi6G5rPJVXJ15yq4ntTOSmlezo6OhbsrI1/H2ss6+zs3OFrwBFZkiRJkiRlZtOmTXVHKG3p0qV1RyhtVdrE9+7+Qd0xSsvpsYVq8lrIkiRJkiQpM21tbXVHKG3WrFl1Ryhl05Y+rl/ycx5MfXVHKS2Xx3abKvK62Lt2Ww6RL8fh35KksczPA5JyNXHixLojlDZ37ty6I5Qyub2NBQcHs6fvW3eU0nJ5bLepIq8jsiRJkiRJysy6devqjlDawoUL645QSqPR4KQj9uahpYvrjlJaLo/tNlXkdbF3STvliKyh5fQ+qtHP11w5vu6G5nOpHJ9L0q7le1M5ZRZ7X7duXXPmzJm7KtJT0tvby4QJE+qOUVpOeXPKCuXzuti7JEmSJEljSF/fCKzj1LMV+rZW3u2aNWsq73Mk5ZQ3p6xQTV7XyJK0U/6nWtq1fM1JksYyf8+V09nZOWSbnp6e6u+4AYyrfrzLypUr2WeffSrvd6TklDenrFBNXqcWSpIkjUFO3yknp8/CknYfO5tWtY1/H2ssc2qhJEmSJEljyMaNG+uOUNrdd99dd4RhySlvTlmhmrwWsiRJkiRJysyILPDduxX6qx+pmtPUN8grb05ZoZq8FrIkSZIkScpMe3t79Z02gf7+yrudM2dO5X2OpJzy5pQVqslrIUuSJEmSpMysXbu2+k7b22B8W+Xd3nzzzZX3OZJyyptTVqgmr4u9S5IkjUEu9l5OTp+FJe0+XOxduzsXe5ckSZIkaQzp6empO0Jpq1atqjvCsOSUN6esUE1eC1mSJEljULPZ9FLiIkm56u3trTtCaatXr647wrDklDenrFBNXqcWSpIkSZI0iji1ULs7pxZKkiRJkjSGbNy4se4IpS1ZsqTuCMOSU96cskI1eS1kSZIkSZKUmfb29rojlDZ37ty6IwxLTnlzygrV5LWQJUmSJElSZsaPH193hNL22GOPuiMMS055c8oK1eS1kCVJkiRJUma6urrqjlDa7bffXneEYckpb05ZoZq8LvYuSZIkSdIo4mLv2t252LskSZIkSWPIli1b6o5Q2sqVK+uOMCy55H10/UbuvOeH5DRAqYrHNp9JtZIkSZIkCYCtW7fWHaG0tWvX1h1hWHLJe851t/K89q2Miz045eA5dccppYrH1kKWdluNRqPuCBojcvoPiEY/35vK8XU3NJ9LqpKvOVXF96ZyUkpDtpkyZcouSFKN+fPn1x1hWHLJ+8KD9+frD/6cSzpm1h2ltCoeW6cWSpIkSZKUmQ0bNtQdobTFixfXHWFYcsn7oRc9j08ccwAHzpped5TSqnhsLWRJkiRJkpSZSZMm1R2htHnz5tUdYVhyyptTVqgmr4UsSZIkSZIyM25cPn/OT506te4Iw5JT3pyyQjV5XSNLuy3XepA0GvnepKr4XJI0GvneVE5nZ+eQbdavX8+sWbNGPkwF7rrrLs4444y6Y5SWU96cskI1eRs5vZF0dXU1Z8yYUXcMSZIkSZJGTGdn5z0dHR0LdtbGv481lu3sNZDPWERJkiRJkgTAli1b6o5Q2ooVK+qOMCw55c0pK1ST10KWJEmSJEmZ6e/vrztCad3d3XVHGJac8uaUFarJayFLkiRJkqTMTJ48ue4IpR111FF1RxiWnPLmlBWqyWshS5IkSZKkzKxfv77uCKUtWrSo7gjDklPenLJCNXktZEmSJEmSlJkpU6bUHaG0+fPn1x1hWHLKm1NWqCavhSxJkiRJkjLTaDTqjlDa+PHj644wLDnlzSkrVJPXQpYkSZIkSZnZsGFD3RFKW7JkSd0RhiWnvDllhWryNprNZgVRdo2urq7mjBkz6o4hSZIkSdKI6ezsvKejo2PBztr497HGsp29BhyRJUmSJElSZrq7u+uOUNry5cvrjjAsOeXNKStUk9dCliRJkiRJGjH9/fnMBNPoZyFLkiRJkqTMTJo0qe4IpWzZ3MseU/dly+beuqOUNm/evLojlJZTVqgmr4UsSZJGkUaj4aXERUOr+2eUy0WSctXV1VV3hFImtLfxwx9/nwntbXVHKe2OO+6oO0JpOWWFavJayJIkSZIkKTPTpk2rO0Ip49rG8bznPZdxbfmUH0444YS6I5SWU1aoJm8+zyRJkiRJkgRAs5nPulN9fX11RxiWnPLmlBWqyWshS5IkSZKkzGzatKn6Tnu3Ql9/5d0uXbq08j5HUk55c8oK1eRt5FTF7erqas6YMaPuGBojXBejnJzeIyRJkqSxoLOz856Ojo4FO2szIn8f926FcQ3IaBqgxqadvQZ8dkqSJEmSlJnNmzdX3+mEthEpYi1btqzyPkdSTnlzygrV5LWQJUmSJElSZsaNy+fP+UmTJtUdYVhyyptTVqgmbz7PfEmSJEmSBMDEiROr77RvK/RXv7TIoYceWnmfIymnvDllhWryWsiSJEmSJCkz69atq77TfqC/+sXeb7vttsr7HEk55c0pK1STd3wFOSRJkiRJ0i40ffr06jttb6u+T+Ckk04akX5HSk55c8oK1eR1RJYkSZIkSZnpH4GRUyNl48aNdUcYlpzy5pQVqslrIUuSJEmSpMx0d3fXHaGU/v4mP7r/R/SPwNpbI2X58uV1Rygtp6xQTd5Gs5nPk6mrq6s5Y8aMumNIkjRiGo1G3RGykNPnl7r4XCrH55Kk0aizs/Oejo6OBTtrk8vfx1s29bL2sY3M2msqEydPqDuOMrGz14AjsiRJkiRJysymTZvqjlBK++Tx/PLxh2iflM8S3UuXLq07Qmk5ZYVq8lrIkiRJkiQpM21tI7Mwe9UajQaxR2Q1UnjWrFl1Rygtp6xQTV4LWZIkSZIkZWbixIl1Ryil2Wyy/34HZDWVe+7cuXVHKC2nrFBNXgtZkiRJkiRlZt26dXVHKKWnu4/bbv86Pd19dUcpbeHChXVHKC2nrFBNXhd7lyRpFMlp2H2dcvr8UhefS+X4XJI0GpVZ7H3dunXNmTNn7qpIT1p/f5MNazcxbdYUxo3L43dTb28vEybksTB9TlmhfF4Xe5ckSZIkaQzp68tjhNO4cQ029azPpogFsGbNmrojlJZTVqgmr4UsSZIkSZIy09PTU3eE0lauXFl3hGHJKW9OWaGavE4tlCRpFHE6WDk5fX6pi8+lcnwuSRqNykwt9O9jjWVOLZQkKRPNZtNLiYuGVvfPKJeLJOVq48aNdUco7e677647wrDklDenrFBNXgtZkiRJkiRlJqcFvvfZZ5+6IwxLTnlzygrV5LWQJUmSJElSZtrb2+uOUNqcOXPqjjAsOeXNKStUk9dCliRJkiRJmVm7dm3dEUrp693KzTffTF/v1rqjlHbzzTfXHaG0nLJCNXkrWew9Ig4APgfsA/QDn04pXRERAVwPzAVWAn+YUuocZP8zgCuANuCqlNJlg92Pi9lJksY6F+gux7WNhuZzqRyfS5JGo7G02Hvvlj7WPraRWbOnMmHi+LrjKBO7YrH3PuBtKaUjgBOAP4+II4F3AXemlA4D7mxdf4KIaAOuBM4EjgTObe0rSZIkSZIG0dPTU3eEUiZMHM8W1mdVxFq1alXdEUrLKStUk7eSQlZK6dGU0g9a368HHgD2B84Grmk1uwY4Z5DdjwdWpJQeSin1AF9q7SdJkiRJkgbR29tbd4TSVq9eXXeEYckpb05ZoZq8la+RFRFzgWcB3wP2Tik9CkWxC9hrkF32Bx4ecH1Va5skSZIkSRrE1KlT645Q2oIFO50lOerklDenrFBN3koLWRExDbgBeGtKqavkboMt4PA7ixVExBvWrFnzVOJJkjTqNZtNLyUuGlrdP6NcLpKUq40bN9YdobQlS5bUHWFYcsqbU1aoJm9lhayImEBRxLo2pfQfrc2/ioh9W7fvC/x6kF1XAQcMuD4HeGT7RimlT++xxx5VxZUkSZIkKVvt7e11Ryht7ty5dUcYlpzy5pQVqslbSSErIhrAZ4AHUkr/OOCmW4ALWt9fAAx2nsXvA4dFxMER0Q68urWfJEmSJEkaxPjx+SyentuglJzy5pQVqslb1Yis3wPOA14YEUtbl5cAlwGnR8SDwOmt60TEfhFxK0BKqQ+4CLidYpH4L6eU7q8olyRJkiRJY05XV9nVfOp3++231x2htB8/2sVtGeXN6bGFavI2cloboKurqzljxoy6Y0iSJEmSNGI6Ozvv6ejo2Omq2P59XL1NW/r45zsfZMbkCfzpKYfWHWe3trPXQD5jEaWKNRqDnWdA28up2C2NBb43leN709B8LqlKvuak0WfLli11Ryht5cqVWazlNLm9jRceuTf96x+rO0ppuTy221SRt9KzFkqSJEmSpJG3devWuiOUtnbt2rojlNJoNHjW3KBt6+a6o5SWy2O7TRV5nVqo3Zb/qS4np/cIaSzwvakc35uG5nNJVfI1J+1aTi3U7m5nrwFHZEmSJEmSlJkNGzZU32nPVuirfqTX4sWLK+9zJOWUN6esUE1e18jSbsv/LEqSxjJ/z0nS2DZp0qTqO21rwAiM6J03b17lfY6knPLmlBWqyWshS5IkSZKkzIwbNwITrNpGZtLW1KlTR6TfkZJT3pyyQjV5nVooSZIkSVJm1q9fX3eE0u666666IwxLTnlzygrV5HWxd0mSRhEX6C4np88vkiQNl4u9D+1nD69lTedmnnXUPrSN8/PTWONi79IgGo2GlxIXSVKe6v79kctFknK1ZcuWuiOUtmLFipHpeITexkcs7wjIKStUk9c1siRJkiRJykx/f3/dEUrr7u6uvM+DD5jFwQfMqrxfGJm8IyWnrFBNXkdkSZIkSZKUmcmTJ9cdobSjjjqq7gjDklPenLJCNXktZEmSJEmSlJmcFntftGhR5X0+tmYjP/lZGpF1M0ci70jJKStUk9ephZJ2yvVDhuai06qSzydVxeeSpNHIz5blpJSGbDNlypRdkKQa8+fPr7zPx9JmOtd1c8gBsxg/vtrn1UjkHSk5ZYVq8lrIkiRJkiQpMzkVBcePr770MO9pe9C3tZ/x46ufaDYSeUdKTlmhmrxOLZQkSZIkKTMbNmyoO0JpS5YsqbzPceMatE9oq7xfGJm8IyWnrFBN3kZOw867urqaM2bMqDuGxoic/oOh0S2n91FJkqQ6+Rm8nJTSPR0dHQt21sa/jzWWdXZ27vA1kNcYNKlCFh8kSZKkXcvP4OV0dnYO2aa7u5tcClnLly9n3rx5dccoLae8OWWFavI6tVCSJEmSJElZsJAlSZIkSVJmJk2aVHeE0nIaMQR55c0pK1ST10KWJEmSJEmZ6erqqjtCaXfccUfdEYYlp7w5ZYVq8lrIkiRJkiQpM9OmTas7QmknnHBC3RGGJae8OWWFavJayJIkSZIkKTM5LZzf19dXd4RhySlvTlmhmrwWsiRJkiRJysymTZvqjlDa0qVL644wLDnlzSkrVJO3kVMVt6urq5nL6UUlSZIkSXoyOjs77+no6Fiwszb+fayRsuKxXzGlvZ39ZnbUlmFnrwFHZEmSJEmSlJnNmzfXHaG0ZcuW1R1hWHLKW3XWnz7+a575D3/NvA++i+7enkr7hmryjq8gh6QxrNFo1B1Bkn5HTiPK6+L7t6rka05V8b2pnJTSkG3GjctnXMqkSZPqjjAsOeWtOuuUCe20t41n1uQptI3Ac6yKvE4tlLRTftiQNBrl9PmlLr5/q0q+5lQV35vKSSk5tVC12dLXS1tjHOPb2mrL4NRCSZIkSZLGkHXr1tUdobTbbrut7gjDklPekcg6cfyEEStiVZHXQpYkSZIkSZmZPn163RFK2drXz/yjjmNrX3/dUUo76aST6o5QWk5ZoZq8rpGl3ZbDmstxKoG0a/nepKr4/i1pNPK9qZzOzs4h2/T351EY2rq1n/Xr1hOzZ9I2Po+xNBs3bmTy5Ml1xyglp6xQTd48nkWSJEmSJOk3uru7645QSvvE8fyqcxXtE/MZR7N8+fK6I5SWU1aoJq+LvWu35aiHcnJ6j5DGAt+byvG9SZI0lu1soett/PtYY5mLvUuSJEmSNIZs2rSp+k77mzAC/yxaunRp5X2OpJzy5pQVqslbydi+iJgEfAeY2Orz31NK742IAK4H5gIrgT9MKf3OZN+IOAO4AmgDrkopXVZFLkmScuNII0mSVEbbSJxVrncrjGvAhGr7njVrVqX9jbSc8uaUFarJW9WIrC3AC1NKxwDzgTMi4gTgXcCdKaXDgDtb158gItqAK4EzgSOBcyPiyIpySZIkSZI05kycOLH6TtvbYAQWZJ87d27lfY6knPLmlBWqyVvJMzSl1EwpbWhdndC6NIGzgWta268Bzhlk9+OBFSmlh1JKPcCXWvtJkiRJkqRBrFu3rvpOG43iUrGFCxdW3udIyilvTlmhmryVlVojoi0ilgK/Br6ZUvoesHdK6VGA1te9Btl1f+DhAddXtbZJkiRJkqRBjMhC782RWSPrxS9+ceV9jqSc8uaUFarJW1khK6W0NaU0H5gDHB8RR5XcdbByrwuESJIkSZK0A319fdV32rMV+vor73bNmjWV9zmScsqbU1aoJm/lk19TSmuBRcAZwK8iYl+A1tdfD7LLKuCAAdfnAI9s3ygi3pDbD0ijW7PZ9FLiIknKU6PR8FLiIkm56unpqb7TCSOzRtbKlSsr73Mk5ZQ3p6xQTd5KnqERMTsiZrW+nwycBiwHbgEuaDW7ALh5kN2/DxwWEQdHRDvw6tZ+T5BS+vQee+xRRVxJkiRJkrI2derU6jsdNzJrZJ1wwgmV9zmScsqbU1aoJm9VpdZ9gW9HxL0UhalvppQWApcBp0fEg8DpretExH4RcStASqkPuAi4HXgA+HJK6f6KckmSJEmSNOZs3Lix7gil3X333XVHGJac8uaUFarJO76CHKSU7gWeNcj2NcCpg2x/BHjJgOu3ArdWkUWSJEmSpLFuwoQJdUcobZ999qk7wrDklDenrFBN3koKWZIkSRpdXOdQksa29vb2uiOUNmfOnLojDEtOeXPKCtXkrX4VN0mSJEmSNKLWrl1bd4TSbr55sOWyR6+c8uaUFarJ28jpv3VdXV3NGTNm1B1DkiRJkqQR09nZeU9HR8eCnbXx72ONZTt7DTgiS5IkSZKkzPT09NQdoZS+3q0sv+9B+nq31h2ltFWrVtUdobScskI1eV0jS5KkUaQxAqe8HotyGlFeF59L5fhckpSr3t7euiOU0uxvsqbzcZ7WPLjuKKWtXr06m7WncsoK1eR1aqEkSaOIxYdycvr8UhefS+X4XJI0Gjm1ULs7pxZKkiRJkjSGbNy4se4IpS1ZsqTuCMOSU96cskI1eS1kSZIkSZKUmfb29rojlDZ37ty6IwxLTnlzygrV5HWNLEmSRhGnOakqPpckaWwbPz6fP+f32GOPuiMMS055c8oK1eR1RJYkSZIkSZnp6uqqO0Jpt99+e90RhiWnvDllhWryuti7JEmSJEmjiIu9a3fnYu+SJEmSJI0hW7ZsqTtCaStXrqw7wrDklDenrFBNXgtZkiRJkiRlZuvWrXVHKG3t2rV1RxiWnPLmlBWqyevUQkmSJEmSRhGnFmp359RCSZIkSZLGkA0bNtQdobTFixfXHWFYcsqbU1aoJq+FLEmSJEmSMjNp0qS6I5Q2b968uiMMS055c8oK1eS1kCVJkiRJUmbGjRuBP+d7tkJv9WtvTZ06tfI+R1JOeXPKCtXktZCl3Vaj0fBS4iJJkiRp9Fm/fn31nbY1oK36MsFdd91VeZ8jKae8OWWFavK62Lt2WxZpysnpPUKSJEkaC1zsXbs7F3uXJEmSJGkM2bJlS90RSluxYkXdEYYll7y9W/v54Y+W1x1jWKp4bMdXkEOSJEmSJO1C/f39dUcorbu7u+4Iw5JL3nOu+xoH9W7gTzv24Fn7zq47TilVPLYWsiTtlFMwh+b0S1XJ11w5vu6G5nNJVfI1p6r43lROSmnINpMnT94FSapx1FFH1R1hWHLJO7Gtja+s2sgbM3pdVfHYOrVQkiRJkqTMjMhi7yNk0aJFdUcYllzy/vu5Z3L9CU/j6H32rDtKaVU8thayJEmSJEnKzJQpU+qOUNr8+fPrjjAsueQd12jw7Gc9q+4Yw1LFY2shS5IkSZKkzOQyTXNrXz+b1/eytS+fNb3Gj89nFaacskI1efM6YqlCrvUgSZL8PCDtWr7myuns7ByyzYYNG5g1a9bIh3mKtvb1c+/9/8sps19I2/g8xtIsWbKE0047re4YpeSUFarJ28jpjaSrq6s5Y8aMumNIkjRicvnvat1y+vxSF59L5fhckjQadXZ23tPR0bFgZ21y+vu42Wz6e0nDsrPXQB7lUEmSJEmS9Bvd3d11Ryjtxz/+cd0RhmX58uV1Rygtp6xQTV4LWZIkSZIkScqCa2RJkjSKOM1JVfG5JElj26RJk+qOUNq8efPqjjAsOeXNKStUk9cRWZIkSZIkZaarq6vuCKXdcccddUcYlpzy5pQVqslrIUuSJEmSpMxMmzat7gilnXDCCXVHGJac8uaUFarJayFLkiRJkqTM5DSFvK+vr+4Iw5JT3lyy/npt4r3XfIoHfv7QU+7LQpYkSZIkSZnZtGlT3RFKW7p0ad0RhiWnvLlk/cC1V/OB667m7nvuecp9udi7dluNRqPuCJKkJymn/0LXxd9zqpKvOVXF96ZyUkpDtpk+ffouSFKNk08+ue4Iw5JT3lyyvvz5p3Dzd79Dz/TJT7kvR2RJkiRJkpSZzZs31x2htGXLltUdYVhyyptL1pOPOZaff+EWTj3sqKfcl4UsSZIkSZIyM25cPn/OT5o0qe4Iw5JT3pyyQjV583nmS5IkSZIkACZOnFh3hNIOPfTQuiMMS055c8oK1eR1jSzttlzrQZI0lvl7TtJo5HtTOZ2dnUO2WbduHTNmzNgFaZ662267jTPOOKPuGKXllDenrFBNXkdkSZIkSZKUmZwWez/ppJPqjjAsOeXNKStUk9dCliRJkiRJmenv7687QmkbN26sO8Kw5JQ3p6xQTV4LWZIkSZIkZaa7u7vuCKUtX7687gjDklPenLJCNXkbOc1R7urqauYyB1iSJEmSpCejs7Pzno6OjgU7a+PfxxrLdvYacESWJEmSJEmZ2bRpU90RSlu6dGndEYYlp7w5ZYVq8lrIkiRJkiQpM21tbXVHKG3WrFl1RxiWnPLmlBWqyWshS5IkSZKkzEycOLHuCKXNnTu37gjDklPenLJCNXktZEmSJEmSlJl169bVHaG0hQsX1h1hWHLKm1NWqCavi71LkiRJkjSKlFnsfd26dc2ZM2fuqkhPSW9vLxMmTKg7Rmk55c0pK5TP62LvkiRJkiSNIX19fXVHKG3NmjV1RxiWnPLmlBWqyWshS5IkSZKkzPT09NQdoZRms8nPHvoZOc0GW7lyZd0RSsspK1ST10KWJEmSJEmZmTp1at0RSunZ3Mch+x9Bz+Z8RpCdcMIJdUcoLaesUE1eC1nabTUaDS8lLpKkPNX9+yOXiyTlauPGjXVHKKV90nhWPvoT2ieNrztKaXfffXfdEUrLKStUk9dCliRJkiRJmcllge/GuAb7z9mPxrh8/nmwzz771B2htJyyQjV5LWRJkiRJkpSZ9vb2uiOUNmfOnLojDEtOeXPKCtXktZAlSZIkSVJm1q5dW3eE0m6++ea6IwxLTnlzygrV5G3kdOaArq6u5owZM+qOIUmSJEnSiOns7Lyno6Njwc7a+PexxrKdvQYckSVJkiRJUmZ6enrqjlDaqlWr6o4wLDnlzSkrVJPXQpYkSZIkSZnp7e2tO0Jpq1evrjvCsOSUN6esUE1epxZKkiRJkjSK1Da1sHcrjGtAm2NeVC+nFkqSJO1mGo2GlxIXScrVxo0bR6bjERjrsmTJkuo7HUE55c0pK1STd3wFOSRJkiRJ0i7U3t5efacT2qrvE5g7d+6I9DtScsqbU1aoJq8jsiRJkiRJysz48fmMS9ljjz3qjjAsOeXNKStUk9dCliRJkiRJmenq6qq+06390F/93MLbb7+98j5HUk55c8oK1eR1sXfttlwXo5yc3iMkSZKksaC2xd57tkKDEZtiKJXlYu+SJEmSJI0hW7Zsqb7TCeNGpIi1cuXKyvscSTnlzSkrVJPXQpYkSZIkSZnZunVr9Z2O0KyVtWvXjki/IyWnvDllhWryWsiSJEmSJCkzU6ZMqTtCafPnz687wrBUnbdzUw/X/M/PebSru9J+Yfd8bC1kSZIkSZKUmQ0bNtQdobTFixfXHWFYqs57508e4ys/fIQbfvhIpf3C7vnY5nO+Tkm1cFH8obkgvqrka64cX3dD87lUjs8ladfyvamclNKQbSZNmrQLklRj3rx5dUcYlqrznvr02Wzs6eNF8/autF/YPR9bC1mSJEmSJGVm3Lh8JlhNnTq17gjDUnXejintXHD8QZX2uc3u+Njm88yXJEmSJEkArF+/vu4Ipd111111RyhtzYYtLMoob06PLVST1xFZkiRJkiRlZubMmXVHKO2MM86oO0Ipm3r6+Ox3HiKmHlZ3lNJyeWy3qSKvhSxJO+XaIZKkscz1esrx84Cq4nOpnM7OziHbbNmyZRckqcaKFSs49NBD644xpEnj2zhivxlM3vJ43VFKy+Wx3aaKvE4tlCRJkiQpM/39/XVHKK27u7vuCKWMG9fgJfP3Z/bUtrqjlJbLY7tNFXktZEmSJEmSlJnJkyfXHaG0o446qu4Iw5JT3pyyQjV5KytkRcTKiLgvIpZGxN2tbRER34yIB1tfO3aw7xkR8eOIWBER76oqkyRJkiRJY9GILPbeuxX6qh/ptWjRosr7HEk55c0pK1STt+o1sk5JKQ2cTPou4M6U0mWtAtW7gHcO3CEi2oArgdOBVcD3I+KWlNKPKs4mSdKo59ohqorPJUka26ZMmTIyHY/A0oHz58+vvtMRlFPenLJCNXlHemrh2cA1re+vAc4ZpM3xwIqU0kMppR7gS639JEmSJEnSIEbkZBUT2qCt+jLB+PF5nWcup7w5ZYVq8lb5DG0C34iIeyLiDa1te6eUHgVofd1rkP32Bx4ecH1Va5skSZIkSRrEhg0b6o5Q2pIlS+qOMCw55c0pK1STt8rS3e+llB6JiL2Ab0bE8pL7DVZGdiy8JEmSJEk7MGPGjLojlHbaaafVHWFYcsqbU1aoJm9lI7JSSo+0vv4auJFiyuCvImJfgNbXXw+y6yrggAHX5wCPbN8oIt6wZs2aquJKNJtNLyUukiRJkkaf7u7uuiOUtnx52XEuo0NOeXPKCtXkraSQFRFTI2L6tu+BFwHLgFuAC1rNLgBuHmT37wOHRcTBEdEOvLq13xOklD69xx57VBFXkiRJkiRJGapqRNbewOKI+CHwP8DXUkq3AZcBp0fEgxRnJbwMICL2i4hbAVJKfcBFwO3AA8CXU0r3V5RLkiRJkqQxZ9KkSXVHKG3evHl1RxiWnPLmlBWqydvIaepQV1dXM6d5wJIkSZIkDVdnZ+c9HR0dC3bWZtWqVc05c+ZUe8f9zWIV64rPiHjHHXdktZZTTnlzygrl8+7sNZDXeRqlCo3I6WrHoJyK3ZIkSdLuYtq0adV32rsVxjVgQlul3Z5wwgmV9jfScsqbU1aoJq+FLEmSJEmSMjMi/3Bur7aAtU1fX9+I9DtScsqbU1aoJm9lZy2UJEmSJEm7xqZNm6rvtNGofFohwNKlSyvvcyTllDenrFBNXtfI0m7LqYXl5PQeIUmSJI0FZdbIyuXv42azyda+ftrGj/NvMJW2s9eAI7IkSZIkScrM5s2b645QSk93Hz/4/lJ6uvOZArds2bK6I5SWU1aoJq9rZGm35UgjSaOR/6ksx/fwoflcKsfnkqRcjRuXx7iUCRPHM23GVCZMzKf8MGnSpLojlJZTVqgmbz7PJEmSJEmSBMDEiRPrjlDKuHENjjxqXt0xhuXQQw+tO0JpOWWFavLmUcKVJEmSJEm/sW7durojlHbbbbfVHWFYcsqbU1aoJq+FLEmSJEmSMjN9+vTqO+3ZCn1bK+/2pJNOqrzPkZRT3pyyQjV5nVooSdIo4no9kiSpjP7+/uo7bQAjsPbWxo0bmTx5cuX9jpSc8uaUFarJ64gsSZIkSZIy093dXX2nE9pgXPUnC1m+fHnlfY6knPLmlBWqydvI6T+/XV1dzRkzZtQdQ5IkadTzrIXl5PRZWNLuo7Oz856Ojo4FO2vj38cay3b2GnBEliRJkiRJmdm0aVPdEUpbunRp3RGGJae8OWWFavK6RpYkSdIY5EgjSRrb2tra6o5Q2qxZs+qOMCw55c0pK1ST1xFZkiRJkiRlZuLEiXVHKG3u3Ll1RxiWnPLmlBWqyWshS5IkSZKkzKxbt67uCKUtXLiw7gjDklPenLJCNXld7F2SJEmSpFGkzGLv69ata86cOXNXRXpKent7mTBhQt0xSsspb05ZoXxeF3uXJEmSJGkM6evrqztCaWvWrKk7wrDklDenrFBNXgtZkiRJkiRlpqenp+4Ipa1cubLuCMOSU96cskI1eZ1aKEmSJEnSKFJmaqF/H2ssc2qhJEmSJEljyMaNG6vvtNksLhW7++67K+9zJOWUN6esUE1eC1mSJEmSJGVmRBb47tkKff2Vd7vPPvtU3udIyilvTlmhmrzjK8ghZanRaNQdIQs5TT+WJEmSdhft7e3VdzqhDUbgz6Q5c+ZU3+kIyilvTlmhmryOyJIkSZIkKTNr166tvtNxDRiBf/jffPPNlfc5knLKm1NWqCavi71rt+WIrHJyeo+QJEmSxgIXe9fuzsXeJUmSJEkaQ3p6euqOUNqqVavqjjAsOeXNKStUk9dClnZbzWbTS4mLJEmSpNGnt7e37gilrV69uu4Iw5JT3pyyQjV5nVooSZIkSdIo4tRC7e6cWihJkiRJ0hiycePGuiOUtmTJkrojDEtOeXPKCtXktZAlSZIkSVJm2tvb645Q2ty5cyvvc+Wqddxz32q29lc/y2wk8o6UnLJCNXnHP/UYUp48a2E5OU0/lsYC35vK8b1paD6XyvG5JClX48fn8+f8HnvsUXmf/f399Pc3odkEqv2dNxJ5R0pOWaGavI7IkiRJkiQpM11dXXVHKO3222+vvM9DDuzguGP2pa2t+rLGSOQdKTllhWryuti7dlv+p7qcnN4jpLHA96ZyfG8ams+lcnwuSRqNXOxduzsXe5ckSZIkaQzZsmVL3RFKW7lyZd0RhiWnvLlk/eqS/2TGOafwsS9+7in3Vdmk2oiYBVwFHAU0gdcBPwauB+YCK4E/TCl1DrLvGcAVQBtwVUrpsqpySXpq/I++pNHI9yZJypPv3+WklIZss3Xr1l2QpBpr166tO8Kw5JQ3l6zf/uE9bOruZk2J5/ZQqhyRdQVwW0ppHnAM8ADwLuDOlNJhwJ2t608QEW3AlcCZwJHAuRFxZIW5JEmSJEkaU6ZMmVJ3hNLmz59fd4RhySlvLlnf90ev54o3/wUXvPyVT7mvSgpZETEDeAHwGYCUUk9KaS1wNnBNq9k1wDmD7H48sCKl9FBKqQf4Ums/SZIkSZI0iA0bNtQdobTFixfXHWFYcsqbS9YZU6fx5y97JY/89GdPua+qRmQdAjwG/FtE/G9EXBURU4G9U0qPArS+7jXIvvsDDw+4vqq1TZIkSZIkDWLSpEl1Ryht3rx5dUcYlpzy5pQVqslb1RpZ44FnAxenlL4XEVcwyDTCHRhskvTvnD4mIt5wzz334FkZVBXPUiRJkiTtWn4GL6ez83eWlv4d48blc+62qVOn1h1hWHLKm1NWqCZvVc/8VcCqlNL3Wtf/naKw9auI2Beg9fXXO9j3gAHX5wCPbN8opfTpPfbYo6K4kiRJkiTla/369XVHKO2uu+6qO8Kw5JQ3p6xQTd5KClkppdXAwxFxeGvTqcCPgFuAC1rbLgBuHmT37wOHRcTBEdEOvLq1nyRJkiRJGsTMmTPrjlDaGWecUXeEYckpb05ZoZq8VY5FvBi4NiLuBeYDfw9cBpweEQ8Cp7euExH7RcStACmlPuAi4HaKMx1+OaV0f4W5JEmSJEkaU7Zs2VJ3hNJWrFhRd4RhySlvTlmhmrxVrZFFSmkpsGCQm04dpO0jwEsGXL8VuLWqLJIkSZIkjWX9/f11Ryitu7u77gjDklPenLJCNXnzWR1OkiRJkiQBMHny5LojlHbUUUfVHWFYcsqbU1aoJq+FLEmSJEmSMpPTYu+LFi2qO8Kw5JQ3p6xQTV4LWZIkSZIkZWbKlCl1Ryht/vz5dUcYlpzy5pQVqslrIUuSJEmSpMw0Go26I5Q2fnxly3PvEjnlzSkrVJPXQpYkSZIkSZnZsGFD3RFKW7JkSd0RhiWnvDllhWryNprNZgVRdo2urq7mjBkz6o4hSZIkSdKI6ezsvKejo2PBztr497HGsp29BhyRJUmSJElSZrq7u+uOUNry5cvrjjAsOeXNKStUk9dCliRJkiRJUmbeeut3+MT37uWhtK7uKLtUXquCSRXKaXFEjW45TdHW6Od7Uzm+7obmc0lV8jWnqvjeVE5Kacg2kyZN2gVJqjFv3ry6IwxLLnm//8tf88Bj63hV10YOiZl1xymlisfWEVmSJEmSJGWmq6ur7gil3XHHHXVHGJZc8t7y2pfy+WcfyIlz96s7SmlVPLYWsiRJkiRJysy0adPqjlDaCSecUHeEYckl7x5TJnHS83+v7hjDUsVjayFLkiRJkqTM5DLlt7+/yYZ1m2j255EXoK+vr+4IpeWUFarJ6xpZ2m3l8sYvSZJGjp8HpF3L11w5nZ2dQ7bZtGkTHR0duyDNU9Pb3cey+++jI17AxMkT6o5TytKlSzn55JPrjlFKTlmhmryNnN5Iurq6mjNmzKg7hiRJI8ZFcMvJ6fNLXXwuleNzSdJo1NnZeU9HR8eCnbXJ5e/jZrNJX89Wxre3+btJpe3sNeDUQkmSJEmSMrN58+a6I5TSaDT48YPLsypiLVu2rO4IpeWUFarJayFLkiRJkqTMjBuXz5/zkyZNqjvCsOSUN6esUE3efJ75/5+9/4+T+6zr/f/n7G52N7822Vdom9K0bCGFCFEDxhrRQ3oEpAJSDgqWI3yqoHg8ePDXUeH4Ww/K1x+AHkXFghREoB7gpAZMpMUUoiyQYJDQBhpkoUub/sg1u5Pdzf6e7x8zS7bbze71bq7JNa+Zx/12G7I7mX3n0WHmvTPXXO/rDQAAAAAAJEk9PT25E6Jt3749d0Ihnno9tUppehnIAgCgiVSrVS4RF6wu9/9HXi4A4NXo6GjuhGgHDhzInVCIp15PrVKaXgayAAAAAABwZuPGjbkTou3duzd3QiGeej21Sml6GcgCAAAAAMCZ+fn53AnRxsfHcycU4qnXU6uUppeBLAAAAAAAnJmcnMydEO3EiRO5Ewrx1OupVUrTW/K0NkClUqn29fXlzgAAAAAAoGHK5fLR/v7+3SvdhvfHaGUrPQeYkQUAAAAAgDMTExO5E6IdO3Ysd0Ihnno9tUppehnIAgAAAADAmc7OztwJ0TZv3pw7oRBPvZ5aHxwJ2rRp0wVvh4EsAAAAAACc6enpyZ0QbWBgIHdCIZ56vbT+1Uc+pMtvfL5+7QPvvOBtdSXoAVwqlUq5EwAAj5GnNT5z4fccUuI5h1TYN8UJIax6m9HRUXlZI2v//v164QtfmDsjmqdeL62nwml1dXTqxU/edcHbYrF3tC1+iQKAX55ev+TC7zmkxHMOqbBvihNCWHWx99HR0WqKw7QuhpmZGa1ZsyZ3RjRPvV5a5+bm9Ikv/Jt2PfEa9fet/rhlsXcAAAAAAFrI7Oxs7oRop0+fzp1QiKdeL62dnZ36z7t2a2ri7AVvi4EsAAAAAACcmZ6eTr/RuXlpPv0MzKGhoeTbbCRPvZ5apTS9HFoIAADQgjh8J46n18IA2sdKh1UtaMj746lZqaMkrfFzRkS0Jg4tBAAAAACghYyPj6ffaHen1JV+mODIkSPJt9lInno9tUppejlrIQAAAAAAzjRkge8GzebdunVrQ7bbKJ56PbVKaXqZkQUAAAAAgDPd3d25E6Jt27Ytd0Ihnno9tUppehnIAgAAAADAmZGRkdwJ0fbt25c7oRBPvZ5apTS9LPYOAAAAAEATybbYO9AkWOwdAAAAAIAWMj09nTsh2vDwcO6EQjz1emqV0vQykAUAAAAAgDMzMzO5E6KdOnUqd0Ihnno9tUppejm0EAAAAACAJsKhhWh3HFoIAAAAAEALGR8fz50QbXBwMHdCIZ56PbVKaXoZyAIAAAAAwJnu7u7cCdEGBgZyJxTiqddTq5Sml4EsAAAAAACc6erqyp0QbcuWLbkTCvHU66lVStPLQBYAAAAAAM5UKpXcCdEOHjyYO6EQT72eWqU0vSz2DgAAAABAE2Gxd7Q7FnsHAAAAAKCFTE1N5U6INjQ0lDuhEE+9nlqlNL0MZAEAAAAA4Mzc3FzuhGgjIyO5Ewrx1OupVUrTy6GFAAAAAAA0EQ4tRLvj0EIAAAAAAFrI2NhY7oRohw8fzp1QiKdeT61Sml4GsgAAAAAAcKa3tzd3QrQdO3bkTijEU6+nVilNLwNZAAAAAAA409Hh5+38+vXrcycU4qnXU6uUptfPIx8AAAAAAEiSzpw5kzsh2p133pk7oRBPvV5aP/+VL2vvL/6UDn7sYxe8ra4EPYBLpVIpdwIA4DHydLKaXPg9h5R4ziEV9k1xQgir3mbTpk0XoSSN66+/PndCIZ56vbT+1Uc/rE8eP6bTlVG9+EUvuqBtMSMLAAAAAABnpqamcidEO3nyZO6EQjz1emn9hZf8V/3wf/o+/eErfuqCt8VAFgAAAAAAzszPz+dOiDY5OZk7oRBPvV5at19xpW79td/XlVsuueBtMZAFAAAAAIAza9euzZ0QbefOnbkTCvHU66lVStPLQBYAAAAAAM54Wuz90KFDuRMK8dTrqVVK08ti72hbLFoKAGhl/J4D0IzYN8Upl8ur3mbdunUXoSSNXbt25U4oxFOvp1YpTS8zsgAAAAAAcMbTGSC7unzNofHU66lVStPLQBYAAAAAAM6MjY3lTog2ODiYO6GQRvSGiemGzEhsx/u25GlqZ6VSqfb19eXOAAAAAACgYcrl8tH+/v7dK92G98d+fPyeh/TmQyf1iu+4Ujc+Y1vuHBdWeg74moMG4KLzNGU5F08fCKD58ZyLw/NudTyWkBLPOaTCvilOCGHV20xOTsrLQNaJEye0Y8eO3BnRUvdu6u1Sd2eHtqzvTrbNBe143zKQBQAAAAAA0CDfcWW/PvSq78qd0TJYIwsAAAAAAGd6e3tzJ0TzNGNI8tXrqVVK08tAFgAAAAAAzlQqlfQbnZ2T5tMfSnz77bcn32Yjeer11Cql6eXQQgArYk0M4OLiOQdcXDzngIuL51yccrm86m02bNiQ/h+er/9PR2fSze7Zsyfp9hrNU6+nVilNLwNZAAAAAAA405BBwe60A1gLZmdnG7LdRvHU66lVStPLoYUAAAAAADgzMTGROyHasWPHcicU4qnXU6uUppeBLAAAAAAAnNm4cWPuhGjXXXdd7oRCvPT+zefu0l8+MKkwMZk7JVqK+5ZDC9G2SqVS7gS0CNZ6QErsm+LwvEMqPOfi8JxDKjzn4oQQVr3N2bNn1dfXdxFqLtzx48e1c+fO3BnRvPS+9VOf1zN7pU8PP6AfePITcudESXHfMpAFAAAAAIAzHR1+DrDq7e3NnVCIl953veQ5uvtLX9Zzt1+ZOyVaivuWgSwAAAAAAJzp6enJnRBt+/btuRMK8dL79Msv0dMvvyR3RiEp7ls/Q7gAAAAAAECSNDo6mjsh2oEDB3InFOKp11OrlKaXgSwAAAAAAJzxtNj73r17cycU4qnXU6uUppdDC9G2WLQUQDNi34RUeCwBaEbsm+KUy+VVbzM/P38RStIYHx/X2rVrc2dE89TrqVVK08uMLAAAAAAAnJmcnMydEO3EiRO5Ewrx1OupVUrTW/I0Il6pVKpeTi8KAAAAAMBjUS6Xj/b39+9e6Ta8P0YrW+k5kGxGlpn9vJl90cyOm9n7zKzXaj5mZvfU/+w/z89eb2ZfMrOTZvb6VE0AAAAAALSiiYmJ3AnRjh07ljuhEE+9nlqlNL1JBrLM7ApJr5O0O4SwU1KnpBslvV7SHSGEayTdUf9+6c92SvpzST8g6amSXm5mT03RBQAAAABAK+rs7MydEG3z5s25Ewrx1OupVUrTm3KNrC5Ja82sS9I6SfdJukHSLfW/v0XSi5f5uWslnQwh/EcIYVrS++s/BwAAAAAAltHT05M7IdrAwEDuhEI89XpqldL0JhnICiF8Q9IfSfq6pPsljYYQ/knSZSGE++u3uV/Spcv8+BWS7l30/XD9OgAAAAAAsIzR0dHcCdH279+fO6EQT72eWqU0vakOLexXbRbV1ZIeL2m9mb0i8sdLy1znZwV6AAAAAAAuMi8Lvc/OzOnaXd+j2Zm53CnRnve85+VOiOapVUrTm+rQwudI+moI4aEQwoykD0l6pqQHzOxySar/+eAyPzss6cpF329T7bDERzCz15w+fTpRLgAAzalUKnGJuGB1uf8/8nIBAK9mZ2dzJ0SpzldVHimrWvUzX8XT2IOnVilNb6qBrK9L2mNm68ysJOnZku6WdJukm+q3uUnSvmV+9rOSrjGzq82sW7VF4m9beqMQwtu3bNmSKBcAAAAAAL+mp6dzJ0RZ09Ol8tiDWtPdlTsl2tDQUO6EaJ5apTS9pVSjomb225J+RNKspH+T9BOSNki6VdJVqg12vTSEEMzs8ZJuDiE8v/6zz5f0VtXOdvjOEMIbl/s3KpVK1cv0SQAAHgtmiMTx9KluLjyW4vBYAtCMyuXy0f7+/t0r3Yb3x2hlKz0Hkg1kXQw8UQEArY7BhzieXr/kwmMpDo8lAM0oZiDr/vvvr15++eVp/+G5ealUkjrS/g45cuSIdu9e8T+nqXjq9dQqxfeu9BzwM7cPAIA2wJtqAAAQY82aNek3OjtfG8Tq6Ey62a1btybdXqN56vXUKqXpZSALAAAAAABnuru7G7DRtANYC7Zt29aQ7TaKp15PrVKa3lSLvQMAAAAAgItkZGQk/UZLpdolsX37ljvvW/Py1OupVUrTyxpZAAAALYg1suJ4ei0MoH2w2Dva3UrPAWZkAQAAAADgzPT0dO6EaMPDw7kTCvHU66lVStPLQBYAAEALqlarXCIuAODVzMxM7oRop06dyp1QiKdeT61Sml4OLQQAAAAAoIlwaCHaHYcWAgAAAADQQsbHx3MnRBscHMydUIinXk+tUppeBrIAAAAAAHCmu7s7d0K0gYGB3AmFeOr11Cql6WUgCwAAAAAAZ7q6unInRNuyZUvuhEI89XpqldL0MpAFAAAAAIAzlUold0K0gwcP5k4oxFOvp1YpTS+LvQMAAAAA0ERY7B3tjsXeAQAAAABoIVNTU7kTog0NDeVOKMRTr6dWKU0vA1kAAAAAADgzNzeXOyHayMhI7oRCPPV6apXS9HJoIQAAAAAATYRDC9HuOLQQAAAAAIAWMjY2ljsh2uHDh3MnFOKp11OrlKaXgSwAAAAAAJzp7e3NnRBtx44duRMK8dTrqVVK08tAFgAAAAAAznR0+Hk7v379+twJhXjq9dQqpen188gHAAAAAACSpDNnzuROiHbnnXfmTijEU6+nVilNL4u9AwAAAADQRFjsHe2Oxd4BAAAAAGghU1NTuROinTx5MndCIZ56PbVKaXoZyAIAAAAAwJn5+fncCdEmJydzJxTiqddTq5Sml4EsAAAAAACcWbt2be6EaDt37sydUIinXk+tUppeBrIAAAAAAHDG02Lvhw4dyp1QiKdeT61Sml4GsgAAAAAAcGbdunXpN1qt1i6J7dq1K/k2G8lTr6dWKU0vA1kAAAAAADhTKpXSb3R6TppNv/ZWV1dX8m02kqdeT61Sml4GstC2SqUSl4gLAAAAgOYzNjaWfqNdHVJn+mGCwcHB5NtsJE+9nlqlNL2lagOmDTZKpVKp9vX15c5Ai2CQJo6nfQQAAADQCsrl8tH+/v7dK92G98doZSs9B5iRBQAAAACAM5OTk7kTop04cSJ3QiGeej21Sml6GcgCAAAAAACAC75WBQMS4pA5AAAAAF719vbmToi2Y8eO3AmFeOr11Cql6WVGFgAAAAAAzlQqldwJ0W6//fbcCYV46vXUKqXpZSALAAAAAABnNmzYkDsh2p49e3InFOKp11OrlKaXgSwAAAAAAJzxtFTK7Oxs7oRCPPV6apXS9CYbyDKznzWz42b2RTP7ufp1ZmYfM7N76n/2n+dnrzezL5nZSTN7faomAAAAAABa0cTERO6EaMeOHcudUIinXk+tUpreUopRXDPbKen9kq6VNC3pgKSflvSTkkII4U31Aar+EMKvLPnZTklflvRcScOSPivp5SGEu5b+O5VKpdrX13fBvQAAAAAANKtyuXy0v79/90q34f0xWtlKz4FUM7K+RdJgCGEihDAr6U5J/0XSDZJuqd/mFkkvXuZnr5V0MoTwHyGEadUGxG5I1AUAAAAAQMs5e/Zs7oRox48fz51QiKdeT61Smt5UA1nHJT3LzLaY2TpJz5d0paTLQgj3S1L9z0uX+dkrJN276Pvh+nUAAAAAAGAZHR0+lryen6+qQ12an/ezpldvb2/uhGieWqU0vV0JOhRCuNvM/n+SPiZpTNLnJcWu4FVa5rpHPcLN7DVHjx4VUyeRSqm03EMPS3laRBIAcA6/5+Lwew6AVz09PbkTosxMzWrLxq2amZpVz9o1uXOibN++PXdCNE+tUpreZEO4IYR3hBCeEUJ4lqQg6R5JD5jZ5ZJU//PBZX50WLXZWwu2Sbpvme2/fcuWLalyAQAAAABwa3R0NHdClO7eLv3b3YPq7k0yj+aiOHDgQO6EaJ5apTS9Kc9aeGn9z6skvUTS+yTdJumm+k1ukrRvmR/9rKRrzOxqM+uWdGP95wAAAAAAwDI2btyYOyFKqVTS3r17Xc0U3rt3b+6EaJ5apTS9KQ+q/aCZ3SXpHyS9NoRQlvQmSc81s3tUOyvhmyTJzB5vZh+VpPri8D8j6aCkuyXdGkL4YsIuAAAAAABayvz8fO6EaOPj47kTCvHU66lVStObbG5fCOE/LXPdaUnPXub6+1RbEH7h+49K+miqFgAAAAAAWtnk5GTuhGgnTpzQ937v9+bOiOap11OrlKa35GmBy0qlUmWxdwAAAABAKyuXy0f7+/t3r3Qb3h+jla30HPBxvk4AAAAAAPBNExMTuROiHTt2LHdCIZ56PbVKaXoZyAIAAAAAwJnOzs7cCdE2b96cO6EQL70Pjk1opOprWCfFfevn/JcAAAAAAECS1NPTkzsh2sDAQO6EQrz03vB3H9GXHh7Rhzdv0d6rr8idEyXFfctAFtqWp9O/orl5WmsQzY99Uxyed6vjsYSUeM4hFfZNcUIIq95mdHRUXtbI2r9/v174whfmzojmpfeZV16un79kjZ6weWPulGgp7lsWe0fb4pcoUvG0H0XzY98Uh+fd6ngsISWec0iFfVOcEMKqi72Pjo5WN23adLGSLsjMzIzWrFmTOyOap15PrVJ8L4u9AwAAAADQQmZnZ3MnRDt9+nTuhEI89XpqldL0MpAFAAAAAIAz09PTuROiDQ0N5U4oxFOvp1YpTS+HFgIAAAAA0ERWOqxqAe+P0co4tBAAAAAAgBYyPj6eOyHakSNHcicU4qnXU6uUppeBLAAAAAAAnPG0wPfWrVtzJxTiqddTq5Sml4EsAAAAAACc6e7uzp0Qbdu2bbkTCvHU66lVStPLQBYAAAAAAM6MjIzkToi2b9++3AmFeOr11Cql6WWxdwAAAAAAmgiLvaPdsdg7AAAAAAAtZHp6OndCtOHh4dwJhXjq9dQqpellIAsAAAAAAGdmZmZyJ0Q7depU7oRCPPV6apXS9HJoIQAAAAAATYRDC9HuOLQQAAAAAIAWMj4+njsh2uDgYO6EQjz1emqV0vQykAUAAAAAgDPd3d25E6INDAzkTijEU6+nVilNLwNZAAAAAAA409XVlX6jDVp6aMuWLQ3ZbqN46vXUKqXpZSALAAAAAABnKpVK+o3OzEszc8k3e/DgweTbbCRPvZ5apTS9LPaOtlUqlXInuOBpHwEAAAC0gmyLvc/NS6WS1MF7JeTFYu8AAAAAALSQqamp9Bvt7GjIINbQ0FDybTaSp15PrVKaXgayAAAAAABwZm4u/SGAjTIyMpI7oRBPvZ5apTS9HFqItsWhhXE87SMAAACAVpDt0EKgSXBoIbCMarXKJeICAAAAoPmMjY3lToh2+PDh3AmFeOr11Cql6WUgCwAAAAAAZ3p7e3MnRNuxY0fuhEI89XpqldL0MpAFAAAAAIAzHR1+3s6vX78+d0Ihnno9tUppev088gEAAAAAgCTpzJkzuROi3XnnnbkTCvHU66lVStPLYu8AAAAAADQRFntHu2OxdwAAAAAAWsjU1FTuhGgnT57MnVCIp15PrVKaXgayAAAAAABwZn5+PndCtMnJydwJhXjq9dQqpellIAsAAAAAAGfWrl2bOyHazp07cycU4qnXU6uUppeBLAAAAAAAnPG02PuhQ4dyJxTiqddTq5Sml4EsAAAAAACcWbduXe6EaLt27cqdUIinXk+tUppeBrIAAAAAAHCmVCrlTojW1dWVO6EQT72eWqU0vQxkAQAAAADgzNjYWPqNVqu1S2KDg4PJt9lInno9tUppekvVBjxIG6VSqVT7+vpyZwAAAAAA0DDlcvlof3//7pVu05D3x1OzUkdJWtOZdrtAQSs9B5iRBQAA0IJKpRKXiAsAeDU5OZl+o10dtUtiJ06cSL7NRvLU66lVStPr62BKAAAAAADQGJ3MdUHz41EKAAAAAIAzvb29uROi7dixI3dCIZ56PbVKaXoZyAIAAAAAwJlKpZI7Idrtt9+eO6EQT72eWqU0vRxaCAAA0II8ndAHAFDchg0bcidE27NnT+6EQjz1emqV0vQyIwsAAAAAAGc8fWAxOzubO6EQT72eWqU0vQxkAQAAAADgzMTERO6EaMeOHcudUIinXk+tUprekqdR3EqlUu3r68udAQAAAABAw5TL5aP9/f27V7oN74/9+LfhEf3+7V/WTz1zQM9+8qW5c1xY6TnAGlloW6VSKXcCWoSnDwQAtA9+zyElftchFfZNcUIIq97m7Nmz8jKQdfz4ce3cuTN3RrTUvacnpjUxM6dTlclk21zQjvctA1kAAAAAADjT0eFnpaDe3t7cCYWk7n32NZfoWy/v0yUbepJuV2rP+5aBLAAAAAAAnOnpST8o0ijbt2/PnVBI6t5SqaTLNjZmwKkd71s/Q7gAAAAAAECSNDo6mjsh2oEDB3InFOKp11OrlKaXxd4BAAAAAGgiMYu9j4yMVDdv3nyRii7M2bNntXbt2twZ0Tz1emqV4ntXeg4wIwsAAAAAAGfm5+dzJ0QbHx/PnVCIp15PrVKaXgayAAAAAABwZnIy/RnwGuXEiRO5Ewrx1OupVUrTy6GFAAAAAAA0kZhDC3l/jFbGoYUAAAAAALSQiYmJ3AnRjh07ljuhEE+9nlqlNL0MZAEAAAAA4ExnZ2fuhGheFqVf4KnXU6uUppeBLAAAAAAAnOnp6cmdEG1gYCB3QiGeej21Sml6GcgCAAAAAMCZ0dHR3AnR9u/fnzuhEE+9nlqlNL0s9g4AAAAAQBOJWex9dHS0umnTprT/8PRcbbpLV9rDFmdmZrRmzZqk22wkT72eWqX4XhZ7B5ZRKpW4RFwAAAAANJ/Z2dn0G+2Q1JF+mOD06dPJt9lInno9tUppehnIAgAAAADAmenp6fQb7eqUOtJ/mD00NJR8m43kqddTq5Sml0ML0baYbRTH0z4CAAAAaAUxhxby/hitjEMLAQAAAABoIePj47kToh05ciR3QiGeej21Sml6uxJ0AC4x0wgAAACAV54W+N66dWvuhEI89XpqldL0MiMLAAAAAABnuru7cydE27ZtW+6EQjz1emqV0vQykAUAAAAAgDMjIyO5E6Lt27cvd0Ihnno9tUppelnsHQAAAACAJsJi72h3LPYOAAAAAEALmZ6ezp0QbXh4OHdCIZ56PbVKaXoZyAIAAAAAwJmZmZncCdFOnTqVO6EQT72eWqU0vRxaCAAAAABAE+HQQrQ7Di0EAAAAAKCFjI+P506INjg4mDuhEE+9nlqlNL0MZAEAAAAA4Ex3d3fuhGgDAwO5Ewrx1OupVUrTy0AWAAAAAADOdHV15U6ItmXLltwJhXjq9dQqpellIAsAAAAAAGcqlUruhGgHDx7MnVCIp15PrVKaXhZ7BwAAAACgibDYO9pdssXezeydZvagmR1fdN1LzeyLZjZvZruX3P4NZnbSzL5kZs87zzbNzD5mZvfU/+wv0gQAAAAAQLuZmprKnRBtaGgod0Ihnno9tUppeoseWvguSdcvue64pJdI+sTiK83sqZJulPS0+s+8zcw6l9nm6yXdEUK4RtId9e8BAAAAAMB5zM3N5U6INjIykjuhEE+9nlqlNL2FBrJCCJ+QFJZcd3cI4UvL3PwGSe8PIUyFEL4q6aSka89zu1vqX98i6cVFmgAAAAAAaDfr1q3LnRBt165duRMK8dTrqVVK09vIxd6vkHTvou+H69ctdVkI4X5Jqv95aQObAAAAAABwb2xsLHdCtMOHD+dOKMRTr6dWKU1vIweySstc52dleQAAAAAAmlRvb2/uhGg7duxIvs2TQ2V9+th9mpubT77tRvQ2iqdWKU1vV4KO8xmWdOWi77dJum+Z2z1gZpeHEO43s8slPbjcxszsNUePHhVnZUAqpdJyY61YytOZTYFWwL4pDvum1fFYisNjCYBXHR2NnJeS1vr165Nvs3tNh3rWdDbk910jehvFU6uUpreRj/zbJN1oZj1mdrWkayR95jy3u6n+9U2S9i23sRDC27ds2dKQUAAAAAAAPDlz5kzuhGh33nln8m1edcUm7XraZeroSD+Q1YjeRvHUKqXpLRX5FMrM3ifpOkmPk/SApN9UbfH3/yPpEkkjko6FEJ5Xv/2vSnqVpFlJPxdC+Mf69TdL+ssQwhEz2yLpVklXSfq6pJeGEB6xoPyCSqVSZUYWUuGT6jh8Ug1cXOyb4rBvWh2PpTg8lgA0o3K5fLS/v3/3Srfh/TFa2UrPgUIDWbnxREVKvMCP42kfAbQC9k1x2DetjsdSHB5LAJpRzEDWQw89VL3kkksuVtIFOXnypLZv3547I1rq3uP3V/SHH79HP/ndA/reJ6Y90qxV79uVngONXCMLQAvgjdDqeBMEAAAQh9eWcc5zkNIjzM+nX+S8USYnJ3MnFJK6996Rszo9Ma17Hh5LPpDVjvctM7LQtvglilQ87UfR/Ng3xeF5tzoeS3F4LAEXF/umOCEEDi1sIdVqVV8NE3pC/zp1NmBNr1a00owsP6c5AAAAAAAAknwt9n7o0KHcCYWk7i2VSnrilvUNGcRqx/uWgSwAAAAAAJxZt25d7oRou3btyp1QiKdeT61Sml7WyELb4lACAM2IfRNS4bEEoBmxb4pTLpdXvY2nwzS7unwNPXjq9dQqpellRhYAAAAAAM6MjY3lTog2ODiYO6EQT72eWqU0vSz2DgAAAABAE1lpoesFvD9GK2OxdwAAAAAAWsjk5GTuhGgnTpzInVCIp15PrVKaXgayAAAAAAAA4AIDWQAAAAAAONPb25s7IdqOHTtyJxTiqddTq5Sml4EsAAAAAACcqVQquROi3X777bkTCvHU66lVStPLQBYAAAAAAM5s2LAhd0K0PXv25E4oxFOvp1YpTS8DWQAAAAAAOFOtVnMnRJmemtWDwyOanprNnRJtdpbWRknRy0AWAABACyqVSly4JLsAaD4TExO5E6J0dnboq8NfVmenn+GHY8eO5U6I5qlVStNb8jKKK0mVSqXa19eXOwMAAKDpMfiAlDy9ZwBaQblcPtrf3797pdvw/hitbKXngJ8hUQAAAAAAIEk6e/Zs7oRox48fz51QiKdeT61Sml4GsgAAAAAAcKajw8/b+d7e3twJhXjq9dQqpentStABAACAJsOhYADQ2np6enInRNu+fXvuhEI89XpqldL0+hnCBQAAAAAAkqTR0dHcCdEOHDiQO6EQT72eWqU0vQxkAQAAAADgzMaNG3MnRNu7d2/uhEI89XpqldL0MpAFAAAAAIAz8/PzuROijY+P504oxFOvp1YpTS8DWQAAAAAAODM5OZk7IdqJEydyJxTiqddTq5Smt+RpIdBKpVLt6+vLnQEAAAAAQMOUy+Wj/f39u1e6De+P0cpWeg4wIwsAAAAAAGcmJiZyJ0Q7duxY7oRCPPV6apXS9DKQBQAAAACAM52dnbkTom3evDl3QiGeej21Sml6GcgCAAAAAMCZnp6e3AnRBgYGcicU4qnXU6uUppeBLAAAAAAAnBkdHc2dEG3//v25Ewrx1OupVUrTy2LvAAAAAAA0kZjF3kdHR6ubNm26WEkXZGZmRmvWrMmdEc1Tr6dWKb6Xxd4BAAAAAGghs7OzuROiPfzQw7kTCjl9+nTuhGieWqU0vQxkAQAAAADgzPT0dO6EKNOTM/ryl05qetLPwNvQ0FDuhGieWqU0vRxaCABAEymVSrkTXPD0+iUXHktxeCwBaEYxhxZ6eX88NzevsZFJbdjcq85O5tIgDocWAgAAAADQQsbHx3MnROns7NA9X73L1SDWkSNHcidE89Qqpen180gCAAAAAACS5GqB761bt+ZOKMRTr6dWKU1vV5Ebm9k7Jb1Q0oMhhJ316/5Q0g9Kmpb0FUk/HkIYqf/dGyS9WtKcpNeFEA4us02T9AFJA5KGJL0shFB+bP85AAAAAAC0vu7u7twJ0bZt25Y7oRBPvZ5apTS9RWdkvUvS9Uuu+5iknSGEb5P0ZUlvkCQze6qkGyU9rf4zbzOzzmW2+XpJd4QQrpF0R/17AADaUrVa5RJxwepy/3/k5QIAXo2MjOROiLZv377cCYV46vXUKqXpLTSQFUL4hKSw5Lp/CiEsnH5gUNLC8NoNkt4fQpgKIXxV0klJ1y6z2Rsk3VL/+hZJLy7SBAAAAABAu9m8eXPuhGg33HBD7oRCPPV6apXS9KZeI+tVkv6x/vUVku5d9HfD9euWuiyEcL8k1f+8NHETAAAAAAAtZXp6OndCtOHh4dwJhXjq9dQqpelNNpBlZr8qaVbSe+tXLXfOZ+ZvAwAAAABwgWZmZtJvdG5emk//tv3UqVPJt9lInno9tUppegst9n4+ZnaTaovAPzuEsPCoH5Z05aKbbZN03zI//oCZXR5CuN/MLpf04Hn+jdccPXpUfX19KZIBlUrLjbViKdYPAQAAAJrP+vXr0290rip1VKWO5Za3fux2796ddHuN5qnXU6uUpveCZ2SZ2fWSfkXSi0IIE4v+6jZJN5pZj5ldLekaSZ9ZZhO3Sbqp/vVNkpZd+SuE8PYtW7ZcaC4AAAAAAO6Nj4+n32h3p9SVdhBLkgYHB5Nvs5E89XpqldL0FhrIMrP3SfqUpKeY2bCZvVrSn0naKOljZnbMzP5SkkIIX5R0q6S7JB2Q9NoQwlx9Ozeb2cIw3JskPdfM7pH03Pr3AAAAAADgPLq7u3MnRBsYGMidUIinXk+tUprekqfDhiqVSpVDC5EKhxbG8bSPAAAAAFpBuVw+2t/fv+IxWKOjo9VNmzZdrKTHrFqtarxyVuv71rp5DzYzM6M1a9bkzojiqVWK713pOZD6rIUAAABoAqVSiUvEBQC8qlQquROiTJ+d1T9/4uOaPjubOyXawYMHcydE89QqpellRhbaFi9e43jaRwAAzuH3XBx+zwFoRjEzsry8P65Wq5qenFV3bxe/mxCNGVkAAAAAALSQqamp3AlRSqWS7n/gG64GsYaGhnInRPPUKqXpZSALAAAAAABn5ubm0m+0Wq1dEhsZGUm+zUby1OupVUrTy6GFaFuePhHIydM+AgBwDr/n4vB7DkAzynZo4dSs1FGS1nSm3S5QEIcWAgAAtJlqtcol4gIAXo2NjaXfaFeH1Jl+mODw4cPJt9lInno9tUppersSdAAAAAAAgIuot7c3/UYbMIglSTt27GjIdhvFU6+nVilNLzOyAAAAAABwpqPDz9v59evX504oxFOvp1YpTa+fRz4AAAAAAJAknTlzJndCtDvvvDN3QiGeej21Sml6WewdAAAAAIAmkm2xd6BJsNg7AAAAAAAtZGpqKndCtJMnT+ZOKMRTr6dWKU0vA1kAAAAAADgzPz+fOyHa5ORk7oRCPPV6apXS9DKQBQAAAACAM2vXrs2dEG3nzp25Ewrx1OupVUrTy0AWAAAAAADOeFrs/dChQ7kTCvHU66lVStPLQBYAAAAAAM6sW7cud0K0Xbt25U4oxFOvp1YpTS8DWQAAAAAAOFMqlXInROvq6sqdUIinXk+tUppeBrIAAAAAAHBmbGws/Ubnq1K1mnyzg4ODybfZSJ56PbVKaXpL1QY8SBulUqlU+/r6cmcAAAA0PU+f1Ofk6bUwgPZRLpeP9vf3717pNg15fzw1K3WUpDWdabcLFLTSc4AZWQAAAAAAODM5OZl+o92dUlf6YYITJ04k32Yjeer11Cql6fV1MCUAAAAAAGgMZvPCAWZkAQAAAADgTG9vb+6EaDt27MidUIinXk+tUppeBrIAAAAAAHCmUqnkToh2++23504oxFOvp1YpTS+HFgIAALQgFjEHgNa2YcOG3AnR9uzZkzuhEE+9nlqlNL3MyAIAAAAAwBlPH1jMzs7mTijEU6+nVilNLwNZAAAAAAA4MzExkTsh2rFjx3InFOKl9/TEpP71s0dyZxSS4r5lIAsAAAAAAGc2btyYOyHaddddlzuhEC+9L3rvfr3is1/VJ4fuy50SLcV9yxpZaFslTi2LRDxN60bzY98Uh+fd6ngsISWec0iFfVOcEMKqtzl79qz6+vouQs2FO378uHbu3Jk7I5qX3u94/KX6rp55XdG3PndKtBT3LQNZAAAAAAA409Hh5wCr3t7e3AmFeOn90xc8SydPntQTbVPulGgp7ls/j3wAAAAAACBJ6unpyZ0Qbfv27bkTCvHU66lVStPLQBYAAAAAAM6Mjo7mToh24MCB3AmFeOr11Cql6S15Ot69UqlUvRwDDAAAAADAY1Eul4/29/fvXuk2IyMj1c2bN1+kogtz9uxZrV27NndGNE+9nlql+N6VngPMyAIAAAAAwJn5+fncCdHGx8dzJxTiqddTq5Sml4EsAAAAAACcmZyczJ0Q7cSJE7kTCvHU66lVStPLoYUAAAAAADSRmEML2/398enyhCpj0xrYtkmlUil3DhLj0EJgGaVSiQuXJBcAAADEyf26zcslxsTERIP/30rn2LFjybd56qFx3ffAmObm0k/OaURvo3hqldL0dl14BgAAAAAAuJg6OztzJ0RrxKL0T3nSFs3MzKurK/38HC+L6Eu+WqU0vQxkAQAAAADgTE9PT+6EaAMDA8m32dXZoa7Oxhxk1ojeRvHUKqXp5dBCAAAAAACcGR0dTb/RarV2SWz//v3Jt9lInno9tUppelnsHW2LtY2Qiqf9KAAAQE68Bo8TQlh1sffR0dHqpk2b0v7DU7NSR0lak/awxZmZGa1ZsybpNhvJU6+nVim+d6XF3jm0EMCKGKQBAABAKry2jFMul1e9zezsbPp/eE2n1ICxxtOnT2vr1q3pN9wgnno9tUppejm0EAAAAAAAZ6anp9NvtKMkNWDW3NDQUPJtNpKnXk+tUppeDi1E22JacxxP+wgAAACgFax0WNUC3h+jla30HGBGFgAAAAAAzoyPj+dOiHbkyJHcCYV46vXUKqXpZY0stC1mGgEAAADwytMC357WcJJ89XpqldL0MiMLAAAAAABnuru7cydE27ZtW+6EQjz1emqV0vQykAUAAAAAgDMjIyO5E6Lt27cvd0Ihnno9tUppelnsHQAAAACAJpJtsfeZudpZC7uY84K8WOwdAACgzZRKJS4RFwDwanp6OndCtOHh4dwJhXjq9dQqpellIAsAAAAAAGdmZmbSb3RNZ0NmY506dSr5NhvJU6+nVilNL4cWAgAAtCBmG8Xx9FoYQPvIdmgh0CQ4tBAAAAAAgBYyPj6eOyHa4OBg7oRCPPV6apXS9HYl6AAAAECTYaYRALS27u7u3AnRBgYGcicU4qnXU6uUppcZWQAAAAAAONPV5WdeypYtW3InFOKp11OrlKaXgSwAAAAAAJypVCq5E6IdPHgwd0Ihnno9tUppehnIAgAAAADAmU2bNuVOiPbCF74wd0IhXno/+MWT+sj8BlWmpnOnREtx3/qZiwgkxtmckArr0CAl9k1xeN6tjscSUuI5h1TYN8UJIax6m6mpqYtQksbQ0JCrtZy89P7WP39G2zvndPhr9+n5Tx7InRMlxX3LQBYAAAAAAM7Mzc3lTog2MjKSO6EQL71/8YPX6St336VnP/HK3CnRUty3JU+frlQqlWpfX1/uDLQIPg1CKp72o2h+7Jvi8LxbHY8lpMRzDqmwb4oTQjja39+/e6Xb8P4YraxcLp/3OcAaWQAAAAAAODM2NpY7Idrhw4dzJxTiqddTq5Sml4EsAAAAAACc6e3tzZ0QbceOHbkTCvHU66lVStPLGlloW0yRB9CM2DchFR5LAJoR+6Y45XJ51dt0dPiZl7J+/frcCYV46vXUKqXp9fPIBwAAAAAAkqQzZ87kToh255135k4oxFOvp1YpTS+LvQMAAAAA0ERWWuh6Ae+P0cpY7B0AAAAAgBYyNTWVOyHayZMncycU4qnXU6uUppeBLAAAAAAAnJmfn8+dEG1ycjJ3QiGeej21Sml6GcgCAAAAAMCZtWvX5k6ItnPnztwJhXjq9dQqpellIAsAAAAAAGc8LfZ+6NCh3AmFeOr11Cql6WUgCwAAAAAAZ9atW5c7IdquXbtyJxTipfen9v2zfu34sO45PZI7JVqK+7brwjMAn0qlUu4EtAhPZ39F82PfFIfn3ep4LCElnnNIhX1TnBDCqrfxdF92dfkaevDSe+Lhsk5PTuuh8bO6Zsvm3DlRUty3zMgCAAAAAMCZsbGx3AnRBgcHcycU4qX3th99gf74W7fpmVddnjslWor7tuTp05VKpVLt6+vLnYEW4ekTDDQ3T/tRND/2TXF43q2OxxJS4jmHVNg3xQkhHO3v79+90m14f4xWVi6Xz/scYEYWAAAAAADOTE5O5k6IduLEidwJhXjq9dQqpen1ceAn0AB8sgigGbFvQio8lgA0I/ZNccrlcu4EoGkxIwsAAAAAAGd6e3tzJ0TbsWNH7oRCPPV6apXS9DKQBQAAAACAM5VKJXdCtNtvvz13QiGeej21Sml6GcgCAAAAAMCZDRs25E6ItmfPntwJhXjq9dQqpektNJBlZu80swfN7Pgyf/c/zaxqZo9bdN0bzOykmX3JzJ53nm2amX3MzO6p/9lf/D8DAAAAAID24Wm9sdnZ2dwJhXjq9dQqpektOiPrXZKuX3qlmV0p6bmSvr7ouqdKulHS0+o/8zYz61xmm6+XdEcI4RpJd9S/BwAAAAAA5zExMZE7IdqxY8dyJxTiqddTq5Smt9BAVgjhE5LCMn/1Fkm/LGnxkPANkt4fQpgKIXxV0klJ1y7zszdIuqX+9S2SXlykCQAAAACAdrNx48bcCdGuu+663AmFeOr11Cql6b3gNbLM7EWSvhFC+PySv7pC0r2Lvh+uX7fUZSGE+yWp/uelF9oEAAAAAEArO3v2bO6EaMePP2p1oqbmqddTq5Smt+tCftjM1kn6VUnfv8xfl5a5zs9BvAAAAAAANKmOjgacu216rjbdpWu5VYEeu97e3qTbazRPvZ5apTS9FzSQJelJkq6W9Hkzk6Rtkj5nZteqNgPrykW33SbpvmW28YCZXR5CuN/MLpf04HL/kJm95ujRo+rr67vAZAAAgNZXKi33mSKW8rRYMgAs1tPTk36jHZIaMEC2ffv25NtsJE+9nlqlNL0X9AgNIXwhhHBpCGEghDCg2uDVM0IIpyTdJulGM+sxs6slXSPpM8ts5jZJN9W/vknSvvP8W2/fsmXLheQCAAAAANASRkdH02+0q1PqSP9ByIEDB5Jvs5E89XpqldL0FhrIMrP3SfqUpKeY2bCZvfp8tw0hfFHSrZLuknRA0mtDCHP17dxsZrvrN32TpOea2T2qnfnwTcX/MwAAAAAAaB+eFnvfu3dv7oRCPPV6apXS9BY6tDCE8PJV/n5gyfdvlPTGZW73E4u+Pi3p2UU6AAAAAABoZ/Pz8+k3OjNXm5HVmfbwwvHxca1duzbpNhvJU6+nVilNbwNWhwN8KJVKXCIuAACfqtUql4gLAHg1OTnZmA03YNd44sSJ9BttIE+9nlqlNL0lT7/AK5VKlcXekQqDNHE87SMAAACAVlAul4/29/fvXuk2vD9GK1vpOcCMLAAAAAAAnJmYmMidEO3YsWO5Ewrx1OupVUrTy0AWAAAAAADOdHZ25k6IMj83r97u9Zqfa8CaXg2yefPm3AnRPLVKaXoLLfYOAAAAHziEPg6H0APwqqenJ3dClJnpOfWvv0Qz03PqWetjLs3AwEDuhGieWqU0vT4eRQAAAAAA4JtGR0dzJ0TpWbtGn/3CYfWsXZM7Jdr+/ftzJ0Tz1Cql6WWxd7QtPqmO42kfAQA4h99zcfg9B6AZxSz2Pjo6Wt20adPFSrogMzMzWrPGz0CWp15PrVJ8L4u9AwAAAADQQmZnZ3MnRDt9+nTuhEK89P7TPV/XW+/4F03MtNdjgYEsAAAAAACcmZ6ezp0QbWhoKHdCIV56f/HAYU08/KD++T+Gc6dES3Hfstg7gBVxaMrqOCwFKfGci8PzDqnwnIvDcw6p8JyLE0JY9Tbr16+/CCVp7NmzJ3dCIV56//B536NDQ9/QdVdfkTslWor7lhlZAAAAAAA4Mz4+njsh2pEjR3InFOKl9/onP0E/bN1a3+1njawU9y0DWQAAAAAAOONpge+tW7fmTijEU6+nVilNLwNZAAAAAAA4093dnTsh2rZt23InFOKp11OrlKaXNbLQtljrAUAzYt+EVHgsAWhG7JvilMvlVW8zMjKivr6+i1Bz4fbt26cbbrghd0Y0T72eWqU0vSVPO5JKpVL18kQFAAAAAOCxKJfLR/v7+3evdBveH6OVrfQc4NBCAAAAAACcmZ6ezp0QbXh4OHdCIZ56PbVKaXoZyAIAAAAAwJmZmZncCdFOnTqVO6EQT72eWqU0vRxaCAAAAABAE+HQQrQ7Di0EAAAAAKCFjI+P506INjg4mDuhEE+9nlqlNL0MZAEAAAAA4Ex3d3fuhGgDAwO5Ewrx1OupVUrTy0AWAAAAAADOdHV1pd/o7Jw0n375oS1btiTfZiN56vXUKqXpZSALbatUKnGJuAAAAABoPpVKJf1G5yXNzyff7MGDB5Nvs5E89XpqldL0stg72haDNHE87SMAAACAVsBi72h3LPYOAAAAAEALmZqayp0QbWhoKHdCIZ56PbVKaXoZyAIAAAAAwJm5ubncCdFGRkZyJ0SpVqv6zFce1tfvezB3SjQv9+2CFL0NWB0OQCvhEMzVcfglgGbE/jsO+3Dg4mLfFCeEsOpt1q1bdxFK0ti1a1fuhChnp+f0yS89pE1rTc/KHRPJy327IEUvM7IAAAAAAHBmbGwsd0K0w4cP506Isq6nSy/+jm16Usd9uVOieblvF6ToZUYWAAAAAADO9Pb25k6ItmPHjtwJ0Z502UZt6nxq7oxonu5bKU0vM7IAAAAAAHCmo8PP2/n169fnTijEU6+nVilNr59HPgAAAAAAkCSdOXMmd0K0O++8M3dCIZ56PbVKaXpLnha4rFQq1b6+vtwZAAAAAAA0TLlcPtrf3797pdvw/hitbKXnADOyAAAAAABwZmpqKndCtJMnT+ZOKMRTr6dWKU0vA1kAAAAAADgzPz+fOyHa5ORk7oRCPPV6apXS9BY6a6GZvVPSCyU9GELYWb/utyT9pKSH6jf7XyGEj9b/7g2SXi1pTtLrQggHl9mmSfqApAFJQ5JeFkIoF/9PAQAAAACgPaxduzb9RqfnatNdujqTbnbnzp1Jt9donno9tUppeovOyHqXpOuXuf4tIYRd9cvCINZTJd0o6Wn1n3mbmS33bHi9pDtCCNdIuqP+PQAAAC5AqVTiEnEBAK8asth7SVIDzoZ46NCh5NtsJE+9nlqlNL2FHqEhhE9ICpE3v0HS+0MIUyGEr0o6Kena89zulvrXt0h6cZEmAAAAAADazbp169JvdE2n1JF+kH/Xrl3Jt9lInno9tUppelMNtf6Mmf27mb3TzPrr110h6d5FtxmuX7fUZSGE+yWp/ueliZoAAAAAAGhJnmaVdnUVWtUoO0+9nlqlNL0pBrL+QtKTJO2SdL+kP65fv9yzqprg3wMAAAAAoK2NjY3lTog2ODiYO6EQT72eWqU0vRc8FBZCeGDhazP7a0n7698OS7py0U23SbpvmU08YGaXhxDuN7PLJT243L9jZq85evSo+vr6LjQZAACg5VWrfH4IAK3M03vj5zznObkTCvHU66lVStN7wTOy6oNPC/6LpOP1r2+TdKOZ9ZjZ1ZKukfSZZTZxm6Sb6l/fJGnfcv9OCOHtW7ZsudBcAAAAAADcm5yczJ0Q7cSJE7kTCvHU66lVStNbaEaWmb1P0nWSHmdmw5J+U9J1ZrZLtcMGhyT9lCSFEL5oZrdKukvSrKTXhhDm6tu5WdJfhhCOSHqTpFvN7NWSvi7ppRf8XwUAAAAAAICWU/I07bxSqVQ9TZ8EAAAAAKCocrl8tL+/f/dKt+H9MVrZSs+BVGctBAAAAAAAF0mlUsmdEO3222/PnVCIp15PrVKaXgayAAAAAABwZsOGDbkTou3Zsyd3QiGeej21Sml6GcgCAAAAAMAZT8sEzc7O5k4oxFOvp1YpTS8DWQAAAAAAODMxMZE7IdqxY8dyJxTiqddTq5Sml8XeAQAAAABoIiz2jnbHYu8AAAAAALSQs2fP5k6Idvz48dwJhXjq9dQqpellIAsAAAAAAGc6Ovy8ne/t7c2dUIinXk+tUppeP498AAAAAAAgSerp6cmdEG379u25Ewrx1OupVUrTy0AWAAAAAADOjI6O5k6IduDAgdwJhXjq9dQqpellIAsAAAAAAGc2btyYOyHa3r17cycU4qnXU6uUppeBLAAAAAAAnJmfn8+dEG18fDx3QiGeej21Sml6GcgCAAAAAMCZycnJ3AnRTpw4kTuhEE+9nlqlNL2larWaIOXiqFQq1b6+vtwZAAAAAAA0TLlcPtrf3797pdvw/hitbKXnADOyAAAAAABwZmJiIndCtGPHjuVOKMRTr6dWKU0vA1kAAAAAADjT2dmZOyHa5s2bcycU4qnXU6uUppeBLAAAAAAAnOnp6cmdEG1gYCB3QiGeej21Sml6GcgCAAAAAMCZ0dHR3AnR9u/fnzshyvx8Vf/wb8O67R/+IXdKNC/37YIUvV0JOgCXSqVS7gQXPJ0QAmgF7JvisG9aHY8lpMRzDmg+nhZ6f97znpc7IcrU7Jy+fP8Z2can5E6J5uW+XZCilxlZAAAAAAA4Mzs7mzsh2unTp3MnRFnb3aUff9YT9X1PWp87JZqX+3ZBil4GsgAAAAAAcGZ6ejp3QrShoaHcCdFsQ4/u/8a9uTOiebpvpTS9JU/ThCuVStXT9Ek0Nw65iONpHwG0AvZNcdg3rY7HElLiOQdcXOVy+Wh/f//ulW7TkPfH81WpJInfIchspecAM7IAAGgi1WqVS8QFq8v9/xGX1roAaD7j4+PpNzozJ83OJ9/skSNHkm+zkTz1emqV0vSy2DsAAAAAAM6sWbOmARvtrM3ISmzr1q3pN9pAnno9tUppehnIAgAAAADAme7u7vQb7WjMIYXbtm1ryHYbxVOvp1YpTS+HFgIAAAAA4MzIyEjuhGj79u3LnVCIp15PrVKaXhZ7R9tiEdw4nvYRAAAAQCvIttg70CRY7B1YRu5FU71cAAAAADSf6enp3AnRhoeHcycU4qnXU6uUppeBLAAAAAAAnJmZmcmdEO3UqVO5Ewrx1OupVUrTy6GFAAAAAAA0EQ4tRLvj0EIAAAAAAFrI+Ph47oRog4ODuRMK8dTrqVVK08tAFgAAAAAAznR3d+dOiDYwMJA7oRBPvZ5apTS9DGQBAAAAAOBMV1dX7oRoW7ZsyZ1QiKdeT61Sml4GsgAAAAAAcKZSqeROiHbw4MHcCYV46vXUKqXpZbF3AAAAAACaCIu9o92x2DsAAAAAAC1kamoqd0K0oaGh3AmFeOr11Cql6WUgCwAAAAAAZ+bm5nInRBsZGcmdUIinXk+tUppeDi0EAAAAAKCJcGgh2h2HFgIAAAAA0ELGxsbSb3S+KjVgssvhw4eTb7ORPPV6apXS9Po5XycAAACilUql3AkueDo6AQAW6+3tTb/RmTmpoySt6Uy62R07diTdXqN56vXUKqXpZSALAAAAAABnOjoacIBVd9oBrAXr169vyHYbxVOvp1YpTS+HFgIAAAAA4MyZM2fSb7RUql0Su/POO5Nvs5E89XpqldL0stg7AABAC+LQwjieXgsDaB8s9t5azs7M6ZNfeVjfNWDa1Lsmd44LLPYOAAAAAEALmZqayp0Q7eTJk7kTCknd+9G7TulPP/kfeu+Re5NuV2rP+5Y1sgCsiE/0V8en+UiJ51wcnndIhedcHJ5zSIXnXJwQwqq3mZ+fvwglaUxOTuZOKCR1754B090PnNFzn3Jp0u1K7XnfcmghgBXxYmN1nvajaH485+LwvFsdjyWkxHMOqbBvihNC4NBCtDUOLQQAAAAAoIU0ZLH3Bjl06FDuhEI89XpqldL0MpAFAAAAAIAz69aty50QZXpqVlddvl3TU7O5U6Lt2rUrd0I0T61Sml7WyAKwIg4lAAC0Mn7PARcXz7k45XJ51dt4OUyzs7NDPb3d6uz0M4+mq8vPUImnVilNr59HEgAAAAAAkCSNjY3lTojS2dWhu+/5d3V2+Rl+GBwczJ0QzVOrlKaXxd4BAGgiXj5dzc3T65dceCzF4bEEoBmttND1At4fo5Wx2DsAAAAAAC1kcnIy/UZn5qS5+eSbPXHiRPJtNpKnXk+tUppeXwdTAgnxSXUcPqkGLi6ec0iFxxIAoLCqJH5/oMkxkAUAAAAAgDO9vb3pN9rdmX6bknbs2NGQ7TaKp15PrVKaXg4tBAAAAADAmUqlkjsh2u233547oRBPvZ5apTS9DGQBAAAAAODMhg0bcidE27NnT+6EQjz1emqV0vRyaCHaFmuHAAAAAPDK0/uZ2dnZ3AmFeOr11Cql6WVGFgAAAAAAzkxMTOROiHbs2LHcCYV46vXUKqXpLXkaxa1UKtW+vr7cGQAAAAAANEy5XD7a39+/e6Xb8P4YrWyl5wAzsgAAAAAAcObs2bO5E6IdP348d0Ihnno9tUppehnIAgAAAADAmY4OP2/ne3t7cycU4qnXU6uUptfPIx8AAAAAAEiSenp6cidE2759e+6EQjz1emqV0vQykAUAAAAAgDOjo6O5E6IdOHAgd0Ihnno9tUppehnIAgAAAADAmY0bN+ZOiLZ3797cCYV46vXUKqXpZSALAAAAAABn5ufncydEGx8fz51QiKdeT61Sml4GsgAAAAAAcGZycjJ3QrQTJ07kTijEU6+nVilNb6larSZIuTgqlUq1r68vdwYAAAAAAA1TLpeP9vf3717pNrw/Ritb6TnAjCwAAAAAAJyZmJjInRDt2LFjuRMK8dTrqVVK08tAFgAAAAAAznR2duZOiLZ58+bcCYV46vXUKqXpZSALAAAAAABnenp6cidEGxgYyJ1QiKdeT61Sml4GsgAAAAAAcGZ0dDR3QrT9+/fnTijEU6+nVilNL4u9AwAAAADQRGIWex8dHa1u2rTpYiVdkJmZGa1ZsyZ3RjRPvZ5apfheFnsHAAAAAKCFzM7O5k6Idvr06dwJhXjq9dQqpellIAsAAAAAAGemp6dzJ0QbGhrKnVCIp15PrVKaXg4tBAAAAACgicQcWsj7Y7QyDi0EAAAAAKCFjI+P506IduTIkdwJhXjq9dQqpellIAsAAAAAAGc8LfC9devW3AmFeOm97cR/6J9OjejMlJ/DTFPct11Fbmxm75T0QkkPhhB21q/7gKSn1G+yWdJICGFX/e/eIOnVkuYkvS6EcHCZbZqkD0gakDQk6WUhhHLx/xSgmFKplDsBLcLTIdpofuyb4vC8Wx2PJaTEcw6psG+KE0JY9Tbd3d0XoSSNbdu25U4oxEvvr94+qG9UxvXt12zXC54ykDsnSor7tuiMrHdJun7xFSGEHwkh7KoPXn1Q0ockycyeKulGSU+r/8zbzKxzmW2+XtIdIYRrJN1R/x4AAAAAAJzHyMhI7oRo+/bty51QiJfe//OCvfqbp12q73uij4E3Kc19W2hGVgjhE2Y2sNzfmVlJ0sskfV/9qhskvT+EMCXpq2Z2UtK1kj615EdvkHRd/etbJB2S9CtFugAAAAAAaCebN2/OnRDthhtuyJ1QiJfe666+Qrr6itwZhaS4b1OukfWfJD0QQrin/v0Vku5d9PfD9euWuiyEcL8k1f+8NGETAAAAAAAtZ3raz7pIw8PDuRMK8dTrqVVK05tyIOvlkt636PvlDn7m4HoAAAAAAC7QzMxM7oRop06dyp1QiKdeT61Smt5Chxaej5l1SXqJpO9YdPWwpCsXfb9N0n3L/PgDZnZ5COF+M7tc0oPn+Tdec/ToUfX19aVIBli0FEBTYt+EVHgsAWhG7JvilMurn/9s/fr1F6Ekjd27d+dOKMRTr6dWKU1vqhlZz5F0IoSweI7YbZJuNLMeM7ta0jWSPrPMz94m6ab61zdJWnblrxDC27ds2ZIoFwAAAAAAv8bHx3MnRBscHMydUIinXk+tUpreQgNZZvY+1RZrf4qZDZvZq+t/daMeeVihQghflHSrpLskHZD02hDCXH07N5vZwjDcmyQ918zukfTc+vcAAAAAAOA8uru7cydEGxgYyJ1QiKdeT61Smt6Sp6mdlUqlyqGFAAAAAIBWVi6Xj/b39694DNbo6Gh106ZNFyvpgszMzGjNmjW5M6J56vXUKsX3rvQcSLnYOwAAAAAAuAgqlUruhGgHDx7MnVCIp15PrVKaXmZkAQAAAADQRGJmZPH+GK2MGVkAAAAAALSQqamp3AnRhoaGcicU4qnXU6uUppeBLAAAAAAAnJmbm8udEGVudl4P3v+Q5mbnc6dEGxkZyZ0QzVOrlKa368IzAJ9KpVLuBBc8HX4MADiH33Nx+D0HwKt169blTogyNzuvgSuu0dzcvDq7fMyl2bVrV+6EaJ5apTS9Ph5FAAAAAADgm8bGxnInROnu7dKXv35c3T1+5tEcPnw4d0I0T61Sml4GsgAAAAAAcKa3tzd3QrQdO3bkTijEU6+nVilNLwNZAAAAAAA409Hh5+38+vXrcycU4qX3XZ+7W7/08SMKE5O5U6KluG/9PPIBZFEqlbiscgFSyv149nIBUsn9WPZyAVLJ/Vj2colx5syZBv+/lc6dd96ZO6EQL71v+dQx7e2Y0KeHH8idEi3FfVvytMBlpVKp9vX15c5Ai+BFGVLxtB9F82PfFIfn3ep4LCElnnNIhX1TnBDC0f7+/t0r3Yb3x/jcfQ/pc/c9qJue/i1a09la85TK5fJ5nwOt9V8KAAAAAEAbmJqayp0Q7eTJk7kTCvHS+4zHX6LrNve4GsRKcd/6+a8FAAAAAACSpPn5+dwJ0SYn/azhJPnq9dQqpellIAsAAAAAAGfWrl2bOyHazp07cycU4qnXU6uUprcrQQfgEms9AGhG7JuQCo8lAM2IfVOccrm86m3OnDkjL2tkHTp0SNddd13ujGieej21Sml6mZEFAAAAAIAz69aty50QbdeuXbkTCvHU66lVStPLQBYAAAAAAM54OgNkV5evg8G89IaJSd0TKrkzCklx3zKQBQAAAACAM2NjY7kTog0ODuZOKMRL74veu1+fO/JZfXLovtwp0VLctz6GGYEG8PQJBoD2wdohSIXfc3F4zgEXF/umOCGEVW/jZX0sSXrOc56TO6EQL73PePyl+uN7vqYDfetzp0RLcd8yIwsAAAAAAGcmJydzJ0Q7ceJE7oRCvPT+6Quepduu/0490TblTomW4r5lIAsAAAAAAAAuMJAFAAAAAIAzvb29uROi7dixI3dCIZ56PbVKaXpZIwttizUxAACtjN9zAJoR+6Y45XJ51dtUKhU362Tdfvvtbtadknz1emqV0vQyIwsAAAAAAGc2bNiQOyHanj17cicU4qnXU6uUppeBLAAAAAAAnPE0u212djZ3QiGeej21Sml6GcgCAAAAAMCZiYmJ3AnRjh07ljuhEE+9nlqlNL0lT6O4lUql6uUYYAAAAAAAHotyuXy0v79/90q34f0xWtlKzwFmZAEAAAAA4MzZs2dzJ0Q7fvx47oRCPPV6apXS9DKQBQAAAACAMx0dft7O9/b25k4oxFOvp1YpTa+fRz4AAAAAAJAk9fT05E6IUq1WdeUVT3C1OP327dtzJ0Tz1Cql6WUgCwAAAAAAZ0ZHR3MnRJk+O6s7Pn67ps/6ObvegQMHcidE89QqpentStABuFQqlXInuODpkxMAwDn8novD7zkAXm3cuDF3QpTutV165nd/j7rX+hl+2Lt3b+6EaJ5apTS9zMgCAAAAAMCZ+fn53AlRSqWSZuenXX3AMj4+njshmqdWKU0vA1kAAAAAADgzOTmZOyHaiRMncicU4qnXU6uUprdUZDq1mb1T0gslPRhC2Fm/bpekv5TUK2lW0n8PIXym/ndvkPRqSXOSXhdCOLjMNk3SByQNSBqS9LIQQnm5f79SqVT7+vqie4GVePpEICcOuQAAn/g9F4ffcwCaUblcPtrf3797pds05P3x9FxtuktXZ9rtAgWt9BwoOiPrXZKuX3LdH0j67RDCLkm/Uf9eZvZUSTdKelr9Z95mZss9G14v6Y4QwjWS7qh/DzRctVrlEnEBAAAA0HwmJibSb7SzJHWkP3Dr2LFjybfZSJ56PbVKaXoLPUJDCJ+QFJZcXZW0MAy8SdJ99a9vkPT+EMJUCOGrkk5KunaZzd4g6Zb617dIenGRJgAAAAAA2k1nZwNmTXV2SB3pZ/Ru3rw5+TYbyVOvp1YpTW+K0wb8nKSDZvZHqg2MPbN+/RWSBhfdbrh+3VKXhRDul6QQwv1mdmmCJgAAAAAAWlZPT0/uhGgDAwO5Ewrx1OultVqt6gtfPamnbHvCBW8rxZzBn5b08yGEKyX9vKR31K9fbhiX45QAAAAAALhAo6OjuROi7d+/P3dCIZ56vbT+4a3v0e6fuUm37f+HC95WihlZN0n62frXfy/p5vrXw5KuXHS7bTp32OFiD5jZ5fXZWJdLenC5f8TMXnP06FGx2DtSYRFcAPCLNfyQCq8H4vCcQyo85+KEsHRFn0fz9N74ec97Xu6EQjz1emnt6upStVrVB77wGb30JT90QdtKMSPrPkl7619/n6R76l/fJulGM+sxs6slXSPpM8v8/G2qDYap/ue+5f6REMLbt2zZkiAXAAAAAADfZmdncydEO336dO6EQjz1emn9+Ze8XMff/n699Sded8HbKjSQZWbvk/QpSU8xs2Eze7Wkn5T0x2b2eUm/J+k1khRC+KKkWyXdJemApNeGEObq27nZzBZOo/gmSc81s3skPbf+PQAAAAAAOI/p6encCdGGhoZyJxTiqddLa6lU0lOufIKG7733wrflaZpwpVKpepo+iebGtGYA8MvT65dc+D2HlHjOIRX2TXFCCEf7+/t3r3QbL++P52bnNTYyqQ2be9XZleKgMLSDcrl83udAijWyAJd4QQYAAABcXLwGj1Mul1e9zfj4uIt1suZm5/WFuz6va6/9TjcDWUeOHNHu3SuOIzYNT61Sml4fjyIAAAAAAPBNa9asyZ0Qpbu3S0+4epu6e/3Mo9m6dWvuhGieWqU0vQxkAQAAAADgTHd3d+6EaFdeeWXuhEK2bduWOyGap1YpTS8DWQAAAAAAODMyMpI7IUp1vqp9+/a5Oqx03759uROieWqV0vSy2DvaFgtNxvG0jwAAnMPvuTj8ngPQjFZa6HqBl/fHUxMzGnloXJsvWa+edT4Oh0R+Kz0HmJEFAAAAAIAz09PTuROidK/t0tm5UXWv9bNG1vDwcO6EaJ5apTS9DGQBAAAAAODMzMxM7oQopVJJD4eHXM0UPnXqVO6EaJ5apTS9HFqItuVpR5qTp30EAOAcfs/F4fccgGbUSocWVqtVzc/Nq6Ozg99NiMahhQAAAAAAtJDx8fHcCVGmJ2d1+JP/qunJ2dwp0QYHB3MnRPPUKqXp9XOQKpAYn8ACaEZ8UhmHfTgAoN11d3fnToiypqdTV115ldb0dOZOiTYwMJA7IZqnVilNLzOyAAAAAABwpqvLx7yUjo4ObXvC49XR4Wf4YcuWLbkTonlqldL0+nkkAQAAAAAASVKlUsmdEO3gwYO5Ewrx1OupVUrTy2LvAAA0EQ4tjOPp9UsuPJbi8FgC0IxaabF34LFY6TngYy4iAABtgjfVSIXHEgC0tqmpqdwJ0YaGhlyt5eSp11OrlKaXQwsBAAAAAHBmbm4ud0K0kZGR3AmFeOr11Cql6eXQQgAAAAAAmgiHFqLdrfQcYEYWAAAAAADOjI2N5U6Idvjw4dwJhXjq9dQqpellIAsAAAAAAGd6e3tzJ0TbsWNH7oRCPPV6apXS9DKQBQAAAACAMx0dft7Or1+/PndCIZ56PbVKaXr9PPIBAAAAAIAk6cyZM7kTot155525Ewrx1OupVUrTy2LvAAAAAAA0ERZ7by3fGDmrd3z6a/qRp1+hp1y6MXeOCys9B7oudgzQLEqlUu4EtAhPHwig+bFvisPzbnU8luLwWAIuLvZNcUIIq95mamrqIpSkcfLkSW3fvj13RrTUvYNfC/rM18t63Pru5ANZ7XjfMpAFAAAAAIAz8/PzuROiTU5O5k4oJHXvC566VX29a/RdT+hPul2pPe9bDi1E2+LTIKTiaT+K5se+KQ7Pu9XxWIrDYwm4uNg3xQkhcGgh2tpKhxay2DsAAAAAAM54Wuz90KFDuRMK8dTrqVVK08tAFgAAAAAAzqxbty53QrRdu3blTijEU6+nVilNL2tkoW1xKAGAZsS+CanwWALQjNg3xSmXy6vextNhml1dvoYePPV6apXS9DIjCwAAAAAAZ8bGxnInRBscHMydUIinXk+tUppeFnsHAAAAAKCJrLTQ9QLeH6OVsdg7AAAAAAAtZHJyMndCtBMnTuROKMRTr6dWKU0vA1kAAAAAAABwgYEsAAAAAACc6e3tzZ0QbceOHbkTCvHU66lVStPLQBYAAAAAAM5UKpXcCdFuv/323AmFeOr11Cql6WUgCwAAAAAAZzZs2JA7IdqePXtyJxTiqddTq5Sml4EsAAAAAACcqVaruROizc7O5k4oxFOvp1YpTS8DWQAAAAAAODMxMZE7IdqxY8dyJxTiqddTq5Smt+RpFLdSqVT7+vpyZwAAAAAA0DDlcvlof3//7pVuw/tjtLKVngPMyAIAAAAAwJmzZ8+m3+jcvDSffrLL8ePHk2+zkTz1emqV0vR2JegAAABAkymVSrkTXPB0dAIALNbR0YB5KXNVqVSVOjqTbra3tzfp9hrNU6+nVilNLwNZAAAAAAA409PTk36jazqkBnwQsn379uTbbCRPvZ5apTS9HFoIAAAAAIAzo6Oj6TfaoNm8Bw4caMh2G8VTr6dWKU0vA1kAAAAAADizcePG9Budr0oNOOR67969ybfZSJ56PbVKaXoZyAIAAAAAwJn5+fn0G52Zk2bTb3d8fDz5NhvJU6+nVilNLwNZAAAAAAA4Mzk5mX6jazqlrvTDBCdOnEi+zUby1OupVUrTW/J0ppZKpVLt6+vLnQEAAND0OGthHE+vhQG0j3K5fLS/v3/3Srfh/TFa2UrPAWZkAQAAAADgzMTERPqNNmhw/9ixYw3ZbqN46vXUKqXpZSALAAAAAABnOjs70290Zr62TlZimzdvTr7NRvLU66lVStPbdeEZAAAAAADgYurp6Um/0c6S1IBD0wcGBpJvs5E89XpqldL0MiMLAAAAAABnRkdH02+0s0PqSD+QtX///uTbbCRPvZ5apTS9LPYOAADQgljsPY6n18IA2kfMYu+jo6PVTZs2XaykCzIzM6M1a9bkzojmqddTqxTfy2LvAAAAAAC0kNnZ2dwJ0U6fPp07oRBPvZ5apTS9DGQBAAC0oGq1yiXiAgBeTU9P506INjQ0lDuhEE+9nlqlNL0cWggAAAAAQBOJObSQ98doZRxaCAAAAABACxkfH8+dEO3IkSO5Ewrx1OupVUrTy0AWAAAAAADONGSB79k5aT79UVtbt25Nvs1G8tTrqVVK09uVoANwibM5xfF0+DEAAADQLrq7u9NvdL7+Px2dSTe7bdu2pNtrNE+9nlqlNL3MyAIAAAAAwJmRkZH0G+3ulLrSDmJJ0r59+5Jvs5E89XpqldL0stg72hYzsuJ42kcAAAAArYDF3tHuWOwdAAAAAIAWMj09nTsh2vDwcO6EQjz1emqV0vQykIW2Va1WuURcAAAAADSfmZmZ3AnRTp06lTuhEE+9nlqlNL0cWggAAAAAQBPh0EK0Ow4tBAAAAACghYyPj+dOiDY4OJg7oRBPvZ5apTS9DGQBAAAAAOBMd3d37oRoAwMDybc5MzOn8YnGrBPWiN5G8dQqpellIAsAAAAAAGe6urpyJ0TbsmVL8m1+6T+Cjt31oGZm55NvuxG9jeKpVUrTy0AW2lapVOIScQFwceV+znu5YHW5/z/ycgEAryqVSu6EaAcPHky+zW2Xb9RVj+9TV2f6fXkjehvFU6uUppfF3tG2ePEax9M+AmgF7JvisG9aHY+lODyWADQjFntHu2OxdwAAAAAAWsjU1FTuhGhDQ0PJt1mtVjU/35gPIxrR2yieWqU0vQxkAQAAAADgzNzcXO6EaCMjI8m3ec9Xy/r0sfs0O5d+jaxG9DaKp1YpTa+f1eGAxDiUAAAAcAhmHF43Ac1n3bp1uROi7dq1K/k216/r0tTMGnU0YD/eiN5G8dQqpellRhYAAAAAAM6MjY3lToh2+PDh5Nu8YmufvvUpl6qjI/1AViN6G8VTq5Sml4EsAAAAAACc6e3tzZ0QbceOHbkTCvHU66lVStPLQBYAAAAAAM50dPh5O79+/frcCYWk7j32jRG97JbP6I4vP5R0u1J73rd+HvlAYqVSiQuXJBcgpWq1yiXiAgDwKffrNi+XGGfOnGnw/1vp3HnnnbkTCknd+9DYtCam53SqcjbpdqX2vG9Lnl4MViqVal9fX+4MtAgGIJCKp/0ogPbB7zmkxO86pMK+KU4I4Wh/f//ulW7D+2M/qtWqHjgzpUs39jRkcfpWVC6Xz/scYEYWAAAAAADOTE1N5U6IdvLkydwJhaTuLZVK2trX25BBrHa8bxnIAgAAAADAmfn5+dwJ0SYnJ3MnFOKp11OrlKa30KGFZnalpHdL2ippXtLbQwh/YmYm6QOSBiQNSXpZCKFc/5k3SHq1pDlJrwshHFxmu+f9+cWYOgkAAAAAaHUrHVa1gPfHaGUpDy2clfSLIYRvkbRH0mvN7KmSXi/pjhDCNZLuqH+v+t/dKOlpkq6X9DYz61xmu8v+PAAAAAAAeDRPi70fOnQod0Ihnno9tUppegsNZIUQ7g8hfK7+9RlJd0u6QtINkm6p3+wWSS+uf32DpPeHEKZCCF+VdFLStcts+nw/DwAAAAAAlli3bl3uhGi7du3KnVCIp15PrVKa3se8RpaZDUh6uqRPS7oshHC/VBvsknRp/WZXSLp30Y8N169b6nw/DwAAAAAAlvB0Bsiurq7cCYV46vXUKqXpfUwDWWa2QdIHJf1cCKGywk2Xe2Zx7l4AAAAAAC7A2NhY7oRog4ODuRMK8dTrqVVK01t4IMvM1qg2iPXeEMKH6lc/YGaX1//+ckkP1q8flnTloh/fJum+ZTZ7vp9f/O++5vTp00VzAQAAAABoOZ4Wen/Oc56TO6EQT72eWqU0vYUGssysJOkdku4OIbx50V/dJumm+tc3Sdq36PobzazHzK6WdI2kzyyz6fP9/DeFEN6+ZcuWIrkAAAAAALSkycnJ3AnRTpw4kTuhEE+9nlqlNL1FD078HkmvlPQFMztWv+5/SXqTpFvN7NWSvi7ppZIUQviimd0q6S7Vznj42hDCnCSZ2c2S/jKEcOR8Pw8AAAAAALDU/HxV8/NVdXU95qW/4VSpWvWzZFWlUql6mj4JAAAAAEBR5XL5aH9//+6VbtPu74/vPvmwyiOTunbX4xnMakErPQf4fxttq1QqceGS5AIAAIA4uV+3ebnEqFRWOu9ac7n99tuTb9M2rZX1r1VnZ/rX443obRRPrVKaXl/naQQAAAAAANqwYUPuhGh79uxJvs3LLlmvyy5Zn3y7UmN6G8VTq5SmlxlZAAAAAAA442mZoNnZ2dwJhXjq9dQqpellIAsAAAAAAGcmJiZyJ0Q7duxY7oRCPPV6apXS9LLYOwAAAAAATYTF3tHuWOwdAAAAAIAWcvbs2dwJ0Y4fP547oRBPvZ5apTS9DGQBAAAAAOBMR4eft/O9vb25Ewrx1OupVUrT6+eRDwAAAAAAJEk9PT3pNzo3L82nX35o+/btybfZSJ56PbVKaXoZyELbKpVKXCIuAAAAAJrP6Oho+o3OVWuDWYkdOHAg+TYbyVOvp1YpTW9Xgg4AAAAAAHARbdy4Mf1G13RIDfgwe+/evcm32Uieej21Sml6mZEFAAAAAIAz8/PpZ041YhBLksbHxxuy3Ubx1OupVUrTy0AWAAAAAADOTE5O5k6IduLEidwJhXjq9dQqpektVavpF3JrlEqlUu3r68udAQAAAABAw5TL5aP9/f27V7oN74/RylZ6DjAjCwAAAAAAZyYmJnInRDt27FjuhEI89XpqldL0MpAFAAAAAIAznZ2duROibd68OXdCIZ56PbVKaXoZyAIAAAAAwJmenp7cCdGuuvKq3AmFDAwM5E6I5qlVStPLQBYAAAAAAM6Mjo7mTogydXZGH/noRzV1diZ3SrT9+/fnTojmqVVK08ti7wAANJFSg0573Wo8vX7JhcdSHB5LAJpRzGLvo6Oj1U2bNl2spMdsfm5eo2Fcm2y9Ojp9zKWZmZnRmjVrcmdE8dQqxfey2DsAAAAAAC1kdnY2d0KUjs4OTc2NuxnEkqTTp0/nTojmqVVK0+vnkQQAAAAAACRJ09PTuROiDQ0N5U4oxFOvp1YpTS8DWQAAAAAAOLN+/frcCVFmpmf1pCufqplpHzPIJGnPnj25E6J5apXS9DKQhbZVKpW4RFwAXFzVapVLxAUAgHY3Pj6eOyFKqVTSPf9xl6v3FkeOHMmdEM1Tq5Sml4EsAAAAAACc8bLAd9eaTl119TZ1renMnRJt69atuROieWqV0vQykAUAAAAAgDPd3d25E6Jt27Ytd0Ihnno9tUppehnIAgAAAADAmZGRkdwJ0fbt25c7oRBPvZ5apTS9JU/rTFQqlWpfX1/uDAAAAAAAGqZcLh/t7+/fvdJteH+MVrbSc4AZWQAAAAAAODM9PZ07Idrw8HDuhEI89XpqldL0MpAFAAAAAIAzMzMzuROinTp1KndCIZ56PbVKaXoZyAIAAAAAwJn169fnToi2e/eKR0k2jWq1qk/d85D6rnhy7pRoXu7bBSl6uxJ0AGhhpVIpd0LT87TWIID2wf4bKfG7Dqmwb4oTQlj1NuPj4/KyRtbg4KD27NmTO2NVZ6fn9K/3PKwrq/fpyS94du6cKF7u2wUpehnIAgAAAADAme7u7twJ0QYGBnInRFnX06Uf+s4rNXXGz2w3L/ftghS9DGQBAAAAAOBMV5eft/NbtmzJnRBt4JINmtnckzsjmqf7VkrTyxpZAAAAAAA4U6lU0m+0Wq1dEjt48GDybTaSp15PrVKa3lKR493N7EpJ75a0VdK8pLeHEP7EzF4q6bckfYuka0MIRxb9zBskvVrSnKTXhRAeVW1mJukDkgYkDUl6WQihvPR2lUql6uUYYKBVsI7B6lg3BEAzYv+NlPhdh1TYN8UJIRzt7+9fcVXshrw/npqVOkrSms602wUKKpfL530OFJ2LOCvpF0MInzOzjZKOmtnHJB2X9BJJf7X4xmb2VEk3SnqapMdLut3MnhxCmFuy3ddLuiOE8CYze339+18p2AYUwi/ROLxwBQCf2H8DaEbsm+KUy4+a1/EoU1NT6f/hNZ1SA94mDQ0NuVrLyVOvp1YpTW+hQwtDCPeHED5X//qMpLslXRFCuDuE8KVlfuQGSe8PIUyFEL4q6aSka89zu1vqX98i6cVFugAAAAAAaCdzc0vnhyTQUZIa8IH/yMhI8m02kqdeT61Smt7HvEaWmQ1IerqkT69wsysk3bvo++H6dUtdFkK4X6oNlkm69LF2AQAAAADQ6tatW5c7IdquXbtyJxTiqddTq5Sm9zENZJnZBkkflPRzIYSVVphbbiiXuaQAAAAAAFyAsbGx3AnRDh8+nDuhEE+9nlqlNL2Fz9dpZmtUG8R6bwjhQ6vcfFjSlYu+3ybpvmVu94CZXR5CuN/MLpf04DL/7muOHj0qFntHKhyfDwAAAMCr3t7e3AnRduzYkTuhEE+9nlqlNL2FZmSZWUnSOyTdHUJ4c8SP3CbpRjPrMbOrJV0j6TPnud1N9a9vkrRv6Q1CCG/fsmVLkVwAAAAAAFpSR8djXinoolu/fn3uhEI89XpqldL0Fn3kf4+kV0r6PjM7Vr8838z+i5kNS/puSR8xs4OSFEL4oqRbJd0l6YCk1y6csdDMbjazhVMpvknSc83sHknPrX8PAAAAAACWcebMmdwJ0e68887cCYV46vXUKqXpLXk6vKpSqVQ5tBAAAAAA0MrK5fLR/v7+3SvdhvfHaGUrPQf8zEUEAAAAAACSpKmpqdwJ0U6ePJk7oRBPvZ5apTS9DGQBAAAAAODM/Px87oRok5OTuRMK8dTrqVVK08tAFgAAAAAAzqxduzZ3QrSdO3fmTijEU6+nVilNLwNZAAAAAAA442mx90OHDuVOKMRTr5fWjx87om3/9QW69f996IK31ZWgB3CpVCrlTgAAAJl5OvER0Ap4DR4nhLDqbdatW3cRStLYtWtX7oRCPPV6af1//3JI958+rb/514/rZS9+yQVti4EsAAAAAACc8TQo2NXla+jBU6+X1l//0Vfrkk39esF3fvcFb4tDCwEAAAAAcGZsbCx3QrTBwcHcCYV46vXSesnmfv36K16tcO99F7ytkqfp1JVKpdrX15c7Ay3C0ycYAACgMTy9FgZaAa/B44QQjvb39+9e6Ta8P0YrK5fL530OMCMLAAAAAABnJicncydEO3HiRO6EQjz1emqV0vT6OJiybm5u7uFyufy13B2LPfTQQ4+75JJLHs7d0eya8X6KWUDxYmvG+6nZcB/F4X6Kw/0Uh/tpddxHcZrxfiqXy7kTHqUZ76dmxP20uma8j3gNHu0Jq91gbm5urFwuf+lixFyojo6Ox5XL5Wa7j8/LU6+nVqlQ73mfA64OLWxGZvaaEMLbc3c0O+6nONxPq+M+isP9FIf7KQ730+q4j+JwP8XhforD/bQ67qM4Xu8nT92eWiVfvZ5apTS9DGQBAAAAAADABdbIAgAAAAAAgAuu1sgCAAAAAKDdmdn1kv5EUqekm0MIb8qctCwzu1LSuyVtlTQv6e0hhD/JW7UyM+uUdETSN0IIL8zdsxIz2yzpZkk7JVUlvSqE8KmsUedhZj8v6SdU6/yCpB8PITymMxYwIwsNYWY8tgAAAAAgsfpAy59L+gFJT5X0cjN7at6q85qV9IshhG+RtEfSa5u4dcHPSro7d0SkP5F0IISwQ9K3q0m7zewKSa+TtDuEsFO1AdgbH+v2mJFVkJm9SNJHQwizuVuaVX3HOmZmm0MIU7l74FPsYGgIYb7RLZ7Uf0k8XtJ9IYRv5O5pNma2S9LpEMK9i667SlJ/COHz2cKAFmVmW0IIp5e5/kkhhK/kaAKAFnCtpJMhhP+QJDN7v6QbJN2VtWoZIYT7Jd1f//qMmd0t6Qo1Yaskmdk2SS+Q9EZJv5A5Z0Vm1ifpWZJ+TJJCCNOSpnM2raJL0lozm5G0TtJ9F7IhFPO7kt5hZh+Q9J4QwqdzBzWbEMKcmX1Z0hZdwIOzXZjZNZJepvrgg6S/DyF8OW9VU5hVbdrpajobHeJBfTDmvZK+W1KoXWWDkn40hPC1rHHN5W8lvWjJdWskvUfSt138HLSKpYPvDLJ/03Eze1UI4R8XrjCzn1bt9dTj8mXBMzMrqfb4eTiEwJmrVsC+6fzqExT2qvZYKi1cH0L4/7JFxbtC0r2Lvh+W9F2ZWqKZ2YCkp0tq5vfQb5X0y5I2Zu6I8URJD0n6GzP7dklHJf1sCGE8b9ajhRC+YWZ/JOnrks5K+qcQwj891u1x+FdBIYRvl/Qc1e78D5rZl8zs1+pPSpzzXkn7zewmM3u2mX3fwiV3WDMxs/8q6d9UewM9LulbJR2tX9/urlZt5/xESf9D0p2Srpf0LfU//1nSz2Sraz63qPbLa1MI4VJJmyV9tn49zrlq4dPLBfVZIQN5cppX/Xdbacl168zsr3I1NRsze4aZfcrMxiXN1C+z9T9R8ypJN5vZ28xsu5n9o6T/JonXA0uY2c1mtm7JdZeb2YFcTc3GzDab2XtUex3+gKSzZvYeM7PMaU2FfdPqzOw3Jf2Vau+HXyrptKTnSRrJmFVEaZnrmnpQ18w2SPqgpJ8LIVRy9yzHzF4o6cEQwtHcLZG6JD1D0l+EEJ6u2vvJ1+dNWp6Z9as2a/Bq1SZwrDezVzzW7TGQ9RiEED4fQvglSVdKeq1qO7+vmNknzOxHWR9KkvTTkvol/ZZqi8+9o365OWNTM/rfkp4fQviREMIvhxBulPR8Sb+XuSu7EMLXFi6qTet9SQjhYyGEL4cQPqba8+5/5q1sKt8h6ZcWPoEJIYxJ+pX69Thn2MyesfiK+vfMHn206yX9i5k9UZLM7JmS/l1SX9aq5nKLaoPqu3Vu4H1hEB6S6jOxvlXS90r6kmpvFr8zhPDvWcOa00ZJ/25m3y1JZnajas+5f8ta1Vz+RtJa1WZ0bKj/2SPpnTmjmhD7ptW9StJzQwg/L2m6/ucPys8HW8OqvRddsE1N/FrGzNaoNoj13hDCh3L3rOB7JL3IzIYkvV/S95nZ3+ZNWtGwpOFFR4n9X9UGtprRcyR9NYTwUAhhRtKHJD3zsW6MQwsfIzN7kqRX1C/zkn5DtWlyPyPphyS9JF9dfiGEq3M3OLFR0tKzSgxKWp+hpZltUu046pFF162rX4+aQdXWS/iXRdft1qMfX+3uLZL2mdkfSPqKpCepNiD6xqxVzelZkt4g6bNm9lHVPqn+2RDC+/JmNZUnSPpVDm06v/on8H+k2v76Laq9efwxSW/PmNWUQgg/YmY/qto+6kuSLpf04hDCv6zyo+3kP0u6PIRwtv793Wb2Y2riN/CZsG9a3eYQwvH619NmtiaE8Bkz25u1Kt5nJV1jZldL+oZqi2Y35REd9dnd75B0dwjhzbl7VhJCeINqr31kZtdJ+p8hhMc8a6jRQginzOxeM3tKCOFLkp6tJl17TLWxkj31mcdnVWs98lg3xkBWQWb2M6oNXm2XdKukV4YQBhf9/QclPZgpD/68WdLvmdmvhxAmzWytpN+uX49zbpF0u5m9VbX1AK5U7awXHDZ3zlckfdTMPqJz99HzJf2dmf3Owo1CCL+Rqa8phBD+2sxGJL1atfvoXtXOpPN/s4Y1oRDCfP132isk/bCkf5C0L29V0/mwpO+XdDB3SBP7vKR/lfRtIYTR+ifb7zGzG0IIL8jc1oy+IWlStZkzd6m2b8c5X1Jtxszis3JdVb8e57BvWt1XzOxpIYQvSjou6afNrCypnLkrSghhtv6+9KBq68W+s/7f0oy+R9IrJX3BzI7Vr/tfIYSP5ktqKf9D0nvNrFvSf0j68cw9ywohfNrM/q+kz6l2qPO/6QI+1CpVqwzUF2Fm+1V787yvflaA5W7z/ReycFkrqJ9B4be0/AKKV2XKajpmdq+kraod015W7XDMkupn9ljQ7vdZ/XDd16h2OOHjVbt/bpX01yGEuZxtzcLM/ibiZtUQwqsaHoOWUH+B/NuqHQL9bklvU226+iM+wGln9RO//KCkw5JOLf47J4sFN5yZvSyEcOuS63ol/V4IoanPBnWx1RfBfYVqa4h9RLVlBm6S9NoQwt/nbGsWZvZ7qr0hfo/OfWjzivr33xz0CyG09aGG7JtWZ2bPlzQWQviEmV0r6e9UO1z1tSGED+atA7AaBrIKMLNOSXdIel4IYSp3TzOrf+K6TbXDCP5WtRcZvyTpgyGEt+Rsayax05dDCHc2ugVoF2b2Kkkv17kzhb5ftU8y+YW4iJn9m6RXLP6E18xeKenNIYRL8pU1j/piwcsKIfz2xWxpZvW1UfZIenwI4QNmtl6SmvGsSjnVZ9S+KoTwwKLrniXpFpZsqDGzf464WTWE0NYnE2DfBKDVMZBVkJl9TdJTQgiTuVuamZk9KOlbQginzWwkhLDZzK6Q9A8hhGZdgA5NysxeLulYCOFuM3uypL+WNCfpv4cQTuSty2dhEe7VLD1LXzurr411g2qnVv6aauuIvE61fdMvZ0xrOvX1Qh51hiszuzKEcO9yPwMsZWbfKuk2SVOStoUQNtRnQtwUQviRvHU+mNnGEMKZ3B1AKzGzEEJ41NkuzezB+tmfATQx1sgq7rcl/WX9k45hLTrNaQhhPltV8+mQNFr/eszMNqt2ONj2bEVNYvF6RStp97WMlvjfOndWiz9WbYHLMdUOdWrnT11PqrYPKumRp1xe+n3nxYxqcj8m6RkhhOGFK+qHjH9OEgNZjzRrZj+p2uy1x4UQvq0+O2Sraof0QJKZ/WfVDnW6QrX1jf42hPDxvFVN5S8k/UYI4T319Wck6U7VPpDAImbWo9rJg14uaUsIYZOZfb+kJ0v6s6xxTWKlM4PzOvyR2Detas3SK+qzR3nNBDjAQFZxN9f/fOWi6xbeNLLjO+fzqq2PdYekT0r6c9UGHr6cM6pJLD5Vbq9qZ7n8rGqzQ65S7cxzHJv/SJeEEB6or6vyvaotPD0j6eG8WXmFEL75gt7Mfly109r+ls7NNPoN1Z6DOOdM/bL0ukqGlmb3O5Keq9rstb+sXzes2iHjt57nZ9qKmf2EausY3Szp06rtw/+ufgIPBmpqnqbaEgNSfYA9hDBeP7kJHumtqh3y/KOS/rF+3RdVe84xkFUzq0d+ULMYr8Pr2Dedn5l9UrXHUK+ZfWLJX29T7eQUAJocA1nFsUZBnJ/UuQXeXyfp9yVtltT2C0yGEL55Jgkze7+kly9eVNLMXqLaouY45yEz2y7pWyV9NoQwVT91a2mVn2snvyvpmkWnJL/HzH5KtcHjd2Wraj5vlfQhM3uTaoMyV6q2ft9bFh+qyeGYkmqz154eQnjYzP6ift1XVTubGmp+WdJzQwifX7iivsjyB8WMowVDkr5Di06xXV9Y+WSuoCb2Yknb6wN985IUQvhGfWkG1Cx9HX65pNerdlZVnMO+6fxuVu3143dKesei66uSHpDErDXAAQayintpCOGPll5pZr8g6c0ZeprS4jeBIYSHJP1Expxm9gOqffK62D5JMWegaye/K+moautiLayp8mzVZv6hpkOPPiX5E8Qn1Ev9Sf3P/7zk+mdL+tP618ywrelUbSatdG4GxIZF10HaIumuJdd9SdKj1l1pY78u6SNm9peSus3sDaqdle8n82Y1pWkteW1uZpdIOp0np/mEEL625KqvmdlNqs1sf8cyP9Ku2DedRwjhFkkys8F2XmcV8I6BrOJ+Q9KjBrIk/ZoYyPomMyupNnj1qLVVlp6Gu82dlPRanXsDLUn/XYtOIQ0phPAuM7u1/vVE/epPS7oxX1XTeYukj5vZ3+jcKcl/rH496hYfjolVfVTSm83s56Vv7td/V8x8WOywavfRr4QQJupn4/t9cWjKN4UQ9pvZD6j2muBO1QbYXxJCOJq3rCn9vaRbFj3nLldtFun7c0Y50CeJM6k+EvumZZjZK0MI76l/+0wze+ZytwshvPMiZgF4DBjIimRmCwtKd9YXT1x8SNMT9eg1V9oda6vE+QlJHzazX1ZtIc4rVFv/4SVZq5pMfXHXyUVfS22+PtZSIYQ/NLN/l/QySU+XdJ9qp3E/kLesOdUP1Xm8pG+EEO7L3dOkfkHSu1U7ccca1WZi/ZM4RHyx/6baIMOomQXVZjv8q2of4qAuhPA51T6kwcr+l6Q/kPQFSesk3aPaYWC/nTOqmZjZe/TINbLWSXqWzq3Dhhr2Tct7uaSFgaxXnuc2VUkMZAFNrlStnm+9RCxmZl+tf3mVpK8v+quqpFOS3hRCuO2ihzUpM7tX59ZWKYcQ+uuf5ocQQn/uvmZSP0PKd6u2zsP9kj613Cnv21l9rZBld1YhBA4B06POhrn4rIVTqg0iHwghPHDRw5qMmV0l6b2qPecWXtwPSvrRZQ5ZgSQzu0y13333hhBO5e5pRmZ2pWr78PsWnxGzXXF23gtXP6Tw4RACL9QXqZ81fLFxScdCCLfn6Gl27JsAtCpmZEUKIVwtSWb27hACn0avjrVVItUHrZaeNQWPxOKuq3uypP8i6TM6d2jhtardRz8o6W1m9kPM0NItqq23dn19QeUNqh0ud4uk63KGNYPznNr+ofrlm3/fzqe5N7PSwuDCovvrG/UL91ENZ+eNtPgkE8vYaFZb0ogTUNSEEJiddh7sm4ozs82SXqDaDO37JH0khDCSswlAHAayClo8iLX0BT+/GB7hH8XaKquqz1xb7tPWhVk0H5L0FyGE2Ysa1mRY3DVKh6QbQwgfXrjCzG6Q9F9DCHvq99ebJLX7QNZ3SPr+hVmPIYQxM/sVsZjygpVObb9YO8+EHFVtTR5p+ftrYUZk295HnJ23kJOqPV4Wz6RdWL5i8WOrbR9PS5nZj6t2WNgVqg3SvCeEwEly2DcVUl825kOqLYK/MMj+5/UP/e7IGgdgVQxkFWRmz5D055K+TbVPGSV+MSzn51Wb4cDaKiv7U0mvqP95r2q/RF+r2oKvQdIvqvbJ9i/nCmxiLO76SM/To9e+2K9za0H8raQ/u6hFzWlQtdkg/7Lout2SPpUnp+ksnv34Akk/rNoCwV9TbZHuXxEzaZ626Ouls0XxaJyddwWLT0BRH6B5jqTf0rnn3G9I4k11nZn9qmqvJf9Y5+6jXzazx4cQ3pg1Lj/2TcX8maTXLD4JlZm9VLX3eTuyVQGIwkBWcbeoNqvoVZImVrltO/st1RZ8/ynVXmSwtsryfkzScxcvNm1m/yjpn0IITzOzf5Z0u9p8IIvFXaN8RdJP65GDVf9N586A+TjV1hJpd1+R9FEz+4jOHYL5fEl/t3hdn3Zdu2fx7Ecz+wVJuxcdZvFlMzsi6Yikv8iQ1xRCCPcu+valIYRHncm4ft9xJuMazs4b73clXRNCOFv//h4z+ylJX5b0rmxVzeUnJF23ZF91ULUlGtp6IGvxvok1H6M8Xo/+YObDqp1gAUCTYyCruCdI+lUW31zVGtUOYXpItRkhDDgs73I9et2wcdV+uUq1F6+bL2ZQkzq55PtxSX/J4q6P8BOSPlQ/TG7hDJhzOncGzKdI+vVMbc2kV7VDCSTpUtUO4/2wpLU6t64P+/eaTaoNGo8sum5d/XrU/IakRw1kSfo1MZC1gLPzxuuQNCDp7kXXPUHM+F9svepr9i1yWrV9OOqW+QBwwcLSFf8vhPD5i1vVdN6tRw+y/3T9egBNjrMWFmRmt0j6uxDCwdwtzc7MOnXukIIXSvq0ar8cPhRCYNF3ffPxdJVqnyIOS9om6Q2SvhFC+P/M7JmS/iqE8K0ZM10ws7eFENr69O71M2DuUW0glDNg4oKY2R+pNlvtrTo3e+11kg6GEH4xY1p29bVVpNoM7Rfq3JpGkvRESb8eQnjCRQ9rUpydN46Z/ZKkX1DtsMuF59yPSXprCOEPMqY1DTN7t6SNqp3w5euqDfS9UdJECOGVOduaiZn9mWrriN2mc4+lH5T0ftU+IH2RpP8WQmjbQRszOyzpuyQ9oHOD7Jeq9n7lm2+QQwjPyhIIYEUMZBVkZh9Q7RfBYUmPOFSOsxmen5k9TdLfSfpW1Q7JfL+k3wwhfCNrWGZm1qvaYZgv1bkzpvy9pN8JIUyY2VZJ3SGEr+er9MHMKiGEvtVviXazylnBvomzgj1S/YQmr9G5/dP9km6V9NchhLmcbbmZ2VfrX16l2pvpBVXVXhu8KYRw20UPa3KcJGd1Zna9ljznONvsOWbWp9oh9C9Tbfb/jGr7pddxtrlzzOyfJP12COFfFl333aq9vnxu/XH21hBC264FVT8JzqpCCLc0ugVAcQxkFWRmv3m+v+OUwI9Uf7HxUtUWM/821Y5Dv0W1F/2/KOn7Qgjflq/QBzN7fQjhTbk7mp2ZnQkhbMzdgeZjZvN69FnBtPT7EAKH76AQM3s3H2KtbKWT5PCcQxH1mf6/Ken3JE2rtvbjwwyIPpqZjUrasvis1/WZkQ+HEDbVzyZ+JoSwIVskAFwABrLQEGb2f1U7i9onVDuc8P+FEKYW/X2HpFEGHlbHTKM43E+IsdJZwUII78pX1pzM7Psl7ZL0iDc77boYPoozsy+odgjme7TkJDksSP1IZtat2qGEu/To5xwDppLM7LSkSxi8WpmZ3anaWXp/M4QwuegIgGeGEJ5Vn6l8KIRwVc7O3MzsMtXOZPw4LTpEPITwzmxRAKKw2HtBi9bFeJQQwscvZkuTG5T0M+c7U2EIYb7+ywOrK61+EwCROCtYpPoaKy+T9M965AAEn4DV1Wce/5akvXr0G6G2foO4CCfJiXeLpG9XbeDvgcwtzeoW1c7I+7bcIU3uJtWW9KiYWZBkqp1x9kfrf2+qnT20bZnZi1U7GdU9kp4m6YuSdqq2fAwDWUCTYyCruHcs+f4SSd2qLdQdtQ5LO1judOTL3GZitdtAEm8aYzHghxicFSzeyyXtWnxKdzzK21Q7ScfvqPaG6BWSfkmPPqV7O/uwpO+XxElyVne9pKtZ62lF10r6H/WzYN4rFuVeVghhSNIzzexK1ddbW7zeagjhSK62JvK/Jf14COHvzawcQnh6fdb203KHAVgdA1kFhRCuXvx9/Xj9X5N0Jk8RgLq/zR0AF94i6eNmtvSsYG/JGdWkTksayR3R5L5f0reEEE6b2VwIYZ+ZHVFtRg2PqZpeSR+unyGMk+Ss7OuSenJHNLm/rl+wCjPbIuk6SZeHEP7AzB4vqSOEMJy3rGlcFUL4+yXX3aLafup/ZugBUAADWRcohDBnZm9UbUbWm3P3oCUx00iSmb1KtRkiC2d3fL+kdy4crhJC+OmMeXAihPCHZvbvqh0y93TVHkuv4qxgy/pjSe81s9/XksOcOMPjN3VIGq1/PWZmm1U709z2bEXN5676Bat7t6R9ZvYnevRzjuUrxBnkYpnZXtVmhh6R9D2S/kDSNaoN0PxgxrRm8qCZXRZCeEDSUP2sjg+LGdqACwxkpfFcSSw6icLMbOty64gtuf6TFzmr6ZjZH0i6QdJbVVug+yrVXow9RdIv5yuDN2b2O/Uvv6HaIFZVtcMvvkO1DyQO1F/UQvqL+p8vXHJ9VbzQX/B51dbHukO1ffWfSxpTbc01iDM6F/Qz9T9/b8n1VbF8haRvfqi1nCnV9uGDi08u1MbeKulHQgh3mFm5ft2nVTs0EzV/Lel7VRvwe4tq60HOq/YhDoAmx1kLCzKzRxyPL2mdatPm/3sI4d15quDV+c60Z2YhhGA5mpqRmT0o6RmLp8PX1334XAjhknxl8MbM3i/pv0j6jM4dWnitaoeCbZP0rZJ+iBlaiFE/81cphPAVM7tE0u9L2ijpt0MIbTsLycyeFUL4RP1rTpKDZMzskKTvVm3G2rBq++3LVJt5NFC/2Q3tvgZUfc2n/vrXIYRg9TOGPxRC2JI5rymZ2VWS1ocQ7l71xgCyY0ZWca9Y8v24pC+HECo5YuDeow4brJ8Fixl+j3RGj16H7owknncoqkPSjSGEDy9cYWY3SPqvIYQ9ZnaTpDdJYiALq1p8iGUI4SFJP5Exp5m8TbWzf0mPPknOAmYZ4bH4oqQPhRD+dOEKM/sZSTtUm13zq5L+j2qDXe3sLjN7Xghh8UkWniPpC7mCmo2Z7ZJ0euGEJiGEr5vZlWb27SGEz+etA7AaZmQ9RvVPNS6T9EAIgUEHFLJoZt/Cek+LbZH0vhACb4jqzOx/SHqxagMMw6rNovklSfskfXThdqzbg9WY2agkCyHMLbquU1I5hNBX/3okhLAxW2RGZnYghHB9/etP6jxnTW3ns4OtcGjTI4QQOH17JDPb1q4LUJvZ3SGEb6l/vXTW/zeFEK66qGFNqn6Y3JbFr73r++2HQwj9ZtYj6cEQwqZskU3AzL5HtddIH1FtTch3q7Y21g0hhM/mbGsWZnZc0osWv3Y0sydJ+nAI4dvylQGIwYysgsxso2prYPyIpDWSZuqHqrwuhDC64g8D57xCtdlYH5X0ykXXV1UbHP1Slqrm9Sf1P//zkuufLWnhU1nW7UGMr0j6aUl/tui6/1a/XpIep9pM23a1+BD5m7NVNLdXrn4TVSUxkBXvLkmPOsy+Tfzkoq+XzvrHoz2g2oDMvkXXvUDSg/WveyXNXOyoZlIf2Ltd0pNUe0y9U7VD6a9t1wHj87hq6Qeg9cPEBzL1ACiAGVkFmdm7VFv/4g2qLTr9BElvlDQRQrgpYxocMrN1IYSJ3B1AuzCzZ0j6kGqDnt+QdIWkOUkvCSF8zsyeJekpIQRO7x7BzN4WQvjvuTvgm5mdaddZkEWZ2UdCCC/I3ZGLmX2/pL+XdFzn1jncKemlIYR/qv/9d7f7SQbM7POSfiCEsHTWP+rM7C5JrwghfG7Rdc+Q9HchhB35ygDEYCCrIDM7JemJiwcfzGyDpK+EEC7LVwaPzOxDkt4SQvjkouv+k6SfDSH8cL6y5mJmW0IIp5e5/kkhhK8s9zPA+ZjZGkl7VDu0935JnwohtPUn+I/V+U5Y0S7qywwsi2UH4rX746gI7qvaawJJz9e5ffhHlnuN0M7M7Jcl3ajajPZhLTpklZMs1JjZT0r6DUl/oNqs7CepdkbsN4YQ3p6zDcDqOLSwuElJl6g2G2vB41Q77S9Q1F5JL11y3ack/b+Ln9LUjpvZq0II/7hwhZn9tKTfVe35B0SrD1p9ctUbIsajTljRZmZ1njWNxKHOQEPUB63ec76/Z7BPUu0Qekn6rSXXc5KFuhDCX5vZiKRXqzaz715JvxhC+L9ZwwBEYSCruJslfczM3qxzhxb+vCQOQ8FjMSlpvR559r0NavP1HZbxKkk3m9k+SW9W7YxEj5d03tO6A7go2n1a99VLvr9c0usl/UOGFgA17T7ArhDC0n0TlhFC+HvVDlVdFofPA82Lgazi3qjauio/qnNnnPuDEML5Ti8NrOSgpL+y/3979x5sWVneefzbIEaRi/5A5N4RkuAgMhGFZCjRKIyiBDHjGMRLuKgBRrnEGRkxQ6sQkCFELobLhIsK1KhMqRFE0CgIWhRB0ehY8TKAYIMQR56GlovChDN/rHU4m9Pd9DnQfd61z/l+qnbttd+9TtWvuvrss/az3vd5k0OranmSjeiaUF/VONegVNWVSV4EfB04FPgU3c47DzcNJmlBq6rbpw3dnuRA4FuA1wUzt+ALD1qjFnqBXWvO2wALWdIArbK3g1bpDODHVbVXVe1YVXsBP0xyeuNcGk//mW6npmVJfgEUsDFwdMtQQ9P3oTuV7t/mNLreGAe1zCQJsACxMhvRtSAQkOTIJKtbAr7jnISZH/ydk+aOv2/SQDkja/YOoGsEOOomup5GR891GI23qloG7JNkc/r1+VV1d+NYQ/Q94Hpg56q6L8klwMVJ9lvIuzdJA3BJ6wAtJbmYx8/+WB94OQv832WavYCTknydrq/R31fV4/qKVtXSFsHG1EmtA0gLiLP7pIGykDV7E6zYwHVdnN2mJynJs4E96ZeqJvlSX+DSlGOr6tLJF1X1T0l2xQt6aY1K8vaqurg/PmRV51XVhf3z4as6Z4G4edrrB4Bzq+qrLcIMUVW9vt9l7s10N/zOTfJZ4KKquq5puAFI8pdVdWJ/fPyqzquqJf3zR+Yq2xhzFo0kzXMWsmbvG8AJSY6pqkf7rbc/hDtg6UlI8irgc8CP6TYP2BY4K8kbq+prTcMNSFVdmmQ94A+BLavqM3QF5OPaJpPmnQOY2g3s7as4ZwK4cG7iDFtVfbh1hnHQ7zJ3Ft3ft53p/o8dnGQp3WY5Z1TV/S0zNrT1yPE2zVKMmSTbAFtV1Q0refu1c51H85ZFUWmgFk1MOGNyNpJsDXyRbmeiycLDXcC+VXVHy2waP0n+GfjQ6GyjJG8CTqiqF7RLNix9o/fLgN8AW1fVBkleBxxYVfu3TSdpIetnrh3A1AYwnwYurCovsEYk2ZOucfJ+wLeBTwI/A44CnldVezSMpzGRZFu6DV9+H5jorwf+I7B3Vb2zaTiNjSTrAh8ETpy+1Hnaeec481gaJgtZT0I/C2s3+p5GwI1V9WjbVBpHSe4FNqmqfx0Zexrwy6p6dqtcQ5Pkm8D/qKqLkyyrquckeRbwk6raqnU+aT5K8mrgtqr6ycjY7wGLq+of2iUbjiSn0BVmTmfq5tZRwOVVdUzDaIOR5FS6ZYX3ARcBl1TVnSPvrwcsq6oNGkVsKsl2Mzmvqm5d21nGQZIr6VZBnAzc018PbAx8v6oWt02ncZLkHuC5foeTxpNLC5+E/gPvhv4hPRUXAe8GzhwZO7wf15QXMtU8eQKgqh5I8sx2kaR57yy6xuWj7u/Hf2/u4wzSQcAuozOyk1wBfAewkNV5BvAnVfWtlb1ZVY8keekcZxqSm+n+ri3i8Y2lp7+e3p91odoN2Kdv7zF5PXBfX8ySZuOTwGHA2a2DSJo9C1lSW7sAhyc5BrgT2ArYDPjHJI81wa2q6V8mF5rbgJfQLUcBIMlurNhoWdKas1lV3TVt7C5g8xZhBupX/WP62PIGWYbq0ZUVsZKcXlVHA1TVj+Y81UBU1WObBSU5mG6Xxw/RzfBbDCwB7Jk55V+A3wFGZ4ruSLdMVZqN3YAj+mvwpYwUjr3ulobPQpbU1nn9Q0/sOOCKJOcCT09yLN1dtHe1jSXNa7cmeVVVXT0y9kfATxvlGaLTgc8lORm4g67lwPuA00aXjC3wZWEHAUeuZPztdLsYasoJwO9W1UP96/+T5FC6os0nmqUallOBLyb5CPC0JAcAH6BbaijNhtfg0hizR5Y0cEnOrqr/1DpHa0l2Ad5Jd4d6KXBeVd3UNpU0fyXZj27pxQXALXSzIA4CDq6qLzSMNhhJZtJbZaKqFtyysL4JPsDfAu+Z9vZ2wJuqaoe5TTVsSX4O7FlVPxwZ+zfA1VW1Rbtkw5LkDcCfM3U9cG5V/X3LTJKkuWUhSxq4JMuraqPWOYYuyRVVtU/rHNJ8kmRX4B3A1nRfGC+oqm8/8U9JkOSa/nAPuubckyboloedUVX2Gh2R5H3Ae4GP0/2+bQMcDJxeVf+9ZTZpvkmyiO4G6QHAplW1c5KXA5uP7iYuaZhcWigN36LWAcaEW7dLa1CSpwMvpis8LAOeBRyZhKr6s6bhBibJtnQ9Du+oqqWt8wxBVb0SIMlfVdV/a51nHFTVXyf5PvCnwO/T9aQ7qKq+3DTYwCTZg+6z6XE7XVbVSW0SaUwdD/x7uiXi5/ZjdwCnARaypIGzkCUNn9MmJbXwSeDfApcDdzfOMkhJtgA+Dfw74B5gkyQ3AG+uqp83DddQkkVVNfm3a0mSdVZ2ntveP15fPF4MPEz3/2k94K1J3mrxuJPkY3SFvm8AD4285bWSZusg4MVV9csk5/RjP6Vb+ixp4CxkSZKkldkbeH5V3ds6yICdA3wPeF1VPZDkWcBJdHf3X980WVv3AZNL4v8fKxYZFvVjC6532GpYPF69twI7LeRCsdaYdYH7++PJz6gNRsYkDZiFLGn4XFooqYWfAb/VOsTAvQzYoqoeAeiLWccAd7aN1dwLR46f3yzF+LF4vHpLgd+0DqF58bRXKQAADpFJREFU4UvAR5P8BTzWM+sEukKypIGzkCUN3yWtA4wJC37SU5TkVSMvLwK+kOQMuubcj6mqq+c02HAtA3akm5U1aQfg3iZpBmK0T1hV3d4yy5ixeLx67wDOS/IpVvxcuq5NJI2p99L9nbuPbhnv/cBXAJfxSmPAQpbUWL9F+QHAlsDP6fqtXDjZX6SqDm8Yb5zY5FV66i5Yydj0360J7CEy6RTgq0kuAG4Hfpuu78pxDTM1l+RiZtCzyL5PFo+fhJcArwVezoo9srZtkkhjqaqWA29Ishldb7qlVeWSXmlMLJqYsDei1EqSU4D96HZMuZ3uIuwo4PKqOqZhtEFJcvyq3quqJXOZRZJGJXklXd+eLehuRnxqoRcdknxwJudV1YfXdpahS/LTGZw2UVUWj4Ek9wD7V9VXW2fR+EvybGAfpm4mf6mqljUNJWlGnJEltXUQsEtV3TE5kOQK4DuAhawp20x7vTnwCuDzDbJIEvDYLnOvBP6I7ovQncDdSa6vql+3zNaSBaqZqyp7iM3OA4BLCPWU9bMhPwf8mKmbyWcleWNVfa1pOEmrZSFLautX/WP62PIGWQarqg6ePpZkb7olmZLUyjl0PbGOYOqL0AeArYBDGuYalL7gtwOwKSP9DBf6zDU9KUuA0/uZ2r8YfaOqHm0TSWPqb4E/r6pLJweSvAk4C3hBs1SSZsSlhVJDSY4A3gCcDNxBN/PofcAX6HZTAaCqbm2Rb8iSrAMsq6qNW2eRtDD1y5y2H91lLkmAm6sqzYINSJKXAf+Lron5RnQ3ajak60fjcjnNSpLJYtXoF5hFdMsv120QSWMqyb3AJlX1ryNjTwN+WVXPbpVL0sw4I0tq64z++ZXTxvcEzuyPJ4AFfXGWZPqXnfWBt9Btwy1JrdxN93l078jYM4G7mqQZptOAU6rqtCTLqipJlgAPtg6mseRSTK0pFwHvZup6G+DwflzSwDkjS9Lg9XdgJ5hakvIg8F3g6Kq6qVkwSQtakvfTFdU/xtSs2ncD/xP41uR5C3kJXZL7gOdU1aN9Ies5/VLDn1bVVq3zaTz1s7KfB/yLSwr1ZCT5JvAHdDuE3km3JHwz4B8ZmfFXVS9vElDSE3JGltRQkk2q6p6VjG9fVbe0yDREVbVO6wyStBKH9s8fmDZ+WP+A7gvRQl5Cdx/dksJ7gbuS7AjcA2zQMpTGU5KN6HobvZnue8wjST4NHFlV9zUNp3FzXv+QNIYsZElt/SDJIVV15eRAksOBE+ia4kqSBsod52bkc8Dr6GapXQBcAzxC1zdLmq0zgWcBO9FtsLAYOLEfP7BhLo2Zqvrk6s5Jcjaw2vMkzT2XFkoNJXktcD5dc/eP0i1P2RJ4e1V9v2W21pIs5fHNXFeqqradgziSpDUgyR50s7G+7JIwzVaSu4HtqurBkbENgFuq6nntkmk+SrK8qjZqnUPSipyRJTVUVVcmeRHwdbolKp8C9quqh5sGG4a3jRzvSnen9Uym7sC+BxtyStJYqapvtM6gsfZr4Ll01wKTNgV+0yaO5rlFqz9FUgsWsqSG+ruIpwIb0+3sdAhwEPB3DWMNQlVdO3mc5CzgNVV158jYlcBVwN80iCdJmoEk32AVs2ttoqwn4XzgH5J8lKkbW3+B101aO1y6JA2UhSypre8B1wM7V9V9SS4BLk6yX1Xt0zjbkGwJ3D9t7H66HWYkScN1/rTXmwPvAC5pkEXj70Tg53S7hW7ZH58CXNgylCRpbtkjS2ooyZ9W1aXTxp4BnFRV720Ua3CSfAJ4Pt0F7FK6Le6PBX5WVTZ3laQxkuR3gI9X1R6ts0jSqiT5VVVt2DqHpBW5pb3UUFVdmmS9JHsk2b8fXhc4rmWuATqMbubaOcBN/fMNdH3FJEnj5U5g59YhNH6SnJlk92ljuyc5vVEkzW/OHJUGyhlZUkN9o/fL6JqUbl1VGyR5HXBgVe3/xD+9sCR5NfBmYLOq+uMkLwU2qqqrG0eTJK1CkkOmDa0P/Afgkap6TYNIGmNJ/i+w1eimOEl+C1haVZu1S6Zx1H8+HcDUMtVPAxdWlV+QpYGzR5bU1jnAkqq6OMmyfuxa4LyGmQYnyRHAUXS9Vt7YDz9Et4vh7qv6OUlSc2+f9voBuhm2pzXIovE3wYorStZdyZj0hJKcAuwHnE63ccC2wH8BdgCOaZdM0kw4I0tqqC9epaomklRVpR9/7FiQ5BZgz6q6LcmyqnpOknWBX1TVJq3zSZKktS/JZ4GfAsdU1aNJ1gFOBn63qv6kbTqNkyS/AHapqjtGxrYBvlNVz22XTNJMOCNLaus24CXAtycHkuwG3Nwq0EBtSNfkHaa2Ql4PeHjlp0uShiDJdjM5r6puXdtZNC8cBXwRuCvJ5Cyau4B9m6bSOPpV/5g+trxBFkmzZCFLaus44Iok5wJPT3IsXWPzd7WNNTjXAe+n27Vw0pHANW3iSJJm6GambkAsGjmefE0/tu5chtJ4qqo7kuwC/AGwNd1Nrhur6tG2yTSGTgc+l+Rk4A66HbHfB5w2WoC3yC4Nk0sLpcb6C7J3AovpLsjOq6qb2qYaliRbAJcDmwJbAbfS3THbt6rubplNkrRqSQ4G9gI+RNeHZjGwBPhaVX2iXTKNu35Z4WMsZmk2kszk/8tEVVlklwbIQpY0cEmuqKp9WudoLckiYFemCn7egZWkgUtyB13/oodGxtYHflJVW7dLpnHU3/w7C9gZeEY/vAgLDpK0oLi0UBq+PVoHGIJ+K+Qb+4ckaTysA/w28MORscW4lFBPzifpZmgfAjzYOIvGWJJNquqelYxvX1W3tMgkaeYsZEmSJGltOQ24OsnH6WbTbgMc1I9Ls7UY+Mv+5pb0VPwgySFVdeXkQJLDgRPoWllIGjALWZIkSVorquqvk/xv4E3Ai+l2mDukqq5qm0xj6vPAq4Evtw6isXcIcH6SLwAfBT4GbAm8qmkqSTNijyxp4JIsr6qNWueQJGltsBekZirJZ4B9gW8Cj9vspar+rEkoja0kAb4OvBD4FF2R/eGmoSTNiDOypOFbtPpTJEkaW/aC1Ez9c/+QnpIkGwCnAhvTLXU+hG7Z8981jCVphixkScN3UusAkiRJLSR5eVVd17/8RtMwmk++B1wP7FxV9yW5BLg4yX7OEJWGz0KW1FCS41f1XlUt6Z8/MneJJEmSBuVsYKf++IJVnDMBbDc3cTRPHFtVl06+qKp/SrIr3kCWxoKFLKmtbaa93hx4BV0zU0mSpAWtqnYaOX5+yyyaP6rq0iTrAX8IbFlVnwHWBY5rm0zSTFjIkhqqqoOnjyXZGzigQRxJklqwF6SkOZXkRcBlwG+ArYHP0N1MPhDYv2E0STOwTusAklbwFeANrUNIkjRHXMojaa6dAyypqhcAj/Rj1wIvaxdJ0kw5I0tqKMn0fg7rA28BljaII0nSGmUvSEkD9ULgkv54AqCqHkjyzHaRJM2UhSyprZvp/nhOLqt4EPgu3bRmSZLGnb0gJQ3RbcBLgG9PDiTZje7aXNLAWciSGqoql/dKkuYte0FKGqjjgCuSnAs8PcmxwGHAu9rGkjQTiyYmJlpnkCRJ0gKRZB1gWVVt3DqLpIUryS7AO4HFdG09zquqm9qmkjQTFrKkOZZkKf1a/CdSVdvOQRxJktaaJ+gF+fqq2qlBJEmakSRXVNU+rXNIWpFLC6W597aR413p+mGdCdxOd0foPcBFDXJJkrSm2QtS0rjao3UASSvnjCypoSQ/AF5TVXeOjG0NXOWdakmSJKmNJMuraqPWOSStyEbTUltbAvdPG7sf2KpBFkmSJEmSBs2lhVJblwGXJTmRrsnkNsCx/bgkSWPHXpCSJGltspAltXUY8EHgHGAL4C7gUuDDLUNJkvQU2AtS0nywaPWnSGrBQpbUUFX9Osk1wPOAzarqj5O8FNgduLptOkmSZq+qrp08TnIWK/aCvBK4CvibBvEkaaZOah1A0spZyJIaSnIEcBRwPvDGfvghujvXu7fKJUnSGmIvSEmDk+T4Vb1XVUv654/MXSJJs2EhS2rraGDPqrotyX/tx34E7NAukiRJa4y9ICUN0TbTXm8OvAL4fIMskmbJQpbU1oZ0F/Yw1Rh3PeDhNnEkSVqj7AUpaXCq6uDpY0n2Bg5oEEfSLK3TOoC0wF0HvH/a2JHANQ2ySJK0RlXVr+n+pl0LXF1V2wOfxeXzkobnK8AbWoeQtHrOyJLaOgK4PMm7gA2T/BhYDuzbNpYkSU+dvSAlDVGS7aYNrQ+8hamVEpIGzEKW1FBV3ZVkV7rtyRfT/fG8saoebZtMkqQ14mjsBSlpeG6ma+uxqH/9IPBd4MBmiSTNmIUsqbGqmgBu7B+SJM0n9oKUNDhVZYsdaYz5CyxJkqS1xV6QkiRpjVo0MTGx+rMkSZKkWUqyBXA5sCmwFXArfS/Iqrq7ZTZJC0uSpUzNDF2lqtp2DuJIegpcWihJkqS1wl6QkgbkbSPHu9L1wzoTuJ3u8+k9wEUNckmaJWdkSZIkSZIWjCQ/AF5TVXeOjG0NXFVVO7VLJmkm7JElSZIkSVpItgTunzZ2P90SaEkD59JCSZIkSdJCchlwWZIT6ZY8bwMc249LGjhnZEmSJEmSFpLDgOuBc4Cb+ucbgENbhpI0MxayJEmSJEkLRlX9GrgGuBa4uqq2Bz4L7N40mKQZsZAlSZIkSVowkhwBnA38BNijH34I+KtmoSTNmIUsSZIkSdJCcjSwV1WdDDzaj/0I2KFZIkkzZiFLkiRJkrSQbEjX5B1gon9eD3i4TRxJs2EhS5IkSZK0kFwHvH/a2JF0fbMkDdyiiYmJ1Z8lSZIkSdI8kGQL4HJgU2Ar4FZgObBvVd3dMpuk1bOQJUmSJElaUJIsAnYFFtMtM7yxqh594p+SNAQWsiRJkiRJkjQW7JElSZIkSZKksWAhS5IkSZIkSWPBQpYkSZIkSZLGgoUsSZIkSZIkjQULWZIkSZIkSRoL/x+CgWvwOb1ZgQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "klib.missingval_plot(df_indicators_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the excel for the next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the dataframe to bue used in the analysis\n",
    "writer = pd.ExcelWriter('data/country_indicators.xlsx')\n",
    "df_indicators.to_excel(writer,sheet_name='data')\n",
    "df_indicators_2018.to_excel(writer,sheet_name='data_2018')\n",
    "df_indicators_imputed.to_excel(writer,sheet_name='data_imputed')\n",
    "df_indicators_imputed[df_indicators_imputed.year == 2018].to_excel(writer,sheet_name='data_2018_imputed')\n",
    "writer.save()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f26333649674f079eee36943c34c754c48500c0c92afa7151671dea441727e40"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('BAIT509')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}