top-user-sentiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top User Sentiments\n",
    "\n",
    "*Prepared by:*  \n",
    "**Jude Michael Teves**  \n",
    "Data Scientist / Machine Learning Engineer, Asian Development Bank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries\n",
    "\n",
    "Note that this notebook is dependent on the CSV files generated by `top-user-tweets-parser.ipynb`.\n",
    "\n",
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\jude_\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "import glob, re, json\n",
    "\n",
    "from itertools import chain\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer, SentiText"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vader Constants / Extra Lexemes\n",
    "\n",
    "The following can be found here: https://github.com/cjhutto/vaderSentiment/blob/master/vaderSentiment/vaderSentiment.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_INCR = 0.293\n",
    "B_DECR = -0.293\n",
    "\n",
    "# (empirically derived mean sentiment intensity rating increase for using\n",
    "# ALLCAPs to emphasize a word)\n",
    "C_INCR = 0.733\n",
    "\n",
    "N_SCALAR = -0.74\n",
    "    \n",
    "NEGATE = \\\n",
    "    [\"aint\", \"arent\", \"cannot\", \"cant\", \"couldnt\", \"darent\", \"didnt\", \"doesnt\",\n",
    "     \"ain't\", \"aren't\", \"can't\", \"couldn't\", \"daren't\", \"didn't\", \"doesn't\",\n",
    "     \"dont\", \"hadnt\", \"hasnt\", \"havent\", \"isnt\", \"mightnt\", \"mustnt\", \"neither\",\n",
    "     \"don't\", \"hadn't\", \"hasn't\", \"haven't\", \"isn't\", \"mightn't\", \"mustn't\",\n",
    "     \"neednt\", \"needn't\", \"never\", \"none\", \"nope\", \"nor\", \"not\", \"nothing\", \"nowhere\",\n",
    "     \"oughtnt\", \"shant\", \"shouldnt\", \"uhuh\", \"wasnt\", \"werent\",\n",
    "     \"oughtn't\", \"shan't\", \"shouldn't\", \"uh-uh\", \"wasn't\", \"weren't\",\n",
    "     \"without\", \"wont\", \"wouldnt\", \"won't\", \"wouldn't\", \"rarely\", \"seldom\", \"despite\"]\n",
    "\n",
    "# booster/dampener 'intensifiers' or 'degree adverbs'\n",
    "# http://en.wiktionary.org/wiki/Category:English_degree_adverbs\n",
    "\n",
    "BOOSTER_DICT = \\\n",
    "    {\"absolutely\": B_INCR, \"amazingly\": B_INCR, \"awfully\": B_INCR, \n",
    "     \"completely\": B_INCR, \"considerable\": B_INCR, \"considerably\": B_INCR,\n",
    "     \"decidedly\": B_INCR, \"deeply\": B_INCR, \"effing\": B_INCR, \"enormous\": B_INCR, \"enormously\": B_INCR,\n",
    "     \"entirely\": B_INCR, \"especially\": B_INCR, \"exceptional\": B_INCR, \"exceptionally\": B_INCR, \n",
    "     \"extreme\": B_INCR, \"extremely\": B_INCR,\n",
    "     \"fabulously\": B_INCR, \"flipping\": B_INCR, \"flippin\": B_INCR, \"frackin\": B_INCR, \"fracking\": B_INCR,\n",
    "     \"fricking\": B_INCR, \"frickin\": B_INCR, \"frigging\": B_INCR, \"friggin\": B_INCR, \"fully\": B_INCR, \n",
    "     \"fuckin\": B_INCR, \"fucking\": B_INCR, \"fuggin\": B_INCR, \"fugging\": B_INCR,\n",
    "     \"greatly\": B_INCR, \"hella\": B_INCR, \"highly\": B_INCR, \"hugely\": B_INCR, \n",
    "     \"incredible\": B_INCR, \"incredibly\": B_INCR, \"intensely\": B_INCR, \n",
    "     \"major\": B_INCR, \"majorly\": B_INCR, \"more\": B_INCR, \"most\": B_INCR, \"particularly\": B_INCR,\n",
    "     \"purely\": B_INCR, \"quite\": B_INCR, \"really\": B_INCR, \"remarkably\": B_INCR,\n",
    "     \"so\": B_INCR, \"substantially\": B_INCR,\n",
    "     \"thoroughly\": B_INCR, \"total\": B_INCR, \"totally\": B_INCR, \"tremendous\": B_INCR, \"tremendously\": B_INCR,\n",
    "     \"uber\": B_INCR, \"unbelievably\": B_INCR, \"unusually\": B_INCR, \"utter\": B_INCR, \"utterly\": B_INCR,\n",
    "     \"very\": B_INCR,\n",
    "     \"almost\": B_DECR, \"barely\": B_DECR, \"hardly\": B_DECR, \"just enough\": B_DECR,\n",
    "     \"kind of\": B_DECR, \"kinda\": B_DECR, \"kindof\": B_DECR, \"kind-of\": B_DECR,\n",
    "     \"less\": B_DECR, \"little\": B_DECR, \"marginal\": B_DECR, \"marginally\": B_DECR,\n",
    "     \"occasional\": B_DECR, \"occasionally\": B_DECR, \"partly\": B_DECR,\n",
    "     \"scarce\": B_DECR, \"scarcely\": B_DECR, \"slight\": B_DECR, \"slightly\": B_DECR, \"somewhat\": B_DECR,\n",
    "     \"sort of\": B_DECR, \"sorta\": B_DECR, \"sortof\": B_DECR, \"sort-of\": B_DECR}\n",
    "\n",
    "# check for sentiment laden idioms that do not contain lexicon words (future work, not yet implemented)\n",
    "SENTIMENT_LADEN_IDIOMS = {\"cut the mustard\": 2, \"hand to mouth\": -2,\n",
    "                          \"back handed\": -2, \"blow smoke\": -2, \"blowing smoke\": -2,\n",
    "                          \"upper hand\": 1, \"break a leg\": 2,\n",
    "                          \"cooking with gas\": 2, \"in the black\": 2, \"in the red\": -2,\n",
    "                          \"on the ball\": 2, \"under the weather\": -2}\n",
    "\n",
    "# check for special case idioms and phrases containing lexicon words\n",
    "SPECIAL_CASES = {\"the shit\": 3, \"the bomb\": 3, \"bad ass\": 1.5, \"badass\": 1.5, \"bus stop\": 0.0,\n",
    "                 \"yeah right\": -2, \"kiss of death\": -1.5, \"to die for\": 3, \n",
    "                 \"beating heart\": 3.1, \"broken heart\": -2.9 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Sentiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check if the lowercase version of a token exists in our augmented lexicon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "lexicon_augmented = list(chain(*[sid.lexicon, NEGATE, BOOSTER_DICT, SENTIMENT_LADEN_IDIOMS, SPECIAL_CASES]))\n",
    "\n",
    "def get_used_tokens_ratio(text:str) -> float:\n",
    "    sentitext = SentiText(text)\n",
    "    tokens = sentitext.words_and_emoticons    \n",
    "    if len(tokens)==0:\n",
    "        return 0\n",
    "    tokens_in_lexicon_mask =  [token.lower() in lexicon_augmented for token in tokens]\n",
    "    return sum(tokens_in_lexicon_mask)/len(tokens_in_lexicon_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_csv_and_calculate_sentiment_and_token_ratio(filename:str) -> pd.DataFrame:\n",
    "    df = pd.read_csv(filename)\n",
    "    df = df[['text']]\n",
    "\n",
    "    sid = SentimentIntensityAnalyzer()\n",
    "    df['sentiment_score'] = df['text'].apply(lambda x: sid.polarity_scores(x)['compound'])\n",
    "    df['used_tokens'] = df['text'].apply(get_used_tokens_ratio)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_statistics_and_graphs(df:pd.DataFrame):\n",
    "    display(df.describe().T)\n",
    "    \n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), dpi=100)\n",
    "    sns.distplot(df['sentiment_score'], ax=ax1)\n",
    "    ax1.set_title('Sentiment Score Distribution')\n",
    "\n",
    "    sns.distplot(df['used_tokens'], ax=ax2)\n",
    "    ax2.set_title('Used Tokens Ratio Distribution')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### March 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment_score</th>\n",
       "      <th>used_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India in space:-\\n1962: INCOSPAR by Jawaharlal...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @NewsHtn: Exclusive interview with #Raghura...</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A salesman at our door! After selling us jumla...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @dhruv_rathee: Read the truth behind how Mo...</td>\n",
       "      <td>0.7184</td>\n",
       "      <td>0.086957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#Dubai Burj Khalifa sends peace &amp;amp; respect ...</td>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                                text  sentiment_score  \\\n",
       "0  India in space:-\\n1962: INCOSPAR by Jawaharlal...           0.0000   \n",
       "1  RT @NewsHtn: Exclusive interview with #Raghura...           0.1280   \n",
       "2  A salesman at our door! After selling us jumla...           0.0000   \n",
       "3  RT @dhruv_rathee: Read the truth behind how Mo...           0.7184   \n",
       "4  #Dubai Burj Khalifa sends peace &amp; respect ...          -0.2960   \n",
       "\n",
       "   used_tokens  \n",
       "0     0.000000  \n",
       "1     0.100000  \n",
       "2     0.000000  \n",
       "3     0.086957  \n",
       "4     0.200000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'top_tweets_2019_03.csv'\n",
    "df_2019 = read_csv_and_calculate_sentiment_and_token_ratio(filename)\n",
    "df_2019.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 77,
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sentiment_score</th>\n",
       "      <td>4338.0</td>\n",
       "      <td>0.162327</td>\n",
       "      <td>0.406923</td>\n",
       "      <td>-0.9539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.493900</td>\n",
       "      <td>0.9742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_tokens</th>\n",
       "      <td>4338.0</td>\n",
       "      <td>0.102434</td>\n",
       "      <td>0.106772</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.7500</td>\n",
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       "                  count      mean       std     min  25%       50%       75%  \\\n",
       "sentiment_score  4338.0  0.162327  0.406923 -0.9539  0.0  0.000000  0.493900   \n",
       "used_tokens      4338.0  0.102434  0.106772  0.0000  0.0  0.083333  0.153846   \n",
       "\n",
       "                    max  \n",
       "sentiment_score  0.9742  \n",
       "used_tokens      0.7500  "
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\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_statistics_and_graphs(df_2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save VADER results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019.to_csv('vader_sentiments_2019_03.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### March 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>text</th>\n",
       "      <th>sentiment_score</th>\n",
       "      <th>used_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @SINGHSAKSHI15: Dear @DrSJaishankar and @PM...</td>\n",
       "      <td>0.3818</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @Drsunil0198: All international indian stud...</td>\n",
       "      <td>0.4995</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The 538 new cases and 10 Deaths reported today...</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>This is to inform you that students are studyi...</td>\n",
       "      <td>-0.6249</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @Sandra68043039: Indian Students stuck in U...</td>\n",
       "      <td>-0.5413</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  sentiment_score  \\\n",
       "0  RT @SINGHSAKSHI15: Dear @DrSJaishankar and @PM...           0.3818   \n",
       "1  RT @Drsunil0198: All international indian stud...           0.4995   \n",
       "2  The 538 new cases and 10 Deaths reported today...           0.3182   \n",
       "3  This is to inform you that students are studyi...          -0.6249   \n",
       "4  RT @Sandra68043039: Indian Students stuck in U...          -0.5413   \n",
       "\n",
       "   used_tokens  \n",
       "0     0.047619  \n",
       "1     0.076923  \n",
       "2     0.090909  \n",
       "3     0.047619  \n",
       "4     0.176471  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'top_tweets_2020_03.csv'\n",
    "df_2020 = read_csv_and_calculate_sentiment_and_token_ratio(filename)\n",
    "df_2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sentiment_score</th>\n",
       "      <td>15284.0</td>\n",
       "      <td>0.090994</td>\n",
       "      <td>0.412610</td>\n",
       "      <td>-0.9623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.427800</td>\n",
       "      <td>0.9805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>used_tokens</th>\n",
       "      <td>15284.0</td>\n",
       "      <td>0.098824</td>\n",
       "      <td>0.104329</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std     min  25%       50%       75%  \\\n",
       "sentiment_score  15284.0  0.090994  0.412610 -0.9623  0.0  0.000000  0.427800   \n",
       "used_tokens      15284.0  0.098824  0.104329  0.0000  0.0  0.083333  0.142857   \n",
       "\n",
       "                    max  \n",
       "sentiment_score  0.9805  \n",
       "used_tokens      1.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5ANnnQ5lMBmtr6wKHPy1fvhz169fH5s2bxd9f+/btERAQgLVr12LFihVFNX++XF1dASDfxU1u376NZ8+eYeTIkeJ3j3r16uHAgQNIS0tDjRo1xHpqy63tlff29sZ7770nvtfvv/+e5/1VKhXWr1+PFi1aAMieV9K9e3d88cUXmDx5sl7l9/T0hJWVVb7XNW0ey5YtQ8eOHXXayNvbG71798YXX3yBsLAwANnB4qRJk8QbuF5eXjh58iS+//578fsTFY49FmZq8uTJ+Omnn7BixQoMHDgQ9vb2OHLkCIKCgvDFF18AAO7evYvHjx+jW7duOndN2rRpA3t7e/zyyy8675nzD0775TrnkCl9tGjRApaWL+NRNzc3NGjQQOdC4OTkJN4hu3z5MjIyMvKUUXv3ImcZq1atqjO2U1vGnENt9OHg4IA1a9bg9OnTmDdvHgIDA/H8+XN8/vnn6Nmzpxj0aLuXc69KsWrVKixZsgS///47FAoF+vTpo/O8j48PatWqhejoaJ3jjRs3Fv/fkHqXJD8/P9y7dw9vv/02NmzYgJs3b6JPnz4YOXJkka/NWf6CBAYGQiJ5edro0aMHNBoNfvvtN6PKndO5c+dgYWGBXr166RzXBhk5271Ro0Y6F/7q1asDMPwzQ1ReCYIAAOJE1fHjx2P+/Pn47rvv4ObmhpkzZ+KVV15BRkYGrly5gs6dO0Oj0YjnpDp16qBhw4biOenixYto1aqVGFQA2cFHzZo1S6S8586dy3fuRd++fcUyal29ehVhYWGoWbMmJk6cqJP+7Nmz8PPzg7W1tViXKlWqwNvbu9BrH5B9bdGeI7y8vCCVSjFgwADxznTTpk0RFhZWaFABZA9fOnDgAPbv348FCxbAwcEBHTt2xJo1a+Dh4SGma9q0KXbv3o1XXnkF9+/fx08//YSIiAj8/fffUCqVerVbamoqbty4gV69eolBBZB9ve3cuXOe61FxaD9LOTVp0gROTk4YO3YsFixYgO+//x41atTAjBkzxPNtQfS5ptSpU0cMKoDsc3jLli1LtIdAG3TnvpbXq1cPLVq0yNN2OT8vNjY2cHJy4jXFAOyxMGOOjo5488038eabbwIAbt68iZkzZ2L58uXo27ev2FX48ccfi5OucoqPj9d5bGtrK/6/9suhoes+53eizfm+uWnLOHbs2Hyfz1nG3O+jPcnlHFdviNq1a2PYsGEYNmwY1Go1Tp8+jTlz5mDRokU4ePCgWLacF9CctGNftXdzcnJ1dc2zRF/OdIbU21BxcXEFrgLVq1cvqNVq7N69G+vWrcPq1atRq1YtTJs2Db179y70fQtqh5xyt4X2Nc+fP9ez9EVLTk6Gs7OzTgALQLybmLPdc39mtJ/r4n5miMyJnZ1doUNCFAqF+Dcgk8mwa9cubNy4EcePH8fevXtha2uLvn37Yu7cuXj+/DnUajW2bNmS75Aj7WpEycnJ4l31nLR/f8Z69uxZgedUQPdccvv2bXTt2hXff/89du/ejWHDhonPJScn48iRIzpLpBdUVhsbG53HgiCI54hXX30VO3bswObNm7Fnzx5s27YNjo6OGDp0KCZPnpzvl22tatWqoXnz5gCyb7rVq1cPo0aNwtSpUxEeHq6TduvWrdiyZQuePXsGNzc3NG3aFLa2tjpDagujvR7l93twc3PLcz0yhHaYW37XFXt7e+zevRsbN27EsWPHsGfPHtja2qJ///748MMPIZVKC3zf4lxTgOybjHfu3DGgBoUrqu1iYmJ0juV3XeE1RX8MLMxMXFwcBgwYgMmTJ2PQoEE6z3l6emLKlCkIDQ3FgwcPUKVKFQDAzJkz810+VLt0nClpy7h8+XLUq1cvz/P5nVSMceLECcyfPx979uxB/fr1xeMSiQQ9evTA+fPnsW/fPp2yJSYm6pxQ7969i8TERLH9njx5kmeYT0JCAurUqVNgOUqr3snJybhx4wb69etXYBptMJqSkoKff/4ZW7ZswYwZM+Dj41PkHaai5A4gEhISAOheQHLPbzC0V8zR0RFJSUnIysrSCS60wZizs7NB70dUXrm6uuL27dv5PqdQKJCYmKhzLmnQoAGWLVsGlUqFq1ev4tChQ9izZw9q166NoUOHQhAEBAcH53uTQftlytnZOd8hMYaMeS+Mk5OTzuRcLe25JOffd5cuXbBp0yZMmTIFK1aswOuvvy6eqx0cHNClS5d8l0TPuZqiPlq1aoUNGzZAoVDg4sWL2LNnDzZu3IjXXnsNgYGBer9Pu3btMHjwYOzZswcHDhzAwIEDAQBff/01li1bhpkzZ+Ktt94S5wB88MEH+PPPP/V6b+31SNtOOcXHxxt1Xjx79izs7e3zndcBAA0bNhQnvF+5cgVff/019uzZg1dffRWjR48udr5A/jelnjx5kicoMea6UpptR3lxKJSZcXV1haWlJXbv3i2u0JDT3bt3YW1tjbp166JBgwZwcXHBw4cP0bx5c/GnRo0aWLFihUEb9OTsWi1JLVu2hJWVFeLi4nTKaGVlhRUrVuis4FESZXR3d8ezZ8/E4WK53bt3T+ye1Y5D1s7D0Fq5ciUWLlyIli1bQiqV5rkjduHCBcTGxsLb27vAcpRkvXPatGkTlEplgUsYTpkyRRyH7ODggJ49e2LChAlQqVTiF/OcQ5kM9dNPP+k8PnbsGARBEANbe3v7PKt3aIeeaRX1e/T19YVKpcLx48d1jmtXptH+3ogqOl9fX8TGxoor8OR0+vRpqFQqcTGKb7/9Fn5+fkhISICFhQW8vLywYMECVKlSBY8fP4a9vT08PT1x9+5dnXOSu7s71q1bJw4H8fPzw+XLl3Uma8fExODBgwclUqc2bdrg/v37eVarOnz4MKRSqdgDALy8AfPhhx9CEAQsWLBA531iYmLg6ekp1qVp06bYvn17nnN6YbZt24bXX38dCoUCUqkU7dq1E0cA/PvvvwbXb+rUqXB2dsby5cvFO+UXL16Es7Mz3nvvPTGoSE1NxaVLl3TuhBd2btT+/o4fP67zmuTkZPz444/FPi9ev34d3333HQYOHJhv78OxY8fQrl07JCYmwsLCAt7e3vjkk09gZ2cnto+FhUWxd72+e/euzvXw4cOHuHr1Ktq2bQvg5SiJnL+LO3fu5OmhKaztGjVqhKpVq+a5lt+7dw/Xrl3jNaWEscfCzFhYWGDBggUIDQ3FgAEDMGzYMDRs2BAZGRn45ZdfsGvXLkyePFmMwMPCwjBv3jxYWFiga9eueP78OTZs2IC4uDg0bdpU73y1cyTOnj2Lhg0bipO0jOXs7IyQkBCsXr0aqampaNu2LeLi4rB69WoIglDgHZLCynjmzBk4Ojrm+9oGDRpg7NixCA8PR2xsLPr27YsaNWrg6dOnOHToEM6ePYvt27cDyB47+sYbb2D58uWQy+Vo2rQpfv75Z5w6dQqrVq0Sx5WuW7cOVlZWeP311/Hw4UOsXr0ajRo1KnSjPmPrnZiYKE50U6lUePr0KU6cOIGjR4/i/fff17n45uTn54f58+dj6dKl6NSpE54/f45169ahXr16Yp5VqlTBzZs3ce7cOZ2xrfq4fv065s6dizfffBPXrl3DmjVrMHDgQLFXpmvXrvjuu++wePFidO/eHRcvXhRX2NIq6vfYqVMntG3bFvPnz0d8fDw8PT1x7tw5bNmyBW+99Rb3vKBKo1evXvjiiy8wZswYjBs3Dk2bNoVarcalS5ewdetW9O7dW7zB4e3tDbVajdDQUIwdOxYymQzffPMNUlJS0KNHDwDZX3rHjh0rrnqjUqmwbds2XLlyRZzcPHLkSBw4cADvvfceJk6cCJVKhVWrVhncC1CQgQMHYvfu3Rg/fjwmT56MWrVq4dSpU/j6668xefLkfIfbVqtWDVOmTMGiRYtw7Ngx9O7dGxMnTkRQUBDef/99DB48GFZWVtizZw/OnDlT6MqGufn5+eGzzz7DpEmTMGTIEEgkEuzevRvW1tbiqk6GqFKlinhdXr16NebNm4cWLVpg3759+PTTT9GlSxc8fvwYW7duRWJios4KRg4ODrhx4wZ+++23fBdRmTp1KsaNG4cxY8Zg2LBhyMzMRHh4OLKyssQNVQujvaZoN8i7cuUKPv/8czRo0CDPHBYtHx8fKJVKTJgwASEhIZDJZDh69Cjkcrn4uapSpQrUajWOHz+OFi1aGLRhq7W1tfhZUCqVWL16NapWrYp3330XQHYvkLW1Nf773/9iypQpSElJwdq1a/Nscuvg4CBe13J/f7GwsMDUqVPx0UcfiZ/9pKQkrFmzBs7OznrNQST9MbAwQ126dMG+ffsQERGBTZs2ITExEVKpFJ6enli5cqX4xwwAgwYNgkwmw9atWxEZGQk7Ozt4e3tj+fLlhQ7Vyc3e3h6jRo1CZGQkzpw5U6KTi6dMmQI3Nzfs3r0bW7duhaOjI9q1a4epU6fqTPouiru7O958803s2rULP/30E44ePZpvuqlTp+K1117D/v37sWjRIqSmpqJKlSrw8fHBgQMHdL7ILlu2DOvWrcOOHTuQlJSE+vXrY9WqVXjjjTcAABMnToSrqyt27tyJ/fv3w8nJCW+88QamTJlS6NwSY+v9ww8/iHt4WFpawtXVFY0bN8amTZsKvdgNHjwYSqUSe/fuxe7du2FjY4N27dphxowZ4heD0aNH47///S/ee+89McjS1/jx43Hz5k28//77cHBwQEhIiM5KLQMGDMD9+/cRFRWFyMhI+Pr6YvXq1RgyZIiYpqjfoyAICA8Px5o1a/Dll18iMTERtWvXRlhYGEaNGmVQeYnKMysrK+zcuRObNm3C/v37sWbNGkgkEtStWxdhYWHily8g+8v31q1bsXr1asydOxcZGRlwd3fH2rVrxV4Nf39/REREYN26dZg0aRKsrKzEu/zaCavOzs7Ys2cPFi9ejNmzZ0MmkyEkJCRPD2Jx2dnZYdeuXVixYgVWrVqFtLQ0NGjQAEuWLCn0Zs2wYcMQFRWFRYsWoX379njttdewa9curFq1CtOnT4cgCGjcuDE2bNhgUEDg6emJDRs2YMOGDZg6dSrUajWaNWuGbdu2oW7dusWq46BBgxAZGYm9e/finXfewcCBA8X9g3bu3Inq1auja9euGDJkCBYsWIC///4b9evXx/Dhw/Hhhx8iJCQEn376aZ4vzx07dkRERATWrl2LsLAwcQWqFStWFLkqn0ql0unpdnJyQs2aNTF27FgMGTKkwInq1atXR0REBFatWiV+rho3boy1a9eiTZs2ALKXwT98+DBmzpyJoKAgzJkzR++28vT0RI8ePTB//nykpaXBz88Pc+fOFYcnOTk5iSteTZgwAbVr18akSZPEIc1a7733HpYuXYqQkBB8+eWXefLJ+V0pNDRUnGg/bdo0veaCkP4ETXH7r4iIiIiIiF7gHAsiIiIiIjIaAwsiIiIiIjIaAwsiIiIiIjIaAwsiIsojMTERAQEBOrvSzp8/H82aNYOXl5f4ExkZWeB7bNmyBZ06dUKrVq0wfPhw3L17tyyKTkREJsLAgoiIdFy8eBFBQUG4f/++zvFr165h4cKFuHz5svhT0J4qUVFR2LFjByIiIhAdHY2mTZti0qRJxV7vnoiIzB8DCyIiEkVFRWH69OkICwvTOa5QKHD79m00a9ZMr/fZt28fhg4dCnd3d1hbW2PatGmIjY3V6QEhIqKKhYEFERGJ/P39cerUKfTq1Uvn+K1bt5CVlYU1a9agffv2CAwMxObNm3V2Ac4pJiZG3OUeyN6ToV69erh161aplp+IiEyHG+QREZHIzc0t3+MpKSnw9fXF8OHD8dlnn+GPP/5AaGgoJBIJQkJC8qRPS0vLs4mkjY0N0tPTS6XcRERkemUeWDx9mgJTDLEVBMDFxcFk+ZcHbKPCsX2KxjYqnDm1j7Ys+urQoQM6dOggPm7RogVGjhyJ48eP5xtY2NraQi6X6xyTy+WQyWTFLzQREZm1Mg8sNBqY9IJq6vzLA7ZR4dg+RWMbFa48ts/p06fx5MkTDB48WDymUChgY2OTb3p3d3fcuXMHXbt2BQAolUrcu3dPZ3iUPso6CDOn4K+0sa4VU2WqK1C56muquhpyI4pDoYiIqEgajQZLlixB3bp14ZIR3+UAACAASURBVOfnh99//x1ffvkl5syZk2/6AQMGYO3atejUqRPq16+PlStXwtXVFT4+Pgbma5ogrDwGf8XFulZMlamuQOWqrznXlYEFEREVKSAgAHPmzMGCBQsQFxcHV1dXTJw4Ef369QMAXLhwAWPGjMGxY8dQs2ZNDBw4ECkpKQgNDUViYiKaN2+O8PBwWFlZmbgmRERUWgRNGS8q/uSJ6eZYuLo6mCz/8oBtVDi2T9HYRoUzp/bRlsXclXVbmdPvqLSxrhVTZaorULnqa6q6GnK94HKzRERERERkNAYWRERERERkNAYWRERERERkNE7eJiqCIAgv/n35uIynJhFRGdP+3efEv3siosIxsCAqhBJAamYWAEAAkJGUjgx5FmRSC3BtG6KKKTlDiUR5FnKHEfb8uyciKhQDC6ICCIKA1MwsXPwnCYosFQDAXmYDZaYS3nWdUdXakncwiSoYQRCQIlfi4j9JyHzxdw8AUksLtObfPRFRoRhYEBVBkaWCXKmGAMAqSw1lji8bRFQxZb74uyciIv1x8jYRERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERmNgQURERERERnNoMDixo0bGDZsGHx8fODv749FixZBoVCUVtmIiMhEEhMTERAQgOjoaPHYiRMn0K9fP3h7e6Nbt25Yt24d1Gp1vq9Xq9Xw8vJCq1at4OXlJf6kp6eXVRWIiKiMWeqbUK1WY9y4cRg7dix27NiB+Ph4BAcHw9nZGaGhoaVZRiIiKkMXL17E7Nmzcf/+ffHY9evXMXPmTKxatQqdO3fG33//jTFjxsDOzg6jR4/O8x4xMTFQKpW4dOkSpFJpWRafiIhMRO8ei+TkZCQkJECtVkOj0WS/WCKBra1tqRWOiIjKVlRUFKZPn46wsDCd448ePcLgwYPRtWtXSCQSNGzYEAEBATh//ny+73Pt2jV4eHgwqCAiqkT07rFwdnZGcHAwli5dik8//RQqlQqvv/46goODDcpQEAwtYsnQ5muq/MsDtpEuQQC0TSFAt31yPqaX+BkqnDm1T0Fl8Pf3R58+fWBpaakTXAQGBiIwMFB8LJfLcebMGfTp0yff97l27RoyMzMxYMAAPHr0CA0bNsS0adPg7e1dovUgIiLzYdBQKBsbG/znP//BwIED8c8//+CDDz7AmjVrMGXKFL0zdHFxKFZBS4qp8y8P2EYvZSSlw15mA6usl+PIZXY2sLWTwsXZzoQlM2/8DBXOnNvHzc2tyDSpqamYPHkybGxsCry5ZGNjgxYtWmDy5MlwdHTErl278N577+Hw4cOoU6eO3uUp6yAs9w0EnedMUJ7SZE6BbmljXSuuylRfU9XVkPwEjXZcUxFOnDiBlStX4ttvvxWPHT58GIsXL9aZ3FeUp09ToF+OJUsQsi/mpsq/PGAb6RIEAYnyLPz61xNkKtUQBEAms0GWQoF2DVxR1cYSev75VBr8DBXOnNpHW5bCeHh44Msvv0Tbtm3FY3fv3sWkSZPg4uKCNWvWwNHRUe88e/fujSFDhuDdd98tdrnLwsOkdETfTURmjhsK1pYStG1QFbV5Q4GIqEB691j8+++/eVaAsrS0hJWVlUEZajQw6QXV1PmXB2yjlzQ5/33xQKPJ/l+2U8HYNoUrr+3zww8/YOrUqXjnnXcwbdo0WFoWfAlZuXIlAgMD4enpKR5TKBSwtrY2KM+yDsIkEgEQJEhLl0OueBlYKK0kyEhX4GmOeYblnTkFuqWNda24KlN9TVVXfW5EaekdWPj7+2PFihXYtGkTxowZg9jYWGzcuLHA8bVERFRx/P777wgNDcWCBQswcODAItPfvn0bFy5cwKpVq+Do6IjNmzcjNTUVAQEBBuVb1kGYRgNAeHkDQec5E5SnLFTEOhWEda24KlN9zbmueq8K1ahRI4SHh+O7775D27ZtMWLECHTr1i3PyiFERFTxbNq0CVlZWVi8eLHOvhQhISEAgAsXLsDLywuxsbEAgCVLluDVV19Fv3790LZtW5w7dw7bt2+Hk5OTKatBRESlSO8eCwBo37492rdvX1plISIiM/Lnn3+K/79p06ZC0/r4+ODy5cviYycnJyxZsqTUykZERObHoJ23iYiIiIiI8sPAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiIiIiIjMbAgoiI8khMTERAQACio6PFY1euXMGgQYPg5eWFbt26Yf/+/YW+x5YtW9CpUye0atUKw4cPx927d0u72EREZEIMLIiISMfFixcRFBSE+/fvi8eSk5MxduxY9O/fH+fPn8fixYuxZMkSXL16Nd/3iIqKwo4dOxAREYHo6Gg0bdoUkyZNgkajKatqEBFRGWNgQUREoqioKEyfPh1hYWE6x0+ePAknJycMGzYMlpaWaNeuHfr06YNdu3bl+z779u3D0KFD4e7uDmtra0ybNg2xsbE6PSBERFSxMLAgIiKRv78/Tp06hV69eukcv3PnDho3bqxzrFGjRrh161a+7xMTE6OT3srKCvXq1SswPRERlX+Wpi4AERGZDzc3t3yPp6WlwdbWVueYjY0N0tPTSyR9QQTBoORG0+YnCEDurAUTlKc05axrRce6VlyVqb6mqqsh+TGwICKiItna2iIlJUXnmFwuh0wmKzC9XC7XO31BXFwcDCtoCUhPSofMzgaWUrV4zNpSAls7KVyc7cq8PKXNFG1sKqxrxVWZ6mvOdWVgQURERWrcuDF++eUXnWMxMTFwd3fPN727uzvu3LmDrl27AgCUSiXu3buXZzhVUZ4+TUFZzveWSARAkCAtXQ654mVgobSSICNdgadqdYWZgC4I2V9QyrqNTYF1rbgqU31NVVdtvvrgHAsiIipSQEAAnjx5gs8//xxKpRK//fYbjhw5ggEDBuSbfsCAAdi5cydu3bqFzMxMrFixAq6urvDx8TEoX42m7H/EfPHyB9r/N0F5yqSuleCHda24P5WpvqY8L+rDoMDi2bNnmDlzJtq2bYs2bdpgwoQJiI+PN+QtiIioHHJ2dsa2bdvw7bffom3btvjoo4/w0Ucfwc/PDwBw4cIFeHl5ITY2FgAwcOBABAcHIzQ0FH5+frh58ybCw8NhZWVlymoQEVEpMmgo1MSJE+Ho6IhTp05BIpFgzpw5+M9//oPw8PDSKh8REZnIn3/+qfO4efPm2Lt3b75pfXx8cPnyZfGxIAgYPXo0Ro8eXaplJCIi86F3YHH9+nVcuXIFv/76K+zt7QEACxcuREJCQqkVjoiIiIiIyge9h0JdvXoVjRo1wr59+xAQEAB/f38sXbq0wKUJiYiIiIio8tC7xyI5ORl//vknmjVrhqioKMjlcsycOROzZs0yaCiUqdYZrkzrHBcX20hXznXsc65frz3OdsqLn6HCmVP7mEMZiIioYtE7sJBKpQCAuXPnwtraGvb29pgyZQreeecdpKWl6b02uanX3jV1/uUB2+iljKR02MtsYJX1ctlJmZ1NhV3PvqTwM1Q4tg8REVVEegcWjRo1glqthlKphLW1NQBArc7+smXImt6mWme4Mq1zXFxsI12CICBDnoXUNDkylWoIAiCT2SAtXV7h1rMvKfwMFc6c2seQdcmJiIj0oXdg0b59e9SpUwcffvghlixZgszMTKxcuRLdu3cXJ3Prw9D1cEuaqfMvD9hGL2ly/ptz/WiwnQrDtikc24eIiCoivSdvW1lZYceOHbCwsEBgYCACAwNRo0YN/Pe//y3N8hERERERUTlg0D4W1atXx8qVK0urLEREREREVE4ZtPM2ERERERFRfhhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0RhYEBERERGR0SxNXQAiIqLyThCEPMc0Go0JSkJEZDoMLIiIqEiHDx/G/PnzdY4plUoAwPXr1/OkDwkJQXR0NCwtX15mVq9ejU6dOpVuQU1ACSA1MyvPcXupBazKvjhERCbDwIKIiIrUt29f9O3bV3wcFxeHAQMGYMaMGfmmv379OiIiIuDr61tWRTQJQRCQmpmFi/8kQZGlEo9LLS3Quq4zqlpbsueCiCoNBhZERGQQjUaDGTNmoEuXLujXr1+e5x88eIDk5GR4enqaoHSmochSQa5Um7oYREQmxcnbRERkkEOHDiEmJgazZ8/O9/lr165BJpMhLCwMfn5+ePPNN3HgwIEyLiUREZU19lgQEZHe1Go1Nm7ciPfffx/29vb5plEoFGjVqhXCwsLg7u6O6OhoTJw4ETKZDD179jQov3zmRJcqbX6CAOTOWsinPDnT6ZPenOSsa0XHulZclam+pqqrIfkxsCAiIr1FR0cjPj4eAwcOLDBN//790b9/f/Gxv78/+vfvj2+++cbgwMLFxaHYZS2u9KR0yOxsYCl9ObTJ2lICWzspXJzt8qTPSEqHvcwGVln6pTc3pmhjU2FdK67KVF9zrisDCyIi0tuJEycQEBAAO7uCvzAfOHAgT++EQqGAtbW1wfk9fZqCspz7LJEIgCBBWroccsXLQEFpJUFGugJP1WqdydiCICBDnoXUNDkylUWnNyeCkP0Fpazb2BRY14qrMtXXVHXV5qsPBhZERKS3ixcvYsSIEYWmSU1NxWeffYa6deuiSZMm+PHHH3H06FFEREQYnJ9GgzK9gGo0AIQX+eZ+roDyaHL9W1R6c1MeylhSWNeKqzLV15zrysCCiIj09vDhQ1SrVi3PcS8vL3z88cfo27cvRo4cifT0dHzwwQd4+vQp6tSpg6VLl8LHx8cEJSYiorLCwIKIiPR2+fLlIo8LgoAJEyZgwoQJZVUsIiIyA1xuloiIiIiIjMbAgoiIKIe453JcfPAMarWZDmImIjJTHApFRET0QoZChUl7fsejZxkQmgvwrGG+yzoSEZkb9lgQERG9sObHu3j0LAMAEJ+SaeLSEBGVLwwsiIiIAET/k4R9l2PFx4npShOWhoio/GFgQURElV66QoVPvv0TANDQTQYASEpXmLJIRETlDgMLIiKq9C4/SkZ8qgLV7KX46E1PANk9Fua6azYRkTkqVmChUqkwfPhwzJ49u6TLQ0REVOa08ykaV7NHfVc7SAQgS61BSmaWiUtGRFR+FCuwWLduHS5cuFDSZSEiIjKJJ6nZw57c7K1hKZHA2c4KAOdZEBEZwuDA4uzZszh58iR69OhRGuUhIiIqc/Gp2T0W1eylAAAXWfa/iWmcZ0FEpC+DAounT59i7ty5WLFiBWxtbUurTERERGUq4UWPhau9NQDAxe5FYMEeCyIivem9QZ5arcaMGTMwatQoNGnSpNgZCkKxX2oUbb6myr88YBvpEgRA2xQCdNsn52N6iZ+hwplT+5hDGcxJgrbHwkG3xyKJgQURkd70DizCw8MhlUoxfPhwozJ0cTHtLqamzr88YBu9lJGUDnuZDayy1OIxmZ0NbO2kcHG2M2HJzBs/Q4Vj+5gfbY9FNW2PhUw7x4JDoYiI9KV3YHHo0CHEx8fDx8cHACCXywEAp0+fNmgi99OnKTDF6n2CkH0xN1X+5QHbSJcgCMiQZyE1TY5MpRqCAMhkNkhLlyMjXYGnajWXosyFn6HCmVP7aMtCgFKlRlJGds+EW645Fs8ylFCr+WEmItKH3oHFt99+q/NYu9Ts//3f/xmUoUYDk15QTZ1/ecA2ekmT898XDzSa7P9lOxWMbVM4to95efJigralRICTrRXkABxsLGEpEZCl1uCZXAk7awvTFpKIqBzgBnlERFSpafewcLOXQngx+UQiCC+XnE3jPAsiIn3o3WORm6E9FUREROZI22Ph9mJ+hVZVOykSUhVIyuA8CyIifbDHgoiIKrV4ceK2VOd4VfZYEBEZhIEFERFVak9eLDXrmqfHgrtvExEZgoEFERFVagX1WDjbafey4FAoIiJ9MLAgIqJK7WWPRe7AIrvH4rk8CyouOUtEVCQGFkREVKnF59ocT0smtYBEyF5eOiUzywQlIyIqXxhYEBFRpaXRaJCQql1uVjewEAQBVWyyey2SMzjPgoioKAwsiIio0kpTqJChVAN4uet2TlVssldlT85gjwURUVEYWBARUaWV8GIYlL21BWyt8u6uLQYWcvZYEBEVhYEFERHp5fjx4/D09ISXl5f4M2PGjHzT/vDDD+jTpw9atWqFnj174vvvvy/j0upHHAYls873+ZdDodhjQURUlGLvvE1ERJXLtWvX0K9fPyxZsqTQdPfu3cPEiRPx2WefoUuXLjh58iSmTJmCkydPonr16mVUWv1oeyzyGwYFAI7iUCj2WBARFYU9FkREpJdr166hWbNmRaaLioqCj48PunfvDktLS/Tq1Qtt2rRBZGRkGZTSMPHaHguHgnosOBSKiEhf7LEgIqIiqdVq3LhxA7a2tti6dStUKhU6d+6M6dOnw9HRUSdtTEwMGjdurHOsUaNGuHXrVlkWWS9PtD0Wsvx7LDgUiohIfwwsiIioSImJifD09ERgYCDWrFmDpKQkzJo1CzNmzMDmzZt10qalpcHW1lbnmI2NDdLT0w3OVxCMKnaREtKyeyyqOVhDEF7mJwiAgJdDoeRZaqRnZsHFRveyqU0HvPwXOR6XdvmNkbOuFR3rWnFVpvqaqq6G5MfAgoiIiuTq6opdu3aJj21tbTFjxgy88847SE1Nhb29vc5zcrlc5/VyuRwymczgfF1cHIpfaD0kyVUAgAavOMLVNTuv9KR0yOxsYCnNXobW1soCGUoVnmep4eJin+c9MpLSYS+zgVWWWjxmbSmBrZ0ULs52pVr+klDabWxOWNeKqzLV15zrysCCiIiKdOvWLRw9ehTTpk2D8OL2lUKhgEQigVSqO4yocePGuHHjhs6xmJgYveZn5Pb0aQo0muKXuyjxyRkAAKlahSdPUiCRCIAgQVq6HHJFdqBQxcYSGUoV7iek4qmLHTQ5CiQIAjLkWUhNkyNT+TKwUFpJkJGuwFO1Wid9ztflll+60iQI2V9QSruNzQHrWnFVpvqaqq7afPXBwIJIT2fuPMGthDQM9q5p6qIQlTknJyfs2rULjo6OGDVqFOLj47Fs2TK89dZbeQKLvn37Yvv27Th+/Dh69OiBkydP4ty5c5g7d67B+Wo0KNULaGJ69qRsZ1url3kJL/J9kaaKjSXiUjIRn5KZb3k0uf7NeTy/9EoAqZl552zYSy1gZVx1iqW029icsK4VV2WqrznXlatCEenhn8R0/HovCYlpChy88i+UKnXRLyKqQGrUqIHw8HD873//g6+vLwYMGIDmzZtj3rx5AAAvLy8cPnwYANCwYUOsX78e4eHhaNOmDTZs2IC1a9eifv36pqxCHhlKFeQvhi852xX8lV67MlR8isLoPAVBQKpChYv/JOHsX0/En4v/JCFVocq3J4OIqLxgjwVREZQqNb75I158/Ph5Jnaff4Cwzg1NWCqisufr64u9e/fm+9zly5d1Hnfs2BEdO3Ysi2IVW9KL3gqphQC7fHbd1tKuDKVdmrYwzzKUOPt3IrzqOKJdIacIRZYKciVvUBBRxcIeC6Ii/BDzFEnpSthbW+Atr1oAgD0XHuLGv89NXDIiMkZSenYPhLOdtNCegpc9FoUHFumKLOy99Ai/P3qOXecf4q+E1JIrLBFROcDAgqgQD5LS8cvdRABAYJNqaFOvKpq94gC1Blj2XYyJS0dExkh6sZt21UKGQQEvA4uEQgILRZYa+y7Hir0gCpUG847+gSdpxg+fIiIqLxhYEBXi7N1EqDVAvaq28KiWvczkG69VAwBc/zcFz9K5Gy9ReaWduO1kW3hg4fhiKFRCaiZU6vxnTEZd/Rf/Ps+EjZUEI9rUhovMCgmpCkz/+joyszjkiYgqBwYWRIW48ih7uFND15fr7zvYWKJu1ey16S89fGaSchGR8bQ3BorqsZBZW0AiAGoN8u2BiE/JxM3HqRAADGpVE7WcbDHMpzYcrC1x/d8UrPievZtEVDkwsCAqQJZagxux2YHFq866uwi3qu0IALjwILnMy0VEJeNlj4W00HQSQRAncD9+Ls/z/G9/Zw+XrOVkg9pO2ecKF5kUc95oDAFA1NXHOHL9sc5rMpQqXH6YjOv/PkdmlsrYqhARmQWuCkVUgNvxqUhXqmBjKUE1B2ud51rWcsShq//iwgP2WBCVV0kZ2b0PRfVYAICjrSWeZSjxOCUTLXI9pw0s3N10dxb3edUZ4zrUw6Zf7mHp/2KgUKnhZCvF77HPEXUlVhwiZSER4FFNhiavVEFVa16Wiaj84hmMqAAXXwQNdavaQpJrxZgWtaoAAP5+mo6naQq4yAq/40lE5kc70bqwPSy0sudZZOBBUobO8TRFFq48zO65bORqn+d1o/1exfV/n+Pnu4n4v9O6Q6JcZVKoNRokpitx83Eq/u/EbYQHtQR3siCi8opDoYgKcEkMLOzyPFfF1kq8O3npIYdDEZVHhgQWtZxsALw8L2hF30uCUq1BVTsruMjyvo9EEPBJzyYI8qoJv3rO8K7tiDZ1nTGkdU2EtHsVY9vXxfA2tWFlIeDyw2TsPP+wBGpGRGQa7LEgyodKrREDhvoueQMLAPCp44Q7CWm4+OAZAjzcyrJ4RFQCEnPsY1GUhq7Z54HfHyVDrlTB5sWGej/+9RQA4FHNvsC9MBxsLDG9WyMA2TtvJ2Zm4exfT8QN8mo72aKnZzUcvhaHDT//DZ9XHfFadQfjKkdEZALssSDKx52EVKQpVLCTWqBGFet807R+1QkAcOE+51kQlTcajQbP9NzHAsgetuQqk0Kh0oirxanUGvz8Yp8bj+p5h0EZwru2I/wbuiBLrcF/jt+CUqWBIAiFbtxHRGRuGFgQ5UPbW9HslSp55ldoedd2hADgn6QMJKQWviMvEZmXNIUKClX2nhTORexjAWT3NHi/uJlw7n4SAOD6v8/xLEMJe2uLPCvHGcrKQoKp3RvB2c4K/yRmYGv0fSRmZiExMwvcLYeIygsGFkT5uPhiGdkWtasUmKaKjZW4ad4lLjtLVK5o51fYWknEYU1F8aqTvcx09D/ZvZSHXywh26auMywkxvUsWEgEWFpYiMMqd5y7j2+v/4uL/yQhVaFizwURlQsMLIhy0Wg0uPpi/4rmNR0LTdvyxepQN+NSSr1cRFRykl4Mg9Knt0LLu052j8Wf8am4cP8ZjlyPAwD0a/FKiZXLs4Y9ajvZQKnS4Jub8VBwjwsiKkcYWBDlEp+qwLMMJSwE3R2386PtsbgVl1oWRSOiEpJkwMRtLWc7KRq9OCfMPnITGgDdG7vB85WCezYNJQgCejSpBgHAH3Gp+OtJWom9NxFRaWNgQZSLNkio7yKD1LLwP5EmLyZs/hmfCrVGU+plI6KSYchSszm1resMAEiWZ8FSIuCDTvVLvGzVHazFYVdRVx6Lk8yJiMwdAwuiXG7HZwcW2t6IwtR3kcHaUoI0hQoPn8lLu2hEVEKKMxQKAHzrOon//45XTdR2Mm7SdkG6urvCRSZFSmYWPj15mzcuiKhcMCiwuHXrFkaNGgVfX1906NABM2fORGJiYmmVjcgk/tQGFnosH2kpEcShEbc4z4Ko3EgUeyz0HwoFAN61neAik8LNXorRbV8tjaIBAKQWErzVogasJAIu3H+GL849KLW8iIhKit6BhVwuR0hICLy8vPDzzz/j6NGjePbsGT788MPSLB9RmdMGFk306LEAdIdDEVH5oJ1joc8eFjnZSi0QObI19oxoDUcDezsM5WZvjV5NqwMANvz0N07eii/V/IiIjKV3YBEbG4smTZogNDQUUqkUzs7OCAoKwvnz50uzfERl6lmGEo9TsvekaKxnYMEJ3ETlT3HnWACAo61VqQcVWl61q+DN5jWgATDvmz/Fnb6JiMyR3oFFgwYNsHXrVlhYvFzv+8SJE2jatGmpFIzIFLS9DrWdbGBvbanXa3L2WGg4DpqoXBDnWBQjsChLgiDgg84N0NOzGlRqDeYcuYnzLzboIyIyN/p9c8pFo9Fg1apV+P7777Fz506DXmuqPX60+XKPoYKxjV5O3G5SzR6CAGibQoBu++R83MhVBkuJgGR5FuJSMvGKo01ZF9ts8DNUOHNqH3MogymJPRZl1PNgDAuJgAU9m0CuVOP7O08w49BNbB3SSpzfRURkLgwOLFJTUzFnzhzcuHEDO3fuhIeHh0Gvd3FxMDTLEmXq/MuDytxGfydnD4Pyqu8CFxd7ZCSlw15mA6sstZhGZmcDWzspXJztxGMeNRxwI/Y5HmWo0Lxh5W0/rcr8GdIH28e0NBpNjh4LwyZvlzVLiQBBEPBcocK0AHc8TVfg6qPn+ODANWwZ2gp1qlTeGxlEZH4MCizu37+PMWPGoGbNmjhw4ACqVq1qcIZPn6bAFKNFBCH7Ym6q/MsDthFw9UH2EIM69lZ4+jQVGfIspKbJkalUQxAAmcwGaelyZKQr8FStFoc+pDtFnwAAIABJREFUNXKxw43Y5zgfkwCfGpX3LiI/Q4Uzp/bRlqUySsnMgkqd/Qsw9x4LC4mAdKUaNx8+Q2aWCr08q+FxshzxqQqEfXUNO4Z7w85K91LOIZlEZCp6BxbJyckYOXIk/Pz8sHjxYkgkxdsCQ6OBSS+ops6/PKisbZSuUOF+YgYAoLGbfXY7vHhOI/4H4vGc7aSdwP1HnOm/MJqDyvoZ0hfbx7S0S83KpBZFboJpLjKzVJAr1RAgYJBXTXx57iH+SczAx9/exswAdwg5xrbZSy1g3uESEVVUegcWBw8eRGxsLL755ht8++23Os9dvny5xAtGVNbuJKRCA8DNXgoXmWHDI5rkWBlKo9HoXOSJyLxo51cYutSsuahiY4VhPrWw+dd/8L8/E+BgbQGv2tk7dUstLdC6rjOqWluy54KIypzegcWoUaMwatSo0iwLkUn9acCO27m5u8lgIWTfCY1PVaC6g3VJF4/I5G7duoWlS5fixo0bsLKyQocOHTB79ux8h8WGhIQgOjoalpYvLzOrV69Gp06dyrLI+Sov8ysKU8/FDkN9X8XO6Ps4dj0ObjJruNqX3/oQUcVQPvqAicrAzRf7ULymx47budlYWaDBixVa/njMHbip4jF0k9Tr168jIiICly9fFn/MIagAgIQXe9UY2jNpbgZ610IDFzso1RocuvYvstTqol9ERFSKGFgQvaANCF6rXrwJrZ4vXnczjoEFVTyGbJL64MEDJCcnw9PT0wQlLZp2E8wa5bxnUSIIeLvVK7C1skB8qgI/xiSaukhEVMkVax8LooomXaHCvcR0AMXrsQCA12rY49B14I/H3IGbKh7tJqk5FbRJ6rVr1yCTyRAWFoZr167B1dUVwcHBGDhwoMH5lsZ0pThtYFHFOs/7596vRue5fMqTe78bY9IXlG9hx6vYWKK3ZzUcuPIvov9Jwms17NG+oX7tZk77qpQ21rXiqkz1NVVdDcmPgQURsjfGU2uAavZSuNoX7y6mZ43sHovslaE4gZsqrqI2SVUoFGjVqhXCwsLg7u6O6OhoTJw4ETKZDD179jQor9JYEvfpizkW7rWc4Oqa9/3Tk9Ihs7OBpfTl0CJrS0me/Wu08tvvxtD0MmtLWFlZ5MlXn+PeDaT4JzkT5+8lIurqY7zjVxcuLvrfIKlMyw6zrhVXZaqvOdeVgQURXg5fKu4wKCB7B24ri+wduB8ly1HbybakikdkNvTZJLV///7o37+/+Njf3x/9+/fHN998Y3BgURp7fjx80TtpBzWePNEduiiRCIAgQVq6HHLFyy/ySitJnv1rAEAQBJ39boqbXqKyhFJpmydffY93buCMv+JTkJiuxLJvbmF5v6ZFrgplTvuqlDbWteKqTPU1VV0N2feIgQURgD+0E7drFG8YFABYWUjg7maPm49T8EdcKgMLqnD03ST1wIEDeXonFAoFrK0N7w0s6T0/slRqPElTAACqO9jkeW+NBoAAnX1sxOcKKI8m17/FTa/NvzjHrSwk6NOsBnacf4Dvbz/BNzfjENikWj6vKOA9KvgXMi3WteKqTPU157py8jYRjJ+4raWdn3GTK0NRBaPdJNXb2xsREREFBhVAdq/GwoULcfPmTajVapw5cwZHjx5FUFBQGZY4fwlpCqg1gKVEKPV9LARB+yPk+Cm9/Go62qBzIxcAwP+dvoN/n8tLLzMionywx4IqvdTMLPyTlL3jdnEnbmt51nDAV1f+xR9cGYoqmKI2SfXy8sLHH3+Mvn37/n979x0X1ZX/j/9173SYYYABaWJDEAURBMWosZdY11TXRBN3E1NMjGsafsxmN2U3cTeJruaXzS89m5iY3Rg1BXU1iSVZjURjQbFQpHcGmN7P94+BkVFEhjID8n4+5CFz586d97nM3Hve95SL++67DwaDAY899hjq6uoQHR2Nv/3tb0hLS/NR9JdVaZwDt8MUEvDdWMsX8s5EQm2yubU08DyH7pwU9uYYFaq0FuRUavHHzPP4/+9KgkhA1xAJId5BiQXp85pvjBcRIOn0DbOap5w9X6WDg7FurbgQ4k3Xu0nqiRMnXL9zHIeVK1di5cqV3gjNI5UtZoTqTgKeg8HqQE5pA8w2u2u5XCpCbHhAt75vxqw4PPrvkzhdrsHfv8/DupmxNJkEIcQr6DIG6fNyuqgbFOC8G65UyENvsaNYbez09gghXauyqXtQmJfuYWG22WGyOlw/1hZJRneJDJTi5fnDwQHYmV2J7acru/09CSEEoMSCkMsDtzvZDQpwdn8Y1q9pnAV1hyKkx6m6QW6Ody3NXbCGRypx//iBAIBXf8jDgYI6H0dGCOkLKLEgfV7zeIjh4V0zL3RChHM7p8o0XbI9QkjXae4KFRYg9XEk3aO5C9bxonr0V0qQFKmA3cHwf1/l4GBe68mF++ByjrpNEUI6jBIL0qdVa80obTCBw+XxEZ2VGh0IAPiluL5LtkcI6TrNLRbe6grlK2abHWYbwy3Dw5AYoYDNwZDxTQ72nq92W6/RaIXaZIPa7P5j9VHchJDejQZvkz7tl+IGAM7WCoW0a74Oo/srIeCAkgYTKjQmRNygV0YJ6Y1u9K5QVxLwHG4bFYHwACm+u1CDZzPP40K1Do9MHAyxkIfWZMXxonq3AeZioQCpA4MQLBFe9yZ7hBDSErVYkD6tuVVh7IDALtumXCJEQoRz1pdfihq6bLuEkM4xWOzQmGwAbvwWi5YEPIcnZ8RiaVp/AMDHv5Ti0S9Oo1bnvFHglQPMLV4YYE4IuTFRYkH6LMYYsppaLMYO7LrEAricqGRRdyhCeozm1gq5RAC5pG812At4Dn+YEoP1C4bDXyzAr6WNuPvj4zhVQhc/CCFdhxIL0mcVqo2o0VkgEfJIilR26bbHDgwCAGQVNcBBXQkI6REqtc6pZsMVfbd74vS4UPzrnhTEhPihTm/B41tP4vAlta/DIoTcICixIH1WVpGzNSE5KgASYdd+FRIjFJCJeNQbrciv1Xfptns7moGG+Eqlxjs3x+vpBgb74cO7UzB3RD/YGcN/z9Vgf24tjacghHQaJRakz2ruBjVmQFCXb1sk4JHS39kKkkXjLFyswFWzz9AMNMRb+sqMUO0hEwnw4tx4rJwSAwD4ubAe/z1fQ8kFIaRTKLEgfZLNwXC8pHvGVzQb25Sw9NZxFl3dqsBxHHQWO44X1eNIfq3r53hRPXQWO7VckG5XSYmFG47jcHf6ACxIDAMAnChtxDdnq2B3UHJBCOmYvjV6jZAmOZVa6C12KKVCxIV2/o7brWlOWH4taYTJaodUJOiW9+kOVgA6s81tmUIsgKiVyr+nVzgtTTPQEOJtVRrnGAtKLNylDQgEBw7fnq3E2Qot7A6GsYNVwDV2U2sXAailgxACUGJB+qj/FTjvQJs2IBACvnuulMeE+CNcIUGl1owfcmsxd0RYt7xPV+M4DjqzDVmX1KjUGNFgsELA8xgQ4g8RzyHIT4QgPzFU/mIIeM4t4Wiub3AcRxUN0uM0t1j09TEWrUmIUEAs5LDjdCXOV+nw7Ndn8Ze5w6/aV61ddAAAuVgAkZdiJYT0XJRYkD7HZnfgqzNVAIBpsSHd9j48x+HWpAi89b9CfHmqoscmFs1XH20OhpOlDTh8SY2fCtQoVBvQVo8IngOC/cQYqPJDpFKK8AApAmUiqAKk4OwOCAUcHDYHrA4Gq935f4PJhtxqLRwOQMhzEAt5hMrFGKa3IEjce1p0SO+jM9tQ1uBssRgQ5OfjaLyP45oTf85tWUuxoXLclRKJL0+W41SZBnd++AseHD8QdyZHQioSuC46HC+qd7vXBd1QjxDSjBIL0uccyKtDnd4Clb8YU7sxsQCAhYlheOdIEU6Xa5Bbo0NsN3W76igrgDPlGuw9V4XvL9Sg3uA+jFok4BAkE0EqEkAuFaFBb4bWbIfeYoODAbV6C2r1FhzvZBzvHSmGUuq8seDICAXGDQrGiHAFuqkxifRB2RUaMABRSilC/MW+DserhLxznJTaZEPLar+A54Ar8vlBwX5YMX4gDuapcaZCg82HLuHNnwoxIkyOxMgAhCgkaNRbECKXdPlseoSQ3o8SC9LnfHGyHACwaGQ4RILuPTGGyCWYMlSF7y/WYvupCmTMiO3W92svm4PhpwI1thwrwakyjWu5n0iA4REKTBkWCmazQyzgwXEclDIhRvQPwonCOpisDjgYg85sg8XugFQiwqnietTqLTBanS0TFqsdATIR/EQCCHkOIgHn3Ncch0aDBTYHg9XOYLbZoTXb0Gi0odFkw+FLahy+pMbbh4uglIkwfnAQJg1R4abBwa4bmtEVUdIRzZ/zUVEBPo7E+wQ8B4PVgZzSBphbtDQopCIMjw66quWin0KC1+9IxOHcOrxzuBAVGjOyK7TIrtC61hHxHIaFyTEqMgBxYV17wYTGcBDSe1FiQXqEK08k3XUSya/V49fSRgg44NakiG55jyvdlhSB7y/WYve5aqyaNAR+Puzy02i04qvsSnxxstzV35zngKEh/kiKDMCQEH8E+4vckojW8ByHAKnIlXCEyIQwWR3gAMjlUljNFtwUE+LWNYLjOKjNNhzJr3XbrlImRGyEEvvPVaGgVo8itRF5tXo0Gq3YnVON3TnVEPAcRoQrMGZgECYNVWFEqD/NIkU8cqq8KbGI7HuJRTPzFRMnSIT2VtcT8hwEPI+JsSEYP1SFSo0Z2eUaFNTqUdZoxMVKHeqNVpyp0OJMhRYjwuUYFh6A4C64mzmN4SCkd6PEgvhcayeS7jqJbGtqrbg5RuW1mWHSBgRiQJAMxfVG7Mqpwh3Jkd3+ni0r3Ywx/FraiJ2nK/BDbi3MNmfFQikTYs6IcEQGiCER+nZ8g0jgHGuhkAgxKkoJu4Oh3mhBjd6G/+XXokZnQXa5BtnlGnxwpAgqfzFuGhSElP5KJEYoMCjYDzwlGuQabHYHzjQlFklRSh9H0/O11sKhEPOYMCQYseEB+PVSLQpqDThdrsHpcg1yKnVY8ekJPDk1BnNH9Otw0k9jOAjp/Six6CM8aVr2ZjN0ayeS7jqJVGhM2JVTDQBeqdw34zkOt4+KwMYDBXjzp0sYPzgYkUppt71fc6Km1luw73w19uRUuQatAkBcqD8Wj47C7Ph+MDjYVS0IPYGA5xAT4o8FyUFIiVKgotGEgjoDCtUGFKmNqNNb8O3ZKnx71jkIXybiER0ow4Ag5090kAzRgTL0D5RB5S/2WosY6Zku1uhhsjmgkAgxRNX3Bm531LVaODiOQ1SgDFGBMoyODsTunCpUaMx4fs8F7DlfjXUzYxER0PFjHE1JTUjvRYnFDai5EuVgDAaLHY1mG9RGKzjOeWXY+cMhWCaC3xVjDBqN1qsG+AFttyB0pNLGGHN7XXefSCw2B9Z+cw4Gqx2JEQqMHdA9N8W7VlJ2V3IkvrtQg+wKLf6YeR7vLE6CsBvGd1TrLNh9vhq7z1biUt3lWZ3EAh7T40OxODkSI8LkrpveGVrpctATBfmJkeonxoQhwUgbFIziWj2OXKrHmQoNciq1MFoduFijx8Ua/VWvlQh5RCiliAiQIlIpxeAQP4yMCECcyq9b/gak52nuBjUyUkEtW10sTCHBivEDUaYx45Ojxfi5sB6LPzqGx24ejDuSI2l/E9LHUGJxAzBZ7ThXpcPFah3y6gzIr9WjRmdGnc4C63XuoCoV8lD5ixHsJ0aIXAylvxg6oxVSIQ+5RACZWAClVIT0ISoMDJBCJHA/SVgYQ63OAoPVDoPFBp3ZDoPZhga9BXV6C+oMFtTqLKgzWFGnt0BvscFodcDuYJAIeUhFPAJlIkiFAihlQkQqpRga6t/l++gfBwuQU6lFgFSIl+cP75b++W32DRbweGlePO75+FdkV2jwzpEirJw4uNPvWd3URehUWSOOFtWjoM7g9nyUUopRUQFI7q/ElGH9vNqV4MrpLbtilwt5DhKRAEPDFIgJUwAA7A6GCo0R1VoLCmr1KG8wobTBiPJGE2q0ZphtDhTWGVB4xb6RCHmMjAzA6Cglxg8JxvAwOVWCblCnyxoBAKMiqRtUdxDwHBan9sec+H74y38v4GSZBq/+kI99F2qwbmYcBneglchqd8Bqd8BPTNUUQnoT+sb2QlVas6tva3a5BuerdbC3kUDwTS0VDsZgdzC3exOYbA6UNZpQ1mi65usBAAcKADgTEaGAA89xsDucLSIdraaabQ6YbQ40Gq+ujH92rAxp0YFIjgrA6GglIgOkHUoGrHYHPv6lxDUT1Itz4jvVRH8t7ekbHKWU4dlZcVj37Tl8eLQEFRoznpgyBEF+15/6kjGGOoMV+TV6XKzR4UyFFtkVGtToLO5xABgWrkB0oBQxKj/Xtr09LWRr01vyPIfOtklda3YbuVSEEZFKwMEQFSDBmAFKyKUiDAqV4+D5KlRqzGgwWqE2WFGrM6NWb4HObMex4gYcK27AO0eK0E8uxs0xKkwZqkJqdGC3zxhGvIMxdnngdh+cEcqbBgX74e3Fo7DtZAXe/PESTpZp8Nt/HcP0uFAsTx+AYf0uzx7V2gWOWr0Fn58ow4GLNSipN8HOGELlYsSHyTEoRI7gfj1rum5CyNUosejh6vQW5NbocLFaj3NVOmRXaFDVNJtPSyp/MRLCFYgKksFitcNPLIBCIkSEUoKkAcE4WaR2dTVijEEo4DB6YDB4B0Ot3gK13gK10YIqvQ2nS+qhMdlgsNhhtNphsjlgstrhYM5EBK30nuEAiIU8ZCIe4QFShCskUPmLofITQyV3/h/oLwLPO1spBDwHi80Bs92Baq0F/8urQYXGjNJ6I2p0FpQ3mvB1YyW+PlMJwNncntJfieFhcsSHyTE42A+BMpEr2bgy6TBY7Mgqqsf/92MBCtVGAMDvxw3AhCHBra7fVVfxr9ela+awUBTWGfDez0XYc64aRy6pMXdEGBLCFRjUdFXPeX8IMyoazShtcM6QlFejR73RetX2eM65b/oHyjBY5YeRUQEYNSC4zRmdvKG1BEAuFSE2vGsqdtfq+91yuURoh0jgbJHzb3HVUy4RYOwQFYrqDDhd1ogTpY04VlSPap0FX56qwJenKuAvFmBSjAoz40ORPiAIYpqvv9eq0JhRo7NAwHNICFf4OpwbVnMLpYDnsXh0FCbFqPDa/jwczKvDvgs12HehBokRAbgloR8mxqjQTy6BCM4uu3k1emw/XYFvzlTCYnc/FtfoLKjRqZFV1IAnp8Zg0cjwXjkjHE2hS/oKSix8jDGGRqMNFVoTKhpNqNA6K5Ql9QZcqNGjTm+56jUCDhgaKkd8uALDwxUYEaFAmEICgYCHzcFwtMVgXFHTfQha4jgOMpEAATIRmIPBTybCgBD/ppslCfBToMSt0iYV8RgXo4IEHBqNVtgcDIwBAgFgdgDZZQ2w251jJqQi/qppRpvfU93KFX25VITEKCWsVpvrPQU8IJWIkF3aiOxyDS5U61ClNWPPuWrsOVfteq2/WICIACkUUiGkIuesRmabA/UGC4pa3DU6SCbCqkmDMT/Beedrb85C1ZoV4wdi/JBg/HXvReTW6LH117J2vY4DEB0kw9AQf4yMDEC0yg/1OrNbC5Soh91R7sqKfk8g4DkYbQy1WjNUfiLMiAvB5JhgVGjMqNRacKSgDmqDFbvPVWP3uWrIJQJMHhqCmcNCMXYAtWT0NsdKGgAAw/rJXccJ0rVaa6GUSIV4bt5wLK7W4e1DBThdpsGZCufPa9/loZ9CgnCFBAV1eujMl48NI8IVGKLyQ5RSCqlIgII6PU6Xa1CkNuLlfbn4Mb8Of5wdh+B2tPT2FDSFLulLKLHoQhzHwWZ3QG2wQm1wjjFQGyyoN1jRYLQ23QTMikajFQ0mGxqNVjSabG12Y+IADAiWIS5Ujrh+coyMCEBihAIGB3NV0i/V6HCpRufRFeFWpxO8xs2Smuc0tzoYZLLLh0Ge5yBzMIgFPEyOy4nIlX3rm5cBV1/Rb62yKZcIMaJ/IIQARoTLYbE5UNJgRIXGDL3FjoIaPSq1zt/zaq8erNusn0KC6XEhWDFuIBRSYVMc3puFqi0J4Qp8snQ0vs+txamyRpyt0KJCYwI4wG5n8BMLECgTIdBPhOhAGSYPC8WAQBkkTRUjnueuSiKvpzvGPPRmV7Z6xIb64zcp/TFhcBDya/U4W6FFTqUWGpMNmWerkHm2CgFSIabGhmBGXChG91dSS0YPp7fY8M7hIgDAxMHBPo7mxtVWF8XY8ADcNioCNw9RIbtCg9NlGtQbrajWmlHd1PouE/FIjQ7EsjHRGBTqj58LLre4JkY4u8NWai344EgRfixQY8m/juO52XGYOETlcazebjmgKXRJX+NxYlFXV4fnnnsOWVlZEAgEWLhwITIyMiAU9p0cxe5gqNSaUFxvRLHaiKJ6IwrrDShWG1GtNXdozIHKX4xQuRhiAQ+FVIggPxEGBfthdmI4pALebZsmAA60r5J+Pe25ony9k0ZLrV25AjrWv75lbFFKGWJC/DF+qLM1xGixo7zRhGqdGeVaM06XNcJud3bxkgh5DAz2w+yE8GsetDs7C1VnK+lWADqbA2MGB2NMU4XnWsmCUiZEdLA/znaiW1F3jXm4EVntDvSTS9AvVoJ5Cf0gl4lx4GItDuXVot7gvMHgV9mVkIl4pEUHYsKQYIwfHNwtY3d6Gk+O/wcPHsRrr72GkpISRERE4JlnnsHUqVO9Gu9bPxWiSmtGpFKKpWP6d3p7bV00IW3fgE8hFWL8YOd3xcEYIoJk0BmsGBTsh6Gh8qZjFFBnuvrKPs9xuD0lClNiVPhj5jnk1xqwZsdZzBoWituTI5ASpWyze5TF5kCjyYoivQ12ixXKFt1oga5vOTBZ7ThVpsGp8kZY7AwmmwN1ejMiA5yz0/E9rGWZkK7kcTbwhz/8AWFhYfjxxx9RW1uLRx55BB999BEeeOCB7ojPZxhjqDdaUaw2orjemTwU1xtQVG9EaYMRVnvbg6X9xUL0U0igbLrqrJQJnXcqlgqhkksgFfIIkIoQIBNCKRVBKhZcValUyoRg4HC8qP66FXpvaM9dWz1JQjx1ZeU4UCFBsFKKoQ4GiYC7qvuWJ5UAT9b1tJLeWhKiM7XeLaytfdSZbkXdPebhRiUS8Ijpp4DZYkdylAJFaiPOVGhwoUoPrdmGHwvU+LFADcA5cHVUVABGhCuQGK7AkBB/CG+wCkR7j/+FhYVYtWoVNmzYgClTpmDv3r34wx/+gL179yIsLMwrsWaXa/CfE85JG9bNiIWsk92guvKiSV8XIBUiMVIJ5mBgADRW5zHpevsyNlSOf90zGv/86RI+O16GvRdqsPdCDaKUUgwMlrm6RzUYnb0EmnsL6C3ux0uxgEOwnxgRSgmig/wwbVgo0qKUnZrkQme24UBeLfaer8Hxkoarxoo0kwh5DA3xR0r/AIwd7HmLCyE9nUeJRVFREbKysnDo0CHIZDJER0dj5cqVePXVV3tVYmF3MDSanAed+uZuSwYrKhpNKG+aIams0QhjG1e0RTyHyEAZ+gdK0T/ID5GBUjTqzZCLhc5uLH4ijOgfdM0Kdk5pA6o1RlRr3Je3pj0V+p6mO2L2pHLsSSXA0wpDZ+No3m5XtDh5qieOeegNzDY7LDaGiKYrjotG8ghRynC0QI1fihqQU6lBodp5A7+vsp0TDkiEPOL7yREdJEOkUorIACmiAqUYIRTCYbFDIrx6/FNP5snxf8eOHUhLS8OMGTMAAHPnzsX27dvx73//G48//rhX4n1tfz4YgHkj+iF9UFCnt9edF036ms7sS4mQx5opMbhleD98eaoCe89Xt2tmQwEH+EuE0JpssNgZKrVmVGrNOFGqwdfZleA5YECQH8IDJAhTOH/6ycUIlUuglAohlwghEfLgOQ6Opln6anUWnK/S4nhpI85UaNwuOPaTi5E2IBBBMjFMdgdyKjS4VGeAyebA2UotzlZq8c2ZKqQPCkL6gCCMiFAgSintdALcUQ7GoDXZ0Giyod5gcXXp1ppssDoYbA4GqZCHv1gIpUyIYD8RVP5ihPiLoZAIe9WxjHQvjxKL3NxcBAYGul1xiomJQXl5OTQaDQICrn9w5XmgI90JNSYr3vqpCPUGKxzMOXjYwZxXO1y/t1wG5+8OB4PZ5oDRaofVzqC32mFuRxcYAc9DIeERKhcjMtBZMegfJENYgARGsx3+Et41572/WIj+Kn9cqGiEuenKi59Y6JzmVcjBwS5/4UQ81+ZyuVQIkcB9Gy2Xebrck3X9Jc5l/hIhhHz3v19Ht9Fy37W23wBALhHBZHPgUrUWFvvlv3fz36rl+u1dl+MAP7EAvAOdiqO1GHrifu7Iulfuo94Qc0e2IZeI0D/ID9ZoOxIjFTBa7ChSG1GhMUFtsCK/Rge9xYH8OgPyr7h/RjMBB/hJBPATCeAvFkIkcCahAp6DgAMEHAee55z/c9xVLWgKqQArbhqEfoqODWL1tB7gyfE/Ly8PcXFxbq8fOnQozp8/73GcHT1n+IkFiOvnjyemxYD34EI0xznbFttz/AGu/d3vis9fVy2/1rpdcczvzPL27kuxUAABj6a/o3P9xMgAJEYG4PFpQ3CsqNHVOgE4K/UqPxGU0qYeA1JnZdjICfDT+UpUay2o1Tkna6jROcd6NBhtqNVbUKu34EyFFp6SCAWIC5Vh+rBQTIkNwcCgy1Ok15tsOFHSAJPV2YU3p0KLnCotDBYHDl+qx+FL9a7tBEgFkAp5CHjn1O4i3jmLIgAwMDT9A2v6H2Cu3y9/Txh4nofN7mh6jrme4zhn1zIOzvEfjDFoTDZozTZc57ZX1yQScAiSiSAW8hALeNf/Ip6DnTmTEpuDuaa8tzmc/zf/AM4Lvqyp/ibgnce85v953nmRjudaLnceIwVNF+9EQgGsLZJU525grgPd9Q537TkcXnnMZFe9jrvmM629vkNxcIBQJIBDbbD0AAAWmElEQVS1RSvcld29Wz6y2p1T+uvMNiwcGY7l6dHXD6K1t/XgfMExD0YNffXVV9i4cSMOHDjgWlZcXIyZM2fi4MGDCA8P9yROQgghvYQnx//ly5cjJSUFq1evdi37xz/+gZMnT+Kjjz7yYtSEEEK8yaMOhX5+fjAajW7Lmh/7+3f93ZIJIYT0DJ4c/2UyGUwm964pJpOJzhOEEHKD8yixiI2NRUNDA2pra13L8vPzER4eDoWCbjxECCE3Kk+O/3FxccjNzXVblpeXh9jYWK/ESgghxDc8SiwGDRqE1NRUvPzyy9DpdCgpKcE///lP3HHHHd0VHyGEkB7Ak+P/woULkZWVhV27dsFms2HXrl3IysrCb37zGx9ETgghxFs8GmMBALW1tXjxxRdx9OhR8DyPRYsW4amnnoJAQHc0JYSQG1lbx/+UlBS88MILWLhwIQDgxx9/xGuvvYbi4mJERUXh6aefxuTJk31cAkIIId3J48SCEEIIIYQQQq7U8bvBEEIIIYQQQkgTSiwIIYQQQgghnUaJBSGEEEIIIaTTKLEghBBCCCGEdNoNn1gYjUYsXrwY27dvb3O9U6dO4c4770RKSgqmTZuGL774wksR+o7BYMD//d//IT09HampqXjmmWeg1+uvuf6f//xnJCYmIiUlxfXz73//24sRd7+6ujqsXLkSaWlpSE9Px1//+lfYbLZW1z148CAWLFiA5ORkzJkzB/v37/dytL7hyT564IEHMHLkSLfPzKFDh7wcsW+o1WrMnDkTR48eveY6ffUz5Gt96XvuSVm3bt2K2bNnIyUlBbNnz8ann37q5Wg7x5OyNrt48SJGjRrV5ve0J/KkrFlZWa76zeTJk/H22297OdrO8aSs//rXvzBt2jSMHj0aCxYswH//+18vR9s1evX5g93ALl68yG699VYWFxfHvvzyy2uu19DQwMaOHcu2bNnCrFYrO3z4MEtJSWGnTp3yYrTet3btWnbfffex+vp6Vltby5YuXcqef/75a65/6623su3bt3sxQu9bunQpe/LJJ5nBYGDFxcVs3rx57N13371qvUuXLrGRI0eyffv2MavVyjIzM1lSUhKrrKz0QdTe1d59xBhj6enp7OjRo16O0PeOHTvGZsyYweLi4tjPP//c6jp9+TPka33pe97esu7bt4+lpaWxEydOMIfDwX799VeWlpbG9uzZ44OoO8aTYxNjjBkMBjZ//vw2v6c9VXvLmpeXx0aNGsW2b9/OHA4HO3fuHBs7dizbvXu3D6LumPaW9cCBA+ymm25i+fn5jDHG9uzZw+Lj41lJSYm3Q+6U3n7+uGETi8OHD7ObbrqJffzxx2zKlCltJhb/+c9/2KxZs9yW/elPf2LPPPNMd4fpMwaDgSUkJLDjx4+7lp08eZIlJSUxg8Fw1fpms5klJCSwixcvejNMryosLGRxcXFuX8zMzEw2ZcqUq9bdsGED+93vfue27P7772ebNm3q9jh9yZN9VFxczOLj45lWq/VmiD63fft2NmXKFJaZmdnmiaGvfoZ8rS99zz0p65YtW9jbb7/ttuzRRx9lL730UrfH2RU8KWuzjIwM9o9//KPXJRaelPXFF19kTzzxhNuygoICVl1d3e1xdgVPyvrBBx+wcePGsby8POZwONi+ffvYyJEjWUVFhTdD7pQb4fzRa7tCmUwmFBUVtfpjMBgQHx+P/fv3Y9myZeA4rs1t5ebmIi4uzm3Z0KFDcf78+e4sQrdrax8VFRXBarW6lTsmJgYmkwmFhYVXbev8+fOw2WzYvHkzxo8fj9mzZ+Odd96Bw+HwYom6V25uLgIDAxEWFuZaFhMTg/Lycmg0Grd18/LybsjPzPV4so+ys7Ph7++PNWvWYNy4cZg/fz62bdvm7ZC9buLEidi3bx/mzp3b5np99TPka33pe+5JWe+55x48+OCDrsd1dXX45ZdfkJiY6LV4O8OTsgLAzp07UVRUhMcee8ybYXYJT8p6+vRp9O/fH0888QTS09MxZ84cZGVlITQ01Nthd4gnZZ03bx5CQkIwd+5cJCQkYPXq1Vi/fj3Cw8O9HXaH3QjnD6GvA+ioU6dO4d577231uTfffBMzZsxo97b0ej1kMpnbMqlUCoPB0KkYfa2tfbR69WoAgJ+fn2tZ8z5obZyFVqvF2LFjsWzZMmzYsAHnzp3Do48+Cp7n8cADD3RD9N7X2ueg+bHBYEBAQECb694In5nr8WQfWSwWJCcnY82aNYiNjcXRo0exatUq+Pv7Y86cOV6N25vae8Luq58hX+tL33NPytpSTU0NHnroISQmJmL+/PndHmdX8KSs+fn52LhxI7Zu3QqBQODVOLuCJ2VtbGzExx9/jI0bN+Lvf/87Tpw4gYceeghKpRK33HKLV+PuCE/KarVaER8fj7/+9a+Ij4/HN998g2effRYxMTEYNmyYV+PuqBvh/NFrE4v09HRcuHChS7Ylk8mg1WrdlplMJvj7+3fJ9n2lrX2Uk5ODTZs2wWg0usppNBoBAHK5/Kr1J0yYgAkTJrgeJyUl4b777sOuXbtumMTCz8/PtQ+aNT++8rMgk8lgMpnclt0In5nr8WQfLVq0CIsWLXI9njhxIhYtWoTdu3ff0IlFe/XVz5Cv9aXvuSdlbXby5EmsXr0aaWlpeOWVVyAU9o5qQnvLajabsWbNGqxbtw6RkZFejbGrePJ3FYvFmD59OqZMmQIAGDNmDH7zm99g9+7dvSKx8KSsL730EkaPHo2kpCQAwO23345vv/0WO3bswNq1a70TsJf05GNTr+0K1ZXi4uKQm5vrtiwvLw+xsbE+iqj7DR48GCKRCHl5ea5l+fn5EIlEGDRo0FXrf/fdd/j888/dllksFkil0u4O1WtiY2PR0NCA2tpa17L8/HyEh4dDoVC4rdsXPzOAZ/to27Zt2L17t9syi8UCiUTilVh7ur76GfK1vvQ996SsgPM7u3z5ctx33314/fXXIRaLvRlup7S3rNnZ2SgsLMSzzz6LtLQ0pKWlAQAefvhhPP/8894Ou0M8+bvGxMTAYrG4LbPb7WCMeSXWzvKkrOXl5VeVVSgUQiQSeSVWb+rRxyZfD/LwhqlTp7Y5eFutVrO0tDT24YcfMovFwo4cOcJSUlLYkSNHvBil9z311FNs6dKlrK6ujtXV1bGlS5eyjIyMVtfdu3cvS0pKYocPH3bNGJKens527tzp5ai715IlS9iaNWuYVqt1zT6xefPmq9bLy8tjI0eOZJmZma4ZGUaOHMkKCgp8ELV3tXcfffjhh+ymm25iZ8+eZXa7ne3fv58lJSWxX375xQdR+0Zbg+/68mfI1/rS97y9Zd2zZw9LSEhghw4d8kGUXaO9Zb1Sbxu8zVj7y3r48GE2YsQItnPnTuZwOFhWVhZLTk5m3333nQ+i7pj2lnXjxo0sPT2dnTlzhtntdrZ79242cuRIlpOT44OoO6+3nj/6bGIxd+5c9tZbb7kenz59mi1evJilpKSw6dOnt5mI3Ci0Wi374x//yMaPH8/GjBnD1q5dy/R6vev5K/fR1q1b2axZs9ioUaPY9OnT2ZYtW3wRdreqqalhq1atYmPHjmXjxo1j69evZzabjTHGWHJyMvvqq69c6x46dIgtXLiQJScns3nz5rEDBw74Kmyvau8+cjgc7M0332RTp05lSUlJbN68eb1qisOucOWJgT5DPUNf+p63t6zz589n8fHxLDk52e3nueee82X4HvHk79pSb0wsPCnrgQMH2G233eaq32zdutVXYXdIe8tqtVrZ5s2b2dSpU9no0aPZrbfe2qsT5d56/uAY6yXtYYQQQgghhJAei8ZYEEIIIYQQQjqNEgtCCCGEEEJIp1FiQQghhBBCCOk0SiwIIYQQQgghnUaJBSGEEEIIIaTTKLEghBBCCCGEdBolFoQQQgghhJBOo8SC3BAKCwt9HQKAnhMHIYQQ39NqtVCr1b4OgxCvocSC9HqffvopnnvuOdfjefPm4euvv/Z5HIQQQm4spaWlGDZsGEpLS9u1/syZM5Gbm9uuddeuXYu1a9d2JjxCfE7o6wAI6awrrwZlZmb2iDgIIYT0bfX19b4OgRCvohYL4lVvvPEGJk+ejLFjx+L222/H999/DwA4e/Ysli1bhjFjxmDWrFn46KOPwBhzvebxxx/HU089hbS0NEyaNAmvv/46AGDHjh14++23cezYMaSlpQEApk2bhu3btwMAli1bhs2bN2PJkiVITk7GwoULcfr0aTz55JMYPXo0pk2bhgMHDrji68o4OrovAOB///sf7rjjDqSkpGDatGnYsmWL67nvvvsOt912G0aPHo3Zs2fjo48+gsPhAOC84vX4449jzpw5GDduHIqLi1FbW4unnnoKEyZMwMSJE/GnP/0JOp3O478dIYT0VK21JLzxxhtYtmwZdDod1qxZg/T0dEyYMAH3338/8vPzXetlZmZiwYIFSE1NxW233YaffvrJ9ZxOp0NGRgZSU1Nx880346uvvmp3TLNnzwYArFixAu+++y6Ato/fLZWVlWH69Ol4+eWXwRiDxWLBpk2bMH36dIwdOxYrVqxAUVGRa/1hw4bhk08+wezZs5GSkoLf/va3uHDhAgDAZrPh+eefx4QJE5Ceno67774bx48fb3c5CPEII8RLjhw5wiZMmMCqqqqYw+FgW7duZenp6ay0tJSlpqayLVu2MIvFwnJzc9nMmTPZ1q1bGWOMbd68mQ0bNozt2LGD2Ww2duDAATZs2DB24sQJ1/NLly51vc/UqVPZl19+yRhjbOnSpWz8+PEsNzeXmc1mds8997CEhAS2b98+ZrFY2Pr169m0adMYY4xVVlZ2aRwd2RcWi4UVFBSwxMRE9sUXXzCr1cqys7NZSkoKO3ToEDty5AhLSEhgmZmZzGq1sjNnzrBJkyaxDz/8kDHGWEZGBktOTmYXLlxgjY2NzG63szvvvJM9/fTTTKvVMrVazR566CG2Zs2azv9BCSGkhygpKWFxcXGspKTEtaz5mLxp0yb2wAMPMKPRyMxmM8vIyGAPP/wwY4yxAwcOsNTUVJaVlcVsNhv74YcfWHJyMrt48SJjjLGnn36aLV68mNXW1jK1Ws1+97vfXfU+bYmLi2M///wzY4y16/idkZHBiouL2dSpU9mmTZtc21m/fj1btGgRKy4uZiaTib3xxhts2rRpzGQyud5n8eLFrLq6mmk0GrZ8+XL2+9//njHG2LZt29jChQtZY2Mjs9lsbMOGDWzBggWd2+GEXAO1WBCvkUgkaGxsxH/+8x/k5OTgzjvvxJEjR7Br1y7ExMTgnnvugUgkwtChQ3H//ffj008/db120KBBWLRoEQQCASZPnozQ0NB2D5SePXs2hg4dCrFYjLS0NAwZMgQzZsyASCTCpEmTUFZWBgD4+uuvuzWO9uwLkUiEzMxMJCQk4I477oBQKERiYiI+++wzJCQkYPv27Zg+fTrmzp0LoVCIhIQEPPjgg/j8889d205OTkZcXBwCAgJw5swZnD17Fn/+858hl8sRFBSEjIwMZGZmUhM9IaRPkEqlOH/+PHbu3Imqqiq8/PLLeOuttwAAW7ZswZIlSzBmzBgIBAJMnToV06ZNw+effw6LxYLdu3dj1apVUKlUCAoKwjPPPNPhONpz/C4rK8OyZcswadIkPP744wAAxhg+//xzPPHEE4iOjoZEIsGjjz4Kq9Xq1uK+bNkyhIaGQqFQYM6cOa5zk1QqRWlpKbZt24ZLly5h9erVPhmHSPoGGmNBvCYlJQVvvPEGPvnkE7z33nuQSqVYtmwZampqcPbsWbcuRA6HAwKBwPU4NDTUbVsikajV5uPWBAYGun4XCARQKpWuxzzPu7o6lZWVdWscLV1rXzzyyCOorq5GZGSk2/rx8fEAgLq6OgwfPtztuf79+7uSIwDo16+f6/fS0lLY7XZMnjzZ7TVisRglJSUICgryOHZCCOlNVqxYAbFYjG3btuHFF19EdHQ0nnzyScyaNQtlZWXIysrC1q1bXevb7XaMGzcO9fX1sFgsiIiIcD0XHR3d4Tjac/w+duwYJkyYgO+//x5r1qyBUqmEWq2GwWDA6tWrwfOXrwdbrVa314aEhLh+FwqFrnPbvHnzYLVa8cUXX2DDhg1QqVR4+OGHsWTJkg6XhZBrocSCeE15eTlUKhXef/99WCwWHDlyBI899hhWrlyJ9PR0vP/++6516+vrodfru+R9OY5r13rh4eHdGkdL19oXCQkJiIiIwMGDB93W//LLL6FSqRAVFYXi4mK350pKStwSnpblDQ8Ph1QqxdGjR10JksViQUlJCQYOHNjl5SKEEF9oPr5ZrVbXsuZW2QsXLmDatGlYvnw5tFotPvvsM6xZswY///wzwsPDsWjRIjz44IOu15WXl0MqlUIul0MikaCkpARDhgwBAFRWVnY4xvYcv+fOnYu///3vWLJkCV544QVs2LABQUFBkEgk+OCDD5CcnOxat6CgAGFhYdd930uXLiEhIQGLFi2CyWTCnj17kJGRgbS0NMTGxna4PIS0hrpCEa/Jzs7GAw88gPPnz0MsFkOlUgEAUlNTcfLkSXz99dew2Wyorq7Gww8/jPXr17druxKJBDqdznV1pqMWLFjgtTiutS+CgoIwb9485OTkYOfOnbDb7Thz5gzWr18PoVCI22+/HT/88AN2794Nu92OnJwcvPvuu7j99ttbfZ+kpCQMHDgQ69evh16vh8lkwssvv4zly5fDbre3f+cQQkgPplKpoFQqkZmZCcYYzp49iz179gAAvvjiCzzzzDOoq6uDXC6HXC6Hn58fxGIx7rrrLnz88cc4ffo0AOex+bbbbsO3334LsViMRYsWYdOmTaisrIRWq8Wrr77qUVxisRharRYA2nX8FolEEAgEeOWVV/Ddd99h165d4Hked9xxB15//XVUVlbC4XBgx44dmD9/vtsA7mvZv38/HnvsMZSWlkIqlSIwMBBCoRAKhcKjshDSHtRiQbxm9uzZKCwsxCOPPIL6+nqoVCqsW7cOY8eOxXvvvYfXXnsNf/nLXyAQCDBlyhQ8++yz7dru1KlTsXXrVqSmprr1N/VUVFRUl8YREBBwzXWvtS9GjRoFAHjnnXfw+uuv46WXXoJKpcLatWsxceJEAMCmTZvw5ptvYt26dQgKCsKSJUuwYsWKVt9HKBTi7bffxt/+9jfMmjULZrMZSUlJ+PDDDyGRSDzcQ4QQ0jOJxWK89NJL2Lx5M95//30kJibirrvuwvHjx/HEE0/gxRdfxLx582A2mzFkyBD885//hEQiwS233AKDwYB169ahvLwcgYGBWL58OZYtWwYAePbZZ/HKK69gwYIFEAqFuPfee7F///52x7V48WI8+eSTWL58OdasWdPu43dMTAxWrVqFF154AampqcjIyMAbb7yBu+++Gw0NDYiOjsbmzZsxYsSI68Zw7733oqqqCr/97W+h0+kQFRWFjRs3Ijw8vP07mJB24lhnL/MSQgghhBBC+jzqCkUIIYQQQgjpNOoKRUg3SE9Ph8ViuebzmZmZV838RAghpHeoq6vDjBkz2lznxIkTXoqGkJ6DukIRQgghhBBCOo26QhFCCCGEEEI6jRILQgghhBBCSKdRYkEIIYQQQgjpNEosCCGEEEIIIZ1GiQUhhBBCCCGk0yixIIQQQgghhHQaJRaEEEIIIYSQTqPEghBCCCGEENJplFgQQgghhBBCOu3/AUDyd8YPYCMYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_statistics_and_graphs(df_2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save VADER results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2020.to_csv('vader_sentiments_2020_03.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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