Applied NLP - Predict Disaster Tweets with Word Embedding and Recurrent Neural Network.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/gloveicg/Glove/glove.6B.50d.txt\n",
      "/kaggle/input/gloveicg/Glove/glove.6B.200d.txt\n",
      "/kaggle/input/gloveicg/Glove/glove.6B.100d.txt\n",
      "/kaggle/input/gloveicg/Glove/glove.6B.300d.txt\n",
      "/kaggle/input/gloveicg/glove/Glove/glove.6B.50d.txt\n",
      "/kaggle/input/gloveicg/glove/Glove/glove.6B.200d.txt\n",
      "/kaggle/input/gloveicg/glove/Glove/glove.6B.100d.txt\n",
      "/kaggle/input/gloveicg/glove/Glove/glove.6B.300d.txt\n",
      "/kaggle/input/nlp-getting-started/train.csv\n",
      "/kaggle/input/nlp-getting-started/test.csv\n",
      "/kaggle/input/nlp-getting-started/sample_submission.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import nltk\n",
    "import spacy\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "from collections import Counter\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing and Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"/kaggle/input/nlp-getting-started/train.csv\")\n",
    "test = pd.read_csv(\"/kaggle/input/nlp-getting-started/test.csv\")"
   ]
  },
  {
   "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</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expand contractions, e.g. hasn't -> has not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decontracted(phrase):\n",
    "    # specific\n",
    "    phrase = re.sub(r\"won\\'t\", \"will not\", phrase)\n",
    "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
    "\n",
    "    # general\n",
    "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
    "    phrase = re.sub(r\"\\'s\", \" is\", phrase) #This confuses contraction with possession\n",
    "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
    "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
    "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
    "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
    "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
    "    return phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['cleanedText'] = train.text.map(lambda x: decontracted(x))\n",
    "test['cleanedText'] = test.text.map(lambda x: decontracted(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove URLs from tweets, save into new column cleanedText "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeURL(text):\n",
    "    for eachToken in nlp(text):\n",
    "        match = re.search(\"((http|https)\\:\\/\\/)?[a-zA-Z0-9\\.\\/\\?\\:@\\-_=#]+\\.([a-zA-Z]){2,6}([a-zA-Z0-9\\.\\&\\/\\?\\:@\\-_=#])*\", eachToken.text)\n",
    "        if match:\n",
    "            text = text.replace(eachToken.text, \"\")\n",
    "    return text\n",
    "train['cleanedText'] = train.cleanedText.apply(removeURL)\n",
    "test['cleanedText'] = test.cleanedText.apply(removeURL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) Tokenize tweets; (2) Removing stopwords (including symbols); (3) Lemmatize and lowercase the remaining tokens for both train and test set and (4) save in column cleanedText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>cleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>deed reason # earthquake may allah forgive u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>forest fire near la ronge sask . canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>resident asked   ishelter place ' notified off...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>13,000 people receive # wildfire evacuation or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>got sent photo ruby # alaska smoke # wildfire ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Our Deeds are the Reason of this #earthquake M...   \n",
       "1             Forest fire near La Ronge Sask. Canada   \n",
       "2  All residents asked to 'shelter in place' are ...   \n",
       "3  13,000 people receive #wildfires evacuation or...   \n",
       "4  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "                                         cleanedText  \n",
       "0       deed reason # earthquake may allah forgive u  \n",
       "1            forest fire near la ronge sask . canada  \n",
       "2  resident asked   ishelter place ' notified off...  \n",
       "3  13,000 people receive # wildfire evacuation or...  \n",
       "4  got sent photo ruby # alaska smoke # wildfire ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopWords = set(stopwords.words('english'))\n",
    "# for each in \"~`!@#$%^&?*()_+?|/<>.,[]-:;'\":\n",
    "#     stopWords.add(each)\n",
    "stopWords.remove('no')\n",
    "stopWords.remove('not')\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "from nltk.tokenize import word_tokenize\n",
    "train['cleanedText'] = train.cleanedText.map(lambda x: ' '.join([lemmatizer.lemmatize(each.text.lower()) for each in nlp(x) if each.text.lower() not in stopWords]))\n",
    "test['cleanedText'] = test.cleanedText.map(lambda x: ' '.join([lemmatizer.lemmatize(each.text.lower()) for each in nlp(x) if each.text.lower() not in stopWords]))\n",
    "train[['text','cleanedText']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate location and keyword into the beginning of the tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate keyword\n",
    "train['keyword'] = train.keyword.astype(str)\n",
    "test['keyword'] = test.keyword.astype(str)\n",
    "train['cleanedText'] = train.apply(lambda x: x.cleanedText if x.keyword == 'nan' else x.keyword.lower() + ' ' + x.cleanedText, axis = 1)\n",
    "test['cleanedText'] = test.apply(lambda x: x.cleanedText if x.keyword == 'nan' else x.keyword.lower() + ' ' + x.cleanedText, axis = 1)\n",
    "\n",
    "# Concatenate location\n",
    "train['location'] = train.location.astype(str)\n",
    "test['location'] = train.location.astype(str)\n",
    "train['cleanedText'] = train.apply(lambda x: x.cleanedText if x.location == 'nan' else x.location.lower() + ' ' + x.cleanedText, axis = 1)\n",
    "test['cleanedText'] = test.apply(lambda x: x.cleanedText if x.location == 'nan' else x.location.lower() + ' ' + x.cleanedText, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a manual counter\n",
    "counter = Counter()\n",
    "for eachWord in word_tokenize(' '.join(train.cleanedText.tolist())):\n",
    "    if (eachWord.lower() not in stopWords)&(eachWord.lower()!=''):\n",
    "        counter[eachWord.lower()] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in train: id, keyword, location, text, target, cleanedText\n",
      "Count of tweets in train: 7613\n",
      "Count of true tweets: 3271\n",
      "Count of fake tweets: 4342\n",
      "Top 10 keywords: nan, fatalities, deluge, armageddon, damage, harm, sinking, body%20bags, siren, evacuate\n",
      "Top 10 locations: nan, USA, New York, United States, London, Canada, Nigeria, UK, Los Angeles, CA, India\n",
      "Total number of words in all tweets: 18513\n",
      "Most common words or symbols used in tweets: ?, #, ., @, :, ,, -, ', %, !, ..., not, ;, &, new, _, fire, ), (, like\n"
     ]
    }
   ],
   "source": [
    "print(\"Columns in train: {}\".format(', '.join(train.columns.values)))\n",
    "print(f\"Count of tweets in train: {train.shape[0]}\")\n",
    "print('Count of true tweets: {}'.format(train.target.value_counts()[1]))\n",
    "print('Count of fake tweets: {}'.format(train.target.value_counts()[0]))\n",
    "print(\"Top 10 keywords: {}\".format(', '.join(train.keyword.value_counts()[:10].index.values)))\n",
    "print('Top 10 locations: {}'.format(', '.join(train.location.value_counts()[:10].index.values)))\n",
    "print(\"Total number of words in all tweets: {}\".format(len(counter)))\n",
    "print(\"Most common words or symbols used in tweets: {}\".format(', '.join([each[0] for each in counter.most_common()[:20]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PuMIfDmV+LoHO/r+nTIZm1vSYett7B6OfzdY7X4iWt97x/LjGPRKntJlDV88BJCRqqi9Clz4YIt7h6hj3M2dUX8SR2udjBADA7gP3I5ermT/r+rh7ZHIl23beERUAQMwuAMBmb4mqe3rMEQEUDHr7604AkKbJSjgmmTz5D9HBD5zo0/OZU3UZ+vR8jLoSDOlFmAzlFOWehlFfgj69gAx9KYb0IgpyFpCmycKQXogubXh9/1lXZg5bdzJvPdmXdNsoY5hoVWkKFlxYxPIbK7ji/xbyy4PnJ3VfwQwD1/xxjBGiJ8kwoviL40pbHEVbUUXxl74RU9b8eh3hQJiS9VUATLtiDk2vHEMKSVReNBMElJ5dTeMrxybU92gULEs80ZuP9uCzTq6OfTRa3mkg6A6M2q54TQVXbr05+vPZt7+IXK1ArlaQOSOxWf3br+RiaSuO+3n3tcHVd9cf7iXs8lB616/Iv/VrALh3j91cWFVSSOEvbqX0L7dR+PPvYX3+tfhGH1Fwz1N6J2BztLDv4MMsmHMjxQVLcDg7aO34kM6uPYSl8ZmSqdUGvH5bXLnPZ+uvN8bVDZ38hyMcHrIaifvnTe4/0+HqxO7uwOHqpKxgOULI8PkdWOxNuL1mppWejc/vxOpoQinXEgr78fgsMeciJ1M5O23YupNpODS1Fi1+d5C9L0XS6E5bmcP885M7NO84aufxW3ZN5dASosgwoS2vHL3hKGjLKlCaMglYzACEg2FsjWb0pUZUejW6YiPmIz1kze3DNCOHtDw9Cq0ysmP4mJBCU2GqNVJ/yX2X2rc042iJ/54DeHoTqzLN5sTvxWwZnGtCNjs9dz8UU99zz8PR1803/2DwvqdejL5u/eGvYu5x7diLa8fwarKPklNaCAD0WWrZd/gRivIWkZU5nZnTLqG0aAUf7LpzknsafjU+0uQZbTPJE32y+AIOBAIQBPt9IxzuTtxeM0LICQRdmPTldPYdIF2bjcuTeMIY03nAFJ5LfBLRlpZP2rM0JeVRIQARlVDW7DyM1ZGdWt+hbrKP9JCzsABDWQYAltpx7MzGgM+S+P8tZAKVIXlV5kdJ18426p4a2wrdbEn8PbcMU/7vwikvBAB6+47S23cUtdrA9Mrzyc2ePe5neX1WNKr41b5aHckxMrAj+KgYiypoKCc6ImZinb3xH/ShZXZXe1SIDScAjFkKMnOVCetOxm4J0tv+6XLEGw110eTpxTXFpTj2745eW2r7KD27mozqLPx2H85WG+Yj3VReMhNjpQkpFMZWbx7hiRPHcizx58ZQYUKmnFrLlfGSd3rhpAmB4cr/XfhECIEBfD47B48+yeplP0lYHw5HdIUy2fATmtlST272bNq7YtUG2Zk1AFisY3OWmSiFFWNfSakrijBsOAPbv7Yg06UhBUOEnW60s6vwNbUDoMw14W/vRTO9FH9TByChnTsNX1M77l1HSFs8C/eOiJlh1ZzkVUGNQ1RBNatz0eqVtB60suGWGVQsyUKulPHUD/dy5K3OuHtNRVpW3zSN6uXZ6LPVHH6rk3fvOU5X3fBepKORZlLx481nR6+b91i474aRbfnLT8/kyt8uJD1TRcAXxtHtpXm3med/HjtpqHUKvvL4CkxFaQR9IVr2W9l8fz0NH8auvJWm5M9TRuPkZ1lre5GrFeSfURI9mDUf6UHIBPlLS7A1WuJs9Sebrp2teM0eMmflRscgU8iY/cUJ+ShNGeYjPRSuKqdswzSaX6+LlivSlOgKDViPJ945JSsELvp8Ni8+8PGp4CabU1oILD3tG6Sn59FnriUU8qFRZ2AwlAyruGlofovc7DnMmXElZcUrcbl7UKv07D5wf7RNV89+pledz/pVv8TuaMPt6UWt0pNhLOPg0Sfp6vloY8Ksuzz5CeTA1shk6WtsI2RzIoXCKLKMOLfsxXjeCkJOD5rppQS6LfhPdKIqzYewhBQKoSqOHG659xwj79Yb6L7j8ehz11+R/BjefCr2C3T5bxaw5cEGHrtld9Ri4xf7zyPoC/P7c97GbYnsGn7w1nr0uRqe+N5unv/ZfgBK5mdw86PLkSS486JN2LvGfsjotvj5yZx/AZGD4Qv+a86wbWesyeP6uxbhsQf41fI3ouVCJsit0sW0vfEvi5l+Zi7/Pe+V6PsqnGXkyw8vQyaX8bMFg9bPygwTk4XiJCHQu78TxwkbBctKo45WtgYzPfs6yFtSzN47YwXeFVtuQsgGvyGn37qK029dFb0ecCpTZ2i4+JXPxdy7/Fdnx7UDCAfCvHjBQ6y/71IypmUR8gbp2HaCPX94f8LOVyc7iw0d7z9X3TeuM4c3v/gsKoOammvnc+7jV5FWoMfd4aBnbwcNLw4fSeB4fWIrq7qTyrVpE7On6fnLPyZ0/2RzSguBppb3KMhb2O8JLAgE3PT2HaWlLfFKz+d3sGv/36gqPxuDrghdeh5ma31cu+2776K8dA3ZmTXodfkEgl527L0Hh7N9it9RPJWzxqeLNz/xBkgSgfaI56HtlfejJmUZl65FVZiD9cVNUfdgX33EpE1ZmINnby1SYPCDXTmGncDJh8JeR4CNdxyNMdk7vLGTORsKmHNOAdufjMS3MuRpOPRGB4feGLTsatln5f2HGln7lWksu66c1/8wtYHDVn4+cnj72u9jJwIpLMXsRnKrdEw/M5e+ZlfM+2o/bGP3c60svXp0e/Zxk8Ai5NWrn4gre+erL8aVAfxz5b0Jy0/GZ/Xy1PJ70Ch0eINOjOp8dKosrN52XIGIIUS6MpMsbSl2fxdahZHd39yOhIRelYNakY7H7GLn/yU29XU0W2MEyXAk0wag/f3mpNsC+O0+Dty9nQN3b0/6nqEHwEM5+Uwg4B/9/M8wcz5Zy9ajzivg6G23xtVr8orwdrVFr0uv+yonHv1r0mOdTE4ZIZA9N4+M6szIwde8PCy1fSgybOx97x/oS43kLSrCUtuLJEkYl2cgGrOZdvlsjj62n3AwTN6iIsyHu5l2eQ1HH3sZWUAWeY6tj+pLZ6LJSuPg3yIqIJ/fwbHjL3GMl0Yd11ubE6uehiJJYfYceCB6bbE1xtzX0Pw2Dc1vJ7y3fAxCoOnIkAk4kflYf5nt5c2DbsEnuQdL/gD2Nwe/GOkGObnFqqT69zhDdJ2IdfZqP2InFIwdS8P2PuZsKKB4jpHtTw6W127u4WSOvdfN2q9Mo+qM5Lxsx4tCLaNkvgkpLHHgtZFNjCv7x9K4I15t0FUbr7oKeSfvoDxR+IipxBuM+K1ISChkaoLSoPmlQqbCG3Ji0hQjE3LU8nRkQk6DdTuVGUs+0nF+FCSrDmpr9CFkMJK9iP3IPuxH9lH9jf9OWD9UAHzcnDJCIGdBPkce3sf8ry3B1mAmc2YO3l43WXNykUISnl4XOfPz6drZjsqgxlJnxlrXh63BQubMHDy9rpiygecMPHfmDfOnZNxynQZVQRaeusF/avG3L45x8BiO7AJl0g5akLxp5tBV/skEe2O9UscUKuKIJ072DKh7huLsjQiK9KzY8w5bZ/z4bZ2RSc9YkPw4xoMuU41cIXD2+vC7R3auMuZHxrLo8lIWXT76oe9kTtzhSRQoyZCuzEQuUyKQEQh5yNQU4/D3IhMyTNoiAiEfEhKh/vM2X8hNkX42MtkpM3VMGskKAXN3YEQB8EnjlPlPei0eKs6bjrWuD6VeTe/+Lmz1Zlb+9hw6P2jF7/AhhSVkcoHP6kUKhdFkpWEoyyBnfn6kfkjZwHMGnitXT85bzTp/MX3/2kHO5SvpfX4bWRcuxd/Wh7/TTN61awj0OZAkkKWpyb3qTKzv7ifQ5yDnshUgoPOBjdFnVcxKXg3j94Zpb5z8kAtjGUPDwfgJKqF100DRyZuVBJ6V47ONGjsD0Z+T8b8ZGGbHETudtfHxbk5mqEnnRJnMZyWDKzDYn93fFWMObfd1IxBx5s8nly297naOvXMf1vZPdh6A4YRAnzlWTdTXNX4Pbd20WeSs2kDXxudxt8THqkorraLgvCtoe+5hvF1tlF5zM6rMHKRwCPP297DsGj2C7Vg5ZYRA48u1Ccs3fefVuLK+wxG1wr6/RNQa9ubB1W2issmk75Ud5N24nt7ntiIFQ9g2H8KwtIbMcxfR9/J2/B1mir5zCdkXLsWxow4Q5Hx2Bb3Pvk/QFuukMpZD4XefsxBO0lEGYMHaDPLKNLz+4KCVzk3/V8W9P4w9IxmLp/DJh8IA1StyUGrkBLyDX5QFFxQBcGxTbMC8xVeUcnxrrEpo8ZWRlfaeF1qTHsd4sLR5aD1gpXhuBgsuKGLvy8Nvx/e80MrK/6hElSbnmf8aPZaQddt7mFZMTpwe67axh9PQ51bi6B7eqk2fW0nV8mvZ+/wvR3xOIn+YRP4vH5dPzFTjdid+X05nbLkpW4G5a3Sv5YTPqjuMs+4waSUVcXXTb/lf6u++jfq7fwOAMiMLdU4kUoKQy8lecRZhvw/bgZ3j6ns4xi0EhBAlwENAPhAG7pUk6U4hxM+BLwMD3/Yf9weT+/dAAplaScjhQVWQiemshWjKcuj4+xvkXHUm/rY+JF8Ayzv7yFgzD8/xDiwb95B73Tokn5+Ovw9apVSM4zxg2ml6Cio02M0Bmg+72fAf+Wx6qhu7OchZ1+eRppdzfI+TrhNe8so0AOgyFFz27WIS5Y4orNAkPYa2hvidiEor57wfzuKlXx6MCqkZ6/Lwe0Ic3Dioe+9tcjHrrHzmfqaQA69FDuCL52aw/IYKAt4QHz7elPQ4xsv7DzZw1e2nce4PZ8UJgezydHqbXAB01Tk48nYXM9floVTLCfgGBZxcKWP6qhyOvD0YcTZoteBpapiw17CnuXFcOwFjQc2IQsBYUDORYU0JMo0S46o5ZF+2HIUhDZlWTdjtI2h30/P0FmxbDiH5E6+4s9bMQp6mwtPcS1pFLl0v70Y3oxBtWQ5hXwCFXoO7oRshk6EuzMBzoo/0qjy6Xt6NpjgT44IyXMe7cB4dnyFI/RR5zLtbG8lZcx6drz4NRHakdX/+35EPHyaBiewEgsD3JEnaLYTQA7uEEAO6jj9KkvT7iQ/v1KTjvkicD3+Hmc4HB9U7bXc8H5lo++fa3mfej5q4tf/1ZcQQdUi6QU5OUXIHsjAYtXPaQh1KtYycEjWVc3W8/VgXF36liKfvaKGgQsPd34+s9EtnDqp5Vl6azeZnejj3i/HZo8aSJCvRTqRxZx/zzy9k2ooc2o/YUKUpkMkEr/zmUMx5weO37OILfz+Dq36/kJX/UUFvs5u55xYgheHpH+7B0jb4xZqzoQCtQYlGpyB/RsSxb81N1XidQbyOAK0HrNEJe8aaPHRZKjR6JVll6ZgKtay5OdLW5wzSftgWtfw58HoHxfMaWPG5Sr7y2ArMrW40OgWZpelkl6dHTU0Bnv3vffxH3lK+98ZaOo7a8TmDGPI05Fbr0egUMW0BbNvfn7AQsH24Jea6cPZ6MkvnodFnI2QKvI4eOg6/Q29jxMChYumVZBTNRKU1UDTnrOh9bQffpHXfqzH1EFHbDK0HkCvUzDz7a8P2AYAQ5E9fQcnCC0CS8LutdB//kI4j7yZ8H0qtnhlrv8zRt+4l4IsPmJi54XTyrl+L3BCripTrtcj1Woq/fTH5N6yj84GNWN87GHe/piCDtse3kn/p4sGykizC/gAhpxchE6RPyydo9+A40IK33YJuRiT0iDxNjb/PiX5OybiFwFTR9txDlF77FbJXnEXv+2/it5rJWraOvq1vAaDJK8TX240UmtyAgeMWApIkdQAd/a8dQogjRHILf2oZak4IJ8VVkYhZiY9lFyCFI8lkgH6rBIlQELQ6OR5XGIUyIlzMnYk9edVaOV5XaEzqpGSxtnl46ZeHOOfbNVQuyUKukvHot3bGrJQhsrq+6/LNrLmpmprVueTPMHJ4Yyfv/b2e9sOxXtpX/m4hMlnsacFZ3xpczb5xx1He+1tE2F300zkYcmN3M2d9c7DtlgcbYkxCX/3dEY5v7eWzv55PwSwjfncQe5eXrQ/F6mc9tgD33bCVLz6wjNIFJuRKGc5eHyd2mzmcwBHOcWAv+VfekMyfLCGexnocB2JjyWQUzsDafgS3uQ2EoGDmGqqWXROdoDuPvEuf1O8VAAAgAElEQVRX7fsUz99A677Xo/cFvPa4el1mCUff+VtMPURiXo3UB0DVGVeTXXk6x7c8QjgURJdVit+d2LNepTUw86yvcvD1PxHyx6+YC764gayLlia4MxZFpp7i712GqiCT7idjVWQh76AqJr0yl/TqfEIuH9qSLCwN3ajzjTgOt6EtziTk8ce0088pJuTwQnhqVVpFl30OlSkbhd5A2Y3fpPO1Z/B1t1N44TWocwuQqbWEXHa63nwRT1vEjFoKBml96u+U3fANAg4btv070BaVU/3NnyJkcvx9XbQ8+TfGGTZtWCblTEAIUQ4sBD4EVgDfEELcCOwksluIi8A2uYnmBQWnbyBv7hrcfW20bH0WT9/ETbCqVkdWD8o0Bao0BX31dmztLpzdHvJmmVh49TR2PnQMmUKGUiNHkiBnupH0LA1b7x45h85YEsm0NXrxeSICRZKgs9lL8bQ0Nv2zh8tvKWb7a/EqhFWX5pBXpqG93sOO181c/PUi/N7J31Yq1DK6jzt45Juj6ykd3V5e+uVBXjpJNf3DuysoKFPznXMjB4s/nZe89vC3695Kqt3QPure7+E3q98c9Z6gP8w91yV5ECdJhNyucYeT7nz60bhT68Mb/1/MtbOvhYWXDJoee+yRM5eQz43bEv95H1ofDocStpHCIVr3DUawPLkPXVYp2ZWLaDu4kb7miJCytMavzsOhAKo0IzPP+hp+tzWhAMhYOy8pATCU3GvX4KnvwLFz0PO387kdMb8BMhZXYt1ej+mMajr++SFSWMJVO6iOrL89snNzHe+cvPDhCdAWmchcXI794GsIuYz0imw6XjlAyZWLaHmqnfaXHqfkykV0v3CUrOXVeNqa0U/Pw9e9CXW2jqzl1TTc9zsMMwsoOG8u/r73CblVuJv6sB2aGrPSCYeSFkLogGeA70iSZAf+ClQBC4jsFG5PdJ8kSfdKkrRoovkxAUxVCyhYeA4yhQpdXgWV626c6CMByCjWkVGiI6NYx/5nGsifnYmzO/Lh7jlmpeeYld7jNmo2lJA320Txadns+2c9Qd/oonpMiWSG6CBf+VsH21818+yfWmmv9/DgTxvZ87YFW0+Ap34/GD/90V8384ebj3Fgs432eg/3/rCB+38ycuasFBOj9d4/EegbezgBf29PXEKZhO1cFsKh8R1IJj2Wk/owFkTi3Pcc/3DE+2RyJTPP/jpeRy/H3rkvYZuCL24Y15gKbz5v1DYdz2wnHAzR8fT2uB15HFMYojl71TTant+Do7YLw+xCOl5JHIHA1+uMxl2SJFDoNTFlA/dqCzLo+Nd+9DPyEz5nMpjQTkAIoSQiAB6VJOlZAEmSuobU3we8PKERJoE+vyrmWm3MQZlmIOAe3bxvJHY9WjvsdTgksfvxyOpk8537o+VCJtj+wMimckqVYOWFyYcaePuZ+JW+4byVuLbtJ2RJ7j0mUgWNKWbQFEcOnZLk8R9xH/7eHpr++GtgINH8TEwr1yBP10USzUuRRPPuuqO4G47jqj08YqL5Jdf+jrYDG7G2HcbvsRMO+ifdPr904QWEgv7h++g/NBotkq4uu5y6TQ8w65xvUL3iemrfezCujVw/Pl8QZW58wMeTCfuDeJo//ng+LU9sR8gEUlii9eld0V1Hy1ODO+WB163PRFRuzrouXA09MWUD9zY/+kFM+VQwEesgAfwdOCJJ0h+GlBf0nxcAXArE7x0nGUdnPdkzl0evfbaeCQuAZEi04hh1FQKU1miRjyF6aFOCCVgIkbQAGI6xOIollU5yAgR8YVZeYOKbvyulu8XPU3d1svnFQS1i+Uwt1363gFmL05ErBMcPuPnva44DMHNROt+/q4J7ftLC1bcUcNtNDVx/awGL1hn54DUrf/7BiZg+rvpWZFV1ch+TScjtwrF3J4694zfnc3Q30nZg0JosPTNxFqtQcOSorqGgH7kisRFCRvFs9r/0f8P2Ye86Dmwgp3IJbQc3JnhCBGdvE25rB3WbH6Jm7ZdGHM+pjKloYqbKusWL8DU1gxBop1Xja21DVVhAoLMLkFAWFKIw6An09eFrakamUWNYuRLbu++iLivD39aOUKlQZmfh7+pGU1lByGrDuXv3qH2Pl4mog1YANwDrhBB7+3/OA34rhDgghNgPrAVumYyBjoSlfi8de94gHPTj7Gqk4e2HRr9pksmbtpy86mXkVi1Fl1mCqXg2pqLZGHIrySqN9VYeS7ygvs4AdsvUpA8cUw6BBI5ixzZ18+T390zKWPLL1FzxzTz+8O0m2pt8fOcPZcxdNhjU7ddPTUNvknPvz1q58/sncFhCLDzTEK3PyFaw6mITWflKfvFoNdaeII/d3sGayzLj+nj2nq6EfQxQoVtIumLsQeGUMjVG1eTlgE3PLCKrbAG67FJyqpZSuexqggl07c7eZkpPuxBjwXRMJXNIMxXG1SvU6ZSedmFcvdvSPmIfju4GLC0HKJ63gcySuZiKZ1M4e12MNdJQbB3HaNr+DDlVsWElhPwUSGIoBKaiWRTURALUDX09Wci0WsJ+HzKNmqDNjqaqEse2D1CVlqCurMSxdSvhQCDazt/Wjq+9HX9HJ44PPkQzrRqFKQNvfQO+pibCXi9CnbwV4XiYiHXQFhI7fE7IJ8Aws4DiKxdz+H/iA2RlLa8ivTSLE0+cHBRKomPXa3TsSpCibdL6GIUhSdX1ORUEAx56G3cxfdV/ULclViiNJV7QVKphxpJNbKrVQTqjnB9ccoyuFj/b37Txl7dncfnX8zmwLbLa97rD/Oz6+ujh9gevWbnrzZl846zB3dBrD/cS9EssWmfgH79pJxySuOa7BQn7ePc5c1wfQylOm4lcKLD4O7D6u/CE7FTqTqfFfYjS9Lk4A2ZkQoZWbkAr19PhPU6mqhBX0IqNLgq009DKDVgDXTgCvVTpFiGXKXEGzDS7kktmb++so2LJ5Qi5Ape5lYZtT1A4e31cu97G3SxccD75NasI+t2c2P0Sbkt7TL3GkEtO5WKyK06PqW/e9QLzL/jBiH3UbX4oYjW08vpIMEKnmY6jwydr7z7+AUuu+S0+Zx/2rogVl1yXvD9KIoRclnRE0ZJ5n0EmV2LvrsfR20zRrLV013+Ix96D321Fo4ukeR36WqnRUVCzmt7m3WgNeajTTajSMuiu/4DcqqUgSfQ07sTr7KNw1jr6mvfitsabmIZdLjSVVSgMekIeT4wOMuxwolu8GJlSScBqRVNZhWvPHhR6PcrcXNSlJYRsNhCCsK/fJ+cj0JOeMh7DydC3tZ6+rfFRQU+FPrqOb4stEAKtPgdrx9HY1JOMzTy08XBiNYw804gi00jQPL4kODK5oKwmuS9mOCTRdHRqhYC5K0BXS0StIYVh3xYHK87PiNbv2WSPs24qKFeTVzK4Surt8OOwBulp80fPQDzOwUP60foYSo+3CWugi7L0echF5EwmXWFChox0RQb1jh1U6BZiC3TT7NrHLONqGpy7ydNEfAU0ch22QDdF2hq6hBJroJNQOIBClvyqLpFevW5zojDEEnue+8UIT5Jo3fdq1DdgKAGPnZ3/jA2SeHIfkhSm/fDbtB9OHATxw0e/F1e2/fEfxFzLJrialWlUhFzJxWjyOnrpaYyo4YrnbqCzbhtFs9bTsP2pYe8JeJ101b1PdsUiwqEgjt5GXOZWyk+/DCGT0bLvFfweO/k1q/C7bRTNXk/d+w/HPce1Zy/SwMQ9xArJvum9mDIhk0Xbmf/1SiQicE9P3KG144ORD+Qng1NgjxaPfnpe9JR8wR+vipbnnTWTyi8Nbt/m/OpSCi9aEL02zi5i7m2fpT/bYuQ1sPiBz6Or7k+y3q/vTLaP7JXTKP7s6ZTfuBzTaWVUfmkVQhG5T8gEZzx+U+L9kCThsXfTVRcb9rqwUk3NwuTNCN9+OrEH6TWaTQTNNq7+bORZC+aq+NqX9BTkyVEkIdrPvMiESpPcv3/Ly9aoiSqAPqOErPzZFFetRpseWUlVzDyP3OLTKKpcGa0bC3Gq7pOt+JI4QgkGIo56fl/ic5lR+xhCrqaSGYZVWPwdTDMsYZp+KWEiAsUbckXb+UJuQlIQEBSnzSJTXUSOpjxaBwKbv4uy9Plka0oJhCc//tMngUDfxM6vkhUAEPGQLpm7AVPRbPqa91I0cy2WtoNoDTlkVyzGkDeNjIKamNdZpQsiO6D+D0l22WmULbwIa/thfE4zRbPPIqOgBkvrQXSZxfQ07EjYtzR05T5CpN9E7abfeDoAlVfOJ2NmLvkrK6LXA1RdszDpv0OynJI7Aefx7mi2JL915BWo7eCg7WxaWRaO2s6ox66zNmKodOhnL1By5WI0BUZan9pJwO5Juo+AzYPSoCHsDxL2B5GpFEz75jrkWiVhfwiFThOR6kluVSvHELDNZQ/R3Rp/6De9Wsm82SoWzB1cXSkU0GcOs+IMNU+/4GZ6tZLa48ObE45lN9JwMHY3YsgsJxjwIElS9FDSaW9HodRiNzeTps9LGKJiJEy5SoqqNLTVe5HJBfNX6GOipp622oBaK4sRRl0tfrpa/GTmJZcac7Q+Bmh0Rs45BDIkwuy1vB5jHVNr3xrTDuCANdbvoIemaPkM4yp2m/9FIOxlbsZ6en0nkhrvx01aTgkaUx6urmYU6jQkKYwyzYCt+RDGstkEXDZy5qzC5zATdNvxWDpxdSY2Q/4oE9If3/ooQiZHCke+3w07nmFgUmjePRjd19pxLOY+c+sBpHCIghlr6KzdjNfRhySFsbQfiVH31n/4JFNB0DP4fc2aW4Df4UNXZsJYnY1xeg7hQAhjVdak93tKCoFkLGyiDGnrauwh+8xp0VWjviZySOdpt3Hs96+j0GtYfP/nOfTT55PvI8FkptBrOPyLl1DoNeSuHVtclvIxOInF5A8YQu3xADd/JxLMbe+ByCS8c4+fvQf8BIODbUaics5YLINix9HWsDnO4aanbS9CyJCkMA5b65htse2WID++r4J//Lqd9VdmkVus4i8/GpwsFUrB/zxSzSsP9eD3hll1USYP/npszjMDfTzz1y6Wnp0R1wfETnxpOcX4nRb8DgvG8jn0Hoo4juXOX0v3vneiv0ej3X2UCt0CwlKYZtf+UdufMkgSclUa4YCP9LJZCCFH6ldtqg3Z2FuO4OlrQ6ZU03tkG7lzzxxWCHzUDAiA/qsx3eN19hAK+AcFv0KOPC2NkNOJLD2dsMuFFA6jNJkIOZ2RVb0kIUtPR1NRgbe+npAjuZSpWedeQMbqdXibG2n4658BaHgqcmY0YGq659eDDpG7fzm6k+NYOSWFwMkIuYyaWz9DWmkminQV6lwDR379r7h29iMd2Pa1suD2q0CAeXsTCMGC26+MrPplgvYXEluzJOqj+eFtCduqs3TM/dVl+M1OnA1js00eU/z+MR7GBpM0IhIieY9lSYrkEUhYEVcUHrZuNN58so/mY16+f1c5XSf83PHdZg5sG4w786Mr6rjhB4Xc9IsS5PKIYNq+cWznIQN9XPXNiInoyX0MjF2uTiMc9KHSmVDpTXR1vo1MPv6E6vZAD/ZAfEKdsWCoyeOMe66e0DNG48gf3qblxSHOTTIZQa8LXUEVAbcDEAiZIHP6YuQqDVI4jEJrIOiZenPsjxJLa6y3v27RIvytregWL0aRacJTW4dMrcLX0kpaeTkhhwN1WRmKTBP+jpETFg0l+8JLMa5Yhbe5kfa/3R1XP6bF8AQQY922T8kghBjTIGRCjkCgkGvwBeMDVJ3K3P/hHIxZycneP996gnefm/z48gXlau56c2ZSbTtP+Pj6uuHzsv67kZZbitqYA1IYTWYBfnsvrs4m8haup+fAZsLhIHkL1tG1923yFqyje/8mPL1TnyXqYxECEHOAOVg4ZBc4hhAMMx+5dVwOY4FuG8e+fGdcuW7R6ShzsrC8+kaCuyYP3emn49y1C93ixYQ9HoIWC6qCfMI+PzKNBmV2FoHePsIeD6qiQhxbt426E8hYtZqs8y8m0NdH611/GNFpcBR2TTTqwidiJ3Ay+boa5DIVcpmSVtt+ggkO2/TrV+LetR8QaBfMwvluZFVv2LAa++ubor8/apIVAMCUOTKtvyJ5veKbT02eELru27nsfd/J/BXptDX46Wj2M2tRGk57CL1Rjj5DTkezH79PQq6IbIUNWQpeuD8+j8FU4e4+gbs7oiJSaOsIeiKLjOZ3Ho+2aX77sZjf/24Y160BScL2ziYMa1cjFHKsr7+J8ay12N58B+P6NRAKY3tnE8Z1a7C99Q6GNauwv7t5xOceuf53SQeQG6D78U10P/HRf0+H4tzV79m7Y/Aw2N+WWPC7DybnG5t1/sXYtm2h94Vn4+ryjDMQQka37RjhyY4Wl4BPpBAQQtDpPEYw5B0xwYXh3HVYHn8ekYy5zClIaAqifsIYPYUPTp6n8KN3RgKaHdo5+Mza/Z5ovtaLP5/FxqcjMXRGy+H6UTAgAJLFuGgZ/u5OJCmMOrcAX2cbqpw8vC3NyNRqMpauwvL+u2jLK/B2tIEE6tx8woEA8rQ0fB1teE58vDp1VX4+qsICPEeOolt8OiGbDZkuYoEWdjjRLVkUsXO39ETrx0LH31/H19qbMJT0UIIWJ533v5EwlPQnHW31dKRQCMvbifX7XbajmNJLWVB+BXsan5zyJD6fyNmxzT5yhE4AhMC5aRvK/FxUJYWoSouQgkGURQUoC/NQFhWgKinE33JqxRT/KBhv8LqpYmCy3/SiLa7sk4Rt5zZMy84EIbBs3UTmynVIoSBhv4+ApQ9fZxu+7g6UmVloyyoJOZ2EgwHCXjdCJtAUlXzsQsDf2UnPI/27npNUPY4PdwyrChptFzAU8+u7sG7aj/HMueRcuhy5IQ2ZVkXY4yNoc9P7zBasm4dPKjMUKRRGO3MGGeesR1VUGFlBP/AQ7sPxKkx1STEZG85CXVmBTK3G+tpGbO9sQhpymFZx5+/p+tsDqAoLMa5fQ7Cnl55HHifnc9cjN+hxbP0Qy8ux/rCKDCMZnzmHtFkzkMIS7v0Hsbz+BmFX4gWUYekyXIcOEHIkPkvJNdaAJLGn6Uk0KgMe//h8gZLlEykEksHxZuRDOXeFgXzv23QU+zB3+JntfYv6TBcbaj7g5S0W5LPSmH6ajtZaD3mlatrqvdTuGVwBPnN8wXBdTDkfZ98DPLBjzoTu93nCXDs3OYsYa9/UhMf4qAk6HBjmLyJoj3x5teVVOA7sQa4zoMrORabVQjhM2OdFlZOLs7MdpSkTz4mmj3fgJzOCnfuw9UkS9gawvLEbyxsTi4mjqa7CdN4GvA1NOLfvRJGVSd5NX6DzL/fiqR0MP502dw65n78BQiHcBw8TcrkwXXAu2pkz6PzrPUiBwc+eftkZKDKMeI/VkTZvDjk3XkegsxMhBBlnr8Nb34DnSCRIpDIvj4JvfRV5ejqeY7UItRrD6pVoZ82g/Y9/ihMEQiYjbXoNPc89Pex7cvssOL3dpKuzcfmmPijev60QGCC/VE3lvHTC4Yj3qKU7QPMRD81HPLTUerj21mJaaj1kFag4ssNBZ/On05nn3w2BQK0ykKbJIttQhUqpQ61MR6VIR6lIQyHXIJMpImat4RBhKYg/4MIXdOIPOPEFnLi8vXSZD+MPukbvsB/LtuE9Q3vf/FckM1dfxDM0c/XZuGoPo6uZjfn9d0YMEeA3u2h6cuqCiAHYj0/MgunjQFszjb7nXojZiVTc+XuM61bHCIGc665G8vlo/8OfCPREJtawy03GZ87GdP65mJ9/KdpWU1FGy89/Rdjno/C738J3ooW+p59DVVxE0a23oJ1WFRUCuTdeizw9nc67/4bnaMTvwHTuBjI+czaZF11A7+OxXsrKnDxkag2+EQR+ec4ZOLxdqBU6ajuSy5cxESYsBIQQTYADCAFBSZIWCSEygSeBcqAJuDJRYpnJoviMi8mdM+ih2vDWg1gbI6vPjY/3wOODH+4BXfOrD0UcyR77XSsyuZiSrFspphalIo0c4zQy9RVkGirQqEYPOZwIIVMgQ4FCriGN2EPzmaXnn9RawupsZc/xxwkERzgv6V8l33C5jrfec2NzhAmFJMJhgc8fqTNvikTl9HWMbl3k7XFSe/f7qIwmAnYrCIFSbyQcCpJeUoXrxPFI4DKVmpDHjTwtnaAjXo1Qccft+Ds7afvN79BUV2FctxZtzXRCDieqhSUoezZGwhckIOfaq9FMq0ZuMCD5/fjbO3Bu34Hjw/44W0JQdtsvESoVTd+9NXpf4Xe+hbq8jMbvDAkvIZNR/n+/pukHP4pRL+kWLiDz0ouRpacjeX2E7Hb87e30PPEUkj/ecdKx7cM4VVTI6URTXR1TJtNq6LjjrqgAALC8+jrK3ByMa1fjOXw0KjTch49GY/d4autw7YyYlftb2yAcRpEV+YxkfOZsVMVF9D33QlQADDw3feF89Gcswf7Oe/g7BzPRqUtKAAiYhze4ONgSiWmm105dDoGhTNZOYK0kSUP3Lf8JvCVJ0m+EEP/Zf/3DsTxw4VINxWUKmo4HuORaPY/dZ6f+WOKQuRrT4B9LksI42uoStoNBXfNQnXNKAHyy0KiM5JpmUlN8DmIsSZInBUGGroTV875Lh/kAzV0f4PR0Ddu6MF/B+Wen43KH6ewJkZ0p58nnk3MkOpmMeUvwdragq5qJPE2Ht7MFf1cbkiShq6hBoTdGBIA2HZUxk/ZXE3u2KnNy0C1ZTM41kXApYY8HRYYR3eLTSZs3h/Y/3tkf+ngQ/fIz0C1ZjBQOE7LZkKeno6mqRFNViXPX7oheXZLwnWhBO33a4F9LqUBVUgyATK2OTq6q3FyEUhmjUsq55ip0SxYT9noJWazI0tNQ5uehzM+j77kXCCUQAglNMSUpYZiRoD1eBz9QJs8YXEAMPSNAkmKvIfpsRUYk7lTWpReTdenF8R0SET4Jbx6B8pxlAOi1uRw48cIorSfOVKmDLgbW9L/+B/AuYxQCeYUK9mz30doUYPYC9bACAEBrGowU6exsQJueRVp+Hg7zCYw51bhs7ShV6Zg7D5OZPwuV1ohKo6ez6UOyCmbT0bB12GenODWQy1TkZc6iMGsBmfoykgomNIXIZAqKshdSlL2Ajr4D1LW9hXeYAzxThgyfX2L1Mi3Pvjx+vxYpFERpzESVkUXQ7URpzEQKBtDk5BPyugnYLARdDpTBAL6e4Z2WhFxOzjVX4dy9B/PzLxJyONBUVJD7hc8h1+sxnXcu3fc/GG2vnVZN9hWX0/vUP3F+uAMpFDFb1NZMJ+e6a8i86AL6nn0eAN+JEzFCQF1WhpDL8dTWoi4rw1MbScykKolNR64qKooImWCQ5h/9JCoc5DodabNnDW93P8Eom2LgczSedWB/nCH3wcPD7p6CtljBE/Z6Bu8d5kylqSdizq7T5IxjUGNnMoSABLzR7/B1jyRJ9wJ5A4llJEnqEELkjufBHlfkH5yVI6esSklzfXwoBLlKizJtMK68tXEfSBIKZRqhoA+/144hqwKp395Wo8um/fh7FE9fi99jQ8jG7wma4qNBn5bPkhlfQD6GCJwfHYKCrHnkmWbx1p5fx2Xg+s2fzMhkkblq4Pd4sR3cCUKGOjsvMsn36za733s1flJJkHxmKL7WVnoeeSx6j7exEesbb5L12UtjJnEA04UXgBA4tn4QU+45Vov5+RfJvu4aLK+8RtjrxXeiJaaNpiISBM2xZSvqivKoEFAXF8e0kxv0AITsjpj3EXI6B9VNE0RhNBLsi1XDyI39/Y7R1BUgZI3c46mtw74pOeuooDni86LIMBG0JFYJZaQVYdKVIkkSTu/Un9NMhhBYIUlSe/9Ev1EIMXJuxX5GSzT/2nODK6a//s4y7JdHO0QVhCRhbdyPWpNBwO/CkF2JTKZAksIE/W5ySxehUGrILV2ETK5Eq88l3ViILqMIp3XqvT5TjI3cjBmU5p1Bpr784x7KqMhkChbXfJ6Djc/h9sV+uQc+u6MJALlCTeHs9WSWzEWlM+G2tNN1bAu9TUMOhKXw4CpfGiFi5SiWO44Ptse18bVEsmrJNIMqDEVGBurSkmGf46lvQMjlqMvL8Bw9hq85NhaTprKCsNuD++Ah8pYvi5arThIC/pZWJL8fRaYJRaaJoHnyjxA106vxNgya4AqZDE1VFVIgGCe8ksFzvJ4MzkY7vTppIeDv6kQK+NGUluEcRgjotfm4fGaU8onlYEiWCQsBSZLa+393CyGeA5YAXQNpJoUQBUB3gvvuBe6F0cNGjPTl0WYPfpjsbccIeBwEPA5c1rbIqiyRXfOQsrpdUxMRMMXEKc9fToau9OMeRtJk6Eo4Y9ZN7D3+JGbH2Oz9hUzOzLO+Qnpm/8Ghx44uqxTd8mtRphnpODx6oLqxEGiPVxdJvvhwzaqiwSxkFXfcPuzz5LpIhraQzUbIZo/uRNQV5fhOtCCFw6jLy6LlqqJCgtbB1XfI6aTn8SfJvvpKSv77v/Acr8e1azeuPXsHE6xMgJDLhXH1mbh27yXQHVldZ2w4C7lBj33LVsLe5ENVD+CtO47vRAtpc2ajW3Q6zp2DeYBlajWK7Cz8bbF+SFIohLuujvRZs3HuSxzHzOxsQiFXo1UlznUx2Uw00Xw6IJMkydH/+hzgF8CLwOeA3/T/nrLTjdxZK6OvGzbeH32dMJjZwOtTIF5SitHZcexBzpx3C2ql/uMeStIo5BoW1XwOCYkDDc/SaT4w+k1A6YILSM8soa95D8fffxQAmVzB4itvo3TB+dg7a3GZB3erebMzSc/RklGmx93j5egrTcy7clrEyEGScPd5katk1G1MvMKN6qZHY0CtFA7TOMTiZyTsW95HO6MGRUYGMo0G2zvvRt6PRoN+6RLCHk+k/M1Y80fXnr249uyNrNBrpqNfdgbZV10BQtD0vR9EzyLGw4kf/wxVQT6m889FO60aoVFj2/gWTbf+OKHVUZTLWWYAACAASURBVLK0334nsrQ0jOvWUPzjHyA3GgjZ7HjrG3BsS5wQpvPh+6m67XZCTie9Lz0fV+/yRVRG6Z+QM4E84LlIznkUwGOSJL0mhNgBPCWE+CJwArhigv0kRK7WojZmR6/DoX8PZ6MUESQpTEv3TqqL1n7cQxkzAsHcikuTFgKhUGS1O/SMSq7URCfhgDf2QLlwQQ5ehx9piGWbFJaikScbN7cjU0z88DwaFVOWvBWW78QJlPn5qHJzIhZDjU3ROnVpKUFbJDTIyaqjAaRwGM+Ro3iOHEVdUkz+179K+sIF0ZV2aaWS05ZrqTt0iIvWGXiiXoVKLbjsRiNP3Gdl/tE/UnfIT3G5kqWr0zi020tvd4iSchvO7U8yJ6Th+GEf82tUZH5NxwN3DKplGr/9/ZixWP4Vm7K28ZbYjGn/n73zDo+jutf/Z7Z3lVXvxb3gDgaDDTbFtBBaKEkgCSSk30ByE/K7Can3hptegFxCIEDovYMpBmNj3OQuy5bV62qlXWl7n/n9MdZK611JuyqE3Jv3efx4debMzNnd2fM951veF0D0+xl85bWkSuIxIcmG2rJ6DZ663YR6El3R+ZY5GLQ5RKIzX60PUzQCkiS1AEtStDuAZDHUaYYxf+azRO763tQFQHQGBTf/qGzijifw4H9243VPP3HU1/87PdfKjk0u9myenlL1WHRqu66u/j3UlKxFIUwtgB+JBfAHHQTDrnghmChGiYlRQEIhqNBrs9GqTRh0eZj0+WhU6SvApYIgKNBpssbMGhoNt63phHrVfNQ6M5Ggh4pll8jH+poI+xOvse/RY3G++WEcemZEKzmnysJg29QpnqMOJ6HOLrTl6T+/oY5ODKcsRlNeRrinJ+5qiTqdaMpLZeF0SYrHIMa9VmcXwZbWeG4+gMGkYKAvximrdDQ1hGhtDKNUCvHXpZVqTlmlQ6GAR/9HNjjf+kkev//RALd8z0rrsTBzF2t55m8uPv3lzF0uOatnMbgjWZc6Ezjf3kTueRspvO6zdN39+wR3lD/kwB3oJdf40bhC/6krho35Ix+St3dmtIfffXbqLJrzV6U/mQS8MV55sH9GPFbpGoGtLw+y442h6R/AJBCO+uhz1lNsPSWj80IRDy5vFy5fN92O/YQjmadmGrS5zK3YiNVSO2kjdErNlew++rcJScDcfc14HR2YrBVUn3olbbufI696JWG/i9adTyf1F1RqlDo9KBTknrYW+1svJRyPhqZvEeF84UWKvvYV8j/7aVxvvRMvflIYjaituejnz2do0widsxgIyER0xSV4PhzJKAq2tmFcvAgkiNjtCROf+YzVKE1m/PX1cT+6oFRiWLQQ/axaPB+MaHucskqPxxVDFCE3X0VFrZqO5kj8tTlLgShCZ2uEa27O5uDuAC884ub8T5ppbghhsig4XBdk45XmtCVWR2POj6+i58kd9G86QLBncgHswXfeRJWVjeXU1RTd8AV6H/hLvB4hy1CKWqkjHPsn2An8o9G77016980sl/h0oHZR+pKSrQ2BGTEAmdRUjcUcmlVuoeyMEuwH+xFUCnJrs/E7AnRs7aLirDLUBjVqg5qBow4WfmoeBx+px1Jmpn1L5pkXo9Fh35mWEYiJEY53vY3D3YwvOIDeIKBUQGmFEnufgMGowOMWOftcHW+8PPEPzB9ysu/4Y2hURsryV1BZeDpqVWZ8+NmmCkrzl9PVXzdBT4mWnU+x6Px/I6dsEdkl8/EP9tC49SFC3mQq7exlpyGGQwS62gnZk4O8nt70qS4mQrC5hf6/P0rBjZ/FtGJ53DcvnBDa8dYl01noaqpBEAi2jgTIQ61tmFYsR1tehnf3noT+qpwcss87l5yLNsppnAoBpckkF5QB/iNH4n2fun8oZdrtfb9yIIrQ1RqJtymUMCwy1nY8nNDWeDg0qZTdhu8+xpyfXEXJNatxH+qg//UDOLcdQ0yD8G40+p9/GjEUJPussyn+wi30/OVuALyhfpBktuSPAv+UojIzDYVWjSpLT/nnz6H5v6ce0/7DpnmU1aaX7vXH73Sw5YXpF5JZe1kO//abyrT6Xjlrf8r2ynXlWMrM2PbbsZSZUSgFEAQEAcwlJkKeMIcfl9kbT/36chpfacbV4eb8X5/Dpts2T2n856/8cVJbfduL2Jz1xMTUgb2v3mZhaFAk16qgrSXKgD1GUYmS4lIlf/7d5Kp2AXLN1ayce2NG5xzr3ER7X2qlOoDSRedRdsr5gEDI60RryuXAS78geMIAqHUmll/xI0Bg52Mn/NYZCLqcjKz1ZxNoOEq414a2ugrDogWo8qy433kPhcmEYeE8FHoDzudeJOfyS5GiURyPJ+9IUkFh0CP6UxtZ/fx5cd6dTJB//bX0P/ZExud9FNAWZGE9ez75G5cgBsLYXqpj4J3DSNHJFYWUW1cQinpRK3V0Ow9M1P3/pqjMTEMMRQjbI0Q9U9+OafUKSqvTz/dtqZ8+/v7RqFmY/m5kLLRv6Yz7ofvrB+KvT/ZNA+y+Zx+SKJFdlUXn9qnXYIQjXjRqE9FYiJ6BfXT078YfHF9s5p7fpvaJZxDjTAmnp5VAaBC9Niftc8rzV45rBMpOuQCA9roX6Tu+nXnrv8ScdZ+n/s0/EYuEsBTOAgSCnlHsLBkaAMvaNaBUotDpkEJhTKetQtBocDz1LKHWNgyLF6IuLMC7uw6FTosYCGJYvBDPtg+TUh3HgqBUkrV2LYNvbMK4bClqqxVVTjbe/QfQlpWhLiwg0HAU47KlhDo6MC1binv7DixnrUGh0xFqa0dTXoagVOLdU0d0yEXOhRdk9D4/SmisJvI2LCTv3EXoSnJw7W2j5lsXUnz5Ko796BlCfZnH1v6pUkT/hYlRvUCftismHBTpbpkZFtPqBZnL+qXC6Ml++HUqLdThtmgoSsNzjVO+b3PvFgRBSc/APqKxqX1GU2QaAGBP48OcNu8mNGpTWv0NOis5pkoGve1Jx4b5j/qOb8d2TC46Or71YVZc+RNqT7+exvcfJLdCzr8Y6sl8FT0MdXER4Z5eBK0GKRQm0HCUYEsbAJZ1Z6KbMxvnM88DoF84n4FHniDrvPWIzS1pp1FKsRjefXL+uyonm1BHJ64t72O96goGHn+S/OuvjR+L2GyoCwoQVErUBQX0/11OjVWazUQcDrLP3UCovR3vzt1knbMu5f3OX3rHpD+PdNBq/4DjPclMnpalVRResoyc02cjhqMMvHOYxp88R6BjAH25ldk/uJyqb1zAsR88leKq40Op1ODyf3Q6Jx8rIzD7jqsYeOcQpTesRVeSQ8g2RM9jH+B4d0RERmM1U/aFs8k9az7+NjvdD2/FtWckKGyoKaD0xnWYF5UjqJQ0/uBJPIc6MrrHRBgeQ9bKWpR6Df42O0e++WDKvpnEA9qPBWeMzG66jECm8E6Tb7rTvnviTh8hAqFBDrY+x4o5nx3hn5kAedmzUxoBrVnOfLE3jQRRoyEfYjRMTtlCqk+9gpyyhSBJ2JvG3k1MBCkSRQpH4uONuT1IEZmKxb1lG769B8g6bz3O514k3NkFkkTM7UFhMiIMuSaVTx/zuJEikWT/tijJzvkTcYXY0EgighgKIUUiDL29GcPCBfLfHwO39TCKLl9F5S0bCHQ4aL/3HQbeOkQsMPLZBDoddD2yjZrbLgLAmFWCz5X+pJ5tKMPt7yXHWPGRGIOPlREAKP3MWdie2kEsECLv/CXUfPcT8QlaZdYz/7efRYqKNP3ieaxnL2TOT6+m8Y6ncO1pQV+Vz/zf3IC/zU77XZsQI1Hm/uI6jv/4aVx7WtK6RzoYHkPn/ZuJegJYz15I1sqahHsMo2ZRZlKOqqI89IvnEGrqQFtTRri9h1BTB5aLzsL92lZM61YihSOE23rQLagl3N6DuqSASI8dpcWEf+8RDCsW4q8beT+F5RqMlvSyW4L+j07Sa+O82wHY1nq/HAwbBZM2nzOrbwLgrcbfJvn9iy0LmF+wAbVST1QME4p68YT6OWx7jZiYyDGVpSumJGshOfpyDJoclIKKoWAPve4GOof2JfH9pAunu4Wegf2U5i1Lq7/VUstxkiUFVRp5oSBGEnc4zTueYPaZN1AwS6ZbGGitI+Aam7F0sjAsXoimtARldja+PXKQ17tTDtz6Dx0m9/JPQExM4sZPBXVeHubTTiPYNH4KZbCtnezzz0X0J7s/dbNqT6SmduI7cJDsC86bUkHXdKPii+tpuP0J3PvbxuwjBsNIMZHSWeswWopp3Jt+PGPA00x1wRnxorGZxsfOCBz+yl/jrwe3N1L1zQvjf8/5+TVE3QHqv/E3AIY+PE7Us5E5P72Guk/+igW/uxHntqO0/uaV+Dmd97/LnJ9dw8HP/5mQbWjCe6SD0WMYHseq175P3Sd/lZAhoNEpWPuJ9P3Gbz/lQFNSjaBUIAgCUjiCdlYFYiCEprwYTUXxiYm+Af2SuYi+ANpZFfg+PEDM5QGFgoLbbqT/j48mXPfca9IXlt/8TGYPnvVzl6IpLUDQaRh6cQv+PUcmPmkKsOgKOaPq84hSjDeP/SrerlEayDfNSjIAABU5K/CE+mh17iIYkeME1dbVLCg8jwWF57Gr4zGc/snVg9S3vYhaqaMgZ/7EYzcUY9IX4A0ksqh4B+TdwaKLbqN11zM4Ow6CJFE4+4yEfuaCalRaA9HQ5OJGzudSJzn4D9XjP5S4CBIDcjxM9PkZeCT9CSwyMIDzpRMCLaMkHvsfe4LcT1yK75CsGRxqa8P+t7aRsb38avz1wOOJVC7DbqJUeHP/TxP+NuaWIYpRlCot+uwi/IM9KFQaJDGG1piDUq3FP9RHNORDpdGDIGDIKUGtM+PqOYokifgc42ez7brov8c9DjC0u4W6q34vjymrZILeifCHnLTaPzpm44+dETgZUXcAQa1EisRQZRnQFmax6vXvJ/VTGrUodGqirsQfSHRIdkeosgxxIzDePdKBYVbRmGMYbQSq5+vlDJo0EI1IdDQGiUaOxPPeQq1d8eDfwL0nVmGCgGHZfFyvvJeUHaIutBI4cCyJ/zyToHBLhprCnvfqCLf1oDQbKP7JV2bcCGiUcs1FKJqY9x+O+el2pZaxPNT7SlJbq2MHRea5ZOmKydaXTNoIANiHjqVlBACMurwkIwAQDQdQafTMOuPTSKdfB8ixgpBvkOYPH6dq5eUYsouZfeYNNLzzP5Me60cFQ14ZBfPPpO/wewgKJcaCKoK7jmCwWBEKqlAolGgtVgJDffJk7exBqdKgtVhxdR0ju3IR/Q0fYMgrR59diKBQotQaiPhdOJvHVljzD/YgSSJF89dha9hC8cJzCPvdOFrrsBTNpufQWxTNW4unvxVzQQ0IAr31myleuB5TfhWCQjGhERgLCo0qKU00nZ3Axot1vPFqau4ia56CwiIlxxoiTIE1Y1x87I2AKksfn5xjngB+T4CO+5IDNVFPkFggjCo7ccJT58iTxsnGYax7pAN/k23MMYxGJq6gzuNBopETE7qYgvfoBKRoDNfr76c8LkUieDYn85VkEg/I1AiE2074LJVKpOCIO6PwlHNwdx4lMNhL5brraN/yeEbXHQvuoI2YGEGvzkKvziIQmXxlsz88SJauGJViamyNLt/Ela/DGKvO4PDrv6Vk4blkF89FrTcTi4bpPrQJe9MOxFiUxvcfZNHGb53IEvr4I+Dswe/sITBoo3TlxQQGbaiN2XhsLYTcA+TWrsBjayG7YiF9h7dQtFierD22FiL+URTvkoRSq0cSRcRIEIVqfDpxSRLRWQqIBNzk1aw4UWktL8RGu9vMBdVIkkgsFCCvZhUKpZqgb3BCCu7xcPIiEKC7aQsGc+G45/3xf3JYtaiP4lIlTY0RRq/htu0uRKmCpuNRPn2Vg0Hn9LtrP3ZGQFduJdgpuyQEhYBlWXX8mKuulfyLlxFo6yfqTp6s3HUtZK+sRaFVI4Zkt0DOmrmEbEMJu4Dx7pEONEXZY45hNDJbgctGSpObj7F2HoGuNrSFJYQH+ogF/BiqZhGydaOx5hMIiFgWLSfqdRPqt6ErLCXY1w2ShGn5AlRGMwPvbwLAWqQmy5re1xwJiXQdz5xNEUHAeuMlDD43UgsgSXIlj6BQTKtmQzjm51Dvqywuvoi1tV/G6e+g130Em7uB6Bj1AkqFmmLLAqyGKkxaK2qlHo3SMGUqimH4Q04kpLQCxGNpIoR8g7TuGjsPP+R1UPfMDyc9xo8akiii1pvRZRUQcPag1OiJRUIJE7EYCRHxu7HOGpmsxUgIXVYBhtwSDNYyBIWCaNCHUq2LazVPhKDbTtBtT9op246+n/B/HFOotxgNpVFLZDA5EUKhmPj398Z7+Vjz5HqWm29w0tEuL0o3vR7g+WcC/OzOLG75mok7fzZ1KpCT8bEzAnN+cjU9T3xI1OMn/4IlaAtHZN9sz+4kd+085v3yM3Te9w4qix7j3BJi3iDdj2yl66H3WfD7G5n339fT9+IexHAU0/xSmn72XNr3EBQCSqMOpVGLyqxHV2Yl5gsS84XiVj7mCTDvl5+h74XdhPvdqCx6dKW5dD+SyClem1FQWDYo5nmLcWyXJ1MxFCDsHEBXUkHU40ZfUYMUiyFGQkiiyNDeD8k9dS3RgA9dcTkKlQrH9s1Yz1gfv24mhqj9WJDYJLKTcq+9gEiXPcEV5LO3U7zsPAKDNsRo5kG98aZTm+cozkAna6q+gNVQidVQybyCDXQM7qWx/72k/utqvoxGZURCwhO04wr0EIx6yDPWYNTkZjy2kyFJIrFYGJVSO2FfpUI95ftNJ3Ktc9BozbgGW6metZGc3FpUah1dHdtpa36LWCzxu9Ppc6isXk+OdTYajZloNMCBuvvweZMD1t11r4EkEXTZEQRFQgDe2SxXUDtb9iVNwpGAh7atI+4Tv6MrbQOQgHQn9gn6rXz21vSuoxDYc/lvE5qKq0/HnFNB497xKet7umN879Yhbv6yidt/aOGrN8t0FC8+F2DL5hD33+vjU9cbuPNn6Q0lE0zaCAiCMBdZTH4YNcAdQDbwRWA43eP/SZKUJr0eHPzCiL9z6MNEreCoJ5BwHEjI6gl2Odh7VeKXsPuiOzO6hyRKRD0Bop7AmNXCJ49hLFTOS88IiDGJzSc4ihzbN8cVo8JOuTAo2NMh/1AaJQSFAkkUcWyTRcqdu96P9wdAEOJGBDILCr/9VObZCFUP/4zgUZkaoOj/fQHHQy8T6e7H19dGS9+DaV1DlWJitOjGF9kOR3282/SnhP6nVlyHN9RPj3vkmcgz1qBRGXmv+Z54UHgYqys/m9b40kG6k3soAw4jpcFIzvkXEHO5UOcVMPDy82SfvQHvgX3EPG6yzzobhcGA64OtWFafHu9nfzoz19u8hVcz0H+EIwcfQxTlhc6Z5/yY8sqz2LvrHtwuOV5y+lnfR6uzUH/wMY4deTZ+/lnrfwqSxK4Pf0soOMo9N2pyHTcDa4JJeFIGYBqhNGrZd8M9hO3jr8K1RcnFXS5HK0P94/Oaud0i3/3WEM1NURrqI7y/qxCFQg4N+n3yZ1O3O8ztP7CMe53JYtJGQJKkY8BSAEEQlEA38DzweeB3kiT9elpG+H8A3S0hwsHRKlEpHnppuDAr1bGxFaZqFk5PPKB2TSHNHySv9tpumLyLIiqGUCm0WHRFDAUS86FLLAszupY7aGPQ34XhpCpLlUKDhJQUSDZocsjSFTMdUCo0aQvej0VxkQrmFSvx7NlNZKCf/CuuxrzyVGIuFznrz6Xv0YdxffgBuuoajAsXJfRLhfnzrqK4aDmSJPHhzl8TDI4Qn0mSSOOR5+IGAMBuO0hJ2amYLWVxI6DVZdHfd4j+vkR67K72rVTWbKC0/Axajr+e9vv7KGGsnUvJNZ9HoZHdcf62ZjofvDutc/0tdqKuiWNlMV+yK7W0di2RkJe2htfHNHb1ByPc9j0zP/2hm3/7jgkEuOsvOXR2xDiwT3ZrFxQqcQ7OjDGcLnfQBqBZkqT2j4r06H8TZooqwpKrIrcwvRVqLCrRcSz5Qdea1Cy7spK8ajO2hiFOu2EW+iw1ux9rIa/WTPkyK/ueacPnCMWPte7sJ6fMiKVIT8Ob3bTtGkhxR3D42ik0z6HWugZ30MZQoAe1UketdQ1WY1XKc8qzl6JRGrB7m/CE5CwbQVBQYJpNrqGCzqFEtSZPyI6AQEX2ctoHR0jLVpZfM6Ug4GhkQiqXSbWzoNEghkKI4TCIIgqtjuiQk6F338G8bAVKs4Vwfx+CQpHQL2l8agOFBTIBn8PRkGAAALyeHsLhRCMZCsoxNJUqMWjudBxLur5j4CiVNRvIyU0/aJ33tevwvPUhoca2lMcG7k5vN6NfPh9NSaGcLTcOfM3H6Hr4z5R++kso9XoMVbWozBainol97Ie++sCEfQBivuTvtrd1O96h8RMHfvULD397NJetuwsIBCRu+byTK67Wc9GlOs44UwNI3HiTkf11M1MrMV1G4Fpg9Lf2dUEQbgD2AN+WJGn6BUOnGfpZxZR9ZSOunY34Dnegqyog2NqH0qwn0Gwj/7LTQADHpn2Euqa3iCPTjJx0kUm1cufxIJFw8kol5I2w46EmPvHz5ficIeqebKF8eR5zzikmGo7xzm/r2fj9U3jlx/vixyxFenoOD7Lz781c+B9LxjQCxwfex2qsQqsysrryBiKxAKoTuqoHe19hUdGFScFbndpCrfUMZuevJRAZQkCBWmVAKciPst2bWKTkC8tutvmF51KVu4pQ1IdBnYU/MkTn4D7mFkxdsMakL0i7bzCcfmDPd+gguRvOI9zfjxiJ4Nm7m9xzLyDm9SKJMdR5eQgaDZIoJfQ7GSXFq+LBya7uHUnHQ6HkMcXdNyfZyWAwORtr2AWk000T100GQdrA3gYCexsm7ggEutrp+vv/UP65r6HQaCi/8at0PPAnYv7pqWpPRZ8Si4bIL11Kf3dqUkaAwwcjXHB2PxvO0/H+eyH6bDHef1c2KL/8XTa/vycHx4DID2+fmeKxKRsBQRA0wCeA4cT5PwM/A6QT//8G+EKK85KE5o//9JmpDmdCjHWPQFMvwfZ++p/9ECkmYlk1G4VOjdKgwzC7hIHX6oj0uyj72kV0/jE57/xkqDXprzKHg8IF83MoXZZP41udLLtuNr6BIL6BAJ5eP+ZiAwXzcvANBAm4QtgOOhjq9HLmN0/BNxBk32PJ/DzT5QoaxuJLyjHmaXG0euP1D9FwDKVakXDMlK/FOxAiGoqNy5vkDQ3wYduD1OatwWqoRKM0MOBtocW5g0F/JzW5qzFp8xLO6R46iFJQk2Mow6KVU+/CMT/OYB897sMp73PEtomy7CUYNDlolAb8kUF2dTyWdO3JwmwYP34xDEmK4QumNoipELb30ffU43ElKgD70yMBU9/hQyOSiwpFQr9hCIKC0pLT4n87B5MreTOpmJ7K3inr8g3oF88hNuhGaZE5l0znnIpx9RJQCISOtjL07FuorNnkf+06lHk5BA8fZ+jZtyi47UaCR5rRzqlCmW3G/ssHEIMhin/yNYL1zQw+NaIAVvj9L4IgELU7cPz12YQxBHs66XnqQUqvvxlNXgFln/4inQ/dI++iZgDmnAokSUwKjJ8Mp0Pk6SeSPQLfvXWIX9/pweGIMVPCidOxE7gQ2CtJUh/A8P8AgiDcB6ScMTMRmgfI11ejUerp9soZKDqVmVxtKXp1Fl2ew8zNXcvB/in6I0f9gMRQWK7ajcVQGrSIgRBSTERQpZdWmG5QGKD1iPzlK5QCgaEQZcvz6W8cYqjTi+O4i0t/dyYv37oNQSEw1Oklp8pMxC8/EcP9UiEzIzCxSyoWFcktN6HRq+Krngv/YwlH3+lBqVHEj4UD6T+tvrCTgz0vpzy2rfWvSW3+yBBH7ck1GuOhY2gfHaPcRIJCYN41C2h4op43jt6Jxqwlb2E+A/X9lK+tILs6h/bNrbg701u1ZxnSqwj1BR2gAAFl+nq54wRFE64xRr886/zpW6EjxwXGagsGxxciMiydR++P7wFBoOQX3wLAePpS+n5xn1wh/b2b0dSUoTDosf3nX0CSKP7xV/Ftl1fRUiRK/x8fSbime9N2NGWJefiDj71KuL1nTHefr+ko/ZtepODCy9GVVlD0iWvpeebh9D6AcZCqWMzv6ZPnkUlSkwDY+2aoSuwEpsMIXMcoV5AgCMWSJA2rXFwOpF6eZYjBUA8LresxqLJxhfvI11cRivniik3B6OT54YfR+adXEdRKiEHfE9sSDwry5NHx2/T0BTLJygn45AfEdtiJ7XCylsDzX9sCwLE35ABdX/1In+G2k2G0KFm9Mb0fvyTC5mdSaxjUnlFA9eoCjr7TS+O7vRx5Y4QWes45xex4cGRlOfrYMF76wdjVnSdDUCnRVJcSG/QQHZg5D+LJ23a9VUfRymIG6vvR5ejo22/D3elm/jULcXe4qL1kNu//x7spr1ViXZp2tXCnfTcKveyii3mn/rxOBKOxkMWLPh3/u6tralQEfv8AcxdcSSwWxm4bqc5esvxmYrEI9QcfGefsUZCkuNESVMp4iqj913+TEx9Gz92jJvLgsVbSQbi9B4XRQMmdt9L1jf9M2Wdw51as685HaTBiXrSU2XMW0Hr3nXH949FQaFQISkUCSVwqaAuzCHQmumwmigcAfPt2M7d8zcTLLyQLSn3nmzOv8DclIyAIggE4D7hlVPMvBUFYiuwOajvp2KQRFUMc6H89vq0aCLQhSiMWsnFw2zhnp48xK4clMmIyzMQfPxPIZBfQ3RokFEi9Umnebqd5ezLNAUDju8mKVpOBwmQg99qNGE9fgqBR435tG87H5axiTUUxlo1rGPjLzLsKT4bapCYWinH4odTCHgIClYWr07qWhETfUAOxSchcThZlpaPHJtHVkxwPyAT1Bx5hyYqbWbD4esor1+L3D6DVmFEoVTQcepJgYHzDHTh0QbwCKQAAIABJREFUnKI7vkK030m0T150eDfvpPB7N51gFRWw/+ZBIt128r96LUprNoEDx4j09qe+oFKBZeMaFDotSms2rhfeIWIboPD2m0EQ8Lw9PuOq471NFFx0BQAKjYb8cy+h99lkQ3bKfV8ESWL/5+TU8NPeuH3Ma+7cmJySPhEuuUzPQ/f7+M8fp9556ovKCdg60RdXEOiduub5yZiq0LwfsJ7UNn3J16OQtfw0FAYDUbeLUE8XCr2eYGc7OWvOYfCDd9HkFaDQ61EZzXiPHsY0fzFR9xBiMIgYCWOat4ihXR/E+wU7kyl9pwsqtUDFnKlREUwVGVUrH/5otExTQWEyUPyjr6AusiKFk4OaUbsT42mLp9UIWCqzKF5VgqttCE+Xh1mfmEN2TQ6HH0rkHmp+tYnai2bhaBgAkndKpfnL044HDHk6MtI5zlIXEJXCKAUNlcbFtHr3IyFi1ZYyFLbH2xSCgmxNER2+Q0nXKCocYTd1Opvw+9OPR6SCz2ujbscfqag+B2v+fArMxUQiAfbuugePe2LhoKFn3oRnEuVgQ41teLcl7hb7/5RMGGf/7UPJF4yJ9N5xV1Jz3y/um3AsAEN7tpO9ag2afNmdZFm0LKURsD23i5OjId2PJ++q1FmTW/jlFyj4YOvYWWP60ioCtk4MZdUEbF2pU8ingI9dxfBYEFQqxFAQhVqDQqtDX1mTMJEPt0UG7Oirak8UVUmI4RBRjzuuh5rq3OlGxRwdKvU/NlV2uuMBM4Xsy9ejLrIy9PxmXK9sofL+nyQcF4MhQsdTr37MJbPx9DahUKozqkp2t7vY/O234n/X/XFEq+D4iyMBdp/Ny+GHDyKmkAnUqs3MLt2Q9j1tg5l5RSUkolIEf9SNJzKAN+ogS11AMObDExmIt82xnIE3kjprRKkcoajo7E7tCnI6GnE6kpMKKmfvomLWPt57ayQbaPlqHXt3uDl+9EWOHx3fLXrhZ/JobQhgyVGx620Xp52fRXa+mtb6AJ6hKMvWWji2z0fzIT8r11voaQux+HQzrfUBzr/Oykv321GqBOYtN9LaEOBo3fRpJg9DEkWcH26h6BOfkhvGiCHYXkzWh+566P2kttHMA5mgrSVGRaUKSG0IvC1HyVu9npDDPu0GAGCKQnsfHSRJkreMgL6yGkQRTUER2qJitEWl8TZvYwO5a87Be6weQalAX1WLJq8AbXEp2uKyeL+ZRCauoP7umclKmEnm0OmEYcUCQsc7GHru7ZQ7AYCoI9kvWrxyI4XLzkUQFNRuvHnGxpfKAAAsqf0UalV6n3Eo4qVnYOwUwVQQBAW5mhIkRDRKI0ZVDjnaEtQKXUKbJzKQsuJ69Mo1EHDidGam7rb1LT8P/zkxHfSWb6cfYPZ5YtQuNqDSCMxZakSpEhjsizB/lZHVF2Tx2sP9NB+SFx/FlVoMJmX8eNvRAB2NQdZcnBO/zkzBfaiOWGDk+VcaJ1aKG0tHIFWdQDr4/a883PL1se8bHuxnYMdmIu6ZiQ/80+wEXLtTr2Rszz4GQMgmb0fV2Tl0P3a/zFnS1UGwS15F2p6TY9eh3vQZHyeL866dWaqGdFBSMzGPDYCtI0T9zo/OT30yBIUCMTj+j0ddmpyHbyqqkRcGH7HilCAoWFx9Bdmm8rT6B0KDbDt8F5KUWYaHK9yHCznRrtEtP/s+74jPPd4WTe2HX3LKjQDEYhF21901ZjxLoQRrvpJ+W+L4Nl5uonaOmrv/e+T6hSWJ04VaLXDvM0V84bJe7n+hmK9cYyMcku+z7ZWhBJW8xv2J7CYKpYAYkxAU8PLfZJ//8PHhtOJHftUT7zdTkCIRbM8/Sun18kKi+JPX0fXo+O6khttT00JHvZMgXwQuu1LP3t1h/nRvsvbIN24ZRGXOIupxIU2Cgysd/NMYgXQRGZpcVsnf9y3GYJ4+tst0cd2txVx36/TQF0wGRRVanm1aOi3XevjOHl78a+og8ljwHziGed1K9ItnEzh0POm4ac1StLXJE+5g8z6Klp/P7E98nYGGyUsuZgKFoOKU2qsoyJ6X9jn17S9nbACmCr3eijV3NgC2vn1Eo2NPTvkFKvp6J5eAHolIbH07wMKlWuo+DMYNAJBy4h7tyRg+LqVgS0nVbybhaz2OFI0iqFQYqmYhKCdI4RUECi9ehkKrSnIh9T6TTOU+Eb5xy/hzliYrF8tcWWM65Mjs95UOPnZGwLT6VCJ9fUQHhzAsXohn63bMZ5xGuKsHSZKwrFuD+50taGfV4Nm6HeOKZSh0WsK9NtSFBUjhCL66fRPf6F+YccxebqZmsZHjez20HErt03U9vxnDigUU/vvn8NfJNSCq4jwsF5yBbkEthuXziQ4kb4MHGj4k4ncTHLQRco/spsxlc9Ba8gi5+hEjYXS5xUQDHlzt9WRVLsTVXk/BKefg62tFby1BrTfTW7dpwveiUupYOutacs1VGX0GTney5OhMo6z0dIbdQV3dE2TI9Mew5itx9MuT3nisylqdMKx3FMfzj3q48atZPPnA9FMcTxaW1aeDJBHq6UZlyUKdl48Ui+I7eACl2YKuuhrfwQNE3fKYpUiE8IAdbVEJglqNxppPyG4b8/q1t11EzplzifmTd7CRxnZy5uXjOGTD2dCP8fSlCBo13i2T18jW5hXh3JMcg5gufOyMQKi5hUi/nMUwXJjl2b6TrHPPwb15C+HuHsK9NnTz5JWOOs/K0Ka3sZxzFjGPNy6L9y/841E534BnKEr1YtOYRiDqdGH7+X3kf+VTGFbKpHGGZfMwLJNX26GWLvrvTt5+Z1UswNUus4Vqs/IxFlYx1HoQrSUPQ345kiii1GixH9xCweK1cj/LSIWwqbiGvv2bKVy6PunaJ0OvzWbZrE9j0udn9P4d7vHZI2cCSqWG4qLlAAwOteDzja9JHI1K/OLPBYTDEioV/PsX7fh9Etd+wYLRJFBYquL+3w/R3hLh7Vd8PPBiCb1dUb7/FXlFOuSMgQC2nhkqZ50MTlgpfVW17O+XRGI+H7qaWiIOR/y1d//IYjHUb0NbJBf9afILxzUCOWfOTaKMHsasKxcSdgXJmZ/P4LEBlDkWRN/k5iSdTiAYlFAZLeSdcR5iODQjxuBjZwTEkGxd1YUFaEpL0ZSXoikuIjrkkn/YZkvCMTE8Yo1VuTn4u0dy1xV6HdkXn493Zx2xIReW9WtRGA2Eu3rw7d6LGAiSdcF6XJs2J43jX5g6ju/zctpFuXQeHT/7KNJjp+eHd6GbV41xzVIEQSDqdBM82kLwSOqVdNmaK1DqjDgbd1N5zvV4e5ooP/Mqwu4BnMd2YSqdQ8jVT+7slURDfnLnrEKp0aHLKUJvLcbb00TuHFlRajzkmCpZOuuatIPAw/AG+jjQPLZIzEyhqHBZnPQtFU9QKnzpquR6j89d2pPU9psfpS4ofO+NzLPLFDo1WWctIu+KM1BZDCj0WkR/iKjbT/8z23Btq0cKT86wuHeNcsmc2NoMU7ADhLuT9QkijpFaBHXO+HQiQ7uaERRCSq4gx6E+ys+tZei4Q85O9AUQlJPLvzGbZSPgazuGobwGMTS5mMNE+NgZgZhbrqSM9NnjAtfhzpEc5MGXZEHqk4+5392KuriIaN+Iz0wMyB+a0mREXVxIzOcj2NxK4HAD1uuvIub14X5ny8y/qf+jmLPCzFO/7mTj59OLeQSPtsa1CSaCv7+TrMqF+OztSNEIPbtepeaCm+KuHa9t7Ou0v/vYhNdXKNQsqLyEEuuStMYzjEB4iD1HHyQQHj+TQ5utY/5nloAAzS8eRaVVUbC8mGNPHkabpWPRzStQ6VXs/Hn6z2dOTi1z51wW/7u/f1qK9SfEgd3pT07FN1+A9dLTUh5TmvUozXrK/u0yyv5Nfh/2J7Zgf3wKv9EUFOyp6NhHZ96oLeOneuor86j5ziVEPQE4yQ5oHDYO3rOTOdedgqCQDdCwm3M0Nm8vYP0Z8lzV2Dn272NOeS8qSw6SBJqcPHJXrp323cDHzghMBZHexC2cuqQIVCpAgFgMVb7Muhg43ECotR1tdSWi7x+XI/+/HYe2DnHJl0roaZl+F13Y60Sh0lKy6iKi4QB5C9agVKeXETURjLo8ltRejUk/vjbsyfCHnNQde3hCAwAw5+qFND1/BJ/Nx6rvncnxp0eEcKounE3ra43Mu25xRveXYwEyRmsDGJYtwX/g0LSlRisMetT5+YTak+s3xrtX9volYxqAsVBw7ToCTb14dmeW4jolTEAxrrGaOfTl+1MeM5VnMe8zS/G0D8kCVXYnuoWz8G1PjFM++NdE9+iTj/pxnqQfnJsr7yAigwOoLTmEBvpwHUmuWZgq/lcZgZMR6bHhfOqF+APpP3AYKSoHwCRRxL/v4Hin/wtThK0tyCt/6aFqoTGhveqhn2d8rbYbf5Dwd/eOEdI5hVpL3rzVdGyduvul2HoKCyovGVMLeDzsOvpA2lXBSp2KiC+CFBNRqBLdBSq9iogvnNLdMBZ0umzyrCNZS312+dnWVJRh2bAOdUEewaYW9AvnIyiV+HbVER0cwrxuDZHePoKNTZjXrUGh051YIFUAAt7tO4h5fPF+gUP1WM4+i4i9n1B7xwmX6wV4d+4BQYjfy7UpmeSv+Kbz034/o1HypQs5NsNGQKEdqfAfdkmPhZbfvoplcQWhAXcSzYwuR48YiWEsMZ+4Vhhliq/x4QcSjcA9f/TS25N4rdIyOSaqLSzFsXMzWQtXTpse8mj8rzYCQMKKZNgAGJafgjLLgm/n9FvVf2EEGz5diEotUDnfyF++NxIkjblGJkopEgWFAlWeXIgU8/ggJqIw6RFU8uMZ7kod3BQU8uQpxSLYD03drbew6jJK85ZN3DEFHO6WjGghWl4+xim3rCIajNC5uYWqi+dgLrPgbhui891WFn1hOdFg+j7x0pLTEtTNhrOCwh1dRHptuN56Vy6wLC0hOuAg64INOJ95AXVBPq7X30JpMaMuyGfgIdlVFu7qJuocxHrdVQy9/Hq8H4Cvbj+GJYsAZHnLt94l5pIzbeL3SgGlKf0q9tFQF6R2zyzaWELF8lzqnu6gaqWV3AoDQ70BPnw484ysRCMwvnur+psbUecYUx7r//OLND45QuGhqSiW551xJu8Pt4VwOpJ3Tl6P3D/U34t11TqiPs+M1MV8rIxA1UVzaXtNVi5aePMqfL0e2l49yro/XYqgUNC/r4f6v8qpVuv+dClKnZq+nZ3xtjnXnoL1lGJ0VgNbb32FqD91Bap/7792AB8F9r49yGBfmOKaxB9/5zdHSLaUORaK7/gygf3HGHrubUKtJ+I/goCmrJDsKzagqUj2mRav3EjB4nUgCAScvTS+8IcpjdWgzZ20Aege2MeR9ok1JkbD3TbE7l9ulWmGRYnubYmulR0/fS+jnUBJ8aqRa7s78XhGcfkkUKSHkCIRXG/KK/XY0EhV8OjXUjAEohinWxl9bDSiDie5V11GoL6BQEPjR1q8l1Wk583fHOHi/7cYMSax5+l2ypckF1ylA03uSDA44ho/b//4z58f81hOiY4Fn1tONBCh8clDRLr6UOXnjPu53Hhd6oC7xzNKN3z3zMUu0zICgiA8AFwC2CVJWnSiLRdZaL4KmS30U5IkDQqyvuQfgIsAP/A5SZLS4hNue+0YRavLCbtDCCoFba8epXhNJZIou2+si4soWFGK45CNwIAfMRxj9rWncORvdUgxkb493TQ+8a8J/uOCwb4ws5aauPwbZfzqpqMp+5T85zfw7zyE46GXEg9IEuFOG/Y/PErezVcknWcqqsHVXk/b5keo3fjFSY+xNG8ZCyovQRAyKxSMRP0cbHk2IQ3UbK3CaClGozPj6K3HkluJ321DZ7Lid9uJRvxk5c/C2XsEa/FCelu2I0kSpbPXMdTfhCW3Ep+rBwkwWoqxtaZfBKdWy9lL4bCHvfv/knDM8djTCCoVkiji27knoRhq8EU50SLm9sRfA8R8PlAoGHj48YR+ICdtuN4cyahzPvdyfMc9+l6jMdkMmYmw8d8XcmxLH9WrrMQiIpFA5oV5gkqFeYGcACDFYngaxp9DPPVdFF2xCpU5eWej9gzganaitmgRFAKmc05DdHvx7TyYloHUaATCJxT+hj9CQaEka+EKAFz1/7iYwIPAXcBo5YXbgXckSbpTEITbT/z9PWSRmdkn/p2GrDSWdjSo/NxZhFwhmp6SvwhP2yBbb30FSZRQqGRSuMLTytn1k3fQWLSUnzuia5rJyulk/OVHXVMifVuw0sT6q3PT6huLSvz5Pzonfa/xcP1txWnrCr/1hINj+6aPmKvpYGKQffm5Oag1Cn7/1bH9uQqtBoVhfDeBoE5+P0GXnNJXfe6NKLWTczNUFa1hTtl5kzr3wyP3Egwnro6zrNV0Nb5L2ZxzyCtbgt9tQ2PIxjXQStA7gCmnnHDATTjgQlAkGp3h/qacchQKFV2NqV0qqZCVVRl/3d2zG1FMngil6IhrKS1Bm0woOU6a8EffaxhK09RYdQWlAimW7DJ57ReHEWMSRzfLSSH2psx1GgxVs+Kux0BnK1IKic7RyNuwiMovbUAMR1FoVPLnJAhEvUEab3sQtVGNsdSCJEp43t5OuG1ihtXVazTcfV8uZrOA2y1y5FCUe/7kYccHYWJBP/7OZrTW9GVMM0FaRkCSpPcFQag6qfky4OwTrx8C3kM2ApcBD0syWckOQRCyTxKaGRchVwhBAH+f7F/1drtZd9cnkE7wjGz799cZPGLnzF9fRMDhx9U8Pdw7W1+amohJWW36D3nn8SDvPpt6CzgVKJUCt/wsPU4bgLeedMRJvGYCbkeEpn3j+8nDHb0YVi5AW1tGqDmZ10lbW4Zh5YKk9s6tT6NQqjCVzsZvz5xjvbZkHbUlk9MX7rTvTjIAAOGQl4LKlSiUavyuXpRqHbFoCDEqBxot1mqiET96cwHGrBJM2aWIYhRDVjFDfcdQqnW4HW0YLEUUVK7E3r4nrfEMZwVJUoye3l2Tek8zDYU280B74vnqpApdR7tvWmgljLNHRIF8x1PvWEej+KpT2XfDPYTtbk599bvUXf0HdKU51Hz7YjwdcmbYMPGgFAxhXL0E347UmhQAl35Sz2/+lM2Tj/oZGhTJyVWwZq2Wh5+wMqe8F+uqswnau1EZLdjff/VjFRguHJ7YJUnqFQRh2EyVAqOXuV0n2tIyAvo8A83PJuY3b/l6oqsgFoyy7TuvJZ3rbpn+iTVdVM5L3wi0HpmZqubyObq0tY1jMYmOxpkpPhlGSa0ejzNK0B/D1Z96dTX0/GYKv3MjxXd8Gf++o/h31wMSCosJ3ZxK9Mvmy/nWJ0FjziXsceLuaMBUMgtvT7J27niYjAGIxkIcaX8Jm7M+5XF7++6EAODJurI9Te/Hjx+vezLefnzPEwn9vYOdE6YpDkOjMVOQL1da2/vrU4rGfxwQC06N/EwMRdDOrkBTXowy24TvwwN0KyoxrStDadITHXQTbu1Gt6CWcFsP6rJCpHCESE8/2tkVeN5OXTgnKJVYFo3EgjxHJmZ71ZXmErYPU07EUOjU+I7bsD2/h7nXy26l7Fm57Pzpu2hqK2T2Y6UCUuxkAL7+LRPvvh3ih7ePLCyUSrj7Pjm+4dy7lZC9J2V9w3RgJgLDqZ7eJNOVSmi+8sI5BOw+Bg6OXbL9cUVVRprCM2MEahalP4auphCR0MxSats7QtQulSlyt7+YWtAkcLAR+x8exfq5yzCsWIBhReKqP+byJscLgMqzr+P4y3eTM2s5efNWczwDI1CUuyiDdyHDG+hnf/MT+IMT7DxHrdJS6sqOs4pL6J/mak/OCpJdSxPxBP0jIfqmtuCQYiK6udW4XtlC1iXrUOh1xAY9KHMtiIGQ7FY80aYuyUeKRBB9AYyrFxMZI7sMwLxoWQJ9dGRw4oXkcJYhQNQTQGM1EXF6CbT3c2yTXGmdVS1P4JEeO4IgjGkAAMoqlPz6zkQ3ViwGzzwhzxNiOITSYMJQXou7Yfp50aZiBPqG3TyCIBQDw6W6XcBon0QZkFSDnkpovv31mcsFVqsFPvtFE2s36CksTg4CXnjG1KQScwrS88MDtBxJdMFUqxeiQEVzZOwtYzrIREOg9cj0uIG++80sfnx7ckbGZ27p51C3hDlbxfG94/tp/XVHCOw/hm5hLfolcwAQ3T5CbT0E65sSfnTD6PrgOQqXbsBYWEXzG+kpSQGYDUUsqrps4o6jYB9s4FDr88TEmaHynSwEQUlpiZwV5PH24HLNnFDSVJHKn58pYm4vprOWI2hUaOdUypw84ojOyHCb6A+iLinA324j3GFDYRhjly4I5J6+LuNxBLtHXMeBTifZp83Cd9yGtjALS54aU1kWaqMG1/17CLd2oakoGfd6XR0xzObktXN2jhxM15dUotQZiPl9CfQX04WpGIGXgBuBO0/8/+Ko9q8LgvAEckDYlW48AMCy4Szc72zFsuEsYm4PCoMeQa1C9AdQGAzEPF6CDcfj9BJpD3ZLEZs3BfjcldNPxZoJettCNOxODsYGpWTfuZlsNIIOhzTxzkihFFh/ZXqBaYB3npoe15mtL3WQ0dYXIxyErHw1hZW6MQnkhiHFYgQONhI4OP5CYOH1P4y/HnafzP/U7dQ/9rMJxzq77Fyqi86csF98TJLIW3U/Tbv/MCpW5tOxRw5cly3LY/l1tbz03cwphsfCV964kD9vfB1JirFt+y8Sjs258MvYj2xjqD19yojF1/yA5rf/ht/RnVb7RJh/2a00vPi7jM5JB97308uMEdQqIjYHUVs/ke6xdwG1t/0IldkCyNmHbX/+VVrXb/juCO3I0f94goqbzuGU+76Iv7Uf//v7QJLw9Yy45VIF4l99J//EfcHrlfj+jyzc+l0zXq+ExaIgv0DBoFPkmSf9hJ39GMprUBlNM+ISSjdF9HHkIHCeIAhdwI+QJ/+nBEG4CegArj7R/TXk9NAm5BTRz2cyIPc7WxP+j3PXTrFSzpqvYMfWmfWBp4NUrqCYFMMrJlMNqAQNFnJxYkdi/C+/tEaLVp9eGp4kQdvR1C6pollrsDV9EP9brTOTX7mCkH8QR2fyTsXjS/2deLwiIT+89XAfc1eZ0xrXmBj13acz2aeCVm2isiB9ygJJEjnU+uyk7nXW1xfw6OdG5XV/xMI3/3ikfr8xtx+lJXOVsLA9M0UtKRIdd/IfxrABABjY/Brh/onPAYgFRu0IJej467t0/FXO5spbXIiiKichjiWok6fZ9rbEDKq+MRZTALrCUsLOfpQ6/T+uYliSpOvGOJQksnoiK+hrmQ4kt8pM+akFePsCNG/poXZdCd7+INZaC45mF8uunc2eh4+hy9KQXW6iZ/8A5acWYD86RO9BBytvnMvR1zuYdXYp+59qil/vwFNyHnd7S5TyyvQ2PrmlixAEJYM99QkcLNOBlhRGIEIYi8KKW3QijfoB6TCgxzihAYDMXEF9nSH8ntQPXen89eSWLqL94Cv4BruZe8aN+F095JYuSm0EvKnH5vOLzFpqwZilwjM4tc9Qv2QOgf3HEtoUai1iJAQIKLU6YqHx4yxVRWeiSCnDmAxJEjnY8gx9gyPEX2d8aR5Vq2UuoaYtvXTtG+DUG+fwwrflgOMnf7OaD/58hNU3zaNoQQ5X3bUGgB33H8NcZOCyX52GpdhA24d9bL37CFfdtYb2nXbKllkx5ul45Ib3WHpVNfM3liMoBDrr+tl69xEu+OEycivNqPVKWrfL5w4jf3YW592+hFfvqMPVnbzTWnjl92h97xH8jm6yKhZQsux8One8iLcvPZK+k6G15FFxxhVoLfkgifQdfp/+hg8wFlRReeZVKNVa3D3Hx1Qx671/E2W3Xp7xfXvvfX1S4x0PliUr4699zcdwfjB2Sq4mz0x4ID3PQ1atlaOP7Kf64rkn6gRWo6kpw3FfIqXJV29KPxvR196EUqtDk239310xrDGq8dmDZJfJQZrschNeewCdRU3/sSH6jw0x0ORi/sWVdNX1o7No8NmDlC3Lo/egHKzz2gMo1YqE6w3j7t+4+cF/ZdPcGGHnB+Nzgzi7D2PJq2Hu6TfQsP1vE37wSlX69QWtKUTdTUI2npMMAMhGoEVKnYlyMmozCAqPF5hWa020Nb9I9bLLObz5LnQmK4c334VSndqv6vOlNgIer0R3UwBJkl1VU4FCn3zvWRd/+USVsETNBTdx/KW7xjxfpdRRnr8i7fs1dLyWYADKllopXZrHYzfJq/ur7z6Trn3Jge7+Jjev/mA3Zcs28szX5d1U2bI8dBY1j31ePveGR86JT+TRsMjzt8lGJLvcyIILy3ns5vdBgmv/chYAb995gFhERFAIfOWNC9l6zxFiEZGSxbmc/sV5PHfbDoKuxFiFJMbIqljA8TfuJeyTV9GujiO4Oo5gKqxO+3M4GTXrb6B921P4B7pQavUs+OS38Q90UnPOZ+ja9QqDrfsxFlQx75Kvpzx/6L1D6GqKybtsddr3tD/2Hp49yYpzU4G+opqiS2Vx+ZDdRu8zfx/3N77g15+m56kd2F+TM4dm/+DyMauGdVY98z6zFKVWiSRK+PfWEzwyftKCQgGnrtawYJE6KSns/nt9CEolgd4OxPD/cirp0mV5BN1hAu4wCy+tQmtSIygFAoNhxJiEIU9HbrXsVogEotSuKyHoDiOKEtbaLPJnZ5NbbSZ/XjaF83Pi1xvG+vN1KBQC9z6WT0drlJPryj55zojfPbdETrk7uv1BtPpsQv7xrXZZbfrslakm4CghjIosOGlxbsBErbCIemni3O+MgsLjCMv7BrtxdB2ksFZWpxrOWIlFUj+AHm/qH4/PJ9J/eHoK0VIZAXGU3qqUojhqNPKzZqe9C+js30NXf2J+vrXWgq1+MO7l6DuS/DwoUqSxDsPZ5o0XMvY3jfiKRxuSvBoL2RUmrr33rHibSqtkw3eXoDGoiIZhpfGVAAAgAElEQVRjaM1qFAoBlU7Jxh+v4MirHUkGAMBcMhvrrJUcePSOtN5zujDkljDrvC8ktOmy8lHpjAy2yhOkz95G0J06EwzA9sCbhLsdFH7mnHFdQ9FBL7YH3mTo/emlw9bkF1J63c0IKhWRISddj9xLLDB+ksTJO4HcM+eO2dfT6UKlUzP8sKhys9HOriTS2z/mOb/4dTaXX62nz5b8HN9/r+9jXScwrah7JHVA0HZYDmBu/YNcQexs9aTs//oP5MDbGz+UJ8y+hsQf6h3fGQQm3oJpjbnklizC5+qhYtGFtB96dcJz1n0y/YCsy5HaNZIqMNwuHUvRMxklNVrmr0pNaJUK7zwzdlC4dd8LnHL+bbQfeJmiWWfQffRdFm/4JqIYpf7de5L62+zJD67bI+IPjP2gVj7wU6RojI4v/YSqv/9XWmP2bE4MrNr2vsXcK25DpTPS8f5T4567oOrStO5xpP2VJAMAcODZVlbfNJfrH1iHIEDzNhs9h5zEIiKffuhsfP3BuA9YjEk0vdfLjY+vx9XjZ88jTUiilOAOimPUSqRpSy+6LA2LL6s8URgpoNaryJ9lIeiJEB2IMXDCgEgxiQeufAuVVsl196/lg/9poGP3yCTjtbXQs+c1ipZswHYgmc1zsggM9eFs3hu/pj63hKDLTsnKi8ipXspg634MeWVoLdZxr+PcVIdz00dL3qivqKbs+i+i0MkLikBHKx0P/Cmtc4f2tFDzrQuxv3GAqEdeDBVdviplX2XUIzMbnJioRX9gwklbo5Wppf/rJ6lrPAb3b8dQXkPMPzMEcsJY/ruPEsMpoh8XmHLK8A31pM7zToE7HqxlyZnpBT+vnJVcjFKpkisW26MNie2CvOKYyBiceWkOt/6uctw+wxjqj3DT6em5mIZhya9BrTXh6ErmVNFpBZytiffuscWYtXxsWgzzubI7wPP2Dqr+/l/49x0lNI6YjHZeNfbfPpzUrjFlE/F7JtwJnL/yx+MeH8bbdT9D/IhF4acbo7ODFlzxHfoObcFxfDdVa69Fn1uMUq0nEnDTtfMlfP0d1Ky/gezKRYS9g0QCHjq2P0vA2cv8T96GPqcooV2MhClbfRkGaymCQklwyE7Tm3/FYC2lYs1VKFQaPD2NqPVmmt56YFrfV8UNZxB2ePG3OVBZdDg/bCb39FrUWQYCHQ4ElRJdSTaBDgfGWQX0vrQffWkOOauqcR/pwTy3CG9TH7riLJR6Df42B6ZZBXib7Ljrx89+0hZlU3PbRZgXlk3IgdT07b+Rt7QISZRofPwg2tmVaGvKcW/aNuY5pWVKHnnaitcj/n/23jNMjupM//5Vd3VOk3Oe0SjnQBAIEEIimGQMOBJsHLAxNuuAsdfedVqbxTZebGyWZLCxwCZjsoRACAWUUB5pcs7T0zl31/uhND3T02F6ggT8972viwv1qVNVp3uqznPOE+4b37gN97VXDJK59GxCLgdKrQ7bkb3jDcE+SZJWMA18ZHYCHxY0WoE3dxVy3pLRUgbXsExfYMgoxm2bOD2ufBKFYokwfvIfgZEM+pmYY6hqfvr3TxSYHg9L/qyYz/a+5D5Zn18iFJIQx8RFXEniBCMYX73p2XcM19bkFAmmJNWmGnMuAbcdhaiOcQ9NFakMgOWs1XJ6niTh7+5GV1WNt6kBbUUV7sMHUJrMhJ0OlOYMRLMZVU4uCAK2rVswLV2Or72N4NAg2Zd8gpDDgdF2kLwaIx0HbQS8YRx9PhZfWojGINLX6KJ4gYXeE04qV2TiHPDT1+giEpEomGVizzMdnHtzJdv+0kLRXDNlSzLZ9aRcI1D/2gPRMR977jfRf7e+G6/TDNC8Jd64AtS9kFhDtynB5C4Oujn+3O/IEYtxRYbxRNILopqV2bjCNtQKLb7IBK7DcITeVw5RfM1obEdXlIF1VxPeLht56+bhONyBt8uGaa7MOpt97iw6n5I9A5ocI5aFJShUStr/vovia5YTtHsxzc6f0Aj4e23UfX8jgkJAtOhZtvGb7Ls+MWtt+bpKXO32KIGcqjg/pQEA+N+/ZGE0Cbz1ZiDhQv//TGB4KkhlldMtTjGZFFgyR69jyirDkFmCoFBiyCimcU/il2cElmyRjJzp/YxVqoWEpVCcMTgqvU+tsIRBeuKCxmNRvWAyRWKpjUDZwkvIKJjD2DS/Q5t+n/Ict0fCYh5jBJJkDCVDxJnaJzsiEzoWhSsuxpBfiau3meqLb6Hh5XhX1UxCGlOUpKuolBO8FQrCHjfaymqC1iG0ldWIJrPsY5YiBAeH0JSWyd/hpBa2v7ub4NAgBrOEzqwi4Anh6JePZZXpefuBJlbfUIEUkWjePUT5sgx2P93B6hsqEASB9x6P3TEtvKSQvob4Sbeq+HysjmYkKYJRn4/T3YNGLe9W/QEnBl0edlcHSqUapUKFzTl5/iWAPLEMV8SGRAQJiVyxjLZAejvNcvU8jvveRyPo8ZHaCIR9o7QjQYeP/PXzEQ0awt7R9pF/G6rzMM7Kx9tupfhTK3Ac6UI0aZEiUsx1lDo1jqPxWsrJIEUkgsNugjY3IUfi96h/b3cMgZwyw4x5wzkpDUF+gYKf/MDOay8njrsp1Rq8PVP7+6SDj6URKLl4DovvXJvSCLy85o9oNAJ+f2rLaTLHBvSc1nb8XjsBrx2daWLWvoq56a/C3Y7EK82wFEIU4gm21GhQIqY0AACV8yaRGZQiKAyQV7mKvS/9NO3rgZwmajGP/i1cSWoHEqHnJ/cT7EkeSISTftVxMBZWI0UiSJFw0pTEmYTj/R0J28eKlvs7Tr6o43K5o+2A66DMqq5YaMEzHKBiRRY9dQ5UOiUBz+jzISgEllxeFNPmHPCx9Ipiuo/ZKag1UTjHTO9xB1pzoqC3hNlQjCAItPXsoKLwHCJSGAHwBxyoRC3hSIDi3GX4g84pGQGTMosiVTUdweN4Ig58khs9cu79bK3sM+8M1BOU/FRqFtIZqCcg+ajRLMUveZCQKFfPxx2xYw8PUKCqRK8wYQsP4AxbqdIsRkTEFbHR9qwcQ+g6+f/xv3H/5tFsrvr/llNKXQ19UUF454nemP5dz+5LKhY/EazbkpPMjRDIWevkGI39hc0TXu+u79i5/GodRw4FGU9g2tsTRldcgbe3A31JJd7eTnkBMoP42BmBM39/FTnLSnjlgj8hhSNc+tatHLrnbTpfP86Z916FUiuy6w65ePmTnzHw5GNywPVAWwnPbnQzPBQ7EWdmx1JIFM8+P/qsGCyFNOx5MuV4zr86/aDwlhQBWa8Uv5oL4KdOmphJ0mBOjwvf4wyz643UhTddx9+R2S+TZAMlQm9fmJKi0UdpJFhcu9RI/QQsolERmRTwHoxPGhhu3E/BsvXUXnk7g3XT58yxOqeWO5+wgjMNo9R52E7n4Vgm0u5jcmBw+19b4/qPb3v6Ljk+03M8cTCxuWu0WE1AoLUndiXq9PQhSREaOjZNONZkcIatDIa6GArFEwK0+A8TkHws1K0hIHlp8R9mtnYVnoid9kAdnoiDhbpz6Qk2k6eSY0pawUB/sINKzQKOhXdFDUJnMIE7Mk3DH53kE/SfKvV865+m/pslwvBwhL8+6iYvP/497u0JY90nF86eKmGZj50RsNTkYK8fiLp7IoEwok5eCe3991dZ+9QN1N60kroHdkQNwAge+oOD3nE6nkUlItd8djSzZqD9AwJe+eXUmwsmHE/57PTZQ5sT1AjACP3BqV/NttR5J3x3CmetpmJJbDbNzn9+N+U541f+I+6g4motDmsQnyeCLQmL6FQxWLeToMeBb7gXv2P6dOLB0Kmj1P6wkWgnmW7SQ7owKCwUq2rQK8y4IzbyxXLChOgNtuCJOJmlWRb9d7VmMe6Ig7AUpkRdi1GRiUuMzdwTEVELWjSCjkxlHoOhyVFXfJywb48czyotU5KVrcA6FKGj/fQlKHzsjIBoUOPuGl1BhTxBVGZ5Ig65Awzu7aDw/BrqHojdvr+/3c9wQh3P2LaA144pqxxzbiWSJOFxJOftUYoCJTXTp5BuDR6lSKxCQJjQ9TMdTOQKAjj0ZjznyyVX6nDYJebMU/HYg07C47JcxweCR4xCX7ufWSdZRLe9ODO6DyNQqDTY2yaX5ZQKofBHixzuw4QqJ5eim76MaJa1fUN2G56mBgZeeCaub2dQ3qW5I3aO+0brWQZDXTHP81Hf6Pt42Lst4XM+cDIJ4rB3G3O0q9jv2UxQ8rNAd06MEVDl5JJxzvnoa2Yhmi3R8dne20pwMHk+/unCVMa35nwND/9t1KvQ0R7mVz9zsPmNU09187EzAiFPEG32aCDUb3VjLB9lsfQPe9DmxufMf/WziX98lzPCj74d66YxZBbjdQ4gqlMHXEtnadOuFg76I3Q1J65ULhfnRX2kpxLpUFiHgrF9jNll6HSDCILA9q0+KqpUNNXHrurHU0eMfPZ5I2TmqbD2Jd8FmNadiZhpxv7yViLe+N9HmWkm+6Yr6L/3iZj2mku/Sv2L9034fdJFMoZQpajhjCt/gcfRx4FNv8GcW0Vx7QVk5M0i6HdhH2iks+4tvK7Ez1fNiuux5Nag1poIhwN47D0cefeBMe4JgTOu+Bl7Xv5pDEXJwgtuQ6FUcXDzqFEWBAVnXPkLdr3wwxn73uNhmDOPgs/eGKPmpsrJxZKTi+f4MdzHj6U4OxbJnud0nvPuYBMV6gVIRGgPjCZMpBqfedlKejc+PqkxzjSmMr5VZ6r5379k8aufORgajJCdo2DDpVr++GAmc8qnx26cDk6N8OcphLNlCFPVaDGKo3GQvDPKUWfIwVHLrFyCrtS0EGMRicArz8e6Auz9DQS8DkRV6oDrZDQEWo/7kqoghQliUJgTHpsIOUXpKzZNhT66YvEVNJwIIYqw5kItnW3xxW7uce4gt0f+XDFXx0sP9SKkeMoMZy3CdNFZCemiAcLDDtTl8VS8Qc8Mi6dM4CfTGXPIK1/BgjVfIzN/NuGQH7XOQm7ZchZd+C305vy4c/IrzySvfAVqnZmAz4lCIWLOqWLe6ltQKEbWXxKu4Q70llHXo0IpYswoQW8uQCmOVqPrTLkolOlTlgMUlGvIyE3vHKXRSP71n0so5wmQf/3nURqNCY/NNBzhIRr8+2j0f4AjPJTW+ASVivzrPz+9GyvkhzXzitWoyyYv55jO+Mb/ht/6roktm3385SE3Lz3v5S8Pufn01UOnZRcAaewEkojM3wNcDgSAJuBmSZJsJyUo64CR6qZdkiR9bbKDKlx8ET0HN5E/bw19x96NObbvx69RfvWi6OeDd29B1KtZ/9KXAPAPe9l5+3MJrztnvooVZ2nIyIiflf74G3lS0RiyKJ69Fre9G5U2dQHYeVfF8+gnw5ZnkrtDBAQSa/FMjIuuT12dORZtJxI/VPPO/xrH3pHzy8/41K/xOUezdbSmHEoLVDyzMXkKX884BsTePtlQdDbI99v2QvLvrqkuxbllN1IwOcmcZ198HYVCKVJ98S3Rz02vP0xZxXm0t27FZC5BozGjM+SgUIgc69+M3dYabRNO/tbD1qZoW0fHu3H3GAtBoaSgejU7nrszxmAUVq+mcslVLDz/Nt5/aZTmetHa2zFmlrLj2e/FXevsa+7hjCt/we6X/5Nw0EdP0w5yy1dE61OKZp0X1SAumrWGjjo5EJlfmT4L6ghWrc8gEpZ4+dGJKdTzr/0sCm3yhY1CqyX/us/T/egDccf02cUY8yrwDHUjag0IgoDX1oepsAbPUBdhvxulWodKZ8bWcZSM0vn4HAMo1TqkSJi8uefQd/QdBIUShVINSOiyilDpTHR/8EZa4xsZ43QgCKPJ0WKGiWDvMJaLVkA4QqDPiuWCpfT8LnmFejrjG/8bzp2v4j9/GC9X+uZrHxEjQGKR+U3AXZIkhQRBuBu4C1lfGKBJkqQlUxmM1pInF/4Ek6/k/cNe6h+NpRDY/9M3KHinEZVJQ++7zQRs8W6PT3xSz89+mzVi6InIad447BF27xi9n99tpbdpe1oVw5MpEkvliukITV1MZzKFYskwYgAAPLYeDm8eLYRZdNEduPURvnK7iQfvS1wEND4mMMInlE5gWPL6UZpSU14oDPHfsen1h+PaPO4BzJZSzBnlsltLkgifLCKzZFZE2wJBD1pdRkxbOuhv3R3X12WTJ+6xBHsaXQbGzNRaz4JCiSmrDFtfPa7hDopmrYkeM+dUEgp4UShFTNmj1diGjOK0xjkWPS0+DBYRQTFxZqEqa+IFRbI+WZVL8A73YsgpIeiVnxOlSkPQ48BUUAUI9B7eQv58+XtqzNkEvfKxvqNb8Q534x3upWTFZQQ9DgSlit7DWyhYuHZS45tJhGxOpEAQhU5DaMCGFAhhfS71YiEdjP8ee98P8PmbDLz0/OgcodMJfOHm2LZThQmNQCKReUmS3hzzcRfwqZkYjM8ur1Y8Q/KLNX4XkAxSRKLn7dRMfTd8xcSRgwG+//UhXtlWyJrF3ZRXivz0N5k8en/s5CYISopq1yBJEt31idOyMnJVWLLTC6lEwlLSVfh0MRNGYCw6j8Wmv3nsPby2KfWDOJ5Ezu2RZ5t0AsP+tm50i2pRGHVEXPH3UWg16BbNimufdfk3cHTU4e5rwzPQQSQUYHBA9rU67B0IghCXcZVuWzK47fH+2XCCBYs+ozD677OvSS5UotLIv03Aa8dgKQQEEMCUVY5ruANBIWLKKmdkl2iwFEUz19KFtT9IXqkmrdRyQT2xazGZYLzH2o1SrcPV34p3WP6dcuecTTjgRYpE8DuHMOSWEfJ7yK5ZiVKtw5hfebLWI4JKZ0JrycNj7cbvGESXWUh2zUoU4qhrJZ3xTRc5X1iPc2esz96x9QDZ112A2GfF19QNrdOTvx3/G/7uv5384/ls7v5dBlZrhOxsBWsu0KStFz5dzERg+IvAP8Z8rhQE4QPAAfy7JEnbEp2USGP4VKK8SuRH37LS2x0mEJDQ6QWOHQ6w8VEX3/sPCzd/ajSwl05guGISwvJdzX4CvplXBMrKU6Xt700Xw92xL0Dj7qe4/gYDKpXAE48kzvkfTyc9YhRCQWnCrCDXO3vRza8h/7s3MfjgMwS7R/8OYl4WOV++JuFOoeFf95NRsZC8RedjKpnNwUe+H3N8ZGLXFpehq6wm7HTg7WgjaB1EU1hC2OXAtHApwzu2knXOWlAqkQJ+hncmX3iEQ+nFmqKxJEnC60peCBceQ3WhVGnRGjJRilqUKi1OazsKpYg5pxK9OY9IOIRSpcXWPzla5Yq5evSm9OpIpMDEGVIRf+I+1uYPokpvI+g7+m5cUZd7oD2hMErnvldBkqILQfdgR1y/dMY3XQz+fTNSMITvxGjxXGjARt/9zyMolQlVwiaL8b/h8WNBrr1ykKdfzEGnF3C7JLZt9XPvPZNTT5wqpmUEBEH4ERAC/n6yqQcokyRpSBCE5cALgiDMlyQpLoqXSGN4JmCqyqb0kjkcu397THsoKEWfJ7stQl6+ksH+MI31QeYtjLXM9v4GlKIWrSH59nMylcLJ6gPWrdew+U0/S5epqJ0tcuxoCL1eoKxcSWNjiO7OMJXVIrt2JH74JyMsnywoDZykiUiOt99soD8Bze0InOOMwIhRSMcd5N51CP3SORjOXkLx3XfI8pISKLMtqIvzQBDw7IvP9ig951MISiX2tiN07YoXoh+BrrxS9v0B0knqBkGpQFdRTcjpxLx4BSGHHaUhDRbWNHcMI7sDCYkP3vzvtM4B0JnyUOvktEzHYAtKUQ2z5J3BSNaWa3hiLqmxqF1i4O1n00vPDVqHZM6jCfokQ0L3aaLfbIpt6YxvupCCIdSF2RiW1uA90YGgVCCFI4hZJlx7TmBcORvXHjnkqZtTiqY8HzHDyOA/3kn7Hol+w/rjIRbP7kWtFggETi+f5pSNgCAINyIHjC88qSaGJEl+wH/y3/sEQWgCaoGJy15nCJbaXKquXxpnBNpaQsxZoOKt1720NgVZc6GWY4cDFJWIMT+6ObeacNCLa7gj5QtXPnsGqBokWHWGmoWLVDzykJuv3Gqgvy/C7vcDtLWG+Y+fm/npj5NnwVROQkOguyX5KrZs4cXycCQJkBBVOiQktMYcbD3H0fmaWX+ZLulOwDXOHTSyE0i3TmDggafxNbRj3rAa3aLaaHto0IZj0w4cr8dTNkiRMAgCUjiMNL5wYQyGd8S783yd7fg6E1M8zAQ8jp6Tl55c8p3WmIvOlAuShNPaFs0EMmaV4j/pBnJZJ2cEGg+7WXi2ieN7U1duA3ibGtHXpl4QeJtjdyI6Yw5KUStzbZkKcNm7onESgOKa8+hq3IpaayHgs2POqkBvKUStMWHtOSq7eySibe3H3xx/y0mNbyZgOnMuQ8/LFdZZV5wlG4KgvAhSF4zm8uvnljP0/HtkX52+bjXE/4YfNqZkBARBuBg5EHyeJEmeMe25gFWSpLAgCFXALKB5RkaaJnR5iTN6brl+gJMLQW79wiDfvsvC81sKaKgLcvWFoxzvjoEmDBnxKYljIaoEVn8iI+0xbXk2MV1ES0uYs85W88hDbhQKePDPsRk4qQwATC4z6K2nk1NWjCWIW3bZD9n/yn/FfM7PVqI3JPdPjheb7xuQP5fU6vA6w2gNE0yGkoRz8644dtFU0OeUYO+oI+ixEw5MI3h2Ciq1fW4rR7b+mflrvsqslZ+h68QWPA75GRPVeopqzkWSItGsHwCfa5Ds4gUYM8voad5BOOgjHPQx2PEBOaXL8Dr78Dr7sQ+kjn2Nx6aNqXmZxmL43S0E+nvj8txH0PP4w3E57npTPgGfA60+i0gkhM6URyQcRFAokSJhDJZCDJYiIpEQRm0xOmMukbC8K9SZ8vC6BzGYCqJt0xmfFAzSu/HxtL9vMgw9/15U29z60hhKEkHA+q+dsf0EIWowQP6NJhrf+N/w8qt0/PYPGfzj7x5swxEysxSsXqOhuERJbemprxNIJ0U0kcj8XYAG2CTIemgjqaBrgJ8JghBC1sn6miRJyWefNHDGb6/g/e+MbvfPvv+alP31BYmNgGdMLrskwb3/Zefe/0ocZHPbUjMLls7SokxTMrG/M5CUOM7nk9j4hGxDE1HQpII5SyS7IP14QLo1AmP93tmliwmHAjQ0hnjtxeQT7Xh3UCgk/9b1+12cfVkWbXUzT8lQ/1J6giAfFhyDzTTs3kjtGZ8nt2xZVPNgJPVzoH1/TH/XcAc5pUsAAefgKI+RY6iVnNKlGDNL6G9LT4hFzJBdW0qjlkCfDVWmkeCwC1WmEUEtEhxyotRpCDk8qPMt+LtGX1H38WO03/cbuWLYIi90QnYbnsb6hEVYQz1y1bZzON6HD1C/L5aF12XriuvnSnJuIoyML/Pc89FVz0K0ZETHN6MVw+lyQo1rm8r4bvu2kbc3+/nxD0bnI6US7n8o/RT06SCd7KBEIvOPJOn7LPDsdAc1FpnzYvl7shYWJul5+jCZ1NDmFFQNXZ1TDzJNhj4aZN6gdNC89xnOuOZXAIT8bup3PcH37zJy4liQjX9xJXw3xheLjSAckth4TydFVdPL3T4VLpvTgcHOgxgyS8jMn43GkIUgKAl4hulp3M5Q18GYvk5rBzmlSwE5HhBtH2o7+S8h7XiA5ew5qAsy8Tb1oqsqIBIIoVeLRAIhFBoVlrPnEnZ6iQSCKI06/F2xKdfBwQHafpOe4lsM0v0bpRsTSILg4AD9zz89cccPCZMdX0mZkt/8OjYIHA7DM0+d+vRQ+BjQRuxIUPjV9tIR2l5MrD1aceVCyq6YH9d++50W6uuCvP7S9FelkwkKT6VKNx1MJjV0oCuAy5aewXEOtXHwDVmMxO8eRpIiPPmYihN1waS7FbcnEq27GItPfCmftjovGbkqNt7TmfjkNKBbXIv3QHpSmzONcMifsOBrBB5Hb8rjbYdfSUuitKdxGz2N8Yl0bltXyusnQtDqAgm0pTnYd9RhXlWLY3c95lW1CKISf9cQwWEXYoYhaaX2/2u47E/r2fX7vQzVJ3ZMXP3Xy9j75wN07Dz9RHWd7WFMpnjPQkbm6SF0+MgbAUdDvE/T1TqcsB3A1ZGYKvmazxp44N6ZoRqYDF1EOnw9U8FkjMBkx+BzjQZx9RmFaDRDfOZGY9LAsCTJhsBkjH1o3/hbP211XsIpMpPSQSKh+cnCqMsnx1KNWV+MWqVHkiQCQRc2dweD9ka8/on1pxOOTSmkzLz6MODYFWswvS39crrqyf+PIHPtIk4xXdVHBq98PXnAebrInp2FudhEy5a2iTsnwH2/c3HbHUaee3r0Pa2qEfnaN408849Tz277kTcC4/Hm5Q8T8iQPIrk7bfgG4ikOmuqDKNJLl54QC89Kjz/FPhRi39szzHEDGC1Kzrw4/cD0W0+nThFMFQivPfsGzpx9PxOlR+fXxouSVMzT03zEw7lXZcdQRxjPWwGhEK7tBzCtnZgKQbegGvfOgxP2SwSLoYQz5t6S9Hhh9igFyXuH78PjH10p5l21Sq7TikhoijLp+HP8RDIdA7Bo9qdp794xoaDLigW3sPdIfIX0eJQUrEQlGmjpfCf2wMjEP87lMrwlXjPavHY5WddfiJiZnmZ2zz0bce9JLI86EQzL51B45+ein0MDNlq/eW/UHz/7yhoW37AAUSuy5/79NLwq55io9CpW3baM0nOKCfvDvPSl1/A7AtH2igvKCLgC0fayc0tY9+vzePUbm+g9MEqfcc2Tl6MyqOje3RvVFjCXmlj9/TOwlJmJhCMc2VhH4xstfPLvn+DoP44z56pZaMwantjwTwz5Bs790ZnkL8wlEoyw8utLefq6F9PSKfjTI7H+/pbmUFxb/YmZpV9Pho+dEQjYU1fe9m1voW97vEDIPT+18as/ZLPjHR8tTclTCmcSp2oXMJnU0HTGsXDdtwj4EhsrtdZMa3MIh13iS183JaSSToaOei+XfTEfW3sxWmwAACAASURBVH/sw5x94xVIoTCu7QfIvvnK9C42BahEHUuqr0u7/6KqT/H+8UeQTmoNh5xeNAUZhN1+Bl7ZT9baBVi3JHZDTgY6bRbVZReSYS5HrTISCvs4UPcEc6uvQK/NQalUMWRrpG/wCBUlazAbi1gyVyZGO1j3d+ZUXx7Tr6n9rWmPCSDv1qsxX7As7f5SKIz3cNOU76ebVx7zWczNQJVrIdgn78p69vfR9k4HPrufG7d8OmoEzrxjBWqDimeuewlthga/IxDT/vdLnsZYYIi2t2/rxNE5jhVAIfDBI4dp3txK/sJcLn9YTpNe9+vzePcXOxmsG0JjVvPJJy6n8Y0WTIVGRK3I09e+iD5Hh6XcjL3Nweu3v8V5/7Ga4SYbh56YmNpcEBUgwTe+akcKjfpXp6pyNhP42BmBqeKsNVp6u0I8/WY+O9+Nz5n/5s3pp9JdU3NgJoc2aRze4ZzRMXjsPUl1hBdddAdF2UJKKulkaDjgpuGAO85oWTe+Gnv/D47jP55c2UszpzKt+41HZcG5aNTps7OaDUUU5yyhc0DOwrG+dTjmuK89/WckFbw+K0fqn2bxnM/S0rkVh0v2Q59ofkWWgBQUnLP8OzS1v8XRhmfJWHYHB+pGqbTj+21hun6djEvPmpQBAPDVtRHxTb2KN2F8eIw2eEaFhUWfmwcKAbVJHXW9lZ1Twuu3bybkC+HqHV2RjLQDuHpTaxabiow0b24FoO/wQNRIZNVksv63FyQ85+g/ZFlJz6CX4soM7G2JF04KlRKVRUfIE0CpU6PUqQgMuZAiEiqTFsuiUkyz8mh9fAeiUUPQ6Sf7rCoG3vlw4l4fOyOgyzfh7Zt8OfXFV8gTUXNDiPzCeL9QzuwzyapaiiAocPY20b3/dUpWXY65qJbhtsNkViymY9fzOHsaqb3k1ph+APkLzsOYV4lKb6LhjQcJB/1Urb0RjTELUaunfefz2DuOxZ1ryCunYMEFiDojAdcwLVs3AhKmwmoKl6xHIaqpf/0BMisWoTFl0b3/DYqWXYzfaWWoYXfc95gKuo6/nfSYzzVIw2CIymqRNRdqefx/Jy46GsH6z+UhqgTK5+r5851jMl7G1QN49h3DtTV5PaFpChONQhApzlk66fNKc1dEjYAqOxeFVougUKAuKMTf3YU6JxdBo8Hf0U7GOedhe28rYbcLw7wFBPp6UWXlEBjoQ52Xj1Knx9NUj7asEn9PF0gSmsIi7An0ihUKkdmVl6FUqolEQojK5HGQ2VWfiOk3Gf6jRFBo1GRduzamTd6pHcZ9oJ5g9xARrw8x04zSbEBdno+mshD3nuRau+nAd6wVrhgttAp09BEakidWbYaGdb8+j+c+9zK2Vjtf3nNDtJ+gEBKavGTt6WBkFR4JRXjyE8/GrMrVJplRIOAa8xymyBAvvW4l2gIz3l47IYcPhVokaPcQsHpwtw6CJOFqGaTkmuVoC8w46npRaD+8qfhjZwQu/OeNDO7vZM9dLxP2pe/Wue7ivpTHay+9hvpX/wxI1F78NQw5MgvkYMMeRLWOzvdfxFw8m4BrmPrXHojp5x7sIBIO0bTlsej1BIWS5i2Po9ToqL3kVuwdx9CYsuPOBWje+gRSOMTsy76BLjMfn62finM/w+F//iJ6PWvTfmZfdhvd+9/AXDyb+lfvjxm/Nq+Y7JXnMbRnK4IoQiSCaDTjbDyKqWY+CpWGoGMYkNDkFiEaTAxslyl6hzqS+9vrd/4NgMMfJJ+IdVqBKy7RM3uWilAIfnWvDUmCvZttWPsCE6aIRpypg18R7+TJ98yGQlTi5Mn1TPpCVKKeYMiDccFChrduiU72mWvWIiiVWN+Wi7z83Z3y5A4ISiWiJQNvaxPBoUE0RcVE/D6MC5cS6OtBW1yKoBIZ3rol4X2zLFWoRB2HTjyFStSRn7NQPiBJKBSqGJWuhP2mAeO5i1AYYv9GXT99NIY/ByDYezJe8v7MKLq5958gNGhHYdThO9HOwCP/im4PVHoVkiSvusejY0cXi29YwLs/34HWosFn9xMOhKPtb/94G4ZcfbQ9EVw9LqrWVdC8uZWcudlYyuQdo6PDyeIbFnDgMXkXmD0rE+cEu4qAK4CpaDRO6Btw4m4ZRKlXEbB60JVmIigVWBaX4B9woq/IxnFEfm7cLYOIZi2iUZPs8lF89vdL2fjtDybsN1kIp0PbdsJBTII7SFAI5KwoZfnPL4lqC3t6HHS+fpz6v0x9ZTznE9/kxCv3I0kRudpRilCy8hP4bP0ICiXe4R4yKxbTf2wbAddwTD8kCV1mAd7hWHZBrSUvSogFoDFlx51ryC3D3S9nFVStvZG+I+/gGexkwafuGmMEBEBC1BooWHRhdAzjkbViDda972KZu0zOrRdkN47KkkXYL79MEb8PJAmVJStqBFJhwdrb6D/256TcQQ/cm8MN18cHyr/xvSE6wlo2PzXA+dfk8M6zM+NKSRezStZRWTC5cv4RHG19ka7BcS/bdGoVTlafpnsd3axaVFk5eJsb0VXXEOjuQhBFxKwcgv19qAsKCfT1Ehq2Ypi/APvO7ZhXnUWgrxciEdQFBUQCQVwH96e8zwjK/3AHqvysmLaiNx7j3UeaWPOlarRmFf0NTsLBCLZeLxkFOqxdXsx5GnLKDYSCEkfe7ME95OfMz1aw428tzF2bT065ge1/bYm2nf2FSnb8rYXVN1ax/fFmzv1iNdsebYq2//8YRdXKLNbdXktGgWycRa2Sw6/38K9fxhXs7ZMkacV07vWx2wlIEYmB3e1suvIRCs6tpmT9bHJWlFB78ypylpXQ8fpxet5uJOSJXbne9l0zTz3uZnAgdjKrnauidq6KnYd3Mevir8mk64JA46aE9XD4nUPUXvL1mH6RUPwqWanSUHb2p0CKoBDVtLy7Eb9jMO7cxN8xTNv2p2XXkUJB4+ZHCfu9hHxuEISEBgDAuldmwLTXjXn5E00649oMGUXRKmlTTqz/XaU1pSSP23BB4tX2hrU6njkgcsWXC1BrT7+AnSqFO2UiJHTFTGexNLbAIo3rqLJy0JaW4u/qJOxwoK2oIux04GuRdxm+9lYs56zB19Yil5YCjt07sZyzhojXSyQYJOJNP7UwLhMoEsFlDbD0yhJEjZLeEw60JhX7X+jgc/et4O+372XV9eX4HHLtiMcWoHJ5Fode68aYoyG30ojOrCISkbOnRtoK55gpmmfBOeBjyeXFOPqT7/BqqkXOXa3h8JEgwZBEIACiCKtWqDlyNIg/ALNniRyvD3LLzUb+9L8u6o6fnmya04HmPVYe/MIuFCeZCSSJUxY4/tjtBBJBk6mj8Pwa5n9rDYJCIOwL8dr6WPWjA20lXH9JHyeOxT4oF2zQ8e//lcGFy2eWoyN//hqZShcoXn4pPscAQw17kva31C7G0XB4QiEbY34lrr6ZXTVNpCw2N1vekTyWIB7g7qpASOAffX+fnwsuP/W8J8kwr/xySnKXT+nc+s43GSwbBqUC186ZE7NPF1nrNuBprEdbUkbY60Gp1xN2ufA01hN2yvEwyzlr8Jw4Tub5F2LfsQ1/VyeWc9YQGh5GnZeH+9hReWeQBmr++fOYzxG3j+abfxmTsaJQCETGTUIjx8fWSoy0jT03UebLRNkwS5eoKchXUF0l8sBDLkIh+MkPzdSdCJGVqWDn+37OOkPDI4+5uOVmIw88lH6s6v8x/N/bCSSCf9hL6/OHCdi8zP7ymRhK0s+h93okLAnkJqcLe2cdNeu+CAolYb+b3kNvodQaKDjnUnxDvQwf3UPeygsRlEpsJ/aTs/x8NBm5uLuacHfFT/LZNSvInbua4//6nwR3mx4mUhZ77JnkL5g/IKHVxFsBr/fDXVyEwlMX8QmF/bh216GbV4Flwyrsb+45rbQV1s2ym87X2pLUfWR/T15g9D/9ZExb5tqL8Bw/hn7u/LSNwHhIITnWNnaSHm8Axh4fWysx0jb23ESTfTIDMGe2ClGEM1eqsdkjRMIQCsGsGpEjx4JYzAp27w0gKgWGrBFCIcjLVTCrRqSh8fSkfp9O5NcYURtGp+mOg4mLYaeDj78RECBrURElF82O0kW42tOv/jz7PA1DAxGMa1aiMOgIDzvwnWhGv3Q+zi07Ma5ZieQPELY5EfOyCPb0oy4tItDejZiTiaDTEOzqw7hmFY7X3yXYJQegffYBGjc/GnMvc2kNjsYjKDQasuavIuiyE7AP4u3rxD/Yy8DeLUhJuBmGGvcy1HjqGbkTKYulwqEjAVYtjw9qHTqaXkZP+V9+lla/tpt/kla/EQSCqYN5qeAPujCsnAMSeI80I+ZYCA1M7uVTW7IpOvtyho7uwt3dRCQ0RVfFJI2P7d23UWXnYH83ecbXRxnHTxZIHTkajAmlNDSGaGgMoVQSLVw8dER+xn7xa8ekCRg/LrjyJwtwWUdT2j+UwHASofn/BL4MjFDi/VCSpFdPHrsL+BIyi+jtkiRNGH2cjDtIk22geN0s5n1DDvqNxAg6XqtLKDG5bJWGs9douOWbZrZu9uGwjT4tXk+Eg/sDvPqCB+P5Z0RzlP0NrZgvOx/nph3oFtTiOXAMdVkRvmONhO1OjOcsR2HQgyThePM9zOvPQWkx4XpvL8Ge5CyGosFE3sp1OFvq8A33kX/mBgL2Ifrf30TWorPRZOZibziIp7s13Z9DhiCgmy/zrIdtdgKdqVlQU8GSHy/laO9LzX9+/dUG7vi6hdmzVASDEp/4dB+796WnwpUMglKBurKYzOsvRmk20HVn4jqGZFAq1Jy/5HsoFZNTXvP4rbx3+D4s61cRGnbg3ntiSruAxbf9Lumx3t1vYG88gM+aOmNtJqGpLkZTmoempgQx04SYZUaZZUbMMJLQnzcBbP/azuDfXk+rb85Nl5Jx6VkT9vOdaKfzxw9NeizjUfLLr6KdVRL93PvbJ3G9LwdUBVFJ1rUXkHHp2Qia1M+Gr6GTzh/976TurV9aS/an16GpTE10GfH6GX5hG7ZXdiAFki8QRLWCUCClhTst7qDHiBeaB7hXkqTfjG0QBGEe8GlgPlAEbBYEoVYaKcGcAax79iYEhYCzeYiO147TtekEfmvyINj+3X727/Zz5XUG/vRbe1xMYATOt2OZFIceHmEBlNAvmYv9lXeix1zb98dMDI4330sr6yN3xYV0v/MCJes/jfPNJ+l886kotbD10A4EpZhSICUZ1EWF5N36RQDce/Yz+NiTE5yRHOWLLgNOyh0asxnuPjahEfjH827+8fzUV96JIIUj+Bs76P/dXym++45Jnx+OBOi1Hpl0rcBIjYD3SDMKveaUuIEKVm2gYNUGfNZebI0HsTV8gH+4f+ITp4Gcz65Ht7DqlN7jowoxR3YPKy0Giu66AU1Var2QEfibJ0cmJ6hEiu76Qlp9FToN2Z9Zh/nC5fT86m8EuhIvHv/tlTV01zmi7rhTsROYktB8ClwJPHVSYaxFEIRGYBWwM/Vp6SPo9NO16QRH70soXZwU2972EfBP/oWWQmHsr43TnZ0iFa6tbg/5Z61n6MDo9UZ45oEpGQAA7dz41ftUMbZy2JxXTU6ZPIlmqQqJSBEihMgQ83GEhtApjXjDLkDCqMzCER5CFMST/cKUaefT6j2EKzw1cjaQV0y+uqkFwpt73iU/cx6icuIcbABvwEbngOxyM52zkEgghK/x1LFKarMKKFhVQMGqDXS/9yLD9fsJeU6Pruz/JYg5FpRGHcX/+SXUxenLU7r3pV/Bq9CqKfrRjZMemyovk+Kff5meu5+Iq8sAePK7p56dYDoxgdsEQbgBWTryO5IkDQPFwNhy0M6TbXGYqtD8pqsfjeHcSBc/u3NqE9GIj38m4O3vwts/85OKdk7txJ2mAEd/E9UrZe4drcJAt7+RWv0qXGErFjEXhaBEqzAiSRFafYep1C3GH3HT7W9EQIEzNDQtAzCCkUDlZOH1D3Os7SUWVV2bVv/Dzc8SCsturJDNJQeET5OWQdE5V1K4+nJcHQ1Y697H3nxkyouCRAi7PITtiQP8Ssu4Og9JIuxIvbOLeNN39zm3HSTYZ0VdlIPSpEdh0qM06VFajGkR1WkLS1Fn5+E4Eiuqk11upHCehSOvpX6nVLkZFHznMzEGQAqGGH7pPcJ2FxG3D4VWjaogC8slZyGISiL+AN6jaS4+BIGCf/s02tllMc2Bzn5cO4/IrqhQGIXZgLa6GMOqeejmjvImKY06Cr/3WVpu+XXcpaWwhMYg0rzHiqg+NanWUzUCfwZ+jkxY8nPgt8AXSVxMnfANGi80X1Sg5NINOmz2CEajguMngnT3hvnkFXpO1Ad5bZOXSy7S4XRFqCwXsQ5HeOUNL5dfouNfr50e8YWPKrQ1U+PWSQ0Bc24FCiGWYsMZHkIUNNhCfeSpy/GEHUQIU6SZhS/ijj4AEhE0Cj0GZQbu8NQzGpQmA7rFUzdyvdajzCq5CJ06dcaY09uHzTUq2uJv6yPzqnMZfmFyO87pQBAUmMpmYyqbTdjnoenFP+MdmJlFQ++9/0h6bHyKaNjhpuXLd8/IfQH8TV34m+K/h6a6mNJffW3C87UFxfJCYJxBLlmcSSQsTUjnLeZkRF1AUjCE7dWd2F56j3CCSnXrP9/GsGouqvwspGB6RtiyYRX6JaO7cSkYYuDRl3FsiXUb0z2I73gbtld2UPX4v6PQje5QlWZDwgXHhjtmA9C6z8qNf17OwzfPDFXMWEzJCEiSFF0eC4LwEPDyyY+dQOmYriVAWlHK7t4warVAZbnIPf/j4OtfNrHrISc9vSEqK+RhVlWIDFrDbN/lZ06titVnatKWefx/GYn0TKeKs64bDfOEQ35a9smiPt1+Oeje42+KUhhkiPl0+evlMYyhNRhBg2dPXNt4ZHzywiRHBJSZJvTL56E0Gab4bWS8d/gPFGYtINtSg0VfhFplQJIi+INu7O4O+m0nGLDVx5zjO9GecHt+uqDU6qm9/jt4+jsYOrIDW/0HCYsSPwqwrDiLQH8vkhRBk1eIv7cLTX4R/oE+CIfIOONchre/g7asgkB/L972ybn3/P09qDKy4ybIgSYngmJiPQd1uaxOGHa46blnY8q/a8QfwLktfdpyVX4mOZ/bEP0shSN0/9dfJ9xFdP/icYp/dguCcnR1b1gxJ46W+5Ev7eZLj6wiHJKYoIRoypiq0HyhJEkjuYNXAyP8ui8BGwVB+B1yYHgWkLbp+tNDzph/V1eK1FSp+OU9dhQKuH/McVEUWLNaw8OPT61IRF1ShGHFUjQ1lYi5OSjUaiIeD6EhK76mVrwHD+NvTU/OLxlM552Nbt4cxOwslGYTgkZNoLOHQGsbngNH8DW1TF5ceBwETXr+7nSx85/fJbt0CSDhGmrD74lfxY9M7G2+I3FtifqlQqgvsdaBJEGgtQv7C1sIDSXWgk4XkhSme+gg3UMHEVU6Vl35c3Y8891pXfN0QZ9Xin7t9ZSuvT7a1vrKo9hbj35kJDfte3eSedYaQi4XUiiEOjsPd/0xQi75fQ25nEiRcLTfZI2AJr8I2/74sGL3URuLryyl+6gtZeGZoFQw/MK7DG3clLTPeOTOOZuB4zvIrlnJUGPiIk+FTkPpPbfFZBm13PxLLrg6g1alTKdxwfV51O9zkleqwTkcYvtLQ/jcYXwNHbR/6/eU/+GOaHZW4fc+S/sd98UEib/82Blkl+n52t/PYvc/T82iZKpC8+cLgiDPFNAKfBVAkqSjgiD8EzgGhIBvTCczyOORooZh/Fzp9Uk8+rcJDIBCQfkf5G2tr76Jvv+Ri6LyvnoTukXxEpRKtQVlhgVNdSWW9RfgO9FA330PTnrcygwLmVdegmFVfMWqpqIUTUUppvPPIdg/wPDTL+I9ln4ASrdgLuqSYtQlhahLihBzsmOOG1Yuw7AyNSWw462tDD/3csJjueXLyas6E0EA/crraNn3HANpCpxPBa7tkwt8GTKK0RqyGeqKF0T5v4KKy75IwGFl8PB2rMd2RXmhPkwM7zyZ7JDApTG4+ZVoW7TfJBDx+8latQbr7thza88vgDTpFGyvTi43xWvtpmDhWkK+5HOMcfVCFFp17Fh9AfyeME2H3Gy4IZ+AN0xxjY6eZi+2gSBavQKfW54Sg/3D+Nv70JSP6qjr5lXEGIFnf3yYvGoj/U0urB2nRmVsRoXmT/b/JfDL6QxqBD19ye1HR+fkgmbqshIQBFQFeQkNQCJoZ89CXVZCoD19fVz9ovnk3Pw5BPXELhpVXi5537iFtm/emdaOIOPyDVguXpf2WKaC0oUXs/9l+c+nM+cz99wvnVIjMFlkFc0n/BF1i5xOqM1ZFK2+nIJVG7DW7abr3Xgt7g8F0xSRT4SgbQjRGB9Adg340BjSc2aEbZP1GAhI4SDe4eTFkrr5iWNx770o727f/FvfhF/d39ITYwS0cyuwbxrdeay8thSVWkHNmfJi7+VfT03FLRU+/hXDaUKh1aCpLCfn5s/FtEfcHsJ2B4JOi5gZHzzMveUGen51LxHvxKst45kryP7ctXGK6xGfj7DNgRQIoC4tjivOyfvKjQw88rcJA1HCTOljpsDYjBSvo29CLqPTBY0+g+pln8KUXUEkEiK/8gwOvHkPGQVzKJm7jsNb7gOgauknCfpd9DZtZ+EF36Sv5X0Kqs6i/egbDLTLxkyKhFm64U5ElZa+1t20H3kNgIUX3IZGn0ko4KHtyKsM99RhzCyhcslVce3LL/khPU3bya88A1Gli7nO6YRCpSZn0TmojBYGDmzF3d08Y9c2FtdQsHJDlASx/tn/ofS8a9Fk5KJQqXG0H6d3d3oFY9OBpqCYsM8Tt8somGMhFAifElUufXYRIb8H/Umq+ETQ1pYlbB9BOrZvvKtTXRi7s2/aOUjIf2rfwf8zRgAg7+tfRKHTQSSCc/v7uN7bFVNdK2ZlYDr/XMxrz41O1GJ2JuZ1a7D9K3Xhs6aiLM4AeA4cxvH2NvxNrdEnQmk0oF+6CMtlF6E0yasb3cJ5ZF17FUMbn0l5D19TC8Lb8X8y0wXnRv8d7OvHN4F7ydeY3Cfb37oXpSjHGXIrljPQemqpKpRmo5yS6IxNSdTOrUJTU0bY5sD9/mH8HhvH3nuYmpWfxmPvpbv+HQCGe+qoWHQ5xswSXMOdZJcs5tBb98rXMOYQ9Lup2/4IC9fejmOwiXDQj6BQ8sEbd6PWmlm64fsMtO0l4HXQcexNbH31aI05LDz/GxzZ+ifmrP4ijXueimvXGLJQKFV88MZ/R6/zYRiBEViqFmKpWsjBP/7bjFxPEBSUrf0MDc/dR9A9OlF1bnsOKSKrms37wr/Tu/sNTrVavae1EWPN3LhZtb/BQUaJ/pSwa7r6W8mqXILHmjivRWk2oMqNXTRKEwlxJ0DEE8txpTDGqvCVL8vCNTiajtu6f/op1+PxkTEChgwVl32zAo1eydO/bGT9V8oQVQLvv9jHisvyMFhUvPNEJ6JKwfU/mcVz/91E5WIz5lw1ap2S/a/1U/9+6lREhU6mPW775p0Jj4esNoaf+xfDz/2L3K/ciH7xAgAsF6/Dc/BoSrdQwfe+GfPZ9uKrWN7qRkGYvjEPb9jlxrltJ85tO1HotBT+4NuIOdkYV5+Bbv4cun7yq6QPk/dIHd4j8dvBsUYg0N6J9ZmXko4zEUTNaPZNX/MuBKX8WAymEJsZi4ysKrT6bDyufkJBD6JKRyjkw2QpJRTw4PfbUSrVSJKE0VSIWmOitVEO0uX92w2IWWY6vnW3rLxVXULhf9xKyOrA9uwmNFUl5HzlU7R+4YdJ7i5xcPPvWLrhTg5u/i1N+5/G77GhOvmdhjoPEg75cdu6MWWVY+sbzQIK+Bx4XYOodRmotOboMZ9rEFt/Axn5swkH/QnbATrrNsdcZyzc3U1osgoQtdPLbPrQIAgnZSxGV6Hm8rlkzVlF6xuPo1BrUWr1p0Ub11A1++SYFDHjKZhjIeg/NTsBY14lnXtfIX/+eQmPq0vz4toEpTIu3XayELNi3V69Jxy4hwOn1M6efqL3JDjjqnx2Pd+LSq3grE8WYO/zc3zHMBd/tYxtG7tp2GNj0YU5tB910t3gpmmfvDo5ts3Ks79uZNWVBRPcQcb4SVRQJP4JXO/FSiBqKlNs/cZdI9Q/iOOtd3FLDtRoEZJo0UW8PoaffyX6WZlhQb9sUarhzzgWnJ/NuZ/OJOR3J/1vImi0FuzWZhy2NpSiBktmJR5XP0qlmmFrI3kFizGaS7BkVdLdsYtwZJS6Q8zJwN/UEV3lWS5bA4LA4IPP4Nq2n6HHXyLQMppjHg760BoyY+4fCQcZaNtLYc05DHfHim7kli9Hb87HkFGE09qW9Du4hzvIKJD5l7TGHDLyZmHrq0cpqhO2T4TG5+7n6MM/pvG5P9K19VmGjuzA3dNCODB1dtPTCSkSpuPtf1Kx4SZqrryVWVffhqevHZXBQtVlX6bo7MvxDp0eqnB303ECw0OMz5Hsq3fI0pqnwAg5uo5TsHAtfmfi7DWFfup6FZNB+dJM5q3NZ96F8n+nAh+ZnYBap8TvDhOJSGiNSqzdfoL+CG882M6yS3LpbfaM1gSM+Zs7BgIEfRGENM2Zc9tOBIWCzMolIEloM/KjOsFj4W+JnTCUFkvSa+rmxhYzObftRAqHcQk2woRTpkp6Dh6J+Ww6bzXuPdPnB9nwlTIyi7Tsf60fU7aa1kMOll+ax6aHO/jE7RWoNAoadtsY7vWTWy7vkLRGkfVfKWPfq/04BgKsvakkugOz9wXY8LUymj9wcODNeJ6TcFgO1loyK5GkCPnFy1Eq1ej02bicPXg9gxiMBXL7GFI3hUFLaHhUsFu3bB6Bth58x5qibf7WUSPQ27SD2jM/z/JL/519r47Kb/Y0bWfp+u/RMYYFNRIOIqr1zF39JZr3P4ffY0NUJRbBCYcClM5dR83yawkFvTTtfxavs5+67Y9StfTquPZ0TELLqQAAIABJREFU4e5ujvPTW6oWosspQptbjC6nGLUpM8nZHy6cHSeIOJrIL9fSfEgOrDY8d1/CvgaLGNNvJhGwDqBQqePaJUniwAunJm1SqTEAEhpTdsLjgnjq43MAr94zquOcUTR5udR08JExAgfeHOCSr5cT8EZ4/4U+Lr2tgsEOL+1HnOSW61DrlVESpfajTqqWJp+Uk0EKBPHV1QMCzp4mgh47Wkv8tg7kVboUCEazfBT65H8AbU0sMZfngJy+6Je8+KUJAsrj/Jya8lIUWg0R3/RYONuOONny106u/0ktvY1uehqU5Ffpmb8mi4FWL7tekLnmS+aOUgaM7MAu/moZj9xxjG0bu6leYWHRhTnseLqH5+5uSnivvu5Ro9XZGl9h63bK93LaO+OCe2GbK6YYTFAqcG4ZV1oypr/X2c/BTfEMnZFwEIUyNiNLoVTRWbc56rYBCAW9MTUCh94a5Uo6/PYf48du60rYPr7OYOx1JoK9+TD25sPRz6LWQNb8M+WagPwyVMb09TBOJUSVwPobCuht9dJ8yMXlXy0iu0jD+68N0XHcwye/VcJwb4DXHu2J6WfOVvGpO0rY9Nc+Ouqnn9aYdeYFaPIL6XlxY0x7bpUJW5cnxmc+U9BnF+OzDyBqEr/3EXf8ex3sG6b7Z48m6J0+Uu1pNny7ln98P/1CtnTxkTECvU0e/vqD49Hqvyd+eBylKBAOSdTvshEKjm4Ftz0lB2uaPxgNWP3tB8eZCMGeXqRwGEGhwFRYnXInALIer/KkEUhVlauuGuUBCTuchKxybKJMKfsy28Pp1wGgUKCuKMd3fGKXQyqM3SFJEihEAaUooDUocdkSM6mO7MDeeLCdVVfkY85Rx+7AZgLjjJ6/uQPdolrUlcXRALFre+xOSMyaaFIUKJp1Xlpumo8iQj43/fvein4W9Sb0+WXkLFyNPr8cZZKJ6JSPKyix65Uhll8k71SaD7t54/FebviPCjrnetj27AB9bb64fo6hIMd3O1m2LnNGjIB119tocuPdvdYON1Vn5nLo5fRTuNOFs6cRpUqDxpSV8HjYFW8EFHoNwTG6E6IoEArFT+tjNRGS4YKvVce1Fc41TzDqqeEjYwRAVijKWnQ2AdsAIZ8HfWElvoEutLmF+Pq7kCJhshafy9C+d9AXVeAb6iUSCkT7aTLz8Fv78PS0Jry+v1XeOkqRCJ7BTvQ5JQn7TQoKRcxOwHd8lHZZEBR4pclvj3XzaqdtBLobZF/+iHFUigKPfXc0HjJiYDvrXHTWyWN87U+jLrDOOheiSkEoGOHIO4n9ojOBgT88iXZeNRmXnweCQMfXY0tMNLXlaGpKk5w9AomOY7HZW0G/e8argqvWldOypR0pIpFVnYG1aeZVngBCHieOlqM4WhJLW5pKa9EXVmIoKEdfUIFSfWr80/nlWs67NpeSWTo6T2Qw2O0n4IugUAgcetfOFbcW0dPiY9fLQzH9NHol+eWaGRN6yVh6JlIohH+wL2YRkVttIuQ7NYHhjNJ5AOiyEtNO+5u7CTvcMufPSShNsZk9d9xqxmqLcPBIgBMNQS69SIder0CvE1AowKBXUFYq8oP/tOJwxv5YAU+Y7X9tjWnLrRxH9DdD+EgZAQBdQSmSFMFQNhv/UA+6/FKCDiv64iqG9m/FN9iFb6gH31AP2cvWIOrN0X7OlmMEbINJrx3xjxYZBb1O7O1H8eint4oY7yaK+Ee3pm2hOkqVtQl5dVJfUz9xp0mi9jvrqbt7dKIMJ1ihjEcoGKHsuuUM7WnF3SIbgrzzahnY1jCjL53vWFNMDGAs/PVttN/6i4THZgLV68sxF5swFujZ8+eDLLlxPoIgUPd8A95hH8u+uJC+QwM4e1ws/vxcLGUmevb3U3l+KaJOSdOmNgbrrCy5aT5Nm9pw9bqZf91sNEY1fYcGsJSbMRboadrURvfemWGkdXbU4+w4uUgQBHTZheQtvxBD4cwSCfa1+Xjq7nif+4N3Np38fzNSREKSSNhvppCMO6jl/UFmrck/JYHh3iPvAKDLSJ5w4q1rw3jGvJTXybQo2PuBn9pqFbNnqWhtC5FhUTJoDTPcHeJIXQCXO95ajjcAAC/89Ehc20zgI2cEho/uxlhai2+gG6VWi6e7FW1OPkgRpEgEUW9Gk5WH3yoH58b2iwQn8A2OWZooRDUZZfNQqnX0Hn57ylWNCnVswEoKjrpaysW5eCLOiQ2AJMUUkCm0M8MHpDJrqbzpbPyDLpAkKr5wBqJBg/1YD7aDnZR/ZiXdrxzGVJOH43gv3h475Z9ZiSpDh6hT42qVJ/7CDfNR6lR0v3KEsuuWoy/JwHaoC9vhU8e1f7pgzDfQf3SIwxvrWHLTAo49U4+r1825PzyDPX86yM7fj1ZLW5tsHHz8KJGwhFKtpO9gP+fcuYrsWZm4B7wsvXkBO36zl4xyM/9fe+cdHcd1nv3fnbJ9URcdIAD2JkrslCiJNqliypbtuChuUZQ4ceI0J/7iNMdfEp8Tx7GPYyf+HNuy5CYXSS6xbBVLikSKqiRFURQ7CaKQKMQCWJTtZeZ+f8xigSUWHSRAa59z9uzunTsz796Znffetzzvs595EYCaLVUc/dFJbvzbLXOmBLIgJdHeTtqefGDujz0JJiNumytongKCp8fShJTWu2l+efxKfrOBp7wBT8ViUrEQ0YHctZqjJ1omVAJf/OpgJnDwzLkkn/2CtXJUFPiDu708/nTEqqM8xRVTQYWD3pa5Ld4EC1AJRDpaiKQLrQuhIKVJ1D8SQtj94ggPSd9r+7L6TQdmMo6qOxCKSknjdQSaZxaRM3rmDyBGKYWOVBNeJbdNMXunbJv7bJ3Cw6i8bTVdTx4n2j7Air/ahauuhBOfsxKaGu+5no5HjlD/4S1EzgdQWq1bwbWohL5XmjGiSTS3HXuZh8DBVgaOdWLGU4Raeml78NVMKc7fBET7oqTiBrpTIxFJWfTEeo5wMzl2H6EIdLdOqCvM4e9YM7WwP5Kz31zD0VhJ6Tu30ffLV3CuqCXWchF7XRmq10mqL4jidtD/xEEKb1qL4rQTv+DH0VhJrOUikZMTkyNORs881T5zgeDpo9R98GNc+HE2j5ezyIZvsRf/2aFx9pw5nMWVXDz6LL5lW8btE3rhDUo/dCuKfeQ/71zVQPRka+Z7rge8acK9351+8aBdf7KMhz4190VmFkyeQC5kHuyjZ+k5ZuwzoTZQ7U5iQz2AINAy84E1I9EsmUbfEDXaUnzKxLVGcx9zboiiVIeOEUlgRBNIUxLvCWZtS0UTKLoK0orKEaqCoilU7FqFZ0kZva9YoY3xQBgznqaTWBjElZcFpx5pYsvHr+X6T26k+X/Hmjd6TgbY+okNVKzLrk519rFmqjdW4PJdWQdurOUi0eaLxFoukuoP4lq9CKFrmJE4ikPPhDHqVSX0P3UIx9IajHAcx9LJyysu2b0YV5kLm9dGUUMh9gI7mkPDWeJEKIKCWi8NuxrQHBquMheaQ8Nd4c5dUWSW8CxfS/tPxkbddBwdINw395FBAEYiTuU1b8XuLUVz5LbFG8EIQ89m82qVf/y3sp4BM8WOP7D8jLd/ckXmVbtmnhzD4xSafwhIp/FRBAxIKa9Ll6E8CQyHw7wipZy8asQ8oLB2FfGh3tlztEtJ9NQZnKus4Rh+B8snMBPETk5c03eqaP3BfstpJmVmBTCMpm/sAwEn/s2KjBKagjRNjv/rE+gFDpLBGKs+dRsnv/BU1n6nvvQ0iq4yU27YOn0FXqUYHQcV2iICRhevxqZG8bvctokGfTUSk6fDP5jSPrXaclbbt2FiEpcRVDRswsFTYatk9pEHRpLL+lsG2fe5/eMdihM/G+us3/PPLwGw97MjLJX7vzqyqiy8rg738koOfvsEdbtX0n/8IrHeMJXbG1HtGgOn/YQ7BnHXFFJ/5xre+I/nqNm1jI5nsu+B8X574FdWUmPwwJlxTZq9D+8b6asImIINXRqSosZCwt0RlrxtMYfvO8LK967AU+kmcLafZCSJois07KwnGU6gu3Sanpg73qLRCJ3JbQtv2Fx62RzDQlWRRopYpHtCJtHe7zxO7PQFKv/SqsCnV5aw+IHPkOwOEHjwGYIvHZ3Q1KxXleJauxjPtrUMPn2A0CtWMMBz91lj+eR/jEQWFpZfngCAGRWal1JmyM2FEF8CRrMgnZNSXjdXAl4uJCOD6K4CkpHBWbMcxptaMw9/xeNG85WS6p1hRI2UmSimucCEf47RC6xRJTvr3r8RmTJp/5/cKyQzOWN2cC4kR27q27S7p7Vvn9FJrbYMvzH18VliW0dXqpnj8ZcxseTWxOxnalOFTJnYChwkBmPEAxESgzEcPjee+mJCFwZwVRVgxFOUrKvGiCXx1BfjbSxBqEqWyW3S3z7Ve3iKD8vRD/RD3zhM2VofJx4+mblnRj94x3sIO1Yswt5YheJ0oLjs6L4iFJcdxeVAcTlQC7IDIOxLaqj/6idJ9Q5gRuOYkVjmPdU7SOToOZIXA5n+s3UMF29/C/0v7s25LTbQjZRyXEaB0Qi9dJSBFXUU7b4+06ZXlFDxiffju+cOBh5/CWMogkwkURw2hMOOa90SbHXlaCUjs/vBpycuvfLTT18e+vRZFZoXQgjgLmDnXArlWlpJ2Z2b8P98P6lQjGRfkNLbr0XzOkn2Bold6EWx64ROzDyyR3cVIE0zw5MzG8ROn4U7R6oLudZfw9DTe6e28yX+gER755QYS8fFFG7aydB8/4uzPsblQJ/RybORH0+5v1N4sAsX55JHMgoAICWvHBV16yPHMw9J/37rAZ4YjHHqPmvFMbwtdGEg84Ad3jYa0/3tc42eY9lRd6MfvOM9hAveuoGCnWNraowHoanoFcXoFbkzqP3f/EWWEnAV22Y5gRvfdjXMHOoqnVoYee93n0CoKoW3ZfsQ1EI3pR+8deYSCi6LP2k0ZvsEvAnollKOXrs2CiEOA0PAP0opcxZpnajQfLTFb73aRjz/iq5hpG2dxTevJhkIzUoJhPxtxIN9Ezp+popLKSa8N91AcM8LUyqQ7lqfzRUUfH56xS8uheq+SgnLLgN0YUVZRc25j6iYDiaaqWa2/Qb7Wi4Xypd4eeWBy2OCKl+5HaGquEqqaXn+wcl3kJKe+36F96Zrs2oHTws57oG/eeatOL06QoGLp4N8/UOzez7kwmynjR8ERk9PuoBFUsr1wCexSk3m9GZIKe+VUm6SUm4as80w0YvdOOpKcdSV4lpaaf1ZDAkSIi1+QidnF99v95biW7Z5SgRp04VWWkzBbW+dtJ/idFD87jsy381whPCB12Z1blt97RXjNVnoUIU1x5H85kQy5TECT5mDbb+zePKOM8DA+WN0H99H1xvPTmu/tk98hYEnXsaMTX21KeNJQi8fI3TgxJhtX7x1L0d/3cXnd+6hu2nueZlgFisBIYQGvAfIrPeklHEgnv58SAhxDlgOTJuUvuO7e7OWepGm3LG6M4XdW0JsoHuMOWamiDU1Z2UOF91xK4OPj+/wVJwOyj/++2ilIyGk/Y88npVnMBMoTifeHdsZemb6ZfzAKhh/neOtFCgl6MKOKU0SMkbQDHAk/tyY/ittmylWK3AKL6rQSMkE55JHuJA8M2cP32vtO6jQRqg5QuYAL0Vz02VvdNyKQ7iwCxeasKg+bnNn+x76jW4OxsbWh1jv2Emh4kMXNpIywYDp5/XY3pzn2eq8g0LFx57IQ6Rkgjp9BUv19QgEURmi22jjXMLieVGFxi7XhzJyb3DsolDxoQmduIwRMC5yLP7CpL97st8OUKj4qNIaKVYr8CjFpGSCsDnIRaN13GvSdNdnMp8bHNewwrmVXBUnvGopW7xvZzDVw6uhyWsn+L/xC/zf+MWk/WaK576em46l/dPfnNL+WsH4/GO+FdswU0kUTafz8MS1REbDGAjR+53HCfzof3FtWI5zTSOeLatR3A6EpmLGk8hYgujxFhLtfqInW4mduYBM5fazmYZEsytEB5N4y+a2nvgwZmMOugU4JaXMTMmFEGVAQEppCCEWYxWan9l6La0ANt9Vj2lKLp4awlvmoLTBzcvfb8Y0JDf+/lJe+HYTADv+aBmJiMHLU1geCkUl0Pz6hARy00X3l7+OY+liyj7+eygOBwhB/de+CIyqLBaPW5XFLrHbx5tb8f/3t2fsC4gcOZapfQBQ/J47KX7320l292DGYghFQTgc6OU+EILAgz8fY3YSCLY430ahUsapxEFOxF8mLqMIBE7Fi0+tyXnuqAzRGjtBTForKgWFW9wfYaVtCwdjT9JvzD5B6kj8OYhDjbaMNfbrJ+x7aFSkUaFSxlbn7kwk0HhYaruOxfo6jsSf4/XUHiQSgaBCa+A2992cTOzPcmiPxrDSOZN4lWcjP0YVGiVKJQk5ljLaoxRxm/tu9kYezmwvUsu5zv4WrrW/hSPxvTl/N0ztt4MVfRU0A5lroqCwSF/FStsWVtq2TDoWEyFo9PHMwMz3v5JQUNhZdDdvhPfgT+amEO95YnwFNdRxGk/FYhKhmRVxMeMJa3b/8jF67vvVjI4xjJ995igrbi7jp/8wT47hXIXmpZT3Ax8g2xQEcDPwWSFECjCAP5ZSBpgFPD4HUkpsLo3YYBJpSExD4mv00NsS4oa7F9N2OEAiMvWIFWkaqLod75INOArLxyWQmy5iTc10f+UblH/891ELR6xgisOBUjl+eFf3V+9FJma+Auj74U+wVVeilflGGhUFvWrq/OMVWj2FShkSyfnkSGirRBIxhzhv5k7IaUtmh8GamAyavRQqPoqUsjlRApcTurDToFs1p7tTIw8LieRiqoV19ptYqq+nK9VMSo69Rl6liJejjxEyrYeFIVP0GOObKofMviwFMWD4aU6+wVJ9bgLqjsWznfomJq3J4+nr6xtnr988FGkVGXPgTBDytxLyt86dQLNAKm5yet/lyYyGmReaR0p5T462nwE/m71YI9jz3yMzsNGhaL0tIXpbQiiKwDQlHUenR+aVjAYZ6jhDpHduGQgTFzro/OwXKbzjVgp23Txh31Sgn4FfPDYrBQCWL6Hri1+l9MPvz1oRTAflqlU0p9eYPRVExAymzR1XLhRzpvCpNSioJMeJGErJJLqwUaJU5QzP9KfaMwpgKuhIjs0BGTL60Gw2FNSsKKa5xPA1ebPAp88BOeSbBAuONmIi5IqyMCeKETZN2v70Uzk3KZpOQc0KNLvLyhzOEWrm+3/HeL3959OW04zF6P/5r0j6e3BdsxqtrBS1wIvQdZIdXcRbzhM5eoL42XMzqkua85zhCD33fg+9ugrP5vW4t260yO2EghmLYUYixI6fItHeSfTk2KQnj2LRNQ8Y05tx1GhLKVWrcSuF2IQdFT1ji78a4FEsu3DYzD2JCMtBCoUPr1KcUwkEzenlgwTl2POksCYBQohZRwmpaFRqDZSq1RSr5ajoKEJFyRED4lVLWebcRIlWxZDRy7nYYcwcWYA24eCtRR/JfB9IdbM/OL6Jw6UUsMFzOw7FjcQkbkYYMPwcC2f7qWzCyXWeXXjUYjShk5JJgkYfB4OP5zxmvWMtpVpN1nFPRl+mLzkycfHpdax13YRdsXIQ1nuywzOf6v92ll9EEzqNjmup0Btwql4CyS5aYm8QSGXXFnarRdxY8D7ORA/SEjvCDQXvwaUUYGIQMvo5EXmRkDH39X+vBK4qJTCXMOJRVN1uZQzPMllsPNy1+Qxvv6sL04RvfqGP/30kxLd+WcuSP7TxR+/q4OwcKYCtO1zsf86imkh2dtH/SBf9j4z9I02E4Vl7Uk49Db9ILWON/QYkkpDZz4DRQ4oExUoFLuXypLjPNVQshTX8IL4URtoENN6qxmDyMODRyGVSmisUqWVcZ38rNuFAIvGnzpMigSFT+NSarGtSqJWz2XMHqtDoSpzDpRSwybObnuRYRZeSCd4I76VYq6TOvnJCGdxqEdu87yJk9DOU7EUXdjxqETW25WOUwCLHarxqCUEjQNyM4FILKNGqqbYtozORvWK6vuC3UIXGYKon67g+rTZLCUhMOhNnKdMX4VGL6U62EjFG57KO/NftiovNnrfjVguJmEP0JM5TbqvHp9dyIvICF+Jja5TYFRcrXdtIyBihZD9utZAirYKEOYvcngmw7YOLaD3Un3lEdZ+dPufQZHjTKgHTSOI/MTYiYzrQC4pxVNWhF1qz6MCB7Jt8150e3nt9tlPqD9/ZzsMvZEd8LAQMmb041EX4tBraU5PXMvCpNWxw7GJf5GcZp/Awtjh2M/dk2JcHA6afRaykSCnLub0gbUIZMKdeUnJiXJ4Jx/D1AMa5Jr7MNSlQfWzzvpOoGeT5gYezWG6vL3j3mGObmHQlmggZ/ZMqgTr7KjShsz84fgTTMJqih2iKZnPvCBRuLb5njBLQhM7xyPO0xycu0NSX7KAv2YFDceNRi+mMnx3XMfyWwg8RMQd5qv/+kTEIwyrXDax23YhdcY+Rr96+hoPBxziVuqQGeV0d7oJCIidPULD9Rgaf30fhzTsY3PccBTdsB9NESklw/ysU3nQzQtMY2DN5+OkrP758FN3DWLBKQCCoK9lIbdG1uGwlxFJDtPe/TmuflU15w+I/oGvoOH2hFlZW3kqBs4rzfQc5498DgKrYqC/ZRKPvehShEo4HaO17hc7BER4Sr6OcpWU7KHJWY0qDgWg7Z/x7iSZGluxl3mUsK9uB215KPBXi4uCJzDm8K9fR/+oLFG+6MSv50OVW+Lf7Kqms1fjBM5at/a8+3EnPxdyzxhXX2PnEP/moqNEIDpp88wt9vPxsZNz2NRscfOpzZbjcCodejLD3idnnOvhTFyhXF1GmTs2WOux0y1U+s1C9emzPvUYnKZkcd6Y/bKboM65MUfWZYiQnQo65Ji6lIOuaFGlWRFwg1TWG5rw/dZGCWVy/i4lzLLKvZrVrOxfiJwka04sLkZgIFARKltlGIlnl2k6B6pvRccdDIDl2DIbNOrnGISWTBFJj7wVHYyNmJIo0TVSvF728HCM4hGfjRpAyQ3Kpl5djq64mcuwYhTfvINbSQvzC+A/62rWF2N0azQcDaDaFZGzufUYLVgmsrXkH1YXX0DFwhKae5yl0VhNPZi+FSt0N1BSt40LgMM29L6ErI3G0pkzh8yzmWOejgKChdCtra+4kFO9hKNaNIlQ213+YoVg3J7p+jUMvxOdZTDKV/QdaU7Wblt6XiST6KfU00ui7njP+PVbhaymRpkGyv5fRle4jYZNPfLCTHzyziI/smlyTf/6+Kj73134OPh+hpl7naz+p4c9/uyNn+3uub+Wf/quCr/9bH3seC7FmgwOPd/ZUEV2pFur11XiVYmrSHDVJGUcgsAsnRWoFF1Mj0ePDztA6fUVWNFGJOn3W1PnEcF7DCtsmKrR6/Knzo0JErRVbS/LoFaWamAmGr4dAZF2TErWK1bZtWX1tisV2GjfHstUmzLGhrdPBQMrPa6Gn2Oi5nTr7KoaMPi7ET9AZP4uZI0dhretmvFoJNuFEEzrKOBE9r4WeYpVrG3X2VVnHnWxlMBlq7SupHWd1M5xxPhpRM7c5ZnDfvkzOUeCJx0FKkn7/mJraAD0PpTOQFSU31/Qo3P5XFidZ66EAv/v1jdz3exPzC80EC1IJFDqrqS68hnO9L9LktxKU/MGxJooSVz0vNn+LcHysc05KkwOtI2yLg9FOdiz7M4rd9QzFurHrXnTVSefgUbqD1o3UFhg7wEc7fklfuBWAnlATxU5rpmwmEwQOWuaf4NncZQCninDQ5ODz1h+yoy3JoRejbN3hytleVatTVKKy5zEre/D4azG27pi98UVicjj2LOsdO1ljv541XI9BCgUVkV7mjFYCYXOIrlQzK22bqddXkZAxnMKDTTg4kzjEcttYzphqbTEFSimasGVm3l6llPWOnaRkkpRMcCpxIGtmVqbWUapW4lDcuITlxHUqHjY4dhGVYVIyQUvy2Kwe0m3JEziFm2vtO0jZkiSJoeNAEzoXUmdoSV6eik6TYaVtc3qs9Kzfvs5+E0mSWb99+HpUaYsz12SYMXXA7KE9cSbnNbkUc5Hg15u8wOHQ09TYl1Om17HGdRMN9mt4YeinmT4CwTr3Tkr1anqT7fiNNpIyTkomuca9I+cxXxhsp0xflHXcQLKLyDjhy1PBkNFHaJxVRcQYe9wJx2f4YT8J9X0GU6gmc/9HD/DR+7dgpCQzYMyfEhakEih1W2XyOvon5vkPxv05FUAuxJJDGDKFTbVmQdHEAIHIeVZX7abQWc3JrtxZgcMKYBjhxNwsQ7MwXtLy5eWNGoOYDLM/+jjrHTstumdhw5BJ4jLGoDk2auhY/EXcShEu4cWmOIiYQdqSJ8c1nVRoDWPMTbqwZbWdTryKHBUmWabVUKstz9pHRctKXmtPniHF7GbqpxIHcSgeipQyHMJNUibwGxc5GX9l8p0vExbpq8a0WdE/I2UkR//2Y/EXGTB7qNGW4RJeojJEW/IkbckTuJWR7NhhJ6ZdjJ082MTc1ETwJ9vwJ9twKG5WOrdRYcsufVlha6DS1siegR+MSazLpQTAMgldetzV7ht5NUc00VQxmPJzIrIwCRNhhNBAUQVCvTwPhAWpBET6l082K4mnxufSWF6xk4aSLbzS8l3iqRCGmUQR2Zw6B0etFDYsuosyz1L2t36fgchEuQOzc+zd/2gtvgqVf/laBYFegy9/pod/+YtuvvaTGiprNUJDJvd9KcDzT4U5cjA2pr2jLcn/ubuT7z1Zh8MpePXFKPt+PbFPoEKtx2+cp15bRa/RSShHmOIwTIysrNu1+g0cS76Us69E8kr00THnCpqBnJmph2PT42EBOBF/hRPTeBDXXXsHQlHpbT1EJDrEyWV9uFqrSEQGqVi+HVV3EOpto2r1W+hvP07Q34zHV4/u8NLceZLIwH5qr7mdRGTKXWdVAAARBElEQVQA00xhby8hHgpQvXonPc0HqFy5A83m4tT519HsncSCQ/gKNtDfcYKatbda5+0fCS9UdQdVa3ZySu2nt/kQ3tq1hE6/QPXaXfQ07ady1Q40u4un9j9A7bVvQ1F1hrrPoag6T7V9n6o1O+k9dyDTr/vUCzRsu4uB9mME/c3EolFqrrmdjjeeRLW7YM1yOgX4zz5DyaJrsLmW42mLMtTdlLkm5+MnqLOvpsa+nKAZoC1mrXSqbUtpcFwz7Ws0GhV6AwOGP2NqiplhWuNHxygBLW1qGa0ANGHjOs8tOY9rV1xZ5qvh465z5+boChr9VAEVtsZxHcP+ZBt1dkvRno7ux5CWz05BwafXEUh1jbvCdN+wHsXjIrRnPxWf+ijdX7gf7+3bARh67DkKdt/M0BP7KHjbTaAqxI43YV/eQOTAUYwBa4VR8LabGPr18xS8fYdVvyCeYOjJ7GCV4loXLz7QSlG1k/vuGb/WxWywIJVAINwGZVBTuI5zvTPT0uWeZfRHLjAUsziHChyVGbNGLrx+4WfcsupvKHbWTqIEpobrb7Lz1f/bTcMSjS032Dl1PImRgs/+xUXueJeTnm6Tc2eSBAcM/BcNHv1BALdb4dyZJCkD3vMBF6eOJwmcj/DlfwzhcguWrtDZ/S4nbrfCVz7dzeFXJ5/9FiglNKircAkvEpNqdQmqUOk22rAJBxeNNhq1NURlCKfw4hAumlJHWKqtIyajgERDp1Fbw0WjjRhh6tQVaNgYlD0UKxWApNOw6Doa1FXEZZSwHKRRXQMIOoyzVKj1mX3sOHEpXpIywbnUSCr8ksod9AWbkUg8jjKC0W68zgp01Ymq6MSTIUIxP4aZpMhdRyjmx2UrJhzvZSBsXbNEdJB4sI/qNbtoeuEBus++SFnjJvxN+3EUlHHupR8BUFS9kq4Te5DSxOOrZ7DrFL7GjUQGqulpfpVYsIfGLe9j8KIVpeIsKCcZC9F99kUKyiyOKFdRFa6iKi4c+TWVK25k6OLZzHmHUbZkM8noILFgH9Vrd9F95iXcJbUYqYR1vDMv4S1vpKh6JbGhXnqbDwJQtfot1nkLy7P71a0lOtBF1/E9GWdjX6tVxKZi5Y34z7xEItxPw9b30df2OqGT+6jf8h6Gupuy7ovjkefZ5NnNSuc2itRyXGohXrWEjsQZamzZK68yfRF2xUmhakVQORQPSxzrScoEKZnIiuRZ4tyARy2mN3mBlEziVDwUamOpWfyJVpY5N7LF+w6GjF5swkmpXpPTBAOwo/CDhIx+YmYo67hHw3tz9u9MnGWx41qqbUvxqEVEzRC6sGXlIBwL72OTdzd19lVU6A1W0p7Q03kLNvYNPjSuElAcduJn23BtXMPgY3txbVqD4rCT8gfQayqw1VViW1RtPfCFglAUzGAEx8pGEm2dCLsNY2AI9/YNGIEh1IJs9t+GDSN02rGhJAVldgrK7LS+Nve5CAtSCfRHLtAdPM2S8pux6156Q+dw230IFJqnqBSCcT8+z2IKndV47WUsKt1M0hiZdfg8i6ktXk9fqJlocpCqQivTNhCZm5CsunoNw4RwSNLTbbBhi439L8SJhCWGAT/9UZiPfNTD4VdDmf73/leQj3zUw9CgSSwGp08mOX0iSdPpJHf/oYfvfyvEx/7Cm+k3FSUwZAYIyQFaU8ep11bRZ3bSb/pZpW8hLK34abcoRCIZMvtoM09Spy2nw2gmYg6xWt9KtbqEmIzSqK3ldOpV3KKAo0nrOpQptZxNHc5EpITkAAOmnyXatVwwzhCTYVbrW1FQM/us1rfSbpylUmnIktUwkxS7F2FKg7aeV2govwHDTJAyrNyF9r5DNJZvR9MchKJ+HHoB/eHzROIjJjojGcc0k3QefwZfw0Z0pxeRdtonIiPx4pdGhCRiQQQCVbNhpGKYqQRSmgihWC9FzRwvOmSFi+oOL1JKENaMf/i8o6HoDhLhfqSRpPPYs0T6O6lbfwddJ/ZS2rgRm9NLdNCPqjtIxUdWtlLKzHlH9xNi/CAAS/a4JbeikYwOYRrJzMp6NAZS3RwIPsoy50bK9EUMGb0cCj5BTEbGKIHVru04lJGHlENxs9Q54l8YrQRaYkeoti3LZOwmzDg9yfOZ1UZmvGWMA8HH2OJ5O4VaGXEzQkf8DOdir3FL0T1j5O1LduBVS/DoRVnH7UqcyzkWcTOS/n2bKVB9OFQ3Q0Z2TYSkjLN/6FfU2VdSaVtCkVZBUsbpT3XjT7YSn4CCPLj3AJgmiZaRCaP3tu2Enre4MnvvfdiS8/zIqjDe1gGjCgUlWjtyOo4Btn3ICkqoXllA97kQvgY3gQsRWl87NKbvbLEglQDAkfb/oaF0K9WFa6kuvIZocoDWvql7xk9dfJpVVbezueEjDEW7ONbxKIt9N2S2RxODaMLG0vId6KqTgcgFDp9/mMFo5wRHnTpKy1RA4nLZGBo0MQ1QNcHGrTYi4ZGL3rhEw+EUWW2hoMnipTpGCnzlCo1LNHr8Bne+x5XVbyaIyygmBgKRiYIR6WzS4W0qGoZMYpBCItGETkyGaTGsP3JMjizLm1Kv06itocfooM8c8QWoaJn9BUrWPiYGpjS51OmhKjqxxCCGTFFdso54Moim2pCYqIpOTcl1JFJhoslBdNVByohjmNmKsKBiCfFQH+G+dqQ0cHhLSUTGzi7DgXYWrX8HgfPZpFyB80epWXtrOovcpGbtLUQHuzFSiczxFM1GqKeFwYtniIcC1F5zOz3n9uNr3Ei4r50II/dQX/OrVK+7jXgoQLjvAvRLFM1GKh5GmgZ2rw9Fs3Hx5D4atr4Xj6+ecN8FQr1t1Ky7zVJGo/pJaRIOtFO34U4C54+QioUoW7qVYHcTPU0HqL12N2YqQeD8kUnvhSGjl0MhyxfmKVAorVeJXjQ4ZPsOmiYQChSVKLwUeJDSMpXyKo3mMwlKy1Q6z6eorNXouZiiYZlOQaHK6WNxEgWtHA82Ew/LSXMww8YAewZ/OKb9yf77xrQdCk2f3ytoBHgtNDEDqIlBW/w4bfGJgzvCxkC2XDmcupFXRsbcs3UT0dNnMcMRi1AScK5eYbWFwqAIXGvXEGs6hzE0NuLowb+2/KGKKjANiVAEv/3v104o40wh5GXKlp2WEELMvxCXEVOIBMvCx/7Cy75nYpw6nszad7rHGUaduhyX8BInmvEJrNVvoN04i0+pxiacRORQZptbFLJYW0tYDuEQblpSR1msraPbaCMo+1mkruRsyjJBLNHWYRMO/EY7fWYndepygrKfpExQr63EkCkCZjdFSllmn5X6JtpTTVSrSziTyj2zGVZS47UJYdVOzrmvoiJNY8znS6EoGqY5NndDCAUpJYu33UXLgZ8iTZNhX9BEx5vJtkvbs7+Lcc8rVA1p5Mo7Eemxmd6N4nIr3HiLi6o6jcF+g0CPweIVNqpqNQ6+ECURl0TCkhOvx7nxFheqBgVFKq+9HKWoVEWaUL9UJxo2sdkFv/jh3Ge2Xk0ovG0nZiQKqgKmRPOVYobDmJEoiseNEQwidBuR117PqQSGMcyXJhTBH/9wG1//4JiiMody1WSZDiZVAkKIOqz6wpWACdwrpfxPIUQJ8BDQALQCd0kp+9MlJ/8TuAOIAPdIKSeslPKbrgTyyONqxfDEY5j9fHgSsvu9HpwuhWceDTHYb2b1m8lE5TcSuUw9o9vGMQWNhq/RTekiF/0XovibcwbCXBElUAVUSSlfE0J4gUPAu4F7sGoHfF4I8XdAsZTyb4UQdwB/jqUEtgL/KaXcOsk58kogjzzyyGP6mLUSmDTVVErZNTyTl1IGgZNADfAu4Hvpbt/DUgyk278vLbwCFKUVSR555JFHHgsM03IMCyEagPXAfqBCStkFlqIQQgzHgdUAF0bt1p5uy8oguqTQfAjoA7Ld9wsTPha+nFeDjJCXc66Rl3NucTXIWS+E+JiU8t6ZHmDKSkAI4cEqGPOXUsqhXGFnw11ztI0x96SFzgguhHh1tsuaK4GrQc6rQUbIyznXyMs5t7ia5GTUs3S6mBLzmBBCx1IAP5RSDldZ6R4286Tfh7l224G6UbvXAnMTd5lHHnnkkcecYlIlkI72uR84KaX8j1Gbfgn8bvrz7wKPjGq/W1jYBgwOm43yyCOPPPJYWJiKOWg78DvAUSHEMKPbPwCfBx4WQnwUOA+8P73tcazIoCasENHfm6IsM17OXGFcDXJeDTJCXs65Rl7OucWbQs4FkSyWRx555JHH/GD21UjyyCOPPPK4apFXAnnkkUceb2LMuxIQQrxNCHFaCNGUzjxeMBBCtAohjgohXk+HYSGEKBFCPC2EOJt+L57sOJdBrm8LIfxCiGOj2nLKlXbQ/1d6fN8QQmyYZzn/WQjRkR7T19MZ5sPb/j4t52khxO1XSMY6IcQeIcRJIcRxIcQn0u0LajwnkHOhjadDCHFACHEkLee/pNsbhRD70+P5kBBWaTkhhD39vSm9vWGe5fyuEKJl1Hhel26ft/9R+vyqEOKwEOLR9Pe5G08p5by9ABU4BywGbMARYPV8ynSJfK2A75K2LwB/l/78d8C/z4NcNwMbgGOTyYXlpH8CK39jG7B/nuX8Z+Cvc/Rdnb7+dqAxfV+oV0DGKmBD+rMXOJOWZUGN5wRyLrTxFIAn/VnHSizdBjwMfCDd/g3g4+nPfwJ8I/35A8BDV2g8x5Pzu8D7cvSft/9R+vyfBH4EPJr+PmfjOd8rgS1Ak5SyWUqZAB7Eop1YyBiPLuOKQUq5D7i0zuWCo/EYR87x8C7gQSllXErZghVdtuWyCZeGvEpoUSaQczzM13hKKeUw05mefklgJzBcZPjS8Rwe558Cu4QYPxP1Csg5HubtfySEqAXeDtyX/i6Yw/GcbyUwHsXEQoEEnhJCHBIWzQVcQpcBjC2bND8YT66FOMZ/ll5Sf3uUOW3e5RQT0KKwgMbzEjlhgY1n2nTxOlYC6dNYq5ABKeUw9/VoWTJyprcPAqXzIaeUcng8/zU9nl8WIl0Hc36v+1eAv4FMvd1S5nA851sJTIliYh6xXUq5AdgN/KkQ4ub5FmgGWGhj/HVgCXAdFp/Ul9Lt8yqnuIQWZaKuOdrmU84FN55SSkNKeR0WW8AWYNUEsiwYOYUQa4G/B1YCm4ES4G/nU04hxDsAv5RydOGNiWSZtpzzrQQWNMWElLIz/e4H/gfrhh6PLmO+cVXQeEgpu9N/PhP4FiMminmTU1wltCi55FyI4zkMKeUAsBfLhl4khBhOTh0tS0bO9PZCpm5CnGs535Y2u0kpZRz4DvM/ntuBdwohWrHM5TuxVgZzNp7zrQQOAsvSnm4bliPjl/MsEwBCCLew6icghHADtwHHGJ8uY75xVdB4XGJH/S2sMQVLzg+koxsagWXA1OuJzlyeq4IWZTw5F+B4lgkhitKfncAtWP6LPcD70t0uHc/hcX4f8KxMezXnQc5ToxS/wLKzjx7PK37dpZR/L6WslVI2YD0fn5VSfpi5HM8r6eHO9cLyup/Bsht+er7lGSXXYqzoiiPA8WHZsOxrzwBn0+8l8yDbj7GW/kkszf/R8eTCWh5+LT2+R4FN8yznA2k53kjfsFWj+n86LedpYPcVkvFGrOXyG8Dr6dcdC208J5BzoY3nOuBwWp5jwP9Nty/GUkJNwE8Ae7rdkf7elN6+eJ7lfDY9nseAHzASQTRv/6NRMr+FkeigORvPPG1EHnnkkcebGPNtDsojjzzyyGMekVcCeeSRRx5vYuSVQB555JHHmxh5JZBHHnnk8SZGXgnkkUceebyJkVcCeeSRRx5vYuSVQB555JHHmxj/H31NVOoknQEHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "allTokens = [each.text for each in nlp(' '.join(train.cleanedText.tolist()))]\n",
    "allTokensTrueTweets = [each.text for each in nlp(' '.join(train[train.target == 1].cleanedText.tolist()))]\n",
    "allTokensFalseTweets = [each.text for each in nlp(' '.join(train[train.target == 0].cleanedText.tolist()))]\n",
    "frequency_dist = nltk.FreqDist(allTokens)\n",
    "frequency_dist_trueTweets = nltk.FreqDist(allTokensTrueTweets)\n",
    "frequency_dist_falseTweets = nltk.FreqDist(allTokensFalseTweets)\n",
    "wordcloud = WordCloud().generate_from_frequencies(frequency_dist)\n",
    "wordcloud_trueTweets = WordCloud().generate_from_frequencies(frequency_dist_trueTweets)\n",
    "wordcloud_falseTweets = WordCloud().generate_from_frequencies(frequency_dist_falseTweets)\n",
    "\n",
    "plt.imshow(wordcloud)\n",
    "plt.title(\"All Tweets\")\n",
    "plt.show()\n",
    "plt.imshow(wordcloud_trueTweets)\n",
    "plt.title(\"True Tweets\")\n",
    "plt.show()\n",
    "plt.imshow(wordcloud_falseTweets)\n",
    "plt.title(\"False Tweets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,2, sharey = 'row', figsize = (10,10))\n",
    "train.apply(lambda x: len(x.cleanedText) if x.target == 1 else None, axis = 1).hist(bins = 20, ax = axs[0,0])\n",
    "axs[0,0].set_title(\"Character Count Distribution for True Tweets\")\n",
    "train.apply(lambda x: len(x.cleanedText) if x.target == 0 else None, axis = 1).hist(bins = 20, ax = axs[0,1])\n",
    "axs[0,1].set_title(\"Character Count Distribution for False Tweets\")\n",
    "train.apply(lambda x: len(x.cleanedText.split(' ')) if x.target == 1 else None, axis = 1).hist(bins = 20, ax = axs[1,0])\n",
    "axs[1,0].set_title(\"Token Count Distribution for True Tweets\")\n",
    "train.apply(lambda x: len(x.cleanedText.split(' ')) if x.target == 0 else None, axis = 1).hist(bins = 20, ax = axs[1,1])\n",
    "axs[1,1].set_title(\"Token Count Distribution for False Tweets\")\n",
    "train.apply(lambda x: np.mean(list(map(len, x.cleanedText.split(' ')))) if x.target == 1 else None, axis = 1).hist(bins = 20, ax = axs[2,0])\n",
    "axs[2,0].set_title(\"Average Token Length for True Tweets\")\n",
    "train.apply(lambda x: np.mean(list(map(len, x.cleanedText.split(' ')))) if x.target == 0 else None, axis = 1).hist(bins = 20, ax = axs[2,1])\n",
    "axs[2,1].set_title(\"Average Token Length for False Tweets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Vectorize tweets with TF-IDF and Make predictions with Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vectorize tweets with TFIDF and Store the Document Term Matrix in tfidf_dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size from all the tweets: 17710\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vect = TfidfVectorizer()\n",
    "vect.fit(train.cleanedText)\n",
    "tfidf_dtm = pd.DataFrame(vect.transform(train.cleanedText.tolist()).toarray(), index = train.id, columns = vect.get_feature_names())\n",
    "print(\"Vocabulary size from all the tweets: {}\".format(len(vect.vocabulary_)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split tfidf_dtm into model-ready variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set has 1599 true tweets and 2207 false tweets.\n",
      "Test set has 1672 true tweets and 2135 false tweets.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "train_index = random.sample(list(np.arange(0, tfidf_dtm.shape[0])), int(tfidf_dtm.shape[0]/2))\n",
    "train_index.sort()\n",
    "train_features = tfidf_dtm.iloc[train_index]\n",
    "test_features = tfidf_dtm.iloc[[index for index in np.arange(0, tfidf_dtm.shape[0]) if index not in train_index]]\n",
    "train_target = train.iloc[train_index].target\n",
    "test_target = train.iloc[[index for index in np.arange(0, train.shape[0]) if index not in train_index]].target\n",
    "print(\"Train set has {} true tweets and {} false tweets.\".format(train_target.value_counts().loc[1], train_target.value_counts().loc[0]))\n",
    "print(\"Test set has {} true tweets and {} false tweets.\".format(test_target.value_counts().loc[1], test_target.value_counts().loc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (1) Train Model, (2) Make Predictions, and (3) Analyze Classification Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.91      0.82      2135\n",
      "           1       0.84      0.61      0.71      1672\n",
      "\n",
      "    accuracy                           0.78      3807\n",
      "   macro avg       0.80      0.76      0.77      3807\n",
      "weighted avg       0.79      0.78      0.77      3807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier = MultinomialNB().fit(train_features, train_target)\n",
    "test_predictions = classifier.predict(test_features)\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(test_target, test_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment: Since the task is to identify true disaster tweets, the metric that we should focus here is recall for true tweets, i.e. the ratio of correctly predicted true tweets to all true tweets. We want to maximize recall, even at the expense of precision (i.e. the ratio of correctedly predicted true tweets to all the predicted true tweets). The reason is we want to be able to identify all true disaster tweets so emergency can be deployed in a timely manner; even if we mistakenly identify some false tweets as true disasters and the emergency service receives some false alarms. However, since we don't have a severe case of class imbalance, we also care about general accuracy. The accuracy here is a fine starting benchmark.\n",
    "\n",
    "#### Here, the recall for true tweets is low. Next up I'll try using pretrained word embeddings GloVe with RNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Import Pretrained Word Embedding Model GloVe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the GloVe model from .txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First line in gloveFile: the 0.04656 0.21318 -0.0074364 -0.45854 -0.035639 0.23643 -0.28836 0.21521 -0.13486 -1.6413 -0.26091 0.032434 0.056621 -0.043296 -0.021672 0.22476 -0.075129 -0.067018 -0.14247 0.038825 -0.18951 0.29977 0.39305 0.17887 -0.17343 -0.21178 0.23617 -0.063681 -0.42318 -0.11661 0.093754 0.17296 -0.33073 0.49112 -0.68995 -0.092462 0.24742 -0.17991 0.097908 0.083118 0.15299 -0.27276 -0.038934 0.54453 0.53737 0.29105 -0.0073514 0.04788 -0.4076 -0.026759 0.17919 0.010977 -0.10963 -0.26395 0.07399 0.26236 -0.1508 0.34623 0.25758 0.11971 -0.037135 -0.071593 0.43898 -0.040764 0.016425 -0.4464 0.17197 0.046246 0.058639 0.041499 0.53948 0.52495 0.11361 -0.048315 -0.36385 0.18704 0.092761 -0.11129 -0.42085 0.13992 -0.39338 -0.067945 0.12188 0.16707 0.075169 -0.015529 -0.19499 0.19638 0.053194 0.2517 -0.34845 -0.10638 -0.34692 -0.19024 -0.2004 0.12154 -0.29208 0.023353 -0.11618 -0.35768 0.062304 0.35884 0.02906 0.0073005 0.0049482 -0.15048 -0.12313 0.19337 0.12173 0.44503 0.25147 0.10781 -0.17716 0.038691 0.08153 0.14667 0.063666 0.061332 -0.075569 -0.37724 0.01585 -0.30342 0.28374 -0.042013 -0.040715 -0.15269 0.07498 0.15577 0.10433 0.31393 0.19309 0.19429 0.15185 -0.10192 -0.018785 0.20791 0.13366 0.19038 -0.25558 0.304 -0.01896 0.20147 -0.4211 -0.0075156 -0.27977 -0.19314 0.046204 0.19971 -0.30207 0.25735 0.68107 -0.19409 0.23984 0.22493 0.65224 -0.13561 -0.17383 -0.048209 -0.1186 0.0021588 -0.019525 0.11948 0.19346 -0.4082 -0.082966 0.16626 -0.10601 0.35861 0.16922 0.07259 -0.24803 -0.10024 -0.52491 -0.17745 -0.36647 0.2618 -0.012077 0.08319 -0.21528 0.41045 0.29136 0.30869 0.078864 0.32207 -0.041023 -0.1097 -0.092041 -0.12339 -0.16416 0.35382 -0.082774 0.33171 -0.24738 -0.048928 0.15746 0.18988 -0.026642 0.063315 -0.010673 0.34089 1.4106 0.13417 0.28191 -0.2594 0.055267 -0.052425 -0.25789 0.019127 -0.022084 0.32113 0.068818 0.51207 0.16478 -0.20194 0.29232 0.098575 0.013145 -0.10652 0.1351 -0.045332 0.20697 -0.48425 -0.44706 0.0033305 0.0029264 -0.10975 -0.23325 0.22442 -0.10503 0.12339 0.10978 0.048994 -0.25157 0.40319 0.35318 0.18651 -0.023622 -0.12734 0.11475 0.27359 -0.21866 0.015794 0.81754 -0.023792 -0.85469 -0.16203 0.18076 0.028014 -0.1434 0.0013139 -0.091735 -0.089704 0.11105 -0.16703 0.068377 -0.087388 -0.039789 0.014184 0.21187 0.28579 -0.28797 -0.058996 -0.032436 -0.0047009 -0.17052 -0.034741 -0.11489 0.075093 0.099526 0.048183 -0.073775 -0.41817 0.0041268 0.44414 -0.16062 0.14294 -2.2628 -0.027347 0.81311 0.77417 -0.25639 -0.11576 -0.11982 -0.21363 0.028429 0.27261 0.031026 0.096782 0.0067769 0.14082 -0.013064 -0.29686 -0.079913 0.195 0.031549 0.28506 -0.087461 0.0090611 -0.20989 0.053913\n",
      "\n"
     ]
    }
   ],
   "source": [
    "os.chdir(\"/kaggle/input/gloveicg\")\n",
    "gloveFile = open(\"/kaggle/input/gloveicg/Glove/glove.6B.300d.txt\")\n",
    "print(\"First line in gloveFile: {}\".format(gloveFile.readline()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
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       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>0.046560</td>\n",
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       "      <td>-0.007436</td>\n",
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       "      <td>-1.6413</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013064</td>\n",
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       "      <td>-0.079913</td>\n",
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       "      <td>0.031549</td>\n",
       "      <td>0.285060</td>\n",
       "      <td>-0.087461</td>\n",
       "      <td>0.009061</td>\n",
       "      <td>-0.209890</td>\n",
       "      <td>0.053913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>,</th>\n",
       "      <td>-0.255390</td>\n",
       "      <td>-0.257230</td>\n",
       "      <td>0.131690</td>\n",
       "      <td>-0.042688</td>\n",
       "      <td>0.218170</td>\n",
       "      <td>-0.022702</td>\n",
       "      <td>-0.178540</td>\n",
       "      <td>0.107560</td>\n",
       "      <td>0.058936</td>\n",
       "      <td>-1.3854</td>\n",
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       "      <td>-0.014359</td>\n",
       "      <td>-0.073794</td>\n",
       "      <td>0.221760</td>\n",
       "      <td>0.146520</td>\n",
       "      <td>0.566860</td>\n",
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       "      <td>-0.232900</td>\n",
       "      <td>-0.122260</td>\n",
       "      <td>0.354990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.</th>\n",
       "      <td>-0.125590</td>\n",
       "      <td>0.013630</td>\n",
       "      <td>0.103060</td>\n",
       "      <td>-0.101230</td>\n",
       "      <td>0.098128</td>\n",
       "      <td>0.136270</td>\n",
       "      <td>-0.107210</td>\n",
       "      <td>0.236970</td>\n",
       "      <td>0.328700</td>\n",
       "      <td>-1.6785</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060148</td>\n",
       "      <td>-0.156190</td>\n",
       "      <td>-0.119490</td>\n",
       "      <td>0.234450</td>\n",
       "      <td>0.081367</td>\n",
       "      <td>0.246180</td>\n",
       "      <td>-0.152420</td>\n",
       "      <td>-0.342240</td>\n",
       "      <td>-0.022394</td>\n",
       "      <td>0.136840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>of</th>\n",
       "      <td>-0.076947</td>\n",
       "      <td>-0.021211</td>\n",
       "      <td>0.212710</td>\n",
       "      <td>-0.722320</td>\n",
       "      <td>-0.139880</td>\n",
       "      <td>-0.122340</td>\n",
       "      <td>-0.175210</td>\n",
       "      <td>0.121370</td>\n",
       "      <td>-0.070866</td>\n",
       "      <td>-1.5721</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.366730</td>\n",
       "      <td>-0.386030</td>\n",
       "      <td>0.302900</td>\n",
       "      <td>0.015747</td>\n",
       "      <td>0.340360</td>\n",
       "      <td>0.478410</td>\n",
       "      <td>0.068617</td>\n",
       "      <td>0.183510</td>\n",
       "      <td>-0.291830</td>\n",
       "      <td>-0.046533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>-0.257560</td>\n",
       "      <td>-0.057132</td>\n",
       "      <td>-0.671900</td>\n",
       "      <td>-0.380820</td>\n",
       "      <td>-0.364210</td>\n",
       "      <td>-0.082155</td>\n",
       "      <td>-0.010955</td>\n",
       "      <td>-0.082047</td>\n",
       "      <td>0.460560</td>\n",
       "      <td>-1.8477</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012806</td>\n",
       "      <td>-0.597070</td>\n",
       "      <td>0.317340</td>\n",
       "      <td>-0.252670</td>\n",
       "      <td>0.543840</td>\n",
       "      <td>0.063007</td>\n",
       "      <td>-0.049795</td>\n",
       "      <td>-0.160430</td>\n",
       "      <td>0.046744</td>\n",
       "      <td>-0.070621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "the  0.046560  0.213180 -0.007436 -0.458540 -0.035639  0.236430 -0.288360   \n",
       ",   -0.255390 -0.257230  0.131690 -0.042688  0.218170 -0.022702 -0.178540   \n",
       ".   -0.125590  0.013630  0.103060 -0.101230  0.098128  0.136270 -0.107210   \n",
       "of  -0.076947 -0.021211  0.212710 -0.722320 -0.139880 -0.122340 -0.175210   \n",
       "to  -0.257560 -0.057132 -0.671900 -0.380820 -0.364210 -0.082155 -0.010955   \n",
       "\n",
       "          7         8       9    ...       290       291       292       293  \\\n",
       "the  0.215210 -0.134860 -1.6413  ... -0.013064 -0.296860 -0.079913  0.195000   \n",
       ",    0.107560  0.058936 -1.3854  ...  0.075968 -0.014359 -0.073794  0.221760   \n",
       ".    0.236970  0.328700 -1.6785  ...  0.060148 -0.156190 -0.119490  0.234450   \n",
       "of   0.121370 -0.070866 -1.5721  ... -0.366730 -0.386030  0.302900  0.015747   \n",
       "to  -0.082047  0.460560 -1.8477  ... -0.012806 -0.597070  0.317340 -0.252670   \n",
       "\n",
       "          294       295       296       297       298       299  \n",
       "the  0.031549  0.285060 -0.087461  0.009061 -0.209890  0.053913  \n",
       ",    0.146520  0.566860  0.053307 -0.232900 -0.122260  0.354990  \n",
       ".    0.081367  0.246180 -0.152420 -0.342240 -0.022394  0.136840  \n",
       "of   0.340360  0.478410  0.068617  0.183510 -0.291830 -0.046533  \n",
       "to   0.543840  0.063007 -0.049795 -0.160430  0.046744 -0.070621  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gloveModel = {}\n",
    "gloveFile.seek(0) # Return the cursor back to the beginning of the file\n",
    "for line in gloveFile.readlines():\n",
    "    lineElements = line.split(\" \")\n",
    "    token = lineElements[0]\n",
    "    tokenEmbedding = [float(each) for each in lineElements[1:]]\n",
    "    gloveModel[token] = tokenEmbedding\n",
    "gloveMatrix = pd.DataFrame(gloveModel).T\n",
    "gloveMatrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring GloVe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findClosestWordToWord(word):\n",
    "    diffMatrix = gloveMatrix - gloveModel[word]\n",
    "    diffMatrix = diffMatrix * diffMatrix\n",
    "    diffMatrix.drop(word, axis = 0, inplace = True)\n",
    "    return diffMatrix.sum(axis = 1).idxmin()\n",
    "def findClosestWordToEmbedding(embedding, excludedWords):\n",
    "    diffMatrix = gloveMatrix - embedding\n",
    "    diffMatrix = diffMatrix * diffMatrix\n",
    "    diffMatrix.drop(excludedWords, axis = 0, inplace = True)\n",
    "    return diffMatrix.sum(axis = 1).idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Father to Mother is like Son to Daughter\n",
      "The closest word to Data is Information\n"
     ]
    }
   ],
   "source": [
    "temp = gloveMatrix.loc['father'] - gloveMatrix.loc['mother'] + gloveMatrix.loc['daughter']\n",
    "print(\"Father to Mother is like {} to Daughter\".format(findClosestWordToEmbedding(temp, ['father', 'mother', 'daughter']).title()))\n",
    "print(\"The closest word to Data is {}\".format(findClosestWordToWord('data').title()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Using GloVe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of vocabulary in GloVe:  400000\n",
      "Size of vocabulary from all tweets:  17710\n",
      "Size of overlapped vocabulary: 12628\n",
      "Since we want to use word embedding to train model, we need to remove tokens that do not appear in GloVe\n"
     ]
    },
    {
     "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>cleanedText</th>\n",
       "      <th>cleanedText2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>deed reason # earthquake may allah forgive u</td>\n",
       "      <td>deed reason # earthquake may allah forgive u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>forest fire near la ronge sask . canada</td>\n",
       "      <td>forest fire near la ronge sask . canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Our Deeds are the Reason of this #earthquake M...   \n",
       "1             Forest fire near La Ronge Sask. Canada   \n",
       "\n",
       "                                    cleanedText  \\\n",
       "0  deed reason # earthquake may allah forgive u   \n",
       "1       forest fire near la ronge sask . canada   \n",
       "\n",
       "                                   cleanedText2  \n",
       "0  deed reason # earthquake may allah forgive u  \n",
       "1       forest fire near la ronge sask . canada  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Size of vocabulary in GloVe: \",gloveMatrix.shape[0])\n",
    "print(\"Size of vocabulary from all tweets: \",len(vect.get_feature_names()))\n",
    "print(\"Size of overlapped vocabulary:\" , len(set(gloveMatrix.index).intersection(set(vect.get_feature_names()))))\n",
    "\n",
    "print(\"Since we want to use word embedding to train model, we need to remove tokens that do not appear in GloVe\")\n",
    "train['cleanedText2'] = train.cleanedText.map(lambda x: ' '.join([each for each in word_tokenize(x) if each in gloveModel.keys()]))\n",
    "test['cleanedText2'] = test.cleanedText.map(lambda x: ' '.join([each for each in word_tokenize(x) if each in gloveModel.keys()]))\n",
    "\n",
    "train.dropna(subset=['text', 'cleanedText', 'cleanedText2'], inplace = True)\n",
    "# test.dropna(subset=['text', 'cleanedText', 'cleanedText2'], inplace = True)\n",
    "\n",
    "# Drop tweets whose cleanedText2 is empty\n",
    "temp = (train.cleanedText2.apply(len) == 0)\n",
    "train.drop(temp[temp == True].index, axis = 0, inplace = True)\n",
    "del temp\n",
    "\n",
    "# temp = (test.cleanedText2.apply(len) == 0)\n",
    "# test.drop(temp[temp == True].index, axis = 0, inplace = True)\n",
    "# del temp\n",
    "\n",
    "train[['text', 'cleanedText', 'cleanedText2']].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'13,000' is in GloVe vocabulary: True\n",
      "The word closest to '13,000' is '12,000'\n"
     ]
    }
   ],
   "source": [
    "# Interestingly, i see '13,000' is reserved in cleanedText2 for a row\n",
    "# Finding out whether 13,000 is in GloVe vocabulary\n",
    "print(\"'13,000' is in GloVe vocabulary: {}\".format('13,000' in gloveModel.keys()))\n",
    "print(\"The word closest to '13,000' is '{}'\".format(findClosestWordToWord('13,000')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Keras RNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert texts into sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a GloVe dictionary where keys are tokens and values are respective numeric indices \n",
    "gloveDict = {}\n",
    "for index, eachWord in enumerate(gloveMatrix.index):\n",
    "    gloveDict[eachWord] = index\n",
    "    \n",
    "# Using GloVe dictionary, convert train.cleanedText2 to sequences of numeric indices of respective tokens\n",
    "def texts_to_sequences(textSeries):\n",
    "    return textSeries.map(lambda x: [gloveDict['.']] if len(x) == 0 else [gloveDict[each] for each in x.split(\" \")])\n",
    "    # if cleanedText2 is empty then fill the string with '.' (to meet submission criterion of full test set)\n",
    "    \n",
    "train['sequence'] = texts_to_sequences(train.cleanedText2)\n",
    "test['sequence'] = texts_to_sequences(test.cleanedText2)  \n",
    "\n",
    "# Padding these sequences with periods '.'. I use the length of the longest sequence in train as the max for both train and submission test sets \n",
    "maxlen = np.max(train.sequence.map(lambda x: len(x)))\n",
    "train['padded_sequence'] = train.sequence.map(lambda x: x[:maxlen] if len(x) >= maxlen else x + [gloveDict['.']]*(maxlen-len(x)))\n",
    "test['padded_sequence'] = test.sequence.map(lambda x: x[:maxlen] if len(x) >= maxlen else x + [gloveDict['.']]*(maxlen-len(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare model-ready variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set has 1648 true tweets and 2158 false tweets.\n",
      "Test set has 1623 true tweets and 2183 false tweets.\n"
     ]
    }
   ],
   "source": [
    "train_index = random.sample(list(np.arange(0, train.shape[0])), int(train.shape[0]/2))\n",
    "train_index.sort()\n",
    "train_features = train.iloc[train_index]['padded_sequence']\n",
    "train_features = pd.DataFrame([each for each in train_features])\n",
    "train_target = train.iloc[train_index]['target']\n",
    "test_features = train.iloc[[i for i in np.arange(0, train.shape[0]) if i not in train_index]]['padded_sequence']\n",
    "test_features = pd.DataFrame([each for each in test_features])\n",
    "test_target = train.iloc[[i for i in np.arange(0, train.shape[0]) if i not in train_index]]['target']\n",
    "print(\"Train set has {} true tweets and {} false tweets.\".format(train_target.value_counts().loc[1], train_target.value_counts().loc[0]))\n",
    "print(\"Test set has {} true tweets and {} false tweets.\".format(test_target.value_counts().loc[1], test_target.value_counts().loc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import metrics\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "METRICS = [\n",
    "    metrics.BinaryAccuracy(name = 'accuracy'),\n",
    "    metrics.Precision(name = 'precision'),\n",
    "    metrics.Recall(name = 'recall'),\n",
    "    metrics.AUC(name = 'auc')\n",
    "]\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor = 'val_auc',\n",
    "    verbose = 1,\n",
    "    patience = 10,\n",
    "    mode = 'max',\n",
    "    restore_best_weights = True\n",
    ")\n",
    "checkpoint = ModelCheckpoint('model-{epoch}-{loss}-{val_loss}.h5', verbose = 1, \\\n",
    "                             monitor = 'val_loss', save_best_only = True, mode = 'auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 72, 300)           120000000 \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               186880    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 120,191,041\n",
      "Trainable params: 191,041\n",
      "Non-trainable params: 120,000,000\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, LSTM, GRU, Bidirectional, BatchNormalization, Dropout\n",
    "from keras.initializers import Constant\n",
    "from keras.optimizers import SGD, Adam, RMSprop\n",
    "model = Sequential()\n",
    "embedding_layer = Embedding(input_dim = gloveMatrix.shape[0], output_dim = gloveMatrix.shape[1],\\\n",
    "                            embeddings_initializer = Constant(gloveMatrix.to_numpy()),\\\n",
    "                           input_length = maxlen, trainable = False)\n",
    "model.add(embedding_layer)\n",
    "model.add(Bidirectional(LSTM(units=64, dropout = 0.5, recurrent_dropout = 0.5)))\n",
    "model.add(Dense(32))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = RMSprop(lr = 0.0002), metrics = METRICS)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3806 samples, validate on 3806 samples\n",
      "Epoch 1/35\n",
      "3806/3806 [==============================] - 22s 6ms/step - loss: 0.6667 - accuracy: 0.5441 - precision: 0.4575 - recall: 0.2680 - auc: 0.5537 - val_loss: 0.6169 - val_accuracy: 0.6251 - val_precision: 0.6132 - val_recall: 0.3115 - val_auc: 0.6558\n",
      "Epoch 2/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.6000 - accuracy: 0.6489 - precision: 0.6735 - recall: 0.3546 - auc: 0.6952 - val_loss: 0.5489 - val_accuracy: 0.6707 - val_precision: 0.6860 - val_recall: 0.4219 - val_auc: 0.7196\n",
      "Epoch 3/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.5645 - accuracy: 0.6837 - precision: 0.6989 - recall: 0.4660 - auc: 0.7346 - val_loss: 0.5279 - val_accuracy: 0.6957 - val_precision: 0.7082 - val_recall: 0.4918 - val_auc: 0.7462\n",
      "Epoch 4/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.5485 - accuracy: 0.7014 - precision: 0.7150 - recall: 0.5089 - auc: 0.7531 - val_loss: 0.5177 - val_accuracy: 0.7081 - val_precision: 0.7191 - val_recall: 0.5233 - val_auc: 0.7608\n",
      "Epoch 5/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.5393 - accuracy: 0.7123 - precision: 0.7244 - recall: 0.5335 - auc: 0.7653 - val_loss: 0.5039 - val_accuracy: 0.7172 - val_precision: 0.7274 - val_recall: 0.5442 - val_auc: 0.7704\n",
      "Epoch 6/35\n",
      "3806/3806 [==============================] - 18s 5ms/step - loss: 0.5241 - accuracy: 0.7200 - precision: 0.7303 - recall: 0.5527 - auc: 0.7740 - val_loss: 0.4970 - val_accuracy: 0.7240 - val_precision: 0.7325 - val_recall: 0.5617 - val_auc: 0.7784\n",
      "Epoch 7/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.5193 - accuracy: 0.7270 - precision: 0.7355 - recall: 0.5695 - auc: 0.7816 - val_loss: 0.4962 - val_accuracy: 0.7298 - val_precision: 0.7376 - val_recall: 0.5748 - val_auc: 0.7850\n",
      "Epoch 8/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.5151 - accuracy: 0.7319 - precision: 0.7408 - recall: 0.5786 - auc: 0.7873 - val_loss: 0.4852 - val_accuracy: 0.7341 - val_precision: 0.7419 - val_recall: 0.5834 - val_auc: 0.7899\n",
      "Epoch 9/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4995 - accuracy: 0.7363 - precision: 0.7444 - recall: 0.5883 - auc: 0.7925 - val_loss: 0.4809 - val_accuracy: 0.7384 - val_precision: 0.7456 - val_recall: 0.5929 - val_auc: 0.7950\n",
      "Epoch 10/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4957 - accuracy: 0.7401 - precision: 0.7476 - recall: 0.5968 - auc: 0.7969 - val_loss: 0.4832 - val_accuracy: 0.7421 - val_precision: 0.7492 - val_recall: 0.6000 - val_auc: 0.7992\n",
      "Epoch 11/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4857 - accuracy: 0.7438 - precision: 0.7520 - recall: 0.6028 - auc: 0.8012 - val_loss: 0.4739 - val_accuracy: 0.7453 - val_precision: 0.7529 - val_recall: 0.6054 - val_auc: 0.8030\n",
      "Epoch 12/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4848 - accuracy: 0.7469 - precision: 0.7552 - recall: 0.6081 - auc: 0.8049 - val_loss: 0.4705 - val_accuracy: 0.7483 - val_precision: 0.7562 - val_recall: 0.6105 - val_auc: 0.8064\n",
      "Epoch 13/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4850 - accuracy: 0.7497 - precision: 0.7581 - recall: 0.6131 - auc: 0.8081 - val_loss: 0.4776 - val_accuracy: 0.7507 - val_precision: 0.7577 - val_recall: 0.6163 - val_auc: 0.8092\n",
      "Epoch 14/35\n",
      "3806/3806 [==============================] - 21s 5ms/step - loss: 0.4727 - accuracy: 0.7517 - precision: 0.7580 - recall: 0.6201 - auc: 0.8105 - val_loss: 0.4668 - val_accuracy: 0.7529 - val_precision: 0.7589 - val_recall: 0.6222 - val_auc: 0.8120\n",
      "Epoch 15/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4772 - accuracy: 0.7537 - precision: 0.7599 - recall: 0.6240 - auc: 0.8133 - val_loss: 0.4655 - val_accuracy: 0.7546 - val_precision: 0.7603 - val_recall: 0.6257 - val_auc: 0.8145\n",
      "Epoch 16/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4655 - accuracy: 0.7555 - precision: 0.7613 - recall: 0.6278 - auc: 0.8158 - val_loss: 0.4626 - val_accuracy: 0.7565 - val_precision: 0.7620 - val_recall: 0.6297 - val_auc: 0.8170\n",
      "Epoch 17/35\n",
      "3806/3806 [==============================] - 21s 5ms/step - loss: 0.4562 - accuracy: 0.7575 - precision: 0.7632 - recall: 0.6318 - auc: 0.8184 - val_loss: 0.4655 - val_accuracy: 0.7585 - val_precision: 0.7643 - val_recall: 0.6330 - val_auc: 0.8194\n",
      "Epoch 18/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4615 - accuracy: 0.7593 - precision: 0.7657 - recall: 0.6338 - auc: 0.8204 - val_loss: 0.4642 - val_accuracy: 0.7601 - val_precision: 0.7661 - val_recall: 0.6354 - val_auc: 0.8213\n",
      "Epoch 19/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4587 - accuracy: 0.7608 - precision: 0.7668 - recall: 0.6370 - auc: 0.8223 - val_loss: 0.4676 - val_accuracy: 0.7616 - val_precision: 0.7671 - val_recall: 0.6389 - val_auc: 0.8232\n",
      "Epoch 20/35\n",
      "3806/3806 [==============================] - 21s 5ms/step - loss: 0.4489 - accuracy: 0.7622 - precision: 0.7676 - recall: 0.6407 - auc: 0.8241 - val_loss: 0.4624 - val_accuracy: 0.7630 - val_precision: 0.7681 - val_recall: 0.6421 - val_auc: 0.8251\n",
      "Epoch 21/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4510 - accuracy: 0.7638 - precision: 0.7692 - recall: 0.6437 - auc: 0.8261 - val_loss: 0.4602 - val_accuracy: 0.7645 - val_precision: 0.7695 - val_recall: 0.6448 - val_auc: 0.8268\n",
      "Epoch 22/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4426 - accuracy: 0.7653 - precision: 0.7705 - recall: 0.6462 - auc: 0.8277 - val_loss: 0.4595 - val_accuracy: 0.7660 - val_precision: 0.7712 - val_recall: 0.6474 - val_auc: 0.8285\n",
      "Epoch 23/35\n",
      "3806/3806 [==============================] - 21s 5ms/step - loss: 0.4370 - accuracy: 0.7668 - precision: 0.7723 - recall: 0.6485 - auc: 0.8294 - val_loss: 0.4634 - val_accuracy: 0.7676 - val_precision: 0.7728 - val_recall: 0.6500 - val_auc: 0.8302\n",
      "Epoch 24/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4381 - accuracy: 0.7682 - precision: 0.7735 - recall: 0.6512 - auc: 0.8309 - val_loss: 0.4646 - val_accuracy: 0.7688 - val_precision: 0.7740 - val_recall: 0.6522 - val_auc: 0.8316\n",
      "Epoch 25/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4307 - accuracy: 0.7694 - precision: 0.7749 - recall: 0.6531 - auc: 0.8323 - val_loss: 0.4632 - val_accuracy: 0.7701 - val_precision: 0.7753 - val_recall: 0.6544 - val_auc: 0.8331\n",
      "Epoch 26/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4398 - accuracy: 0.7707 - precision: 0.7761 - recall: 0.6555 - auc: 0.8337 - val_loss: 0.4554 - val_accuracy: 0.7712 - val_precision: 0.7764 - val_recall: 0.6564 - val_auc: 0.8343\n",
      "Epoch 27/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4193 - accuracy: 0.7718 - precision: 0.7774 - recall: 0.6574 - auc: 0.8351 - val_loss: 0.4636 - val_accuracy: 0.7725 - val_precision: 0.7779 - val_recall: 0.6583 - val_auc: 0.8357\n",
      "Epoch 28/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4184 - accuracy: 0.7730 - precision: 0.7787 - recall: 0.6592 - auc: 0.8364 - val_loss: 0.4636 - val_accuracy: 0.7736 - val_precision: 0.7792 - val_recall: 0.6601 - val_auc: 0.8370\n",
      "Epoch 29/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4205 - accuracy: 0.7741 - precision: 0.7798 - recall: 0.6611 - auc: 0.8376 - val_loss: 0.4590 - val_accuracy: 0.7746 - val_precision: 0.7800 - val_recall: 0.6619 - val_auc: 0.8382\n",
      "Epoch 30/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4191 - accuracy: 0.7750 - precision: 0.7807 - recall: 0.6626 - auc: 0.8387 - val_loss: 0.4635 - val_accuracy: 0.7755 - val_precision: 0.7809 - val_recall: 0.6635 - val_auc: 0.8392\n",
      "Epoch 31/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4189 - accuracy: 0.7760 - precision: 0.7814 - recall: 0.6645 - auc: 0.8398 - val_loss: 0.4604 - val_accuracy: 0.7765 - val_precision: 0.7818 - val_recall: 0.6653 - val_auc: 0.8403\n",
      "Epoch 32/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4141 - accuracy: 0.7769 - precision: 0.7823 - recall: 0.6662 - auc: 0.8408 - val_loss: 0.4626 - val_accuracy: 0.7774 - val_precision: 0.7827 - val_recall: 0.6669 - val_auc: 0.8413\n",
      "Epoch 33/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4112 - accuracy: 0.7779 - precision: 0.7834 - recall: 0.6676 - auc: 0.8418 - val_loss: 0.4612 - val_accuracy: 0.7784 - val_precision: 0.7839 - val_recall: 0.6684 - val_auc: 0.8423\n",
      "Epoch 34/35\n",
      "3806/3806 [==============================] - 19s 5ms/step - loss: 0.4085 - accuracy: 0.7789 - precision: 0.7846 - recall: 0.6691 - auc: 0.8428 - val_loss: 0.4666 - val_accuracy: 0.7793 - val_precision: 0.7848 - val_recall: 0.6700 - val_auc: 0.8433\n",
      "Epoch 35/35\n",
      "3806/3806 [==============================] - 20s 5ms/step - loss: 0.4023 - accuracy: 0.7797 - precision: 0.7852 - recall: 0.6709 - auc: 0.8438 - val_loss: 0.4644 - val_accuracy: 0.7801 - val_precision: 0.7855 - val_recall: 0.6715 - val_auc: 0.8442\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_features, train_target, epochs = 35, batch_size = 32, \\\n",
    "                    validation_data = (test_features, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the model loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "historyDF = pd.DataFrame(history.history)\n",
    "historyDF.loss.plot()\n",
    "plt.title(\"Train Loss\")\n",
    "plt.show()\n",
    "historyDF.val_loss.plot()\n",
    "plt.title(\"Test Loss\")\n",
    "plt.show()\n",
    "# plt.title('Visualizing Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions and Creating Classification Report test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.85      0.83      2183\n",
      "           1       0.79      0.72      0.75      1623\n",
      "\n",
      "    accuracy                           0.80      3806\n",
      "   macro avg       0.80      0.79      0.79      3806\n",
      "weighted avg       0.80      0.80      0.80      3806\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_predictions = pd.Series(model.predict(test_features).reshape(-1,),name = 'test_prediction')\n",
    "threshold = 0.45\n",
    "test_classPredictions = pd.Series(np.where(test_predictions > threshold, 1, 0), name = 'test_classPredictions')\n",
    "print(classification_report(test_target, test_classPredictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comment: Recall appears to increase for true tweets with low enough threshold, although the overall accuracy of the model is the same with Naive Bayes"
   ]
  }
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