3.5. Analysis_Q5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcb5368b",
   "metadata": {},
   "source": [
    "# 3. Analysis\n",
    "## 3.5. Q5: Frequency of Terms Related to the Ukrainian Side in News Coverage Since the Start of the SMO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c3a8b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e7685be",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"0.1. Intro_Visualization style.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2e2e23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run \"3.0. Analysis_Functions.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a04f58",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "948ec4b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'data/df_tokenized.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cf84dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tokenized = pd.read_csv(file)\n",
    "df_tokenized.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "df_tokenized['dat'] = pd.to_datetime(df_tokenized['dat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f51cd21",
   "metadata": {},
   "source": [
    "## Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd859cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make all columns lower case\n",
    "for col in ['tags_top', 'title', 'body', 'tags_bottom', 'body_token_final', 'title_token_final']:\n",
    "    df_tokenized[col] = df_tokenized[col].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af6f7165",
   "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>id_in_source_file</th>\n",
       "      <th>date</th>\n",
       "      <th>tags_top</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>body</th>\n",
       "      <th>tags_bottom</th>\n",
       "      <th>video_duration_seconds</th>\n",
       "      <th>file_key</th>\n",
       "      <th>datetime</th>\n",
       "      <th>dat</th>\n",
       "      <th>year</th>\n",
       "      <th>year_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>whole newscast</th>\n",
       "      <th>body_token_final</th>\n",
       "      <th>title_token_final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>общество</td>\n",
       "      <td>выпуск программы «время» в 21:00 от 27.01.2023</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446104-vypu...</td>\n",
       "      <td>смотрите в этом выпуске: 79 лет назад была пол...</td>\n",
       "      <td>общество</td>\n",
       "      <td>2422.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:45:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>['смотреть', 'выпуск', '79', 'год', 'полностью...</td>\n",
       "      <td>['выпуск', 'программа', 'время', '21:00', '27....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>культура</td>\n",
       "      <td>в москве проходит церемония вручения премии «з...</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446121-v_mo...</td>\n",
       "      <td>событие в мире отечественного кино. в москве в...</td>\n",
       "      <td>культура,кино,марина зудина,екатерина климова,...</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:35:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['событие', 'мир', 'отечественный', 'кино', 'м...</td>\n",
       "      <td>['москва', 'проходить', 'церемония', 'вручение...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>спорт</td>\n",
       "      <td>в туле появился новый футбольный манеж</td>\n",
       "      <td>https://www.1tv.ru/news/2023-01-27/446120-v_tu...</td>\n",
       "      <td>футбольный манеж в туле — искусственный газон ...</td>\n",
       "      <td>спорт,регионы,футбол,алексей дюмин,тульская об...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0-15</td>\n",
       "      <td>2023-01-27 21:34:00</td>\n",
       "      <td>2023-01-27</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>['футбольный', 'манеж', 'тула', 'искусственный...</td>\n",
       "      <td>['тула', 'появиться', 'новый', 'футбольный', '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_in_source_file                 date  tags_top  \\\n",
       "0                 15  2023-01-27 21:45:00  общество   \n",
       "1                 16  2023-01-27 21:35:00  культура   \n",
       "2                 20  2023-01-27 21:34:00     спорт   \n",
       "\n",
       "                                               title  \\\n",
       "0     выпуск программы «время» в 21:00 от 27.01.2023   \n",
       "1  в москве проходит церемония вручения премии «з...   \n",
       "2             в туле появился новый футбольный манеж   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://www.1tv.ru/news/2023-01-27/446104-vypu...   \n",
       "1  https://www.1tv.ru/news/2023-01-27/446121-v_mo...   \n",
       "2  https://www.1tv.ru/news/2023-01-27/446120-v_tu...   \n",
       "\n",
       "                                                body  \\\n",
       "0  смотрите в этом выпуске: 79 лет назад была пол...   \n",
       "1  событие в мире отечественного кино. в москве в...   \n",
       "2  футбольный манеж в туле — искусственный газон ...   \n",
       "\n",
       "                                         tags_bottom  video_duration_seconds  \\\n",
       "0                                           общество                  2422.0   \n",
       "1  культура,кино,марина зудина,екатерина климова,...                   290.0   \n",
       "2  спорт,регионы,футбол,алексей дюмин,тульская об...                    48.0   \n",
       "\n",
       "  file_key             datetime        dat  year year_month  hour  weekday  \\\n",
       "0     0-15  2023-01-27 21:45:00 2023-01-27  2023    2023-01    21        4   \n",
       "1     0-15  2023-01-27 21:35:00 2023-01-27  2023    2023-01    21        4   \n",
       "2     0-15  2023-01-27 21:34:00 2023-01-27  2023    2023-01    21        4   \n",
       "\n",
       "   whole newscast                                   body_token_final  \\\n",
       "0            True  ['смотреть', 'выпуск', '79', 'год', 'полностью...   \n",
       "1           False  ['событие', 'мир', 'отечественный', 'кино', 'м...   \n",
       "2           False  ['футбольный', 'манеж', 'тула', 'искусственный...   \n",
       "\n",
       "                                   title_token_final  \n",
       "0  ['выпуск', 'программа', 'время', '21:00', '27....  \n",
       "1  ['москва', 'проходить', 'церемония', 'вручение...  \n",
       "2  ['тула', 'появиться', 'новый', 'футбольный', '...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tokenized.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc323332",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter rows that are needed for analysis\n",
    "\n",
    "# Mark rows about the special military operation\n",
    "df_tokenized['SMO'] = df_tokenized['tags_bottom'].str.contains('специальная военная операция|вооруженные силы')\n",
    "#Filter\n",
    "df_SMO = df_tokenized[(df_tokenized['SMO']==1)&(df_tokenized['dat']>=pd.to_datetime('2022-02-24'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1f3774",
   "metadata": {},
   "source": [
    "## Estimating frequency of terms related to the Ukrainian side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3cb17e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Terms related to the Ukrainian side\n",
    "names_dic = {\n",
    "    'Nationalist': ['националист', 'нацбатальон', 'нацформирован'],\n",
    "    'Bandera': ['бандеров'],\n",
    "    'Mercenary': ['наемник', 'наёмник'],\n",
    "    'Nazi': ['нацист', 'нацизм'],\n",
    "    'Chastener': ['карател'],\n",
    "    'Fascist': ['фашист', 'фашизм'],\n",
    "    'Radical': ['радикал'],\n",
    "    'Saboteur': ['диверсант'],\n",
    "    'Militant': ['боевик'],\n",
    "    'Enforcer': ['силовик'],\n",
    "    'Enemy': ['враг', 'враж'],\n",
    "    'Adversary': ['противник'],\n",
    "    'Armed forces of Ukraine': ['всу', 'вооруженные силы украины', 'вооружённые силы украины',\n",
    "                                'воруженных сил украины', 'воружённых сил украины',\n",
    "                                'воруженным силам украины', 'воружённым силам украины',\n",
    "                                'войска украины', 'войск украины', 'войскам украины',\n",
    "                                'украинские войска', 'украинских войск', 'украинским войскам',\n",
    "                                'украинский батальон', 'украинского батальона', 'украинскому батальону'],\n",
    "    'Ukrainian servicemen': ['украинские военнослужащие', 'украинским военнослужащим', 'украинских военнослужащих',\n",
    "                             'украинский военнослужащий', 'украинского военнослужащего', 'украинскому военнослужащему',\n",
    "                             'украинские солдаты', 'украинским солдатам', 'украинских солдатов',\n",
    "                             'украинский солдат', 'украинского солдата', 'украинскому солдату',\n",
    "                             'украинские бойцы', 'украинским бойцам', 'украинских бойцов',\n",
    "                             'украинский боец', 'украинского бойца', 'украинскому бойцу'],\n",
    "    'Armed formations of Ukraine': ['вфу', 'формирования украины', \n",
    "                                    'формирований украины', \n",
    "                                    'формированиям украины', \n",
    "                                    'украинские формирования', 'украинских формирований',\n",
    "                                    'украинским формированиям'],\n",
    "    'Criminal': ['преступ', 'криминал', 'убийц', 'убийств']\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71f3af9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate how often different names for Ukrainian adversaries are used\n",
    "names_dic_count = {}\n",
    "for name_key, name_values in names_dic.items():\n",
    "    \n",
    "    count_name = 0\n",
    "    for name_value in name_values:\n",
    "        # We count all terms that were used - in news title and news body\n",
    "        count_name += len(df_SMO['body'].str.extractall(\"(\"+name_value+\")\")) \\\n",
    "                     +len(df_SMO['title'].str.extractall(\"(\"+name_value+\")\")) \n",
    "        \n",
    "    names_dic_count[name_key] = count_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da9cd936",
   "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>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bandera</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chastener</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fascist</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed formations of Ukraine</th>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saboteur</th>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukrainian servicemen</th>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enforcer</th>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mercenary</th>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criminal</th>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enemy</th>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radical</th>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nazi</th>\n",
       "      <td>1458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adversary</th>\n",
       "      <td>2314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Militant</th>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed forces of Ukraine</th>\n",
       "      <td>4987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationalist</th>\n",
       "      <td>5064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             frequency\n",
       "Bandera                             42\n",
       "Chastener                           70\n",
       "Fascist                            113\n",
       "Armed formations of Ukraine        210\n",
       "Saboteur                           406\n",
       "Ukrainian servicemen               650\n",
       "Enforcer                           684\n",
       "Mercenary                          883\n",
       "Criminal                           886\n",
       "Enemy                              913\n",
       "Radical                            971\n",
       "Nazi                              1458\n",
       "Adversary                         2314\n",
       "Militant                          2650\n",
       "Armed forces of Ukraine           4987\n",
       "Nationalist                       5064"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_count_df = pd.DataFrame.from_dict(names_dic_count, orient='index', columns=['frequency'])\n",
    "names_count_df.sort_values(by='frequency', inplace=True)\n",
    "names_count_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a078334",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efce8765",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Terms with neutral connotation\n",
    "neutral_names = ['Armed forces of Ukraine', 'Armed formations of Ukraine', 'Adversary', \n",
    "                 'Enforcer', 'Enemy', 'Ukrainian servicemen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "be900acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zx3sdHN1Z9vzbnIjezOWUM1SUFaFWG2Hv6E7v4BEMHjm93fscOiuo31BGRszn/OloKsqLDR7DhaYhHE/vXpyO2UtuViq1NVWYmVtibeuIl28fevXrXJB1p7hdu1k6J/MSFeXF1NVVY2KiwcmlB8EDxxE6eMJt1X8nrp0+wSOpq61umqOnrBBbO+dWc/QYHJOnP5NmPcXJozsoLy00uBcJIGzoJJxdvTl1bAeZGcnUVJVjqjHH2sYBT+8gg14eT+8gHnni9xzc8w15OVfQaCzo3X84vj1D2Li6dWDZnpu1afDI6Ti5el2bx+QStbVVmJlZ4t6jJ4NHTMO3Z0in99WdqfR6vf7mxYQQP2RdeeT1QbFvxxecOrYTlVrN0y/+0+DGTyFE9yU9JkKIH5Q1n71OUUG2ch9McNg4CUqEeIBIYCKE+EEpLc6jqqIEC0sbAvsOZcK0pfe7SUKILpChHCGEEEJ0G/K4sBBCCCG6DRnKEeI+aHmz6Y0zv96YgfaV19e0Wu7l24dFy/90z9rbUW6XzspIO69k0u3sbLf3W3vZle+EltmVTUw0/OQX/8bK2l5Zv/IfP1Um0lr+4j9wdPZss54H3Z24tu61k0d3UFvblHQyfLzhlPz36zqvr6vlxOEtJJ+PoawkDwCNuRXWNg44u3oxdNQsZYblG//GqFQqfvKLdw1mAAZY9d7LSnoIaMpFFDbUcD6g0uI8Th3byZXUBMrLCtHptFha2eLpFcSAoZNu6dF2CUyEEOI+a2io41jUJibNeup+N0V0wsljO5Sg8cbA5H7QabWs+ex1crNSDZY3VpRQVVFCblYq/oEDDFI/tKTX6zl9Yg8RUxcry66kxhsEJW25EH+UnZtW0thgONV/eWkh5aWFXIg/yuCRMxg/bUmXjkcCEyF+ZBrq6zAx1XRpm+nznr/tb37efv2U3p8Hxb1s79lT+xgSPrPVt9Yfujtxbf3YpSSdVIISv8BQJs9ajpW1A5WVJRTmZ5J8/gSmZuYd1hEfd4Dw8QuUvw1xx3d1WD43O43t3/1XmZ12+JiHGDRiOiamGlISY9i95X80NtRz8uh27BxcGNiFSQklMBHiAXcmNpI9W1eBXo+jcw8eeeJ36LSNBsM+Q8JncmT/dxTmX6VP8Aimz3ueM7GRJCUco7gwh7qaKrS6Riwsbenh3YvhY+fg4nZ90rL2uttbDnU89OgviI5cQ3ZGMhozCwJ6DyJiymIlm297XdxXr1wg9sg2CvOuUl1dQUNDHWZmlri4+zJw6GSDiazi46LYuWklACPHzcfMwpLTJ/ZSUVaIrb0rw8fOoV9ouFK+oryY6L1ryLicQFVVGUZGxlhY2uLs6k3YsEk3nb67raGcrrahM1RqNTqtliP71jPj4RduWv7qlQucPLqD7KvJ1NRUYmZmSQ+f3gwfMwe3a4n8igqyWfXeL4GmDLQPLfwFAIci13Ls4CYAnvrp33By9aKhvo5333wKnVaLq4cfy557SznWM7F7KSrIprGhHjNzS2zsnJsmApv5VIe5a+pqq4neu4bLKWeoLC9GpVZjbmGNk0sP+oWOpu+1c3Qnri1oSix59uQ+Es8epjD/Kg0NdVhY2ODm6c/k2U8rw2T1dbXEHtlGcmIMpcV56PU67Bzc6BsazpCRMzAybv9jseV736y5ndB2IJuSGMvRgxspys/EysaRsGGTGDxiusG5q6os48ShLVxOPk15aQEqlRpHF09CBo0ndPCEDs8zQElhjvJzD+/e2F4Lbm3tnLG1cyYgKKzdbTVmlqhUUFtTReK5w4QOnkBZST6pF+Oa6rB3oayN3FjHojYqQUlAr4GMmbRIWddvwBgqy0s4eG2Cw6MHvmPA4ImojYxa1dMWCUyEeIDFHd9F5I4vQK/HzTOABUt/g7mFtcEfkoK8DDat/js3PoB3OfkMGZfPGyyrLC8mKeEYqcmnWfb8Wzg4uneqHdVV5Xyz6s9KqviGhjrOxkaCXs+Uh37S4bZ52WlcSjplsKymuoL01HjSU+OZueBn9A0Z1frYT+yitqZKeV1UkMn2De9jZ++Mp3cvAL776h3yc68oZXRaLWX1+ZSV5OPs6nXbeUU604bO6D9gDPFxUSSeO8yw0bNxcvVqt+zpmD3s3fYZtHg/q6vKSU6M4dLFU8xZ9Et69hqEo7MH1raOVJQVcfVKklL26pVE5eeMK4k4uXqRdTVZ+ZDx8e8PNH2g3vghXF1VTnVVOblZqUyc/gSqDj5odmz8kJSWOZi0TVPbV5QVYW5urQQmN9OZa0vb2MiGr/9Kemq8wbaVFSVcSjrFqPELsLK2p6a6km/+92eKCjINyhXmXyV677dcTjnDo8t+32Fw0hVpKWeVoAugtDiXAzu/wtLKTrmmS4vzWP2/P7fKP5WbdZncrMtkpCUy+5EX6Yh1i+nxD+9fR0baeXr49Mbdqyee3r3QaNrvLTE2NqZvaDixR7YTd3w3oYMnEHdiD3q9HicXL9w8/VoFJjqdjiup55TXIQMjWtUbMniCEphUV5WTl5OGe4+eHR6H0qZOlRJCdDsnj+5g/84vgaYPk7mP/crgG2Sz2poqevcfQcTUxZiaWSh/AMOGTWLkuHnY2juj0VhQV1fDyaPbOXZwEw31tZyJiez02HBjQz29g0cyccYTlBbn8e2n/4e2sYHzZ6KZPPvpDr/xefn15dEn/4CTSw/MzKzQ6bSkpyUoOWJij2xrMzCpq6th1oIX8Q8awLGDm4g5vBWAhDOH8PTuRU11pRKUBPUdyrS5K1Cp1VSUFZOZfgELi67lMWnLzdrQWb36DSM/N5287DQO7VvH3MdebrNcRXkx+3d+BXo9rh5+zHz4p9jZu1KQl8GGr96muqqc3Zv/h//LA1AbGeHj35+E0wepriqjKD8LG3tncjJTlWy4V9MSGThsClfTrgcrzUkM09OuB62Ln3kdVw8/aqorKSrIIi3lzE2/xadfTgCacgvNX/wKJiYaKitKyMpIbjUFf0c6c23FnditBCWWVnZMnfMMXr59qa2t5NKFk0q23yP71ytBycSZT9JvwBjQ6zm8bx2nju8i88oFzp7a1+6wQ/DAcQQPHGdwY3JHw31VlWVETFtCyMAIzp+JJnL75wCcPx2tXNP7dnxBVUUJarURsxb8DP9eA6mvqyFy22dcPH+cpPij9AsNJ6DXwHb3E9RnCEcd3SgpykWv15N+OUE5/0bGJvQJHsn4aUsxM7dsc/uwYVM4eXQHBXkZXE4+TXzcAQAGDp9C9tXkVuVrqitoqL+eK8vWofXwo7mFFRozS+qu3SRcVloogYkQP2S52ZeVJzqC+g5l5oKftZu/xVRjztS5z2J6LbNw87cnSys7jkdvJivjItWVZa3yctz4rbIjKpWKybOewszcCgtLG5xcvMjLvkxjYwNVlWVYWdu1u621jQMJpw+yd+unlJcVtrqRrii/7Xb07DWIPiFN+VP6DRijBAXl11LAm5lZYGZuRW1NJVkZyRyN+g5HZ08cnT3pN2DMbee76UwbOk2lYvTER9nw5dukXIglJ/NSm8UuJ59Reg7ystNY9V7rAKaqooT83HTcPP3xCQgm4fRBoKl3xKHSA622kZ69B5N26axyDWVcC0yMjE2UTLl2LT5sjh38nh7eQTg4eeDq6YeP/+M3PSQ7e1fyc69QmJ/Jkf0bcHTpgaOzB4F9hyjXYudOzc2vrZQLMUr5sZMXKR/iphozBg6fqqxrmUX7xkSQzdJSznbpfoiOuLj7MmTkDAD6h41TApOy0qbro7GhXsmMrdNp2bz2323Wk5ZytsPAxMRUw5Jn/8KR/RtIOn/CoPdF29hAwumD1NfVMGfRL9vc3s7ehYBeA7mUdIrt331AbU0VGjNL+oaGtxmYQGenP7u1adIkMBHiPjA2vp5QrqHe8IO45TeR9j48W5bx8e/f4Yesg5NHqw+CstICVn/yR4N6bnRjgNARSys7zMyvp2xveXOttrH9evQ6HWs/+wsFeRntt+PaB/GNWj4+23J/jdf2p1Krmf3Ii+zZ8j9KS/KVTK3QNK4+edZyJai4VTdrQ1f4Bw6gh09vMtOTiN7b9rfw6sqyTtXVnE3XN6C/suzqlQtUVZYCTTdI1tZWkXnlAjlZqcqNk57eQZhcS3YYNmQyeVlpXEg4SurFU6RevD7c1lEPXbNpc59lx8YPKcjLIO7EbmW5sYkpoyc8ypBRMzp1LJ25tiorrp8XJ1fvduvqzPlrKxPxrbrZ9VFTU9mp3qPqTrTJzNyKCTOeYPz0ZRTlZ5KZcZGE0wfJvpoCNN0g29jY0O7fioHDp3Ip6ZRy/MEDx7UbQJpb2GBiqlH+fpQV5xvckwZQU11JXW218tq2xXDTzUhgIsR90DJl+o0fyvm56S3KObe5vYOTBw31tVSUF7N3+2eojYwJHTy+zbImbWTVTbkQq/xR8fbvx4z5L2Bt48ClpFNdyoba7Mab2jru5L+uIO+qcvyWVrY88sQfcHT2pKGhjnf/8mSn99ne/nx7hvDML9+jpDiX4sIciguziTm0larKUnZt/ohe/YejVt/6PJOdaUNXjJm0iG/+9yfSLyegaqNdFla2ys+hQyYyZfbTrcro9XplmMXSyg4nFy8K8682BSbXvkl7+/alsqKEzCsXOH7we6W3zNc/WKnHyNiYGQ+/wKTZyynIzVBuiLwQf5T0ywnEndjN8DEPtXssrh5+PPnTd6goK6KwIJPSojxOHd9FcWE2B3Z/Te/gEVjbONz0nHTm2rKytqW0OBeAwryruHn4tVmXhZUtleXFoFLx/P/7wGDemGadmQxd1cl326jl9dHG0Je5uRVqtRE6nRZTjTk/+80nbd7fcrM21dVWK8NVKpUKJ1cvnFy9CB44jnffeIrGhnr0Oh11tVUYW9m1WYdvQDCOzj0oKshEpVIRNmxSm+UA1Go1vgEhyj1E5+KiDG5Sb1p2QPnZwtIGV/e235M26+90SSHEHeMbEIzxtYChuDCb3Vv+x5XUeM6fOaTcMAbQs/egNrdv/hC3sLQFvZ7dWz4hPi6q0/tXq6//wTQyMsbU1IyS4lyOHdx4awd0i1oGBSqVGlNTDXW11RzY+dUdqX/v1k+5nHIGIyNjfP2D6d1/BFY2TR9GDfV1d/Tb8Z3Qw6cX/teeoNDrdK3W+wcNwOjaN96EuCgSTkdTV1tNQ0M9eTlXOBS5ltWf/NFgG59rvSZVFSVkpidhYWmLo4sn3tcmvkpJOtmi7PXA5OL5E5w8toOykgIcnT0J6jfMYH15WWGHxxK991uSE2PQ6bR4+fShV//h2Dm4Nq3U65sChDsksO9Q5eeDe7/lcvJp6uuaAvfTMXsovXbzZlCfIcr+d3z3AUX5WWi1jVRWlHIx4Tjrv3iL82cO3XR/ZhbWys95OVduud3GJqb4BTbdgF1fV8POTSspK8lHq22kvKyQhNMHWf3JH5Uht/YkJRzns/dfuXafyFUaG+qpr6sl8exhpefTwtKm6e9FB8InLKBn78EMDZ+NvUPHiS9HjJ2rBI2pF09xKHIt1VXlNNTXcf7sYY7u36CUHTluXqefyAHpMRHivrCwtGHc5MeUMeezsZFNTxq0YO/o3uE3UkdnDx554nes+fT/qK2pYtf3H6FSqekfNuam+/cPCsPE5BsaGupISznLu288pezzXnJw9lS+0VdWlCiPON+pdpw5GcnpmD1trnP18MfSquM/1PfD6ImPcjnljMFTN82sbRwYP20pe7d9ilbbyI6NH7QqY3NDl7lvQDCnju0Emr55N8/E6eHdCyMjY6W3xMzc0qCnoaggi8P71rXbTv+gAR0eR1LCcY5Hb25zXdNspO0PuXTVwKFTuJx8hvTUeKoqStjw1V8N1i97/m0ARo1fQEbaeQrzM7mSGs+q/7S+R6d38Iib7s/TO4i87MsAfPHBb4Bbn415wownyMu5QmV5MYnnDpN47nAbpW7ei1OQl6HcDN+WUeMfvukNy736DaNXv2E33ReAm6c/M+a/oEywduzgJuUx9JYGj5xucJ9PZ0hgIsR9MnD4VBydPTl1fBc5mZeoqa7EyNgYO3tXevYexNDwWUr3bHtc3Hx4eMlvWPfFm9e+cX2ISq2mh3dQh9vZ2bswf8mvid77LQW5GZiaWdA3ZBS+AcGs//KtO3mYHVKr1cxf/P/Yv/Mrrl5JRKfT4RsQzIQZT/Dh325/0q3hox8iM+MixYU51F7rHbG2dcQvMJRREQ/fdv13g6u7L737jyAp/mib68OGTsLZ1ZtTx3aQmZFMTVU5phpzrG0c8PQOatWl7uXbF7WRkfI4sJdfU2BiYmKKm2cAWRkXgaYJ8FoOH/n496ekMIecrFSqKkupr6tBY2aBs5sPg4ZPpWevtnvzmg0cPoW05LMU5l+lproCnV6HpZUdPv79GBnxsNJjeCcYGRuzYOlvOXsyksSzR5R5TMwtbHBrEYCaW1ix+Nm/cPLoDpITYygpykGn02FpZYuDozt+gQNuGnABjIp4mNrqStIvx1NVVd5mENlZdvYuPPH828Qc3krqxdNN08mrVFhZ2eHo4oV/0ICbDoP4B4YydsrjXE1LpLgwm5rqCurrazE3t8LV3Y8BQycR2GfwLbexPX2CR+Lm6U/csV1cSY2nvLRpSnoLK1s8vXsRdotT0kt2YSGEEEJ0G3KPiRBCCCG6DQlMhBBCCNFtSGAihBBCiG5DAhMhhBBCdBsSmAghhBCi25DARAghhBDdhsxjIsQ9pNPpqKwowdTU7KaTHQkhuk6v11NfX4uVtf1tpRsQ948EJkLcQ5UVJaz8+wv3uxlC/OCt+NV/sbF1vN/NELdAAhMh7qHmbJ0rfvVfNBrz+9waIX546upqWPn3F9rNjCu6PwlMhLiHmodvNBrzm043L4S4dTJU+uCSATghhBBCdBsSmAghhBCi25DARAghhBDdhgQmQgghhOg2JDARQgghRLchgYkQQgghug0JTIQQQgjRbUhgIoQQQohuQyZYE/ddRtp51nz6Oi/+bhVm5pb3bL+H96/n0oWTPPHCXwHYsfEDamuqmff4r+76vl/avhq9kfz6CdEVH8158n43QdwD0mMiOmXHxg9459WFHI/ebLA8JTGWd15d2Ol6vl31Gvt2fGGwzNOrF8+/svK+z4Q6YfoTTJ/3XKfK7tj4ARtX//0ut0gIIX58JDARnWZsbMKJQ1uoram8o/UaGRtjZW1336eQ1phZ3NMeGyGEEK1JX7LoNJ+AYEqKcjkevZlxUx5vtb6muoK92z4jMz2J2ppK7OxdGT52Dn1DRgFNvQxXr1zg6pULnDq2E4Bnf/keZaUFrYZyLp4/weF96yktzsXS2o6Bw6cydNRMZV8r//FTQgdPoLQ4j6SE45iZWzJi7FwGDJmolInavZqUC7FUlBdjaWVH35BRjIyYj1E7Qyg3DuVcTDjOkQPfUVqci7GJBld3X+Y+/itiDm8l4XQ0gNJbtPCpV/H263e7p1gIIX70JDARnaZSqRkzaSHb1v+HQcOnYn1DSvHGxgbcPPwYNno2Go05qcmn2f7df7Gzd8HDK5AJ05+guDAHJ1cvwsc/AoCFpQ1lpQUG9eRmXWbL2n8zKuJhegePICsjmb3bPsXc3IrggeOUcrFHtjN6wiMMHzOHi+dPsHfrKrx8++Do7AmAqcac6fOew8ranoK8q+ze/DGmGnOGjZ5902OtrChh6/r/MHbKYwT1GUJ9XS2Z6Umgh6GjZlFUkEV9XQ3T5jYN/ZibW7VZT2NjA9rGBuV1XV3NzU+0EEL8iElgIrokqO9QXNx9Obx/PdPmrjBYZ23jwNDwWcrrQcOnkpZylovnj+PhFYjGzAIjI2NMTDRYWdu1u4/Yo9vx8e/PyIj5ADg4eVBUkEXM4W0GgYl/0ADChk0GYNjo2Zw8uoOMtEQlMBk5bp5S1tbehaLCmSTFH+tkYFKKTqclqO9QbO2cAXB281bWm5iYom1s7PA4AI5Hf8/RA9/ddH9CCCGaSGAiumzs5MdY89nrDGkxtAKg0+k4Eb2ZpIRjVJQXo9U2oG1sxMRE06X6iwqyCOw92GCZp3cvTh7bgU6nQ61uujXK2c1HWa9SqbC0sqW6qlxZdjHhOCeP7aS0OJf6+lp0Oh0ajXmn2uDi5oOPf38+e/8V/HqG4NszhF79hmHWTs9Ie4aPmcOQkTOU13V1Naz8+wtdqkMIIX5MJDARXebl2we/nqFE711DcNhYZXnskW2cPLaD8dOW4uzqjYmphn07vkCrbezaDvTAjTfC6vWtihmpjQxeq1Qq9HodANlXU9iy/j3CIxbgGxiCxsyCpHNHiT26vVNNUKvVPPLE78nKSObKpXPEHd/Noci1LH72L9jZu3T6UIyNTTA2Nul0eSGE+LGTp3LELRkzaSGpF0+RdTVZWZaZnkTP3oPoN2A0Lu4+2Nm7UFKUa7CdkZExep2uw7odXTzJSk8yWJZ1NRkHR3elt+RmMjMuYmvrxIhxc3H3DMDB0Z2yssJOHl0TlUpFD59ehE9YwLLn30ZtZExKYiwAaiNjJQgSQghx50hgIm6Ji5sPfUPCiTu+S1lm5+DKldR4sjIuUpSfxe4t/6OqstRgOxt7Z7IzL1FWkk91VXmbQcqQUTNJv5zA0QPfUVyYTcLpg8Sd2N1q6Kgj9g5ulJcVceHcUUqKczl1bKcSVHRG9tUUjh3cRE5WKuWlhSQnxlBTVY6jswcAtnbOFORmUFSQTXVVedd7hYQQQrRJhnLELQuf+AhJ548rr0eOm09ZSQHrvngLExMNoYPHE9hnCHW11UqZoaNmsmPjh6z6z69obKjn2V++16peNw8/Zj/6cw7vW8/RgxuxsrInfPwCgxtfbyawz2AGj5xO5PbPaGxsICAojJHj5nHkwIZOba/RWJB5JYlTx3ZSV1eDja0TEVMX4x8UBkDo4AlcTUvky5W/o6G+tsuPC7874/H7PqGcEEJ0Ryq9vo3BeyHEXVFXW827bzzFS7//VAITIe4C+R178MlQjhBCCCG6DRnKuUu+XfUaLu6+TJi+rN0yZ2IjORa1kYqKEsZPXcLgkdPvYQvvrpTEWA7s/pqyknwGDp/a4Xm4mc6cy1txv5IHAmQdmIWJsXRWCtERr0n773cTxH3wwAYmWRkX+eZ/f8Y3IIQFy357v5vTZXW11URu/4zxU5cQ1G8YGs0Pq8tx95b/ETxwLAOHT8W0nblD3nl1IXMXvUxg3yEGy/ft+IL8nCssWv6nu9rG7pI8UAghxHUP7FDOubgoBg6fSmZGEuWlHT8Gqtfr0Wm196hlnVNeVoROq8W/10CsrO0xMe3aJGTNuuPTIPV1tVRXleHbMxRrG4dOT2p2p2gbO3dOukvyQCGEENc9kD0m9fW1XEw4zpJn36CqopT40wcZdW36crjeRb9g6W+JjlxLQV46C5b+jqMHNuDs6o1KrSbhdDRGRsaET1hA39BwIrd9xsXzJ7C0tGHizCeVpy8ACvMzidr1NVfTL2BiYoZvz2DGT1uKhaWN0p69W1aRfCEGU1NzhoR3/FhrfFwUOzetBODjf74INCWzs7V34XTMHmIPb6O8vAhbOxdGjptLvwFjlG3feXUhk2YtJy3lDOmpCQwZNZPwCQtIuXCSo1EbKcy/iqmpGT18ejP3sZeBpg/qQ/vWknj2MHW11Ti59mDs5MeUp0jKSguI3PYZmekX0WkbsbFzZtzUxwlocQ5aqq2pZN/2L7h0MQ6ttgEv3z5MmPEEDo7uyrkHWPtZ0/93OsHd5ZQzbFn7LhNnPEn/sDFK8j2PHj05dWI3RkZGrHj5fc6fOcTJYzspLszG1FSDt18/xk9fhqWVLdB6KCc+Lor9O79k9iMvsW/HF1SUF9HDuzfT5q3Aytre4P07cWgLZaUF2No5M2j4VGVqfCGEELfngQxMkuKP4eDkjqOzB30HhLNv2+eMHDev1TffqN2riZi6GFsHF8zMmu4hSDgTzdDwWSxZ8ReS4o+xd+sqLl04SWDfIQwfO4eTR3ew/bsPWPHy+5iYaqisKOHbVa8ROng8EdOW0NhQz8E937Bl7bssfOpVZT8ZaeeZu+hlLK3siI5cQ252Gi7uvm22v3fwSGxsHVn7+RssefYNrG0dsbC0ITkxhn07vmDCtGX4BPQn9eJpdmxaiZWNIz7+1z/Yj+zfwJhJCxk/bSkqtZrUi3F8v+afjBgzl5nzX0CrbSQ1OU4pv2PTh5SXFjDrkRexsrEnJTGW9V++zZM/fQcHR3f2bv0UnbaRx5b/CRNTDYUFmZiamrV7/nds/JCSolzmPf4rNBoLDu75hg1f/pXlL/4dT69ePP3SP/nfu79kzsJf4uEd1G6Cu1tx4dxRdm/5hBnzXyCwz/Vp69MvJ6DRmPPost/RfOeGVtvI6AkLcHDyoLqqnP07v2Tnxg95eOlv2q2/oaGOmCPbmPHwC6hUarZveJ8Du75m1oKfAXD25D4O79/ApBlP4uLhS372FXZt/hgTUw39W8yC20yS+AkhRNc8kIFJfNwB+oaGA+DfcwA762tJv5yAb0CwQbnwCY/g2zPEYJmLm7eS3G34mDmcOLQZcwtrQgdPAJrm4jgTs5eCvAw8vAI5HbMXVw8/xkxapNQxbe4KPvz7CxQXZmNl7UD8qQNMn/+8sq/p857nw78/3277TUxMMbOwBsDC0lpJBBd7ZBv9w8Yq374dnDzIvppC7JFtBoFJn5CRhAyKUF5vXfceffqPJHzCguvH6d6UR6akOJcL8Ud57lf/xdrGAYCh4bNISzlLQlwUYyYtoqKsiKC+Q5UkdXYOru22vbgoh0tJp3j8J6/h6d0LgJkLfsqHf3+BlAsn6d1/OBaWTT0SZhaWN01y1xWnT+whOnINcx/7lcH5ADA11TB1zrMYGV+/pFueIzsHVyZMf4KvPvo99XW1mGraDrx0Wi2TZy/H3sENgIHDpnA06noSvqNRG4mYupigfkOb6rV3obAgkzOxkW0GJpLETwghuuaBC0yKCrLJyUxlzqJfAqA2MqJ38AjiTx1oFZi4efq32t7Z9XriN7Vajbm5NU4tssY2d/NXVZYBkJedRkbaef71eusnQkqL82hsaECrbcTTK0hZbm5hhYOjxy0cW5YSIDXz9Ani1LFdBsvcPAMMXufnprfarlle9hXQ6/nfu78wWK5tbMTMoqknY+Dwqezduoorqefw8Q8mqN9QXFokyDNoY34WarUR7j0ClWXmFtY4ODZlAL5bks+foKqqjMeffg33Hj1brXdy9TYISqDpvTtyYAP5OenU1FQq+XbKywpxcunR5n5MTDRKUAJgaW1H1bXEgNVV5VSUFbHr+4/YvfljpUxTcsC2b6CVJH5CCNE1D1xgEh93AJ1Oywd/a9EjodejNjKmtqbSIPtrW1lt1UaGid9QGSaDU4aDrn2I6fU6evYaxNjJj7Wqy9LarlUumNt3Y/K6VkswveG4jI1N261Nr9ehUqtZuuKtVnlmTK4N14QOHo9fYAipF09z5dI5jh/6noipSxg0fGoX2q1v1c6bMdWYU1dX3Wp5XW1VqydlXNx9yctOIz4uCjfPgFbDdje+1/X1taz74k18e4Yw4+EXsLC0obyskPVfvNXhDcM3Xh8qVAbXAsCUh57B44bgSNVODh9J4ieEEF3zQAUmOq2W82eiiZi6pNUQzfff/pPEs4cZ2KUP05tzdfcjOTEGWzvn1kENTTlZ1EZGZF9NwcbOCWi6ObSkKAcvvz5d2pejsyeZGUn0D7t+s2tWRjIOzp4dbufs5k16akKbU7a7uvui1+morirDy7f99tjYOhE2dBJhQydxcM+3nDu5v83AxNHFE51OS05mijKUU1NdQXFRDo43aeeNHJw8yMlKNRgC0ev15Gal4R80wKCsnYMrEVMX8+2nr6NSq5k086kO6y4uyKamuoKxkxdhY9v0vuRmXe5S+25kaWWHlY0DZcV59Ls2lCiEEOLOeqAeF069GEdtTRUhgyJwdvUy+Ner3zDOnYq64/sMGzaZ2ppKtq5/j5zMS5QW55F26Sw7N61Ep9NhqjEjZGAEUbtXk54aT0HeVXZs/BBu4RHUoaNmkXD6IKdj9lJclEPske0kX4hh6E2e8hkVMZ8L8Uc4vG89RflZFORmcOLQFqDpw79vSDg7vvuA5PMxlJbkk5OZyonozaQmnwaa5g1JSzlLaUk+udlpZKQl4ODc9lCUg6M7PXsPZtf3n5CZnkR+TjrbNryPlbUDPVvcjNq5451J/KkDxB3fRXFhNvk56URu+4zSkrw2n3JxcPJg4ZOvkny+6SbhjtjYOWFkZEzc8d2UFucpTy3drlERD3P80GZOHttBcWE2BbkZxMdFEXtk+23XLYQQ4gHrMTkXdwCfgOA2J8QK6jeM49Hfk5uddkf3aW3jwGM/eY2Du79h3RdvodU2YGPrhF9gqDKcMG7KYurra9m4+u+YaswYPGqmQeK6zgrsO4QJ05cRe3gr+3Z8jq2dC9Pnrrjpo7befv146NFfcDRqIycObcZUY27QOzJt3gqORW3iwK6vqKgoxtzcGg+vQOWRaL1Ox95tn1JRXoxGY45fz1DGT1/a7v6mz1vBvu1f8N3X76DVNuLl04eHl/4aI6OuXU69g0egR0/skW1ER67F2NgUV3dfHlv+Z2ztnNvcxtG5KTj59tP/Q6VSM37akjbLWVjaMG3ecxzau4ZTx3fh6u5LxNTFbFz9ty618Uahg8djYmJKzOFtHNz9DSamGpxcvRk8Ytpt1SuEEKKJJPET4h6SBGNC3F3yO/bge6CGcoQQQgjxw/ZADeUI8UPx0vbV6Ls49CXuj4/mPHm/myDEj4r0mIgHyrerXjO48XXlP37KyaM7lNfvvLqQlMTY+9E0IYQQd4AEJuK+27HxA955dSG7t/yv1bo9W1fxzqsL2bHxAwDmLHqZ8AmPtFvX86+sxO/ao8ZlJfm88+pC8nKu3PE2Z6Sd551XF1JbU3XH6xZCiB8zCUxEt2Bt60hS/FEaGuqVZY0N9Vw4d1SZhwSaZtXtKFuxlbWdTGgmhBAPMBnkFt2Cq7sfZSX5JCfGKJOXJSfGYGPriK29i1Lu21Wv4eLuy4TprVMEQNNQztxFLxPYdwgfXcvc/MUHTUn7vHz7sGj5n8jJTCU6cg35OVfQahtxcfdl/LSluHn4GdQz5aFnuJx8mrRLZ7GydiBi6mIC+wymrCRfyaD83pvLAegfNobp89rPjySEEKJzpMdEdBv9B44lIS5KeR0fF9XmbLadteTZNwB49Inf8/wrK5mz6GUA6utr6D9gDI8t/zNLnvkL9o5ufPfV260y/x49sIFe/Yfz5AvvEBA0gG0b3qemuhJrWyfmLGzK1fT0S//k+VdWMmH6E222obGxgbra6uv/JLuwEEJ0SHpMRLfRL3Q00XvXUFaSDyoVWRkXmfXIi2SkJd5SfRaWTRmczSysDbIc+/j3Nyg3ZfZPeO/N5Vy9kkjPXoOU5f3DxtI3ZBQAoyct5NSJ3eRkXcI/cABmFpbX9mGLmbllu22Q7MJCCNE1EpiIbsPC0oaAoDASTkejR49/UBgWljZ3fD9VlWUc3r+ejMsJVFWWodfraGiop6K0yKCcc4sMy6amZpiamlFdWd6lfUl2YSGE6BoJTES3EjxwHJHbPgNg4qyOE/Xdqp0bP6S6upzx05dha+uEkbEJX3/8aquswy2zTkNT5unmDMOdJdmFhRCia+QeE9Gt+AUOQKttRKttxK9n6G3Vpb42gZleZxhMXE1PYtDwqQQEheHk6oWRsTE11RVdqrs5L1BXAxUhhBAdkx4T0a2o1WqWv/gP5efbYWlpi7GJKWkpZ7G2dcDY2BSNmQX2Dm6cP3MIN48A6uqqidq9GmMT0y7VbWPnDCoVqRfj8A8Kw9jYFFON2W21VwghhAQmohu6U4m31EZGTJj+BEejvuPw/nX08OnNouV/YtrcZ9m95RM+//A32Ng6MmbiQg7s/rpLdVvbOBAe8TAH93zLjk0r6T9gdJceF353xuOSYEwIIdog2YWFuIck86kQd5f8jj345B4TIYQQQnQbMpQj7pnD+9dz6cJJnnjhr/e7Kfdd1oFZmBhLZ+W95DVp//1ughCiE6THRNyWrIyL/O2Pi1j/xVv3uylCCCF+ACQwEbflXFwUA4dPJTMjifLSwvvdHLSNjTcv1EU6na7VI8dCCCHuDhnKEbesvr6WiwnHWfLsG1RVlBJ/+iCjIuYr649Hb+bk0R00NNTRu/9wg1lc01LOsvGbv/PCKysNpnSP3P45+bnpPLb8T0BTj8zBPd+Sm5WKuYU1gX2HMmbSQkxNmx7NXfmPnxIyaDylxbkkJ8YS2GcIUx96hv27viT5fAy1tVVYWtkyYPBEho+dA0Dske3Ex0VRVpKPmbklAb0HMW7y48rjvvFxUezf+SUzHn6Bg7u/obgoh0ef+APrPn+DFb/6r8H09vt3fkVuViqPPf3nu3SWhRDix0UCE3HLkuKP4eDkjqOzB30HhLNv2+eMHDcPlUpFUvwxjuxfz8SZT9HDpzeJZw9x6vgu7K5lCvYJCMbMzILkxBOEDBoPNPVMXEw4xqjxCwAoyM1g3RdvMXrCI0yd+yw1VeXs3fYZkds+Y/q855R2xBzeyshx8xgxdh4Ap47v5FLSKWY/+hI2dk5UlBVRXnZ9unmVSsWEGU9ga+9MWUk+e7d+StSe1UyetVwp09BQx4nozUyd8wxm5tZY2zpga+/C+bOHGBY+q6m9Wi2JZw8zdvKids9RY2MD2sYG5bUk8RNCiI5JYCJuWXzcAfqGhgPg33MAO+trSb+cgG9AMCeP7SB44DhCBzcFHaMnPsqV1HjlQ1qtVtO7/wgSzx1RApP0y/HU1lTRu/9woCng6BsyisEjpzft0NGdiTOe4NtVrzF51nJlUjQf//4MvRYsAJSXFWHv6EYPn96oVCps7ZwN2q3UB9jZuxA+4RH2bl1lEJjotFomzVyOi/v1fDkhgyJIiItSApPU5NM0NNTR61p72yJJ/IQQomskMBG3pKggm5zMVOYs+iXQNJlZ7+ARxJ86gG9AMEUF2QwYMslgGw+vQK62yBTcNzScrz9+lYryYqxtHEg8ewT/oDDMzK0AyM1Oo7Q4l8Rzh69Xoge9Xk9ZSQGOLp4AuHn4G+ynf9hY1n3+Bv979xf4BQ4goFeYwfT26ZfPczz6e4ryM6mrq0Gv09LY2EB9fa0yRGRkZIyzm3ereg/tW0v21RQ8vAKJjztA7/7DlW3aIkn8hBCiayQwEbckPu4AOp2WD/7WYrZTvR61kTG1NZWdqsO9R0/sHFxJij/GgKGTSLkQy7S5K1pUpyN0yEQGDZ/aalsbWyflZxNTjcE6Nw8/nv3le1xOOUN6agJb1r6Lj39/5iz6JWWlBXz31duEDpnI6AmPYGZuSWb6RXZ9/xE6rVapw9jEFJVKZVCvpZUtPXsNIj4uClt7Fy4nn2HhU3/s8BgliZ8QQnSNBCaiy3RaLefPRBMxdQm+PUMM1n3/7T9JPHsYR2cPsjNT6B82RlmXk3mpVV19QkaReO4w1jYOqFQqAoLClHWuHn4U5l/F3tGty23UmFnQJ3gkfYJH0qvfMNZ/+RY11ZXkZl1Gp9MxfuoSVNdy8SQlHO90vSGDItiy7j2sbRywc3Clh0+vLrdNCCFE++RxYdFlqRfjqK2pImRQBM6uXgb/evUbxrlTUQwaMY34uCjOnTpAcWE2h/etpzA/s1VdfUPDyctO49jBTQT1G2aQTG/Y6NlkX01h79ZPycu5QnFRDikXThK57bMO2xd7dDsXzh2lqCCL4sJsks4fx9LKDjMzC+wcXNHptJw6sYvS4jzOn4nmTGxkp4/dr2coGjMLjh3cRHDY2M6fNCGEEJ0iPSaiy87FHcAnILjNPBRB/YZxPPp77B3dGTluHgf3fENjYwO9+g1lwJCJXLl0zqC8g6M7bp4B5GalMn76MoN1Lm4+LHrqTxyKXMM3//szoMfO3pXewSM6bJ+pqRknDm2hpDgHlUqNu2cADy/5NSq1Gld3XyKmLeHEoa1E712Dl08fxk5ayPbvPujUsavUavqHjeV49Pf0a9Eb1FWeEVslj4cQQrRBkvgJ0UW7vv+Yqsoy5i/+f13eVhKMCXF3ye/Yg0+GcoTopLraaq6kxpN47jCDhk+5380RQogfJBnKEaKTNq7+GzlZqYQOnoBvzxBlhtiXfv9pl+t6aftq9EY//F+/j+Y8eb+bIIR4wPzw/zKKH6UdGz8g4XQ0YyYtYviYh5TlKYmxbPr2H7zy+pou17no2jT5zXoHj8S/xVNEQgghbp8M5YgfLGNjE04c2tLpeVW6ysTEFEsr27tStxBC/FhJj4n4wfIJCKakKJfj0ZsZN+XxVutrqivYu+0zMtOTqK2pxM7eleFj59A3ZBQAZSX5fPTPF1tt5+Xbh0XL/3RbQzlCCCHaJoGJ+MFSqdSMmbSQbev/w6DhU7G2dTRY39jYgJuHH8NGz0ajMSc1+TTbv/svdvYueHgFYm3rxPOvrFTKV1WWsu7zN+jh26fTbZAkfkII0TUSmIgftKC+Q3Fx9+Xw/vUG090DWNs4GCT/GzR8KmkpZ7l4/jgeXoGo1WqsrO0AaGyoZ9M3f8fDK5DwiIc7vX9J4ieEEF0jgYn4wRs7+THWfPY6Q0bNNFiu0+k4Eb2ZpIRjVJQXo9U2oG1sxMRE06qOnd9/RH1dLY888XtlKvvOkCR+QgjRNRKYiB88L98++PUMJXrvGoNp5GOPbOPksR2Mn7YUZ1dvTEw17NvxBVpto8H2R6M2kpZyliUr/oJGY96lfUsSPyGE6Bp5Kkf8KIyZtJDUi6fIupqsLMtMT6Jn70H0GzAaF3cf7OxdKCnKNdju4vkTHI36jocefQl7h64nExRCCNE10mMifhRc3HzoGxJO3PFdyjI7B1eSE2PIyriImZkVsUe3U1VZiqOzJwAFeVfZ8d0HDAufjaOLF5UVpQAYGRljbmF1Pw5DCCF+8CQwET8a4RMfIen8ceX1yHHzKSspYN0Xb2FioiF08HgC+wyhrrYagNysVBoa6jh2cBPHDm5Stmt+XFgIIcSdJ0n8hLiHJMGYEHeX/I49+OQeEyGEEEJ0GzKUI8R90J2T+EniPSHE/SQ9JuIHbcfGD9i4+u/K629Xvca+HV/ctfqFEELcnu75lU386DRnAwZQqdVYWdsTEBTGmEkLMTO/c0/AzFn0MmojoztWnxBCiDtLAhPRbfgFhjJt7nPodVoKCzLZuekjamurmf1I60R6t0oe8xVCiO5NAhPRbRgZmSi5aaxtHendfwQJpw8CTdPH7978MRmXz1NVWYq1rRNhwyYxeMR0ZXudTkfU7q+Jj4tCpVITMiiCG585+3bVa7i4+zJh+jKgKcne4X3ruHDuCNVV5VjbOjF8zGxCBo3v1D6FEELcWRKYiG6ptDiPtJSzGF0bdtHrdVjbODL70Z9jbmlNVkYyezZ/gpWVPb2DRwBNU8zHx0Uxdc6zODn3IOboNlIuxOLt16/d/Wz/7r9kX01hwowncHHzoawkn+rqik7v82Yku7AQQnSNBCai20hNjuNfry9Dr9PReO3DPGLaEqBpttXwCQuUsnb2LmRnJJOUcEwJEk4e28nw0Q/Rq98wAKbMeporKWfb3V9xYTYXE47zyBO/xzcguKleB1dlfWf2eTOSXVgIIbpGAhPRbXj79WPyrOU0NNRx7tQBSgpzGDRsqrL+dMxezp06QHlpAY2N9Wi1jbi4+QJNkypVVZTg4R2klFcbGeHmGUB7cwjm56SjUqvx8u3Tbps62mdnSHZhIYToGglMRLdhYqLB3rEpUd7EGU/w7af/x5EDGxg98VGS4o9xYOeXRExdgodXIKYac2IObyUn89It78/YxLTD9Xdin5JdWAghukbmMRHd1qiI+cQe2UZFeTGZ6Ul4eAcRNmwyrh5+2Du6UVqcp5TVmFlgaW1P9tUUZZlOqyU3+3K79Tu7eqHX67l65UKb62+2TyGEEHeeBCai2/L264ejixfHo7/HztGV3KzLpKWcpbgwm0ORa8nJSjUoP3j4VE4c2kxyYgxFBVns2bZKScjXFlt7F/oPGMPOTStJSYyltCSfjLTzJMUfA+jUPoUQQtxZMpQjurUhI6ezc9NKnv75v8jPSWfLuncBFX1CRhI2dDJpKWeulx01k8rKUnZs/BCVSk3wwHEG2YLbMnnWcqIj17Bn26fUVldgbevEiLFzABgwZNJN93mr3p3xuCQYE0KINkh2YSHuIcl8KsTdJb9jDz4ZyhFCCCFEtyFDOULcB1kHZmFi3H06K70m7b/fTRBCCEACE9ENtUzo15Jfz1AWLPvtfWiREEKIe0UCE9EtNSf0a8nYWC5XIYT4oZO/9KJbapnQ70bvvLqQKQ89w+Xk06RdOouVtQMRUxcT2GewUqYwP5OoXV9zNf0CJiZm+PYMZvy0pVhY2gBNyfycXb1RqdUknI5Wpp/vGxpO5LbPuHj+BJaWNkyc+ST+QWHo9Xo++ffPGTBkIkPDZyn7Kci7ymf/fYWf/Pxf2Du43dVzIoQQPwZy86t4IB09sIFe/Yfz5AvvEBA0gG0b3qemuhKAyoqSa1mEfVi64k0WLP0N1ZVlbFn7rkEdCWeiMbewZsmKvzBw+BT2bl3FljX/xtM7iGXPv4VvYCjbv/uAhvo6VCoVwQPHER930KCO+LgD9PDp3W5Q0tjYQF1t9fV/ksRPCCE6JD0moltqTujX0rDw2YyMmA9A/7Cx9A0ZBcDoSQs5dWI3OVmX8A8cwOmYvbh6+DFm0iJl22lzV/Dh31+guDAbBycPAFzcvBk5bh7QlNPmxKHNmFtYEzp4AgAjx83nTMxeCvIy8PAKJHjgOA7vX09O5iXce/REq20k8exhxk15vN3jkCR+QgjRNRKYiG6pOaFfS2bmVsrPzm4+ys+mpmaYmppRXVkOQF52Ghlp51sFNgClxXlKYOLser0OtVqNubk1Tm7eyjJLK1sAqirLALCyticgKIxzcVG49+hJ6sU4Ghsb6NVveLvHIUn8hBCiayQwEd1Sy4R+bTFSGxm8VqlU6PU6APR6HT17DWLs5MdabWfZ4r4VtZFhHagM61WpVFyrUFkWMmg827/7L+OnLSU+7iC9+4/AxFTTbjsliZ8QQnSNBCbiB8fV3Y/kxBhs7ZxbBx+3yT8oDBMTDWdi9pKWcoZFy/90R+sXQogfO7n5VXRLWm0DlRWlBv+qq8o7tW3YsMnU1lSydf175GReorQ4j7RLZ9m5aSU6ne622qVWq+kfNpbovd9i5+CKp3fQbdUnhBDCkPSYiG4pLeUsH7yzwmCZg5MHT7/0z5tua23jwGM/eY2Du79h3RdvodU2YGPrhF9g6PXhmdsQMiiC49HfEzwo4pbr8IzYKnk8hBCiDZLET4guyky/yJpP/4/n/t9/sbSy69K2kmBMiLtLfscefNJjIkQnNTY2UFFWxOF96+jVf3iXgxIhhBA3J4GJ6PYy0s6z5tPXefF3qzAzt7zlelb+46cMHjGdwSOn39L2F84dYdf3H+Hi7suMh5se+d2x8QNqa6qZ9/ivulTXS9tXozfqHr9+H8158n43QQghFN3jL6P40aisKOX4wU2kJp+msrwYC0sbXNx9GTxiGj4BwW1u4+nVi+dfWXnb3bJLV7yJiUn7j/beTPDAcQQPHHdbbRBCCNExCUzEPVNWks/qT/6ExsyScZMfw9nNG51WS9qlc+zd9lmbN7ZqtY0YGRu3mzenK5rz5AghhOi+JDAR98zerZ+CSsWSFX/B1NRMWe7k6qX0RLzz6kImzVpOWsoZ0lMTGDJqJt7+fQ2GcuLjoti/80tmPPwCB3Z+TUV5Ef6BA5g+/3mSz5/g8P711NXW0C80nPHTl6FWNz0Vf+NQzs2SAep0OnZv/piMy+epqizF2taJsGGTGDzi1oaChBBC3JwEJuKeqKmu5PKls4yZ8KhBUNKs5b0jR/ZvYMykhYyfthSVWk1ZSX6r8g0NdcQd28XsR16kvr6W77/9J99/+080ZhY8vOQ3lJbksfnbf+Hp04s+wSPbbdfRAxsYO+Vxxk15nLjju9i24X1WvPw+5hZW6PU6rG0cmf3ozzG3tCYrI5k9mz/Bysqe3sEjOnXcjY0NaBsblNeSxE8IITomgYm4J0qLc0Gvx8HZ46Zl+4SMJKTFHCFtBSY6rZZJs5crWX2D+g0j8cwhXvj1R5hqzHBy6YG3Xz8y0hI7DEw6SgZoZGRM+IQFSlk7exeyM5JJSjjW6cBEkvgJIUTXSGAi7onm6XJU3HyCMzfPgJuWMTHRKEEJgKWlLTZ2zphqrvfGWFjZUn0tAV97OkoGCHA6Zi/nTh2gvLSAxsZ6tNpGXNx8b9q+ZpLETwghukYCE3FP2Du6g0pFUUEWgQzpsKxpJ56caTMBn9GNif2uB0Tt6SgZYFL8MQ7s/JKIqUvw8ArEVGNOzOGt5GReumn7mkkSPyGE6BrJlSPuCXMLK/x6hhAXs4f6+tpW62trqu5DqzqWmZ6Eh3cQYcMm4+rhh72jG6XFefe7WUII8YMmgYm4ZybNWo5ep+OrlX/g4vkTFBflUJSfxaljO/n641fvd/NasXN0JTfrMmkpZykuzOZQ5FpyslLvd7OEEOIHTYZyxD1jZ+/Csuff4tjBTRzY9RVVFaWYW9rg5uHH5NnL73fzWhkwZBL5OelsWfcuoKJPyEjChk4mLeXMbdf97ozHJY+HEEK0QZL4CXEPSYIxIe4u+R178MlQjhBCCCG6DRnKEeI+kCR+QgjRtu7xl1E8EHZs/ICE09GEDpnIlNlPG6zbs3UVZ2L20j9sDNPnPX+fWiiEEOJBJ0M5okusbR1Jij9KQ0O9sqyxoZ4L545iY+t0y/Xq9Xp0Wu2daOId153bJoQQPzTSYyK6xNXdj7KSfJITY+gXGg5AcmIMNraO2Nq7KOX0ej0xh7dyJjaSqooS7B3dGTluHr36DwcgI+08az59nQVLf0t05FoK8tJZsPR3ePv2IebwVs6e2k9FWREWVrYMGDyREePmAlBRXsz+nV9y5VI8KpWKHj69mDB9mbLvHRs/oLammh4+vYg9sh2ttpE+wSMZP30pRteGTs6fOcTJYzspLszG1FSDt18/xk9fhqWVbbttmzL7J+z8/iOWPPsX3FvMTHvq+C5iD2/j2Zf/g0p181lthRBCdEwCE9Fl/QeOJSEuSglM4uOiCB44joy0RKXMoci1JCfGMHnWcuwd3bh65QLbvvsv5pY2ePv1VcpF7V5NxNTF2Dq4YGZmycG933Lu1H7GT1uKp08vqipKKSrIBqChvo41n/4fPXx689jyP6FWG3H04EbWf/k2T77wDkbGTZdzRtp5rKztWPjUq5QU57J17Xu4uPsQOngCAFptI6MnLMDByYPqqnL27/ySnRs/5OGlvzE4zhvb5uvfn4S4gwaBSXxcFP3DxrYblEgSPyGE6BoJTESX9QsdTfTeNU3J9VQqsjIuMuuRF5XApL6+lpNHt/Pok6/i6R0EgJ2DK5kZFzkbG2kQmIRPeATfniFA04f2qeO7mDjjSfqHjQXA3sGNHj69AbgQfxSVSs3UOc8qgcD0uc/x7ptPkXHlPH49Q4GmTMUTZz6FWq3G0dkT/6Aw0lMTlMCkZYJAOwdXJkx/gq8++j31dbUGuXZatq1pu/Hs2fo/IqYtwdjYhPycdPJz05m76JftnitJ4ieEEF0jgYnoMgtLGwKCwkg4HY0ePf5BYVhY2ijri/KzaGxsYN0Xbxhsp9U24urua7DMzdNf+bm4IAttYwM+Af3b3G9edholxbn8+y9PGCxvbGwwmCreycULtfr67VNW1nYU5F01qOfIgQ3k56RTU1MJ16byKS8rxMmlR5ttAwjsM4TI7Z+RkhhLn5CRxMcdwNuvr8EQ1o0kiZ8QQnSNBCbilgQPHEfkts8AmDjrKYN1zUnw5i/+NdY2Dgbrmodbmpm0SNhnbGza4T71eh1uHn7MfPhnrda1DIzU6hsT/F1PzFdfX8u6L97Et2cIMx5+AQtLG8rLCln/xVtotY3ttq257X1DRxN/OoqgvkNJPHeE8dOXddhmSeInhBBdI4GJuCV+gQOUD/LmIZRmji49MDI2oaKs0GDY5mbsHd0wNjElPTUBu8HjW6139fAjKeEYFpY2tzyjY3FBNjXVFYydvEh5iig363Kntw8dNJ5P3/8Vp2P2oNNpCeo79JbaIYQQom0SmIhbolarWf7iP5SfW9JozBk6aib7d36FXq/H06cX9bU1ZF1NxtTUTLl/5EbGJqYMC5/NwT2rMTIyxtMniJqqCgrzrxIyaDx9Q8KJObyVjd/8nfDxC7C2daS8tJCUxBiGhs/C2tbxpu22sXPCyMiYuOO7GTBkIgV5VzkatbHTx+3o4omHVyAH93xD8MBxmJh03MsjhBCiayQwEbeso16L8AmPYGFpw/HozZSW5GFmZomrux/Dx87psM6R4+ahVhtxeP86KitKsLKyZ8CQiQCYmGpYtPzPHNzzDd9/+0/q62uxtrbHO6A/phrzTrXZwtKGafOe49DeNZw6vgtXd18ipi5m4+q/dfq4gwdGkJWRTPDAiJsXFkII0SWSxE+ILjoWtYkL8Ud56medD2aaSYIxIe4u+R178MnMr0J0Un1dLTmZqcSd2MWgEVPvd3OEEOIHSYZyhOikyO2fcuHcUXr2GXzbwzhZB2ZhYnxvOiu9Ju2/J/sRQog7QQIT8cDITL/Ini3/o7gwG/+gMOY9/qt7uv/p856XBIVCCHGXSWAi7onmzMQ38usZyoJlv+1UHQd2fYmLuw8Llv4GE1Ozm28ghBDigSOBibhn/AJDmTb3OYNlxsadvwRLi/MZMGRSpx4Lbo+2sbHVJG93glbbqCQJFEIIcevkL6m4Z4yMTLCytmtz3TuvLmTKQ89wOfk0aZfOYmXtQMTUxQT2GUxZST4f/fNFAHZuWsnOTSuZNneFkjgwavdqCnLTMTO3on/YGEZPeBS1UdPsr9+ueg0nVy+MjIw5f+YQji49eGz5nyjMu0rUnm/ITE9Cr9fj6u7DtHnPYe/gBjQl5ztxaAtlpQXY2jkzaPhUwoZNBlDaM/uRlzgds4fszEtMnrWc4IHj7vo5FEKIHzoJTES3cfTABsZOeZxxUx4n7vgutm14nxUvv4+1rRPPv7KS/737C8InLKB3/5FozCyoKC/mu6/+Sv+wscyY/zzFBdns2vwxRsYmhI9foNSbcDqasKGTeOzp1wA9FeXFfLPqNbz9+vLok39AozEnKyMZnbZp2vqzJ/dxeP8GJs14EhcPX/Kzr7Br88eYmGoMJoc7uOcbIqYuYZqHb7u9JZJdWAghukYCE3HPpCbH8a/XDXPLDAufzciI+QD0DxtL35BRAIyetJBTJ3aTk3UJ/8ABWFnboVKp0GgslF6XY1F7sLZ1ZOLMJ1GpVDg6e1JZUULUnm8YNW4+qmsz0to7uDFuyuPKPqP3fovGzIJZj7yoBBQOTh7K+qNRG4mYupigfk3TzdvZu1BYkMmZ2EiDwGTQyGlKmfZIdmEhhOgaCUzEPePt14/Js5YbLDMzt1J+dnbzUX42NTXD1NSM6srydusrKsjCwysQlUqlLPP07kVDfS0V5cXY2DXlwrkxS3B+Tjo9fHq32ctRXVVORVkRu77/iN2bP1aW63Q6NBrDyZrcPAI6OlxAsgsLIURXSWAi7hkTEw32jm7trje6ISuwqkVW4Pa0DEoA9FybG6TFYhNTwyzBxh3kt2ne35SHnsGjR0/Dfd2QE+jGetsi2YWFEKJrJDARDyxHZ0+SE2PQ6/VKgJKdkYypxhxra4d2t3N29SbhTHSbT9JYWtlhZeNAWXEe/ULD72r7hRBCtCZT0ot7RqttoLKi1OBfdVX7QzU3EzZsMhVlRURu/4yigixSLpzk8P71DB45vVXvRksDh0+hvq6GreveIycrleKiHM6fiaaoIBuAUREPc/zQZk4e20FxYTYFuRnEx0URe2T7LbdVCCFE50iPibhn0lLO8sE7KwyWOTh58PRL/7yl+qxtHJi/5NdE7V7N5yd/jZm5FSGDIhg5dl6H25lbWPPok38gavdq1qz6P1RqNS5uPnh69wIgdPB4TExMiTm8jYO7v8HEVIOTqzeDR0y7pXa2xTNiqyQYE0KINkh2YSHuIcl8KsTdJb9jDz4ZyhFCCCFEtyFDOR04vH89ly6c5IkX/nrH637n1YXMXfQygX2HdKp8fFwU+3d+yUu///SOt+V+a55Jddnzb+Pq7nu/m3NP3IvswpJVWAjxIPpR9ph8u+o19u34otXylMRY3nl14T1pw/OvrMQvaECny/cOHsnTL/3r7jXoPmqe2dXZxet+N0UIIcR9Jj0md5BOp0NF6/ku2tJezpj2mJiYYtLB/BvdVWeS26nV6i6fDyGEED9MEph0QWlJPus+fwPfgGAmzXyKhDPR7N/5JTMefoGDu7+huCiHn/z839RUVRAduYb8nCtotY24uPsyftpS3Dz8lLpaDuU0D2XMWfhLTp3YRU7mJewd3Jg8+2k8vYOA1kM5JcW5HNj5FdlXL9HQUIujsydjJi3CNyBY2cfKf/yU0METKC3OIynhOGbmlowYO5cBQya2e4wXE45z5MB3lBbnYmyiwdXdl7mP/wpTUzOlHV1Jbjd28iKi93zLnMdexj9wgLKf5PMxbP/uv7zw64+oqS5vNZRzp5LsxZ3YRW7WZZxcvZj58E+pq61hz9ZVFBdm0cO7NzMefgELSxulXZ2pt6P3SQghxO2RwKSTCvKusu6LN+k/YAxjJy9Sljc01HEiejNT5zyDmbk1FpY2lJXk03/AGNymPwFAzNFtfPfV2zz983+j0Zi3u4/oyDVETF2MvaMbhyLXsnX9ezzz83eVTLktNdTV4R8UxugJj2JkYsL509Fs/Podnn7pX8pU7ACxR7YzesIjDB8zh4vnT7B36yq8fPvg6OzZqs7KihK2rv8PY6c8RlCfIdTX1ZKZnkTzZKq3mtwu80oSiWcPGwQmiecO07P3IEw1ZtRUG85lcqeS7B3ev54J05dhY+vEzu9XsnX9fzDVmDNh+jJMTEzZvPZdDu9bx+TZT3ep3q68T5LETwghukYCk07Iykjmu6/fYfiYhxgaPstgnU6rZdLM5bi4X8/z4uPf36DMlNk/4b03l3P1SiI9ew1qdz9Dw2cS0GsgAKPGL+DT//yKkuLcNoMIF3cfg32OnvgoyYmxXEo6ycDhU5Xl/kEDlG/8w0bP5uTRHWSkJbYTmJSi02kJ6jsUWztnAJzdvJX1t5rcrm9oONu/+4CG+jpMTDXU1VZzOfk0Dy36ZZvn4fSJ3Xckyd7Q8Jn4BYY2tWn4NLauf49Hn/wDPXya5isJGRRBwumDt1RvZ98nSeInhBBdI4HJTZSXFbLu8zcIn/iIQTK2ZkZGxgYf3gBVlWUc3r+ejMsJVFWWodfraGiop6K0qMN9ObteDzSa77moripv8wOvvr6Wowe+I/ViHJUVJeh0Whob6ikvM9xHy8R4KpUKSyvbdmdbdXHzwce/P5+9/wp+PUPw7RlCr37DMDO3uq3kdgFBA1GrjbiUdIo+ISNJTozBVGOOX0BIm+24U0n2Wp5PCyvba8uuv1dN56Lstuq92fskSfyEEKJrfpSBianGgrra6lbLa2urML1hqMXcwgYrG3uS4o8SMjCi1YQ9xiamrRLJ7dz4IdXV5YyfvgxbWyeMjE34+uNX0WobO2yX4VBAU53tJbGL2r2aKylnGTd1MfYObhibmLJ5zb9a7aMrifHUajWPPPF7sjKSuXLpHHHHd3Moci2Ln/2LcuPtrSS3MzI2ple/YSSeO0KfkJEknjtC7/4j2hz6gDuXZK9l/c1vkfqG89E8v+Ct1nuz90mS+AkhRNf8KB8XdnT2IDfrcqvluVmXDYYMoOlpmPmLX8HI2JT1X77VqXsErqYnMWj4VAKCwnBy9cLI2Jia6oo71n6AzCtJ9A8bS1DfoTi7eWNpZUdZacFt16tSqejh04vwCQtY9vzbqI2MSUmMNUhuZ+/oZvDPzt7lpvX2DR1F2qUzFOZdJSPtPH07SJDn7OpNZnpSm4Hc7bajPXerXiGEEF3zowxMwoZOorQkj71bPyU/J53iwmziTuzmXNyBVveQAJiamvHw4l+jVqvZ8OXb1NfVdli/vYMb588coig/i+yrKWxb/36HvQC3wt7RjeTEWPJyrpCfk87W9f/hdrMLZF9N4djBTeRkpVJeWkhyYgw1VeU4OjcFa7eT3M7Lty+WlrZs2/A+tnbOeHgFtlv2fiXZk+R9Qghx//0oh3Js7V14bPmfORS5lnVfvEljYz0OTu5Mn/scvfsPb3MbU40ZDy/5Leu/fJMNX73Nw0t/02790+Y+y+4tn/D5h7/BxtaRMRMXcmD313f0GMZPW8LOTR+x+pM/Ym5hzbDRs6mvaz081RUajQWZV5I4dWwndXU12Ng6ETF1Mf5BYcDtJbdTqVT0CRlFzOGtjBw3v8Oy9yvJ3r1I3tdMkvgJIUTbJImfEPeQJBgT4u6S37EH349yKEcIIYQQ3dOPcihHdH8/5KSFAC9tX43+JlP1d+SjOU/ewdYIIUT3IYGJuCuqKss4vG8dl1POUF1Zhpm5Jc5uPoyKePieTt/e1SzOQggh7i8JTMRdsXnNP9FqtUyf9zx2Di5UV5aRnppAbU3l/W7aXaHX69HrdO3OzSKEEKJzJDARd1xtTRWZ6RdZ+NQf8fbrC4CtnTPuLSYuiz2ynfi4KMpK8jEztySg9yDGTX4cU42ZQV0pibFE7VlNeVkRPXx6M23us9jYXs8FdDpmD7GHt1FeXoStnQsjx82l34AxQFMSQ4BN3/4DABs7J1a8/D4Al5JOceTABgrzM7Gytqf/gDGMGDsXtZGRkqyvZVLB2poq3ntzOQufehVvv35kpJ1nzaevs2Dpb4mOXEtBXjoLlv4OH/9+d+ekCiHEj4QEJuKOMzU1w8TUjJQLsXh4BbY586lKpWLCjCewtXemrCSfvVs/JWrPaibPWq6UaWio41j0JqbPex4jI2P2blvF1nXv8fhP/g+A5MQY9u34ggnTluET0J/Ui6fZsWklVjaO+Pj3Y+mKN3n/7WeYNncFfoEDUF+bwTUt5SzbNrzPxBlP0MOnN6XFeeze/AkAo8Y/3KVjjdq9moipi7F1cMHMzLLVekniJ4QQXSOBibjj1EZGTJ/3HLs3f8zZ2EhcPfzw8u1D7+CRuFzL3TN45HSlvJ29C+ETHmHv1lUGgYlOq2XijCeVydimz3ueVe+9TE7mJdx79CT2yDb6h41VkhQ6OHmQfTWF2CPb8PHvh4WlDQBmZpZKThuAYwc3MXz0Q0piPjsHV8InPELUnm+6HJiET3gE355t5/wBSeInhBBdJYGJuCt69RtGQFAYmelJZF1NIS3lDCcOb2XqQ88QPHAc6ZfPczz6e4ryM6mrq0Gv09LY2EB9fS2mpk3DOWq1EW6e1xMCOjp7ojGzpKggC/cePSkqyCJ08ASD/Xr6BHHq2K4O25aXnUZuVirHojcpy/Q6HY2NDTTU13XpON08/TtcL0n8hBCiayQwEXeNsYkpvteyFI+KmM/O7z/iyP4NePv347uv3iZ0yERGT3gEM3NLMtMvsuv7j9BptQZ1qNqsWdV+CX1727QootcxavwCgvoObd1mYxNUKnVzQWW5TqdtVRbAxETT5vKW9UkSPyGE6DyZYE3cM07OPWhoqCM36zI6nY7xU5fg4RWIg5MHlRUlrcrrdFpys68nWywqyKautkrJ3ePo7ElmRpLBNlkZyTg4eyqv1UZG6G7I/Ovq4UdxYXarZH32jm6o1GrMrw0BVVaWKtvk51y53cMXQgjRCRKYiDuuprqCNZ++zvkzh8jPTae0JJ+khOPEHN5Cz96DsHNwRafTcurELkqL8zh/JpozsZGt6lEbGRG5/TOyr6aQm53Gzk0f4uEVqDzdM3TULBJOH+R0zF6Ki3KIPbKd5AsxDA2fqdRha+dM+uUEKitKlUeVR46bz/kzhzi8fz2FeVcpys/iQvxRDkWuBZoySnt4BXIiejOF+ZlcvXKBQ/vW3oMzJ4QQQoZyxB1nYmqGe4+enDy2g9LiPHRaLda2joQMGs/wsXMxMTElYtoSThzaSvTeNXj59GHspIVs/+4Dw3pMNAwLf4ht6/9DRXkxnj69mDZ3hbI+sO8QJkxfRuzhrezb8Tm2di5Mn7sCb7/rj+xGTF3C/p1fce7kfqxs7Fnx8vv4BYYyf/ErHI36jpjDW1GrjXB09iBk0Hhlu2lzVrDz+5V8ufJ3ODh6MG7KY6z74s27f/KEEOJHTpL4CXEPSYIxIe4u+R178MlQjhBCCCG6DRnKEeI+yDowCxPj9jsrvSbtv4etEUKI7uMH2WPy7arX2Lfjiw7LnImN5MO/Pc87f1zEyaM77lHLbt/Kf/y027dXr9eza/PHvPfmct55dSF5t/FES1lJ/m3X0Z4dGz9g4+q/3/F6hRBC3Lpb6jHJyrjIN//7M74BISxY9ts73aa7rq62msjtnzF+6hKC+g1Do+l+45DxcVHs3/klL/3+U4PlS1e8edO5M+63tJQzJJw+yKKn/oitvSsWFtatyrR3fADvvvEU46ctJXjguLvazgnTn0BusRJCiO7llnpMzsVFMXD4VDIzkigvLeywrF6vbzVp1v1WXlaETqvFv9dArKztMTG9tQ96rbbxDrfs5iwsbW65vfdKaXEeVlb2eHr3wsra7p5n3O3s+6Ixs8DMvHV+GyGEEPdPl3tM6utruZhwnCXPvkFVRSnxpw8yKmK+sr69rKtHD2zA2dUblVpNwulojIyMCZ+wgL6h4URu+4yL509gaWnDxJlP4h8UptRXmJ9J1K6vuZp+ARMTM3x7BjN+2lIlD0p9fS17t6wi+UIMpqbmDGkxh0Vb4uOi2LlpJQAf//NFAJ795XvY2rt0mKkW4J1XFzJp1nLSUs6QnprAkFEzQQWXLpxk4PCpHDmwgdqaSvqFjmbizKeIPbKNk0e3o9frGTR8GiPGzVXq6ii7bkbaeaWN77y6EICREfMJH7+Alf/4KYNHTFdyzZSXFhK5/TPSLyegUqnx6xnKxJlPYGllB8Dh/eu5dOEkQ0bN4NC+ddTWVOEfOIApc55BozEH4GLCcY4c+I7S4lyMTTS4uvsy9/FfKVPD3ygjLZGo3aspyE3HzNyK/mFjGD3hUdRGRuzY+AEJp6OVtrfM6Hsn6HU6dm/5H1evJPLIE7/H1s65zfdlZMR8dm/+mIzL56mqLMXa1omwYZMYPOJ6jp4dGz+gtqaaeY//CmgaAnR288HY2IRzp/ZjZGRM6JCJhI9foGxTV1vNgd1fc+nCSRobG3Dz8Gf8tKW4uPvcsWMUQogfsy4HJknxx3BwcsfR2YO+A8LZt+1zRo6bh0plOBF4W1lXE85EMzR8FktW/IWk+GPs3bqKSxdOEth3CMPHzuHk0R1s/+4DVrz8PiamGiorSvh21WuEDh5PxLQlNDbUc3DPN2xZ+y4Ln3pV2U9G2nnmLnoZSys7oiPXkJudhsu1dPU36h08EhtbR9Z+/gZLnn0Da1tHLCxtbpqpttmR/RsYM2kh46ctRaVWEx8XRUlxHmkpZ1iw9LeUFuexec2/KCspwN7JnYVP/Ynsq8ns3LQSn4D+SkK6jrLrenr1Yvz0pRzet56nX/oXQJtBgl6vZ+M3f8fUVMOip/6ETqdl77ZP2bL2XRYt/5NSrqQ4j5QLJ5m/+NfU1lSyZe27nIjezJhJC6msKGHr+v8wdspjBPUZQn1dLZnpSdDOCEdFeTHfffVX+oeNZcb85ykuyGbX5o8xMjYhfPwCJkx/Ajt7V86e3MeSFW8qGX3vBG1jI1vXv0dpcT6PPf0alla27b4ver0OaxtHZj/6c8wtrcnKSGbP5k+wsrKnd/CIdveRcPogQ0bOYPGzfyE7I4Udmz6kh3cvfHuGoNfr2fDVXzEzt2L+kl+jMbPgbGwkaz//C0+/9C/MLaxa1SfZhYUQomu6HJjExx2gb2g4AP49B7Czvpb0ywn4BgQblGsr66qLmzcjx80DmpKbnTi0GXMLayUR28hx8zkTs5eCvAw8vAI5HbMXVw8/xkxapNQxbe4KPvz7CxQXZmNl7UD8qQNMn/+8sq/p857nw78/3277TUxMMbt2z4OFpbWSdfZmmWqb9QkZScigCMNK9Xqmzl2BRmOOk0sPvP36UVyYzcNLfo1KrcbR2YMTh7aQkZaoBCYdZdc1MjZGo7FApVIZZMW9UXpqPAV5GTz7y/ewsXUCYMb8F/j0P78iJzMV9x4BSvumzXtO6SHpN2A06ZcTAKisKEWn0xLUdyi2ds4AOLt5t7vP0yf2YG3ryMSZT6JSqXB09qSyoqQpM++4+WjMLDDVmKNSqztse1c11Ney4eu/0thQz6Llf2w1P0Fb70v4hOs9HXb2LmRnJJOUcKzDwMTFzVvJMOzg6E7cid1N13fPEDLSzlOQl8FPf/Oxkv8mYuoSUi6c5OL54wwYMrFVfZJdWAghuqZLgUlRQTY5manMWfRLoGnK8N7BI4g/daBVYNJW1lVn1+vd3Wq1GnNza5xafAg2fwOuqiwDmrLAZqSd51+vL2tVV2lxHo0NDWi1jXh6BSnLzS2scHD06MphXTu2zmWqbZnttpmNnbPyoQ9gYWWLSq1G1aK3wMLKluqqMuV1Z7LrdqbNNjaOSlAC4OTSo0UG3oA222dpbUd1VTkALm4++Pj357P3X8HvWsK9Xv2GYWbe+tt/8z49vAINesg8vXvRUF9LRXkxNnZObW53u7auew9rW0cefeIPbd5j09b7cjpmL+dOHaC8tIDGxnq02kZc3Hw73E/LaxTAqsW5ys1Oo6G+lv+89bRBmcaGekqL89qsT7ILCyFE13QpMImPO4BOp+WDv7XokdDrURsZU1tTafBh1taTI61uglSBkfr6MuXD7tqTEnq9jp69BjF28mOt6rK0tqOkKLcrze+Em2eqNW3juIxuOC6VCtTqG5aB8gRIWWlBp7PrdkSPvmlnbaxpubhV+1Chv5bYTq1W88gTvycrI5krl84Rd3w3hyLXsvjZv2Bn79Lmfm8cttM3j/vcLK1vCxqNBfX1teh0OoPhHp1OR319baseEf+gMBLPHiY7MwUf//6t6rvxfUmKP8aBnV8ScS1RoKnGnJjDW8nJvNRhu1rfqHv9XKHTYWltz6Kn/tj6eNqZYVKyCwshRNd0OjDRabWcPxNNxNQlrYZovv/2nySePczA4VPvaONc3f1ITozB1s65zSc77B3cUBsZkX01RfmmXltTSUlRDl5+fbq0r+ZMtf3Drt/semOm2julZXbd5l6VpITjBmWMjIzR6XRtba5wcu5BeVkh5WWFSq9JYX4mdbXVXWq3SqWih08vevj0YmTEfFb+46ekJMYyZNSMVmUdnT1JToxBr9crAUp2RjKmGnOsrR06vU8HZw/0Oh15OWm4t+jtyMtOQ6/T4eBk2Os1YOgknFy92Lj6b8xf/Gu8/fp2WH9mehIe3kHK0BzQbq9GZ7l6+FFVWYparca2naBNCCHE7en0nYmpF+OorakiZFAEzq5eBv969RvGuVNRd7xxYcMmU1tTydb175GTeYnS4jzSLp1l56aV6HQ6TDVmhAyMIGr36mv3W1xlx8YP2+lF6FhnMtXeKZ3Jrmtr70xDfS3pqfFUV5XTUF/Xqh6fgGCcXb3Ztv59crPTyMm8xPbvPsDLt4/Bh31Hsq+mcOzgJnKyUikvLSQ5MYaaqnIcndseDgsbNpmKsiIit39GUUEWKRdOcnj/egaPnG4wdHUzTi498A8cwK5NH3ElNZ7SknyupMaz6/uP8A8cgJNLj1bbDBo+ldETHuW7r99pukG3A3aOruRmXSYt5SzFhdkcilxLTlZqp9vXFp+AYDy9Atn0zT9ISzlLWUk+WRkX70jdQgghmnS6x+Rc3AF8AoLb7LIO6jeM49Hfk5uddkcbZ23jwGM/eY2Du79h3RdvodU2YGPrhF9gqPJtfdyUxdTX17Jx9d8x1ZgxeNRM6mqru7yvzmSqvVNc3X1vml3X07sXA4ZMZMu696iprlAeF25JpVIx77FfEbn9M75d9WeDx4U7S6OxIPNKEqeO7aSurgYbWycipi42eGS7JWsbB+Yv+TVRu1fz+clfY2ZuRcigCEaOndfl8zDrkRc5cmADe7asorK8CCsbRwJ6hbU6zpYGj5yuPB2zYOlv8PTu1Wa5AUMmkZ+TzpZ17wIq+oSMJGzoZNJSznS5nc1UKhXzl/yGQ5Fr2blpJdXV5Vha2eHl08fgCaHO8IzYKgnGhBCiDZJdWIh7SDKfCnF3ye/Yg+8HmStHCCGEEA8myS4sxDU3zgR7N720fTV6o+u/fh/NefKu71MIIR4EEpiIbqnl1PYt/eTn/8be0e2u7LMrSf3uZRAjhBA/JhKYiG7LLzCUaXOfM1jWnCPpbpDxaCGEuP8kMBHdlpGRSatp7TtKfghNk9dFbvuMzPSL6LSN2Ng5M27q4wRce8qoMO8qUXu+ITM9Cb1ej6u7D9PmPYe9g1urXpD2khvGHN5qkKgQYOFTr96VJ7iEEOLHRgIT8UDpKPkhwN6tn6LTNvLY8j9hYqqhsCBTmeK/oryYb1a9hrdfXx598g9oNOZkZSSj07aeyK6j5IZDR82iqCCL+roapUfHvJ0p/CWJnxBCdI0EJqLbSk2OM8iT5B84gIcW/kJ5fWPyQ4CKsiKC+g5VEhHaObgq5U+f2I3GzIJZj7yI0bUbT2+cYbbZzZIbmpiYom1svGmiQkniJ4QQXSOBiei2vP36KQEHgImp5qbJDwcOn8rerau4knoOH/9ggvoNxcWtKTFffk46PXx6K0FJR7qa3LA9ksRPCCG6RgIT0W2ZmGgMnsDpTPLD0MHj8QsMIfXiaa5cOsfxQ98TMXUJg4ZPxdjEtNP7vpXkhm2RJH5CCNE1MsGaeGC0TH7o4RWIg5MHlRUlrcrZ2DoRNnQScx97mSEjZ3Lu5H4AnF29yUxPQqtt7NT+mpMbhk9YwLLn30ZtZExKYiwAaiPj61mHhRBC3DESmIgHRmeSH+7b8QVpKWcpLcknNzuNjLQEHK4lJBw4fAr1dTVsXfceOVmpFBflcP5MNEUF2a32dbPkhrZ2zhTkZlBUkE11VXmngx0hhBAdk6Ec8cDoTPJDvU7H3m2fUlFejEZjjl/PUMZPXwqAuYU1jz75B6J2r2bNqv9DpVbj4ubTZiLAmyU3DB08gatpiXy58nc01Nd2+XHhd2c8LvOmCCFEGySJnxD3kCQYE+Lukt+xB5/0mAgh7rhnv//srtTbHXMKlZXkk3bpHAOGTFSWbfjybSbMfAJ7h7uTPuF+OXl0B31CRmFpZXvTsvFxUXh6BymP5KdcOElmehIRUxff7WaKB5zcYyLumndeXajcLCoMvbR99V378Bb3VllpAWev3WDd7OGlv/nBBSUAJ4/toLqqvFNlE04fpLgoR3kd2GewBCWiU6THRNyyyopSjh/cRGryaSrLi7GwtMHF3ZfBI6bhExB81/d/eP96Ll04yRMv/PWu70s8WN55dSFjJy0i+UIM1ZXljIyYT/DAcQAUF+Wwf8eXVFeVoW1sZMCQiYQNmwzAxfMnOBS5FmNjU4L6DeXwvnX8/A+fY6oxY9v69ykqzEanbcTa1pFpc1dgaWXLni2rKC8r5PP//hprWyfmL/5/rPzHT5m/+NfU1VaxZ8sqnvrZ35S2fbPqNYaMnEFgn8GkpZzlaNRGGhvrUavVjJuyGC/fPq2Op6K8mH3bP6ekKBeAnr0HMXrio1RVlrJny6pry/UMHD5V6blZ+Y+fEhw2jrRLZ6mqKCV4UAQjx80D4NtVr+HeoyfZV5OpLC/Bp2cIU2Y/DdBunUcOfEdlRQmb1/wLY2MTps17jtrqCg7tW0djQwM6XSNDRs0keOA4zp7cT272ZfZt/4LDkesYPWkh1ZVlpF6MY86iXwJw4tAWEk5Ho1KpcHbzZvKs5WjMLDi8fz0lhTnU19dSWpyHpZUdDy38BeYWXZtDSDy4JDARt6SsJJ/Vn/wJjZkl4yY/hrObNzqtlrRL59i77TOefumf97uJ95Rer0ev06E2MrrfTRHXGJmYsOTZNyjKz+LLj35Pv9DRoFKxbf1/mDH/BRydPWmor+Prj1/F3SsQaxsHdm/+hMXPvo6Dozsnj+4wqG/89KVKEsnj0Zs5GvUdk2Y+xeTZyzmwazXLnnuzVRt6+PRGq20kJysVd88ASovzKCnMISAojNLiPI4c2MCCpb9FY2ZBSVEu3656jWdf/k+rSQC3bXgf/6Aw5UO9udcicvvnODh5MPexl6mqLOPLD3+Lq7sv7j16AlBbW8XiZ16nuqqcj//1EsEDx2Ft4wBAaXEeC5/6E1ptI5++9zJZGcl4ege1W+eoiPnExx3goYW/wNnVq6n+mkoee/o11Go1NdWVfPHhb/ALDCV08HgSzx5iSPhMevYaBDQN7TS7nHya+LgoFj/zOmbmluza/DHRe9cwadZTAORkXmLJijcxt7Biy9p3ORsbyfCxc27nchAPEAlMxC3Zu/VTUKlYsuIvSi4aACdXL+WbKUB1dQWbvvkHaZfOYmXtQMTUxQT2GQyATqdj9+aPybh8nqrKUqxtnQgbNonBI6Yr22eknSdq9zcU5WeiNjLCyaUHMxf8jIzL55Wp3psT6U2bu4LggeOoq63mwO6vuXThJI2NDbh5+DN+2lJc3JtmgG3uaRkyagaH9q2jtqYK/8ABTJnzDBqNOdAUaMQc3sqZ2EiqKkqwd3Rn5Lh59Oo/XGnXmk9fZ8HS3xIduZaCvHQWLP0dPv6SyK+76BsSDoCjiydqtRFVlaXU1dVQmJ/J1nXvKeXq62ooys+koqwIVw8/HBzdAQgeOI79O79UyiWeO8z5M4fRNjbQ2FiPpZVdp9oRHDaWhNMHcfcMIP70QfqGjkJtZNT0WHtxHt+ues2gfEVZkUEqhfq6WrIzknl02e+VZc0BUnpqAuNfWAKApZUtQX2Hkp6aoAQmfUPDlfJ29q6UleQrgUmf4JGo1WrUalNc3H0pLc7D0zvopnW2VFNdyc5NH1FSlINabURNdQWF+ZlYWdt3eE6upCbQNzQcM3NLAMKGTGJLi/fEL2iA0kPi4R1IQd7Vm51m8QMigYnosprqSi5fOsuYCY8aBCXNmv/YABw9sIGxUx5n3JTHiTu+i20b3mfFy+9jbmGFXq/D2saR2Y/+HHNLa7Iyktmz+ROsrOzpHTwCnVbLpm/+Qcig8cx65GfoGrXkZF1ChYrewSMpzL9KWspZHnniDwBozCzQ6/Vs+OqvmJlbMX/Jr9GYWXA2NpK1n/+Fp1/6l/LHrqQ4j5QLJ5m/+NfU1lSyZe27nIjezJhJTUHOoci1JCfGMHnWcuwd3bh65QLbvvsv5pY2ePv1VY4vavdqIqYuxtbBBTMzS24kSfzuH2Pj6zP9qtVqdDot6PWYW1i3OfyXcuEkqnbqykxPIu74HhY/839YWNqQcuEkR6M2dqod/cLG8MUHvyFiymLOn47m4SW/BkCPHr+eocx4+DZTFKhU7b5ueQ5UzefgGiMTkxabqAzWdVRnS3u2/I+A3gOZs+iXqFQqPv/gNzQ21nei0XpUN5xtlUG7r7dNrVKjb9k28YMnN7+KListzgW9Xpm4rCP9w8bSN2QU9o5ujJ60kIaGOnKyLgFgZGRM+IQFuPcIwM7ehX6h4fQPG0tSwjGg6UO8rraagF4DsXdww9HFk/5hY7Gxc8LExBQTUzPUaiOsrO2wsrbDxMSUjLTzFORl8NDCn+PuGYCDozsRU5egMbPg4vnj1xum1zNt3nM4u3rh5duHfgNGk345AYD6+lpOHt3OtLkr8AsMxc7BleCB4+gbGs7ZGyZ0C5/wCL49Q7B3cMPcwrrV8R+P/p5333hK+Sd5cu4vBycPTEw0JJyOVpaVFOVSU12Jh1dPcrPTlPs4Ek4fVMrU1lShMTPHzNwKbWMjZ0/uU9aZaiyoq61ud5/WNg64efizb+eXWFrb4nRtGMS3Zwhpl84a9AbkZF5qtb2pxgxPn17EHrs+tNQ8lOMT0J+zsfuUZckXYm+7166jOjU3HGttbRW2ts6oVCquXrlAQW56i3ZbUFfbdiDuGxDMhfijSqB+9uQ+fAL631a7xQ+H9JiILmue+ubGbzxtcb6WQA/A1NQMU1Mzqiuv39V/OmYv504doLy0gMbGerTaRlzcfAEwt7Cif9hY1n/5Fr4BwfgE9Kd3/xEddhPnZqfRUF/Lf9562mB5Y0M9pcV5ymsbO2dl2AbA0tpO+WNflJ9FY2MD6754w6AOrbYRV3dfg2Vunv4dHr8k8ete1EZGzFv8/9i/40tij2xDp9NhYWnNzAU/w9rGgcmzn2bDV3/F3MKKnr0GoTYywsTEFP/AAZw/e4j/vftLrG0d8PQKIu3SOQBcXL1xcHLn0//8Clt7V+Yv/n+t9tt/4Di2rP03k1okpXRwdGfGwy+w6/uPaGxouvZdPfyYteBnrbafMf8F9m3/nFXv/Qq1Wk1gnyGET1jAxBlPsGfLKj57/xX0eh0jxs5tc8ilKzqqc+Dwqezc9CEmJhqmzXuOsZMWsWfrpxw/tBknlx4G+w4dMp4DO78m9vBWRl/riWzmHxRGQd5VVn/8KnD95lchQAITcQvsHd1BpaKoIItAhnRY1khteDOoSqVScswkxR/jwM4vibiW+8ZUY07M4a0G3xqnz3uOQcOnkpZylqT4YxyKXMejT/weD6/Atneo02Fpbc+ip/7YalXLyZaMbrhJVcX1djX/P3/xr5XxeGU7Y8NfGRMTTUeH/6NN4ne/5xt55fU1Bq9/9ttPlJ8dHN2V4ZQb+QWG0vvafUTxcVG4e/ZEpVajAh569OcGZZuH/dRGRq3qW/Hy+wave/cfTu/+hm0C8OsZil/P0Jsej7WNg3Lja0uWVnbMfezlNre5sQ0tb85dtPxPButa1t1RnaGDxxM6eLzBsmd+8e82y/bsNUi58bVZy/vPho2ezbDRs1ttFz5+gcHrgcOntlm/+OGSwER0mbmFFX49Q4iL2cPAEVNb3WdSW1NlcJ9JezLTk/DwDlIe1QQMejWauXr44erhx/Cxc/j641dJPHcED69AjIyM0el0rcpWVZaiVqux7UIW4JYcXXpgZGxCRVmhwf0k4ocv7tguLp4/jk6nxczciqkPPXO/myTEj44EJuKWTJq1nNUf/5GvVv6B8AkLcHbzRq/VcSX1HKdj9nbqcWE7R1cSzkSTlnIWW3tnzp85RE5WKnbXAorSknzOxu6jZ+9BWNnYU1yYTXFhDv0GjAaaEumVleaTl3MFaxsHTDXm+AQE4+kVyKZv/sHYyY/h4OROZUUJl5PP0LPPYNw9A27aLo3GnKGjZrJ/51fo9Xo8fXpRX1tD1tVkTE3N6B829vZOnui2Royby4hxc+93M4T4UZPARNwSO3sXlj3/FscObuLArq+oqijF3NIGNw8/Js/u3FjxgCGTyM9JZ8u6dwEVfUJGEjZ0MmkpZwAwMTGluDCb79dEU1tdgaW1HQOHTWHA4KYJpIL6DSM5MYY1n75OXW2V8rjw/CW/4VDkWnZuWkl1dTmWVnZ4+fTp1DTazcInPIKFpQ3HozdTWpKHmZklru5+d2wuBUniJ4QQbZMkfkLcQ5JgTIi7S37HHnzyuLAQQgghug0JTMQP3rerXmPfji/udzMMvLR99f1ughBCdEtyj4m4J3Zs/MBgUiszcyvcPQMYO+UxXFrMdSKEEOLHTQITcc/4BYYybe5zQFMG00ORa/nu63d47lf/vc8t65hOp0NF05TeonOu7h1/80K3wGvS/rtSb2ecPLqDPiGjunQTdbOykny+XPl7g/lUupuUxFisbOw7NUFbRtp5tNpGZQ6WivJitm14v835g4ToKglMxD1jZGSClbUdAFbWdgwbPZtvV71GdVU5FpY2RO1eTcqFWCrKi7G0sqNvyChGRsxXMq12JvlefX0te7esIvlCDKam5gwJn9mqHdrGRg7tW0vi2cPU1Vbj5NqDsZMfw9uvadrt+Lgo9u/8khkPv8DB3d9QXJTDT37+b2qqKoiOXEN+zpWmGWrdfRk/bSluHn735gSK++rksR34BAS3GZjor82n8yAHrylJsbh5+HcyMEmkob5WCUysbRwkKBF3jAQm4r6or6sl8dwR7BzcMDdvSqxnqjFn+rznsLK2pyDvKrs3f4ypxtxgdsibJd+L2r2ajLTzzF30MpZWdkRHriE3Ow2XFlPJ79j0IeWlBcx65EWsbOxJSYxl/Zdv8+RP31EyyzY01HEiejNT5zyDmbk1FpY2lJXk03/AGNymPwFAzNFtfPfV2zz9838bTG/fkiTxuz/eeXUhYyctIvlCDNWV5YyMmK/MOlpclMP+HV9SXVWGtrGRAUMmKpP8vfPqQn7+h88x1TRNGvift37C0hVvkHDmEJUVJWxe8y+MjU2YNu85Ui7EUlqcR0N9HaXFeTy89DecPLqDq1cS0Wm1mJpZMHXOM8o11R5tYyPRkWtISzmLSqXCytqeBct+i06n4+Ce1aSlnAXA268fEVOXYGRszI6NH2BsoqGkMIfyskKcXLyY/chLGBkbc3j/ekoKc6ivr6W0OA9LKzseWvgLzC2s2q3zyuV4LiWd4kpqAudOHSBs2BQCeg1k6/r3qK+robGhAZ+A/kyYvoz83HTOxkai1+tJT00gsO9Q+g8YbdAjdDnlDNF716DX6dCYWzJ51nKcXHqQkXae/Tu+xMM7iKz0i+h0OqbPf65T8wuJHw8JTMQ9k5ocx79eXwZAQ30dltb2zF/8ivItc+S4eUpZW3sXigpnkhR/zHDa6mvJ95oDAYPke3W1xJ86wPT5z+PbMwSA6fOe58O/P69sXlKcy4X4ozz3q/8q080PDZ9FWspZEuKiGDNpEQA6rZZJM5fj4n79/hcff8MkY1Nm/4T33lzO1SuJrabebnY8+nuOHviu6ydL3DYjExOWPPsGRflZfPnR7+kXOhpUKrat/w8z5r+Ao7MnDfV1fP3xq7h7BXbY8zUqYj7xcQd4aOEvcL6WhC/lQiwZaYkse+4tpRdl2OjZRExdDMCFc0fZv+PLdqe/b3Y8+ntKi/NY+tybGBubKDmbzp6MJDc7jaXPvYVapWbj6r9x8vhOhoXPAiA/5wqPPvkqRkbGfLvqz1xMPEHfkFFAUzLAJSvexNzCii1r3+VsbCTDx87psM6evQfh5uGvTAHf2FDP/MdfwVRjhk6nY+Pqv5F8/gS9+g8ndMhEGupriZi6BGgaqmpWVVnG9g3/ZeGTr+Ls5s35s4fZsvbfPPWzvwNQmJ/JlDnPMHnWck7H7OVQ5FoeWfa7rr254gdNAhNxz3j79VMSddXWVHI6Zg8bvnybJSv+gq2dMxcTjnPy2E5Ki3Opr69Fp9O16onoKPleaXEeWm0jnl5BynpzCyscHK9nQc7LvgJ6Pf979xcG9WobGzGzsFJeGxkZ4+zmbVCmqrKMw/vXk3E5garKMvR6HQ0N9VSUFrV7zJLE7/7pGxIOgKOLJ2q1EVWVpdTV1VCYn8nWde8p5erraijKz7ylIbmAXgMNhnauXDpH3Ind1NfVoNfrO9VDlpocR8TUJUpOJQtLGwDSUxMIGRihLA8ZPJ4zsZFKYBLUdygmJqYAuHkGGKRz8AsagPm169nDO1DJYHyzOlvS6/Uc3PMNmelJAFRXleHq7kuva7mE2pOTeQkXNx/l96dfaDiR2z6lsqIEAAcnd6WHxNMriNgj2256jsSPiwQm4p4xMdFg7+imvHb18OfdN57k3Mn9BPQayJb17xEesQDfwBA0ZhYknTtK7NHtBnV0mHyPm88VqNfrUKnVLF3xFuob7gcwaZHzx9jEFJXKMHvyzo0fUl1dzvjpy7C1dcLI2ISvP34Vrbax3f39WJP4dQfGxqbKz2q1Gp1OC3o95hbWPPHCX9vcRqVWK9cTQGNjfYf7MDW9nsSxvLSQff+/vTsNjqJM4wD+n8xM5s455JgwuQnIESTJCnghAhICKCh4lYBSiiirbpVuKWVZ5dZalrrW1lYhSq27QAR3sYQVJQQjgXBI5IigQQjhDoFAApEcc2dmej9Mpp0h3MhMT/j/qvIh3U/efvvJzOTpN2+/XV6KGc+/g7iEZLScacAXS9657v4LggBc8BoMfKJ34OtKPL+L7ZNFQejed6U2A+2qXgu7rRMznn8HCmU0Nq777Ir5uNQx/EcCAHnA70V2Qb+JAK5jQmEkAyCTRaGry4WTJ+oRG2vEyPumIjUtBwmJqWhvP3dN7cUnpCBKLkdT4yFxm8NuwfnW0+L3yamZELxe2KztiE9MCfryT8y9lMaGAygcUYycvGEwJpshVyhgt3VeUx8pvBKMJiiVqqBb18+3noHdZgEAxMUno+mk7/VzcN9OdLmcYpxKpYXTYbtk206HDVFyBXT6OAiCgN07Kq6qT7n9C1FTXQ5391wk/whgZu4Q/LJnMzxuN7weD2p/rEJGzuDLNXVFl2szWqWF0/HbCI/DboXOEAeFMhpWSxvqf9ku7lNdEBsoLT0PLaePo7XlFADfv7QMMQlXfH8R+XHEhELG4+mCpbMNAOC0W31D3i4HcgcUwOmwo6O9FXW11Ujpm42j9XtwaP+ua2o/WqVGfsFobKr4HBqtHlp9HLZWrgi6ekswmjAw/26Ur/oYo4tnIMmUCbu1EyeO/gJjSjpy8oZdsv34hBTs+2krUkw5cDpt2FTxORTK6EvG38rCeVvv5UTJ5Xj4qT9jY/ln2LWtDF6vF1qdAZOmvwQAGFMyE+vXLIFOH4P0rEHQaA3izxaMKMa6rz6BUqnChIdf6NF2n5R0DBg0HIsXvAZDnBGZOUOuqk/D73kIWypXoPTjNyCXK6A3xGPazDcwtGgs2n5tRuknbwAAzFkDUThiwg2d/+XaHDT0HpT/7xPU79uOYcPHo3BkMb5e8Q8sXfg69DEJyAg4n34D/4B9/9mKpQtfFye/+ml1MZg4bR7WrFwgTn598LE/3VC/6dbCZ+VQSFy4wFq0SoMEownD73kQ/QcNB+C7o2bv7k1wu7uQkzcMJnM/bKtaiVfeXAzgt9uFA4fha6rLUfNDOea++hEA3wTY79b8C4f270K0So2iuybhaP1uJKVmYkyJb+Ktx+PGD5u+wr6ftqCz81doNAaYzP1w9/2+pyT7bxf2H9evuekYKr75FGebGxETm4h7xz6OqorlKBpZgqI7S64qD/7neBy9fQwWPfLc9SeUiC6Kz8qJfCxMiEKIH5pENxffY5GPc0yIiIhIMliYEBERkWSwMCEiIiLJYGFCREREksHChIiIiCSDhQkRERFJBgsTIiIikgwWJkRERCQZXJKeKIT86xlezVNnieja+d9bXDs0crEwIQohu933sLhFH84Lc0+IejeXywG1RhfubtB1YGFCFEJqte+D8vlXP4Kay2VfN6fTjkUfzsPc1xZCpdKEuzsRqbfmUBAEuFwO6A3x4e4KXScWJkQhFBXlm9alVmv5HI/fgUqlYR5vUG/MIUdKIhsnvxIREZFksDAhIiIiyWBhQhRCcoUSd45+BHKFMtxdiWjM441jDkmqZALvqSIiIiKJ4IgJERERSQYLEyIiIpIMFiZEREQkGSxMiIiISDK4wBpRiOzZ8R12fr8GFksbjEl9cf+EmTBn3hbuboXF9s2rcbBuJ1rPNkGpjIbJnIdRDzyJxD4mMUYQBGyrWomfazbCabcgtW8uxk2aDWOyWYxxu7uw6dvlqNtbDXeXC+nZg/HA5NkwxCaKMQ67BZVrl+LwgR8BALkDCjF24jO9bhGu7ZtXY0vlChSOnIAxJbMAMIcUmThiQhQCdXursWFdKUaMmoqnX3gPfTMGYOWy99DRdi7cXQuLxuN1GHbHA5gx5694dNab8Ho9+LL0XbhcDjFm59ZvUFNdjnETn8GMue9Cp4/DF6XvBj0AcWN5KQ7W7cLkR1/Gk8++jS6XA6uWfwCv1yvGrPlyAVpON2D6zPmYPnM+Wk43YO2qhSE935vt9Mkj+LlmA/okpwdtZw4pErEwIQqBmuq1yC8YjaFF9yMxKQ1jSmbBEJOIPTvXh7trYTF91nwMKbgPxmQzklIzUPLwC+hoP4fmpmMAfFf6NT+sw8h7pyBv0B3ok2xGySMvwt3lRF3tNgCA02FD7e4qjC5+Cpk5Q5BsysLEafNwtvkEGo7sBQC0tpzCsUM/o3jKHKSl5yEtPQ/FU+bgSP1utJ5tCtv5/55cTgfKVi7A+ClzgkYwmEOKVCxMiG4yj9uNM03HkJmbH7Q9KzcfpxoPhqlX0uJ02AAAao0eANB+vgVWS1tQzhQKJcyZt+HUCV/OzjQdhdfjQVZAjCEmAcYksxhzqvEgVGotTOZ+YozJ3A8qtRZNvST368sWIztvGDJzhgRtZw4pUrEwIbrJbLYOCF4vdPrYoO1afSysnW3h6ZSECIKAjeuWoW9Gf/TpnvtgtbQB8OUokFYfK+6zdrZBLleIxYyfLjDG0gatLqbHMbW6mF6R+7raajQ3HcWocU/02MccUqRiYUIUMrLgbwVAJpNdPPQWUlm2BGebGzB5+ss99vXIjwDILswjeoQAQT/XM14QLoyJPB3t57ChvBSTpr0EhTL6knHMIUUa3pVDdJNptTGQRUWJV6B+Nmt7j6vZW01l2RIcPlCDJ559O+guEJ0+DoDvil5viBe3B+ZMZ4iDx+OGw24JuuK3WdqRZs4T27FZ23sc127r6DGCFWmaTx2DzdqO0kXzxW2C14vGhgPYvaMCz77ydwDMIUUejpgQ3WRyhQIppiwc755M6Hf8yF7xw/9WIwgC1pctxsH9O/HY7LcQF58UtD82Pgk6fVxQzjxuNxqP1yEt3ZezFFM2ouRyHD/8W4yl8zzOtTSKMWnmPDgdNpw+eViMaWo8BKfDBlOE5z49ZzCe+ePf8PSL74tfKWnZGJh/F55+8X3ExSczhxSROGJCFAJFd07E2lULkWLKRpo5Dz/VVKKj/Rxuv2NsuLsWFuvLFqOudhumPvkaoqM1sHTPVVCptVAqoyGTyVA0cgK2b1mN+MQUxCemYvvmr6BQqnBb/l1ibH7BaFR9uwwarR5qjR5VFcvRJzkdGd0TQROT0pDVbyi+Xf1PjH/oOQBAxdefIqd/QdCaKZFIpdKIc3L8lEoVNFqDuJ05pEjEpwsThcieHd9hx/ffwNrZBmOy+ZZeYO2Dtx6/6PYJU+diSMF9AAIWB9u1AQ6HVVwcLPCPsbvLhU0Vn2N/7Ta43S5kZA/GuMmzERNrFGPsNgs2rF2Kw/Xdi4P1L8TYSb1zcbD//vsvSErN7LnAGnNIEYSFCREREUkG55gQERGRZLAwISIiIslgYUJERESSwcKEiIiIJIOFCREREUkGCxMiIiKSDBYmREREJBksTIiIiEgyWJgQERGRZLAwISIiIslgYUJERESSwcKEiIiIJOP/SItB818aEyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 280x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart\n",
    "plt.figure(figsize=(2.8, 3))\n",
    "plt.barh(names_count_df.index, \n",
    "         names_count_df['frequency'],\n",
    "         color=color_4,\n",
    "         label='negative connotation')\n",
    "plt.barh(names_count_df[names_count_df.index.isin(neutral_names)].index, \n",
    "         names_count_df.loc[names_count_df.index.isin(neutral_names), 'frequency'],\n",
    "         color=color_2,\n",
    "         label='neutral connotation')\n",
    "plt.legend(loc = 'lower right', frameon=False, fontsize=8)\n",
    "plt.title('Frequency of terms related to\\nUkrainians in News since the SMO', loc='left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48ad8f5e",
   "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>frequency</th>\n",
       "      <th>connotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bandera</th>\n",
       "      <td>42</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chastener</th>\n",
       "      <td>70</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fascist</th>\n",
       "      <td>113</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed formations of Ukraine</th>\n",
       "      <td>210</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saboteur</th>\n",
       "      <td>406</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukrainian servicemen</th>\n",
       "      <td>650</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enforcer</th>\n",
       "      <td>684</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mercenary</th>\n",
       "      <td>883</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criminal</th>\n",
       "      <td>886</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enemy</th>\n",
       "      <td>913</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radical</th>\n",
       "      <td>971</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nazi</th>\n",
       "      <td>1458</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adversary</th>\n",
       "      <td>2314</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Militant</th>\n",
       "      <td>2650</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed forces of Ukraine</th>\n",
       "      <td>4987</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationalist</th>\n",
       "      <td>5064</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             frequency connotation\n",
       "Bandera                             42    negative\n",
       "Chastener                           70    negative\n",
       "Fascist                            113    negative\n",
       "Armed formations of Ukraine        210     neutral\n",
       "Saboteur                           406    negative\n",
       "Ukrainian servicemen               650     neutral\n",
       "Enforcer                           684     neutral\n",
       "Mercenary                          883    negative\n",
       "Criminal                           886    negative\n",
       "Enemy                              913     neutral\n",
       "Radical                            971    negative\n",
       "Nazi                              1458    negative\n",
       "Adversary                         2314     neutral\n",
       "Militant                          2650    negative\n",
       "Armed forces of Ukraine           4987     neutral\n",
       "Nationalist                       5064    negative"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_count_df['connotation'] = np.where(names_count_df.index.isin(neutral_names), \n",
    "                                         'neutral',\n",
    "                                         'negative')\n",
    "names_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c49d9021",
   "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>frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>connotation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>negative</th>\n",
       "      <td>12543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>9758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency\n",
       "connotation           \n",
       "negative         12543\n",
       "neutral           9758"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connotation = names_count_df.groupby('connotation')[['frequency']].sum()\n",
    "connotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bf779ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pie chart\n",
    "fig1, ax1 = plt.subplots(figsize=(2, 2))\n",
    "ax1.pie(connotation['frequency'],  \n",
    "        autopct='%1.1f%%',\n",
    "        colors = [color_4, color_2],\n",
    "        startangle=90)\n",
    "ax1.axis('equal'); "
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}