christopherkullenberg/coronatroll.ipynb

## coronatroll.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "from nltk import TweetTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize\n",
    "from collections import Counter\n",
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET DATA: http://genuskollen.se/files/coronatroll.xlsx\n",
    "\n",
    "# Read data\n",
    "df = pd.read_excel(\"coronatroll.xlsx\")\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_str</th>\n",
       "      <th>from_user</th>\n",
       "      <th>text</th>\n",
       "      <th>created_at</th>\n",
       "      <th>time</th>\n",
       "      <th>geo_coordinates</th>\n",
       "      <th>user_lang</th>\n",
       "      <th>in_reply_to_user_id_str</th>\n",
       "      <th>in_reply_to_screen_name</th>\n",
       "      <th>from_user_id_str</th>\n",
       "      <th>in_reply_to_status_id_str</th>\n",
       "      <th>source</th>\n",
       "      <th>profile_image_url</th>\n",
       "      <th>user_followers_count</th>\n",
       "      <th>user_friends_count</th>\n",
       "      <th>user_location</th>\n",
       "      <th>status_url</th>\n",
       "      <th>entities_str</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-14 15:11:36</th>\n",
       "      <td>1238845239617519618</td>\n",
       "      <td>Ks47656475</td>\n",
       "      <td>@Isavonschedvin1 MSB har såklart inte förberet...</td>\n",
       "      <td>Sat Mar 14 15:11:36 +0000 2020</td>\n",
       "      <td>2020-03-14 15:11:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.005081e+18</td>\n",
       "      <td>Isavonschedvin1</td>\n",
       "      <td>1224242710221152259</td>\n",
       "      <td>1.238765e+18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1227576564...</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://twitter.com/Ks47656475/statuses/1238845...</td>\n",
       "      <td>{\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14 09:34:12</th>\n",
       "      <td>1238760329745780736</td>\n",
       "      <td>Ks47656475</td>\n",
       "      <td>@Moftan @Folkhalsomynd @johanssonmorgan @SvD I...</td>\n",
       "      <td>Sat Mar 14 09:34:12 +0000 2020</td>\n",
       "      <td>2020-03-14 09:34:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.601968e+07</td>\n",
       "      <td>Moftan</td>\n",
       "      <td>1224242710221152259</td>\n",
       "      <td>1.238750e+18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1227576564...</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://twitter.com/Ks47656475/statuses/1238760...</td>\n",
       "      <td>{\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14 09:33:07</th>\n",
       "      <td>1238760056948002816</td>\n",
       "      <td>Ks47656475</td>\n",
       "      <td>@Folkhalsomynd @SvD Varför rekommenderar ni at...</td>\n",
       "      <td>Sat Mar 14 09:33:07 +0000 2020</td>\n",
       "      <td>2020-03-14 09:33:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.888583e+08</td>\n",
       "      <td>Folkhalsomynd</td>\n",
       "      <td>1224242710221152259</td>\n",
       "      <td>1.238735e+18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1227576564...</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://twitter.com/Ks47656475/statuses/1238760...</td>\n",
       "      <td>{\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14 09:30:53</th>\n",
       "      <td>1238759497457381376</td>\n",
       "      <td>Ks47656475</td>\n",
       "      <td>@alicemedce Anders Tegnell rekommenderar att s...</td>\n",
       "      <td>Sat Mar 14 09:30:53 +0000 2020</td>\n",
       "      <td>2020-03-14 09:30:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.731344e+08</td>\n",
       "      <td>alicemedce</td>\n",
       "      <td>1224242710221152259</td>\n",
       "      <td>1.238716e+18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1227576564...</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://twitter.com/Ks47656475/statuses/1238759...</td>\n",
       "      <td>{\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14 09:30:53</th>\n",
       "      <td>1238759497457381376</td>\n",
       "      <td>Ks47656475</td>\n",
       "      <td>@alicemedce Anders Tegnell rekommenderar att s...</td>\n",
       "      <td>Sat Mar 14 09:30:53 +0000 2020</td>\n",
       "      <td>2020-03-14 09:30:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.731344e+08</td>\n",
       "      <td>alicemedce</td>\n",
       "      <td>1224242710221152259</td>\n",
       "      <td>1.238716e+18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1227576564...</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://twitter.com/Ks47656475/statuses/1238759...</td>\n",
       "      <td>{\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  id_str   from_user  \\\n",
       "time                                                   \n",
       "2020-03-14 15:11:36  1238845239617519618  Ks47656475   \n",
       "2020-03-14 09:34:12  1238760329745780736  Ks47656475   \n",
       "2020-03-14 09:33:07  1238760056948002816  Ks47656475   \n",
       "2020-03-14 09:30:53  1238759497457381376  Ks47656475   \n",
       "2020-03-14 09:30:53  1238759497457381376  Ks47656475   \n",
       "\n",
       "                                                                  text  \\\n",
       "time                                                                     \n",
       "2020-03-14 15:11:36  @Isavonschedvin1 MSB har såklart inte förberet...   \n",
       "2020-03-14 09:34:12  @Moftan @Folkhalsomynd @johanssonmorgan @SvD I...   \n",
       "2020-03-14 09:33:07  @Folkhalsomynd @SvD Varför rekommenderar ni at...   \n",
       "2020-03-14 09:30:53  @alicemedce Anders Tegnell rekommenderar att s...   \n",
       "2020-03-14 09:30:53  @alicemedce Anders Tegnell rekommenderar att s...   \n",
       "\n",
       "                                         created_at                time  \\\n",
       "time                                                                      \n",
       "2020-03-14 15:11:36  Sat Mar 14 15:11:36 +0000 2020 2020-03-14 15:11:36   \n",
       "2020-03-14 09:34:12  Sat Mar 14 09:34:12 +0000 2020 2020-03-14 09:34:12   \n",
       "2020-03-14 09:33:07  Sat Mar 14 09:33:07 +0000 2020 2020-03-14 09:33:07   \n",
       "2020-03-14 09:30:53  Sat Mar 14 09:30:53 +0000 2020 2020-03-14 09:30:53   \n",
       "2020-03-14 09:30:53  Sat Mar 14 09:30:53 +0000 2020 2020-03-14 09:30:53   \n",
       "\n",
       "                     geo_coordinates  user_lang  in_reply_to_user_id_str  \\\n",
       "time                                                                       \n",
       "2020-03-14 15:11:36              NaN        NaN             1.005081e+18   \n",
       "2020-03-14 09:34:12              NaN        NaN             1.601968e+07   \n",
       "2020-03-14 09:33:07              NaN        NaN             3.888583e+08   \n",
       "2020-03-14 09:30:53              NaN        NaN             2.731344e+08   \n",
       "2020-03-14 09:30:53              NaN        NaN             2.731344e+08   \n",
       "\n",
       "                    in_reply_to_screen_name     from_user_id_str  \\\n",
       "time                                                               \n",
       "2020-03-14 15:11:36         Isavonschedvin1  1224242710221152259   \n",
       "2020-03-14 09:34:12                  Moftan  1224242710221152259   \n",
       "2020-03-14 09:33:07           Folkhalsomynd  1224242710221152259   \n",
       "2020-03-14 09:30:53              alicemedce  1224242710221152259   \n",
       "2020-03-14 09:30:53              alicemedce  1224242710221152259   \n",
       "\n",
       "                     in_reply_to_status_id_str  \\\n",
       "time                                             \n",
       "2020-03-14 15:11:36               1.238765e+18   \n",
       "2020-03-14 09:34:12               1.238750e+18   \n",
       "2020-03-14 09:33:07               1.238735e+18   \n",
       "2020-03-14 09:30:53               1.238716e+18   \n",
       "2020-03-14 09:30:53               1.238716e+18   \n",
       "\n",
       "                                                                source  \\\n",
       "time                                                                     \n",
       "2020-03-14 15:11:36  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2020-03-14 09:34:12  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2020-03-14 09:33:07  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2020-03-14 09:30:53  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2020-03-14 09:30:53  <a href=\"http://twitter.com/download/android\" ...   \n",
       "\n",
       "                                                     profile_image_url  \\\n",
       "time                                                                     \n",
       "2020-03-14 15:11:36  http://pbs.twimg.com/profile_images/1227576564...   \n",
       "2020-03-14 09:34:12  http://pbs.twimg.com/profile_images/1227576564...   \n",
       "2020-03-14 09:33:07  http://pbs.twimg.com/profile_images/1227576564...   \n",
       "2020-03-14 09:30:53  http://pbs.twimg.com/profile_images/1227576564...   \n",
       "2020-03-14 09:30:53  http://pbs.twimg.com/profile_images/1227576564...   \n",
       "\n",
       "                     user_followers_count  user_friends_count  user_location  \\\n",
       "time                                                                           \n",
       "2020-03-14 15:11:36                    26                  80            NaN   \n",
       "2020-03-14 09:34:12                    26                  80            NaN   \n",
       "2020-03-14 09:33:07                    26                  80            NaN   \n",
       "2020-03-14 09:30:53                    26                  80            NaN   \n",
       "2020-03-14 09:30:53                    26                  80            NaN   \n",
       "\n",
       "                                                            status_url  \\\n",
       "time                                                                     \n",
       "2020-03-14 15:11:36  http://twitter.com/Ks47656475/statuses/1238845...   \n",
       "2020-03-14 09:34:12  http://twitter.com/Ks47656475/statuses/1238760...   \n",
       "2020-03-14 09:33:07  http://twitter.com/Ks47656475/statuses/1238760...   \n",
       "2020-03-14 09:30:53  http://twitter.com/Ks47656475/statuses/1238759...   \n",
       "2020-03-14 09:30:53  http://twitter.com/Ks47656475/statuses/1238759...   \n",
       "\n",
       "                                                          entities_str  \n",
       "time                                                                    \n",
       "2020-03-14 15:11:36  {\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...  \n",
       "2020-03-14 09:34:12  {\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...  \n",
       "2020-03-14 09:33:07  {\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...  \n",
       "2020-03-14 09:30:53  {\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...  \n",
       "2020-03-14 09:30:53  {\"hashtags\":[],\"symbols\":[],\"user_mentions\":[{...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make index from timestamp with proper formatting\n",
    "pd.created_at = pd.to_datetime(df.time, \n",
    "                               format='%Y-%m%-%d%H:%M:%S')\n",
    "df.index = pd.created_at\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline \n",
    "\n",
    "This figure resamples the timestamps into day-for-day chunks to make it possible to plot the number of Twitter messages on a readable timeline.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tweets')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAHTCAYAAACeHQFJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeXxW9Zn//9cnO0kgQBKyscoiS4BEAVEU3FjiRrS1g5W6tB1bta2t09p2ps5vptpO+3Xq1ql1WtdR6lKtiaKAUBdEK4skLAFkkSUkJJCEJWRP7s/vj3OiIQYIkDvnvu+8n4/H/Qj3uc99znUvCec6n+tcH2OtRUREREREREJLmNcBiIiIiIiISNdTsiciIiIiIhKClOyJiIiIiIiEICV7IiIiIiIiIUjJnoiIiIiISAhSsiciIiIiIhKClOyJSJcyxthO3HadwfZ/Y4ypb3M/yRjzH8aYCR2s+2NjzDWnuy+R4zHGvGiM2eJ1HMHKGPOxMWZZB8sfcP9G/OI0t3uL+/zt7ZbPOcnfpKx268caY35ljNlhjGkwxpQZY143xoS1Wec3x9nWi8eJ7ZvGmLXGmDpjzEFjzHJjzOgTvJZ3O3ov3O/e8V5HYZv1Yk6w3nH3KyKhJcLrAEQk5Jzf7v5rwDrgP9osaziD7f8B+Fub+0nA/wdsB9a3W/fHwELg9TPYn4j4mTHGAI8A3wd+bK393WlsIwn4b6Csg4f/wZf/NgE8B0Ti/I1q3U4MsAxIBf4L+BQYAMwCTLvntwAXtltW0UFsDwK3Ab8F7gbigalA7HFey63A8RKyXwAPt1s2CniWjv/W/S/wTLtlu46zbREJMUr2RKRLWWs/bnvfGNMAVLRffgbbLwaKu2Jbp8MYE22tPZNkVQKMPtMzY4yJstY2nsHzDU5C8m3ge9baP5zmph4EPgTqgEltH7DWHgba/20aBYwA7rfW2jYP/Qwn0Rpnrd3XZvkrHe30ZH/bjDEXAz8Ccqy1i9s8tPA46ycBD+Akvn/pYH/bcU5utX3O1e4//6+DTe7tqr+/IhJ8VMYpIp4wxtxojPEZY5LbLPuDW2I0v82yq91lw937n5dxuqVIm91Vn2tTojTPGFMGpADfarP88TbbPdcYs9AYc8gtq1pujDnmzL9bLrXdGDPdLTurA355gtf0sTFmmRvzene7a9x9RbolauXGmEpjzJ+NMb3aPb+3MeZ3xpjdxphGt4TsHvdguHWdBGPMY8aYYre8rNwY87YxZkSbdX5sjNni7r/KGLPKGHNVm8evMMYsdkvTaowxG4wxP2hbouauF+/GWWWMqTbG/NUYM6P1PW637uXGmPeMMUfd25vGmDHt1rnKfY+OuOtsNsb87Hjvp/uc77r7u8D9vGqMMRXGmIeNMdGn8f61lvNdbYx5xhhTCew+SQxzjDHrjDH1xphtxphvHme9/zLGFLqv74D7XZjU5vHBxpgWY8x3Onjub9z3uPeJYungeR2Wk7rv8+I29zvzvYk0xtxrjNnqrrPXGPNbY0xUm3VGu+/ft40xDxlj9gH17b/LpxB/GPA08C3gtvaJnjEmwxizwBizz42p1DjllP3arXcp8BXgB6ew+5vcn58nSO535XbghXaJ3pm4A9jULtE7kQdxEtPXOrOy+x5+A/jQTQRFRD6nZE9EvPIuTknUJW2WXYpzVv7Sdsv2WGt3dLCNXUBr0vEfOGVa5wNLgSuAKpyyptblvwUwxkwFVgBxOAeZXwVqgHeMMePb7SMJp9Tr/4AcjnN2v42xwH3A/cA/AX2AfODPQALOAeZvgFuBn7c+yT2gXoZz0Pbfbvz/527n/jbb/x9gLvDvwEycA9NN7n4wxnwL+LX73Cvc7eUB/dts4yxgMXALcDWwwI3p39u9lmeA+TilbNfhJEXPtn/BxpjrgCU45Wtfd/eZDCw3xqS564zGKb/dAlzvvoZHgM4mNy8AG4Br3ffge+7zW2Po7PvX6nGgHrgBp7yuQ8a5lusN4BDO5/nvOCM/7Uv3wCn7ewC4BvgmcBhY4b52rLV7gDeBY5I9Y0wkzvdhgbW2+vhvwRk54ffG9TLwU5zP/Uqc9/F2nGSsvf8EBuKMxn0FaDZfXCP2eAfrdyQC57s3H7jZWvtEB+u8CGTjlD7OBH4IlAMxrSsYp+zycZwRuhMm7m2eY3C+Kx9Za7e1eWgkTsnmbvdkQLV70uRtY0xmB5sKd0+atBhjdhpj7m9/EgLnu7LeGPNvbtLa7J48yO0grstw/h6dStJ6CTCIDn43XT90T37UGGOWmnYntUQkxFlrddNNN938dsNJyJ4/zmOfAn90/50KWOB3wM426xQCz7S5/xugvs390e7z5new/TLgiQ6Wf4hzjU5Em2WRwA7gxTbLXnS3PbuTr/VjnARiUJtlX3O3sbDdum8Bm9vc/2fAB5zXbr37cBLgvu797cCvTxDDEzgHsJ39fAzOQfd9QHmb5RPcuH/Qbv0/ucvnuffDcMpq32q3Xn+cBOk37v357uuLPsXvz3fd/T3cwfvSBAw9xfdvjru9Fzq5/1eBfUBMm2XDgWZgywmeF+5+p3YBv22zvHX/kzv4jmSdxu/Xix3F4X4XF7e5f7LvzUw3hq+1W/4td/mYdr9vX/qOAdHu+/KHTv6uWPf2ryf4bjbijPidaFv34ySukW3ek+0nec4l7r5va7f8Ynf5EZwTIjk4yewmoBJIa7PurTjXBc/EuZ7vIfc7ubDNOmHu9/KI+xn8k7tunrt8Tpt1Y4BtwC/a3Let90/wWp4FaoGEdsujcBL3r+EknDcBG9339IJT/a7ppptuwXnTyJ6IeOkdvhjFuxQnOfsDMNQYM8wYk4iTdLzbVTs0xvTBGeV7yb0fYYyJwDmoegeY3u4ptdbaJaewiyLrXFfYqrXErv02tuCcjW81B9gKfNIakxvX2zgHfVPc9VYDtxljfmqMOce0K710Hz/PLbG7tKPyOmPMQGPMk8aYPTgHp004TR8GGGP6uqtNdX/+td3T249sjsMZ4Xm+XdxH3Fha38+1OAe3fzXGXGec65JOxcvt7r+Ik6S2lkl29v1r1akSOZzvyhvW2s87wFpnlHl1+xXdcs/lbmloM85B9RDg7DarLcE5qdB2dO87wCprbSH+c7LvzRyc0e38Dt4/gIvarZ/XfgfW2gZrbYS19s5OxlSEM1r8I2PMuA62Z4FPgH81xnyvo3WMMWOBnwB3WGubOrlfgJtxTsy81G556/tyGJhrrV1krX0VZwS8D87Jh9b4nrbW/re1dqm19m1r7Y9wfo+uNMa0Hfk1OA1Z5lprX7LWvo0zeredNqP77nN9wP/r7IswxsThJKN51rku8XPW2kZr7S3W2pettSustf+Hk/RVcIJydBEJLUr2RMRL7wKjjDHpOGfa37PWfoZzAHgJzll2g5OEdZVkd5u/4otEp/X2bSCx3foddfY7kYPt7jeeYHlMm/sDcJKC9jEtdx9vjes7wFPuz0+AcuNcC9i6rT/jlIBdhFPWWGWca+0GgpPc4pQSXo5TincxMBmn/JA2MaW5P/e3i7u83f0B7s8FHcR+eWvc1tpNOKMkMThNJ8qNMR8aY6bROe3323o/o00cnXn/Wp30eiy31C+lg31/KR63NG4hzujPrTjJ8mScpP7zz9lNYB4H5hlj+hinScgl7jJ/Otn3ZgBOWXM9x75/e9zHT/n964QynJM8DcDfjTFnd7DOtTgjbP8GbHSvI/y5+9mAU576JlBojOnrnqyIBMLc+zHtN3iiBAnn8wNYbts07XET/M9wSkpP5AX352T3eT6cEe591tqiNttrxvn7l+3GNAK4x32dse7rSHBXj3FfS0fHbF/B+dyOV8J5DGvtIZz3c3Jn1heR4KdunCLipfdwRtQudW+/cZe3jvgdBna0Gyk7U1Xuz9/hjA61Z09y318qccpa5x/n8c8ArLVHcA4K7zHGDMMp0foVThnX/+ceXP4B+IM7MjoH57UuAGYAY3BGS6+31n4+SmeMub7d/loP5gdw7IF9SgdxA/wLXyRWbbUdEVsKLHUPwC90437LGDO4g4Pu9lJwRsTax1HSJo6Tvn9tnPRztdZaY0w5X37NdLDsq8BR4KvW2pbWhcaY/ny5AcxTOOWl3wCG4XzP248wdVY9Trlee4k4SQZw8u8NzvtXzbHXy7ZV0u5+l/xeWGs/c5urvI+T8M2wba7PtdaW4YymfdcdxbsV55rUMpxrCcfifBbXdrD5gzjX6bZvAnQdzkhbRwnSVpwk93h8nXldHPv+FOF8zifa3gicJLX9SDo4CeC/4fzutm/GczNQinNi51R01981EfGYkj0R8Yy1dr8xpginUchZfDGC9w7OQdphTl7C2Xr2vaNugA3tl1trDxpjVuIkPD9xR1oCwWKcxOyg7bgZzZdYa3cCvzXG3Ax8qXmEtbYSWOCOnv2Tu7h1Xq/PD2jdhhI3tHt6a6v264FH2yxvnxRuwDnYHGOtfbCTcdcDy9xE6CVgsLudE/ka8FGb+/NwSiVbyylP+f3rpH8AVxtjYlpLOY3TGXYyx7a/j3Xj+fz7ZIy5gi9GPj9nra0yxryE06UxBXjOWlt7mvHtBjKMMX3dURuM0wV1GMcmx23339H3ZjFwF841lR+eZiynxVq71RhzOc7v+jvGmOm2g0Yr7ujwT4wxd/BF3Nfx5WT333FGeW/ki5HJtm7GOYGxtIN91BljlgAz2n3mI3Cu1exoaoO2bnR/rmqz7DXgAWPMeGvtBnd7EcBlfPH9XcWxzapwX9cSnJMDz7V/LcaYQe5zHmh7guFEjNPFNKddfCISwpTsiYjX3sWZT6ptx813gXT3dt9Jnr8X5/qwG40xn+KMVOyw1h7EaapwiXvQvR/Yb52OiD/ESSjfMsY8gzNKkIxz/VeTtfbeLnx9nfU0zkHou8aY3+E0UojGOeN/DU6TmBZjzBqc69eKcK6xuhynacYjAO7rOYCTrB1wH5vHF9cMrsdJzv5fm7Kwf+GLclMArLXrjTGv4iQF0TiNcmbjNJcAd0TCjel7ONfixeI0NKnEabgzDdhqrf0fY8wPcBKkxTifWTLwrzgHsF+aOqAD1xpn6ot3gAtwrnX6c5ukoFPvXyf20959OAfkS9ztxuGUv7Yv712MMwL1pDHmeZxRmH/j+OWOjwEr3X9/qYTTGPNd4I/A+fbEc6S9jHOt1/PGmEdxksef0W5i75N9b6y1i40xf8O5Zu9BYI371GE4nTm/31EC1m4f0e62//cUrtvD3X+RMWYmzuf7d2PMdJwJy/Nxyn4/de9/FecEzlL3eR+135b73g221r7XwWMDcRKk353g+3AvTpL/hjHmEZxE/pc4pbuPu9uJweno+xzOaKDBOdlwB055aNu4Hsf5buQbY36BM+J6B871nLe6r6MKp9KhbaytJag7O3otOCPDhuOUcBpj/g3nuuD3cb6Hw3BGd/u5r1FEegKvOsPopptuPePGCbpxuo9fizMa8ky75Z+6y9PaLT+mG6e77HqchKGJYztFjsfpvFnrLn+8zXPG45RMHcAZASzGOQM/q806J+3q1y6Oj4Fl7ZZ12C3UfR3N7ZbF4nQW3OrGVImTEPw7YNx1HsRJvA7jlA2uA25vs41v45RTHsAp8fsM53q8+DbrTMI5mK11X/e9OAefFkhts15vnO6eh3BK/P4G5NJBh1KcawQX4ZTO1QM7cQ7Sp7R5/A2cRK8BJ+F8ARhxkve0tRtn6zVxNe778gjtOnt28v1r7YZ54Sl8rjk4SXIDzmjerXTQBRMnad6N0/1zJU7Z7DFdMdutvxv44DiP/QtOQn1WJ+L7Gs6JjTqcRjiXtN/vyb437jrh7n43uJ/hIaDA/a7Gn+j77D7W2j3y8U7E/KXflTbfzUM4v89DcK5B3eTG3Dox+vUn2fZxf29xThJYnAnTT7SNC3B+j2rd/b4CDGvzeARO8vyZu049zgmGn+N2BW23vYFuXIfcz+lD4JKTxHDCbpzue7T6BM+/Dmc0vBLnb2MFzt+4czr73ddNN92C/9b6n5+IiMhJuSMT/wmkW2s7alzS1ftrHeEaZK3d6+/9dRfjzOe4HidpWtDB438DsNZe192xiYhI6FAZp4iIdMgYcy1OGeR6d9EMnMmtn+uORC8UuddZjcBpjrKbDhpyuJ0mL8IpmxURETltSvZEROR4qnFKZH+BUyJZDPw3mqPrTNyJMy/cFuDr1trG9itYp+QmubsDExGR0KMyThERERERkRCkSdVFRERERERCkJI9ERERERGREBQU1+wlJSXZoUOHeh2GiIiIiIiIJz755JMKa+0pXdMdFMne0KFDWbNmzclXFBERERERCUHGmN2n+hyVcYqIiIiIiIQgJXsiIiIiIiIhSMmeiIiIiIhICFKyJyIiIiIiEoL8muwZY/oaY14xxmwxxmw2xpxvjOlvjFlqjNnm/uznzxhERERERER6In+P7D0CLLbWjgYmApuBnwF/t9aOBP7u3hcREREREZEu5LdkzxiTAEwHngSw1jZaaw8Bc4Fn3dWeBXL9FYOIiIiIiEhP5c+RvWHAAeBpY0yBMeYJY0wckGKt3eeuUwak+DEGERERERGRHsmfyV4EcA7wR2ttNlBDu5JNa60FbEdPNsbcZoxZY4xZc+DAAT+GKSIiIiIiEnr8meztBfZaa1e691/BSf7KjTFpAO7P/R092Vr7J2vtJGvtpOTkZD+GKSIiIiIiEnr8luxZa8uAYmPM2e6iy4BNwOvAze6ym4F8f8UgIiIiIiLSU0X4efvfBxYYY6KAz4BbcRLMl40x3wJ2A1/zcwwiIiIiIiI9jl+TPWttITCpg4cu8+d+RUREREREejp/z7MnIiIiIiIiHlCyJ2ekxWdZsa0Cp7GqiIiIiIgECiV7ckb+uqaY+U+u5INtFV6HIiIiIiIibSjZkzPyWkEJAIs27vM4EhERERERaUvJnpy2kkN1rNxZRVR4GG8XldPiUymniIiIiEigULInp+31wlIAfjRzFJU1jazaWeVxRCIiIiIi0krJnpy2/MISzhncl5vOH0J0RBiLVcopIiIiIhIwlOzJadm87whbyqrJzc4gLjqC6aOSWVJUjk+lnCIiIiIiAUHJnpyWvMISIsIMV45PAyAnM5WyI/UU7j3kcWQiIiIiIgJK9uQ0+HyW1wtLmT4qmcT4aAAuG5NCZLhh8cYyj6MTERERERFQsienYeXOKvYdrmduVvrnyxJ6RXLB8CQWbdynCdZFRERERAKAkj05ZfmFJcRFhTNrbOoxy3MyUymuqqOo9IhHkYmIiIiISCsle3JK6ptaeHPDPmaPS6VXVPgxj80cm0KYQaWcIiIiIiIBQMmenJL3Pt1PdX0zc7MzvvRYYnw0U4b1Z3GRkj0REREREa8p2ZNTkldQSlJ8FNOGJ3b4eE5mGtv3H2X7/upujkxERERERNpSsieddriuiXe27OfqielEhHf81Zk9zrmOb9EGje6JiIiIiHhJyZ502qIN+2hs8ZGb9eUSzlapCTGcM7gvi3TdnoiIiIiIp5TsSaflFZYwLCmOCQMTTrheTmYam/YdYU9lbTdFJiIiIiIi7SnZk04pPVTHyp1V5GZlYIw54bpzMt1Szo37uiM0ERERERHpgJI96ZTX15ViLcdMpH48g/rHMi69j7pyioiIiIh4SMmedEpeQQlZg/oyNCmuU+vnZKZSsOcQ+w7X+TkyERERERHpiJI9OaktZUfYUlbNtR3MrXc8czLTAFiiRi0iIiIiIp5QsicnlVdQSniY4coJaZ1+zogB8YwcEK+unCIiIiIiHlGyJyfk81leLyzhopFJJMVHn9JzczJTWb2rioqjDX6KTkREREREjkfJnpzQ6l1VlB6uP6USzlZzMtPwWXi7qNwPkYmIiIiIyIko2ZMTyissJTYqnJljU075uWPSejO4f6y6coqIiIiIeEDJnhxXQ3MLb64vZdbYFGKjIk75+cYYcjJT+Wh7BYdrm/wQoYiIiIiIHI+SPTmu9z49wJH6ZnJPo4Sz1ZzMVJp9lmWbVcopIiIiItKdlOzJceUXlpAUH8WFI5JOexsTB/YlLSFGXTlFRERERLqZkj3p0JH6JpZt3s9VE9KJCD/9r0lYmGH2uFSWbzvA0YbmLoxQRERERERORMmedGjxhjIam31nVMLZKiczlcZmH+9u2d8FkYmIiIiISGco2ZMO5RWWMDQxlokDE854W5OG9icpPkpdOUVEREREupGSPfmSssP1/OOzSuZmZWCMOePthYcZZo5N5d0t+6lvaumCCEVERERE5GSU7MmXvL6uBGvpkhLOVjmZqdQ2trB864Eu26aIiIiIiByfkj35kryCUiYO6suwpLgu2+b5wxNJ6BXJYnXlFBERERHpFkr25Bhby6vZtO8IuVnpXbrdyPAwLh+TwtLN5TQ2+7p02yIiIiIi8mVK9uQYeQUlhIcZrprQtckeOKWc1fXN/OOzyi7ftoiIiIiIHEvJnnzO57PkF5Zy4YgkkntHd/n2LxyZRFxUOIs37uvybYuIiIiIyLGU7Mnn1uw+SMmhOnKzu35UDyAmMpxLRg/g7aJyWnzWL/sQERERERGHkj35XF5hCb0iw5k1NtVv+8jJTKOyppFVO6v8tg8REREREVGyJ67GZh9vrt/HrHEpxEVH+G0/F5+dTHREmEo5RURERET8TMmeAPDep/s5XNdEblbXza3XkbjoCGaMSmZxURk+lXKKiIiIiPiNkj0BIL+wlP5xUVw4Msnv+8oZn0r5kQYK9x7y+75ERERERHoqJXvCkfomlm0u5+oJaUSG+/8rcenoFCLDjSZYFxERERHxIyV7wuKNZTQ0+5ib7d8SzlYJvSK5YHgSizbuw1qVcoqIiIiI+IOSPSG/sIQhibFkD+rbbfvMyUyluKqOotIj3bZPEREREZGeRMleD1d+pJ6PdlQyNysDY0y37Xfm2BTCDCrlFBERERHxEyV7PdzrhaVYC7lZ/plI/XgS46M5b1giizQFg4iIiIiIXyjZ6+HyCkuYMDCBs5Lju33fOeNT2XGghu37q7t93yIiIiIioU7JXg+2rbyaotIjfp9b73hmjU0FYNEGlXKKiIiIiHQ1JXs9WF5hCWEGrpqY5sn+UxNiOGdwXxbpuj0RERERkS6nZK+HstaSX1jKtBFJDOgd41kcOZlpbNp3hD2VtZ7FICIiIiISipTs9VCf7D7I3oN1XNtNc+sdz5xMt5RTjVpERERERLqUkr0e6rWCEmIiw5g1LtXTOAb1jyUzo49KOUVEREREupiSvR6osdnHmxv2MXNsKvHREV6HQ05mGoXFh9h3uM7rUERERER6lBaf5bl/7OKBJVu8DkX8QMleD7R86wEO1TZxbXb3zq13PLPd0cUlGt0TERER6Tbbyqu5/vGPuDe/iD+8u4Ot5ZoOK9Qo2euB8gpL6B8XxUUjk70OBYARA+IZOSBepZwiIiIi3aCx2cejf9/GlY+u4LOKGv7j6rGEhxnyCkq8Dk26mJK9Hqa6vomlm8q5cnwakeGB8/HnZKayelcVFUcbvA5FREREJGStKz7ENf+zggeXbmV2ZirL7p7BLdOGcdHIJPILS/H5rNchShcKnKN96RZLisppaPaR63EXzvbmZKbhs/B2UbnXoYiIiIiEnLrGFn715iaufexDDtY28uebJvH7G7JJio8GIDcrg5JDdazZfdDjSKUrKdnrYfILSxjcP5ZzBvf1OpRjjEnrzZDEWBYXqZRTREREpCt9tL2C2Q8v588f7GTelMEsvXsGM8emHLPOzLEp9IoMJ69QpZyhRMleD7L/SD0fbq9gblY6xhivwzmGMYY5mal8tL2Cw7VNXocjIiIiEvQO1zXxs1fX8/UnVhJm4MXbpvLra8fTJybyS+vGRUcwa1wKb67fR2Ozz4NoxR+U7PUgr68rxWdhblZglXC2mjMulWafZdlmlXKKiIiInIklRWXMfPB9/vrJXr4z4ywW/3A6U89KPOFzcrMzOFzXxHuf7u+mKMXflOz1IPmFpYzPSGDEgHivQ+nQxIF9SUuIUVdOERERkdN0oLqBOxes5TvPfUJifDR5d0zj5zljiIkMP+lzLxqRRGJcFPmFpd0QqXQH72fUlm6xff9RNpQc5hdXjvE6lOMKCzPMHpfKX1bt4WhDc0BM+C4iIiISDKy1vLq2hPsWbqKuqYWfzD6b26afdUrd1yPCw7hqQhovri7mSH1Th+WeElw0stdD5BeWEGbgmomBMZH68eRkptLY7OPdLSofEBEREemM4qpabnpqFT/+6zpGDojnrR9cxJ2XjDitabZyszNoaPaxWJVWIUHJXg9grSWvsIRpI5IY0CfG63BOaNLQ/iTFR6krp4iIiMhJtPgsT3+4k9kPL2ft7oPcN3ccL3/n/DO6ZCdrUF+GJMaSr66cIcGvdXLGmF1ANdACNFtrJxlj+gMvAUOBXcDXrLWa0MOP1u45SHFVHXddNsrrUE4qPMwwa1wqeQUl1De1dKq+XERERKSn2VZezT2vrqdgzyEuPjuZX107noy+vc54u8YY5mZl8Pt3tlF+pJ6UAB8okBPrjpG9S6y1WdbaSe79nwF/t9aOBP7u3hc/yisoJSYyjNnjUk6+cgCYMy6V2sYWlm894HUoIiIiIgGlsdnHI8u2ceWjK9hVUcND/zSRp2+Z3CWJXqvcrHSshdfVqCXoeVHGORd41v33s0CuBzH0GE0tPhauL+XyMSn0DpKLbM8fnkhCr0jViouIiIi0sa74EFf/fgUPLdvK7MxUlt49g2uzB3b5/MlnJcczcWCCJlgPAf5O9izwtjHmE2PMbe6yFGvtPvffZUBwDDcFqeVbD3CwtoncAJ1bryOR4WFcPiaFpZvLNamnhLwWn+WBJVsoLD7kdSgiIhKg6hpb+NWbm7j2sQ85XNfEEzdN4vc3ZJMUH+23fc7NyqCo9Ajbyqv9tg/xP38nexdaa88BcoA7jTHT2z5orbU4CeGXGGNuM8asMcasOXBA5XynK6+wlL6xkUwflex1KKckJzOV6vpmPtpR4XUoIn71/tb9/OHdHdz81Cp2HDjqdTgiIhJgPtpeweyHl/PnD3Yyb8pg3r57OpeP9f9YydUT0+VcZ7AAACAASURBVAkPMxrdC3J+TfastSXuz/3Aa8AUoNwYkwbg/uywx7619k/W2knW2knJycGVqASKow3NLN1UxlUT0oiKCK7GqxeOTCIuKpwl6sopIe75j/eQFB9FZLjh1qdXU3G0weuQREQkAByua+Knr6zn60+sJMzAi7dN5dfXju+2ue+Se0czbUQS+YWlOOMzEoz8lgEYY+KMMb1b/w3MAjYCrwM3u6vdDOT7K4aebsnGMuqbfEFVwtkqJjKcS0YP4O2iclp8+gMjoam4qpZ3P93PDVMG88TNk9lfXc+3n11DXWOL16GJiIiHlhSVMfPB93ll7V6+O2M4i384nalnJXZ7HLlZ6ew9WMcnu9U4P1j5c7gnBVhhjFkHrALetNYuBn4DzDTGbAMud++LH+QVljCwXy/OHdLP61BOS05mGpU1jazaWeV1KCJ+8eLqPRjghimDyRrUl0fmZbNu7yF++FKBTnKIiPRA+6vruWPBJ3znuU9Iio8m/85p/CxntGdTUc0el0qvyHBeK1ApZ7DyW7Jnrf3MWjvRvY2z1v7KXV5prb3MWjvSWnu5tVZH8n6wv7qeD7dXkJuV0eUdmrrLxWcnEx0RxuKN+06+skiQaWz28dLqYi4dnUK62y579rhU7r1yLEuKyvn1W5s9jlBERLqLtZa/rilm5oPLWbZ5Pz+ZfTb535tGZkaCp3HFRUcwc2wKb27Yp6Z5QSq4LuSSTntj3T58FnKz070O5bTFRUcwY1Qyi4vK8GmUQ0LMkqIyKo42cuPUwccs/+aFw7h12lCeXLGTZz7c6VF0IiLSXYqrarnpqVX85JX1jEqJZ9FdF3HnJSOIDA+Mw/Tc7HQO1TZp/uMgFRjfIuly+YUljEvvw4gBvb0O5YzkjE+l/EgDBWpLLyHm+Y93M6h/L2aM/HIDql9cOZZZY1P4z4WbeFtNikREQlKLz/LUip3Memg5a3cf5L6543jptvMZnhzvdWjHuGhkMv3jonhNXTmDkpK9ELTjwFHW7z3MtdnB15ilvUtHpxAZbtSVU0LK9v3VrNxZxdenDCEs7Mtl1uFhhkfmZTMhI4EfvFjAOp3sEBEJKdvKq/nq4x/xy4WbOO+s/rx99wy+cf7QDv9P8FpkeBhXTUhj2aZyquubvA5HTpGSvRCUX1CCMc78KMEuoVckFwxPYtHGfWr7KyHj+Y/3EBlu+Nqkgcddp1dUOE/cPJnk3tF869nVFFfVdmOEIiLiD43NPh5Zto0rHv2AXRU1PPxPWTx9y2Qy3Gu3A9XcrAwamn0sKSr3OhQ5RUr2Qoy1lrzCUi4YnkhKnxivw+kSOZmpFFfVUVR6xOtQRM5YbWMzr67dS05mGonx0SdcN7l3NE/fMoWmFsutz6zmcK3OqIqIBKvC4kNc/fsVPLRsKzmZaSy7ewa52cHRSO+cwX0Z3D+WPHXlDDpK9kJMQfEh9lTVBuXcesczc2wKYQYWb1QppwS/N9aVUl3fzPypQzq1/ogB8fzpG+eyp7KW255bQ0Oz5uATEQkmtY3N3L9wE9c99iGH65p48uZJPHpD9klP+AUSYwy5Wel8tKOC/UfqvQ5HToGSvRCTV1BCdEQYczJTvQ6lyyTGR3PesEQWaQoGCQELVu5hVEo8k4d2fv7L885K5IHrJ7ByZxU/fWW9SppFRILEh9srmP3wcp5YsZMbpgxm6d3TuWxMitdhnZa52Rn4LLy+rtTrUOQUKNkLIU0tPhau38flY1LoHRPpdThdKmd8KjsO1LB9f7XXoYictvV7D7F+72HmTx1yymU7c7My+Mnss8krLOXBpVv9FKGIiHSFw7VN3PPKOm58YiURYWG8eNtUfnXt+KA+PhueHM+EgQnkqStnUFGyF0JWbKugqqaR3BDowtne7HHOSOWiDSrllOD1/Me76RUZftq/o3dcPJx5kwfx+3e28/Lq4i6OTkREusLijWVc/tD7vLq2hO/OGM6iuy5i6lmJXofVJeZmZbCx5Ajb9x/1OhTpJCV7IeS1ghL6xkYyY9SX5+0Kdil9YjhncF8W6bo9CVKHa5t4fV0pudnp9DnNM7vGGO7LzWT6qGT+9bUNfLBNE9yKiASK/dX13LHgE777/Cckx0eTf+c0fpYzmpjIcK9D6zJXT0wjzDjzOUtwULIXImoamlm6qZwrxqcRFRGaH2tOZhqb9h1hT6Va0Evw+VvBXuqbfNx4XucasxxPZHgYf/h6NiMGxHP782vZvE9dakVEvGSt5a9ripn54HKWbd7PPXPOJv9708jMSPA6tC43oHcM00YkkVdYouvHg0RoZgU90NubyqhragmJidSPp7XpjBq1SLCx1rJg5R6yBvXtkv/8e8dE8vStk4mPjuCbz6ym7LA6o4mIeKG4qpabnlrFT15Zz6iUeBbddRF3XDyCyPDQPcTOzcqguKqOtXsOeh2KdELofhN7mLyCUjL69uLcwZ3v8BdsBvWPJTOjj0o5Jeh8/FkV2/cf5cbzBnfZNtMSevHULZM5UtfErc+s5mhDc5dtW0RETqzFZ3lqxU5mPbSctbsPcl9uJi/ddj7Dk+O9Ds3vZmemEhMZRl6BunIGAyV7IeBAdQMfbDvA3Kx0wsICf2LOM5GTmUZh8SH2Ha7zOhSRTnt+5W4SekVy9cT0Lt3u2PQ+PDb/XLaWV3PngrU0t/i6dPsiIvJlW8ur+cofP+KXCzdx/vBElt49g29MHRLyx2Ct4qMjmDk2lYXrS2nS/zsBT8leCFi4vhSfJaRLOFu1lnIu0eieBIkD1Q0s2VjGV88d6JeL9GeMSuZXuZm8v/UA9+YX6RoKERE/aWz28fCyrVz56AfsrqzhkXlZPHnzJNL79vI6tG6Xm5XOwdomlm9Vo7BAp2QvBOQVljI2rQ8jU3p7HYrfDU+OZ+SAeJVyStB4eU0xzT7L17uwhLO9eVMGc+clw3lh1R7++P4Ov+1HRKSnKthzkKt/v4KHl23jivFpLLt7BnOzMk55ztRQMX1UMv1iI8krVClnoFOyF+R2VtSwrvgQudldWx4WyHIyU1m9q4qKow1ehyJyQi0+y19W7uGC4Yl+v47jX2aezTUT0/l/iz/l9XX6z1dEpCvUNjZz38JNXPfHjzhS38RTt0zikXnZJMZHex2apyLDw7hqQjpLN5XpmvEAp2QvyOUVlGAMXDMx9Es4W83JTMNn4e2icq9DETmh9z7dT8mhOuZPPbPpFjojLMzwwPUTmDK0Pz9+eR2rdlb5fZ8iIqHsw+0VzH54OU+u2MmN5w3m7R9N59LRKV6HFTBys9Opb/Lp0poAp2QviFlryS8s4fyzEklNiPE6nG4zJq03QxJjNQWDBLwFK/cwoHc0M8d2z8FBdEQ4f7rpXAb278Vtz61hx4Gj3bJfEZFQcri2iXteWceNT6wkIiyMl26byv254+kdE+l1aAHlnMH9GNS/F3maYD2gKdkLYoXFh9hVWUtuVs8Z1QMwxjAnM5V/7KjkcG2T1+GIdKi4qpZ3P93PvMmDunW+pb6xUTxzyxTCjeHWp1er3FlE5BQs3riPyx96n1fXlnD7xcNZdNdFnHdWotdhBSRjDHMnZvDh9gr2V2u+10ClZC+I5ReWEhURxpzxqV6H0u1yMtNo9lmWbVYppwSmF1btweA0T+lugxNjefKWyeyvrufbz66hrrGl22MQEQkm+6vruf35T/ju82sZ0Dua/Dun8dM5o/3SRTmU5Gan47PwxjpVWwUqJXtBqqnFxxvrSrl8zAD69MCyggkZCaQlxKgrpwSkxmYfL68p5tLRKZ615M4a1JdH5mWzbu8hfvhSAS0+TckgItKetZaX1xRz+e/e5+9b9vPTOaPJu3MamRkJXocWFEYM6E1mRh/yVcoZsJTsBakV2yuorGlkbg8r4WwVFmaYPS6V5dsOqAuUBJwlRWVUHG1k/tTuH9Vra/a4VO69cixLisr59VubPY1FRCTQ7Kms5RtPruKeV9YzOrUPi+66iNsvHt6tpfehIDcrg/V7D+s68QClb3OQyi8ooU9MBBefnex1KJ7JyUylsdnHu1v2ex2KyDGe/3g3g/vHMn2k97+f37xwGLdcMJQnV+zkmQ93eh2OiIjnWnyWJ1fsZPbDyyksPsT9uZm8eNtUv0+RE6qunphOmHGOTSXwKNkLQjUNzSwpKufKCelER/TcWvJJQ/uTFB/FYpVySgDZVl7Nyp1VfP28wYSFBcZku/deNZZZY1P45cJNLN2k61xFpOfaWl7NV/74Efct3MT5wxN5+0fTmT91SMD8vQ5GKX1iuGB4EnmFpVirSwYCjZK9ILR0Uzl1TS3kZvWcidQ7Eh5mmDUulXc/3U99kxpQSGBYsHIPUeFhXH/uQK9D+Vx4mOGRedmMz0jg+y+sZV3xIa9DEhHpVo3NPh5etpUrH/2APVW1PDIviydvnuTZddWhZm5WOnuqainQ/y8BR8leEMorLCE9IYbJQ/t7HYrncjJTqW1sYfnWA16HIkJtYzOvrt1LzvhUEuOjvQ7nGL2iwnni5skk947mW8+upriq1uuQRES6RcGeg1z1+w94eNk2rhifxtIfTWduVgbGaDSvq8zJTCU6Iow8lXIGHCV7QabiaAMfbKtgbnaGSg6AqWclktArUqWcEhDeWFdKdX0z86cO8TqUDiX3jubpW6bQ2Ozj1mdWa55KEQlptY3N3LdwE9f98SOq65t56pZJPDIvO+BOxoWC3jGRXD42hYXr99HU4vM6HGlDyV6QWbiulBaf7XETqR9PZHgYl49JYenmchqb9cdFvPX8x3s4O6U3k4b08zqU4xoxIJ4/3TSJPZW1fOf5NTQ0qwRaRELPim0VzH54OU+u2Mn884bw9o+mc+noFK/DCmm5WRlU1TSyYluF16FIG0r2gkxeYSmjU3tzdmpvr0MJGDmZqVTXN/PRDv1xEe+sKz7EhpLD3Dh1cMCXBk09K5EHrp/Ax59V8bNXN+iCehEJGYdrm/jJX9cx/8mVRIaF8fJ3zue+3Ex698A5ibvbjFHJ9I2N5DWVcgaUCK8DkM7bVVFDYfEhfp4z2utQAsqFI5OIiwpn8cYyLj57gNfhSA+1YOVuYqPCuTY7OEbd52ZlsPdgHQ8s+ZSB/XrxL7PO9jokEZEzsmjDPv799SKqahq54+Lh/OCykcRE9tyu5d0tKiKMK8en8be1JdQ0NBMXrTQjEGhkL4jkFZZgDFzTw7twthcTGc6lY1J4e1M5LT6NUEj3O1zbxOvrSpmblRFUZ4/vuHg48yYP4vfvbOfl1cVehyMiclr2H6nnu899wu0L1jKgdzT5d07jnjmjleh5IDc7g7qmFt7epF4KgULJXpCw1pJfWMp5w/qTlqA2we3lZKZSVdPIqp1VXociPdCra/dS3+TjxvMGex3KKTHGcF9uJheNTOJfX9vAB9vU1VZEgoe1lpdXF3P5g+/z7qf7+emc0eTfOY3MjASvQ+uxzh3cj4H9evFaQanXoYhLyV6QWL/3MDsraoKmRKy7zRiVTHREGIs37vM6FOlhrLUsWLmbrEF9g/IAIzI8jMduPIcRA+K5/fm1bCk74nVIIiIntaeylm88uYp7Xl3P6LQ+LLrrIm6/eDgR4Tq09VJYmGFuVjorth3gQHWD1+EISvaCxmsFJUSFhzEnM83rUAJSXHQEM0Yls7ioDJ9KOaUbffxZFTsO1ATsdAud0TsmkqdvnUx8dAS3Pr2assP1XockItKhFp/liQ8+Y/bDyyksPsT9uZm8+M9TOSs53uvQxJWblYHPwsL1Gt0LBEr2gkBzi4+F60u5dPQAEnoFz/VA3S1nfCrlRxooKD7kdSjSgzy/cjcJvSK5akJwn4hJS+jFU7dM5khdE7c+s5qjDc1ehyQicoxPy6r5yh8/4v43N3PB8ESW3j2d+VOHaN7hADMypTfj0vtogvUAoWQvCHy4o5KKo43kqoTzhC4dnUJkuGFJkS4Klu6xv7qeJRvLuP7cgSHRCGBseh8em38uW8uruXPBWpo1Ma6IBICG5hYeWrqVq37/AXuqann0hmyeuHmSehgEsNysDNa5lyCJt5TsBYG8ghL6xERwyehkr0MJaAm9Ipk2IolFG/dp3jDpFi+vLqbZZ/l6kDVmOZEZo5K5PzeT97ce4N78Iv0uiYin1u45yFWPruCRv2/jyvFpLLt7BtdMTA/4+Ux7uqsnpmMMGt0LAEr2AlxtYzNLisq4Ynwa0RHBP3LgbzmZqRRX1VFUqiYT4l8tPssLq4qZNiIx5K4VuWHKYO64eDgvrNrD4+9/5nU4ItID1TY288s3NvGVP35ETUMzT98ymYfnZdM/Lsrr0KQTUhNiuGB4InmFJTpp6DElewFu6aZyahtbVMLZSZePSSHMwOKNKuUU/3rv0/2UHKpj/nnB25jlRH4862yumZjObxdv4fV1usheRLrPim0VzHpoOU99uJNvTB3C23fP4JLRA7wOS07R3KwMdlfWUqheCp5Sshfg8gtLSU+IYcrQ/l6HEhQS46M5b1giizQFg/jZ8x/vZkDvaC4fm+J1KH4RFmZ44PoJTBnanx+/vE5zWIqI3x2ubeInf13H/CdXEhUexsvfOZ9fzs0kPjrC69DkNMzJTCUqIoz8Qp0w9JKSvQBWebSB97ce4OqsdHWaOgU541PZcaCGbeXVXociIaq4qpb3th5g3uRBRIbwnE7REeH86aZzGdi/F7c9t4YdB456HZKIhKhFG/Zx2YPv87eCEu64eDhv3XURU4bpRHcw6xMTycwxKbyxrpQmNfzyTOgepYSANzfso8VnNZH6KZo9LhVQKaf4z19W7cEA86aETmOW4+kbG8Uzt0wh3BhufXo1FUc1Sa6IdJ39R+r5znNruH3BWlITonn9e9O4Z87okOhwLDA3K53KmkZWbK/wOpQeS8leAMsrKGF0am9Gp/bxOpSgktInhnOH9GORkj3xg8ZmHy+vLuayMSmk9+0Zbb8HJ8byxM2T2F9dz7efXUN9U4vXIYlIkLPW8vLqYi5/8H3e+/QAP8sZTd4d0xiXnuB1aNKFLj7bmSM6X105PaNkL0Dtrqxh7Z5DzM3SqN7pyMlMZdO+I+yprPU6FAkxi4vKqKxpZP7U0GzMcjzZg/vxyLxs1u09xA9fLKTFp+5qInJ69lTWMv/Jldzz6npGp/Vh8Q+n890Zw4kI4bL4nioqIowrJ6SxpKicmoZmr8PpkfRbFaBaL2adm5XucSTBqbWUU41apKs9//FuBveP5aIRSV6H0u1mj0vl3ivHsriojF+/tdnrcEQkyLT4LE988BmzHn6fdcWH+dW1mbz4z1MZlhTndWjiR7lZGdQ1tbB0U7nXofRISvYCkLWWvMISzhvWv8eUiXW1Qf1jyczoo1JO6VJby6tZtbOKr583uMc2TfrmhcO45YKhPLliJ898uNPrcEQkSGwpO8J1f/yI+9/czLThSSy9ezo3njekx/4t7UkmDelHRt9e5BWqlNML6mUbgDaUHOazAzX880VneR1KUMvJTOOBJZ+y73AdaQlKmuXM/WXlHqLCw7j+3IFeh+Kpe68aS8mhOn65cBMZ/WKZGaLTT4jIqalpaGZXZQ27KmrZVVnDzooadlXUsKuyloqjDSTGRfHoDdlcPSENY5Tk9RRhYYa5Wen87/LPqDjaQFJ8tNch9ShK9gJQXkEpUeFhXJGZ5nUoQW1OZioPLPmUJRvLuGXaMK/DkSBX29jMq5/s5YrxqST28P+owsMMj87LZt6f/sH3X1jLS7edz8RBfb0OS0S6QW1j8+fJnJPYOcndzsoaDlQf2613QO9ohibFcdnoAZyVHMf1kwbRPy7Ko8jFS7nZGTz23g4WrivVMVk3U7IXYJpbfLyxvpRLRieTEBvpdThBbXhyPKNS4lmkZE+6wOuFpVQ3NHNjD2vMcjy9osJ54ubJXPfHD/nWs2t47Y4LGNQ/1uuwRKQL1DW2sLvKSeR2VtS6o3POrfzIsQldUnw0w5JiuXhUMkOT4hiWFMeQxFiGJsYRp8nQxTUqpTdj0vqQV6hkr7vptzDAfLSjkgPVDeSqC2eXmJOZxv+8s01lA3JGrLU8v3I3Z6f0ZtKQfl6HEzCSe0fz9C1TuO6xD7n1mdW8+t0LdJJKJEjUN7Wwu7L2i9G5z8suayk7Un/MuknxUQxNjOPCEckMS4plaFIcQxPjGJoUR7wSOumka7PT+fVbW9hZUaOmPN1Iv6EBJq+whN4xEVwyeoDXoYSEOeNSefTv23i7qJyvnxf6E2CLf6zfe5iNJUe4b+44XWfSzogB8fzppknc9OQqvvP8Gp795hSiIzQZskggqG9qobiq1kniKp1r53a519HtO1KPbTODSv+4KIYmxnLBiESGJcYxJCnO/RlLnxidxJEzd83EDP5r0RbyC0v44eWjvA6nx1CyF0DqGltYsrGMqyakExOpg6WuMCatN0MSY1m0cZ+SPTltz3+8m9iocHKzNeLekalnJfLA9RO468VCfvbqBh782kQlxSLdpKG5heKqumNH59wmKaWH645J6PrFRjIkMY7zzkp0R+ZiPx+hS+ilhE78KzUhhqnDEskvLOWuy0bq/4luomQvgCzdXE5NYwtzszW3XlcxxjAnM5UnP9jJ4domlZjJKTtc28Qb60u5NnsgvXV2+7jmZmVQXFXLf7+9lUH9enH3rLO9DkkkZDQ2+yg+WPt5Z8u2iV3poTp8bRK6hF6RDE2KY/LQfgxJHMiwpDi37DKWvrFqjiLeys1O56evbmDd3sNkqbFXt1CyF0DyC0pI7eOc9ZCuk5OZxv++/xnLNpfzlR7eMl9O3Str91Lf5GP+VI0Mn8ydl4xg78E6Hn1nOwP7xfK1yYO8DkkkaDS1+Nh7sM5tinJs2eXeg7XHJHS9YyIYlhTHOYP7cd05AxmWFMuQRKfssp+6XUoAm5OZxr35ReQVlCjZ6yZK9gJEVU0j7289wLcuHKYJRrvYxIEJpCfEsGhjmZI9OSXWWhas3E324L6MS0/wOpyAZ4zhvtxMSg7V8a+vbSCtbwwXjUz2OiyRgNHcmtBVfjH/XGtit/dgHS1tMrr46AiGJsUyYWACc7PSPy+3HJYUR7/YSJXASVBK6BXJZaMHsHB9Kb+4cgwR4WFehxTylOwFiDfXl9Lss8xVF84uZ4xhdmYqC1bu4WhDszqHSaf947NKPjtQw++un+h1KEEjMjyMx248h+sf/we3P7+WV24/n9GpfbwOS6TbNLf4KD1Uz842XS5bE7viqlqa2yR0cVHhDE2KIzMjgasmpDE0Me7zssvEuCgldBKS5mZlsGhjGSu2V3Dx2WpI6G866g0QeYWljEqJZ0xab69DCUlzxqXy9Ie7eHfLfq6eqGsipXMWfLyHhF6RXDkhzetQgkrvmEievnUyuX/4kFufXs1rd0wjNSHG67BEukyLz1J66IsRup0VX0xhUHywlqaWLxK62KhwhiTGMSatNzmZqW2mLYglOT5aCZ30OJeMTqZPTAT5haVK9rqBkr0AsKeylk92H+SeOWfrj76fTBran6T4KBZvLFOyJ52y/0g9S4rKuOWCoeqOexrSEnrx1C2T+drj/+Cbz6zm5e+er1F1CSo+n6X0cB27KmrZWVnD7jZNUYqr6mhs8X2+bq/IcIYkxjIqpTezxqU6c9G5o3TJvZXQibQVHRHOlRPSyC8spbaxmdgo/d/gT3p3A0B+YQkA1ygJ8ZvwMMOscankFZRQ39Sig3c5qZfXFNPss9w4dYjXoQStcekJPDb/XL75zGruXLCWJ2+epOszJKD4fJayI/XO6FybUbrdlTXsrqqlsfmLhC46IoyhiXGMGBDP5WNTnNE5N6FL6aOETuRUzM3K4IVVxSzdVK5LmPxMyZ7HrLXkFZYwZWh/BvaL9TqckJaTmcpfVu5h+dYDzBqX6nU4EsBafJYXVhVz4YgkhiXFeR1OUJsxKpn7czP5+d82cG9+Eb++NlMHxdKtfD5LeXU9Oytq2O12t2xtirK7spaGNgldVEQYQ/rHMjQpjktGDzhmLrrUPjFqoCbSRaYM7U96Qgx5BSVK9vxMyZ7HikqPsONADd+68CyvQwl5U89KJKFXJIs3linZkxN6d8t+Sg7V8Ysrx3gdSki4Ycpgiqtqeey9HQzuH8vtFw/3OiQJMdZa9lc3OEmcO0q3u/U6usoa6pvaJHThYQxOdBK46SOTP+9wOTQpjjQldCLdIizMcE1WBn/+4DMqjzaQGB/tdUghS8mex14rKCEy3HDFeCUf/hYZHsbMsSksKSqjsdlHVITKyaRjz6/czYDe0Vw+NsXrUELGj2edzd6Ddfx28RYy+vVS2bp0ieYWH68VlPA/725nd2Xt58sjww2D+scyLDGOaSOSPp9UfGhiHOl9exGuhE7Ec7nZ6Tz+/g7e3LCPm84f6nU4IUvJnodafJY31jmdiPrGahLU7jBnXCqvfLKXj3ao3a90rLiqlve3HuD7l44kUteXdZmwMMMD10+g7HA9P355HWkJMUwe2t/rsCRINbf4yC8s5ffvbGNXZS3j0vvwH1eP5azkeIYlxZGWEKPrQ0UC3OjUPoxO7c1rBSVK9vxIfwk99I8dleyvbuDabNUqd5cLRyYRFxXO4o1lXociAeovq/ZggBumDPI6lJATHRHOn246l4H9e/HP/7eGHQeOeh2SBJkWn+W1gr3MfGg5//LXdcRGRfCnb5zLwu9fyC3ThjF9VDKD+scq0RMJErnZGRTsOcTuyhqvQwlZ+mvoodcKSugdHcGlozXC1F1iIsO5dEwKb28qp6XNxLYiAA3NLby8upjLxqSQltDL63BCUt/YKJ65ZQrhxnDr06upONrgdUgSBFp8lryCEmY++D4/emkd0RFhPD7/XN78wYXMGpeqpj8iQeqaiekYA/mFpV6HErL8nuwZY8KNMQXGmIXu/WHGmJXGmO3GmJeMMT2yfrG+qYUlRWXMyUzVNADdLCczlaqabjjuowAAIABJREFURlbtrPI6FAkwizeWUVnTyHxNt+BXgxNjeeLmSeyvrufbz66hvqnF65AkQLX4LPmFJcx66H1++FIhURFhPD7/HN76wUXMyVSSJxLs0vv24rxh/ckrKMFanYT3h+4Y2bsL2Nzm/m+Bh6y1I4CDwLe6IYaAs2xzOUcbmlXC6YGLz04mJjKMxRv3eR2KBJgFK/cwJDGWi0YkeR1KyMse3I+H/ymbdXsP8cMXCzXSLsdo8VleX1fK7IeXc9eLhUSEhfHYja1JXpo6ZoqEkNysDD6rqGFDyWGvQwlJfk32jDEDgSuBJ9z7BrgUeMVd5Vkg158xBKq8ghJS+kRz3lmJXofS48RGRTBjVDKLi8rw6QBTXFvLq1m1s4qvTxmsA8luMiczlV9cOZbFRWX811ubT/4ECXk+n2Xh+lLmPLycH7xQQJiBP3z9HBbddRFXjFeSJxKKcsanERUeRl6BSjn9wd8jew8D9wCtE9wkAoestc3u/b1AjxvaOljTyHufHuCaielq/+yROZmplB9poKD4kNehSIBY8PFuosLD+Oq5A70OpUf51oXDuOWCoTyxYifPfrTL63DEIz6f5a0N+8h55AO+95cCLPD7G7JZfNd0rpygJE8klCX0iuTS0QN4fV0pzS2+kz9BTonfpl4wxlwF7LfWfmKMufg0nn8bcBvA4MGDuzg6b725YR/NPkuuSjg9c+noFCLDDYs37uPcIf28Dkc8VtPQzN/WlnDF+FRN7OqBe68aS8mhOv7zjSLS+/ZipuY37DF8PsuSojIe+fs2tpRVMzw5jkdvyObK8Wk6GSrSg+Rmp7O4qIyPdlQyfVSy1+GEFH+O7E0DrjHG7AJexCnffAToa4xpTTIHAiUdPdla+ydr7SRr7aTk5ND60PMLSxg5IJ6xaX28DqXHSugVybQRSSwuKtMFwcIb60qpbmhWYxaPhIcZHp2XzfiMBL7/wlrWacQ95Pl8lsUby7ji0Q+4fcFaGlt8PDIvi7d/NENVLyI90MVnD6B3TAR5hR2mBXIG/JbsWWt/bq0daK0dCswD3rHW3gi8C3zVXe1mIN9fMQSi4qpaVu86SG52hrqIeSwnM5XiqjqKSo94HYp4yFrL8yt3Mzq19//P3p2HR3md5x//nhltaEX7SANi3weQzJp4A2wMMtgSiZ3YhtZx0qRt0iVNnLhpk675dUnSdE3apknspBA7S4PAdsDGDt5is9kS+2ZsJKF9QQsSWkZzfn8gJ9hGIECjd5b7c126kDSad27zWsM8c573OVrlddCYODfffXARWcnxfOIH+6hu7XY6kgSBtRdW8tb++yv83sbX6fUH+OePzmfHn9xKSaFXRZ5IlEqIdbNmbh7PHKrnfJ8mNI8kJ/bZewT4nDHmTS5cw/c9BzI4Zuv+Cxef3j0/3+EksnK2B7fLaIP1KLf/TDuHajpYv6RAb8A4LDslnsceWkSff4CHHttLe3e/05FkhFhr2XGkgbX//gq/+7+v093n55sfmc+OP7mFdUXjVOSJCCWFXrr6BthxtMHpKBFlVIo9a+0L1tq1g5+/Za1dbK2daq2911obNTvqWmvZXF7DoonpjM9IdDpO1MtIimPJpAy2aQuGqLZxVyWJcW5dQxsipuak8J3fXkhVSze/u3EfvX69wxvOrLU8f7SBu//jV3zyh/s41+vnG/fO57nP3cqHbhhHjNuJ95xFJBQtmZRBXloCW8rVyjmS9Cw7ig7XdvBm4zlKCvWiMlSs9nk41dTFyYZOp6OIA9q7+3lyfy2lRV5SEmKdjiODlk7O5Ov3zmPXW6386f8d1HW1Ychayy+PNVDyrV/xiR/so/18P1+/Zx7Pf+5W7lmgIk9E3s/lMtxdmM+LJ5po7epzOk7E0LPtKNpSUUOMy7Bmbp7TUWTQqjkeALaplTMq/eyNM/T6A2xYosEsoaak0MvDd0xnc3kN/7zjhNNxZJistew83kjpt1/l44/t42x3H1/78Dye//yt3LtwvIo8Ebms0kIv/oDl6QPac2+kBG3rBXm3gYBl6/5als3IIT0pzuk4Mig3NYEFE9LZfqieP7ptmtNxZBRZa9m0u5KigrHMztdk3FD0meVTqW49z7/98k3GpSfykUXjnY4kQ7DW8uKJJv7luZNUVLfhHTuGf/jQXD68YByxKvBEZJhm5aUyIzeFsopafusDE52OExH0DDxKdr3VQkNHL6VFGswSaop9Ho7UdVDVoul/0eS1Uy281dSlVb0QZozhq+t83Dwtiz/bfJCXTzY5HUnew1rLSyea+NB/vsrHHt1LU2cvf/+huex8eBn3LS5QoSciV620yMvrlWf1umyE6Fl4lJSV15AcH8Pts7RZcKj5TSunBrVEk427KxmbGMuaeWqrDmWxbhffXn8DU3OS+fTGNzhWr61SQoG1lpdPNnHPf73Gb39/Dw3tPfy/dT52PryM+xcXEBejlxcicm3uLrywMLJFe+6NCD0bj4Ke/gG2H6pntc9DQqzb6TjyHuMzEpnrTdN1e1GksaOHZw83cO+CcfqdDAMpCbE8+tAiEuPdPPToXurbe5yOFLWstfzqzWY+8t+v8Vvf20Nt23m+Wupj5xeWsX7JBBV5InLdvGPHsHhSBmUVNRrQNQL0rDwKnj/aSGevn1JN4QxZq30eKqrbqGs/73QUGQU/3luNP2B5QC2cYSMvbQzf/9giOs738/HH9nKu1+90pKjz6qlmPvrfu1j/3d1Ut57nb0vm8MIXlrFh6QTiY/SmiYiMnHVFXk41dXGoRt0c10vF3igoq6ghJyWeD0zJdDqKDGG170Ir5zNa3Yt4AwHL43uquGlqFpOykpyOI1dhTn4a31p/A8cbOvnMpjfwDwScjhQVXjvVwkf/+zUe+J/dVLZ28dd3XyjyfusDE1XkiUhQ3OnLI87tokytnNdNxV6QtXX38cLxRu6en4/bZZyOI0OYkp3M9NxktXJGgV8ea6S2vYcNSwucjiLXYNmMHL5a6uPFE018ZcthtfgE0e63WrjvO69x///s4u3mLv7yrtm8+IXlPPjBiWp/FpGgSkuMZdmMbLbur2UgoOf563HFYs8Y8/fGmFRjTIwx5hljTIMx5oHRCBcJnj5YR/+ApbRILZyhbrUvj72nW2k+1+t0FAmiTbsryU2N5zYNSwpb9y8u4NPLpvD4nir+68W3nI4TcfaebuWB/9nFR7+zi1NNXfzF2tm89MXlPHTjJBV5IjJqSou8NHX28uqpZqejhLXhrOwVW2s7gLVALTATeCSoqSLIlvJapmQnMUf7eIW8Yp+HgIVnDzc4HUWCpLq1mxdPNPHRRRoJH+4evmMGd8/P5x+3H2Prfm2+OxL2nW5lw3d3c+9/vcaJhnN8ec0sXv7icj5+k4o8ERl9K2bmkBIfQ1m5nuOvx3A2VX/nZ+4EfmqtPWuM0XrqMJw5282e0608fMd0jFELZ6ib6UlhYmYi2w7V8cAStfhFok27q3AZw/2LtTl3uHO5DF+/dx717T08/JP95KUlsGhihtOxwtLrlWf5l+dO8PLJZrKS4/jymlmsXzKBMXEq8ETEOQmxbornevjFwXq+2ufTc9I1Gs5b29uMMYeAJcAOY0wWoD63YdhSceGdiBJN4QwLxhhW+Ty8dqqF9u5+p+PICOv1D/CTfdXcNjOHvLQxTseRERAf4+Y7v72AcRlj+OQP93Gq6ZzTkcLKG1Vn+e3v7+HD//kqR2o7+LM7Z/LSF5fzOzdP1osqEQkJpYVezvX6ee6ouq6u1XCKvS8BK4AF1tp+oAf4cFBTRQBrLVsqalgwIZ3xGYlOx5FhKvbl4Q9YPalEoO2H6mnt6mPDUm23EEnGJsbx2McW4zaGhx7dS4uuub2iiuo2Hvz+Hj707Vc5VNPOl4pn8vIjy/nULVNIjBtOw4+IyOhYMjkTT2qCNli/DsMp9vZYaxuttX4Aa+05YGtwY4W/o3WdnGg4p8EsYWb+uDTy0xI0lTMCbdpVxYTMRG6amuV0FBlhBZmJfPfBhTR29vA7P9xHT/+A05FC0v7qNh56dA+l3/oVB8608cjqmbz8xeX87q0q8kQkNLldhrsL83nheBOtXX1OxwlLQxZ7xpgcY8x8YIwxZq4xZt7gx02AlqquoKyihhiXYc3cPKejyFV4p5XzpZNN2rQ5ghyv72TP6VYeWFyAS1ugRKSignT+5aNFVFS38dknKjSq+yIHz7Tzicf2UvKtX1Fe3cYXVs3g5UdW8PvLppAUryJPREJbSWE+/oDl6YN1TkcJS5db2VsD/AcwDvg28K3Bjy8BXwl+tPA1ELBsrajl1unZZCTFOR1HrlKxL48+f4CdxxqdjiIjZNPuSuJiXNy7UINZItlqn4cvr5nN9sP1/P0vjjodx3GHatr5nR/s5a7/eIV9lWf5wqoZvPLICj6zfCrJKvJEJEzMzktlem4yW8rVynkthny2t9Y+CjxqjPmItfYno5gp7O1+u4X6jh7+fM0sp6PINVgwIZ2s5Hi2H6rnrvn5TseR69TV6+fnb9SwZm6e3nyJAp+4aRLVrd1895W3GZ+RyIMfnOh0pFF3qKadf33+JDuONJCaEMPnV07nYzdOJCUh1uloIiJXzRhDSaGXrz9znOrWbs3CuErDuWbvBWPMfxtjngIwxsw2xnwsuLHCW1l5DUlxbm7Xps1hye0y3DEnl53HG3XtTwTYur+Wc71+NizVdhrR4itrZ7Nydi5//eRhdhyJnmFLR2o7+NQP97H2319h91stfG7ldF750xX84W3TVOiJSFgrKbzw5rv2Vb16wyn2HgVeBN7pfzoJfD5oicJcT/8A2w7Ws8rn0ejqMFbs89DdN8BLJ5qcjiLXwVrLxl2VzPSkcENButNxZJS4XYZ/u6+Iud40/ujxcg6caXM6UlAdrevg9/73de78t5d57a0WPnv7NF5+ZAV/dNs0UlXkiUgEGJeeyOKJGWwur8FaXZN9NYZT7OVYa38EBAAGt18IBDVVGNt5rJHOXj/rNIUzrC2dnEnamFi2aypnWKuobuNwbQfrl07AGA1miSZj4tx898FFZCbH8fHH9lHd2u10pBF3rL6D39/4OsX/+jK/erOZP7ptGq88soLP3j6dtDEq8kQkspQU5fNm4zkO13Y4HSWsDKfY6zLGZAAWwBizCNDf8hA2l9eQnRLPB6dovHs4i3W7WDk7lx1HG+jz672NcLVpdxVJcW69+RKlslPieeyhRfT5B3josb20d/c7HWlEnGjo5DOb3mD1v7zMyyeb+aMVU3nlkRV8bqWKPBGJXGvm5hHrNtpz7yoNp9h7GHgSmGyMeRF4HPjDoKYKU+3d/bxwvIm75uXj1nj3sFfs89DZ4+fVU81OR5Fr0Nbdx5P7aykt8mryYBSbmpPCd357IZUtXfzuxn30+sP3OtyTDZ38wY/eYNW/vMQLxxv5g+VTeeWR5XzujhmkJarIE5HINjYxjmUzcthSUavtda7CFYs9a+0+YDlwK/DHwGxrbUWwg4WjXxyqo28goFWECHHj1CyS42PUyhmmfvb6GXr9AdYvmeB0FHHY0smZfP2e+ex6q5U//b+DYXe9x5uNnfzh4+Xc8S8vsfNYI59eNoVXHlnBw6tmMDZRE2ZFJHqUFnpp7Oxl11stTkcJG1d8u9sYM4YLRd5Ea+3vGWOmGmOmWWu3BT9eeNlcXsPk7CR83lSno8gISIh1s3xmDs8eaeCrpQFi3MNZCJdQYK3lR7uruKFgLLPz9fsoUFrk5czZbr7x7AnGp4/hc3fMcDrSFb3ZeI5//+VJtu6vZUysm9+7dQqfvHmythARkah126wckuNjKCuv4capumRqOIbz6vX7gz930+DXtcDfBS1RmKppO8+et1spLfRqEEQEKfZ5aO3qY+/ps05Hkavw2qkW3mruYsNSrerJb3xm+VQ+unA8//bLN/nJ3mqn4wzpraZzfPaJcu745xd59nADn7plMi9/cTmPrJ6pQk9EolpCrJtin4dth+q1PdYwDedClmnW2vuNMfcCWGu7jaqZ99lacWHfj9JCtXBGkmUzskmIdbH9UB0fmJLpdBwZpo27KxmbGMudc/OcjiIhxBjDV9f5qG0/z59tPkje2ARunpbtdKxfe7u5i39//iRlFTXEx7j55M2T+dQtk8lMjnc6mohIyCgt8vLT18/w/NFG1szTv/NXMpyVvT5jTAK/mcY5CegLaqowtKWihhsKxlKQmeh0FBlBiXEx3Do9m+2H6wnoYuCw0NjRw7OHG7h3wTgSYrXXpbxbrNvFt9ffwNScZD698Q2O1Ts/XPp0cxef/8l+bv/mi/ziUB2fuGkSLz+ynC/dOUuFnojIeyydnElOSjxlmso5LMMp9v4G2A6MM8b8ANgJfCmoqcLM0boOjtV3UqrBLBGp2JdHQ0cv5dWRvTFzpHhibzX+gOUBDWaRIaQkxPLoQ4tIjHfz0KN7aejocSRHZUsXD/90P7d980WeOlDLQx+cyMtfXMGfr5lNloo8EZFLcrsMJYX5vHC8kbZurT9dyXCmcW4H7gU+CWwGFltrnw92sHBSVlGD22VYo5axiLR8Zg6xbsP2Q3VOR5Er8A8EeHxPFTdPy2JSVpLTcSSE5aWN4fsfW0TH+X4eenQv53r9o/bYVS3dfPFn+1nxTy/y5P5aHvzARF5+ZDlfXjub7BQVeSIiV1JS6KV/wPL0Qb02u5IrFnvGmMeAtcBha22ZtbYx6KnCSCBg2VpRy63Ts9VuE6HSxsRy49Qsth2qD7uR7dFm5/Em6tp7tN2CDMuc/DS+tf4Gjg9uUu4fCAT18apbu3nkZwdY8U8vUFZRy28tncDLX1zOX9w1m5yUhKA+tohIJJmTn8rUnGS2lNc6HSXkDaeNcxMwCfgfY8wpY8yPjTGfCXKusLH77Vbq2nsoKcx3OooEUbHPw5mz5zlc6/z1PTK0jbsqyU2N5/ZZOU5HkTCxbEYOXy318eKJJr6y5XBQ3tA5c7abL/38AMu/8QKbK2rYMFjk/dXdc8hJVZEnInK1jDGsK/Ky53QrZ852Ox0npA2njXMH8JfAF4DvAB8A/iTIucLGlooaEuPcrJyd63QUCaKVsz24XUYbrIewqpZuXjrZxH2LCrQnolyV+xcX8OllU3h8TxX/9eJbI3bcmrYLUz+Xf+MF/u/1Gh5YUsBLX7hQ5OWqyBMRuS53z7+w0LKlQqt7lzOcTdWfAdKAvcDLwFJrrf5WgZ7+AZ4+WMfqOR4S44azi4WEq4ykOJZMymDboToeXhX6mzFHox/tqcJlDPcvLnA6ioShh++YQfXZ8/zj9mN408f8+kXEtahtO8+3dr7JT/ZVYzDct6iATy+fQl7amBFMLCIS3cZnJLJwQjpl5TV8etkU7XM9hOFUKCeAImAa0ADUG2OarbVRP/7mheONdPb4KdEUzqhQ7PPwlS2HOdnQybTcFKfjyEV6/QP8ZF81t8/KwZOmFRO5ei6X4Rv3zqOhvYeHf7KfvLQEFk3MuKpj1LWf59s7T/HjvdVYLB9ZOJ7PLJ9K/lgVeSIiwVBa5OXLZYc4UtfBnPw0p+OEpOG0cf6htfYm4KNAO/C/g39GvbLyWrKS47hRm21HhTvmeADYplbOkLP9UD2tXX0azCLXJT7GzXd+ewHjMsbwyR/u41TTuWHdr769h7/Ycohbv/YCT+yt4p6F49j58DL+37q5KvRERIJozdw8YlxGrZyXMWSxZ4yJGfzz94wxm7jQxvlh4IdAyejEC13t5/v55bFG7pqfr+uDokRuagILJqTrur0QtHFXJRMyE7lpapbTUSTMjU2M47GPLcZtDA89upeWc71D/mxDRw9/tfUwt3x9Jz/aXcWHF3jZ+fAy/m7dXMalJ45iahGR6JSeFMeyGdlsrahlIKCJ6ZdyuSplz+CfY4FvAz5r7TJr7Vestc8GP1po23awjr6BAKWFauGMJsU+D0fqOqhq0eSnUHG8vpO9p8+yfkkBLpf69eX6FWQm8t0HF9LQ0cPv/HAfPf0D77q9saOHv37yMLd8bSf/u6uSdYUXiry//9A8FXkiIqOstMhLfUcPu99qcTpKSLpcsWcArLX/YK39la7Re7eyihomZSUxb5z6g6PJql+3cmoTz1CxaXclcTEu7lkw3ukoEkGKCtL51/uKqKhu47NPVDAQsDR29vA3Tx7h5q/t5IevVVJSmM/Ozy/jH++Zx/gMFXkiIk64fVYuyfExlFXUOB0lJF1uQEu2MeZzQ91orf1mEPKEhdq28+x+u5XP3jZdk3+izPiMROZ609h2qJ7fvXWK03GiXlevn5+/UcOauXlkJMU5HUcizGqfhy+vmc3fPnWE+/9nFwfOtNE/YFlX5OUPV0xlQmaS0xFFRKJeQqybVXM8bDtYz9+U+EiIdTsdKaRcbmXPDSQDKUN8RK2t+2uxFm2kHqVW+zxUVLdR137e6ShRb0tFLed6/WxYqu0WJDg+cdMkPn7jJPadbmXN3Hye/9ytfOPe+Sr0RERCyLoiL529fn55rNHpKCHncit7ddbavxm1JGGkrLyGwvFjmZilf+yj0Wqfh68/c5zth+p56MZJTseJWtZaNu6qZKYnhRsK0p2OIxHsK2tn8Scrp5GSEOt0FBERuYQPTMkkJyWesvIa7pyb53SckHLFa/bk3Y7Vd3CsvpN12lsvak3JTmZ6brKmcjqsorqNI3UdbFg6Qe3UElTGGBV6IiIhzO0y3DU/nxeON9HWrTEjF7tcsXfbqKUII2XltbhdhjXz9K5BNFvty2Pv6VaaLzOWXYJr464qkuLclOqNFxERkahXWuilbyDALw7qzfiLDVnsWWtbRzNIOAgELFsrarh5WhZZyfFOxxEHFfs8BCw8e7jB6ShRqa27j6cO1FJa5CU5/nLd6CIiIhINfN5UpmQnaSrne2g38Kuw93Qrte09auEUZnpSmJiZqC0YHPKz18/Q6w+wYekEp6OIiIhICDDGUFroZc/brdS0aYjeO1TsXYWyihoS49ysnJ3rdBRxmDGG1b48XjvVQnt3v9Nxooq1lk27q1gwIZ1ZealOxxEREZEQUVJ4YUFmi1b3fk3F3jD1+gd4+kAdd8zOJTFObWNyYSqnP2DZcVStnKPp1VMtvN3cxfol2m5BREREfqMgM5EFE9LZUl7rdJSQoWJvmF443kRHj1/DIOTX5o9LIz8tQVM5R9nGXZWkJ8ZqtLKIiIi8T2lhPscbOjla1+F0lJCgYm+YyspryEqO46apWU5HkRBhjGGVz8NLJ5s41+t3Ok5UaOjo4dkjDdy7cDwJsW6n44iIiEiIWTMvnxiXoaxcrZygYm9YOnr6ef5YI2vn5RPj1l+Z/EaxL48+f4CdxxqdjhIVfry3moGA5YHFauEUERGR98tIiuPW6dls3V9LIGCdjuM4VS7DsP1gPX3+gFo45X0WTEgnKzlerZyjwD8Q4PE9Vdw8LYuJWUlOxxEREZEQVVLkpa69h91vayc5FXvDsLm8homZicwfl+Z0FAkxbpdh1Zxcdh5vpKd/wOk4Ee2Xxxqpa+9h/RJttyAiIiJDWzkrl6Q4t1o5UbF3RfXtPex6u4WSQi/GGKfjSAha7fPQ3TfAiyeanI4S0TbursKTmsDts3KcjiIiIiIhbEycm1U+D784VBf1b8ar2LuCrftrsBa1cMqQlk7OJG1MLM+olTNoqlq6eelEE/ctHq/rZkVEROSKSgu9dPb4eeF4dM9V0KumKygrr2X++LFM0jVCMoRYt4uVs3PZcbSBPn/A6TgRadOeStwuw32LNJhFREREruyDUzLJSo6nLMr33FOxdxknGjo5UtdBaWG+01EkxBX7PHT2+Hn1VLPTUSJOr3+An+47w+2zcvCkJTgdR0RERMJAjNvF3fPz+eWxRtq7+52O4xgVe5dRVl6D22VYO0/FnlzeTdOySI6P0VTOINh2sJ7Wrj42LNVgFhERERm+0qJ8+gYCbDtU53QUx6jYG0IgYNlSUctNU7PITol3Oo6EuPgYNytm5vDskQb8A2rlHEmbdlcyMTORG6dkOR1FREREwshcbxqTs5Ioq4jeqZwq9oawr/IsNW3nKS3Sqp4Mz2qfh9auPvaePut0lIhxrL6DvafP8sCSAlwuTcMVERGR4TPGUFrkZddbrdS2nXc6jiNU7A2hrKKGMbFu7pjtcTqKhIllM7JJiHWxPYpbBUbapl1VxMW4uHfBeKejiIiISBgqGZy9sXV/dA5qUbF3CX3+AE8fqOOOObkkxcc4HUfCRGJcDLdOz2b74XoCAet0nLDX1etnc3kNa+fmkZ4U53QcERERCUMTMpMoKhgbtRusq9i7hBeON9J+vp/SQu2tJ1en2JdHQ0cv5dVtTkcJe1sqajnX62e9BrOIiIjIdVhX5OVYfSfH6jucjjLqVOxdwpaKWjKS4rhpmgZCyNVZMSuHWLdRK+d1staycVclMz0p3FAw1uk4IiIiEsbWzM3D7TJRueeeir336Ojp57mjDdw1L49Yt/565OqkJsRy09Qsth2qx1q1cl6r8uo2jtR1sGHpBIzRYBYRERG5dpnJ8dwyLYutFTVRd6mNqpn32H6onl5/gJIitXDKtVnt83Dm7HkO10Zfq8BI2birkqQ4N6X6PRQREZERUFrkpba9hz2nW52OMqpU7L3HlooaJmQmUjRerWNybVbO9uB2GW2wfo3auvt46kAd627wkqwBSSIiIjICVs7OJTHOzZYo23MvaMWeMSbBGLPHGLPfGHPYGPPXg9+fZIzZbYx50xjzY2NMyIzZa+jo4dVTLZQUetU6JtcsIymOJZMy2Kbr9q7Jz14/Q58/wPolGswiIiIiIyMxLoZVczw8faCOXv+A03FGTTBX9nqBFdba+UAhsNoYsxT4R+CfrbVTgbPAJ4KY4apsrajFWigt1Ebqcn2KfR5ONXVxsqHT6ShhJRCwbNpdxYIJ6czKS3U6joiIiESQ0iIvHT1mbeBwAAAgAElEQVR+dh5rcjrKqAlasWcvODf4ZezghwVWAD8b/P4PgNJgZbhaZRU1zBuXxuTsZKejSJhbNceDMbBNrZxX5dVTLbzd3MWGpQVORxEREZEIc+OUTLKS46KqlTOo1+wZY9zGmAqgEdgBnALarLX+wR85A1xyAoMx5lPGmH3GmH1NTcGvvk82dHK4tkN768mIyElNYEFBuoq9q7RpdyXpibEU+/KcjiIiIiIRJsbtYu28fJ4/dmFP7WgQ1GLPWjtgrS0ExgGLgZlXcd/vWGsXWmsXZmdnBy3jO8oqanAZWDtfLzJlZKz2eTha10FlS5fTUcJCQ0cPzx5p4N6F40mIdTsdR0RERCLQuiIvff5A1OyJPCrTOK21bcBO4APAWGPMOyP2xgGOr6Naa9lSUcuNU7PISUlwOo5EiFVzPACayjlMT+ypZiBgeWCxWjhFREQkOOaNS2NSVlLUbLAezGmc2caYsYOfjwFWAke5UPTdM/hjDwJbgpVhuF6vPMuZs+dZpz29ZASNz0hkrjdNrZzD4B8I8PieKm6elsXErCSn44iIiEiEMsZQUpjPrrdbqGs/73ScoAvmyl4esNMYcwDYC+yw1j4FPAJ8zhjzJpAJfC+IGYZlc3kNCbEu7hhciREZKat9Hiqq26LiyeR6/PJYI/UdPWxYqu0WREREJLhKC71Ye2ESf6QL5jTOA9baImvtPGutz1r7N4Pff8tau9haO9Vae6+1tjdYGYajzx/g6YN1rJzt0QbOMuKKfWrlHI6Nu6vwpCZw28wcp6OIiIhIhJuYlUTh+LGUqdiLfC+daKKtu591RdpbT0be5OxkZuSmqJXzMipbunjpRBP3LR5PjDvqn5JERERkFJQW5nO0roPj9ZG9J3LUv7LaXFFDRlIcN08L/sRPiU6rfB72nm6lqdPRReyQ9aPdVbhdhvsWaTCLiIiIjI618/NxuwxlEb7nXlQXe509/Tx3pIE1c/OI1YqCBEmxz4O1sONIg9NRQk6vf4Cf7Ktm5axcPGmahCsiIiKjIys5npunZbG1opZAwDodJ2iiusJ55nADvf4ApZrCKUE005PCxMxEtkXJfi5XY9vBes5292swi4iIiIy60kIvNW3n2Vd51ukoQRPVxV5ZeQ0FGYncUDDW6SgSwYwxrPbl8dqpFtq7+52OE1I27qpkYmYiH5yS6XQUERERiTIrZ+cyJtbN5vLIbeWM2mKvsaOHV081U1KYjzHG6TgS4Yp9HvwBy46jauV8x7H6DvZVnmX9kgm4XPodFBERkdGVFB/Dqjm5/OJgHX3+gNNxgiJqi72t+2sJWCgpVAunBN+8cWnkpyWwXa2cv7ZxVyVxMS7uWTDO6SgiIiISpUqKvLSf7+eF441ORwmKqC32yipqmOtNY2pOstNRJAoYY1jl8/DSyWbO9fqdjuO4c71+Nr9Rw9p5eaQnxTkdR0RERKLUzVOzyEyKi9ipnFFZ7L3ZeI5DNR2UFGpvPRk9xb48+vwBdh6LzHeOrsaWihq6+gZYv0SDWURERMQ5MW4Xd83P57mjjXT0RN5shags9rZU1OAycPd8FXsyehZMSCcrOZ7tUb7BurWWjbuqmJWXquFIIiIi4riSwnz6/IGIfI0WdcWetZayihpunJpFTqr29ZLR43YZVs3JZefxRnr6B5yO45g3qto4WtfBhqUFGo4kIiIijiscP5YJmYmUReBUzqgr9t6oOkt163kNZhFHFPvy6O4b4MUTTU5Hccym3ZUkx8fod1BERERCgjGG0kIvr73VQn17j9NxRlTUFXtl5bXEx7hYNSfX6SgShZZMzmBsYizPRGCbwHCc7erjqQN1lBblkxwf43QcEREREQBKi7xYC0/ur3U6yoiKqmKvfyDAUwdqWTk7l5SEWKfjSBSKdbu4fVYuO442ROx+Lpfzs9fP0OcPsGGpBrOIiIhI6JiUlcT8cWkRN5Uzqoq9l040cba7n1K1j4mDin0eOnv8vHqq2ekooyoQsGzaXcnCCenM9KQ6HUdERETkXUqLvByu7eBkQ6fTUUZMVBV7ZRW1jE2M5Zbp2U5HkSh207QskuNjInLi0+W8eqqF0y3dWtUTERGRkLR2Xj5ul4mo1b2oKfbO9frZcaSetfPyiIuJmv9sCUHxMW5WzMzh2SMN+Aeip5Vz465K0hNjWe3zOB1FRERE5H2yU+K5cWoWWypqCQSs03FGRNRUPc8cqqenP6AWTgkJxT4PrV197Dnd6nSUUVHf3sOOow18ZOF4EmLdTscRERERuaR1RfmcOXue16vOOh1lRERNsVdWUcO49DEsmJDudBQRbp2RTUKsK2qmcj6xt4qBgOWBJQVORxEREREZ0h2zPYyJdUfMnntRUew1dvbwqzebKS30ahNnCQmJcTHcOj2b7YfrI6ZNYCj+gQBP7KnmlunZTMhMcjqOiIiIyJCS4mNYOTuXpw/WRcTk9Kgo9p7cX0fAQmlRvtNRRH6t2JdHQ0cv5dVtTkcJquePNVLf0cN6reqJiIhIGFhX5KWtu58XTzQ5HeW6RUWxt6Wihjn5qUzNSXE6isivrZiVQ6zbsP1QndNRgmrjrkry0hK4bWaO01FEREREruimaVlkJMVFxFTOiC/2TjWd48CZdtYVaTCLhJbUhFhumprFtkP1WBuZrZynm7t4+WQz9y0qIMYd8U83IiIiEgFi3S7WzsvjuSMNdPb0Ox3nukT8q68t5TUYA3fNVwunhJ5iXx5nzp7ncG2H01GC4vE9VbhdhvsWj3c6ioiIiMiwlRZ56fUHwn5f5Igu9qy1lFXU8sEpmeSmJjgdR+R9bp+di9tlwv6J5FJ6+gf4yb5qVs7K1e+fiIiIhJWi8WOZkJnIlopap6Ncl4gu9sqr26hq7dbeehKyMpLiWDIpg20ReN3etkN1nO3uZ8PSCU5HEREREbkqxhhK5ufz6qlmGjp6nI5zzSK62CsrryE+xsVqn8fpKCJDKvZ5ONXUxcmGTqejjKiNu6qYlJXEB6dkOh1FRERE5KqVFHkJWHhyf/iu7kVssdc/EOCpA3XcPiuXlIRYp+OIDGnVHA/GwLYIauU8WtfB65VnWb+kAJdLe1uKiIhI+JmSncy8cWlhPZUzYou9V04209rVR6mmcEqIy0lNYEFBekQVe5t2VxIX4+LDN4xzOoqIiIjINSsp9HKopoM3G8OzAytii73N5TWMTYzl1unZTkcRuaLVPg9H6zqobOlyOsp1O9frZ/MbNaydl0d6UpzTcURERESu2V3z83AZKCsPz1bOiCz2unr97DjSwJ1z84iLicj/RIkw71xXGglTOcvKa+jqG9BgFhEREQl7OSkJ3Dg1iy37a8JyX+SIrISePVLP+f4BbaQuYWNceiJzvWlh38pprWXT7ipm56VSNH6s03FERERErltpoZfq1vO8UXXW6ShXLSKLvc3ltXjHjmFBQbrTUUSGbbXPQ0V1G3Xt552Ocs3eqGrjaF0H65cWYIwGs4iIiEj4W+XzkBDrYnN5+A1qibhir6mzl1dONlFSmK8pgBJWiiOglXPTrkqS42O0t6WIiIhEjOT4GFbO9vD0gTr6BwJOx7kqEVfsPXWgloBFLZwSdiZnJzMjNyVsWznPdvXx1ME61hV5SYqPcTqOiIiIyIgpLcznbHc/L51ocjrKVYm4Yq+svIbZealMy01xOorIVVvt87D3dCtNnb1OR7lqP3v9DH3+AOuXFjgdRURERGRE3TI9m/TE2LBr5YyoYu/t5i72n2mntCjf6Sgi16R4rgdrYceRBqejXJVAwLJpdyULJ6Qz05PqdBwRERGRERXrdrF2Xj7PHW3gXK/f6TjDFlHFXll5DcbA3fPVwinhaUZuChMzE9l2qM7pKFflV6eaOd3Sre0WREREJGKVFuXT0x/gmTC65CZiij1rLWUVNXxgciaetASn44hcE2MMq315vHaqhfbufqfjDNvGXZVkJMVRPNfjdBQRERGRoLihIJ3xGWMoqwifVs6IKfYqqtuobOnWFEAJe8U+D/6AZcfR8GjlrG/v4bmjjdy7cBzxMW6n44iIiIgEhTGG0kIvv3qzmcbOHqfjDEvEFHtbKmqJi3GxWisLEubmjUvDO3YM28OklfOJvVUMBCwPLNZgFhEREYlsJYVeAhae3B8er9MiotjrHwjw5P5abp+VQ2pCrNNxRK6LMYZVczy8dLI55C8A9g8EeGJPNbdMz2ZCZpLTcURERESCampOMj5vKmVhMpUzIoq9V95spqWrjxK1cEqEKJ7roc8fYOexRqejXNZzRxup7+hhwxKt6omIiEh0KC30crCmnVNN55yOckURUextKa8hNSGGZTOynY4iMiJuKEgnKzme7SE+7WnT7kry0hJYMTPH6SgiIiIio+Lu+fm4zIUaJNSFfbHX1evnmcMNrJmXr+EQEjHcLsOqObnsPN5IT/+A03Eu6XRzFy+fbOa+RQXEuMP+qURERERkWHJSE/jglCzKKmqx1jod57LC/hXajiMNnO8foLRQG6lLZCn25dHdN8CLJ5qcjnJJP9pThdtluG/xeKejiIiIiIyq0iIvVa3dvFHV5nSUywr7Yq+soob8tAQWTcxwOorIiFoyOYOxibEh2crZ0z/AT/dVc8fsXHJTta+liIiIRJdVc3KJj3GxJcT33AvrYq/5XC8vn2ympMiLy2WcjiMyomLdLlbOyuW5ow30+QNOx3mXXxys42x3PxuWTnA6ioiIiMioS0mI5fbZuTx1oI7+gdB6nXaxsC72ntpfy0DAaiN1iVjFcz109vh59VSz01HeZdPuKiZlJfGByZlORxERERFxxLpCL61dfbx8MjQvuYEwL/bKKmqZ6UlhhifF6SgiQXHj1CyS42NCqpXzaF0Hr1eeZf2SAq2oi4iISNS6ZXo2YxNjKSuvdTrKkMK22Dvd3EVFdRvrirSqJ5ErPsbNipk5PHukAX+ItAhs3FVJfIyLexaMczqKiIiIiGPiYlysmZvHs0fqOdfrdzrOJYVtsVdWUYMxcLemcEqEK/Z5aO3qY8/pVqejcK7XT1l5DWvn5TM2Mc7pOCIiIiKOWlfkpac/wLOHQ6cL62JhWexZa9lSUcuSSRnkpY1xOo5IUN06I5uEWFdItHKWldfQ1TfA+qUFTkcRERERcdyCCemMSx9DWUVotnKGZbF34Ew7bzd3qYVTokJiXAzLpufwzOF6AgHnNu601rJxVyWz81IpGj/WsRwiIiIiocIYQ0lhPq+cbKKps9fpOO8TlsXe5vIa4twuVvvynI4iMiqK53po6OilvNq5jTvfqDrLsfpONiydgDEazCIiIiICUFroJWDhyf2ht7oXdsWefyDAUwdqWTEzh7QxsU7HERkVy2fmEOs2bD9U51iGjbuqSI6PoUTXyYqIiIj82rTcFObkp4bkButhV+z96lQLzef6KFULp0SR1IRYbpqaxbZD9Vg7+q2crV19PH2wjnVFXpLiY0b98UVERERCWWmhl/1n2nmr6ZzTUd4l7Iq9svIaUhNiWD4z2+koIqOq2JfHmbPnOVzbMeqP/bPXq+nzB9iwdMKoP7aIiIhIqLu7MB9jCLlBLWFV7HX3+XnmcD13zs0jPsbtdByRUbVydi5ulxn1qZyBgGXT7ioWTUxnhidlVB9bREREJBzkpibwwSmZbKmocaQLayhhVeztONJAd9+AWjglKqUnxbF0cgbbRvm6vVfebKaypVureiIiIiKXUVLopbKlmwoHB+q9V1gVe2XlNeSnJbB4YobTUUQcsXqOh1NNXZxs6By1x9y0u5KMpDhW+zyj9pgiIiIi4Wa1z0N8jIuy8tAZ1BI2xV7LuV5eOtnMXYX5uFwa+y7RadUcD8bAtlFq5axv7+G5o43cu3CcWqdFRERELiM1IZbbZ+Xy1IE6+gcCTscBwqjYe/pgHQMBq43UJarlpCawoCB91Iq9x/dUEbCW9YvVwikiIiJyJSWF+bR09fHKm81ORwHCqNjbXF7DTE8KMz2pTkcRcdRqn4ejdR1UtnQF9XH6BwI8sbeKW6ZlU5CZGNTHEhEREYkEy2bkMDYxNmRaOcOi2OvzByivaqOkUKt6Iu9cOxfsqZzPH22koaOX9UsKgvo4IiIiIpEiLsbFnXPzePZwA129fqfjhEex13a+H7iwf4VItBuXnsi8cWlBb+XctLuSvLQEVszMCerjiIiIiESS0kIv5/sH2HGkwekowSv2jDHjjTE7jTFHjDGHjTF/PPj9DGPMDmPMycE/0690rLbuPpZMysA7dkyw4oqElVVzPFRUt1HXfj4ox3+7uYuXTzZz/+ICYtxh8Z6QiIiISEhYOCEd79gxbA6BVs5gvorzA5+31s4GlgKfMcbMBv4UeN5aOw14fvDry+r1B7S3nshFioPcyvmj3ZXEuAz3LRoflOOLiIiIRCqXy1BSmM8rbzbTfK7X2SzBOrC1ts5a+8bg553AUcALlAA/GPyxHwClVzqWAe705QUpqUj4mZydzIzclKC0cvb0D/DT189wx5xcclITRvz4IiIiIpGutMjLQMDy1P5aR3OMSn+WMWYiUATsBnKttXWDN9UDuVe6f0pCLGmJsUHLJxKOVvs87D3dSlPnyL5j9IuDdbR197N+ibZbEBEREbkW03NTmJWXyuaKCC/2jDHJwP8Bn7XWdlx8m7XWAnaI+33KGLPPGLMvdiA41yWJhLPiuR6sZcQv/t24q5LJWUl8cErmiB5XREREJJqsK8pnf3UbbzcHd7usywlqsWeMieVCobfJWvvzwW83GGPyBm/PAxovdV9r7XestQuttQvzc/SiU+S9ZuSmMCkriW2H6q78w8N0pLaDN6raeGBJAcaYETuuiIiISLS5e74XY2BLhXODWoI5jdMA3wOOWmu/edFNW4EHBz9/ENgSrAwikcwYw6o5Hl471UJ7d/+IHHPT7kriY1zcs2DciBxPREREJFp50hJYOimTsvIaLjQ0jr5gruzdCPwWsMIYUzH4cSfwD8BKY8xJ4PbBr0XkGhT7PPgDlh1Hr7+V81yvn7LyGtbOy2dsYtwIpBMRERGJbuuKvJxu6Wb/mXZHHj+Y0zhfsdYaa+08a23h4McvrLUt1trbrLXTrLW3W2tbg5VBJNLNG5eGd+wYto9AK+fm8hq6+gbYsLRgBJKJiIiIyOq5HuJiXJQ5tOeedksWCWPvtHK+dLKZc73+az6OtZZNuyqZk59K4fixI5hQREREJHqlJsRy28wcnjpQi38gMOqPr2JPJMwVz/XQ5w+w89glZx0NyxtVZzlW38mGpRM0mEVERERkBJUWeWk+18crbzaP+mOr2BMJcwsK0slOiWf7dWywvnFXFcnxMdw9P38Ek4mIiIjIshnZpCbEsMWBPfdU7ImEOZfLcMfsXHYeb6Snf+Cq79/a1cfTB+r40A1ekuJjgpBQREREJHrFx7hZMy+PZw7X09137ZfdXAsVeyIRoNiXR3ffAC+eaLrq+/50XzV9AwE2LJ0QhGQiIiIiUlropbtvgB1Hrn+C+tVQsScSAZZMzmBsYuxVt3IGApYf7ali8cQMpuemBCmdiIiISHRbNDGD/LSEUZ/KqWJPJALEul2snJXLc0cb6PMPf9LTK282U9nSzXpttyAiIiISNC6X4e5CLy+dbKb5XO/oPe6oPZKIBFXxXA+dPX5ePTX8SU8bd1WSmRTHap8niMlEREREZF2Rl4GA5ekD178/8nCp2BOJEDdOzSI5PmbYrZx17ed57mgD9y4cT3yMO8jpRERERKLbDE8KMz0plFWMXiunij2RCBEf42bFzByePdIwrE07n9hTjQUeWKwWThEREZHRUFrkpbyqjcqWrlF5PBV7IhGk2OehtauPPadbL/tz/QMBnthbxS3TsinITByldCIiIiLR7e75+RgDZeWjs+eeij2RCHLrjGwSYl1XbOV8/mgDDR292m5BREREZBTljx3DkkkZbKmowVob9MdTsScSQRLjYlg2PYdnDtcTCAz9BLJxVxX5aQmsmJkziulEREREpLTQy1vNXRysaQ/6Y6nYE4kwxXM9NHT0Ul7ddsnb327u4pU3m7l/cQFulxnldCIiIiLRrXhuHnFuF5tHYc89FXsiEWbFzBzi3C62H7r0WN8f7a4kxmX46KLxo5xMRERERNLGxLJiZg5P7q8b1lC966FiTyTCpCTEcuPUTLYdqn9fL3hP/wA/ff0Md8zJJSc1waGEIiIiItGttCif5nO9vHqqJaiPo2JPJAIV+/I4c/Y8h2s73vX9pw/U0dbdz4YlGswiIiIi4pRlM3JITYihLMitnCr2RCLQytm5uF2Gbe9p5dy4u5LJWUl8YEqmQ8lEREREJCHWzZ1z83jmcD3n+waC9jgq9kQiUHpSHEsnZ7xrC4bDte2UV7XxwJICjNFgFhEREREnlRR66eobYMfRhqA9hoo9kQi12pfHqaYuTjZ0ArBpdxXxMS7uWTDO4WQiIiIismRSBnlpCUFt5VSxJxKhVs3OxRjYdqiezp5+yspruGt+PmMT45yOJiIiIhL1XC7D3YX5vHSiidauvuA8RlCOKiKOy0lNYEFBOtsO1VNWXkN33wAblmowi4iIiEioKC304g9Ynj5QG5Tjq9gTiWCrfR6O1nXw7RdOMSc/lfnj0pyOJCIiIiKDZuWlMtOTErQN1lXsiUSw1T4PAHXtPWxYOkGDWURERERCTEmhlzeq2qhq6R7xY6vYE4lg49ITmTcujZT4GEoK852OIyIiIiLvcffga7QtFSO/uhcz4kcUkZDyDx+aR/v5fhLj9OsuIiIiEmq8Y8eweFIGmytq+IMVU0e0E0sreyIRbnZ+qjZRFxEREQlh64q8vNXUxaGajhE9roo9ERERERERB93pyyPO7aJshFs5VeyJiIiIiIg4KC0xlmUzstm6v5aBgB2x46rYExERERERcdi6Ii9Nnb28eqp5xI6pYk9ERERERMRhy2fmkJIQQ1n5yG2wrmJPRERERETEYQmxbop9HrYfquN838CIHFPFnoiIiIiISAgoLfLS1TfAc0cbRuR4KvZERERERERCwNJJmXhSE0Zsg3UVeyIiIiIiIiHA5TLcXZjPC8ebaO3qu/7jjUAmERERERERGQGlhV78AcvTB+uu+1gq9kRERERERELErLwUpucms6X8+ls5VeyJiIiIiIiECGMMJYVe9lWepbq1+7qOpWJPREREREQkhJQU5gNc96AWFXsiIiIiIiIhZFx6IosnZrC5vAZr7TUfR8WeiIiIiIhIiCkpyudUUxeHazuu+Rgq9kRERERERELMmrl5xLoNZdcxqEXFnoiIiIiISIgZmxjHshk5bN1fy0Dg2lo5VeyJiIiIiIiEoNJCL42dvex6q+Wa7q9iT0REREREJATdNiuHlPgYNl9jK6eKPRERERERkRCUEOtmtc/D9kP113R/FXsiIiIiIiIhqrTIy7le/zXdV8WeiIiIiIhIiFo6OZPc1Phruq+KPRERERERkRDldhnKPnPjNd1XxZ6IiIiIiEgIy0sbc033U7EnIiIiIiISgVTsiYiIiIiIRCAVeyIiIiIiIhFIxZ6IiIiIiEgEUrEnIiIiIiISgVTsiYiIiIiIRCAVeyIiIiIiIhFIxZ6IiIiIiEgEUrEnIiIiIiISgVTsiYiIiIiIRCAVeyIiIiIiIhFIxZ6IiIiIiEgEUrEnIiIiIiISgVTsiYiIiIiIRCAVeyIiIiIiIhHIWGudznBFxphO4LjTOWRIaUC70yHksnSOQp/OUWjT+Ql9OkehT+cotOn8hL4Z1tqUq7lDTLCSjLDj1tqFToeQSzPGfMda+ymnc8jQdI5Cn85RaNP5CX06R6FP5yi06fyEPmPMvqu9j9o4ZSQ86XQAuSKdo9CncxTadH5Cn85R6NM5Cm06PxEoXNo492llT0REREREotW11EThsrL3HacDiIiIiIiIOOiqa6KwKPastSr2QoQx5vvGmEZjzKGLvvdXxpgaY0zF4MedTmaMdpc6R4Pf/0NjzDFjzGFjzNecyhfthvgd+vFFvz+njTEVTmaMdkOco0JjzK7Bc7TPGLPYyYzRbohzNN8Y85ox5qAx5kljTKqTGaOZMWa8MWanMebI4L85fzz4/QxjzA5jzMnBP9OdzhqtLnOO7h38OmCMUVddiLmWmigsij0JKY8Bqy/x/X+21hYOfvxilDPJuz3Ge86RMWY5UALMt9bOAb7hQC654DHec36stR995/cH+D/g504Ek197jPc/z30N+OvBc/QXg1+Lcx7j/efou8CfWmvnApuBL4x2KPk1P/B5a+1sYCnwGWPMbOBPgeettdOA5we/FmcMdY4OAR8CXnIynIyckCv2hlqVGLzt88YYa4zJciKbgLX2JaDV6RwytCHO0e8D/2Ct7R38mcZRDybA5X+HjDEG+Ajw+KiGkncZ4hxZ4J2VojSgdlRDybsMcY6m85sXqDuAD49qKPk1a22dtfaNwc87gaOAlwtvOv5g8Md+AJQ6k1CGOkfW2qPWWm135rDLrLx+fbBL64AxZrMxZuyVjhVyxR5DrBwZY8YDdwBVox1IhuUPBv/H+77aMkLSdOBmY8xuY8yLxphFTgeSS7oZaLDWnnQ6iLzPZ4GvG2OqubAy/iWH88j7HeZCMQFwLzDewSwyyBgzESgCdgO51tq6wZvqgVyHYslF3nOOJDQMtfK6A/BZa+cBJxjGv0UhV+xd5l3vfwa+yIV3VyW0/CcwBSgE6oB/cjaOXEIMkMGFJ4wvAD8ZXEWS0HI/WtULVb8P/Im1djzwJ8D3HM4j7/dx4NPGmNeBFKDP4TxRzxiTzIXW9M9aazsuvs1eGAev13QOu9w5EudcZuX1WWutf/DHdgHjrnSskCv2LsUYUwLUWGv3O51F3s9a22CtHbDWBoD/ATS4IPScAX5uL9gDBAC1Q4cQY0wMF66T+LHTWeSSHuQ311L+FD3PhRxr7TFr7R3W2snOShAAAAcHSURBVAVceNPklNOZopkxJpYLRcQma+07vzsNxpi8wdvzAF1S4KAhzpGEmMusvH4c2Hal+4d8sWeMSQT+jAsXxEsIeueJe9A6LlzcK6GlDFgOYIyZDsQBzY4mkve6HThmrT3jdBC5pFrg1sHPVwBqtQ0xxpicwT9dwJeB/3I2UfQa7Bz5HnDUWvvNi27ayoU3Thj8c8toZ5MLLnOOJIQMtfJqjPlzLrR6brrSMWKCF2/ETAEmAfsHu87GAW8YYxZba+sdTRaFjDGPA8uALGPMGeAvgWXGmEIutGOcBn7XsYAy1Dn6PvD9wcFHfcCDgy00MsoudX6std8D7kMtnCFhiN+hTwL/OrgC2wN8yrmEMsQ5SjbGfGbwR34OPOpQPIEbgd8CDl60lcyfAf/AhcsIPgFUcmEglThjqHMUD/w7kA08bYypsNaucihjVBtq5dUY8zFgLXDbcF7LmVB8vTe4XPmUtdZ3idtOAwuttVqVEBERERGRiDK48voDoNVa+9mLvr8a+CZwq7W2aTjHCrk2zsF3614DZhhjzgy++yMiIiIiIhIN3ll5XWGMqRj8uBP4Dy4MoNox+L0rtquH5MqeiIiIiIiIXJ+QW9kTERERERGR66diT0REREREJAI5XuwZY6wxZuNFX8cYY5qMMU85mUtERERERCScOV7sAV2AzxgzZvDrlUDN1RxgcBS2iIiIiIiIDAqFYg/gF8Cawc/v56K9powxi40xrxljyo0xrxpjZgx+/2PGmK3GmF8Cz49+ZBERERERkdAVKsXeE8B9xpgEYB6w+6LbjgE3W2uLgL8A/u6i224A7rHW3jpqSUVERERERMJASLQ/WmsPDG6kfj8XVvkulgb8wBgzDbBA7EW37bDWto5KSBERERERkTASKit7AFuBb3BRC+egvwV2Wmt9wF1AwkW3dY1SNhERERERkbASEit7g74PtFlrDxpjll30/TR+M7DlY6MdSkREREREJByFzMqetfaMtfbfLnHT14C/N8aUE1rFqYiIiIiISMgy1lqnM4iIiIiIiMgIC5mVPRERERERERk5Kvb+f3v3EnJVFYZx/P9gJqEiRiXiJQs0IlADKQgHNjAyAoUudMEcBEFQFBQUTWpQ4EgqLAoskogiUKhJSYMGkRiSeEElkyQyTCONLJLw821wVnIQNaL6zuc+/9/k7P2utRd7jQ4Pe6+1JUmSJKmDDHuSJEmS1EEDCXtJZiX5NMmeJLuTPNbqlyb5JMnX7Xdqq9+fZGeSXUk2J1nQN9atSb5Ksj/J04OYjyRJkiSNNQPZoCXJdGB6VW1LMhn4ElhB79MKR6tqdQtuU6vqqSQ3AXur6liSZcBzVXVjknHAPmApcBDYCtxbVXtGfVKSJEmSNIYM5MleVR2qqm3t+DiwF5gBLAfWt27r6QVAqmpzVR1r9S3AzHZ8A7C/qr6pqj+A99oYkiRJkjTUBr5mL8kc4HrgC2BaVR1qTT8A085yyYPAR+14BvBdX9vBVpMkSZKkoTbQj5QnmQRsAB6vql+SnG6rqkpSZ/S/mV7YWzyqNypJkiRJF5iBPdlLMp5e0Hunqja28uG2nu+vdX1H+vrPB9YBy6vqp1b+HpjVN+zMVpMkSZKkoTao3TgDvEFv05U1fU0fAqva8Srgg9Z/NrARWFlV+/r6bwXmJrkqycXAPW0MSZIkSRpqg9qNczHwGbALONXKz9Bbt/c+MBv4Fri7qo4mWQfc0WoAJ6tqURvrNuBFYBzwZlW9MGoTkSRJkqQxaiBhT5IkSZL0/xr4bpySJEmSpP+eYU+SJEmSOsiwJ0mSJEkdZNiTJEmSpA4y7EmSJElSBxn2JElDKclIku1JdifZkeSJJOf9X0wyJ8l9o3WPkiT9G4Y9SdKw+r2qFlbVdcBSYBnw7N9cMwcw7EmSLgh+Z0+SNJSS/FpVk/rOrwa2ApcBVwJvAxNb8yNVtTnJFuBa4ACwHngZWA0sASYAr1TV66M2CUmSzsOwJ0kaSmeGvVb7GbgGOA6cqqoTSeYC71bVoiRLgCer6vbW/yHgiqp6PskE4HPgrqo6MKqTkSTpLC4a9A1IkjQGjQfWJlkIjADzztHvFmB+kjvb+RRgLr0nf5IkDZRhT5IkTr/GOQIcobd27zCwgN769hPnugx4tKo2jcpNSpL0D7hBiyRp6CW5HHgNWFu99Q1TgENVdQpYCYxrXY8Dk/su3QQ8nGR8G2dekolIkjQG+GRPkjSsLkmynd4rmyfpbciyprW9CmxI8gDwMfBbq+8ERpLsAN4CXqK3Q+e2JAF+BFaM1gQkSTofN2iRJEmSpA7yNU5JkiRJ6iDDniRJkiR1kGFPkiRJkjrIsCdJkiRJHWTYkyRJkqQOMuxJkiRJUgcZ9iRJkiSpgwx7kiRJktRBfwJCD0w9h9BfQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweetsperday = df.resample('D').count()\n",
    "\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.title('Twitter messages per day, \\\n",
    "user: Ks47656475', size=16)\n",
    "\n",
    "tweetsperday['id_str'].plot() #Plot each individual tweet per day\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Word frequencies\n",
    "Creates a word frequency chart by counting the most frequent words. This is a very simple method for detecting the main content of discussion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordlist = []\n",
    "\n",
    "# Extracts tokens minus Swedish stopwords\n",
    "tokenlist = []\n",
    "for d in df['text'].iteritems():\n",
    "    #print(stopwords.words('swedish'))\n",
    "    tknzr = TweetTokenizer()\n",
    "    dtokenized = tknzr.tokenize(d[1])\n",
    "    #print(dtokenized)\n",
    "    for token in dtokenized:\n",
    "        tokenlist.append(token)\n",
    "             \n",
    "for t in tokenlist:\n",
    "    if t not in stopwords.words('swedish'):\n",
    "        if len(t) > 2: #remove short special chars like :, ;, #\n",
    "            if t.startswith('http'): #remove links\n",
    "                continue\n",
    "            if t.startswith('@'): #remove users\n",
    "                continue\n",
    "            #print(t[0] + \"\\t\" + str(t[1]))\n",
    "            wordlist.append(t)\n",
    "\n",
    "word_freq = Counter(wordlist).most_common(10)\n",
    "\n",
    "wordfreqdict = {}\n",
    "\n",
    "for w in word_freq:\n",
    "    wordfreqdict[w[0]] = w[1]\n",
    "\n",
    "dfwords = pd.DataFrame.from_dict(wordfreqdict, orient=\"index\")\n",
    "dfwords = dfwords.sort_values(by=0, ascending=False)\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "plot = sns.barplot(x=dfwords.index, y=dfwords[0], data=dfwords, color=\"royalblue\")\n",
    "for item in plot.get_xticklabels():\n",
    "    item.set_rotation(45)\n",
    "    item.set_fontsize(16)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mentions network\n",
    "- which users are frequently mentioned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mentioned_users = []\n",
    "\n",
    "for tweet in df['text']:\n",
    "    match = re.findall(\"(?<=@).*?(?=[\\s|\\:|\\.])\", tweet, re.IGNORECASE)\n",
    "    if match:\n",
    "        for m in match:\n",
    "            mentioned_users.append(m)\n",
    "            G.add_edge(\"Ks47656475\", m)\n",
    "    nx.write_gexf(G, \"Ks47656475_mentions.gexf\")  # use this file for network analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('pwolodarski', 26)\n",
      "('svtnyheter', 22)\n",
      "('Folkhalsomynd', 18)\n",
      "('mjau10303930', 17)\n",
      "('alicemedce', 16)\n",
      "('hanifbali', 11)\n",
      "('ThomasDiLeva', 10)\n",
      "('Bardissimo', 10)\n",
      "('Ivarpi', 9)\n",
      "('rooshv', 8)\n",
      "('CarlSchlyter', 8)\n",
      "('DrippingCreati1', 7)\n",
      "('carlbildt', 7)\n",
      "('MelLilja', 6)\n",
      "('MonaJohansson9', 6)\n",
      "('Panshiri_M', 6)\n",
      "('chrisforsne', 6)\n",
      "('Jerlerup', 5)\n",
      "('BengtHojer', 5)\n",
      "('ltenstedt', 5)\n",
      "('andreasnurbo', 5)\n",
      "('MagnusRanstorp', 5)\n",
      "('tuffse', 5)\n",
      "('Vallamnius', 5)\n",
      "('anderslindberg', 4)\n",
      "('nymananders', 4)\n",
      "('TomHedrup', 4)\n",
      "('AgnesWold', 4)\n",
      "('FawAzzat', 3)\n",
      "('robertrea', 3)\n"
     ]
    }
   ],
   "source": [
    "for user in Counter(mentioned_users).most_common(30):\n",
    "    print(user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retweets\n",
    "- which accounts/users are often retweeted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "retweeted_accounts = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('MelLilja', 4)\n",
      "('robertrea', 3)\n",
      "('nntaleb', 2)\n",
      "('andreasnurbo', 2)\n",
      "('TomHedrup', 2)\n",
      "('MagnusRanstorp', 2)\n",
      "('NorbertElekes', 2)\n",
      "('ThomasDiLeva', 2)\n",
      "('DellAnnaLuca', 2)\n",
      "('MariaHolm72', 1)\n",
      "('geofflemon', 1)\n",
      "('bradleyziffer', 1)\n",
      "('Baphometpomade', 1)\n",
      "('Andromake000', 1)\n",
      "('Per_Laber', 1)\n",
      "('P4Jamtland', 1)\n",
      "('WHO', 1)\n",
      "('somethingbrite', 1)\n",
      "('cmuggla', 1)\n",
      "('BeleGunnar', 1)\n",
      "('gorberg', 1)\n",
      "('CoronaSverige', 1)\n",
      "('JohanErnelind', 1)\n",
      "('evinbdya', 1)\n",
      "('Ivarpi', 1)\n",
      "('anckarsater', 1)\n",
      "('PeterKolchinsky', 1)\n",
      "('HillaryClinton', 1)\n",
      "('scopedbylarry', 1)\n",
      "('martinklepke', 1)\n",
      "('salonium', 1)\n",
      "('nimagap', 1)\n",
      "('FredricMorenius', 1)\n",
      "('BjorklundVictor', 1)\n",
      "('pwolodarski', 1)\n",
      "('ROGSAHL', 1)\n",
      "('denfundersamma', 1)\n",
      "('MaryvanSet', 1)\n",
      "('hanifbali', 1)\n",
      "('mattiasreporter', 1)\n",
      "('Tedekeroth', 1)\n",
      "('gustavkassel', 1)\n",
      "('DrEricDing', 1)\n",
      "('jenanmoussa', 1)\n",
      "('sjunnedotcom', 1)\n"
     ]
    }
   ],
   "source": [
    "for tweet in df['text']:\n",
    "    match = re.findall(\"(?<=RT\\s\\@).*?(?=\\:)\", tweet, re.IGNORECASE)\n",
    "    if match:\n",
    "        retweeted_accounts.append(match[0])\n",
    "\n",
    "#print(retweeted_accounts)       \n",
    "for user in Counter(retweeted_accounts).most_common():\n",
    "    print(user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}