week3_4_assignment_albert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     C:\\Users\\Albert\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package movie_reviews is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Albert\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import time\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import numpy as np\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "import nltk\n",
    "# Import all the libraries required\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import rgb2hex\n",
    "from descartes import PolygonPatch\n",
    "from shapely.geometry import Polygon, MultiPolygon\n",
    "from textblob import TextBlob\n",
    "from textblob.sentiments import NaiveBayesAnalyzer\n",
    "import nltk\n",
    "nltk.download('movie_reviews')\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tweets_data = []\n",
    "#\n",
    "##with open('tweets_reduced_10000.jsons', 'r') as tweets_file:\n",
    "#with open('geotagged_tweets_20160812-0912.jsons', 'r') as tweets_file:\n",
    "##with open('customTweets.jsons', 'r') as tweets_file:\n",
    "#    for line in tweets_file:\n",
    "#        try:\n",
    "#            tweet = json.loads(line)\n",
    "#            tweets_data.append(tweet)\n",
    "#        except Exception as e:\n",
    "#            print (e)\n",
    "#            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_data = []\n",
    "\n",
    "#with open('tweets_reduced_10000.jsons', 'r') as tweets_file:\n",
    "#with open('geotagged_tweets_20160812-0912.jsons', 'r') as tweets_file:\n",
    "#with open('customTweets.jsons', 'r') as tweets_file:\n",
    "#with open('customTweetsWithSentiment.jsons', 'r') as tweets_file:\n",
    "#with open('customTweetsWithSentiment2.jsons', 'r') as tweets_file:\n",
    "with open('customTweetsWithSentiment3.jsons', 'r') as tweets_file:\n",
    "#with open('customTweetsWithSentimentWithNotEmptyCleanedText.jsons', 'r') as tweets_file:\n",
    "    for line in tweets_file:\n",
    "        try:\n",
    "            tweet = json.loads(line)\n",
    "            tweets_data.append(tweet)\n",
    "        except Exception as e:\n",
    "            print (e)\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "print(tweets_data[0]['sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweetsIdAndRetweetCount = []\n",
    "\n",
    "#with open('tweets_reduced_10000.jsons', 'r') as tweets_file:\n",
    "with open('geotagged_tweets_20160812-0912.jsons', 'r') as tweets_file:\n",
    "#with open('customTweets.jsons', 'r') as tweets_file:\n",
    "#with open('customTweetsWithSentiment.jsons', 'r') as tweets_file:\n",
    "#with open('tweetsIdAndRetweetCount.jsons', 'r') as tweets_file:\n",
    "    for line in tweets_file:\n",
    "        try:\n",
    "            tweet = json.loads(line)\n",
    "            tweetsIdAndRetweetCount.append(tweet)\n",
    "        except Exception as e:\n",
    "            print (e)\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    " import json\n",
    " # with open('customTweetsWithSentiment2.jsons', 'w') as file:\n",
    "with open('customTweetsWithSentiment3.jsons', 'w') as file:\n",
    "    for i, tweet in enumerate(tweets_data):\n",
    "        twt = {}\n",
    "        twt['id'] = tweet['id']\n",
    "        twt['in_reply_to_status_id'] = tweet['in_reply_to_status_id']\n",
    "        twt['text'] =  tweet['text']\n",
    "        twt['place'] =  tweet['place']\n",
    "        twt['talk'] =  tweet['talk']\n",
    "        twt['cleaned_text'] =  tweet['cleaned_text']\n",
    "        if 'sentiment' in tweet:\n",
    "            twt['sentiment'] =  tweet['sentiment'] \n",
    "        twt['sentiment_num'] =  tweet['sentiment_num']\n",
    "        twt['talk_num'] =  tweet['talk_num']\n",
    "        if 'replies' in tweet:\n",
    "            twt['replies'] = tweet['replies']\n",
    "        file.write(json.dumps(twt) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hillary': False, 'trump': True}\n"
     ]
    }
   ],
   "source": [
    "def whoYouTalkAbout(tweetText):\n",
    "    tweetText = tweetText.lower()\n",
    "    keyWords = {\n",
    "        'hillary' : ['hillary', 'clinton', 'imwithher'],\n",
    "        'trump' : ['donald', 'trump']\n",
    "    }\n",
    "    talk = {\n",
    "        'hillary': False,\n",
    "        'trump': False\n",
    "    }\n",
    "    for candidate in talk.keys():\n",
    "        for word in keyWords[candidate]:\n",
    "            if word in tweetText:\n",
    "                talk[candidate] = True\n",
    "                break\n",
    "        \n",
    "    return talk\n",
    "\n",
    "answer = whoYouTalkAbout('@theblaze @realDonaldTrump bla bla https:\\/\\/t.co\\/TY9DlZ584c')\n",
    "print(answer)\n",
    "    \n",
    "#\n",
    "# probably talks about hillary:\n",
    "# @HillaryClinton\n",
    "# #hillaryclinton\n",
    "# #hillary\n",
    "# #crookedhillary\n",
    "# #imwithher\n",
    "# #neverhillary\n",
    "# \n",
    "# probably talks about trump:\n",
    "# @realDonaldTrump\n",
    "# #donaldtrump\n",
    "# #dumptrump\n",
    "# #nevertrump\n",
    "# #trump\n",
    "# #trumppence16\n",
    "# \n",
    "# neutral:\n",
    "# #maga\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapIdToIdx = {}\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    mapIdToIdx[tweet['id']] = i\n",
    "def getTweetThatThisIsAnsweringTo(tweetIdToFind, tweets):\n",
    "    global mapIdToIdx\n",
    "    idx = mapIdToIdx.get(tweetIdToFind,None)\n",
    "    return None if idx == None else tweets[idx]\n",
    "\n",
    "def evaluateTweet(idx, tweet):\n",
    "    global justHillary\n",
    "    global justTrump\n",
    "    global both\n",
    "    global neither\n",
    "    global error\n",
    "    global jt\n",
    "    \n",
    "    tweetText = tweet['text']\n",
    "    if tweetText == \"\":\n",
    "        error += 1\n",
    "        return\n",
    "    answer = whoYouTalkAbout(tweetText)\n",
    "    \n",
    "    if not answer['hillary'] and not answer['trump']:# if neither, check quoted_status['text']\n",
    "        if 'quoted_status' in tweet and tweet['quoted_status']['text'] != None:\n",
    "            tweetText = tweet['quoted_status']['text']\n",
    "            answer = whoYouTalkAbout(tweetText)\n",
    "        else:\n",
    "            in_reply_to_status_id = tweet['in_reply_to_status_id']\n",
    "            if in_reply_to_status_id != None:\n",
    "                tweet = getTweetThatThisIsAnsweringTo(in_reply_to_status_id, tweets_data)\n",
    "                if tweet != None:\n",
    "                    evaluateTweet(idx,tweet)\n",
    "                    return #ignore for now, next call to evaluateTweet should work\n",
    "            neither += 1\n",
    "            if neither < 5:\n",
    "                print(tweets_data[idx]['id'], tweetText)\n",
    "            tweets_data[idx]['talk'] = 'neither'\n",
    "            return\n",
    "    \n",
    "    if answer['hillary'] and answer['trump']:\n",
    "        both += 1\n",
    "        tweets_data[idx]['talk'] = 'both'\n",
    "    elif answer['hillary'] and not answer['trump']:\n",
    "        justHillary += 1\n",
    "        tweets_data[idx]['talk'] = 'hillary'\n",
    "    elif not answer['hillary'] and answer['trump']:\n",
    "        tweets_data[idx]['talk'] = 'trump'\n",
    "        justTrump  += 1\n",
    "        jt.append(idx)\n",
    "    else:\n",
    "        neither += 1\n",
    "        tweets_data[idx]['talk'] = 'neither'\n",
    "        if neither < 5:\n",
    "            print(tweets_data[idx]['id'], tweetText)\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "764040263132180480 Come to Jesus meeting!!!! What on earth is that supposed to be? 😔 https://t.co/a3lOpTtFig\n",
      "764040676354908161 I don't hate Democrats. I hate the evil ideology of abortion and immigration they so proudly present as good America https://t.co/eR6GLDhj1M\n",
      "764040797318582272 Hey,stop using the media.If they are so biased against you don't use,print or cable. https://t.co/TOtgdmW7wC\n",
      "764040884086185984 Preach! https://t.co/0E1MDwU48r\n",
      "\n",
      "tweets that talk about both: 100658\n",
      "tweets that talk about trump: 354151\n",
      "tweets that talk about hillary: 139297\n",
      "tweets that talk about neither: 63201\n",
      "empty tweets: 0\n",
      "tweets that talk about trump num: 354151\n"
     ]
    }
   ],
   "source": [
    "justHillary = 0\n",
    "justTrump = 0\n",
    "both = 0\n",
    "neither = 0\n",
    "error = 0\n",
    "jt = []\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    evaluateTweet(i, tweet)\n",
    "            \n",
    "print('\\ntweets that talk about both: %d' % both)\n",
    "print('tweets that talk about trump: %d' % justTrump)\n",
    "print('tweets that talk about hillary: %d' % justHillary)\n",
    "print('tweets that talk about neither: %d' % neither)\n",
    "print('empty tweets: %d' % error)\n",
    "\n",
    "print('tweets that talk about trump num: %d' % len(jt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "354151"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    if tweet['talk'] == 'trump':\n",
    "        t +=1\n",
    "        \n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOTAL = len(tweets_data)\n",
    " \n",
    "labels = ['Both', 'Just Trump', 'Just Hillary', 'Neither']\n",
    "sizes = [both/TOTAL, justTrump/TOTAL, justHillary/TOTAL, neither/TOTAL]\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
    "pie = plt.pie(sizes, labels=labels, shadow=True, autopct='%1.1f%%')\n",
    "plt.legend(pie[0], labels, loc=\"best\")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function that removes the hyperlinks from the tweet's content.\n",
    "def remove_link(tweet):\n",
    "    return re.sub(r\"http\\S+\", \"\", tweet)\n",
    "\n",
    "def remove_hashtags(tweet):\n",
    "    return re.sub('#', '', tweet)\n",
    "\n",
    "def remove_entire_hashtags(tweet):\n",
    "    return re.sub(r'(?:\\s|^)#[A-Za-z0-9\\-\\.\\_]+(?:\\s|$)', ' ', tweet).strip()\n",
    "\n",
    "def remove_mentions(tweet):\n",
    "    return \" \".join(re.sub(\"(?:\\@|https?\\://)\\S+\",\" \",tweet).split())\n",
    "\n",
    "\n",
    "def remove_non_az_characters(tweet):\n",
    "    return \" \".join(re.sub('[^a-zA-Z]',\" \",tweet).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Come to Jesus meeting!!!! What on earth is that supposed to be? 😔 https://t.co/a3lOpTtFig\n",
      "😔\n",
      "Come to Jesus meeting!!!! What on earth is that supposed to be?  https://t.co/a3lOpTtFig\n"
     ]
    }
   ],
   "source": [
    "# https://pypi.org/project/emoji/\n",
    "\n",
    "import emoji\n",
    "\n",
    "def extract_emojis(str):\n",
    "    return ''.join(c for c in str if c in emoji.UNICODE_EMOJI)\n",
    "\n",
    "def text_without_emojis(str):\n",
    "    return ''.join(c for c in str if c not in emoji.UNICODE_EMOJI)\n",
    "\n",
    "text_with_emoji = \"Come to Jesus meeting!!!! What on earth is that supposed to be? 😔 https://t.co/a3lOpTtFig\"\n",
    "print(text_with_emoji)\n",
    "print(extract_emojis(text_with_emoji))\n",
    "print(text_without_emojis(text_with_emoji))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "import string\n",
    "\n",
    "# Create a set of stopwords\n",
    "stop = set(stopwords.words('english'))\n",
    "\n",
    "# Create a set of punctuation words \n",
    "exclude = set(string.punctuation) \n",
    "\n",
    "# This is the function makeing the lemmatization\n",
    "lemma = WordNetLemmatizer()\n",
    "\n",
    "# In this function we perform the entire cleaning\n",
    "def clean(doc):\n",
    "    doc = doc.lower()\n",
    "    doc = remove_link(doc)\n",
    "    #print('without links:', doc)\n",
    "    doc = remove_mentions(doc)\n",
    "    #print('without mentions:', doc)\n",
    "    #doc = text_without_emojis(doc)\n",
    "    #print('without emojis:', doc)\n",
    "    doc = remove_entire_hashtags(doc)\n",
    "    #doc = remove_hashtags(doc)\n",
    "    #print('without hashtags:', doc)\n",
    "    stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\n",
    "    #print('stop free:', doc)\n",
    "    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n",
    "    #print('punc_free:', doc)\n",
    "    normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n",
    "    #print('normalized:', normalized)\n",
    "    normalized = remove_non_az_characters(normalized)\n",
    "    #print('non-az removed:', normalized)\n",
    "    normalized = \" \".join(word for word in normalized.split() if len(word) > 3)\n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords \n",
    "set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'!',\n",
       " '\"',\n",
       " '#',\n",
       " '$',\n",
       " '%',\n",
       " '&',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " '*',\n",
       " '+',\n",
       " ',',\n",
       " '-',\n",
       " '.',\n",
       " '/',\n",
       " ':',\n",
       " ';',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '?',\n",
       " '@',\n",
       " '[',\n",
       " '\\\\',\n",
       " ']',\n",
       " '^',\n",
       " '_',\n",
       " '`',\n",
       " '{',\n",
       " '|',\n",
       " '}',\n",
       " '~'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "set(string.punctuation) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'waaaaayyyy much'"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean('@remy: This is @hello waaaaayyyy too much. For you!!!!!! #angry https://google.com 😔')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, tweet in enumerate(tweets_data):\n",
    "    tweet['cleaned_text'] = clean(tweet['text'])\n",
    "    tweets_data[i] = tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lower-casing\n",
    "# removing all URLs & mentions & stop-words & tokens that consist only of non-alphanumeric characters (no emoticons) & short tokens (<3 characters)\n",
    "# filter out all non-English\n",
    "# remove very long conversations (>200 tweets) to avoid excessively long documents\n",
    "# filter out all re-tweets (i.e., reposting tweets posted by other users) (they serve more as an endorsement rather than an original content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import TweetTokenizer\n",
    "\n",
    "tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[':',\n",
       " 'This',\n",
       " 'is',\n",
       " 'waaayyy',\n",
       " 'too',\n",
       " 'much',\n",
       " '.',\n",
       " 'For',\n",
       " 'you',\n",
       " '!',\n",
       " '!',\n",
       " '!',\n",
       " '#angry',\n",
       " 'https://google.com']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = '@remy: This is waaaaayyyy too much. For you!!!!!! #angry https://google.com'\n",
    "tknzr.tokenize(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['remy', 'hello', 'waaayyy', 'much', 'you', 'angry', '😔']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tknzr.tokenize('remy hello waaaaayyyy much you angry 😔')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>candidate</th>\n",
       "      <th>candidate_confidence</th>\n",
       "      <th>relevant_yn</th>\n",
       "      <th>relevant_yn_confidence</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_confidence</th>\n",
       "      <th>subject_matter</th>\n",
       "      <th>subject_matter_confidence</th>\n",
       "      <th>candidate_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>relevant_yn_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>sentiment_gold</th>\n",
       "      <th>subject_matter_gold</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No candidate mentioned</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.6578</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I_Am_Kenzi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @NancyLeeGrahn: How did everyone feel about...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-08-07 09:54:46 -0700</td>\n",
       "      <td>629697200650592256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scott Walker</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.6333</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PeacefulQuest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @ScottWalker: Didn't catch the full #GOPdeb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-08-07 09:54:46 -0700</td>\n",
       "      <td>629697199560069120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No candidate mentioned</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.6629</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>0.6629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PussssyCroook</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @TJMShow: No mention of Tamir Rice and the ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-08-07 09:54:46 -0700</td>\n",
       "      <td>629697199312482304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No candidate mentioned</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>0.7039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MattFromTexas31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @RobGeorge: That Carly Fiorina is trending ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-08-07 09:54:45 -0700</td>\n",
       "      <td>629697197118861312</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.7045</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sharonDay5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @DanScavino: #GOPDebate w/ @realDonaldTrump...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-08-07 09:54:45 -0700</td>\n",
       "      <td>629697196967903232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 candidate  candidate_confidence relevant_yn  \\\n",
       "id                                                             \n",
       "1   No candidate mentioned                   1.0         yes   \n",
       "2             Scott Walker                   1.0         yes   \n",
       "3   No candidate mentioned                   1.0         yes   \n",
       "4   No candidate mentioned                   1.0         yes   \n",
       "5             Donald Trump                   1.0         yes   \n",
       "\n",
       "    relevant_yn_confidence sentiment  sentiment_confidence     subject_matter  \\\n",
       "id                                                                              \n",
       "1                      1.0   Neutral                0.6578  None of the above   \n",
       "2                      1.0  Positive                0.6333  None of the above   \n",
       "3                      1.0   Neutral                0.6629  None of the above   \n",
       "4                      1.0  Positive                1.0000  None of the above   \n",
       "5                      1.0  Positive                0.7045  None of the above   \n",
       "\n",
       "    subject_matter_confidence candidate_gold             name  \\\n",
       "id                                                              \n",
       "1                      1.0000            NaN       I_Am_Kenzi   \n",
       "2                      1.0000            NaN    PeacefulQuest   \n",
       "3                      0.6629            NaN    PussssyCroook   \n",
       "4                      0.7039            NaN  MattFromTexas31   \n",
       "5                      1.0000            NaN       sharonDay5   \n",
       "\n",
       "   relevant_yn_gold  retweet_count sentiment_gold subject_matter_gold  \\\n",
       "id                                                                      \n",
       "1               NaN              5            NaN                 NaN   \n",
       "2               NaN             26            NaN                 NaN   \n",
       "3               NaN             27            NaN                 NaN   \n",
       "4               NaN            138            NaN                 NaN   \n",
       "5               NaN            156            NaN                 NaN   \n",
       "\n",
       "                                                 text tweet_coord  \\\n",
       "id                                                                  \n",
       "1   RT @NancyLeeGrahn: How did everyone feel about...         NaN   \n",
       "2   RT @ScottWalker: Didn't catch the full #GOPdeb...         NaN   \n",
       "3   RT @TJMShow: No mention of Tamir Rice and the ...         NaN   \n",
       "4   RT @RobGeorge: That Carly Fiorina is trending ...         NaN   \n",
       "5   RT @DanScavino: #GOPDebate w/ @realDonaldTrump...         NaN   \n",
       "\n",
       "                tweet_created            tweet_id tweet_location  \\\n",
       "id                                                                 \n",
       "1   2015-08-07 09:54:46 -0700  629697200650592256            NaN   \n",
       "2   2015-08-07 09:54:46 -0700  629697199560069120            NaN   \n",
       "3   2015-08-07 09:54:46 -0700  629697199312482304            NaN   \n",
       "4   2015-08-07 09:54:45 -0700  629697197118861312          Texas   \n",
       "5   2015-08-07 09:54:45 -0700  629697196967903232            NaN   \n",
       "\n",
       "                 user_timezone  \n",
       "id                              \n",
       "1                        Quito  \n",
       "2                          NaN  \n",
       "3                          NaN  \n",
       "4   Central Time (US & Canada)  \n",
       "5                      Arizona  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv(\"Sentiment.csv\", sep=\",\", index_col='id')\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>RT @NancyLeeGrahn: How did everyone feel about...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Positive</td>\n",
       "      <td>RT @ScottWalker: Didn't catch the full #GOPdeb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>RT @TJMShow: No mention of Tamir Rice and the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Positive</td>\n",
       "      <td>RT @RobGeorge: That Carly Fiorina is trending ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Positive</td>\n",
       "      <td>RT @DanScavino: #GOPDebate w/ @realDonaldTrump...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment                                               text\n",
       "id                                                             \n",
       "1    Neutral  RT @NancyLeeGrahn: How did everyone feel about...\n",
       "2   Positive  RT @ScottWalker: Didn't catch the full #GOPdeb...\n",
       "3    Neutral  RT @TJMShow: No mention of Tamir Rice and the ...\n",
       "4   Positive  RT @RobGeorge: That Carly Fiorina is trending ...\n",
       "5   Positive  RT @DanScavino: #GOPDebate w/ @realDonaldTrump..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = dataframe[['sentiment', 'text']]\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframe2 = dataframe\n",
    "#dataframe2['sentiment'].iloc[0] = np.array('bla')\n",
    "#dataframe2['text'].iloc[0] = np.array('text bla')\n",
    "#print(dataframe2.head())\n",
    "#del dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = []\n",
    "\n",
    "for index, row in dataframe.iterrows():\n",
    "    sentiment = row['sentiment']\n",
    "    tweet_text = row['text']\n",
    "    # cleaning\n",
    "    cleaned_text = clean(tweet_text)\n",
    "    # tokenizing\n",
    "    tokenized_tweet = tknzr.tokenize(cleaned_text)\n",
    "    \n",
    "    training_set.append((tokenized_tweet, sentiment))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the separate words in tweets\n",
    "# Input:  A list of tweets\n",
    "# Output: A list of all words in the tweets\n",
    "def get_words_in_tweets(tweets):\n",
    "    all_words = []\n",
    "    for (words, sentiment) in tweets:\n",
    "        all_words.extend(words)\n",
    "    return all_words\n",
    "\n",
    "# Create a dictionary measuring word frequencies\n",
    "# Input: the list of words\n",
    "# Output: the frequency of those words apearing in tweets\n",
    "def get_word_features(wordlist):\n",
    "    wordlist = nltk.FreqDist(wordlist)\n",
    "    word_features = wordlist.keys()\n",
    "    # print (\"Word frequency list\\n\")\n",
    "    # pprint(wordlist)\n",
    "    return word_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_features = get_word_features(get_words_in_tweets(training_set))\n",
    "\n",
    "# Construct our features based on which tweets contain which word\n",
    "def extract_features(document):\n",
    "    document_words = set(document)\n",
    "    features = {}\n",
    "    for word in word_features:\n",
    "        features[word] = (word in document_words)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we apply the features we constructed to our tweets data.\n",
    "training_set = nltk.classify.apply_features(extract_features, training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Printing the resulting training set shows the features we are going to pass to the classifier.\n",
    "# pprint(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the line of code that we use to train our classifier. Training is performed in a streamlined way so no output is visible.\n",
    "classifier = nltk.NaiveBayesClassifier.train(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "          donniewahlberg = True           Positi : Negati =     87.3 : 1.0\n",
      "              imwithhuck = True           Positi : Negati =     69.6 : 1.0\n",
      "               lrihendry = True           Positi : Negati =     63.7 : 1.0\n",
      "                libertyu = True           Positi : Negati =     46.8 : 1.0\n",
      "             wilberforce = True           Positi : Negati =     44.3 : 1.0\n",
      "            kimguilfoyle = True           Positi : Negati =     36.7 : 1.0\n",
      "                 forward = True           Positi : Negati =     35.2 : 1.0\n",
      "                    imho = True           Positi : Negati =     31.6 : 1.0\n",
      "            monaeltahawy = True           Negati : Positi =     29.1 : 1.0\n",
      "               obviously = True           Negati : Positi =     28.0 : 1.0\n",
      "              transcript = True           Neutra : Negati =     27.9 : 1.0\n",
      "               influence = True           Negati : Positi =     27.5 : 1.0\n",
      "                  makeup = True           Negati : Positi =     26.2 : 1.0\n",
      "                   punch = True           Negati : Positi =     24.0 : 1.0\n",
      "                    sets = True           Neutra : Negati =     22.5 : 1.0\n",
      "                   icymi = True           Neutra : Negati =     22.5 : 1.0\n",
      "                 raising = True           Positi : Neutra =     22.0 : 1.0\n",
      "                 enjoyed = True           Positi : Neutra =     21.1 : 1.0\n",
      "              rossramsey = True           Neutra : Negati =     20.7 : 1.0\n",
      "                   loved = True           Positi : Neutra =     19.4 : 1.0\n",
      "                  listen = True           Negati : Positi =     19.2 : 1.0\n",
      "                 merited = True           Neutra : Negati =     18.9 : 1.0\n",
      "             livetweeted = True           Neutra : Negati =     18.9 : 1.0\n",
      "       democraticdebates = True           Positi : Neutra =     18.9 : 1.0\n",
      "                 reminds = True           Negati : Positi =     17.9 : 1.0\n",
      "                   music = True           Negati : Neutra =     17.4 : 1.0\n",
      "                elevator = True           Negati : Neutra =     17.2 : 1.0\n",
      "                 thehill = True           Neutra : Negati =     17.1 : 1.0\n",
      "            goldietaylor = True           Neutra : Positi =     16.7 : 1.0\n",
      "               delivered = True           Positi : Negati =     16.5 : 1.0\n",
      "                   adult = True           Negati : Positi =     16.2 : 1.0\n",
      "               fantastic = True           Positi : Negati =     15.9 : 1.0\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print (classifier.show_most_informative_features(32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "# The tweet we are about to classify\n",
    "tweet = 'This dish is horrible'\n",
    "print (classifier.classify(extract_features(tweet.split())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='neg', p_pos=0.35008478632074624, p_neg=0.6499152136792535)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"This dish is horrible\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "tweet = 'Ivo listens to horrendous electronic music'\n",
    "print (classifier.classify(extract_features(tweet.split())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='neg', p_pos=0.4883445582310797, p_neg=0.5116554417689199)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"Ivo listens to horrendous electronic music\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "tweet = \"Come to Jesus meeting!!!! What on earth is that supposed to be? 😔\"\n",
    "print (classifier.classify(extract_features(tweet.split())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='pos', p_pos=0.5917171731855415, p_neg=0.4082828268144588)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"Come to Jesus meeting!!!! What on earth is that supposed to be? 😔\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "tweet = \"I'm so happy today\"\n",
    "print (classifier.classify(extract_features(tweet.split())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='pos', p_pos=0.7367024460316827, p_neg=0.2632975539683174)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"I'm so happy today\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='pos', p_pos=0.5608742774566471, p_neg=0.4391257225433527)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"I like math\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polarity score is a float within the range [-1.0, 1.0]. The subjectivity is a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4357142857142857\n",
      "0.39166666666666666\n"
     ]
    }
   ],
   "source": [
    "testimonial = TextBlob(\"Textblob is amazingly simple to use. What great fun!\")\n",
    "print(testimonial.sentiment.subjectivity)\n",
    "print(testimonial.sentiment.polarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "negatiu [-1.0, -0.1]\n",
    "neutral [-0.1, +0.1]\n",
    "positiu [+0.1, +1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzerNaiveBayes = NaiveBayesAnalyzer()\n",
    "\n",
    "def getSentimentValue(txt):\n",
    "    global analyzerNaiveBayes\n",
    "    \n",
    "    sent = TextBlob(txt, analyzer = analyzerNaiveBayes)\n",
    "    val = sent.sentiment.classification\n",
    "    if val == 'neg':\n",
    "        return 'Negative'\n",
    "    if val == 'pos':\n",
    "        return 'Positive'\n",
    "    return 'Neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tweets positive:  426130\n",
      "Number of tweets neutral:  0\n",
      "Number of tweets negative:  231177\n"
     ]
    }
   ],
   "source": [
    "positive = 0\n",
    "negative = 0\n",
    "neutral = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    text = tweet['cleaned_text']\n",
    "    sentimentText = getSentimentValue(text)\n",
    "    if sentimentText == 'Negative':\n",
    "        negative += 1\n",
    "    elif sentimentText == 'Positive':\n",
    "        positive += 1\n",
    "    else:\n",
    "        neutral += 1\n",
    "\n",
    "print('Number of tweets positive: ', positive)\n",
    "print('Number of tweets neutral: ', neutral)\n",
    "print('Number of tweets negative: ', negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='neg', p_pos=0.325051595491348, p_neg=0.674948404508652)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"@realDonaldTrump is such an asshole!! https://t.co/TAinjWbfZh\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='neg', p_pos=0.2200817281023955, p_neg=0.7799182718976042)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"Hey, check out our new Video!\", analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sentiment(classification='neg', p_pos=0.3048559624200577, p_neg=0.695144037579942)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(clean(\"Hey, check out our new Video!\"), analyzer = NaiveBayesAnalyzer())\n",
    "print(sent.sentiment.classification)\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.17045454545454544, subjectivity=0.45454545454545453)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(\"Hey, check out our new Video!\")\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.0, subjectivity=0.0)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent          = TextBlob(clean(\"Hey, check out our new Video!\"))\n",
    "sent.sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are our tweets positive, negative or neutral?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_TWEETS = len(tweets_data) # len(tweets_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
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      "100000\n",
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      "200000\n",
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      "300000\n",
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      "330000\n",
      "340000\n",
      "350000\n",
      "360000\n",
      "370000\n",
      "380000\n",
      "390000\n",
      "400000\n",
      "410000\n",
      "420000\n",
      "430000\n",
      "440000\n",
      "450000\n",
      "460000\n",
      "470000\n",
      "480000\n",
      "490000\n",
      "500000\n",
      "510000\n",
      "520000\n",
      "530000\n",
      "540000\n",
      "550000\n",
      "560000\n",
      "570000\n",
      "580000\n",
      "590000\n",
      "600000\n",
      "610000\n",
      "620000\n",
      "630000\n",
      "640000\n",
      "650000\n"
     ]
    }
   ],
   "source": [
    "for i, tweet in enumerate(tweets_data[:NUM_TWEETS]):\n",
    "    if i % 10000 == 0:\n",
    "        print(i)\n",
    "    cleanedText = tweet['cleaned_text']\n",
    "    tweet['sentiment'] = getSentimentValue(cleanedText)#classifier.classify(extract_features(cleanedText.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "found = False\n",
    "for i, tweet in enumerate(tweets_data[:NUM_TWEETS]):\n",
    "    if 'sentiment' not in tweet:\n",
    "        print(i)\n",
    "        found = True\n",
    "        break\n",
    "print(found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_data = [tweet for tweet in tweets_data if tweet['cleaned_text'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "607531"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweets_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Positivity - Negativity - Neutrality per state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "426130 231177 0\n"
     ]
    }
   ],
   "source": [
    "positivesTweets = 0\n",
    "neutralTweets = 0\n",
    "negativesTweets = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data[:NUM_TWEETS]):\n",
    "    sentiment = tweet['sentiment']\n",
    "    \n",
    "    if sentiment == 'Positive':\n",
    "        positivesTweets += 1\n",
    "        s = 1\n",
    "    elif sentiment == 'Negative':\n",
    "        negativesTweets += 1\n",
    "        s = -1\n",
    "    else:  # neutral\n",
    "        neutralTweets += 1\n",
    "        s = 0\n",
    "    tweets_data[i]['sentiment_num'] = s\n",
    "print(positivesTweets, negativesTweets, neutralTweets)\n",
    "\n",
    "mapStateToSentiment = {}\n",
    "for tweet in tweets_data[:NUM_TWEETS]:\n",
    "    if tweet['place'] == None:\n",
    "        continue\n",
    "    countryCode = tweet['place']['country_code']\n",
    "\n",
    "    city = tweet['place']['full_name']\n",
    "    if countryCode != 'US' or ',' not in city:\n",
    "        continue\n",
    "    cityAndState = city.split(',')\n",
    "    state = cityAndState[1].strip()\n",
    "    if len(state) != 2: # skip Philadelphia\n",
    "        continue\n",
    "    tweet['place']['state_name'] = state\n",
    "    s = tweet['sentiment_num']\n",
    "    if state not in mapStateToSentiment:\n",
    "        mapStateToSentiment[state] = 0\n",
    "    mapStateToSentiment[state] += s\n",
    "    \n",
    "values = mapStateToSentiment.values()\n",
    "min_sentiment = min(values)\n",
    "max_sentiment = max(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mapStateToSentiment.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': 2065,\n",
       " 'LA': 1266,\n",
       " 'MD': 2065,\n",
       " 'CA': 19248,\n",
       " 'NJ': 3974,\n",
       " 'TX': 11423,\n",
       " 'MI': 2060,\n",
       " 'KY': 805,\n",
       " 'IA': 999,\n",
       " 'FL': 11771,\n",
       " 'MA': 3023,\n",
       " 'PA': 3900,\n",
       " 'VA': 2354,\n",
       " 'AZ': 3904,\n",
       " 'NY': 11143,\n",
       " 'UT': 574,\n",
       " 'OR': 1467,\n",
       " 'CT': 1027,\n",
       " 'SC': 1224,\n",
       " 'DE': 88,\n",
       " 'OK': 764,\n",
       " 'IL': 5940,\n",
       " 'TN': 2497,\n",
       " 'KS': 642,\n",
       " 'MS': 734,\n",
       " 'NC': 2876,\n",
       " 'GA': 2562,\n",
       " 'OH': 3115,\n",
       " 'MN': 1120,\n",
       " 'WI': 1350,\n",
       " 'ME': 565,\n",
       " 'CO': 1578,\n",
       " 'AR': 438,\n",
       " 'WA': 2800,\n",
       " 'RI': 504,\n",
       " 'NV': 2063,\n",
       " 'DC': 1531,\n",
       " 'NE': 296,\n",
       " 'WV': 252,\n",
       " 'IN': 1294,\n",
       " 'HI': 852,\n",
       " 'ID': 414,\n",
       " 'AL': 2401,\n",
       " 'NM': 725,\n",
       " 'NH': 946,\n",
       " 'VT': 70,\n",
       " 'ND': 27,\n",
       " 'MT': 90,\n",
       " 'SD': 105,\n",
       " 'AK': 202,\n",
       " 'WY': 42,\n",
       " 'Mo': -1}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapStateToSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_DIR = 'states_21basic/' \n",
    "BLUE = '#5599ff'\n",
    "\n",
    "with open(os.path.join(S_DIR, 'states.json')) as rf:    \n",
    "    data = json.load(rf)\n",
    "\n",
    "def displayUSMap(mapp, cmap = plt.cm.inferno):\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "    ax = fig.gca()\n",
    "    for feature in data['features']:\n",
    "        geometry = feature['geometry']\n",
    "        props = feature['properties']\n",
    "        NAME = props['STATE_ABBR']\n",
    "        POP = mapp.get(NAME,0)\n",
    "        norm = matplotlib.colors.Normalize(vmin=min_sentiment, vmax=max_sentiment)\n",
    "        col = cmap(norm(POP))\n",
    "        #print(NAME,POP,col)\n",
    "        if geometry['type'] == 'Polygon':\n",
    "            poly = geometry\n",
    "            ppatch = PolygonPatch(poly, fc=col, ec=col,  alpha=0.5, zorder=2)\n",
    "            ax.add_patch(ppatch)\n",
    "        elif geometry['type'] == 'MultiPolygon':\n",
    "            for polygon in geometry['coordinates'][0]:\n",
    "                poly = Polygon(polygon)\n",
    "                ppatch = PolygonPatch(poly, fc=col, ec=col, alpha=0.5, zorder=2)\n",
    "                ax.add_patch(ppatch)\n",
    "        else:\n",
    "            print('Don\\'t know how to draw :', geometry['type'])\n",
    "\n",
    "    ax.axis('scaled')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayUSMap(mapStateToSentiment, plt.cm.inferno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hillary vs Trump?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, tweet in enumerate(tweets_data[:NUM_TWEETS]):\n",
    "    talk = tweet['talk']\n",
    "    if talk == 'hillary':\n",
    "        s = 1\n",
    "    elif talk == 'trump':\n",
    "        s = -1\n",
    "    elif talk == 'both':\n",
    "        s = 0\n",
    "    else:  # neutral\n",
    "        s = None\n",
    "    tweets_data[i]['talk_num'] = s\n",
    "\n",
    "mapStateToTalk = {}\n",
    "for tweet in tweets_data[:NUM_TWEETS]:\n",
    "    if tweet['place'] == None:\n",
    "        continue\n",
    "    countryCode = tweet['place']['country_code']\n",
    "    city = tweet['place']['full_name']\n",
    "    if countryCode != 'US' or ',' not in city:\n",
    "        continue\n",
    "    cityAndState = city.split(',')\n",
    "    state = cityAndState[1].strip()\n",
    "    if len(state) != 2: # skip Philadelphia\n",
    "        continue\n",
    "    tweet['place']['state_name'] = state\n",
    "    s = tweet['talk_num']\n",
    "    #print(s)\n",
    "    if s == None:\n",
    "        continue\n",
    "    if state not in mapStateToTalk:\n",
    "        mapStateToTalk[state] = 0\n",
    "    \n",
    "    mapStateToTalk[state] += s\n",
    "\n",
    "\n",
    "values = mapStateToTalk.values()\n",
    "\n",
    "min_talk = min(values)\n",
    "max_talk = max(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayUSMap(mapStateToTalk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-21766 -1\n"
     ]
    }
   ],
   "source": [
    "print(min_talk, max_talk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': -1741,\n",
       " 'LA': -1680,\n",
       " 'MD': -4188,\n",
       " 'CA': -21766,\n",
       " 'NJ': -4609,\n",
       " 'TX': -12196,\n",
       " 'MI': -2854,\n",
       " 'KY': -1107,\n",
       " 'IA': -630,\n",
       " 'FL': -12823,\n",
       " 'MA': -3863,\n",
       " 'VA': -1852,\n",
       " 'AZ': -5527,\n",
       " 'NY': -14978,\n",
       " 'UT': -706,\n",
       " 'OR': -2636,\n",
       " 'CT': -1244,\n",
       " 'PA': -4566,\n",
       " 'SC': -903,\n",
       " 'DE': -126,\n",
       " 'OK': -786,\n",
       " 'IL': -6191,\n",
       " 'TN': -2722,\n",
       " 'KS': -972,\n",
       " 'NC': -2888,\n",
       " 'GA': -2625,\n",
       " 'OH': -3334,\n",
       " 'MN': -1083,\n",
       " 'WI': -1443,\n",
       " 'ME': -465,\n",
       " 'MS': -398,\n",
       " 'AR': -313,\n",
       " 'WA': -3777,\n",
       " 'RI': -484,\n",
       " 'NV': -1889,\n",
       " 'DC': -1469,\n",
       " 'CO': -578,\n",
       " 'NE': -738,\n",
       " 'WV': -628,\n",
       " 'IN': -1371,\n",
       " 'HI': -1046,\n",
       " 'ID': -526,\n",
       " 'AL': -3209,\n",
       " 'NM': -932,\n",
       " 'NH': -646,\n",
       " 'VT': -63,\n",
       " 'ND': -84,\n",
       " 'MT': -81,\n",
       " 'SD': -123,\n",
       " 'AK': -331,\n",
       " 'WY': -66,\n",
       " 'Mo': -1}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapStateToTalk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import json\n",
    "# \n",
    "# with open('customTweetsWithSentiment.jsons', 'w') as file:\n",
    "#     for i, tweet in enumerate(tweets_data):\n",
    "#         file.write(json.dumps(tweet) + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which kind of tweets are replied the most? (Albert) Are neutral tweet more or less likely to be replied than positive and negative tweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tweet1 - positive = 2\n",
    "# \ttweet2 - negative = 0\n",
    "# \ttweet3 - negative = 1\n",
    "# \t\ttweet4 - neutral = 1\n",
    "# \t\t\ttweet6 - positive = 0\n",
    "# \n",
    "# tweet5 - negative = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_test = [\n",
    "    {\n",
    "        'id': 1,\n",
    "        'in_reply_to_status_id': -1,\n",
    "        'sentiment':'Positive'\n",
    "    },\n",
    "    {\n",
    "        'id': 2,\n",
    "        'in_reply_to_status_id': 1,\n",
    "        'sentiment':'Negative'\n",
    "    },\n",
    "    {\n",
    "        'id': 3,\n",
    "        'in_reply_to_status_id': 1,\n",
    "        'sentiment':'Negative'\n",
    "    },\n",
    "    {\n",
    "        'id': 4,\n",
    "        'in_reply_to_status_id': 3,\n",
    "        'sentiment':'Neutral'\n",
    "    },\n",
    "    {\n",
    "        'id': 6,\n",
    "        'in_reply_to_status_id': 4,\n",
    "        'sentiment':'Neutral'\n",
    "    },\n",
    "    {\n",
    "        'id': 5,\n",
    "        'in_reply_to_status_id': -1,\n",
    "        'sentiment':'Negative'\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapSentimentToRepliesCount = {'Positive': 0, 'Negative': 0, 'Neutral': 0}\n",
    "\n",
    "def f(tweet, tweets, isParent):\n",
    "    id = tweet['id']\n",
    "    in_reply_to_status_id = tweet['in_reply_to_status_id']\n",
    "    sentiment = tweet['sentiment']\n",
    "    \n",
    "    parentTweet = getTweetThatThisIsAnsweringTo(in_reply_to_status_id, tweets)\n",
    "    if not(parentTweet == None or isParent == True): # parent found\n",
    "        f(parentTweet, tweets, True)\n",
    "    elif isParent == True:\n",
    "        mapSentimentToRepliesCount[sentiment] += 1\n",
    "        if 'replies' in tweet:\n",
    "            tweet['replies'] += 1\n",
    "        else:\n",
    "            tweet['replies'] = 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "50000\n",
      "100000\n",
      "150000\n",
      "200000\n",
      "250000\n",
      "300000\n",
      "350000\n",
      "400000\n",
      "450000\n",
      "500000\n",
      "550000\n",
      "600000\n",
      "650000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Positive': 13007, 'Negative': 6356, 'Neutral': 0}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = len(tweets_data)\n",
    "\n",
    "for i, tweet in enumerate(tweets_data[:num]):\n",
    "    f(tweet, tweets_data[:num], False)\n",
    "    if i%50000==0:\n",
    "        print(i)\n",
    "    \n",
    "    \n",
    "mapSentimentToRepliesCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202073"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "for i, tweet in enumerate(tweets_data[:num]):\n",
    "    if 'replies' not in tweet:\n",
    "        print(i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "764042476487217152 in mapIdToIdx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapIdToIdx.get(764042476487417152,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapIdToIdx.get(764042476487217152,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "637944"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_without_reply = num - sum(mapSentimentToRepliesCount.values())\n",
    "tweets_without_reply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOTAL = sum(mapSentimentToRepliesCount.values())\n",
    " \n",
    "labels = ['Positive replies', 'Negative replies', 'Neutral replies']\n",
    "sizes = [mapSentimentToRepliesCount['Positive']/TOTAL, mapSentimentToRepliesCount['Negative']/TOTAL, mapSentimentToRepliesCount['Neutral']/TOTAL]\n",
    "pie = plt.pie(sizes, labels=labels, shadow=True, autopct='%1.1f%%')\n",
    "plt.legend(pie[0], labels, loc=\"best\")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61580"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sumEmptyTweets = 0\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    if tweet['cleaned_text'] == \"\":\n",
    "        sumEmptyTweets += 1\n",
    "sumEmptyTweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Positive': 13007, 'Negative': 6356, 'Neutral': 0}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapSentimentToRepliesCount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## POSITIVE TRUMP  + NEGATIVE HILARY vs NEGATIVE TRUMP + POSITIVE HILLARY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pro Trump vs pro hillary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaned: 48072\n",
      "bothNeither: 163859\n",
      "trump: 354151\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'trump': {'Positive': 233547, 'Negative': 120604, 'Neutral': 0},\n",
       " 'hillary': {'Positive': 91624, 'Negative': 47673, 'Neutral': 0}}"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapSentimentToCandidate = {'trump': {'Positive':0, 'Negative':0, 'Neutral':0}, 'hillary':{'Positive':0, 'Negative':0, 'Neutral':0}}\n",
    "\n",
    "cleaned = 0\n",
    "bothNeither = 0\n",
    "trump = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    candidate = tweet['talk']\n",
    "    sentiment = tweet['sentiment']\n",
    "    \n",
    "    if candidate == 'trump':\n",
    "        trump += 1\n",
    "        \n",
    "    if candidate not in mapSentimentToCandidate:\n",
    "        bothNeither += 1\n",
    "        continue\n",
    "        \n",
    "    if tweet['cleaned_text'] == \"\":\n",
    "        cleaned += 1\n",
    "    \n",
    "    mapSentimentToCandidate[candidate][sentiment] += 1\n",
    "    \n",
    "print('cleaned:', cleaned)\n",
    "print('bothNeither:', bothNeither)\n",
    "print('trump:', trump)\n",
    "mapSentimentToCandidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "657307"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweets_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOTAL = sum(mapSentimentToCandidate['trump'].values())+sum(mapSentimentToCandidate['hillary'].values())\n",
    " \n",
    "labels = ['Pro Trump', 'Pro Hillary', 'Neutral Trump', 'Neutral Hillary']\n",
    "sizes = [\n",
    "    (mapSentimentToCandidate['trump']['Positive']+mapSentimentToCandidate['hillary']['Negative'])/TOTAL, \n",
    "         (mapSentimentToCandidate['hillary']['Positive']+mapSentimentToCandidate['trump']['Negative'])/TOTAL,\n",
    "    mapSentimentToCandidate['trump']['Neutral']/TOTAL, \n",
    "    mapSentimentToCandidate['hillary']['Neutral']/TOTAL\n",
    "        ]\n",
    "pie = plt.pie(sizes, labels=labels, shadow=True, autopct='%1.1f%%')\n",
    "plt.legend(pie[0], labels, loc=\"best\")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': 785,\n",
       " 'LA': 548,\n",
       " 'MD': 1133,\n",
       " 'CA': 11138,\n",
       " 'NJ': 2934,\n",
       " 'TX': 3433,\n",
       " 'MI': 1266,\n",
       " 'KY': 389,\n",
       " 'IA': 1509,\n",
       " 'FL': 5983,\n",
       " 'MA': 1931,\n",
       " 'PA': 2104,\n",
       " 'VA': 2072,\n",
       " 'AZ': 2174,\n",
       " 'NY': 6761,\n",
       " 'UT': 372,\n",
       " 'OR': 1227,\n",
       " 'CT': 655,\n",
       " 'SC': 868,\n",
       " 'DE': 116,\n",
       " 'OK': 702,\n",
       " 'IL': 2724,\n",
       " 'TN': 913,\n",
       " 'KS': 246,\n",
       " 'MS': 516,\n",
       " 'NC': 1616,\n",
       " 'GA': 1516,\n",
       " 'OH': 2363,\n",
       " 'MN': 822,\n",
       " 'WI': 1020,\n",
       " 'ME': 201,\n",
       " 'CO': 1932,\n",
       " 'AR': 182,\n",
       " 'WA': 1466,\n",
       " 'RI': 362,\n",
       " 'NV': 1353,\n",
       " 'DC': 633,\n",
       " 'NE': 272,\n",
       " 'WV': 36,\n",
       " 'IN': 780,\n",
       " 'HI': 226,\n",
       " 'ID': 118,\n",
       " 'AL': -285,\n",
       " 'NM': 305,\n",
       " 'NH': 414,\n",
       " 'VT': 24,\n",
       " 'ND': 69,\n",
       " 'MT': 78,\n",
       " 'SD': 15,\n",
       " 'AK': 242,\n",
       " 'WY': 44,\n",
       " 'Mo': 1}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pro trump = -1\n",
    "# pro hillary = +1\n",
    "mapStateToProTrumpProHillary = {}\n",
    "for tweet in tweets_data:\n",
    "    if tweet['place'] == None:\n",
    "        continue\n",
    "    countryCode = tweet['place']['country_code']\n",
    "    city = tweet['place']['full_name']\n",
    "    if countryCode != 'US' or ',' not in city:\n",
    "        continue\n",
    "    cityAndState = city.split(',')\n",
    "    state = cityAndState[1].strip()\n",
    "    if len(state) != 2: # skip Philadelphia\n",
    "        continue\n",
    "    tweet['place']['state_name'] = state\n",
    "    \n",
    "    #print(s)\n",
    "    if s == None:\n",
    "        continue\n",
    "    if state not in mapStateToProTrumpProHillary:\n",
    "        mapStateToProTrumpProHillary[state] = 0\n",
    "    \n",
    "    talk = tweet['talk']\n",
    "    sentiment = tweet['sentiment']\n",
    "    if (talk == 'trump' and sentiment == 'Positive') or (talk == 'hillary' and sentiment == 'Negative'):\n",
    "        val = -1\n",
    "    else:\n",
    "        val = 1\n",
    "    mapStateToProTrumpProHillary[state] += val\n",
    "\n",
    "mapStateToProTrumpProHillary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayUSMap(mapStateToProTrumpProHillary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proportional to number of tweets per state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mapStateToNumberOfTweets {'MO': 6303, 'LA': 4706, 'MD': 8999, 'CA': 67370, 'NJ': 15852, 'TX': 34437, 'MI': 8810, 'KY': 3081, 'IA': 3481, 'FL': 40241, 'MA': 11733, 'PA': 13804, 'VA': 9806, 'AZ': 14686, 'NY': 42179, 'UT': 2288, 'OR': 7045, 'CT': 3989, 'SC': 3992, 'DE': 482, 'OK': 3356, 'IL': 19672, 'TN': 7825, 'KS': 2358, 'MS': 2334, 'NC': 9832, 'GA': 8984, 'OH': 12235, 'MN': 3798, 'WI': 4414, 'ME': 1805, 'CO': 6520, 'AR': 1518, 'WA': 10764, 'RI': 2060, 'NV': 7155, 'DC': 4415, 'NE': 1594, 'WV': 1070, 'IN': 4822, 'HI': 2540, 'ID': 1402, 'AL': 6171, 'NM': 2601, 'NH': 2484, 'VT': 298, 'ND': 287, 'MT': 392, 'SD': 323, 'AK': 1030, 'WY': 358, 'Mo': 1}\n",
      "mapStateToNumberOfTweetsTrumpOnly {'MO': 3225, 'LA': 2472, 'MD': 5586, 'CA': 36046, 'NJ': 8326, 'MI': 4809, 'IA': 1184, 'VA': 4567, 'AZ': 8103, 'NY': 23235, 'UT': 1223, 'TX': 19039, 'SC': 1848, 'DE': 239, 'FL': 21330, 'MA': 6371, 'GA': 4639, 'PA': 7436, 'OH': 6278, 'NC': 5090, 'MN': 1940, 'WI': 2147, 'MS': 1069, 'AR': 730, 'CT': 2141, 'IL': 10597, 'TN': 4284, 'DC': 2391, 'CO': 2538, 'NE': 980, 'OK': 1623, 'WV': 762, 'IN': 2528, 'KS': 1399, 'WA': 5989, 'RI': 1022, 'ID': 815, 'AL': 3953, 'KY': 1745, 'NM': 1490, 'ME': 916, 'NV': 3555, 'NH': 1252, 'OR': 4120, 'ND': 145, 'MT': 187, 'SD': 199, 'AK': 565, 'VT': 153, 'HI': 1466, 'WY': 176, 'Mo': 1}\n",
      "mapStateToNumberOfTweetsHillaryOnly {'TX': 6843, 'FL': 8507, 'MA': 2508, 'OR': 1484, 'PA': 2870, 'KS': 427, 'MD': 1398, 'MO': 1484, 'CA': 14280, 'NJ': 3717, 'ME': 451, 'MI': 1955, 'NC': 2202, 'WA': 2212, 'WI': 704, 'RI': 538, 'OK': 837, 'TN': 1562, 'AZ': 2576, 'GA': 2014, 'OH': 2944, 'IL': 4406, 'VA': 2715, 'NY': 8257, 'DC': 922, 'CO': 1960, 'CT': 897, 'SC': 945, 'AL': 744, 'AR': 417, 'MS': 671, 'IA': 554, 'LA': 792, 'KY': 638, 'MN': 857, 'UT': 517, 'NM': 558, 'NV': 1666, 'NH': 606, 'HI': 420, 'IN': 1157, 'WV': 134, 'WY': 110, 'NE': 242, 'ID': 289, 'SD': 76, 'DE': 113, 'AK': 234, 'VT': 90, 'MT': 106, 'ND': 61}\n"
     ]
    }
   ],
   "source": [
    "mapStateToNumberOfTweets = {}\n",
    "mapStateToNumberOfTweetsTrumpOnly = {}\n",
    "mapStateToNumberOfTweetsHillaryOnly = {}\n",
    "\n",
    "for tweet in tweets_data:\n",
    "    if tweet['place'] == None:\n",
    "        continue\n",
    "    countryCode = tweet['place']['country_code']\n",
    "    city = tweet['place']['full_name']\n",
    "    candidate = tweet['talk']\n",
    "    if countryCode != 'US' or ',' not in city:\n",
    "        continue\n",
    "    cityAndState = city.split(',')\n",
    "    state = cityAndState[1].strip()\n",
    "    if len(state) != 2: # skip Philadelphia\n",
    "        continue\n",
    "    if state not in mapStateToNumberOfTweets:\n",
    "        mapStateToNumberOfTweets[state] = 0\n",
    "    if candidate == 'trump' and state not in mapStateToNumberOfTweetsTrumpOnly:\n",
    "        mapStateToNumberOfTweetsTrumpOnly[state] = 0\n",
    "    if candidate == 'hillary' and state not in mapStateToNumberOfTweetsHillaryOnly:\n",
    "        mapStateToNumberOfTweetsHillaryOnly[state] = 0 \n",
    "\n",
    "    if candidate == 'trump':\n",
    "        mapStateToNumberOfTweetsTrumpOnly[state] += 1\n",
    "    if candidate == 'hillary':\n",
    "        mapStateToNumberOfTweetsHillaryOnly[state] += 1\n",
    "    mapStateToNumberOfTweets[state] += 1\n",
    "    \n",
    "print('mapStateToNumberOfTweets', mapStateToNumberOfTweets)\n",
    "print('mapStateToNumberOfTweetsTrumpOnly', mapStateToNumberOfTweetsTrumpOnly)\n",
    "print('mapStateToNumberOfTweetsHillaryOnly', mapStateToNumberOfTweetsHillaryOnly)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': 12454.386799936537,\n",
       " 'LA': 11644.708882277942,\n",
       " 'MD': 12590.287809756639,\n",
       " 'CA': 16532.58126762654,\n",
       " 'NJ': 18508.705526116577,\n",
       " 'TX': 9968.928768475767,\n",
       " 'MI': 14370.034052213394,\n",
       " 'KY': 12625.770853618955,\n",
       " 'IA': 43349.61218040793,\n",
       " 'FL': 14867.920777316667,\n",
       " 'MA': 16457.85391630444,\n",
       " 'PA': 15241.95885250652,\n",
       " 'VA': 21129.920456863147,\n",
       " 'AZ': 14803.213945253983,\n",
       " 'NY': 16029.303681927026,\n",
       " 'UT': 16258.741258741258,\n",
       " 'OR': 17416.607523066003,\n",
       " 'CT': 16420.15542742542,\n",
       " 'SC': 21743.486973947896,\n",
       " 'DE': 24066.390041493774,\n",
       " 'OK': 20917.75923718713,\n",
       " 'IL': 13847.09231394876,\n",
       " 'TN': 11667.73162939297,\n",
       " 'KS': 10432.569974554708,\n",
       " 'MS': 22107.96915167095,\n",
       " 'NC': 16436.12693246542,\n",
       " 'GA': 16874.443455031167,\n",
       " 'OH': 19313.44503473641,\n",
       " 'MN': 21642.969984202213,\n",
       " 'WI': 23108.29179882193,\n",
       " 'ME': 11135.73407202216,\n",
       " 'CO': 29631.9018404908,\n",
       " 'AR': 11989.459815546772,\n",
       " 'WA': 13619.472315124489,\n",
       " 'RI': 17572.81553398058,\n",
       " 'NV': 18909.85324947589,\n",
       " 'DC': 14337.485843714609,\n",
       " 'NE': 17063.989962358846,\n",
       " 'WV': 3364.4859813084113,\n",
       " 'IN': 16175.860638739112,\n",
       " 'HI': 8897.63779527559,\n",
       " 'ID': 8416.547788873038,\n",
       " 'AL': -4618.376276130287,\n",
       " 'NM': 11726.259131103421,\n",
       " 'NH': 16666.666666666668,\n",
       " 'VT': 8053.6912751677855,\n",
       " 'ND': 24041.811846689896,\n",
       " 'MT': 19897.95918367347,\n",
       " 'SD': 4643.962848297214,\n",
       " 'AK': 23495.14563106796,\n",
       " 'WY': 12290.50279329609,\n",
       " 'Mo': 100000.0}"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapStateToProTrumpProHillaryProportional = mapStateToProTrumpProHillary.copy()\n",
    "factor = 100000\n",
    "for state in mapStateToNumberOfTweets.keys():\n",
    "    mapStateToProTrumpProHillaryProportional[state] =factor* mapStateToProTrumpProHillary[state]/mapStateToNumberOfTweets[state]\n",
    "    \n",
    "mapStateToProTrumpProHillaryProportional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "cdict1 = {\n",
    "\n",
    "         'green': ((0.0, 0.0, 0.0),\n",
    "                   (1.0, 0.0, 0.0)),\n",
    "\n",
    "         'blue':  ((0.0, 0.0, 1.0),\n",
    "                   (0.5, 0.1, 0.0),\n",
    "                   (1.0, 0.0, 0.0)),\n",
    "    \n",
    "        'red':   ((0.0, 0.0, 0.0),\n",
    "                   (0.5, 0.0, 0.1),\n",
    "                   (1.0, 1.0, 1.0))\n",
    "        }\n",
    "RedBlue = LinearSegmentedColormap('RedBlue', cdict1)\n",
    "displayUSMap(mapStateToProTrumpProHillaryProportional, RedBlue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayUSMap(mapStateToProTrumpProHillaryProportional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "autocolorscale": false,
         "colorbar": {
          "title": "More positive means more prohillary"
         },
         "colorscale": [
          [
           0,
           "rgb(255,0,0)"
          ],
          [
           0.5,
           "rgb(255,255,255)"
          ],
          [
           1,
           "rgb(0,0,255)"
          ]
         ],
         "locationmode": "USA-states",
         "locations": [
          "MO",
          "LA",
          "MD",
          "CA",
          "NJ",
          "TX",
          "MI",
          "KY",
          "IA",
          "FL",
          "MA",
          "PA",
          "VA",
          "AZ",
          "NY",
          "UT",
          "OR",
          "CT",
          "SC",
          "DE",
          "OK",
          "IL",
          "TN",
          "KS",
          "MS",
          "NC",
          "GA",
          "OH",
          "MN",
          "WI",
          "ME",
          "CO",
          "AR",
          "WA",
          "RI",
          "NV",
          "DC",
          "NE",
          "WV",
          "IN",
          "HI",
          "ID",
          "AL",
          "NM",
          "NH",
          "VT",
          "ND",
          "MT",
          "SD",
          "AK",
          "WY",
          "Mo"
         ],
         "marker": {
          "line": {
           "color": "rgb(255,255,255)",
           "width": 2
          }
         },
         "text": "Result",
         "type": "choropleth",
         "uid": "4931bd40-c7c3-11e8-a054-54271e5d0a77",
         "z": [
          785,
          548,
          1133,
          11138,
          2934,
          3433,
          1266,
          389,
          1509,
          5983,
          1931,
          2104,
          2072,
          2174,
          6761,
          372,
          1227,
          655,
          868,
          116,
          702,
          2724,
          913,
          246,
          516,
          1616,
          1516,
          2363,
          822,
          1020,
          201,
          1932,
          182,
          1466,
          362,
          1353,
          633,
          272,
          36,
          780,
          226,
          118,
          -285,
          305,
          414,
          24,
          69,
          78,
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       ],
       "layout": {
        "geo": {
         "lakecolor": "rgb(255, 255, 255)",
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          "type": "albers usa"
         },
         "scope": "usa",
         "showlakes": true
        },
        "title": "Pro Hillary vs Pro Trump"
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       "<div id=\"3c4d3990-2f39-4373-ad0c-08370f08fc24\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3c4d3990-2f39-4373-ad0c-08370f08fc24\", [{\"autocolorscale\": false, \"colorbar\": {\"title\": \"More positive means more prohillary\"}, \"colorscale\": [[0.0, \"rgb(255,0,0)\"], [0.5, \"rgb(255,255,255)\"], [1.0, \"rgb(0,0,255)\"]], \"locationmode\": \"USA-states\", \"locations\": [\"MO\", \"LA\", \"MD\", \"CA\", \"NJ\", \"TX\", \"MI\", \"KY\", \"IA\", \"FL\", \"MA\", \"PA\", \"VA\", \"AZ\", \"NY\", \"UT\", \"OR\", \"CT\", \"SC\", \"DE\", \"OK\", \"IL\", \"TN\", \"KS\", \"MS\", \"NC\", \"GA\", \"OH\", \"MN\", \"WI\", \"ME\", \"CO\", \"AR\", \"WA\", \"RI\", \"NV\", \"DC\", \"NE\", \"WV\", \"IN\", \"HI\", \"ID\", \"AL\", \"NM\", \"NH\", \"VT\", \"ND\", \"MT\", \"SD\", \"AK\", \"WY\", \"Mo\"], \"marker\": {\"line\": {\"color\": \"rgb(255,255,255)\", \"width\": 2}}, \"text\": \"Result\", \"z\": [785, 548, 1133, 11138, 2934, 3433, 1266, 389, 1509, 5983, 1931, 2104, 2072, 2174, 6761, 372, 1227, 655, 868, 116, 702, 2724, 913, 246, 516, 1616, 1516, 2363, 822, 1020, 201, 1932, 182, 1466, 362, 1353, 633, 272, 36, 780, 226, 118, -285, 305, 414, 24, 69, 78, 15, 242, 44, 1], \"type\": \"choropleth\", \"uid\": \"4936c73a-c7c3-11e8-a195-54271e5d0a77\"}], {\"geo\": {\"lakecolor\": \"rgb(255, 255, 255)\", \"projection\": {\"type\": \"albers usa\"}, \"scope\": \"usa\", \"showlakes\": true}, \"title\": \"Pro Hillary vs Pro Trump\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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       "<div id=\"3c4d3990-2f39-4373-ad0c-08370f08fc24\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3c4d3990-2f39-4373-ad0c-08370f08fc24\", [{\"autocolorscale\": false, \"colorbar\": {\"title\": \"More positive means more prohillary\"}, \"colorscale\": [[0.0, \"rgb(255,0,0)\"], [0.5, \"rgb(255,255,255)\"], [1.0, \"rgb(0,0,255)\"]], \"locationmode\": \"USA-states\", \"locations\": [\"MO\", \"LA\", \"MD\", \"CA\", \"NJ\", \"TX\", \"MI\", \"KY\", \"IA\", \"FL\", \"MA\", \"PA\", \"VA\", \"AZ\", \"NY\", \"UT\", \"OR\", \"CT\", \"SC\", \"DE\", \"OK\", \"IL\", \"TN\", \"KS\", \"MS\", \"NC\", \"GA\", \"OH\", \"MN\", \"WI\", \"ME\", \"CO\", \"AR\", \"WA\", \"RI\", \"NV\", \"DC\", \"NE\", \"WV\", \"IN\", \"HI\", \"ID\", \"AL\", \"NM\", \"NH\", \"VT\", \"ND\", \"MT\", \"SD\", \"AK\", \"WY\", \"Mo\"], \"marker\": {\"line\": {\"color\": \"rgb(255,255,255)\", \"width\": 2}}, \"text\": \"Result\", \"z\": [785, 548, 1133, 11138, 2934, 3433, 1266, 389, 1509, 5983, 1931, 2104, 2072, 2174, 6761, 372, 1227, 655, 868, 116, 702, 2724, 913, 246, 516, 1616, 1516, 2363, 822, 1020, 201, 1932, 182, 1466, 362, 1353, 633, 272, 36, 780, 226, 118, -285, 305, 414, 24, 69, 78, 15, 242, 44, 1], \"type\": \"choropleth\", \"uid\": \"4936c73a-c7c3-11e8-a195-54271e5d0a77\"}], {\"geo\": {\"lakecolor\": \"rgb(255, 255, 255)\", \"projection\": {\"type\": \"albers usa\"}, \"scope\": \"usa\", \"showlakes\": true}, \"title\": \"Pro Hillary vs Pro Trump\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "from plotly.offline import iplot,init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "scl = [[0.0, 'rgb(255,0,0)'],[0.5, 'rgb(255,255,255)'],\\\n",
    "            [1.0, 'rgb(0,0,255)']]\n",
    "\n",
    "# df['text'] = df['state'] + '<br>' +\\\n",
    "#     'Beef '+df['beef']+' Dairy '+df['dairy']+'<br>'+\\\n",
    "#     'Fruits '+df['total fruits']+' Veggies ' + df['total veggies']+'<br>'+\\\n",
    "#     'Wheat '+df['wheat']+' Corn '+df['corn']\n",
    "\n",
    "data = [ dict(\n",
    "        type='choropleth',\n",
    "        colorscale = scl,\n",
    "        autocolorscale = False,\n",
    "        locations = list(mapStateToProTrumpProHillary.keys()),\n",
    "        z = list(mapStateToProTrumpProHillary.values()),\n",
    "        locationmode = 'USA-states',\n",
    "        text = 'Result',\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            title = \"More positive means more prohillary\")\n",
    "        ) ]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Pro Hillary vs Pro Trump',\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)'),\n",
    "             )\n",
    "    \n",
    "fig = dict( data=data, layout=layout )\n",
    "iplot( fig, filename='d3-cloropleth-map' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ratio pos/neg Trump vs pos/neg Hillary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we dont have enough data for state  ND 145 61\n",
      "we dont have enough data for state  SD 199 76\n",
      "we dont have enough data for state  VT 153 90\n",
      "state  Mo not in mapStateToSentimentHillary\n"
     ]
    }
   ],
   "source": [
    "mapStateToSentimentTrump = {}\n",
    "mapStateToSentimentHillary = {}\n",
    "\n",
    "mapStateRatiosTrumpEstimated = {}\n",
    "mapStateRatiosHillaryEstimated = {}\n",
    "\n",
    "mapStatePosDividedByAllTrumpEstimated = {}\n",
    "mapStatePosDividedByAllHillaryEstimated = {}\n",
    "\n",
    "for tweet in tweets_data:\n",
    "    if tweet['place'] == None:\n",
    "        continue\n",
    "    countryCode = tweet['place']['country_code']\n",
    "    city = tweet['place']['full_name']\n",
    "    if countryCode != 'US' or ',' not in city:\n",
    "        continue\n",
    "    cityAndState = city.split(',')\n",
    "    state = cityAndState[1].strip()\n",
    "    if len(state) != 2: # skip Philadelphia\n",
    "        continue\n",
    "    candidate = tweet['talk']\n",
    "    sentiment = tweet['sentiment']\n",
    "    if candidate != 'trump' and candidate != 'hillary':\n",
    "        continue\n",
    "    if candidate == 'trump' and state not in mapStateToSentimentTrump:\n",
    "        mapStateToSentimentTrump[state] = {'Positive':0, 'Negative':0}\n",
    "    elif candidate == 'hillary' and state not in mapStateToSentimentHillary:\n",
    "        mapStateToSentimentHillary[state] = {'Positive':0, 'Negative':0}\n",
    "\n",
    "    if candidate == 'trump':\n",
    "        mapStateToSentimentTrump[state][sentiment] += 1\n",
    "    elif candidate == 'hillary':\n",
    "        mapStateToSentimentHillary[state][sentiment] += 1\n",
    "\n",
    "mapStateRatio = {}\n",
    "\n",
    "thresholdNotEnoughData = 100\n",
    "for state in mapStateToSentimentTrump.keys():\n",
    "    if state not in mapStateToSentimentHillary:\n",
    "        print('state ', state, 'not in mapStateToSentimentHillary')\n",
    "        continue\n",
    "    \n",
    "    sentimentTrump = sum(mapStateToSentimentTrump[state].values())\n",
    "    sentimentHillary = sum(mapStateToSentimentHillary[state].values())\n",
    "    if sentimentTrump <= thresholdNotEnoughData or sentimentHillary <= thresholdNotEnoughData:\n",
    "        print('we dont have enough data for state ', state, sentimentTrump, sentimentHillary)\n",
    "        continue\n",
    "    \n",
    "    ratioTrump = mapStateToSentimentTrump[state]['Positive']/mapStateToSentimentTrump[state]['Negative']\n",
    "    ratioHillary = mapStateToSentimentHillary[state]['Positive']/mapStateToSentimentHillary[state]['Negative']\n",
    "    \n",
    "    mapStatePosDividedByAllTrumpEstimated[state] = mapStateToSentimentTrump[state]['Positive']/(mapStateToSentimentTrump[state]['Positive']+mapStateToSentimentTrump[state]['Negative'])\n",
    "    mapStatePosDividedByAllHillaryEstimated[state] = mapStateToSentimentHillary[state]['Positive']/(mapStateToSentimentHillary[state]['Positive']+mapStateToSentimentHillary[state]['Negative'])\n",
    "    \n",
    "    mapStateRatiosTrumpEstimated[state] = ratioTrump\n",
    "    mapStateRatiosHillaryEstimated[state] = ratioHillary\n",
    "    \n",
    "    mapStateRatio[state] = 0 if ratioTrump > ratioHillary else 1\n",
    "    #mapStateRatio[state] = ratioTrump - ratioHillary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': 0,\n",
       " 'LA': 0,\n",
       " 'MD': 1,\n",
       " 'CA': 1,\n",
       " 'NJ': 1,\n",
       " 'MI': 0,\n",
       " 'IA': 0,\n",
       " 'VA': 1,\n",
       " 'AZ': 0,\n",
       " 'NY': 1,\n",
       " 'UT': 1,\n",
       " 'TX': 0,\n",
       " 'SC': 0,\n",
       " 'DE': 1,\n",
       " 'FL': 0,\n",
       " 'MA': 1,\n",
       " 'GA': 0,\n",
       " 'PA': 0,\n",
       " 'OH': 1,\n",
       " 'NC': 1,\n",
       " 'MN': 1,\n",
       " 'WI': 1,\n",
       " 'MS': 1,\n",
       " 'AR': 0,\n",
       " 'CT': 1,\n",
       " 'IL': 1,\n",
       " 'TN': 0,\n",
       " 'DC': 1,\n",
       " 'CO': 1,\n",
       " 'NE': 1,\n",
       " 'OK': 0,\n",
       " 'WV': 0,\n",
       " 'IN': 1,\n",
       " 'KS': 0,\n",
       " 'WA': 0,\n",
       " 'RI': 0,\n",
       " 'ID': 0,\n",
       " 'AL': 0,\n",
       " 'KY': 0,\n",
       " 'NM': 1,\n",
       " 'ME': 0,\n",
       " 'NV': 1,\n",
       " 'NH': 1,\n",
       " 'OR': 1,\n",
       " 'MT': 0,\n",
       " 'AK': 1,\n",
       " 'HI': 0,\n",
       " 'WY': 0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapStateRatio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MO': 2.3247422680412373,\n",
       " 'LA': 2.7397881996974283,\n",
       " 'MD': 1.596931659693166,\n",
       " 'CA': 1.8106042884990254,\n",
       " 'NJ': 1.6600638977635782,\n",
       " 'MI': 1.7184850197851893,\n",
       " 'IA': 2.165775401069519,\n",
       " 'VA': 1.7184523809523808,\n",
       " 'AZ': 1.861228813559322,\n",
       " 'NY': 1.775654043722375,\n",
       " 'UT': 1.687912087912088,\n",
       " 'TX': 2.224216765453006,\n",
       " 'SC': 1.9951377633711507,\n",
       " 'DE': 1.5978260869565217,\n",
       " 'FL': 1.9662077596996246,\n",
       " 'MA': 1.6950084602368867,\n",
       " 'GA': 1.8495085995085996,\n",
       " 'PA': 1.8644067796610169,\n",
       " 'OH': 1.63449433487201,\n",
       " 'NC': 1.9421965317919074,\n",
       " 'MN': 1.7635327635327636,\n",
       " 'WI': 2.209267563527653,\n",
       " 'MS': 1.8891891891891892,\n",
       " 'AR': 2.3031674208144794,\n",
       " 'CT': 1.699873896595208,\n",
       " 'IL': 2.002833663927458,\n",
       " 'TN': 2.102099927588704,\n",
       " 'DC': 2.03041825095057,\n",
       " 'CO': 1.7408207343412527,\n",
       " 'NE': 1.3786407766990292,\n",
       " 'OK': 1.617741935483871,\n",
       " 'WV': 1.5570469798657718,\n",
       " 'IN': 1.7719298245614035,\n",
       " 'KS': 1.7813121272365806,\n",
       " 'WA': 1.7999064983637214,\n",
       " 'RI': 1.784741144414169,\n",
       " 'ID': 1.9422382671480145,\n",
       " 'AL': 2.937250996015936,\n",
       " 'KY': 1.752365930599369,\n",
       " 'NM': 1.7850467289719627,\n",
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       " 'NV': 1.9575707154742097,\n",
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       " 'OR': 1.3925667828106854,\n",
       " 'MT': 1.5972222222222223,\n",
       " 'AK': 1.3061224489795917,\n",
       " 'HI': 2.2362030905077264,\n",
       " 'WY': 1.5142857142857142}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapStateRatiosTrumpEstimated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{'MO': 1.9444444444444444,\n",
       " 'LA': 1.955223880597015,\n",
       " 'MD': 1.8072289156626506,\n",
       " 'CA': 1.9172625127681306,\n",
       " 'NJ': 1.9429928741092637,\n",
       " 'MI': 1.6707650273224044,\n",
       " 'IA': 2.147727272727273,\n",
       " 'VA': 1.7704081632653061,\n",
       " 'AZ': 1.615228426395939,\n",
       " 'NY': 1.9022847100175746,\n",
       " 'UT': 1.7210526315789474,\n",
       " 'TX': 1.8897804054054055,\n",
       " 'SC': 1.8549848942598188,\n",
       " 'DE': 2.138888888888889,\n",
       " 'FL': 1.8451505016722407,\n",
       " 'MA': 1.8053691275167785,\n",
       " 'GA': 1.785615491009682,\n",
       " 'PA': 1.8415841584158417,\n",
       " 'OH': 1.8280499519692603,\n",
       " 'NC': 1.9438502673796791,\n",
       " 'MN': 2.428,\n",
       " 'WI': 2.2146118721461185,\n",
       " 'MS': 2.1952380952380954,\n",
       " 'AR': 1.6226415094339623,\n",
       " 'CT': 1.811912225705329,\n",
       " 'IL': 2.1337126600284493,\n",
       " 'TN': 1.8245931283905967,\n",
       " 'DC': 2.190311418685121,\n",
       " 'CO': 1.8739002932551319,\n",
       " 'NE': 1.6021505376344085,\n",
       " 'OK': 1.5833333333333333,\n",
       " 'WV': 1.528301886792453,\n",
       " 'IN': 1.85679012345679,\n",
       " 'KS': 1.66875,\n",
       " 'WA': 1.7684605757196497,\n",
       " 'RI': 1.7731958762886597,\n",
       " 'ID': 1.7788461538461537,\n",
       " 'AL': 1.6666666666666667,\n",
       " 'KY': 1.7148936170212765,\n",
       " 'NM': 1.8911917098445596,\n",
       " 'ME': 1.7668711656441718,\n",
       " 'NV': 2.04014598540146,\n",
       " 'NH': 2.672727272727273,\n",
       " 'OR': 1.904109589041096,\n",
       " 'MT': 1.5238095238095237,\n",
       " 'AK': 2.1621621621621623,\n",
       " 'HI': 1.9166666666666667,\n",
       " 'WY': 1.1568627450980393}"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mapStateRatiosHillaryEstimated"
   ]
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  {
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   "execution_count": 16,
   "metadata": {},
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      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"6fdb208e-853f-4467-bb2c-4ca598fab258\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"6fdb208e-853f-4467-bb2c-4ca598fab258\", [{\"autocolorscale\": false, \"colorbar\": {\"title\": \"Blue = Hillary\"}, \"colorscale\": [[0.0, \"rgb(204,61,61)\"], [0.5, \"rgb(255,255,255)\"], [1.0, \"rgb(26,128,196)\"]], \"locationmode\": \"USA-states\", \"locations\": [\"MO\", \"LA\", \"MD\", \"CA\", \"NJ\", \"MI\", \"IA\", \"VA\", \"AZ\", \"NY\", \"UT\", \"TX\", \"SC\", \"DE\", \"FL\", \"MA\", \"GA\", \"PA\", \"OH\", \"NC\", \"MN\", \"WI\", \"MS\", \"AR\", \"CT\", \"IL\", \"TN\", \"DC\", \"CO\", \"NE\", \"OK\", \"WV\", \"IN\", \"KS\", \"WA\", \"RI\", \"ID\", \"AL\", \"KY\", \"NM\", \"ME\", \"NV\", \"NH\", \"OR\", \"MT\", \"AK\", \"HI\", \"WY\"], \"marker\": {\"line\": {\"color\": \"rgb(255,255,255)\", \"width\": 2}}, \"text\": \"Result\", \"z\": [0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0], \"type\": \"choropleth\", \"uid\": \"66fb71c8-c8a3-11e8-b8cc-54271e5d0a77\"}], {\"geo\": {\"lakecolor\": \"rgb(255, 255, 255)\", \"projection\": {\"type\": \"albers usa\"}, \"scope\": \"usa\", \"showlakes\": true}, \"title\": \"Hillary Clinton vs Donald Trump\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "from plotly.offline import iplot,init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "scl = [[0.0, 'rgb(204,61,61)'],[0.5, 'rgb(255,255,255)'],\\\n",
    "            [1.0, 'rgb(26,128,196)']]\n",
    "\n",
    "# df['text'] = df['state'] + '<br>' +\\\n",
    "#     'Beef '+df['beef']+' Dairy '+df['dairy']+'<br>'+\\\n",
    "#     'Fruits '+df['total fruits']+' Veggies ' + df['total veggies']+'<br>'+\\\n",
    "#     'Wheat '+df['wheat']+' Corn '+df['corn']\n",
    "\n",
    "data = [ dict(\n",
    "        type='choropleth',\n",
    "        colorscale = scl,\n",
    "        autocolorscale = False,\n",
    "        locations = list(mapStateRatio.keys()),\n",
    "        z = list(mapStateRatio.values()),\n",
    "        locationmode = 'USA-states',\n",
    "        text = 'Result',\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            title = \"Blue = Hillary\")\n",
    "        ) ]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Hillary Clinton vs Donald Trump',\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)'),\n",
    "             )\n",
    "    \n",
    "fig = dict( data=data, layout=layout )\n",
    "iplot( fig, filename='d3-cloropleth-map' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>State</th>\n",
       "      <th>Hillary</th>\n",
       "      <th>Trump</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>34.36%</td>\n",
       "      <td>62.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>36.55%</td>\n",
       "      <td>51.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>45.13%</td>\n",
       "      <td>48.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>33.65%</td>\n",
       "      <td>60.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>61.73%</td>\n",
       "      <td>31.62%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Hillary   Trump\n",
       "0     Alabama  34.36%  62.08%\n",
       "1      Alaska  36.55%  51.28%\n",
       "2     Arizona  45.13%  48.67%\n",
       "3    Arkansas  33.65%  60.57%\n",
       "4  California  61.73%  31.62%"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentagesRealDataDF = pd.read_csv(\"voting_percentages_per_state.csv\", sep=\",\")\n",
    "percentagesRealDataDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>State</th>\n",
       "      <th>Abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Abbreviation\n",
       "0     Alabama           AL\n",
       "1      Alaska           AK\n",
       "2     Arizona           AZ\n",
       "3    Arkansas           AR\n",
       "4  California           CA"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usCodesDF = pd.read_csv(\"us-states-codes-to-names.csv\", sep=\",\")\n",
    "usCodesDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Hillary</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.36%</td>\n",
       "      <td>62.08%</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.55%</td>\n",
       "      <td>51.28%</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.13%</td>\n",
       "      <td>48.67%</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.65%</td>\n",
       "      <td>60.57%</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.73%</td>\n",
       "      <td>31.62%</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.16%</td>\n",
       "      <td>43.25%</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.57%</td>\n",
       "      <td>40.93%</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53.09%</td>\n",
       "      <td>41.72%</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90.48%</td>\n",
       "      <td>4.07%</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47.82%</td>\n",
       "      <td>49.02%</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45.64%</td>\n",
       "      <td>50.77%</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62.22%</td>\n",
       "      <td>30.03%</td>\n",
       "      <td>HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27.49%</td>\n",
       "      <td>59.26%</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>55.83%</td>\n",
       "      <td>38.76%</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37.91%</td>\n",
       "      <td>56.82%</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>41.74%</td>\n",
       "      <td>51.15%</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>36.05%</td>\n",
       "      <td>56.65%</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32.68%</td>\n",
       "      <td>62.52%</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38.45%</td>\n",
       "      <td>58.09%</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>47.83%</td>\n",
       "      <td>44.87%</td>\n",
       "      <td>ME</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60.33%</td>\n",
       "      <td>33.91%</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>60.01%</td>\n",
       "      <td>32.81%</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>47.27%</td>\n",
       "      <td>47.50%</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>46.44%</td>\n",
       "      <td>44.92%</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40.11%</td>\n",
       "      <td>57.94%</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.14%</td>\n",
       "      <td>56.77%</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35.75%</td>\n",
       "      <td>56.17%</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33.70%</td>\n",
       "      <td>58.75%</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>47.92%</td>\n",
       "      <td>45.50%</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46.98%</td>\n",
       "      <td>46.61%</td>\n",
       "      <td>NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>54.99%</td>\n",
       "      <td>41.00%</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>48.26%</td>\n",
       "      <td>40.04%</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>59.01%</td>\n",
       "      <td>36.52%</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>46.17%</td>\n",
       "      <td>49.83%</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>27.23%</td>\n",
       "      <td>62.96%</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>43.56%</td>\n",
       "      <td>51.69%</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>28.93%</td>\n",
       "      <td>65.32%</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>50.07%</td>\n",
       "      <td>39.09%</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>47.46%</td>\n",
       "      <td>48.18%</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>54.41%</td>\n",
       "      <td>38.90%</td>\n",
       "      <td>RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40.67%</td>\n",
       "      <td>54.94%</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>31.74%</td>\n",
       "      <td>61.53%</td>\n",
       "      <td>SD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>34.72%</td>\n",
       "      <td>60.72%</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43.24%</td>\n",
       "      <td>52.23%</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>27.46%</td>\n",
       "      <td>45.54%</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>56.68%</td>\n",
       "      <td>30.27%</td>\n",
       "      <td>VT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>49.73%</td>\n",
       "      <td>44.41%</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>52.54%</td>\n",
       "      <td>36.83%</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>26.43%</td>\n",
       "      <td>68.50%</td>\n",
       "      <td>WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>46.45%</td>\n",
       "      <td>47.22%</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>21.63%</td>\n",
       "      <td>67.40%</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hillary   Trump Abbreviation\n",
       "0   34.36%  62.08%           AL\n",
       "1   36.55%  51.28%           AK\n",
       "2   45.13%  48.67%           AZ\n",
       "3   33.65%  60.57%           AR\n",
       "4   61.73%  31.62%           CA\n",
       "5   48.16%  43.25%           CO\n",
       "6   54.57%  40.93%           CT\n",
       "7   53.09%  41.72%           DE\n",
       "8   90.48%   4.07%           DC\n",
       "9   47.82%  49.02%           FL\n",
       "10  45.64%  50.77%           GA\n",
       "11  62.22%  30.03%           HI\n",
       "12  27.49%  59.26%           ID\n",
       "13  55.83%  38.76%           IL\n",
       "14  37.91%  56.82%           IN\n",
       "15  41.74%  51.15%           IA\n",
       "16  36.05%  56.65%           KS\n",
       "17  32.68%  62.52%           KY\n",
       "18  38.45%  58.09%           LA\n",
       "19  47.83%  44.87%           ME\n",
       "20  60.33%  33.91%           MD\n",
       "21  60.01%  32.81%           MA\n",
       "22  47.27%  47.50%           MI\n",
       "23  46.44%  44.92%           MN\n",
       "24  40.11%  57.94%           MS\n",
       "25  38.14%  56.77%           MO\n",
       "26  35.75%  56.17%           MT\n",
       "27  33.70%  58.75%           NE\n",
       "28  47.92%  45.50%           NV\n",
       "29  46.98%  46.61%           NH\n",
       "30  54.99%  41.00%           NJ\n",
       "31  48.26%  40.04%           NM\n",
       "32  59.01%  36.52%           NY\n",
       "33  46.17%  49.83%           NC\n",
       "34  27.23%  62.96%           ND\n",
       "35  43.56%  51.69%           OH\n",
       "36  28.93%  65.32%           OK\n",
       "37  50.07%  39.09%           OR\n",
       "38  47.46%  48.18%           PA\n",
       "39  54.41%  38.90%           RI\n",
       "40  40.67%  54.94%           SC\n",
       "41  31.74%  61.53%           SD\n",
       "42  34.72%  60.72%           TN\n",
       "43  43.24%  52.23%           TX\n",
       "44  27.46%  45.54%           UT\n",
       "45  56.68%  30.27%           VT\n",
       "46  49.73%  44.41%           VA\n",
       "47  52.54%  36.83%           WA\n",
       "48  26.43%  68.50%           WV\n",
       "49  46.45%  47.22%           WI\n",
       "50  21.63%  67.40%           WY"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedDF = pd.merge(percentagesRealDataDF, usCodesDF,  on = 'State')\n",
    "mergedDF = mergedDF[['Hillary','Trump', 'Abbreviation']]\n",
    "mergedDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AL': {'Hillary': 0.3436, 'Trump': 0.6208},\n",
       " 'AK': {'Hillary': 0.3655, 'Trump': 0.5128},\n",
       " 'AZ': {'Hillary': 0.45130000000000003, 'Trump': 0.4867},\n",
       " 'AR': {'Hillary': 0.33649999999999997, 'Trump': 0.6057},\n",
       " 'CA': {'Hillary': 0.6173, 'Trump': 0.31620000000000004},\n",
       " 'CO': {'Hillary': 0.4816, 'Trump': 0.4325},\n",
       " 'CT': {'Hillary': 0.5457, 'Trump': 0.4093},\n",
       " 'DE': {'Hillary': 0.5309, 'Trump': 0.4172},\n",
       " 'DC': {'Hillary': 0.9048, 'Trump': 0.0407},\n",
       " 'FL': {'Hillary': 0.4782, 'Trump': 0.4902},\n",
       " 'GA': {'Hillary': 0.45640000000000003, 'Trump': 0.5077},\n",
       " 'HI': {'Hillary': 0.6222, 'Trump': 0.3003},\n",
       " 'ID': {'Hillary': 0.2749, 'Trump': 0.5926},\n",
       " 'IL': {'Hillary': 0.5583, 'Trump': 0.3876},\n",
       " 'IN': {'Hillary': 0.3791, 'Trump': 0.5682},\n",
       " 'IA': {'Hillary': 0.4174, 'Trump': 0.5115},\n",
       " 'KS': {'Hillary': 0.3605, 'Trump': 0.5665},\n",
       " 'KY': {'Hillary': 0.3268, 'Trump': 0.6252},\n",
       " 'LA': {'Hillary': 0.3845, 'Trump': 0.5809000000000001},\n",
       " 'ME': {'Hillary': 0.4783, 'Trump': 0.4487},\n",
       " 'MD': {'Hillary': 0.6033, 'Trump': 0.33909999999999996},\n",
       " 'MA': {'Hillary': 0.6001, 'Trump': 0.3281},\n",
       " 'MI': {'Hillary': 0.4727, 'Trump': 0.475},\n",
       " 'MN': {'Hillary': 0.4644, 'Trump': 0.44920000000000004},\n",
       " 'MS': {'Hillary': 0.4011, 'Trump': 0.5794},\n",
       " 'MO': {'Hillary': 0.3814, 'Trump': 0.5677},\n",
       " 'MT': {'Hillary': 0.3575, 'Trump': 0.5617},\n",
       " 'NE': {'Hillary': 0.337, 'Trump': 0.5875},\n",
       " 'NV': {'Hillary': 0.4792, 'Trump': 0.455},\n",
       " 'NH': {'Hillary': 0.4698, 'Trump': 0.4661},\n",
       " 'NJ': {'Hillary': 0.5499, 'Trump': 0.41},\n",
       " 'NM': {'Hillary': 0.4826, 'Trump': 0.4004},\n",
       " 'NY': {'Hillary': 0.5901, 'Trump': 0.3652},\n",
       " 'NC': {'Hillary': 0.4617, 'Trump': 0.49829999999999997},\n",
       " 'ND': {'Hillary': 0.2723, 'Trump': 0.6296},\n",
       " 'OH': {'Hillary': 0.43560000000000004, 'Trump': 0.5169},\n",
       " 'OK': {'Hillary': 0.2893, 'Trump': 0.6531999999999999},\n",
       " 'OR': {'Hillary': 0.5007, 'Trump': 0.3909},\n",
       " 'PA': {'Hillary': 0.4746, 'Trump': 0.4818},\n",
       " 'RI': {'Hillary': 0.5440999999999999, 'Trump': 0.389},\n",
       " 'SC': {'Hillary': 0.4067, 'Trump': 0.5494},\n",
       " 'SD': {'Hillary': 0.31739999999999996, 'Trump': 0.6153},\n",
       " 'TN': {'Hillary': 0.3472, 'Trump': 0.6072},\n",
       " 'TX': {'Hillary': 0.4324, 'Trump': 0.5223},\n",
       " 'UT': {'Hillary': 0.2746, 'Trump': 0.45539999999999997},\n",
       " 'VT': {'Hillary': 0.5668, 'Trump': 0.30269999999999997},\n",
       " 'VA': {'Hillary': 0.49729999999999996, 'Trump': 0.44409999999999994},\n",
       " 'WA': {'Hillary': 0.5254, 'Trump': 0.36829999999999996},\n",
       " 'WV': {'Hillary': 0.2643, 'Trump': 0.685},\n",
       " 'WI': {'Hillary': 0.4645, 'Trump': 0.4722},\n",
       " 'WY': {'Hillary': 0.2163, 'Trump': 0.674}}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapRealPercentatgesToState = mergedDF.to_dict('records')\n",
    "mapStateToRealPercentatges = {}\n",
    "for i, record in enumerate(mapRealPercentatgesToState):\n",
    "    hillary = float(record['Hillary'].replace('%', ''))/100\n",
    "    trump = float(record['Trump'].replace('%', ''))/100\n",
    "    mapStateToRealPercentatges[record['Abbreviation']] = {'Hillary':hillary, 'Trump': trump}\n",
    "mapStateToRealPercentatges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AL',\n",
       " 'AK',\n",
       " 'AZ',\n",
       " 'AR',\n",
       " 'CA',\n",
       " 'CO',\n",
       " 'CT',\n",
       " 'DE',\n",
       " 'DC',\n",
       " 'FL',\n",
       " 'GA',\n",
       " 'HI',\n",
       " 'ID',\n",
       " 'IL',\n",
       " 'IN',\n",
       " 'IA',\n",
       " 'KS',\n",
       " 'KY',\n",
       " 'LA',\n",
       " 'ME',\n",
       " 'MD',\n",
       " 'MA',\n",
       " 'MI',\n",
       " 'MN',\n",
       " 'MS',\n",
       " 'MO',\n",
       " 'MT',\n",
       " 'NE',\n",
       " 'NV',\n",
       " 'NH',\n",
       " 'NJ',\n",
       " 'NM',\n",
       " 'NY',\n",
       " 'NC',\n",
       " 'ND',\n",
       " 'OH',\n",
       " 'OK',\n",
       " 'OR',\n",
       " 'PA',\n",
       " 'RI',\n",
       " 'SC',\n",
       " 'SD',\n",
       " 'TN',\n",
       " 'TX',\n",
       " 'UT',\n",
       " 'VT',\n",
       " 'VA',\n",
       " 'WA',\n",
       " 'WV',\n",
       " 'WI',\n",
       " 'WY']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = list(mergedDF.to_dict('dict')['Abbreviation'].values())\n",
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trump scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hF4/PfVOgjEXidLV2U7W0nKd+91EcLscEVSvE2GmvD/L8t39FuD9CXsCHw2W/9jmtNQM9IQa6QyxYO5fHvrJpTH8P0g1WsnBECDFpHd5xjFQildbZjg6XHZfXyXu/3D8OlY2tY++cZPcL+ymuLMKb57nl1IbDZaekKkD9mSZe+95OTDPzxrdCTHaBCj9f+tanuPsTtxMLx2irD9Je30F7Qwdt9R3kFnp58muP8OTvPDJp3lzIfIcQYlKKhmMc23WK/GJf2vfkFuZQf7qRjuYuisqmZkPXeDTOOz/bQ9GsghGnNJRSBMqLOH/kEo21LdNyjZkQ3jwPtz+2lurNK2m91E40FMOwDE4PTsZ1sBKshBCTUuPZZlKJVEbr3ZRSKENx8VjdlA1W54/WkYgl0z5CSSmFw2Xn8M5jEqzEtGa1WZm9sGyiyxiRTAUKISalyEB0sNtfhmw2K/1dA9kvaJwcfesE7tz0z0cD8PlzOX+4jnB/ZIyqEkKkS4KVEGJSMiwGjGKEX2swxmhX0Hjo7ejLeK2IYRgoQw2GUSHEhJJgJYSYlLz5nlHdl0wkKUyj781kNdrVIlprJtlSEyFmpLSClVLqEaXUWaXUeaXUfxzimk8qpU4ppU4qpX6S3TKFEDPN7IWlePLcGR2omkqmMCyK+avnjGFlYyuvOI9oBoeEA5ipwR2BrpzMphCFENk34qpQpZQF+C7wENAIHFRKvaK1PnXdNQuAPwE2aq27lVKBsSpYCJG5RDzBxeP1HN55nK7mblKmSU6eh5X3L2Xx+gW4J+ELssViYf2jq3nzx+9ROie9p5TOlm6W3bV4Un4/6Vr94HJe+e42cgtz0r6nu72XJRsW4PI4x7AyIUQ60tlusx44r7W+CKCUeg54Ejh13TX/Fviu1robQGvdnu1ChRCjU3voAtv+5W1ioRiuHBcurxOlIBaO8+aP3uXtn77H7R+r5o4nqifdmYjL717C6X21tNd34p9dOOy1PcE+3Lku7nxy3ThVNzbmrqjA4XYQC8dwuEdea2Wamng0waoHlo1DdUKIkaTzLDoLaLju741XPna9hcBCpdQepdQ+pdQjt/pCSqmvKKVqlFI1wWBwdBULIdJ2Ys8ZXvzOVpwuB8WVfnILvNjsVqw2K+5cF8WVfvKL83nvl/vZ+cNdk67JpMPl4OlvPEZxpZ+WS2233PUWi8RpuxzE4bbzqT9+ityC9Ed6JiOb3camL9xDV2sPiXhy2GuvHuWx9M6FlM0rGacKhRDDSWfE6lbLIT+8CdoKLADuA2YD7yqllmmte264SevvAd+DwSNtMq52kjFNk3BfhEQsgdVuxeNzT7p3/GLmarsc5PV/fpPC0oJheyJZbRaKq/wcefMEgQo/q+6fXCMfHp+HT/77Jzi9v5b9W48MnptnKK6exuXOcXLPs3ew/O4lU3oK8Hq33b6QaCjKjh/sxuVxkFuUi2Hc+FQc7ovQ09HLkvULePjL90+6JolCzFTpBKtGoPy6v88Gmm9xzT6tdQK4pJQ6y2DQOpiVKieZSChKbc0FDmw9Qm+w78qTvCanwMv6R1ezaN38afMEL6auQzuOYbNZ02o0aRgG+cV57H2lhuX3LMFimVztCuxOOyvvXcbyu2+j9VI7od4wWmtcXidl80rG7NDVibTmwRX4Zxey/7XDXPqgfnDXHwp95X1tQUkeW35zE0s3Lpp0/7+EmMnSCVYHgQVKqTlAE/Bp4LMfuuYl4DPA/1VKFTE4NXgxm4VOFk3nW/jl320lOhAlp8BLoKLo2uciA1F2/nA3u5/fy1Nf30LlktkTWKmYyUK9IU7traUwg+7jTo+DtstB6k83MWdZxRhWN3qGYcyoKa/yRbMoXzSL7vZems+3EgvHsDls5JfkUTavWEbIhZiERgxWWuukUuprwHbAAnxfa31SKfUtoEZr/cqVz21WSp0CUsC/11p3jmXhE6HlYhvP/eVLuHMG16Z8mMvrxOV1Eu6P8PP//jKf+uOn5IgJMSFaLrWjtcZiyeyF12qzcOmD+kkbrGaq/ICP/ED6ZyYKISZOWodwaa23Als/9LFvXvffGviDK3+mpWQiyYt/vxWX14nH5x722qvTgC/9w+t89dtfxO60j0eJQlwTjyZGdZ/FZiUix6IIIcSoyThymupONDDQE8abl143aHeOi1goxvmjdWNbmBC3YLVZRtXB20yZ2F3yRkCIqSDcH6H5Qiv1Z5porWsnmRh+F6kYH+kfGz/DHdx+FLc3s+Z7Hp+bA1sPs2TDAtmxI7Ii1BcmMhDFMBQen3vIM+UKywrQ+uoxJ+n/7CViCcrmFWerXCHEGGita+fo2yc4uefM4B59pdCmxuFxUL15JUs3LprybUemMglWadBa03SuhaJZwzco/DCPz01bfZBUMoXVJv/UYnRSqRT1pxo5uP0Y9acauHognGEolm5czMr7llJc6b8hQBWW5lO+qIyOpq60O3gn4kmsduuUPg5GiOlMa83+rYd59/m9WO02Ckryb9gRG4vE2fPiAfZvPczT33hM1vhOEJkKTIOZMjFNfVMfmZEopTCUMWKTPyGGEu6P8LO/epnn/8ertF1qx19eRODKn/ziPE69f5Yf/Oef89ZP3iWVSt1w77pHVzPQG752jtxwtNZ0Nnex+sFlQ46CCSEm1oFtR3jnufcpnFVIYVn+TW1GHC47gYoinG4Hz3/7FZovtE5QpTObBKs0GBYDq81CKpka+eLrmKZGo7E7Ru4jJMSHRcMxnv/2K7ReaKO4sgifP/eGUSmL1UJhWQGB8iJqth9j5492o/Wv++7OXVFJ9eaVtF5uJzVMuNJaE2zspGxeCXc+MbWPgxFiuupq7Wb3z/cSqCjEahu+b5k7x4XT4+TVf9px0xsuMfYkWKVBKcWi6nn0BPsyuq+vo4+5KyqnZfNCMfbef/kg7fUdFM0uHHadlGExKK7yc/TNE1w4Vnft40op7v/MRjY8tpZgQwfBxs4bFreapkl3Wy9tl4OUL57FM3/wMdnBKsQk9cG7p1GGSntZSU6+h572PhprW8a4MvFhsvAnTaseWMbJ989mtBg4Fomz9qGVY1yZmI6i4RjH3j5BYVl+WtcbhoHH5+bgtqPMX/XrNVIWi4X7PnknyzYu5viukxzffYpU8tejV/NWVrHmoRWULyqTZpNCTFKJeIIjb35AfnFeRvc5XHaOvHlcmlWPMwlWaSqbV8KsBaW013dQWDryi113Ww/+8kLKF5eNQ3Viujl3+CLJRGabHnIKvDScaaajuYui6zquK6Xwzy7kwc/dw92fuINIfwTT1Dg9DlyezHa6CiHG30BPmGQihc2e2Uu2x+eirS44RlWJochb1DQppXjydx7B43PT0dx1w1qW62mt6Wrtxu608/Gvb5EzvMSoNF9oxZbh2jylFIYB3a09Q15jd9jwFeWSH/BJqBJiijBT5qha9ijDIJmQNVbjTYJVBrx5Hj77p08za0EJ7fVBgo2dxCJxkokU8WicjqYu2uuD+MuL+NyfP4OvKHeiSxZTVCKawMjwOBoArZEnUiGmGYfLjmmaQ76hH0oilsCd6xqjqsRQZCowQ948D5/8oycJNnZyfNcpLhy9RGQgit1pY+nGRbfsKSSmnkgoSm3NeY68eYK+zgFQkOfPZc2mFSxYM2fMWxK4c92kRtFFWSmF3Sm7UGeKRDxB3YkGulq6ScSTuHNdVN5WntZyBTF1eHxuyuaV0N3WS26BN+37BroHWLt54xhWJm5FgtUoKKUIlBex6fP3sOnz90x0OSKLtNbUvHGMd1/YSypp4s334PG5GegZ4Mz+c9RsP4rFZuX2j63hkS8/MGajknNXVFKz/WhG96SSKZShKJ0rndOnu1gkxqE3jlHzxjGioRiGYaAMda0lTMWS2Wx8ah3li6RB5HSglGLdI6t46R9eTztYDbZYUSzZsGBsixM3kWAlxBVaa979xT7ef/kg/vIibHYrXa091NZcINwfQRkKi8VCPBrhte/t5NCO42x8Yh33fPJO8gO+rNZSvriM3AIv0VAMpye90bGe9j6Wblx07RBwMT2F+sL88m9fpeViOwUl+eT5b/zZ01oTbOjkp//1RR7+8v2svHfpBFUqsmnO8gqKZhXQ1dpDQcnwuwO11rTXd1C9eUXa59uK7JE1VkJcUXvoIu+/XENxVQCb3UrrpXZO7a0lGU/iyXXjyXHjdDvw5LopLM2nu7WHY7tO8uO/eIFgY2dWa7FYLNz51Hq62nrS6pwei8RJJpOs2bQiq3WIySWZSPLyP7xOe30nJVWBW077KqXwFeVQWJrPtn9+i3NHLk5ApSLbbHYbz/zex3B6HHQ0d2Gat15vlUwkaa1rZ+Haudz7yTvHuUoBEqxElpimSagvTG9HH6G+cMaLLCea1pr3Xz5AbqEXi8Wgq7Wb80frcOe6sDvtN62ZMwwDu9POQE8YgOe//Qqh3lBWa1p+9xLWPbxyxFPrIwNRult7ePyrmwmUF2W1BjG5XDhaR+PZFopmFYx4rc1hw+fP5c0f7Zbu29OEryiXz/35M8xZVk6woYP2hg4GekKE+yP0dfbTWhekN9jHHU+s44nffljOqJ0g8q8uPpKri7z3bz1Cb7APdeWU9cJZ+ax/dM24LPTOhtZL7QQbOimu9KO1pu5kIw6XHcswO/Mcbju9HX1YHZX0dw7wwXtnuP2xtVmrabBz+l148zzsefkgqUQKb54Hm9MGWhMZiBIZiOLJc/OJP3ycuSsqs/bYYvLRWnPg9SN48txpb45x57hoq2un8WwzlbeVj3GFYjzk5Hv5+NcfoyfYy8k9Z2mobSYRTeDKcbJo3XwWrJmL0z35n3OnMwlWYtTqzzTx4ne2Eo/EySnwUlzhBwZfAMJ9Ebb+n524c9w883uPTfoF1Y3nWlBKoZSir2uAyEAET6572HsUCjQMdA2QF/BRs/0o1ZtXZvVdomEYbHhsLSvuW0ptzQWO7zrFQE8Iw1CUzitm9QPLqVpaLscmzQA97b201rVnPCppd9k5/u5pCVbTTJ7fx8an1k90GeIWJFiJUWk428TP/uolcvK9Ny3cVkrh8bmv7KYL8dxfvshn/vRpSqoCE1TtyCL9kWvhpKOpE0MZ6Y0KKEgmUzhcdnqCfbRcbBuTnVguj5OV9y6VhcgzWKgvMrj7L8NWLg6Xg972zM45FUKMnqyxEhmLR+O8+Pevk5PnHXEHmjfPg91l56W/f/3aVvDJyO6yXVskHo/EsVjT/NXQXOuurxREQ7GxKlHMcEoNNoAd3c1ZLUUIMQwJViJj549cIhaKpd3RNyffS1/XAJdPNY5xZaNXXBm4tuBeGQbpvH7pK1e5cn59NIwy5BVMjA13rhs9iu7b0XCM/OLstgMRQgxNgpXIyNUFtJn2RnF5HNRsPzJGVX10FYtn4fG5iYZjuHOcpNI4FiYRS+LKdZFT4EVrjTZ1Rl2RhchEnj+XWQtK6O8ayOi+RCzB8rtvG6OqhBAfJsFKZCQRTxJs6Mz4/KmcAi8NZ1smbRsGi9XC+i2r6WrtpmhWARo9bK1aa2LhGLMXlKJQhHrD+MsL8Uu7AzFGlFKsf3RNRu1MQn1hfP5cZi8sHePqhBBXSbASGUklkihDZbyAVilFKpXCNEdudjlRVj+wnAWr59DbMbjLLxqK3vI6rTUDvWECFUX4Zxeitaava4D1W9bIGZFiTM1ZXkHVsgqCDZ0jhqt4NE5/1wAPffFeDEOe6oUYL/LbJjJic9gGp70yHHkyUyY2m/XaQu/JyGK18PhvP8LiDfPx5nmIReJEI79ejG6aJuH+CKHeMCVVfuavngNAe30Hc1dUsnDt3IkqfdxorYnHEkRC0Um9GWG6slgtPPHbDzNrYSmtde1EwzdvljBNk+62HrrbevnYVzczZ1nFBFQqxMwl7RZERqw2K5W3ldN2qR2fP/0DiHs7+lm4bt4YVpYddoeNx7+6merNK9n1/F52/nAXAz0hHE47FqtBoMJPcZUfd66LSF+Uvs5+qpZXTPsux9FwjNqaCxx4/QjdbT0oQAPzV89hzaYVlC8qk1GRceLyOHnm9z/G8V0nOfD6EVrr+gY3/anB4KuUYv6aOdz+2NpJ3z9OiOlITdSal+rqal1TUzMhjy0+mkt+JvsYAAAgAElEQVQfXOaFv36V4kp/WtdrrWm73MHn/vxpZs2fWms9Opu7OLTjOMd3nSKVSl3rxK6BwrLB7vKL18+f1qHqwrE6fvW/3iARTZCT7722vs40TXqDfcQicUrnFvPU7z5KTr4s3h9PqWSKhrPN9LT3kkgkcXmclC8qw1eU/pseIUR6lFKHtNbVI14nwUpkKpVM8X+/+RyhnjB5gZG3cXe2dOMvL+Rzf/bMlF2DlIgnaD7fSjQUw7AYg53mK/1T9vtJ17nDF3nxO1vx+XNxeZxDXtfZ0o3H5+azf/p0xjtGhRBiKkg3WMnYvciYxWrh6W88hsVmobutZ8j1VlprOlu6cec4efJ3HpnSIcRmt1F5W/m1s7hKqgJT+vtJR19XP7/6X2+QF/ANG6oACkvzGegOse1f3hqn6oQQYnKSYCVGJb84j8/+2TPkl+TRVt9BV0s3iViCVDJFPJqgo6mLtvoOSuYE+OyfPSNTRFPQqb21mEkz7QNdC8vyuXS8ns6W7jGuTAghJq/puzBEjLn8gI8vfPNZWi62cfTtE1z6oJ54NIHDZWfZXYtZed9SAhVF035kZzpKJpLUbDua0QYFpRTKUHzw7mnu++SdY1idEEJMXhKsxEeilKJsXgll80omuhSRRb0d/UTDMXILczK6LyffS92JepBgJYSYoWQqUAhxk2Q8iRrFyb2GxSB2i95KQggxU0iwEkLcxOawXjtkOhOpZApHmmuyhBBiOpJgJYS4ic+fizvHdcvO3sMZ6Ald60gvhBAzkQQrIcRNLBYL6x5dTV9HX9r3mKbGTJksu2vxGFYmhBCTmwQrIcQtLdmwAKvDRrg/ktb1HU2dLFo3nzz/yE1jhRBiupJgJYS4JW+eh6e+9ij93QOE+4YOV1prgk2dFJTms/lL945jhUIIMflIsBJCDKlqaTmf/g9PkUymaLscpL9rANPUaK1JJlJ0NnfRdjlI+aJZfPo/PIXL65rokoUQYkJJHyshxLDKF83it779BS4er6dm2xFaLrVjmiZ2h43b7lzEyvuWzohzE4UQIh0SrIQQI7LZbSyqnsei6nlorTFNE4vFMtFlCSHEpCNTgUKIjCilJFQJIcQQJFgJIYQQQmSJBCshhBBCiCyRNVZCCPERaa1JxBIk4klsdis2h00W8wsxQ0mwEkKIUYpH41w4dpkDWw/TXh8EpUBD6dxi1j26mrkrKrDZbRNdphBiHEmwEkKIUWi52MYv/+41wn0RPD43gYrBlhNaa3qDfbzy3W3kFHh5+htbCFT4J7pcIcQ4kTVWQgiRoZaLbfz0v76IUoriSj/ePM+1qT+lFDkFXoor/SQTKX7yX35Je0PHBFcshBgvEqzEhEjEEwz0hAj1hUmlUhNdjhBpi8cSvPidrTg9Drx5nmGvzS3wYrVZeenvXyeVlJ9zIWYCmQoU40ZrTcvFNo6+fYJTe2tBg0Zjd9pYs2kFyzYuJr84b6LLFGJYl45fZqAnRElVIK3rcwtzaLscpP50I3OWV45xdUKIiSbBSoyLWCTGa9/byfkjl7A7bBSWFWCxDA6YxmMJ9r92mH2vHmLjk+u4/fFqDEMGU8XkdOD1w3h87ozucbodHNx+TIKVEDPAjAhWWmvi0TjJRAq70ya7dMZZIp7gxb/bSmNt8y3PlLM7bATKi0glU+x+YR/xWJJ7n71DtquLSSeZSNJ6KUigoiij+3IKvdSfbkRrLT/XQkxz0zpYRQYinK25wIGtR+gN9oECNMxdWcnah1ZSvrhMjuYYB/tePcTl002UVA1/UK/FaqG4KsC+Vw9Redts5iyrGMcqxUyktSYaipKIJbHYLLi8zmFHSxPxJEqpjMORYRiYpsZMmVis8pwjxHQ2bYNV7aELvPa9HSTjqWs7dABMU9NU28LFo3UEqvx8/OtbyC3ImeBqp694NM6hN45RVJaf1ouRxWLgyXFRs/2oBCsxZuKxBBeO1nHg9cO0X+5AGQZam/iKclm/ZTWLqufh8rpuus/usGFqnfHIUyplYrUaGBaZ4hZiupuWv+VnD57npe9sxePzUFzpx53z6ydIw1DkBXwUVwXoaunhp//llwz0hCaw2untwrHLJGJJbI70p19zi3KoO9FAd1vPGFYmZqr2hg7++U9/zK/+53YGusMEKooIlBdSXOHHTJns+Nd3+N6//yF1JxtuutditVB12yz6OgcyeszeYB/zVlbJNKAQM8C0C1Z9nf289r0d5Bfn4XQ7hr22sDSfUF+E17//1jhVN/M01jZnFKqAa1MtHU1dY1TV5JBMJGm+0MqlE/XUn2mir7N/okua9oKNnfz0v/ySVDxFSVWAnHzPDWHHneOiuDKA0+Pk+W+/wqUT9Td9jbWbVxENRTJ63Hg0zpqHVnzk+oUQk9+0mwo8secMpqlxjBCqrioszefS8ct0tnRTWJo/xtXNPPFIfFTTHxpNIp4cg4omXn/3ACf2nKFm+zFi4di1j2utmbeyirUPraBiyWwZ3cgy0zR5+bvbMCwWcguHn/53eZ0AvPKP2/jKf/8iLo/z2ueqlpaTF/DRG+zD588d8XG723oIVBQxa0HpR/sGhBBTwrQasUomktS8cYy8NJ7srlJKYbFa+ODdU2NY2czl9DpIJTJvjKhQ2DMc6ZoKmi+08i9//hx7fnkAp9tBoLzo2h//7CIazjbz3H97iTd//K40Ts2yhrPNdLf24CtKb02ly+skHklQW3Phho9brBae/r2PgRqc4htOd1sPNoeNp373UWkhIsQMMa1+03s7+olH4tid9ozu8+Z5qDtx83oK8dFVLa0gmUhkdI+ZMgEorppe56u11wf52V+9jM1uJVBRhMN148+pYSjyAz6KK/zUbD/GOz/bg9Z6gqqdfg7vOJ7xc0NOgZcDW4/c9P+hsDSfz/35M3jy3LTWtdPd1kMqmUJrTSqZoqulm9a6dvKKfXz2z54mz+/L5rcihJjEptVUYDKeRJH59IlhMW6YkhHZU7W0HHeum1gkflOQGEpPey+L1s8nJ987xtWNH601r3//Law2y4jNJQ2LQUmVn0NvHGfRugXMlimkrKg/00ROwfBH0HyYO8dFe32QaCh60y7BgpJ8fuMvPk1jbQuHdx7j0gcNJGIJ7E4b81ZVsXrTCsrmFctIlRAzzLQKVjaHFU3m7/BTyRROr3PkC0XGLFYLGx5bw84fvUtJVQDDGD74JmIJ4rEE1ZtXjlOF46O1rp32yx1pN5Y0LAY2h42jb30gwSpLErHE6NodKEVyiOlswzCoWDyLisWzAKQBqBBiek0F+vy5uHNdREOZjT4N9IRYuGbOGFUl1mxawfK7FtN2uZ3UlWm+W4lF4nQ0dbH5S/dROrd4HCscex/sPo3VZsnoRTc/4OPMgfOE+sJjWNnM4fRkvt5Pa402ddo7WyVUCSGmVbCyWCyse2Q1vR3DLyi9nmkOPnEu3bh4DCub2QzD4JF/8wDrt6yms6mT9voOouEYpmmSSpmEesO0XQ4S6g3x+L/bzKr7l010yVnXdrkdV87NDSeHc3V0pb8rs55JQzFNk5aLbdQeusDZmgvUn2kimZieOy9vZeHaefR2ZNbSor87RNn8krSnsYUQYlpNBQLcdvsC3n/pAOH+yA2NQYfS0dTJovXz8BWlv5NQZM5isXD/p+5i7UMrOb3vHEff+oCulm6UYVBYmsc9z97O/NVzcLjSa5Mx1SQTqVGNZih+vZh/tOKxBKf31XLw9SN0t/cO1qEHJ82dHifVD69kxd1L8PgyW3801ay8bylH3z6R0XRduD/M5i/dKyNRQoi0Tbtg5fF5eOrrW3j+26+gTT3kQmGtNcGmTopmFfDQF+8b3yJnsNyCHDZsWcOGLWsmupRx5c3z0NYTvNYfKR1aa0ydfk+2Wwn3R3jpO1tpONuMryiX4oobd1rGInHe++V+jrz5Ac/+4RP4ZxeO+rEmu0BFERVLZtNyoY3CspF71vV29OErymXOcjlaSQiRvrSmApVSjyilziqlziul/uMtPv8bSqmgUurolT+/mf1S01e5ZDaf/g9PYZomrZeD9HX2Y6ZMtB5sOtnR1EVbfQdVS8v51B8/eUPzPyHGwtKNi4kMRDO6J9IfJb84j4KSvFE9ZjyW4KXvbKXlYjslcwK4c28ewXW47INHuSRNfvZXL2U0jT7VKKV4/KsP4S3w0NncPWwri95gH1rDM7/3GDb79OunJoQYOyOOWCmlLMB3gYeARuCgUuoVrfWHO2r+TGv9tTGocVRmLyzjt779RepONHBw+1FaLrSSSprYXXaW37OYlfcuxV9eJEP8YlzMX1WFzWElEUukvRC6r2uAR//N/aP+GT219ywNtc2UVAVG/Bq5hTl0NHXx7i/28bHf2jyqx5sKPD4Pn/2Tj/Pq93Zw+WQjVpuV3KIcrFYLqZRJf9cA8WiCotkFPPk7j8hpDEKIjKUzFbgeOK+1vgiglHoOeBKY9K3KrTYr81fPYf7qwR1/shVaTBS7086dT67jrZ+8R8mcwIi9jXqDfeQFclmwdt6oHs80Tfa/dpg8vy/tn/mCkjzOHDjPfZ/aiDdv+q638vg8fPKPniTY0MGxXSeprblIXySO3Wlj7spK1jy4nLL5JdJ/SggxKukEq1nA9W3JG4ENt7juGaXUPUAt8Pta60nXylxClZhI1Q+vorejn8M7jlNYln/LLuCmqelu7cbhdvCJP/jYiAeJD6X5Qhu9Hf2UVKbfvT4WjdMb7GPHD95h2V1LKK70j3im3lSllCJQ4eehL9zHQ1+4b6LLEUJMI+kEq1ulkQ8vTvgV8FOtdUwp9VXgX4EHbvpCSn0F+ApARYUsCBUzi2EYbPr8PRSU5LHn5YN0tfXgdDmw2q1oUxMJRdGmZt6qKjZ9/p6PFGoGugdI532ERtMb7KPpfCs97b3EI3EGekJcOHoZrTUL1s6h+uHV0qRUCCHSlE6wagTKr/v7bKD5+gu01p3X/fV/A//tVl9Ia/094HsA1dXVcgiamHGUUqx9aCUr7r2NuhMNnDlwjlBfBJvdSuncYm67Y2FWzpUzU+bNb38+RKNpONNE/ZkmbHYbnlw3FquFnHwvgYoiTNPk8slGzh68yAOfvYvqzStl1FcIIUaQTrA6CCxQSs0BmoBPA5+9/gKlVKnWuuXKX58ATme1SiGmGZvdxoI1c1mwZi5aa9rrOzi+6yQv/f3rJGJJXDlObrtjIYvWzceTO/zZgrfi8jpvPdZ8naZzrdSfbsLjc19bT2QmTRzuwSlKwzDIL84jEU/y5o92Y3faWHnv0oxrEUKImWTEYKW1TiqlvgZsByzA97XWJ5VS3wJqtNavAF9XSj0BJIEu4DfGsGYhpo3Olm62/p+dtFxow2qzXgk5iv7OAXb+6F3e/ukeVj+4nHs+cTtWW/pt52YtKMVmt5KIJ7HZb74vFolRf6rxhlCltUajKSi5cSeczW6laHYhb/5oNwur50l7EiGEGEZaz9Ra663A1g997JvX/fefAH+S3dKEmN7aGzp47i9fAq0prvTfMM3mADw+N6lkioPbjtLd1sOTv/NI2uHK7rSz+sHlHNx2lED5zQc/tzcMzt5fv/MtHk2Qk++9Zb8ru8NGKmlSW3OelfdOvyOHhBAiW2Q/sRATIBqO8Yu/fhXDUOQFft0SIZlIEuoLM9ATIjIQxbAYlFT5OX+kjt0v7MvoMVY/sByHy85AT+iGj2utabnQitPz6x2HqWSKRCxBxZJZQ66jyinwcnDbsQy/UyGEmFmm3ZE2QkwFZw+eZ6AnRPGVdghXD6Juuxy8oSO40+2gbH4pBSU+jrz5ARseW5P2mqvcwhw+8YeP8/O/epmulm7yivMwDEUqmSIZT+FwOdBaE48mSMQSLFg7d9iF8y6vk/aGDukHJ4QQw5BgJcQ4M02Tg9uOklPgRWtN07kWLp9uwlAKp8eBYfn1QHIiluDisUvYnHb85YWcPXieNQ+uSPuxSucU8/lvPsuun7/PhaN1oMBitRCPxkENjl55fG4Wrp1LXmDk3YhaawlWQggxDAlWQoyz3mAfPW29BCqKaKht5vLJBjy57hsC1VU2hw2bw0YsHKOptoWaN45nFKwACkvzefobj9ET7OXMgfN0NHZSf6qR/GIf/vIiPD53WkEpHk0M1ikdyYUQYkgSrIQYZ/FoAqUGp//qTzcOGaqu53A7SCaSnN57dtQjRnl+H7c/tvbK17Pzwe4zGR1d09vRx51PVGf8uEIIMZPIW08xbaVSKbrbemi7HKSzpZtEPDHRJQFgsRqgFK117ShDjRiqrrI57YT6wgQbO0e+eAQr711KMpG8YT3XcMyUiTY1Szcu/siPLYQQ05mMWIlpJ9wf4cyBcxzYepiB7jBKKTQam8PG2oeWs3TjEvLTWE80VnIKvJimSWtdO+6cm1sbDCUZT+Lxuflg9yke/Nw9H6kGf3kRS25fyJn95whUFA07Aqa1pq0+yNqHVuIryv1IjyuEENOdBCsxrTSdb+GXf/sa0XCM3IIcAhW/7uEUjyXY96vD7Hv1MI/8Pw+wbIJGXxwuB/NXzeHknrN4felNxWk0Zspk9oIymi+0fuQalFI8/OX7iYaiXDxeT2FpPnan7abrYpE4nS3dLL1zEfd/euNHftzpIhqOEQ1FUUrhynFhd9z8byeEmJkkWIlpo+VSGz/7by/j8joprrh5ZMXusBGoKCIejfPqP72BUrD0zokJVwvXDh5lk0qZWNKYCoyGYnjzPXjz3SRiyazUYHfY+PjXt3Bw21EObjtCd1scq92KxWIMtmRIJHHnunnwc3ezZtNyLBZLVh53qjJNk8baFo7sPM65I5cAUAqUYbDinttYce9t+GcXyo5JIWY4CVZiWkilUrzyj9txuO14fMP3ebI77RSWFrDt+29TsWQ2Ofnecary14rnBCiZE6C7tQd3rnvYcBWLxAFYsGYuybiJrzD96cORWG1W7ni8muqHV3LxeD2NtU3EwnGcHieVt82mamk5FuvMDlQAkVCUV/9pB5eOX8bhslM0q+Da7shkIsXxXSc5vPM46x5ZxT3P3jHjQ6gQM5kEKzEtNJxppi/YR3FVIK3rHS47pmlyel8t6x9dM8bV3aygJI85yytweZ20XGwbnFLyOm9oZZCIJ4hF4tiddpbfuRh3jou2y0Huenpd1uux2W0sqp7Houp5Wf/aU108GufFv32N5gttNx09BGC1WSiaVUgqZbJ/6xES8SQPfeFeGbkSYoaSXYFiWji08zh2t2PkC6+TV5TLwe1HSaVSY1TV0AzDYMOWNbhz3Kx+YBmlc4uJhmKE+sKDf3pDKMNg3qoqVj2wDI/PQzKRwmI1WFg9f9zrncn2bz1M47kW/OXDT/NZrhw/dHjH8cFmrEKIGUlGrMS00HK+NaOeTDDYG6q3o5/oQBRPmovIs2nx+vm8//JBUgmTOcsqKF9cRjwSx0xpLFYDp8d57YVca02wsZM7Hl+Ly+Mc91pnqng0zuEdxyksK0hrBMowDLx5HvZvPcy8VVVp3aO1puViG+cOX2SgO4TVbiVQUcTC6nlpH18khJg8JFiJaSERT+IxRjH1ogbXyEwEl9fFJ/7gcX76ly/S096Lz5+LO+fmF9JUMkV7QweL1s1j41PrJ6DSmevCscvEowls9vSfKnMKvDSfa6GzuYuiWYXDXnvuyEV2v7CPzqZurFYDq92K1prju07x5o/fZemdi7jr6Q0Tsg5QCDE6EqzEtODyOq9MlaW/aPjquXe3ajMwXoor/Xz+//0Er//vnbRcasdmt+LOdWMYikQ8Sag3jGExuP1ja7nr4xtkIfk4a77QgjWDUAWDrSyUoehs6Rk2WB3cfpQ3f7wbX2EuxZU39xJLpUxO7a3l8qkGPvXHT5FfnDeq70EIMb4kWIlpYcntC6jZdgx/+fAjBNfr7xpg1oJSXN7s7bIbjaKyAj7/zWdpuxzk2K6TtFxoIxlP4vPncueT61i0bt6E1zhTxaOJtDvjX08z2NB1KGcOnOPNH+0mUFGE1Xbrp2GLxcA/u5Ce9l5e+OtX+fw3PyHTwEJMARKsxLSw/O4l7N96JKNz9ML9ER555IExriw9SilKqgKUpLmrUYwPd46L1CimihUMORKaSqV452d7yC/OGzJUXS8v4KO1rp2zB8+z6r5lGdcihBhfsitQTAsFJfksWT8/7XP0etp7KSzLp2pZ+RhXJqayqqXlpBKZNWQ1UyagKJlz65DceLaZ/s4BXN70R598Rbnsf+0wpmlmVIsQYvxJsBLTxubfuI+SOQHa64NDvgBprels6cbqsPL0730srREDMXPNXlRGTqGXSCia9j097b0sWj9/yAXnp/bWYnPaM6rD5XXS19lPsOGjH8AthBhbEqzEtOFwOXj2Dx/ntjsX0dHYRVt9kHB/hFgkTmQgSrCxk/bLQUrnBvj8n39iQg9iFlODxWLhzifX093ak9ZoUTyWIB5PUv3wyiGv6e3sH9WGCaUUkYH0A54QYmLI23Ux5XQ0d3Fyzxla69pJxlN48zws3rCAuSsqcLgcbPnNTdz19AZO7a2l9uB5IqEYdpeN1WuXs/zuJRTNSq8n0UwQ7o/QcLaZaCiKxWKQU5jD7IWlciTLdZbfvYS2y0EO7ThGoNyP1Xbrf5toOEZ3aw9b/u2DlM4pHvLrWSwGWo+uFjWaliJCiHElwUpMGV2t3bzxg13Un2rEYrXg8g420Oxs7qa25gIOt4ONT61jzaYV5BbkcPtja7n9sbUTXfak1NnSTc32o5x47zRmSt+w6N+b52H9ltUsv3sJ9gynrKYjpRQPfu5uPD43e1+pwTRNcvK82F02tIbIQJRIfwSH28FTX3+URSN0xi+aVUD96SZy8tNvSqu1Rps6o3uEEBNDgpWYEtrrg/zsr17GTJm3PK8NBrtk7/jBLnrae3ngs3fLqNQQ6k428OLfvYbWg4v+P9wbKxqKsfNHuzm9/xxPf+Mx3DnS6sEwDO58Yh2r7l/G2YPnOfbOSfq7BjAMg4LSPNZ+4R7mrqzEZh95im/pxsUc3H4sox2sg61BSqSXlRBTgAQrMemF+yO88NevogyD/KLcIa+zO+2UzAlwcNtR8gI+1j409DqXmaq1rp1f/M2rePM8Q+5Kc3oclFQFaLsU5KW/38qzf/REWoFhJnDnuFj9wHJWP7B81F/DP7uQsrnFdLf24PMP/fN8ldaaUG+Yh798v7xZEGIKkMXrYtI7va+WUG+Y3IKRj/UwDIOiWYXseekgiXhiVI+XTCTpbush2NhJT7B32mxx11rz5o93Y3faRtzqr5SiaHYBDWeaqa25ME4VzgxKKR764r0k4knCfZFhr9Va017fwYK1c5m7onKcKhRCfBQyYiUmtVQqxYGtR/ANM1L1YXanje62Hi59UM/CtfPSvq+vs58Te85Q88YxEpE4KIU2Nbn+HDZsWc3C6vlTuvN1sLGT5vOtBCr8aV2vlCK3MIcDW49w2x2LZLQki4or/Tz7R4/zi795lVBfmLyA74bzCLXWhPsi9Hb2sWDNXDZ94R5O7z/HiffOEOoNYbFZKanys/LepZTMCcj/GyEmEQlWYlLraOxioDdEcZph4Cqn28GpvbVpB6tTe8/y+vffwkyZ5Ad82K8LcuH+CG/8312894sDPP37jw2742syO72vFsNiyehF2J3ror2+g2BDR9qBTKSnfNEsvvStT3P07Q84+taJwcPANSgFWg8ucr/rmU30dgzwz3/yY5LxFO5cFza7jWQ8xul95zi+6xSBiiK2/OaD8v9HiElCgpWY1KLh2KjejVsdVkK94bSuPb2/ll/9zzcoKM3H4bp5F5w7x4U7x8VAT4jn/vIlPvdnT0/JF7HO5u5bfn/DuXqgcLr/liIz+QEf93/qLu58Yh3N51uJhuMYFoPcQi+BiiJ2/Osujr1zkqLZhTeMaMFg01CtNX2dA/z4//sFn/zjJ5k1v3SCvhMhxFWyxkpMaharhdFMcmhTk0wkOVtzgRN7znD24Hk6mm7uWj3QE+L1//PmkKHqet48Dza7lZf/cfuUXHel9ZXhkAwp1Kj7Lon0OFwO5iyvZMmGBSyqnkfpnGIO7/yAo++coLgqcFOoukopha8oB6fHyS/+5jX6uwfGuXIhxIfJiJWY1HILvZimxjRNDCO99wF9XQPU1lzA7rASrO8YDAUKMDWzFpayfssa5q2sQinFqb1nSaXMtEdycgtzaK1rp+FsM5VLZo/+G5sAef5c6k81Znyf1jqjc+3ER5eIJ3j/5YMUzSrESKMpqMfnpq0+yMn3z0rvNiEmmIxYiUkttyCH+Wvm0BvsT+v6lkttfLD7JH2d/cxdVUWgwk9xpZ/iCj+BSj9dLT288D9+xVs/eZd4PMHB7UfJy2BhPIDDZefIm8dH8+1MqMUbFpCIpwZHrtIUGYiSU+iluGrqTX1OZRePXSYWjmF3pN/mIs/vo2bbUZIZHhothMguCVZi0luzaQXRcHTE6bf2hk4uHK1DGQaBikK8vhu7VF/d5VZSFeDg9mO889P3iPRHcbgdGdXjzffSdK414+9jopXNK6FwVn5G66V6O/pY/+jqtEcLRXacOXgeZ4Y7UB0uO9FwTA5qFmKCybOlmPQqFs9i9YPLaasLDhmuUskUF4/VYbFasFgN5q2sGvLrGRaD4ooiDm47SiwSy7gew1AkYqPrkTWRlFI8+Nm7GegOEY+OXH93Ww8FpfksuX3hOFQnrhfuiwy5rmo4SilikfgYVCSESJcEKzHpXQ0Eqzctp+1ykO62m5t2tlxqv9Lfx8KyjYtxeYc/hsVitWC1W+lq6c5oagwgmUjhnKJrjqqWlv//7d15dFznfeb551cr9n0jQXDf9wUiZcmiJGuxKMmWrSiJ7ER23OlWuydKx8eTPu1kuj1nnOmZLDOJJ9OepJVlTtITS3ZsSZFlSrJky5YlWdxJkRK4gDu4ACB2FLaquu/8AZAGSRfvKXgAACAASURBVICoAi9QAPj9nMNDVNV7q364vLx48N73vq8e+bf3q/VimzoudY34vSfiCTWduaTs/Gw98dVHlZVmjx5uXjgSGt8NEs4pGOK0DmQSg9cxLQRDQT3w1N1adtti7X3jA9XvP6nA0B1uzkkXTzRq/uq5qllenfK4lLLqEp3+qEGdl7pSWlrkss5LXap9aPoul7PqjuUqKM3Xuy/u1JnD52UBKRQKyTmnZDyhYCSk9Z9YrY99apNyC1n0NxNmLazUqQ/PKr947NUGLvM8T84prWMZgP8IVpg2zEzzVszRvBVz1Nnapc5LXVfu6Hvuj19UUVnBdQsK30g4Elbp7GJ1tXen/MPIOaek52nNXSvG+21MCTXLqvXk1z6rS+dadPLgGXW1xRQKB1Uyq1iL1s+f1jPMzwQr71imd1/aKc9zKd0VKEntTZ1asnGBCkryJ7g6ADdCsMK0VFCSf/UPEKdxzdGUX5ynnMIcdTR3phSumhtatOy2RSqpKk77s6aisupSlVWXZroMXKO4olCL1i/Q6Y/Oqmx2yZjtk0lP/b392nD/2kmoDsCNcDEeM0J+ca4GUhi06+QU64jp+AentPtHB3Rk93ElE0ldONWkCycbRx1v5XmDi+FWzivXJ3/rXr/LB67zwFNblZUTVUdz5w3bJZOeGk83a/O2DapZNnuSqgMwGnqsMCOs/8RqvfXcuzecyLK/t19H95xQ56UuBYIBeUlPFTVlysqJKr8oVycPntHZI+c1d3m1iioKFQgGlIwn1dk6OIfWytuX6v6ntiqazWBuTLyC0nw9+bXP6Ht//oounm5SflGecgqyryzxlEwk1d7cqXh/XFse2aitT9zOYszAFGDp3hHll9raWrd79+6MfDZmnlhHTH/11X9Q6aziEcdZ9fX06eDPDysRTwyGLyfFOnq0ZutKFZQMDhD2PE9nj55Xf0+/5iyZLeecsnKjWla7SKvuXK7CNCcSBfzQ39uvY3tPasf2vWo93yYLBK5c9V75saVaf+9qVS2oIFQBE8zM9jjnasdqR48VZoTcwlzd/ugmvfviTlXOL79qQkvP81T3/jElE0nl5GXLOaeezl6VzCpWfvEv73oLBAKat3yOms+1qHR2sZ746qf4YYWMi2ZHtfrO5Vp1xzK1Nbarv3dAwWBA+SV5Y04rAmDyEawwY9zx2G2KdfRo/08Oqaiy8MqdbR3Nnerp7FVeUa6SiaR6u/tUWFagJRsXjhicymaX6OTBM2o+e0kVc1nKBVODmc2YmyaAmYzB65gxAoGAHvjC3XroX90r53lqPNWk5oYWHT9wSol4Ut3tMcX7E6pZXq0VH1uqUHjkqRnMTKFwSAd+9uEkfwcAgOmOHivMKIFAQOvuWa3Vd63Q2cPndfzAKZ053KCa5bNVUJKn4sqilOa6KiwvUN2Oej3w1D0TXzQAYMYgWGFGCgaDmr+qRuVzSrTvzYOqmFuW1vahcFD9Pf1yzjHOCgCQMi4FYkYLhkNySv/OV89zCoVDhCoAQFoIVpjRotkRFZYVqKerN63tutu6Vb1k1gRVBQCYqQhWmNHMTJsf3qCu1u60tuuN9av2wem70DIAIDMYY4Wb5pzT+eMXdbrunHo6ehTJCqtyfoUWrp2rcCSc6fK0rHaRfvr8u+qL9Ssrd+xZ07vbY8ovydW8lXMmoToAwExCsMK4Oed0ZFe93n1pp1rOtykQCCgUDsrznBLxhKI5UdU+uE63bdugSDRzASs7L1uPfvlBvfDNH6rICpWVM3q4inX0qDfWp8//weMp3T0IAMBwLGmDcXHO6ecv7NB7L+1SYXm+cvKzrxvoPdAXV8uFVtUsm63P/t4jVybszJT6fSf18l+9rmQiqaKyAkWHBaze7j51tnQpKzeqx7/yiKoXM74KAPBLqS5pQ7DCuOx544De+Me3VTmv7IY9O845NZ9t0bxVc/T4Vx5RMJjZXqCutm7VvX9Uu17br97uPpmZnOdUUJavzQ9v0LLaRSwTAgC4DmsFYsL09/br7e/9QuVzSsa8XGZmKq8p1cmDZ9Rw5LzmrayZpCpHll+cp83bNmrTA+vU1datxEBC4WhY+SV5V60vCADAeBCskLZje08qPhRIUmFmiuZEteeND1IKVslEUs0NLYOLzYaCKq4oUG5h7pjbpSMYCqqovNDX9wQAgGCFtB346YfKSzPoFJYV6PiBU+rp6lVO/siX2mIdMX343pGrL9M5Sc5p2ebF2nDfGlUvrrpqLFcykdSZugYdeuewOlq6FAiYSqtLtPaulapaUMEEnwCASUWwQto6W7oUyY6ktU0gYDIz9Xb3jRisLpxo1Pf+/Afq6+lXUVmB8ovzrrzmJT0d339Kde8f1eaHN2rrE7crEAjoo/eP6q3n31FPR68i2RFFsyJyzqnx1CUdeOtDldeU6sEv3sNAdADApCFYIW1mpnGsEjO07fXPNZ29pOf/5CVFsyOqnFt+3euBYEAlVUVKJj29/4PdMpOy8rL01rffUXFVkSrnXb1NbmGOnHPqbovpuf/9RT3+lUe0cM288RUMAEAaGK2LtJVUFakv1pfWNsmkJzMppyDnquedc3r1b3+sUCiovKIbX14MBgOqnFeuN/7xbb36N2+qYm7ZqFM4mJnyS/KUX5ynl/7yVbVcaEurXgAAxoNghbRtuG9N2mvvtTd1aMXtS6+bnPPiqSY1nm5WQVl+Su8TCAV06VyLerr6FAqP3eGanTcYvPa+cSCtegEAGA+CFdK2YM1cZeVlqS/Wn1J7z/MU749r/b2rr3vtg7c/UjgSSnmQeXdbTF7CU2dLl+IDiZS2Ka4s1Ac/r1Nvd3phEACAdBGskLZQOKRP/tY9amts10B//IZtnXNqPH1Ja+5aoVkLK697/eLJJmWPcpfgSNoa2xUIBWQyDfQOpLRNMBSUl/B07tjFlD9nNN3tMTWdvaSms5cU64jd9PsBAGYWBq9jXJbVLta2f32fXvv7nyiaHVFhecFVE2w65xTr6FFnS5dW3blcD3zh7hF7pRLxZFoTcw70xRUIBuQlPXmel1bN/SkGsWslk0mdqTun3T86oNOHzkqmoRnbPS1cN1+bHlirmuXVTDAKACBYYfzWbl2psuoS7Xp1n47tPSnnnJxzQ/NPOZXXlOqeJ+/U8s2LRw0duQXZajnfpqzc0RdGHi4QDMh5npxTSmOsrjApGEo/+PT19OuVv/6RThw4pay8bJXNKVUgMBgQPc/p7JHzOrb3pJZuWqiH/819iman9n0AAGYmghVuyuxFVXrsmW3qbOnS+eMXNdAXVygcVHFVkarmjz1B56o7l+vVv/3JVfNW3UhOQbYG+uLKL8lPOYw55+S8wUlK0xEfiOulv9yuhqMXVDnC9xIImIorCuWcU/2+E3r5r5L67O9uSy/wAQBmFH4CwBcFpfkqKE3tzr7hlmxcqDcjPxtcIicy9uFYOqtYiXhSsxamPqt6T2evSquLVbWgIq3a9v34oE7XNYwZEM1MFXPLdWL/KX3w9kfaeN/atD4HADBzpHRtxMweMrMjZlZvZl+7QbsnzMyZ2ZirPwOSlJUT1ZZHNqq5oUWeN/aso7GOXlUvrkp5glLnnDoudWrztg1pLW+TTCS167X9KqkqTmk7M1NRZaF2bt+rZDKZ8ucAAGaWMYOVmQUlfUvSNkkrJX3OzFaO0C5f0r+XtMPvIjGzbXl0k9ZuXaHGU02Kj3KXoXNOLRfaFAwG9O+++SWVzC5W68UbT/o5eEdis5ZsWqiVty9Nq6bTHzWop7NH0TSW7snOzVJXS7cajl5I67MAADNHKj1WmyXVO+dOOOcGJD0v6bER2v2RpD+VlN6U3DPI5d6RpjPNunSuRX09qc3zdKsLBoN66F99Qvf8+h3qautW46lmdVzqVKyzR93tMTWfbVHjmUuas3SWfuM//4pqls7Wr/+Hx1RQlq+LJ5sU6+iRc7/swrr873DxVJOWbFqoT335QQVDwbRqunSuRWbjucvP1HaxfRzbAQBmglTGWFVLOjvscYOkLcMbmNkGSTXOuVfM7Pd9rG9aGOiP6/j+U9r56l41nb6kQHDoB7JJaz6+QuvuWaWKuWVpXYq61QQCAW15ZJM23LdGxw+c1uEdRxXr7FU4GtLsRVVadccylVQVX2lfUJqv3/xPT+jonhPasX2vms62aOhmPXme07yV1dr04HrNX12jYDC9UCVJ8f6ELJD+v5cFbNReNwDAzJdKsBrpp8uV7gEb/LX+LyT91phvZPa0pKclae7cualVOMW1N3fo+3/xilrOtym3MPeqAJVMJHXoncPa/9Yh3f7oJn388S3MdTSGSFZEK7Ys0YotS1Jqu/rO5Vp1xzK1nG9Vb3efLBBQXlGOisoLb6qO7PwsJRO/HCuVTHpqb+pQ4+lmDfQOyMyUXZCtqnnlyi/Ju/Jv7nmeskZZvxAAMPOlEqwaJNUMezxH0vlhj/MlrZb006EfLlWSXjazTzvndg9/I+fcs5KelaTa2toUhx9PXV1t3frOn7ykvli/quZff8dZMBRUWXWJkomk3vuXXUomPd3za3fQc+UzM1NZdamv71mzrFqmwaDUdPaSTn/YoMRAQqFIaPCyonPqPd+r5rOXlJ2bpcUbFiiveDBgzVk6y9dappr+3n4d23tSR3bVq6ezV+FoWNVLqrT6zuUqrizKdHkAkFGpBKtdkpaY2QJJ5yQ9Kenzl190znVIKrv82Mx+Kun3rw1VM9FPvv2OYh29KqsuuWG7YCioyvkV2rV9nxavn6+aZdWTVCHGq6y6RLOXVOmj946quaFV2XlZ1y0gHY6GJUkDfQM69E6d5iydrWWbF8/YcJFMJvWLH+zRrlf3Kj6QUE5etkKRkLykp3NHL+j9H+zRgrVz9eAX7hnX1BsAMBOMeV3KOZeQ9Iyk1yXVSfquc+5DM/uGmX16ogucqjpbunR093GVzErth2gwGFAkO6K9bx6c4MrgBzPTrEVVOlN3Tll5UYXCo4/TimRFFIqGdGzvCS3bvHgSq5w8yURSP3z2Tb37wg4VlBaoal6FCkrzlZOfrbyiXJXXlKq8pkxn687p//uj76mtkQH8AG5NKQ34cc5td84tdc4tcs79l6Hnvu6ce3mEtvfcCr1VH71/VGaW1pipoooCHdtzQp2tXRNYGfzgeZ5OHDitRRvmq6+7T4l4YtS2iXhCA71xzVk2WxdPNE1ilZPn3Zd26vD7x1S1oGLUiVwDAVPp7BIl4kl97y9e0UDf+NZmBIDpjJHU43Su/qKy8tIbpBwIBGQBU3tT5wRVBb+cP96o1gttWrhmnpbdtkiJRFLd7TH19fQrEU8oEU+or6df3R0xJRNJLd+yRIvXL9DBd+rU292b6fJ91dPVq12v7Vd5TWlK4wOLygvUfrFD9ftPTXxxADDFsKTNOMX74lcW402Hc07JG/R+YGo4ceDUlbmvyueUqXR2idoaO3TxVJP6Y4Pzk+WX5KlqfrmKKgqv9Fy6pNO5+otavH5Bxmr325Fd9fKSXlpzgeUW5Wjn9r1asWUJN2sAuKUQrMYptyBbTaeb09/QOUXSmM0bmRHr6FFo2CWvQCCg0lnFKp1VfIOtJMlpoHdmXQI79O4R5RXlprVNbmGOms62qLOlK+3FrwFgOuNS4DgtrV2U9szqA31xRbIjqphbNnZjZFQoEpJLYe3C61nas7xPdT2dV4fMVJiZzKR+Vh8AcIshWI3TgrXzFM2JaiCNWbbbmzu06YF1CkfCE1gZ/FAxtyztGdSdc3LOU1HFzU1OOtWEwsGrlgxKlZkpMMNCJgCMhWA1TpFoWLc/ukkt51rlpdCz0dPVq1A4qDV3rZiE6nCzltYuUjAUUCKeHLvxkFhHjyrmls+4HsmqBRWKdfSktU0inpQFTPnF6V1CBIDpjmB1E257aL3WbF2hxlNNN/wB3NXWrVhHjx7/yqNMnDhNZOdmac3WFWq50JpSe+ecOlu7tPnhjTNusPa6e1Yr3h9Pq9eqvalda7euVDQ7OnZjAJhBGLx+EwKBgD75pXtVWF6gHa/sUTLhKbcwR+FoWM5z6unqVbw/ruKqIj3+e49o1sLKTJeMNNz5mS06U3dOLedbVTp79Nn1Pc+p8XSzVt6+VEtrF05ihZOjenGVyqpL1NXandIvBslEUom4p7V3r5yE6gBgarHxjJ3wQ21trdu9e+bMI9ob69PR3cd16J3DinX0KBgOqnJumdbfu1qzF1ex+PI01dXWrRe++UNdPNWkvKI85RXlDFtw2amjuVN9PX1adccyffJL987Y8XONp5v17f/yfWXlZim3MGfUdslEUo2nL2nrE1t0x2ObJ7FCAJhYZrbHOVc7ZjuCFXBjA/1xnThwSju371PjmWYFzCQzOc9p0fr52vjAWtUsmz3jw/O5+gt64Zs/VH/PgArLCxQdNm1IMumpvalDA31xffyzm/WxT9fO+P0B4NZCsAJ85pxTe1OHerv7ZAFTbmGOuttiOn/iovq6+xXNjapqXrmql8yasaGiuz2mj35xRLte26/erj5paFoF55xW3bFM6+9draoFFTNunBkAEKyACeKcU937R/WLV/ao5VybzKRAMCAv6ck5p6KKQt3+6Cat/vjyGRuwkomkLp1rVX/vgELhoArLC5RbMPolQgCY7lINVgxeB8bQ2dqlWHuPPM9TNCeifT8+pD0/OqDCsgJVziu7rnemp6tX2//mTZ09ck4PfekTM27CUEkKhoKqnFee6TIAYMohWAEj8DxPpz9q0J4fHdDJQ2cUMJOT6eLJRnW2dGrJpkXKyouOeMkrJz9bWblZOvTzw8rKieq+39iage8AAJAJBCvgGgP9cb32dz9W3Y5jys7NUvmcMgUCpv7eAR3be1zhaFj1e0/qwvFGrbh9yYhzNQUCpsp55dr75kGt/8SaFNYYBADMBDNzAAgwTslkUtv/5g3V7ahX1fwKFVUUKhAY7JVqbmiRJGXlZCmvKFe9sT59+N4RxQdGXvomEAzIAqZD79RNWv0AgMwiWAHDHNlZr8M76lU1v/y6y3wXTjQqK+eXvVM5ednq7e7TmcPnRn2/4soi7fvxQXmeN2E1AwCmDoIVMMQ5px3b96qgNP+6UOWc00DfwHUD0XPys9V0ulnxeGLE9wxHQkoMJDXQl96CzgCA6YlgBQxpPN2sSw2to88sPsLMJIFAQJ7n1Hq+bdT3zdSUJgCAyUewAoa0N3VocFL16+/0MzNFcyJKjrDYdjAYUKyzZ8T3jPfHFcmOKJI1M5e6AQBcjWAFDBmc4HP012ctrFJ/78D1L5gpmbg+cElSW1OHNt6/ZsZOFAoAuBpne2BIZNjadyMpn1Mi2WAAG85LeopkXb9tMunJ8zytvnO5r3UCAKYughUwpHpxlQLBwKi9T5GsiOYur1ass0eeN9i15eTkPKfiysKr2nqep8bTzdq8baOKK4smvHYAwNRAsAKGZOdla81dK9TW2DFqm+ols1S9ZJZiHTH19w5ooC+u7IJs5ZfkSRocqB7r6NHFk03a8InVuutXtkxW+QCAKYCZ14FhNj6wVgffqVNvrE/ZuVnXvW5mmr+qRvkleWo4fE7N59s0b8VsNZ9puTJXVXlNqe7+9Ye0YsuSEQfCAwBmLoIVMEzZ7BJ99ncf1ve/+YoSA0nlFeVcF47MTPlFuSqfW6YHvniPZi+uUn9Pv6I5UVXUlKlqQQWBCgBuUQQr4BoL187T5/7gcb3+//5EjaebFY6GB3uvTBroi6u/p1/Z+Vl69MsPavWdywlRAIArLFOTF9bW1rrdu3dn5LOBVDjndK7+og6+/ZFazrfJS3rKL8nTmq0rNH9VjUJhfi8BgFuFme1xztWO1Y6fDMAozExzlszSnCWzMl0KAGCa4K5AAAAAnxCsAAAAfEKwgm88z9NAf5xFhwEAtyzGWOGmxAfiOvHBGe16dZ8unGiUJFnAtLR2kTbet0azF1exTh4A4JZBsMK4NRy7oH/5r6+qp7NXOfnZqphbJjOTl/R04sBpHd5Rr1kLK/TYMw+poCQ/0+UCADDh6ErAuDQcPa/v/PGLCgQCqpxXrvySvCvzOQWCAZVUFalqfrkunWvVc//bC+pq685wxQAATDyCFdLW292rF/6v7copzFFuYc4N25bOKlZPZ69++OybjL0CAMx4BCuk7eie4+rv6VNuwY1D1WUls4p1pq5BzQ0tE1wZAACZRbBCWjzP044f7lN+GmOmzEyhcFAf/OyjCawMAIDMI1ghLbGOHnW2dCknPzut7fJL8nV8/8kJqgoAgKmBYIW0xPvjGs+aw8FQQP29cf8LAgBgCmG6BaQlHA1rPGPQkwlP0eywb3U453T++EXtf+uQztSd00BfXFk5ES29bZHW3LVSZbNLfPssAABSRbBCWnILc5Rfkqfe7j5l52WlvF1Xa7fWbF3uSw2tF9v0g7/6kRpPX1I4GlJ+cZ6i2VElE0ntfeOgdr16QIvWz9PD//o+Zeeld8kSAICbwaVApCUQCGjLwxvV2dKV8jbOOSXiCa27e9VNf/6l8636p//1++po7lTlvDKVzipWJCusUDioaHZE5XNKVTmvTKcOndVzf/yierp6b/ozAQBIFcEKaVt22yJFsiMph5bWC22qWV6t8pqym/rc+EBcL3zzh5KkoorCKxOSXsvMVD6nVG0XO7T9b5k/CwAweQhWSFt2XrY+++8fVnd7TLHOnhu2bb3Ypuz8bD3y9P2jBqFUnfjgjDqaO1VYVpBS+7LqEp08cFqXzrXe1OcCAJAqghXGZe7yaj35Hz+jZDypi6ea1NUWu9Iz5Hme2hrbdfFUk4qrivT5P3z8ptcKdM5p5/a9aU3zYGYKhIL64G3mzwIATA4Gr2Pc5iydraf/jy/oxIFT2rl9ny6eahrslTJpycYF2nj/Os1ZOkuBwM3n976efl081aSKNC8nFpYV6MjOet33+btuugYAAMZCsMJNiUTDWr55iZZvXqJkMqlkPKlQJORLmBou3heXWSDty4mhcFCdrf2+1gIAwGgIVvBNMBhUMBicmPcOB+Wcl/Z2yaSnSNS/+bPG0hvrU39PvwLBgHLysxUK818MAG4lnPUxLWTnZSm/OP35s7rbujVv1ZwJrExKJpM6U3dOe984oJMHz8gCJuekUCSkjfev1eo7l6mkqnhCawAATA0EK0wLgUBAmx/eoB//08/TClb9vQPaeP+6Casr1hHTS//1NZ07dkFZOVGVzSlTIDB4uTLeH9eu7fu045U9uuuJ27Xl4Y03fWckAGBq465ATBvLNy9RKBJSXyy1MVMdl7pUMqtYc5bOmpB6ert79Z0/+xc1nmpW5bxyFVUUXglV0uDyP+U1pSqdXaKfPv+ufvHyLubUAoAZjmCFaSMnP1uf+vKDam/uGDNcdbV2K5lI6LHfecj3gfSX/eyff6GW820qqy65YU9UKBxU5bxyvfPiTp2rvzghtQAApgaCFaaVxesX6PGvPKJYR0yNp5uvCljOOcU6etR4ulkWNH3+Dx9X+ZzSCakj1tmjQ+8cTnmx52AoqEhWRHvf/GBC6gEATA2MscK0s3j9Aj39Z0+pbscx7Xp1v5rOXpJJ8pxT2ewSbf3V27V4wwJFs6MTVsORXfVynlMwlPpdkEXlBTq6q15dT96p/OK8CasNAJA5BCtMS7mFuap9cL023LdGPZ29SsSTikRDyinImZQB4mfqzikrN/VB9JIUCA7Ow9V6sZ1gBQAzFMEK01owGMxISIn3xxUIpn8l3UlKDCT8LwgAMCUwxgoYh6zcqJLx8QWkSNbkTVgKAJhcBCtgHBZvWKi+nvSWyokPJBQMBVQxN731DgEA0wfBChiHRevnK5IdUbw/nvI27U0dWnfP6gkdVA8AyCzGWAHjEImGtXnbBv38+++ran7FmAPmL/durbtn1WSUhwzyPE8XTjSqq7VbnueUnRtV9ZJZimRFMl0agElAsALGacsjG9V8tkWHdx5TxdxyBUcZzN7T1avOli595ne3qXQWawbOVAP9cdW9f0Q7frhPHc2dMhu8WcFkCkdD2nD/Wq2/Z5UKSvMzXSqACUSwAsYpGAzq0X/7gPJL8rT3jQ/k5FRQkq9wNCznnHo6e9UX61N2frZ+9fc/rQWr52a6ZEyQnq5evfR/b9fZIxdUWJavynnlV70e749r5/a9OvDWIT3xP35KsxZUZqhSABPNMrV2WW1trdu9e3dGPhvwW1dbt+reP6oP3q5TrKNHwVBA5XNKtenBdZq/qkahML/DzFTxgbj++f/8gc4fv6jy6tIbXhbubo8p3h/Xb379V+m9BKYZM9vjnKsdqx1ne8AH+cV52rxtozZv25jpUjDJDu84prN151S1YOyxdnlFuWq90Kaffvc9/crvPTJJFQKYTCndFWhmD5nZETOrN7OvjfD6l83soJntN7N3zGyl/6UCwNTinNPO7ftUWFaQ8oz/RZVFOrH/lNqbOya4OgCZMGawMrOgpG9J2iZppaTPjRCcvu2cW+OcWy/pTyX9ue+VAsAUc/FUk1ob25Wdn/ryRoGAyQKmwzvrJ7AyAJmSSo/VZkn1zrkTzrkBSc9Lemx4A+dc57CHuRq8GQYAZrTutphMSnt9ykhWRC3nWiemKAAZlcoYq2pJZ4c9bpC05dpGZvY7kr4qKSLpEyO9kZk9LelpSZo7lzukAExvnuekcSz6bZK8pOd/QQAyLpUeq5HOGtf1SDnnvuWcWyTpP0r6TyO9kXPuWedcrXOutry8fKQmADBtZOdljat/Pt4fV0EZ81kBM1EqwapBUs2wx3Mknb9B++clfeZmigKA6WD2okpFc6Pq7x1IeRvnnBKJpJbWLprAygBkSirBapekJWa2wMwikp6U9PLwBma2ZNjDRyQd869EAJiaQuGQaj+5Tu1Nqd/h193eo8p55aqaXzGBlQHIlDGDlXMuIekZSa9LqpP0Xefch2b2DTP79FCzZ8zsjwakhwAAEMdJREFUQzPbr8FxVl+csIoBYApZe9cK5RblqLOla8y2A/1xxdpjuvvX7kh7wDuA6YGZ1wHgJjU3tOg7f/qSBnrjKqkqUuCadSOdc+puiynW0aNt/+Y+rfn4igxVCmC8mHkdACZJ+ZxS/eZ/fkI///4OHdlZL+ecwtGwLGCK98flJT3NWlihR7/8gOatrBn7DQFMW/RYAYCPutq6dXT3cbVcaFMykVR+cZ6Wblqo8poyLv8B0xg9VgCQAfnFedr0wLpMlwEgQ1JaKxAAAABjI1gBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4JKVgZWYPmdkRM6s3s6+N8PpXzewjM/vAzH5sZvP8LxUAAGBqGzNYmVlQ0rckbZO0UtLnzGzlNc32Sap1zq2V9D1Jf+p3oQAAAFNdKj1WmyXVO+dOOOcGJD0v6bHhDZxzbznneoYevi9pjr9lAgAATH2pBKtqSWeHPW4Yem40vy3p1ZFeMLOnzWy3me1ubm5OvUoAAIBpIJVgZSM850ZsaPabkmol/dlIrzvnnnXO1TrnasvLy1OvEgAAYBoIpdCmQVLNsMdzJJ2/tpGZ3S/pf5J0t3Ou35/yAAAApo9Ueqx2SVpiZgvMLCLpSUkvD29gZhsk/TdJn3bONflfJgAAwNQ3ZrByziUkPSPpdUl1kr7rnPvQzL5hZp8eavZnkvIk/bOZ7Tezl0d5OwAAgBkrlUuBcs5tl7T9mue+Puzr+32uCwAAYNph5nUAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJykFKzN7yMyOmFm9mX1thNe3mtleM0uY2RP+lwkAADD1jRmszCwo6VuStklaKelzZrbymmZnJP2WpG/7XSAAAMB0EUqhzWZJ9c65E5JkZs9LekzSR5cbOOdODb3mTUCNAAAA00IqlwKrJZ0d9rhh6DkAAAAMk0qwshGec+P5MDN72sx2m9nu5ubm8bwFAADAlJVKsGqQVDPs8RxJ58fzYc65Z51ztc652vLy8vG8BQAAwJSVSrDaJWmJmS0ws4ikJyW9PLFlAQAATD9jBivnXELSM5Jel1Qn6bvOuQ/N7Btm9mlJMrPbzKxB0q9K+m9m9uFEFg0AADAVpXJXoJxz2yVtv+a5rw/7epcGLxECAADcsph5HQAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwAAAB8QrACAADwCcEKAADAJwQrAAAAnxCsAAAAfEKwAgAA8AnBCgAAwCcEKwAAAJ8QrAAAAHxCsAIAAPAJwQoAAMAnBCsAAACfEKwATJhEPKGB/ricc5kuBQAmRSjTBQCYWWIdMdXtOKbdr+9XZ0u3zEzhaEjr7l2lNXetVNnskkyXCAAThmAFwBfOOe37yUG99dy78jxPhaUFqpxXLjNTfCChvW8c1K5XD2jNXct1/1NbFY6EM10yAPiOYAXAFztf3ae3nntH5TVlCkeuPrWEIyGVzymV5zkd/Hmderv79NjvPKRgKJihagFgYjDGCsBNazh6Xj/77nuqmFt+XagaLhAwVc4r17E9J7TrtX2TWCEATA6CFYCbtuv1/YpmRxUKj90DZWYqnV2ina/uV3wgPgnVAcDkIVgBuCkdlzpVv/ekCsvzU94mkhVWX0+fTh48M4GVAcDkI1gBuCnNZ1tkZgoE0judRCJhnak7N0FVAUBmEKwA3JSB/ric0p+nKhAKqi/WNwEVAUDmEKwA3JRINCyTpb2dl0gqKzdrAioCgMwhWAG4KeU1pfKcJ8/z0tpuYCCumuXVE1QVAGQGwQrATSksK9CSDQvV0dyV8jYDfXFl5WRp4dq5E1gZAEw+ghWAm1b7yXXq7+1XIp4cs61zTi3nW7V523pmXwcw4xCsANy0mmXVuvvX7lDTmWbFBxKjtvM8p8bTzVqyaaFue2jDJFYIAJODJW0A+GLztg0KR0ODawUmPRWWFSiaE7myVmB7U4e8pKc1d63Q/U9tZTkbADMSwQqAL8xMG+9bq2W1i1S345h2v75fjaebZWYKR0Pa+MAarblrpcpml2S6VACYMAQrAL7KLcxV7YPrVfvgeiUTSXmep1A4JLP0p2QAgOmGYAVgwgRDQQXFJT8Atw4GrwMAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+IRgBQAA4BOCFQAAgE8IVgAAAD4hWAEAAPiEYAUAAOATghUAAIBPCFYAAAA+IVgBAAD4JKVgZWYPmdkRM6s3s6+N8HrUzL4z9PoOM5vvd6EAAABT3ZjBysyCkr4laZuklZI+Z2Yrr2n225LanHOLJf2FpD/xu1AAAICpLpUeq82S6p1zJ5xzA5Kel/TYNW0ek/QPQ19/T9J9Zmb+lQkAADD1pRKsqiWdHfa4Yei5Eds45xKSOiSV+lEgAADAdJFKsBqp58mNo43M7Gkz221mu5ubm1OpDwAAYNpIJVg1SKoZ9niOpPOjtTGzkKRCSa3XvpFz7lnnXK1zrra8vHx8FQMAAExRqQSrXZKWmNkCM4tIelLSy9e0eVnSF4e+fkLST5xz1/VYAQAAzGShsRo45xJm9oyk1yUFJf29c+5DM/uGpN3OuZcl/Z2k/25m9RrsqXpyIosGAACYiixTHUtm1izpdBqblEm6NEHlzFTss/Swv9LHPksP+yt97LP0sL/Sl+o+m+ecG3McU8aCVbrMbLdzrjbTdUwn7LP0sL/Sxz5LD/srfeyz9LC/0uf3PmNJGwAAAJ8QrAAAAHwynYLVs5kuYBpin6WH/ZU+9ll62F/pY5+lh/2VPl/32bQZYwUAADDVTaceKwAAgCkt48HKzP7ezJrM7NAor/+GmX0w9Oc9M1s37LVTZnbQzPab2e7JqzqzUthn95hZx9B+2W9mXx/22kNmdsTM6s3sa5NXdeaksL/+w7B9dcjMkmZWMvTaLXeMmVmNmb1lZnVm9qGZ/d4IbczM/nLoOPrAzDYOe+2LZnZs6M8Xr912Jkpxn3EuGybFfca5bEiK+4tz2TBmlmVmO83swNA++19GaBM1s+8MHUc7zGz+sNf+YOj5I2b2yZQ/2DmX0T+StkraKOnQKK/fIal46OttknYMe+2UpLJMfw9TcJ/dI+mVEZ4PSjouaaGkiKQDklZm+vvJ9P66pu2nNLhywOXHt9wxJmmWpI1DX+dLOnrtcSLpYUmvanCd0Nsv/7+UVCLpxNDfxUNfF2f6e5oi+4xzWfr7jHNZGvvrmvacywbPT3lDX4cl7ZB0+zVt/gdJfz309ZOSvjP09cqh4yoqacHQ8RZM5XMz3mPlnHtbI6wrOOz195xzbUMP39fgWoW3tLH22Q1sllTvnDvhnBuQ9Lykx3wtbgpKc399TtJzE1jOlOecu+Cc2zv0dZekOknV1zR7TNI/ukHvSyoys1mSPinpDedc69D/2zckPTSJ5WdEKvuMc9nVUjzORnPLncvGsb84lw3qHnoYHvpz7cDyxyT9w9DX35N0n5nZ0PPPO+f6nXMnJdVr8LgbU8aDVZp+W4O/JV/mJP3IzPaY2dMZqmmq+thQ9+erZrZq6LlqSWeHtWlQ6ieyGc/McjQYAr4/7Olb+hgb6hbfoMHf9IYb7Vi65Y+xG+yz4TiXDTPGPuNcdo2xjjHOZb9kZkEz2y+pSYO/9I16LnPOJSR1SCrVTRxjY64VOFWY2b0aPBl9fNjTdzrnzptZhaQ3zOzwUO/ErW6vBqfe7zazhyW9JGmJBrtFr8Vtob/0KUnvOueG927dsseYmeVp8MT8Fedc57Uvj7CJu8Hzt4Qx9tnlNpzLhhljn3Euu0Yqx5g4l13hnEtKWm9mRZJeNLPVzrnh4219P5dNix4rM1sr6W8lPeaca7n8vHPu/NDfTZJeVIrddDOdc67zcvenc267pLCZlWkwcdcMazpH0vkMlDhVPalrus5v1WPMzMIaPHn/k3PuhRGajHYs3bLHWAr7jHPZNcbaZ5zLrpbKMTaEc9k1nHPtkn6q64cmXDmWzCwkqVCDQ0fGfYxN+WBlZnMlvSDpKefc0WHP55pZ/uWvJT0oacS7vm41ZlY1dI1YZrZZg//OLZJ2SVpiZgvMLKLB/3wvZ67SqcPMCiXdLelfhj13Sx5jQ8fO30mqc879+SjNXpb0BRt0u6QO59wFSa9LetDMis2sWIP77PVJKTyDUtlnnMuuluI+41w2JMX/l5zLhjGz8qGeKplZtqT7JR2+ptnLki7fvfyEBgf8u6Hnnxy6a3CBBntKd6byuRm/FGhmz2nwzo8yM2uQ9D9rcICZnHN/LenrGrze+f8M/f9KuMHFEis12K0nDX4f33bOvTbp30AGpLDPnpD078wsIalX0pNDB0rCzJ7R4A+6oKS/d859mIFvYVKlsL8k6bOSfuSciw3b9FY9xu6U9JSkg0NjEyTpDyXNla7ss+0avDOwXlKPpC8NvdZqZn+kwR98kvSNay5HzFSp7DPOZVdLZZ9xLvulVPaXxLlsuFmS/sHMghoM5d91zr1iZt+QtNs597IGw+p/N7N6DfZUPSlJzrkPzey7kj6SlJD0O0OXFcfEzOsAAAA+mfKXAgEAAKYLghUAAIBPCFYAAAA+IVgBAAD4hGAFAADgE4IVAACATwhWAAAAPiFYAQAA+OT/Bxd4/+RgO56rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "x = [] # estimated from tweets\n",
    "y = [] # real data\n",
    "\n",
    "for state in states:\n",
    "    if state not in mapStateRatiosTrumpEstimated:\n",
    "        continue\n",
    "    x.append(mapStateRatiosTrumpEstimated[state])\n",
    "    y.append(mapStateToRealPercentatges[state]['Trump'])\n",
    "    \n",
    "\n",
    "colors = [1]*len(x)\n",
    "area = 200  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0991459881466935 0.41720045410398887\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "x = [] # estimated from tweets\n",
    "y = [] # real data\n",
    "\n",
    "for state in states:\n",
    "    if state not in mapStatePosDividedByAllTrumpEstimated:\n",
    "        continue\n",
    "    x.append(mapStatePosDividedByAllTrumpEstimated[state])\n",
    "    y.append(mapStateToRealPercentatges[state]['Trump'])\n",
    "    \n",
    "\n",
    "colors = [1]*len(x)\n",
    "area = 200  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "\n",
    "p = np.polyfit(x, y, deg=1)\n",
    "# p[0] y-intercept\n",
    "# p[1] b\n",
    "print(p[0], p[1])\n",
    "x1 = x\n",
    "slopeList = []\n",
    "for xVal in x:\n",
    "    slopeList.append(p[0]*xVal)\n",
    "y2 = p[1] + slopeList\n",
    "plt.plot(x1, y2, '--')\n",
    "plt.title('Donald Trump', fontsize=20)\n",
    "plt.xlabel('Percentage of positive tweets per state', fontsize=16)\n",
    "plt.ylabel('Election results per state', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hillary scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "x = [] # estimated from tweets\n",
    "y = [] # real data\n",
    "\n",
    "for state in states:\n",
    "    if state not in mapStateRatiosTrumpEstimated:\n",
    "        continue\n",
    "    x.append(mapStateRatiosTrumpEstimated[state])\n",
    "    y.append(mapStateToRealPercentatges[state]['Hillary'])\n",
    "    \n",
    "\n",
    "colors = [1]*len(x)\n",
    "area = 200  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8931122187339373 -0.777094907470649\n"
     ]
    },
    {
     "data": {
      "image/png": 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I+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+ETKg5uZLTazDWZWYWa39HD8x2a2Ov7PRjOrSXWNIiIiIkOR5+BmZrPN7K9mtt/MwmY2J77/22a22OM9gsCdwKXATOB6M5vZ+Rzn3Oecc6c7504Hfgr81WuNIiIiIsczT8HNzM4FXgZmAPd2uy4KfMzj8+YDFc65Lc65NuA+4Mo+zr8e+KPHe4uIiIgc17y2uN0BLAVOBj7f7dhKYI7H+4wGdnba3hXfdwQzGw9MBJ72eG8RERGR41qax/PmAO9yzjkzc92O7QdKPd7HetjX/X4drgP+4pyL9Hgjs5uAmwDGjRvn8fEiIiIi/uW1xa0FyO7l2Eig1uN9dgFjO22PAfb0cu519PGa1Dl3l3NunnNuXmmp19woIiIi4l9eg9s/gc/GBxd06Ggp+1e8v85cDkw1s4lmFiIWzh7ufpKZTQeKiPWrExERERG8vyr9GvAisAb4C7HQ9kEz+xEwFzjDy02cc2Ezu5lYf7kgcI9zbp2ZfRMod851hLjrgfucc729RhURERE54ZjXbBSf/uP7wPnEQlcUeAH4vHNuVdIq9GDevHmuvLx8MEsQERER8cTMVjjn5g3kWq8tbjjnVgIXmlkmUAzUOOeaBvJQEREREek/r/O43WNmEwGccy3OuT0doc3MxpvZPcksUkRERES8D064gd6n/CgBPpiQakRERESkV/1Zq7S3znAjgOYE1CIiIiIifei1j5uZXQ1c3WnXN8xsf7fTsoDzgBVJqE1EREREOulrcMI4YqEMYq1tpwOt3c5pBV4Cbk18aSIiIiLSWa/BzTn3E+AnAGa2FbjKObcmVYWJiIiISFeepgNxzk1MdiEiIiIi0jfP87gBmFkRMBXI7H7MOfd8oooSERERkSN5Cm7xSXfvAd4LWC+nBXvZLyIiIiIJ4HU6kK8BC4jN12bAzcCHiS0+vxl4RzKKExEREZG3eA1u1wDfBO6Lb7/qnPu1c+4CYgvPL05GcSIiIiLyFq/BbRywzjkXAdqBnE7H7gGuTXRhIiIiItKV1+B2AMiNf94JzOp0rITYRLwiIiIikkReR5W+AswGHgMeAL5lZnlAGPgCsb5uIiIiIpJEXoPbd4m9LgX4D2AKsT5vQWKh7hOJL01EREREOvM6AW85UB7/XA9cY2YZQIZzri6J9YmIiIhInKc+bmZ2m5mN6rzPOdfqnKszs5FmdltyyhMRERGRDl4HJ3wdGNPLsVHx4yIiIiKSRF6DW2+rJQAUAa0JqEVERERE+tBrHzczWwAs6rTro2bWfYWELOByYF3iSxMRERGRzvoanHAB8NX4Zwfc2MM5bcAbwKcTXJeIiIiIdNPrq1Ln3DeccwHnXIDYq9KzOrY7/ZPpnJvjnHs5dSWLiIiInJi8TgfitS+ciIiIiCSJ1+lAzu7cv83MhpnZH81srZn9wMyCyStRRERERMD7qNLvAnM7bX8fuAzYCHwc+HKC6xIRERGRbrwGtxnEV04ws3Tg3cDnnHPXAF8B3pec8kRERESkg9fglgt0LG01H8gBHolvr+StdUxFREREJEm8BrfdwKz450uB151zVfHtIqAp0YWJiIiISFeeRpUCfwS+HZ+U9zK6LnE1B9iU4LpEREREpBuvwe12oAU4C7gD+FGnY7OA+xNbloiIiIh053Uetwjwn70cuyqhFYmIiIhIjzSxroiIiIhPKLiJiIiI+ISCm4iIiIhPKLiJiIiI+MRRg5uZBc1slpmVpqIgEREREemZlxY3R2y5q9lJrkVERERE+nDU4OaciwI7iS1zJSIiIiKDxGsft18CnzWzUDKLEREREZHeeV05IQ+YDGwxs38Ae4m9Qu3gnHNf7/FKEREZshrrmmhpbCUQDJCTn0UoU7+fiwxlXoPblzt9/lAPxx1d1y8VEZEhKhKOsP2NXZQvXcX2N3YTCAbAOQLBAKddMJPTLjiZktHFmNlglyoi3Xhd8krThoiIHAcaahp58KePsWdzJVm5mZSNKzkc0MLtYVY/s44VT6zlrHfO5dyr5xMI6Me/yFDitcVNRER8rrmhmT9//yFqqusYMaHsiONp6WmUjhlGJBzhpQeXEQlHWPDes9XyJjKEeA5uFvsv953A+cAw4Hbn3HYzuwDY5Jzbk6QaRUQkAZ67/2UO7D3E8HGlOOeoPVBP1Y5qWhpaAcjMzWD4uFLyh+UxfEIZy5asYtKp4xg/c+wgVy4iHTwFNzMrApYAZwJ1xAYr/BTYDnwEOAh8Okk1iojIMWqsa2LdixsoGVXMoX01bH5tO62NrQTTggTTgwA01TVTveMAmTkZTJo1nsycDMqfeE3BTWQI8dp54fvAWOAcoATo3G7+JHBhgusSEZEE2rC8gmg0yoG9h1j38gaIOnILc8jKzSSUkU4oI52s3ExyC3OIRh1vvLSRtuY2tqzeRu3+usEuX0TivAa3K4GvOOdepus0IAA7iIU6EREZonZv2ku4LcymFVvIzs0iPSO913M7QlzF6q00N7ZwsLImhZWKSF+8BrdcYHcvxzLp2gInIiJDTFtrmMrt1aSF0gimBY96fjAtSDAtSNWOaiLtkRRUKCJeeA1uG4CLezl2AbA2MeWIiEgyuEiU2uo6MrK8T7CbmZ1B/cEGWppakliZiPSH11GldwJ3mlktcG98X6GZ3QjcDNyUjOJERCQxsvOyiIaj/Zraw0UdmNHa1JbEykSkP7xOwPsrM5sMfAP4Znz3E0AU+J5z7g9Jqk9ERBIgb1gu6aE0IuGIp1elAC2NrZSMLCLcFk5ydSLiled53Jxzt5jZL4i9Mi0FDgBPOOe2JKs4ERFJjMzsDErGFHNoXy25hTlHbXkLt4dxQFFZYZ8DGUQktfq1coJzbjvwqyTVIiJywnHO0dbSRntrmLRQGhlZoaSsVFA0opDiUcWEMkPs215NTkF2r8tZtbe209rSxoz5Uwm3tVNYVpDwekRkYPqzckIQ+BfgbcBoYqNMXwJ+75zTkCMRkX5oa21n62vbWfbYSiq3VmMBIxp1lIwuYv6lc5g6ZyIZWRkJe96Ek8eSnZdFYWkBGVkhdm3aC86RnhkiGIwFuPbWMOH2MKHMdGa+bTrZeVm0NrcxfuaYhNUhIsfG68oJ44GlwDRgF7APOBX4MPDvZrY43honIiJHUbWjmgd+8ij1BxrIKcg+vNC7c46mumYeu/spns3N5KpPX8aYqSMT8sy09DTmXTKL5+9/hXEnjWHkpOHs33OQfduraW9pBzPyinMYOWkEBaV5mBmV26pZcO3ZnvvEiUjyeW1x+xmQD5zrnHupY6eZnQPcT2z5qysSX56IyPGlaud+7v32X7FAgGBakG2v76SlKb5WaHYGIyaVUTK6mJbGVu67429cd8vVCQtvc99+GhUrt1K1fT8lY4oZOXE4IycOP+I85xzVuw4wdtpIZi86JSHPFpHE8DqP2yLg1s6hDcA59yLw5fhxERHpQyQS4W//vYSaqlo2LKtgY/lmmhtaCAYDBIMBmhta2Fi+meVLV9NY20h2XhYP/nQJbS2JmY4jlBnims+9g5FThlO5rZr6gw0499ZiOM456g82ULm1ijFTR3L1Zy4jlOl93jcRST6vLW4NQFUvx6qApsSUIyJy/NqxfjcVq7ZSW11Hdl4WwbTMLseDaUEyskKE2yNsXLGFiaeOIy0tjc1rtnPSmVMTUkN2Xhbv/bcr2LxmO8uWrKRyWxUBMzAjGokyavJw3v4vFzB51njS0vs1fk1EUsDrf5X/B3wMeKyHYx8FfpewikREjlNLf/MM1bsOUFRaQCDY+wuPtPQgOfnZbH19B5NOHc+yJSuZMX9KwkabpqWnMX3eZKbNncShfTU01cdWRsjJz6KwrCApo1pFJDG8BrcK4D1mthZ4gNjghOHAu4E84DEz+1DHyc65e3q7kZktBn4CBIG7nXN39HDOe4HbiS1ov8Y59z6PdYqIDEnRaJTXnl1HTn52n6GtQyAYID2UzoG9hwimB2lvCxNK8HxqZkbxiCKKRyT0tiKSRP1Z8gpgDHByD8d/3umzA3oMbvEpRe4ELiI2OnW5mT3snHuj0zlTgVuBc5xzh8yszGONIiJDVvWuAzTUNjFsVJHnazKyQtTtr6egNJ9wEoKbiPiP1+A2MUHPmw9UdKy2YGb3AVcCb3Q65yPAnc65QwDOud761omI+MaeisrYhLcO8PgmsuOVZWNNI+kZietv5pzT61ARn/K6Vmmi5mgbDezstL0LOLPbOdMAzOxFYq9Tb3fO/SNBzxcRGRTN9c0UlOTT2tRKVm6W5+vaW9spHlFIeujYWttq99ex7qUNrHlmHY11TQQCRvHIIuZdcnrCJ/sVGajnNlbzwXuWsek/LyXdQ5eCE1Gqhwz19Cue67adBkwFFhB7NfuCmZ3inKvpciOzm4CbAMaNG5f4SkVEEig9K0TxyAL2VOzD4TCPzW7hcISZZ08f8HMj4QjP3Pciq55eC0BhaQElo4eBczTXt/DY3U/xZEYal9y4kJPOnDbg54gci+0HGrng+88e3n7mzSouPlmdL3uS6uC2CxjbaXsMsKeHc15xzrUDW81sA7Egt7zzSc65u4C7AObNm9c9/ImIDCmlo4eRlZtFblEOzfXNnlrdWppaychMZ+ZZAwtukUiER375BBuWVVA2vqTb2qRGTkE2OQXZtDa38dCd/6C9Ncxp588c0LNEBqK5LcJFP36OXYeaD+975FPncsporY/bm1S3Qy4HpprZRDMLAdcBD3c750FgIYCZlRB7dbolpVWKiCTY2BmjyC3KYcIpYwmmp9Hc0Iw74oXDW1qaWgm3hzn5nJMYf/LA1got/8dq3ly2ieETSntdUB5igyBKRg9j6a+foXKbuhVL6jS1hQ+Htv+69nS23XG5QttRpDS4OefCwM3E1j1dD/zZObfOzL5pZh1LZi0FDpjZG8AzwBedcwdSWaeISKIFg0HmXzqbxtpmTj1vBtn52TTWNtHc0EwkEsXhiEajNDe00BAfjDB6yggWXHt2n6GrN+1t7by6ZBXDRhZ7GogQykgnLZTGqifXDuSPJ+LZ/72ynQm3PMqhxjaG5Wbw2GfOY+t3LuOq2aMHuzRfSPm02M65JcCSbvtu6/TZAZ+P/yMictw4feEpbHt9J1vW7uCU82bQWNNE5dYqDlbWEG4PE0wLUji8gBETS2mub2HqnEmcet5JA3rW1rU7aG1qpbA03/M1hWUFrHt5A+e/5yxyCnIG9FyR3pRvO8i7/+flw9tPv1nFNXPHcNJI739HxWNwM7MAEIi3mHXsuwQ4BXjaObcqSfWJiBw30tLTuOITl/DY3U+xoXwzocwQk2dNYOqcAA6HizhqqmppaWjlpLOmsfhDiwimBQf0rK2v7yC9n/O+BeOj+Kp27GfiqQpukhhNbWFm3ra0y75Xbr2QEQWZvVwhffHa4vZHoBX4FwAz+xhvTbrbbmaXO+eeTEJ9IiLHlVBmiHd+4hLmVlSy8qm1bCzf3OX49DOmMPvCUxk1efiAXpF2aK5vGVjoc4621vDRzxM5io75AtM6/T2+/2Nv44wJxYNYlf95DW5nAf/eafuLwN3AF4iN7PwKoOAmIuJBIBBgzLRRjJk2iuZA4zfeAAAgAElEQVTGFprrY52zs/KyyMpJTCtERnaIaCTa/wvNSEsfWCufCMQC2zf+/ga/eWkbSz59HjNH5bPm6xdTkKWVPxLBa3ArA3YDmNkUYisp/Mw5V29mvwbuTVJ9IiLHtayczISFtc7GTh/N2hfW9+uaaDRKNBqlZLRaRGRgHly1m8/+afXh7dd21TBzVL5CWwJ5DW51wLD45wXAfufca/HtCKAX1SIiQ8jUORNJD6XR3hYmPeTtR31tdT1TZ0+ioESdxaV/WtojzPjaW4scjSnKYulnzycngUu1SYzX7+hLwC1mFgY+S9dRoVOITZorIiJDREZWBrMvPJVlS1YzfHzJUacEiYQjtDS1MPfiWSmqUI4HLe0RMtODZKS91Y/tuS8uYPwwDW5JFq89X78EFBObLDcTuL3TsWuBl3u4RkREBtHZV85nzLQRVO88QGympZ6F28Ps217NuVefydjpo1JYofhVOBLl/Xe/woyv/YOHVu/GzNjy7cvYdsflCm1J5nWR+U3ANDMb1sNkuJ8B9ia8MhEROSahjHTe9dl3sORXT1KxehuhUBoFZQWHp/1oa2njUFUtOFj4vnOZv3i2p8l65cT24yc28pOnNh3ebo/EfikIBPR3JxW8zuN2D/At59zWHg7XAd8DPpTIwkRE5NhlZmdw9acvY++Wfax+5nXWv7KRaDQ2TUNGVohzrprPyWdPV782OapI1DH5y2/1lFo4vZS7P3gGQQW2lPLax+0G4H+AnoJbCfBBFNxERIYkM2PU5BGMmjyCiz+4gNbmNgIBI5QVIhjU1B/Stz01zQzPzyQYMDLTA6QFArz474soyNZI0cHQn+EevXWQGAE0J6AWERFJsrT0NNLSNdJPjq6+pZ2FP3iO/Q2tfPBt4/nGlafw5rcuHeyyTni9/tdrZlcDV3fa9Q0z29/ttCzgPGBFEmoTERGRFItGHTf/cSVL1lYe3nfOlJJBrEg66+vXrnHEQhnEWttOJ7bsVWetxKYKuTXxpYmIiEgqOeeY1Kkf20cvmMStl540iBVJd70GN+fcT4CfAJjZVuAq59yaVBUmIiIiqVG+7SAnjcwnJyONRTPKqG1u548fOYtQ2sDXy5Xk8DodyMRkFyIiIiKptbummXPueBqAGSPy+Mdnz+eeG84Y5KqkL331cTu/Pzdyzj1/7OWIiIgklnNO89N109Ie4Z0//SebqhoO77vjmtMGsSLxqq8Wt2d5aySp0fuo0o5jGlMuIiKDLhKJsPPNPax4fDU73txDe2s7GdkZTJszidMXncKIiWUnfJDrvK7o9645jfeeMXYQq5H+6Cu4LUxZFSIiIglQtXM/D/3sMQ7tqyUzO4PC0nwCwQCRcIQ3l1ew9p/rGTN1JO/8xCXkFeUOdrkp9ZcVuzhzYjFji7P54iXT2XWoiW9ffeoJH2L9xvpav84v5s2b58rLywe7DBERGURVO6q599t/JS09jfxheT2e45zjYGUNOflZXP/ld50Q4W31zhquuvPFw9vb7rh8EKsRADNb4ZybN5BrNVxERER8r621nQf+61HSQum9hjaIrSIxbGQRjXXNPPLLJzgeGi96s+NAExNuebRLaHvhS3qZ5nde1yp9+iinOOfchQmoR0REpN82r95G/cEGRkwo83R+8YhCdr65m33bqz1f4xftkSjX3fUKK7YfOrzv3g+fydmaRPe44HXdkwBHDk4YBkwHqoGNiSxKREQGX0tTK831sRUNs/KyyMzOGOSKeuacY9ljK8kpyPF8jZmRHkpj9TOvs/jGRUmsLrUm3PJol+2zJw/j3o+cNUjVSDJ4ncdtQU/7zWwy8CDw7QTWJCIig8Q5x57Nlax++nXeXLbp8K/sDph+xhRmX3gqo6eMGFId2luaWqnasZ+ysf1rUcoflkfFqq1wY5IKS6Hfv7yNrz207vD2ZaeO4GfXzyEQGDr/niQxjmmlYefcZjO7A/g+MDsxJYmIyGBoa21n6a+fYf0rG0nPSKd4ZDHBYKwrdDQSZfOqrax/ZSMz5k9h8YcWEcoMDXLFMe2t7ZhZv8NkMD1IW3NbkqpKjRcr9vP+u1/tsu/lWxcxsiBrkCqSZDum4BZXDUxLwH1ERE5Izjlqqmppqm/GzMgtzOmzg30yRMIRHv3lE2xauYXh40uPCEGBYIDikUU453hz2Wba28JcdfOlBNMGfwrP9FAaLtr/QQaRcJT0jPQkVJR8tU3tnPvdp6lvDR/e9+Tnz2dKWWr/3kjqHVNwM7Ni4PPA5sSUIyJyfIiEIzTWNhEJRwhlhcjOyzoiDLW3tbNlzXaWPbaSym3VBOLHo84xfuZYzrhkFuNmjiEYTH44WvvCejYsr2DkpOF9tlyZGcPHl7BpxRbWPLeOORcO/mz7mTmZFA0voKmumex87y1N9QcbmDRrfBIrS7xwJMqUrzzWZd9HzpvIVy6fOUgVSap5HVW6lSMHJ4SA4fHP1ySyKBERv6rdX8e6lzZQ/viaw6/hXNQxYmIZ8y+dzaRZ40kPpdNQ08jf/vtR9m6uIqcwm7KxJYcDk3OOfVur+PMPHmba3Mlc9pELychK3sCAaDTKq4+uoGh4oafXjWZG0YhCli1ZyawFJ6ckWB6tnjMunc3jv3m2X8GtraWdOReemsTKEuvbS9Zz1/NbDm9/etEUPn/x9EGsSAaD1xa35zgyuLUA24H7nXNqcRORE5pzjrUvrOeJ3z2Hc47C0nwKS/IPH6vbX8/DP19K4fACLv/oRSy95xkOVdYwYuKRU1GYGQWl+eSX5FGxagt//0WEqz51KWnpiejdcqRdG/dSd6CB4eNLPV+TlZNJ5bYqdr65hwknD/5ySdPPmMKzf3qJpvpmsvOOHt5qqusoHTuMUVNGpKC6Y3PX85v59pI3u+x781uLyUwf/NfUknpeR5XekOQ6RER87bXn3+Cx/32aktHFhLr1mzIz8opzySvOpaa6jp9+8m5yC7IZO310n/c0M8rGlVKxaivrXtrArAtOTkrt+3cfHNB1Zsb+PQeGRHDLzM7g6k9fxp++9yA4+mx5q62uwwyu/OQlBAJDdx767QcaueD7z3bZt/JrF1GcMzQGhcjgGPDfWDMrNrO5ZjY0J/YREUmRg5WHePy3z/YY2rrLK8ph37ZqDlbWeJq138woLCtg+WOriEajiSq5i7aW1gFN7xEIBGhrbk9CRQMzbsZorv3SVbS1tlO5vYrG2qbD32PnHHUH6tm3vZrM3Ayu//K7KB5RNMgV96y2qZ0JtzzaJbT96L2z2HbH5Qpt4rmP21eBHOfcrfHt84FHgBxgt5ld6JzblLwyRUSGrrUvrMfMjhraAA5W1pAWCtJwqJGm+mZy8rOPek12Xhb7tlezZ/M+xkwdmYiSu8jKzRpQKIxEIp5eS6bSuBmj+egP/h+bVm7l1SUrqdqxHwxwMHraSOZfOpsJJ49N2mvnY+Gc4+P/t5J/rKs8vO+/rj2dq2b33TIrJxavf3M/APyw0/b3gDXxr7cB3wKuS2xpIiJDX1trO6ueWkvR8EJP5zfVNcU68zuo2l7NxFO9j2qsra5LSnDrmFDXOee55S12LkOyj1hGVgannDODk8+eTltLG+H2COkZ6Z6C9WD58G/LeXL9vsPbowuz+Oe/LxxSEx3L0OA1uI0GNgGYWSlwBnChc+5ZMwsB/52k+kREhrS6/XVE2iOkh7z9OI1EophBWkY6tfvr+/WsSDgykBKPqmT0MEZPG8nBPYc8zx9Xf6iRUZNHUDpmWFJqSgQzIyMrg4yh1SjYRfm2g7z7f17usm/DfywmYwjMjydDk9fgFiE2/QfA+cRGlL4Y364GihNcl4iIL4TbI9CPVpFQZjrRSKxlKxLp3+vJjKzk9W86+4ozuO+7fyM7P+uorxHD7REaahq59F8XqUVogDZXN3DhD5/rsu+RT53LKaMLBqki8Quvwe114ANm9hLwIeA551xHj9SxQFUyihMRGepCmemeBhl0KCwtYDu7iEYjZHhcMioSiWIBY8y0xL8m7TDh5LEsvP5cnrn3n7FBFr3U1tbSzv7dB1hw7dlMnjUhafUkknOO9rYwaenBQR9F2twW4ao7X2TDvrdaW//80bcxf6LaP8Qbr8HtW8BDwPuBduCSTscuA1YmuC4REV8oKM0ntzCb5oYWsnIzj3p+TmE2OQXZ1FbVM2qyt/5htVW1zHzbNHIKco613D7NXzyb7NxMnvrDC7S31JJdkE1mdizAtTS10VTXRHpGGov/dVHSpiZJFOccuysqWfXUWjYsrzi8JNaIiaWcsXg2k2ZNSHmft9NuX0pdy1tLVL1n7hi+/55ZKa1B/M/rPG5LzewkYA6wutuEu88TG6ggInLCCQaDzL9sDk/94QVPwc0wxkwbxf5daykafvTXYq1NrYTDEeZdnPz/wZsZp543k+lnTGHzmu2sfPI1aqvrcEBhaR4Lrz+HKadPGDKLy/em/lADD/98Kbs37SWUkc6wUcUEg4H4lCCN/P0Xj5OZk8FVn7r0qHPpJcLfVu3ic39663+Tw3JClH/17XrNLANi/WniH6rmzZvnysvLB7sMETlBNdU3879fvpdgMEBuYd+tYs45qnbsJ7c4l9rqOopHFJKZ3fN0mI21TdQfauDKmxczfd6UZJR+3GmsbeQP//lXGmubGDay93namuqaqa9p4L1fvJLxJ41JSi3Pbqjihl8vP7w9piiLRz91HgXZQ3d0q6SGma1wzs0b0LVeg5uZjQa+QGxwQjFwhXPudTP7LPCyc+7VgRSQCApuIjLY9myu5E/ffZC0jHQys0K0t4Vjc7tlppMefyUXjTqqdu5n3PRRvOtz76Bi5Raeue9FGmubyMzOIBQffNDS2EpbSxuFwwu45IaFSQsWx6P7f/gwO9/cQ8noo/cZa6pvprW5lQ/f8QFP8+l5tbummXPueLrLvqe+cAGTS3MT9gzxt2MJbl4n4D0ZeIHY6NKXgdm8Ncp0PDAfeN9AChAROR6Ujh3GWe+cx5++9xCHKg8RTAuSFkrDLDYgIbsgm6y8TE45ZwZv/8D5hDLSmfm26Uw/Ywrb1u1k7QvrqdtfjwVg7PRRnHbBTEZPHTnonen9ZP/uA2xdu5Ph40s8nZ+dl0X9oQY2LK9gzoWnHfPzw5EoU77yWJd937jiZD549oRjvrdIB6+DE34IrCc2KKEFaOt07CXguwmuS0TEN+oPNfC3nyyhcmsVU06fQDQ6jsptVdQfbCTSHqGmuo5IJMKsBSdz8QcXdJluI5gWZPKsCb4ZoTmUrX1hPcG0QL/6juUPy2PZkpXMWnBybGLkAfr6Q6/z25e3H95eNKOMe244Y8D3E+mN1+B2LnC9c67BzLr/zd4HDL2ps0VEUqCpvpk/f/8h6g40MGJi2eH9haVdBx5Eo1E2LKsgIyvEJTcuVEtaEmxevY284v69jszKyaRqRzUNhxopKMnv9zN/9fwW/nPJ+sPbC6eXcvcHzyAY0MADSQ6vwa2vWSJLgOYE1CIi4jv//OurHKysYfi40j7PCwQCDJ9Qxprn3mDqnElMmT0xRRWeOFqb28jMOfrI3u7MjPbW9qOf2MmK7Qe55hddVzxY9pULKcvr//NF+sNrcFsG3Aj8vYdj7+WtVRRERE4YzQ3NrH1hPSWjvE2eGggYuQXZvLpkJZNPn6DpIBIsIytEJOx9+bEOzrnDA0iOpr6lnVNvf7zLvj98+EzOmeKtX53IserPBLxPmtnjwL2AA95uZp8BriY20lRE5ISyccVmopEowX6sK5lXnMueTXs5sOcgJaOH7jqffjRp1gRWPbW21+lVetLc2EJOQQ65RX1P49LTwIPPXDiVz100bUC1igyU1wl4nzOzq4D/Au6J774D2AZcNZhTgYiIDJY9m/f1ezJaM8MCAWqq6hTcEuy0809ixdLVOOc8t2bW7a9n0fvO7XNgwoRbHu2yfdP5k/jyZScdU60iA+W5Pdk59yjwqJlNAcqAA865DUmrTERkiAu3hgkE+/+60+EIt4ePfqL0y7BRxYw/ZSy7Nu719Pq6uaGF9Mx0Zpw5tcfjX/jzGh5YuavLvte/cQm5Gf17FSuSSEcd1mRmITNbaWYXAzjnKpxzLym0iciJLrsgi3DbwAJYRtbQXjbKj8yMyz78dnILczhYeajPc5vqm6k7WM+Vn1x8xOS7r245wIRbHu0S2h74+NvYdsflCm0y6I4a3JxzbcBEQL8eioh0MuX0if0ObuH2CMFggBGThiepqhNbbmEO1996NcNGFVO5vZqDlTVEI7GJEZxzNNQ0sm9bNW2t7Vz7pauYeMq4w9fuq2thwi2Pcu1drxze9/EFk9l2x+XMHe9tAIpIsnn91eEJ4GLg6aOdKCJyohgzfRR5w3JpbmjxtMA8QE1VDaddcDJZA5i2QrzJK8rl/V+9hj0Vlax8ai0bllcAseA2fFwpC68/l8mzxh/un+icY+KtS7rc4/SxhTz4yXNSXrvI0XgNbj8F/s/M0oAHgb3ERpYe5pzbkuDaRESGtGAwyDlXzefRu54kI6uMQLDvlxgtja044PRFp6SmwBNYIBBgzLRRjJk2iuhHLyLcFiaYHjxiEEL3gQcAW79zmaZqkSHLa3B7Lv7188Dnejln4GuFiIj41CnnnsT+PQd59ZGVlI4Z1ut8YI21TTTUNHL1Zy7zPO+bJEYgEDhi9O8PH9/AT5+u6LLvhS8tZGxx4habF0kGr8HtxqRWISLiU2bGgveeQ25RLi/+9VXaW8Nk52URykzHudgkva3NbRSW5nPdLVcxdvrowS75hHZ/+U6++JfXuuz74Xtmcc3cMYNUkUj/mHPu6GcNcfPmzXPl5eWDXYaInODaWtqoWL2N1557g4ZDDQSCAUpGFzNr4SmMnT5K65MOogMNrcz9jye77Dt/Wim/+9D8QapITmRmtsI5N28g12pcs4hIgoQyQ8w8axozz9Js+kNJT/3Ytt1x+SBUInLsFNxERE4gkUiE5voWwu1hQpkhsnIzj9uO+D0Ftje/tZjMdHXJFv9ScBMROQE01jbyxiubWP7YKprqmzGDaMQxYmIp8y+dw6RZ40kPeVtofai76EfPsamqocu+Bz5+NnPHFw1SRSKJo+AmInKc27C8gkfvepJoJEJ+ST5lY0uA2PxldQcaeOjOf1BYVsA1n3sHw0b6N9z84/VKPvZ/K7rsu2BaKb9VPzY5jii4iYgkUN3Bet54eSPb1u2krbmNzOwMJp8+gRlnTj1iaaVU2LC8ggd/9hjFI4qOWGbLzMgryiWvKJfa6jru/fZf+cBXr6FoeGHK6zwWTW1hZt629Ij96scmxyONKhURSYDmhmae+sMLvPnqJjAjOy+LQDBAJBylqa4JCxinnT+TBdeefcScYslSf6iBX33p9+QV53laG7Wmqpai4QV84Lb3+KbfmwYeiB+lZFSpmU0C3guMA7qv1eKcc//q8T6LgZ8Qm7D3bufcHd2O3wB8H9gd3/Uz59zdXusUEUm1xrom/vS9Bzm4t4aS0cOOWEEhryiHSCTKmmfXsX/XAa75/DvIyMpIel3rXtpANOo8L2hfUJrP3q1VVG6rYuTEob2Wak+BbeXXLqI4JzWhWGSweApuZnYlcD+xRemrgNZup3hqtjOzIHAncBGwC1huZg87597oduqfnHM3e7mniMhgikajPHTnP6jZV3e471hPgsEAZeNK2L25ksf+92mu/OTipLZqRcIRypeupqAk3/M1ZkZ6KJ3Vz7w+ZIPbJ+9dyaOv7e2y78fXzuLq2ZpAV04MXlvc/gN4Fni/c676GJ43H6joWNfUzO4DrgS6BzcREV/YU1HJrg17GT6+99DWwcwoG1vCpvLNHNhzkJLRw5JWV0NNIy2NreQX5/XrutyiHHa+uSdJVQ3cyh2HeNfPX+qyryQ3RPlXLxqkikQGh9fgNgn4wjGGNoDRwM5O27uAM3s47xozOx/YCHzOObezh3NERAbdyqfWEspM99x6ZmYE0oK89vx6Fl1/btLqam8LD+i6QDBAS0NLgqsZuEjUMfnLS47Yr35scqLyGtzeBBLxq2FPP9m6v2b9O/BH51yrmX0M+C2w6Igbmd0E3AQwbty4BJQmItI/kXCEjeWb+71ofGFZAWtfSG5wi62V6nDO9euVbKQ9QkZO8vvfedFTP7at37nMNwMnRJLBa3D7EvBfZvZqx2vOAdoFjO20PQbo0ibvnDvQafNXwHd7upFz7i7gLoiNKj2GmkREBqStpQ0cRwxGOJq09CCtza1Eo9GkrV+aV5RL8fBCmuqb+zUNSd3Bes68fE5SavKqp8D2wpcWMrY49dOpiAw1XoPb7cRa3Nab2SbgYLfjzjl3gYf7LAemmtlEYqNGrwPe1/kEMxvpnOvoeXoFsN5jjSIiKRULbP3/vdE5RyAQSGrLkZkx//I5PHb3056DWzTqiEajnHLuSUmrqy8/e3oTP3h8Y5d9/3bxNG5eNHVQ6hEZirwGtwiw4Vgf5pwLm9nNwFJi04Hc45xbZ2bfBMqdcw8DnzazK4AwsYB4w7E+V0QkGUKZIXKLcmlubCErp/ssSb1rrG2mbFxJ0l/5TZ0ziecLX6b+UAN5RblHPX//rgPMmD+ForKCpNbV3a5DTZz73WeO2K9+bCJH8hTcnHMLEvVA59wSYEm3fbd1+nwrcGuinicikixmxvzLZvPUH17oZ3Br5ML3J69/W4eMrBDnv/tt/OE//kI04sgpyCY7P4vikUWkp7/14985R/WuA5SNK+HiDy5Mel2dnzvxVg08EOkPLXklInIMpp8xhWf/9BKtzW2eJrptbmghMyeTSbMmJLSOxrom3nx1E6ufWUdDTSN1B+up3VdLRk4G2XnZ7NlcSdWOajAjIzPE8PGljJhURlNdMy7qmDp3EpfcuJDM7NQMTOipH1vFf15KWj/7C4qcaPqzcsJI4AvABUAxcIDY3G4/cs5VJqU6EZEhLic/m8U3LuThXyylZNQwQpnpvZ7b0tRK7f463v2FdxLK6P28/ohGo7z00HJeeWQFLurIK86loaaRHW/sIhAMUHeogbT0NKbOnUQoI529W6qo2V/Hzg27ObD3EO/42EXMv3ROyhaXX/D9Z9h2oKnLvnvedRLTCjM5uPsARSMKSQ8l5nsjcjzyunLCNOAFoAh4EagARgCfAf7FzM5zzm1KWpUiIkPYzLdNJxKJsuTuJ2k42Hh4f1pGGkVlBWTlZVJbXU8gEODqT1/GpFPH9/sZzjkqt1Wx883dNDe0kJ6RTumYYWxauYW1z6+nbFwJwbQg1bsOsH3dTnILcg6Pdg23h9mwrIKpcycxY/6Uw/c8sPcQlVurySvKOfZvwlE88toebr53VZd9E7KCXLl7Gyt/uZ1VxIZ5hDJDzLtkFqecM4P8Yf2bPFjkROC1xe27QB1wpnNuW8dOMxsPPB4//q6EVyci4gNtLW0c3HuIaNjRWNdETVUdOEfUObZEo2RmZXDeu8/i8o9e3O+O/845KlZt5aWHlrNvezVmRlp6kGjEUb37AJXbqpgyeyKYEY06tq3bSVZOZpcpStLS08jOz6Ji1Vay87PIK4wNVBg2sojKbVVsXLGFU86ZkdDvSYfapnZmffPxI/Z/smobgUCAguEFpHXqb9fW0saLDy7j1UdXcOXNlw4o5Iocz7wGt4XAxzqHNgDn3HYzux34eYLrEhHxhebGFh748SPsqaikbFwJo6eMINweobmhhWgkSjAtAAa7N1VSvnQ1F77/PM9ztznnePnh5Tz/wKvkF+dSMrqYaMQRCAawIGx7Ywe5BdnsWL+b2uo6Rk4aTltLG7kFR7agBYNBAsEAe/8/e/cZGNV5JXz8/9zpM5pR70IIEIjeMc0G4wYG3GM7sTc96ySbYm8Sp2+STTavvYl3E6/X2U3iTbLJpifuBoOxjQu26b2IjgCh3keaep/3g0BBqM0Idc7vi607d+6cEaOZM08551g53jl/22HqTU5g69qdTFlU1Oe7XDtbx/bC6rG89LMNJOemdDpdbHfaycxPp6Wp9fd675dvJ39ibp/GJcRwFmviZgcau7it8fztQghxRTFNk+d/so6yExVkjk5vS3ysNkuH6Ue318X2V3bjSXSz6NZ5MV1/9xv72fin93B67Jzaf5q6qkYUoGnthtDc0EJqdjJ2p52G6kaqz9XidHf9duzyOKk6U0PBlFHYna3nuX0uKkqq+rR3amcJ2/5/Xo5uCfLTL/2atJzOk7Z2sSY40abmhf9axycf+1C7UTkhrmSx/iXsAj6nlFqrtTYvHFSt71L/cP52IYQYdNFolNOHStm/6RB1lQ0oQ5Gak8zUxRPJGZfVp6NKp4tLObX/dLukrSuGYZAxKp13n9/GzGVTcXtd3Z4fCoRY94vXOHe8jFAghNVmxeNztT1O5dkammr9BFtCpGYn4/G5OXO4FKstqctrXrhvU52flCx72zHDYtDSB/1JP/u7Hby451y7Y4/cOY0PXNXalnDLa3swTbPbDRwXc/tclJ2q5OS+063TwUKImBO37wIv0to54Y/AOVo3J9wNjAek6I4QYtAd3n6M1373Fo01fuxOW9uoUvnJSna/foC03GRu+vC15E3I6ZPH2/HKHhwuR8zJYOvaNJPirUeZdd20bs/d9fp+ircdx+lxdDr1aRgKm9OKMhRVZ6tJy01BWQz89X68Sd1tNtBEI9FLDmnUZbTeevNwJR/6xZZ2x2aOSuLZzyxu+9k0Tbau20VSmi+ua7sTnGxdt0sSNyHOi7UA78tKqdXAvwDfgPOj9bAdWK217rjyVAghBtDO1/ay/lcbScpIJHN0ervbPIlutNY01TXz+0ef4Y7Pr6Rw5uUlAi3+AMd2nyQtN74G897kBHZv3N9t4qa15i///gIWi9FlYV/DUKDBYjFQQHVpLU6Pg8ZaP9FoFIvF0vm1Ue02LmitMc3W4rzxCkVMJnxzbYfjnRXQDfiDtDQG8I7quYPDxUHzqRkAACAASURBVDyJbspPVsQdmxh+tNaUHitj36ZD1JyrQ2tNUrqPKYsnMqoop9/6+g43MS8a0Fq/DLyslHLTWhakVmvd3MPdhBCi353YV8L6X20kLS8Vm73ztzWlFN5kDzaHlef+82U++O27yRiV1uvHDPiDrdOMcX6Y2Jw2Gmuauj2n7GQFNedqu02m7C47jXWtpUcMi0EkEsWwWNC6dUSts8RNowGNK+Fv07QN1U2Mmpgb927XztaxddfxwIyaXd7WHWUoIuFozyeKYe108VnW//oNqs/WYLNbcZ7/wlJxqop9bxeTmO7luvuuZvyssYMc6eCLe7Xn+WRNEjYhxJCgtebtpzeTkOTpMmm7mNPtwF/fzLZ1u1j5iRt6/biGodA6/gbzQLsRr87s3ri/dedoN1OwrgQnhmqNQSmF1WqhpSmAxWohHAxjd3TcpBBqCZGY6m1bX6e1prmhmXkrro859s4Stre/soy85O5H7OxOW9vonmHEvs4wEorg8sbeTkwMP0d2HOfZJ9biSXR3WC964ctLc2MLT//oJVZ8/DpmLJ0yWKEOCV2+yymlvgU8pbUuPf//3dFa6+/1bWhCCNGzyjPVnDtRQWZ+7KNnSRmJHHj3MEvvWYTHF/8UIYDL62od6QpHsdo6n5bsTEtTgLTc7rsUnNx3Gm9KAsHmIBZr59c2DIOEZA8N1U3YnTYMi0EoEMab7MGMaiKhCFZ7+36koUCYwtlj236uKKli/JyxFEwd1WPctz+5iV2n69od+/4dU7l/fmx11uxOO2On5XP2SBlJcYzu1Vc1Mnf5jJjP7y/RaJRgcwgzamJ32fus80V/M02z7bUwFKcaK0oqef7Jl0lM93Xbbs3tdWGzW1n3i9dIzky6okvEdPf19DvAy0Dp+f/vjgYkcRNCDLgTe06hFHHtFrVYDLSpOVNcStG8wp7v0Am7w8b0JZPZ88b+uMpoNDc2M+v6pd2eEwqEyR6byZEdx3G4uv4w86V5CbaECLWEsDlsmJEoGflpZI/NonjLUULBUOuUk1L46/zkFGaRlOGjuaGFusp6CmeNYdUDN3S5Hg7g4LkGbn78rXbHHFaD4n+5OebnfMGcm2ZybPdzMZ+vtSYaNZl2zaS4H6uvNNQ0cuDdw2xbt4uAP3ghMMbPHsusG6aTNyF7yCVE0WiUkoNn2b5+Nyf2lbR+QgN5RTnMWzGTgimjhkx5lc1rdmJYLTH1yLU5bLh9bjY9u4X8r94xANENTV3+y2mtjc7+XwghhpKmen+vPoS01gRbQpf12NOXTmbnhj1EoyaWGJqjB/zB1gbz0/O7Pc/hsmF32bDarUTCkS6fn6EM0vNSqS6tobmxhXA4SsboNFKykpixbAqni0s5e+Qc2jRJzUvF7XNRWdLaD3TVAzcwacGELq+ttWbM19Z0ON7dOraejJqYQ/bYDKrO1vbYG/XCiODkBeNJyRqYPqqXPv6u1/fx6m/fOr9IPhFfSmsLLtM0Obn/NMXbjjFqYi63fWZFj+VdBkpNWS3P/Mcaas7V4XDZSc9La5vWrz5bwzOPr8GbksCdD64kIz+95wv2o6Y6P8Vbjsa1wcebksDpQ6Xnd1L3Td3B4SbWXqX5wDmtdbiT26xAjta6pK+DE0KIntjsNrQZ/1qzC/XLLkd6XipzV8xk85odZBVkdDvyEgqEqS2v444HV/bYRH38nHHsfHUv42YUcGjzEdw+V5ejYoZhkJabStXZGhLTrEQjJhWnqwBIy03hmrvmk5qd3LomzmohMc3bYz27ztaxfabiFLmFmRzdeYKCqb0bsbFYLNz+uZX84V+fpfJMNak5yZ3+zqKRKFVna8idkM1NH7427sfpC9vW7+bV/3uT9LxUbJdMixqGQXJmElprzh0t408/fI57v3J7lzuAB0pteR2/+/7TaFN32FmtlMKX6sWX6qWxponffv+v3Pf1uzqcN5BOF5eitRnX32Hr67a1tZskbt07ASwEtnRy24zzx2Nf5CGEEH0kLS+VSCTSq/smpcdXU+xSSimW3L2QSDjKjlf24El0401JaJcURaMm9RX1hEMRVn3yRibMGdfjdacvmcT2dbvIGJ1O4awxHNt1EqvNgsPdsWZcOBgm0BwkMc3Lxx+9j0nzJ7TueDUUrgRnj0nixTpL2D5lbyHHMNGjUqk5V8fT/7GG9LxU7npoVa+awHuTE7jv63ey4Tdvcnj7MQASEj1t6wX9Dc1YLAYzr5vC0nsWD8pasrKTFbz++02kj0rrdsOLUoq0vFQqSqrY+MdN3Pyx2Dd59DXTNHnmibWYpu7xde1NSaCxtomnH3+JTzx6f1yvkb4U8AdorS4WH5vdir/+yt0jGWvi1t1v1gb0bp+3EEJcpnEzRmOzWwmHIjHtKoXWHWq+NC85hVmX/fgWi4Ub/m4JY6bls2XtTs4Un0MZCoUGFCiYNH88c26aQVZBRkzXTM1JoWD6aE4fPEtWQQZur4szR89RW3Z+c8D5S6PB5XWSNyGb5MwkJl41AYfL0e26uM488eoR/u2Vw+2OZason3b8rZvCxSM2NWV1/P6Rp7n/m+8jodtiv53z+Nzc9pkV1Fc1sP+dYk7uP00oECbR42DhLXMouqqw15tG+sKu1/dhs1tjfj2l5qawf1MxS+5agKeTYskD4czhc1SdqY75NeZNTqD8VCXH95RQNLfnLxP9wWK19GpntmnqIbNGbzB0t6s0Cbh44jlXKXVpARUX8GGgrB9iE0KIHjlcDmZeN5WtL+8ia3TPH1paa+orG1j+sWV9tqhcKUXhzDEUzhxD1dlqasrqiIQi2J12ssZkYHfZKTlwhp3H96LPF7vNn5zX5dSaUoqVn7ie3z/yDFVna0jNSWby/AkEmgM0VDcRjUQxLAauBCcoCLeEed8XbolpgffFTlb5ufaxjR2Of9fp7/Z+KVlJVJ2tZsNv3uD2z62M6zEvlpjmY9Gt82Lu3ToQmhtb2L+pmJSsrluHXerCZpeDm48w96aZ/Rhd13Zs2I3DFV/bcLfXxda1O5kwZ2yftoKLVWK6r1ePa0ZMUnMGft3jUNFdyvog8G04X7ER/tLFeer8eUIIMSgWrJrDiT0lVJfWkJrT9UJnrTWVp6spmJbPlEVF/RJLWm5q29obf0Mz21/Zw44NewgHwqAvFMFVWGwG05dMYu7yWZ0Wv/X43Hzgq7fzwn+/QsnBM9jsVnxpPtLzUjFNTVNNEy2NAXypCbzvH2+JeaTlgs6mRR+sORPzB2JKdjJHdp6gvqqBxDjbWA1lNedq0Vp3WYalKy6vi1MHzw5a4nZibwlJ6fEVUU5I9nDueDnhUGRQpqTzJmTjTfac37QT25eOcCiCzWll7PTYytCMRN0lbs8CJ2lNzH5Ba7urY5ecEwQOaK339Et0QggRA1eCi7u/dCtP//glyk5W4E1OwH1RQ3atNY01Tfjrmxk3s4DVn7qp39f11FbU8+cfPkd9VSPJWUkdPhgj4Sh73jjIwfeOcPcXbyV7bGaHa3gSPdz75dsoP1XJ7o37ObTlKKGWEFa7ldzx2cxbPoP8SXlxJRmdJWxbH17Cr7/yG5Li2L15YbRy/zvFQ2rE7HKFQ71bL2mxGIQuc5dyb5mmSTgUiXuzjVKtU/mtBZsHPnGzWCzMu3kWG/7vTbIKMmIafasurWH+qtltfYivRN2VA9kN7AZQSmngRa119UAFJoS4cl3oBhAPb3ICH/j6nRzZfowta3ZSUVLV9kFmmia5hVms+Nh1jJmWH/doSrz8Dc38+bHnCfiDXe7as9ospOel0lTn508/fI4PfvvuTsteKKXIKsgg6yMZLP/Isl79bqDzhO0rKyby6WvHcfbouda+p3H+Xjw+N2eKS+OOZSi7dAdprCLhaMyjRn3NMAxsditm1Izr31BrDbr3z7kvTFsymSPbj3PmyDnS81K7fW1Xl9aQlpfKVStnD2CEQ0+sq/veBaYCb1x6g1JqCa2lQo70ZWBCiCtHJBzh5P7TbFu3m3PHy1o/BN0OJi2YwPSlk3t8Q7/A7rAxZdFEJi8sorq0hpamAEopPIlukjNjX7N0uXa/vo+6ivqYpi8TkjxUl9ay6Zkt3PLp5T2eH2/S9viGI/xow+EOxy+uxxYJR+lN8y7DaO3WMJKk56W0tg2LY7MLtHbEKJxZ0H+B9WDMtHxO7T8T1+u8qdZP1piMuJ5nX7M7bNz2uZt5/ifrOLmvhIREDwnJnnaj5f76Zhpr/WSPyeDOh1YNetmVwRbrv9aPgQN0krgBq4HJ5/8rhBBxOXv0HM89+TJNtX5cCS6SMpIwDEUkFGH3xv3sfHUPY2cUsPLvb4j5DVspNWg1nsKhMNvW74mraGxyZiLFW49x7fub8CYn9Ekctf4Qs773SofjnRXQtTtt0IvdfZHwyOsj2rrZZQo7XtlLel5sr6FIOILNbmF8DKVe+svsG2ZweNvxuO7T3NjCDR9aOigbEy7m8ji566FVHN9ziq1rd1J6vBzDMNAatGmSPiqVa9+/mMJZY4ZNq7H+FGviNhf47y5ue5PWnaVCCBGX08Vn+dMPnsPtc3cYnbI5bKTnpaK15uS+0/zph89xz8O3Dflv22eKSwk2B+OqEWdYDLTWHN11glnLpl12DJ1Ni3bX8SAtNwW7y0EoEIpr7VBzY4AJg1RKoj/NWNqauAWbgzh62KmrtabqTA3zV8+Oe1dvX8qbkE1aXip1lQ0xvfYaa5rwJLl77OIxUKw2KxPmjGP87LHUltfhr29Ga3B7naTmpAx6cjmUxLqS0QsEurgtDMS3lUUIccXzNzTz9OMv4Un04EnsumaXUor0vFQqS6rZ8Js3BzDC3vHXN9ObeUerzUpdef1lPXbBV1/qkLRt/NK1PbapstltzF0+g9qK2B+/bZRp9qVVooa/lKxkVn/qRmor6mlp6uqjr7WeWEVJJWOm57P49qsGMMKODMPgjs/djGEo6iobuj23saaJUDDMXQ+tHrTiu11RSpGSlcyoolzyJ+aSlhvbMokrSayJ23Ggq5LQ19G6+1QIIWJ28L3DhFrCuH2x9XhMy0vh0JYj1FVeXnLT3/SF4ri9vW8vdJaw3T4zh5OPrqIgLbaCsFMXT8Rmt9Lc2NLjuVprKs9UM2f5zEEdZepPRXMLed8XbiEYCFF2soLGmtb6eabZ2uO2oqSKytNVTL1mErd/7uYhURA2OTOJ+75xJ55EF2UnK6irqMc83w5Oa01DdSPlpyqx2Czc/407B7Xdlei9WF9pvwa+p5QqAZ7SWgeVUg7gE8BDwHf6KT4hxAgUjUbZunZXXPW/LpSfOPBOMYtuG9zRje64fa5eJW7hcDjuFly/31LC157e2+F4bxrB+1K93PHgKv7y2POYUbPLjghm1KS8pIoJc8ax+LaRUwakM2Onj+ZT//ZhTuw5xbZ1u6g8U00kHMXtc7HgltlMWTyp0xp8gyklK5mPfO/9nD5UyrZ1uzixr6RtBHhUUQ5zV8ykYErves2KoSHWf7nHgHnAE8DjSqkaWrsqGMBfgX/tn/CEECNRfWUDzQ3NpI9Ki+t+3uQEDm8/PqQTt1ETc1tbcAXDMZdZMM3WroHjYtyVGI6ajP/G2g7He5OwXWz0pDze/7U7ePG/11N+sgK724HH50YZinAwTGNtE0op5t40naX3LOr3sipDgd1ho2heIUXzCgc7lJhZLBYKpoyiYMooTNMkEopgtVv7rFOIGFwxJW5a6yjwPqXUdcBNtCZtVcB6rfXG/gtPCDESRUIRejMsZbFaCDYH+z6gPmR32Jh94wy2vLSDjPzYEtO6igbGzxob0whkZxsPTjyyss/WAeUWZvOJf/07zhSXsn3DHipOVhKJRHF7XSy9eyGTFxb1qj+pGByGYVzRxWpHorjGSrXWrwGv9VMsQogrhMVmPd/6KT5m1MQeZz/GwTDruqnse+sg9VWNJKZ5uz23ubEF0zRZfEf3o4idJWxP/8MiZuf3fc9Gi8XC6MmjGD15VJ9fWwhxeWIeN1WtblVKPaaU+qVSavT540uVUjn9F6IQYqRJTPPicNkJBeJrEdRU56dg6tBPJrzJCdzz8K0YFkXF6SqikWiHc8yoSfW5WpobW7jrH1eT0cW08eRvvdwhaZuQmcDJR1f1S9ImhBjaYhpxU0olA2uA+UADreVBngBOAX8P1ACf76cYhRAjjNVmZc5NM9j07BYy82Pb2aa1xoyaTLtmcj9H1zfSclP54Lfu5r0Xt7P37YOYkSiGpXVNWDRiYhgwYV4hC2+Z22mh171n6rnlP9/ucPxy17EJIYa3WKdKfwiMAhYDW4GLvyZvAB7u47iEECPclEVFvPvCNoItIRwxTH/WlNUxekoeaTkpAxBd3/Clernpw9dyzV3zOb6nhPrK1vIM3mQPY2cUdNolIRqJMu6bL3c4/pVgJfNunom/3o8nUdaYCXGlijVxuw34ktb6XaXUpduISmhN6oQQImaJaT5WP3Ajz/7nWpIzk7qtB1ZTVofb6+Tmj3dVTnJocyW4mLKoqMfzOlvH9i2HH6uCQNTC63/YxJt/fo+Vf389E68a3x+hCiGGuFgTtwTgbBe3Oel1uUkhxJWsaF4hd3x+JS/99BXqKhpITPPiSmhtaWWarQVDA/4gGfmp3Pngqj7r4znUdJaw3WcLMNHyt7VxTo8DpyedYEuI5/7zZcxPm0xe2HUyqLWm9FgZu17bx+niUsLBMK4EJxPnFw7J+mNCiNjEmrgV01oGZEMnty0FOlaAFEKIGEyYM468f8uheOtRtqzdSXlJZesNGgqm5TP3phnkT8rFYhl5NcMeWXOQn77ZsTH4d53+Lu/jcNlJyU5m7f+8Rk5hFknpHROwqtIaXviv9VSersLmsOFN9mBz2IiGo7z34g7efX47E+eP56YPL8XhGpmdD4QYqWJN3J4EnlRK1QO/O38sSSn1UeCzwAP9EZwQ4srg9rqYdd00Zi6bSrAlRDQcwea0Y4+xgO1wU90UZM6/dPwe/M+OppjqsTlcdrTWnXaRqDhdxR8efQZQZI5Ob3c9m92K0+PANDXFW45SV1HPPQ/fKsmbEMNITOVAtNY/B/4d+Gfg6PnDrwA/A36stf5t/4QnhLiSKKVwuh14Ej0jNmkr+OpLHZK2X8xI5HNVJXEV0U1KT2Tb+t2EQ+G2Y8GWIH/90YsYhkFSuq/L6xmGIiM/jfKTlaz/3zd690SEEIMi5gK8WuuvKqX+C7gRyACqgVe01h3H+YUQQrTT2Tq23d+6iUS3jV/+0+/j7kZgd9qoqwzTUN1EanZrPbcjO07QVOuPuXl4Wl4qB987zDV3ze90ylUIMfTE2znhFPBUP8UihBAjzuJHX+NsXUu7Y7fMyOGJD8xq+znUEmqr8RYPhSIajgCtmxG2rt0ZVwJoGArDYrDv7UNcfcf8uB9fCDHwukzclFL58VxIa11y+eEIIcTIsG5/GZ/8zfYOxzsroOtMcNLSGIj7MbTWbY3sm+r8VJXWdNmBoSvelAQObT4iiZsQw0R3I24nIa5mgiNvy5cQQsQpEI4y8Z86FtDtruPBhDnj2PTMFtxeV8yP0+IPkJDsxne+F2ooEMYwjLibzVttVlqaWno+UQgxJHSXuH2M+BI3IYS4onW2ji2WFlVTFhXx9tObMU0Tw4ithXR9VSM33H9NW5kUq82CNs34Aqa1Z6rNHteqGSHEIOryr1Vr/asBjEMIIYatzhK2V7+4lHHpsRUM9qV6mbxwAgffO0JGfs9Tnf6GZpxuB0VXFbYdS0jy4PQ4Y24h1natOj8T5o2L+XwhxOC67K9ZSikDSNJa1/RBPEIIMWx88jfbWLe/vN2xjy4u4Nu3TIn7Wjd8cAnV52qpKKkmPS+lyynPpjo/LU0B7v3ybXh87rbjFquFOctn8PbTm8nMj21XqdaaUDDCzGVT4453uKmtqOdMcSnNDc1Y7VZSspLIn5SHxSqrfMTw0t3mhBrgBq31jvM/K+A54KFLSoDMA95B1rgJIa4Q207W8L7/frfdsSyfk/e+3vteqg6Xg3sevo21//MqR3ecwLAYJKZ5sdqsmKamqc5PwB/El5rAfV+/k+yxmR2uMWVREe88uzXmUbeGqkYy8lM7vdZIcfboOd57cTvHd5/C1BqLYaBNjUbj8bmZu2Ims66fNmLrBoqRp7sRt6RLbjeA1cB3+jMgIYQYqrTWjPnamg7HY1nHFgun28Edn1tJVWkNe986wMF3j9BQ3YjFZiWnMIu5N07vdpTIl+Ll5o9fxwv/tZ6U7ORuk7eG6kY0cMunl8e9oWG42LfpEGt+vgGn20H6qDQMo/3zDDYH2fiHTRzbeYI7HlyJKyH2zSFCDBZZkSqEEDHobB3bsf+3EovR90lPWk4Ky+69mmX3Xh33fS80nl/7i9fQpiYxzdeWwGmt8dc3469vxpuSwF3/uLqteO9Ic3zPKdb87BVSslOwOzsfTXO4HWSNyaD0eDnP/2Qdd/3jaqw2+VgUQ5u8QoUQohudJWx//fQi5oweugnP5IVF5BXlcODdw2xbt4v6qkZAo7UmPS+Va9+/mMKZBdidsW9iGE5M0+SV37yBL9XXZdJ2gVKK9LxUTu4/zYm9JYyfPXaAohSidyRxE0KITvzb+mKeeO1ou2MP3TCeh26YMEgRxceX4mXBqjnMWz4Tf30zkXAEu9OOJ9E9YqdGLzhdXEp9ZSNZBbFt0lBK4Un0sGXtTgpnjRnxvx8xvPWUuOUqpS58/bBcdKzuonPy+j4sIYQYHPXNYWZ8d32H4321jm2gWawWfKnewQ5jQO1762CPI22X8iZ7KD1WRl1lA8kZ0rdVDF09JW5/6eTYs5f8rJBCvUKIEaC3BXQHg9aalqYA4WAYm8OGK8EpI0Xn1ZTV4XQ74rqPUgqlDJobmiVxE0Nad4nbRwcsCiGEGESdJWyHvrcCp23oVTkKNAc5suM4W17aQW15PcoAbUJydhLzV85m/OwxOFzxJS1CiOGju84J/zuQgQghxECb8q2X8Yei7Y795VMLmVuQMkgRde/MkXM88x9raGkM4E3xkD4qFaUUWmuaG1pY89QG3F4Xdz64ipxxWYMd7qBJzkyk5lwtbl/s5T201mjTjKtfrBCDIbameEIIMYLsKKml4KsvtUvavA4rJx9dNWSTtrNHz/HHf30Wq9VCVkE6Ht/fNhm0Lq53kzU6A8Mw+MOjz3LueHkPVxy5pl0ziVAgHNd9Gmv95IzLJEmmScUQJ7tKhRBXjJZQlEnfernD8aG6ju2CUDDMs0+sxe114Ul0d3tuQpIHgGeeWMMDP/jgFVmXLK8oB29KAs2NLTGNoLXWt/Nz44eWto1g1pTVEWwOYlgMvCkJ7dqLCTGYrry/aCHEFWk4bTy41Ik9p/DXN5NVkBHT+QlJHspOVXJy32kKZ43p5+iGHovFwo0fWsqf/+0FrHZrt+2stNZUnq1m9KQ88iZks/ftg2xZs4PasjqUYbSdM/GqQmZeN43cwizZBCIGlSRuQogRrbOEbfe3biLRPXx6U25Zu6PHkbZLuROcbFm744pM3ADGzShg1QM3sPapV3F6nCSmeTskXMGWEDVlteQWZrPk7oX87vtPU1tRhzc5gfRRaW3nm1GToztOcODdw8y+YTrL3r9YmtOLQSOJmxBiRHry9aP8cF1xu2NfXlHEP1xbOEgR9U40EuXc8QoyR8dWTPYCb0oCZ4+UobW+YkeIpl09ieTMJN59YRsn95agtcZisWCaJgCuBCdL71nE+Flj+NMPnyccipA1uuOopmExSMlOxoyabF+/G601N/zdkiv29yoGlyRuQogRpaw+wIJHXm13LD/FzZtfXjZIEV2eSDiCYai4k4QL50fCEWz24TO62Nfyxmdz9xduoba8jpKDZ/E3tmBzWEnJTGL05DysNivPPrGGlqYAabndb0wxLAaZo9PZsWEP42ePpWDKqAF6FkL8zYAnbkqpFcDjtHZieEpr/WgX570P+DMwT2u9bQBDFEIMQ6apGfv1NR2OD5d1bF2xOWxoWp+fEUdDezNqogx1RW5O6ExyZhLJmUkdjtdXNXBk5wnS81Jjuo5hMXB5nGx/ZbckbmJQDOhftFLKAjwJ3AicAbYqpZ7XWh+45Dwv8Hlg80DGJ4QYnjpbx3bikZUjYirLMAzGzijgTHFpXBX96yobGD977Ij4HfSnA+8eBlp/z7Hypfk4vvsU9VUNJKb5+is0ITo10F/FrgKOaq2PAyil/gDcBhy45LzvAT8AvjSw4QkhhpP7fv4e7xyrbnds6zduIN07sjoHzL1xOsd3nojrPqFAiNk3TOuniEaOc8fLcSXEV3TXMBSGoairlMRNDLyBTtxygdMX/XwGmH/xCUqpWcAorfWLSilJ3IQQHbx7rJoP/Py9dsf+875ZrJ6eM0gR9a+8ohxS81KpLa8nObPnUbea8joyC9LJHZ89ANENb5FQJK4p6As0EA1HezxPiL420J0TOvvraGtQr5QygB8BX+zxQko9oJTappTaVllZ2YchCiGGquqmIAVffald0laU6eXko6tGbNIGrXXJ7nxwJTaHlZqyOrTWnZ7XWji2FofLzu2fvTmu6b8rlTvRTTgYif+OWmN32fs+ICF6MNAjbmeAi1dz5gGlF/3sBaYCG8+vy8gCnldK3XrpBgWt9c+AnwHMnTu383cxIcSIMZwL6PaFpPRE7v/mXTz/k5c5d7wCm8OGLyUBw2rBjERpqG4iHAqTU5jFrZ9eji/VO9ghDwsT5xW2rXOLVSgQwu5ykDk6rZ+iEqJrA524bQXGK6XGAGeB9wP3XbhRa10PtP0lKKU2Al+SXaVCXLk6S9iO/b+VWHoxvTXcJab5+Lt/upuyExXsem0fx/ecIhgI4XDZmbigkJnLppJVkCEbEuJQMHUU7gQnwZbW32MsaivqWXz7VVd0mRUxeAY0cdNaR5RSnwXW0VoO5Bda6/1Kqe8C27TWzw9kPEKIpW4kBAAAIABJREFUoeunbxzjkbWH2h3b8IWlFGYkDFJEQ4NSiuyxmWSPzRywx6wtr2PfpkOcPVpGJBTB43NRdNV4CmcWYHcO7+lCq83Kotvm8cqv3yCzIL3H6WV/fTN2p42pV08coAiFaE91tVZiOJk7d67etk0G5YQYCfacqePW/9zU7thDN4znoRsmDFJEV676qgbW//oNTuwtwWIYuLxODMMgHIoQ8AewOazMXzWHq1bOwmIZvi2gtNZs+L832fHKHtJyU7B10ttUa902HX3Pw7eRJxs/xGVQSm3XWs/tzX2lMqMQYkjwByNM+fa6dseuLUrnVx+9apAiurKVn6rgZ1/5LQ2VDbgSHFgdNmxOG75UN25DkZjmJRyK8Maf36WmrJYVH7tu2CZvSimuv/8akjIS2fTMZsLBCK4EZ2vxY61paQwQDoXJGJ3OzR+7Lu72Y0L0JRlxE0IMuit940Fvaa2JhFt3RFpt1j5Z2xYOhdn07BZ+/8izhFtCODwOlFJo08TUGofLTl5RDpn56SilME1N+ckKrr5rPotvG/5JdigQ4tjuU+x7+yBNdX6sNgsZo9OZvmSyrB8UfUZG3IQQw9I3ntnLbzeXtDt26HsrcNqG58jNQKmvamD/O8Vsf2UPgaYAAE6Pg9k3TGfK4iKS0mPvsHCxYEuQZx5fw+43D2BGoyRndWwRFQ5FOLrjJP66ZsZOH41hKNJHpbL5pR3MuXEGTvfwLn5sd9qZNH88k+aPH+xQhOiUJG5CiAH31+1n+OKfd7c7tu6hJRRlSQmL7pimydvPbGHzS9tRSpGU7iPxfNmPUCDMuy9s453ntnLVyllcfef8uKYuTdPkpZ9t4PThUpobW3B6nJ2eZ7NbsdosbSVJ8ifmYrVZiYajHNl+jGnXTO6T5yqE6JwkbkKIAXOorIEVP36r3bEf3zuT22flDlJEw4fWmld/+xbbX9lDZn4aFmv7pMzutJExKo1o1OS9F7YTbA5y44eujXlqr/RYOUd3nsCX5iXQFMCT6O7yXKUUnkQXZw6XkjUmA7vDhtvnZt/bxZK4CdHPJHETQvS7qKkZ9/U17Y4VZiSw4QtLBymi4efg5iNsX7+brDEZ3ZassFgMMgvS2bFhL3kTcpi8sCim6+98dS92h41oJIpSCtVpo5u/MQwDrTXVZ2vIHpuJzWGluaE5ruckhIifJG5CiH714B928tyu0nbHZONBfLTWbH5xO4lpvpjaWBmGQWK6j3df3M6kBRN6HHULBUIUbz1KWk4KLf5AzHE5nHbOnSgne2wm2tRYnLI2UYj+JombEKJf/GX7Gb50yTq2Pd+5CZ9Tqs3Hq+xEBZVnquMqQ+H2uqgoqaL0WBm5hd3XHAv4g4DCsBht/TdN0+wxSbTYLASbQwA0N7YwekpezPEJIXpHEjchRJ/aWFzBR365td2x1790LWPSPIMU0fBXfqoSIK5SFBfOLT9Z2WPipgwFtJaGstmsZOSnUXm6GrfX1f2DaFDqfFmSUIQZS6fEHJ8QonckcRNC9In65jAzvru+3bGf3D+bldOkwvzlCjQHMCw9T5FeyjAMAjFMfTo9DixWC+FQBJvdSlZBBuUnK3scdYuEIji9LhqqG0nPTyNrTEbcMQoh4iOJmxDismitWfToa5yr/1uCMDrVzRsPLxvEqEYWp9uJGTXjvp9pml2W9biYzW5jxrVT2PnqXtLzUvEkuckryuH0obN4Et1dJm+hYJjMgnQi4SgrP36dFKcVYgBI4iaE6LXvvnCAX2w60e7YiUdWygd4H8sc3dqlQGsd8+9Wa43WkFkQ27q46UsmsW3dbqJRE4vFIH9iLmjNmcPnsFgNnB5nu8cONgcJBcIkJHu45+HbyMgfPm2g/PV+Dm8/TnVpLdFoFG+Kl/GzxpCWmyKvXTHkSeImhIjbTzYe5QcvF7f97LFbePfr18vGg36SNSaD9FGpNDe0dFtf7WLNDS2k5iaTMy4rpvNTc1KYd/NMNr+0nayC1pIj+ZPzSM5K4tzxCqpLa9rONaMm4VCEVQ9cz4qPXU9C0vBYv1hf1cDbz2zm4HtH0KbG5rChFISDETY9/R45hdksed8CRhVJXUExdEniJoSIWXFZI8t//Ga7Yy99/mqm5PSuxZKIjVKKBavn8OwTL+NKcPa43s00TeqrGrj1H5bHPIKklGLJ+xYQCUXYvn43CckJeJM9+FK8+FK8hIKjaG5sob6qETNqsvqTNzLnhhl98fQGRNXZav74g+cINgdJzUnBcsnvUGtNbVkdv3/kGVZ/8saY698JMdAkcRNC9CgYiVL0zZfbHXtgyVi+vnLSIEXUvXAojL++mWjExOGy40l0D/spsKJ5hcxdPoPt63eT0UnnhAuikSjlJVXMvnE6kxZMiOsxLBYLN/zdEsZMzWfzmh2UHjkHRusUraEUoJh+zWTmrZgZ80jeUOCv9/Onx57HjGrSclM7PUcphS/Vi9Pj4MWfvoInycPoSVLeRAw9krgJIbp1+5Ob2HW6ru3nVI+d7f904yBG1LXa8jr2vn2IHRv2EAlFgNaRlKyCdK66eTZjZ4zGZh+e07lKKa6772qcHgfvvbAdgMR0H/bz09PhYJi6yga0qVl02zyuvuOqXiWrSikKZ41h3MwCqs7WUF1aQyQcxeawkT02A1/K8Osnu+etg/jrmmOqg2d32vH43Lzxp3f44LfuHvYJvxh5JHETQrSJhCMEW0Iopfj8X/ex4VBF2203TMrkpx+cg8UYeh9kWmu2v7Kb1/+wCUMZJGX4sDlsbbc1VDfx3JMvk5qTzJ0PrSY5Y3hO7RqGwdV3zGf6ksnsf7eY7et3U1teh1IKV4KTRbfNY8qiIhLTfJf9WEop0vNSSc/rfIRquIiEI2xbt5ukOP7NE5I9lJ2spKKkKq6ix0IMBEnchLjCaa0pO1nB7o372b+pmApl4Y+p7Rdnb/3GDaR7HYMUYc+2rd/Nq//3Jumj0rDZ27+tKaXwJifgTU6grqKePzzyDPf/013DcuToAl+ql4Wr57Jw9Vwi4daRRatN3s47c/ZoGQF/gMTU2P+9lVJYDIPirUclcRNDjvylC3EFCwVCrP2f1yjeehRlt3EoI4NXzb/V/bq5voKxgWaKN+wi9bZ5MfXJHGgVp6vY+IdNnSZtl0rKSKS6tIZ1v3ydu7946wBF2L8kYetewB+kN2PENqeNhurGPo9HiMslf/FCXKHCoTDPPLGWkgNnyBydTgDFu0EHacpkmTXENEsUnB6iESdvP72ZcDDMtfcuHnJrfva8sR+L1egxabsgJTuZE/tOU1NWS0pWcj9HJwabUqB7k7ppjRqCX1SEkFelEFeorWt38m5xOW9n56JRuBR8xtHC5x0trUnbeRarhcyCDLas2cmJvSWDGHFHgeYge948SFJGUsz3UUphsRjsfetgP0YmhorWGnM67vsFAyFScySxF0OPJG5CXIGOltXz7TdLeDYtl+OmhRrdOiLhVZ1/wFksBm6fmy1rdw5kmD2qq6hHR02sts5LY3TF7XNx5vC5fopKDCVZYzJITPfR3NgS831MU4OGiVcV9mNkQvSOJG5CXEFaQlH+5cUDrHj8bU5YHSyzhnjQ0UKa0fOIhC81gZKDZ6g+VzsAkcYmEo72ahrMMAzCwXA/RCSGGsMwmL9yFvVVjWgd28hbXXkdY2eMJil9eO4+FiObJG5CXEFsFsVbR6pYkGzjw3VlLLOGsceY9yilMAxF1Znq/g0yDq01zOKfBouEozgTem6+PthiTTRE9yYtKCJ3fBZVZ2t6/J021fnBUCy9Z9EARSdEfGRzghAjmNaa9QfKeeqt4/ziI/PwOm0899nFvP6bjRw+0fP9O16vNekZKlKzk3F5nQSbgzjcsZcraW5oZvEd8/oxst7RWlN5ppo9b+znwHtHCDYHsdos5BZmMeemmRRMGdVlxwTRNbvDxh2fX8kzj6/h7NFzJKUn4rokcQ+HItSW12F32rn34dtIy0kZpGiF6J4kbkKMUHvO1PEvLx1ky4kaCjMSKG8I4HXacNosuL0uopHeJWA2x9B527BYLcxbPpM3//wuGfmx1duKRqIYFoOiueP6Obr4tDS18NLPN3B8TwlWq4XEdB+JqV5MU1N+qpqnf/winiQPt3/25mHVbmqo8Pjc3PPwrex/p5jNL+2gvKSydbD2/IizzW5l7vKZzLpuap8UMBaivwydd2AhRJ8IRqJ89a97eWbnWVI9dr53+1Q+MG8U1ouaaudPymPzS/FtNDBNE4Csgow+jfdyTVlUxHsv7sBf34wn0U1LUwuNtX6ikSgWi4HL5yIhyYNCtY1ozVsxE1eCa7BDb9PiD/DHHzxHVWkNmflp7UquWCyKxDQviWlemur8/P6RZ7j3y7eRNyFnECMenuxOO7Oum8b0pZM5d6ychpomtKlxehzkTcjG4Rq6RaaFuEASNyFGiKipsRgKh9VCYyDCp68dx6evHYfP2bE3Z/6kXBKS3QT8QZye2D6s6isbGD9nDL44KtAPBE+ihzsfWsUvvvZbirfW0tIU/NuNCtDgSXSTXZiJGTEZN6OAa+5aMGjxdmb9/26k6mwNGaPSuj0vIcmDMhRPP76GTzx6P27v0Ek+hxOLxSKJrxi2ZHOCEMNcJGryu80lLPnB65yuaQbg5x+aw1dWTOw0aYPWD64Fq+dQU17bWvqgB+FQaw/Tuctn9WnsfUFrzdkj5zBNk2BLCFTrFKorwYkrwYXdZaO+qoG9bxzEarWw+lM3DaluA7UV9Rzeeoy03Nh6gnp8boItQQ5tOdLPkQkhhiJJ3IQYxt44XMnK/3iLrz+zl+xEJ8Hz69Zi6W4wc9lUpi+ZTPnJCqJRs8vzQoEwlaerue6+q8kbn91nsfeV3W/s5/Xfv03+pFEsWD2XqYsnkpSRiGmaREIRrDYr42aOYfEd84mEo2x6ZvOQ2q25f9MhlNG6YzdWvhQvW9bsJBodOhtFhBADY+h87RRCxCxqav7+19t47VAFo1Pd/Nf9s1kxNSuudlSGYbD8o8vwJLrZunYnoPClebE77aA1Lf4AjTV+bHYLqx64nqlXT+q/J9RLgeYgr//ubdLyUtuK8Cam+bpcXO4Ync6OV/YwfekU0vNiG+Hqb0d2HMebnBDXfVwJTipKqmiobiI5Q2qNCXElkcRNiGGkMRDG67RhMRQTMr0sGpfKBxeOxtHLEhEWi4Wldy9i1vXTOPjeEXa9tpeqs9UYhiI5K5mr75zP+NljccZRaiMWTXV+Dm4+wp439uOvb8GwKNLzUplz0wwKpoyisdbPib2naKr1Y7FaSM5MZOyMgg5xHNl+jHAoit3R+ZTwpQyLgWG1sOfNA1x/3zV9+px6K9gc6tXUrTKUFBEW4gokiZsQw0BLKMpTbx3np28e538/dhVzRifz1Zsn9tn1fSle5q+czfyVs9Fa91sj+WgkyltPv8e2dbtBa7wpXhKSPK27PU9X89vv/5W68jq8yV68yQlYbBa01kQjJja7hRnLpjJ/5Sw8iR4Atr+yB2+yJ64YkjMT2fPGfpbevXBIrHWzOW1EQ72Y8tQam33w4xdCDCz5qxdiCDNNzTM7z/LY+mLO1QdYPiWTtAR7vz5mvyVt0ShrnnqVA+8UkzE6HYul/RLbhlCY0qNlaK1pbgiQnJlI6kVFUCPhCDte2cPRHce5++HbSM5IpK6iPu6aW1ablWjEJOAPkpA0+G+BBVNGseu1fTHv7gUItoRwuB14U+KbYhVCDH+yOUGIIUprzf1PbeaLf95NWoKDPz6wgJ9+cC6jU+MbYRoqtq3bzf53iskak9Exaatp5NCWozjdDrxJCTg9Dg5vO9auMbjVZiUjP42WpiB/+fcXaGlq4XL2GAyVDQrTl0zGjETjiqeusp65K2YOiRFDIcTAksRNiCHmdE0zptk6XXnrzBx+dO8MnvvMYuaPHRqL6XsjHAqz+aXtpOWkdBjR02hO7C3BZre2JSKt/1WUn6zocK3kzERqy+rY/04xvtSE1hIgcYhGoiil4hrh6k9puSnkT86j5lxtTOcHm4NYLAaTF07o58iEEEORJG5CDBG1/hDfeX4/yx7byAt7SgH4wFX53DErL65SEUPR8d2nCDaHzjeFb6+5oYWmOj92V/spYKfHQdnJSiLhSIf7JGUksmXtTmZcO4XG2qa4YqmraGDK4iJs9tg2NPQ3pRQ3f+J6XF4XNeV13Z4b8AepLa9j1SdvxJcytAohCyEGhiRuQgyyYCTKz988ztIfvs6v3z3J3XNHsXDc8B1d68zRXSday4x0oupsDUopFO2TU8NioE2Nv665w32cbgf++mYS030YhhFz43utNeFQmBnXTon/SfQjX4qXD3ztDpLSfZSdrKSusqFdYeSWpgDlpyrxN/i586HVFM0tHMRohRCDSRZICDHIPvarrWw6Ws3SCel8feUkirJG3khKwB9sq7PW8bYAlq7KmajWqc0ubkKbmsW3z2PjH98hqyADw9L1d1GtNeWnKpm0YEKn/VZN0+R0cSm7XttLRUk1ZjSKJ8nDtGsmMWHuOFweZ4/P83Ikpvn44LfvpuTgWbat383JvSVtDdB9qV5u/PBSiuaOG1I9VoUQA08SNyEGwa7TdUzM8uK0WXhgyTgeWDKOpRPSBzusfmNz2DC76c7QnS6TMaVQSnHVytk0N7awZc1OEtN9uL2uDuvogs1BqstqGT97LMs/uqzD7WcOl7Lm5xuoq2zE4bLj9jqxWC00VDWy/lcbefX/3mTB6jksuGUuhtF/ExUWi4UxU/MZMzWfUDBMOBDCYrPicNn7bbevEGJ4kcRNiAF0uqaZH6wr5oXdpXxj5ST+fsnYEZ2wXTCqKIfiLUfprMa/K8FFdWnHhflaa7TWuLpopK5NjSfRjWEYLHv/1WSNyeTd57ZSfqoSpRRWmxUzahKNRvEkubn+/iXMvn5ah9G943tP8dcfvYjH5yaroP2/hd1pJyHJQyQc4a2/bqahuombPnJtvyZvbY/tsMVcWFgIceWQxE2IAdAQCPPk60f55aaTGAo+f10h983PH+ywBkzRvEJe+91bRCPRDolTWl4Kpw+dRaPbrXML+IOkZifjcHVcG9fc2EJimo+sMa1TnkopJi+YwKT54yk9Vsbp4rO0NAVxuB1kF6STPymv0+nY2op6nntiLb4UL66ErqdCrTYrmQUZ7N64n7S8FObeNLO3vwohhLgskrgJMQC+8MfdbDhYzp2zc3l4eRHZiVfWOiW318XUqyex580DZOa3H9VyJ7hITPfhr/PjPL+OzDRNopEo2WOzOr1efVUDyz+6rMPIl1KK3MJscguzY4pr98Z9mKbuNmm7wDAUKdnJvPfCdmYumyo11IQQg0J2lQrRD7TWvHqwnIrGAABfvGkCL3z2av79nplXXNJ2wZK7F5KWm0rV2ZoOxWbHTB1F1DQJh8KYURN/fTN5RTn4Ujt2Bqg6W0PWmAwmzR9/WfGEAiF2vrqPpDiatDtcdpqbApzcd/qyHlsIIXpLEjch+tj+0nruf2ozH//fbfxq00kAJmX7mJYXe4IwErk8Tu55+FayCtKpOFVJXUV9W8kLT6KHCXPH0VjTRG1FHaOKcsifmNtuQX6wJUTZyQpSspO488FVOFyXV0C37GQl0Ug07n6fDqeNw9uPXdZjCyFEb8lYvxB9pLwhwGPrivnLjjMkumx8+5bJ3D9/9GCHNaR4fG7u/ertnD5Uyvb1uzixtwRlKLQGq83CXV+4hYA/wKn9Z6goqQI43/S+dRPDkrsXMnPZVJzuy+96EAqEoBddr6w2Ky0XteISQoiBJImbEH3ksXXFPLerlL+/ZiyfubaQRLfsCOyMxWKhYMooCqaMosUfINgcxLAYuBKcbd0MmhtbKDl4hpamAIbFwJeSwKiJuX26rsxitUAvKmyYpomti2LCQgjR3yRxE6KXoqbmrzvOMC03kUnZPr60vIjPXTee/FT3YIc2bLg8zk4L27q9LiZedXlr2HqSkpWENjWmqeNqKRbwB8kZl9mPkQkhRNdkjZsQvbDpaBWrn3ibL/9lD3/edgaATJ9TkrZhJDHNx9jp+dRXNcR8n2jURBmq35NKIYToioy4CRGHoxVNPLLmIK8eqiA3ycUTH5jF6umxlZ4QQ8/c5TM5svNZElO93bbLuqCmtJZJC8aTkOQZgOiEEKIjSdyEiMNzu86y5UQNX715Ih9ZVICzi/6bYnjIn5THVTfPZMvaXWSOTsfSTfJWfa4WX7qXa+9dPIARCiFEe5K4CdGNQDjKLzedZGK2l2VFGXxq6Tg+sqiA1ITL39UoBp9SiqX3LEIZBlvX7sRqs5KcmdjWZUFrTWOtn+aGZjLy07jzoVV4fDIdLoQYPJK4CdEJrTXP7y7lBy8Xc7auhY8sKmBZUQYehxWPQ/5sRhKLxcK19yxi0vzx7HnjAHvfPog2NUopzKhJzrhMln/kWgqmjmrb9SqEEINFPoGEuMSOklr++YUD7D5dx+RsHz9833QWFaYNdliiHymlyCrIIKsggyV3L8Rf34wZNXG47XiTE9oVAhZCiMEkiZsQlzha3kRZfQs/fN907pydhyWOUhFi+HO6HX1S4FcIIfqDJG7iilffHOaJ144wOs3DBxeM5q45eayekY07zlZIQgghRH+TTyZxxQpHTf7vvVM8/uoR6lvCfOLqMQBYDCVJmxBCiCFJPp3EFemdY1V845l9nKjys7gwlW+snMzkHN9ghyWEEEJ0SxI3cUVpbViuQLeOrP3iI3NZVpQhi8+FEEIMC5K4iStCaV0LP1xXTKrHzjdXT2ZRYRrrHloiGw+EEEIMKwPeq1QptUIpVayUOqqU+mont39KKbVXKbVLKfW2UmryQMcoRo6mYITH1hWz7LGNvLT3HO6LarBJ0iaEEGK4GdARN6WUBXgSuBE4A2xVSj2vtT5w0Wm/01r/9/nzbwX+HVgxkHGKkeHNw5V84U+7qWoKctvMHB5eXkReslS9F0IIMXwN9FTpVcBRrfVxAKXUH4DbgLbETWvdcNH5HkAPaIRi2AuEozhtFvKSXRRmeHjqw3OZOSppsMMSQgghLttAJ265wOmLfj4DzL/0JKXUZ4AvAHbguoEJTQx3xWWNfH/NQZxWg599aC5j0xP4wwMLBzssIYQQos8MdOLW2aKiDiNqWusngSeVUvcB3wQ+3OFCSj0APACQn5/fx2GK4aSyMci/v3KYP24tIcFh5fPXj//b7lExaKLRKCUHzrBjwx7OnaggEorg8rqYungiUxYXkZSeONghCiHEsKO0HriZSKXUQuA7Wuvl53/+GoDW+pEuzjeAWq11t+/wc+fO1du2bevrcMUw8PaRKj75m20EIyYfXDiaz183nmSPfbDDuuKdPXqO5/9rHU01fhxuB55EN4ahCIciNFQ3ok3N1Gsmcv39S7A7pHG7EOLKopTarrWe25v7DvSI21ZgvFJqDHAWeD9w38UnKKXGa62PnP9xFXAEIS5impoqf5AMr5OpuT5umpLF564rZGx6wmCHJoCSQ2f58w+fw+V1kTk6vd1tFqsFp9uBaWr2vnmIhqpG7nxoFTa7JG9CCBGLAS0HorWOAJ8F1gEHgT9prfcrpb57fgcpwGeVUvuVUrtoXefWYZpUXLneO17NbU9u4qO/3IppapLcdn5070xJ2oaIxtomnn78JTxJHhKSPF2eZxiKzNFpnDpwhjf//O4ARiiEEMPbgBfg1VqvAdZccuxbF/3/gwMdkxj6jlc28ejaQ6w/UE52opMvryga7JBEJ/a/U0w4ECYls+ddvEop0vNS2fX6PhbeOg+31zUAEQohxPAmnRPEkPfe8Wr+7qnNOKwGDy8v4uNXj8Fpswx2WOISkXCEbS/vIikj9k0HFqsFM6o5tOUIs6+f3o/RCSHEyDDgnROEiEUwEqW4rBGAOaOT+eTSsWx8eBmfWVYoSdsQVXOulkBzEIcrvs0hbp+bw9uP91NUQggxssiImxhStNas3VfGo2sPEQhHefPLy3DaLDy8fOJghyZ6EApGelWCxWqzEGgK9ENEQggx8kjiJoaMnSW1fP+lg2w7VUtRppd/uX2qjK4NI1abBXpRXsiMmtidsqtUCCFiIYmbGBL2nqnnjp+8Q1qCg0funMY9c0dJE/hhJikjEcNiEAlHsNpif2vxNzQz9ZqRPaJ6oV6mFIUWQlwuSdzEoGkIhNl9uo5rxqczNdfHI3dO45YZOSQ45GU5HDndDqYvncyu1/eTnpca031MU7cW4716Uj9HFzutNRUlVezbdIjKM9WYURNfqpcpCyeQPzkPi+X/t3fnYVZUd/7H39/eG5pm625AdpTGna1BiMoSUYjEJdGok0jiJMZsJjHbJDNJHKLZM8bxNxOTOGZiNEYTY8YY44JRUFxQQDGKCgJCQKSbpVm66fX2+f1xqqG43Nt9Lw136f68nqee7qo6VXVunapb33tO1anEaoH372tgzfJ1rHz87+zZ7l/BXDqwhInnnMZJZ4yld9/43aWIiMST0jcnHCt6c0J2aY20cc/yzfzn42tpaInw/L+eQ99iNZV1Bzve2cmvv30vAwb1Iz+BNyLseGcXo04dzge/OD8Fuetc9abtLPrNYt7dUENeXi5FJUWYGc2NzTQ1tFDSvxezLnsPJ02rjFt75pxjxaJXePq+52mLtNFnQAlFvQsBaNrfzN6d+7Ac48yLp3LG/Enk5OgZMZGeJpvenCA9mHOOxWtq+P7Db7Kupo4zRg/gW/NPVtDWjZQNHcicK2fw2K8XUz5sYIfB265tu+ndt5jzPjYrdRnswOY173Dff/yF/MI8Bo0sPyQw6923FwAN9Y08+PNF7NtVxxnzJx+2Ducczz7wAs/8aTnlwweSX3DoV2xxSRHFJUW0tkR4+r7naahrYPYVZ6kJVUQSpsBNUmbjzv184jcrGD2wN7ctmMy5Jw/SBasbmjD7VHJyc3j8zqfAQb8X86+SAAAeIklEQVSKUgqKfBchzjn27qyjsb6B8uFlfPCL8zt8w0Kq1Nbs4f6bH6JXn2J6lcbvCLi4dxH5w/NZ/Pvn6DeoL+OqTjhk/vpXNvLM/y1n0MgycvPiN6nm5ecyaGQ5Lz78MkPGDOKkMyqP2mcRke5NgZscU9V7G1n8Zg1XTB3B6LLe3PXxMzhjzADyc9U81F2ZGeNnnsLoU0ew+vk1rHzsFXYH93i5NseIk4ZSNXcCo04Z3mFwk0qrFr9KpLWtw6CtXV5+Lv3KSll6/wuMnTTmQFOnc44XHlpJSb/eCX2unNwc+paVsuwvKzlx6lj9iBGRhChwk2Nif3Mrtz29gV8+tYGIc8w+sYJBpUWcNbYs3VmTFCkd2Ifp769i6ryJNNQ10trSSmFxAcUlmfVqq6aGJlY9uTqpNz4U9ymietN2tq6vZtjYIYC/X2/r+m1UjChPeD29Soup3rSddzdUc9zxg5POu4j0PArc5KiKtDnuf2kLNy1aQ/XeJuafNoSvzzuRQaVF6c6apEluXm5GNIfGs3V9NZGWyGH3o3XEzMjNy2XdyxsOBG7b3q4BZ0nVnJn59Fs3bFPgJiIJUeAmR9Wu+mYWPriaykF9uPUjk5g8ckC6syRJiLRG2PT6Fja9sYXG+kYKiwsZOnYIY04fQX5B93yIpGl/E5D80/UFhfnU1dYfGG+sb4QjaO3Myc2hsa4p+QVFpEdS4CZdtq6mjvtf2sK/zB1HeZ9C/vy5MzmhokT37GSRtrY2Xn7iVZY9tJL6vQ3k5+eSk5dLW2sbKxe9QmGvAqbMm8iUeROS6lw3G+Tk5sARHKttbY68UC1dQXEBR9K9UlukLen3u4pIz9W9voElpXbWNfGff3uL3734D3rl53JZ1XBGl/Vm7KA+6c6aJCHSGuGRXz3Ba8+uYeDgfvQZWXJYmubGFp7+4zK2rN3KRde+j4IE+mjLFn3LSnFtbTjnkvqx0dzUTMWIg/ds+k6HLen1OAcVIxO/L05EejY92idJa2qN8Iun1jPrJ0v43Yv/4MNTR7Dka7MYXZa59zFJfE/d9zyrn13DkNEVFPYqjJmmoCifwaPK2fjaZh779eIjqlnKVBUjyqgYUU79nv0JLxOJtGFmjKs6/sC0IWMGMXBof/bvbUh4PQ11jfQtK2H4uOOSyrOI9FwK3CRpzsGdz21k6ugBPHbd2dx48akMLIl9wZfMtmfHXlYsWnVYh7OxmBkVI8p444W32L55R4pyeOyZGVPPn8S+XXUJB6S73q3l5OmVh7y2ysyYNn8Su3fspa2t8/U456it3s0Z8yfr7QkikjB9W0hCVm7axefufomm1ghF+bk89IWz+dVVUzihQs2i2Wz1c2swM3+fVwLMjLy8XFYtXn2Mc5ZalZPHUFl1PNWbtncavO2u2UPvvsWcfcm0w+adNK2SCbNOoXpjDW2RtrjraGtrY9vG7Zw8vZLTZmTOe1pFJPMpcJMObdpZz2fvXsklP3+e5Rt38fYO/xTdgN66mbo7WLX4NfqVJ95/GUD/QX157Zk3iEQixyhXqZebl8v8a+ZQWTWGbRtr2LNj32EB3P59DVRvrKG4tJjLv/4B+vQ//F7AnJwczvvYLKrmjqdm8062b9lJS3PrgfmtLRG2b9nJ9s07GD/zZM7/5JyEX1ovIgJ6OEHiaGyJcNOiNdzx3EbycnK4bs5Yrpkxhl5J9HUlmc05R/3u/YfcYJ+I3Lxc2iKO5obmjOtMtysKigq44DNzOe3szSx/dBWb33zH10Q6h3NQWtaH8/55FuOmjKUozr2A4PfPOR+ZwfhZp/DqM2/wyuLVB4K3vLxcxs86hdNnnkz5sIF68lpEkqarsMRUkJvDik21fGDiUL5y3jh1oNtNWY7hXPK9YTgc1g3vy8rNzeX48aM4fvwoamv2UFdbR1ubo7h3IWXDBiZ1L1rZ0IHMvvwsZlwynaaGZpxzFPUqzJjXfIlIdlLgJoCvfVn0ejW3Ll7H/141hYElhfz+mukU5HW/i7N4Zkb/QX1prG+kV5/Ea86aG1so7FVIQVH36RIklv4VfemfxGuw4snNy01q/4qIdERXZeHVLXu44rZlfOquldQ1tVK91/firqCt+6uaO559u+qSWqa2ZjdV556uJyFFRNJANW49WGukjX/549/508vvMKB3ATdedApXTB1BfoJPGEr2q6w6gSd+u5TmppaEOtVtbfEPJJxy5onHOmsiIhKDrtA9UEvQTUFebg4YfHrm8Sz52iwWTB+loK2HKe5dxJwFM9kR9fRjLJHWCNs37+DMi6fSt6w0RTkUEZEw1bj1IJE2xx9WbOaWv73FnZ+YSuWgPtz0ofF6sq2HO+3sk2hqaGbxPc9QWFxI3/I+hzSDOufYu2Mf9fsamH7BZKa9f3Iacysi0rMpcOshnlq7ne//9Q3WVO+jamR/2ruoUtAmZsaUuRMYMrqC5Y+tYt1Lb4MBwTHigFEnD2PK+yYy6pThOmZERNJIgVs355zjU3etZNHr1Ywc2Iuff2QS804drIuvHGZY5XEMqzyOPTv2Ur1pOy1NreQV5FE+bAADBvdPd/ZERAQFbt3W7v3N9OtVgJkxfng/po4ewILpIylUH1LSib5lpbqHTUQkQylw62YaWyLcvnQDP1+ynluvnMzMynI+N/uEdGdLREREjgIFbt1EW5vjgVXv8JPH1vDunkbOO3kQIwf0Sne2RERE5ChS4NZNXH3nCp58s4bThvbl5ssnMG3MwHRnSURERI4yBW5ZbOOOeob1LyYvN4eLJhzHBeOHcNH4oeTk6MEDERGR7kiBWxaqrW/mlife4rfLNrHwwlO4ctpILpowNN3ZEhERkWNMgVsWaWqNcNfzm/h/T7xFXVMrl08ZwdxTBqc7WyIiIpIiCtyyyOfufpm/vVHNzMpy/u38kxg3uE+6syQiIiIppMAtw63avJsx5b0pLcrnUzPHsGD6SGZWlqc7WyIiIpIGeqN4BttR18Rlv3ie25e+DcCUUQMUtImIiPRgqnHLYGUlhfzyo5OZMmpAurMiIiIiGUCBW4abPa4i3VkQERGRDKGmUhEREZEsocBNREREJEsocBMRERHJEgrcRERERLKEAjcRERGRLKGnSjNQJBLhnbe2UVdbh3NQXFLEsMohFBQVpDtrIiIikkYK3DJIU0MTry59gxcfeZn6PfsxwDk/L78wj4nnnMbEc06jdIBedSUiItITKXDLEHW767n/5ofYtnE7/Sv6MmjEoW9IaGluZfmjq3j16Tf40FcvZNBIvUFBRESkp9E9bhmgqaGJ+29+iJ3v1jJkdAVFvQsPS5NfkEfF8DIsx/jDT/5MbfXuNORURERE0kmBWwZ47Zk3qd64nbLjOn+1VZ/+JbQ2R1j6pxdSkDMRERHJJArc0iwSifDiwy/Tr6Jvwsv0H9SXtcvXsa+27hjmTERERDKNArc027puG3W762M2j8aTk5uDA9auWH/sMiYiIiIZR4Fbmu2rrQdLfrn8gnx2bdN9biIiIj2JArd0cw5c8ouZQVuk7ejnR0RERDKWArc0Ky4pwiz5KreW5lZKy9Sfm4iISE+iwC3NhlYeR35RHi1NLQkv45yjLeIYO2nMMcyZiIiIZBoFbmlWUJjPpDmnU1u9J+Fl9u7cx/ATj0uo+xARERHpPhS4ZYAJs0+luKSIvbs6796jaX8TjfubmHHptBTkTERERDKJArcM0Kd/CR/66gU459jxzq6YDx0459izYy+7t+/lws/OZegJQ9KQUxEREUknvas0Q1SMKGfB9ZfyzJ9e4M0X14Fz5OXngRmtza20tbUx4qRhzLh0GscdPzjd2RUREZE0MOeOoC+KrmzQbB5wC5AL3O6c+2HU/C8DVwOtwHbg4865TR2ts6qqyq1YseIY5Tj16nbXs3blemq37aYt0kbpwD4cP3G07mkTERHpBsxspXOu6kiWTWmNm5nlAj8DzgW2AMvN7EHn3OuhZC8DVc65/Wb2GeDHwOWpzGe6lfTrzaRzTk93NkRERCTDpPoet6nAOufcBudcM3AvcFE4gXNusXNufzC6DBiW4jyKiIiIZKRUB25Dgc2h8S3BtHg+ATxyTHMkIiIikiVS/XBCrFcExLzJzsyuBKqAmXHmXwNcAzBixIijlT8RERGRjJXqGrctwPDQ+DBga3QiM5sDfBO40DnXFGtFzrnbnHNVzrmq8vLyY5JZERERkUyS6sBtOTDWzEabWQFwBfBgOIGZTQR+iQ/aalKcPxEREZGMldLAzTnXClwLPAa8AfzBObfazG4wswuDZD8BSoD7zGyVmT0YZ3UiIiIiPUrKO+B1zj0MPBw17frQ/3NSnScRERGRbKBXXomIiIhkCQVuIiIiIllCgZuIiIhIllDgJiIiIpIlFLiJiIiIZAkFbiIiIiJZQoGbiIiISJZQ4CYiIiKSJRS4iYiIiGQJBW4iIiIiWUKBm4iIiEiWUOAmIiIikiUUuImIiIhkCQVuIiIiIllCgZuIiIhIllDgJiIiIpIlFLiJiIiIZAkFbiIiIiJZwpxz6c5Dl5nZdmBTuvPRTZUBO9KdCTmqVKbdj8q0+1GZdj/hMh3pnCs/kpV0i8BNjh0zW+Gcq0p3PuToUZl2PyrT7kdl2v0crTJVU6mIiIhIllDgJiIiIpIlFLhJZ25LdwbkqFOZdj8q0+5HZdr9HJUy1T1uIiIiIllCNW4iIiIiWUKBWw9mZvPMbI2ZrTOzb8SYf5WZbTezVcFwdWheJDT9wdTmXOLprEyDNJeZ2etmttrMfhea/jEzeysYPpa6XEtHulimOk8zUALfvTeHym2tme0OzdN5moG6WKZJnadqKu2hzCwXWAucC2wBlgP/5Jx7PZTmKqDKOXdtjOXrnHMlKcquJCDBMh0L/AF4r3Ou1swqnHM1ZjYAWAFUAQ5YCUx2ztWm+nPIQV0p02CeztMMk0iZRqX/PDDROfdxnaeZqStlGowndZ6qxq3nmgqsc85tcM41A/cCF6U5T9I1iZTpJ4GftX/Rt1/ggbnA4865XcG8x4F5Kcq3xNeVMpXMlOx37z8B9wT/6zzNTF0p06QpcOu5hgKbQ+NbgmnRLjGzv5vZH81seGh6kZmtMLNlZnbxMc2pJCqRMq0EKs3s2aDs5iWxrKReV8oUdJ5mooTPNTMbCYwGnkx2WUmprpQpJHme5nUlp5LVLMa06HbzvwD3OOeazOzTwG+A9wbzRjjntprZGOBJM3vVObf+GOZXOpdImeYBY4FZwDBgqZmdmuCyknpHXKbOud3oPM1EyZxrVwB/dM5FjmBZSZ2ulCkkeZ6qxq3n2gKEa9CGAVvDCZxzO51zTcHo/wCTQ/O2Bn83AEuAiccys5KQTss0SPNn51yLc+5tYA3+op/IspJ6XSlTnaeZKZlz7QoObVLTeZqZulKmSZ+nCtx6ruXAWDMbbWYF+IPpkKdZzGxIaPRC4I1gen8zKwz+LwPOBGLehCkp1WmZAg8As+FA2VUCG4DHgPOCsu0PnBdMk/Q64jLVeZqxEilTzGwc0B94PjRZ52lmOuIyPZLzVE2lPZRzrtXMrsWf9LnA/zrnVpvZDcAK59yDwBfM7EKgFdgFXBUsfhLwSzNrwwf/P4z39IykToJl2v7F/zoQAb7mnNsJYGY34r+AAG5wzu1K/aeQsK6UqZm9B52nGSfBMgV/A/u9LtT1g3Nul87TzNOVMuUIrqfqDkREREQkS6ipVERERCRLKHATERERyRIK3ERERESyhAI3ERERkSyhwE1EREQkSyhwk6xhZleZmQsN+8zsFTO71syyumsbM5tlZgvNrFuck2Y23cxeMLP6oKwmpDtPcGA/OzObFZp2nZl9MEbahWaW0sfuzexiM/tyKreZqO52jHamq2XR0/aXpI4OKMlGHwKmA5cALwL/BVyf1hx13Szg3+k+5+Sv8P1EXoAvq7Xpzc4BL+Hz81Jo2nXAYYEbcHuQNpUuBjIycKP7HaOd6WpZzKJn7S9JkayupZAea5Vzbl3w/yIzOwF/8e1S8GZmufi+DVu7msGeLKhhGAd8zzn3ZGfpU8k5txdYlmDaLfhX2UiWMrPC0Gv7RLoH55wGDVkx4N/c4IAToqb/OJheEZr2SeAVoBHYga8BGhC1nAO+B3wDeBvf6/zEYF45cCuwGWgK/t4FFIaWH49/rUkt0AA8C5wdtY078Bf/icBSYD/wFvDpUJqFQV4OGULzv4OvIdoTfJYngWkx9s+kYBuNQX7/LVjWRaXLA/4VeDP4bFuBm4CiBMqgFPjvYJkm/Hsxv8TBzrzbyyg8bOxgfe2f/TRgcbB/3gVuAHKi0o4D/g/YHezvZcC8qDSVQZqaYD/8A7gPyAvmzwq2NysY3xgjv3eE8xZa92rg/hif4YxguYuTOTZirOeOWPsO3xP7buBbobSnBfOfiVrHFuDHofFewI/wx3dz8PebMfZtGfBz4J2gXN8ErknkGA2OpxuB9Rw8354Bzurk8y4J0l0EvBba7mUx0iZzrk0HngvS3dLB9ucG69kD1OGP5es7KotgXhFwc5DnOmAb8BfgxCTO6YTKRYOGWEPaM6BBQ6ID8QO3+/Cv5eoVjP8QaMEHI+cB/xxckF4AckPLuWD6Unyz6zxgEP5dcm8BO/FByTkEryoB+gTLTgLqgwvPpcD5wYWlCZgc2sYdwF78e14/BZwL/C7Y9uwgzTB8s5zDv6duGqHALJi3AP8+yvcH+WgGTg+lKQsuaquBy/DNPE8Bm8IXjCDtvUHerwfmAJ/HBwaHBSVRy+UE+6oe+Eqwb28J8v39IE158BlckO9pBMFwnHW2X+DWBxeu84Jyc8DCULrjgO3496peiW+CfRQfbL8vlG4tvvn8EmAm8GHgt0BBMH8WhwZuE/GB4qPt+x04Ppy30Lq/EZRv/6jP8F/BsdK+jYSOjRj74njgr/igsz0v7T8kHgSeDKX9Ij7IbQZ6B9PGBZ/tfcF4XlBeO/E10ucE+7gRuCm0rlJ80PIP/A+eOcBPgn37+c6O0WCddUGeZgZl8x3gwk6OpyX4oGcT/hydDzwEtBGcG0dwru0L1vf5oKzPiLPtMcHyd+PP+/fiz88fJVAWfYN9cUXweT8API4/hwYnsL8SKhcNGuINac+ABg2JDhwM3MYFX379gy/bCPBAkGZUMH591LLtwUS4VsTha46Ko9LeQKj2LU5ensAHYwWhabnBtAdC0+4gFKQF0wrxtRK3haYtDNLldbIPcoPPvoZQbQLwffxFfFhoWjFQzaHBx9nBdj4atd6PBNMndLDt9wdproqafntwESwLxvOICrw6WGf75/5G1PT/wV+E+wXj/4EPzk8IpckN9sNLwXhZsK64AQNRgVswbSPw23h5C40PD46LT4Wm5eMDyluTPTbi5O8OYEuM6V/C1yAVBuMP4GvI6oC5wbRP43+wlATjC4LPOiNqXd8MjpWKYPzb+KBhbIwy2MHB2sqYxyg+2PrTEZzPS4L1hX+k5OJr3ZZ24Vy7KIFtXxqkLU22LOKck72C4/VLMY7t6P2VULlo0BBv0E2Tko3exF+gduGbM+8GPh7MOxdfM3S3meW1D/jatr3AjKh1Peqca4iadh6w3Dn3cqyNm1kx/pf2fUBbaBsG/C3GNvY75xa3jzh/z81bwIhEPqyZzTGzxWa2Ex+8tOCbBMeFkk0Dnnf+vqz27TTgaw3C5uEvDvdH7Z9FwfzovIfNwNeG3BM1/bdAAV27kf8PUeP3AiXAqaFtL3MH723EORcJ8jLBzErxNRgbgB+a2SfNbGwX8nMY59xmfC3mgtDkefiA8U44omMjUYvxTXTvCe4hnIl/ofWz+Noigr/LnXN1obxtAp6LUdb5+GOmPd0LwNtR6R4DBgInd5K35cD5ZvY9MzvLzAqS+FybnXMH7jkMyvQ+YKqZ5RzB/mzFB5KdWYU/j+41s0vNrCKJPGNmlwVPTe8OtlmPP17HdbwkkHi5iMSkwE2y0QeAKcCJ+GaijzrndgXz2r+A1+G/mMNDKf5CFPZujPUPpOOb0gfgf2V/O8Y2rgX6R3UBUBtjHU34C3GHzGwS8DC+ZuUT+C/1Kfj798LLD8E360SrjhqvwAdZdVH5bl82ev+EDQB2ucNv9t4Wmn+kovPZPj40tO5YZbUNfxHv75xz+MB9BfADYK2ZbTCzz3QhX9HuBM40s9HB+AJgXSj4SPbYSNQr+MB0Nr55txQfRC4GZpuZ4WsTF4eWqQBGxsjHi8H8gaF0M2Kkuy8qXTzfxz89eSFBE6CZ/drMyhL4XNHl3j6tAN/snuz+rAmCvw4FPwDm4q+BdwHbgkBsZmfLmtkFwO/xNX4fxt/jOAVf89rpOU3i5SISk54qlWz0WrjmJcrO4O95xA6YdkaNuxhpdnAwYIhlN77m6WcENS3RnHNtHSyfjEvwv+g/6JxraZ9oZv2DfLR7l4NBa9igqPGd+Gaxs+Nsb2sHedkFDDCzAudcc2j64NC6j9QgfG1ZeBz8PYjt2x7M4Qbjy3AXgHNuA/DRIJAZj7+432pmG51zj3Qhf+3ux5f7lWZ2C/5+rh+E5h+TY8M558zsKXyt2j78k9W1ZvYk8F38rQDlHBq47cTf9H5ZnNVuDKWrwd+jFsuaTvLWgr/R/kdmNhjfpP5TfPPh5R1/ssOOz/ZpzfhAqJjk9mes8zlevhcDi82sEL//bgD+amajnHM7Olj0CnywflX7BDPLJ/EfLomWi0hMCtyku3kc/0U/wjn3+BGuYxHwLTMb75x7JXqmc67ezJbiA4OXjlKQ1l6LVYy/MLfrhb+v6sAFyczei29mfTuUbhnwVTMb1t5cGjQzzY/azqPA14G+zrknkszjU8DX8P3o3R2a/hH8hTahbjbiuAz/UEm7K/C1gq+Ftn1dcFHdCAe6b7kceNk5F95nBLVvq4IOVD+Bb3KNF7g14fd7p5xz+8zsz/iatq34Gpa7QvO7emx0lJfF+IAogn+yGGAlvpluIb4Mng2lfxQf+Nc5597sYJuP4m/m/4dzLlatbThvcPgxeoBzbhtwu5mdz8Fm7o4MN7Np7TWWQZl+CHgx2HfH4lyLznMT8KSZlQB/Bkbjf7zFK4te+B9TYQvwNYNh8fZXouUiEpMCN+lWnHPrzexHwH+b2Tj8Bb8Rf2P5ucDt4fvN4rgZ3wTyNzP7LvAq/j6mi/DdeOzDd8z5NPCYmf0KX+NVhn8CLtc5940ks/568PcrZvYIEHHOrcB/yV8H3GFmv8bf2/ZtDtZEtfsp8JkgP9/BXzS+HPw9EPQ555aY2T3AH83sp/jmmTb8Qx3nA193zsXrLPcR/JN9vzCzcvwTrOcDVwM/6KSWojOfDJq8luObsK7GP9zQXqt4M/7hlMfN7N/x9yt+Fr8/5gOY2en4p1x/j28qzw2WaeVgoBPL68DZZvZ+fNPrjvbgMI478U8ZfwffHcfbUfO7cmy8jq/V/Ay+ybfROfdqMO9J/D1QM/A1XDjnImb2NL6W6+mo+zXvxj+t+YSZ3YRvbi3APzF5If5Bnf34fXs5sNTMbsbXsPXG34pwtnPuolDeIOoYDQLZV/Bd1tTim3LnAb/s4HO2qwZ+H5TpdvwxXBn8bXe0zzXM7NP4/fgwvuucMnwXOVs5+GMhXlk8Clwc7KuHgMnAFzi0Brx9eTj8nE60XERiS/fTERo0JDoQpzuQOGkX4GuA6vE1N2/g+x8LP3XpgO/GWb4CuA1/kWjGf7n/hkP7cTsJfxN9DT5A2oLvpuD8UJo7iP2U4BJgSWg8F98cVIMPpFxo3ufxtWsN+MBmTvTyQbpJ+MCqER/YfRsfyNRGpcvBN4u193O3J/j/x/iauI72a3s/bu37ZS2hftyCNEfyVOmp+BqlBnzwdCOx+3F7IMhvI1H9uAVl9psgT/vxzadPETx1GaSZxeFPlZ7IwT72HHH6cYsqq3eDtNfE+VydHhtxluuNf+Cilhh94AX7poWgW5pg2pfi7W98jeBCDvbZtys4hhYSetoR/4T2zRzsV6wm2CfXdXaM4ruGWYZvAmzAB34LgfxOPusS/PF6IQf7cVsDXH4k+5MEnwIN0k7H166199P4Lv6evnGdlQX+/PkuPsjbHxxjE/FNnHckeE4nVC4aNMQa2jvNFJFuJmh2eglfg3ROuvMTi5ktxN/Ynu/0xooexcyW4IOUs9KdF5FsoqZSkW7CzG7ENxFuwj+ZdjVwOr45U0REugEFbiLdh8O/DeG44P+/4++XORpPU4qISAZQU6mIiIhIllAHvCIiIiJZQoGbiIiISJZQ4CYiIiKSJRS4iYiIiGQJBW4iIiIiWUKBm4iIiEiW+P8hrhyqaMm8WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "x = [] # estimated from tweets\n",
    "y = [] # real data\n",
    "\n",
    "for state in states:\n",
    "    if state not in mapStatePosDividedByAllHillaryEstimated:\n",
    "        continue\n",
    "    x.append(mapStatePosDividedByAllHillaryEstimated[state])\n",
    "    y.append(mapStateToRealPercentatges[state]['Hillary'])\n",
    "    \n",
    "\n",
    "colors = [1]*len(x)\n",
    "area = 200  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "\n",
    "\n",
    "p = np.polyfit(x, y, deg=1)\n",
    "# p[0] y-intercept\n",
    "# p[1] b\n",
    "print(p[0], p[1])\n",
    "x1 = x\n",
    "slopeList = []\n",
    "for xVal in x:\n",
    "    slopeList.append(p[0]*xVal)\n",
    "y2 = p[1] + slopeList\n",
    "plt.plot(x1, y2, '--')\n",
    "plt.title('Hillary Clinton', fontsize=20)\n",
    "plt.xlabel('Percentage of positive tweets per state', fontsize=16)\n",
    "plt.ylabel('Election results per state', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3743"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emptyTweetsAfterBeingCleaned = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    if tweet['cleaned_text'] == \"\":\n",
    "        emptyTweetsAfterBeingCleaned += 1\n",
    "        \n",
    "emptyTweetsAfterBeingCleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117475"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "\n",
    "for i, tweet in enumerate(tweets_data):\n",
    "    if tweet['talk'] == \"both\" or tweet['talk'] == \"neither\":\n",
    "        s += 1\n",
    "        \n",
    "s"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}