NLP_Project_NC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Project-3---Classifying-Reddit-Posts-by-Subreddit\" data-toc-modified-id=\"Project-3---Classifying-Reddit-Posts-by-Subreddit-1\">Project 3 - Classifying Reddit Posts by Subreddit</a></span><ul class=\"toc-item\"><li><span><a href=\"#Problem-Statement\" data-toc-modified-id=\"Problem-Statement-1.1\">Problem Statement</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1.2\">Imports</a></span></li><li><span><a href=\"#Accessing-&amp;-Scraping-Data\" data-toc-modified-id=\"Accessing-&amp;-Scraping-Data-1.3\">Accessing &amp; Scraping Data</a></span></li><li><span><a href=\"#Exploratory-Data-Analysis\" data-toc-modified-id=\"Exploratory-Data-Analysis-1.4\">Exploratory Data Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Tokenizing-&amp;-Lemmatizing-Data\" data-toc-modified-id=\"Tokenizing-&amp;-Lemmatizing-Data-1.4.1\">Tokenizing &amp; Lemmatizing Data</a></span></li><li><span><a href=\"#Using-CountVectorizer-to-Find-Frequently-Used-Words\" data-toc-modified-id=\"Using-CountVectorizer-to-Find-Frequently-Used-Words-1.4.2\">Using CountVectorizer to Find Frequently Used Words</a></span><ul class=\"toc-item\"><li><span><a href=\"#TheOnion-(no-stop-words)\" data-toc-modified-id=\"TheOnion-(no-stop-words)-1.4.2.1\">TheOnion (no stop words)</a></span></li><li><span><a href=\"#TheOnion-(bigrams,-no-stop-words)\" data-toc-modified-id=\"TheOnion-(bigrams,-no-stop-words)-1.4.2.2\">TheOnion (bigrams, no stop words)</a></span></li><li><span><a href=\"#TheOnion-(stop_words-=-'english')\" data-toc-modified-id=\"TheOnion-(stop_words-=-'english')-1.4.2.3\">TheOnion (stop_words = 'english')</a></span></li><li><span><a href=\"#TheOnion-(bigrams,-stop_words-=-'english')\" data-toc-modified-id=\"TheOnion-(bigrams,-stop_words-=-'english')-1.4.2.4\">TheOnion (bigrams, stop_words = 'english')</a></span></li><li><span><a href=\"#Conspiracy-(no-stop-words)\" data-toc-modified-id=\"Conspiracy-(no-stop-words)-1.4.2.5\">Conspiracy (no stop words)</a></span></li><li><span><a href=\"#Conspiracy-(bigram,-no-stop-words)\" data-toc-modified-id=\"Conspiracy-(bigram,-no-stop-words)-1.4.2.6\">Conspiracy (bigram, no stop words)</a></span></li><li><span><a href=\"#Conspiracy-(stop_words-=-'english')\" data-toc-modified-id=\"Conspiracy-(stop_words-=-'english')-1.4.2.7\">Conspiracy (stop_words = 'english')</a></span></li><li><span><a href=\"#Conspiracy-(bigrams,-stop_words-=-'english')\" data-toc-modified-id=\"Conspiracy-(bigrams,-stop_words-=-'english')-1.4.2.8\">Conspiracy (bigrams, stop_words = 'english')</a></span></li></ul></li><li><span><a href=\"#Create-list-of-stop-words-common-to-both-subreddits\" data-toc-modified-id=\"Create-list-of-stop-words-common-to-both-subreddits-1.4.3\">Create list of stop words common to both subreddits</a></span></li></ul></li><li><span><a href=\"#Combine-DataFrames\" data-toc-modified-id=\"Combine-DataFrames-1.5\">Combine DataFrames</a></span><ul class=\"toc-item\"><li><span><a href=\"#Export-Combined-Dataframe-to-CSV\" data-toc-modified-id=\"Export-Combined-Dataframe-to-CSV-1.5.1\">Export Combined Dataframe to CSV</a></span></li></ul></li></ul></li><li><span><a href=\"#Modeling\" data-toc-modified-id=\"Modeling-2\">Modeling</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-X-(features)-&amp;-y-(target)-datasets\" data-toc-modified-id=\"Create-X-(features)-&amp;-y-(target)-datasets-2.1\">Create X (features) &amp; y (target) datasets</a></span></li><li><span><a href=\"#Baseline-Accuracy\" data-toc-modified-id=\"Baseline-Accuracy-2.2\">Baseline Accuracy</a></span></li><li><span><a href=\"#Function-for-Evaluating-Vectorizers-&amp;-Classifiers\" data-toc-modified-id=\"Function-for-Evaluating-Vectorizers-&amp;-Classifiers-2.3\">Function for Evaluating Vectorizers &amp; Classifiers</a></span></li><li><span><a href=\"#Pipeline-&amp;-GridSearchCV\" data-toc-modified-id=\"Pipeline-&amp;-GridSearchCV-2.4\">Pipeline &amp; GridSearchCV</a></span><ul class=\"toc-item\"><li><span><a href=\"#Train-Test-Split\" data-toc-modified-id=\"Train-Test-Split-2.4.1\">Train Test Split</a></span></li><li><span><a href=\"#Logistic-Regression-Pipelines\" data-toc-modified-id=\"Logistic-Regression-Pipelines-2.4.2\">Logistic Regression Pipelines</a></span><ul class=\"toc-item\"><li><span><a href=\"#CountVectorizer\" data-toc-modified-id=\"CountVectorizer-2.4.2.1\">CountVectorizer</a></span></li><li><span><a href=\"#TF-IDF-Vectorizer\" data-toc-modified-id=\"TF-IDF-Vectorizer-2.4.2.2\">TF-IDF Vectorizer</a></span></li></ul></li><li><span><a href=\"#MultinomialNB\" data-toc-modified-id=\"MultinomialNB-2.4.3\">MultinomialNB</a></span><ul class=\"toc-item\"><li><span><a href=\"#CountVectorizer\" data-toc-modified-id=\"CountVectorizer-2.4.3.1\">CountVectorizer</a></span></li><li><span><a href=\"#TF-IDF-Vectorizer\" data-toc-modified-id=\"TF-IDF-Vectorizer-2.4.3.2\">TF-IDF Vectorizer</a></span></li></ul></li><li><span><a href=\"#Random-Forest\" data-toc-modified-id=\"Random-Forest-2.4.4\">Random Forest</a></span><ul class=\"toc-item\"><li><span><a href=\"#CountVectorizer\" data-toc-modified-id=\"CountVectorizer-2.4.4.1\">CountVectorizer</a></span></li><li><span><a href=\"#TF-IDF-Vectorizer\" data-toc-modified-id=\"TF-IDF-Vectorizer-2.4.4.2\">TF-IDF Vectorizer</a></span></li></ul></li></ul></li><li><span><a href=\"#Evaluation-&amp;-Interpretation\" data-toc-modified-id=\"Evaluation-&amp;-Interpretation-2.5\">Evaluation &amp; Interpretation</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 3 - Classifying Reddit Posts by Subreddit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import time\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer,HashingVectorizer\n",
    "import regex as re\n",
    "from sklearn.feature_extraction import text\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing & Scraping Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.reddit.com/r/conspiracy/ & https://www.reddit.com/r/TheOnion/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_onion = 'https://www.reddit.com/r/TheOnion/.json'\n",
    "url_conspiracy = 'https://www.reddit.com/r/conspiracy/.json'\n",
    "user_header = {'User-agent': 'JaneJacobs'} # header to prevent 429 error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subreddit(url, n_pulls, header):\n",
    "    # Create empty templates\n",
    "    posts = []\n",
    "    after = None\n",
    "    \n",
    "    # Create a loop that does a max of 25 requests per pull\n",
    "    for pull_num in range(n_pulls):\n",
    "        print(f'Pulling data attempted {pull_num+1} times')\n",
    "        \n",
    "        if (after == None):\n",
    "            new_url = url         # base case\n",
    "        else:\n",
    "            new_url = url+\"?after=\"+after   # subsequent iterations\n",
    "            \n",
    "        res = requests.get(new_url, headers = header)\n",
    "        \n",
    "        if (res.status_code == 200):\n",
    "            subreddit_json = res.json()                       # Pull json\n",
    "            posts.extend(subreddit_json['data']['children'])  # Get subreddit posts\n",
    "            after = subreddit_json['data']['after']           # after = ID of last post in iteration\n",
    "        else:\n",
    "            print(f'It would appear there has been an error. Status Code: {res.status_code}')\n",
    "            break\n",
    "        time.sleep(1)\n",
    "    \n",
    "    return(posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pulling data attempted 1 times\n",
      "Pulling data attempted 2 times\n",
      "Pulling data attempted 3 times\n",
      "Pulling data attempted 4 times\n",
      "Pulling data attempted 5 times\n",
      "Pulling data attempted 6 times\n",
      "Pulling data attempted 7 times\n",
      "Pulling data attempted 8 times\n",
      "Pulling data attempted 9 times\n",
      "Pulling data attempted 10 times\n",
      "Pulling data attempted 11 times\n",
      "Pulling data attempted 12 times\n",
      "Pulling data attempted 13 times\n",
      "Pulling data attempted 14 times\n",
      "Pulling data attempted 15 times\n",
      "Pulling data attempted 16 times\n",
      "Pulling data attempted 17 times\n",
      "Pulling data attempted 18 times\n",
      "Pulling data attempted 19 times\n",
      "Pulling data attempted 20 times\n",
      "Pulling data attempted 21 times\n",
      "Pulling data attempted 22 times\n",
      "Pulling data attempted 23 times\n",
      "Pulling data attempted 24 times\n",
      "Pulling data attempted 25 times\n",
      "Pulling data attempted 26 times\n",
      "Pulling data attempted 27 times\n",
      "Pulling data attempted 28 times\n",
      "Pulling data attempted 29 times\n",
      "Pulling data attempted 30 times\n",
      "Pulling data attempted 31 times\n",
      "Pulling data attempted 32 times\n",
      "Pulling data attempted 33 times\n",
      "Pulling data attempted 34 times\n",
      "Pulling data attempted 35 times\n",
      "Pulling data attempted 36 times\n",
      "Pulling data attempted 37 times\n",
      "Pulling data attempted 38 times\n",
      "Pulling data attempted 39 times\n",
      "Pulling data attempted 40 times\n",
      "Pulling data attempted 41 times\n"
     ]
    }
   ],
   "source": [
    "onion_posts = get_subreddit(url_onion, n_pulls = 41, header=user_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(onion_posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pull_posts(posts):\n",
    "    \n",
    "    post_titles = []\n",
    "    for i in range(len(posts)):\n",
    "        titles = {}\n",
    "        titles['title']     = posts[i]['data']['title']\n",
    "        titles['subreddit'] = posts[i]['data']['subreddit']\n",
    "        titles['id']        = posts[i]['data']['id']\n",
    "        post_titles.append(titles)\n",
    "\n",
    "    return(pd.DataFrame(post_titles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>balg7j</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>Treasury Department Issues Emergency Recall Of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>babanf</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>NicoDerm Introduces New Nicotine Eye Patch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ba4l1w</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>Tom Izzo Calls 2019 Spartans Best Team He’s Ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b9szn0</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>Paramount Executive Snaps Up Script That Begin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b9szmp</td>\n",
       "      <td>TheOnion</td>\n",
       "      <td>New ‘Game Of Thrones’ Trailer Provides Sneak P...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id subreddit                                              title\n",
       "0  balg7j  TheOnion  Treasury Department Issues Emergency Recall Of...\n",
       "1  babanf  TheOnion         NicoDerm Introduces New Nicotine Eye Patch\n",
       "2  ba4l1w  TheOnion  Tom Izzo Calls 2019 Spartans Best Team He’s Ev...\n",
       "3  b9szn0  TheOnion  Paramount Executive Snaps Up Script That Begin...\n",
       "4  b9szmp  TheOnion  New ‘Game Of Thrones’ Trailer Provides Sneak P..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_onion = pull_posts(onion_posts)\n",
    "df_onion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pulling data attempted 1 times\n",
      "Pulling data attempted 2 times\n",
      "Pulling data attempted 3 times\n",
      "Pulling data attempted 4 times\n",
      "Pulling data attempted 5 times\n",
      "Pulling data attempted 6 times\n",
      "Pulling data attempted 7 times\n",
      "Pulling data attempted 8 times\n",
      "Pulling data attempted 9 times\n",
      "Pulling data attempted 10 times\n",
      "Pulling data attempted 11 times\n",
      "Pulling data attempted 12 times\n",
      "Pulling data attempted 13 times\n",
      "Pulling data attempted 14 times\n",
      "Pulling data attempted 15 times\n",
      "Pulling data attempted 16 times\n",
      "Pulling data attempted 17 times\n",
      "Pulling data attempted 18 times\n",
      "Pulling data attempted 19 times\n",
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      "Pulling data attempted 21 times\n",
      "Pulling data attempted 22 times\n",
      "Pulling data attempted 23 times\n",
      "Pulling data attempted 24 times\n",
      "Pulling data attempted 25 times\n",
      "Pulling data attempted 26 times\n",
      "Pulling data attempted 27 times\n",
      "Pulling data attempted 28 times\n",
      "Pulling data attempted 29 times\n",
      "Pulling data attempted 30 times\n",
      "Pulling data attempted 31 times\n",
      "Pulling data attempted 32 times\n",
      "Pulling data attempted 33 times\n",
      "Pulling data attempted 34 times\n",
      "Pulling data attempted 35 times\n",
      "Pulling data attempted 36 times\n",
      "Pulling data attempted 37 times\n",
      "Pulling data attempted 38 times\n",
      "Pulling data attempted 39 times\n",
      "Pulling data attempted 40 times\n",
      "Pulling data attempted 41 times\n",
      "Pulling data attempted 42 times\n"
     ]
    }
   ],
   "source": [
    "conspiracy_posts = get_subreddit(url_conspiracy, n_pulls = 42, header=user_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b6bp4k</td>\n",
       "      <td>conspiracy</td>\n",
       "      <td>Announcement: /r/conspiracy Is Accepting Moder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>baf93n</td>\n",
       "      <td>conspiracy</td>\n",
       "      <td>What does the following quote from 1984 mean?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bahjhq</td>\n",
       "      <td>conspiracy</td>\n",
       "      <td>Another scientist just got \"Wakefielded\". Prof...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baifvm</td>\n",
       "      <td>conspiracy</td>\n",
       "      <td>1989 \"White House Call Boy Scandal\" Reagan/Bus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bacr2l</td>\n",
       "      <td>conspiracy</td>\n",
       "      <td>Philanthropy cannot be used as an excuse to gl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id   subreddit                                              title\n",
       "0  b6bp4k  conspiracy  Announcement: /r/conspiracy Is Accepting Moder...\n",
       "1  baf93n  conspiracy      What does the following quote from 1984 mean?\n",
       "2  bahjhq  conspiracy  Another scientist just got \"Wakefielded\". Prof...\n",
       "3  baifvm  conspiracy  1989 \"White House Call Boy Scandal\" Reagan/Bus...\n",
       "4  bacr2l  conspiracy  Philanthropy cannot be used as an excuse to gl..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conspiracy = pull_posts(conspiracy_posts)\n",
    "df_conspiracy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_onion.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_onion.drop_duplicates(subset = 'id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(974, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_onion.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1044, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conspiracy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_conspiracy.drop_duplicates(subset = 'id',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(893, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conspiracy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenizing & Lemmatizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def tokenize_lemmatize(text_col):\n",
    "    # Instantiate tokenizer\n",
    "    tokenizer = RegexpTokenizer(r'[A-z]+')\n",
    "    \n",
    "    # Tokenize text\n",
    "    tokens = [tokenizer.tokenize(post.lower()) for post in text_col]\n",
    "    \n",
    "    # Instantiate lemmatizer\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    tokens_lem = []\n",
    "    # lemmatize words\n",
    "    for words in tokens:\n",
    "        lem_list = [lemmatizer.lemmatize(i) for i in words]\n",
    "        lem_post = ''\n",
    "        for word in lem_list:\n",
    "            lem_post += (word+' ')\n",
    "        tokens_lem.append(lem_post.strip())\n",
    "    \n",
    "    return (tokens_lem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma_token_list = tokenize_lemmatize(df_onion['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tom izzo call spartan best team he s ever threatened with violence'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemma_token_list[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvec = CountVectorizer()\n",
    "lemma_features = cvec.fit_transform(lemma_token_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12381"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemma_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'treasury', 'treat', 'tree', 'trend', 'trick', 'tried', 'trigger', 'trojan', 'trolley', 'troop', 'trophy', 'troublehacking', 'troubling', 'trout', 'truck', 'trudeau', 'true', 'truly', 'trump', 'trunk', 'trust', 'try', 'trying', 'tsa', 'tucker', 'tuesday', 'tumbleweed', 'tumor', 'tuna', 'tunnel', 'turbulent', 'turn', 'turner', 'turning', 'tv', 'twain', 'tweet', 'tweeted', 'tweeting', 'twitter', 'two', 'tylenol', 'type', 'typist', 'ugly', 'ulta', 'ultrasound', 'um', 'un', 'unable', 'unaware', 'unbridled', 'unconstitutional', 'uncover', 'uncovered', 'uncovers', 'under', 'undergrad', 'underneath', 'underrated', 'understand', 'underwear', 'unemployed', 'unexplored', 'unfair', 'unfortunate', 'unhappily', 'uniform', 'uninspired', 'uninvited', 'union', 'unisex', 'united', 'universe', 'university', 'unknowable', 'unless', 'unlicensed', 'unmasked', 'unnecessary', 'unpaid', 'unplayed', 'unplugged', 'unprepared', 'unrealistic', 'unsafe', 'unsettling', 'unsolved', 'unsure', 'unsuspecting', 'until', 'unusual', 'unveiled', 'unveils', 'unwanted', 'up', 'upbeat', 'update', 'upon', 'upset', 'upside', 'urge', 'urged', 'urinal', 'usa', 'usc', 'usda', 'use', 'used', 'useless', 'user', 'using', 'vacation', 'vaccinate', 'vague', 'valentine', 'validated', 'valley', 'value', 'van', 'vanilla', 'vaping', 'variant', 'variety', 'vatican', 'vaulter', 've', 'vegan', 'vehicle', 'venezuela', 'verdict', 'verge', 'very', 'viable', 'viciously', 'victoria', 'victory', 'video', 'vietnam', 'view', 'viewer', 'viewership', 'vigilante', 'village', 'villain', 'vinyl', 'violence', 'violent', 'virgin', 'virginia', 'virginity', 'visit', 'visited', 'visiting', 'visitor', 'visualizing', 'vital', 'vivisection', 'vj', 'vladimir', 'vodka', 'voice', 'volatile', 'volunteer', 'vomit', 'vortex', 'vote', 'voted', 'voter', 'vow', 'vp', 'wa', 'wait', 'waiting', 'wake', 'waking', 'walgreens', 'walk', 'wall', 'wallace', 'wand', 'want', 'war', 'warehouse', 'warm', 'warming', 'warmth', 'warn', 'warren', 'wartime', 'wary', 'washing', 'washington', 'wasn', 'waste', 'wasteland', 'wasting', 'watch', 'watched', 'watcher', 'watching', 'water', 'waving', 'way', 'waylon', 'we', 'weak', 'wealthy', 'wear', 'wearable', 'wearing', 'weather', 'web', 'website', 'weed', 'week', 'weekend', 'weekly', 'weepy', 'weighing', 'weight', 'weinstein', 'weird', 'weirded', 'weirdo', 'welcome', 'wendy', 'went', 'were', 'weren', 'werewolf', 'west', 'whale', 'wharton', 'what', 'whatever', 'wheel', 'wheelchair', 'wheeled', 'when', 'whenever', 'where', 'whether', 'which', 'while', 'whipped', 'white', 'whiteboard', 'who', 'whoa', 'whole', 'whose', 'why', 'wide', 'widow', 'wife', 'wikipedia', 'will', 'willed', 'william', 'williamson', 'willing', 'win', 'winded', 'window', 'windshield', 'wine', 'winfrey', 'winnie', 'wiper', 'wire', 'wireless', 'wisconsin', 'wish', 'with', 'witherspoon', 'within', 'without', 'witness', 'witnessed', 'wizard', 'woefully', 'woman', 'won', 'wondering', 'woodcut', 'woos', 'word', 'work', 'worker', 'working', 'worksheet', 'world', 'worldwide', 'worm', 'worried', 'worse', 'worst', 'worth', 'would', 'wouldn', 'wow', 'wrapped', 'wreath', 'wrestler', 'writer', 'writing', 'written', 'wrong', 'wrote', 'wwii', 'yankee', 'yard', 'yeah', 'year', 'yearly', 'yeezy', 'yell', 'yellow', 'yes', 'yet', 'yoda', 'yoga', 'yogurt', 'york', 'yosemite', 'you', 'young', 'your', 'yourself', 'yousafzai', 'youtube', 'zara', 'zendaya', 'zero', 'zion', 'zoo', 'zookeeper', 'zuckerberg']\n"
     ]
    }
   ],
   "source": [
    "vocab = cvec.get_feature_names()\n",
    "print(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using CountVectorizer to Find Frequently Used Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def term_counts(df, stop_list = None,n_gram = (1,1)):\n",
    "    \n",
    "    # Count Vectorize\n",
    "    cvec = CountVectorizer(stop_words = stop_list,ngram_range= n_gram)\n",
    "    title_cvec = pd.DataFrame(cvec.fit_transform(df['title']).toarray(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "\n",
    "    # See word counts\n",
    "    counts = title_cvec.sum().sort_values(0, ascending=False)\n",
    "    \n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Words')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onion_bigram_counts = pd.DataFrame(term_counts(df = df_onion,n_gram=(2,2),stop_list = 'english'), columns=[\"count\"])\n",
    "\n",
    "onion_top_bigrams = onion_bigram_counts.head(15).sort_values(by = 'count',ascending = True)\n",
    "onion_top_bigrams.plot(kind = 'barh',figsize=(20,12),color = '#006b3a',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Bigrams in Posts on r/TheOnion',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Words', size = 25)\n",
    "#plt.figtext(.4,.4,f'Number of Unique Bigrams: {len(onion_bigram_counts)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TheOnion (no stop words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Words: 4503\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "to        370\n",
       "of        301\n",
       "the       225\n",
       "in        162\n",
       "for       139\n",
       "on        107\n",
       "this       98\n",
       "and        96\n",
       "he         96\n",
       "it         84\n",
       "that       83\n",
       "new        80\n",
       "with       73\n",
       "by         68\n",
       "has        68\n",
       "at         64\n",
       "man        60\n",
       "what       58\n",
       "his        58\n",
       "they       55\n",
       "just       55\n",
       "is         53\n",
       "be         52\n",
       "after      51\n",
       "you        50\n",
       "as         46\n",
       "said       45\n",
       "trump      45\n",
       "from       44\n",
       "who        42\n",
       "out        41\n",
       "about      40\n",
       "up         40\n",
       "will       38\n",
       "all        36\n",
       "into       35\n",
       "not        34\n",
       "report     33\n",
       "have       33\n",
       "was        31\n",
       "your       31\n",
       "like       31\n",
       "year       30\n",
       "her        27\n",
       "him        27\n",
       "if         26\n",
       "now        26\n",
       "an         26\n",
       "can        26\n",
       "old        25\n",
       "off        25\n",
       "when       25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer()\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "onion_cvec = pd.DataFrame(cvec.fit_transform(df_onion['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Words: {onion_cvec.shape[1]}')\n",
    "onion_cvec.sum()[onion_cvec.sum() >= 25].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TheOnion (bigrams, no stop words) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Bigrams: 10006\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "said what     44\n",
       "of the        22\n",
       "in the        15\n",
       "year old      14\n",
       "to be         14\n",
       "to get        11\n",
       "will be       10\n",
       "super bowl    10\n",
       "out of        10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(ngram_range=(2,2))\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "onion_bigram_cvec = pd.DataFrame(cvec.fit_transform(df_onion['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Bigrams: {onion_bigram_cvec.shape[1]}')\n",
    "onion_bigram_cvec.sum()[onion_bigram_cvec.sum()>=10].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22451465198>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onion_bigram_cvec_top = onion_bigram_cvec.sum().sort_values(ascending = True).tail(25)\n",
    "onion_bigram_cvec_top.plot(kind = 'barh',figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TheOnion (stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Words: 4289\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "new       80\n",
       "man       60\n",
       "just      55\n",
       "trump     45\n",
       "said      45\n",
       "report    33\n",
       "like      31\n",
       "year      30\n",
       "old       25\n",
       "time      24\n",
       "day       23\n",
       "woman     22\n",
       "people    20\n",
       "know      20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(stop_words='english')\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "onion_cvec_stop = pd.DataFrame(cvec.fit_transform(df_onion['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Words: {onion_cvec_stop.shape[1]}')\n",
    "onion_cvec_stop.sum()[onion_cvec_stop.sum() >= 20].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W2NYpHaLMq8YtAv69T16PBHbqMrxea+5y4DF98ng+HXfQmbwabUmpAdO31gBjq9E20W1xz1o+twCbdUmzCUvWmphIjbb6PL/fZfy+tfHrV8O2rQ17Wpdpfl0b/69dxq9HuXmQlOYKDumSZiVKsKyVz3k9yl+vWZWUC7kV+/zeLWjvP+cBu3RJ87hqPdbz7VajbU417rbO/05HunWAp05gHY2lRlsClwLrdkn3ulqaX463PF2We88abZQg0MJa2m07xl9SG3d8t3VHudBvpfk7sHbH+OfVxn8biD7l2Rp4RJfhrZptdw/x21v/v1uAjQes9y3GuXxXZvHzkI92/i5KUObztTTX0KUmD4vX+EpKkH7lLuk+Wktz0gS2jbU75rcA2GPIaev707619nv8vvHUaGutyyX+o5Sakq1j4DxgrS5p1qNdM+wuuhwHq3RbUPb9SQnQdh7rjq2VZ2CN+B7zuLOWxzMn8TcPrNHG4udxPwNW75Kmfj47H3hilzR7d5T7GuBxXdLtRjmeJOXY/rDxbsO+fPV7+WyypIdk5nzK45sAr4lamzfVneDWHflTqrTDeF/t8xsy8/dd5vsXyqNBWQ06PJZsn2iT6v387HP3LjPvycyrhyxb026I7m22tF6zhsjjOzn4Tt2RlNoG84A9M/OGboky81TK3XeAnSNim44k+1BqhEAJeC1RcybbbXrcPETZGxGld6xW7cWfZuZh2aWB3yx3YA+gnChB6UFzkFd3W16ZeSEloAilBuFUNMT935QTR2j/3m4OrH3+f+Oc12Nrn3u2g9VLlsbKb6wNekxHkv+gBOoBPpaZX++T1x2Z+Yv6sIjYhFJDEcoFxosy8//65HFWZnZth2qSvKnffmecJrItvrP2+eAsHVd05vMn+m9XY3Uh7VqbO0Us0U7bztX7tZl5W/X5ato1k3auJ46IdWnXGurVPttBlCAEwAmZ+V+dCap9wxspvWAD7BID2uij1GA7ODMX9UlzGO2avh/KzFld5v03Su2T7BzXUh07H199PSv71LbLzLsy8ze9xjdsPqVDlCV6Ec7Mb1HWHcDTq3U1aaoazP9Nu0bW9dR6to2InSg1vwB+BxzUbd1l5pcpATQoQY3O7X+T2udvZGbP9ZaZV2fmEjWphhURq9DeT/48S2P0veZ1V2b+dpyzehXt7evMzPxI5+/KUpP0cMp/GMqNkZfR39+A12X3mtafpARqoPQU25RPZebPBicD2r2N3sTibftNtgO7/Ucz83eUADCUY+FOXaZ9O+1am6/vPA7W8vot8O/V10dSzsHqJnQ8r9SPZ53H804T+c19RcQTKDdpoAQhX52Z/+wyr1OBz1RfV2HxY2A3Cbyq2kd35nUOpQYolGX5lLGWWxqGgTZJnWZW74+ktDvTshflxLWepq+ImEG7t7nf9DuBysxf0n6c8/GUmhB1rUc7N60uXJZVX+k3Mkoj68+vvn4vB3fN/j+1zy/oGFc/0f5Crwyqi40lLnAn0atpX+B+vl/C6iLge9XXp0aXRuhrLqm2s17qjxMP7KBgojLzVspjSwDPjYh/6UxTBbtbJ9k3sniHDGNRv1C+u2eq/urTPbL1oSrjK6qvdwGfG0fer6Z9TnJUZo73kczJcF1mntlwnuPeFqN0JNF6RH5OZp7eK5PMPIPxX4h15jWXdvBlvc5y0Q6knV+b5gFKMwH18S070X4cdFaP2e5T+/yZHmlaQfejaoP26ZW2clyPAELd3tX7PPrs/zLzUtoBjG4W0e5MZLMuN5FG5Xv9gtmUR2RbNu+ZamyeXjVY3nrtFxFHUoJnrUbZE3h3Lv6YcX19fnFAgPTTtc+d20G9iYjJvri+n7Luoaz3ybrmGvY/kh3jB/1HvtEt4FHldR+lqQqAdaNPT/Vj0HqMflh7Ve8/6hcwbdgN2fE4codB5xCvq96vG5APlFrorUBv57nbpB3Pu5jobx7kpbSP/cdmZr/fcxTtnokHbb+zMvNXfcZP6fmelk+20SZpMZl5UUT8kXYbWK32fQ6o3q/LzIu7TdvF9rXPw1yonkmp0g2lraL6hehZlLtemwNnR8TngbPHULNuKhxMuSPXy6B2sRZSHpnrZ4fa5/sjYu+eKYvVap87L5ae3sqH0u5GP+dQHheZCjvWPm84xG9cs/Z5c8pjDt1cOiCf+kXnOj1TNetYStuEQal98YGO8S+jHeA+vgpcjEqvNtG2pV2b7axx/ifr2/WU9R43pEH/jfGYyLa4De0Lk1lDzOt8Sg2WJpxP+ybIzpQASauNndbFSmctjV9QHvPaKarGdWrTt8zqnFHVk12rTdC/DFGjsH6MecaAtH3XaRX0bl18Xtor4FBzDovvtx6SmRkR51ACSdsAP4uIo4Bzhwj2TaZR7A/fO2D8P4G3ZWZn74RDn0tk5m8j4mZgQ2CbiFixFpg7jxL8WpHSvthGwLcy8w9D/4IhZeYDEXEuJUiyPfDTqgfHc7N3e1jj0Vo2C1jyv9fpHEpAawUG/0fGun3cOiD9INfkkG2HVrXzW7W6pvJ4Me7/TEQ8ltK2H8CtQ5zXQKkN/AiaC3QvVqQh0032fmIs/+07IuIKSluaG0bE47rVWKtMx/M9LWcMtEnq5pvAJ4AXRcT61bA9auOGtWHt8zCPXdXTbNgx7n2Ui/GNKDUhdgIWRsRsyp3VcyknsEs8YjiFzuz3WNAQbh1wpx7aJ2pQGjt/0xjy7zyZaHUf/7chLvj+NIb5TNSM2udv90rUQ78TpmECnS2rjHG+43Uu0ApsHxARH+7YBlqPPj1I+xGN8biT9nJde5x5rFX7fEftc/0xlvFesLbySBqqgdWgfrV+xmsi2+JGtc/XDzGvYdIMaxbtR7R3oV0DZWfaF26dF/utoNa6lM4sWp0o7NKRb6f1aJ+nDjx+ZOb1EdEKonQePzoNWqf1ZTzMvm9QmndRbmw8ilIj+fnAgoi4nFIb7hxKDYypDKRPh/3hfZSaNb+nLIPjM/OmLula63N+Zt7YZXyn66ppVqZsd7dBedQ3Iv6DUrNrFUqD8O+vAnMXUbbVn1aPXTfhnZT/wyMpj1i+EJjfsd7PH+96rx7fbtUmu2HQ+UNmzo+IGylPDQz6j0z19jGW/WzrsdF/UJbhVJnIMplR+7wjPQLzPXSe19Qf9276eN5psreD8VwntDqt2ZB20yGdpsP+Tcs5Hx2V1M2JlAv7FSntz7y2+vxgNW5Yj6h9HlQjAMpJU7dpqQJYT6P0HtSqWr4y8BzKXfIzgBsj4rAubQctLYapCbTW4CQ91dvcW4Gy/GC4HleHWX9NaeQ3dtHZ4+HIVbV7jqu+bkhpcB54qO2SVg3PM4d4TLif+sXpoB4SlxARq7F4QK1+UVSvUTjedtNaeczLJXumHLXJqDU7kd+4eu3zVP93f0G77PUaaa3PN3SpYfBLSm2bh9JFxNrAltWwXu2zjfX4UU/3iL6pBq/TNWqfJ7yMq9+3NaUGa+txsFUoF9vvp/Sk+reIOGSIeTVlFP+zV2Zm1F4rZ+YGmblrZn6iR5AN2utz2O2g37nEZyk3Di+g3bbehpTH378E/DEizomILZmgLG3Sbk1pW7NVplZbVh+gBIn+GhEHjXMWq9O+lhvrsll1wGPMU719jGU/2+pt+cwprhU6kWXS5HnNhI7nlU1rn/sFOSd7O2jsOqHDdDuP0HLIQJukJVQXSq32Cw6g3Uj5OUPeTW6pNyS8es9UbfWLmyUaIc7M2zLzbZQGwp8NvIfSxlWrse31KW04HDuGMi5t6icZ+3VctAx67d6asLqD3jpBXY3Bhll/wxp07Gn9xvmU3qDG8htPbrCcU+UE2uui3vHBm2nXEhpvJwgt9ce9n9UzVW9PpzxuBHBTR83Ne2qf6//hsWjlsdoktmVUtzSf/9QvRqb0v1u109Zq92b9iNis+rxE+2y1ae4DLutItyODH38d6/Gjnm7cjdhX6vvZRpZxZt6UmYdQaurtSKml/aPavDYEvh4RXxxjWZcHrfU57HYw6FzijMzcidLT6SuALwJX0g68PRe4NCK2G19xF5vXjZl5EFVvy8ARwI9p/483Ao6NiM+OI/t/0g4ojHXZzB9xUwTjEhGPo91BzHRrZqCf+j7l6DGe13TWWpvQ8Twi/pV2O2/zKdv+qDR6nSBNJ0vziaakyTWzet+Sds2DmV1T9lbvqXLTnqm6p+l1Z5vMvD8zL8nML2TmyyiP47yRWrAiIp46xrIuLep3HifamHNrGT+u3sNsD5sMGH8f7YuUQXmtN2B86zeuSrtX1GVWZt5Bu0OH3SPiMVVNgwOqYbcy8QuKs2qfXxQR/Ro/7qbe69lZHePqwffxtiXTyiMYf3ti9ZoNE90Gp7P6vnGJDjS6GCbNWMyqfd656pGytb/t1UZUa3irt9K+7bNVbqfdmPzA40dVA7T1qGnP48eQ6tMP2vcNmwYogcfMvDAzP5uZL6Ecvw6l/Vvf0a1jlOVc61xi1YgY1EMitLeXhSz+mN1iqpt338/Md2fmtpT/SmtfuyqLd6wwIdV6vyAzP5OZe1HW+1tp9+T77oh4fO8cuuaZtNtGe0JE9G0SqOoJtVUzeaL/kVFpPTb6IPCTURZkjJo8dzuHdoD1mVWv3WNRP57/ouE2A8dqUq4TpOnAQJukXk5l8Zoq9wA/GGMe9c4Mnt8zVVu9Z6V+PfItpjqBPYHFe+x8TkeyejXypfXRUlj8onSYxnT7aS3jlRjcXshu/UZWj/u17i5u1C8tgxthrteKeVnPVFNvMrehVi3MFShB4z1pL8cThmi7r6/MvJJ2L3ErUx5XG0pEbAq8pjboyx1JrqD92M/zI2JVxq7eOP1Leqbqr95b2US3wensStrb4i5DpO/s7XOiZtU+78LivYcOCrQ9itJpwi498ntI9UhYq/bcjCEuJsd1/Ogx7+tpt1v0jIgYVNOi7/5xwLwWZObXgW9Ugx5GqbGttqHPJSLiKbT//1eOZd9Z1dR9Fe3aRzt0Sdb6703oGJCZ8zPzq5QazVDW+3hqG7eWzSp0L2/dbrRrJk/oPzJCrePDxcN2njAdVO3+tYJtO43jZlc9rxtpn48HcOSw01Y9xNYfUe88nk+1sfy316XdGc/NY3zCRppyBtokdZWl58CjKY/8XAYclWPsTbA6aW1VSd8qIjq7KH9I9YjGc6uvf6FcvI/VnNrnzju79Wr7TT4GOaWqE4vzqq9PjYjX9Us/QD1w+q5eiSJiDWCYNmR+X70/qVetg6o2y9sG5HMS7dod74mIRw0x76kwadtQZl5A1YMjpYOLg1ujgP9uaDYfpl3r8LCI2KNfYnho3f8P7caCf1AF7R5S3Q3/bvV1HeDwcZTtO7RrdrxznOv897XPz+2VKCJ2o91D5lInM++lvQ+YEREv7ZU2InanuR5HWy5g8XbaWoG8/8vMP/eY5hLa/+m9aD/61at9tpbv1z733K6qGrnv7DHdeLX2j6vTZ/8XEdszOMAxjDm1z3ZWtrj6+nzngLbF3tdjuqFk5gLaPVev0KXN13obZ01cR82pfR7Peh/qP1Kp9/raxH9kSkXEI2gH6Zemx0ZbWm0crwx8bIJ5fZzyJAHAv0XE6/slhof2kzNpPzZ6cWb+dILlmKjTaB9PDo6INfukPYx228JL3far5Y+BNkk9ZeZHMvOZ1evIcWbzmdrnmbU2fR4SERsDJ9PeJ32u3nZIRGwYEZ+vHg3qqmqsff/aoF91JLmh9nmbYQs/TX2A9kXrsRHxqn6JI+IJEXFU1QB53am0eyR8UUS8r2M8EbEy5cRsmMd1zmhNxuLrvZVXUB7F6Vt7rrpYb9XwejTws4iY0St9RDwsIl7QrfwNm+xtqPWbHw+8qPp8XjbUA15mnkt7vawA/CAiDul1sVi143I2sH016G8s3oZc3ado12r7cET8e69yRMS6EbHYNlCt81aPxhsAP+33iFhE7FYFAesupH0R/Lpuj49Xv2lmr3yXIkfVPv9X9bsWU9UA+0bn8InKzLto9xy6Ie3ajr1qs5GZ/6R90+WdtGvVzBowu+OAu6rPB0bEmzsTRMRKlDYMW8tgVmZe1pluHL5Eez/7iYjYqcu8H0vpGbln7aaI+JeI+FTVtlSvNGtSOh5q6Tx+LdeqGxGHDU+6AAAgAElEQVSXVF+3pLRlt0RQKiLeArRuPt1Ox02KiHhzRLymOq51FRHPp/0o8K+rxzPrWseBlYAt+uSzaUR8stpGeqVZi8VrC49nvZ9CO1j3ooj4UJf5RER8mlL7FOAaSoBjabM77WYBlsZA2xeoesAFDo2Ij/Z73Dci1omIwyNiiZqOmfkrFg+cnhARR1T7w255PY7SNmCrrd67WHzbG4nqhvwp1ddHAydV5/OLqW4oHVF9XUCpCCBNa94xkzSpMvO7EbE38GrKRdmVETGTctL8ALAdpQZP6y7WmcDXOrJZGXg3pQ2Tyyk1Kv5AeVRsLcoF1mtoB4MuoFx01/2GcoKzPvDaiPg7cCnt4MD8zFyiIe/pKDMvjYh3AF+ltCNzckQcTjnx/DPlLuc6lPaydqAsY4BPduRzf3XheiblePDpqpbTdykXKU8A3kBZvj9g8GOcx1Fqxq1JucP6WEpNpTspXdu/mtJz7MnAfgPyehflgmpHyqMC10bEaZR1eyvlZHsDSs2Y51NO0H5ClwBfUzLz5oj4A2W57h4Rx1BqFrUatX4gMzvbLxuLb1ECkfWTzON6pB2v/6A0JvxWyv/q68C7IuJU4FpKD4sbAruy+EXNDcAeVXtyS8jM6yLiYEqwbAXga9W21QrmJqVtoOdU+X6TxR8XBXg7ZV1vRdlmr4uIUyj7irso29VTKI/V/mtVzodqGWbmPyPi65RaHasAF1bfr6q+P5vSNs0iyrbyUA+vS5vM/ElEtP5Hj6bsV0+g7NOgdF7xJsq29H3g5dXwpnpim0W7Vtr61fug/ecvKEHb9WvDZvWbIDPviNIj43cpwaz/FxGvpAQJ7qDsV/anXUPxTso+a8Iy87cR8SlKTdDVgHMj4tuU//xCyr7sQGBt+u8fV6M8qn1ERFxGOTZdA8ytpt2ccvx6dJX+jMy8qonfsIzZH5hN2Q8cCDwrIr5FCTKtB+xD+xHeB4E3ZubdHXlsAbyDEqg7s8rvRso+YQPKfu/FtfSf6lKOc2i3cXVSRHyFchOi9d/6Q5Yeolen3BR7f0RcSlnv11LW+zqUbfY1tP8PP87M3wy7MFoy876qNtPZlP31x6rj+CmU9q82qubz9GqSBcBrl8aOEGg/NnrdgJqw01K1P9uHcs61GmXfsn9E/C/wW8rxbE3giZTmDXamBHT36pHfl6obqEdSblT/J/DvEfF9Sg35eynb106U7brVrMNtwItzYj2ZN+ntlHO9x1KOy7+rjmfXUnoW3YPy/255T5/a09L0kZm+fPlaTl+Ui98ErplAHte08umTZkVKjYMc8PpfYNUu0z9+iGlbr3OBR/Yox0F9ppszzt8/p5bHjHHmcct41gPlxOPvQy6X24A1e+TzKkqApde051DaVmp9P6NPmfamXIT2yutUSnB0mLxWBv6LEpAd5jce2yWP3WvjjxiwPAemrX5fr/Is6Eh7SG3cfkOu0+Nr09wOPHyi//Me83kDpSHhQcv0Qcqjo+uPYZu8Y4h8v9Zj+rWA04dc30uUiXLxcl6fae6itOX16dqwZ05ku6lNc3JtmkdPwba48oBldT8loPrW2rA9Gtp+9u4yv80HTLNXl2n+dcj57Ue5aOy3PfwJeEqfPL5XS7vekPMNSs22fvP9T8pFbOv7ezryePKQ23MCPwPWmsB6mV3l848e4+vbwisG5DV02gH5fK+JfKq8tqYE/fstw7nAPj2m/+KQ62E+cGif/92VfaY9rEq35RjW+4+BR0xw2TyPwecDtwA79Mnj6FrarQfMb+i0ffJYu5bHaQPSrkD72PK5cc6vXubDxrDNtaY5uom0lJtJvx9y25gHPHvAfPeh3GQdJr/TGXCu2uRvHnYdU87zrx5Q9gXAW/rkUT8u9V2/Y0nry9d4Xz46KmnSZeaizDyQ0tDvf1MuiP5JOZm9gXIhv1tmvjK7tAOX5a7bxpTgwImU2mlzKQGPecAfKTWn9srM52bvWjfHUS5gT6PcxV7YLd3SIjNPpdToeAvlRP1vlGV6HyWwdjHlInFP4DGZeU+PfE6h3On/L8r6WEg5Yb+AUnPgBQzZjXpmnkZ5rPJEyjJuleVs4NWZuQ9DLvfMXJiZh1AuVD8LXF6VaxFl+7meUjPpCMoF9sG98mpK9ft2ov24zoKGZ3F27fM3c5J6A8vSecgmlJ5Nv0v5D82lrK+bKLXIPgZsmZmvzczbemTVme+plJ77DqcEvG6lBHzmU/7336XUTHlPj+nnZuZLKXfyvwFcR7nLv4gSeLyYEiTbsluZMnMeZXt9G6VtyXuqeV9Hedxyq8w8c5jfMt1V/4+XUgLlZ1KWz0JKG5cnAs/KzGOAeqPbPXtgHKNfUC5QWv6emX8YMM2FLF6jblD7bA/JzJMptTw+Tgkm3UnZrm6l9IJ7KPDkzPxdz0zGIYt3UGpK/YASqLiPsm/7PuW41bdjkcz8PaV28Jsp7U/+jrJdPkDZj11Lqc26e2bukZlzm/wNy5LMvJpSA/CtlH1la/9yJ6VR9Y8CT6z2Q928l1LL+8OU7eavlH34oiqPS4BPAJtlZmfN+lYZFlKOAR+u5nk3XWqKZuavKdvsgZTzk9+z5Ho/EXhhZr44S9uL45aZZ1fzez+l45vbKcumdSw/HNgkMztr+y8tdqDdttjS+NjoQ7I89rkF8ErKo+d/ppxjLaLcDLqK0knGayk3bS4ekN+plP/FfpR9ybVVPvdT9lmXU2r7b5+ZL83yuOa0Up3nb0upLfpjynnIfZTzkl8Bn6PcmPnqyAopjVFk5uBUkqTlWkSsQvsx259n5u790mt8IuJ7tB/ze/IQwQupr4j4CaXNvwcptaX+MWASSZpWIuILlCYlbqcEn5bGR18lLUes0SZJ0jQQERvRboPmFwbZNFERsSnwwurr5QbZJC2lWu2U/dQgm6SlgYE2SZKmhw9SGj4G+PIoC6LpLyKeFBEb9hm/MaVNxFYPn8f2SitJ01VEbA5sWn1dqh8blbT8sNdRSZJGICIeR+lFc3VKu2IHVaN+TQmQSP3sROk9cRalDabrKe1NrUdpD/OVtHuZO5/S06skLVWq2t0x6nJI0lgYaJMkaTT2BL7eMWw+8Ma0AVUNZ0VKb4PP65PmTGDfzFyiwXZJkiQ1z84QljHrrbdezpgxY9TFkLSMefDBB7nqqqsAWHPNNdl0000HTKFB/v73v/PXv/4VgJVWWonVV1+djTbaiFVXXXXAlBIsWrSIuXPncs899zBv3jwWLVrEAw88QEQ8tD2tu+66rLXWWqMuqiRJ0jLhiiuuuD0zHzUonTXaljEzZsxg9uzZoy6GJEmSJEnSMiMi/jJMOjtDkCRJkiRJkhpgoE2SJEmSJElqgIE2SZIkSZIkqQEG2iRJkiRJkqQGGGiTJEmSJEmSGmCgTZIkSZIkSWqAgTZJkiRJkiSpAQbaJEmSJEmSpAYYaJMkSZIkSZIaEJk56jKoQbH+Gsm+W426GJIkSZIkaTmUx1w06iJMioi4IjO3G5TOGm2SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQFsXETEjIjIiZlafT46I2yNiQUTMjogX95ju1RFxXkTcVaX9Q0R8MCJW7kh3U0Tc2GX6v1Tz/VDH8BdVwz/W7C+VJEmSJElSUwy09fd44JfADOBbwCnAFsDpEbFrPWFE/DdwErAJcCrwVeBO4OPAGRGxYi35ucBjImKz2vSbABtXX3frKMdzq/dzJv6TJEmSJEmSNBkMtPW3C/DVzHxmZr4zM/cHXkpZboe3EkXEAcAbgR8AT8rMN2XmuzPzOcBHq3zeUsv33Oq9HlBrfT4LeFZErNYxbj5wSUO/S5IkSZIkSQ0z0NbfX4BP1Adk5s+BvwLb1wa/A1gEvDEz53fk8XHgDuDfasNaNdM6A223AV8GHg7sABARjwS2Ai7MzPu6FTIiDqoeaZ3N/PuH/3WSJEmSJElqzIqDkyzXrs7MB7oM/xvwLICq5tlWwO3AYRHRLZ+FwOatL5n5l4i4Htg1Ih4GJKXW29nA+ZSg3W7AmcCuQNCuBbeEzDwOOA4g1l8jx/QLJUmSJEmS1AgDbf3d3WP4Itq1AdehBMIeBXxkDHmfAxwIbAPcX01/TmbeGxGX067ttlstvSRJkiRJkqYpHx2duLnV+1WZGf1eHdO1aqg9j3Yw7dza+9MiYt1q3Fzgysn8EZIkSZIkSZoYA20TlJn/AH4HPKUKjA3rXMojo7tRehW9PjNvqMadQ1k3rwM2BWb1eIRVkiRJkiRJ04SBtmZ8kdKBwfERsXbnyIhYJyK2qQ/LzNsoAbrnADux+KOhFwMLgA9U33u2zyZJkiRJkqTpwTbaGpCZx0fEtsChwJ8jotUz6brAEyiBtBOAQzomPQfYova5ld/CiLgI22eTJEmSJElaalijrSGZ+RZgL+ASSrtr7wJeAqwFfA44ustkrQBaAuf1GHdrZv6u8QJLkiRJkiSpUZGZoy6DGhTrr5Hsu9WoiyFJkiRJkpZDecxFoy7CpIiIKzJzu0HprNEmSZIkSZIkNcBAmyRJkiRJktQAO0NYxmy78WbMXkaraUqSJEmSJE1n1miTJEmSJEmSGmCgTZIkSZIkSWqAgTZJkiRJkiSpAQbaJEmSJEmSpAYYaJMkSZIkSZIaYKBNkiRJkiRJaoCBNkmSJEmSJKkBBtokSZIkSZKkBhhokyRJkiRJkhpgoE2SJEmSJElqgIE2SZIkSZIkqQEG2iRJkiRJkqQGGGiTJEmSJEmSGmCgTZIkSZIkSWqAgTZJkiRJkiSpAQbaJEmSJEmSpAYYaJMkSZIkSZIaYKBNkiRJkiRJaoCBNkmSJEmSJKkBBtokSZIkSZKkBhhokyRJkiRJkhoQmTnqMqhBsf4ayb5bjboYkiRJkpZRecxFoy6CJE25iLgiM7cblM4abZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAWyUiZkRERsTMiHhiRHwvIu6IiHsj4syI2KJK96iIOC4ibo6IBRFxeUTs2pHXRhHx4Yi4KCJuiYj7IuKmiDgpIjYfMO8ZEXFyRNxe5T87Il48VctBkiRJkiRJ42OgbUkzgMuADYCZwJnA84BZEbEpcCnwdOAU4LvAVsDPImLjWh47AUcAdwPfB46qpnsFcHlEbNVj3o8HflmV4VvVPLYATu8M5kmSJEmSJGl6WXHUBZiGdgY+mJmfbA2IiA8BH6ME4L4LHJqZD1bjzgJOBN5ZvQDOBTbIzHvrGVcBtouATwN7dJn3LsCRmfnR2jQnAWcAhwPnNfD7JEmSJEmSNAms0bakOZRAWN03q/eVgcNbQbbKScAiYOvWgMy8rTPIVg3/FSUIt2tErNRl3n8BPtExzc+BvwLb9ypwRBxUPWI6m/n390omSZIkSZKkSWSgbUlXZ+YDHcNuqt6v6wygVWlvBR5bHx4Re0bEj6q23O6v2mBLYC9KwG69IecN8DdgnV4FzszjMnO7zNyOVbvF7yRJkiRJkjTZfHR0SXM7B2TmoojoOq6yCHgowhURbwe+BNwFnEWpkTYPSGBvSrtuK3fJ5+4++RsUlSRJkiRJmsYMtDUsIlYEPgrcAmyTmTd3jH/WSAomSZIkSZKkSWUtqeatB6wNXNwlyLYGsM1ISiVJkiRJkqRJZaCtebdRHhPdtgqsAVB1fvAlurfNJkmSJEmSpKWcj442LDMfjIgvA0cAv4mI04GHA7sC6wLnVZ8lSZIkSZK0DLFG2+T4EPBuYD5wMLAPMBvYntIxgiRJkiRJkpYxkZmjLoMaFOuvkey71aiLIUmSJGkZlcdcNOoiSNKUi4grMnO7Qems0SZJkiRJkiQ1wECbJEmSJEmS1AA7Q1jGbLvxZsy2KrckSZIkSdKUs0abJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNSAyc9RlUINi/TWSfbcadTEkSZKkZUoec9GoiyBJGqGIuCIztxuUzhptkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBA25AiYkZEZETMHHVZJEmSJEmSNP0YaJumIuLIKrC3y6jLIkmSJEmSpMFWHHUBliL/B2wOzB11QSRJkiRJkjT9GGgbUmbeD1wz6nJIkiRJkiRpevLR0SF1a6MtImZFRPZIf0CV/oCO4VtGxHciYk5ELIyIv0fElRFxdESsVKWZA3ykmuS8Kp/sNS9JkiRJkiSNnjXaplBEbAlcBiTwQ+AGYE1gE+BQ4IPA/cDRwN7AzsA3gTkjKK4kSZIkSZLGwEDb1NofWAXYOzNPr4+IiHWAeQCZeXRErE0JtM3MzFlTXVBJkiRJkiSNjYG20ZjfOSAz7xpvZhFxEHAQAGs8fPylkiRJkiRJ0rjZRtvUOgV4ADgtIk6MiNdHxBMnmmlmHpeZ22Xmdqy60sRLKUmSJEmSpDEz0DaFMvOXwI7AucArKO2v/SkiromIV4+0cJIkSZIkSZoQA20T8yBARHR7BHftbhNk5iWZ+WJgHeA5wMeBDYCTIuJ5k1VQSZIkSZIkTS4DbRPTalftcV3GbddvwsxcmJkXZ+aHgbdXg19aS/JA9b7CxIooSZIkSZKkqWCgbWJ+Wb0fWB8YEbsBSzwKGhE7RsRaXfLZoHqfVxt2R/W+8UQLKUmSJEmSpMlnr6MTcwJwOPD+iNgK+D3wJGAP4AfAyzvSvxt4QUTMAq4H/gE8pUp/F3BcLe15lEdT/zMitqjGk5mfmKwfI0mSJEmSpPEz0DYBmXlbROwMfA7YCdgZmA08H3gCSwbavkYJmD2D0j7bisCN1fAvZOZfann/ISL2B94DHAqsUo0y0CZJkiRJkjQNGWgbXivQtbA+MDN/B7yoS/rzgZkdac8Ezhx2hpn5P8D/jKmUkiRJkiRJGgnbaBvek6r3G0daCkmSJEmSJE1L1mgbICK2BP6tej1IaXtNkiRJkiRJWoyBtsG2Ad4GXAMckpm/HXF5+tp2482YfcxFoy6GJEmSJEnScsdA2wCZOZOOttYkSZIkSZKkTrbRJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ2IzBx1GdSgWH+NZN+tRl0MSZKkpVoec9GoiyBJkqaRiLgiM7cblM4abZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUgGUi0BYRMyIiI2LmqMsiSZIkSZKk5dMyEWiTJEmSJEmSRs1AmyRJkiRJktSApT7QFhFHAjdUX/evHiFtvQ6IiF2qz0dGxPYR8ZOIuLMaNqPKIyNiVo/8Z9bTVsMeelQ1Ip4YEd+LiDsi4t6IODMitqjSPSoijouImyNiQURcHhG7dvsNVX67RMT+EXFVRMyPiNsi4viIeHSTy0ySJEmSJEnNW3HUBWjALGBt4B3Ar4DTauOursYBPAt4P3AhcDywHnDfBOc9A7gM+AMws/r+MmBWRDwLOAO4BzgFWBfYD/hZRDwpM//aJb93Ai+o0p8B7AC8AdglIp6RmX+fYHklSZIkSZI0SZb6QFtmzoqIOZRA29WZeWR9fETsUn18AXBIZh7b4Ox3Bj6YmZ+sze9DwMcoAbjvAodm5oPVuLOAEykBtXd2yW8P4BmZeVUtv6OAw4BPA29qsOySJEmSJElq0FL/6OgYXN1wkA1gDiUAVvfN6n1l4PBWkK1yErAI2LpHft+qB9kqRwJzgddExMrdJoqIgyJidkTMZv79Yyi+JEmSJEmSmrI8Bdp+OQl5Xp2ZD3QMu6l6vy4z762PqNLeCjy2R37ndw7IzLmUR2BXATbvNlFmHpeZ22Xmdqy60ljKL0mSJEmSpIYsT4G2WyYhz7mdAzJzUa9xlUVAr2jYrT2Gt8q+1vBFkyRJkiRJ0lRangJtOWBcr/bq1u4xfDJs0GN4q9fRXsE7SZIkSZIkjdiyEmhrPb65wjinvwt4XOfAiFiB3u2pTYadu5RhraoMCyi9m0qSJEmSJGkaWlYCbXdRaqVtPM7pfwlsHBEv6Bj+QeDxEynYGL0uIp7WMexIyiOj38nMhVNYFkmSJEmSJI1Br8cllyqZ+Y+IuAzYMSK+DVxHqeX2wyGz+DzwQuD0iDgFuBN4NvAEYBawS9Nl7uFnwEUR8V3gZmCH6jUHOGKKyiBJkiRJkqRxWFZqtAG8DvgJsDvwEeDjwDbDTJiZ5wB7A78D9gP2pwS3tgf+Mgll7eUo4FDKo6KHAZsBM4FnZ+ZtU1gOSZIkSZIkjdEyUaMNIDP/BOzVY3QMMf0P6V4D7oDqVU87p1+emdlv3IwB5ZhJCa5JkiRJkiRpKbIs1WiTJEmSJEmSRsZAmyRJkiRJktSAZebRURXbbrwZs4+5aNTFkCRJkiRJWu5Yo20ayMwjMzMyc9aoyyJJkiRJkqTxMdAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDIjNHXQY1KNZfI9l3q1EXQ5IkaamWx1w06iJIkqRpJCKuyMztBqWzRpskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNA2hSLigIjIiDhgDNPMrKaZMWkFkyRJkiRJ0oQZaJMkSZIkSZIasOKoC7Cc+QFwKXDzqAsiSZIkSZKkZhlom0KZOReYO+pySJIkSZIkqXk+OtpDRLwkIs6JiJsjYmFE3BQR50fEobU020bElyLiVxFxZ0QsiIg/RsQXImKdLnn2bKMtIp4XERdExD+rvE6LiM0m+WdKkiRJkiSpIdZo6yIiDgKOBW4BfgTcDqwPbAm8AfhalfRA4GXA+cDZwArANsC7gD0i4hmZee8Q83sFcApwX/V+M7ADcAnw68Z+mCRJkiRJkiaNgbbuDqYEvbbKzNvqIyJivdrX/wTekpkPdKR5E/AN4FDgM/1mFBFrUIJ6DwI7Zubs2rijgMMm8DskSZIkSZI0RXx0tLdFwP2dAzPz9trnv3QG2SrHA/cALxxiPi8F1gVOqgfZKkcyRJtuEXFQRMyOiNnMX6LIkiRJkiRJmgIG2rr7NrAa8LuIOCoi9o6IR3UmioiVIuKtEXFh1a7aAxGRlNppawKPGWJe21Tv53eOqDpPuHpQBpl5XGZul5nbsepKQ8xSkiRJkiRJTfPR0S4y84sRcTvl0c+3Ux7fzIg4Hzi8VvPsFEobbdcDp1PadFtYjTsMWHmI2a1Vvd/aY/wtY/8FkiRJkiRJmmoG2nrIzBOBEyNibeDZlIDaG4GfR8TmwMbVsLOBF2XmQ89sRsTDgPcOOavWo6Eb9Bj/6HEUX5IkSZIkSVPMR0cHyMy7M/OnmXkgMJPSntqOwCZVkh/Wg2yV7YFVh5zFldX7zp0jImItYOsxF1qSJEmSJElTzkBbFxGxe0R0q+23fvU+D5hTfd6lY9r1ga+OYXanA3cBr4mI7TrGHUn70VJJkiRJkiRNYz462t3JwIKIuJASUAtKLbanA1dQHhd9ELgI2CciLgYupDz+uQdwLXDTMDPKzH9ExEGU9t4uiIhTgJuBHYAtgF8AOzX2yyRJkiRJkjQprNHW3RHAJZQeQQ8F3gCsBLwP2DUz78/MB4CXAF8HNqJ0mrAD8A3ghUDn46Q9Zeb3gN0pQbx9gUOAO4FnATc085MkSZIkSZI0mSIzR10GNSjWXyPZd6tRF0OSJGmplsdcNOoiSJKkaSQirsjMzia/lmCNNkmSJEmSJKkBBtokSZIkSZKkBtgZwjJm2403Y7aPOkiSJEmSJE05a7RJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSZIkNcBAmyRJkiRJktQAA22SJEmSJElSAwy0SZIkSZIkSQ0w0CZJkiRJkiQ1wECbJEmSJEmS1AADbZIkSZIkSVIDDLRJkiRJkiRJDTDQJkmSJEmSJDXAQJskSZIkSZLUAANtkiRJkiRJUgMMtEmSJEmSJEkNMNAmSZIkSfr/7N15uK7nfDf87y+D8NgEqU1II1Q0xiDbTDOYleJBvIqKvBpDKdp61FCioWirlE2JIhQ1q+nBSwbDNu6dUEOUIEiIOQmROb/3j/tasrKstdfaa19732vtfD7HcR/nWud1Xuf1u9f673uc53UCMAJBGwAAAACMQNAGAAAAACMQtAEAAADACKq7p10DI6q1azqH7j/tMgBgFL1+w+Wb+WIAACAASURBVLRLAACAVNWm7l632Dgr2gAAAABgBII2AAAAABiBoA0AAAAARiBoAwAAAIARCNpWqKrqqjph2nUAAAAAsDSCNgAAAAAYgaANAAAAAEaw6oO2qtpn2GZ5TFXdqKreXlU/qapLquqgYcw1quqFVXVyVZ1bVWdV1bFVdY955jtsmO+wqvrjqvpMVZ1TVb+sqndV1b4L1LFnVb2yqk6tqguq6qdV9Z6qOmCRZ9yrqk4Yaprp62HogUPfzOfIEf90AAAAAIxol2kXMKI/SPL5JN9M8pYkV0pydlVdL8kJSfZJ8qkkH0ly5ST3TfKRqnpsd792nvn+d5J7J3nvcP8tkzwoycFVdcfu/p+ZgVV1/SSfTnKdJMcl+c8kv5/kIUn+uKoe1N0fnOcZD05yryQfTvLqocYvJXlekucm+V6SY2aNP2HJfw0AAAAAtqsdKWi7c5IXdvczZ3cOBwpcL8nDuvtts/qvlklw9fKqen93/3jOfPdLcr/ZAVlVPTnJy5K8KsldZ419dSYh27O7+wWzxr8qySeTvLGqrtfdv57zjPskuU93f2RO/5eq6rlJTu3uI5fy5QEAAACYrlW/dXSWH2eyEuy3qmr/JAcmeffskC1JuvvMTFaNXTGTlWpzHTfPKrT1Sb6d5JBhpVyqaq8k90jy/ST/OOcZn8lkdds1MlkhN9f75gnZtlhVHVFVG6tqY869cGunAwAAAGAZdqQVbV/u7vPn9N1haHdf4P1m1xzaG89z7RNzO7r74qr6dCbbVG+VydbOWw2XP9Xd86VcxyV5xDDuTXOufWGe8Vusu49OcnSS1No1vchwAAAAALaBHSloO2Oevj2G9u7DZyFr5umbu5V07nN2n9P+aIHxM/1X28xcAAAAAKxyO1LQNt9KrrOG9snd/fItnO9aC/Rfe87cZ83pn2vPOeNms/oMAAAAYAexI72jbT6fG9q7LOPeA+d2VNXOmRy6kCQnzWnvXFXzBZcHD+2JW/j8S5LsvIX3AAAAADAlO3TQ1t0bk3wqyf+uqsPnG1NVN6+qtfNcOqSq7jun74mZvJ/t+O7+3vCM05J8LMk+SZ4yZ+7bJfnTJL9M8t4tLP/nSX5/C+8BAAAAYEp2pK2jC/nTTA4keF1V/WWSzyc5M8leSW6R5GaZHJrwkzn3fSDJe6vqvUlOSbJ/kvsk+UWSJ8wZ+7gkG5L8U1XdI8nGTEKyh2SyMu3R3f2rLaz72CT/T1V9IMmmJBcl+WR3f3IL5wEAAABgO9jhg7buPq2qDkjypCQPSvLwTLZknpHk60lekeQr89z6nkxO8nxWkj9OcuHQ94zu/uacZ3ynqtYleXYmYdxBSc5O8pEkL+juLy6j9Cdn8g63uw5z7pTkeUkEbQAAAAArUHV7H/9sVXVYkjdksgrtmOlWs+Vq7ZrOoftPuwwAGEWv3zDtEgAAIFW1qbvXLTZuh35HGwAAAABsL4I2AAAAABjBDv+OtsubA/beLxttswEAAADY7gRtcwzvZTtmymUAAAAAsMrYOgoAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADCC6u5p18CIau2azqH7T7sMAHZgvX7DtEsAAIDtqqo2dfe6xcZZ0QYAAAAAIxC0AQAAAMAIBG0AAAAAMAJB2xaoqn2qqqvqmFl9xwx9+2xuHAAAAAA7NkEbAAAAAIxA0Lb1npHkxklOn3YhAAAAAEzPLtMuYLXr7h8l+dG06wAAAABguqxo20rzvaNtM2N3qqqXD+PfU1VXnHVtl6p6QlV9rqrOrqrfVNVJVfXEqvJ/AgAAAFjhBDjbyRCqvSPJk5K8MsmDu/u84dquST449F8tyVuTHJ3J/+cVSd44jZoBAAAAWDpbR7eDqrpGkvcluVOSv+3uF88Z8qwk90yyPslTuvvi4b6dMwncDq+qd3X3+7Zj2QAAAABsASvatrGqul6SDUlul+SRc0O2YVvoE5OckeSpMyFbkgw//3WSTvLwzTzjiKraWFUbc+6F2+BbAAAAALAYK9q2rT9M8tkkV05y7+4+dp4xN0qyR5JvJXl2Vc03z7mZnGw6r+4+OpOVb6m1a3orawYAAABgGQRt29aNklwjyZeSnLjAmD2Gdt8kz93MXGtGrAsAAACAkdk6um19IMkzk9wyybFV9XvzjDlraN/b3bWZz/W3W9UAAAAAbDFB2zbW3S9M8tQkt0pyfFVda86QbyQ5M8nth9NHAQAAAFiFBG3bQXe/LMnjk9w0ySeq6jqzrl2U5BVJ9kzy8qq60tz7q2rPqrrJ9qoXAAAAgC3nHW3bSXe/uqrOS/K6JJ+sqkO6+/vD5aOS7J/kcUnuV1XHJTk9ydpM3t12pyTPSvL17V85AAAAAEuxTYK2qrpKkku6+5xtMf9q1d3HVNX5Sd6US8O273T3hVX1gCSPSHJYkvtmcvjBT5N8N8nfJXnLlMoGAAAAYAmqu7fshqo9kxyc5Kfd/bE5126c5A1JbjN0fTLJn3f3KSPUyhLU2jWdQ/efdhkA7MB6/YZplwAAANtVVW3q7nWLjVvOO9oOT/IfSe4254FXTPLhTEK2Gj4HJvn4sMINAAAAAHZYywnaZgK2t8/p/7Mke2dygubjM9kC+cMkv5/kL5ZZHwAAAACsCst5R9s+Q3vynP4HJekkz+zu1yRJVf0kyf9N8idJXrTMGtkCB+y9Xzba0gMAAACw3S1nRds1k5zd3efOdFTVTknumEnQ9s5ZYz+W5JIk+21NkQAAAACw0i0naNs5yW5z+m6W5MpJvt7dv5jp7O6Lk/xyuAYAAAAAO6zlBG1nJNmtqq43q+8eQzvfnsU1mYRtAAAAALDDWk7Q9tmhfU5N7JHJ4QedyVbR36qq62ey+u1HW1UlAAAAAKxwywnaXj60h2WyUu0HSa6f5HtJ3j9n7MwJpScupzgAAAAAWC22OGjr7s8lOSLJb5JcNckVk3wryYO6+6I5wx85tMdvTZEAAAAAsNLtspybuvt1VfW2JLdIcmaSbw4HH/xWVV0hyTFJ3pjkQ1tZJwAAAACsaMsK2pKku8/Jpe9rm+/6BUlev9z5AQAAAGA1Wc472gAAAACAOQRtAAAAADCCzW4draoLRnpOd/duI80FAAAAACvOYu9oW/Y73ObokeYBAAAAgBVpsSDt7gv0753kJUnWJHlvkuOSnDZcu26SQ5I8MMmvk/xNku9vdaUAAAAAsIJtNmjr7mPn9lXV7yV5Y5JzkhzU3f89z61HV9XNk3woyfOS3GqEWgEAAABgxVrOYQjPTrJnkscsELIlSbr7K0mOSLLXcA8AAAAA7LCWE7TdL8l53f3RJYz9aJJzk/zJMp4DAAAAAKvGcoK26yS5aCkDu7uHsddZxnMAAAAAYNVYTtB2VpI1VbXoe9eq6tZJrjLcAwAAAAA7rOUEbccnqSSvraqrLzSoqq6W5OgkncmppAAAAACww9rsqaML+PskD8zkJNGTq+qVmYRvp2cSqu2V5OAkT0hyrSTnJ3n+KNUCAAAAwAq1xUFbd59cVQ9K8p9J1iY5cvjMVUnOSfKn3f31ragRAAAAAFa8mpxXsIwbq/ZO8uwkD0my+5zLZyV5Z5IXdPf3tqpCtkitXdM5dP9plwHAKtHrN0y7BAAAWPGqalN3r1ts3HK2jiZJuvv7SY5IckRV7ZvJ6rYk+UmSU3q5CR4AAAAArEJbHLRV1R2HH7/W3WclSXd/K8m3xiwMAAAAAFaT5axo+3SSS3LpCjYAAAAAuNxbTtB2dpKLu/sXYxcDAAAAAKvVTsu455Qka6rqCmMXAwAAAACr1XKCtrcn2TWT00YBAAAAgCwvaHtZki8kWV9V9xi5HgAAAABYlZbzjranJfl4kpsk+XBVfTnJhiQ/SXLxQjd19z8sq8JtrKr2S3JykuO7+5AFxnwlyX5Jfr+7zxj67pnkyUlum+QqSU5L8p4kL+juM+fcf3CShyW5c5K9MlkR+O0k70zy4u4+b874I5M8N8nBSa4zPOemSX7W3fts9ZcGAAAAYHTLCdqen6SHnyvJLZPsv4T7VmTQ1t3fqKrjkxxcVTfq7m/Ovl5Vd0xysyTvnhWyPSfJ85L8IskHMwkZb5Hkb5Lcp6ru0N1nz5rm6ZkEdZ9J8qEkV0xypyRHJjmoqu7W3fOFlH+d5O5JPpDk+CS7j/OtAQAAABjbcoK2z+TSoG1H8apMVo8dkUlYNtsRQ/ua5Ler056X5LNJ7jN79VpVHZbkDcP1p86a4wlJvtvdl/m7VdVRSZ6d5MGZvPturkOS3KG7T1rWtwIAAABgu6k52c/lUlXtkuR7SXZLct3uPn/ov1qSHw6ffbu7q+q9SR6Q5Gbd/bV55jppmGPtEp67R5KfJXlDdx8+q//ITLaOvqy7n7rA7bPnOSIzgeCaKxyQR61b7BYASJL0+g3TLgEAAFa8qtrU3YsGLstZ0bbD6e6LqurfkzwnyYOSvHW49MgkV0py9KzVaHdIcmGSh1TVfCevXiHJNatqj+7+eZJU1ZUzec/aA5PcKJN3utWse667QGlfWGL9Ryc5Oklq7RrJKQAAAMAUCNoudXSSZyZ5bC4N2o5IckEm20Fn7JHJ3+25i8y3JsnPq2rXJMdlcmjCVzPZIvrTTMK6DPPstsAcZ2zZVwAAAABgWrYqaBtCpEOSrEuyNpN3t/00yRczOcXzws3cvqJ09+lV9YEkD6yqGye5eiaHILy9u386a+hZSXbq7msscer7ZxKyvbG7D5t9oar2zOYDO6vTAAAAAFaJZQdtVXV4JieQXmuBIT+uqmd19xsWuL4SvSqT7Z1HZBK0JcMhCLN8LskfV9VN53tH2zxuOLTvnufagcuqEgAAAIAVZ6fl3FRVL0jy2iTXzuRdYz9JcuLw+cnQd+0k/z6MXS2OTfLNJI9KcmiSb3b38XPGvHRoX1tV15k7QVVduapuP6vr1KE9aM64GyR58Qg1AwAAALACbHHQVlV3SfKMTMK09yW5RXfv2d23GT57Jrl5kvcOY/62qu40ZtHbynDgwaszWc12pfzuarZ097FJ/jbJ7ZN8q6reWVX/WFWvqqoPJflxkiNn3fKBJKck+auq+v+q6sVV9Z9Jvpzks9v0CwEAAACw3SxnRdsTh/aY7n5gd3917oDu/lp3PyiTQwQqyZO2osbt7ZgklyQ5P8kb5xvQ3S9O8kdJPpTkTkmekuQhmZweenSSZ88ae04m77F7a5KbJvnLJLdIclSSR2yj7wAAAADAdlaTRVxbcEPVaUn2THKd7v7xImOvneT0JD/q7r2WXeV2VFUHJTk+yZu7+5FTLmeL1do1nUP3n3YZAKwSvX7DtEsAAIAVr6o2dfe6xcYtZ0XbNZOcuVjIliTdfUaSM5P83jKeMy3/Z2jXT7UKAAAAAFaV5Zw6+qskV62qK3b3eZsbWFVXTHKVJGcvp7jtpapunuS+SQ5Icu8kH+zuz0+3KgAAAABWk+UEbf+d5MBMTub8ncMC5nj08Iz/XsZztqcDkvxDJoHgO5M8YbrlLN8Be++XjbYBAQAAAGx3y9k6+pZMDjh4aVU9aqFBVXVYkn9J0knevKzqtpPuPqa7q7t37+5Du/tn064JAAAAgNVlOSva3pDJarY7J3l9VT03yXGZHHrQSX4/ycFJrpdJIPepTE7yBAAAAIAd1hYHbd19SVXdL8kbk/xJkn0y2SI6Ww3t+5Ic1t2XbE2RAAAAALDSLWdFW7r7rCQPqKo7JnloknVJ1g6Xf5JkY5K3dfdnR6kSAAAAAFa4ZQVtM7r7M0k+M1ItAAAAALBqLRq0VdWnM3kH2wlJPtPd523rogAAAABgtVnKirY7JrlDkmcluaCqPp/k+OHz2e6+cBvWBwAAAACrwk5LGPOxJOdkcsDBbkn+KMlzMgnazqyqj1fVM6vqDlW187YrFQAAAABWrkVXtHX3PYcA7TZJDk5yUCar3K6c5EpJDhn6k+Q3w1bTmRVvm5w4CgAAAMDlwZIOQ+jui5N8bvi8sKp2SXLbTEK3gzPZWvq/Mgnf7pnkHsOtv6qqTyU5rrtfOm7pAAAAALByVHdv/SRVu2YSvB08fG6fyWq3Gd3dtpVuB+vWreuNGzdOuwwAAACAHUZVberudYuNW8o72hbV3Rd294bufn533zWTbaZvTnLxGPMDAAAAwEq3pK2ji6mq6+fSbaQHJbnuzKWhPWOM5wAAAADASrWsoK2qrpdLQ7WDk+w1c2loT0vyiZlPd39r68oEAAAAgJVtSUFbVe2dy65Y23vm0tCemssGa98ds0gAAAAAWOkWDdqq6ttJ9pn5dWhPyWWDtR9sk+oAAAAAYJVYyoq26yfpTN6z9k9J3tbd3rkGAAAAALMs9dTRSrJnkpckObaqXlVVh1bVtbddaQAAAACweixlRdtNMnkv20FJDkxy4+Hz2CSpqm9lsoX0hEy2kf5wG9QJAAAAACvaokFbd38jyTeSvDpJqmq/XDZ4u9Hwecxw/du5bPB22vhlAwAAAMDKUt29dRNU3TiXDd7WDpdmJv5ukhO6+zFb9SCWpNau6Ry6/7TLAFjxev2GaZcAAACsElW1qbvXLTZuKVtHN6u7T05ycpJ/Gx48E7zdPcn9ktwgkwMVBG0AAAAA7LC2OmibUVU7JVmXSch2cJI7ZemHLQAAAADAqrbsoK2qKskBmYRqByW5c5I1s4cM7blJ7M8BAAAAYIe25KBtCNZunUvfx3aXJFeZPWRoz0/yuSTHD5/PdfeFI9S66lXVqUnS3fsscfw+mbzj7o3dfdg2KgsAAACAESwatFXVUzNZtXaXJFedfWloL0zyhVwarH2mu88fuU4AAAAAWNGWsqLtJZmcIDoTrF2UZGMuDdY2dPe526Y8AAAAAFgdlhK0XZLkxFwarH2qu8/ZplUBAAAAwCqzlFNBr9Hdt+3up3f3R4Rsv6uqDq2qT1bVWVV1blV9paqeUVW7LfH+q1TVv1TVaVV1XlV9o6r+Kk5tBQAAAFg1Fl3R1t1nb49CVquq+ockz0jysyRvTfLrJPdO8g9J7llVd9/cYRBDGHdsktsk+XKStyS5WpK/S3Lgtq0eAAAAgLEs+dRRfldV3SGTkO0HSW7b3WcM/c9I8t4k903ytExCt4X8dSYh23uSPKS7LxnmeFGSTduuegAAAADGZGvi1jl8aJ8/E7IlSXdflEmAdkmSxywyx6OHcf9nJmQb5vhukpcvpYiqOqKqNlbVxpy74OI5AAAAALYhQdvWufXQHjf3Qnd/M8lpSa5fVVeb7+aqukqSGyY5vbu/Pc+QE5ZSRHcf3d3runtdrrTrkgoHAAAAYFyCtq2z+9D+aIHrP5ozbqH7f7zA9TMW6AcAAABghRG0bZ2zhvbaC1zfc864he6/1gLXF5oXAAAAgBVG0LZ1Thrag+ZeqKobJtkryXe7+8z5bu7uXyU5Jcl1q+oP5hnyO/MCAAAAsDIJ2rbO64f22VV1zZnOqto5yT9n8vd93SJzvGEY9+Kq+u3/o6qun+Qvxy0XAAAAgG1ll2kXsJp192eq6h+T/J8kX62qdyU5J8m9k9wsyaeT/NMi07wkyQOSPCjJiVX10Uze3fbQJJ9M8ifbqHwAAAAARmRF21bq7qcneViSbyX5s0xWoe2U5NlJ7t7dFyxy//lJ7pbkpUmumeTJmWwZfX6Sp26zwgEAAAAYVXX3tGtgRLV2TefQ/addBsCK1+s3TLsEAABglaiqTd29brFxVrQBAAAAwAgEbQAAAAAwAoch7GAO2Hu/bLQdCgAAAGC7s6INAAAAAEYgaAMAAACAEQjaAAAAAGAEgjYAAAAAGIGgDQAAAABGIGgDAAAAgBEI2gAAAABgBII2AAAAABiBoA0AAAAARiBoAwAAAIARCNoAAAAAYASCNgAAAAAYgaANAAAAAEYgaAMAAACAEQjaAAAAAGAEgjYAAAAAGIGgDQAAAABGIGgDAAAAgBEI2gAAAABgBII2AAAAABiBoA0AAAAARlDdPe0aGFGtXdM5dP9plwGw3fT6DdMuAQAA2MFV1abuXrfYOCvaAAAAAGAEgjYAAAAAGIGgDQAAAABGIGhbRFUdVFVdVUdOuxYAAAAAVi5BW5Kq2mcI046Zdi0AAAAArE67TLuAVeALSW6c5GfTLgQAAACAlUvQtoju/k2Sb0y7DgAAAABWtsv91tHh3WvfHX591LCFdOZz2ELvaKuqE4b+XavqOVX17ao6r6q+UVV/Pmvc46rqK1V1blWdVlXPq6p5/+5VdbuqeldVnVFVF1TVD6rqNVV1nW31/QEAAAAYhxVtyQlJrpbkyUm+nOS/Zl370nBtc96W5HZJ/m+SC5M8OMnRVXVhklskeVSSDyY5NsmfJHlOkt8kefHsSarq0Ulem+T8JO9P8oMk+yZ5TJL7VdXtu/v7y/2SAAAAAGxbl/ugrbtPqKpTMwnavtTdR86+XlUHLTLF3klu1t1nDuNfkslW05cmOTPJLbr79OHakUlOSfI3VfWS7r5o6L9RktckOTXJgTPjh2uHJPlYkn9N8sCt+KoAAAAAbEOX+62jI/jbmZAtSbr7O0k+nclKuKNmh2bDuA8k+b0k1501x+OT7JrkybPHD/ccl8kKt/tV1VXmK6CqjqiqjVW1MedeONLXAgAAAGBLXO5XtI1g4zx9PxzaTfNcmwnS9kryveHnOwztgVV1m3nuWZtk5yQ3mm/O7j46ydFJUmvX9NLKBgAAAGBMgrat1N1nzdN90dBu7tqus/r2GNqnLfK4NVtQGgAAAADbkaBtZZgJ5Hbv7rOnWgkAAAAAy+IdbRMXD+3OU3r+54b2LlN6PgAAAABbSdA28csknckJotOwPsmFSV46nEB6GVV1haoSwgEAAACsYLaOJunuX1fV55PcparekuSbmaxye/92ev43qurwJK9P8rWq+shQw66ZhH93SfLTJPttj3oAAAAA2HKCtks9MslLk9wrycOSVJLTkpy6PR7e3W+uqi8n+eskBye5R5JzMjnB9F1J3r496gAAAABgeaq7p10DI6q1azqH7j/tMgC2m16/YdolAAAAO7iq2tTd6xYb5x1tAAAAADACW0d3MAfsvV82Wt0BAAAAsN1Z0QYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjqO6edg2MqNau6Ry6/7TLAFiWXr9h2iUAAAD8jqra1N3rFhtnRRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbSOoqlOr6tRp1wEAAADA9AjaAAAAAGAEgjYAAAAAGIGgDQAAAABGIGhbopp4YlV9rarOq6rTq2p9Ve0+z9jdq+ppVXVcVZ1WVRdU1U+r6v1Vdfs5Y69eVb+pqm9XVS3w7A9WVVfVAdvq+wEAAACwdQRtS/eyJK9IcvUkRyd5W5J7Jfl4kivMGXvjJC9IckmSDyX5lyQfS3JIkk9V1b1mBnb3L4e5bpDkbnMfWlV7Dc/Z1N2bxv1KAAAAAIxll2kXsBpU1R2T/GWSbye5bXf/Yuh/VpLjk+yZ5Huzbjk5yXW6+2dz5tkryReSvDTJR2ZdelWSRyd5bCaB3GyPSbJzkteM9X0AAAAAGJ8VbUvz6KF9wUzIliTdfV6SZ8wd3N1nzQ3Zhv7TkrwryX5Vtfes/o1JNia5f1Vde6a/qnZO8v8m+VWS/1youKo6oqo2VtXGnHvhFn85AAAAALaeoG1pbj20n5jn2qeSXDS3s6ruVFXvqKofVNX5wzvWOsmThiHXnXPLqzJZYXj4rL77JNkryZu7+9cLFdfdR3f3uu5elyvtusSvBAAAAMCYbB1dmpkDD34890J3X1xVP5/dV1UPzGTl2nmZbAX9dpJzMnln20FJDkyy25yp3pbkJUn+vKpe1N2XZLKVNLFtFAAAAGDFE7QtzVlDe60k35l9YdjeuUeS02d1H5XkgiTruvvkOeNfk0nQdhndfW5VHZPkqUnuUVVfzeQQhM9395dH+h4AAAAAbCO2ji7NiUP7OwFZkrvkdwPLGyb5+jwh205J7ryZ5/xbks5kJZtDEAAAAABWEUHb0hwztM+qqmvMdFbVFZO8cJ7xpybZt6quM2tsJXlukpss9JDu/laSY5PcN8njkpyZ5O1bWTsAAAAA24GgbQm6e0OSVyT5gyRfraqXV9VLknw1k9VsP5pzy0uTXCXJSVX1qqr61yRfTPK0JB9Y5HEzhyJcK8l/dPdvxvsmAAAAAGwrgrale3ImJ4aelcnWzocl+WiSu2XyPrbf6u7XJHl0JgHco5I8PMkPktwul25DXcj7k/xs+Nm2UQAAAIBVorp72jUwS1XdIMkpSTZ09122+P61azqH7j9+YQDbQa/fMO0SAAAAfkdVberudYuNs6Jt5fmbJJVk/bQLAQAAAGDp5p6WyRRU1d5J/jTJvplsOf1ykndOtSgAAAAAtoigbWW4QSanl/4myceSPL67L1nORAfsvV822noFAAAAsN0J2laA7j4hk+2iAAAAAKxS3tEGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACZHjdAAAAIABJREFUQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIygunvaNTCiWrumc+j+0y4DIL1+w7RLAAAAGEVVberudYuNs6INAAAAAEYgaAMAAACAEQjaAAAAAGAEgjYAAAAAGIGgDQAAAABGIGgDAAAAgBEI2gAAAABgBCsuaKuqNVV1QVVtmNN/pao6r6q6qh4559oThv7DZ/XtW1VvqqrTh/l+OPy+7zzPPHK4/6CqelhVbaqq3wz3/EtV7TaMO6SqTqiqs6vql1X1H1W1xzzzHVxVR1fV14ex51bVV6vquVV1xUWe/+Cq+sLw/F9U1duq6rpb8zcFAAAAYNtbcUFbd/86yReS3LaqrjLr0p2S7Db8fNc5tx0ytMcmSVXdJsnGJI9I8sUk/5zkc0kenmRjVa1b4PFPSvK6JP+T5N+S/DzJU5O8pqoemOTDSX6R5OgkJw/zv3meeZ6e5B5JvpTkNUn+PckFSY5M8uGq2nmB5z9hmO/UJK9M8tUkD03y8ZmwDwAAAICVaZdpF7CA4zIJ1v4oyYeGvrsmuTjJJzMraKuqnZIclOQ73f29qqokb0py1SSP6O63zBr70CRvS/LmqrpJd18y57l3S3JAd588jN8tyYlJHpnkfknu0d2fmPXcjya5V1Xdsru/NGueJyT5bnf37Mmr6qgkz07y4CRvn+d73yvJbbr7K7PueWuShyW5f5J3LPgXAwAAAGCqVtyKtsGxQzt75dpdk2xK8u4ke1XVjYb+WybZY9Y9d0yyX5LPzg7ZkqS7357k00n+MMmd53nuy2dCtmH8+ZkEYjsl+dBMyDZcuySXrmbbf85zvjM3ZBu8bGjvOc+1med/ZU7fa4f2tgvck6o6oqo2VtXGnHvhQsMAAAAA2IZWatD22STnZgjaqmr3JLfOJEw7bhgzE8LNbBud6b/1nN/nmum/1TzXNs7T98Oh3TTPtdOHdq/ZnVV15ap6ZlV9sarOqqpLqqqT/GwYstA71+Z7/g+G9uoL3JPuPrq713X3ulxp14WGAQAAALANrcito919QVV9Osndqmptkjsk2TnJsd19clX9MJOg7d+GtnNpgLb70P5ogeln+q82z7Wz5um7aAnXfptuVdWuQy23zeQda29P8tMkM0vNnptL3zU315mbecZC73UDAAAAYAVYkUHb4Lgkd89kxdodk5yfZOYk0uOT3Ht4h9pdknytu38yXJsJxK69wLx7zhk3tvtnErK9sbsPm32hqvbMJGgDAAAAYAezUreOJpd9T9shSTZ093mzrl0jyeOTXHnW2CQ5aWgPWmDemf4Txyp0jhsO7bvnuXbgNnomAAAAAFO2koO2TZlspbx/kpvmsmHazM/PGNrZ72PbkOR/kty5qh48e8Lh9z9K8s1MDkXYFk4d2oPmPPsGSV68jZ4JAAAAwJSt2K2j3X1JVX0ik6AtmRW0dff3q+rbSf4gycVJZp8G2lX1qCQfS/L2qnpfkm9kctLoA5L8KsmfDaeGbgsfSHJKkr+qqptnssJu7yT3TfKh4WcAAAAAdjAreUVbcmm4dnZ+90TOmWubuvsy71vr7s8nuU2St2ZykMLTMnnP238muc1wfZvo7nMy2er61kxW4v1lklskOSrJI7bVcwEAAACYruruadfAiGrtms6h+0+7DID0+g2LDwIAAFgFqmpTd69bbNxKX9EGAAAAAKvCin1HG8tzwN77ZaNVJAAAAADbnRVtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMILq7mnXwIhq7ZrOoftPuwxgher1G6ZdAgAAwKpTVZu6e91i46xoAwAAAIARCNoAAAAAYASCNgAAAAAYgaBtO6uqrqoTpl0HAAAAAOMStAEAAADACARtAAAAADACQRsAAAAAjGDVBG1Vtc/wfrNjqmq/qvqvqvpFVZ1TVZ+uqnsscN/Dqur4qvplVZ1XVSdX1bOrarcFxt+1qj4yzH1eVX2zql5UVbvPM/aEoabdqur5VfXdqjq/qr5dVc+tqitswffbpaqeUFWfq6qzq+o3VXVSVT2xqlbN/wkAAADg8mo1BjjXT/LZJHskeU2SdyY5IMmHq+qhswdW1euSvDXJDZO8J8krk/wiyVFJPlJVu8wZ/9gkH0typyT/leRlw/inJ/lMVV1tgZrekeTwJB9Isj5JJzkyyburqhb7QlW1a5IPDvVdbaj56Ez+P69I8sbF5gAAAABgunZZfMiK80dJ/rm7nzbTUVXrMwnfXl1VH+7us6vqsEzCr/cmeXh3nztr/JFJnpvkL5L869B3vSQvT/LrJLft7m/MGv+qJI9P8o9JjpinphsnuWl3/3IY/6wkxye5b5JHJPmPRb7Ts5LcM5OQ7indffEwz86ZBG6HV9W7uvt9i/51AAAAAJiK1bii7awkfz+7o7s3JnlLJqvBHjh0PznJRUkOnx2yDY5K8vMkD5/V94gkV0iyfnbINnhWkl8leeQCW06PmgnZhnrOS/KM4dfDN/dlhm2hT0xyRpKnzoRswzwXJ/nrTFbIPXz+GZKqOqKqNlbVxpx74eYeBwAAAMA2shpXtJ3Y3b+ap/+EJI9KcquqemeS/ZP8LMlTFti9eX4mK9Fm3Hpoj5s7sLt/WVUnZbKabr8kX54z5BPzzP+pTIK+Wy34TSZulMk22G8lefYCtZ47p9a59R2dycq31No1vcjzAAAAANgGVmPQ9uMF+s8Y2t2TXD1JJblmJltEl2LmsIMfLXB9pn++97T9Tk3dfXFV/TzJ2kWeu8fQ7pvN17pmkXkAAAAAmKLVuHX0Wgv0X3tozxo+SXJSd9fmPrPuP2vOPHPtOWfcZmsa3q+2R5KzF/wml53vvYvUev1F5gEAAABgilZj0HbrqrrKPP0HDe1J3f3rJF9LctOqusYS5z1pzjy/NZw2essk5yU5eZ57D5yn7y6ZrBg8aZ5rs30jyZlJbj+cPgoAAADAKrQag7bdkzxndkdVrcvksICzMjllNEn+JZPDDV4/BGWZc8/Vq+rWs7renOTCJE+qqhvOGX5UkqsmeXN3nz9PTX9XVVefNfcVk7xw+PUNm/sy3X1RkldksmLu5VV1pXlq3bOqbrK5eQAAAACYrtX4jrZPJnlMVd0uyYZMAqqHZhIaPra7z06S7n59VR2Q5AlJvl1VH03y/STXSHL9TA42eEOSxw3jT62qpyR5ZZITq+odSX6ayWq1O2Sy8uzpC9R0cpKvVdW7Mgnr7p/kD5J8KMl/LOE7HZXJ4Q2PS3K/qjouyemZvN9t3yR3yuTk068v5Q8EAAAAwPa3GoO272YSSL1oaHdLcmKSv+/uj84e2N1/UVUfHsbdLZODDH6RSeD2T5msYps9/lVVdUqSv0nyoCT/K8kPhrH/0N1nLlDToUn+LpNVddfJJCQ7MsmLunvRU0C7+8KqekCSRyQ5LMl9Mzn84KfD9/27JG9ZbB4AAAAApqeWkAOtCFW1Tyah0xu7+7CpFjOoqhOSHDjnUIWpqrVrOofuP+0ygBWq12+YdgkAAACrTlVt6u51i41bje9oAwAAAIAVR9AGAAAAACNYje9oYzMO2Hu/bLQ1DAAAAGC7WzVBW3efmmTFvAstSbr7oGnXAAAAAMDKYOsoAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADACQRsAAAAAjEDQBgAAAAAjELQBAAAAwAgEbQAAAAAwAkEbAAAAAIxA0AYAAAAAIxC0AQAAAMAIBG0AAAAAMAJBGwAAAACMQNAGAAAAACMQtAEAAADACARtAAAAADCC6u5p18CIau2azqH7T7sMYIXq9RumXQIAAMCqU1WbunvdYuOsaAMAAACAEQjaAAAAAGAEgjYAAAAAGMHlImirqn2qqqvqmGnXAgAAAMCO6XIRtAEAAADAtiZoAwAAAIARCNoAAAAAYASX66CtqnaqqpcP7297T1VdsaqOHH4/qKoeXFVfqKrfVNUvquptVXXdBebat6reVFWnV9UFVfXD4fd954x77DD/n8/pP3zo/01V7Tbn2heq6ryqutL4fwUAAAAAxnC5Ddqq6opJ3pHkSUlemeTB3X3erCFPSPLmJKcO17+a5KFJPj5PEHabJBuTPCLJF5P8c5LPJXl4ko1VtW7W8GOH9q5zSjpkaK+U5A6z5t49ya2TfLa7z13OdwUAAABg29tl2gVMQ1VdI8n7ktwpyd9294vnGXavJLfp7q/Muu+tSR6W5P6ZhHSpqkrypiRXTfKI7n7LrPEPTfK2JG+uqpt09yXdfUpVfT/JIVVV3d3D8EOSHJfkoExCuBOG/oOS7DxcAwAAAGCFutytaKuq6yXZkOR2SR65QMiWJC+fHbINXju0t53Vd8ck+2Wy4uwtswd399uTfDrJHya586xLxyW5ZpKbDzXdJMmeSd6V/7+9O4+WrSzvPP79MQjILHq9IgqoTGI3ooiCClzRGKMQQSTqAiVpRF0SY7dEW2MEkqg4Jah0QitRHIgSBRxajQiCDCquqxgbUFABm1kZZb4oT/+xd3m3ZdUd9xlu1fezVq1d9b7v3uc9q561T9Vz3gF+wO+Pdhs8P5sxkhyRZHGSxdz7wLhmkiRJkiRJmkHTlmjbAfgOsCXwguHE2JDFI8quaY+bd8qe0h7HjTgblO86omyQRBtMGz27rXtako07dXcB3xvX0ar6SFXtVlW7scG645pJkiRJkiRpBk1bom17mpFjV9KMHFuW20eU/aY9rt0p27Q93jDmOoPyzTplw+u07QtcW1VXtHXrAHsneSSwM3BeVf0GSZIkSZIkzVvTlmj7MvA24MnA2Uke3sM172iPC8fUP2qoHVV1PXA5TTJtPZp12AbJtwuAJcBzWZqIc302SZIkSZKkeW7aEm1U1buB/04zlfOcdtTY6ri4Pe4zpn5QPjyC7mxgI+B1NKPdzm77dw/NjqX78vtTSiVJkiRJkjSPTV2iDaCqjqdJcO0MfCvJlqtxuQtpRqc9K8lB3Yr29V7AFTQj1boGo9TeOvR68PxJwP7ALcB/rkb/JEmSJEmSNAumMtEGUFUnAn8BbAecl+Sxq3idAl4F3AmcmuT0JO9Kchpwalv+yqp6cOjUc4AHgQXAT6rquk7dYATbI4Bz2p8hSZIkSZKkeWxqE20AVXUycAiwNU2y7XGreJ2LgKcB/wbsAfw1sCfwGeBpbf3wObcCP2xfDq/BdhFw95g6SZIkSZIkzUNxsNRkyYKNioN3metuSJqn6oQL57oLkiRJkrTGSfL9qtptee2mekSbJEmSJEmS1Jd15roD6tdTH7sjix2xIkmSJEmSNOsc0SZJkiRJkiT1wESbJEmSJEmS1AMTbZIkSZIkSVIPTLRJkiRJkiRJPTDRJkmSJEmSJPXARJskSZIkSZLUAxNtkiRJkiRJUg9MtEmSJEmSJEk9MNEmSZIkSZIk9cBEmyRJkiRJktQDE22SJEmSJElSD0y0SZIkSZIkST0w0SZJkiRJkiT1wESbJEmSJEmS1AMTbZIkSZIkSVIPTLRJkiRJkiRJPTDRJkmSJEmSJPXARJskSZIkSZLUAxNtkiRJkiRJUg9MtEmSJEmSJEk9MNEmSZIkSZIk9SBVNdd9UI+yYKPi4F3muhuSZkGdcOFcd0GSJEmSpkKS71fVbstr54g2SZIkSZIkqQcm2iRJkiRJkqQemGiTJEmSJEmSemCibTUl2SZJJTm5U3ZyW7bNiPZvSHJZknvbNm+cxe5KkiRJkiRphqwz1x2YJkleBnwQuBg4Hrgf+O6cdkqSJEmSJEm9MNE2M94KHAdcN1T+osGxqq6f3S5JkiRJkiRpJplomwFVdQNww4iqLdt6k2ySJEmSJEkTxjXaZsDwGm1JjklSwKL2dQ0eQ+ft2J57TZL7k9yU5N+S7DDrv4QkSZIkSZJWiiPaZse57fEwYGvg2OEGSf4YOB1YF/gy8DNgK+BA4IVJFlXVD2ajs5IkSZIkSVp5JtpmQVWdC5ybZB9g66o6plufZHPgM8A9wF5VdVmnbmfgIuAk4Cmz1GVJkiRJkiStJKeOzg+vBDYDju4m2QCq6lLgo8CuSZ446uQkRyRZnGQx9z4w872VJEmSJEnSH3BE2/ywR3vcJckxI+q3b487AZcNV1bVR4CPAGTBRjVcL0mSJEmSpJlnom1+2KI9vno57Taa6Y5IkiRJkiRp1Zhomx/uaI+7VNWP5rQnkiRJkiRJWiWu0TY/fLc9PntOeyFJkiRJkqRVZqJtfvg4cDtwdJLdhyuTrNXuWCpJkiRJkqR5yqmj80BV3ZLkIOAM4LtJzgYuBR4EHkuzWcIWwPpz10tJkiRJkiQti4m2eaKqzk7yX4GjgOfTTCNdAlwPfBM4bQ67J0mSJEmSpOUw0baaqupqIENlhwGHjWi7zwpc68i++iZJkiRJkqTZ4xptkiRJkiRJUg9MtEmSJEmSJEk9cOrohHnqY3dk8QkXznU3JEmSJEmSpo4j2iRJkiRJkqQemGiTJEmSJEmSemCiTZIkSZIkSeqBiTZJkiRJkiSpBybaJEmSJEmSpB6YaJMkSZIkSZJ6YKJNkiRJkiRJ6oGJNkmSJEmSJKkHJtokSZIkSZKkHphokyRJkiRJknqQqprrPqhHSe4ELp/rfmjeeDhw81x3QvOKMaFhxoSGGRMaZkxomDGhYcaEuiY1Hrauqkcsr9E6s9ETzarLq2q3ue6E5ocki40HdRkTGmZMaJgxoWHGhIYZExpmTKhr2uPBqaOSJEmSJElSD0y0SZIkSZIkST0w0TZ5PjLXHdC8YjxomDGhYcaEhhkTGmZMaJgxoWHGhLqmOh7cDEGSJEmSJEnqgSPaJEmSJEmSpB6YaJMkSZIkSZJ6YKJNkiRJkiRJ6oGJtgmQZKskH0tyfZL7k1yd5Pgkm8913zQzkhyU5MNJzk/y6ySV5NPLOWfPJF9NcmuSe5L8KMkbk6w9W/3WzEiyRZLDk5yR5GdJ7k1yR5ILkvy3JCPv9cbEZEvyniRnJ7mmjYlbk1yc5OgkW4w5x5iYIkkObf9+VJLDx7R5UZJz23vKXUkuSvKq2e6rZkb7mbHGPG4cc473iQmX5NlJTktyQ/vd4oYkZyb5kxFtjYcJleSwZdwfBo/fjjjPmJhwSV7Y3hOubT9jXpnkc0n2GNN+6mLCzRDWcEkeD3wbWAB8EfgJsDuwCLgceGZV3TJ3PdRMSPJDYBfgLuBaYEfglKo6ZEz7PwVOA+4DTgVuBfYDdgA+X1UvnY1+a2YkeS3wL8ANwDnA/wMeCRwIbErz3r+0Ojd8Y2LyJVkC/AC4DPglsCHwDGA34HrgGVV1Tae9MTFFkjwG+L/A2sBGwKur6qShNkcCHwZuoYmJJcBBwFbAB6rqqFnttHqX5GpgM+D4EdV3VdX7h9p7n5hwSd4O/D1wM/B/aD5bPBzYFTinqt7caWs8TLAkTwZePKb62cBzgK9U1Ys65xgTEy7Je4A303w2+ALNveIJwP7AOsArq+rTnfbTGRNV5WMNfgBfBwr4y6Hyf2zLT5zrPvqYkfd9EbAdEGCf9r3+9Ji2m9B8yb4f2K1Tvj5NkraAl8317+RjteLhOTR/sNYaKl9Ik3Qr4CXGxHQ9gPXHlL+zfY//2ZiYzkf7t+Ms4OfA+9r39/ChNtvQfCi+BdimU7458LP2nD3m+nfxsdqxcDVw9Qq29T4x4Q/gpe37+A1g4xH16xoPPtr3+Tvte7y/MTE9j/a7xW+BG4EFQ3WL2vf4SmOinDq6JkvyOOCPaD4k/a+h6qOBu4FDk2w4y13TDKuqc6rqp9XeqZbjIOARwGeranHnGvcBb29fvm4GuqlZUlXfrKovV9WDQ+U3Aie2L/fpVBkTU6B9P0f59/a4XafMmJgub6BJ0P85zWeFUf4CWA84oaquHhRW1W3Au9qXr53BPmr+8T4xwdplJt4D3AO8oqruHG5TVQ90XhoPUyrJk2hGyF8HfKVTZUxMvq1plh+7qKp+2a2oqnOAO2liYGBqY8JE25rtOe3xzBFfsO8ELgQeSnMj1PQaxMl/jKg7j+YD1Z5J1pu9LmkWDT4U/6ZTZkxMt/3a4486ZcbElEiyE3Ac8MGqOm8ZTZcVE18baqM123pJDknytiR/lWTRmHVzvE9Mtj2BbYGvAre1azC9pY2JUesuGQ/T6zXt8V+rqrtGmzEx+X5Ks4zE7kke3q1IshewMc2I+YGpjYl15roDWi07tMcrxtT/lGbE2/bA2bPSI81HY+Okqn6T5CpgZ+BxwI9ns2OaWUnWAV7Zvuz+gTMmpkiSo2jW4NqUZn22Z9Ek2Y7rNDMmpkB7T/gUzZTyty2n+bJi4oYkdwNbJXloVd3Tb081yxbSxEXXVUn+vKq+1SnzPjHZntYeb6JZ3/O/dCuTnAccVFW/aouMhymUZAPgEOBB4KShamNiwlXVrUneQrNM1WVJvkCzxMTjadZo+wZLE7EwxTFhom3Ntml7vGNM/aB8s1noi+Yv42R6HQc8CfhqVX29U25MTJejaDbHGPgP4LDOlyUwJqbFO2gWNH9WVd27nLYrEhMbtu1MtK25Pg6cD1xKM+XnccCRwBHA15LsUVX/2bb1PjHZFrTH1wJXAc8FLqKZKvYB4PnA51i6FIXxMJ0OpnlPv1KdDZVaxsQUqKrj2410Pga8ulP1M+DkoSmlUxsTTh2dbGmPbi2rZTFOJlCSNwBvotmJ+NCVPb09GhMToKoWVlVoRq0cSPNF+uIkT1mJyxgTa7gku9OMYvtAVX2nj0u2R2NiDVZVx7brfN5UVfdU1SVV9Vqa0QobAMesxOWMiTXbYLpwaEaunV1Vd1XVpcABNLvc7z1mGukoxsNkOqI9/u9VONeYmABJ3gx8HjiZZiTbhsBTgSuBU5K8d2Uu1x4nLiZMtK3ZBhngTcfUbzLUTtPJOJkySV4PfBC4DFhUVbcONTEmplD7RfoMmiUFtgA+2ak2JiZYZ8roFcDfruBpKxoTv16Nrmn+Gmyks1enzPvEZLutPV7ZGcUIQDsCdjAyfvf2aDxMmSRPpFnL71qatfyGGRMTLsk+NJumfKmq/kdVXdn+k+YHNAn564A3tZs2whTHhIm2Ndvl7XH7MfWDHeXGreGm6TA2TtovX9vSLJR/5Wx2SjMjyRuBE4BLaJJsN45oZkxMsar6BU0SdufOQrbGxGTbiOa93Qm4L0kNHjS7lAN8tC07vn29rJh4FM1/sK91fbaJNZj609253vvEZBu8v7ePqR8k4jYYam88TI9xmyAMGBOT70Xt8ZzhivbzwPdocky7tsVTGxMm2tZsgwD/o3ZL7t9JsjHwTOBe4Luz3THNK99sj388om4vmp1pv11V989elzQT2sVJ/wn4IU2S7ZdjmhoT2rI9Dj4oGxOT7X7gX8c8Lm7bXNC+HkwrXVZMvGCojSbPYHpg98uP94nJdh7NF97tkjxkRP2T2uPV7dF4mCJJ1qdZiuRBmr8VoxgTk2+wO+gjxtQPype0x6mNCRNta7Cq+jlwJrAN8Pqh6mNp/gv5yaq6e5a7pvnl88DNwMuS7DYobP9g/kP78l/momPqT5K/pdn84PvAvlV18zKaGxMTLsmOSRaOKF8ryTtpFr3+dlUNRigYExOsqu6tqsNHPYAvtc0+0Zad2r7+OE2C7sgk2wyulWRzlu5YOpheqDVQkp2TPGxE+dY0I6MBPt2p8j4xwdrPDafSTPF6R7cuyfNoNkO4g6W7mBsP0+WlwOY0G2wNb4IwYExMvvPb4xFJHt2tSPICmoE+9wHfbounNiZSNXHrzk2VJI+nCeQFwBdptsV9OrCIZsronlV1y9z1UDMhyYuBF7cvF9J8+LmSpTe/m6vqqKH2n6e58X0WuJVmC+Yd2vKDy5vBGivJq2gWJP0t8GFGr3NwdVWd3DnHmJhg7RTi99GMUPg5zdbrjwT2ptkM4UaahOxlnXOMiSmU5Bia6aOvrqqThur+EvgQTfycSvMf6oOArWg2VTgKrbHa9/5/0syQuIpm19HHAy8E1qdZg+mAqlrSOcf7xARLsgC4EHgCzWfK79HsOnoAzWLlr6iqz3XaGw9TIsn5wLOA/avqy8toZ0xMsHYW3ddpdiW+EziD5jPlTjTTSgO8sao+2DlnKmPCRNsESPIY4O9ohmRuAdwAfAE4dsQi6JoAnS9G4/yiqrYZOueZwN/QTAdZn2YL5o8BHxqzzoLWECsQDwDfqqp9hs4zJiZUkicBr6P5z+JWNNum303zD5iv0LzHf/D3wZiYPstKtLX1+wFHAU+hmQlxGXBCVX1iNvup/iXZG3gtzVo6C2lmQtxOs/zAp4BPjfry431isrWjHN9Ok1x7NM2X6QuAd1fVHyxHYzxMviQ70dz7rwW2Wd77akxMtiTr0symexnwRJrpn7fSJOY/VFVnjjhn6mLCRJskSZIkSZLUA9dokyRJkiRJknpgok2SJEmSJEnqgYk2SZIkSZIkqQcm2iRJkiRJkqQemGiTJEmSJEmSemCiTZIkSZIkSeqBiTZJkiRJkiSpBybaJEmSJEmSpB6sM9cdkCRJ0sxJsjbwEuBFwDOABcBDgduBK4DzgVOq6pI562SPkjwZeDFwe1UdP9f9kSRJ0yVVNdd9kCRJ0gxI8gzgE8D2neIHgDuBzfj92Q2nAy+vqiWz18P+JTkM+Djwi6raZm57I0mSpo1TRyVJkiZQkv2Ac2mSbLcAbwW2r6qHVNUWwEOApwHHAb8GDqQZ6SZJkqRV5NRRSZLGVEA0AAAFrElEQVSkCZNkO+DTwHrAZcDzq+rabpuq+i2wGFic5H3Ax2a9o5IkSRPGEW2SJEmT5x+ATYD7gAOGk2zDqurWqnoxcEe3PMnCJO9LcmmSu5Lc3T5/b5JHjrpWkn2SVJJlrk8yaJNkn2Wdn+QJST6W5Jok9ye5NslHkzx61DVppo0CbN35GYPHMUPtn5/k9PaaS5L8OsmVSc5MclSShy3rd5AkSRrmiDZJkqQJ0ibADmpfnlJVV6zoudVZvDfJ3sAXaNZyA7gHKOCJ7ePwJPtX1QW9dHyEJIuALwEb0awrtxbwaOBw4E+S7F5V13VOuQnYgCbJ+CDwq6FL3tW59juAYzt19wABtm0fz6MZ8Xduf7+RJEmadI5okyRJmiyLWPoZ74xVuUCSx7A0yXYZ8Kyq2rCqNgL2Ai4HNge+OGpkWY9OA74J7FRVmwAbAn9Gk3TbEnh3t3FVLQT+qn15TVUtHHq8v/39tgaObtv9I/Do9vfbuP2dnw38c/tzJEmSVpiJNkmSpMmyc+f5xat4jbfRJJxuA/atqgsHFVV1PvBcmg0UHkazycJM+SHN1NeftD97SVX9O/A3bf1BSVZlhsbTaT4HX1FVb6qq6wcVVXVHVV1QVa+vqu+v7i8gSZKmi4k2SZKkybJF5/mtK3tykgAHty9PrKobh9u0a76d2L582Ur3cMW9q6oeHFH+xfa4AbDdKlz39va4cZINV6lnkiRJI5hokyRJmixZzfO3pRmpBnDWMtp9oz1ukWTb1fyZ41w0pvz6zvNV2bDge8DNwKOAi5IcmWTHNskoSZK0yky0SZIkTZabO89XJQm1oPP8urGtoLuT6YKxrVZDVY1cI62qftN5ue4qXPd24OU0myXsDHwY+DFwW5IvJTkkyUpfV5IkyUSbJEnSZLm083zX1bxWLb/JSrWbN6rqLJrRe68EPgH8FNgU2A/4FHDxDG/0IEmSJpCJNkmSpMlyDjBY1+yAVTj/l53nj1lGu606z3/Vef670WZJ1h91YpJNV6Ffvauqu6vqU1V1WFVtT/M7vQW4j6Uj3SRJklaYiTZJkqQJUlU3Aae1L1+RZPsVPbddo+wqlm6isO8ymj+3Pd5SVVd1ym/rPB+XqHv6ivZpFQySjCu93lpVXVdV7wU+0BY9r7deSZKkqWCiTZIkafK8HbiLZlfO05c3BTLJ5klOAzatqgJObatek2ThiPZbAq9pX35mqPoK4N72+UtGnLsW8NYV/UVWwa/b42bjGiRZbznXGPT/t730SJIkTQ0TbZIkSROmqq4ADgWW0EyB/GGStyR5wqBNkrWT7Jrk74ArgQM7l3gXcDvNZgpnJdmzc94zaXYj3Yxm5NtxQz/7AZaOqHtbkoOTPKQ9dwfgDGCXPn/fIZe0x02SHDymzVuSfC3JoUl+NwU2yXrtOX/dFn11BvspSZImUJp/WkqSJGnStEmxk4EndIqX0Ix224yl/3Qt4LPAq9pEGUn2Br5Is0EAwN3tccP2eDuwf1WdP+LnbgVcBGzZFj1AM0psE+BOmg0Hzm3rFlXVuZ1z96FZZ46qGjv9M8ngQ+zvnd/WncXSaa93snQq7PFVdXySY4CjO6fc2z42Z+mU0x8Dz6mqG8f1QZIkaZgj2iRJkiZUVV0I7Ai8HDgF+BnNQv8b0ySfLgDeCexUVa8YJNnac7/VnvsBmqTTWjRJqB8D72/P+YMkW3vutTTrsJ0EXNcW3wV8EnhKe+2ZdBDwTzTTWNcFtm4fg+mkHwGOoJn2eglwD00S8DbgfOCNbT9NskmSpJXiiDZJkiRJkiSpB45okyRJkiRJknpgok2SJEmSJEnqgYk2SZIkSZIkqQcm2iRJkiRJkqQemGiTJEmSJEmSemCiTZIkSZIkSeqBiTZJkiRJkiSpBybaJEmSJEmSpB6YaJMkSZIkSZJ6YKJNkiRJkiRJ6sH/B39oFrI35p4SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onion_cvec_stop_top = onion_cvec_stop.sum().sort_values(ascending = True).tail(15)\n",
    "onion_cvec_stop_top.plot(kind = 'barh',figsize=(20,12),color = '#006b3a',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Words in Posts on r/TheOnion',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Words', size = 25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TheOnion (bigrams, stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Words')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dmnwzHNeJNfCAF0ya30t3g166zy7wHPtsL49510Sg/dFwXi+PNUPzr08LDlWX7v9MyOuyjKhZwbq1Tb/H5NpDd2eo5h0bryZo0WrIjL3udMvMpybohp6L9+nlczxwgxFprkMLIvTXudFqgnafx+vSgnKDmnNnMqHmFlNqgtKCZoN1fgO45Ig092P9GkPTaoIW7Yf1FaZs812AS02Yf8mhz9ZtZjjfilbDfcuhNJvT/pwbpDm6+yyNqpH2b8zVXjsB2Hxo/lV7+RzFmBYWXdprAVdd4Dkx35qgRfsjYb1aZLQ/2gZpptaOn1KumWqC0oLXg3Sn9z9rtODQyd28C4AnjVh+C1qAc5DHYSPSvLQ3f+x3Oy3Ic8cR0/s1Qr8wZbvX9NJ+Hbj4hLTXZsI1dsp6btj7zJ0N3HdMuQ/pled9Y/Lq15Qs4Pkj0mxO++Nw6n6coey7D63v98B1Zlz2/zF3D7zljMtsaE3QogWFtx+R7lG9NN8dk9edmPs++SFj7lNpf0QMvuO/NmL+T3vretAC9vuNe8ufxfrXrMXc5ok1QbvP7W97af4HyFCasO51+9fDZe7SvW6o3J8d9bkD/m8vzccWev76uGg8lr0APnz48LGaH70v5F/0pg2anJ/Uv8nrbhpO7Obt15s+LQjab453rwlluTVzTUjWMvQjibmmqAsNAu3VK8dei7gP1w7dAE16HD4mj/4Ptg/PsM6vdGn/BVxjStrX9PK+xdC8PXvzvjQhj0sy1wSs2MhBUFqNwUHw9qApeW3FXLPb9bpxYP3A060n5PWJXrpbTclrMYKgV2Duh+R6Pzp66V7fy2f3BZ6nx/TyuP8inOt3GJp3QG/eixeQ93WY+6F2PHD5BeSxMYOg95hh/fMNgm7Iufi1WY7niHUuVhB02uNMWk2paV1xjA2C0gLvp3bz/8nkoPhwkHFaEPQ84IYL3RdD+V6Gue+nkU1kh8r3J2CbMel2GdqOscHAoc/crYbm7dybN/YPiUXY9vkGQX/IiEBCl2575gK7P93Ack0NgtJqKfYDIR8Ymt//k+o9E9a1Ja2m7cjrCXNB0vOZEJSckP98gqB366V9wkY87v3ujdZr2txLd1nmuhs4lxHBdtYNEo69v6PVlp2aboayDwdBd53Hsj/qlpk5iMWGB0HPYPK1r99dxqig4eCPlX8CV5uy3jf08rrp0Lx+1y03XsB+32Jovw93Z7OY2zwtCPrw3vxDppT7sF7aUfdx/SDonxnTLUq3/YOugk5lKOjqw0f/4cBIkrT09u+eL0u7kRi4L61mRD/NRGkjAg+a4/24qr40Lm21ZiaDJurXoDWh6hs0V79upgwGsYl7y6SZaQNLDJolfaLaCLeTfLD3eri53QN7r18/LoOq+gfwjinrWUwPY655/usmJazWhHwwOulNMmJAlJ5v1eTmTP0uEqYOVrShqupvtKaVALsmudZwmq655n90b//EuoMzzUe/O4VJTRIn6S932cGLroyDQSROodXem6+HMdeM/o1VtdBm5hvDr6rqK4uc54LPxW6ghkG3H2ur6rPjMqmqg2kBmqV2Pi0wON8mk327MDeg0ieq6s8T0r6F9ifarA6pqvk0nx+rWlP/QV63mWGR/avqX2PmfYt1t+OtE/I5svd6+HrV797lxjOUaam8tarOGzWjqk6mBUkBrj9tQJ152C1t8KfB41FJ/hf4Ma0WLLRr20uHlntQ7/Wrx2VeVefQ+ngdtRzMHYuL0Wr1b0xLddwH9w5n0brcGKmq/k77Yx3ad/r9xqXtvHlCXj+g1cyFxft+/llVDXeNNFJ3P3uT7u1Sjgo/7dp3WO/1Dfsz0gaNG3xXHFhVf5yyrpH3i2mD9PUHCJr3PUS1wfL6173LjkvLBmzzjGb6bI+YP/zZHvaeqvrnqBnd9g+u2dvRmtxLI9knqCQtsao6KsmvmetzcdCf3F7d86+q6pszZnfr3utZgghfAe7avb4NrQ+2ga/SOma/IfC1JK+j1Z47c8ayLIUn0v7pHWdaP4xn05oBT3LH3utzkzxgSvr+aJ/DN4uDvuDOpTU1neQQ4GVT0iyWO/VeX2mGbez36XZDWi2gUab1wdS/6b7MlLSL5Z20Zmih9Xn7gqH5D2Tuz4f31vz74VpM4/rgvCWtKw2Ary7wM9k/r5fyB+Yspn02FmJDzsVbMBcwPnyGdR1B665gMX2Z9f8Y2Yw2gNrtaef0g4EHJnlSVb1nAevo91l42NhUQFX9OclvgOvNmPfMx7Trn/ihtADOTWl9FG7L6M/DVWfI8jvjZlTVWUlOp/XdeHJN7hfzb73Xw+fID2jfN5ej9Z23Ba0v1e9VVc1Qxo1llvP+VrRz6VIs/A+bvvdNmf8H4KH9fZ1kK+b6P/99Vf1qSh79+5vhQPhXmevT+YtJXgF8sqr+OiXPhTiWFijcHnhqtx3vAY5ZrOPe/Vk3+E76TlWdNmWRrwDP7V7fhtYH8jjTzo+/0LZtsb6f53NtHwRwzwe+uEjrn8WGfFf076XOm+Feqh/o7N8vLlb/27Pms7Hv1Qa/Tc5h+nfoYbR75C2Y/ifXfMs9KdCrizCDoJK0PN5P6/vo3kl26KbdqzdvVlfqvZ72I2I4zZWG5j2XFii5MnDn7nF2kqNpfZ4dChza/du6XL5SQ4MizNPfxtWS6VnTe/247jGr4ZvFwT/Rf6zJg/LAugMvbGxreq8/NM9lJ90QzxKEHrj4PNe7UIfS+pq6LrBXkhcPnQODwcAuoPWZuVAnM7dfL73APLbrvf5773U/8LPQ2nWDPIrlqbk4ycb4obIh52K/BslxM6xrljTztbaqPjNm3juSvJL243IHYL8kP6mqscG/MRaynbMGQWc6pkluQatpfs0Z873U9CTrfHZGGRz7WdPB0DlSVeckeRKtz+QtgL27xylJvkmrkXRwV7tuKa2Ea/AZtH37Q9ofLh8eUTP3csz9Dp1671JVxyU5r1tm+N7lk7Qa/w/o5r0FeEuSX9IGiDyC1sx92vGeqgui/x/mBjt8Uvc4eei4/3BCNtMs5n1d35lVNW2AysH5sVjnxnyu7YMg6De6WstLZUM+M2t6rwfXgFldeC9VVecl+Qdz17dLM8/vxSSbs+6f8pPO9419nRi0GPp9V5N7rKo6O8kfaH3oTmppBCvj+qZVwObwkrQ8DqAFXTYHHgE8snt9QTdvVv1/lcc1/+vrNyPpL0sXXLw5sC9ztUO2Au4APIc2yuOfkjwtyVKNGr3YZqlBt930JGNtOXjRNW8ajIo57YcHzHb8FsuibOMI82kuuyS62jn7dW+vRBvwBoAk12SuZvRXZuj6YJI/9V7PGii6UJJLsG6ws/8DqB/4GdkUbAaDPM6oqpV2nDZGbfMN2cZteq9X2mcXgK6p+Yu7txcDXriAbDbmdk49pt0fgF9lLgC6ltY8/am07hseRKup/UDmAj2bzbDuWY/9Bn0OquqTwO1oI8sP/li5DO0a80rg+0l+kOSuY7LYGJbjs32lqkrvsU1VXb2q7ltV7xrTNcF871366YbvXS6gjXa9D+v+wXN94DG07oWOT/K+JJdjA1UbLfv2wEHMdUexPa17o1cBP0hybJJdxmQxzaLd1w1ZjnNjpmt7ku1of7zD0rdU2JD9spj3Uht0DzG0zFlMDoJutHMhycVpfwzB7J/twTm8ZVe7epyVdu+iTZRBUElaBl2/QYN+kvaiNYuH1o/an0YuNNo/eq+3GZtqzrZjlh2U64SqegqtdtHtgWfRalic3iXZgdY31zvnUcZNTf8HxZ5DP+6mPXYbLNg1qx78K30Jppvl+M1q2vf7YBvPBC42z2386CKWc6m8j7lj8YTe9Mcz13zsXWyYfhcWt1vA8oMmqgB/GarxfHrvdf8zPB+DPC6xiH0BTrIp32P2f7gt9Wd3Pr7ce73rAo7rcm/n05jrS/ddtBGkn1xVb6mqj1bVp6vqM12N2Im1iZZLVR1TVfej1Wy8D20U5G8wFxTdEfhqkj3GZHFRNd97l366UfcuF1TV26vqBrRg0GOBdzNXw3lz2r3Wt5NscFPvqvpeVe3OXN/ur6DVAh0ERW9O61ZoWvPoURb1vm4TcW/mAmcrrbuWSfr3i3vM815q96G8NvQeor/Md2do9bRRVNVZtObtMPtne3AOnzNDqylpg23KN6iStKnbv3u+affoT5tVv8+r686Qvp/mL+MSVdW5VfWtqnp9VT2Q1g/dY+kFkpLcZNzym7h+DbwNHfhgsI+v1g1uM8l1pswfjHIOk2tjwlx/YuMMtnFrZm+GusnqmkEOBnfaLclVupq6e3XT/saG//D6au/1vZNMGpRglP/ovf7q0Lz+HyMLGaSgn0dYeP+V/R8nG3oOrmT9a+N6g2mNMEuajaFf02cb5l8rabm3827d89nA08f1x9u1PLjaIq97UVXVaVX1xap6UVXdmdbVwGDQpQBv3IRbUGwMJzEXKJ5679LV2h80nx977wJQVb+uqvdV1ROq6tq04NDPutnXBv5rYUUeua7TquqgqnphVd2JdtwHfflejHUHdJrVRrmvW+EGTeF/NqWf3pVmMe8X+9/7D+/uUeZj0j3EUhv0G3+NaQOtdvfGV+/ebox+fKX1GASVpOXzKdat4XU68Ol55tEf2OjuY1PN6Y9ePmnk5HVU1TlV9T7WHVn9DkPJ+s1UNuUfe4f3Xi+kFkffYB9vwbod6I8ysclk1+RvUMtj2qiX0zqXP6L3+oFjUy29jXkODWovb0YL6N+Huf34vg2tNVFVx9L6zoXWDcLzZ102yXWBh/cmDY8GfAxzzQrv3g0kM1/9ASqmjSA8Tn8QlQ09B1eyY5k7F3eeIf1dNl5RJhoOtM/SpL3v6N7riU13u1GQp/1RM19X6J6PH9NkeuC2bFiz0yVXVSdW1ZOBn3STrgpcYxmLtKJ0tb0G/WauSTLt3FrQvUu3rm/TrvkDdxxKsmjfO1V1Aq1Z/qDv5jVJZhnMq5/Hccz1fXibJJOauMMG7JuVoOvLctCKZlOqBQrr3i9u6L3Up4HB6PJXYd1WKxMl2ZW57gTOYq4LoOUyOA+3ZPr34y7M1QLe5M5fbZoMgkrSMqk2wvObaCPZfgd4Y81z1Oeuyeyx3dsdk9xjXNokOwG7dm9/TwuszNfa3uvhwfX6zYKWq3noBuu6IxiMlHyTJI/agOz6Qe1njEuUZFtm61B/UJvlel1QYlReAZ4yJZ8PM1cL51lJLj/DupfCRjuHquobwE+7t48DnjiYRRvddzG8mLnauk9Lcq9JieHCY/9B5jrx/3QXUL1QN7DAx7q3lwGevYCyfYS5pppPX+Ax/1nv9a7jEnV9IN5oAfmvCFX1D+auAWuS3H9c2iS7sfgjw8/qnr3Xxy2gGeFhzP0Rt0eSSYHtp7D4vxsGQdurdP3IjfPiCfNWurW91w5Iu65P9l6PvaZ1NcWePma5Wa3tvV7nOHSDBQ0CoRv8vdP1Q93vX3ohx/1T3fPWwJPHJUqyPXOD+53HphdEhBYkGwwmuEmVv7sHH/zBeLMkD9uAvM4F/rs36TVJbjZtue663b+HeUsXjF9OM322O88Zs5y00RgElaRlVFUvqarbdo+XLjCbV/de75/kBsMJklwd+Chz1/3X9pseJrlSktd1Tc5G6gZueXRv0vDop7/rvb7FrIVfoV7AXJDwnUkeOilxkmsmeWOS4VHBP8Vcn2T3TvLcEctuResGYWRQc8jBg8VY97gP8gptYIaJtU675maDmpFXBL6UZM249EkuluQeo8q/yDb2OTTY5mvQ+iADOKyqfrMYmVfVocwdl82ATyd50ri+GpNcH/gacOtu0h8ZX/vjFczVBn1xN0LxSEm2T7LOOdAd8/d3b68AfHFcIL3L465dgLbvSOYC1Y8a1SVGt037j8t3E9JvxvqObrvW0dVee/fSFWmddd+QdX8wf2S+eXTBn0GNoW2Aj46qdZbkvkz4E2cDfK973hx42Yj1XizJa5irJbZiJNk9yZOTjB2tvvsuHtSwPZV1A2Nq594p3esnJHn8cIKuKe27mPuj4fCq+s5Qmv+XZOcp3Q3s03s9auT2td3zv01qvpvk/kn2mVQ7M8mNmauV93fW7c5kVm9krh/clyS593CCJNvQ7usGXY+QX3qOAAAgAElEQVR8cJ59yq8Ug5YJJ9AqBGxqXsDcH4zvmtb/b5JrJXnzqHOoqt4NfLx7e0ngkCRja5gmuS2tZc+abtLRwIvmV/yN4hPM3fveI8nLhj+faf6HuT9Uf41BUC0R/5GUpE1cVX2s63z/YbTRr49Nsj/wLdqN2U60mm+DH2tfAd42lM1WwDOBZyb5Hu2f7Z/TfrhtR/sB8nDmAnXfoAVE+n5Mu4ndAXhkkhOBbzMXuDmzqo5gE1BV307yX7Q+3bamBQeeTaul8Fvaj5PL0PpnvCNtH0MbFKOfz7ndD7uv0L5zX9XVDvwYrbnbNWmj116fVmt0WnOq/WjBiEsBj+ia2X0EOJl2E/ww2oAMHwX2nJLXM2h90d4JuCXwyySfoR3bv9GaMV0BuBmtq4Ur0kbDXS/4uliq6q9Jfk7br7sl2ZdWW23QVPb8qtqQvq4+QAsS9weBWexmYy+kdfL/ZNrn6u3AM5J8ijZy8Rm0z+kutODOoG/N3wH36vovXU9V/SrJE2mBzM2At3Xn1iDQXrQmt3fo8n0/6zaBhzbq9i1pg7XsBPwqyYG0a8UptPPqxrSuAq7flfPC2rlV9a8kb6fV7Lg4cGT3/vvd+9vT+iU7j3au3Gce+21FqaqDkgw+R1ekXVffR7umQRvI6nG0c+mTtNGpYfFGr12T9QdV2YwW8Lg98BDmag//ioV/Ll9G6/bjOrRrwc+SvId2/d+WVtt0D9r16mfMNW1cjO18K/BI2p86z+laK3yWdv25BvAI2jXqh12am47JZzlcldY9zOuSHEoL3vyO9n13eVp3EHswd615XVfTS52q+nuSvWnfh6EFkB5MG4zx77TvtEczV6v8ZNr35bDdaNfdPyX5KvAD2jm0Oe2e5YG0LhWgfZcMdzcCcAitz9vLAp9J8uFufYOa/d+sqtNpfdO+BXh977ivZe6435Z23Addlrx2IV2tVNUvkjwfeD3te+QLST5JGwztH8ANaE38B30p/o51a8tuSu7bPX+h6/Znk1JVRyZ5Ou282gb4eHcf/Xnad3P/fnFwvwXwkjFZ7kW7L7g/beC4TyU5lnb/+Tva9+tVadfmXZjrwuFoYPeu5ciy6u59H0m7f9uKVpv/nkk+Quv380q0+9VBtzlnA49crsGcdBFUVT58+PDhYyM9aDfQBfxiA/L4xSCfCWk2p9WWqCmPjwNbj1j+GjMsO3gcClx2TDn2nrDc2gVu/9peHmsWmMfxCzkOwIOAE2fcLycAlxqTz0Npwa9xyx5C+wE1eH/whDI9gHbDOC6vT9EC17PktRVtEIfzZ9zGd47IY7fe/OdN2Z9T03bbN648Zw2lfVJv3p4zHtP39pY5CdhyQz/nY9bzGNogFdP26QW05vA7zOOc/PsM+b5tzPLb0QJNsxzv9cpEC+ocNmGZU2h91L2qN+22G3Le9Jb5aG+ZKy7BubjVlH11Li3Y/eTetHttwDnzpAnrGvc4ArjKhDynXvto1/9fTljHibQf75/oTRv1PbKQz+PTus/AuHX/iBYM+zYjrgG9fCaeb/PdJ9POESZ/1/Uf5wOvAbLAc+LivbxGXsunfS4WmnbGfbhB+XR57UkL7E3aj78Bbjxm+e/OeCz+Ctx5TB7Xol27xi27U5dun3kc91cu9Lj3yvUMJn/fFy34NekacHSX7p8zrG/mtBPy2L1XtmdNSXuTXtr7L3B9RzN0nGZYpn/N3mMx0gIPpt1TzHJ+/A3YZkJeFwOeR6uIMC2vs2lda43Nb7G3edZjTAvSnjDDvhj5uezyeN2sx3c+aX1ctB82h5ekVaCqzquqJ9BGQX0P7QfDv2i1E35HC7LctaoeXCP6Ha2q39NqFDwGOIBWq/M02o38GbRmKh8B7ltVu9b42mr70X44fobWBGy+fdStKFX1KdoP8P8EvkBrrnwm7Z/9E4BvAm+m1Xi7SrWaIqPyORD4N1rA8Xe0/XIirabeE2hBo3+MWnZEXp+hNRU/gLaPB2X5GvCwqnoQM+73qjq7qp5Eq2nzGlrz1BNpNQ3+RavFcBDtZvzGVfXEcXktlm777gwcSAuAn7XIq/ha7/X7ayPVmqg2kNh1aLU6Pkb7DJ1GO15/odW+/G/gplX1yJqxD6/unLwWrTbmYbQfEOfSzsvfdOt6NPCsMcufVlX3p9XoezetFuE/acf8JNo5/aquXOuVqVoT6nvQ+oj8Dq1PyTO7fN4I7FhVX5llW1a67vNxf9qfGF+h7Z+zac2aDwBuV1X7su4ARSdvxCJdQDuHfkrrcmA3YOeq+vOkhabprv870s6po2nXon/RaoO+BrhZtT51B9t51qjvkQWu+020z/unaOfyebTr2ZG0UbxvXa3fvZXmXbRa989irsbXGbTvzNNotaPfTNt3z6mqWq6CrnRV9VHaqO0vp51/J9OuaX+jjXK9D3CjqvrpmCx2pX0Hv4F2/TqhW/4cWuDzq7Rz6XpV9fUxZTiO9r36Vtp5/y9aIGXY27t0z6bdEwwf92NpAakdq+r5G3rcq+oNtBqEr6f9ITD4DvkzrWbgI2mfkQ26BiyjQVP4M1n+Ec03SFV9nHa/+GTafdOfaPcvg3u0o2jnxr1p94tjB4Orqguq6lW0z8Xg/vN3tO/qs2j3oofR7s2uV1VPm5Tfcqmqw2j3Qc+lXdNPYu5e4xu0/kCvM+5zKW0s8TtZkqTl1w0MMggsfLmqVlw/eKtBkk8w13T5RlX180nppWmSHET7YXsBsF1V/XPKIpuctBGcT6LVJP5uVd1myiKSNFGS79D6xP5CVd13WnpJWgzWBJUkSRcJ3Siqg5onXzcAqg2V5LrMjdL+vdUYAO08ihYAhVYDSZIWLMkVaX0rwyY2KrykTZtBUEmSdFHxImAw8u+owTGkCyW5XpIrTZh/dVoz7s26Se9ckoItsiS3SnKJCfN3pjXjhNbs991LUS5Jq9p9aYP6FK1LCUlaEo4OL0mSVqUkV6ONdr4NrR/LvbtZP6IFr6RJ7gy8PcnhtP7LjqP1x3Y5Wv/LD2ZuFOgjgPcvQxkXw38B901yMG2QmT/TAhNXAe5Oq+k6GIH4lVX1m2UppaRVo6reRetbV5KWlEFQSZK0Wt2HNpBF35nAYx2oRDPaHLhb9xjnK8BDquqCpSnSRnEp4CHdY5QCXgu8eMlKJEmStMgcGElaIpe73OVqzZo1y10MSSvUBRdcwPe//30ALnWpS3Hd6153mUu06TvxxBP5wx/+AMAWW2zBNttsw5WvfGW23nrrKUtKcN5553Haaadx+umnc8YZZ3Deeedx/vnnk+TC82n77bdnu+22m57ZCnb22WdfuJ1nnXXWhdu52WabseWWW7Ltttty+ctf3s+NJElasY455piTqury09JZE1RaImvWrOHoo49e7mJIkiRJkiStGkl+P0s6B0aSJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqpaqWuwzSRUJ22LZ4yI7LXQxJkiRJknQRVPsetdxF2CiSHFNVO01LZ01QSZIkSZIkSauaQVBJkiRJkiRJq5pBUEmSJEmSJEmrmkFQqZPkpUkqyc7zWObwJHasK0mSJEmStIIZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBBUASW7Q9Yd56IQ0P05ybpIrDk2/Z5IvJjkpydlJfpvktUkuPSKPXZLsl+RnSU5PcmaSnyR5SZKLj0h/YT+dSR6e5DtJ/plk7Yzbdd0kByT5c5Jzkvyle3/dWZbv5bNnkmO68p6Q5ANJrjyfPCRJkiRJkrQ8Nl/uAmhlqKpfJDkM2CXJ9arqV/35SW4P/Bvwyao6vjf9xcDLgJOBLwAnADcFngXcO8ntqur0XlbPBW4AfBM4CLg4cAfgpcDOSe5WVeePKOIzgbsDnwcOA7abtk1JbgV8Dbgk8DngZ926HwHcP8ldq+roGfJ5OvAG4FTggO75nt02nDZteUmSJEmSJC0vg6DqexuwC7A3LYjZt3f3/M7BhCS70AKg3wLuXVWn9ubtBbyvm//0Xj77AL+rqnVGVE/ycuBFwB7AgSPKtitwu6r6/iwbkiS0gOWlgEdW1Yd68x4KfBT4YJIbVdUFE/JZA7wKOAW4RVWt7aY/H/g48KBZyiNJkiRJkqTlY3N49X0G+AuwV5KtBhO7Zu0PAX5Lq1k58NTu+Qn9AChAVe0P/IBW67I//bjhAGjnTd3zPceUbb9ZA6Cd29NqfX6rHwDtynAgcCRwfeCOU/J5BLAl8JZBALTL4wLg2cDYACpAkr2THJ3kaM48dx7FlyRJkiRJ0mKxJqguVFXnJXk38GLg34EPd7MeBWxNC0T2A5i3A84FHpzkwSOy3BK4fJLLVtXfAZJsA/wX8EDgerSm6uktc5UxxfvuPDfnFt3zuD5OD6UFQG8OfH2GfI4YnlFVxyX5I3CNcQtX1X7AfgDZYdtRwV9JkiRJkiRtZAZBNWw/4AXAE5kLgu4NnENr3t53Wdo59JIpeW4L/D3JFrTg462Bn9CavZ9IC6TS5bPVyBzg+DHTxxn0GfrXMfMH09cbvGlMPn8bM/94JgRBJUmSJEmStPwMgmodVfXnJJ8HHpjkhsBlaAMiHVhVJw4lPw24WFVtP2P296cFQN9fVXv1ZyS5EpODqfOtRTkYsOiKY+ZfaSjdtHyuAPx0xPxx+UuSJEmSJGmFsE9QjfK27nlvRgyI1PNt4DJJbjxjvtfpnj85Yt5dZi/eTAb9h+48Zv5g+rFT8hnMX698Sa4FXG2+BZMkSZIkSdLSMgiqUQ4BfgU8mjYg0q+q6rAR6d7YPb8ryZWHZybZJslte5PWds87D6W7FvDqDSzzsKOAXwJ3TLLH0Pr2AO5M28Yjp+TzIVpz/ad0I8UP8rgY8Fr8DEmSJEmSJK14NofXeqqqkrwDeEM3aVQtUKrqkCTPA14J/DrJF4Hf0foAvQat9uSRwG7dIp8HfgM8I8lNaLU1rw7sDhzUvV7MbXg08FXgwCSfBX5BGxH+AcA/gP/oRnmflM/abhtfD3w/yYG0JvL3pPUn+iPgpotVbkmSJEmSJC0+a7FpnP2BC4CzgfePS1RVr6bVqjwIuAPwNODBtFHe9wNe1Ev7L2BX2oBLNwaeSgsgvhx45GJvQFV9B7hVt77bAc8Gbg98BLhVN3+WfN4APJwW4N0LeCxtYKfbA6csdrklSZIkSZK0uFI13/FmdFGQZGfgMOCDVfWoZS7OqpAdti0esuNyF0OSJEmSJF0E1b5HLXcRNookx1TVTtPSWRNU4zyne953WUshSZIkSZIkbSD7BNWFun46dwduCdwL+MKsTcYlSZIkSZKklcogqPpuCbwCOB34OLDP8hZndbnl1W/A0au06rkkSZIkSdJKZhBUF6qq/WkDIkmSJEmSJEmrhn2CSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtVSVctdBukiITtsWzxkx+UuhiRJkqQNVPsetdxFkCR1khxTVTtNS2dNUEmSJEmSJEmrmkFQSZIkSZIkSauaQVBJkiRJkiRJq5pBUK1ISdYkqST7L3dZJklyeBI71pUkSZIkSVrBDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIKucEnul+SQJH9NcnaSvyQ5Isk+Q+nWJlk7Jo+Xdv1r7jw0vbo+La+c5ANJTkhyZpJjkjx8QpnumeSLSU7qyvTbJK9NcukRadd2j0sleUP3+twkL53HPrhBks8kOTnJv5IcmeQeY9JuleR5SX6U5Iwkpyf5RpKHDKXbNsk5SY4amr51krO6ffOooXn7dNMfO2vZJUmSJEmStPwMgq5gSfYGPgvcCPg88Hrgi8DWwGMWaTWXAb4J3AR4H3AAcC3gQ0mePaJMLwYOBm4DHAT8L/Ab4FnAUUkuNWIdWwKHAg8AvgK8GfjdjOW7JvAt4LLAO4GPA7cEvpTkoUNl2xL4MvBKYAvgrcAHgOsBByZ5xSBtVf0T+C5w6ySX7GVzB2Cr7vVdh8qya/d8yIxllyRJkiRJ0gqw+XIXQBM9ETgH2LGqTujPSHK5RVrHTWmBxT2r6oIu71cBxwD/k+STVXVcN30X4GW0oOS9q+rUXnn2ogVRXwY8fWgdVwJ+Btylqv41z/LdGXhdVV0YkE2yb1eGdyT5UlWd3s16JnAX4EvA/arqvC79y2gBz+cn+UJVfbNLfygt6HlnWkAXWuDzfODr9IKgSS4G7AwcV1W/n+c2SJIkSZIkaRlZE3TlOw84d3hiVZ20SPmfDzx3EADt8v4drYbnFkC/SfhTu+cn9AOg3TL7Az8AHjFmPc9cQAAU4DTgv4fWdTTwIeDSwAN7sx4LFPCMQQC0S38C8PLu7eN76Qc1Ovs1Pu9KCwB/Erhqkut1029Gq406r1qgSfZOcnSSozlzvcMoSZIkSZKkJWBN0JXtQ7Qm8D9NciBwBHBUVZ24iOv4Qxf0HHY48BLg5r1pt6MFZB+c5MEjltkSuHySy1bV33vTzwJ+tMDyHVtV/xhTvkd35Xt/16T9OsCfq+oXI9If2j33t+dbwJl0QdAk2wG3AF7TS39X4FfMNYU/lHmoqv2A/QCyw7Y1n2UlSZIkSZK0OAyCrmBV9YYkJwH70GphPg2oJEcAz+5qRG6ov42Zfnz3vF1v2mVp58xLpuS5LdAPgp5QVQsNAM5avsHzX8ekH0y/cPCmqjonyZHA3ZLsQAvybgYcUlU/T/IXWhD07d1zMc8gqCRJkiRJkpafzeFXuKo6oKpuSwtA3gd4D60Pyy93gbuBCxgf1F5v1PaeK4yZfsXu+bTetNOAU6oqUx7DfWZuSA3IWct32tD0YVcaSjdwKBBaTc+7AmcDgxHjDwN2SbIVcCfgp8N9s0qSJEmSJGnlMwi6iaiqU6vqi1X1BGB/YHtaYG7gFOAKSbYYsfhOE7K+epI1I6bv3D1/vzft28Blktx4xmIvhlsMjd4+sHP3/H2Arsn8b4GrJLnuiPS7dM/HDk3v9wu6K627gbN687YH/g+wDY4KL0mSJEmStEkyCLqCJdktyajanYMaoGf0pn2XVhP0MUN57EUbAX2czYBXd6OfD5a5Jq35/XnAB3tp39g9vyvJlUeUd5skt52wroXYDnjx0Hp2og3AdBrw6d6s99Jqdb42yWa99JcD/m8vTd8xwKnA/YEbs26gc/D6+d2zTeElSZIkSZI2QfYJurJ9FDir67dyLS3AdyfgVrTg3dd6ad9CC4C+PcldgT8COwK3B74A7D5mHT8CbgMck+QrtKDjQ2lN6J9TVb8dJKyqQ5I8D3gl8OskXwR+R+sD9BrAXYAjgd02eMvnfB14fJLb0JqpX6kr38WAJ1bV6b20rwPuRQto/rAr3yWAB9MCx6+pqiP7mVfVBV0fq/fvJh3Sm/eHJL8Frg2cTxuYSpIkSZIkSZsYa4KubM+jjWB+C9rgSI8BtgCeC+xSVecOElbVz4C70QKF9wX2Bs6hDfZzzIR1nEILlP60y38vWmDzEVX12uHEVfVqWp+kB9FqmD6NFmS8Cm0U9BctdGPH+F1XvlOAJwEPoTVpv3dVHThUtnOAuwMv7CY9hTaC/K+Bh1fVc8esYxD4PB0YHmxqMO+YqhruT1SSJEmSJEmbgCx80G5t6pIUcERV7bzcZbkoyA7bFg/ZcbmLIUmSJGkD1b5HTU8kSVoSSY6pqknj4QDWBJUkSZIkSZK0yhkElSRJkiRJkrSqOTCStERuefUbcLTNZiRJkiRJkpacQdCLsKrKcpdBkiRJkiRJ2thsDi9JkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVUtVLXcZpIuE7LBt8ZAdl7sYkiRJ0npq36OWuwiSJC1IkmOqaqdp6awJKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIqnlLsjbJ2o28jqcm+VmSM5NUkqd10yvJ4Rt53S/t1rPzxlyPJEmSJEmSlsbmy10AbbguKHiXqspyl2UxJNkTeDPwfeBNwNnAt5e1UJIkSZIkSdpkGQTVSrT74Lmq/jI074bAGUtcHkmSJEmSJG3CDIJqJboywIgAKFX1i6UvjiRJkiRJkjZl9gm6giW5X5JDkvw1ydlJ/pLkiCT7dPPXJCngLt376j0O7+Uzth/NJPt389cMTU+SJyf5aZKzkvw5yb5JthuRx5O6PF48Zh1XTHJukh9P2d6Xdtuzy/D2TNqWfh+eSfZI8t0kZyQ5OclHk1xlzPpumeTgJP9IcnqSryW53YTy3SnJ55P8qTsexyf5dpKXTNouSZIkSZIkLS9rgq5QSfYG3gkcD3weOAnYAbgp8BjgbcCpwMuAvYBrdK8H1m5gEd4EPBX4K7AfcC5wf+A2wJbAOb20HwReDTw+yf9U1flDeT2Wdq69c8o6D++e92L97ZnFPsD9gM8BR3RlfSiwY5KbVdXZg4RJbg98rduWTwG/AW7WleHQ4YyT7AYcBJze5f9nYHta8/x9FlBWSZIkSZIkLRGDoCvXE2mBxh2r6oT+jCSXA6iqU4GXdqOYX6OqXroYK+4ChE8FfgvcuqpO7qa/EDgMuBLw+0H6qvpnkg8A/wncC/hCL68Aj6f14/mBSeutqsOBwzdge3YDblVVF9Y4TfJh4GG0AO7HemV6L7A18ICq+mwv/X/RAsDDnkCrOb1zVf2wP2NwPCRJkiRJkrQy2Rx+ZTuPVgNzHVV10kZe72O65/8ZBEC79Z4FPH/MMm/vnp84NP0ewDWBA6vqtEUt5fr+tx8A7byre751b9rtgesDX+8HQDv70oK/45w5PGHS8Uiyd5KjkxzNmesdSkmSJEmSJC0Bg6Ar14eASwA/TfLGJA9IcvklWvctuucjRsz7Bi04u46q+inwdeBeSa7Wm7V39/yORS3haEePmPbH7vkyvWljt69ryn/kiHw+1D1/J8k7kjw0yVWnFaiq9quqnapqJ7beYlpySZIkSZIkbQQGQVeoqnoD8GjgD7Sm6Z8G/pbksCQ7beTVDwY/+tuIcp0P/H3Mcm8DNqM1fyfJFWl9dP6gqr67Eco57NQR0wYB281608ZuX+f44QlV9Slgd+D7tD5OPwr8savlefeFFVeSJEmSJElLwSDoClZVB1TVbYHLAvcB3gPcGfhykh3mkxXj+3+99Ihpg2brVxiekWSzrjyjfIoWWHxcl27WAZGW2tjt61xx1MSqOqiqdqXVKr0r8EbgxsAXktxo0UspSZIkSZKkRWEQdBNQVadW1Rer6gnA/rRRye/US3I+XBigHOUU4GrDE7v0NxuR/tju+S4j5t2JMQHVqjoXeDdwFeC+tBqh/2SuKflKMXb7un1yx0kLV9W/qurQqnoG8AraCPP3WvRSSpIkSZIkaVEYBF2hkuyWZFSwcVAD9IzetEHz9KuPye67wNWT3GNo+ouAa4xIv3/3/MIk2/fKdHHglZPKDexHC8ruSxsQ6cNV9Y8pyyy1bwK/BO6c5P5D854MXHt4gSR3TbL1iLwGtUnPGDFPkiRJkiRJK8C4JtJafh8FzkpyJLAWCK0W5q2AY4Cv9dIeAjwY+FSSL9JGMP99VX2gm/864J7AZ5McCJxMGyH9msDhwM79FVfVUUneAjwF+EmST9BGqb8/rVbpX8cVuqr+kOQgWl+gsPKawlNVleRxwFeBTyb5FPAbYEfgbsDBwG5Di70eWJPkcNrxOAe4JbAr8Hva8ZIkSZIkSdIKZE3Qlet5wLdoI5nvAzwG2AJ4LrBL1/R84N20GprbAc8BXg48bjCzqg4BHgD8FNiTNuDSWuDWtADeKP9FC4KeBjwReBjwZVqQ8JwpZX9v93x0VR07MeUyqaqjaEHlr9Gasj8F2IoWEP7OiEVeAXyJ1gfo44En0WqBvgK4VVWdsvFLLUmSJEmSpIVIVS13GbTKJHkp8BLg8VX1nmUuzoqRHbYtHrLjchdDkiRJWk/te9RyF0GSpAVJckxV7TQtnTVBtaiSXJJWS/Jk4CPLXBxJkiRJkiTJPkG1OJLch9Z0/760ZuLPqioHC5IkSZIkSdKyMwiqxfJgWl+jf6P1T/rG5S3OynPLq9+Ao21mJEmSJEmStOQMgmpRVNVewF7LXAxJkiRJkiRpPfYJKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKsxOcrUAACAASURBVEmSJEmSJGlVMwgqSZIkSZIkaVVLVS13GaSLhOywbfGQHZe7GJIkSRcJte9Ry10ESZK0BJIcU1U7TUtnTVBJkiRJkiRJq5pBUEmSJEmSJEmrmkFQSZIkSZIkSauaQdAVKsleSSrJXstdFkmSJEmSJGlTZhBUkiRJkiRJ0qpmEFSSJEmSJEnSqmYQVJIkSZIkSdKqZhB0ESRZ0/XfuX+S6yU5MMkJSS5IsnMv3fZJXpnk50nOTHJakkOS3GOe67tqkn2THJfk7CR/T/K5JLcakfbKSV6c5Kgkxyc5J8lfknw4yQ3H5H+/rlx/7fL/S5IjkuwzIu3M25RkyyRPTXJsklOSnJFkbZLPJrnbjNu+f7ev1yR5YpIfJzkryd+S7Jdkuw3ZZ12eleQJQ9Mf200/I8lWQ/O+25Vh61m2QZIkSZIkSUvLIOjiujbwHWAN8CFgP+B0gCTXAI4BngecCLwDOBC4IXDwcNBtnCS3AH4A7AP8EngL8HngzsCRSe49tMidu3WeCnwSeCPwbWAP4HtJdhzKf2/gs8CNunxfD/+fvTuP+72e8z/+eGoRTto4RFowM5niUEdEqVMqW9aRsY1qKLLFjB8RshsGTbI1SpYoU0OMEtrUqeSUrCEpLUqlOu376/fH+/PVt2/f61zLuc51nb497rfbdftc5/N5f96f1+dz9dez98JRwP2AXQbaTvadDgb+C1gJ+AqwH/Bj4LHAMyby/n0+1v38HPgMcDHwGuBbgw0n+c2O7Y7bDnSzTXe8H7B5X9+rAZsAp1bVjZN8B0mSJEmSJM2AFWe7gBGzBfCRqnrnkGtfBtYDXlpVh/ZOJlkdOAHYL8l3quovY3WeZEXgm8AcYEFVndh37WHAT4EDk6xfVTd3l44DHlJV1w70NQ9YCHwUeGbfpd2BW4B5VXXZwD0Pmuo7dWHhP9NC0ydV1e0Dfa811nuP4cnAY6vqgu7+Fbt3XZBks6o6ve/8hL9ZVf0hyQXANklSVdU136brf2taQHpCd35rYIXumiRJkiRJkpZDjgSdXn8B3jd4sgsctwKO6A8LAarqauC9wCrAi8bp/9m00aaf7g/zun7+TBsZ+VD6RjFW1WWDAWh3/ufcGRquNHD5NuDWIfdcsRTvVECAm4E7hvT91+GvPKb39wLQ7v7bgC91/9ysr92kvxntuzyYNkKVJP8IrA0cDpw50Lb3+7EMkWS3JIuSLOLGu31SSZIkSZIkzQBHgk6vn/eNwOzXmz69WpJ9hlx/cHccukbnkH7WG6Ofv+vr56jeySTPBl4LzAcexN3/7g8CLul+P4Q2Bf7XSQ4DTgQWVtXlY9QyoXeqqmuSfBfYETgryRHAScBPquqGoW+7ZIuGnLuwO64xpM7JfLPjgJ1pAecvuHMq/LG0pQ7emmTVLlzeBrgOOH1YkVV1AG1ZBDJ3Tg1rI0mSJEmSpGXLEHR6XTrG+d5U7+26n7HMGaf/Xj8vHqfd3/pJ8ibaOpxXAT8ELgBuoI3MfD4wD/jbRj9V9ckkV9DWz3wTsCdQSU4E3lZVvfBxKu/0EuDtwMu4c8TsTUkOB/59SUsBDHH1kHO3dccV+s5N+ptx13VBP9UdL6qq3yc5Fvh/wFZJfgpsBBzVjUSVJEmSJEnScsgQdHqNNdJvcXd8c1XttxT99/p5XlV9Z7zG3XqY76OFs5tU1SUD1zcfdl9VfQX4Sre251OAFwC7AsckeUy3Vuik36nbOGgfYJ8kj6BtTLQz8AraCMstJ9LPJE3qm0GbJp/kd7Sg8760dT+P7C6fTFsz9enAA7tzrgcqSZIkSZK0HHNN0JlxWndc2pBvsv08CFgdOGVIADqHtqv5mKrq6qo6qqpeQ9vZfc2+Zy/VO1XVhVV1CLADcA6wxRQ2R5qIqdZ5LG106Oto3/BYgG7q/mm00aH90+QlSZIkSZK0nDIEnQHdFPKTgBcm2XVYmySPTTJ3nK6OBM4FXp/kWWP0s3mS+3f/vIw29X3TLvTstVmJNkV+cLd3kjyjG0E6qFfbDVN5pyQPTvKkIc0eAKxKm8p+y7B+ltJkv1lPb3TnXgP/7v2+MfBc4K/Az6evXEmSJEmSJE03p8PPnJfRwrMDu3U6f0Jb13Id4HG0UG1zWnA5VFXdmuSFwDHA95KcApxFCyYfATwReCRtJ/MbquqOJPsB7wB+meRIYGVgAW1U5/Hd7/0Opa3TeTJwPm1H9y27vs8AfjTFd3o4cFqSs2k7rF9Im07+HNru7PsN28V+aU32m/XdejxtF/u5wG+r6uK+a8fSpvU/GDi8qtzwSJIkSZIkaTlmCDpDquqiJJsCbwReBLyctoHPpcBvgE8Dv5xAP79IMg94Ky1A3IUW1l0C/Ax4L3BF3y3vBi4HXg3sTlsj84fA3ty5OVG/d9CmqG8CPAu4CfgTbUOjz1XVrVN8p/O72ramBa8PAq4Eftc989Dx3n2qpvDNqKork5xF+w6Da37+BLieNorV9UAlSZIkSZKWc3EQmzQzMndOsdO82S5DkiTpXqH2XzjbJUiSpBmQ5Iyqmj9eO9cElSRJkiRJkjTSDEElSZIkSZIkjTTXBJVmyKbrbsgip2VJkiRJkiTNOEeCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkpapmuwbpXiFz5xQ7zZvtMiRJ0j1U7b9wtkuQJEla7iQ5o6rmj9fOkaCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYKOoCQ7JzkiyR+T3JjkmiQLk7xijPYnJKkkKyd5T5LfJbk5ycED7V6a5PgkVyW5KcnZSfZOct8hfT4/ydeS/D7J9UmuS3JGkjclmfB/d0m27mrbJ8n8JN9Psrir4Ygkj+jaPTLJoUku7975+CTzhvT390k+mmRR1/bmJH9KckCSdcZ5/uOTfC/J1UluSHJikqdM9F0kSZIkSZI0OwxBR9PngPWBHwP7AocC6wFfTfKBJdx3BLAHcEp33y97F5IcCHwdeDTwv8BngCuBDwDfT7LiQF8fBTYBfgJ8GvgqMAf4L+DLU3inJwIndb//N3A68ELg2CQbdv9eB/gK8D1gK+CHSeYM9PNC4LXAhcA3utp+A7wa+GmSh4/x/Pm077IK8EXg/4Atuuf/wxTeR5IkSZIkSTNkMLjSaNi4qs7tP5FkZeBo4B1JPl9VFw+5b73u3isG7t0Z2BX4FvDyqrqx79o+wHuB19MCzp5nD6nhPsCXgH9Jsn9V/WQS7/Qs4BVVdUhffwd2dZ0CfKKqPtR37d3A+4F/Hajrq8Cnqurmgdq2p32fvYHXDXn+s4Fdqurgvnt2Bz4PvJkWHkuSJEmSJGk55EjQETQYPnbnbqGN3lwR2HaMW989GIB23gzcBuzaH4B2PgD8FXj5BGq4gzsDyR2W9A5DnNwfgHZ6I0oX00ae9vtKd3z8QA0XDwag3fkfAL9eQl0L+wPQzkG077LZWEUn2a2ber+IG28dq5kkSZIkSZKWIUeCjqAk6wJvp4Wd6wL3G2gy1pTv04f0dX9gHnAFsGeSYffdDDxm4L61gLfRRnA+EnjABGsYy6Ih5/7cHc+qqtsHrvVGut5lnc+0F3g5sDPtvdYAVuhrcstEn19Vtyb5S9fHUFV1AHAAQObOqbHaSZIkSZIkadkxBB0xSR5JCzPXoK2h+QPaSMnbaeuEvgq420ZGnUuHnFsDCPBg2rT3idSwOvBTYIOulq/Q1g+9DVidNrJ0rBrGsnjIudvGulZVt3WB7UoDlz4J7AlcAhxDC0t7o1t3pi0JMMzVY5y/jbuGqJIkSZIkSVrOGIKOnrcCazGwfiW03d1pIehQVTVspGIvYPxZVW0ywRpeTQtA31dV+wzUsDktBJ1xSeYCbwJ+BTylqq4duP7S2ahLkiRJkiRJy5Zrgo6eR3fHI4Zc22qynVXVdbS1MjdKsuZs1DCNHkn7b/4HQwLQdbrrkiRJkiRJGjGGoKPn/O64df/JJDvQRmhOxSeBlYGDuqnud5FkjST9o0THquEJwF5TrGE6nN8dt0jytynsSeYA/40joyVJkiRJkkaSoc/o+SywC/A/SY6grXm5MfAM4JvASybbYVUdlGRTYA/g3CTHABcAa9KmvT8N+BLw2u6Wr9A2Rdo3yQLgHODvgOcA/zuVGqZDVV2a5FDgn4GzkvwAWA3YDrgJOIuB3eQlSZIkSZJ0z+dI0BFTVb8AFgCn0HZmfx3wQOCFwOeXot/XAzsCpwJPp609+lxaiPhxYN++tn8GtgS+B2wBvIG24dAewDumWsM0+Vfgw8D9gNcDOwD/BzyF4ZsvSZIkSZIk6R4uw/fCkTTdMndOsdO82S5DkiTdQ9X+C2e7BEmSpOVOkjOqav547RwJKkmSJEmSJGmkGYJKkiRJkiRJGmlujCTNkE3X3ZBFTmOTJEmSJEmacY4ElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNIMQSVJkiRJkiSNNENQSZIkSZIkSSPNEFSSJEmSJEnSSDMElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNJSVbNdg3SvkLlzip3mzXYZkiTpHqr2XzjbJUiSJC13kpxRVfPHa+dIUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00u7VIWiS9ZNUkoNnu5bJSnJ+kvNnu47lgd9CkiRJkiRJS3KvDkElSZIkSZIkjb4VZ7sATdm2s12AJEmSJEmSdE9gCHoPVVXnznYNkiRJkiRJ0j2B0+E7STZM8u0kVya5PsnJSbZfQvuXJjk+yVVJbkpydpK9k9x3SNtKckKShyb5YpKLk9yeZOfu+sFdm/WT7J7kl12ff0lyQJLVhvQ55jqYk6ltCe/3kCT/meR33fe4uvv94CSP7Gu3cpI3JDkqyZ+S3Nx9wx8leeYYfZ/f/dw/yceTXNDd94ckb0+SIfeke86vu3e6OMn+w77NVL/FBP5OE/omkiRJkiRJWr44ErTZADgV+BXwBWBt4CXA0UleVlWH9TdOciCwK3AR8L/A1cCTgQ8A2ybZrqpuG3jGmsBpwHXdPXcAfxlo8zFgB+C7wA+ABcBrgEcD20zkRaZY22Af9wcWAo8CftjVE2A94HnA4cAf+97rv4BTuraX077fjsBRSV5TVV8c8piVund8GHA0cBvwfOCjwCrA+wba7wu8CbgEOAC4tavlScDKwC3T9C2G/p0m+U0kSZIkSZK0HDEEbZ4G/GdVva13Isn+tGD080mOrqpruvM704K1bwEvr6ob++7ZB3gv8HpaMNjvscBXgV2XEEI+GXhsVV3Q9bcicBywIMlmVXX6kl5iKWobtC0t7Nu3qt4y8IyVgf5RlFcB61XVRQPtVqOFhh9Lckh/LZ2HAT8HtutdS/I+4PfAW5J8uKpu7c4/hRaAngtsVlVXduffBRxPC13/NE3fYujfKcmOk/gmkiRJkiRJWo44Hb5ZDLy//0RVLQIOAVYHXtB36c20UYu7Dgn2PgD8FXj5kGfcAvz7OKMw398LQLsabgO+1P1zswm8x1RrG8tgH1TVLVV1bd+/bx4MQLvzi4GDgDWAJ47R/5v666yqy4AjgdWAf+hrt0t3/FAvAO3a3wTsNUbfy+rvNO436ZdktySLkizixlvH6FKSJEmSJEnLkiNBmzPHCLFOAF4FPAH4cjcleh5wBbDnkKUrAW4GHjPk/PldyLcki4acu7A7rrGkG5eytkEnAhcD70iyCXAUbVTnWVV1+5BnbwS8jTaidm3adPZ+Dx/yjMVV9Ych54e97yZ9dQ06iRZ29tezLP5Ok/omPVV1AG36Ppk7p8ZqJ0mSJEmSpGXHELQZXJuz59Lu2Nt8Zw3aOpAPpk2nnoxLx2/C1UPO9QK+Fca5d2lqu4uquibJk2nrcj6Xtk4pwBVJPgt8sG+q+pNpU/ZXBI4FvgNcQ1tL8/G09TKHTRUf9q4w/H173/9uf6equj3JXwdOT/vfaTLfRJIkSZIkScsXQ9DmIWOcf2h3XDxw/FlVbTKk/ZIs61GAS1Pb3XRT3P+126n9H2kbM70eeA9tGYV3d033Bu4HLKiqE/r7SLIXLQRdWr13ewgDmw8lWQFYizZKc7D9tP6dJvFNJEmSJEmStBxxTdBmkySrDjm/dXf8GUBVXQf8GtgoyZozVNuELKvaqvl1VX0a2K47/fy+Jo8GrhwMQDtbTVMZZy6hvy0ZCPOX9d9pAt9EkiRJkiRJyxFD0GY12mi+v0kyn7ZxzmLaDuM9nwRWBg5KsvpgR0nW6NaMnA3TUluSjZOsP+RSb8TsDX3nzgfWTPK4gT7+lTunjC+tg7vju/pDzSSrAB8Z455p/TtN8ptIkiRJkiRpOeJ0+ObHwKuTPIm22c3awEtoIfHuVXVNr2FVHZRkU2AP4NwkxwAXAGsCG9A2B/oS8NqZfYVpre3pwCeTnAL8FrgMWIc2tf0O4ON9bfelhZ0nJ/kmLTSeD2wBHA780zS818IknwbeCPwqyeHArV09VwGXDLlnuv9Ok/kmkiRJkiRJWo4Ygjbn0cKwj3bH+9KmYL+/qo4ZbFxVr09ydNf26cDqwJW0kO3jwNdmqO67mabajqGFm0+jhXwPpAWNPwQ+WVWn9D3v+0l2pK0N+hLgduB0YAHwSKYhBO28Gfg9bQ3O3YG/0kbovhP4+bAbpvnvNOFvIkmSJEmSpOVLqpb1fj2SADJ3TrHTvNkuQ5Ik3UPV/gtnuwRJkqTlTpIzqmr+eO1cE1SSJEmSJEnSSDMElSRJkiRJkjTSXBNUmiGbrrshi5zGJkmSJEmSNOMcCSpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkZaqmu0apHuFzJ1T7DRvtsuQJEn3ULX/wtkuQZIkabmT5Iyqmj9eO0eCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZgh6D5PkhCSTWsg1SSU5YRmVNJHnn5/k/Nl6viRJkiRJku7dDEHvpZLs04WjW892LZIkSZIkSdKytOJsF6AZ8RjghtkuQpIkSZIkSZoNhqD3AlX129muQZIkSZIkSZotToefIUnmJLklycKB8/dLclM3Nf2VA9f26M7vOqS/FZO8M8k5SW5OcmGS/0iy8pC2d1kTtFuf873dP4/vrtfgWqNJ7p9kryRnJbk+yXVJTk3y0il+g/sn+XiSC7qa/5Dk7UkyRvudkvw4yeIkNyb5ZVfPfcd7x4FrB3fX1x84/9wkxya5pKvnz0lOTLLHkD7WTPKRJGd3tSzu7t1+Kt9CkiRJkiRJM8eRoDOkqq5LcjrwpCSrVtW13aWnAr1Qb1vgq323bdMdjx3S5deBLYGjgWuAZwH/D5gL7DJOOfsCzwe2Ar4MnD/YIMnqwHHAE4AzgYNoofkOwNeTbFRVe4/znH4rAT8AHtbVfFtXw0eBVYD3DTz/w8BewBXdu14HPBP4MLBDku2q6tZJPH/w/XYDvgBcCny3e85c4HG07/fZvrbrAScA6wMnAd8HHgA8B/h+kt2r6r+nWoskSZIkSZKWLUPQmXUcLfR8GvC97ty2wO3Aj7vfAUhyH2Br4I9V9achfT0K2Kiqruzavwv4OfAvSfaqqkvHKqKq9u1Czq2Ag6vqhCHN9qUFoG+vqo/11bUK8G3gnUkOr6qzJvLitPDz58B2VXVj19f7gN8Db0ny4V6omWRzWgB6IbBZ712S7AV8ixY+vo0WiE7V7sAtwLyquqz/QpIHDbT9MrAe8NKqOrSv3eq0cHS/JN+pqr8sRT2SJEmSJElaRpwOP7N6Izq37Tu3LXAGcASwTpK/784/HliL4aNAoYWTV/b+UVXXA4fQ/qbzl6bIJGsBrwAW9Qeg3XNuAt4OBHjZJLt+Uy8A7fq6DDgSWA34h752ven/H+wPc6vqNuDfgDuAV0/y2cPcBtxtNGlVXdH7Pck8Wlh8RH8A2rW7mraswCrAi4Y9IMluSRYlWcSNUx64KkmSJEmSpKXgSNCZdSpwI10ImmQ1YBPgY7RRonTXfs+dU+GPY7hFQ85d2B3XWMo6nwisAFSSfYZcX6k7PmYSfS6uqj8MOT+s5k26493evap+n+QiYIMkq3dB5FQcAnwC+HWSw4ATgYVVdflAu82742pjfIsHd8eh36KqDgAOAMjcOTWsjSRJkiRJkpYtQ9AZVFW3JDkZeHqSubSAbQXg2Ko6O8mfaSHo57pjMUYIOkb4d1t3XGEpS12rOz6x+xnLnEn0OVZYOazm1brjJWPccwmwbtduSiFoVX0yyRXAHsCbgD1poe+JwNuqqhcy977Fdt3PWCbzLSRJkiRJkjSDnA4/846jTSXfhhZ03gz0dow/HljQ7X6+JfDrwfUqZ8ji7vipqsoSfhYs4+c/dIzraw+0gxYYjxXqrz7sZFV9paqeTAs6nw0cSFuv9ZgupO5/xpvH+RbjbUYlSZIkSZKkWWIIOvP61wXdhjYF+6a+a2sCr6PtPj7WeqDT4fbuOGzU6Om0dTe3XIbPX5KfdcetBy8keTSwDnDewGjYq4BHDGm/Am191TFV1dVVdVRVvQY4mPY36L37ad1xtr6FJEmSJEmSlpIh6Mw7gzaF+3nARtw16Oz9vld3HGs90Onw1+647uCFbvTpIcD8JO9OcrcRlkkelWSDZVTbQd1x7yS9NTd7geZ/0v67PXDgntOBdZNsP3B+b9rO7neR5BnD3gvojQC9AaCbFn8S8MIkuw5pT5LH9o0clSRJkiRJ0nLGNUFnWFXd0a07+bzu1LF91y5Ici7wKNpIzROXYSnH00Z7fiTJxrSRlFTVB7vrbwD+Dng/8MpuLdO/AA+jbQL0ROClwHnTXVhVnZLkY8D/A36V5HDgeuCZwMbAycDHB277T2AH4Mhuo6MrgacAGwAncPdRpYcCN3XvdT5tiYItu/c6A/hRX9uX0QLpA5O8CfgJLcheB3hcV9PmwGwsXSBJkiRJkqRxLJORoElWTfKAZdH3iOgFn9dw913ee9fOqKrFLCNVdTbwKuBS2uZAH+h+etevAbYC3ghcAbwIeCuwALgWeAvww2VY39tpIes5wL/QNi+6D21k53ZVdctA+2OB5wO/Bv65e7fzgc2APw15xDuAU2k70e8B7ELb9f7twIKqurWv74uATYF30cLpl3f1PAW4ANgd+OXSv7UkSZIkSZKWhVTV5G5I1qYFYZdX1Q8Hrj0G+BJ37ij+Y+A1VfWHaahVukfL3DnFTvNmuwxJknQPVfsvHL+RJEnSvUySM6pq/njtpjISdFfgq8DTBx64CnA0LQBN97MV8KMkq07hOZIkSZIkSZK01KYSgvbCz8MGzv8LbZOdq2m7m+8M/Jm2Y/frp1ifJEmSJEmSJC2VqWyMtH53PHvg/IuAAt5ZVV8ASHIZcBTwXOCjU6xRGgmbrrshi5zGJkmSJEmSNOOmMhL0wcA1VXVj70SS+9A2iSngf/ra/pC2A/mGS1OkJEmSJEmSJE3VVELQFYD7DpzbGHgA8JuqurJ3sqpuB67qrkmSJEmSJEnSjJtKCHopcN8k6/Wd2747DpvrO4cWhEqSJEmSJEnSjJtKCHpqd3xPmrVoGyEVbfr73yTZgDZq9JKlqlKSJEmSJEmSpmgqIeh+3XFn2gjPC4ENgD8B3xlo29tJ/sypFCdJkiRJkiRJS2vSIWhVnQbsBtwAPBBYBTgHeFFV3TbQ/JXd8filKVKSJEmSJEmSpmrFqdxUVQcmORR4HHA18PtuE6S/SbIycDDwZeB7S1mnJEmSJEmSJE3JlEJQgKq6njvXBx12/RbgoKn2L0mSJEmSJEnTYSprgkqSJEmSJEnSPYYhqCRJkiRJkqSRtsTp8ElumabnVFXdd5r6kiRJkiRJkqQJG29N0CmvGTqgpqkfSZIkSZIkSZqU8ULO7cY4vy7wCWAO8C3gOOCi7trDgW2AFwDXAf8OXLDUlUqSJEmSJEnSFCwxBK2qYwfPJXkQ8GXgemDrqvrFkFsPSPJY4HvA+4AnTEOtkiRJkiRJkjRpU9kYaW9gbeDVYwSgAFTVL4HdgHW6eyRJkiRJkiRpxk0lBN0RuKmqjplA22OAG4HnTuE5kiRJkiRJkrTUphKCPgy4bSINq6q6tg+bwnMkSZIkSZIkaalNJQRdDMxJMu46n0k2AVbt7pEkSZIkSZKkGTeVEPR4IMB/J1ljrEZJVgcOAIq2e7wkSZIkSZIkzbgl7g4/hvcDL6Dt+H52ks/QgtGLaYHnOsACH9Wt/AAAIABJREFUYA/gIcDNwAenpVpJkiRJkiRJmqRJh6BVdXaSFwHfAOYC+3Q/gwJcD7ysqn6zFDVKkiRJkiRJ0pSl7V00hRuTdYG9gRcDqw1cXgz8D/ChqvrTUlUojYjMnVPsNG+2y5AkablR+y+c7RIkSZJ0D5fkjKqaP167qUyHB6CqLgB2A3ZL8ne0UaEAlwF/qKmmq5IkSZIkSZI0jSYdgiZ5Svfrr6tqMUBVnQOcM52FSZIkSZIkSdJ0mMpI0JOBO7hz5KckSZIkSZIkLbfuM4V7rgEWV9WV012MZk6S9ZNUkoNnuxZJkiRJkiRpWZpKCPoHYE6Slae7GGm6JNm5C3l3nu1aJEmSJEmSNLumEoIeBqxE2xVe91wXA48B9prtQiRJkiRJkqRlaSoh6L7A6cD+Sbaf5no0Q6rq1qr6bVVdMtu1SJIkSZIkScvSVELQtwE/AlYAjk5yZpJPJ3l3kneO9TO9ZS9/kuyU5MdJFie5Mckvk+yV5L5jtF8nyX5JzklyU5Irk5ye5N0D7RYkOSDJb5Jc0/X9qyTvTbLKkH5X7f4Wv+raX5vk3CSHJdm0r92Ya4ImWTvJZ5Kcn+SWJJcn+d/++yfxXTZMclDX181JLktyUpLXDWm7bZLvd9/ipiS/T/LRJKsNaXtCV//KSd6T5Hdd/wcnOQH4Utf0S1273s/6fX2smGSPJKd13+qGJD9L8oYk9xl43vkD/Qz+3O07SpIkSZIkafmQqprcDckdQO+mdMdxO6mqFSZX2j1Hkg/TppVfARwOXAc8E9gIOBHYrqpu7Ws/HzgGWBP4MXAacH/gH4Gt+79Vku8DGwKn0KawrwI8FXgCcALw9Kq6vWsb4GTgKcCpXb+3AY8AtgY+VFX7d23XB84DvlxVO/c9b4Ouj4cBx9FG/T6CO5c/eFFV/d8Ev8uzgf8B7gt8H/gFsDowD1i7qjboa7s78Dng+u6ey7qanwT8BnhqVV3d1/4EYCvg/4AnAkd391wG/BV4PvA84EjgrL6y9q2qq5OsBHwX2AH4XfctbwIWAI8DvlZVr+x73p5d7YN2BDYBPldVeyzxe8ydU+w0b0lNJEm6V6n9F852CZIkSbqHS3JGVc0fr92KU+j7FCYQet5bJNmcFoBeCGxWVZd25/cCvgU8hzZ69sPd+ZVpId+awMur6usD/T1i4BF7AOfVQFqd5APA3sA/0dZpBdiYFoB+u6peMND+PsDdRlQO8XlaALp3VX2o7/7P0gLbLydZr6quW1InSR4EfJ3239g2VXXiwPV1+n5fD9iPFh5vVlW/HXju64CPAbsNedR6wMZVdcVA/9BC0G9X1cFD7nsXLQDdH9izL0heATgA2DXJ4VV1JEBV7TvkHbfr+vkD8J6xvoUkSZIkSZJm16Snw1fVFlW15WR/lkXxy4ldu+MHewEoQFXdBvwbcAfw6r72OwLrA98ZDEC7+y4c+PcfBwPQTi+U22HItRuH9HtHVV21hPfoBZPbAxfQQsf++08BvkELb1+4pH46rwIeSBsheeLgxaq6qO+frwBWBvbvD0A77wKuBV45xtIC7x4MQMfTBcJvAC4F3tILQLu6bqf93Qp4+RL62Jg26ncx8KyxakiyW5JFSRZx463DmkiSJEmSJGkZm8pIUN3VJt3xuMELVfX7JBcBGyRZvZvO/eTu8tET6TzJA4A3Ay8A/h5YlTuXIQB4eN/vv6FN/X5pN7rySNrU9kVVdcsEHveE7nhS//T9PsfRAssnAF8Zp6/JvOeSvuFVSX4GPI22LMDPB5qcPoH+B/09sBZwDrB3N2p00I3AY4ZdSLI28D3aNP/nVNU5Yz2oqg6gjSxt0+ElSZIkSZI04wxBl15vivlYu6xfAqzbtbuaO9eVvHi8jrt1K48DNgN+RZv2fjnQCyjfSwvigDaKMck2tKnZ/wT8R3fp2iRfBvYaZxr7RN4Fhq+NOWjC77mUz710yLnxrNUd/472DccyZ/BEF0r/H22d1JdX1UlTeL4kSZIkSZJm0FKFoF1Itw0wH5hLm0J8OfBT4PgxRhOOmsXd8aHAuUOurz3Qrre5z8OHtB30PFoAepfNi+BvoxHvFuB1U97fArwlyaNpmwftTpv+vTrwysF7+vS/yzCD77Ik/e/5y3Ha9j/315N57hhLBYyn18+3qmoiU/uBv02j/wZt5Oq7quobU3i2JEmSJEmSZtik1wTtSbIr8CfgKOD9tJDtjd3vRwN/SrLLdBS5nPtZd9x68EIXQq5D29ioFwqe1h2fOYG+H90djxhybavxbq6qP1TVgV3b62ih6pL03mWLJMMC8gXd8czxns3k3nNJ33B14PG0ndvPnkBfPb11PlcYcu23tJD2yV2QP1H70tZ0PaiqPjyJ+yRJkiRJkjSLphSCJvkQ8N+0kXsBLqMFY2d2v6e79sWu7Sg7qDvuneTBvZPdLuP/SfvGB/a1/y5wPvDcJC8d7CxJ/wjR87vj1gNtHsmdU937z2+QZKMhNa5BmzZ/tw2T+nWbFf2QtnHTngN9Pwl4GXAVbdf78XwZuAZ4XZKnDal1nb5/fo02xf+NXXDc7wO0DZa+VlU3T+C5PX/tjusOXug2rfo0bYTpfknuN6S+tZP8Y9+/96SF/McCr51EHZIkSZIkSZplk54On2RLYK/un0fSduf+1UCbjWgjQl8AvCPJUVW1cGmLXR5V1SlJPgb8P+BXSQ4HrqeNgNyYtjHRx/va35LkxcAPgK8n2Z02anIV2kY823Ln3+W7wB+AtyZ5LG3E5LrAc2gb8wwGfPOAbyU5g7aG6J+BB9NGgK7EkOB0iNcCC4GPJ9keWERb//LFtJ3ud6mqayfwXa5I8jLaDurHJzka+AUt0Hxc1+cGXdvzu5DxM8CZSb5JW1ZhK2Bz2sjNt0+g9n6nAjcAeyZZE/hLd/7TVbWYFq7O6953xyTH0dYvnUtbK/SptJ3pf5PkocAnaMs9/BJ415DNlM6qqm9PskZJkiRJkiTNgEx2ScUkh9ECsYOratdx2h4I7AJ8s6r+ecpV3gMk+WfakgDzaIHjucDXgU9U1U1D2q8LvIMWlj4cuJYWeH6nqj7U1+4RwEdpo0HXBP5IG2X5SdroyROrauuu7TrAHrTw8FG0EaCX04K7/arq6L5+1wfOY/h6ow8H9gaeRRsteQ0tzP1QVf10kt9lI1qAuS0tkL2KFmoe0u2c3t92e+DfgScC9wcuBP4X+HDfcgK9ticAW1XV0K3duzbPoK2b+ljgAd3pDarq/O56aLvd70zb8X4O7XudR1vm4atVdWHft1qSu33Hu9Uzd06x07xxupEk6d6j9h/J/0cuSZKkGZTkjKqaP267KYSgF9GCsYdV1V/GaftQ2ui6S6pqnSW1lUadIagkSXdlCCpJkqSlNdEQdCprgj4YuHq8ABSgqi6lbUDzoCk8R5IkSZIkSZKW2lRC0GuBVZOsMl7Drs2qtJ3JJUmSJEmSJGnGTXpjJNrmNlsBrwK+ME7bXbpn/GIKz5FGyqbrbsgip/1JkiRJkiTNuKmMBD0ECPCpJK8aq1GSnWmb9xTwtSlVJ0mSJEmSJElLaSojQb9EGwW6BXBQkvcCx9E2QCrgEcACYD1aWHoScPB0FCtJkiRJkiRJkzXpELSq7kiyI/Bl4LnA+rRp7/3SHY8Edq6qO5amSEmSJEmSJEmaqqmMBKWqFgPPT/IU4CXAfGBud/kyYBFwaFWdOi1VSpIkSZIkSdIUTSkE7amqU4BTpqkWSZIkSZIkSZp244agSU6mrfl5AnBKVd20rIuSJEmSJEmSpOkykZGgTwE2B94F3JLkJ8Dx3c+pVXXrMqxPkiRJkiRJkpbKfSbQ5ofA9bTNju4LPA14Dy0EvTrJj5K8M8nmSVZYdqVKkiRJkiRJ0uSNOxK0qnbows0nAguArWmjQx8A3A/YpjsPcEM3fb43UvQMd4aXJEmSJEmSNJsmtDFSVd0OnNb9fCTJisBmtEB0AW26/P1pwegOwPbdrdcmOQk4rqo+Nb2lS5IkSZIkSdL4UlVL30myEi0UXdD9PJk2SrSnqsqp8rpXmz9/fi1atGi2y5AkSZIkSRoZSc6oqvnjtZvImqDjqqpbq2phVX2wqralTZ3/GnD7dPQvSZIkSZIkSVM1oenw40myAXdOjd8aeHjvUne8dDqeI0mSJEmSJEmTNaUQNMl63Bl4LgDW6V3qjhcBJ/Z+quqcpStTkiRJkiRJkqZmQiFoknW560jPdXuXuuP53DX0PG86i5QkSZIkSZKkqRo3BE1yLrB+75/d8Q/cNfS8cJlUJ0mSJEmSJElLaSIjQTcAirau58eBQ6vKNT4lSZIkSZIk3SNMdHf4AGsDnwCOTfLZJDsleeiyK02SJEmSJEmSlt5ERoL+I20d0K2BrYDHdD+7AyQ5hzYt/gTa1Pg/L4M6JUmSJEmSJGlKxg1Bq+q3wG+BzwMk2ZC7hqJ/3/28urt+LncNRS+a/rIlSZIkSZIkaWJSVUvXQfIY7hqKzu0u9To+Dzihql69VA+S7uEyd06x07zZLkOSpOVG7b9wtkuQJEnSPVySM6pq/njtJjIdfomq6mzgbOBz3YN7oeh2wI7AI2mbKxmCSpIkSZIkSZpxSx2C9iS5DzCfFoAuAJ7KxDdekiRJkiRJkqRlYsohaJIAm9ICz62BLYA5/U26442Ac50kSZIkSZIkzYoJj9RMs2mSf0vyXeAq4CfAR4FnAKvSgs9bgB8D+9DWCF29qraf7sIHajshydItbnoPlOTgJJVk/Uned6/8XuNJsk/3Pbee7VokSZIkSZI0fcYdCZrkLbTRnlsCD+y/1B1vBU4Hju9+Tqmqm6e5TkmSJEmSJEmakolMh/8Ebaf3Xuh5G7CIO0PPhVV147IpT8vIvwD3n+0ilkP7A4cCF8x2IZIkSZIkSZo+EwlB7wDO5M7Q86Squn6ZVqVlqqoM+YaoqiuAK2a7DkmSJEmSJE2viawJumZVbVZVb6+q789EAJpk5yRHJPljkhuTXJNkYZJXTKGvHZIcleSKJDcnOTfJx5OsPtDurd16kEcM6ePpSW5P8ssk9+s7//wkX0vy+yTXJ7kuyRlJ3pTkbt+2fw3PJLt3/d2U5C9JDkiy2hjv8PQkJ3XPuDLJt5NsOEbb9btnHJzk75McluSyJHf01roctiZokpWTvKH7Vn/qvtWVSX6U5JkT+th37W+1JB9J8rvuHa9KckySpy/hnu2TfLer9+YkFyY5sv+eqdSZ5HFJvpHk/K795UnOTLJvkpX62o25JmiSbZN8v3vWTd3f/KNj/c0kSZIkSZK0/Bh3JGhVXTMThQz4HPAb2gZLlwBrAc8CvprkH6rq3RPpJMl7gPcBVwL/B1wGPA74d+BZSTbvvV9VfbILv16YZI+q+mzXx0OBrwE3AS8ZmPr/UdpI2Z8AFwOrAdsA/wU8EXjlGKV9DNgB+C7wA9qaq68BHt3d3/8O/wQcRttw6rDue2wBnAr8Ygmv/6iurt8DhwD3A5b0t1yzq/sU4IfA5cDawI7AUUleU1VfXML9/TWvDiwE/hH4KbAv8CBgJ+AHSV5XVV8YuOd9wHuA64BvAxcCDwOeArwC+NFU6kzyuO47FPAd4Dza2raPBvYA9qata7uk99md9t/k9cD/0P472hp4O7BjkqdW1dUT+TaSJEmSJEmaeROZDj8bNq6qc/tPJFkZOBp4R5LPV9XFS+ogyQJaAHoq8Kz+kCrJzsCXuutv6bttF+As4BNJFgK/pAWgDwF2rarfDDzm2UPqvE/X978k2b+qfjKkvCcDj+1NS0+yInAcsCDJZlV1end+DvAFWtC6ZVUt6nvOp4A9l/AJtgA+UlXvXEKbflcB61XVRQPvsxot0PxYkkMmuP7rf9AC0AOA11ZVdX39B2092f2SHFNV53fnt6cFoOfR3vMuf9sk6yxFna8CVgGeX1VHDtyzBnDDkl4kyXrAfrRwdrOq+m3ftc8Cr6OF2rst8YtIkiRJkiRp1kxkOvyMGwwWu3O3AJ+hBbfbTqCbN3XH1wyO0quqg2lh58sHzv8VeCmwEm3U5Qe7Zx1SVV+aYJ130EYqQhvtOcz7+9flrKrbaMEpwGZ97Z5HG/n49f4AtLMPsHiM/gH+Qgt5J6Sqbh4MFrvzi4GDgDVoo1uXqJte/gpaaLhXLwDt+jqHFiiuTNucqeeN3fHfhoXb/XUtRZ13C2+r6qru77Ukr+jq3b8/AO28C7gWeGWS+w67OcluSRYlWcSNSxxwKkmSJEmSpGVkuRwJmmRd2lTjbYF1aVO5+z18At1sTpvm/OIkLx5yfWXgwUnW6sJPAKrq5CTvpQWgewHnAK8do861gLfRpuo/EnjABOscDDShTf+GFuL1bNIdTxxsXFWLk5wFbDXGM35eVTePcW2oJBvR3udptCnmqww0mch335C28/zCqrpyyPXjaFPQn9B37sm06erfXwZ1Hga8Gfh2ksNp0+oXDguwx9D7Gxw3eKGqrkrys66ODYGfD2lzAG1ELJk7pwavS5IkSZIkadlb7kLQJI8ETqeFgSfR1sxcDNwOrE+b3jx01N2AtWjv995x2s0B/jpw7n+B99NGyn6xqq4bUufqtPUuN+jq/Qpt7dHbgNVpwdtYdQ5bP/K27rhC37nepjt/GaOfS8c4P961u0nyZFrQtyJwLG39zGtoU/EfTxuVOpHv3qv5kjGu9873b0y1OnDVRKbaT7bOqjo9yZa0UZv/RLdOa5LfAe+rqm8sg/eRJEmSJEnScmS5C0GBt9ICzF26aet/k+SltBB0IhYD96mqNSfz8CSrAL1g7CrgPUmOrKrfDTR9NS0AfV9V7TPQx+a0EHRp9aa7P2SM6w9dwr2THXW4N23E7YKqOqH/QpK9aOHiRPRqHqu2tQfaQQuF10pyvwkEoZOus6pOBZ7TTVnfFHgGbQr+15NcXlU/GrxnjPf59QTfR5IkSZIkScuR5XFN0Ed3xyOGXBtr6vcwpwFrdFOnJ+OTwDzgI8A/06Z2H9aFo8uiziU5c6z+uo2AHj9Nz4H2PlcOBotjPX8JfkfbbOjx3cZDgxZ0xzP7zp0GhBZOLrM6u/VET6mq93DnmrHjhbs/645bD17oRgM/HrgJOHucfiRJkiRJkjRLlscQ9PzuuHX/ySQ70EZfTtSnuuN/J3nY4MUkD+imVvefexFtt++FwHur6ge0nb/n0cLRidT5BNpaotPhSNpo1JclmT9wbR/unKo9Hc4H1kzyuP6TSf6VsTd4uptuA6tDaMsMvH+gr0fRwsdbga/2Xfp0d/xEkrutOzpwblJ1JtmyC4wH9UbXLnF3eOBrXb1vTPLogWsfAB4IfG2y669KkiRJkiRp5iyP0+E/C+wC/E+SI4CLgY1powS/CbxkIp1U1bFJ3kEb0XlOkqOA82jh3Hq0UYMnd/2SZH3gi3ShY1Xd3nW1N23jm9clObaqeiM/v0LbnGffJAtoGyj9HfAc2pqiE6pznHe4LslutM19TkpyGG0Nyi1o3+THXW3TYV9aiHhykm/SpnfP7551OG09zYl6B7Al8IYkTwSOBx4E7ASsCryhqs7rNa6qHyT5APBu4Owk36ZtFPWQ7vmnATtPsc5/A7ZPcgLwR9qu9RsBz6T9rQ9Y0otU1flJ9gQ+A5zZPfNy2n8/mwO/pW3iJUmSJEmSpOXUcjcStKp+QZsyfQpt1/XX0UbbvRD4/CT7+g9aSPg94KnAnsCLabuHH0ALOEmyEnAobXObV1fVBX193Aa8lLZu5YFJNujO/5kW9H2PFsC9gRau7kELAadFVR1OC2rPoIWIr6VtwLQ5LdSdrud8H9gR+A0twP1X4Gba3+J7k+yrV9/HaOu7vpX23U8HnlFVnx1yz3uAZ9P+7s8B/p0Wdp5NC5ynWudnaSHy+rRNkf4/e3caLVdZpn38f8kohmaOighBRbEdIhJRASUYcFYcEF+lkaEVbUfUdmpFUJyxbRoRFRUQGxQHBrVFtAlRCSomICiCrdiAAUEGmWSG+/2wd0FRVCUnJyenTir/31pZ+5y9n/3se9cp+HCtZ3gL8Oj2/FZj2SW+rfc5NGHsy9v3mQ4cDDy9fV9JkiRJkiRNUala2v1zJI1Hpk8rdps57DIkSZoy6rD5wy5BkiRJK7gkC6uqdxnJ+5lyI0ElSZIkSZIkaSIZgkqSJEmSJEkaaVNxYyRpJG296ZYscNqfJEmSJEnSpHMkqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJGWqhp2DdJKIdOnFbvNHHYZkiRNGXXY/GGXIEmSpBVckoVVNWtJ7RwJKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoNAZJ5iVxAV1JkiRJkqQVkCGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqArgSQzklSSo9ufv5Hk6iS3JlmQ5IWLufdVSU5P8re2/QVJPpBkjZ52lydZ1Of+S9pn799z/vnt+Q8vofbntu2OHHB9jfZdru6uqT3/3iTnJbk5yQ1JfpZktyV8Po9OcnySvya5O8nsJdT3rCTXt+//pMW1lSRJkiRJ0nAYgq5cNgPOAmYAXwOOBx4PnJxkx97GSb4CHAc8CjgB+BxwLXAQ8MMkq3Y1nws8LMmWXfc/Cti0/XVOT/fPao+nLaHmU4GLgFcmWafP9ZcDGwBHV9Vt7XNXb+/7OLBaW/fXgEcDxyf52IBnPRL4Jc3ncyxwBHDDoMKS7A6cAlwOPL2qfr2Ed5EkSZIkSdIQGIKuXGYDn6uqp1XV26tqT2AXmu/Bu7obJtkL2Ac4EXh0Vf1zVb2zqrYDPtT29aauW+a2x+6ws/Pzj4GnJ1mr59otwM8XV3BVFfAFYC1gjz5N9m2PR3SdeyewA01A+YSqeldVvQl4AnAJ8L4k2/bpa3vg81X11PbzeUNVnd2vriTvpglWfwVsV1WXLO49JEmSJEmSNDyGoCuXS4CPdJ+oqlOBS4Ftetq+DbgT2Keqbum5dhBwDbB717nOiM7eEPSvwKHA6jQhI0k2AGYCZ1TV7WOo+yjgVuD13SeTPIYm7Dy9qv6369I+QAHvqKo7u971r23tAK/t85wraQLegZI8IMlhwCdpAuKdquraxbTft11yYAG33LG4riVJkiRJkrScrLrkJhohv66qu/qc/zPw9M4v7YjNmcDVwH5J+vV1G/DYzi9VdUmSPwE7JnkATQg5G/gf4Cc0geoc4EfAjkC4d/ToYlXVNUm+CbwmybZVdWZ7qTMK9Atdta9NM33/sqq6sE93nWdu1efauZ0p9YvxHeAlwGeB/arq7iXUfgTtKNVMn1ZL6FuSJEmSJEnLgSHoyuW6Aefv5L6jgtejCSk3Ag5Yiv5PA14HPBm4o73/tKq6McmvuHeU6Jyu9mN1OPAamtGgZ7abIO1JM9L0pK52nXVD/zKgn875dftcu2IMdTyT5vP63pICUEmSJEmSJE0NTodXP9e3x3OqKov713NfZ5TlTtwbdM7tOm6VZP322vVA3/U2+6mqX7btd0uyHvduiHRUz5T6Tu0PGdDVQ3va3ecxYyhlR+BvwPeSvGAM7SVJkiRJkjRkhqC6n6q6CTgfeFwbWo7VXJogcQ7N7u9/qqr/a6+dRvN92wPYApg3YGr+4nweWJNmROi+7bO+1FP7jTS7yT8syRZ9+tixPY45gO3p/zyadUivBU5I8pLx9CNJkiRJkqTJYwiqQT5Ds5nRkUnuN3U8yXpJntx9rt146HxgO5pp493T3c+k2dzo39rfx7QeaI/jaEZwvpsmiPxxVV3Up92RNNP5D06ySlfNGwL7d7UZl6q6gOb9rgS+leSV4+1LkiRJkiRJy59rgqqvqjoyydbAG4GLknR2kV8f2JwmBDwKeEPPracBj+/6udPfbUnmM771QDt93Jzkq8Bb21NfHND008DzgF2Ac5P8AFgLeAUwHfhUVZ2xtM/vqeWPSZ5BE+Yem2SNqjpmWfqUJEmSJEnS8uFIUA1UVW8CXgT8nGadz3cAL6bZfOhg4JA+t3XCzQJOH3Dtyqo6f5xldUZw/gX47oC6bwd2Bt7fnnoLzSZKfwBeXVXvGeeze59zCU0Y/EfgqCSvm4h+JUmSJEmSNLFSNZa9YKSpIcleNCNQP1JV+y+h+ZSS6dOK3WYOuwxJkqaMOmz+sEuQJEnSCi7JwqqataR2jgTVCiPJqjSjUe9k8FR4SZIkSZIk6T5cE1RTXpLtaTZCmg08ATisqhYNtahx2HrTLVngiBdJkiRJkqRJZwiqFcFOwAHAtcCXaHaHlyRJkiRJksbEEFRTXlUdCBw45DIkSZIkSZK0gnJNUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNIMQSVJkiRJkiSNNENQSZIkSZIkSSPNEFSSJEmSJEnSSDMElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNJSVcOuQVopZPq0YreZwy5DkrQc1WHzh12CJEmStFJJsrCqZi2pnSNBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVFiMyl10rAAAgAElEQVTJgUkqyexh1yJJkiRJkqTxMQTVyEmyVxtc7jXsWiRJkiRJkjR8hqCSJEmSJEmSRpohqCRJkiRJkqSRZgg6xSWZluT2JPN7zj8wya3ttO89eq69sT2/T9e5rZP8Z5Jzk1zb3vuHJP+eZL2e+9/Q3v/BATU9JMkdSX4zBWufBxzV/npU21fn34yudqu07zk/yfVJbknyxyRfTrLFgPfZNclZSW5u6/hGkoct7jOQJEmSJEnS8BmCTnFVdRNwFrBNkrW7Lm0HrNH+PKfntme1x9O6zr0O+H/A72lCwi8AfwHeAczv6fu/gBuA1yZZpU9Z+wCrAl+cgrUfDZzc/nwy8KGuf9cBJFkd+CHweeDhwHHAocBC4KVtfb3eSPO5XAx8Dvgt8Ergf5Ks0ae9JEmSJEmSpohVh12AxmQuTTD3TOC/23NzgLuAn9IVJCZ5ADAb+FNVXdLVx8eBN1XVXd0dJ/ln4Ms0Id8noQkvk3wNeBPwPOD7Xe0DvBa4GfjaFKz96KZEdgFOqqqj+9R0ILAT8D3gFVV1W1efawD/0Oee5wJPqarfdLU9DnhV+6xvDv4IJEmSJEmSNEyOBF0xdEZFdo+anEMzcvE7wCZJHt2efxKwAfcdSUlVXdIbIraOpBn1+Zye859vj6/vOf9sYHPg+Kq6forWPlA7svWNwC3AG7oD0PZZt1XVVX1uPbQ7AG19qT1us5jn7ZtkQZIF3HLHWMuUJEmSJEnSBDIEXTH8nCa0mwOQZB3gyTRh4dy2TSdk7Ewnn9vdQZLVkrw5yRntepZ3JSngbpqRj/dZ27KqzqcZqfm8JA/vurRve/zCVK19CbYE1gHOq6rLl+K+BX3O/bk9rtfnGgBVdURVzaqqWTxwtaV4nCRJkiRJkiaK0+FXAFV1e5IzgJ2STAeeDqwCnFZVFyS5nCZI/Hx7LHqCROB4mvUu/0SzVuYVQGcU5H7cu0Znt8NpprG/FjggyUOAFwO/rqqzpnjtg6zbHi9binugXU+0x53tsd+6qZIkSZIkSZoiDEFXHHOBnWlGS25LEwJ2dl0/nWbE5hrAM4Dzq+qvnRuTzKIJEf8HeH5V3dF17QHAuwc88wTgSuCfk3yYMW6INEVqH6QTZrqruyRJkiRJ0krC6fArju61NZ8FzK+qW7uurQ/8C/AgetbUBB7VHr/bHSK2tgEe2O+Bbdsv0wSGL6IZEXoTcOwUr72zfmi/EZoX0gShT0yy8ZjfQJIkSZIkSSssQ9AVx0Ka8G4X4HHcNyzs/Py+9tg7nfzi9ji7+2Q7Pf1zS3juETSh4mE0GyIdV1U3LkXdMPm1X9MeN+290G6wdDhNePqFdgRqd7+rJ9loQL+SJEmSJElaATkdfgVRVXcn+QlNkAhdQWJVXZrkIuCRNIHlT3pu/xXN9POXJTkTOAN4MPA84PfAwA2C2r7/m2YtUFj6qfDDqP3nwM3AfknWp5nSD/DZdkf7DwFPpRnd+r9Jvg/cCDwceDbwLuDopX1PSZIkSZIkTU2OBF2xdMLDG7j/buWdawvboO8e7ejHF9NsPrQx8FZge5qp7s8BeqeZ9zqyPS6oqrPHV/rk1V5VfwNeDvwO2Bs4qP23Xnv9duC5wFtoAtI925+3AU6kCVolSZIkSZI0IlJVw65BU1ySA4EDgNdW1VeGXM4KK9OnFbvNHHYZkqTlqA6bv+RGkiRJkiZMkoVVNWtJ7RwJqsVKsjbwBuBa4OtDLkeSJEmSJElaaq4Jqr6SvAB4Ms26mQ8G/rWqbh5uVZIkSZIkSdLSMwTVIK+gWSvzSuDjwH8Mt5wV39abbskCp0lKkiRJkiRNOkNQ9VVVewF7DbkMSZIkSZIkaZm5JqgkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRlqqatg1SCuFTJ9W7DZz2GVIkpajOmz+sEuQJEmSVipJFlbVrCW1cySoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRppK1UImmRGkkpy9LBrmUij+l6SJEmSJEnSRFipQtCJlOTiJBcPuw5JkiRJkiRJi7fqsAuYZJcBjwWuH3YhkiRJkiRJkibHShWCVtUdwIXDrkOSJEmSJEnS5FmppsMPWjszydHt+RlJXp/kN0luTXJlkiOSrNPVdnaSAjYDNmvvq95+29/nJXlIki8nuSzJXUn26mrz0CSfa6fW357kqiQnJNl6QP1rJ/lMkkVtfRcmeQcD/o7t82vAtb3aGvfqc22TJIcm+UP7nGuTnJVk/wFtD0vypyS3JbkmyXeTPGVA/fsn+W2SG5LcmOSiJMcPeudB75Rk1ST/1tZ4W5I/J/lkktUH3Ldl+3f+c9v+yiTHJXlMT7uPt/3v3HP+w+35i/r0fUWSS8dSvyRJkiRJkibfSjUSdAw+BTwH+B7wI2BH4HXAo4BntW0uBj4E7Nf+fkjX/b/u6W994BfATcAJwN3AlQBJNgfOADYG5gJfBx4OvAJ4QZKXV9X3Ox0lWQM4DXgKcC5wLLAusD+wwzK9dZcks4BT29p/2ta9FvCPwIHAQV1tn0zzOa3f3nMCsCHwEuCMJC+tqh+0bQP8ENgW+DnwZeDO9p1nAz8DFi5FqccBzwBOAW4Ang+8G5gO7N3zTs9ta1uN5m/7R2AT4GU0n/WOVXV22/w04L3AHODHXd10/v6PSDKjqi5u+3488GDg6KWoXZIkSZIkSZPIEPS+ngY8oaouBUiyKk1AuWOSbarqrDb8OrAzgrKqDlxMf08AvgbsU1V39lz7Ak0A+oGq+mjnZJLDacLHrybZrKpuai+9kyYAPQF4RVXd3bb/BEsXHg7UjqL8Fk2ouXtVHddz/eFdP68KfBOYBuxYVT/purYx8CvgK21geBvweJoA9KSqemlPvw8A1mHpPBJ4XFVd2/bxfppw+DVJ3ldVV7Tn16MJmG8GnllVv+t67uOAX9IEsk9uT88HbqMJQTvtpgHb0ISiO7fXvtJe7rSbu5T1S5IkSZIkaZKsVNPhx+DDnQAUoA0uj2p/3WYc/d0O/GtvAJpkE+DZwKU0o0/vUVVn0oR269OMVOzYm2Yk6bs7AWjb/v+AQ8dRWz8vAmYA3+0NQNtn/bnr1xfQBJGf7Q5A23aX07zXQ+gKE1u39On37qr621LW+p5OANr28Xea0bEPAGZ1tXsNzYjZA7oD0Pae84EvAVsl+cf23C00I1Wf3AaoAM+kGUV6CHBVzzstNgRNsm+SBUkWcMsdS/mKkiRJkiRJmgiOBL2vBX3OdYK/9fpcW5KLq+qvfc5v1R5/1m7W1Gsu8E9tu2OSrE0zJf/PVXW/NSmBecAB46iv19Pa4yljaPv09rhZkgP7XN+iPT4W+AHwO5rlAl6VZDPgZJrlABZU1e3jqHWsf6tOnTMH1Pnorjo7Ielcmin6s4ETaabC30EzQvf09neSrEITkP6+qi7rV2RVHQEcAZDp0/quzypJkiRJkqTlyxD0vq7rc64zinOVcfR3xYDznanffxlwvXN+3Z72Vy7lc5ZW53l9A70eG7THVyyh3TSAqrorybOADwK7Ap9sr9+Y5KvA+7qm/i9RVY31b9Wp83VjqbN1GvBhmlGeJ7bHX1bVTUlOA3Zr1wJ9EM3f5tix1i1JkiRJkqTJZwi6fA0a+Xd9e3zIgOsP7WnXOT54QPtB/XTWDV21z5qk6/Zp3wkWHzagv26dmnapqu+OoT3tlPe3A29P8iiaDZ1eD7y5rWePsfSzlDp1zqyq88Z4z1k0m1ntlGQDYCZNKAr3TnvfiWbDqO5zkiRJkiRJmoJcE3T87mJ8o0MBzmmP27cbDPXasT2eDVBVN9LsaP6wJI/s0372gOd01tl8eJ9rs/qc+0V7fN6A/vq1fcYY2t5PVf2xqr5CE4TeBOwynn7GYKnrbAPjnwKPoQlmQzM6lKr6I81arnNopsXfTTNFXpIkSZIkSVOUIej4XQNslOSBS3tjVS2i2Wl8BrBf97UkTwVeTRNgnth16Siav9cn293UO+03B9464FFntcf7TAVPMgd4VZ/23wMuBl6c5H7Xk3SPED0ZuAh4U5Ln93t4kqcnWatTZ7sbe6/1gDXos2HSBDmKZoTrAUnut7lVkgckmd3nvs7ozvcBf6fZRb772g7AdsC53Rs0SZIkSZIkaepxOvz4nQY8Bfhhkp8Ct9EEYt8b4/1vAOYDByd5Ns1GPw+nWWPzbmDvdgRox78DLwFeDpyd5FSa9ShfSTNq8cV9nnEU8C7gfUlm0mz882iakZ4ntn3do6puT/IK4EfAcUleTzOSck2ajYPm0H5nquqOJC8DTgX+O8mZNBsf3dy+x1OAR9BM7b+ZZkr5iUkWAr8FLgc2ohkBuhr3rhE6oarqmiS7tu/7i3ZNz/NpPuNNaTZO2qB9x26ntcfpwA97Nm86Ddirp50kSZIkSZKmKEPQ8fsIzTqWL6IZEbgK8FWa0ZRLVFV/SjIL+ADwfJop7TcAPwQ+WlW/6ml/W5KdgANpgs+30Yza/AhNwHe/ELSq/ppkB+Bgml3Md6AJW3cGNqcnBG3vWZDkScB7acLSbYHOdPwDetqe14ar7wBeCOxNEy7+hWbK/wHA1W3zBcDH2xqeSzMC9CpgIXBoVY1lR/pxqarTkjwR+FfgOTRT42+nCWLnAt/pc9u5be0bcv81P+cO+FmSJEmSJElTUKoG7d0jaSJl+rRit5nDLkOStBzVYfOHXYIkSZK0UkmysKr67X1zH64JKkmSJEmSJGmkOR1emiRbb7olCxwhJEmSJEmSNOkcCSpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkpaqGXYO0Usj0acVuM4ddhiRpOarD5g+7BEmSJGmlkmRhVc1aUjtHgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIqgmTZIskJya5IkkluW7YNY1XW/+8YdchSZIkSZKkZbfqsAvQaEiyCnAS8Cjga8Ai4NahFiVJkiRJkiRhCKqJsznwj8CXqmrfYRczAR4L3DzsIiRJkiRJkrTsDEE1UTZuj5cPtYoJUlUXDrsGSZIkSZIkTQzXBJ0ESWa0a0wenWTLJCcluTbJ35OckeTZA+5bI8l7k5yX5OYkNyT5WZLdJuoZ7b2vSnJ6kr8luTXJBUk+kGSNMb5fAT9pfz2graOSHNheP7D9fXaSVyf5ZZKbklzc1cdeSb6T5E9JbmnfdX6SfxrwzHltn6sm+bckf0hyW5I/J/lkktUH3LdlkiOTXNy2/2v7mf5L7zv1rgmaZO0k+yf5bVvfjUkuSnJ8kq3H8llJkiRJkiRp8jkSdHJtDvwc+C3wReChwCuBU5K8uqqO7zRsQ7xTgR2AC4HPAWsBuwLHJ3lSVf3bsjyjfc5XgH1o1vA8AbgOeBpwEDAnyc5VdecS3utDwAxgT5owdF57fl5Pu3cCOwPfA04H1um69nngd8BPgb8AGwDPB76W5DFVtf+AZx8HPAM4BbihvefdwHRg7553fQHwLWAN4IfA14F1gZntPZ8f9IJJ0t6zLc3n+2XgTuDhwGzgZ8DCQfdLkiRJkiRpeFJVw65h5CWZAfxf++unq+pdXddm0YRqNwGbVdUN7fn3AR+jCfde3Akik0wHzgI2A7arqjOX4Rl7AUcBJwK7V9UtXfccCBwA7FdV/zmGd5xNE2x+qKoO7LnW6etmYPuqOqfP/Y+sqot6zq3evv8zgRlVdVnXtXk0AfHZwM5VdW17/kHAuTRh8MOq6or2/IbARcAD2/Y/6XoUSTapqkVdvxfwk6qa3f7+BOA84KSqemnPvQ8A1qmqvy32M5o+rdht5uKaSJJWcHXY/GGXIEmSJK1UkiysqllLaud0+Ml1PfDh7hNVtQA4lmZEYne4tg9QwDu6R2JW1V9pRmkCvHYZn/E2mtGM+3QHoK2DgGuA3cfyYmN0RL8AtK3xoj7nbqcZAbsqMGdAn+/pBKDtPX+nedcHAN3/AewJ/APw+d4AtL1vUe+5AXo/J6rq7kEBaJJ9kyxIsoBb7hjjIyRJkiRJkjSRnA4/uc6uqhv7nJ9HE9JtBXw1ydrAo4DLBmzQM7c9brUMz1iLZhr41cB+zWzv+7mNZpf0iXLWoAtJNgXeQxN2bkozYrPbwwbcuqDPuT+3x/W6zj2tPZ6y5DL7+h3wa+BVSTYDTgbOABa0YW1fVXUEcAS0I0ElSZIkSZI06QxBJ9eVA85f0R7X6Tn+ZUD7zvl1l+EZ6wEBNqKZqj4Zruh3MskjaALS9WjW1vwRzYjWu7h3rdG+mzRV1XV9TndGzq7Sda7zWV3GOFTVXUmeBXyQZl3WT7aXbkzyVeB9VXXTePqWJEmSJEnS8mUIOrkePOD8Q9rj9T3Hh/RpC81mR93tluUZ51TVkwfcM9EGjYR8B81GSHtX1dHdF5K8iiYEXVadsPRhwG/G00E75f3twNuTPIpmTdLXA2+mCVn3mIA6JUmSJEmSNMFcE3RyPbmd6t5rdns8B6Cdzn4R8LAkW/Rpv2N7PHsZnnETcD7wuCTrj6n65edR7fE7fa7tMEHP+EV7fN5EdFZVf6yqr9DUdxOwy0T0K0mSJEmSpIlnCDq51qGZTn2Pduf23WlGZp7YdelImunqBydZpav9hsD+XW2W5RmfAVYHjkxyv6n1SdZLMhmjRC9uj7N7nv8c+m/+NB5fBW4A/iXJM3svJtlkcTcn2TzJ4/pcWo9mqv79NkySJEmSJEnS1OB0+Mn1U+C1SZ4KzKeZ1v5KmjD69VV1Q1fbT9OMWtwFODfJD4C1gFcA04FPVdUZy/KMqjoyydbAG4GLkpwKXAqsD2wOPBM4CnjDBL3/IIcDewPfSvIdmnU7Hw88F/hmW/8yqaqrk7wa+DZwepJTgPNodox/IvBwmnceZCZwYpKFwG+By2nWU90FWI171wiVJEmSJEnSFGMIOrn+jyZQ/ER7XINmSvuHq+rU7oZVdXuSnWnWy3w18BaaDX/OBfarqq8v6zPa57ypDQTfAOxEs7bltTRh6MHAfy3LC49FVZ2XZEfgI8Dzab6X5wIvo1nLc5lD0PY5/92Oiu3sQv9s4G/AhcDHl3D7grbNDjTh7HrAVcBC4NCqGu+u85IkSZIkSVrOUjVorxpNlCQzaMLJr1bVXivqM7RsMn1asdvMYZchSVqO6rD5wy5BkiRJWqkkWVhVs5bUzjVBJUmSJEmSJI00Q1BJkiRJkiRJI801QaVJsvWmW7LAaZKSJEmSJEmTzhB0ElTVxUBW9GdIkiRJkiRJKyKnw0uSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppKWqhl2DtFLI9GnFbjOHXYYkaTmqw+YPuwRJkiRppZJkYVXNWlI7R4JKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCDqFJZmRpJIcPexaJEmSJEmSpBWVIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoEOSZJskxye5LMltSf6S5EdJdhvQfkaSbyS5OsmtSRYkeWGfduskeVeSuUkWJbk9yVVJvpvkaQP6riTzkmyY5Ii2ltuSnJ9k7wH3rJHkwCR/atv+X5KPtOcrybw+96ya5I1JfpHkhiQ3JzknyZuTjOm7mOTrbf9b9Jw/pj1/Ws/5tZPckeSn4/mMkqzX1nlRkgyo6fvts7ceyztIkiRJkiRpchmCDkGS1wFnAi9pj/8O/DcwHXhjn1s2A84CZgBfA44HHg+cnGTHnraPBT4K3N32+Rngx8CzgJ8lee6AstYF5gNPB74NHANsDByZZM+e+gN8BzgAuBM4DPgesBfwjQHvvBrwfeBz7bOOA46g+Q5+FvjqgLp6dULOOT3nO5/DtknW7Dq/A7Bq132wFJ9RVf2tfadHADv1ea9NgOcCC6tq4RjfQZIkSZIkSZNo1WEXsLJJ8o/A4cANwDOq6vye65v0uW02cGBVfair3XHAD4F3Aad3tb0A2Liqru7T71nAf7T39ZoJfAV4fVXd1d7zH8B5wHu4b0j5T8ALgJ8BO1XV7W37DwK/GPDq7weeQxOY7tf1jFVowtB9kny7qk4ecH/H3PY4B/hC28djgE1ogsydge24f1g6t6uPpf2MDgf2Bl7fPqPba4FVgC8uoW5JkiRJkiQNiSNBJ9+/0ITPB/UGoABVtajPPZcAH+lpdypwKbBNz/nre8O9rn6/DWyZZNM+z7gZeEcnnGzv+R3N6NDHJlm7q21nZOgHOgFo2/464KDejtup7m8GrgDe3vOMu4B3AgXs3qeu3vf4E3AxsGPX9PRO0PlB4C7uO0p0DvB34JddfSzVZ1RVC4AFwC5JHtL1XqsA/wzcCHy9X71J9m2XLljALXcs6fUkSZIkSZK0HDgSdPJ11pw8ZSnu+XV3cNjlzzTT1+8jyXbA29pr04HVe5o8jCZA7faHqrphwDOgmcJ+Y/vzVjRTyc/s0/6MPuceDWwA/AH4wIClNW+hmaY+FnOBfYAnAefQTGP/S1X9IslC2hA0yUY0ywb8qDusba8t7Wd0OHBk+9yPteeeTzMC9fNVdVO/QqvqCJqRrmT6tBrj+0mSJEmSJGkCGYJOvnXb42VLcc91A87fSc9o3iQvpRnNeCvN1O2LaEZC3k0zrX4HYI2lfAY0U7471gGurao7+7S/ss+5DdrjFjTriA4ybTHXup1GE0bOSXIuzXud0nXt3UnWoQlHw33XAx3vZ/QNmrVbX5fkE1V1N830eHAqvCRJkiRJ0pRmCDr5OmHjw4ALl0P/BwG3A7Oq6oLuC0m+SBPwLasbgPWTrNonCH1wn/bXt8cTq+plE/D8zvqeO7U/b8C9Qedc4H00GyX1Ww8UxvEZVdUtSY4G3g48O8lvaTZE+mVVnbusLyRJkiRJkqTlxzVBJ19n46DnLaf+HwX8rk+49wBg+wl6xjk0351t+1zr94wLacLfp7W7xC+TqroC+B3wDJogEu4NOucDt9EEoM8C/tbW2228n9HnadYufT1uiCRJkiRJkrTCMASdfJ+nmWK+f7tT/H0M2B1+aVwMbJFk464+QzMN/X7PG6dj2uNHktyzlmY7BX3/3sbtaNHPAg8FDk3ywN42SR7a7/NYjLnAWjTrev6hqi5tn3UL8HNgN+CRwLx26nq3ixnHZ1RVf6AZcfpC4A00we7xS1GzJEmSJEmShsDp8JOsqn6X5I3AF4BzkpxMs2HQBsAsms2HdlyGR/xHV9/fAe4AtqMJ974HvGgZ+u44Bvh/NKMwf5vku8BqwMtpdlF/DM36mt0OAmbShIcvSjKXZl3U6TRrhW4HvJ9mhOdYnEaz4/x04IQ+12Z3/dxrWT6jw2mm4T8Y+GxV3TzGeiVJkiRJkjQkjgQdgqr6Es206+/ThHXvAl4MXA18bhn7/iKwN/AXYE9gd5od3p8KnL0sfXc9o4CX0gSbqwFvAXYBvgq8qW12Q889dwAvAV4D/J5mNOU7aYLUB9CMID12KcqYx71Ba++an93BZ++1Zf2MvkvzdwKnwkuSJEmSJK0Q0uRZ0sRIsjPwI+ATVfW+Ydcz0ZI8AvgjML+qnrFU906fVuw2c/kUJkmaEuqw+cMuQZIkSVqpJFlYVbOW1M6RoBqX7vU0u85tAHyi/fXEya1o0vwrEOCwYRciSZIkSZKksXFNUI3XZ5LMBM4ErgI2odnxfn3gi1V11jCLm0hJNgVeTbN26d7AucC3lrafrTfdkgWOEJIkSZIkSZp0hqAarxNoNgd6EbAucCtwPnAk8OUh1rU8PAL4OHAz8GPgX/rsOC9JkiRJkqQpyhBU41JV3wS+Oew6JkNVzaOZAi9JkiRJkqQVkGuCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkZaqGnYN0koh06cVu80cdhmSpOWoDps/7BIkSZKklUqShVU1a0ntHAkqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkT/cg4UAABq4SURBVGYIqikryewkleTAYdciSZIkSZKkFZchqCRJkiRJkqSRZggqSZIkSZIkaaQZgg5ZkhntlO+jkzw6yfFJ/prk7iSzu9ptkeSYJJcluT3J5e3vWwzo96FJjmr7uiXJr5Psubgp5knWT/LxJBe091yf5LQkz+7Tdq+2n72SPDfJvLZ9jeGdH5zk00l+n+TvSa5rfz46ySPaNkcDp7e3HNA+q/Ov+3NZI8l7k5yX5OYkNyT5WZLdlvBZz0jyjSRXJ7k1yYIkL1xMza9KcnqSv7XtL0jygSRrLOl9JUmSJEmSNFyrDrsA3eORwC+B/wWOBR4I3ACQ5CnA/wBrA98FfgdsCewO7JJkTlUt6HSUZDpwJjAD+Gn780OAw4Ef9Xt4ks2Aee09PwN+CDwIeCHwwySvr6ov9bl1V+C5wCnAF9r7B0qyFjC/fd8fA98DAmwG7AJ8G/gTcFJ7y57AT9raOi5u+1odOBXYAbgQ+BywVlvT8UmeVFX/1qeMzYCz2ud8DVgfeCVwcpKdqur07sZJvgLsAywCTgCuA54GHATMSbJzVd25uPeWJEmSJEnS8BiCTh3bAx/vDe2SBDgG+Afgn6rq2K5rrwS+AfxXkn+sqrvbSx+nCSM/VVXv6Wp/CE34189XacLBV1XVN7ruWZcmgDw0yXer6sqe+54PPL+qfjjG95xDE4AeUlVv73nX1YE1AKrqpCTX0YSg86rqwD59vZMmAD0FeHEniEzyofY935fk+1V1Zs99s4EDq+pDXc8+jib4fRf3jkAlyV40AeiJwO5VdUvXtQOBA4A3Af85xveXJEmSJElaanfccQeLFi3i1ltvHXYpQ7HmmmuyySabsNpqq43rfkPQqeNK4EN9zm9LM+rz590BKEBVHZ/kzTQB6vbAT9sg8VXA9cBHetqfm+QY4LXd55PMpAkTv90dgLb3XJfkAJqRmS+nGU3a7eSlCEC73dJ7oqpuB25fij72AQp4R/dIzKr6a5KDgC/TvGtvCHoJ9/9sTk1yKbBNT9u3AXcC+3QHoK2DgDfTjMjtG4Im2RfYF4Bpq4/5xSRJkiRJkrotWrSItddemxkzZtCMmVt5VBXXXHMNixYtYvPNNx9XH4agU8e5VXVbn/NPbo9zB9w3lyYA3Ypm6vtjaKbSL6iqG/u0P4OeEBR4entcp99aocBG7fGxfa4NGlk6yE+Ay4D3Jnky8AOa6fG/rqq7xtpJkrWBRwGXVdWFfZp0Pq+t+lwb9Kw/c+9n0Zm6PxO4GthvwP9gbqP/5wJAVR0BHAGQ6dOWuF6qJEmSJElSP7feeutKGYACJGGDDTbgqquuGncfhqBTxxUDzq/THv8y4Hrn/Lo97XunrbOY8xu0x53bf4NM63NuUN19VdUNSZ5GM+r1xcBz2ktXJzkc+EhV3TGGrpb2c+l23YB77uS+m4WtR7Ne6UY0094lSZIkSZKGZmUMQDuW9d3dHX7qGDRK8Pr2+JAB1x/a0+6G9vjgAe37ne/c+7aqymL+7b0UdQ9UVYuq6p+B6cDjgbcC1wAfbP+NxdJ+LuPRufecJXwuK+//gSRJkiRJklYAjgSd+s5pj7MHXO+cP7s9Xkiz3uYTk6zdZ0r89n36+EV7fAZw6PjKXHpVVcD5wPlJTgIuBV4C7N826UxZX6XPvTcmuQh4RJItquoPPU12bI9nM05VdVOS84HHJVm/qq4db1+SJEmSJEkTKW/ebkL7q8PmT2h/43HIIYew7777stZaa014344EnfrmA78Htk+ya/eF9vdnAv9Ls9ZnZ3Oh42mmi3+gp/1M4DW9D6iqBcDPgJcl2adfEUmekGT6sr5MkscnmdHnUmeE6s1d565pj5sO6O5ImunqBye5JyhNsiH3BqlHjrvYxmeA1YEjk9xvan2S9dq1TSVJkiRJkrQMDjnkEG6++eYlNxwHQ9Aprh0tuSdwI3B8khOSfCzJd2jCzhuB11TV3V23vZdmVOW7k8xr2x9Fs0v6D9o23e0BXg1cBHwlya+TfDHJJ5Mcm+Q3wHnAIybglXYC/pTkjCRfbms7Bjitrengrra/p9lE6f8l+VKS/ZN8IMlm7fVP04S/uwDnJvlUksNoRpfOAD5VVWcsS7FVdSRwePuMi5Icl+QTSY5I8mOaNVH3XZZnSJIkSZIkrSiOOeYYnvjEJzJz5kz22GMPLrnkEubMmcMTn/hE5syZw6WXXgrAXnvtxbe//e177ps2rdlqZt68ecyePZtdd92VLbfckt13352q4tBDD+Xyyy9nxx13ZMcdd+z77GXhdPgVQFX9MslTaEZ27gS8iGbH8q8DB1XV73vaX5lkW+BjwPOBp9IEim8E/k4z5fyGnnsWJdkaeAvwcmB3mmnoVwC/Az4L/GYCXudU4BCaEay7AP9As4nRj4HPVNWZXTXdleSlwCeA3YC1aUZ+ngFcUlW3J9kZeAdNiPsWms2NzgX2q6qvT0C9VNWbkpwCvIHm818XuJYmaD4Y+K+JeI4kSZIkSdJUdv755/PRj36U+fPns+GGG3Lttdey55578prXvIY999yTI488kre+9a2cdNJJi+3nnHPO4fzzz2fjjTdmu+22Y/78+bz1rW/lM5/5DKeffjobbrjhhNduCDpkVXUxTbC3pHa/B/ZYin4voxlBeh9JPtr+eEGfe26kCU4/Nob+jwaOHms9XfddQBNajrX9r4A5i7l+K2Ov+WIW81lX1ezFXPs+8P0lPUOSJEmSJGlUzZ07l1133fWekHL99dfn5z//OSeccAIAe+yxB+9+97uX2M8222zDJptsAsCTnvQkLr74Yrbfvt82NhPH6fAjKsnGfc49gWYn9muBn0x6UZIkSZIkSVphVRXJ4sfyda6vuuqq3H333ffcd/vtt9/TZo011rjn51VWWYU777xzOVR7X4ago2tBkp8mOTzJwe3u62cDawFvbUdQSpIkSZIkSWMyZ84cvvnNb3LNNc1e1tdeey3bbrst3/jGNwA49thj7xnROWPGDBYuXAjAySefzB133LHE/tdee21uvPHG5VK70+FH1xdp1v58Fc1amtfRrMf56aqaN8S6Vlpbb7olCw6bP+wyJEmSJEnSCKghZAyPe9zjeP/7388OO+zAKquswlZbbcWhhx7KPvvsw8EHH8xGG23EUUcdBcDrXvc6dtllF7bZZhvmzJnDgx70oCX2v++++/K85z2Phz70oZx++ukTWnuazcclLW+zZs2qBQsWDLsMSZIkSZK0Arrgggt47GMfO+wyhqrfZ5BkYVXNWtK9ToeXJEmSJEmSNNIMQSVJkiRJkiSNNENQSZIkSZIkaQWwMi9ruazvbggqSZIkSZIkTXFrrrkm11xzzUoZhFYV11xzDWuuuea4+3B3eEmSJEmSJGmK22STTVi0aBFXXXXVsEsZijXXXJNNNtlk3PcbgkqSJEmSJElT3Gqrrcbmm28+7DJWWE6HlyRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjLSvjjlLSMCS5Efj9sOvQSNoQuHrYRWhk+f3S8uT3S8uT3y8tT36/tDz5/dLyMqrfrc2qaqMlNXJjJGny/L6qZg27CI2eJAv8bml58ful5cnvl5Ynv19anvx+aXny+6XlZWX/bjkdXpIkSZIkSdJIMwSVJEmSJEmSNNIMQaXJc8SwC9DI8rul5cnvl5Ynv19anvx+aXny+6Xlye+XlpeV+rvlxkiSJEmSJEmSRpojQf9/e3ceLFlZ3nH8+wMFZFhFcaJSCLJIMFFMBARlWF1QkFWBiEsCaoIFuEQiIriLC4qQRFREUIigsplIFBFGQBMsVEwQdJgMGEFAhmFgYIBBePLHOe20N/cOc+/c2z3d9/up6jqnz/ue009Xnep+++l3kSRJkiRJkjTUTIJKkiRJkiRJGmomQSVJkiRJkiQNNZOg0hRK8swkZyT5bZKHk9yS5OQk6/c7Ng2uJBskOSzJhUnmJnkwyb1Jrk7yN0n8bNekSnJokmofh/U7Hg2+JC9Jcn6S29vvx9uTXJpkz37HpsGW5JXtvXRr+/04L8k3kryo37Fp5ZfkgCSnJrkqyX3t997Zj3PODkkuSbIgyeIk/5Xk6CSr9ipuDYbx3F9JNk9yTJLLk/wmyZIkdya5OMkuvY5dK7+JfH6NOP9LXe39zaYy1n56Qr8DkIZVkmcDPwI2BC4GfglsCxwFvDzJjlV1dx9D1OA6EPgccDtwBfC/wNOA/YDTgVckObBc+U6TIMlGwKnA/cBafQ5HQyDJccCHgPnAv9F8lj0F2AbYGbikb8FpoCX5OPBu4G7gIpp7bDPg1cD+SV5fVcv9g1DT0nHA82i+824FnrOsykleDZwPPAScBywA9gI+A+xI02aTOsZzf30IeC1wA8334gJgS2BvYO8kR1XVKVMbrgbMuD6/uiXZC/hrpkF739XhpSmS5LvAS4Ejq+rUruOfBt4OfL6q3tqv+DS4kuwKzAC+XVWPdR2fCfwY2Ag4oKrO71OIGhJJAnwP2AS4AHgXcHhVnd7XwDSwkhwIfB24DNivqhaNKH9iVT3Sl+A00NrvwNuAu4A/r6rfdZXtAlwO3FxVm/YpRA2A9l65FZgLzKL5s/mcqnrdKHXXaeutC+xYVde2x9egud9eBBxcVef2KHyt5MZ5f70R+HlV/WzE8Vk0bbMCnlVVt0913BoM47m/Rpz3VOC/gdnAzPbczatq7pQG3CcOmZSmQJJNaRKgtwD/NKL4BOAB4NAkM3ocmoZAVV1eVf/anQBtj98BnNY+3bnngWkYHQnsCryJ5nNLmrB2qo6PA4uBQ0YmQAFMgGoFbEzz2+aa7gQoQFVdASwCntqPwDQ4quqKqrppOUfTHEBzT53bSYC213iIpkcWwN9OQZgaUOO5v6rqzJEJ0Pb4D2iSVasBO0x+lBpU4/z86vaFdnvEZMe0MjIJKk2NXdvtpaMkqhYBPwTWBLbvdWAaep0Ewu/7GoUGXpKtgBOBz1bVlf2OR0NhB5pexZcA97RzNx6T5Cjna9QkuAlYAmyb5CndBUl2Atam6YEsTZZOe/87o5RdSfOHzw5JVu9dSJombO9rUrQ9jvcB3jpdpupzTlBpamzZbueMUX4TTU/RLYDv9yQiDb0kTwBe3z4drUEuLZf2XvoqzXyzx/Y5HA2PF7bbO4GfAn/WXZjkSpqpPO7qdWAafFW1IMkxwKeBG5JcRDM36LNp5tD7HvCWPoao4TNme7+qfp/kZmBrYFPgxl4GpuGVZGNgN5oku39Sa8Lae+mzwNlVdVG/4+kVk6DS1Fi33d47Rnnn+Ho9iEXTx4nAc4FLquq7/Q5GA+14mkVqXlxVD/Y7GA2NDdvtW4Gbgd2Ba2iGMZ8EvAz4Bk7noQmqqpOT3AKcARzeVTQXOHPkMHlpBdneV0+1vYrPAVYH3l1V9/Q5JA2odoqis2gWQjqyz+H0lMPhpf5Iu3VlMk2KJEcC7wR+CRza53A0wJJsS9P786Sq+o9+x6Ohsmq7DU2Pz+9X1f1V9QtgX5rJ/Gc5NF4TleTdwDeBM2l6gM4A/gKYB5yT5BP9i07TkO19TZokq9KM0tkROA/4VH8j0oB7O80CSIdPt2S6SVBpanT++V13jPJ1RtSTJizJETRDGW4AdqmqBX0OSQOqaxj8HOB9fQ5Hw6fTyJ5XVT/vLmh7HHd6sG/b06g0FJLsTLPw1req6h1VNa+qFlfVT2mS7LcB72wXr5Qmg+199USbAD0bOBD4OvC6CSx+IwGQZHPgI8CXq+qSfsfTayZBpanxq3a7xRjlm7fbseYMlZZLkqOBfwSup0mA3tHnkDTY1qL53NoKeChJdR7ACW2dL7bHTu5blBpUne/GhWOUd5KkT+pBLBo+r2q3V4wsqKrFwI9pfvts08ugNNTGbO+3fypuQrNwzbxeBqXh0t5LXwMOAv4FOKSqXBBJK2JrmikV3tTd1m/b+7PaOje1x/bpX5hTwzlBpanRaYC/NMkq3SvEJ1mbZhjDg8B/9iM4DYd2AYgTgeuAPapqfp9D0uB7GPjSGGUvoEkeXE3zw8+h8hqvK2kSApsnWa2qlowof267vaWnUWlYdFbgfuoY5Z3jI+87aaIuB/4KeDlNkqrbTsCawJVV9XCvA9NwSLIaTc/PVwNfAd7U/btSmqBbGLu9/0pgJs0c7fcxhG0yk6DSFKiq/0lyKc0K8EcAp3YVf4BmjqrPV9UD/YhPgy/J+4APAj8BXuoQeE2GdkjyYaOVJXk/TRL0rKo6vZdxaThU1fwk59EkDY4HjuuUJdmDZmGke4Hv9CdCDbirgLcBb07y+aq6rVOQ5BU0f0A/BPyoT/Fp+HyTZgqGg5KcWlXXAiRZA/hwW+dz/QpOg61dBOkCYE+ahNWbTYBqMlTVdYzd3p9NkwQ9tqrm9jKuXjEJKk2dv6NpaJ+SZDfgRmA7YBeaYfDv7WNsGmBJ3kCTAH2U5kffkUlGVrulqs7scWiS9HjeQfNd+N4kO9EMUd6YZs7GR2km6B9ruLy0LN8ELgN2B25MciFwB830Hq+iWaTmH6rq7v6FqJVdO/SzM/xzZrt9UZIz2/35VfUugKq6L8nhNPfe7CTnAguAvYEt2+Pn9Sp2rfzGc38Bp9EkQOfTzGl8/Cjt/dlVNXvKAtZAGef9NW2ZBJWmSNsb9C9pklUvp/kSux04BfiAPfe0AjZpt6sCR49R5wc0q+NK0kqjqn6XZDuaXqD7AtsDi4BvAx+rKqeJ0YRU1WNJ9qQZgXMQzf21Jk1S6hLglKq6tI8hajA8H3jDiGObtg+AXwN/SCJU1UVJZtF0btgfWAOYS/OHzykuXqMRxnN/ddr7T6EZPTGW2ZMVnAbeuD6/pqv4uSxJkiRJkiRpmLk6vCRJkiRJkqShZhJUkiRJkiRJ0lAzCSpJkiRJkiRpqJkElSRJkiRJkjTUTIJKkiRJkiRJGmomQSVJkiRJkiQNNZOgkiRJkiRJkoaaSVBJkiRJkiRJQ+0J/Q5AkiRJmo6SrArsD7wK2B7YEFgTWAjMAa4Czqmq6/sW5CRK8nxgH2BhVZ3c73gkSdL0kqrqdwySJEnStJJke+AsYIuuw48Ai4D1+OMRWxcAB1fVkt5FOPmSvBH4MvDrqnpWf6ORJEnTjcPhJUmSpB5KshcwmyYBejfwHmCLqlqtqjYAVgNeCJwI3AfsR9NDVJIkSRPkcHhJkiSpR5JsDpwNrA7cALysqm7trlNVjwLXAtcm+SRwRs8DlSRJGjL2BJUkSZJ658PAOsBDwL4jE6AjVdWCqtoHuLf7eJKZST6Z5BdJ7k/yQLv/iSRPG+1aSXZOUkmWOR9Wp06SnZd1fpLNkpyR5DdJHk5ya5IvJnnGaNekGQoPsHHXa3Qe7x9R/2VJLmivuSTJfUnmJbk0ybuSPHlZ70GSJGkke4JKkiRJPdAmJw9on55TVXOW99zqmsg/ySzgIpq5QwEWAwX8afs4LMneVXX1pAQ+iiS7AN8C1qKZx3QV4BnAYcCeSbatqtu6TrkTeBJNAvgx4K4Rl7y/69rHAx/oKlsMBNikfexB01N29uS9I0mSNOzsCSpJkiT1xi4sbX9fOJELJNmIpQnQG4AXV9WMqloL2An4FbA+cPFoPTIn0fnA5cBWVbUOMAN4LU1C9OnAx7orV9VM4Kj26W+qauaIx6fa97cxcEJb79PAM9r3t3b7nl8C/HP7OpIkScvNJKgkSZLUG1t37f9sgtc4liYZeA+wW1X9sFNQVVcBu9MspvRkmgWXpsp1NMP5f9m+9pKq+jrw3rb8gCQTGXW2Hc1vlDlV9c6q+m2noKruraqrq+qIqvrJir4BSZI0vZgElSRJknpjg679BeM9OUmA17RPT6uqO0bWaecYPa19etC4I1x+H62qx0Y5fnG7fRKw+QSuu7Ddrp1kxoQikyRJGoVJUEmSJKk3soLnb0LTwxPgsmXU+1673SDJJiv4mmO5Zozjv+3an8jiRT8G5gN/AlyT5G1JntMmgCVJkibMJKgkSZLUG/O79ieSINywa/+2MWtB94rzG45ZawVU1ahzclbV77uePnEC110IHEyzcNLWwKnAjcA9Sb6V5HVJxn1dSZIkk6CSJElSb/yia3+bFbxWPX6VcdVbaVTVZTS9Xl8PnAXcBKwL7AV8FfjZFC/6JEmShpBJUEmSJKk3rgA682juO4Hzf9e1v9Ey6j2za/+urv0/9NJMssZoJyZZdwJxTbqqeqCqvlpVb6yqLWje0zHAQyztISpJkrTcTIJKkiRJPVBVdwLnt08PSbLF8p7bzol5M0sXVNptGdV3b7d3V9XNXcfv6dofK4m63fLGNAGdBPC45/esqtuq6hPASe2hPSYtKkmSNC2YBJUkSZJ65zjgfprV0y94vGHdSdZPcj6wblUVcF5b9JYkM0ep/3TgLe3Tr40ongM82O7vP8q5qwDvWd43MgH3tdv1xqqQZPXHuUYn/kcnJSJJkjRtmASVJEmSeqSq5gCHAktohnVfl+SYJJt16iRZNck2ST4IzAP267rER4GFNAsrXZZkh67zdqRZNX49mh6jJ4547UdY2hP12CSvSbJae+6WwIXA8ybz/Y5wfbtdJ8lrxqhzTJJ/T3Jokj8M60+yenvO37eHLpnCOCVJ0hBK84eyJEmSpF5pE5ZnApt1HV5C00t0PZZ2VijgXOANbRKTJLOAi2kWCwJ4oN3OaLcLgb2r6qpRXveZwDXA09tDj9D0rlwHWESz+NDstmyXqprdde7ONPOaUlVjDmlP0vmB8Ufnt2WXsXQo/yKWDu8/uapOTvJ+4ISuUx5sH+uzdBj9jcCuVXXHWDFIkiSNZE9QSZIkqceq6ofAc4CDgXOAuTSL/qxNkxi8GvgIsFVVHdJJgLbn/qA99ySahOAqNAnCG4FPtef8vwRoe+6tNPN+ng7c1h6+H/gK8IL22lPpAOAzNEPznwhs3D46Q+S/ALyZZij/9cBimgTtPcBVwNFtnCZAJUnSuNgTVJIkSZIkSdJQsyeoJEmSJEmSpKFmElSSJEmSJEnSUDMJKkmSJEmSJGmomQSVJEmSJEmSNNRMgkqSJEmSJEkaaiZBJUmSJEmSJA01k6CSJEmSJEmShppJUEmSJEmSJElDzSSoJEmSJEmSpKFmElSSJEmSJEnSUPs/g//MDZF3QG0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onion_bigram_counts = pd.DataFrame(term_counts(df = df_onion,n_gram=(2,2),stop_list = 'english'), columns=[\"count\"])\n",
    "\n",
    "onion_top_bigrams = onion_bigram_counts.head(15).sort_values(by = 'count',ascending = True)\n",
    "onion_top_bigrams.plot(kind = 'barh',figsize=(20,12),color = '#006b3a',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Bigrams in Posts on r/TheOnion',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Words', size = 25)\n",
    "#plt.figtext(20,20,s = f'Number of Unique Bigrams: {len(onion_top_bigrams)}');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Bigrams: 6824\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year old             14\n",
       "super bowl           10\n",
       "study finds           9\n",
       "releases new          8\n",
       "white house           7\n",
       "bernie sanders        7\n",
       "area man              7\n",
       "ocasio cortez         6\n",
       "new york              6\n",
       "alexandria ocasio     6\n",
       "way watch             5\n",
       "things know           5\n",
       "roger stone           5\n",
       "pope francis          5\n",
       "introduces new        5\n",
       "change way            5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(stop_words='english',ngram_range=(2,2))\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "onion_bigram_cvec_stop = pd.DataFrame(cvec.fit_transform(df_onion['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Bigrams: {onion_bigram_cvec_stop.shape[1]}')\n",
    "onion_bigram_cvec_stop.sum()[onion_bigram_cvec_stop.sum() >= 5].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dmnwzHNeJNfCAF0ya30t3g166zy7wHPtsL49510Sg/dFwXi+PNUPzr08LDlWX7v9MyOuyjKhZwbq1Tb/H5NpDd2eo5h0bryZo0WrIjL3udMvMpybohp6L9+nlczxwgxFprkMLIvTXudFqgnafx+vSgnKDmnNnMqHmFlNqgtKCZoN1fgO45Ig092P9GkPTaoIW7Yf1FaZs812AS02Yf8mhz9ZtZjjfilbDfcuhNJvT/pwbpDm6+yyNqpH2b8zVXjsB2Hxo/lV7+RzFmBYWXdprAVdd4Dkx35qgRfsjYb1aZLQ/2gZpptaOn1KumWqC0oLXg3Sn9z9rtODQyd28C4AnjVh+C1qAc5DHYSPSvLQ3f+x3Oy3Ic8cR0/s1Qr8wZbvX9NJ+Hbj4hLTXZsI1dsp6btj7zJ0N3HdMuQ/pled9Y/Lq15Qs4Pkj0mxO++Nw6n6coey7D63v98B1Zlz2/zF3D7zljMtsaE3QogWFtx+R7lG9NN8dk9edmPs++SFj7lNpf0QMvuO/NmL+T3vretAC9vuNe8ufxfrXrMXc5ok1QbvP7W97af4HyFCasO51+9fDZe7SvW6o3J8d9bkD/m8vzccWev76uGg8lr0APnz48LGaH70v5F/0pg2anJ/Uv8nrbhpO7Obt15s+LQjab453rwlluTVzTUjWMvQjibmmqAsNAu3VK8dei7gP1w7dAE16HD4mj/4Ptg/PsM6vdGn/BVxjStrX9PK+xdC8PXvzvjQhj0sy1wSs2MhBUFqNwUHw9qApeW3FXLPb9bpxYP3A060n5PWJXrpbTclrMYKgV2Duh+R6Pzp66V7fy2f3BZ6nx/TyuP8inOt3GJp3QG/eixeQ93WY+6F2PHD5BeSxMYOg95hh/fMNgm7Iufi1WY7niHUuVhB02uNMWk2paV1xjA2C0gLvp3bz/8nkoPhwkHFaEPQ84IYL3RdD+V6Gue+nkU1kh8r3J2CbMel2GdqOscHAoc/crYbm7dybN/YPiUXY9vkGQX/IiEBCl2575gK7P93Ack0NgtJqKfYDIR8Ymt//k+o9E9a1Ja2m7cjrCXNB0vOZEJSckP98gqB366V9wkY87v3ujdZr2txLd1nmuhs4lxHBdtYNEo69v6PVlp2aboayDwdBd53Hsj/qlpk5iMWGB0HPYPK1r99dxqig4eCPlX8CV5uy3jf08rrp0Lx+1y03XsB+32Jovw93Z7OY2zwtCPrw3vxDppT7sF7aUfdx/SDonxnTLUq3/YOugk5lKOjqw0f/4cBIkrT09u+eL0u7kRi4L61mRD/NRGkjAg+a4/24qr40Lm21ZiaDJurXoDWh6hs0V79upgwGsYl7y6SZaQNLDJolfaLaCLeTfLD3eri53QN7r18/LoOq+gfwjinrWUwPY655/usmJazWhHwwOulNMmJAlJ5v1eTmTP0uEqYOVrShqupvtKaVALsmudZwmq655n90b//EuoMzzUe/O4VJTRIn6S932cGLroyDQSROodXem6+HMdeM/o1VtdBm5hvDr6rqK4uc54LPxW6ghkG3H2ur6rPjMqmqg2kBmqV2Pi0wON8mk327MDeg0ieq6s8T0r6F9ifarA6pqvk0nx+rWlP/QV63mWGR/avqX2PmfYt1t+OtE/I5svd6+HrV797lxjOUaam8tarOGzWjqk6mBUkBrj9tQJ152C1t8KfB41FJ/hf4Ma0WLLRr20uHlntQ7/Wrx2VeVefQ+ngdtRzMHYuL0Wr1b0xLddwH9w5n0brcGKmq/k77Yx3ad/r9xqXtvHlCXj+g1cyFxft+/llVDXeNNFJ3P3uT7u1Sjgo/7dp3WO/1Dfsz0gaNG3xXHFhVf5yyrpH3i2mD9PUHCJr3PUS1wfL6173LjkvLBmzzjGb6bI+YP/zZHvaeqvrnqBnd9g+u2dvRmtxLI9knqCQtsao6KsmvmetzcdCf3F7d86+q6pszZnfr3utZgghfAe7avb4NrQ+2ga/SOma/IfC1JK+j1Z47c8ayLIUn0v7pHWdaP4xn05oBT3LH3utzkzxgSvr+aJ/DN4uDvuDOpTU1neQQ4GVT0iyWO/VeX2mGbez36XZDWi2gUab1wdS/6b7MlLSL5Z20Zmih9Xn7gqH5D2Tuz4f31vz74VpM4/rgvCWtKw2Ary7wM9k/r5fyB+Yspn02FmJDzsVbMBcwPnyGdR1B665gMX2Z9f8Y2Yw2gNrtaef0g4EHJnlSVb1nAevo91l42NhUQFX9OclvgOvNmPfMx7Trn/ihtADOTWl9FG7L6M/DVWfI8jvjZlTVWUlOp/XdeHJN7hfzb73Xw+fID2jfN5ej9Z23Ba0v1e9VVc1Qxo1llvP+VrRz6VIs/A+bvvdNmf8H4KH9fZ1kK+b6P/99Vf1qSh79+5vhQPhXmevT+YtJXgF8sqr+OiXPhTiWFijcHnhqtx3vAY5ZrOPe/Vk3+E76TlWdNmWRrwDP7V7fhtYH8jjTzo+/0LZtsb6f53NtHwRwzwe+uEjrn8WGfFf076XOm+Feqh/o7N8vLlb/27Pms7Hv1Qa/Tc5h+nfoYbR75C2Y/ifXfMs9KdCrizCDoJK0PN5P6/vo3kl26KbdqzdvVlfqvZ72I2I4zZWG5j2XFii5MnDn7nF2kqNpfZ4dChza/du6XL5SQ4MizNPfxtWS6VnTe/247jGr4ZvFwT/Rf6zJg/LAugMvbGxreq8/NM9lJ90QzxKEHrj4PNe7UIfS+pq6LrBXkhcPnQODwcAuoPWZuVAnM7dfL73APLbrvf5773U/8LPQ2nWDPIrlqbk4ycb4obIh52K/BslxM6xrljTztbaqPjNm3juSvJL243IHYL8kP6mqscG/MRaynbMGQWc6pkluQatpfs0Z873U9CTrfHZGGRz7WdPB0DlSVeckeRKtz+QtgL27xylJvkmrkXRwV7tuKa2Ea/AZtH37Q9ofLh8eUTP3csz9Dp1671JVxyU5r1tm+N7lk7Qa/w/o5r0FeEuSX9IGiDyC1sx92vGeqgui/x/mBjt8Uvc4eei4/3BCNtMs5n1d35lVNW2AysH5sVjnxnyu7YMg6De6WstLZUM+M2t6rwfXgFldeC9VVecl+Qdz17dLM8/vxSSbs+6f8pPO9419nRi0GPp9V5N7rKo6O8kfaH3oTmppBCvj+qZVwObwkrQ8DqAFXTYHHgE8snt9QTdvVv1/lcc1/+vrNyPpL0sXXLw5sC9ztUO2Au4APIc2yuOfkjwtyVKNGr3YZqlBt930JGNtOXjRNW8ajIo57YcHzHb8FsuibOMI82kuuyS62jn7dW+vRBvwBoAk12SuZvRXZuj6YJI/9V7PGii6UJJLsG6ws/8DqB/4GdkUbAaDPM6oqpV2nDZGbfMN2cZteq9X2mcXgK6p+Yu7txcDXriAbDbmdk49pt0fgF9lLgC6ltY8/am07hseRKup/UDmAj2bzbDuWY/9Bn0OquqTwO1oI8sP/li5DO0a80rg+0l+kOSuY7LYGJbjs32lqkrvsU1VXb2q7ltV7xrTNcF871366YbvXS6gjXa9D+v+wXN94DG07oWOT/K+JJdjA1UbLfv2wEHMdUexPa17o1cBP0hybJJdxmQxzaLd1w1ZjnNjpmt7ku1of7zD0rdU2JD9spj3Uht0DzG0zFlMDoJutHMhycVpfwzB7J/twTm8ZVe7epyVdu+iTZRBUElaBl2/QYN+kvaiNYuH1o/an0YuNNo/eq+3GZtqzrZjlh2U64SqegqtdtHtgWfRalic3iXZgdY31zvnUcZNTf8HxZ5DP+6mPXYbLNg1qx78K30Jppvl+M1q2vf7YBvPBC42z2386CKWc6m8j7lj8YTe9Mcz13zsXWyYfhcWt1vA8oMmqgB/GarxfHrvdf8zPB+DPC6xiH0BTrIp32P2f7gt9Wd3Pr7ce73rAo7rcm/n05jrS/ddtBGkn1xVb6mqj1bVp6vqM12N2Im1iZZLVR1TVfej1Wy8D20U5G8wFxTdEfhqkj3GZHFRNd97l366UfcuF1TV26vqBrRg0GOBdzNXw3lz2r3Wt5NscFPvqvpeVe3OXN/ur6DVAh0ERW9O61ZoWvPoURb1vm4TcW/mAmcrrbuWSfr3i3vM815q96G8NvQeor/Md2do9bRRVNVZtObtMPtne3AOnzNDqylpg23KN6iStKnbv3u+affoT5tVv8+r686Qvp/mL+MSVdW5VfWtqnp9VT2Q1g/dY+kFkpLcZNzym7h+DbwNHfhgsI+v1g1uM8l1pswfjHIOk2tjwlx/YuMMtnFrZm+GusnqmkEOBnfaLclVupq6e3XT/saG//D6au/1vZNMGpRglP/ovf7q0Lz+HyMLGaSgn0dYeP+V/R8nG3oOrmT9a+N6g2mNMEuajaFf02cb5l8rabm3827d89nA08f1x9u1PLjaIq97UVXVaVX1xap6UVXdmdbVwGDQpQBv3IRbUGwMJzEXKJ5679LV2h80nx977wJQVb+uqvdV1ROq6tq04NDPutnXBv5rYUUeua7TquqgqnphVd2JdtwHfflejHUHdJrVRrmvW+EGTeF/NqWf3pVmMe8X+9/7D+/uUeZj0j3EUhv0G3+NaQOtdvfGV+/ebox+fKX1GASVpOXzKdat4XU68Ol55tEf2OjuY1PN6Y9ePmnk5HVU1TlV9T7WHVn9DkPJ+s1UNuUfe4f3Xi+kFkffYB9vwbod6I8ysclk1+RvUMtj2qiX0zqXP6L3+oFjUy29jXkODWovb0YL6N+Huf34vg2tNVFVx9L6zoXWDcLzZ102yXWBh/cmDY8GfAxzzQrv3g0kM1/9ASqmjSA8Tn8QlQ09B1eyY5k7F3eeIf1dNl5RJhoOtM/SpL3v6N7riU13u1GQp/1RM19X6J6PH9NkeuC2bFiz0yVXVSdW1ZOBn3STrgpcYxmLtKJ0tb0G/WauSTLt3FrQvUu3rm/TrvkDdxxKsmjfO1V1Aq1Z/qDv5jVJZhnMq5/Hccz1fXibJJOauMMG7JuVoOvLctCKZlOqBQrr3i9u6L3Up4HB6PJXYd1WKxMl2ZW57gTOYq4LoOUyOA+3ZPr34y7M1QLe5M5fbZoMgkrSMqk2wvObaCPZfgd4Y81z1Oeuyeyx3dsdk9xjXNokOwG7dm9/TwuszNfa3uvhwfX6zYKWq3noBuu6IxiMlHyTJI/agOz6Qe1njEuUZFtm61B/UJvlel1QYlReAZ4yJZ8PM1cL51lJLj/DupfCRjuHquobwE+7t48DnjiYRRvddzG8mLnauk9Lcq9JieHCY/9B5jrx/3QXUL1QN7DAx7q3lwGevYCyfYS5pppPX+Ax/1nv9a7jEnV9IN5oAfmvCFX1D+auAWuS3H9c2iS7sfgjw8/qnr3Xxy2gGeFhzP0Rt0eSSYHtp7D4vxsGQdurdP3IjfPiCfNWurW91w5Iu65P9l6PvaZ1NcWePma5Wa3tvV7nOHSDBQ0CoRv8vdP1Q93vX3ohx/1T3fPWwJPHJUqyPXOD+53HphdEhBYkGwwmuEmVv7sHH/zBeLMkD9uAvM4F/rs36TVJbjZtue663b+HeUsXjF9OM322O88Zs5y00RgElaRlVFUvqarbdo+XLjCbV/de75/kBsMJklwd+Chz1/3X9pseJrlSktd1Tc5G6gZueXRv0vDop7/rvb7FrIVfoV7AXJDwnUkeOilxkmsmeWOS4VHBP8Vcn2T3TvLcEctuResGYWRQc8jBg8VY97gP8gptYIaJtU675maDmpFXBL6UZM249EkuluQeo8q/yDb2OTTY5mvQ+iADOKyqfrMYmVfVocwdl82ATyd50ri+GpNcH/gacOtu0h8ZX/vjFczVBn1xN0LxSEm2T7LOOdAd8/d3b68AfHFcIL3L465dgLbvSOYC1Y8a1SVGt037j8t3E9JvxvqObrvW0dVee/fSFWmddd+QdX8wf2S+eXTBn0GNoW2Aj46qdZbkvkz4E2cDfK973hx42Yj1XizJa5irJbZiJNk9yZOTjB2tvvsuHtSwPZV1A2Nq594p3esnJHn8cIKuKe27mPuj4fCq+s5Qmv+XZOcp3Q3s03s9auT2td3zv01qvpvk/kn2mVQ7M8mNmauV93fW7c5kVm9krh/clyS593CCJNvQ7usGXY+QX3qOAAAgAElEQVR8cJ59yq8Ug5YJJ9AqBGxqXsDcH4zvmtb/b5JrJXnzqHOoqt4NfLx7e0ngkCRja5gmuS2tZc+abtLRwIvmV/yN4hPM3fveI8nLhj+faf6HuT9Uf41BUC0R/5GUpE1cVX2s63z/YbTRr49Nsj/wLdqN2U60mm+DH2tfAd42lM1WwDOBZyb5Hu2f7Z/TfrhtR/sB8nDmAnXfoAVE+n5Mu4ndAXhkkhOBbzMXuDmzqo5gE1BV307yX7Q+3bamBQeeTaul8Fvaj5PL0PpnvCNtH0MbFKOfz7ndD7uv0L5zX9XVDvwYrbnbNWmj116fVmt0WnOq/WjBiEsBj+ia2X0EOJl2E/ww2oAMHwX2nJLXM2h90d4JuCXwyySfoR3bv9GaMV0BuBmtq4Ur0kbDXS/4uliq6q9Jfk7br7sl2ZdWW23QVPb8qtqQvq4+QAsS9weBWexmYy+kdfL/ZNrn6u3AM5J8ijZy8Rm0z+kutODOoG/N3wH36vovXU9V/SrJE2mBzM2At3Xn1iDQXrQmt3fo8n0/6zaBhzbq9i1pg7XsBPwqyYG0a8UptPPqxrSuAq7flfPC2rlV9a8kb6fV7Lg4cGT3/vvd+9vT+iU7j3au3Gce+21FqaqDkgw+R1ekXVffR7umQRvI6nG0c+mTtNGpYfFGr12T9QdV2YwW8Lg98BDmag//ioV/Ll9G6/bjOrRrwc+SvId2/d+WVtt0D9r16mfMNW1cjO18K/BI2p86z+laK3yWdv25BvAI2jXqh12am47JZzlcldY9zOuSHEoL3vyO9n13eVp3EHswd615XVfTS52q+nuSvWnfh6EFkB5MG4zx77TvtEczV6v8ZNr35bDdaNfdPyX5KvAD2jm0Oe2e5YG0LhWgfZcMdzcCcAitz9vLAp9J8uFufYOa/d+sqtNpfdO+BXh977ivZe6435Z23Addlrx2IV2tVNUvkjwfeD3te+QLST5JGwztH8ANaE38B30p/o51a8tuSu7bPX+h6/Znk1JVRyZ5Ou282gb4eHcf/Xnad3P/fnFwvwXwkjFZ7kW7L7g/beC4TyU5lnb/+Tva9+tVadfmXZjrwuFoYPeu5ciy6u59H0m7f9uKVpv/nkk+Quv380q0+9VBtzlnA49crsGcdBFUVT58+PDhYyM9aDfQBfxiA/L4xSCfCWk2p9WWqCmPjwNbj1j+GjMsO3gcClx2TDn2nrDc2gVu/9peHmsWmMfxCzkOwIOAE2fcLycAlxqTz0Npwa9xyx5C+wE1eH/whDI9gHbDOC6vT9EC17PktRVtEIfzZ9zGd47IY7fe/OdN2Z9T03bbN648Zw2lfVJv3p4zHtP39pY5CdhyQz/nY9bzGNogFdP26QW05vA7zOOc/PsM+b5tzPLb0QJNsxzv9cpEC+ocNmGZU2h91L2qN+22G3Le9Jb5aG+ZKy7BubjVlH11Li3Y/eTetHttwDnzpAnrGvc4ArjKhDynXvto1/9fTljHibQf75/oTRv1PbKQz+PTus/AuHX/iBYM+zYjrgG9fCaeb/PdJ9POESZ/1/Uf5wOvAbLAc+LivbxGXsunfS4WmnbGfbhB+XR57UkL7E3aj78Bbjxm+e/OeCz+Ctx5TB7Xol27xi27U5dun3kc91cu9Lj3yvUMJn/fFy34NekacHSX7p8zrG/mtBPy2L1XtmdNSXuTXtr7L3B9RzN0nGZYpn/N3mMx0gIPpt1TzHJ+/A3YZkJeFwOeR6uIMC2vs2lda43Nb7G3edZjTAvSnjDDvhj5uezyeN2sx3c+aX1ctB82h5ekVaCqzquqJ9BGQX0P7QfDv2i1E35HC7LctaoeXCP6Ha2q39NqFDwGOIBWq/M02o38GbRmKh8B7ltVu9b42mr70X44fobWBGy+fdStKFX1KdoP8P8EvkBrrnwm7Z/9E4BvAm+m1Xi7SrWaIqPyORD4N1rA8Xe0/XIirabeE2hBo3+MWnZEXp+hNRU/gLaPB2X5GvCwqnoQM+73qjq7qp5Eq2nzGlrz1BNpNQ3+RavFcBDtZvzGVfXEcXktlm777gwcSAuAn7XIq/ha7/X7ayPVmqg2kNh1aLU6Pkb7DJ1GO15/odW+/G/gplX1yJqxD6/unLwWrTbmYbQfEOfSzsvfdOt6NPCsMcufVlX3p9XoezetFuE/acf8JNo5/aquXOuVqVoT6nvQ+oj8Dq1PyTO7fN4I7FhVX5llW1a67vNxf9qfGF+h7Z+zac2aDwBuV1X7su4ARSdvxCJdQDuHfkrrcmA3YOeq+vOkhabprv870s6po2nXon/RaoO+BrhZtT51B9t51qjvkQWu+020z/unaOfyebTr2ZG0UbxvXa3fvZXmXbRa989irsbXGbTvzNNotaPfTNt3z6mqWq6CrnRV9VHaqO0vp51/J9OuaX+jjXK9D3CjqvrpmCx2pX0Hv4F2/TqhW/4cWuDzq7Rz6XpV9fUxZTiO9r36Vtp5/y9aIGXY27t0z6bdEwwf92NpAakdq+r5G3rcq+oNtBqEr6f9ITD4DvkzrWbgI2mfkQ26BiyjQVP4M1n+Ec03SFV9nHa/+GTafdOfaPcvg3u0o2jnxr1p94tjB4Orqguq6lW0z8Xg/vN3tO/qs2j3oofR7s2uV1VPm5Tfcqmqw2j3Qc+lXdNPYu5e4xu0/kCvM+5zKW0s8TtZkqTl1w0MMggsfLmqVlw/eKtBkk8w13T5RlX180nppWmSHET7YXsBsF1V/XPKIpuctBGcT6LVJP5uVd1myiKSNFGS79D6xP5CVd13WnpJWgzWBJUkSRcJ3Siqg5onXzcAqg2V5LrMjdL+vdUYAO08ihYAhVYDSZIWLMkVaX0rwyY2KrykTZtBUEmSdFHxImAw8u+owTGkCyW5XpIrTZh/dVoz7s26Se9ckoItsiS3SnKJCfN3pjXjhNbs991LUS5Jq9p9aYP6FK1LCUlaEo4OL0mSVqUkV6ONdr4NrR/LvbtZP6IFr6RJ7gy8PcnhtP7LjqP1x3Y5Wv/LD2ZuFOgjgPcvQxkXw38B901yMG2QmT/TAhNXAe5Oq+k6GIH4lVX1m2UppaRVo6reRetbV5KWlEFQSZK0Wt2HNpBF35nAYx2oRDPaHLhb9xjnK8BDquqCpSnSRnEp4CHdY5QCXgu8eMlKJEmStMgcGElaIpe73OVqzZo1y10MSSvUBRdcwPe//30ALnWpS3Hd6153mUu06TvxxBP5wx/+AMAWW2zBNttsw5WvfGW23nrrKUtKcN5553Haaadx+umnc8YZZ3Deeedx/vnnk+TC82n77bdnu+22m57ZCnb22WdfuJ1nnXXWhdu52WabseWWW7Ltttty+ctf3s+NJElasY455piTqury09JZE1RaImvWrOHoo49e7mJIkiRJkiStGkl+P0s6B0aSJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBJUkSZIkSZK0qhkElSRJkiRJkrSqpaqWuwzSRUJ22LZ4yI7LXQxJkiRJknQRVPsetdxF2CiSHFNVO01LZ01QSZIkSZIkSauaQVBJkiRJkiRJq5pBUEmSJEmSJEmrmkFQqZPkpUkqyc7zWObwJHasK0mSJEmStIIZBJUkSZIkSZK0qhkElSRJkiRJkrSqGQSVJEmSJEmStKoZBBUASW7Q9Yd56IQ0P05ybpIrDk2/Z5IvJjkpydlJfpvktUkuPSKPXZLsl+RnSU5PcmaSnyR5SZKLj0h/YT+dSR6e5DtJ/plk7Yzbdd0kByT5c5Jzkvyle3/dWZbv5bNnkmO68p6Q5ANJrjyfPCRJkiRJkrQ8Nl/uAmhlqKpfJDkM2CXJ9arqV/35SW4P/Bvwyao6vjf9xcDLgJOBLwAnADcFngXcO8ntqur0XlbPBW4AfBM4CLg4cAfgpcDOSe5WVeePKOIzgbsDnwcOA7abtk1JbgV8Dbgk8DngZ926HwHcP8ldq+roGfJ5OvAG4FTggO75nt02nDZteUmSJEmSJC0vg6DqexuwC7A3LYjZt3f3/M7BhCS70AKg3wLuXVWn9ubtBbyvm//0Xj77AL+rqnVGVE/ycuBFwB7AgSPKtitwu6r6/iwbkiS0gOWlgEdW1Yd68x4KfBT4YJIbVdUFE/JZA7wKOAW4RVWt7aY/H/g48KBZyiNJkiRJkqTlY3N49X0G+AuwV5KtBhO7Zu0PAX5Lq1k58NTu+Qn9AChAVe0P/IBW67I//bjhAGjnTd3zPceUbb9ZA6Cd29NqfX6rHwDtynAgcCRwfeCOU/J5BLAl8JZBALTL4wLg2cDYACpAkr2THJ3kaM48dx7FlyRJkiRJ0mKxJqguVFXnJXk38GLg34EPd7MeBWxNC0T2A5i3A84FHpzkwSOy3BK4fJLLVtXfAZJsA/wX8EDgerSm6uktc5UxxfvuPDfnFt3zuD5OD6UFQG8OfH2GfI4YnlFVxyX5I3CNcQtX1X7AfgDZYdtRwV9JkiRJkiRtZAZBNWw/4AXAE5kLgu4NnENr3t53Wdo59JIpeW4L/D3JFrTg462Bn9CavZ9IC6TS5bPVyBzg+DHTxxn0GfrXMfMH09cbvGlMPn8bM/94JgRBJUmSJEmStPwMgmodVfXnJJ8HHpjkhsBlaAMiHVhVJw4lPw24WFVtP2P296cFQN9fVXv1ZyS5EpODqfOtRTkYsOiKY+ZfaSjdtHyuAPx0xPxx+UuSJEmSJGmFsE9QjfK27nlvRgyI1PNt4DJJbjxjvtfpnj85Yt5dZi/eTAb9h+48Zv5g+rFT8hnMX698Sa4FXG2+BZMkSZIkSdLSMgiqUQ4BfgU8mjYg0q+q6rAR6d7YPb8ryZWHZybZJslte5PWds87D6W7FvDqDSzzsKOAXwJ3TLLH0Pr2AO5M28Yjp+TzIVpz/ad0I8UP8rgY8Fr8DEmSJEmSJK14NofXeqqqkrwDeEM3aVQtUKrqkCTPA14J/DrJF4Hf0foAvQat9uSRwG7dIp8HfgM8I8lNaLU1rw7sDhzUvV7MbXg08FXgwCSfBX5BGxH+AcA/gP/oRnmflM/abhtfD3w/yYG0JvL3pPUn+iPgpotVbkmSJEmSJC0+a7FpnP2BC4CzgfePS1RVr6bVqjwIuAPwNODBtFHe9wNe1Ev7L2BX2oBLNwaeSgsgvhx45GJvQFV9B7hVt77bAc8Gbg98BLhVN3+WfN4APJwW4N0LeCxtYKfbA6csdrklSZIkSZK0uFI13/FmdFGQZGfgMOCDVfWoZS7OqpAdti0esuNyF0OSJEmSJF0E1b5HLXcRNookx1TVTtPSWRNU4zyne953WUshSZIkSZIkbSD7BNWFun46dwduCdwL+MKsTcYlSZIkSZKklcogqPpuCbwCOB34OLDP8hZndbnl1W/A0au06rkkSZIkSdJKZhBUF6qq/WkDIkmSJEmSJEmrhn2CSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIJKkiRJkiRJWtVSVctdBukiITtsWzxkx+UuhiRJkqQNVPsetdxFkCR1khxTVTtNS2dNUEmSJEmSJEmrmkFQSZIkSZIkSauaQVBJkiRJkiRJq5pBUK1ISdYkqST7L3dZJklyeBI71pUkSZIkSVrBDIJKkiRJkiRJWtUMgkqSJEmSJEla1QyCSpIkSZIkSVrVDIKucEnul+SQJH9NcnaSvyQ5Isk+Q+nWJlk7Jo+Xdv1r7jw0vbo+La+c5ANJTkhyZpJjkjx8QpnumeSLSU7qyvTbJK9NcukRadd2j0sleUP3+twkL53HPrhBks8kOTnJv5IcmeQeY9JuleR5SX6U5Iwkpyf5RpKHDKXbNsk5SY4amr51krO6ffOooXn7dNMfO2vZJUmSJEmStPwMgq5gSfYGPgvcCPg88Hrgi8DWwGMWaTWXAb4J3AR4H3AAcC3gQ0mePaJMLwYOBm4DHAT8L/Ab4FnAUUkuNWIdWwKHAg8AvgK8GfjdjOW7JvAt4LLAO4GPA7cEvpTkoUNl2xL4MvBKYAvgrcAHgOsBByZ5xSBtVf0T+C5w6ySX7GVzB2Cr7vVdh8qya/d8yIxllyRJkiRJ0gqw+XIXQBM9ETgH2LGqTujPSHK5RVrHTWmBxT2r6oIu71cBxwD/k+STVXVcN30X4GW0oOS9q+rUXnn2ogVRXwY8fWgdVwJ+Btylqv41z/LdGXhdVV0YkE2yb1eGdyT5UlWd3s16JnAX4EvA/arqvC79y2gBz+cn+UJVfbNLfygt6HlnWkAXWuDzfODr9IKgSS4G7AwcV1W/n+c2SJIkSZIkaRlZE3TlOw84d3hiVZ20SPmfDzx3EADt8v4drYbnFkC/SfhTu+cn9AOg3TL7Az8AHjFmPc9cQAAU4DTgv4fWdTTwIeDSwAN7sx4LFPCMQQC0S38C8PLu7eN76Qc1Ovs1Pu9KCwB/Erhqkut1029Gq406r1qgSfZOcnSSozlzvcMoSZIkSZKkJWBN0JXtQ7Qm8D9NciBwBHBUVZ24iOv4Qxf0HHY48BLg5r1pt6MFZB+c5MEjltkSuHySy1bV33vTzwJ+tMDyHVtV/xhTvkd35Xt/16T9OsCfq+oXI9If2j33t+dbwJl0QdAk2wG3AF7TS39X4FfMNYU/lHmoqv2A/QCyw7Y1n2UlSZIkSZK0OAyCrmBV9YYkJwH70GphPg2oJEcAz+5qRG6ov42Zfnz3vF1v2mVp58xLpuS5LdAPgp5QVQsNAM5avsHzX8ekH0y/cPCmqjonyZHA3ZLsQAvybgYcUlU/T/IXWhD07d1zMc8gqCRJkiRJkpafzeFXuKo6oKpuSwtA3gd4D60Pyy93gbuBCxgf1F5v1PaeK4yZfsXu+bTetNOAU6oqUx7DfWZuSA3IWct32tD0YVcaSjdwKBBaTc+7AmcDgxHjDwN2SbIVcCfgp8N9s0qSJEmSJGnlMwi6iaiqU6vqi1X1BGB/YHtaYG7gFOAKSbYYsfhOE7K+epI1I6bv3D1/vzft28Blktx4xmIvhlsMjd4+sHP3/H2Arsn8b4GrJLnuiPS7dM/HDk3v9wu6K627gbN687YH/g+wDY4KL0mSJEmStEkyCLqCJdktyajanYMaoGf0pn2XVhP0MUN57EUbAX2czYBXd6OfD5a5Jq35/XnAB3tp39g9vyvJlUeUd5skt52wroXYDnjx0Hp2og3AdBrw6d6s99Jqdb42yWa99JcD/m8vTd8xwKnA/YEbs26gc/D6+d2zTeElSZIkSZI2QfYJurJ9FDir67dyLS3AdyfgVrTg3dd6ad9CC4C+PcldgT8COwK3B74A7D5mHT8CbgMck+QrtKDjQ2lN6J9TVb8dJKyqQ5I8D3gl8OskXwR+R+sD9BrAXYAjgd02eMvnfB14fJLb0JqpX6kr38WAJ1bV6b20rwPuRQto/rAr3yWAB9MCx6+pqiP7mVfVBV0fq/fvJh3Sm/eHJL8Frg2cTxuYSpIkSZIkSZsYa4KubM+jjWB+C9rgSI8BtgCeC+xSVecOElbVz4C70QKF9wX2Bs6hDfZzzIR1nEILlP60y38vWmDzEVX12uHEVfVqWp+kB9FqmD6NFmS8Cm0U9BctdGPH+F1XvlOAJwEPoTVpv3dVHThUtnOAuwMv7CY9hTaC/K+Bh1fVc8esYxD4PB0YHmxqMO+YqhruT1SSJEmSJEmbgCx80G5t6pIUcERV7bzcZbkoyA7bFg/ZcbmLIUmSJGkD1b5HTU8kSVoSSY6pqknj4QDWBJUkSZIkSZK0yhkElSRJkiRJkrSqOTCStERuefUbcLTNZiRJkiRJkpacQdCLsKrKcpdBkiRJkiRJ2thsDi9JkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVUtVLXcZpIuE7LBt8ZAdl7sYkiRJ0npq36OWuwiSJC1IkmOqaqdp6awJKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIqnlLsjbJ2o28jqcm+VmSM5NUkqd10yvJ4Rt53S/t1rPzxlyPJEmSJEmSlsbmy10AbbguKHiXqspyl2UxJNkTeDPwfeBNwNnAt5e1UJIkSZIkSdpkGQTVSrT74Lmq/jI074bAGUtcHkmSJEmSJG3CDIJqJboywIgAKFX1i6UvjiRJkiRJkjZl9gm6giW5X5JDkvw1ydlJ/pLkiCT7dPPXJCngLt376j0O7+Uzth/NJPt389cMTU+SJyf5aZKzkvw5yb5JthuRx5O6PF48Zh1XTHJukh9P2d6Xdtuzy/D2TNqWfh+eSfZI8t0kZyQ5OclHk1xlzPpumeTgJP9IcnqSryW53YTy3SnJ55P8qTsexyf5dpKXTNouSZIkSZIkLS9rgq5QSfYG3gkcD3weOAnYAbgp8BjgbcCpwMuAvYBrdK8H1m5gEd4EPBX4K7AfcC5wf+A2wJbAOb20HwReDTw+yf9U1flDeT2Wdq69c8o6D++e92L97ZnFPsD9gM8BR3RlfSiwY5KbVdXZg4RJbg98rduWTwG/AW7WleHQ4YyT7AYcBJze5f9nYHta8/x9FlBWSZIkSZIkLRGDoCvXE2mBxh2r6oT+jCSXA6iqU4GXdqOYX6OqXroYK+4ChE8FfgvcuqpO7qa/EDgMuBLw+0H6qvpnkg8A/wncC/hCL68Aj6f14/mBSeutqsOBwzdge3YDblVVF9Y4TfJh4GG0AO7HemV6L7A18ICq+mwv/X/RAsDDnkCrOb1zVf2wP2NwPCRJkiRJkrQy2Rx+ZTuPVgNzHVV10kZe72O65/8ZBEC79Z4FPH/MMm/vnp84NP0ewDWBA6vqtEUt5fr+tx8A7byre751b9rtgesDX+8HQDv70oK/45w5PGHS8Uiyd5KjkxzNmesdSkmSJEmSJC0Bg6Ar14eASwA/TfLGJA9IcvklWvctuucjRsz7Bi04u46q+inwdeBeSa7Wm7V39/yORS3haEePmPbH7vkyvWljt69ryn/kiHw+1D1/J8k7kjw0yVWnFaiq9quqnapqJ7beYlpySZIkSZIkbQQGQVeoqnoD8GjgD7Sm6Z8G/pbksCQ7beTVDwY/+tuIcp0P/H3Mcm8DNqM1fyfJFWl9dP6gqr67Eco57NQR0wYB281608ZuX+f44QlV9Slgd+D7tD5OPwr8savlefeFFVeSJEmSJElLwSDoClZVB1TVbYHLAvcB3gPcGfhykh3mkxXj+3+99Ihpg2brVxiekWSzrjyjfIoWWHxcl27WAZGW2tjt61xx1MSqOqiqdqXVKr0r8EbgxsAXktxo0UspSZIkSZKkRWEQdBNQVadW1Rer6gnA/rRRye/US3I+XBigHOUU4GrDE7v0NxuR/tju+S4j5t2JMQHVqjoXeDdwFeC+tBqh/2SuKflKMXb7un1yx0kLV9W/qurQqnoG8AraCPP3WvRSSpIkSZIkaVEYBF2hkuyWZFSwcVAD9IzetEHz9KuPye67wNWT3GNo+ouAa4xIv3/3/MIk2/fKdHHglZPKDexHC8ruSxsQ6cNV9Y8pyyy1bwK/BO6c5P5D854MXHt4gSR3TbL1iLwGtUnPGDFPkiRJkiRJK8C4JtJafh8FzkpyJLAWCK0W5q2AY4Cv9dIeAjwY+FSSL9JGMP99VX2gm/864J7AZ5McCJxMGyH9msDhwM79FVfVUUneAjwF+EmST9BGqb8/rVbpX8cVuqr+kOQgWl+gsPKawlNVleRxwFeBTyb5FPAbYEfgbsDBwG5Di70eWJPkcNrxOAe4JbAr8Hva8ZIkSZIkSdIKZE3Qlet5wLdoI5nvAzwG2AJ4LrBL1/R84N20GprbAc8BXg48bjCzqg4BHgD8FNiTNuDSWuDWtADeKP9FC4KeBjwReBjwZVqQ8JwpZX9v93x0VR07MeUyqaqjaEHlr9Gasj8F2IoWEP7OiEVeAXyJ1gfo44En0WqBvgK4VVWdsvFLLUmSJEmSpIVIVS13GbTKJHkp8BLg8VX1nmUuzoqRHbYtHrLjchdDkiRJWk/te9RyF0GSpAVJckxV7TQtnTVBtaiSXJJWS/Jk4CPLXBxJkiRJkiTJPkG1OJLch9Z0/760ZuLPqioHC5IkSZIkSdKyMwiqxfJgWl+jf6P1T/rG5S3OynPLq9+Ao21mJEmSJEmStOQMgmpRVNVewF7LXAxJkiRJkiRpPfYJKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKkmSJEmSJGlVMwgqSZIkSZIkaVUzCCpJkiRJkiRpVTMIKsxOcrUAACAASURBVEmSJEmSJGlVMwgqSZIkSZIkaVVLVS13GaSLhOywbfGQHZe7GJIkSRcJte9Ry10ESZK0BJIcU1U7TUtnTVBJkiRJkiRJq5pBUEmSJEmSJEmrmkFQSZIkSZIkSauaQdAVKsleSSrJXstdFkmSJEmSJGlTZhBUkiRJkiRJ0qpmEFSSJEmSJEnSqmYQVJIkSZIkSdKqZhB0ESRZ0/XfuX+S6yU5MMkJSS5IsnMv3fZJXpnk50nOTHJakkOS3GOe67tqkn2THJfk7CR/T/K5JLcakfbKSV6c5Kgkxyc5J8lfknw4yQ3H5H+/rlx/7fL/S5IjkuwzIu3M25RkyyRPTXJsklOSnJFkbZLPJrnbjNu+f7ev1yR5YpIfJzkryd+S7Jdkuw3ZZ12eleQJQ9Mf200/I8lWQ/O+25Vh61m2QZIkSZIkSUvLIOjiujbwHWAN8CFgP+B0gCTXAI4BngecCLwDOBC4IXDwcNBtnCS3AH4A7AP8EngL8HngzsCRSe49tMidu3WeCnwSeCPwbWAP4HtJdhzKf2/gs8CNunxfD/+fvTuP262e9z/+emsQNo22KZXhEGGTpJDahUzhcOSYE8o8HicRFY4hU4dMIYmQU4ifFBrQDtlRnHRkqkiDpp007vr8/viui6ur6x6ve+97d/V6Ph77se691nd912et+/7r/fgOHAXcBnjRQNuZvtPBwH8DawCHAB8Bfgg8EHj8dN6/z37dv9OAjwHnAi8Fvj7YcIbf7NjuuMNAN9t3x9sAW/f1vTawOfDjqrpqhu8gSZIkSZKklWD1+S5gzDwKeE9VvWXItc8DGwPPrqqv9E4mWQc4AfhIkm9W1QUTdZ5kdeCrwAJgcVX9oO/aXYGfAZ9NsklVXdNdOg64U1X9baCvRcAS4L3AE/ou7Q5cCyyqqgsH7tlgtu/UhYX/TgtNH15V1w/0vf5E7z2BrYAHVtU53f2rd++6OMmWVXVy3/lpf7Oq+l2Sc4Dtk6Sqqmu+fdf/drSA9ITu/HbAat01SZIkSZIkrYIcCTq3LgD2HTzZBY7bAkf0h4UAVXUZsDewFvCMKfp/Em206Uf7w7yun7/QRkbemb5RjFV14WAA2p0/jX+GhmsMXF4OXDfknotGeKcCAlwD3DCk74uHv/KE3tELQLv7lwOf6/67ZV+7GX8z2ne5I22EKknuD9wFOBz4+UDb3s/HMkSS3ZIsTbKUq27ySSVJkiRJkrQSOBJ0bp3WNwKzX2/69NpJ9hly/Y7dceganUP62XiCfv6lr5+jeieTPAl4GbAFsAE3/b1vAJzX/XwobQr86UkOA34ALKmqv05Qy7TeqaouT/ItYCfg1CRHAD8CflpVVw5928ktHXLuT91x3SF1zuSbHQfsQgs4f8k/p8IfS1vq4A1Jbt+Fy9sDVwAnDyuyqg6kLYtAFi6oYW0kSZIkSZK0YhmCzq3zJzjfm+r92O7fRBZM0X+vn2dO0e4f/SR5DW0dzkuB7wHnAFfSRmY+DVgE/GOjn6r6UJKLaOtnvgZ4HVBJfgC8qap64eNs3ulZwB7Ac/jniNmrkxwO/MdkSwEMcdmQc8u742p952b8zbjxuqAf7o5/rqozkxwL/CewbZKfAZsBR3UjUSVJkiRJkrQKMgSdWxON9FvWHV9bVR8Zof9eP0+tqm9O1bhbD3NfWji7eVWdN3B962H3VdUhwCHd2p6PAP4V2BU4Jsn9urVCZ/xO3cZB+wD7JLk7bWOiXYDn0UZYbjOdfmZoRt8M2jT5JL+hBZ23pq37eWR3+UTamqmPAe7QnXM9UEmSJEmSpFWYa4KuHD/pjqOGfDPtZwNgHeCkIQHoAtqu5hOqqsuq6qiqeiltZ/f1+p490jtV1Z+q6lBgR+C3wKNmsTnSdMy2zmNpo0NfTvuGxwJ0U/d/Qhsd2j9NXpIkSZIkSasoQ9CVoJtC/iPg6Ul2HdYmyQOTLJyiqyOB3wOvTPLECfrZOsltu/9eSJv6/tAu9Oy1WYM2RX5wt3eSPL4bQTqoV9uVs3mnJHdM8vAhzW4H3J42lf3aYf2MaKbfrKc3unPPgf/3fn4A8BTgYuC0uStXkiRJkiRJc83p8CvPc2jh2We7dTp/SlvXckPgQbRQbWtacDlUVV2X5OnAMcC3k5wEnEoLJu8OPAy4J20n8yur6oYkHwHeDPwqyZHAmsBi2qjO47uf+32Ftk7nicBZtB3dt+n6PgX4/izf6W7AT5KcQdth/U+06eRPpu3O/pFhu9iPaqbfrO/W42m72C8E/q+qzu27dixtWv8dgcOryg2PJEmSJEmSVmGGoCtJVf05yUOBVwPPAJ5L28DnfODXwEeBX02jn18mWQS8gRYgvogW1p0H/ALYG7io75a3AX8FXgLsTlsj83vAXvxzc6J+b6ZNUd8ceCJwNXA2bUOjT1TVdbN8p7O62rajBa8bAJcAv+me+ZWp3n22ZvHNqKpLkpxK+w6Da37+FPg7bRSr64FKkiRJkiSt4uIgNmnlyMIFxc6L5rsMSZKkW4Q6YMl8lyBJklaCJKdU1RZTtXNNUEmSJEmSJEljzRBUkiRJkiRJ0lhzTVBpJXnoRpuy1GlZkiRJkiRJK50jQSVJkiRJkiSNNUNQSZIkSZIkSWPNEFSSJEmSJEnSWDMElSRJkiRJkjTWDEElSZIkSZIkjTVDUEmSJEmSJEljzRBUkiRJkiRJ0lgzBJUkSZIkSZI01gxBJUmSJEmSJI01Q1BJkiRJkiRJY80QVJIkSZIkSdJYMwSVJEmSJEmSNNYMQSVJkiRJkiSNNUNQSZIkSZIkSWPNEFSSJEmSJEnSWDMElSRJkiRJkjTWDEElSZIkSZIkjTVDUEmSJEmSJEljzRBUkiRJkiRJ0lgzBJUkSZIkSZI01lJV812DdIuQhQuKnRfNdxmSJOlmqg5YMt8lSJIkrXKSnFJVW0zVzpGgkiRJkiRJksaaIagkSZIkSZKksWYIKkmSJEmSJGmsGYLezCQ5IcmMFnJNUklOWEElTef5ZyU5a76eL0mSJEmSpFs2Q9BbqCT7dOHodvNdiyRJkiRJkrQirT7fBWiluB9w5XwXIUmSJEmSJM0HQ9BbgKr6v/muQZIkSZIkSZovTodfSZIsSHJtkiUD52+T5OpuavrzB669oju/65D+Vk/yliS/TXJNkj8leV+SNYe0vdGaoN36nHt3/z2+u16Da40muW2SPZOcmuTvSa5I8uMkz57lN7htkvcnOaer+XdJ9kiSCdrvnOSHSZYluSrJr7p6bj3VOw5cO7i7vsnA+ackOTbJeV09f0nygySvGNLHeknek+SMrpZl3b2Pm823kCRJkiRJ0srjSNCVpKquSHIy8PAkt6+qv3WXHgn0Qr0dgC/03bZ9dzx2SJdfArYBvgNcDjwR+E9gIfCiKcrZH3gasC3weeCswQZJ1gGOAx4C/Bw4iBaa7wh8KclmVbXXFM/ptwbwXeCuXc3LuxreC6wF7Dvw/HcDewIXde96BfAE4N3AjkkeW1XXzeD5g++3G/Ap4HzgW91zFgIPon2/j/e13Rg4AdgE+BFwNHA74MnA0Ul2r6pPz7YWSZIkSZIkrViGoCvXcbTQ89HAt7tzOwDXAz/sfgYgya2A7YA/VNXZQ/q6F7BZVV3StX8rcBrwgiR7VtX5ExVRVft3Iee2wMFVdcKQZvvTAtA9qmq/vrrWAr4BvCXJ4VV16nRenBZ+ngY8tqqu6vraFzgTeH2Sd/dCzSRb0wLQPwFb9t4lyZ7A12nh45togehs7Q5cCyyqqgv7LyTZYKDt54GNgWdX1Vf62q1DC0c/kuSbVXXBCPVIkiRJkiRpBXE6/MrVG9G5Q9+5HYBTgCOADZPcpzv/YGB9ho8ChRZOXtL7T1X9HTiU9jvdYpQik6wPPA9Y2h+Ads+5GtgDCPCcGXb9ml4A2vV1IXAksDZw3752ven/7+oPc6tqOfBG4AbgJTN89jDLgZuMJq2qi3o/J1lEC4uP6A9Au3aX0ZYVWAt4xrAHJNktydIkS7lq1gNXJUmSJEmSNAJHgq5cPwauogtBk6wNbA7sRxslSnftTP45Ff44hls65NyfuuO6I9b5MGA1oJLsM+T6Gt3xfjPoc1lV/W7I+WE1b94db/LuVXVmkj8D90iyThdEzsahwAeB05McBvwAWFJVfx1ot3V3XHuCb3HH7jj0W1TVgcCBAFm4oIa1kSRJkiRJ0oplCLoSVdW1SU4EHpNkIS1gWw04tqrOSPIXWgj6ie5YTBCCThD+Le+Oq41Y6vrd8WHdv4ksmEGfE4WVw2peuzueN8E95wEbde1mFYJW1YeSXAS8AngN8Dpa6PsD4E1V1QuZe9/isd2/iczkW0iSJEmSJGklcjr8ynccbSr59rSg8xqgt2P88cDibvfzbYDTB9erXEmWdccPV1Um+bd4BT//zhNcv8tAO2iB8USh/jrDTlbVIVW1FS3ofBLwWdp6rcd0IXX/M147xbeYajMqSZIkSZIkzRND0JWvf13Q7WlTsK/uu7Ye8HLa7uMTrQc6F67vjsNGjZ5MW3dzmxX4/Mn8ojtuN3ghyb2BDYE/DoyGvRS4+5D2q9HWV51QVV1WVUdV1UuBg2m/g967/6Q7zte3kCRJkiRJ0ogMQVe+U2hTuJ8KbMaNg87ez3t2x4nWA50LF3fHjQYvdKNPDwW2SPK2JDcZYZnkXknusYJqO6g77pWkt+ZmL9D8AO3v9rMD95wMbJTkcQPn96Lt7H4jSR4/7L2A3gjQKwG6afE/Ap6eZNch7UnywL6Ro5IkSZIkSVrFuCboSlZVN3TrTj61O3Vs37VzkvweuBdtpOYPVmApx9NGe74nyQNoIympqnd1118F/AvwDuD53VqmFwB3pW0C9DDg2cAf57qwqjopyX7AfwL/m+Rw4O/AE4AHACcC7x+47QPAjsCR3UZHlwCPAO4BnMBNR5V+Bbi6e6+zaEsUbNO91ynA9/vaPocWSH82yWuAn9KC7A2BB3U1bQ3Mx9IFkiRJkiRJmoIjQedHL/i8nJvu8t67dkpVLWMFqaozgBcC59M2B3pn9693/XJgW+DVwEXAM4A3AIuBvwGvB763Auvbgxay/hZ4AW3zolvRRnY+tqquHWh/LPA04HTg37t3OwvYEjh7yCPeDPyYthP9K4AX0Xa93wNYXFXX9fX9Z+ChwFtp4fRzu3oeAZwD7A78avS3liRJkiRJ0oqQqprvGqRbhCxcUOy8aL7LkCRJN1N1wJKpG0mSJN3CJDmlqraYqp0jQSVJkiRJkiSNNUNQSZIkSZIkSWPNjZGkleShG23KUqexSZIkSZIkrXSOBJUkSZIkSZI01gxBJUmSJEmSJI01Q1BJkiRJkiRJY80QVJIkSZIkSdJYMwSVJEmSJEmSNNYMQSVJkiRJkiSNNUNQSZIkSZIkSWPNEFSSJEmSJEnSWDMElSRJkiRJkjTWDEElSZIkSZIkjTVDUEmSJEmSJEljzRBUkiRJkiRJ0lgzBJUkSZIkSZI01gxBJUmSJEmSJI01Q1BJkiRJkiRJY80QVJIkSZIkSdJYMwSVJEmSJEmSNNYMQSVJkiRJkiSNNUNQSZIkSZIkSWPNEFSSJEmSJEnSWDMElSRJkiRJkjTWUlXzXYN0i5CFC4qdF813GZIk6WaqDlgy3yVIkiStcpKcUlVbTNXOkaCSJEmSJEmSxpohqCRJkiRJkqSxZggqSZIkSZIkaazdokPQJJskqSQHz3ctM5XkrCRnzXcdqwK/hSRJkiRJkiZziw5BJUmSJEmSJI2/1ee7AM3aDvNdgCRJkiRJknRzYAh6M1VVv5/vGiRJkiRJkqSbA6fDd5JsmuQbSS5J8vckJyZ53CTtn53k+CSXJrk6yRlJ9kpy6yFtK8kJSe6c5DNJzk1yfZJduusHd202SbJ7kl91fV6Q5MAkaw/pc8J1MGdS2yTvd6ckH0jym+57XNb9fHCSe/a1WzPJq5IcleTsJNd03/D7SZ4wQd9ndf9um+T9Sc7p7vtdkj2SZMg96Z5zevdO5yY5YNi3me23mMbvaVrfRJIkSZIkSasWR4I29wB+DPwv8CngLsCzgO8keU5VHdbfOMlngV2BPwNfAy4DtgLeCeyQ5LFVtXzgGesBPwGu6O65AbhgoM1+wI7At4DvAouBlwL3BrafzovMsrbBPm4LLAHuBXyvqyfAxsBTgcOBP/S9138DJ3Vt/0r7fjsBRyV5aVV9Zshj1uje8a7Ad4DlwNOA9wJrAfsOtN8feA1wHnAgcF1Xy8OBNYFr5+hbDP09zfCbSJIkSZIkaRViCNo8GvhAVb2pdyLJAbRg9JNJvlNVl3fnd6EFa18HnltVV/Xdsw+wN/BKWjDY74HAF4BdJwkhtwIeWFXndP2tDhwHLE6yZVWdPNlLjFDboB1oYd/+VfX6gWesCfSPorwU2Liq/jzQbm1aaLhfkkP7a+ncFTgNeGzvWpJ9gTOB1yd5d1Vd151/BC0A/T2wZVVd0p1/K3A8LXQ9e46+xdDfU5KdZvBNJEmSJEmStApxOnyzDHhH/4mqWgocCqwD/GvfpdfSRi3uOiTYeydwMfDcIc+4FviPKUZhvqMXgHY1LAc+1/13y2m8x2xrm8hgH1TVtVX1t77/XzMYgHbnlwEHAesCD5ug/9f011lVFwJHAmsD9+1r96Lu+F+9ALRrfzWw5wR9r6jf05TfpF+S3ZIsTbKUq66boEtJkiRJkiStSI4EbX4+QYh1AvBC4CHA57sp0YuAi4DXDVm6EuAa4H5Dzp/VhXyTWTrk3J+647qT3ThibYN+AJwLvDnJ5sBRtFGdp1bV9UOevRnwJtqI2rvQprP3u9uQZyyrqt8NOT/sfTfvq2vQj2hhZ389K+L3NKNv0lNVB9Km75OFC2qidpIkSZIkSVpxDEGbwbU5e87vjr3Nd9alrQN5R9p06pk4f+omXDbkXC/gW22Ke0ep7Uaq6vIkW9HW5XwKbZ1SgIuSfBx4V99U9a1oU/ZXB44FvglcTltL88G09TKHTRUf9q4w/H173/8mv6equj7JxQOn5/z3NJNvIkmSJEmSpFWLIWhzpwnO37k7Lhs4/qKqNh/SfjIrehTgKLXdRDfF/cXdTu33p23M9Erg7bRlFN7WNd0LuA2wuKpO6O8jyZ60EHRUvXe7EwObDyVZDVifNkpzsP2c/p5m8E0kSZIkSZK0CnFN0GbzJLcfcn677vgLgKq6Ajgd2CzJeiuptmlZUbVVc3pVfRR4bHf6aX1N7g1cMhiAdradozJ+Pkl/2zAQ5q/o39M0vokkSZIkSZJWIYagzdq00Xz/kGQL2sY5y2g7jPd8CFgTOCjJOoMdJVm3WzNyPsxJbUkekGSTIZd6I2av7Dt3FrBekgcN9PFi/jllfFQHd8e39oeaSdYC3jPBPXP6e5rhN5EkSZIkSdIqxOnwzQ+BlyR5OG2zm7sAz6KFxLtX1eW9hlV1UJKHAq8Afp/kGOAcYD3gHrTNgT4HvGzlvsKc1vYY4ENJTgL+D7gQ2JA2tf0G4P19bfenhZ0nJvkqLTTeAngUcDjwb3PwXkuSfBR4NfC/SQ4HruvquRQ4b8g9c/17msk3kSRJkiRJ0irEELT5Iy0Me293vDVtCvY7quqYwcZV9cok3+naPgZYB7iEFrK9H/jiSqr7JuaotmNo4eajaSHfHWhB4/eAD1XVSX3POzrJTrS1QZ8FXA+cDCwG7skchKCd1wJn0tbg3B24mDZC9y3AacNumOPf07S/iSRJkiRJklYtqVrR+/VIAsjCBcXOi+a7DEmSdDNVByyZ7xIkSZJWOUlOqaotpmrnmqCSJEmSJEmSxpohqCRJkiRJkqSx5pqg0kry0I02ZanT2CRJkiRJklY6R4JKkiRJkiRJGmuGoJIkSZIkSZLGmiGoJEmSJEmSpLFmCCpJkiRJkiRprBmCSpIkSZIkSRprhqCSJEmSJEmSxpohqCRJkiRJkqSxZggqSZIkSZIkaawZgkqSJEmSJEkaa4agkiRJkiRJksaaIagkSZIkSZKksWYIKkmSJEmSJGmsGYJKkiRJkiRJGmuGoJIkSZIkSZLGmiGoJEmSJEmSpLFmCCpJkiRJkiRprBmCSpIkSZIkSRprhqCSJEmSJEmSxpohqCRJkiRJkqSxZggqSZIkSZIkaaylqua7BukWIQsXFDsvmu8yJEnSzVQdsGS+S5AkSVrlJDmlqraYqp0jQSVJkiRJkiSNNUNQSZIkSZIkSWPNEFSSJEmSJEnSWDMElSRJkiRJkjTWDEElSZIkSZIkjTVDUEmSJEmSJEljzRBUkiRJkiRJ0lgzBB1DSXZJckSSPyS5KsnlSZYked4E7U9IUknWTPL2JL9Jck2SgwfaPTvJ8UkuTXJ1kjOS7JXk1kP6fFqSLyY5M8nfk1yR5JQkr0ky7b+7JNt1te2TZIskRydZ1tVwRJK7d+3umeQrSf7avfPxSRYN6e8+Sd6bZGnX9pokZyc5MMmGUzz/wUm+neSyJFcm+UGSR0z3XSRJkiRJkjQ/Zh2CJlknyaOTPGTItbskObwLqy5J8oUkC0crVTPwCWAT4IfA/sBXgI2BLyR55yT3HQG8Ajipu+9XvQtJPgt8Cbg38DXgY8AlwDuBo5OsPtDXe4HNgZ8CHwW+ACwA/hv4/Cze6WHAj7qfPw2cDDwdODbJpt3/NwQOAb4NbAt8L8mCgX6eDrwM+BPw5a62XwMvAX6W5G4TPH8L2ndZC/gM8P+AR3XPv+8s3keSJEmSJEkryWBwNRMvBvYDPg68uneyC8O+C9wfSHf6OcCDkzy0qq4d4ZmangdU1e/7TyRZE/gO8OYkn6yqc4fct3F370UD9+4C7Ap8HXhuVV3Vd20fYG/glbSAs+dJQ2q4FfA54AVJDqiqn87gnZ4IPK+qDu3r77NdXScBH6yq/+q79jbgHbS/0/66vgB8uKquGajtcbTvsxfw8iHPfxLwoqo6uO+e3YFPAq+lhceSJEmSJElaBY0yHf5x3fHLA+efBWwGXA38Fy1UupwWiu42wvM0TYPhY3fuWtrozdWBHSa49W2DAWjntcByYNf+ALTzTuBi4LnTqOEG/hlI7jjZOwxxYn8A2umNKF1GG3na75Du+OCBGs4dDEC7898FTp+kriX9AWjnINp32XKiopPs1k29X8pV103UTJIkSZIkSSvQKCNB790dfzVwfmeggL2r6gMASX5Hm5L9b8ABIzxT05BkI2APWti5EXCbgSYTTfk+eUhftwUWARcBr0tyk5uAa4D7Ddy3PvAm2gjOewK3m2YNE1k65NxfuuOpVXX9wLXeSNcbrfOZ9gLPBXahvde6wGp9TSYaqXyT51fVdUku6PoYqqoOBA4EyMIFNVE7SZIkSZIkrTijhKAbAFdU1d8Gzj+6O/aP2vsGLRjdbITnaRqS3JMWZq5LW0Pzu7SRktfT1gl9IXCTjYw65w85ty5tWYM70qa9T6eGdYCfAffoajmEtn7ocmAd2sjSiWqYyLIh55ZPdK2qlneB7RoDlz4EvA44DziGFpb2RrfuQlsSYJjLJji/nBuHqJIkSZIkSVrFjBKCrsXAqLlug5i1gTOr6rze+aq6NsmlwB1GeJ6m5w3A+gysXwltd3daCDpUVQ0bqdgLGH9RVZtPs4aX0ALQfatqn4EatqaFoCtdtznXa4D/BR4xGOB330eSJEmSJEljZpQ1QS8Ebpvkzn3nHtMdTxrS/jYMH82nudVbpuCIIde2nWlnVXUFba3MzZKsNx81zKF70v7mvzskAN2wuy5JkiRJkqQxM0oI+rPu+Ab4x9qRL6NNez+2v2GSu9FC0PPQinZWd9yu/2SSHWkjNGfjQ8CawEHdVPcbSbJukv5RohPV8BBgz1nWMBfO6o6PSvKPKexJFgCfZrSR0ZIkSZIkSVpFjRKCfoq2VuQbk5wBnElb8/OvwNcG2i7ujoObKGnufZy2TMH/JDk0yX5JjgK+Axw+mw6r6qCu36cCv0/ypSTvTXJgku/R1hLdre+W3hqg+yf5WpL3Jfka8NOujnlRVefTNujaEjg1yQeTfIY20vWewKnzVZskSZIkSZJWnFmHoFV1DLAPbeTnfYG70nYQf25VXTXQ/Dnd8fjZPk/TU1W/pIXOJ9F2Zn85bS3WpwOfHKHfVwI7AT+mLXvwBuAptDVg3w/s39f2L8A2wLeBRwGvom049ArgzbOtYY68GHg3bWTyK4Edgf8HPAKXa5AkSZIkSRpLGb4Xzgw6SDYCHk7bPfvkqlo2cH1NYA9a4PqpbjSedIuThQuKnRfNdxmSJOlmqg5YMt8lSJIkrXKSnFJVW0zVbuQ1EKvqHOCcSa5fC7xz1OdIkiRJkiRJ0myMsiaoJEmSJEmSJK3y3A1bWkkeutGmLHUamyRJkiRJ0ko30kjQNLsm+W6S85Nck+T6Sf4tn6vCJUmSJEmSJGk6Zj0SNMkC4CjgkUDmrCJJkiRJkiRJmkOjTIffB3gUcD3wJeAY4ALA0Z6SJEmSJEmSVhmjhKDPBAp4bVV9fI7qkSRJkiRJkqQ5NcqaoAtpoz4/M0e1SJIkSZIkSdKcGyUEPQ+4sqqunatiJEmSJEmSJGmujRKCHgPcIcmmc1WMJEmSJEmSJM21UULQ9wAXAx9JssYc1SNJkiRJkiRJc2rWGyNV1TlJngR8FTglyQeBpcDfprpvts+UJEmSJEmSpJkaZXd4gN8A3wJeBRw0jfY1B8+UJEmSJEmSpGmbdSCZZAPgBOB+vVPTuW22z5MkSZIkSZKk2RhlVObewP2BK4EP0jZKugBYPgd1SZIkSZIkSdKcGCUE3Yk2vX3XqvrqHNUjSZIkSZIkSXNqlN3hFwLXAkfMUS2SJEmSJEmSNOdGCUH/AlxXVdfPVTGSJEmSJEmSNNdGCUG/CdwuyRZzVYwkSZIkSZIkzbVRQtB30UaDfjLJOnNUjyRJkiRJkiTNqVE2RnoA8Bbgv4FfSuMJ0QAAIABJREFUJ/k0cDLwt8luqqofjvBMSZIkSZIkSZqRUULQE2i7wwOsDew1jXtqxGdKkiRJkiRJ0oyMGkhmBbeXJEmSJEmSpJHMOgStqlHWE5UkSZIkSZKklSJVNXUrSSPLwgXFzovmuwxJklYZdcCS+S5BkiRJN3NJTqmqLaZq52hOSZIkSZIkSWPNEFSSJEmSJEnSWJuTndqTbAg8AtgQuB2TbIBUVe+Yi2dKkiRJkiRJ0nSMFIIm2QD4JPA0pt75PUABhqC62UlyArBtVU31dy5JkiRJkqRVzKxD0CS3A04A7gdcC5wGbNn9fDJwZ+DeXfNLgF+NUqgkSZIkSZIkzcYoa4K+Erg/8BvgnlW1VXf+kqp6dFXdB7gH8FVgHeDoqlo8UrWSJEmSJEmSNEOjhKD/SpvevmdVnTesQVWdXVX/TgtC351khxGeJ0mSJEmSJEkzNkoIuml3PHrg/BpD2r6Vtiboq0d4nmYpySZJKsnB3c9fSXJRkquTLE3y5EnufXaS45Nc2rU/I8leSW490O4vSf485P6zu2e/beD8E7vzk64Rm+TxXbuDJrh+6+5dLuqvqTv/5iS/THJlksuT/CjJzlN8n/skOSzJhUluSLLdFPVtn2RZ9/4PnqytJEmSJEmS5scoIehawGVVdU3fuauBBYMNq+qPwDLamqGaPxvT1mvdBPgCcBjwAODIJDdZqiDJZ4Ev0dZ2/RrwMdr6ru8Ejk7Sv6bsccDdkmzad/+9gY26/w6OAt6+Ox47Rc3HAL8HnpVk7SHXnwGsDxzc+1tMsmZ333toofzHuve9D3BYkndP8Kx7AT+lfZ9DgQOByycqLMlzge8AfwG2rqpTp3gXSZIkSZIkzYNRQtALgDsk6e/jr8CaSTbsb5hkNeB2tLBK82c74GNVtVVVvb6qXgg8lfZ38Kb+hkl2AXYFvg7cp6peXFVvrKpHAvt2fb2y75bjumN/2Nn7+XvA1kluO3DtKuDHkxVcVQV8Ergt8PwhTXbrjgf2nXsjsC0toHxgVb2pql4JPBA4G9gzySOG9PUo4BNV9fDu+7ysqn4+rK4k/0kLVn8GPLKqzp7sPSRJkiRJkjR/RglBz+7uv2vfud5IuH8daPsU2k70F47wPI3ubOBd/Seq6hjgHG46Sve1wHJg16q6auDaO4GLgef2neuN6BwMQS8EPgKsSQsZSbI+sAg4saqunUbdn6ONMt69/2SS+9LCzuOr6sy+S7vS1qt9Q1Ut73vXC7vaAV4y5DkX0ALeCSW5VZIDgPfRAuLHVNUlk7TfrVtyYClXXTdZ15IkSZIkSVpBVp+6yYSOpYVa2wOHdOcOA3YC3pNkLVoougjYixZKfWeE52l0p1bV9UPO/wnYuvefbsTmIuAi4HVJhvV1DXC/3n+q6uwkfwAWd6ODizZa9PvAD2iB6g7Ad4HFtDVij2MaquriJF8FXpDkEVV1UnepNwr0k3213542ff/cqvq/Id31nvmQIddOG1jeYZgjgKcBHwVeV1U3TFH7gXSjVLNwQU3RtyRJkiRJklaAUULQbwH70EYD9kLQL9NG2G0HvLevbYDzu/aaP5dNcH45Nx4VvC7td3ZHYO8Z9H8s8FJgc+C67v5jq+pvSX7GP0eJ7tDXfro+DryANhr0pG4TpBfSRpp+o69db93Q8ybop3d+nSHXzp9GHY+mfa9vTRWASpIkSZIkadUw6+nwVfWLqrpVVe3Yd66AJ9E2pPkjLSy6GPgisFVV/WXEerVyLOuOv6iqTPZv4L7eKMvH8M+g87i+40OSrNddWwYMXW9zmKr6add+5yTr8s8NkT43MKW+V/udJ+jqLgPtbvSYaZSyGLgU+FaSJ02jvSRJkiRJkubZKGuCDlVVV1XVW6vq3lV166paWFUvqKpz5vpZWjGq6grgdGCzLrScruNoQeIOtGUS/lBVf+yuHUv7e3s+8C/ACRNMzZ/MJ4C1aCNCd+ue9emB2v9G203+bkn+ZUgfi7vjtAPYgf5/SVuH9BLga0meNpt+JEmSJEmStPLMOgRN8oLu353msiCtMj5E28zooCQ3mTqeZN0km/ef6zYeOh14JG3aeP9095Nomxu9pfv/tNYDHfAl2gjO/6QFkd+rqt8PaXcQbTr/+5Os1lfzBsDb+trMSlWdQXu/C4D/SfKs2fYlSZIkSZKkFW+UNUEPpk13H7a2om7mquqgJA8FXgH8PklvF/n1gHvQQsDPAS8buPVY4AF9P/f6uybJEma3HmivjyuTfB54TXfqUxM0/QDwBOCpwGlJjgJuCzwTWAjsV1UnzvT5A7X8Lsk2tDD30CS3rqpDprpPkiRJkiRJK98o0+EvAS6vqivnqhitWqrqlcBOwI9p63y+AXgKbfOh9wP7D7mtF24WcPwE1y6oqtNnWVZvBOd5wDcnqPta4LHAW7tTr6ZtovRb4DlVtccsnz34nLNpYfDvgM8leelc9CtJkiRJkqS5lbaX0SxuTE4EHg6s260hKa1wSXahjUB9V1W9bYrmq5QsXFDsvGi+y5AkaZVRByyZ7xIkSZJ0M5fklKraYqp2o4wEPRhYDXjJCH1I05Zkddpo1OVMPBVekiRJkiRJupFZrwlaVZ9J8jjgfUmuBQ6squVzV5rUJHkUbSOk7YAHAgdU1Z/ntShJkiRJkiTdbIwyHb63A/czgNsBlwI/Ay4Erp/gtqqqF8/qgbrFSrIPsDdtHdojgNdW1VXzWtQsbLHFFrV06dL5LkOSJEmSJGlsTHc6/Cgh6A20zW8yjea9dlVVq83qgdLNnCGoJEmSJEnS3JpuCDrr6fDAIbRwU5IkSZIkSZJWWaOsCbrLHNYhSZIkSZIkSSvEKLvDS5IkSZIkSdIqzxBUkiRJkiRJ0lgzBJUkSZIkSZI01ma9JmiSP8zwlquBy4DTgaOBb1TV9bN9viRJkiRJkiRNxyi7w2/S93MBmaDd4LWHA7sCZyR5ZlWdMUINkiRJkiRJkjSpUULQFwHrAG8H1gV+BJwAnEsLPe8KbAdsA1wCvIM2/X4L4OnA/YGjkyyqqstGqEOSJEmSJEmSJjRKCHo4cDJwA7B9VZ0wrFGSR3dtdwW2rqr9k9wHOA7YEHgl8F8j1CFJkiRJkiRJExplY6Q9gU2B3ScKQAGq6ofAy4AHAXt0584E3kgbMbrTCDVIkiRJkiRJ0qRGCUH/DbgW+MY02h4JXAPs3HfuW7RRpPcZoQZJkiRJkiRJmtQoIehGwFVVdcNUDbtd4K8CNu47dyVtt/jbjVCDJEmSJEmSJE1qlBD078DaSe41VcMk96ZtonRV37kAt6dtmiRJkiRJkiRJK8QoIehPuuMBSdaYqFGS1YGPAgX8uO/ShsAawHkj1CBJkiRJkiRJkxolBH1/d3wc8PMkz0+ycZI1kqyeZKMkLwBO6doA7Nd3/1O640kj1CBJkiRJkiRJk1p9tjdW1Q+TvA74MLAZcPAETUMbBfqGqvpR3/mFtA2TDpttDZIkSZIkSZI0lVmHoABV9dEkpwDvABbTAs8bNQGOA/auqiUD9+49yrMlSZIkSZIkaTpGCkEBquok4DFJ1gUeAtyRFoZeCPyiqi4d9RmSJEmSJEmSNFsjh6A9Xdh53Fz1J0mSJEmSJElzIVU13zVItwhZuKDYedF8lyFJ0iqjDlgydSNJkiRpEklOqaotpmo3yu7wkiRJkiRJkrTKm9Z0+CR/6H78XVU9buDcTFRV3WsW90mSJEmSJEnSrEx3TdBNuuPVQ87NhHPvJUmSJEmSJK1U0w1BX9Qdlw05N++SnABsW1WZ71pWpiQHAy8E7lFVZ83gvhO4BX6vqSTZB9gbWFxVJ8xvNZIkSZIkSZor0wpBq+rz0zknSZIkSZIkSaua6Y4E1Xh5AXDb+S5iFXQA8BXgnPkuRJIkSZIkSXNnzkPQJAuAJwL3AP4OLK2qn8z1czR7VWXIN0RVXQRcNN91SJIkSZIkaW7daqY3JNkqyXuSfDTJq5Lcoe/aU2ij6L4MvBv4b2BJkhOT3HkGz9glyRFJ/pDkqiSXJ1mS5HmzqHfHJEcluSjJNUl+n+T9SdYZaPeGJJXkiCF9PCbJ9Ul+leQ2feefluSLSc5M8vckVyQ5Jclrktzk2yY5uHvGJkl27/q7OskFSQ5MsvYE7/CYJD/qnnFJkm8k2XSCtpt0zzg4yX2SHJbkwiQ3JNmua3NCkhq4b83u93lUkrO7b3VJku8necK0PvaN+1u7+zv5TfeOlyY5JsljJrnncUm+1dV7TZI/JTmy/57Z1JnkQUm+nOSsrv1fk/w8yf5J1uhrt0/37bYb0scOSY7unnV19zt/70S/M0mSJEmSJK06ZjQSNMnbaRvH9HtLkocDdwYOA2495Natge8keVhVLZ/Goz4B/Br4IXAesD5tdOkXkty3qt42g3r3BS4B/h9wIfAg4D+AJybZuqouB6iqD3Xh19OTvKKqPt71cWfgi8DVwLOq6qq+R7wXuAH4KXAusDawPS38fRjw/AlK2w/YEfgW8F1gMfBS4N7d/f3v8G+073ptdzwPeBTwY+CXk7z+vbq6zgQOBW4DXD5J+/W6uk8Cvgf8FbgLsBNwVJKXVtVnJrm/v+Z1gCXA/YGfAfsDGwA7A99N8vKq+tTAPfsCbweuAL4B/Am4K/AI4HnA92dTZ5IHdd+hgG8CfwTuQPvWrwD2Aq6b4n12p/1N/h34H9rf0XbAHsBOSR5ZVZdN59tIkiRJkiRp5Zt2CJpka2CfvlMX08LJOwH/RQsAbw18Hfg0cDawIfBc2hqUD6KFgp+bxuMeUFW/H3j+msB3gDcn+WRVnTtFvYtpAeiPgSf2h1RJdunq2Bd4fd9tLwJOBT6YZAnwK1oAeidg16r69cBjnjSkzlt1fb8gyQFV9dMh5W0FPLA3LT3J6sBxwOIkW1bVyd35BcCnaEHrNlW1tO85HwZeN8kneBTwnqp6yyRt+l0KbFxVfx54n7VpgeZ+SQ4dCIEn8j5aAHog8LKqqq6v9wFLgY8kOaa3o32Sx9EC0D/S3vNGv9skG45Q5wuBtYCnVdWRA/esC1w52Ysk2Rj4CC2c3bKq/q/v2seBl9NC7d0m/SKSJEmSJEmaNzOZDr97d1wK3L2q7gjcvfv/TsDjgc9U1TOq6uiqOqOqvldVu9Cmxgd45nQeNBgsdueuBT5GC253mEY3r+mOLx0cpVdVB9PCzucOnL8YeDawBm3U5bu6Zx1aVTcJbyeo8wbaSEVooz2HeUf/upzd6Nhe/1v2tXsqbeTjl/oD0M4+wLIJ+ge4gBbyTktVXTMYLHbnlwEHAevSRrdOqpte/jxaaLhnLwDt+votLVBckxaM97y6O75xWLjdX9cIdd4kvK2qS7vf12Se19V7QH8A2nkr8Dfg+UmGjYAmyW5JliZZylWTDjiVJEmSJEnSCjKT6fCPoE0pfm0vqKqqc5O8jjYCr2i7aw/zUeAtwKLpPCjJRrSpxjsAG9Gmcve72zS62Zo2zfmZSYaFr2sCd0yyfhd+AlBVJybZmxaA7gn8FnjZBHWuD7yJNlX/nsDtplnnYKAJbfo3tBCvZ/Pu+IPBxlW1LMmpwLYTPOO0qrpmgmtDJdmM9j6Ppk0xX2ugyXS++6a0neeXVNUlQ64fR5uC/pC+c1vR/n6OXgF1Hga8FvhGksNp0+qXDAuwJ9D7HRw3eKGqLk3yi66OTYHThrQ5kDYilixcUIPXJUmSJEmStOLNJAS9G3A9MLjT+0+787cCBkfKAVBVFyRZRps+P6kk9wROpoWBP6Ktmbmse8YmtOnNQ0fdDVif9n6Da5gOWkCb2t/va8A7aO/0maq6Ykid69DWu7xHV+8htLVHlwPr0IK3ieoctn5kb63U1frO9TbduWCCfs6f4PxU124iyVa0oG914Fja+pmX06biP5g2KnU6371X83kTXO+d79+Yah3g0ulMtZ9pnVV1cpJtaKM2/41undYkvwH2raovr4D3kSRJkiRJ0ipkJiHobYAL+6c3Q5v+neQSYINuyvpErqZtSDOVN9ACzBd109b/IcmzaSHodCwDblVV602zfe8Za9F2t4e2/uTbkxxZVb8ZaPoSWgC6b1XtM9DH1rQQdFS96e53muD6nSe5d6ajDvei/Y4XV9UJ/ReS7EkLF6ejV/NEtd1loB20UHj9JLeZRhA64zqr6sfAk7sp6w+lLd3wauBLSf5aVd8fvGeC9zl9mu8jSZIkSZKkVchM1gSFNtpuJudn497d8Ygh1yaa+j3MT4B1u6nTM/Eh2rT99wD/TpvafVgXjq6IOifz84n66zYCevAcPQfa+1wyGCxO9PxJ/Ia22dCDu42HBi3ujj/vO/cT2pqxj1+RdXbriZ5UVW/nn2vGThXu/qI7bjd4oRsN/GBawH/GFP1IkiRJkiRpnsw0BF0ZzuqO2/WfTLIjbfTldH24O346yV0HLya5XTe1uv/cM2i7fS8B9q6q79J2/l5EC0enU+dDaGuJzoUjaaNRn5Nki4Fr+/DPqdpz4SxgvSQP6j+Z5MVMvMHTTXSjgQ+lLTPwjoG+7kULH68DvtB36aPd8YNJbrLu6MC5GdWZZJsuMB7UG1076e7wwBe7el+d5N4D195JG938xZmuvypJkiRJkqSVZybT4VeWjwMvAv4nyRHAucADaKMEvwo8azqdVNWxSd5MG9H52yRHAX+khXMb00YNntj1S5JNgM/QhY5VdX3X1V60jW9enuTYquqN/DyEtjnP/kkW0zZQ+hfgybQ1RadV5xTvcEWS3Wib+/woyWG0NSgfRfsmP+xqmwv700LEE5N8lTa9e4vuWYfT1tOcrjcD2wCvSvIw4HhgA2Bn4PbAq6rqj73GVfXdJO8E3gackeQbtI2i7tQ9/yfALrOs843A45KcAPyBtmv9ZsATaL/rAyd7kao6q9v862PAz7tn/pX297M1bR3cPWbwbSRJkiRJkrSSzXQk6J2SXD/4D1gIMOxaX5uJ1rW8kar6JW3K9Em0XddfThtt93TgkzMptqreRwsJvw08Engd8EzaJk8H0gJOkqwBfIW2uc1Lquqcvj6WA8+mrVv52ST36M7/hRb0fZsWwL2KFq6+ghYCzomqOpwW1J5CCxFfRtuAaWtaqDtXzzka2An4NS3AfTFwDe138e0Z9tWrbz/a+q5voH33k4HHV9XHh9zzduBJtN/7k4H/oIWdZ9AC59nW+XFaiLwJbVOkVwP36c4/ZDq7xHf17kgLY5/Rvc9C4P3A1t37SpIkSZIkaRWVgX2OJm6YzMW6n1VVq03dTBo/Wbig2HnRfJchSdIqow5YMt8lSJIk6WYuySlVNbiM5E3MZDr8viPUI0mSJEmSJEnzYtohaFUZgkqSJEmSJEm62VkVN0aSxtJDN9qUpU77kyRJkiRJWulmujGSJEmSJEmSJN2sGIJKkiRJkiRJGmuGoJIkSZIkSZLGmiGoJEmSJEmSpLFmCCpJkiRJkiRprBmCSpIkSZIkSRprhqCSJEmSJEmSxpohqCRJkiRJkqSxZggqSZIkSZIkaawZgkqSJEmSJEkaa4agkiRJkiRJksaaIagkSZIkSZKksWYIKkmSJEmSJGmsGYJKkiRJkiRJGmuGoJIkSZIkSZLGmiGoJEmSJEmSpLFmCCpJkiRJkiRprBmCSpIkSZKk/8/efYfbVdZ5G7+/gmAJQ9OI0hUUa1QiFlSCFBFFbOAgowLjCKKO6IyFEQTFruM4gI1RBAuISrMB+hJRCCqTgCgijKIoIKBICUiH3/vHs45sNvsk56Scnezcn+vKtc5Z61nP+q2dLeD3eookjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00lJVw65BWiFk+rRi1xnDLkOSpGVGHT5n2CVIkiRpOZdkXlXNXFg7R4JKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCLqCSrJRkkpy1LBrkSRJkiRJkpYmQ1CNpCR7dCHvHsOuRZIkSZIkScO18rAL0NBcATwWuGHYhUiSJEmSJElLkyHoCqqq7gAuGnYdkiRJkiRJ0tLmdPglJMmuSX6c5IYktyT5ZZL9k6w6Tvv1khya5DdJbk1ybZJzkhzY127rJEckuTDJ/K7vC5IclOQBA/pdLcmBXZv5SW5MckmS45Js3tNu3DVBkzw8yaeSXJrk9iR/SXJC7/2T+Fw2S3Jk19dtSf6c5MwkbxjQdpskp3afxa1J/i/Jh5OsPqDtGV39qyR5T5KLu/6PSnIG8MWu6Re7dmN/NurpY+Uk+yb5afdZ3ZzkvCRvSnK/vudd2tdP/5/7fI6SJEmSJElaNjgSdAlI8kFgf+Aa4BjgJuAFwAeB5yfZrht5OdZ+JnAasBbwY+AE4EHA44CDgUN6un8nsBlwNvBd4AHAll27WUm2raq7un4DnAo8C/gJ8HngTmB9YBZwJjBvIe+yMXAW8AhgNnBsd/8uwAuTvLyqvjPBz+WFwDeAVbu6jgXWAGYA7wA+09N27+73v3X3/Lmr+Z3ATkm2rKrrBzzmeOBpwCnASd19ZwDXAzsDJwM/72l/ffe8+wPfBp4PXEz7e7sV2Bo4DHg68Oqe+z7Z1d5vJ+CpwM0L+zwkSZIkSZI0HIagiynJM2kB6GXAFlV1VXd+f+BE4EXA22mBKElWoYV8awG7V9Uxff2t3/eIfYHfV1X1tTsEOAB4BXBcd/oJtAD0pKp6aV/7+wH3GVE5wGdpAegBVfWBnvs/TQtsj06yYVXdtKBOkjyEFiyuDDyvqn7Ud329np83BA6lhcdbVNVFPdc+DbwB+Cjw+gGP2hB4QlVd09c/tBD0pKo6asB976YFoIcD+/UEySsBRwB7JflmVZ0MUFWfHPCO23X9/BZ4z3ifhSRJkiRJkobL6fCLb6/u+P6xABSgqu4E/g24G3hdT/udgI2Ab/UHoN19l/X9/rv+ALQzFso9f8C1Wwb0e3dVXbeA9xgLJrcH/kgLHXvvP5s2knMt4GUL6qfzWuAfgM/0B6Bdf5f3/PpPwCrA4b0BaOfdwI3Aq8dZWuDA/gB0YbpA+E3AVcBbxwLQrq67aH9vBey+gD6eAHyTtrHUjuPVkOT1SeYmmcstdwxqIkmSJEmSpKXMkaCL76ndcXb/har6vySXAxsnWaObzv2M7vIpE+k8yYOBtwAvBR4NrAakp8m6PT9fSJv6vVs3uvJk2tT2uVV1+wQe95TueGbv9P0es2mB5VOALy2kr8m854I+w+uSnAc8l7YswPl9Tc6ZQP/9Hg2sDfwGOKAbNdrvFuCxgy4keThtaYJVgRdV1W/Ge1BVHUEbWUqmTxsUZkuSJEmSJGkpMwRdfGNTzK8c5/qVwAZdu+u5Z13JKxbWcbdu5WxgC+AC2rT3vwBjAeVBtCAOaKMYkzyPNjX7FcBHuks3Jjka2H8h09gn8i4weG3MfhN+z8V87lUDzi3M2t1xU9pnOJ5p/Se6UPo7tHVSd6+qMxfh+ZIkSZIkSZpChqCL74buuA5wyYDrD+9rN7a5z7oD2vbbmRaAHl1Ve/Re6EYj3ifA66a8vxV4a5JNgK2AvWnTv9fg3pv99Ot9l0H632VBet/zlwtp2/vcX03mueMsFbAwY/2cWFUTmdoP/H0a/bG0kavvrqpjF+HZkiRJkiRJmmKuCbr4zuuOs/ovdCHkerSNjcZCwZ92xxdMoO9NuuPxA65ttbCbq+q3VfWFru1NtFB1Qcbe5dlJBgXkW3fHcxf2bCb3ngv6DNcAnkzbuf3XE+hrzNg6nysNuHYRLaR9RjfadqI+SVvT9ciq+uAk7pMkSZIkSdIQGYIuviO74wFJHjp2sttl/OO0z/gLPe2/DVwKvDjJbv2dJekdIXppd5zV1+aR3DPVvff8xkkeP6DGNWnT5u+zYVKvbrOiH9A2btqvr++nA68CrqPter8wRwPzgTckee6AWtfr+fUrtCn+b+6C416H0DZY+kpV3TaB5475a3fcoP9Ct2nVYbQRpocmeeCA+h6e5HE9v+8HvBk4HdhnEnVIkiRJkiRpyJwOv5iq6uwkHwXeAVyQ5JvA32gjIJ9A25joYz3tb0+yC/B94Jgke9NGTT6AthHPNtzz9/Jt4LfA25I8kTZicgPgRbSNefoDvhnAiUnm0dYQ/RPwUNoI0PszIDgdYB9gDvCxJNsDc2nrX+5C2+l+z6q6cQKfyzVJXkXbQf2HSU4BfkELNJ/U9blx1/bSLmT8FHBukq/T1j7dCngmbeTmOydQe6+fADcD+yVZC7i6O39YVd1AC1dndO+7U5LZtPVLp9PWCt2StjP9hUnWAf6TtmP8L4F3D9hM6edVddIka5QkSZIkSdIUyKItqah+Sf6Rtu7mDFrgeAlwDPCfVXXrgPYbAO+ihaXrAjfSAs9vVdUHetqtD3yYNhp0LeB3tFGWn6CNnvxRVc3q2q4H7EsLDx9FGwH6F1pwd2hVndLT70bA7xm83ui6wAHAjrTRkvNpYe4Hqup/J/m5PJ4WYG5DC2Svo4WaX+12Tu9tuz3w78DTgAcBlwEnAB/sWU5grO0ZwFZVNXBr967NDrR1U58IPLg7vXFVXdpdD223+z1oO95Po31evwe+B3y5qi7r+awW5D6f433qmT6t2HXGQrqRJGnFUYfPGXYJkiRJWs4lmVdVMxfazhBUmhqGoJIk3ZshqCRJkhbXRENQ1wSVJEmSJEmSNNJcE1SaIptvsBlzHfEiSZIkSZI05RwJKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBm7y4QiAAAgAElEQVSCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaSlqoZdg7RCyPRpxa4zhl2GJGkpqsPnDLsESZIkaYWSZF5VzVxYO0eCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgmqZlWRWkkpy8LBrkSRJkiRJ0vLLEFSSJEmSJEnSSDMElSRJkiRJkjTSDEGHLMlG3ZTvo5I8OslxSf6c5O4ks3rabZrkS0muSHJ7kj91v286Tr8PT/LFrq9bkvw8yWsXNMU8yVpJPpTk1909NyQ5Pcn2A9ru0fWzR5IdkpzRta8JvPPDknw8ycVJ/pbk+u7no5I8smtzFPDD7paDumeN/en9XFZN8q4kv0hyc5L5Sc5MsutCPuuNknwtyTVJbk0yN8mLFlDzbkl+mOS6rv2vkxyQZNWFva8kSZIkSZKGa+VhF6C/exTwM+D/gK8CDwTmAyR5GvD/gNWAbwEXApsBuwM7J9mmquaOdZRkOnA2sBHw4+7ndYBPA98f9PAkGwJndPecCZwKPBh4EXBqkr2r6n8G3PoKYAfgFOCz3f3jSvIgYE73vj8Avg0E2BDYGfgm8DvgpO6W1wI/6mobc2nX1yrAacBWwEXAp4AHdTUdl+TJVfUfA8rYEDine86XgbWAVwInJ9m2qn7Y2zjJF4C9gMuBE4DrgWcAhwDbJNmuqu5c0HtLkiRJkiRpeAxBlx3PBj7UH9olCfAl4B+Af6qqr/ZceyXwNeArSR5XVXd3lz5ECyM/WlXv7Gn/SVr4N8jRtHBwt6r6Ws89a9ACyEOTfKuqru67b0dgx6o6dYLvuQ0tAP1kVb21711XAVYFqKqTklxPC0HPqKqDB/T1b7QA9BTgxWNBZJL3du+5f5LvVNXZfffNAg6uqvf2PPsYWvD7du4ZgUqSPWgB6InA7lV1S8+1g4GDgDcC/z3B95ckSZIkSdIUczr8suNq4L0Dzj+LNurzJ70BKEBVHQecBTyGFqKOBYm7ATcA7+9rfz4tUL2XJDNoYeLxvQFod8/1tKDvAcDLB9R38iQC0F639J+oqtur6sZJ9LEXUMDbekdiVtWfaaM0AV434L4/cN/P5jTgj8AWfW3fAtwJ7NUbgHYOAf5KG5E7UJLXd1Pt53LLHQt/I0mSJEmSJC1xjgRddpxfVbcNOP/U7jh7nPtm0wLQp9Cmvj+GNpV+7jiB4lncNxh8ZndcfdBaocBDu+NjB1wbb2TpeH4EXAG8K8lTge/Rpsf/vKrummgnSVYDNgGuqKqLBjQZ+7yeMuDaeM+6jHs+i7Gp+zOAa4D92qDc+7iNwZ8LAFV1BHAEQKZPW+h6qZIkSZIkSVryDEGXHVeNc3717njlONfHzq/R175/2joLOL92d9yu+zOeaQPOjVf3QFU1P8kzaKNeXww8v7t0TZJPA++vqokMmZzs59Lr+nHuuZN7j45ek7Ze6UNpo2ElSZIkSZK0HHI6/LJjvFGCN3THdca5/vC+dvO748PGaT/o/Ni9b6mqLODPnpOoe1xVdXlV/TMwHXgC8K+0aeXv6f5MxGQ/l0Uxdu95C/lcBg4RlSRJkiRJ0rLBEHTZd153nDXO9bHz53bHi2jrbT6pmzLe79kDzv20Oz5nEepbZNX8qqoO454RqC/paTI2ZX2lAffeCFwCrJtk0wHdb90dzx1wbaL13QT8Cnh8krUWtR9JkiRJkiQNlyHosm8OcDHw7CSv6L3Q/f5c4P9oa31SVbcDx9Gmix/Q134G8Jr+B1TVXOBM4GVJ9hpURJInJpm+uC+T5AlJNhpwaWyE6s095/7aHTcYp7sjadPVP5bk70FpkocAB/a0WRyfAFYBjkxyn6n1Sdbs1jaVJEmSJEnSMso1QZdxVVVJXgv8ADguycm00Z6PoY2avBF4TVXd3XPbu4DnAe9I8nTgbNr08F1pGxG9BOhtD/Aq2mZCX0jyr8DPaGtnrgc8iTZt/ZnAnxfzlbYFPpHk7O49/tw9Y+eupo/1tL2YtonSPya5nbZ7ewFfrqo/AB8HXtDde36S7wEPAnahTbX/aFWdtTjFVtWRSTYH9gUuSTK2i/xawMa0EPqLwD6L8xxJkiRJkiQtPYagy4Gq+lmSp9FGdm4L7ETbsfxY4JCquriv/dVJngV8ENgReDotUNwX+BstBJ3fd8/lXdj3ZuDlwO60aehXARcChwG/XAKvcxrwSVp4uDPwD7RNjH4AfKKqzu6p6a4kLwU+TAtwV6ON/DwL+ENV3Z5kO+BttBD3zbTNjc4H9quqY5dAvVTVG5OcQgs6t6VttnQtLQz9GPCVJfEcSZIkSZIkLR2pmvS+NlqOJfkA8B/ADlV12rDrWZFk+rRi1xnDLkOStBTV4XOGXYIkSZK0Qkkyr6pmLqyda4KOqCSPGHDuibSd2K8FfjTlRUmSJEmSJElD4HT40TU3yW+BC2hT4DcFXkgLvvepqluHWZwkSZIkSZI0VQxBR9fnaGt/7kZbS/N62nqcH6+qM4ZY1wpr8w02Y67TJCVJkiRJkqacIeiIqqr3Au8ddh2SJEmSJEnSsLkmqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGWqpq2DVIK4RMn1bsOmPYZUiSlqI6fM6wS5AkSZJWKEnmVdXMhbVzJKgkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoMuwJBslqSRHDbsWSZIkSZIkaXllCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpoh6JAk2SLJcUmuSHJbkiuTfD/JruO03yjJ15Jck+TWJHOTvGhAu9WTvD3J7CSXJ7k9yV+SfCvJM8bpu5KckeQhSY7oarktya+S7DnOPasmOTjJ77q2v0/y/u58JTljwD0rJ9k3yU+TzE9yc5LzkrwpyYS+i0mO7frftO/8l7rzp/edXy3JHUl+vCifUZI1uzovSZJxavpO9+zNJ/IOkiRJkiRJmlqGoEOQ5F+As4GXdMf/BL4LTAf2HXDLhsA5wEbAl4HjgCcAJyfZuq/tY4EPAHd3fX4C+AHwPODMJDuMU9YawBzgmcA3gS8BjwCOTPLavvoDHA8cBNwJHA58G9gD+No473x/4DvAp7pnHQMcQfsOHgYcPU5d/cZCzm36zo99Ds9K8oCe81sBK/fcB5P4jKrquu6dHglsO+C91gN2AOZV1bwJvoMkSZIkSZKm0MrDLmBFk+RxwKeB+cBzqupXfdfXG3DbLODgqnpvT7tjgFOBtwM/7Gn7a+ARVXXNgH7PAf6ru6/fDOALwN5VdVd3z38BvwDeyb1Dyn8CXgicCWxbVbd37d8D/HScV3838HxaYLpfzzNWooWheyX5ZlWdPM79Y2Z3x22Az3Z9PAZYjxZkbgdsyX3D0tk9fUz2M/o0sCewd/eMXq8DVgI+t5C6JUmSJEmSNCSOBJ16b6CFz4f0B6AAVXX5gHv+ALy/r91pwB+BLfrO39Af7vX0+01gsyQbDHjGzcDbxsLJ7p4LaaNDH5tktZ62YyNDDxgLQLv21wOH9HfcTXV/E3AV8Na+Z9wF/BtQwO4D6up/j98BlwJb90xPHws63wPcxb1HiW4D/A34WU8fk/qMqmouMBfYOck6Pe+1EvDPwI3AsYPqTfL6bumCudxyx8JeT5IkSZIkSUuBI0Gn3tiak6dM4p6f9waHPS6jTV+/lyRbAm/prk0HVulrsi4tQO31m6qaP84zoE1hv7H7+Sm0qeRnD2h/1oBzjwbWBn4DHDDO0pq30KapT8RsYC/gycB5tGnsV1bVT5PMowtBkzyUtmzA93vD2u7aZD+jTwNHds/9YHduR9oI1M9U1U2DCq2qI2gjXcn0aTXB95MkSZIkSdISZAg69dbojldM4p7rxzl/J32jeZO8lDaa8Vba1O1LaCMh76ZNq98KWHWSz4A25XvM6sC1VXXngPZXDzi3dnfclLaO6HimLeBar9NpYeQ2Sc6nvdcpPdfekWR1Wjga7r0e6KJ+Rl+jrd36L0k+XFV306bHg1PhJUmSJEmSlmmGoFNvLGxcF7hoKfR/CHA7MLOqft17IcnnaAHf4poPrJVk5QFB6MMGtL+hO55YVS9bAs8fW99z2+7ntbkn6JwN7E/bKGnQeqCwCJ9RVd2S5CjgrcD2SS6gbYj0s6o6f3FfSJIkSZIkSUuPa4JOvbGNg16wlPrfBLhwQLh3P+DZS+gZ59G+O88acG3QMy6ihb/P6HaJXyxVdRVwIfAcWhAJ9wSdc4DbaAHo84Drunp7Lepn9Bna2qV744ZIkiRJkiRJyw1D0Kn3GdoU8wO7neLvZZzd4SfjUmDTJI/o6TO0aej3ed4i+lJ3fH+Sv6+l2U1BP7C/cTda9DDg4cChSR7Y3ybJwwd9HgswG3gQbV3P31TVH7tn3QL8BNgVeBRwRjd1vdelLMJnVFW/oY04fRGwDy3YPW4SNUuSJEmSJGkInA4/xarqwiT7Ap8FzktyMm3DoLWBmbTNh7ZejEf8V0/fxwN3AFvSwr1vAzstRt9jvgT8I20U5gVJvgXcH3g5bRf1x9DW1+x1CDCDFh7ulGQ2bV3U6bS1QrcE3k0b4TkRp9N2nJ8OnDDg2qyen/stzmf0ado0/IcBh1XVzROsV5IkSZIkSUPiSNAhqKr/oU27/g4trHs78GLgGuBTi9n354A9gSuB1wK703Z4fzpw7uL03fOMAl5KCzbvD7wZ2Bk4Gnhj12x+3z13AC8BXgNcTBtN+W+0IPV+tBGkX51EGWdwT9Dav+Znb/DZf21xP6Nv0f6ewKnwkiRJkiRJy4W0PEtaMpJsB3wf+HBV7T/sepa0JI8EfgvMqarnTOre6dOKXWcsncIkScuEOnzOsEuQJEmSVihJ5lXVzIW1cySoFknvepo959YGPtz9euLUVjRl/h0IcPiwC5EkSZIkSdLEuCaoFtUnkswAzgb+AqxH2/F+LeBzVXXOMItbkpJsALyKtnbpnsD5wDcm28/mG2zGXEcISZIkSZIkTTlDUC2qE2ibA+0ErAHcCvwKOBL4/BDrWhoeCXwIuBn4AfCGATvOS5IkSZIkaRllCKpFUlVfB74+7DqmQlWdQZsCL0mSJEmSpOWQa4JKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkaaIagkSZIkSZKkkWYIKkmSJEmSJGmkGYJKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRlqoadg3SCiHTpxW7zhh2GZKkpagOnzPsEiRJkqQVSpJ5VTVzYe0cCSpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqJaYJJsmOTHJVUkqyfXDrmlRdfWfMew6JEmSJEmStPhWHnYBGg1JVgJOAjYBvgxcDtw61KIkSZIkSZIkDEG15GwMPA74n6p6/bCLWQIeC9w87CIkSZIkSZK0+AxBtaQ8ojv+aahVLCFVddGwa5AkSZIkSdKS4ZqgUyDJRt0ak0cl2SzJSUmuTfK3JGcl2X6c+1ZN8q4kv0hyc5L5Sc5MsuuSekZ3725JfpjkuiS3Jvl1kgOSrDrB9yvgR92vB3V1VJKDu+sHd7/PSvKqJD9LclOSS3v62CPJ8Ul+l+SW7l3nJPmncZ55Rtfnykn+I8lvktyW5LIkH0myyjj3bZbkyCSXdu3/3H2mb+h/p/41QZOsluTAJBd09d2Y5JIkxyXZfCKflSRJkiRJkqaeI0Gn1sbAT4ALgM8BDwdeCZyS5FVVddxYwy7EOw3YCrgI+BTwIOAVwHFJnlxV/7E4z+ie8wVgL9oanicA1wPPAA4BtkmyXVXduZD3ei+wEfBaWhh6Rnf+jL52/wZsB3wb+CGwes+1zwAXAj8GrgTWBnYEvpzkMVV14DjPPgZ4DnAKML+75x3AdGDPvnd9IfANYFXgVOBYYA1gRnfPZ8Z7wSTp7nkW7fP9PHAnsD4wCzgTmDfe/ZIkSZIkSRqeVNWwaxh5STYCft/9+vGqenvPtZm0UO0mYMOqmt+d3x/4IC3ce/FYEJlkOnAOsCGwZVWdvRjP2AP4InAisHtV3dJzz8HAQcB+VfXfE3jHWbRg871VdXDftbG+bgaeXVXnDbj/UVV1Sd+5Vbr3fy6wUVVd0XPtDFpAfC6wXVVd251/MHA+LQxet6qu6s4/BLgEeGDX/kc9jyLJelV1ec/vBfyoqmZ1vz8R+AVwUlW9tO/e+wGrV9V1C/yMpk8rdp2xoCaSpOVcHT5n2CVIkiRJK5Qk86pq5sLaOR1+at0AvK/3RFXNBb5KG5HYG67tBRTwtt6RmFX1Z9ooTYDXLeYz3kIbzbhXbwDaOQT4K7D7RF5sgo4YFIB2NV4y4NzttBGwKwPbjNPnO8cC0O6ev9He9X5A7/8AXgv8A/CZ/gC0u+/y/nPj6P+cqKq7xwtAk7w+ydwkc7nljgk+QpIkSZIkSUuS0+Gn1rlVdeOA82fQQrqnAEcnWQ3YBLhinA16ZnfHpyzGMx5EmwZ+DbBfm+19H7fRdklfUs4Z70KSDYB30sLODWgjNnutO86tcwecu6w7rtlz7hnd8ZSFlznQhcDPgd2SbAicDJwFzO3C2oGq6gjgCOhGgkqSJEmSJGnKGYJOravHOX9Vd1y973jlOO3Hzq+xGM9YEwjwUNpU9alw1aCTSR5JC0jXpK2t+X3aiNa7uGet0YGbNFXV9QNOj42cXann3NhndQWLoKruSvI84D20dVk/0l26McnRwP5VddOi9C1JkiRJkqSlyxB0aj1snPPrdMcb+o7rDGgLbbOj3naL84zzquqp49yzpI03EvJttI2Q9qyqo3ovJNmNFoIurrGwdF3gl4vSQTfl/a3AW5NsQluTdG/gTbSQ9dVLoE5JkiRJkiQtYa4JOrWe2k117zerO54H0E1nvwRYN8mmA9pv3R3PXYxn3AT8Cnh8krUmVP3Ss0l3PH7Ata2W0DN+2h1fsCQ6q6rfVtUXaPXdBOy8JPqVJEmSJEnSkmcIOrVWp02n/rtu5/bdaSMzT+y5dCRtuvrHkqzU0/4hwIE9bRbnGZ8AVgGOTHKfqfVJ1kwyFaNEL+2Os/qe/3wGb/60KI4G5gNvSPLc/otJ1lvQzUk2TvL4AZfWpE3Vv8+GSZIkSZIkSVo2OB1+av0YeF2SpwNzaNPaX0kLo/euqvk9bT9OG7W4M3B+ku8BDwJ2AaYDH62qsxbnGVV1ZJLNgX2BS5KcBvwRWAvYGHgu8EVgnyX0/uP5NLAn8I0kx9PW7XwCsAPw9a7+xVJV1yR5FfBN4IdJTgF+Qdsx/knA+rR3Hs8M4MQk84ALgD/R1lPdGbg/96wRKkmSJEmSpGWMIejU+j0tUPxwd1yVNqX9fVV1Wm/Dqro9yXa09TJfBbyZtuHP+cB+VXXs4j6je84bu0BwH2Bb2tqW19LC0I8BX1mcF56IqvpFkq2B9wM70r6X5wMvo63ludghaPec73ajYsd2od8euA64CPjQQm6f27XZihbOrgn8BZgHHFpVi7rrvCRJkiRJkpayVI23V42WlCQb0cLJo6tqj+X1GVo8mT6t2HXGsMuQJC1FdficYZcgSZIkrVCSzKuqmQtr55qgkiRJkiRJkkaaIagkSZIkSZKkkeaaoNIU2XyDzZjrNElJkiRJkqQpZwg6BarqUiDL+zMkSZIkSZKk5ZHT4SVJkiRJkiSNNENQSZIkSZIkSSPNEFSSJEmSJEnSSDMElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNIMQSVJkiRJkiSNNENQSZIkSZIkSSPNEFSSJEmSJEnSSDMElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00lJVw65BWiFk+rRi1xnDLkOStBTV4XOGXYIkSZK0Qkkyr6pmLqydI0ElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0khboULQJBslqSRHDbuWJWlU30uSJEmSJElaElaoEHRJSnJpkkuHXYckSZIkSZKkBVt52AVMsSuAxwI3DLsQSZIkSZIkSVNjhQpBq+oO4KJh1yFJkiRJkiRp6qxQ0+HHWzszyVHd+Y2S7J3kl0luTXJ1kiOSrN7TdlaSAjYENuzuq/5+u9/PSLJOks8nuSLJXUn26Gnz8CSf6qbW357kL0lOSLL5OPWvluQTSS7v6rsoydsY5++xe36Nc22PrsY9BlxbL8mhSX7TPefaJOckOXCctocn+V2S25L8Ncm3kjxtnPoPTHJBkvlJbkxySZLjxnvn8d4pycpJ/qOr8bYklyX5SJJVxrlvs+7v+bKu/dVJjknymL52H+r6367v/Pu685cM6PuqJH+cSP2SJEmSJEmaeivUSNAJ+CjwfODbwPeBrYF/ATYBnte1uRR4L7Bf9/sne+7/eV9/awE/BW4CTgDuBq4GSLIxcBbwCGA2cCywPrAL8MIkL6+q74x1lGRV4HTgacD5wFeBNYADga0W6617JJkJnNbV/uOu7gcBjwMOBg7paftU2ue0VnfPCcBDgJcAZyV5aVV9r2sb4FTgWcBPgM8Dd3bvPAs4E5g3iVKPAZ4DnALMB3YE3gFMB/bse6cdutruT/u7/S2wHvAy2me9dVWd2zU/HXgXsA3wg55uxv7+H5lko6q6tOv7CcDDgKMmUbskSZIkSZKmkCHovT0DeGJV/REgycq0gHLrJFtU1Tld+HXw2AjKqjp4Af09EfgysFdV3dl37bO0APSAqvrA2Mkkn6aFj0cn2bCqbuou/RstAD0B2KWq7u7af5jJhYfj6kZRfoMWau5eVcf0XV+/5+eVga8D04Ctq+pHPdceAfwv8IUuMLwNeAItAD2pql7a1+/9gNWZnEcBj6+qa7s+3k0Lh1+TZP+quqo7vyYtYL4ZeG5VXdjz3McDP6MFsk/tTs8BbqOFoGPtpgFb0ELR7bprX+guj7WbPcn6JUmSJEmSNEVWqOnwE/C+sQAUoAsuv9j9usUi9Hc78O/9AWiS9YDtgT/SRp/+XVWdTQvt1qKNVByzJ20k6TvGAtCu/e+BQxehtkF2AjYCvtUfgHbPuqzn1xfSgsjDegPQrt2faO+1Dj1hYueWAf3eXVXXTbLWd44FoF0ff6ONjr0fMLOn3WtoI2YP6g1Au3t+BfwP8JQkj+vO3UIbqfrULkAFeC5tFOkngb/0vdMCQ9Akr08yN8lcbrljkq8oSZIkSZKkJcGRoPc2d8C5seBvzQHXFubSqvrzgPNP6Y5ndps19ZsN/FPX7ktJVqNNyb+squ6zJiVwBnDQItTX7xnd8ZQJtH1md9wwycEDrm/aHR8LfA+4kLZcwG5JNgROpi0HMLeqbl+EWif6dzVW54xx6nx0T51jIels2hT9WcCJtKnwd9BG6P6w+50kK9EC0our6opBRVbVEcARAJk+beD6rJIkSZIkSVq6DEHv7foB58ZGca60CP1dNc75sanfV45zfez8Gn3tr57kcyZr7HkDA70+a3fHXRbSbhpAVd2V5HnAe4BXAB/prt+Y5Ghg/56p/wtVVRP9uxqr818mUmfndOB9tFGeJ3bHn1XVTUlOB3bt1gJ9MO3v5qsTrVuSJEmSJElTzxB06Rpv5N8N3XGdca4/vK/d2PFh47Qfr5+xdUNXHrAm6RoD2o8Fi+uO01+vsZp2rqpvTaA93ZT3twJvTbIJbUOnvYE3dfW8eiL9TNJYnTOq6hcTvOcc2mZW2yZZG5hBC0Xhnmnv29I2jOo9J0mSJEmSpGWQa4IuurtYtNGhAOd1x2d3Gwz127o7ngtQVTfSdjRfN8mjBrSfNc5zxtbZXH/AtZkDzv20O75gnP4GtX3OBNreR1X9tqq+QAtCbwJ2XpR+JmDSdXaB8Y+Bx9CC2dBGh1JVv6Wt5boNbVr83bQp8pIkSZIkSVpGGYIuur8CD03ywMneWFWX03Ya3wjYr/dakqcDr6IFmCf2XPoi7e/rI91u6mPtNwb+dZxHndMd7zUVPMk2wG4D2n8buBR4cZL7XE/SO0L0ZOAS4I1Jdhz08CTPTPKgsTq73dj7rQmsyoANk5aQL9JGuB6U5D6bWyW5X5JZA+4bG925P/A32i7yvde2ArYEzu/doEmSJEmSJEnLHqfDL7rTgacBpyb5MXAbLRD79gTv3weYA3wsyfa0jX7Wp62xeTewZzcCdMx/Ai8BXg6cm+Q02nqUr6SNWnzxgGd8EXg7sH+SGbSNfx5NG+l5YtfX31XV7Ul2Ab4PHJNkb9pIygfQNg7ahu47U1V3JHkZcBrw3SRn0zY+url7j6cBj6RN7b+ZNqX8xCTzgAuAPwEPpY0AvT/3rBG6RFXVX5O8onvfn3Zrev6K9hlvQNs4ae3uHXud3h2nA6f2bd50OrBHXztJkiRJkiQtowxBF937aetY7kQbEbgScDRtNOVCVdXvksgqZa0AABhrSURBVMwEDgB2pE1pnw+cCnygqv63r/1tSbYFDqYFn2+hjdp8Py3gu08IWlV/TrIV8DHaLuZb0cLW7YCN6QtBu3vmJnky8C5aWPosYGw6/kF9bX/RhatvA14E7EkLF6+kTfk/CLimaz4X+FBXww60EaB/AeYBh1bVRHakXyRVdXqSJwH/DjyfNjX+dloQOxs4fsBt53e1P4T7rvk5e5yfJUmSJEmStAxK1Xh790hakjJ9WrHrjGGXIUlaiurwOcMuQZIkSVqhJJlXVYP2vrkX1wSVJEmSJEmSNNKcDi9Nkc032Iy5jhCSJEmSJEmaco4ElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNIMQSVJkiRJkiSNNENQSZIkSZIkSSPNEFSSJEmSJEnSSDMElSRJkiRJkjTSDEElSZIkSZIkjTRDUEmSJEmSJEkjzRBUkiRJkiRJ0kgzBJUkSZIkSZI00gxBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVJEmSJEmSNNJSVcOuQVohZPq0YtcZwy5DkrQU1eFzhl2CJEmStEJJMq+qZi6snSNBJUmSJEmSJI00Q1BJkiRJkiRJI80QVJIkSZIkSdJIMwSVFiDJwUkqyaxh1yJJkiRJkqRFYwiqkZNkjy643GPYtUiSJEmSJGn4DEElSZIkSZIkjTRDUEmSJEmSJEkjzRB0GZdkWpLbk8zpO//AJLd2075f3Xdt3+78Xj3nNk/y30nOT3Jtd+9vkvxnkjX77t+nu/8949S0TpI7kvxyGaz9DOCL3a9f7Poa+7NRT7uVuveck+SGJLck+W2SzyfZdJz3eUWSc5Lc3NXxtSTrLugzkCRJkiRJ0vAZgi7jquom4BxgiySr9VzaEli1+3mbvtue1x1P7zn3L8A/AhfTQsLPAlcCbwPm9PX9FWA+8LokKw0oay9gZeBzy2DtRwEndz+fDLy358/1AElWAU4FPgOsDxwDHArMA17a1ddvX9rncinwKeAC4JXA/0uy6oD2kiRJkiRJWkasPOwCNCGzacHcc4Hvdue2Ae4CfkxPkJjkfsAs4HdV9YeePj4EvLGq7urtOMk/A5+nhXwfgRZeJvky8EbgBcB3etoHeB1wM/DlZbD2o1qJ7AycVFVHDajpYGBb4NvALlV1W0+fqwL/MOCeHYCnVdUve9oeA+zWPevr438EkiRJkiRJGiZHgi4fxkZF9o6a3IY2cvF4YL0kj+7OPxlYm3uPpKSq/tAfInaOpI36fH7f+c90x737zm8PbAwcV1U3LKO1j6sb2bovcAuwT28A2j3rtqr6y4BbD+0NQDv/0x23WMDzXp9kbpK53HLHRMuUJEmSJEnSEmQIunz4CS202wYgyerAU2lh4eyuzVjIODadfHZvB0nun+RNSc7q1rO8K0kBd9NGPt5rbcuq+hVtpOYLkqzfc+n13fGzy2rtC7EZsDrwi6r60yTumzvg3GXdcc0B1wCoqiOqamZVzeSB95/E4yRJkiRJkrSkOB1+OVBVtyc5C9g2yXTgmcBKwOlV9eskf6IFiZ/pjkVfkAgcR1vv8ne0tTKvAsZGQe7HPWt09vo0bRr764CDkqwDvBj4eVWds4zXPp41uuMVk7gHuvVE+9zZHQetmypJkiRJkqRlhCHo8mM2sB1ttOSzaCHg2K7rP6SN2FwVeA7wq6r689iNSWbSQsT/B+xYVXf0XLsf8I5xnnkCcDXwz0nexwQ3RFpGah/PWJjpru6SJEmSJEkrCKfDLz9619Z8HjCnqm7tubYW8AbgwfStqQls0h2/1RsidrYAHjjogV3bz9MCw51oI0JvAr66jNc+tn7ooBGaF9GC0CclecSE30CSJEmSJEnLLUPQ5cc8Wni3M/B47h0Wjv28f3fsn05+aXec1Xuym57+qYU89whaqHg4bUOkY6rqxknUDVNf+1+74wb9F7oNlj5NC08/241A7e13lSQPHadfSZIkSZIkLYecDr+cqKq7k/yIFiRCT5BYVX9McgnwKFpg+aO+2/+XNv38ZUnOBs4CHga8ALgYGHeDoK7v79LWAoXJT4UfRu0/AW4G9kuyFm1KP8Bh3Y727wWeThvd+n9JvgPcCKwPbA+8HThqsu8pSZIkSZKkZZMjQZcvY+HhfO67W/nYtXld0Pd33ejHF9M2H3oE8K/As2lT3Z8P9E8z73dkd5xbVecuWulTV3tVXQe8HLgQ2BM4pPuzZnf9dmAH4M20gPS13c9bACfSglZJkiRJkiSNiFTVsGvQMi7JwcBBwOuq6gtDLme5lenTil1nDLsMSdJSVIfPWXgjSZIkSUtMknlVNXNh7RwJqgVKshqwD3AtcOyQy5EkSZIkSZImzTVBNVCSFwJPpa2b+TDg36vq5uFWJUmSJEmSJE2eIajGswttrcyrgQ8B/zXccpZ/m2+wGXOdJilJkiRJkjTlDEE1UFXtAewx5DIkSZIkSZKkxeaaoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCpJkiRJkiRppBmCSpIkSZIkSRpphqCSJEmSJEmSRpohqCRJkiRJkqSRZggqSZIkSZIkaaQZgkqSJEmSJEkaaYagkiRJkiRJkkZaqmrYNUgrhCQ3AhcPuw6NpIcA1wy7CI0sv19amvx+aWny+6Wlye+Xlia/X1paRvW7tWFVPXRhjVaeikokAXBxVc0cdhEaPUnm+t3S0uL3S0uT3y8tTX6/tDT5/dLS5PdLS8uK/t1yOrwkSZIkSZKkkWYIKkmSJEmSJGmkGYJKU+eIYRegkeV3S0uT3y8tTX6/tDT5/dLS5PdLS5PfLy0tK/R3y42RJEmSJEmSJI00R4JKkiRJkiRJGmmGoJIkSZIkSZJGmiGoJEmSJEmSpJFmCCotRUnWS3Jkkj8luS3JpUk+mWTNYdem5VeStZO8LsmJSX6b5JYkNyQ5K8k/J/Gf7Vqikrw6SXV/XjfserT8S/KcJMcnubL79+OVSb6fZMdh16blW5IXdt+ly7t/P/4uyTeSPHPYtWnZl+QVSQ5LcmaS+d2/976ykHueleR7Sa5NcnOSXyTZL8lKU1W3lg+T+X4l2TTJO5PMTnJZktuTXJ3k5CRbT3XtWvYtyj+/+u7/Qs9/72+yNGsdppWHXYA0qpI8CjgbmA6cDFwEbAG8BdghyZZV9dchlqjl1y7AZ4ArgR8CfwQeBrwM+DzwgiS7lDvfaQlIsj5wGHATMG3I5WgEJDkAOAS4BvgO7Z9lDwGeAswCvje04rRcS/IR4B3AX4GTaN+xTYCdgZcneU1VTfj/EGqFdAAwg/bvvMuBzRbUOMnOwPHArcBxwLXATsB/AVvS/ptNGjOZ79chwCuBC2n/XrwWeAzwYuDFSd5SVYcu3XK1nJnUP796JdkJ2IsV4L/33R1eWkqSnAZsD/xrVR3Wc/4TwFuBz1XVPsOqT8uvJM8DHgx8t/5/e/ceNGdVH3D8+wMJlwABEZqCTAwlQYptxVZAsIRwEUuRcpMCBYEZLrY4SIVKBQV0vFAEi9ALKsUgpALl3im1ECHc2sKg4BSIhjTgSJRLIIFAgHD59Y9zlqzbd0PezfvuZvf9fmZ2zrPPOc+zv515ZvfZ355L5ptN+ycC9wFbAgdn5rU9ClEDIiICuBWYDFwHnAocl5mX9DQw9a2I+DhwNTALODAzl7TUr5WZr/UkOPW1+h24AHgG+N3MfLqpbjpwG/BYZm7VoxDVB+q18gQwD5hG+bN5ZmYeMUTbDWu7CcAumXl/3b8O5Xr7EHBYZl7ZpfC1mhvm9XU08JPMfKBl/zTKvVkC78nMX4123OoPw7m+Wo7bFPgfYDYwsR47JTPnjWrAPeKQSWkURMRWlATo48Dft1SfBbwEHBkR47scmgZAZt6Wmf/anACt+58ELq5Pd+t6YBpEJwG7A8dQPrekjtWpOv4GWAoc3poABTABqlUwifLb5t7mBChAZt4OLAE27UVg6h+ZeXtmPrqSo2kOplxTVzYSoPUcr1B6ZAH8+SiEqT41nOsrM2e0JkDr/jsoyapxwM4jH6X61TA/v5p9u5YnjnRMqyOToNLo2L2WtwyRqFoC3AOsB+zU7cA08BoJhNd7GoX6XkRsC5wDfDMz7+x1PBoIO1N6Fd8MLKpzN54WEZ92vkaNgEeBZcAOEfGu5oqI2BXYgNIDWRopjfv9HwxRdyflD5+dI2Lt7oWkMcL7fY2I2uN4f+CTY2WqPucElUbHNrWc26b+UUpP0anAD7sSkQZeRLwD+ER9OtQNubRS6rV0OWW+2dN7HI4Gxwdr+RTwY+B3misj4k7KVB7PdDsw9b/MfC4iTgO+ATwSETdQ5gb9LcocercCJ/QwRA2etvf7mfl6RDwGbAdsBczpZmAaXBExCdiDkmT3T2p1rF5L3wSuyMwbeh1Pt5gElUbHhFo+36a+sX+jLsSiseMc4H3AzZn5H70ORn3tTMoiNR/OzJd7HYwGxma1/CTwGLAncC9lGPP5wN7Av+B0HupQZl4QEY8DlwLHNVXNA2a0DpOXVpH3++qq2qt4JrA28NnMXNTjkNSn6hRFl1EWQjqpx+F0lcPhpd6IWroymUZERJwEnAL8FDiyx+Goj0XEDpTen+dn5n/1Oh4NlDVrGZQenz/MzBcz82HgAMpk/tMcGq9ORcRngWuAGZQeoOOB3wfmAzMj4tzeRacxyPt9jZiIWJMySmcX4CrgvN5GpD73l5QFkI4ba8l0k6DS6Gj88zuhTf2GLe2kjkXEiZShDI8A0zPzuR6HpD7VNAx+LvCFHoejwdO4yZ6fmT9prqg9jhs92HfoalQaCBGxG2XhrZsy8zOZOT8zl2bmjylJ9gXAKXXxSmkkeL+vrqgJ0CuAjwNXA0d0sPiNBEBETAG+Anw3M2/udTzdZhJUGh0/q+XUNvVTatluzlBppUTEycDfAQ9REqBP9jgk9bf1KZ9b2wKvREQ2HsBZtc136r4Lehal+lXju3Fxm/pGknTdLsSiwbNvLW9vrcjMpcB9lN8+23czKA20tvf79U/FyZSFa+Z3MygNlnotfR84FPhn4PDMdEEkrYrtKFMqHNN8r1/v96fVNo/Wffv3LszR4Zyg0uho3IB/JCLWaF4hPiI2oAxjeBn4714Ep8FQF4A4B3gQ2CszF/Y4JPW/V4F/alP3AUry4G7KDz+Hymu47qQkBKZExLjMXNZS/75aPt7VqDQoGitwb9qmvrG/9bqTOnUb8GfARylJqma7AusBd2bmq90OTIMhIsZRen7+CfA94Jjm35VShx6n/f3+HwMTKXO0v8AA3pOZBJVGQWb+b0TcQlkB/kTgoqbqL1LmqPpWZr7Ui/jU/yLiC8CXgB8BH3EIvEZCHZJ87FB1EXE2JQl6WWZe0s24NBgyc2FEXEVJGpwJfL5RFxF7URZGeh74QW8iVJ+7C/gUcHxEfCszFzQqIuKPKH9AvwL8Z4/i0+C5hjIFw6ERcVFm3g8QEesAX65t/rFXwam/1UWQrgP2oSSsjjcBqpGQmQ/S/n5/NiUJenpmzutmXN1iElQaPX9BudG+MCL2AOYAOwLTKcPgz+hhbOpjEXEUJQH6BuVH30kR0drs8cyc0eXQJOntfIbyXXhGROxKGaI8iTJn4xuUCfrbDZeXVuQaYBawJzAnIq4HnqRM77EvZZGav87MZ3sXolZ3dehnY/jnxFp+KCJm1O2FmXkqQGa+EBHHUa692RFxJfAcsB+wTd1/Vbdi1+pvONcXcDElAbqQMqfxmUPc78/OzNmjFrD6yjCvrzHLJKg0Smpv0D+gJKs+SvkS+xVwIfBFe+5pFUyu5ZrAyW3a3EFZHVeSVhuZ+XRE7EjpBXoAsBOwBPg34GuZ6TQx6khmvhkR+1BG4BxKub7WoySlbgYuzMxbehii+sP7gaNa9m1VHwA/B95KImTmDRExjdK54SBgHWAe5Q+fC128Ri2Gc3017vffRRk90c7skQpOfW9Yn19jVfi5LEmSJEmSJGmQuTq8JEmSJEmSpIFmElSSJEmSJEnSQDMJKkmSJEmSJGmgmQSVJEmSJEmSNNBMgkqSJEmSJEkaaCZBJUmSJEmSJA00k6CSJEmSJEmSBppJUEmSJEmSJEkD7R29DkCSJEkaiyJiTeAgYF9gJ2AzYD1gMTAXuAuYmZkP9SzIERQR7wf2BxZn5gW9jkeSJI0tkZm9jkGSJEkaUyJiJ+AyYGrT7teAJcBG/PqIreuAwzJzWfciHHkRcTTwXeDnmfme3kYjSZLGGofDS5IkSV0UER8DZlMSoM8CnwOmZua4zNwEGAd8EDgHeAE4kNJDVJIkSR1yOLwkSZLUJRExBbgCWBt4BNg7M59obpOZbwD3A/dHxNeBS7seqCRJ0oCxJ6gkSZLUPV8GNgReAQ5oTYC2ysznMnN/4Pnm/RExMSK+HhEPR8SLEfFS3T43In5jqHNFxG4RkRGxwvmwGm0iYrcVHR8RW0fEpRHxi4h4NSKeiIjvRMQWQ52TMhQeYFLTazQeZ7e03zsirqvnXBYRL0TE/Ii4JSJOjYh3rug9SJIktbInqCRJktQFNTl5cH06MzPnruyx2TSRf0RMA26gzB0KsBRI4Lfr49iI2C8z7x6RwIcQEdOBm4D1KfOYrgFsARwL7BMRO2TmgqZDngLWpSSA3wSeaTnli03nPhP4YlPdUiCAyfWxF6Wn7OyRe0eSJGnQ2RNUkiRJ6o7pLL//vr6TE0TElixPgD4CfDgzx2fm+sCuwM+AjYEbh+qROYKuBW4Dts3MDYHxwJ9SEqKbA19rbpyZE4FP16e/yMyJLY/z6vubBJxV230D2KK+vw3qe/5D4B/q60iSJK00k6CSJElSd2zXtP1Ah+c4nZIMXATskZn3NCoy8y5gT8piSu+kLLg0Wh6kDOf/aX3tZZl5NXBGrT84IjoZdbYj5TfK3Mw8JTN/2ajIzOcz8+7MPDEzf7Sqb0CSJI0tJkElSZKk7tikafu54R4cEQEcUp9enJlPtrapc4xeXJ8eOuwIV95XM/PNIfbfWMt1gSkdnHdxLTeIiPEdRSZJkjQEk6CSJElSd8QqHj+Z0sMTYNYK2t1ay00iYvIqvmY797bZ/8um7U4WL7oPWAj8JnBvRHwqIt5bE8CSJEkdMwkqSZIkdcfCpu1OEoSbNW0vaNsKmlec36xtq1WQmUPOyZmZrzc9XauD8y4GDqMsnLQdcBEwB1gUETdFxBERMezzSpIkmQSVJEmSuuPhpu3tV/Fc+fZNhtVutZGZsyi9Xj8BXAY8CkwAPgZcDjwwyos+SZKkAWQSVJIkSeqO24HGPJoHdHD8003bW66g3bubtp9p2n6rl2ZErDPUgRExoYO4RlxmvpSZl2fm0Zk5lfKeTgNeYXkPUUmSpJVmElSSJEnqgsx8Cri2Pj08Iqau7LF1TszHWL6g0h4raL5nLZ/NzMea9i9q2m6XRN1xZWPqQCMBPOz5PTNzQWaeC5xfd+01YlFJkqQxwSSoJEmS1D2fB16krJ5+3dsN646IjSPiWmBCZiZwVa06ISImDtF+c+CE+vT7LdVzgZfr9kFDHLsG8LmVfSMdeKGWG7VrEBFrv805GvG/MSIRSZKkMcMkqCRJktQlmTkXOBJYRhnW/WBEnBYRWzfaRMSaEbF9RHwJmA8c2HSKrwKLKQsrzYqInZuO24WyavxGlB6j57S89mss74l6ekQcEhHj6rHbANcDvzeS77fFQ7XcMCIOadPmtIj494g4MiLeGtYfEWvXY/6q7rp5FOOUJEkDKMofypIkSZK6pSYsZwBbN+1eRukluhHLOyskcCVwVE1iEhHTgBspiwUBvFTL8bVcDOyXmXcN8brvBu4FNq+7XqP0rtwQWEJZfGh2rZuembObjt2NMq8pmdl2SHtENH5g/NrxtW4Wy4fyL2H58P4LMvOCiDgbOKvpkJfrY2OWD6OfA+yemU+2i0GSJKmVPUElSZKkLsvMe4D3AocBM4F5lEV/NqAkBu8GvgJsm5mHNxKg9dg76rHnUxKCa1AShHOA8+ox/y8BWo99gjLv5yXAgrr7ReB7wAfquUfTwcDfUobmrwVMqo/GEPlvA8dThvI/BCylJGgXAXcBJ9c4TYBKkqRhsSeoJEmSJEmSpIFmT1BJkiRJkiRJA80kqCRJkiRJkqSBZhJUkiRJkiRJ0kAzCSpJkiRJkiRpoJkElSRJkiRJkjTQTIJKkiRJkiRJGmgmQSVJkiRJkiQNNJOgkiRJkiRJkgaaSVBJkiRJkiRJA80kqCRJkiRJkqSB9n+p2Oe47SudBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onion_bigram_cvec_stop_top = onion_bigram_cvec_stop.sum().sort_values(ascending = True).tail(15)\n",
    "onion_bigram_cvec_stop_top.plot(kind = 'barh',figsize=(20,12),color = '#006b3a',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Bigrams in Posts on r/TheOnion',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Bigrams', size = 25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conspiracy (no stop words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Words: 4211\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "the           662\n",
       "to            342\n",
       "of            314\n",
       "and           255\n",
       "is            222\n",
       "in            199\n",
       "for           134\n",
       "on            119\n",
       "that          104\n",
       "with           97\n",
       "it             95\n",
       "was            84\n",
       "this           82\n",
       "are            81\n",
       "what           65\n",
       "by             62\n",
       "be             61\n",
       "from           60\n",
       "you            59\n",
       "about          59\n",
       "conspiracy     57\n",
       "all            51\n",
       "at             49\n",
       "why            47\n",
       "trump          47\n",
       "has            46\n",
       "as             46\n",
       "how            44\n",
       "he             43\n",
       "they           43\n",
       "people         42\n",
       "an             41\n",
       "biden          38\n",
       "have           38\n",
       "who            37\n",
       "we             37\n",
       "us             37\n",
       "not            34\n",
       "can            34\n",
       "more           31\n",
       "their          31\n",
       "or             31\n",
       "out            30\n",
       "like           30\n",
       "joe            30\n",
       "if             29\n",
       "think          29\n",
       "his            28\n",
       "up             28\n",
       "government     28\n",
       "just           28\n",
       "nipsey         28\n",
       "being          27\n",
       "world          27\n",
       "no             27\n",
       "new            26\n",
       "so             26\n",
       "media          25\n",
       "over           25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate CountVectorizer\n",
    "cvec = CountVectorizer()\n",
    "# Fit CountVectorizer on the training data and transform training data.\n",
    "conspiracy_cvec = pd.DataFrame(cvec.fit_transform(df_conspiracy['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Words: {conspiracy_cvec.shape[1]}')\n",
    "conspiracy_cvec.sum()[conspiracy_cvec.sum() >= 25].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conspiracy (bigram, no stop words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Bigrams: 10579\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "of the            65\n",
       "in the            48\n",
       "to be             25\n",
       "on the            24\n",
       "joe biden         22\n",
       "to the            21\n",
       "is the            21\n",
       "this is           18\n",
       "from the          18\n",
       "that the          17\n",
       "for the           16\n",
       "nipsey hussle     16\n",
       "at the            14\n",
       "and the           13\n",
       "sandy hook        13\n",
       "why is            13\n",
       "has been          12\n",
       "the real          11\n",
       "with the          10\n",
       "the same          10\n",
       "the world         10\n",
       "donald trump      10\n",
       "you think         10\n",
       "julian assange     9\n",
       "the government     9\n",
       "the truth          9\n",
       "do you             9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate CountVectorizer\n",
    "cvec = CountVectorizer(ngram_range=(2,2))\n",
    "# Fit CountVectorizer on the training data and transform training data.\n",
    "conspiracy_bigram_cvec = pd.DataFrame(cvec.fit_transform(df_conspiracy['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Bigrams: {conspiracy_bigram_cvec.shape[1]}')\n",
    "conspiracy_bigram_cvec.sum()[conspiracy_bigram_cvec.sum() >= 9].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conspiracy (stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Words: 3994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "conspiracy    57\n",
       "trump         47\n",
       "people        42\n",
       "biden         38\n",
       "like          30\n",
       "joe           30\n",
       "think         29\n",
       "nipsey        28\n",
       "just          28\n",
       "government    28\n",
       "world         27\n",
       "new           26\n",
       "media         25\n",
       "real          24\n",
       "amp           23\n",
       "years         23\n",
       "video         22\n",
       "america       21\n",
       "know          20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(stop_words='english')\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "conspiracy_cvec_stop = pd.DataFrame(cvec.fit_transform(df_conspiracy['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Words: {conspiracy_cvec_stop.shape[1]}')\n",
    "conspiracy_cvec_stop.sum()[conspiracy_cvec_stop.sum() >= 20].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conspiracy_cvec_stop_top = conspiracy_cvec_stop.sum().sort_values(ascending = True).tail(15)\n",
    "conspiracy_cvec_stop_top.plot(kind = 'barh',figsize=(20,12),color = '#e5862d',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Words in Posts on r/Conspiracy',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Words', size = 25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conspiracy (bigrams, stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Bigrams: 6824\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "joe biden              22\n",
       "nipsey hussle          16\n",
       "sandy hook             13\n",
       "donald trump           10\n",
       "julian assange          9\n",
       "conspiracy theories     7\n",
       "years ago               6\n",
       "year old                6\n",
       "rapper nipsey           6\n",
       "new zealand             6\n",
       "dr sebi                 6\n",
       "alex jones              6\n",
       "jussie smollett         5\n",
       "hussle death            5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate CountVectorizer\n",
    "cvec = CountVectorizer(stop_words = 'english',ngram_range=(2,2))\n",
    "# Fit CountVectorizer on the training data and transform training data.\n",
    "conspiracy_bigram_cvec_stop = pd.DataFrame(cvec.fit_transform(df_conspiracy['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "print(f'Number of Unique Bigrams: {conspiracy_bigram_cvec_stop.shape[1]}')\n",
    "conspiracy_bigram_cvec_stop.sum()[conspiracy_bigram_cvec_stop.sum() >= 5].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conspiracy_bigram_cvec_stop_top = conspiracy_bigram_cvec_stop.sum().sort_values(ascending = True).tail(15)\n",
    "conspiracy_bigram_cvec_stop_top.plot(kind = 'barh',figsize=(20,12),color = '#e5862d',fontsize=20)\n",
    "plt.title('Most Frequently Occuring Bigrams in Posts on r/Conspiracy',size = 30)\n",
    "plt.xlabel('Counts', size = 25)\n",
    "plt.ylabel('Bigrams', size = 25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find all common words in TheOnion and Conspiracy Posts\n",
    "common_words = []\n",
    "for i in list(onion_cvec_stop):\n",
    "    for j in list(conspiracy_cvec_stop):\n",
    "        if i == j:\n",
    "            common_words.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1447"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(common_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create list of stop words common to both subreddits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_english_common = text.ENGLISH_STOP_WORDS.union(common_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "conspiracy    57\n",
       "nipsey        28\n",
       "hussle        18\n",
       "assange       18\n",
       "fbi           14\n",
       "5g            14\n",
       "vaccine       13\n",
       "truth         13\n",
       "sandy         13\n",
       "theories      12\n",
       "propaganda    12\n",
       "reddit        11\n",
       "vaccines      10\n",
       "julian        10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(stop_words=stop_english_common)\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "conspiracy_cvec_stop_en_com = pd.DataFrame(cvec.fit_transform(df_conspiracy['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "conspiracy_cvec_stop_en_com.sum()[conspiracy_cvec_stop_en_com.sum()>9].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nation           18\n",
       "super            16\n",
       "work             13\n",
       "beautiful        12\n",
       "mom              12\n",
       "onion            10\n",
       "bowl             10\n",
       "teen             10\n",
       "announced        10\n",
       "week              9\n",
       "car               9\n",
       "ftw               9\n",
       "pope              9\n",
       "dad               8\n",
       "fans              7\n",
       "let               7\n",
       "heartbreaking     7\n",
       "ep                7\n",
       "players           7\n",
       "roger             7\n",
       "rowling           7\n",
       "space             7\n",
       "awesome           7\n",
       "introduces        7\n",
       "fan               6\n",
       "francis           6\n",
       "heartwarming      6\n",
       "grandmother       6\n",
       "happy             6\n",
       "drive             6\n",
       "bezos             6\n",
       "issues            6\n",
       "unveils           6\n",
       "tom               6\n",
       "owner             6\n",
       "stone             6\n",
       "horrified         5\n",
       "sleep             5\n",
       "announce          5\n",
       "apartment         5\n",
       "basketball        5\n",
       "students          5\n",
       "store             5\n",
       "bottle            5\n",
       "smoking           5\n",
       "singer            5\n",
       "hour              5\n",
       "quit              5\n",
       "personal          5\n",
       "oscars            5\n",
       "okay              5\n",
       "normal            5\n",
       "mountain          5\n",
       "hard              5\n",
       "hare              5\n",
       "iowa              5\n",
       "absolutely        5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvec = CountVectorizer(stop_words=stop_english_common)\n",
    "# Fit our CountVectorizer on the training data and transform training data.\n",
    "onion_cvec_stop_en_com = pd.DataFrame(cvec.fit_transform(df_onion['title']).todense(),\n",
    "                          columns = cvec.get_feature_names())\n",
    "onion_cvec_stop_en_com.sum()[onion_cvec_stop_en_com.sum()>=5].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_combined = df_onion.append(df_conspiracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined['subreddit'] = df_combined['subreddit'].map({'TheOnion': 1, 'conspiracy': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined.reset_index(inplace = True,drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export Combined Dataframe to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined.to_csv('./combined_data2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\noaha\\Anaconda3\\envs\\dsi\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score, confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create X (features) & y (target) datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_combined['title']\n",
    "y = df_combined['subreddit']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.521693\n",
       "0    0.478307\n",
       "Name: subreddit, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function for Evaluating Vectorizers & Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "vectorizer = [CountVectorizer(), TfidfVectorizer()]\n",
    "classifier = [KNeighborsClassifier(),LogisticRegression(), MultinomialNB(), RandomForestClassifier()]\n",
    "vec_param = { 'CountVectorizer': { \n",
    "                                    'CountVectorizer__max_features': [None,1200,2000,2250,3500],\n",
    "                                    'CountVectorizer__min_df': [2,3,4],\n",
    "                                    'CountVectorizer__max_df': [.3,.35,.4,.5],\n",
    "                                    'CountVectorizer__ngram_range': [(1,1),(1,2)],\n",
    "                                    'CountVectorizer__stop_words': [None,'english']\n",
    "                                    },\n",
    "               'TfidfVectorizer': {\n",
    "                                    'TfidfVectorizer__max_features': [None,1200,2000,2250,3500],\n",
    "                                    'TfidfVectorizer__min_df': [2,3,4],\n",
    "                                    'TfidfVectorizer__max_df': [.3,.35,.4,.5],\n",
    "                                    'TfidfVectorizer__ngram_range': [(1,1),(1,2)],\n",
    "                                    'TfidfVectorizer__stop_words': [None,'english']  \n",
    "                                    }\n",
    "              }\n",
    "clasf_param = { 'KNeighborsClassifier': {'KNeighborsClassifier__n_neighbors':[3,5,7]},\n",
    "                'LogisticRegression': {'LogisticRegression__penalty': ['l1','l2']},\n",
    "                'MultinomialNB': {},\n",
    "                'RandomForestClassifier': {'RandomForestClassifier__max_depth': [90, 100, 110],\n",
    "                                           #'RandomForestClassifier__max_features': [None, .5, .75, 'auto'],\n",
    "                                           'RandomForestClassifier__n_estimators': [100, 200, 300]}\n",
    "               }\n",
    "vect_names = ['CountVectorizer','TfidfVectorizer']\n",
    "clasf_names = ['KNeighborsClassifier','LogisticRegression','MultinomialNB','RandomForestClassifier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def grid_pipe(X,y,vectorizers,classifiers,vects_names, clasfs_names, vec_params, clasf_params):\n",
    "    t0 = time.time()\n",
    "    score_list = []\n",
    "    # Train Test Split Data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y,stratify=y,random_state=42)\n",
    "    \n",
    "    for n_vec,vec in enumerate(vectorizers):\n",
    "        for n_clasf,clasf in enumerate(classifiers):\n",
    "            t1 = time.time()\n",
    "            # Empty Dictionary used to create dataframe of model performance\n",
    "            df_dict = {}\n",
    "            \n",
    "            # Create dictionary of Pipeline Params for Vectorizer-Classifier Combination\n",
    "            pipe_params = {}\n",
    "            vec_dict = vec_params[vects_names[n_vec]]\n",
    "            clasf_dict = clasf_params[clasfs_names[n_clasf]]\n",
    "            pipe_params.update(vec_dict)\n",
    "            pipe_params.update(clasf_dict)\n",
    "            \n",
    "            # Instantiate Pipeline for Vectorizer-Classifier Combination\n",
    "            pipe = Pipeline([\n",
    "                (f'{vects_names[n_vec]}', vec),\n",
    "                (f'{clasfs_names[n_clasf]}', clasf)\n",
    "            ])\n",
    "            \n",
    "            # Run GridSearchCV to determine best params for Vectorizer-Classifier Combination\n",
    "            gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3, n_jobs = -1,verbose = 1)\n",
    "            gs.fit(X_train,y_train)\n",
    "            \n",
    "            best_estimator = gs.best_estimator_\n",
    "#             print(f'Best Estimator: {best_estimator}')\n",
    "            print(f'Vectorizer: {vects_names[n_vec]}, Classifier: {clasfs_names[n_clasf]}')\n",
    "            print(f'Best Params: {gs.best_params_}')\n",
    "            print(f'Best Score: {gs.best_score_}')\n",
    "            print(f'\\n')\n",
    "            \n",
    "            # Fit using Best Estimator\n",
    "            best_estimator.fit(X_train, y_train)\n",
    "            \n",
    "            # Make predictions using Best Estimator\n",
    "            pred_train = best_estimator.predict(X_train)\n",
    "            pred_test  = best_estimator.predict(X_test)\n",
    "            \n",
    "            # fit vectorizer\n",
    "#             vec.fit(X_train)\n",
    "            \n",
    "            # transform data through fit vectorizer\n",
    "#             X_train_vec = vec.transform(X_train)\n",
    "#             X_test_vec  = vec.transform(X_test)\n",
    "            \n",
    "            # fit classifier\n",
    "#             clasf.fit(X_train_vec,y_train)\n",
    "            \n",
    "#             pred_train = clasf.predict(X_train_vec)\n",
    "#             pred_test  = clasf.predict(X_test_vec)\n",
    "            \n",
    "            # create confusion matrices for Train and Test\n",
    "            tn_train,fp_train,fn_train,tp_train  = confusion_matrix(y_train,pred_train).ravel()\n",
    "            tn_test,fp_test,fn_test,tp_test  = confusion_matrix(y_test,pred_test).ravel()\n",
    "            \n",
    "            # Name of Vectorizer and Classifier\n",
    "            df_dict['model_vectorizer']  = vects_names[n_vec]\n",
    "            df_dict['model_classifier']  = clasfs_names[n_clasf]\n",
    "            df_dict['model_best_params'] = gs.best_params_\n",
    "            \n",
    "            # Calculate Accuracy of Train and Test Predictions\n",
    "            df_dict['train_accuracy'] = accuracy_score(y_train,pred_train)\n",
    "            df_dict['test_accuracy']  = accuracy_score(y_test,pred_test)\n",
    "            \n",
    "            # Calculate Sensitivity of Train and Test Predicitons\n",
    "            df_dict['train_sensitivity'] = recall_score(y_train,pred_train)\n",
    "            df_dict['test_sensitivity']  = recall_score(y_test,pred_test)\n",
    "            \n",
    "            # Calculate Specificity of Train and Test Predicitons\n",
    "            df_dict['train_specificity'] = (tn_train/(tn_train+fp_train))\n",
    "            df_dict['test_specificity']  = (tn_test/(tn_test+fp_test))\n",
    "            \n",
    "            # Calculate Precision of Train and Test Predicitons\n",
    "            df_dict['train_precision'] = precision_score(y_train,pred_train)\n",
    "            df_dict['test_precision']  = precision_score(y_test,pred_test)\n",
    "            \n",
    "            score_list.append(df_dict)\n",
    "            t2 = time.time()\n",
    "            print(f'Run Time for {vects_names[n_vec]} & {clasfs_names[n_clasf]}: {t2-t1}\\n')\n",
    "    t3 = time.time()\n",
    "    print(f'Total Run Time: {t3-t0}')\n",
    "    return(pd.DataFrame(data = score_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 720 candidates, totalling 2160 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   22.6s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   38.0s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   59.4s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 2160 out of 2160 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: CountVectorizer, Classifier: KNeighborsClassifier\n",
      "Best Params: {'CountVectorizer__max_df': 0.3, 'CountVectorizer__max_features': None, 'CountVectorizer__min_df': 4, 'CountVectorizer__ngram_range': (1, 2), 'CountVectorizer__stop_words': 'english', 'KNeighborsClassifier__n_neighbors': 5}\n",
      "Best Score: 0.5935714285714285\n",
      "\n",
      "\n",
      "Run Time for CountVectorizer & KNeighborsClassifier: 212.6096315383911\n",
      "\n",
      "Fitting 3 folds for each of 480 candidates, totalling 1440 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.5s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   22.7s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   40.0s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1440 out of 1440 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: CountVectorizer, Classifier: LogisticRegression\n",
      "Best Params: {'CountVectorizer__max_df': 0.35, 'CountVectorizer__max_features': None, 'CountVectorizer__min_df': 2, 'CountVectorizer__ngram_range': (1, 2), 'CountVectorizer__stop_words': None, 'LogisticRegression__penalty': 'l2'}\n",
      "Best Score: 0.7878571428571428\n",
      "\n",
      "\n",
      "Run Time for CountVectorizer & LogisticRegression: 107.08188796043396\n",
      "\n",
      "Fitting 3 folds for each of 240 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    9.9s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   18.9s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   33.8s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   50.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: CountVectorizer, Classifier: MultinomialNB\n",
      "Best Params: {'CountVectorizer__max_df': 0.35, 'CountVectorizer__max_features': None, 'CountVectorizer__min_df': 2, 'CountVectorizer__ngram_range': (1, 1), 'CountVectorizer__stop_words': None}\n",
      "Best Score: 0.7964285714285714\n",
      "\n",
      "\n",
      "Run Time for CountVectorizer & MultinomialNB: 51.33518695831299\n",
      "\n",
      "Fitting 3 folds for each of 2160 candidates, totalling 6480 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   39.3s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  5.4min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  9.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed: 14.9min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed: 21.6min\n",
      "[Parallel(n_jobs=-1)]: Done 2442 tasks      | elapsed: 29.4min\n",
      "[Parallel(n_jobs=-1)]: Done 3192 tasks      | elapsed: 39.0min\n",
      "[Parallel(n_jobs=-1)]: Done 4042 tasks      | elapsed: 46.8min\n",
      "[Parallel(n_jobs=-1)]: Done 4992 tasks      | elapsed: 53.6min\n",
      "[Parallel(n_jobs=-1)]: Done 6042 tasks      | elapsed: 60.4min\n",
      "[Parallel(n_jobs=-1)]: Done 6480 out of 6480 | elapsed: 63.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: CountVectorizer, Classifier: RandomForestClassifier\n",
      "Best Params: {'CountVectorizer__max_df': 0.4, 'CountVectorizer__max_features': None, 'CountVectorizer__min_df': 2, 'CountVectorizer__ngram_range': (1, 2), 'CountVectorizer__stop_words': None, 'RandomForestClassifier__max_depth': 110, 'RandomForestClassifier__n_estimators': 100}\n",
      "Best Score: 0.7778571428571428\n",
      "\n",
      "\n",
      "Run Time for CountVectorizer & RandomForestClassifier: 3806.0263612270355\n",
      "\n",
      "Fitting 3 folds for each of 720 candidates, totalling 2160 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    9.7s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   18.9s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   31.7s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:   51.1s\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 2160 out of 2160 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: TfidfVectorizer, Classifier: KNeighborsClassifier\n",
      "Best Params: {'KNeighborsClassifier__n_neighbors': 5, 'TfidfVectorizer__max_df': 0.3, 'TfidfVectorizer__max_features': None, 'TfidfVectorizer__min_df': 4, 'TfidfVectorizer__ngram_range': (1, 2), 'TfidfVectorizer__stop_words': 'english'}\n",
      "Best Score: 0.64\n",
      "\n",
      "\n",
      "Run Time for TfidfVectorizer & KNeighborsClassifier: 127.82720518112183\n",
      "\n",
      "Fitting 3 folds for each of 480 candidates, totalling 1440 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   12.2s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   22.5s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:   37.1s\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:   55.6s\n",
      "[Parallel(n_jobs=-1)]: Done 1440 out of 1440 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: TfidfVectorizer, Classifier: LogisticRegression\n",
      "Best Params: {'LogisticRegression__penalty': 'l2', 'TfidfVectorizer__max_df': 0.35, 'TfidfVectorizer__max_features': 2000, 'TfidfVectorizer__min_df': 2, 'TfidfVectorizer__ngram_range': (1, 2), 'TfidfVectorizer__stop_words': None}\n",
      "Best Score: 0.7821428571428571\n",
      "\n",
      "\n",
      "Run Time for TfidfVectorizer & LogisticRegression: 64.52485251426697\n",
      "\n",
      "Fitting 3 folds for each of 240 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   12.4s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   22.3s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   34.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: TfidfVectorizer, Classifier: MultinomialNB\n",
      "Best Params: {'TfidfVectorizer__max_df': 0.35, 'TfidfVectorizer__max_features': 2250, 'TfidfVectorizer__min_df': 2, 'TfidfVectorizer__ngram_range': (1, 2), 'TfidfVectorizer__stop_words': None}\n",
      "Best Score: 0.7942857142857143\n",
      "\n",
      "\n",
      "Run Time for TfidfVectorizer & MultinomialNB: 34.96168565750122\n",
      "\n",
      "Fitting 3 folds for each of 2160 candidates, totalling 6480 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   15.7s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   54.5s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  7.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed: 15.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2442 tasks      | elapsed: 24.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3192 tasks      | elapsed: 30.5min\n",
      "[Parallel(n_jobs=-1)]: Done 4042 tasks      | elapsed: 42.9min\n",
      "[Parallel(n_jobs=-1)]: Done 4992 tasks      | elapsed: 52.0min\n",
      "[Parallel(n_jobs=-1)]: Done 6042 tasks      | elapsed: 66.1min\n",
      "[Parallel(n_jobs=-1)]: Done 6480 out of 6480 | elapsed: 73.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizer: TfidfVectorizer, Classifier: RandomForestClassifier\n",
      "Best Params: {'RandomForestClassifier__max_depth': 110, 'RandomForestClassifier__n_estimators': 200, 'TfidfVectorizer__max_df': 0.5, 'TfidfVectorizer__max_features': 2000, 'TfidfVectorizer__min_df': 4, 'TfidfVectorizer__ngram_range': (1, 2), 'TfidfVectorizer__stop_words': None}\n",
      "Best Score: 0.78\n",
      "\n",
      "\n",
      "Run Time for TfidfVectorizer & RandomForestClassifier: 4431.473654985428\n",
      "\n",
      "Total Run Time: 8835.86712050438\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_best_params</th>\n",
       "      <th>model_classifier</th>\n",
       "      <th>model_vectorizer</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>test_sensitivity</th>\n",
       "      <th>test_specificity</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>train_precision</th>\n",
       "      <th>train_sensitivity</th>\n",
       "      <th>train_specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'CountVectorizer__max_df': 0.3, 'CountVectori...</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>CountVectorizer</td>\n",
       "      <td>0.623126</td>\n",
       "      <td>0.809091</td>\n",
       "      <td>0.364754</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>0.735000</td>\n",
       "      <td>0.894505</td>\n",
       "      <td>0.557534</td>\n",
       "      <td>0.928358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'CountVectorizer__max_df': 0.35, 'CountVector...</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>CountVectorizer</td>\n",
       "      <td>0.837259</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.836066</td>\n",
       "      <td>0.838565</td>\n",
       "      <td>0.995714</td>\n",
       "      <td>0.991848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'CountVectorizer__max_df': 0.35, 'CountVector...</td>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>CountVectorizer</td>\n",
       "      <td>0.835118</td>\n",
       "      <td>0.849372</td>\n",
       "      <td>0.831967</td>\n",
       "      <td>0.838565</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.943989</td>\n",
       "      <td>0.946575</td>\n",
       "      <td>0.938806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'CountVectorizer__max_df': 0.4, 'CountVectori...</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>CountVectorizer</td>\n",
       "      <td>0.783726</td>\n",
       "      <td>0.773946</td>\n",
       "      <td>0.827869</td>\n",
       "      <td>0.735426</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'KNeighborsClassifier__n_neighbors': 5, 'Tfid...</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>TfidfVectorizer</td>\n",
       "      <td>0.646681</td>\n",
       "      <td>0.772414</td>\n",
       "      <td>0.459016</td>\n",
       "      <td>0.852018</td>\n",
       "      <td>0.754286</td>\n",
       "      <td>0.876953</td>\n",
       "      <td>0.615068</td>\n",
       "      <td>0.905970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'LogisticRegression__penalty': 'l2', 'TfidfVe...</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>TfidfVectorizer</td>\n",
       "      <td>0.837259</td>\n",
       "      <td>0.820611</td>\n",
       "      <td>0.881148</td>\n",
       "      <td>0.789238</td>\n",
       "      <td>0.923571</td>\n",
       "      <td>0.902975</td>\n",
       "      <td>0.956164</td>\n",
       "      <td>0.888060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'TfidfVectorizer__max_df': 0.35, 'TfidfVector...</td>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>TfidfVectorizer</td>\n",
       "      <td>0.820128</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.836066</td>\n",
       "      <td>0.802691</td>\n",
       "      <td>0.945714</td>\n",
       "      <td>0.937166</td>\n",
       "      <td>0.960274</td>\n",
       "      <td>0.929851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'RandomForestClassifier__max_depth': 110, 'Ra...</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>TfidfVectorizer</td>\n",
       "      <td>0.798715</td>\n",
       "      <td>0.815126</td>\n",
       "      <td>0.795082</td>\n",
       "      <td>0.802691</td>\n",
       "      <td>0.997143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994521</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   model_best_params        model_classifier  \\\n",
       "0  {'CountVectorizer__max_df': 0.3, 'CountVectori...    KNeighborsClassifier   \n",
       "1  {'CountVectorizer__max_df': 0.35, 'CountVector...      LogisticRegression   \n",
       "2  {'CountVectorizer__max_df': 0.35, 'CountVector...           MultinomialNB   \n",
       "3  {'CountVectorizer__max_df': 0.4, 'CountVectori...  RandomForestClassifier   \n",
       "4  {'KNeighborsClassifier__n_neighbors': 5, 'Tfid...    KNeighborsClassifier   \n",
       "5  {'LogisticRegression__penalty': 'l2', 'TfidfVe...      LogisticRegression   \n",
       "6  {'TfidfVectorizer__max_df': 0.35, 'TfidfVector...           MultinomialNB   \n",
       "7  {'RandomForestClassifier__max_depth': 110, 'Ra...  RandomForestClassifier   \n",
       "\n",
       "  model_vectorizer  test_accuracy  test_precision  test_sensitivity  \\\n",
       "0  CountVectorizer       0.623126        0.809091          0.364754   \n",
       "1  CountVectorizer       0.837259        0.850000          0.836066   \n",
       "2  CountVectorizer       0.835118        0.849372          0.831967   \n",
       "3  CountVectorizer       0.783726        0.773946          0.827869   \n",
       "4  TfidfVectorizer       0.646681        0.772414          0.459016   \n",
       "5  TfidfVectorizer       0.837259        0.820611          0.881148   \n",
       "6  TfidfVectorizer       0.820128        0.822581          0.836066   \n",
       "7  TfidfVectorizer       0.798715        0.815126          0.795082   \n",
       "\n",
       "   test_specificity  train_accuracy  train_precision  train_sensitivity  \\\n",
       "0          0.905830        0.735000         0.894505           0.557534   \n",
       "1          0.838565        0.995714         0.991848           1.000000   \n",
       "2          0.838565        0.942857         0.943989           0.946575   \n",
       "3          0.735426        0.985714         0.973333           1.000000   \n",
       "4          0.852018        0.754286         0.876953           0.615068   \n",
       "5          0.789238        0.923571         0.902975           0.956164   \n",
       "6          0.802691        0.945714         0.937166           0.960274   \n",
       "7          0.802691        0.997143         1.000000           0.994521   \n",
       "\n",
       "   train_specificity  \n",
       "0           0.928358  \n",
       "1           0.991045  \n",
       "2           0.938806  \n",
       "3           0.970149  \n",
       "4           0.905970  \n",
       "5           0.888060  \n",
       "6           0.929851  \n",
       "7           1.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df = grid_pipe(X = X,\n",
    "                     y = y,\n",
    "                     vectorizers  = vectorizer,\n",
    "                     classifiers  = classifier,\n",
    "                     vects_names  = vect_names,\n",
    "                     clasfs_names = clasf_names,\n",
    "                     vec_params   = vec_param,\n",
    "                     clasf_params = clasf_param)\n",
    "model_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline & GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,stratify=y,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=2)]: Done 196 tasks      | elapsed:   23.2s\n",
      "[Parallel(n_jobs=2)]: Done 446 tasks      | elapsed:   46.1s\n",
      "[Parallel(n_jobs=2)]: Done 648 out of 648 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7778571428571428\n",
      "Best Parameters: {'cvec__max_df': 0.33, 'cvec__max_features': 900, 'cvec__min_df': 3, 'cvec__ngram_range': (1, 1), 'cvec__stop_words': None, 'lr__penalty': 'l2'}\n",
      "Train Accuracy Score: 0.935\n",
      "Test Accuracy Score: 0.8137044967880086\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>177</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>41</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   177               46\n",
       "actual onion                         41              203"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_cvec_lr = Pipeline([\n",
    "    ('cvec', CountVectorizer()),\n",
    "    ('lr', LogisticRegression())\n",
    "])\n",
    "\n",
    "pipe_params_cvec_lr = {\n",
    "    'cvec__max_features': [850,900,950],\n",
    "    'cvec__min_df': [2,3,4],\n",
    "    'cvec__max_df': [.3,.33,.5],\n",
    "    'cvec__ngram_range': [(1,1),(1,2)],\n",
    "    'cvec__stop_words': [None,'english'],\n",
    "    'lr__penalty': ['l1','l2']\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_cvec_lr, param_grid=pipe_params_cvec_lr, cv=3,n_jobs = 2,verbose = 1)\n",
    "gs.fit(X_train,y_train)\n",
    "\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 324 candidates, totalling 972 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=2)]: Done 273 tasks      | elapsed:   18.9s\n",
      "[Parallel(n_jobs=2)]: Done 773 tasks      | elapsed:   36.0s\n",
      "[Parallel(n_jobs=2)]: Done 969 out of 972 | elapsed:   43.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done 972 out of 972 | elapsed:   43.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7785714285714286\n",
      "Best Parameters: {'lr__penalty': 'l2', 'tvec__max_df': 0.31, 'tvec__max_features': 1100, 'tvec__min_df': 3, 'tvec__ngram_range': (1, 2), 'tvec__stop_words': None}\n",
      "Train Accuracy Score: 0.9035714285714286\n",
      "Test Accuracy Score: 0.8179871520342612\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>178</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>40</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   178               45\n",
       "actual onion                         40              204"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_tvec_lr = Pipeline([\n",
    "    ('tvec', TfidfVectorizer()),\n",
    "    ('lr', LogisticRegression())\n",
    "])\n",
    "\n",
    "pipe_params_tvec_lr = {\n",
    "    'tvec__max_features': [900,1000,1100],\n",
    "    'tvec__min_df': [3,4,5],\n",
    "    'tvec__max_df': [.3,.31,.33],\n",
    "    'tvec__ngram_range': [(1,1),(1,2)],\n",
    "    'tvec__stop_words': [None, stop_english_common,'english'],\n",
    "    'lr__penalty': ['l1','l2']\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_tvec_lr, param_grid=pipe_params_tvec_lr, cv=3, n_jobs = 2, verbose = 1)\n",
    "gs.fit(X_train,y_train)\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MultinomialNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 540 candidates, totalling 1620 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  50 tasks      | elapsed:    4.5s\n",
      "[Parallel(n_jobs=3)]: Done 350 tasks      | elapsed:   14.6s\n",
      "[Parallel(n_jobs=3)]: Done 850 tasks      | elapsed:   34.1s\n",
      "[Parallel(n_jobs=3)]: Done 1550 tasks      | elapsed:  1.0min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7964285714285714\n",
      "Best Parameters: {'cvec__max_df': 0.32, 'cvec__max_features': None, 'cvec__min_df': 2, 'cvec__ngram_range': (1, 1), 'cvec__stop_words': None}\n",
      "Train Accuracy Score: 0.9428571428571428\n",
      "Test Accuracy Score: 0.8351177730192719\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done 1620 out of 1620 | elapsed:  1.1min finished\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>187</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>41</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   187               36\n",
       "actual onion                         41              203"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_cvec_mnb = Pipeline([\n",
    "    ('cvec', CountVectorizer()),\n",
    "    ('mnb', MultinomialNB())\n",
    "])\n",
    "\n",
    "pipe_params_cvec_mnb = {\n",
    "    'cvec__max_features': [None,1000,1100,1200,1300],\n",
    "    'cvec__min_df': [2,3,4],\n",
    "    'cvec__max_df': [.3,.32,.33,.34],\n",
    "    'cvec__ngram_range': [(1,1),(1,2),(1,3)],\n",
    "    'cvec__stop_words': [None, stop_english_common,'english']\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_cvec_mnb, param_grid=pipe_params_cvec_mnb, cv=3, n_jobs = 3, verbose = 1)\n",
    "gs.fit(X_train,y_train)\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 216 candidates, totalling 648 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  58 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=3)]: Done 358 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=3)]: Done 648 out of 648 | elapsed:   24.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7921428571428571\n",
      "Best Parameters: {'tvec__max_df': 0.32, 'tvec__max_features': 1500, 'tvec__min_df': 2, 'tvec__ngram_range': (1, 2), 'tvec__stop_words': None}\n",
      "Train Accuracy Score: 0.92\n",
      "Test Accuracy Score: 0.815845824411135\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>38</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   175               48\n",
       "actual onion                         38              206"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_tvec_mnb = Pipeline([\n",
    "    ('tvec', TfidfVectorizer()),\n",
    "    ('mnb', MultinomialNB())\n",
    "])\n",
    "\n",
    "pipe_params_tvec_mnb = {\n",
    "    'tvec__max_features': [1300,1400,1500],\n",
    "    'tvec__min_df': [2,3,4],\n",
    "    'tvec__max_df': [.3,.31,.32,.33],\n",
    "    'tvec__ngram_range': [(1,1),(1,2)],\n",
    "    'tvec__stop_words': [None,stop_english_common,'english']\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_tvec_mnb, param_grid=pipe_params_tvec_mnb, cv=3,n_jobs = 3,verbose = 1)\n",
    "gs.fit(X_train,y_train)\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 972 candidates, totalling 2916 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed: 10.4min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed: 18.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed: 28.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed: 40.7min\n",
      "[Parallel(n_jobs=-1)]: Done 2442 tasks      | elapsed: 55.1min\n",
      "[Parallel(n_jobs=-1)]: Done 2916 out of 2916 | elapsed: 65.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7664285714285715\n",
      "Best Parameters: {'cvec__max_df': 0.45, 'cvec__max_features': 2500, 'cvec__min_df': 5, 'cvec__ngram_range': (1, 1), 'cvec__stop_words': None, 'rf__max_depth': None, 'rf__n_estimators': 1350}\n",
      "Train Accuracy Score: 0.9921428571428571\n",
      "Test Accuracy Score: 0.7837259100642399\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>183</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>61</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   183               40\n",
       "actual onion                         61              183"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_cvec_rf = Pipeline([\n",
    "    ('cvec', CountVectorizer()),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "pipe_params_cvec_rf = {\n",
    "###### Count Vectorizer Params ######   \n",
    "    'cvec__max_features': [2500,3000,3500],\n",
    "    'cvec__min_df': [3,4,5],\n",
    "    'cvec__max_df': [.4,.45,.5],\n",
    "    'cvec__ngram_range': [(1,1),(1,2)],\n",
    "    'cvec__stop_words': [None,'english'],\n",
    "###### Randaom Forest Params ######\n",
    "    'rf__max_depth': [None,2,5],\n",
    "    #'rf__max_features': [None, .5, .75, 'auto'],\n",
    "    'rf__n_estimators': [1200, 1350, 1500]\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_cvec_rf, param_grid=pipe_params_cvec_rf, cv=3,n_jobs=-1,verbose=1)\n",
    "gs.fit(X_train,y_train)\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 192 candidates, totalling 576 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:   12.6s\n",
      "[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:   51.4s\n",
      "[Parallel(n_jobs=3)]: Done 444 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=3)]: Done 576 out of 576 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.7464285714285714\n",
      "Best Parameters: {'rf__max_depth': 10, 'rf__n_estimators': 500, 'tvec__max_df': 0.98, 'tvec__max_features': 3300, 'tvec__min_df': 3, 'tvec__ngram_range': (1, 1), 'tvec__stop_words': None}\n",
      "Train Accuracy Score: 0.8307142857142857\n",
      "Test Accuracy Score: 0.7773019271948608\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>150</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>31</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   150               73\n",
       "actual onion                         31              213"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_tvec_rf = Pipeline([\n",
    "    ('tvec', TfidfVectorizer()),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "pipe_params_tvec_rf = {\n",
    "    'tvec__max_features': [3300,3750],\n",
    "    'tvec__min_df': [2,3],\n",
    "    'tvec__max_df': [.9,.98],\n",
    "    'tvec__ngram_range': [(1,1),(1,2),(2,2)],\n",
    "    'tvec__stop_words': [None,'english'],\n",
    "    'rf__max_depth': [5,10],\n",
    "    #'rf__max_features': [None, .5, .75, 'auto'],\n",
    "    'rf__n_estimators': [300,500]\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(pipe_tvec_rf, param_grid=pipe_params_tvec_rf, cv=3,verbose = 1, n_jobs=3)\n",
    "gs.fit(X_train,y_train)\n",
    "print(f'Best Score: {gs.best_score_}')\n",
    "print(f'Best Parameters: {gs.best_params_}')\n",
    "\n",
    "print(f'Train Accuracy Score: {gs.score(X_train,y_train)}')\n",
    "print(f'Test Accuracy Score: {gs.score(X_test,y_test)}')\n",
    "pd.DataFrame(confusion_matrix(y_test,gs.predict(X_test)),\n",
    "                              index = ['actual conspiracy', 'actual onion'],\n",
    "                              columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation & Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of all my models, the one that seems to have the highest accuracy in predicting the classification of posts based on their titles is the TF-IDF Vectorizer with Multinomial Naive Bayes Classifier.\n",
    "\n",
    "The parameters for TD-IDF were:\n",
    "- max_df: 0.31\n",
    "- max_features: 1400\n",
    "- min_df: 2\n",
    "- ngram_range: (1,1)\n",
    "- stop_words: None\n",
    "\n",
    "This resulted in a training set accuracy score of 0.9466950959488273,\n",
    "and a testing set accuracy score of 0.8361702127659575."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy Score: 0.9228571428571428\n",
      "Test Accuracy Score: 0.8222698072805139\n",
      "Train Sensitivity Score: 0.947945205479452\n",
      "Test Sensitivity Score: 0.8565573770491803\n",
      "Train Specificity Score: 0.8955223880597015\n",
      "Test Specificity Score: 0.7847533632286996\n",
      "Train Precision Score: 0.9081364829396326\n",
      "Test Precision Score: 0.8132295719844358\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>predicted conspiracy</th>\n",
       "      <th>predicted onion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual conspiracy</th>\n",
       "      <td>175</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual onion</th>\n",
       "      <td>35</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   predicted conspiracy  predicted onion\n",
       "actual conspiracy                   175               48\n",
       "actual onion                         35              209"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvec = TfidfVectorizer(max_df=0.31,max_features=1400,min_df=2,stop_words=None,ngram_range=(1,1))\n",
    "tvec.fit(X_train)\n",
    "X_train_scaled = tvec.transform(X_train)\n",
    "X_test_scaled  = tvec.transform(X_test)\n",
    "\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(X_train_scaled, y_train)\n",
    "\n",
    "pred_train = mnb.predict(X_train_scaled)\n",
    "pred_test  = mnb.predict(X_test_scaled)\n",
    "\n",
    "cm_train = confusion_matrix(y_train,pred_train)\n",
    "cm_test  = confusion_matrix(y_test,pred_test)\n",
    "\n",
    "tn_train,fp_train,fn_train,tp_train  = cm_train.ravel()\n",
    "tn_test,fp_test,fn_test,tp_test      = cm_test.ravel()\n",
    "\n",
    "print(f'Train Accuracy Score: {accuracy_score(y_train,pred_train)}')\n",
    "print(f'Test Accuracy Score: {accuracy_score(y_test,pred_test)}')\n",
    "print(f'Train Sensitivity Score: {recall_score(y_train,pred_train)}')\n",
    "print(f'Test Sensitivity Score: {recall_score(y_test,pred_test)}')\n",
    "print(f'Train Specificity Score: {(tn_train/(tn_train+fp_train))}')\n",
    "print(f'Test Specificity Score: {(tn_test/(tn_test+fp_test))}')\n",
    "print(f'Train Precision Score: {precision_score(y_train,pred_train)}')\n",
    "print(f'Test Precision Score: {precision_score(y_test,pred_test)}')\n",
    "\n",
    "pd.DataFrame(cm_test,\n",
    "             index = ['actual conspiracy', 'actual onion'],\n",
    "             columns = ['predicted conspiracy', 'predicted onion'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>title</th>\n",
       "      <th>pred_sub</th>\n",
       "      <th>actual_sub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>Nany Pelosi has another \"senior moment\" thinks...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>Incredibly Relatable: Alexandria Ocasio-Cortez...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1773</th>\n",
       "      <td>The Eye Of The Sahara - Mauritania's Richat St...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>Hopefully this puts an end to the conspiracies...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>Former CIA spy and torture whistleblower John ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  pred_sub  actual_sub\n",
       "1387  Nany Pelosi has another \"senior moment\" thinks...         1           0\n",
       "813   Incredibly Relatable: Alexandria Ocasio-Cortez...         1           1\n",
       "1773  The Eye Of The Sahara - Mauritania's Richat St...         0           0\n",
       "1830  Hopefully this puts an end to the conspiracies...         0           0\n",
       "1073  Former CIA spy and torture whistleblower John ...         0           0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_pred = pd.DataFrame(data=X_test)\n",
    "df_test_pred['pred_sub'] = pred_test\n",
    "df_test_pred['actual_sub'] = y_test\n",
    "df_test_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wrong = df_test_pred[df_test_pred['pred_sub']!=df_test_pred['actual_sub']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Next World Reserve Currency Will Not Be A National Currency'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wrong['title'].values[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conspiracy: \n",
      "    Nany Pelosi has another \"senior moment\" thinks it's Thanksgiving on Valentines Day.\n",
      "Conspiracy: \n",
      "    The Next World Reserve Currency Will Not Be A National Currency\n",
      "Conspiracy: \n",
      "    Project Blue Beam\n",
      "The Onion: \n",
      "    Report: White House Overruled Intelligence Officials For Rejecting Saudi Prince’s Top Secret Security Clearance\n",
      "The Onion: \n",
      "    Embarrassed Comcast CEO Just Tells People He Does Digital Media Stuff\n",
      "The Onion: \n",
      "    Elliott Abrams Defends War Crimes As Happening Back In The ’80s When Everyone Was Doing It\n",
      "The Onion: \n",
      "    Treasury Department Issues Emergency Recall Of All US Dollars\n",
      "Conspiracy: \n",
      "    Scientists cannot See Ghosts\n",
      "Conspiracy: \n",
      "    SS: Satan uses high profile celebrities to push evil as funny, cool and hip to attract more foot soldiers as evidenced by the recent push to stop Georgia’s Fetal heartbeat abortion bill. (Whew!)\n",
      "The Onion: \n",
      "    National Endowment For The Arts Provides $80 Million To Discourage Talentless Hacks\n",
      "Conspiracy: \n",
      "    Strange 1000 Mile Long Anomaly On The Floor of The Red Sea\n",
      "Conspiracy: \n",
      "    England, still the greatest enemy of America.\n",
      "Conspiracy: \n",
      "    Michael Jackson’s Neverland ranch has asking price slashed by 75 per cent to £23m as desperate family try to cash in\n",
      "Conspiracy: \n",
      "    Humanizing robots on alexa commercial\n",
      "Conspiracy: \n",
      "    The CIA smuggles drugs into the USA (broadcast by the CBS News show called \"West 57th\" with Jane Wallace\n",
      "Conspiracy: \n",
      "    Tucker Suggests Imposing All kinds of Taxes on the Rich .\n",
      "The Onion: \n",
      "    Girl Power! 7 Women Who Are On This List\n",
      "Conspiracy: \n",
      "    Rising Dem star: Pete Buttigieg's father was a Marxist professor who lauded the Communist Manifesto - Washington Examiner\n",
      "The Onion: \n",
      "    Cutest Guy In Whole Office Not Even Particularly Attractive\n",
      "Conspiracy: \n",
      "    Alexandria Ocasio-Cortez hit with FEC complaint for alleged 'subsidy scheme'\n",
      "Conspiracy: \n",
      "    After Gaza slaughter, Pete Buttigieg praised Israeli security responses as 'moving' and faulted Democrats for easy judgment\n",
      "Conspiracy: \n",
      "    Has Donald Trump donated to NAMBLA? (North American man boy love association). Is that why he won’t release his tax returns? The trump camp has neither confirmed nor denied the accusations. Interesting article I found.\n",
      "The Onion: \n",
      "    We Want To Know More About Our Readers!\n",
      "Conspiracy: \n",
      "    Nikola Tesla's Time Travel Experience: 'I Could See The Past, Present And Future All At The Same Time'\n",
      "Conspiracy: \n",
      "    Albert Pike The Grand Old Man Of Masonry\n",
      "The Onion: \n",
      "    Facebook: ‘Identifying Hate Speech Is Difficult Because Some Posts Actually Make Pretty Interesting Points’\n",
      "Conspiracy: \n",
      "    French authority rejects pre-emptive 'yellow vest' protest bans.\n",
      "Conspiracy: \n",
      "    You elected them to write new laws. They’re letting corporations do it instead.\n",
      "The Onion: \n",
      "    Pros And Cons Of Salary Transparency\n",
      "Conspiracy: \n",
      "    \"I asked him what was going on, and he told me he's been kidnapped and he's been traded through all these people,\" said the woman, who asked not to be identified. \"He just wanted to go home. He needed help.\"\n",
      "Conspiracy: \n",
      "    The vast majority of cute puppy/dog pictures and gifs that hit the front page are fighting dog breeds\n",
      "Conspiracy: \n",
      "    The healthcare industry is one of the main enclaves of the elite. Artificial scarcity, in healthcare licensing and schooling, easily makes enough $$ for corporate PR that suppresses and memory-holes anything critical about this.\n",
      "Conspiracy: \n",
      "    The sun is actually dead\n",
      "Conspiracy: \n",
      "    Mueller may have a conflict — and it leads directly to a Russian oligarch\n",
      "The Onion: \n",
      "    It’s Not An Easy Thing To Admit When You’re Wrong, And That’s Why I Won’t Do It\n",
      "The Onion: \n",
      "    NASA Administrator Resigns After Leak Of Offensive Anti-Moon Email\n",
      "Conspiracy: \n",
      "    Subliminal messaging in the Punisher Season 2 - no spoilers, promise.\n",
      "Conspiracy: \n",
      "    US Secret Service arrests Chinese woman for entering Trump’s vacation property\n",
      "Conspiracy: \n",
      "    Former NSA spies hacked BBC host, Al Jazeera chairman for UAE\n",
      "Conspiracy: \n",
      "    S.S Song writing about king and queen of england and how they use symbology to infect you brain any ideas on how to finish it i was messing with cards and came up with it\n",
      "The Onion: \n",
      "    Doctors Assure Recovering Patient He Has Many More Years Of Looking At Phone Ahead Of Him\n",
      "Conspiracy: \n",
      "    Money\n",
      "Conspiracy: \n",
      "    A second sun?\n",
      "The Onion: \n",
      "    How To Feel Emotions When Holding Your Newborn - Troublehacking with Drew Cleary\n",
      "Conspiracy: \n",
      "    Summaries of The Mueller Report will be publicly released as soon as tomorrow.\n",
      "Conspiracy: \n",
      "    Allison Mack and Seagram’s Heiress Have BFF Reunion at Court Today\n",
      "The Onion: \n",
      "    Most people don’t know this!\n",
      "Conspiracy: \n",
      "    Anomalous Soil Accumulation\n",
      "Conspiracy: \n",
      "    Chinese woman with 2 passports, malware arrested at Trump's Mar-a-Lago Club\n",
      "Conspiracy: \n",
      "    “He Died As A Direct Result of the HPV Vaccine” – Mother Of Dead Teenager Speaks Out\n",
      "Conspiracy: \n",
      "    Is Graham Hancock a Freemason shill? Ad Hominem Attacks Build in month before book release. Josh Reeves bombshell accusation\n",
      "Conspiracy: \n",
      "    The Poverty of Capitalism - Economic Meltdown and the Struggle for What Comes Next (book -pdf)\n",
      "The Onion: \n",
      "    Group Of Friends Engage In Passionate, Incoherent Discussion About Current Events\n",
      "The Onion: \n",
      "    Reconstruction Finally Completed On Field Destroyed By United Flight 93\n",
      "The Onion: \n",
      "    Bouncer Who’s Not That Big Must Be Fucking Crazy\n",
      "Conspiracy: \n",
      "    Louisiana's incarceration rate is nearly five times Iran's, 13 times China's and 20 times Germany's\n",
      "The Onion: \n",
      "    17-Year Cicadas Horrified To Learn About 9/11\n",
      "The Onion: \n",
      "    Sighing Banksy Methodically Kills Another Few Kids Who Stumbled Upon Him Doing Graffiti\n",
      "The Onion: \n",
      "    Congress Agrees To $1.3 Billion For Protective Border Fencers\n",
      "The Onion: \n",
      "    Matter Of Life And Death: First Responder Describes His Most Intense Rescues\n",
      "The Onion: \n",
      "    So-Called ‘Atheist’ Doesn’t Even Barge Into Churches Screaming ‘You’re All Brainwashed Fools’\n",
      "The Onion: \n",
      "    The Power Of People: When These Parents Came Together And Demanded That There Never Be A School Where All The Teachers Are Pedophiles, The School District Released A Statement Saying They Agreed\n",
      "The Onion: \n",
      "    Encouraging Report From Radical Extremist Think Tank Finds America No Safer Since 9/11\n",
      "Conspiracy: \n",
      "    Everyone inside this ice shelf fully believes in what Freemasons create, human unappointed leaders due to the free-ness of it, and barely know the ways of G_d, or the almighty spirit.\n",
      "Conspiracy: \n",
      "    Establishment democrats blame Bernie for bringing up bidens creepy actions. Sanders campaign has denied the accusations. Just more Bernie hate from the DNC.\n",
      "The Onion: \n",
      "    Furloughed Federal Employee Starts Online Search For New Government\n",
      "The Onion: \n",
      "    God Purges Millions Of Souls From Heaven Now That Sexual Assault Being Taken More Seriously\n",
      "Conspiracy: \n",
      "    Supreme Court will take up case of citizenship question on 2020 Census -OANN. Will SC rule by June 2019 when census forms go to print?\n",
      "The Onion: \n",
      "    The Key To Getting Pregnant Is Letting Everyone Know You’re Trying\n",
      "The Onion: \n",
      "    Aging Mount St. Helens Starting To Think Erupting Days Are Behind It\n",
      "The Onion: \n",
      "    He’s Running: Joe Biden Just Made A Pinterest Board Called ‘President Style Ideas’\n",
      "The Onion: \n",
      "    Creepy Older Brand Clearly Targeting Female 18-To-24-Year-Olds\n",
      "Conspiracy: \n",
      "    Kamala Harris Whistleblower, SF Public Defender Jeff Adachi, found dead at 59 of suspected heart attack\n",
      "Conspiracy: \n",
      "    Emissions cheating devices by automobile manufacturers and shipping companies still run rampant\n",
      "Conspiracy: \n",
      "    Scientists have Observed Epigenetic Memories being Passed Down for 14 Generations\n",
      "The Onion: \n",
      "    Study: No Two People Have Listened To Same Band Since 2003\n",
      "Conspiracy: \n",
      "    I love irony.\n",
      "The Onion: \n",
      "    Why do you love that smell?\n",
      "The Onion: \n",
      "    ‘The Last Of Us 2’ To Be Released In 2019\n",
      "Conspiracy: \n",
      "    Massive pharmaceutical recall continues with little press coverage\n",
      "Conspiracy: \n",
      "    Opinion Survey: Has Tim Pool Sold Out?\n",
      "Conspiracy: \n",
      "    The Army can't recruit enough soldiers, and it may start talking to kids as young as 12 to find them\n",
      "The Onion: \n",
      "    Scientists Genetically Engineer Lab Rat Predisposed To Think Anything Wrong With It Might Be Cancer\n"
     ]
    }
   ],
   "source": [
    "for i,title in enumerate(df_wrong['title']):\n",
    "    if df_wrong['actual_sub'].values[i] == 1:\n",
    "        print('The Onion: ')\n",
    "    else:\n",
    "        print('Conspiracy: ')\n",
    "    print('   ',title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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