04_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How can a country be more generous?\n",
    "\n",
    "In the previous analysis, we showed that `Generosity` is the best area the UAE could improve.\n",
    "\n",
    "In this survey, a person was considered generous depending on how she answer the three following questions:\n",
    "\n",
    "In the past month do you\n",
    "- helped a stranger, or someone they didn’t know who needed help?\n",
    "- donated money to a charity?\n",
    "- volunteered your time to an organisation?\n",
    "\n",
    "But, how to create public policies that elevate the generosity of a population? \n",
    "\n",
    "I believe that the first step is to identify the countries by their generosity. Thus, this notebook will use Time Series Clustering to identify the groups of countries by their generosity trend.\n",
    "\n",
    "Finally, it will show the UAE trend of generosity among all groups. We hope this clustering analysis help policymakers to identify countries with efficient public policies that could be reproduced in the UAE.\n",
    "\n",
    "source: https://www.cafonline.org/docs/default-source/about-us-publications/caf_worldgivingindex2015_report.pdf?sfvrsn=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import silhouette_score\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ts(df, group, must):\n",
    "    random.seed(2021)\n",
    "\n",
    "    group_df = df.loc[df[\"group\"] == group].copy()\n",
    "    group_df.drop(columns=\"group\", inplace=True)\n",
    "    group_df[\"avg\"] = group_df.apply(lambda r: np.mean(r), axis=1)\n",
    "    \n",
    "    # selects the top 5 countries in a group\n",
    "    countries = group_df.sort_values(by=\"avg\", ascending=False) \\\n",
    "                        .index \\\n",
    "                        .tolist()[:5]\n",
    "    \n",
    "    group_df.drop(columns=\"avg\", inplace=True)\n",
    "\n",
    "    # adds UAE to the chart\n",
    "    if must in countries:\n",
    "        countries.remove(must)\n",
    "    else:\n",
    "        must_df = df.loc[[must]]\n",
    "        must_df.drop(columns=\"group\", inplace=True)\n",
    "        group_df = pd.concat([group_df, must_df], axis=0)\n",
    "    if len(countries) > 4:\n",
    "        sample = random.sample(countries, k=4)\n",
    "    else:\n",
    "        sample = countries\n",
    "    sample.append(must)\n",
    "        \n",
    "    group_df = group_df.reset_index() \\\n",
    "                   .melt(id_vars=\"Country name\", var_name=\"Year\", value_name=FEATURE) \\\n",
    "                   .set_index(\"Country name\")\n",
    "\n",
    "    plt.figure(figsize=(12,7))\n",
    "    for s in sample:\n",
    "        temp = group_df.loc[s]\n",
    "        plt.plot(temp[\"Year\"], temp[FEATURE], label=s)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "UAE = \"United Arab Emirates\"\n",
    "FEATURE = \"Generosity\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/prepared.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>year</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Logged GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.983</td>\n",
       "      <td>7.702</td>\n",
       "      <td>0.529</td>\n",
       "      <td>53.200</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.220</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.559</td>\n",
       "      <td>53.000</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.662</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.491</td>\n",
       "      <td>52.800</td>\n",
       "      <td>0.427</td>\n",
       "      <td>-0.121</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.694</td>\n",
       "      <td>7.692</td>\n",
       "      <td>0.508</td>\n",
       "      <td>52.600</td>\n",
       "      <td>0.374</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.375</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.420</td>\n",
       "      <td>52.400</td>\n",
       "      <td>0.394</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>2021</td>\n",
       "      <td>3.512</td>\n",
       "      <td>7.926</td>\n",
       "      <td>0.787</td>\n",
       "      <td>48.700</td>\n",
       "      <td>0.715</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>2021</td>\n",
       "      <td>3.467</td>\n",
       "      <td>9.782</td>\n",
       "      <td>0.784</td>\n",
       "      <td>59.269</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2021</td>\n",
       "      <td>3.415</td>\n",
       "      <td>7.676</td>\n",
       "      <td>0.552</td>\n",
       "      <td>61.400</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021</td>\n",
       "      <td>3.145</td>\n",
       "      <td>7.943</td>\n",
       "      <td>0.750</td>\n",
       "      <td>56.201</td>\n",
       "      <td>0.677</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>2.523</td>\n",
       "      <td>7.695</td>\n",
       "      <td>0.463</td>\n",
       "      <td>52.493</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>962 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country name  year  Ladder score  Logged GDP per capita  Social support  \\\n",
       "0    Afghanistan  2015         3.983                  7.702           0.529   \n",
       "1    Afghanistan  2016         4.220                  7.697           0.559   \n",
       "2    Afghanistan  2017         2.662                  7.697           0.491   \n",
       "3    Afghanistan  2018         2.694                  7.692           0.508   \n",
       "4    Afghanistan  2019         2.375                  7.697           0.420   \n",
       "..           ...   ...           ...                    ...             ...   \n",
       "957      Lesotho  2021         3.512                  7.926           0.787   \n",
       "958     Botswana  2021         3.467                  9.782           0.784   \n",
       "959       Rwanda  2021         3.415                  7.676           0.552   \n",
       "960     Zimbabwe  2021         3.145                  7.943           0.750   \n",
       "961  Afghanistan  2021         2.523                  7.695           0.463   \n",
       "\n",
       "     Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                     53.200                         0.389       0.080   \n",
       "1                     53.000                         0.523       0.042   \n",
       "2                     52.800                         0.427      -0.121   \n",
       "3                     52.600                         0.374      -0.094   \n",
       "4                     52.400                         0.394      -0.108   \n",
       "..                       ...                           ...         ...   \n",
       "957                   48.700                         0.715      -0.131   \n",
       "958                   59.269                         0.824      -0.246   \n",
       "959                   61.400                         0.897       0.061   \n",
       "960                   56.201                         0.677      -0.047   \n",
       "961                   52.493                         0.382      -0.102   \n",
       "\n",
       "     Perceptions of corruption  \n",
       "0                        0.881  \n",
       "1                        0.793  \n",
       "2                        0.954  \n",
       "3                        0.928  \n",
       "4                        0.924  \n",
       "..                         ...  \n",
       "957                      0.915  \n",
       "958                      0.801  \n",
       "959                      0.167  \n",
       "960                      0.821  \n",
       "961                      0.924  \n",
       "\n",
       "[962 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specific Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index([\"Country name\", \"year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Logged GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country name</th>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <th>2016</th>\n",
       "      <td>5.341</td>\n",
       "      <td>9.362</td>\n",
       "      <td>0.749</td>\n",
       "      <td>65.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <th>2020</th>\n",
       "      <td>6.260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.806</td>\n",
       "      <td>74.1</td>\n",
       "      <td>0.763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong S.A.R. of China</th>\n",
       "      <th>2020</th>\n",
       "      <td>5.295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <th>2019</th>\n",
       "      <td>7.533</td>\n",
       "      <td>10.931</td>\n",
       "      <td>0.982</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Iran</th>\n",
       "      <th>2018</th>\n",
       "      <td>4.278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.674</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>5.006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.698</td>\n",
       "      <td>66.3</td>\n",
       "      <td>0.623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4.865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.757</td>\n",
       "      <td>66.6</td>\n",
       "      <td>0.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kosovo</th>\n",
       "      <th>2020</th>\n",
       "      <td>6.294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <th>2020</th>\n",
       "      <td>6.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.938</td>\n",
       "      <td>72.2</td>\n",
       "      <td>0.931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">North Cyprus</th>\n",
       "      <th>2015</th>\n",
       "      <td>5.843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5.827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5.608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>5.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Palestinian Territories</th>\n",
       "      <th>2018</th>\n",
       "      <td>4.554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>4.483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <th>2015</th>\n",
       "      <td>6.375</td>\n",
       "      <td>11.486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Somalia</th>\n",
       "      <th>2015</th>\n",
       "      <td>5.354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.599</td>\n",
       "      <td>50.1</td>\n",
       "      <td>0.968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4.668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.594</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">South Sudan</th>\n",
       "      <th>2015</th>\n",
       "      <td>4.071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.585</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2.888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.532</td>\n",
       "      <td>50.6</td>\n",
       "      <td>0.440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2.817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.557</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Taiwan Province of China</th>\n",
       "      <th>2018</th>\n",
       "      <td>6.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6.537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>6.751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Venezuela</th>\n",
       "      <th>2018</th>\n",
       "      <td>5.006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.887</td>\n",
       "      <td>66.5</td>\n",
       "      <td>0.611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>5.081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.888</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4.574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.805</td>\n",
       "      <td>66.9</td>\n",
       "      <td>0.612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <th>2017</th>\n",
       "      <td>5.175</td>\n",
       "      <td>8.876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yemen</th>\n",
       "      <th>2018</th>\n",
       "      <td>3.058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.789</td>\n",
       "      <td>56.7</td>\n",
       "      <td>0.553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>4.197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.870</td>\n",
       "      <td>57.5</td>\n",
       "      <td>0.651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Ladder score  Logged GDP per capita  \\\n",
       "Country name              year                                        \n",
       "Algeria                   2016         5.341                  9.362   \n",
       "Cyprus                    2020         6.260                    NaN   \n",
       "Hong Kong S.A.R. of China 2020         5.295                    NaN   \n",
       "Iceland                   2019         7.533                 10.931   \n",
       "Iran                      2018         4.278                    NaN   \n",
       "                          2019         5.006                    NaN   \n",
       "                          2020         4.865                    NaN   \n",
       "Kosovo                    2020         6.294                    NaN   \n",
       "Malta                     2020         6.157                    NaN   \n",
       "North Cyprus              2015         5.843                    NaN   \n",
       "                          2016         5.827                    NaN   \n",
       "                          2018         5.608                    NaN   \n",
       "                          2019         5.467                    NaN   \n",
       "Palestinian Territories   2018         4.554                    NaN   \n",
       "                          2019         4.483                    NaN   \n",
       "Qatar                     2015         6.375                 11.486   \n",
       "Somalia                   2015         5.354                    NaN   \n",
       "                          2016         4.668                    NaN   \n",
       "South Sudan               2015         4.071                    NaN   \n",
       "                          2016         2.888                    NaN   \n",
       "                          2017         2.817                    NaN   \n",
       "Taiwan Province of China  2018         6.467                    NaN   \n",
       "                          2019         6.537                    NaN   \n",
       "                          2020         6.751                    NaN   \n",
       "Venezuela                 2018         5.006                    NaN   \n",
       "                          2019         5.081                    NaN   \n",
       "                          2020         4.574                    NaN   \n",
       "Vietnam                   2017         5.175                  8.876   \n",
       "Yemen                     2018         3.058                    NaN   \n",
       "                          2019         4.197                    NaN   \n",
       "\n",
       "                                Social support  Healthy life expectancy  \\\n",
       "Country name              year                                            \n",
       "Algeria                   2016           0.749                     65.5   \n",
       "Cyprus                    2020           0.806                     74.1   \n",
       "Hong Kong S.A.R. of China 2020           0.813                      NaN   \n",
       "Iceland                   2019           0.982                     73.0   \n",
       "Iran                      2018           0.674                     66.0   \n",
       "                          2019           0.698                     66.3   \n",
       "                          2020           0.757                     66.6   \n",
       "Kosovo                    2020           0.792                      NaN   \n",
       "Malta                     2020           0.938                     72.2   \n",
       "North Cyprus              2015           0.791                      NaN   \n",
       "                          2016           0.808                      NaN   \n",
       "                          2018           0.837                      NaN   \n",
       "                          2019           0.803                      NaN   \n",
       "Palestinian Territories   2018           0.819                      NaN   \n",
       "                          2019           0.833                      NaN   \n",
       "Qatar                     2015             NaN                     68.3   \n",
       "Somalia                   2015           0.599                     50.1   \n",
       "                          2016           0.594                     50.0   \n",
       "South Sudan               2015           0.585                     50.2   \n",
       "                          2016           0.532                     50.6   \n",
       "                          2017           0.557                     51.0   \n",
       "Taiwan Province of China  2018           0.896                      NaN   \n",
       "                          2019           0.893                      NaN   \n",
       "                          2020           0.901                      NaN   \n",
       "Venezuela                 2018           0.887                     66.5   \n",
       "                          2019           0.888                     66.7   \n",
       "                          2020           0.805                     66.9   \n",
       "Vietnam                   2017             NaN                     67.7   \n",
       "Yemen                     2018           0.789                     56.7   \n",
       "                          2019           0.870                     57.5   \n",
       "\n",
       "                                Freedom to make life choices  Generosity  \\\n",
       "Country name              year                                             \n",
       "Algeria                   2016                           NaN         NaN   \n",
       "Cyprus                    2020                         0.763         NaN   \n",
       "Hong Kong S.A.R. of China 2020                         0.705         NaN   \n",
       "Iceland                   2019                         0.959         NaN   \n",
       "Iran                      2018                         0.603         NaN   \n",
       "                          2019                         0.623         NaN   \n",
       "                          2020                         0.600         NaN   \n",
       "Kosovo                    2020                         0.880         NaN   \n",
       "Malta                     2020                         0.931         NaN   \n",
       "North Cyprus              2015                         0.785         NaN   \n",
       "                          2016                         0.796         NaN   \n",
       "                          2018                         0.797         NaN   \n",
       "                          2019                         0.793         NaN   \n",
       "Palestinian Territories   2018                         0.655         NaN   \n",
       "                          2019                         0.653         NaN   \n",
       "Qatar                     2015                           NaN         NaN   \n",
       "Somalia                   2015                         0.968         NaN   \n",
       "                          2016                         0.917         NaN   \n",
       "South Sudan               2015                         0.512         NaN   \n",
       "                          2016                         0.440         NaN   \n",
       "                          2017                         0.456         NaN   \n",
       "Taiwan Province of China  2018                         0.741         NaN   \n",
       "                          2019                         0.814         NaN   \n",
       "                          2020                         0.799         NaN   \n",
       "Venezuela                 2018                         0.611         NaN   \n",
       "                          2019                         0.626         NaN   \n",
       "                          2020                         0.612         NaN   \n",
       "Vietnam                   2017                           NaN         NaN   \n",
       "Yemen                     2018                         0.553         NaN   \n",
       "                          2019                         0.651         NaN   \n",
       "\n",
       "                                Perceptions of corruption  \n",
       "Country name              year                             \n",
       "Algeria                   2016                        NaN  \n",
       "Cyprus                    2020                      0.816  \n",
       "Hong Kong S.A.R. of China 2020                      0.380  \n",
       "Iceland                   2019                      0.699  \n",
       "Iran                      2018                      0.703  \n",
       "                          2019                      0.728  \n",
       "                          2020                      0.710  \n",
       "Kosovo                    2020                      0.910  \n",
       "Malta                     2020                      0.675  \n",
       "North Cyprus              2015                      0.659  \n",
       "                          2016                      0.670  \n",
       "                          2018                      0.614  \n",
       "                          2019                      0.640  \n",
       "Palestinian Territories   2018                      0.814  \n",
       "                          2019                      0.829  \n",
       "Qatar                     2015                        NaN  \n",
       "Somalia                   2015                      0.410  \n",
       "                          2016                      0.441  \n",
       "South Sudan               2015                      0.710  \n",
       "                          2016                      0.785  \n",
       "                          2017                      0.761  \n",
       "Taiwan Province of China  2018                      0.736  \n",
       "                          2019                      0.718  \n",
       "                          2020                      0.711  \n",
       "Venezuela                 2018                      0.828  \n",
       "                          2019                      0.839  \n",
       "                          2020                      0.811  \n",
       "Vietnam                   2017                        NaN  \n",
       "Yemen                     2018                      0.793  \n",
       "                          2019                      0.798  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[pd.isna(df[FEATURE])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country name</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th>2015</th>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lesotho</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <th>2021</th>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>962 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Generosity\n",
       "Country name year            \n",
       "Afghanistan  2015       0.080\n",
       "             2016       0.042\n",
       "             2017      -0.121\n",
       "             2018      -0.094\n",
       "             2019      -0.108\n",
       "...                       ...\n",
       "Lesotho      2021      -0.131\n",
       "Botswana     2021      -0.246\n",
       "Rwanda       2021       0.061\n",
       "Zimbabwe     2021      -0.047\n",
       "Afghanistan  2021      -0.102\n",
       "\n",
       "[962 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[FEATURE]].copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are using time series data, **interpolation** is the best approach to fill the missing values.\n",
    "\n",
    "Because we have few years to analyze, the interpolation method will be **linear**, i.e., missing fields will be filled by a point that belongs to a line formed by its two contiguous points.\n",
    "\n",
    "Interpolation must be used with temperance, or it may produce artificial data. So before using it, we removed the countries that don't have at least four years of information.\n",
    "\n",
    "source: https://en.wikipedia.org/wiki/Linear_interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bhutan',\n",
       " 'Burundi',\n",
       " 'Central African Republic',\n",
       " 'Comoros',\n",
       " 'Congo (Kinshasa)',\n",
       " 'Jamaica',\n",
       " 'Maldives',\n",
       " 'North Cyprus',\n",
       " 'Qatar',\n",
       " 'Somalia',\n",
       " 'South Sudan',\n",
       " 'Swaziland',\n",
       " 'Syria',\n",
       " 'Trinidad and Tobago']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove countries with more than one date missing\n",
    "remove = []\n",
    "temp = df.reset_index()\n",
    "for col in df.columns:\n",
    "    t = temp[[\"Country name\", col]].groupby(\"Country name\").count()\n",
    "    remove += t[t[col] < 4].index.tolist()\n",
    "remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country name</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th>2015</th>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lesotho</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <th>2021</th>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>2021</th>\n",
       "      <td>-0.102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Generosity\n",
       "Country name year            \n",
       "Afghanistan  2015       0.080\n",
       "             2016       0.042\n",
       "             2017      -0.121\n",
       "             2018      -0.094\n",
       "             2019      -0.108\n",
       "...                       ...\n",
       "Lesotho      2021      -0.131\n",
       "Botswana     2021      -0.246\n",
       "Rwanda       2021       0.061\n",
       "Zimbabwe     2021      -0.047\n",
       "Afghanistan  2021      -0.102\n",
       "\n",
       "[930 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df.index.get_level_values(\"Country name\").isin(remove)]\n",
    "df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the missing years for each country\n",
    "\n",
    "The countries have different missing years. The code below create the correct missing years for each country. Below, the explanation of each line of code:\n",
    "\n",
    "1. convert country and year to columns\n",
    "2. convert each year to a column, NaNs appear\n",
    "3. convert country to column\n",
    "4. group the several years columns to one \n",
    "5. set original index\n",
    "6. sort index by country and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index() \\\n",
    "       .pivot(index=\"Country name\", columns=\"year\", values=FEATURE) \\\n",
    "       .reset_index() \\\n",
    "       .melt(id_vars=\"Country name\") \\\n",
    "       .set_index([\"Country name\", \"year\"]) \\\n",
    "       .sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-0.102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value\n",
       "year       \n",
       "2015  0.080\n",
       "2016  0.042\n",
       "2017 -0.121\n",
       "2018 -0.094\n",
       "2019 -0.108\n",
       "2020    NaN\n",
       "2021 -0.102"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an example with missing data\n",
    "df.loc[\"Afghanistan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[\"Afghanistan\"].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interpolate\n",
    "df = df.interpolate(method=\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-0.102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value\n",
       "year       \n",
       "2015  0.080\n",
       "2016  0.042\n",
       "2017 -0.121\n",
       "2018 -0.094\n",
       "2019 -0.108\n",
       "2020 -0.105\n",
       "2021 -0.102"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the example after interpolation\n",
    "df.loc[\"Afghanistan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[\"Afghanistan\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we format the dataset in such a way that the years are now features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>year</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>-0.1210</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>-0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>-0.081000</td>\n",
       "      <td>-0.017000</td>\n",
       "      <td>-0.0290</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>-0.075667</td>\n",
       "      <td>-0.121333</td>\n",
       "      <td>-0.1670</td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>-0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>-0.174000</td>\n",
       "      <td>-0.192000</td>\n",
       "      <td>-0.1860</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>-0.203000</td>\n",
       "      <td>-0.170000</td>\n",
       "      <td>-0.1470</td>\n",
       "      <td>-0.163</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>-0.170</td>\n",
       "      <td>-0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>-0.117000</td>\n",
       "      <td>-0.155000</td>\n",
       "      <td>-0.1690</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-0.169</td>\n",
       "      <td>-0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0.086000</td>\n",
       "      <td>-0.090000</td>\n",
       "      <td>-0.0655</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>-0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>-0.139000</td>\n",
       "      <td>-0.151000</td>\n",
       "      <td>-0.1470</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>-0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>-0.039000</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>-0.123000</td>\n",
       "      <td>-0.095000</td>\n",
       "      <td>-0.0980</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year              2015      2016    2017   2018   2019   2020   2021\n",
       "Country name                                                        \n",
       "Afghanistan   0.080000  0.042000 -0.1210 -0.094 -0.108 -0.105 -0.102\n",
       "Albania      -0.081000 -0.017000 -0.0290  0.009 -0.099  0.007 -0.030\n",
       "Algeria      -0.075667 -0.121333 -0.1670 -0.146  0.005 -0.031 -0.067\n",
       "Argentina    -0.174000 -0.192000 -0.1860 -0.211 -0.211 -0.122 -0.182\n",
       "Armenia      -0.203000 -0.170000 -0.1470 -0.163 -0.172 -0.170 -0.168\n",
       "...                ...       ...     ...    ...    ...    ...    ...\n",
       "Venezuela    -0.117000 -0.155000 -0.1690 -0.169 -0.169 -0.169 -0.169\n",
       "Vietnam       0.086000 -0.090000 -0.0655 -0.041 -0.126 -0.112 -0.098\n",
       "Yemen        -0.139000 -0.151000 -0.1470 -0.147 -0.147 -0.147 -0.147\n",
       "Zambia       -0.039000  0.122000  0.1400  0.048  0.077  0.056  0.061\n",
       "Zimbabwe     -0.123000 -0.095000 -0.0980 -0.068 -0.064 -0.009 -0.047\n",
       "\n",
       "[143 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index().pivot(index=\"Country name\", columns=\"year\", values=\"value\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling the data is particularly important for clustering because many methods are distance-based and can be affected by different scaled values, producing unfeasible results. For the data, I used the **MinMax Scaler**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.325926</td>\n",
       "      <td>0.179787</td>\n",
       "      <td>0.284534</td>\n",
       "      <td>0.212941</td>\n",
       "      <td>0.190712</td>\n",
       "      <td>0.224096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.198958</td>\n",
       "      <td>0.263492</td>\n",
       "      <td>0.277660</td>\n",
       "      <td>0.406139</td>\n",
       "      <td>0.223529</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>0.310843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.204514</td>\n",
       "      <td>0.153086</td>\n",
       "      <td>0.130851</td>\n",
       "      <td>0.223140</td>\n",
       "      <td>0.345882</td>\n",
       "      <td>0.282353</td>\n",
       "      <td>0.266265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.102083</td>\n",
       "      <td>0.078307</td>\n",
       "      <td>0.110638</td>\n",
       "      <td>0.146399</td>\n",
       "      <td>0.091765</td>\n",
       "      <td>0.169659</td>\n",
       "      <td>0.127711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>0.071875</td>\n",
       "      <td>0.101587</td>\n",
       "      <td>0.152128</td>\n",
       "      <td>0.203070</td>\n",
       "      <td>0.137647</td>\n",
       "      <td>0.110217</td>\n",
       "      <td>0.144578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.161458</td>\n",
       "      <td>0.117460</td>\n",
       "      <td>0.128723</td>\n",
       "      <td>0.195986</td>\n",
       "      <td>0.141176</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>0.143373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0.372917</td>\n",
       "      <td>0.186243</td>\n",
       "      <td>0.238830</td>\n",
       "      <td>0.347107</td>\n",
       "      <td>0.191765</td>\n",
       "      <td>0.182043</td>\n",
       "      <td>0.228916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>0.138542</td>\n",
       "      <td>0.121693</td>\n",
       "      <td>0.152128</td>\n",
       "      <td>0.221960</td>\n",
       "      <td>0.167059</td>\n",
       "      <td>0.138700</td>\n",
       "      <td>0.169880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>0.242708</td>\n",
       "      <td>0.410582</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>0.452184</td>\n",
       "      <td>0.430588</td>\n",
       "      <td>0.390093</td>\n",
       "      <td>0.420482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>0.155208</td>\n",
       "      <td>0.180952</td>\n",
       "      <td>0.204255</td>\n",
       "      <td>0.315230</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.309598</td>\n",
       "      <td>0.290361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year              2015      2016      2017      2018      2019      2020  \\\n",
       "Country name                                                               \n",
       "Afghanistan   0.366667  0.325926  0.179787  0.284534  0.212941  0.190712   \n",
       "Albania       0.198958  0.263492  0.277660  0.406139  0.223529  0.329412   \n",
       "Algeria       0.204514  0.153086  0.130851  0.223140  0.345882  0.282353   \n",
       "Argentina     0.102083  0.078307  0.110638  0.146399  0.091765  0.169659   \n",
       "Armenia       0.071875  0.101587  0.152128  0.203070  0.137647  0.110217   \n",
       "...                ...       ...       ...       ...       ...       ...   \n",
       "Venezuela     0.161458  0.117460  0.128723  0.195986  0.141176  0.111455   \n",
       "Vietnam       0.372917  0.186243  0.238830  0.347107  0.191765  0.182043   \n",
       "Yemen         0.138542  0.121693  0.152128  0.221960  0.167059  0.138700   \n",
       "Zambia        0.242708  0.410582  0.457447  0.452184  0.430588  0.390093   \n",
       "Zimbabwe      0.155208  0.180952  0.204255  0.315230  0.264706  0.309598   \n",
       "\n",
       "year              2021  \n",
       "Country name            \n",
       "Afghanistan   0.224096  \n",
       "Albania       0.310843  \n",
       "Algeria       0.266265  \n",
       "Argentina     0.127711  \n",
       "Armenia       0.144578  \n",
       "...                ...  \n",
       "Venezuela     0.143373  \n",
       "Vietnam       0.228916  \n",
       "Yemen         0.169880  \n",
       "Zambia        0.420482  \n",
       "Zimbabwe      0.290361  \n",
       "\n",
       "[143 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mms = MinMaxScaler()\n",
    "df = pd.DataFrame(mms.fit_transform(df), index=df.index, columns=df.columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "Before clustering, we use **dimensionality reduction** in the data. There are two reasons for that. \n",
    "\n",
    "First, the reduction for 2 or 3 dimensions will allow us to create charts to understand how the data is grouped. Such a chart will be only a **projection**, i.e., we will lose some details that could be only noticed with more dimensions.\n",
    "\n",
    "Second, we can cluster the dimensionally reduced data, making things easier for the clustering algorithm, which will deal with fewer features.\n",
    "\n",
    "Later, I reduced the dimensionality using the **T- Distributed Stochastic Neighbor Embedding** (TSNE) algorithm. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data.\n",
    "\n",
    "source: https://scikit-learn.org/stable/modules/manifold.html#t-sne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, perplexity=11, random_state=2021)\n",
    "axis = pd.DataFrame(tsne.fit_transform(df), columns=[\"x\", \"y\"], index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.scatter(axis[\"x\"], axis[\"y\"])\n",
    "c = plt.Circle(tuple(axis.loc[UAE]), 0.01, color='r')\n",
    "ax.add_patch(c)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clustering algorithm is the **Agglomerative Clustering**. It performs a hierarchical clustering using a bottom-up approach: each observation starts in its cluster, and clusters are successively merged. \n",
    "\n",
    "Some of its advantages are: clusters may have different sizes, and it works well with non-globular data.(what is the case for this dataset).\n",
    "\n",
    "The score selected to determine the best number of clusters was the **Silhouette Coefficient**. The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.\n",
    "\n",
    "The Silhouette coefficient checks at the same time the distance **between clusters** and the distance **inside the cluster**.\n",
    "\n",
    "source: https://scikit-learn.org/stable/modules/clustering.html#clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 0.52887005)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score = -1\n",
    "best_groups = []\n",
    "best_n = 2\n",
    "for n in range(4, 12):\n",
    "    cl = AgglomerativeClustering(n)\n",
    "    groups = cl.fit_predict(axis) \n",
    "    score = silhouette_score(axis, groups) \n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_groups = groups\n",
    "        best_n = n\n",
    "axis[\"group\"] = best_groups\n",
    "df[\"group\"] = best_groups\n",
    "best_n, best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ka28WOKeiUp+JTaBRFNUxaKVUG6AvMBdoXJK8AXYDjaP5LCGEEMc472zwLeBgcg5ygetHtG9trKKKmqglaKVUEvAN8FetdX7ZczrY3xCxz0EpdZNSKksplZWdnR2tcIQQQtRz2jMDdHGEMwZ459R6PNEWlQStlLISTM6faK3Hlxzeo5RqWnK+KbA30rVa67e11gO01gMyMzOjEY4QQohjgSkdsIUfVxYwpdV2NFEXjVncCngXWKW1fqnMqe+Bq0t+fzUwobrPEkIIIQ5SznOJnMZMYB9Z7ftr3yqMvMcwDtyEUfQZWruOflEUVXuSGHACcCWwTCm1uOTYw8BzwJdKqeuBLcDFUXiWEEIIAYAyN4H0V9G59xAcRdWgHKi0N1GmhGrd23D9AHmPAF6CXeZz0cUfQsOvUabEKER/dNGYxT0DUOWcHlHd+wshhBDlUfZh0Gg2+JYAFrD2Qilzte6ptRfyHwfcZY66ILAdXfwJKummat2/oqSSmBBCiDpNKRvKdhzK1rdSyVn7t6FdP6K989HaOHTCt5LI7U4PuH+tdrwVFY0ubiGEEAII7tc8d/VW/ly0nkSnjXMGd6Nd04axDiuE1gY6/1Fw/QBYgrnY1BAafIQyNwVTEuhA5ItNqbUWpyRoIYQQUWEYmgf++yOzVm7B5fFhNik+n7yY+y4axgUn9op1eIe4xoPrJ8AT/KWBgAudcwcq42swtwdzMwhsAsq0rHGiEq6stTCli1sIIURUzFixqTQ5AwQMjcfn54Uvp5BX5D7K1bVHF39MaHETAAP8a9CB3SilUOlvBZO0SgCVBNgg8RqU45Rai1Na0EIIIaLi96y1pcm5LIvFzNzVWzi1f+cYRBVBxOImAKbSc8rSCjImgW8hGAfA2hdlzqi9GJEELYQQopIKit0s3rCTRIeNPu2bYzIFJ1TZbRZMSmEctlmFAuyWOEo3jtOg6H2CS6jKMCWBuU3pj0opsPWv1dDKiqM/MSGEEDVt4679fD9rBQUuD8N7t+eE7m1LE2xFfD5lEf8ePx2L2QxoEhw2Xr/jfNo3y+DcId35ed4q3F5/yDUazaCuraP8JqADO9GFb4J3PpiboRJvQtkHlTm/D138QbDsp7kVKvE6lLU7KvFGtPsXCGQT7Oq2AhZU6j9QKn5GflU8bcs1YMAAnZWVFeswhBCiXvpu5nKe/2Iy/kCAgKFx2q3069icl28Zjdl09MS0dOMubv7312EJODM1kZ+fuQGzycR/f5nLf3+ei9mkMJlMaK15+dbRDOjUMqrvov3b0fvHlHRJH4zHCSlPYko4Dx3Yjd43GnQRwZayCbCh0v6FcoxAaxe6eAJ4Z4KlJcp5abBbOwaUUgu01gMOPy4taCGEOAYUuDw8/8VkPL5DydXl8bFw3Q4mL17PyH6djnqPr6ctCbn+oCK3l8UbdtK/YwtuOGMQ5wzuxuxVW3DarJzUsx1OuzWq7wKgC18FXUjoLGsXFDyDdp5Tcj4fOLhcygDc6PzHwH4ySjlRiZdC4qVRjy1a4qctL4QQosYsWLsdizn8r3yXx8fErIptzZhX7CFSp6tSikKXp/TnxunJjDm+B6cN6FwjyRko2a3KCD+uvRDYAZ5pHErOZRiFENhZMzFFmSRoIYQ4BtiskStsKcBpq1hn6il9OkT8rD8QoG/75tUJr/LM5e1+GAjuZFXublYGmJIjntFGLkbR/zDyHsUo+gJtlDfbu3ZIghZCiGPAcZ1aRpwMZrdZGHNCjwrd44zjOtOuaUMcJUlaAQ6bhdtHDyUl0RHNcI9KJd4EOA87agt2X5tSUYnXRThvBfsJqAjVwLR/Azp7JBS8BK4voeBZ9L7T0IGIOyXXChmDFkKIY4DVYuaVW8dw+2vfAhrDgIBhcNWoAfTr2KJC97BZLbx778X8Mn8NkxauJSXRwUUn9aZ3+2Y1G3wEynEqOmkbFL4CygTaB/ahqNTngh9wjAHfeij+EJQteN7aE5X6QsT76bxHQBcQLCsGUAyGB13wPCrtxdp4pTAyi1sIIY4hbq+fmSs2UeT2MrhraxqlJcU6pGrR2gX+TWDKREXo9tZGDvjWgrkJmFLQRR8Ex6fNjYPLrmzHobUHvac3Ece0VSKmxotq9B1kFrcQQggcNgsj+nYs9/z+/CI+mbSIrLXbaJ6RwpUj+9OtdZNajLBylHKCtRsA2igCzxTAA7ahKHMjlCkd7IPQxgH0vnPAyAW84F+O9sxEpzyCcp5PcMQ3QoKmhia5VYAkaCGEEADsySngsqc/psjjxec3WLllD1OXbmTcNacfManHA+2Zic69jeDIuAbtRyffgynxuuD5ovcOJedSbih4DpxjwH4yeCZzaE01gD14LkZkkpgQQggA3vpxDgUuDz5/sCVpaI3b6+eZTycRMCK1LuODNoqCyVkXBwuT6GLACwUvo30rgx/yTCGstCcACvzrUKnjwNw6uDkGDsAJ1u6o5Ltr6zXCSAtaCCEEALNXbSFghM9Lcnl97D5QQPOM2tsLuVK80wi2nIHcAPg1ZFgAL9r1LcraDUwZQIT13toHKg1lagAZP4F3LgS2gKULWHsH63HHiCRoIYQQAKQlOtiTUxB23DA0SU57yDFfIMDkxeuZtWIzmamJjD6+By0y02op0sPoQ0VS8GtwH/ySYYAObnOpEq9DexcRus2kBaw9UJbgLHalTGAfAgypjaiPShK0EEIIAK4Y2Z+nP/0jpNa21WxiUNdWpJZZ5+zx+bnhxS/ZuPsALo8Pi9nEx5MW8fyNZ3FSz3a1H7htKOiSmDPKpjUnynEaAMp+Ejr5Lih4GZQl+HlrF1Taa7UebkXJGLQQQggAzhzYhctP7ovNaibJYcNutdCrXTP+fs3pIZ8bP30ZG3btL9372R8w8Pj8PPa/X/EFIpTXLCOn0MXX05bw4e9ZbNi5LypxK3MGJN9HcOz4YFpzguMUsJ1Q+jlT4nWoRrNR6e+gMn7A1PBLlLlhVGKoCdKCFkKIOsrnD/Dmj7MZP2MZbq+P4zq35L6LhtOqUXqV7qeU4vYxQ7ly1ADW79hHo/QkWkbotv5l/uqwHa0g2BW+euteerZtGvH+05dt5IH//gRAIGDwxg+zOW9oD/7vouHVHus1JV6Ntg1Cu74F7Qq2nG3Hh91XmRJjusdzZUiCFkKIOur+d35izqotpTtMzVyxmSUbP2P8E1fTMCWxyvdNTXTQv1P51cXKq91tGBq7NfI5l8fHg+/+HJrYAwYTZq5gWK/2DOpS/a0elbULyvpQte8TL6SLWwgh6qCte3NCkjOA1uDx+vlq6pIaffaFJ/XGaQst4KGABikJdGyeEfGauau3YorQSnZ5ffw0d1VNhFnnSYIWQog6aMPO/VgjbB/p9QdYvmVPjT57ZL+OnD24KzarGafNSqLDSlqSk3/fOrrcrmrjCGWldYSlXUK6uIUQok5q3TgdXyC8eIjVbC63FRstSikeumwEV4zsz6L1O0hLcjKkW2us5shbWgIM6tKKQIR4nTYrZwzqUpPh1lnSghZCiDqoXdOG9GzbBJslNClaLSYuGdanxp+/+0ABn0xayMeTFvLdzOWs2nLkbRkTHTb+ds1p2K0WbBYzSgXrgp86oBNDurau8XjrItnNSggh6qhit5cXvprCz/NW4w8YdGvdmEcuH0GXlo1q9Lnbs3MZ++ynuLw+/AEDRXBf6b9dfRoj+3U64rV7cwv5LWsNRR4vQ7u3pXub+N2Io7bIblZCCFFP+AIBvp66lAmzV6C15rZzj+fCE3uS4LAf/eIoeOOH2RS5vaXjyprgNpbPff4np/TpiMlU/pKpRmlJXDGybixzijVJ0EIIUYdorbnrP9+xeMPO0iVLb/4wm2nLNvLO3RdVez3xtuxcdu7Pp2OzDBqkJET8zPw12yJO+ip2+9iTW0DTBinVikEESYIWQog6ZNH6HSzZsCtkPbHb52fV1r3MW72VQVUczy1ye7n3rR9YsmEHVosZry/AmBN6cP/FJ4e1iNOTnezLLwq7R0Brkp2104o/FsgkMSGEqEOWbNyJ1x9excvl8bFow84q33fcx7+zeP0OPL4AhS4vXn+A72ev4Otp4Wuqrz51AI7DipXYLGaG92oXtqmGqDpJ0EIIUYdkpCRii1Cty2GzkJFatephLq+PyUs24PWH1tF2e/188ueisM+fcVwXrho5AHtJzW6b1cygLq144spTq/R8EZl0cQshRIxs2LmPjbsO0KZJgwqvXR7RtyMvfDU17LjZZOK0AZ2rFIe7ZNOLSAqK3WHHlFLcfM4QrhjZj817cmiUlkSjtKQqPVuUTxK0EELUMrfXzz1vTmDx+p2YzSYChkGPNk34961jcNqtR7w2wWHjnbsv5P/e/rF0HDg9KYF/3HhWlcd/05KcZKQksutAfshxpThijewkp50eskyqxsg6aCGEqGUvfDmZb6YvC+lStlnMnDO4G4+MHRny2SK3FwgW+ihLa83WvblorWndOL3as7fnrNrCPW9+j9cfwDA0VrMJp93KJw+NpXlGaunnvD4/E7PWMHPFZhqlJXH+0J60adKgWs8+1pW3DloStBBC1LIT7/5PaeIty261MPuVO4BgMZDHP5jI8s27Aejepgl/u/q0iNs/Rsv6Hfv4eNICNu/JoW+H5lx+Sl8yUw91Xbs8Pq554XO278vD5fFhNiksZjPPXHcGJ/fpUGNx1XdSqEQIIeJE2R2oyvL6/Wit8foDXPvPL8gpcJWuN162cRfXvPA5P/39hrAZ1NHSoXkGT151Wrnnv5q2hK17c0vjDxiagOHnyQ9/Y2jPtkesxS0qT2ZxCyFELRvQqQWH90groG+H5iil+HPRelweX0gxEENrPF4/fyxaW7vBlvFb1tqIXy4MrVmzLTsGEdVvkqCFEKKWPXDJKSQ57KUbXdgsZhKdNh669BQAduzPw+UNn1nt8vjYtT8/7HhtSXBEnsBmGAYJR5ncJipPuriFECLKtNb8tmAtX05ZTJHHx6j+nbhseB8SSiZ6tW6czrdPXsM305eyYuseurRsxIUn9ipdx9ylZSOcNivFhy1/ctqtdGqRWevvc9Alw/qwYvOekC8PSkHj9GTaykSxqJMELYQQUfbCl5OZMGsFrpJynFv2HOCXeav45KGx2EuKjDRISeDGswZHvH5It9a0yExl0+4cfCUzva0WE80apjC0R9vaeYkITunbgUUbdvD1tKVYzMEO2CSnnZdvHV3tWeQinMziFkKIKNp1IJ/znvhfWFUup83C/11yMmOO71Gh+xS5vbz5w2x+mb8a0Jx+XBduPntIXJTS3HUgnyUbdpKenMCATi0wm2pvtNTj8/P7grUsWLedZg1TGHN8DzLreJEUmcUthBC1YOmGXVjMprAE7fL6mbVic4UTdKLDxr0XDePei4bVRJjV0rRBSkx2rCpwebjq+c/Ym1OIy+vDZjHzv4lZvH7n+fRu36zW46lpMklMCCGiqLwtGs0mRZP05FqOpn55/9f57NyfXzoG7vUHcHl9PPr+L8RTb3C0SAtaCCGiqF/H5qQkOHB7/SHLpKxmMxec2CuGkdV9vy1YUzomX9bevCJuf/VbVm7dQ1qSk6tHDWD08d3r/Lh4VFrQSqn3lFJ7lVLLyxxroJT6XSm1ruSf6dF4lhBCxDOzycTbd19I2yYNcNgsJNitpCTYefaGM2ndWP4arA57hF28AHz+AHNWbyGvyM2WPTn848vJvP79rFqOLvqiMklMKXUSUAh8qLXuUXLsH8ABrfVzSqkHgXSt9QNHuo9MEhNC1Cdb9uTg8vro0CyjdNazqLpP/1zEaxNm4PZGrsRWlt1q4ffnb4qLSXVHU6OTxLTW05RSbQ47PBoYXvL7D4ApwBETtBCiZizO2cxPOxbhN/yc2rQ3gzM61vnuv7pAWszRdfGw3ixct52ZKzajVHBc3+0NEDCMsM9azCa27s2hW+u6u9tWTY5BN9Za7yr5/W6gcQ0+SwhRjtfXTuTzzbPwGH40mj/3rGB4o2482esiSdKiRvgCAd7/dR7fTF+G2+dnaPe23HneUBpXc5KcxWzin385h3U79rFi824y05L4ZtoSpi7dyOF9wT5/gEZpdXtSXq1MEtNaa6VUxL50pdRNwE0ArVqVv++oEKLythfv59PNM/Eah7oEXQEvk/eu4LzcgfRJbxO74ES99cA7PzF75ZbSut2/LVjD3NVbGf/E1aQkOqp9/47NM+jYPAOA1EQHc1ZtxV2mRrjNaubEHm1LK7PVVTU5KLJHKdUUoOSfeyN9SGv9ttZ6gNZ6QGZm7ErYCVFXFfk9bCjYTaHPHXZudvZaIrWRPQEf0/euqvngxDFn0+4DIckZgrteFXu8fDdr+RGurJoebZrw7A1n0igtCZvFjM1i5vQBnRl3zRlRf1Ztq8kW9PfA1cBzJf+cUIPPEuKYY2iD19ZO5KstszErM34d4NwWA7i369mYVfC7t9Nsw6TCv4eblZkEc/xPnhF1z9rt2ZhN4V8L3V4/SzfuinBF9Q3r1Z6TerYjp8BFgsNWY9tx1rZoLbP6DJgNdFZKbVdKXU8wMY9SSq0DRpb8LISIks82z+TrrXPwGH6KAx68hp8fty/gv+snlX5mWONuEQs4mJXi9GZ9ajFacaxokZFKpMVBVoupRjfUUErRICWh3iRniFKC1lpfprVuqrW2aq1baK3f1Vrv11qP0Fp31FqP1FofiMazhBBBH2+ajjsQutuR2/Dx+eZZpUk52erk+b5jcZptJJrtJJjt2E0WHuw+huYJdWP3oYJiN1OXbiBr7baIs3Xrm/35RXwzfSlfTl3CrgOx21qyqrq1bkybJulYD1tWZjWbufAkKdRSGfXnq4YQx5g8nyvi8eKAh4A2sKjgXsNDMjvx68kPM3f/evw6wKCGHUi2OgFYkbuN9zdOYWvRPnqmteKadsNpmdiw1t7haL6YupiXv5mGxWwGNA6blf/ccX6Vt1z0Bwx8gQBOW3zuXfzzvFWM+/h3lFJoDS99M5U7Rw/l8hH9Yh1ahSmleP3OCxj38e9MW7YRraFd0wY8ceWokFncWmtWbd1Ddm4R3Vo3rvMbXtQE2c1KiDrqutlvsDxvW9jxNomZfHni3Ue9fsbe1Ty8+DM8hg8NmFHYzVbeG3IL7ZJivypy5Zbd3PDiVyGzcwHSk5xMfO6mShX+cHl9vPDlFH6euwq/YdCmcQMeHTuCPu2bRzvsKtufX8TZj74XMrkKggU3Pn14bJ3cb9nj8+PzB8KKhWTnFXLrv8ez80A+JqXw+QOcP7Qn/3fx8GNy6V95hUqktI0QddTdXc/CYbKiSuZpK8BusnJft3OOeq3WmudXTsBdkpwBAmhcAS+vrvm15oKuhG+mLwvbEQrA4wuwYG34F5MjeeCdH/l57iq8/gCGodm4az+3vfItW/bkRCvcapuyZAORcpM/EGBi1praDygK7FZLxEpeD7zzE5v3HMDl8VHk9uL1B5gwazk/zavcygKXx8dXU5dw/zs/8vL4aWzPzo1S5PFBErQQdVTPtFa8O+RmhjfuRnNnA4ZmduGtQTcysGGHo16b73NxwFMYdlwDS3K21EC0lZdf7A7ZbOIgpaDQ7a3wfXbsy2P+mm1hyd7r9/PxHwuqHWdlaK3ZtPsAa7dnh42nBwwdcXKV1hAI1J+x9+zcQlZu2UPACH1Zl9fPZ38uAoJ/TkVu7xHnHOQXubl43If8a/w0/li4jk//XMTFf/+Iuau31mj8tUnGoIWowzomN+X5vmMrfZ3TYgt2JUZICGm2+CjucHKfDsxasaV0a8GDfP4A/Tq2qPB9tmfnYrWY8fhCE3TA0KzfuS8qsVbExl37uefN79mbW4hJKRw2K09fdwaDugQLNJ3Ysy0vfTM17DqbxcyIfh1rLc6aVuT2YjaZgPDekUKXl0mL1vHCl1PYn1+EzWLh0uG9uXX0CSXXHPLexHnszSsq3d3KHzDwBwwe/9+v/PrsjfWiq1xa0EIcg2wmC2c264vdFPod3WGycmXbE2MU1SGGodmbW4i/TMtRAQ6bhVvPOZ70JGeF79WuaUO8vvBkYDWb6Nm2abVjnb5sI9f/8wvOfew9/vbR7+zcHz7z2ucPcONLX7Ftby5ur59ij48DBcXc88YE9uYGezKaNkjhjtFDsVstmE0qmMStFi4Z3ocuLRtVO8540bJRWsSlUFazia4tM3ns/V/Zm1tIwNC4vD4+m7KYf309Lezzfy5aH3HryQKXh23ZeTUSe22TFrQQx6h7u55Nkd/N1L2rsJrM+I0Al7Q+njEtjot1aDz96R/8Mn81vsChv4DNZhPPXHsGw/scvQu/rMy0JE4/rjO/Za0tnXCmFNhtFsZWcnZ0TkExvoBBZmoiSik+n7yIV747tLvSrgP5TFq8js8fuYKmDVJKr5uxfBNefyCswyJgaL6ftYIbzhwEwNgR/RjSrTW/LVhLIGBwSt8OdG0V+wl71VHg8vD2j3OYuGANJqU4Z3A3Hr58BI/971d8/gABQ+OwWkhLcrIrpyBsUqDb6+ebGcu4bcwJIbPvnfbIM/ENQ+O014/UVj/eQghRaXazlaf7XMYBTyF73Xm0SGxIkqX6dZKrKzuvkJ9KJnSVpYDFG3dWOkEDPHbFKFo1SueLKYspdHkZ0KkFf73gpApv3rBzfz4PvfsTq7dlo4AmDZJ58qpTeXXCzJCtDwOGptjt5f1f5/Pw5SNKj+8vKI44juz1B0pb0Ae1a9qQm88eUul3jEe+QIBrX/icbdm5+PzB9/940gK6tGzERw9cxpdTl7Bjfz6DurTivBN6cMFTH0S8j0kpDuQX0zwjtfTYxcN68+LXU0P+/M0mRZeWjchMrR9LtiRBC3EM+m3nEv6z7jd2u3Jp5Ejhlo6j6JIaH0uONu7cj81qDkvQvoBR5VKRZpOJ604fyHWnD6z0tf6AwfUvfkF2blHppLWte3O57ZXxRCp0HjA089aEzjLv275ZpOF+nHZr6Rh0JFprducUYLOYaZgSH3MDKmPako3sPlBQmpwhOAt/7Y595Ba6eeiyESGf79yyEfvyNoX9WSkFmYdtfHHeCT1ZtmkXv2atwVIyPt0wJZHnbzyrRt4lFiRBC3GM+X3XUv6+fDxuIzj5ao87j+dWTEADZzWPfUGMZhmpIX+hH2Q2qZisBZ69cjMFxZ6wGeUBQ4eMkZfVOD20Bde+WQan9OnAlCXrcZW0+BxWC22bNGBY7/YR77Fkw04eff8X9uUXY2hNl5aZPHfDWSFd5/Fu2eZdFHt8Ycf9/gArt+6hf6fQyX63nDOErLXbQlrFDpuFG84YhM0amq5MJsWTV53GDWcMYsXmPWSmJdK3Q/N6MTnsIJkkJkQl5XmL+W7bfD7bPJPNhRE3aYtrr6/9rTQ5H+Q2fLyx9rcYRRSqZWYafTo0w2Yxhxy3WsyVHjOOht0HCiIu9/H6AzRKTwqL02GzcO1p4eP44645nQcvPYWebZvSpWUmt5w7hP/ec3HEgivZuYXc9up4duzPLy32sWLLHm548as6Ve60RWYazkgTwixmmkX4otG1VWPe+uuF9O3QDKfdSsvMVB689BSuPjWshkfIM047rjP9OraoV8kZpAUtRKXMzl7LA4s+QSlFQBu8vnYi57caxF87n1ln/nLY5Y5cnGOvJx+tNUop/EaASbuXM3HXYuwmK+e2GMDgjI619o4v3nQOz342id8WrkNrTbOGKTw2dhTtmtZ+GdLubZpEfO8Eu5WbzhzM1KUbmL1yCxazCaUUd19wEoO7tg77vMmkOGdId84Z0v2oz/xu1vKw1rlhaPKK3MxbvY0h3cLvH49OH9CZ10om0R3sfzApRYLDxkm92kW8pmfbprx77yW1F2QckwQtRAW5A14eWvxpWOvz223zOCGzc4UKhMSDJo40drrCk3QjewpKKQxtcO/Cj1icsxlXIFgQZOa+NVzYcjB3dqmdPXYTHDbGXXsGj10xCo/PT5LTHrMvQN1aN6Z/xxZkrd1eWobTajGRmZbEmQO7MOaEHuQUusgpKKZlZhrWw1rUVbFjX37EKmqGNtiTW1Dt+9eWJKed9+67hEff/5UNJWvOu7VpwtPXnh6VP6f6ThK0EBU0f//G0rKaZbkDPn7esbBWEnSh34034CfdlljlhHVbp9MYt+ybkC8aDpOVWzqNAmDOvnUhyRmC7/jV1tlc0GpQre6CZbNawsYeo23B2u28+eNstu7NoUPzDG4953i6t2kS8pmXbjmXTyctYvyMZfj8AUb178QNZx4aF01PclZqbfbR9O/YnN8XrsV1+Pithh6tm0S+KE61a9qQTx8eS26hC5NSpCTGfqVAXSEJWogKMnT5Y3/+Gt50JtdbxJNLv2Le/g0ooKkzncd7XkCv9Mp3dY5q2gtDa15f9xu7XTk0dqTxl44jSyeIzdi7OiQ5H6RQzN+/oc5sU1kR05Zt5MF3fipde5udV8Si9Tt4/c7zQzbSsJrNXH3qgCOOhVbVwWGFsk4d0Jl3f53HrgP5pRPmHDYLx3drQ4fmGVGPoTakRfELzLFCErQQFTSgYXsCEZK002zj9Ka9Q47tdefx4qofmZW9Bosyc1qz3tzR+QwSLeEbBxyN1prb5r/LpsK9+Euev7V4H3dkvc/nQ++iqTO90vc8rVlvTmvWO+K5FKsTizKVPusgkzJVKf549sIXkyMWxnjp62l8+MBlNfZcf8DgnZ/n8PnkxRS6PHRqmckDl5xc+qXAbrXw0QOX8e6v8/hj4TpsFjMXntiLi4f3qbGYRPyRWdxCVFCixc7jPS/EbrJgNZmDpSfNVoY36sYJmZ1LP+fye7lm9utM3bMSj+GnKODhh+0LuHXefzm4vavWmg0Fu1mdtyNi0i9rZd52thcfCEuYPiPAN1vnRv09z27RH7MKHx9UCk5s1CXqz4sVr8/PzgORx3PXbs+u0Wc/9/kkPvpjAQUuDxpYsy2bW18Zz7odh2qDJyc4+Ov5J/Hj369n/JPXcPmIfpXaYlPUfdKCFqISRjbtSc+0lkzctYRCv4ehmV3omdYypIvy152LKfK7McqUW/DpAJuLslmcs5kUq5P7Fn7Mfm8BJhQ2k4VxvS9hUEbkDRF2unIijn37dYCtRdHf7KFFQkMe73kB45Z/g7nkO7zFZOal/lfhMNui/rxYsVrMJNitFEXYGatBSkKNPTe30MWPc8IrpXl9Ad77dR7PXn9mjT1b1C2SoIWopMbONK5qN6zc82sKduIKhBdnMLRmTf5O3tswmVxfcenx4oCX/1v4MV+ddA+NHalh13VOaYZfh8/odZis9E5vU7WXOIpRTXsxNLMLi3M2YzWZ6ZPeBoupfs26VUoxdkQ/Pvw9K6wwxnWnVb7iWEXt3J+P1RJeKc3QmnU13HIXdYv0lwgRZe2TGuMwhxfyNytFgd+NzwhPtgEMftweeW/iVokZDGvUFYfp0D3NykSS1cHoFtGftHSQ02JjSGYnBjRsX++S80E3njmIi07qhd1qwWmz4rRbue70gVxwYs8ae2bzjNSIuzCZlKJTy8wae66oe6QFLUSUndG8L2+vn4Qn4EeXdHNblIkmzjQa2BIjjjn7jADZnvLXtz7V62I+2zyTb7bNxRXwcmJmV27uOJIkqyxZqQ6zycTdFwzj5nOO50B+MZmpiTW+rCs10cG5Q7rz45yVIRPUbFYz11ehVriovyRBCxFlSRYH7w+5hWdXfMeC/RsxKcXJjbvzf91Gk+MtLE3aZTnNNgY1jFyTGYJjwFe2O4kr251Uk6Efs5w2a8hOSTXtgUtPpmFqAp/9GZzF3aVVI+6/+GTaN6ubS6hEzVC6htdvVsaAAQN0VlZWrMMQImoC2kARXKJ00JNLv+LPPctxl4xTO0xWOiQ34e1BN9XbruR4snN/Pjv359GuScManQzm9voJGAaJjvozsU7UDKXUAq112HiVtKCFqEFmFT7N4/GeFzAoowPjt87Da/g5vVkfzm85UJJzDXN5fTz435+Yt3orVosFr8/PuUO68+Clp2AyRa+MaE5BMU9+9BuzV25Ba+jYPIMnrzqVTi1kfFlUjrSghRDHhKc++o1f5q/G6zs0Qcths3DLOcdz5cj+UXmG1pqLxn3I1r25IZtdJDpsfP+3a0lPrrkWu6i7ymtByyxuIUS95wsE+GVeaHKGYDf0p38ujNpzFq7bwe4DBWE7UfkDAb6btSJqzxHHBknQQoh6z+cLlLuPcoHLU+X7Ht4DuX1fHpE6JT2+AJt2HwBgX14R05ZtZNXWPWHXC1GWjEELcQzZ7yng1TW/MHXvKqzKzJnN+3Jzx1H1qkJYJAkOGy0y0tiyN3SbTaWgf4cWlb7fkg07ef6LyazZtpckp52Lh/fmL2cNoXPLzMiz9G1WerZpwotfTeGraUuxWcwEtKZFRir/ueN8MlITq/xuov6SFrQQxwh3IFgjfOKupRT5PeT6ivl661zumP/eMdGSe2TsCBw2C+aSCWEWs4kEu42/XlC5pWvrd+zjlle+YfW2vWiCLfBPJi3k2c8m0aVlI/q0b4a9zFpqs0mRnGDHYjHxzYxleP0BCt1eXB4fG3ft5/53fozma4p6RBK0ENVU4HNR5K96N2lt+W3XUvJ9rpBCKV7Dz9qC3SzL3RbDyGrHgE4t+eiByzhrUFd6tGnCRSf15tOHx7JlTw6fTFrIgnXbK/RF5d1f50Ucy/5p7ipyC13865bRXDmyHw1TEkhOsHPGwK58/ODlfDN9WUhJUYCAoVm5ZQ/ZuYVRfVdRP0gXtxBVtKFgD08t+4r1BXsATe/0NjzZ66KI9bTjwcrc7RH3eTa0Zm3BTnqlt4pBVLWrfbMMnrzqNAB2Hyjg2n9+TkGxB3/AwGw20alFJq/feT5OW3ip1oPW7cjGiJDIbRYzO/bl0b1NE2499wRuPfeEkPMFxZG/xJnNJgrdXmQRljictKCFqIICn4ub5r7Fmvyd+HUAvzZYnLOZG+e8hT9Cre140CYpM6Se90FmpWiZ0DAGEcXWYx/8SnZeEcUeH15/AJfHx6ote3j35yNv4dm5RSNMKnzdtNcfOGI1smG92mGNsF2k3WqhVaO0Sscv6j9J0EJUwS87FuEzAiHTgQLaIN/nYs6+dTGL60jObN4Pq8kSsnGlRZnIsKdw3BHKjNYFlR1DL3R5WLJhJ4YRep3XH+D7OSuPeO11px+HzRpaVMZhs3DOkG6kJTnLve7a0wfSICWhdHzabFI4bBaeuGIUZpP8VSzCSRe3EFWwrXg/biN8S0m/DrDTlRPhithLsTp5d/BfGLd8PCvztqOAIRmdeKTH+SGlSOuSpTlbeGHlD6wt2EmCxc7FrYZwY4cRR63KdqSEbpSzHOug9s0yePOuC3nhy8ms2hqcxX3ZKX244YxBR7wuPcnJl49dxbczljFn1RaaNUzl0uF96NBc6m+LyCRBC1EF3dNa4tyxIGxM16xMdE5pFqOojq5NUiPeHXwz7oAPk1LYTHX3r4ANBXu4Peu90prmRX4Pn26eyT5PAY/1vOCI1yYnOOjYPIPVW/eG9IJYzSZG9e901Gf3ateUjx68vNIxJzvtXDVqAFeNqrltQkX9UTe/NgsRY6c06UFDexJWdailZjdZ6JTSlF5p8T/ZymG21unkDPDBxil4A6Gzoj2Gj4m7lpDrLTrq9eOuOZ3kBDsOW/DPwWm30qxhKrecfXyNxCtEZdXt/0OFiBGbycL7g2/lrfW/M2n3cizKzNnN+3Ft+5NRESYQiehbV7AbI0JREKvJzI7iA6TZjlz8o13Thvzw9+uZOH81W/fm0r1NE07p0wGrRTYtEfFBErQ4ZrkDPj7dPIOfdyzCrBTntBjAJa2HYK1gyzLVlsD93UZzf7fRNRxpZDneQv63YQpT964isWT89dwWA+LqC4I74OWP3cvZVLiHDslNOKVxD+zm8pcwHeTye5mRvRp3wMfAjA4Rl651SmnKpsK9YUnaZwRoXsFZ6clOOxee1LtiLyNELZMELY5JAW1wy7x3WF+wG48R7CZ9e90fzN63ltcGXFdukgvO0l6LQjEksxNJFkdthl2q0O/mqln/Yb+nEL8OLut6cfWPrMrfwYPdx8QkpsPtceVy7Zw3KPJ7cAW8OM02Xl/7G+8PvoUMR0q51y08sIl7FnwIgEYT0AbXtT+Z69qfHPK5a9oNZ8qelSHzABwmK6c360OaTXaNEnWfJGhxTJqdvZaNhXtLkzOA2/CxPHcbS3K30Ce9Tdg1v+5cxNPLvy3Z41kR0Abjel3M8CbdqxXLyrztvLTqR1bl7SDZ6mRsm6GMbTv0iDOrJ2zLItdbXJqcIdgj8OOOhVzb/uS4KJby3MoJHPAUlrZwXQEv3oCfl1b9yDN9I0+w8gR83LvwQ4oDoUU9/rdhCgMatA8pptI2qRGvH3c9L676kZX520m2OLm49ZCwRC5EXSUJWhyTluZujVhVy2cEWJ67LSxB73bl8vTyb0MSOsBjS79gQvr9NLAnVSmODQV7uHneO6UzkQ94C3ln/SSyPfnc0/Xscq+bv389ngjLvKzKzKq8HTFP0FprZu9bG9b9HMBgWvbqcq+bt38DEYaV8Rh+ftixIKzaWfe0lrw35JaoxCxEvJFZ3OKY1MiRErGqls1kJtMe3v36x+5lESckgeLPPcurHMf7EWYiuw0f47fNo8DnKve65gkNMEf439dA0+gI3ce1yUTkYYLyjgN4DV/EP2WNxhPhC5UQ9ZkkaHFMOq1p77DqTQqwmiwMa9wt7PMuvzfifsKGNvAEwluyFbUmf0e5M5GPVPDk4lZDsB5WjMOMiabOdLqmNK9yPNGilGJYo24lwwGHWJSZEU16lHvdgAbtCejwUqlOs42RTXtFPU4h4pkkaHFMSrY6eeO4G2iR0BC7yYLdZKFtYiPeGnQjjgizjIc26hJx3bBJKU7I7FzlONonNUFFaFH6jABNHGnlXtc6KZPn+46loT0Zh8mK1WSmd3prXjuu/AluNaHQ72ZDwR6KI+zmdX/3c2nmTCfBbMOizCSYbbRMaMjdR+i6T7UlcG/Xs7GbrKU9BE6zjYENOzC0Gn/OQtRFKp72gR0wYIDOysqKdRjiGKK1ZqcrB7My0cSZdsTPPr9iAj/sWIC3ZBzapixc0mYId3Q+o8rPX5O/kxvnvBVSNtRusnJ6s9480uP8o15vaIOdrhycZhsN7clVjqOyAtrgX6t+4rvt87EoMwEd4KLWQ7i902khk9sC2mB29lq2FGXTLqkxgzI6VKis6MbCPfy0YyFFPg/Dm3RnUMMOcbV8TIhoUkot0FqHlZeTSWLimKaUonlCgwp9Nt2WUFrDWREcF61uUuyc0oyX+l/FP1f9wKbCbBxmKxe1GszNHUdV6HqTMtEiBjtRvbv+T77fnoXX8OMl+IXl6y1zSLclcmXbk0o/Z1YmhjbqwlC6VOr+7ZIaV+uLjxD1QY0naKXU6cC/ATPwX631czX9TCGibUPBbj7aNANfyfioBnw6wOtrf2N4o+40S0iv8r0HNGzP50P/it8IYFamo7YUXX4vP+1YyOx9a2niTOPCVoNpm9Soys+vis+3zArbLMRt+Phk04yQBC2EqLoaTdBKKTPwH2AUsB2Yr5T6Xmt95P3chIgzk/eswH/YEquDpu9dxSVtql+/+Wg7MEFwH+prZr9Oticfd8CHGcUP2xfwdJ9LObFR12rHUBGGNij0uyOey/MV18gztdYsytnE5N0rsJktnNmsL+2Tm9TIs4SIFzXdgh4IrNdabwRQSn0OjAYkQYs6xaxMoBQcNmdDEZwoVhFew8/vu5Yyfe8qGtiSOK/VQDomN61UHJ9unsEedy5eI9iSD6AJGD7+tuwbfjn5oQol+eoyKRPtkhqxsXBv2LlOlXyfitBaM275N/yxexmegA8Tii+3zOG2TqdyaZsTov48IeJFTc/ibg5sK/Pz9pJjQtQppzTugSXC5CYNDG989EpinoCPG+a8yT9Wfs+fe1Ywfts8rp/9Jj/vWFipOP7cvaI0OZflM/xsKgpPmDXl3q7nYDdZS+efKxQOk/WIxVWqauGBTfyxexnuQHCNdACNx/Dx2tqJ7PMURP15QsSLmC+zUkrdpJTKUkplZWdnxzocISJqnZTJLZ1OxVayJMtusmAzWfi/rueQWYHCID/uWMDmwuzS6mUGGrfh4/mVE3BXogBHebW/A9og0Vx7dcGPa9ietwbdyNDMLjRzpjOsUVfeGfwXeqe3jvqz/tyzPOJac7NSzM5eG/XnCREvarqLewfQsszPLUqOldJavw28DcFlVjUcjxBVdnmboQxv1J3pe1dhUophjbvRqIIlNX/fvSxsUhUEu4uX525jQMP2FbrPxa2HsG7FrtLSoBCszNU2qVG1JqpVRbfUFrzY/6oaf47NZEGh0Bw+vKDCirUIUZ/UdIKeD3RUSrUlmJgvBSJXyReiDmiWkF6lCWFJ5bRuDa1JsNhDjuX7XHyxZSYz9q6hgT2Jy1qfwMCMDgCc2rQXK/K2MX7bPKwmM1prGtiTeaHvFZV/mRqWU1JXfOqeVdjNVi5sNYiLWw2p9Dj5mc378vXWOWF10A00QxtVbvmWEHVJjSZorbVfKXU7MJHgMqv3tNYravKZQsSjC1sPZt6B9SEtXwWkWhNCSnMW+FyMnfkKOd6i0oIoCw5s5JaOp3JZmxNQSnFP17O5ou2JLM/dRoY9mZ5preKuiEex31OyHWYBfh0skfrm2t9ZnruNZ/pcVvq59QW7WZa7lQx7MkMyOkVM3h2Tm3Jzx1N5Y91vmJQJE8Hk/Gyfy2O23acQtaHG10FrrX8Gfq7p5wgRzwZndOSKNify4aZpWJUZDSRYbPx7wDUhyfXLLbNDkjMEt5F8fe1vnNtiAIklre1GjlROaRK5ez2gDd7fMJkvt8ym0O+mW2oL7u16Dl1Ta29+5s87FpHnLS5NzhBcJz197yq2FO2jRUIDHlv8OdOz15TMhDfhNFt5a9BNtErMCLvf2LZDObVpL2btW4vdZGFoZheSrJKcRf0mlcSEqCU3dRzJ+a0GsThnMylWJ/0btAvbTGJG9pqQ5HyQxWRiTf5O+jVoe9TnPL9iAr/uXFw65r00dyt/mfc2Hw25ndZJmdF5maNYmLMp4pi7WZlYlbedBfs3MGPfmpAtM10BLw8s+oTPht4V8Z6ZjhRGtwirhihEvRXzWdxCHEsy7MmMbNKTgQ07hCXng+cjCRgG6bbEo94/11vEzzsXhSVHnxHgg01TqxZ0FbRMaIhVhXdXa6CJM43x2+aFdPcHz2m2F+9nZ3H5u3gJcSyRFrQQceSyNicwd9+6kARrRtE6KbO0nOcBTyG/71pKvt/FwIYd6FVmDHpb8X6sJnNYKzygDdbk74xanAsPbOLNdb+zuXAvrRIzuLnjqJCZ6Oe3HMjnW2bhCxxas23GRCNHKr3TWkfsJYBgbXRvhJa3EMciaUELEUf6NWjLXV3OxGG2kmixYzdZ6ZjSlH/1Cy5nmrdvPWOmvcBra3/lv+sncWfW+zy4+FMCJWO9zZzp+CIUMjGh6JAUndKYc/et469Z/2NxzmZyfcUszd3K3Qs+ZMbe1aWfaexM45UB19Lc2QCbyYJVmenboA1vDLwBpRSnNe0dcfvOZIuT1om10w0vRLyTFrQQceaCVoM4q3lf1ubvJtWWQOuSSVM+w89Diz8N6Rp2BbzM2beWP3cvZ1TTXjQs6UKftHt5yPiuzWzhqnbDohLfy6t/DutC9xg+Xl79c8iyp97prRl/0r3s8xRgN1tJsTpLz13eZih/7lnO9uIDuAJebCYLZmViXO9L4m5GuhCxIglaiDjkMNvold4q5Niy3K0YEfZvdwV8/LBjAaOa9gLg0R7n09CexDdb5+EKeOiY3JT/63Yu7ZMbRyW28kqKbi3eh9Y6JMEqpSJWWnNabPxvyK1M2bOSBfs30siZyjnN+1eoKpsQxwpJ0ELUGeW3LFWZcxaTmTs6n8HtnU5HozFFmIxWHQ1sSRFrYKdaEyrV+rWaLIxq2qv0i4UQIpSMQQtRR/RKa4XZFP6/rMNs5dwW/cOOK6WinpwBrmk3HIfJGhbDNVHqQhdCBEmCFqKOsJjM/KPvWJxmGw6zFbMy4TBZGd6oOydXYEetaLmo1WCubX8yCebgJLYEs42r2p7E5W2G1loMQhwLlI4wphUrAwYM0FlZWbEOQ4i4lu9z8efu5eR5i2memE4zZwM6JTetlb2gy/IbAXK8RaTZErBGmJEthKgYpdQCrXVYFR75v0qIOibF6qRvg7bcu+BD9nryMGHCrBSP97yQYY271VocFpNZJnUJUYOki1uIWqa1ZlXeDhbs31ipvaAPCmiDW+e9w7bi/bgDPooDHgr8bh5b8gVbi/bVQMRCiFiQFrQQtWhz4V7+uuADcrxFmJTC0Ab3dxvNWc37VfgeWfs3UOT3hu2P7NcBvts2jzu7nBntsIUQMSAtaCFqSUAb3Db/PXa5cnAFvBT5PbgCPp5bMYG1+bsqfJ8cbxEQPnfErw32uvOjGLEQIpYkQQtRSxYd2ESR3xOWWn2Gn/Hb5lX4Pr3TW4ds43iQ02zj+MzO1YxSCBEvpItbiFqS73NFPG6gOeANL/xRnqbOdMa0OI4fdmThKin7aTdZaJHQgJFNe0Yl1kjm7V/Pv1f/zMbCvaTbErmm3XAuajVYSnMKUUMkQQtRS4It3/CNLBxmKydldq3Uve7tejZ9G7Th661zKPZ7GdW0Fxe2GhRxA4poWJKzhXsXfFRa33ufp4DX1v5Kod/Nde1PrpFnCnGskwQtRC1paE/mqrYn8fHm6aUbXjhMVlonZnJqs96VupdSihFNejKiSc21mMt6c93vIZtvALgDPj7cOJUr254o66CFqAHyf5UQteimjiPpld6ar7fMocDvYmSTnpzZvB+/7VzCd9uzCOgAZzbry5iWx8VV0ttUuCficUNrDngKaexMq92AhDgGxM/fAEIcIwZndGRwRsfSnx9e/BkzsleXtqo3FOxh0u7lvD7w+hqppV0VrRIzOeAtCjuuFKTbk0KObSnax8IDG0mzJnB8ZmfsZmvYdUKIo5MELUQMrcnfyYy9q0P2V3YbPlbl72DOvnVxMyv75o4juSvrg5BubofJymWth5aOe2uteW7FBH7euRBFcKMOi8nE68ddT6eUZrEKXYg6Kz6+ngtxjFp4YBMBwpdMuQJesvZviEFEkfVr0I7n+l5Oq4QMILi15A0dTuGmjiNKP/PnnuX8umsxHsOP2whWOMv3ubhn4UcYEZaFCSGOTFrQQkTZyrztfLBxKluL9tEjrRXXtBtG84QGET+bbkvEqiz4CJ3dbTNZyLDHV53rEzI7c0JmZwxtROx6/3bbPFwRSpcW+Fysyd9F19TmtRGmEPWGtKCFiKJZ2Wu4ee47TNmzkg2Fe/hx+wLGznyVTYV7I35+eONumCOsIzYrE6c361PD0VZNeePinoA/8udR+IzI54QQ5ZMELUSUHByDdRu+0jrZAQxcAQ+vrvk14jUOs43XB95AE0cqTrONBLONdFsiL/W7igaHTb6qjiU5W7h9/nucM+V57l3wIavzdlTpPpsK9/LBxql8unkGe1y5IedOb9YHhyl8QphSim6pLar0PCGOZdLFLUSU5Ptc7PeEVwTTwOKczeVe1zmlGROG3c+Gwj0EtEGH5CaYozh7e1b2Gh5Y9GnpBK+97jzm79/Aa8ddR6/01hW+z5trf+eTzdMJlHRxv7H2N+7vNppzWvQH4NwW/Zm4azFr8nfhCnixKjNmZeJvvS6u9b2qhagPJEELESVOiy1Y9jJ8HwtSrQlHvFYpRYfkJjUS1z9X/hAy+1oTnCn+8uqfeW/ILRW6x+q8HXyyeQaeg13VJZO+nl85gRMyO9PAnoTVZOGNgTcyY+9qZu9bR0N7Emc370dTZ3q0X0mIY4IkaCGixGaycEazPvy6c/GhREawlOeVbU+MSUx+I8AO14GI59YWVHwHrd93L4s4jmxSiunZqxndYgAQHDsf1rgbwxp3q1rAQohSkqBFjdBas3jyctZmbaRx6wyOHzMQm73+F6y4t+s5FPjczMhejdVkxmcEuKjVYM5rOTAm8ZiVCafZTnHAE3YuzZYYg4iEEBUlCVpEnbvYw/0jn2Lz8m143T5sTiuv//V//Hvm32narnGsw6tRDrOV5/pezj5PAXvdebRKyCDJ6ohZPEopLmtzPJ9smhFSDMVhsnJV25MqfJ9RTXry5ZbZYfW4Da05MbNL1OIVQhwis7hF1H3+7Hg2LN6Mq9BNwB/AVeAmNzuPZ6/4d6xDqzUZ9mS6pbaIaXI+6IYOIxjT8jjsJisJZhsOs5Ur2p7IRa0GV/geXVKbM7bNUOwmCxZlwqrM2E0WHug2OqqzzYUQhyitI8xoiZEBAwborKysWIchquny1jeTvW1/2HGLzcIXO98mpUFyDKKKf34jwI87FvLLzsXYTGbGtDyOUxr3iNp+y8V+D9meAho7UnFUsT72psK9TNu7CqvJzIjGPWSTDCGiQCm1QGs94PDj0sUtos4IlF/WURuV/0KYvX0/hblFtOrSHLOlfi7XMbTBnVnvszx3W2lX9NLcLczZt55HepwXlWckWOy0ttirdY+2SY1om9QoKvEIIY5MurhF1J1y+YlYD5sQphS06d6S1IyKl688sDuHu4Y+yjWd7uCu4x/hosbXM+3r2dEONy7Myl7LirztIePEroCPX3cuKrcKmRCifpMWtIjI6/Ex9ctZLJ26ksZtMjn9ulPIaBa5nvThrnjsQrImLmb3pr24Ct04Eu1Y7VYe/OiOCj9fa81Dpz/NlpXbCPgNIJi4/nH1azTr0IQOfdpW5bXi1tz96yLWsQZYcGBjxFar3wgwf/8GCvwu+qa3JdMRX7W7hRDVIwlahCnKL+bOIQ+zd9t+3IVurHYLXzz/Hc/+8gg9hnY96vUJyU7eWPAP5v60kDXz19O4TSOGX3I8CcnOCsewYclmdm7YXZKcD/F5fHz3ys/c995tlX6veJZuSypdllWWWZkjFjnZULCb2+a/hycQLCvq1wZj2wzllk6n1lbIQogaJl3cIsyX/5jAro17cRe6AfB5/LiLPDx7xStUdFKh2WLm+NHHce3fL+PMG0ZUKjkD5OzOxWwJ/8/TMDR7tuyr1L3qgrOa9cUc4X9Hs1Kc2Cj0S5GhDe5a8AEHvIUUBTwUB7x4DT+fbZnJ7Oy1tRWyEKKGSYIWYaZ8MQufxxd2PG9fPrs27jnitZuWbWHW9/PZsyW7WjF07N8Onye8cpXdaWPAab2rde/a8t22+Zw1+TkG/fow50/9J5N3Ly/3s42daTzb93KSLA4SzXYSzDYy7Mm8dtz1YTOuV+btoNDnCruHO+Bj/La5UX8PIURsSBe3CGNzRF6CYxi63HOFuUU8fOYzbFy6BbPVjN/j46SLhnDfe7diNld+5nVaZirn33UW3732C+6iYBUsq81CSkYyZ900qtL3q21fb5nDK2t/wR0IftHZ7jrA40u/4hmTOaxFfNAJmZ2ZeMrDrMzbgdVkpktKs4hbO7oC3nKXXhX5wyuGCSHqJmlBizBn3zwKe0LochyTSdG2R0symjeMeM2LN7zBuoUb8RR7KM4rxuv2Mf2bOYx/+acqx3HdM5dz37u30m1IJ1p0bsZ5d53FmwtfICktvktUaq15a/0fpcn5II/h4z9rfzvitVaThd7premW2qLcfZd7prXEiDDU4DBZGdmkZ9UDF0LEFWlBizBn33wqS6euYu5PC1AmhTIpElMTefSLeyJ+3l3sYc6PC/B7Q7ukPcVeJvznVy6699wqxaGUYtjFxzPs4uOrdH2suAM+Cv3uiOd2FEfeuKIyHGYbD3YbzTMrvsNv+AmgcZpttEtqxNklWz8KIeo+SdAijNls5rEv72Hzim2smrOWjOYN6DeqV7ld1V63N+IWiwCugsiJqj5zmK0kWRzk+YrDzjVPqNhStaM5o3lfOqU047vt8zjgKeLERl0Y0aQHVpP8Ly1EfSH/N4tytenekjbdWx71c8npSTRuk8mOdaHbF5rMJo47o29NhUdudh7a0KQ3TquxZ1SFUoq/dBgZMgYNYDdZua3TaVF7Tvvkxtzb9Zyo3U8IEV9kDFpUm1KK+969BUeiHbM12Mq2Oawkpydy3dOXRf1529ft4raBD3B5y5sZ2+YWbup9L5uWb436c6rjwtaDuafL2WTaU1BAi4SGjOt1MSc2kp2fhBAVI5tliKjZuWE33736C9vW7KDH0C6c/ZdTK1XasyK8bi9j29xK3r780rreSkFiaiIfb36dxJTwoh5CCBHPZLMMUeOatW/CrS9fW6PPmDVhPh6XJ2TTDa3B7/Mz9YtZnHnjyBp9vhBC1JZqJWil1EXAk0BXYKDWOqvMuYeA64EAcKfWemJ1nhUvtNZM+WIWP771G16Xl1MuH8pZN43C5rDFOrQK2bF+F9O/mQtaM/T8QbTo1CzWIVXK3q378LrCi6i4izzVLo4ihBDxpLot6OXA+cBbZQ8qpboBlwLdgWbAH0qpTlrrQPgt6paXb36bPz+dXlo8Y9Pyrfz56Qz+NX0cFmt8d0h8/a8fef+RzzACATTw0bivueqJi7jk/jGxDq3COh/XAavDSqAw9D8lZ5KDzgM7xCgqIYSIvmpNEtNar9Jar4lwajTwudbao7XeBKwHBlbnWfFg+9qd/PHR1NLkDMG1vltWbmfmd/NjGNnR7dq4h/cf+RSv24vfFyDgC+B1efnwqa/YvnZnTGLy+/x8NO4rLm52I+emXMlTF/yTXZuOXEq017ButO/dGpvzUEUzq91K0/aNGXRmv5oOWQghak1NzeJuDmwr8/P2kmN12rLpqzCZI5ReLHST9dtiINiF/J+73uOBU//GR3/7itzsvFqOMrKZ382LuNFFwB9gxrfzYhARPHvFK3zx3Hfk7M7FVehm1oR53Hbcg+TsLf/PTCnF8789xqUPnEfj1plktszggrvP4uXp4zBbKl9SVAgh4tVR+2SVUn8ATSKcekRrPaG6ASilbgJuAmjVqlV1b1ejUjNTMJnCE7TFZqFh03SWz1jFQ6c/jc/rJ+APsHzGar579Rdez3qexq0zYxDxIcHazeH1m1Xpudq1c8Nu5vyQhdd9aDzZMDSeYg8/vDGRq564uNxr7U47Vz5+EVc+flFthCqEEDFx1Ba01nqk1rpHhF9HSs47gLIVLlqUHIt0/7e11gO01gMyM2ObxI7muNP7YI2wWYTZYua0a0/mn9e/gbvYQ8AfHB/1un0U5hTy7sOf1HaoYU44byCR8rDJbGbo+RUbfVg4aRnPXvFv/nbxi0wfPxfDMI5+UTk2L9+GxRb+/dDr9rFqzroq31cIIeqLmprV9D3wqVLqJYKTxDoCselHjSKrzco//3ySx0c/R86ePEwmE2aLiQc/vovE1ISIs4gNQ5M1cUkMog3VpE0jbnrhSt7+v48I9nRrlFJc98xlNO/Q9KjXv/PAR3z/+sTS8ff5vyyi/6m9eeLr+yrUAjcMg8WTV7B52VaadWhCo9YZpV9kyrJYLbTpcfTqZUIIUd9Vd5nVecCrQCbwk1Jqsdb6NK31CqXUl8BKwA/cVh9mcEOw/OUH615j8/KteFxeOvZrh9lixuMqf5s/Z7KzFiMs3+jbzmDQWf2Z+W1wPPqE8wbStG3jo153sABJ2e5od5GHBb8vZfHk5fQ95cg7KBUXuLj35CfYsXYXfq8fi91KakYybXu2ZsPiTSH7PlvsFsbcfkbVX1IIIeqJaiVorfW3wLflnHsaeLo6949XSina9mwdcszutDPk3AHM+T4LX5ldnewJNkbfdnpth1iuJm0accHdZ1fqmgW/LSFS/7i70M2cH7KOmqDfffhTtizfVvrn4vP68bo8tOjcjJMuHMK0r2YTCBi06dGSu9/6S8zH64UQIh7E98LdOubed27mkV3PsX7RRixWC16PjxMvHMIFd58V69CA4LKm1XPXYTKb6DywQ7m7Ux0uISUBc4TZ6xarmcQK7M385yfTQ760AAT8Bov+WMaPRR9z33u34vcFcBy2B7UQQhzLJEFHUWJqIi9PH8fmFdvYvWkv7Xq1olGr+GgNLvxjKeMueQkjYIAGq8PKU9/eT/fjOx/12iHnDuDft7wddtxkMTPqymFHvd4IRJ5MprVGa43VZo37Ii9CCFHbZDerGtCme0sGn90/bpJzzp5cHh/zDwpziijOd1Fc4CIvO5+Hzvg7RfnhexYfLiHZybgfHiQxNYGEFCcJKU7sCTbu/e/NNG139DHsE8YMDFujrEyKnid2xWoLnxUvhBBCWtDHhMmfzURHWBKltWbG+Lmcds3JR71H72Hd+XL3f1kyZQV+r58+J3fHmVSxyW83vnAlS6etJH9/Aa5CN45EO3annXveubnS7yKEEMeKYz5BG4bBvu37SUhJIKkC46l1Ud7+/JAZ2Af5vQHy9xdW+D42u5XjTutT6eenN0rlvVUvM/2buaxbtIlWnZtx8mUnVDjBCyHEseiYTtCzvp/Pyze/TXFeMYZhMOC0Pjzwwe0kptavRN33lJ6Mf/mnkBriAGaLib6n9KiVGGwOGyPGnsiIsSfWyvOEEKKuO2bHoNct3Mgzl79Mzu5cPC4vPo+frF8X88R5L8Q6tKjrPbw7vYd3x5F4aJa0I9HOCecNokPftjGMrGr2bMnm1dv/y1/63se4i19k7YINsQ5JCCGirl63oHdu2M3vH06h4EAhg87qT/9Te5fW0v7qxR/Cun19Xj+r5q5j54bdNGsfqfx43WQEDC65fzQd+rZl+YzVmK1mzrx+BCdeOLj0M8tnruant3+nMKeIky4cwvBLj484gcvn9fHZc9/x01u/4S7yctzpfbjpH1dUeUKc1+Nj0aRleIo99DmlBykNko/4+e1rd3L7oIeCJVV9ATYt3crcnxfy2Jf3ym5WQoh6pd4m6KlfzuKFa/+D3x/cWnHiB1PpeWJXxn3/AGazmV0bdqON8N2drDYL2dv315sEvXTaSp664J/4S9Yhm21mnvjqPnoP7176ma9e/J4PnvgCr8uH1prFk5fz09u/88/JT4Ytf/r7pf9iwcQleFxeAKZ/PZvFfy7nvdUvHzW5Hm7FrDU8evazwZreOrhO+y//vIpzby2/sMu7D39KcYGr9N+d1hpPsZd/3/I29713Gx899SU71u2iXe82XDvuUjofJ3tECyHqpnrZxe0qcvPCda/jcXkJ+IIVRt2FbpZNW8m0r+YAwW5fazmbNbTtUbVdtdzFHnze8MlYsVKQU8gjZz9L/v4CiguCy6sK9hfy6DnPkb+/AIC8ffm8/+jneIq9pdtRuos8bFiymalfzg6537Y1O8gqk5whWGvcVeTm53cmVSo2r9vLI2c9Q2HuoaVfXrePt//vIzYs2VzudUumrIj4xWr/rhweO/c5ls9YTc6ePBb8toR7T36C5TNXVyouIYSIF/UyQS+fvgqzJfzV3EUeJn0yDYDz/3oWzmRnyP7OjkQ75915BikNgy3BgD/A0mkrWTx5OV5P+Yl33cKN3NL/fkanXsW5KVfx9GX/ojC3KMpvVXlTv5wNEfaA1tpg6pezgOAe11Z7+BcVd5GH6ePnhBzbuHQrlgh7LntdXlbNWVup2LImLomYaH1ePxPf/7Pc6w7+uzmc4TfwlvniAOAp9vLWfR9WKi4hhIgX9bKL22ovv/iF3WkDoEGTdN5Y+A8+fPJLsiYuIaVhEhfdey4jrzwJgOUzVvHEef/A7zu0x8fDn/41bJxz34793HvyE7gK3EBwvHfmt/PYvTmbV2c/E+1Xq5SCA4URl1d53b7S5VUJKQkRr1UmRXKDpJBjzdo3jrjFpNVmoXW3FpWKzVXoLm2xl2UEDIryXOVed+G95/DmPR/gKT40I91qtwRLiYbfjo1HaI0LIUQ8q5cJusfQLlis4S09R6KdM24YWfpzo5YZ3PfurWGfK8ov5uGznilNugeNu+hF/rfuVTKaNSg99sMbv+H3hNaZ9nn9bF6+lXULN9KxX7vqvs4R5R8owOawRaxj3fvk7tjsVtzFocur7E4bfUqWV/Ue1g2bw0ZxfmhStDmsnH3TqJBjHfq2pXW3lmxYsrl0TBvAYrNwzi2nVSruviN64I+w3aQjycHQ8weVe91ZN45k5/rdfPfaL9jsVnweH/1G9WLJlJW4CsITe3qTtErFJYQQ8aJednFbrBbGff8gCSlOnMkO7An2YMK5+VT6j+p11OtnjJ8buXVnaCZ/NiPk2OYV28I2ggAwmU3sXL+76i9xFMtnrubaLndySbObOK/BNTx1wT8pyAktOtJ1UEeOO6Nv2PKq/qf2ptuQTgCYLWaem/go6Y1TSUgOlvG0Oazc+PwVYROslFI8N/FRjh99HBabBbPFTLverXnhzyfJbNGw9HM5e3JL/lzKHxZo0CSdq5+6BHuCDWVSpbH1Oqkbg84qfza2Uoqb/nElX+x4m2d+eYQP1r3KuAkPcsFfz8J+2JcUe4KdsY9ccJQ/SSGEiE8qUiKKlQEDBuisrKyo3c9d7GHOD1kU5RXTb2SvCtWNBvjmXz/y7sOfhOxTfNClD4zh+mfHlv78xT++48Onvgob/7Q5bby58B+07Ny8ei8Rwa6Ne7ip970hhUcsNgsd+rYN61Y3DIMpn8/k1/9NBkNz2rWnMPzS48N2sgoEAiyfvpriAhc9T+x61KpqPq8Pv9cfUg2sKK+IZ8b+m0WTlmOxmlEmxc0vXs0Z148o9z6r5q7j1/f+pLjAxbCLhjDk3AEV3mXr8Pjfe/hTJvxnIgpQZhNjHzmfi/9vNCrCVplCCBEvlFILtNYDwo7X5wQdSfb2/Xz90g+smLmGVl2bc9F954bN2t60bAt3DH44ZLYyBFt4f//hIXoP705udh5r5m/A5rDy9KX/ouBAIUbJpCe700a/Ub3423cP1Mg7vHnv/5jw2q8h4+MH43t5xt9p37tNjTz3aB464+8smbzisP2w7fxtwgP0G3HkPaOjxePykLs3nwZN02QjDiFEnVBegq6XY9Dl2b5uF7cPfBBPsQe/L8C6hRuZ9vWcsATStmdrTr5sKFO+mFnaSnUk2uk7oic9T+rK+49+xtcv/YDFZsEIGDRomk7XIZ1YOmUl9gQbZ900issfOb/G3mPrqh1hyRmC3eq7N+2NSYLet/MAS6auDOvu9xR7+PKFCbWWoO1OO41bx8cuYkIIUR3HVIJ+5/6PKM53lY4vGwEDT7GHf9/8Nv9b+0pIV+g979zMoLP68et7f2IEDEZeOYxhFw9h5nfzGf/vn/C6faUzpHdv2ktSWiIT8mpnSU+PE7uyZMqKsBnafq+fdr1bH/X6/bty+OTvXzP3p4UkpSdywV/PZtRVw6rVFZy7Jw+rzYIvwqzx7O37q3xfIYQ4Vh1TCXrJlBURJ3/t3ZpNUV4xiakJuIvc2Bw2zBYzQ88bxNDzQmcUf/vv8E0njIDB5hXb2LVpD03bVmycuzrOvmkU41/+Cb8vgBEILnuyJ9g4YczAoz4/f38Bt/T7P/IPFBLwBdi7dR+v3v5fNi7ZzM0vXVPlmFp0blYaS1lmq5l+p9RO61kIIeqTejmLuzzlTXxSJhMrZq3hmk53cF6Dazg35Speuf2/EYuTHD5T+iCzxURRbnFU4y1PSsNkXs96npMvG0pygyQatc7gqicv4f7/3X7Ua79//VeK8opLK6xBsCjJ92/+Rs7evCrH5Eiwc+3Tl4Us9zJbzCQkO7nkgdFVvq8QQhyrjqkW9Pl/PYv3HvkstMiFw0q/kT0Zd9GLpZPCAn4vE9+fTGFOEQ9/clfIPU4YM5Ad63bjOyx5m8wm2vRoWfMvUaJRywwe/PCOSl+3eHJ41zgE93pev2hTlfZ7Puj8O8+iefsmfPHCBPbvzKH/qF5c9tD5ZDRvePSLhRBChDimEvSYO85g+7pd/Pren6VFLvqO6InZbA5LWl6Xl5nfziVnbx7pjVJLj19w99lM+ng6OXuC21SaTAqrw8rdb/4lbGOJeNS0bSOWTV8V1h3t9/lD1jJX1aCz+jPorP7Vvo8QQhzr4j+jRJHJZOLO127gyscvYuuq7TRp04jGrTO5ocfdEcemrXYre7fuC0nQyelJvLn4BX757yTm/7qIRi0zGH3HGXToUzf2VT7/r2cx+YtZIb0IFquZtj1a0aZ77fUACCGEOLJjKkEflN4oNSTpdh3UkW1rdoa1Kn0eH807hG87mZiSwIX3nMOF95xT47FGW9uerXn087t56aY3Kc4vxggY9DqpGw8d1pUvhBAituplgt6ychufPfcdG5dspkPftlzywBhady1/M4dLHzqPqV/NxlV4qPa2PcHOObecetSKWnXR4LP78/n2t9izOZuEFCepGSmxDkkIIcRh6l0lsZWz13D/qHH4PD6MgIHJbMLmsPLCpCfoMrBjuddtWraFt/7vI1bMWkNKw2QuvOdsxtx+hpSJFEIIUaOOmVKftwy4n/ULN4Ud7zKoY8y3fxRCCCEOd0yU+tRas2HR5ojn1mZtqN1gKsHr9rJ02ipMJkXPk7oetYa0z+tj2ldzmP1DFqmZKZx140ja9Tp6BbGqMAyDn9+ZxA9vTMRd7OGkC4dw6QOjSUytf13/QggRT+pVglZK4Ux2hO1tDJCYmhCDiI5u7k8LePryl0u70pVSPP71feXWrvZ6fNw7/Ak2L9+Ku8iDyWxi4vt/cufrN3LqVcOjHt8L1/6H6d/MLZ31/c2/fmDG+Dm8uegF7M7wPaiFEEJER72rJHbubadjT7CFHLMn2Bhzxxkxiqh8B3bnMO7il3AVuCnOd1Gc76Ior5gnxjxfbsWy3z+YwqZlW0vLjQbriXt55db/4ipyR7ymqrav28W0r+eELMnyefzs23GAr1/6kUfPfY7RaVdxeeub+eZfP2AY4aU+hRBCVE29S9BXP3kxwy85AavDSmJqAlaHlVMuP5Gxj1wQ69DCTPl8VsT112iY/vWciNdM/Wp2SMI8yGwxsWr22qjGt2rOWszm8P9E3EUePvrbV8z7aSHF+S6yt+3n/ce+4LU73o3q84UQ4lhWr7q4ASxWC/e9eys3PDeWnRv20LxDk1pbRpR/oAC/10+DJukV+nxhXhE+jz/suN/npzC3KOI1CSnOiMe11jiSHBUPtgIaNmsAESaxK5PCCBghXy48xR4mvj+Zq568mLTM1PCLhBBCVEq9a0EflJaZSrfBnWolOWdv38/dwx7nkmY3cUXb27iu612sqcCktP6jeod1x0NwB6j+p/aOeM25t5yGIzF87DcxJYEuAztUPvgj6HNyd1IaJmOK0IrWRuTKa9tW74xqDEIIcayqtwm6tgQCAe4+6TFWzlqD3+vH5/Gxbc1O7h/xFDl7co94bbchneg1rBtmq7n0mCPRzsmXDqV97zYRr+k3shcX3nsuVruVhGQnCclO0hql8swvj2AyRfdfp8lk4qUpT9GxX1tsDiuOBDsNmzeg34hemEzhTWufx0fjNplRjeFoAv4AM7+bxxt3v8+X/5xw1D9zIYSoK+pdF3dtW/jHMvL3F0bcfGLi/6Zw6QNjyr32l3cnsXjyitLWqMlsoueJXbn77b8c8ZlXP3kx59w8iqVTV5LcIIk+J/fAbDEf8ZqqatQqk9fmPse+HftxF3tp1r4xW1Zu547BD4eMhdscVvqf2ptGLTNqJI5IPC4P9w5/gi2rduAudGNzWPnoqa959peH6TG0a63FIYQQNUFa0NWUvXUfRiAQdtzr9rFzw+5yr8vNzuM/d76H1+UtTe5GwGDZ9FWsmLnmqM9t0CSd4ZecQP9RvWssOZeV0bwhLTo2xWQy0bZHK/7+w4O06NQUsyVYqW3E2JN4+NO/1ngcZU147Vc2Ld+Gu6REq9ftw13k5unLXpYZ5UKIOk9a0NXUaUD7iMcdiXZ6nNCl3Ovm/7oYk8UMhG5z6Sn2MOXLmfQ8Mb5bgH1O7sH7q1+huMCFzWGN2labxQUupn8zh7zsfHoP707n48ofV//j42l4S/bwLqswt4htq3fQupvsziWEqLskQVdTh75t6T28B0smL8dTkiysNktJC/f4cq8zm01ELPOtVK20iKMlITnyrPKqWD1vHQ+cOg4jYODz+rFYLRx3eh8e/eJuzObwPxOLNfKfk9Yacx3Ym1sIIY5Eurij4Mnx93HF4xfRtF1jMpo34JxbT+PVuc9gc4TP0D5o4Jn9MPzh3bAHu4uPNYZh8OR5L1Cc78Jd5CHgC+Ap9pA1cTGTPp4e8ZpTrzkFuzP0z1gpyGyZEXGbUCGEqEukmREFVpuVSx8Yc8QJYYdLSkvkoU/u4tmx/y5dVwxw6QNj6FxOt3l9tnHJFooLwku0uos8/PLuJE69enjpsX07D/DS9W+wcNJSAgEDZVJYLGYsditWu4Unv7lPdiETQtR5kqBrmd/nZ9pXs5nx7TySGyTxt+8fYPembLxuL4PO7EfTdo1jHeJRbV+3i9fvfI9Fk5djd9o4/bqTufbvl1WrNrdhGBGLogAhM+T9Pj93nfAI+7YfCDlusVm48z83cNJFQ7DZj7zZiBBC1AWSoGuR3+fnvlOeZMPizcGNLkyKSZ9M4y//vIoxt8dfrfBIcrPzuGPwQxTlFqO1xu/188Mbv7F5xXae+/XRKt+3fZ822J02XAWh9cTtCXZOu+bk0p/n/rSQggOhy9q01mjA7wtIchZC1BsyBl2LJn8+szQ5AxiGxlPs5c17PqQoL3Jpz3jz41u/43V5Q8p8et0+lk1fxZaV26p8X7PZzGNf3osjyYGtZFzZkeSgx9AunHrN8NLP7Vy/G5/bF3a9u9DN9jU7qvx8IYSIN9KCrkXTvppdmpzLstjMLJ22iiHnhO3XHXfWzF+PN0KCtFjNbF6xvVpLm3qd1I2PN/6HyZ/PJC87n17DutHn5B4h48nterfGarfi94WuPXcmOejQt22Vny2EEPFGEnQtSkpPRCk4fAMrraO7XKkmdejblgW/LcXnCU3SAb9Bqy7Nqn3/1IyUI3b39x3Rk2YdmrB11fbSjUYsVjNpjVI54byBFOUXM+Xzmezduo8ugzoy8My+EZdoCSFEvJMu7lp01k2jsEWYSOVIsNHjxPKLmsSTs/9yKlZ76Pc6q91K5+Pa07Zn6xp/vslk4sUpT3HGDSNJTk8iMTWBkVeexGtzn2X7mp1c0eZW3rz3Az59ZjzPjv03tw96KOr7ZAshRG1QEfcjrujFSr0AnAN4gQ3AtVrr3JJzDwHXAwHgTq31xKPdb8CAATorK6vK8dQFX7wwgQ+f+AKLzQI6uO75ud8eK3dzjHi0aflWXrn1HVbMWoPVZmHE2JO45V9X40yKbS/AjT3vYfOK0HFwm8PKRfedyzV/uzRGUQkhxJEppRZorcPGOKuboE8F/tRa+5VSzwNorR9QSnUDPgMGAs2AP4BOWuvwotVlHAsJGiBvXz5Lp64kMTWB3sO716nKYWUZhoFSKi7WHO/beYCrOtwecQJZ4zaZfLzx9RhEJYQQR1degq7WGLTW+rcyP84BLiz5/Wjgc621B9iklFpPMFnPrs7z6ovUjBROvGBwrMOotmhvb1kdJpOCcr5rxlOcQghRUdH8m+s64JeS3zcHyvY1bi85JkSNaNAknVbdmoe15m1OG6ddOzwmMQkhRHUcNUErpf5QSi2P8Gt0mc88AviBTyobgFLqJqVUllIqKzs7u7KXC1Hq0c/uJiUjGWeSA7PFjCPJQecB7bno3nNjHZoQQlTaUbu4tdYjj3ReKXUNcDYwQh8a0N4BlF0Q26LkWKT7vw28DcEx6KOHLERkLTo149MtbzDj23ns276fzsd1oNewbnExRi6EEJVVrTFopdTpwP3AMK11cZlT3wOfKqVeIjhJrCMwrzrPEqIibA4bp1w2NNZhCCFEtVW3UMlrgB34vaSVMkdrfbPWeoVS6ktgJcGu79uONoNbCCGEEIdUdxZ3hyOcexp4ujr3F0IIIY5Vsv5ECCGEiEOSoIUQQog4JAlaCCGEiEOSoIUQQog4JAlaCCGEiEOSoIUQQog4JAlaCCGEiEOSoIUQQog4VK39oKNNKZUNbIl1HOXIAPbFOogaUF/fC+rvu8l71T319d3kvaKjtdY68/CDcZWg45lSKivShtp1XX19L6i/7ybvVffU13eT96pZ0sUthBBCxCFJ0EIIIUQckgRdcW/HOoAaUl/fC+rvu8l71T319d3kvWqQjEELIYQQcUha0EIIIUQckgRdQUqpe5VSWimVUfKzUkq9opRar5RaqpTqF+sYK0MpNa4k7sVKqd+UUs1Kjtf193pBKbW6JPZvlVJpZc49VPJea5RSp8UwzCpRSl2klFqhlDKUUgMOO1fX3+30ktjXK6UejHU81aGUek8ptVcptbzMsQZKqd+VUutK/pkeyxirQinVUik1WSm1suS/w7tKjtfpd1NKOZRS85RSS0re66mS422VUnNL/pv8Qillq+3YJEFXgFKqJXAqsLXM4TOAjiW/bgLeiEFo1fGC1rqX1roP8CPweMnxuv5evwM9tNa9gLXAQwBKqW7ApUB34HTgdaWUOWZRVs1y4HxgWtmDdf3dSmL9D8H/9roBl5W8U131P4L/Hsp6EJikte4ITCr5ua7xA/dqrbsBg4HbSv491fV38wCnaK17A32A05VSg4HngX9prTsAOcD1tR2YJOiK+RdwP1B2wH408KEOmgOkKaWaxiS6KtBa55f5MZFD71bX3+s3rbW/5Mc5QIuS348GPtdae7TWm4D1wMBYxFhVWutVWus1EU7V9XcbCKzXWm/UWnuBzwm+U52ktZ4GHDjs8Gjgg5LffwCMqc2YokFrvUtrvbDk9wXAKqA5dfzdSv6uKyz50VrySwOnAF+XHI/Je0mCPgql1Ghgh9Z6yWGnmgPbyvy8veRYnaGUeloptQ0Yy6EWdJ1/rzKuA34p+X19eq/D1fV3q+vxV0RjrfWukt/vBhrHMpjqUkq1AfoCc6kH76aUMiulFgN7CfbCbQByy3zZj8l/k5bafmA8Ukr9ATSJcOoR4GGC3dt1zpHeS2s9QWv9CPCIUuoh4HbgiVoNsIqO9l4ln3mEYJfcJ7UZW3VV5N1E3aa11kqpOrt8RimVBHwD/FVrna+UKj1XV99Nax0A+pTMWfkW6BLbiIIkQQNa65GRjiulegJtgSUl/xG2ABYqpQYCO4CWZT7eouRY3CjvvSL4BPiZYIKu8++llLoGOBsYoQ+tI4z794JK/Tsrq0682xHU9fgrYo9SqqnWelfJkNHeWAdUFUopK8Hk/InWenzJ4XrxbgBa61yl1GRgCMHhPUtJKzom/01KF/cRaK2Xaa0baa3baK3bEOzm6Ke13g18D1xVMut5MJBXppsn7imlOpb5cTSwuuT3df29Tic4X+BcrXVxmVPfA5cqpexKqbYEJ8HNi0WMNaCuv9t8oGPJrFkbwQlv38c4pmj7Hri65PdXA3WuN0QFWynvAqu01i+VOVWn300plXlwtYdSygmMIji+Phm4sORjsXkvrbX8quAvYDOQUfJ7RXDm6QZgGTAg1vFV8l2+ITgreCnwA9C8nrzXeoLjmYtLfr1Z5twjJe+1Bjgj1rFW4d3OI/gl0QPsASbWo3c7k+Cs+w0Eu/NjHlM13uUzYBfgK/n3dT3QkOAM53XAH0CDWMdZhfcaSnDy1NIy/3+dWdffDegFLCp5r+XA4yXH2xH8orse+Aqw13ZsUklMCCGEiEPSxS2EEELEIUnQQgghRBySBC2EEELEIUnQQgghRBySBC2EEELEIUnQQgghRBySBC2EEELEIUnQQgghRBz6f1fFWAXRX2zvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.scatter(axis[\"x\"], axis[\"y\"], c=axis[\"group\"])\n",
    "c = plt.Circle(tuple(axis.loc[UAE]), 0.01, color='r')\n",
    "ax.add_patch(c)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 1, 3]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = axis.sort_values(by=\"x\")[\"group\"].unique().tolist()\n",
    "sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axis.loc[UAE, \"group\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ['Argentina', 'Armenia', 'Azerbaijan', 'Belarus', 'Botswana', 'Bulgaria', 'China', 'Colombia', 'Costa Rica', 'Croatia', 'Czech Republic', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Estonia', 'France', 'Gabon', 'Georgia', 'Greece', 'Hungary', 'Japan', 'Jordan', 'Latvia', 'Lesotho', 'Lithuania', 'Mexico', 'Morocco', 'Palestinian Territories', 'Panama', 'Peru', 'Poland', 'Portugal', 'Romania', 'Russia', 'Saudi Arabia', 'Slovakia', 'Tunisia', 'Turkey', 'Venezuela', 'Yemen']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 ['Austria', 'Bahrain', 'Bosnia and Herzegovina', 'Cambodia', 'Cameroon', 'Chad', 'Denmark', 'Ethiopia', 'Germany', 'Ghana', 'Guinea', 'Honduras', 'Hong Kong S.A.R. of China', 'India', 'Israel', 'Kyrgyzstan', 'Laos', 'Liberia', 'Malawi', 'Mongolia', 'Mozambique', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Pakistan', 'Paraguay', 'Rwanda', 'Serbia', 'Sierra Leone', 'Singapore', 'Switzerland', 'Togo', 'Uganda', 'Ukraine', 'Zambia']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 ['Afghanistan', 'Albania', 'Algeria', 'Bangladesh', 'Belgium', 'Benin', 'Bolivia', 'Brazil', 'Burkina Faso', 'Chile', 'Congo (Brazzaville)', 'Cyprus', 'Finland', 'Guatemala', 'Iraq', 'Italy', 'Ivory Coast', 'Kazakhstan', 'Kuwait', 'Lebanon', 'Libya', 'Luxembourg', 'Madagascar', 'Mali', 'Mauritania', 'Mauritius', 'Moldova', 'Montenegro', 'Namibia', 'Philippines', 'Senegal', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Taiwan Province of China', 'Tajikistan', 'Uruguay', 'Vietnam', 'Zimbabwe']\n"
     ]
    },
    {
     "data": {
      "image/png": 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pQkND/3H7vffey7x58xg3bhwFBQXs37+fJUuW8MMPP3D48GFOnTqFlZUV3bp1Y+TIkTg7OxMbG8uSJUvo2bPnf17z9ddfZ9u2bbi7u5Ofn38jT1HUUuMaVhJC1NqRjYlcOJ1D38m+tPax0zrODTE2MWLAnZ3oNcGHuGOZrPnwOCUFFVrHEkKIv7j55puJjY0lKyuLn3/+mfDwcExMasYjBw8ejJOTE5aWlkyYMIG9e/cC0K5du6sWyAC9e/dm+vTpfPnll+h0unp9HqKGjCQL0QQlRGYRsTmJTje1xr+fu9Zx6oSiKHQd0g77llb8+s3pS1tZB+HsYat1NCGERq424ltf/P39Wbly5RXvmzZtGj/88ANLly7l22+/vXy7oih/edwfX1tbW1/xdoDy8vLLf164cCGHDh1i06ZNhIaGcvToUZycnG74uYh/JyPJQjQxeeklbF98hpbtbLn51g7/eGFu7LxDXJjwVCiqCqveP0biyWytIwkhmpmBAwdSUVHBokWLLt928uRJ9uzZw/Tp05k/fz4Afn5+l+//9ddfyc3NpaysjLVr19K7d+8rntvV1ZWzZ8+i1+tZs2bN5dvj4+Pp0aMHr7/+Oi4uLiQnJ9fPkxOXSZEsRBNSWV7NloVRGJsYMeyBQExMm+a8XZe2tkyaE4ZjKys2f36SyO0XUFVZ0CeEaBiKorBmzRq2b9+Oj48P/v7+PPfcc7Rq1QpXV1c6d+7M3Xff/ZdjunfvTnh4OEFBQYSHhxMWFnbFc7/zzjuMGjWKm266idat/79l59NPP01gYCABAQHcdNNNBAcH1+tzFKAY2htLWFiYGhERoXUMIRodVVXZuugUiSeyGfNYCB4dHbSOVO+qKnX8tvgM8cey8OvjRr9bO2BsIFttCyHqx9mzZ+ncubPWMf5VaWkpgYGBHDt2DDu7mvUgixcvJiIigk8++UTjdM3blX52FEU5qqrqFX9jkXcTIZqIY9vOk3A8i5sm+DSLAhnA1MyYoTMCCB3ejjN7L7LhoxOUl1RpHUsI0Uxt376dzp078+ijj14ukEXjJSPJQjQBF07nsOGTE/iGtmTwvf5Nbh5ybcQcTGPHD9G0cLJk5ENB2LtaaR1JCFEPDH0kWRguGUkWopkpyCrjl69P4+Rmw4A7OzfLAhmgY8/WjJ3dhfKSKla+G0FqTJ7WkYQQQjRiUiQL0YhVVerY8kUUAMNnBmBq3jQX6tWWW3t7Jj4bhlULM9YviOTMvotaRxJCCNFISZEsRCOlqio7v48mJ7WYwff6Y+ci0wsA7FwsCX82DPdODuz8Ppr9q+LQy1bWQgghrpEUyUI0Uid3pBB7JIMeo71p5y8N5f/M3NKEUQ8HEXizO8d/vcDWL6KoLK/WOpYQQohGRIpkIRqh1Jg89q2KwyvYmdBh7bSOY5CMjI3od2tH+k7pQNLJbNZ8cIzivPKrHyiEEP8hJyeHkJAQQkJCaNWqFe7u7pe/rqys/MtjFy5cyHfffQfAyy+/zPbt2wHw9PQkO/ufGyHddNNN/3ntt99+u46ehaiNWnW3UBRlGLAAMAa+UlX1nb/dPx14H0i9dNMnqqp+dem+u4AXL93+pqqqS/7rWtLdQoj/VpRbzor/HcHC2pSJz4ZhZim7y1/N+dM5bPvyFKbmxox4MAhXzxZaRxJCXCdD6m7x6quvYmNjw1NPPXVNx3l6ehIREYGzs/M1HWdjY0NxcfE1HSP+X513t1AUxRj4FBgO+AG3Korid4WHLlNVNeTSf38UyI7AK0APoDvwiqIozaOBqxD1oLpKx9Yvoqiu0jN8ZqAUyLXUzt+J8GdCMTYxYs0Hx4g7mql1JCFEE/Lll1/SrVs3goODCQ8Pp7S0FKgpoufOnQvA9OnTWbly5V+OKysrY/jw4Xz55ZdATREMkJaWRr9+/QgJCSEgIIA9e/YwZ84cysrKCAkJ4fbbbwdg3LhxhIaG4u/v/5ctsm1sbHjhhRcIDg6mZ8+eZGRk1Pv3oCmqzTtsdyBOVdUEAEVRlgJjgTO1OHYo8KuqqrmXjv0VGAb8fH1xhWi+VFVl99JzZJ4vYvjMQBxaWWsdqVFxcrNh4rNhbFkYxbYvT5Gf4U3o8HbNtmWeEE1B+ttvU3E2uk7Pad65E62ef/6ajpkwYQL33XcfAC+++CJff/01jz766H8eU1xczNSpU5k2bRrTpk37y30//fQTQ4cO5YUXXkCn01FaWkrfvn355JNPiIyMvPy4b775BkdHR8rKyujWrRvh4eE4OTlRUlJCz549eeutt3jmmWf48ssvefHFFxHXpjZzkt2B5D99nXLptr8LVxTlpKIoKxVFaXMtxyqKcr+iKBGKokRkZWXVMroQzcvpPRc5uy+N0OHt8A5x0TpOo2TVwoyxj4fQoYcrh9YnsH3xGXRVeq1jCSEauVOnTtG3b18CAwP58ccfOX369FWPGTt2LHffffc/CmSAbt268e233/Lqq68SFRWFra3tFc/x0UcfXR4tTk5OJjY2FgAzMzNGjRoFQGhoKElJSdf/5JqxuvqsdgPws6qqFYqiPAAsAQbW9mBVVRcBi6BmTnIdZRKiyUhPKGDPsnO09XOk+2hvreM0aiamxtwy3Q8HVysOrU+kKLuc4TMDsbQ10zqaEOIaXeuIb32ZPn06a9euJTg4mMWLF7Nr166rHtO7d2+2bt3Kbbfd9o9PtPr168fu3bvZtGkT06dP54knnvhHMb1r1y62b9/OgQMHsLKyon///pSX1yxONjU1vXxOY2Njqqulu8/1qM1IcirQ5k9fe/D/C/QAUFU1R1XViktffgWE1vZYIcR/KymoYOsXUdg4mDP4Xn+MjGR6wI1SFIWwEV4MvS+AzAtFrHw3gpyLshhGCHF9ioqKaN26NVVVVfz444+1Oub111/HwcGBhx9++B/3nT9/HldXV+677z5mzJjBsWPHgJrit6qqCoCCggIcHBywsrIiOjqagwcP1t0TEkDtiuQjgK+iKF6KopgBU4H1f36Aoiit//TlGODspT9vA4YoiuJwacHekEu3CSFqQafTs+3LU1SUVjN8ZiAW1qZaR2pS2oe2ZPwTXamu1LP6vaNcOJ2jdSQhRCP0xhtv0KNHD3r37k2nTp3+ct9/rXtYsGABZWVlPPPMM3+5fdeuXQQHB9OlSxeWLVvGY489BsD9999PUFAQt99+O8OGDaO6uprOnTszZ84cevbsWfdPrJmrbQu4EcB8alrAfaOq6luKorwORKiqul5RlP9RUxxXA7nAg6qqRl869h7gj89D3lJV9dv/upa0gBPi/+1edo6onSkMvtePDt1aaR2nySrKLWfTZyfJTS2mz+QOBA3w0DqSEOJfGFILuKt59NFH6dq1K3fffbfWUQTX3gKuVkVyQ5IiWYgaMQfT2L74LMGD2tBnkq/WcZq8yvJqfv3mDEknswm82Z0+k30xMpb9loQwNI2lSH7ppZfYtm0bW7ZswclJdkU1BHXeJ7m50OtkhbswHFkXitj5YwzuHey5aYKP1nGaBTMLE4bPDCRkcFuifk9l06cnqSiTxS5CiOvzxhtvcPjwYSmQGzEpkgG9XmXTZyfZvzpOimWhubLiSrYsjMLSxpQhMwJkNLMBGRkp9A5vz4A7OpESnceq945SkFWmdSwhhBAakHdfQNWr2DpZcvyXC6xfEElJQcXVDxKiHuh1en756jSlhZUMeyAQqxbSlkwLfn3cGP1YCKUFFax8N4KLcflaRxKNSGlhJVG7UtjwcSSJJ6T3vxCNlRTJgLGJEf1v68ig6Z3JSCxk+dtHSJM3RaGBg+sSSInOo9+tHXD1bKF1nGbNo6MDE58Nw9zKhHXzjxNzME3rSMKAVZRWcXb/RdZ/FMniOfvYvfQcafEFbF10iqSobK3jCSGuQ11tJtIkdOrZGmcPW7Z+EcXaD49zU3h7ggZ6yLa1okHEHc3k+C8X8O/njl9vN63jCMDe1YqJz4axdVEU2xefJS+jlB6jvVGkV7UAqip1JJ3MJvZIBudP56CvVmnhbEHXIW3x7eaKjaMF6+cfZ+sXpxj5UBBt/By1jiyEuAYykvw3zh42THoujHaBTuxdEcsvX52mslwW74j6lZNazG/fnaWVdwv6TpZOFobEwtqU0Y+G4Ne7NUe3nGfbV6eoqtRpHUtoRFetJ+lkNr98fZpvnt7LL1+dJiOpkIB+7oQ/G8odb/Si5zgfnNxtMLc0YfSsEOxdrdj8+UlSz+VpHV/UgaSkJAICAv5y26uvvsrcuXP/87iIiAhmzZoF1PRB3r9//zVf29PTk+zsK38yERkZiaIobN269ZrPe7Vz/2Hx4sW4uLgQEhJy+b8zZ87U+hoLFy7ku+++u6Zc1/u9qgsyknwF5lamDH8gkOO/XuDg2nhyUosZ9kAgjq2ttY4mmqCK0iq2LIzCzNyYYfcHYmwiv7saGmMTI/rf0Qn7VtbsXx1HUc4xRjwUhLWdudbRRAPQ61Uunssj9kgG8cezqCitxtzKhA7dXPHt5oqbr/2/7oRpYW3K2NkhrPnwOBs/PcmYWSG09rFr4GcgDEFYWBhhYTWdxnbt2oWNjQ033XRTnZ3/559/pk+fPvz8888MGzbsH/erqoqqqhgZ3dh7zJQpU/jkk0+u69iZM2de8fbq6mpMTK5cktbH96q25N34XyhGCl2HtmPM7C6Ul1Sx4p0IYiMytI4lmhhVr7L92zMU5ZQz9P4ArO2l6DJUiqLQZXBbRswMJDe9lJXvRJCVXKR1LFFPVFUlPbGAPcvPseS5faybH8m5iEzaBTgx8qEg7n6vDwPu6IRHR4erbhVvaWvG2NkhWNuZsfHjSDLPFzbQsxBa6N+/P88++yzdu3enQ4cO7NmzB6gp9kaNGkVSUhILFy5k3rx5hISEsGfPHrKysggPD6dbt25069aNffv2AZCTk8OQIUPw9/dnxowZ/NveFqqqsmLFChYvXsyvv/5KeXk5UDPq3bFjR6ZNm0ZAQADJyck8+OCDhIWF4e/vzyuvvPKX87z33nsEBgbSvXt34uLiav2cd+3axc0338zYsWPx9vZmzpw5/Pjjj3Tv3p3AwEDi4+OBv4649+/fn9mzZxMWFsaCBQvYsGEDPXr0oEuXLtxyyy1kZGRc0/fq999/vzy63aVLF4qKbvz1WUaSr8KjowOTn+/Oti9P8ctXp0lPKOCmCe1ltE/UiSObEkmKyqHf1A64tbfXOo6oBa9gF8Kf7sqmT0+yeu4xBt/th3eIi9axRB3JSS3m3JEM4iIyKMwux8hEoZ2/E77dXPEMcsbUzPi6zmttZ864x7uw5oNjrF8QybgnuuDsYVvH6ZufPcvPkZ1cXKfndG5jQ9/JHW7oHNXV1Rw+fJjNmzfz2muvsX379sv3eXp6MnPmTGxsbHjqqacAuO2223j88cfp06cPFy5cYOjQoZw9e5bXXnuNPn368PLLL7Np0ya+/vrrK15v//79eHl54ePjQ//+/dm0aRPh4eEAxMbGsmTJksvbVr/11ls4Ojqi0+kYNGgQJ0+eJCgoCAA7OzuioqL47rvvmD17Nhs3bvzHtZYtW8bevXsvf33gwAEATpw4wdmzZ3F0dMTb25sZM2Zw+PBhFixYwMcff8z8+fP/ca7Kykr+2EAuLy+PgwcPoigKX331Fe+99x4ffPBBrb9Xc+fO5dNPP6V3794UFxdjYWFxTX9nVyJFci3YOJgz7sku7F8Vx8kdKWQmFTH0vgBsHGTUT1y/xJPZHNmURKeerQi42V3rOOIaOHvYMnFOGJs/O8mWL6K4aXx7Qga3kUW+jVRBVimxRzKJjcgg92IJipGCRycHwkZ44R3ijLmVaZ1cx8bBgrGzawrldfMjGf9EVxzdZBpfY/Nv/87/fPuECRMACA0NJSkp6arn3L59+1/m9hYWFlJcXMzu3btZvXo1ACNHjsTBweGKx//8889MnToVgKlTp/Ldd99dLpLbtWt3uUAGWL58OYsWLaK6upq0tDTOnDlzuUi+9dZbL///8ccfv+K1/m26Rbdu3WjdujUAPj4+DBkyBIDAwEB27tz5r+f6Q0pKClOmTCEtLY3Kykq8vLyueMy/fa969+7NE088we23386ECRPw8PC44vHXQorkWjI2NqLv5A608rJjxw/RLH/7MENmBODR8co/sEL8l/yMUrZ/cxqXtrbcfFtHKa4aIWs7c8Y/2ZXti8+yf3UceRkl3HxrR/mUqZEoya8gNiKD2CMZZJ6v+Vi2tY8d/aZ2wKdry3rrUd7C2bKmUP7wGOvmH2f8k12xd7Wql2s1Bzc64ns9nJycyMv76yLM3NzcvxR15uY1g2jGxsZUV1998b9er+fgwYPXNfqp0+lYtWoV69at46233kJVVXJyci5PN7C2/v9fxBITE5k7dy5HjhzBwcGB6dOnX56aAX8t9K/1femP5wxgZGR0+WsjI6N//R78Odujjz7KE088wZgxY9i1axevvvrqFY/5t+/VnDlzGDlyJJs3b6Z3795s27aNTp06XdNz+Dt5Nb9Gvt1cmfRsGBbWpqyff5xj287/6xwhIa6ksryazQujMDIxYtgDAZhc58e3QnsmZsYMneFP2AhPzu5LY8NHkZSXVGkdS/yL8uIqTu1OZe2Hx1j83D72rYxDr1fpNcGHaW/fxISnQwns71Hvm/jYu1oxdnYXVFVl7bzjsqtjI2NjY0Pr1q3ZsWMHUFMgb926lT59+tT6HLa2tn+ZMztkyBA+/vjjy19HRkYC0K9fP3766ScAtmzZ8o/iHOC3334jKCiI5ORkkpKSOH/+POHh4axZs+Yfjy0sLMTa2ho7OzsyMjLYsmXLX+5ftmzZ5f/36tWr1s+nLhQUFODuXvOp6pIlSy7fXtvvVXx8PIGBgTz77LN069aN6OjoG84kRfJ1cHSzZuKcMLy7tOTAmni2LIyiokzaxImrU1WVHd+dJT+9hCEz/GnhZKl1JHGDFCOFHmO8ueVuP9ISClj5bgT5GaVaxxKXVJZXE3MonY2fnODbZ/by+08xlBRU0m2EJ7e92oMpL3Sn65B22Dre+PzFa+HY2poxj3WhukrHuvnHKcotv/pBwmB89913vPHGG4SEhDBw4EBeeeUVfHx8an386NGjWbNmzeXFaB999BEREREEBQXh5+fHwoULAXjllVfYvXs3/v7+rF69mrZt2/7jXD///DPjx4//y23h4eH8/PPP/3hscHAwXbp0oVOnTtx222307t37L/fn5eURFBTEggULmDdv3hWzL1u27C8t4OqqPdurr77KpEmTCA0NxdnZ+fLttf1ezZ8/n4CAAIKCgjA1NWX48OE3nEkxtFHQsLAw9Y9J3IZOVVVO7khh/6o4bJ0sGPZAIM4eNlrHEgbs2LbzHFgTT68JPnQd0k7rOKKOpcXls+WLKPQ6lWH3B+DRSTaP0EJ1lY7zp3KIPZJBUlQOuio9Ng7m+IbVtGxzbmNjMFOcMs8Xsm5+JJY2pox/sqt0uKmFs2fP0rlzZ61jiEboSj87iqIcVVU17EqPlyK5DlyMy2fbl6eoLK2m/+0d6diztdaRhAFKPpPLho8j8enakiEz/A3mTVrUrcLsMjZ+epKCjFL63doB/76yKLMh6HR6UqLziDuSQUJkFpXlOixtTWnftSW+3Vxp5W1nsDslpicUsH5BZM0i8Se61vt0j8ZOimRxvaRI1khJQQW/fHWai7H5+Pdzp+8kX4xNZTaLqFGYXcby/x3B2s6c8GdCMbOQNbNNWUVZNb98dYoLp3MJvqUNN01of9VeuuLaqXqVtPgCYo9kEHcsk/LiKswsTfDu4oJvWMuaHsbGjeN1+GJsPhs+jsTOxZJxj3fFwqZuOmo0RVIki+t1rUWyvFPXEWs7c8bODuHgugSO/3KBrPOFDHsgsMHnuQnDU12pY8sXUah6GD4zUArkZsDc0oSRDwWxd2UcJ7YnU5BRyuB7/eXvvg6oqkrWhaKawvhoJsV5FZiYGuEZ5IxvN1fa+jtiYtr4FsO6+doz4qEgNn1ykvUfRTJ2dkidtZ5rilRVlU/jxDW5nkFhGUmuBwnHs9i+5AzGxkYMvtePtn5OWkcSGlFVld8WnyXmcDojHwrCM9D56geJJiVqVwp7lsfi2NqakQ8HyS/O1ykvvYRzR2pathVklmFkpNDW3/HyJh9N5ReQ86dy2Pz5SVza2jLmsZAm87zqUmJiIra2tjg5OUmhLGrlz23x/t5/WaZbaCA/o5QtX0SRm1ZC91FehA33NNj5cKL+nNyZzJ5lsXQf7UW3kVdujC6avgunc9j25SmMzYwZ8WAgrbzstI7UKBTmlBEXkcm5IxnkpBSDAu4d7PENc8Wna0ssrJvmSGtCZBbbFp3C1bsFox8NwdS88Y2M16eqqipSUlL+0t9XiKuxsLDAw8MDU9O/vm5IkayRqgodu36K5tyhDNr6OzH4Hr8m+6Iu/ulibD7r5h2nbYATI2YGyi9JzVzuxRI2fXaCkoJKBt3VGd8wV60jGaTSwkrijmYSeySD9IQCAFy9WuAb5kr7sJZY2zWP7g+xERn8+vVp3Do4MOrhIOmnLkQ9kSJZQ6qqcnp3KnuWx2Jtb87wBwJxaWurdSxRz4rzKlj+9mHMrUyZOCcMc0v5yFRAWVElW76IIi2ugO6jvQgb4SkfFwMVpVXEH88i9kgGqTF5qCo4uVvTPswV3zBX7FyaZz/xmEPpbF98hrZ+Nb9oy2JwIeqeFMkGID2xgG2LTlFWVEW/Wzvg19tN60iinuiq9Kz58Bi5F0uY+GwYjm7WVz9INBu6Kj07f4wm5mA6vt1cGTitU6NcaHajqip0JJ3MJjYig/Onc9BXq7RwtsC3W00vYyc36TkPcGbvRXb+EI1XsDND7w/AuJF06xCisZAi2UCUFVXy6zenST6bR+ebWtNvagf5CK0J2vljNGf2XGTY/QH4dG2pdRxhgFRV5di28xxcm0Ar7xYMnxnULHrj6qr1XDidQ2xEJoknsqiu1GNtZ3Z5xLilp62MrF9B1K4Udi89V9Nj/V6/RtPWTojGQFrAGQhLWzNGPRrCkY2JRGxOIiu5iGH3BzbbjxKbojN7L3Jmz0W6Dm0nBbL4V4qiEDrMEzsXK35bfIaV70Qw8uEgnNyb3uipXq+Sei6P2CMZJBzPoqK0GnNrEzr0aEWHMFda+9pLD+mrCOzvga5az76VcfxmqjDoLj/5ngnRAGQkWSNJUdls//YMALdM98MzSFqDNXbpiQWs+eAY7h0cGPVIsLyJiVrJPF/Ips9OUlWhY+iMANoFNP6WkaqqkpFYyLlLvYzLCisxNTfGK8QZ3zBX2vg5yrSB6xCxJYlD6xLw692a/rd3ksXAQtQBmW5hoAqzy9jyRRTZycWEDm9H99HeUlg1UqWFlSx/+whGxgqTn+smu2WJa1KcV86mz06Sk1JM70m+BA3waHTTDlRVJSe1hNgjGcRGZFCUU46xiRHtAp3wDXOlXaATpjK97IYdWp9AxOYkAm92p+/UDo3u50QIQyPTLQxUC2dLwp8OZffScxzdcp6MxEKG3OuPpW3Tn5vYlOh0erZ9eYqKkiomPBMqBbK4ZjYOFox/sivbvz3D3uWx5KeX0meKb6MYbc3PLK0pjI9kkJdeimKk0KaTA91HeeEV4iKdXepY99Fe6Kr0HP/1AkamRvQOby+FshD1RF69NGZiZszAaZ1p5WPH7p/PsfztIwy9P0A2G2hE9q+K42JsPrfc7YdLG2nvJ66PmYUJwx8I5MDaeI7/coGCrFKG3hdgkFsTF+eVX+5lnHm+CIDW7e24+dYO+HRtKb/o1yNFUeg1wYfqaj0ntidjYmpEz7E+WscSokmS6RYGJOtCEVu+iKIkv4I+k3wJuNldRggMXMyhdLZ/e4aggR70ndxB6ziiiTi7/yK7fozBzsWSkQ8HYedipXUkyooriT9W08v4Ylw+qODS1vbyJh+y3XbDUlWVXT/FcGbPRXqM8SJshOzoKcT1kDnJjUh5SRXbvz3D+VM5dOjuSv/bO8mWpAYqK7mI1e8dpaVnC8bMDmkUH42LxiP1XB5bvohCQWH4zEDcfO0bPENlWTUJJ7KIPZJJytlc9HoVh1ZWNb2Mw1yxd9W+eG/OVL3Kju/OEn0wnV4TfOg6pJ3WkYRodKRIbmRUvcrRrUkc2pCIY2trhj8QKG9GBqa8pIoV/zuCrlpl8vPdmkWPW9Hw8jNL2fTpSQqzyxhwRyc69Wpd79esrtRx/lQOsUcySDqVg65Kj42jOb5hNZt8OHvYyCdcBkSvV9n+zWliIzLpO8WXoAFttI4kRKMiRXIjdeFMDr9+fQadTs+guzrj00X67hoCvV5l4ycnSD2Xx/gnu8r8cVGvykuq2PblKVKi8+g6tB09x3rXeesvnU5PytlLvYxPZFFVrsPS1pT2oTWFcSuvFtJuzIDpdHp++fI0CZFZ9L+9I/593bWOJESjIUVyI1aUW87WRafITCqky+C29BznLbstaezA2niObT0vb0aiweh0enYvPceZPRfx7uLCLdP9bngalqpXuRiXT2xEJvFHMykvqcLM0gSfLi74dnPFvYO9vNY0IrpqPVu+iOL8qRwGTevcIJ86CNEUSJHcyOmq9OxdEcup3am4+dozZIY/1nbmWsdqluKPZ7L1i1P49XFjwB2dtI4jmhFVVTm5I4W9K2NxaWPLiAeDsHG4ttcBVVXJulBUs8lHRCYl+RWYmBnhFeRM+zBX2vk7YWwqhXFjVV2lY9OnJ0mNyWPwPf74dnPVOpIQBk+K5CYi5mAau36MwczKhKH3BeDW3l7rSM1K7sUSVr4bgaObNeOf6CrFhNBE0slsfvn6NGYWxox8OBiXtldvO5h7sYTYiJpexgVZZRgZK7T1d8K3W0s8A50xs5BuoE1FVaWOjR+fIC2+gGH3BeDdxUXrSEIYNCmSm5Cc1GK2LIyiMKecmyb4EDyojSyiaQAVZdWsfCeCitIqJj/fDRsHaXcltJOdUsymz05QXlzF4Lv9r1gIFWaXXSqMM8lJLUZRwL2jA77dXPEOccHC2vD6L4u6UVlezYaPIsk8X8TwmYF4BjprHUkIgyVFchNTUVbNb4vPkHgiG5+uLRk4rZOMBNUjVa+yeWEUF07lMPbxENx8HbSOJAQlBRVsWRhFRmIhvcb70GVIW0oLKy9v8pGRWAiAq1cLfLu50j60pUzTakYqyqpZP/84OakljHwoiDZ+jlpHEsIgSZHcBKmqyvFfLnBwbTx2La0Y/kAgjm7WWsdqko5sSuTwhkT6TPYleKC0VxKGo7pSx47vzhIbkYlDa2vy00tQVXByt8G3W0t8w1xp4WypdUyhkfKSKtZ+eJyCzFJGPRqMewf5BV+Iv5MiuQlLjclj21enqKrUM/COTrJQo44lRWWz6bOTdOjuyi3T/WRqizA4qqpydEsSiSeya+YZh7nKL8zisrKiStZ8eJyi3HLGzAqhtY+0rBTiz6RIbuJK8ivY9uUp0uILCBrgwU3h7TE2kUVlNyo/s5QV/4ughbMFE54OxdRMdj4UQjQ+JQUVrPngGGWFlYx9vAst27XQOpIQBuO/imSppJoAa3tzxj7RheCBbTi5M4W1Hx6nOK9C61iNWmV5NVsWRqEYwfAHAqVAFkI0WtZ25ox7vAsWNqasXxBJdkqR1pGEaBSkSG4ijI2N6DPZlyEz/MlOLWb524dJic7VOlajpKoqO7+PJi+thKH3BsicTiFEo2fjYMHY2V0wNTdm3fxIci+WaB1JCIMnRXIT4xvmyqQ5YVhY14wYHNt2HkObUmPoIn9NJu5oJj3H+ciKcCFEk9HC2ZKxs7tgZKywbv5x8jNKtY4khEGTIrkJcmxtzcQ5YfiEtuTAmni2LIyiorRK61iNQnJ0LgfWxOHT1YUuQ9pqHUcIIeqUvasVY2d3QVVV1s47TkFWmdaRhDBYUiQ3UWYWJgy5158+k3w5H5XD8v9FkJ1SrHUsg1aYU8YvX57GvpU1A6d1lk4WQogmybG1NWMe60J1lY5182s6Xwgh/qlWRbKiKMMURYlRFCVOUZQ5//G4cEVRVEVRwi597akoSpmiKJGX/ltYV8HF1SmKQvCgNox7ogu6Sh2r3o0g+mCa1rEMUnWljq1fnEKv0zNiZqBsziKEaNKcPWwYMyuEitJq1s07Tkm+LPYW4u+uWiQrimIMfAoMB/yAWxVF8bvC42yBx4BDf7srXlXVkEv/zayDzOIatW5vz+QXuuPq1YLfFp9l14/R6Kr0WscyGKqq8vvPMWRdKOKWe/yxd7XSOpIQQtS7lu1aMPrRYEoLK1k3/zilhZVaRxLCoNRmJLk7EKeqaoKqqpXAUmDsFR73BvAuIJ/bGCCrFmaMeSyErkPbcnrPRVbPPUphjsxFAzj1eyrRB9IJG+mJV5Cz1nGEEKLBtPK2Y9QjwRTllrN+wXHKi2X9ihB/qE2R7A4k/+nrlEu3XaYoSlegjaqqm65wvJeiKMcVRfldUZS+V7qAoij3K4oSoShKRFZWVm2zi2tkZGxEr/HtGT4zkPyMUpa/fYQLp3O0jqWptLh89i6PpV2gE91HemkdRwghGpybrz0jHgoiP6OM9R9FykJvIS654YV7iqIYAR8CT17h7jSgraqqXYAngJ8URfnHVj+qqi5SVTVMVdUwFxeXG40krsI7xIVJz3XDxt6cDZ+c4MimRFR982sTV5JfwdZFp7B1smDw3X4oRrJQTwjRPLXp5MjwmYHkpBaz4eMTVJZXax1JCM3VpkhOBdr86WuPS7f9wRYIAHYpipIE9ATWK4oSpqpqhaqqOQCqqh4F4oEOdRFc3Bh7VyvCnw2jY/dWHN6QyMZPT1Je0nxGD3TVerYuOkVlhY7hMwMxtzLVOpIQQmiqXYATQ+8LIOt8ERs/OUFVhU7rSEJoqjZF8hHAV1EUL0VRzICpwPo/7lRVtUBVVWdVVT1VVfUEDgJjVFWNUBTF5dLCPxRF8QZ8gYQ6fxbiupiaGTNoemduvq0jKTG5LH/rCJnnC7WO1SD2Lo8lPaGAgXd2wsndRus4QghhELxDXLjlHj/S4wvY9NlJqiulUBbN11WLZFVVq4FHgG3AWWC5qqqnFUV5XVGUMVc5vB9wUlGUSGAlMFNVVdkr2YAoikJAP3cmPBmKqqqsev8op/ekNuld+s7uv8ip3al0GdwW3zBXreMIIYRB8Q1zZdB0P1LP5bHli1PSDUk0W4qhFUNhYWFqRESE1jGapbLiSn79+jTJZ/PodFNrbp7aARMzY61j1anM84Wsfv8YrdvbMfrRYIyMZT8dIYS4kjN7L7Lzh2i8gp0Zen8AxvJ6KZogRVGOqqoadqX75CdeXGZpY8aoR0MIG+lJ9P40Vr53lIKsUq1j1ZnSwkq2LIzCqoUZQ2b4S4EshBD/wa+PG/2mdiDxRDa/fn0GvU5GlEXzIlWC+AsjI4Ueo70Z+XAQxbnlLH87gsST2VrHumF6nZ5fvjpFWXEVw2cGYmljpnUkIYQweIH9Peg9sT3xxzL57buz6JthJyTRfEmRLK7IM9CZyc93w87Fks2fneTg2vhG/eK4f008qefy6X97R1za2modRwghGo2QW9rSY6w35w5l8PuP0c2yZahonqRIFv+qhbMlE57uil/v1hzdep4NH0VSVtT4ti09dySdE9uTCezvQaeerbWO0+jp8vNRdbLiXYjmJGy4J2EjPDmzL409y8416cXdQvxBimTxn0xMjRlwZ2cG3NmJtLgClr99hPSEAq1j1Vp2SjE7v4umdXs7ek9qr3WcRq8qLY24wUNIeeRRVL3MTxSiOek+2osug9sS9Xsq+1bFSaEsmjwpkkWt+PV2I/yZUIyMFdZ8cIyoXSkG/wJZXlLFloUnMbcyYeh9sjL7RqmqSvrrb6AvKaF4505yvvxK60hCiAakKAq9JvgQOMCDE9uTObRetj0QTZtUDaLWXNraMum5brTxc2T30nP8+s0Zg92RSa9X+fWb0xTnVTDsgUCs7cy1jtToFf3yK8U7d9LyySdpMWIEWQsWUHLwoNaxhBANSFEU+k72xa+vG0e3nCdic6LWkYSoNyZaBxCNi4W1KSMfDOLo1vMc2pBATmoxwx8IxN7VSutof3FkYyIXTudy820daeVtp3WcRk9XWEjGm29i7tcZx7umoVZUUB4TQ+oTT+K1ZjWmrrIpixDNhaIo9L+1I/oqPYfWJ2JkYkTXIe20jiVEnZORZHHNFCOFsBGejHk0hNLCSpb/7wjxxzK1jnVZQmQWEZuT6Ny7Nf593bSO0yRkfvgh1Tk5tH79DRQTE4ysrfH4aAH68nJSH38CtapK64hCiAakGCkMmNYZ37CWHFgdz4kdyVpHEo1UXnoJv/8cQ36m4e3LIEWyuG5t/ByZ/Hw3HFpZs3XRKfatjNW82XxeegnbF5+hZTtb+k3tgKIomuZpCkqPHSN/6TIc77wTywD/y7eb+/jQ+o3XKTt2jMy5H2iYUAihBSMjhUF3++Ed4sLe5bGc2p2qdSTRSKiqSvKZXDZ+coKfXj3EmX0XyUwq1DrWP8h0C3FDbB0tmPBkV/aujCVyezKZ54sYMsNfkznAlWXVbFkYhYmpEcMeCMTEtGltqa0FtbKStJdfxsStNS6zHv3H/XYjR1J27Di5S5Zg2aULLYYN1SClEEIrxsZGDJnhz5Yvovj9pxhMTI3o1EtabYorq67Uce5wBid2JJN7sQTLFmZ0H+2Ff193rFoY3iZfUiSLG2ZsasTNt9bM/d31QzTL3zrC0PsCcPO1b7AMql7ltyVnyc8sY+xjIdg6WjTYtZuynK+/pjIuHo+Fn2NkbX3Fx7g++wxlp6JIe+EFzDt2wNzLq4FTCiG0ZGxixLD7A9j06Ul2fHcWIxOFDt1aaR1LGJCS/Aqifk/h9O6LlJdU4eRhw6C7OuMb5oqxqeFOalAMrY1XWFiYGhERoXUMcZ1yUovZ8kUUhdnl3DTBh+BBbRpkykPEliQOrUug98T2hNzStt6v1xxUJCaSOHYcNoMG4jFv3n8+turiRRInhGPi4oLnsqUYWRnWQk4hRP2rqtSx8eMTpMUXMPQ+f3y6tNQ6ktBY5vlCTuxIJi4iE71exSvImeCBbXDrYG8w0yEVRTmqqmrYFe+TIlnUtYqyanYsOUtCZBY+XV0YeGdnzCzr70OL86dz2PjJCXzDXBl8j5/B/MNrzFRV5cJd0yk/exafzZswcXG56jHFe/eRfN99tBg9Crd335W/ByGaocryajZ8FEnm+SKGPxCIZ5Cz1pFEA9PrVRJPZHHit2TS4gowNTemc+/WBA3wwM7F8AZQ/qtINtwxbtFomVuaMOyBAG6a0J6EyGxWvBNBzsXierlWQVYZv359Gic3Gwbc2UkKszpSsHoNpYcP0/Kpp2pVIAPY9OmN8yMPU7h+A/nLltVzQiGEITKzMGHUoyE4e9iwZVEUF87kaB1JNJCKsmoit1/gh5cOsPWLUxTnVdB7Ynvueqc3fSd3MMgC+WpkJFnUq9RzeWz76jRV5dUMuLNTnc5Tq6rQseq9oxTnlTPpuW7YuVjW2bmbs+qcHBJGjMSsfXvaff8dilHtf5dW9XqSH5hJ6cGDtPvpJywDA+oxqRDCUJWXVLH2w+MUZJYy6pFg3Ds6aB1J1JOCrFJO7kjh7P40qip0tG5vR/CgNngFu2BkZPgDVzLdQmiqJL+CbV+dIi2ugMABHvQOb4+xyY19iKGqKr9+c4bYiAxGPRJMO3+nOkorUp9+hsKtW/FeuwZzH59rPr46L4/E8HAUFDxXrcTEQd4chWiOyooqWfPhcYpyyxkzK4TWPrKxU1OhqioXz+VzYkcyiSezMTJS8A1zJWigBy3btdA63jWRIlloTqfT1zSc/y2ZVt4tGHpfADYO19+BInL7BfatjKPHWG/ChnvWXdBmrnjPXpLvuw/nhx66Ysu32io7eZKk2+/AuldP2ixceE2j0UKIpqOkoII1HxyjrLCSMbO74OrZuAoo8Ve6Kj3njtS0cMtJKcbCxpSAfu4E9HPH2r7hW7/WBSmShcGIO5rJju/OYmJmxOB7/WnTyfGaz5ESk8f6BZF4BTkz7IEAmYdcR/RlZSSMHoNiaorX2jUYmd/YC17uTz+R8fobuDw2C+cHH6yjlEKIxqY4r5w1HxyjorSasY93waWNrdaRxDUqLazk1O5UTv2eQllRFY5u1gQPbEOH7q6YmDXuPQn+q0iWPsmiQbUPbYmjmzVbv4hiw4JIeoz1puuQdii1nLdUlFvOL1+dwr6lJYPu6iwFch3K/vRTqlJSaPvdkhsukAEcbr2VsuORZH30MRZBQdj07l0HKYUQjY2NgwVjZ3dhzQfHWL8gknFPdMHJzUbrWKIWslOKOLEjhXOH09FXq7QLdCJ4YBs8Ojk0i/dfGUkWmqgsr2bnD9HERWTiGeTMLdM7Y25l+p/HVFfpWDP3GHkZpUyaE4ZDqytvbiGuXfnZsyROnITd+HG4vflmnZ1XX1pK0pQpVGfn4LV6FaatZScuIZqr/IxS1nx4DFWFCU92xd618XU7aA5UvUpSVDYndiSTGpOPiVnNLopBAzya5PuuTLcQBklVVaJ2pbBvRRw2ThYMuz/gXz+GU1WVnd9Hc3Z/GsNnBuIdUru2ZOLqVJ2OpKm3UnXxIj6bNmJsb1+n569ISCRp4kTMfX1rumWYGd7Wo0KIhpGbVsLaD49hZGzE+Ce7SlciA1JZXk30gTRO7kihIKsMGwdzAvt74NfHDQvr/x7EasykT7IwSIqiEDSgDeOe7IqusqadW/SBtCs+9vSei5zdn0bYCE8pkOtY3o8/UR4Vhevzz9V5gQxg7u1F67ffouzECTLen1vn5xdCNB6Ora0Z81gXqqt0rJtX0/lCaKswu4x9K2NZ8tx+9iyLxcLGlCEz/LnjzV50HdquSRfIVyMjycIglBZW8svXp0iNycevrxt9J/tiYlqzGCA9oYA1HxzDo5MjIx8OahR9FxuLqrQ0EkaOwjI0lDaLvqjXOWbpb79N3nff4/7hB7QYMaLeriOEMHyZ5wtZNz8SCxtTJjzZtdF2RmisVFUlLb6Ak78lkxCZBYpC+64uBA1sQyvv5tWqT6ZbiEZBr9NzaH0ix7adp2U7W4beF4CxqRHL3z6CiZkxk+aENevfaOuaqqqkPPQwJQcP4r1hPWYeHvV7vcpKzk+7i4pz5/Bcsfy6ejALIZqO9IQC1i+IxMbBnHFPdMWqhUzFqm+6aj1xRzM5uSOZzPNFmFuZ4N/XjYCbPbB1vP62rI2ZFMmiUUmIzOK3xWdQjBVsHS3Izyhl4rNhOLnLaui6VLjtF1Ife4yWzzyD0z13N8g1q9LTSZwQjrGDA17Ll2Fk3fQWgQghau9ibD4bPo6khbMl457ogqWNFMr1oay4ktN7LnJqVwolBZXYu1oRPKgNHXu0wtS8cbdwu1FSJItGJz+zlK1fnCIntZgh9/rj281V60hNiq6oiIQRIzF2ccZr+XIUk4brBlly4AAX7p1Bi+HDcZv7frNoIySE+HfJ0bls+uQkDq2tGPd4l6t2OhK1l3uxhBM7kok5lI6uSk8bP0eCB7ahrZ9jrVuvNnXSJ1k0OvYtrZj4bCj5maU4e0jj+bqW+eGHVOfk4PHZZw1aIANY9+qFy6xHyZq/AMuuXXC8/fYGvb4QwrC06eTI8JmBbP78JBs+PsGYWSGYWUp5cr1UvcqFM7mc2JFM8plcjE2N6NizFUEDPKQ/9TWSn0JhsEzMjKVArgelx46R//NSHO+6C8vAAE0yON1/P2XHI8l4510sAwKwDA7WJIcQwjC0C3Bi6H0BbFt0io2fnmD0oyHNfhrAtaqq0BFzKJ2TO5LJSy/Fys6MHmO98e/rJtNYrpNMtxCiGVErK0mYMAF9aSk+GzZoOidYl59PYvhEVL0er9WrMHFw0CyLEMIwxEZk8OvXp3Hr4MCoh4Ma/ZbHDaE4r5yoXSmc3nORitJqXNraEjyoDe1DW2JsIp1+r0amWwghAMj55hsq4+LxWPi55ovmjO3tcZ8/n/O33cbFp56uaUFnLG+IQjRnvmGu6HUq2xefYcsXUYyYGYSxqRR6V5KeWNPCLe5YFqgq3l1cCB7YhlY+drLWo45IkSxEM1GRmEj2Z59jO3wYtv37ax0HAMvAAFxffJH0V14h+7PPcXn0Ea0jCSE01rFHK3RVenb+EM3WL08x7IEAjI2lUIaaVqnxx7M48VsyGYmFmFkYEzzQg8D+HrRwlt0L65oUyUI0A6qqkv7qayjm5rR6/nmt4/yF/eRJlB07RvZnn2EZEoxN375aRxJCaMyvjxu6aj27l57j16/PMOReP4yacaFcXlLFmb0XidqVQnFeBS1cLOk7xZdOvVpjZiGlXH2R76wQzUDBmrWUHjpEq9dew8TFsLb1VhSFVq++QvnZs1x86mm81qzG1M1N61hCCI0F9vdAV61n38o4fjNRGDTdr9ntuJqXXsLJnSlEH0ijulKPe0cH+t3akXYBTs3ue6EFKZKFaOKqc3LIfPddLENDsZ80Ues4V2RkaYn7gvkkTZxEyuzHaffD9xiZyWpsIZq7kFvaUl2l59C6BIxNjRhwe6cm399XVVVSovM4sSOZ81E5GJkodOjeiuCBHtLxqYFJkSxEE5fxzrvoSktp/fprKEaG+3GluZcXrf/3NqmzHiPznXdp9fJLWkcSQhiAsOGe6Kr0RGxOwtjEiH5TOzTJhWnVlTrOHc7gxI5kci+WYGlrSrdRXgT0c5ctuzUiRbIQTVjx3n0UbtiA80MPYe7jo3Wcq2oxZAhl06eTu3gxll26YDd6lNaRhBAGoPtoL3RVeo7/egFjEyN6T2zfZArlkoIKTv2eyqndqZQXV+HkYcOguzrjG+YqnT00JkWyEE2UvqyM9FdfxczLC6cH7tc6Tq21fPIJyqKiSHv5ZSw6dcTc11frSEIIjSmKQq8JPlRX6znxWzLGpkb0HOvdqAvlrAtFnPgtmdiIDPR6Fa8gZ4IHtsGtg32jfl5NiRTJQjRR2Z9+SlVKCm2/W4KRubnWcWpNMTXF/cMPSQwPJ2XWY3iuWIGxjbY9nYUQ2lMUhb6TfdFV6zm29TwmpkZ0G+mldaxroterJJ6oaeGWFleAqbkxAf3cCRrogZ2LldbxxN9IkSxEE1QeHU3Ot4uxmxiOdffuWse5ZqauLXH/4AMu3H03aS+9iPuHH8rIihACRVHof2tH9FV6Dm9IxNjEiK5D22kd66oqyqo5u+8iJ3emUJRTjq2TBb0ntqdzbzfMLaUUM1TyNyNEE6PqdKS99DLG9va4PvWU1nGum3WP7rjMnk3Whx+S16UrjtPu1DqSEMIAKEYKA6Z1Rlet58CaeIxNjQge2EbrWFdUkFXKyZ0pnN2fRlW5jtbt7eg9sT1ewS7Swq0RkCJZiCYm78efKI+Kwm3uXIzt7bWOc0OcZtxL2fHjZLz3HhaBAVh16aJ1JCGEATAyUhh0tx+6apW9y2MxNjEioJ+71rGAmhZuF2PzOfFbMoknszEyUmgf1pLggW1o2a6F1vHENVBUVdU6w1+EhYWpERERWscQolGqSksjYeQoLENDabPoiyYxRUFXUEBi+ETUqiq81qzGxNFR60hCCAOhq9az5YsozkflMOiuznTq1Vq7LFV6YiNqWrhlJxdjYW1KwM3uBPRzx9q+8awLaW4URTmqqmrYFe+TIlmIpkFVVVIeepiSgwfx3rAeMw8PrSPVmfIzZ0iaeiuWoV1p+9VXKMbGWkcSQhiI6iodmz49SWpMHrfc40eHbq0a9PqlhZWc3pNK1O+plBVW4uhmTfDANnTo7oqJmbxWGbr/KpJluoUQTUTRL79SvHMnLZ9+ukkVyAAWfn60evkl0l58iaxPPqHlY49pHUkIYSBMTI0Z8VAQGz8+wfZvz2JsYoRPl5b1ft3slGJO7Ejm3OF09NUq7QKcCB7UBo9ODk3iUzwBtepSrSjKMEVRYhRFiVMUZc5/PC5cURRVUZSwP9323KXjYhRFGVoXoetala6Ktw+9zfKY5VpHEeK66IqKyHjzTcw7d8bxrmlax6kX9hMnYjdhAjmfL6T499+1jiOEMCCmZsaMfDgIV09bfvnqNEkns+vlOqpeJfFkNmvnHWfZm4eJi8jAr7cbt73ag1GPBNOms6MUyE3IVUeSFUUxBj4FBgMpwBFFUdarqnrmb4+zBR4DDv3pNj9gKuAPuAHbFUXpoKqqru6ewo0zMTIhsSCRDfEbGNh2IM6WzlpHEuKaZH74IdU5OXh89hmKSdP9gKjVyy9RfuYMqc88i9eqVZh5GMZCHSGE9swsTBj1aAjr5x9ny6IoRj4URFs/pzo5d2V5NdEH0jm5I5mCrDJsHMzpNd4Hvz5uWFib1sk1hOGpzUhydyBOVdUEVVUrgaXA2Cs87g3gXaD8T7eNBZaqqlqhqmoiEHfpfAZFURRe6PECFboK3j/yvtZxhLgmpceOk//zUhzvvAPLwACt49QrIwsLPBbMB52O1Nmz0VdWah1JCGFAzC1NGD0rBAdXa7Z8HkVqTN4Nna8wp4x9K2NZ8tx+9iw7h4WNKUNm+HPHm73oOrSdFMhNXG2KZHcg+U9fp1y67TJFUboCbVRV3XStx146/n5FUSIURYnIysqqVfC65mnnyYzAGWxO3MyBiwc0ySDEtVIrK0l/5WVM3FrjMmuW1nEahFm7dri98z/KT50i4+23tY4jhDAwFtamjJ0dgq2zJRs/O0lafME1Ha+qKmlx+WxdFMUPLx7gxI4U2vk7Ev5MKBOfDcM3zBVj41rNVhWN3A3/LSuKYgR8CDx5vedQVXWRqqphqqqGubi43Gik63Zv4L20tW3LW4feokJXoVkOIWor55tvqIiNo9XLL2Nk3Xy2bra95RacZtxL/tJlFKxbp3UcIYSBsbQ1Y+zsEKztzNj4cSQZSYVXPUZXrefc4XRWvhPB6rnHSInOo8uQttz5Zi+GzAiglbddAyQXhqQ2RXIq8OetbDwu3fYHWyAA2KUoShLQE1h/afHe1Y41KObG5rzQ4wXOF57nm1PfaB1HiP9UkZhI9mefYztsGLb9+2sdp8G5zJ6NVbdupL3yKuUx57SOI4QwMNZ25ox7vAsWNqZs+CiSrOSiKz6uvLiKiC1JfP/Cfn795gyV5Tpuvq0jd/2vN73Gt8fW0aKBkwtDcdU+yYqimADngEHUFLhHgNtUVT39L4/fBTylqmqEoij+wE/UzEN2A34DfP9r4Z4h9El++ven2XFhB2vGrqFti7aaZhHiSlRV5cL0uyk/cwbvTRsxbVn/7Y4MUXVWFgkTJmBsZY3nqpUY29hoHUkIYWAKs8tY88Exqqv0jHuiC05uNa8TuRdLOLEzmZiD6eiq9LTxcyR4YBva+jmiyJbRzcZ/9Um+6kiyqqrVwCPANuAssFxV1dOKoryuKMqYqxx7GlgOnAG2Ag8bWmeLK3m629OYGpvy1qG3MLTNVoQAKFizltJDh2j51FPNtkAGMHFxwePDD6lMSSHt+Rfk36sQ4h9aOFsydnYXjIwV1s2PJOZgGus/iuTn1w8RczCdjj1aMfXl7oyZFUK7ACcpkMVlsuPev/jx7I+8c/gd3r/5fYZ5DtM6jhCXVefkkDBiJGY+PrT74XsUI1lAkvP112S+P5eWc57Fafp0reMIIQxQbloJaz88RllRFVZ2ZgT298C/rxuWNmZaRxMakm2pr4NOr+PWTbeSXZbN+nHrsTGTj3GFYUh9+hkKt27Fe81qzNu31zqOQVBVlZRHH6V41++0W7IYq9BQrSMJIQxQfmYpOSnFeAY5Y2wiAwziBqdbNFfGRsa83Otlssuy+STyE63jCAFA8d59FG7YgPN990mB/CeKouD2v/9h6u5G6uzHqc6un922hBCNm31LK3y6tpQCWdSK/JT8hwDnACZ3nMzP0T9zJufM1Q8Qoh7py8pIf/VVzDw9cXrgfq3jGBxjW1s8FixAV1hI6pNPoVZXax1JCCFEIyZF8lXM6joLB3MH3jjwBjq9wa85FE1Y9qefUpWSQqvXX8PI3FzrOAbJolMnWr3yCqWHDpH10cdaxxFCCNGISZF8FS3MWvB0t6c5lXOKledWah1HNFPl0dHkfLsYu4nhWHc3uJ3dDYr9hPHYT5pIzqJFFO3YqXUcIYQQjZQUybUwwmsEPVr3YMGxBWSXyVzHhlBepePNjWfo//5OYtKv3AC+uVB1OtJeehlje3tcn3pK6ziNguuLL2Lu15mLc+ZQmZysdRwhhBCNkBTJtaAoCi/0eIFyXTlzI+ZqHafJO34hjxEf7eGrvYnkFFcy7ZtDJOeWah1LM3k//kR5VBSuzz2Hsb291nEaBSNzczwWLAAg5bHH0FfINvNCCCGujRTJteRl58U9AfewKWETh9IOaR2nSaqo1vHu1mjCP99PRZWeH2f0YOWDN1FWqWPaN4fJLm5+hU5VWhpZ8+dj3bcvLUaO0DpOo2LWpg1u77xDxZmzZLz5ptZxhBBCNDJSJF+DGYEzaGPbhjcPvkmlrlLrOE3KqdQCxny8j893xTMptA1bZ/eld3tnOray5du7u5FWUMb0bw9TVF6lddQGo6oq6a+/garX0+qVl1EU2QXqWtkOHIDT/feTv2Il+avXaB1HCCFEIyJF8jWwMLHghR4vkFSYxLenvtU6TpNQpdMzf/s5xn26j/yySr6d3o13JwZha2F6+TGh7Rz5/PZQotOKeOD7o5RXNY8uI0W//Erxzp24PPooZh4eWsdptFxmPYpVjx6kv/Ya5dHRWscRQgjRSEiRfI16u/dmqOdQFp1cRHKhLAi6ETHpRYz/bB/zt8cyOtiNX2bfzIBOLa/42AGdWvL+pCD2x+cwe2kkOr1h7RRZ13RFRWS8+SbmnTvjeNc0reM0aoqJCe4fzMW4RQtSHnsMXVHzXggqhBCidqRIvg7PdHsGU2NT3jr8Foa2rXdjUK3T89muOEZ/vJf0gnIW3hHKvCkh2FmZ/udx47t48NIoP7aeTufFtVFN+nuf+eGHVOfk0Pr111FMTLSO0+iZODvjPn8eVSmpXHzuuSb9syOEEKJuSJF8HVpateTRLo+yL3Ufv5z/Res4jUp8VjETFx7gva0x3OLXkm2z+zEsoFWtj7+3jxcPD/Dh58PJfPDLuXpMqp3SY8fJ/3kpjnfegWVggNZxmgyr0FBaPvUUxdt/I/ebb7SOI4QQwsDJENV1mtJxCuvi1vHe4ffo7dYbGzMbrSMZNL1e5dv9Sby3NRpLM2M+urULo4NaX9ditKeGdCS3pIpPdsbhaG3GPX286iGxNtTKStJfeRkTt9a4zJqldZwmx3H6XZQdP07mh/OwDArCqls3rSMJIYQwUDKSfJ1MjEx4qedLZJVl8Wnkp1rHMWgXckqZ+uVB3th4hr6+zvzyeD/GBLtdd7cGRVF4c1wAw/xb8frGM6w5nlLHibWT8803VMTG0eqllzCyttY6TpOjKAqt334LszZtSHniCaoyM7WOJIQQwkBJkXwDAl0CmdxxMj9F/8TZnLNaxzE4qqry/cHzDFuwm7MXC5k7KZgvp4XR0tbihs9tbKQwf2oIvbydeHrFSXZGN/5ipyIxkezPPsd22DBsBwzQOk6TZWxjg/uCBeiLirn4xJOo1dVaRxJCCGGApEi+QbO6zsLe3J43Dr6BTt88WpPVRmp+GXd+fZiX1p4itJ0D2x7vx8RQjzrt9WthasyiaaF0am3Lgz8e5ej53Do7d0NTVZX0V19DMTfH9fnntI7T5Fl07EDr116lNCKCrPnztY4jhBDCAEmRfINamLXg6W5PE5UdxarYVVrH0Zyqqiw/ksywebs5diGPt8YH8N093XGzt6yX69lamLL47u642Vly97dHiElvnO29CtaspfTQIVo++SSmLa/cBk/ULbuxY7GfMoWcr76maPt2reMIIYQwMFIk14GRXiPp0aoH84/NJ7ssW+s4msksLOfeJRE8s+okfm4t2Da7H7f3aFfvO8U525iz5J7uWJoZM+2bQyTnltbr9epadW4ume++i2XXrthPnqR1nGbF9fnnsPD35+Kc56g8f17rOEIIIQyIFMl1QFEUXuj5AuXV5XwQ8YHWcRqcqqqsi0xl8Lzd7I/P5pXRfvx8X0/aOFo1WIY2jlZ8d08Pyqv03Pn1IbKLKxrs2jcq45130JWW0vr111CM5J9kQzIyN8d9wQIwNiblsdnoy8u1jiSEEMJAyDtyHfGy8+LugLvZmLCRw2mHtY7TYLKLK3jwh2M8tjQSHxdrNs/qy929vTAyqt/R4yvp2MqWb6aHkV5YzvRvD1NUXtXgGa5V8d59FK7fgPN9MzBv317rOM2SmYc77u+9S0V0NOmvv6F1HCGEEAZCiuQ6dF/gfXjYePDGwTeo1FVqHafebYlKY+i83eyIzmTO8E6smHkT3i7a9osObefI53eEEp1WxP3fHaW8ynAXU+rLykh/9VXMPD1xeuABreM0azY334zTgzMpWL2a/JUrtY4jhBDCAEiRXIcsTCx4oecLJBUmsfj0Yq3j1Jv80koeW3qcB388hpu9JRtn9WHmzT4YazB6fCUDOrZk7qRgDiTkMHtpJDq9YW5BnP3ZZ1SlpNDq9dcwMjfXOk6z5/LII1jf1Iv019+g/MwZreMIIYTQmBTJdayPex8GtxvMopOLSC5K1jpOndsRncGQebvZdDKNJwZ3YPVDN9HB1VbrWP8wros7L4/yY+vpdF5cG4WqGlahXB4dTc4332IXPgHr7t21jiMAxdgYt7lzMXZwIOWx2egKCrSOJIQQQkNSJNeDZ7s9i7FizNuH3ja44ux6FZZX8fSKE9yzOAJHazPWPtybWYN8MTU23B+he/p48ciA9vx8OJm5v8RoHecyVacj7aWXMbazw/Xpp7WOI/7ExNER9/nzqEpL4+Kc51D1eq0jCSGE0IjhVjiNmKu1K490eYS9qXvZfqHx91/dE5vFsHm7WXUshYcH+LDukd4EuNtpHatWnhzSgVu7t+XTnfF8vTdR6zgA5P30M+VRUbg+/zzG9vZaxxF/Y9WlC67PPEPxzp3kfPW11nGEEEJoxETrAE3VrZ1uZX38et45/A43ud2Etam11pGuWUlFNf/bcpYfDl7Ax8Wa1Q/1JqSNvdaxromiKLw5LoD80kre2HgGBytTJnT10CxPVVoaWfPmYd2nDy1GjtAsh/hvDnfeQenxY2TNn49lUBDWPXtoHemKKsvyOJ+8n4SMo1QpxgzsPhsrs8b3WiOEEIZIMbTpAGFhYWpERITWMerEyayT3LH5Du7wu4Nnuj2jdZxrcighh6dXniQ5r5QZfbx4ckhHLEyNtY513Sqqddz97REOJeby5bRQBnZybfAMqqqS8vAjlOzfj/fGDZh5aFesi6vTFZeQNHkyuoICvFavxtRVo50Q9XrKcmJJSj1AfOYJEvLjSSjLIF5XQrIR6P60WY+NYsyYDpOY0vlWvO28tckrhBCNiKIoR1VVDbvifVIk1683DrzBytiVLBu1jE6OnbSOc1XlVTre2xrDt/sTaetoxdxJwXTzdNQ6Vp0orqjm1kUHic0s4od7exDWwM+r8JdfSJ31GC2ffhqne+9p0GuL61MRG0vi5ClY+PnRbvG3KKam9XexsnyKM6JIvHiY+OzTJBSeJ74ih3gquGhsjHqpGDZWVdpigo+pHV7W7vg4dsSnVShl57ay/PxmttnYUIVKj1Y9mNJpCgPaDMDESD40FEKIK5EiWUMFFQWMWTsGDxsPvh/xPUaK4U4DP3Yhj6eWnyAhu4S7erXj2eGdsDJrWm+uOcUVTFp4gOziCpbP7EWnVi0a5Lq6oiISRozE2NkZrxXLUUya1ve1KSvYsJGLTz+N49134/rsDX4ipKuCvCQK0k8Qn36U+NwYEkouklBVSLyxSsaffi5MVfBUzPGxcMK7hSc+zv54u/ekXcsQTE3Mrnz+I1+Rs/VZ1ri1Z0ULWy6WZtDSqiUTO0xkou9EXKxcbiy/EEI0MVIka2xD/Aae3/s8L/V8ickdJ2sd5x8qqnXM+zWWRbvjaW1nyXsTg+jd3lnrWPUmJa+U8M/3o6qw6sGbGmT77LTXXiN/2XI8ly3FMjCw3q8n6lb666+T99PPuC9YQIuhQ/77waoKJVmoWefIyThBQuZJ4gsSiC/LJEFfRoKpCTkm/z91yVJV8DKxxseqFd527fF2DcHHvSfudu2ubwQ4ejOsvAedjQt7Bj/P0ou/s+/iPkwUEwa1G8TUjlMJdQ1FUQyjr7kQQmhJimSNqarKvb/cS3RuNBvGbcDJ0knrSJdFpRTw5IpIzmUUM7VbG14Y2Rlbi3r8SNlAnMsoYtLCAzhYmbLywZtwtqm/zTxKjx3n/O234zjtTlyfe67eriPqj76ykvN33EllfDyeK1dg7uUFVWWQE4+afY6MjJPE55whvugCCRV5JBjriTc1pdD4/4thG4zwNrPHx6YNPo6d8GrdFZ+WwbS2bl33nzClRMBPU0DVw61LuWDfmmUxy1gbt5bCykLa27dnasepjPIZ1SgXFQshRF2RItkAJBQkEL4+nBFeI3irz1tax6FKp+eTHXF8sjMOZxsz3gkPYkBHjRYmaeTo+Tzu+OoQ3i7WLL2/Z738cqBWVpIYHo6uuASfjRswspaCpFHR66EwFXJiKY+OIPGlH6i2hqOT9MQalZJoakK8mSmlRv9f5NorpvhYuODdoh0+LoF4twrFx6E9LpYuDTt6m5sAP4RD4UUI/wo6j6asuoytiVtZGrOUMzlnsDKxYrTPaKZ2nEp7h/YNl00IIQyEFMkG4qNjH/Fl1Jd8M/QburXqplmO6PRCnlx+gtMXC5nQxZ1XRvtjZ9X0R4+vZGdMJvctiSDM04HFd3ev8w4e2QsXkjV/AR6ffYbtwAF1em5Rh8oLIScWsuOoyo4hOes0CYVJJJRlEW8CCaamJJqa0CkJnlumZ0+AwvKx1nhbt8bHwRefliF4OXXCx94HRwsDWuhakg0/T60ZWR72DvScCdR8unUq+xRLY5ayNXErlfpKwlzDmNppKgPbDsTUqHm+Hgghmh8pkg1EeXU549aNw8zYjFWjV2Fq3LBvRNU6PV/sTmD+9nPYWZry1vhAhvq3atAMhmjt8VRmL4tkqL8rn97WFZM62kWwMimJhDFjsRk4EI/58+rknOIG6Koh/zxkx0JOLJVZMSTlxpBQnEy8rpR4s5pCOMnUlOo/jfi6mbbAy8YDH6fO+LgE4rv6OCaLV9HqtddwmGJ4awz+obIUVt8H0Ruh1yMw+A3408h3Xnkea+PWsixmGanFqbhYuhDeIZyJvhNxtW74VolCCNGQpEg2ILtTdvPwbw8zq8ss7gu6r8GuG5dZzFMrThCZnM/IwNa8PtYfp3qch9vYfLsvkdc2nGFqtzb8b0LgDX8srqoqF6bfTfmZM3hv2ohpy+Y1lUUzqgqlOZcLYbJjKc2OJTE/loSyTBJMFOJNTUkwMyXZxAT9pb9nIxQ8zB3xtvPCxzkAb0dffOx88LLzwsr0rws7VZ2O5PsfoPTwYdr99BOWgQFaPNNro9fB1jlweBH4jYPxX4CpxV8eotPr2HdxH8tilrEnZQ9GihED2w5kasepdGvVTRb6iSbhh4Pn2Rmdyatj/Btk0bYwfFIkG5gndj3B7pTdrBm7hja2ber1Wnq9yjf7Enl/WwyWZsa8MTaA0cFu9XrNxuqDX2L4eEccD/X34ZlhN9bTOn/1GtKef55Wr76Kw9QpdZRQXFZVXjPn9lIhTE4cRTnnSChIIkEtJ97MlIRLxXDqn9qqmWBEO+tWeNv74n1peoS3nTeedp6YG9f+l8bqvDwSJ4SjKApeq1c1ju3FVRX2fwy/vgRte8HUn8DqylNDkouSWRGzgtVxqymoKMDbzpspHacw2mc0tma2DRxciLpxLqOIUR/tpVKnx9bchLcmBDJG3g+bPSmSDUx6STpj144l1DWUTwd9Wm8jNOdzSnh6xUkOJ+VyS+eWvD0hkJa2Flc/sJlSVZUX1p7ip0MXeHFkZ2b0vb4dy6pzc0kYPgIzHx/a/fA9ipHh9sY2aKpas+jsciEcDzmx5OWcI6E043IhHG9mQoKZBZnG///vyEwxwcu2Dd6OHfGxb4+3vTc+dj60adGmzubblp04QdIdd2J9Uy/afP554/l7PrUK1swEB0+4fSU4tPvXh5ZXl/PL+V9YGr2UqOwoLE0sGeU9iqmdptLBoUPDZRbiBun0KhM+309ybinfTO/G6xtOc+xCPhNDPXhtjD/W5tK7vrmSItkAfXf6O96PeJ95/edxS7tb6vTcer3Kj4fO8/bmaEyMFV4Z7U94V3f5uLQWdHqVR38+xuaodD6YFEx46LVvHZ36zDMUbtmK95rVmLeXjgFXVVEEOXGQHVdTEOfEoWafIzsvkXil6tJcYVPizS1IMDMjV/n/1yxLY3N87LzxdvDF284bH3sffOx8cLNxw9io/rdRz/3xRzLeeBOX2Y/hPHNmvV+vziTtg6W3gokF3LYc3EKuesjp7NMsjVnKlsQtVOgq6NqyK1M7TeWWtrc0+PoKIa7Vl7sTeGvzWT66tQtjgt2o1un56LdYPtkZRzsnaxZMDSHIw17rmEIDUiQboGp9NVM3TiWvIo/149bXWa/S1Pwynll5gn1xOfT1dea9iUG0trOsk3M3FxXVOu5ZfISDCbl8OS2UgZ1qv3ipeN8+ku+dgfNDD+Iya1Y9pmxk9LpLi+b+vxAmOxZ9ThzpZZk1I8KXpkfEW9qQYGJEEfrLh9ua2uBj3x4f+5p5wn8Uw67WrpruYqmqKhefeprCLVto+/VXWPfqpVmWa5YZDT9OhNJcmLwEfAfX6rCCioLLC/2Si5JxsnAivEM4kzpMopW1LAQWhicxu4Rh83fTr4MLi+7860Y6hxJymL0skuziCp4a0pH7+npjZCQDSs2JFMkG6kTWCe7cfCd3+t3J092evqFzqarKiogUXt94pmbawEg/bu3eRkaPr1NxRTW3LjpIbGYRP9zbgzDPq7f10peVkTBmLIqxMV7r1mJk3gwXRpbm/v+iuUuFMDlx6HITSFX0xJvVFMOJFlbEW1iRYKSn7E/FsKOF4+UR4T//39nS2WB/lvUlJSROnoIuLw+vNasxdW1EHSGK0uHHSZBxGkbNg9C7an2oXtVz4OIBlkYv5feU31EUhQFtBjCl4xR6tu5psH9fonnR61WmLjpIdHohvz5xM64t/jnlML+0kjmroth6Op2+vs58MCmYlld4nGiapEg2YK8deI01sWtYNmoZHR07Xtc5MgrLmbPqJDtjsujp7cj7E4Nl1W4dyCmuYNLCA2QXV7B8Zi86tWrxn4/P/OADcr78irZLlmDdo3sDpdRAdeX/L5r78zSJ7FiqynJJNjUh/o/pEdZ2JJiZkahWUvmnYrilVUt87Hz+MjLsbeeNg4WDhk/s+lXEx5M4aTIWHTrQ7rslKGZmWkeqvYoiWH4XxP8GNz8L/Z+DayxwU4tTaxb6xa4mryIPzxaeTOk4hTHtx9DC7L//3QhRn747kMTL607z/sQgJoX9+0J5VVVZeiSZ1zacxsrMhLmTgq7pU0TReEmRbMAKKgoYs3YMbWzb8N3w767po2NVVVkXeZFX1p+molrHs8M6cVcvT/moqA6l5JUy8fMD6FWVVQ/e9K+/fJRHR5MYPhG7cWNxe0v7HRXrha4aIr6BXW9TUZ5H0h9TJKztSbCyJd5Y4YK+nOo/FcPuNu7/GBn2svNqkh0SCjdvJvWJJ3GYdietnn9e6zjXRlcFG2fD8R8g5HYYvQCuY55xpa6SbUnbWBazjBNZJ7A0sWSE1wimdppKJ8cb6xgjxLVKzi1l6PzdhHk6suTu2rUxjMss4tGfIzmbVsj0mzyZM7xTnW8yJQyLFMkGbn38el7Y+wKv9HqFiR0m1uqY7OIKXlgTxbbTGXRta8/cScF4u9jUc9Lm6VxGEZMWHsDBypQVM2/Cxfav0yhUnY6kW2+jKiUFn82bGkc7sGsV9xtse56CnBgea9ee41Sip+a1w0gxoq1t278UwT72Pni28PxHj+GmLv2tt8n7/nvc531Ii+HDtY5zbVQVfn8Xdv0PvAfA5O/A4vpHgc/mnGVZzDI2JWyiXFdOsEswUztNZUi7IZgZN6KRdtEoqarKnV8f5viFPH554mbc7Wu/Nqe8Sse7W6P5dl8SnVrZ8vGtXfB1bXq/2IsaUiQbOFVVuWfbPZzLO8f6cetxsnT6z8dviUrjhbWnKC6v5skhHZjR1xtjGT2uV0fP53HHV4fwdrHm5/t70sLi/0fZcr//gYy33sLt/fexGz1Kw5T1IDsOfnkBzm2l3MGTB9q0Jaoklen+0+ng2AEfOx/atWgnRc8lamUl56fdRcW5c3iuXIG59/W1EdTU8R9gw2Pg0hluXw4tbqyPbGFlIevj1rMsZhlJhUk4WjgywXcCkzpMws1GetSK+rHsyAWeXRXFm+MCuKPnv7c5/C87ozN5asUJSiqreWmUH7d1bytz7ZugGy6SFUUZBiwAjIGvVFV952/3zwQeBnRAMXC/qqpnFEXxBM4CMZceelBV1f/sk9Qci2SAhPwEwjeEM8JrBG/1ufLH9fmllby87jTrT1wkyMOODyYFy2+3DWhXTCYzlkQQ2s6BJfd0x8LUmKq0NBJGjsKya1fafLmo6byAluXD7+/B4S/AxBJdvyd5sjKRHcm7eO/m9xjmOUzrhAarKj2dxPETMHF2wnPZMoysGuFoetxvsHwaWNjDHSuhZecbPqVe1XMo7RBLo5eyK2UXAP08+jG141R6ufXStEuJaFrSC8oZ/OHv+Lu34KcZPW9oCmJmYTlPrjjBnthshvq78m54EPZWMijQlNxQkawoijFwDhgMpABHgFtVVT3zp8e0UFW18NKfxwAPqao67FKRvFFV1Vrv29pci2SAj459xJdRX/LN0G/o1qrbX+777WwGc1ZHkVdSyWODfJnZ3wdTY3lTaWjrIlN5bGkkQ/xc+fS2LqTPmkXJ/v14b9yAmce191Q2OLpqOLYEdr5V06mi652oA17krdNfsixmGXO6z+H2zrdrndLgFe/bR/KM+2gxahRu773bOH95SjtZ0/miqgym/gBe/ers1Okl6SyPWc6q2FXklufS1rYtkztOZlz7cdiZ29XZdUTzo6oqM5ZEsC8+m22z+9HO6cbbq+r1Kl/vTeS9bdE4WZszb0oIvXz++xNf0Xj8V5FcmyqrOxCnqmqCqqqVwFJg7J8f8EeBfIk1YFhzOBqJ+4Luw93GnTcPvkmVrgqAwvIqnlpxgnuXROBkbca6R3rz6CBfKZA1MjbEnVdH+/HLmQwWvfUNxTt24PLoI02jQE74Hb7oB5ueAJdO8MDvMOZjvkxcx7KYZdwdcLcUyLVk07s3zo8+QuGGDeQvXap1nOvTOghm/Aq2reD7CXByRZ2dupV1K2Z1ncX2idt5t++7OFs6MzdiLoNWDOLlfS9zOud0nV1LNC/rIi/yW3QmTw/tVCcFMoCRkcJ9/bxZ/WBvLM2Mue2rg8zdFkOVTn/1g0WjVpuR5InAMFVVZ1z6+k6gh6qqj/ztcQ8DTwBmwEBVVWMvjSSfpmYkuhB4UVXVPVe4xv3A/QBt27YNPX/+/I0+r0Zrd8puHv7tYR7r+hgdzMfy7KqTZBZV8ODNPswa5IuZiRTHhuCjdcfo+sqDmDg50WPbehSTRrylaU48/PoyRG8E+7Yw5E3oPAYUhTWxa3h5/8uM9h7Nm33elI/Er4Gq15M8cyYlBw7i+eMPWAYFaR3p+pTlwdI74PxeuOVV6D37mlvE1UZMbgzLYpaxMWEjZdVlBDoHMrXTVIZ6DsXcuBn2HBfXLKuogsHzfsfb2ZoVM2+ql7U6JRXVvLr+NCuOptClrT0fTe0iLVcbuRudblGrIvlPj78NGKqq6l2KopgDNqqq5iiKEgqsBfz/NvL8F815usUfHv3tMfak7KUgbjY+Dm35YFIwwW3stY4l/iTt1dfIXbaMx/s9yuQ7hjKjbyNcoFVeCLvfh4Ofg7EZ9HsSej4MpjVN9Hen7GbWjln0aN2DTwZ9gqmRbD18rarz8kgKn4iKiteqVZg4NM4+0FRXwNoH4dQqCLsXRrwP9bTtd1FlEevjaxb6JRYkYm9uz3jf8UzuMBkP2ybwiY2oNw/9eJTtZzPZPKsv7VvWb7enDScu8vyaKFDhzfEBjA1xr9frifpzo9MtUoE/d+D2uHTbv1kKjANQVbVCVdWcS38+CsQDHWpxzWbrYEIOx473o1oPHfy2s+GR3lIgG5jSY8fJX7YMxzvuoH2/7ry56SyrjqZoHav29Do4ugQ+7gr7P4agKTDrGPR98nKBfDLrJE/9/hQdHTvyYf8PpUC+TiYODrgvWIAuK5uLzzyLqm+kH8+amMOEr6D3YxDxNSy7AypL6+VStma23N75dtaNXcfXQ76mW6tufHf6O0asHsHDvz3M7pTd6NVG+n0U9WZzVBqbo9KZfYtvvRfIAKOD3dg8qy8dWtny2NJInlgeSXFFdb1fVzSs2owkm1AzXWIQNcXxEeA2VVVP/+kxvqqqxl7682jgFVVVwxRFcQFyVVXVKYriDewBAlVVzf236zXXkeSySh3vbavpy9jOyYrBPaNZlvAZ8/vPZ1C7QVrHE5eolZUkhoejKy7Be8MGqi0suGfxEQ4m5LLozlAGdTbwHZqS9sLWOZAeBW16wvB3wK3LXx9SkMSdW+7E1syW74Z/h7Ols0Zhm468pUtJf/U1nB99BJeHH9Y6zo05/CVsfhrcu8Kty8DGpd4vmVGSwcrYlaw8t5LssmzcbdyZ0nEK49uPx97Cvt6vLwxbXkklg+f9Tms7S9Y8dBMmDbhmp1qn56MdcXyyI5a2jlYsmNpFBrYambpoATcCmE9NC7hvVFV9S1GU14EIVVXXK4qyALgFqALygEdUVT2tKEo48Pql2/XUFM8b/utazbFIPno+j6dXnCAhu4S7erXj2eGdMDOBKRunUFBRwPpx65vdpgyGKnvhQrLmL8Djs8+wHTgAgOKKam778iAx6UX8MKMH3TwdNU55BXlJ8MtLcHY92LWBwa+B/4R/zC3NLsvmjs13UFZdxvfDv6dti7ba5G1iVFXl4rPPUrhhI22+/BKbPr21jnRjzm6EVfeCbWu4YxU4+TTIZav0Vfx24TeWRS8jIiMCMyMzhnkNY2rHqQS6BDZIBmF4Hl8WyYYTF9nwaB86t9ZmG/TDibnMXnqczKIKnhrakfv7esvut42EbCZioCqqdcz7NZZFu+NpbWfJ+xODuKn9/4/aRWZGcueWO7nL7y6e6vaUhkkFQGVSEgljxmIzYAAeC+b/5b6c4gomfXGArKIKlj/QS7MX6n+oKII9H8KBT2vmkPZ5HG56FEz/uftUcWUxd2+7m/OF5/l26Lf4O/trELjp0peWkjRlCtVZ2XitXoWpWyPfSCP5CPw8pebPty6DNt3++/F1LDYvlmUxy9gQv4HS6lL8nfyZ0nEKw72GY2Fi0aBZhHZ+O5vBvUsieGyQL48P1nY2Z0FpFc+tOcnmqHR6t3fiw8khuLaQn0VDJ0WyAYpKKeDJFZGcyyjm1u5teH5EZ2wt/jnv89X9r7I2bi3LRi2jo2NHDZIKqBkJvDD9bsrPnMF700ZMW7b8x2NS88sI/2w/OlVl9YM3abviWa+HEz/Bb69DcQYETYVbXvnX3dOqdFU8+NuDHE0/yseDPqaPe58GDtw8VCQkkjRpEmbtffD8/nsUs0a+KUFOPPwQDkVpEP41dG74HSdLqkrYEL+BZTHLiMuPo4VZC8a3H8/kjpPlk5AmrqCsiiHzfsfe0owNj/YxiO5Pqqqy7Egyr204g6WZMe+FB3GLn4FPw2vmpEg2IJXVej7ZGcenO+NwtjHjnfAgBnT8Z8H1h4KKAkavGU3bFm35bvh30oJLI/mr15D2/PO0evVVHKZO+dfHxWYUMXHhAeytTFk58yZcbDVoXXX+QM2847RI8OgGw94Bjyv++wdqdkKbs2cOWxK38FaftxjjM6bhsjZDhVu3kTp7Ng63306rl17UOs6NK8mGn6ZA6lEY/h70uF+TGKqqcjTjKEtjlvLb+d+oVqvp7d6bqR2n0te9L8b11I1DaGfOqpMsj0hm7cO9CfKw1zrOX8RlFjPr5+OcSSvkrl7teG5EZyxM5WfQEEmRbCCi0wt5YtkJzqQVMqGLO6+M9sfO6updA9bFrePFfS/yaq9XCe8Q3gBJxZ9V5+aSMHwEZj4+tPvhexSj//5F5diFPG7/8hBeztYsfaAnLa7wCUG9yL9Q0+/49BqwdauZdxwwEa6Sd+6RuSw5s4TZXWdzb+C9DZO1mcv43zvkLlmC2wdzsRs5Uus4N66yFFbNgJhNNdN5bnn9qj939SmrNKtmoV/MSjLLMnGzdmNSx0lM8J2Ao4UBrhkQ12xPbBZ3fn2YmTf7MGd4J63jXFFFtY73tsbw9d5EOrra8vFtXejgaqt1LPE3UiRrrFqn54vdCczffg47S1PeHh/IEP9WtT5eVVXu3nY3sXmxbBi/QV7kG1jqM89QuGUr3qtXYe7rW6tjdsVkMmNJBKHtHFhyT/f6HUGoKIZ982vauUFNm67ej4HZ1XebWnJ6CXMj5nJbp9uY031O49w+uRFSq6o4f9d0yqOj8Vq+DPP27bWOdOP0OtjyDBz5qmZR6PiFNa3jNFSlr2JX8i6WRS/jUPohTI1MGeo5lCkdpxDsEiw/741USUU1Q+btxtzUiM2z+hr8CO3OmEyeXnGCovJqXhzlxx092srPngGRIllDcZnFPLniBCeS8xkZ2Jo3xgXgaH3t8xDj8+OZuH4iI71H8mafN+shqbiS4n37SL53Bs4PPYjLrFnXdOy6yFRmL4tkcGdXPru9a923JdLrIWo5bH+1Zk5owMSaHdHs21ztSAA2J2zm2T3PMqTdEN7r9558HN3AqjIySJwQjrG9PV7Ll2FkXTdb6GpKVWHfAtj+CrTrDVN/BEvD2EAlIT+BZTHLWB+/nuKqYjo7dmZKxymM8B6Bpck/F7IKw/XKulN8d/A8Kx7oRZghdhO6gqyiCp5ccYLd57IY4ufKu+FBOFxHLSDqnhTJGtDpVb7dl8j722KwNDPmjbEBjA6+sdXs84/O5+tTX/Pt0G8Ja/Xvc0xF3dCXlZEwZiyKsTFe69ZiZH7to2KL9yXy6oYzTAlrwzvhgXU3epB8BLY+WzMP1K0LDHsX2vao9eEH0w7y4PYHCXEJYeHghbLtr0ZKDh7kwj330mLYMNw+mNt0RpeiVtbs0OfgBXesrNnu3ECUVpWyMWEjS2OWEpsXi62ZLWN9xjKl4xQ87Ty1jieu4nBiLpO/OMDdvT15ZXTj6sCj16t8sy+Rd7dG42htxrwpIdzkI33otSZFcgM7n1PCUytOcCQpj1s6u/L2hABa2t54G5iy6jLGrxuPhbEFK0avwNRYdkGrT5kffEDOl1/RdskSrHt0v+7zfPhLDB/tiKubuXMFKTUjx1ErwKZVTceKoKnXNP/zbM5Z7t52N242biwetpgWZgbSrq6Zyl74BVnz5+P6wgs43nmH1nHqTtJeWHobmFjA7SugdbDWif5CVVWOZx5nacxSfj3/K9X6anq17sWUTlO42eNmTIxMtI4o/qasUsfwBbvRqSrbZvfDyqxx/h2dSi1g1tLjJGaX8FB/H2bf0gHTBtwARfyVFMm1seJusHaBLndA66DrOoVer/LjofO8vTkaE2OFV0f7M6Gre52ODv2e/DuP7HhEFlnVs/LoaBLDJ2I3bixub711Q+dSVZUX157ix0MXeGFEZ+7r533tJ6kshf0fwd75oOprFkf1eRzMr2371ZSiFO7YfAdmxmZ8P/x7XK2lNZHWVL2elIcepnjfPjy//w7LkBCtI9WdzLPww0Qoz4fJS6D9LVonuqLssmxWx65mxbkVpJek08q6FZM61Cz0kx0nDcfbm8+yaHcCP83o8Zc9BRqj0spqXlt/hmURyYS0seejqV1o6ySbhmlBiuSr0etg9f01u5HpKqFVEHSdBoETaz2fLiWvlGdXnWRfXA79Orjwbnggre3qZ57b7J2z2Ze6j7Xj1uJu414v12jOVJ2OpFtvoyolBZ/NmzC2t7/hc+r0KrOWHmfTyTTmTgpmYqhHLcOoNR9db38FClPBbxwMfh0c2l1zhtzyXKZtmUZeeR7fD/8eb/vrKNZFvdAVFJAYPhG1uhqv1aswcWwc8yxrpTANfpwEmWdg9ALoeqfWif5Vtb6a3Sm7WRq9lANpBzAxMmFwu8Hc2ulWQlxCms50mEbo+IU8wj/fz9TubXl7fNPZXXHjyYs8tzoKVYU3xwUwrou8pzc0KZJrqzS3piA5/j2knwRj85rm+F3uAK/+V/xIW1VVlkck88bGszUjhqP8mNqtTb2+mKaXpDNm7Ri6t+rOxwM/lhfuOpb7/Q9kvPUWbu+/j93outscoaJax72LIziQkMMXd4RevcF86lHYMgdSDtf84jbsHfC8vu2MS6tKmfHLDM7lneOrIV8R0jLkus4j6k/Z6dOcv/U2rMLCaPPlIhTjJrSQsrwQlk+DhJ1w8xzoP+cfW6IbmqSCJJbFLGNd3DqKqoro4NCBKR2nMMp7FFamMuLXkCqqdYz6aC/FFdX88ni/K2681Zil5JUye2kkEefzmNDFndfG+je552jIpEi+Hmkn4PiPcHJZzUeFdm0g5DYIuf3yKF5GYTlzVp1kZ0wWPb0deX9icIPtsvZH6675A+YzqO2gBrlmc1CVlkbCyFFYdu1aU6jU8Rt5cUU1t395kOj0Ir6/twfdva4wYliYBr+9Bid+BuuWMOjlmp+96+w+UaWv4rEdj7Hv4j7m9Z/HwLYDb/BZiPqSt3w56S+/gvNDD+Ey61Gt49QtXRVseAwif4SQO2D0fGgE6ypKq0rZkriFpTFLic6NxsbUhjE+Y5jSaQredvJpTEP44JcYPt4Rx7d3d/vPzbcas2pdzUZjH/0Wi4eDFR/d2oWQNvZax2oWpEi+EVXlNQ3yj/8A8TsBFdXrZiIcR/DQUXeKdCbMGdaJab08MTJquJGRKn0VUzZOoaiyiHVj18nIRh1QVZWUhx+hZP9+vDduwMyjllMirlFuSSUTF+4nq6iCZff3ws/t0sK5qjI48AnsmQf6Kuj5EPR9Eiyuf2Gdqqq8sv8V1sSt4aWeLzG54+Q6ehaiPqiqStpzz1Owbh1tvliITb9+WkeqW6oKu96B398Bn0E185TNG8fmCqqqciLrBMtilrEtaRtV+ip6tOrBlE5TGNBmgCz0qyenUgsY++k+xoW488Fkw1r8WR+OJOUye2kkGYXlPDGkAzP7+TRobdEcSZFcV/KTKTn8PaWHv8OlOo0SxRqd/0Ra9Jpe04argT8+PJ55nGlbpjHdfzpPhj3ZoNduigp/+YXUWY/R8umncLq3fhdFpuaXEf7ZfnSqyqoHetE2fRv8+goUXIDOo2vmHTve+CjVx8c/ZtHJRcwMnsnDIQ/XQXJR3/RlZSRNvZXq9HS8Vq/C1L0JzlE89h1smA2ufnDbCmjRWutE1yS3PLdmoV/MCi6WXKSlVUsmdpjIRN+JuFi5aB2vyajS6Rn7yT6yiiv49fF+2Fs1j77CBaVVPL8mik1Radzk48SHk0NoZXfjHbLElUmRXEc2R6Xx4tpTlFRUMrdbESOrf8Po7HqoLoeW/jVzl4OmgLVTg2V6df+rrI1by/LRy+ng0KHBrtvU6IqKSBgxEmNnZ7xWLEcxqf9RodiMIl5a+CPPKUsI1p8B1wAY9j/wqpvRw2XRy3jz0JuE+4bzSq9XZO56I1KZlETixEmYeXrS7qcfMTJrgsVB7PaaecpWjjUt4lp21jrRNdPpdexJ3cPSmKXsS92HiWLCoHaDmNpxKqGuofJv7gZ9siOWub+c44s7Qxl6DbvUNgWqqrIiIoVX1p/GwtSI9yYGM/hq61jEdZEi+QbllVTy8vrTbDhxkSAPOz6YFIzvH/uvl+XD6dVw7Hu4eAyMTKHTCOhyJ/gMvO55pLWVX57PmLVj8LTzZPGwxRgp0mvxeqS//jp5S5fhuWwploENsHK6KAN+ex018kdyVVt+tJ7G9EdeooVV3YwWbD+/nSd2PcHNHjczb8A8+Si4ESr89VdSH52F/a1Taf3KK1rHqR8XI+GnyTUDDVN/As8+Wie6bhcKL7A8Zjlr4tZQWFlIe/v2TOk4hVva3SJt5K7DuYwiRn20lyH+rnxyW1et42gmPquYWT8f5/TFQu7s2Y4XRnY2+G24Gxspkm/A9jMZPLcmivzSSmYN9OXB/j7/vr1wxulLi/2WQmkO2LpByK01i/2cfOot49q4tby07yVeu+k1JvhOqLfrNFWlx45z/vbbcbjzDlo9/3z9XqyqHA5+Bns+gOoK6PEAe93v5e6fo+nS1oHv7ul+wy+ARzOOcv8v99PJqRNfDflKttxtxDLee5/cb77B7f33sBs9Wus49SPvfE2LuLxEGPd5TevNRqysuoytiVtZGrOUMzlnAGhv357urbrTvXV3wlzDsDO30zilYdPpVSZ8vp/k3FJ+fbwfTjbNe0fQimodc7fF8OWeRDq62vLRrV3o2KpxzOVvDKRIvg4FZVW8vuEMq46l0KmVLR9ODvn/BVZXU10J57bWLPaL+7Vm84d2vWumY/iNBTPrOs2qqirTt04nviCeDeM24GBRu97OAtTKShLDw9EVl+C9YQPGNnX7d/P/F1Lh7Ab45UXIPw8dR8CQNy//8rQuMpXZyyK5pbMrn9/e9d9/EbuKuLw4pm2dhpOFE98P/x57C/s6fBKioanV1ZyfPp3y02fwWr4Mc19frSPVj7I8WHo7nN9XMx//plkG3yKuNqJzo9l/cT+H0w5zLPMYZdVlKCh0cuxEj9Y96N6qO11du2JtWk+vO43Ul7sTeGvzWT66tQtjgt20jmMwfj+XxZPLT1BY/n/t3Xd4VNXWwOHfSe+d0EIg9BpCAqEoIE0IiqAUAVHB3lGuvV/rp9yryLWgIs0ColSRAKIoSgudUEJLQk0hvU+m7O+PE0JvYZKZJOt9Hh5hZnJmjSfJrFln7bWNvHpLG+7u1rjat/QYLUb2Ze5jW9o2+oT2obHPte8BcL0kSb5Gaw+c4oUFu0jPN/DYTc14sm8LXJwq2MaQd1If5bX9O8hKBBdvaH+73o4R0sVqbwSHsg8x8peR3NrsVt6+4W2rHLM2OL0lcMjnn+Pdt0/lPEnKLlj5MiT/DXXawKD39Fac88xen8wbS/cwqnMIHwwPv+ZffqmFqYxbPg6LsvDt4G9lo5kawpieTtIdw3H08qLJzz/h6HVtuyxWG8YSWPwI7FkEXR6EmA8qvV2tKhnNRuIz4tmUuonNqZvZkb4Do8WIk+ZE+6D2dKnXha71u9KxTkfcnGrvIq2kjEIGTVlLr5Z1+Opu6es+36l8A8/9vJM/95+if5u6fDginADP6rNmodhUzK5Tu9iWto2t6VvZdWoXxaZiAJtdDZck+SoVGEy8t3wfP2w6SvNgL/47siMdrTWnUCk4ukFPlvcsAmMRBLXSq8sdR4PX9c9+/Hjrx8zYPYNZg2YRVTfKCkHXbKXJySTeNhSvPn0I+WSK9Z+g4BT88ba+kt/dH/q8DFETwPHS/cEf/XaAqb8f5OHeTXkp5uoXMuUachm/YjyphanMGjSLVgGtrPEKhJ0o3BTH0QkT8B4wgIZTPq65iYPFAqtfh/X/g1a3wPDp4FIzx1uWmErYcWoHcSlxbErdxJ6MPZiVGRcHFyKCI8rbM9oHtcfZwf7nSVuDxaIY/dVGElLz+G1Sb+r61N4PC5djsShmrk/mg9gE/D2d+XhUhN1u051ryGV7+vbypHhvxl5MyoSGRkv/lkTWjSSqbhSRwZE2mwwjSfJV2HA4k+d+3smJnGIe7NmUSQNaVl5zvCFfT5S3fwfHNoHmCC0H6QlziwEVHrBfZCzi9iW34+Hswfwh82vNL9aKUEpxdPwESvbupemvy3AOtuKAelMpbJoGayfrH4aiH4Lez1/VFudKKV5fsodvNx7h5cGteajXlXvZDWYDD616iPiMeKb1n0Z0/WhrvAphZzKnTyf9P/+l7ksvEnDvvbYOp3Jt+hJiX4CGUTD2R/C0zwTAmgqNhWxN20pcShxxqXEkZCWgULg7uRNZN5Ku9boSXT+a1v6tcaxBFfazfbshmdeW7GHyiHBGdm5k63Ds3u4TuTw1bztJGYU80rsZkwa0xLmCrXrWklaYxrb0bWxN28q29G0czD4IgJODE+0D2+sJcd1IIoIj8HGp+B4A1iRJ8hUYTGZumvwnrk4O/GdkRzo3ucguaJXl1AF9G+yd86AwXd9hreNovR2jzrWPdPvz2J88+ceTPBP1DPe1v8/68dYQOYsWk/LSS9R78w38R4+2zkGVgv2xsOoVvbWmxc1w87vXfB7NFsVT87bz666UK75ZmC1mnv3rWVYfXc3kXpMZFDboel+FsFNKKY4/8SQFf/1F4zmz8Yis4Sv+9/0CCx4AnwZw18+VuvjZHuUactmSuoVNqZuIS4njcO5hALxdvOlct3N5T3Nzv+Y14srCsawiBk5ZS+cmAcye0KVGvKaqUFRq4u1le5kbd4yOIb5MHdOJxoFV0+OulOJI3pEzSXHaNo4XHAfA3cmdiDoR5Ulxh6AOdttGJEnyVdiXkkfjQA88XGw0KstshIO/6dXlAytAmaFRV7263O72a9qV6qk/nmJjykYWD11MAy9Z9HA+U1YWiTGDcWnalMbff4fmYIVP3ml79L7jxD8hqCUMfB9a9K/w4QwmM/fP2sKGxEy+HBdF/4vMx1RK8d6m95i3fx4vdHmBcW3HXccLENWBOS+PpBEjUSUlhC1aiGNAANuO5jA37iipuSV8OrZTzdpw4Vgc/HCnvnZj7HwIuej7WK2QUZxRXmWOS43jWP4xAALcAuhSrwvR9aLpWr8rod6h1S7BVEpxz4w4th3JZtWk3jT0k4k812p5fAovLtiF2aJ4e1h77oi0/o6xZouZA9kHzkmKM0syAfB39adTcCei6kYRVTeKVgGtqs3oUUmSq5v8NNj1o15hzjgAzh56otxpHIR2v+Jiv5SCFIYuGUrX+l35X9//VVHQ1ceJ558nL3YFTRcuuP5pAYWZsOZd2DoTXH30vuPO91W4ZeacQxtMjP16Iwmp+cy5L5quTc/dpObrXV8zdftUJrSbwKTOk677+UT1ULJvH0mjx5Ab1po3ez5CwqlCPF0cMZoV4SG+fPdA15o1RzXjEHw/XP+9OOIbaH2LrSOyCycLTuoJc1lPc3pROgB1PeqWV5mj60VT38v+dzOcv/kYzy/YxTvD2jOuW9VPN6gpTuQU88y8HcQlZzEsogFvD2uPt1vF34tKzaXszthdnhTvSN9BgbEAgPqe9cv7iaOCowjzDat2H85OkyS5ulIKjm/Rk+XdC6E0HwKaQae7oOPYy27lOnP3TD7a+hGf9PmEvqEXTlKorQrWrePY/Q8Q+OgjBE+cWPEDmUph83T46//AUABd7oebXtJ3D7OirMJSRk5bT3qegR8f7l4+hvD0bOxbm97Kuze+K5vI1AJKKbYdzeaHTccoWbqIJ7b8yB9RgwmYOJEhHRvw5/50nvhhOzHt6/Hp2EgcHarnG9ZFFZyCuXfCye0Q8yFEP2jriOyKUoqj+UfZlLKJuNQ4NqduJqskC4BG3o3Kq8xd6nWxu41NUnNLGPDxX7St78PcB7vhUJO+b23AbFF8tuYQn/x+kAZ+bnwyuhORoVc3FrbQWMiO9B3l/cTxp+IptZQC0NS36TlJcXX48HW1JEmuCUoLYe8SvR3jyDrQHKD5AL263HIQOJ17idVoMTLql1EUGgtZPHQxHs41c4X4tbAUF5N421A0BwfCli7BwbUCA+qVgoOr9NaKzEP6KLeB71XqlroncooZ8cV6jGbFwkd7kFy8haf+eIqu9bvyad9PcbZC1VrYr9wiI4u2H2du3DH2p+Xj6eLI0E4Nueuvb3FY8QshX3yOdx99fOH0vxN559d9jO/RhDeGtK22lZ2LKi2En++HA7Fww9PQ7w2wRqtUDWRRFg7lHGJz6mY2pWxiS+oW8o35ADTzbUZ0/Wi61utK53q23dhEKcUDs7ew7nAGKyb2okmQzIu2lq1Hsnhq7g5S80qYNKAlj/RudsEH56ySLH3qRFlSnJCVgEVZcNQcaR3Q+pzJEzV5/wVJkmuazMOw43vY8QPkp4BHIISP1hPmum3LH7Y9fTv3xN7DhPYTmBQll+PT//tfMr+eTujs2Xh2rcAEiPQEPTk+/DsENteT4xY3V8mmB4fS8xkxbQMeXicxBn9GmG8YMwfNlE0IaqjTVePvNx3l110pGEwWwkN8GRsdypCODfB0dcJSUkLymLEYT5wgbOECXEL0HsS3ftnLjHVJvDK4DQ/2amrjV2JlZhPEPg9bvoH2I2DY5+BUu3djuxpmi5mErITyRYDnb2xyetxcVN2oKv2dsmTHCSbO28Grt7ThgZ417HvVDuQWG3llUTzLdqXQrWkAL95al2PFe8qT4qTcJABcHV3pENShPCnuWKdjrXpvkSS5prKY4fAfejtGwnKwGKFBpJ4stx8O7n68sf4Nlh5ayvwh82nhX0N367oKJfv3k3THcHyHDqXBe+9e2xcXZcGf/6e3V7h4wU0v6JsdOFXtAqnlCbt4fv1DOOHOotvnEuZfr0qfX1S+3CIjC7cfZ27cUQ6kFeDl6sTQiAaMiQ6lfcMLK36lx46RdMdwXBo1ovHcH3BwdcViUTw5dzu/xqfUzB3LlIJ/Pobf/w1NesKd34G7n62jqlaMZiO7M3eXt2ec3tjEUXOkfVD78qQ5ok5EpU0kOJVvYMDHfxEW5MnPj/SoWe1BdsCiLCTmJLI1bSuLE/4hPmMHmnMOAN7O3kQERxBZN5LOdTvTNrAtLo41aMHvNZIkuTYozIT4+bDtW0jfA05u0HYoOe2HMWTb+zT1bcrMQTNrZe+qMpv1itvx4zT9dRlO/ld52chshC0zYM17YMjTNwLp87JNZrZmFGcwbvk4cksKyTz4IBH1WzDnvuiatUCrllJKsfVINj/EnakadwzxZcxZVePLyf/jD44/9jg+Q4YQ/OyzONcNpsRo5p5v4thxLIfZ90XTvVngZY9RLe36CRY/qo+Gu+tn8JO5uhVVYiph56md5Unz7ozdmJUZZwfn8o1NutbvSvvA9lZr73r8+238tjeN5RNvpHnw1U9vEhdntBhJyEwoX2S3PX07OYYcAILcg2jlG87epCCOp9RjdMdoXh/SXt4/ykiSXJsopS9u2f4dxP8MhlwWBYfyuie8FfE0t3e839YRVrmsb78j7d13aTB5Mr5Dbr26Lzq0Gla8DBn7Iaw3DHof6rar3EAvoaC0gPtW3kdyXjIzBs4g8UQAE+dtp1/rukwbF4mTjYfHi4q51qrx5ZyaOpWMz78ATcMjKgrvmEGoXn2586cDpOWV8PMjPWhVrwYmIklrYd44cHaHu36C+uG2jqhGOL2xyeme5nM2NgmOLO9pbh1QsY1NYuNTePT7bTw3sBWP92leCa+g5is2FRN/Kp6t6footp2ndpZv79zIuxGRwZHl49gaeTdC0zRKTRb+s2o/X61NpEWwF/8b24nW9exjQw9bkiS5tjIWw75lWLbPYUJJAonOzvzi1By/yHv1LV+d7XOwtzUZU1JIvOVW3Dt1otH0r6+8kCnjoN53fHAV+IfBwHeh1eAq6Tu+GKPZyGO/P8aW1C38r9//uLHhjQDM2ZDM60v2MDIqhA9HhNesBVo1WHnVeNNRfo0/UzUe2zWUW8OvXDW+HENiInmxseTFxlJ66DA4OODYKYrpTk3Z3qQT304aSD3fGvgzn7YXvh8BJbkwag4072friGqcS25s4uxNVL2o8t0Am/s1v+LVyuzCUgZ8vJZ6vq4seuwGm+8QV13kGnL1yRNlSfGezD2YLPr2zi38W+hJcT19kV2wx+V3kF174BST5u8kr8TIK4PbcE/3xrX6PUSSZMHBI38x6s8nua3Ewr9TjoGbH4SP0nf2q6HVF6UUxx9/gsL162n6y1JcGl3mcmxxNvz1IcR9BU7u0Ps56PqITRcFWZSFl/5+ieVJy3nnhncY2nzoOfd//NsBPvn9IA/3aspLgytvuoa4frlFRhZs06vGB9P1qvGwTg0Y3eXaq8ZXo+TAAfJXrCBveSylycmYNQcONmxNt/tHERwzEEc/P6s/p03lnYTvR8KpBBgyVR+TKSpNRnFGeZX5UhubRNeLprHPhcnXpB93sHTnSZY+cWP5SEtxofSi9HMmTxzMPohC4eTgRLvAduVV4o51OlZoQklGgYHnftrJmv2n6N8mmA9HdCTAs3b2JUuSLAD4aOtHzNw9kznhE+l0eD3sWwZmA9QL15PlDiOsPufXlvJWreLEUxMJfu5ZAu+/RJuJ2QTbZsEf7+qJcuQ90PdV8Lr8J/Gq8N8t/2XWnllMjJzIAx0euOB+pRSvL9nDtxuP8FJMax7uXbu27bV3Sim2HMlm7tlV40Z+jI1udN1V42uJwZCQwJ4fFpK7PJb6hZng5ITnDT3wGRSDd/9+OHrXkDaMkjyYf7e+6+VNL0Pv5212Bai2SSlIKd8JcGPKxvKNTYI9gsurzNH1okk47sh9s7bwVL8WTBrQ0sZR24/Tc67PTopPf/Bwd3KnY52O5Ulx+6D2uDtZZ0dCpRSz1ifz/vIE/Dyc+WhUBDe2sK852lVBkmQBQJGxiGFLhuHp7Mn8IfNxLsmH3Qv06RgpO8HRBVrfqk/HaHoTVKDXzF6Y8/NJvOVWHAMCCPtpPprzRRabHF6jt1ak74XGN+p9x3ZSVZ+zZw6Tt0xmTOsxvBT90iUvhZktionztrNsVwqTR4QzsrMsXrK1nKJSFm47cUHVeEx0KO0a2G4m7cKtx/jiq1+5z3iYzsnbMJ08iebsjGfPnvjEDMKrT18cvar52CdTKfzyFOycq3/wv/Vjq+x+Ka7e2RubbE7dTFxqXPnGJpopEHdTK17pdxs9Gna1u41NqorZYuZgzsHyrZ23pW8jozgDAD9Xvwu2d3Z2qNzv4b0n83hy7jYSMwp5qFdT/jWgFS5OtacNRpJkUW7N0TU8teYpJkVNYkL7CWfuSNmlz17e9aNeUfVtBBFj9T/+TWwWb0WlvvUW2XPn0WT+j7h36HDunZmHYdWrsH85+IXCze9Am9vspuoUmxTL82ufZ0DjAUzuNfmKC2NKTRbun72Z9YczmTYuigFt61ZRpOK001Xj073GpWVV47uiQ7m1Y308XCq/anw1PltziMkr9/NIr6ZMbFhK3vJY8laswJSWhubiglfvXvjExOB10004eFTTDYiU0ifSrP0QmveHkbPAtYZUy6shpRSHcg7x2spF7MzYio//UYpM+tbGzXyb0aVel/LdAG25sUllKjWXsidzT3lSvCN9R/nmLvU8652zyC7MN8wmU6iKS828tWwvc+OOEh7iy9TRnWrN5i6SJItzPPnHk2xK2cSSoUsu3FrSZNCTx23f6jOYURDWS6/KtBmiryK3c0Xbt3Nk7F343z2Oei+/fOaOklxYOxk2TtN7jXtOgm6P29UCxo0pG3l09aNE1Ilg2oBpuDpeXU90ocHE2Omb2JeSx7f3RdO1aQ0c+WWHcopKWVBWNT6UXoC3qxPDOjVkdHQjm1aNL0UpxauLd/P9pqO8NbQd93RvgrJYKN6xQ0+YV67AfCoDzd0dr5t64zMoBq/evXBws5+fkau2dTYse0afSnPXT+Atc8Vt5Z+DGYz7ZhMP927K8wNbkpCdQFxKHJtSN7Et7eIbm0QGR+Ll4mXr0Cuk0FjIzvSd5Yvs4jPiMZgNAIT5hp2TFDfwsq855it2p/DCgnhMZgv/Htqe4ZENa/yiPkmSxTlOFpxk2JJhdKvfjal9p176gbnHYcdc2PEdZCeDqy90GK63YzSItJvK69lUaSlJw0dgzs+n6bJl+uVji1lvKfn9bSjKhIi7oN9rdvemmZCVwPgV46nvWZ/ZMbPxcbm2RS1ZhaWMnLae9DwDPz7cXRbFVBKlFJuTs5kbd6ZqHNHIj7F2VjW+FJPZwiPfbeX3hHSmjYtiYLszPwfKbKZo61byYmPJX7kKc1YWDh4eePXti0/MIDx79sTBpRot7jn4G8y/V9+VdNzPUKeVrSOqdQoNJgZOWYuLowPLJ/a8YDbv2RubbE7dzI70HZRaSnHUHGkX1K68p7kyNza5XlklWWxP216eFCdkJWBWZhw0B31752B9045OdTsR4Gb/635O5hTz9I87iEvK4raODXjn9vb4uNXctiVJksUFZuyewcdbP2Zqn6n0Ce1z+QdbLHBknT57ee8SMBVDcDs9WQ4fZZPNNS4lY9qXnJoyhZDPP8e7bx9I+htWvARp8dCoG8T8HzToZOswL3A8/zh3x96Nk4MT38V8R13PirVMnMgpZsQX6zGaFQse7U7jwNpxuawqXKpqPCY6tNp9ICkuNTPm643sS8njhwe7EtX4wjduZTJRtHkzectjyV+1CnNuLg5eXnj364d3zCC8evRAqw4J88nt8P0oMJfCmLnQuIetI6pV3ly6h9kbkvnp4e50bnLlBPFyG5t0rNOxfEZzh6AOVtvY5FqdLDhZvsBuW9o2EnMTAXBxcKFDnQ7lSXHH4Oq7vbPZovh8zSGm/H6QBn5ufDK6E5GhV7kRVzUjSbK4gNFiZNQvoyg0FrJ46GI8nK+y/7AkF3Yv1BPmE1vAwRlaxejtGM36gqPtqmilyckk3jYUrz59CHljIvz2Guz7Re+vHvBvaHeHXVa/s0uyuSf2HrJKspgTM4dmftc3peJQej4jp23A282Znx/pTrCPfVZfqoPTVeMfNh1h+e5USk0WOoX6MSY6lFvD7b9qfDmZBQaGf7GenGIjCx7tQbM6l760rYxGCjdu0ivMq1djycvDwccH7/798YmJwbNb14svjrUX2cnw3QjIOQK3fwnt77B1RLVCXFIWo77cwPgeTXjztoptxlRoLGRb2jbiUuMu2NikU3Cn8t0A2wS0qdDGJleilCIxN/GcpDilMAUAL2cvIoIjylsn2gW2q3HbO289ks3EedtJyS3hmf4tePSm5jVuC3FJksVFbUvbxr0r7uW+9vfxTNQz136AtL36Yr+d86AoA7zrQ8cxeoU5sGrHkSmlODrhPkp2x9P0xX4475sBDk5w4yTo8YTd9lIXGYt4cNWD7M/ez9c3f02nYOtUuXccy2Hs1xsJDfDgx4e74+tuxwmMHcouLC2fa3z4VCHerk7cHtmQ0V2qX9X4co5kFnLH5+vxcHVkwaM9CPa+8gcqVVpKwfr15MfGkr/6dyyFhTj6+eE9YAA+g2Pw6NIFzckOPzwUZcG8sXB0g75Yt/sTdvmhuaYoMZqJ+eRvTBYLK5/uZbUPlLmGXLakbSEuRR85dyjnEHBmY5PTM5pb+Leo0AI4k8VEQlZC+SK77enbyTZkAxDoFkhk3TP9xC38WlRKYm5v8kqMvLpoN0t3nqRrWABTRkdQ39c+31MrQpJkcUmvrXuNZYeX8dOQn2juX8HtQU2lcHClXl0+uAqUBUJ76Mlyu2HgUvmXm3IWLiTl5Veo18OMf2gahI+G/m+Aj30tijibyWJi4pqJ/HPiHz666SP6hVp3p7C/D57ivlmb6dTInzn3R1/QCyjOpZQiLimLuXFHa1zV+HJ2Hsth9FcbaR7sxbyHul3T/GaLwUDhP//oLRlr1qCKinAMDMRn4M14DxqER1QUmqMdfd8ZS2DRw7B3MUQ/rI99rAVJji28v3wfX65N5PsHunJD88pryTt7Y5PNqZs5mn8UAH9X/3MmZzTxaXLRBWglphLiM+LLk+Kdp3ZSZCoCIMQrhMi6eutEZN1IQr1Da/witktRSrFg2wleX7IbZ0cHPhgezqD29rWup6IkSb4KSqla+c2fXZLNkMVDaObbjFmDZl3//4O8FH1G6fbvIOswuHjplzY73Q0hXSqlcmPauZLEe5/BxctA43vD0GI+gJAoqz+PNSmleGP9Gyw6tIjXur3GqFajKuV5ftl5kqfmbadf67pMGxeJk2wBe4ELqsZuTtzRqSGjo0NpU7/mVI0v5/d9aTw4Zwu9Wtbh63s6V2irYEtJCQV/rSUvNpaCP/9ElZTgVKcO3gMH4jM4BveICDQHO/j+s1j0VqwNn+pz4YdPt9srTdXVjmM53PH5Ou7sEsr7d3S48hdY0dkbm2xK2URaURoAwe7B5ZuaBLgFlLdO7M7cXb69c3P/5mcW2QV3qvDakJosKaOQifO2s+t4LmO7hvLaLW1xd6neHzQlSb4KSSNGYikpxjUsDJcmYbiEheHSpAkuYU1w8q+ZzeqnLTy4kDfWv8HbN7zNsObDrHNQpeDoRj1Z3rMIjIUQ1LJssd9o8LbCL5/sI7D6DU5MX0PeMQ+aTn4C18GPVYtLqJ9u/5Qvd33JIx0f4fGIxyv1ub7dkMxrS/YwIiqEySPCa+WHwfMppdhUVjWOjU+l1KxXjcdGh3JreINq/0u/IubGHeWlhfGM6hzCB8Ov7/vEUlREwZ9/6gnzX2tRpaU41auHT1nC7BZuB9+HG7/QF/WGdIEx88BTxiZag8FkZsj//iG/xMTKZ3rZdCqCUopj+cfYlLqpvD3j9MYmTpoTbYPaEhWst05EBEfU2DnN1lZqsvDf3/bz5V+JtAj2YuqYThUvKJhK9SvQu+ZBz2ehQYRVY70akiRfhVNTp1KSsJ/SpCRKjx0Dk6n8Pkc/v7OS5jBcwprgGhaGc2ho9RqHdAkWZWH8ivEk5Sbxy7Bf8HPzs+4TGPJhz2I9YT62ETRHaDlQT5hb3HztO2IZCuCfj2H9/yhIdeHY794EPnQ/wZOetW7clWT+/vm8vfFthrcYzhvd36iSZGHK6gNMWX2Qh3o15eXBbSr9+ezV6arxD3FHSaylVePL+WjVfqb+cYin+7fg6f7W2TbYXFBIwZo/yFseS+E//6CMRpwbNMA7ZhA+MYNxa9fWdgnz3iWw8CHwaaiPiAtoaps4apDT30Mzx3ehT+tgW4dzjtMbm+QYcqy6vXNt9c/BDJ6Zv4PcYiMvxbRmfI+Lt7RcQCk4vkVPjHcv0Dcw86yj75DZZkjlB34eSZKvkTKZMB4/jiEpidKkZEqTkylNSsKQnIT5VMaZBzo44NywYXnSfCaJDsMpONj2lZJrcCD7AKN+GcWw5sN4s8eblfdEGQf1ZHnnXChI038wOo7W2zGuNMPUYtF3BPz935CfgqXVHSROP4Lm5ELY0iU4uF7dxhu29PuR35n01yR6NuzJlD5TcHKomj5XpRRvLN3DnA1HeCmmNQ/3rtqFlbZ0sapxZKgfY7s25pYO9Wtl1fhSlFI89/Muft56nA+Gd+DOLqFWPb45L4/83/8gb0UshevWg8mEc2goPoMG4TM4BtdWrar+9+bRTTD3Tv3D+9j5dt+qZc/2nMxl6KfruC2iAR+NirB1OKIKZBYYeO7nXfyRkE7f1sFMHhFOoNcl3ouzk2HXfH2xf9ZhcHKD1rfoC/6b9rHZdCxJkq3InJ9PafIRSpOT9MQ5Kans38mo4uLyx2keHrg0aYzr6daN00l0kyb6Bhd26KMtHzFzz0zmxMyx2pSFSzKb4NBvesJ8YAVYTBASXbbY73ZwO6+qd3QTrHgRTm7TNzKJ+YD0+X+T+fV0QmfNwrNb18qN1wq2pW3jwVUP0jqwNdNvnl7lVQyLRfHUvO0s25XChyPCGdW5UZU+f1XLKixl4UWqxmO6htK6nlSNL8VotnDfLH2b8+n3dqZPq8qpBppzcshfvZq82BUUbtwIZjMuYWH4xAzCJyYG1xYtKuV5LyrjIHw3HArSYeRMfayluCZGs4Vhn60jLc/A6km98POo/ldZxdVRSjF7fTLvxSbg6+7MR6M60rNFHf3O4hz9is3OeXB0vX5bk54Qfie0HXrhe70NSJJcBZTFgiktrazinKxXoJP0RNp48qR+eaGMU3DwOT3PrmVJtHPDhjZdCV5kLGLokqF4u3jz460/4uxQRb1kBel6hXjbt5CxH5w9oO0wiLxbn3G8+k3Y/TN41YP+b0L4nZQcPEjSHcPxHTqUBu+9WzVxXodD2Ye4Z8U9BLoFMidmDv5utulzLzVZuH/2ZtYdymDauChublczViefdrpq/MOmo6zYrVeNoxr7MyY6VKrG16DAYOLOLzeQlFHIvIe6ER7iV6nPZ8rKIn/Vb+StWEFRXBxYLLi2aI73IL0lw7VpWKU+P6D/HvphFKTshMGTocsDlf+cNchnaw4xeeV+po2LqjFTD8S12ZeSx1Nzt5OUnsN7HdIZ7vQPjgdiwWyAwBb6VePwUeBn3StU1+u6k2RN0wYBnwCOwHSl1P+dd/8jwOOAGSgAHlJK7S277yXg/rL7nlJKrbzcc1XXJPlyLAYDpUeOnJM4lyYnY0hOxpKbW/44zdkZ59DQs9o39P5nl7CwKls8+MfRP5i4ZiL/ivoX49uPr5LnLKcUnNiqbyEdvwBK8/XbHV2hx5Nw4zPg6oUym0keMxbj8eM0/XWZ3S+sTC1MZdzycZiVme8Gf0dDr4Y2jafQYGLs9E3sS8ljzn3RdGta/RcsZRWWsmCrPqEiMUOvGg+PDGF0dCOpGldQel4Jt3++HoPJzMJHbyA08Co3HLpOplOnyFu1ivzYFRRt3QpK4dq6dXlLhktoJb7BlhbCTxP0kZY3PgN9Xwd7mMhh5w6m5XPL1H8Y0K4un42NtHU4whaUgpPbMW2fS8mO+XiZcsjVfKDDCHy7jtOvANtpC+p1JcmapjkCB4ABwHFgMzDmdBJc9hgfpVRe2d9vAx5TSg3SNK0tMBeIBhoAq4GWSinzpZ6vJibJl6KUwpydXZ406+0byWcWDxqN5Y919PW99OJBK/fiPvn7k2xK3cTSYUup52mjikBpob5bXvpe6Hw/+Dcuvyvr2+9Ie/ddGkz+EN8hVd/kfy1yDbmMXzGe1MJUZg2aRauAK/RdV5GswlJGTltPep6BeQ93o12D6reqWynFxkS911iqxpXjUHoBw79YT4CnCwse7UGAZ9VeQjempZG/ciV5sSso3r4dALd27fCJGYT3oBhcQirhA6fZBMufha0zocMoGPoZOEnrwKWYLYrhX6znSGYhv03qTdCl+lFFzZRzDOLL+owzDoCjC7SKYavfIB7e4EeR2YF/39aOEVEhdrtO63qT5O7Am0qpgWX/fglAKfX+JR4/BrhHKRVz/mM1TVtZdqwNl3q+2pQkX44ymTCeOHHB4sHSpCRMp06deaCDA84NGpQnzi5NzrRvONWtW6FvyhMFJxi2eBg3NLyBKX2mWO9FWYExJYXEW27FvVMnGk3/2m5/6AAMZgMP//YwO0/tZFr/aXStb1990ydzihnxxXpKzYoFj3ancaB99sqf7/yqsY+bE3dEhjAmOpRW9bxtHV6NsyU5i7HTN9G+gQ/fP9DNZh8+jCdPkrdiJXkrVlCyaxcAbuHh+MTE4DNoIM7161vvyZSCfz6C39/S+yfv/A7c/ax3/Bpk+t+JvPPrPj4ZHcHQCNteJRNVpCQP9i3VE+PkfwAFod31PuN2w8Bdv7qbklvMMz/uYGNiFkM6NuCdYe3tcvfX602SRwCDlFIPlP37bqCrUuqJ8x73ODAJcAH6KqUOapr2KbBRKfVd2WO+AWKVUj+f97UPAQ8BhIaGRh05cqQCL7P2MBcUnJs4J5dVoM9fPOjuXpY0Nzkz+7msGn2lxYPfxH/DlG1T+LTvp/Ru1LuyX9JVUUpx/PEnKFy/nqa/LMWlkf0uPDNbzDy39jl+O/Ibk3tNZlDYIFuHdFGH0gsYOW09Xm5OLHikB8E+V96W2BaUUmxIzGRu3DFWllWNO5dVjQdL1bjSrdidwqPfb2NAm7p8MS4KRwfbfjgtPX6c/BUryFseS8le/aKme6dO+MTE4D1wIM51rbTYcOePsORxCGyuj4jzDbHOcWuIpIxCBk1ZS88Wdfj6nii7LlqI62Q2QeKf+mSqhF/BVKyPTAwv6zMOuPi6AbNFMe2vw3z02wHq+bgxdUwEUY0Dqjb2K6iSJPmsx48FBiql7r3aJPlsUkmuOKXUmcWD5S0ceiJtPHHi3MWDdeqcO3XjdPtGw4ZoTk4YzUZG/jKSYlMxi4cttot5knmrVnHiqYkEP/svAh+w30U1Sine2/Qe8/bP4/kuz3N327ttHdJl7TiWw9ivNxIa4MGPD3e3q0/6mQWGst3wjpEkVWObmrUuiTd/2cs93Rvz79va2U1CVJqcrFeYY2Mx7N8PmoZHVBTeg2PwuflmnIKuc0vkxD/hx7vBxRPu+gnqVe0OcvbKYlGM/noj+1LyWD2pN3Xt9AO2uA5KQWq8XjGO/wkK08HND9oP1xfhXcMuutuOZjNx3nZO5pQwsV8LHu/T3OYftk+r6nYLByBbKeUr7Rb2w2IwYDx69Ez7xll90OazFg/i7IxLo0a4hIWRFezGN7mxRHa+lXGDXsDR399mb4zm/HwSb7kVx4AAwn6aj+ZsP4nc+abHT+eTbZ8wod0EJnWeZOtwrsrfB09x36zNRDTy49v7u+LmbLvK7KWqxmO76lVjW8ZW2723fB9frU3khUGtefQm+5u1bUhMJC82lrzYWEoPHQYHBzy6RuMzKAbvmwdUfJFv2h74boS+MdKd30KzPtYNvBo6vZNnbRgnWevkndST4p3z9HVBDs76BmAdR+sbgDlVrO88r8TIa4t3s2THSaLDAphyZwQN/GxfgLveJNkJfeFeP+AE+sK9sUqpPWc9poVS6mDZ34cAbyilOmua1g74gTML934HWsjCPftiOr14MCm5rHWj7O9Hj56zeNDB1xfXslnP5VXosCa4NG5c6Rt5pL71Ftlz59Fk/o+4d7DfSs7iQ4t5bd1r3NL0Ft678T0ctOqzMn7ZrpM8OXc7/VoHM21cFE6OVRv7xarGw6P0qnHLulI1tgcWi2Lijzv4ZedJptwZwbBO9tuDWnLgQHlLRmlyMjg64tmtGz6DY/Du3x9H32tcrJp7Ar4fqY+pvO1TiBhTKXFXB8eyihg4ZS1Rjf2Zc1+03VxVENfBUAAJy/TEOPFPQOmV4o6jod0d4GG9FomF247z2uLdODk68MHwDgxqb8X1BBVgjRFwg4Ep6CPgZiil3tU07S1gi1JqqaZpnwD9ASOQDTxxOonWNO0V4D7ABDytlIq93HNJkmw/lMnEqaS9vD73AdoX+TPctXt59dmUnn7mgZp21uLBMH0TlbMXD17nCKWi7ds5MvYu/MeNo94rL1/nq6o8fx//myf/eJLoetF81u8znK91u2078O3GI7y2eDfDI0OYPCIch0q+HKaUYsPhTH6IO8rKPakYzYouTc70GkvV2P4YTGbunRHH1iPZzJoQzQ3Nr7OdoZIppTAkJJAXu4K82FiMx46BszOePbrrPcz9+uHofZUfwkpy9daLpL+gz6vQ61m7HWtVWZRS3DMjjm1Hsln5TC9C/KtmNKCoBBaz/r2880d9mpSxEPwa6wvwOo6GwMq7WpScUcjEedvZeTyXMdGNeO3Wtni4yI57VyRJsv1ZcGABb254k3dueIehzYcCYC4opPRI8kVnP6uiovKv1dzdcWnc+JxNU05Xoh29vK743Kq0lKThIzDn59N02TK73a0w/lQ896+6nyY+TZg5aCaezvYZ59WYsvoAU1Yf5MGeYbw8uE2lVIkyCgzlEyqSM4vwdXfmjsiGUjWuJnKLjYyatoGTOcXMf6Q7bepXj1nUSilKdu8hb0Us+bErMJ48iebsjGfPnvjExODVp8+Vf8eYSmHpk7BrHkTeA7d8bLPtdG1h/uZjPL9gF28Pa8/d3Rpf+QuE/Unbqy/Ai/8J8lPA1VefStFxDIR2q7IPfqUmCx+vPsC0vw7TNMiT/42JpG2Dqv9dIkmyuC4WZeHe2Hs5kneEpcOW4ufmd8nHKqUwpaefN/tZb98wnjgBFkv5Yx3rBJ3ZtvvsxYMhIWhO+ptOxrQvOTVlCiGff453X/vsAzySd4S7l9+Np7Mn3w7+liB3+66sXYlSijeX7mH2hiO8GNOaR3pbp5pgsSg2Jl5YNR7bNZSY9lI1rm5O5hRzx+f6NrMLH+thF72F10IpRcmuXeQtjyVvxQpMaWlorq549eqFz+AYvHr3xsHjElVSpeCPd+Dv/0DzATByFrhe+UN/dZeaW8KAj/+ibX0f5j7YrdKvNAkryk/Tk+Jd8/TFeA5O+vduxzuhZQw4227h5bpDGfxr/k4+HBFOr5Z1qvz5JUkW121/1n7uXHYnw5oP480eb1boGJbS0ksvHszJOfNAJ6fyxYOF//yD1003ETL1E6u8DmvLKM5g3PJxFBmL+HbwtzT2qRmVlbN7Tz8cHs6oLhVfmJNRYODnrceZd1bVeHhkCGOiG9FCqsbVWkJqHiO/2EB9Pzd+eqSHXU1GuRbKYqF4xw49YV65AvOpDDR3d7xu6q1XmHv1wsHtIknElpnw6yR94sXYn8C7btUHX0WUUjwwewvrDmewYmIvmgRV36tltUZpEexfrleND/8ByqLvfNdxtD6hwtN+CjolRrPNCiWSJAur+M/m/zB772y+jfmWiOAIqx5bXzx4/uznJJTRSOM531pv7qkVFRoLmbBiAsl5ycwYOIP2Qe1tHZJVlZos3D97M+sOZfDFuCgGtrv63RctFn1CxQ9xR1lVVjWObhLAmK6NpGpcw6w/lMG9M+OIDPVnzv3RuDpV73OrzGaKtm4lLzaW/JWrMGdl4eDhgVffvvgMjsHzxhtxcDlrB74DK+Gn8XrCcdcCqNPSZrFXpiU7TjBx3g5evaUND/RsautwxKVYLHDkH73PeO8SKM0HnxC9Yhw+usZ+f14PSZKFVRQZi7ht8W34uvry460/4uRQe/rwzmc0G3n898eJS43jf33/R8+QnrYOqVIUGkzcNX0Te1PymHNfNN2aBl728ZeqGo/t2ojmwVI1rqlOJ1C3htdn6uhONeYyvDKZKNq8mbzlseSvWoU5NxcHLy+8+/XTE+bu3dFcXODENvhhFJiNMGYeNO5u69Ct6lS+gQEf/0VYkCc/P9LDbubbirOc2q9Pptg1H/KOg4s3tB2qV40b3wDXuYC+JpMkWVjN70d+5+k/n+bZzs9yb7t7bR2OTViUhZf/eZlfE389ZzFjTZVdWMrILzeQllvC3Ie60b7huaOzLlU1Hts1lEHt60nVuJaY9tdh/i82gYd6NeXlwW1sHY7VKaORwo2b9Arz6tVY8vJw8PXFu38/fGIG49kyGO3H0ZBzDO74EtrdbuuQrebx77fx2940lk+8UT7s2pOCU7B7gd5nfHI7aI7QrK+eGLcaDC4yeeRqSJIsrEYpxZN/PElcahxLhy2lnufVX4KvKT7a8hEz98xkYuREHuhgvzv/WdPJnGJGfLGeUrOFnx/pQZMgzwuqxn4eZ3qN5Y209jl7wecbQ9oy4YaLb1NbE6jSUgrWryc/Npb81b9jKSzE0c8P77698XHagAc70WLehe6P2zrU6xYbr29J/tzAVjzep7mtwxHGEjgQq1eND60GiwnqhZf1GY+o0X3xlUWSZGFVJwpOMGzxMG5seCMf9/nY1uFUqW/3fsuHmz9kdKvRvNz15Vo1RP9QegEjp63Hy82J8IZ+rNpbVjUOC2BstFSNBZgtise+38qqvWl8PjaSmA623SSgKlgMBgr/+UdvyVizBlVUhKOnEz71c/EZdDNu932Mg0f1/NCYXVjKgI/XUs/XlUWP3YBzFW8wJMpYLHBso74Ab88SMOSCd30IH6X3Gddta+sIqzVJkoXVnd56+bN+n9ErpJetw6kSsUmxPL/2eQY0HsDkXpNxdKh9CeHOYzmM/Xojzk4OZVXjUJoH1/zRV+LqlRjNjP16I7tP5vH9A13p0sR6O3XZO0tJCQV/rSVv+XIK/liNMppBU7j4OuBa3xfXJg1xbdUG1/BoXDr2QvO8xl3/qtikH3ewdOdJlj5xo03m19Z6mYfL+oznQc5RcPaENkP0qnFYL6iF70GVQZJkYXVGs5GRv4ykxFzCoqGLcHeqXjNSr9WmlE08svoROtbpyJcDvsTVsXK34bZnWYWleLg4StVYXFJ2YSnDv1hPZmEpCx7tXivbbyxFRRTM/4ySHXEYjhynNCWP0hwzUHb1yUHh6qfhWs8H1yYhuLRqjWuHaFzCb0Dzsv1orj8S0rhv1hae6teCSQNkIkKVKcoq6zP+EY5vBs0BwnrrG320vqVWzOOuapIki0qxJXULE1ZO4IEODzAxcqKtw6k0CVkJjF8xnvqe9ZkdMxsfF6moCHElx7KKuP3z9bg6ObDosR4E+9huswJ7YSnMp3THOgy7NmJI2Ish+TiGlFyMeWc2WdIcFC7+4FrPG9fGIbi2aoVrh2ic29+A5h1cJbuh5ZUYufmjtfi6O/PLkzfi4iRtFpXKZNBHCe76Uf+vxQjBbfWKcYeR4NPA1hHWaJIki0rzyj+vsDxpOQuGLKCpX82bnXmi4ATjlo/DUXPku8Hf1cqFikJUVPzxXO78agNhQZ78+HB3vFxr79jIy7Hk52PYuR7Drk1lyfMxDCm5mPLN5Y/RHC24lifPDXBt2RrXDl1watsNzTfEqsnzSwt38ePmYyx67AY6NvKz2nHFWZTSK8U758LuhVCSA1519aQ4/E59g5patObFliRJFpUmqySLIYuG0NK/JTMGzqhRC9myS7K5J/YeskqymBMzh2Z+1tmeWYjaZM3+dB6YvYUezQKZMb6LLP66Bua8PD1x3rWR0oS9GJKOYTiZg6nwTPLs4GTBxU/hWs+rLHlupSfPrbqg+Te55r7Vfw5mMO6bTTzcuykvxdS8UX42l5WkzzLeNQ+yEsHJHdrcqi/Aa3oTOMoHyaomSbKoVD8f+Jl/b/g37974Lrc1u83W4VhFsamYB1Y9wP6s/Xw14Csi60baOiQhqq35m4/x/IJdDI8M4T8jw2vUh2lbMOfkYNi9FcPODRgS9mBIPIYhJQdz0VnJs7MFVz8zrnVPJ88t9eS5RWcIaAqOF24hXmgwMXDKWlwcHVg+saesO7CW4mzYs1hfhHdsI6BBkxv1PuM2Q8BNWvhsSZJkUaksysI9sfdwLP8YS4ctxdfVvldsX4nJYuLpNU/z94m/+eimj+gX2s/WIQlR7U1ZfYApqw/yZN/m/OvmVrYOp0YyZWVh2LMDw46y5Dn5KIYTOVhKziTPjq5mXH3NuNb1xDW0Pq4tW+LSoQtOYRG8s8nEN5tS+Onh7nSuRVNJKoWpVJ9jvGse7I8FcykEtdK3h+4wCvwa2TpCUUaSZFHp9mft585ld3J7i9t5o/sbtg6nwpRSvLnhTRYeXMhr3V5jVKtRtg5JiBpBKcVLC+OZt/kY793egbFdQ20dUq2glMJ06hSlCbsx7NioJ89JRzCcyMZSembBoKObGVdfEyrAFd9mDXFt0RLX9lE4No6AOi3BtfZNKLlmSulblO+ap0+oKMoEjyDoMEJfhFc/QvqML0EpBUqh2WD77MslydL8IqyiVUAr7mpzF3P2zmFos6FEBEfYOqQK+Xzn5yw8uJCHwx+WBFkIK9I0jXeGtSctr4RXF8dT18eVfm1kd7DKpmkazsHBOAf3xbNX3/LblVKYUlMx7N9H4fYNbF29nqCcU/gcKqR43wngBLAGJ3czrr5GXOu44RpaT+95btcJh0bhUKcVeEjFmZyj+mSKnT9C5kFwdIXWg/U+4+b9LtraUhsoiwVzbi7mzExMGZmYMjP0v2dm6X/PyMSUlYU5IwNTZiYN3n8Pn8GDbR32OaSSLKym0FjI0MVD8XP1Y96t83ByqF6fwebvn8/bG9/mjhZ38Gb3N6VvUohKUGgwMfqrjRxKL2DuQ92IkOkJNvf+8n18uTaR7x/oSo+mARhPnsSwfz+G+M0Y9sVjSDpC6clslOlM5dnZw4SrnwnXQGdcQuvj2qI5rm0jcQhpB3Va65MaavLv0JJc2LtET4yP/KPf1vgGfTJF26Hg7mfT8CqLMhoxZWVjzsw4k+yWJb7mzIyyZDhTvy07G0ymCw/i6IhjgD9OgUE4BQbiGBiAU2AQPrfcgnv7dlX+mqTdQlSZ1UdW88yfz/Bc5+e4p909tg7nqv1+9Hcm/TmJng17MqXPlGqX4AtRnZzKNzD8i/UUGkwseLQHTYI8bR1SrbXjWA53fL6OO7uE8v4dHS75OGU2Yzx2DMOBgxj2bNWT58RkSlOyUebTeYTC2aus8hzoiGujerg2b45L20gcGrTVK88+IWCDS+pWYTbB4T/0sW37l4OpBAKa6QvwwkeCfxNbR1ghlpISTBmZZYnvWUluRibmrMwziW9GBubc3IseQ3N1LUt4A/X/BgXiFBCIU9Dp24JwCgzAMSgIR19fm7RVXIokyaLKKKV44o8n2JK6hSXDllSLucLb07fz4KoHaeXfiq9v/hoPZw9bhyREjZd4qoDhX6zH192ZBY/2INCr9u5iaSsGk5kh//uH/BITK5/phY/btbcFKJOJ0qNHMRw4gGHvTgx7d+nJc2o2WMryC03h4mXC1deEa4CGa0gwri1a4NK6I1r9tvqCNv8m9jn+TClI2am3U8T/BIWnwN0f2g/Xk+OGUXZXMVdKYcnPPyfZNWVl6u0NmafbHrLKE19LUdFFj+Pg5XVh4ns62Q0MxCkoCKcAPfF18PSstldfJUkWVep4/nFuX3I7PUN68tFNH9k6nMs6nHOYe2LvIcAtgDkxc/B387d1SELUGluPZDP26420ru/DvAe74e4iI8eq0ker9jP1j0PMHN+FPq2DrXpsVVqKITkZw8GDGPbFU7ovHsPhJErTsuF02uGgcPU24eJjwtXfoifPzZvh0qojWt3WettGYDNwssEHqNwTED9fb6c4tQ8cXaDlQD0xbj4AnFyqNBxlNmPOyTk38T0r2T0n8c3MRJWWXngQTcPRz6+suns6wb1I4hsYiGNAAA5utWOXTEmSRZX7etfXTN0+lc/6fUavkF62DueiUgtTuTv2bkwWE98N/o6GXg1tHZIQtc6qPak88t1W+rYOZtq4KJxks5EqsedkLkM/XcdtEQ34aFRElT2vpaSE0qQkPXlO2KO3bRxOwpieU/4YzUHpibOvUR9XFxKIa/MWOLfogBbcWm/bCGoBLlZu0zHkw75f9HnGSWsBBY266n3G7W63+iJFVVqKKSvrvLaGs5Pdsh7frCzMWVlgsVx4ECen85LdMz2+TkGBOJa1PDgFBuLo74/mZIfVehuTJFlUOaPZyIhfRmAwG1g0dBHuTu62DukceaV53Bt7LymFKcwaNIvWAa1tHZIQtda3G5J5bckexnYN5d1h7avtZdvqwmi2MOyzdaTlGVg9qRd+HlVbFb0YS1ERhsOJGA4dwrB/b3nybMo40wOrOSpcfYx624avCZcGfrg1b45Ts3ZowW30ynOdluB2DbP6LWZI/FNPjBOWgbFIb/0IHw3ho/RK9jW+DlNmJqaMy09yMGVlYblUf6+7+7nJ7vmJb1n7g1NgIA6+vvLzcp1kBJyocs6Ozrza7VXuW3kfX+/6mqcin7J1SOUMZgNP/fEUyXnJTOs/TRJkIWzs7u5NOJlbwhd/HqahnzuP92lu65BqtK/WJrLnZB7TxkXZRYIM4ODhgXuH9rh3aA8MK7/dXFBA6aFDevJ8YD+GffEUHk4iNzkPdlqAAzg47cfFx1hWeTbhWs8H1+ZNcQprd6byXKc1eAadecLU3foCvPifoSBVT6zD79TnGTfqWt5nrJTCkptbVvG9SOJ7uv2h7I8qLr746/PxKU92XVu2xPOii9vKEl9PWchqLyRJFpWmS70u3NbsNmbumcmtTW+lqV9TW4eE2WLmpb9fYmvaVj7s9SFd63e1dUhCCOD5ga1IzS1h8sr91PNxY3hUiK1DqpEOpuXzyeqD3BJen0Ht7X9htaOXF+4REbhHRJxzuzk3V0+cDx7CcPAAhn27KTicSG5SQdkjknBwTsTVd+GZ5LmOO67NmuLoWIj56D5Mpc6YAqMx1x2DybkB5v15mNYtwZQ540zim5UFRuOFgTk44Ojvrye2QYG4N2p0kakOp1seAnBwsY8PI+LaSLuFqFSZxZnctvg2WgW04pubv7HpZSGlFO/Hvc/chLnVbkSdELVBqcnChFlxbErMYuaELvRsUcfWIdUoZoti+BfrOZJZyG+TehNUAyeKmLKy9MT50EH9vwl7KD18GHP+xSc4nE1zdr7yCLPTia+fH5qjLDStCaTdQthMoHsgT0c9zVsb3mJZ4jKGNBtis1i+2f0NcxPmMr7deEmQhbBDLk4OfDEuilHTNvDod9v48eFutGtwDf2l4rJmrktix7EcPhkdUSMTZACngACcukbj2TW6/DalFOaMjLLK80HMObln9fueSXwdvL2lv1ecQyrJotJZlIW7Y+/meP5xlg5biq9r1b/pLTm0hFfXvcotTW/hvRvfw0GTFfRC2KvU3BLu+HwdRoti0WM9CPGX2eXXKzmjkEGfrOXG5nX4+p4oSQaFKHO5SrJkCqLSOWgOvN7tdXINuUzdNrXKn//v43/zxvo36Fa/G2/3eFsSZCHsXD1fN2bdF43BaGb8zM3kFF1k5qu4ahaL4oUFu3B2dODd22V6iBBXS7IFUSVaBbRibJux/HTgJ3ad2lVlz7s7Yzf/+utftPRvyZQ+U3B2vPYdpYQQVa9lXW++uqczRzOLeGjOVkqMZluHVG19H3eUTUlZvHZrW+r61I4NIoSwBkmSRZV5POJx6njU4e2Nb2OymCr9+Y7kHeHx3x8nwC2Az/t/jqezjNURojrp1jSQ/47qSFxyFpPm78Bisa/2wOrgeHYR/7d8Hz1bBDFSJoYIcU0kSRZVxtPZkxejXyQhK4G5CXMr9bkyijN4+LeHUUrx5YAvCXIPuvIXCSHszpCODXj1ljYsj0/lnV/32TqcakUpxUsL4wF4/44O0mYhxDWSJFlUqf6h/bmx4Y18uv1T0grTKuU5Co2FPLb6MbJKsvis32c09mlcKc8jhKga998YxoQbmjBjXRLT/060dTjVxk9bj/P3wQxeHNxGFj8KUQGSJIsqpWkaL3d9GbMy88HmD6x+fKPZyDNrnuFA9gH+2/u/dKjTwerPIYSoWpqm8dotbRncoR7v/LqPX3aetHVIdi8tr4S3l+2la1gAd0WH2jocIaolSZJFlWvk3YiHwh/ityO/8ffxv612XIuy8Pr619mQsoE3e7xJz5CeVju2EMK2HBw0PhoVQZcm/vxr/k42JmbaOiS7pZTilUXxGM0WPhgejoODtFkIURGSJAubGN9uPGG+Yby36T1KTCVWOeaUbVNYlriMpzo9xbDmw6xyTCGE/XBzduTrezoTGujBQ3O2cCAt39Yh2aWlO0+yel86z97ciiZBsmBZiIqSJFnYhIujC692fZXjBcf5Ov7r6z7ed3u/Y+bumYxuNZoHOjxghQiFEPbIz8OFWRO64OrsyPgZcaTmWudDdk2RUWDgzaV76BTqx4QbwmwdjhDVmiTJwmai60czpOkQZuyeQWJuxRfjrEhawYebP2RA4wG8GP2irOAWooYL8fdg1oQu5BYbGT8zjrwSo61DshtvLN1DocHM5BHhOEqbhRDXRZJkYVP/6vwv3J3ceXfju1Rki/S4lDhe/udlIutG8n7P93F0cKyEKIUQ9qZdA1+m3R3FofQCHv1uK6Umi61DsrkVu1P4dVcKE/u3oHmwt63DEaLakyRZ2FSgeyBPRz5NXGocvyb9ek1fuz9rPxPXTKSxT2M+6fMJro6ulRSlEMIe9WxRhw+Gh7PuUCYvLNhVoQ/aNUVOUSmvLt5D+4Y+PNSrqa3DEaJGkCRZ2NyIliMIDwpn8ubJ5Bpyr+prThSc4NHVj+Lp7MkX/b/A19W3kqMUQtij4VEhPHtzSxZtP8HklfttHY7NvLVsLzlFpXw4vCPOjvLWLoQ1yE+SsDkHzYFXu71KjiGH/23/3xUfn1OSwyO/PUKJuYRp/adRz7NeFUQphLBXj/dpztiuoXz+52G+3XjE1uFUuTUJ6SzcdoLH+jSnbQMfW4cjRI0hSbKwC20C2zC29Vjm759P/Kn4Sz6u2FTM4388TkphCp/2/ZTm/s2rMEohhD3SNI23bmtH/zbBvLFkN6v2pNo6pCqTV2Lk5UXxtKzrxRN95PehENYkSbKwG090eoI67nV4e+PbmCymC+43WUw8/9fz7M7YzQc9PyCybqQNohRC2CMnRwemjulEhxA/npy7na1Hsm0dUpV4f3kCaXklfDiiIy5O8pYuhDXJT5SwG57OnrwQ/QL7svYxL2HeOfcppXhn4zv8efxPXun6Cv0a97NRlEIIe+Xh4sSMeztT39eNB2ZvJvFUga1DqlTrDmUwN+4oD/ZsSkQjP1uHI0SNI0mysCsDGg/ghoY38OmOT0kvSi+//YudX7Dg4AIeCn+IUa1G2TBCIYQ9C/RyZdaEaBw0jXtnxnEq32DrkCpFocHEiwt3ERbkyTMDWto6HCFqpKtKkjVNG6Rp2n5N0w5pmvbiRe6fpGnaXk3Tdmma9rumaY3Pus+sadqOsj9LrRm8qHk0TeOV6FcwWUx8uPlDAObvn88XO7/g9ua380TEEzaOUAhh75oEefLN+C5k5Jdy/+zNFBoubN+q7iav3M/x7GI+HBGOm7PMhxeiMlwxSdY0zRH4DIgB2gJjNE1re97DtgOdlVLhwM/Ah2fdV6yUiij7c5uV4hY1WCOfRjzY4UFWJq9k8ubJvLvpXXqF9OL17q/LbnpCiKsS0ciPT8d2YveJXJ74YRsmc83ZbGRzchazNyRzb/cmdGkSYOtwhKixrqaSHA0cUkolKqVKgXnA0LMfoJRao5QqKvvnRiDEumGK2mZC+wk08WnCnL1zaB/Ynsm9JuPk4GTrsIQQ1Ui/NnV5Z1gH1uw/xauLd9eIzUZKjGZe+HkXDf3ceW5gK1uHI0SNdjVJckPg2Fn/Pl5226XcD8Se9W83TdO2aJq2UdO0YRf7Ak3THip7zJZTp05dRUiipnNxdOG9G98jJiyGT/t9ioezh61DEkJUQ2O7hvJk3+bM23yMqb8fsnU41+3j1QdIzCjkg+HheLpK4UCIymTVnzBN08YBnYHeZ93cWCl1QtO0psAfmqbFK6UOn/11SqmvgK8AOnfuXP0/6gur6FCnAx/W+fDKDxRCiMuYNKAlJ3NK+Hj1Aer7uTGqcyNbh1QhO4/l8PXaRMZEN+KG5kG2DkeIGu9qkuQTwNm/UULKbjuHpmn9gVeA3kqp8uXESqkTZf9N1DTtT6ATcPj8rxdCCCEqg6Zp/N/wDqTnl/DSwniCvV25qVWwrcO6JgaTmed+3kmwtxsvDW5j63CEqBWupt1iM9BC07QwTdNcgNHAOVMqNE3rBHwJ3KaUSj/rdn9N01zL/h4E3ADstVbwQgghxNVwdnTgi3FRtKrrzWPfbyP+eK6tQ7omn605zIG0At67oz0+bs62DkeIWuGKSbJSygQ8AawE9gHzlVJ7NE17S9O009MqJgNewE/njXprA2zRNG0nsAb4P6WUJMlCCCGqnJerE7MmdMHfw4UJszZzLKvoyl9kB/aezOPzNYe4o1ND+raua+twhKg1NHtb7du5c2e1ZcsWW4chhBCihjqUns/wLzYQ6OXCgkd64O/pYuuQLslotjDss3Wk5RlYPakXfh72G6sQ1ZGmaVuVUp0vdp/suCeEEKJWaR7szfR7O3M8u5j7Z2+mxGi2dUiX9NXaRPaczOOdYe0kQRaiikmSLIQQotbp0iSAT+6MYPuxHCbO247ZYl9XVUGveH+y+iC3dKjPoPb1bR2OELWOJMlCCCFqpZgO9Xn91ras3JPGW7/ssavNRswWxXM/78LT1ZE3b2tn63CEqJVkErkQQohaa8INYZzMKebrv5No4OfOw72b2TokAGauS2L70Rw+GR1BHW9XW4cjRK0kSbIQQoha7aWYNqTklvB+bAL1fN0YGnG5TWUrX3JGIf9ZtZ/+bYK5rWMDm8YiRG0mSbIQQohazcFB47+jOnIq38CzP+2kjpcrPWy0o53FonhhwS6cHR14Z1gHNE2zSRxCCOlJFkIIIXB1cuSrezoTFuTJw99uJSE1zyZxfB93lE1JWbx2S1vq+brZJAYhhE6SZCGEEALwdXdm1oRoPFwdGT9jMydziqv0+Y9nF/F/y/fRs0UQIzuHVOlzCyEuJEmyEEIIUaaBnzuzJkRTaDAxfmYcucXGKnlepRQvLYwH4P07pM1CCHsgSbIQQghxljb1ffjy7iiSMgp5+NstGEyVv9nIT1uP8/fBDF6MaU2Iv0elP58Q4sokSRZCCCHO06N5EP8Z2ZGNiVk8+9MuLJW42UhaXglvL9tLdFgAd3VtXGnPI4S4NjLdQgghhLiIoRENOZlTwgcrEmjg68ZLg9tY/TmUUryyKB6j2cKHw8NxcJA2CyHshSTJQgghxCU80rspKbnFfLk2kfq+boy/Icyqx1+68ySr96Xz6i1taBLkadVjCyGujyTJQgghxCVomsYbQ9qRmlvCv5ftpZ6vG4Pa17fKsTMKDLy5dA8RjfyYYOXkWwhx/aQnWQghhLgMRweNqWM60amRHxPn7WBLcpZVjvvG0j0UGsxMHhGOo7RZCGF3JEkWQgghrsDN2ZHp93ahoZ8798/ewqH0gus63ordKfy6K4WJ/VvQoq63laIUQliTJMlCCCHEVQjwdGHWhGicHTXunRFHen5JhY6TU1TKq4v30K6BDw/1amrlKIUQ1iJJshBCCHGVQgM9mDG+C9lFpUyYuZkCg+maj/HWsr3kFJXy4YhwnB3lbVgIeyU/nUIIIcQ1CA/x47O7IklIzeex77dhNFuu+mvXJKSzcNsJHrupGe0a+FZilEKI6yVJshBCCHGN+rQK5v3bO7D2wCleWhiPUlfebCSvxMjLi+JpWdeLx/s2r4IohRDXQ0bACSGEEBUwqksjTuYWM2X1QRr4ujHp5laXffz7yxNIyyvhi3E34OrkWEVRCiEqSpJkIYQQooIm9mtBSk4JU/84RH0/d8ZEh170cesOZTA37igP92pKRCO/qg1SCFEhkiQLIYQQFaRpGu/c3p60/BJeXbybuj6u9G1d95zHFBpMvLhwF2FBnjwzoKWNIhVCXCvpSRZCCCGug7OjA5+NjaRtfR8e/347O4/lnHP/5JX7OZ5dzIcjwnFzljYLIaoLSZKFEEKI6+Tp6sSM8V0I8nbhvlmbOZJZCMDm5Cxmb0jm3u5N6NIkwMZRCiGuhSTJQgghhBXU8XZl9oRoLEpx74w4TuYU88LPu2jo585zAy+/qE8IYX8kSRZCCCGspGkdL6bf24WU3BJu/ngtiRmFfDA8HE9XWQIkRHUjSbIQQghhRVGN/Zk6phNFpSbGRDfihuZBtg5JCFEB8tFWCCGEsLKB7eqx9vk+1Pd1t3UoQogKkiRZCCGEqAQh/h62DkEIcR2k3UIIIYQQQojzSJIshBBCCCHEeSRJFkIIIYQQ4jySJAshhBBCCHEeSZKFEEIIIYQ4jyTJQgghhBBCnEeSZCGEEEIIIc4jSbIQQgghhBDnkSRZCCGEEEKI80iSLIQQQgghxHkkSRZCCCGEEOI8kiQLIYQQQghxHkmShRBCCCGEOI8kyUIIIYQQQpxHkmQhhBBCCCHOoymlbB3DOTRNOwUcsdHTBwEZNnpucXFyTuyTnBf7I+fEPsl5sT9yTuyTrc5LY6VUnYvdYXdJsi1pmrZFKdXZ1nGIM+Sc2Cc5L/ZHzol9kvNif+Sc2Cd7PC/SbiGEEEIIIcR5JEkWQgghhBDiPJIkn+srWwcgLiDnxD7JebE/ck7sk5wX+yPnxD7Z3XmRnmQhhBBCCCHOI5VkIYQQQgghziNJshBCCCGEEOep0UmypmmNNE1bo2naXk3T9miaNrHs9gBN037TNO1g2X/9y25vrWnaBk3TDJqmPXvesZI1TYvXNG2HpmlbbPF6agIrnxM/TdN+1jQtQdO0fZqmdbfFa6oJrHVeNE1rVfYzcvpPnqZpT9voZVVrVv5ZeabsGLs1TZuraZqbLV5TTWDl8zKx7JzskZ+TiqvAOblL07RdZe/p6zVN63jWsQZpmrZf07RDmqa9aKvXVBNY+bzM0DQtXdO03VX6GmpyT7KmafWB+kqpbZqmeQNbgWHAeCBLKfV/ZT8E/kqpFzRNCwYalz0mWyn1n7OOlQx0VkrJAPLrYOVzMhv4Wyk1XdM0F8BDKZVTpS+ohrDmeTnrmI7ACaCrUspWGwRVW9Y6J5qmNQT+AdoqpYo1TZsPLFdKzarq11QTWPG8tAfmAdFAKbACeEQpdaiKX1K1V4Fz0gPYp5TK1jQtBnhTKdW17HfWAWAAcBzYDIxRSu21wcuq9qx1XsqO1QsoAOYopdpX1Wuo0ZVkpVSKUmpb2d/zgX1AQ2AoMLvsYbPRTxpKqXSl1GbAWPXR1g7WOieapvkCvYBvyh5XKglyxVXSz0o/4LAkyBVj5XPiBLhrmuYEeAAnKzf6msuK56UNsEkpVaSUMgF/AXdU/iuoeSpwTtYrpbLLbt8IhJT9PRo4pJRKVEqVon+IGVolL6IGsuJ5QSm1FsiqmsjPqNFJ8tk0TWsCdAI2AXWVUilld6UCda/iEApYpWnaVk3THqqcKGuX6zwnYcApYKamads1TZuuaZpnpQVbi1jhZ+W00cBc60ZXO13POVFKnQD+AxwFUoBcpdSqyou29rjOn5XdQE9N0wI1TfMABgONKivW2qIC5+R+ILbs7w2BY2fdd7zsNnGdrvO82EytSJI1TfMCFgBPK6Xyzr5P6f0mV9NzcqNSKhKIAR4vK/2LCrLCOXECIoEvlFKdgEJA+seuk5V+Vihrf7kN+MnqQdYy13tOyvr9hqJ/sGwAeGqaNq6Swq01rve8KKX2AR8Aq9BbLXYA5koJtpa41nOiaVof9GTshSoLshaqzuelxifJmqY5o5+c75VSC8tuTivrlTndM5N+peOUVWNQSqUDi9Avy4gKsNI5OQ4cV0ptKvv3z+hJs6gga/2slIkBtiml0qwfae1hpXPSH0hSSp1SShmBhUCPyoq5NrDi+8o3SqkopVQvIBu9H1ZUwLWeE03TwoHpwFClVGbZzSc4t5ofUnabqCArnRebqdFJsqZpGnrP6j6l1Edn3bUUuLfs7/cCS65wHM+ypnPKLunfjH6pTFwja50TpVQqcEzTtFZlN/UDZHFFBVnrvJxlDNJqcV2seE6OAt00TfMoO2Y/9N5AUQHW/FkpW9SHpmmh6P3IP1g32trhWs9J2f/vhcDdSqmzP5hsBlpomhZWdjVsdNkxRAVY8bzYTE2fbnEj8DcQD1jKbn4ZvSdmPhAKHAFGKaWyNE2rB2wBfMoeXwC0BYLQq8egX+b/QSn1blW9jprEWudEKZWnaVoE+idOFyARmHBW07+4BlY+L57oiVlTpVRu1b6SmsPK5+TfwJ2ACdgOPKCUMlTl66kprHxe/gYC0Rf1TVJK/V6lL6aGqMA5mQ4ML7sNwKSU6lx2rMHAFMARmCHv9RVn5fMyF7gJPR9LA95QSn1T6a+hJifJQgghhBBCVESNbrcQQgghhBCiIiRJFkIIIYQQ4jySJAshhBBCCHEeSZKFEEIIIYQ4jyTJQgghhBBCnEeSZCGEEEIIIc4jSbIQQgghhBDn+X+qkoNQ5Y+Q3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 ['Australia', 'Canada', 'Gambia', 'Haiti', 'Iceland', 'Indonesia', 'Iran', 'Ireland', 'Kenya', 'Kosovo', 'Malaysia', 'Malta', 'Myanmar', 'Nepal', 'Netherlands', 'New Zealand', 'Norway', 'Sri Lanka', 'Sweden', 'Tanzania', 'Thailand', 'Turkmenistan', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uzbekistan']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = df[\"group\"].unique().tolist()\n",
    "labels.sort()\n",
    "for label in labels:\n",
    "    countries = df.loc[df[\"group\"] == label].index.tolist()\n",
    "    print(label, countries)\n",
    "    plot_ts(df, label, UAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clustering produced four groups, where UAE belongs to the same clusters as the most generous countries, like Myanmar, United Kingdom, and 23 others.\n",
    "\n",
    "These countries may provide (by their culture, history, laws, or public policies) insights to improve the generosity among the UAE people."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "metadata": {
   "interpreter": {
    "hash": "e7206334e5c5b210d15f8086395462bb9dd4ce3199b865cbf88a285d37c0a69b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}