wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangle Data from WeRateDogs with the Twitter API\n",
    "\n",
    "\n",
    "**Goal:** Wrangle WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations.\n",
    "\n",
    "> Find at least **8 quality issues** and **2 tidiness issues**. Provide at least **3 insights** and **1 visualization** after data wrangling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "- [Gather](#gather)\n",
    "- [Assess](#assess)\n",
    "- [Clean](#clean)\n",
    "- [Store](#store)\n",
    "- [Analyze](#analyze)\n",
    "- [Visualize](#visualize)\n",
    "- [Sources](#sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pylab\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import json\n",
    "import dill\n",
    "import spacy\n",
    "import requests\n",
    "import itertools\n",
    "import statsmodels.api as sm\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "from wordcloud import WordCloud\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment to save or load environment accordingly\n",
    "\n",
    "#dill.dump_session('notebook_session.db') #save\n",
    "dill.load_session('notebook_session.db') #load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "# GATHER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc = pd.read_csv('./data/twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "#install breed predictions of a neural network for dogs\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "r = requests.get(url)\n",
    "\n",
    "with open(os.path.join('./data/', url.split('/')[-1]), mode='wb') as file:\n",
    "    file.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions = pd.read_csv('./data/image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the last row in the `image_predictions` DataFrame:\n",
    "\n",
    "- tweet_id is the last part of the tweet URL after \"status/\" → https://twitter.com/dog_rates/status/889531135344209921\n",
    "\n",
    "- p1 is the algorithm's #1 prediction for the image in the tweet → **golden retriever**\n",
    "\n",
    "- p1_conf is how confident the algorithm is in its #1 prediction → **95%**\n",
    "\n",
    "- p1_dog is whether or not the #1 prediction is a breed of dog → **TRUE**\n",
    "\n",
    "- p2 is the algorithm's second most likely prediction → **Labrador retriever**\n",
    "\n",
    "- p2_conf is how confident the algorithm is in its #2 prediction → **1%**\n",
    "\n",
    "- p2_dog is whether or not the #2 prediction is a breed of dog → **TRUE**\n",
    "etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Use Tweepy (Twitter API) for each tweet's JSON data using the tweet IDs under the WeRateDogs archive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#handle environment variables\n",
    "from os.path import join, dirname\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "path = './weratedogs-env-vars.env'\n",
    "load_dotenv(dotenv_path = path, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create API object\n",
    "import tweepy\n",
    "\n",
    "consumer_key = os.getenv('CONSUMER_KEY')\n",
    "consumer_secret = os.getenv('CONSUMER_SECRET')\n",
    "access_token = os.getenv('TOKEN')\n",
    "access_secret = os.getenv('TOKEN_SECRET')\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save JSON tweet data to tweet-json.txt\n",
    "tweet_ids = arc.tweet_id\n",
    "num_success = 0\n",
    "num_errors = 0\n",
    "\n",
    "start_time = datetime.now()\n",
    "\n",
    "with open('./data/logs/error-file.txt', 'w', encoding='utf-8') as errfile:\n",
    "    with open('./data/tweet-json.txt', 'w', encoding='utf-8') as outfile:\n",
    "        for tweet_id in tweet_ids:\n",
    "            try: \n",
    "                tweet = api.get_status(tweet_id)\n",
    "                tweet_json = json.dumps(tweet._json)\n",
    "                json.dump(tweet_json, outfile)\n",
    "                outfile.write('\\n')\n",
    "                num_success += 1\n",
    "                print(tweet_id)\n",
    "            except tweepy.TweepError as e:\n",
    "                err_string = '{}: {}'.format(tweet_id, e.args[0][0]['message'])\n",
    "                errfile.write(err_string)\n",
    "                errfile.write('\\n')\n",
    "                num_errors += 1\n",
    "                print(err_string)\n",
    "                continue\n",
    "        \n",
    "time_elapsed = datetime.now() - start_time\n",
    "\n",
    "print('Time elapsed (hh:mm:ss:ms): {}'.format(time_elapsed))\n",
    "print('Valid tweets: {}'.format(num_success))\n",
    "print('Invalid tweets: {}'.format(num_errors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read tweet_json data into a list of tweet JSONs\n",
    "all_tweets = []\n",
    "\n",
    "for line in open('./data/tweet-json.txt', 'r', encoding = 'utf-8'):\n",
    "    json_data = json.loads(json.loads(line))\n",
    "    all_tweets.append(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'created_at': 'Tue Aug 01 16:23:56 +0000 2017',\n",
       " 'id': 892420643555336193,\n",
       " 'id_str': '892420643555336193',\n",
       " 'text': \"This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU\",\n",
       " 'truncated': False,\n",
       " 'entities': {'hashtags': [],\n",
       "  'symbols': [],\n",
       "  'user_mentions': [],\n",
       "  'urls': [],\n",
       "  'media': [{'id': 892420639486877696,\n",
       "    'id_str': '892420639486877696',\n",
       "    'indices': [86, 109],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'url': 'https://t.co/MgUWQ76dJU',\n",
       "    'display_url': 'pic.twitter.com/MgUWQ76dJU',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'medium': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'small': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'large': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},\n",
       " 'extended_entities': {'media': [{'id': 892420639486877696,\n",
       "    'id_str': '892420639486877696',\n",
       "    'indices': [86, 109],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'url': 'https://t.co/MgUWQ76dJU',\n",
       "    'display_url': 'pic.twitter.com/MgUWQ76dJU',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'medium': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'small': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'large': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},\n",
       " 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       " 'in_reply_to_status_id': None,\n",
       " 'in_reply_to_status_id_str': None,\n",
       " 'in_reply_to_user_id': None,\n",
       " 'in_reply_to_user_id_str': None,\n",
       " 'in_reply_to_screen_name': None,\n",
       " 'user': {'id': 4196983835,\n",
       "  'id_str': '4196983835',\n",
       "  'name': 'WeRateDogs™ 🏳️\\u200d🌈',\n",
       "  'screen_name': 'dog_rates',\n",
       "  'location': '「 DM YOUR DOGS 」',\n",
       "  'description': 'Your Only Source For Professional Dog Ratings Instagram and Facebook ➪ WeRateDogs partnerships@weratedogs.com ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀',\n",
       "  'url': 'https://t.co/N7sNNHSfPq',\n",
       "  'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHSfPq',\n",
       "      'expanded_url': 'http://weratedogs.com',\n",
       "      'display_url': 'weratedogs.com',\n",
       "      'indices': [0, 23]}]},\n",
       "   'description': {'urls': []}},\n",
       "  'protected': False,\n",
       "  'followers_count': 8164701,\n",
       "  'friends_count': 12,\n",
       "  'listed_count': 6272,\n",
       "  'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "  'favourites_count': 142918,\n",
       "  'utc_offset': None,\n",
       "  'time_zone': None,\n",
       "  'geo_enabled': True,\n",
       "  'verified': True,\n",
       "  'statuses_count': 10421,\n",
       "  'lang': None,\n",
       "  'contributors_enabled': False,\n",
       "  'is_translator': False,\n",
       "  'is_translation_enabled': False,\n",
       "  'profile_background_color': '000000',\n",
       "  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_tile': False,\n",
       "  'profile_image_url': 'http://pbs.twimg.com/profile_images/1112594177961844736/qQK8NJT-_normal.jpg',\n",
       "  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1112594177961844736/qQK8NJT-_normal.jpg',\n",
       "  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1560181807',\n",
       "  'profile_link_color': 'F5ABB5',\n",
       "  'profile_sidebar_border_color': '000000',\n",
       "  'profile_sidebar_fill_color': '000000',\n",
       "  'profile_text_color': '000000',\n",
       "  'profile_use_background_image': False,\n",
       "  'has_extended_profile': False,\n",
       "  'default_profile': False,\n",
       "  'default_profile_image': False,\n",
       "  'following': False,\n",
       "  'follow_request_sent': False,\n",
       "  'notifications': False,\n",
       "  'translator_type': 'none'},\n",
       " 'geo': None,\n",
       " 'coordinates': None,\n",
       " 'place': None,\n",
       " 'contributors': None,\n",
       " 'is_quote_status': False,\n",
       " 'retweet_count': 8130,\n",
       " 'favorite_count': 37424,\n",
       " 'favorited': False,\n",
       " 'retweeted': False,\n",
       " 'possibly_sensitive': False,\n",
       " 'possibly_sensitive_appealable': False,\n",
       " 'lang': 'en'}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pick interesting fields to add to DataFrame\n",
    "all_tweets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dictionary to use in pandas DataFrame\n",
    "tweets_json = []\n",
    "for tweet in all_tweets:\n",
    "    tweets_json.append({'tweet_id': tweet['id'],\n",
    "                     'created_at': tweet['created_at'],\n",
    "                      'favorite_count': tweet['favorite_count'],\n",
    "                      'retweet_count': tweet['retweet_count']\n",
    "                     })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tweet_id': 892420643555336193,\n",
       " 'created_at': 'Tue Aug 01 16:23:56 +0000 2017',\n",
       " 'favorite_count': 37424,\n",
       " 'retweet_count': 8130}"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_json[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create Pandas DataFrame\n",
    "tweets = pd.DataFrame(tweets_json, columns = ['tweet_id', 'created_at', 'favorite_count', 'retweet_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to `tweet_id`, `created_at`, and `retweet_count`, I also decided to add the additional fields `created_at` and `followers_count` for the user. This is because I want to examine how the other variables such as `favorite_count` and `retweet_count` could impact the fluctuation of followers, and also see which time of the day under `created_at` gives the biggest spike in followers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "# ASSESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>857393404942143489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-27 00:38:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Instead of the usual nightly dog rate, I'm sha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/meeko-needs-heart-sur...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>668274247790391296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-22 03:46:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Sophie. Her son just got in the car to le...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668274247...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Sophie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>672980819271634944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-05 03:28:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Extraordinary dog here. Looks large. Just a he...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/672980819...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-24 00:19:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Oliver. You're witnessing one of his m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889278841...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>705102439679201280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-02 18:48:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Terrenth. He just stubbed his toe. 10/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/705102439...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Terrenth</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>847251039262605312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-03-30 00:56:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Furzey. He's doing an elevated sandy z...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/847251039...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Furzey</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>779377524342161408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-23 17:50:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Have you ever seen such a smol pupper? Portabl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/779377524...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>732726085725589504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-18 00:14:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Aldrick. He looks wise af. Also except...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/732726085...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Aldrick</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>753420520834629632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-14 02:47:04 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we are witnessing an isolated squad of bo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/753420520...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>674606911342424069</td>\n",
       "      <td>6.744689e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-09 15:09:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>The 13/10 also takes into account this impecca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "177   857393404942143489                    NaN                  NaN   \n",
       "2226  668274247790391296                    NaN                  NaN   \n",
       "1979  672980819271634944                    NaN                  NaN   \n",
       "15    889278841981685760                    NaN                  NaN   \n",
       "1337  705102439679201280                    NaN                  NaN   \n",
       "236   847251039262605312                    NaN                  NaN   \n",
       "748   779377524342161408                    NaN                  NaN   \n",
       "1115  732726085725589504                    NaN                  NaN   \n",
       "934   753420520834629632                    NaN                  NaN   \n",
       "1905  674606911342424069           6.744689e+17         4.196984e+09   \n",
       "\n",
       "                      timestamp  \\\n",
       "177   2017-04-27 00:38:11 +0000   \n",
       "2226  2015-11-22 03:46:11 +0000   \n",
       "1979  2015-12-05 03:28:25 +0000   \n",
       "15    2017-07-24 00:19:32 +0000   \n",
       "1337  2016-03-02 18:48:16 +0000   \n",
       "236   2017-03-30 00:56:03 +0000   \n",
       "748   2016-09-23 17:50:56 +0000   \n",
       "1115  2016-05-18 00:14:46 +0000   \n",
       "934   2016-07-14 02:47:04 +0000   \n",
       "1905  2015-12-09 15:09:55 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "177   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2226  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1979  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "15    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1337  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "236   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "748   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1115  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "934   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1905  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "177   Instead of the usual nightly dog rate, I'm sha...                  NaN   \n",
       "2226  Meet Sophie. Her son just got in the car to le...                  NaN   \n",
       "1979  Extraordinary dog here. Looks large. Just a he...                  NaN   \n",
       "15    This is Oliver. You're witnessing one of his m...                  NaN   \n",
       "1337  This is Terrenth. He just stubbed his toe. 10/...                  NaN   \n",
       "236   This is Furzey. He's doing an elevated sandy z...                  NaN   \n",
       "748   Have you ever seen such a smol pupper? Portabl...                  NaN   \n",
       "1115  This is Aldrick. He looks wise af. Also except...                  NaN   \n",
       "934   Here we are witnessing an isolated squad of bo...                  NaN   \n",
       "1905  The 13/10 also takes into account this impecca...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "177                        NaN                        NaN   \n",
       "2226                       NaN                        NaN   \n",
       "1979                       NaN                        NaN   \n",
       "15                         NaN                        NaN   \n",
       "1337                       NaN                        NaN   \n",
       "236                        NaN                        NaN   \n",
       "748                        NaN                        NaN   \n",
       "1115                       NaN                        NaN   \n",
       "934                        NaN                        NaN   \n",
       "1905                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "177   https://www.gofundme.com/meeko-needs-heart-sur...                13   \n",
       "2226  https://twitter.com/dog_rates/status/668274247...                10   \n",
       "1979  https://twitter.com/dog_rates/status/672980819...                 5   \n",
       "15    https://twitter.com/dog_rates/status/889278841...                13   \n",
       "1337  https://twitter.com/dog_rates/status/705102439...                10   \n",
       "236   https://twitter.com/dog_rates/status/847251039...                12   \n",
       "748   https://twitter.com/dog_rates/status/779377524...                12   \n",
       "1115  https://twitter.com/dog_rates/status/732726085...                11   \n",
       "934   https://twitter.com/dog_rates/status/753420520...                11   \n",
       "1905                                                NaN                13   \n",
       "\n",
       "      rating_denominator      name doggo floofer  pupper puppo  \n",
       "177                   10      None  None    None    None  None  \n",
       "2226                  10    Sophie  None    None    None  None  \n",
       "1979                  10      None  None    None    None  None  \n",
       "15                    10    Oliver  None    None    None  None  \n",
       "1337                  10  Terrenth  None    None    None  None  \n",
       "236                   10    Furzey  None    None    None  None  \n",
       "748                   10      None  None    None  pupper  None  \n",
       "1115                  10   Aldrick  None    None    None  None  \n",
       "934                   10      None  None    None    None  None  \n",
       "1905                  10      None  None    None    None  None  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "arc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>835246439529840640</td>\n",
       "      <td>8.352460e+17</td>\n",
       "      <td>26259576.0</td>\n",
       "      <td>2017-02-24 21:54:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>832088576586297345</td>\n",
       "      <td>8.320875e+17</td>\n",
       "      <td>30582082.0</td>\n",
       "      <td>2017-02-16 04:45:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-15 17:52:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>The floofs have been released I repeat the flo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/820690176...</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-19 23:06:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspir...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/sams-smile,https://tw...</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Sam</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>775096608509886464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-11 22:20:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: After so many requests, this is...</td>\n",
       "      <td>7.403732e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-06-08 02:41:38 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740373189...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "313  835246439529840640           8.352460e+17           26259576.0   \n",
       "342  832088576586297345           8.320875e+17           30582082.0   \n",
       "433  820690176645140481                    NaN                  NaN   \n",
       "516  810984652412424192                    NaN                  NaN   \n",
       "784  775096608509886464                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "313  2017-02-24 21:54:03 +0000   \n",
       "342  2017-02-16 04:45:50 +0000   \n",
       "433  2017-01-15 17:52:40 +0000   \n",
       "516  2016-12-19 23:06:23 +0000   \n",
       "784  2016-09-11 22:20:06 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "313  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "342  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "433  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "516  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "784  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "313  @jonnysun @Lin_Manuel ok jomny I know you're e...                  NaN   \n",
       "342           @docmisterio account started on 11/15/15                  NaN   \n",
       "433  The floofs have been released I repeat the flo...                  NaN   \n",
       "516  Meet Sam. She smiles 24/7 &amp; secretly aspir...                  NaN   \n",
       "784  RT @dog_rates: After so many requests, this is...         7.403732e+17   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "313                       NaN                        NaN   \n",
       "342                       NaN                        NaN   \n",
       "433                       NaN                        NaN   \n",
       "516                       NaN                        NaN   \n",
       "784              4.196984e+09  2016-06-08 02:41:38 +0000   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "313                                                NaN               960   \n",
       "342                                                NaN                11   \n",
       "433  https://twitter.com/dog_rates/status/820690176...                84   \n",
       "516  https://www.gofundme.com/sams-smile,https://tw...                24   \n",
       "784  https://twitter.com/dog_rates/status/740373189...                 9   \n",
       "\n",
       "     rating_denominator  name doggo floofer pupper puppo  \n",
       "313                   0  None  None    None   None  None  \n",
       "342                  15  None  None    None   None  None  \n",
       "433                  70  None  None    None   None  None  \n",
       "516                   7   Sam  None    None   None  None  \n",
       "784                  11  None  None    None   None  None  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at denominators that are not equal to 10\n",
    "arc.query('rating_denominator != 10').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "80        2\n",
       "20        2\n",
       "2         1\n",
       "16        1\n",
       "40        1\n",
       "70        1\n",
       "15        1\n",
       "90        1\n",
       "110       1\n",
       "120       1\n",
       "130       1\n",
       "150       1\n",
       "170       1\n",
       "7         1\n",
       "0         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check counts of denominators != 10\n",
    "arc.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>835246439529840640</td>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>832088576586297345</td>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>775096608509886464</td>\n",
       "      <td>RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>740373189193256964</td>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>722974582966214656</td>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>716439118184652801</td>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>686035780142297088</td>\n",
       "      <td>Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>684225744407494656</td>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>682962037429899265</td>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>682808988178739200</td>\n",
       "      <td>I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "313   835246439529840640   \n",
       "342   832088576586297345   \n",
       "433   820690176645140481   \n",
       "516   810984652412424192   \n",
       "784   775096608509886464   \n",
       "902   758467244762497024   \n",
       "1068  740373189193256964   \n",
       "1120  731156023742988288   \n",
       "1165  722974582966214656   \n",
       "1202  716439118184652801   \n",
       "1228  713900603437621249   \n",
       "1254  710658690886586372   \n",
       "1274  709198395643068416   \n",
       "1351  704054845121142784   \n",
       "1433  697463031882764288   \n",
       "1598  686035780142297088   \n",
       "1634  684225744407494656   \n",
       "1635  684222868335505415   \n",
       "1662  682962037429899265   \n",
       "1663  682808988178739200   \n",
       "1779  677716515794329600   \n",
       "1843  675853064436391936   \n",
       "2335  666287406224695296   \n",
       "\n",
       "                                                                                                                                                                           text  \\\n",
       "313   @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho                                                                          \n",
       "342   @docmisterio account started on 11/15/15                                                                                                                                    \n",
       "433   The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd                                                                         \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "784   RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…                                \n",
       "902   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                                              \n",
       "1068  After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ                                \n",
       "1120  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                                                   \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                                            \n",
       "1202  This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                                       \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                                               \n",
       "1254  Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                                \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK                          \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                                          \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                                                \n",
       "1598  Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating                                    \n",
       "1634  Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3                                              \n",
       "1635  Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55                                \n",
       "1662  This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5                                 \n",
       "1663  I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible                                 \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq                                                                                                      \n",
       "1843  Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw                                                                 \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                    \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "313   960               0                   \n",
       "342   11                15                  \n",
       "433   84                70                  \n",
       "516   24                7                   \n",
       "784   9                 11                  \n",
       "902   165               150                 \n",
       "1068  9                 11                  \n",
       "1120  204               170                 \n",
       "1165  4                 20                  \n",
       "1202  50                50                  \n",
       "1228  99                90                  \n",
       "1254  80                80                  \n",
       "1274  45                50                  \n",
       "1351  60                50                  \n",
       "1433  44                40                  \n",
       "1598  4                 20                  \n",
       "1634  143               130                 \n",
       "1635  121               110                 \n",
       "1662  7                 11                  \n",
       "1663  20                16                  \n",
       "1779  144               120                 \n",
       "1843  88                80                  \n",
       "2335  1                 2                   "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check corresponding text with denominators != 10\n",
    "denoms = arc[arc['rating_denominator'] != 10][['tweet_id', 'text', 'rating_numerator', 'rating_denominator']]\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "denoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>832215909146226688</td>\n",
       "      <td>RT @dog_rates: This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wu…</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>786709082849828864</td>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>680494726643068929</td>\n",
       "      <td>Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "340   832215909146226688   \n",
       "695   786709082849828864   \n",
       "1712  680494726643068929   \n",
       "\n",
       "                                                                                                                                              text  \\\n",
       "340   RT @dog_rates: This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wu…   \n",
       "695   This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS               \n",
       "1712  Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD                                     \n",
       "\n",
       "      rating_numerator  \n",
       "340   75                \n",
       "695   75                \n",
       "1712  26                "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check non-int numerators\n",
    "arc.query('rating_numerator == 75 or rating_numerator == 26')[['tweet_id', 'text', 'rating_numerator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2204    an     \n",
       "1916    life   \n",
       "1854    a      \n",
       "1368    a      \n",
       "822     just   \n",
       "1002    a      \n",
       "193     quite  \n",
       "2314    a      \n",
       "1815    the    \n",
       "1923    a      \n",
       "1382    a      \n",
       "2347    a      \n",
       "2353    a      \n",
       "1797    the    \n",
       "1193    a      \n",
       "1049    a      \n",
       "992     his    \n",
       "1071    getting\n",
       "1063    just   \n",
       "2333    an     \n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at nonsensical dog names\n",
    "mask = arc.name.str.islower()\n",
    "bad_names = arc.loc[mask]['name']\n",
    "bad_names.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for number of dogs that fall under more than 1 dog stage\n",
    "def mask_duplicate_stages(df):\n",
    "    stage_mask = []\n",
    "    for i, row in df.iterrows():\n",
    "        sum = 0\n",
    "        if (row['doggo'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['floofer'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['pupper'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['puppo'] != 'None'):\n",
    "            sum += 1\n",
    "\n",
    "        if sum > 1:\n",
    "            stage_mask.append(True)\n",
    "        else:\n",
    "            stage_mask.append(False)\n",
    "    \n",
    "    return stage_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_single_stages(df):\n",
    "    stage_mask = []\n",
    "    for i, row in df.iterrows():\n",
    "        sum = 0\n",
    "        if (row['doggo'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['floofer'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['pupper'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['puppo'] != 'None'):\n",
    "            sum += 1\n",
    "\n",
    "        if sum == 1:\n",
    "            stage_mask.append(True)\n",
    "        else:\n",
    "            stage_mask.append(False)\n",
    "    \n",
    "    return stage_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_all_stages(df):\n",
    "    stage_mask = []\n",
    "    for i, row in df.iterrows():\n",
    "        sum = 0\n",
    "        if (row['doggo'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['floofer'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['pupper'] != 'None'):\n",
    "            sum += 1\n",
    "        if (row['puppo'] != 'None'):\n",
    "            sum += 1\n",
    "\n",
    "        if sum > 0:\n",
    "            stage_mask.append(True)\n",
    "        else:\n",
    "            stage_mask.append(False)\n",
    "    \n",
    "    return stage_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create masks\n",
    "dup_stage_mask = mask_duplicate_stages(arc)    \n",
    "single_stage_mask = mask_single_stages(arc)\n",
    "all_stage_mask = mask_all_stages(arc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of dogs with more than 1 stage\n",
    "arc[dup_stage_mask].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of dogs with only 1 stage\n",
    "arc[single_stage_mask].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of dogs with >= 1 stage\n",
    "arc[all_stage_mask].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>817777686764523521</td>\n",
       "      <td>This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>808106460588765185</td>\n",
       "      <td>Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>802265048156610565</td>\n",
       "      <td>Like doggo, like pupper version 2. Both 11/10 https://t.co/9IxWAXFqze</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>801115127852503040</td>\n",
       "      <td>This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>785639753186217984</td>\n",
       "      <td>This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>781308096455073793</td>\n",
       "      <td>Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>775898661951791106</td>\n",
       "      <td>RT @dog_rates: Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>770093767776997377</td>\n",
       "      <td>RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>759793422261743616</td>\n",
       "      <td>Meet Maggie &amp;amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>751583847268179968</td>\n",
       "      <td>Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>741067306818797568</td>\n",
       "      <td>This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>733109485275860992</td>\n",
       "      <td>Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "191   855851453814013952   \n",
       "200   854010172552949760   \n",
       "460   817777686764523521   \n",
       "531   808106460588765185   \n",
       "565   802265048156610565   \n",
       "575   801115127852503040   \n",
       "705   785639753186217984   \n",
       "733   781308096455073793   \n",
       "778   775898661951791106   \n",
       "822   770093767776997377   \n",
       "889   759793422261743616   \n",
       "956   751583847268179968   \n",
       "1063  741067306818797568   \n",
       "1113  733109485275860992   \n",
       "\n",
       "                                                                                                                                                                      text  \\\n",
       "191   Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel         \n",
       "200   At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk   \n",
       "460   This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7   \n",
       "531   Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho                       \n",
       "565   Like doggo, like pupper version 2. Both 11/10 https://t.co/9IxWAXFqze                                                                                                  \n",
       "575   This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj                       \n",
       "705   This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd   \n",
       "733   Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u                                                                                                             \n",
       "778   RT @dog_rates: Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda                                                                              \n",
       "822   RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                             \n",
       "889   Meet Maggie &amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll                       \n",
       "956   Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8                               \n",
       "1063  This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                                            \n",
       "1113  Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda                                                                                             \n",
       "\n",
       "      doggo  floofer  pupper  puppo  \n",
       "191   doggo  None     None    puppo  \n",
       "200   doggo  floofer  None    None   \n",
       "460   doggo  None     pupper  None   \n",
       "531   doggo  None     pupper  None   \n",
       "565   doggo  None     pupper  None   \n",
       "575   doggo  None     pupper  None   \n",
       "705   doggo  None     pupper  None   \n",
       "733   doggo  None     pupper  None   \n",
       "778   doggo  None     pupper  None   \n",
       "822   doggo  None     pupper  None   \n",
       "889   doggo  None     pupper  None   \n",
       "956   doggo  None     pupper  None   \n",
       "1063  doggo  None     pupper  None   \n",
       "1113  doggo  None     pupper  None   "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for text associated with each tweet showing > 1 stage\n",
    "arc[dup_stage_mask][['tweet_id', 'text', 'doggo', 'floofer', 'pupper', 'puppo']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-21 01:02:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-07-19 00:47:34 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>777641927919427584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-18 22:54:18 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This is Arnie. He's a Nova Scotian Fridge Floof. Rare af. 12/10 https://t.co/lprdOylVpS</td>\n",
       "      <td>7.504293e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-07-05 20:41:01 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Arnie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>877611172832227328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-21 19:36:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @rachel2195: @dog_rates the boyfriend and his soaking wet pupper h*cking love his new hat 14/10 https://t.co/dJx4Gzc50G</td>\n",
       "      <td>8.768508e+17</td>\n",
       "      <td>5.128045e+08</td>\n",
       "      <td>2017-06-19 17:14:49 +0000</td>\n",
       "      <td>https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>836397794269200385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-28 02:09:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This is Buddy. He ran into a glass door once. Now he's h*ckin skeptical. 13/10 empowering af (vid by Brittany Gaunt) https:/…</td>\n",
       "      <td>8.178278e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-07 20:18:46 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/817827839487737858/video/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Buddy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>804413760345620481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-01 19:56:00 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This is Rusty. He's going D1 for sure. Insane vertical. 13/10 would draft https://t.co/AsykOwMrXQ</td>\n",
       "      <td>7.848260e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-10-08 18:41:19 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/784826020293709826/photo/1,https://twitter.com/dog_rates/status/784826020293709826/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Rusty</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>752309394570878976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-11 01:11:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: Everyone needs to watch this. 13/10 https://t.co/Bb3xnpsWBC</td>\n",
       "      <td>6.753544e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-11 16:40:19 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675354435921575936/video/1,https://twitter.com/dog_rates/status/675354435921575936/video/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>866094527597207552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-21 00:53:21 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: Here's a pupper before and after being asked \"who's a good girl?\" Unsure as h*ck. 12/10 hint hint it's you https://t.co/ORiK…</td>\n",
       "      <td>8.378202e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-03-04 00:21:08 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/837820167694528512/photo/1,https://twitter.com/dog_rates/status/837820167694528512/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>798628517273620480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-15 20:47:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7</td>\n",
       "      <td>6.675094e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-20 01:06:48 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667509364010450944/photo/1,https://twitter.com/dog_rates/status/667509364010450944/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>788552643979468800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-19 01:29:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX</td>\n",
       "      <td>7.363926e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-05-28 03:04:00 +0000</td>\n",
       "      <td>https://vine.co/v/iEggaEOiLO3,https://vine.co/v/iEggaEOiLO3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>mad</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>800855607700029440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-22 00:17:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @Lin_Manuel: 11/10 would recommend. https://t.co/pnUF69K4xk</td>\n",
       "      <td>8.008540e+17</td>\n",
       "      <td>7.992370e+07</td>\n",
       "      <td>2016-11-22 00:10:52 +0000</td>\n",
       "      <td>https://twitter.com/littlewiewel/status/800852955880628224,https://twitter.com/littlewiewel/status/800852955880628224</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "19   888202515573088257 NaN                    NaN                    \n",
       "767  777641927919427584 NaN                    NaN                    \n",
       "78   877611172832227328 NaN                    NaN                    \n",
       "303  836397794269200385 NaN                    NaN                    \n",
       "552  804413760345620481 NaN                    NaN                    \n",
       "949  752309394570878976 NaN                    NaN                    \n",
       "137  866094527597207552 NaN                    NaN                    \n",
       "603  798628517273620480 NaN                    NaN                    \n",
       "682  788552643979468800 NaN                    NaN                    \n",
       "577  800855607700029440 NaN                    NaN                    \n",
       "\n",
       "                     timestamp  \\\n",
       "19   2017-07-21 01:02:36 +0000   \n",
       "767  2016-09-18 22:54:18 +0000   \n",
       "78   2017-06-21 19:36:23 +0000   \n",
       "303  2017-02-28 02:09:08 +0000   \n",
       "552  2016-12-01 19:56:00 +0000   \n",
       "949  2016-07-11 01:11:51 +0000   \n",
       "137  2017-05-21 00:53:21 +0000   \n",
       "603  2016-11-15 20:47:30 +0000   \n",
       "682  2016-10-19 01:29:35 +0000   \n",
       "577  2016-11-22 00:17:10 +0000   \n",
       "\n",
       "                                                                                 source  \\\n",
       "19   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "767  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "78   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "303  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "552  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "949  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "137  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "603  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "682  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "577  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                             text  \\\n",
       "19   RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX     \n",
       "767  RT @dog_rates: This is Arnie. He's a Nova Scotian Fridge Floof. Rare af. 12/10 https://t.co/lprdOylVpS                                         \n",
       "78   RT @rachel2195: @dog_rates the boyfriend and his soaking wet pupper h*cking love his new hat 14/10 https://t.co/dJx4Gzc50G                     \n",
       "303  RT @dog_rates: This is Buddy. He ran into a glass door once. Now he's h*ckin skeptical. 13/10 empowering af (vid by Brittany Gaunt) https:/…   \n",
       "552  RT @dog_rates: This is Rusty. He's going D1 for sure. Insane vertical. 13/10 would draft https://t.co/AsykOwMrXQ                               \n",
       "949  RT @dog_rates: Everyone needs to watch this. 13/10 https://t.co/Bb3xnpsWBC                                                                     \n",
       "137  RT @dog_rates: Here's a pupper before and after being asked \"who's a good girl?\" Unsure as h*ck. 12/10 hint hint it's you https://t.co/ORiK…   \n",
       "603  RT @dog_rates: This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7            \n",
       "682  RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX             \n",
       "577  RT @Lin_Manuel: 11/10 would recommend. https://t.co/pnUF69K4xk                                                                                 \n",
       "\n",
       "     retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "19   8.874740e+17         4.196984e+09              2017-07-19 00:47:34 +0000   \n",
       "767  7.504293e+17         4.196984e+09              2016-07-05 20:41:01 +0000   \n",
       "78   8.768508e+17         5.128045e+08              2017-06-19 17:14:49 +0000   \n",
       "303  8.178278e+17         4.196984e+09              2017-01-07 20:18:46 +0000   \n",
       "552  7.848260e+17         4.196984e+09              2016-10-08 18:41:19 +0000   \n",
       "949  6.753544e+17         4.196984e+09              2015-12-11 16:40:19 +0000   \n",
       "137  8.378202e+17         4.196984e+09              2017-03-04 00:21:08 +0000   \n",
       "603  6.675094e+17         4.196984e+09              2015-11-20 01:06:48 +0000   \n",
       "682  7.363926e+17         4.196984e+09              2016-05-28 03:04:00 +0000   \n",
       "577  8.008540e+17         7.992370e+07              2016-11-22 00:10:52 +0000   \n",
       "\n",
       "                                                                                                                                                                                                                                                           expanded_urls  \\\n",
       "19   https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1       \n",
       "767  https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1,https://twitter.com/dog_rates/status/750429297815552001/photo/1       \n",
       "78   https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1,https://twitter.com/rachel2195/status/876850772322988033/photo/1   \n",
       "303  https://twitter.com/dog_rates/status/817827839487737858/video/1                                                                                                                                                                                                       \n",
       "552  https://twitter.com/dog_rates/status/784826020293709826/photo/1,https://twitter.com/dog_rates/status/784826020293709826/photo/1                                                                                                                                       \n",
       "949  https://twitter.com/dog_rates/status/675354435921575936/video/1,https://twitter.com/dog_rates/status/675354435921575936/video/1                                                                                                                                       \n",
       "137  https://twitter.com/dog_rates/status/837820167694528512/photo/1,https://twitter.com/dog_rates/status/837820167694528512/photo/1                                                                                                                                       \n",
       "603  https://twitter.com/dog_rates/status/667509364010450944/photo/1,https://twitter.com/dog_rates/status/667509364010450944/photo/1                                                                                                                                       \n",
       "682  https://vine.co/v/iEggaEOiLO3,https://vine.co/v/iEggaEOiLO3                                                                                                                                                                                                           \n",
       "577  https://twitter.com/littlewiewel/status/800852955880628224,https://twitter.com/littlewiewel/status/800852955880628224                                                                                                                                                 \n",
       "\n",
       "     rating_numerator  rating_denominator    name doggo floofer  pupper puppo  \n",
       "19   13                10                  Canela  None  None    None    None  \n",
       "767  12                10                  Arnie   None  None    None    None  \n",
       "78   14                10                  None    None  None    pupper  None  \n",
       "303  13                10                  Buddy   None  None    None    None  \n",
       "552  13                10                  Rusty   None  None    None    None  \n",
       "949  13                10                  None    None  None    None    None  \n",
       "137  12                10                  None    None  None    pupper  None  \n",
       "603  12                10                  None    None  None    None    None  \n",
       "682  13                10                  mad     None  None    pupper  None  \n",
       "577  11                10                  None    None  None    None    None  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for retweets and duplicates\n",
    "arc[~(arc.retweeted_status_id.isnull())].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  \\\n",
       "19  888202515573088257   \n",
       "23  887473957103951883   \n",
       "\n",
       "                                                                                                                                          text  \n",
       "19  RT @dog_rates: This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX  \n",
       "23  This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX                 "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check 1 instance to verify that these retweets are, in fact, duplicates\n",
    "arc[arc.text.str.contains('Canela')][['tweet_id', 'text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>797971864723324932</td>\n",
       "      <td>https://pbs.twimg.com/media/CxL3IWeVEAAAIE2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.489845</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.305760</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.072799</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>878057613040115712</td>\n",
       "      <td>https://pbs.twimg.com/media/DC98vABUIAA97pz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.839097</td>\n",
       "      <td>True</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.078799</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>813051746834595840</td>\n",
       "      <td>https://pbs.twimg.com/media/C0iKPZIXUAAbDYV.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.914804</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.083550</td>\n",
       "      <td>True</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>684460069371654144</td>\n",
       "      <td>https://pbs.twimg.com/media/CX-wzZEUwAA4ISM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.673691</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.194897</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.059471</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>669203728096960512</td>\n",
       "      <td>https://pbs.twimg.com/media/CUl9PGBVEAUV3Wz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.910452</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.055090</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.014897</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>685321586178670592</td>\n",
       "      <td>https://pbs.twimg.com/media/CYLAWFMWMAEcRzb.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.972483</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.025469</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.692517</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.058279</td>\n",
       "      <td>True</td>\n",
       "      <td>fur_coat</td>\n",
       "      <td>0.054449</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>683828599284170753</td>\n",
       "      <td>https://pbs.twimg.com/media/CX1ye7HUMAADDzh.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.577376</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.287131</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.117563</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>841439858740625411</td>\n",
       "      <td>https://pbs.twimg.com/media/C61lFFiWoAAJdiL.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>military_uniform</td>\n",
       "      <td>0.853684</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.048200</td>\n",
       "      <td>True</td>\n",
       "      <td>groenendael</td>\n",
       "      <td>0.015394</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>675517828909424640</td>\n",
       "      <td>https://pbs.twimg.com/media/CV_r3v4VAAALvwg.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Scottish_deerhound</td>\n",
       "      <td>0.240591</td>\n",
       "      <td>True</td>\n",
       "      <td>groenendael</td>\n",
       "      <td>0.156916</td>\n",
       "      <td>True</td>\n",
       "      <td>flat-coated_retriever</td>\n",
       "      <td>0.090899</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1586  797971864723324932  https://pbs.twimg.com/media/CxL3IWeVEAAAIE2.jpg   \n",
       "2008  878057613040115712  https://pbs.twimg.com/media/DC98vABUIAA97pz.jpg   \n",
       "1668  813051746834595840  https://pbs.twimg.com/media/C0iKPZIXUAAbDYV.jpg   \n",
       "695   684460069371654144  https://pbs.twimg.com/media/CX-wzZEUwAA4ISM.jpg   \n",
       "176   669203728096960512  https://pbs.twimg.com/media/CUl9PGBVEAUV3Wz.jpg   \n",
       "712   685321586178670592  https://pbs.twimg.com/media/CYLAWFMWMAEcRzb.jpg   \n",
       "7     666055525042405380  https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg   \n",
       "681   683828599284170753  https://pbs.twimg.com/media/CX1ye7HUMAADDzh.jpg   \n",
       "1856  841439858740625411  https://pbs.twimg.com/media/C61lFFiWoAAJdiL.jpg   \n",
       "488   675517828909424640  https://pbs.twimg.com/media/CV_r3v4VAAALvwg.jpg   \n",
       "\n",
       "      img_num                              p1   p1_conf  p1_dog  \\\n",
       "1586  1        American_Staffordshire_terrier  0.489845  True     \n",
       "2008  1        French_bulldog                  0.839097  True     \n",
       "1668  1        golden_retriever                0.914804  True     \n",
       "695   1        Labrador_retriever              0.673691  True     \n",
       "176   1        pug                             0.910452  True     \n",
       "712   1        Boston_bull                     0.972483  True     \n",
       "7     1        chow                            0.692517  True     \n",
       "681   1        malamute                        0.577376  True     \n",
       "1856  3        military_uniform                0.853684  False    \n",
       "488   1        Scottish_deerhound              0.240591  True     \n",
       "\n",
       "                            p2   p2_conf  p2_dog  \\\n",
       "1586  Chihuahua                 0.305760  True     \n",
       "2008  Boston_bull               0.078799  True     \n",
       "1668  Labrador_retriever        0.083550  True     \n",
       "695   Chesapeake_Bay_retriever  0.194897  True     \n",
       "176   French_bulldog            0.055090  True     \n",
       "712   French_bulldog            0.025469  True     \n",
       "7     Tibetan_mastiff           0.058279  True     \n",
       "681   Siberian_husky            0.287131  True     \n",
       "1856  Labrador_retriever        0.048200  True     \n",
       "488   groenendael               0.156916  True     \n",
       "\n",
       "                                  p3   p3_conf  p3_dog  \n",
       "1586  Staffordshire_bullterrier       0.072799  True    \n",
       "2008  toy_terrier                     0.015243  True    \n",
       "1668  kuvasz                          0.000453  True    \n",
       "695   American_Staffordshire_terrier  0.059471  True    \n",
       "176   Chihuahua                       0.014897  True    \n",
       "712   boxer                           0.000458  True    \n",
       "7     fur_coat                        0.054449  False   \n",
       "681   Eskimo_dog                      0.117563  True    \n",
       "1856  groenendael                     0.015394  True    \n",
       "488   flat-coated_retriever           0.090899  True    "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for tweet_ids in common between 2 DataFrames\n",
    "def find_common_tweet_ids(df1, df2):\n",
    "    common = df1.merge(df2, on = ['tweet_id'])\n",
    "    return common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for tweet_ids NOT in common between 2 DataFrame\n",
    "def find_missing_tweet_ids(df1, df2):\n",
    "    missing = df1[(~df1.tweet_id.isin(df2.tweet_id))]\n",
    "    return missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for common and missing tweet_ids between arc and image_predictions\n",
    "common = find_common_tweet_ids(arc, image_predictions)\n",
    "missing = find_missing_tweet_ids(arc, image_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2070    666049248165822465\n",
      "2071    666044226329800704\n",
      "2072    666033412701032449\n",
      "2073    666029285002620928\n",
      "2074    666020888022790149\n",
      "Name: tweet_id, dtype: int64\n",
      "2038    671550332464455680\n",
      "2149    669684865554620416\n",
      "2189    668967877119254528\n",
      "2212    668587383441514497\n",
      "2298    667070482143944705\n",
      "Name: tweet_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(common.tweet_id.tail(5))\n",
    "print(missing.tweet_id.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 2356)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the sum of len(common) and len(missing) equals the entire length of arc\n",
    "len(arc), len(common) + len(missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for missing tweet_ids between arc and tweets\n",
    "common1 = find_common_tweet_ids(arc, tweets)\n",
    "missing1 = find_missing_tweet_ids(arc, tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2329    666049248165822465\n",
      "2330    666044226329800704\n",
      "2331    666033412701032449\n",
      "2332    666029285002620928\n",
      "2333    666020888022790149\n",
      "Name: tweet_id, dtype: int64\n",
      "566     802247111496568832\n",
      "784     775096608509886464\n",
      "818     770743923962707968\n",
      "932     754011816964026368\n",
      "1726    680055455951884288\n",
      "Name: tweet_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(common1.tweet_id.tail(5))\n",
    "print(missing1.tweet_id.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 2356)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the sum of len(common1) and len(missing1) equals the entire length of arc\n",
    "len(arc), len(common1) + len(missing1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for missing ids in common between image_predictions and tweets (IDs not present in arc)\n",
    "common3 = find_common_tweet_ids(missing, missing1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    872668790621863937\n",
      "1    869988702071779329\n",
      "2    866816280283807744\n",
      "3    856602993587888130\n",
      "4    845459076796616705\n",
      "5    827228250799742977\n",
      "6    812747805718642688\n",
      "7    775096608509886464\n",
      "8    770743923962707968\n",
      "Name: tweet_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(common3.tweet_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check number of missing ids in common\n",
    "len(common3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "266"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(image_predictions.query('p1_conf >= 0.95'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>created_at</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>716447146686459905</td>\n",
       "      <td>2016-04-03 02:08:05 +0000</td>\n",
       "      <td>Sun Apr 03 02:08:05 +0000 2016</td>\n",
       "      <td>13421.0</td>\n",
       "      <td>6141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>856543823941562368</td>\n",
       "      <td>2017-04-24 16:22:16 +0000</td>\n",
       "      <td>Mon Apr 24 16:22:16 +0000 2017</td>\n",
       "      <td>16237.0</td>\n",
       "      <td>2903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>766793450729734144</td>\n",
       "      <td>2016-08-20 00:26:19 +0000</td>\n",
       "      <td>Sat Aug 20 00:26:19 +0000 2016</td>\n",
       "      <td>5299.0</td>\n",
       "      <td>1440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>727524757080539137</td>\n",
       "      <td>2016-05-03 15:46:33 +0000</td>\n",
       "      <td>Tue May 03 15:46:33 +0000 2016</td>\n",
       "      <td>4582.0</td>\n",
       "      <td>1273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>747594051852075008</td>\n",
       "      <td>2016-06-28 00:54:46 +0000</td>\n",
       "      <td>Tue Jun 28 00:54:46 +0000 2016</td>\n",
       "      <td>3807.0</td>\n",
       "      <td>1106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>673345638550134785</td>\n",
       "      <td>2015-12-06 03:38:05 +0000</td>\n",
       "      <td>Sun Dec 06 03:38:05 +0000 2015</td>\n",
       "      <td>2507.0</td>\n",
       "      <td>1285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>752334515931054080</td>\n",
       "      <td>2016-07-11 02:51:40 +0000</td>\n",
       "      <td>Mon Jul 11 02:51:40 +0000 2016</td>\n",
       "      <td>3950.0</td>\n",
       "      <td>1167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>698989035503689728</td>\n",
       "      <td>2016-02-14 21:55:47 +0000</td>\n",
       "      <td>Sun Feb 14 21:55:47 +0000 2016</td>\n",
       "      <td>3477.0</td>\n",
       "      <td>1024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>747242308580548608</td>\n",
       "      <td>2016-06-27 01:37:04 +0000</td>\n",
       "      <td>Mon Jun 27 01:37:04 +0000 2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>747816857231626240</td>\n",
       "      <td>2016-06-28 15:40:07 +0000</td>\n",
       "      <td>Tue Jun 28 15:40:07 +0000 2016</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>1219.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                  timestamp  \\\n",
       "1201  716447146686459905  2016-04-03 02:08:05 +0000   \n",
       "183   856543823941562368  2017-04-24 16:22:16 +0000   \n",
       "842   766793450729734144  2016-08-20 00:26:19 +0000   \n",
       "1142  727524757080539137  2016-05-03 15:46:33 +0000   \n",
       "1008  747594051852075008  2016-06-28 00:54:46 +0000   \n",
       "1965  673345638550134785  2015-12-06 03:38:05 +0000   \n",
       "948   752334515931054080  2016-07-11 02:51:40 +0000   \n",
       "1411  698989035503689728  2016-02-14 21:55:47 +0000   \n",
       "1012  747242308580548608  2016-06-27 01:37:04 +0000   \n",
       "1004  747816857231626240  2016-06-28 15:40:07 +0000   \n",
       "\n",
       "                          created_at  favorite_count  retweet_count  \n",
       "1201  Sun Apr 03 02:08:05 +0000 2016  13421.0         6141.0         \n",
       "183   Mon Apr 24 16:22:16 +0000 2017  16237.0         2903.0         \n",
       "842   Sat Aug 20 00:26:19 +0000 2016  5299.0          1440.0         \n",
       "1142  Tue May 03 15:46:33 +0000 2016  4582.0          1273.0         \n",
       "1008  Tue Jun 28 00:54:46 +0000 2016  3807.0          1106.0         \n",
       "1965  Sun Dec 06 03:38:05 +0000 2015  2507.0          1285.0         \n",
       "948   Mon Jul 11 02:51:40 +0000 2016  3950.0          1167.0         \n",
       "1411  Sun Feb 14 21:55:47 +0000 2016  3477.0          1024.0         \n",
       "1012  Mon Jun 27 01:37:04 +0000 2016  0.0             3021.0         \n",
       "1004  Tue Jun 28 15:40:07 +0000 2016  5003.0          1219.0         "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if timestamps in twitter archive and scraped tweets are consistent\n",
    "time_const = arc[['tweet_id', 'timestamp']]\n",
    "time_const = pd.merge(time_const, tweets, on = 'tweet_id', how = 'left')\n",
    "\n",
    "time_const.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality ##\n",
    "\n",
    "**`arc`**:\n",
    "\n",
    "**Missing Data**\n",
    "\n",
    "- `arc` contains 281 tweets that are missing from `image_predictions`.\n",
    "- `arc` contains 22 tweets that are missing from `tweets`. After dropping previous entries, there should be 13 tweets to be dropped.\n",
    "\n",
    "\n",
    "- retweeted data indicates duplicated tweets.\n",
    "- unnecessary anchor tags under **source**\n",
    "- erroneous datatypes under **tweet_id**, **in_reply_to_status_id**, **in_reply_to_user_id**, **retweeted_status_id**, **retweeted_status_user_id**, **timestamp**, and **retweeted_status_timestamp** in `arc.`\n",
    "- inconsistent & inaccurate readings mapped from text to numerator & denominator fields (e.g. 24/7 being mistaken as a rating).\n",
    "- some numerator values contain a decimal point (e.g. 9.75/10).\n",
    "- **rating_denominator** has values besides 10\n",
    "- nonsensical dog names such as *a, an, quite, by, actually, such, not, one*.\n",
    "- inconsistent timestamp format between `arc` and `tweets`.\n",
    "- there is one entry with no rating.\n",
    "- Under `image_predictions`, dog breeds under the **p1** column have untidy formatting.\n",
    "\n",
    "## Tidiness\n",
    "\n",
    "**`arc`**:\n",
    "\n",
    "- **doggo**, **floofer**, **pupper**, and **puppo** all are not under 1 category variable.\n",
    "- **favorite_count** and **retweet_count** fields under `tweets` need to be merged with `arc`.\n",
    "- **p1**, **p1_conf**, and **p1_dog** under `image_predictions` need to be merged with `arc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "#before cleaning, create a clean copy of each DataFrame\n",
    "arc_clean = arc.copy()\n",
    "img_clean = image_predictions.copy()\n",
    "tweets_clean = tweets.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean'></a>\n",
    "## CLEAN\n",
    "\n",
    "### Quality\n",
    "\n",
    "> **Issue 1:** `arc` contains 281 tweets that are missing from `image_predictions.`\n",
    "\n",
    "**Define**\n",
    "\n",
    "Drop the tweets_ids from `arc` that are not present in `image_predictions`.\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_clean = find_missing_tweet_ids(arc_clean, img_clean)\n",
    "\n",
    "missing_ids = missing_clean.tweet_id\n",
    "arc_clean = arc_clean[(arc_clean.tweet_id.isin(img_clean.tweet_id))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "281"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify that 281 entries have been dropped from arc_clean\n",
    "arc.shape[0] - arc_clean.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 2:** `arc` contains 22 tweets that are missing from `tweets.` After dropping the previous entries, there should be 13 tweets to be dropped.\n",
    "\n",
    "**Define**\n",
    "\n",
    "Drop the tweets_ids from `arc` that are not present in `tweets`.\n",
    "\n",
    "From the previous step, there 9 dropped tweet IDs in common with the missing IDs in `tweets.` Excluding these tweets that were already dropped, there should be 22 - 9 = **13 tweets** dropped after this step, accounting for a **total 294 entries dropped.** (281 + 13)\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_clean2 = find_missing_tweet_ids(arc_clean, tweets_clean)\n",
    "\n",
    "missing_ids2 = missing_clean2.tweet_id\n",
    "arc_clean = arc_clean[(arc_clean.tweet_id.isin(tweets_clean.tweet_id))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify that 281 + 13 = 294 entries have been dropped from arc_clean\n",
    "arc.shape[0] - arc_clean.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 3:** retweeted data under `arc` indicates duplicated tweets.\n",
    "\n",
    "**Define**\n",
    "\n",
    "All nonduplicate tweets have null values under **retweeted_status_id**, **retweeted_status_user_id**, and **retweeted_status_timestamp.**\n",
    "\n",
    "- drop all rows from `arc` where **retweeted_status_user_id** is not null.\n",
    "- drop the 3 columns as they are no longer necessary.\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. drop entries where retweeted_status_user_id is not null\n",
    "arc_clean = arc_clean[arc_clean.retweeted_status_user_id.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check that there are no duplicates\n",
    "len(arc_clean[~(arc_clean.retweeted_status_user_id.isna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. drop unnecessary columns\n",
    "arc_clean = arc_clean.drop(columns = ['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1988 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                 1988 non-null int64\n",
      "in_reply_to_status_id    23 non-null float64\n",
      "in_reply_to_user_id      23 non-null float64\n",
      "timestamp                1988 non-null object\n",
      "source                   1988 non-null object\n",
      "text                     1988 non-null object\n",
      "expanded_urls            1988 non-null object\n",
      "rating_numerator         1988 non-null int64\n",
      "rating_denominator       1988 non-null int64\n",
      "name                     1988 non-null object\n",
      "doggo                    1988 non-null object\n",
      "floofer                  1988 non-null object\n",
      "pupper                   1988 non-null object\n",
      "puppo                    1988 non-null object\n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 233.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#check that columns don't exist\n",
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1988"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new size of DataFrame\n",
    "arc_clean.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 4:** Unnecessary anchor tags under source in `arc`.\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Use RegEx to reformat the strings under **source** column and only display the text between the anchor tags.\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean.source = arc_clean.source.str.extract('<a[^>]*>(.*?)</a>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1950\n",
       "Twitter Web Client    28  \n",
       "TweetDeck             10  \n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.source.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 5:** Under `arc`, erroneous datatypes under **tweet_id**, **in_reply_to_status_id**, **in_reply_to_user_id**, **retweeted_status_id**, **retweeted_status_user_id**, **timestamp**, and **retweeted_status_timestamp**.\n",
    "\n",
    "**Define**\n",
    "\n",
    "- get rid of scientific notation under **in_reply_to_status_id** and **in_reply_to_user_id**\n",
    "- change data type of **in_reply_to_status_id**, **in_reply_to_user_id**, and **tweet_id** of all DataFrames to string.\n",
    "- change data type of **timestamp** to DateTime.\n",
    "\n",
    "*The retweeted columns can be ignored now that they've been dropped.*\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "#suppress scientific notation of reply IDs, convert to string\n",
    "arc_clean = arc_clean.fillna(0)\n",
    "\n",
    "arc_clean.in_reply_to_status_id = arc_clean.in_reply_to_status_id.apply(lambda x: '%.f' % x)\n",
    "arc_clean.in_reply_to_user_id = arc_clean.in_reply_to_user_id.apply(lambda x: '%.f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace all 0s with 'None'\n",
    "arc_clean.loc[arc_clean['in_reply_to_status_id'] == '0', 'in_reply_to_status_id'] = 'None'\n",
    "arc_clean.loc[arc_clean['in_reply_to_user_id'] == '0', 'in_reply_to_user_id'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert tweet_id of all DataFrames to string\n",
    "arc_clean.tweet_id = arc_clean.tweet_id.astype(str)\n",
    "img_clean.tweet_id = img_clean.tweet_id.astype(str)\n",
    "tweets_clean.tweet_id = tweets_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert timestamp to datetime\n",
    "arc_clean.timestamp = pd.to_datetime(arc_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1988 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                 1988 non-null object\n",
      "in_reply_to_status_id    1988 non-null object\n",
      "in_reply_to_user_id      1988 non-null object\n",
      "timestamp                1988 non-null datetime64[ns, UTC]\n",
      "source                   1988 non-null object\n",
      "text                     1988 non-null object\n",
      "expanded_urls            1988 non-null object\n",
      "rating_numerator         1988 non-null int64\n",
      "rating_denominator       1988 non-null int64\n",
      "name                     1988 non-null object\n",
      "doggo                    1988 non-null object\n",
      "floofer                  1988 non-null object\n",
      "pupper                   1988 non-null object\n",
      "puppo                    1988 non-null object\n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(11)\n",
      "memory usage: 233.0+ KB\n"
     ]
    }
   ],
   "source": [
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None                  1965\n",
       "667152164079423488    2   \n",
       "675349384339542016    1   \n",
       "691416866452082688    1   \n",
       "674793399141146624    1   \n",
       "670668383499735040    1   \n",
       "703255935813287936    1   \n",
       "671561002136281088    1   \n",
       "684481074559381504    1   \n",
       "759099523532779520    1   \n",
       "729113531270991872    1   \n",
       "692041934689402880    1   \n",
       "667806454573760512    1   \n",
       "671729906628341760    1   \n",
       "855818117272018944    1   \n",
       "675707330206547968    1   \n",
       "674752233200820224    1   \n",
       "684222868335505408    1   \n",
       "675497103322386432    1   \n",
       "746885919387574272    1   \n",
       "703041949650034688    1   \n",
       "674999807681908736    1   \n",
       "733109485275860992    1   \n",
       "Name: in_reply_to_status_id, dtype: int64"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.in_reply_to_status_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None          1965\n",
       "4196983835    23  \n",
       "Name: in_reply_to_user_id, dtype: int64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.in_reply_to_user_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null object\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "img_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2334 entries, 0 to 2333\n",
      "Data columns (total 4 columns):\n",
      "tweet_id          2334 non-null int64\n",
      "created_at        2334 non-null object\n",
      "favorite_count    2334 non-null int64\n",
      "retweet_count     2334 non-null int64\n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 73.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tweets.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 6:** inconsistent & inaccurate readings mapped from text to numerator & denominator fields (e.g. 24/7 being mistaken as a rating).\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Under `arc`, compare the untruncated **text** with each rating where tha denominator is not equal to 10, and correct them accordingly.\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>740373189193256964</td>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>722974582966214656</td>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>716439118184652801</td>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>684225744407494656</td>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>682962037429899265</td>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "433   820690176645140481   \n",
       "516   810984652412424192   \n",
       "902   758467244762497024   \n",
       "1068  740373189193256964   \n",
       "1120  731156023742988288   \n",
       "1165  722974582966214656   \n",
       "1202  716439118184652801   \n",
       "1228  713900603437621249   \n",
       "1254  710658690886586372   \n",
       "1274  709198395643068416   \n",
       "1351  704054845121142784   \n",
       "1433  697463031882764288   \n",
       "1634  684225744407494656   \n",
       "1635  684222868335505415   \n",
       "1662  682962037429899265   \n",
       "1779  677716515794329600   \n",
       "1843  675853064436391936   \n",
       "2335  666287406224695296   \n",
       "\n",
       "                                                                                                                                                                           text  \\\n",
       "433   The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd                                                                         \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "902   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                                              \n",
       "1068  After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ                                \n",
       "1120  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                                                   \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                                            \n",
       "1202  This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                                       \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                                               \n",
       "1254  Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                                \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK                          \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                                          \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                                                \n",
       "1634  Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3                                              \n",
       "1635  Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55                                \n",
       "1662  This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5                                 \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq                                                                                                      \n",
       "1843  Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw                                                                 \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                    \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "433   84                70                  \n",
       "516   24                7                   \n",
       "902   165               150                 \n",
       "1068  9                 11                  \n",
       "1120  204               170                 \n",
       "1165  4                 20                  \n",
       "1202  50                50                  \n",
       "1228  99                90                  \n",
       "1254  80                80                  \n",
       "1274  45                50                  \n",
       "1351  60                50                  \n",
       "1433  44                40                  \n",
       "1634  143               130                 \n",
       "1635  121               110                 \n",
       "1662  7                 11                  \n",
       "1779  144               120                 \n",
       "1843  88                80                  \n",
       "2335  1                 2                   "
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean[arc_clean['rating_denominator'] != 10][['tweet_id', 'text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Tracking***\n",
    "\n",
    "Correct Tweet IDs (where rating was recorded correctly):\n",
    "- 820690176645140481\n",
    "- 758467244762497024\n",
    "- 731156023742988288\n",
    "- 710658690886586372\n",
    "- 709198395643068416\n",
    "- 704054845121142784\n",
    "- 697463031882764288\t\n",
    "- 684225744407494656\n",
    "- 684222868335505415\t\n",
    "- 677716515794329600\t\n",
    "- 675853064436391936\n",
    "\n",
    "Wrong Tweet IDs (along with correct ratings):\n",
    "- 810984652412424192 (NA)\n",
    "- 740373189193256964 (14/10)\n",
    "- 722974582966214656 (13/10)\n",
    "- 716439118184652801 (11/10)\n",
    "- 713900603437621249 (99/90)\n",
    "- 682962037429899265 (10/10)\n",
    "- 666287406224695296 (9/10)\n",
    "\n",
    "***So far, there are 7 corrections we have to make.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids_to_correct = ['810984652412424192', '740373189193256964', '722974582966214656', '716439118184652801',\n",
    "                  '713900603437621249', '682962037429899265', '666287406224695296']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_rating(tweet_id):\n",
    "    if (tweet_id == ids_to_correct[0]):\n",
    "        return [0, 0]\n",
    "    elif (tweet_id == ids_to_correct[1]):\n",
    "        return [14, 10]\n",
    "    elif (tweet_id == ids_to_correct[2]):\n",
    "        return [13, 10]\n",
    "    elif (tweet_id == ids_to_correct[3]):\n",
    "        return [11, 10]\n",
    "    elif (tweet_id == ids_to_correct[4]):\n",
    "        return [99, 90]\n",
    "    elif (tweet_id == ids_to_correct[5]):\n",
    "        return [10, 10]\n",
    "    elif (tweet_id == ids_to_correct[6]):\n",
    "        return [9, 10]\n",
    "    else:\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in arc_clean[arc_clean.tweet_id.isin(ids_to_correct)].iterrows():\n",
    "    rating = correct_rating(row['tweet_id'])\n",
    "    if (rating is not None):\n",
    "        arc_clean.at[index, 'rating_numerator'] = rating[0]\n",
    "        arc_clean.at[index, 'rating_denominator'] = rating[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     1975\n",
       "80     2   \n",
       "50     2   \n",
       "170    1   \n",
       "150    1   \n",
       "130    1   \n",
       "120    1   \n",
       "110    1   \n",
       "90     1   \n",
       "70     1   \n",
       "40     1   \n",
       "0      1   \n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 7:** some numerator values contain a decimal point (e.g. 9.75/10).\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Under `arc`, correct numerators of the isolated tweet IDs based on the text, then round them to account for the columns' int datatype.\n",
    "\n",
    "I isolated these Tweet IDs through visual assessment.\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make corrections to wrongly recorded numerators.\n",
    "ids_wrong_numerator = ['786709082849828864', '680494726643068929']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>786709082849828864</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-10-13 23:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS</td>\n",
       "      <td>https://twitter.com/dog_rates/status/786709082849828864/photo/1</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>Logan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>680494726643068929</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2015-12-25 21:06:00+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD</td>\n",
       "      <td>https://twitter.com/dog_rates/status/680494726643068929/photo/1</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id in_reply_to_status_id in_reply_to_user_id  \\\n",
       "695   786709082849828864  None                  None                 \n",
       "1712  680494726643068929  None                  None                 \n",
       "\n",
       "                     timestamp              source  \\\n",
       "695  2016-10-13 23:23:56+00:00  Twitter for iPhone   \n",
       "1712 2015-12-25 21:06:00+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                  text  \\\n",
       "695   This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS   \n",
       "1712  Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD                         \n",
       "\n",
       "                                                        expanded_urls  \\\n",
       "695   https://twitter.com/dog_rates/status/786709082849828864/photo/1   \n",
       "1712  https://twitter.com/dog_rates/status/680494726643068929/photo/1   \n",
       "\n",
       "      rating_numerator  rating_denominator   name doggo floofer pupper puppo  \n",
       "695   75                10                  Logan  None  None    None   None  \n",
       "1712  26                10                  None   None  None    None   None  "
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.query('tweet_id == @ids_wrong_numerator[0] or tweet_id == @ids_wrong_numerator[1]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Tracking:***\n",
    "\n",
    "- 786709082849828864 (9.75/10)\n",
    "- 680494726643068929 (11.26/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean.at[695, 'rating_numerator'] = round(9.75)\n",
    "arc_clean.at[1712, 'rating_numerator'] = round(11.26)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1988 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                 1988 non-null object\n",
      "in_reply_to_status_id    1988 non-null object\n",
      "in_reply_to_user_id      1988 non-null object\n",
      "timestamp                1988 non-null datetime64[ns, UTC]\n",
      "source                   1988 non-null object\n",
      "text                     1988 non-null object\n",
      "expanded_urls            1988 non-null object\n",
      "rating_numerator         1988 non-null int64\n",
      "rating_denominator       1988 non-null int64\n",
      "name                     1988 non-null object\n",
      "doggo                    1988 non-null object\n",
      "floofer                  1988 non-null object\n",
      "pupper                   1988 non-null object\n",
      "puppo                    1988 non-null object\n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(11)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>786709082849828864</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>680494726643068929</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  rating_numerator  rating_denominator\n",
       "695   786709082849828864  10                10                \n",
       "1712  680494726643068929  11                10                "
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.query('tweet_id == @ids_wrong_numerator[0] or tweet_id == @ids_wrong_numerator[1]')[['tweet_id','rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 8:** rating_denominator has values besides 10\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Under `arc`, normalize the ratings using a common ratio so that they all have a common denominator of 10. Create a new column **normalized_numerator** to account for these new numerators.\n",
    "\n",
    "e.g. for a rating of 200/50, calculate 200/50 = x/10 where x is the new normalized numerator.\n",
    "\n",
    "$$\\frac{numerator}{denominator} = \\frac{x}{10} $$\n",
    "\n",
    "$$ 10 * \\frac{numerator}{denominator} = x $$\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in arc_clean.iterrows():\n",
    "    if(row['rating_numerator'] != 0 and row['rating_denominator'] != 0):\n",
    "        normalized_numerator = round(10 * (row['rating_numerator']/row['rating_denominator']))\n",
    "        arc_clean.at[i, 'normalized_numerator'] = int(normalized_numerator)\n",
    "    else:\n",
    "        arc_clean.at[i, 'normalized_numerator'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert normalized_numerator to int\n",
    "arc_clean.normalized_numerator = arc_clean.normalized_numerator.astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>normalized_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>684225744407494656</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  rating_numerator  rating_denominator  \\\n",
       "433   820690176645140481  84                70                   \n",
       "516   810984652412424192  0                 0                    \n",
       "902   758467244762497024  165               150                  \n",
       "1120  731156023742988288  204               170                  \n",
       "1228  713900603437621249  99                90                   \n",
       "1254  710658690886586372  80                80                   \n",
       "1274  709198395643068416  45                50                   \n",
       "1351  704054845121142784  60                50                   \n",
       "1433  697463031882764288  44                40                   \n",
       "1634  684225744407494656  143               130                  \n",
       "1635  684222868335505415  121               110                  \n",
       "1779  677716515794329600  144               120                  \n",
       "1843  675853064436391936  88                80                   \n",
       "\n",
       "      normalized_numerator  \n",
       "433   12                    \n",
       "516   0                     \n",
       "902   11                    \n",
       "1120  12                    \n",
       "1228  11                    \n",
       "1254  10                    \n",
       "1274  9                     \n",
       "1351  12                    \n",
       "1433  11                    \n",
       "1634  11                    \n",
       "1635  11                    \n",
       "1779  12                    \n",
       "1843  11                    "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check ratings where denominators are not 10\n",
    "arc_clean.query('rating_denominator != 10')[['tweet_id', 'rating_numerator', 'rating_denominator', 'normalized_numerator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>normalized_numerator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10</td>\n",
       "      <td>1776</td>\n",
       "      <td>1776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_denominator  rating_numerator  normalized_numerator  count\n",
       "0   10                  0                 0                     2    \n",
       "1   10                  1                 1                     4    \n",
       "2   10                  2                 2                     9    \n",
       "3   10                  3                 3                     19   \n",
       "4   10                  4                 4                     15   \n",
       "5   10                  5                 5                     33   \n",
       "6   10                  6                 6                     32   \n",
       "7   10                  7                 7                     51   \n",
       "8   10                  8                 8                     95   \n",
       "9   10                  9                 9                     151  \n",
       "10  10                  10                10                    420  \n",
       "11  10                  11                11                    398  \n",
       "12  10                  12                12                    449  \n",
       "13  10                  13                13                    258  \n",
       "14  10                  14                14                    36   \n",
       "15  10                  27                27                    1    \n",
       "16  10                  420               420                   1    \n",
       "17  10                  1776              1776                  1    "
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for denominators with 10, verify that raw and normalized numerators are unchanged and have consistent counts\n",
    "arc_clean.query('rating_denominator == 10').groupby(['rating_denominator', 'rating_numerator', 'normalized_numerator']).count().tweet_id.reset_index(name = 'count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 9:** invalid dog names such as a, an, quite, by, actually, such, not, one.\n",
    "\n",
    "**Define**\n",
    "\n",
    "Remove all lower case strings under the **name** field in `arc`. Invalid names all share 1 common property: the first character is always lower case.\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_mask = arc_clean.name.str.islower()\n",
    "arc_clean['name'] = arc_clean['name'].where(~name_mask, other='None')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1988\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify that all names are capitalized\n",
    "arc_clean.name.str.islower().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 10:** inconsistent timestamp format between `arc` and `tweets`.\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Drop the **created_at** column under `tweets`. The format for timestamp under `arc` is recognized as legitimate by Panda's datetime().\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_clean = tweets_clean.drop(columns = 'created_at')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2334 entries, 0 to 2333\n",
      "Data columns (total 3 columns):\n",
      "tweet_id          2334 non-null object\n",
      "favorite_count    2334 non-null int64\n",
      "retweet_count     2334 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 54.8+ KB\n"
     ]
    }
   ],
   "source": [
    "tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 11:** there is one entry with no rating.\n",
    "\n",
    "**Define**\n",
    "\n",
    "Drop the entry in `arc` where I previously set the numerator and denominator to 0 as a placeholder for NaN.\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_index = arc_clean.query('rating_denominator == 0').index[0]\n",
    "arc_clean = arc_clean.drop(drop_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     1975\n",
       "80     2   \n",
       "50     2   \n",
       "170    1   \n",
       "150    1   \n",
       "130    1   \n",
       "120    1   \n",
       "110    1   \n",
       "90     1   \n",
       "70     1   \n",
       "40     1   \n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 12:** Under `image_predictions`, dog breeds under the **p1** column have untidy formatting.\n",
    "\n",
    "**Define**\n",
    "- replace all '_' characters with a space (' ')\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_clean.p1 = img_clean.p1.str.replace('_', ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1712    Norwegian elkhound       \n",
       "766     cocker spaniel           \n",
       "1256    Tibetan terrier          \n",
       "1192    Irish water spaniel      \n",
       "1923    golden retriever         \n",
       "545     Blenheim spaniel         \n",
       "963     Staffordshire bullterrier\n",
       "1690    Border collie            \n",
       "781     standard poodle          \n",
       "1965    Eskimo dog               \n",
       "Name: p1, dtype: object"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_clean[img_clean.p1.str.contains(' ')].sample(10).p1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness\n",
    "\n",
    "> **Issue 1:** **doggo**, **floofer**, **pupper**, and **puppo** all are not under 1 category variable.\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Create a new column **dog_stage** and set values to where prior dog stage columns are not equal to None. Separate dog stages by comma if a tweet falls under more than 1 stage.\n",
    "- Drop **doggo**, **floofer**, **pupper**, and **puppo** columns.\n",
    "\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. create dog_stage column\n",
    "arc_clean['dog_stage'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in arc_clean.iterrows():\n",
    "    stage_string = 'None'\n",
    "    if row['doggo'] != 'None':\n",
    "        stage_string += 'doggo, '\n",
    "    if row['floofer'] != 'None':\n",
    "        stage_string += 'floofer, '\n",
    "    if row['pupper'] != 'None':\n",
    "        stage_string += 'pupper, '\n",
    "    if row['puppo'] != 'None':\n",
    "        stage_string += 'puppo'\n",
    "        \n",
    "    arc_clean.at[i, 'dog_stage'] = stage_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create masks for testing\n",
    "all_mask = mask_all_stages(arc_clean)\n",
    "single_mask = mask_single_stages(arc_clean)\n",
    "duplicate_mask = mask_duplicate_stages(arc_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#strip commas and replace None values for entries with >= 1 dog stage\n",
    "arc_clean.dog_stage[all_mask] = arc_clean[all_mask].dog_stage.str.replace('None', '').str.strip(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop doggo, floofer, pupper, and puppo stages\n",
    "arc_clean.drop(columns = ['doggo', 'floofer', 'pupper', 'puppo'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doggo, pupper     9\n",
       "doggo, puppo      1\n",
       "doggo, floofer    1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify counts for duplicate stages\n",
    "arc_clean[duplicate_mask].iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     203\n",
       "doggo      62 \n",
       "puppo      22 \n",
       "floofer    7  \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify counts for single stages\n",
    "arc_clean[single_mask].iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper            203\n",
       "doggo             62 \n",
       "puppo             22 \n",
       "doggo, pupper     9  \n",
       "floofer           7  \n",
       "doggo, puppo      1  \n",
       "doggo, floofer    1  \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify counts for all stages\n",
    "arc_clean[all_mask].iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "644"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check that None is preserved for entries with no recorded dog stages\n",
    "arc_clean.name.value_counts()['None']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1987 entries, 0 to 2355\n",
      "Data columns (total 12 columns):\n",
      "tweet_id                 1987 non-null object\n",
      "in_reply_to_status_id    1987 non-null object\n",
      "in_reply_to_user_id      1987 non-null object\n",
      "timestamp                1987 non-null datetime64[ns, UTC]\n",
      "source                   1987 non-null object\n",
      "text                     1987 non-null object\n",
      "expanded_urls            1987 non-null object\n",
      "rating_numerator         1987 non-null int64\n",
      "rating_denominator       1987 non-null int64\n",
      "name                     1987 non-null object\n",
      "normalized_numerator     1987 non-null int64\n",
      "dog_stage                1987 non-null object\n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(8)\n",
      "memory usage: 281.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#check that columns have dropped\n",
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 2:** favorite_count and retweet_count fields under `tweets` need to be merged with `arc`.\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Perform an inner merge between `tweets` and `arc` on **tweet_id**, adding **favorite_count** & **retweet_count** fields from `tweets`.\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean = pd.concat([arc_clean.set_index('tweet_id'), tweets_clean.set_index('tweet_id')], axis = 1, join = 'inner').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make sure size is preserved\n",
    "arc_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1987 entries, 0 to 1986\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                 1987 non-null object\n",
      "in_reply_to_status_id    1987 non-null object\n",
      "in_reply_to_user_id      1987 non-null object\n",
      "timestamp                1987 non-null datetime64[ns, UTC]\n",
      "source                   1987 non-null object\n",
      "text                     1987 non-null object\n",
      "expanded_urls            1987 non-null object\n",
      "rating_numerator         1987 non-null int64\n",
      "rating_denominator       1987 non-null int64\n",
      "name                     1987 non-null object\n",
      "normalized_numerator     1987 non-null int64\n",
      "dog_stage                1987 non-null object\n",
      "favorite_count           1987 non-null int64\n",
      "retweet_count            1987 non-null int64\n",
      "dtypes: datetime64[ns, UTC](1), int64(5), object(8)\n",
      "memory usage: 217.4+ KB\n"
     ]
    }
   ],
   "source": [
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>normalized_numerator</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>706291001778950144</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-03-06 01:31:11+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>When you're just relaxin and having a swell time but then remember you have to fill out the FAFSA ...11/10 https://t.co/qy33OBcexg</td>\n",
       "      <td>https://twitter.com/dog_rates/status/706291001778950144/photo/1,https://twitter.com/dog_rates/status/706291001778950144/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>1749</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>683852578183077888</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-01-04 03:28:54+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Tiger. He's a penbroke (little dog pun for ya, no need to applaud I know it was good) 10/10 good dog https://t.co/Yei0HzS3JN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/683852578183077888/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Tiger</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>1986</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>709918798883774466</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-03-16 01:46:45+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Watson. He's a Suzuki Tickleboop. Leader of a notorious biker gang. Only one ear functional. 12/10 snuggable af https://t.co/R1gLc5vDqG</td>\n",
       "      <td>https://twitter.com/dog_rates/status/709918798883774466/photo/1,https://twitter.com/dog_rates/status/709918798883774466/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Watson</td>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "      <td>3068</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>715680795826982913</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-03-31 23:22:53+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Zeus. He's downright fabulous. 12/10 https://t.co/sSugyyRuTp</td>\n",
       "      <td>https://twitter.com/dog_rates/status/715680795826982913/photo/1,https://twitter.com/dog_rates/status/715680795826982913/photo/1,https://twitter.com/dog_rates/status/715680795826982913/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Zeus</td>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "      <td>4437</td>\n",
       "      <td>1686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>711363825979756544</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-03-20 01:28:47+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>\"Please, no puparazzi\" 11/10 https://t.co/nJIXSPfedK</td>\n",
       "      <td>https://twitter.com/dog_rates/status/711363825979756544/photo/1,https://twitter.com/dog_rates/status/711363825979756544/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>3760</td>\n",
       "      <td>1177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id in_reply_to_status_id in_reply_to_user_id  \\\n",
       "1023  706291001778950144  None                  None                 \n",
       "1305  683852578183077888  None                  None                 \n",
       "972   709918798883774466  None                  None                 \n",
       "925   715680795826982913  None                  None                 \n",
       "958   711363825979756544  None                  None                 \n",
       "\n",
       "                     timestamp              source  \\\n",
       "1023 2016-03-06 01:31:11+00:00  Twitter for iPhone   \n",
       "1305 2016-01-04 03:28:54+00:00  Twitter for iPhone   \n",
       "972  2016-03-16 01:46:45+00:00  Twitter for iPhone   \n",
       "925  2016-03-31 23:22:53+00:00  Twitter for iPhone   \n",
       "958  2016-03-20 01:28:47+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                              text  \\\n",
       "1023  When you're just relaxin and having a swell time but then remember you have to fill out the FAFSA ...11/10 https://t.co/qy33OBcexg             \n",
       "1305  Say hello to Tiger. He's a penbroke (little dog pun for ya, no need to applaud I know it was good) 10/10 good dog https://t.co/Yei0HzS3JN      \n",
       "972   Meet Watson. He's a Suzuki Tickleboop. Leader of a notorious biker gang. Only one ear functional. 12/10 snuggable af https://t.co/R1gLc5vDqG   \n",
       "925   This is Zeus. He's downright fabulous. 12/10 https://t.co/sSugyyRuTp                                                                           \n",
       "958   \"Please, no puparazzi\" 11/10 https://t.co/nJIXSPfedK                                                                                           \n",
       "\n",
       "                                                                                                                                                                                        expanded_urls  \\\n",
       "1023  https://twitter.com/dog_rates/status/706291001778950144/photo/1,https://twitter.com/dog_rates/status/706291001778950144/photo/1                                                                   \n",
       "1305  https://twitter.com/dog_rates/status/683852578183077888/photo/1                                                                                                                                   \n",
       "972   https://twitter.com/dog_rates/status/709918798883774466/photo/1,https://twitter.com/dog_rates/status/709918798883774466/photo/1                                                                   \n",
       "925   https://twitter.com/dog_rates/status/715680795826982913/photo/1,https://twitter.com/dog_rates/status/715680795826982913/photo/1,https://twitter.com/dog_rates/status/715680795826982913/photo/1   \n",
       "958   https://twitter.com/dog_rates/status/711363825979756544/photo/1,https://twitter.com/dog_rates/status/711363825979756544/photo/1                                                                   \n",
       "\n",
       "      rating_numerator  rating_denominator    name  normalized_numerator  \\\n",
       "1023  11                10                  None    11                     \n",
       "1305  10                10                  Tiger   10                     \n",
       "972   12                10                  Watson  12                     \n",
       "925   12                10                  Zeus    12                     \n",
       "958   11                10                  None    11                     \n",
       "\n",
       "     dog_stage  favorite_count  retweet_count  \n",
       "1023  None      1749            488            \n",
       "1305  None      1986            370            \n",
       "972   None      3068            1102           \n",
       "925   None      4437            1686           \n",
       "958   None      3760            1177           "
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Issue 3:** **p1**, **p1_conf**, and **p1_dog** under `image_predictions` need to be merged with `arc`\n",
    "\n",
    "**Define**\n",
    "\n",
    "- Perform an inner merge between `image_predictions` and `arc` on **tweet_id**, and adding **p1**, **p1_conf**, and **p1_dog** columns. \n",
    "- rename columns to **breed**, **confidence_rating**, and **is_dog** (respectively).\n",
    "\n",
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean = pd.merge(arc_clean, img_clean[['tweet_id', 'p1', 'p1_conf', 'p1_dog']], on = 'tweet_id', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean = arc_clean.rename(columns = {'p1': 'breed',\n",
    "                            'p1_conf': 'breed_confidence',\n",
    "                            'p1_dog': 'is_dog'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1987 entries, 0 to 1986\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                 1987 non-null object\n",
      "in_reply_to_status_id    1987 non-null object\n",
      "in_reply_to_user_id      1987 non-null object\n",
      "timestamp                1987 non-null datetime64[ns, UTC]\n",
      "source                   1987 non-null object\n",
      "text                     1987 non-null object\n",
      "expanded_urls            1987 non-null object\n",
      "rating_numerator         1987 non-null int64\n",
      "rating_denominator       1987 non-null int64\n",
      "name                     1987 non-null object\n",
      "normalized_numerator     1987 non-null int64\n",
      "dog_stage                1987 non-null object\n",
      "favorite_count           1987 non-null int64\n",
      "retweet_count            1987 non-null int64\n",
      "breed                    1987 non-null object\n",
      "breed_confidence         1987 non-null float64\n",
      "is_dog                   1987 non-null bool\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(1), int64(5), object(9)\n",
      "memory usage: 265.8+ KB\n"
     ]
    }
   ],
   "source": [
    "arc_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='store'></a>\n",
    "## STORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "arc_clean.to_csv('./data/twitter-archive-master.csv', encoding = 'utf-8', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analyze'></a>\n",
    "## ANALYZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = arc_clean.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>normalized_numerator</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>breed</th>\n",
       "      <th>breed_confidence</th>\n",
       "      <th>is_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>37424</td>\n",
       "      <td>8130</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>32155</td>\n",
       "      <td>6028</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id in_reply_to_status_id in_reply_to_user_id  \\\n",
       "0  892420643555336193  None                  None                 \n",
       "1  892177421306343426  None                  None                 \n",
       "\n",
       "                  timestamp              source  \\\n",
       "0 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU                                                        \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name  normalized_numerator  \\\n",
       "0  13                10                  Phineas  13                     \n",
       "1  13                10                  Tilly    13                     \n",
       "\n",
       "  dog_stage  favorite_count  retweet_count      breed  breed_confidence  \\\n",
       "0  None      37424           8130           orange     0.097049           \n",
       "1  None      32155           6028           Chihuahua  0.323581           \n",
       "\n",
       "   is_dog  \n",
       "0  False   \n",
       "1  True    "
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **For tweets that feature actual dogs, what are the top 5 most common dog breeds?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs = df[df['is_dog'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden retriever      138\n",
       "Labrador retriever    93 \n",
       "Pembroke              88 \n",
       "Chihuahua             79 \n",
       "pug                   54 \n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.breed.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.093878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Labrador retriever</td>\n",
       "      <td>0.063265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.059864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.053741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.036735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 breed  proportion\n",
       "87  golden retriever    0.093878  \n",
       "35  Labrador retriever  0.063265  \n",
       "47  Pembroke            0.059864  \n",
       "15  Chihuahua           0.053741  \n",
       "99  pug                 0.036735  "
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_grouped = (dogs.groupby('breed').count() / len(dogs))['tweet_id'].reset_index(name = 'proportion')\n",
    "dogs_grouped.sort_values('proportion', ascending = False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** The top 5 most common dog breeds are the golden retriever, labrador retriever, pembroke, chihuahua, and pug at proportions of 9.39%, 6.33%, 6.00%, 5,37%, and 3.67% respectively. As for raw counts, they are 138, 93, 88, 79, and 54."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **For confidence ratings on breeds that are less than 95%, do they have less or more dogs compared to confidence ratings equal to or greater than 95%?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_ratings = df[df['breed_confidence'] < 0.95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "greater_ratings = df[df['breed_confidence'] >= 0.95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8721690991444389"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check ratios\n",
    "less_ratings.shape[0] / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12783090085556115"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_ratings.shape[0] / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_dog\n",
       "False    0.257357\n",
       "True     0.742643\n",
       "dtype: float64"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_grouped = less_ratings.groupby('is_dog').size() / len(less_ratings)\n",
    "less_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_dog\n",
       "False    0.279528\n",
       "True     0.720472\n",
       "dtype: float64"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_grouped = greater_ratings.groupby('is_dog').size() / len(greater_ratings)\n",
    "greater_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4852856318522793, 0.44094488188976383)"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#different in proportions\n",
    "less_prop = less_grouped[1] - less_grouped[0]\n",
    "greater_prop = greater_grouped[1] - greater_grouped[0]\n",
    "\n",
    "less_prop, greater_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04434074996251547"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_prop - less_prop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** In both cases where the confidence rating for breed predictions is less than 95% *and* equal to or greater than 95%, there are more dogs than non-dogs, with a difference in proportions of 48.5% and 44.1% respectively.\n",
    "\n",
    "The difference between these 2 proportions is 4.43% in favor of the case where confidence rating is less than 95%. The difference between these proportions is too small to make any practical conclusions to predict whether 1 case tends to have more dogs or non-dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which months have the highest favorite count and retweet count? How about day of the week?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "#examine month\n",
    "months = df.timestamp.apply(lambda x:x.strftime('%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>633613</td>\n",
       "      <td>1887312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>418885</td>\n",
       "      <td>1564243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>379544</td>\n",
       "      <td>1387904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>301596</td>\n",
       "      <td>1148810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>425534</td>\n",
       "      <td>1557542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>593842</td>\n",
       "      <td>2011650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>519589</td>\n",
       "      <td>2095136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>214300</td>\n",
       "      <td>649641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>213842</td>\n",
       "      <td>677563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>256584</td>\n",
       "      <td>815006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>390269</td>\n",
       "      <td>1098641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>791663</td>\n",
       "      <td>2081166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       retweet_count  favorite_count\n",
       "month                               \n",
       "01     633613         1887312       \n",
       "02     418885         1564243       \n",
       "03     379544         1387904       \n",
       "04     301596         1148810       \n",
       "05     425534         1557542       \n",
       "06     593842         2011650       \n",
       "07     519589         2095136       \n",
       "08     214300         649641        \n",
       "09     213842         677563        \n",
       "10     256584         815006        \n",
       "11     390269         1098641       \n",
       "12     791663         2081166       "
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_grouped = df.groupby(months).sum()[['retweet_count', 'favorite_count']].sort_index().rename_axis('month')\n",
    "months_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "12    791663\n",
       "01    633613\n",
       "06    593842\n",
       "07    519589\n",
       "05    425534\n",
       "02    418885\n",
       "11    390269\n",
       "03    379544\n",
       "04    301596\n",
       "10    256584\n",
       "08    214300\n",
       "09    213842\n",
       "Name: retweet_count, dtype: int64"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#month with highest retweet counts\n",
    "months_grouped.retweet_count.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "07    2095136\n",
       "12    2081166\n",
       "06    2011650\n",
       "01    1887312\n",
       "02    1564243\n",
       "05    1557542\n",
       "03    1387904\n",
       "04    1148810\n",
       "11    1098641\n",
       "10    815006 \n",
       "09    677563 \n",
       "08    649641 \n",
       "Name: favorite_count, dtype: int64"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#month with highest favorite counts\n",
    "months_grouped.favorite_count.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [],
   "source": [
    "#examine day of the week\n",
    "days = df.timestamp.apply(lambda x:x.strftime('%w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>640750</td>\n",
       "      <td>2156089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>801891</td>\n",
       "      <td>2656310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>753403</td>\n",
       "      <td>2592189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>845238</td>\n",
       "      <td>2776301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>642443</td>\n",
       "      <td>2180460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>741639</td>\n",
       "      <td>2377756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>713897</td>\n",
       "      <td>2235509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     retweet_count  favorite_count\n",
       "day                               \n",
       "0    640750         2156089       \n",
       "1    801891         2656310       \n",
       "2    753403         2592189       \n",
       "3    845238         2776301       \n",
       "4    642443         2180460       \n",
       "5    741639         2377756       \n",
       "6    713897         2235509       "
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_grouped = df.groupby(days).sum()[['retweet_count', 'favorite_count']].sort_index().rename_axis('day')\n",
    "days_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day\n",
       "3    845238\n",
       "1    801891\n",
       "2    753403\n",
       "5    741639\n",
       "6    713897\n",
       "4    642443\n",
       "0    640750\n",
       "Name: retweet_count, dtype: int64"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#day with highest retweet counts\n",
    "days_grouped.retweet_count.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day\n",
       "3    2776301\n",
       "1    2656310\n",
       "2    2592189\n",
       "5    2377756\n",
       "6    2235509\n",
       "4    2180460\n",
       "0    2156089\n",
       "Name: favorite_count, dtype: int64"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#day with highest favorite counts\n",
    "days_grouped.favorite_count.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** Since November 15, December had been the month with the highest retweet counts at 791663, while July had the highest favorite count at 2095136. However, December came as a close second at 2081166.\n",
    "\n",
    "On the other hand, Wednesday has both the highest retweet counts and favorite counts at 845238 and 2776301 respectively. Additionally, the descending order of the days for both retweet and favorite counts is the same at Wed, Mon, Tue, Fri, Sat, Thu, and Sun."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which 5 dog breeds tend to have the highest mean ratings out of 10? Is there statistical significance in the correlation between proportion of dog breed and mean rating out of 10?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add normalized_numerator to dogs_grouped df\n",
    "dog_breeds_mean_score = dogs.groupby(['breed']).mean().normalized_numerator.reset_index(name = 'mean_normalized_numerator')\n",
    "dogs_grouped = pd.merge(dogs_grouped, dog_breeds_mean_score, on='breed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>proportion</th>\n",
       "      <th>mean_normalized_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>clumber</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>briard</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Tibetan mastiff</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>12.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Border terrier</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>12.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              breed  proportion  mean_normalized_numerator\n",
       "80  clumber          0.000680    27.000000                \n",
       "52  Saluki           0.002721    12.500000                \n",
       "76  briard           0.002041    12.333333                \n",
       "61  Tibetan mastiff  0.002721    12.250000                \n",
       "9   Border terrier   0.004762    12.142857                "
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_grouped_ratings = dogs_grouped.sort_values('mean_normalized_numerator', ascending=False)\n",
    "dogs_grouped_ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>breed</th>\n",
       "      <th>proportion</th>\n",
       "      <th>mean_normalized_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.093878</td>\n",
       "      <td>11.572464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Labrador retriever</td>\n",
       "      <td>0.063265</td>\n",
       "      <td>11.086022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.059864</td>\n",
       "      <td>11.443182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.053741</td>\n",
       "      <td>10.708861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.036735</td>\n",
       "      <td>10.240741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 breed  proportion  mean_normalized_numerator\n",
       "87  golden retriever    0.093878    11.572464                \n",
       "35  Labrador retriever  0.063265    11.086022                \n",
       "47  Pembroke            0.059864    11.443182                \n",
       "15  Chihuahua           0.053741    10.708861                \n",
       "99  pug                 0.036735    10.240741                "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_grouped_props = dogs_grouped.sort_values('proportion', ascending=False)\n",
    "dogs_grouped_props.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>mean_normalized_numerator</td> <th>  R-squared:         </th> <td>   0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                       <td>OLS</td>            <th>  Adj. R-squared:    </th> <td>  -0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>Least Squares</td>       <th>  F-statistic:       </th> <td>  0.2092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                 <td>Wed, 03 Jul 2019</td>      <th>  Prob (F-statistic):</th>  <td> 0.648</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                     <td>07:49:49</td>          <th>  Log-Likelihood:    </th> <td> -223.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>          <td>   111</td>           <th>  AIC:               </th> <td>   450.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>              <td>   109</td>           <th>  BIC:               </th> <td>   456.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                  <td>     1</td>           <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>          <td>nonrobust</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>  <td>   10.7588</td> <td>    0.209</td> <td>   51.364</td> <td> 0.000</td> <td>   10.344</td> <td>   11.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>proportion</th> <td>    5.9553</td> <td>   13.020</td> <td>    0.457</td> <td> 0.648</td> <td>  -19.849</td> <td>   31.760</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>179.101</td> <th>  Durbin-Watson:     </th> <td>   1.882</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>15271.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 6.147</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>59.132</td>  <th>  Cond. No.          </th> <td>    75.0</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                OLS Regression Results                               \n",
       "=====================================================================================\n",
       "Dep. Variable:     mean_normalized_numerator   R-squared:                       0.002\n",
       "Model:                                   OLS   Adj. R-squared:                 -0.007\n",
       "Method:                        Least Squares   F-statistic:                    0.2092\n",
       "Date:                       Wed, 03 Jul 2019   Prob (F-statistic):              0.648\n",
       "Time:                               07:49:49   Log-Likelihood:                -223.47\n",
       "No. Observations:                        111   AIC:                             450.9\n",
       "Df Residuals:                            109   BIC:                             456.4\n",
       "Df Model:                                  1                                         \n",
       "Covariance Type:                   nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept     10.7588      0.209     51.364      0.000      10.344      11.174\n",
       "proportion     5.9553     13.020      0.457      0.648     -19.849      31.760\n",
       "==============================================================================\n",
       "Omnibus:                      179.101   Durbin-Watson:                   1.882\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            15271.556\n",
       "Skew:                           6.147   Prob(JB):                         0.00\n",
       "Kurtosis:                      59.132   Cond. No.                         75.0\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_grouped['intercept'] = 1\n",
    "lm = sm.OLS(dogs_grouped['mean_normalized_numerator'], dogs_grouped[['intercept', 'proportion']])\n",
    "lm.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** The top 5 dog breeds with the highest mean rating out of 10 are the clumber, saluki, briard, tibetan mastiff, and border terrier at 27, 12.5, 12.33, 12.25, and 12.14 respectively. Each of these mean ratings is greater than every rating for the top 5 most common dog breeds.\n",
    "\n",
    "After creating a simple linear regression model between **proportion** and **mean_normalized_numerator** of dog breed, the coefficient was found to be 5.955. This suggests a positive linear relationship. However, in comparison to a Type I error rate of 0.05, we obtained a p-value of 0.648. This suggests that for each dog breed, its commonality is *not* statistically significant in predicting its mean rating out of 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which dog stage appears the most often?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper            203\n",
       "doggo             62 \n",
       "puppo             22 \n",
       "doggo, pupper     9  \n",
       "floofer           7  \n",
       "doggo, puppo      1  \n",
       "doggo, floofer    1  \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['dog_stage'] != 'None'].dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     203\n",
       "doggo      62 \n",
       "puppo      22 \n",
       "floofer    7  \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get counts of single dog stages\n",
    "single_counts = df.loc[(~df['dog_stage'].str.contains(', ')) & (df['dog_stage'] != 'None')].dog_stage.value_counts()\n",
    "single_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['doggo', 'puppo', 'doggo', 'floofer', 'doggo', 'pupper', 'doggo',\n",
       "       'pupper', 'doggo', 'pupper', 'doggo', 'pupper', 'doggo', 'pupper',\n",
       "       'doggo', 'pupper', 'doggo', 'pupper', 'doggo', 'pupper', 'doggo',\n",
       "       'pupper'], dtype='<U7')"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separate stages of tweets with duplicate dog stages\n",
    "duplicate_strings = (df.loc[df['dog_stage'].str.contains(', ')].dog_stage.str.split(', '))\n",
    "flattened = np.asarray(list(itertools.chain(*duplicate_strings)))\n",
    "\n",
    "flattened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['doggo', '11'],\n",
       "       ['floofer', '1'],\n",
       "       ['pupper', '9'],\n",
       "       ['puppo', '1']], dtype='<U21')"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get counts of dog stages from array of duplicated stages\n",
    "flattened = np.array(flattened)\n",
    "unique, counts = np.unique(flattened, return_counts = True)\n",
    "counts_np = np.asarray((unique, counts)).T\n",
    "\n",
    "counts_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doggo      11\n",
       "floofer    1 \n",
       "pupper     9 \n",
       "puppo      1 \n",
       "dtype: int64"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#turn 2d array into a pandas Series\n",
    "duplicate_counts = pd.Series(counts, index = unique)\n",
    "duplicate_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     212\n",
       "doggo      73 \n",
       "puppo      23 \n",
       "floofer    8  \n",
       "dtype: int64"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get total counts of all dog stages\n",
    "single_duplicate = pd.concat([single_counts, duplicate_counts], axis = 1, sort = False)\n",
    "all_counts = single_duplicate['dog_stage'] + single_duplicate[0]\n",
    "\n",
    "all_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** The most common dog stage, with entries containing multiple stages included, is *pupper* at 212 counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Which is the most used Twitter source?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1949\n",
       "Twitter Web Client    28  \n",
       "TweetDeck             10  \n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0.980876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>0.014092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TweetDeck</td>\n",
       "      <td>0.005033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               source  proportion\n",
       "2  Twitter for iPhone  0.980876  \n",
       "1  Twitter Web Client  0.014092  \n",
       "0  TweetDeck           0.005033  "
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#proportions\n",
    "source_props = (df.groupby('source').count() / df.shape[0])['tweet_id'].reset_index(name = 'proportion').sort_values('proportion', ascending = False)\n",
    "source_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9667840966280826"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference in proportions between Twitter for iPhone and Twitter Web Client\n",
    "source_props.proportion[2] - source_props.proportion[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9758429793658783"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference in proportions between Twitter for iPhone and TweetDeck\n",
    "source_props.proportion[2] - source_props.proportion[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** Twitter for iPhone is the most commonly used source, with a vast difference in proportions from Twitter Web Client and TweetDeck of 96.7% and 97.6% respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **What are the most common adjectives used to describe dogs vs. non-dogs?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "nondogs = df[~df['is_dog'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "#turn all text into a single string\n",
    "dogs_text_array = np.array(dogs.text)\n",
    "nondogs_text_array = np.array(nondogs.text)\n",
    "\n",
    "dogs_text_string = ''.join(string for string in dogs_text_array)\n",
    "nondogs_text_string = ''.join(string for string in nondogs_text_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(str, str)"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dogs_text_string), type(nondogs_text_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')\n",
    "\n",
    "dogs_doc = nlp(dogs_text_string)\n",
    "nondogs_doc = nlp(nondogs_text_string)\n",
    "\n",
    "dogs_adjs = [token.text for token in dogs_doc if token.pos_ == 'ADJ']\n",
    "nondogs_adjs = [token.text for token in nondogs_doc if token.pos_ == 'ADJ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('good', 80), ('rare', 29), ('other', 28), ('happy', 27), ('great', 22)]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = Counter(dogs_adjs)\n",
    "dogs_common = dict(c1.most_common())\n",
    "\n",
    "list(itertools.islice(dogs_common.items(), 0, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('good', 29), ('rare', 17), ('great', 11), ('many', 10), ('other', 9)]"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = Counter(nondogs_adjs)\n",
    "nondogs_common = dict(c2.most_common())\n",
    "\n",
    "list(itertools.islice(nondogs_common.items(), 0, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Conclusion:*** The 5 most common adjectives to appear in texts of tweets containing dogs are *good, rare, other, happy,* and *great.* On the other hand, the 5 most common adjectives to appear in texts of tweets not containing dogs are *good, rare, great, many,* and *other.* \n",
    "\n",
    "It appears that non-dogs are perceived with the same or similar levels of cuteness as dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='visualize'></a>\n",
    "## VISUALIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (10.0, 10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "breeds_plot = dogs.breed.value_counts().head(5).plot('barh', title = 'Top 5 Most Common Dog Breeds');\n",
    "breeds_plot.set_xlabel('Count')\n",
    "breeds_plot.set_ylabel('Breed')\n",
    "plt.savefig('./visuals/breeds-dist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1, sharex = True)\n",
    "\n",
    "plt.plot(months_grouped.favorite_count)\n",
    "plt.plot(months_grouped.retweet_count)\n",
    "\n",
    "plt.xlabel('Month');\n",
    "plt.ylabel('Count');\n",
    "plt.legend(('Retweets', 'Favorites'))\n",
    "plt.suptitle('Favorite and Retweet Trend by Month', fontsize = 24, y = 1);\n",
    "plt.title('From Nov 2015 to Aug 2018', fontsize = 16);\n",
    "\n",
    "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "months_i = [i for i in range(0, len(months))]\n",
    "plt.xticks(months_i, months)\n",
    "\n",
    "plt.savefig('./visuals/months-trend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1, sharex = True)\n",
    "\n",
    "plt.plot(days_grouped.favorite_count)\n",
    "plt.plot(days_grouped.retweet_count)\n",
    "\n",
    "plt.xlabel('Day');\n",
    "plt.ylabel('Count');\n",
    "plt.legend(('Retweets', 'Favorites'))\n",
    "plt.suptitle('Favorite and Retweet Trend by Day of the Week', fontsize = 24, y = 1);\n",
    "plt.title('From Nov 2015 to Aug 2018', fontsize = 16);\n",
    "\n",
    "days = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n",
    "days_i = [i for i in range(0, len(days))]\n",
    "plt.xticks(days_i, days)\n",
    "\n",
    "plt.savefig('./visuals/days-trend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "xi = dogs_grouped.proportion\n",
    "y = dogs_grouped.mean_normalized_numerator\n",
    "\n",
    "#create regression line\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)\n",
    "line = slope * xi +intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xi, y, 'o', xi, line);\n",
    "plt.xlabel('Dog Breed Frequency')\n",
    "plt.ylabel('Mean Rating (out of 10)');\n",
    "plt.suptitle('Proportion of Dog Breed vs. Ratings out of 10', fontsize = 24, y = 1);\n",
    "plt.title('with Regression Line', fontsize = 16);\n",
    "\n",
    "plt.savefig('./visuals/breed-ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_counts.plot('barh')\n",
    "\n",
    "plt.suptitle('Top 5 Most Common Dog Stages', fontsize = 24, y = 1)\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Dog Stage')\n",
    "plt.savefig('./visuals/dog-stage-dist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAE5CAYAAABS724NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvVeTnUeen/lk5uuOP+WrgEIVAIIEAdA325vpaY1paWZkVytpFQopdiMU+zX2Vncbe7URe6mIjdjYjZB0oZWbVfeoZ9qQbDY9CEPY8lXHn/PazNyLPCiggCoQAEHTPecJgiTO613mL/8uhbWWCRMmTJgwYcKECZ8d+WWfwIQJEyZMmDBhwu8KE2E1YcKECRMmTJjwlJgIqwkTJkyYMGHChKfERFhNmDBhwoQJEyY8JSbCasKECRMmTJgw4SkxEVYTJkyYMGHChAlPiYmwmjBhwoQJEyZMeEpMhNWECRMmTJgwYcJTYiKsJkyYMGHChAkTnhLel30CAEKISfn3CRMmTJgwYcJvBdZacdSyr4SwmvAgMgzxag2s1uh4hEniL/uUHo6UCM9DACbLvuyz+cxIAaWS4G//uMK3vhYdWNbpGf7LT0d8dCljt2UeeZ9+2WPmuWnyuCBuJWT9lMVXF5CeJB/mWGvJhwV+xaO6UGHzN9uUZ0sEtYC1X21gtcWv+JRnSgRVnzwusNpQni2jAkXSTend7lPEOdNnpqgdq5H2UwZbQ+K9BGstx19fYrA9pIgL5s7PsP3+LsOtETrTh5+0EHiNMs1vnsEkOel2l3SjjR4k2OLRr/1JCWaqlI43Sbd6ZN0Yk+RPtB9VDgjn63iVgKw9IlnvACA8iYx8vHJI1h5i80PugxRUz8xjC0O2OyDvxzAZCv61REgFiPE/AukFWF1gjEZ6PrbI99fTRcbkRfnryURYfRoCkGOPqTFfyHciwwiv0SScXaAY9LBF8VSElfB8ZBii4xjMER3pp55bCFJh4tHBfUuJCiOwvxvCSkgoRYIffa/Ev/gn9QPL1jYKtnYK1jeLxxJWKvSYPjPFaNcJmSLOmTs/i1/2GO3GFKkmaSV4JY/asQqtKx3qJ2pU5stsvLWJ1hYhBX7FY+b5aXSqSXsZypfUjtcY7sSMdmN0UtBYqbPw0jz9zQHWQhEXWG1ZeHkO8b5gsDGgulAhG+YoXzHaHZENMuy9l6MEqhwSHZ+m8bXTZLs9rDFkOz0QAuFJhO+aEOEpMAY9St03oiTSV8gowKQ5JitAj3f+kGV+o4RXLyEEFHFOtFCncmYerxrhd0YUvZi8lyCUwGqDSXJMrvFqJaSvyDsj/KkyKvIB0HFOMUgRviKYrhAt1kFKJ6wEBNNVgukKMlAUwxRdaGToE85VEVKi0wI9yiivzIAU+I0SeTcm7yfoQYKOH1/oqWpEMFdHhj55Z4hJMoL5BqoUIpTEGoOOM/LWgKJztNhT5QB/po5XjRChhxACU2h0LybvDCnawwc282dqeM0y0vdINzvI0MOfriJLAQA21+hRRrbTxQxTrP78xLNQCizYJ2yLvmik8gnKTYSQWKuxxuAFJYwuMLpAeT7WWqTyEVIx6m5iJuLqryUTYXUUgnHnoZChD0JgRuOG5okmrr7Xavjw7f2pGcKFRbxqnaLfxeriCY73IF61RrCwRHzruhNGT3Ad/tQsMoqIb15z21s7vk8eMgjdlQ2eyun+zpInmnyYo3PXaUlPIn2JGWTowhCvDWhdbpP2UopYk/Xz/UeV9TN6t/usfv8EQS2gc6PH9T+/QVALEQJ0rjHjZ4IELEgpkEpiMQglKeKC9rUucSfl1O+vUD9eY+fDPXYv7qHTu52cDH2ipSnKZxbAWrK9Aelml7w9AG3xGmX86QpY8BpldJwxuroJxqIiH3+6SnR8mnSzQ7bTQw9TALdspkZ0bOrgMiGonFmg+fIywlMMb+xitUH6HtVzS6jQp+gn9D5YQ5UCikFCvNYhbw+pPjNPMF2h9atPaL66Qun4FNZCfLNF/+K6E0PdmGC2evdBSEnlmTnKx6dId/vImy2Mpwhnq8z/4QVU5JNs9uh9sIYMFMFMjcqpOWSgGFzaon9xg9GNvcd+/tGJWWb+6GXCpSbdX10mXW8z++NXKa3OISshJslIbu3R/m8f0XvzKvlu/4F9yNAjOjHL9I9eoHL2OMF8A+FLiu6IwXs36f7qCt1fXMLqg9947aVV6t84gz9dZfvf/Ipgrs7U984RrcyCgKIzJL6+w+5/+A2jK5vo/udnKVdReSwiHxSAX0VUUKaxeBYBFNmQLBngByUQEmsNWENQbuKX6oAgT4dko/ajCUdxpFfpLk/S79y/3yfqu+7fj310rXjvdg879sOu/0nP+VH3/7SOcQ8TYXUIweIU5bPHqLywSjBbR4Ye6Uabvf/316TrLfxmhdKZRbKdHtlGm6JzeMMghaLkNZgprxJ5dTzpo21BkvdpJ7cZZntoe49oUgqvWic6vkI4v4jVBapSRQ4HCM8jWj6JDENsnlH0uiAEOh5R9HuYeES0vApSku1sUTn9HKpURicx6eYa1hiipWUqz57DrzVId7cpem30cEB0bAWv0QQERa9N3mmDEJRPnEIEAUW3Q7q9DkJSPnkaf2oWr9Ygb+9RdNuYIqdy6jkQgrzbJgeChSX8ehNVKiOjiLy1R9baRY+GlE+ewavVsVj0YECyfpOi2/lCnu1Xkd5an/7agMVX5ilN5wy3Rww2h+TDw60hVlvidoIKFVIKslHOzke7NFcbnP3TZ8iTAlNYtt/bIe2kzD4/zey5GbK+s275VZ+p0w1mzs5Qmo5IOulB3T9GSOkaJGMxhcHmBVbf7SSq547T+MYZhh+vY9Icm2sEziJTef440cos8c1d6q+eIu8O6fzVJWTguWXLM8Q3d6i/doq8NaT7xmWihQYq8hhe22VwZRs9SgkXGwSNMqMbe2S7fYSSNF5aphimJOsW6SsQ4FVD/KkywpP4zTLp7oDOr2+g4xw9TBHeIXk62hDfaiGAcL7u3IKeRJUDVOQzuLLN4ONNstaQ8olpstaA7OqIdKdP7dkFyidnn0hYuXsr8KerTP3gPPleH5NkdH55CYwTrKVnFpn+0Qv4zQpb//fPD7hdha9ofu8czW8/5+7jjR0GH90GbfCaFcLlGWZn64SLTdp/8ZGzMN6DCn2iY1PM/53X0f2EdKvD8OM1ZOARLDQoP7vEfOTT+vP3aP/sIpinbHGREumHlJdPo5MRwxuXn+7+PyeEEM4qZTTGaEye4NXmKLIhpkiJavNYo0kHexR5is5HWHO0xU+WSngz0wQnlvGmp1D1OrJcQnie+x61++ZMPEL3BxTdLsXuHsX2DkWvD/ohgk0I/LlZSi9eoHT2OQDMaET88WUGP//lY1137fvfJTpzGlkqARB/8CGjDz6i2H34ux89+wzRc88Srq4A0P/FL4k/+AibOo+GiCL86SmC1RP4c3Ooeg0RRSBwbU2coPt98p1d8o1NsrX1xxNAUuA1mwTHj+EvLKCmGshKBRkGrk3LMsxoRLGzR7a2Rr65he4/HavARFjdg/AV3lSF+jefpXJhhWChiRCuAVSViE41QngSVY2ITi4QLE3T1+ZIYVUL5mhEi5S8OhYojHuhIq/KVLSMJwNa8a27G1iLLQpsUaCTGD3oU/SdK1BGJaKFY+S9NsVwiMkygtk5hPIwaYJJYvzmNMLzKPpdgtl5rNbkvQ4mz3E+Hov0fLd+GmO1RigPf3oWGYZkO1voOMbmOSiFyTO8Uhmv3sDqgqy1A0I6l8NwgElT19Faiy1yZLmKitzH59caeNUa1lr0aIRXayDDiGxvG1UqI/wAYQ2Uygh19GsoBfiBIApdz5/nliyzFL8d3oMDFEnB3qUWSTclH+VYbdl+f4e0n5G0E1SgMIUh7WUkrYRsmNO71WO0F2Pv6dx0btj5cI/++oB86Fxhg3XXIFhjMNqStBOSTorONMKXeKEiHxUknYTe+oB8VDDcHpJ2EtJ+xmh79IDbx+YFxTAh7wzRw2TfNXVntKqqEX6zQtYaoHsxRXcECIKFJqXVOaLlGUyaU1qdw+uUGF7awG+UKZ2cI1yawmQ5pZNzeNWI0dVNgpkKQgqy1pB4rQ3G4tdLWGPJ9wbEax28akgwXcEai/CUc0dKiQgUcuwOw0LRT/bjqACUFxz+TIYpepQhAw8hnXux6CUMLm85F2OjhMk1Qgn0MCXd7BKvtak9t4BXDZ/8ZZDOzYqAdL1N782rZLt9rNb4zQoIQenUPJWzx/BnauTtITYrEIFH6eQ8tRdXCJemGH68Tv/dG2SbHWyh8ZoV6q+eonR6geZ3nye+tYeOU/Qg3T/0nTZMlUPiq9sMPrxFvjdA+IrSyXmE7xGtzFI6NU//vZsHnvlnRfgBfq1BNH+cyoln0ImLVyuGPfJBF6E8ysdOolP3u1ASHQ9dGyYEKiyR7m1hixxVKhPNHyfd28JkKeHMgmtXlIctctLdTXQyemoWf52nDFq3wRqKdEiRjZBdnyJPMDrDGovWKUbnmCJH54e7AYXv4x9bJFxexj9+DH/eiQpVKSPC0LlIhQBjsIV27fUoRg+H6E6PotUiu3GL+KOL++3vgwcRyCgkWFqk9LwTVkW3R9FqP/Z1+wvzRM+cRtVrbj97e8ir1z51O1WvES4f3z9+8sk10iufoNMMf2GBYGWZcHUFf3Eer9lEVsqIYPydao1J7wifXUaB74TVo6AUqlp1+18+jn9syQnXahVZihC+D8aOQ2wSim6X4MRx8vUN0pu3yNY3McPPZkWdCKt7kOWQyrkTNL71HF6zSnJzh2y9ReXCCady9xHIwKP0zCLp2h7xpcMfeCNaohkdpxOv0c92yHWCkh61YJ5mtESgooPCyhj0sE/e3sMWOXm3Tba7hTUWVa4go4jsyhbxrWsIpQhmF5BBiJCeO6fQvTRCCGyeU/S7ZLvbmHiEyXOKXpe81yG+dc1ZpaRClUou7mo4ZHj5I3d1nudE0PYmJsvw6g386VnitZsU/R5Yy+j6lbujMalId7YIjwUIPxjfnxCrNdneDunuFrXzLxPMLaDjIbZw52byDIzeD/g8jGpFsLTosXrCvapb25rb6wWtjnmwPRHSjfTM2BXGHVfl2Cdmufv7+DkeDCp6PDxPUK0KKhXBYGApCosQEAQCrXExUULgeS48T+uC7qUdPE8QAEJZtn+9ge+7dTZ/PsRYgVLge1AMLb1Riu8LahUocjG+HMveR7t4njuHwAfPFiS3O/SvtSn0wcsabo2wWLj3UgX01/tgwNrDTfsmLSi6I7LdPsUgIdvtk7fuNji20OTtIcOP1tCDxO3WkwQLDVQtAmHx6mVMUWCyAq9WIlyaQlUjhACvXsLmGpNrVCXAjp+MDDz8RgmTaUTggbXoJMemBbYUOLf8WOz4zTImK1xM1djqZpJx3NYdpECGHl41RJV8VOQjQw+rDaoUoCohMvJQUUAuYvJeTP/jTZqvrBAtNhhe3UGGPsUgQ8dOyLoTfQT3zREIXFxattWj99YntH7ywb5lSEYBQkq8Rhlvukp4bAo9ytBZgYx8ai+vEq3MUnRHbP/bN0jXW5jR3bhG3RuBgNkfv0rl2UXy7S7xYOeegwtQktG1bTq/uszgnRv7i5xVUDD3Z6/jz9YJZmsUvfhufNxnRPo+fm2KyvIzBFNzmCzFFjnJzjomS1GVGlOvfodk8zYmyxBKkbV3MHmGDAKC5gzFqI8eDfFrUzTOv0b34jsU/Q61Zy6MwxJcELlQimRrjWLY+/QTewR0HtPbOmhdy+K7+076u5+6DxH4eDMzVF59mfL5c/gL8+7TM8YNbLTGjEeNQgqQElWtoup1grE7yyQJo3c/ILk8boOfshvr80IGAbJSxlpL6fzzlF9+gfDUSbdQ6/1rEUKCH6DCEFWvIUsR+c7uo12nUqhGnej0SSpf/xrh6iqyNE4+KpzF3aSZ6wakRNVqqGaDaHWFotfD//gKgzffIrt5CzN6cjf4RFjdg9eo0PzBBUxa0P7zd2n9f+9iC81C5FM9v7K/nh4m5Dtdqi+uoirRkfuTwqPQCeuDDzFWY61BCMEwbxF6VTx5+Cj6UKxBx/ePvuydAyGkcK4brSm6HfoX3yM6doLqcxdIN9dItzcObnMPJk0x2d0RrT89Szi36FyJ8RCM3s/4e5yh650RgS0KsBaTpqTbW5ReP40MQtLtTUbXLqNHR48Ozj4b8Hf/VoV/+t+50dJ/+PMR/+f/M+Cv3ojJ770V4+B5qXyKZIT0ArAGU+RjkVc4gRoEmKJwWT1SjkfMT9YwTU9Ljp0I+O53A37yk5TtbUMUCU6dUrRahl7P4imYmZXEMbTbzmo4P6ewwMa6Jk4sx5YUMzOS69cLOl1Loyk5dVLxs5+lBIFg5YRiaUmxuanJCyfcPvggZ25OsrCg6PUM8/OSNIWbNzU7O5r8nn7QHubKsWCLz9ggG3vANbi/61yTbnQYXdmi//5Nt9644/AbZdL1NqNLG26ZxQWhZwVeyWfq66dovLhM+eQM6WYXoQ4vtZft9FGlgPkfPU+y3cerhhT95NB1vWpE6cQ0zVdXCeereOVwbIkbUTu7SO3sIuF8nWSji4x8wFI7t0Q4V0PHn28iRnJ7j+HHB10cVmuy7S56kOA3K3iNCtJXaED6HuUzi8jAI76+Q3Jj54HszPj6DuHlDfibrxKdnCe4ukV8fefggbVhdHGddK114GcTp6TrbcxYxKmqSyR4Wl23jkckO24gWl4+Td5r07/8HkY7S71Xn8KvNOi2fs1o7RomibFGEzRnCWcXDt2nX605q6WUdD76NTpNCKfnqJ0+j0mTpyasngb+/DyV116m8spLqPrdpBgzitHdLkW3h0lThBCIwEdVK6ipKVSp7EZbgO71KVptTJr+1ogqcKLSX1ig9HyTytdfw1+Yd+evNUWvjxmNQGtEuYyquBASpKTYa1HsfrpoBfCmmpSef476D3+AatSddQrXFxWtNrrVQo8ShJKoWhVvehpVq+5bucovXUBGIYO33mb09jtPfK0TYXUPMvSJTswyePc68SebFK2xeyU7aEo2ucsUEpGP8NWR+ytMRm5TpFBok2MxYAVShmibH97hHYXFdVDjD8la61x+M/NUnz1P3m3hVWvo0RBVrRPMzOE1ppB+4F4uITFZhh6NKK08g1fbdYHxReHMG/d8oEJKZBThNaedadZarDZYQMcxXq1B9dxLZHs76NEQIeU4LmwJU+QErR1EEGLTdBzUafcvQmDHcVclVKWKV6uPR62Hm+tPrfhcOBtwfNG9qtNNSRiKgzFBUhLNLOJVas41mmXoNEYohVeu4ZUqmDzDGo1XqlAkI0yRY7KUZHcdkyZP1EANBpY4s8QjS7tlqNUEZ896GA3z84qppiVNLf2BZXfXkGWWCxd8tracxePceR9duGUffZTT7VqS1FKrCeo1gacEMzOS6RnJx5cKlpcV09OSPLNcugSNhmR+XpLElkZdMhhavsgBrL1jYrr3N21Jbu66bMKVGYTvhFG23WP48TrxjR0qpYBoddZ9OwKyrS6Di2sUQ0P/ow3SnR4m0xSDBIxF+Iq8E6PjDGsMW//5A7LdAdJXJJtd9ChzVqw0pximdN+9jb6nLINJcpL1Dq3iE1TkUQxSsr0BOi0YXN4i3e27QPWtHkU/HV/bBircwaQFeWdEut1DpzlFz70r3fdvc2hg2mOiB8mDrjbrrIVWG2dtC7x965hQEn+6ijWWojc6NGNQxxlFL8akOV6jjKoeMvizkLcH+5bG/Z+1xaS5sxxIl735NK7z7gGcC0ZnCbbIMFmKTu7NMLbO1TbsHUjceSAAXNw9LRVVkH5IMD1P9eRZdJa6DGWsa3++KkiJvzBP6dzzqFoNoRRFu0N88WOytXX0WFTZQruBn1II30eWSk4ETE3hzc1S7OyS3rr9WyWqAPzZWYTnESwfR9WqzvV2/Qb59i5mNHLZ5NaOPSYhslrFn50h39kh29j81P2LKCI68wyV117Bm55y4Sz9AdnaOsnVTyh299CjETYvnCHC95HVKuGJZaJnTuMvLSLCkODkCqXBkGKvRb6x6UJjHpPfCmEVzpTxygEm06TtEeaomjuH4JUDgmYJnRYUw+xAg3s/QglkOUQPE2f+Pmq9/X89HGs1voxYqD5HWgzQJkcKReTVKfsNjNVMl+6xhJmMfubEirV2bKFyPaVOE7K97btlF6wl77RRURkZhEg/IO+6YHQXdCzRwwFaa/RwgNUFejQk3VpHlStjsSUwRUHeaR+wWOkkIe92xmIsdebpzMVTFd020vNRlYqzYt0JcLYGPRpgihyhPPSgh04S7PhjybttsJZgdt65Ewc9TDwknFtEj4YPlG+4w+qyx+oJ/6H3WQiJV23i1xoIqbBFQbK3ifR8guYcKgiw2lkMpR8iw5IbyY4Grg5Nnh1qefk0RiNDMbS024ZW27AUKep1SadjEIDvC6SEm7c0W5sa5Tm34Z32cKopMcbS61nSDIpxBlcYCppNSb0hqNclUSSIY0sUCWpVQZzAwoJibk5Sq41r6oxdkJWKoNU6+pwfF1toin5Mcmv3AYtQvtcnvrbtOoL9DSzZTg9/poo3VUaVQhfvEY6wxpJt9/ZT/lU5cIHM4Wh8LEO81nbxVVIcGjRtck3v3dvuLwJGt1rj7NS764xuHgyqNVlBtjsg230wMDUepMS3H4w7ybuj/es5zFwT33w6N9lkBeYQq9i9AxIh71ERUqCiAGstJj0idshYbKGxuUYGHtJ/sJm3WEx8n8vUHdgN+KwL1v4s7s6juSeb+IGstXGcYJYdtM6Pn4OQyllzpEKOww6483fP3w9BMHlGvHGTYvTVSVGWUYg3NYW/uADSDXTzrW0Gv3yTfHMLEx/R5wiBLJf2t9X9wSNbcL5KePNzqKkm3vQU6bXrxB9fJvn4MvnW9oPiRUpkqYQ/P4dJk08PKleKYGmR6MxpgtUVJ6pGMemt2wzffofk48voft/FZNy3Xb655eKNAx9vbhavXidcPUHRbqPbHfTvqrBqvrBE/fQsyc6A7Z9fJ917tMAyoQSlpTpz31gh3urTu7LD8ObR2WfWuNGa8D1XYuEIZOTj1UrYOHvAmnUvSvo0wiWO1y+QFiOMLZAoQq9CpmNykzBdOrG//ihv8+HOn5Ptbj2wr6Lbpt+9pwOwlqLbZtDrjIWNPTCCye+se8/vJokp+l1QintNG6NPLh08VqdF0Wm5+l33vYjZ7jbZ3s7dZeN95O2DnVnKQeJrV9DzS9Rf+hr9D98h39tBlstMff17pLtb5K0HGwopYWXZY+X4o7ym1sUpAKbIKAYdhPTwyjWyIhs3yBJTpAjlMnt0PHQNu5DAk0XDjy3ZWAvdrmVjQ1MuC3Z2NVkKs7OSLLUUBSAsnY5heVkhgN1dTZpaZmYlC4sBly8XDAaWxUXn+jux7LnSCcDrX/Ox1rkThYQLFzwaDYnR7vGPRpZSSXDypGJrS5NlT2c0a+Kc9HaL9PaDQmLw4W0GH95+8J5ow+CD2ww+XEP4ahw74t4jCwzev8Xgg9tuWWEebOzg0TLRLJ/fqP1pZ8IdhTaPXSvKumDBB0XJAcR+ivlRV2K1/uKu88CB3bGFVC4DTsoD7dRhZ3RnYCT8EKE8VFTCq9RASnTiCiin7R26H75F2h67Pe9rE79sZKXiArSV83Lo4Yhsa4v02vWHb2gtZjgiG47Ibq99/if6OeHPzjhvQq9H76d/SXLlKja9v6cYYwxmOCS99oh9vedRunCO4MQycuz+y7e2GL3/AcNfvXn0hlqT3bzlrIOeojYzjZUSb26W8gvnGb3zHnoweOz36LdCWDWem2f+m6v0r7Vov7fxyMLKaktpvsrxP36eeKOHNfahwsokGfHVTaITsxQvrpCu7VHcV8dFBj7h8iyVF1dJN9vkraOVdCu+RVoMUdLDjLPywJVhMNY41+A9FDrdzxx8ZI5qPB6S5vvQNN1H2Ye1PInPSQ/7jG5cJZxfJFo8hi0KRjeuHlpqIQwESwuK+VlFufToo2aTpsTbt102kLWYW5ed+/SOmdGafSFqjXZZmJ+hQOH2luY//qeEVsvQ7Vp6PYPnQZJYjHYZjcOhIU1BpPDmmzlK3Y2xNwbGxkOGQ4vWsLdnuHQppzUuPup5Yr/vGfepLvh7HBQ/GFhu3wKpBHkOcfwV6Uysda6qw96Thy2bcDTaUHRHLlP5MBcfLiheRgEy8tHDFDM6ovP6kjA6J++1QSjKy8/glarE27dJd4929xRDF7Ae1Js0L7yOzhKE5yOEpBj0KAZdovljNJ5/xX33eU7a2ibZuk0x6H6BV/cQjHNz3kGEIapUAnVnkPolntsXRNFqM/zlG+RbW86b8TRQClWtEK4s401N7f+cXL5K+gjZiwD59jbp9RtUv/E6IggQnoeqVfGXFjGjEbr3YC25h/FbIayCekQ0VyXrxEj/8eaNloEinKmgIo9wuvzQdXUvpvfLSzS/d47qC6sIT5Hv9ohW51HViOqLq64Y39IUXq1E+62rZBtHp6/GeZdUP3raprUGY39L6gg8QYdo0oR0ax3TnEb6PjZ3mY+HFQiMIsHzzwbMTCs87+HCylpD1t1zrkhdkA86LtPQWoqHZBw+DZIUNjfvNJaWOLb7BsQ7v91L64hK7fduMxpZ9g4YAO/f54O47GD71dMpDzuhr9zJfvUxuSa5sYtXLxPM1vDn6g9UZw8WGkTHpxFSkm48fPD3pWAMJksZrX2CX6lj8sxlC+uCYtCl+8EbD4ghk2dknV36Vz90rr4id7MAtHZI97bQaczgxmVUVHLhALpAjwZYffT3H6wu4s02wFiSSzcxwwSBIlAlcpMghSJUZeKi91TaZRPHmFGMyfNxQWUfb36Oyqsvk964he50nyie57cFqzW602X04UUnVJ7S9y+jEG9uBtVoIEIX9mHihHxr+5HLS5hRTNHuoHt9VLOBDAJEEOIvzLv6Vr+LwuqzYLVBJzneuOjfwyj6Mb03rhAem6b22mmm/+BldD/Gq5cRoUf99TOIwMOkOemtXQbvXCfbfogFzBZgBIFytZ0Kk5KbBBAoMc7wsL+7H9L92KJ5PfsRAAAgAElEQVRA97ro3qePIMslwYvnAqabjyCkjSFrbT+FM3w6PEl78WnbfNblE343MFnO8OIa0cos/myd2surjC5vULSHWAvSV1TPLVN+dhE9TIivbZNtf0UsNvdiDcPrHz/ws05iWr/+2WEbkPfadN5/48hdDm9cOnLZYZRfOE3pxdPYoiDfamGGCVJIIlVFCQ8lfSJVJdWjg8JKyP1ruFPK5c68gUbfsYAfEh8Yu867aLXxZ6YRvk+wsED9+99lWPkN6Y1bFN0uNkldIPcTxH1+lbFZRtHtkj9CIPoBpDhYwN0cjHuUpRL+woKzNI3jhot2G93vP7pVzFpsmlG0OshyGYIAoSTezLT7+2Pyuy+sjHMJykAhD6u+fC/aoPsxu//+LUaX16m9eppoZc4VIhzHUsVXNhi8f5PBO9fI9/qHz+M1xpMh9XCRY/ULLqZmdI31/kd4MqQWzADQTn57feafJ+WS5MXzIVPNo7MuJ0z464aJc3pvXyNanaP5/XMs/dPv0/3lFZKbLoMxXJqi9sop/KkK/fdvMfp4/YHK6xMcwfIcpfOnMKMEGbnSN8YaEj1gNlpFCEEn3bhHVLkgeeWFWCy2yJBeiLUaqQIqU8skgx2yuDueI/A+rCXb2GT09jvUvvdtVLWKiEJXeX1+jmJ3j/TmLZJLV8hu3aZod36nRky6P0B3H/9dVJGHCu72Azop0Mnd2GYZRS7jcJykYfMC3ersV3h/VFzc7ehuIpOUqHJ5P2brcfidFlbCk6iSj18NxtkmjxIQayk6Q0YX18j3Bnj1EjL0QLiqzLoXk+/1naj6lPLfjegYjWiRYbZHM1zCly4F2NicSjCDJ4OJsDqEwIeZacm5Z32ajcdz/U6Y8DuNtehhSveXl9GjlNorJ12V9JNzbrkQ6GHM8OJtem9cId3sfDkB6l9xhFKIwEeGviv1MjaJuALOsyjpI4Cy1yDRA4wtUH5IpXkcXaQIqQjCGl5YochjjM4IohrZqMPDUsaLVpvR+x8ilCI6+yz+4gLS91HlMmJBIStlgqVFZ9na2yPb2CZfX6fodI8O9P4twWb5E11DNFOhNF9FhR4y8BjcbNP/5G6shPA8lxQgnfiSYYC/fIzGH/7IBZ4/IrJcJjh+7G5BUSEQYQDe4w/uv1rCSgikLxFKHsh4EZ4c1/VwQskrf0phTQlSKUqLNWqnZ1CRT9ZLDqjchzKepuaoqWoelYo/RaRqrPXfoxbMIIR7QMZqAlUmUOXx6So84aPwKMgRCIw1FGT4IgSEm89MlMlsMnYfCjzhu/n2bE7Bo7kUpYR6VdJsuD+NuiQIBMG4+jdAUbipY9LM0ukZ2h33ZzgyD42Jfxx8H0qRpFIWVMr3/LciaNYl588GrJzwKN0XuH58yeN734yYakqKxyxwubOn+cWbCXnxFAaCh2xfqQhmmorpKUm9JimXJEEASroSC4V22YGj2DAYWro9w/ZuwSi2n7vVXymIQsFUUzHddOdXisR+1XcpBUVhyXNIUsNgYGl3Ne2Oodc3T6vw9j7lkuDUisfxJY9a9a54/vhKzuVPctJDshp9H2pVyfysolmXVCruGjwl7kxpiNaWJLGMYstgaOj0DJ2uYTj8bNfgqxJhUCPwylhrSPI+WT5EP26yCVD0RgwvbWC0IVl7cL41ayx6mDH8eJ28MyTd7GDSHDl2T+U6Ib62RTGIKfox0YkZ/EbFTf8ySklu7ZLc3CXb6rh5D0MPC6jQc+7BT7bwapFrSxtlTJy5Aq31Ev5UBRl6JDe2MXGGXw0J5+suI09KVzJj1xVn9eolZ4VpDdzUX40yxTB1ta+kcLW08i8p8/BhSOEq+t8pFXMPYtyuJtrF1Aihxq0vSOUTVqaI+ztI5RNVZ5BeiPJCjHbWKyEfPhC0SUK+vsHQaPRwSHjqJP7sDKpR37e8+LOz47CJHtn6JtmtBbLNTYrtHfK9lnNvPa2G+AvEjovAPgky9Cgt1pC+Im0NnXa981opNS4mOq715vv4M9P4M9Of8YzF3ZJCj8lXSlhJXxJMlfGr4bgwncOvhvuiqrRYx+QPf6mEEviVkLlvrTDz6jIIQbI9IH9IbSq3oRNvVj9Cmq4YF6k8os4NgBQSY3M6yQbHqucPHOjeROhARFRkg0CWGJmeE1g2p2f2qMgGAolEMuedoK23GJouCkVFNtAUjEyfgWmP07CPOBfpssgqJcm553xeuhDy8vmA88+HzEzJ/Y7KZacZ2l1Dq2N478OUX7+b8pv3Mq5ezxmMzH55gSfFWaQUy8c8Tp7wOHnCZ/XE+P9XfBbnPRr1w1/mb7wW8Y3Xjq52/zD+4ucx//B/2qTbM59ZyJh7XhEpXRbj6RWf11+N+OarIc+d8TlxzGNmWhEGAm3GgmpguL1ecOVazgcXc37685jrt3J6fcMTtjmfiuc5MX1sUfHayxGvvRRy7lmfYwuuZEO1IvA9wWBo6fUNm9uaq9dyfvOBe/YffpwxGFmK3D61PnJuRvHf/90q/+BPq5w9c3eg9K/+tzb/6//eZael9+/vnQzIuRnF2TMBP/h2xEvnA06t+izOeVQqAqUEWluGQ8v2bsGttYKr1wve/yjj1++lXLnm7vGTIJBUS3PM1p+lWT1OoTN2updo9W8wSh9/EuZ0rcXO2kPqYBlDvttj598dTBOP/DpR2KQ32sAUOdlGh9bG4TGewVyNxssr43pXOVYbwrkGvfdv0fovLWx/QDhdwZ6cJVlrk7UGlJaniZaaWGtp//R9wpka0UIdIVzslggUNje0f3GF6FiT6tkljDZ037yG8BT1F5YZXtvBb5SQgUf/wzXy9ujBWllfNkIgQv/Qqv7GakZFl7joAYKSV3OxsoyTi4qMbORKufhhFVNkSOmh/Gg8cP70DGZbFGS318k3tog/+Ijo+econ38ef2kRVam4BkVK1NQU5akpyhfOke/skly64mpe7ey4mle/ZW5Ce6dG2mMSb/YQUhBOlynigiLOD/S5rtint2+MsXcy5T/r/TFP3tF9pYRVNFtl8ffPMP3SsQMZfKX5GqrkU12Z4sw/ex19VGG8Owg3xUtQj/CqIbbQtH5zm8GNh2cIeI0Kta89Q3x1k3SjjU0PtwLJcog/XcWfrpJudsiPCA411mCtxRMuLRhcqYWy30DJYN93X5VTGAr2ijW01TTULL4IXJFJEZHblJHpE4//AEyp+bF1yyeSFQamw8PydU+v+nzn6xF/8HslnjnpM910QqpclvgeeMrNUQfge4pqVbK04Ap0/vC7JVptw/sfZfynn474xZsJG1tPpkw8BX/vT6r8we+VePlCSBS6CZbD8X+jyFnPvspYC2nqrEy1quDUqs8//LMqr78SsrLsLDDlkiAMBJ4vkAIU4ClnmWs2JM+c8vnetyz/4M8qvPVOyp//txH/+Scj4sNnZXkilHKWoR99v8wPv1vi66+ENBry7vn5AuW55y4FNJQrMDozrXjmpMd3vhmxu6e5ci3nv/xFzF/+Mub6reJzbc/LJUGtJtgZ6w4lXYX5P/mDMr//vTIvng+Yakoq5bsWtzvlKzwl8DxBueyztODx4nnLH/6wxIcfZ/zH/zri//jXj5fZcwchBPXyMQqTcGP7V8RpmzQfUuin+LAegUo0y2z9GUbJHpl5uIXaaoNJc/xmhTzT5J0hKgqIFpuApejFWO1mVDCFmwnA5K7it1+LyHb7pLt9rLVIX5F1hmAsqhJSObOADDyKfoIFSivOKyAjH+FJF3caeJRWZzHZ5ldOWAkpkIHvXq77UMKj4k+RmwSBpOJPkxR9tC3QWUx3+yq6cAlI3e3xlDLCFSkW0qPIRhj9aFZMq7VzDf7mXZLLV52V5dgS4eoKwbElVKPuRBagmg1KL5zDP7bI8I1fE1/8mGLvKVYCfhhKHnqvviikf3eC9XzwoOfJjidU3p+VJM8pOl10u41NnvwbtYWm2Gs9UVzYV0pY6awg2RqQ7o3wKyHBdIlwqowMPaSSyEpAtfLo5r1ilJFs9+ld3WX3rduMNh9+g7xGmeb3z2Oywk33cJSwCn3CxSmqr52m/+aVI4XVMG+hpMex+gUq/hS+DDH1l4i8GtYa+tnd+bs84ROKMjkJEokSHlU5RSgiCpuhcS+TFBL2jWQCY/VDMwunm5KvvRzyvW+V+OZroQsIb0j8h4gXpSAYj7zqNfdB5bnl2JLH4oLizCmf//qzmEtXH98KIAQcX1JcOBvw6ovhY237VcEYN/XM7IziwvMBf/pHZb7zjYjVZZ9q5egGSEnwEUQhNOpuZLW67DE3q5ibVUw1FD/9q5gbtz+7eKlVBc+e9vkbPyjzjdciXjwXcPKEN/Z+HP7spXTV4ksRNOqSBeDEcY+TKx7zc4qVZcV/+0XCL99KyPPPZ8BcKklqNWc59T04eybgR98v8Qe/V+LFcyFLC54zFh/x+ioFgS+o3JPIs7WjUerxxbqUPpFfo1ZepFlZxtgCa4ybA9Tk5MUIISRT1VUEAm1yAq+MlD5p3qMzvI21hshvUI6mCb0KCEGhU5KsxzDZRQhFFNSoRnPjOcKddVqbnH68RZJ38VREJZplpnaaZnWF+bRNXoxI8wGd4c3Dz91TqGrkpgWyFlWLiG+19jsfNruAoBgkrvK7teOJrAVZa4hXiyj6CYNLm0hfORFlxuvcqcZ+T/V04SnS7R7p7sB5F0JvXHD54aJK+B4i+mLbARl4qOmaE1f3YTBkJibyaijhOyvLuLW11lBkd8NDivSzl7GweY7u5uhuD93ukO/ukW9s4s3O4s/P4s/P4y/MI8slVKOBqlaxmZvaafDGm+PqxJ/5NB6K9HyE+vKkwvg1Q4WemyLr/o/faBeobu4Iq4JiZ5fk4sfkn0V8GoNJEor2o5VsuJevlLDKOjG7b94i3RtSXZ2isjJF5UST2qkZgik3232yO/x0ixU4338npnd1l703b9G5uEUxePhIQlUiKhdWGLx7Hel7R9biFr7Ca1aovnSS9PbRroB+uo0UisXq8wghCL0Kc+VTgGBrcIlO7CYjTe2QiAplWWNkGIsoQSQrGIxr0DHjOCqBwTC0XSQeuU1JzOBQN+DstOLVlwL+xT+q8d1vlji+dPBx32lksxyK3O7HoCjlilK6dsfFr/i+YOW4x4ljHufPBszOKP7tvx/ywcWM7mOIK4ur07Tb0qxvHv4chXCd41RTIu+bUiNODKORPTQG59PYbemnGprw4rmA738r4n/4B7Wx9V64hthCPo5Tu3M8T4HnC3zvrrARwsU3nV71mZtRnDzhkaSW/mDEXvsJ3VYCSpHg3HMBf/NvlPmf/3mDel0+YAU0xpIXUBR347vuCCt/HHMF7jkszHn84e95nF517sPBwHD1Wk5v8PRb9HJJ0KhJpISTJ3z++PfL/I//tM6J4x5RePcanKX/7tyI41mc3BRy9zW8N9ec6/VxkUIR+lUa5SWioIExGluyhEGDJOuRZj2EEMzWzyCEIs16+F4JT4X04y16o3UQimppnunaKp5y8ZLa5KR5j1zHCCS10iLHZl4iyboYo1EyQMlx9Wg9wlMh1WiOSjRL6NdolI9RmJRhvHOksLpzk9KdvhuYhj7xrb1PFTo6zki3ukTHpiiGKaOr2wfjWR6F/WK8n76Rmq4Tnj72GDv/7AjPw5uuouoPptEbq0mKAY1gAU8GJPrwtvXzwMQxJo5dOQLfw5ueJjp9itL55wmXj6OmmqAU0enTmCQlvnjRTfVyRBKVvX9mAvdxPN5JjYP8xSHTIn1hWHDxTgK/GqKi+/qxvHBB6ncKPRuDGcUk126Q3bz1hZ8ufMWElS0MWTem9f46nY82XVZf4HH2X36buW+dZHirzdV//Rb9G5+iQo11k4kWGpsbTF5giqfYoxqDLTQyONxPf4dUj9gdXaeTbFDy6njSH3+4fXKdUIwtTQPTYUjPVePG4GKwxH575sZMhs38GhaDxTIyXQRivOROSe67KAU/+n6Jf/FPanzra9GR1cu1hq3tgt09Q39okBJqFcnMtOTYosdhcXsrxz3+5T+rMzMl+b/+zYD/9JNPiV07eOt4/2IGAt794HCh6/uC5SWPP/txmVJ0Xyd5u+CdD1Ju3i4eO07q2s2cNLWfWVx5nmBh3uOH34n4wbdL+y5UcE+h0LC1U7Czq+kNDJ4SNOqS+RlnmVKHJJlUyoKXXwj5Oz+uECeWf/cfnixxwvfh7Bmff/6Pavy9P6mMxemD66WZZWdXs7unGYycuKpWXLX7mWn1wH0H5xb+2z+usHrC43/5V21+8356VJv+xJQiQb0mCQPBP/77Vf7+n1Q5teI9cM+shSyDUeJiv6JIUCqJQxN4btwquHT18YVVoRM6wzWGyS4ISV4k7PWuEqdtCpNhrUaJO1YPQ5x3WGu9PS70a7DWUA5nqJZcxt6V9Z9gbMFUdZWFqfNU4x2yYoSUHlHQ5Pbur2n3b+B7JU4v/oDQr+LJkCTtspG/Bwg8r8SN7V84EcbRL3K2N6D1l5fxaiVMXlD0k091yQ0ub+JVQlQlpPv2DfSdOQwfV1fYR9+odP4ks//sx495gKfAHXfgAz8rIlVFCoUSPiWvgRQbX/z5ja0uw3aH9Np1qt/8OrXvfgsRhk50NRqEK6skV65iiiPaCmPuWijBBWE/ztyP44rmIgw5tBH5gvBKPkIJsnZMOFvBrxxMXjNJQrG7h8kLFM4K6s1MIcNPSXL7HPlKCStgPNWFRY8D1AuRkewOyTqxm4S5MyLdebgJdl+pf04DDVUtERybHk+J8rCexWJsQaY1xuQIIV0Wn8kPTGfjhJM+sN1hp37HHXj0Go4ggN//bokf/6jMKy+EVMpi3wJhjMuYevfDjDd/k/DxlZy9lmY4suS53bdOVcqC2WnFuecCXn0p5JUXAsLA7cfzBFVP8MPvlOj3Lbt7hg8uZaTpp99wY+Di5YyNrYJS6fCPtRwJXnkx5I9/VKJ0X5z67fWCn/5Vwhtvu+y+x2E4NGTZZ69O3mxI/uHfrrC85FEpu/va61s+uJjwq7dTLl/N2WtrBiNDnrtBYhi4CZSPLXp86/WIV18MObVyt2GXUhBI+MbXQm6s5fzkL0duipvHEIFBIFheUvzzf1zn975TYqqp9l1/xliGI8vb76a8/V7Kpas527uaJDX7WZK+B5WyZH7OuXu//80Sp096NBtOrXieYGZK8coLIX//TysICW+8/XRTwMslybOnfRbnFL/37RKrJ5yoSjPL5U8yPvw45+r1nI2tgl7f3V9j7Di2ymU9rhzzOLXqkiLqNcHaRsHu3pPOBakpdIYxGmMKtMkodPrAdFRZEROnHYri7rI7Fi+ANB+Q6wRrNVkxIi9GBH4FYw3GaNKs737XsSuZYDKXJS09LAZtcozNnTvKZBTm4ffdakMxTF38lLaPFOdk0pxcG3ScYdLisecwfBKE76Gqpc/9OI+KNjmDfI+46BF5NUpe/csLEjfGFdXca5HdXiNbWydcOYHwfUQQoJoNN9fiYViLzYsDRUZFECDviKRHGF3KKNwvP/DweSk/X3RWkO6O0KOcYGdAcl//b0Yx2fqmcwdai/B8/LlZVLOJCMMvpUzFV09Y3Y+FrJuQtWMnlLSzRj0NZBS4IPSFBjLwiU7MIpQgWpmj9uppit7okI0E0YlZys8do2gPHphL8OCqCl+WKAdTCCAp+ozyznjaBGeGzvQhx/gMRKHg2JLib/1hhW+/HjI3c3cIn2WWVkfzV28k/MVfJfzirYSLlzOS5MEO/E7g87lnA65cy9nedTFa01Nq3620esLn+9+OaLU1Oy3N5lbxSGJnt2XYPWJqF2A/wPswi1Svb7h+s+D9ixlf1uwPpUjw2oshUkKeQ7ur+dkvE/7i5zE/fyPhyrWcUfygZczzXMzbJzdydvc0f/yjMgtzB11cy0seF84GPH8m4MNLGYPho7/rxxYVv/+9Mn/wgxKnVny8cVyR1pabawVvvZPyX38W88bb7hz7h7jyhHDxVadWPNY3Cn74vRLfeDWi2ZAoJQgCwdyM4o9+WGZz2wW2d7vmqWULzs8pvv5qxOyU5NzZACmdlfI377uBwDsfZGNhpRkMzYE+LwyhWVesLDu35alVj2Zd8v5HGXHy+XaO2mTkOn5st5FLLNbkOsaYux/P3aKU8sDaQtzJKH4EjMXEj/CRSIGMQsT/z96bxUaW5el9v3PuFnswIriTuW+1V1dXV093z/SM0JJHGI3GNiRBlm0IEmR4lQ34xX72i18MW4YhL4IhSAZs6UGSZQ880miWnpF6m+rq6dqzKrNyYTK5k8HY4+73HD+cIJlckklmsqorW/MBhUoygjdu3Btxz3f//+//fTkXtEYFEVppZLmAzLmkzS7CtZF5D5lzUUE8ClGWyIKHipLRgmaDJYx4Pc3AtlB932w/nyPrDznuTkEFESqKj33OmUAIsKR5P0dUrQw0Wmejm+Cf7fSdThKy4dA4iSttzr80urbjWnsqDFCR0WMhjHWALBSwx6qkvd5jW4g7kMUiuevXsMqls31Dp4SVc7Dy9mja7zDPVVFE0myStlrYYxVkPo9VLOKdmyfd2iJ6cEy7/HPCl59YAXHbJ9js45S8pxrXfBzsaoHiqxcY++WXcBplpGfCFytfv0bx1Yt7PdtHIGxrZCqX0fneJ8fGRTgyx1hultnKy0gkm8O7+EkXSzpUvElA0PRPFhJ5UtRrkre+kuPf+JU8Vy7tlUK11nS6inc/jPjv/naHm7diwmMqTFkG/YHmnfcibt6O+eE7If/Nf1XjrTdyNOrmYi+E4KXrppL19rshw6GxaPh5x442Smvj9fST9yP+1v/W4YOPY+Lk8cc0TWGzqfjN3/ZZWctwXcGv/ekC3ri1e0doWYK5GZtvfzPP0mrKYHiySouU8NqLHn/9r5SZn7Vx3b0Lbn+g+N6PAv723+1y+25y7HnXGjpdxXsfxdy8HfNwJaWQl7z1hkc+Z967ZZm25S/cS3jnvYh3Pww5qzzVSxcc5mZsGnWJVrC4nPL9Pwr4n/73LnfuH7/vUWSE6htbGT95Lxpp9Z64fnxu0FoTJX1gBs8pY1seSqW4dgHPLtFKFkiyCNsyxsFP2JohLwhs6WJJd9RyfPaJO2Hb2ONj2BNjJh5qtUnW6uHMNHAm6ww7N7HHyjgzDezxKslWBx1Exi9opkHa7qEzhVU0gcJqGKDDBJFziRZWEY6NM9MgvP0QHTy+epC2esRr28c+50wwIoTehWlkfT+xsqRD2W3gyiJSWKMIsjNacyxr1xNJq2y/Z8sT9le4rmnJ7cwNZJmxXHhcgLzWZIMhKgjQaWqqXFJiVSp4Fy+gPruLOsY8U7gudr1O4eUXsSqV077TM0V+okh+umz+PV1Ga42/8si6m2WowZDowSJWtYI7PwdCkH/hOsr3STa2TETQjiDzOEhpztGOdcNT3r0/F8Sqe2uTuBti5x2i1tlVeJL2gP5790h7PrkLExSuzuDUiiTtAUmzb+7EDkCPYm/itTb9d+8dS6zq+fNUctNs+w9o5C+M8gE1mYopOg0cyztzYnXxvMNf+PUi9dp+sUmcaH78bsj/8L92uLuQEJ9C/B2EmnsPEv7Hv9Phb/4HVX79V4u707eWbUTyv/Fni3R7infefb7dgU8DP9D89IOI//Zvtbm7kJCcwrD07v2Ev/8Pe7z8gkt9zOLR1IRa1eKl6+4hc9TjcGHe5rWXXV5+wd1XAQP4rd/z+X/++ZA7C0cbbz4OSQJv/3GI6wounq8zN23zaOfhxWsOvzqyM4jjsyHUxYKgkDNVmQfLKb/520P+7v/VY3ktPfXAgtZmMONn1cnRKIKowyDYolYucHXmT7EzydsPNxmEW2YBoHqi7UVJnyDucGHqm6RZRM9fY7X1wZntrXBtSDPjkH3goDmz4zhTdVQQYtcrxgdKStQwxKoUEbaFVhB+ch+rUcWZHEP1fZzxqiEUJ2glBZ8uMvjhh6StzzeCx2hwqtT+zV/Eru8nDVpnRJnPMDEO6rZwyc4ggBkgd/UyzuQkwrKIl1dItraM+Py4tpwQeOfPkb9+zbQBR19A5QfEy6umwvc4KEXa6ZJsbuHOTINl4UxOUPrm18n8gPjhEso/sJ4KgSwUyL/4AsWvvGraaY9rN35BCLYGxP0IlWT4a33i9mEOoNOU4ONPsKpVnIlxhOdh1WsUXn8VWSzif/Qx8eoaavAYPZqUyHwOu1HHmZ7CKpXRScLw3fdQw9NzjueCWIXNAckgQtqSdHhGt8aAjlOS7QE6WSHe6JD1QwovzuPfXsG/tUJ6hKGouVOISTtD4o3OsVmBrl3AEjatYImqN7Mb3qlRWNLBlk9ndPk4VCuSa5cc3nrDozQy+9zBp58l/OgnIR98HOGHpzN6VMpUPd79KObtn0ZcveTw0g1TDZNCUCoJfvkbOd55N+TjT2P84PkyrntafPpZzPffDvn40+jUC3hvoLh9L+H2nYT5GZuZqb2vYqVsfK4OEqTj8PrLHq+/7O5zMY9iTbuT8f0/Cnj3wwjfPz0x2WhmfHAz4oObMTnPTAjuYH7O5s3XPepjEj84G4NT2xIoqUkz+MMfBPz2d33u3E+emhw9M6myLEQhT78ckKUxWWYjrLwR9UqJDiI6oolueFjj81grpgWmkxTVHyBqJUInpiubFBqzqDAk7rUJ/CZ6qooeBgz8LWjvVLeMzqfZu0uSBiTp3kV9GG2z3r6JYxfQOiOIzzBcWQjTgrMs7EZlVN0oYVVLWGMlQ5xGc+8qiJAa0zqUAhXFSFxkqYA1VkbmPHSSEa+3yF2ZRWeKaGHtiaXDrNMnXtki6zy7jcGxb9Wx0VGC8g/fBFrCoexOEGc+WmssaVzYz6IyaJXLeBfO40yO4108T9rtofp9Mt9HhxE6SUzbbuT4LXMeslTEnZ7GPT+/GwacbreIV1dJm9tPrKikG5tEd+/jjI/vkgd3fkrPEdMAACAASURBVI7yN79OfH6etNVGhZHx4rJtrHweq17DOzePMzVpKl79FOm6WJXyMx+Dp4GKU+y8Q36yjFNyycIj3rNSJM1tws8+wyoVyb94w1TdxsfJj/Y92d5G9QaoMBwdZ2MILhzHHOt8AatcwqpWQWuSza2nJpXPBbE6GLp4thtXRivVNrEMyWYX//Yq/XfvkTSfzlBwBwKJ0hnDuLXPa8oSjpn502fbNpubtrl+xeHc3F75Y2dh+cl7Ee+8GzI45eK6A6Wg21O8+0HE9St7xAqMMPvGVZfrl10mx0MeLH25DAHPGjuWCu9+GPGDHwecMusTMMfT9zW37sa8dMPdR6wKBcHcjHFsPwmkhK++5vHyjf1TMIOh4uNbMR9/Gj+1oWuSQHNb8c5PQy7O2/uIVa1qcem8ad21RtE3Z4E4huZ2xne/b/RgP0uDaeHYyHKRfsEHNFgl7Ng1i5xSpKpDK1nBK5/Dqk0iWhtY1Qo6jIh9H2digiTL6KQt/JxDpnxS1SQN2xReeI1kfYNha4lhuOdpp1TCdu/eoX0J4y7hWZKpHWiNihOy3nCUn+ehcjFgjBdlziXtDlFphrCk0VzZFrKUR7gOWauHLOXxqmWsagkdJyRbbdJmF/n6VXQQkaxvP1E7pZP0yC7Bmb9dpUxV7ohKkRQ2eauCJRwylY4CNs5IuC0lVqWMd/EC3sUL6Eyho4is3zNtuzBCZ8ZqR7ouslTEro0hc7lRnNDIRuDeAuGde4erTUdgx7Hdu3QRe3IcmcthFQoUv/Ia3uWLpO0Oqj8wlSrPxSqVseo1kIKs1ye6dx80Rgz+MyJW0rPJjRcpX2pgebZxXn/YPtSh1XFMdH8RNMh8HmdqEqtUxJkYx5kYRyUJKgxR/eHoOI9yBnM5ZD6/N0UoBMn2Nmnz9KkKO3guiNUXhSyICe6ukXaHZzIRo3ec16WzlzclbIpuDdtyHxGong2uXXG4evmwGFNr+OiTiE9uP/tF69bdmHc/iPjrf2X/l8yy4MolmxevO/8aECvTYrp1N+HmraevoCplphxbnf2fA9sy03G2bTzEjiMWUpqBhRtXHS6c23/uW23Fv/xBSLP1bJ+zINR8fCvmO98+vB3jmeWwvJaeGbHq9jL+1Y8CFh4kDE8h3v9cIM0drY4jZLmEM2baduHtuyQbm7vuhVapCEqRtdo49Ro6nzNmn6WiER8PhmS+j1U2Bo9p6/Smg58XdJKSbrTImqb9pUdalGSjhZDCTD7vuLJKuTdptvuzwp1tkI1XCT97aAiaY2ONlchaPbK+fyJBuo5TM9n1eeORmJ+DSFRIM1gkzAa7+qozu04rZbRVO5ACkc9h5zzs8fHDX/Sd47vz535A8OktBu/8MeHCg5O95HBItPiQ3vd+QPlb38C7eH7Pzb1cxiqVHnndnXMsyLpdovsLdH/vD/AuXUB+wSaujyI/WaYwP4ZVGF3fjpn4zwYDgs/ukrZalL/1DXIv3sBpNIBRRa5YxCoc8C87wnFYJylZEDx1zuWfEKtHkLYGbP/OeyTtIdng2bVC3WgdIQSXa9+k6k1RcMbI2SVcq4CfdGgHK2ew13s4N2txbnb/KY1ixcZmxupGRrf/7BeI7bbi4UrKxlZGbcxUVXb0E3PT9mhxP7mv1fOIOIGFxYSt7QyloVQSu0abShkjUKOP0gS+5txFhzjSNLcyAn/P7kFrTX+gCA9MrAkhdgmT44hj9XD5vODKBYexqtyn0wLTvn3v4/CZCU+cmKnC/uDwdjxPcPWic6baul5f8fZPDSE87rImbUGu6jH3lQb1i2VKEzncom1c0FNF4qcMmiEPfrTO5u3jKz1Cwtwb48y+WqdQz7Hww3W27nSJtIczOWGE45Y9mpoLsCcaZtKt3TUTVqM2i1Uqo0c6Fe/SBTNRpZQZc9caIS2j/6iNYdfHDMlqt03V4GcJpfcv+mAcrXd/2PnXwWuI+TntDODuMmoQmJafEKAU4cLqsWJ0HSdk3QE6zVB++MUENuuR/cQRr6XRpNrsr2cVcGSeYbJNdgatwPD+AjqOSVY3sCca2NUKslRE5kzlT1ijKT89imiJY7KhT9rukG5uEq+uk6ytk2w1OY3XTOb7hHfuoqMI9/w5vHNz2PU6slhEeK6phiUpKgjI+n2SjU2ixYfED5dGk3bVE1XHPi/4K13iboi0DSGMu8fE1GiNjiKSrSb9H/2YcOEB7uwMzsQEVr2GVSkjPW+vxacUKo5RfkDWH5B1Ru736xtGkxU83Vr23BEru+zhFD3swvHmnEch2h4eK35XYUxwd/1Zd3EXw7iFFJJGPk8/3kYgsIRNkoX0og368eaZvRbA7LTNzPT+UxpGmsWllHYnOxN7gijStNoZy6sp+ZzAc/dE8tNTNufnvviPlJRQb0gcV5DEmnbr2UOWj0Mcax4sJQSRYmLKYmbOotNWBL4mDDX+UFFrSAoFQ4ouXLJpbSt6HUVwYKmKIuMfdhAC43puW3DcPXwhL7h+1aFSlvsEwlqb0Oe7CwnDp2z/7iBLNdut7EjLAscRTE/ZjzWgfRoMhpoPb8Z0escTwupskXNvTXDxG1PUL5Up1DyENB12rTRpmNFdGdK8130ysRKCmVfqXPvOHMXxHLGfMmyGhJsZ2cAs/GJ0Z6uCwExoPWKEqQYD0m0HrRRZp4f2PJCCtNlip6Wm0wzRHxhNS5aRbDWNY/QXCNuzcAo2bsFmsBWQndHQgfJDkiQ13kkwaluFqDA+VluVrG3T/94H6DQjXtl67PPOFBrIjAfhwXagmbr0cGQOSzjY0iFIu2dCrLJWmzAISbZbOI0GVrW8S6ykY7ILd7wOdWqqd8r3jQB9a4t0q4nynyJ8OcvIOl2CICRpNknWN7DrNWSxiHRdsB4lVgOSzU2S9Q2ytgn4TprbhHf2WtPRg8U9oiWliSWyLFQQ7tu3dLtF8NkdsuFwdzvJxsapj1vcDY8nUwehNTqKiVfXyAZ9sm6XeHllNxJIjKb/ja4wQ8cxKgjJBgPSTpe0uU3W7T01qYLniFgJS2IXHMpXximdr5GfKptgxlM4yW79eJGtHy+ezQ5JgVXwUHGKfoz5XqICWsEy7WCVvF3Gkg5KK6K0R6bTM/VHEQImJ2wmx/dPA0aRqTY86+L6KIJQ82ApZX7WpvrIUM3kuMXszBf/kbIsOH/BolqzCAPNndsJga+JIn0mguqDSFLNylqKtOHqDZuvfcNj4V5Kp6PodxW3bybUG5LZczaOI/A8GB5R7QGz5jxuKMg63qYGgELODCwclVGotZnGrpQlpeJp3+X+/SgWTGvy0GMSyiW5z97hWaC1JggU9x4kDIaPX/QtRzLzap2v/7UblKfzZIki6MSGLEQK6Qhs1yIJM/RJpjWloDpToDiew8nbjM0XcUsO6t424adHaC13TsxoIUnWNkjWzKKRdToneq/PouF4WhRqHrULZcbmi9z93irDrTMKklYaHSeANncFSpnq1U6bZSfwbWfhHf07vLNEeG9lr1VomXF3nY1G4wW7+iLUgW08C7QmGwSk7b7RW2V7pq55q4wtPTSaJDOBy2cFFQSolYBkZfV0fyikWeue4b3rKCJZXSdZPV3xIG1uM2huM/jxTw49JvM5Qw49h2RtE63UbuUtXl0jXjWu9VoZ82mEQHiuaS/vCMh3SA7anOdMGcI2ilHQWbb7+RCO/UhVLzPHQ4q9ClSmdq0sZM5DuC4qCokePDTPl2KP6EuJsCx0mprP3SjeQR93UT4hng9iJaB0qc65P/cS1Rcm8eoFpGNO3k4s1UkQbPTPhlgJgVMvMf1X/xS9d+7S/eGnxzzZuKqHWR+ykfu5zjhLW/idtlHO41CkR5LAZjM91v/ntIhizfpGeshpPZ8XlArypMa+Z4Y0hVufpszMaq5et/kb/0mJxYWUmx8m3Pzo6SfKHocs0zRbilJFkrelMV5tmrZgrSapjkkmJi3GapJ+T+ONWnqfBxxHMN6wjiQ2r7/s8Y//3vSJ7FuOgxCGXM3PHr5cWJagXDo+1Ps0yDIIIxj6iuwYUlydK9C4XKE0mUMrza1/scRn312hv+7vruFCCnSmGTRPQB6UZnuhT+NKhdJEno1P2vit41sOzyMmblS5/EvTNC5XWPu4dSbEygQpu0jPQQ3DXdKpwghZNK7qOor3FtRMGaPROMGqFLEbVSNuB6xSAXtijGS1ifJD44E1XSfZ6pB1B0jPJRsGZ2Ii2vu9dxi+88lIT2ZeX+mMMBsgRsbNiuxMJgKfCULgVhs4pQr+2uKu8PpnDkviXT6PLJXIej3YaOJMT+LOz2CViqihvzdJ2usb5/diAZnPEd5dIN3uIKTAu3YZWcijk5h0q0W6voU1Xse9MIeQkmR5jWSziY5i8q+9iFUpofyQaOEhWa+PPVYl9+K10XncIl5ZR3V7uJfOYVcroyrcNs5EA6tcInqwhI5j7Ik67rk5ojsL2FPjuHMzIAXx4rLxvuo/XawYPCfEKj9Tof7aLBPfuEhuvIjOFEkvJPUTvPEids4h8WPSvhkbtfIObtVMUkStIb27TaLmkMGDZ0i6fgTCkljFHPnL0yduHR4UQBadGkJYDOLmM++PlMb/x3H2omt2kGWa/kCTnsJj6UlIU6ODOVjht6TAdU17Kgj1/nackCOX6bNnXFJCrS4plQRBoPnw/Zhw5HxeLAn84bPnAz4KpczCv7qSsdXJuHNbsLGaIoTA9QTdjuLenYTV1YzQ1+QLgk5LEYTPHqdzELZlKlIHM1KFML//yiufr+hUSsjlxJH5h0+DJNGEkWnlHneoCo0cxUYOaUl66z4btzqsfrBNPDyw6JwwQFgpzfK7Tfx2hFd2WPuwhd/6+fNkG5svMnl9jHzNxXLPKP9NgMx7OJNmmgytIVWoJDHVfCmQjoMKY2TORZbzWKUCabtvpgsLOdJWF5nzcCbGsMerCClINtuoYYgseLjnJmF+EquYI/j0AWmr/8x3b8lGm2RrVF0cXayUzvDT/W3jsx4yOi3sQpnCzAW8+iTh1irZl4VYKY3yA2TBOJ0jJdZYBataIev2EK6Dlc8hHAflOqgwIhv6ZJ0uztSE+X1vgDM9SbptCFU2DLAm6tiNuvnMxDHO7BQynyNaWsVu1NBpRtrqmLaokMhyCbtRI15cJuv2jXXFaN90sYgsFY1sSIPwXNy5abLewIj3KyWc+RmsSgnh2Kgowj03i7Ak4e37T31ongtiVb5Yp/bqDIXZClHLZ/iwzWCxRdwJmfzWRfJTZfylDt27W6g4w63mKM5Vyc9UiNoB3dubNN95yODhgUmcA+X8E8OSu3donFLntYOSO44tvTMhVpaEwmNaNUpBECqyM4oBAsiUHhGng6JrkydXLEjiOHtkeMjCyVeQ0iaNh2RJeKYES0qYnLJwXVhfzbh/N6VckbiuqegIcbZsRimjXVtafHy4b2t77/2dVffiKEjLkGrL+tlledmW8TM7CyTZyNTzCc/zSg5u0UZlisFWwLAZHiZVcPLCsIatO1227nwOdgZfBgiwbEl5qkB5Kk8anyFZkOZG05mqIXKume4bEap0u2tuRMtF4uVNrHoZd3YCWfCQpbwxJFUKYVtYlQJWvYLIudiTNXSUEA9N1cqulMzf5FyiB2sgzkCbphQHc6w16sxjxp4NArdSJz81j1ebNAMUXxZoTbrdMUMb9ZoZunFdEBAvr+FMT5icQcdGeB6q2ydZ3yRrtih/5xdNu3DgIz2XdLNJdG8RpKTwxitYpQLZcIjq9nGmJ5Flo2VQQx8VRGTdnmk9S4lOU7Jen7TTRfm+ae0pRdbqjmwXxkFI1HCIKhVwL5wj63TN3w187CkzkZn1B2TdPu65WWSx8IQ3fzy+RGfp8SjOj1G+1EBnmq23H7D2h3dpfbACGnITRaycTfvTdZZ+6ybBWg/p2uQmilz6y28w9uIU1RtTbHzvHunwkTtQS47E78IILk+x8glbIl2bJwpgjoFnl3DOyCBUSIHniiM5ntLGF+isKzZRfHQVyJKmJdjtC0g0CInt5hmbvIabrzBoLTFoL5HGZ3fx0hr8oWJtVbO5nqEUdNpq3+NnCa0hiQ8Ty+Oe/3nBkoJC3trNBXzeobVGnWAyzLIl0hKgIYsU6gwrsj+PkJYgX/MoTeTIVV0GW2c3uStz7shItEzW7pP1fXSWIYt5ZD4HwgwGJRttI99wHXScID3HPMe1jYdSpYhVKZg2n2Mjizmsagm7UUWnmYnSiRKyns/nOp3yZYIAtzaBUxr7We/JyaAUu+PRaba/ZZupXSKNGA3bKGXI0CNTjmJUftexGfiI7j80pKfdwX//E7yrFyi8/hLx4grx0irxwxXUYEj+lRewG3Xz++XDGrasN0Dk89hTE1j1KvHiMtH9RfKvvACC3dDq6M590u6zeVg+F8TKqeRwqjlUktH6cJXeqDIFoBKFzjRCCHSiUHGGShRBmrHxw/sIIWi8OU/jzXOoJGOwaKpW9e+8Sv76LCqIaf5/PyHZ6uHNN5j8y79oSNMxELaFVcxhjx1WBNdyc0yVbjzxPVW8SYbJ2XjZaKUJI33ktUYIcN3DLcJngZQ72zz8WKYgDB9ZHLU2jtVJhPYyLMf4+5wlpIRaQ+I4mng0ZRf4n49wfQdflmVca4hjhTrA3tJU0+0pFh6evcbsUQShZmExodv7fBY6y5UUGzmu/PIM5ak8btHGKTiMzRepzhWxXMnEtSq/8Ddu8MpvXNi/b92Im7/1kO37PaL+/spioe7x2l+4xNh8CbdwYJK2H/PwJ1usfrBNb+1kNwBXfmWGK788g8o0H/2/D9i83cErOcy/0WD8apXKdAGnYKMSRdhP6G/4LP54k+7K8MhKm5CCsfkiE9eqTFyrkqs4OEUHy5GkUUbspwTtiO7qkO2FPr1Vn7C7Nz9q5yyK4zlmXq0zNl+k2MhRmswz/WINyzWPffs/f2Xf3+wgiVLe+fuf0XrQP1E2qxqGRA/WSJqG+OyIio1b+86TFCo0RqFZd2DEzDtBwlKSdYdkvSHR4vquSF2HMSqMzRSlUmaKL9Nk/hkJ7k8LIbFcj+LcFXKTc7iVMYS0yeKQuNNkuHyPqNNExXv7V5y7QnH+Ck6lTvvjHxNsraDT0TEXAq8+Re2ltwDwVxfoL3yCVorC3GUKMxfxxsbxGtO4lRoIwdyf+cvozHyWtVIMVx8wXLlH1Fzbfc3cxCxj198gbK0Td5popSlduI5THkPaLjpNCLaWGa4sEG2PpCzSwi3XyE/N4dWmsItlpOMhhCSLfKL2Jv76Q8LmGjpNwLKQxTzupXOmwlPI456fQ5ZLR1wczdrjTE9gT9R3p/DS1qgN++jztSZeXsM9P48zM4kqFkjbHXSWYeka7vwM9kQDMZqilKUCllvFmZvZ9YwTro1VKeFemMc9N2vyA883iZfW0EmCGgx2zUBVf0C88BD3/BzOzCRZMU/W6iIG5nsvHHtPUH8KPBfEyso7WJ5tDDzXesSdvbstMz0C1qMTglqThSndTzcpzFSZ+vZlaq/OMFzq7BIrq5zHaZRRw2h3+sCuFKh+8wWUH5ENQjONcBSkRHoOwj5MEDy7zJg3Qz9uPkH0KM7M0TdT4PvqSB3VjrD9KTuWR8KSxhjyYPtJa8jSkfB499CZlPgkGmC7BRy3sBtEetaojEmkBWmiWXqY0e99WejP54dMafzgMKlOM3i4kvIP/sngkQqQgEfOmRiZAe4scN5UlaQ9JO0FCGs0mSUEwjaTWsIy01lZYKa/BKZ11+5pVpujeAhLmudblhnbGEa7+gb9FGnI0hK4JYfJF8Z2SZBTsPDKLl7RRgiBV3KonS9Rnszv+9thM+ReZQ3riO+ptASFukd1rkC+5mHZknzNxS3YBJ2YoBOzfe/kmXX1C2Wu/qlZhBAs/3SLJEg59+YE5782QfVckfyYh5OzkJYgjTK6Kz7Nuz36G4crR07OYubVOrOvNZh+qUbjcgXpCKQtd718VKqIBgn9dZ8Hb2+ypDb3kSTLkRTqHlM3xmhcrlCoexQaOfJjLkKC5Vo0LpePtFuI/fQQ2TwOOknJOoMTRdGoYWgE7iMICXI0ZJAOj66iqcf8/guFkDilCqXzNyjOXsIuVdhhBHahhFOq4pRr9O5+RNBcRUVmn7VWWF6e8sUXSIddtEoJNpYAcCt1SvNXqV59DX99cV8HRIwm1pAW0nER0gK0mZDUO4JG4yF4cB2xCxVKF65jF8vEYxOgMpxqA8vNIW1jdpcGfSx3T38pALtUoXjuGnauiLDs0fVB4lbruGPjOOUaKg6Juy0z0a41OghImy2EbaGieGQvYlp26XbbxB2ViljlIqCNrUOSkDU3ydo9dJoSL63u+bhpbbbnOKAyVBih+iaGBmWc6tOtbbRSRmcVx0gpUb5PvLxmIoL6A2P3EYSk222ywRAdRqAVOgiJ7i8icx7pdgcdJ6SbzZEJsCHxWX9gEgeKeZyZKVQUkXX6p/Kaey6IlbRN2TANElSi9jFclWTG3bzg7ls0wPhWBWs90iChdKFOfrK0+5h/Z420H5iLwmD/HZB/d43wwQaZf7SDkHRtnPEy1W+9eOgxpVP8tMNy70OiY3r185VXkOJsFL8qg6GvSVJQWu/Tu1gWlIqCs8zRtCxBuSgPCZa11iSpZugfbBMK0tgnTQLypTrijN733utCMNRMTksmJm2EgE5b0/+cqihfJmSpMQI9GACtNSwup/yd/6O7N2QwsgjZgXQshGPOhVWQjH19nMFnCcFiF+nYqNgI8qVnm3ZNzkIlEG+HkClDyBwb4Vio0MIqWlhFz2zXs0FDvNVD5hx0kpG0Tq+L0dpUpYfNcF8CVHW2YMjUVJ6gG9G816O3tn8Bjnoxw+2I9Ig8z9hPefBHG7Qe9CnUPNySw9zrDWrnS4eee1JIS+AWbUqTeXJVl6/+u1dx8jZhL6a7MkQrKNRdchWXfM0lS9QhYiMtQaHh8fpfuszcGw1yFZeonzDYCoj6pvqYq7jkKg7FRs68/05M+0F/n1eXWRMNiRtshfjtiNxWwNQLNUqTOdIwY+3j9pEC/TTKCHvxoSDms4YQRjZQKEr8oSI6OPDyJYJ0XHLjs0y89R10muBvPKT/4DY6ifHqkxTnrlB/9ZvoLEElEcHmMgDBxhJCWhRmL1E6fx0VR0StTbTKzO8uXAfLonf/JsPle7u+WmFzjWTQRVgOtZffojh7EZ0pNn/yB2TBzrSaJosDsuiICp6QuGMTuGPjZKHPYPE2cb9jihD5AiqOSIO99UlrhbRsLC9PuL1O3N0mDYYIAeWLL1Kcv0JufJZgc4ksCkiHfVR/SHjrcPTSDrKOuTGRhTz5N14ha3VI1jbIuv19+ojgo/03MDqMiBceEi88NJUBrXefv7PNfb8Txu7EXCz2thv1Bhw1fhLe/GzfzyoIie4uEN2FnZF24TpYjRq5V26QDYbEC0vEg+GJdR3PBbHa8RixCy7iAHnKIqOP8hoF5EGvASCLU5JugFcr7FniA8HdNcIHJpIiOxDGOfxkid6PPztEuHYg8y658xOUv3L50GNB0qMVLBGkXZLs8VNFcepjW2czsaW0EVP7gSKKNPnc3jFyHcH0pEUud3ZVopwnmJm2D4UEB6Fm4KtDpEoIgWW7SCmJgz5KnW2PzrIFFy7Z1BsShBGse7mza31+mZFmmk5XHTIZdR0znek4gkyZaUTp2BSuTAGmuuRNVcmilGS7T9oLiLf7uONlnGoenSoTbTGMSPvhyDNOYhUl7mSFbBhhl3LY1QIqTog3ekjPoXB9huDBlgnqdc3r2WMF4vUunbfvGMfrUyzYaZTRXR3y/j+6t1utAbj4rSmufWeOQsOjszzk1u8s8/Cd/Ya7KtOEvZjsCKF2EqQsv9fE+lgiLWkm5LQRxXvlw7FQJ4WwJC/82XMMtgK27nS5890V2g8Hu+0+27MoTeYpTuRoLfRID2Sg5use0y/VmLhexXIslt9t8sf/52eGIIbZ6DUEubJDZbbAxPUxVj/YpnmguhYNUjZvd+isDLFsie1Z1C+UcP89h0LdI+zGfPybpmV5EFpp/Hb0ufe7iyXBC696vPwVjzufxty7FbO5np2+db0bt/OU3/mRYehxyDWmKZ2/jl0o03z3X9K79zFxrw1aE2wuE3W2cWvj5GcuEPc7u8RKZylRa4OtP/59xt/4FQpzl0mGPZJBh9L5a9jFCtvv/4BgY3kfQcrCABVHCGmRhUNUkoDKSHotUn9P/6P10V4qQkjcsXH6Czdpf/IT4nYTlZoWoql6adPS29sQ/voiUaeJzlLz3+hCHvc7qDSh9vLX8WqTRNsbpMOTa5B0plC9vpnS2/GeOikOCnmPEvZqzsR+49Ht6zRD+yZXEK2M7usU+/1cEKvUT8jCBCvnYBddpGvtaqzSQYSKM3KTJZyyh3As9CN3qEIKcwdt73emVsHhapSKE+L1NvF6h3irh35MIKjwI6xiziw+BxCmfVSQkmQR+lD8wx7a4TJSnN3h1xo2tjK2morz83sLkOcKzs05FAtnRzRyOcGFeXsfgQNotjI2Nve/ZyktcqUJbLeAkBaW7Z65xgrM+19Zyej3FJevOGc6BfllhjFrTfAPGMBKKSgWBOfmbJZXU/xA71as7HLOtOcsY1Ou0wwVp6T9ALtSwCqNvIfSjLTrk3SG2KUcTq2IVcohHRtnzIwwa63RmcIeK2JX8lhFz4hAhcAq5kw1K0rJgnh0g3TK86IhixWDA35LfjsypERDGin8VnhiPRQYd/Z4sEdqpC2IBgnZEd/pE0OYlkqh7rG90Of27y6x8WmHoBPtiuuFhN66j1d2CHqH9W/OSBvlFh2SIKX9cMDaRy3iwOiLdmC5ku7KkPbiwDjE9/Zfq7TSJEFGEpjvo52zcIu2IWcaVKYYbp/umJXny0y/OUthsoDlnqzqvPH+Bu072/ibh19np5KaywvG6hZeTpxo/ueZyQAAIABJREFU7bKnarjTDZzpOrJc2HUtf1plhY4Set/9Ken24ydCnUoNrz5BFvmE2xtE7a1dYqLikKS3TdTexKtP7eqhdisqoY+/+oDB5Dz5yXPUXvwaaTDAyhcJm2v0798kHXbZV5LVyoQ0K7Vrlmq+a+kJfaw0Oo2Juy3CrbV9uq/HQcURKj5cDIg7TZL+SELjFRD26W48dJqSrG6g4gQVPR8WJkKa62PW6yNLxnvrNHguiFXSC4k7IcVzOXLjJZxKjqhpyqFROyAZRJQuNShfGSdsDgnWe+hM4ZQ8vEYRp+yhM416wkUz6wf0fnKHeL29b0rhIHSmyPyIaK1F1t/ffkhUQKKerAnox2cf37CymrKylnJ+fu+05jxDgsYqEtvmmQXdrgNjVYvzczY5bz9BWt/MWFo58ALSTAUqnZHGgdEKnODqeZrlVylNt6Po9zXDgWJi8nhzyZ8n+IHm1p2E3kDtGmOC+X+pKHn5hku3p/ADM6mT9gKEbSGkIO35ZEFM2gvI/IikOUDFKVbORdgWWRgTN/skrQE6Ts1QhxQoIqxyHh1npP2ALIiwy3kyxyJcbhE3+4hRmzHtB6gkJWkNn0pj9Twi6ic073ZZ+OE6B11FtDKPHxTT7z3BVNp2FlIpBbmKi8oiknDP3CuLFcPtiOH2F7dQFadLXPrVy9RfaOCW3BP9jbA/JmwFRxIrKc2gy/ZWhmXxRMsQ4TnY9Qr5V6+Qv3Ee79IMVr1i9K7PYKSWDXyGP719LLGy80WcUhWdpbjlMQrT+wclnPKYiSzz8li5AsKyd4mXVhmpP2Dw4DZ2oUztxa8Z4fnDzxg+vEPUOn3My5OgtSYNBqR+/0SkCkDYDlaugOXlkbZjrtVSIhA45TF28jKFHEVCjIS7wrKNxioMjVeUbSNzHiqMdq0P0k5v97ovbHsvSkgIc5M3yqLSSbrX/vsiXaYPQppOiwqM3cdp9+W5IFZhc0iw0ad4vkb5coPBg9YusQo3+wTrPRpvnmPuV28AmrU/uEMyiKjcmKT+6gxOJY+/2iX1jw/Lize6bPyD75lJmOMW/0yRtge0fud9wpUvPpbicVhaS1le288oPM9ULWambColSavzbB/W2pjF/IzF5IR1yG1ifeMwsdJZStDfQqsUISS2WyBNTiBGPU21OIONjYzLVx0aDYfh8CzDgr7cGA4VN2/HdLr7iRUYg9A3X/e4eTtmYytDRSmDT5b3Wia7KfHm/2nX38/MdnQMo8fSfsCu4+bO83Y0DWLvIfPdEQTiEXfOf11OCLD1WZetz7qHSNVJEPZiOksD0khRnS1w7msTdNd87n9vjfbS4Myy/b4MmJiysG1YeZhQa9gIa1+h5xDs8TEq3/kqpbdewp4YG7UAv5h9FZaNlStiF8pM/dKvH95JIUwlXgiEtJC2a4w8H9USbSyRm5hFX0+Rjke4vc5w9elNKJ8EFYX7231PgFMao3T+GsXZy7hj49iFEtL1THC4ZZtpxJGgXbgOImeqOHa1glUtEy08hEyNpvDmiJdXTW5mmo5c983aID3PkDCM75XIeUjPOPFn2y1EzjNZieHPrrplshND48XW6pC1ThZTtYPnglgNlzr0F7aZ+Pp5chMlnOpeWW7wsEPvbpMsSCjMVDj3519m4hcuoBKFN5YnN1FCK0Xn0w381Seb/+kT9mpVlDC8tYQKH//BdWQeKSyibAhobOmRs8vk7ApB0iFIe2cal/DZnZhbd2KyTCPlaOoLY4vw6ksuN2+7/ODHzzaqfP2qw1df80ayBnNV21l/7y8m3Lp7hOBfa3KlcdxcGWm5pEmAyh5/3NLU+GQddX21bYHn7Z+DyTJYeZjRaSlcT5Cm0N7++VmAjkOmYDA02XrLawnn5/bK9I26xZ/9ToE/+EHAwmJCkjIa3XzMyrVLinZ+OAClH/n9gccPPf0JNyc/x/DbEUHn6RaF2E9pPRjw2e+vcPnb01RmCrzyGxc4/9YE7cUBW3e6bH7WobM0PNIq4XlCHBtrFDsWLN5P6Gw/Xl9lVUvkrsxReuslrFp5tw1NNgp79k11xB4fM9E6SUrW6puqn2sjCzlDBiyJihLSrY6xiFhtkmy2SbeOt77RWUYWBag0Ybh01+irHoOwuYpK40Of/9L56xSmL5iJtmGf3MQs5Ysv0vrobX7Wdx7F+SuULr5Ice4y0fYGvfs3yYIBKk3QSlE6d5XqtddBgzs7jZirIUsFwlt30JkyMo9yCXu8gd2oI6TAnZlGNer7hOY6SVDDIcJ1sOs17HqdpLkNSmGXSrjzs1ilIsnaOtH9xV0y9oXDsnbDmmWxAEKQbp68iPJcEKtgo0/7ozXW56p0bq7vVqsAkm5A726TrR8vUnt1htL5OuVLDfM5FZAFCcOHbVrvLeOvnI51HgulSdvHZwlVvAkcmWfDv4tAUHLHqefPAZCzSzjxNp1w5cx2aXUj4869hAcPE+ZmbHI5sVtYePN1j7sLCe99FBGEp494kQIKBcFXXvH42lf2i+6TVLO+kXF3IWFlbX+7RwgLN1cmV6jj5MoIIUejw49Hmmqi2MTwKKX3eXCVS5JGbRRIOroYaQ2DgWYw+NdvIdfaENH3Pop48Zqzj1gV84JrVxy++bUc65sZn9x+vhfi5wVpmJm23VNApZrhdsi9f7VK7KfMvlandr7I9Ms1GpcqjF+tMPnCGK2FHtsLfVoLffx29FxWsqJQ4ziC6XmbXjujtSU5ZIU+gj1Zw7s8izNdByDr+yQbLeKlTbLuEBWEaKWpfOdNnKkaqu/T/9FH6DBGODay4GGPV3HnJrBqZUATr2wRfHiXeLV5pOb2UWShTzLo4pZr+BtL+CsLJuz3CKg43KeDEraDXaxQvvgCdqHMYOkOmT/AHZugeO4qweYKUXvz8S27Uenz89Cm7iA3MUdh+jyW6xFsPMTfWCId9lBJjLQd0wrcKWbncliObSaCbTPUIosF7PE69kQDq1JB+T5WpYxU2V4HSCuy/gC13cKqjWFPjGNVyiRbzV0CY5VLCNcBaZ0B1dxZN55+S8KykJ6L9k7W+t7Bc0Gskl5I64MVgo0+/mp35KNjoJVmsNhm8Z9+iJCCyrUJ7KJrDOYyRbDep/XeMtvvm78/FlIgc65Jak+zZ9aEVHOz5O0Km8N7uHaesdw0E4VLtIIlxnJzeHbpTInV0NfcfZDwgx9H/NqftvA8vVtVeu0lj6WVlD/8QcC9BwlD/+S5dUIYgemNqw7feNPjtZf2iJXWmiBQvPNeyGf3Enr9/RdGISVuvoK0HLTKUPrJNvDZKDJm6GtqVXAf+UyP1yUXz9m4DsSf/+DSc4N33g25fsXhV76V3zWEtSxBMS/4c3+mQG+gWF1P6Q/UM4+073ijaUy238/qpvLLCqX0iYw1H4csVqx8sE1rsc/K+00uf3ua2dcaVGYKTN4YY/b1BmEvYfPTDp/+zhIr7zXpbwbGiuY5QhRqXFdw4bKD1jadlmJt+egPkzvTwD0/ZapUShGvbDH4o48Z/Ohjsr5vRuQdm8Irl7HHq6TdId1/8faet5Ylcc9PUf7mKxS//iJWrYLMe2ZizY+eWF1N+h3i9ha5xixCSFQSkYXDXUsKc8Mo0Wjjf7izPSGwCyXKF66TnzlP3G3R+vBHpMMe9dd/icLMRcZeeJP2zbcJWxtHXhtVkhg7BMfD8vKoJBq5lxvn8sdNBp4GTmkMy/WIe9sMVxeIOyZqTUiJU67iFMu7sgEdBCY+Jk7QSYKslE3EGyWTHzgYomNTsdOpcU8XjmVafb0BaauDPTmBkIKsNyDr9rCqFRND0+6g45h0u/XMgmDHLSKlRZbFqCw1mY+n6c/v6L/g1NYjzwWxAkiDhOFSG3WEJ03qx/TubvHZ332bwlyV/FQZ4VrEbZ9g3Wiw4m74xFXYHitS/eYLpF2faKlJuLh5/B88AVqbD70io+JNIbDYGN5hc3iX2fLL2OJ0LPgkeLCU8k//2YCvvu5Rr8ld/yrbgbfeyPFf/xdj/Pf/S4dbdxKC8GQflrwnuHrB4b/8j8b41lu5ff5VWkOnq/it3/W5u3C4vZelMb3mwq7+ACBLntyOjCLN/QcJlbJkvL73gufnbd78isdEwyaOU6L4T6gVGDPQD2/GfPhJzMsvuPumQF9+weUv/UYRz4V/+H8P2NjMnno62bGNduutr+ZIEs39xYSHy+mX1n/oeUbYT1i/2aa12CdfXaRxqczsaw3OvTVBebrA3BsNGpfLvPeP7nH/++ts3z+5oemX4Y4kiTXLiylSBsxdcIiOuR5Z1SJ2vQJA2uoTfHyf/g8/Qg2CfWREK0MyhBT7jYgzRbKyRf/7H5B1Boz9hV+h9AsvAZpkfRsVxvs8kA4i3F7DeligdO46YzfewKuNM1y5Txb4pg2WL+JW6qgsJtxaZbhs/J2k65GfnGP8jV8m7rYYPrxDsP4QrRSDxVtYjsvYjdeJ2htkUbg7fbcHTdJvkUUBufoUYy++SbCxRBoOEcIiDYekw94j3lZPhzToo7MMrzZJYfYilptDa4VTqlK6cIPC9AVUYshSsrk1MjnVkGWk2y0TIbMr8hSGwDyq0xz9W6dGdxYtLBIvrYDWqCgm6/bMHdtI73kWLcDZi9+kUJpi0Ful337IcLBBEp3MJsKqlnDPzZgcS8tC5k5njfTcECuU3rVYOOqxLEzxV7vE3YDBYhthCzI/IfVjsvBkJ8ku56m8ddWYh7afLStoB0JIcnaJsdwsmUrY8heNJYNW2J+DA3m3m/HRJzH//PeHCFHklRcMeZNCMNGQfOutPP/Z39B8/+2Q9z6MuPcgIYwOtwZ3qhJXLjq88arHt7+R4xd/IcfEuL3PtuLBw5Tvft/n3Q9Dmq2jzo8mS0+vN/F9zQefRJyft/cRq2JB8sJVl//wr5b57e/6fPRpTPsEgnzXMRYEYfQlWFE+B8QxfPhJzD/4JwP+479W4fIFe9e7rFiQvHjNxXUEM1M2H9yM+exezNJKynZbET5mQSsVBZWypDZmMTkumZmymZ+xOTdrc/6czfsfR3S6iuXVPyFWnwd0pkmClCRICVoRfjuiu+qz9nGL+a9OMPeVBhPXqlz61jTDrfB0xOpLAMcx15F2S9Hajthce/x1WhZyuzYgydq2ad/1jiATmdqNxMGxdg0fAXSckmy0CfPLxA/WcOcn8S7Nkn/xIsGtxX2O8Ic2G4UEW8s03/8e+cl5nEqdsVKVnXaTqWhows2VvTagEBRmLlG+9BLScRks3WW4ct8QFCDYXMbKFchPzhvz0CSme/dDdPJIW1JrwuY6w+X7WF6BwswFcuMzRvuUpgyW7uCn6TMTK3/1AZabp3T+GpVLL6Hmr6Ky1GjLggHBxkPc2iRaZegkRYf7j5WOTicz0GGEfsS+8/PQUvU7S2RpjLRsqo3LVOoXSZOA0N8mGDaJo/5j1yYdp8ZtPYqxysXHp7A8Bs8PsToBtNIk/Yik/3TCUZlzyV+cJFprncn+xJlPzi5Ty83jWgV66QbDZGfb2pRwzxhxAutbKf/sd32qFcl4XTI5biGlwPMks9OCv/jnS1yYt7l4zubDmxGtjiII9K6vjGUZc8n6mMVrL7t86+s5vvFmjnxuf+Zgq53xk/dDfvO3hzxcTh+7QD8N+kPF238c8bXXc1y+qHdDhi3LkIN/598ukc9LZqZCHi4nRLGJd0Gba6klBZYNjm1MMl1H0O0p3nk3PO7G9LnG4lLCP//9ITeuOkiZ5/JFB8c2Qwa1MYs3XpVcu+zy4c2I9z+O+OxewupGymC4o2czx05Kc5xrVUmjZiZA52dtLl+wuXjeYX7GxrKM1UM+Fzy1L+Of4ORQmcbfjvC3I9Zvto2vl4CJ61UmX6hSe+/JjvFaYaJIpEmz+KIm6h4HKQWuJ8jnBeurGWHw+C+mcB2E55qKSbPz2PgcnY4CgIVAjsTq+tGKVpyQbncJ7yxj16vY42PkX75E9HADkYjdSs2uFlQI0nCItByksBk8uIVOEnITM9ilKtJxjddUHJIOe0StDZLBDsEVWK55vL94m3DtIXro4zhF0iwiGw4J1pfo3b+JW65huTks20NIjywJUcp0AJJ+m+HyPSPJmJzHzuWRjovaaQEeWEeycIi/toiKA5Lhych2sLUjSdG41QbScUFK0qjHcOU+CEF+Yp40GJBFX4KIoROg3byLP9ikWJ6mWJnBy1XJFWq4XhmvUCMKusRhlyQeksQ+KtsjhzqOyTo9VBRDkp7afPbnilg9M4QAxyYbRmTH3L2cFN1oA1vmOF/9Clv+Ap1wDaVSHGvk66Q+nzDRJIE/+uOQakVQH5P8W79WYkd7Z/yNBL/yi3l++Zt50lSzvJqx2czoDRQCIxCfaFicm7OxbWMxctTH6oObEb/9XZ/f/ZfBmQ+A9XqK737P59f+dIGvvu5RLj7iJu/C/KzNf/rXK/z7f7HE2nrG2laKP9KNuY6gWJCUy4JqxVRc0lTzhz8I+Gt/MyQ++QTyc4UkhZX1lP/573XRWjPRKFOv7VVFpYRKWfBLo+rjjlVMGGmGgSKONa7z/7P3Zk+SZFma1+/eq6vt5nuER0TGlntVZlV1T3fRPT3dM8AgM4ggwAjwwDwgCLwgwp/AO+88IyAgAggIIPRAdwPT3TO9VlVX1pJLZGTsER6+u+1mut6FBzX38Fg8tszIzCj6E8lId3M1NbWrqle/e853viOIIkEcVhqtw8i+4Nj//xbfOA5ujtn5bAD/HkTNAL/+jKncOYy2OOuQSuDH6ittzP4yyDJLECjOX/ZZO+Nz5Zc5926dYMrsqaOernaWVam7x+BwZdUEWkiBiILKa+mRXdq8pLi7g3n/AsGZZYLzp5FhgL/Qpb5yHltmcz1TDekFjO5dIWx08RsdZnt30OMhg517ZMO9Rz/+8J/575bRtV8yuv4xAI3WaZrtMzjnmE22sdZgphP2f/zHKBVUeiYZUmuvMpvukCUPqtDy3jZ5b/thPxV4orYq3d2onN8fPZ6nwJUFydZtkq07j9/k88+Y3vniuff3bYCzmizpkyV9erufo7yQuL7E4tr7nDr3m/h+jWS6T3/vKoOD6ySTnQdvFgLh+3j1WhWx0gZ9MHhuP6vXklgJJVC1oLJeqB2W0Bom9/qYZ3hVPQ2uKCl2+khPoeov5rT6JKTliD17nVG+RWESCpPicJQ2Z3d67aGU2qvAR7/MKUvoDy3/8PdqXDjnH32mAJysqnJOrSkWFiRaV68rD0Jf4PtVtOMxv6o9zV/8KON//v0pP/lZ9kqq6o2tIiK//0cz4ljw7/9bjSMLiaPvMDfBPHtGsLKsjiJWQgo8VbW68b0q5ZAkL9/x4nWC1pVR7H//v0zZ3DH8h/+kybkzHs3Gw50HDuUPUkJNQhAonK20KWoetTpuqfG3+HrQXI1ZvNTCacdoe8ZkN32s4k95gtV3Opz+oKqQm/WyZ1ovGO2YHaQPWuvEHqc/WCTp5wzuvXgPx68CQSjoLinW1j1u3yix1p3oY+VM5UAuvMMm30+QUbiKNLnSgFJ4rTo68DGPkjBjMdOqT6zwfbx2HTyJzUp0NsUUKcpoTFmJ2v1aCxXVsLqgTCYE9TbSD59fMD7fzvdqGFOSJX3qjTWEqMTuAonDUuQTdJk8ffHygp/54nBP4U4vt08RhUTvvYmMQsxkRrmxhZnOHljvv8w+wwD/1AqyXqvSlYMRpj983NhbSJQKqDfXaLbXiRsr+EGN4cFNymKKNSVh3GHl9IdMxyscbH/CIUG3WU6wuoSMI8x4+kImoa8VsRJK0jjfpX62S+10i3Cxjhf7OOMoBin5MCE9RqziUy2ixTrSl0zvDijG2RPb0BxCTzMmP72J164Rnlkivb33TFHj02BcQaoLUv2wf5Zz5rHXXgX2e5af/iKnKB2DoeWHvxbxzps+iwuKwBdHpKkWC2rx0/elTbWPG7dLPvplxh//WcpPfpaxd/DqKpGMqchhHAlqkeAHH4SsLCnCY47vnidoeIJG/en7EuL1WWl9GThXEdJPr+bMkioK9evfC3nvrYDz56rWRocO14ecSSkxL0h4NokqS8cssWxsaq5eKzjov7wQ/ptC2PCoLUQEDQ/lV70CvVDROVsnqHmoQNFer3PquwtEbR+rHdY48mlJNipIB/krs+iqLUac+f4SjaWIZFgw20/JZxqTV83mpZIEDY/V97qsvdfFasf2J/1n6qucdRRTzfD+lPF2QmutxoXfXiNs+vRuj7FlRaqlJ8DB7b/eJR28WoPGuc78iMw/zRzUlSWuKBBeVHlShY+3VXGATXJsXlkseEsd5P09zCNarCqQc+gCLpFxiJASnYxJcdiyRHp+VVnuLMoP0dkUZzTlbFQJrvVLWJcIiUSgVIAfNJCq+g5HvrrWYE2B8iOkfK0ezU+FjCNq338f2Wqid/awk+nclf3LEKuQ4OI5/PVVMJbi7ibpx58/RKzqzTWi+hJB0ECpAKk8imxEMt1lNtmlLKYIIag11mh1z9Fsr9Pb+fSBP1qWY0YT7CzBJi+W/nxtzp4MPaLFOmu/c4nlH56n9eYSQlWrcJ0UzDaG7PzFTdKdB6Lz1qUlFr+3TrBQ4/4ffs742l5VHXgCzCRl8rNbLP7jX6P29mnyrT7l/hhblCeS9epGPjlKJpAoeXgDPfzgss5g3KvNS40mlj//Ucatu5pPPy/4d/7NStC+0JGEoSAIBEoKpHoQ0bGucjM31lEUlVnnZGq5er3g//p/E/70LxJu3P56aux39gx//Ocp27uG/+SfNvn+d0NWljzCsIqoeUoc6YIO/S3dXL9qjMOYigwMx7ZyJ/9ajvqbR57D1esl126O+M0fhPyD36nxD34n5uy6R6MmCQIenPtjEaojs/Wq4AdjHKUBXVbeYuOJZXNL8xc/yfizv0rZ2Hz9hOuNlZj17y2xcL5BUPfxQoUfKxYvtog7IcqXrL3XpdYJSPo5ZWHQmWF4f8be5wM2JyXuFVkbqEBSX45444erxO0AHBSpJp+UWONQviRqB0glMIVleH/GjT/bZufK0w0ucWBKy85nA9qn61z63VOc/nCRlXfaZOOSMtFIT6J8ic4NB7fGr5xY6cIxmzomo8rY1/NOJvY2zbGzDFmLUe3GkZD9ITiHGc+waY5XjwjOLJPduA+7D49N5bsUVanFY61ZymRMmTxbk2SylxOKG53h+zXCqDPXT1Vmi9bpIyIlpEJKb+5ZdaxzwWsMEfiEl8/jLS+QxyHpp9cQB/0v9c1E4OOfWiF67y2Eksg4Ir9+u2qaPEeze47u0pv4fp3x8B6j/m2mo03K4uEI7Wy8g+/HxPXlhz+kLCk3tl7q+F4PYiWgeX6B8//kQzrvrxEtN54cCn7sfYJwsc7C99ePKgafRqxkHBBfWgUpiM6scOY/+0cUeyPMOD2RPA3/4grTX9x+8v6ER+y16ESnUfLx5sNJOWR/duPZ3+MrwO6+5o/+JOEnP8t486LPd98L+c67Ae++6bPQUbRakkZNYl0VkRhPHMOR5eqNgitXCz69WlWS9fqWWfL1hihGY8vPP8n5L/7LkvfeCvj+d0M+eD/kwjmPtRWPVksSzyPzRemYpY5k5hiMLDt7mo1Nze27mo+v5K8dCfiysBZ+8WnBjdsl/+s/m/L2ZZ933wp4+82Aty76dNuSVlNSr1dE2xhHUTjSzNEfWPYODNu7mrv3NV/cKCuH9y1NklqSpNrudTNYb52qcfHvrrH2nQX8SFXaMSFQgUR6EjHfpr4U4cy8+a2D/S+G4Bw7VwbYV7QeGm8l3PyX2zjjWLrUorFSI2r6BCteFZHRlmJaMtiYsnNlwJ2/2qV3e/LcTu/3f3ZAPilJhjnrHy7RPl2nvhjhug6dapJBTv/uFJ2/+hsligVSQJZYanVJODc0ftL1ZMYJejDBW+niry7MTT4fgQPdH2MmCcGZFaJ3z5N8fJP89tZDWQcReATry8h6VEUmivJr6RIwHW/NLSAOCZOYr2YsCFlV3DlLno4wR8Trb/FEGIOdJaA1otZELXYR3sN0Jpv12c1/TjrZQ+sMawqMfXJAYDrepsgnL+xXdRJeC2IVr7XofOcUC99bR8U+09s9Rtf3yXsJK791nvqZzhPflx1MyfsJfiOkeWmJ4ee7jK+d3PxY1UJq75zBX2hUbRBCH3+xiWpEJ7a6Uc2Tc2jd6DStcA0pFLWgi3WGwiTEXhttMwrz/J3lvyy0hvHEMp1aRhPLvfuav/l5xtKiIo4EUVhVzzlXRXjywpFljoO+Yf/AsNczDEfPNpdUtTrhqTMEyyuUwz7pnVvYPPtSE5ed663SzJAkORtbmp/8LKfTljTmhODwnjLGUZYVwcoyxzSxjMaW0cjSH9rnTpNbC9PE8T/+71N+8dnDYf/ZzPLRxzkHX7JtjjFw7UbJf/s/TfjzR1oNZZnl6vWSaLXB4tkO9ZUaUgpMaUgHGdsfVY1bm6fqLLy1wPZPd5juzPBijzM/PE02zJjuJggBC5e7RO0Q5cHkdMgVobn2ixn2DwfEkaB7usbyWx0Wz7cQviSbakbbCRsf7dPbTimkT+u7p9jxcoZdTXwqpBl59K4PyD7ef2p6/ST0h4Y/+OcJ126WdNoP7DSKwrGxpSnLp18v+9dGfFLc4e6P9pjuZxzcfH6rgf6dKZ/9n3e586Nd5PMs0OZI+jnDjSnmCdGquz/ZI5+WqECy8+mApP90oiOqWDYOiz3mNq5HjskVza1hj3udHqoBXqiQ8/StNQ6dG7JRwXQvZXh/RpnqqnHzc6CYaQ5ujtGFYesXPaJWgArmovDSVtYOo4LZwasprDmOw7nECwSeL1hb95hNLBt3Hn/4md6Icm9A9PY5vE4Db7GNbNYqH6vDucU5yu0DTH+M8BX+UofaD97CWUt+cxOnDapRI7x4mviDS3jdZtXepjf+WhqEW1ueZCz/EPQJD/9DrLzVxo8Vg3tTrHXozKB63AFBAAAgAElEQVTzasdRy0f5kjI3lIlBKqh1Q1be6TDdSxnvpKTDAi9SLLzRoNYNGd6fMTvIKDODkBB3qlVqmRnK1CBVlSJWviRo+BTTknz6DbsCz/2vnKnMR2U9nvtgPUAyrYoLinzCs0hqOtunyCf4fu2xbY0psM84J4/itSBWjTcW6L5XRapGX+yy/6O77P/4LsnOhNp6+2RitT8l25+CFNROtQg6TxcSOW3QowSb5DxvENyMTg4LN4JlWuEKB8kdmuEyxpYk5QCJREofTzyuE3jVsA4GQ8tgaPni5itYdiuFqtWIz19G7W2Tb21ii2c7Gz8PnKuE+P2h5fNrrzaF6hxkmeNP/jzlT/781ZQXWwsbW5qNrZNv2nO/0+bMD08DDlMYypmuROdKoCKP1rkWZ3/rNMPbQ6Y7M1SoWPv+CuP7E6x2CAnrv3EKL/KYbE6YyYDMV4xmki/+dAecY+ndGqeLGHnVIKRFKoFQTa7/aI/h7Zx4QfCD31igWMpQIiMMFV7NZ7I9e+kywcnU8eOPcn780culm0b3Z4zuv1xKZrQ5Y7T55Xx/HsXe1SF7V5+vZdYhqfIJ0JRHxMrDx8sj7J7P/t6U1M0oKebbFQ8RsC+DfFKye2XI7pWvsMXXS8LaiiyKufVCGD35gir3h5RbB2AdIgrwFlr4yx2K9JhWxznKvSHl3gCbZMhaRO39C8gowFts4QqN12kQnD9FdHEdEfrovSHF3Z2nyjm+beierVNfijCFwYs9pvsZ092UsOVTX4wI6x7OwcGtMU476osh536wyM6VIfmkJB0V+KFi8XyDhXMNdKrJxgUOqHUC6ksRXqgoZiUHtyYEsUfcCagtVKXlo01HPtPfaEDNOVfpqax7YOD5SHXSAzujZx9oWcyw1lCvL6NUgHMOazWeF5LMDsiyZ6TaH8FrQqy6NC8t4oxl+0+vs/sXt0k2K/H3k5zYD6GnBeU0RwiB34rw4qcTmWJnyPZ//c+/suOWQlGYlM3Jp8R+i7ScsDn5FIHijc73Cb0nhLNfc5jJmMmnv8BfWMJmr4ffybcZrfUmjZUav/jvPmV4e0QxLY6yB/W157t9o3bI4NaQn/83nwDwwT99n4XLHRqrdayxdC90aJ1p8pP/6iOSg5SV7y7zg//0Q9pvtMgGDyIXzjgGN4fc/9HL6Q7+FocQKBQ+/pwsVQ/1Gk3qooWPT0aCxeAT0GGJET1yfrXupyAUGO3o7RtOR5LNeyU3v3gywdH7Q4r7+9i8QIY+qlUnOLNCsXXwUHWZnSQUm/vk93aJLp/FW12gubZI87c/eGyfzjn0cEL22W1s8uojdF8l/EjRXq+x9k6H3asjtgrD6Q8WUL6i1g1on67xye9rpgcZ0pMEcTVXWOMQVJX1XqDwAnlkw9FYilj/cAEctE/XsNoy3c+odUPW3umw8laLjZ/3GG+n33yW0vGg+kHKqm/hIxXMre4b4By9vas8T6jQUyGt1hnieAlrNXkxodFYY2/3k19NYhW0I4JOjMkNs3tDsoPnW23aQmPyEmcsXuyjwq/36z5sDaBQ0kMKhXW2WrOKKgS/flqxflrRaAiu39Ac9CxCwPKyJE0ck6kjTR1KwepKlfoajx2jsUVrCENYP+Wxvq4oS8fN25rBwHLxgsfqiuLK1ZLx2CIl1OuC0agqa/b9ypE9y93Xoj3yFxapvfku0g8QSmGzlNn1qwjPw+8uEq6eqnpieQHOaJJb1yh6B3iNJvW33gUEXquFDCKKg12mVz+j7J2c2v1VwNZPdyiTkjO/cYo3fucMs92E3U/2Gd19clWp4NAi4cEkkw0z8nFxNBlmo5x4WlJbinHOUV+t0b3Y5sP/6DvoVBM0g0rTG0ikL3HzNNNsP2W699VGel4neEGNqLlMc/kCo+2rJKOdE7cNCKnTxhcBHj4Ox9QNyUgICGmLRTQa7QwSSUhMRywRizraaQJCHJZQxKyIM0SuxpQRhcsJRUTqZmTMsFi6YhntSiZ88xGoF0GaVJIDa+H65wW9XXNyVaA2lNsHjP+fn2AmKcX9PfI727gnmNLlt7aY/tkv8botvMU2InjyvF9uHpB+dof0i7vY9NUI9WWjhvAUZvjVdPI4RDYu2Pp0iBcodG6oL0V0zzaqNLWDWa+qXNWFJR0WZJOCyW5K0q9ezyYF6aggGfoMNxPK1NA549Fdr5FNNba0ZJMSqSRCQDLM2bk6YveLEbNnpLm/DghPolrNqmGztZUb/CPXTq1ZOcWz93yRXmMLZrN9yjKjLGfk2Qilgrlx6IsVErwWxEpFPjJQmKSgnOTY/Pnync66o4eCOKznfREIkGGACKrqEVeaKmT8nPXlhUmQKJTwyfWMQNVYb34H40oir0VUy3l3qRJfX7rosbAg2d21BIGgVhN0O5JaTbBxX3PrlsH3od2WnFpV1OuCv/zrAilhbU3RbgneedsjSRxb24aRhHZLsrYquXm7ckzvdAQXL3hcv65pNgWNumB7x1KWr55YqXoDv7uE31mgHPSqpqmeR3z+EjZPUY0mweopso07mLJEhjHR2fM4Yyrd1tpp0o2qx5aMY2QQPtwL7FcUk60JRhsWL3eJOhFRJ+T8753l8/8trYTV1uGF3tFYCCUJWyFe9EC/Jz1ZldEf/q5EVVVWmsr7R1tMYUn2U4ppATsJ/RtDRnfHlLMSL6qmCZ1pyuT/vx2XhfLxwwa19hrT3r2nbqvwqYlG1ZR3vlpui0WM0/NXHB4BirkoHYPFUFKQk1K4DI0mxOHhYTBoV2IxCASRiJFOUpAREs9F0F/DIHyFEAIaDcnyquLuzfLpc5Bz6N6Y6Y+vYGcpZpKc2ILGDCakn99BdZtEl8/gry2gmjVQEqcNdpZV7uuf3yX59NZT5RxfFuGbbxCcO0W518f0BujeCDOafCkPJ+eq6tDmSkzUDCimmjI1pIOcsOkjvUpjpef/mdJijSPuBMTdgMluhi0rw1jpCVprMUNt0bklG5d484KOw31Y688JWk46LB7zVfva4XmoZpPg7ClkPa7OaZI95jOlVIAV5uk+HsdgTUmS7AMCKRV+0KDIx2id8aI312tBrIDqAVCaF1LtCyUQ3rw7tTYnCtAfe5+vkHGAasT4C43KLFRKbJqjBzP0JMHO8ifaMBxyN+dgVgwoVYYSHtPigMX4DU4338M4TaYnRI19vvfrIVvbhjgStFsCIeHiBY/lZYm1cOmCRxQK9vcttVjQbEjOnfP43gc+n31e0mpJ3nvXZ2vb0Gw8buZ5HI264NxZxWzmOHtG0WhIdve+nhC41+7itTvYZMbsiyuYNCFcXaPzw79HtrWBKwtcUTC7fhWbJoRr6zQ//DXKfq9ywZWSfGsDPR5ik4R8bwfzaKpRVKXU87BNtZI5RqyFFBXZnt+AQorq74fbH3rbSIlQAlse6xovBUIeRoJcJdg+7k9xNPBufrzHtjX2pR960pPY0rL7yQFepDjzw3Uu/KvnuPFHt8mGOSY3Vfh/MaKxVifqVuTLCx8IwoNGQLwYU1+tIaQk7kYIAbP9BCxMt2cM74zY/NEW2by6TEhBOsjQmaYxJ1ZHPgzfQkgvQKoq0iakVzVAtwapAqzOsabE4VBehLP6qBG48iOEVJgyx1lTRZmVj+dHDy3Eynz6wH1eSLygRhC3j7yOTJljj/Udk0gkHgkTEjdGojgn32Ts+iRMmTGizRJybg6Zk5EwpXA5mZsxY4LFoJwiJ2PsekwYIpDgHA3RIRZ1EqY4HMa9foQ3CAWdBcnaGZ/x0BJG+qnPQJtk5Dc3n/zHY3DaUO72Gf3hjyi+c4HorXMEpxbB93BZQdkbkl+/T3ZtA31wsp+g8gR+KCkyg31JHhS9c5HG7/0Guj8iv36H/MY9intb2EmCy+bPkEdNLZ+BfFJSXwxZvtQkavkMNizj3ZTtK0MWLzSJ2wF5UpJPS0xRkap0VBJ3Q5orMZPd6trXmcFqx+L5BsmgIBnk7Hw+ZOliC+kLsklBPisJGh5lqjCeOPn5K0AEldP9o9XvshYfzZVCSmQUVq+96FQiAClRzQbB+TME588g63XsLEH3h4/1G9RlipAeUbyA0Rn2kZPorDlqGwRU6b9sRBR1ieIOSgXMpnsU5YsT79eCWJlMY7XBb0XIQD13VM6fpxCFFBSjDJM+n0AxWOvS/MFF2r/1Dv5CEzkPJVc5+SmzTzcY/fVV0hsPix6VAjk/vLKEUb6DQGCcZphtkZYjesltpPRJ9Zg3Ohmrq4q//OucXs+Q5T5aO5aWJOunFcOhZTyxJKmlVhO8cU6xuqJYW60iWVJWRCuOBL/8ZUHgQXSC+BNgZ9fy0c8KPvwgmO/fMRrZr6XFi4pipOdTjqobwBUFNstQ8fz12YxyNKj+ZgxWl0hPVYSrUmvT/OD7CM/HZhnFwd5jGi4Z+ni1ABX5IAUmKSgGSSWMXazj1UP0OKUcZ6AEXhxQjjOkrxCexBYaEPjtGL8Vk24PMVlZObnXQ1TsI5TCGUPem1a9yAToaY6KfXAViZKRX0VZ/Yrc5L0p9jkbgT8EAWd+a53zv3cOqy1CCnSm2fzxFuWspJgUzPYSsmHGpX/jAuf//jlMbqqS/NmDk6pzTfdih+//xx8QtkPyccH+lQNmu1VUq3d9QNSNeP8/eAfpV4ZgzsEn/8MV+tdfTFvwTaG5+AaNxTcQShE1ljBlRp4MaSycYbR3k2nvHs4auqfeIR3v0b9f6c06p94lrLUZ7lwjmx6gvIj6whkWznwHqapOBc45tj//F9VELSQqiFk8+wHd9fdQXkiZzxhsXmG0c/WRo3IYSgx67q79cIr2ZeCwpMxoskCdNgExfbdDwlebavo64BykqWM8NPiBQHniq+Pt1mHTnOTjm6Sf333gWeUczhhcoXHF0+/J7orP2Xdq3PzllOlAv4jx9kMQgY+/0sXrNIk/eBs7TShub1ZE69YG5f3dF9rf5scDdq5WhFAogckrn7WDWxMG92dIVY1jMY8uz/o5n/yzewjBQxWt258N2L9RVdMWicZZx87VIfu3JtW22mG1Y7KbMt3PqrnuhGiV8H2C82dQ3TYyDB/6m2w1qj6PVKnR8M3zqIU27oltiU6AoBKpN+sEZ07hnz2FbNRACux0RnFv87FG0LPJLq2F85x7619jOryPLpOH+vOmsx7T0f3HPiqZ7ZNnY4QQlOUMY17cDPa1IFZ5f0Z+MKN+rkvz4hLpzoR0+9nl1e3Ly7TfWsZZR7I5Ih883d5ABB7+QpPu775P7Z0zSF+R3dvHJnnVYyv08Rca1N46jdep0/ujn5Pd2cVMH0R9Wm3F6mlFXBMMe5aDPcNo6LBOk+kppc0QQmJsyXjm2NgIefstj5VlRbstUUqwu2sIAsFCV6J1ZRtQlo7lJUWzKfEDcVRZOhxZpjPHD38z5M3LHuOxQ8qSxQXF+rri0iWPJHX84uOS/X1Df+DotCXGwP6++TIR6ReCLXKs0aharYoeeQrh+9iiKpnF2XkH8co3qCLO8xXSfLbNNjcwsyk2z9GT8WMrlNrZLo3LK0hPYbKKVHn7E/Q4Q8UB4UqTzgfr9H58m3KS4TUjamcX8JoRJskZfny/6tfVjqmfX0QnOc5Y/GZE99feQPoKqw16/t6gU8PkmtEn96md6eKMRacFrbfXULUArEMnOX4zItsdUzzj+nsMDnpf9NGpxrnqsawzQ9pPyScFzjqmOzO++P0bR+k6o6uoy2w/QSea2nJMOdOkvSHbP9tFzat9ZnvJUTRvsjVl4682GdwcPPCHc5DsJTjryMc51//gFsl+Umm1ngNeK8ZfqKPqESYtKnfzTg2blVUvzqQA51DNCKyjHCfoYUKw0sarh8xu7OI1IvxuDdWM0aOEcjDDzJ6s76giVh5lNqVQPgKBEJLZYAspFHFjiWzaQwVV/7dDKD9EBTXE3JSx3l2ns/YW04O76DKtNBrOUaQjhPJAKLygxvTgLslwC+kFdE69Q1jv4gV1dPGoy7ebrwGrfz3hUXMNWixQF00suopS8eRVscWQMaMpOnjOJ2VGTkpJjhYlgQsxVaIQeO6sxzcOIWFpVdFsSYrC0WhJovgrbp/kHC4rXuwBfgxhTXHqQsz65ZhJX7O3kbFxLSWdGswz7ECOQ0gB0kN4HiIOUY0aMg7xTy0RvX8Zvd+n3Nqn3Nqj3Ovj0qfb0xym+B6FKe0TrUCcceST8jFOr/Mq/Xf8mjl87TisdjwrklGZgL5BcP4sqtV85G/eEdmSjTrRe5erjiYv0rJhnlWQgY9s1pH1OkiBKzXlXo/synVs+nD2RXkBSvkIIIw7+EH9oe9hTAHHApZi3v6m2VrH82KsKbDOkMz2yLIX0y++FsRqdn/I5E6fxvlFFr+3jp7l9KyjHKXzlEsFAUhf4TUCoqU6S79+jvaby9jSMLq2/5Ar+5OgaiGN77xB7e11hJKMP7pJvnGAmaQVsYp8wjNL1N5eJ760SuODNzBJdkSshIBGU3LqjIfvCzzPUJSO0bC6gBwGfSwcOR4LPv2s5MwZhTGO/QPDbGa5dccxnTrOnVMUuWNvvyJP/b5lljhmM8ugb5nNLOMJxLHgzLoiSRy9vpkL3QWzmeNg3nJGVpkxytLh+9DrW27ffflV2BMhJTKM8FsdVKOJDAKCpZVKqF7kmGRKsH6OYPUUrizxGk2K3W1McswJ99HUqlQcptecLrFZhjMGv92p0oL5gwdt0K1RP7eAc458f1L1FlOSIvAqYrXUoPX+OuPPdyiHVbSr8dYqKvRINgdVmtBZpK/w2zEq8o76UtbPL+KcoxxnOGOJVppEa23Kccro0yoiZksDSlA7t4AKfXSSY40lWG6g59GzF8Xw9ojh7ZPTFcWkYOunTxZRBw2f2nKMKQyjjcmJ1XzFpKCYFAxuPnnyKBPN9gmfcRK8Vkz8xjLhcouiP6uihksN0o0eMvQJlg6jB9UJV82IWVIQLDTwFxvMbu0RrrYJT3dQcYBuxThtTyRWAEaXZNMexuR4fiXMzyZ7hI0l/KhBNu2d+F6oUohhfYGwscjO9b8kn/UrYjWHH7epiL8lGW0z2Poc6fk0l87PCVqMLpNKD4QmZUbpCuw8ZjV1Q/ScADkshcswVOJ1gMLlGKEpKY/omKZkTB+fECU8hKvmO+1KCqp5x2DmaU5Be9Ejzyx5YrH2Qd+3KsU5rwoT4PkSXdrqYSWqTgvWPdj+6yBnR3NS4QiCp8sYvgkUmWXcK1k+F9JennfPEJBMDLORYTbWZFPzQnOoEAJ8D2+pi7fUJbiwjk1zinvbFPe2Ke/voA+GVSuVaYKdJl/dyTipMOArO9cCGQaobgsZR4jAn0szjuk7oxC5tvKlP8lZi8tyyvs75NduUW5sPxax0mVKMt0HnlzgdCgHODp6IfG8mDDqEAQNrCkRQqLL5FeTWI2vH1A73Wbtdy+z8q+cx6sHSF8x+OXWUaWfEJU2KlyIaV1eYvV3L7P44TrhUp28n9D/eJPpxtPTGl6nTufvvYcrDaO//oLeH3z0+EY/vk7jexdY+se/RuO7b5BvHJDdqkK5yqu8WISA29cL/FAQP2UVNp44fvlJySefVWmb4zfonTvw059VPx8Gbf7kX+ZHkarj2+7tFXz0swfVp4fY2n54VSNlla7UGu5vaK5ff7yS4stA+D7B8grN9z4kOrU+F5rXyLY2SG/fpBz0qZ2/TPM7VUrPJFPGP/sJwvfxO93KQ+Do7q+0UDKoogtCShrvfVj9yRj0eMz4k48odrePPt8WmnKcYgtDcq+P1ZagWyNabeHVQ8KlRpWyUwKTlZTjlKBboxgk5LuTo544Ji/RyeEDXOCMpZxm2MKQ7Y7J98aEy83qyTDvdSaURJiql46e5Zi0IO/NSO71CJebz9cp4MVG+2icHv99/vNc0OyOHrDHr8XD7Y5fAE/6HU6ckZ/4ngoyDggWm4RrHYSSmLTAFprp55uEq20a76wDMPybmwhP0fzOWdK7vSMXdCEgOrtIsNigHMwIFhvk+8+IUjuD0RlKhxghcdZirZkvduVD3+jB4YujiV/5VUGE0Tm6SB4iVUcfYTX5bIAu54JWB0bn4Oy8LUk1HjkJuXtApA2a++5Bl4Wxe3wumjJ8bChLCnpu96gdlju2QUlB4qZoqgdKEAne/H6d3nbB/v2CIqsMcYWsemr6oSRPTSUhWPCYDDRCCLxAUCRVEUtlNOow+tWSK2fh3u0SIaHZlswmliz5doXa9u/n7N+v5oGFtYBz79T47t/roJTgYLPg5i+nbN5IyJKnaCjn8oDqOqteeqgZuuehmh7x+5eJ37uEzUvKzV2yz2+RXb1FfuNu5ddk7Ks9IV8SNitIP/4cM5kSXDhLcPY03mIXGYdH/bIO0+ov9T3cw7+4vKDc6zH985+QfXYNlz2+4Boe3ACev7OJEBLlhZWhqLN4XoSUqrqBfhWrAstxyvCzHTb+j09Y+bsXaV1eIlqqc+YfvUvtVBsZeMSn27z3n/8OtrR4NZ+gW8OvB0zv9Nn659eY3h08U+ciA4/gdJfJT2+S3T84+Xj6E5LrW3T+/ndRjejo9SAUlKVjd8uwvWkwxuGeYzVz0ornSa+ftO3zVPV1O5IL5z0+/axk4/5TmufKeaouCHDaVKTB97BJiup2kGGAPugjfL/qAp4XlRjRWop+j9HPf8z0xpUqJKwU+d272CIDXTD6+U8e3GTWYsscNx1R9g8QysPMZjhrKfZ26P/5HxMsLiPjGpNPf0HZO8DqEr/TpfW9v4OKntE5mor0eO14rs17MKEFi3Xq5xaZXNvFb8dEp1rM7hygYp/4dIfGhWWk54HYOnHQy1GK8BWr//p71M52Se71Kfqv1o7AC2pE9UU8v4a1JaasRJl+2MA5gy4S/LBeCbmlIz9IufcvJqA9Fk5VmqA8GWB0TljrUGQTrCmRskpx6TLDlBnOOVpLF9BlSjreRfkReTLAWUPUWCKd7BFEbcJam9HBbdwjzsQq8ADH5JN7eK0YGfmVxvEwImIsJi2IziwghMAkOX4nJlxtEyw1CU91MVmBnmUIJch2R+jxy/s4OeewRiOVj1TzVKAQ+GEDP6pSF6bM5013I7wgrsbhSarlb0DEf0ioJIoGbWqiicUyZoCZ+2ApJWi0FXE9orvsU2SWdGaJm4pW16MsLEXukBLihqJILZOhJksszY6HLi2jA83eRo75GkoMdQmbdzXDXtXDczb5lnbzFuCFgqiuiOsKXToWTwd0Vrpc/KDOF38z4d7VJ0ej00+vgZIE59fxTy1XDuFPCc2JwMM/tYxqN4jev4QZTSnvbpHf2aLc2Eb3hic/BL5JGIPe62EnM/Lrd5BhgGzU8ZYWCM6dpvbr30XUYuxkRnH3PmY8fSHRvoOKpBYldjpD90fo/R6mN8BMv5oOJsaUZGkfP6gjVQflhSSz/XlvwV/BqkBbWpKtEdt/egOrLd33T1E/16F+toOQEuccXi2g/fYqiGrSLic5gys79H52n72/vkPer/QiT4UUyCjAFiU2PTkv7wqNmeVITyHUg+oraxxSQb0pWT2l2N0yjL9Fk0VROgZzQfxkcvJYqHYT1ajjSo2dJYgwQHXauLJEtVvVKn04rn5WEleUVWuBeTjNFiXmYA9VZngLHUQzrgioc1V1ZlHMxYgesh6jsww7e5iQ2Dyj2NtBhhFhXEPVm1WHe2tRcY1yOKgc3Y8h25twGGEq+jOcddV5Cn2EkqjQI9seUfRm2NJQDBOy3TF+O660MMZV/i17Y0ZXttHjFDPLsaVhfGW70lBNcvQsqyw/pEQGCr8VVxGqvQnFIGFybReoyEIxTLCFQU++Gu8X5YVE9SXCWgddpuh8hgN0MUNKRa21hufHlPkEXWb4QQc9ElhdYE1GVF9EegFWF0jlIZWPNRrnqj5l1mqMqXqnKa/ycHHOEda66CLBWkNUXyJPBig/xA+bFUl+5DiL3hSubVP0pnjtGir0sXlZad96U2CnKhY4fMZYh5nmZJt9ymGCSQuy+33KgwlCCspRgp68fAWrtZoym2B0QdRcYvHc93DWEMStI/LkrCab9sgm+7TX3sYU6XxcHNODu9XKtZrhX/o4viwcDk1J6qYY9FG0Cpj7FjmQEMSSuKHorIAXSKQEax01v4pchbEkCAXTkSaZGGpNRWvBAwfbd/Kv5Rs6V3lZpclzrAqVRMYhqlVH98e4/CX7+3kKGfrIOKqeFXmJmTw53dZc8Dh9Maa97BOEEiHh5i+npLPKpqTW8rj0YYP9+/mJxKrc2MFlBcXdLbyVRbzl7lEaUDZqyOCYafV8wSlqEbIWVQJvbfCWuvhn1ih3DtB7PfTBAH0wwPRHVTTr2xDJclUUyeQFDKvIsvA9ys1dzHhC9M4lRBhgpzOyL26ht/ce00Q9+yMcaINNsypNOnt6mlQIhR/UCKJ2FXl6JF5d5BOy5Lg8wGFMQZZWC0ilfLJs+KtbFQhUROmzbYpxRrI9ZvnXzxKvt1GhX/XRkqKqBDEOnRQkmyP2/uo2/Y+3nkvoDlXKxCbVg1jVwhO3k1GA14xxpXmox1RZOqJY8MZFj7XTCq1zxqNvD7GaTByTybNXCarTxlvoUu7sVmQzjvGWlyrLAd/HTmcgBKrdRDYa1Wql10fGMSIKcUVBuXtQdR2PIoJmY259YOZNTyvhshAClMLOkseI1SHKQR9Vq1NfO42qN6oqkKIguXUNPX74vGZbI7KtE/RIh7Xyx25EPa5u7HTrQf7cFprpJGN6be+htz8aicqZPtivFHCsV1vRe3jb4jkNbZ8Hcu6lpLwI5gJtIQTZ9ACpfOrtDlIqZqMtymxCvX0aISTpZI9s1iNurlTRLV9TZBOkFyJVgNYZ2XiXIh1VqS0ERTqmzKfoMkGpAC+Icdbihw2kCo6I2SE5O0448p0h+U41rvn2w/qEIi0p9p58TxYHD3SQR3WNzxGFN2VGmc8wusDZjMAHP3DorESpdILDgqoAACAASURBVB7Nm1Ik+9Q6p+mefhtTZgg0Jh8S1wxF4sjGW0x9SXv1XZT3oCCgTHuU2QyjM4pkhNWHhMZRZhN0kc5Lt1/tQ85hT6wA1NpxsF0cBdQaLUVz0aPMLKO+Jp0aml0PzxdY7UjGhuG+pr9d4PuCWqPqSWj0t+BB/QhkLSJYXyJ68xz57S3KnR5mNKuIxYvsJ/DxljuEF04jPIXeH5Jd36gMQh9ZeHeWfN79YYul9YDhXsmdT2d88dMJycRgtKPR8Tj/fh0/ODkCZUZTzGhKfuMeIgzwVhcJL54lvHxuHplqIqIAEQZVluCYN5+QEhFIgjOrBGdWcVpjhhPyW/cpbt4jv30fM5pikxSXF5VZ6rfo1LlSYwZDSk9ixhNkq4HNcsrNXYrb9yr92CtEGLWoNVepNdcq4bqba2iVj7Oa8eDeI8SqImNFPqEsE6RQCCFfqjHza0OsgKpSaWtEfjBj7y9vVfqZlQZBJ0YFHjbX5IO0ar7cm6GTcl5C/5y7z0qyjQPC04vULp1i9tnGY805hacIT3Wpf/cNyv4EPX5wcfiBoN2pxOtl4bj5xctVo3zjMAabpJS7B6h6DdVqoNpN7HRW3exhNRGodhMRhJjpDNmoI3wPGUfI5QVskiJrcfW+JAXPQ8yjW/7yImY4xozG+GfXEdHeyYcym5DcukZ67zZHuiDn5rYMLzCpvqpIg+MhUvWqIaWHUh7TwQZeUMMLquhUrbkCAnSRoPzoobJiYwq8sEZz6QKeH2OKFGMKpPLJkwEgkMrDWf3Q+46+orMYUxDVl5DKR3kBQdwirC0Q1heIaguk7uAxMehXhucY3snBXab9+zir8USP1Ysx594M+Jt/MabdHCIlHASQ9j8hHVzBaEA4/EDQWRC8+a7gi18Y8jzBpLfYuXqPqD53nZ5aoqjECMPkYMpsuHVErKwp2b/908o361ULk56BIrVc+/kDEi+lQKrqkOxclnDpuzVaXYW1sHUrp79TID1Bve0xPCgZ7X87e+Z5S21qH75J+x/+BmaaMvubzxn/yUeUu/0Xqi4ToU9wdpWFf/d3kXFIevUuNivI7+7gHnFfL3LH/kbOT/6wx7ivKTJLmdsjeUeZW774mzF7954vGu2KgnJrD73XJ/nos6rf4Zk1wrcvEF4+h7fUPbIleCKUQnVbxB+8TfTuRVxWkN/ZJL9+l/zGXYqNbXiB593XBacNuj9ELXa/1s9dXPsO9eYqZZnSaK9TZCPKIqHROk06O3gsJSulRxA08f2YMGwTxQv4QZ3+wReMRndf6LNfL2IFOG3RukCnBcU4J9ufoubO085YTK4xaYl5Tnf249DjhNFfXqX7u+/T+s038Tp18p1BVYnkbFXNtNohurCK164x+NNPHtJiGQ37O4arnxQsrijKb+cc9Uzo3gAxmoDW2DSl2KrCubYoKx8nazGTKfmNOyBllaKbR6CEqrRZZjQC5WEGw2pVOb+InTGVdivPq9BxkmCGJ1e94Vy1/9d1ML9i5OmQwe5VyjxBej5S+lXLhWPO20IqynyCNZrh3nVwFkd1ftLxLnZOoIRQmDI9eo+eNyKt4JgO72ONxpqS6WADIasVnJRetarLp6STPfJ0iDXf7PlxVh/pvJQCaySzsSWZFDRajqilWFhRXHpX4fmC2aRqn5LODEoJgkjxzvcjwlji+YKtu8VRitBah1KS/W1LMikxjzy8XhmhfEE4B2V+nNg9TvK27uSMehohYbivyTOLEIKduxnWQJ5+eyLsx+EvtgnWl5GNeB7h8aso0wsSWZvmmNG0SgnWY/yVLuGl05S7fcwjxCqZaLZupvR3CrLZg3HprPg4C5NByY2fz1ODzwMH6CrL4bKcMi8wk4Ry54D04y/wVhbw15YJ1lfxlheQzdrDEax5hF8oBaGPq8VEvoe33CV65wK6N6wsGzb3KLf3sLP0xSwNXhGcMejekODs19sKxwtqGFMw2LtKEDaZjrcZ9+8y7t+m1X0DP6g98g5RkauwTRA2kVJWcyUAkufpN3j02V/d1/ia4cDmmuIlCNRJMLOM6Sd3jiJSzV+7SDxOKxNQ6xCBwmvGVdXZ9W2mH9+hfKRSKc8cg17lx5R9SyepZ8HOHkThXFFiiiFm8Hi5aXk8R36CgY7pP70S04xfP2PDbxK6SNDF84fQ08nTzAefnmOrolmP/3yI8ltqSllpdyyjvsFZCEJJu6twJuCNN0OCSDAdGcrCsXu/pCwdtYYiXBJEsay6JowNflC5+KczS70hyRLL3rONv7/VGPc0496jc6ZjuP/ti3Qch+o28ZbaQKWLsrOsamnzLN3sI3BZgRlOMYMJqhYh6zHB2VVE8MVj2waRpLPic7CZk6cWpapqyosfNJiNDaMfawZ7rpr2xAPH8cP0uC5m8yISdawQ4sHxuqLE9EeY/ggEyFYTf22J8twa/ulVvOVupXdtNSrd1aN6LCVQnSaq08SdO1WRta39B7YN+wP0cIwdTzGT2TdHsoxBH/TRg9FcCGi+lsiulApdZkxHWyyuJRTpiOlog+kIGu3Kq+phuCMzUG1yimJanUf94gun15dYvQK40lDuj+n93z8n2+rR+e13ic+vVsJrKXC5Jr29w/ijm4x+9AW6N6lam8wRBILukmJ13eNgV5POXuHFMy9LR879X4Q4khFVf39C3v/wYnbzKqNjpfgPTDlfEt8GAeVXBTG3UDgskRbHPHYeGuQ5jo+re3g8n1kw8Sox/x7umJ/R8UMX4vmqVr/KY3nimFYH8/D2j44px67VZ4xpnjpmY0sQWppdxcKyx9KqT1yv2iLo0iGVqFp9zCwI6C4q+nuagx1NMrWMeoY33gpQSrB5S7O0FhE3viW9KR8Zy6MxhUfG8RFfKjc337Uvf11+U1ezbMSoVh0A3RtVAvaXbBnhipJyp4/XbVa2IKsLSP/xR+Ha+Yjf/reXGPc0+mZKWJO8/XdavPubTbZu5lz7aYof1quorjWoebWpHzcJ4jbT/j3CWhflRySj7RMtPKqDAjuakI8m5F/cRgQ+3soC4dsXiN+9hH/uFHRbVQRLiiOh+yGElIhaTHj5HOHlc1UB1/Ye+fW7lXXD9Tu4WVaZKtuvt6rVlRq9vUe5sVVVl6fZc7eX+3IfTGVTIgRCqMow1IvmVdDeXND+ANZq8nyE79dwzpJlQ6KoQ1nOeJFoFfwtsXoi9GjG9Oe3yW7tIqMA4c+bMGuDzQrMJEWP0scujqKoLtZ6Q1BkVdXNq4CKFPFCTP1Ug/pKnXgxJuzEBA0fv+7jxz7Cl1Vncll5hzjjMIVBp5oyKclHGVk/JT1ImWxOmO1OyQbZt0r8+E3Ar/nESzGN003qq3WibkzYDglbISr2UIFC+hLlq6rYQVtsadGZppgW5MOMfJgx20+YbU+Zbk4oZsWREebXAeVX5z1qBSxdbjHcmJKOKqf2oO4hlUQqgRcpJrspVlukLymm5SshWl7sES8+YUzbIV7so8JqTKU3b6czv1ZNXo1pNsrJhxnJfEwnmxPKaYHVTz7YPLXsbZb09zW6dIz7hiAUGFP5OSGq9J41kKUWAWzfK9GFxdhKj6QL6O+XCAHpzDE40N98BFqAChSNUw2aZ1rU1xpEizFRJ/r/2HuvJ0uy+87vkyd9Xu/Kd1VXu+kxPYMxoANAEABjydWSq9WDtHrQPuhBsf+EnvQn6EGKkCIUsREbUuhlgyvukiK5BAGCXIAwg8H4aV++6lZdb9KeTD2cW9Vd3VVdt9qMofCN6DFddfPmPffkOd/zM98vVt7CmMxPTajosYxTZCSJR4fPe8C4OWKwPWCw0ScannNeZnw+G+IJEJZ5VH8k+2NluvuUyGRKOhyTJRIx6cDjBJ253XsBP/3zNq9/q8TVt/LEUcrydY+Pf9xn85ZJsXGZOBigWw7GZDP2B/uQZQhhUKguo5sOMjl/rW0WJyT7HdKRT/jpPfRKEWOujn1xEeviIkajguac3mCF0DDqVUTOw766QqE/ItrYJbq/Rbi2hdxvn2np87yQxQnRxg6y0wNNQ/aHqjP8BWM8aqKhIXSTYW+TfGmBSuMlpIzQdRN//LhgsKYJ4sQ/0rSSafyYlMw0+EoQK2+xhDtbQNgG483uUWH688ahpU3SG5F01R/1g8k/zmD5QkCWqlorN/f8iJVu69hlh8JkY/JmPNyah1N1sEsOVsHCzFsYjoFh62px1ZWR8KEo2xEJiFKSMCEZxxPF7RC/5RN0A+Jh/FQdEOdBJjPWf7jGcGd4LNr3hUBTm75Xz5Gbz5Gfy6txrbm4k7E18xamZ2J65hGp0nQNoYsjwvqAXMXEo3iykYUEHUVcx60xo90hw50ho92hsqh5QURLNwW1y0VMR8fyTMoXcuimoLs1YtwOKMx56qSbZQhT4JQsTNdA02DrvRbR6NnJ1SGRys3lyc+rMXVrrpqvExJwNKa2rmRLjIfG9GHCOjkIxKOIsB8StAOC1mRM90YMtweMdofEo/hoTNNUpeTDQP1/6J9dAzPsP/6h/Ycyrv7oCyIUhsDMWxSWChQWC+Tm83iNHF7dw6k4mAULK2diOJP5aShZAFWwrsZQhonylhxGal62fUVSmyOGWwN1sNodKqL6hGmZpUqO5IuIwmqmgbBNFdkZ+09tUwNAmiJHAVks0Qwd4drHHDwO0W/H3H1/hJvXWbziohsad94bcfeDEaN+Ea+UI8skhulhWB4ZEPn9SedujjRNELr1dGvqQ/IFstNXtje7+yTbTcI76xiNqvpTL6NXS+j53MQAWX0OTdPQHAvhWGTlAsyl6PUy5uIM1pULJM22+rPfJjnoKPmKF6WPlWVkfkByTnmFZ8Wgu4GGIE1C+p11sizFy8+QAcG4zbB3PK+v6kdNLNPDMF0l12B6DAbb504HfiWIVfFag8Y7y1hll+3v36T7oXwhxErPOxTevkS41Sbe7yu7mnGkiq+nWEyEACnVgm67GrpxaunRmdCEhrAEdtEmN5undLFM47UG1et1CosFrMITukcevRYa6KCbOrhg84STzgtGEib01nqM98fIL4hYCUNgeAZ2ySE/l6NytUbtlTq1l+t4de/Id+8sHI2rpULKdunxcc3SjPH+iM6dDgcf7dP66IDh7pCg4xMNnn8kS7cEMy+VsfMmWZahm4J8wyEax0TDmOKsRxJKYj9Bt3VKCx65qkMSSZqf9YjHyTF172mhGQLDMbBLNrnZHJUrVWov16m/UsebyWF65tkX4YS5Wjx5ro73R3TvddWYfnzAYGtA0PYJB6Ea06945FXTNay8hdvwKC6XmH1jlvprDcqXKxi2cSIROPZ61DzHBvIWbu34z5XP5JDWpy2av9qj9WFz4gUZkvgnn9APm4O+iLT/ob8oqAjIo93a50GWZqRhrNJyukCzjaMUquUIagsWYjK+Qtfot2Iu3chTqBr8/Z8cYDsCoUMYjFRkcBKR0g5bMIE0S0kiH6EnyCSceE4+/efPohh50EUedOHjO4hCDnNhBnt1CXNlHnOujsi5aLalonumqQrdD1PGho45U8OcqeG8clkJda7vEN7eILyzjuz0kIMRmR9+ebSxnhHjh+pLk2FAGPQwzXuqGzroIU8gS4eRKsPw0HWLYmmJOBoxHp3euX4SvhLEKrdYpvrGInbVo/3BtlLRfgGwGiVm/utvkMWSYH2f0YfrDD9YI9xpkz7Bo+wQYZiRpmCaIGOeyRbCcA0KiwVWvnuR2bfnKV0sH6VLxHO3R/n/F5yKQ+PGDEvfWmbm9Rmsov3gtK8/5/StBm7dwy47zNyYJQkTmu/tsfXjTbb+fkMRgeepG6SBYQuEoRGPJeEgJuhHyCjFrdgUZl3aawOicUJ9IYcMJZ2NIX43IhrFyl/uKeCUbGovN7jwu8s0Xp/Brbno5iTCdwYJeKr3q7rMlhzqrzSQQcLBR/ts/WSLjR+uEfZD0hPMaL8q0ISGXXJY+K1FLnxrmdk3546nS5/Lm0BuNodbdZl7aw6/7bPxgzU2/26dg49Odp1Ik5R4HH8hEatsooOnCaG64p5lDdSUBdVRJ+1DU2Vm2eZf/Y8rOLkHe0wGODnVTfrf/0+rpBm8+9dd/p//9faxOic07aiGatTZ5FAeRokPH9d5e1akwzHhnXWitS0000TkPawLc1gr85hL85gLMxiVAogTDjSahijkcK6vYl9eJot+g+j+FsEnd/E/vk2ye/DU9WtfZsgkQMpIteycIiuTJAG97vrEN9DBND1kGvOP0tLGzNtYZRdh6oQHI6Leiwkpxp0h7b96D2epjlHJUfrmy+RurBDtdQk3WwRrTcKtFknPPzFs6uUEpglxDIWS9kThuNOgCY3q9RqzX5tj9q05CktFvIaHlZ8+QvVrPA5N13BrHjNvzDLzxiyVKxXy86pGRRji+AL5PN930r0jdEFmZZg5k5k3ZvFmPGa/NsvOz3c4+GifwcZ0IrZnIQkk6z/dRxgaMk5JZYaMUmSsOuTu/3iPYBgjQ0k4qanK0kn9XXC+U7UmNEUYJ2NavVolv5jHqboqHf0CXXWFLkAHYQpMz6T+2gxuXX2/uz/fZv+DJr213rn3MmO2gXVh4aHoSIwcjIg2tsmiCM2y0D0XORw9Zvr6POBUHSpXq1z45jKVa1UKS0Xssj0pwn1+7/PwvNQtHcM1WP7OCoULRfY/bLL1dxuM9kbHCKoSX06+GGIVJWRhjGaZ6EUP3XPOftEp0HSBXsohTFPVWwXR0Wfq7MX8v/9mF+OIwKquv0P/ykM5vP3NQEl8nHa/PH1EbSpMuuuyRJKFyvlCFnPIfhF9okqe5T3QH49uaof+mEKgGQY4NtalJfRKEfvaiopk3bpPtL5DOnixIp7PG8XqKqXKxal+d9jfprP/oBtU0wS6bpMvzGOaHmgaSRJM0oDnm/NfCWIlTIEwBalMCbs+yRTRo6dB0h3R/eFHOMsNnJUG9lINs1bAuzyHvVDFXZ0hWNsn2u0StwfEnSFyGBwVAeoCxqOMnc2EONaPajymgSY0zIJF5UqFxd9aYu6deWrX6w+6fn6Np4MGZs6kuKxSqQu/uUjt5TpO1fncx/Wwe8trqJqj6tUqTs3Fq7s0f9Wke6dDEjzbxpUmGe37p8sgBL0HZMDvRce7Bc/xtmbOpLBUpP5Kg/nfXKD+SgNvxvvCxtStuTgVh+q1Gm7Nxa177P1yl+7dzrH6q7OglwpYC7OKOEmJ8FyMRo3koA22pSyaTFOpSkcxmqGjVysI24IsU3ZO3T5ZeP41Kr9YOJqjC7+1iF12PpfotCY0DNugvFrBa+QoLhXRLZ29X+zSu9clnpRdHEWsPsdGjEOk4wA59BEFD71aRC/nwdBV6/55oCnbMnO2ivBslWLrDY/MVke9hF/+9ePSMpOXHuELz5TpAmHb6OU8oqgkF8yZKsZMFb1aRDiW6h48C5PnRy/m0Yt5zKVZzIUZ9FoZvVIiurdF0uqeaHL8zBACzTIROQ9hW2imoUToNMjGAXIwVFpc5xhsQ7ewJt6fyvPPgiwljlVHpqbpGKYLZJNuv0egHbpZ6GSZZDw+II7+kVraKAPVbGLaK1/Yg51FCeFmi3CzRe8nn6F7Nt71RfKvLeNdX6L8zZfJvvEy0V6P8WebDD9cZ/zZFnFTCVwGQUZzV3LQlOxuCbrtcwiKeSaVyxVe+1evU7tew6mcbTD8a5wBDQzHoLRS5sofX2X591awy58/oTrx1oSG4Zos/c4Fqldr1K7v8uG//YDB5oB4FJ1OcjRAU/pKz9w2/WQtyVPfX5g6hQtFLv3BZVb/4BJO2VGplS8YmtDQhMb81xcoX67QeG2GD//tB3TvdIgG4dSfMQ1Cgo9vIcc+5vwMubdfPzKVtVcvIPsjklYHdB2Rz+O+9hJGrQIyJen08D/8lKR5jo1IqDq9xd9Z4uLvrzL7tbmnG4DnACtvUX2pRm4hT66R4/af3qJ7r0MapxNiFX0hESvZG5G0+pjzNcx6GXOmgp53kf3RubSsNMtAL+WxLswgPIekOyButk+0xtEEGKaGbmiIydwCFeFNoowo/ALSzbpKhYqcizFTxX3lMva1VczleYTnHBMUPYSS2MgglUffnaYLVRT8ULG7+nsdc76BMVPDeWmV8T+8z+hnHxBvN89PYp8EQ0d4Hka1jHVhHr1eUXpdjo0mBPHmDsGndwjvbTz+vkIDcdj9qtLEh8R4ONg5Ikyl6iUsp4RMAoa9LZLYRzcc8sUF0CAKjmcJsixFJiHdzj0y0mdaW78SxCoeRCTDEKvkKJV1Q7z4jrI0Q44jxp9uEawfYPzoE8xqAediA3uhhr1YI/fyBfb/5Cd0vv8BoLRwiiWBaWtUa4I7N2MG/fTM78dwDRZ/e5Erf3SN6ku1cxWm/xqnw2t4NG7McvmfXaF6tYpV+OKK9p8Eu2wz8+Ycb1dc7vzHW+z+fJtx8+QQvCjkMOfraEIjafWRnb5SvRfaUe1JFqpIivJVjBG2RZakz+XU6VRUXdPlf3aV2it1VVz+AmqonhV2waL+aoM3//Vb3PuLO2z9eIvR7nCq1wrPxX3z1YmbgyS8t0468kmDCM00MWcbaIaybzJm65OT9VhtXppSmj4P8nMFVv/gEkvfuEBptfw0H/f5QgMrZ7H0rQtYRZtP/u+P6K/1lHH5M9ThPQvi/Q7xzgHua6tg6NiXFij8zg36P3iXdDz9vLYvzpP7jZfRcy7ognTgE93fIT2hy3Bm2eHGN0tcej1HsWpiWKoQvLcf8/7fdvm7Pzm5Fu2FQGgIz8W6uIB9eRlrZQFjporwXNXVaFsnaxeiCt+TvRbhrTWSgw4Igb26iHlhHqNeVgTrhPfTK0Vy33gTvVJk/IuP8N/79NlDdRNLNO/NV7FfuoS1NK/IlGmo9UtMDo0aRNt7yqvvkbSqOT+LfWkZvV4FKYnWt/Df+xhA+YVOnBAqjevIJGB342eqgWDiDjLqbzO3/Js4XvXYdXXdwnEqZFlKnIyfKlJ1iK8EsfJ3eow2u9jVBdy5InbFI9ifbpF8JqQpchioPwOfLMsw58oI28CaKWHWCuiFB5ElTQPDAtvWGA0zoiA7cx4KQ6gi1W+vUH+1jpmzzl3se9jOGw9jolFE4sfqhBkrUicMgW4KdEfH9EysvIX2AuuKHr23LM2QQULsJ4S98HMpgM0vFph7e47l37tI48aM+sxfQgIAqgPOrbiYOZM0lpieycaP1gna/mNF2JoQCNNEeLY6uboWaRSj5z0VSp9AuI4iX92BUpvuj5DPSKxyczlm3phl5XurNF6bwak4X9oxFaaOXRLUrtdJkxTDNVn7/n2Cto+Mnkx8skQi28qKSXNs9FIRdJ0sikj9YHLa18iShHTsYy3OAhqy2ydptcnO0VZeXC4x/xsLLP/eCoWl4peillLTNDRDw2vkmH1zlsSPufeXd/FbPvF5ta+eE5K9DtH6Huk4RDgm5mwV753rpGFMcHuTpNlRFjcnQRcIz8FemcN78xrua5fQLAMSSdLq4X+2cSKxKtdNLt3IKS/ADEp1k7VPRlRmLZz8i2mgOn7fOsJzMOfqGLM1jNk65lwNc66OXi2j5x+1ZFHI0lT58+23SfZaxLsHJLsHxDv7yMEITdOIN3cfSDY0KhhzdYxyUWl6MZkDlomolZUvYZIgh2Pi9W0lzfA0MAzM2RrujevY1y9jLs5hlItM3vDYr2qOo+q/TlpeDB1RLuLeeEkJZLsO0f0t5GCoas0mBxuhm0BGEgcTY/kMJqbKhuE8JtZqGC7lyipZlpEk/hGxGo8PCMMn2K6ddIvn+u0vCIP7bbqf7FG8XKd0pYG/3Sfq+qTxCywQFBrCNBA5G92zMat57JUG7koDveipB3pt/4HWFcpGIxdoOHaG72fE8ZMXIMM1yM3mVPj/rflzRVSyLCON1AkyGoQk44Tx/hi/5RP1AxI/IYlU2lS3DQxHx8pbOBUHbyantITy5oOOuKdI5WRpdqSVkyaP/Hno72SYEA0iwl6gNLNa/gtbnA/b1OfemmPlu6ss/vbSM13v6DNG6rNkMlVkMZs885MUgTDEUdfWUYvzOe/bdE3mv76A6ZokfszuL3bxW+Pj5CpNlXqy4aFXiohijixOMCpFNEMnS+TEDFvNJVHIEW01YfD0py9NaJh5S5Gq715k+dsrT30teGTexKnaCNJHxlR7aEzNpxxToWG4BrNvzmEVLOJRzO67O0qz6Uldg0lCctBBDkeYjRr2pRWEa5PpQhFYx0a4DnR6yF4fo15DMwxV6H5vSDqNr6Wm0m4zb86y/J2LVK/VnomkZvKwUUFO5qlSWVcSfIdzVEOYqvt1mu5CYQi8Ro7VP7xMOIjYe3fnmWsAnxZJp0+4tkt4bxt7ZQ5R8HCuLiEcC6NWJLi5odTYEwnygTcmuo7u2Ri1Et7bL+G8tIw1qyIV8X5HXXNt98Q0l50TFCoGf/q/bbN01WPhksNf/19NvvFf1olelFisEEqU2nXQS3mMRg3n2grW5QuYizNopnnic5Cl2cTbNUAOx8jegOjOJuGtNaK1LVVH9tApP95uqrEp5rCWF7Cvrago2FxdpeRM4yitaNQrONcvkYUR/U4fGXfPbSUEYFRLOK9cJf+9byjdLUNX62kUqe9N11Uk8QxkfkA69pWfomOTDseYi7Okd0OyhzTrZBJi2XmKlRXlnZpKhG5g2XmErh/Z1zwYeh3LVvVZhnRVATsQx6N/pMTqbgsjZ1G61qDyxgKJHzHa6hI0By9sgxaOhT1XJn/jIrnXLihrm6JH0h3i32/S/+lNRh9tEO0+KHQc9FMcT9CY07j2mk2aBnRa0alRq9xcnit/dJXayw2c8jm7XNKMcXPM7rs7bP/DFp1bbaJBhIzkA3uayb8On8PDTiAzZ1FcLjLz+ixLv7usdLGe4qQsY0nUVwKjflsRJr/tE7QD/NYYvxUQdHzCXkiayKPFXoYvrk7OKtjMvTPP5f/iDsN+pAAAIABJREFUKvXXGs98PRlLol7IaG90dGJPwoQ0TlV3kS0wHBO36irh1rqL4ZhP3cGl6aor9LXqG2Rpxu7PdxjvP5IWFNqEOGloGOhztUnbeApCkI58ZHdAlqZouk5y0EX2niGs7ZnMvjnHpT+8wtzb8099nUOksSQaREdjGg1CkiAhTVI0oakuNcfAqbp4DQ+vMdEWe0opDE3XKF+q8Mb/8DW0/0Nj6z9vPjEtqE1SgaCiV3HzQFmMzNZxrqxi1KtKpDKRpKMxwlapGL2Yx5qfIUwS0vjJEXXd0pl5Y5alb15g5o2ZZ+74SyJJ2AkY7gwJ2j7RUK0Fh6TfcA3ssoPXUE4NU683kzrFi9+7iF2w2PrPm892o0+LDKKdFt0//Xuq/813sXMOmmViX5zHWmqQjgKSdp+k1ScdB2ruGzpGKY9eLWLUioqUGA8iTeP3bjL+5c1Ta4cyCXGUkR56yGsa4Vhi2oL0RaxfmoZwbOzLS9ivXMa+uqKioYfGy086/EpJ0u0TfHyX4OPbhLfWSP1QmdfLU+qFpER2B/iDWwSf3cVoVHFeWiX/u29jzNXR7AcHfaNRxf3ay4x/9ZnqODwtOvgEODeuk/vtt9BLBTWgaUo69ok2d5DdPnohh3vj+pnXkd0+yU7zKOUu8jmslUXirV3kQ9HiXusOtblXufzKHxPHQ0WshIFlF9jfeZ9+Z+3YdcOwz87Wz9E0DSnjI1HQLDt/AOcrQaw0oRG2xuz84Dbz37lK5bV5nFqO/p0DgoMRySiaSjButNVjvHU689RzNu7lOZyVGeylGtZsGT3vkEUJo8+2CDcPCHc6xPt94s6QpDMifUTvwzLBdZVhaxydngq0SzaVq1WWvnUBtzp9SiWVKYPNATs/3WL/gyb99d6RsF8aP1k5+RDxOCHxY8b7Y/Y/bFJ/tcHc2/PMvDF7pNY+DQabA7b/YYudf9hChhIZq9b+JExIpQaGQybKyLQHukZGivTHCNuZaLtMvrPJvzVDRzNMFQLOQA4Hqksky8jis1vb7ZJN/dUG1/6rlyhdKiuRyXMiCRJGu0O6dzv07vUYNYeEXZW+lKEkjeWkkSI7KpYWuqaigq6BVbBw6x6l5RKl1TLl1TK6a0wdEdQ0RSxyMzmu/PE1NKGx/sN14qH6/KkfEm/tk7QfFF5qlok1X1MkYLetIlqHi6kQqmX6KWsjrIJF9VqNK398lepLtSMx1PMgCVQ0VY1p98g+KR4nyCB5MKZZhoYi/5quOtUMx8DMm7h1j+KFIuXVCuXLZQzXnFrTSdM0pTJfcVn9w8ugwf2/undiQXu801SpvMn3lcmULI5JByNkf4hs99BsC9kfImwL+6XL+B+pzQZNw1q9QNLpkfZPJ1a6pZOby7Py+6vUXqo/1TxNk5Sg49O51aFzu81ga4DfGpP4yYN5mk7GUygB10PCauZU5Lq4XKR8uUJppXRqA8LhWuDWPeZ/Y5HSShm3/sU01qTjgPDuFoMf/JJ05OO8fFEppxv2UZTWqJfVXnBYd2iZaLZ5ZGCcZRlJd8D4l7cY/eIzoq39U98vClL6B/GknAEKFYPv/rezLF11Wf/k6Q8qx6DrCM/GujCPuTyPtTg3UVMvohfzCO+Usc5UM1ey1yLa3FXGy9tNZercGSD7w+mU1B+SbUiaLfwwQvYGeO+8hvPqFUTemxzIBSLn4ly9SNofEW/uTv8ZTQNztoG1sojRqCkZg/024c17BJ/ennTRRthXV6ciVlmckI7GyG5faXh5DsZcQ4miPoTxcJ90+1eMejvohjVJA6akMmI82CPwjxvLp6kkigYIYUykF9T1pMxO1L16Er4SxKr88izFKw3Mgo3hmeSWKhQu1civVglbY5JRRDoFsdr92ztPJlalHOVvvYo1W1ZWAFIS7nRUp+BaE/9+U5GpE3LMmgblqk6pqh8VOT5pLyuulJh5fYbihdJUp9XDWqXOrTY7P9th44drdO50nkqBPpMpYS8k7IVqo9sdTaJKKfVXpq/z0oRG1A9pf9Yi7IUIL69IU6I6UIyigVEqoudTNamjiDQK0YtFxIQ8aaZJMhgAGUapTBqFSrNHaOieh7Ad0iAgbO48caEQpqByrcqFb12gcWNGEYApowBZlhGPYka7Q3r3e3Tvqs2qd7+HfzAmHk0/xmoDdygulyivlqlcrVJcKVFYLODWvKnkMzShods69VcbKnXaCdj5+TZplKo2/94Qeo9s3HFCJiVJs3PyRZ8CwhCUVsss/e4yM6/PqqaKc4xpEiSMdob01np073bp3m7Tvd/Fb46JhtNrQGm6hlNxKS4VKF2qULlSoXSxTGGpgNfITTemmlJ0r12vEfVC/AOf3Z9vKymBh57TdDAkGpxCikZjZPtBhNqYraMZBtLvkQYBmmkpVfAzSKw347HwGwvM3Jg5N0lJE/Xs9ta6dG62OfjkgM7tNqPd0RH5PgvCEEouY7lIeTKelUsVisslnKp7ImE1bCVYXFgsnOt+nysSieyNGL93S22u4xBzvoZezqNPPP/ECfpWqps8Ud2cBz3C+7uMfvox0fou6ej0erhuM+LTnw8IhpLWTsjemkm5YdHaDtlbf4Z6RSEQeQ+9XMColTEaFazlecwL85izdTT75PU3m5Ag2R8iOz2SVo94a0+Rqs091an6DGnaLIxJmm1kb3jUnOHcuDqRZNDQTBNrZYHw7sa5iJUwTazVJcyZGpptkUUxwSe38d/9kPDmPRVVA/TDeqszbzQjDSNkp4deKiCOZFCO05kkHjPs+YwHTUzLQ9N00kySRCPSVHJSFELTdBy3iqHbpJNIVeB3iKLTJWxOwleCWM1/9xoX/ujVx/4+f7FG/mLthFecjNFGl70f3T3150bBpfD2FeL9HqNPNxh9sMbo5jZy4D95wiqtNRaWDKp1feIVeLpAqCY0Gq/NMPfW/NSRqkxmRIOI+//pHmt/fY/h9vMr3u/d7xJ0fAYbfb72r9+ifKmC4Z49NcqrZRo3Zmh9ckDzvT2MUhmzXEOGAXF70jEjBMJ2lPWDTFRqoVDCyBcQloWRLxLsKM8md3kVf+OeimpZNlatgZ7LE7X2idr76gE8YdM6FKpc+M0lVr63OrU45WHKNAkS+us91r5/n80fbTDY7J9q8HsW0jhl3Bwzbo7Ze3dXdXz+zhLL37nI3NvzKuWqT0cEDMdg7p15IKNzu03QDk69r3jn+Xco2SWbubfnufxPr2AVpiPbR2MaJgy3Bqx9/z4bP9qgN2nZfxpkMsM/GOMfjNn71R66pbPw20ssf3uFxd9exMxbU4m8apqKgjVen0ETGv21LoPtAWn0dPcl+0Oie+tYq8sId0EZ5n52B9k9XexV0zXKV6pc/uOruHXvXLWNqUyJBhEHH+1z989vs/OzbaLB+UVKD8lZ+ME+Bx/sYxZMZm7McumfXmH2rbmjpoQvgyzJSYj32iTdAcFn63jvXMe9voK52EDPu5OOMu2BLFuWqcPI0Ce8vcn4vdv4H99/rOboJOytKwIlBPgjSXs3Yv6SS2s7pLd/zgOthkrpGQbCc7BWFnFeuYRz7SLWysLkd04hU6mK7mdRrBTXb90n+Og2wcd3kcPxUcT/eSELI4JP7qpU6/VVsMxJQ4PAnKuhF04umj8NmmViXbygmkASSToYMf75+4Q37z39vUuJ7A3IohitkFfpxceeJU0p9U+EPo/+VujoQidLJelDBstCGFhWgUr1MrpuE/htAJIk+MdJrML2iNF6+5mvE/X8J7/PVovN/+XPlAFzf0w6DpF+dOYp4PBx2NlKaB1INE1je12jufv4pBGGwK27lFbL5M9x+gu6AXf//A67v9h9vObmOSAaRrRvt7nzZ7dZ/YNLzLw+O9XrKpcrXPz9VTq32mSahnAcjFKFNAzQHRfDy+O39tFzBYxcgTQIMAslEAI5HpMBwvWUCnISI0dD0DR0x0H6Y+R4RNw+UOmtUxZC3dZZ+e5FZt6YwcxN50kHQKo+99rf3Gf9B2t0breJ+iGpfD6FqVmakfgJOz/dZrA9YP/DJtf+xXVys7mp/Qjtok3lao3VP7jM+g/WnptC+5kQGkvfWmbunXk1pufYY6NRxNbfbXD/r+/RudUm7IZPTVQfQwYykuy9u8toZ8j+B02u/YuXKFwoTu1HaOUtypfLrPz+Kut/s0b3ztNF+bIwVMa43d5EmiElCyOy5PRNt7hSov5yneJy6dxp1cHmgJ2fbHH3L+8w2OgfCXc+K5JRwv4HTfyWT/tWm6v//BrejIdhf3m3hyxKiPc7DP72PUY/+wQ97yo1dc9RER9dJ0sSMj9EDn2S7pAsCJGjgHQcTJ0aFzrU5m3cgk4qM9Y/GSGTDHmeGitdTGqnLmBdWcFeXZx09bloztm1bkm7S3R/W6mh39tC9gZK/sMPVMr/BUAORsR7B8h2D71aUnIOQiAKOTTrnPW4uo7ZqCI8Bzn2Ce+ukw6Gz0QIszQjC0Nlc6SLidzEcWJlWjnyxXmK1VV000Zox5+3fvs+ze33jl+XFH/cQsqI4WAXyJDy/NHJL++T8xAOfrFB0Hz2CE3/1un5dAA5Dhl9vKFSfeeYsFmmslTBOMNxNap1gZcTtFuPX0O3dWov1cjP56feXKOBStlt/t0Gg43eC/FBy2RG2AvZ+ek2haUC+cUCbtU989R6qHZdWCoyaI4JdjYRlk3S75GOR0h/TNLvkoahqvcJfMLdLVVbH0UqfKsp0TtNN1SkS+ikga86XVKJDE62EIKJqvpKifmvL1BcLk0dAZCxJGj5rP9wjc2/26D1ycFTnf6PcDhOjyzYWarGNQkTklEMKSz/3grVa1XM3NkLlDAE3ozH8u+tMNjsE3aCc6XRngaH3apzb89TXq1MXcuUxpJwELExGdPmB02i/vTCnFMjg6gf0gsTNRYaLH97hfqrDczcyV1TD0MYqt7qwjeXGe4MGe+PiPpPMaZppjqaoulf27gxQ+PGDKZ7jgMA0N/osfMPW9z7T3fp3G4jg+cXpchSFQ3v3uuSStWleemfXKa4XMQ4530+C8ozJsWqgWFqjPqSJEoRQsMr6oTjlGEvYdiVLF11CX1JazvCkWNKToxh+PjtHrpvYuctDFsw7sX09gL8TkB2gpzCWSg1TJave8xfcnHzOp29iB//6QEvvVMgjjI++9nZUQxzeR57dRFjtoE5V1OyCbXyJN13upin7PSI91oku/vEuwdKMmGvhWz11CHzRUNK0nFActBB5D2Y6GQJ20Y7Z02gpqvUp2aZZMMxyX7r6SUbjpAd1dId2fM88tiXaqvkCvMqYhWNjmo4D5E8YsKckZFlSp3dsgrkCyruOR4fEAYnq/Gfhq8Esep+uEv3w3MUyz0t0ox0HKKZOsJx0OxJy+kT1mk5DEh91fknZUYuL1heNbFdjf3dRPlLPbSx6I5B/bUZvMb04dRxc8zBh/t0brXOVe9zXmRJSn+9R+uTAypXq7gV98xIhW4bODWX6vUafnuTwcbJKSk5elDsKcdnF34mU85jt+Yy9/a8soeZstMpSzP8lk/zvV1u/4dbDDYGJKFq9yVNj2welODj5L9lqn4Ok8JYZccAGmkYqnli6GhCqJPkISaEUAaS/kYfv31b6YrZOtWXalOlXEzPpP5Kg8aNGUa7I1qfvFhhQrvsMPfOAtVrVdzadDVAWZoRdEMOPmpy58/v0LnZIvFf7AYgQ8lwe8C9v7yr0nyuQf2VxlSp1sPxn7kxw2C9z/4H53OvPy9UN65J49UZypcrU79OSX1Imr/cY+NH6+y//+LuM40kvXtdgpaPU3EQlqC0Uv7cbLVqcyZzKw6Bn1KsZpPOuwzDFCRJhrUvGPV8Fq849NsJnWZMoWIwt2Lj5gSDTgLEGJaad0kYo41CsqckobU5izd+V33+Ys0kXzb4h/+ocfWtAsFITkWs7MvL5L/9dawLc0o1/bR0n0xJg1BFogYjoo0dwtsbhLfXkO3eF2OKnCTI/vC4Kr0uHosMnQ1Vn4UQirD5wWMaUueHNiGmh0bajwtxF0oXsOw8B7sfEgX9Y2k/UDVYx5ClSBmRZRLTLGBbeWSaEEdDzhuz+koQq88bRiWPvVjDWawiXOtkZdoJhu/dZfzZNgBhmJFKsCxIIhXpfPTLNmyd6vU6TnX6otX+Ro/dX+wgX6Ru10Po3u2y/36T2TfnjjH802C4BnNvzdO72/38UlUTFJaLrP6TS9jnkKtIk5T9D5p8/H9+RH+9R5qokxiWqdTJNU1pQoWRIk9CkI59dM9VwReZKg+5WhU0jWhHkX6jVELkXMJ76yo1REY2fpB+zmRG1A+595d3SZP0KCKgTSMjoMHCby7it/wXTqxyczku/9FV3HOQ/1SmtG+2+ODfvE9/rffCSdURMuXMsP6DNWQkKa+qjkHNmG5MZ9+cw2/5L5xYmZ5J7Xqd4oWiUqufEjKUjHaHrH3/Ps1f7b3AO1Q4jFzf+pObGJZBYak4dcTyWeF4OjLOeO+HPd75XpnavMmwK3n3b7qsvpJjZslm47MHz5OmgeUK4ijDLWgsXfUY9RNaOxF7GyGdvZgkefpwqVcyqM5b/Lv/eZOrbxWYv+SSTvTWpr2qMVvFWp4/050gHfuE9zYJPryF/9Ft0u6ANIxOl0r4HJBlqgPvWLYgfZr7mXSAH0aXtIm0+jNAmwiDaoau6s/CCB7r3MsI/C7t5qdH9XZPvMssJYl9up17WFYew3Dw/Q5J8uQSopPwa2L1EIRn4azMUP7my7iX59A9W+lACTHxJVITI5MpqR8RNXv4N7ePXp9K5Rc4GGR4ucdrEYUhsAo2XsObKhVwqPk02B7SudN+fnUqZ2C4M6R7p4MMJJqjnbnx66ZO+VL5XOTmeaCwVKB6tUZ+sYAwp1/8m7/aVXVPm31kKDHn5zHqNfVgptmkADudFMtPIlaT7z4dDEnHY4xqGb1cUilMQyfa3iVLU4Tr4r58DeG5yP6Q4NObj9WHBR2fg4/2ufcX91j+9jJu/ckE5vCUm5/PU7lSpXixzGh7cKaC+NMgN5+ndq1G8ULxXDU2Bx/ts/2Tzc+XVD2EsBfQ+vSA2//hNsvfXia/8OT6xcMx9WZyVK5UKV2qMNoekAQv5t6tos3Cby3iNrxzCYGOmyNu/fub9NZ6L+T7PglZmjFujmh+0KS4UmL2rbmnkoQ4LwxTo9wweemtPJYtCEZK9HTluodX1EnijOq8RX3ewnYE/QObhVUH3dQwLTHZszVkkhH5qaqDegZOcvgtJVGGTLKjyN1hEHuqa2jicRmLyVqQdPrEO/tE97eIt/ZI9tvI7kBp0MWn15R+ntD0B36CqhEgPrdlU5ZmSvctTtBMA71cVJI6z3JfloUxW0e4DlkUkXR6j0kuxdEIIQxst0IY9MiyJz/bmiYQwqJYuoBhOEgZ4eUa9PubjEfnO3j9mlg9BD3vUnz7MrnrS2iWQbhxgAwivCvz6MU8w/fvo1kGZiWPXnBJbm4jH/Kp0iatKHGcMehlhOHxB8PwDNyai5UzpyMCGQS9gHFzRNAJnn+tyimIhxHj/THjgzG5mdyZHYJKodnDqdjoto4MH3/w9GIRTQiSbhfhOKpTUBcqjaaJIw2rNFa2IVl0dvC1crVK7XoNa4paJVCRqiRI2P3FLvsfNI/Sqno+h9moTwiQUgFHSuRgqHRwhCAdjxGeR6JpZKlEr1TQc57K7RsGSauDsEz1d4aBZpqkp1jIpHFKf6PH+g/XKF+uYOTMqYi2mbMorZSYfXOW9W7wQjba8mqZ+quNqQVjU6nGtPneHnvv7r7QVPUT7yNOGW0PWfv+PUorJeyyM1Uxu+mZFC4Umf/6Aut/c/+FECslF+Ew88bsuYSAo0FI736Prf+8SdD2P7fnH1SkrHOzxc7PctReriN08cLti1StqkoBNjdDtSGnqoC8vRcR+UpRvrkREoxSZJIx6CQqspKBaWlEfkq3FRP6Z3u0noVgJOntx6y+lmPxiktt3uL1SWqw3z7/PMmkVOK9nT5Jq6tqpyZyCUmz/Vy8PJ8nVLnDQxmbLFPiqycYVj8RUiJbXdJ6Fc2xMRdmETlHXXcara1HIHIexkxNSSxYFmmnS7J3cCTbcIgsy3C8GrNLbxGM28gk4uGHKBi3GfYfBEY0TWCaHqbhkZGRJCH5wjymf/7Gua8ksVICgucLTyvxzDMsZgouhbcvk8WS/t9/SusvfknSHzP3332b3EuLbP/vf4nwbApvX6b49auMPt0ibj3Is+uGhu1q2LbG7pZkPDz+flbeIjeXR5sytJ5lGePdEWH38yNVh4j9mP56D6tgnUmslHWIiV12sUsO4+bjNVTW7JzSrOr1MMoVjHIZzTSRwyEIHd1z0fMFkl6XaG+XpPXkRUYTGrWX61SvTS+3IcOEwUaf5q/26N1/qIjrKDKdTU6KSvtG9vsq3ScESaeHVcirE5JvIxxbTac4BjI0y0I4DhgGaX9IGobIbvexh/0QYTdk//092p8e4NVdzKXpioRz83mWvrHE3i92CHvPd15oQlNk9ZXpFevTOGW4pToeO0/ZXfe8EA0j9t9v0vr0gPxCntLF6QyNvbrL8reX2X9/l/H+6Lk/a6Zr4jW8qWVMDjHcGdL69ID+Ru/FPf8Ph9UfWR8HmwOav9ojaPvotv7CuwSDUUpzM+Kzd4fHszpH2gkK7b968OzuroUPwkfnHCPNNlXzTKaEdx/d5LsHMfc/HnHjWyVmlh1sV/Cdf9ngox/32b49ZXpoIvegfCUD4s1dgk/u4n94m2T/y0emjkHT1PikqRLCjhMVTTvnPWdxQrS1i7kwi7k0r4RC61WSZot0eM4Od13HnG9gX1tF5HOgC+RwTLS+9VhBvBA6ucIs1dnrxPGYNImPqai3dj9+jFjphkUUD4ljnyga4OXqk9qsRybhGfjyEasp7t+dLZBbnr4ANJMp/VsHxP3giT5XmqFjVgv0fvIZo082kP2xynEf+piZBkl3xPjmNkbepfj1K6RBRHyg6oosG+ozOqtXTEI/Y3dLO1a8brjm+YxrMxjtjQi6n//DJ4OE/kaP8qXpx9mpOLh190Ri9QAaRrVCGoQkzSbupUtHWldmRW2ESffJG7QwBU7VpXiheGYa7WH4bZ/7f3WX0d7xzTMNQuLdPcL1zWOLaxZFJAdtlf6NQmRf2cSQJCS947VkWRgRC4G2sXlUF3FWyDyNU7Z+vIU3m6OwNJ04nl20KV+qkpsvKIud5xQh0nQNp+pSWCqSm81N/bqwHyoZiM3B507+T8Puz3fIzU1PrMy8ReVKhdx8gcHm4Nm6Q0+AN5ejfLk6XS3dQ+jc6dB8f+8FjauGsOxjnWlpHB6bs6lMCdo+u+/usvBbixTOSK8+K3bXAgxLO6lU5sl4yvHJf+N13OsrZImk8+//lmTv+LrT2Yv45fe7bN70qS1YWK6gvROxvxHSa0333KWjMdHdDYLP7hPeWVcCnBNLmHNHfj5nZFFEvL1PFqha0yyRxDt7JAfnO0BlUUR48x725RXMCwsI2yL322+DEPjvfjR9V60QWEtzuG+9hvf2DUX6pES2OgSf3jneNAS0mp8w7G+hCUOt2xx3JgmD42LhUsb44xaWVaBYXELTBFE0JIl9zjvJvjTEyizY5C6Uqby+wPBem+FaG3/35K6L4pUGi394tvT9IdJIsvnnn9D7rEnUfcJJQwOEhvQj5DAgm0guHBp76jlbaaJ0RkTNLoV3LmNW8kcvT2LlLSUlOK6GYRw/BBqOgV2yp15gs0wVOz+qrp6rmNRWcuzdGpDEGW7BoDTv0tkcEwcpuYqJUzQRQtUE2HmDJEoJRwnhMCFNoTznYHk6e7eHjHsx8hGRRBmnBJ3gXEbXpmc8nkISAuG46IWJIGixgCZ0hG1jFJRnlLAsyDKi/X00w0Azn5yGMj2T+qt13IY3tRaQjCXjfZ+dn+8Qdo4/gEm7g+wPSPuPz7eHu3EeXghPi0Rlpws5P4ZUpnRutejd7xENwqkU71WdnkXtpRrj5ojevfO1AZ8G3TaoXa+Rm81NPabpZI7svbuLfzA5eWoadqmBbiupjjSOSMIxmiYwnBxJOCaTiRLpsx2S4PD/DXTbJU1CZKi83sx8iUzGRIMuVqGCDH0ymWDmK6RJhNANhOUgwzGJPySN1QGke69L916HsB9i5swzJTiELjA9i+rVKoPNPtHg2TXzHkZuNk/5UhkhzhYxhYn+WZAwWO89t+/3MWgaZrGM7nrKRko3CJpbJP2H3i+DaBjTfG+P6tXqCydWo4E8SSPzhcG+MIN34xJpENH7y8dTtEmUMezGmI5GmmZYtmA8VPeo0o5nv4f/8R3i3RbJXouk1TmXhtbzgm7Y5EoLZFmKTEJkHJDKBMNyELpFEo0RuoXQjYk0gY8mdAxRQF8bItd7yDQmlQmmNBFDiSZ0TDOHaefIyJCxihzqupJlSOKAOBySpSpal+wdEN3fxJxrYMw1jkRRjWqZeKeJ7A0wag8O8ZppoOdzGI2asi0qFjDqFayVRazVidiophFt7hLeXVeivPI4UQ3GbaLgdKeV9LHOxIw0jfH9FjIJ0IROHI2I4vPbF31piJUzk6f+zjIX/vlr7P/kPplMTyVW3mKJ2W9cAtTmdKqA58TVXYYJw7U2wcHwicQqSzPSIFa55Yfk8dMwJkskZqNE3B2pXHmYoOeVJMPR70kY9FJ2txJ63ZTokRor3RJq85x29cg48qh7GG7ZYvHVEp0tn3QQ4xRMFl4uEA5jgkFCbSVHvqbIidA1yvMu3W1fEag4ZdiKaFzKk6tadLZ9gkHCY1NMZsTDmPQcXTW6ZZygzaVNxD8lqRZP0n8DZYhqWSTdDsL1IEmIOx3Mev1U0nIIM2eqepXK9J2VUT9iuNWnd7/72Himp1mYvGhk4Ld8Bhs9hjtDypcqU0UzdUvZ3XRutZ/bxms4OvXXGueSAYmGEcOtAb213pG2lqb1dyogAAAgAElEQVQJ7HIDK18GNGQUEPVbinAVayTBCBn5gIZdrBJ095GRj2662MUqsd8nzNqkcYRdapBGAfGoj1WokhgDZBRilxtkqUQYJrrlkvgD/DQ9IlZBW7kIDDb7lC9XptM2E8rupnOrTefm8yVWXsOjtFKCKasXUpkybo4mhsrnYOrngaahGQZGvoiRK6AZJnGvTcLx+ZT4Ma1PDwg6PqlMz6UUf17I+HMkHEKgFzyMahE58k8sLXELOrV5i8qcKpbXhEaxrlL2+5shoymMzaM7G0R3Np777Z8HumFTqCwThwPiaExmS+JwhOnkMa0cSTRCN5S0TppEmFYe3bDQhIFcb0GWkgZDomiEW7uEHgl03cYrzmLZBbIsJY7GyoFE00lTqSytIiUATZqRjsaEt++jlwp4eQ/heTjXL2MtzRGtbZHstxClB8Rdz+cwlxdUiYXnYNSrmIuz6LUKwrbUftIfEXxym/DWPTgh+qdM6C1MW11Xff7RJOVnoz0iG3HoD5jEY6JwoGQXrPxTEeEvDbHKr1QpvzqHU/PILVewa9OlI2SQkEYnh1SFIRCWgaZr5C9WcT7eY3Cndeq1sliSdIbono1RerDByFFAGiW4V+eJdtpkMsUouio6+NCgO57GeJzxyfsR6/diAv/4F6LpSr/oPKcyGSXIJ/ggxkFKMIzVhqxpmK5OsWGTphAHEqGr09b2pyp1deH1Mnu3h7Q3xkR+osjZCeQpS1MSP5mEUKeDsATi0WhHKkl6XZIP359cOCNutU60byDLiPZ2z66Fm7Su26Xp29YHWwPaNz+/zsrzYLg95ODjAyVwOkX9nTCUL+J55BDOgm4ZVK/WziUDMtobcvDJATJMjkXKNU1MOislmq4jDItUJsg4wCpUiEc6SThGmPaDlKkFmmEBGmkcKlHYLD1qzdY0gekW0W0V7XLKM6o9Ohiqaw67xMPOQ/c2Yv+DJoWl6bobNaFRulQhN5c/83fPBU1preUXCtMbrUcprc9ajJvP32HhwZtIwoM9NCEQlg1ReKJivAwl/Y0efjtAhhLhfT7SCy8UmoZmGSdYoBzH0lWX7/zLWQpVQ3UaBg/Wjo9/0uP+h8/JiPlzQJZlDHvbZFlGqXaJNFVCsKQpbn4GTQjGgz36rTVq869i2nkiv0eneYvq7EsYloc/OpjUs2kYlkuxdoko6JGlEjdXQ8qQYNwmGLWJAuX/+jDC2/fJpESvFLEvX0QUcohyEaeYfyDFMIExW1eGzVl2ZFP0QKoBUt8n+Ogm/nsfE93fOvEzG6ZLuXaJxsLXQIO9zXdp732MYbrkS4skccCgu370+0KYuG4doRtE0ZAoHFKpXmY03GM42Hns8zwJXxpiZddzuHNFQGNw+4DRE8ySH8bmn33Mwc/XT/yZ08hT+9oSc793BXe2gFV6ckdO6oeMPtvCLOcxaw/Yc7TXI+mNKP/Oday6qoWxF6ok/TFJ/0EELAozTAsWVwwKJY21Own7ew9IkSY0hKGdTCpOgYxSsofIgFsyKc87lBccKosuQtfI1ywqix7VJZ9ROyKJU7yyRRxI/H5MEkpknCF0VfOVygxharhFk2LDZtiOSMLjhCNLM2SUPLEm7VHoho5xWhrpUYJ2Gnk6g1Tplo5TdsjPF86lXj3eG9K73z3TIPeLwKg5onu3QzalTYama7hVF6/hYRWtp1MNfwjCFNhlm9x8/lyWQOP9Mb07p3gATsR+NDTMXPGBO/zh4piBjCIMr4CmG8q3K4nQLRenNEOkd7DyFeU4P2gR9vbJza9iuHl69z6ELMNwc2hoRIMOSXg8Eh20lT3LyvemT2U7FQev4WGXbMLnoBivCQ235uKUHXR7ermCNJHKv7P7gqJVE2i6ge4VMAtlwnbz9ENUqkj0uDmaum7tSw2hIcxD8efT12LbFbh5wY/+3T6d3Qj50Fo4aH2566MeRpZlKiU3+X41oePkKirqq+loaBNZgjKFyuFhJsO085SqFxG6OhhZTgnHq5ImMaHfIQq66jmWMVEwwDAd9RzLhBMfHpkSbzfp//kPcV/bwb5+GWt5Ec2yHiuP0YR4LMKbZRlZGBFu7BB+chv/g09J9g5O3TMqjau4uQbD3ibF6iqGYatIWhKSLy6SJMeJVZalyDTCcctomkGaSiyrQKB3Tv48T8CXhlhZBQe74kKWMVzvEOxNZ3o4uHPA/k/WTvyZU89h5m3mv3MFq+Sie0+u3ZHDgOGv7mPNlIj2HoTEw50249s7uFcXcFdnQdfIkpThL+8S7Tw4JUdhxqCX0j6QJKf6SZ2viEB7REwtSzPG3ZjdzwaEo4Q0zfj/2HuzH8mSNLvvZ3ZXv77GvuSelVVZXcvUVDdnpmc4w3UEUIAgCiAJ6X/Qg174v+hRD4KeJFECRFDUEEMOOUNOL+zp7tqrsir3zNh9v/s1Mz2YR2RExuYeGVmdHPIABWTBPdyvX/dr99j3ne+cPFZsfTMi6dvKlVdzKJIKIe2k4ua9MdnIPrbzIKbMFONd2zZR1WleL2L28epX9307F37Lp75Sx58E756H/VDgZCexAuvvqWDlBy3CsIMfNCjy8ZkJ6Xk/Y/R0SJWVOKFzbrtlP6C5thhRX65bsbXBOr971uRUuA5IaY3zzjvWybRq0PSn8ivaP6dZN2XwaHAkW9EYQzHqUmXxwYInJo71RlUIx0XlKbosMMpWSHRVoqvClu6FQFclGE0+2rMj3kqhsphy1ENXJeW4j5ASN60DgiodofKj1Z18kDN8PKSMS/Tc+S2s/YDmcKFGfa1BmZSvHB0lpCBarhO0g9nClkvN6PHQTgNPCafTnnixMblu7c3RaIPq9am6J7Q3929IQp5rBLxvv/LXglgJgQi8cytWYNfHx18lbD78/iezLwtaFcTDzYnTuCAZbdpfh9YYDFK6hPUFOwmtCtLxDkQLOF6ANpW1KihztCpJR9uU+YiqTBn3nyEdD10VVGWG69co8xitT5dymCSluP8YU5ZUvQHl823cxTmcRh1RC5GBb8OqpbA/z/3w6TRDjWOq3R7lo6fk9x9TPts6066hVl/EcXy6e9/RnLs+af1ZkukFdYQ8utbttzRVWOG5IdQWKMvkWPTNNHhjiJUTejg1HwPke/GZgclGmwNR9VkViGKUUw4zEAI38nH8sz+uinPGnzwCrY5UasqtAeNfP8RtRUR31hCOJHu6R//ffU5+iIApBVvPFVvPT94lG21sK2qGC1T6zhECkY0qnn8x5PkXL6bSRuTsPrRl6aVbdbxAsnVvTK3l4YUO3/3ZtiVQwGjHEqqte2O2vh2feizCEbihN5OthVFmJk3WRRDO1Wheac40YaVKRbKbEG99f1qqWm2eucW36XRuMho+Z2/ny1OJVRmXJDsJWd8K2Ke9CUeLEc2rLfr3+zbMuh7hdtpUe11kGIAjqbZOb33vI2gHtK+1kO4MVZVSkXZT4s3x0Uqb0aS7J5fmj+HlvCfRPfL/5eOvjjx9vHH/4N95f4ecHU4bI67SinTbWpVES9HULazaXI3W9TbDh6+eySkcYTcBMzit20qxjerJh9NPA7uLC0S/9SFyog21bR4DUpJ9fe9EYmW0QucpKkvs0MgZCRPZXkq2N7sD9bkQWC84z7W/B60xxQnmmI488/hmekvfRdZrdvNxBqpJ+68555GO1RENWFlo8uR72qWdYYkxDVSVM9x7ce0Mdo6vQ21jMEYz6toihdEaL2gw7D7m8Jhmnr48EXjI62KGznX5ZIPy6SbpX32Gd+MK/toK7tK8NQ/dD9GeVKjUcEy106V8vkXx6Bl6HE/lfyWkg1I58WgTrV6QPSEcTlo3jFGUZUyWdanVFgjDNuPRs5lzAuENIlaHN0ym0me2oMpRTvzUftgqPn1HbpS2REaAU/OO63+O/YGZuG0ff6jqjuj96ScM/uJLAHRZoUbpMbfXs6ArjcqqmdpRbuAg/ekXlP5Gyld/toMfOQy2MitMP63FdMZhSClwIxc5C4Gp1GuP3QlaAdFSfepuqlGGZCehGOZTt9ouA6PRc7SuJhf0+QerCsXo6ZCwE04dzh10AqLluhWN1kL8a2uEd29RbXcRnosaJ1Tb3XMXY6/hE600bJt6GhhI9lLyfv5qmrWXj+tCbdrT/0ZXmtHzkW1xTmEWCrYi2lhtzGyNcBKEFIQLtanCtvehCkU+yCnicqZzWzx+ih7H1H5wl3J7h3JnB6M00Q/uok8aZxcCr2mrT+VgDxlEZ7bFsn72WlqTwvdw51uEd64ifA/VH5Hde2KNlw+trd5SB3d+OkuSc9/Tc3Hm2ziNcxIPEEQtl3/4P64z7lXWjHRyX/ru12N++i8ud8jh+AEIcB2k59lIHK0nnlgnZKW9IpLRFoevpSzZo8iGR0jVyXiF4zAGnWYU959QPtmwbuz7eYoTuYDNUVSYSlnX96Kc2lTUaGUrco7VboKdWgzrS0jpnjAVaJGlPfJ8iEDaNVzP3vZ9Y4iVLqwI3al5uI0AJ/Soxifv2PpfbPLd//YLgDPF6I7v4uwLV/fteScQgTWGMy8L30/jIJWm6r+aWFEXinJcTq9bErZNM+2NFqDMNaPdHL/moEpNmc9WIduH9CS1uRpyhiiLKq0ozyC6lwG/6VvR9rRCYKVJtuIj3kRz83dw3ZCySvH9Bo4TUBZj9va+wWiF79ep1Zeo1eaR0kXpkiIfMRo+oyoTHCegPXcD328gpZ3yzPMh8XiLNNmdvG8xKSOnOM75btt6EijcuT03dfBx2A6JliOEEJjSBqaWGztUO11r6pcddRo+DX7DtlenrU4aY0h3EmtQ+gZDV5p4c0w5LmB5umEYvxnMdC7OgpBWCzeLbq1KS5LdxLrqz3DdmqJAjccvjrtSUFUIz0P63vHqoP0r2/7J7bCAyk//Poth/spavpPgX1+h+Qcf4l9bthPDo5jg5hqjv/iEcusFcam9f4vo43cu5T2FlIjQx10+26NvsFvw5U+HRE3nRYVqcgpHF3BeP/lgrAmnuzSPM9/CaTWQjToyCibaI+dQrIyxRYdJe8wkGWqcoIZjVH9oDUeL8vQp+TNQlcmR35sqM5R4vWs5YD0is/y1GKWOB0+JGiusXvsdavVFDAY/bOEHLcpiTDx4fuLfaV3BBcjUYbwxxKocFxSDjKjmEa21CBfrjE8hVuOHXcYPz98t+PM1/PkIjF2wDkeAhNeWkL5LvtFDDROM0ojAI1jpUPZjVJxZo8dLRJlWZP2zTUoPQwhr2nhWvIhT93Eb9sat0sJqTxqBnZSUFa5ncCIf6TmotKToxVNdeI7vUF+t484gui3HxWtZfA/Db/rUFqfPWzuoWI1fHFerfZ0gbJPEOziOj+MGSOnYHDDpEkYLzM3fnpAmu6iJps2eipU9x/saKscJkNKl3lxFCHFArGaFKjXxZkyVTm/46bcCa5AqrN+W6g0ohMCk2cEOb5qbs1f3iJaj6XVAE5uIyzbSvGzoSpNsJzOZqPoNf3IuLqFiJWyUzbTVMrBrRLqbHBlYmRpao+LE+sUtLEwCcznZvsQYdFXiGGOjpsbjE6cC91GMiiPX0GXBX12g8eP3cdoNhOugswL/yhLJFw+OECv/xhqN333v0t//JOxz0N52yad/bjsjridxPYGQUGT61YmV6+A0GzhzTZxOC//aGt7qAs5CB2euZTVHoX9i+9NojckL9Cih6g2p9npU213KJxtUvaHNGxyMjlT8zoN5qXpjjJ6iWvVmYzR4hhAO88vvUuRjHMcnaq4C0Nv55ojr+mXjjSFW+V5MtjUiWmsx9/4q48e9qcjTWWjcmKd5cx5jDHk3OdI2nPs7H+CvdBj8hy8Z/uI71DDFX2yx9I9+n8G//5L4y6eo0eVqCopxwXhzPPWiKYQN3j1rBD66vkDrgysIKYjv7yJDl/YHV8m2BmTPB+iionFnGW++TvJoj51/8xU6Pz/g0615tG90cGe4KRTD4pj55mXDq/vU5sKpvcCMNmT97FgwsDGGskzY3PiribMugMH3m9Try4TRAg/u/Ql5PqTVusrNO39MVF+iLGPybMD2xi8np9Agpcfd9/8RtWj6KJiXoStN2k1PzFk8DX7TpzZfezE9IwSyFuLduGJbgd0+ya++OJdceZFHbaE2ffvLGPJB/hvLBZwWRhmyXjZT/p9X96gtRJfSCkQKglY4U4yNyiryQT7TNO4+dJqR/PpTgls38K9dQUiH/OEjiq3tE693U5UIx8Ft2BabSmL0KVWrMi5fTzXacRCBf9CGFK6DbEYHAw/fN4QAx7NBzulIkY7s9dhacGkveniBpLtVEvdfgVhJiVOvUfv4XaIfvUfw9g2E406q8FMkPAuBCAOcMMBZnCN4+7qt/FSK4v4Tkl99SfyTX6PHqRX+/meKIhuyt/UFvd1vCWtzVmivSrK0h1bFi0nl14A3hliNH/UY3Nth/uMrtO4us9xPKYcZvc83LRGYATJwidZaLP3udTrvrWIqzfDeDunWC8G3Uw8QvoMaZzDR3sh6QP0HV0m+foZ4DWnu5bgg3hyjSiuOP7fqIgXRSp3aQg3Hd04M3TWVRsV2ospr13AawaRaoSj7CeUwJVhuUsU52Ubfiv7PszQIrKVBfb0xVRtyX3Cb9tLXPiLuhA5ew596+tBoQ34CsaqqlDTZnWigXpwPz68ThB3q9RWu3vhDlCrshMhkJFlKF8+rs7D0Lu7EVA8EtWiRJD5dTH0edKXIuulMwcrCETi+g1/3yascGQbIes36xRTV1BUrx3fwG+e7vu/DAPkwo0zebGKltSbrpTMRq/1z6tW9V9aQCQFe3Z3JakHlyr7vRfSAxqCznPzRU8qtHRACp15HBj76hLkJXVX2OUF4QLJOg1aaKleUSYkbupcWyFw83mT4r35OePc6suZT9cekXz2m3Dk5NsUYQ/F4i6o3OpKKMAuElIhagH9lCbdz1LdsYd3n7/73y/ziT3s8+tyqsdduhfzgxy2uvxuRp5rNhxn3Pxlz/5PZpSHOfJvgrevUPv4B/tUVnPk2wrOt2mk3i0eeN1l/jLD3E+/6Go1mRHDnBulffUH+7SOqU87lX3/Y86Qq669lS5EapctL16i9jDeGWKWbQwbfbBM/6RMuNZh7fw2jDMFCRPykT7YbUw4ydHWC9kBY802vERAsRERXOsy9t8L8b13Ba4fkvYTeZxskR7yx7O7AvtSEWLkO7lwDGXqXNoFyGCpXZL2MeCvGbwX4U4havbpHY61B63qL/sM+5qWpu6IXE9/fwWiN9F1k4JFvD6kGGfnOCJXkk8cN5SCdqr0ZLddp32rj14/7i5wEq2WJybqzVQdmhfQlbuBOHbkCgDGU4wL1kpZO65KqSo/tWozRB/9VZUpVpZTFmCTZO2jz1WrzNJrrExO50cHY7stOvrPAVIZ8mM9GrIRAehK/GViSI4XV1EQ1tFtONaUjPYkTTrSI094rja1gqCk3PLWOT2Mlorlm2/L5qCTeSRk8G796t0GA40nmbjRxfIetz19UuY0yFKPZz6lwJX4jQHrJqxErx4aTT2NhsQ9VKopxMZMxL2C/91podTmeZ0mSEHjrq4idXard41pU6UwmCI3BqTXsDf40GGsDUWUVju9cGrEqt3uMf/oFxeYeMvBQo5T80SbqNFdzY8i+fkx+/zlqfLGOgvAcnE4DWfOPEauw7nDntxt884sxBvA8wds/bNKa9+huFoz7FcvXA/JUz0ys3KU5gndvE/3QVqlkvXZ5k44TwbfTiA6cymXgIWoB2effTnSX/4n6RVwQjfY6UrpkSQ8EqDJHKVt1DcIOYX2eoNbBqIok3iVL9lDV5Wi93hhiVfRThvd22P7LByz/+CbRepv15Xdov7tM99fP6H++yfhRjyopJlWXyR8K60TthB7Reov23WXmPlin8/4qwhHkewmDr7bofbpBesgbS0/8fbzFJk4rsuPqoWdfz3eRkY+Mzh+TNkU19WSg0YYyLund6xEtRucSKyEEwhG0rrdZ/u1Vxhtjypd0EMXumGL3bBuB8TdbUx0fAFLQud1h6cNlhDPdLkoVit69Lln3NYxj70OAV/Osc/0Mi7rRUCbVsSzEg4mTl2BbfX2SeIe9na8OLBIEgrJK8L06QdjG9UIG/YcM+49x3ICllQ+PkDTHCfC8CMcJcNwA16vhejVUVRxJWN+H1poyLma+kUtXErQDsq6NHMEYgjs3MEpTbe1S7XXPrFq5oYsbzF6BqNJqqralkDB/u8XaR4ssvdPBaBg8G7P5aZfhRnyhlteR1xcCP3K59jvLhO3gCLHCQJVUM+Vdgo2B8lsBju8cq3ROfVxS2HUpcGf2sCqTYubzIoIAd37eRn6EoW0tCUFwdZ3slHaQ8DyMUqgkxmu2z73JG2XTGM7SfM4KPU7Jx8/I709p0WEgu/eU5K++Ro0u5kxvxeIdovdvw+2XHhP2u1PKeoCFdZfbH9V58GnMz/7fPeKB4p/802vMLU8vkdgXqAc/uE399z6i9sHbJ380Y+zkn9K2hafNxDPuSKwB+1FtONIS6JcyKPcrcrWP3kWEAQKIf/qJDSk+7XflSCuSl9JuvvdtMIT93o2yLu0i8KFSVpPsuzY/VQiEI+2/z/rZTqprM1eLHMdaYziO/VttBfznacjml9/F8xv0d78FIciSnvXkqlIanavML71DvbWGUgX9nW/p7nxNfEm6qzeGWAGkG0Me/p+fgIGlH9+kcX2O+tUO4WKdlT+8jS4U1TinOhRjIz0HJ/TwGgHSk8jAxQk9pOeQ7cXs/vwxj/7ZJ6SbwyNferk3Iry1wvwf/zbefItqb4i3ZBeX8PoyraykGp5/4Sb3NsifTC9YVnnF7ufbzN2Zo3l1uvHh1o02a7+7zrP/8ASVq9cXyzKZQpy/u8jiB8tTVzCqrGLnsy3i7dcX8SDEZPd/jhfZyzDGUKXl1DfXskwZjzbw/AZXrv3eCxM5A8+e/HvSpEeWDXAcS6bac7fQqkBg24tCCIRwWFh6l7n5O9SiBYR0iOpL1KIFdrc/J8+Gx0z0jLKke9bvVjg2lFm6ElWU6HGMjkLUKKbq9s9tBboTsjoTjA0JVuf4PAlH4Nc9rv6NZRzf4S//589RpUIVtq30uj3PjDYXMvoUjsBvTGHPcs5r2JbZbH+3b8kyq5GtyXNUEuNfv4rqD6j6fXvD8z1UcvKGRxe5zQeMxxTdHarx2abMWmmqdIap5tcEkxfoE7Lhpv57Y9D5yVXBw/f8WsNh6aqPqgzxoCIZKvu4Ps1U+WTIKMS7vkb9dz8iuHP99OMqK/Q4odrpofpD9DhB5zlmX74hbb6jDANko4bTaU3MNSPwTyZ6/s0rICXV3oD8wRP08OQ12ptv4i52cJoR5W4fGXg49RrCd6l6Y8qdPmqUUP/oLYqNParuiPDWKvnDLYRviWp+f+PM9qxteUpMPltVyF2csxE3C3OgNNXATkBWWztndmBq0QLNznU8P7L3gTIlHm6w9fQ/0upco6py7n36z/CDFs3ONRaW3yUebV6KaP+NIla6UGQ7Yzb+zT3S7TFzH67RvrNEsBjht2vWMKzU6EodeBIJx5bunYkxnspL8l7K+MEevc836X26wfhJ/9iFGH/xFKce0vjhbZo/vA1KIUMf4Uhq76zhr7Snunh1Uc1ErKqsYvfzHVb/xjoL7y7gThHL4jd8OrfnuPUP3uLxnz1icP/1JN57kcf1v3OD5Y+Wp87hq7KKZDtm78u911uxwratpvZaOgRVqiPu4N29bwAoi/FxAaPRZGmP7u43JGHnyC4+z0coVZClXTae/UeknASO6orh4Alp2p1UwTTxeAulChwnQAhhYx+KMWWZnlixwthW8axeW0IKnEnFSbgesh5hysqKgGvhKWP2L2DP6Wx3f4M1CNXn7BjdwGH+ZhNjYLSZMHw2PqYdWv1wgfaVOo4nEY6g+2DI3v0hxbhk7cMFWusvHtu7P6Q7eezKDxdprtbt9Q801+qUyfEBBZWrmfVKQrw4pxeFkAK53zKb4WWMMlS5mjl6yVTWbiN/8AidJOgkBSEofB81Pn3DI4MQt9HG8QNUGqPS059rtLH60N9ULNSkeqOL0pKNi0Ib22k44aZc5pqNBylvf9zg9od1Gh2X59+mbD7I0JO0F+kKxLScWwichQ6Nv/kx3pVla9x7+FCynKo7oHj4jGpzl6o7QMcpJs3RRQnq0HEKYdcj10H6HmKiqXTnWrgri/g313EXOva6n0AGPt7KIvUff4SOU/JRcuJ64C60EYFHud3DW57DadYwpSJ/tIm70MbtNNBxilMPJ3FA1r3eXWghPAcZ+qdb4EwIfvTDD/DWV6h2uxQPn1LtdO3v9CRIidNqEn54F//GFdzFOfu5JjpCNRhRPt0g++Ie5ebJBKuqcuLRBjvPPwEM9dYafmhj86R0qaqcZLRNkQ2pN1fww9YFFbLH8UYRKwC0YfDVNtluTPKsT/rRkPrVDsF8ZE0+PevrcbDoaYNWGl0qVFZR9BOSZwP6X2zR/2KTdHt04plKH2yBY8up/nIbJwoQ+6xfmxflz/Mw4yKjS83o2Yj+/R7jHyzSeetsLxWw7Z5oKeLG379FOS7RhWa8Mbo0w0sh7Vj43Nvz3Ph7N5l7a35qXUi6l7D35S6jp8PXOyU2afnOfLMz1u3+8C57NHx65p9UVUo1TonHGyc+XpYx3d2vTnzMvqUhHm8SjzenP0xt0OXZxrgnQUw0RkhxsPAKRyIcHxMG5xMrR1xoAu7lc3oSXN+hfbVBEVeMt+yNfv5WE7/uopVhvJOydLdDa71OMS7xaq4NTnclvUcjlu52aK7WKZJDjzmCve+GrLw3T2MlIu3nSGkrTC8TK4wl1VNdx4chxLHfmsTFoDFopHAxxpoPnvoSk1bgrBFPRmtbYZv10p6YG+skBcdBNhv2JrFvu3EKnDDC7ywgXZe8t3POsVkt4G8i2sUUJWqc2pFhETIAACAASURBVKpOVrzatJsxmLy0N+OXro1kpPjyp0OuvBXRWffRyvDgs5idp7m91hxBf7skS6Z7fxmFeKuLhB++g1OPDt4fQA3GFBvb5PcekX11n/LpNnowXZTbAYRANut460uEO7cJ3r6Bd2UZt9N68XgUEr5/x77H5g56fLwTIyZFBZ3myChA1kOq3oj8yTbuYhvZqNlKWegjazbVQQ0TvNV5W8HuDk837nQk7nyH8MO7hO+/Q7W9Z+/bSXoqsXLaDYI7N2n8wY/w1leQ9aOT8booqG5eBYRtUW8e/+1WpdXHdre/gkn2YaO1ThC2EI4HVWbd1osYrcpX0si+jDePWE2Q78bs7Mbs/OQRfqdGbaVJ/XqHYMHm/zm+FduqrKIa5+S9lPhpn2RjSNE7v4Wnk5z4k0fEnzyyoZyhT+PDG1z/p/8dvX/zGcOffE25d/6PXGWzjyAbZdj9fIfmldZUxArspN7cW3Pc+W/fwW/4fPW/f0GZlJdCrpzQYfm3V3j3n7zH/N2FmcbDBw8GPP6zR9/L6L105YVIgFHme8sIfBXoSs9eDZgI2IUQ6CSl2u0S3r1tWx1lda5LsbgIWcWe0/OIlXAEQdNHV5oqrwgaHr/1T+6w9G6HfFjw1b94hBdawfl3f/aMaCHkzt+7wrv/9XUe/eUWbuiy+dke9//dc6KFkLf/+Crv/FfXuCee4EUu21/2+PZPn7L4Tptbf7jGSSzGKDP7ANCErMpD5yVwIpQpqXRBKOsUOqUyp1/7Qgikc34G37HjNRNNy4yHDCDDgNoH7+E0G0cm/LJvvztRvG4zGY2ND1HqfEdrYw50fN83qu6Q/OEmOsvR8StOHhtjDTZPINyjbsVP/nmXzvII1xUkY0UWK7SyekHHE3z65wPKlzWbp8Bd6OBfX8NpN489ln3zkPg//JL0l1++0mfRwzH5cEz+1QPCD9+m/vu/TeMPPn7xHEciW3X8a6sUTzYpTiBWJitwltqEd69Zc+E0P6IdljUfb6mNtzyHzgvK3QHZt8+Z/0d30KOE8S++OVXzJDwP/8YVnE4LGdXwr18h67QQ/ulaPf/mNep/9Du2lekevx8Jz8NbX6H++z8EIRidQKzAIISD44VgNJ5fp1ZfpLN4hyBoURWHzsO0UR5T4o0lVodRjnN0qch2x0jfPVKxMtq60epSofLS6hNmhTborECNU3ScodMclebWJPQcXFRv0H/QZ+tXm6z97jrRUnSurcG+OLG53uTGH9+idb3F5i822f18h+GT2XPNhCMIOyHzdxdZ/dEqix8s0bk9Z8XhU/7IBg/67Hy6RffrvZkmry4KManKzAqzLwJ9DTjQ0jjSagDzcwSc52Eiqp9+9BrkRGQKAl2UJH/1GSIIphqqEPICYdv7wv/z7sPaUMQlQdPD8R3ycckn/8e33P0H11n9YIH6Uo0yU2R9GzeU9XOEENQXazRWatZQd1AceSxaCIkWQoqkIhsV1ll/L6dIFH50yjVkzHT2JvvnBI608ASSeX8dKRxKnePJGt3iKVV1xqZK2N/GzBUrY9DaXIy8TMxAdWLbPWZS1TmtKiBd34qVDQjPPxZKe/zYJtfS7Ef2yoh//iXZ149Ba8rty4mS0WlONUps9eqldXzcrxACVAX73plG21bh7vN8ahmOszSHt74MvFjDdV5QfPeE9JdfkH/7+FI+yz6KB88Qvo+sRwR3ruM0ooP3dVcW8VYXKO4/OfZ3+ZNtiq3eQYVbSDERrBuye89ACkxRof7Vf0RnBTovcecaVHsDVD8+s4IoPBd3dclW7IzBVBXlxrbVgB57MriL8/g3r+JfX7eCdaUp97rk3z7C5AXu8gL+tTVks4G7NI9/8wrelZUXzvMTDPtPaM/d4M77/xAhJGURM+w9xq+1KYoRjutz5dYfYYymVl9EXSBs+TT8J0GsTKWpquLMXMBXhjaoOCd7sks1iNH5yT34y0IxzOnd6/LoTx9w449v0VxvTrXwe3WP9vU2tcUatcWIzltzDB8PyLop+TCnGBeorEJXGl1ZQiEd25aQvoNXc/FbIeFcSGO1QeetOebfmSdark8dnaMrTZmWPP/pM7Z+uUk+uPw4ghNhuBhpERcjZOdBepLaYkT79jy6UKiiohjljJ4MXznAdxaYSXdGBB6yEVF89/iFJuGcVuCFz+kURoaqUAyex1z94RKNpRpGa7r3h8Q7mdU/lfrA3uCAiACq0qhCW9I3ISf7Tui60mhlkFIcVJSEI84Xic/eQT76WUyFNopSZyhTWS+cNw3C3sR0mlpd1YRY6/h03ZQQchKb4uI226gsoRoPT37yPpn+DTCraqdPtXO52tL00+9Q4wQqRTU4OlldFSd/SK2ZKXzZaTdxFg91JYzBpDnJr78mv/8UPbrcgR89TigePiOp1/BWFqylw2Ttc+bbOHPtk/8uzuCUQoI6JHgvEvsc4buIRkjxeNsaaZ/xmxCOg7s4h4hqNnqr20cNhifH2AhpSdW1dZyGjaHKHz4h++pbsq++haLCXZpH9QZEf+MjRBjgLi3g37yGGo6PEKtkuIlATHS0gjTeIUv7eF6NeLiJ69UIJpqrLOmSxjuXtgH/T4JYfV9Q44zRrx9QbA1sG+V1wsD4+Yhv//k9Wtdb+A3/QDB+XrVCepKwHbL2O+ss//aKdXR/OmL4dEiyFVun8ayaiKE10ndwAxe/4RPOhzSvtWhdaxEt1WeqUMGkCjHK6d/v8/jfPmL3y4tFuFwEWl+gVcakKnP5vAq35tFYb7LyozWKoTWTLAYZyVZ8IWK1Xz2a6fswHLRnhOfhNBvIRoTTbr5wtD7jnE3T0jt+oPvVmLOPs8oVvYdDrv3OMs21iNZaHVVp3NChiEuGz8d0rjdpLIU0lmrU5gKMMfQfjRg8HbPwVpvGUs0+Nh9gtKH/dEy8nbJwu020EFJfqtFcifAjD31CK+tVzul+VcJgGJa7KFOR65hpmIVt6c3ehhSImQXvL/5YIH2fKs3Q6QvPOlOdvJYZrez7TDyv3HoTlcSnE6u/Zoh//iXxz1+hDTcFZL2G03rhlWWURo0Tsi+/o9p9Pcadqj8k++we9b/5Mc7iHGIy2OU0I2Tz7ODpqaENJq/I97bOv1c6EqfdQgY+JsutzivNT1yXhOcS3LmJt7486UZVZJ9/TfzTXx3oqIrHz6i6fYJ3buO6HZxGHf/WNbKv78MhoppnffKsT3f75O/YD1vUG6tIxyOJd8jiPS5r1/BfiNUhlL0x3f/vl7Za9SpTJ1NCFYp4c8y3/889tDLc+Hs3Z27LSFcStAK8tz3atzr2hqBsK8HsVyPExBNrUgGQrrTTYBcImi2Tkt0vd/nsf/k1/Yd99JRag1fGxKBwdk3ZpLLxGpiVEzhoZRg86Fk/qNpkRP8i73XB1hHaoCdO/jqOqbZ3Ce/etoLS3d65o8O2AnRxEnj2axuyfsHmp3tc+XiJP/yfPkJNRO/d+0M2P+tSX6qx8t48K+8v4AaS7S96fP0vHzN4FtO6Umf1g3lWP7SPbX3e5d6fPGHwNGbto0VW3ptn4XabfFwQzQeMd463vKYhgMdx0iCBnfaceuGdDE3MzKzkpA15EWalNWo0wluYw19ZPmgFZvcfUHWP38SFOwmiLwuEF5DvbZHvTT9w8V9wPoTv2Ym5CXSSUm3voeN0piy/maA0Os2otrt4ywvIeVulEoGPDC7Hg8xUimoQn6/Lw/pqyXrNVlPjFLXbO3GgQnguTruJd2UVp9PClCXlxjbF4+fW4HT/vYsSNRhRPHqG8K0Jqre8eEAgp0WZjxmUDwCB0erkae0L4o0iVsL18GpNymSEUb+BUrvSqNHrjWQ5gglZ2PtqFzfyEALWf+8qXt2bXg8yMRG9CEmaBVpp8kHOxk+f8eTPH9P7rvdaXdZPPIZqyknNwxAgfedSQnVfRjkuyAcZ9dUGbsdD5RXxxvhCPmP7I/6zHqcxNk7I6Mno9kRnIMIAkxfn8gCtZrd4gIm4ewrrC6MNu/f65KNi0g6EqlCkvZx8VPL8V7sMnsTWUkFObBmeJ5RpxfNf7tJ/ND54bLiRMNpIqHLFk59tsfvtAAGUmcL1JcXLU4HCxvXMfE41E7KqJy8jaHlLCAS5toLXcdWj0KcPyZjJZPGs2HfTvwg310VBfv8hZVQDx30xfdY/pYWm9YTQu5OKVRuVJpT940J3mJBp9/VUf/+6QkhpK4ITmLywrbXXRar2oTR6HB/xjDowAL0sTJsOIGwiBNIOSag4OSD9hyEbEcFbN3BadvhCjxPyr76j2u0e03CZoqTa6+JfX0dGNWTr6MAGWJPmWmOJZucaAKP+Y8aD5ziOR1CbQ+vSRt28BrxRxMqLWrRv/xZ5f4cyHqCyMVVqRyFf/ySKmHhYnFCePIi/eT3Vmaybsf2rTXShEFIyf3eB2mINN3gDvh5jq1TJTszuF7s8+tcP2P7l1vcewGs9iarZDTQFuKGD9C6feFZpRd7LSHasg3g5Lhg9Gczs9A32prUvgp8FRluzTlwXWXesm3ZeIKMa4pCfzWlQuZr5eAVYEui+iPLZFz6fFGwa7xYkuwVbYogxGsfxkNJFGIfB44z+I+vtJZDWLkI4uJ5H72FM78EYhMToo6Rp994AGBx7ryPHKQVO6FoN1yyYGKAeJpyO8JBCooz93UvOcSnXVkM2a5tVOtJGNgmBjCKE56PTZJL7eM5rVYpy27ZLZBDizs9T7u1iipO1qcYYhHSQgf2dSNc9My9QSEv6/guzmgHa2JbsxMnHKIXJi+/FC8ymgry4Ho02oA1CQNSe+D6W5sBl3ujJcw6+XmFnG4ydiJSODaeWk1Z1VWi7aZnMWkjHVqlP5Fv77W2t4ZTfsmw2CN59C1m3npU6Scm/eYDqH29NG6XQoxhTltazLwqPkcaosUyzc416e50gbB8YhAohaXauUpXZfx7EKpxbYf3H/w1lMiDdfsLo2T1GT+9RxgN0eXJP9rLgCAchHCp9VFAnkDjSXhUvP3aZSHdTnv3kGYNHA+7+4x9w7W9dp75cfzGZ9D0vZgcXvobxxpinf/GYb/7vry2JqCYX3+Gv47z/PwnnPefw4xOCpw6ZJ051ToTArbnI1xCqjYB8mLP9Vxt2smrSHrtIBUg4Ai/yZjbr1EpTjgpko0Gw2sZZsKamzlwbNYrJv3105nVTpdURQ8qpz2no4PgSEEjHx/VrICT6hEk5Pak+O26AqjLC+gJe0CQZbSKli9aKqkiQjo/j+rhugHR98riLVhXC8ahOMnM99zAFXs3FmdUAVRvKuEBNCKfB0Cueo0x5ULE69zWUoUqriafl9FOe0p0QbAH+yjre3BzZk8dUg/5xx+r913w58gRwFxdp//gP6P/5n1Fub5/8XlIi/QA3jDCqoujvUuyd/FyYEFXf+c3xKjFZEH6zxu8zweQFJssRB+3AC+rnLgJx9L1MXmDyAscVXH07QkjBuF+RjO2GtSoMVW4Q+2RKgFeTqNLg+ZIgksSDCj+0ViTDbolfs1OlujJ4oSSL1XFxv+GFZ5g1A+PYSRBYQ9C7t5G1ich9OKJ4/OxE360DywytbfSOe/x3Obf8Dn7QZNh9wPL6xziOZzNgq4x6a52qytjb+vwyzvQxvFHEymiNrkpcPyJavUXQWWburY/JB7uk3Q3Snadk3U2qPL50krXYvEMzXOJJ91cUKpn0WwWu47PUfBvfrfNo92evrWoFtv2QbMV88399xd4XO6z8cI3Vj1eJliMbkvs9okorxs9HPP/pc3Y+2aL3bZdsL8Vt1vDnI/xORL49QhcKJ3QJV1pUcW4nN6XAa4WYUlEOUspxTrjaBmMYfbNFsNwkWKjjhB75Xoz0HFRakm4MqF+ft7lTWuN1IspuQt6NqUYZKlM2zqjUU1eghBC4kT9TEO60COdqBG0bpTR6MiRaqlNfa7D9662ZbT+EFPgNf3YX9El4czEYY3oJstvHZDmyUedgK3kGVK6oUpunNwv5tFE4+yG+1iPGD9tI6eD6EVpXaFXh+ZE135PWTyZPugjhYIxGAF7QRAhB5YU0566jqpwiGyGEoLlwC8+vI6Rk7/nnlPlwJnIlpMCr+zOTaq00xbA4oh8UCBruPB25RqULxtXemSTLaIOuFCq3lS8xZWKAdCVe7YUUQEZ1orffsZEc3S7F5ga6yPFX1/CXlhGeR/boIVW/h5AOtbdtDt1+FeosEqKriqK7A0LgNedOFbkfnANH4obTyxQuE+H7t/DXF9FxRvr5A9Tg7HzUmSAl7nwT2apbPZTSqGGMGiXoCwY970NnOWqcIDvWx0q4js3ve93nUAhkLTgSrK2TDJ1mCCloL/s0Oi7xUPHsm5ird5uEdYe4X6E1xANrN3HtbsQXfzmgKgzz6wHpOKbe8ai3XRxfcPvDJmHDIRsrykLz7F7CzuOMqjz0wzNW83WQCNGIjrft5ufw1paRrSa4Dmpzh+L+Ezvld9IaJgS4jjX1PGWyWUqXqswY9h6zsPLeS49e0NJkSrxRxKoYddn97C/wm/P4zQ5evYPfWsBvLxDMLVNbWCcf7FKM9ihGPYpRjyobY6pXb0vVvDaNcJmjJ9ygjSL0WtSD+emqMK8CYyNihg8HFMOcYljYKobvUF95vV+VVjajLNlJiLdiRk+H9O/3D1zV9y0V3LbEa4bUVttUoxy3KamttZC+i9sM7I1kooUq0/Lg/71GcOAOHq60kJ6DLiqiqx2k75Hvjsk2hwRLDUCgspLaSguTWzd9eNH2qpISvxVMtfMTEoKmjxNePrESjsBvBkQrdfJhTrgU0bjSYvfzbWZtBu6HKTsz5NPtR4wUowIjXZDSLuS9IWIUT9VWNNq2WMu4xG9NacAqwG/6k8qKwHF8/KCJHzbRqsJxA4S2i57jhdblGINA4HoRQjo2X1G6+EEDxw1RVUYQzdnJHKNx/QauV8NxPbRWnNykP+cwpSBoBTNlIe474BfjFxUrAbgyAASVKfBlDSnOvx73q1aqVFMTZuk7eE3/xY3XaExVofMcGQQEV6+RPXqAqSqM1sggxFtawqlFdvcuJDrPbGtWnt22E44luLosOJhyOevYXGn1n69Zz3kSwlvrRD98BzVKKJ5uXwqxkrUAd2We4PY63socTjOy6RtKo5MM1R9TbnXJH25Q9UbW72pGVD2ba+ddXQFAhAFOp3VEd/U6IByJ027aFtn+sez1qbq2fW6z8+wVtbAesLAWEDZcao2KIteEkYN0YX4tIGq5qMowv+qz9yyjteAxv+rjh5KFKwGuN/GUHIHrSRxPUh2SFxilUYMR7uI8IgjwVpcPVfAsvCur+LeuIicC9Gpn4lt1SmqAEAIZWAd4e42oE3mSMRWqyg9V5B27WZts7l4X3ihilfd32Pz5v6S2eIVo6RrR8jXC+TWcMCJoL1JbWMcYTTHcI9l+Qrz5kKy3ZVuFVY4uCztCfJGRfGF1VJXOj1Sl9v1qji6kAimkJcpGIxB4TojSJRo1qVZb74yjuq39heuc4xO2tJr2UtK9lDI5/YI2E/NDXeoXo+UnjWvvm/tN/tNKT4xVNarUlElB1k3Z+7rL7mc7dL/eY/z8ZOf5A4GtK/FaIeFqm6IbI30X6bvoUhE/6pJtDamGmXX3diYB2b5LMBdRDjLy3TGNmwt2WiQrcRsBXjtCFxU6LxGT9zi8u6vSknyY25vPFMxqP67HmyKTcVbs+4UF7YBoqY7fsIvFRTZC0pWEC7WZSIAuNVVaUaYl7uo8stGwVYdKYcrqRIHoSaiyiqyf4zV8mOLthRAE7RCv7gHC7j6lRKuSIhuiqgxtNEZXVGWGmfzb6Mq2Db3wyKImpQOuT1XEFOmAskjwggZlEVNkA1SZUVXpuROOx47Tsd/9LEkCupqc05c0Vq70UbokVwmeF0zVzTEG21LM1dS/PydwCVrBAXnReUG5s025s42/vk7tzjvkG89QoxEFArcocFst3FYbnWXkG8+p+j28uXnCW7fP5EpCOkg/wAlCXowQn3FsvpxpsOYy4S7PUXv3BirJGP7Jz8CZ+G95Ljhi4ldk7O++rM4M5wVASrwri9R/9C7Nv/URTqd5cFPfh4pTyud7jP78V6SfP6DY2JtZdF7tdCmebVH76F2MtO747nwbWY9Qg9FrEbELz0U267gLbWQUHpCKanOXanMXrQ39rYLBTolWhvm1gHSsKDJNWRryWKG1wRWS3maBrgzuZGjB9SWOI3BcQdR0SEYVZabpbxekY0WeqGNc3lSKamcPb20ZZ3EO7+oq3vIieji2VazAJ3z7JsGta/aemheUWzvkj56ebnLsOMh6ZCdblbY6wpfWh7KIcdyAWmMRx/Xx/AZRc4WovoQxhrKYrq1/EbxRxAoMRlWku8/Ielv073+CGzaIlq9SX71JfeUmXnOOoLOM35yndeM9qmRI2tsk3rhPvPGAYtSzeqxZ33kS8CmFi6Jif5ERByLVo4ts4NbRRlOqFM8JWW2+Sy99Sl5ZLUhexTjSs46vKkVONFxgzjUXDOesR9Xdf/wDGutN/OYZI7LGtu1Gz4Z4kYff8K2myHUOFsD9kXqVK8q4pBjlZL2MdDdhvBkzejJk9GxIvBVPjC7V6U7qwi5gVZwTLjVACPLdMdJzKPZidFHhzUWYUmEqjXAl/kKDcLWFU/MI19roSuN1aritEJWVUCicus/cx9cI5iPyvRgMqKTAn4tsBuQkpqgYFSS7CY314xERJx6ulAQz3lynRTEuiLfH+O2A1s0O5bhg55NNdD77Yik9SW2hNlPLtxgXZL0MjBV++jev4DTrVFtdTFmi+kPSz745/3XikmQ7prHaOBDZnoegHeDVPYxRFNmI/vY9hBCTqsnh0u7hVdZWRYQQdgFV5aTtJw8eM8ZO4+XZ4IChmsm6MCuEIwnna7jh9KS6TErSbnqEVBmgUAk1t0Xdm0MbNV31TBvyQU6VVtCZ7v3dwCHsBEhH2O2dMZiqtBtGQAYB4fWb9pxJgUriyWd1bAWqqjBlaUn1vnPsaYdX2NgSoxTp1lOq5PQqkPQkju/OVFG9dDgSpxYgAh+nVceda+JfWbKtJSksCd3Yo9jYQ3XP8OJyJE4U0vyjj2j83vs47fqJ03KyFuDfWKEz/0fIVh39b39Ftd2baedU7XQpn2xiisJGuDjWesC/dQWdJKjdyzU9BWtKGv7gLSsHmDjroyrKZ9uUGzvo0vDwszH7LZinX8eHKqT7H8/qAoVgEt8jePRFTFlodh5nOK6YDD8IO0WrrBm1VhwLPTdlSfHwGf7Nq9aBvdWk8Ue/g7e+TNUb4F+/QvDObZz5DhhD+WyT8tkWejQ+9VwL17F60sC3AeTj5BiZ7u99R2fxDtfv/H2ixjJB2GFu+R1ct8bW018w7N6/5DP/Am8YsbIwqsKoCl1kqDxBFSlZb4vh46/wmwsEnUXCzjJBZwmvOYcT1glaizSv3qUY7pH1d8h7W+SDXars9C/nMPIqptIFa+33GKTPycoxUjq0a+v4bkRaDMBA6DYJvTa+E1GoBKVLBBJXhoRuk8Bt4AiP3fg+odvCkR6jfJtGsIg2irTsHyFuhyF9SWO1ybW/fZ0rv3/1IGLmJCsFow35IGPn0222frXF6NkI6Qgcz0EGHrX1axhVUQ0HVMn4IPpHVdpGr2SV3ZnHBcWooBgX9gZwCqQfUL/1DlXSI3nSo9iNbdXBwH5obZXa/C1nc0g5SK0fmDFUo4z+J08RjkPRjVFJbrUGh8/CxGsredJDZSUqLcm79qZx2HG/GBakO8nU01bCEdSX62eT04vC2OPpf9sl3hhjlKYYFTaSZEY4nkNjrYF3WizLCciHOeluYoX9G9uYqsJpN6m2LbEy+XRJBeW4sKamU9oDCCmoLUa2HQtWQ6EulopgTvm7aeJ4zoN0JfXVOn5jemJVjAqS7fglqwRDZUoyFVOZEl+ETKMJMMaQ9bIzK84vw6151Bajg0lGGYYEV6/hr6yClBTbWwA4jQYi8BGeh3Bc1HiMGo+o3biJWl6x06GqOnPtk36A9DyE6xHML6HiEeoUcuU3fPym/70P0RyGEALjSBp/8AH1j9/GnW8hm5GtWgkBSqHGqW3fffec5JffoMbpMVsAWQuofXCb4MaqJVVCUG33KLd6VL0haIO72MZbmcdd6uB2mkQf3EaPUwZ/8rOZqkwmLyk3doh/8mtqv3UXd6GDjEKiH72HHsVkw/iIW/grn6NaiHd9jfrvfojTqiOEQI1j8q/uUzzbtMacQJm/qFDaf5/7SdivV6hytvXNFCX5gycE79zCu7KKrEd419dxOi10luO0Gshmw07/FgXZ5/coHj07FjP04kMKRGi9q2QtnLRtB8cq9FnSpb9zjzIbEdTmQIBSJarKGPWfkqeXT2r38UYSq8MwqqIc9yjH1uDOrTUI2kuE82uEC6v4jQ5evY3fmKO2sI4qUit2331O1tukGOxSxkPKZIAq8lPbCUnRJcwbtGpruE5IqRKkcKj7S5Q6ZZBuYCahjp4MCN0GUjgU1QunV200rvQI3SauDJDCwZUBdX+eZrBEVo5IipPHO53Aob5S5+ofXePG373J/N2FU3UZVV6R9zO2frnF0794zOYvNsl6L2IFhOvRercOQlKNrGWFSmN0nmG0wm20kZ6HUVCNM4yqkJ6PvzCPkBJdlugiQ+cZbrODcF2kH1C7dov4u5x07ymFTI7+8M/Qn6mkIH7wwhunPHtK/tBrHncNz4c58fZsxCpaquM3JxqvC2Y7nvziVj9QDAuKYYHf8vFbAcUon1kPJAOHxpUmbjQDCRhkJDuJ3Sl2B+gkQ0YhajCy/jlTetYUo8JWK6e1shAQLUYE7XCyY30zx7SkJ6mvNWyLc0qU431idXTE1REuAoE2E7fyKZqBRhuybjqTNYkbuUSLEY7nUCYxVb9vjR0dB5UkVJubk8nTChnU0HmGGseo0QCVJJZwOQ6mLMmfRDz8NAAAIABJREFUPkW/PEl46DPthy/rPEW6rTP1WH4rwG+9hs3JrBCC6MPbCNfFOcVFXA1j/LVFjFJkXz2i2j264MhaQO29m7gLbVsZHyckv/6W7NunVHsDjDZ4y3OEb12h9v4t3KUO/tVlau8lxD/7EjUYTW8grTVqr0/yk19bfybfQ9ZrBG9dt0aZeU7xeBOdZGfm7Z0L18Fp1vGvr1P78G38t64hfA+d5pQbu8Q//4xqa+/V3uOiUAq11yP/7hHu4hzB27eQ9QinediRXqGHI4pnm2Rf36faOdlLDaxOze20bLqE52GyPtX23jEHeFXlxKMt0ngXz28gpIPRFUURTyrA/5mI16dBlY6p0jHx5kMQkqC9SH3tFu2b71NfvYFba+LWmtRXb2FUSd7fZfTsGwYPPiXrbaPy5MRd3CjbQukS322w1HyLwG2gjWKYbrIz/pa9+CFgyMohnhNQD+apyTZp2adU1lQ0LnZt6zAUCCFtdcqULNRv4kqfrBxR6ZN36OF8jeWPV/nB//A+tYXaqYafRhvyXsbWLzf57H/9lMHD/qnj/V5njmB5FeE4pBtPKXY2UGlC8+338FpzqCxl/N0XVOMRXmeB+s23EY5DOexT7G2T727ReOtd3EYLXZZIL3jx4i/fTA+VnsR+cJsxrzZFecL3lA8y4q3xQbDyeTtoIQW1hdqBMPxCId2nQDoSt+YdVG5aN9qEixHpbkI1Q5UCAW7g0lxvzqQFywc56U58cO5NXqAmu1/ZaiA87yAG4iwUo4LR85E1YJ3SGiCcC23LyndQrxo8/TogrCaosdrAr09PCPJRTrx1lFgJIHQahE4DgfWy2ve2O+uDG21IuxllMn01zw1dwoUabs2ler5FsbkxOYiXrBX28y9fqsaUO9tHn3cGjFJU8ZAqGSO9Par4ZE0l2NZv0ApOffz7ghACd6410ZbakOCXz4lsRoTvXsdp1+kWJVVvdKRNJAMf//oqslE7aB8O/uRnFI+3Dp6Tff6A/N4TVH9M6+//CGe+hbc6T3BrleybEjWYPuNPxynZVw9wVxYQoU/47m1kvUb0e7+FuzDH6F//lOLRc5vvqCeVpIP5qZe+x8Okft9OQUic/5+993qSJDuz/H6u3UOLzIxUpatad6MF9M5iBpjFLNf4QjPyYV5II/8yPpI2tkaxnLEZzpIjgQYGQKOBFqVl6oyIDO3a73U+3MgslVkZWZVVXZjlMevu7KqISI8bHtePf9/5zikXcN+5RPGHH+FcOqvirKbpC9G1O4S/v3F4Nt+rQp4TfXkDMoFRq2I0qlMXePUdkmFMcn+D8d99Srq+9cxqu1GrYC0toJlKWiP9gHRz5wi/thwpM+LoyerU8d/fF8EfHLEC0C0Xu1yjsHCWYus8bnMZp9LEsF1kliBTNQVg2C52tUnN+RbF1nkmW7cZPbiK3157akOSuSBMBqz3PmN78BW6bqg2lkxIMh85NSh0zAKW7kGuYZsehm4dEKsnIfKMPBdYukucTUjE4WI5s2Cx+MkSb/w3b+JUnz2KG3QCtv5lk2t/cZXJ9viZnknZeETmj0iHA7zV81j1OeK9q/j3b2M35jCLZdyls4Sb9zEcB912GF37Hdl4iG7ZFM9dJh0Pidrb5FJQffdjFU+gm5iGhxCJuoMnRwN03ZrqyMBz6ggRE8b9x4TKL2pXEfUjxhsjsihTOYczTrEV5guUV8oM7vZP7bvkNj0qZ6uUz9VI/YTCXAHdNk4s7nUqjmoDntBuIegGjDbG6lyvV5UotqLCvI16FRnGZLvdYy+y8TBivDZUrVzpzrymXsOjerbK4G7/udzmXybskj3VJzonMoeNehGjB8PH3k8OxCJA5hmg9GFZnnLciZSLHH93QnLCkHLdMiivlgk6AVFvOu7/5GeYHzEuPqv+R9exak3MUgXDdtAtG3/9Dkl8+F7mNTy8pneCd/FyISch6c4e0Z1NRXKERC842Gdb2KsLB60898oZsvaA+P72wycbOka1iGZbiMGYZLODPGTiL+0MmXx2HffdC7gFF91zcC+tkmztnYhY7SP8/Q0V6dIfU/j4bXTXxb50hlqjStbtke50yXb2yPojpB+QhzEySR9eq/b9miwL3XPQK0XMWgWz1cRanMNo1DBqZTXZKCXx3Q2CX39F+LvrICWa56JZhpoYfYGOrgyimWUGjz0viohu3kUMhlirSxj1qtJIBSHZ7h7pboess4c8hgDqroPmOqTbbTTDIL67Rnz7wbHPexTV5gVyKRj1H5z4fcyCPxBipWHYDlapjrNvv1Cdx6nOY1caGJZDFgcEnXXioXJtz/Mcu1jFrszhVJu4jUWlKTBtZJYSD7tPidxFnhIkT2dqGbqNZbikIkTIjCgbk+cS01BVqFRG9MI1EhGioTGOO6QiRObZVIKkE6R9ouyQO0INmm81aX3YUpoq+/BQ5P3pv84Xu2z8bJ3h3cGxLRgRR6SDPkmvQ+HMBaxKHbs2p0azhUAmMVa1jm6qKonSZA3IJiOsah2zUiXa2SQd9oGcXApsqwJOE5kLbLOgjguBqTvIXCBETCoiTMPBMl103UTIjDTzybIXv2PKooyoH+HvTDAc49hqxP5aFheKVM9VGd4bnJrrsUwF8ShG2xyThSlZkGLYxolJhtv0qJyrqs9+BlKW5zkiEoTdkKCtKlZ5miLjBC2OkdOSuCqNH/9eRSKIBhH+jo9TdY+tTOyvqTdXoHaxxnBtCK8ZsXJqLrULNWUJMeuaxoJwL2Sy86TeLCeWExI5FW5rynbh2NeUOf6uTzSMkULOFAataRqGpVM5q87VA2J12shzZBxCsYzuuGqy8xnH5s0X8OZOKcD3BZDnOaI/Jrz+gOD3t0m3ukhfTZ7qto15exPv3fMU3r+MtdhQRGtt9zFipekaumsrv7wkQwwmh+qm8jghaw9I1ttYzSp6wcFamUcvzF65M6plRXY8B911MecbyitL05QAu1xELxUw5+tYywuI/ggxCcijRBlgTm019oc+MHQlzXAsNM9Vgeu1MkZVVaj3z69cCDTTxFqaVxOmpoFmmqcSaxP+7hrxjfsnf6KQyNGEeOKT9YZq6MA0pzE/Y2QQHj/NCYjRhPj2fZUfqKkBATEczfTcfRRKC4gs+S+QWGkaumGpcWDbwyk38BbOUFq6iFNvYXolZBIj0pCov0PQ3cLfvkvY2SAZKyJgFWt4c8uUli9TWrmEU5mjcv69Ayf3eMY7Sc+qYBoug2CTVEakSUxIH/lI9WXPv3fw835lStdMNE0nEQF+0ifOnhCGakq0vPSdZebencd0j/44cqGMILd/s83u5zsz6VpykT20oAAMr4C7uIJRLCu91SOTVvtEa7+6lMucPE3RTQvD9dSXOwfXKmN5c4TxANeqKD8QBKbhESUDQhEjZTa1oChg6g45MAl2yET04tWiHFI/pX+rj1v3Zm7zFBdLVC/U0P5p7dQ0QfEoJvVThvcGkOdYReXtdNKImOJCkdrl+sx5j7nICXshQTcgGasLvJwE5JlAhrEKKR1NZq90TydLB3f7FFvFmVs+hfkCtct1Nn6+zhFFWwA0w0Sf+mzlUjyccDNtVV2WyozXsF3kdHBFN0w0Q31/0CCLT2a34M15NN5ozm5fkXMwKZuMnt4XEnnyDNF9jVU8iMiiDGtG/ZxuGlQv1uh82Ya7T7cwtOlkLvDUz2iaigbSNDTNQMiHE4WPPRZIRwO1xxomuVTf/8Og6RqFhdeDWJFDvLaL/6urTD796qm/ju9tIYYTNMPEbFawWg2sxebjD5qSGk3XyYVEPiNiJs8E6Uab7PwiTrWINV87UZCxtdrCffuiIljVMkathFGZVpUODkdTJMlzsRbnZ37tZ0LXsRYaWC1lZ6PMNE9n8EAMx89HrPYhc0RvgOg9n3hc7PURe08XQABMq4BlF499jUJpgTh6xuToC+K1JVaG7eJU5ymtXKG0dBG33sJwPDTDJM8lqT9ksnWHycZNgu4mWTCabsoPRWlpMCTb8AnaawTtNRpvfpvi0kWqF94n6GwQD7szHUu1sELRbjAMtqaeVDlyhiuWa5axdIeuf+/QNqBu6jg1l7l35ymvVp75WmmQ0vlil9HakGTyfBNY+141hlfEcAvTit3hXzYZh4Qb9yheehvvzAVEGKKbagPW0HGsErpuYZkuhm6TZD5xMibNQly7gutUESIlTkeUvNa0tXo6VY3UT+l81abxRoPSUun4JwCl5TL1Kw10x1DViFPgVnmmzgPDMTBdFUcjs5O/dmmlzNzb8zO7c8tMMrjdUxOBj0CzLcxmDbNZn7Y5RtNW4PGvmcWC7tUujTeaVM5WZzqOQqtI8605TM8k9dMjCatTqmOXm5hOgTQckwYj8jynOLfKZPc+yaSHZpiUl68Qj/eIhx3c6jx2sY7pKcPYwf0vVOLCjCi2Ssy/vzAzscrznOG9Af7OKTp6MyXBeyH+zoTq+dpMbVbd1mm+2WRr/nEio2k6uq5yFmWuJm4Nw0KIVJmxGja6YWHoFqblYdkl/PEOSTImlwLDtA8kDYZhI0RKNhohJz5ZFiIPMVrWdA2zYFJslSi8DsSKnOjqPeL7O0c+It3qEn55h9L338GoFDGqz9gj8vxh3MphkFK15oJIVZhqJUVUZoT79kXKf/oDNTijTz35jjFuPS0cZIV+c4Ocrxxzi+/SWv32sY9zvRrdlxRnA68ZsTIcD7fewm0u49YWsKtz2MUaVrGCphuk4YRwb5Oot0Pcb5NM+qSTAVnkH+5zk+fkIiULM/yde8p4dOEsdrWJ69WJDfdAH2UZHpZxeGitZ1WxzJNvKokIyGSCyFPkId5Vdtlh7u053IZ3rA5ERBm9m70D36JnIRcZweZ9dTcWKbG+f/8WumUj4pC4152ujfK6ySZDNN0g88fIqcZCZinJoEd+5zq6aSGzjNA0wY8gjFEHoaNrBvr0zjjJJtP/+vTHDzANB10zCeI+aXbyO/6jkPoJnS/anPnRWeqZnEmXZLhq6nL+/QV617vEgxdvS1pFi9JKmcbb8ySjGJlKUj8hHsXkR/mAPQJN1ygtlaicqeA1vJm1WSIRdL7uMNl+nATojo1eLCghZ5Yh42RmkifijL2rXfwfrCAzcRCw/CwYtoE3V2Du3XnaX7SJ9g5vWxlOEafcxPIqaLpJFvnIJEI3HTW9yNR01rSUaaVp49ZaxGOVFehW5k50cSgulaicreDNF2auAiJh74ZKGVDHo0gMgBAJpuEoPy2ZoWmmirzSNHTNUL5WuVRmwZr21A1E0PYZ3htQPVudyYBVN3QK80VKy2Xcpnewro5bo1heVFW/A8MhdasnRYLIEnTDRtd1TNPDcSpATkE20XUTyyqSpBOSeILIYorlCrpmkGUhk/Hh+4rpmTTfnFPn5yzau5eG6fvNIeuPEeOjzR1FGCun9Eyglxw05wXMgfMc4UfKEkHXD1qIs0KzrMfcz18VtCdyAv9LgaYZCBHT79yYJjYcjsbC22Tp6V2TnsRrRaysYpX6lY/x5laxK00M2yELJ8SjPZJxj7jfJuiqvMBk3D9BayAn9Yek/gCRxpheibLXInPmGAQbAJSceWqFlalA9XGU3Hm1WWraiaoRKrT56Au4XbKpv9HAKljHlmlFKphsjWcb3c5z0v7j46qPhqumg96hd2jZ5JHSqJTIOCLe3eTwntLRfaZMxGQixjIL2GYRIVPVBjwliEQw3hwz3hgR9UMK88eXfnVDGXCu/nCVsBOcCrEybAO74lJoFUknCSIRyPQZd79PQDM05t6dV1qgGQ1MZSpIRjH9W09XrDAMNQ2kTS0QTqA5kKlksj1mvDEm3Aspto6vBOqGjlNzWP7eitISHUWsLAfdsJAixbBdDMtBihTTKWDYBQzbVQHMtodpewjbw7CVUDqf1YzzETTfbFK/3Jh5wlJmKsJmcLuPv+ujaQaOVcIw1NSSzLOp1YKafnSdGkImSJEqCxY0ZC4PzvP8CRdzf3fC4E6fM//27EzHo+kqkLtytkrtQo2d6boapoPj1hBZhKYZj1SBc6JoSJqE6HqOrlsYhgWahmk6aFoB03QxDHta8Veu07ZdUi1a1OCJ1LJpRupDWEWbhQ8X8ZreN+phhcwPtEZ5Jp59bgsVA5Tnai2fIkIneB85TLVOctpCNFX16f/Ha4k09ZkMN+nuXD20mLEP16urNIeXhNeKWNmlOs13f0guJXmWkk4GTLbuMl6/gd9+QDp5MUMvOTUdzU2bqreI9JYPiFWjdI6zjU8I0wFPEgbbLOFHs7UNTwLTM6mcqWDOkGOXi5zET5GnEYFwYvH2YY8//jXSLCDNXkJsQK4IRv92n/rlxoH247iN3615nPnjc2z/aovR2lCRoBc5jFz5Hg3v9Ol8uUs8rSbOJF7X1Gj90veWqV6sz/w70yBVpHJ9dKCvOoBUFRP37Usgc7JOj+DLG8q7ZobPXKaS4b0B/dt9CguKrB63pnbJZuXfnGH3d7v0b/UOXVNNN8nigHjUwWssYzgF0HQsr4RdrJKLFM0wsNyy0k0mEVkc4pQb6Oa+CekMi6OpC2nr40UabzaPf/wUWaQCx/czMU3DpVxYVNUzzUDXTYJob2rHUqBRu0yaBoRRT2kNnSoaGkkWMJ5sPTX36u/6DO4OkJlEt2ezswCoX6qz8EFLaSpFjhQJaTwiTUNMy8O0PDUckkWk8Zgo3MOyS5iWqgQm8YgsDVU1Ooum+YwGaeIThl0ct4JhWKSJj6bp02rb4+vpVGwWP1n8xvVVuRDkqVBxLY6NZptwxNaimQaa82imYf7QE2+faM1KrvIcRK4m8zTA0E5GzIRQleN/TTjBDdurRujvIURCEo2eukl4FHE0em5T41nwWhErKVKSUQ9/5z5B+wFhd0v5VsXBkcLKkyAXGVnkI+KQQfszxsNbB3+XiYh+sM69zi+mFgIPsVR9F9sscNqeF7qpq8y7GUrLmqHjVOxvNlLiNUP3qzaV1TKLHy/OdBepWzpew2Ppu8vEo5jOF+1jn/MsyGkbsrRawW14SCFJhjGbP18/1i/LaxZY+FaL+pUGbm32VsFka8LGz9aP1NnlQpB1+8gwgjyn+O33iG7cQ/Rnc2Xdu9GltFJi5fsrs62poePWXBY/WiTsBOx8tv3UYybteyp4OUuJx3tIkYGUqoKcREih7iy70S+RWYYUCfF4b+rGr74bIjn+7tKpKr1i440mXmN2a4Cg7fPg7+8TDaZt8FwQpSNK3gKm4ZKJCMv0sLUSllVUE3UyI8kCgqiLlBme28AyvQP7kUeRBRlBx2dwb0D1XG3mFIDSSvlAfxns+sTRkCwNp9VzHU03DmKEhEiU9jTxGY82le1Jnk8fCxr6tLqm3h95zmS0Oa1uSqTMnmphFuYKNN5sUjlXnVl4/7Iggxjphxj1MtZiA7NeVtN8h8CoFrGX5qYTcJoKW56vIfpjNMtQxqL75EjXHjq3HwZt+vfGdD1TcSKT4eCzq2Ttw02h/1AR3177pg/hSIR+lzjsP5NUAXS3v3xh659n4bUiVsloj84X/0g86BAPuyT+EJ7RJz0pwr1tejd+DTn4e/dIHrE/8OMeeS4ZRbtPfSgVb5HHTNtOC5q6MM1yA2R6Js235ujf7uPvnNxD5ZuGroNpgm1pLC2YxEnOXl+QJCrgEw2yLCfPwbE1Cp6ObUN/IAnCwxfe3/Xp3+kzuDegvFp55lQlqGqGYRu0Plok7IWMN8fEg+iZXmDPgkgEQcend7VzYNaXBekTcShPw3RNahdrnP93Fyi2SjOT5TRIGa2N2Pls+/CYFE3djedCqLgT1FSTNvV9mcUgMOgE9G/36d/uUV6tYB1nZzFd0/kPFgj7IcMHQ+Jh9FjlSsQPSwvyEV2DeELj8ChZPGnep+EYVM5WuPDvL1Jercy8pipnc8zWv2weTAnnuSBOVFvc0C0yoQJeNd0gTidEUZ9MRMTJGCESIEcINVF72GadSxVts/vbHZyKMzOxMj2TytkK5/70Avf/813GG6MDK42jkOeCLJ2txZE9Q/eo6Rq1S3WWv7eCUz6Zv9rLgBhOyHojjEYF942zSmc1CpT55yOehHrJw7m4gvfBJTTbRNM0rFaD8o8/Jrr+AN22cN88OzWXBM0yVfTLETe3mqZhVArKIkFIZBidKG4p227PfFPzvDB0MAztwNxXV+4KpClouuKE+5DyYQE7z3N0XcN1NeJprI1lQpJCo67jeRqdriSK5GNuFDI4xRaarqMXXPRiAd1z1bSkMZ1gnOqA8yRFhhHSD5RDvTx6f5UimYkyJPHLmwiE141Yjft0v/r5S3v9aG+LaG/r0L+bxB2idHTo5Nr+n+enzazyHJGKmTpzVsFi4Vst9q7vEez66u76dXO7fgZcR6Ne1VmYN7hy0WY0ltx7kBJGOdb0ZjiKIU0lxYJOrarjuTpJkhxJrLIwY/hgyPavtnAqzsw+ULVLdRYHyghy9/NdklH8XBYMMhEEuz7B7uxEVzM0ymcqtD5eZPn7K7PbAQD+jk//Vu9ok9M8B5lP2yWOqgp19pTOxDJnIlYiEow3xmx+usH5n17E9KyZ1rR6vsbiJGFwp8/u73aJnggyfpnQDI3ySpnWh4us/tGZYwn2owg6ak37t3oHa5rnkiT1SdJHP9f9NXj8PWlo5OQkqU8qwiPH9pNxwtavtmi+M0dppTyTqF7TVCbjhZ9eZPRgSDKa3SLmhTA11F34oMXiJ0vo1jdfJU/bfZKtLs6lVZxLK8g4RcYpyYMd8jhReipDx2rVKXx0Be/dC2iWifAj9KJH9ccfYzWraJaJfWYBzTLIZY7u2FgLdSUw1/WnL9qmgbXYwKgUyTNBNpg8FZ3yLMggguDliaQBCiWNckknzXJcV8OxNXQDBpHEs3XKZY0kUTe2WQa+L0lTdRNrGRolSyfN1T5cKOj0B4IzNZNSUcffSoiGgtPummkFD73gYpSKmM26MjetltELnmrzauqzyNMMGYSI0Zhsb4DYGyAmE/IgOqjKv454rYjVN4koHaMx4bArVs9fm0ZYnO6HKFPVOppFk2M4BrWLdc7/9AKarnHv/76LSGbTzrwOqFV1PnjX4Y9/6DIc5wxHkta8gW5AmuakqapYDUaSTOQHXSj9mIv6ZGPM/f/nHvPfauHU3JkqFZquMff2POafG8TDmN7NHunzWlicAJqhYZdszv/0Auf/9MLMgvV97Hy2xe7vd448DWUUk/VHmPUyeRSBruOcP0P4xTW1wc+IYNfn7l/fofnuPN58AdOZ4Tg1pQl673/4gCz8Le0vdklGr2BNdQ2raHHmj89x8T9cUoL1E2iL21+02fnN1gxf7SN8jsgJQtW2fFK0/ihSP6X7ZZvJ5pjGG82ZvcL2q1Znf3wOEWds/Gx9pue9CAzL4OxPzrP03WXcuvdaTJcl622iWxuUf/g+mmXgvXcB99Iy2ShAjibkmUQvuJj1MnrRVRdnNKLrD8iFoPidtyn94F1V1Z3maOZphu45OJdWsBabZL0R8tFpQ11D92zcN89iterkSUa20zuRw/erwPKSyZuXLYZDyZkVg1JJZzSR3L6TcfG8yZVLFhtbGfWaQZbmrG1mDIcSy1IkLBM5Sy2TVsugXNK4c1dZeYwm+cv57A0d5/J5vPfewLlyHqNWUUMB+zFNB7E90y7RNGUgFwLRHxHfvk/01Q3Ca7dVee01xGtLrHTLwfRK2KW6ckzXn9EHfwIiDphs3TkwxpsN+ZEVqeeZTJoFaZgx2hix8K3WsY/VNA3N1Gi80cQq2sy9M8fgTp/x5pigGxAPY7IgJYsz8ix/7YJxl1omZ1ZMDEPD0HM8V8M0dOIkx7E1hIAozplrGmxuZ+x2BGdXTVz32XeH+8LjjZ+to5vK/+c4aJry5qleqPH2n7/L2t/fZ+c32wSdlyC0n8IsmFTOVDn/04ssf3cZb64ws4hZCdZHtH+/y/D+M9oKufqXZttgGshJQPjVDbL+6EQtdZEI/LbP1i82sTyL+fcXjn2OpmmYrkl5pcwb/+1beM0CW7/cUCHRL+lcNFyT0lKJ8z+9yMr3V9Qko3a84B5UkPl4Y0zni136dw43G5wVOcd7l+UyJ4sz2r/fpbhYYvl7KzO99v73fuGDlpo6lZL273bJgtPLvHwUpeUSS99d4cyPzlI5V/2GLRYeQk4C4rubjP7hc4ofXcFsVslLBSzXJq+XVKXWNNBsVWEVfkh49T6TX35NnmbkSYb3zgWMWknpEDsDoptr6J5D4cM3qP70O9hnFkjubZGNfHU+z1Vxr5zBOb+EXnBIu0PCG2vPtHr4JmCaUKvoLLUMpISJnzOZ5KwuGyzMGWhAFOX4vkTXoVTUmW8adHsCP8i5cFbRgL09wd4eCJFTKGicoDA344EaWEsLFD5+H/vCKmZr7sAsdZbvbJ7nyr7CdTAXmtgXzxJ+/jXJxvahzvnfJF5DYqXh1Bdw64u4jQXsUh3DctGM2YlVMuzit9dm8hLaR9FpYhkew2DzKYJVsBtYhsMwfFqY+yJIJwn9Wz2SqbniLC0Xr6EChWsXagwvDxhvjZV9wDAmDRKyWJBnUl3MTng9y0GNNee5mkLKJDKViESQRRlZmJH4CamfnHhjD0LJ5k7GaCyJohwhc6SEMMoxlS6UOMmpVXV2O4KdtsBxNKLomDw2mas2yy83KMwXKC4UcWrOsV9U3dCxyw5L31meEi2Lzpdt/O3JM80uTwrN0CgsFKldqtP6VotzPzmPN+fNVgVCCeTDvZD1f15X58ohruAHv8sy0D33oIqZpxnp5u6Jq5q5zMmijJ3PtvHmPEorZZyqc2z7SjN0zIJF66NFdF3HcA06X7aZbI1JRsnpramu4c0XqF2oMf/+Aud+cp7SUmnmFqAUkngYs/mLDfau752K9cZMyKF7tUt5taKSFjxzZp+tYqtI66NFQDmz92/1CDvBqWQ0arqG6ZmUVyuayZXIAAAgAElEQVQsfNji7I/O0rjSnFkL9iqQp4J0p8f4519AnuNcWsFsVNBLnjrnNZRFTBAh+mPi9V0mv7pGdGMNhCCPU7LBBLNZUS3ynR7RzXWspSbulTO4V1YxaiWS1XmVA6hrmI0KzvklzFqJPBVk7QHRrXXk+OWN6T8PhiPJ2mZGraozGktGo5wozmnUddIsQ9NgayfDsTVMU2mxqhWddkcyGkscW8MPJH6Qk6U5jqNai3GSE0b5qQ0BWsstvA/eovj9D9GrFXTbUlXeTCCiSMX4pPsa1fxhddGylEef56JZFuZ8A6NWxpxvqtZtnpM82Hzq93kFDcfTsWwNx1XXA12HQkkn9HMCXxJH08qdq6MbkKU5/kQSBeo6uHTWUoOhKRgWdLczoiOkKY/i9SJWmoZu2tQufovqxffxmssPy4AnYAmhW1RBkyfAXOkiFW+JUbhD/oSX1Xz5EiV3ntHm7qlOEiTjhL2rXaJeiEjFzBdb3dCxSzbz7y/MVE2YFXk+JVOZRMSCLEhJRglRX8Wn+Ds+wwdDRutD/K0JIhUHjz/u47l2M+XazcfLtvsT0PscaP/nfR6wsZXNxAlkJtm72qW8UqYyvTjoln58LtvUL2j1j86oaJZ/XmPtHx4wvDcgDVLkcxJU5Z2jYVgGVsli+XsrnPvxeVofL850XMCBVicNUgZ3+9z5y1uEx1TUdNvGKBeRQagyuF7kljOH3o29KYGps/BhC82bLevO8iyWvres1vSf1lj7xweq3eonSCGV9uqkHEtX571u6cqm4ttLnPmTc6x8fxXd1GfOAwSlzRutDbn3N3cYrb1cYfGTGN4f0L3aYbQ2pHahhuYev6b7KC+X8eoepaUy9/7mNtu/2iLshc9/nhoa+pQMl1bKXPoPl1j5weqRKRD7eaWzZB6+DMhJSHT1PqI/xnvnAoUPL2OfbakLrq6RJxnJVpfw63sEv79FvLZ7UMkIfneL4Ou76LYFhq7IUZ7jRgnx7Q2cN85gr87jnH28e5DnymohbQ+Ibm+Q3Ns+kXj9VWBjU7CxKR7bO4/Do4+9fis9+PnRPz/J6x0Ly8R7/y1KP/w2xlwDYGpinCrn/3aXrNtDDCfKjDWXyql+uqeZc3Ws1jx6qQC2DaaJOd+g8P2PQddJNndBZI99BxoLJvNLJpWaQXPBRNPAtKC1atHeytheS+m1BaWqTmPewPF0xgPJ+p2YnY0UITS++6MiModgInE8nV/+3YRo8/h99bUiVmahTHnlCuUzb+JU58ilIAtGpP4IEYczt/bi0d402uZ0oGsmhmbNnr02I6SQJOOEnc+2caoOc++cUk7UC0A3dHRDV6SgYOHUXEorJUQqkYkgi4VyFx/G9G7s0fmqw961LlH/5GLl/S/to1/eo36eBbuf76IbOsVWkUKrOLuIWVOj5ed+coH59xcY3OnT+bpD7/oek60x8Sie+XPXLWVEWrvcYO7tOebem6e0WMJteiefrMpzdj/b5s5f3SbcU+T7WRB+QLK5A7qO2ag9lkf2vNi72uGmoeE2XCpnTzZ271QdzvzxWZrvzDG8NzhY0/HG6ETDF5qh4TVV4HPz7Tnm3lugvFzCaxbUmp7kGp/ndL9sc+s/3cTfmSid4qtEDv3bfW7+b9d5/3/8FqXl8omO33AMGm808Orvsfz9Fdq/36XzVYfx+mhmYfv+JGflXJXGGw3m3pmj8dY8hYUCzjOqVFmYEQ8jvGbhG7V9ybpD/N9cJ7x2X4URT+0S9lt+MogQQfR0eygTSDHVDU03l2SjTf8vf071z76L9+Y5zLkn4pyEJFnbZfzpl/i/vnbsxO83iZPsl7PsuadFqjTHxrl0Dvv8KkZdkfY8TojvPCD8/TWStU01aZhmirQ++os17SBAWvdc7HMreO+/ifPGRTTHxqyVsc+t4L51kfjOGnn4UEu6sGTSWrUQ08/cdjXSJOfzT0PmWop0Fcs6WZpjmA893JTeF6TM6exklKoGIoO12zGhP9vn/3oRK7ekSFWlQRb6hJ01/J0HZOEYmc3eShBJSC6OF7WZuoNjlTF1G8+qYRuFJ9zXNQzdxrOrp5Zz9xhy5ai+/est3LpLYaGIW3dnj+E4ZTwag7BfAThsA5VCkaxiS7W4Fr7VYnCnx96NPSabY0T8zdzRRf2Qzldtbv/lLc795Dy1izWMGaqAmqYuNIX5Ak7NwWt6qi3yQYuwGxANIpJRTBpkiDhTVTqhzDgPqigFC7toYVcdvIZHsaUiSUrLZQzHOPFnKlNJ96s2m7/YoPt1BxFnxxIRzdDRXQcsa1qxSnnRO4F4ELN3tcvdv77DuZ+cp/FWc6bKqqapip3XLOBUHLy5AqWVMvPvLag17UcqxDpI1Zqm8uD7rRkahmlgFkysoo1TsXEbHoUFtabl1TKmYz6XBUD3qw7tX3QIvojIfQ0916fhTNq0Kq5hYGDhkBKTkaKh4eAhkWSofWXfE0rHQJA9ptDU0VHKq8P3jKgX0v6izfZvtln6rkZ5uTzz8Wu6GoAwPRNnumc0357H350Q9kKSUfKY1nLfNFU3dQzHxCpY2GUbp+qo1vliidJSicJCUZ2jR5A8f9enf7vH4O6Ai//VJQrz35xhaJ5miOEEMZz6WO3vW8ddH3J4Mq1D+hHx/W3G//g74vs7WK0GelFJCWSSIYY+yUab+PYmabv/6oeFDB3NMA7KR7k4xnX+VUHTlDxH19T0XnZ0FUezLexL5zDnG0oQJgThF9cJv7xOcvs+WW8w23vSNcRoggxCpB/iffSuIlfNOs4bF0i32ohHiFV7OyPwJUKAaWnqV2ew185obxnYjo7IctI0RzfUnpVEkn5XkMSqOnvnWoxX1JEiZ9gTRMFsn//rRawcj8LCWXTLIejcZu/6r5ls3SY/JBz0NGDoNkW7jmtV8ewajlmiUTx3YBCqaRqm4aq8u3TwcuwNcujf7OHWXYoLRea/1cKdcbrtm4Ju6OieTu1indqFOkvfzujd3GPj03V2P9th+GBI6ievbNx+H7nI8Xd97v71beySNfU2UgLcWVsXhmVQbJUotkq0PlLEN52k0wGBiNRPEbFAZmJ6wTIwHQO75uLVXdy6O7NFwVFIg5TJ1pgHf3ef3c93CHuzaTo0x8Fo1KaePBr55MXDpnOZE/ZC7v/nu1gFE9MzqV2sn2hNdcugMFegMKdG+GUqSYNErekgVpq9WLWVNY0DEuBUHNyGi1f3MD3zRBltTyKLMoJuwNo/rjH89Qija+HikWEBOTo6EjWkYmBRpEyET0KEhkaRCgJBTEiOJCNDR8PGJSNFoPYMHR0TC4EgxOewD0DEAn9nwvo/PcAqWjhlB6s0m4D3YE2nxqxuzWX+vQWyOCMZJQQdX61pkCAStZ6aoWPYOlbJxqm6B2s6i9A/l8oSZu9qh42fb9C/02PlByt4c99wxM2jyF/AYzDPycOE4He3iG5tYNRKmPUS6DoyiMn2RoiRr8yfXhZ0Hd1zlPecrSpwmmkoPyfTVP+vK21UnqSQCeXvlIlplS4ljxM1rfgqSJemoTsuVk0lRsgsQ0YRIpgc6jGlWRb2mWWMShmyDDH2CX77FfG120j/BIMAMkd0e4RBiAwiVQFr1tFLRexzq+ju5+SGdkCSum1Be0eQpRJ9Sr51Q8N2dQZ9xSksWycKhGr5KtUR+nStpYDN++nUYPdknPq1IlaaYWEVq+QiI9zbZrJxi1y+nOkXAJmnJFmAZ9fQNQNDtx6rTuXkCJmx59+fitpfzkmby5zdz3eJehFvJYLFT5YoLR6f1fa6wHAN5t6dp3q+RuvDRW78x2t0vmq/OlHwI5CpJOgG3PpPN0nDlHf+/L2pu/3zXQR0U8epOtgVeyrsP+RB2sNq34sQqn2M1obc/r9usf5PDwi7swtlNctELxYgPd6k9CTIhSJXd//6Domf8MH/9CF2yUZ7Tn8jzdSwyw5WyUYN1B1yYTzlNZ1sT7j1f95g45/X0LdsqjTRD7Y/VV3S0UhISEnRMShSoUAZDQ0DE8hxcAjwEQgMTAqUAA2JQEPDpYCNQ8CEhAjB4fuXiAXb/7KFU3Xw6i6tj5deaLTdsA28podbdw8uEk9C01BO4ycI6JWpIGgHPPj7B2x+uo7hmIhIgGSmQOk/JEg/RIYR6c40Z/WRUf+XBkNHcxycy+ewzy5hLjaVOLteQS+405DyRz+snFxIZBgjByPSdo90t0u6sUN8a0211F4yudJtB3uuRfmdD8gCnzxLkFHE5MbXyPBpoqQZOma9gu7YSD8kufOAbKdzMlL1CPIgJGt3Se6u4dgWRqWE2axhOBZWycArGpiOjuPpRL6gt5PgFnUMQ8MtmaxcKbBzL8QwNeqLNve+GJOlOboOSSRxCyYikwSjKZl+Dt7+WhGrfYgkUpl+L5FUAWQywU96JCLE1B1SGbHR+91jkTZ5LklEQPIyMu8egYgzRusjbvzHawS7PsvfX3ktNFfHQlMmiZqpYZUsmm81efe/f597f3uXnd9sM3pwtDjYK+m88XGJrTsR3a3k9PavXDmIb/58A5FILv77S5TPVJ4rluPg4o72Ui8k+5OYO7/ZZuNn62x+uk7Uj042SSeluqN99AJ6agcI4V7I9q+2ySVc+LOL1C7WsUsnnxx71Wva/mKXjZ+ts/HPawSdgKI00dAwsQ6qVCYGEkFGQkyIi4eGrs7taXtQIhAIomlInYWDg0fIBO2RSlVEQERwZCtwHzKT7H6+q6byChaVM9XnnsQ7TRK6jzRIGd4fcPN/v0Hnqzapn6JpGmEvJA0S7PJsXlx/UJBHe5GdGjQVs2NfPINz6QzWakuZY5YK6K6D7jpKH2kah36H8zxHdx3yoofRrGNfWEH6byB+MCbdapPcXSe69YA8Sp7pUv7chz8Nok6HfbXPmBa6w9FT+5oG0/cj44S03SN/wfzEPElJOz2s86sYtQqm57D8RhFjr4CmKX2UrmuUGxZn3ylRqlts3fLp7ybohkYaSaSlYVo65YZNY8mhOmcx6Cb4/YxBO3lIrJ4DrxWxyqWYitRfTQ85zyWpCElFSD9YJ0xHDMLNl6OnegZMz1QakvnCtJVknuoG+aqgGzpu3cOpushUogFZmBLuHS5sL5QM3v9BmdgXdLdO10xSxILh/SGJn6IZGkvfXqZxpYFTd1+fFsYUIhXEg4jezR7r//iA3c93mGwdnoP2LEg/JN3and7lKjH7qR5nIhhvjEgmCZqusfy9hOZbTZzaN6cLPAoykyTjmN6NHus/W2PnN9uMN1SEVUqCj4q0kKhEBR0dQUZEOKVWiiwBj5GwhJiUGB2TjJSQCSH+AbFSrydJiGeqcPs7E3Y+28Eq2Zz5o7Mnzo58GcjznDRI6d/ssfnpBps/XyceqnQCKSRhNyCZ/CslVi8ZmudiztVxzi/jvHUB+8Iq1uKcmoCbcV/SNA0sc1qh9oDqwfRidmYRq9XEqFdI7m+RdXpI/3TtIVSwtGr9GaUySIkIgmeTOCkVaZWSPIpe+BqfS6mSJIQ80M7Zrk5xzkbXobcdY3k65YZFuWlhewa2a2DZGl7JwHJ1TEujUDWZW3WYP+NRaVrohobfz8iSFzu+14pYySwlGfewSzUMx0MzrJlE6KeBYbjNOGq/MlJ1ICh1DcorFebfm6f1yRLNt5q4de9EsRyvGzRdY+k7y5BDPIrZ/HRDZds9wa1sT+f8Ox7XP5u8lGq7SASTrTHX/+IqQTtA/tlF5t9fUP5B5uwb2cvA/ui/TCRRP6R7tcO1//Uqg7t9kvHzkUwxHCOG4+Mf+AKQqSTsBNz6P24QtH1kdpn59xewS/bMVhIvCwdrmkriQUT/Tp9r/8vX7N3oPtaWDvGn+qejMeLZpqGCFJ/0gKA9/0GDvz3h+l9cQ8RK6zH/3gKGY3wjtgZSKKuV0YMR9//fe9z/27uPTcXmMsfvBOocXXqlh/aHD9PAWprD++gdSv/mI4xq+SCz8EWhaRoYBtbSPOZCA/fdK/g//4zgt9eI721wmm6fMgoRhkHenEPTdEQUknR2jtRC51IiJ4EaptE0NOPwStxJoIxzVeUszzLEOGDUjnALEqdiMuqmOAVlmNrbjklCyWSQ4hZNvLJJqWZiWDrFiomua+iGxmSQEYwE/Z2ESf/F1uu1unpn4YTx+g3qVz7BrbcoLV1ksnP3pYnXH4WQKfIIPcTLgNtwab41x8oPV6lfaVCYL2AVbUz3+aadXkc03mpyWb7BaG3I6MGILHp8fXOJGl/NwTA1RPYS2FWuqldbv9hgsjmm9cki5/7kPNULtW+cvMpUsvu7HTZ+vs7Or7eZ7EwQ0as7B18EIhHs/GYbf3tC65Mlzv7xORpvNr/xNSWHzldtNj/dYOsXm/g7E9Lw9Yy9eAwyZ/0fHhDs+oz+zYizf3IOt+6+cufzqB/R+bLNnb+6Te96VwVjPzqCL3PCjv/c5P+lYD8K5Xmr/Hn+SkTf7pVzFL7zHt7H72KUCio9+WVA19GLLsUffIheLKDZFtH1u6fqTi7CgODebTTjPqCqWEdaHKUZ6U4bs1lHsy2MZl2lPb8AtKmPle46yCAk2dzFX/cZjEcYBoQTwWSQKhsFqW669q8ve1sRcaAGO7Zu+eRSRRPmgEhyIv/F1+m1I1ajB9ewK3M4lTr1N7+N4RaJB7uk/hCRRLN5WR1S/tD0hxMw8pC2VNFuYJtFJnGHTMa4ZoVaYQXXqhAkPUbhLmE6eLE3qKkw5cabTRa+1WLu3Xlql+p4zdmduJ+EFGpM/Xkn8PbvivdNLU8TdsmmerHG6h+dZS29z+Du4+sXR5K16yErl1wcT6ezmSjPkUfeyu56zGTwgid6DvEwVs7lk4SwG9J8e47GGw1qF2qYBeuVtLL2KyrBrjJa7d/q0b3aoXezx2Rr/MqnKF8IuTK4HdwdkIYZ4V7I3NtzNN5sUj1fw67Yr3RNw27AaG1E72aPvWtqTUdro9fad+hJRP2I7tUOaZASdgPm31ugdrlOsVV8qZUrKSSpnypfui/bdL5s073aJR4+7TWWy5xgv2L1DcFcqGMtNrFadYxyQU3NGcZzi/9lnDL++8/I9l6w+ngENMfCaNTwPnkX953LmPUjDFizDBknyHGADCLyKCbPMnUOT92TNUNHsyw0x1YhxuUCmuM8Vvnar14ZtQrOWxcVqRj7ZLt7MwWxH/t+DEOt95RMGcUyRq1EvLN1aBFEJinxrQdYSy3ss8vYZ5cxqxVEt/dcJsaabWHUq1hnltALHulul+jGXZJhiBw/fL3sCI+6OHi4J4STlzPt+VoRKzXxkJH6Q7zmEpVzK5hukbCzTjTokEUTcpEdO6Uh0phob+uxPq6maziege2pL2ASCJJIIFL1WmV3gbK3QJSOgJySO0er+jam4eDFNTTNIBwOeV5ho6ZruHWX+uUGZ398jsVPliivVmbSUolURcqkfoqIMkQsEIlQmodp7IwUzzFaPxW77rcldUNXY/TG9P9NHcM2MFwT0zUPtF8zawF0DaficObfnmV4f8BofYRMH34mWZrT20m4/EGRlUsuG3ciRPr45F3kyxcnVvu/L8oY3hsw2RzTu7FH6+NFFj9apLhUwqm52GVb+SM9w8/npMjzfGovkJKME5JxTP9mj/bvd9n57Q7hXvDYmvyhQSSC0YMhk+0xvRtdWh8t0fpokfJqBbfuYJech1XYU+QGIhFkYUoySUhGCf07PTpftNn+9TbhXvCNeam9KOJBTOeLNqP1EeP1EYvfXqL59ryaTC3bagBjxjzEI5FPJ56jTN1o9CImm2M2P11n9/OdAy3aoU89IFavfuJXXVDLFN6/iPvWOdwLyxjNijIJNZ6/pSYmAcHnN18asdLLJbz3r+C+cxlr6fGBpDwTyDhGjv2pN9cYsTdEDCdIP0Amyq1SVVWUWabu2OhFD6NSwmhWMapljEoJvVxEd23VIpvCWmig6RrZ7h5BkpJtd178/XgFzELx4e9ozGFW6yR7nUOJVZ6kxPfWcC6ewWzNYS7OY19YRfg+WXvvREJ2zXWwWnM4l89jLs6TC0G6tUt8674Knn9N8FoRK6c6x8JHP8EpN7GKVQzbpbx6hdLKZdU32u/zH8Mgwu4m9/7mf0ZEj+go8pzKvM3i5RLlOZvdOz7t+wHDXbVBuHaVojNHJiNss4hn19A0jQfdX1EvrNIsnmd3eP1E0TqPwrANlr6zzJv/3dvULtVVy2TGvTEexgzvD+h81Wa0NmKyOVYtjmBqrJhPJ1mOOrT933PISPv+D8rvRsP0LOySMhD0Gh6l5RKVs1XlWXWxhulZJ3aKrr/RoHqhRvfrDv7Ow8/EsjTmV2yyLCfLcpqLT0/tucXTr3qIRNC/02f0YMj9v71L4805RbI+WaR6tnq6otxcGULuXd9j9/Mddn+7jb/rq3zI5yHDrylkIhneHzLeUP5b9ct1Wh8tsvjJEtXztVMXZMeDiN6t3nRNd1Qe4Th5vhuM1wy5zIl6IQ/+/j47n21TPlNl5QcrLH6yROPNpvK4e1GSKmGyNWHn8222/mWTzhcdFTl0TPagIlY+8TdQsTLna1R+/BHF77yDNV9Trb/XbBDlKRg65kKD8k++h9GoPfXXMoxI1rbwf/Ul8a0HiE5/apfxiM3Do+ezNv2XBvtZekazhnNxleJ3P8A+v4xRfdxwVq+UKP3oO6TbHbL23gu3Pe1GE2fpYYi4Uaqi2/bR5FYIRLdP+OUNtIJH8QcfU/rR99BLRfxf/JZ0uz2bT5hhYC0vUPzuh3ifvI/uOoRfXif66ibZbvfVm7c+A68VsTKcAqWli2p807SnI8TGifcQ3Xz6Aq0bGpZroGnQvucjZa6qV09A5pKSM4+pO+xN7jGJOlTcFq5Veb5IG02ZTl76ry9z5kdnqZ6vYrrHT/0lfoK/NWHrV5v0b/aY7ExU0LKfkoUpWZSpluYpBdvuH2sWCZJxQtgNGK+P6N3cwyra2GWb4kKBhQ8Xmf+gRfV8daa7Zk1TFbDq+Rq1i/XHiNVkkPHzv+xjWEe/TmfzJW3gMkckAikke9e7+LsTtn65gdf0KMwXKS4WKcwXcWsOdkX9Yzomhm0o4buhqaBqMc1WjDISPyUZxySjhHAvwG8HBFNH7KgfTf8JlUj5D6ntNytypRtLxqrtGnQCdn6zfTDxerCmdRenYqs1dU1028AwDbWm8tG8SlWlTcYJ8Tgm2gvxOwH+zoSoFxL1IqJBRNQLyaLsX9ea5so/LB7FyLt9klHEzm+2lYP9coniYonCQgG35uJUHKyiheGYaoBA19T5namEhDTISMYx0SAm7Kr1G2+OCfcCwm5IuBeQjOLZrD1ySIOMO391i84X7WMfPl4f4u8+e1BgFhjVIu6lZYrfeQezoUTf+5Nw0o+QYUyeCsz5KrptIdMM0R9DPvV3KygLA83QkUlK1hkQP9gh3eqStftknReUeRwBs1HDXl3EqFdV9A4cEID49hrh17eJvr5FtjdETnxll3IC5IDo9omimGyni/v2Rdz3ruC+eUE9QNPQTAOjXsY+s0i61Sbb6b7Qe0q6HbLJw6llo1BE9zxk8uy9Ouv2SO48wFpQXl3eR+9gLbcQe32y/hA5CZBxPNWC5aDpaKaB5rkY5SJGvYrRqGHNN9Adm6y9R9bpo1km3gdvqYRlbbaOSnznAWLwciqU8JoRKxEHTLbvvvDrJKM91TLch4ZyHnZ0dEMj9lUbMAkePibPlcFf0a5T8RbJ85y+f5tE+Cq+QnvSqG02OBWHxptNVv7oDM135meqhvg7E3o31d34zm+3GW+MSCevQICbQ55JRCYRT1ZVNbA8C39XRbxo+jkKC0UsbzZvqMpqheqFGpufbhz8WRLnrN96eeVbs1rAXWlgt6ro9sNTPe1NiLb6xJs9dfEaRMSDiOE9VVl0ai6FhcI0jsXGLjvYVYfqR+ewyh5yMiFa7yIipX/YD61OA9WaSicJYT8i7AYEnYAsTF/ZRV8zDdzVBs5iDbP6MHZE+DFJZ0Rwr02evFyBfC5y4mGMFscsWBOGOzp9LNxmgXPvlHBaLqJkM9ItpG7iFg2qdQM/ULo7Lc8pFiAJMib9FL8f42oZWT8kbYeITsDeeoSW55TLGmEsyf8wO3/HIhe5andOEoYPhgfRS4WFojo/H2kRGo5xMO16EKieSrIww8gFK+dNso6P3Jig7U5Ybug48zpoRSLfY3ctYm87IRw/vZhOQae+YLN4zlGTVKZGGo8Y9TK274aMein7W+65dwo0F23Wb4X4nZQ0lhTKBu/+sKKGCzZi7l8LIIfGosXcssP9qwFJdHQlxVyo41xYwV5qAiDGAWm7T7rRIRtOkEEMuaT8Jx9jterIccj406/I42RKrFzMZgV7eR6jXoI8J1lvE35xh2SriwxfTmvTnKtjr7aUq/r0gp8LQdYdEH51i+Czr0nXd17od+RJiugNEb0hMozIhUSfBhfrjo2m62jO/8femz3JlWRnfj/3u8ee+4bEvlShtm529VLd7B6Ss5AzIsdmNDTTPyGbN/0beteD9DQmsxFNJtOMTCMOt+Y0u5td3VVde6FQ2JF7RmTsd3d3PXhkJhJIAIkC0CzO8JiVFYCIuHHvDb/un5/zne/z8Zbn8RZmnhtYqXiMiscgrTK8zlLoicc2mYkoJHz9Mm6riTs3bcGS7+M1G7gzU9bbcWBLnybLLXdrf811HKvtVavYLkrfRzgSUxQIR+LNTeNUIgtaJ44TJ1mmy27/vx1glXa32fjZf3zu4xhdoouj6Nl1Ja4v8SJJ1PTY20gZtg/fU6oMbRTT1bPU/BkG6Q6jdHcCqJjIMDzb4igcQW2lzqV/eZmZyzMEjSeDKqMNKlPsfLjNnT+/zf2f3HuxZQ3HQXgeJsuePW1qrGDg1nubxO0Yx3VY/Z3TuMvuiXYI1cUq9ZX6I1k/IcAPJV4gcF17HKWgzDVZop9L386frdN65xLN71zCa5ESXeIAACAASURBVEYI10FGPsNP7tP584/J1vce+YzKFfHOmHjngV22sIDlzP+4iD8fMvq8zfb/9S5q+PWp6e+H9F3qr6/S+t5logsLlj8X+eRbfXq/vEG206d8ycBqP6pVyTe/6XPzRsndeynJ3YTGwpB6KclzQ9zV9HqaRkNSP+PQ3VB0Ohop4coVl6Rv6HU0o5HhyhWXqDBE0uCvCpIehKHk9GmH4bCgfFxHqbCWGgc2Iftj1RjLwdy3BTmuY8p1EN7EVmSyG973pDNKYfLy0QyDY+1JAHSaW6sSKSe6O3a+Ea6DCDyb4csLTFY8xgpkYmfiObb7BkBrxnsFw63dZ+rymlsN+O3/aZXEVfRDgblS5/Jv1Zhe8JFSMOqXvPcXXT78L31ufzI+8oxKCXOnAq5+r8G3/nGL2aUA1xckI8XmnZSf/YcO198f0m/bcfXN32nx1o+a/Nn/vsPnvxgw6hpmln3++N+eAuBXf97l3hcxWsHpKxXe/mdT7K6vPxFY+Uuz+KcXDrJU+Uab0c8/YfTTj1FDq6EkPJfo6jnc2SZlf0T/P/0c1Rsd/C7+6UXq33+d2neuWmXz0Ld2LHHywPVaDzyjFQ/7Cn6VcGZauA/yqoxBpznppzdIPr7+3KDq4SjWd6wAaa1K9Z23bJZuMubd+Wnc+ZkX8j3C9XCiCjIMrf5kHD92TXEaNVr/6vctqJJHqR3CcWw2ql499rOP/X7Pw52bwZ37atcz/vn7X+lzJ42vFbAyqqRMnl+H54BzdPAPUOQKJIQ1lyLTuA+Vn3rJOiBoVpbpJuv0YptZCbw6pcoYZbvPDHKqC1XmXp9n6bsrJ1L9LsY5W+9tcfvPbrP9680XzhXxlheJXrnE6BfvoQdf/T7H22O+/L+/oHmuSTRTOVGLvV+3nC2v4h2UbYSEIJR840cNrn63xsqFCIDOVs7Nj2J+8ac9hr3yoMHgWSPb7tH5q08Zfb6B16pQObfA7B+89ZWOtR/igDj89eR2qKyg/94tkvU9/NkGbi1k9vffemRC+01Ev6/58Y8zLl10+fbbHlluWF11uHGjZH1d8d3v+EhhJXZGI/vaYGDY3VX4vmBjo+TePYXjwKuvunQ6mt1dzcWLLqdWHDxP8MTLEgIZ+oRXzxJdPYe/Oo+MLNdLpzmqPyK7vUF6/R7Zl2tHPysFwekFgounCM4v47bqyGoIjvWQy+/vkH5+h/iD65hCgTEIz8WdadL8F9/DaMPoJx9S/8dv4821KDsDhv/l12AguLBM9bdeweQl4/evWeL0w6UoKQkurBC9dp7g4imcWoTBoAcx2a114g++JLu7CU/hRD0cZ6/aLGZvt+CTn/bpt0uqTYc3f9jit35viqjmsH4jocg0RoPjCepTLr/zx3NceKtKb7fgL/56hzRWTC/6vPnDJj/6N7PUplz+6t9bYvSoVzLql8wu+QSRgy4Nc6cCilzTmHGZWTqcC6Oaw8xSgOM++XlymlXcacsdKrtDko9vMvrpR6hRcgSUGm076GxTzgODQ2mK9V2GP/kQ1Rsx9a9/RPW7VwHobu2hsxy0wfFDanNnSXpbFHH/uXUNZb2C03qA82TAxCnx+59Rbj5f5uhxUbZ7xL/8iPC1C/a7J8DKadSQjWcDMMeF8AP82XmqF69QjoYIx3ZlDj56Dx0/puz794EP9wLjawWsgJPJKTxj2KyIYzWNCkN92scLj/Kr0mJI19wjK8dk5YC0GGIwlCqlG99HIJ7ZK7B1for5txaemqkCmykZb4+5+xe32bvWphiXNn27uICsRGAMajhCD0cIz8VbWEB4LmWvj+oPkJXIWiEEAcJ1Kdt7qNEQIR381RVMqXBnpnCaTWQY4tSquFMtEFDstO2k0qzbHcjE5FM4DvnaBnp89GEps5LhxpDerR6N003qp45vH34wpCtxKx7RdMR4Z4xSimrd4a0fNphd9hnsleyu9xGAF0rqUw7f/qdNPv7ZkK07Xy1Nr+KcbKNLsTfGqQQYbZjRbz77gQwYpdn7yee41YC8M0JnJyzNSkF0ehbhOahRSrbdfyZenD/fwJuuodOCfKePip/COVOavDOkHKVk612cakjrnUt4rdpvnNTtutBoCBoNQaslUdrK1ySJYTw2BIFgYdEhywyuC0EgWFyUVCqCMBT4vmB2VjI9LanV7N+LwnDzZsl3v+OT5/DrD3Ly/PgLk1FA9TtXCV89iztVR41i1CA+5J00KrgzTZzjFhtj8FcXCC+tIquhBWJxinAkshoRXjqFU69gjCH9/A56lFh7D8+1mYGpOibOQBuE4xCcWcT84E0LhFyJ6o/wVuaIXjuHyQqGP/ngYFyI0Kfy1kWi1y/gLc5g0pyy3bPHD3yiNy8iGxWcj2rE733xTL9Jpe7S28n55Z/ucffzmFGvJKo5FKnhnT+aZulsyMyST2cjI08NlZrDa+80OHUpYtxX/PQ/dNi8lZBnhtachyoNb/ygyYU3qnzy0z7d7cICq17JzJJPEEmEcJlZ8tm+n+FHkmrLo1J3yRJNVHdozHhPBVayElrtJ6DY7JBvtFH9YxZxNVH4FgI8xy7ok/tq8oJia4809MnubuGfmiM4t0T46hnSL+6hxynS8Qhq04SNOVSekMc9kv4OZTr+ShZrMvAPwDyATjPKTo+y3UW/pC42k9nxUra7uLNTB/dNRMGRc/mqsS/wqbOMsreHDCt4U9OPBU4my8mu3aJsPp2T95uKly2k/LUDVo+PZ0G7j060jitJhyW9rZTmfPAIUVPpnDgvyMoxUjgYYwl01vImPfaYTzpVx3eYujTNzKsnS1VmvZTul3sHLfhIBxEE+GdWkWGAGlhNHpNmyCjEnZ/BqdWQtSplpYJTr9q0r+uCkMgopOz1QWm8pQV0kiKjyHapTLcQQYA71bKibpMSoTs1Za9Ta0AgwoCy23sEWO2TlAd3+4y3RicCVmDvSTgdkXRTVKao1B2+8aMGazdSPvn5kHtfWOuF+dWAi29VeeuHDTZuZV8ZWKENOi3QaYEaZxTdMV8ZXWjD4FfPzv8TUlJ7bRUZuKT32uTt4TNtHsLVGWqvrJBt9lDj7OnACjC5leNQ4xw5StHZ343oqJSCSkUQx4ZyklkZDQX9viHPDZubCtcTlKVhNDKkqaEsIQwFu7uasjREkaBeF/R6mjQ1KAU7O5pmS9Ltau7dU49tKJLVkOp3r+JONyi29hj/8ho6SRFCIGsR3sK0Jcwex68xNvuhhmPyzTblbt9ag0iBtzBN9duvEpxbxihFfn/bAqtJCCGQ1Qh3fork45voUUJ09SzVb1ym2Nkj/fI+6ae3qLpv4M62CK+cZvg3HwKTrNdsi+o7r+PNTqG6Q+IPrlP2R5ZTMj9F5e1XiV49h/Bc0i/X0OP0ZF1VgCo12/czfvGf9mypXYHrC7JEc/WdOrWWy8yiT3+3IE8VlYbD699vElQkd3415Nd/2T3gUvXbBfGw5PJv1ZhfDVg+HzHqKYbdklG3ZPF8iB9KXF8ws+yzdSel3nJxXEFr3iMeKCo1dwK+njy/C99DhD4YQ7nbOyzxPfyzlcrOX0IgfSvD8CAgMnlB2emTfnkfd7qBOzdF5fXz5Pd30GOrk1jmCVFzHi+qI70AEBTBCJUnqCJFF9mJM1nCda3v3yT2gZX18XtJOx1jDnhXOk4OgdWkJP684YQRMoxQSYLOM5CO5Vk9phSo04zk/U8QwVfzwXwZofae7KzwvPH3A1iJZ7fKeHDxMgbSUUmWlOxtpFRbHsPOowuUI10q/jShVyMvY/rJhjVi3RcWPSFDVjqSaCaicbpJbbn+9A8A/Xt91n+xTjGeKB1PpP+deo1ic5v4g4/Yd1sXvo/OMrylRZxWE3/VTvCq26PYaaP6Q+q//V28hXmK3Q7pjduoXh9/eRFveYHw0gWyO/eIP/wENRxRe+fb+CtL6Cwn/fwLRBDgzU7bB/EJ3YvDzSFx++R+dNKTBM0AOREidTzB1LzHu3/W44v3xgfP5d3PE1Rp+P5/N4Uf/v1OHwtHUHt1BeFIyt4YIcUzQbtwZYbaG6fReYnwn/Nx/Q3fyjg2fPJJyX4X9n7FZr+T/M//wgKafeHsBzvM998HcO3a4d/DULC0JNncUOy29ePxhBSIwMNfmqXY7hC/f43xLz45xNWTdvX9Z+q4GP30I0Y/+/iBk97/rM1MVb9zleDCKWT4aEZa9Ue2VPjhl3ibHUTg0ji3TPbLzxj/8hrF+i6yVqH63ddwph4o17RqhJdXCS+tknx6m+GPf036xd1JFsZmbtQwof473yQ4s0x48RTpjTX04GSdd/12SWcjIxkdAgOtDMNuSZEZXFdQqTvISQbJDyUrlyJG3dIK+D6A0YtUs3M3IxlrZpd8ZlcCbn8yZtQrGXZLXlvy8SPbMDS7EnD9VyO276VUGy6LZ0NGvZJKw6HM9yVjHh/ClQcimCpO0elxGwyDKQpMqWwpMPQtofmh5LLOCvK726ir5/BX5vDPLiEni36Zjdm782trvB1UCZvz1OfP4ngRKosZ7t4h6W+hshPOew+VJE1eWJL2y/bDNcaKjD6QWX9RFknezBzR6XMgBP7c/MQrcPzY58ikGcknz5ZZfenxkqUZvpbAym9MU5lbpTJ/Bq/aRE6yMCeNvN9m81d/is5tqlVIqM/6BBUHL3CozXjkiSJ9QHW14k9bvaraOVwnoDO6Qz/ZxHerTFVXcaXPevfDE+lYSU9a7Z5nMPxNdmO61/ceEos01rAyP9zduAtzuDPTyEqEKUtL2pSObTvOctsVMvFkwrW1b5PnmDxHl+Wk/dizx85tB4aQ0j78StlJySltJuspp16Mi0dsap4UQgoc3zkC1owBL5D4oSRL7LW7viCIrMeA69isRxDYEpKUAteBvDAHC7UjwfMFqgQ/EFw877C5pejsaZIX6z/6TCE8B7dVxZutTzrxnmFSE+BUQ7yZGl6r+nfCkfqqYYVlrcBsOfG/k44grHosvjFL3ElJehkLr8+w80mHwcboiXZGD86BWhuKAj74sGA0esLipA2mKFH9Ee50k/DVs+QbHYqNts08GXhq9lKb499jDKo/wiQp7nTjWL83kxeU3SGmKNFphh7ZrLfqjexnlZp0stks1SGwqhNeWEE4DsX2Hvn97UPdIQMmK8jvb6MGY/zVBYIzi+RrOycGVnmqyZOj9+1ANgkzEQ0+HKnSEVSqDtPzHq05j7d+2DzyWSHh3GtVht2SWtM58Fwbdktac7YUGESS2eWAX7a7eIFg8UzI4pmAQd1ButDezJ9qZ2WUbTYQjpz8d8yzZCxoMoXNwruNKqXvoR4GYUqjhjGmKBG+h9uswoM2YsZMutIEjuvjeKGV8BFVmitXqEwtEe+tM9q989T7bfICnWbI2n6H7hP0Bl90PLT2mKywgqPPGdnmGmVvkvER2EFgtF1PHhdfI42p30R8rYCVkA5OUKFx5ir1lctEsyu4Uc2S454CUKzCdWZtb1T5yPsdV+BPgFWZm0eacGrBDI1oiawcEfpNArc2Oa6i4k8Reg3Wux9xkqdCeg6NM02C5tPr2cYYVKpIOgnjrZEVODx4kQO+08Gxowh3egqn2UD1B5PU7uSclLL/TdLUOknQWYa/tEBZreA2G5iyoOx2EZ6Hv3oKnWe25DEaHcywtvPm6de5rwJ/4phoWu3P2lmiuf1ZzMJqwHd/v0WvbR/6WtNlasHj3hcJFV9z5rRDUUAQcNA5mOeGNDN4rqDZEBgs/ybw4ew5lzg19AePWRhPcqqeQ7DUIlyZwWsdcnBUkpHvDhl/uXmsbIHwXYLFFv5sHW+mTjDXIFhoYnJF/a0zOLUAMwHPRmuyzS7p+h5Fx5Y2/PkG/lwTf7aGN12jenkZr1WhemUJHEHZPdwp550h2UaXdL3zQidrGXn4c/Y83EaE9F3bsRpnFJ0R2WYXNUqPtYsRUlBfruJ4Eq0soFK51e2SniSaDtGllQKozkQ4gfNMc65SMBxqxmMLsJ4UOs6IP7xBdPUs/so89X/0TYr1XYrtPcpOn2K3i0mP78gDy3VymjW8mSayGiL2Fb6lJLx8CqdZs8/fMcRcUyqbMVDaPsMT6w6T5gdK0/suEpazYn9CpxriLU4jPAd/dZ7a914/uihJgVOLcGeaCN/FmW48U3lHKXN8B+UD3/HwVGuwPmtloSmPaSS5/v6Iva2c7XsZRa5JY8Ngr8DzBY1pj7AqiaoOvXaOVoZa02V+NSCIJI4j6KxnT21QMYXtnhQVyxM6rqxksL+5nkgsuLMt5P0d1EOg0zxwvcKViCg82LhI1ydszOGGNRw/RDoe6bCNUSVGK6TrE7UW0ao8EbDSaYYeJ4fASjpHgPRLC8FhJ+z+uSQZ5gXISqjxCJUkNuGBVWJ3osrXtZ/n7yS+VsBK+iHR7DJTl94mml22bZx5gtEa6bg4YRVd5ugyn2RUBFI6VlDU8SjGfZK9LcYbNx8xhMxihetLVGEYdnKSwdFZOfKn8N0K1zb/Mxfnf2fyr4Zc2YXMc6LD0sHTrsOT1FfqBI2T1ZSzQUbaTSnGD5zTvvhdOslA7UdR2EyVlIggAFWi4sRmm4ryoCtGxwlqMET1+0RvXMWJE7vT7vbJbt3FnZ0mvHgOrUrK7V2KJMVp1q0WibTERBEnT/RaU6VGP4txsjE8qFoRDxS/+vM+v/0vp3jtuzW6O/Y6q02XPNX85Z+0WZlSnD/vsnZfceWKSxgK8txQKltqqkSChQWH4dCWhVzXlou8J4iOniSk71K5uMj0D1+lemnJLmjVgKI9ZPD+bdK1zrGyBU7oUXtlmfobZ4jOzeHN1nEqARiYmqnR/M7Fg0ndFIruz67T/ZtrB8AqXJ2l8c1z1F5dwZ+t4zQipOdQ/8ZZaq+tHvk9Rp+u0f3pF6Qbey9sRygDj3B5mubb56ldXcVfbNnz15qiOya+uUXvFzeIb2xS9OJHALiQgunzTXSpyfoZ9eUaKldWnLKbkXRSitjqeo12YvLRs2l8lSX0eid7v45Thj/5AFMqKt+8TPXbr8A3LpFvW55T8tENis2Obdd/ULpAWG6MtzRLePEU4ZXTuK0aohpa3o7rWBmFKMDk5fHriTaW7zPB9vulLqP14W/4IO6fLLTC95D1KsJ1qX7jMpWr5x9/L3rDZy8pPeM40aVh3FfsJTnXfjnk3f9v79g5sMgNyViRjBRawbBbkiWa6UWfoCJRpWG4V5LFmvnVgLOvVfB8yXioaG9kxwK2I+eR5Og4tST2Vg2nFh17bWowRicZbjXEX5kj/fI+7Bzl0wgpkZXwQH7Dgo9JxtCPaCxdImouoFVJvLfGYPMGedw/4Gotv/lPcfyTkcD1OEENRrgLlmsrPBdZjU5kZfZcISSyEiH8wzVIj8ao0cmpG489tOsigxCnavXAvKkZvKlpin7Xrk3/EF8vYOVV6jTPvYFXbaCSMXH7Pr1bH1OMukRzqyx9558zWr/BcO06SXsdxw8JZ5aoLZ2ntnyBtLNJ/9ZHDNe/RD8gVmY0DNs5424Bwk4WDxsx7w/zUhfHdP8Z4OQ6VlIKglaIcwIZAgwUo5wyeWhAKoUajUk+/uyIxk6xs0vZ6yN875CoOTmOKUs7sI1h/MtfW56Z0pSd7gHgAlvzLnbapDdu288Vhb1+R9rdtBAU65vgOFbj5XHX6coDLsZJwmhDmZUHjQNZorl7LWHYK5ld8pia9zHGTsqdzZz2Zk71DRcB3L5TcnrVoVaTuK4gjjWnVuxubLet6PcNQQBTLUklsuXC5wmdFow+vk/RHuLN1HEin/k/+tYRodFjP5eXpOtddF4S39khWGzR+t4ldJIzvr5pBTonv6fRhmytQ7Z92GpftIeMPrtPttXDn6tTf/MM4dIUo2vrJLd2KB7ohMp3h6RrnReaZq+9scrUb79C81vnybd6xF9uUnTHSM8hOjtH/c0z1K6eYutPfs7gw7sHgPAwrKJ92s0YtxPqyzXqi1WkKxlsjMlGOdmoIB8XZMMcVbxEZU+lKfeGDH/8PsnHN/HPLBBePo1/eoH6j75B9e1XGP3NR4x/dY1i7bBjSfge/uoCjX/ybYILK+hhTHrtDsV2FzWyJaTo6jnbtbf0YnSBjsS+1tFnt0gfloF4IHSaUbatr9zLiizVrH0Zs3A6xPUFO2sZHOcaZGyZdp/amqea3bWcmSUfIWHnfkaRGdJxwbBbMrtsuZbjYcLuev54HbJJ6MGYsjvEnWtZ8+XWMdxVA+XeADWM8U/NE756hvjjm2R3No9sAITv4q/MWvkMZSkU+1l+oxVFMmK0c4c87qGKHF1mRzi7487aiTsEy90uxfoOwaUzAMhKhDM7ZdtjX1YIYWkIsy1k/VAkuNhqW+uX5wynWsefWyBYWEKnqVVeD0NLSfl7GQ+KK0qO7ni+WnytgJXjR0SzK0jXY7x1h87n75K011BZghNUDjJY+aBDsruGcFyyQYdi1EPlGV6lTtCaO/jMgwuOKs0T6/jKlGhdErhVpHBB5EjhUA8XcJ2QvIxPfq+lwKv4ON7JBlqZFKjjRBu1fgTYmKK0ZYVYPHFBfbCTTx1X+y4KzAMEpIePZE7gJONXfesdeMLQylDGxQGwMsaCq921nOFeyfZ9+6XpWBMPFWVhuHXL3pfR2PDJZwXVuwohbCkwiiwRfDzSZJnNVkWRoBIJ7q8r0vSrPxxGaYreGJXkyPU9pO8y/Y+u4s8+uRlBF4p0fY+8PcBtVij7MfU3z6BGCfHtHQbv3ULv/9bGlhb1A51+eWeIijNk2CFYnrLK8TM1svU9hh/fOwLCdFagkvyFlAGFI3GnqtRfP03l7Bzp/Q69n18nuddGxTnCkQSLTepvnmH6R6/S/M5FymFK/yFgZZShe7tPkSiKpGT32h5+1aNISrJBTu/eEJVrVKHQpSZ/2Y4CSh1kMdRgRLG1h7c4g39mkco3LxO9dh6dZEeAlYwCKq9fIDiziEkzhj9+n3x91x4ny6FUeEuzL2V3booSPU5xphrkW3vEH3z5WK6X0dryZo4lcr+YGPdLPvhxnx/+9x6nX6nwvX8+zdqNhHhgs3FBVTKz6DMeKO58GlNojTEWWO3cS5le8ilzzcbNlCLXJEPFcEJaR/hs3k5pr+dPLQWWnT7l9h5cXsVt1XFnm8h6xXZjHnjqGZuB3BsgPAdvrkXlm5cwSpHd2sCUCqcWEZxbJnrzIm6rbp+hzuBgs6PLgnSwSzbqUKaHY9sNKgjHo4j7xN2NAyD2tCh398jXtuycPRGcdepV/OV5siRFD58/g/RwyFqF4NyKLVXvW/8oTbGxS/GcqusAJs8o+11MWaDzHCcMkUGEKR8zDqXEadR4sujc856UwYwShDKAQBuFEHKi56uRwrXVLeEgpUNZJmitkNIjDJqUKqMsE0Dg+zW0LsjyAVK6aK0mKgEnj68VsBKuh1+fstowe1sM164f/FhGlRObmn27hhzKHJXFqCxGlwUL3/qnVBfPkg865KP+E8tYD0eaD0jdOq3KKUKvhiNdZmrnaEUrCATDbJeTrmBCCNzQOXE2R5X6kQwawo5DKQ/JpVIKtDYIwHGhLCxvcP/fHUdgjAWR0pnMN8YSULWytHtHCqSLfa+2Le7O5DtO2LF9EOF0iH/CcidY0JENsiNGr9KBxoxLY9olqlogmqeaUb9kb7tgZ0cfzJu37yhAHXSQPS6e9vpJwxQKVSjUiEl5tHz6cZWm7FlQq5IcpxrYxTIrKbtjss3uE+UP1ChFjVJwpNW+ijNMqSmHCdlOn2zjOdqEn3TPfJfo7ByVCwtI36Pzl5/Q+/l18vah3kt8extdKBrfOk/18jLxjW36798+YupqDAw3DxeL3t2jejHFA5nZYvybKxuYoqTsDCg7A7JbG3j3tvCWZvCXZgjOLR15r/A9/DMLyGpI+uV9Rn/7yYEIKGDV1QMPWXmxxtJgy5fFbhdvdR6BsICw+3I1d54U6Vhz/b0hq1cizr9Z5Zu/02L+dEg8sM9CUJG0Zn3WbyTc/yKhyLFZ+Eyzcz9j+UJIPITteyllbsgSTTwsKXNDtWG5s3tb2VPJ60W7R77ZsbpgoY871cCbbZEl2WEZ1xhrc7PTRccZshJQef08cvJ+U5Q4rRrB2SXC88uI0Leegfe2D3TphJS4fkgh3QPqhxNERK0lHDeglwwp4v6J75/qDynWtynub+ItzVm9wUpI8OoFK+URp89tinwkXAd3fprozStWgkdKdJaj2l2KjW3U3snP/XGhshSdZ+SdXds85ftIP0A/hvQow4Dw6qUXoqH12CgV+rN1GOW2KqVL5KThTU+yjVK6SOniSJfMsu4J/AZh0KIoxyRZj7JMCH0rH+RID9eNSLM+WT7gWXawXy9gJSWOH1GM+1aQ7QEEbIyZiFa6j7hol/GQ4doXTF1+m6AxQ3XpAv27n2HUyXfDvWQdBJyf+z6VYAZX+szWLpCVQ9b2PmBn8OWJOgLBPo+O7yCckyF0IcQjXEZHYjtqQkFZYonZgSDPDFJCpSYZ9hWeLwhCSRobKnVh+Q19RVix5GGtIIgEaWLPPQwllZokqgrKwrB+pyCqSsrSMB482wNeXagRTR/DdXhMqEwR78aoCXlbTq7xm/+owbd+r3mgCr1zP+OzX4z4yz/p0GsXlA8JQD4N3Pw31oDyQkIGLrUry3gzNYremMGvblMOHsqW7oPDjT0q5xfwpms4kY8ap7+5TqcXECYvUN0hxfou3mwTGT60ORDYsrhSmKw42qQgBTIM8BZn8OZaJzMufoYou0Oym2tU3riAf3qe8NIq43c/e6Hf8SyhSsNgr+Sv/v0uG7dSvv+HM/zwX81Qa3kYYzNa967F3P08tlplk9uRp4adexlCCFRp2L6bUeb2uc9jzdbdlJULEarQdHeKpyaAyp0e+doOJi8QvofTquGvzpNvto9QJfQwJt/YJb+3RXBpFXdhmtrCNLXvT4SBH5pny+6QJu27JAAAIABJREFU9NPb6Nh2kHtRg/lXfsDu9b+l3LKyCNWZVZrLVxBC0t+8/mxq7KWi3Gwz+stfUP+D38afAKvqd95A7e5Rbk26VF9QOLUKwblTVN/5BmLfWmk4ZvzzD2zm7AV0BaL1kcfd5PnxVZFJyHqVxh/9Y9zZ6ef/7sdFnCPG76LX2ge+v/qBcm2cWR6qEBY7eF6FKJimWV+lKGI8N0QIh27/NtpoqtEMzfppAq9Ku3udoozR+uT37msFrA7iOIdqrVBljuMFOO6jujFGa8p4SNCcxas1Dzz+ThqlyujHG1zf+kt8t47reBhjyIoRcd6lUCcf/AbQpT7xpOtVvUf4WFNzLq+9HaFKQ7etyFPDK2+F7LVLRn0Nk6yMANJEs34nZ2rGY2rWyhlobfB8gesJityws1FSqUnOXPRZv1OQZwY/EJy5HNCcdti8V/DBz2KK3DwdmAir1dU636J+Qp0ulZUUE4Pi/UxifcrlB380RVh1+PhnQ378f1rvvuaMS3PW5Xf/eIZf/Oce9679HWom/NcUT0igCkfiTdVwQh//fI0z//YP0Hn5yBh26yHhyjQy8hG+gxN6qPgreE++5JDVEG9plurbr6DGKWWnb8tGWiMrId7yLJU3L1hNo7XdI581eUl+bxtvYdrysX7vWxRbNlvizjSIXj2LvzKHzooXIrj4YKjeiPTaPZIv7uEvztD4J9/GnWtR7vbQqS3JylqEtziDTlKGf/2BBQVPyXr0dwv+w/+ygZSCYe9wwRHCwZEuRnn8p/+1jRtodu9nxKOj6etRv+SLXw7ZuZcS1RxcT06y45pkpOl37AZofxiM+yWf/LzP9v2UsrAgK8/sOW7dzfg//uc1wopDb/fpoApsl2Wx0ab/Z++ihwn52g7p7c1jgUJ2a5Ph33yEM9XAnZkYsB8z9vP1XdLP7pB8cfdAJLZMh+ze+CVhc56gMUuRDKlMr5AOdhnv3v1KziBqFJN8egN3cQ4QeKcWrIbZO99ANqokv/qUYmcPk371jj0RBvhnloneukL46gVEGICUlJ0e6Re3iX99DbX3YkyHZRDiRBEyCHEbzQmJHUZffIpOj5+rBS9GQ+uxIaxemCM9tJFoXSClhzGaUiWkqc30+16dKJxCiBqeWz0QApfSxXVCfK9C6DeQ0iMvRuT5kDQfPLPq/tcLWGmNKnKk61ndkCMvKVQ6xg0rOGGFo4Qz+0ejCoSQtmPjWQVF0eQqJo9jHOkjhQMYSpU/s5UN2lCm5ZGS12NDQNAM8Wu+FY+cLGRhRbJ4yqPIDUFou2oWVj3ywmCMoN6U9PcUXiCoG4fOTsnUnMv8kmuBnbZlNqNhPNTMLri0ZhzmljzW7xS4HtSbDq0ZhyAUByXHk4RX8WicaVJfbeCfwK4HIO2mxO34iE5XWJFceqvK9Q/GfPaLEdv37MQyNe9x/o0KP/jDaa796uURc/8hHgghkKF3mGUVAuG7j6xHulDEdywQyTa6Lzxjc7gCPtxcIicZ45N+nz1/b3kW3/cwWY4pJlZNroMMbaYtvX6P5PM7Rz6pk4z0szu4Uw381Xmqb7+K6g8nGm8SEQbk67voLCe4cOp5L/hImLyg2Oky/vkn6Dcu4C/PEr1+3so0TDIzwpUI3yNf333K0SZ3QjiYMmD7Zog2JeWEGyaEQxg0CcMWUjjs3hmR5QOKSUnHlk3sPCzxGPcUvd0YsDt/1w1wnICyTFCqnNAVPAQCjEPSD7m9l6NUfpA9kNKjzAI2vvCt96HSSOlNXn/Cb2sM5d6A0d9+ih6lqOHYKs4fE2pvQPLpHasJdvEU3uK07SJ0nAMZDNXpk1y7S/zxrSMq7qrIGO3eQToelaklosY8yWCXcec+cW/ryef4uChLVHdA8uE1jFZERYF3ahFvdRER+DjVCsXmLmWnN+EDTrQLJ+bg+x2lQlh+iHAc+2wGPrISIutV3JkW/uoi/vnTuPNTIAWq3SX9/Bbxrz+n3N2zOmCh/0yakMeF9EOcSg23NYVbqyOjCqYsrevHMWGUpuz2n02OYV81WErbibvvKrJ/zKJEDUao/sAKrg5TgmGfMutTFCOUKRETIrpSmeVIw0SfzI79vBiTZN2DsadUjtIFSdYly4colaGNIssHJ65W7cfXClhpVVLGA/xaE8ePkK5vuVSALnLyUY/K3Cn8WgsnrFj1W2PsouC6SNd/IZ0JSuc8T6+S0YZ8mJ9Y4yloBoRTEV7VIx9NyMjG7v7KwuB5As8X9DuK9mZJURim5xz6e4p6S1JvOgShtCKZGuKhwvMleaaJR5rxUDM1a8X7Ojslezs2e6W0YXezIEsNO+sFRWGeOm8IKajMV1n90WlqizUc/2T3e7w9ZrR+lCsiXUFj2qW9kXPvi+QA2G3dzXB9wT/5H6wdxj/EbyCMnQCNMeTtIZ2/+BhdqCfqmaWbXZuteoHgyhMBUjhk+rD5QiAIZY3SZBQm2//HCcFQHJVK2L+cSbkvv7+Df2bRdkhFAUZry1vqDBi/+znJJzePzVilN9YRUYDOC4ILK7izTUxRUG53iT+8QdHuEZxewKlXJxpVlghpSmWNgbGTPxMKg45Typ2eVcKekAB1mlN2B7bd/4FdjUlzxu9+RtnpE712jvDKGdyFaYTnYspyUu5qk93etJpNjmM7N4SwXb0P7ZB8r0oUzeB5EWWZkWY9kiTDcTyiaIZmYxUDeGkXxlAU9t77fp0obKFUgeuGFEUyIfhKfL9GGDbxvCp5NiTN+hTFGM+r4joBrhvgeRWUykmzPkmyhxCCwK8ThlO4bmCzAvmQ8XiXwoyfqr6uxynZjfWnjiFTKortDv3/9+fkb5wnunIab2kW4bnoLKfc7VlboS/uUbb7gMCLagh5uBzm4x5hY5aodZ69ux+hshgvqFKkz8h3c61ulRV87Vi5hTCYWJJV8FcX8U4toAcjiq02xdo25V4PPRyj49Q2S+xnIx1ppQ58D1mNkM067kwTb3ked3F20pknDu/BVofs5n2KjR3c2SkLTjwXHOe5NKfMMAPj4FRqmLJEDQeWXzVZix/ZoRcF+c17lNsTax0hDn9rISwQn3zuICMohN3keS6yVsWpVax8ROAjAx9TKspOl+zaDfK76+i9ASqOyMZd4rTz2HPP8mLCl3p85MXzb+a/XsCqyEm7O7hRFbfaIJyaJ9nbwqgSXaSke5tUZleoLJyhdeEtul++h84zHC8gbC0QTi0gXY8yHZ+4a+OlXIcyJJ2YIjlZTVZ6kupilamL07Q/3UXlijwztDdLrn2UMOjaDhwpBXluydy3r6VkqcFxLDk9SzTd3RIpD7NVWttzMdoKpEpnv3Roye3OdTG5TYay4ESbMb8ZMH15mvP/4iJh6+RkxNHGkMH9Rwe01pZnFVYkydj+Zn4oqdScxwpf/72J5z3/3+C1G227IPd9FeNbOxSdIeoJZtOm1AfCly8qZv1Vqk6Lm/F7B5liV/icDq+yV2yyW9wFrHuArEbIIKBo7z0CrkxRUuz26P/p39qF7aFOEKP0RBU7PyoQOtGocup18tubZDfWrJXQ/sSvFCYtrEfg3S3G731hyeXGArJit8vwrz/CYDvULGm+z/jdzyagQCB9n3IYk35+l/zu1sH5Phz5vW2K7T1GP/2Ig4cXLMelUFZFu1S4K4u4M1MIKUg+/fJAgBRshqjROMVU8zx7vZtI6eA69rkVwpZOpPQoVYbW5ZFSV6t5ltmZV+j2bqFUhlI5IAiCBq3mWaJomsFgjenpy2TZgN32p1SrczTqp3CcgG7vJtNTl0izHlv5r3GcgFbrHFE4Tbd3k9mZc6Rpl+Fw46mg6plDG3SSEX90g/Ta3YmHKhPwO/ntJ2VE6fksv/X7RM35Bw4gkG6AG0SsfOMPMLok7m5y/73/OGmiOll4i3P4q4s4rTpOq4E727Ig6CGBU1mr4J9dwTu1AGqidXYgiX8IQvb/L6S0QMuR1mHDfQgsSYG3Mk9j5gfU/9n37fW/IEub/v/z1yS//BwZRRR7tsvQCULrG2hsV550bBlOCAFpSfJf3sMYhTHgeCGqmHh2uj7S8Q6SKOnIgiLx4PVKaZ/JqSbhKxcJX72At7yItzhHdu0m5V6fcnOHwsivVKp9GfG1AlYqi4m37xDNLlnphOkl0t4uRpWUyZjx5i0ap18laMwwdelbhFMLqCxBuh5+Ywa/NUcZD0g7m08VzpurX8QYQ3t08+DvM7XHi/EdnKPKGGVtevEaaTk8lsioS8VoY0Q+fHob9P5Ab55psvzOKfp3e6ieYtBTXPswobNdkh0jG5AlR+iDAJTFk675uInr5JOZkAK/7rPyzinO/N5ZKjOV420lHv4GbXWN+nf7DO4e7UhJhoqPfjrg9OWIhdMB3e2J8vqUS63p8Pm7QzpbL6+V/KWH1ujUyktI38Wp+CcvURtjOU6ltfFwqgHSfXntyiYriW9sHSi91147Re9n19Hdx1il7LeSvuD10BEunvARPOipKPBkgCMm05UU+KtLyHoVk2aU3T6yXsOZahyocus4RbX3UMMx7vQU7syU7RrbamOUwqmGiDBAhj46zSk2dzBxgjMzhbc0B0qT399EDccI38dbWcCpVmxHct+WIETg4y0v4J9ZpdzrUXZ61rZpbsb6B5aasjtAD4aAwGm17L1W+5pJBlmr485OAVBu7WAKhaxVrABpGNid+U4HPY5xalXcpTmE69rSzm7HlmL3rV6CR8fXfpu44wZE0ZTNDk3KIkrlFGVCXowpy5gk3SPLDzMyQthW/eFog7JMKUu7GFYrcwghGY+3Gcc7BEEDKSRROIXrhChVUBQxcbxLrbpofz83AuEgpQU4RXHYOar0CXd1zxrGYJLcSpI86W2qpHf/U8YPKKmbCUCw87M9tyIZPbMga3DpDNXvvYkMAkToI8Pg0MNwEkIIcBybeXxRRsVCWPPleuWgvfxFcZxE4KGzhGJvF53nCCFRWh/cG8cLrcBqmSEdDyeo2PmszO14LCWBN22rUo5LNupCXmDyBN0/PiOoHInqDVDDMarXJ3ztMsGlcwSvXkCPY0Y7bQuUvyZcz68VsCqzmPH2HaK5FZuyfyCNooqUpLNJsnsfN6xSXThNZW4FXVhBS8ePUEVG0tlkvHXnqcg1cKtHdkmNcJGFxhXGWfuJuyeDxnVChJDsDL6kNI/W+XWh6d/pkfVSjDEnGtDVxRqL31pk58MtOp+3SfdSNu6+ZI2fE4YbukSzETOvznLmd8+y8FuLCOdkux9VKIbrQwb3+sS7RzVb4pHio78Z8Fu/22TxTEC9ZYejH0rikeLDvxnQ2fqK90Aw4SI4Vmk58HCq1qleug5OxcdpRFYh+8B25IExIwXSd+2OUFp1ZuE59roDF7cR2Ylk4mGm8/KRkpguFOUwQSc5bj0iPD1LuDpj7WC0HRcqzVFx/qg9jgE1TlHjDIQgPDVDeHoWXSgLtqRAFyUqyY/oYD143sJzcKLA7mYdgQw8e96ltZhBaXRhz1vnJfHNbZK7u3itM7S+dxk1Sknu7KKS3J6vIy1AjHyM0uSdIUX7BUsBTHzapHDQk02L5Ts+ONYEMgpxGnW051qdnFYdf3nBCi9KiVOvWj/NO2t4K4v4y/OUnR5lb4B0Xdy5abvjlxJZKstZunMf6Xs49RoYjdj27G45CAjOnkL4HqrbtxmhsYsMfNzpJrJes4DOkdabsNVAVkLKrTZynKAdae266lUQwmrTORJvbgp3bhqnWQdVIsS8pTVEIbjOQYbNqVXIbt1HRAHudAunXkP4vh134wSTT4yHj6E7GqNJ0z6j8SbGGDwvwisrpKnllpQqpSwTiiIhz0eTUt/+ZxVFMWY83p5kqyzvyur8lMRxmzTtkWdDPK+K51URQlIUY0v6Tftk+RApHKTjH3KtjEE6HknaJU27z6wR9KLDaEV//fOXcmxvcZbwladv2F90CCHAP7nG4DMd25kor0cVZFiZZHLLg82QdD3C2gzGKJv18yNL88nGaFXguAGOH+F4ARhDHvetUrzzhPNVdqzr8TomTjClwl9dwl9dQo9isht3KLZ2j2Rr/y7jawWsdJ4S79yj/Yn9c9rdfuBFjUpj9q6/B0IyfflbCMfDCb2D17PeLqP1G4zWv3wqsNoZ3ODBXVKhU7rxPa5t/sVkB3V81IJZZuvnWWpepTu+T6kfBVYqV/Rudkk6E7PXE2wUvIpH80yLy//6CtcKzdZ7m89k9fEyozJfZeX7p7j8b155Jl6VMVYQdPPdDYbrw0eIznlquHstZed+zvSCx+LZAGNgb6tg535OMlZfeQMiHIlTD/Gma8jQw63abjYhwKkEBIstKhcX0WmBTnLUMDnUbBIWnPhzDZyKjwx9ZOTbrJHv4k3ViM4v4HVHtnSW5OQ7gwMtnIPrz0uKvSF5e0i4Mk3ru5cQArJda84rXIfkXofkzg759kP6MsZQ9mOKzhCjFI1vnAVtiE7PUg5TZOCRtwck99okt7btOJMWPPmzdWQ1wIl8vHp0eN7TNaJzC7iNir3uOCNvD61xbanINroMP7iDP12j9f3LeNM1kts7JHfb6KzAqQYE8w3C1RmyzR57P/mc3osGVoDEwRfhQTbYEwESefgYaU2+voUvmJTAJtkax6HcblNs7OCtLhG+ehHV7iIDHzVOiN//FDWK8U9Z0nCxuUO51caZalD7wduUOx3KThccSXDxzKTjT4ArkY0axf1N4l99fHCeeuSh08xms6ZbuPOzZLfXKNu2tTt+/5ODHbTwXNIv7xCcX7VZC88jvHKestsn/vUn6MGY+u9+D2dmCrXXpdjZo9zcRVQC6r/7ffK1LYrNXWv8vLyIO93EW5yjuLfx1Ps5HK0zjrcJgxZLS9+iWl1gOJxwlcxhdubhMEYfm5FXukBgF0IhBFJaPSo9yTwd9zljDHk+QqsC141o1E/RH9wjSR7PhzlxPG8i5tg5Rhzyfx540zNJLfxXGk4YEiwsU7v6JkW3g0oTdJZS9rqHZVIh8CvTSOlgjEYiUGWG0SVRc5EiG1GMhlbwO+4R1GZxvJNJ95S7e2TXb1PudPBWFnHnZwhfv4LqD1H/AKweE8ZYQHVsytW+1vns54w3bxO05pCuh1ElRTwk3dsi6++eqM5aaOs2f/i1atIVkB/Rv3g4xlmbejiPU318WccoQzbIGNzrM7jfp3mm9dTzAXAjl7nX50FDbbnOvR/fJR/lmJN0F77gcCse9VMNlt5eYvb1OaYuTFGdqzxTOUrlitHWmLWf3H2EuP5gZKmmvVkw7NnfLc80eaqfK6vrzdRpfvsCM7/7us04ORK3GoAj8ecbtN65TO3qKYw26CRn9NkaG//uJ4A1IY5Oz7H4x9/Fm67ZhduRBEsthOdQvbiIN1076DLTScH9/+2vLMB5KEyuaP/njyi6I+pXV6m+eorqa8IKiY4yVJqTbewdfxEGBh/cASFofe+SNUZetGNJZwXDj++R7w7Y75B1ayHRuXnm//BbeDM1m7VyHYKFJsJ1qL2ybA2hC4XOS9Q4ZePf/YT45uF5jz5fR8U5yf0OlXPz+AtNwtMzVhemVOg0J+8MGX2+9nxipY+JgergyoBL1W+jTIHtB5QMyjYj1XviZ41SqHGCzie8KWNQoxHp5zfxFmapvP0Gxfq2bUU3BvYNzifZuMcfGEycHtkNe8vzuHPTyCiyxF1l+SRi8v4TVbZc155HUdrzFcKSj43BJNZM2Ak8EOCvLuHOtJBhMMmO6hNljF03olZdpF6zIqiOtJmi/SjKGFWmTE9dpFqZpT+4T69/+7HH01oxGKzRbJ5hduYV6rVlgqBBkuwRJx08r4KUx2cehJB4XpVKNGNFlN2IwWCNbu8WxpyAtyQAxyE4NW87/WabONXIkrIdOSFuPzvK0llO/0/fpWz3cIMqjaXLVGdX8SstpGOzpWWeMO6ssXv9Z18bHs/fVRhj0GlCtrlGunEfNRphtLLVI2zlqb91Hem4+JUp/KjOuLc5UbI3pMOOXWO1BeC6zCnzo04pTwud5+Rrm8hWE6dWxT+zcmTTc1wIVx7oS5ZJ8VLX1a8fsMJmq570Wrq3RT7s4u1tIB0XoxRlFlMmoxMTCx9OP2fFiLHcQxv1RHmFUmd2whfyiY+wLjTdL/dof9qmeXoCrJ7yzEtXEk5FzL0xj1f1Cacierf2GK4NiXfGhx2DLzqELfcFjYBwJqIyV6G6VKd5usH0lVnqp+rPRFTfL6UO14Zs/O06vds98vGjOwkhwPUEC2cCZpd9ag0Hg1V77u0WrN9KyRN9PMZ+Sui8JN8dML52fBeRdD0cz6ajyyRBd0ui1qK1PAgcXFkhXx8jxrajR+Upo0/XcbwIL6ySDtr8/+y9V49dS5bn94uIbY/Lk5ZJ7y7Ja8t1dVd3T7WbUY+DHjSQIEiC9B0E6FPoYwgQBOlJAw1mRtNmembaVHV1uVt1LclLn94cv31E6CF2nsxkJslMMu9tDqBFJDJ57N6xTaxY62+sqVB+RBB1ES8oclptSO47S4t8rY/fbSICxwLTSUH2ZMcJbL4g8vU+g7/7imqUTgU5hZKYvCR9vE25625We99lixKRJlQbBTqrEJ4kvbdKOBMSzsbkK0PCbownYPx40+k6BQprLMqXmCQjfbQJVUWx0Sdc7ODNxK5KaSw6qci3S4qnGSYBoRwN2lYO3yD8AC+MMWVBldWs3VOctKke0kfWFSp3jWkqetUamRnX5uMB3tI83rkFVMdhlGQcTZMl9oC/QiD8mqrdiPCW5jFjhxkTnoe3tIBsNhC+wywJKVFzXfwL5/Dmunjzs1PhSFtVh/A1Io5Qs11Ut40ejNwY1DpEJs1Q7SbhO1epegNslk/xWN7iHDoIUN0OujdAhAHhzauYPHeJ4XDsoGtag9lXe5eNGBmFyFYTPRy5pEwpZCN2Y7E071qT87NU27vTJNBag9Y5eeHwoHkxJM/3SSRVlZKk21MGn9b7WkpJsoXR5VS5uj6jyfMhSbLlcDO2oiwTsqxX46q2kdKj0jlgSSabCCExpqDZWMJYzWD4lDTbIQw6BEGTMOyQ54OXtgSF7+EtzRK/f43g0hL+Yhc103RYNM9zbV2HeD51bqUnGaO/+hVsgwoi2uduYKoCXWYEzWXS/jpCeQSNDnsfLoRCqaCu2Pn1uIx4/lwvHq8y+fHHp9ugtzzK1U13LQiBarYRnu/sbbLU2SyZijJ155guC8psTD7emWpBVflRGx9zCjFv9yGaqjfEFgWi08JbmEO8wkJOSIHXCGhe6jioRVZRDDKy3QRTnG2y/FYmVkAtEuqwCcdVhqzRFMPDK30hlXv9nuXNKWJS9Ch19ko2oVN0LcnLIeYV2IDe/R7Ncxtc+uFl/IZ/4mpPNBdzrhux8OEimx9vsPnxBtufbzNeGaGzCl061sieCKk1tTBfDSaeFvb3SCR7bBDpVsRSCYQSSCURnsQLPaK5iPbFNrM355i7M0/n2gzRbPTagMdiVLD1q00e//lDB+I/Zli9QLB0OeT9H7S5/n5Md8GtdMeDirWHGQhYf5QzGZ7+pK96EwZ/e5/B394/9nkvbBK1FwhbcxTJAKNLWgtXkSpwRqzjMTv/8hP8RgfPjynSIUlvhag1Tzx7nt6TX6HLnLC9wOzF96l2XizupycZ48+eMf7sxYa6LwqTlWTPdpzZ8it3usL0RyQ//YJse0K2k+K3Q6q0oH25y8w782z96Akz12fxWwG9v3mC1wxonGtRpSXSV1htHO6oyEg/fURqLdJTSF+iQg9sSDlpOZyFDZFBiN9oofMMazQqjAlnFp1zgt3CFNmpVvilzelXG/SrDTwRApbKljV+w7pkIg5RXYdtEr6P6nZq7FI2lTgweYkZTxBh6EDtrYbTPspyRM2i8hbnYGEWPZqQP3iCNRpvtoM3O+NYh+0mstnADEcOy3SgYmXzApvnYGo/tqpCTxKstejdPjIKCa5fdnpCg5FLBusE0JYVqtWkeLZOcGmZ4NrFmh6/VSdbocNNGce61IMRZpw4gHPTgqewZYnNC2fq2+04/JaUqG4HM55MWyJa54zGa4wn63WC4wQR90LrgkmySZLsecjtPzcarzIaH201WqsZjVYYjVZqgPu+Qf0k2Tz02r33KxUw130HrTN2dldJsx6z3esOs+U3KYrRSxMrNdum8dFNuv/V76HaDaTnFmFo7SAGb0ik2KtYSuUTthdY//QvMLpk7uq32br3I+LZi4St2el+SqnwvAjPbxD4DYypKIrxEXxu9tlXFE/XD3+XFI6YUJ+rMo72hU6lrPXK7D6TVTtIhJC1z5kxjp16jMzINxE2yVBehPR8wuULLklJxpS7O0f8M6t8TJWfvRahtRaK0lVufQ/ZiF/pRWiNdQz8izNE802nybc2ZHB/h7yXorPyzOA3b21iJf0Qv9HGi1q1PpU8MaNKFxnJxulUcpNil3QqQvjisMAgXSUvx1PRsRdF1kvZvbvD6o9XOPe9ZRoLjZe+/lDUtjhL3zrH3J15qrQk280YPhkyWh2R7iSkOynFIKdMSmdqWxpM6ZKtvQRKem5CVJHCb/j4rcBVprrRfnXqXJOgE+KFnptAAzeRvnZYWP3bFVb+5imDR/1DoqAHY2be55/8jwsYDU/vZvz437oWxcKFgPPXI/7Z/7TIn/2f23zxsxcw0944xBQwaY0GobBGO0yA8l01tD6HGjPL5KM3NzD9OiNeaDL3wTmnhzbMUZFH61KHZG2EzivSrTFCCMpJgd8KuPgH1wm7MVVaMl4dkmyMaSy1aF1oE87GZL2UclxQJSVZP3UVq9RiyhzP96e076A9h5gNMEVOlQwRnkfQmUd6AZPVr157f6raCVwAgYzRtqSqCnR/RPrx52Sf3nXaTQfUt22lQWvKJytU65uYvKTa2kZ89cQ9XzhxRtluUt5/7JhGSerEGLVB7w4oHj1zE1xZuYnCWJJffnbITLNcd4KxjOBaAAAgAElEQVSO0ncV8z12HpWmeLJKubblBCnz3LEAhUBvOfwW2rjEyFh0f0j2xYN626tppc1WlWsX5wWjP/lLB06XwlWq9kQjjXEJ3c8+cQSFev+OmkNbrLUvxQedWgR5730nBJ5bY0jSbVqtCywsnKOqcjwvYjLZIEm3Xgq/AIjfvUrzN9/Fm2k57TKAsqLqjzFp7sgfr+m5Z9J8io+01jrZib2xkhJT1cLTviO/AGhdIkRKs7WMp8I6MTw6d+jhGD0+fP+ScUT84buUK+uYoiR69w7lsxWstcgootrt19VfHzXTodrpO3KD56M6LfQ4Qe/2HJbv7yOMRasJJv81qtV2wPRhH5O/vnL8acOB8x1RxbU+ji/AHAxbGfLdlJ1frDL7wTka5zvM3FqgebnL4O4Wg3vb5Ltn4/LxViVWQnl4cYvO5XeJ5pfx4s40qXKVk5MlVvlgi3RnFXuK8p615rmby/PfZae/iyqh0sUrK1ZWW8arI+79q7sELR8vVATtkymVC+FK2l7s4cUetk6G4sUGc3fmKNOSKq3QuZ4mU1bXv/e2flqhkghPoDyJDBQqUHiR534aPn7Dd71n+XrVqYOR7qRsfeIqVdufb78wqQJXsVq8EPLLvxzy6Y9GDHfdzXXjScG4r/nj/2GBqPnmgq8vC2Mqx1CpbZL2Vt9CKsLWnBvLmgrtRU38xgxB3MVvdJFFShB33GONLrrM0OWL23pfdwgl8SKFF/t4kYdpBjSWW6QbY8qkRA1zdFo6a6FmgeyE5L106hCQ7SQErYAqq9AbY4w26EJTJgXlKCeciRDNAHoW6QWoIMZWFX5rdjoRKdusK80S6XkI5dUttNdZCdbVAXzm/YuMdY9h5YxfbZa/9BNtWU21oWxVYcW+cKZNM8xo7Kjbo8khKxGr9bE2KUfsRipXMdDZUUHE6Xc/J5ZojiQ89ffxkglJW1etAqwQwFH7IKtfPhZnGlLgL3YRUVBfF5Jqd+ja2dUe2cTWrFQ3vThdsCZZb4Kt1klFnypJkUGAaVi89jx2u4+ZZC+swniLXYILi3UFcEx29ynppw/RowRbVPu6T68TWk+Nrq3RVOnIQUrqrkn38odE7QV3jk+llTw8L3YJlxfivWguMOZotV4bRODjLS1gRhPK1XVXpRxPKDe28JcWXFKepBSPn+EtLbhWc1lS9Qfo/gg9Gh8rjPu60WoJbr3j8b3vBAgBP/lpwaeflcSx4NJFRZZZHj2ulf+Vh9dqE1266nQkhcDvdEke3scW30xyJXwff3kJGcfuflCUrxQqVqFHOBfTujZLtpOQrNXJcN09DjoReS89E7jNW5VYeVGL9qU7zN52GlVCeei9VsJpgG1VfmqvQE+GBF6TRtBFCHXo/cZqimrCMF2b/v9VSdVe5MOczV+s0702gwoU8x8s4oXeqZMYIYWbMGMflpqneu/XHda6dmSylbD9yRaP//1DNj/eINt9RZJhoaosu+sFG09yytpsORlp4pakzC3mzC1T6q82mqpIYOxAtQiBney6m2edmUrpueNsLVWeYHWFLnOKGj8gpHItgEm/rnh9fTpTJ4kqKZisjfDbBcUwp8pK8p2UYpSjC5eAF+PcHS9tyHs+utAInLdltpsifeWS9UIjfImpDFVSUozyep8NVaYRXlBfmxVVOsZUBabIHUVfV4BwYNYTXLdK+AQiIjcTPBHgy8N4Pl+EzPnn0bZkyMlsXI7EwQRnkjptqJq2/Ubxsv170XN7LZ4ap+I2yuy3Mp4ThXRq3VFNlMgOVc6+8ZASb8kxMa21+EtdZBxQ9ccuwSl1rRAuwYDJcmQzxlucIRusYNQQGeToInWT40yHYG4JPEm50UMPJseOm4xDZNOdF+XaDskv7jL661/XSvNnt3u6zBhvPaLKJyAkWX8DP25jTEU+3tlnedZJl65ydA0fOF2IaQu5fLJNeP1K3QasEHGE9Dx0UTpl/SBAhiFaG8d8GwzPXFbg9i2XVH34vs/584rdXcOnn5VICd/5ts9gcCCx8n1Us4U306XYXEeGIV6nS/b0EfobSKxkq4F/fong0jKy4dqoZjh69XUhmZIc0o0R2XaCLty9Klpo1FCHs9nGtyqxCjpzLH70ewSdOazR5P0tst46Ok8x+hVeUgeinAxOpY4L0Ai6LHZuc6n7bXwvRtbieAhBqVO2Rw/4dPVfn55uax2Q/at/fR9dGqL5mPaFNjJQX68p5TcUe0lVlVWs/ugZD//kARs/X3/1G3GCprtrBUEo6S769DZdpaAz7zGz4P5fFhbl7QNS95Tk3zT2q0vPeU6+KgYbjDb2cVvFpMdk+8mbb9AZRLqdkG4nh3Zp+GCfAZZuupZEMciZrO5JS9RP1q8fPx0wfjo4dliynWT62vyAFEreP5jsnHI8gVi2mPMvsJ4/oOMtMBdcONRWUcKjrebpi6Osy9cJNzmdvUzEiUIpJxYZBs4Prk6mbF44yQiLIwLUuCHhe6h2C395CZPlFGsbDoD/OoyOMwghBKrdoNzsoftjwqvL+BcX8Ra6mKLETDJkI0RISbm26ypKe0mAtah2A39prtaAM8hmhOo0CQO/9vHLj60YOjVyjbAe+aM1imdb2OzsqfVVNmb7/k/qfZVsJwPi7nl0me4voHDsyLJMHVC/ymqdr5Od99ZabFlSPFujfOYW63phFjXTIWg1sHmOznLwFOE712rMYF5XZqqv5dj/oz+MWFiQ/PgnBf/1v4gJQ4HWMBxaPvwgoNcz/Nt/5xbK0g8AQbG5Trb6DK8zgwxfQW4SpycVPPcB7pcU+FcuEn/vA7zz55CB78R5N4/iu54PnVUkq0MmqwMwrlghPbd4zLbOFm7yViVWUvn4rS5GVwwffcLOFz/B1KDY01geWF2dmmXQjs7RCGa5t/mfWGjfxJiS7fFDuo2LSCEZZ1uvX2oGl3j8eIVyVHDzv7zF3J154rmT6Xa8zZEPcvoPejz+i0dsf7LF6BjbmheFUs4r8I//+wX+8L+ZZ7hTOmXnGUVrxik0t7uL/MG/mGPvwvq7P+vzsz8fvPyDTxVvh1bYmcZpdulFr31tof7Tj6e2FZmZYNCEskEkm/TKfcNbhU+lCsxr4oDepgivX8U/fw4ZR5jJpPaCq1tozoPKJRhZ7ipVcYSMY/e7NrrNHz91IqN/L8mVSwq82TaqGaEnKTbNaxB2iHd+HlNpqu0Bxdq2Y/MtzODPz1Bt9FDdNjIOyZ9sEFyYR4Q+1WDs2oClBk/CMflSubFD8XST6PYVvNkOqn0KvOprhB938MIYIRT5eOcIO9LJRQR0OpcRQjq5iWSLk5z/Ni/IPv0Sc6C9XD5bo9zY2m8f7+E5DraTjXXv+RoqlmEoGAwsP/tFwT/7J88lSc/tkk4mCM/Dn1+gefs9x8rv7b4wsRFxRPzBLSdz8lpRCzTXHov+8mLdHnUwET0YkX35wFVzXxLBTEQ4GyM9RbI2xO9EdK7PsfPxKuX4bJP0tyqxssbJJkjlUU6GJJtPHeX4GwhfxSgZsDt5TDOcR5uc7fFXZOWA2cYllHRZ+utOxFZbko0J61mFxXJu5TyLHyzSvTmL9OSZ4Ju+qdClphwXDB4N2L27w/Zn22z9aoN0Jz2x8TRAlhju/nLC0sUAL5BkiUYIwbhfseUVJCONmbI03Phkk//8J9f/Pw5HaXPGVQ9jNZUtSPWIYbU1rQ57IqDtzaNPonX0locMA2Tg6PkiCBDGOnX3yDEgqV0A1KybUIWQ+7pb1k6NeIWUp7ZXOYuw2lKu7jiFe2uphg7jJATOsiUOayajMxGWUYjujcgtzpxagB6MKTf72NqKx2SFa+fUOK3jInuwhpq9j5pp4Z9zsgt6nFJu9jDj9Pgq12uE9ELi7jLRzBJ+1MRqze7jjwlbc3hRi+HavQPMcSeOeuow5kjF1KQZnA1u+rXCWigKy3hsp3lbEMD8vHJ8ieqA5qOu0GlC2dvBa7UxRU65s113lY6GjELi73zgHAZeN5RCBI4BLBtxfQ2B7g8pn6ySf/X4KA7yudiTW2icb1OlJeFMRPPSDL3Pz6YSfjDeqsSqyhMmaw9pLFxEBhFBew5dY1umANgTVo1O3fOuVwalTrG2crRpU9JPVug2LtEOF94kr6q3yZL1Mh7/2SNGT4ck6w4gHM83CNoBXuztSyO8ReEwOa7dV6UVWS9l9HTIyo9X2Px444gH4EljMtT85E/6qJd4DsrArZ5MmaPCmMkAhBdgqwKhnHiio9wGDqSsK4RSqCByDJ+yxgYppw5ttXbVzK/bU0rJfQ2lPZNUVZMwDh7fGu80pVDXTDRbVmcKTj1RSAc4Fp6aGrs6Sx9ZA3nrbZ+uqm3NTrN1peXAPpTlibe/ssWUATjRA0qTM6i22bvYPOHTrrYpzMtZuF9L1BgnEfj12HhTK5wjY1EzA63WNbi9OsQsBBzoGOuwXTUgGWtRrSbCr4H+lXY+iDUZBa0xkwQzSdCT5HSVque2f8+TTqgDLOv6vmbN3nloHEuyrJz46UGDaGMoVrann/2q68ikOcVKDvV7qp39e8UeYPwkUTzdcEzpmSaND28Sf3ADEYVkXz6h2uq7ypl2ZvWnvraNpdoZYMsKL2zQvfQ+XhijggZCSAarX9KYv0zcPcdo46up1yPUKvTi1Wzytz12djSNhuCdmx6tlmBpUfLeuz43b3poA7u7B865+rjr8RhbFK6jpKsXjrsIfMKbV528yRmEa6VWmDQj/+ox2ZdfUa2/GnupC4cdDdoh0UITL/bqe9iZbNaheKsSq2K4zdbHf8HiR79HPHee87/5jxk9u0sx7FFlCVafDAxrdEWZDE91ge2xAj0ZAM71PfJalCZHSR8hzpad1n/QZ7I+4elfPeXS717i/G9dZOGDRfym/4a96LMPqy3FuGD3i202P95k65Mtevd2qPIK/QbCalVpp7iqF0X76g0EgvHmfTo3rqHmBZHYJt1aIewuIJRHlY6Ily5T9LfJB9sEnTnal29jdMVk5SsQAr81iwpCymGPvL+FLr7e5aHqtPAvLBFcueD84GZnUJ2WW9XXVil7DDSTZI6WPRhRbe1QrW9Trm9T9QbfXHIlpTP53fOvm+/iLcyiZtrIhlO3lmHgJmfplONNVWHzwtHVRxO3/bt9qo0dZxez3Ts1BX6i+yTPrWAqW7KS3z0xYeQsQ0Qh3uIc4bULeMuLePOzbtUch3XSrKAymFpXStfjoHf6VJvbFGtb6J0+ZuLOt2JlzSVldexBHIQ8kHBbV8XyLy7jLy0CUG5uo3d2XevQnJwBJ6IAf2kB/9Iy/vlF1FwXNdNCtV0iJ5SayjuYLMeME/Rw7I7h6ibF6gbVxvbxx/GbNLzVhvLZFr3/+z9h0oLGd96h/Q8+ovVb79XXUeUqX7WS/qnITknO9v/2byiebSG9gLh7jq37P0FKxdy179aElprUcuBjhRCEYQelolNBVd7G+Mu/yfmDH0b8L/9zm3dueFw4r/gv/mFEpyP5P/6vhB//ZL9VJoIQf/EcrdsfUI369cLRZ/jzv0VPzl6z6khUmqrXJ/v1lyQ/+zXFCWydAKpJQbo5Zvwsonmhjc41279YoUrOHqv3ViVWVjuqazkZEs0u0zx/g6A979hHujpxFaoY7rD+sz+dVitOEpN8ByEkUniM8y060TlunftDtK2QwiMpds8UjmNKQz50GlRP//IJu/d3aV9o07napX25TftCm+ZyCxUq5MvsNs4wbL3yLicl6W5Gsjlhsj5hvD5msjYi2UpIthLSXaefdRbxqkNqrcIKga4sxnqoIMJr5A5EGzt2pBnuON8ypWq69LgmPJToIqN14QYyCLHGEMwuUCaDo4mVdC2X9u9/H//80uFtMG6FNPqLH1GtvXhlJNtN/PNLhDev4C8voOa7bgKLI5eU7E3EB9lf2kwrAy5JyTBJ5ia43oDiyRrFoxXKtc0znciE7yFbTfxzTrXbW5xzk24zdtsbh4godNu9V6U5UL3CWlRd3bBV5fSTihKb5S5RHE3Qu33KtS23/Vu72FdgIAB8EaGER2oOYvWcZ1us2ggEiR5geIlKdxgQvXuTxvfeP/S4Bag06af3SH/+6UvHxlucJ7hxyVnJLM6hOi2XYMZuTFAHqnlmbyw0Xj0OJssxSYqZpC7h3Ngm/+op5erGNMk6sm3PRbWxjRlNEFKihyOHrzlBtUq2GvjLCwTXL+EvL7qkvt1ENmNEGCJC31VTpdzXhLK2NiKvsEWBuXLBVciGE6rdHuXjNYqnq5SrW38v2C4RBgSXl2j94D2id6/hL3SRcQhxOK2c2krv45NOk1hNMsRBw2KhMFWBrVm+Yu/fgUqztdaJr45WiaJu7ZP4hi2Nv8d48kTzH/5TxtqG5tJFhRCQJJbhyPDxr0pWVw/gy6REWEs1HFBsriEbTfzZ+Rfuucly0l9+hmy/PpvdVXKr/UXobt8tQrd3X9kC3P8QqNKSycqAalJgLZTj4gWioI71eVD89jTxViVWMoiIFy/jN2dQcRO/0UGFDbcisSdfpakgcgrsp4hJvkOpU4ytGGUbKOHRCGaR0mecbdFLnp59ubdmDA6fDBk+HbIVeXSuzNC50qF9uUP7QptwJsJv+fixj4o8VKhQvkIFjtIslUQosd9CPMjysnXZtG7lWW0cq047IVFdanThtIp07tp8VVqS93OS7YTx+pjxap1UbSZTvaNvMqzRri3c2S8jqyDEb3fxGm3XEpTSua2HDaTy0VmCLvPpyl4GUZ105bywHCglIvCJPnqX+P2bz22D00lJP/6can376HmoFP65eYKrFwlvXSO8cw1vrlvjZl4RNXZAcpjIYI3BJCmy+QV6OKZcfzPyxN4+ynYDNdPBm+/iL83jn190lZgllzwIdcLrRjgVaOEBYQDP3TOtdttfrm7gnVugePiM4tk6erf/UjxMy+vSUnNMdJ/cTMhNgrYVM/4SkWhgMQQyZFTtUtrjb6jC9wiunqf1+795eJtqy5u9G/0R3RshUN02/oVzhO9cJbpz3Rksd1ovHwsl6tavB8ccc5PllCsbmCRz4o/HJFbHhRlPphY8rwwhaqufOYJL5wmuXyK6dbX2M4xOJK4slHLHkgbUl9uexENxcQXvq3nyr55QrW05653nj6MQeJ3YsQPTsvaqdMmnEI7q7lTSQXgSUzgjchl4VMO9Nq84VgLDX56j8e13aP/+d1CthpMq0AaTFw70rvWhFt1pwiTZ9L3WVJRJn6A5ixc28MIGzcUrqCCmTA9b1lhrqcqUQnoY/RpVD0+5hUscTrXyXqUe/qZhkhS9exS6MRpbPv+y4quHmoV5ie8LisKyvaMpj0FOWGOwVbEvMWMtwdwCledjsuSQWKhNM5JffHI4eT1N2AOJ1SRFj8ZucXLKBF/6Er8VEs03p+17vx2S7yY8Dw/z/QZh1KWqMowpMbqkqk7e5XirEquwM8/it/6AxuIlVBA5n6FxH50nmLI4cbk1H2yfWhYhLftk5QCEpNQpeTlmZ/wQKX0qkzm7mxclVsJZxGBqzMlhvPXxb3v+OeuYg7t3d9j9srYvkdBYbNK+2KF9qU280CCei4lmI8KZEL8V4DecGKQzl3TbIRCHkyhtpvioMi2dLtGwIB/kZL2MbDcl2UqYbE6YrI+p0srdAN+C0GWO1+zQvHjDeakVTk6gsXwVFTXR6QSv0cFvdrC6whvuUgx2kFIh/BAVNSgnQ1QYOxukwc6ptVaElBD4yNBVnOwBzInwFLLVoPnb3yX+zrsEl8+fyX4LIVDNhqPgn0VIiWzGhO9cJf7wDtHta/gXll79vtcMoSSq3UTduUF46xrlygbJTz9h8uNfUu28uEU4oxa5FL1LYXN2yhW2y6ckVZ8L4TtYa8htypy4xGPzK0p9yuMoBPgeMnDYt+c1kGQcEt25QfOHv0F469oUIPumIcMA2Yz52nr8wlV0vHPztP/gt4g+uIW/NH82H60UqtUk/vC28z7c2mX05z8i++IB1dbuoclNeJLG9UWKrSHZWh8ZeXjtGmgsBSr2nXG5sajYp+gleK2QYL7N+Iv9dk7ZO5pMRncu0/z+u6iuA0DbSmPS3Mk+DBNX5c1fD8Bu82LqCanLjNHmQzrLN4m7y6iwyeLt32G8+Yjh2t391q2QKBURx3NO57BMOFFlQwDCVX/VTNu13pcXkc0IGUUI34evMbcqHj5j8je/PPY5ayHPLSurhxPbvbXWlIxYZyHeTBd/tmZsW0vz5h2K7Q3y9VWKfN/ayBYl+d2HZ70rpw6vEdC+PsuFP7hBsjaiHGXotKqdKQ7vc9SYY37hXbKsT1lMyLM+49F/pomVDELiuWWEVAyffEHv3s8oRj1sVZ6qh22qcuq0fdIIvRbNcJ52tIynfKRQCATU5cCk2GWl92uev3hkqIiXO8x/9zKjB9skz/rkuwnCV4RzDaQnSTdGtQebo41KX+G3Qqy1ZJsjsOC1QvxOiE5LqknhTCGtA/4Rh+SVpP/zTaqxW11J70Clqv4B9svVtq6v7VWtzIGfvYSrqn9Kgy4NptDoQr81SRVAtr1GOeqjophw9hzluE/R25pWTNAaUxXsfvYTbFWic5cAj5/dByHQRUaVjGoPSZyQZfV6N2ARx05/aC+x8j2Caxdp/f5vEd64jJqbObsdr0P3BmfSBgyunKfx/Y+I3ruJmptxVYxvKoTAW5yj+TvfQTYikp9+Qn7/8bEvNWjGepcn2Wd0vXM0ZJsJfWfeW22R6CGXo/ffjOAR+MhGw4kDWifMKeOI9h/9FtGHt/EvLU+p3GcVJsspHj87tg34pqFmOsQf3abxg2/jn19Etr4eAWER+HiLc3T+8Q/xLyyR/PxT8nuP95MrIZCBtz92BvzZJqoRuuqUFOAr9nzvTFqglUCGHjO/cR2vHVENU3b/6i6mrA5VFL3ZDv6iM7PX/THJx/cY/+RzdG/k2pfavFJ5+4VhDbrvsEG6SBmu3SUfbU+ZgEXSJx/tUEz6U0agtQZjKmd9hY+nTiYloGY6BNcuEn90C295AdVpIoLAVfUOtma/xnhRYvWi+N3fDsgyy9/9zN03TVlS7Gyhfzlh35TWjb0tS8w3pL5+2rA4nNXg7jb9u1tOl0/bYzFWWdpjZ/sLB9LXBWV5OuLMW5VYYe2Uspn1Nxk9u4vO028EJNmOzjHXuoY2BdYatD08+ZoXgIGEkvjtiOa1WfLtMWl9UxFAtNjGbwWUo5wqKVCBR7jQpHGpiyk1+dYYBMTLHYJujN+O8Bo+owc7lMOMcLGF1wjwu028dkj2+Rbpxhj9NYDtXjek9FAqrG80JdYapPTRpqhxCQprXSvXrfJCpFRoXQKWZmuZohhTlW7C0To/VG00ReaSodw5p+ssoUqPAiRNedg36+Br9GsmUkf2tRG5CtI4ASkJ37lK47vvE33wzpTVdVZhra1xVkPMcPzG18DexOMtzL66tXXGIYQznRWLc8Qf3XH4ozRzCeNzk6G2JamZMKi26HiL+DLCFyECQWkyCpO6Bc8bVH9k4CObMXo4AivwFmeJv/Uu8bffw7947syTTpPl6N6QandwZrIAAHgeqtWg8f0Pib/9LuHNK07C4WuanIWUiDBAnl90VbL6fC+fbWAmiVvrBIpgvoUpNSYrCeZaCCXJJhlBt4lOnW6XN+MSLq8Z4c3E7l4hhWOWGnuk+COUnJZOiqcbpJ89Iv30gRMJPcPpwRpDlY0dvsZoVDpGlymmKg65cewlVlWZ4fkxnh/jB02qMuVF/on+5WWiO9eJ3r9JcPUCstM6s6roScNVTk8Xt97xGY3NNLESnof03HaruIEMY8CSrTzFnrKg8Y2GNlhtkZ6kca6F3woxhaa6X6Kf6wVqnVPkI6Ko6+YsoSiLkwPz36rESucpycZjgpkFR5uXHlL5WLPXmvr6EqxmOEc7WmJ98Bl5OUKbwzfAF7YC64ekkjWds/YnM65/G843ULGPzkpU5BEttujcXmTypEe6NgAErWtzBLMNhHLO2+W4QIUeMx8sk62PnDFy7B+LkxC+hwwdy8xkrl0qA7/ugetpy0W1Iqx1QM2zCil9gqBFELTQuiDPh1gMcTxHmuwghMLzY5ftW4NSIWHUQSBr01JDd/YGyWSDLBuipMdkskFVPbeNxqCLDL1zMkX3rytkDULXnsKb69L4zns0fuNDvNmjlarp+JelA3jrmllkbF1tcwbGeGqfxi8OAGSNodrapeoPD7UeXzd0f0j+1RMa3//QgZhfgKWy1tH7bVHLPtSyAU68cq/V7fbh4PZPwfkvwWgJpfAvLBF9cMvZymz3jlhzVLZC24pYtlBCoVC0vC5SKJTw8cSbt0ZF4KOaMZUQiE6L8J1rtP/oB6i5rgOmPz8eNcbOVvo51pmdVk6Fqo+j79WYov1rVQ/HThn6hODzE4WUqJkW0e1rNH/nuwRXzrtz6JiweySJosAU5VTKY2rfRF3pVgrhq9pCpQboP5+k1fvln19CxBFYy0T/kuLJqgN0ZyUiUPjdBuVOXQVKC6pegteK0EmOzkq8mQYy9BC+whaaYpBgspKiNzn2fNeTDDNKkHFAudGj3Oxh07OfxIX08OM2QWMGFcQIKVGBq0ZVecJ461F97IXDWFUpUiqkUIThDMZU6OcxYsrplMUf3aHx/Q8Ib14+8+1+WUzP4bI6lNgvzEvm517dd7x9y+PZyv4+ST/E68zgtdt43Tm8VgdrDMXWBvotTqxMZTCFk1KKl9rEwoHZR0966Oy5c65eyAZhG99vUhQjxuN90eJXxVuVWOWDbTZ//u+Z/+gfEM4sMHfnNxk/u0sx6aPz9NQ2NaeJrBwxzjYpdUpWjlwiddBf7AWrEJ2VlIOMclyQbY4oBi4psNpQpQUqObwiMYVm/HCH8YMd0lXHfAoXmoSzDaqkoBimmFKjYp9wtsnOT59SDDNaV2aPFQT0l2aILjuTzuTLZ9hSE15eQI8zyki9kSAAACAASURBVJ0hugaFNr91HasNo7/98kzGCyBuzBM35vG9mCztUZYJygtZWHiP9bVfoFRAs7XEaLSKUgFhOEMUzZJlPYpyMt0fP2gRBG3ixjzVaobW5QtXfX9/IVzFKgpRzQatP/ptog9vTzEfz4ctSnRvSLm6iR6MnC9dlmOK0ulD1bgbb76LtzjvJvV4v51gtaFY2UD3Tq5k/7Iw44RiZZ1ybRPZjF9YtbKVxozGlGtbVNs9qu0eeneASRJM6oQc0ZVLohoxXreNd27RMSEvLKFOwPwJLp/HZgXpLz6jqjWf9qKyJYGMeL/1Q1IzQduS5cAl4w3VIZQNPOHVVavXCxH4iGYMUhJ/eJvm734Xb2n+eOCw1ugko1rZoNrpo0djbFZg8wKLRfg+sh4HtTCLd24BFUdOQHPvI3oDytXNM22xyzgkvHmF7r/4x8hOax8Ic1wYg5kk5I9XKZ+uUW1sUw2GmLSAqjYbDkNUp4m3OEdweZnw2mV3br+koqI6LZq/+z30OHEA/WfrDD9+UmvLOdhB0XPef1Yb97cxDsuzOYTKeSVO7q7vJ+wvqMwWj9ZI7z6hvTSL6rZQra/HtcJvdDj37g8Jml3nBXiAWZ4Nt5hsP8FajZQKzwtRXkBRjqnKDN+LyIU6wleVzQbhras0vvcewdWzwWCeKoxxxInNXcqNnenD//yfRvx3/+2rr9dLFxT/6t8caGFLiQxD/O48KInJUkyeHdZ1ewtDZxXjZwOSjbryVHcwdX40rzDGYHSFEF79f81pCjtvVWLlxS1aV+7gNzoErVmi2XO0zt/AVDlGa7DmRFirYrTL1sf/8VRyC8ZWeCrgUvfbFDqhMq5VtRdJ3uPp7s+OMgNrDBMCWtfnMaWeyuXH59o0LsyAtfQ/W5+ucm3psE3WuPelq0OkUnit0LFwcoezStYGdG4tEi408WIfcfco1V/FIcGFecIL85Q7Q6r+BG+25fBFowQT+gRLM9iionoefCcEwlf48x1k6GPygnJn5PyolETFId5sE1sZqv4Ykx2mhxT5CN9vEIUzxPEceTECaxHSm5qUur8lQdBGSp/RcIWiGLkkTAUIIE/7GFPheZFj+EmF/vs0mj0uhKtY+ecX8Zfmid67gTc3c0iTyLH3th1zamsH3R9hJqkT0asOMJekcBWO2qZBxhGy1UDNzuCfW3B6QzNtyqfr6P7ZJFZYi00y0s/uo2baqE6rZslVVL0h5foW1YajL+veEFNPlibLXRJRVVCZqYjkngJ4GfjIh8/IPr+Pv+wSrPDdG45h+AKGk/B9vPku0Ue3yX591+ld1THS21RZTigblDbHWI1EIoUikDGeCFjN75GZ19fLEb6PNztDeOsq0Xs3CC6fP1RpM0VJtd2jePiM8tk61W5/Oh62rA4w0GzNjPQcsSEKkXGMmu3gLc7hLy/iX1ii3NilXNua4nPeOKQkeu8dGr/1LVS34ypLx1SzrbUUj1coHjylePisTgwnmDSrz8m6ioo7Hyvfo3iySv7lA5LZGcJrFwnfuUpw/dJU7+rQONZMxPjb72KzgmqnV7fmDjDniur4vw+QF05ihJ0/Xq+xWxZ/oUvju7cRnqJY2UYPJ/seg284qUvlETZnGW48IBtsHJL40UVawxQEnhfTaCzgeTG+36CQE8ajVfTzhAoB3twMrR9+D29p/mhVsca/mtGkPr9qwdhaImP63ZMUM6pB/aK2eAl8ZBS6cXledLjSZJ/dp1jZRO8MHJMuyRxxpA6lBP2+4c//IuNlRfF/+scRw+EBeEaWUg0HqGaLalQb0it1YtP1Q6GUI7l0Wshmw0EGvForz9h9fbVJgh6MHUv2NecGGSoa59rMf+s8KnJi3FVasvYfH1AMDx+3vXlob4EvxekYBW9dYtW58h5e1MSLW6gwJuw+x1yy9pWyB+n2Ctuf/DWcIrFSMkDJgNJkaFMdYRW+7Dt1WjC6v+3K4EpOESBFP0V6CpM7IKYpNUU/RecVOi33PpjJ0z6m1IRzDadv1UupJjmj+1tES22qSUE5zKgm+ZGbkPAVMgoQgYe/0HHg+MhHBnVLQklUu4FqRZiD+A4Bqh0Tv3N+ipUwaUE1SMBavE6T6Po59swz/XMzpPdWMcn+xXMQOxVGM3jjkKKcUFUZYdTF9xv4fgNXNncZv1QeygudaalwI1WWCVWVUpaOySROeRJ/U6G6HaJb1xBxiH9uARmFUwp/ubrpJqWHz8jvPaLa6Z9It2kanoeaaeMvL+BfWMKb65I/fIoenp3gnilK8ruPCC4uI1sNzDih2ulTbmxTrm5SrW9T7fQw4+REN0iLu770DpRP1ymerlNtOs+w8PZ1/D2l5ecnZCmQrQbxB7conqzBgcQqNwm5SRyz9QC9ViJpqBl8EZCYIaV5/Za2bMYEl5bxL54jvH4J1W5OFeR1b0DxbJ3i4TPy+48pVzZObdgs2y28hVmXhF9apni8Srm58/rg6oPhqWliH926diyuz1qLmSSUa1ukv/qS/IsHFE/XjrRdD71n748+VDi2a7W+RdUbYiuNf2kZ1WocPpb138GFc+g71yker5A/eHpyXaHThBCYsqLa6hNcWiK+fQWv26ZY2XITbpJh8/q+fcpxtpUm/fwRZpxCTVhK+2uM1u+/7F1YDAKBUiFK5lN4w6HNjiO8pTnC29eOyK/o0QS906fc2EX3h66qXToF88b33ke2GtOEuVzdJPv0vrsu9xZmoY9qNpDtJmrGnXMiDNyCxlpMklE8eEb25UOXlD13TW/vGH71Scm/+9OMPH/xmF27ohgMDybCJToZU/Z2qcZD53yx5yJwkhAC1e245HF2Bm++64SIW02nn1fDIlxipTFZres3GDr9qt0Buj9A90enaq17sU84GxMtNAm6ESr0KMd5DaU55px9A6jiW5VYqSAinF0C42xHTFLui3RZW6+AXf1un/bq9v4g9kMX2akz59BrIhDc3fgPlDo7mli9JKHLdxLW/uxLJ7lgHb6q6Kds/+1R5lPRP8oKyjZHjh0o9rwl3OOj+9uMHuwwFb17QZg0J727gtdt4c00DyVQVht0khPdPI8312byq0eAu3EG52eZ/+e/yfhXDym3hqg4dMyeOCS8ssjM733I5JNHyFZEdGWRamdEUfScbgzg+Q2UCjDWIFWAkAqjS9Jkl1brAmHUxlqLEIIs6xPHkrn5W5TFhMHgKVnWw9RJrNPb0vU4v53h1UmP6rTA99izVtDDMeO/+inpr7+kOlBqP1VUFXqnh97pkX167+spqVcV5dM18nuPsHlO8XiF7N5jVxV7TQ2gg2EGI9JP7qIH7vO82RmHwTrmtTIKCW5cOtI6dGIh0lU7kXXLz50TrxIGPWmoTovwnauo2RlE6E/vHSZJST+9x/gv/47i8eprq96b0ZhiNKZ4+PTMj6OMQuJv3SG4cfkl7dyKcmWT0Z/99f6kesqwlaZ4ska1M0DvDmj/o99B3rp2LFtS+B7BxXM0f/Bthwv8GhKr6OZF4m+9g7cwgwwC1Fyb4PISzd+4c2Cj7bQCdJox1+OUtf/1fycfp44hWOUI5aECJ9Gyv5C0WONKO2U5YTLW+J4zg66q/Nhuijc7g39hyeEaD85V2lA+WSf52aeMf/xLl4weuAbV3AzB9UtQj3d+7zGDf/nnR/ZLhD7+8iLRnes0fvAR3rkFB1D3FMHNyxQrm4h7xzNwHz6qmEwM6xuaF/gnA7C2rhmPD3+v1Ro9Hjl9QGupJpOTJTnKwSDCOzeJP7pDePv6ifXzrDGYwYj8/iPSX39B+uu7zoz8hBUsFXhYbdn+5QqN5TZ+O8LkldNnfF7btT6PZO24clr5prcmsVKNFkQ+o+ETksdfUQ52QQjCxWXy7Q1sURCeu4DwPEyeo5MxpizwZ2ZdGXs0oBz08LvzqEZjegGcNLJyjDYlV+a/T16OqXTBwZHOqzHb44e8LMM5XsH1uHiJQq/de75+zQlWX6aoKFZ2iG9dRDZDyvX9CoAtKoq1XQdaP3DiezNNvHaD9ME6yRfPKNYcq86kOdH1ZcIri8g4ILq6hIwDVCsmWJpFD1OqgbtRF/mQqsqYTDYda6tMMKai33/gbICkAmsoiglgKYuEZOI0xiqdY3TJxsbHjkmDZWf7LloXmLfUbNfrdtxEuaeEX5YUD54y+rMfUTxeOXVl46VxlirrUwCyxRpIP7lLfu8RplZ6f2VSdRpBaWMo17dJv/gKNd8lunPjEN5oGp5CtVvuphpH0+pex1tg3r/IjLeEEj4S58OWmQlr+T22y2cn3e0Xhmw42Yz9loMTMx396V+TfnLPsRXPqhV9lsmx76HmZoi/8x7+uYUXvqy4/4TJT39N9sUDd3zfIEyakX3xAG9xtvZ8u3Ls69RMi/DWVbzlBfQkwSYHvldMh/m1I3znIq0fvD9tgb1QDkMIJ9h6imEXnjxUiRMIlm7/Dvr6dw9Zo+XjXba/+jus0c6lQzr8mZASpXznRfrc8VZzM/jLh4+VLUqKh88Y/83PyX59D5seQ2owru0uqFnmStZA/+q5z6oo17fRwzHZ/cc0f+c7xB/ddlIO3Q6N73+ADDyG/+9fOa2uA9v34GHF02ccEcd8Pv6ff50euhxEEBDMLdJ85110lmKrEpOlJA/vO6zVi8L3CK44eZrg8nnUbC37clJRVCGQ7Sbhu+/gLS8SvnuTyV//jOLBkxMRfIqBwy97DR/lOyNzoWRNRjn82iBo0Wotu6qkUCjvdEzhtyaxslVJlYzJ+htM1h9S9rYRfoBoRuT9DUxZoOZnKTY23QD7AdV4gKFEhhHWVGS9DYzQBHLxVLpXAJXOyKsJUnj4KkKKw0OzN9kfblOJ6f8doNGr3RQ0UqhpJUZKVYPfBUp6BGGHspxQlqlLoYTC9xsoL5yWk5UK8PwGedp3bTQhnHnwcWEsOi0od0eEoU94eZFqWFfGrMXmpaMxHxyS2o7DFhV6kqEP4K+E5062fGWb/MkWpqiQvqLY7B+qhhlTYYxGVxkWCMIWnmxS5mNXfarMFKPgRx2kVBTZ2IlfeiF+2CJP+1Mgclm+emUtPIkMfbx2jCkrdFI4EO7XHEKIfSBvPZDZoxWSX3xO9uWDkyUoLwjVjvFmmsg4oFjvHToWJw1/oeMIE72jrUMVKsJOgB97jNcn2CKDMsMWBj/2sL7AlAYv9qY6ZsqX6NIQtgOCtk+6k1Gm1YkWDzYvKJ6skc09cBIAz2NAqMfT81DdNqrdpKoTgFh2iFWHfrXBjOdgAIkeEqv2kWvydUN4an9itpaqNyD99V3ST+9Rrm5gX1No8mAEIqYpOqR2QmkzNG++WPBmO4S3ruEtziOOUXi3WqOHE7IvH5B9/pVr6b5p1MD37POvkJ22E8D11BH8nPA8VKdNeOOyI20kjsHrR5KZcyHdcyErX4zJE3c/EAKUJxxT2ViUJ7HaorXT2ZsaQ9enm5lkVNv9N9+f43YxyZ0HI06QeLL9GC9ug7Xocj8ZOfi3q7Cb+v4vXtjNUJ0maraz/4C12Cwn/fhLiq+evnAxZrWpk/v6nlNj+WypDyfr1jqPyrxAD8dusWAszWaMbMT4ywvYsiJa2ST/8uEhMkySnGyO3Ng4fF+Tno8IAiwWGQQQRcggfDmBAghvXKHxvQ+J37+FPGA6bovSESCSxBFDtJ52n1AO6iIbMarVdIuLtmsZylbTXatKkX9276XfLQOFNZZynLt7nLZkuwnCk0cZgbg5vKxSsnyIH0xObKe3F29NYmWKnGoypBq7H51leJ6P9AOEHyCFQAYhRW8br9kimFuoWYICIZUDv4URwnM+WNIPHAD6hCW8Qk8YZy/2gcsrd5NyAGtX/lR1+wtr0VWO8gL2BEWlVPXBEHh+RJ4PEUjCsE0Yd8nSnnutqQiCNlE8i+fHJJNNyjLBUyGN1rkaa+J6+Umy/dIDnD/bRsUB7dt3SO+vuVVO6KNaEbIZIcoKb66NHqfYUmOKEhmH+POdGlxtqQZjh7XaHWGNIV/Zoeq7Mm/VnxwDNLVYBH7QJG4tIlVAfiDBNNppWzVai0gvRIgtdJURRB2CqF0LEfsYU5JNdnjVUlO1IqILczRuLFH2JqTPdsgeb5/oGJ9VWOPaRtnnX5H+6suTTWA1UUC1YmftUWn0JMNWGtmMCC7M4y92HB19lIKSeO14at2hkxy0dkDpRlhr/mhMWWErQ3RjGawl/WoNk5UuAa4tiKQSxN2Q1gXXOpKedKSJXkbcjTDaUqUVUTekylw7Nmj55MOCeDYknoswhRORPSmpQG/3KB4+xSSp86V7QYVBtlvOQ2zTtVB96TTRHqefcCl6F2M12+UzrkTvfy0mzCbLKZ6uM/6rn1GubU2TKoFEoQCBQR9qQSo8RF1JM2gs+/cYiXIMRtFmTi2zqzcwtjqbxGpxnviDWw6HchxYvSgpn6yQ33/yUk/L14ni2Qbe4lOq3/gAb7ZztMpQq9qHt65RPF2jfOYSqyBWzF+KufRem6RfMe4V08eVJ7CA0RblCXRpKXODLgxFZqgKM03k8werX5twsS0rTL2Y0UXKYO3usThPUx10/7D1wllTlSllkRy7mJeNGNneb9laYzDjxCnX77wkUax9OPdiT9rkpaEN+f3HCE/iX1wivHEZ2Yzxzy/S/O1vOXPw4fjYBaCU0GwKZruSKBJ72stkmWW3Z0hTe6ioZquKatBDNVuuUldVvPDeLSWyERN9cJv4Ox8gux1XDBjXhuWDEdXWLro3wIwTbOkEwYV0NlGy2UDNdvAX5pHdNqrjjOFVt0P87fewlaZ8suragseURYUSRAtNTKmpJgUYS7o1xq6/pPtkLUaXlMXE4YH5z7QV+HzIIMTrdAm68+g0oZqMsNU+60Moj2B+EXMAMOc1W/jdufpnHru7hclPtvofZ9tM8hfjY2xtvhc3F/H9BsZUxI0FwJlxal06QTkvxPMbGFMipYfvOeG43e27KBXSai+T5QMa0icI2mRpn9n5d/D8iKrKkMpn1H+KMSVCCLpzN/CDJkaXFKs/pSqSF/Z7i/VdvJmGA7QKkKFPeHGe1v/H3ps+x5WlZ36/c+6eOxI7N5BFVhVZS3f1PprulqZntFkeLyE5Yr7Y/gf80RH+kxwTYUfYMfbIIakljVpLq7tVXfvCIosrQOyJ3O96zvGHczMBkAABFFndxZaeiCqyConMm5n3nvuc933e53nrJaKry6A0apgw/OVtO0G428ephTR+67r92Thl7y/fJd/qIQOP9rdfJjjXtjf2OGPvz39pKyKPLXCO49For+D5VTsBGNZL77F9Ez0/bOJ6Ia4b0N+5i9EK1w2ZWbyBF1RJx3tsJYMyc+v4Ez4816b92zeY+Z3XGN9ap/OTT371xCrLSG/dJ711n2LrdK8tPAd/oUXtOy/j1ivkO32G790h3+5h0hw1SvDmSusGR+LUIurffRW3XUMNE8Yf3afojvDmmlTfWEEELkVnSLbVRfVG1L7+ErIS4M03SR7ukK3uTKtX2Sgnjwu80OXib50jnAkxWrN3t4df8a0pYqbwKz6OL/GqHmEroPegb135M32kh9rTP6O89G/q2FH+WuXoz6WMl5n+ntFToiKQgEKZAkd4OM+pYnUQ+fo2yc27ZPfXpjccgSQQEZGsIXFI9ZjYDFEUSBwqso4vQrRRjHSfnNTu3nEIRQVfhFREnWdSvz4OR+LOzxC8vHJszJFOUsbvfEKx+SVcD0pR7HVJb91Dfu1VHO9JGwbhOviXz+POzky1ZY4rKFLNYDdj8VqV5jBAOoL5lYgsVggpcH1JNlbkqaZINVmseHRzyGB3vxIdf/qA5Nazt4GPhpnqRrXKSfrHfX77XopCOEjHI0l6ZNmQLOtz1LologB5wBrCxKmdzjwqa/EgtD7cjpYS4bgnt+W1IV/fYfi3b+POzSCrEbISEr12zba5VzeP1NxFkeDN1z3+8PdDrl31iEKbH3j784L//P/FfHKzmFa41Hhsj2VuHhmE6CQh3do4VrwufI/g5cv4K+en1TudpaQ37zD62bukdx6UHm/7/nDWKQz774nvn+dZU+Zvf43oa68iwhCn1cBfOU94/SVbpR09ucmVnkP79UVUWhBvDu3k/ubQkqxjEEZt2nOvkMRdinxMmvaAvWMf/zi+UsSqGA0Zff4pKrYeR/neDr0P30bFY3SRU/S79u9T23wx9UISQlhX7vGQZGONot89kwus4bFW2TGQ0sELakjh2v1qkVIUCapICaM2nl9BCgctbPsvz8fk+RjPq+B6dqpmsrNx3JBqbQHHtaJxIRxcJ8T1Ioz28YMGWhVkqbUnsNWfwwc5qSipYYyOM+Lb62z9x78h2+ighglCCAZv3yb+fB0MFHtD8s4AnebkO332fvyOFSBibFuwN0ZnOdl6h70/e5vJlayzAjVMjtV8CSGRjmed1lWKdEMboGxc/LCBlHbSw/EiHN+STTeoolVBGvemlgsnVay82TrhyjxONbAj7s6vfoJQxynxhzcpzjDpZQpF0RsRf7aGvziDDD2iq8uoQbw/eFHCbVWJrp1DeI6d0tSG+ndeYfjeXetW3aoy+OVt8u0eOskxWU7RHUJnwOiD+6hhjDpgBBvUfSpzEVE7pMgUo62xdSB2ZJmhKqZ/V4VGdVOSboouNE7glAHfZ/+cTJaTb+3izrfhacTqgAarMDmZTiZnHVWnxYXwOpGslUTr+SJ78MjG60xJlSAQFZbdy6QmJjMxvgxJlF2wHVxCUUUgCWWNtnOOteIWuUmpyDpzznkSM8LDpyab9PTzqRy5C7O4C7PHBirr0jctu7/2XCdJD71Gb0j2+UPCV67AUfZtQiDDwE57tZuo3S5FpkmGBcmwwAsdWksBjivIYkXctzE3fuQQ9+1jpAOt5ZCN2yP0wbazKs1Zv2S4YY3W+esMtu5SZDFRY57m+esUWcxoZ5Xh9l3bfjO67DZU7druBoxGm09+JK6LPEBCdZrZ9t8JgxFTU+Hp81ht2XTA6SlQwzHZvTWK3S7OTMOavfoe/vlF8uV10iOI1b/7UchrN1zSFP7LT1Ly3BAEgrk5ye/9bsjsbM6f/3iyphhUMiZ+cI90Yx2jCtR4VFatnoTwfYKrK7jtlj3+omD803eIP/iU7P4ja6FwRKXp8Xdp0oz08/u29dntU/vt70zJVfDyFdJ7q3AEsTJKM14fUL8yQ/1KG1Nosm5CkeaopGDnnUdPkKw07dHr3sNoRVGkp5KpHMRXiliZPCPr7C9EajxCjfffkBrZfrQpcrLk6PbLwcd/GcjSYblb8Sny8bRapVVelo+tw3iWDiiKFK0ytFE4ToBbJKRpjzwbTfvzUrrooS2bT9p8EzF3Mt6jKGKUylBFWsb9HD7dVH88NQEFKDoDis7hvr0axqRHDIboOCO+9Wg/n+oASVDDhPGnq1aLZXiq8tRoRTLuoIoUhESrbCpcN8ZYolguRmDbpkIIktEuRR6jVUGRjeCkVo8UeDNVgqXWlxbbcRJ0ltkb2N3VM4nVZeTjNCq2BD4YI5wa3mzdClIfK807lQB/aQad5qh+bDVP9chyXCkw2pCu7lDs7r++GiboJCNd2zmS7OWjnP7agHSQoVKN40tqi9V9T8bSjy3eS0k6CXlS4PgOXlTGlpxSX3UQplDWyys7foMzdSsvMdY9lLHmmyPVReLgy5CB2iPVz+/aNkrbWJ21TYqNwxUKiUNFNABBYTIKM/G0E2WL0IrqI1Gj5SywWdxDCEEk6rj4FKZbTjXC86paeYtzuHPtY6en9Cim2Nq19gjPMzbn4GuMY7LVDft9TjQwBzBxb3daddzZGVSnR5boaeXJixyiuot0BONeTjpWCCFwfUEWa6SEsOaSjhRFfrqN7vOG44XUF6+R9HeQjkd98SqOX8ENa2BgtHN/6m0khCQIbH6h3RQ+SXomfnUTmLyw1hAnbMimiQcTuNZe4VTnU16gugOKnT2bHVnq8dyFWdz5WdLPnrwZ3HjVZbbt8Jd/nXD3XkGcGGpVyZXLDn/weyGDvuHPf1y+Jz9A+r6dPh30TzTuFp6Dd27RaqKyHNXpknx0i/T2/bNZ0hiD7g9thcsYwusvTacgvfNLSN87cmZYK8PoUR+35tsEEw1aadvdO+ZryPMRw0GBFBKtVfn9nh5fKWL1ImA82mI82jryZ2nSJay0iaJZer0H6OLZxo7Hwyd3QF8KnnaRn0KQrXXBsPtllen3IUMPr13Dn6ufuTX1vKCHY/L1rTPnvnmzDaKXz+E2KlarpktzSSlATGJtStNQUd6kJjYAw5jhO5+jBrFdPEZPVg7t8wlk4GHy0sByMs3Uz0j7GTs3y1K2gMpsRNSOELKwAuJy5HiwNqT3oE8eF/uLzlmmAg99WJa8PG13Llz3kGliv9gnOZ38ESPVI3LqjFWPXD+/MX5TFNa3a3vvUPvAYFDkpCbGxcMVAUPdQ6PLQRP7YTjCwxM+srSE8EWILwJ6eoe+7hCIiKpoPjddmLs4hzvbOvbnqj8kW9vkqXPzzwidZKjdro33KW1UjoJTr+LMtuC2bQP2tzP62yW53h9QfQJzlyK8ULL6yZBk+OsxCBbSwQ2rSNfD8UKqsxd59MGPqS1cJmzMT9cdm3vqEwQNisLmAx41FXgw4xAoo65OXlMfj5+RrmMJ0mnXPWOsbqk/mp43bruJ024c+XDfF2xtKf78x8mBPbTi5mc53/22j3eg++xEEW5zBukHpJuPbDHDiuWOfG4hHetTFfi2mnbnIcX27tlI1cG3lqSo7Q7Z3YfISmXqiYVzDJ3RhrQzZvfdR3Q/3UZIQT5I0fnx55jRikJ/8eGPfyFWzxlp0ifPxujiq5uZ9ELCkQTLM3gz1V9JAvxx0MMx+dbO2cfxyxuR06pa76SJF1ijituuU3nlvBWvD2LS1R1Gnz6k9vUruDN1is7ATmQ+Zfqx2BvgL7dp/ZuvEd/ZIFvvHKpkHj4W/HHdMgAAIABJREFUSLopa79YP+CtA0JCEStUpg7f+L5g5cAYY2/CT6l2TlIwpv9dEhUrCjekejx1YDdnFJA+9djygnxj60hNRmYSVoubeCKkLme47L3OvfwjwFCXbVzh09c7ZCYmFDXbRkdjMDg4pReXOLNb89Pgzs0cni57DHo4It/YPpWL+ReG1ugsswLjLD9yMhFshIu1Jjnih085l7obKYOdzA5T5I9916Lk97+CKpYxBjesY7QiTwaoLEEr9dgwnqIoEvJ8hMGG0R8lXjda282pnNgmOFMN7FOPIS8OWSuIwEfWK3Zo5ZTvQQ9jTLq/GRHlpOBR2NnVuA4sL0m2tjV5DmEAS4sOo5Gh0zngvJ6mYAzRpStIP7BtwDwj3Vy3OujHIYR9z47EZBn5buewWfUXgCkKa2acpjhS2jbpCfcFlSp0bjeQj3cJnjf+hVg9ZxhdoM5YNvwXnAzhCKKLc7Z99muqVoHVLxSbu2e+gRXdMfHnG+R7Q1tRKpT1fOoO7WSf0sh7ZcBsZ4gpCkZlBUuXk4JqGNsWQVag48PVm+T+FmoQIzwH1Rs94XfzOHShSXu/AvKv9AnEbOLZZtF05wlkha3s3pSwqOcVBXMApijIN3dQ48PDLRKJJ0KqsoWLTyAinDKb0ABSOFRlE4PGxZ8eeWYSUhPTkvO2moGHJ4JnTxEo40ucZt0aPx4DPU5Qnd5TSexzgdY27D0v7J33CMgwQNZs4sJZUGSa4/ajzcUQL3QYbCcUubFnjSPQyiAdqwFUhZlaOGCs1UOeKJvA5AlUppGOwPEljiNxPEGRacbdg4bKBemwQ3X2AkUyZLB5F5UnB6qVE0wiuxxc6eN5R2fuTVp6wiuJlVsmZZywhpksP+SUL6IQp1k/veeTMehJlFYJGfiH3N+//U2Pt75uS1Fvvu5RrQrOnXPY2NRkmaESCZaXHBoNweb2Qef1AhWPybsd2waU1rrn2K/bmOkahzFQuss/E8rYHrQpA6bzk/WuxkzlDNF8FaMNye7Yakhd2z0wStup7TLf0o18dDbZbJ7+mF9sYuVInEqAU/FxQt8mszvyC1U0in5sReCjY9oNZZslWGgiK76dvuiNKXrjQ5UE4Tk4UYBTC5C+u39MZr8MrLPCjsTHmR2jPyukQPouTjXEiTxEKeKe6I6MKqMA0hw1zqZj/c9kaS4FTugjIw8Z2Mgc4ZbvbSKoLINXdV5YR9vy/ZmnlFwfh3Al0veQoWfjeny3fK8Btdcv4M/v79qdSkBwbobqjfMnPq8ep+Q9+x0/C/Q4puh094cmpMANJFHTRxWGdJiDATeUdqAit15RQmh0b498tzRizRV5rPCrLmo4In+0Q9QKyAc5utC4oYNZW0drqxGQEoq4zM1KRrjSIBrWlLBIbNRHvtW1C9yXvBs7NUz5rzMsSDV3hrrTZjt7gHkOLuvHolAUO0/GDgkkLh4V0UAgUEbR17sU5Bg0qRmjTQtfhIBgZPooCjKTEOsBLTmPLyIEglSPKUyGfpZKm2NH1WU1sp5Bx8CkGbo/fDYXzlPAmPKm/7T2buCXIvvn97qt5ZD6vBW+j3s5ri8JGx55YicLBaCUxnGl1WilisZ8QJHZCkVQ8xjtpiDACx2kI/ACh7ifHyJWKk8Ybd+jOncRozXDrbuoIiNPRzjDvf2p9DILVascxwmQjs9RPXOT5+g0s6kW2GENWaucSJBMkh4yd5UVG9gupDz9Mv6YA/1EbjDBpYsu3/8tH2NASkGlIvjWN3y6PU1RgO9DqyW5e08xHB4gVlqjRkOSh/f2/59Sx5L6iWO6abes/CEInl0jWwZB24zC3Opdz9BFiBZr0/i4oB3hhB7SsfpV4Uh0oW3MXDMi2RujO/GZNKYvLrGSAqfiU3/jArUbF4guL+DP1XHqkb3pO2f74vb+4Sa7f/EBg48eHsl8ZegRXZzl3P/429RunEeNUzo/+ZTOTz5m/Nn69HHeTI3qq+eof32F8Hwbv13DaUTWNyrJKHoxyfoe8b0tRp+tM/xkFc4oCnYin2B5htobF6m+vER4vo3bqCADD8zELHRI8nCX0c1H9N65S9Ebn4ngHH7zAifyqby0SOXqItHKHP6Sbcs51QBZGr1NJgez3QHJaofRzUeMPn1Etjs43c1egFsLCS/OEl6aw19sESw2CRab+PMNnEow3fkBRJfnCS/OsvBff/PEpx5+tErnbz9h968+fKbMNpPm6MF4egPzIofWhRrXfmeJUSdl7Z1dVKFpX6rhhg6DzZiFV5tIVxJ3M/rrY1xfEvcydm73mbvaIO6mZKOCa7+zzMN/2mbczWiv1GgsVUiHOckgI6z7bN3sIh3J3NU68SAnbPg4rqBzb8BwOyGP1VeHVH1BaKMoTH6s4eLzgtEaPRg9oZNTFIxMn7gYIcsqlUFNydGe2qKvdstqmj3GicfVyPS5k38wNbw92CL8ohCehzPbOjIT8ND7STPU4JSxIs8Ik+dPvYkJz0WGE6LxfOBXXFrnIsKax/a9Ec2lkHPXGwz3MrJxUW5mHPJYsXl7QHctoXW+QmMxoNb2iRo+6zf7xL0cVZQeWQbS0eHKbpGO2HvwAXsPP7IVDm0zTnurH9MTYjpgZIymKGK63btUKvMct2s1cYYZxdCyY5QyDHDazeOd40uo0dh+nyWcRhV3eR7c01ZAJ+23A69T2hZM8Pc/Tbl1Oz/088cTmKQApQ+3AjEanSZk6WPRccdsoExh2+7uwiwi8HFnZxBH2HWcBcJ1cefbiDDAjGPrQXfarEKsBQMIpO8w++YSbuQjpMBrBBSxnRZEG5yKT+ejTZLds+mtXkhi5c3Vqb68ROt71mfJm6laE8zAs+aHZVnyLC0j6bv7USVHQCBKluziVMopi2YFJ/Cm1ZzW91+l/sZFwouz9piiABmUuwRjMCrEbVXxFxpEF2dxKgHDjx6e7gCFLSPX37xE/c1LVF9Zxm1VceshThTsV8YApxrgNiL8hQaVa0s0v3uN/vv3GX74kPju0cL7I+EI6m9covbqOaLL87gzNdxGhFMNbIXQd/c/7zJbya1HeO0a4flZajfOk2106b/3gMH790nXn+4DIqQkurpE67tXqb120YZJhx5O5FvSKMWh71Q4ZSSBf/JpLAP3VHlUJ8Eohcmy6TpapIr+xpjhdsJoN2HcSanOBtZN2pPMvlTHi1y6D4f01sbMv9LE8aRdg6Sg0g5QuSYbKxzPVluDukfzfJWd232iGZ+5lxoIR9C5P8BxJdX5iCxReIHE8Rx0YZ6veaIjbdugUcOpVaap8zLwp1EwwnNs7pczqRKX34U88N+Bjzvbwl2YPfVLpzomkBVmvfMkemhJVrlgFyZD8Zwm3oyx+ox6iL/QsM73oUfRHVLsjfAXWqgy9FxWfYTnouMMPUoBY41efQeTK4pBjFMJkKE/rYjKwENGHkXftm+F6+A2KxT9MSiNcB3SRx3ceoRTj9BJhlOvWO1IZ0gxiEEb2wasVw8J/I9C+MbLzDXrX3orUDgS7+KyNXV9ymNONLM8AOlAY87n27/fZu58wAd/2+X+RyOG3X3Sk40Lxt2cZJDTWAiozwW2+hRIhHDBgPQE0oGo7pHNKFpLtmo23EkZbKX0NxOCmkvY8Nm4OaA26xPWjwix1goeq5Y+bsxsjEapnCTpkueTdvKTn70eWRNM97xNEcCRyCjAaTVQveGxwdh6OLYVyMlnFAX2WpqfJc+KE4OuhbRBx4fax4U6VGnc62pGp3Rfz/P9x8moilOpIKQk7+5hsqcfi0kz0k/v4J1bIri2gn/5Ak67RbGzZ9fSM0KEAe58G2/lPLIa2fzTj2/ZyJ5TQnoOwUyEcARO5OGElmgVo4x8mNpJ7MAFYaZDRGfBC0es3FaF+msXmPn+depvreBUAivUG2dkWz3U2Frii7JN6DYi3Kb10JlObWhDutEl747QaY5OC0afPiLvDE/fLhPCVmsiH3+uTv3NFdo/vE7l5WW8hn09U+Y9GaVti8u1bTRqIRhjd3WneT0pcBsRtevnaX7vGvU3LhGea9vXMJME8FIkLATSc+x7r0X4C02iKwu4rQpuLURIQbK6i05PpwOrXFui+b1rVK4u2huq3N/SqDRHxZmdchGUrU8XL/LxWlVYbqEuzeHO1ACDUYpsq3/8iwkQvmtbm6UNgRqltj0rBV4jQkY+sty9qyS3TuXDky+odKt/yNvpC0NZx/rJZ6ALQzrISAYZ6SCnyBSVWasHka5tEQpHlFoZYduCDgR1j5mLVartgDy2rYuw4RM1fYpE4QW2XeF4EjdyMBpq8xFu4BDWy7I1VmcSNn2Sfk6RfrGbqggDGxMxIVLlP06tWhoMRsgwsN+L79mbvDshVc4BUiWtQ7RT/jn5+1mOBYEnQtreMokeo0w+vUT6xRZDdXqTvqeiFNa7zQpuI8KkBd58ozzvDG67itOI7PVU8cl3Bji1EG+mii6UrZ46DjrL8dp1RODaNacWTrUkE4Gs24hwaiE6zvFm68jQAwPZdg+nWcFfaJLvDPAXmnbybpBM2+vCkcha5cRNgbc499T8wF8pJufBKb94IQVR1eHqW3UuXa+weT9h/U4MB4zJ+1spRabJE0Wl6ZPFiuFuStT0GHdzjDbUZn36WymDnZRsXNBZHZeGowVFZkiGBZWWR1B16W8naGWs+e0XhCVXKUodTyxUf0Sx12OibBJSInwfd3neGoUeR6z6I9Re33p3lXE2TrNGeOMldJxQPHrKBllKRBTgLc0dCurWWX6oQptlkGX26nJdaDYl83MSz3vSt263o1lbK20mpMCJKvhzCwjHteJ1rVCjo1vRJs/JHj4iu/sAd7aJO9smfO0aFAXZw3XrmH4a4iIEslbBv3SO8I1XcWdn0MMR2f1V0rsPraj+lEh2R6i0QGeK0Vof6dmihEoKVFLg1XyipTpoO9Rj0zJOf668WMRKCipXF2n961eZ+cF1ALRSFN3RtO2VbvXRSYbwXcLlGSpXF6m8cg6n4k89mYzSDD96yOCDB2TbA4reiHxvTDFMTs1MhRS41QC3VaHy8jLL/+G38No1hCMphrHVUqU5OtsvKQrfLr7CdUge7ZGsdU71Wk7kU7m8wOIff5doZd4u3kZbnVaSUwwT8t2BfS0pcWsBXrtemmjailn99Yt4zSpuNWTrP/+SdLN7cktMmZKYVm20SqHQpfDaKEXWGaEG1j8JAW6jYglcPbIaKdcSvMbXVzBZjk5zOlsfH/tyRhuyrR7Dj1cp9g5roYQrqb12gfBcG9ksiVV/THx/m/Gdk6tw6UbXvudnFE0aZbOtHn8eldrIF+kKqnM2csQoTTYqcAKHykyA40uSQQYapCOZf6WJGzh4oUPUCgiqLtFMwHg3Ie5nNC9YAfBoJ0UIqC1E+JGD1gajIBtZfUljqcJwO7H6rtNiEhcR+LjzbfyVc/hXLuBdWMKdax/rlP5lwxUeobSvHcrDVZFcx8+ZWBU4C02E5zL64AHurL1+pe+i4gxvpmavNW0Y/vJz3Had4JVzqHE2/X2TFQQrC9OIInemiskVeWdgzXs7Q6JrSzhRwOijh9S+fhl/vomKM2Row82dWkS+M9jXLB48tRzH6pV+DUa4XxjCVve/qEvHUdhbi9lbm1SG7NoQVB0uvTXDzr0RKtece63Bw/d6jMrYnM7DA4MJ5cHsre7/fbjz5Q9vqN7QZhya0k28nJALVpbJVzdQx8TaqMHIWrqMU0QUgOsgKxHV77xJsbuH6vZtePPj65kQyGqItzSHd2ERp3mAWI2TY6s67bbk+ssuX/+aT7M1yb+1+elFAW+/k02Jlc5SjNb484v2WMdjTJYSJwlGH/GZKo3a65F8chtZq1L9fpvqd76OjOzGJV/fsjE2+ojqUDk2LMrJP//SOSrfepPwzesI1yW9t0py+x56MLKbj1N2Jfp3u0+SwAMnbPV8g6BdKeuWtlihfiOJVVkRaf3WK1Sv74uVkwc79H7xObt/+YGtnuRq6rHS9xz8+QbV6+dY+PffJlhqTVtqKs5IN7qM72ztu9yepYwuBU4tpPntqwgp8ecbFMOU8ecbDN69x/j+tiUdue3/S9/FbVUIllpUry2RrHWIH5zOlbl6/Rxzv/c1S6oqga3kDBL2/u5TBh8+sBWorJyQEGW5vlWh/uYlGt96ieor58AR+AsNmv/qGsmjDv1f3iXdODnUdPjxKv5CE7cWMrq1zvjOJvH9bdK1PVRSVqtKgiZcgduqUrmyQPtHbxBdmsOJrOC28vIy2c6A7k9v2c/kKFKnDcnqLvl2n753+AKRoW0Huo3KtAKZdYYM3n/A7n/56MT3YXJlq3TPutIfURY2Glbf2bFj4pnmzt9t7u/4xKRSyrRaOoGQAiHttJLWht7aiGxcoAvN7t3BtDo49WIUwu6ehCBPlO14O9KGi8ZnmER1HdzZFuGNq4TXr+ItzdmqiO/vV6R+TdjJ1+gVR18XuXl+PlZWPKUxSY6sR9S+fhm3aYmc8G3YrXCkXRsKNW21Gm3QaW5DwI2h2ByTbfXsxs0YsrUOTjUEZdMKjDH2vBOC2hsrCN+zFVYDlVfO2U2LY88DneWIwCO40EaNEmu0K2yUx6/LEPerjCxWPHivS5FaK4R7b++RjY/Rfh1lH/IrsG5QnS7FxjZG62nVUfiebYd9dPv4Xyw1gOm9VfwrF3BqNqrMXZ6j9oNv4dQqjH/xIWo4PuQT5zRqBDdeovaDb+LOzxzyMlHdPqp7dMfg3/9XEa++4jIeG157zWNzU9HraV677nHn3mNri9KoYZ/xnVuYIi+HsnKemssrBMVul+zz+7gLs/gXloneeg3/pYuoTs+Sxd4AEyd24hQzrdTJqs0FdOfadjq2XkU4DvnGNmpnDxmGVL71hiVV4nQpEfHHt1A7j23SDpwP8daQrG83tCpTqPQFDWE+CTL0CZZbduS+VbGCUKUZfviQ7s9ukaweXf1RoxQ1zqi+vIz03dJcEvz5Bm6r+lRvoKdBOBJ/toZTDTC5Itvu0/3HWww/XiV+sEO+Z9uM0xtpqcNK1jok93coRgl554QJNQHBYovadSvQn2i7koe7dP/hJv33H5A83LGxJ48tEtlWj2KQUAwShOsQnGvjVgP82TrN714j2x2UFZynH0J8Z4uOMYxvrZOud8m2++SdIXlvfHSY51affHcIQjDzW69Q/9oKAG7dtiWD5RbpRhedHF1dMWlBcUSbUoaeJc4HXtPkqqzWfTkRHmdBOtw/5uQL2hjk44Pv+5QX8lnKAo7EbbcIrl4iuHoJ79Iy3uKc1VCdQRPzZaIwKcXzJFAnINvcoxjENmmj1EdNyNRkwtiGbqfk2327hiy30bGddvXmGmTrnWn7Tw0SROCWzu428DXvDKy25WAcSbn65zt9+5i9IcUosfFSebGvhSntFn5dhrhfZRj92HU3+OrZ3Ogko+gOKDZ37VRfaFvI7kIbb36WtF49MrsPrOlr8tEt3AVbQRalbtG/tIwMfLyleYpuH5NkoDXC921Y8fkF/Mvnbfv+wHlTbOxQbBydh7u85GAM/PRnKRcuOHx+p+Dd93I++bTgtRseM639iqnRCjUek248KjeOZYXrmMKErFWofv/buO2WJUjt1lSzKasR7kwTb2kenZSkSqlpdQ9pHedlFNp1ynPtZGRRIGtVgmsreBeWppIE234++VqZkLLjoHONzr/4OvTCECunElgBdbOC9F2bvTZIGN3aYPz58Q7lOslJN7qMbj4iWGxZYgUE52YIlme+8PEIKXEbFZyqJt3s0f35bXb/+iPiBztHT2ZpY/VA45Rs/eRK0eQ1Ki8vU31leWozkHeGjD5dY+fP3yfvjo6d9NNpQXxvGxVnuDNV2pGPW7Ui99qN8ww/XmX40eqJ+qRsxxKp/rv37Ul/wk1cpwXpoz26/3gLr1Wl+uq5aQvUqYUE59qWdCa5rUK6EunaqstZdwX/Ak5NqkTg47abBDeuUfnGawRXLyGjo32InniJstJmisLGbByo8BpdVnrLSt6hcr48aBVwuimgQFRwhMtY7++sBYJI1slN+nyrVkC+O4Dtp+j+DkAn1mbAnalZEqYNbqtqtZ2DmEle7MGFXboCM4zJBjGiHOeGsoqpja1WCVC5hu7Ebf+xFxaHvb5eVDiuoN52qbc9wqol8nmqyRON68sTuaMfSRptj0rDwY8c6yyiDMlQMewW9HfzY4smUkJ91qPWcgmrDo4n0AqyRDPuF/R2cvL0sOdaVHNoLXi4vmTULcgSTWvRJ4gksvTQGvcV/d2ccb84WmWgNbo/Ir39wJKDMLB2OWGAbNZwnkasBiPiT+4QvvmqFaKX4dtOw/6ef/UiejCynnZaI6LwyGvNKI1JU/LVTfJjAro9D/b2NL/4p4wf/U7I3XuKv/uHFCnhf/tfG7QOECvrIZWj9URbGyKD413hZbVC7be/Z6cCH49BkhJROd649DgI18WdaeA+xTT3aRjUjh++eB54cYhV5BOebyMDe8g6VySP9p5KLqbQmmS1Y8XpJbxWFa/17DoSnRUk97fZ+D/+0ZKU5znu7gjqb14kXNkXpY5ubzB4/wHZTv9UN9WiO2bvJx9TfWWZ8MKsFd3XQsLzbcILbUY315+uOyo1aWd9X9lWj+TRHllnSDDfsBoB38Wbq0+rI8IRBM2AoBGg0oLB2q+/8vSbCnehTeXbb1D7wbfPZjQIpZ4oK1sJA9RgZHeXcYqOU5sdlxW2LVBWW0xeIMOA4NUrhDeuIk85Gdj2z1NzWtwevz11WneFz8XwBp18ne38wRd5+88NapjQ/8Ut3EYFDIw/W5tWlwTg+M6hm4dfcVG5nvqZqVyVGwqHfJTjVV0cTxLvpZZcHXEpmtL04UWGkFBpOLz1oxm+9fttLt2oghB0HqXceW/Ah3/Xw3GPZ1ZSwuy5gO/8wSzXv9dg8XJIWHEY9QvufjDi/Z/s8fM/3aHIzBPkSghrFvrmD1u88YMWV96sUp/xSMaKjXsJN3/R56f/aYedtYSD3s7nrkb88H9YYGbR58O/77F1P+Hf/IcFzl2LCKsO8UDx2dsDfv6nO3z68z5FdvR3pAYjkvduEqycg3YTtEZNgoefUik2sSVD2d013HYTuTz/xBuTjRqyXuqojvn4TJaRrW6SrW6gOr1jP18prTwhCARRZMlWkoDnCnxfHHqwdD1kZO+ffnsOrz3L4KP30Co+8vnFWdab3wC8MMRKuBKnGk6/IDsxlpzKm8kYUOPUirtLTEwunxXJ6i7DTx+hhslzTV8XrsRrVGyMS3OfACYPd4nv75x6ndV5QbrZo+iOUUmOE9rdjDdbIzg3w+jWxpl9tE4FY0Oei94Yf7ZeOiVLnMjf9xjT1vSycaFO0KxTmYvo3O6Sj76cENl/rvAuLFH5xutUv/t1nHqtnNg6bhU26HFMvrFDvrljg313uujRGJNlVrBdKDt+rrQ957W2lRhtrG6prFo5jRru4mypmTgdyjCYx/6fxJcRrng275vnAmNsu3rSNjyw/vh1n+WvzyEn7UNlQ63d0MGvedaOJHSmxrFFUuCFLnlcEHeO1pUZY6x6+DiPIGMwSUryyeeM3zl+MORXCq1R3cE+4RRQn/H43f9piVe/0yCqOXzysz7jXoF0BfVZjx/88QIXr1ds1egIvP79Fm/92xle/kad7bWED37Spcg0lYbL7PmAf/3fzrNwIeDv/q9tth8ermouvxTx3T+a5ZVvNdDa8Nk/DYiHirDq0Jr3+dbvtmnOevziz3b55B/3N6xCWjuHufMB3/nDWXYfpfQ7Obs/SfEjSWvB5+pbNfxQUmu5/PxPd4+U6epxTPr5A9LPH1J0euSrm6T31yjWdyiOITpTKM34Fx/YQZNaBVmNptfv9Ap+SqVPpxn5+jaDv/xH8qdMEn5+p0AbCALBhx/lvPm6z7/6bsB4bJifkzx4uH8NO5UafnsOf34BnaU41TpOpXp8QHicMv7lBzhfcpXoLFC7z2kI5hi8MMTqSOgz7OSO9KJ49vJ6ttElvrf13DO6ZOjhLzbxmtb4c9KOybb7ZDsnXIwHoQ06tpODOsmmxMqtR1PC82XthXVpAzF1K5YCeYReJGpHVBcrSClwvP7zcir6F5QTSOGNq0RvvIy3NH/sQ41SdhJpY4f80aZtG2x3ULt7FHt967dzxolKIW022ElDIRKXUNaoOk2a7hyRrLMcXC0rVgIXz6bMP8eswGdCORH4ONzAoXXRVg9UpsnjguF2TBj61BcrGGOQrkTnmmyc49dcvMAl7qb2+j6mlWTS/Ok+ZcaQb+wwfvvDZ35r1q/PTkFNWrsTET+w/3fDdPmctn8p/zBmvz0MVBou51+u8Pr3W3i+4P7HI975qw7jgSKsOCxdCXnzhy2CinyCWDmuoDnncf27DS6/XqWzkfHxT/ts3ovJYk215fLG91tcfqPKN39vlrsfjhj1C8Y9ux7XZlxWXq/yzX/XZrBXcOf9IZ+/N2DUVUR1h5UbVd787RavfLvB3mbGo9sx/Z18eqpLYY/fGNhINB/9Q4/Bbo7jCuYvhnzvj2Y5dy0iS5p88rM+w26Byh/7rgqF6g4Yv/cp0vPIN3fIN3fhNHl5xpCtbSI/uImMAoJXLuPM7LcFj/01bdCDEdnDdZKPbpN8eufYliPAu+/lCAlJYvinX2YIAa++4uI48O77GR99fOBYjcEoG2ujU+v5ZLL02MKCSRLidz9+ZlPQ54kTCS32WghklUjaPNBucbzk6HG8MMTKFNrGoxyIEpGRfyrRrRBW/C4OVKh0VpyYp3YaZJ2hFYE/ZzhRQHh+FhGUJ6OxNz+E/ZlzSn3MBFbwd2B6JPJxa+GZRbHCdWyu0sScs3TznbY/yuk1sOTtsPBWHJ5uErZ1ErYCgrrPYHXwgjc89uEEDk7o4YRHX2I6U+SjzCasf0lvWnguzmyL6Guv4l+5cOzjTKHQwzHp7QeMf/HdS2wIAAAgAElEQVQ+6c07qN7zasuefH65wsVzmiz6V2i6i/gyxJcRBlNWrzTdYov0GdLmfxUwxqBzg1aWVKX9jOHGCCEgaofWcX8vJRvm5GlBpR3ieCeYuyptR+Sf6nRu1wgTPwf9mStxIg838OxpqbQdNS/bXNKT6OwAkQJMrm1IcaGP9PppzXu8/M06s8s+H/+0x9//39vc/Hl/Sl7qMy7Vhktr/kmy4AWSlTeqrLxhqx1/+b9vcPf94SHz0FGvQDjw23+ywOXXq+yupYx79lxZXAl56Ws1Fi6H/MN/WuWdv+qweW9fV7p5LyFLNb/3Py9x8XqV89cqDPf6qGJStgLXF2zci3n7L3b54G+7TLxCo9oQP5R8749muXS9ytLlkNXPxox6R39XyXs3T/stHEZekN68h+oOqWUZwbUVK4R3J4JtUW6QDShTmhjnZPfWGP/iQ+L3PkWPjm7RTfDeB/vEaW9P8+BhQXtGEoWSh2sF/f7+OariETqNSTbWQGukHyDDEH2MUajJctJb977Ye/81QuJQd2ZZDFZQRv1mEiud5mRbfWsrgL1pBMszOBNy8LTdtJQES63pmD5A0RuT9599odZxjho+/ykm4Tt47WppvU9pE+Gy9CffY/4P3zrz87ktGz8zfX7P2SdtpzogaxkRLM/YqJ6FJl67ilsrTTsjD+k5JfGyxpFuPcRtHj9xZrQhH+ckvRSv6uGG7m/M8FP79UWWv7/C4ncvHvnzvU+3ufv/fELv9i7F+Mup0TnNOtXvvIk723qqv0uxu0fy0S0GP/kFansPfYKr8/NGblLifIux6rLoX6HiNHiYfDrVWGEMBQWF/tUe11mR7KV89hcPykqPnZLKY8Xe/QGDjXEZ8mowWiNdSfiWh6h5SEfiVTzyUY5+rC0/qSQ+tSLuOja+xHUOjd5/EXjNiPq1eYwxuNUQt+KjUpt/aYy9cU90rhNfO5XkpDtD4rUu5ogSeG3G49zLEa4v2XqQ8PDT0aHlOks1dz8ccuVrNWYWDpMrP5RceaNGfcZjdz3l83cHJKPD5G13PWP7YYqQgoVLIc15j4clh1m6HLJ4OSKLNQ9vjuisH57YHfUKVm+OyGJNc85jYSXk1i/7qMf23J1HKWu3xof0W3mmufP+kGvfqHPhlQoXX62y+yg7llg9C0xeUGxs0/9//wZvaR7v4hL+yjKyXrPTf45Epxm6NyDf3CVb3aRY30J1B4cyB0+LwcAwHiukUE8W1iZDK5P/zFJ0nv9K4pReFLwwxKoYJYw/30T1x5iihXAkbjOynlCru8THmEQKzxKU2o3z1seqRLLWIT2lQefTYAOHn/+NUTjWXHOS7SSEsDYRcw14DgbLQsqymnTC43yXYKm1nxE4Z20q3FqwH9kzIVQTx21H7OsAnuK/I4TA8RxUpsiGGXmcnyno8qsMlRakvYR46+jKT7oXo9JiGtfyZUDWqoSvvYxs1I6MdzLGYMYx6e0HjH72HsX69hPZec8EIawv1kmBsxgUOUrn7OWbjFWfkeruE6sXBFoZku6T5E+lT/rgCEew+3kPd22EyjQqVUeeCyYvrBnkMTq1yfcqAh+nWnnmvECjNbrQhPM1ZOBiCoPJlY2V8l2EtDIFowzFMCHrxXiNEJ0VjO8fLb4PIklz1kPldopuPDj8WWhl6G3nJKMnCYnjCmaXfapNh6AS8d/9Lxee2EMHkWTpip0qq9Qdgmh/E1Fve7TmPYJI8sM/WeDNH7YO/a7rCWotl8asR55qqo3J8MGBFzEwHir6ncOTf1oZupsZ8aDA9QWtRR8veDaRtpRQqwjOLbk4jmB1vWB50aESSfLcsLY+JN3IMaMeYneDwvFwAg/HtWHsgckQgzHpRh8/S9BaYXzwXEmSaI6TO750xUUpw/0H9jtQar9IennFoShgde04jzAD5l8mug/ihSFWOs4tgXqwg79QhvIGHrXXLqDGKRisqV5WWDG2FMjAxWvXiV5aoPrqObx2zYre44zx5xvED48ePT0LjDZnsro/LYQU1qagJCYTjZXOiuei51LjtHQQP+YBUiA9l+ilBepvXKLxzStUry3aEX2BtY9IMkxa2GOKM0uKtC6PFZyqPw2HPopgSVcSzUU4vs2707l+VmP0rwzirRE7764TbxxNrJK9mGRnjM6/JPLguTiNGv6FJUR4fNs43+qQfn6f9Pb9ZwqnPhJS2NimM3hkjXWPRI9eOFJ1Vhhl6K2e4GOHrQqpntW4Ga2PJakyDHBmmqhR/EzESicFyWYfN/RgmJIPUmvZUg0sufIchJdZu5txRjFIcCOvrMQdff44riCIpBXsZ/qJqT1jIB3rJ7VJWAF5VHdxPUlUlbz2r5vHHvvqZ2P2NjPSeH99DCJnao9w5c0qWXL0WH93O2N7NWXcP5rgFpkhTw4fuDGQxooiN0hHEFQk8hnt4DxXcOGcx2uv+Chl2NpRrFzwWJx3UAoCX7C1kyN0lyUzYDwwmD4UhWFzW1Gfc3CkQIiCcysuAoc0N7gOrK4XdHv6yNPj9dc8snyfWB3EG697xLE5nlg9/h5EQN2dI1EDhBB4IrIDGzonMzGJ3j/vBQJXBHjCByFQpiCQEQ4eBk2mE1I9nmaETiKvAlmZDrMoCnKdkurxNBAdoObMAIKx6qMpyt+XeDLAFyHKFMR6MH1eR/hEsoYj3GmQui+DUo7wG2oQakNTc3o/v43bqEx9nao3zuMvNqm8vMzw41WyzR46zhC+Q3iuTfX6eZspGPogBWqcEd/dYvjxKsnDo83SvhKYJpFPFKJWMJtudK0h6DMi741Jt/rHtlCl5+LPN1j8779D/a3LVo81Qek+nTzYtZmLuwPUMEHFGSrNMZkdt6+8vEzze9cIz7URR6w4TujQvtqismBbtEUcIJ3fjF5gvD0i3h6x+8HG8Q/6EkmkU6vitJuWVD2lapjevk/2YP35kyrKqmgYnin8ujAZ8KTBqngseu6fTddBKfRgjBrHmCw/liQ7tQru4iz5xrb1GvuiLxfnxKtdkke9cmkwB4Tq4oDqwuBWA2pXF0h3hqS7pyCJHHPKly/xtE1VnmrW78T89X88XudiDPR3crYfHmh9CVAFDDo5P/k/tw//7DGM+wWdjexJgre/BD+JgxZjz+ESCkPBGzcCLiw7PHxUsLunyDJDGEgadUm1IrlzL6NalfzoBxU6e4peX7O2UbDTiVmYc2jUJLWq4A9+VCUKBXtdzWCk+fufx4zHOUn65IG+dsMljg1/9udPHtM33vLpdjV/+dena8U33Hm+2fhD7sXv4QiPBX8FR7gMig5b2X0eJh9PN06OcGl688y4SzjCY6j2OBdco+q0KEzOVnqPR+ktBmq3fLzHrH+eZf8qdXcWS5x67OSrrKe3SfT+Rval6BtI4fDZ6BckeoBG4YmAWe88c/4lhkWHu/G7AEjh0nBnWQnfpObOIJEkekiv2MEVHoU5WyX/xSFWANowvPkIWQkQrqT+5goy9HDrNqA4PN9GlxM0QojyZ+GUVOU7A4afrLH74w+I721/pa1hJs7Nk1620YYizuj8zccM3r//7M+fK4pRynFuucFSk8U//h6Va0vTWBpTaEafrjJ4/yGjW+u2Qpjk0yraRD+CNhhjbPxOcvw0mSk08V6CyjVu6OB4ct+K4TcFv6ZzTFZCnHqVSaTOccjXt4/NK3tWCNfBbTcRJ0wwnQTXE1x6OUArawjZbLvc/yxh0P3n035QOzYfTh4z2SnrVbyleZIPb53eiB+XKg0CIvbYQqHszh2XSFdQVtlGSIWxGZKTHrqUVZwzuruNUcbGVB2DPNOM+4q5CxI/3DfXnB67I6g2HPzwyWqcVoZBp0ApQ5Zobr8zRCtzbAu9yAxZsn9ejAeKZKyotV0efR5z74Mh+phNhCoMeXp0RcePJFHdeldNj1sKqk0XP5So3DDsFhRHVN3OgglP29hSdPY0Kxc8Ll3waDXtZ1OrClYuegSBIM8M3Z5ie1fT7WuadcnyosvinMNs28HzBIORZrtT0O1rCmWsN1XJj4IAKhVBGAgadYnvGZaX978D1xFUKoL5OUkSn/59WcMUSd2dpZOv88noH5BI2t4yTXeWLLhCJ3tEZuLp40NZJ5ARmY55kHyMNgUGKHQ2HVqROFPSNdJd1kafYTBUymliZXI6+TpDVUp8jjXWFWVFav9nM+4is/4FNIr78QekeoQnAub9FQJZITnj4MyLRaywhpfDj1cRUiJDn+jyPF6zgvCsWHoS2ojWVlgZZySru2Q7A1up+mSNwUcPj41U+arAFMqK4idjzkIgXEm22WN089GXXO0ICS/OUn/rsrV7cB1UkjP67BHdn96i/+5dW+07ocqh4uypx6mVIRvYxxSpDZ9VX1Zr7J8ZROAjK+GxPzel/YjqDVCjL2HaTkpEFNrppWckVkJaF2yMQSmozzi43pdPwCtVgetCnkGaGqQEzxfIUppYFJDEBs+HhUWHRlOys2Uz1vIMXBfqDUmaGuKxQWuQZU6s6wjCSJAk9mcA1ZpASshzSBNziMTkmzsUu91jLTMmbV8ZBVasfIqSnoOLh09ASETN6tzQgMEjxJAgUAREpMRP2KAYpcn7Jwuj46Fi51HKyutVmvM+7SWfnbX96ofjCZYuh9RaT96Oisywfifm0o0KQcWh3nbZvJccqcc6CnvrKZ2NlOWX7PMbA73ts6/9zVmP+QsBD2/uC9gdV7B8JaLR9sgSzdaDhHT8bOtXmhlu383ICxiONFluuPcgZ3O7QGkQGPICHEewtl6w01EMhppRbBiNtZ3gG2jS1LDX1YxjzV6v/LN7WGNVr0tWLjlcu+ry0mUXbeC/+aP9VqkQ4HmC4dDwYPXsVdBCZ4zUHrvZWtkS9JnxlpnzzjModskOmIlO2noD1aFXbJHr1BI04aJMXrYMfWa8cxQmYyddpVM8whhouAmhrNJyl0h1vE+szoCaM0PNadPJ19jNV0nUiFBWaboLeOJsE/jwAhIrgHxvxOj2OtGVebzZGk7ko0YpxTAGU3qt5DZtPusMSdY6NsPv7raNsHgBoLOCfG+InuippMCtBMiKtY0wR+TpPS947RrhxTn8ds1+lsbG8ez95BN6b98h2zydj5ZwbCL500b9jJlU4wqyfjqd+jz6wWd9J/98IRzH3tmPg5mMZWccq2h9BsgwwGnVcZqNU8fZHAtjb7DSsX/P0yfdtZ8nhIAgFMwvOFSqgjQ1bDxS+IGgNSOREvxAMBpo1h4qokhw/XWPl655/NPPUrJbBarQBJFg5SWXbkfzaFWRpoYgENTqgnpdUqkJtjZtq8f3BQtLDmEoiMeG9Uf28ZNzPl/fptjeszqmI6qQsl7FO7+AM9tCjcansl4QCBSKQuTUaKAoSE1CQsxkgF+XXmLP4vk36BSs3hzzxvebLF0JufaNGvFIoQpLVlvzPiuv12jO+xTZ4S92MjF47Rs1li5HfOv32rz9Fx12VpPSEsGSUccTuJ5k1C/I030d18a9hLVbMTe+1+SVb9UZ7uXceltPK0tC2Nghz5dkiSIZqamdwhQG5i6EXPlaje52brVWwtpEXPtmg+a8z3AvZ/WzMcnw2a6lODG8/f7h725tff85xdPaksDG9umruFEoOLfs8q1v+CwvO/g+SLm/GVPKkCTw13+T8PYvz05G+2qHYbFndU8GBmqPyKkz763gisc2WwJSPWY72+/GGECXongHF19GVGSD3XyV3fzRVDc11j06+SNeir5BRdZOPK7Hz2SBJJBVXOGxk6+S6HF5LcR0i+0vZEz8QhKrypUF2j+8zswPb+A2KySru+z++AOGn6xSDModlLETLpNAVf3/s/feP5YlV57fJyKue/7lS59Z3nQ127FJDsmZ4Q53Z2fWChAECJIgA6yEhQD9oD9HgAQIEvYHrVbA7mohaKGd0YyG5BhyOG3Zrrq8zUqfz18fEfohXpqqyqyqrK5qds3OKVSbZ+67NuLEOV+TFk4z6GuPp0lwHv6+HmcktzcerqwpSTDbJJhpvhBG41Hhtap7ZtUwUbkfpYyvPDiW4bGs+E5u4QiMjxcqps61SHZSrLHMvDFDNiwo0ydrrvxNPEMcKoh78H1cNfQlYKsAvNkpwrMnQX11KwshHPPLaIvGElUl8iWOXNWa4J3vBFSqgtaUYm5e8cd/ENPpKM5f8llfLTl52mNn27CzleAHgjASBJGgLC3GuGqTFIIz530265pez5Dnlk5HculNn/Ov+Vz5omBrw9BsSt7+ToDnCzrTknpD8sf/LmZjVZNP4GbF6gbF+ha2KJxu1WOzg0BWK1TeuIAZJxT3n4Dtm4Rr9RVofDx8QqpIPEpyIiqTdEpQoUrMs/kpHhbd9Zwr7w2clc3rNWpNj+Z0wHCnoNJQLJ2vcOaNGtWmYrD1cGJVpIbbn424+sGA9lzA3/5P55leCrnzxZidtQwhHKtv9mTE0vkKf/C/POD2ZyPSSeVo/U7CtQ8GnH+3zqUfNJmaDzj5eo312wlGQ1hVtGd9Tr1R4/Iv+rz3B1uM+g9ry5WFZflihd/2Z2nPBmytpChPsnS+wnd/v0OWaG59OmLjbkqevNyK+4sk96ytaX7aS/mr9zP+6T8x5Dn8H//y4eq1MTCODckxWoG7oU25lxgBaFuircaTAeKRG1jbYoKvPDykUPgydAxiW+4lVeCSr8KkKKGQE+C5feqcux+e8JFCYbEUJsVO9tliKW2OPiZwHV7BxMqfctIJze+fx5uqka32GHx8m95fXSffGr4Q0c8XFVHURqmQNO1hrUEIiRAKrVOk9PH9CoFfJ836lGWCOWBUZfKSfGtIvjkgWppC1dxKonp2juT8/EtNrGTgIaMDWfoErK6T/JkZiX6nRjDdwKs/GTxtLQT1ACFB+U+Wf9i1T3loP1+ALdFfx7BliS2esMqUYlLVUk/XgTtuKIm/PE/4+jnEk6pmzxjWMqkwuISlOMQP7kVGGApOn/NdNcN1qPE8QbUm8H24d7ukVpNoY4kqkiw1xGPLeGTYWDOkqWv7laV1VRXr8Djg2ke6hJ1tw8q9kkHf0GpLzl7wSVODP2lx+r6YEDkmQpxpRrmxTX7nAcHppcfaq0IIRCUievsSxcY2ZbePfYoopEaTEVPaHIlC4WHQ5GT02EK7ehZdNsh4/sVOmRu2H2T86b/e4K0ftVk8V+F7/6BDHhuyRBMPSt77w23e/p02jc4jBsITxuDnf9EnTy1v/qhFs+Pxzo/bLoE1u0LvljTWFLlBH1gsFJnl3pWY/++fr/HWj9pML4Vc+o0GF79bd+Lwk+/r0pJnhrK0DyVVu+uTe5dj7l8Zs3Shwpk3awRVxzbsbeR8+d6AT37WJU9eLVZzUUIxsgxHlp/+afZkSYXnCCkUQsi98ymFRAiJtvqxxMdO/hwVBoO2E2afkAjkHgBeIFHCx1iDxexvxz5e3RVCukTqgG3W7v44ZqC3J0q8t88cf3H4as1KQhAstKmcm6Ny0ok5ZQ92GF9dJXvwcr1/nifCoEUYtRBCkudDpPTwvCq6TFBeRBA0iMIWUvkkyTZZNmTvLtTGVa1ubRAtTVGZJFaV07PUX19m/OUDiu7ombwSHwslJ6PRETfyIfYaQop9pfWnjR5KUr2wQHRqBvkEEVJdGEZrY7xQIZSgGBWP6f3s7xOYrHyo6qjqkROIleKlVV6eNYTyEUJgrcFO1AWFVEgvwJoSozVY49iRByhujsFlXcXBC1F+4Fqvuau8WuO+tw/EfEolahI2Lxxx4Kj9FQLrKWS1goiCF6PaDSAl/sIswdmTBCcWXkjFyhhLf7tEqkmSlTsg88uKXYZaVHH/Hg+NS+YslAWs3NN0pkuqNYkfwGBgSRNLWUIQgpICo1zbz/cFjaZgqiMZ9DRCQBy71uLaA008tjRb7ncqFeF+b2QoikcA2sZSbmyTXb6BPz+NDfzH2oHC9whOLhK+dpZyp0929TaYo5X9LYaCnOIQFubB13KOLzD50O9YSMaaX/2kSzLQvPYbDeZOufFssF1w70rMJz/rkaWGpXMVtldzikeYayvXEka9ksFWwek3q8wshVQanvNjHGv6Gzlrt1O664+z+vqbBZ/8rMu4V3LmrTrLFyoOb4WriI37JWt3Uh5cjx/73V0jibuXx3z8J13e+dttZpZDdGnprRvuX425/Ms+9y7Hx0+qhHD6f2Hg9N4mSuriIBv8BYeJU3Tv8erj55eLF54UVlSdSNbIzBgQRLJGIEISM3iokvUs4apSCaXN8URAVbWItYOkBLJC3WuT25Tc7N+r2pYo4RHKKpkZYa3FFyE11UIdaO8ZNIXJMBjqqkNpC4zRKHwqsoEvgidW0w6LVyyxwql+Tzf2XioHyQuRH3gZoVRAFLbxvIhu9yZSetRqsy7DVgFS+UjhUZ/0NYoiwZj9KoPVhsHHt53a+WkHWg0X2jTeOUW20aP7Z18eqzUHgBSoSoApyiNxWiYvHfB89yuBh6pXnAm2r55cFZSOjdn+rUvU3zixP6gfMk6UScnmp4ebzz4a1lr0KMUc2Gd/qoY/3djHnP0al4t+tYlUHrrMKcZ9wKLCCmFzhjIdUyZDdJGjwipSeQipsAKKUQ+rS6TyiVozRO15jC6It1bAGsoswZSZS9ykxBqNLZ+OdzBZsae4bK09khnoTU+hWg3KF5RYCd+j9pvfJvrWeeQT9LOOE7qElVtfn+p6mli++DTn9Td9pqadMON4bElTu+fp99Ct5vJiWm3JD38U8Zd/ntLbNiwsKc6c95wtii/YXNd7AvgHvz8aGj79KOP1N30aTcl4ZBmNLNkjh1yubxP/6jLV772JrFUf0wcTQoCnqL7zOlhLubLudK1eoDn884Y1EA80H/+ky8c/OXwR/JP/3UkpiF0w3SOLuP5mwYd/vMOHf3z8ar0u4Or7Q66+f0yMrXBkg2RYcv2jIdc/enEYXeF5qFYdf3EWNTOFbFSRYeASrZdkQZHdvMf4Lz567PV+/8WPnW1vnlgPGOkuAknTm6WimvSKtWMnKhZDZlKG5Q6hrDIXnGElvYzFUldtZv3TDPX2XrIFUNiUUFQn+9FDoKmqFh1/+RHfUUtqhhQmZT48S2rGEz2tKlP+AqGskpjjzbOvVmLFRJDzwMNWvbBA0Ru7vzsjTJzvGYb+ukPrjDwfUpYpUirCoE4QNCiLGGsNRhdom1KWKVrnSOlN2oGT4zOW5M4Wo8/vEZ2YpnJ2DuErwoU2s3//XcKFKUZf3Ce+vk6+2XdtugPPh/AkshLit6sE8y2ipQ7hQhurDb33rjP65O6h+11sD10F8MC2VC1k+vfeQniS8ZcPDm0J+lM1qhcWaP/2JRpvncRrHC7G91xhDOnKDkV3/waXoU/t4gJz//i79H5+xbWCj2pVTnR4XnTyJYOIsDlDUJ/CGk0+6lHGA4TykX6IUB7Nk6+T7KySbK/SPHEJXWYI6RHUWvRuf4qQiqDeASHw61PoPEXINaL2EkXcp0zHVGaWyXob5KPuM2H4zWiE3u457aMnEAjCC6co17co1766WK43P0Pl268Tvfmas9F5RSNLLbeuF2xuaILAJXa9bU0aW+7eLskyy9XLBUo5xlRZwuXPCh7c15SlZWfbkGfus//6Xzh9p/HIMB45nNX2lkEI9ijsSWy5frVgfVXj+Y6FOB48rmJuyxLdHTD+4DOq332L4OTCofsvaxWi188j/rP/gNGfv09+5wE2fgm4RSGcs8XiHP7SHLJRZ/yXH2MGj05CzolBBuGksmyRQYjOM7AWISWmyJF+gFA+1mjCRgddpOTDLtLzMWWxVwl+lUNEAd50m/D1c4Snl/FmpxCV0FWs1MGK1csJq/WhiVWrKXj7rYC/9aMAAfzkZxnvfZBTqwnOn/OIx5Yr1453/mM9oCKbfKv2IzzhY7EMyi3W81tk5vj3o0XzILtKx1+k6c3yRv13sBOCxVDvsJHdZqj3k/at/B4WQ8dfpO3NoW1BYQs287vUvamHtt0tHCZxLjjDucq7GDSlKRiWOxh1/Pvu1UqsLBSbA8pu7JInKQjnWjTfPYMMfcp+7HSsHk2sdoHsE/mFcpCQb/QpumNMcrzM+TiRZn20LjCmoNRu+SliD0+FKCUpdUaadCnLdIKx0jw6mupRyujyCqpRQYYewVwLVQ2JTk0jQ2c3U3tt0SWVWTlxnneO9DL0kJUQrx7hd+oEs028dpVic8D46oMj97voxc7yZ7WLP9NARQEy9Gl++zRCCqLlDkVvjN3zbVSoWkg436Jydo76mycRUpCv99FJTuXM7FfGQllt9/apHCSoeoRQzgNy6keX8JoV8o0+ejxxWRfSmUV7Chl45JsD0vvbL75lPHF6l36IVB7WGrL+Jiqs4ldbeJUGYWuOIhkh1QZ+rY3urmF0jvQCpBfgVxoE9Tbx1n3KLHbLeyy6SFFBhF9tELXmyIc7rjX4LLuVF+jhGL3VRc1MHSksGZxcpLx4mmJtk2Jt8/hec1IgopDgxCLhpbNU37mEvzDzwqpVv44QvodabJMC47SgTApsqMlDH+P7BJ2MwlfoQBE2BWpckGpDFkvCRkB0WuGlmryfspn56KxEZyWqKZHVgNJayrSkshwgA2ehorOSblogtcSr+HhTOWaQotMDg7q1mHFM8skV1FQL1aqjmo+zoITv4U23ndyGtWQLMxT31yjWtzFx8vx+gp5CViJko4Zq1FDNOmqqhT8/jeq0QQiSX11+LLFSYYRfayGUR5k6y52g0XnouHSRo/wQazVZbxMVVvCqDbxKAxVVybrrZP1tXiq47iWH6rQITi8RvX6W4OxJ/IUZpzX3EhOpR0PWq4e+/u63A95606dZl1y44HH1Wsl7H7hi57ff8en3j59YjXWf1IwIbRUlPDITMyx3iPVgDwdlrCHWA7bye3sYqqPCYhnrnmOz2pJI1kFAblLGusdQ71DY/TLvUHchh9KWeCLAWE1mYka6yzSoABEAACAASURBVEh3Jy1KF6kZ0y3WJwzBKgJBYTOGustQV47dunzFEitLes/Z2hS9MX67hqwG1C4uUru4eMR3XCvEFpoyTim7Y9IHXUaf32d89QHpvW1nifMSMDpZ1ifL9kuTeT4kTbs0m6fwbIWyjBnH62idY58wYCR3tzBZgdes0HjnNNHSFDIKCBbahItT2O+f3wOY20I7RlLgEoqHDJCtxRSaYnPwxOM1SU72YIfBR7dpfucMYqGN9BXRiWmC2SbN750je9DdsxJS1cCZMk/VkKGPyUuSWxuMr61SjlLCxfZXB5lbS7E1JL65QXx7g9rFRWTo4TUq1F5fonphAT1MKIcJJisQSk0SywBVCxl8cJPtP/mMbK33Qq+1KQvycZ9oagFVbRAItzr3ohpBYwovaiCDaN+KRAjycQ+dp/hV5+EnvQAhJGUypEyGk5afId1ZpTK9TDS1gFAKq4+xardgxjHZzXtE1ejQREcIgTfdJrp0DjNOiN//DN0fYPLiyRIMUiJ8D+F7yGoFb3aK2g9c+8+b3Z8wrTbYLHfWTEo9UXbjmxRe1WfmO8uUSUHeTSjGOfkgJexUiTpV0q0xft0lRaYwlGNn5yQ8gV8LiGZqFOOC4a0dqosNsp2ErBsTtit49QBrLDotqczW8GohQkA+zMj7CTJQhK0K6eaYwa0dkvThJMUWJfnNe6TzM3idFtGlc6DUY1UO4SlUvUr9t94lPHeC9Npt0k+vUm7sODmGsnSsULvb3nTSBWIXVDRxfnDen8pVnKoVVKeFvzSHvzg7qVTNu1Z8qSnXtw4lK6igQtieQwiJNRZT5gSNaVRUcfdpMalceT5lMiLdXnXV20abKKzihVVMkZEPu9+YbsSxQgiE7xFeOEX1B+9Qffd1Z5z9DXoefvj9gKm25C//Kuf8eY8gEGgN47Hlwnmfbvf45z0zYzbzu3tCoIeFQTPWPcb62USKDZqB3mKgD1TYhbNHwxcILSfWT4LCpOyYNbr52p5oOMJ1vfp2HelJvKqPzjXWGlI5ZE3FzsN18vmjbJqeFq9WYgUUg5jBr26j6iGz//A7qPozrIwFiEDhe1W8WkS4MEXjzZMkt9bp/vwq3Z9foex9PTgtrXP6/TsToLN9uPV3VBhLvjFg7V/9JcmdLdq/dZHmu2eRwYHJateXLZxsa3ITPRpWG4ruGB0/uVKXbQxY/zd/BVLQ8j3CeefRJXyPoFPHa1b322rCAduFkuhxSnJnk43/633SlR3CE50X4m24G6PP7iIA7z+JCBenUFXHjhKexGtVUY3oAK5rYgK9S+96CSG9gLAxhVSeS5CkGzBVUMELq46t9ZRtFMkQ4Xm0z71L0OhQjLrEG/fQRTY5BkXW33Ktk2OEHowYv/cp/okFvM7RrTl/aY56o4p/YoH0i+tk1+9QrG+5ysajrVNPIetVB1A/vUR49iTBmWVUo4YIHmaqmSQhvXyD4PQy/szUK5NYAWAsg+vblEnB9NsLjO5Z0E50uH6qjQo8xg8GbH+8wsz3ThBOVcgHKZvv3Wf63SW8igPHqtAD6TTd6qfbbH+yhvQl0+8susTTOCaZChTN89PY0lAMMyrzdZKt8ZEm3ulnV8EYvJkOaqp5tAirEHizHWqtBpW3X8cMR5TbPYq1TcxwjEky50NYFCCVM1T3PYTvI2sVV5lqN1CtBrJWdfgfb7JgU8olUk+5rLrIKJMRlekl/CLD5ClBY4oyHaPTMabMkX6ElC6B82tNgnobKX3K8YBisOOwiM9Yrf2mhaiE7hn7W98lfO3Msbwzv64IQ0FvYPjgo5z/6D98BMJh+UZrCHoVn2i6ihd5ZL2UfJC5hCkr3f0fKoqkQHoK6Ul0Vk4WQFWiToXBrS5l6l5rnGrRu7pNMc6RvqIYZs+VXL0yiZXwFV49ov7WSerfOkHt0hIydLtvS4PJD2kBwmTSdysGoSRSeRCAqviIC64SUPZjRpdXKHaOCQR/zjgIUH/WsNpQ9mKGn9yh2Bkx+vQe0ekZwvkWfqeB13StQjmhz9tSO4mEOKMcpRTbI/LNAdlql+T2JsndJ2NqbFGSbw3Y+clnZA+61F5bJFyawu/UUbUI6av99mqcUXTH5Os9kjtbjK+tEl9bQ8cZMvTRSYHXfDErzXKQMLq8wuq//AW11xapnJ4lXGjhNarIyN8TNLWF3pOIKPsJya0NB/R/wRgrqwuKeIA1rgVorcGUBVl/A52NQUiS7RXyURedpwzuXXY4KaMZrd2iTMdOxTsd4UU7JNsr6DylzMY4x3iLLjKSrfvo7HjJv0kz8rsPyO88QLUbRyZXwvdQrQbhxdN4U02iN85jRrGbdPPCtV/kZMINfWQUIetV1wpqN1DNuqtsHEicyp0e2fW7jP78fWqldoKhh7StvolhJw4AeqJ9J0OPynwDU2o3OPuO1WlKTZmUe8m79BX1U22CRrgn5GkKjV8NkEpiCkM0U3WYoly7FpCdSEcIgU4mBAwhyHoJZXL0OGHGMfmNuwz+4E+p/ea7BKcWkdVHJsTJ9XCJkKsumnoF1W7iLcxAXmCLCXxgAq3Yr1JJhO8jAh8ZBYgwdNs5guX5pKfKFBlZbxOdp5g8xejSMWWLHKMLhJBE00tuO8ZiypJ44577bllgrXEtxAPPbujVqQRtjNEkRQ8hJM1ogawckRYDCn18DE/oN4i8BtYakqLH5r2MP/s/N/n8533uffn8C2+v06b+49/AP7HgKsePLjAmx2UmTF5bls4W7Ll/8egww8OPQ2uLfoT/E0WChXmJ70NRfnMzKxkownaEVNJVhas+YSsk3UnIeillUlA/2cLkmnyYofOSsFOhttggbEcIIRjdH2BKjRf5TL0+iwocU339vRXyfnrs5OqVSKyErwjn2zTePknrBxeonptHNSpk633K3phymDpfPf2oM/mkYqEEKvCRlQCvUSGYdzglr1mlemmR5oOzFL34iYmV1Ro9Shh9cd9ZzUwiW9lxN6On9ts9xu4boUqJCH2YCJViDCjlJiNPYcYxZuKnJ4IA1W5iswyTpNjMVZVE4LtVTqnJNweUo5y8V5A86BPONQhmGqh6iIoCVL2KatYoe0P0cEw5SCj6MWUvIVvrk97fohzEe1Y5e2cqCpHVKrrvqLgyDBBRRHKvTzkqyHcSqhcX8Ts1VDVA+h4ohS0MepSQre6QrQ9IV3ZIbq1hUmfHk28PGbx/A3+6TtGLSe5vP8TsO27Y0pBvDih2RmQrO0SnZ4lOdPDbNVQlQHjKJVZ5iUlyyrFL+pI7m+Rbgxe+8rJGUyYjdBYfaKlAMe47duAjzKZk6/7ef2f5ARp7OiYf7hz4rEBFVQeIH+6QDbePD94tNWYwIr18wzGOqhUHkj2keieUwms38dpNwkl7yCSZq2QYg1DKfdf3nqhNZY1BD0Zk124Tf/AF6Zc38Zfm8ZfmX5nEyuSa8UqfYuSIMPGDwSSRMlhtKSbPf7oVY40l3RxTjHJMqVGBIh+4wbwYZsRrQ5eMWMt4pY+QTrxwdK+H9F3lYveS21K78UpK0q3Rk+1ijKXc6RO/9wki8LFFQXB62VWVniBxIX0ffP9rvRZWl3tt7t0o433Kv1AeKqxiihSdZ+g8pYiHT8RThV6dVmURYzTa5kihmKmfo5+sYq1GGzd2GmsQMCEG6QPaRwKlQiQKi6bUOYGqUgunnWQKhrg75MovYrQd8LwDh6hE+IszVL/9OrJWeSipsmWJiVPKnb5byIxizDhxya59nLzwIiK/vXLo6/fua6Y7kh98P2C6I7l4wePHfyvkzGmPJIGVB89eLcxNwkZ+h8SMnOr61xECVOQRdSpEnQpeJUBFzsKoGOdUZmtkvZSsnyJ9RWWm6qpcoYdclOSDjGyQuupXp4JX8TGlQQVP1lY8Kl6JxMprVmm+e4blf/JjZOR0fvKNPjs/+Yz++zdJ7m1js6dUgYSzaqlfWmbmH79L9fw8slVD+h6Nd04zvrrK6PN7R37dZCXpvR1W/tnPDtlBh2fA953qa1GgByM3IYUB/mwHk2QO25BmyCgkunQO2ayTXbtNsboB2uB12lS//w7F2gb57RXKjS1Q0oFFmw3MaIwejFBTLcI3L5F8cY345qYDKg9cNcZbnCW6dJ7syk3KnZ5LzoSj1Qvfx1jJYXeKN90hPHeK+MNPHb5hdhp/eYlyp4vNMpK1hDzZxIzvoOMEAajOFML3sFlO2evjtVuI6hT+giV/sIZNUoqtIXf/pz863gV/hrDaEN9YJ75xtNv91xlHmVkfq0J24LNCSsLmLKbIXSXsK1Dm44++AE/hL8zizU07LMKT2nLCva9qFeDZmJ27CxpbarJrdxj94mPSX30JTAyEt7tweum5j+HrjDLO2XxvfyxY+4vb+28eYpSw/cnq0e9PqlKPyY48TRj6maifBhOnjH76S8qtLvUff5/o0lnnrvsU8+0XGXuL2edsGVldEm8czlA+KpQMiPwmAIN0DSkU9WiWtByQFhUCL8NaS6FTVxHzG+RljDYOz6pUQD2cxpcVCpMxyjZd214IpPCphTP4qkpaDEiLAeYpwOqjwptp459cRLbqe9dj93yZOCW7eZ/xzz8iu3EP3R382qQx/uLnGb//exH//X9XZ2lRsbys+Hu/F1GtCv6H/3HEL//q2UleA73FR8M/PPzNp7Gzn0OsWGclWTehMu2A+fH6iDIpUYEi6yWMVgZU5+ukWzHpdkxltk7UqeLXAmypkYHCqwcgBZW5GulOTLoTk/cz8lGG1ce/qV+JxKrx9ilaP7jg1MClILm2RvfnV+n94uqzq63bSRvp6gOs0cz/x7+J33KMDH+64aQBnlNoUjXqVN5+HT0YIoIAGYUkn1x2YM+pJsL3CaZalJs7ZDfuuupW4LuV/y6ORToNmj0Nk8lE7cTjAlSjRvTGRZJPLu+1C1SrgTczjZCC5FdfYA7RIhKViODUEjIM3Gq2EpF88iW61z+SHeSAxhITx6hmA2wdk6YUmzvo/gAhBcHJEw5knRdgLeHpk9hSY8sSb3aGcmsHnXw1YcF/n8MaTdpbmzBanwGH96TQmuzqLfrGUP/R9whOL734ioWxFOtbJJ98SfLxZYqV/YS33O5Sbn3zBHyfK552GR59/9Hx5Fku4zEvtS012fU7e5XCytuXnDp75WgT7hcaxqC7fbJbdzHZy2NZPxRi7x8o6fBlpXHJVOjV8FSItYZhuoGSPp3aafrJKmnRR8mAE1PfJi2G5GVMIBUicxO6J0Ma0Ty9eAUvCJipn+N+92OSvLdX7TpOeNNt/IWZh180Bt0fMv6Lj4g/vEy51Z0YZ//6MGSra4Z/94cpn31RcHLZQ3lOyHZ7x3DzVsnW1lffN292Cm+mjaxFpFfvYgbjh99fmKb6nUuMf/k5eufZ/GgBTKZJNsaU42JvDp9MkehMYwrNzuUNdKbRuSHZHLP6i7soX+1p/BVJgS0NycYIUzgrPKPNw6zc4xzrc33rawzhSSqnZ6iem3MTPpA+6DL48BbZahdbPvvNbgtN2RszvrpGOXA9eCEEKvIRoWMuWfP4iZSNOv7cHOGpEyAEJk4ot7fJ7tzD5rnDqHTalNtu8hDNOrIS4c128GamMHmBqlUxw7FrVe1iG7IcPRjuVSNs7vrrOk4cLVoIVKOOajdR7Sb+wizZ9duYseuTm3GCqktEvYqsVA5NMGUY4C/Pg7ETjEyADAOMdCVwABGGroVYr6MadUQYIpt1hxsJXYVQ5DkmjjHj2LUJpcNgWF1ikhTVau6tzk2eH13B+Zt45jD5C0pMrUX3BmRf3gQg2tohOHMCf34GEfp7z9XzbNcWJWV3QLGyRnbjLumXNykebGDT/SRf7/SdppbWrjX+KoHYX4WwFjMckycpNk7R/SHB/WX8xTm82Q6qWX9h8hd24thgkhQ9GKF7ffRWl2Jti/z+mhu3vsYwVhN4Fay1pMUQY0uk9JD4WKuRE/+40KujZICvKgSqiidD8nKDOHdjtjHlRHHfUOiUtBigpI+nQnwZkUlvr714nHCL34c1k2yak356jeSza+R3Hhy7SiWkwqs1iabmyPvbFPHQMSthop2n3LhcOvyaE1x1i7WjCABJarm/olnf0Fy/XuJ5gqKw9PqG8jDt5YkYbbA4g2y4SpFJMld1MxZ/cQYE6GFMud13LhBKIioharqF8D2QE53FE3NgwZtuEizPklRDhGy7z0lB2R1i82KC9Qv25iS93UcPxq5jk2l0duDee6Tqm/cPjEcT+ZPDohi9mIXBNzuxkgIZ+PidOv5Ufa+EWuyMSO5uHSup2g1rrDM2nmCMdrcpEEeylrx2m+qb36L5uz9GKEWxsUnyxZcUG5voXZdUASZJEZnE1KqOMRMGiEqEVApTFJg8B2uwaeYwSJ7CFg6oiDF7uCqbFxMtJhzQtNNGNhsOq7WLj7EWO06c97znwYT+LnzfTZaVyN28SiKj0D0Yk9aBS3oOtJ1C3xm7AiJy1Hzp+5hJMmUnA6nN8r1VlcVOsDqu9VlsbiMDH1uUFDvdPXzYKxPWsb7KrR3y+7UjP6Z7gxcOgP9aQrtVcvzLX1GubRFtbBO9eRE11XLYq937R0qXIIsDz8MudmzX1FxrKDQ2z12V5PYKyceXye+sYEaPg2P1aEyxvkV+d9WxyQ6C3Le6L3wydjivMfkTjIjLzZ3HBHVf+Sj1xKx5k/TLm4TnTxK+do5geR411XKyFwcZfQev9W7sti2tdeOEmVzvCUbUlqXzLdzuU6xukN9dIb91H73Tf6Hs32cJi6HUKaHXwGKJ852H/FZdiMkf6djEMsRTEUneZ5xtMsoeJvGUJmeYrjPOtvBVhawYIaVLzvQh1j9PC1mvotr7TiHWGPQoJv7gc4r768/V+hOeT9iepXXhXYa3v8BqTaFLhOfj15pIP8RqTT7YRno+XlQDISizGJ0lhzo3SOnA6s2GIAgEUjpfy46UDIa7rgMHvqAUqlElOH8Cf67jkqjekPz+OpSG8LVTDgfYHSA8RX5/w81rByA7Igrw5jpEl067lpzvgZCoehUx1yE8uwxAsbaFiVNkvYKqhIgwdPI7rS7Z9XuHV7d+zc/1NzqxEkIgJ4DkvTAWW5jnNlsWnsLv1B/ysNtjFebHZ+sdjPDcqT2Ab3r5OsXK2gT3NAdFiS1cdWe/ldckevM10is3nKjeRAMpWF4EY8hu3EXWqshK9PAqfwIuDi+eQdQqjgH42QgRBnizHfylBQSCFCi3dshu3cdfnENWI2zurE7sAZ0iMxyTXr1BdvMOJi/cbz0qWmfsngmyaPp4nQ7F6hooiTc3S/L5l5PzZ90A+w2w0ThWaI0Zjen9qz9wCekRsQvKf5UjX1mj2Nxm/MtPCE4t4p9YwF+cxZtzgoWyGiHDYI8WbkuNzQrMOHZVim6fYn2b4v4qxcoGejSeUPaPeCaNJb18g/zuAxz4Z/8t+wKeu0fDJhnjv/yY5JMvj/6M1o4h9SomyU8LY9HdAcmvrpB+cQPZqOFNTxEsz+PNzzjx0HYDVau58dX3naejOZA85W6sMsMxuj9E94eUOz30ZpdifWsfZF2W+5pYX/thauK8S7u6jBQem6MbeNJHiIklzuReE0LiKacVZzFYa5DSdwbBh4Q9xCT4eUMEPiLal8KweYHuDVyy8BST7KeGMRjtNJikH1JdOL2XSPm1Fr3rH6OiKpWZZaTnk/U2SXfWyHqP24hVIsHbb/n8/t8NOXfWpxIJhiPXBvzDP0q5crUkTvbPiYx8/KU5TJyS31l12lFK4i/MYPOS+KOrmFFMcHqByrcvUm49rlPlz7QJTs6R3VhBdwcOj7Y8R3D+hOvm9Ifo/pjg5DyyElJu9yi3+5SbdwFB7bffptzqHqtt+HXFNzqxcuyuR2QUhEDVQrypmvMIPC4Nslmh/ZsXCWYnqwhjydd7lP2vvmou1jb3VuxmNMYkCSbLKTZ33EMwGO0NQPndFfR2F1vsm+XaOCX+5LITzRs7enF+98Ge8F524zblqsv8k19dnojqeW6SSDNEUVLcfYAZJdgkoewNMUlKsbKG7g8d0Lwonb3FwQll9zxPJrinnVE9jkmvXncsNYtLSuIX40kmAp/w3DnCM6fQwxHp1WuUm1/dbuXQ3wpDat96A6/dphwMGH34AWY4fvoXD4lgaZlweQkZRgw/+ACTfE3+lRNxvNaZNs1TLWoLdYJWiBd5CCkwhSEf5SSbMcOVAb3rO+Sj3LWM4pSsLCk2tlG1KrJW4cw/eo2F104gQ4+da102P12ne2UbtHYLg7zAZhk2TZl/Y4qlv39pci9ZBnf63P/ZHeLNGJ0/fC/4oaCxGHLid04RdSLi9TE3/9110lFG1I6YeesEM2/OIj1J/1aX6//2GrY0BM2QxnKD1tkpqnNVgmaIF3pYYymTgqyfMVod0bu5w3h15Ey8rcWmGTp9Qf6CQuAvdPBPzOMvzqCatT1mpC315BnbJL+7SrG2/diYFL1+hujN86h2/bGquBmMye6sEr/3ORiLatcI5qewpaHsDtHDmF3nCOF7SN9zLGLlnAXAYURtqdHjFH+6SdkdoUcJwhPYIscMLaU1UOaYfpdceeg432d4TiQW7KQqibETKRUnJaHaDfz5KaJzi3BxeW+scIu0jOz2A4p764dOnoeezijAX5gmOLWANzOFrFdcO3rymw7UvUJ+f+OJk6Y1miTv0aws4MkQowus8CgndinTtTP4qoIUisCrIoSk0Kkz41UhndoZGtE8xmq647svpUXtdMEOGP4mjgW4i019nrBlgclSJ8sy7qOz2FlrtWfdnCCEG5uFREjXcrPGELSmMbo8NLH6278T8sYbPiD44KPcGYoH0G5Lfu93I6amcv7kp/vPk81L9FaXYPkishJRbvXIVzYITs47ktRojBklYAyyEh3aDdqFppgkRY9iZN0RZWQlwpQaPU6cZMzKBqpWmXw2Qw9ihK+Qgf9EBuyvM77RiRXWotMCPUrRcYaqObZLuNim8fYphp/eRQ+Tp7cEBahqiD/doHZxgdYPLuBPN9yAVWjG19fIN58/67WlRg9GFGubjv104IHRSYbe7j32EOmtLnqr+xBg3hYFxd0HD7Vg9NaOQ0I9wpYwd5OHWzWT/Sg3dyg3HzYpNcVoryL2IlboNk3J768gqxXXGtqtwn3VEALVaBBdPEft3W9TbGxSrm+8tMRKej7RmTMES8vk62uMPvzg+TYkBH6nQ+XcBVS9zvizzzBp8mLOyRNCRR6VmQrtc1PMvDFL+3yH+nKDaCrCq/iTxEqTDTLi9TH92z22lhp0r20zWhmSD3MnEjkcU4J7tn67xky9QdAIMayzfeca47+49dhvB42AqZNLnP/HF5xOnBBs/GqdwZ0+2TB/LLEK6gGdS9Oc+fvnqC822L68yd0/ue1+sxUy8+YsZ//Befyaz9r7q9z6f29SWagzdbHD7NtzTL02TW2hTtiO8CLPCd3GJWkvYXRvwNYXLbY+32D78iZlUj4Xk+ewEKGPmmpRefcS0aXTBCfnUc3aZLKyYDQ2zYk/voruDSnWd3h0aaKmGoTnl/EXZ1wrbqIHJnyPcqMLvkf8/heARdUrhCdnnNWWZG8C2WuzSYHXqSOrTiur2OjhTTcxSYbJS/zZNrISYrIcWYsw48wlZUJAkaG7JXqckt9/+jMlq04qIDw7T3ByDm9hGtWoAsK1CUt37AiB7g7hGRIrWavgn5ij+p1LhJM2kptQJ9ssSucgYQxld3BkYlWUCaNsi7QYMEjWCVSFrBxjbOlA7FjycLLItSXDdIO8jCnKBC1yxtk2UiiUDBC2BISzGCv6WGvQpqAgZZxtkevkuVmB8DAD1+YFZjj+ShhUazSmzDFlMTFpL/baf9J31TFdZFhdTkzbS3RZELZn8avNQ7f51ls+83OKn/5pyvUbmiS1NOrOJ/D3fjciTuzDiVWp0YOxs1WaarkO0p1V91qzhr8wg0kyRBRihvFEK6+O12k6/buma0/aNMfrtBBSopp1VyQYjV1rsBo5SYphjM0L/IVp5whRlq7C+g2Ob3hi5RKffKNPvtEnOjMLQO21RYQS6DgjubXhgOh28oW9mLQchEB4iuj0LO0fXKD1/fNUTs04IUlj0WnO8Fd3SO8f3zF9N0ySkt+656o2h02mT5pgD6u4Pes2jjtxv8iJXpvnru4cGULgzc3gz846eYlx/ETNpG9SWOuwKKbU+/ikl5lYCajN11j6zRN86794m2gqcsKVE0bMLnZQBYrqXI3afJ3Zd+Y58/vnuPWHN7j9RzfY/GTjYeE7C2k3JdmKCZohlekqYfMQ0LOA6lyNqFNBqH0KuV/zaZ5us3N1+7GveFWf+mIDFShMacjHBfFW7CpMBz8XOXG/oB5w8u+c5tTvnmH6jVm3n5PjshMvzLAZELZDWmfazH9viY1P1vnkf/6Awd3BCwOhqnaD6vdep/n7P8SbnQKjMXnpKtOldolPNUL4CnNEK7RY3yH59DrF2jayGqGmGvjz03hTj09yQgpXhbY5ql7Bn2rgL0xhssJVooYxlQvL+DNNiu0BvZUtZ0xfaEw8YcWdmcdrO3eEsjdCj1P0KKXsDt1zmz7bufEWZqj+8C2af++HrtpdatciHI0B68RDW5MJsny2xMNfnqX2m2/T/Hs/BA60mUexG6sDH9Wqu2v9BMzWKNtklLnKy/rg8qGfuZX9AoHEIUIffhbvdz+evMdDbL9dMDtAoROS4tmqcEdG6Sq9e3CWSUL6VTuN1toJEH0i3VDkxBv3qMws4VVqmNJgigxTBJgyQ6cJgpkjiSqVSLC2rvm3/0/60LD12ecF333XJwwfqTgpiYgm5tGBh6xGeIszDqiuDbUfvonJS8qtHtmdVWQlJDi5QHhuGdmqEZycJ1/ZpNjYpvbDt7FZ4aq/k2ql12kRvnbKVcPEzgTbadxxA8JOICdHdKx2MXX2kGv/dcQrMWsNcx89egAAIABJREFUP7+HP10nOjUNUqIqAdXzC5z4r/8O2caAYmtA0XMGzBgnXSB9tScC6nfqeM0KXru6L6sAZKtd+u9dZ/TlCkXv+ZMEm+UUD9ZfOFbk37uQcpJUHb6q+saGtaS3b1NsbCKUQg8He3IZLyOEJ2icaHH2H57n1O+ecUmV50TuRitD+nf6ZP0Mqw0qVDSWG65NuNhARR7Lv3OKoBkipKB7fYd8sD/Rpjsp8VZM69wUlZkqYesQj0EpqC3WiaacfZDONdKTLrE61cQLHx9W/KpPfamO8hVZPyVeHx/p+1ZbqPPGf/k2s2/PUT/RpBjldK/tMFod7u1r1IlonmrTPj+F9CQqUkxdmOKt/+ZdPv1fP2bny60XAmD1ptvUf/gWqlml3OqSXrnD+C8/c8xcY0BIZBSg+yPXCjvkmIoHW+jecE+cNTgxT/W7ryO/c+mQXxTsWjHten3avHTioZ5C1Stu8rnvWiKqUSWYbqCjAFtogllnPVVs9ibVM+EcCIYJZXeI12kga88mwxCcnKf6zkWEpyjub5B8doP4owmW0rJHjCk3dii7g6duDyA8s0TlzXMgBPmdVZJPrpN8cm1v7BSeQkQBxeoWuvfVnTCeJJHwPPIJxw2TF5g0Q1Umz9Gu0O5X7DoWwy7dK+9Txk501eiCrLtOOe4jpMJa7SyD8pRi3J94j64dKTK8uWWQEubnJFvbjgkYhjA/pxiPLTs7jyyAmnWq33+D5ItbrlppLdVvX8SMYvKbK+5ZmAg17+IvzSghvXrHCWMPY+dJKgR68Ms9AhdSTro/22S3H7iFYpY77z5f7blB2CRj8Ee/RA8en7cjapwQ54moMrA73OXqVzvZzxGvRGKVPegy+PA2/lSd2reWCSa6U16jQjjfphyn6FHmVj+THrNQEhl6qGqIqkfugZUCqw165Pzshp/dY/D+DfKNAbb4CvggYx6il/9NPF8IIfaqVV9rHBRwfM4w47HDxb3kEFIQNEJO/vgUSz9YpnmyBQJ2Lm+x9fkmO1e2iDdiithhE6WvqExXaJ5uMf3GLHPfnqcyXWH2nXl0VnLt33xJ99rOXusu7SYkm66FEtQDgkaICpV7f3KOhBDUlxpUOhXKrKR/s0dtsY5fC2ieaqGix1fFXtWntthABop4KyZeHx1uEyEgaIYs/fYJvMhjcKfH+oer9G/1SLYTythNwH49oL7cYPr1GZZ+6yTRVETUjph7Z572uSnitRFp96vJVYjAc/ii5VlEEFCs3CF+/zLpFzcfZr16ao81eVg8ivcSgYcejg/9fDkYk95YdZPOZKIRfg/0hKVnLeX2wOns5KXDON3fwhauxZfeXncr/9zp8gglJ2bYBTqZjJHP0CYVYYA33cKb64CUZDfvE3/0JenlW/tVAgEotT8pPnGDk23OtF3lD8iu3iX5+Arp1TsHtuk6DI4o8+oTC2ySOVmcSXVSBD6yXv3K3qWmzDHDA/egtZMK1cPzkNXl3ms6PXp8khK+9brPf/tP66yva/ICqhXB0oJi+YRiZkbxX/3n+5/fzHx+sZM5c+7aZIE1GKNHCWacHArM13kB/ceT5TJ+/Dm1SeZ0ro6o/FttKNcP7zIpFHVa1ESTkvzXwhB8JRIrPc4YX32AHqdMxxnVi4sEE2af8BX+VI1gurHPZJu0Ba1xFHpbaHSSY/OScpSSb/Tp/dV1Rp/eI73/eNvi2eLVf+i/USEEIgjwpjuo+tdvfSKkQFYmDEzfMYas0Zg0xaQpNn+8fSJrNVS1ivAOMkwLiu3tJ080SiHDEFmpOMyNPEQN3xr0cITJ0ofaLCryaCw1OPnj07TOT2G0oRjm3P/zu9z72R16Nw4X4gynInau7hDUA9oXO1RmKpz6vbPsfLlFshUzXneDbtp1FStnJK3w6wFhKyLZjvcnZAH1xQZRp0I5Llj/cJXFHyzTOtemttTArzp8lz0wAftVn+p8DelLsn7GeH18pP+WChWNE012rmxz72d3uPF/XyUf5o99XoWKrc82qc7WmH5jhrAVEbYU7XNteje7e4mVlD5SKrDO2kSbAq2fvhASUYiqVxz4FijXt8mu3nm8Mv0CZQZ0b0zypOr5IarsSX+fKFFuP7lylA+fgaQjBLIWIesVZ+wO5HfWyO+sPpzsWJ792IVE1iru78QwOru1Qn5v7ZFt2qPZpa9g6NEY3Rvgn5gHQIYhqt10NmUvGy5wjMgyy0xH8tabPr2uodSWMBBMTbkKVhgIlhajPV3Wz64ZfvrP7uKdXMCfmsGmOdmtB+jtF8zQ+4acn+PGK5FYAeg4I765Qfa//TnRyWlqlxapnp8nXGi7Vl+9ggiUY0RMTHh1mqMHCfnOiGJjQHJ3i/jWBsm9bUycYb5Clco+p33D38Th4ZKqKVSzgQiDQxOZlxbGIMOIyvlz1N58m2BxCRmG6PGI+OoV4itXyO7cfuxrtTffovHud/Dn5/deK9bXWf8X/xw9HD72+d1Q9QaVs2epvf0OfmfaJViPSDzYLKP/Z39GfPUKxfY+0Lg2V2Ph+0tUZquoQJFuJzz45Qqrv1yhf+foQS3rpWx/scmtP7zBOSWZfWcOL/SY++4i4/XxfmK1s1+xAle1qs5VSXvJXmIlpKC24FqByVbC+oertM60mbrYIagHRFNVvKq/h3OSvkvQoqkIIcV+K/BJz4+Fez+5zb2f3iEbZIc+azrTjNdGrPz8Ln4jYLblEqDaQmPP3gIEtcoMUeSqJGHQZBxv0O3ffMKPu5CB04TbDZMVjun06xzsv6aflmHwEL7RJNmhzg7PHAIn8HhAOsfEmQPp/zWOcmOH4v4a0VsXAceIVFNNvE4LPRhhv8o5fYHxx3+S8tHHOUoJzMQA2qnuuP9/dA00HFryVUO+0ZuQqKwjkf2NMDTwDU+spFBUww6Bqk40S3YoRhnpnW0YlOgbI/qhwfqu/7pHvbQTMHFpMLl2A2KSUw4TymHiTJS/yuC4V/a3E7Bli+DEEt70NKpeQwRuRWazHD0aUW5tk99fQfcHzwzyfCyEQPg+/vwc/vwsXqfjJuRgIhpalphxQtnvU25tkT9YPRpM/yw/5/uodgt/fg6v00E16sgocoPtxHLHFAU2TdHjGD0YUO500d3eE5MKANVq4U21Ue0Wql5H1muoRgNvqo033XEsGs/Dm+nQ/Ls/pvrdbz9xezbNGPzpnznl++c4XhGE+LNVau+8i4ljkhvXEH4wYfqdR1WrmCSm7PUeSviyu3ewWYbXbhOeOk24tIys1Y4Umt29hvW33iI6ew4hBPHlzzFliddsUblwEVWtkm+sM/zwA7J7d9Hjh0vnlRnXxvNrzvoo66es/PldRmujJ7NjrVMVXv9wldl35pl+YwbpSTqvTbP9+eZeJSQf5aQ7CWVaoCYJUXWuRu9GF4NBBopo2mGvhJLko5zhyoDR2ohskOGFitpCjbAV7SVWUTtyOLDJ85n3U8broyNbPTotGa+N6d3qEW+On5hM6FwzuDsg6++3E4JGgF/bTYgERZlA6syPS52R5U++P/dCyYfBvtZ+Jc/GVyYEzl7rAPPKavPVtKqEA+U/xOaa6OL9dY5yu0t+f90lUGHgICrViOidSxMNqAe/7l0EYG3dsL397NeiLHELrVdc0+9lxTc2sRJCEXh1WtVlQq9OoRNKk6PzEpVKvG2J6Y4Zp9sUOiXwqkjhIaU7JGe4mTk7A7+BRCB2B4evuOK0EyVqWa/jLy4Qnj5FcPoU/uw0qu4qLuCqDno4otzcIp2dIb9zl/zBGiY+njChiCK8dotgaZHg5AmCxQW8mWmnmB04exlbFOg4Qfd6FBubeLfvkv//7L1pkxxZeqX33OtbuMeekfuGrYBa0VXsbnazOdRwRovRTKRkMuMP0Cf9C/0W6ZtMHySTKIkzJkokh5wWu5vF7tqBQqGwI/eMjD18vffqw42MzEQmgMRS1VVkH1gtiEzfIsLdj7/vec95vEG+t49JXkBrIgTuTBNvcQF/dQV/edESxloVWQqPnLMn0TU6TtCjEarXJ99vk29vkz54RNFun+2+LgT+yjLB5Yv4S4uWWFUr1kepdCSqFY6DU6kQvnWWwPck1GDA6DcfURx0XuqJXvr2gqfThOT+PYpuB6QkWFqm/N51SusXyHZ3GN+4QXGMWGU7O+TtNjKwo+9OtYoMnh4dIhwHt96gdPESXrPJ8LPPGH91Cz0e4TabIAThpcsU3S7jmzdQ4/FJbzAJQaNE43ITJ7DTdUk3pX1zj2zw/CdfXWiGmwNG20OyQUapWSKaLxMtlPHKPvkoQ2eKbJARt2PC2chWrObK0+k/N7StSK/io3NN0onJ+hnj3RHJQUxlpUp5yVo+DDdsW6rULFFq2tgRU2jSXkrSjp/aCiySgt69DvEZU4NPwihN3B5PtVdg26WOf0iIDHkeo42a5McdTVIdfTCA4+CvzCOrkdVjui5uq463vjj9NW/ZTrSd3AGDHifk2/sUe684QfY8COvxE1xbR5YC6y5/b2MytHP2DU4EHt5iC2/JTlSn9zasxcPh9UeIaazIoSms8D28uSbuwsx0PcEbq6d86oyyU1zZo52TuW9CTG0VpOeC6yADH3e+eSLeJXjzgn0wPL7OyWRY9nDLVgcn8JZmcRdmkIFPsd+l2O+iztDrPAlnpmZ1Xc0aOknJt9oUuy8/Af6i0MOYfGuP9PZ9/EurOLUKIvApvXOFYq8zMdw9n/D/m0QcG77dMKJvGq+um30VfGeJlSsDwqBJPVqxD9OZxnNKpMIhCprUy6tk+Yg46+MLSaO8TuBGeE6IAfrxJqO0je+Wma2+gRCCUbJPb2xff6V33RhwJMHaKuF7bxO+/ZZ9GjuM/gD7hBZFyHIZb3GB0ptXiW98yeAffkV631Y6zgUp8eZmid57h8rPfmL1R45ztK3DSB7Px20GuDNNgksXia6/x+g3HzP88J/IHp8zj0oIRCkgfPtNKj/+Ef6lC0c/08cI6eS/MggsGZppwhpEQpDv7dvt/upDivQM/ZqUBBfWCN9+C29x/uTbqpSdmuGIvKL11DrgaTB58dzfeeZhOw5Fr0f3b/4GNRhMIx+Su3eRUUTl+g+oXH+f9OEjiu6xm6cxltAWCh1bJ2qeRaw8D39+HqdapRgMGH78kd2eKtBJytj7HH9hARmGOFHZaruOfW6O6+DXAqL5MgjIx9YgM27H6Px8T45GmUm7b0Q4E+L4DkG9RDgXodICnWuKJGfweIBf8fEqHtF8NNGBWa1U/VIdt+SSDVLGu3a6L2mPiffHVNdqVJas39QhSjMhpRkrcD3c5+wZdghFUtB/0KMYP79NZLQhG570zZKOQE6TAwza5DgElIIG5WiekdhjHB/zcZpkllX++IcEV9asrqoSWa+pY+2w6MfvEP3o7ZM7oDXZw236f/VLhnsfPXd/XwlS4s7Waf23f4a30CL58j77/8NfYPa7U5PhJ+HUKlT+6Peo/+kfAdD+H/8PBv/h10c6JkfizjWp/5d/ZANyK8d0UMeqS7X/4qfwn//0xLp1nJA/2qHzv/w1Sf/I60y4Dt7SLI3/+l/j1KtH6/S9E+us/+kfnboM61FM9mibzv/8V6TDx9PXww/epPpvf4y3PMfoF58y/PnHxJ88f9oruLZO5V99QPTBm+Qbu/T+718w/BaJFVpT7B4w+JtfUf+voun7EFxcQXX66HFM/OsvbEXwW67eCY7ifjTWbd7aFNg/h7CGBfpM2wJx7A/nXOasJU8va547tSmR0/UcLqkxCIS9RT3ROBBIJHKy5pOxbqd/8/B90S8Xvv3CS3xLKFTCOG0zGG+hTME46zCMd1E6Z5z1kMIjCmaQwsWAzXUqhvTjLQBmKpcoeTW0LsiLEZmK8Z0yrepl4qz7CoZvllD4i4uUrlzBbdbtpM7BAUX7AD0aY7TGCUPcuVmcZmNiCuhRunIZ4fv0/t+cfHMbHT/nGUEKgksXKf/eD4jee3dKqmxGW59iv40exxhjkEEJb34Wp1FH+D4yLBFdfxdZLtP/D39HvrP33MqVCAIqP/kx0fX38JYnT+rGoIYje2zDgR19NQbhechKBbdRx6nXpjcgPRqT3r3/9GPTmuTuPdR4fEKkLnwft1GndPWKfV0p1GhE8vVdiv1nDxiYzMYfvCxX1lmGGg1Rw8GpceSi26UYDPDn5k84KL8UJu0QIaUlZfp4dMbR1Jf922m4oYtb8qYXjGJcnKtS9STycX6C2DiBQ1ANGE3ISBEXDB71qF+sTzRWFeSkYuWVfWoXm7gll6QTW2KlDXE7Jm7HR1YMx4nVYcVKG0Y7I9Les/fZFJqkG6Oyc17QTukdxRMXVYGUDq4bUqiUQp1xHkhrcYBSqMEYPU4mE1zh1G9K94cU3SfaiFqT77RfPZ7kt4XD+CopbNxKT6GHsTVGrZVxqjY3U7V7qCeO0SQZ+c7BaV8swWSd0sohCuv7JX0PWa/gVKz+rdjr2oivY9DjhGL3wFbhjiF7tE165xHeyhzBG6tk9zeJP7393Mq/vzKPv7ZgrztfP6LYftlhpZeHHsektx8w/vUX1hD3yjoAwdULyHKIvzTH+OMvybf2vtXp8ipNZligKho8NF+RElNnloaYo0SEMyFcfQ44MLt0OW0qW6VJXbSo0cSnhEBQkBObEW22GdIl4/QxlalSo0mFBoEIcfGRCDSa1IzpcUCXfWKGp8iZQBAQMcsSVdGgRIhAkhLTZZ/MpDx5UZA4LHGRWbHE2AzY4j5DztaklqkyK5ZoMs+e2aDDLmNezPrjO0usDBqlcnKVonRGXowptP2AtM4pVIoQh2J1gZzEFcRZHynkJMpAkhVjCp2RF7HVXb0G1bmMQryFeWS5TLG/T/rwsdVQHRIPbZC+h9ts4q8sU3rzKrJSRlbKBGsrlN+/zjAvyB4+evpGXBenWiF67x1K167iNBtgDNmjDbJHj8g2tmy1I8vAGITn4zRq+MtLBBcv4s61bBbhpQvo4Y8Y/upDso3Np1+IHAenViV88xr+8hLC9210zle3yR49pmgfYBKbMWgDmB1kWEKWI5xqFadex6lVKPb2yba20E8LYTaGfGsb1e2dICoyivCXF/HXVnAqFYzW6FFMevce6b0Hz/w8bEvi5XPfTFGgswyTn/Yh00mCTlNkFFmB+ZmTPOfbrikKim4XncTIKCK8eIn4wX10kuBUawRr6wjXQ3c66PFpnyfpOzi+nDo560KdsEE4L3SmTrTYHE/ihu5UG1bEOYNHfVSqCGolwtkI6dnWmlf2qF9s4IYu6QMbJ6OVIWnbKpgQgmguImgECEdglKHUDKfEarw9JO0/m+BrbSbu6S/5BP8EqYrCFlHYQkoHpVK0fuJz1gaT5Iw/uU16b+vofamVCa6s4v7kXQDSuxuMPrxxclljbJVl83RMyPcCSqO6A0a//OwoiF0KnEaF8L03cN6+BMD4s69Jbz9xvVIKNYxRnZM3KKM0RbvH6P/7ZGorIBxp1/n+NZyrlliMP/qS7MHJoGwz0Yqq3kkCm2/tk93bxPz0PZxaBXfRtgaLvc7ZOh/XsSLxhRZOrYzJcrK7G+QvWq0SAhmVbJfgpWAm2jxN+tV9AHSSEVxZt2HDa0vIsITTapBv7VG0u+j+ED1O0ElqK4taTx66XnIXju9Nnk8F8x4+ZVGlwRwDOuRkNMQcDjZr0WBw8fEo4XLyodLDp8oMs2KJiAoOzqTqZX9WEhE+JQ7MDgdsk5GeIEh10aKBJXCHlS2DtUqoiRkCInxTYov7ZCQnlq3QoCUWabE42a6mIMfFo8kcRoBPMDWAZXI0EkFASCQqdM0+Y4ZoTndyKtRpMEeVOjs8Qp3xO8/Dd5ZYPQ1CSBzp47khruPjOgEKaSPrdDGpRLkIQGuFNmoSXeCSFkOyfIQ2r1ZylaUSwvdRwxHxV7cZ/frjM0mS8Dz8lWUMEL55FadRtzfU6++SbW2Tb+88dfrNiUKCixcI37qGNz8HSlH0+sRf3GD86eeWJJ3aMYm3vEh5OKb8ow+sQLxWo/zDD8g2NikOOpaAnHVMgY/bbOAtzONUyraK0+sx+qePSG599dTlDgXZ7mwLb27Wtsa6zx65Vf0Bqn/ywulUKza647BFYQwmyyj2rPD/qXAkwrNBstb/RoPRJ7LcwCCCACGlrfAVxUlyZOwyZ8FoW6IXUj41tPW8MEVBtrdLtrND6eIlytffB9dFjUa41RqlCxfQ8Zhsews1Ou1zJOTJeAwML+X1Y12bjy0nhI2lmfw1HxcMHvUpkgKn5BDUA7zIw/Ed/LJPbbWGE7g2p29zYHVOBzHx/hgwBLWAUqOEF3lko2xCrEq2YrU9JHtOxQpj9WCvZ/hOEPhVAr9Kmg8pioSieGL7xpoQxh/dOvGyO98EKShPiFX2YIvh371k7NF3FVqjOgNG//Dp0WtS4C3N4bQalCbEKrl5zxKl80Bp1H6X4X881hp1HbylObylWZgQq/izO8QfPT0o+8QqD/pkG7sUux3c+Sbe4gzB5VVUZ4BRZ1ihBD7B5WXc2QYIQdEdkD/eRT1ZcXwOhOvgX7bt4ZeCsdeQw2Br1R2Qb+zgX1gGfOtIvrqAv7pgjTH3Dih22qhOHzUY21zOQk20wa/eKiz2OqRfPzx+hDi4NMQsGSkSSc8ckE+qTAEROcn073YJSUiFZXGRCnVyMnoTogIQiJA6LWaYxxUehck4YBdFMV3DIVFLGDEyA/IJ8fIJmBELlKnhixJds4+ioMA+DB3u6yLr+AR02advOqSMLdkTTao0cfGQwpmSUYNmxIAhPRZYI6LCgBIJJ61NBIIKDUpEZKRPrbg9D987YuU5EbVwgWZlndBv2imf/OyTZZjuYYxmtnqFtBjZFqDKbQ/2Fem/yTKSL28Rf/El2cbZN36T52SbW/T+6q9xGzVktWIFzM0m3qIVoOebW2cu67ZmqPz0xziNunWnHY0Z/fojxp99Qba1feYyaE2+tcNg9Au8hTmrgaqUkeWIYH3NiujvnD1mLoIAt9GYTkHpOCG9/5Bib+/ZLcsJAcq3dyh29771+AAZRXhLi7i1KjpNUcMRJstwZ1sAqE4PoxX++hpOOWL8+Q3UQeeEsF447gkvqhPrd21Qrc6y04TsRaE1Oo4ZfvYpCEHtpz8jfOMNdJ6jk5ii02H48UfE9+6eqbfQmUIdm/wTjrAxNi8I6coTy2mlUama6tSKpGDwuI9K7IVQepJwNiIbpnhVn3DWphek3YTh5gCtDGk3IT6IUZnC8Rz8eolooYJ62CNolPDrATpTDHdG1j7hW4NmMNoiTrtI4VCohKJ4NePQ3+G3AGNQnQHxJ7cp/8F7uPMzlN65RPzZ7TOHZETJp/TWJdxmFdUfk9x6QDEYvfAUmygF1P/sj/HXll5t9ydiajvx7JwpK5DVMkE5wl9fPtK0TjwZX9dldfSrT08QKzHRE4VU6NPlsfkaRTG9jh/eK4/rjAJK1GgyyzIddtgxj9ljY7qMNJIdHvKm+CEV6iyIdXrm4BixMmya+5N168k/R/szMgOWxEXbKhR1UjOmIJ+QnjpVmngEdNhjw9yhx8F025GpMCuWuMjbJ3RiACP69Mw+C2KNqphhZAYniJWYEL6KqOHg0DH7k0rbPyONFYAyGd3x40kopj15CpXQj7dJ8gGO9MmKEUrnOHKXrIhROkOIjI3Op6SFZdA2nFOhVGrTzV9DlIHJc5Kv75Dv7T3zZDV5jur1SB8+xmk08BcXQAi8+Tn8xYUziZUolXBbLYK1VTtlphSq1yO++SXF88wnJ22x9N593EadoFqx21tewtvYfCqxenKU3I4Fh7aUfx4ycViu/pYhfVtpc2dn0OPYGnz6vt0fpaDVJHv4GD0c2tO4PzhlQCh8Dycq49TrtlJ0aIkhhG1xVioUBwfoM1qFLwxjcOsNZBiRbm4y+uJzil7XtkDimOKgjRqe3c8vkoIitgG1CIEbuvgV/5RI83lwI88uN4FK1QkDTp0p4oOYbGC93qQrieYiirSgNGNJVT7MSLoJWT+1rTSsVcNwY0BluUpQD6iuVCnGGV7ZRToSpQtG28PnaqxeN8ykIimkh1I5Sv0ueur7CNUfMv7kFsHbF/FXrXbKnW2QZznmuCbLkbaNe3kFp1Yh29gl+eLuiSnDc2My0CPLL1mxOuc27H+EDd7mZduO59hUcNYDpGBAl745IOP5Dx0lylREAweHgekwoHOMNIFGoVHEZkgoKkRU8PCnVSlgUoE6+74yZkBGgkQSUEJOaYogokqJEEXBgdlhzPDEtmNGDEyXQuQ4T9CbnJQxA8b0iShToU6brek+ufg0aOFTIiOlx96Jdb8IvtPEyhhNkp9sK2lTkOR9kvwZI6qGqYgdmFS0zrAsftn9UspOxGzvoJ9yEzz6ZTs5lm9uUawsW2IFlgzMzZ65iFOr4s62cKo22kUNBtbKYGfvlNjzzE0Wimxzi+DSRQ5n1NzZGZzWzNMjAvIcNRrZkvVEnO7OtvBXl9GjEUW3d77Jwm8ZwnNxypElS1Iiy2WbRRUnU58cHcdWH1YKntI6EzjVKuV33iV9/Bg1HFhSVasTLK8gXI/4ztenK3dS2sgRxw4nCMe2C2UQoD1v2kZ88v32Wi2cSmVi4fAF+f759DkqU+TDjLSX4lcD3JKLXwvwyj7ZID1XVAkS26o7tD/QhnyUk3SO7A+MNhRxTtJJyIY50rUVK6004UwIBuJ2TNpJTkwj5sOc/qO+9bmqBVRXq6S9BLfkYYzVTSXtmHz07ZpCGqMBie9XJv8PRfE9FZv/C4aOU7KHOxQ7B3gLM7gzNYJLK+hhbLVWE8hKhLfYstE5QqDaPdI7j38XO/YMjBkSn1OgHRASYgePAhHRMHNUaZz+PRFO9Fq+bc0hp3olicQnwJ/otyQOcjKL5+FTIuKwTSmnc4OCEhEeARrFkN60RXgIRUFGQkZKiZPVfI0mIaZLmyZzRKI/ACAIAAAgAElEQVRKYEJSbLHFw6cpFnDxGdChz8GZGqzz4DtNrF4vXl+LyuQ5aji0badzxi/k+21U74gkOtUKTv3ssGG32cCbOfKQUf0B+c7u1Abg+TtoKDon9VROpYJTrVgdUlGcHnNOUtTBgc27a9Qnk48LlH/vfYTjMP78piUoRf5avMBeG4RNr88ePEJWIltd2u/Z9l2SkO8fWFG6EAjPR1YrGKVOaNt0luFEEbU/+EPGX94kb+8jpKR0+QrBygpFr8fos08s4TqE6+KUQqu3CwLcWg3pBwjXxZudO2ofZtkpPypZLiPDELO/jywFOOXyxF6Co2nBQoF+0jfIkPZS+o/6NN+YwYs8gnpAZblC/4EiHz3n+yHALbmEs5Ft52GrYGlnoo86/pEaGO2OSLsJpaYVsAspCFuWkA23BiQHJ8lJPs4YPOzRensWvxZQWakx3h/jlBxUqiZELT23NcTrgtYKIQRhqYXvVZGjHQa/I1bfP6jJQMu9DdyFGbylWcJ3r5Bv7Z8gVt5sg+CNNWTgoYcj8t2Db9W76vuI3GTknO/+4uLiYSvei1xgUaw/e91kSJyJmFxNCVKdFg0xSzipaDl4E3olkbinSJPdtiVhVq6enkl8DIacDJ/T1jcFGW2zRVU0CCf70Gab4pBYMYdEEjNiyMv7i/0LIlavDybLbRzBC7S+1HBondAnkGGIc+jS/QRJsVN2R6RLJ6kVe59XqGzM0UTJIYRAeh6yFNp215NCSKVQwxHjL26C6xKsrQIQrK/hNptE198jffCA9P5Dss0tVOcbNkM8J4r9NqNfj9FZZm0MHMeSGCltOzDLoVB2cnNrBxPHE0H7EfK9HZLRiKLXo7S6RnjlijX9VJrkwQPir2+Tbm1NpwaF71NaXaP8/geU1tdBSJwoQoa2ddr60z+zlb+iQI2GHPz7f0e2vX1se7sEi0tU3n+f8Mob1uJhMjmkRyPSrU3GX90i29465XcW74/Z+3SH6kp1QqxKrP7ROvdGd8hHzx4acEsu8+8vUl2pISfRIt07HQaPT1tVGGC8OybtJoSzEUGzhBM4lFohaMNoa0jSeYJYjXJ6D+00oV8NqCxXGW4OcEsu+Ti3uq3nGH5+E5DSxXMjorDFaLQzMQn9Hb6XMIb0q4f4q/MEF5cJrq3jfnILcduZntfufJPg6hrCdcke7diMw9/hOdCTf14MbbYmovWn35tyMlLGaDQOLiEV1sU1KtSndg4pCYXJ0Ghc4dJikSozT12nOfbPi6Agp0ebhBEV6syIBbqmjUASUSWkTI8DxubFhhyexO+I1UvAFIX1hHoBYmWSBJ0dF0xPBIyuO8kHOOa5USohwiMfILdRJ3rnLdyZ5rnbccL3CC6sHf1dCGu+GQQQx2eLo5OU+MaXlpQojb+6bPVeQYBTr+HUqvjLS+T7bYr9NvnuPvnePqrb/VbN7QQSTwSUZd2O8Pf7uAhc4SGFS27SyXitg0NgdQMJeKlDJJcZix4ZCaIQOF/tkJKQxwP8VJL0Rni1BoFXIU9HsN/D2+8S5C6ZLuylR2mKwYDkwX1U/+lPNcZoTJaiJi1EWSpRungJf34BnWUk9+5hivxYWLHAiSKC1TWcWo3ez//eaviOvbfjvTE7v9li8cfL+BWfoB6w/LNVhpsDiqRgvHN2gK8bedTWa6z9mwvULtZB2ArY/me7dO+e8TRvjHVT7yZIR1BqlDC1gLAVYQxnV6xGOYOHPYq0IJqLKC+UKc+XLbEa2eib40ae3xaMmQhkjbIPMq843fm9giOnlgf/XJBvt8k391DDMbIa4S3P4y60yDd2keUQd34Gb6EFQpA92iV7+JRhn3PAZDnjf/z0iUm67y+yu8+w+DknFAo1qSYNTY8OuxTP0CIZ9LTdFhAxwwJ1ZsjJaZsterRJSSeieU1oIqqieea6Cgr0pOp11F588t4jcHFPidftvthq1oAeJSKqNAgIcZBUqOPgMDRdRq9QrYLvOrGSAlnykVFgPYSMQTgOOk5Ro8RGT3juxNQvtqLFSogMffQoQQQ+wrMjl8KR6HGCGsaYVwhfhsPx2fyF6LLJiyNRNEyN+aTvoZU6QayE5yH9I3Gx25rBbc0QXX/3lfZbSInwXOv9ddYvKEX2eMNqb7IcUxQ49ZptXZUCvKVF/OWliRVDn/T+A5J7D8gePUb1+tZ7KfvmhcECgSNcqk4LMGQmIRAhgQyRwiXVMYXJcIWLL0KGqoNCEciIlreMzguULhC5xvl6F6UOyPWYsrPAYHuDAhcpaygUngjwhETKBsrso43CqIJ8b5d8b/cFdlrgVGtUfvgjnFKJbHeX8c0b6CzFaD0hvi6lCxeJrl2j/O57jD//zOYTHqtapf2Eg1tt2jf28Cs+leUqM2/OsvyzNYyB/S92KWLroI4xIAVuyaWyVGHu+gLLP10lnI0o4oLRzpDdT3foPzzjImJgvGdbgcKVlFoh0pH4FR9daEbbI5LuyWpaEeeMtgcUI6vLKs2EVFdruJFP3B4zeDxA/5aIlVLWy04Igfw+E40n03iedSxiEqbsfbcv8y8KPRyTb7XJN3YJrq7jry3gX1gk39yzpGp+BhmVpnEyxW7n+Sudwup6rA+SQGUa+YsHGPcRxaT2EhDi4mEwpIwpTD5x8raQOLjCxSPAmdzgDaDIycnIjT1vnpygDkSIT4mUMYbDllswXV5TkDBGmeLEshKJI1x8StPtga3O2O1l04Gtpzn0vwhyUpJJAI59D1LG5yQiLj510cLDp0ebbR6SMD52PIKIysRV/UkYMhIKcoIJLcpITgjMBRIPD58A+YwhgIHpUBY1WixSpopPQEXUUShG9M+tN3v6cX6HIX2P0pVlorfX8OYbmELhViPGtx4z+uQOTr2Cv9BA9ccMfvUlTlSi8sM3CK+tMvr0LsGFBbxWDaM0bi1i9Pl9hh/eIt/vv5pGaNK2eRFmZbQ+lY0mhJw4cYsTnTmbVfYNTIYIzuXFlG9tU7QPGH/2OdF77xC+/RbBxUmu16FvVWsGt9kgfOct1EGH0UefMP7iJvn2zjeuv9IoYj0k1n17YusxLX8ZKSSFyYhklbHuISYXHI0iMzFGacayT6yHJHpkvVSmu2qjEUqyMiFuHhVnhsJkKJMTyRoj1T3h5/IiEJ6HW68TXr7C6MbnjG58TnL//ulfVAq3XidYXUOGIdLzUMfbgRqyfsrtv7iF47tEc2WkL1n9T9aZebNF5+sDDm61SQ5szI1TcqhfatJ6q0XzjRZO4ICA0faQr/7Xm3RutadhyU9ivDsi6STTqUDHcxBSkA0z4v3xqVgaowz5MCdux+RxgRd5zLw5i1vx6D/qMfwtVawEtt2uVGaHy8U3N3X1jeLQf+zwOysFeEfGrqchkNWyNbj8Z4Z8p03yxV38C0t4awv4F5cZ/epzgkvLuAstTJrbXMT97gs97Dm4VGmyKNdwhMvQ9FgcXERR0DG77OhHrMpFGmKWgpwH+ku6po8yR9VbV4RUabEg16iIOp7wUSh6Zp99vcGe3p+IuE9eJ5uyxYq8wn19AwNURZN5sYonfLTRDE2Xu/oLxgxQx5JDpAiIRJNluUKFBr6w4u6+PmDHPGJft6cVptehNR4zZGi6INZoijkSM3oqsToemXP8NWBS98pOkESBoMYMEdVT6zIYxvTJiClTpSUWiM2Q9FjKYUCJSNSmhPRpGNChYurMiiVqYoaQiJAyY0bEDMl5NQL6nSZWCFtpwnXI2wNGv76NN1/HqZcpv3+ZbKdrKzCH+VNiQkoCz7baXEm+32P08R28+QZOrUz03kX6P//i3KLzs/dLTLZ5/jl3IaWNzDgGG0pbnCJcRusTrtNFr0/RbqMGw1ciLdnDR6gkfr42TGsbIF0U1jfr8QbuzAze8iL+8jLe/JyNznFdpJSIOYfyj3+IOzdLfPMW408++xbE7ebYH01mEjwCDIZYD1FG4QiBRhHKCmoiBHeljydKBKLAFT6hrFCQU5iMvm4z563Z/1dtO+GHgzaG1AxeevQWJu3jLMXkOd7sHKULlzBJOmkPT5zzqxXCq9cIVlYpej2KXg99RqakLjTDx33u/z93yMcZ6//pJYJaQDgX4YYutfU6KlM2AcAReBWfoBZMg5v3Pt1h4x8esfGLx6d0UsdRjHKyfkoxznFLHsIRtt33qG9Dj8/Q/OmJCWjaTfArNbyqj3QkxSj/rRErg5mk3Eik40yD2r930AZTKJt+oLQNS55vova7qPgMwi/Av7CIO/90rcr3FardJ/36Ebo/QtYrePMzuHNNgjfW8Oab6DgluXEPdfBiLR2beCcIRISHT0JMR+1QEXVmWcYTAZlO6LJHWdSZYYHcpGTGDgqViGixxIJcpzA5B3qLnBwpbKtpjhV8UWJL3z/1kCaMwDcBM8ZOjhtj2OUhYHDw0BRkeoym4JAguXjMiiXmWUVoQcfsUJAhhUOFBnOs4AmPTX3vpSfcnkRGTI8D9tgiosKiuECJMrEZUlAgAAePkrDRRbEZsc8migJFzpDuxI+qwQpXGNCZmoNGVKmIOg4eT5JAg2FEnwFdKjRpMIcWmqppkJJYUiwa1GiSk52yWziOgpyYESP603agQdNl96UMQZ/E9+IKY7ICNRgTf/WYvN2j8sOrBG+uku8fE5gJJvoJcWK5vN0n/mqDvN2n8vvX8NfmwBGccwDiTAgpwfNehFfZcvyxQNdpwHB+2nTyUPh8CNXvk969T/ro8cl24gui6A/spOB59FAT0lfstynaBwj/Md7WFvnGFt7iAt78nLWEqNdxyhH+8pKNfXEcq7/a2z9/0PRLItEjmBCrke7iiQBjNKmeaJqEQ2GsX4oxCo1hpLoo8klpXTHSfXKdoIwiNwmpHpObjFgPEEhc4aGNJtPjE0+JLwytKYZDxre+xG00CFaWJwJ5e7ETroP0A9xmEzUeEd/5mrxzcGbMDsbqmdo39ijGOSpVNC43KS9WCBoB5aUKjudMdVRFUpANMnoPegw3B2z/0xZ7n2wz2ny2QFMXmrRvQ56rK1VLkMY5vQc9iuTs98IcCtu7CbX1Oo7v2O33U5JOgi6+fa8zW6UVE9G688wn2e86TFFQdPo4MzWcSkTp7UsU+110kh0ZZUqBLFvLgdIba7iz9d/uTn8D0HFCvtshe7hN8OYF3Nk6pTcv4K8tIKMS+c4B6Z3HqN7LtXQOb8pjBiRmZCvYokFkKmyaXUbY60uZGgFHHldV0aQh5whESFtv0zdtMpPiCAclCupiljmxQl+0GRh16mFN4lAVTfrmgL45YGg6VtCNZ2PbjlV4BIKKaNAQ1jpgWz+gZ/ZJTWIr9UJTFy1aYokDdkgYvxZypVCM6bNtHjArlgipWIIpGtOWqEDi4pISk5FOz7mcjI7ZIxJVQirMikUiqhi0bWnikjBC4hBROVVfy0jpmTa+CGkyR40moShPPLKYZhWO6BOZ01WvQxgMMSO6E8NQX5QYmT4ds/vSXYnj+H4QqzRHJ/YGo8YpRhuc8Jgx4qHB2iRM9BA6zW2YpzGoUQIGZOhPe9YvDdex6e8vIIIVQYA85rY7jTnI89PEKs1OkBI9HpNtbhF/fuPsG+03DWMwaUr28DHZw8c2MLnZIHr/OuFbbyLWV22rq1EnuHSRqNNl+KsPKb5hYjXUR9qJoTpDR2HgSf+ynfz+iZ/H+iS5OP7zgXq9ga2q36f7H/+e8jvvEl6+TPmdd+0wgTiy8Mi2t0ju3yO5+zVCFwjfxWRnk5hskLH3+S7tm/vMf7DI3A8WaL7ZorxQxq/4CEei0oK4HdN/2OPg5j5bH24Qt+Nza52STkL3TgcncHACl3E7ZrSfopQ52xpOGwYbfYYbfaqrNYQjyccFaawQJR/HUeiswBwjWFoZ8nFO0k2so3svsU7w55yCPfTiiidi+qyXUhybPhTC+uMonQMCrb/Fqpnr2Or25KFPCIEsTTSjk4dB4brIqHQ0rXoYxJ0Xp82H84L84TZuq4E336T843codjuYXFG07aSu8H381XnKv/8u/vqiXfd3xR7lNUKPE+Ibd3FmG7itOuUfv4M738QoRbHfIdvYRY9fzmXfhvom7OtNCjJqpkVKTGwGjEwPhSYwIWVRO1EZaYg5QioMTIdt/YCUieWNgdQkSClZlpepiRaZsYaVJ7cr8IRHV++xY54QzD+pr0PQFHNEVIjNiA1958jg0wiUKfBlQFPOUxENCpOTHSNWhxl7Vqd0PBD++chI2eUxiRnRZH6yH1Uc3KlAPGbIwHTpczD1r8rJ6LCLY1xaYmEafaNRJIzomTbbPKJOizmxMtGvndyvLm1Sk0yyDVuUKBNRJcYGPw9Mlwo1EOKZovqUMV32WWANDGQkdGlTvGIbEL4nxEqWA5xJlcWtWj+doje2vXNHWIG6EDj1Mk545F3hlEs2eV0K3JotS+pB/MoXGen7OLXaUQvyHHAqFetkPoEex+jh6MyWmRoMKHpHJWwZhrj1+ndmusfkOUX7gOEvPyTf3iH6wXWi6+8iwhJOpUz41jXGn38B+99+kvxpfHduKKYoUL0uw49/w/jmFzbP8JCcH/pX5TluxaP2gxVq11fpffSAzi9uP2OltrLUvrlH734Xt+QiPYlwbFMDY9CFRmWKIi7IR9kLVY32P9th+LiPU3IREipvr7Lw539AcKNL0s1Qw5M3Ll1o9j7bof+gx63/7RbhygyNn76Bd+0q1/77C/Q/fUjnH+8yunU0Aj/c6PP1X9zi4d/eR7oClSmSg5h8/PwKoVGGtJNw43/6jDt/+RUARaLIekf7pXVhM0OlezQd+G3AdQiurOIttnDqFYTvIX0PZ6aGv7qAKAU4rkvpzQs0/vw/sw9UWW5bfVlOcusB2f2tE9cInaSMPrxhycRcA1mJqP3JH1D+2Q9sDmaukGGAUwkRUYnxb77EbdUJr1898dD5zwE6SYk/v0Pp7Uv464sEb15AhgHZ/S3Srx6+mtwDMCgK8klNXE2ck1L0JIZFY/3RhBB2QApBSUQIBEPTPVUdykhITIwyirKo0hMBPDHWr1EMTO9cDuiHTuQCwdj0n9ieIWFEPsn/i0SFoemeWG+fA2Iz5DF3rAj/Jdo4Q/okxOyZDQTOtNZx+J7ZnL+CJ9NOuuwxNN0TYnt97F3OSemaffREh3UcBk3CiE3usmMeToXuh0RRoRjSZd9sPVO+cbiPTIYQLGHOX4hgPg3fD2IV+Li1MtV/9S5eq2pH7D+/T7bVxl+aIVhfoPaHGhn6OPXyVPshA59gbY7aH76D26phCk381cYL50U9CeF5OOUyTqWC6vXPVUVyWzM4tSNvKjUcPnVUv+j2KA6OKjBOrYq3OI/w3G+8vXYuTNqEqt8nvf8Q4bqUrl5BlAJbzWq1EJ7//PWcWOWhMPdYapTr/LO7GZiiQA0GqMHT23BOUMetBARLDdw755s8zEf58w1CXwKn1js3y1Kjiiz5T32wyIc5+TBntDvC2xwzbGfUrq9SeWcVtx7ZavMxqFQRp+NJiPOLQxea8c7oqVYTQhwJ1qX49sTrwnEILq1Qeusi7twkh3NS7ZZRYL/fjsSbbyJD3+qnlAZlDWLVYEz2YPsEsTK5It/YZfSrzzFxgn9pBbdRxVu02Zgmy9Fphur0yW7cZfzrLwmurRNcXkW+bJDwdxVKoQ765Bt7qMsreBMtWbHXIbn96BWJ1ZF+8/grmpMTeWLyx9a4nEn1yk4qP3mDPqRnmgKXYOJKfnqrmUmmFZ7nwRX+lMxdlu+dEok3xBxi4nL+5JSc1TwVwNF5J4SkFDQoh/NEpRk8N2S/e5vReIdCnaH3RJGhXrjGU5A/k8hpsmcKyDX6hGj9SahJJe5Z8PApU0XiMKRHn86J6c5XwfeCWKENwvfwl2eQYUB6b4fxzYd2nLY9wKlG+EszqHFC0RtZSwWtMcbgBAHeUgsnDIjvbBLf3jghDH8ZCMdBhiHewjxFr/d8s0zHwVtcsD5UE6huj7x9thuw6vUo9tvoOEb4PrJcxl2cx5ubJSuU9dD6jkANBmSbW0ctTSmRvndKqP9caHMy5FgK2yZzvqcTXP8i8OwnO1Nosv0B2f6AYhDj1stPbWt+k7DEajKfJF1cN8BzQ/Ii4VnHYLKCYr9HcvMeAPnei4ztT9ahja1AJdl0S8+6+ogwwKlGqKG1hji9QoMeJySffY1q9yjtdfEWWji1sn3wyq0GK3u0TXLjnrUakIJ4cRanXqboDJ4/WGIsQSu229Njf1mt0vH9NmlGvrk/XacevhyRPrWf7S5Fu4c7P4MZJ+Q7B+Qbu6/8AP3ku3R8XOZJiOm/rczE8DT7Srv8IRk76+eaYhq99DyIiT1ESZRxhHvmvo1Mj4zsnKRB4DkhUWmGZu0izdolsnxEknbPJFavE/b89BDSsVVm/c3KXgIi6qJlK34MGPL6TK+/F8SqOBiQPNhh8A83AFvdODxpBr+8yfBXXx69LgROFBC+sWIvirc3GBz/+SuebIcQvkfp2hsUBwfPJlaOgxOFBOurJ7IB87098u2zjev0cESxt0e2uYm/PDHpbDaJfu8DTP5PZI83XssxvBZIOWlpTQYHtEbn+QsHMhul0McmFoXj4FQrEy3b2fmGv8NvEYcRPN8DGG194qT0EMKhFDTQFUW3/+CZF2/VHTD65aeMP/zCrucFszJNmjH4618x/NsPn2GJcBLBG6uE198g/uQrso2T5rDHoccJ6Z3HpPc3LQETMBW9GSY5lbYCHH96m+SLuyDEJCrpOeemMRT7HXr//h/o/9Uv7UuvMDQDWFPdvQO6/+ff0/vLn9t1vmKrbgptpseabe2Tb++jR99uZNFhW8mgJ2THO3NIQkziWtQZ7bGXgaJgbIYMTIcd82gyMXgSGk1Odq72ojGK/miTwXiL/nCDcjiP/pZa547jUwqaBH6VJO0yis+XofoyOBTHN5lHUTA0PUa8mtv6cXwviJU5nKArzviA1ZPSNmxJ/dgF5szlXhHC8yi9cRnV76PTjOzR41M3f+G6eIsLlH/8Q7zFBYTr2hJ/f0C+tUPxDA1S3j5g+I+/pvbHEd7iAk4YEb33DkJAXK2Q3Htg24JPIxwTYuLOtvAXF8geb5Dv7p/IDzwOd2YGf3UFjKZod8gPDp5fGZMSf3WF8gfXkVGIAFSSkG1sHU0onRMmz63JaJLYi/jEmT64dJGifUB678ELre+3BeG7lJabVN9dIVxv4VZL1t/pYMj47h6df7xjdX+HhbnQp/r2MtXrawRzVVSSo0YpOlfT2JlDSN8lvDBL7f11gsU60nUoBgnje7v0P3tMtt+frtefrxGutwjXWqQ7PbxGROXaItL3GD/YZ3hjg/5n1oXZrUeEF1rUrq/htyrIwLNxPhsdBl88ZnBj46S1ghSE67P4czWCxTrCdUi3ugxvbdH/+MFLV4SFKylfXaT6zgqllRlr79AZM767S+/jBxSD+KXInDaKOO2yf3ATOx1oDUO1PseNXelXq3C/4PLZw21Ub4jqDuz35FkwBgr1/LdEG8x5jvXEurEtyaeQyUOPOGs/ED9Vy1JzJ9lramC95AqFmbSryrKBFurUAMkLwZW4Sy285VnQhvTOBvnWb0fbadAkZoQnfCqizp45ef56BAQixBUOsR5OjUJfZYuxGeIJH0e4JPps/6WjpuZ5v4fGSjPMaa+tbxKuG1KvrgKS/DVneYqJHu0wPLoi6tNcwB0eMeTZcWAviu80sTKFIm/3bTUjPv+N2hSKYr+PyRV69PrbZjpJ0OMYWa1QuvoGwnFJZlvo4RCTZRhtJoaQNfy1VaLr71p9lRDoZEx86yuyrS10/PR908MRye07tmLlOrhzc7itGcK33rStwfl5VL+PSdMJcTTT6pHwPGRYwqnVcGeaeHOztkXQ7cNTiJWsVgguX8SpVdGDIflBBz0YWKKTZei8sE+6QliPMN/DqVYJ1tcoXXsD6QcYY1D9AfHNL20e4Qu9qdqOUO/s4rVatm3qOASXLmKURpbL6DiZtguFlJOJKwe0Jn30zaTXu16I64VI1yMZddDq2d9DIQQycJGevaiqUYqQkmCpgdcsk2x1SDY69nVHUrm2SPW9VcLVGYqBFR875YBSPUIGJ0/P6MoC1beXCdda6DRHpQUy9Ki9f8F6W92CbMfq9pxyQLjeYuZnVxl+uYmKc1ScY5SZfo7TfXYlju8hPZu3prSx+/b2MoBt5+0NpgRBCCitNEn3BugkR/qGcL2F9F2K/pj40cF0ive8EK60x/fuCqXVFjrJEULg1kNqH6yj4ozRnR3ygxf8XgFgUCplfJbX01lwJE69gtusIkrBRIrgojoD8t0DTJLhrS3gVEN0nNmw3yxH98e2JTXXwGlYsbqQkuKgb40qkwynUcFpVHFq5em0suoNUe0e7mwDd7aBLJfQcYrIiul77q3M2WUc29bM97ro4RgR+NZlfBSjOn2E51q9lZRk97fwVudxKqE9V4SkaPcoOn3Mud4LGxNVktYJ26DJTUamYwIZWjNdIVGmsL5xuIROFVdYV3JlcspOHQcXT5QoTEZmYnKdYtA0vHkA+oVHplNyk55ZcXnq3nku7mILb3EWWQnRcUJ2f4Ni77cXuNw3HQIRUhUNGmKWoelRkCOR1MUsFVG3kSqmc06B+tNhMPTNAYGIKBFREzOMTH9qySCRU6NMG9Hy+kiSlB4lv0ZYauI6pWlXIcl6JEmXNB9Mfs/Fc8tUonkc6U/1jVk+JE4PSNIeUrpEpRb16hoz9StoXeB7ESXf2oT0Rxsk6au16QSSKg1aYhGBJCBEIumwR9tsvbLT+pP4bhOrXJE92iN79GIlQZMVpA9fIG7kBVF0e2SPNwjW1/Dm5/EWFgjfe4d8e9uGJSuNKEf4S4u4rRmbCQiYNCXf22f064/It5+9f4eTd6PffAxSUg4jZFjCnZ/DnZ8j+uAHFAcd9GCIThJLNlwXGZaQlQpOtTLdLiWVJggAACAASURBVED8xc1n6p6E5+I2G5TevIYThbaCNBhSdDqoif+VyQtL3koBbqWCt7iArFZsK9AY9Dgm39xm/Mln6MGLf1GNUqR37uE2mzi1qiVWqyu4M01Kly+S77VtiLLRCNezY+u+b3UW/9e/o3iCWAnhIB2XwyqFMQqjNdLxJt5GBq2snkEIiXQmn5NRdiTfGLxSjag6jx/WUfnnpM8h+EYpis6I3m/uo7OCbG+AUw6Y+5PrNP/gDapvL6OGqSVWnkPzZ1fxZsqM7uyw85cfI4Sg8fuXWfrz3z9VJWr85DLhWovR7W32//YGapQQXZpn6c9/Qv2DC6A1BztHAxFOySdcmyXZ6ND/7BG9jx9Oid/xi6yOc5LNA7L2gOxghE4y3GrI+n/3bwkvzFK+Mk/eHR9VXoTArQR0fnGbzi++RjiSxf/mR4SX5mj85Ap5LyZ7QWIlA4+ZP7yK36oSP2qz85cfgTLUf+8ic39yncbvX0anOXlnjJh8prYdebRPh63ow4u8OSTgxoCQSNdFF/m0NfjUffE9/LV5wncu4TRqqFGM26iQ3tuEj2+Tbe4RvX8Vf32e7PGe1UT1R7baNBhTurqGf2nZkh7fI727QXLjHtnmnp1eu7qGNz+DUYp8u016ZwN10MdbniX64Vv46wt0//e/I727iSkUwnMJ37uMtzI/ST5wSb68T765j1MJ8RZbZI93ibtDZFgifP8qwnPJHm4TvX/VVnOw/nvxF3cxXxUU5yBWEkkgIua9dYQQZDphrPook1OSVWpui9ykjEUfaSS+DGl5KwQymvjJje15JZxpokGsB4x1n0wn1Nw5HBwEkpHqMlKa7LxecVIgqxHRD67iLcxAoSl2D8ge7aI6r6+l86Lomj1KJqImmyzIdUqmTWLGuMJlTqziEUw9ql7ViNJg6Jo9IlOlKhssyUv0zD5jM0Cj8YQ/rdCkOplOM74OuE5AtbLCbPMaUdBECAdjDP3RBu3u16QdK7/x3DL16hqLsz/Ad8tTvWN/tMnewU3StI8rfZq1C8w236QSLaJ1QSWap6hZ4nl/8+ckaY/zE0MbeXbYwpTSRRgIRcWK+Y2wflgcWHsGehRGIYX9Lk6vH2i0UdPXXmSa+DtNrL6rKPbbjD78Dcntr4l+cJ3SG1dwG9Yocxp1IyXCsw7wYIlS8vVdhh/+E9nGJjo5R6nTGLKNTYwqUJ0OlZ/8GKfZtE/CjoPXbGLqtckN2OrLkNLeTI5bMyhlPbOepRE51GUcfnldF6dWRR4ek55kzx167ziOJVSOA9qgx2NGH3/C+ONPKTovGcqsFPGXXyHLEW69hre0CI6DLJWs+H+2dfJYhQSjUd2+3ZcnEFZnqc9dRQqHNOmRDPdJRm3q81cJoga6yOm375GnQzy/QmPxGgDJsM2w+4g8GaKLFFWc/wJoCk12MDw6OZWmGCak2z3SnR5uNZxWoqTnEF2cJdnqWiIW22pn/KhN/9OHlC/bJ3rhStxqSLg6g8kL2n93k7wzsu26rS4Hf3+TmX/9FtHFOQ5+/tXRzghACnofP2DwxYYVUSNs9fH4255k6L3CXjyUAm3Ie2Pix228ehm3Xj5Byo3W9L/YYHRvD5XkIGB4a+v/b+9Me+TIsvP83Htjzb12FtlsLtM9vcyi0YxkSyNLhmEYBgT7kwD7F/nf+IsMGDAMyIYgyxY0m3q6p7fhTlax9so1trv5w00WySabXeypkVpSPECBrKqojMjMyIj3nvOec1DdlP57V54/hvMgQKYRvfcuU9w5ZPzTu7gyDKcud0M6cvCDayTrfVSak2++iYxinG4w5QxvLXFvgIgSzGyMiGKsrnF1SdQb4q0hyrp0r9xk/uBzqpN9bPXVkS9vHXrngNlf/5J4a4Xk6hb5999CH5wiuxleW8oPb+PmZUh9C0G8tYIadNGPjyg/uIUa9oJv6ofvYCdz0rfeAOs4/a9/EapgjTnrtVd9/jBMjYijM/uC7Gak1y4hooj6s/tUnz9EDjpk71yj84O3qW89esXrKom2VrGTOfP/+yG+bnBFjTtnmv7JTM5UdjjQD5iaMCvTYSjcFGklXbWynI2XkKs+tVtQuwKWBu1IpFinmbkFpZsxjDboqhET/TmlDaNZDpr7WG9eK1qlhn2yd67R/cPvE19axxyeMv/rX2LH/3CiCqCi4NA9wviGVXmJS/IaijBWq/IFB/4hR273QppQQugLdeAeUfmCdbHNmtjmkrwGSKwP3aQm/mSZfr24iJW1DYtin6I8Wi5aHUncY2vtu6yPvs3RUlh1slVG/TeZLXaZznco6/FZZW6j5yEKakr2jj6krMdc3vhdqmbKZPaI6SJ4iRs9f61jT1TOWvc643IXKSSj/A3m9SGlNOzKPSozQ7saKSOSeERHe5w3KBmTRn1iFc7ZQo+ZVQdkcR8lYmb1+YM1rbB6Tby12NmcemcHeXCw7AB8SHLlMtHKCNnthpl6gG8a7HiCOT2lebQbuqffv48ry5eOA3np/poGvX+IbzR2OiO+sh26no9GyF7v6ZBVKZed3DW2KHFFgZ3OMOMx5viE6s69V6YezfExxd/9EjefE62vo4Z9VKeLSNPl2KDk6T5sGKthZ2PsJFQ36sd71PcehFmBr2nyfRZXFFSf38IbQ3bjBtHmeniunRyZLnuUOYczBl/V2PkCfXDwQsuLJB8SZwO8dxTzQ5pqhveewdoNvHeUs0O8s/RW3sBZg7M6pPpMjYwSBms3ON37dOk1OL9IFJEkHnXpXF8n2Rwg03Au5NfWSVZ76ONFqHhUEpnFqE6Kqw3N0Tyk6bzHFg3N4Yz8aiihF0oSDTJkFqOPa5qj2VmDTVc2lLtjZBITDfLnjP7eOmxZo8cFdvHkQv6i6VxlMcl6n87NTaJ+FtoACOi9dQnXGGT0hbYKHvTpIvSwWprY9aTEVpp4tXuWBj3/a6aIuilRP6f37mWEFNgijPpRvYx0c0i6OUBmMTKKSYar1KcH4D3Z6hZCqnBueo+MYprJEXFvSHrlJovH97DV4ixy6XQTBqifA19r7HSBPZmAscSXN4gvjc5agLiqweyf4KoanEd2c6K1IdHmCgiJb3ToVTXs4a1FDruhaet8gTl8MbXhqxq3qEJ/vuV7KJI4VLtpc5ZSFLMF+XduEq0NXxRWz06f8J769iPUsEf2zjVcWdPc28UdvJ7/0WHRrkL7Z3qDeYv1Zjl7UeB82GYQr+IJUxEW9pSeWAvH4Qoqt6Dv10hEhCUINEND419dnQmQfvtNkmuXkXkaPjvDHvGlNeLtddyipL71kPKXv/6NKg3j4QpxZ0gzrdhp7j3n8xm7Q2pRUvnFmSga+0OMM2Fu3hKPo2SB9Y+pXUksMhQqRD+GOQtTUMwWTyNHQiBUhEwzZnbB3eZjCj/F9mKy7hVkmqEnJ5higdcv90/pXFD0FJNMMDk9hKJCsHxPqKkolr2kLk5YCSFQMqaTbxCpYANRKqHb2cCYiieFFMZWaL2gk6/jnA3X3voUY2bYpa0iiKuCuplhbI02JXUzoay+nldOCEkadVnrvIm2FY0t6CSrSKFoaPBCYYxFCo8SKZ1kBBDEVHPCMN8mUimxy8jjIYl6/TYl3yBh9bQzsZBRSNvYsIIRMkJIuRxkHJS3kJIo6+F0gzXNWUpAqAghVWi26GwIbihFlPVCdKOpcKZZPq4KK18bthVSEncGy200+LA/V9XYkzF29whbFSHiNJvjvMctKuzRKebgmHhzDTnonwkAX9e46Rx7cEp5+zb65AivNVncxzq9PLE8zj+JFImzOWZPUlcANAZ3cEpxcIx6+Ij40hbJ1hb5xmVclmCVx0sQFnxTY4oFbjrHnJyiDw8xewdENiLxGUay7EAd9qdkjPcON5lRfvwJ5vgkCLe1VdRwEAYBpylE4T1g2THe1Q12OsUcHdPs7S0N668w058X79F7+6GX18EhyZUrRBvroSHrUrBibfC0FIsgHpetKZ4lyfpEUUZTTZie3MfqirSzSnd0hcnhLYrZPgLJYP0mSsWU80NO9z9D13P6q9fpb77F5PD2ax++6qYMf3j9qXF9OTQ33eijOunyprw815UM1x/3haHeT74/a+klnkY+n/T7Onu5wvy4MKBLPtcN3TsfIj+vMk8LSLdHDH9wjezyCkLJUCziIR510JNieaP+QhrZfeE4lhHNs+f0GojlayEExMM8eMj0M+Lce8r7R8FfJSQyTtGzMTKKyTeuIKME25Q43SCjGO8cKslIhussdu9iqxKhIlxT4nS9vIZ8NeF6E94Hb5bpw+Vx4nzoGfVsZ28BxFFIUacJ0Xpor2JnC+x4vryG+derLhShAbJ/YoJ/8n4DqHBNfBKpDi1K4qf9xZyj+vgu6c0rJDcuh6IEY4M/7/h8Zl2Px/jnmyZKJLFISWROLBNikVJTon1Nrgahy73XaN+cXcOsN2dm6Cenh/UmVGfJPtrXy/28/FxNrm3T+/H3idZHIWIfqbAImRfUn96n/PAWevc3qyKLekPSzcvU6SGnxQJbFogqwVtDkTZUaUht+irB6YY5c8oOyCgmtquYYo6MIrz3VLqitI+QafBnuqamu/Uu3g1QYoZrarxuQuZhuIpMU4r5jEn1GBHH5GvXyDcuIdP8LDrv6iosDKxBqAjV6WKLOaQRbpgxH0oWxT7NfP83eh2+GkEcdxkNbpDGPYSUOBfsFJHKlveXQN1MmcwfkaYDsnSIlIo06VOUx5T16TIadfHHB4Ik6uLxFM0peTwCPMY1wfelsjNTf6pCirI2cxpb4JwJ3kGnGWSbGKep9OuZ278xwkpIGXwzMjoTN838FPBEeRcVp5imxlZzvLPIOGfwxrtUp/tU432srkBI4rxPlPcw5RxTLfDeEWU9Vm78DrYuWBw+oJocAh6Vdkj7K5hqgS7nqLTD6ls/Yr5/n2Z2hLcGU80xh4eUpSGdKBb796gnh2cCQskIsdBUH3zMvC7wy7EZEELpscrI01WUrrBWIFTG5sp3KKojFtUhzlm0WYTO0EKeGfaMrc5OujjKSaIOjSkw+8fovX0qGZGuvIs2JbNyD2MqoigDD8aWYRWxvDFIGbE2uomUEZP5QxbVMc5plIzJ0xWsa9CmxOiK5tEOzc7u8+XhX9JP56X/vvS9JdycXkNz+aqiun2X6s5dngiRL2zxVHj4Fx9cyjAyxDZPxZ4QAqmiIFqdARm8VkKFdhHONiE6JZ4c9GsiBenGgMv/6V8y/eABR//rY2YfhYjCyo/fZuUP3z4TOd5abNHgjUMkCtVJllVvPpjJ83TZPT2kF/WkCBGZNApm6UovhYwiHoR0rSubc0dCnyCUZPDdq2z96Q/Y+++/YPLTu1Q7offR5f/8ByRrvZf+ncoTZBI9972IFWZeh8jba+C0wSxqnHGMf/Y5+3/+c5rTL0wl8EEoxt1hiDgtF1LeOZrZOAhCa2kmRyT9FbxzzB/dIh1t4nSDEBKVdYi6Q0xdYMuvTgWejZkRAjXohBvIvDgTy18MAHizbFh5cIqdLpj975+dHbyQEpGnyCzB9zrnfm28NpjjKfH2ejChKxlM7M7j5iVoGwbPRwohJWrYR6TBdwhgDseYownlJ/fo/OBt4rUBmG2KcwqrFxHEMmMQrbESXSJTPZqoDN2XvCYSYWRYInN6akQkEox/eYSsdgX9aJXt9C3Gep+ZPaHxL7dIPOlIL5IYkcb4usEcjik/uk3xk1/R3Nv9ms/neWSa0732NkJF6PExi/u/xhQL8svXyK9cQ0hF8fAOzfEB3jv673yPuD/ClAvmv/6YqN/HG01zeoxZzMi2rhAPV6n2d+je+DYySkgGK9SHe9QnB8gkpf/t76LnU5zWy8/zCvnlq6Qb29iqJOr0kFFYVNanh5j5lLg3pPut95h9+gFOa1xdXWSm79WvkYzo5Ztcv/In3Lr/Pzkaf0ajF8RRhlL/jjR52gi70QtOJncYzx7Q72wxGtzg8uYPafSCw+OP2Tv+8LdwhKGi8ah4gEAy6lxB2zJ4pXAsmlO6ySpKJLilp886jRCKtc410rjHvD6mqE/ZHrzH8eIe8/r1omffGGGl0g7ZcJN87TL19BirD8KKdHUblWSopEOU95g++pRmdrpcRIsn4hQAGafIKCHKevS332Ly8FPq6VGITMUpVtfLcTYeGSWoOEVGKYOr1yiOHqIXk6U46xGlOSrpMH34MbqchQu5DV/eOYSKyFe3iZIOKs2JO0Omjz6lnh2fRdqkDGWwEsnq4CZFdcys2EMIQRzl9DtbpPGQo8mvcU6TJUOydIgxFWXjafSCQfcykcqQQjLsXWUy36GojsJK1T5tQ+Gtod/ZwAPj+QO6nU2sran1LHgdVALIIBy8J09XyJMVpIpJ4x51M+V0fh8ZGaI4vK66diSZxFnQjSPNJc56vIcolujGAQKpBEY74jhEK+rSkWYh8iGUYPtGzuxEMz3W1MXTFamQijQbkiRhWGZTz2jqKWm+QpL2mZ7eJ447JNmAOOmhVEox36c4x4qsXByj4pzhxluknRWacoI1DePD2+SDLbqjK3inqYpjTBMM8etXfxdng2ApJo9RcUbe26Q3ukLaWaEuTvHe01QvvykJGaILMo2xRYOeFHjnSNb65G+skV1ZRZ8ub+ie4NF5cEzUSRn+4BrHf/kJQGjX8N0ryGWHcm8tZlpSPjgh2eiz9ifvMP7JHWzRkF0asvrH72AmJeWD1w+dCyWRSbgx69MCszTVpxsDOtfWkUlEee/oC89T0nv/CtXeBH2yQChB7+1LpBsD5p/tYovX9I94cJVm/ukuUTdj+KMbHP/Vp9iyCWnCTorMY8yswhRzpvc+xhQzhFhgdQ3OLv12Hqdrmtkpy1Dg2UofITj99GeYYo5rzleNJdOY6Po2wz/9MWrUw5zOKH95+0vbt/hGo3ePQspwa5Xhf/gjgCC2Hh/RPNynvrtLfHmD0Z/9G9AGczpD7x+jHx2Qfvsa+fe+RXLtEnhPtLWK3j2kvr1DtD4iffsqyRubiCzBHE9Z/O3H6MdHZO/fCOb2S6tBBPY72NMpspPR+f33iVb6oeBgbRjaERycr2rOYSndlMf1bRr/JMXm0a7m1Owxt6coEdG4ikRmrETb7Nd3qH2JQrESX+bUPGZhxxgXGlSe6j2mIhRPTM0RhZshkTSuXA5MfznlR7fRu4fIPDuzJIRKzAV2PHt1VPY1cLqhGR9jFlNUmtF7+7uY6SkiTsK5U5WkG8HztrjzKcWDOyQr66i8S375KkIq9GKKiKbhvchyZJKixyfo6RivG+a3P8HWZYhaGUOxcy9ErZIU7yxmNqY+2g8LjvmEen8H1e2TrKwjZ5OwGIwT4v4IoSK8e3pf+/tACoWUEVIIjK0wpkIKxbD3JlkyfC66GaksZA70gkV5jHWaupmyvfE7ZOnoucf13uGcJlIpSr3e5I5n0bbkYH4LbcPnvLHFMlq6TE+6JtwXxdPCFv/Myl8KhccTqZRFc0ptnkndnpNvjLCSKj4TRlZXOF0hVES2cmlZsRUR5X1UnIaU1AsIoqxHlPeJO0OylW0WB/epvTurBHK6xtYF+CDkos6AOO+TjS7RzMfoxSRcjnUNiCDo0hzbvLiKElKRjbaQUYJYijGVZCENuRRWkcpI4z5J1KGbrWNtw4zQFNQ6jXWWWGXEKkMvT8ZY5SiZYJ2mFKd00hWcs9RmTi7CiAGlUox98eYQx6GEWyBIog5GSLRZYGyzHDxraUyBx5HGfbr5BtZpkqiHtZokidm4npBkwXtuGofRHqkEWUfRHUY0taUpHULA5EiTdRWD9ZjF2NDUDgGkHUVvGNHUjqZybL0ZUmKLyRcmucuYvLMe0r5Ap7eJMSVRnJPmQ8RYkGRDkrSHNZq8u05Tn8+cauqCqjghSrpYU+GsxjQFZhHER5x0cFbTlBN0U4TzT8bgHVoXNOUEZxpMs6BcHKHrObpZvLoyxHtsUbO4fYDqZ4x+7ybdty8hlSQa5phJ8VxEyRnL5Bf36b1zid672yHloy3xShdbNE9HvyxF2PTDB/Te2ab79iVUnuC0Je5nRL2U6UePWNx6/RSAd47meE65e0rn5gZRPwstFGKFdx5TfCHa4H1InRtL9sYKUT9DpqG/lplVTH5+DzMPwkp1Ejrf2iIa5HRubJBdHuGqLq6xRL009OC6d4SZFNjaMP7JHXpvXyK/ts6afC8sYJap0+Z4RnH3CDur0NOTJy/L8rN6PuqT13t9XK3xPnQI99oEcfRoH6yjvvPoxSiqC13Rmwd7+LpBdoM3w5V1eCxtae4+xlcN0foopOXKGt+YkCKsG/Tj0NzSzYrwVdTYyYzmwR5RMQztVJb7SKfHvHXFkcaHTOsOh0VD7BpcVZDWJTffVJRJg/cVOEfxqEQdHZBWM7yCKBJEESSxII4Fs7lDa08cC7TxDHqCTsdzcjrF154n01IcltoVTwcME0YFhVaUoUEmQmB8TWXnSzP78j3wxdn9v/EVjQ1+II8nigW9nmK4FuOcp1w45hMTrkHzKX4xpdYeFQmkFKgIRqOIeWypVHjYNJNESXhfiplFytAJ3TTn9LRag5lPaU4OSNY2g5hRCluV4WsxQ0ShjUY0GCGiCNfUoWfhYIS3BlkF24QQImRhVISrS1xdYcsFzfj47CbuhMTMJsSDleAV9B7X1NiiQEYxZjYJKcYsDzYXFdpmCKlQafYl98KvjxCKQfcycdyh19lCqYRB9zLgqeoJ82If5zTalMyLA3qdzTCD0zvSpB9SaM9405Kkx6D3xjJa5JfpwpRGz2n081FjYyvmxT797mVGg+vEUY7zluk8tFsQUqCSJ+l0j4yW/19eU6NMoWKJSiRxbmHiaQpDZRvUcls8yFSgbRDxQgpMY1/QpJFMSaMuk3KXUr989Nyr+MYIKwgXyWp8QD0+wFRzoqyHSvKlZ8qE1J5zy8q3UEovowShYoTUJN0BSX+VuNNHRklQos4G/0VTBcFmasAT533SXtg2iLWnhtuwH0uUdYO/K4rDvypCxklIJzmHSnJUnOCsQVfFUrw8JY46ZMkKSdwhUtlZDw8BaFOhzYImGSKExDlD3czo5VskURcbhxuGFDHG19TNlDoOkZ1IJTinkVIhfYySMdZWeO+QQhJFWfiZa3DehVXFchyBtQ1+2YU6jnKEVTinsa4hShU3vpuTdgS6CSfs47sVeU9x9Z0OvVFEXTiKmaEpHfsPKkabCVff6XD4qGb/bon3cP17PXrDiHJhmR5pkkxytPviDVAuI1bz6Q7eO7qrN1nM9pBSIYRCyIgkDb1yFrPHZJ0Vzrsq895STvcpp/tn0Ywnfzs5mPHUCPT08crZ/gs/b6ops5PzNSf11tOcLDj+y08Y/f4N1v7kXbwLjTbrvQnjn9456xUFQSyNf3oHhGf1x99m899/D7toKO4fMf7JHQbfv4p9pr3D9O/u48qG1T9+l9U/+jYiiTDjBbNPdpn84j7VztNIhDcWO18a3V8xRsZrx+LOAfHP7jL6vZuo37mGmVdUO6cUt/Zw2mKKJytisGVD+fCY2UePiIY56//2fVQnpbx/xPyDHaZ/9+DMQ6S6KaMf3SC/uka82kV1U+hnqF5G5/o65aNTzLRcDlQ3TH52F1cbRj+6wfq/fg/VTc6aq578v1sI9ffbnyg06pyz+JuPXvhd8fPPvvTvmnuPw/Dkl6AfH6EfH730d9Un96g+ufelv/siW29E/Kvf61CWd7l9TzO/qxkOJEXp6XUF33s/5YNffUbdeNJEUB1Z8kwgEsGsK1gdKbJMkMSCTi44OLI0DfS7gvHU8caViI01xQe/qjk6dthXpHhrVzI1R6GFghihfcWx3qH+ktTeszyJcHT6ku1rKTe+08Foz9Fuw+69mnJuSTKJUoKqdCRpuElHseDauzkHDxuOHjc4B4PViE4viI2T/XDtMdpzsqfPZ0VY+v1kkoZokDVn3rYnWZL68DEiiskvv4nKe7i6DOf8k6paIVBZB1dXZyk8AJY+XpXlZ9MphIrCvuI43GeW+3zhNVr6i1Wa4/IOKkkJ5bQSGcXhMaIk7E+qEMX9GkgZsTK8TjffIk36WFvT62ySJgPqZoq1DfPigKI65uDkE4b9q4wG19GmYDx9QFEdL83rgVjl9LvbDLqXQyTIO6xrODj5lPH84XP71nrB6fQenXyDQe8Ko/6bWBtsKo2dkHQjkm4cvKPaEaUKqx1WO7yHzkpK2o1I+zG9rZyTuzPmhyW2caT9GNs4TGOJEoWzHiGDPWR+VGGb5yNS3ju0rSma8ddqUfGNElbAc74KZxqKo0dkoy2itIMzDVbXSKlI+muko60gvIRkfnCPKO0FkXQ2GPMZhCQbbuKtYb53J4iiJ8bAs117vLN01t9AqhiV5swe30IISdJfJVvZWvY/EpQnu5THu+HYsi7eGZxugul9SaTCTLIvunmdt/TyTYQQZMnw7AQLil8QqZRIZXjvKeoTsmTIxuhd0rjH0eQW1mrydEQ3WyfMnZKczu6iTUknXWV79fvk6QqTxdOKISEEaTxgtX+D0/l9qmbMosrppGt4glHeugohc+I0hEaLieHkcU3WUQxWI453azqDiKyjEFKw+WZG1lXoxqFrx+lBg64da5dTTveaYEZOJUkeVpcvvtUOYyqyTqh+M7okSjqk+QpZZ5W8sxo6PEcZo7WbxHGH6pyjQZ7f0cs+GF92lf3Nwul2UTP+29vMPnqIiNWZ8dlre7ayelYs2aJm/Ld3mP1qBxkpvHM4bXG1YfLze89t6xrD/PM9yp3T4G8SgHXYyoT02zPRsHpvwsn4UyY/u4sev9pPVO2ccnj6ISf/57Ngrncep+3SsL00wS/7Us0/2eXWf/lvwQ+mJEd/8avgPdJm6Rl7ekHXJwv2/vxniFgFc/qyms47D9bhjMXMn16EvXHMP92lvH+ETMO8SY8PPYqK5rWbjv5TxxrPYuF5Yy8gRwAAB2hJREFUsBME1Z/9xx6b65LH+5bp3NHrSqIIOrnk2tWY99+B/UPLeOIYDSV/8gcZZeW580AjgH/xw4ztTcXKSHL7nsF7qBt/rmk8xmvmdkzp5oA46wHkzjlMGGB9O+GNtzI6/XDjU1FCb6ioiiDqVCTo9BTGeI52GsZHBu9g62rCaD2iWDiUAikFcSq48X7OaD3mcLfhb/7HhKZ2L78UPINKMzpv3ISrAtdUlI/u0oyPSTe2STe2UZ0Zenxy1qhYdXrILA8Vex70dILKc4bf+SHN5IQo72IWIcquZ1OSlXUG3/kR1d5DbLEIEzWuvR3SiVkHW8yoD14U5bZc4KqS3lvvY6vgt/TOoPIuamWD/Mp14sEIuzS4Nydfr4+jtQ07+z9fpvpkMOvjz1oqaFPinMG6ht2Dn7N//OFZxNHa5ix69eQ6Oiv2KHfGKBk90x/KY2x9VhV4tm9nKKoT7u38Veg9RRDdWhfkqylb743w3hOniihVOONwxqMrS1MYZBSK36JUkQ8TNt4asHajT9yJ6KykVJOGyW7BbL9k9GaXpBNRTzSPPjiiHDc48/T6ab0Okfmv2ffrGyOsTB2iREJFwTcBOGuCMb1aIOME5+xZNKmZnzK5/xHOaEwxxVtDebpLMz9GyIjF0SPqaVgZemeZ791BKIWti9C8bnKAqRZIKSmPd4OvqymZPvoMEAgZxIOpFuAdzfSY8Z0PsE2FLiY4o6kmB5i6QMXL3PgTL9aSsj7FugYhBIvykEoHxX86vRceH8lM7lM3M5zXVE0okZ2Xh8tUn6eojtCmWPqsFGV9cva7o8ktABqzwDnz3Lbzcn+5v/BaThe7ROrkbJRHref4xQ5VM8F7R6MX+MhSzAynBw3jg4amdEyPNYtlON5Zz9b1jM4gYv9+iTMhTagiQTmznB40WOO59YsZ1oYLchQHYTXe1zTV8yeptQ3z6U6I5AkB3mF0hXeWqjihqWchf7/8UFblmKZ+/bDs3yvOYxf1M+0Nvv72dv6FdO/Si3QegeG1xWiLmZ1jPpi2mEmJmXx1dMFVmmr36TDiV3Ue8tY99ZSdk/M+v98mThv0zhF2PA89pr6hRJFg0Jdc2owIPXo9RycO66DXlfR6gk4mGfQlV7Yj5nNH03i6uaDfk6yvKR7sGMrSc/lSRJYJau05PHaoCLJUoMqX1668SJiV95vMlYuXKbyjxw2DlbCAA5hPLL2hojtc5vsElDNFVTpGaxHOQ97zrG0Ldu+E8707iMJ1aW5ZTGyItn3FmklPTlg4GzIgSoYmzYvZmR9Kj4/xxmDLAu9CZZ6ejsOC3BrwQQDJJMXMpti6DKmxpaevPtjFzKcIpdDTCV7XOK0pHtym2t/B1SVmPsU7h54cY4v50oelsc5RHewGW4sxyyp2hR6fYOMZrqmQcYKeTc5VmPHleLQ5R483H4quvmrco3Oa5tzDlIPpvNEv2j28S7GNY3ApJ0qfZpdsY0n7YRF2fG9GlEiSToQuLbqyYTHnoZo0mCZEHueHJb3NjDhVWG1RsUQq8ZywCkfz9X17wp8rPvrbRQhxjoN4po78qx/wS0rQlmb3Z3/3qm3PHb34qm2/7Pcv/lwsw6Uvbn8xx/NkdfGybaNEcPWdLpOjhpO95qWHsPVmRpJJHn5enP8pv86ht7S0nIvtLcUf/2FOXXuaxlPXnqr2xAn0OkFQ/fLjmkFfcu2NmOMTS1V7lIJBXzIcKO4/1DzYMXzreowUoLWn1pBngn5PYAz85BcV44mj+S1rzDe+lTLaiJlPLGuXYrKOpKmDHWFlK6Y/iqgKSxQLJseG+dhy/b2cpg5d9jt9xe0PC6JYsLIZnxXdTI4Mu/fqi7kGLZsSP/3+S+4fr7wHiS+Jop9n/2L5PP55XVDjXNHbyFm72UcqidUOFQtM7ZCRIM4j9j4+Jc4iumspzjia0oRCsTzCNMFv1xSGg8/GrN0ckPZimoVmcVxTz/UL6cCvwnv/pUuOf0TCqqWlpaXlVchn7IRCvjgA4YnX+YsdSs7+Dp6bDPRk238QntUgz6yJX6otvmTbl1gpW/4x88z7KaQ4834KIYLF4Mve79/CedAKq5aWlpaWlpaWC+JVwupiazVbWlpaWlpaWv4Z0wqrlpaWlpaWlpYL4huRCmxpaWlpaWlp+adAG7FqaWlpaWlpabkgWmHV0tLS0tLS0nJBtMKqpaWlpaWlpeWCaIVVS0tLS0tLS8sF0QqrlpaWlpaWlpYLohVWLS0tLS0tLS0XRCusWlpaWlpaWlouiFZYtbS0tLS0tLRcEK2wamlpaWlpaWm5IFph1dLS0tLS0tJyQbTCqqWlpaWlpaXlgmiFVUtLS0tLS0vLBdEKq5aWlpaWlpaWC6IVVi0tLS0tLS0tF0QrrFpaWlpaWlpaLohWWLW0tLS0tLS0XBCtsGppaWlpaWlpuSBaYdXS0tLS0tLSckG0wqqlpaWlpaWl5YJohVVLS0tLS0tLywXRCquWlpaWlpaWlguiFVYtLS0tLS0tLRdEK6xaWlpaWlpaWi6I/w9nj5ZqWccZOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud().generate_from_frequencies(dogs_common)\n",
    "plt.figure()\n",
    "plt.imshow(wc, interpolation = \"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.savefig('./visuals/dogs-common-adjs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud().generate_from_frequencies(nondogs_common)\n",
    "plt.figure()\n",
    "plt.imshow(wc, interpolation = \"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.savefig('./visuals/nondogs-common-adjs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqMAAAJOCAYAAACDRQ40AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xm8bGV95/vvLxzBCUFAEYl4RNE4AxGjXIduJU4k7ZC+0cSBGI1tMK22sRMSb7x62+7G7o56YyfaapxNtJOYaGKMqDGCEVCQ0QEFg1EEnBBxQsFf/1HPjnU2Z+YcnjO8369XvXbVqlWrnvXs2md/zqpV51R3BwAAZviJ2QMAAGD3JUYBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKMAOrqp+pao+MnscG3NDjLGqLqqq+2/P5wBueGIU2Gaq6uKq+kFVHbBq+dlV1VW19npuv6vqTptY56Cq+uOqurSqrqqqz1TVi6vqZtfnuXdGVbV2zNmaVcvfWFUvuYHG8MCq+va4fGeM59tLl0M2d1vdfcfuPnVs98Sqet2q5zqtqp60jcd/76r6YFVdMS4fr6pjtuVzwO5OjALb2j8l+aWVG1V1zyQ3uSGeuKr2S3LqeL77d/feSX42yb5J7nhDjIF1dfcp3X3z7r55kruPxfuuLOvuf545vmVVtceq2z+R5D1J3pXk1kluk+Q3k3xnOzz3mk2vBbsmMQpsa29J8pSl28clefPyClW1T1W9uaq+WlVfqKr/Z/ziT1Xdqao+XFVXVtXXquodY/nJ4+HnjCNqj1/Pcz8vyVVJntTdFydJd3+xu5/T3eeO7Rw9jm5dOb4evTSuf6iql1TVR8dz/HVV7V9Vb6uqb4311y6t31V1fFV9bhyF/U9VdceqOnWs/7+ras+l9X+tqi6sqm9U1bur6rartvXMsa0rquoPq6pW7+BY/vurlv11VT13w9+SDdvQfI/7fqqq3j/Ge0FV/eLSffuPffhWVX0sWxn7VfXIqvr40u2PLH2vU1VnVNUjxvXLquoBVfWYLL7Xx43v08fGnByV5HVj2e+Px9yjqv5+zOmnx2NXtv32qvqDqjqpqr6TZPUpALdNcnCS13b3D7v76u4+eeXo7NjGs2px+sDXq+qdVXXg0txds2pf/+XI7fhe//34fl6R5ISx/PhaHM2/qqrOG3+ZS1XdrqreNb5Hn6+qZ27NfMMOqbtdXFxctsklycVJjklyQZK7JtkjyReT3D5JJ1k71ntzFkeb9k6yNslnkzxt3PenSV6QxV+Wb5zkAUvb7yR32sjzn5bkxRu5f78kVyR5cpI1WRzBvSLJ/uP+f0hyYRZhtU+ST42xHTPWf3OSN6waz7uT3CKLo35XJ/lgkkOXHn/cWPchSb6W5MgkeyV5ZZKTV23rb7I4intIkq8mecS471eSfGRcv2+SLyf5iXH7gCTfTXLgevZ37djumlXL35jkJRub7yQ3G9+7p459P3KM/+7j/rcn+d9jvXskuWRljBuZ/+uMZ7wGrh5zeOMklya5bFzfO8n3k+w91r1saXwnJnnder7/T1q6fYuxvSdm8Vo8Ksk3Vl5DYx++keRnxv7vtWp7a7J4Tf9lkkcnufWq+x81xnSvMd7XJHn/uO+nklyzofEleWaSa5L82hjbTbJ4XX4hyRFJKsldkvzkuP+8JL+dZM8kd07yz0kePPtn3sVlW1wcGQW2h5Wjoz+b5DNZhEqSf3kr9PFJfqe7r+rFEczfz+IXcZL8MIt4vW13f7+7t+RDMftnER8bcmySz3X3W7r7mu7+0zG+n19a5w3dfVF3X5nkvUku6u4PdPc1Sf4si1BY9tLu/lZ3fzLJ+UlO6u7PLz1+Zf0nJnl9d3+iu69O8jtJ7l/rnkd7Ynd/sxdvXX8oyeGrd6C7P5bkyiQPHYuekOQfuvvyjU3MRmxovn8uycXd/YYxV59I8hdJ/u34Hv5Ckhd293e6+/wkb9qaJ+/uq5Kcm+QBSe6X5ONJPjauPyDJuWOdrfHYJOd399u6+9ru/niSvx5jX/Hn3X16d/9ofF+Wx3ZNkgcnuTzJK5JcWovzR+8wVnliktd097nd/f0kv5XkoVV1m80c3+e7+7VjbN9L8vQk/6W7z+qFC7r7S2MebtzdL+3uH3T3Z5O8IYvvPez0xCiwPbwlyS9ncUTvzavuOyCLoztfWFr2hSzeDk0Wv9Aryceq6pNV9atb8LxfT3LQRu6/7arnXf3cySI8VnxvPbdvvurxm7v+Os/d3d8e411+7suWrn93Pc+14k1JVj6o86Qs5nt9Vt4mvtGq5TfKIkKTDc/37ZP8TFV9c+WSRXzdJsmtsjhq+MWlba6e1y3x4ST/KsmDxvV/yCICHzxub63bJ3nQqn34haz7Gvni+h+60N1f6O5ndvcdsjjinSSvH19Xf0+/meRbWfd7ujGrn/t2SS7awH6sXbUfz8viewE7PTEKbHPd/YUsPsj0qCTvXHX31/Ljo3ErDsk4etrdl3X3r3X3bZP8uyR/VJv4BP2SDyR5bI3zT9fjy6ued53n3s7Wee5afLp//6187rcmeXRV3TuL0yH+agPrXZrFXK9dtfwOGRG1kfn+YpIPd/e+S5ebd/evZ3EKwTVZxNOKzf5U/HqsjtEPZ9Mx2pux7ItZHKlevQ/P3chjNmi8rl+VxWkJyXW/p/tkcWrAJVl8yGmPqtpraROr43F9413fubdfTPKZVfuxd3c/dnPHDjsyMQpsL09L8pDuXueTx919bRbnGv7nqtq7qm6fxVGetyZJVf3fVfWTY/UrsviFfe24fXl+fHRqfV6WRQy8aWw3VXVwVb2squ6V5G+T3Lmqfrmq1tTiQ1B3y+Jcze3tT5I8taoOH4HyX5KcPk5T2CLjrduPZ3FE9C/GW7zrW+/aLN5a/8/jA0c3qqpfymKf35tsdL7/Jou5evJ43I2q6qiquuvY7juTvKiqblpVd8vig2pb65Qk984i8s4al7tmcYrDhk7TuDzJHVZ9yGv16+OvkhxRVY8f49+zqu5XVXfenEFV1YFV9cKqOrQWbp3F0f7Txip/muTXxoekbpzkpUn+vrsvyyJUv5rkiVW1R1Udn00fMX1dkhNq8c9JVVXdeXxvPjLG89yquvF47d6rqo7cnP2AHZ0YBbaLcd7lGRu4+99nceTo81n8ov2T/Pitz6OSnF5V387iw0HP6e5/Gve9KIvQ/GYtfbJ76Tm/keToLI4Gnl5VV2XxgaIrk1zY3V/P4lzI38ziLfLfSvJz3f2167u/m9LdH0zye1nE4aVZHAG7Puf8vSnJPbPht+hXHJ/Fh3TOTfKVJL+R5Nilc0zXO9/jPM2HjTF+OYtTCF6axYevMrZz87H8jVmcw7hVxtvbn0py1jh/8kdJzkzy6XHf+rw9yU2TfKOqPjqWvTzJU8Yn5/9bd1+R5OFZfAjr0rEfL8l1T1vYkO8nOSyL0wauSnJOFsH+9DHuv0nyX7OYty9nceTzyeO+a8d6/28W7wbcbuzTxubhLVn8herPs3i7/8+z+GewfpjFuwxHZ3FE+6tZHKHd0GkcsFOp7s1+hwKAHURVPSiLo8lrR7wB7JQcGQXYyVTVjZI8J4t/2kiIAjs1MQqwE6mquyb5ZhafCH/F5OEAXG/epgcAYBpHRgEAmGbN7AGw+Q444IBeu3bt7GEAAGzSmWee+bXuvtWm1hOjO5G1a9fmjDM29C/lAADsOKpqs/5nNm/TAwAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnE6E7kvEuuzNoT3jN7GAAA24wYBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYJo1swewpapq/yQfHDdvk+TaJF8dt+/b3T/Yhs/1q0n+trsvG7c/kuRWSa5OsmeSk5L8XndfuRXbfkmSr3X3K7bVeAEAdjY73ZHR7v56dx/e3YcneXWSl6/c3pYhOvxqFsG77PHdfa8k90ryoyTv3MbPCQCw29jpYnRDqup3q+r4cf2VVXXSuP7wqnrjuP7Iqjq1qj5RVe+oqpuN5UdV1Yer6syqem9VHVhVj09yeJJ3VNXZVbXn8vON8H1+ksOq6u5jO8dV1cfG+n9UVT8xlh87nvOclXGtGvuvV9V7qurG222CAAB2QLtMjCY5OckDx/Ujk+xbVWuSPCDJKVV16yQnJHlodx+Z5Nwkz6mqvZL8/0l+obt/Oslbk/yn7n5HkrOzOBK63qOu3X3N2M5PVdU9kjw2ydHjqO2aJE+oqtskeVWSx3b3vZM8YXkbVfXcJA8b939/9XNU1TOq6oyqOuPa727x2QAAADu0ne6c0Y34eJKjqmrfJN9OcmGSI7II1LckOTrJ3ZJ8tKqSxTmfH0ly1yR3T/KBsXyPJF/aguet8fWYJEclOWNs5yZJvpjke0k+1N1fSJLu/sbSY5+a5AtJHjfC9jq6+zVJXpMkex10WG/BuAAAdni7TIx299VV9eUkT0nyj0k+m+ShSQ7p7s+Ot9L/rrufvPy4qjoiybnd/cDrbHQTxpHXeyT5dJJDkry+u39v1TqPS7KhiDwvi1MBDs4iSgEAdiu70tv0yeKt+uePr6ckeVaSM8d9H03y4Ko6NEmq6mZVdViSTyU5uKruO5bvuXIOaJKrkuy9vica55C+NMmF3f2pJB9I8otVdcC4f/+qOiSLMH5IVd1+LN9vaTNnjDH+9Xg7HwBgt7KrxegpSQ5Mcnp3X5Lkh2NZuvvyJE/L4gNJ52QRp3fu7quT/NskLxvLz0ryM2N7b0jyulUfYHpHVZ2bxVHNPZM8bmz/vCQvzuLt/nOz+GefDhzP++tJ3jW2/7blAXf3h7M4l/U9q0IVAGCXV91OQ9xZ7HXQYX3Qca/IxSceO3soAAAbVVVndvd9NrXernZkFACAnYgYBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGdyL3PHifXHzisbOHAQCwzYhRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMs2b2ANh8511yZdae8J713nfxicfewKMBALj+HBkFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgmo3GaFXtX1Vnj8tlVXXJ0u09N/HY91XV3lW1X1U9c2n5oVX1hOsz6Kr6zar6H0u3/7iq/m7p9n+oqpdt5PFrquqbm/lcv1JV51fVJ8flP4zlb62qx4zrb6iqu2zlvjykqu63NY8FANjZbTRGu/vr3X14dx+e5NVJXr5yu7t/sInHPry7r0qyX5JnLt11aJItitGqWrNq0UeTHL10+55J9q+qlf05Osk/bslzbOB5fy7JbyQ5prvvnuQ+Sa5avV53P7W7L9jKp3lIEjEKAOyWtupt+qr63ao6flx/ZVWdNK4/vKreOK5/qar2TXJikruMo6knjtv/etx+9jhK+bKq+lhVnVtVTx+PP6aqPlBVb09y1qohnJnkblW1V1Xtl+RbSc5Pcrdx//2zCNZU1XFj22dX1R8tBWuq6uVV9Ymqen9V7b+eXf3dJM/r7suSpLu/192vW898fKSqDh/XH1lVp47tvqOqbrY0Hy+qqrPGft65qu6Y5OlJ/uMY39Grtw0AsCvb2nNGT07ywHH9yCT7jqOXD0hyyqp1T0hywTiaesK4/aFx+w+SPCPJV7r7vkmOSvKsqjpkPPZ+SX6ru++5vMFxVPb8JD+dRXieNi5HV9Xtk/yguy+tqnskeWySo8fR3TX58VHZfZKc1t1HJjk1ye+tZz/vnkX4bpaquvXYv4eO7Z6b5DlLq1ze3UckeV0WkXvRuP7fx3x8dD3bfEZVnVFVZ1z73Ss3dygAADuF1W9/b66PJzlqHPn8dpILkxyRRaC+ZQu39bAkd106j3SfJIeN66d29z9v4HH/mMXb8bfM4ijoF5M8L4u30Vei7pgsAveMqkqSm4z1kuSaJH82rr81yZ9s4bjX5+gsjs5+dDzfnkk+snT/O8fXM5M8anM22N2vSfKaJNnroMN6G4wRAGCHsVUx2t1XV9WXkzwliyj8bJKHJjmkuz+7hZurJMd39wfXWVh1TJLvbORxH01yXJJ9k7wsyRVJ7pFFjK6cL1pJXt/d6xz1HEdxV4fd+kLvU1kcfT15s/Zk8Xx/191P3sD9V4+v12br/yIAALDLuD7/tNPJSZ4/vp6S5FlZ/1vaVyXZeyO335fk+JUPKVXVXarqJpvx/CtHRvcdH7T6UZJvJjk2Pz4y+oEkv1hVB4xt7790CsCNkjxuXP/lrHsEc8V/TfI/qurA8fgbV9W/38iYPprkwVV16Fj/ZlV12EbWT647HwAAu43rE6OnJDkwyendfUmSH+a654umuy/P4m3y88YHmM5KskdVnVNVz07yv5J8LsnZVXV+kldlM44advfXklyZxXmZK05LckCS88Y65yV5cZIPVNW5SU4aY8547JFV9YksznV9yXqe491jfH9fVZ9MckY2MmdjX5+W5B1VdU4WcXrnTezKu7II5rN8gAkA2N1Ut9MQdxZ7HXRYH3TcK9Z738UnHnsDjwYAYMOq6szuvs+m1vM/MAEAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATLNm9gDYfPc8eJ+cceKxs4cBALDNODIKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBp1sweAJvvvEuuzNoT3jN7GADATuriE4+dPYTrcGQUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAabYoRqtq/6o6e1wuq6pLlm7vuYnHvq+q9q6q/arqmUvLD62qJ2ztDixt5wlV9emq+sBWPn6PqjplXL9TVX1v7NenquoPa+GYqvqr6ztWAAAWtihGu/vr3X14dx+e5NVJXr5yu7t/sInHPry7r0qyX5JnLt11aJItitGqWrOexU9P8ozuPmZrttHd13b3A5cWXTD2895JDk/y81syRgAANm2bvE1fVb9bVceP66+sqpPG9YdX1RvH9S9V1b5JTkxyl3HU8cRx+1+P28+uqjVV9bKq+lhVnVtVTx+PP6aqPlBVb09y1qrn//+S3C/J66rqxKq6SVW9qarOq6pPVNWDxnpPr6q3V9XfJHnvqm2sqapvrt637v5hklOT3Gks2ruq3llVF1TVm5ce/7NjH86rqteuHCke+/2iqjpr7M+dx/KbV9Ubx36eVVViFwDY7azvCOPWODnJs5L8UZIjk9xoHHl8QJJTVq17QpI7jaOOqapjkvxGdz9m3D4+yVe6+75VtVeS01biNovgvFt3//PyBrv7hVX1kLGds6vqt5P8oLvvWVV3T/K3VXXYWP3+SQ7v7is2Z8eq6mZJHpLkt8eiI5PcLclXxtjul+TcJK9P8q+6+6KqeluSZyT5n+Mxl3f3EVX17CTPy+LI8AuT/F13/0pV3TLJ6VX1/u7+/qrnf8bYVva4xa02Z8gAADuNbfUBpo8nOWoc+fz2uH1EkgfmujG6KQ9L8tSqOjvJ6Un2TbISkqeuDtENeECStyRJd38yyZfz4yObJ21miN5ljOGUJH/Z3e8fy0/r7ku7+9okZydZm+SuST7X3ReNdd6c5EFL23rn+HrmWH9lP18wnuNDSW6c5JDVg+ju13T3fbr7PnvcdJ/NGDYAwM5jmxwZ7e6rq+rLSZ6S5B+TfDbJQ5Mc0t2f3cLNVZLju/uD6yxcHEH9zhZsY0M2dxsr54yudvXS9WuzmMONPd/yY1bWz3jMY5YCFgBgt7Mt/2mnk5M8f3w9JYu37c9cz3pXJdl7I7ffl+T4lQ8YVdVdquomWzGWJ47H3zXJQUku3MJtbIlPJTmsqg4dt5+U5MObeMz7kjx75UZVHbGdxgYAsMPaljF6SpIDk5ze3Zck+WHW8xZ9d1+e5IzxQZ8Ts/gw0h5Vdc44p/J/JflckrOr6vwkr8qWH8F9ZZKbVNV5Sd6W5Cmb+rT/9dHd303ytCTvHM95dZLXbuJhL05y0zEPn0zyou01PgCAHVV19+wxsJn2OuiwPui4V8weBgCwk7r4xGNvsOeqqjO7+z6bWs//wAQAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgmjWzB8Dmu+fB++SME4+dPQwAgG3GkVEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGIUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKYRowAATCNGAQCYRowCADCNGAUAYBoxCgDANGJffNeiAAAGmUlEQVQUAIBpxCgAANOIUQAAphGjAABMI0YBAJhGjAIAMI0YBQBgGjEKAMA0YhQAgGnEKAAA04hRAACmEaMAAEwjRgEAmEaMAgAwTXX37DGwmarqqiQXzB7HDuSAJF+bPYgdiPlYl/lYl/m4LnOyLvOxLvOxrq2Zj9t39602tdKarRsPk1zQ3feZPYgdRVWdYT5+zHysy3ysy3xclzlZl/lYl/lY1/acD2/TAwAwjRgFAGAaMbpzec3sAexgzMe6zMe6zMe6zMd1mZN1mY91mY91bbf58AEmAACmcWQUAIBpxCgAANOI0Z1AVT2iqi6oqgur6oTZ47khVNXtqupDVfXpqvpkVT1nLH9RVV1SVWePy6OWHvM7Y44uqKqHzxv99lFVF1fVeWO/zxjL9quq91fV58bXW47lVVV/MObj3Ko6cu7ot62qusvSa+DsqvpWVT13d3t9VNXrq+orVXX+0rItfk1U1XFj/c9V1XEz9mVb2MB8/Peq+szY57+sqn3H8rVV9b2l18qrlx7z0+Nn7cIxZzVjf66vDczHFv+M7Cq/gzYwH+9YmouLq+rssXx3eH1s6PfsDf9nSHe77MCXJHskuSjJoUn2THJOkrvNHtcNsN8HJTlyXN87yWeT3C3Ji5I8fz3r323MzV5J7jDmbI/Z+7GN5+TiJAesWvbfkpwwrp+Q5KXj+qOSvDdJJblfktNnj387zsseSS5Lcvvd7fWR5EFJjkxy/ta+JpLsl+Tz4+stx/Vbzt63bTgfD0uyZlx/6dJ8rF1eb9V2Ppbk/mOu3pvkkbP3bRvOxxb9jOxKv4PWNx+r7v/9JC/cjV4fG/o9e4P/GeLI6I7vvkku7O7Pd/cPkrw9yaMnj2m76+5Lu/sT4/pVST6d5OCNPOTRSd7e3Vd39z8luTCLudvVPTrJm8b1NyV5zNLyN/fCaUn2raqDZgzwBvDQJBd19xc2ss4u+fro7pOTfGPV4i19TTw8yfu7+xvdfUWS9yd5xPYf/ba3vvno7pO6+5px87QkP7mxbYw5uUV3n9qL37Rvzo/ncKeygdfHhmzoZ2SX+R20sfkYRzd/Mcmfbmwbu9jrY0O/Z2/wP0PE6I7v4CRfXLr9pWw8ynY5VbU2yRFJTh+LfmO8RfD6lbcPsnvMUyc5qarOrKpnjGUHdvelyeIPliS3Hst3h/lY8YSs+wtkd319rNjS18TuNDe/msWRnRV3qKqzqurDVfXAsezgLOZgxa44H1vyM7K7vD4emOTy7v7c0rLd5vWx6vfsDf5niBjd8a3vXJTd5t/jqqqbJ/mLJM/t7m8leVWSOyY5PMmlWbytkuwe8/R/dfeRSR6Z5FlV9aCNrLs7zEeqas8k/ybJn41Fu/PrY1M2NAe7xdxU1QuSXJPkbWPRpUkO6e4jkjwvyZ9U1S2y68/Hlv6M7OrzseKXsu5faneb18d6fs9ucNX1LNsmrxExuuP7UpLbLd3+ySRfnjSWG1RV3SiLH5C3dfc7k6S7L+/ua7v7R0lemx+/1brLz1N3f3l8/UqSv8xi3y9feft9fP3KWH2Xn4/hkUk+0d2XJ7v362PJlr4mdvm5GR+o+LkkTxxvrWa8Hf31cf3MLM6LvHMW87H8Vv4uNR9b8TOyO7w+1iR5XJJ3rCzbXV4f6/s9mwl/hojRHd/HkxxWVXcYR4GekOTdk8e03Y3zd/44yae7+2VLy5fPe3xskpVPRb47yROqaq+qukOSw7I4yXyXUFU3q6q9V65n8aGM87PY75VPLh6X5F3j+ruTPGV8+vF+Sa5cedtlF7PO0Yzd9fWxypa+Jt6X5GFVdcvxlu3DxrJdQlU9IslvJ/k33f3dpeW3qqo9xvVDs3hNfH7MyVVVdb/x59BT8uM53Oltxc/I7vA76Jgkn+nuf3n7fXd4fWzo92xm/BmyvT6l5bJNP/H2qCw+5XZRkhfMHs8NtM8PyOIw/7lJzh6XRyV5S5LzxvJ3Jzlo6TEvGHN0QXbSTzduZD4OzeJTrOck+eTK6yDJ/kk+mORz4+t+Y3kl+cMxH+cluc/sfdgOc3LTJF9Pss/Sst3q9ZFFiF+a5IdZHJ142ta8JrI4l/LCcXnq7P3axvNxYRbns638OfLqse4vjJ+lc5J8IsnPL23nPllE2kVJ/mfG/1a4s102MB9b/DOyq/wOWt98jOVvTPLMVevuDq+PDf2evcH/DPHfgQIAMI236QEAmEaMAgAwjRgFAGAaMQoAwDRiFACAacQoAADTiFEAAKb5P0yA5KGrwnHeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arc_clean.source.value_counts().plot('barh')\n",
    "plt.title('Most Commonly Used Twitter Source')\n",
    "plt.savefig('./visuals/source-dist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sources'></a>\n",
    "# Sources\n",
    "\n",
    "- **WordCloud:** https://medium.com/@agrimabahl/elegant-python-code-reproduction-of-most-common-words-from-a-story-25f5e28e0f8c \n",
    "\n",
    "- **Error file:** https://github.com/gouravaich/wrangle-analyze-weratedogs-twitter/blob/master/Solution.ipynb \n",
    "\n",
    "- **String formatting:** https://stackoverflow.com/questions/3395138/using-multiple-arguments-for-string-formatting-in-python-e-g-s-s"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}