Project_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from scipy.stats import spearmanr\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import seed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/almagunn/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Session</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Client IP Block</th>\n",
       "      <th>ASN</th>\n",
       "      <th>Country</th>\n",
       "      <th>NAT</th>\n",
       "      <th>Outbound Private Status</th>\n",
       "      <th>Outbound Routable Status</th>\n",
       "      <th>Adjacent Spoof Prefix Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>966236</td>\n",
       "      <td>2020-08-31 23:48:14</td>\n",
       "      <td>103.132.11.x/24</td>\n",
       "      <td>138528 (EMPIRETECH-AS-AP)</td>\n",
       "      <td>khm (Cambodia, Kingdom of)</td>\n",
       "      <td>yes</td>\n",
       "      <td>received</td>\n",
       "      <td>received</td>\n",
       "      <td>/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>966208</td>\n",
       "      <td>2020-08-31 23:21:12</td>\n",
       "      <td>177.190.91.x/24</td>\n",
       "      <td>262977</td>\n",
       "      <td>bra (Brazil)</td>\n",
       "      <td>yes</td>\n",
       "      <td>rewritten</td>\n",
       "      <td>received</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>966170</td>\n",
       "      <td>2020-08-31 22:18:40</td>\n",
       "      <td>2804:4e08:20xx::/40</td>\n",
       "      <td>268235</td>\n",
       "      <td>bra (Brazil)</td>\n",
       "      <td>no</td>\n",
       "      <td>received</td>\n",
       "      <td>received</td>\n",
       "      <td>/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>966159</td>\n",
       "      <td>2020-08-31 22:00:03</td>\n",
       "      <td>2804:4e08:20xx::/40</td>\n",
       "      <td>268235</td>\n",
       "      <td>bra (Brazil)</td>\n",
       "      <td>no</td>\n",
       "      <td>received</td>\n",
       "      <td>received</td>\n",
       "      <td>/16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>966158</td>\n",
       "      <td>2020-08-31 21:59:07</td>\n",
       "      <td>2a02:27b0:45xx::/40</td>\n",
       "      <td>9146 (BIHNET)</td>\n",
       "      <td>bih (Bosnia and Herzegovina)</td>\n",
       "      <td>no</td>\n",
       "      <td>received</td>\n",
       "      <td>received</td>\n",
       "      <td>/16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Session            Timestamp      Client IP Block  \\\n",
       "6   966236  2020-08-31 23:48:14      103.132.11.x/24   \n",
       "23  966208  2020-08-31 23:21:12      177.190.91.x/24   \n",
       "44  966170  2020-08-31 22:18:40  2804:4e08:20xx::/40   \n",
       "53  966159  2020-08-31 22:00:03  2804:4e08:20xx::/40   \n",
       "55  966158  2020-08-31 21:59:07  2a02:27b0:45xx::/40   \n",
       "\n",
       "                          ASN                       Country  NAT  \\\n",
       "6   138528 (EMPIRETECH-AS-AP)    khm (Cambodia, Kingdom of)  yes   \n",
       "23                     262977                  bra (Brazil)  yes   \n",
       "44                     268235                  bra (Brazil)   no   \n",
       "53                     268235                  bra (Brazil)   no   \n",
       "55              9146 (BIHNET)  bih (Bosnia and Herzegovina)   no   \n",
       "\n",
       "   Outbound Private Status Outbound Routable Status  \\\n",
       "6                 received                 received   \n",
       "23               rewritten                 received   \n",
       "44                received                 received   \n",
       "53                received                 received   \n",
       "55                received                 received   \n",
       "\n",
       "   Adjacent Spoof Prefix Length  \n",
       "6                            /8  \n",
       "23                         none  \n",
       "44                          /16  \n",
       "53                          /16  \n",
       "55                          /16  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(\"recent_tests.csv\", sep=';', dtype={'Sesssion': 'str'})\n",
    "\n",
    "#Resetting types\n",
    "test_df.astype({'Timestamp': 'str',\n",
    "                'Client IP Block': 'str',\n",
    "                'ASN': 'str', \n",
    "                'Country': 'str', \n",
    "                'NAT': 'str', \n",
    "                'Outbound Private Status': 'str',\n",
    "               'Adjacent Spoof Prefix Length': 'str'}).dtypes\n",
    "\n",
    "df_received = test_df.loc[test_df['Outbound Routable Status'] == 'received']\n",
    "\n",
    "df_received.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASN</th>\n",
       "      <th>Country</th>\n",
       "      <th>IP Block</th>\n",
       "      <th>First Spoofed Timestamp</th>\n",
       "      <th>First Fixed Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>7377 (UCSD)</td>\n",
       "      <td>usa (United States)</td>\n",
       "      <td>2607:f720:fxx::/64</td>\n",
       "      <td>2016-02-12 19:35:38</td>\n",
       "      <td>2016-06-14 19:32:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>262426</td>\n",
       "      <td>bra (Brazil)</td>\n",
       "      <td>177.39.131.x/32</td>\n",
       "      <td>2016-05-25 22:11:10</td>\n",
       "      <td>2016-05-26 13:02:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>36352 (AS-COLOCROSSING)</td>\n",
       "      <td>usa (United States)</td>\n",
       "      <td>107.172.23.x/24</td>\n",
       "      <td>2015-10-23 14:44:30</td>\n",
       "      <td>2016-04-23 18:25:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>16276 (OVH)</td>\n",
       "      <td>deu (Germany)</td>\n",
       "      <td>51.254.23.x/24</td>\n",
       "      <td>2016-02-28 20:58:58</td>\n",
       "      <td>2016-04-17 18:58:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>8816 (IT-STCOM)</td>\n",
       "      <td>ita (Italy)</td>\n",
       "      <td>212.45.144.x/32</td>\n",
       "      <td>2016-03-31 15:03:36</td>\n",
       "      <td>2016-04-08 05:03:56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                         ASN              Country            IP Block  \\\n",
       "814              7377 (UCSD)  usa (United States)  2607:f720:fxx::/64   \n",
       "815                   262426         bra (Brazil)     177.39.131.x/32   \n",
       "816  36352 (AS-COLOCROSSING)  usa (United States)     107.172.23.x/24   \n",
       "817              16276 (OVH)        deu (Germany)      51.254.23.x/24   \n",
       "818          8816 (IT-STCOM)          ita (Italy)     212.45.144.x/32   \n",
       "\n",
       "    First Spoofed Timestamp First Fixed Timestamp  \n",
       "814     2016-02-12 19:35:38   2016-06-14 19:32:14  \n",
       "815     2016-05-25 22:11:10   2016-05-26 13:02:54  \n",
       "816     2015-10-23 14:44:30   2016-04-23 18:25:01  \n",
       "817     2016-02-28 20:58:58   2016-04-17 18:58:38  \n",
       "818     2016-03-31 15:03:36   2016-04-08 05:03:56  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remedies_df = pd.read_csv(\"remedies.csv\", sep=';')\n",
    "remedies_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get remediation blocks\n",
    "remedies_count_df = remedies_df.groupby('Country')\n",
    "remedies_count_df = remedies_count_df.count()[['IP Block']]\n",
    "remedies_count_df = remedies_count_df.rename(columns={\"IP Block\" : \"Blocks Fixed\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get other spoofable blocks\n",
    "spoofable_df = df_received.groupby('Country')\n",
    "spoofable_df = spoofable_df.count()[['Client IP Block']]\n",
    "spoofable_df = spoofable_df.rename(columns={\"Client IP Block\" : \"Spoofable Blocks\"})\n",
    "\n",
    "spoofable_country_list = df_received['Country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to calculate remediation rate\n",
    "#Input: list of countries\n",
    "#Outputs remediation rate per country\n",
    "def calc_remediation_rates(countries):\n",
    "    remediation_rates = []\n",
    "    \n",
    "    for country in countries:\n",
    "        remedies_row = remedies_count_df.loc[country]\n",
    "        spoofable_row = spoofable_df.loc[country]\n",
    "\n",
    "        remediation_rate = (remedies_row['Blocks Fixed'] / (remedies_row['Blocks Fixed'] + spoofable_row['Spoofable Blocks'])*100.0)\n",
    "        remediation_rates.append(round(remediation_rate,2))\n",
    "        \n",
    "    return remediation_rates\n",
    "\n",
    "#Average calculation\n",
    "def calc_average(lst):\n",
    "    return round(sum(lst)/len(lst),2)\n",
    "\n",
    "#Get in the country-value tuple and splits the list into two lists, higher and lower than the median\n",
    "def compute_split(combo):\n",
    "    median = statistics.median(x[1] for x in combo)\n",
    "    print(median)\n",
    "    \n",
    "    higher = []\n",
    "    lower = []\n",
    "    \n",
    "    for value in combo:\n",
    "        if value[1] > median:\n",
    "            higher.append(value)\n",
    "        else:\n",
    "            lower.append(value)\n",
    "            \n",
    "    return higher, lower\n",
    "\n",
    "#Get in the country-value tuple and splits the list into two lists, higher and lower than the median\n",
    "def compute_average_split(combo):\n",
    "    median = statistics.mean(x[1] for x in combo)\n",
    "    print(median)\n",
    "    \n",
    "    higher = []\n",
    "    lower = []\n",
    "    \n",
    "    for value in combo:\n",
    "        if value[1] > median:\n",
    "            higher.append(value)\n",
    "        else:\n",
    "            lower.append(value)\n",
    "            \n",
    "    return higher, lower\n",
    "\n",
    "#Extraction of the remedation rate column\n",
    "def extract_remediation(values, df, column, col_of_interest):\n",
    "    rates = []\n",
    "    for value in values:\n",
    "        df_current_row = df.loc[df[column] == value[0]]\n",
    "        rate = df_current_row[col_of_interest].item()\n",
    "        rates.append(rate)\n",
    "        \n",
    "    return rates\n",
    "\n",
    "#Write out the new grouping into the dataframe\n",
    "def set_group(dataframe, values, col_to_change, tag):\n",
    "    for value in values:\n",
    "        dataframe.loc[dataframe[\"Countries\"] == value[0], col_to_change] = tag\n",
    "        \n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Remediation rates</th>\n",
       "      <th>continent_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Remediation rates</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.257143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent_encoded</th>\n",
       "      <td>-0.257143</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Remediation rates  continent_encoded\n",
       "Remediation rates           1.000000          -0.257143\n",
       "continent_encoded          -0.257143           1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#General continental analysis\n",
    "unique_countries_remedies = list(remedies_count_df.index)\n",
    "unique_countries = [x for x in spoofable_country_list if x in unique_countries_remedies]\n",
    "\n",
    "europe = ['aut (Austria)',\n",
    "          'bgr (Bulgaria)',\n",
    "          'bih (Bosnia and Herzegovina)',\n",
    "          'che (Switzerland)',\n",
    "          'cze (Czech Republic)',\n",
    "          'deu (Germany)',\n",
    "          'esp (Spain)',\n",
    "          'fra (France)',\n",
    "          'gbr (United Kingdom)',\n",
    "          'grc (Greece)',\n",
    "          'hun (Hungary)',\n",
    "          'irl (Ireland)',\n",
    "          'ita (Italy)',\n",
    "          'ltu (Lithuania)',\n",
    "          'nld (Netherlands)',\n",
    "          'nor (Norway)',\n",
    "          'pol (Poland)',\n",
    "          'prt (Portugal)',\n",
    "          'reu (Reunion [French])',\n",
    "          'rou (Romania)',\n",
    "          'svn (Slovenia)',\n",
    "          'swe (Sweden)',\n",
    "          'ukr (Ukraine)' \n",
    "         ]\n",
    "\n",
    "north_america = ['can (Canada)', \n",
    "                 'usa (United States)']\n",
    "\n",
    "south_america = ['arg (Argentina)',\n",
    "                 'bol (Bolivia)',\n",
    "                 'bra (Brazil)',\n",
    "                 'chl (Chile)',\n",
    "                 'cri (Costa Rica)',\n",
    "                 'hti (Haiti)',\n",
    "                 'mex (Mexico)',\n",
    "                 'pan (Panama)',\n",
    "                 'pry (Paraguay)',\n",
    "                 'ury (Uruguay)']\n",
    "\n",
    "asia = ['bgd (Bangladesh)',\n",
    "        'idn (Indonesia)',\n",
    "        'ind (India)',\n",
    "        'irn (Iran)',\n",
    "        'irq (Iraq)',\n",
    "        'isr (Israel)',\n",
    "        'jpn (Japan)',\n",
    "        'kaz (Kazakhstan)',\n",
    "        'kor (South Korea)',\n",
    "        'mmr (Myanmar)',\n",
    "        'npl (Nepal)',\n",
    "        'pak (Pakistan)',\n",
    "        'phl (Philippines)',\n",
    "        'rus (Russian Federation)',\n",
    "        'tha (Thailand)',\n",
    "        'tur (Turkey)',\n",
    "        'twn (Taiwan)']\n",
    "\n",
    "africa = ['ken (Kenya)', \n",
    "          'khm (Cambodia, Kingdom of)',\n",
    "          'mar (Morocco)',\n",
    "          'mus (Mauritius)',\n",
    "          'tza (Tanzania)',\n",
    "          'zaf (South Africa)']\n",
    "\n",
    "australia = ['aus (Australia)', 'nzl (New Zealand)']\n",
    "\n",
    "remediation_averages = []\n",
    "europe_remediation_rates = calc_remediation_rates(europe)\n",
    "remediation_averages.append(calc_average(europe_remediation_rates))\n",
    "\n",
    "na_remediation_rates = calc_remediation_rates(north_america)\n",
    "remediation_averages.append(calc_average(na_remediation_rates))\n",
    "\n",
    "sa_remediation_rates = calc_remediation_rates(south_america)\n",
    "remediation_averages.append(calc_average(sa_remediation_rates))\n",
    "\n",
    "asia_remediation_rates = calc_remediation_rates(asia)\n",
    "remediation_averages.append(calc_average(asia_remediation_rates))\n",
    "\n",
    "africa_remediation_rates = calc_remediation_rates(africa)\n",
    "remediation_averages.append(calc_average(africa_remediation_rates))\n",
    "\n",
    "australia_remediation_rates = calc_remediation_rates(australia)\n",
    "remediation_averages.append(calc_average(australia_remediation_rates))\n",
    "                            \n",
    "continents = ['Europe', 'North America', 'South America', 'Asia', 'Africa', 'Australia']\n",
    "\n",
    "data = {'Continent': continents,\n",
    "       'Remediation rates': remediation_averages}\n",
    "\n",
    "continent_df = pd.DataFrame(data, columns=['Continent', 'Remediation rates'])\n",
    "\n",
    "enc = LabelEncoder()\n",
    "enc.fit(continent_df['Continent'])\n",
    "continent_df['continent_encoded'] = enc.transform(continent_df['Continent'])\n",
    "\n",
    "corr_continent_df = pd.DataFrame.corr(continent_df, method='spearman')\n",
    "corr_continent_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:      Remediation rates   R-squared (uncentered):                   0.537\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.444\n",
      "Method:                 Least Squares   F-statistic:                              5.790\n",
      "Date:                Wed, 04 Nov 2020   Prob (F-statistic):                      0.0611\n",
      "Time:                        11:32:34   Log-Likelihood:                         -26.433\n",
      "No. Observations:                   6   AIC:                                      54.87\n",
      "Df Residuals:                       5   BIC:                                      54.66\n",
      "Df Model:                           1                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "continent_encoded     7.0444      2.927      2.406      0.061      -0.481      14.570\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   0.643\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.526\n",
      "Skew:                           0.373   Prob(JB):                        0.769\n",
      "Kurtosis:                       1.756   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/almagunn/anaconda3/lib/python3.7/site-packages/statsmodels/stats/stattools.py:71: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "#Linear regression of continental analysis\n",
    "X = continent_df['continent_encoded']\n",
    "Y = continent_df['Remediation rates']\n",
    "\n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "\n",
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GDP related analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>GDP 2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>19101353832.7371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>15278077446.8643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>169988236398.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>ASM</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>3154057987.23833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Series Name     Series Code    Country Name Country Code  \\\n",
       "0  GDP (current US$)  NY.GDP.MKTP.CD     Afghanistan          AFG   \n",
       "1  GDP (current US$)  NY.GDP.MKTP.CD         Albania          ALB   \n",
       "2  GDP (current US$)  NY.GDP.MKTP.CD         Algeria          DZA   \n",
       "3  GDP (current US$)  NY.GDP.MKTP.CD  American Samoa          ASM   \n",
       "4  GDP (current US$)  NY.GDP.MKTP.CD         Andorra          AND   \n",
       "\n",
       "           GDP 2019  \n",
       "0  19101353832.7371  \n",
       "1  15278077446.8643  \n",
       "2  169988236398.126  \n",
       "3                ..  \n",
       "4  3154057987.23833  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtain GDP data\n",
    "GDP_df = pd.read_csv(\"2019_GDP.csv\")\n",
    "GDP_df = GDP_df.rename(columns = {\"2019 [YR2019]\":\"GDP 2019\"})\n",
    "GDP_df = GDP_df.dropna()\n",
    "\n",
    "GDP_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc916c6850>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generating countries which appear in unique countries and spoofable data\n",
    "GDP_countries = GDP_df[\"Country Name\"].unique()\n",
    "\n",
    "unique_countries = list(remedies_count_df.index)\n",
    "\n",
    "unique_countries = [x for x in spoofable_country_list if x in unique_countries_remedies]\n",
    "\n",
    "unique_countries_short = []\n",
    "\n",
    "GDP_scores = []\n",
    "\n",
    "country_GDP_combo = []\n",
    "\n",
    "for unique_country in unique_countries:\n",
    "    for country in GDP_countries:\n",
    "        if country in unique_country:\n",
    "            unique_countries_short.append(unique_country)\n",
    "            GDP_curr_row = GDP_df.loc[GDP_df['Country Name'] == country]\n",
    "            GDP_curr_score = GDP_curr_row[\"GDP 2019\"].item()\n",
    "            GDP_scores.append(float(GDP_curr_score))\n",
    "            \n",
    "            country_GDP_combo.append((unique_country, float(GDP_curr_score)))\n",
    "            break\n",
    "\n",
    "unique_country_remediation = calc_remediation_rates(unique_countries_short)\n",
    "\n",
    "data = {'Countries': unique_countries_short,\n",
    "       'Remediation rates': unique_country_remediation,\n",
    "       'GDP': GDP_scores}\n",
    "\n",
    "GDP_short_df = pd.DataFrame(data, columns=['Countries', 'Remediation rates', 'GDP'])\n",
    "\n",
    "enc = LabelEncoder()\n",
    "enc.fit(GDP_short_df['Countries'])\n",
    "GDP_short_df['Country encoded'] = enc.transform(GDP_short_df['Countries'])\n",
    "\n",
    "GDP_corr_df = pd.DataFrame.corr(GDP_short_df, method='spearman')\n",
    "\n",
    "GDP_corr_df.head()\n",
    "\n",
    "f, ax = plt.subplots(figsize =(9, 8)) \n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(GDP_corr_df, ax = ax, cmap =\"YlGnBu\",linewidths = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "292444706937.8925\n",
      "72006516176.9143\n",
      "1014130587956.7915\n"
     ]
    }
   ],
   "source": [
    "#Compute the four grouping values\n",
    "higher_GDP_half, lower_GDP_half = compute_split(country_GDP_combo)\n",
    "low_GDP_quarter, lowest_GDP_quarter = compute_split(lower_GDP_half)\n",
    "highest_GDP_quarter, high_GDP_quarter = compute_split(higher_GDP_half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get remediation rates based on grouping\n",
    "lowest_GDP_quarter_remediation = extract_remediation(lowest_GDP_quarter, GDP_short_df, \"Countries\", \"Remediation rates\")\n",
    "low_GDP_quarter_remediation = extract_remediation(low_GDP_quarter, GDP_short_df, \"Countries\", \"Remediation rates\")\n",
    "high_GDP_quarter_remediation = extract_remediation(high_GDP_quarter, GDP_short_df, \"Countries\", \"Remediation rates\")\n",
    "highest_GDP_quarter_remediation = extract_remediation(highest_GDP_quarter, GDP_short_df, \"Countries\", \"Remediation rates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.8056518565165799, pvalue=0.4964484171683027)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One way ANOVA testing\n",
    "stats.f_oneway(lowest_GDP_quarter_remediation, low_GDP_quarter_remediation, high_GDP_quarter_remediation, highest_GDP_quarter_remediation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add grouping to the dataframe\n",
    "GDP_short_df['GDP group'] = ''\n",
    "\n",
    "GDP_short_df = set_group(GDP_short_df, lowest_GDP_quarter, \"GDP group\", \"Very low\")\n",
    "GDP_short_df = set_group(GDP_short_df, low_GDP_quarter, \"GDP group\", \"Low\")\n",
    "GDP_short_df = set_group(GDP_short_df, high_GDP_quarter, \"GDP group\", \"High\")\n",
    "GDP_short_df = set_group(GDP_short_df, highest_GDP_quarter, \"GDP group\", \"Very high\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERDgfBt5btY/k9FwAktNzeW/VPjbvTvDK85lMJgYPjmPFik8LtC9btog+fQYQGBiIy+Vi0qSHsVqtfPTREv797/kcOXKI2bNfyt9///699O8/kGXL1jB06N3Url2HdevW5G9fvfozunfvddEw8FuPP/4YSUlJvPfeh3z88VICAwN57LGHcLlctG/fkS1bvgPgl18OkJmZwY4d24DzH9ibN3/Lrbfe5qmh8QifB4JPPvmE5557jt69ezNnzhxCQoqf5mjfvj0Aa9b8782z2+18/fXX+dtERMT7Fn99CPvvZhjsDheLvz7ktefs128gp06d4KeffgDg9OkkNm/+ltjYOwDYt28PBw7s5+GHJxIYGERYWDhjxz7IihXLsNvtANhsftx+ew/8/Pzw8/Ojd+9+rFmzCoDs7Gw2bPiKPn36XbyA/zpxIp5du3byt789QlhYGIGBQTz00GP8+uth9u7dTadOndm6dTMAW7dupk+fAZw+fZqEhFP89NOPWCwWmjS5yVvDVCo+PWSQnJzM9OnTqVWrFsOHD2fPnj0FttetWxebzcbBgwepW7cu4eHh1KpVi8GDB/Pss8+SlZVFvXr1ePfdd0lLS+O+++7z0SsREbn6XJgZuNR2TwgNrUL37r1YvnwpN93UjE8/XUKrVq2pU6cuAKdOncLlchEb26fA48xmMwkJpwAICwsrsN6sV6++vP32G/z66xH27t1NrVp1aNiwcbF1nD2bAkB0dI38toCAAMLCwklMTKBTp86kpCQTH3+crVu/4847h3Ps2FG2b9/K0aO/0qlTl8te8+ZtPg0EGzduJDs7mxMnTjB8+PBC259//nmio6MZMWIEM2bMIDY2FoCnn36a0NBQ3nrrLbKysmjSpAnvvvsu9erprl0iIuUlItTvoh/+EaF+Xn3euLhh/PnPo/nrXx/hs88+zT8uDxAVFYW/vz+fffYFZvP5BYY5OTkkJiZQq1Ztfv75x0IfxJGRUcTEtOGLLz5n3749Jc4OwP+CwKlTJ7nmmmsByMrKIiUlmfDwCPz8/ImJacuGDV+xZ89umjVrwaFDB9m+fSsHDuzjoYce89RweIxPA8GgQYMYNGhQifvt37+/wL9tNhtTpkxhypQp3ipNRERKENu5Pu+t2lfgsIHNYiK2c+FTxj2pYcNGNGzYmJdffgGbzY82bdrlb7vxxqZER9fgX/+azf33j8PlcvHSSzPZvfsn5s//pMg+e/fuz7///X+cPn2aKVP+XmINkZFRtG7djtmzX2Lq1Kfx87Px8suzqFGjJjfffAsAt97ahddff4VGjRoTGBhI69Ztee+9d7BabTRv3rLM4+BpPl9DICIilVO7JtHc0/v6/BmBiFA/7ul9vdfOMvituLhhrF79GXFxQwt847dYLLzwwiskJp7ijjsGEBfXj/T0NGbNmp0/Y3AxnTp15uzZs7Rs2arIs9x+78knnyEyMpKRI+8kNrYvGRkZvPzyv/IXI7Zv35GMjHRatWoNQMOGjfH3D6BDh04lLlj0BcPt7as4VHA67bBy09j7jsbedzw19gkJR4mO1qHWC0aMGMb994/LP1XwYiwWU5lP1fS2ot7Xkk47rHgRRUREpBwdO/Yr27ZtJSsri3btOvq6HJ9RIBARkavazJnTiY8/zlNPPVshp/LLy9X7ykVERIDXX3/b1yVUCFpUKCIiIgoEIiIiokAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIpXEQw89wJNPTr7otnnz5jJ8+BC8ffHdtm1bsHPn9otu+/zzVfzhD0MvqZ8HH/wT77zzf54srcwUCEREpFKIixvKxo3rSU1NLdDudrtZvnwJsbFDfXpL4R49ejN//sc+e/6yUiAQEZFKoX37TkRGVmP16hUF2rds2UxaWiq9e5+/bXFy8hmeemoK/fv3YPDgPrzyyovk5uYAsHLlckaP/iN/+9uf6dWrC/PmvUvnzm1ITj6T39+2bVvo1687DofjonVs3fod99xzF927d2Ls2FEcO/Zrft9DhvTP32/VqhUMHTqQ3r27MnXqJKZMebTArMCJE/GMH3++jjvvHMw333ztkXEqLQUCEREpNfsvm8hc+AgZb40kc+Ej2H/Z5LXnMplMDB4cx4oVnxZoX7ZsEX36DCAwMBCXy8WkSQ9jtVr56KMl/Pvf8zly5BCzZ7+Uv//+/Xvp338gy5atYejQu6lduw7r1q3J37569Wd0796ryMsY//TTD7z88ussX76WwMCgAn1f8MMP3/PCCzN49NEpLF/+Oa1atWbDhq8K7LN9+1YeeOBvrFz5Jd279+LZZ58qMoSUBwUCEREpFfsvm8jdOBd3ZjIA7sxkcjfO9Woo6NdvIKdOneCnn34A4PTpJDZv/pbY2DsA2LdvDwcO7OfhhycSGBhEWFg4Y8c+yIoVy7Db7QDYbH7cfnsP/Pz88PPzo3fvfqxZswqA7OxsNmz4ij59+hVZw9ChdxMWFo6/vz+dO99GfPzxQvusWrWCLl26EhPTBovFwsCBsdxwQ5MC+3Tt2p2GDRtjMpno3r0XmZmZnD2b4pFxKg3dy0BERErFvm0ROOwFGx127NsWYWvY3ivPGRpahe7de7F8+VJuuqkZn366hFatWlOnTl0ATp06hcvlIja2T4HHmc1mEhJOARAWFlZgrUGvXn15++03+PXXI+zdu5taterQsGHjImsICQnJ/7vVar3ot/qkpCQaNSrYR82aNYvtB/DpDIECgYiIlMqFmYFLbfeUuLhh/PnPo/nrXx/hs88+5bHHHs/fFhUVhb+/P5999gVmsxmAnJwcEhMTqFWrNj///GOhhYeRkVHExLThiy8+Z9++PcXODlyq6tWj8wPIBYmJidSte02Z+/YWHTIQEZFSMYIjLqvdUxo2bETDho15+eUXsNn8aNOmXf62G29sSnR0Df71r9nk5OSQlZXFrFkzmDTp4WL77N27P19+uZYffthF9+69y1xjv34D2bDhK7Zt24LT6WT16s/4+ecfy9yvNykQiIhIqdhi4sBiK9hosZ1v97K4uGGsXv0ZcXEFTzW0WCy88MIrJCae4o47BhAX14/09DRmzZqdP2NwMZ06debs2bO0bNmKsLCwMtfXpElTHn74MZ577hn69u3G5s3fcsMNN+YfGqiIDLe3r+JQwSUnZ+JylW0IoqJCOH06w0MVyeXQ2PuOxt53PDX2CQlHiY6uV6Y+7L9swr5tEe7MZIzgCGwxcV5bP+BtI0YM4/77x9GpU5ci97FYTDgcrhL7OnbsKAB16/5vfEeP/iMDB8YyYMDgMtdanKLeV5PJICIiuMjHaQ2BiIiUmq1h+0obAC44duxXtm3bSlZWFu3adfRIn0eOHOKVV17kX/+aQ/Xq0WzYsJ4jRw7TqlVrj/TvDQoEIiJyVZs5czrx8cd56qlni7z2wOXq3LkrBw/+wrhxo8nMzKROnTpMn/48NWvW8kj/3qBDBjpkUKlp7H1HY+87FemQwdXmUg8Z+FJpDxloUaGIiIgoEIiIiIgCgYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiKVyJAh/Vm5cnmh9jVrVjJkSH8SEhLo3r0TCQkJJfb1zjv/x4MP/skbZVZKulKhiIhcMaKjo1m7dqOvy6iUFAhERKTUtibs5NNDqzmbm0qYX1UG1O9F6+gWPqvn1KmT3HHHAD755FNq1KjJ3r27eeml5zl69FeuvfY6mjW7hT17dvPaa28BkJ2dzXPPPcOmTd/gcrmIixvKvffe77P6fUmBQERESmVrwk4W7ltEnisPgLO5qSzctwjAq6HgxRefY/bsFwu0ORwOqlYteNvijIwMHn30bwwb9gfuuusP7N27m0cfHU+DBg3z9zlwYB933jmciROfYMeObTz00AO0adOOG29s6rX6KyoFAhERKZVPD63ODwMX5Lny+PTQaq8GgkcemUSfPv0LtK1Zs5K3336jQNumTRux2fz4wx/uwTAMbrqpGQMGDGbPnp/z97nuugZ0794LgFatWhMeHkF8/PGrMhBoUaGIiJTK2dzUy2ovb0lJSURFVcMwjPy2mjVrFtgnJCSkwL+tVisOh6Nc6qtoFAhERKRUwvyqXlZ7eatePZrExAR+e1PfxMREH1ZUsSkQiIhIqQyo3wuryVqgzWqyMqB+Lx9VVFDHjrfidDqZP38uDoeDffv2snz5El+XVWFpDYGIiJTKhXUCFeksg98KDAxkxoxZvPTSTN5//980aNCQ1q3bcfZsiq9Lq5AM92/nUq5CycmZuFxlG4KoqBBOn87wUEVyOTT2vqOx9x1PjX1CwlGio+t5oKKKKS0tlePHj9G06c35ba+99jLJyWd46qlnS9WnxWLC4XB5qkSvKOp9NZkMIiKCi3ycDhmIiMgVyeFw8Je/jGHHjm0AHDlymLVrV9GuXUcfV1Yx6ZCBiIhckSIiInnyyWf45z9fIDExgapVqzJs2B/o0aNirHGoaBQIRETkinXbbd247bZuvi6jUtAhAxEREVEgEBEREQUCERERQYFAREREUCAQERERFAhEREQEBQIREalEOnZsxc6d231dxhVJgUBEREQUCERE5MqxfPlShg8fQo8enRk1ajjffrsRgFdf/SeTJ0/I3++NN16le/dbcTgcAOzcuZ0BA3riclXs+xR4kwKBiIiUWvp3mzj82CMcuG8khx97hPTvNvmslpUrl/P666/w2GOPs2rVl9x77/1MnTqR3bt/5tZbu7Bz57b8ALB162ZcLie7d/8MwLffbqBjx1sxma7ej8Wr95WLiEiZpH+3icT35+JISQbAkZJM4vtzfRYKPvvsUwYOjKVZs+aYzWY6depC585dWbFiGTfd1Ayr1cbu3T+RkpLM6dNJdO3anR07tgLw7bcb6dy5q0/qrigUCEREpFTOLF6E224v0Oa22zmzeJFP6klJSaZGjRoF2mrUqEliYgImk4n27TuyZctmtm79jubNW9GqVWt27NjG4cOHSE09S8uWMT6pu6KoUIFg7969NGnShISEhGL3W7ZsGY0bNy705+mnny6nSkVE5MLMwKW2e1t0dA1OnjxZoO3EiXgiIiIA6NSpM1u2bGbbti20atWamJg27NnzM+vWraF9+05YLFf3/f4qzKs/fPgwY8aMyT++U5x9+/ZRr149nn/++QLtkZGR3ipPRER+xxIecdEPf0t4hFefNzU1laSkxAJtkZFR9O07gFmznqNDh1tp2vQmNm3ayIYNXzFr1mwAWrduy9NPTyUh4SSjRv2J8PAI6ta9ho8/XsjUqfpC6fNA4HA4+Oijj3jxxRexWq2X9Jj9+/fTpEkTbrnlFi9XJyIiRYmMjSPx/bkFDhsYNhuRsXFefd4nn5xUqG3ZstV069aTzMxMnn/+WZKSEqlZszZPPvlM/qEAPz9/WrVqzcGDB6lVqzZwPiQsXnyMNm3ae7XmysBwu91uXxawZcsWxo4dy+jRo6levTpPPPEEX3/9NdHR0UU+pn379owYMYKxY8eW+fmTkzNxuco2BFFRIZw+nVHmWuTyaex9R2PvO54a+4SEo0RH1ytTH+nfbeLM4kU4UpKxhEcQGRtHaNsr98PVYjHhcFTsUxOLel9NJoOIiOAiH+fzGYL69euzbt06IiIiWLx4cYn7JyUlkZyczJ49e+jVqxfHjx+ndu3ajBs3jkGDBpVDxSIickFo2/ZXdAC4mvg8EFzucf99+/YBEB8fz6OPPoqfnx9Lly5l4sSJOJ1O4uK8O1UlIiJyJfJ5ILhcTZs25c033yQmJobg4PNTHx07diQ5OZlXXnnlsgNBcdMnlyMqKsQj/cjl09j7jsbedzwx9klJJiyWCnWyWaVQ0cfMZDKV6uej0gWC8PBwbrvttkLtnTt3ZtOmTaSkpBAeHn7J/WkNQeWmsfcdjb3veGrsXS5XhT8eXtFUhjUELpfroj8fJa0hqNgx5yK+//57Pvnkk0Ltubm5WCwWQkL0jUVERORyVbpAsGvXLp544on8tQRwPg2tWbOGFi1aXPKpiyIiAj4+0Uw8rCzvZ4UPBCkpKezatYvMzEwAYmNjqVWrFg8++CArVqzgq6++YsyYMRw4cIAJEyaU0JuIiFxgNlvIy7OXvKNUGnl5djFo6PoAACAASURBVMzm0q0GqPCBYP369QwbNozdu3cDUKVKFebNm8fNN9/MjBkzGD9+PFlZWcydO5dmzZr5uFoRkcojOLgqqamnsdtzNVNQybndbuz2XFJTTxMcXLVUffj8wkS+pkWFlZvG3nc09r7jybHPzj5HZmYqTmfJl42X8yv4Xa6KuajQbLYQHFyVgICgi26v8BcmEhER3wkICCryA0QKu5KDcIU/ZCAiIiLep0AgIiIiCgQiIiKiQCAiIiIoEIiIiAilOMvAbreza9cu4uPjOXv2LGazmYiICGrUqMEtt9yCxaITF0RERCqbS/70/uqrr5g/fz5bt27F4XAUuoiFYRgEBATQtm1bhg4dSpcuXTxdq4iIiHhJiYFgw4YNTJ8+naNHj9KoUSNGjRpF48aNqVOnDsHBwbhcLlJTU0lMTGTXrl3s2LGDsWPHct111/Hwww/TrVu38ngdIiIiUgbFBoK//vWvbNu2jREjRtC/f39q165dbGd9+vQB4OjRoyxZsoTHH3+cpUuX8tprr3muYhEREfG4YhcV3nDDDXzxxReMGzeuxDDwW/Xq1WP8+PF88cUXXH/99WUuUkRERLxL9zLQvQwqNY2972jsfUdj7zuVeey9ei8Dp9PJkSNHyMvLo0GDBlit1rJ0JyIiIj5S4nUI1q5dyz333MOBAwcKtC9dupQOHTrQv39/YmNjad++Pe+//77XChURERHvKXaGYMGCBTzzzDNUrVqV9PT0/PaNGzcyadIk6tWrx5gxY3C73axYsYIZM2bg7+/P0KFDvV64iIiIeE6RawicTidt2rShZcuWzJ49Gz8/v/xto0ePZufOnaxZs4Zq1arl7z9ixAjOnDnDmjVryqd6D9AagspNY+87Gnvf0dj7TmUe+1KvIZgxYwaZmZmEhITw9ttvF9i2bds2atasyccff1ygPSgoiF27dvHaa6/Rpk0bYmJiyli+iIiIlIciA0F0dDSGYVC9enVq1aqV3/7999+Tl5dH165dC7QDHD58GMMwqFWrFiEhId6rWkRERDyqyEAwevRo3n77beLj45kwYQKGYWC32/nggw8IDg7mL3/5CwEBAfn7Z2Rk8M4773D99dczePDgcileREREPKPIQGAYBmPHjmXmzJn069ePxo0b88MPP3Dy5EkeeeSR/DCQlJTEvHnz+PTTT0lKSmLWrFnlVryIiIh4RrGnHd57771MnToVp9PJ559/jmEYTJgwgfvuuy9/H7vdzttvv01eXh5PP/00ffv29XrRIiIi4lkeuVLhvn37qF+/fqW8MJHOMqjcNPa+o7H3HY2971TmsffqlQov0P0KREREKrcSr1QoIiIiVz4FAhEREVEgEBEREQUCERERwQOBIC0tjdzcXE/UIiIiIj5SYiDIy8vjww8/ZPLkyQXat2/fTt++fWnbti3Nmzfnvvvu49ixY14rVERERLyn2EBgt9u55557+Pvf/86KFStwOBwA/Prrr4wePZrDhw/TqVMnRo4cyZEjR7jzzjs5c+ZMuRQuIiIinlNsIHjvvff4/vvvefTRR9m2bRsWy/nLFrz66qvk5ubSt29f3nrrLR577DEWLVqE2WzmzTffLJfCRURExHOKDQSrVq2iZ8+ejB49Gn9/f+D8rMGXX36JYRiMHj06f9+qVasSGxvL+vXrvVqwiIiIeF6xgeDo0aO0atWqQNuuXbvIzs4mKiqKG264ocC2unXrkpSU5PkqRURExKuKDQQulwuz2VygbfPmzQC0b9++0P4ZGRkFboksIiIilUOxgaBu3brs3bu3QNu6deswDIMuXboU2v+bb76hbt26Hi1QREREvK/YQNC3b1+WLVvGunXryM7OZu7cufzyyy9ERETQtWvXAvt++umnfPvtt9x+++1eLVhEREQ8r9i7HY4cOZKNGzfy4IMPYhgGbrcbq9XK9OnTsdlsAKxdu5b58+ezdetWrr32WkaOHFkedYuIiIgHFRsIbDYbc+fOZeXKlezatYugoCAGDBhAgwYN8vf5+eef2blzJwMGDGDSpEn5ZyOIiIhI5WG43W53WTrIzs7Gz88Pk6ly3hYhOTkTl6tMQ0BUVAinT2d4qCK5HBp739HY+47G3ncq89ibTAYREcFFbi92huBS6KwCERGRyq/YQPDaa68Vuc0wDPz8/AgNDaVhw4bcdNNN+VcyFBERkcql1IHgtwzDoEaNGjzzzDN06NDBI4WJiIhI+Sk2ELz//vvFPtjpdJKWlsb+/ftZsmQJDzzwAB9++CHXX3+9R4sUERER7yrzosIL0tPTGTx4MM2bN2fWrFme6LJcaFFh5aax9x2Nve9o7H2nMo99SYsKPXZqQGhoKIMHD2bnzp2e6lJERETKiUfPFaxZsyZnzpzxZJciIiJSDjwaCM6cOUOVKlU82aWIiIiUA48FgtzcXJYtW0aTJk081aWIiIiUk2LPMjh58mSxD3Y6nZw7d45ffvmF999/nyNHjvD44497tEARERHxvmIDQdeuXTEMo8RO3G43/v7+PP7447Rv395jxYmIiEj5KDYQDBo0qNhAYLPZqFKlCvXr16dDhw5ERkZ6vEARERHxvmIDwXPPPVdedYiIiIgPFbuo8MSJE2V+guPHj5e5DxEREfGuYgNBXFwczz77LImJiZfd8fHjx5k6dSpDhgwpdXEiIiJSPooNBMuWLSMpKYnbb7+dkSNHsmDBgiK/8bvdbvbv38+CBQsYPnw4PXr0IDk5mWXLlnmlcBEREfGcS7qXwbZt25g7dy7r16/H5XIREBBArVq1CA4Oxu12c/bsWZKSksjJycEwDLp27cqoUaNo0aJFebyGMtG9DCo3jb3vaOx9R2PvO5V57Eu6l0GxiwoviImJISYmhoSEBDZs2MDOnTs5fvw4qampmEwmatSoQcuWLWnbti0dO3YkPDzcYy9AREREvO+SAsEF0dHRDB06lKFDh3qlmL179zJkyBC++OILoqOji9zv3LlzzJo1i88//5ysrCxatWrF448/zjXXXOOVukRERK50Hr2XQVkcPnyYMWPG4HA4Stz3oYceYvXq1UyYMIGZM2eSmJjIiBEjyMionNM4IiIivubzQOBwOFiwYAFDhgwhNze3xP23b9/O119/zcyZMxk8eDA9evRg7ty5ZGRk8MEHH5RDxSIiIlcenweCHTt2MGvWLEaNGsWECRNK3P/bb78lKCiIDh065LeFh4cTExPDhg0bvFmqiIjIFcvngaB+/fqsW7eOBx98ELPZXOL+hw8fpl69eoX2rVu3LkeOHPFWmSIiIle0y1pU6A2Xe/+DzMxMgoMLnzYRFBREZmamp8oSERG5qlxSIMjLy+PgwYM4HA4aNGhAQECAt+sqUnGXTTCZLn/Co7hzMi9HVFSIR/qRy6ex9x2Nve9o7H3nSh37EgPB3Llzef311/O/fdtsNu6++24eeeQRLJbyn2AIDg4mPj6+UPu5c+cuOnNQEl2YqHLT2PuOxt53NPa+U5nHvkwXJlq6dCnPPfcctWrVYuDAgZhMJrZs2cLcuXNxOp1MmTLF4wWX5Nprr2Xz5s243e4Ct2Y+evQo1157bbnXIyIiciUodo594cKF3HLLLaxcuZInnniCKVOmsHTpUnr27MlHH32E3W4vrzrzdezYkfT0dDZt2pTflpKSwvbt22nfvn251yMiInIlKDYQHDp0iP79++Pn55ffZhgGI0eOxG63c/jwYa8XmJKSwq5du/IPWcTExNC6dWsefvhhPvnkE9auXcvIkSMJCQnhrrvu8no9IiIiV6JiA0F2djYhIYUXT9SuXRu32016errXCrtg/fr1DBs2jN27d+e3vfbaa3Tt2pXnn3+eSZMmER0dzdy5c6lSpYrX6xEREbkSFbuGwOVyFThOf8GFawA4nU6PFhMbG0tsbGyJbVWqVGHGjBnMmDHDo88vIiJytfL5hYlERETE90o8bzA1NZWTJ08WaEtLSwPOH9///TaAmjVreqg8ERERKQ+Gu5gr/Vx//fUXPWQAFDrtL79Dw2DPnj2eq9DLdB2Cyk1j7zsae9/R2PtOZR77Ml2HYPDgwR4vSERERCqeYgOBFu2JiIhcHbSoUERERC7t5kbZ2dksWrSIjRs3sm/fPlJTUzEMg/DwcBo3bky3bt3o378/NpvN2/WKiIiIF5QYCLZt28b48eNJTk7GZrNRt25datWqhcPhIDU1lfXr1/PVV1/x2muv8eKLL9KiRYvyqFtEREQ8qNhAcPDgQe677z6Cg4N5/vnn6dWrV6FZgMzMTFavXs3s2bO57777WLJkCfXq1fNq0SIiIuJZxa4heOuttwgICGDx4sUMGDDgoocEgoODGTJkCP/5z3/w8/Njzpw5XitWREREvKPYQLB161bi4uKoXr16iR1Vq1aNQYMGsWvXLo8VJyIiIuWj2ECQnJx8WdP/1113HadOnSpzUSIiIlK+ig0EeXl5BAQEXHJnfn5+nDt3rsxFiYiISPnSdQhERESkdDc3KsrZs2fLXJCIiIiUvxIDwT/+8Q/+8Y9/lEctIiIi4iO6uZGIiIjo5kYiIiKiRYUiIiLCJd7c6OjRoyxevJhx48bh7+9Penr6RQ8nTJ48mW7dunm8SBEREfGuEmcIFixYQL9+/Xjrrbf44YcfAHA6nZw4cYLg4GBq1qxJzZo1SUlJYdq0aeTm5nq9aBEREfGsYgPBrl27eOaZZ4iJiWHNmjW0adOmwPZJkyYxb9485s2bx7Rp0zh9+jRLlizxasEiIiLiecUGgvfee4/atWvz5ptvUrdu3WI7GjBgAI0bN2bt2rUeLVBERES8r9hAsH37dgYOHHjRuxxeTPfu3dm7d69HChMREZHyU2wgSE1NpVatWoXaAwICuPfeewtti46OJjMz07MVioiIiNcVe5ZBREQEqamphdr9/f2ZOHFiofbTp08TGRnpuepERESkXBQ7Q9CgQQPWr19/yZ2tW7eOpk2blrUmERERKWfFBoLY2Fi2bt3KsmXLSuxo4cKF7Nmzh7i4OI8VJyIiIuWj2EMGvXv3ZvHixUyePJnvvvuOMWPGcM011xTY5/jx48ydO5cPPviAHj160LlzZ2/WKyIiIl5QbCAwDIOXX36ZJ598kiVLlrB06VKioqKIjo7G7XZz5swZEhIScLvd9OnTh2eeeaa86hYREREPKvHSxcHBwbz00ksMHz6c5cuXs23bNg4dOoTL5aJatWoMGjSIAQMG0K5du/KoV0RERLzgku5lANCyZUtatmzpzVpERETER3S3QxEREVEgEBEREQUCERERQYFAREREUCAQERERFAhEREQEBQIRERFBgUBERERQIBAREREUCERERAQFAhEREUGBQERERFAgEBERERQIREREBAUCERERQYFAREREUCAQERERFAhEREQEBQIRERFBgUBERERQIBAREREUCERERAQFAhEREUGBQERERKgggWDFihX07duXm2++md69e7N06dJi91+2bBmNGzcu9Ofpp58up4pFRESuLBZfF7Bq1SomTJjAiBEj6NSpE+vWrWPixIn4+/vTq1eviz5m37591KtXj+eff75Ae2RkZHmULFcwq9UMQF6e08eViIiUL58HgpdeeonevXszZcoUADp16kRaWhqvvPJKkYFg//79NGnShFtuuaU8S5UrmNXsJsDIJmvfFkx+gVS5rjmZTn+cTpevSxMRKRc+PWRw/Phxjh07Ro8ePQq09+zZk8OHD3P8+PGLPm7fvn00bty4PEqUq4BhGPi7Mjj51njOfvEeySvf4NT7Uwgy5/i6NBGRcuPTQHD48GEArr322gLt9erVA+DIkSOFHpOUlERycjJ79uyhV69eNGnShJ49e5a47kCkKH4WSN+8BLfDnt/mTD9D7rHdWCwVYpmNiIjX+fSQQUZGBgDBwcEF2oOCggDIzMws9Jh9+/YBEB8fz6OPPoqfnx9Lly5l4sSJOJ1O4uLivFy1XHncuPNyC7W68uyAUf7liIj4gE8DgdvtBs5P2V6s3WQq/O2sadOmvPnmm8TExOQHiY4dO5KcnMwrr7xy2YEgIiK45J0uQVRUiEf6kcvnibG3tRvMub2bgf/+TPoFEtywOZaQwDL3fSXTz73vaOx950ode58GgpCQ84P6+5mAc+fOFdj+W+Hh4dx2222F2jt37symTZtISUkhPDz8kmtITs7E5XJfTtmFREWFcPp0Rpn6kNLx1Nj7+YdTY9TzZGxbgeEXRGjrfmTk2XDofS2Sfu59R2PvO5V57E0mo9gvwT49QHph7cCxY8cKtB89erTA9t/6/vvv+eSTTwq15+bmYrFYLhoiREqS6zCTZatGQOdR+LW9kwxnIA6deSgiVxGfBoJ69epRu3ZtVq9eXaD9888/55prrqFmzZqFHrNr1y6eeOKJ/LUEAC6XizVr1tCiRQusVqvX65Yrk8vlJsfuJtfuwl22SSMRkUrH59cheOCBB5g8eTJVqlShS5cufPnll6xatYp//vOfAKSkpHDs2DEaNGhAcHAwsbGxzJs3jwcffJDx48cTFBTEwoULOXDgAAsWLPDxqxEREamcfH5OVWxsLNOmTeObb77hgQceYOvWrcycOZM+ffoAsH79eoYNG8bu3bsBqFKlCvPmzePmm29mxowZjB8/nqysLObOnUuzZs18+VJEREQqLcPtvronR7WosHLT2PuOxt53NPa+U5nHvkIvKhQREZGKQYFAREREFAhEREREgUBERERQIBAREREUCKSCM5mMQve6EBERz/P5hYlELsZsNggy5+JIOYlh9cMUEklmno2r/CxZERGvUSCQCscwINjI5tTcSTgzUwHwq9GAyLiJpNt1aWoREW/QIQOpcGwWg/RtK/LDAEDuqYPYEw5hNutHVkTEG/TbVSocEy4caYmF2p1ppzGZtJ5ARMQbFAikwrG7zAQ371mw0WQmoH4L8vJ0T2IREW/QGgKpcJxOF6aIa4ga9BDpWz7FsAUQ1mU4uaZAcPm6OhGRK5MCgVRI5xwWLDWbU3XQDWCYyHbbcDqUBkREvEWBQCosh8OFA9t//6UwICLiTVpDICIiIgoEIiIiokAgIiIiKBCIiIgICgQiIiKCzjIoN2azCT8jB5PbjcPkR06erysSERH5HwWCcmA1g39uEslr3saRlkjQDR0JbTuI9FwN/5XGbDYIMuXizsvGZPUj120lx2H2dVkiIiXSJ1I5CDDbOTl/Km57NgDp21aAyYRfq1hyNVNwxTCZDIJcaSTMe/L8jZkME2Gd7yLgxtvIdnjuv5phnL8BlIGLPJcZp1PXaBCRstMaAi8zDANn2un8MHBB1t7NWF25PqpKvMHflEfK5+/87y6Nbhdn1y/E6rZ77DksFhOhllzs2/7DuXVv4nf2IIEWh8f6F5Grl2YIvMztdmMOqlKo3RIejcvQVPKVxIQT+5njv2t14zyXihEQiNtd9ucINOVwau4knJlnAcjav4WoQQ9hqdkchy7tLCJloBmCcuAw+RPaZmD+v00BwYR3H02Oy+rDqsTTHIaNwIatCrQZVn/MoZEeCQMmk4Ej5VR+GLggbcun2Nw5ZX8CEbmqaYagHGQ5LAS27E9Ii564cjIxBVUly+mHy+mBTwmpMHIdBqEd7sCdZ+fcvu+whlUnovc4sp2eCX5uN5istkLtJqv/+YUFIiJloEBQTrIcFsCCYQvk/CFlhYErjdsNGXYrAbeOIPTWu3G5IQd/j03lu91ujJBIbDXqYz916HyjYaJql+HkuP3QDaBEpCwUCMqZJ6aOpeJyuyEnz0SOl+7SeM5hIypuEvZTB3GkJRLYoBW5RqDONBCRMlMgEKlEXC436XYr5mpNMEU3JcPhxK0sICIeoEAgUgk5nS6cTl9XISJXEp1lICIiIgoEIuI9NqtBoDkPP51hK1Lh6ZCBiHicYUCIzcG5XZ+Teeh7bLUaUaXNQDIcfrhcWlkrUhEpEFxFzGYTZrOBw+HSL2XxKn+zk9Qv5nJuzzcA5J78BfuJA4QPeoxMXZBLpELSIYOrRLA1D7+UX3DuWk5A1gld/168ymo4OLd3U4G23JO/YHLrbl4iFZVmCCowm82MYRjY7U7cZbiAQaDFQcaGBWT+tP58wzcfE3b7SPwadyH3Ks8FhmEQYLZjwQmGQa7bRq5DV/0rKzdg8g/ElZ35v0aTBUwm0NkRIhWSZggqILPZoIotF9fuNdi3fESwOxU/c+lPNreQR+ZPXxdoS/3mY2yG7rYYYrWTtvJVTvxrLCff+huuQ5sIsOgTq6xy8Se826gCbVU7xGJ363CBSEWlGYIKKMicW+COdunbPqPGyOfI86tehmP/v3uc08HV/j3YzwoZW5aR8+uPALjzckhZ8za1xtxMNiE+rq5ys+e58a/TjFpjXyX31CFsUfVw2kLIcugOnyIVlWYIKhiz2URu/P6Cd7Rzu0jbtAhbKWcJnFgIuLZZgbaQlr2wU/hGOVcTs8tOzvHdhdrtp49iNuu/RlnlOMyku0Jw1GxBpjnsv/fzEJGKSv9DK4syrCHIcloJ7/cXsvd/R+7xPQQ0bout9o1k2K/uOQKnyYb/NbfgzDiL81xqfrstqh455XBvAJPJuCrO9tB9FkQqBwWCCsbpdBFUuzHm4Ko4M//7IWWYqNJhCFlOE6W5S6LbDem5FqwNbyWwYSccboMMu35Jm90OQpvdRkCdxpgDQzi7aSmB9ZuTZw4ALy62DLQ4sDizcaSexhpRi2yXFbtTMxIi4lsKBBXQOacfNUbO5NzPG3GeSyX4lm7YraG4HGX7NpmX5+L8SV8V/1up2WzCMPDYrYN/L9DiIOu7T8jYuQYAwxZAzRHPkucfSbbde+MTYHGS++MaEr/55HyDyUL1u6biqnKt116riMil0NeSCsjpdJNm98PUtCe2tneSaQoj13F1vFVms0GoLQ9r4k+Yj28n1JqLxez5QxsWZ3Z+GABw27NJXvsuhsu758lbjTxSv130vwaXg5RV/4c/OV59XhGRkmiGoAKz2y//9Der1YxhlO6xFUGQ2U7igqk4ziYAYPILpMaoWWQYgWVZRlGAYRg4s9ILtTtSkzDcTrz638Jh5/f3K3akn8G4updziEgFcHV87bwKmM0mQm12jCObcf28hlDTOWzmin9o4LcsFhO5x37ODwMArtws0rd8is3iuU9Mt9uNuUoUJv+gAu1BTW/FYfL32PNc9Lkt/liqVCv4vE06kudWNhcR39JvoStEkDmHxHlP4EhLAuDsxg+pee8LOCzhlWYlu2EYBa9s91+u7AwMD697yHL6Ef3H6Zxd9y55qYkENelE8C09Sffi+gGALJc/1e/+O6lfzcee9CsBjWIIielPhl3ZXER8S4HgCmA2m7CfPJgfBgBwOkj79hMCu95PjqtyzEfn5TkJbdgK46t5uB32/PaQ1v3Idhp4cjGkw+nmnDmM0N5/wXA7cZj8vR4G4PxZJJmmIAK7jibE7cBh8iNdF4wsltVqxjCB0+HWKYwiXqRAcAUwDHA77YXaz3+ouqESXZMw2+1PjXufJ23TItz2bELbDsYZVK3MZ1hcjMvlJstlASzgLL9ZFJfLTY7LDJh1Xf9iGAYY/k6+T/qZPacP0KZWC66tUgdXjq52KOINCgRXAIfDRVCdGzEFBP9myt0gtF0sOS4L4LlvVRaLKf85vSHPaeA0hxHYZRTgJsdlwanT8a5ONidv71zIzlM/AfD1r98Rd2MfutftwkXyr4iUkQLBFSLT6UfNUbNI374KV1YqIa364vCP8NgUq9ls4LLl8fOZXwC4IbIhJrsVpxe+WZ//Bm1wfmaj5PrNNgMneVjdNvLyFB6uFE7DkR8GLlhx4Atuv7Yj+tUl4nn6X3WFcDrdpLv8scbEYeAmywEuD35Yu2x2Jq97jrTcDACq+Icy4/aJkO27u9eZTAYp2aksPbiGI6nHaVOrOe1rt8KZrQV6VwLjIoe6TBiV4bpaIpWSfnOWkclkkJaTgcMvG7e/HcPv8g8Km80mbDYLJlPZjvW73WC3u8i1uz16ZoHNZuHro9/lhwGAtJx0vjm2HZvNd8dz3bY8nl0/m9UH17P/zCHe/+E/fHrgc8xX9z2bimQyGZXqegcmt5l2tVsWaBt0Qy9MTn2PEfEG/c8qK38HMza8weGzxwDoUDeG4U1jcV/CwqcLi6YOnj3EwbNHial5M2F+YbhzK9aiKcOAjNxzhdoz7JkYPvyEsbvziE8/VaDtiyPf0q9hN/Sj/T8mixuXNY9f005SLSiCAFNghfsZuxh3rpkRzYbQvm4r9p7+hZhazageUA2nzsoQ8Qr91iwDq9XEuiMb8sMAwLfHtnH7tR2oYa1V8rd0Pyf//v5Dtp38AYCle1dzX4u7iIlqgSPv4o81m024rHZyXLnYTFZMLgvuyziH3WwxcFnyyHXZ8TPbMBxmXHnFf6jb7U66XdeR1QfX4/rvVfbMhonbrm2P3e7FuwCVwGIq/KEWagvC7aFLGprNJlwul8eukOgLFouJU/aTPLPuFZyu87NXfRp2pV/97rjtFT8UuLLNNApuyA1VGuNwOHHmlv3NMJtNWCwmnE43DodO8xC5QIGgDFyGi0NnjxZqP3L2OLVr1MHlKvqXjeHvxO7OzQ8DF3yyewXNb78J8gq/NYYBDmsOz3z9MonnzgDQo/6tDGrU+5K+8ZnNJjJI5dkvZ5OWk47NbOXPMffQuErDYkOB2+3G3x3EjG6TWLJ3NQYw6IbeBLgD8eU1j0xOCz0a3MrnBzcA5485j2w+FIvLD0cJixFNFnBbHeQ4cvC3+BcIRobNhcOwcyj1OLVCaxBgBFSKD8+LcZhzeef7D/PDAMDKX76kT6OuGHj/NVmsJvJMdnC7sbr8SnV2isPh8thZLSY/J/Hn4tl8aCfXRzagaVRj3DnmSh36RDxFgaAMTG4zbWu3ZMfJgiuhb46+odhfYIbNyYe7l9K9QSeuDatDzxqtHVuVQgAAIABJREFUCTT78W3ybnafPkhRl+AxWeHjPZ/lhwGAzw9toNt1nQg2qpT4S81psfP65rmk5Zy/jr/dmcfr297jlV5/h7zCiwMN43yIcDpduB0GoaYwRja9EwC3w8D1u1kMs9nAZc3D7s7DarJgOM2XNXtx/jmN/357c5U4w+K2mxjWtD+3XdOe+PRTNIq47vyHTglnGpgtBkl5icz46jWy83Kwmiz8pc0oGoY0wGQY7Ek9wOzv3sX933dhWNP+dKndEZe9Eh2Av8CAlKzUQs25Djv+eHexhWFzsjt1P4v+v707D6/h+h84/p67JTcrEUtJiC12SSwhdkHR2lvSNI0uWqVKUWqplrbU1mobUS2+lFajaKv5ae1FQ1FLrbV8FUlsCSHIdrc5vz/S3LoSW2Qh3/N6Hs8jn9nOPXPnzmfOnDnz189oFA1P132Smp7V7/s7UVB0BoUtZ3cRc2gVAJtObSPosfq8EhgBj8AtFOl/j6JQpMmq7FT4ACwWG/XL1qZPnS4Y9c6UNnryevALuGru3Gxt09j4LX4XwmxhxGNPUGbJRgzRK+h9pQLvNR+MQc07T7NiJeHauVzxC2nJaDQajIoVV1sGLlnXcMGM9pa3BCoaiL9leYvNgimPh7qdFSvOmdcw79+Dc3oqzooVVRXYzGAzk+tkrShgM5iZsi2K4Wsm8vovE1h35tf76mTprFhxSkshfdtWdJfPY1TufjvC3ckNT1GGhqUaoDMZEZa7f6VtWjPRf3xFpiX7DYMW1crnuxej6i1YtWYW/rncngwArPzrF1Rt8d0aeRBaVU9rv2YOsTIupTFq8//OBr1Og4swYb6ail6fd31rNAop5itE7fwP565fJPHaeT7ZMZ9US+oDd57NL5vGwqpjax1if144jO0evmcFTVHASSswqiacFVux1Yn0cNJpBG6Y0V8+j6stA2elaG5tPRQtBKtXr2bu3LkkJiZSqVIlXn31VXr16nXb+dPT0/noo49Yv349GRkZNGnShLfffhs/P7+iK/Q/1EwNfeo9QYeqbUCI7OZq852vUFVhQ6/VU9qq4++JHyJs2Ts7c+HX1PAYiqFBKUx5XL0ZhIHmPo0d+ixoFA3VSlfByWTi7OIlXP4tDgBjpUrUee9d0nD6dwU2hYDyddh/8S97yN3JDSetk0OLhEEruLFzF6e/nG+PVXmhPx5t2mO6zTDIGj2sOLqGc9ezX0wkhOCnY+tp59cCD4MrqlAw3+HK3aCFG7v+4PQX8+yxx3p0x7tnL0zizldvQgis9zOSoQLJN7WyAGRZTVhUK3qNjjSzYwdKm2rDptrQUnyPWOaXzQy9/DvjbnBl59l9+Hg8RniDXmithrveVsmLUbGStmcfJ5evRNEo+IT1w6VhAFm3vJxJr9ey9cSOXMtvS9hFnxrdMZke7CSsKMp99xXJHrOz+E+8igKuWDi3bAVX9+7D1a8KfgNeIsvZ/aEemllRFIyYUcwmUBSE3kAmenm7pYDpdBq0l86z/51JqFnZFy2+Ec9SKrTDXX8LH1SxtxCsWbOGUaNG0bJlS+bMmUNwcDBjxoxh7dq1t11mxIgRrF27llGjRjF9+nSSkpLo378/N27cuO0yhcmg1UOWDkz6uzZXA+jR08K3MTcOH7UnAwCKTkdGfALW9DT7iIA3s1hU2lRuxpP+objqXajoXp5xrV/HoBqwJCfZkwGAzHPnuLj6Z4e3BGpVAwMbhdOgXG0UFHw9KzK53ZtorAa0Og2KoqAoCgZsnF2+wmHbid8uQ3dLS4JGo9ivbKxYMeqM9KvThzZVQjBo9bxUpyfOCUlc+M8Crq35GTfMaLV5f+X0VhMJX3/jELuw+mf0quWu9XnfhIY6ZWs4hMq6lkGv6NEJLc0rBTlMq1a6MgblHvpo6LQIrQah0eS5/4qLLVNDR992jAkZQv96/dCZnPN1T16jUbAmXeDv6M8xJSeTdTGJk5/NRr18KdcVrqoKfD0q5VqHr0fFB3okVqdVcFPM6JISMGZdw8i9fz90qp6+9brRtXpHXm80iH51etPOLwTtfb5pUqvV4OSku+13+W6csBG/aBFJ69ZjvnyZq3v28tfE93C2ZeVrfUXFVTFzff8BUi0KqWa4tn8/rsXQulLS6S1ZnPr8C3syAJAYswy9Wvh1XewtBLNmzaJr166MHz8egNatW3Pt2jU+++wzunTpkmv+PXv2sHXrVubPn0+bNm0AaNKkCR06dCAmJoaBAwcWaflvpSggNBrMVhWzRcXNqEeL40tZFIueZxv0xnziJEn/xNzq1qXsK4PYffo6mngLzRso9vv3N7NlauherQtP1OgAQkFnM4AKmYlnc5Ul/fQZylgt5OxmvTWL89HzeLZxA4yNO6F1Ls2RIzfYcOAk5b1cCOtYC6tq49CZ61QbP4msXb9zeeVyAFSzGYQKSnZfAZui4XxKOqBQsYwLiqLD6Uod9hxLobpvQ95v0xnDyf9ybPp79vIk/7qZulMnk55H64eiKKhZtzxPpqoIVaWg+75pLDqGBL/Agr0xHEk+TtXSlRnUNBKtRY+zYuWFBr0p71yaAyknqOFRmT51OuNi1XCndFOj1/L91r/ZtDuR0u5OvNq7AT7eLogifEfCnVjNKgr6f9oE8lcmvV5L8uatueIpcXF4hz3rcNVvsdgIrhTIplPb7Le5/Er5EFihPpbM/DV/ajQK+rSrHBg7Hlt6BgBlQ9tT8dlnybyH1hubRdCsQnOWHTzOwqMXqVrRg1d6tUBrU+65tUTRaTl57hoHTl6mSZ3y+JZzQ9znkwpaYcN87TpVJk/FajCiV2xcW/MLwmwCw8M5iIZeryUty8Je58r8sOQgKNCvQ02aWFR0TppCG8r8f5EGQdbFi45BVcWWlQlGp7wXKiDFmhAkJiaSkJDAyJEjHeKdO3dmzZo1JCYm4uvr6zBt+/btuLq60rJlS3vMy8uLpk2b8ttvvxV7QoBWy4KfDvP7oezn48t7ufDh4JZobuocoqoCNV3Bw9cPz8AAbvx1lLKvDGLkwgOkZ2Zf8Xy7/gSfjWybZwNn9oV69g+gFRVFAY/69XL1QCnTqiU2nQH+OVi1CFL37oM9eynVtAmnQ3ry5S/ZQxEf/juFPUeTGPd8MLNi9gMwvFcgj4UkcnXHDjwCGmJGl33iVjSMn7udiynZP8oDetTj9Plr/LonOyk5ciqFE/HXGRbs7lBuU3Iy5uRLKOV8cjX3WjU6yoa2J3nDRnvMo349VG3BN9OrqkBrcmJgYCRCo4JQ0Fp12cMwCwsJH35MI9+KBNcKwHbwPGe+eo9670+E27QS6PRaNu49S+xvpwBIz7Qwcf5O5o3tgKYEDaunqgKX6tVgg2PcpWrVvK/6s3SMbfk6Nyw3UFBw07uBSUd+ExKDsBC/8Ct7MgBw6dfNVOzdC4yed1+BRsOCVUfYfvA8ACnXskhM2sHUwS3uafuKVkPM+uOs/yP7ll1s3Cn6htbkyRZVUO/jhKhotRj7D2Tc0kOkpplw0msZ2qMTlZ1dCvK1IwVKp9MSf93Klz8etsfmrDzI9NdbUdlNKxOCAmTV6inTIoTkTZvtMX2pUmiMLoW+7WJt1zx1KvsHtGrVqg7xKlWqAHD69Ok8l6lSpQpareOPc+XKlfOcvyhpNAoXr2TYkwGApCsZrNp6Eo0u98kkXeipNmwoQXM+Y8uJa/ZkACAt08Kve89iMNw9ZxMCrE4u1B4/FueKj6Hz8MCnX188Gzd2OFCtKHjUrQOAc3AI6/5McljPtTQzV69n4eWR3eFs0cZTePbsjXePnnj0f5mf9yXh4qIn7sB5ezIAUMOnFFv3OXZWPHbmKppyFXLXkS7vE0KWqsEn/Bn8Br5MqcAAfJ99hhojh5OlFM4Vk6qK7N7uWTowaR3eyZB24r+kbNpK0ucLufzzWrIuXEC13L5p2mxT7SeZm9d/8mxqvpuVH0YWi43SwU1xq/nv7Rb32rXwDArEYsl9layqApGlxV0thZvqicjSPtDtAsVmw5R8KXe5Uq/eU6c8oSjsOOw4kFXSlQxM93gyU4GNuxMcYj/99vd9vygzDT1RsSdITctuETNZbMyOPU6m5uHto6LVKmz5M3eH5rj950rUd/xhYBYafCKepXzXLhi8vPAMaEi9ye+TpS3c1gEo5haCnHv+bm5uDnFXV1cA0tLSci2TlpaWa/6cZfKavyhpNArnknOXISEpLc+XAAkB6UKHk7MzWZbcP3Sm+xj0xyy06GrWofZ77wECm96J9FsWN2sMVB/2Ov/9aBYiIx03l9xXVS5GvX27aRkW1FLerDPWYev8ffR/sg5CQNIVx053JrMNF2cdNzL+PWlqNAp6NxeHVgvXqlXReXlxu7Fl0lQdri3a4NEsBFWrI80qKOqB621oMPr6kpmYaI/p3N3AcPuDUadR8Kvgwd9nrznEH/N2RVVL1pVTBgZqjB2DyMxAq9GgOjmTLgzcaT8VVKczm8EZ73ZtSVz6rT2mNTrj/FhF0u4h0RCqoHxpFy6k/Pv91WoUDDoN3GNnvlu3kr3Z++yoqCjEX7zuEDJZbJjMKk4P6bnVZlOpVtEjV7xqRU/ZOlDAhIB09JTrF0aF3r0RGi1ZGn2Bvpvmdoo1IchpNr51+NucuEaT++i4U8/ivOa/mzJlcicX+VG2bHbzeIA/aBQcBuwJbeKDt5fLHYf57Rrix//FncL6zw+TXqehc3M/PD2N91kS17tMd6PuO28jhMqA64LR0dvs22xY05u0DDPpWdkJQZugSmzZf54Ne89TrrSR1oE+uLo68XgzP37efsa+xl/3JvJit3pELd9vjz3VvgauRgNB0Z9xedvvGCtWwLNhAwylSlHQDV85dV9Qar/1Jn+9PwXTpUvoPDyoPXY0zqU9cdHd/nCJ6FKbw6dSSLqS3XLSrWVVypZywd314bwn/OC87P+72zeuILl07oQiVJJ/3YJTWW+qvjIAp9KeGO+wb3IIIRj2TCDvfrkDizX7VtuL3evh7uKE0fnuy6dlmmkdWInfbrpSfqKlH+6uBoxO9/5TeiPDTGDNsvx54t+LAE83Ay5Gvb117l4U9Pf+blo0rMjWfec4nnAVgLpVvWhcuxzu7s64u+f/MdZHUVHXfVFRREGN85oPW7Zs4dVXXyU2NpZatWrZ40eOHKFPnz4sXLjQoa8AwLBhwzh79iw//PCDQ3zy5MmsW7eOuLg47kdKStoDvwiobFl3Ll3Kbu1QtBoSL6Xxn9gjpGVaeKKFHx0a+6LepeORRqMhw6oSG/c3CtCjdXWMeg1qIT6GpNVpsaiCvceSKe3hTNWKHqzfeYZDp1IIrluB0Ca+JFy8gRCCyuXd0YjszpHZnzGdZRuOA/Ds47WpVNaVTJOVY/FXqVbJEw8XPdiyf3T1+uym4sK4kri57guKVqPgpJrAYkHR6TDrnMijRdxxGa0GK9mtJQa9Fq0C4iF+hKwgFEbd3wuDDrRmE0KjxaK9v1dwa7QaVEXh0tVMvDyd0d3nftLotRw8mcL+/16iWb0K+PuWuu9OhRqNgk1RiF5xgP3/vUSVCh6MeCYITxfdPV8FFkfda/+puwyTFUVRcHbSolXFQ/2oZGEoru99QdBolDteBBdrC0FO34GEhASHhCA+Pt5h+q3L7NixAyGEwxV3fHx8nvMXNWFT8SvnzrsvNUOQ/Xy99W5nE0BVVYxahYhO/gBYLWqhJgMANqsNnUYhpH4FrKoAIejRuhpdmvuh14DVbKWyd/b1vGq1kfMpsj+jK6PCG4ECOgWsVhtGnUITf29sNmH/kc1+A+OjNV68TRVkYAD9P1f391B8m01FAZy1SvbTEYVawv9tZiug+ecWzn02o+YcU+U8DIh87CfVYiOwehka+Xuj2tR8vQtBVQVaDQzrG4D455Za9jH0cJ9Yc078Lv88yiwstns5NKRHSLHesapSpQo+Pj65xhxYv349fn5+VKxYMdcyrVq14vr16/z+++/22JUrV9izZw8tWtxbb+HCZrXaUFQVjare07gEOYQQWMw2LGZbgb2g525UVWA1W8FqA5uKxWRFUf8dO15V836VstWqgqqC7d95hciOF2OjkyTdkwf5ilqtNswm6wOdwFX1n6T5n+PuYU8GbiZE0Q6nKxWdYh+HYMiQIYwbNw5PT0/atWvHr7/+ypo1a/jkk0+A7JN9QkICNWrUwM3NjaZNmxIcHMzIkSMZNWoUpUqVYvbs2bi7uxMeHl7Mn0aSJEmSHk3FnhD06dMHs9nMwoULWbFiBb6+vkyfPp0nnngCyO5nMG7cOJYsWUKzZtljskdHRzNt2jRmzJiBqqo0btyYTz/9FE/Pe3gWWZIkSZKkXIq1U+HDoKA7FUpFS9Z98ZF1X3xk3RefR7nu79ap8CF96lWSJEmSpKIkEwJJkiRJkmRCIEmSJEmSTAgkSZIkSUImBJIkSZIkIRMCSZIkSZJ4CMYhKG738trUolyPdP9k3RcfWffFR9Z98XlU6/5u5f6fH4dAkiRJkiR5y0CSJEmSJGRCIEmSJEkSMiGQJEmSJAmZEEiSJEmShEwIJEmSJElCJgSSJEmSJCETAkmSJEmSkAmBJEmSJEnIhECSJEmSJGRC8EBWr17Nk08+ScOGDenatSurVq0q7iKVOKqqEhMTQ/fu3QkKCqJjx45MnTqVtLQ0+zyHDh0iMjKSoKAgWrVqxaxZs7BYLMVY6pLp9ddfp1OnTg6xbdu28dRTTxEQEEBoaCgLFy4sptKVTLt37yY8PJyAgABatWrFBx98QHp6un26rP/CExMTQ9euXQkMDKR79+7ExsY6TC+Jda+dNGnSpOIuxKNozZo1vPnmm3Tv3p3BgwdjMpn49NNPqVmzJjVq1Cju4pUY8+fPZ8aMGfTp04dXX30VPz8/Fi9ezL59++jZsyfx8fGEh4fj4+PDmDFj8PPzY+7cuVy5coW2bdsWd/FLjJ9++okvv/wST09P+vfvD8C+ffsYMGAAwcHBDB8+HHd3d6KiojAajTRq1KiYS/zo279/P88//zz169dn7Nix+Pv789VXX3Hy5Em6dOki678Qfffdd0ycOJHevXvz2muvoaoqM2fOpEaNGtSsWbPk1r2Q8qVjx45i+PDhDrE33nhDdOnSpZhKVPKoqiqaNm0qJk2a5BD/+eefhb+/v/jrr7/E+PHjRdu2bYXJZLJPX7p0qahTp464ePFiURe5RLp48aJo2rSpaNOmjejYsaM9/vzzz4u+ffs6zDtjxgzRpEkTh/0h5U9ERISIiIgQqqraY998843o0KGDyMjIkPVfiMLCwkRkZKRD7NlnnxXPPfecEKLkfvflLYN8SExMJCEhgccff9wh3rlzZ06dOkViYmIxlaxkSU9Pp0ePHnTr1s0hXq1aNQASEhLYvn077du3x2Aw2Kd36dIFm83Gtm3birS8JdWECRNo2bIlISEh9pjJZGLPnj15HgPXr19n3759RV3MEuXKlSvs2bOH8PBwFOXfN9RFRESwceNGNBqNrP9CZDKZcHV1dYiVKlWK1NTUEv3dlwlBPpw6dQqAqlWrOsSrVKkCwOnTp4u8TCWRm5sbEyZMoHHjxg7xjRs3AlC9enUuXLiQaz94eXnh5uYm90MBWLFiBUeOHOGdd95xiCcmJmKxWOQxUEhOnDiBEAJPT0+GDx9OYGAgjRs3ZuLEiWRlZcn6L2T9+/cnLi6ONWvWkJaWxtq1a9myZQs9e/Ys0XWvK+4CPIpu3LgBZJ+wbpaTUd7c4U0qWAcOHGDevHl07NgRDw8PIPd+gOx9IffDgzl37hxTp05l6tSpeHl5OUyTx0DhunLlCgBjx46lU6dOzJ07l+PHj/Ppp59iMpkICwsDZP0XlieffJKdO3cyfPhwe6x37968/PLL/Pnnn0DJrHuZEOSDEALAoSnv5rhGIxteCsPevXsZNGgQPj4+TJ48GbPZDOTeD5C9L+R+yD8hBOPHj6dt27Z07tw5z+mQd92DPAYeVM5TMo0aNWLixIkAhISEIIRg+vTp9OvXD5D1X1gGDx7Mn3/+ybhx46hbty4HDhzg888/x83NjSeeeAIomXUvE4J8cHd3B3JngjmPA+VMlwrOL7/8wtixY/Hz82PBggWULl3aXt95ZeQZGRlyPzyApUuXcvz4cf7v//4Pq9UK/JsEWK3W2x4DOX/Lun8wOVebbdq0cYi3atWKadOmcejQIUDWf2HYt28f27ZtY+rUqfTp0weA4OBgPDw8ePfdd3n66aeBkln3MiHIh5x7RwkJCdSqVcsej4+Pd5guFYxFixYxffp0goODmTNnjv2Ac3V1pXz58vZ6z5GSkkJaWprcDw9g3bp1XL16lVatWuWaVq9ePSZNmoRWqyUhIcFhWs7fsu4fjJ+fH4C9FSxHTsuBj4+PrP9Ccv78eYBcjw82adIEgKNHj5bYun902zaKUZUqVfDx8WHt2rUO8fXr1+Pn50fFihWLqWQlz4oVK5g2bRpdu3ZlwYIFubLvli1bsnnzZocfznXr1qHVagkODi7q4pYY7733HitXrnT41759eypUqMDKlSvp0qULTZo0Yf369faWA8iue3d3d+rXr1+MpX/0Va9enUqVKvHLL784xDdv3oxOpyMoKEjWfyHJOaHv3r3bIb5//34g+ymnklr3cmCifHJ3d2fu3LlcvXoVRVFYtGgRP/74IxMnTqRmzZrFXbwSISUlhZdffpny5cvz5ptvkpKSwsWLF+3/DAYDdevWZeHChezZswdPT0+2bNnCzJkz6du3L927dy/uj/DIKl26NOXLl3f4t23bNpKTkxk1ahRGo5EKFSrwxRdf8Pfff2M0Glm1ahXz589n6NChNGvWrLg/wiNNURTKli3LokWLOHPmDG5ubqxZs4Y5c+YQGRlJp06dZP0XknLlynHs2DG+/fZbnJ2dMZlMrFu3jo8//piQkBBeeeWVElv3irg5xZHuy7Jly1i4cCEXLlzA19eXgQMH0qtXr+IuVomxatUqxowZc9vpM2bMoGfPnuzZs4cZM2Zw9OhRSpcuTa9evRg6dCh6vb4IS1vyjR07lr1797JhwwZ7bMOGDURFRXH69GnKly9PREQEL730UjGWsmTZuHEjc+bM4eTJk5QpU4awsDBeffVVe8c1Wf+Fw2w2Ex0dTWxsLCkpKVSqVIlu3boxcOBA+5gnJbHuZUIgSZIkSZLsQyBJkiRJkkwIJEmSJElCJgSSJEmSJCETAkmSJEmSkAmBJEmSJEnIhECSJEmSJGRCIEmFYuzYsdSqVSvXv4YNG9K+fXvGjx/P5cuXi7uY9+Xs2bPUqlWL2bNn22O1atVi7Nix+VpfYmKiw9+RkZGEhoY+UBmLQ1pamv3thJL0KJPvMpCkQjRu3DhKly5t/zstLY0dO3bw/fffc/jwYVauXGkf6ORRNGPGDCpXrnzfy33//fe89957HDx40B4bNGgQmZmZBVm8Qnf48GEGDx7MRx999EiPUCdJIBMCSSpUHTt2xMfHxyEWERHBpEmTiImJYePGjfbXqT6Kevbsma/ldu/ejclkcoi1bNmyIIpUpE6cOEFycnJxF0OSCoS8ZSBJxaB3794AHDhwoJhLIkmSlE0mBJJUDIxGIwC3jhy+efNmnnnmGQICAmjatClDhw7l9OnTDvPUqlWL+fPnM2/ePNq1a0dAQACRkZHEx8dz+vRpBgwYQGBgIKGhoSxZsiTXtn/44Qd69epFgwYNaN68OWPHjs11lWu1WomOjiY0NJSAgACef/55Ll68mGtdt/YhsFgsfPnll/To0YPAwEAaNmxIjx49WLlypX2eyMhIfvzxx1zL59WH4Pjx47z22ms0adKEhg0b0q9fPzZu3OgwT2RkJAMGDOC3336jT58+NGjQgHbt2jF79mxUVc17B9yy7CeffEJQUBAhISEcP34cgLVr1/Lcc8/RuHFj6tevT2hoKDNmzLC/WXP27NmMGzcOgP79+zuU/eLFi7z11ls0b96cBg0a0KtXL2JjYx22LYQgOjqazp0706BBA1q0aMHo0aO5cOHCHcssSYVF3jKQpGIQFxcHQN26de2xH374gfHjxxMSEsLo0aO5du0aMTEx9OvXj+XLlzu8Z/3rr7/GaDTy0ksvkZKSwoIFCxg6dCipqam0a9eOTp06sWLFCqZMmULt2rXtr4KOjo5m9uzZdO7cmX79+pGUlMQ333zDH3/8wcqVK/Hy8gJgwoQJ/Pjjj3Tr1o1GjRoRFxfHoEGD7vq5xo0bx5o1awgPDycyMpKrV6+yfPly3n77bSpXrkxwcDCDBg1CVVX7S6lu1wfh4MGD9O/fHzc3N1588UVcXV356aefGDJkCO+++y4RERH2eU+cOMHw4cMJCwsjLCyM1atXEx0djZeXl8N8edm3bx/x8fGMHj2as2fPUqNGDVasWMGECRMIDQ1l1KhRWCwWNmzYwH/+8x9cXFx4/fXX6dSpE5cuXeK7775j0KBBNGjQAICkpCT69u2LEILIyEg8PT3ZtGkTo0ePJjk5mZdffhmAL774gjlz5hAREUGtWrU4e/YsS5Ys4fDhw6xevRqtVnvX+pakAiUkSSpwY8aMEf7+/uLIkSMiJSXF/i8+Pl588803IjAwUHTt2lWYzWYhhBA3btwQjRo1EiNGjHBYT3JysmjatKl47bXX7DF/f38REBAgLl26ZI8NGzZM+Pv7i5kzZ9pjZ86cEf7+/mLWrFlCCCESEhJE7dq1xUcffeSwjePHj4t69eqJKVOmCCGEOHbsmPD39xeTJ0/O8zNFRUU5lGXMmDH2staqVSvX+v/++2/h7+8vPvjgg1zrutlzzz0n2rdvb/+7b9++IjAwUFy4cMEey8rKEr179xYNGzYUKSkp9uX8/f3Fpk2bHOZr2rSpCAsLE3eSs+zOnTsd4l26dBFhYWFCVVV7zGKxiDZt2ohu3brZY99//32u5ceMGSOCg4NFUlKSwzpHjhwp6tfAaN1nAAAHRklEQVSvLy5fviyEEKJr165i4MCBDvPExMSIHj16iPj4+DuWW5IKg2whkKRClNNX4GZGo5HQ0FDeeecd+yuat2/fTlpaGh07dnR4hE2r1dK8eXO2bt2K1WpFp8s+ZIOCgvD29rbP5+fnB0CnTp3ssZzOjDm3AzZs2ICqqoSGhjpsw9vbmzp16rBlyxbGjx9vb7145plnHMrdv39/e1N/XsqWLcvevXvtr+aF7GZxq9UKQHp6+m2XvdXly5c5cOAA4eHhVKhQwR53cnJiwIABjBw5kt9//51u3boB2XXarl07h/mqVq16T492Ojs707RpU4dYbGwsmZmZKIpij6WkpODh4UFGRsZt16WqKhs3bqRZs2bodDqHen788cdZvXo127dvp0ePHlSoUIFdu3axePFinnzySby9vXnmmWdy1bskFRWZEEhSIZo5cybe3t5YLBbi4uJYunQpXbt2ZdKkSTg5OdnnS0hIAGDEiBG3XdeVK1coV64cAGXKlHGYlpMo5DT5A/YmZ/FPP4WcbdzuhJOTnJw7dw4AX19fh+nVqlW700cFwGAwEBsby7Zt2zhz5gzx8fH2REDcx5vWc8pw822SHNWrVwfg/Pnz9lipUqUcEpGcstytD8HtltXr9ezevZvVq1dz6tQpEhISSElJAaBSpUq3XdfVq1e5ceMGGzduzNXXIUdOH4G33nqLwYMH8+GHHzJ16lTq1atHaGgo/fr1o2zZsncttyQVNJkQSFIhatSokf1KvW3btlSpUoXJkyeTmprK559/br8CzTlxffDBB7keU8zh6elp/39OAnCrm69ob5Wzjblz5+Ls7Hzb+XLWYTKZHMZIuNvJ1Ww2M2DAAPbu3UuzZs0ICQnhhRdeIDg42OHq/V7cKXnIKUdOAgPkOqHfj7zu1X/88cfMmzePunXrEhgYSM+ePQkKCuKDDz64Y6c/m80GQOfOnW+beOUkWrVr12bdunXExcWxefNm4uLiiIqK4quvvmLZsmX2xEeSiopMCCSpCEVGRrJjxw42bdrE4sWLeeGFF4B/rzq9vLxo0aKFwzK7du1CVdUHHsAoZxuPPfYYderUcZi2detW3NzcgH9PWGfOnLF3lIPcIwve6pdffuGPP/5gypQpPP300/Z4UlJSvst66tSpXNNynrq4+VZCQTp37hzz5s2jZ8+ezJgxw2Ha3W5BeHl5YTQasVqtufbj+fPn+euvvzAajdhsNo4dO4abmxsdOnSgQ4cOQHYdjhgxghUrVuR7BEhJyi/52KEkFbH3338fT09PPv30U/tJtkWLFjg5ObFgwQIsFot93qSkJF577TU++uijO17934v27dsD8OWXXzpcgR89epTBgwezePFiADp06IBWq2XRokUOyy9duvSO609NTQWgRo0aDvGcRx9z+hLAv1f0t2t1KFu2LPXr1yc2NtbhcUez2cyiRYswGAyFNpDRtWvXgNyfY+vWrZw5c+aOn0On09GmTRu2bt3KsWPHHJafNm0aQ4YM4erVq9hsNvr378+HH37oME9AQIDDeiWpKMkWAkkqYt7e3owaNYp33nmHiRMnsnDhQry8vBg5ciRTp04lLCyMHj16YLVa+fbbbzGZTIwZM+aBt+vv709kZCRff/01qampdOzYkdTUVL755htcXV154403AKhcuTIvvvgiCxYsICMjg9atW7N3715+//33O66/RYsW6HQ63nrrLSIiItDpdGzevJlt27ah1+sdOhXm9HWIioqy31641YQJE3j++ed5+umnCQ8Px9XVldjYWI4cOcKECRPw8PB44DrJS40aNahYsSJffPEFJpOJChUqcPDgQX788UecnJzy/BwxMTFcvnyZ7t27M2rUKHbt2kVERAQRERFUrFiRLVu2sHnzZsLCwqhZsyaQ3Vo0d+5chgwZQuvWrcnKyuK7777DaDTy1FNPFcpnk6Q7kQmBJBWDvn37smrVKrZv386qVavo1asXL7zwAuXLl2fRokV88sknODs7U69ePWbOnEnjxo0LZLtvv/021apVY9myZUyfPh13d3eaNGnCG2+84XDPevTo0ZQrV46lS5eyfft26taty7x58+jbt+9t1+3v709UVBTR0dHMmjULV1dXatasyaJFi/j222/5448/sFgs6PV6wsPD2blzJwsWLODQoUN5JgRBQUHExMQQFRXFwoULUVWV2rVrM2fOHDp27Fgg9ZEXg8HAvHnzmDZtGkuWLEEIQeXKlRk/fjxWq5UpU6Zw+PBh6tevT0hICF27dmXz5s3s3LmTxx9/nMqVK7N8+XKioqJYvnw5GRkZ+Pr6Mm7cOCIjI+3bGTZsGKVKleL7779n+vTpaLVaGjVqxMyZM2X/AalYKOJ+uv5KkiRJklQiyRtVkiRJkiTJhECSJEmSJJkQSJIkSZKETAgkSZIkSUImBJIkSZIkIRMCSZIkSZKQCYEkSZIkSciEQJIkSZIkZEIgSZIkSRIyIZAkSZIkCfh/uN1y+0q5gMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize distribution in a scatterplot\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.set(font_scale=1.25)\n",
    "sns.scatterplot(x='Remediation rates', y='GDP', hue='GDP group', data=GDP_short_df) \n",
    "\n",
    "plt.ylabel(\"GDP (US $)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the distribution without the states\n",
    "GDP_exUS_df = GDP_short_df.loc[~(GDP_short_df['Countries'] == 'usa (United States)')]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.set(font_scale=1.25)\n",
    "sns.scatterplot(x='Remediation rates', y='GDP', hue='GDP group', data=GDP_exUS_df) \n",
    "\n",
    "plt.ylabel(\"GDP (US $)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IDI score related analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc91511990>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set up did database and visualize correlation\n",
    "IDI_scores_df = pd.read_csv('IDI_2017_Rank.csv',sep=';')\n",
    "IDI_countries = IDI_scores_df['Economy'].unique()\n",
    "\n",
    "unique_countries_remedies = list(remedies_count_df.index)\n",
    "\n",
    "unique_countries = [x for x in spoofable_country_list if x in unique_countries_remedies]\n",
    "IDI_countries_short = []\n",
    "unique_countries_short = []\n",
    "\n",
    "IDI_scores = []\n",
    "IDI_scores_combo = []\n",
    "\n",
    "for unique_country in unique_countries:\n",
    "    for country in IDI_countries:\n",
    "        if country in unique_country:\n",
    "            #IDI_countries_short.append(country)\n",
    "            unique_countries_short.append(unique_country)\n",
    "            IDI_curr_row = IDI_scores_df.loc[IDI_scores_df['Economy'] == country]\n",
    "            IDI_curr_score = IDI_curr_row[\"IDI 2017 Value\"].item()\n",
    "            IDI_scores.append(IDI_curr_score)\n",
    "            IDI_scores_combo.append((unique_country, IDI_curr_score))\n",
    "            break\n",
    "\n",
    "unique_country_remediation = calc_remediation_rates(unique_countries_short)\n",
    "\n",
    "data = {'Countries': unique_countries_short,\n",
    "       'Remediation rates': unique_country_remediation,\n",
    "       'IDI scores': IDI_scores}\n",
    "\n",
    "IDI_scores_df = pd.DataFrame(data, columns=['Countries', 'Remediation rates', 'IDI scores'])\n",
    "\n",
    "enc = LabelEncoder()\n",
    "enc.fit(IDI_scores_df['Countries'])\n",
    "IDI_scores_df['country_encoded'] = enc.transform(IDI_scores_df['Countries'])\n",
    "\n",
    "corr_IDI_df = pd.DataFrame.corr(IDI_scores_df, method='spearman')\n",
    "\n",
    "f, ax = plt.subplots(figsize =(9, 8)) \n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(corr_IDI_df, ax = ax, cmap =\"YlGnBu\", linewidths = 0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'IDI score')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make histograms to evaluate distribution\n",
    "seed(1)\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, tight_layout=True)\n",
    "\n",
    "axs[0].hist(GDP_short_df['Remediation rates'])\n",
    "axs[0].set_title('Remediation rate')\n",
    "axs[1].hist(GDP_short_df['GDP'])\n",
    "axs[1].set_title('GDP')\n",
    "axs[2].hist(IDI_scores_df['IDI scores'])\n",
    "axs[2].set_title('IDI score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.13\n",
      "4.157826086956522\n",
      "7.593225806451613\n"
     ]
    }
   ],
   "source": [
    "#Compute the groupings\n",
    "higher_IDI_half, lower_IDI_half = compute_average_split(IDI_scores_combo)\n",
    "low_IDI_quarter, lowest_IDI_quarter = compute_average_split(lower_IDI_half)\n",
    "highest_IDI_quarter, high_IDI_quarter = compute_average_split(higher_IDI_half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get distribution based on groupings\n",
    "lowest_IDI_quarter_remediation = extract_remediation(lowest_IDI_quarter, IDI_scores_df, \"Countries\", \"Remediation rates\")\n",
    "low_IDI_quarter_remediation = extract_remediation(low_IDI_quarter, IDI_scores_df, \"Countries\", \"Remediation rates\")\n",
    "high_IDI_quarter_remediation = extract_remediation(high_IDI_quarter, IDI_scores_df, \"Countries\", \"Remediation rates\")\n",
    "highest_IDI_quarter_remediation = extract_remediation(highest_IDI_quarter, IDI_scores_df, \"Countries\", \"Remediation rates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.4030108154678864, pvalue=0.751452859917694)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One way ANOVA tests\n",
    "stats.f_oneway(lowest_IDI_quarter_remediation, low_IDI_quarter_remediation, high_IDI_quarter_remediation, highest_IDI_quarter_remediation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add column for grouping to database\n",
    "IDI_scores_df['IDI group'] = ''\n",
    "\n",
    "IDI_scores_df = set_group(IDI_scores_df, lowest_IDI_quarter, \"IDI group\", \"Very low\")\n",
    "IDI_scores_df = set_group(IDI_scores_df, low_IDI_quarter, \"IDI group\", \"Low\")\n",
    "IDI_scores_df = set_group(IDI_scores_df, high_IDI_quarter, \"IDI group\", \"High\")\n",
    "IDI_scores_df = set_group(IDI_scores_df, highest_IDI_quarter, \"IDI group\", \"Very high\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create scatterplot of distribution\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.set(font_scale = 1)\n",
    "sns.scatterplot(x='Remediation rates', y='IDI scores', hue='IDI group', data=IDI_scores_df) \n",
    "\n",
    "plt.ylabel(\"IDI scores\")\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}