Twitter Sentimental Analysis_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query based Sentiment Analysis using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General:\n",
    "import tweepy           # To consume Twitter's API\n",
    "import pandas as pd     # To handle data\n",
    "import numpy as np      # For number computing\n",
    "\n",
    "# For plotting and visualization:\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Twitter App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Twitter App access keys for @user\n",
    "\n",
    "# Consume:\n",
    "CONSUMER_KEY    = 'yzxNyCyjkVJJrpNY0L3AphSVV'\n",
    "CONSUMER_SECRET = 'e7xhbuIqTM520PCtwGrSrSIXWyrQbH7ckr9s0ToOlQE6tek4OP'\n",
    "\n",
    "# Access:\n",
    "ACCESS_TOKEN  = '2186056028-BPexxHqPrML7hcvwIuLfX3itD8IZPqguuksDHtn'\n",
    "ACCESS_SECRET = 'vvhbgIqa7jK5nBAO9P4Mh1MTFvJdWH3Fl5bbzGhai9Dah'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import our access keys:\n",
    "from credentials import *    # This will allow us to use the keys as variables\n",
    "\n",
    "# API's setup:\n",
    "def twitter_setup():\n",
    "    \"\"\"\n",
    "    Utility function to setup the Twitter's API\n",
    "    with our access keys provided.\n",
    "    \"\"\"\n",
    "    # Authentication and access using keys:\n",
    "    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n",
    "    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n",
    "\n",
    "    # Return API with authentication:\n",
    "    api = tweepy.API(auth)\n",
    "    return api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#LaAcademia\n",
      "Nordeste\n",
      "#الرياض_الان\n",
      "#SpaceX\n",
      "#LaVozMexico\n",
      "#FicaTemer\n",
      "Taylor Swift\n",
      "Jason Garrett\n",
      "Alexandre Frota\n",
      "Romero Jucá\n",
      "体育の日\n",
      "Câmara\n",
      "JJ Watt\n",
      "Max Muncy\n",
      "NFC East\n",
      "Bolsonaro e Haddad\n",
      "Matías Almeyda\n",
      "#MondayMotivation\n",
      "#CyberMondayCL\n",
      "#AirForceDay\n",
      "#Texans\n",
      "#TheWalkingDead\n",
      "#ChopOn\n",
      "#افضل_هديه_جتك\n",
      "#pazartesi\n",
      "#AgoraÉHaddad\n",
      "#TWD9naFOX\n",
      "#ConsequencesMidnightTomorrow\n",
      "#مرات_شعوري_يحتاج\n",
      "#PasapalabraCHV\n",
      "#PrayForBrazil\n",
      "#اشياء_ماتكرر\n",
      "#FelizLunes\n",
      "#IPCC\n",
      "#Mahalaya\n",
      "#技術書典\n",
      "#이_해시를_보면_무조건_프사캐입\n",
      "#彼氏からの予測変換でリア充がバレる\n",
      "#出雲駅伝\n",
      "#EnSuPropiaTrampa\n",
      "#世代がバレる系ボカロビンゴ2018\n",
      "#DeshBachaoYuvaBachao\n",
      "#SpeechlessOnSpotify\n",
      "#SegundaDetremuraSDV\n",
      "#90DayFiance\n"
     ]
    }
   ],
   "source": [
    "extractor = twitter_setup()\n",
    "trends1 = extractor.trends_place(1) # from the end of your code\n",
    "# trends1 is a list with only one element in it, which is a \n",
    "# dict which we'll put in data.\n",
    "data = trends1[0] \n",
    "# grab the trends\n",
    "trends = data['trends']\n",
    "# grab the name from each trend\n",
    "names = [trend['name'] for trend in trends]\n",
    "# put all the names together with a ' ' separating them\n",
    "trendsName = \"\\n\".join(names)\n",
    "print(trendsName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "query=\"SpaceX\" # Enter user or query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tweets extracted: 95.\n",
      "\n",
      "5 recent tweets:\n",
      "\n",
      "RT @BobBassen28: @EmilYunusov @SellBlock12 @elonmusk @_TomCross_ @SpaceX Really careful\n",
      "\n",
      "#spacex launch or alien invasion? Who knows? https://t.co/gpM0slCkYE\n",
      "\n",
      "RT @SamuelTsui: Ummm this @SpaceX display in the sky above Santa Monica right now is totally crazy and beautiful https://t.co/rDgeuItBpe\n",
      "\n",
      "RT @BobBassen28: @EmilYunusov @SellBlock12 @elonmusk @_TomCross_ @SpaceX Really careful\n",
      "\n",
      "RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We create an extractor object:\n",
    "extractor = twitter_setup()\n",
    "\n",
    "# We create a tweet list based on query:\n",
    "tweets = extractor.search(q=query, count=100)\n",
    "\n",
    "# We create a tweet list based on user\n",
    "#tweets = extractor.user_timeline(screen_name=query, count=100)\n",
    "\n",
    "print(\"Number of tweets extracted: {}.\\n\".format(len(tweets)))\n",
    "\n",
    "# We print the most recent 5 tweets:\n",
    "print(\"5 recent tweets:\\n\")\n",
    "for tweet in tweets[:5]:\n",
    "    print(tweet.text)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#spacex launch or alien invasion? Who knows? h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT @SamuelTsui: Ummm this @SpaceX display in t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RT @WhoIsGev: #SpaceX yo this was cool https:/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SpaceX Achieves First West Coast Falcon 9 Land...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT @leticiaVShep: @1Jesse1Sandoval https://t.c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @KTVU: VIDEO: SpaceX Falcon 9 launches from...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...\n",
       "1  #spacex launch or alien invasion? Who knows? h...\n",
       "2  RT @SamuelTsui: Ummm this @SpaceX display in t...\n",
       "3  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...\n",
       "4       RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA\n",
       "5  RT @WhoIsGev: #SpaceX yo this was cool https:/...\n",
       "6  SpaceX Achieves First West Coast Falcon 9 Land...\n",
       "7  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...\n",
       "8  RT @leticiaVShep: @1Jesse1Sandoval https://t.c...\n",
       "9  RT @KTVU: VIDEO: SpaceX Falcon 9 launches from..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create a pandas dataframe as follows:\n",
    "data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "\n",
    "# We display the first 10 elements of the dataframe:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_api', '_json', 'author', 'contributors', 'coordinates', 'created_at', 'destroy', 'entities', 'favorite', 'favorite_count', 'favorited', 'geo', 'id', 'id_str', 'in_reply_to_screen_name', 'in_reply_to_status_id', 'in_reply_to_status_id_str', 'in_reply_to_user_id', 'in_reply_to_user_id_str', 'is_quote_status', 'lang', 'metadata', 'parse', 'parse_list', 'place', 'retweet', 'retweet_count', 'retweeted', 'retweeted_status', 'retweets', 'source', 'source_url', 'text', 'truncated', 'user']\n"
     ]
    }
   ],
   "source": [
    "# Internal methods of a single tweet object:\n",
    "print(dir(tweets[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding relevant info to our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We add relevant data:\n",
    "data['len']  = np.array([len(tweet.text) for tweet in tweets])\n",
    "data['ID']   = np.array([tweet.id for tweet in tweets])\n",
    "data['Date'] = np.array([tweet.created_at for tweet in tweets])\n",
    "data['Source'] = np.array([tweet.source for tweet in tweets])\n",
    "data['Likes']  = np.array([tweet.favorite_count for tweet in tweets])\n",
    "data['RTs']    = np.array([tweet.retweet_count for tweet in tweets])\n",
    "data['Language'] = np.array([tweet.lang for tweet in tweets])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Tweets</th>\n",
       "      <th>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171558453989377</td>\n",
       "      <td>2018-10-08 05:36:06</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#spacex launch or alien invasion? Who knows? h...</td>\n",
       "      <td>68</td>\n",
       "      <td>1049171555408699393</td>\n",
       "      <td>2018-10-08 05:36:06</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT @SamuelTsui: Ummm this @SpaceX display in t...</td>\n",
       "      <td>136</td>\n",
       "      <td>1049171554226114561</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>612</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171553882202112</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA</td>\n",
       "      <td>44</td>\n",
       "      <td>1049171553571807232</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>3185</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RT @WhoIsGev: #SpaceX yo this was cool https:/...</td>\n",
       "      <td>62</td>\n",
       "      <td>1049171544138702850</td>\n",
       "      <td>2018-10-08 05:36:03</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>2598</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SpaceX Achieves First West Coast Falcon 9 Land...</td>\n",
       "      <td>73</td>\n",
       "      <td>1049171542616158213</td>\n",
       "      <td>2018-10-08 05:36:03</td>\n",
       "      <td>dlvr.it</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171540347179008</td>\n",
       "      <td>2018-10-08 05:36:02</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT @leticiaVShep: @1Jesse1Sandoval https://t.c...</td>\n",
       "      <td>58</td>\n",
       "      <td>1049171539373879301</td>\n",
       "      <td>2018-10-08 05:36:02</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>und</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @KTVU: VIDEO: SpaceX Falcon 9 launches from...</td>\n",
       "      <td>140</td>\n",
       "      <td>1049171537738194944</td>\n",
       "      <td>2018-10-08 05:36:01</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "1  #spacex launch or alien invasion? Who knows? h...   68   \n",
       "2  RT @SamuelTsui: Ummm this @SpaceX display in t...  136   \n",
       "3  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "4       RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA   44   \n",
       "5  RT @WhoIsGev: #SpaceX yo this was cool https:/...   62   \n",
       "6  SpaceX Achieves First West Coast Falcon 9 Land...   73   \n",
       "7  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "8  RT @leticiaVShep: @1Jesse1Sandoval https://t.c...   58   \n",
       "9  RT @KTVU: VIDEO: SpaceX Falcon 9 launches from...  140   \n",
       "\n",
       "                    ID                Date               Source  Likes   RTs  \\\n",
       "0  1049171558453989377 2018-10-08 05:36:06       Kim Bakıyor Ki      0    62   \n",
       "1  1049171555408699393 2018-10-08 05:36:06   Twitter for iPhone      0     0   \n",
       "2  1049171554226114561 2018-10-08 05:36:05   Twitter for iPhone      0   612   \n",
       "3  1049171553882202112 2018-10-08 05:36:05       Kim Bakıyor Ki      0    62   \n",
       "4  1049171553571807232 2018-10-08 05:36:05   Twitter for iPhone      0  3185   \n",
       "5  1049171544138702850 2018-10-08 05:36:03  Twitter for Android      0  2598   \n",
       "6  1049171542616158213 2018-10-08 05:36:03              dlvr.it      0     0   \n",
       "7  1049171540347179008 2018-10-08 05:36:02       Kim Bakıyor Ki      0    62   \n",
       "8  1049171539373879301 2018-10-08 05:36:02  Twitter for Android      0     1   \n",
       "9  1049171537738194944 2018-10-08 05:36:01   Twitter for iPhone      0    33   \n",
       "\n",
       "  Language  \n",
       "0       en  \n",
       "1       en  \n",
       "2       en  \n",
       "3       en  \n",
       "4       en  \n",
       "5       en  \n",
       "6       en  \n",
       "7       en  \n",
       "8      und  \n",
       "9       en  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display of first 10 elements from dataframe:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght's average in tweets: 96.16842105263157\n"
     ]
    }
   ],
   "source": [
    "# We extract the mean of lenghts:\n",
    "mean = np.mean(data['len'])\n",
    "\n",
    "print(\"The lenght's average in tweets: {}\".format(mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0f66b6d8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gtSbEYmZmHaJhtZKk97P83cJESZvVFBsOHAj8dwtiMzOzinTX5rAfcGo2HaS3wNXzGHB0M4MyM7NqdVetdAYwkjS4TcBu2Xz+s1JEvDcibmp1oGZm1j4N7xwi4nXg9Wy2V09vNTOz/s0nfTMzK3ByMDOzAicHMzMraJgcJI2R9M52BmNmZp2huzuHx0hPXUXSLXXGOJiZ2QDVXXJ4FVg5m96Vku9rMDOz/q+7QXB/Bs6XdGM2/0VJcxuUjYiY0tzQzMysKt0lh6OA75De9hakN7U1eoZSAE4OZmYDRHeD4P4G/BOApLeAfSPi3nYFZmZm1Sn7PoeNgEZVSmZmNsCUSg4R8bikoZIOIr1Deg3gBdKb4a6NiDdaGKOZmbVZ2XdIrw3cALwfmAM8C3wY+AJwv6RPRMT8VgVpZmbtVXaE9DnAmsD2EbFxRHw4IjYGts+Wn9OqAM3MrP3KJoeJwJSImJlfmM2fBOzZ7MDMzKw6ZZPDSsCiBusWAcOaE46ZmXWCssnhbmCKpFXyC7P5Kdl6MzMbIMp2Zf0ycCvwpKQbSA3SawOfJL0lbteWRGdmZpUodecQEfcB44BpwCjg46TkcAkwLiLub1mEZmbWdmXvHIiIBcDUFsZiZmYdwi/7MTOzAicHMzMrcHIwM7MCJwczMyvoMTlIWknSKZK2bkdAZmZWvR6TQ0S8BpwCrNb6cMzMrBOUrVa6B/hQKwMxM7POUXacw1eBn0paBlxPGiEd+QIRsaTJsZmZWUXKJod7su8LgPMblBmy4uGYmVknKJscPkvNncKKkDQEmAU8HRF7SdoIuJL0hrk/AZ+JiGXNOp6ZmfVO2deEXtHk4x4LPAysms2fBZwbEVdKugQ4Evh+k49pZmYl9Wqcg6QtJH1G0smSRmfLNpE0shf72ID0cqAfZvMCdgOuyYpMB/btTVxmZtZcZd8h/S7gcmAS8Hq23QxgHnAG8ARwYsljnkdq4O5KKGsCCyPijWz+KWD9BnFMBiYDjBkzpuThzMyst3rzDukdgQmkk7py664H9iizE0l7Ac9FxOz84jpF67ZvRMS0iBgfEeNHjRpVKnAzM+u9sg3S+wPHRsStWWNy3uPAe0ruZydgb0kTgeGkNofzgNUkDc3uHjYAnim5PzMza4Gydw4jgOcbrBsJvFlmJxFxUkRsEBFjgYOBWyLiENJb5iZlxQ4DrisZl5mZtUDZ5DATOLTBuknAXSsYxxTgBEmPktogLlvB/ZmZ2QooW630NeAmSTcBPye1CUyUdDwpOezc2wNHxG3Abdn0P4DtersPMzNrjbLvkL6D1Bi9EnAhqRH5dGBjYPeImNmyCM3MrO168w7pO4GPShoBrE7qfurnKZmZDUB9ednPUtJYh1ebHIuZmXWI0slB0kRJd5GSwzxgqaS7JO3ZsujMzKwSpZKDpKOB3wCLSc9FOiD7Xgz8OltvZmYDRNk2h5OBaRHxbzXLL8kelHcKcGlTIzMzs8qUrVZaE7i2wbpfkB61bWZmA0TZ5HArsEuDdbsAf2hOOGZm1gkaVitJ2iI3ewHwQ0lrAr8CngPWBvYDPgV8rpVBmplZe3XX5vAgyz8dVcDR2SdY/mmqM/BrQs3MBozuksPH2haFmZl1lIbJISJ+385AzMysc5R+fEYXSUOBYbXL/SgNM7OBo+wguHdLuljSXNII6UV1PmZmNkCUvXO4gtRl9QfAo8CyVgVkZmbVK5scJgBHR8TPWhmMmZl1hrKD4J4A3KZgZjZIlE0OXwW+JmlMK4MxM7POUKpaKSKul7Q78KikOcDCOmX8mk8zswGiVHKQdDZwHDATN0ibmQ14ZRukPwecEhHfamUwZmbWGcq2OSwBZrcyEDMz6xxlk8P5wGRJ6rGkmZn1e2WrldYCtgcekXQbxQbpiIgpzQzMzMyqUzY5TALeAN4JfLzO+gCcHMzMBoiyXVk3anUgZmbWOcq2OZiZ2SBSdpzD53sqExEXr3g4ZmbWCcq2OVzYzbquV4k6OZiZDRClqpUi4h21H2AN4J+B+4EtWhmkmZm1V6/fBNclIhYCV0l6N3ApsGuzgjIzs2o1o0H6MWB8E/ZjZmYdYoWSg6R1gS+TEoSZmQ0QZXsrzefthucuw4CRpHdK79/kuMzMrEJl2xwuopgclgJPATMi4vmmRmVmZpUqO0L6tBbHYWZmHcQjpM3MrKDhnYOkW3qxn4iICT0VkrQh8GNgNPAWMC0izpe0BnAVMBaYAxwYES/24vhmZtZE3VUrlWlHWBfYkWJ7RCNvAF+OiD9JGgnMlnQjcDhwc0ScKWkqMBU/5dXMrDINk0NEHNBonaQxpJP3XsAC4NwyB4uIucDcbHqRpIeB9YF9eHsQ3XTgNpwczMwq06sR0pI2AU4C/hV4Lpu+NCJe7e2BJY0FPgDcA6yTJQ4iYq6ktXu7PzMza55SDdKStpT0U+Bh4GPAscB7I+K8PiaGdwG/AI6LiJd7sd1kSbMkzZo/f35vD2tmZiV1mxwkfUjStcADpKv8zwHjIuKSiFjWlwNKeicpMfwkIq7NFj+bjbbuGnX9XL1tI2JaRIyPiPGjRo3qy+HNzKyEhslB0m+Be4GNgIMjYvOImB4Rb/b1YJIEXAY8HBHn5Fb9Gjgsmz4MuK6vxzAzsxWniPodjSS9lU2+QOp22q2I6LGdQNJHgNuBv+T2eTKp3eFqYAzwBHBARLzQ3b7Gjx8fs2bN6umQzSO171hVaPB/YGYDi6TZEdHjw1K7a5A+vYnxABARdwCNzrI9jpMwM7P26K4ra9OTg5mZ9Q9+fIaZmRU4OZiZWYGTg5mZFTg5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg5mJlZgZODmZkVODmYmVmBk4OZmRUMrToAs5aTqo6gtSKqjsAGIN85mJlZgZODmZkVODmYmVmB2xzMrLO5zagSvnMwM7OCjkkOkvaQ9IikRyVNrToeM7PBrCOSg6QhwEXAp4AtgH+WtEW1UZmZDV4dkRyA7YBHI+IfEbEMuBLYp+KYzMwGrU5pkF4feDI3/xSwfW0hSZOBydnsYkmPtCG2qqwFLGjb0QZ6o197+W/Xvw30v997yhTqlORQ77dTaMKPiGnAtNaHUz1JsyJifNVxWO/5b9e/+e+XdEq10lPAhrn5DYBnKorFzGzQ65TkMBMYJ2kjScOAg4FfVxyTmdmg1RHVShHxhqR/B34HDAEuj4i/VhxW1QZF9dkA5b9d/+a/H6Do0NF5ZmZWnU6pVjIzsw7i5GBmZgVODmZmVuDkYGZmBU4OZi0gaTNJ+0par+pYzPrCvZU6RHYS2Ys0AHB4zeqIiCntj8rKkHQp6W90TDZ/EPBfpG7Zi4E9IuKuCkO0GpIO7U35iPhxq2LpVE4OHUDSfsDPSCeT54BlNUUiIjZue2BWiqTHgZMi4qfZ/H8DdwNfBb4HrBEREyoM0WpIeqtmUdeJUHWWERFDWh5Uh+mIQXDGGcANwOER8ULVwVivrU324EhJ44BNgP0jYp6kacBVVQZndY3MTW8GXA1cBlxLukBbG/g08FngwLZH1wGcHDrDhsAXnRj6rReAdbLp3YF5EfFgNi/SHaF1kIh4pWta0neBiyLinFyRF4BvSloKnAPs0uYQK+cG6c5wF7Bp1UFYn/0W+L+SvgBMJV2FdtkKmFNFUFbadkCjx/U8CGzbxlg6hpNDZzgBmCzpMEnrSVq59lN1gNatL5PaGI4Bfg98PbduP2BGFUFZaU8CRzRYdyTpqdGDjhukO0BN41jdP8hgbBDrbyRtCXyQVE14edbmMI5UzbSo2uisEUmfJr198hHS06C72hz2JrVHHBQRv6guwmo4OXQASYfTICl0iYjp7YnGekvSu4DLSQ2Yb5Da8raNiD9Juhp4PCK+UmWM1j1JHyRVCW4LjAbmkV4lcFZEzK4ytqo4OXQQSVsAH2L5K89NgGd95dm5sh5JE4HPAHcCS4HxWXI4HDgxIraqMESzXnNvpQ4gaRXgRyx/5TmDdPVyBvA44CvPzrU/cGxE3CqptvrvcUq+s9eskzg5dIZzgR1J3SC7rjy7XA+ciJNDJxsBPN9g3UjgzTbGYn0gaRIpydd7QgERsV3bg6qYk0Nn8JVn/zYTOJT6vZImkboqW4eSdBqph9n9wEMUn1AwKDk5dAZfefZvXwNuknQT8HNS54KJko4nJYedqwzOenQkcGZEnFx1IJ3E4xw6Q9eVZz2+8uxwEXEHMAFYCbiQNCr6dGBjYPeImFlheNazkcDNVQfRadxbqQNI+ghwE3AH6crzYuBU0qjpScDOPsH0D5JGAKsDCyNiSdXxWM8kXQIscnfj5Tk5dAhJOwFnAjuQnsUTZE/2jIg7q4zNbCCTdABwFml0+43AwtoyEXF9u+OqmpNDh/GVp1l71Xl8d60YjE8ocIN0h4mIV4FXq47DbBDZqOoAOpGTg5kNdqtUHUAncrWSmQ1qWbVST882c7WSmdkg87E6y9YAPpF9jm1vOJ3Bdw5mZg1I+gYwJiIajUMasDwIzsyssVuBfaoOogpODmZmje1JnXEPg4HbHMxsUMteyFRrGOktcOOAQfnMJbc5mNmgJunWOouXkt4d/cvBODoanBzMzKwOtzmYmVmBk4OZmRU4OVi/JOk0SQuqjsNsoHJyMDOzAicHMzMrcHKwAUfSKpIulPSIpCWSHpN0kaRVa8qFpGMlnSFpvqTnsnIr1ZTbVdIDkpZKmilpO0kLshfTd5WZI+nsmu0Oz47xrl7GtbqkKyW9IukZSVMknS1pTk25MVm5F7L9/U7SpjVlTpL0aBb7s5JmSBq9Ir9fGxw8CM4GopVJb9M7BZgPbJhN/xz4ZE3ZLwO3AP8KvB/4FvA48G0ASesD15Pe430yMBr4CTCihXFdAXyE9MC3ecDxwPuAN7sKSFqD9FrZ54FjgCXAVOAmSe+LiFclHZrFPAX4K7AmsBt+RLWV4ORgA05EzAf+rWte0lDgMeAOSWMi4olc8TkRcXg2/bvsda37kyUH4DjSifefshcxIell4KpWxCVpK2Bv4MCI+HlW7mbgSWBxbnfHk07y20TEC1m5O4E5wGeBi4DtgBsi4uLcdtf2Nm4bnFytZAOSpM9I+rOkxcDrpKtsSFfgeTfUzD8EbJCb3xa4sSsxZH7dwrjGZ9+/6domO/ZNNbvanfS+45clDc0SzSJgdm4f9wETJZ2eVYUNuncSWN85OdiAI2k/4MfAH4EDgB2A/bLVw2uK1z5UbVlNmdGkKqD/FRFLWf4qvplxjQYWZcfIm18zvxZwECnB5D8fI1VXAVxOqlY6ELgHeFbSfzpJWBmuVrKB6ADgnoj4fNcCSbv0cV/zgFH5BZKGA++qKbeU9LC2vDX6ENc8YKSk4TUJYlRNuRdIdzD/WSfmRQAR8RZwLnCupA2BQ4BvAk8Dl9TZzux/OTnYQDQCeK1m2SF93NdM4AhJI3JVS3vXKfcUsHnNso/3Ia5ZuWNcDSBpRLavRblyN5PuCP5aU+VVV0Q8CZwp6Qhgi57Kmzk5WH82TNKkOsvvA06TdAqpOmUiMKGPxzgP+ALwG0nnkqp9ppIaqd/Klfsl8D1JJ5MSyv7AljX7uhG4qLu4IuJBSb8Bvi9pJOlO4oQ6xzuH1MPqFknfI90NrAPsAtwRET+TdCnpDuNu4CVSldM4Uu8ls245OVh/NpLUDbTW7sB3SV1Bh5NOyv9COkn2SkQ8LWlP4HxST5+HSb2BbgRezhWdBrwX+BKwEqlt4RvApbkylwIbl4jrcOD7wAWkto2LgH+QGse74logaQdSNdG5wGrAXFID9wNZsT8CRwFHZ8d7FDgqIn7V29+DDT5+ZLdZL0n6CHA7sFtE1HsXQLOPNxR4kNRecVirj2cGvnMw65Gks4A/k6p4NgX+g3R1/vsWHe8AYD3gL8CqpKv/ccCge8m9VcfJwaxnKwHfIdXpLyKNjTgh6w3UCq8ARwCbkEZU/4U0CO/eFh3PrMDVSmZmVuAr/EeDAAAAJ0lEQVRBcGZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlbg5GBmZgX/H0Vi0bgnFwJ1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_by_lang = data['Language'].value_counts()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.tick_params(axis='x', labelsize=15)\n",
    "ax.tick_params(axis='y', labelsize=10)\n",
    "ax.set_xlabel('Languages', fontsize=15)\n",
    "ax.set_ylabel('Number of tweets' , fontsize=15)\n",
    "ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')\n",
    "tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tweet with more likes is: \n",
      "RT @BobBassen28: @EmilYunusov @SellBlock12 @elonmusk @_TomCross_ @SpaceX Really careful\n",
      "Number of likes: 0\n",
      "87 characters.\n",
      "\n",
      "The tweet with more retweets is: \n",
      "RT @jarrettzackary1: They’re saying it’s a #SpaceX launch, but we all know what’s really going on... https://t.co/wxfadjOUMn\n",
      "Number of retweets: 5592\n",
      "124 characters.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We extract the tweet with more FAVs and more RTs:\n",
    "\n",
    "fav_max = np.max(data['Likes'])\n",
    "rt_max  = np.max(data['RTs'])\n",
    "\n",
    "fav = data[data.Likes == fav_max].index[0]\n",
    "rt  = data[data.RTs == rt_max].index[0]\n",
    "\n",
    "# Max FAVs:\n",
    "print(\"The tweet with more likes is: \\n{}\".format(data['Tweets'][fav]))\n",
    "print(\"Number of likes: {}\".format(fav_max))\n",
    "print(\"{} characters.\\n\".format(data['len'][fav]))\n",
    "\n",
    "# Max RTs:\n",
    "print(\"The tweet with more retweets is: \\n{}\".format(data['Tweets'][rt]))\n",
    "print(\"Number of retweets: {}\".format(rt_max))\n",
    "print(\"{} characters.\\n\".format(data['len'][rt]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series of Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create time series for data:\n",
    "\n",
    "tlen = pd.Series(data=data['len'].values, index=data['Date'])\n",
    "tfav = pd.Series(data=data['Likes'].values, index=data['Date'])\n",
    "tret = pd.Series(data=data['RTs'].values, index=data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lenghts along time:\n",
    "tlen.plot(figsize=(16,4), color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0LYaxpwUbi4i8v0QFbFvuwziVsPRx6OpwPWQlslIzqimPP/74ntvXXXcd11133X5f87vf/e4996+88so9RZ1yc3PZuXPnez5/2WWXcdlll+33PGeddRZvvPHGfuevvfZarr322j330z3L2lNf+riKV+qqIbsfDMrgqsriWuGUTXXLfCT9VKBJ4qpnZWERCa9EJTSs8b41W6ICbBds37+YjkiUKHENo/pVUDJOyxczWccu2LZM+1u9NGQC5PTf2ytXJC4Kh8OAoVoaKBJ2iUro7oCm9d6PA9CwyttxRDymxDWM6qu1TDjTbXnXXR0tU+LqmewcKJ2sGVeJH2PccmG1xBEJtz2tajxOKNUSJ21cKR85Un3991PiGjZdnW7ZiAozZbZNb7ujCjN5q2yqS1yTRQREYqOsyq3a6GwPOhIReT8lPVrieKmgBPIHKXHto/z8fOrr65W8HiFrLfX19eTn5x/xc6g4U9g0rnPLRjTjmtlqF0JBAgaOCjqSeCubBm/82lV1HKLfOYmR0inub8m2pboAJhJWA4ZA3kB/EspEpfczuzE3atQoampq2LZtW9ChRFZ+fj6jRh35e1slrmGTevFKaMY1o9WqMJMvUnuIaxcocZV4KU0VH1ukxFUkrIxxhZP8SCgTlbD2Ze/HibHc3FzKy8uDDiOjaalw2KiHq3Tshq1Ltb/VD0OPcQ3ga1WgSWKmZBzkDlCBJpGw82smNFEBO2qgvdX7sUQ8osQ1bOqroX+J248gmWnrEujuVEVhP2TnwvBjVVlY4icryxUfU0sckXBLVELTBtdNwNNxkvtpt6/xdhwRDylxDZv6VdrfmulSs39a3uePsqluOaWKLUjclFa5GVcVHxMJr0QFYF1hTk/HUWVhiT4lrmFTV61lwpmudqGr/jdoTNCRZIYR06CtSVehJX5Kp0B7i362RcLMr4SyZJw/44h4SIlrmOzeAS2bNeOa6TapMJOvUnuJ1c9V4qasyh21XFgkvBI+tcTJK4LCUqhf7e04Ih5S4homeyoKK3HNWJ3tbo+r9rf6Z9hEyMrVPleJn6ETIStHBZpEwiyvCAqH+1dZWDOuEmFKXMMk9aKlpcKZa9tS6GpXRWE/5eTB8EmqLCzxk5sPQya4PdwiEl5+JZSJcUpcJdKUuIZJfTWYrL37ECTzbFJhpkCUTXVLhVWgSeKmrEpLhUXCLlEBDT7NuLbWwa5G78cS8YAS1zCpq4ZBo90MkGSm2oWQN1AXL/xWNg12bYfG9UFHIpJepVOgZQs0bwk6EhF5P4lK2LnN+4QytRXNjyRZxANKXMOkvlr7WzNd7QI3Q6LCTP4aoQJNElOlqQJN2ucqElp+JZQlqUJQSlwlmpS4hoW1yR6u2t+asbo6YPNiLRMOwrBjXREb7XOVuCmd4o5aLiwSXnta4niduJYDRomrRJYS17DYsQk6WmGIZlwz1rbl0NUGI44LOpLMk5vvKrCqsrDETf9BbguKEleR8Bo81tU48bpwUk4eDDpKBZokspS4hkV9tTtqxjVz1aowU6BUoEniqrRKlYVFwiwnDwb6lFCqJY5EmBLXsKhLJa6acc1YtQuhX+HePSjirxHTXLXFHRuDjkQkvUqroGE1tDUHHYmIvB/fWuJUutcDXaSVCFLiGhb1qyB3ABSPCDoSCcqmBe4NZpZ+LQNRpgJNElNlVYCFLe8GHYmIvJ9EJdT7kFCWVEDbDlfFWCRi9A45LOqrXR8vVZPNTF2drupnqrqt+G/4sW6Pkfa5StykKgtrubBIeCUqob0ZWrZ6Pw5oubBEUq8TV2NMtjHmbWPME8n75caY140x1caYPxhj+iXP5yXvr0x+fmyP57gxeX65Mea8dH8zkVanVjgZrb4aOnftnfUT//UrgKHHqLKwxE/xCOhfogJNImGWSLWq8TihTKgljkTX4cy4Xgcs7XH/h8CPrbXjge3AlcnzVwLbrbWVwI+Tj8MYMwn4BHAscD7wM2NMdt/Cj4nONmhcD0NUmCljbVJhplBIFWgSiRNj3HJhJa4i4eXXTOjAoyArVzOuEkm9SlyNMaOADwG/Tt43wFnAI8mH3Ad8JHn74uR9kp8/O/n4i4GHrLVt1to1wErg5HR8E5HXsBqwqiicyWoXQm6BLl4ErWwatGyBHbVBRyKSXqVVsHWp6xctIuEzcBRk53mfUGbnuH6uSlwlgno743oH8J9Ad/J+Ami01nYm79cAI5O3RwIbAJKfb0o+fs/5A3xNZktVFFYP18xVuwBKp0CWFiEEaoQKNElMlVZBV7vrFy0i4ZOVDSXj/FnCm6osLBIxh0xcjTEXAVuttfN7nj7AQ+0hPnewr+k53lXGmDeNMW9u25YhFc9SPVzVBiUzdXe5oina3xq84ZMBo32uEj9lyQJNWi4sEl6JCn9mQlMJcnf3oR8rEiK9mXE9DZhtjFkLPIRbInwHMMgYk5N8zChgU/J2DXAUQPLzA4GGnucP8DV7WGvvstaeaK09cejQoYf9DUVS/SooLIX84qAjkSDUr4KOnaooHAZ5hTDkaFUWlvhJVEJOf1e9XETCKVEB29e4C9qejlMJXW2wo8bbcUTS7JCJq7X2RmvtKGvtWFxxpeestZ8EngcuST7sCuDR5O3HkvdJfv45a61Nnv9EsupwOTAemJe27yTK6qq1tzGT1aowU6iMmKalwhI/Wdmu5ZNa4oiEV6LSLelv2nDox/Z1HFBlYYmcvvRx/X/Al40xK3F7WO9Onr8bSCTPfxm4AcBa+y7wMLAEeAr4grXW40tKEVGvVjgZrXYh5OTDkAlBRyLgLiA0b/K+l56I38qq3Iyr3W+XjoiEgV+Vhf1qvSOSZoeVuFprX7DWXpS8vdpae7K1ttJae6m1ti15fnfyfmXy86t7fP33rLUV1toJ1ton0/utRNTOeti1XYlrJtu0wO2tzM459GPFe6m9xlouLHFTOgXamqBxXdCRiMiB+DUTWlTmOhmoQJNETF9mXCUdUle7tFQ4M3V3uxlX7W8Nj9Ip7qjlwhI3pcntCFouLBJOA4ZCXrH3M6HG+FcISiSNlLgGLVVRWDOumWn7GmhvVkXhMMkvdr+PqiwscTNsIpgsFWgSCSs/E8oSJa4SPUpcg1ZXDVm5MGhM0JFIEDa97Y4qzBQuZSrQJDHUr8BVzVZLHJHw8iuhTFTC9nXQ1eH9WCJposQ1aPUroaRc+xszVe0CyO7nZkIkPMqmuqqOO+uDjkQkvUqrtFRYJMwSldC4ATp2ez+O7XLJq0hEKHENWv1KSGh/a8aqXehaVGTnBh2J9JTac1z7drBxiKRb6RRXNXtnXdCRiMiBJCoBC9vXejyOKgtL9ChxDVJ3l6voNkT7WzOStS5x1f7W8CmtckctF5a4KUv+bGu5sEg4+ZVQpmqrNKiXq0SHEtcgNa5zjaZVmCkzbV8Lu5tUUTiM+g+CweVqiSPxk7ooowJNIuHkV+JaUAL9B2vGVSJFiWuQUn26tFQ4M6Wq1qowUziNUIEmiaGCEigepX2uImGVPxAGDFNlYZEDUOIapLpkKxz1cM1Mmxa4itLDJgUdiRxI2VS3KqK1IehIRNKrrEpLhUXCLFG5d3LD83FWez+OSJoocQ1SfTXkD4KCRNCRSBBqF7pqwjl5QUciB5Lae6xZV4mb0inuwmn7zqAjEZED8auXa6ISdtRAe6v3Y4mkgRLXINVVuxcNY4KORPxmrVsqrP2t4ZVawq3EVeKmtAqwsGVJ0JGIyIEkKmDnVlcHw9Nxxrljg2ZdJRqUuAapfpWWCWeqpg2wa7sqCodZQQkMGr13L7JIXOypLKyLMiKhlCra6fVyYVUWlohR4hqUthbXS08VhTNTqlqtEtdwK1OBJomhgUe5bSqqLCwSTnsSSo9nQkvUy1WiRYlrUFIvEppxzUy1C8Bkw/Bjg45EDqZsqnvj4PVyLRE/GeP2uaqysEg4DS4HjPcJZV4hFJb6UwhKJA2UuAYl9WKkGdfMlCrMlJsfdCRyMCNUoEliqrQKti6Brs6gIxGRfeXmw6Cj/CvQpMRVIkKJa1DqVwIGSsYFHYn4zVq3VFjLhMNPlYUlrsqqoHO3q24vIuGTqPQpcVUvV4kOJa5Bqat2V9Ny+wcdifhtxyZorVNF4SgYMASKR+3dkywSF6XJAk1aLiwSTqmZUGs9HqfCvSfZ1ejtOCJpoMQ1KPXVWiacqVJValPtViTcRqhAk8TQkPGQnQeblbiKhFJJBbTtgJ3bvB1HlYUlQpS4BsFadxUtocJMGWnTAjBZMHxy0JFIb5RNdcuo2pqDjkQkfbJzYfgkJa4iYbWnJY7Hy3j3VBZW4irhp8Q1CM2bob1FFYUzVe1CGDIB+hUEHYn0Rtk0wGpJpcRPaZVrieP1UkQROXwJnxLKEp8qGMt7dXdpefYRUOIahFQxDC0Vzky1C7S/NUpUWVjiqnQK7NoOTTVBRyLpYIw7du4KNg5Jj0GjISvX+4QyJ8+NpRlX76WKcz79dbh9Evz3BLXbO0w5QQeQkeqUuGas5s3QskUVhaOkcBgUle3dmywSF6l99psXuWKBEm1Zue5ou4ONQ9IjK9t1nlBl4ejbvhbe+T9Y9H9Qt9z9rg4aDS2boa0F8gcGHWFkaMY1CPWrIKc/FI8MOhLx2yYVZoqksmmqLCzxM2wSYNxyYYmXjt1BRyDp4FePVb8qGGeS1gZ449dw93nwP1PhuVugIAEX3QFfXQGnXRd0hJF0yBlXY0w+8CKQl3z8I9bam4wx5cBDQAnwFvApa227MSYPuB84AagHLrPWrk0+143AlUAX8O/W2qfT/y1FQKqicJauG2Sc2gWAcUv0JDrKpsKKp6B9J/QbEHQ0IumRV+j+Fmn/dvzUzIPymUFHIX2VqICVz7r9kFnZHo5TCe3NroJx4TDvxom79lZY8aSbWV35DHR3wtCJcPZNMOUSN8sqfdKbpcJtwFnW2hZjTC7wsjHmSeDLwI+ttQ8ZY36BS0h/njxut9ZWGmM+AfwQuMwYMwn4BHAsMAJ41hhztLW2y4PvK9zqqrXHMVPVLnRFufIKg45EDseIZIGmze/A6FOCjkYkfcqqYMO8oKOQdFvzohLXOEhUQleb24c+eIx34+ypLLxSievh6u6CNX93yerSx90FgKIRcMrnoeoy10Eitf9c+uyQU37WaUnezU1+WOAs4JHk+fuAjyRvX5y8T/LzZxtjTPL8Q9baNmvtGmAlcHJavoso6WyHxnXa35qpNi3Q/tYoKlOBJomp0inQtMEta5P4WPNi0BFIOiR6JJRxGCcuUkWWnvqaK7L024/Csifg2IvhisfhPxbDube411clrWnVq+JMxphsYD5QCdwJrAIarbWdyYfUAKkNmyOBDQDW2k5jTBOQSJ5/rcfT9vyanmNdBVwFMHp0DKfUt69xhRPUwzXztGyF5k2abY+iolIYMEz7XCV+SqvccfM7MO70YGOR9Nk43/WezisKOhLpiz29XFdB5dnejbOngrEqCx9Uwxp45xF452GoW+H+zY4+D6r+CcafB7n5QUcYe71KXJPLeacZYwYBfwYmHuhhyeOBLi3Yg5zfd6y7gLsATjzxxPjtEk9VFB6iGdeMk5qtU2Gm6DHGXXBQZWGJGyWu8TN2Bqx9Cda/BuNnBR2N9EXhcOhXCA0eJ5RZ2a6fq2Zc97ezHt79k6sKvOF1d27MaTD9CzDpYug/ONj4MsxhtcOx1jYaY14ATgEGGWNykrOuo4BNyYfVAEcBNcaYHGAg0NDjfErPr8kc6uGauVKzdak3ihItZVNdkYz2VuhXEHQ0IulRONS1e9qsAk2xcdQH3BvsNX9X4hp1xvjXqsavCsZR0N4Ky//mktWVz7oiS8MmwTnfhsmXqH1YgHpTVXgo0JFMWvsD5+AKLj0PXIKrLHwF8GjySx5L3n81+fnnrLXWGPMY8DtjzO244kzjgcyrCFG/0i05VM+mzFO7wBVAyC8OOhI5EmXT3DL/Le/CUScFHY1I+pRWqbJwnOT2h1Ena59rXCQq3dJvz8epgJVzobs7M7tedHW6iz3vpIostSSLLF3jiiyVTg46QqF3M65lwH3Jfa5ZwMPW2ieMMUuAh4wxtwBvA3cnH3838FtjzErcTOsnAKy17xpjHgaWAJ1zrKp7AAAgAElEQVTAFzKzovBKV1VWMk/tQjgq8+qRxUZqb3LtAiWuEi+lU9ysQscul/RI9JXPhBd+4IpuFZQEHY30RaIS3v0zdLZBTp5345RUuArGO2oyp22Lte5v+qKHYfEfoWUL5A2EYz/qktUxp2VmEh9ih0xcrbWLgOMOcH41B6gKbK3dDVz6Ps/1PeB7hx9mjNRXwzEXBR2F+G1nvavcefJVQUciR6p4pGsern2uEjdlVWC7YOsSGHlC0NFIOpTPhBe+D+tegYkfDjoa6YtEpVvts30tDJ3g7TjglgvHPXFtWONmVhc97N6XZ/eD8ee6ZHX8uSqyFGKHtcdV+qi1AVrrtb81E6WSHRVmii5j3HLhTWqJIzHTs0CTEtd4GHkC5Ba45cJKXKOtZ49VXxLXlVBxpnfjBGVnnZu5XvQw1CR3Ko75IJx6LUyarSJLEaHE1U+pTe9aKpx5lLjGw4hp8Mr/QMduXZGV+Bg0BvKKtc81TnL6wZhTYfXfg45E+ioxzh29LtBUVAq5A+JVoClVZGnRw7BqbrLI0rEqshRhSlz9tKeisBLXjFO7EAaPhf6Dgo5E+qJsqvvDt/VdzUxJfGRluX2uqiwcL+Uz4ZlvQfNml5RINPUfDAVDvE9cjXFJstetd7yWKrK06GFY9oQrslQ80rWvmfJPKrIUcUpc/VRXDVk5MHhM0JGI3zYtgBH7bRWXqClLFWhaqMRV4qV0Crx1P3R3uZ6OEn3lM91xzUtQdcDSIxIViUqoX+3POLUR3A5jLWx6Cxb9nyuytHOrK7I0+WNu3+roU1VkKSaUuPqpvtrNumXnBh2J+GnXdmhcByd+JuhIpK8GjYb8QXt78orERWkVdLS6ZYJDjw46GkmH0irXem/N35W4Rl2i0lX+9lpJBSx5DLo6ovFetWG1S1bfedjNSGf3g6PPczOrKrIUS0pc/VS/SsuEM1Hq6qX2t0afMW6fqyoLS9yUpQo0LVLiGhdZ2TB2hvq5xkGiAhY8AG3NkFfk4TiVrsL49nUwJKSFRHfWweI/uWS15g3AwNgPwqn/riJLGUDz5n7p7nKJa1hfCMQ7qdm51DLTKGhcD6ueDzqKcCqbBluWQGd70JGIpM+QCZCVq32ucVC3Yu/t8pluxc/2tYGFI2nQs1WNL+N4vJ/2SKx7FR68FG47Gp683vWdPudm+I/F8Okn4IQrlLRmACWufmna4Bo7qxVO5qldCANHR6sJ/Gu/gAcvccuc5b3KpkJ3h+t5KRIXOf1g2ERVFo6yzl3uuOgPe8/13Ocq0eVXQpno0XonbJ7/Hqx/zbWv+fw/4POvwAe/BANHBR2Z+EiJq19SLwJaKpx5ahfAiIgtE+7udB/Lnwo6kvAZkSrQpOXCEjNlVa6Xq7VBRyJHortr/3NDj4EBQ7VcOOpKyt3R6xnXghI3axnGysK22104nnUzDD826GgkIEpc/VKXTFzVwzWz7G5yxQOitEy4p6WPBR1B+Awud9UKo1h5UeRgSqugtQ6aa4OORNLFGDfruuZFXZCIstz+MPAof2ZCE5XhnHEVQYmrf+qr3ZvdAUODjkT8lFp2F9XEdeVcVwxC9jLGzUypsrDETWmyQJOWC8dL+Uxo2exa8kl0JSr8mQktqfB+ZlfkCClx9UtdtXvRMSboSMRPqeWkUa0o3NUG1XOCjiJ8RkyDLe+6lgESLV2d8Pz3g44inFLL7za/E2wckl7lp7vjmr8HG4f0TWom1OuZ80Ql7NgI7a3ejiNyBJS4+qV+lZYJZ6LahVA8EgojONOeVwwDhrmebvJeZdNcUr9tWdCRyOF64fvw9x/Cu38JOpLwyS+GknGwWcvgY2XwWFcgUIlrtCUq3faj1nqPx0kWaGpY7e04IkdAiasf2nfCjhoVZspEmxZEd5mwyYJjPgTVz7iy87JX6v9Uy4Wjp7UhefT4zV9UlVZpxjVu9uxzfQm6u4OORo6UKguLKHH1RWqvgHq4Zpa2ZvfCPyKiiSu4Zt4dO91eV9mrZBz0K1KBJomf0imu5+fupqAjkXQqnwm7G2GLLkpEll8JZUlqxlX7XCV8lLj6oT5ZEEE9XDPL5ncAG939rQBjZ0D+IFUX3ldWlivQpJY4Ejep1yvNusZL+Qx3VFuc6Bo4GrJyvE9c8wqhsFQFmiSUlLj6IfXLn7qKJZkhtYw0qkuFAbJz3XLh5U9BZ3vQ0YRL2TTYvNgV+xGJi9Ip7qjENV6KR7jtSkpcoys7x7VjU0scyWBKXP1QV+36b/UrCDoS8VPtQigqg6LhQUfSNxNnQ1uTCnvsq2wqdO6CuhVBRyKSPkWlriibWuLET/lMWPcPVUOPskSlPzOhCbXEkXBS4uqH+motE85EtQuivUw4peJMt59zyaNBRxIuqb3LWi4scVNWBZuVuMZO+Uxob4FNbwcdiRypRIWr9ut1ka1EBbTWwa7t3o4jcpiUuHrNWnfVSolrZmnf6WbiorxMOCUnD44+D5b9Vctie0pUQu4AFWiS+Cmd4lo9dbYFHYmk09jUPletnomsRCV07nZ9Vr0eB6BeLXEkXJS4eq1lK7TtUA/XTLN5MdjueMy4gqsuvKsB1r0SdCThkZXt3uCrJY7ETWkVdHfC1qVBRyLpNCABw6don2uU+dYSJzmOKgtLyChx9ZoqCmem1PLRKLfC6anyHMjpr+rC+xoxzRWx6e4KOhKR9FFl4fgqnwnrX4eO3UFHIkfCr8R18FjXy10FmiRklLh6rU6Ja0aqXegKnBSVBR1JevQbAOPPgaVPqIF9T2VTXZ9b/XGXOBlcDv0Ktc81jsadDl1tUDMv6EjkSBSVui0qXhdOyslzRUX1t01C5pCJqzHmKGPM88aYpcaYd40x1yXPlxhjnjHGVCePg5PnjTHmJ8aYlcaYRcaY43s81xXJx1cbY67w7tsKkfqVkJPvXgAkc2xKFmYyJuhI0mfixdCyWW94ekrtYdZyYYmTrCwYPlmVheNo9HQw2bBa+1wjyRhIjPOxJY6WCku49GbGtRP4irV2InAK8AVjzCTgBmCutXY8MDd5H+ACYHzy4yrg5+ASXeAm4APAycBNqWQ31upXuv6tWZrczhgdu1xhk7gsE045+jzI7gdLtFx4jyFHuyXUKtAkcVM6BbYs1gqLuMkvhpHHa59rlPnVYzXVEsda78cS6aVDZlPW2lpr7VvJ283AUmAkcDFwX/Jh9wEfSd6+GLjfOq8Bg4wxZcB5wDPW2gZr7XbgGeD8tH43YVRXDUO0TDijbHkXbFd8CjOl5BfDuDNh6eP6Q5aSnQOlk9USR+KnrMq1Ttm+JuhIJN3KZ8LG+dDWHHQkciQSldC4HjrbvR+nvdkVGRUJicOaBjTGjAWOA14Hhltra8Elt8Cw5MNGAht6fFlN8tz7nd93jKuMMW8aY97ctm3b4YQXPp3tsH2t9rdmmlSPvDi0wtnXpNnQtF59AHsqm+aWVGpmSuKktModtc81fspnuour614NOhI5EolK9//XuM7jcSrcUZWFJUR6nbgaYwqBPwJfstbuONhDD3DOHuT8e09Ye5e19kRr7YlDhw7tbXjh1LjOvbgk1Aono9QuhIIEDBwVdCTpN+FCtz9K1YX3Kpvqrko3qN+dxMiwiZCVo32ucXTUB9y2D/VzjSa/KguXVPgzjshh6FXiaozJxSWtD1pr/5Q8vSW5BJjkMbWWoAboWYloFLDpIOfjK1VRWD1cM0ttDAszpRSUQPkMt89Vy4Wd1F5mLReWOMnJg6HHaMY1jnL7u+RV+1yjqWScO3qdUA4aDVm5SlwlVHpTVdgAdwNLrbW39/jUY0CqMvAVwKM9zv9LsrrwKUBTcinx08C5xpjByaJM5ybPxdeeHq4VwcYh/ulsg61L47lMOGXibLd0aOuSoCMJh6HHQHaeEleJn9Ip6uUaV+Uz3f9ta0PQkcjhKihxq7q8Tiizsl2SrMrCEiK9mXE9DfgUcJYxZkHy40LgVmCWMaYamJW8D/A3YDWwEvgVcA2AtbYB+C7wRvLjO8lz8VW/EgqGQP/4F0+WpC3vQndn/Aoz9XTMRYBRdeGU7FwYfqxa4kj8lFZByxZo3hJ0JJJu5TMBC2tfDjoSORIlFf4klAmfxhHppZxDPcBa+zIH3p8KcPYBHm+BL7zPc90D3HM4AUZa3UotE840qVm3uLXC6alouOsFuPQxOPPGoKMJhxHT4J0/coBt+yLRVdajQFPRrGBjkfQacTzkDnDLhSfNDjoaOVyJSlj9vA/jVMDKua74oNo6Sgjop9BL9dWqKJxpahdC/iAYNCboSLw1abZbKlynvS+Am2Fva3JVxEXiYvhkd9Q+1/jJ6Qdjpmufa1QlKqC5FtpaPB6nErraYEeNt+OI9JISV6/saoSd25S4ZppNMS7M1NPED7vj0kcP/rhMkdrTrOXCEif9kxfhVFk4nspPh7rl0Lw56EjkcKXeW3pdzV6VhSVklLh6JbUnQEuFM0dnu5uFjPMy4ZSBo2DkCdrnmjJskqu+WLsw6EhE0qusSgWa4qp8pjtq1jV6/GqJs2cc7XOVcFDi6pU9FYWVuGaMbUuhqz3ehZl6mjjb7end7nET9CjI6QfDJ6mysMRPaZWrIt7WHHQkkm6lU9zWFvVzjZ49LXE8TiiLSt1eaCWuEhJKXL1SVw0mGwaPDToS8UtqmWicW+H0lCrosfTxYOMIi7JpWios8VOaKtC0ONg4JP2ysmHsBzXjGkX9CqB4lPczrsZAYpyWCktoKHH1Sn01DB7jZmIkM9QuhLyBe6+Exl3JOBg+xVUXFrdEvLsj6ChE0qt0ijtquXA8lZ8OjetVWC6KEhX+JJSJSrfqQiQElLh6pX6VlglnmtoFbj9Y3Asz9TRpNmx4HXbUBh1J8DJlibhkluIRUJCAzdq/HUva5xpdiQo3SWI9bsOWqHRbgjrbvR1HpBeUuHqhu9slrirMlDm6OtxSukxLXiYmlwsveyLYOMJg2LFBRyCSfsa45cKacY2noRNgwDAlrlGUqITdTdDa4O04JRVgu6BR9SwkeEpcvbCjBjp3qRVOJtm23PU6y5T9rSnDjoEhR8MStcUhNx8GlwcdhUj6lU6BrUvdBTqJF2PcrOuaF72fuZP02tMSx+NlvKosLCGixNULqT0HSlwzR6qabCa0wtnXxNmw7hXYWRd0JMHb8/+vN4ASI2VTXcX0bcuCjkS8UD4TWrZA3YqgI5HD4VtLHPVylfBQ4uqFuuQvt5YKZ47ahdCvcG+z7kwyaTbYblj216AjCV5qxr2jNdg4RNJJBZriTftco2nQaMjK8T6hLCiB/oOVuEooKHHti+VPwrpX9z9fXw39iqBwuP8xyXutfx06dns/zqYFbh9YVgb+SpVWwaAxqi4MmbfHOZ3aWqBmftBRpNe8X0H7zqCj6LtEJeQWQO2ioCNJr7YWeOPXQUcRvJJylwSpn2u0ZOe6louqLCwZJAPfZafRnG/CvLv2P19X7ZZWZFJ12TBa/hTcc64/hYPqq91+zziw1s2s5BX37vHGuFnX1X+HXY3exhZ2wyYFHUF0PXoN/OZ86OoMOpL02LoU/vZV+PPVQUfSd1nZMPxY2ByzxPWPn4W/fgW2aYms2+f6EnR3BR2JHI5EpT97T0sqtMdVQkGJqxdUUTh4nW3w9I3udscu78ez3ZAdk569Sx+D9f+AD17X+6+ZeLHrYbriKe/iioLs3KAjiKZVz7kCX13txGZ/cOoiTsu2YONIl9Ip7oJWnAr47Ez+3+xuCjaOMCg/HXY3ajl41KQSyu5ub8dJVMKOjdCubTASLCWu6daxC5o2qIdr0F77OTSsDjqK6OnYDXO+4WYOj/90779u5AlQNAKWaLmwHKbONvjb9UFHIYdSWgVtO2D72qAjES+MneGO2ucaLYkK18WieZP344DeV0nglLimW/0qwMIQVRQOTPMWePFH7o2WHJ7X7oTG9XD+DyA7p/dfl5UFEz8Mq+a6fWMivfXqnW6P1uhTg45EDqYs+XqqGbl4Ki5zrc2UuEaLKgtLhlHimm711e6oVjjBmXuzm8U597tBRxItzZvhxf+GCR+CcWcc/tdPmg2du6F6Trojk7hqqnEXmY65CCrPCjoaOZhhk8Bkx2+fq+xVPhPW/UP9eqPErx6rJUpcJRyUuKabergGq2Y+LHgQpl+Tma1p+mLud9wewyNN+EdPhwFDVV1Yeu/pr7k9k+f/IOhI5FBy+7sZuc2Lg45EvFI+Ezp2wsa3go5EequozFX89jpxzSt0Y2mpsARMiWu61a2E4pHQb0DQkWSe7m548j9dG6KZ2jN3WDa+5RL+Uz6/d0nQ4crKhmM+BCvm+FMQS6ItVZBp5lddKw4Jv4ISaNdWgNjSPtfoycpKFmjyYSbUr3FEDkKJa7rVV2u2NSjvPAwb34Szb4K8oqCjiQ5r4akb3WxpXxP+SRe7K/arnktPbBJPqYJMJRVw6rVBRyMi4C5MlE5RP9eoSfiUUPo1jshBKHFNJ2vdL7USV/+1tcAzN7nqtlMvDzqaaFn8R9jwGpz1TcjvZe/W9zN2BuQPUnVhObhUQaYL/gty8oKORkRSyk+HDfO0aiZKEpWu2rfXe5MTldBaD7u2ezuOyEEocU2nnXWuH5x6uPrvpf+Gls1w/g/d0hnpnfZWl/CXToHj/rnvz5ed65YLL38SOtv7/nwSP40bXEGmiR+G8ecEHY2I9FQ+E7raXPIq0ZCoANsF29d5Pw5Avfa5SnD0Dj+d9lQUVuLqq4bV8OpPoeoTcNRJQUcTLf/4X9hRk0z4s9PznBNnQ1uT9knJgc35uludcp4KMomEzphTXfVovX5Hh28tcXwaR+QgDpm4GmPuMcZsNcYs7nGuxBjzjDGmOnkcnDxvjDE/McasNMYsMsYc3+Nrrkg+vtoYc4U3307A6lKJq6rZ+mrONyErF875dtCRREvTRnjlDrcvdexp6XveijOhXxEsfTR9zynxsHJuj4JMRwUdjYjsK6/IbbnRPtfo8CuhHDwWTBY0eFzBWOQgejPjei9w/j7nbgDmWmvHA3OT9wEuAMYnP64Cfg4u0QVuAj4AnAzclEp2Y6V+JWTnqUKmn1Y9D8uecG+Ei8uCjiZanv02dHfBrDT3u83Jg6PPg2V/ha7O9D63RFdnm6v6rYJMIuFWPtNVmt+9I+hIpDcKSqD/YO8Typw8GHiUZlwlUIdMXK21LwIN+5y+GLgvefs+4CM9zt9vndeAQcaYMuA84BlrbYO1djvwDPsnw9FXvxJKxqVvyaUcXFcHPHWDuwp4yjVBRxMtG95wVZhP/SIMHpP+55802xVxWP+P9D+3RNOrP3WvkReqIJNIqJXPdHsm178adCTSW4lKnyoL+zSOyPs40j2uw621tQDJ47Dk+ZHAhh6Pq0mee7/z+zHGXGWMedMY8+a2bduOMLyA1FXDEFUU9s2b98C2ZXDe9yE3P+hooqO7G576f1BYCh/8sjdjVJ4DOf1VXVicxg3w4m2uIFOlCjKJhNpRJ7vVY9rnGh2JSqj3YQlvotIVZ7LW+7FEDiDdxZnMAc7Zg5zf/6S1d1lrT7TWnjh06NC0Bueprg7YvkatcPyysx6e/x6MOxMmXBh0NNHyzsOwcT6ccxPkFXozRr8BrmLs0sddoiyZ7emvqSCTSFTk9nfJq/a5RkeiAnZshPad3o/T3gwtW70dR+R9HGniuiW5BJjkMfUTXAP0rLgxCth0kPPx0bgeujtVUdgvz9/iereefyuYA10XkQNqa3F7W0cc76owe2nixa5FUc0b3o4j4bZyLix9TAWZRKKk/HTY/A607rtTTEKpJFkUtMHjVjV7WuJoubAE40gT18eAVGXgK4BHe5z/l2R14VOApuRS4qeBc40xg5NFmc5NnouPVEVh9XD13uZ3YP69cPK/wbBjgo4mWl65A5prXcLvdb/bo8+D7H4uaZHMpIJMItFUPtMd174UbBzSO363xFFlYQlIb9rh/B54FZhgjKkxxlwJ3ArMMsZUA7OS9wH+BqwGVgK/Aq4BsNY2AN8F3kh+fCd5Lj729HDVUmFPWQtP3gD5g+CMGw79eNmrcb3r2zr5Ehj9Ae/Hyy92S7mXPKb9MJlKBZlEomnk8ZA7QPtco6JknDt6nbgOPMpdkNaMqwQk51APsNZe/j6fOvsAj7XAF97nee4B7jms6KKkrhr6l7iy5OKdJX+BdS/Dh2535d+l9575FmBg1s3+jTlpNlQ/DbULYMRx/o0rwWvcAH//kQoyiURRdi6MOVWJa1TkFULRCFc4yUtZ2TC43J9CUCIH4PFawQxSv0rLhL3WsQvmfBOGT4ETPh10NNGy7h/w7p/htOtg4Cj/xp1wIZhsVRfORE9/zR1VkEkkmspnQt0K2FEbdCTSG4kKH1viKHGVYChxTZf6ahVm8torP4GmDXDBreqVezi6u12/2+KRLnH1U0EJlM9w+1y1XDhzrHzW/Z+ffr0KMolE1bjT3VH7XKPBt16u41wRKHUMkAAocU2H3TugZYt6uHqpcQO8/GOY9BEY+8Ggo4mWBQ9C7UI452boV+D/+BNnuz+mW5f6P7b4r7MN/pYsyDT9i0FHIyJHavgUV09itdriREKiEnY1eF8JOlEJXW2wo8bbcUQOQIlrOqSucEW+MFOIZ8SevQmwcO53g44kWnbvgLnfgVEnw5RLgonhmIsAo+rCmeLVn7qKkyrIJBJtWVluxcyav2vFTBTsqSzs8TJevyoYixyAEtd02JO4RnipcPMW6NztbtuuYGPZ17p/wOI/wmlfgkGjg44mWl76b9i51S2vDqrfbdFwGD1d+1wzwZ6CTLNVkEkkDspPd1t0tq8NOhI5FL96rKZ6xmqfqwRAiWs61FWDyYKS8qAjOTKrnoNfnLb3vg3RvoXuLtcHsniU//szo65hNbz2M5h6OYw8IdhYJs2Gre/qD13cPX2ju0By3veDjkTk0Lo7/Btr7Sv+jZVOqX6uqi4cfoPGuGKIXieuRaWuVZL+nvdN2CaJIkKJazrUV7uZwKgti+vqdMtIf/sxKEgEHc2Bvf1b2PwOnPudYPZnRtmcb0JWLpx9U9CRuJYoAEseDTYO8c7KZ2Hp4zDzqyrIJOG3+R239x9gd6O3Y1U/C/deCK/e6e04XhhyNBQOV+IaBTn9YPAY7xNXY/yrYBxn7/7FHf/6FejYHWwsEaLENR3qV0ZvmXBTDdz7IbeU9PhPwb89H3RE+9vV6BLr0afCsR8LOppoWfMiLHsCZvwHFJcFHY1rwTPyBO1zjatUQaZEpQoySbh1dcDf/wvuOmPvufad3o65fY07RnGGyhg367rmRe1zjYJEpasx4Pk4Slz7LPW6s+JJ+PU5UKd/z95Q4tpXtjt6PVyXPwm/+CBsWQwf+zXM/t9wzmb+/YeuOl6Q+zOjqLsLnroRBo4OVxIxcTZsehsa1wcdiaTbP/7XvVm6QAWZJMS2vAu/Phue/56rUC+9Uz7T1UrYtjzoSORQ9vRY9fgiQ6LS/S3vbPd2nEww83rYsRHuOh0W/V/Q0YSeEte+aq6FjtZoVBTubHcJze8/AQOPgqtfhKpLg47qwLYth3l3wQlXQNnUoKOJlrfucxclzv0O5PYPOpq9Js12x6WPBxuHpFfjBnjxtmRBprODjkZkf10drmjYL0+HHZvgsgfgkruDjio6tM81OhIV7j1p82aPx6l0ezQb13k7TiYYfx587mUonQJ/+iw8+kVobw06qtBS4tpXddXuGPbEtWE13HOuK9Zz8tXw2Wf3VqALG2tdgp07AM76ZtDRRMuuRnjuFre8OmwzCiXjXF9AVReOFxVkkjDbsiQ5y3qLu3h2zet799xL7wwe6+p4rFE/19Dzq1VNiU8VjDPFwJFwxRMw4yvw9gPwq7Ng67KgowolJa59tSvZ6DnMS4UX/wl+MdMlr5c9GP7+iiueglVz4YwbYMCQoKOJlhd/FO7l1ZNmw4bXvb8aLP5QQSYJq65OtxLglzOhaSP80/1wyT0wIKSFCMOufCasfdltRZHw8iuhTKglTtpl58DZ34J//iPs3Aa/OhPefjDoqEJHiWs69CuEohAUwNlXxy54/EvwyGdg2DFuKcLEi4KO6uA62+Dpr7lKhif/W9DRREvdSnj9F3DcP4d3efXE2YDVcuE4UEEmCautS+Huc+C577q/eV94HSZdHHRU0VZ+hqu+vPmdoCORgykeCTn53ieuBSXQf7BmXL1QeTZ8/hVX0PLRa+DPn4O2lqCjCg0lrumQqAjf7Na25fCrs2H+b+C0L8FnnnRLfcLutZ+7meHzfwDZuUFHEy1zvg45/d0Vu7Aadoy7KKHqwtGngkwSNl2drlL+L2e6wjGX3geX3quVO+lQPsMdtVw43LKy3KyrHzOhiUolrl4pKoV/eRTOuBEWPuRmXzcvDjqqUFDimg5h29+64Heu1H/LFvjkH2HWzdFIAps3u6WuR18AlecEHU20rJzrlljP/CoUDgs6moObOBvWvgI764OORI5U43oVZJJw2boM7p7lWqhNuMDtZT02ZPv8o6yoFIZMUIGmKEj4mLg2rPZ+nEyVle22zF3xGOxucnv13/xNxrelUuKaDmHp4drWAn+6Gv7yebfE4HMvw/gIJYBzv+OWH573vaAjiZauDre8enA5nPL5oKM5tEmzXTXC5X8NOhI5Uk9/TQWZJBy6OuHlH8MvZ8D2tXDJb9x+1sKhQUcWP+UzYd2r8WuB0rgeXvghPPOteCQFiUro3OX9OCUVro2L132QM135TPjcKzDmVHjiS/DHK2H3jqCjCowS13QIQ2Gm2kWuB9Q7D8MZX3NLDIpDuO/2/dTMhwUPwvRrwlvtOKzevAe2LYNzb4nGks3SKhg0RtWFo6o6VZDpehVkkmBtW+6q5T/7bTj6fPjCPJj8saCjiq/ymdCxEza9FXQkfdexC955BO6/GO6oghe+D6/8D3R3Bh1Z3/m1CjD1Xk2zrnG0g6wAABvVSURBVN4rHOpWUJ79LXj3L+79/qYFQUcVCCWu6RBkomUtzPsV/PocN+P6L4/BGf/PLTGIiu5uePI/oXC4ezMsvdfaAM9/372hOOZDQUfTO8a4WdfVL7j2PRIdnW3w5PXRLsj09m/dccPrwcYhR667yyUZv5gBDWvg43drltUPYz8ImOguF7YWNs6HJ/4DbpvgZq4aVrvlmCf+a9DRpY9viWuq9Y4qC/siK8u1y/n0X6Fjt9sa8fpd8VglcBhygg4gFoLa47qrER671hW6qZwFH/1FNIpQ7Kxz6/VTCf87D8PGN+EjP4e8omBji5oXfgBtO+D8kLa/eT8TL3bFfVY8DVMvCzoa6a1//K97o/fPf4KcfkFHc2RSMypblgQbhxyZbStcpc2aN+CYi+CiH4d/X39cFJRA6RSXuJ7+n0FH03st22DRH1x/zG1LXdXdSRfDtE/C2BkuIXjxR0FHmT5+vSctGeeOKtDkrzHT3VbAv3zeXUhe+yLM/in0HxR0ZL5Q4tpXRWXBJFs18+GRT8OOTTDrOzD9WvfiGxbWQnOtW8K6bUXyuBzqlkNrsijPtW+5NxzP3OT25FZ9ItiYo2brUnjjbjjh0zD82KCjOTwjT4CiEe6iixLXaEgVZJp0cTQKMrXvhB217nWoefPeo0RTdxe8eic8dwv0K4CP/RqmXBKtC3ZxUD4T5t3lltrm9g86mvfX1QHVz7gtSCuechesRp4IF93hlpPnDww6Qu8UlPgzTl6yFaRmXP03IAGXPwSv3em2SvxyBlxyL4w6IejIPKfEta/8nm3t7t77g1o0Aj7zFBx1kr8x7BtP0/r9k9Nty91MYEr+IBh6jLtC3tUBC3/nZl3ffgBaNsMnHgxX4h121roCOf0K4cyvBx3N4cvKgokfhrfuc0vc8wqDjkgO5akbw1GQqbMtmYj2SEabN+1zf/N7X39Scgv23tZe+uioW+lmF2rmwYQPuVnWouFBR5WZyk+HV3/qltqPOyPoaPa3dRkseAAW/gF2boUBw+CUa9zs6rBj/ImhrRm2r3MX+xrXQ+O6vcdUH9wdm2Dk8d6Mb4zbetWyxZvn7ylR6dqiif+ysuDUa+GoU+CRf3X7/c+5GaZ/IdYX9JS49sXx/+JvcZKd9fCXz0H1HJcAXvxT1wDaD12drmLjtmXuoy6ZqNZVQ0fr3scNGAZDJ0DVZe44dIJLWAcM3fuLtPwpl7huX+P+AE69HEad6M/3ERcrnoZVz7kkIgrLww9k0myY90tY+Qwc+9Ggo5GDqX4Wlj0BZ98EA0d5O9bGt6C1bv+Z0tTs6a6G/b8mK9dd+S8qda83FWe526lzqWNeMdw+ySW62pYQft1drrf3c991yzs/9iuYcmn03pRlJd9qvXk3bF3ielkPneDayww9GopHRefC7Zjp7vtZ82J4EtfdTbD4j/D2g27bUVaOK9Z13D+71nrpbgfYvrNHUrrevTfqmaTu2v7ex+cWwKDR7iOVuHqdVCYq/UlcS8a5vw0SnKNOgs+9CI9+EeZ8Hda+5Lbe+TXz7jMlrn1x2r/7N1bLVvjFaW6Z7YW3wUmf9e6P99al7mpgz9nT+pXQ1aMEfvEo94f3hA+6P7xDj3F/jA/nF2Xud9wbzrNvSv/3EGdd7W62NVEJJ/1b0NEcudHT3QWNJY8pcQ2zPQWZxntbkGnB793xnnP3njNZbuagqBQGj4HRH+iRjI7Ym5T2H+zNG39r3RLDzjb3e9fVnrzdAV3Jc53J811tPW63Q82b6Y8nk9SthEe/ABtec729P3yH+/+OouGT3bEoWel/2RNutUlKboF7PU8ls0PGu9slFeHbS55X5LZ6BF2gqbvb7e17+0G35aRzNwydCOd+z10470uhro7d0LQhOWu6rseM6Xp3rrXuvY/PznNJ6eAxbhZ10GhXOX/QGHd7wJC979ce/xLM/82Rx9ZbiQpY94r7d/F0nEr3vnTXdv8mUmR//QfDZQ+4ZfxzvuEK111yN4w+JejI0s73xNUYcz7wP0A28Gtr7a1+xxBJuxuhIAGf/QOUTfV2rHvOS94wMHis+wM6fpZLTodOcAlqOmYrtq91SWvY2/a0t7pZntaG5At08vau7e5+a4O/fcw2zAMs/H8Ph+9NzeHIynaVkBc+BPfN9nasTW+7Y1T2OHbsTv6cJX++9vz89bi9L2tdUtW5e+9HR+p2m+vr19nm9qYdzuOaa11Bpk/92duftx2b3PGSe1xP4qIytwfeqwrpb/8Wtry7T0Lavk8y2g70sWJjX5e0hu31Z+sS+PPn3UXK/oPd36WCEuhf4o4FCXe7Lz8rq56H31/unuOjv3SJSNRmWXtKXVT58P/A0cm/rzvr914YrlvhPta/Du/8396vM9lQUv7eZDZ1O7/Y/+8jpXymK2Z015n+rFyoPGfvRMH2dbDgd+6jaT3kDXTLgI/7JIw4/sh/TjYmW/zcdvT+qzqyct3qukGj3d+sQaPde6PULOqAYeGbMU9tY/O6VU1qnDs/4N4n7vs6sOe1IeFeL7I1X+YZY+ADV8NRJ8P/fRp+cyGc9Q33M/u36+Gsbwa7tTBNfP0JMsZkA3cCs4Aa4A1jzGPWWpV3PJhjLoKCIXDOTf78kbj4Ttdrc8h4b4svDC53a/H9Yq3b97ZvArBfglAPrdv3fv5gjbzzit0Lc9lUV9nZn28EKs6G8ece+qFhd9K/ucIOnW3ejpPa71i70Ntx+g1wx0Fj3NFaaG/ZJ+nY/t4E5EDJSM/l9/uNUQQFPa5s3zp6byLaF1k5kNPf9QLOTR5T98/4mlt+66Xzf+Caqx/7MW+TlI/8DH77ETcLVpCA7H4uQcru8ZGT1+N26nyeW3KYk7fP7dzk/dRjk7d3boXfXLD3dSEOrz/HfhTeut8tRWttcD0930+/wvcmtKk3r+9JcvdJeHML3P/98ifd8ZrX/bmwuXmRuyCbleMulGTnJm/3+Oh5Lh0/nwMSMOBUGHPqe8+373RbcFLJbCqxrZ4D3R17H1dU1mPJcfI4eKz7XOr1xyupxHXTW25/nZfqVrgZz6JSd7FpzYuAccuUz7nJvSlPx/uUVc+7Y+U57r1ParZ00Gj3bx22xPRQhhztji1bvR0nVSugZQusnOteF7oO8vc8b+A+v/vv95rQ43xuvrffQ1DOvBEe+DiUVaX3eUccB1e/CI9fB3NvdpMDdcthl4/vtz1krI/9f4wx04FvW2vPS96/EcBa+4MDPf7EE0+0b74Z3uVWNz/+Lks2HaAASESNb1/Cx1t+x60lt3g6zjHti7m5/qv81+CbmJ8/3bNxEl1b+dnWf6Exy73JL+zeQQ5dB3xsN1m0ZBXRbIpoySpmR1YxLVnFNGcVJY/uo8Xs/VxLVhFdxt+rhzfVX8+E9ne5fsjP2Zjr8ZuTGDl35+NcueNO5hRcxN0DvVvumm07+N3mD7MjayBdZFHU3UwOB25o341hpyl0P1dZRTRnDdzv563Z7Pu5IjqNm8n6Q+35ADxZMJsO0492088d6Ue7yaPD5CaPyc/hjnset+fx7rHdJkK9nyMgjq8/+8q17RR276CoewdF3c0UdTdR2N3s7lt3vnCfzxXalvd9vnZyackqpp91b3yvLH3E0/hTv0OHq4ssusim22TTSQ7dJjt5Locuk00X2XSZbLrJptPk0M+2c1TnOm4dfDNv53/giMbMtp0M69rMyM4NjOxcv+c4orOGAvveC11L+03m24nbjmic3si17Tyw2a2SuazsKc/GAbhu+/c5dbdblrwlu5QX+s/i7wWzqM9Obwuk+2svJo+2/7+9ew/Sq67vOP7+7G42CUsS7hcJ4SJXwYpKrYMzCCpCRS1So4yWEQSpVS4z1gJatUhRKC2IXKyAUdQBb1WsWtrUW6sErKAD1RQEFZVLgECAJIaQ7O6nf/zOYR9iQpLdfa77ec1kdp/nnPPs75n55HfO91x+P47f6eusUfMKpZOfuJQjVt3A1bNP4ztDzZt7vc8jHLfiGhYOvZ5H+5s3v/GA13BtYxZspvspZo0+wSwvr/qDJ6o+YHlDf7GcLb3i6d9nesMn6FZrOiv65jB75Al+Obgv5257YdO+T0+xedWqG3jb8k8yyFqu2+sfectfnNLuVm2QpJ/Y3uiAN63e6+0C3Nvw+j7gGb24pFOAUwDmzZvXupYFdw8+r+lFK8Cd0w7gXTt8btJ3POt6rG9bfjDzFQx6TXVQ2HAAWB0UloJgNqs0hNX5Z1T/e+YR3Dzj0BStm+mmmS/npOVX8K2hY5v6d0YYYOEWr2XrkWVjxecf5K0UIyu1JZ5AsXjfwDzuGDyQa+a8axK/QUyWXux/1rVWgzzWvx2P9W/6AHF9Hnm6uN3SY0XtrNEV1UFt+f3eFvRx525zAR9adjYf2+r9GNHPCP0epp/RUoZ6uPpZlaoeoZ9h+hmhz6MMMEyfR6rtqp9Pf0ZZp17/wf6duWfa+GchGNEASwbmsmRgLrfScMLXZuvRR5lbFbMnLv9nvjtzfAX5plqrQT436x08OPCcpv4dgEUzD2eVhlg08zDuGHx+0/6ffGrOqbz7iYtYw/SmfH7t34aO5YhVN3DzzEOb+ndG1c91s09q6t8AGNYg1846iTsHqyn5JJ7SDJ7qm8EjbPpjEgNeU/ULDSfA3FDkVifAfjzjkI1/WBQS3xk6mrsH9+f1K7/Cki32bXeLJkWrr7jOB460fXL1+njgJbZPW9/6nX7FNSIiIiIiIsZvU6+4tvoU731A4/wxc4EHWtyGiIiIiIiI6CKtLlxvAfaWtIekQeA44BstbkNERERERER0kZY+42p7WNKpwELKdDiftr24lW2IiIiIiIiI7tLyIQlt3wDc0Oq/GxEREREREd2p+4YxjIiIiIiIiCklhWtERERERER0tBSuERERERER0dFaOo/r5pK0FPjtODffDnhkEpsT3StZiFqyEJAcxJhkIWrJQtSShdbbzfb2G1upowvXiZB066ZMZBu9L1mIWrIQkBzEmGQhaslC1JKFzpVbhSMiIiIiIqKjpXCNiIiIiIiIjtbLhetV7W5AdIxkIWrJQkByEGOShaglC1FLFjpUzz7jGhEREREREb2hl6+4RkRERERERA9I4RoREREREREdresLV0lqdxsiorOkX4iIdUnq+mOemLjsH6KWLHSfruzEJW0h6WRJQ7ad4E1dkoYkvVjS9Ha3Jdor/ULU0i9ETdJMSWdJmmN7NMXr1FT1CUdJ2sLV4C7ZR0xNOVbobt3agX8I+BhwcrsbEu0j6QRgMXAWcLGk57W3RdFm6Rci/UKs6yTgfEoeADIi5RQj6a3Az4C3A+dLei2AMzrpVJVjhS7WVYWrpIHq199SQne4pAOrMyb9bWxatJikHYHXA4fafhPwO+CM+iA1Z9CmDknTql/TL0xxknYi/ULwjH7hHuDvgSMlvaTqFwaeZdPoIZLmAK8Gjqr6hB8CJ0o6pFqePmGKaDgeyLFCF+v4wlXSzpJ2ALA9XL29N/AQcCPVGRPbI+1pYbRKYxaAJ4Fdge2q1zcBhwBvhpxJ7XWS9pD0JwC211Zvp1+YgqosvLR6+SQwl/QLU1KVhRPgGf3CyyjF69XA+6tlw+v9gOgJVQ5OBLD9BHAwpV8A+Hn1+ynV8vQJPUzSTtXJi8bjgX3IsULX6tjCVdI0SR8BvgtcKenshsWPV+9/AXiupM9IOqod7YzmWycLV0k60/Zy4JvAByRtAxxOOUidLenANjY3WuOHwN+scxto+oWp6YfAeyXtTylcv0H6hSmlYR/xL8D06r36qupvgPttfxLYUdK3JB3XnpZGM62Tg8GGRVdQbhGeBxxHKVhGJL2yDc2MFlBxIXAr8BlJZzQszrFCF+vIwlXSFsAlwC6UM2V/C5wtaWa1yrbADpTbP15KOaO6qNo2t330kA1k4X2SBoHLgPuBqyhXXy8B9gIebk9ro9mqndF2wC+Au4BXVFmA0i9sT/qFKWF9WQBGSL8wFV0FnGj7xbavhGdcVd0N2ErSMcDulP3If0D6hR70BzkAsP0J4HvAOcA84FLKs87pE3rXwcCBtucCHwaOkfSGatkQsCM5VuhKnfqcx2rgPNtLACTtDVxP2encASwDvkg5WPlL4BjKWbSrc9tHz1k3C3sB/wrsaftOSacDs6orsEgy5TbB7JB6UPUsynLgbuBRSkFyEPBj4BHgy6RfmBLWk4V9gBfZvgU4TdLs9AtTxieA/QAkvQg4ALjb9o8otwkvAG4DjgXOBE4Hzk2/0HM2mAPb75M0w/bqavk2lAImetOTlDtttrB9u6RrgVdJWgI8RrnammOFLtRxV1wl9dkeBR6S1CfpWOBy4EHguurWjtuAt9s+zPbXgK9QzrpHD3mWLCyhZOF1gGwvl/T8qmOaRjlQiR6yzlnQvYE+2xdRnlP5c0lvBx4A3mb75ekXetezZOFBShZOk7RD1S8cmH6hd6ma2qY6WXGTpNXAx4E9gK9JOpoywvSf2T7U9s3ARyknuqJHbCQHX5X0hup4YrWkAyRdB2xNKVyiyzU8FtA4V/MA5f/5C6vX11bv7UHpE05ODdGd2n7FVdI5wMJqh0JVqDz9U9KNtnetfr8D+IDtwxu2l+1vtrzhMek2MwuLgTNtf7PqqD4K/MT2Oe1oe0yu9WSh8SzoXcBKlVEAD6QMvHOV7Xc1bJ9+oUdsZhbeCHzW9mVVv3A+6Rd6xob2EZWLKHdjXWB7RNJvgPfYfmXD9v1VcRNdbDNz8GvgDNvXV33CqcD/2X5Lq9sdk0/SecCjkhbYXt6QhcWUi3MvlPQL249IupFyK/mrG7bPsUKXadsVV0lvlLQIeB7wq4b3nyPpHEn7AthuvLXr84BVpkKhWp7L+l1unFm4FhiWtEvVUc3PwWn320gWPizpuZQzqAdTRoccBC6kXJWfV6+ffqH7jTMLFwH3S9oj/ULv2IQs7Gv7AdsfaRgd9LqyytMj0Wfk0C43zhx8ERiVtHPVJ5xh+7w2ND8mkaRdJX0b2Jfy+Fh9C/guVTG7FfDvwP6UZ1mxXdcQ29Sfk2OF7tOWwlXSfpQDjE/bfpPthzU2h9JTwALbv6jW7at+vhi4gXKA8kgbmh1NMMEs/Ixyqyj1cyvRvTYxC7+y/WPKQArvsD2f8qzKqmqd6AGTkIVVkH6hF2zOPqJhm4OBb1H2ETle6AGTkIOHAWyvaWGzo3m2AX5ne77tXwN1FlYCn7K91PZC4L+AEySdL+lmypgIj7elxTEp1KqTDSrzKM0F7rK9VtJlwK8pgy6dQgnSt4HFjR2LpCHgrZTbOy62fU1LGhxNkyxEbXOzUN3ql6smPShZiNoE9hGDwHzgbMo+4jMtb3xMmuQgalUWdqFkYVjSScC+ts+UtIByIe5HwJdsP964f6hOerwOWGZ7Qbu+Q0yOlhSukv6a0sksBlbYfpuk3SmX9/spndBMyhQ33687GUlHUubbmg0sdSYN73rJQtQmmIX/sZ2zpj0iWYjaBLOwqFq2LCc1ultyELV1srDc9glVMfo94EuUq+m3Ui5srLX9jmq7I4FbbC9rT8ujGZo+OJOkU4HDgFfZvlfSUkkH2b5N0geB+2z/VNI0yrDUe6mMELYn8Byg39VUKNHdkoWoTTALOwOjG/zw6CrJQtQmYR/RZ3tp275ATIrkIGobycLXKSOG71mtezewQGUcnC0p+4cRScqzrL2jFaMKf9b25QCSDqCcFdkBwPY36pWq20DmAKuq2wDutp2hyntLshC1ZCFqyULUkoWA5CDGrC8L21fLzgVOlPRSlzmb9wd+afshSQ/b/tX6PzK6WdMGZ9LYXEq/r14fQLm1YzFwpqT3StqlWjZP0j8AbwLWN+VBdLFkIWrJQtSShaglCwHJQYzZSBbOknS27QcptxC/U2Vu3o9SbhNPFnrYpD3jKul0ynDUC9b3TIHK6G/TXCaAfgHwT8Dptu+Q9EXgUeAs2ysnpUHRNslC1JKFqCULUUsWApKDGDOOLFwMvNv2nZK2Ag4Hvp0s9L4JX3GV9DJJPwCOABZ5bBSvGZI+JOloKPOnuZqawPbtwDBlOGuAk2y/O4HrbslC1JKFqCULUUsWApKDGDOBLKwBtq5eP277+mRhahj3Fdfq7Ec/8FXgftvvrN6fbvupavmOth+o3pdtS9oH+CCwLXC87Ucn44tE+yQLUUsWopYsRC1ZCEgOYkyyEOO12YWryshtFwAzgE8Dzwf2AP6TcsZkK+D7lCkKHpLUZ3u02u5Q4ELgy7YvnLyvEe2QLEQtWYhashC1ZCEgOYgxyUJM1GYVrpIEXEGZS3Mh8GbKPEovAfajTP57W/V6le1Tq+2OBO6hTBw9I5fzu1+yELVkIWrJQtSShYDkIMYkCzEZNnc6nFnAQcCRtldIWgb8MXA/cJ7tnwNIupUyAtyuwFLKMwl32R4GErjekCxELVmIWrIQtWQhIDmIMclCTNhmDc5keznwG+CE6q0fUM6CTAceaVh1e2Cl7Xttr7b9Bdv3TLy50SmShaglC1FLFqKWLAQkBzEmWYjJMJ5Rha8HDpK0s+0VwM8oo3vtJGl7Se+n3IN+Mzx9a0D0pmQhaslC1JKFqCULAclBjEkWYkLGU7jeSJk76wQA2z+l3I8u4HjKg9avsX11tTyTAPeuZCFqyULUkoWoJQsByUGMSRZiQjb3GVdsL5H0deACSb8EbgHWVv8usT06yW2MDpUsRC1ZiFqyELVkISA5iDHJQkzUROZx/VNgPnAIcLntyyezYdE9koWoJQtRSxailiwEJAcxJlmI8Rp34QogaRrlSv7w5DUpulGyELVkIWrJQtSShYDkIMYkCzEeEypcIyIiIiIiIpptPIMzRURERERERLRMCteIiIiIiIjoaClcIyIiIiIioqOlcI2IiIiIiIiOlsI1IiKiySSNSLpN0mJJt0t6j6Rn3QdL2l3SW1rVxoiIiE6WwjUiIqL5nrR9kO0DgCOA1wB/t5FtdgdSuEZERJDpcCIiIppO0krbWza83hO4BdgO2A34PDBULT7V9k2SfgTsD9wDfBa4FLgAOAyYDlxh+8qWfYmIiIg2SuEaERHRZOsWrtV7jwH7ASuAUdurJe0NfMH2wZIOA95r+7XV+qcAO9g+T9J0YBEw3/Y9Lf0yERERbTDQ7gZERERMUap+TgMul3QQMALss4H1Xw38kaQ3Vq/nAHtTrshGRET0tBSuERERLVbdKjwCPEx51vUh4AWUsSdWb2gz4DTbC1vSyIiIiA6SwZkiIiJaSNL2wCeBy12e15kDLLE9ChwP9FerrgBmNWy6EPgrSdOqz9lH0hARERFTQK64RkRENN9MSbdRbgsepgzGdHG17BPAVyXNB74P/L56/3+BYUm3A9cAH6eMNPxTSQKWAse06gtERES0UwZnioiIiIiIiI6WW4UjIiIiIiKio6VwjYiIiIiIiI6WwjUiIiIiIiI6WgrXiIiIiIiI6GgpXCMiIiIiIqKjpXCNiIiIiIiIjpbCNSIiIiIiIjpaCteIiIiIiIjoaP8PPxS+A5/LnakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Likes vs retweets visualization:\n",
    "tfav.plot(figsize=(16,4), label=\"Likes\", legend=True)\n",
    "tret.plot(figsize=(16,4), label=\"Retweets\", legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie charts of sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of content sources:\n",
      "* Kim Bakıyor Ki\n",
      "* Twitter for iPhone\n",
      "* Twitter for Android\n",
      "* dlvr.it\n",
      "* Twitter for iPad\n",
      "* Twitter Web Client\n",
      "* Youtube Analiytc V11.2\n",
      "* Hootsuite\n",
      "* Instagram\n",
      "* Twitter Lite\n",
      "* Tweetbot for Mac\n"
     ]
    }
   ],
   "source": [
    "# We obtain all possible sources:\n",
    "sources = []\n",
    "for source in data['Source']:\n",
    "    if source not in sources:\n",
    "        sources.append(source)\n",
    "\n",
    "# We print sources list:\n",
    "print(\"Creation of content sources:\")\n",
    "for source in sources:\n",
    "    print(\"* {}\".format(source))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiIi0qVFJWy/z/smxUeUODPOh3wXwfXdBXnWYj0vUIix77ZVpXA/7f55cpENE7dZKa9CCq7y/n+hBcmqE3uILABey8JFOLHvtkWncAOB5sOgRUTtlKZTc679x8z8CZ0fj6gOfw774MgsfacGy196Yxo0AngOLHhG1U6Uqbe2F3gc671LdcqL4tp8BuIiFj3TgN/z2xDRuAoseEbVTSsH6ODBh3jjPMyOiXPQA4CzY1+KL1HQxUaM4stdemMYPATwLQHRHISKKtoCSAz/z3bH3Y+uUcZqjfAJ7e7UazTmoHWHZaw/qRvRY9Iio3dmvsr/K8zzQ/wiMLrqzBH0M4BIWPooWlr1EZxozAfwbnLolonZGKXj/EThr8R/8N04DJNb+s/sR7HP4fLqDUOJj2UtkpnEmgA8ApOiOQkQUTV7lLLrG+7uqr9Swk3RnacI/AVzrLsjjN2KKKI72JCrTmADgbbDoEVE7s8XK+XKc55lOMV70AOBqAH/XHYISH0f2EpFpDAGwEEBX3VGIiKJFKVT+r/+KVY8HLp2qO0sL/cpdkMfSRxHDspdoTCMHwJcA+umOQkQULVUqZfMl3v9J2aL69NedpRUUgOvcBXn/0B2EEhPLXiIxjWwACwCM0B2FiChallgnzb/Om3+qF0nxfNqKD/YuGx/rDkKJh2UvUZhGEuzl/GfojkJEFA2WkqP3+G/+5o3AjFN0ZwmTCgBnuAvylusOQomFCzQSx2Ng0SOidqJYpa+e6nnUk0BFDwDSARTm5hf21R2EEgvLXiIwjdsB/Fh3DCKiSFMKgfcCk+aO9zw9ei+69NSdJwK6wd5WLU13EEocLt0BqI1M41wAD+mOQUQUaX7l2Hez7xcH51jjZujOEmFjAbwI4ErdQSgx8Jy9eGYawwAsAWDojkJEFEm7VZelF3ruH1KCjGzdWaLod+6CvAd0h6D4x7IXr0yjE4BlAAbqjkJEFClKwfN84Pyl9/l/ME13Fg0UgIvdBXnv6Q5C8Y1lLx6ZhgPAJwC+ozsKEVGkeJRr+5Xee32r1aChurNoVAZgkrsgb5PuIBS/uEAjPv0RLHox609zPZA/lWH9wQAAQP5UhtFPVWDs0/bXugOBBl93oMLCOa9WYshjFRjzdAWW7vaH9BhRItpg9Vs4zvNM93Ze9AAgE8C7ufmF7Wn6msKMCzTijWmcDeD3umNQw1buC2DJHj/6GnLM/Yt+2BHpydLIq2y/+dyDaf1c+OQHKVi4049r/lONrbelQ0SafIwokSiFsgf816x/NpAXb1ueRdIgAK8A+K7uIBSfOLIXT0yjF4D/A3/fYpLHr3DrBzV48oIOaE0F+/cGH35ychIAYGpfF1JdguV7rWYfI0oUFSp145nev5c8G8ibrDtLDLowN7/wTt0hKD6xNMQL03ACeA1AV91RqGH3zvHg2lFJ6J994l+rGS9VYuzTFfjNZzXw+E88T/ZIlQUFoEta3Wv7Gg7sKrOafIwoESgFNT8wat5YzzODd6hevKBw4/6am184XncIij8se/HjfgCn6w5BDVu8y4+v9gbws4lJJzy28850LL8lHfNv7IiNhy38eb5HQ0Ki2GQpOXSn79YV1/l+M90P14l/gai+ZACv5eYXpusOQvGFZS8emMYFAH6tOwY1bl5RAJsPW+j/aAVyHynH7jKFc/9RhU+2+dHHsP+aZaYIfjQuCV/uOnGBRufgqN3hqrrRup2lFvpkOpp8jCieHVaZK0/zPKbesaacrDtLHBkM4AndISi+8LtFrDONrrCvpM4z8WNY/tQU7P1FBtx32l+9MwUfX5uGib2cqPbZ07Z+S+HNTX6M7e5s8BhXDE/C08t9AICFO/2o9itM6OVo9jGieKMU/G8FTp830fPk2APo1E13njh0XW5+4Q90h6D4wevsxTrTeAvAZbpjUMvkPlKO969OQ7lH4cfv10AE8AWAyX2ceOS8VKQnC/aWW7jg/6qw+if2jMz+CgvX/qcaRaUKHVzA0xemYnIfV7OPEcUTn3LuvtH36+KF1qhRurPEuQoA490FeVt1B6HYx7IXy0zjWgCv6o5BRBQObqv74u967xtejo7c4jE8vgJwmrsgr+GLdxIFcR4oVplGDoDHdMcgImorpVD9hP+iBTO8D5/GohdWEwH8QncIin0c2YtVpvERgHN1xyAiaosalfTNTK+JDar/IN1ZElQNgDHugrwtuoNQ7OLIXiwyjZ+ARY+I4twqa+D8sZ5nclj0IioVwPO5+YVcxEeNYtmLNabRH8CDumMQEbWWUii913f94ku9f55Wg5QOuvO0A1MB/Fx3CIpdnMaNNabxPoA83TGIiFqjTKWtu9B7f/ZO1b237iztTCWAke6CPLfuIBR7OLIXS0zjUrDoEVEcUgrWZ4Hx88Z5Zp/EoqdFRwDP6g5BsYkje7HCNDoC2ASgj+4oREQtEVBy4Fbf7Xs+sk7lvq363eQuyHtRdwiKLRzZix1/BIseEcWZ/Sp7+SmeJ50sejGjIDe/MGqXtxERt4iMDP48TUQ+FpEXRcQpIs+JSJv2dBeRGSJSJSKrg1/rROTKEF6XKyKH2/LerXX8e4vIIBHZJiI3B2+vFpGonsvKshcLTGMEgDt1xyAiCpVS8P2f/8x5kzyPTzgCo4vuPPStbgDujfabiogB4BMAmwHcpJQKKKV+pJRaEIbDb1RKjVVKjQVwJYDnRSRm+ouINLwHpv3YKABfAPitUupZAAh+lupo5QNY9mLFUwCSdIcgIgqFTzmLrvT+Yevv/D+aDggv+RF7bsvNLxwSxffrBmAugC+UUneo4PlhIjJXRC4M/vwlEZktIl+ISJGIPCwiZ4rIguDo4B0hvpcBoFQpZQWP+3cR+UpE1ojI5yLS7/gXiEiKiPxbRB4S21wRuVBEeonIPhFJrffcd0Xk6uDPrwuOJK4Vkf+KSLfg/TeIyEci8qqIrADQ4NZ/InIqgA8B/EQp9Xq9+5WIpIf4ecOCZU830/gBgDYNcxMRRctWq9eXYz3PdFqmThquOws1KgnAw1F8v38DeE8p1dyI4ggA5wM4CcA1AK4FMB3AFAD3N1GAhgenPjfDHiWrXwwLlFITlVJjAPwLwF/rv1BEOgH4GMCXSqm7Vb2FCkqpvQDmwR4tRLAongzgzeDUdAGAc5RSowGsx7G7Wk0FYCqlJiilVjeQOQPApwCuV0p90MyvS8Sx7OlkGqkA7tcdg4ioOUqh8iHf5QvP9v59SiU6ZOjOQ826IDe/8PwovdcHAK4UkZ7NPO9tpZRHKVUF4GsAHyilLKXUHgDFABpbxV07jTsMwHgAT4hITvCx80VkiYisB/BLAGPrvS4VwEIATyilHm3k2LMA/Cz4858CeEEp5QVwRjDfvuBjswGcVe91C5VS25r4rFWwRzt/JiLaZ+5Y9vS6HVyUQUQxrkolf32et+DgrMBlU3VnoRZ5ODe/MBpF428AXgIwt5nCV1Pv54EGbruaeyOl1CYAbgCTgyNxDwO4Sik1EsBNsAteLS+AJQAuauy8OqXUIgBOEZkC4HrYpQ4ABMDxlyupf7uimagBAJcDSAbwmu7Cx7Kni2l0AvAb3TGIiJqy1Bo2b6zn2dyvVd/+urNQiw0FcFs03kgp9RcAL6P5wtcmItILwBAAWwFkwi50+4MLNn5y3NMtAD8EUAbg9SYK12MAXgOwWCm1K3jf5wAuEJEewds3A/isJVmDI4QzAaRAc+Fj2dPntwCydIcgImqIpVD8a9/Ny6703jvdi6QU3Xmo1f6Qm18Yle81SqkHALwCu/D1CuOha8/ZWw27cP1BKbVaKbUOwBsANsA+l29HA5mUUupW2KOBb9dfjFHPawCyATxZ73UbYA/IfCoiawGMwbHnCoYkWPgug+bCx4sq62Aa/WCfr8B/QIko5hSr9DUXeu7vtgddIzZCQ1F1v7sg7/e6Q8QqEZkK4GkAo1SCliKO7OnxZ7DoEVGMUQqBwsCpcyd4nh7JopdQ7sjNL+yqO0QsEpHnAfwTwM8TtegBIZwMSWFmGqNhLzknIooZfuXYd4vv7oNfWONn6M5CYZcOe0rybt1BYo1S6oe6M0QDR/ai7/fgrzsRxZA9qvOyiZ4nU76wxo/RnYUi5qe5+YU9mn8aJSKWjmgyjSGwV+YQEWmnFDwv+M+bN8Xz2CnFyOykOw9FVCqAX+sOQXpwgUY0mcYLAG7UHYOIyKtcO670/sG7Sg0eqjsLRU01gP7ugrwDuoNQdHFkL1pMow/srWGIiLTaaPVdONbzTDcWvXanA+xdJqidYdmLnl/B3q+QiEgLpVD+F99VX17gLZhahdSOuvOQFrfk5hdyu7t2hmUvGkyjK4Af6Y5BRO1XhUrdeJb3waOzA9+dojsLaZUJe1cJakdY9qLjTtjD50REUaUU1ILAyHljPc8M3qZy+unOQzHhttz8Qn7/b0d4nb1IM41UnLhfHxFRxFlKDt/t+6n7bWvqdN1ZKKYMAHARgLd1B6HoYLOPvO8D4CUNiCiqjqiMVZM9jwXetqaerDsLxaS7dAeg6GHZi7yf6Q5ARO2HUvD/JzB17smep8bsR6fuuvNQzJqWm184TncIig5O40aSaUwAMFF3DCJqH/zKsfsG3z3FC61RM3RnobhwF4DrdIegyOPIXmRxVI+IoqLI6rZkvOfpjIXWqFG6s1DcuDI3v7Cb7hAUeSx7kWIaWQCu0h2DiBKbUqh5yv/dBdO9j0wqQ7qhOw/FlWQAV+sOQZHHadzIuRG83AoRRVCNStp2hfeP1jo14HTdWShuXQ/gEd0hKLI4shc5N+sOQESJa7U1cMFYzzO91qkBg3Vnobg2Nje/cIzuEBRZLHuRYBonAzhJdwwiSjxKofSPvusWX+L98+k1SOHsAYXD9boDUGSx7EXGtboDEFHiKVMd1s/wPlT+cuC803RnoYRyTW5+IU/rSmAse+FmGk5wYQYRhZFSUJ8Hxs0d53lmWJHq0Vt3Hko43QCcrzsERQ6bfPh9B/ZfHCKiNgsoOXib7/bdH1inztCdhRLa9QDe0x2CIoMje+H3fd0BiCgxHFRZy0/1PCEfWKeO152FEt53c/MLM3WHoMhg2Qsn00gGcKnuGEQU35SC7zX/GfNO8Twx4TCyuurOQ+1CMjiVm7A4jRteZwPI0h2CiOKXTzl3/sCXX77EGjFdd5ZwKf7ieVRuWYRA6QH0vOlxJHfNRaC6DIff/1/4S/ZDnElwZfdE53N/DmfaideFtnw1OPLBo/Du/wZwOJF9xk1IG3RKs49Ri10E4HXdISj8OLIXXhfpDkBE8Wub1XPROM/srCXWiBG6s4RThyGT0OPqAjgz65/OLDBOnYmcm2ej102PIymrJ4rnvtTg68uW/ReO5A7I+fGz6DbzDzjy0SxY3upmH6MWu4CrchMTy154XaA7ABHFH6VQ9Yj/sgXf8f7v5AqkJdx5U6m9R8CVeexstLNDBlL7jv72dnKvoQiUHWzw9VWbFiB9rD3DmNQpByk9BqN6+4pmH6MWywKQMCPKVIcNPlxMYzQAXhKBiFqkSiV/fZn3f5I2q77tdsszpSxUrPoAHQad2uDj/vJDcBl1o4LOzK4IlB1q9jFqlYsBfK47BIUXR/bCh6N6RNQiX1lD5o/zPNNvs+o7QHcWnY5+OhuS3AEZEy7UHYXsskcJhmUvfPJ0ByCi+GAplPzG98OlV3jNaR4kp+rOo1PxF8/DX7wXXS+6ByINf0tyZXSFv7RuijdQdgjO4LRwU49Rq/TNzS8cqzsEhRfLXjiYRhYAbl9ERM0qUR3XTvM+WvWvwHcanrNsR4rnvwLPgW/Q9bLfQVxJjT4vbdgUVKz+EADgO7oHnn1b0aH/+GYfo1bj4EWCEaWU7gzxzzSuBPCa7hhEFLuUgvWhdcr8n/tuP92Cw6k7TzQd/Ww2qr5ehEBlMRxpmXCmZqLLxfdg3wu3wtUpB+JKBgC4jO7odtnvAQB7X7wN3S434croDMtbgyMfPAzvge2Aw4HsGTcibfAkAGjyMWq1T90FeefoDkHhw7IXDqbxPICbdMcgotgUUI59P/HdeeBT62ROj1E8qACQ7S7I8+sOQuHBadzwmKo7ABHFpr2q07KTPU+msOhRHEkHMEF3CAoflr22Mo2uAIaCjutYAAAgAElEQVTojkFEsUUpeF/2nzNvsufxU4qR2Ul3HqIW4vX2Egivs9d2k3UHIKLY4lWuHd/3/t6zUg3hN0yKV9MA/E13CAoPjuy13RTdAYgodmy2+iwc55nddaUaMkx3FqI2mJqbX8iOkCA4std2LHtEBKVQ8Vf/99c8HbiI5/BSIjAAjAWwUncQajuWvbYwjRTwJFaidq9SpWy6xPvnDltVb/7njxLJZLDsJQQO0bbNBAApukMQkR5KQX0ZGDFvrOfZgVtV71zdeYjCjCvIEwRH9tqGl2knaqcsJUd+5fvx9resaVyEQYlqtO4AFB4c2WubUboDEFH0HVEZq6Z4ZvnesqZN1J2FKIJG5uYXtqvdXhIVy17bjNQdgIiiRykE/huYMvdkz1Nj9qFzD915iCKsA4DBukNQ23Eat21G6A5ARNHhV449N/l+dWS+NWaG7ixEUTQGwGbdIahtOLLXWqbRB/bSdCJKcDutrksmeJ7uON8aw3OYqL3hn/kEwJG91uP5ekQJTinUPBPIW/YX/zXTdGch0mSM7gDUdix7rcfz9YgSWI1K2vY9773WWjWQRY/aM36vSwCcxm29k3QHIKLIWGv1XzDO80zPtWogT06n9q53bn4hB4biHH8DWy9XdwAiCi+lUPZn/w/WvxA4/3TdWYhihBNAbwBuzTmoDVpc9kTEASBdKVUWgTzxpI/uAEQUPuWqw4bveu/LdKuek3VnIYox/cCyF9dCmsYVkX+KSKaIdASwEcDXIvKryEaLYaYhsP+nQ0RxTimoOYEx88Z5Zg9xq578TxzRifrpDkBtE+o5e8ODI3mXAPgAQF8AP4hYqtjXHdwTlyjuBZQcus1328obffdM98OVpDsPUYxi2YtzoU7jJolIEuyy97hSyiciKoK5Yh3/908U5w4qY8WFngf6HkT2BN1ZiGIcy16cC3Vkbzbs+fqOAOaLSD8A7fmcvb66AxBR6ygF3+v+GfNO8Tw5/iCyu+rOQxQHWPbiXEgje0qpWQBm1burSETOiEykuMCRPaI45FPOXdf77ilbZI2crjsLURzhAEecC3WBRncReV5EPgzeHg7g+ogmi23cAJ0ozmy3ei4a73k6c5E1kntaE7VMJ90BqG1CncZ9CcDHAHoFb28BcGckAsUJ7olLFCeUQtWj/ksXnOn938nl6Mi/u0Qtx783cS7UstdFKfVvABYAKKX8AAIRSxX7+AefKA5Uq+StF3j/su9h/xW8SDJR6yXl5hem6Q5BrRdq2asUkc4AFACIyCQApRFLFftY9ohi3Apr8Pyxnmf6bFL9BurOQpQA+H0vjoV66ZW7AbwLYKCIfAmgK4DLI5Yq9mXqDkBEDbMUSu7137j5H4Gzp+nOQpRAsgDs0x2CWifU1bgrRWQ6gKEABMDXSilfRJPFNv4PhygGlaq0tRd6H+i8S3WbpDsLUYLh9704Fupq3Fth74e7QSm1HkC6iPwsstFiGv/QE8UQpWB9HDh57jjPMyN2qW45uvMQJaAs3QGo9UI9Z+9mpVRJ7Q2lVDGAmyMTKS5wGpcoRgSU7P+x7641P/bdPcOCw6k7D1GC4iBHHAv1nD2HiIhSqnaBhhNAcuRixbz2/NmJYsY+1emrPM/9A47CGKc7C1GC4/e9OBZq2fsEwL9F5GnYK3J/AuCjiKUiImqCUvC+Gjh78b3+G6YBIrrzELUDHDWPY6GWvV8DuAXAT2Ev0PgEwHORChUH+M2FSBOvcrmv9v6uerkayi3PiKIn1NO+KAY1W/aCU7YvK6WuBfB05CMREZ1IAWqLv+snM/1/6VmBtI4AtunORNSOVOgOQK3XbNlTSgVEpKuIJCulvNEIFQc4skcUJVuSknY8lm3sXKeyOv/k6yvL83pv37MjO2nDisqT1gf2W8vcBXklzR+FiKj9kuCai6afJDIbwHjYF1aurL1fKfVQ5KLFMNOoBpCqOwZRoip2OI4+m5W5/j8Z6Z0rHY4RAHDOhrM+6buzaOR5/X5a/Z/UpWXeLiX+Dem9du7w9zq6v6TLDucRzxoAC90FeWWa4xMRxZRQz9nbG/xyAMiIXJy4wZE9ojDzAt7/ZqSvesHIlL0u5ziIfLsDhsNSgYGHT0mtVlt7Haj4et41cvrk9w6tWDL8SNnMCR12FXn6V29cOmT0lM17BtzW94FPjkq5b4coLIBd/iqbeFsiooQX0sgeHcc0KgFwU2iiMFiSmrLh8eysI2tTkkcpkeyGnjN9XdLiYaX35njKnuvrFFfVzH53VYo4ui52fT1/g3P3FAicTqevIqfv+hXbevTyfa7OdO7Z29UpezwiVX6PKCwDMAfAl+6CvOrofkIiIr1CncadA/uSK8dQSp0ZiVAxzzQOAOimOwZRvNrlcu1+PNv45tOOaf18Iv2be/5dH5xZWJlx6hBv+T8HA8CEzufMG5Q5bjoAbHccWPFF0vpBkNqLvirVpUvRym79v66en3q6+lSdl1p+MFVcOyurpNTbTxR2ApgLu/wtdhfk1UTqcxIRxYJQy96EejdTAcwE4FdK/TpSwWKaaWwFMEh3DKJ4Ui5S9oqRufZfmekZpQ7HaEho18fre1Btn7nu9wcqXKUdfJXvjQUAp7iqZ/a7q0zE0R0AjkrFjreTl4klKrf+azt0KC0aNGiZ25Oler4tl+9bjKk5/hLlcxVVHHAc8vQUS/UDsAR28ZsDYKm7II8L0YgoobR6GldE5iml2ud1rkxjBewFK0TUhAAQ+LBj2qrZWYbXneQaB5EOLT3G715zfXGk799P9XtWrfVXzz2t9v5xnb4zf4hx8rfn9dXAV/JmyuLtNeI74e+mw+Gr7Ndv7YpeOZtztjiGet/AVYe+xknDUWXVOHdWbHPuq86EzxolgA/AItSVv6/cBXm+1n16IqLYEOrIXqd6Nx0AJgCYpZQaGqlgMc005gCYoTsGUaxam5K85bFsY9+y1NSTLJFWn/KQ4lWVf3596ortg743zVc1d37As7Ju0Qacnpm5dx91iKNn7X0WLH9h8spFBxyl0xo+ItCli3vlgAErAo4Uz+gvZfqad3FZ4AB6jIdPVTv3VG5w7q50SFVgpNiL0SoBLETdtO9yd0FeoLWfh4hIh1DL3g7Y5+wJAD+AHQD+Rym1MLLxYpRpvAPgIt0xiGLJAafzwFNZxubC9LSeNQ7HkHAc8+o5gfk9Ar93VXXsMdlb8f48y7flmNmEMZ3OmD/MOOWEYrfM9c38tc6iyZDGrzjQoUPpzoGDlu3Iyto/tko64j1csvZznJNVJemjYCmvY3/1WtfOiiop9Q0RoEfwZeUAFqBu5G+VuyDPCsdnJSKKFK7GbQ3TeBXAtbpjEOlWI1L9r8z0Va9kZqYcdjrGwt5xJ2xefEg2LJ30cF+IZHjKXpuvAnuPKXYOOLwzc39xyCGOnONf63YcXPVZ0rpcCBpc4fvtMepN8Toc1sA9yCl6E1e5V2DigIC4+kApJcXeTa6iioOOwzU5YmFwvZeXApiPuvK3xl2Qx39UiSimhDqylwR7X9zaf2jnApitlGqf57KYxhMAfqY7BpEOClBz0zqsfTLLKN+cnDQGIhG59ua4b6w11y+c5Nk89JpTAMBT+sJiZZWcdvzzRmVPWzA867TTGzpGiVQW/Td5mT8g1sBQ3rN2ijc5pWoCBLIa49e9hStLd2DgaIgYACCVvp3Oosodzv3VRvA8v/oF9yiOLX/rWf6ISLdQy95zAJIAvBy86wcAAkqpH0UwW+wyDRPAH3XHIIqmb5KSdszKNormp3UYFBDpHen3e2S2f/Gugb+zKjv2mgIANSVPrYaqHnv88wQO3+W5d+93iLNPQ8fxwFf6VsqSrVXiPTnU964/xSsCw4ukmi9w9qpCXOw6is7jIGJPD3sDR527qjY691S6pDowSoCOxx3qEIB5sIvfXHdB3sZQMxARhUuoZW+NUmpMc/e1G6bxIwDP6o5BFGklDkfxs1mZ696qt21ZNGSXq4NPPoG0edMeDdSOqNUUz9oK+Ac39PwRWVMWjsyeOrWx41lQgQ+TVi3c5yxu0RUEjp/iBYASZB16G5dvmo8zunkkddi3Tw6oGsf+qnWunZU1UuYbKg1fi/MA6hZ7zHEX5G1pSR4iotYIteytBHCFUmpb8PYAAG8qpdrn5UdM4zwAH+qOQRQJXsD7dkb6queNTAS3LUuOdobb3wnMHXj0lI6bhl03sfa+muKHDwKqwZW9AgnMzP3Fbqc4+zV13BWubQtWOd2TIEhqaaYuXdwr+w9Y4U9JqTpZBA4A2I4BW9/A1XvXYfRQJc4e3z5ZKeU46tnoLKo45Dji6SMWGptG3otjy9+2luYiImpOqGXvOwBeBLA9eFcugBuVUnMiFy2GmcYIAOt1xyAKp6XBbcvWpCSPVMdebimqHJby/9/fAodWTPjN9or03lNq768pfsiPJvbzPsmY9OXoTtOnNPZ4rZ2Ow2s+SVrTG4LOrcl3/BQvAFgQaxlOW/VfXFGzG33GQuSY6Vyp8LmdRRVFzv3V2fCrkWJfwqohuxCc8oVd/tytyUhEVF+TZU9EJgLYpZTaLyIpAH4M4CwA+wHkK6WORidmjDENA0CJ7hhEbRXctmzbpx3T+oaybVk0nL3SWvLDTzBh7rRZlRDJAgBl1ZR6Sp80mnqdPbp3d5FTXAOae49Sqdr9n+Sl1QGxGpwWDkVwindlr5zNvWqneAGgGqkVHyNvzcfISytD5hiIHFvsPIEjzt2Vm5y7q5KkJjBKmt5n2426xR5z3AV5u1ubl4jar+bK3koAZymljorINACvAbgNwFgAJymlLo9OzBhkGmWwL7pKFFcqRMpfMTLX/CszPaOkBduWRcuzj/pXVadPDGwcfsO3CyqswBG3t+zl3OZeOzRz4qKxnc+cHMr7eOEvfytlyaZK8ZzS+rS2Ll2KVvYfsPyYKV4AOISue9/ClVuXYEpvnySfOJUbUNXOfVXrnDsrvVLuGypA12beahuOLX/72pqdiBJfc2Xv20UYIvIEgENKKTN4e7VS6oSVce2GaWwEcJLuGEShCACBjzqmrZ6dZXh2tHLbsmjod0Bte/CFwMBlE+5ZWJHR99sFFwGfe72v4j8jQziEdXm/u7c7HUkh7V2toKyPk9bM3+08MqO1metraIq31iYM3/gGrjq8BcNGKHGcOIWslOU44lnvLKoodhzx9BWFUEZav8ax5/wdDMfnIKLE0lzZWw9grFLKLyKbAdyilJpf+5hSKpR/fBOTaXwE4FzdMYiasi45ecusTm3ftixa/vQP//yhux2T506bVYZ65w36PeuW+as+DWkEbnDm+CXjO589qSXvu9q5Y+Fy1/aJEKS0NHNDGpviBQA/nL6FmL7qHcy0DqL7ONinyJxAyn07nEUVRc4D1Z3hVyOaOM+vvo2oG/mb5y7IO9z2T0NE8a65svc7ABcAOAygL4DxSiklIoMAvKyUavZk6IRlGo8CuF13DKLjHXQ6Dz6dlbnpvfSOYdu2LBpSvari5f8NWAe7jv9mw4gfHrPS31e9aEGgZkmDF05ugJrZ7+6tLkdSiz77bseRdR8lre4BaXYqtUUam+IFgAqkl7yHS9Z9gXOyq6Rj4/959gQOuXZWbnburUpBTWC0AKkhvLWCvZCsfvkrbstnIaL41OxqXBGZBKAngE+UUpXB+4YASFdKrYx8xBhlGrcAmK07BhFgb1v2ekb6qpeNjORDTue4cG9bFg3XzAnMv3iJmvbV+F8tKM/MPabY+So/mhfwbgz5GnkDM8YuPbnLuae2NEOZVO/5T/KSCr9YQ1v62uY0NcULAHuQU/QGrnavxMn2Nm2NCVhVzr3V65y7Kn1S7jtJEPKqYgvAGtRN+853F+SVtuKjEFGc4d64rWUaUwAs1B2D2q/abcueyjLKNtnblmXqztQWrz7o35rslwFzps8qhji61H/MW/7mPMu/s0UXRL6s312bkhzJLT6v1gd/5VvJS9dVOGpaNBUcqqameAH793U1xq99C1eW1d+mrUFKBRyHPeudRRUljqOeXFFo8jqDxwkAWIW6S70scBfklbfw4xBRHGDZay3TyALAKRGKuuC2ZTvnp3UYGI1ty6Jh/DfW2vw3rNEHu4xdtX7kzeOOf9xT9spCFTjc6A4ZDemfPuqrU7peMLH5Z55IQanPktbOL3IeblHBbKkuXYpW9R+w3NfQFC8AeJFU8znOWVWIi13F6FS3TVsjpMy7zVVUsctxsKYr/Gq4AC1Zae0HsAJ1075fugvyKlv0gYgoJrHstYVp7AHQS3cMSnwlDkfxc8FtyyqiuG1ZtDwy27+o11FMXj7uFwvKjAEnnJtXUzJ7OVRlyHvb1rqs350bkxwpw1uba62zaNEy1zfjISGdI9dqzU3xAkAxsg69jSs2LsCM7sds09aYmsAB186KLc69VanwWKMFLV584gPwFerK3yJ3QV51C49BRDGAZa8tTOMTAGfrjkGJKRa2LYuG7HJ18OnHA9mAOOdMn3UE4jhhgURN8WMbAV+LS1u/jsOXT+r23RaXxPr2SfHGD5JXdlaC7m05TigcDl9l337rVuTkbMppaIq3VqPbtDXGb1U491atd+6qDEiFf7gA2a2I5wGwFHXTvovdBXmeVhyHiKKMZa8tTONhAHfqjkGJJVa2LYuWO94OzJuySU0/1HnUmnWjfjKmoefUFD+yG7BaNWV9ad871iU7U0e1JWMFava9mbKkxC+BqF1b057iXeFLSalscIoXsLdpW4rJq/+Ly2v22Nu0NbUbR/BFyu84XLPeWVRR6ij2DhCFxheDNK0GwGLUjfwtcxfkeVt5LCKKIJa9tjCNG2DvGUzUJrtdzj2PZ2d980kMbVsWDbX74DoVeq4Ye9f80qxB0xp6Xk3xQ5UAOjb0WHP6dBy6cnK3S8Y3/8ym+RGo/k/y0tVljurT2nqslkjtULZr0KCl25ua4gXsbdo+woVrPsYFaeUNbdPWCCn1bnUVVexxHKzphoA6qYXn+dVXBWAR6srfV+6CPH8rj0VEYcSy1xamMRTAZt0xKD7F+rZl0XDOCmvJjz6xJimINWf6rEMQxwlTpUr5azwls9p0ztwlfW9fk+Ls0OCoYUsoKPVF0vr5OxwHp0FaXYpaJdQpXqBum7bFmNLHL8nN7hX8rWr/PtfOyq3OvVVp8FqjBWjLqQMVsK9YMBd2+VvhLsgLtOF4RNRKLHttZRqHEfp1rqidi5dty6LluUf8qzKrMe5wpxFr147+2eiGnmMFSvd6y55v00KonLTBq6d2vyxs2ztucO5avNi1ZQwEzU+bRkAoU7y1mt2mrTE+q8y5t2qDc1elJZX+kYLGRxVDVAZgAepG/la7C/KsNh6TiELAstdWpvEegAt1x6DYtj45eeusbGPv0g6pwyyRiJ/oHw9q98EFgJVj75xXkjW4wcucWP49m73lrze/+rQZl/S9bXWKMy1shW+/lGwqTF6RpQQ9w3XMlgp1ihewt2lbgBmr3sVl1kF0H9+iBT+W8jkO1qxz7awolxLvIFHIaXN4+9JV9cvfWndBHr8hEUUAy15bmcZvADygOwbFnnrblvWocTjCviNDvPvTq/55J+3GdAWoOdMf2w9xNFiaAt7NK3yVH0xo6/v16DBg7fQeVzQ4ethalfAcfDNl8SGfBLReDic4xbsyJ2dTgxdqPl7I27Q1Qkq8X7vcFfsch2t6SEC1qogXf/E8KrcsQqD0AHre9DiSu+YeATAPdat9NxT99UIHgFkAzoO9/VuBUuo5ABB7l5gGHyOiY7HstZVpTIf9DxNRQmxbFg21++AKkHkke9j6NWNua7Rw+Gu++tJfvSAs+3Bf1OfWlR1c6W1erFGfH4Gat5O/WlHiqIyJvcI7dylaOWDACn8oU7xAC7Zpa4RU+fc4d1Z849xXnQGvNUqApFBeV7N7A1yZ3bD//+5Bt8vvRXLX3OOfcqh0yZs7KtZ8lNHzxsdm7nr4isOwd/yYqpRyi8h1AK4BcD7sU2m+fayln4Eo0TV5NXYKyVewrzzPX8t2SgFqXocOa5/MNso3JSeNhshk3Zli3eULrZUCTAOAnX3POdLUc5VVFraT+pceej95Rs/vh+twAAAXnKmXeydNmZu0Ye43jv3To71w43hHDvcbf+Rwv+AU77LtWVn7mpzizcGefnfiwX4KUKvVhOA2bQNGh7r9nkpz5fiHZeX4h2UBPqvUubtyg3N3FaTKP1KARo+R2rvZwdCunl3ru2ZNvwGO5A4b+93z/v6D/32gHFB/z80v/A2AKwE8q5SyABwSkbcBXAHgwVByE7UnLChtZZZWwTRWA2jThVsp/mxLcrkfy84qmmtvW9bmlZ7tybkrVE/ALsrFWYMGN/VcZVWE7ST+AzVFI6v85cvTXBlh//s6wzdiRg9n1tKFrs0jIEgP9/FbqqY6s8/6dWf1CU7xLmhuilcAGYcVo8dhhb1Nmzp3USEuSgplm7ZvJTmMQP+MyYH+GYClvI4D1StcOysrpcQ7WNDycxv9ZYfgMrrV3uyR0mtIj0D5kcEAZro69fZnn/lDyc0vNGBP/e4EWn3NQKKExrIXHp+BZa9daGDbslzdmeLN+K3WmhQ/xgBAcdaQjRBnk0M8yqoM61T4kkPvpp3Z85pwHvJbwwI5p3ay0re8l7yioxIVjkUMbWZZSR3dO8af7t4xHp27FK0aEMIq3mT4Us/H+5PPx/v2Nm3qio0LMKOHR1JDP/fUIclWz7QJ3p72gmUp9mxyuSsOOA57eomlhrT1c4nD6XKmGefDnsaFMfWaEu/B7fty8wuvBzDHXZC3s63vQZQoWPbC40MA+bpDUGT4AN87GR1XPWcYao+9bVmDF/6l0Fz3uVVV+/Odfc8+3OwLVFVYt4k7VLN7eKW/dFlHl3FKOI9bq5syhlzlmXLozZQla73iD+uCkLY6crjfuJZM8QJANkq63ohnp9+IZ7FNDdz6Bq7aux5jhqkGronYFJWdcpIvO+UkAJBK/y7nzortzn3VmQDGNfYaV2ZX+EsPIqWn3Q0DZYfgzOzW4GNWVUlWSq9hWQBeAoDc/MIdqFvpO8ddkLenJXmJEgnLXngsAlCKtl+HimLIstSUjY9nG4dXp6SMVCIRKQbtTacydaBncd0oeHHW0GZXjirlbdMFlRuy+OC7xnd6XqskQheyTkNK12s8pxvvJH+18KijYmok3qMtWjrFW2sgtg3Ox32DLTgCS9TkFW/j8po96D0upG3a6lEdXX38J2X18Z+UBTXbYfl6uNa4kp01Uh0YJaibAk8bNhUVaz5G2tDJsKrLUbVlCbpf89dmHwvqH/y6CQBy8wu3ou4Cz3PcBXn7W5KZKJ5xNW64mMabAGbqjkFt0163LYuWO94OzJ2ySc0AgBJj0KaV4+5qdq/ZmuJHtwOB0HeBCFFe7x8vTU/KOjXcxz3eAtemeV87956OEFbG6hTqFO/xqtGh/CPkrf0YF3QMbtPWaIEuf+oh1Cz4HNb+vUj9zvnwrlkB6+gROIwsSKaBLs+96Tn68xtrHAGH139wTyeIw+nK7IJAuT0AnHnKZfAeKkLNjhVQCjjmsVMvR8bY81rykTejbuRvrrsg71BLXkwUT1j2wsU0fgiA13iKQxUi5a8aGWv+mZmRXuJwNPnNilovuA/uYadCDwBYM+qnc490HjmjudfVFD90FECncOfJTu6x9exe1w2K1OhefVsc+76an7RxGAQZkX6vtmrJFO/xDqLbnrdw5TdLMLnBbdq861bB2b0njt5xE7IfmAVX/0EnHKP6k/dQ89mHyPrLYwo79n99+K5r+ne/9sF9yVm9civWf47KDXPR7Xt/glVdjn0v3o4e1/4NLqPN1ylXADairvzNcxfkNblKnCiecBo3fD7SHYBCFwACHwe3Ldue5BoLkZibaks0Z69UXzkVTqu9fTR7WLMjp8HLamRFIk+xd//gCn/x4oykTqc1/+y2GWL1nNjJ2/Gbd5OXF1ui+kb6/dqiborXX9W379oFvXI293Q6Aye2sgZ0w8Gcn+KxnJ/iMWxUIza+gasOb8XQkUocnQAgeVSjp+fVvf+cT9Ah71KI0ykYlDMs5azzUK425qZPGVtU/sZnGemjziqDOPo40wxn2pBJqNy8EMapbZ5UEQAjgl8/B6By8wvXom7ad567IK+krW9CpAvLXriYpXtgGusAjNIdhRq3Pjl562PZxt4lHVKHWiJt3pWBQnfFQuvbfYBLM/t/rRyu5ld2qqqjALpEKtOig+90O6fXDZaIRHyKtYvKHHSVZ+rRN1OWrPaIL2zbtkWKZbnS3O7xp7vd367i9aakVE4MdYp3ODYM/yN+b2/Tps5Y+i4uVaFs02Yd3A9n97qtkJ3deyBwaD9UelI/S1UD50/o5Onf44hzd+UmfJne1196sCdCvJBzCwiAMcGvOwBYufmFq1G3u8d8d0FeWZjfkyhiWPbC6z2w7MWcQ07HoaezjI3vpnfsWeNwDAHQ5HXdKPxy96ttmdX4tuAU9T1nH4Bmy56yKksRwbJX4j04sNx3ZFFmcpeoXAi7A5I7XeOZmvFu8vIFhx3lp0fjPcOhNat4a7kQSDoDn516Bj5DOTKK31OXLH3Z75uEthS0FGfnwMDMqWp4DwT27wv4RmYtc+6s9EiZb5gAXVt93MY5AIwPfv0CQCA3v3Al6qZ9F7oL8ioi8L5EYcGyF16vA/it7hAEeAQ1r2dkrHrZyEg6aG9bNl13pvbspk8DewB8u9rzSKfhuaG8TlkVEf8GuujgOz3PzbkpIFHa2s4BR9Il3lNOX+T6ev5G5+4pEMTNlnptmeIFgAyUZ1+NV08vdJXgZ76/7FmmbvlmJSYMtMTVu/Y5jm49EDiwF0nD7MsvBg7sh7N7z8Yf69HLGcjpeEogpyOglOU44lnnLKo44jjq6SsWwr6wJ8gJYGLw69cA/Ln5hctRV/6+dBfkVTXxeqKo4gKNcB5lDgkAACAASURBVDONDQCG647RHilAze+QuvbJbKN8Y3LyKIjwUjgxoINHlb/0UAACe3FCWUa/rcsn/Dqk0VV/zeol/uovJkU2IXBuzk1fZiV3jfrettsc+5fPSdowGC1cCBFLOnfeuWrAwOWNruKd/fQRLFhQif37/Xj2ud7o3z8Z11y9E/fd3wP9+ydDAWpFYPzaBwv2dyveuL27qqlxOIwsdH7+DaiyEhy55SokjT0Z/g1rYVVX4fjHsh95Hq5evRuKBin37XAWVRQ5D1R3gl+NFERtRbQXwDLUTfsuchfk1UTpvYlOwLIXbqbxBwD/oztGe3LctmUN/6tP2lz7eWD+RcvUtxeiXjfi5rmHuo6dEcprfVXzFwQ8yyM+3ZmR1Lno/Jwf5kio24KF0REp3/5O8lcOS1RutN87nFI7lO0aNHDZ9qzsfWNE6hbVrFtXg+7dXbjrzr0YMSIVa9dW4+jRAAzDicxMB55/oQ9uuXk3klMED87KrX7n0Gnrn7/hnxMkq5NDnC4kjRkP6/AhZBU8Dqv4CA7/4GI4Mg2I04W0q25A2oUhLs7wBA65dlVudu6pSkFNYJQAHZp/Udh4ACxB3cjfEndBnjeK70/tHMteuJnGYABbdMdIdKUOR8lzRua6NzPTsyscjpG681Dj/vGg/5tkP76d6psz7ZHtypEU0vSat+KDuZZv84yIhavnnF43LMxO6a5lVXYNfCVvpizeXiO+8TreP5yCU7wrjp/irT+ad7zf/nYfzjs3A9Om29dTfmzWYXTonF7huOb2FW/98o2TUy+6smPq9LMBAGWPFsDZvSc6fv/6/2fvvMPjqq69/a7RqFiyLFe5yQUbbLCRsY0btmVjbEowEFpCDRNISHLDvbkJKV9CCpMbbq7SICEhhIRATEhIAoQAoTf3gruPewG5yVVW7zNnfX+cI1u21TUzZyTt93n0WHP2Pnv/RrJm1uy91/q1XWTYrkg4VGkl7CuvldLa8yWK50IboRJYwang78O83Pm1MdZg6EKYM3uRJli8i2DGOpyDvIYIUgu1r3RPW/9kzx72Ab9/IiId5oB7V+XiXfaGpNCpxIzS7kP2qC+xWaeGOlTLYlbzcPnRl4ddnXVvrYhEOrOzWVJI7Hl79cxx/05at/ior7hD2/GdlsXbZ9+GESPX1CQnl0+iiS3Uo0dDZPY/9XaUmenn2PGy7v/J72cvL9rPzb18+zZov483M+78hP4D+oePtdP8IsGXGs5KmxrOcs/5Ha/elLC3rNB3onqYaEz8rrsBl7lfAOXDv/3aMk6VelmTlzs/FAMdhi6CCfaiw3OYYC9i1LMtG2tsyzoWd71nV9Z/vHfo5fupl6jRLHZFzAKvslDhkBM1h5f0SR7oyYcIHz7/dTWTZq3y71psJeybjnT81+eCgqHjCwqGkpJScqC6+jf9wmGtBnq0dpzhiQeHXsWPhtr4wg+VdzuwuaxvAqoZrbVpaxARn90vZZzdz3Hlk5KaPf695ft9Ryv7EtKx4pRhiTZpwBXuF0Dp8G+/tpRTZ/7W5eXOD8dAh6GT0uFfTOKUvwM/IXaHgTsdB/0J+Y/17Lnrre6pQ2pETMJLB6R3iR4ZUMjk+teO98lu1ZlK1arkyKpqmhVHXx4xP+uLNdJMLbhoMjV03qxMO2Pde4nWCCQ6BaVjTVVVjyzVVDZtvNpOTjqyZNDgbQMSEsInk3QyM/0cPRLi/POdx/VX+uq3+bATepXtzbrpnINcx2dK39Brlr7N1d1LSY+Y8432SBpZm500EnpBVfiof3/ZjoSDFSlU29kCEfdpboR04BPuF0Dx8G+/toRT274b83Ln2zHSYugEmGAvGgSL9xPMeBtolVFjV6dcpMy1LUsrdGzLBjV/lyFeues9e5vApXWPy9IGfWQnJLW4TAcAWtM90rqaojxUPPh49cHF/VKyPN1KPcfOnHhTzbS9LyWtOmGLRqt8SMxR9XfLy5uQk5c34bQt3tmzuvtee72UmTlplJTYLFtWzsOPOH/+s2d1p6G2blSm38jzM2/keY6SefAFvXX3KqYPDUli5DytUxIyQ+dlZIbOy4CQXZ6QX7EyYX95SMpCYyQKFn5NkAFc434BFA7/9muLORX8WXm5880BfEOjmASNaBHMuB54yWsZ8Y4N9ltpqeuf6Nmjek9i4viIbMsYPOdMH1yALRd8duGR/pMvbc04VYWPHAYd0HzPyJGakH7omiH/0UtEYrWK0yjV1Ba/kLxyV6XUTPJaS1t544032LZtG2VlZaSmppKamsqXv/xl/vKXvzBnzhxGjOh+4JxzVu1Z8Ix1yYb1lUkAt9zak2uucXZ7w2Hl178+zto1zomA+m0NsZWxW57ntoL6Nm0RRzXsO1ZlJewtL/YVVg8XZVhU5mk5x4FFuNu+ebnzt3isxxBnmGAvWgQzEoC9wGCvpcQjW5KSdj16yrYspm/mhuhz1Rp75T3v2KfVx1uY88hOOyFpVGvGqSp8uBqI6VYuwJwBty3O7DY0LhIlbDT8RuK6pYcSijp1YXAni9dae+YWb1sJkVC7mDnrX+FGPUbmhOZs2tqDlNTs9ueVHfQdrepHWC+I0Tm/pjjKqWSPD/Jy5+/wVo7Ba0ywF02CGUHgQa9lxAvHEnzHnnBsywZU+nzN+6IaOixP/jK0ob49WnnqgLxVU74/vDVjqFaXVhc9lh5xcS0gJaH70euGfDldRGJZi61J1vj3LNmQkDcNibgPbNzhbvFWt8aLtylKSS98leut97m8d6WkRbdUU1XosH9v+c6EQxWp7jm/mH9YaYBDnB787fZWjiHWmGAvmgQzBuOs7nUYO6RIc4Zt2Xg8KFpriC3nHNbdP3n6dAutrefftfDwgKmXtmYcO3xif03Jn4ZEVFwrmD3glkUDug2Pq9W0fb7jG99O3JiF0MdrLbEgJaXkwLnnfrjnzELN7eEAWXnPc/veM23aokLILk04WLE5YX+5LeWhsULcJNwcwAn8fp6XO3+T12IM0ccEe9EmmPEv4JNey4glrm2Z9dteGSXGtqzr8aNnQotHH+S0LdCFOQ9vtxOSz2/NOOHa/Vtqy54fG1l1LSclIe3YdUPuSxWRNK80NESxVOz/Z9Kq6rDYrUt26cD4fKGKIUOttYMjtMULzuvUeiZtepFbSvM4ZxwirS4J0ypsDfmOVVn+vWUlUlQzUhSv3X7CwPC83PkHPNZhiAEm2Is2wYyrgDe8lhELPkr07/11r555C1O7jQiJeLYiY/COM31wASq6Ze5bOfXBoa0dK1y9ZXVtxVuTm+8ZPXL637xoUOrIuFrdA6ghVPpi8srt5VLt6c/HC1wv3ppIbfECVJNU+S5Xrn+D65IK6TUBkajvxkhRzU7/3rJ837Gq/hLWC6I9XwO8mJc7/2YP5jV4gAn2ok0wQ4AtgBd/zFHnpG1ZeveeZQm+bK/1GLzlM++FF19bzwcXYNvoOxYeGjj90taOFapcuSxUtXxGxMS1gSRftxPXD/2vRBHx5OxgUyhqv5m4YcnBhBNxF4zGgpSUkgMjz129u1ev/PGR2uIFKKTX0Zf41LYlzB5YIymtSihqM5WhQ/595bsS8iu6U2NnCzE5lzkrL3f+khjMY4gDTLAXC4IZnwWe9lpGpDjDtmwCIvFwANkQBzz709CepPDpDhmLZv5ia9if0urC2LXl7ywK11ieBzIzM29cNDjtPM91NMa6hI+XrvN/NAXBs0LQXhKNLd469nDuzue57dBmxp2v4usfybEbpdYuTjhYviXhQAXuOb9oHINZlZc7f1rz3QydBRPsxYJgRiKwB+jQW5urXduy9Y5tWZc4IG5oOZN22hu+9aI9vv61ypS+B1ZM+2GbzibVlP5zoR3KuzQi4tpBki+l6PqhX/FJtM90tYMDvgLrzcQNAxD6ea3FS+pt8U4SiVxinI0vvILpG/7Fp6rzGTyBWGVp21rrO1pl+feWlUpxzXmiRKrQ/Kfycue/EKGxDB0AE+zFimDGfwO/9FpGa3FsyzJ2vpWWNqTGJy33NDV0OX71u9DKgYWctlqwfdRti/IHzWzTqlh1yZ+XavjYzMioax/TMz+5aEja+XG7ugdQIhUHXkxaVREWO2pbj7/85S/x+/34/U5S/bx58zj33NPzRGpra3n55ZfJz8/H5/NxxRVXMGrUqGbbIkm0tngBKulW+gbXbIy0TVtLkMLq7f69ZYd9x6sHSljbWr5qNzDa2K11LUywFyuCGanAPoj/kgln2JaNQ8R4/BqapE+JHv7tY+G+coYF46KZP98S9ndrU0ZtVdEfPkRLp0RGYftI9CUX3zD0v5E4zyyvJVT+YtKqzWW+qqnRGP+Xv/wlt99+O5mZmY32WbRoEcXFxVx33XUUFBTw9NNP85WvfIWkpKQm26JBNLd4AY7Q/8CL3LIn4jZtLUAqQgcS9pbtSThcmU6NPe7Mv70m+GJe7vzfR1yPyCqcmoJJwChgs9u0XlXvjvR89ea9DPCp6rvu43OBpaqtc94RkYnACFVtcMXTddR5GRgIvKWq32yH5oeA7wJXqepb7rUewGFgk6pGfIvdvInHimBxBfBrr2U0hg32m2mp664fPGD5tGFZvsd69ZxR6NTFM/9HDM1y13v29jPfbCpTeueHE1p/Vu8U1XFjnVdrV2fsK9+2wWsdzZGIP+2WmulThob7LvJKw5YtW5g0yXF369OnD4MGDWLXrl3NtkUD2/an7s2bkLN82e3nbd0ye0NVVdoqVcKRGr8/R7K+zKOzF3DrOQ/og1tG6fbFovaJSI3fFJrqzwpd0HN29ZyBE6svG1heO6rHMjs1YYVCaRO3HQQWREWP6lRVHQ9cDRSp6nj3K2qBnstlwLwIjDMRaCo7+WJggKqOa02gJ43Xll0PBOo9vgXY2tJxW4spcBtbfg18A4ipuXtTbElK2vXrXhn5Kxzbsole6zF0PHy2hqbuOLt0xL4h83Yh0vYzRhqKqzNya46/NXFo2gWFItLLay1NIYhcUXvR7E323mUf+ndfjBBRj99//vOfqCpDhw5l7ty5pKScPnxxcTEZGacWQDMyMigpKWm2LdoUFAwdX1AwlJSU0gMjz/1wT69e+REr1Awwls1jx/JdQvhrFumcVa9ygx4jc2I0bdpOkujLCJ+TPiN8TjrYWuM7UrnGv7e8QoprzhNnJaqO3Lzc+dVR13MGIvI88FdVfUlEHsB5H+yrqraI7MRZ4fpIRO4BvoQTmxQCX1LVXeJslX8HuB4nU3kfcC8wCPi8M4VcBfwF15NeRB4GZgIpwH+o6jL3+t3A/YACu9z5BPgBkC4iG4APVPVr9fSPwQmSB7rtDwFvAb/BCRIFeFpVf+H2X4rjWDIdKAOua+DH8i5wvYhkqGoxTuD3J+BOd4xk4BWc3cBuwEr351Hr/jweAG4FbKBMVZusXGCCvVgSLD5BMOO3wLe8lHE8wXfsd6fblkV8e8PQdbhina72KZecef1I5uR2mtDbcbVlGtKa9LyyzYvOSc+O67N7dYwLD5vRV9O3vJG4vp8Kje+7toK7776bjIwMQqEQb775Jq+//jo33nhjJIaOGVVV6VlbNs/Ncrd4l0R6i9dPKGku70ydyzuUkl74it6w8gMu71MpqbEpEO6TJHtg6qSagamgqlJYs9W/t+yor6A6WcL6h5hoOJv3gLk4gdhcYDswUUSOAIluoHcpTjA3U1VrRORa4ElgNk4glAVMcwPE/wJ+pqoBEXkS8Kvqt+HkNm5/YI2q3i8ic4HnRGQkMAb4ETBJVQ+LyP8Bv1TVO0Tkf4B5qnrrmeJVdauIfAl4qG6LVUR+gVOYehzQA1glIptU9R33trHA5ara2EqyDbwAfFpEFuI4bW2v114L3KqqheLssP3Z/Tk8CdwDfAKYrqqlItK3uV+ACfZiTy7wBWJsm1MtVP0jPX39nzLS/UcTEiYg0iHesAzxz6eW2mdlJlYl9zwc8ndrswepariG6JScaBfrCt65eHj3C4+35MU1Hhhk9x776erph15MXrUtJOF21/qsW5Xz+/1MnjyZv/3tbw32KS4uJi3NMR4pLi5m+PDhzbbFmrot3r15Exwv3hFrq5JTyiZHMos3ndJed/DMrDt4hv065OPnuW3feiaNtCUhNu4ZIqK9k8fU9k4eA3z58JzxMV/Vc3kf+Kp77i0T+BXO1usRnEAQnNWvicCHbs6LcKo4+3XAeGCd2+YHCpqYrxJ4DkBV3xORMM6ixmXAv1X1sNvvCWBVG5/TPOCL6iQ+FIvI39xrdcHeX5oI9Or4E/BHYDhnb6/7gG+LyJU4gWBvnNVOgGuA36pqKYCqHm9OrDmPFWuCxYXAT2IxlWtbtunWQf2XTBo2pPqnfXpdctTvn2z8aQ2RYsQh3ZVeyfgzr+8bMm9Hu7IUtaKw+U6xJ6S13T8q27TFax2tIZ1uA++ozhnWw+62oj3j1NTUUFVVBYCqsnnzZgYMOPsM/JgxY1izZg0ABQUF5Ofnn8zYbarNSwoKho5fvfqGaWtWX3/oxIlBi1QpivQcQ9h/zv38dPYzfHrw/Zq7cZh+tBT3zToG5OGsCHmCqu4EUnG2HZfhbGHOdb/ed7sJ8Pt6Z/0uUtUR9dqC9douVNUmFyz09OxTwdm2rfv3tK5tfFrNjVXW3ADuz8UHfBY485PTZ4ApOCud2TiBad2ZiVa/tppgzxt+BeRHa/CPEv17v5bZd9HE4UMO3Dcgc9yW5OQc409riAZ3vxM+1ND1w/2ntOtcm9plxe25P5qsL3h3sqp9zGsdrSGRhNRP1Vwy7Zxw5kK0bW9u5eXlLFiwgMcff5zHH3+cEydOcPXVVwPwu9/9jtJSJ26ZPn061dXVPProozz33HNcc801JCcnN9sWD7hbvLOXL7stad++C5eEwwkRzx4RkItZfdGP+ebMp7jdf7v+aXlPPbGG5leB2sMPD88ZXxvF8VvC+0AQeFdV83DOEs7jVLD3ChAQ95yviCSIyMX12u4TkZ5uW4qIjHPbSjh7F6CbiNzq9p2DE+vsxllFvFZE6o413IsTeDY2TlO8495fl0n76XpjtYZvAd9U1TM/YPQEjqtqmXtO+LZ6ba8CXxaR7u78ze40mNIrXhHM+AJOpB4Rin2+oqcyeljPp3fvVZrga/P2mcHQUhrywQWoTso4uuyS/+3bnkzucM3OdbXl/47bhKGJfS5ffF6PibOa7xl/bE7Yt2Klf9d4hNgUBu7gRGuL90wK6XX0n3x621JmD6yR5EgWH7SA8YfnjI9JXT0RGY5zXq7vGdc/g7NV2U9VC0Tk98AMVR1br89dwNdwgrNE4O+q+kO37RvAXW5XH/BrVX3CPYv3onu9LkFjKc5K5hU0naCxG2cr9pgbUL2OswL5fv0EDfe+eZx+Zi8deAyYQMMJGg+p6puN/Iweot45w4bmcPW8iLPtfRBndTZBVT/vJmh8D2eltBYnQaPJmqQm2POKYIYfxzO3zX/UtVD7qmtbtt/YlhlizF3vhhdfs1rPCnh2jbxh8f4h89oVCIWq1q4IVS46K+kjXvBJQtVNw+4v8omvVbW84oXDUrTttaS1PVVOy9Q0NEG9LN5xIkQ1I3s35+18ntsObSH7AhVfe5Nrrj48Z/wbERFm6LCYYM9Lghk3A8+39rbVKclbH+uZcWxdSvJY7SAHxQ2dj4Z8cAGWTP/Jhtqk7med42sNtRULF4Wr18V1EtH43pctHp0xuUOu7gGUU3XkheSVx2slHJss0U6CzxeqHDLUWhOtQs31cWzaZqz/FzfXtNGm7Z3Dc8ZfERVxhg6FCfa8JpixAmi2Wna+P+HQYz0zdr6ZlpZlbMsMXjN5p73hm2f44AJUJ6YfWzb9/3oj0q7trpqyVxfatbsubc8Y0cZHQvVNw+8v8IkvUn6lMSdEuOqlpA/XFfsqpnutpSPSu8/+DSNGrKlOSSmLqBdvQ1SQWvIG12x6m0+kl5E+rgUJUGGc7dvNzfQzdAFMgob3/BdOvZ2zKBcpeyKjx7JZQwevvzJrUP9X0rvPNoGeIR74zHt2ZUPX9w+5bFt7Az0Atcvi/rXJJpy8o/jDPV7raA9+ElI+VXPJ9JHh/m1O3OjKnCgYMn7N6humulm8C1WJWhZ5KhU9buIfM5/g7ose5r6D03XxIr/WftzELU9GK9ATkVUiskFEtopIyP1+g4g83cL7p4rIM+73vd3zePXb73Hr5UVC6zwRWdlaDZ0Ns7IXDwQzfo+b1WOD/XZa6vonevao2p2YOB6RNI/VGQyn0adYD/32t+F+DXlxLpn+f+trk3pMaO8c1cVPrVC7KG7P7NUh+GpvHn7/EV+s6qZFkW0JB1Yu8+/IRjCvOW0kllu8dWwme8sL3Fqwi9HZnHJ3OQGMOjxnfFO16NpNY8kYrRzjLC/b5hIcmhgrAbDrl105M7GipRo6G6beWnzwwNakxEmP9upZsqJbyij7VLq5wQMO/e0QJWtKqD1ey7kPnUtKllPaaO+v9lJ7vBYEfCk+Bt4xkG7Dzj5Co7Zy6NlDlFqliAh95/el9+zezbZ1FALv2TvPsGACoCaxe0FtYnp2JOZQreoQmaKKnbitaFXe2F7TO3ywd0E4a1ofO33Hq0lru6voYK/1dERs29+trlBz7z77N44YsaYq2lu8F2KNvRCrvk0bBfR5Kv+ySVEN9BqjJdZowAhOBWCPAX1cG7JSnIzd8cBjIlKKk527kAbs0lT1qJvZOgSnVMlI4BKa9geu01k/CDxNg6rmiMhg4FF37G7An1X1pxH4EXmCCfbigWDx8VsWZP8eeNxrKQboMbEHfS7vw8c/Pn2HJOveLBJSndfsknUlHHzqIOf+8OydhqIVRdQcrWHUT0YRLguz+8HddB/TnaR+SU22dQQSwlo7Zaee31DbgcGXbkUkJyITaW3c+Ec3x5aipdMu6Dl1n08Shnqtpb1kasbo26pnHHsheaVVI6GIBO5dlRMFQy46UTCkLot3t+vFG7Us3no2bauBJ8GzEwYtsUYbUa//fTiraifPAIvIZ6m3suc+PssuDcc+DGAWjgVaWwPcszQAzwLfVdXl4vgbLxSR1ar6QRvn8JS4PxfThXgCx+jY4DFpo9JI6nN28FUX6AGEK8ON1jAvXlVMr9m9EJ/g7+Gnx8QeFK8ubratI3DFOl3jU/o31JY/cHpq5GaKL1/cplDUv6Vo+X6vdUSKVJL73VGdM6qXnbbUay2dAbdQ86XLl92WEq1CzfWwgf+Ye9memNTUa4T3gXn1rNGewimePI9T1mit5TqcFcF17urbF3Esxup4rR2B3lm4RZJnAr915/sQx2+33ZaDXmFW9uIEK2Bp9oLsLwFrIbpZXYa2c/Cpg5RtLkNRhn99eIN9ak/Uktgn8eTjxD6J1J6obbatI3DzUrvBgK7Wn1ZYk9TjokjM4Zy30ajWMYs024pWTBvTc3pegiQM91pLJEjAl3xTzbSZi/1bF+1MOJSDmIWB9hKjLd7fzr1sz9oIjtdqVHWniJxpjfYHHB/c19s4bJ1d2jONtDdrTdZKfDiB8yRVDUV4bE8wf8BxhBWwNuJYqRnilMH3DGb0w6Ppf1N/Dv/9cPM3dCJGHNJd6VU0GNAdGDx7S8Q8l7WqmA72QVTRhM2FS6JmgegVs0JjZueELliDNn8GytByThQMucjN4j3sevFGIot3H865tnigOWu0+pQAaXJ6Fv+Z1mVN2aVFgtM0uNZlK4Fv1nUQkWEi0uCuRkfABHvxx/dxLFwMcUyvGb0o31ZOqOzsD32JvROpLTi1WldbUEti78Rm2+KdxnxwAQ4OmpHSWFtrUS2PWvmKaLK9eNW0sIY+8lpHpBkdHjTlkzWTD4tKp9mqjheqqtIHu168Kfv2Zi8JhxN2tmO4L829bE+kV7jaynvAUKDufNtyoEBVz/qErKpHgReAzSKyxL38BPA/bjmXOar6NI4BwWIR2QSswUnEaCkTRORAva8nW6DhVmC8iFgishn4K9CjFXPGFab0ShySvSD7EmAJZjvXU3Z8fQfDvjaMlKwUwlVhwuXhk2f5StaXkL8gn9GPjD6rtmnhkkKKVxUz7P5hJ5MwRnxnBEmZSU22xTPdqrTkT4+EfQJnJU7U+rsVL5nxs244h5jbTbj24021ZS9F8lN7zBjVY9KKCX3mxn3JmLZQSU3BC8krDlRLKCLb9YaGcbd4K1Na58X7zNzL9gSa72boqphgL07JXpD9Y+JnSb5Lkf9sPiVrSwgVh/B395PQPYFz/t857P3VXuwaGxEhoXsCA24ZQLfhToWQvIfz6H9Df7qd080pr/LnQ5Rudna++s3vR+9L65VeaaQtngm8G140f7U2aF+WN/TKpR+NuK5JE+7WEKre9GGo4t0pkRovxtg3Dbt/j9+XGJMaa7HGxq59OWnNygJfaWSyrg2NkpJSetDN4m3Oi/cQMHbuZXs65Iq4ITaYYC9OyV6QnYSTAWQ+RRs8pzEfXIBl0360ujql9+RIzVVbuWxJuGpVhw0mzk2fuPLivpc3a4HYkVnm37FoW8KBmUTZIszgFmoesnnt4KytmQkJ4VENdLl27mV7/h1zYYYOhTmzF6dYAasG+AxQ47UWQ9dm8g57fWOBXighpaQ6uVdEt1zVLg1HcrxYs7t03dSQXbvDax3RZEZo9OxLa8euR+k4dYM6KLbt77Z37/iZy5fdPmrL5ks3VFZ2X6lK3d/I76MZ6IlInohc2MZ7gxKhox2G9mOCvTjGClgW8AOvdRi6Np95365urO3goJkWIskRndAua87gPd6R9Sfei2gQVBWq5jtv/YKc39/GvD8G+H9v/uysPmE7zHfffpgZT9zKzCdu47mN/25RW1s51x4w6YaaKQU+lb3tHszQIk6cGDJ+zeobpq1Zff3hwsKBLwL3e62pCR4EIhrsSaQy/rsgJtiLf36GU6vIYIg5fYr1UP8iJjXWfnDwrIinEqtd3uFf0D8qXFivrQAAIABJREFU3Til1q7ZFqnxfrzwdyT7k1h8719593ML+EbO587q89LWd8grPMiSL/yVlz/zOA8vfZr9xYeabWsPfTR9xO3VM3ukaOL6dg9maDFVVemZm615/zf3sj3lsZhPRBaKyM9EZKmIfCQiufXaHhSR7W7m7HoR6Skij7nNy93rPUXkdhFZ5fZZLyJz642R42a9bhKRX4nI3roVRXd18fsi8gHwhIgMEJEPRGStiGwRkZ/WGycoIn8TkddFZLeI/F1EJojI+yKyR0TO/pTURTDBXpxjBSwbxxImXlLqDV2IwHv2Dmmk5l0oIbmsKrl3xLNmVasiu1LoEesK3o7I32x5TQUvbn6Lb+Z87mTmd7+0s5N6Xt32PrdfdC0+8dEntSdXjprJa9sXNtvWXlJI6nV79czsTDtjcUQGNLSEB4LBYKyLJw/FsSWbAHxeRM4TkV443rcTXKuxWUCZqt7n3jNdVce7devewrE7m4BT1mQBgDg7A88BX1bVcTg+uGdaDw5U1Tmq+jmgCLhWVS/G8dCdJCJX1et7MXAbMBo4H8gFPgGMAwIi0imTp5rDBHsdACtg7QHu9VqHoWvh+uCOaaw9f+CMTTiWSJFFa9IiPqYH5JVtmVxrV29p7zh7i/Lp2a0Hjyz7E1cvuJdP/fUrfHhg01n9DpYcYXDGqZqvg3v0J7/0aLNtkcCHz39dzaRZF4aGLEbpFI4DccxbwC88mPd5VbVVtRjYBozEKUa8A3hWRO4FujfhODESeEtEtgB/BwaIyACcoKxSVZcAqOpLOAFdfeo7ZyQAPxORjTiOUxfiBH11vKWqxaoaBjYB76hqtaqWu1obPH/c2THBXgfBClh/A37jtQ5D1+HKtbrap2Q21n5g8KwovX6E0qMzbuxZc/ytqvaOEbLD7CvKZ2z/83g98AceuPRLfOGl71FaHZMdvFYxLTRq1mW1F25Cz3qzNkSGI0AgGAx6UUaj/v/lMOB3A6ppwKNAFrC2CWeL54DfqupYYCIQAlJwrNCaez71V8nvB3oBU92VwH+54zSm8yzdzczVKTHBXsfifhwLF4Mh6ty0zD6rgHIdYV9SeVVK3yiVBdL4LzzYQvaVb7u4Jlx19jJcK8jq0R+/L4HrL5gHwIRBY+jdLYOPTpxuaDG4R38OFh85+fhgyREGpWc22xZpRtj9J95UM7XIp/JxVCbouihwVzAYPNJszxghIulAP1VdpKoPAptxVtoASjnd8qwnUPd/4nNA3XGN7ThWZTPcMT/p9m2MnsAhVa0SkcHAJyPyZDo5JtjrQFgBqxb4FHDMay2Gzo3rg9voebz8gZdsQqRbpOdVra0EUiM9rpesPv5Gu0rJ9E7tySVDJ7A4bzUAH53Yz/GKIob3Gnxav/nnz+GvG1/FVpuCiiLe2rmUT4ye3WxbNOil3YffUZ3Tu5smxfpcWWfmp8Fg8G2vRZxBBvAvN7FiM3AY+Kfb9gvg/boEDeCrbt+lwHCgAEBVq4Hbgd+JyCqclcIj0GhZn0eBGSKyHngcx5rN0AymqHIHJHtB9lzgbUywbogSDy0ILR6Vz6zG2ldMeXBFZWpmxG3B7HDxwZqSPw5uvmfH4vqh/7UhOSF1fPM9G2ZvUT7feD2XwspiEhP8fCvnXuaMnMZdz3+Tr8/8HBcNPJ+wHeZ77/zyZFD45am3c8f46wCabIsmNhp+PWnd0sO+ouhFll2DfwOfDAaDttdCooGIpKtqqfv9HJzkjeGq2imfrxeYYK+Dkr0g+7vAQ17rMHQ+mvLBBQj7EisX5TxiIxLxRAq79sC2mrJ/XBDpcb1mULeRG3MG3Nxl3XBW+3cv2ZiwdxpCxEv1dAE2ATOCwWCnrcggIp8FvoazgFEFfFVVTcmxCGJWhjouP8b5tGcwRJRPL7E3NBboARwaMG1jNAI9ANXSimiM6zX5lXsuqgqXd9ladJND5+ZcXjtuC8oJr7V0MI4A13bmQA9AVf+kqheparaqTjaBXuQxwV4HxQpYimOnttNrLYbOxeXrtclt1AODL43a1orape3OXo1XVh17rUv7yA6z+42/uWZaWYL69nitpYNQhbN1u89rIYaOjwn2OjBWwCoCrgaOe63F0DmY0oQPLkDY56+qSO2fHa351S7ttDXaDld+PK4yVNalExZ6atrQO6pz+qVq8mqvtcQ5Cnw2GAyu8lqIoXNggr0Ojltw+ZOcXkvIYGiQQ387xI5v7GDzZzdTdeDs/zKfed+uDqvyoyOHufKjPVz50R5eKDpVMi0/c9LGvy99ND343J0En/sMy7e9FlF9apdGdLx4Y+WxVyNfhLqDkYS/x23VMy4eHO69yGstccwPg8Hg370WYeg8mGCvE2AFrOXAXTRfmNLQxekxsQfnfOccEvucfU6+T7Eeyixi8r9LSthXU8sb54zguaHDeKzgOAdrawBYUB7ufawknx/c+gxfv/7XvL72GQpKD0dMn9rlnfo16WjVvrEVodIuv6oliO8TtRNmT6w9ZylKjdd64ozngsHgD70WYehcdOoX1q6EFbCeB77jtQ5DfJM2Ko2kPkkNtn32XXuHQMIbpSXc3DMDnwi9/X7mdu/Om6Wl2OKvXn942zkzLrgan/hI79aTccNnsH5PBBdotLJhcZ2IFUdfaTT5pasxMTxi5pW147ej5iiKy0rgHq9FGDofXdI2pLNiBayfZC/IHgF8wWstho5FQlhrJ+9yfHAP1YYY5D+18jfQn8jh2hCH+0/aVHR00eRFm//Fm2ufRcRHRU0p52SebZ9r22GeX/Ybth1YDQhXjL+V6RfMb7ZNtToiBZVPlFfwp2WnjsdV1tRSFQrxo+uvOEOn8q/1W9hx+BgIXHb+SKaOGNpsW3s4Xn3ggvJQ8Ydp/owp7R6sEzDE7jPu0zWXHHgxadWusNhd0qTeZS9wfTAYNEdyDBHHBHudj/uAYcCVXgsxdByuWqtrfEqTRZL3Z11Wg7WIqy++i1GDJwDwzPu5bN3/4Vl9V+9+j7rt3vKqEn7y4hcZnXUxfdIHNNmGhiKy6tU7LZX7r8g5+fjl9VsIN1BTdN2+gxwvK+f/XX0pFdU1PPLOUs7r35feaalNtrWXFUdfyZg78E4VEWn3YJ2AHpqadWd1TtmLyatWlUnVVK/1eEApTomVuLFCM3QuzDZuJ8MKWCEcS7WNXmsxdBxuXGafjGAGJvrJD9WebDsUqiUzMSlcnjbowt7dMymvLjnZVlxxHH/C2ef/1u1ZSGPbvU21QbgpT8w2EQrbrNuXz5RzhpzVtmF/PlNHDMUnQveUZMYO7s/G/YeabWsvBdX5o8tDRSbTsh6J+LvfUj19ypBwn66WuGEDtwWDQctrIYbOiwn2OiFWwCoFrsAxmDYYmmSk44N70t3hyvR0XigqxlblRCjEe2VlZA+duhWRjAkjZrFs2+s8u/BnfPfPt7AzfyO3z/76WWOeKDtK7+79Tz7u1T2TwvJjTbap2mGaNkBvE1vyj5DRLYWsXhlntRVVVNEr7ZTFb6/UbhRXVDXbFgmWH325jxoLo9MQRK6sHT97cu3IZSjVXuuJAQrcGwwGI5vWbjCcgQn2OilWwDoKzAVMAVPDSfKfzWf717ZTW1hL3k/z2PXALu55O3zoiwf2s7mqEoDremSQlZjIJz7+iNv27eU/+vTFN/rGYoAp511O3x4D2X1oI4n+RKacdzkLrZciI04rTwAR39Zc/fF+Jg/PivSw7aaw5sh5paHCFV7riEcuCg+f8YnaCbtEOeq1lihzXzAYfMprEYbOjzmz14mxAlZ+9oLsucBioP0nyw0dnkF3DmLQnYNOPu5WpSXnPhK++ImsU1ucCSI8OGDAyce2+EILu2eNBfD5Erg156unjfm1Jz9BWVUx3VNOrZz17p7JibIjDMs8H4DCeqt5jbWpXVYM9Ivk8y2urGLPsRPcNnV8g+09U1MoLK9kaG9nQbGwopJeqd2abYsUy4/8a8CVg++2RcR88D6DwXbvCz9dMz3/xaSV20Nin++1nihwfzAYfNxrEYaugXmB6eRYAWsvzgpfZA4bGToVtyyx1ws06XN7rN+EjYj0AqiuraSw7NRii5W3nNTkdNKSe5x2T912r602pZVFbMpbxvgROU22qV1aHunnt+bjA1wwMJO05IYrulyUNZBVH+3DVqWsqpotB48wLmtgs22Rorj22IiS2oKVER20E5Gu3QbdUT1raLrdrbP9jB4IBoOPeC3C0HUQc2Ska5C9IPsCYBERXjkxdGz+8tPQR4lhRjTVZ/XEby0p7TEsB6Ck4gS/f+sHVIeq8ImPtOR0bpj2RYb0G8VvX/8O8yd/lmH9RmPbYf6x7NdsP7AGgHkX3crMMdcANNoWqlq/IlT5QZMZwa0l9/WFXD9hDOcPzDx57cnFH3LlhaMY0rsntq28tH4zOw87Zd7mnD+SaSNPlV5prC2S9Ejsk3fV4M8NEZEu7Z3bFIrqe4nW4ryEY7O91hIBfhQMBn/gtQhD18IEe12I7AXZFwEfAL281mLwnqnb7XVff8me2FQfW3yhhbN+VYz4+kRbT23F4sXh6jWzoj1PPHLloLuX9UzOnOG1jnhnc8K+5Sv9uyYgRHY/PXbkBoNBU/zeEHPMNm4XwgpYG3Hq75U019fQ+bnzfbu2uT7H+15kxSLQA1C7tMt+8lx+9F9DVDXktY5458Lw0OnzayZ+LErkPPpix49MoGfwChPsdTGsgLUamAPGnqgr07dYD2UWM6m5fvuGzC2LhR4A1ZhNFXeUhgqHFtYc6Wzn0qLCQO015pbqGSRqwlavtbSCH5itW4OXmGCvC2IFrHXALOCA11oM3lDng9tUH0XCJenDYpcFaVecXZ25C7H86MvDVbXZ1VYDdCdlwB3VOedk2KnLvdbSAr4TDAZ/5LUIQ9fGBHtdFCtgbQNmAru91mKILQlhrZ20S8c21+9432wL8cUsoUe1qqOew4oI5aGirBPVh8zqXgvxk9DtUzWXTB8R7r8IJV6PAHw9GAzmei3CYDDBXhfGLcsyE9jktRZD7Lhqra72afNZ2fuGzIvt2U6tbb/pbAdn+bFXRqpqV3COiBiX1V44e3po9CqUiJfuaQcKfCUYDD7stRCDAUyw1+WxAtYRYDbQEbZDDBHgpmV29+b6KGIX9zgnxoVsw2f7mXUxKkLFg45XHzCeua1kTDhr2rU1kw6Iku+1FqAKuDUYDP46mpOISJ6IXHjGtTUicmkbx+spIt9qp6ZBIvJBvcdBEWm4yKUhpphgz4AVsIqAy4G3vdZiiC4j83Vn9yrGNdevoPfYzYgvs7l+kUV7x3a++GTF0VdHqWrkTHi7CP01Y/Rt1TMTEzVhs4cyjgJzgsHgPzzU0FZ6Au0K9lQ1X1Xn1Lv0IGCCvTjABHsGAKyAVQFcC3TEFylDC7nn7fCRlvTbN3ReUbS11Ee1ugTzpgBAZbh0wNGqfR96raMjkkpyvzurZ53Xy05b5sH024BpwWDQ83OXItJfRF4SkU0iYonIXfXaJovICrdthYhMdpseA3qKyAYRWe72fVBEtrvX1rurf8NF5Hi98U4+PuP7x9wuy937e4pIDxF5UkQ+dOf/lSkmHhtMsGc4iRWwaoBbgf/zWosh8qRWafG5h2iyiDKAghZljDwvFppOzmmXF8Zyvnhn5bFXL1DVCq91dEQS8CXfVDNtxnmhgQtR7BhN+x5wSTAY/DhG89XxghtIbRCRDcAY9/qjwGZVHQdcAfxERC50t1RfBL7vtn0PeNG9fh9QpKrjVXW6OBaJ3wAmqOp4nAoOLa6PpKr3ud9Od8csAh4GFqnqFGA8kAnc086fgaEFmGDPcBpWwFIrYD0ABIAar/UYIscti+0NzfngApzodcFmxBdZE9hmULu06xbZa4CqcHm/I5V5q73W0ZGZHRpzaU7ogjVoywOUNvJH4BPBYLA4yvM0xM1uIDXeDcjqag/OA54AUNVDwGs49VVHAzWq+q7b9h7O6/zoBsYuAXYAz4rIvUD3CBT+vg74phuYrgMuBka1c0xDC/B7LcAQn1gB65nsBdkfAS8Bfb3WY2gnqjpvg7bI2HXf0MtPRFvOmahdFk+ZlHHBymP/HvvJof9ZLiLNBuiGhhkdHjSlt9191ytJa7qpaFaEh1fggTgurXJmORoFpIHrDfVFVcMiMg2YAVwGrBWRq4ATnL5QlNIKTQJcr6ofteIeQwQwK3uGRrEC1lJgKs5ZFEMHZtoO3ZAY5pzm+iloYc9zY7qFC4BdYlaRz6Daruh7qPKjNV7r6Oj00x7n3V49MyVJ/ZEsMVUF3BLHgd67wBcARGQAcDWOL/p2IFlE5rhtc4BEYCfOSl6qiPjdtnSgn6ouUtUHgc3AhcBhIFFEznXnur0JHaVA/Sz7V4Bv153TE5G+ItLs65Kh/Zhgz9AkVsD6CLgEk6nbobnjfbtFtdsKe47eiiQMiraeM1G7NBzrOTsCq469lq2qpV7r6Oh0I6nvHdU55/ex05dEYLijwKXBYPD5CIwVLb4CXCQim4B3gG+r6hZVrQFuAn7stv0YZyu4RlVPAH8BLDdBIwP4l5tIsRknyPunu5X738A7IrIQaOpv9xfA+3UJGsBX3f4bRcQC3gQGR/7pG85EVOO18LghnshekJ2Ac+j3y15rMbSOfkWa/5vHw/2bs0cD2DDuPxed6H3B7Fjoqk9N6T8X2qG8S2M9b0dgRuYNC7PSRl3qtY7OwlL/9kXbEw7mIG1a7NgKzA8Gg3kRlmUwRBWzsmdoEVbAClsB6z7gi4Cp8N+BCLxr72pJoAdQ2HPUyGjraQjVcnN+uBE+PP76BFX14vB/p2Rm6PzZl9aOWYfSWoeYd4HpJtAzdERMsGdoFVbA+j3Otq45YNsB8Ie1ZtJuHdN8TyjMOHer+hIifYi9RahdmezFvB2BWrs6Y3/59g1e6+hMnGsPnHRDzZRjPpW9LeiuOOWorvIo49ZgaDcm2DO0GitgrQcm4mTqGuKYq9bompb44ALsHXrFsWjraRSt6fK+uE2x5vibE9w6ZYYI0UfTR95WPTM9WRObCqSP45RVeSAYDJpzpYYOiwn2DG3CCljFVsC6Efg6UOu1HkPD3NgCH9w6CnuN9jArLpTu3dzxT63W9NhbvnWj1zo6G91I6n1H9cyx/eweixtoXgqMDwaDb8Val8EQaUywZ2gXVsB6GLgUOOCxFMMZnHtQd3Svbt4HF6Cox4jt6vO3qA5fdLB7eTd3x2Dt8bcudjMmDRHEhy/xkzWTZ40NDVmMEsbZts3F8bg96LE8gyEimGDP0G6sgLUcmACYT8BxxD3vhI+2tO/eoZe3yDM3GqiGawCzstcMIa3t/nGZZXmto7NySWjUrNm1YxbhZNt+JxgMttctwmCIG0ywZ4gIVsA6jlO487uYbV3PSa3S4pEt8MGt40TvMcOiqadJtLzAs7k7GOsK3p2kant3trJz88559sA7g8HgG14LMRgijQn2DBHDCli2FbB+jJOtu91rPV2ZlvrgAhSnD9+pPv/wKEtqFLXLTNHgFhLW2rQ9pZu2Nt/T0ApqgW8BV2bl5hzyWozBEA1MsGeIOFbAWouTrfuY11q6JK3wwQXYO/RyT9/g1C6LtlF9p2LDifemqNot3qI3NMlW4JKs3JyfZeXmGIcBQ6fFBHuGqGAFrEorYP0ncBVgDjnHkGnbdX1LfHDrKOgzdkg09TSH2iVVXs7f0QhrqNvukvVm5bx9hHGSMCZm5eas9VqMwRBtTLBniCpWwHoLxzz7Ga+1dBXu/MBu8ZnJku5DdqsvcUQ09TSH2qXmIHwr2VD4wRRbbbPl2DbqVvO+k5WbY9yADF0CE+wZoo4VsIqsgBUAPgl4lvXZFehXpPn9ipnU0v77hl7h+aqr2qW21xo6GraGU3aVrNnltY4ORhjHCWNiVm7Oaq/FGAyxxAR7hphhBaxXgLHAnzyW0mn57Lv2zpb64AIc73Ph4GjqaQlql5vXoTaw6cSiabba+V7r6CBsAaZl5eY8YFbzDF0R8yJriClWwCqwAtbdOIWYt3ksp1PhD2vNxbt1bEv7l6YN3mMnJJ0bTU0tQiuSvJbQEbGxk3YUf7jHax1xTghnNe/irNycNV6LMRi8wgR7Bk+wAtYiYDzwPaDSYzmdgk+s1tUt9cEF2Df08rhwPVGtTvFaQ0fFKlwyzdZwXPwe45BFwHizmmcwmGDP4CFWwKqxAtb/4iRwGPeNdnLjcrtHa/of63vRwGhpaRVaa9wz2ohiJ24rWpnntY444zDwmazcnEuzcnO2eC3GYIgHTLBn8BwrYH1kBayrgFsAk2HYBs49qDvSqsluaf+y1IEf2wlJo6KpqeWEM7xW0JHZUrRsmq3hvV7riAPCwKPA+Vm5Oc96LcZgiCdMsGeIG6yA9Q/gfODXOC/chhbSGh9cgH1DL98XLS2tQVUV6OW1jo6Mov7Nhcv2e63DY5bjnMv776zcnGKvxRgM8UZcBHsiskpENojIVhEJud9vEJGnW3j/VBF5xv2+t4h844z2e0QkYgfRReTPIrJFRP4SofEeFpFqEenTxvv9IqIi0uqzTyLyvyJycyNtD4lIbls0tRUrYJVYAesrwDjg9VjO3VFJq9TikYe4uDX3HO03vn+09LQKrSykFdnDhobZXrzykrCGPvZahwccBe4BZmbl5mz0WozBEK/4vRYAoKpTAURkOLBGVce38v5VwCr3YW/gG8DP63W5B8gHdrdmXBFJAGx39aHu2mDgGqB3/estGMuvqmcVjxWRROB2nE+md+BsQ0SMxuatQ1W/G8n5IoUVsLYC87MXZM/D+V1e5LGkuOWWxfZGgVkt7V/erf9eOyH5/Ghqailqlxfj/M0a2oGiCVbhkkPje89psXNKB6cCeBj4aVZujvFWNhiaIS5W9ppCRJ4XkRvc7x8QkRMi4nMf7xSRESIyT0RWurc8BvRxVwaXiMjncbI+H3OvzRGHB0TkQxFZLyIvi0imO+ZDIrJARF4GNgLd62npCbzjXlsvIl9xV9UecVf6NovIT90gERF5VkR+JSJv4QRzDXEtTgmSH+IEpXVz1a3WfUdEVovIHhG5vl77p0Rkh4gsB77TwH3fEJFFwHdboPFLdc9PRP7p9nsTWm65FS2sgPUujs9uXcBuqI+qzt2orbI72zf08rwoqWk1qmXmjTpC7Cj+cFrYDnX2Uixh4I/AeVm5Od83gZ7B0DLiPtgD3gPmut/PBbYDE0VkCJCoqh+d0f8+oEBVx6tqjqo+CWwA7nOvfQAEgCxgmqpOAN4FflZvjFnAPap6oaqefDFR1SLgunrjPwr8BzAGJ6C8GJhCvaANmAbcoKpTGnl+9wBP45QJSBeRiWe0F6rqZLffrwBEZCDwO+AaVZ1Ow+fbVFVnq+oPW6Cxjh8Cx1V1LHAXMLsRzTHFCli2FbCeBs4DHgTKPZYUN1yyXde1xgcX4Gi/CS0uzxJt1C6p8FpDJ8K3sXBhq85udjBexyml8vms3Bzzwc9gaAUdIdh7H5jnnkfLBJ4C5rlf77VxzOuAq4B1IrIB+CIwvF77a6pa0MKx5gFPq2qtqlbjuEPMq9f+vKo2+IbmBm0zgBfdLeFnODsI+5v770pgqLvtewnwoarW2SX9voHhF7RCYx1zcD41o6pHgX81pNsrrIBVYQWs/wHOBZ7EJHFw5/t2q3xlK7r1PRD2p4yJlp7WonZpi318Dc2zq2TttJBd29ls1NYCl2Xl5szPys3Z7LUYg6EjEvfBnqruBFKBW4FlOKtwc92v99s4rABBd3VuvLuCV38Vq6yVY515dq/+46bGCuCcm9wiInnAvcBtIpJcr0+V+29dYJPgztkc9edtTmP9fnGPFbAOWwHrXhzrtb/QRYO+zCI92Lek5T64APuGXB5X23xql3bJ310UkQ0n3j/htYgIsQvnHPPkrNycD7wWYzB0ZOI+2HN5HwgC76pqHjAQZ2WqoWCvBEirO5NW71r9Wl6vAPe5Z/AQkRQRGddGbe8Ad7vn4pJwtj/fbeG9nwWuVdXh7lcWzpbzDc3ctxyYLCIj3cefj5DG94C7AUSkL/DJlj0Nb7AC1g4rYN2Js0X9LF0s6Au8a+9qjQ8uwJHMi/tGS0+bsMs6xAeMjsSe0g1TQnbNdq91tIPdOB+EL8jKzflrVm5OixPhDAZDw3SUYO89YChQ9+luOc65ucNndnS3H18ANovIEvfyE8D/1CVoqOrTwPPAYhHZBKzB2RptC4/jJFhsANbhbDk0WzJGRGYAacDiM5r+QsPn6U6iqodwzuG9LiLLgOasgFqqMQj0F5EtOFu9bzczblxgBaydVsD6DF0o6GutDy5AZUqfg2F/t1bdE23Urkj0WkMnRNYVvFfitYg2sAfnw+YFWbk5z2Tl5nT6v2ODIVZIK6qHGAwdguwF2ecB38cpadMpa7hdt9JefucH9vTW3LPjvFsWHRw8Ky6SbuqoKnpiDVreqq1oQ8u4cdjXtib6kuLmfGYTbAN+DDxnAjyDITp0lJU9g6HFWAFrlxWw7gIuwEl66XRJADcst1vtJ3skc1L81bPTmjSvJXRW1ha8He+ZzhuBTwMXZuXmPGsCPYMhesRFUWWDIRpYAWsXEMhekP0A8F/AF+gE1lzntdIHF6AqudehkL/bhdHS1HZCxhc3Suwt2zJpYp95m5N8KfH0e1fgLeDhrNycd7wWYzB0FczKnqHTYwWsg1bA+jYwBCfoa5WTSrxxz9ut88EF2Ddk7i5E4jAZQjt88B3PrDn+Vo3XGlyqcMolXZiVm/MJE+gZDLHFBHuGLoMVsMqtgPUbYDRwPWcnx8Q9aZVaPOJw63xwAQ73nxJ3K2iqNeVAN691dGb2l2+fWBOu3OShhCM4hdCHZuXm3JuVm7PVQy0GQ5fFbOMauhxWwLKBl4GXsxdkXwzcD3wKiPvM0FsW2xuklc4mVUk9j4T8qa3a9o0FalcU4WSkG6LIh8fTUuH8AAASxklEQVTfsGf2vzHW024GHgH+kpWb01y1AIPBEGVMsGfo0lgBay1wR/aC7G/ilLz5HKe7qcQPqjp3gw5r7W37h1y2A5H+0ZDULuyyYmCw1zI6Owcrdo2vDldsSE5IHR/lqapwSlr9ISs3Z0lznQ0GQ+ww27gGA2AFrHwrYD0EjACuxHnTiqss3unbdF2i3fpA9HD/qT2iIKfdqF0a79minYZVx16P5nnNzcB/A4OycnPuMoGewRB/mJU9g6EeVsBSnGLSb2cvyO6HU8n/8zjn/Dzljg/sVpemqE5MP1abmNZWd5ioonaJ2d6LEYcq91xUFS5fn5KQNiFCQ1YAf8dZxVsRoTENBkOUMMGewdAIVsA6Bvwc+Hn2guwcHO/im/EgqcD1wW11Ysb+IZdtRyQnGprai9qlIa81dCVWHv23/9KBt7R3mLXAUzhn8Yrbr8pgMMQCs41rMLQAK2AtcQs1D8I51/cOMbRl++w7rffBBTg04JK4TYBQLTX2PTHkSFVedmWodE0bbt0F/BAYnZWbMykrN+e38RLoichTIvKTM669KyL/0Y4xbxSRFrm6iMjnReRvbZ3rjLH6iEi1iPw8QuMtFZGr3O//V0Rubqa/T0SCItKqRSD35/25BsbaJyLTReRiEVkuIhVn/qyaajuj3wUislBEtovIZhF5UkSSW6Ozq2OCPYOhFVgBq8gKWE9ZAesKnMDvP4GlOMVio4I/rDUTd2urC+PWJHYvqE3sflE0NEUCtcvNzkKMWXHs1dQWdj2Ek007OSs3Z1RWbk4wKzdnZxSltZWvAp8WkakAIvJFnL/F37VjzBsBLyz8PgMsA+4UkYhWBlDV76rqC8108+GUyWnt3+VTOJ7G9ZkLVKjqcuAwzu/pmw3c21RbfaqAr6rq+cBFQE+cKgqGFmKCPYOhjVgB66gVsB6zAlYOMAznBWttpOe5erWu9kHf1t63P2vOVkTi1htYtSruS910No5V7R9TESpZ3UhzEc4b9zwgKys35/6s3Jy2rATGDFUtwXHGeVpERgHfAz6nrum7iDzgrgRtFpE/ikiqe/1ZEflS3Th1j0XkauBq4LsiskFE7jhz9a6B1bxeIvJPEdnornINdPuJO/+HIrJeRF4Wkcwmns7dOCuo24H59eZ7SET+IiJvuCtbr4pIN7ftChFZ4Y5vicinGhq43vNLFZHDUi87X0R+KyLfAh5zL61yn3u6iPQSkT+5Y28UkV82MPw/gTHuz7/+c3kKQFUPquqHwFlndJtqO6Pfx6q6wf0+DKzGec01tBAT7BkMEcAKWPutgPVzK2BNAs4Dvo/j/dlublhutymb9tCAS1q6iuMNWh3f+jopy4++Ut9X+SiOs8V8YEBWbs7nsnJz3svKzbG9Udd6VPUdYBFOAPCgqu4DEJFrgVuBS4BsIAX4bjNjvQ68Dvyvqo5X1b+0QEIO8C1VvQhYgbMiCk5yVxYwTVUnAO8CP2toAHfbuDtOofenccpA1WeS+1wuwKlNeat7fTUw0x3/CuAREWn09UJVK4BncZLOEJF0nHPIfwTuc7tNdZ97KfAoUAiMc5/fQw2MWQX8DfisO2YGcC2OL3nEEZE0d65XojF+Z8VsoxgMEcYKWLtxXhQfyl6QPQS4xv26DOcNp8WMOqDbW+uDC1DrTyusSeoRt1u4AGhtevOdDJGmoPqgv6jm6A96JmV+ACzvSIFdE/wcuEVVn6p3bR7wVzdoQUT+APyEZgK+NrBIVessGJ/ECcAArgPGA+tcp0I/UNDIGPcAC1RVReQF4JciMkBVD7vtb6hqsfs8VgEj3ev9gT+JyEggBPQBRgFNrcj+BvhARHJxAtLXVbWgkbN61wBj61ZKVfV4I2M+BfxLRL4H3AZ8UE97xHC3t/8OvOkG5oYWYoI9gyGKWAFrP/A48Hj2guxUnLMs1+KspAxq7v673wk39uLaJPuzZm9BZGZb7o0ddk+vFXQRwjhnwV4FXv363/+9w2M90SAMnBm0Cmefpa17HOL0na2mPoS1pm/9OQUIqmqTK1wikoKzUlchInUren7gLuCn7uOqereEOfXe/QTwD+C3bqD4UTP6UNU8EdmI8zr0ZZyAr12o6hoRKcBZXbwb+L/2jnkmbjD6HI4Fnzmv10pMsGcwxAgrYFXgvuFmL8gWYALOC+41wETOOFbh+uBObMtc+QNntGoFMdao2iEg7vx6OxEfA+8D7wFvff3v/z7hsR4veAf4kYg8BpTjZNG/67btASYDiMggHAvCurYSTv+/uQe4SESScAK4m3ACjjpmicgIVf0IZ3vxA/f6K8B9IvKKqha5Qd0oVT3Tq/hGYJOqXlp3QZxySX/gVLDXGD2BPDfQ+wRwTjP96/g18CfgkKquBlDVkIhU4Dz3uuDy38A3ReR+d46+TazuPQ38CBgKvNZCHS1CRHzAn3HqO95bt9JoaDkm2DMYPMAt3rzO/fph9oLs3sAcnJW/y4DRty5qvQ8uQK2/W3FNUkacb+FWFAL9vJbRiTiCE9y9D7z39b////buPdjO6Yzj+PeXRHNBxjWVhJSWNlFRYhCExsigU9UyWqadNqiatGkNjaE1poNBzaBt6EFRcakZZzAuDaXIXSIhEY3IxSVKXYOSiCRy5Okfa+1ztt19bsk5Oee85/eZ2XPe237f9e6zZvaz11rPeiev6ODydLiI+Luk4cBTedNc4PK8fD1wb27hWpr3ldwO3CLpFOCqiLhT0kzSk0JWAIv5fMLUVOByScOAlaSsWiJikqQdgRm5G7cHKciqDPZOAyrHBs4C+kg6tJnbPB/4c+4+XZjL2KyIeELSBuC6il1X5/KuJY1FPAuYCDyfj58KnNPIae8gdZNfGxH1Tx/KXczTgX75nv4DXBgRtzaz7wTgmIgYR/pRfEq+v1K3+IyIOKsl92sgB8hmnc/w24YP/sONdaN2fZ+jgdGkx7i1yIovHTtrxR7f6dRduBvr3nnx09V37tXR5ejCPgKm0RDcLe7Y4lhXUhZk7ZkTLKzg3LJn1gktGrvoDcZSSxqMzJKhw3YFDsuvUcC+NDLJ8psDR3X6yUZj4+qPO7oMXcyrwDxSC9QsYP6E2slbbFJvKw5Jl5HG6f3agV734ZY9sy5oydBhW5PGHB0CHJyXB9X17LNqxqir+pDGF3VadesWzKlbO+2Qji5HJ/UhKaNzLjnAm1A7+d2OLZKZdWVu2TPrgoYtXbKG1I03rbRtydBhgz7YYdi+SCNJCR8jgMEdUsBmxMbVG5o/qltYRxqHVB/YAcsn1E72r3AzazNu2TMrsJpxU3YmZf3uQ5qQde/8d/uOLNenH0+etnHD8tEdWYYtbD0pEWBxfr2Q/77i7lgza28O9sy6oZpxU75IQ+BXeu0NDNwS11+/+q4ZUffmEVviWlvYWuAlGoK60utlB3Vm1lEc7JlZvZpxU7YhzdW1BykDuLQ8JL/apEVw/UeTZsfG/zY3rURntB54Pb9eJU3F8Urp74TayW3+1AAzs83lYM/MWiwHg0OA3UiPahrQyGtnmpjJf92H1z9LrN2/3QvcMp8C7+XXyip/3wZey6+VHk9nZl2Ngz0zaxc146b0JwV+2wP9STPz9wf6r/vo5p5sXLUT6aHupVc/oC9p8tkepKcVNPW39MSR9aSZ9deW/W1s+RNStmt9MDehdvKq9voMzMw6Awd7ZmZmZgXWo/lDzMzMzKyrcrBnZmZmVmAO9szMzMwKzMGemZmZWYE52DNrJUkhaZvS38081wmSrsjLX5Z0RtuU0szMLHE2rlkrSQpgW2A1sG1EfNyK9/aMiKpPUpA0Brg0Ika2TUnNzMzcsmfWLEknSloqabakC6vs/7Gk+8rWe0l6U9Lukk6V9IikOyTNB4ZXvPcMSXfl1RpguKSFkmrb9abMzKzbcLBn1gRJA4CbgO9GxKGkpy1Uuhc4XNJOef1bwNKIeDWvjwIuiogDImJhE5cbDyyKiP0i4uS2uQMzM+vuHOyZNW0ksCAiluX1GysPiIhPgAeAH+ZNpwKTyg6ZFREvt2chzczMGuNgz7o8SXNz1+cLkury8kJJk5p/N0g6WNLteXkHSeeW7wYGSdqzmdPcCoyVtCPwTVJrX8nnxvTlLt3Fku5sSfmaKPd4SWfl5TGS1uT7fl7So5KGtPJ8vXLSSaPPtLXGtXM9RNLpLaiHLS3rW5IOKFu/X9KcsvVdJK2V1LuZ8/xN0rgWXrO/pBpJL+c6ukjSb/K+S8sSleqTljaVpBGSTtqcc5gVSa+OLoDZ5oqIgwEk7Q48ExH7tfL9c4G5eXUH4Fzgqrw+B/gqcATwElA1WzYiZkrqD/weuD8iPpHUs/I4SYOB44AdIiIqsm9XkZ4f+38k9YqIuopr1lQctqiU3CHpmnwPP6h+19bW2rkeApwOvEmqhy2W6+HG+Hw23nRgNDBfkoB9gTWStskJR6OBORGxvjXXaqIMPYBHgKeBoRGxQVI/4KeVx0bEfcB9ldtbaQQwBrhnM89jVghu2bNCk3S3pBPy8gWSPshfPEhanqc7GSPpqfyWGmDH3CIzEzgeqANukLQG2D0fV2p1eVLSA3ls323Az4DBkh4AngPqW8kkbQc8BvQH/p1b5XoA+0taDNwC9M6tHrW51WSipEeB2VXurb41pIrHga/l486T9HS+pzmS9i07x/clLZM0G/htiz9Ya5XNrYf5R8F+QE3edqSSCyTNk/RsWT0s1Y3byuph5RRBU0kBHaSkoReAWcBhedtoYFo+V29JV+frPJfP26/sXCMkPZHr0Q2StqryERwNDAQmRMQGSMMfIuLaKp9VedJSqUVznqQF+Tp7lR33j/zZLs6f04D8GfwOOCZ/Vn9s4l9j1i042LOiewI4Ki8fBSwlfTntBmwVEa9UHD8eeD8nSRweETcD84HjI2LriPglcBowAOgZEd8gBVZXRsSlwGXAUOD0iNgnIm6IiJMAIuJDUvC4MiKGRMQ1QG/gVdIX+QHAa8DEsgSNkcAJEXFQS284t+ScCDybN90SEQfmlqaLgevycQOBG4DjcvJJ1SlhrE20RT1cCIzP26YCY4FdgZERsT+5Hpad4wga6uHqivNPIyUV9SAFdtOBGTQEgKNJASGkHwErI+KgXN/fA84vO9eBpHq9D7AXVVrrSC1t8ytbp5sjaTTwPWBURIwA/gTcXHbIQcDZEfF1Uovn+Ih4F7gEeDR/Vue05ppmReRuXCu6KcDZSuPQBgATSd0775C+gDfF8aTgbEHqAaMX8H7Z/oci4v1qb6xiDDCp1Noh6VZSNu9Nef/dOQGkJYZLKmX7PkdD6+PBeWzU9kAAe+TthwDzIuLFvH4jKVi1ttep6mFELMst1fuTxpheAbwB/ErSLsAQGrqUjwf6STolr/cm/QAquSsi1gAojTn8NulHRDlt0h2ma48A5uV7FGmOy5KZEfFGXn4KOHwTr2NWaA72rNAiYnnucjoFeJLU+nET6Uv24U08rUhTqdzeyP4WT7Kcz1U5s3n5emvOVT9mr/7kUl+gltQyslApaaPUirSpX8DWSp20Hk4HjiQHjBHxmaTtScHa7IgoTTMk4MyImNHCMlWbqX8+cIaamFS8ifPdGBGXNLJ/XdnyZ/g7zawqd+NadzAFuAh4PM99N5DUqjKlyrGrgK0rkisqEyceBMbnMXhI6lM+Dq6VHgNOU8qE/QLwE1Ig0Fb6Aj2B1/P6L8r2zQYOlPSVvO5HtbWvzlYPpwJnAsvKArA5pC7aaRXXmZBbJUtZtUPL9p8sqV8eq/cjGrp/y/2TFNheWRrTJ6mvpPOaKeODpCz3Qfk9PVWWRdyERpOdzLojB3vWHTxB6pYqfQnNJo2HervywDze5x7g+ZygAfAX4JLSwPiImATcDcyQ9C/gGVKX6Ka4HlhCGo+1gNQC0qKpOloiIj4gjV+aL2k66UuwtO8t4OfAw5KeBNok89Ia1dnq4VTSGLvpZdum523TyrZdRqqjT+frzCCNSy2ZSQrKnie1Gv+1yv1sBI4l/fhYKmkRDd3EjcpjEy8GHpL0HLCIlM3enMeA7XJCiRM0rNvzs3HNzMzMCswte2ZmZmYF5mDPzMzMrMAc7JmZmZkVmIM9MzMzswJzsGdmZmZWYA72zMzMzArMwZ6ZmZlZgTnYMzMzMyswB3tmZmZmBeZgz8zMzKzAHOyZmZmZFZiDPTMzM7MCc7BnZmZmVmAO9szMzMwKzMGemZmZWYE52DMzMzMrMAd7ZmZmZgXmYM/MzMyswBzsmZmZmRWYgz0zMzOzAnOwZ2ZmZlZgDvbMzMzMCszBnpmZmVmBOdgzMzMzKzAHe2ZmZmYF5mDPzMzMrMAc7JmZmZkVmIM9MzMzswJzsGdmZmZWYA72zMzMzArMwZ6ZmZlZgTnYMzMzMyswB3tmZmZmBeZgz8zMzKzAHOyZmZmZFdj/AH3/khAU8nXYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create a numpy vector mapped to labels:\n",
    "percent = np.zeros(len(sources))\n",
    "\n",
    "for source in data['Source']:\n",
    "    for index in range(len(sources)):\n",
    "        if source == sources[index]:\n",
    "            percent[index] += 1\n",
    "            pass\n",
    "\n",
    "percent /= 100\n",
    "\n",
    "# Pie chart:\n",
    "pie_chart = pd.Series(percent, index=sources, name='Sources')\n",
    "pie_chart.plot.pie(fontsize=11, autopct='%.2f', figsize=(10,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "import re\n",
    "\n",
    "def clean_tweet(tweet):\n",
    "    '''\n",
    "    Utility function to clean the text in a tweet by removing \n",
    "    links and special characters using regex.\n",
    "    '''\n",
    "    return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n",
    "\n",
    "def analize_sentiment(tweet):\n",
    "    '''\n",
    "    Utility function to classify the polarity of a tweet\n",
    "    using textblob.\n",
    "    '''\n",
    "    analysis = TextBlob(clean_tweet(tweet))\n",
    "    if analysis.sentiment.polarity > 0:\n",
    "        return 1\n",
    "    elif analysis.sentiment.polarity == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Tweets</th>\n",
       "      <th>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
       "      <th>Language</th>\n",
       "      <th>SA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171558453989377</td>\n",
       "      <td>2018-10-08 05:36:06</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#spacex launch or alien invasion? Who knows? h...</td>\n",
       "      <td>68</td>\n",
       "      <td>1049171555408699393</td>\n",
       "      <td>2018-10-08 05:36:06</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT @SamuelTsui: Ummm this @SpaceX display in t...</td>\n",
       "      <td>136</td>\n",
       "      <td>1049171554226114561</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>612</td>\n",
       "      <td>en</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171553882202112</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA</td>\n",
       "      <td>44</td>\n",
       "      <td>1049171553571807232</td>\n",
       "      <td>2018-10-08 05:36:05</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>3185</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RT @WhoIsGev: #SpaceX yo this was cool https:/...</td>\n",
       "      <td>62</td>\n",
       "      <td>1049171544138702850</td>\n",
       "      <td>2018-10-08 05:36:03</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>2598</td>\n",
       "      <td>en</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SpaceX Achieves First West Coast Falcon 9 Land...</td>\n",
       "      <td>73</td>\n",
       "      <td>1049171542616158213</td>\n",
       "      <td>2018-10-08 05:36:03</td>\n",
       "      <td>dlvr.it</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>en</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RT @BobBassen28: @EmilYunusov @SellBlock12 @el...</td>\n",
       "      <td>87</td>\n",
       "      <td>1049171540347179008</td>\n",
       "      <td>2018-10-08 05:36:02</td>\n",
       "      <td>Kim Bakıyor Ki</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>en</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RT @leticiaVShep: @1Jesse1Sandoval https://t.c...</td>\n",
       "      <td>58</td>\n",
       "      <td>1049171539373879301</td>\n",
       "      <td>2018-10-08 05:36:02</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>und</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @KTVU: VIDEO: SpaceX Falcon 9 launches from...</td>\n",
       "      <td>140</td>\n",
       "      <td>1049171537738194944</td>\n",
       "      <td>2018-10-08 05:36:01</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len  \\\n",
       "0  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "1  #spacex launch or alien invasion? Who knows? h...   68   \n",
       "2  RT @SamuelTsui: Ummm this @SpaceX display in t...  136   \n",
       "3  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "4       RT @SpaceX: Liftoff! https://t.co/rzFcHcEvnA   44   \n",
       "5  RT @WhoIsGev: #SpaceX yo this was cool https:/...   62   \n",
       "6  SpaceX Achieves First West Coast Falcon 9 Land...   73   \n",
       "7  RT @BobBassen28: @EmilYunusov @SellBlock12 @el...   87   \n",
       "8  RT @leticiaVShep: @1Jesse1Sandoval https://t.c...   58   \n",
       "9  RT @KTVU: VIDEO: SpaceX Falcon 9 launches from...  140   \n",
       "\n",
       "                    ID                Date               Source  Likes   RTs  \\\n",
       "0  1049171558453989377 2018-10-08 05:36:06       Kim Bakıyor Ki      0    62   \n",
       "1  1049171555408699393 2018-10-08 05:36:06   Twitter for iPhone      0     0   \n",
       "2  1049171554226114561 2018-10-08 05:36:05   Twitter for iPhone      0   612   \n",
       "3  1049171553882202112 2018-10-08 05:36:05       Kim Bakıyor Ki      0    62   \n",
       "4  1049171553571807232 2018-10-08 05:36:05   Twitter for iPhone      0  3185   \n",
       "5  1049171544138702850 2018-10-08 05:36:03  Twitter for Android      0  2598   \n",
       "6  1049171542616158213 2018-10-08 05:36:03              dlvr.it      0     0   \n",
       "7  1049171540347179008 2018-10-08 05:36:02       Kim Bakıyor Ki      0    62   \n",
       "8  1049171539373879301 2018-10-08 05:36:02  Twitter for Android      0     1   \n",
       "9  1049171537738194944 2018-10-08 05:36:01   Twitter for iPhone      0    33   \n",
       "\n",
       "  Language  SA  \n",
       "0       en  -1  \n",
       "1       en  -1  \n",
       "2       en   1  \n",
       "3       en  -1  \n",
       "4       en   0  \n",
       "5       en   1  \n",
       "6       en   1  \n",
       "7       en  -1  \n",
       "8      und   0  \n",
       "9       en   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create a column with the result of the analysis:\n",
    "data['SA'] = np.array([ analize_sentiment(tweet) for tweet in data['Tweets'] ])\n",
    "\n",
    "# We display the updated dataframe with the new column:\n",
    "display(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We construct lists with classified tweets:\n",
    "\n",
    "pos_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] > 0]\n",
    "neu_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] == 0]\n",
    "neg_tweets = [ tweet for index, tweet in enumerate(data['Tweets']) if data['SA'][index] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of positive tweets: 33.68421052631579%\n",
      "Percentage of neutral tweets: 35.78947368421053%\n",
      "Percentage of negative tweets: 30.526315789473685%\n"
     ]
    }
   ],
   "source": [
    "# We print percentages:\n",
    "\n",
    "print(\"Percentage of positive tweets: {}%\".format(len(pos_tweets)*100/len(data['Tweets'])))\n",
    "print(\"Percentage of neutral tweets: {}%\".format(len(neu_tweets)*100/len(data['Tweets'])))\n",
    "print(\"Percentage of negative tweets: {}%\".format(len(neg_tweets)*100/len(data['Tweets'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Data to plot\n",
    "labels = 'Positive Tweets', 'Neutral Tweets','Negative Tweets'\n",
    "sizes = [len(pos_tweets)*100/len(data['Tweets']), len(neu_tweets)*100/len(data['Tweets']), len(neg_tweets)*100/len(data['Tweets'])]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral']\n",
    "explode = (0.07, 0.07, 0.07)  # explode 1st slice\n",
    " \n",
    "# Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}