sahkprotests_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis of Tweets on the 2019 Hong Kong Protest Movement \n",
    "\n",
    "In this pet project, an end-to-end sentiment analysis was performed on twitter tweets regarding the 2019 Hong Kong Protest Movement. We want to gain a better understanding of the sentiments on this social media platform using data science and machine learning techniques. Specifically, we want to find out what is the sentiment towards the Hong Kong Government and Police that are at the center of this mess. A positive sentiment would mean that a particular tweet supports the Hong Kong Government and/or Police, whereas a negative one would indicate the opposite.\n",
    "\n",
    "The beginning of this notebook will focus on scraping twitter tweets that are dated from 25th October 2019 onwards. We will then clean up the tweets and do some word processing procedures so that the dataset can be used for subsequent data visualization and model training.\n",
    "References:\n",
    "- https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed\n",
    "- https://towardsdatascience.com/extracting-twitter-data-pre-processing-and-sentiment-analysis-using-python-3-0-7192bd8b47cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tweetcreated_at = 8 hours time difference. SG local time is 8hrs ahead. Take note."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generic ones\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# Word processing libraries\n",
    "import re\n",
    "from nltk.corpus import wordnet\n",
    "import string\n",
    "from nltk import pos_tag\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import WhitespaceTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "# Widen the size of each cell\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dataset\n",
    "The .csv files each contains 15k tweets and the search parameters used are stated below:\n",
    "1. search_words = \"#hongkong OR #hkprotests OR #freehongkong OR #hongkongprotests OR #hkpolicebrutality OR #antichinazi OR #standwithhongkong OR #hkpolicestate OR #HKpoliceterrorist OR #standwithhk OR #hkpoliceterrorism\"\n",
    "2. date_since = [\"2019-10-28\",\"2019-10-29\",\"2019-10-30\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_1st = pd.read_csv(os.getcwd() + '/data/raw' + '/20191103_131218_sahkprotests_tweets.csv')\n",
    "tweets_2nd = pd.read_csv(os.getcwd() + '/data/raw' + '/20191103_153932_sahkprotests_tweets.csv')\n",
    "tweets_3rd = pd.read_csv(os.getcwd() + '/data/raw' + '/20191105_010011_sahkprotests_tweets.csv')\n",
    "tweets_4th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191105_222815_sahkprotests_tweets.csv')\n",
    "tweets_5th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191107_000333_sahkprotests_tweets.csv')\n",
    "tweets_6th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191108_001436_sahkprotests_tweets.csv')\n",
    "tweets_7th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191109_030106_sahkprotests_tweets.csv')\n",
    "tweets_8th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191109_120954_sahkprotests_tweets.csv')\n",
    "tweets_9th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191110_021422_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_10th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191110_134433_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_11th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191111_223912_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_12th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191112_231846_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_13th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191113_225544_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_14th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191115_000508_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_15th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191116_121136_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_16th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191117_001011_sahkprotests_tweets.csv', engine='python')\n",
    "tweets_17th = pd.read_csv(os.getcwd() + '/data/raw' + '/20191120_015710_sahkprotests_tweets.csv', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of 1st set is: (15000, 11)\n",
      "Size of 2nd set is: (15000, 11)\n",
      "Size of 3rd set is: (15000, 11)\n",
      "Size of 4th set is: (15001, 11)\n",
      "Size of 5th set is: (15000, 11)\n",
      "Size of 6th set is: (15000, 11)\n",
      "Size of 7th set is: (15000, 11)\n",
      "Size of 8th set is: (15000, 11)\n",
      "Size of 9th set is: (15001, 11)\n",
      "Size of 10th set is: (15001, 11)\n",
      "Size of 11th set is: (15000, 11)\n",
      "Size of 12th set is: (15000, 11)\n",
      "Size of 13th set is: (15000, 11)\n",
      "Size of 14th set is: (15000, 11)\n",
      "Size of 15th set is: (15000, 11)\n",
      "Size of 16th set is: (15000, 11)\n",
      "Size of 17th set is: (15000, 11)\n"
     ]
    }
   ],
   "source": [
    "# Shape of dataset\n",
    "print('Size of 1st set is:', tweets_1st.shape)\n",
    "print('Size of 2nd set is:', tweets_2nd.shape)\n",
    "print('Size of 3rd set is:', tweets_3rd.shape)\n",
    "print('Size of 4th set is:', tweets_4th.shape)\n",
    "print('Size of 5th set is:', tweets_5th.shape)\n",
    "print('Size of 6th set is:', tweets_6th.shape)\n",
    "print('Size of 7th set is:', tweets_7th.shape)\n",
    "print('Size of 8th set is:', tweets_8th.shape)\n",
    "print('Size of 9th set is:', tweets_9th.shape)\n",
    "print('Size of 10th set is:', tweets_10th.shape)\n",
    "print('Size of 11th set is:', tweets_11th.shape)\n",
    "print('Size of 12th set is:', tweets_12th.shape)\n",
    "print('Size of 13th set is:', tweets_13th.shape)\n",
    "print('Size of 14th set is:', tweets_14th.shape)\n",
    "print('Size of 15th set is:', tweets_15th.shape)\n",
    "print('Size of 16th set is:', tweets_16th.shape)\n",
    "print('Size of 17th set is:', tweets_17th.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10515 non-null object\n",
      "location          5694 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10566 non-null object\n",
      "location          5909 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10941 non-null object\n",
      "location          5770 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15001 entries, 0 to 15000\n",
      "Data columns (total 11 columns):\n",
      "username          15001 non-null object\n",
      "acctdesc          11491 non-null object\n",
      "location          6577 non-null object\n",
      "following         15000 non-null float64\n",
      "followers         15000 non-null float64\n",
      "totaltweets       15000 non-null object\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null object\n",
      "text              15000 non-null object\n",
      "hashtags          14999 non-null object\n",
      "dtypes: float64(2), object(9)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10900 non-null object\n",
      "location          5730 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          11180 non-null object\n",
      "location          5663 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          11409 non-null object\n",
      "location          6077 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10803 non-null object\n",
      "location          5548 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15001 entries, 0 to 15000\n",
      "Data columns (total 11 columns):\n",
      "username          15001 non-null object\n",
      "acctdesc          11594 non-null object\n",
      "location          5692 non-null object\n",
      "following         15000 non-null float64\n",
      "followers         15000 non-null float64\n",
      "totaltweets       15000 non-null object\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null object\n",
      "text              15000 non-null object\n",
      "hashtags          14999 non-null object\n",
      "dtypes: float64(2), object(9)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15001 entries, 0 to 15000\n",
      "Data columns (total 11 columns):\n",
      "username          15001 non-null object\n",
      "acctdesc          10903 non-null object\n",
      "location          5903 non-null object\n",
      "following         15000 non-null float64\n",
      "followers         15000 non-null float64\n",
      "totaltweets       15000 non-null object\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null object\n",
      "text              15000 non-null object\n",
      "hashtags          14999 non-null object\n",
      "dtypes: float64(2), object(9)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10368 non-null object\n",
      "location          5799 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          9964 non-null object\n",
      "location          5297 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          9402 non-null object\n",
      "location          4419 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10628 non-null object\n",
      "location          4111 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          11536 non-null object\n",
      "location          5027 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          11997 non-null object\n",
      "location          5322 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 11 columns):\n",
      "username          15000 non-null object\n",
      "acctdesc          10879 non-null object\n",
      "location          5528 non-null object\n",
      "following         15000 non-null int64\n",
      "followers         15000 non-null int64\n",
      "totaltweets       15000 non-null int64\n",
      "usercreatedts     15000 non-null object\n",
      "tweetcreatedts    15000 non-null object\n",
      "retweetcount      15000 non-null int64\n",
      "text              15000 non-null object\n",
      "hashtags          15000 non-null object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 1.3+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(tweets_1st.info())\n",
    "print('\\n')\n",
    "print(tweets_2nd.info())\n",
    "print('\\n')\n",
    "print(tweets_3rd.info())\n",
    "print('\\n')\n",
    "print(tweets_4th.info())\n",
    "print('\\n')\n",
    "print(tweets_5th.info())\n",
    "print('\\n')\n",
    "print(tweets_6th.info())\n",
    "print('\\n')\n",
    "print(tweets_7th.info())\n",
    "print('\\n')\n",
    "print(tweets_8th.info())\n",
    "print('\\n')\n",
    "print(tweets_9th.info())\n",
    "print('\\n')\n",
    "print(tweets_10th.info())\n",
    "print('\\n')\n",
    "print(tweets_11th.info())\n",
    "print('\\n')\n",
    "print(tweets_12th.info())\n",
    "print('\\n')\n",
    "print(tweets_13th.info())\n",
    "print('\\n')\n",
    "print(tweets_14th.info())\n",
    "print('\\n')\n",
    "print(tweets_15th.info())\n",
    "print('\\n')\n",
    "print(tweets_16th.info())\n",
    "print('\\n')\n",
    "print(tweets_17th.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of concatenated dataset is: (255003, 11)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 255003 entries, 0 to 255002\n",
      "Data columns (total 11 columns):\n",
      "username          255003 non-null object\n",
      "acctdesc          185076 non-null object\n",
      "location          94066 non-null object\n",
      "following         255000 non-null float64\n",
      "followers         255000 non-null float64\n",
      "totaltweets       255000 non-null object\n",
      "usercreatedts     255000 non-null object\n",
      "tweetcreatedts    255000 non-null object\n",
      "retweetcount      255000 non-null object\n",
      "text              255000 non-null object\n",
      "hashtags          254997 non-null object\n",
      "dtypes: float64(2), object(9)\n",
      "memory usage: 21.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Concat the two dataset together:\n",
    "data = pd.concat([tweets_1st, tweets_2nd, tweets_3rd, tweets_4th, tweets_5th, tweets_6th, tweets_7th,\n",
    "                  tweets_8th, tweets_9th, tweets_10th, tweets_11th, tweets_12th, tweets_13th, tweets_14th,\n",
    "                 tweets_15th, tweets_16th, tweets_17th], axis = 0)\n",
    "\n",
    "print('Size of concatenated dataset is:', data.shape)\n",
    "\n",
    "# Reset_index\n",
    "data.reset_index(inplace = True, drop = True)\n",
    "data.head()\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Duplicated Entries and Removing Them\n",
    "Since we are performing the scraping close to each other, it is possible to scrape the same tweets as long as they fall within the search window of 7 days from the search_date.\n",
    "We will remove these duplicated rows from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xzxzanalazy        651\n",
       "belleng324         540\n",
       "wdamidoinhere      399\n",
       "natalie_hoyin      286\n",
       "hky2147            264\n",
       "                  ... \n",
       "LS_0301              1\n",
       "LamSiuC              1\n",
       "J3theWebDev          1\n",
       "jinyi_whale          1\n",
       "eastriverrunner      1\n",
       "Name: username, Length: 51955, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the number of unique users in the dataset\n",
    "# It can be seen that some users are more proactive on the social media platform than others.\n",
    "data['username'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>following</th>\n",
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       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10271</td>\n",
       "      <td>Phy32833861</td>\n",
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       "      <td>2019-11-03 04:24:41</td>\n",
       "      <td>12</td>\n",
       "      <td>@JoachimWatson @Treerisu @SolomonYue @HawleyMO...</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <td>10292</td>\n",
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       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-03 04:24:33</td>\n",
       "      <td>2</td>\n",
       "      <td>@JoachimWatson @SolomonYue @HawleyMO SOS! Plea...</td>\n",
       "      <td>[{'text': 'StandWithHongKong', 'indices': [86,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10436</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-03 04:23:16</td>\n",
       "      <td>15</td>\n",
       "      <td>More &amp;amp; more cases like Hoi-lam Chan will h...</td>\n",
       "      <td>[{'text': 'hkpolice', 'indices': [74, 83]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10463</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>418</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-03 04:23:04</td>\n",
       "      <td>34</td>\n",
       "      <td>This clip is a bit old but this happens on eve...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10549</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>418</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-03 04:22:28</td>\n",
       "      <td>2818</td>\n",
       "      <td>Arrestees ALL GIRLS. \\nHard to believe they're...</td>\n",
       "      <td>[{'text': 'HK', 'indices': [102, 105]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71978</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1112</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-06 14:53:28</td>\n",
       "      <td>2</td>\n",
       "      <td>@SenRickScott Other than interest, #ccp and it...</td>\n",
       "      <td>[{'text': 'ccp', 'indices': [56, 60]}, {'text'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72020</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1112</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-06 14:52:52</td>\n",
       "      <td>590</td>\n",
       "      <td>“The UK Government says that it is “fully comm...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72062</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1112</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-06 14:52:07</td>\n",
       "      <td>378</td>\n",
       "      <td>Top Pro-CCP figure in #HongKong, Junius Ho was...</td>\n",
       "      <td>[{'text': 'HongKong', 'indices': [39, 48]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72153</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1112</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-06 14:50:59</td>\n",
       "      <td>132</td>\n",
       "      <td>Just like the pro-Beijing HK lawmaker Junius H...</td>\n",
       "      <td>[{'text': '何君堯', 'indices': [62, 66]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72334</td>\n",
       "      <td>Phy32833861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1112</td>\n",
       "      <td>2019-09-28 03:09:49</td>\n",
       "      <td>2019-11-06 14:48:53</td>\n",
       "      <td>3506</td>\n",
       "      <td>#hongkong is this generation’s Berlin. It repr...</td>\n",
       "      <td>[{'text': 'hongkong', 'indices': [14, 23]}]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          username acctdesc location  following  followers totaltweets  \\\n",
       "10271  Phy32833861      NaN      NaN        7.0        7.0         418   \n",
       "10292  Phy32833861      NaN      NaN        7.0        7.0         418   \n",
       "10436  Phy32833861      NaN      NaN        7.0        7.0         418   \n",
       "10463  Phy32833861      NaN      NaN        7.0        7.0         418   \n",
       "10549  Phy32833861      NaN      NaN        7.0        7.0         418   \n",
       "...            ...      ...      ...        ...        ...         ...   \n",
       "71978  Phy32833861      NaN      NaN       11.0        9.0        1112   \n",
       "72020  Phy32833861      NaN      NaN       11.0        9.0        1112   \n",
       "72062  Phy32833861      NaN      NaN       11.0        9.0        1112   \n",
       "72153  Phy32833861      NaN      NaN       11.0        9.0        1112   \n",
       "72334  Phy32833861      NaN      NaN       11.0        9.0        1112   \n",
       "\n",
       "             usercreatedts       tweetcreatedts retweetcount  \\\n",
       "10271  2019-09-28 03:09:49  2019-11-03 04:24:41           12   \n",
       "10292  2019-09-28 03:09:49  2019-11-03 04:24:33            2   \n",
       "10436  2019-09-28 03:09:49  2019-11-03 04:23:16           15   \n",
       "10463  2019-09-28 03:09:49  2019-11-03 04:23:04           34   \n",
       "10549  2019-09-28 03:09:49  2019-11-03 04:22:28         2818   \n",
       "...                    ...                  ...          ...   \n",
       "71978  2019-09-28 03:09:49  2019-11-06 14:53:28            2   \n",
       "72020  2019-09-28 03:09:49  2019-11-06 14:52:52          590   \n",
       "72062  2019-09-28 03:09:49  2019-11-06 14:52:07          378   \n",
       "72153  2019-09-28 03:09:49  2019-11-06 14:50:59          132   \n",
       "72334  2019-09-28 03:09:49  2019-11-06 14:48:53         3506   \n",
       "\n",
       "                                                    text  \\\n",
       "10271  @JoachimWatson @Treerisu @SolomonYue @HawleyMO...   \n",
       "10292  @JoachimWatson @SolomonYue @HawleyMO SOS! Plea...   \n",
       "10436  More &amp; more cases like Hoi-lam Chan will h...   \n",
       "10463  This clip is a bit old but this happens on eve...   \n",
       "10549  Arrestees ALL GIRLS. \\nHard to believe they're...   \n",
       "...                                                  ...   \n",
       "71978  @SenRickScott Other than interest, #ccp and it...   \n",
       "72020  “The UK Government says that it is “fully comm...   \n",
       "72062  Top Pro-CCP figure in #HongKong, Junius Ho was...   \n",
       "72153  Just like the pro-Beijing HK lawmaker Junius H...   \n",
       "72334  #hongkong is this generation’s Berlin. It repr...   \n",
       "\n",
       "                                                hashtags  \n",
       "10271                                                 []  \n",
       "10292  [{'text': 'StandWithHongKong', 'indices': [86,...  \n",
       "10436        [{'text': 'hkpolice', 'indices': [74, 83]}]  \n",
       "10463                                                 []  \n",
       "10549            [{'text': 'HK', 'indices': [102, 105]}]  \n",
       "...                                                  ...  \n",
       "71978  [{'text': 'ccp', 'indices': [56, 60]}, {'text'...  \n",
       "72020                                                 []  \n",
       "72062        [{'text': 'HongKong', 'indices': [39, 48]}]  \n",
       "72153             [{'text': '何君堯', 'indices': [62, 66]}]  \n",
       "72334        [{'text': 'hongkong', 'indices': [14, 23]}]  \n",
       "\n",
       "[166 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is acceptable to have repeated username since they can tweet multiple times throughout the day.\n",
    "data[data['username'] == 'Phy32833861']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial size of dataset before dropping duplicated rows: (255003, 11)\n",
      "Current size of dataset after dropping duplicated rows, if any, is: (248652, 11)\n",
      "          username                                           acctdesc  \\\n",
      "0     five5demands  HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...   \n",
      "1   Dejavu53328974  #followbackhongkong\\n#StandwithHK\\n#HKHumanRig...   \n",
      "2         tsksimon                #FightForFreedom #StandWithHongKong   \n",
      "3  JimmyWo67187904                                                NaN   \n",
      "4         ARCHI418                                                NaN   \n",
      "\n",
      "    location  following  followers totaltweets        usercreatedts  \\\n",
      "0        NaN      437.0      260.0         466  2019-10-05 13:40:37   \n",
      "1  Hong Kong      581.0      360.0        7835  2019-08-27 15:47:34   \n",
      "2        NaN      825.0      456.0        6728  2014-10-05 15:57:07   \n",
      "3        NaN       22.0        1.0         656  2019-10-08 12:58:51   \n",
      "4        NaN       19.0       11.0         121  2014-06-19 16:19:36   \n",
      "\n",
      "        tweetcreatedts retweetcount  \\\n",
      "0  2019-11-03 02:57:49          292   \n",
      "1  2019-11-03 02:57:49         1315   \n",
      "2  2019-11-03 02:57:49          105   \n",
      "3  2019-11-03 02:57:48          660   \n",
      "4  2019-11-03 02:57:46            0   \n",
      "\n",
      "                                                text  \\\n",
      "0  Disgusting: Police in #HongKong are marking pr...   \n",
      "1  #China-made teargas canister stuck in the road...   \n",
      "2  This is only tiny bits of #HKPoliceBrutality.\\...   \n",
      "3  A fireman complained to the #HongKongPolice fo...   \n",
      "4  #police threw a #Teargas hand #grenade. It bla...   \n",
      "\n",
      "                                            hashtags  \n",
      "0        [{'text': 'HongKong', 'indices': [36, 45]}]  \n",
      "1  [{'text': 'China', 'indices': [11, 17]}, {'tex...  \n",
      "2  [{'text': 'HKPoliceBrutality', 'indices': [42,...  \n",
      "3  [{'text': 'HongKongPolice', 'indices': [41, 56]}]  \n",
      "4  [{'text': 'police', 'indices': [0, 7]}, {'text...  \n"
     ]
    }
   ],
   "source": [
    "# Let's drop duplicated rows:\n",
    "print('Initial size of dataset before dropping duplicated rows:', data.shape)\n",
    "data.drop_duplicates(keep = False, inplace = True)\n",
    "\n",
    "print('Current size of dataset after dropping duplicated rows, if any, is:', data.shape)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Non-English Words/Tokens\n",
    "\n",
    "Since it might be possible to remove non-english words that are used in daily english conversations such as names etc, it might be better to filter by the chinese language.\n",
    "\n",
    "##### Remove empty tweets first! \n",
    "If there are empty tweets or 'nan', the following codes will break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset = ['text'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The unicode accounts for chinese characters and punctuations.\n",
    "def strip_chinese_words(string):\n",
    "    # list of english words\n",
    "    en_list = re.findall(u'[^\\u4E00-\\u9FA5\\u3000-\\u303F]', str(string))\n",
    "    \n",
    "    # Remove word from the list, if not english\n",
    "    for c in string:\n",
    "        if c not in en_list:\n",
    "            string = string.replace(c, '')\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>five5demands</td>\n",
       "      <td>HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.0</td>\n",
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       "      <td>466</td>\n",
       "      <td>2019-10-05 13:40:37</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>292</td>\n",
       "      <td>Disgusting: Police in #HongKong are marking pr...</td>\n",
       "      <td>[{'text': 'HongKong', 'indices': [36, 45]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Dejavu53328974</td>\n",
       "      <td>#followbackhongkong\\n#StandwithHK\\n#HKHumanRig...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>581.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>7835</td>\n",
       "      <td>2019-08-27 15:47:34</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>1315</td>\n",
       "      <td>#China-made teargas canister stuck in the road...</td>\n",
       "      <td>[{'text': 'China', 'indices': [11, 17]}, {'tex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tsksimon</td>\n",
       "      <td>#FightForFreedom #StandWithHongKong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>6728</td>\n",
       "      <td>2014-10-05 15:57:07</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>105</td>\n",
       "      <td>This is only tiny bits of #HKPoliceBrutality.\\...</td>\n",
       "      <td>[{'text': 'HKPoliceBrutality', 'indices': [42,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>JimmyWo67187904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>656</td>\n",
       "      <td>2019-10-08 12:58:51</td>\n",
       "      <td>2019-11-03 02:57:48</td>\n",
       "      <td>660</td>\n",
       "      <td>A fireman complained to the #HongKongPolice fo...</td>\n",
       "      <td>[{'text': 'HongKongPolice', 'indices': [41, 56]}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARCHI418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121</td>\n",
       "      <td>2014-06-19 16:19:36</td>\n",
       "      <td>2019-11-03 02:57:46</td>\n",
       "      <td>0</td>\n",
       "      <td>#police threw a #Teargas hand #grenade. It bla...</td>\n",
       "      <td>[{'text': 'police', 'indices': [0, 7]}, {'text...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          username                                           acctdesc  \\\n",
       "0     five5demands  HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...   \n",
       "1   Dejavu53328974  #followbackhongkong\\n#StandwithHK\\n#HKHumanRig...   \n",
       "2         tsksimon                #FightForFreedom #StandWithHongKong   \n",
       "3  JimmyWo67187904                                                NaN   \n",
       "4         ARCHI418                                                NaN   \n",
       "\n",
       "    location  following  followers totaltweets        usercreatedts  \\\n",
       "0        NaN      437.0      260.0         466  2019-10-05 13:40:37   \n",
       "1  Hong Kong      581.0      360.0        7835  2019-08-27 15:47:34   \n",
       "2        NaN      825.0      456.0        6728  2014-10-05 15:57:07   \n",
       "3        NaN       22.0        1.0         656  2019-10-08 12:58:51   \n",
       "4        NaN       19.0       11.0         121  2014-06-19 16:19:36   \n",
       "\n",
       "        tweetcreatedts retweetcount  \\\n",
       "0  2019-11-03 02:57:49          292   \n",
       "1  2019-11-03 02:57:49         1315   \n",
       "2  2019-11-03 02:57:49          105   \n",
       "3  2019-11-03 02:57:48          660   \n",
       "4  2019-11-03 02:57:46            0   \n",
       "\n",
       "                                                text  \\\n",
       "0  Disgusting: Police in #HongKong are marking pr...   \n",
       "1  #China-made teargas canister stuck in the road...   \n",
       "2  This is only tiny bits of #HKPoliceBrutality.\\...   \n",
       "3  A fireman complained to the #HongKongPolice fo...   \n",
       "4  #police threw a #Teargas hand #grenade. It bla...   \n",
       "\n",
       "                                            hashtags  \n",
       "0        [{'text': 'HongKong', 'indices': [36, 45]}]  \n",
       "1  [{'text': 'China', 'indices': [11, 17]}, {'tex...  \n",
       "2  [{'text': 'HKPoliceBrutality', 'indices': [42,...  \n",
       "3  [{'text': 'HongKongPolice', 'indices': [41, 56]}]  \n",
       "4  [{'text': 'police', 'indices': [0, 7]}, {'text...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply strip_chinese_words(...) on the column 'text'\n",
    "data['text'] = data['text'].apply(lambda x: strip_chinese_words(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect @Users mentioned in each tweet\n",
    "\n",
    "We want to obtain this useful information from each tweet because it could allow us to analyse what are the popular figures in the protest movement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>mentioned_users</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>five5demands</td>\n",
       "      <td>HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>466</td>\n",
       "      <td>2019-10-05 13:40:37</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>292</td>\n",
       "      <td>Disgusting: Police in #HongKong are marking pr...</td>\n",
       "      <td>[{'text': 'HongKong', 'indices': [36, 45]}]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Dejavu53328974</td>\n",
       "      <td>#followbackhongkong\\n#StandwithHK\\n#HKHumanRig...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>581.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>7835</td>\n",
       "      <td>2019-08-27 15:47:34</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>1315</td>\n",
       "      <td>#China-made teargas canister stuck in the road...</td>\n",
       "      <td>[{'text': 'China', 'indices': [11, 17]}, {'tex...</td>\n",
       "      <td>[@hkgetv]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tsksimon</td>\n",
       "      <td>#FightForFreedom #StandWithHongKong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>6728</td>\n",
       "      <td>2014-10-05 15:57:07</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>105</td>\n",
       "      <td>This is only tiny bits of #HKPoliceBrutality.\\...</td>\n",
       "      <td>[{'text': 'HKPoliceBrutality', 'indices': [42,...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>JimmyWo67187904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>656</td>\n",
       "      <td>2019-10-08 12:58:51</td>\n",
       "      <td>2019-11-03 02:57:48</td>\n",
       "      <td>660</td>\n",
       "      <td>A fireman complained to the #HongKongPolice fo...</td>\n",
       "      <td>[{'text': 'HongKongPolice', 'indices': [41, 56]}]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARCHI418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121</td>\n",
       "      <td>2014-06-19 16:19:36</td>\n",
       "      <td>2019-11-03 02:57:46</td>\n",
       "      <td>0</td>\n",
       "      <td>#police threw a #Teargas hand #grenade. It bla...</td>\n",
       "      <td>[{'text': 'police', 'indices': [0, 7]}, {'text...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          username                                           acctdesc  \\\n",
       "0     five5demands  HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...   \n",
       "1   Dejavu53328974  #followbackhongkong\\n#StandwithHK\\n#HKHumanRig...   \n",
       "2         tsksimon                #FightForFreedom #StandWithHongKong   \n",
       "3  JimmyWo67187904                                                NaN   \n",
       "4         ARCHI418                                                NaN   \n",
       "\n",
       "    location  following  followers totaltweets        usercreatedts  \\\n",
       "0        NaN      437.0      260.0         466  2019-10-05 13:40:37   \n",
       "1  Hong Kong      581.0      360.0        7835  2019-08-27 15:47:34   \n",
       "2        NaN      825.0      456.0        6728  2014-10-05 15:57:07   \n",
       "3        NaN       22.0        1.0         656  2019-10-08 12:58:51   \n",
       "4        NaN       19.0       11.0         121  2014-06-19 16:19:36   \n",
       "\n",
       "        tweetcreatedts retweetcount  \\\n",
       "0  2019-11-03 02:57:49          292   \n",
       "1  2019-11-03 02:57:49         1315   \n",
       "2  2019-11-03 02:57:49          105   \n",
       "3  2019-11-03 02:57:48          660   \n",
       "4  2019-11-03 02:57:46            0   \n",
       "\n",
       "                                                text  \\\n",
       "0  Disgusting: Police in #HongKong are marking pr...   \n",
       "1  #China-made teargas canister stuck in the road...   \n",
       "2  This is only tiny bits of #HKPoliceBrutality.\\...   \n",
       "3  A fireman complained to the #HongKongPolice fo...   \n",
       "4  #police threw a #Teargas hand #grenade. It bla...   \n",
       "\n",
       "                                            hashtags mentioned_users  \n",
       "0        [{'text': 'HongKong', 'indices': [36, 45]}]              []  \n",
       "1  [{'text': 'China', 'indices': [11, 17]}, {'tex...       [@hkgetv]  \n",
       "2  [{'text': 'HKPoliceBrutality', 'indices': [42,...              []  \n",
       "3  [{'text': 'HongKongPolice', 'indices': [41, 56]}]              []  \n",
       "4  [{'text': 'police', 'indices': [0, 7]}, {'text...              []  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define function to sieve out @users in a tweet:\n",
    "def mentioned_users(string):\n",
    "    usernames = re.findall('@[^\\s]+', string)\n",
    "    return usernames\n",
    "\n",
    "\n",
    "# Create a new column and apply the function on the column 'text'\n",
    "data['mentioned_users'] = data['text'].apply(lambda x: mentioned_users(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Text Cleaning\n",
    "We had to remove chinese words and @users from our tweets first before we do the usual text cleaning and processing. Otherwise, we will not be able to obtain these information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Emoji_patterns\n",
    "emoji_pattern = re.compile(\"[\"\n",
    "         u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "         u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "         u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "         u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "         u\"\\U00002702-\\U000027B0\"\n",
    "         u\"\\U000024C2-\\U0001F251\"\n",
    "         \"]+\", flags=re.UNICODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function to implement POS tagging:\n",
    "def get_wordnet_pos(pos_tag):\n",
    "    if pos_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif pos_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif pos_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif pos_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN\n",
    "\n",
    "\n",
    "# Define the main function to clean text in various ways:\n",
    "def clean_text(text):\n",
    "    \n",
    "    # Apply regex expressions first before converting string to list of tokens/words:\n",
    "    # 1. remove @usernames\n",
    "    text = re.sub('@[^\\s]+', '', text)\n",
    "    \n",
    "    # 2. remove URLs\n",
    "    text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))', '', text)\n",
    "    \n",
    "    # 3. remove hashtags entirely i.e. #hashtags\n",
    "    text = re.sub(r'#([^\\s]+)', '', text)\n",
    "    \n",
    "    # 4. remove emojis\n",
    "    text = emoji_pattern.sub(r'', text)\n",
    "    \n",
    "    # 5. Convert text to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # 6. tokenize text and remove punctuation\n",
    "    text = [word.strip(string.punctuation) for word in text.split(\" \")]\n",
    "    \n",
    "    # 7. remove numbers\n",
    "    text = [word for word in text if not any(c.isdigit() for c in word)]\n",
    "    \n",
    "    # 8. remove stop words\n",
    "    stop = stopwords.words('english')\n",
    "    text = [x for x in text if x not in stop]\n",
    "    \n",
    "    # 9. remove empty tokens\n",
    "    text = [t for t in text if len(t) > 0]\n",
    "    \n",
    "    # 10. pos tag text and lemmatize text\n",
    "    pos_tags = pos_tag(text)\n",
    "    text = [WordNetLemmatizer().lemmatize(t[0], get_wordnet_pos(t[1])) for t in pos_tags]\n",
    "    \n",
    "    # 11. remove words with only one letter\n",
    "    text = [t for t in text if len(t) > 1]\n",
    "    \n",
    "    # join all\n",
    "    text = \" \".join(text)\n",
    "    \n",
    "    return(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>cleaned_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>five5demands</td>\n",
       "      <td>HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>466</td>\n",
       "      <td>2019-10-05 13:40:37</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>292</td>\n",
       "      <td>Disgusting: Police in #HongKong are marking pr...</td>\n",
       "      <td>[{'text': 'HongKong', 'indices': [36, 45]}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>disgust police mark protestors pen write numbe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Dejavu53328974</td>\n",
       "      <td>#followbackhongkong\\n#StandwithHK\\n#HKHumanRig...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>581.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>7835</td>\n",
       "      <td>2019-08-27 15:47:34</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>1315</td>\n",
       "      <td>#China-made teargas canister stuck in the road...</td>\n",
       "      <td>[{'text': 'China', 'indices': [11, 17]}, {'tex...</td>\n",
       "      <td>[@hkgetv]</td>\n",
       "      <td>teargas canister stick road citizen try pull f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tsksimon</td>\n",
       "      <td>#FightForFreedom #StandWithHongKong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>6728</td>\n",
       "      <td>2014-10-05 15:57:07</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>105</td>\n",
       "      <td>This is only tiny bits of #HKPoliceBrutality.\\...</td>\n",
       "      <td>[{'text': 'HKPoliceBrutality', 'indices': [42,...</td>\n",
       "      <td>[]</td>\n",
       "      <td>tiny bit \\nwe've protest month yet govn't stil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>JimmyWo67187904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>656</td>\n",
       "      <td>2019-10-08 12:58:51</td>\n",
       "      <td>2019-11-03 02:57:48</td>\n",
       "      <td>660</td>\n",
       "      <td>A fireman complained to the #HongKongPolice fo...</td>\n",
       "      <td>[{'text': 'HongKongPolice', 'indices': [41, 56]}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>fireman complain unnecessary firing tear gas l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARCHI418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121</td>\n",
       "      <td>2014-06-19 16:19:36</td>\n",
       "      <td>2019-11-03 02:57:46</td>\n",
       "      <td>0</td>\n",
       "      <td>#police threw a #Teargas hand #grenade. It bla...</td>\n",
       "      <td>[{'text': 'police', 'indices': [0, 7]}, {'text...</td>\n",
       "      <td>[]</td>\n",
       "      <td>threw hand blast citizen’s back make large are...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          username                                           acctdesc  \\\n",
       "0     five5demands  HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...   \n",
       "1   Dejavu53328974  #followbackhongkong\\n#StandwithHK\\n#HKHumanRig...   \n",
       "2         tsksimon                #FightForFreedom #StandWithHongKong   \n",
       "3  JimmyWo67187904                                                NaN   \n",
       "4         ARCHI418                                                NaN   \n",
       "\n",
       "    location  following  followers totaltweets        usercreatedts  \\\n",
       "0        NaN      437.0      260.0         466  2019-10-05 13:40:37   \n",
       "1  Hong Kong      581.0      360.0        7835  2019-08-27 15:47:34   \n",
       "2        NaN      825.0      456.0        6728  2014-10-05 15:57:07   \n",
       "3        NaN       22.0        1.0         656  2019-10-08 12:58:51   \n",
       "4        NaN       19.0       11.0         121  2014-06-19 16:19:36   \n",
       "\n",
       "        tweetcreatedts retweetcount  \\\n",
       "0  2019-11-03 02:57:49          292   \n",
       "1  2019-11-03 02:57:49         1315   \n",
       "2  2019-11-03 02:57:49          105   \n",
       "3  2019-11-03 02:57:48          660   \n",
       "4  2019-11-03 02:57:46            0   \n",
       "\n",
       "                                                text  \\\n",
       "0  Disgusting: Police in #HongKong are marking pr...   \n",
       "1  #China-made teargas canister stuck in the road...   \n",
       "2  This is only tiny bits of #HKPoliceBrutality.\\...   \n",
       "3  A fireman complained to the #HongKongPolice fo...   \n",
       "4  #police threw a #Teargas hand #grenade. It bla...   \n",
       "\n",
       "                                            hashtags mentioned_users  \\\n",
       "0        [{'text': 'HongKong', 'indices': [36, 45]}]              []   \n",
       "1  [{'text': 'China', 'indices': [11, 17]}, {'tex...       [@hkgetv]   \n",
       "2  [{'text': 'HKPoliceBrutality', 'indices': [42,...              []   \n",
       "3  [{'text': 'HongKongPolice', 'indices': [41, 56]}]              []   \n",
       "4  [{'text': 'police', 'indices': [0, 7]}, {'text...              []   \n",
       "\n",
       "                                        cleaned_text  \n",
       "0  disgust police mark protestors pen write numbe...  \n",
       "1  teargas canister stick road citizen try pull f...  \n",
       "2  tiny bit \\nwe've protest month yet govn't stil...  \n",
       "3  fireman complain unnecessary firing tear gas l...  \n",
       "4  threw hand blast citizen’s back make large are...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function on the column 'text':\n",
    "data['cleaned_text'] = data['text'].apply(lambda x: clean_text(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(248651, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>mentioned_users</th>\n",
       "      <th>cleaned_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248646</td>\n",
       "      <td>zizihui1</td>\n",
       "      <td>Five demands, not one less 🇭🇰#FollowBackHongKo...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>505.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>1301</td>\n",
       "      <td>2019-09-25 17:05:21</td>\n",
       "      <td>2019-11-19 17:19:37</td>\n",
       "      <td>72</td>\n",
       "      <td>@marcorubio CCP is sending PLA to Hong Kong al...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[@marcorubio]</td>\n",
       "      <td>ccp send pla hong kong already use exactly tac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248647</td>\n",
       "      <td>Candy96023268</td>\n",
       "      <td>#手足互科 #StandWithHongKong</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>15.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>116</td>\n",
       "      <td>2019-09-12 06:32:09</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>796</td>\n",
       "      <td>As violence continues to escalate in Hong Kong...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>violence continue escalate hong kong cause fre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248648</td>\n",
       "      <td>Vincent03231883</td>\n",
       "      <td>Hongkonger never surrender\\n願榮光歸香港\\n#followbac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>248.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>362</td>\n",
       "      <td>2019-09-28 14:29:13</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>1162</td>\n",
       "      <td>“We call on Chief Exec. Carrie Lam to promote ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[@SecPompeo]</td>\n",
       "      <td>“we call chief exec carrie lam promote account...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248649</td>\n",
       "      <td>terryyipomni</td>\n",
       "      <td>Hongkonger 🇭🇰 Stand with Hong Kong! Fight for ...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>4627</td>\n",
       "      <td>2015-03-09 06:57:20</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>9</td>\n",
       "      <td>Huh excuse me😃, Hong Kong government lie again...</td>\n",
       "      <td>[{'text': 'SOSHK', 'indices': [74, 80]}, {'tex...</td>\n",
       "      <td>[]</td>\n",
       "      <td>huh excuse hong kong government lie \\n.\\n\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248650</td>\n",
       "      <td>lajiaer1</td>\n",
       "      <td>言論自由是一切自由之母。川普总统支持者；支持爆料革命；</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5042</td>\n",
       "      <td>2019-06-01 23:58:30</td>\n",
       "      <td>2019-11-19 17:19:35</td>\n",
       "      <td>10</td>\n",
       "      <td>@y345a678 @YuriStoyanov I can’t stop crying wa...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[@y345a678, @YuriStoyanov]</td>\n",
       "      <td>can’t stop cry watch clip show young protester...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               username                                           acctdesc  \\\n",
       "248646         zizihui1  Five demands, not one less 🇭🇰#FollowBackHongKo...   \n",
       "248647    Candy96023268                           #手足互科 #StandWithHongKong   \n",
       "248648  Vincent03231883  Hongkonger never surrender\\n願榮光歸香港\\n#followbac...   \n",
       "248649     terryyipomni  Hongkonger 🇭🇰 Stand with Hong Kong! Fight for ...   \n",
       "248650         lajiaer1                        言論自由是一切自由之母。川普总统支持者；支持爆料革命；   \n",
       "\n",
       "         location  following  followers totaltweets        usercreatedts  \\\n",
       "248646  Hong Kong      505.0      326.0        1301  2019-09-25 17:05:21   \n",
       "248647  Hong Kong       15.0      155.0         116  2019-09-12 06:32:09   \n",
       "248648        NaN      248.0      170.0         362  2019-09-28 14:29:13   \n",
       "248649  Hong Kong     4653.0     2490.0        4627  2015-03-09 06:57:20   \n",
       "248650        NaN      348.0       45.0        5042  2019-06-01 23:58:30   \n",
       "\n",
       "             tweetcreatedts retweetcount  \\\n",
       "248646  2019-11-19 17:19:37           72   \n",
       "248647  2019-11-19 17:19:36          796   \n",
       "248648  2019-11-19 17:19:36         1162   \n",
       "248649  2019-11-19 17:19:36            9   \n",
       "248650  2019-11-19 17:19:35           10   \n",
       "\n",
       "                                                     text  \\\n",
       "248646  @marcorubio CCP is sending PLA to Hong Kong al...   \n",
       "248647  As violence continues to escalate in Hong Kong...   \n",
       "248648  “We call on Chief Exec. Carrie Lam to promote ...   \n",
       "248649  Huh excuse me😃, Hong Kong government lie again...   \n",
       "248650  @y345a678 @YuriStoyanov I can’t stop crying wa...   \n",
       "\n",
       "                                                 hashtags  \\\n",
       "248646                                                 []   \n",
       "248647                                                 []   \n",
       "248648                                                 []   \n",
       "248649  [{'text': 'SOSHK', 'indices': [74, 80]}, {'tex...   \n",
       "248650                                                 []   \n",
       "\n",
       "                   mentioned_users  \\\n",
       "248646               [@marcorubio]   \n",
       "248647                          []   \n",
       "248648                [@SecPompeo]   \n",
       "248649                          []   \n",
       "248650  [@y345a678, @YuriStoyanov]   \n",
       "\n",
       "                                             cleaned_text  \n",
       "248646  ccp send pla hong kong already use exactly tac...  \n",
       "248647  violence continue escalate hong kong cause fre...  \n",
       "248648  “we call chief exec carrie lam promote account...  \n",
       "248649        huh excuse hong kong government lie \\n.\\n\\n  \n",
       "248650  can’t stop cry watch clip show young protester...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the shape again and reset_index\n",
    "print(data.shape)\n",
    "data.reset_index(inplace = True, drop = True)\n",
    "\n",
    "# Check out data.tail() to validate index has been reset\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the Column 'hashtags'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import ast to convert a string representation of list to list\n",
    "# The column 'hashtags' is affected\n",
    "import ast\n",
    "\n",
    "# Define a function to convert a string rep. of list to list\n",
    "## Function should also handle NaN values after conversion\n",
    "def strlist_to_list(text):\n",
    "    \n",
    "    # Remove NaN\n",
    "    if pd.isnull(text) == True: # if true\n",
    "        text = ''\n",
    "    else:\n",
    "        text = ast.literal_eval(text)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>mentioned_users</th>\n",
       "      <th>cleaned_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>five5demands</td>\n",
       "      <td>HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>466</td>\n",
       "      <td>2019-10-05 13:40:37</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>292</td>\n",
       "      <td>Disgusting: Police in #HongKong are marking pr...</td>\n",
       "      <td>[{'text': 'HongKong', 'indices': [36, 45]}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>disgust police mark protestors pen write numbe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Dejavu53328974</td>\n",
       "      <td>#followbackhongkong\\n#StandwithHK\\n#HKHumanRig...</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>581.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>7835</td>\n",
       "      <td>2019-08-27 15:47:34</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>1315</td>\n",
       "      <td>#China-made teargas canister stuck in the road...</td>\n",
       "      <td>[{'text': 'China', 'indices': [11, 17]}, {'tex...</td>\n",
       "      <td>[@hkgetv]</td>\n",
       "      <td>teargas canister stick road citizen try pull f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tsksimon</td>\n",
       "      <td>#FightForFreedom #StandWithHongKong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>6728</td>\n",
       "      <td>2014-10-05 15:57:07</td>\n",
       "      <td>2019-11-03 02:57:49</td>\n",
       "      <td>105</td>\n",
       "      <td>This is only tiny bits of #HKPoliceBrutality.\\...</td>\n",
       "      <td>[{'text': 'HKPoliceBrutality', 'indices': [42,...</td>\n",
       "      <td>[]</td>\n",
       "      <td>tiny bit \\nwe've protest month yet govn't stil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>JimmyWo67187904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>656</td>\n",
       "      <td>2019-10-08 12:58:51</td>\n",
       "      <td>2019-11-03 02:57:48</td>\n",
       "      <td>660</td>\n",
       "      <td>A fireman complained to the #HongKongPolice fo...</td>\n",
       "      <td>[{'text': 'HongKongPolice', 'indices': [41, 56]}]</td>\n",
       "      <td>[]</td>\n",
       "      <td>fireman complain unnecessary firing tear gas l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARCHI418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121</td>\n",
       "      <td>2014-06-19 16:19:36</td>\n",
       "      <td>2019-11-03 02:57:46</td>\n",
       "      <td>0</td>\n",
       "      <td>#police threw a #Teargas hand #grenade. It bla...</td>\n",
       "      <td>[{'text': 'police', 'indices': [0, 7]}, {'text...</td>\n",
       "      <td>[]</td>\n",
       "      <td>threw hand blast citizen’s back make large are...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          username                                           acctdesc  \\\n",
       "0     five5demands  HKer Fight for freedom 當獨裁成為事實 革命就是義務 #followb...   \n",
       "1   Dejavu53328974  #followbackhongkong\\n#StandwithHK\\n#HKHumanRig...   \n",
       "2         tsksimon                #FightForFreedom #StandWithHongKong   \n",
       "3  JimmyWo67187904                                                NaN   \n",
       "4         ARCHI418                                                NaN   \n",
       "\n",
       "    location  following  followers totaltweets        usercreatedts  \\\n",
       "0        NaN      437.0      260.0         466  2019-10-05 13:40:37   \n",
       "1  Hong Kong      581.0      360.0        7835  2019-08-27 15:47:34   \n",
       "2        NaN      825.0      456.0        6728  2014-10-05 15:57:07   \n",
       "3        NaN       22.0        1.0         656  2019-10-08 12:58:51   \n",
       "4        NaN       19.0       11.0         121  2014-06-19 16:19:36   \n",
       "\n",
       "        tweetcreatedts retweetcount  \\\n",
       "0  2019-11-03 02:57:49          292   \n",
       "1  2019-11-03 02:57:49         1315   \n",
       "2  2019-11-03 02:57:49          105   \n",
       "3  2019-11-03 02:57:48          660   \n",
       "4  2019-11-03 02:57:46            0   \n",
       "\n",
       "                                                text  \\\n",
       "0  Disgusting: Police in #HongKong are marking pr...   \n",
       "1  #China-made teargas canister stuck in the road...   \n",
       "2  This is only tiny bits of #HKPoliceBrutality.\\...   \n",
       "3  A fireman complained to the #HongKongPolice fo...   \n",
       "4  #police threw a #Teargas hand #grenade. It bla...   \n",
       "\n",
       "                                            hashtags mentioned_users  \\\n",
       "0        [{'text': 'HongKong', 'indices': [36, 45]}]              []   \n",
       "1  [{'text': 'China', 'indices': [11, 17]}, {'tex...       [@hkgetv]   \n",
       "2  [{'text': 'HKPoliceBrutality', 'indices': [42,...              []   \n",
       "3  [{'text': 'HongKongPolice', 'indices': [41, 56]}]              []   \n",
       "4  [{'text': 'police', 'indices': [0, 7]}, {'text...              []   \n",
       "\n",
       "                                        cleaned_text  \n",
       "0  disgust police mark protestors pen write numbe...  \n",
       "1  teargas canister stick road citizen try pull f...  \n",
       "2  tiny bit \\nwe've protest month yet govn't stil...  \n",
       "3  fireman complain unnecessary firing tear gas l...  \n",
       "4  threw hand blast citizen’s back make large are...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply strlist_to_list(...) to the column 'hashtags'\n",
    "# Note that doing so will return a list of dictionaries, where there will be one dictionary for each hashtag in a single tweet.\n",
    "data['hashtags'] = data['hashtags'].apply(lambda x: strlist_to_list(x))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since each 'hashtag' contain a list of dictionaries, we need to loop through the list to extract the actual hashtags in the tweets.\n",
    "# Define a function to perform this extraction:\n",
    "def extract_hashtags(hashtag_list):\n",
    "    # argument:\n",
    "    # hashtag_list - a list of dictionary(ies), each containing a hashtag\n",
    "    \n",
    "    # Create a list to store the hashtags\n",
    "    hashtags = []\n",
    "    \n",
    "    # Loop through the list:\n",
    "    for i in range(0, len(hashtag_list)):\n",
    "        # extract the hashtag value using the key - 'text'\n",
    "        # For our purposes, we can ignore the indices, which tell us the position of the hashtags in the string of tweet\n",
    "        # lowercase the text as well\n",
    "        hashtags.append(hashtag_list[i]['text'].lower())\n",
    "        \n",
    "    return hashtags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                           [hongkong]\n",
      "1                                 [china, policestate]\n",
      "2                                  [hkpolicebrutality]\n",
      "3                                     [hongkongpolice]\n",
      "4    [police, teargas, grenade, hkger, policestate,...\n",
      "Name: hashtags, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Apply function on the column - data['hashtags']\n",
    "data['hashtags'] = data['hashtags'].apply(lambda x: extract_hashtags(x))\n",
    "\n",
    "# Check out the updated column 'hashtags'\n",
    "print(data.head()['hashtags'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning up the Column 'location'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN (empty) values with n.a to indicate that the user did not state his location\n",
    "# Define a function to handle this:\n",
    "def remove_nan(text):\n",
    "    if pd.isnull(text) == True: # entry is NaN\n",
    "        text = 'n.a'\n",
    "    else:\n",
    "        # lowercase text for possible easy handling\n",
    "        text = text.lower()\n",
    "        \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          n.a\n",
      "1    hong kong\n",
      "2          n.a\n",
      "3          n.a\n",
      "4          n.a\n",
      "Name: location, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Apply function on column - data['location']\n",
    "data['location'] = data['location'].apply(lambda x: remove_nan(x))\n",
    "\n",
    "# Check out the updated columns\n",
    "print(data.head()['location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n.a                              157316\n",
       "hong kong                         49093\n",
       "香港                                 6829\n",
       "united states                      1058\n",
       "hk                                  912\n",
       "                                  ...  \n",
       "com em vallès no hi ha res...         1\n",
       "foot of himalaya                      1\n",
       "sipping coffee somewhere              1\n",
       "█████████                             1\n",
       "廣義開山里                                 1\n",
       "Name: location, Length: 6218, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a quick look at the value_counts()\n",
    "data['location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, most of the users are probably from HongKong. Slightly more than half of the users in this dataset did not reveal their locations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning up the Column 'acctdesc'\n",
    "\n",
    "Likewise, we will clean up this column by removing NaN values and replacing them with 'n.a'.\n",
    "For now, this is all we do to this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    hker fight for freedom 當獨裁成為事實 革命就是義務 #followb...\n",
      "1    #followbackhongkong\\n#standwithhk\\n#hkhumanrig...\n",
      "2                  #fightforfreedom #standwithhongkong\n",
      "3                                                  n.a\n",
      "4                                                  n.a\n",
      "Name: acctdesc, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Apply the function already defined above: remove_nan(...)\n",
    "# Apply function on column - data['location']\n",
    "data['acctdesc'] = data['acctdesc'].apply(lambda x: remove_nan(x))\n",
    "\n",
    "# Check out the updated columns\n",
    "print(data.head()['acctdesc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering - Rule-based Word Processing\n",
    "So far, we have removed duplicated rows, extract important information such as hashtags, mentioned users and users' locations, and also cleaned up the tweets in the previous section. In the coming section, we will focus on Rule-based word processing for our sentiment analysis. We will postpone some exploratory data visualization till later once we have all the ingredients.\n",
    "\n",
    "### Generating Sentiments from Tweets with NLTK Vader_Lexicon Library\n",
    "We will be using the Vader_lexicon library from NLTK to generate sentiment for each review. Vader uses lexicon of words to determine which words in the reviews are positive or negative. It will return a set of 4 scores on the positivity, negativity, neutrality of a text, and also an overall score whether a text is positive or negative.\n",
    "1. Positivity - 'pos'\n",
    "2. Negativity - 'neg'\n",
    "3. Neutrality - 'neu'\n",
    "4. Overall Score - 'compound'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing VADER from NLTK\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "# Create a sid object called SentimentIntensityAnalyzer()\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Apply polarity_score method of SentimentIntensityAnalyzer()\n",
    "data['sentiment'] = data['cleaned_text'].apply(lambda x: sid.polarity_scores(x))\n",
    "\n",
    "# Keep only the compound scores under the column 'Sentiment'\n",
    "data = pd.concat([data.drop(['sentiment'], axis = 1), data['sentiment'].apply(pd.Series)], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional Features: no. of characters and no. of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>mentioned_users</th>\n",
       "      <th>cleaned_text</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>numchars</th>\n",
       "      <th>numwords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248649</td>\n",
       "      <td>terryyipomni</td>\n",
       "      <td>hongkonger 🇭🇰 stand with hong kong! fight for ...</td>\n",
       "      <td>hong kong</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>4627</td>\n",
       "      <td>2015-03-09 06:57:20</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>9</td>\n",
       "      <td>Huh excuse me😃, Hong Kong government lie again...</td>\n",
       "      <td>[soshk, standwithhongkong, hongkongprotests]</td>\n",
       "      <td>[]</td>\n",
       "      <td>huh excuse hong kong government lie \\n.\\n\\n</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248650</td>\n",
       "      <td>lajiaer1</td>\n",
       "      <td>言論自由是一切自由之母。川普总统支持者；支持爆料革命；</td>\n",
       "      <td>n.a</td>\n",
       "      <td>348.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5042</td>\n",
       "      <td>2019-06-01 23:58:30</td>\n",
       "      <td>2019-11-19 17:19:35</td>\n",
       "      <td>10</td>\n",
       "      <td>@y345a678 @YuriStoyanov I can’t stop crying wa...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[@y345a678, @YuriStoyanov]</td>\n",
       "      <td>can’t stop cry watch clip show young protester...</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.163</td>\n",
       "      <td>-0.8176</td>\n",
       "      <td>141</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            username                                           acctdesc  \\\n",
       "248649  terryyipomni  hongkonger 🇭🇰 stand with hong kong! fight for ...   \n",
       "248650      lajiaer1                        言論自由是一切自由之母。川普总统支持者；支持爆料革命；   \n",
       "\n",
       "         location  following  followers totaltweets        usercreatedts  \\\n",
       "248649  hong kong     4653.0     2490.0        4627  2015-03-09 06:57:20   \n",
       "248650        n.a      348.0       45.0        5042  2019-06-01 23:58:30   \n",
       "\n",
       "             tweetcreatedts retweetcount  \\\n",
       "248649  2019-11-19 17:19:36            9   \n",
       "248650  2019-11-19 17:19:35           10   \n",
       "\n",
       "                                                     text  \\\n",
       "248649  Huh excuse me😃, Hong Kong government lie again...   \n",
       "248650  @y345a678 @YuriStoyanov I can’t stop crying wa...   \n",
       "\n",
       "                                            hashtags  \\\n",
       "248649  [soshk, standwithhongkong, hongkongprotests]   \n",
       "248650                                            []   \n",
       "\n",
       "                   mentioned_users  \\\n",
       "248649                          []   \n",
       "248650  [@y345a678, @YuriStoyanov]   \n",
       "\n",
       "                                             cleaned_text    neg    neu  \\\n",
       "248649        huh excuse hong kong government lie \\n.\\n\\n  0.000  0.794   \n",
       "248650  can’t stop cry watch clip show young protester...  0.378  0.458   \n",
       "\n",
       "          pos  compound  numchars  numwords  \n",
       "248649  0.206    0.0772        40         7  \n",
       "248650  0.163   -0.8176       141        22  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column: number of characters in 'review'\n",
    "data['numchars'] = data['cleaned_text'].apply(lambda x: len(x))\n",
    "\n",
    "# New column: number of words in 'review'\n",
    "data['numwords'] = data['cleaned_text'].apply(lambda x: len(x.split(\" \")))\n",
    "\n",
    "# Check the new columns:\n",
    "data.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Word Embeddings - Training Word2Vec using Gensim\n",
    "\n",
    "Word embeddings involve the mapping of words in the corpus to numerical vectors, where similar words sharing similar contexts will have similar vectors as well. It will involve a shallow two-layer neural network that will train a matrix/tensor called the embedding matrix. By taking the matrix product of the embedding matrix and one-hot vector representation of each word in the corpus, we will arrive at the embedding vector.\n",
    "\n",
    "We will be using Gensim - an open-source Python library - to generate doc2vec. <br/>\n",
    "\n",
    "To confirm: apparently, doc2vec should be used over word2vec to obtain the vector representation of a 'document', in this case, our 'reviews' from the travellers. Word2vec will only give us the vector representation of a 'word'."
   ]
  },
  {
   "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>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>...</th>\n",
       "      <th>doc2vec_vector_0</th>\n",
       "      <th>doc2vec_vector_1</th>\n",
       "      <th>doc2vec_vector_2</th>\n",
       "      <th>doc2vec_vector_3</th>\n",
       "      <th>doc2vec_vector_4</th>\n",
       "      <th>doc2vec_vector_5</th>\n",
       "      <th>doc2vec_vector_6</th>\n",
       "      <th>doc2vec_vector_7</th>\n",
       "      <th>doc2vec_vector_8</th>\n",
       "      <th>doc2vec_vector_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248649</td>\n",
       "      <td>terryyipomni</td>\n",
       "      <td>hongkonger 🇭🇰 stand with hong kong! fight for ...</td>\n",
       "      <td>hong kong</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>4627</td>\n",
       "      <td>2015-03-09 06:57:20</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>9</td>\n",
       "      <td>Huh excuse me😃, Hong Kong government lie again...</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021183</td>\n",
       "      <td>-0.019724</td>\n",
       "      <td>-0.008041</td>\n",
       "      <td>0.057916</td>\n",
       "      <td>0.005414</td>\n",
       "      <td>-0.055156</td>\n",
       "      <td>0.090390</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>-0.095140</td>\n",
       "      <td>-0.023267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248650</td>\n",
       "      <td>lajiaer1</td>\n",
       "      <td>言論自由是一切自由之母。川普总统支持者；支持爆料革命；</td>\n",
       "      <td>n.a</td>\n",
       "      <td>348.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5042</td>\n",
       "      <td>2019-06-01 23:58:30</td>\n",
       "      <td>2019-11-19 17:19:35</td>\n",
       "      <td>10</td>\n",
       "      <td>@y345a678 @YuriStoyanov I can’t stop crying wa...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024503</td>\n",
       "      <td>-0.092746</td>\n",
       "      <td>0.057546</td>\n",
       "      <td>-0.056595</td>\n",
       "      <td>0.075753</td>\n",
       "      <td>0.086547</td>\n",
       "      <td>0.135315</td>\n",
       "      <td>-0.009363</td>\n",
       "      <td>-0.096228</td>\n",
       "      <td>-0.130499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            username                                           acctdesc  \\\n",
       "248649  terryyipomni  hongkonger 🇭🇰 stand with hong kong! fight for ...   \n",
       "248650      lajiaer1                        言論自由是一切自由之母。川普总统支持者；支持爆料革命；   \n",
       "\n",
       "         location  following  followers totaltweets        usercreatedts  \\\n",
       "248649  hong kong     4653.0     2490.0        4627  2015-03-09 06:57:20   \n",
       "248650        n.a      348.0       45.0        5042  2019-06-01 23:58:30   \n",
       "\n",
       "             tweetcreatedts retweetcount  \\\n",
       "248649  2019-11-19 17:19:36            9   \n",
       "248650  2019-11-19 17:19:35           10   \n",
       "\n",
       "                                                     text  ...  \\\n",
       "248649  Huh excuse me😃, Hong Kong government lie again...  ...   \n",
       "248650  @y345a678 @YuriStoyanov I can’t stop crying wa...  ...   \n",
       "\n",
       "       doc2vec_vector_0 doc2vec_vector_1 doc2vec_vector_2  doc2vec_vector_3  \\\n",
       "248649        -0.021183        -0.019724        -0.008041          0.057916   \n",
       "248650         0.024503        -0.092746         0.057546         -0.056595   \n",
       "\n",
       "        doc2vec_vector_4  doc2vec_vector_5  doc2vec_vector_6  \\\n",
       "248649          0.005414         -0.055156          0.090390   \n",
       "248650          0.075753          0.086547          0.135315   \n",
       "\n",
       "        doc2vec_vector_7  doc2vec_vector_8  doc2vec_vector_9  \n",
       "248649          0.019608         -0.095140         -0.023267  \n",
       "248650         -0.009363         -0.096228         -0.130499  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the Gensim package\n",
    "from gensim.test.utils import common_texts\n",
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "\n",
    "documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(data[\"cleaned_text\"].apply(lambda x: x.split(\" \")))]\n",
    "\n",
    "# Train a Doc2Vec model with our text data\n",
    "model = Doc2Vec(documents, vector_size = 10, window = 2, min_count = 1, workers = 4)\n",
    "\n",
    "# Transform each document into a vector data\n",
    "doc2vec_df = data[\"cleaned_text\"].apply(lambda x: model.infer_vector(x.split(\" \"))).apply(pd.Series)\n",
    "doc2vec_df.columns = [\"doc2vec_vector_\" + str(x) for x in doc2vec_df.columns]\n",
    "data = pd.concat([data, doc2vec_df], axis = 1)\n",
    "\n",
    "# Check out the newly added columns:\n",
    "data.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TD-IDF Columns\n",
    "Next, we will compute the TD-IDF of the reviews using the sklearn library. TD-IDF stands for Term Frequency-Inverse Document Frequency, which is used to reflect how important a word is to a document in a collection or corpus. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general.\n",
    "\n",
    "1. Term Frequency - the number of times a term occurs in a document.\n",
    "2. Inverse Document Frequency - an inverse document frequency factor that diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely.\n",
    "\n",
    "Since NLTK does not support TF-IDF, we will use the tfidfvectorizer function from the Python sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>...</th>\n",
       "      <th>word_use</th>\n",
       "      <th>word_video</th>\n",
       "      <th>word_violence</th>\n",
       "      <th>word_vote</th>\n",
       "      <th>word_want</th>\n",
       "      <th>word_way</th>\n",
       "      <th>word_work</th>\n",
       "      <th>word_world</th>\n",
       "      <th>word_year</th>\n",
       "      <th>word_young</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248649</td>\n",
       "      <td>terryyipomni</td>\n",
       "      <td>hongkonger 🇭🇰 stand with hong kong! fight for ...</td>\n",
       "      <td>hong kong</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>4627</td>\n",
       "      <td>2015-03-09 06:57:20</td>\n",
       "      <td>2019-11-19 17:19:36</td>\n",
       "      <td>9</td>\n",
       "      <td>Huh excuse me😃, Hong Kong government lie again...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248650</td>\n",
       "      <td>lajiaer1</td>\n",
       "      <td>言論自由是一切自由之母。川普总统支持者；支持爆料革命；</td>\n",
       "      <td>n.a</td>\n",
       "      <td>348.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5042</td>\n",
       "      <td>2019-06-01 23:58:30</td>\n",
       "      <td>2019-11-19 17:19:35</td>\n",
       "      <td>10</td>\n",
       "      <td>@y345a678 @YuriStoyanov I can’t stop crying wa...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            username                                           acctdesc  \\\n",
       "248649  terryyipomni  hongkonger 🇭🇰 stand with hong kong! fight for ...   \n",
       "248650      lajiaer1                        言論自由是一切自由之母。川普总统支持者；支持爆料革命；   \n",
       "\n",
       "         location  following  followers totaltweets        usercreatedts  \\\n",
       "248649  hong kong     4653.0     2490.0        4627  2015-03-09 06:57:20   \n",
       "248650        n.a      348.0       45.0        5042  2019-06-01 23:58:30   \n",
       "\n",
       "             tweetcreatedts retweetcount  \\\n",
       "248649  2019-11-19 17:19:36            9   \n",
       "248650  2019-11-19 17:19:35           10   \n",
       "\n",
       "                                                     text  ...  word_use  \\\n",
       "248649  Huh excuse me😃, Hong Kong government lie again...  ...  0.000000   \n",
       "248650  @y345a678 @YuriStoyanov I can’t stop crying wa...  ...  0.413149   \n",
       "\n",
       "       word_video word_violence  word_vote  word_want  word_way  word_work  \\\n",
       "248649        0.0      0.000000        0.0        0.0       0.0        0.0   \n",
       "248650        0.0      0.397212        0.0        0.0       0.0        0.0   \n",
       "\n",
       "        word_world  word_year  word_young  \n",
       "248649    0.000000        0.0    0.000000  \n",
       "248650    0.368334        0.0    0.458794  \n",
       "\n",
       "[2 rows x 129 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Call the function tfidfvectorizer\n",
    "# min_df is the document frequency threshold for ignoring terms with a lower threshold.\n",
    "# stop_words is the words to be removed from the corpus. We will check for stopwords again even though we had already performed it once previously.\n",
    "# We will only consider a maximum of 100 features since in our previous runs.\n",
    "tfidf = TfidfVectorizer(\n",
    "    max_features = 100,\n",
    "    min_df = 10,\n",
    "    stop_words = 'english'\n",
    ")\n",
    "\n",
    "# Fit_transform our 'revi`ew' (the corpus) using the tfidf object from above\n",
    "tfidf_result = tfidf.fit_transform(data['cleaned_text']).toarray()\n",
    "\n",
    "# Extract the frequencies and store them in a temporary dataframe\n",
    "tfidf_df = pd.DataFrame(tfidf_result, columns = tfidf.get_feature_names())\n",
    "\n",
    "# Rename the column names and index\n",
    "tfidf_df.columns = [\"word_\" + str(x) for x in tfidf_df.columns]\n",
    "tfidf_df.index = data.index\n",
    "\n",
    "# Concatenate the two dataframes - 'dataset' and 'tfidf_df'\n",
    "# Note: Axis = 1 -> add the 'tfidf_df' dataframe along the columns  or add these columns as columns in 'dataset'.\n",
    "data = pd.concat([data, tfidf_df], axis = 1) \n",
    "\n",
    "# Check out the new 'dataset' dataframe\n",
    "data.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "Now that we have done most of the required feature engineering on the dataset, we try to visualize the data with data visualisation libraries - seaborn and matplotlib. \n",
    "### 1. WordCloud - What are the top trending or popular words used in this protest movement?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show_wordcloud(data, title = None):\n",
    "    wordcloud = WordCloud(\n",
    "        background_color = 'white',\n",
    "        max_words = 100,\n",
    "        max_font_size = 40, \n",
    "        scale = 3,\n",
    "        random_state = 42\n",
    "    ).generate(str(data))\n",
    "\n",
    "    fig = plt.figure(1, figsize = (20, 15))\n",
    "    plt.axis('off')\n",
    "    if title: \n",
    "        fig.suptitle(title, fontsize = 20)\n",
    "        fig.subplots_adjust(top = 2.3)\n",
    "\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.show()\n",
    "    \n",
    "# print wordcloud\n",
    "show_wordcloud(data['cleaned_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected Vocabulary \n",
    "\n",
    "Looking at the wordcloud, the only obvious word that has a strong negative connotation would be 'disgust', appearing at the bottom. Most of the remaining words thrown out align with what one has been seeing regularly on news and social media. From our wordcloud, we see that these words are - police, tear gas cannisters, protest, protestors etc. \n",
    "\n",
    "Outdated as more tweets are taken into consideration: Interestingly, 'good' appears in the wordcloud. But as of now, it still remains to be seen as to what or who is mentioned or associated numerously with 'good'. Let's see if we can find out more.\n",
    "\n",
    "### 2. No. of Positive Sentiments vs No. of Negative Sentiments\n",
    "Next, we look at what is the overall distribution of positive and negative tweets. Based on the SentimentIntensityAnalyzer from the NLTK Vader-Lexicon library, this analyzer examines the sentiment of a sentence, on how positive, neutral or negative it is. We can interpret the sentiment in the following manner. If a sentiment is positive, it could mean that it is pro-government and/or police. Whereas, a negative sentiment could mean that it is anti- government and/or police, and supportive towards the protesters.\n",
    "\n",
    "The analyzer returns 4 scores for each sentence namely, 'positive', 'negative', 'neutral' and 'compound'. The score 'compound' returns the overall sentiment of a sentence with range of [-1, 1]. For our current purpose, we want to classify each tweet into 5 classes and assign a range of values for each of them:\n",
    "1. Very positive '5' - [0.55, 1.00]\n",
    "2. Positive '4' - [0.10, 0.55)\n",
    "3. Neutral '3' - (-0.10, 0.10)\n",
    "4. Negative '2' - (-0.55, -0.10]\n",
    "5. Very negative '1' - [-1.00, -0.55]\n",
    "\n",
    "Note: the range of values for a neutral sentiment is more stringent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248646    5\n",
       "248647    2\n",
       "248648    3\n",
       "248649    3\n",
       "248650    1\n",
       "Name: sentiment_class, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Focus on 'compound' scores\n",
    "# Create a new column called 'sentiment_class'\n",
    "sentimentclass_list = []\n",
    "\n",
    "for i in range(0, len(data)):\n",
    "    \n",
    "    # current 'compound' score:\n",
    "    curr_compound = data.iloc[i,:]['compound']\n",
    "    \n",
    "    if (curr_compound <= 1.0 and curr_compound >= 0.55):\n",
    "        sentimentclass_list.append(5)\n",
    "    elif (curr_compound < 0.55 and curr_compound >= 0.10):\n",
    "        sentimentclass_list.append(4)\n",
    "    elif (curr_compound < 0.10 and curr_compound > -0.10):\n",
    "        sentimentclass_list.append(3)\n",
    "    elif (curr_compound <= -0.10 and curr_compound > -0.55):\n",
    "        sentimentclass_list.append(2)\n",
    "    elif (curr_compound <= -0.55 and curr_compound >= -1.00):\n",
    "        sentimentclass_list.append(1)\n",
    "\n",
    "# Add the new column 'sentiment_class' to the dataframe\n",
    "data['sentiment_class'] = sentimentclass_list\n",
    "\n",
    "# Check out the new column\n",
    "data.tail()['sentiment_class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>compound</th>\n",
       "      <th>sentiment_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.1779</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.8074</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.7430</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.1779</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compound  sentiment_class\n",
       "0    0.2732                4\n",
       "1   -0.1779                2\n",
       "2   -0.8074                1\n",
       "3   -0.7430                1\n",
       "4    0.1779                4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify if the classification assignment is correct:\n",
    "data.iloc[0:5, :][['compound', 'sentiment_class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Distribution of sentiment_class\n",
    "plt.figure(figsize = (10,5))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.countplot(data['sentiment_class'])\n",
    "plt.title('Countplot of sentiment_class')\n",
    "plt.xlabel('sentiment_class')\n",
    "plt.ylabel('No. of classes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 random negative original tweets and their sentiment classes:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>86970</td>\n",
       "      <td>This evil bill has long since been withdrawn, but obviously, pro-Beijing trash and CE Carrie Lam (also trash) will not let their loss go down easy, taking their bureaucratic revenge once again on these democratic lawmakers. #HKPoliceState  #HKTotalitariangovernment https://t.co/cVlwOVedHO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22382</td>\n",
       "      <td>According to StandNews, the casualty is a first aider. Two rounds of tear gas were aimed at  journalists &amp;amp; first aiders who were leaving the scene. There were no protesters. #HongKongProtests #PoliceBrutalityHK #PoliceState https://t.co/wpUCvSvIso</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116368</td>\n",
       "      <td>HK is burning thanks to the violent HK prodemocracy rioters. Tuen Mun\\n#HongKongProtests #HongKongProtesters #ViolentHKprotesters #stophkriots https://t.co/HSqXu2SdB2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241753</td>\n",
       "      <td>[English Subtitle Available]\\n\\nAn arrestee in #PolyU successfully live-streamed the process of arrest, recording #HKPolice uttering death threats, and pepper-spraying them while they were detained. \\n\\n#SOSHK\\n#StandwithHK \\n#HongKongProtests https://t.co/2XfJybwHP8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47566</td>\n",
       "      <td>A young student in #HongKong has just lost his life due to injuries sustained while fleeing the police's indiscriminate firing of tear gas. Nothing but respect for the people all over the world putting their lives on the line for a free world #StandwithHK https://t.co/IijReiMaUU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14693</td>\n",
       "      <td>According to StandNews, the casualty is a first aider. Two rounds of tear gas were aimed at  journalists &amp;amp; first aiders who were leaving the scene. There were no protesters. #HongKongProtests #PoliceBrutalityHK #PoliceState https://t.co/wpUCvSvIso</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51443</td>\n",
       "      <td>A Ming Pao reporter appears to have been slightly injured after the police shot at journalists with water cannon. Protesters have long since left Chatham Road South by 9pm.\\n\\nPhoto: RTHK screenshot. #hongkong https://t.co/MRU5qmf34R</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119012</td>\n",
       "      <td>Sai Wan Ho #HongKong. Residents said they just wanted to go home. #Police argued with the crowd &amp;amp; lost control, almost leaped at a man. Later on cops ran after &amp;amp; arrested a man in white shirt while pepper-sprayed others, when crowd was seen dispersing. #PoliceState \\n\\n@cityusucbc https://t.co/F2dm1Wptrg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190574</td>\n",
       "      <td>#HongKongPolice jumping over ticket gates of #MTR station.\\n\\n#CarrieLam said #HongKongPolice are here to \"maintain law and order\"...\\n\\n...they're not paying for #MTR tickets tho.\\n\\n#HongKong\\n#HongKongProtests https://t.co/HIIZfW88VV</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206485</td>\n",
       "      <td>Tiananmen Square Massacre 2.0\\nThe government and polices are trying to kill all the students. On 12th November, they shot 2356 tear gases! You can see area in purple is the CUHK and it is very emergency! There are many animals inside and some birds dead! #StandWithHongKong #SOSHK https://t.co/8v60Jj6M13 https://t.co/mq3NYRlcRy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                             text  \\\n",
       "86970   This evil bill has long since been withdrawn, but obviously, pro-Beijing trash and CE Carrie Lam (also trash) will not let their loss go down easy, taking their bureaucratic revenge once again on these democratic lawmakers. #HKPoliceState  #HKTotalitariangovernment https://t.co/cVlwOVedHO                                           \n",
       "22382   According to StandNews, the casualty is a first aider. Two rounds of tear gas were aimed at  journalists &amp; first aiders who were leaving the scene. There were no protesters. #HongKongProtests #PoliceBrutalityHK #PoliceState https://t.co/wpUCvSvIso                                                                                 \n",
       "116368  HK is burning thanks to the violent HK prodemocracy rioters. Tuen Mun\\n#HongKongProtests #HongKongProtesters #ViolentHKprotesters #stophkriots https://t.co/HSqXu2SdB2                                                                                                                                                                      \n",
       "241753  [English Subtitle Available]\\n\\nAn arrestee in #PolyU successfully live-streamed the process of arrest, recording #HKPolice uttering death threats, and pepper-spraying them while they were detained. \\n\\n#SOSHK\\n#StandwithHK \\n#HongKongProtests https://t.co/2XfJybwHP8                                                                 \n",
       "47566   A young student in #HongKong has just lost his life due to injuries sustained while fleeing the police's indiscriminate firing of tear gas. Nothing but respect for the people all over the world putting their lives on the line for a free world #StandwithHK https://t.co/IijReiMaUU                                                     \n",
       "14693   According to StandNews, the casualty is a first aider. Two rounds of tear gas were aimed at  journalists &amp; first aiders who were leaving the scene. There were no protesters. #HongKongProtests #PoliceBrutalityHK #PoliceState https://t.co/wpUCvSvIso                                                                                 \n",
       "51443   A Ming Pao reporter appears to have been slightly injured after the police shot at journalists with water cannon. Protesters have long since left Chatham Road South by 9pm.\\n\\nPhoto: RTHK screenshot. #hongkong https://t.co/MRU5qmf34R                                                                                                   \n",
       "119012  Sai Wan Ho #HongKong. Residents said they just wanted to go home. #Police argued with the crowd &amp; lost control, almost leaped at a man. Later on cops ran after &amp; arrested a man in white shirt while pepper-sprayed others, when crowd was seen dispersing. #PoliceState \\n\\n@cityusucbc https://t.co/F2dm1Wptrg                   \n",
       "190574  #HongKongPolice jumping over ticket gates of #MTR station.\\n\\n#CarrieLam said #HongKongPolice are here to \"maintain law and order\"...\\n\\n...they're not paying for #MTR tickets tho.\\n\\n#HongKong\\n#HongKongProtests https://t.co/HIIZfW88VV                                                                                                \n",
       "206485  Tiananmen Square Massacre 2.0\\nThe government and polices are trying to kill all the students. On 12th November, they shot 2356 tear gases! You can see area in purple is the CUHK and it is very emergency! There are many animals inside and some birds dead! #StandWithHongKong #SOSHK https://t.co/8v60Jj6M13 https://t.co/mq3NYRlcRy   \n",
       "\n",
       "        sentiment_class  \n",
       "86970   1                \n",
       "22382   1                \n",
       "116368  2                \n",
       "241753  1                \n",
       "47566   1                \n",
       "14693   1                \n",
       "51443   2                \n",
       "119012  1                \n",
       "190574  2                \n",
       "206485  1                "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display full text:\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "\n",
    "# Look at some examples of negative, neutral and positive tweets\n",
    "\n",
    "# Filter 10 negative original tweets:\n",
    "print(\"10 random negative original tweets and their sentiment classes:\")\n",
    "data[(data['sentiment_class'] == 1) | (data['sentiment_class'] == 2)].sample(n=10)[['text', 'sentiment_class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 random neutral original tweets and their sentiment classes:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31386</td>\n",
       "      <td>#092 Gastly don’t need no mask #pokemonforHK @uwu_uwu_mo @hkwuliff #StandWithHongKong https://t.co/hyUWGuuw3h</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143334</td>\n",
       "      <td>Officer dashed and pepper sprayed citizens who were just standing\\nSource - BarryMa TV\\n#HongKongRioters #PoliceBrutality #PoliceState #StandWithHongKong #FreeHongKong  #FightForFreedom #5DemandsNot1Less #police #HongKongPolice #HongKongPoliceTerrorism https://t.co/Q7ZHqXJV1n</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128820</td>\n",
       "      <td>A sea of light at Tamar Park. #HongKongProtests https://t.co/WaYU1faSdH</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72065</td>\n",
       "      <td>We are working with everyone to pass our #HongKong bill but we aren’t going to let anyone run out the clock on us\\n\\nAt some point very soon it will be time to act\\n\\nOne way or another we will have a vote &amp;amp; those who oppose it will have to explain why\\n\\n https://t.co/9qOEGvJpLi</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237807</td>\n",
       "      <td>@Kennedy_J123 @Y_LHongKonger #hkpoliceterrorist = mobs</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6950</td>\n",
       "      <td>This is why the Senate should vote on the Hong Kong Human Rights &amp;amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106121</td>\n",
       "      <td>Lunch break in Central.  These are not the typical young protesters.  You're looking at people in dress shirts and business attire speaking up against #HKPolice during their lunch hour in memory of Chow\\n\\n#StandWithHongKong #HongKongProtests https://t.co/wRqW4THqMS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4355</td>\n",
       "      <td>This is why the Senate should vote on the Hong Kong Human Rights &amp;amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155377</td>\n",
       "      <td>@RepMcGovern @senatemajldr Thanks Rep McGovern for raising the awareness on #HKHumanRightsandDemocracyAct.\\n\\nWe are facing human rights crisis and becoming police state. Please #StandwithHK</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248441</td>\n",
       "      <td>“We call on Chief Exec. Carrie Lam to promote accountability by supplementing the Independent Police Complains Council review with an independent investigation into the protest-related incidents.” - @SecPompeo on the political unrest in #HongKong. READ: https://t.co/RZMCdcgLQN https://t.co/i00X8iM8ky</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                 text  \\\n",
       "31386   #092 Gastly don’t need no mask #pokemonforHK @uwu_uwu_mo @hkwuliff #StandWithHongKong https://t.co/hyUWGuuw3h                                                                                                                                                                                                   \n",
       "143334  Officer dashed and pepper sprayed citizens who were just standing\\nSource - BarryMa TV\\n#HongKongRioters #PoliceBrutality #PoliceState #StandWithHongKong #FreeHongKong  #FightForFreedom #5DemandsNot1Less #police #HongKongPolice #HongKongPoliceTerrorism https://t.co/Q7ZHqXJV1n                            \n",
       "128820  A sea of light at Tamar Park. #HongKongProtests https://t.co/WaYU1faSdH                                                                                                                                                                                                                                         \n",
       "72065   We are working with everyone to pass our #HongKong bill but we aren’t going to let anyone run out the clock on us\\n\\nAt some point very soon it will be time to act\\n\\nOne way or another we will have a vote &amp; those who oppose it will have to explain why\\n\\n https://t.co/9qOEGvJpLi                    \n",
       "237807  @Kennedy_J123 @Y_LHongKonger #hkpoliceterrorist = mobs                                                                                                                                                                                                                                                          \n",
       "6950    This is why the Senate should vote on the Hong Kong Human Rights &amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d                                                                                                                                           \n",
       "106121  Lunch break in Central.  These are not the typical young protesters.  You're looking at people in dress shirts and business attire speaking up against #HKPolice during their lunch hour in memory of Chow\\n\\n#StandWithHongKong #HongKongProtests https://t.co/wRqW4THqMS                                      \n",
       "4355    This is why the Senate should vote on the Hong Kong Human Rights &amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d                                                                                                                                           \n",
       "155377  @RepMcGovern @senatemajldr Thanks Rep McGovern for raising the awareness on #HKHumanRightsandDemocracyAct.\\n\\nWe are facing human rights crisis and becoming police state. Please #StandwithHK                                                                                                                  \n",
       "248441  “We call on Chief Exec. Carrie Lam to promote accountability by supplementing the Independent Police Complains Council review with an independent investigation into the protest-related incidents.” - @SecPompeo on the political unrest in #HongKong. READ: https://t.co/RZMCdcgLQN https://t.co/i00X8iM8ky   \n",
       "\n",
       "        sentiment_class  \n",
       "31386   3                \n",
       "143334  3                \n",
       "128820  3                \n",
       "72065   3                \n",
       "237807  3                \n",
       "6950    3                \n",
       "106121  3                \n",
       "4355    3                \n",
       "155377  3                \n",
       "248441  3                "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter 10 neutral original tweets:\n",
    "print(\"10 random neutral original tweets and their sentiment classes:\")\n",
    "data[(data['sentiment_class'] == 3)].sample(n=10)[['text', 'sentiment_class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 random positive original tweets and their sentiment classes:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25088</td>\n",
       "      <td>Thanks Nottingham for standing with #HongKong 💪\\n#StandWithHongKong https://t.co/68RjxTcLjH</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90928</td>\n",
       "      <td>My most important meeting today in the Danish Parliament: My meeting with the representative from the democratic movement in Hong Kong. Thank you, Mo, for meeting with me and my MP-colleague @khoenge plus @amnestydk. #StandWithHongKong #FiveDemandsNotOneLess #dkpol https://t.co/MKxoX72220</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89015</td>\n",
       "      <td>@joshrogin @SenSchumer @senatemajldr Thanks so much for #StandWithHongKong .The suitation in #HongKong is getting more serious,#hongkongpolicebrutality happen everyday, one university student lost his life today ,he was only 22. Please pass the act to #SOSHK https://t.co/mNFfXe37hP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69731</td>\n",
       "      <td>Chris Patten, last governor of HK: China’s behaviour in HK “piece of authoritarian vandalism of a great city and its way of life.” World leaders need to “replace cringeing with values-based diplomacy” when it comes to China @unimelb #hongkong https://t.co/EZyFYymDW8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223246</td>\n",
       "      <td>Dear foreigners, here are the ways to help stand with us. It wouldn’t take a long time. Please help Hong Kong\\n#HongKongProtest\\n#StandWithHongKong https://t.co/zIF9IFzyM7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56976</td>\n",
       "      <td>Speaking to parliamentarians this morning about an UK #HongKong Human Rights and Democracy Act. Lots of enthusiasm. UK based lawyers who love HK and want to help should DM me. #StandWithHongKong</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214582</td>\n",
       "      <td>I fully respect the decisions by our brothers and sisters at the forefront of #HongKongProtests.\\n\\nIf they decide to stay, we stay together and pour in supplies to support them.\\n\\nIf they decide to retreat, we evacuate every one of them and leave no one behind.\\n\\n#BeWater #HongKong https://t.co/95ybULJGMi https://t.co/j8JmQR3rO8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198182</td>\n",
       "      <td>I hope it will not be too late to #PassHKHumanRightsAndDemocracyAct. Many #HongKongers have gone through a lot and sacrificed lives and happiness for the days of freedom and democracy. Thank you for #StandwithHongKong. https://t.co/aeB4CdWZth</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183018</td>\n",
       "      <td>\"Hey Popo, Congrats! You finally managed to go into university and work in Central\" - on a day #HKPolice tries to storm into univ campuses and again confronts protesters in Central financial district. #HKPoliceState https://t.co/g7xKBrFYeK</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141611</td>\n",
       "      <td>I’m excited to be coming to HK on Nov 14-19 to hear your brave stories and share them with Congress, the Trump Administration and the American people. #StandWithHK ⁦@Andychanhotin⁩ ⁦@FreedomHKG⁩ ⁦@Fight4HongKong⁩ ⁦@Stand_with_HK⁩ ⁦@HKWORLDCITY⁩ https://t.co/oe3mI9bJS7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67280</td>\n",
       "      <td>At Birmingham Cake International 2019, an artist paid tribute to the Hong Kong protesters with this cake, adorned with mask, umbrellas, teargas etc. I understand this entry has been disqualified but an appeal will be lodged. #StandWithHongKong #HongKongProtests https://t.co/NJTduVLp4D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12449</td>\n",
       "      <td>Disgusting: Police in #HongKong are marking protestors with a pen, and writing a number on their hands just like the Nazis did in Germany. #China https://t.co/aAF6Jw7yXN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60021</td>\n",
       "      <td>Chief Executive Carrie Lam &amp;amp; human rights violator President Xi have been working together to restrict the rights of the people of #HongKong.\\n\\nXi &amp;amp; his puppets have no interest in allowing Hong Kong to have the autonomy &amp;amp; human rights they were promised.\\nhttps://t.co/CMYZwkIqMH</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76186</td>\n",
       "      <td>These are very important projects which can significantly help #FreeHongKong in this current critical stage of the movement.\\nI hope they will be successfully released at the right time. #HongKongProtests https://t.co/BIh3QM84ow</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45170</td>\n",
       "      <td>If you have dash cam footage at Sheung Tak Parking Structure A second/third floor from NOV 4th 00:30-02:00, please share it with the contacts listed in below poster. \\n\\nIt will be an anonymous submission according to poster. Thank you 🙏\\n\\n#HongKong #HongKongProtests #LIHKG https://t.co/WRkJaFQWMB</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173149</td>\n",
       "      <td>Filmed by HKU’s Campus TV: a journalist in yellow vest appeared to be shot by tear gas. A group of #CUHK students went over to help move the journo to a safer area, while another round landed at the exact same spot. Does this still look like a “protest” scene to anyone? #HongKong https://t.co/TVoylGoxg9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138248</td>\n",
       "      <td>@shujamtaro Thank you so much 😊! #StandWithHongKong</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136663</td>\n",
       "      <td>Hong Kong Police claims to pop Champagne to celebrate the death of the recently passed away student (Chow Tsz Lok) - 8th Nov\\n\\nhttps://t.co/1wCBkimvVx\\n●：i-cable TV\\n# # # #AntiELAB #LIHKG #HongKongProtests https://t.co/dpoBN7onRS</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13224</td>\n",
       "      <td>Pouring rain in Nottingham, UK didn't stop Hong Kong students and their friends showing solidarity in their #humanchain with the #HongKongProtests on\\n#WorldResistanceDay https://t.co/rmUTeZ52nY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151791</td>\n",
       "      <td>The violence from Beijing’s puppets in #HongKong continues to grow. Our hearts go out to the brave pro-democracy fighters as they mourn the passing of one of their own.\\n\\nHis family and friends deserve the truth. There should be a full &amp;amp; open investigation.\\nhttps://t.co/yE6pErm4qb</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                 text  \\\n",
       "25088   Thanks Nottingham for standing with #HongKong 💪\\n#StandWithHongKong https://t.co/68RjxTcLjH                                                                                                                                                                                                                                                     \n",
       "90928   My most important meeting today in the Danish Parliament: My meeting with the representative from the democratic movement in Hong Kong. Thank you, Mo, for meeting with me and my MP-colleague @khoenge plus @amnestydk. #StandWithHongKong #FiveDemandsNotOneLess #dkpol https://t.co/MKxoX72220                                               \n",
       "89015   @joshrogin @SenSchumer @senatemajldr Thanks so much for #StandWithHongKong .The suitation in #HongKong is getting more serious,#hongkongpolicebrutality happen everyday, one university student lost his life today ,he was only 22. Please pass the act to #SOSHK https://t.co/mNFfXe37hP                                                      \n",
       "69731   Chris Patten, last governor of HK: China’s behaviour in HK “piece of authoritarian vandalism of a great city and its way of life.” World leaders need to “replace cringeing with values-based diplomacy” when it comes to China @unimelb #hongkong https://t.co/EZyFYymDW8                                                                      \n",
       "223246  Dear foreigners, here are the ways to help stand with us. It wouldn’t take a long time. Please help Hong Kong\\n#HongKongProtest\\n#StandWithHongKong https://t.co/zIF9IFzyM7                                                                                                                                                                     \n",
       "56976   Speaking to parliamentarians this morning about an UK #HongKong Human Rights and Democracy Act. Lots of enthusiasm. UK based lawyers who love HK and want to help should DM me. #StandWithHongKong                                                                                                                                              \n",
       "214582  I fully respect the decisions by our brothers and sisters at the forefront of #HongKongProtests.\\n\\nIf they decide to stay, we stay together and pour in supplies to support them.\\n\\nIf they decide to retreat, we evacuate every one of them and leave no one behind.\\n\\n#BeWater #HongKong https://t.co/95ybULJGMi https://t.co/j8JmQR3rO8   \n",
       "198182  I hope it will not be too late to #PassHKHumanRightsAndDemocracyAct. Many #HongKongers have gone through a lot and sacrificed lives and happiness for the days of freedom and democracy. Thank you for #StandwithHongKong. https://t.co/aeB4CdWZth                                                                                              \n",
       "183018  \"Hey Popo, Congrats! You finally managed to go into university and work in Central\" - on a day #HKPolice tries to storm into univ campuses and again confronts protesters in Central financial district. #HKPoliceState https://t.co/g7xKBrFYeK                                                                                                 \n",
       "141611  I’m excited to be coming to HK on Nov 14-19 to hear your brave stories and share them with Congress, the Trump Administration and the American people. #StandWithHK ⁦@Andychanhotin⁩ ⁦@FreedomHKG⁩ ⁦@Fight4HongKong⁩ ⁦@Stand_with_HK⁩ ⁦@HKWORLDCITY⁩ https://t.co/oe3mI9bJS7                                                                    \n",
       "67280   At Birmingham Cake International 2019, an artist paid tribute to the Hong Kong protesters with this cake, adorned with mask, umbrellas, teargas etc. I understand this entry has been disqualified but an appeal will be lodged. #StandWithHongKong #HongKongProtests https://t.co/NJTduVLp4D                                                   \n",
       "12449   Disgusting: Police in #HongKong are marking protestors with a pen, and writing a number on their hands just like the Nazis did in Germany. #China https://t.co/aAF6Jw7yXN                                                                                                                                                                       \n",
       "60021   Chief Executive Carrie Lam &amp; human rights violator President Xi have been working together to restrict the rights of the people of #HongKong.\\n\\nXi &amp; his puppets have no interest in allowing Hong Kong to have the autonomy &amp; human rights they were promised.\\nhttps://t.co/CMYZwkIqMH                                           \n",
       "76186   These are very important projects which can significantly help #FreeHongKong in this current critical stage of the movement.\\nI hope they will be successfully released at the right time. #HongKongProtests https://t.co/BIh3QM84ow                                                                                                            \n",
       "45170   If you have dash cam footage at Sheung Tak Parking Structure A second/third floor from NOV 4th 00:30-02:00, please share it with the contacts listed in below poster. \\n\\nIt will be an anonymous submission according to poster. Thank you 🙏\\n\\n#HongKong #HongKongProtests #LIHKG https://t.co/WRkJaFQWMB                                     \n",
       "173149  Filmed by HKU’s Campus TV: a journalist in yellow vest appeared to be shot by tear gas. A group of #CUHK students went over to help move the journo to a safer area, while another round landed at the exact same spot. Does this still look like a “protest” scene to anyone? #HongKong https://t.co/TVoylGoxg9                                \n",
       "138248  @shujamtaro Thank you so much 😊! #StandWithHongKong                                                                                                                                                                                                                                                                                             \n",
       "136663  Hong Kong Police claims to pop Champagne to celebrate the death of the recently passed away student (Chow Tsz Lok) - 8th Nov\\n\\nhttps://t.co/1wCBkimvVx\\n●：i-cable TV\\n# # # #AntiELAB #LIHKG #HongKongProtests https://t.co/dpoBN7onRS                                                                                                         \n",
       "13224   Pouring rain in Nottingham, UK didn't stop Hong Kong students and their friends showing solidarity in their #humanchain with the #HongKongProtests on\\n#WorldResistanceDay https://t.co/rmUTeZ52nY                                                                                                                                              \n",
       "151791  The violence from Beijing’s puppets in #HongKong continues to grow. Our hearts go out to the brave pro-democracy fighters as they mourn the passing of one of their own.\\n\\nHis family and friends deserve the truth. There should be a full &amp; open investigation.\\nhttps://t.co/yE6pErm4qb                                                 \n",
       "\n",
       "        sentiment_class  \n",
       "25088   4                \n",
       "90928   4                \n",
       "89015   4                \n",
       "69731   5                \n",
       "223246  5                \n",
       "56976   5                \n",
       "214582  5                \n",
       "198182  5                \n",
       "183018  4                \n",
       "141611  5                \n",
       "67280   4                \n",
       "12449   4                \n",
       "60021   4                \n",
       "76186   5                \n",
       "45170   5                \n",
       "173149  5                \n",
       "138248  4                \n",
       "136663  4                \n",
       "13224   4                \n",
       "151791  4                "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter 20 positive original tweets:\n",
    "print(\"20 random positive original tweets and their sentiment classes:\")\n",
    "data[(data['sentiment_class'] == 4) | (data['sentiment_class'] == 5)].sample(n=20)[['text', 'sentiment_class']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the sentiment scores for sentences are not accurate to capture the sentiments of the tweets. Supposedly neutral tweets from above are negative, in which they are about liberating Hong Kong and against police brutality etc. On the other hand, the deal breaker comes from the supposedly positive tweets. A glance at the 20 positive tweets from above shows that they are all actually negative - pro-protesters and anti-government and China. \n",
    "\n",
    "#### Hence, this short exercise reveals a shortcoming in using a rule-based/lexicon-based model such as the SentimentIntensityAnalzyer from VADER Lexicon library. Since words contained in the tweets are measured against a pre-defined dictionary of positive and negative words, it fails to examine and take the context of the tweets into account. As a result, most of the 'positive' sentiment tweets could mean that they are showing support to the protesters and their cause, NOT to the Hong Kong government and/or China. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Popularity of Hashtags\n",
    "\n",
    "The tweets were scraped on the basis of pre-defined search term i.e. specific hashtags. However, the tweets can still contain other hashtags that are not defined in the search term. We want to see amongst this collection of scraped tweets, what are the most and least popular hashtags used by Twitter users in their tweets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# the column data['hashtags'] returns a list of string(s) for each tweet.\n",
    "# Build a list of all hashtags in the dataset\n",
    "\n",
    "hashtag_list = []\n",
    "\n",
    "for i in range(0, len(data)):\n",
    "    # Obtain the current list of hashtags\n",
    "    curr_hashtag = data.iloc[i, :]['hashtags']\n",
    "    \n",
    "    # Extract and append the hashtags to 'hashtag_list':\n",
    "    for j in range(0, len(curr_hashtag)):\n",
    "        hashtag_list.append(curr_hashtag[j])\n",
    "    \n",
    "# Print out the list\n",
    "print(hashtag_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of hashtags used in 248651 tweets is 306161\n"
     ]
    }
   ],
   "source": [
    "# No. of hashtags\n",
    "print('No. of hashtags used in {} tweets is {}'.format(len(data), len(hashtag_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            hashtags\n",
      "0  hongkong         \n",
      "1  china            \n",
      "2  policestate      \n",
      "3  hkpolicebrutality\n",
      "4  hongkongpolice   \n",
      "Shape of df_hashtag is: (306161, 1)\n"
     ]
    }
   ],
   "source": [
    "# Now we have the list of hashtags, we can convert this into a simple DataFrame, which will be used for visualization.\n",
    "df_hashtag = pd.DataFrame(\n",
    "    {'hashtags': hashtag_list}\n",
    ")\n",
    "\n",
    "print(df_hashtag.head())\n",
    "print('Shape of df_hashtag is:', df_hashtag.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Visualization: All-time top N hashtags used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               hashtags  counts\n",
      "2142  hongkong           73039 \n",
      "4041  standwithhongkong  18842 \n",
      "2250  hongkongprotests   16316 \n",
      "1988  hkpolice           11380 \n",
      "4033  standwithhk        10084 \n",
      "3955  soshk              9638  \n",
      "2209  hongkongpolice     8653  \n",
      "2034  hkpoliceterrorism  6711  \n",
      "1024  cuhk               5587  \n",
      "1459  fightforfreedom    5552  \n",
      "2163  hongkongers        4903  \n",
      "796   china              4834  \n",
      "1592  freehongkong       4372  \n",
      "2026  hkpolicestate      4308  \n",
      "1994  hkpolicebrutality  3568  \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The top 10 hashtags\n",
    "N = 15\n",
    "top_hashtags = df_hashtag.groupby(['hashtags']).size().reset_index(name = 'counts').sort_values(by = 'counts', ascending = False).head(N)\n",
    "print(top_hashtags)\n",
    "\n",
    "# seaborn countplot on the top 30 hashtags\n",
    "plt.figure(figsize=(30,8))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.barplot(x = 'hashtags', y = 'counts', data = top_hashtags)\n",
    "plt.title('Barplot of Top ' + str(N) + ' Hashtags used')\n",
    "plt.xlabel('Hashtags')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the top N hashtags are mostly showing support for the protesters instead of the Hong Kong Government and Police. \n",
    "\n",
    "#### Advanced: Time-series of Top N Hashtags\n",
    "In this advanced section, we want to see the growth in usage of hashtags from 25th October 2019 onwards since tweets in dataframe are scraped from this date onwards.\n",
    "\n",
    "Did one or few hashtags become more popular over time? Let's find out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "ind_to_drop = []\n",
    "date = []\n",
    "\n",
    "# First find out which 'tweetcreatedts' is not a string or in other weird formats\n",
    "for i in range(0, len(data)):\n",
    "    ith_date_str = data.iloc[i,:]['tweetcreatedts']\n",
    "    ith_match = re.search(r'\\d{4}-\\d{2}-\\d{2}', ith_date_str)\n",
    "    if ith_match == None:\n",
    "        ind_to_drop.append(i)\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop these rows using ind_to_drop\n",
    "data.drop(ind_to_drop, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248648, 130)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check new shape\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Len of date list:  248648\n"
     ]
    }
   ],
   "source": [
    "# Create a new list of datetime date objects from the tweets:\n",
    "for i in range(0, len(data)):\n",
    "    ith_date_str = data.iloc[i, :]['tweetcreatedts']\n",
    "    ith_match = re.search(r'\\d{4}-\\d{2}-\\d{2}', ith_date_str)\n",
    "    ith_date = datetime.strptime(ith_match.group(), '%Y-%m-%d').date()\n",
    "    \n",
    "    date.append(ith_date)\n",
    "    \n",
    "# Size of list 'date'\n",
    "print('Len of date list: ', len(date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append 'date' to dataframe 'data' as 'dt_date' aka 'datetime_date'\n",
    "data['dt_date'] = date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-11-03    29892\n",
       "2019-11-09    29133\n",
       "2019-11-13    15000\n",
       "2019-11-19    15000\n",
       "2019-11-11    15000\n",
       "2019-11-16    15000\n",
       "2019-11-14    15000\n",
       "2019-11-15    15000\n",
       "2019-11-12    15000\n",
       "2019-11-04    14644\n",
       "2019-11-08    14422\n",
       "2019-11-06    14398\n",
       "2019-11-07    14184\n",
       "2019-11-05    13624\n",
       "2019-11-10    13351\n",
       "Name: dt_date, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['dt_date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique dates available in this dataset are: [datetime.date(2019, 11, 3) datetime.date(2019, 11, 4)\n",
      " datetime.date(2019, 11, 5) datetime.date(2019, 11, 6)\n",
      " datetime.date(2019, 11, 7) datetime.date(2019, 11, 8)\n",
      " datetime.date(2019, 11, 9) datetime.date(2019, 11, 10)\n",
      " datetime.date(2019, 11, 11) datetime.date(2019, 11, 12)\n",
      " datetime.date(2019, 11, 13) datetime.date(2019, 11, 14)\n",
      " datetime.date(2019, 11, 15) datetime.date(2019, 11, 16)\n",
      " datetime.date(2019, 11, 19)]\n"
     ]
    }
   ],
   "source": [
    "# Create a new dataframe first\n",
    "timeseries_hashtags = pd.DataFrame(columns = ['hashtags', 'count', 'date', 'dayofnov'])\n",
    "\n",
    "# Obtain a set of unique dates in 'date' list:\n",
    "unique_date = np.unique(date)\n",
    "print('Unique dates available in this dataset are:', unique_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to create a visualization of top N hashtags for T number of days\n",
    "    \n",
    "def visualize_top_hashtags(main_df, timeseries_df, N, T, unique_dates):\n",
    "    # main_df - main dataframe 'data'\n",
    "    # timeseries_df - a new and empty dataframe to store the top hashtags \n",
    "    # N - number of top hashtags to consider\n",
    "    # T - number of days to consider\n",
    "    # unique_dates - list of unique dates available in the table\n",
    "    \n",
    "    # Returns:\n",
    "    # timeseries_df\n",
    "    \n",
    "    # Start counter to keep track of number of days already considered\n",
    "    counter = 1\n",
    "\n",
    "    # Starting from the latest date in the list\n",
    "    for ith_date in reversed(unique_dates):\n",
    "        # Check if counter exceeds the number of days required, T:\n",
    "        if counter <= T:\n",
    "            \n",
    "            # Filter tweets created on this date:\n",
    "            ith_date_df = main_df[main_df['dt_date'] == ith_date]\n",
    "\n",
    "            # From this particular df, build a list of all possible hashtags:\n",
    "            ith_hashtag_list = []\n",
    "\n",
    "            for i in range(0, len(ith_date_df)):\n",
    "                # Obtain the current list of hashtags:\n",
    "                curr_hashtag = ith_date_df.iloc[i,:]['hashtags']\n",
    "\n",
    "                # Extract and append the hashtags to 'hashtag_list':\n",
    "                for j in range(0, len(curr_hashtag)):\n",
    "                    ith_hashtag_list.append(curr_hashtag[j])\n",
    "\n",
    "            # Convert the list into a simple DataFrame\n",
    "            ith_df_hashtag = pd.DataFrame({\n",
    "                    'hashtags': ith_hashtag_list\n",
    "            })\n",
    "\n",
    "            # Obtain top N hashtags:\n",
    "            ith_top_hashtags = ith_df_hashtag.groupby(['hashtags']).size().reset_index(name = 'count').sort_values(by = 'count', ascending = False).head(N)\n",
    "\n",
    "            # Add date as a column\n",
    "            ith_top_hashtags['date'] = ith_date\n",
    "            ith_top_hashtags['dayofnov'] = ith_date.day\n",
    "\n",
    "            # Finally, concat this dataframe to timeseries_hashtags\n",
    "            timeseries_df = pd.concat([timeseries_df, ith_top_hashtags], axis = 0)\n",
    "\n",
    "            # Increase counter by 1\n",
    "            counter += 1\n",
    "        \n",
    "        else: # break the for loop\n",
    "            break\n",
    "    \n",
    "    print('The newly created timeseries_hashtag of size {} is: '.format(timeseries_df.shape))\n",
    "    timeseries_df.reset_index(inplace = True, drop = True)\n",
    "    \n",
    "    # Visualization\n",
    "    plt.figure(figsize=(28,12))\n",
    "    ax = sns.barplot(x = 'hashtags', \n",
    "                   y = 'count',\n",
    "                   data = timeseries_df,\n",
    "                   hue = 'dayofnov')\n",
    "\n",
    "    # plt.xticks(np.arange(3, 6, step=1))\n",
    "    # Moving legend box outside of the plot\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    # for legend text\n",
    "    plt.setp(ax.get_legend().get_texts(), fontsize='22')\n",
    "    # for legend title\n",
    "    plt.setp(ax.get_legend().get_title(), fontsize='32') \n",
    "    plt.xlabel('Top Hashtags')\n",
    "    plt.ylabel('Count of Hashtags')\n",
    "    plt.title('Top ' + str(N) + ' Hashtags per day')\n",
    "    sns.despine(left=True, bottom=True)\n",
    "    plt.xticks(rotation = 45)\n",
    "    plt.show()\n",
    "    \n",
    "    return timeseries_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The newly created timeseries_hashtag of size (70, 4) is: \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 39321 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 28207 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 39321 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 28207 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2016x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call function\n",
    "\n",
    "timeseries_hashtags = visualize_top_hashtags(main_df = data,\n",
    "                       timeseries_df = timeseries_hashtags,\n",
    "                       N = 10,\n",
    "                       T = 7,\n",
    "                       unique_dates = unique_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'visualize_top_hashtags' function allows us to vary the top number of hashtags per day and the number of days to consider. e.g. we can consider the top 5 hashtags per day for the 2 latest dates in our dataset. A plot of top 10 hashtags per day for the past dates reveal that some hashtags - such as #hongkong, #hongkongprotests, #hongkongpolice, #standwithhongkong #hkpolice - are consistently used a lot. This could be because they are more generic in nature and hence, allowing people to easily search for related tweets using them.\n",
    "\n",
    "On the other hand, we see that there are some hashtags that had a spike in usage - such as # blizzcon2019, #周梓樂 (rep. by squares in above graph), #hkust. These hashtags were used probably after specific events, as suggested by these hashtags, took place. It is expected that major incidents will trigger corresponding hashtags, other than the usual ones, to be used. Diving into these events, there are:\n",
    "1. #blizzcon2019/blizzcon19 - https://www.scmp.com/tech/apps-social/article/3035987/will-blizzcon-become-latest-battleground-hong-kong-protests\n",
    "2. #周梓樂 - https://www.bbc.com/news/world-asia-china-50343584\n",
    "3. #hkust (related to #周梓樂) - https://www.hongkongfp.com/2019/11/08/hong-kong-students-death-prompts-fresh-anger-protests/\n",
    "4. #japanese (A japanese was mistaken to be a mainland chinese and hence, was attacked by the protesters) - https://www3.nhk.or.jp/nhkworld/en/news/20191112_36/#:~:targetText=Hong%20Kong%20media%20reported%20on,take%20pictures%20of%20protest%20activities.\n",
    "5. #antielab (protesters boycotting the elections because Joshua Wong was banned from it) - https://www.scmp.com/news/hong-kong/politics/article/3035285/democracy-activist-joshua-wong-banned-running-hong-kong\n",
    "\n",
    "Finally, other hashtags are actually quite similar to the popular ones. They differ because some words in these hashtags are spelt out in full.\n",
    "For example:\n",
    "1. #hkpolice vs #hongkongpolice\n",
    "2. #standwithhk vs #standwithhongkong\n",
    "3. #hkpoliceterrorism vs #hkpoliceterrorists\n",
    "\n",
    "### 4. Most Popular Tweets\n",
    "In this section, we will focus on what are the most popular tweets around. There are 2 indicators that can help us achieve this - a tweet's retweet count and favorite count. Unfortunately, we managed to extract only the retweet count because there was some trouble in retrieving the favourite count from the mess of dictionaries in the .json format (Please feel free to tell me if you know how to do it!).\n",
    "\n",
    "We will perform the following:\n",
    "1. Top N tweets of all time (in this dataset only)\n",
    "2. Top N tweets for particular day\n",
    "3. Top N tweets for the past T days\n",
    "\n",
    "#### Top N Tweets of All Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current data type of \"retweetcount\" is: object\n",
      "Current data type of \"retweetcount\" is: int64\n"
     ]
    }
   ],
   "source": [
    "# It turns out that some of the entries of 'retweetcount' is either in str or int\n",
    "# We convert the data type of the column to all int using pd.to_numeric()\n",
    "print('Current data type of \"retweetcount\" is:',data['retweetcount'].dtypes)\n",
    "data['retweetcount'] = pd.to_numeric(arg = data['retweetcount'])\n",
    "print('Current data type of \"retweetcount\" is:',data['retweetcount'].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>username</th>\n",
       "      <th>acctdesc</th>\n",
       "      <th>location</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>totaltweets</th>\n",
       "      <th>usercreatedts</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "      <th>...</th>\n",
       "      <th>word_violence</th>\n",
       "      <th>word_vote</th>\n",
       "      <th>word_want</th>\n",
       "      <th>word_way</th>\n",
       "      <th>word_work</th>\n",
       "      <th>word_world</th>\n",
       "      <th>word_year</th>\n",
       "      <th>word_young</th>\n",
       "      <th>sentiment_class</th>\n",
       "      <th>dt_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248148</td>\n",
       "      <td>BrianLe89224997</td>\n",
       "      <td>hongkonger🇭🇰</td>\n",
       "      <td>n.a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28</td>\n",
       "      <td>2019-11-17 16:56:44</td>\n",
       "      <td>2019-11-19 17:22:10</td>\n",
       "      <td>64205</td>\n",
       "      <td>The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp;amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>235579</td>\n",
       "      <td>SzeNgaCindy1</td>\n",
       "      <td>a hongkonger😷\\nmay glory be to hong kong 🇭🇰💛</td>\n",
       "      <td>n.a</td>\n",
       "      <td>44.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>116</td>\n",
       "      <td>2019-11-13 13:12:29</td>\n",
       "      <td>2019-11-19 15:26:53</td>\n",
       "      <td>64201</td>\n",
       "      <td>The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp;amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-11-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               username                                      acctdesc  \\\n",
       "248148  BrianLe89224997  hongkonger🇭🇰                                   \n",
       "235579  SzeNgaCindy1     a hongkonger😷\\nmay glory be to hong kong 🇭🇰💛   \n",
       "\n",
       "       location  following  followers totaltweets        usercreatedts  \\\n",
       "248148  n.a      1.0        1.0        28          2019-11-17 16:56:44   \n",
       "235579  n.a      44.0       14.0       116         2019-11-13 13:12:29   \n",
       "\n",
       "             tweetcreatedts  retweetcount  \\\n",
       "248148  2019-11-19 17:22:10  64205          \n",
       "235579  2019-11-19 15:26:53  64201          \n",
       "\n",
       "                                                                                                                                                                                                                                             text  \\\n",
       "248148  The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK   \n",
       "235579  The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK   \n",
       "\n",
       "        ... word_violence word_vote word_want  word_way  word_work  \\\n",
       "248148  ...  0.436332      0.0       0.0       0.0       0.0         \n",
       "235579  ...  0.436332      0.0       0.0       0.0       0.0         \n",
       "\n",
       "        word_world  word_year  word_young  sentiment_class     dt_date  \n",
       "248148  0.40461     0.0        0.0         1                2019-11-19  \n",
       "235579  0.40461     0.0        0.0         1                2019-11-19  \n",
       "\n",
       "[2 rows x 131 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the sorted dataframe\n",
    "data.sort_values(by = ['retweetcount'], ascending = False).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that will pull out top N tweets of all time:\n",
    "def alltime_top_tweets(df, N):\n",
    "    # Arguments:\n",
    "    # df - dataframe\n",
    "    # N - top N tweets based on retweetcount\n",
    "    \n",
    "    # Sort according to 'retweetcount'\n",
    "    top_tweets_df = df.sort_values(by = ['retweetcount'], ascending = False)\n",
    "    # Drop also duplicates from the list, keep only the copy with higher retweetcount\n",
    "    top_tweets_df.drop_duplicates(subset = 'text', keep = 'first', inplace = True)\n",
    "    # Keep only N rows\n",
    "    top_tweets_df = top_tweets_df.head(N)\n",
    "    \n",
    "    # Print out only important details \n",
    "    # username, tweetcreatedts, retweetcount, original text 'text'\n",
    "    return top_tweets_df[['username', 'tweetcreatedts', 'retweetcount', 'text']]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All-time top 10 tweets:\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248148</td>\n",
       "      <td>BrianLe89224997</td>\n",
       "      <td>2019-11-19 17:22:10</td>\n",
       "      <td>64205</td>\n",
       "      <td>The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp;amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36545</td>\n",
       "      <td>taterto66916070</td>\n",
       "      <td>2019-11-04 15:08:27</td>\n",
       "      <td>47503</td>\n",
       "      <td>Disturbing video taken in #Shenzhen just across the boarder with #HongKong. Something extraordinarily bad is about happen. #China #HongKongProtests #Democracy #SaveHongKong \\nhttps://t.co/Gad5R5HVZL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161213</td>\n",
       "      <td>Sergio74409673</td>\n",
       "      <td>2019-11-12 13:28:02</td>\n",
       "      <td>34101</td>\n",
       "      <td>Sen Marco Rubio joined @foxandfriends this morning to discuss the situation in #HongKong. \\n\\nRT 🔃 if you agree it's time for Congress to pass Rubio's #HongKongHumanRightsandDemocracyAct https://t.co/JQD1tMyviy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248156</td>\n",
       "      <td>BrianLe89224997</td>\n",
       "      <td>2019-11-19 17:22:07</td>\n",
       "      <td>32209</td>\n",
       "      <td>In #HongKong, government police continue their assault on universities into the night, making mass arrests and now threatening live fire against largely defenseless students. Beijing is pushing Hong Kong into a state of siege #StandWithHongKong https://t.co/88poKjgKt8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>229761</td>\n",
       "      <td>H45888752</td>\n",
       "      <td>2019-11-16 15:19:33</td>\n",
       "      <td>26585</td>\n",
       "      <td>RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp;amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>238617</td>\n",
       "      <td>Mj201915</td>\n",
       "      <td>2019-11-19 15:49:04</td>\n",
       "      <td>25576</td>\n",
       "      <td>Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201418</td>\n",
       "      <td>yutung19401791</td>\n",
       "      <td>2019-11-14 15:41:54</td>\n",
       "      <td>24789</td>\n",
       "      <td>#HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218890</td>\n",
       "      <td>GarySo13</td>\n",
       "      <td>2019-11-16 13:52:08</td>\n",
       "      <td>23697</td>\n",
       "      <td>Hong Kong's youth protestors are kind. I post this clip to say thanks. @CBSrandy  was rolling when the police fired tear gas at the group we were in.  An umbrella and helmet were forced on me. I'll prob never know who they are but I'm so grateful for their care. #HongKongProtests https://t.co/NNAsFjpXOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>237523</td>\n",
       "      <td>Ka38413923</td>\n",
       "      <td>2019-11-19 15:52:31</td>\n",
       "      <td>22399</td>\n",
       "      <td>The United States proudly stands with the people of Hong Kong as they rightfully resist China’s Communist Regime. # #FreeHongKong https://t.co/8legnSDaVF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239244</td>\n",
       "      <td>haeq</td>\n",
       "      <td>2019-11-19 16:19:57</td>\n",
       "      <td>21143</td>\n",
       "      <td>Mainland #China soldiers appearing on #HongKong streets now. Bad development - Beijing about to escalate further? #HongKongProtests https://t.co/9MErNXkbHc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               username       tweetcreatedts  retweetcount  \\\n",
       "248148  BrianLe89224997  2019-11-19 17:22:10  64205          \n",
       "36545   taterto66916070  2019-11-04 15:08:27  47503          \n",
       "161213  Sergio74409673   2019-11-12 13:28:02  34101          \n",
       "248156  BrianLe89224997  2019-11-19 17:22:07  32209          \n",
       "229761  H45888752        2019-11-16 15:19:33  26585          \n",
       "238617  Mj201915         2019-11-19 15:49:04  25576          \n",
       "201418  yutung19401791   2019-11-14 15:41:54  24789          \n",
       "218890  GarySo13         2019-11-16 13:52:08  23697          \n",
       "237523  Ka38413923       2019-11-19 15:52:31  22399          \n",
       "239244  haeq             2019-11-19 16:19:57  21143          \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                    text  \n",
       "248148  The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK                                                                         \n",
       "36545   Disturbing video taken in #Shenzhen just across the boarder with #HongKong. Something extraordinarily bad is about happen. #China #HongKongProtests #Democracy #SaveHongKong \\nhttps://t.co/Gad5R5HVZL                                                                                                            \n",
       "161213  Sen Marco Rubio joined @foxandfriends this morning to discuss the situation in #HongKong. \\n\\nRT 🔃 if you agree it's time for Congress to pass Rubio's #HongKongHumanRightsandDemocracyAct https://t.co/JQD1tMyviy                                                                                                \n",
       "248156  In #HongKong, government police continue their assault on universities into the night, making mass arrests and now threatening live fire against largely defenseless students. Beijing is pushing Hong Kong into a state of siege #StandWithHongKong https://t.co/88poKjgKt8                                      \n",
       "229761  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong                                                                                                                                           \n",
       "238617  Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong                                                                                                          \n",
       "201418  #HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81                                                                                                                                                         \n",
       "218890  Hong Kong's youth protestors are kind. I post this clip to say thanks. @CBSrandy  was rolling when the police fired tear gas at the group we were in.  An umbrella and helmet were forced on me. I'll prob never know who they are but I'm so grateful for their care. #HongKongProtests https://t.co/NNAsFjpXOP  \n",
       "237523  The United States proudly stands with the people of Hong Kong as they rightfully resist China’s Communist Regime. # #FreeHongKong https://t.co/8legnSDaVF                                                                                                                                                         \n",
       "239244  Mainland #China soldiers appearing on #HongKong streets now. Bad development - Beijing about to escalate further? #HongKongProtests https://t.co/9MErNXkbHc                                                                                                                                                       "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All-time top 10 tweets\n",
    "print('All-time top 10 tweets:')\n",
    "print('\\n')\n",
    "alltime_top_tweets(data, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top N tweets for a particular day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define another function as well\n",
    "def specified_toptweets(df, spec_date, N):\n",
    "    # Arguments\n",
    "    # df - dataframe\n",
    "    # N - top N tweets\n",
    "    # date - enter particular date in str format i.e. '2019-11-02'\n",
    "    \n",
    "    # Specific date\n",
    "    spec_date = datetime.strptime(spec_date, '%Y-%m-%d').date()\n",
    "    \n",
    "    # Filter df by date first\n",
    "    date_df = df[df['dt_date'] == spec_date ]\n",
    "    \n",
    "    # Sort according to 'retweetcount'\n",
    "    top_tweets_date_df = date_df.sort_values(by = ['retweetcount'], ascending = False)\n",
    "    # Drop also duplicates from the list, keep only the copy with higher retweetcount\n",
    "    top_tweets_date_df.drop_duplicates(subset = 'text', keep = 'first', inplace = True)\n",
    "    # Keep only N rows\n",
    "    top_tweets_date_df = top_tweets_date_df.head(N)\n",
    "    \n",
    "    print('Top ' + str(N) + ' tweets for date ' + str(spec_date) + ' are:')\n",
    "    # Print out only important details \n",
    "    # username, tweetcreatedts, retweetcount, original text 'text'\n",
    "    return top_tweets_date_df[['username', 'tweetcreatedts', 'retweetcount', 'text']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 tweets for date 2019-11-05 are:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>tweetcreatedts</th>\n",
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       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>56313</td>\n",
       "      <td>flora30639385</td>\n",
       "      <td>2019-11-05 13:56:16</td>\n",
       "      <td>14866</td>\n",
       "      <td>Outside the Kowloon mosque, Philip Khan condemn HK police and CE Carrie Lam in rage, saying the government is insulting Islam by spraying blue dye to the mosque using water cannon #HongKongProtests https://t.co/DTVf6DjzFo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56394</td>\n",
       "      <td>bellenico1</td>\n",
       "      <td>2019-11-05 13:55:07</td>\n",
       "      <td>11038</td>\n",
       "      <td>Man driving by in a Porsche was pulled out of his car and arrested by a dozen riot police, only for playing “Glory to Hong Kong” in his car.\\n\\nKid was asking the woman shooting the video : “Why was he arrested only for playing a song?”\\n\\n#HKPoliceState \\n#StandWithHongKong https://t.co/qn4whKuacW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48110</td>\n",
       "      <td>vkydfgtjgdvxcjy</td>\n",
       "      <td>2019-11-05 12:02:36</td>\n",
       "      <td>9126</td>\n",
       "      <td>Today I’m introducing new legislation to impose Global Magnitsky sanctions on Beijing officials &amp;amp; their collaborators who seek to repress Hong Kong. The situation there is urgent. Proud to be joined by @SenRickScott &amp;amp; @JohnCornyn #StandWithHongKong https://t.co/Owa6mMcjak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53045</td>\n",
       "      <td>r2wqn0p00t</td>\n",
       "      <td>2019-11-05 12:49:21</td>\n",
       "      <td>5591</td>\n",
       "      <td>BREAKING: Hong Kong bans activist Joshua Wong from standing in district election race https://t.co/J8b7RA2uON #HongKong #China #HongKongProtests #StandWithHK #antiELABhk #antiELAB @krislc @joshuawongcf @demosisto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56875</td>\n",
       "      <td>carbonwithhk</td>\n",
       "      <td>2019-11-05 13:48:26</td>\n",
       "      <td>5476</td>\n",
       "      <td>Disturbing reports that Beijing is planning new steps to “safeguard national security” in #HongKong. If so, that will only make things worse.\\n\\n@Senatemajldr McConnell: bring the #HKHumanRightsandDemocracyAct to a vote as soon as possible to support #HK!\\nhttps://t.co/CtAx5vr2Rj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58114</td>\n",
       "      <td>IdaLi40260586</td>\n",
       "      <td>2019-11-05 13:27:57</td>\n",
       "      <td>5019</td>\n",
       "      <td>After #HKPoliceTerrorists have arrested a #hkprotester tonight, his face was covered in blood.  #PoliceBrutality #HKHumanRightsandDemocracyAct #StandWithHongKong https://t.co/tzjI6rwpYq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56544</td>\n",
       "      <td>shortbr50041064</td>\n",
       "      <td>2019-11-05 13:52:52</td>\n",
       "      <td>4916</td>\n",
       "      <td>This is why the Senate should vote on the Hong Kong Human Rights &amp;amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53814</td>\n",
       "      <td>ethirica</td>\n",
       "      <td>2019-11-05 13:36:49</td>\n",
       "      <td>4496</td>\n",
       "      <td>Police officers have been filmed shoving a fireman around in Central after he complained they had shot tear gas straight at his truck. The officers also pepper sprayed a journalist who dared to report on the bust up. #HongKong\\n\\nhttps://t.co/rubbu7eQe2 https://t.co/Yt1FKU4BRm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57973</td>\n",
       "      <td>vycpwpeBfM4AJgg</td>\n",
       "      <td>2019-11-05 13:30:45</td>\n",
       "      <td>4485</td>\n",
       "      <td>In Tai Po Megaball just now, a female citizen lost consciousness and police did not let First Aid treat her, but continued to drag her body on the ground! 😭😭😭 What crime did she commit to deserve this? Is being a HongKonger a crime? \\n\\n#HongKongProtests #PoliceBrutality #SOSHK https://t.co/1VVbkz6HZt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57580</td>\n",
       "      <td>Sophie24842362</td>\n",
       "      <td>2019-11-05 13:37:54</td>\n",
       "      <td>4431</td>\n",
       "      <td>#LIVE: The ambience at New Town Plaza has intensified again as police have attempted to storm into the mall a 2nd time today. The chaotic scene has led to an arrest of a few. In this footage, a cop covers a man’s face as he tries to shout out his name. #antiRLAB #HongKongProtests https://t.co/vRhNYhtgPR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              username       tweetcreatedts  retweetcount  \\\n",
       "56313  flora30639385    2019-11-05 13:56:16  14866          \n",
       "56394  bellenico1       2019-11-05 13:55:07  11038          \n",
       "48110  vkydfgtjgdvxcjy  2019-11-05 12:02:36  9126           \n",
       "53045  r2wqn0p00t       2019-11-05 12:49:21  5591           \n",
       "56875  carbonwithhk     2019-11-05 13:48:26  5476           \n",
       "58114  IdaLi40260586    2019-11-05 13:27:57  5019           \n",
       "56544  shortbr50041064  2019-11-05 13:52:52  4916           \n",
       "53814  ethirica         2019-11-05 13:36:49  4496           \n",
       "57973  vycpwpeBfM4AJgg  2019-11-05 13:30:45  4485           \n",
       "57580  Sophie24842362   2019-11-05 13:37:54  4431           \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                   text  \n",
       "56313  Outside the Kowloon mosque, Philip Khan condemn HK police and CE Carrie Lam in rage, saying the government is insulting Islam by spraying blue dye to the mosque using water cannon #HongKongProtests https://t.co/DTVf6DjzFo                                                                                     \n",
       "56394  Man driving by in a Porsche was pulled out of his car and arrested by a dozen riot police, only for playing “Glory to Hong Kong” in his car.\\n\\nKid was asking the woman shooting the video : “Why was he arrested only for playing a song?”\\n\\n#HKPoliceState \\n#StandWithHongKong https://t.co/qn4whKuacW       \n",
       "48110  Today I’m introducing new legislation to impose Global Magnitsky sanctions on Beijing officials &amp; their collaborators who seek to repress Hong Kong. The situation there is urgent. Proud to be joined by @SenRickScott &amp; @JohnCornyn #StandWithHongKong https://t.co/Owa6mMcjak                          \n",
       "53045  BREAKING: Hong Kong bans activist Joshua Wong from standing in district election race https://t.co/J8b7RA2uON #HongKong #China #HongKongProtests #StandWithHK #antiELABhk #antiELAB @krislc @joshuawongcf @demosisto                                                                                              \n",
       "56875  Disturbing reports that Beijing is planning new steps to “safeguard national security” in #HongKong. If so, that will only make things worse.\\n\\n@Senatemajldr McConnell: bring the #HKHumanRightsandDemocracyAct to a vote as soon as possible to support #HK!\\nhttps://t.co/CtAx5vr2Rj                          \n",
       "58114  After #HKPoliceTerrorists have arrested a #hkprotester tonight, his face was covered in blood.  #PoliceBrutality #HKHumanRightsandDemocracyAct #StandWithHongKong https://t.co/tzjI6rwpYq                                                                                                                         \n",
       "56544  This is why the Senate should vote on the Hong Kong Human Rights &amp; Democracy Act - and my Be Water Act - now #StandWithHongKong #HongKong https://t.co/DokQaccB2d                                                                                                                                             \n",
       "53814  Police officers have been filmed shoving a fireman around in Central after he complained they had shot tear gas straight at his truck. The officers also pepper sprayed a journalist who dared to report on the bust up. #HongKong\\n\\nhttps://t.co/rubbu7eQe2 https://t.co/Yt1FKU4BRm                             \n",
       "57973  In Tai Po Megaball just now, a female citizen lost consciousness and police did not let First Aid treat her, but continued to drag her body on the ground! 😭😭😭 What crime did she commit to deserve this? Is being a HongKonger a crime? \\n\\n#HongKongProtests #PoliceBrutality #SOSHK https://t.co/1VVbkz6HZt    \n",
       "57580  #LIVE: The ambience at New Town Plaza has intensified again as police have attempted to storm into the mall a 2nd time today. The chaotic scene has led to an arrest of a few. In this footage, a cop covers a man’s face as he tries to shout out his name. #antiRLAB #HongKongProtests https://t.co/vRhNYhtgPR  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try: top 10 tweets for 2019-11-05\n",
    "specified_toptweets(data, '2019-11-05', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top N tweets for the past T days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique dates available in this dataset are:\n",
      "[datetime.date(2019, 11, 3) datetime.date(2019, 11, 4)\n",
      " datetime.date(2019, 11, 5) datetime.date(2019, 11, 6)\n",
      " datetime.date(2019, 11, 7) datetime.date(2019, 11, 8)\n",
      " datetime.date(2019, 11, 9) datetime.date(2019, 11, 10)\n",
      " datetime.date(2019, 11, 11) datetime.date(2019, 11, 12)\n",
      " datetime.date(2019, 11, 13) datetime.date(2019, 11, 14)\n",
      " datetime.date(2019, 11, 15) datetime.date(2019, 11, 16)\n",
      " datetime.date(2019, 11, 19)]\n"
     ]
    }
   ],
   "source": [
    "# Obtain the list of unique dates again\n",
    "unique_date = np.unique(date)\n",
    "print('Unique dates available in this dataset are:')\n",
    "print(unique_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define another function, which is similar to the one from above 'visualize_top_hashtags':\n",
    "\n",
    "def past_toptweets(df, T, N, unique_date):\n",
    "    # Arguments:\n",
    "    # df - dataframe 'data'\n",
    "    # T - last T days \n",
    "    # N - top N tweets\n",
    "    # List of all unique dates in dataset\n",
    "    \n",
    "    # Create a df to store top tweets for all T dates, in case there is a need to manipulate this df\n",
    "    past_toptweets_df = pd.DataFrame(columns = ['username', 'tweetcreatedts', 'retweetcount', 'text'])\n",
    "    print(past_toptweets_df)\n",
    "    \n",
    "    # Filter data according to last T dates first:\n",
    "    # Do a check that T must not be greater than the no. of elements in unique_date\n",
    "    if T <= len(unique_date):\n",
    "        unique_date = unique_date[-T:] # a list\n",
    "    else:\n",
    "        raise Exception('T must be smaller than or equal to the number of dates in the dataset!')\n",
    "    \n",
    "    # Print out top N for each unique_date one after another, starting from the latest:\n",
    "    for ith_date in reversed(unique_date):\n",
    "        # Filter tweets created on this date:\n",
    "        ith_date_df = df[df['dt_date'] == ith_date]\n",
    "        \n",
    "        # Sort according to 'retweetcount'\n",
    "        top_tweets_date_df = ith_date_df.sort_values(by = ['retweetcount'], ascending = False)\n",
    "        # Drop also duplicates from the list, keep only the copy with higher retweetcount\n",
    "        top_tweets_date_df.drop_duplicates(subset = 'text', keep = 'first', inplace = True)\n",
    "        # Keep only N rows\n",
    "        top_tweets_date_df = top_tweets_date_df.head(N)\n",
    "        # Keep only essential columns\n",
    "        top_tweets_date_df = top_tweets_date_df[['username', 'tweetcreatedts', 'retweetcount', 'text']]\n",
    "        \n",
    "        # Append top_tweets_date_df to past_toptweets_df\n",
    "        past_toptweets_df = pd.concat([past_toptweets_df, top_tweets_date_df], axis = 0)\n",
    "        \n",
    "        # Print out the top tweets for this ith_date\n",
    "        print('Top ' + str(N) + ' tweets for date ' + str(ith_date) + ' are:')\n",
    "        # print only essential columns:\n",
    "        print(top_tweets_date_df)\n",
    "        print('\\n')\n",
    "    \n",
    "    return past_toptweets_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [username, tweetcreatedts, retweetcount, text]\n",
      "Index: []\n",
      "Top 2 tweets for date 2019-11-19 are:\n",
      "               username       tweetcreatedts  retweetcount  \\\n",
      "248148  BrianLe89224997  2019-11-19 17:22:10  64205          \n",
      "248156  BrianLe89224997  2019-11-19 17:22:07  32209          \n",
      "\n",
      "                                                                                                                                                                                                                                                                                text  \n",
      "248148  The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK                                     \n",
      "248156  In #HongKong, government police continue their assault on universities into the night, making mass arrests and now threatening live fire against largely defenseless students. Beijing is pushing Hong Kong into a state of siege #StandWithHongKong https://t.co/88poKjgKt8  \n",
      "\n",
      "\n",
      "Top 2 tweets for date 2019-11-16 are:\n",
      "         username       tweetcreatedts  retweetcount  \\\n",
      "229761  H45888752  2019-11-16 15:19:33  26585          \n",
      "232997  NovaJK6    2019-11-16 15:39:35  25504          \n",
      "\n",
      "                                                                                                                                                                                                            text  \n",
      "229761  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong                                   \n",
      "232997  Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong  \n",
      "\n",
      "\n",
      "Top 2 tweets for date 2019-11-15 are:\n",
      "       username       tweetcreatedts  retweetcount  \\\n",
      "218476  mlcdu    2019-11-15 16:28:22  24707          \n",
      "216370  c_079ty  2019-11-15 16:40:58  21667          \n",
      "\n",
      "                                                                                                                                                                                                            text  \n",
      "218476  Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong  \n",
      "216370  The United States proudly stands with the people of Hong Kong as they rightfully resist China’s Communist Regime. # #FreeHongKong https://t.co/8legnSDaVF                                                 \n",
      "\n",
      "\n",
      "Top 2 tweets for date 2019-11-14 are:\n",
      "              username       tweetcreatedts  retweetcount  \\\n",
      "203264  Vickiehooo      2019-11-14 15:38:06  26185          \n",
      "201418  yutung19401791  2019-11-14 15:41:54  24789          \n",
      "\n",
      "                                                                                                                                                                           text  \n",
      "203264  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong  \n",
      "201418  #HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81                \n",
      "\n",
      "\n",
      "Top 2 tweets for date 2019-11-13 are:\n",
      "         username       tweetcreatedts  retweetcount  \\\n",
      "188015  lin_tenny  2019-11-13 14:29:19  26030          \n",
      "188088  1Kekster   2019-11-13 14:29:10  24574          \n",
      "\n",
      "                                                                                                                                                                           text  \n",
      "188015  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong  \n",
      "188088  #HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81                \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "      <th>retweetcount</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>248148</td>\n",
       "      <td>BrianLe89224997</td>\n",
       "      <td>2019-11-19 17:22:10</td>\n",
       "      <td>64205</td>\n",
       "      <td>The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp;amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>248156</td>\n",
       "      <td>BrianLe89224997</td>\n",
       "      <td>2019-11-19 17:22:07</td>\n",
       "      <td>32209</td>\n",
       "      <td>In #HongKong, government police continue their assault on universities into the night, making mass arrests and now threatening live fire against largely defenseless students. Beijing is pushing Hong Kong into a state of siege #StandWithHongKong https://t.co/88poKjgKt8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>229761</td>\n",
       "      <td>H45888752</td>\n",
       "      <td>2019-11-16 15:19:33</td>\n",
       "      <td>26585</td>\n",
       "      <td>RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp;amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>232997</td>\n",
       "      <td>NovaJK6</td>\n",
       "      <td>2019-11-16 15:39:35</td>\n",
       "      <td>25504</td>\n",
       "      <td>Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218476</td>\n",
       "      <td>mlcdu</td>\n",
       "      <td>2019-11-15 16:28:22</td>\n",
       "      <td>24707</td>\n",
       "      <td>Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216370</td>\n",
       "      <td>c_079ty</td>\n",
       "      <td>2019-11-15 16:40:58</td>\n",
       "      <td>21667</td>\n",
       "      <td>The United States proudly stands with the people of Hong Kong as they rightfully resist China’s Communist Regime. # #FreeHongKong https://t.co/8legnSDaVF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203264</td>\n",
       "      <td>Vickiehooo</td>\n",
       "      <td>2019-11-14 15:38:06</td>\n",
       "      <td>26185</td>\n",
       "      <td>RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp;amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201418</td>\n",
       "      <td>yutung19401791</td>\n",
       "      <td>2019-11-14 15:41:54</td>\n",
       "      <td>24789</td>\n",
       "      <td>#HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188015</td>\n",
       "      <td>lin_tenny</td>\n",
       "      <td>2019-11-13 14:29:19</td>\n",
       "      <td>26030</td>\n",
       "      <td>RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp;amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188088</td>\n",
       "      <td>1Kekster</td>\n",
       "      <td>2019-11-13 14:29:10</td>\n",
       "      <td>24574</td>\n",
       "      <td>#HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               username       tweetcreatedts retweetcount  \\\n",
       "248148  BrianLe89224997  2019-11-19 17:22:10  64205         \n",
       "248156  BrianLe89224997  2019-11-19 17:22:07  32209         \n",
       "229761  H45888752        2019-11-16 15:19:33  26585         \n",
       "232997  NovaJK6          2019-11-16 15:39:35  25504         \n",
       "218476  mlcdu            2019-11-15 16:28:22  24707         \n",
       "216370  c_079ty          2019-11-15 16:40:58  21667         \n",
       "203264  Vickiehooo       2019-11-14 15:38:06  26185         \n",
       "201418  yutung19401791   2019-11-14 15:41:54  24789         \n",
       "188015  lin_tenny        2019-11-13 14:29:19  26030         \n",
       "188088  1Kekster         2019-11-13 14:29:10  24574         \n",
       "\n",
       "                                                                                                                                                                                                                                                                                text  \n",
       "248148  The Chinese and #HongKong governments must immediately deescalate the situation and exercise restraint at #PolyU. More violence &amp; bloodshed will only make things worse.\\n\\nThe world is watching. #StandWithHongKong #DemocracyForHK                                     \n",
       "248156  In #HongKong, government police continue their assault on universities into the night, making mass arrests and now threatening live fire against largely defenseless students. Beijing is pushing Hong Kong into a state of siege #StandWithHongKong https://t.co/88poKjgKt8  \n",
       "229761  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong                                                                                                       \n",
       "232997  Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong                                                                      \n",
       "218476  Great news. The #HKHumanRightsandDemocracyAct just hit the Senate hotline. Passage should be imminent. @marcorubio has been a champion on this and I’m proud to join him as cosponsor #StandWithHongKong                                                                      \n",
       "216370  The United States proudly stands with the people of Hong Kong as they rightfully resist China’s Communist Regime. # #FreeHongKong https://t.co/8legnSDaVF                                                                                                                     \n",
       "203264  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong                                                                                                       \n",
       "201418  #HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81                                                                                                                     \n",
       "188015  RT to tell @SenateMajldr McConnell: bring the bipartisan Hong Kong Human Rights &amp; Democracy Act to the Senate Floor this week! #HongKongProtests #StandWithHongKong                                                                                                       \n",
       "188088  #HongKong police now raiding universities in the city - major new escalation from the Beijing backed government #HongKongProtests https://t.co/YpXKl77p81                                                                                                                     "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "past_toptweets(data, T = 5, N = 2, unique_date = unique_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One flaw of this function 'past_toptweets' is that it could return tweets that are identical. For instance, a popular tweet on day 1 can be retweeted again on subsequent days by other users. This function can then pick up such tweets because no logic is implemented as of now to consider only tweets that have not been chosen from earlier dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Behavior of Twitter Users\n",
    "\n",
    "#### No. of Tweets Daily\n",
    "Next, we want to know who are the users who have been relatively more active in tweeting tweets related to the protests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loop:  2019-11-03\n",
      "           username  noTweets  noFollowers     dt_date\n",
      "9374  zzzqqq9999     10        171.0        2019-11-03\n",
      "9373  zyxpcy3        2         0.0          2019-11-03\n",
      "9372  zyx_yny        1         1741.0       2019-11-03\n",
      "9371  zxlinux        1         3575.0       2019-11-03\n",
      "9370  zxcvbnvbnvbnm  15        2.0          2019-11-03\n",
      "...             ...  ..        ...                 ...\n",
      "4     037hk1         2         70.0         2019-11-03\n",
      "3     034angie1      3         47.0         2019-11-03\n",
      "2     02468Hkers     2         10.0         2019-11-03\n",
      "1     0203Thend      2         4.0          2019-11-03\n",
      "0     007nash        6         55.0         2019-11-03\n",
      "\n",
      "[9375 rows x 4 columns]\n",
      "Current loop:  2019-11-04\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "4490  zzxxcc11vv       1         4.0          2019-11-04\n",
      "4489  zwzw_22          1         171.0        2019-11-04\n",
      "4488  zwei55699        1         25.0         2019-11-04\n",
      "4487  zukashooter      3         903.0        2019-11-04\n",
      "4486  zuboi3           2         7.0          2019-11-04\n",
      "...      ...          ..         ...                 ...\n",
      "4     0UMfzLy5iFALoCE  4         182.0        2019-11-04\n",
      "3     0Kaylo2          7         1682.0       2019-11-04\n",
      "2     0830welcomepap1  4         2.0          2019-11-04\n",
      "1     034angie1        3         47.0         2019-11-04\n",
      "0     01_2501          6         7.0          2019-11-04\n",
      "\n",
      "[4491 rows x 4 columns]\n",
      "Current loop:  2019-11-05\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "3852  zukashooter      8         934.0        2019-11-05\n",
      "3851  zu00743015       1         162.0        2019-11-05\n",
      "3850  zstheshittycook  3         145.0        2019-11-05\n",
      "3849  zo_sanz          10        525.0        2019-11-05\n",
      "3848  znvv13           2         313.0        2019-11-05\n",
      "...      ...          ..           ...               ...\n",
      "4     0325Eternal      2         214.0        2019-11-05\n",
      "3     0308_jackie      2         14.0         2019-11-05\n",
      "2     0202Khk1         2         94.0         2019-11-05\n",
      "1     01_2501          4         10.0         2019-11-05\n",
      "0     007Vincentxxx    2         332.0        2019-11-05\n",
      "\n",
      "[3853 rows x 4 columns]\n",
      "Current loop:  2019-11-06\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "4441  zzzqqq9999       1         170.0        2019-11-06\n",
      "4440  zyyyyyyyy11      1         1.0          2019-11-06\n",
      "4439  zxj0858          1         6.0          2019-11-06\n",
      "4438  zukashooter      1         946.0        2019-11-06\n",
      "4437  zombot19         4         37.0         2019-11-06\n",
      "...        ...        ..          ...                ...\n",
      "4     0x46524545484b   2         272.0        2019-11-06\n",
      "3     0r3d_l8r1N0aM1o  4         2927.0       2019-11-06\n",
      "2     0830welcomepap1  6         2.0          2019-11-06\n",
      "1     0202Khk1         5         94.0         2019-11-06\n",
      "0     007Kayan         1         321.0        2019-11-06\n",
      "\n",
      "[4442 rows x 4 columns]\n",
      "Current loop:  2019-11-07\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "3950  zyyyyyyyy11      2         1.0          2019-11-07\n",
      "3949  zyx_yny          3         1757.0       2019-11-07\n",
      "3948  zxm10086         1         76.0         2019-11-07\n",
      "3947  zwzw_22          2         174.0        2019-11-07\n",
      "3946  zumih0708        6         19.0         2019-11-07\n",
      "...         ...       ..          ...                ...\n",
      "4     0TypeSpeed0      1         184.0        2019-11-07\n",
      "3     0830welcomepap1  3         2.0          2019-11-07\n",
      "2     05rKknRcyZTtYEz  1         856.0        2019-11-07\n",
      "1     02468Hkers       1         14.0         2019-11-07\n",
      "0     0202Khk1         1         97.0         2019-11-07\n",
      "\n",
      "[3951 rows x 4 columns]\n",
      "Current loop:  2019-11-08\n",
      "           username  noTweets  noFollowers     dt_date\n",
      "4007  zyyyyyyyy11    2         1.0          2019-11-08\n",
      "4006  zynismusjetzt  1         17.0         2019-11-08\n",
      "4005  zwzw_22        3         173.0        2019-11-08\n",
      "4004  zorakwong      3         0.0          2019-11-08\n",
      "4003  zombot19       3         38.0         2019-11-08\n",
      "...        ...      ..          ...                ...\n",
      "4     099gold        4         1.0          2019-11-08\n",
      "3     0803Advil      1         2.0          2019-11-08\n",
      "2     06130613_7     2         86.0         2019-11-08\n",
      "1     0531Kimmy      1         62.0         2019-11-08\n",
      "0     0306sasa       4         6.0          2019-11-08\n",
      "\n",
      "[4008 rows x 4 columns]\n",
      "Current loop:  2019-11-09\n",
      "         username  noTweets  noFollowers     dt_date\n",
      "8173  zzap         1         5875.0       2019-11-09\n",
      "8172  zyx_yny      3         1762.0       2019-11-09\n",
      "8171  zxlg01       1         566.0        2019-11-09\n",
      "8170  zxcvbn963    1         1.0          2019-11-09\n",
      "8169  zwzw_22      1         173.0        2019-11-09\n",
      "...       ...     ..           ...               ...\n",
      "4     034angie1    8         49.0         2019-11-09\n",
      "3     0308_jackie  3         13.0         2019-11-09\n",
      "2     0224chieko   1         327.0        2019-11-09\n",
      "1     012_ylsh     1         25.0         2019-11-09\n",
      "0     00sallie     1         157.0        2019-11-09\n",
      "\n",
      "[8174 rows x 4 columns]\n",
      "Current loop:  2019-11-10\n",
      "       username  noTweets  noFollowers     dt_date\n",
      "3420  zyx_yny    3         1765.0       2019-11-10\n",
      "3419  zxd123     1         1224.0       2019-11-10\n",
      "3418  zwzw_22    1         173.0        2019-11-10\n",
      "3417  zshing1    2         1.0          2019-11-10\n",
      "3416  zozozoie3  6         1075.0       2019-11-10\n",
      "...         ... ..            ...              ...\n",
      "4     0Kaylo2    16        1745.0       2019-11-10\n",
      "3     0991_ak    1         3.0          2019-11-10\n",
      "2     0803Advil  3         2.0          2019-11-10\n",
      "1     037hk1     2         80.0         2019-11-10\n",
      "0     0315_alex  2         36.0         2019-11-10\n",
      "\n",
      "[3421 rows x 4 columns]\n",
      "Current loop:  2019-11-11\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "7285  zzzzerot         1         15.0         2019-11-11\n",
      "7284  zzzqqq9999       9         169.0        2019-11-11\n",
      "7283  zzz_snorlax      1         0.0          2019-11-11\n",
      "7282  zyyevian         1         0.0          2019-11-11\n",
      "7281  zxvi_weiyi       1         6.0          2019-11-11\n",
      "...          ...      ..         ...                 ...\n",
      "4     0512_yukiko      2         11.0         2019-11-11\n",
      "3     043342f7a8594c0  3         5.0          2019-11-11\n",
      "2     0226_1989        2         213.0        2019-11-11\n",
      "1     0124palin        1         131.0        2019-11-11\n",
      "0     007_latif        1         211.0        2019-11-11\n",
      "\n",
      "[7286 rows x 4 columns]\n",
      "Current loop:  2019-11-12\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "7090  zyx_yny          2         1846.0       2019-11-12\n",
      "7089  zxxsss           1         1.0          2019-11-12\n",
      "7088  zureeal          1         2477.0       2019-11-12\n",
      "7087  zubedog          1         160.0        2019-11-12\n",
      "7086  ztV8Qm44O202viX  1         224.0        2019-11-12\n",
      "...               ... ..           ...               ...\n",
      "4     0226_1989        3         213.0        2019-11-12\n",
      "3     01SsSsS10        1         14.0         2019-11-12\n",
      "2     0123topgun       1         116.0        2019-11-12\n",
      "1     00sallie         2         157.0        2019-11-12\n",
      "0     006Jin           1         3.0          2019-11-12\n",
      "\n",
      "[7091 rows x 4 columns]\n",
      "Current loop:  2019-11-13\n",
      "          username  noTweets  noFollowers     dt_date\n",
      "6589  zzzqqq9999    1         169.0        2019-11-13\n",
      "6588  zzcvbnm22331  1         2.0          2019-11-13\n",
      "6587  zxxjackie     2         2.0          2019-11-13\n",
      "6586  zxswrd        1         48.0         2019-11-13\n",
      "6585  zxlg01        1         566.0        2019-11-13\n",
      "...      ...       ..           ...               ...\n",
      "4     03inkeisei    1         867.0        2019-11-13\n",
      "3     034angie1     1         51.0         2019-11-13\n",
      "2     0325Eternal   1         215.0        2019-11-13\n",
      "1     0315_alex     1         36.0         2019-11-13\n",
      "0     0147yyfel     2         0.0          2019-11-13\n",
      "\n",
      "[6590 rows x 4 columns]\n",
      "Current loop:  2019-11-14\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "6167  zzzzz91290225    3         178.0        2019-11-14\n",
      "6166  zxcvbn963        2         1.0          2019-11-14\n",
      "6165  zwzw_22          1         179.0        2019-11-14\n",
      "6164  zuzzazi          1         6.0          2019-11-14\n",
      "6163  zuizai98         2         72.0         2019-11-14\n",
      "...        ...        ..          ...                ...\n",
      "4     00uvfbPXoyE0TuC  1         0.0          2019-11-14\n",
      "3     00_10_03_        3         14.0         2019-11-14\n",
      "2     009artwork       1         22.0         2019-11-14\n",
      "1     001winnie001     4         4.0          2019-11-14\n",
      "0     001Discord       1         37.0         2019-11-14\n",
      "\n",
      "[6168 rows x 4 columns]\n",
      "Current loop:  2019-11-15\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            username  noTweets  noFollowers     dt_date\n",
      "5505  zzwvic          1         12.0         2019-11-15\n",
      "5504  zxxlol_tyra     4         33.0         2019-11-15\n",
      "5503  zukashooter     2         1783.0       2019-11-15\n",
      "5502  zoomersrevenge  1         21.0         2019-11-15\n",
      "5501  zombie_lai      1         230.0        2019-11-15\n",
      "...          ...     ..           ...               ...\n",
      "4     04Mystars       2         6.0          2019-11-15\n",
      "3     0416lfh         5         315.0        2019-11-15\n",
      "2     0308hker        4         147.0        2019-11-15\n",
      "1     00HKer1         6         227.0        2019-11-15\n",
      "0     001winnie001    1         11.0         2019-11-15\n",
      "\n",
      "[5506 rows x 4 columns]\n",
      "Current loop:  2019-11-16\n",
      "          username  noTweets  noFollowers     dt_date\n",
      "5878  zxdcvasdf     1         429.0        2019-11-16\n",
      "5877  zwww69697391  5         428.0        2019-11-16\n",
      "5876  zuosa11365    1         229.0        2019-11-16\n",
      "5875  zukashooter   1         1821.0       2019-11-16\n",
      "5874  zrroyip000    5         0.0          2019-11-16\n",
      "...          ...   ..         ...                 ...\n",
      "4     0227_ccy      4         462.0        2019-11-16\n",
      "3     0213_yyy      2         73.0         2019-11-16\n",
      "2     020416com     4         64.0         2019-11-16\n",
      "1     01SsSsS10     1         14.0         2019-11-16\n",
      "0     00nolimit     1         243.0        2019-11-16\n",
      "\n",
      "[5879 rows x 4 columns]\n",
      "Current loop:  2019-11-19\n",
      "             username  noTweets  noFollowers     dt_date\n",
      "6544  zyxw46347590     4         313.0        2019-11-19\n",
      "6543  zxlg01           1         568.0        2019-11-19\n",
      "6542  zxcvz100087      1         427.0        2019-11-19\n",
      "6541  zx97010720       1         14.0         2019-11-19\n",
      "6540  zuosa11365       3         233.0        2019-11-19\n",
      "...          ...      ..           ...               ...\n",
      "4     0830welcomepap1  2         2.0          2019-11-19\n",
      "3     0512_yukiko      2         16.0         2019-11-19\n",
      "2     0308hker         3         219.0        2019-11-19\n",
      "1     01SsSsS10        1         14.0         2019-11-19\n",
      "0     0131Mws          3         1428.0       2019-11-19\n",
      "\n",
      "[6545 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "top_user_df = pd.DataFrame(columns = ['username', 'noTweets', 'noFollowers', 'dt_date'])\n",
    "\n",
    "# Convert datatype of 'totaltweets' to numeric\n",
    "pd.to_numeric(data['totaltweets'])\n",
    "\n",
    "for ith_date in unique_date:\n",
    "    print('Current loop: ', ith_date)\n",
    "    \n",
    "    temp = data[data['dt_date'] == ith_date]\n",
    "    \n",
    "    # pd.DataFrame - count number of tweets tweeted in that day - noTweets\n",
    "    temp_noTweets = temp.groupby(['username']).size().reset_index(name = 'noTweets').sort_values(by = 'username', ascending = False)\n",
    "    \n",
    "    # pd.Series - count max followers - might fluctuate during the day\n",
    "    temp_noFollowing = temp.groupby(['username'])['followers'].max().reset_index(name = 'noFollowers').sort_values(by = 'username', ascending = False)['noFollowers']\n",
    "    \n",
    "    # *** NOT WORKING\n",
    "    # pd.Series - count max totaltweets - might fluctuate during the day. Note this is historical total number of tweets ever since the user is created.\n",
    "    # temp_noTotaltweets = temp.groupby(['username'])['totaltweets'].max().reset_index(name = 'noTotaltweets').sort_values(by = 'username', ascending = False)['noTotaltweets']\n",
    "    \n",
    "    # Concat series to temp_noTweets, which will be the main df\n",
    "    final = pd.concat([temp_noTweets, temp_noFollowing], axis = 1) # add as columns\n",
    "    final['dt_date'] = ith_date\n",
    "    \n",
    "    print(final)\n",
    "    \n",
    "    # Append 'final' dataframe to top_user_df\n",
    "    top_user_df = pd.concat([top_user_df, final])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top_user_df is:\n",
      "           username noTweets  noFollowers     dt_date\n",
      "9374  zzzqqq9999     10       171.0        2019-11-03\n",
      "9373  zyxpcy3        2        0.0          2019-11-03\n",
      "9372  zyx_yny        1        1741.0       2019-11-03\n",
      "9371  zxlinux        1        3575.0       2019-11-03\n",
      "9370  zxcvbnvbnvbnm  15       2.0          2019-11-03\n",
      "9369  zxcv_73        1        434.0        2019-11-03\n",
      "9368  zwzw_22        2        168.0        2019-11-03\n",
      "9367  zukashooter    7        867.0        2019-11-03\n",
      "9366  zu00743015     1        159.0        2019-11-03\n",
      "9365  zsazsa99       1        738.0        2019-11-03\n"
     ]
    }
   ],
   "source": [
    "print('Top_user_df is:')\n",
    "print(top_user_df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Standardize the columns 'noTweets' and 'noFollowers', so that the graphs will look proportionate\n",
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# MMscaler = MinMaxScaler()\n",
    "# scaled_top_user_df = MMscaler.fit_transform(top_user_df[['noTweets', 'noFollowers']])\n",
    "# top_user_df[['noTweets', 'noFollowers']] = scaled_top_user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cyZDys3hjRHxwMB18ZlB7AHhm023ODWqSJEmSJEmS7gFDyht+EngEeAVwAXjToB673DZ3e4CIeH1EPB4Rj1++fHlvupQkSZIkSZIOGUPKgcy8mJn9zCyAf86NKd3ngIc23fRB4PxNHuMtmflYZj42Ozu7tw1LkiRJkiRJh4Qh5UBE3L/p8C8A13f+/nXg6yOiGRHPBx4F3rvf/UmSJEmSJEmHVa3sBsoQEW8HXgOcjIhzwA8Ar4mIV7A+lftJ4G8CZOaHI+KXgD8GesC3Z2a/jL4lSZIkSZKkwygyd11eUXfpsccey8cff7zsNiRJkiRJkqQDISLel5mP7XbO6d6SJEmSJEmSSmVIKUmSJEmSJKlUhpSSJEmSJEmSSnUkN86R9lpmQc5dgMWrJElMniBOnCHCzwUkSZIkSZK2M6SU9kDOXSDnL904XrgMWRCzZ0vsSpIkSZIk6WByWJe0F5bmdpRy6VoJjUiSJEmSJB18hpSSJEmSJEmSSmVIKe2FyeM7SjE5U0IjkiRJkiRJB59rUkp7II7fv35l28Y5kiRJkiRJ2smQUtoDERXixANw4oGyW5GkA6PIgrn2JZa7izSrI8yO3E+tUi+7LUmSJEkHgCGlJEnaF59e+ijznasbx1faF3nJ9CsMKiVJkiS5JqUkSdp7y93FLQElQKff4krr2ZI6kiRJknSQGFJKkqQ91ylau9bbxdo+dyJJkiTpIDKklCRJe26iNgXEjvpk/dj+NyNJkiTpwDGklCRJe65RHeHB8eezOaicaZ5kpjFbXlOSJEmSDgw3zpEkSfvi9OgDzDROstxbZKQ6ylhtouyWJEmSJB0QhpSSJGnfNKpNjlcdPSlJkiRpK6d7S5IkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSqVIaUkSZIkSZKkUhlSSpIkSZIkSSpVrewGJEmS9NyeWZ3jQmuBfhacaEzwgvGT1CrVstuSJEmS7okjOZIyIn42Ii5FxIc21X40Ij4aER+MiF+JiGOD+sMRsRYR7x9cfqq8ziVJ0lF0YW2eJ1ev0i569LLgYnuRjy9fLLstSZIk6Z45kiEl8FbgK7bVfgf43Mx8OfBx4Hs3nftkZr5icHnDPvUoSZIEwLPtxR21uc4K3aJfQjeSJEnSvXckQ8rMfDcwt6327zKzNzh8D/DgvjcmSZJ0i7LsBiRJkqR76EiGlLfgrwO/ven4+RHxhxHxroh4dVlNSZKko+lUc3JH7XhjnLprUkqSJOmQcOOcbSLi7wM94OcHpQvA2cy8GhGvAn41Il6WmTvmXUXE64HXA5w9e3a/WpYkSYfcA6Mz9LPg/GDjnJONCR4Zny27LUmSJOmeMaTcJCK+Gfhq4MszMwEysw20B9ffFxGfBF4IPL79/pn5FuAtAI899pizsCRJ0j1zduwEZ8dOlN2GJEmStCec7j0QEV8B/D3gazJzdVN9NiKqg+svAB4FPlVOl5IkSZIkSdLhcyRHUkbE24HXACcj4hzwA6zv5t0EficiAN4z2Mn7S4AfjIge0AfekJlzuz6wJEmSJEmSpNt2JEPKzPyGXco/c5Pb/jLwy3vbkSRJkiRJknR0Od1bkiRJkiRJUqkMKSVJkiRJkiSVaqhDyoj4wogYG1z/hoj4kYh4qOy+JEmSJEmSJN26oQ4pgbcAaxHxcuD7gIvA/1NuS5IkSZIkSZJux7CHlL3MTOC1wD/OzDcBkyX3JEmSJEmSJOk2DPvu3isR8V3ANwKviYgKUC+5J0mSJEmSJEm3YdhHUn4dEMAbMvMC8CDwY+W2JEmSJEmSJOl2DPtIyjdm5vddP8jMpyPi0TIbkiRJkiRJknR7hn0k5VfsUvtz+96FJEmSJEmSpDs2lCMpI+JvAm8AXhgRf7Dp1CTwvnK6kiRJkiRJknQnhjKkBH4J+D3g/wS+Z1N9KTMvldOSJEmSJEmSpDsxlNO9M/NaZj6Rma8DZoEvysxPAr2IOFtye5IkSZIkSZJuw7COpAQgIr4f+CLgEeBtwCjwr4AvLrMvSZIkSZIkSbduKEdSbvKXgK8CVgAy8zPAVKkd6VDJLMhum8yi7FYkSZIkSZIOraEeSQm0MzMjIgEiYqzshnR45OIV8up5sugT1RqcfIiYOFZ2W5IkSZIkSYfOsI+kfEdE/BNgOiK+Bfh3wM+W3JMOgWyvUlx+hiz668f9HsWlJ8lup+TOJJUhs2B57SLXlj7FSuuSo6slSZIk6R4b6pGUmfmPIuIrgQ7wecD/kZm/XXJbOgRyZX6XYsLqAkzP7n9DkkqTWXDx2h/R7i5u1EYaFzl17HOJiBI7kyRJkqTDY6hDyoE/AlqZ+R8iYiQixjNzpeymNNyiUiV3O1Gp7ncrkkq22r66JaAEaHXmaXWuMdo8XlJXkiRJknS4DPV074j468CvAz89KJ0Ffq28jnRoTBwntgWSUavDuGtSSkdNt7d6W3VJkiRJ0u0b6pAS+FvAFwKLAJn5ceBUqR3pUIhanXjgUWJihmiMEJPHiTMvJCrD/r+MpNvVrE/uWm/cpC5JkiRJun3DPt27lZmd62uCRUQVcIEw3RPRGCVOP1x2G5JKNto8ztjISVZbVzZqE6OnGWlMl9iVJEmSJB0uwx5S/ueI+G5gJCK+FPh24DdL7kmSdMjMTr+E1ugC3d4KjfrkTUdXSpIkSZLuzLDPXf1uYAn4KPAdwO8Bf7/UjiRJh9JIY5rJsTMGlJIkSZK0B4Z9JOUXAT+bmT9ZdiOSJEmSJEmS7sywj6R8A/DhiPhPEfFDEfGVETFVdlOSJEmSJEmSbt1Qj6TMzL8CEBHPA74W+GfA/UC9zL4kSZIkSZIk3bqhDikj4uuBVwOvABaAnwL+Y6lNSZIkSZIkSbotQx1SAj8JfBz4CeA/ZOa5kvuRJEmSJEmSdJuGfU3K48DrgWPAmyLiPRHxL0ruSZIkSZIkSdJtGPaQcgw4BZwG7gNOAI1SO5IkSZIkSZJ0W4Z9uvd7gf8M/CfgZzLz0yX3I0mSJEmSJOk2DeVIyoj4IYDMfFlmvj4z33a7AWVE/GxEXIqID22qHY+I34mITwy+zgzqERE/HhFPRMQHI+KV9/YnkiRJkiRJko6uoQwpga+4B4/x1l0e53uA38vMR4HfGxwDfCXw6ODyetY37JEkSZIkSZJ0DwxrSFmNiJnByMcdl1t5gMx8NzC3rfxa4OcG138O+NpN9bfluvcAxyLi/nvxg0iSJEmSJElH3bCuSfli4H1A7HIugRfc4eOezswLAJl5ISJODeoPAM9sut25Qe3C5jtHxOtZH2nJ2bNn77AFSZIkSZIk6WgZ1pDyjzPz8/fx+90sDN1ayHwL8BaAxx57bMd5SZIkSZIkSTsN63TvvXLx+jTuwddLg/o54KFNt3sQOL/PvUmSJEmSJEmH0rCGlP94jx7314FvHlz/ZuDXNtW/abDL9xcCC9enhUuSJEmSJEm6O0M53Tsz33q3jxERbwdeA5yMiHPADwA/DPxSRHwr8DTwusHN3wl8FfAEsAp8y91+f0mSJEmSJEnrhjKkvBcy8xtucurLd7ltAt++tx1JkiRJkiRJR9NQTveOiH80+Pq657qtPrvMJLs91nNYSZIkSZIkaf8NZUgJfFVE1IHvLbuRYZbLq+STn6F46jPkp8+Ri8tltyRJkiRJkqQjaFine/9b4AowHhGLQAB5/WtmTpXZ3DDIbo/i2cs3jouCvHSVSrNBNBsldiZJe6/TXWa1fYVK1BgfOUW16t89SZIkSSrTUI6kzMzvysxp4LcycyozJzd/Lbu/obCyums5b1KXpMNiafUCF+b+kIWVZ7i2/GnOX30f3Z5/+yRJkiSpTEMZUl6Xma+NiNMR8dWDy2zZPQ2Nyu7/6eMmdUk6DDIL5pef3FIrssfCytPlNCRJkiRJAoY8pBxsnPNe4HXAXwbeGxF/qdyuhsTEGFGtbilFpQKT4yU1JEl7r9dvU2RvR73TWymhG0mSJEnSdcO6JuV13w98QWZeAhiMpPxd4N+U2tUQiEoFHrwP5uah1YFmnTh+bEdwKUmHSa3apFqp0y+6W+rN+mRJHUmSJEmSYPhDysr1gHLgKkM+OnQ/Rb1GnD5ZdhuStG8iKhyf/ByuLHyUJIH14HJ6/GzJnUmSJEnS0TbsIeW/jYj/F3j74PjrgHeW2I8k6YAbGznJmfoXsNa+SqVSY7R5gko4ilySJEmSyjTUIWVmfldE/EXgi4EA3pKZv1JyW5KkA65WbTI5dqbsNiRJkiRJA0MdUgJk5juAd5TdhyRJkiRJkqQ74/qNkiRJkiRJkko19CMp9dyy1SavXCNbbaJeJ04cIybGym5raOS1i+TCJbLoEWPTxMmHiFq97LYkSZIkSZIODUdSHnJZFOT5S2SrvX7c7VI8e5lsd0rubDjk4hWKufNkvwcJubJAXnqy7LYkSZIkSZIOlUMXUkbEPyy7hwNlZY0sih3lXF4toZnhk8vXdtbWlsluu4RuJEmSJEmSDqfDON37fWU3oKMgym5A0l3o9FZYWHmaXm+NRn2S6fGz1KrNstuSJEmSpCPr0IWUmfkbZfdwoIyPEpXK1tGUEcTkeHk9DZGYPEGuLW+tjU4Q9UZJHUm6W71+m4tzH6TIHrAeWLY6C5w58UoiDt0EA0mSpM+q00kAGg0HYkgq11C/G4uIByPiVyLickRcjIhfjogHy+7rIIlKhXjgNDE6sh5ONhtU7p8lGm78citi8jiVkw+uh5KVCjE5Q5x+ftltSboLK62LGwHldb3+GmvtuZI6kiRJ2n+9XnLx2T6XLhZculhw8dk+/X6W3ZakI2zYR1L+C+BfAa8bHH/joPZnS+voAIpmg3jgdNltDK2YniWmZ8tuQ9I9UhS93eu5e12SJOkwmp9Put0bx90uXLuWnDzpiEpJ5RjqkZTAbGb+i8zsDS5vBUyTJEk3NTZyckctCEYbx0voRpIkqRyt1s5Rk7vVJGm/DHtIeSUivjEiqoPLNwJXy25KknRwNetTzEw+QiXWJxNUKw1OTr+EatW1ZiVJ0tFR3SUNqFb3vw9Jum7Yp3v/deAngDcDCfyXQU2SpJuaGjvDxOhp+v0OteoIEU5rkiRJR8vkVDB/bevIyclJXxNJKs9Qh5SZ+TTwNWX3IUkaPpWoUqmNlt2GJElSKSYmKlSrycrKelA5Ph6MjhpSSirPUIaUEfEPPsvpzMz/fd+akSRJkiRpCI2OGkxKOjiGMqQEVnapjQPfCpwADCnvUPYLaLWhXiMa9bLbkSRJkiRJ0hEwlCFlZr7p+vWImAS+A/gW4BeAN93sfvrscnGZ4vIc5Ppw/5gYJ06fcK02SZIkSZIk7amhDCkBIuI48L8A/yPwc8ArM/NauV0Nr+wXWwJKgFxeIcZGYGqixM4Ojly+Ri5fg0qFmDxJjPq86GDqdZdZWzlPFj0aIydojp7ywwbpALrSXuZye4lKBPeNTDNdd41USZIkHV1DGVJGxI8CfxF4C/AnMnO55JaGX6u1JaC8LldbhCElxbVnybkLG8e5fI3KfY8QY1MldnU4FL02UakSlaH8c3TgdDtLLFz9ADn4/7ndukqvu8TE9OeU3JmkzZ5ZnePJ1asbx5fbS7x06gzHG+MldiVJkiSVp1J2A3foO4EzwPcD5yNicXBZiojFknsbTrWbBES16v72cQBlFjB/aVsR8tqz5TR0SBTdFdae/UPWzr+X1c+8l878p8tu6VBYWzm3EVBe11p5lqLfKakjSdsVmZxb2zr5I4Fn1ubKaUiSJEk6AIZy6FJmDmu4emBFs0GMj5ErqzdqlQoxPVliVwdEUZBFf2e9Z+hzN1qXP0L21tYPsk938RxRG6E+cX+5jQ25ot/eUUuSouhQqTZK6Ohg6hddVlqXKYouY80TNOo3HzHe67dYaV0GkrHmLPWaU3J1d/pZ0MtiR73d75XQjSRJei79dtJbSaICtcmgUnUpJWkvDGVIuVci4kXAL24qvQD4B8Ax4G8Alwf178vMd+5ze3su7jtJLC6Tq1rmizAAACAASURBVK313b2nJ4m6vyJRrRHNUbK9tvWEU73vWL+zfCOg3FxfvWpIeZcazeN0O0tbapVqg2rNKaTXdXtrXLz2AfpFF4CFlaeZmXyEqbEzO27b6ixwaf5D6yOqgYXlpzl57CWMNU/sa886XOqVKhO1Jsu9rR8qzDTGSupIkiTdTGexoHX5xkylmE/GzlSoNgwqpXvNEYmbZObHMvMVmfkK4FXAKvArg9Nvvn7uMAaUABFBTE9SuX+WyskZA8pNYvZ5RK1+43hkjDi+M9DQrYm4yTICFZcXuFujEw/QaM5sHFcqdaaOvdiNczZZXHlmI6C8bmH5SYrcOWJ6fvnJjYAS1kelXltyaQLdvRdOnKa5aS3eqdoID4+dLLEjSZK0XWbSntu6lFL2oXNt534Oku6eKdTNfTnwycx86ii9uc9OF9odaNSJplNDr4vmKJx9GdFaWd/du+lol7tRqY9SHZ2hv21NNkdR3r2IKtMnPpded4UsetQak0T4edRmnf7KjlqRfXr9Fo1tI067vfUlMDKTbn+VLPpkrU9m4fOquzJea/IFMw+z2GtRjWCiNlJ2S5IkaZvsrYeS2/U7hpTSXjCkvLmvB96+6fiNEfFNwOPAd2bmtd3vNryKK9fI+Rv7DsXUBJVTTmm8LiJg1J3O75XmiRfTXXyG3tocUalTn3qA6sixsts6NGp1p3ffTLM2Sae7vKVWiRr16s61Jhv1CVZbV1hYeYpuv7Veq47R6iwwumnEqnQnIoLpumucSpJ0UEVt/ZLblo2uNo/OQCZpPzkMZBcR0QC+BvjXg9JPAo8ArwAuAG+6yf1eHxGPR8Tjly9f3u0mB1a22lsCSoC8vj6ltAeiUqNx7PmM3f8qRk+/nNqogfi9ktlndelp5q98kKVrH6O3LZA76qbGH6JW3TxqLZiZfMGuIyNnJp7PWntuI6AMKoyNnGJu6Ykdu6hrp4XOHJ9c/GM+sfBHXGldLLsdSZKk2xIRjJyowKZMslKD5owhpbQXHEm5u68E/iAzLwJc/woQEf8c+M3d7pSZbwHeAvDYY48N17vXtZ07AgPkWosYcwqaNEwW5z5Cp70+2LsLtFtXODb7+dRqLlMAUKs2uf/EK1lrX6Vf9BhrHt8WWt7QqE8wNf4QlUodSJr1KSqVGr1+i37Rvun9BHPty3x66aMbx4vdeTpFmzNjZ0vsSodVryhoF33GqjXX4JUk3VP1iaA6Uhns7h3UxiEq/lsj7QVDyt19A5umekfE/Zl5YXD4F4APldLVXrrJJjluniMNl153eSOgvC6zoLVynonpzympq4OnElXGR07d0m2b9Qn6xdap3ZWoDoJL3czFtXM7apfWPsN9ow9ScT1P3UNPrSzymbVlikwalQqPTs4w0/ADBEnSvVOpBY1pg0lpr/kuYZuIGAP+LPCOTeUfiYg/iogPAl8K/J1SmttL46PESHNLKRoNmHRdO2mYFNt2rd6o93ev67lNjT+0Yyr41PhDVG62S70A6BadHbV+9rbsli7drbn2Gs+sLlEMll/oFAUfWZyjV/h7JkmSNGwcJrdNZq4CJ7bV/mpJ7eybiIAzp4ilFbLVIZp1mJpwypQ0ZOqN9enIRbF1de/GyPGSOhp+zfok9x9/JctrFyiyz1jzpJvm3ILpxnGutJ7dUpuoT1Ot+NJD987Vzs61s4tM5rttTjbdlEiSJGmY+E5BG6JSgelJYrrsTiTdqYgqkzMvZunaxyiKLkHQHDtNc/TWpjZrd/XaKDOTLyi7jaHywNjzafdbLHXnARipjvG8iUdL7kqHTb2y+6Sgm9UlSZJ0cBlSStIh02jOcPz0n6TXXaFSbVCtNp/7TtI9VqvUeOH0n6DVX1sfgVqbKLslHUKnR8a5sLZCP2/sVzhRqzNd9++eJEnSsDGklKRDKKJCvTFZdhsSI1Wn3GrvjFZrvPzYLOdWl1jr95iuN3lozL99kiRJw8iQ8ojLxWVycRmAmBwnpn1hL0nSQbTa6/DM2hwr/TZTtVEeGj1Os+pLufFanRdNue6uJEnSsPOV7RGW84sUV67dOG61iX5B5biLUkqSVIZeUbDabzNabVCv3NhBvlP0+ODiObpFH4CVXodr3RVedexhKm5yJ0mSpEPAkPIIy/mlncX5RTCklCRp3z3bWuDTK1foZUElgodGZzg7dgKAS+2ljYDyula/x9XOMrNNZ0FIkiRp+BlSHmHZL3bWip01aT8U3VW6y89C9qmOnqA26tQ9SUdHq9/lieVLXN/+pcjkqdU5puujTNfH6G0LKK+7WV2SJEkaNpWyG1B5YmLnZgYxPlZCJzrq+u0l1p59P72lz9Bbfpb25Q/TWXi67LYkad9c666Su9TnOisAnGjs3B09gJnG+N42JkmSJO0TQ8ojLE4eJ8ZuBJUxOkLMOnpN+6+7+Azk1tFA3aVzpCOEJB0RjajuXq+sT3qZrI/wyPgstagM6lVeNHkfI9X6vvUoSZIk7SWnex9hUa0QZ06RvR4kRN1fB5Wj6K3tUuyTRZeo7P7GXZIOk5nGOOO1Biu9zkatWalxqjm1cXxm9BinR6Zo93uMVOtumCNJkqRDxVRKRM1fA5Wr2pym113dUovaCFFtltSRJO2vSgQvn3qQc2vXWOq1GKs2eGB0ZssO3wDVqDBWa5TUpSRJkrR3TKeOuOz3yavzsNaCWg3qNeh0od+H8THi+DRRcVUA7a3G9PMoOosUg7XXqNRoHn+UcJSQpCOkVqny8PjJstuQJEm3qLuSdOYLsg+1saA5E0TV9zDSnTKkPOLy/CWyvT61LJdWyLkFKidnoFGH+UXo9Yj7ZkvuUoddVOuM3vdK+q0FsuhRHZkmKv55kiRJw2Wp2ycTJusVP2yVDrneWrJ2seD6znedhaTfScbPuFyVdKdMAY6wbLU3AkoAVtbXBczVFtFYX4g/l1fJXp+o+YdWe686Ml12C5IkSbet3S/44/k1VvsFACPVCi+dHmW05owk6bDqLuZGQHldfw36naTa8EMK6U4YUh5lxba/qDk4zmL3uiRJuqcutZeY6yxTjQr3j0wzURvZcZt2v8eF1jyr/Q5T9VHuH5mmGgYf0kHy6eX2RkAJ0OoXfGq5zcuOjZbY1fDrFcmllWStB2N1mB0LahXDHx0M2982b7hZXdJzMqQ8xLLfh4ibryk52iSq1fXbAYw0odMlRm68QYpmw12/tecyk+x3iGrDqVGSjownV67wzNq1jeNL7SU+d+oM0/WxjVq36POBhWdoFz0ArnZWuNpZ5vOmH9r3fiXd3Hynv0utR2b62uYOFZl87GpBa/DUzrdhvpW86ESFis+pDoDaBPS27v1JpQ4V9/6U7pjp0yGU3R558QrZaq+HlFMTxMmZHS+QIgLOnIKLV8hOFybGqUyMQwCZxEiTOO0C/tpbvZVLdOY/vRFSNmYeoTbm752kw61XFJxvzW+pFZmcW7u2JaS82F7cCCivW+y2uNZZYaYxvi+9SnpujUqw1s8dNQPKOze3lhsB5XWrPVhowYwDVHUANCYrFN2CzkJCAdUmjJxyPVrpbhhSHkIbASVAJrmwtL6m5MzO9f6i2YBTJ8jFZSqNOjE5cSOkrLoOpfZW0V2lPffxjSUFst+hffVjVBoTVHaZ8qib6/fbdNaukNmnOTpLteard+kg62Wf/i7LqbT7vW3H3S3Hmclir8WnVi7zfOC4QaV0IDw03uDji60ttbPjDqe6G7sMTl2vF8n6GxapfCPHKzSPJVlApebvpXS3DCkPmez1bgSUm+vLq8QuIWVeW6C4uj6SIwHml4gHTxM1fzW093qrV3aueZoF/bWrVCYfKKepIdTtLLFw9Y/IXH81v7r0NJMzL6Y56ohU6aAaqdYZrdZZ2xZCHmuMbTmeaYxxvrUArAeUT61eYbXfZaza4MOL5znVnORFk/ftW9+Sdjc7UqdeCS611qd4nxqpM9P09fTdmGoGF1Z2fpgz6YYkOmCiErhUtHRv+L/SYXOzoeW71LNfUMwtbK31euS1xb3oTNohKjd58R6O4r0dq0tPbgSUAEmysvjpEjuSoF/0eHr5CT4w9x4+fO1xrrSeLbulA+eFE6dpVG78vZuqj3B29PiW2xxvTHD/yDQBLPRWWSt63DcyTX1wv0vtJRa7a/vZtqSbONao8cKpEV40PWpAeQ9MNIL7xmNjzGQFODMRjNUNKSXpsPJfz0MmqlViYpxcXtlan57ceeNud/edu9vdnbUjLHtdaK1AvUE0x577DrpltbFZOgtPwab11qLacE3K29Trruyo9fstiqJH5WZBsLTHnlz+OPOdqwD06PLU8ieoRIXjzVMld3ZwTNVH+YKZ57PYXaNWqey6s3evKJipjzFZa3J+bZ6J6ii1bRvirQx2/Zakw+aByQqzY0mrB6M1qFcNKCXpMPPd6yEUp09AvQbLq1CtENOTxOQua1bV6+sjLLcHlSON/Wl0COTiFYor5zaeoxifJk4/TDie/56Iap3R0y+nM/8URXeFSmOSxvTZm4+w1K5qjUk6rbkttWpt1IBSpen02xsB5WaXW88aUm5Tidgxxfu6uc4KH1t6ll4WAPSKHpVd/v2ZrLnunaTDq1ENGk6ykaQjwXewh1BEECeOwYljn/121QqVkzMUl2+EG1GvEcem9rrFoZC97paAEiBXFoilOZhypN+9UqmPMzL70rLbGGrjk8+j11miKNZHQUcEjeYMi3N/TJI0R2cZGTUY0t5Z7S1zce0z9IoOU43jTNdndr1dDsI2PbfM5InlSxsBJUAlqqz127SLPku9FtWo8NLJ+3cdgSlJkiQNG0PKQyx7/fVduus3/88c05NURkdgdQ2qVZgYI262ruVR017ZdTp8ri0ThpR3rOi1gKDiyJ97plafYObUY3RaV8gsKIo+q0tPbpzvtOYo+h3GJh4sr0kdWqu9ZT668H5y8PdysTvPanOWifoUy92taxw7inKrzKRVdKlHlVpl6zCh1X6HdrF1p+9KBJ2iR6NSY6LWZLRaZ7nfZrG75nRvSZJ0aGU/oZfQCPOKQ86Q8hDKoiAvXiVXVgGIkSZx+uRNw8po1KFR388Wh8PNQrS64dqdKHpt2lc/StFeDy2qo8dpnniRU7vvkUqlxsjY+g6/1y69b8f5teVzhpTaExfXzm0ElNfNtS/zoqnP40I8zWLnGpWocmrkDKdGz5TU5cGz0F3j48vP0ur3qERwZmSa54/PbpxvVmpUIig2Pbfdos9av8tMY4KJQa3I5Hxr3pBSkiQdSsXlHnmtD0VCPajcVyfGXX7tsDIdOITy6vxGQAmQrTZcuko8cLrEroZPNEeJiWPk8vyNWq1OTJ0osavh1bn2iY2AEqC/Nkdn/kmaxz+nxK4Op6Lo7FLrklm4nuot6Pc7LLcu0i+6jDWPM9L47EtnHHXdYvfN1iKCR6c+lyL7BBU/9d6kyOQjSxfoFv2N43Nr84zXRjjVXN/orlap8sDIMZ5enWOx12K112a136EawUJ3lana6MZzun3EpSRJ0mGQS33y6qbXOd2kON+l8kiDqPja8jAypDyMVtZ2lHKtRRYFUTGguB1x6mFibI5cW4Jak5g6SdQcdXq7sujTX7u2o95fuwoYUt5rjeZxWmuXttWOGVDegl6/xbNz76c/CN6WVj/D9PhZjk08r+TODq7pxnGWuvNbavVKg9Ha+oZtlXC3g+0Wu2sbAeVmV9vLGyElwMPjJzm/Ns9yr8WzrQVqUWGht8Zyv8NkbZXnjZ6ACI7Xd9kcT5Ikacjl0i7rmfcTVguY8DXmYWRIeRjtFkRGrF+2yX5BXpkjl1eJSoU4NkXMuHHOdREBkyeISUdP3pWoQKUK29+UO9V7T4xPv4B+f41uZwmAWn2cielHS+5qOCyunNsIKG/UnmFy7AzVih9Q7GZ25H5We0vMtS8D6wHlCyZfvOsu1FpXu8kHhvVt61IudFfpk0zURhitrn8AOVkbpdPvcanfZbG3xvH6BMfqo5zsTzBabex575IkSfvmZjmkoygPLROCQyiOTZKXrm6tTU/uOtUuL14hV9ff+GS/T169RqVaIaYmdtxWulMRQX3iDN3FZ7bU65MP7Hr7LPr0W3OQSXX0uOtW3qZKpc6xk6+g110Bklrd/59vVbe3uqOWJGvtOSCpVUcZaUzvf2MHWCUqPH/yxTww9jDdostYbcKp3c9hojbCdH2Uhe6NmQ+VCO4f2fq7tdpfD8w7m4LzZqXGSKXGQq/FbGOK+0amme+u8eHF87zq2PN87iVJu+r1knYrqdWC5oj/Vmg4xLEquVCsr0d53UiFGPPD8MPKd/6HUExNUIkgF5fXd/eeGIPpyR23y15/I6DcUl9cNqTUPdc49jBRbdBbvQRUqE/cR218506/RWeF1uUPkf3BuorVOiOzn0u14e/k7ao5BfS2NRtTtLoLW2qrrSsU2d+YtjzWPMHJ6ZcYBm3TqI7QqI6U3cbQeOnkGc6tzXGtu0qzUuPB0RnGt23YNlVbfz63j5DsZUGzUmOqfuP5XhuMrJyuj+1985KkobK0WLCwcD3kSZpNODnretE6+KJZofJQnZzrk90kxirECad5H2aGlIdUTI4Tk88VUORznD/aMhMWLpMr1yCqxPRJYtwNNO5GffIM9cnPvrtve/5TNwJKgH6XzrVPMnr68/a4Owmmxh5krT1Hp7cCrG+iU6nUtqyruNq+ymr7MuMjO0N26VbVKhUeHj9Jc22eS50lnlq9yv0jxzjZvPGBzHityYOjxzi3Nr8x8nKyNkKFoFIJxqpbQ830n3VJ0jb9fm4KKNe127Cykvz/7L3JkxzZlt73O9en8JhzTsxATW+q1+Prllq0ppkkmsy0kEkbaaOFFjLjTmtxrRX/BXHHjcykDU3URqJabJmMapHsx35svqnq1YAZyDlj9Nnv0cITOQOFeq+qEkDcXxksEZ7hgRu3PML9fv6d83W7TqR0vPlIbJAbzjm5KDiR8hJE5AEwBWqgUtWfiMgy8D8Dd4EHwH+lqheTQN5AtCgbR2V01okhvo/ELTTNzu7QbTeJ4L6P+It7l0L3n6Lj3ZPH6RSzcQ/pfrVQqUXjUJUw/tbG965yOgH8Vdscjm8DY3w2l/+QtDjA2pK6LhjNH154Xl5MnEjp+J15MN/j8alQsVGZ8j3dYL110hv6XmeN9ajPB911amvxjEHV8sV878xrtTyfQeDOOQ6Hw+E4S1Fcvj3PoesKlX4rVBVbgHhgfCf0Ot4ctLZQWgi9tzb93ImUL+c/VNXTK4B/APxfqvoPReQfHD3+769maK+HVjW6tdsIjoBEIbK5hgQn/9tlcxV2D9F5E5xD6MPBGGubFC0Z9DBry1cy/qtEbY1O9i5uH++8UqTUqkC376NZ09dOojayeQ/xXZjB62KCNraYndkmrnzR8R0iIrSjJiwrLydwiUjp++6YdPxuWFWeZ+ML259kh2dESmgcledLwX3xeJQekNUlw6DN+901V7bncDgcjgv4L1nxBy4P8LeizpR022Kr5nHQFVrr4s7BjivH7s3Rw6wprfEMZr2D9KKv3vENw3lmX5//HPjHR3//x8B/cYVjeS107/BYoATQvEB3D848RzwPs7mK9/5t5M51NCvRI4ESQMdTdDr/zsb8xmDt5XVzdfXK3XTvybFACaB5gu49+aZH904TDO40aeAvECEc3L2y8TgWmyjoHwuWLwj8Nl3nojxmVk54NPucx7MvSKrZV+/gABqRslJ7YXtlL247v9/zbMyoTNlsDfj3l9/j48ENl+ztcDgcjksJAqHdOSugeT50Ok5U+7qoKunOiUAJUM6UYuT6rXxTaGax2yV2q0STV18TOU7QWYEepCcaRm2xWzO0evvm0DkpL0eBfyYiCvyPqvqPgA1VfQ6gqs9F5I1foer8klCcJG16LcLFuz1pBpcsmHSevkZ/y3cL8QOk1T4jOALQeXWqryYXy5Iv2+Z4OX68jNn8A6r5TvO4vY4JF+v4c7xZrA5+QJLvkhcTAr9Np7WOcYnzABzkO9yffnr8eCd7zvv9HzAMV16xlwOanpTDIGZUnj1XL4cdamsxctGVoar8YvL0JBU8h51swu8Pb+GJu+/scDgcjstZWhLiWMgzPRYozVtaCnqV2LL5c54qUaKl73487xo6t9gnxXF0ho5qZDPADBe3Bd3rovNL+jqoQlJC/+1yU7pV1uX8HVV9diRE/p8i8snr7CQifx/4+wC3b9/+Nsf3Wohv0PKc6Jjl6IOnaF0jrRayvoyER15/7yUffm8xFz6yfhe276P5UX/JzgBZuvbqfTwfrcoL2xxfDxN0CIf3rnoYDgfQ3NDptNZdD8pLeJqcL4VXniePnEj5mnzY3eCT6XOmVY5Vy6RKeZTs8Ze7n7Acdvij4W3uddaOn39YJicC5RHzumA3n7LZevVNNIfD4XAsLiJCHEMcO2Hyd0EMIFzInxXPzes3gd2rLsyt7lXowCXRfyX+SzSbl21/g3n7RvwdoKrPjn7uAP8E+FNgW0SuARz93Llkv3+kqj9R1Z+sra2d//V3iuYFGANpfmL5zfKmT2VdN8/JMvTZzomzshUhrdbZFxKDDHrf5dDfGCSIMDe/j7n9Q8ydH2E232v6dr5qn+HGa21zOBxXS21LpslzZukW1r66jYPjclSVos4ubM/qiy5+x+W0vIA/GN7mT5bushb1GBUps6rAqrKXz/jrwwdn+lZm9SX2DSCtX5KKsOCUtmYrnbOVzV9aRp/XFc/TGTtZQn1JNYnD4XA4HC8wvhD0zollAuHQCWhfB60UO6qxoxqtT6mS5SVl85WCOz1/JTJoXTCXSctH2m9f81ln8TqHiHQAo6rTo7//J8D/APxT4L8B/uHRz//16kb5avRwgt1v0kJVgFmCrC03guO5C3CtKiTLIW7ESbm+BqMJJDkEPjLsnTgtFxQJXt8eLYM1jBeg0/3mcW8Z6Trvv8PxJpEVY3ZHv8Rqc8PmUO6zsfRjwsBFXH4dRIRu0GdWnm1p0Quco+/r0vICdvMpyTmxcVKm7OQTrh25JF+W3j1w4WIXGJc5vxzvY49uxD4wYz4erNI9FWS3l6f8ZnqAPVoThXPD7w3XaLkKCIfD4XC8hNaq4AVQJorxhGAg+C0nUr4umljsk5IXJ1/dFczNAIkNEht0Wp/dITLOqfoaiG8wtwboYQpFDXGALLW+esc3EHcVdpEN4J8c2Yl94H9S1f9dRP4a+F9E5L8FHgH/5RWO8aVoVWMPRk0/yuTIzRK3IG4hgX95CM5p6/QsgSRrgmOCCBZcoPxtkO7wlQngjpdjq4xy/JA6n2KCNsHgNl7ohCPHN8vh9MtjgRLAasXhrBEqX0VVZ4xmD8jLGaHfZtC5vfDC5q3O+3w2+QXViwZNCpUt+cXhT2n7Ha6379LyLhfWHGcxL+kpKZycozt+xO32Mo+SkxC8zVafZde39wJfzsbHAiVAZZUH8wkfD1aBxgn85Wx0LFACFNbyKJnwUW/5ux6uw+FwfKPkuTKdKFWlRJHQHwieE3q+EUSEcCiEbrn3W2F3Ks6cfGvF7lR4d0Jk3UdzC8XR7z3BbDrJ6nWR0EM23v61ifs/fg5V/RL4/Uu27wP/8Xc/oq9JUaDTOTo5lbBazpDdgya9+5xIKWGItBqnoE5m2J39k18ejKGqkXXXW8zx7aNqyXZ+jlZN+WhdpdT5iHjzjzD+23kXyPHmoaoUlyRQF+X0lftZrdk6+Ftq2zjdqjolK8ZcX/ljvAVOVW77XX689KdMikMqLXk8/5J51cxlXqdMyzE/Gv4E3wUNfSW34iU+m20zKVJe6JIrYZeVczdq7rRXWI96TKucjhfS8d+uZujfBVaVeXWxNH5anjhVC1tTXFICPi0vL6l3OBzfParKvAQj0A6cwPa6lKWyu2uPe/tVlZIXysaG6+vneAPILqndPtomgWDuhTA/On47BnEBTwuH60n5rhEGl6d6ZwUStzCba40waQzSbTfl3S+eM764SNfpHH1JHyeH45ukTg+OBcpjbH2c8u1wfBOICMElzr7Af3W5bJrvHwuUL7BaMc/c8WnEMIxWsGrPOFShcVWOir0rGtnbQ2Ut0yojNB5P80N+OXnGw2SPSZXyxWyHx6eckwCxF7Ie9ZxA+RKMCPElJdsd/6Q6JDAewSV9pk8/x+FwXB1pqfxyz/LpgeXX+5ZP9mvK+pJ+dY4LzGd6IXykKiHPr2Y8DscZokskqFPbRATpekjPcwLlguJEyncM8X2kE5/b5iHtCFVFum3M7WuY9241gqV/6iLeXnLiVz0J3nE4vkX0ZeEl50QPh+N3Zan3HmU5Z3f0a57t/RsOZ/fpxtdfuc/LwnXOi3KLTK2Xz1F9ao4qW/E8ecQXk1/zPHlE5UKLAPjNdItfTJ7y+XQHH0PXj+gFLXwxbOVjHiT7TMuLIUWOl3Ov0+f02saIcLfTf+ljAN8It9qLGRbocLxpPJxY8lOn2HkJT2duTfI6vGzp5pZ0jjcBs+7Dae1RwKy5ihvHCe5oeAcx19fRwEfzEvFM05Oy225EyFf1Ium24XB8ZpO0WojnfcsjdjjAj5cpxMC5cCcvXr2iEb1bWFsi4iEv6Xu3aMyybbJiBEBeTHi69695//rfw/cuby0QRyvI9Ev0nDWhHbl2GC9YCld5ljzirH1DGIbNHFmt+XT8t2R1AsCo2OOw2OP7gz94aT/GRWBUJPz16D5fzHfZysYUR8JtbivWwj6zqrG+jMqEXuBaX1xGrRZV8E85I/tBxO8N1zksMgRYi2Kic+7KjVaHrh+yl6f4xrAWxYTGXfOcRlUpVQlEXJmo4zujsk2Z93mmuVPZXoe4LcznZ+fKGIic+d7xBiAdg7kXHQfkSM9DQnd+cZzgRMp3EFldgtrCPGnck3kJswSbPEbaMbK+gvgXL8JleQBVjc7moNoIlJtuAe74bhAvJFr9AcXh52iVgxcQ5MD4owAAIABJREFUDu7gRc7V8rtQlTOmo8+oyhkiHu3uTdq921c9rCtlb/wpZZ0ShScuqlm6xWT+jOX+e5fu43sRq4MfcDj7gqrOMSZgqXtv4YNzTtPy29zrfcTj+ZdUtiQwITc77xEdCb+H+d6xQPmCtJozKvZYjtavYshvBI/TA8ZFxm46ZVblVFqjQGQC8rpkGDbVEZHr63kBq8oXsxG7eYJVGIYRd+I+D9MJoyLHCKxHbd7rDjEvEdg6fuBKvF/Cfp5zfzYnt5bIGO52Oqy2nMrh+PbxBHyB6pwmGbh7CK9FqyUMBsJ0qlgLvg9LSwbjSmcdbwgSCrLirmscl+OOjHcQMQa5tobWFt0/PBOio0kKO/vI9bMLQlWFeQq+h6ytQBxhgsU+PLQqYDYCFDpLSLC44RjfJnV2eJTmHePFK8TX/gStC8QLnOvvd0RVmRz8mrrOjh7XzKcPMX6LVry4otBlwTlWK8p6fsmzT2i3VoijZWpb4Bl3fF7GcrTOUrhGaQsCE55xXhX28mZYRb2YTbKsKnvFjPvzXQ7KGVaU0HjY2pLZCt8YFFgNe7S9gJWoS1IVPEh2SeqCa9GQa/HLxbdF4HEyZTs7Eb5HRc6Xs0csHwm7VmErSwiMx51zpd2OV5PVNZ9Opse+6NxaPp1O6QQ+sauwcXzLiAgbHTlT3i3AZsedd1+XXt/Q7TUipUv1/vaxlVIeHa8mAFuAeBB0BHHzD4CmFp1b8AXpu0Acx8tZbBXqHUc8gyYXe1hpkqLWIkdlUaqKPttB05PnSqcN19Yu7LsoaDrDPv/ipPT44Dlm8z2k7RY53yT5/qdngnFM1Ke1/mOMC4P4RqjK6bFAeZoi3VtokbIX3+Bwdh891Vog8GI6ra/+zhMRfM8dn69CRAgvmaNeMAQeXtjeD5e+g1G9WdRq+cX4KZMqYzefkdYlgTG0vDatukQw3IiX+cnSXd7rrnG9NWS/mPP/7H3Kft6I6Z484Yf9a/zp0ntnypwXib38rDO3VstWljAIIrxTNxH28tSJlF+Tg7w4n7sBNO7Km+1XB405HN8Em11D5CuHqWIEVttC15WEfi1EBHdP4dunSpVky4KFKrWUEwiHghcJ+aHSuW4wC55Ob/crdPekD7keCOZ2iPiLPS+Oy3Ei5bvOZXcoRJo/L5inZwRKAJ0naJIi7YspuIuAHjw7Fii1KmF2QL3/FHP9Q2T5GhK7EuTflTqfXEjutvmEOtnD7yyugPZN8lKn34I7AJf77zNNn/L84GcU5QwjPjfW/j06rY2rHtpbR2VLnib3GReHBCZkI77JcnRW7H0y+5JfjX9GUk0xeCxH68R+GxCutW/R9hevZH43n3JQzNnKxjxJD9nJJ1i1LIVt1qM+a1GPH/evs97qs5WNGRcJz7LxsUAJjSD3MDngTnuFG/HiCb3AkRDZ9LSyquxmKVvZDF+ElTBmJYqPnucWQV8XI42bcifLSOqatKoJjCGtKgJj2Gi5/qiOb5+llrDUcp/fb4u6VkYjJc8U34d+39CK3Xx/XfL9RqBUVcpJE1BUzhQvErSCfKTEa4s7r1opuncuKLFQ9LBGXGCO4xLcUfGOI4Meuntwdlu/i4igVQ1Zjj0Yo1kOgX82JCcvYFFFyjxtfqqih8+hbrp3azpDn3+Bufk9JLw4N5rOwNYQ946dqo7LscUlJbd1QTF5gon6GN8tgH5X/KBLEPYpi8nxNkGIO9eucFRXj+9FLHXvYeuSok4Igy6qNXvjT1jqvUdRzvD9Fkb847+Hfueqh/1G8sX0V8zK5vgqbcH96SdUtiQ0EW2/y6Q45P/b/QvsKdfqfr7Nny/9p3T9/qWOy0VgVuU8Svd5mo4o6orYhBRa0ffbfNDdYODHlKrsF40oOa9yPp0+xxcP/1SwS1aXx8E6i8hm3OHzaROAtZ0lHJYZK2FMpcr2kctyJYrZjC///M7LgmfZnEEQsd5y7sDTDIKAp0lCbpW9LCe1Nb7A+90On09neIjrT+lwvMWoKrs7lupIOyoK2NuzrG8YQudY/VrURTOfVaLUpWJ8wZ4KfrKLHviUK5dZ83Vm0VYNoSDR2XWzqkKqTd+WtisN/yq0tpBW4Buk9fZLfG//O3C8Ehn0MICOZ40zsNtBlgfoeIrdO0QPJ+h4AkUJnTb0u0j36EJ9gSPgpNVuBMciPRYoxY8aB6oqOj1AVm4cP1/rCn3++bG4KcaDzXvOcfkKTHh2bsrZc+r0kKB/g7RKCPq3CQeLHfDyTdBf/hHJ9AFFdojxQuLuTYJwcNXDulJULbNsmzDsQWWYzJ9gtWJ/8hta4RLDzm2yckxdF3TjTUSETmuNlf73XLrtKdJqfixQvmAne8pW+pjN+BYg7GXPzwiUAFmdkNXzC47LRUJRkqokqQtEhEEQM69zFGUrH9P2Ar6cj+n6EbfiZQLxaHkB4zJjYE5ukLW8gN4C39DZbDXi49OkKZlfjWJWwhb7ecakyslsxQe94fHzTvPvRjv89cE2tTYrp9vtHn9v487Cls6fZ1SW3Gy3eZamFGqJPY9+EJDWNbHvs5VlTqR0ON5iipxjgfI085kSLrtrna+Hku0qtlaquYJAdKrAwUQLPp+RNE1lTwmVdlYjkxqbN9eI0veQa35jpCoV+6RoxE0ATzA3AyR25+fL0EmO3Z41Fl5AOiFyvfdWr1mcSLkAyKCHDE4EIa0q7O4BmuVNkE4QQF5CXjYhO60QM+wj7cVd+MjydfT55yc9KTFI/1TSuT276NbDrWOBEkBtDbuP4NYP3+oviG8SW8wpZ8/QKseLlzCtZdRWlNOnqCq2mGHCLrauqEcPKCdPEb9F4Eq/fyeM8ekOPoDF1iUvUFYJabbP4fwBIh6B1yLND/C9FpPkGWU1Q1FCv0MU9plnu8TRMp2WOx6hucO9kz7jWfIATwKG4TKVrZgUIwITsZM+o9KS58kj2l6XwDsbPFbb+opG/mawEnRp+8HxBbsnho3WAA9hLewSmSZtejufMKlSloI2y2EHESjrmmmVoSjXWwNWosUrlz/NZqvDetQms9WL63PWW23WaRMac6lAOSpyfnqwcyxQAjxKpnwy3efjweKK56dRhcjzuNFuk9Yn1zwvZszqgjuDHI63CFVlNlOyDDwPul25tOcsXGp4c3wFagE96gEaK3WqiDFH6xswOaQ7lnDQ9KlcNMQXZM1HdxpV3FaK3auRtiA1SMfApEa6BvpeUxp+2n1aK3arxLvnboydR2uL3TkRKAF0XsAoQ5be3opYJ1IuIulRaVhxyofebYPvY9otZNjHbC72Rbq0OpjbP0In+83JOojAaxaNWuYQRKjqiQCZTi+8hpYFUhXNvguOLeek238L2ggTVbJHle4RdDbw22sUk0c0Z3ePer59vF/y9F/Svfsf4YWLvQh3fLOUVUKS75Pk++RHTsAcQxg0x1laHOCZAGsrsmJMFDaBG1kxciLlEQ9mv+Eg36GyFYmdM6vGRKZNVqVkJJR+c54xYtjLt7nWvnW8b+S1uNZebJf0MGzz/e41irpmVCZEJkCP/hsEMb2gxWSWMS4SWl6AwSDAn69+xC8nz+hWEb0gpuO1+GT6nN8f3PrKf/NdxoiwfOSgfEGtlkFw+QX6k2RCXld451yTz9K5EymPWIlCHsznhMbQ9jySukaAXtAsHZyL0uF4ezg4UNLkRMRIE2VtrQnVqc/dM2y3m7WNtYqtwfNxhotXYKtmPdhagzpTwCABoIJ4YEKweVPyXc6bEJ1FFCrNso+2DZpY5FGBeCAFUFg0sbDmN7/re83j8+SKVuqCds6TVU1J/HnSEpxI6XirOLrAxDt7cS5xC7ptpO9KlAHE85GlDTTuoruP0XSC3bqP1iWy/wTtLmE++hNM3G+EyOJcirKY5szuoJw+OxYoAep8RJ3s47WWMEEbv7NBOX5MPd/GBCd9wUR8qulTvJXvXcWwHe8o0+QZ3dYmamvm6RYWizE+gdc4rjwTMs+2qeqMskpQLP32DXzv7T3Zf5MUdcZB3oRebbZvsZM+4zDfZad6RmVLjBgslrbfpRcM6QVDPPGotWYQLvPHK38X/8gpuKgYET4e3MAT4TezHVJbsBR0qLVmNeyhQMv4zI1HYAyB8ViP+jxOD1kKO8zrgt182vwp2tyOl1kKF7tv6ofdJeCQ/TxlK5uDNvOcHFZ81Fui4wekdcWnkwMeJROeZ3Pans9S2DpegPf98NX/yAIReR7f7/e5P59xox2zn+e0fZ/Y87kWt7geu+9Dh+NtoKrOCpTQmK5mc1hZNYwOLUUBxkB/ILRawnhsmU0V1WYps7xkiFyA0aWIeZFHKfjtkzkyUSNOntF3LRSTxQ3RkZYBT8CcE7616U/JjaNtgUB5Tng72s9xjsD7etvfEpyCskDYaQK7jTNQAeIWzFPIcqgt2q2hsug8aZqU9DruzhmNq1JufZ/qN/8aNQbxmjJ4nR1iP/8b5OO/C90ldOch2BppdSBoIUvrTW9KB1oX5x4fNcGxFXghJuxj64w6G+OjSNDGC9pH5d/FJa/ocPz2VDbHGI9B9za+12KW7SAiGPFRrfFMCNo8jvwuRTUjLQ7oxptXPfQ3gvJUN/jQRGzEN0jrOcNwld38OfNywrg4IDAh/XCJG+27fNj/MUaEaTmmsCnzckonWOwbYh0/4ifL9/iD4R1AMWL4fLbDdj6htpbQ+NxsL3EnXiYyASLCQTFjVKaMy5S0LjAIlVoeJPsLL1L6xvCD/gqP5hOUJiQMYF6VfDo94I+WNvh0csCsKlkKW6yGLXaLDL8q6Qchbc/nR4PVq30TbxjLUchytExlLb4x1KoIjfjrcDjeDs47JU9vD0NhfcPDWkWkEY7SVJlOTgSiuoL9fcvmNYNx4SUXECOEQyE/OCWqCQRdIb8kMEcv6QO6UFSKhAZtWcjOzpkMmnWzWfGxaXGm94CseC485xIk9JB+hE5OhSh6Bhm+3W37nEi5INidfexnD5rkJwAjmBvrMOyjB2PwPZgk2CQHUcTzkMkcbqw7oRLQqoTpwcW5SCboeA8OtyBsQzpBkwnm+gZmabETlE/jtZao05OUeS/sUucjJIhRlHLyGOPFENbYMsX4Ef7gNiKC31p6xSs7HF+fOFomK5pE4E68ju/HVFXC+tLvYcTwbP+nrA1/iBFDVWd4JqIdreAtuPvvBbHfITAhpW1uIMyrKYLQDft0gh6PZp9TaUnsd7kW38Y3AUY8Ppv8HHvkqN5Kn3Cn+yGrLSf8ng5q+bC7zjCIOSwTclsQGp/AnFyqrYd9PpvtMCqS423TOmdapjgaDsv8WKB8QVJVHBYZs+ooCA/h95fWeZJMSeqSjwerfDxYpRc4J+VlvDhGPXc96HC8dYRh45I8106f+JSGcVp8PO+6hGbfPAdnoL6caMlgwiY0RwwEfcEEUIz0dCEZAH778tdYGCIBX5AVD+aKFrbpWXk3PC7llo7B3AnRcQ0WpGeQrjP+vAzZ6CLtsOlFGXjIIEKck9LxpqPWYp9sNQJlVUGSo2lGtb2PLA0w/Q4EAVqUiLWNu7LfRbMMmadNv8pFxxi4zBUpBp3sNg7KIITgyIWRzVC1iDhfOoDfvYYtplTzpkTUxEPa/evYfEqdjdAqJejfRASynV9QT5+TqxBf+0P83vUrHr3jXaMXX6Mop8yzXQDa0TKr6/8BrXAIQJLvU9uzDl7POPHiBUYM93rf58vpr5vybjy6QZ9BsIwglO2CaTliGC4TehH3ut9jO318LFC+4GnygOVoHeO+J4/ZLWY8Tg/YzWeMipRZnRF7IZutAWtRj/fbq/yLg8+On28EhkGbcZW94lXfbQ7ylJ+P93iUTPFFCIxhOYwJT52zRSA0BhGorLKdzZlWBYLwXnfIn62684zD8aaTlMqTqWVeQsuD6z3DYAF7+31dRITlFcPBvj0WKuO20OlePnfmJafkl213NAQdIeicndN43ZDu2GOhMugKQX+xj1kxgrkeYJ+V0FWoBApFD2rqucWs+E3Sd8s05eGOr0REoB8h/XenV7QTKd9RNC8gLyA6WlgXJViLHc8aEbIsm56U1mKtYnyvWe0AVCcLSS0KBCdSivGQ9Tvog59z2nsuy5sIFxPytK6RqgLnygBokrujIVF7DTEeJuwhYtC6IDv4HBN0QAzF4ReYeAkTdvHaK2ArbDHFi1w09delrnPKfITxIsJoeNXDeaMQMawOvs+we5eqzomC3vENhawY45mQopqdESZ7bSdinKYXDPjx0p+SVLOmTHnyy2Nn5UZ8g/X4Oh/0fkg/XMaI4dH8iwuvUdmSyhaE3ttdkvJNMS4TPp1ukdYFj5KmNUtoPFbDLh6GzajP/WSPJb+Nh5BrTdeLCI1P13t3Lky/DvOq5G9GO3wxHZHWFYbG7Tcpcz7sLR87KlfDmI4fsha1+ZuDbSZVc6yWdcXD2Zh/5T3nT5Y3MG4V7nC8kVhVPj+0lEciW1LBl4eW768Y4kBISiWtlNgX2sFii0CX0WoJ164biqJJ9/ZfET7S6QqzuZ4ptQ1DiJwg/NpUqaIVeDF07xjqvPG6GHdsAiBtg3kvRDOLPq4Q/+hgyxX7rMT4grTd+XiRcSLlO4jdPUDHp9Kmex2IQuzWLuyNGoHy6FZa011ojI1C6HcQY06CdQBx6Y3HmOsfIl6A3b4PapHlG5gbH6L7T6HMzzxX/AB8VxqqquT7n1Inu8fbgv7NY9FRvJCwd40s3ccWc7QuEAx4EX6reU4133Ei5dckS7aZjT5Dj64wg7BHf/ljjHFf+afxvRb+ix6zatkd/Yq0OASgKOcEnqUTr9FpbdBru/YN5zFi6AZN8vn3Br/P8+QRST0j9tpca9+hdSpoqOP3yOuzJcmhiQiMO8e8YDtrztujMjleGxa2ptCah/MDfj17hih8Ot2mxtL1Iyprudte5mZ7Mdti7GQJT5MZT9PZ8Zz5InwULuGLIfZ8lqMWN+Om/+nddp9fTfYpbc1+kbGVzQmMx5NszsNkwn92/T1id+52ON44xhnHAuULLHCQKVWi7KUvvgGUtbZwu+8EjvOICNFrnHKDQFhbM0ynSl0pUST0B05cex3UKslzS/2iuEEgXjMEPTd/5xEjTQK6XtK3c1w7kXLBcSvWdwxNs7MCJcB0jm6swi9PSsSoFUIP8hKk2Y/RBK1qGPSQWxt4t68jbdd85AUigmzew2zeO/uLpWuQTpu+lc0TkdVbrpcnUKcHZwRKgHLyBLWKLWeIGPzuNfzOOkVx//g5XmuICTqorSjGj7DFHBO0Cfq3MIE7Jl9FXRdsPfo/mI2/wNYFrfYm3eH7hNEy7d7tqx7eG0FVZ4znjymqGZHfo9+5RVYcHguUAHG0BAirg+8fC5mOlxN5Le72Pnrp7zdaN7k//YRJOSI0IUvRGu/3f+i+J0+hp2wrsypnWqUkdcGTZJ8KZTXsNt+ZxqOwNb54rEZdSrWshYsZQmRV2SvSM9UM86rkYTLhTrfPtbjDRuskUMiIsBbFDP2IL+djghcl4QqjMudvDnf4O2s3vts34XA4vpIXn3GrymEGaaUEBnyE6lwP2t1EWW4p3dCdX74u87klmTez3ekY2h0nFH0dirFSZ41YWc4UW0I5tQx/4OGFbi4djtfFiZTvGJrml243dY29tga7h5BmYIpGqLQVmBaMxk2zkXbciJqPtmBt5Tse/duJBCF66wfIaAdQpL/WOCkd2GICNItvW8xQBS0TytkWXryMGI9y9pzW2sfEN/8MRVFbYoIuilKMHuDFS9RI09MyO6C9+ceI5+ZX1VJXCcaLMKcCXbYf/TMOd/6Gupqj2rgqi/wQ47WcSAlYW7F18LfHPSeLckZaHBL5l4k8Sl5OnUj5EipbUdiMwASUtiQyLbxTbt28zqhtBQJfTH9FPxjiiYdViy8hsbfYadQAlbUcljNaJmQj6rOVjZlXOXvFlFGZIgoTtRRaY9UyDDpExscXQy+I2Qj7rERdslOJ64vEMIyI5KT3ZFqVJLaipyGVVT6bjiit5Wa7+Xz7xrActvh8OqI6cm9YVQLPYFXZzpNL/x2H43dlXtV4IrQ8J1S8LmnZJE63fGEQgW/g0VhJj9KRC+CZKsMYQu+sIDkrnEh5GaradPzywDs3Z9OJZTw++l60ynxes7Iq9HpvdwDHd8kLgTLbPwnMsSXMHlr674tLpz6HtkAVsIqcOh5fpHw7FhcnUr5jSBhc6I8IQLeNiSK0227ckmkOdQ2tFmQ5VBY8mr+LwLim/tUX4BnMjU3EXVS9FM0T2L6PlkdBG2UO63dcaA4gfkxdzMh2/h11PkFtRZ3uEy5/D0b3oc7x4hXK2Rbd23+OCXuU4/tU0+cgHrZqSsArtjFRj6B3g2q+TdC/edVv7UrJ0z1m48+xtkREiDs36fTvUlUZyewxts6o65K6mh/3phQx9Abv0+nf++p/4B1mnu1cCMWp6gwjl58OA885dy9jK33C8+Qhh/keB8UeK9E6w3CZ6+27rEYbfDn7hO30CdvpE0pbYm3NUrTGWuvasXtyL3vOjc7iHo/353v8i/3PSKoCT4RhEBMan0fJAfOqAFU6fsTTbERWl6R1wa43YzXqEnshgyBmOepgRIi9xex/vBS2+Kg/RKawl6dUWNaimNvtPuboOHuazo5FSoAPu0uUdc2/OdwmqUosilSQ1hWrkbsh4fhmSSrLJ+OUtG5qlZdCn+8NWi4p/RXklfLlyJIciZG9EN4bGm50hYejZltgYCUWUBjnTYn3aVqv6Lm4qGSZcnhgqY/Es05XGA7l+Jw8nSqqynyuZEflyvO58v4HhlbLzefrYAKo84uJ3iiUcyV0Zd/H6Nyiz0uoFB3VaCCYNR+z6rtSb4cTKd85OjHSitDslKPS96G2zc+8QLpt1FooiqYT8mQKakEFyroRKaUJ1dF5iu4fIuvOVfkydPvBiUAJ6GwEYYwsbV7hqN4MvHiVfO9TyukzAGyVo1VGNXuK8TsgQNaEu8we/t+EgzuEg7sAJNv/Fq1yOHKx2XxK7e2hg8V2A1pbMh19gh65gFSVZPaYIOwjxm8uNo1PXSXUx01xlLpKmY4+ww/7RK3F/TzXtrp0eytawmpFWaXk5ZiySujGm2dclHk5ZZ7tAEK3tU4YdL+jUb9ZzMoJT+f3Keqc3ew5ADvpM2KvzZP5l4yLQ8bFPlvpEypbcJDvMatG5JoRmJClaBWAShfT/Qcwr3L+av9zkqrAYtnPE34+fspH3Q3Woh6Kclgk5HVJywRYaxEEq5btbMKd9ioeglVlLeowWNA2GEaEjwdrtI9E2k8m+4BwPT75bFZ6tpGdbwy/t7TOQZHxFzuPyOuKWVUQeR6x8ZlXJR1XDeH4hvjNJDsWKAEOi4rH84K7XdeP92U8nJwIlADTAp5NlWFLuHGu16SqUp0rIusGMHDTewZrld2dmiRR1EIYCcyaZWCnI6gq1kKenwiUAFUFB/uWzWsG41yAX0k4EJIt4XTqkN8RxJOLwuUCopWi47oJzNmrIQQJBFlrJCmz1iR7OxxOpHzHEBG4sYFM59g0Q4zBjqfIaIL4Htptg1W4vgF7BzBNGkFSpTkT+T6I14hH3RgJ/CYNfIFRVUTkwk8ALTK0zBuv+um74skYnEhJNduizsfNMWUrtG5ER5tPMV4E4mOrFLwQLRPqYobfGqJ1iWCwdQachObUxRQvXlyBDaDID48FytPk2T7dwQcE4RK+36X0Rti6QIwhiJbxgy5VOaPI9hdapGxHy4xmD44fN59lodvaYNC+yaOd/5eySgj8DiA8P/gZ15b/kLQ4YG/86fF+0+QZ68MfEkfL3/l7uGrGxQEA82p66vtQmVczhuEyz9OHeOJR2ZK9bJvSFszLOT57qOqxSDkIF/c43MtnzKoci7KdTxllczJb8mWygy8+qJLVBYdFiu8ZVsIunjFYAGv5oLNKyw8pbMn77dWrfjtXyvW4S9cPGielrbE0qegvWA5bZ87bL/igt8QvJ/s8S+Z0QmEljNktUnazhE7XhbU5fnfy2jKvLioTB3nlRMqXYFWZFhe3j3PlZl/wBapTl0Aiwo/XG+EyLaEdwHIsrt/xOeZz5WD/pPNxlivtthLHHp1OM4+tWJhMOPN9GYZN1mpRNMV3Di49n7zABELvPcPkM4taxQsFryUg4LcX+5jUSrEPCyj1RKRsC7J8IkfpzDqR0gE4kfKdQ1XRgzGMp5AX2L1DbF1jgqAJdIlbIILptLGzGZoXEBx9GeQFFCX4FjoxKOhoCmuLmRqq8zG6/xQdbaN5isY9BEXCGAkiGK5Dq40ePEOLtHGx9ZYg7oPrmQhANX+OeB4miLFVhtYFqhUiIWprBEG1oM7G1OkOJhpAawhiEC/A72w24q+1IIawfwsv6l/127pSjLm8rFPEMBt/RhD2wdbUVYpqjZGQqLVOEA0R8c70r1w0qjrj+cHf8HT/p4xnjwi8iGH3LjdW/5TAj8nLaVMW375+Zp9p+pxZunXu1ZTR/OFCipSC8OvRz/hs8gtm5YRO0OV250M24qYNQ2RaKJDWcypbIgi9oI8nPqUtSKs593rfZ7jAImXk+Xhi2E5HbGdjsrqk0prI86koyW1JakvmNoNaqK1lrdXDqiIGZnXOuErZNzPS7ZI/HN7mTntlYRfm/SCiH0Tcavf4ZHLAuCxQlLSu0Fz5q+IZK2HMB90hvmkEjbyuCIzHne7JOaWwlvF5W5bD8VviiWBoUqhPEzhH2ksRuCBEQtOP0ohwb2h4OLYUFgyw1hbWXGnoVzKf64V2YGkCrJ5s7XXhySNlOlWMKO2O0DkKzjFuiikmlvxQ0Qq8GFqrBu+SvqdB29C7C/lB41oVD6JlufS5i4SOaiiPjrcX34GJoj1FgqPHTp90HOFEyneN0RQ9GKFpju4eYA8nUNfYYR/CAEGbbslF0bgmex2oavA9KMKm3Dv0IQqa1zKCrCyeo0DLHLt9H9LURN7+AAAgAElEQVQp9eEWVAW68wDTX0XFYAaraDZFFPD8pjQ+n6LptEkAv/7BVb+FNwO1BN0bZHu/xhZTxPhoXRMM74JaVJWgu4bxAtTvUEyeIMbD7240Kd4S4LUGjQDshbTWf3zV7+jKCcLBsSvyBcb41FV6FJAT0B2+D8ZQVwle0MYYwTMBYWuFVvvaFY7+anm691O2D39Bmu8BUGtFWWcU1YxJ8hTvJQJuWSVUdXZhe1Utpsv8WfKQL6e/xqo9dksm5Yz3ez8gMCF3Oh/ycP4ZgQQUNkdRhuEKw3CF9dYN3u/9kGudxW7bsBr26Hghh+Wcoq5QlEotuS2prRIYj1A8+n6LaZXjG8OoSChtzXvdNQ6KhHGZEhmfyAQ8Tg/xjcfNePFuKqoqh0XGpCqwqmy2OqxFbe7PR02RA4Iq7GYJ4yLnVqfHIIiI/YBQDMWpcvBADLHnLo0d3wy+ETbjkGfpWWvgjfblNxvz2nJY1BiBlchfyL6VIsJ6R3g2OyupbRyJZf1I+HjNkFWNx8J3gu9XYq2Sp02/yapUFMHzwPeEOD6qDFPl4MDSbgt5rngeGCOUFfT7QrjgAluVKNnuyTFZp5A+t3Rum0tvDoYDg4ks5VTxW4LfXez5A6A4alNVg5Z6XBUmlUIgjXAZCvawQloGLGhhkdg0jx0LhbsSe8ewo0kjTiYZ+nwX5gmIoEWJtFrorQ28MMC2Wk3St0jTr3KWNLfJPANlBbVt+lqKoksLKFLODkAVu/cYpgdND89kjE0nEHWgrkDrZvvGPfRwG8qsKZ1PJs5JeYSJ+oTL71MVM3RUQdgjbA0IB/ewRdML1UR9UItEPSgSyskTqnQfv3cT0Zo62cOLl4iv/QR/ARfg5xERBis/Jp09oSzGeH5Mq3OD8d7PACiLCQi0e7epyim+F6PUdIYfsrT2h3j+YtbrFOWM0fwhk+QpSbbbbKyFNNunKOfM0x3Whj+g8XGcXRy1wiFVnZKX0zPbo3DxvhsrW/Fg9htaXodJ8ZSknqNaU9mKf7X7l/zXH/x3rMZNq4tHs8/xxDsOERuGK/TDIcNocR2UL/CN4aPuJp9Otzgs5/hiGAZ99os5ta3pBC1yW5HbmlB8PITM1vS8iI4J2crHlHUNAv2sxSCI6fjhwomUtVp+Md7nWTLjSTo9/uT6YlBVapSeH3K91eFJOmNel2S2whPDahRzpzNgv0jJ6oqW57MSxiwFi/kd6fh2uNsNaXnCfl7hGeFaHDAMLy6/DvKKT8fpsevyoRE+HraJ/cVbnF/rGgJjOcgUI8JqLAxPBbeICLG7zH4tikLZ27WkqSVJlDyDIFD8ADqDE5EySZTd3aYvpR8IZQ5+oASesLLiBLZydrHNkq2aNG//kpbQ+ciS7zf7lBPFGyvt62ax073bBt2t0L3q6DJbILNoLEjfQKbodtVUhe7X4AlmyUMBWfIwG+5Dv0g4kfIdQ2dztKrRPIdZ2vRKrCuIIjTPkazAfPwRZDk6nWK3DtC6btyAgQdJ1jgrAw/CGMSg23tX/bauAGl6TWbJyaa6hrpsfjc7bM5Otka9AME24UMoFDl29yESxpDOIGwhS5tI1L6qN3NlhMN72GJG2N3E80IwhrB/GxO0seUMtYqEbdJnP8UWMxCD315vAiKKCcZ4qNbU+YS6mCy8SFkWY5Lp4yYQRzm6ATElN7uoQpHtk0wfU+YjvKBDFK/SG34PgN7wQ/ygc7Vv4IpJ80P0TOdyJa8mKE1/oVm6w8HkC6bJE1rhEiv9D+l3btBprRH6HbZHv8DaJuzF9yKWuu9dzRu5YowIWT0nqaeAYsTDNz5ZnfKXz/8pP1n9u+RVys32e0RezLRs4lin5YgfDv+YrE55NP+cylYsRatsxrcwsngL8f1iim88lsMOtbUUtmZcJXRMSGUrUltSq6XrRdxoLzM6ck7uFTP28ynFUcuMtgmpsFyLFk8038oSJkXOVjZHaUq4d/OUa60OSV3SCyKmVcHzDOZ1CSj7ecqsKrk/H7MexeS2wqriibDRarMaLWYIkePbQUS41g659hL3JEBZW/5qZ8ZBURIYw2rkge/xaJ7zvcFiHo+rbUPkK9sz5enUMi2Ea11xzsmvyXhksRY8X0AbcdIYaMeGsCWUZdN3cjZtBEoAzxO89tHzuuICc2icptmeJT+sqUvBjyBaNnhtyBtfC0FXCAdNQE5+cFbUrHMoJko0XNy5lL5BCz32AYiAXPORtofpedjJUZhiqo1gWVnq3CIABxV0DKbr6sEXBSdSvmOI7zef/Vl6lNhNU95tLXghLA3QqkL3DqHbQTe0aerSipo+llXdPLdUSDI0jpFLQjredaS7DE8/a4TasgkgUmOacKF8Bn6Eel5zVtp5AEdJomotmozhi3+LufFR82JljqZTzM3vN70sFwjjt4iv/TF+d5Ns5+eYoHPsqjKtZUDI9z6hzg6P9hCq+VZzF21SE3avNXNMTfrsrwk6G5gFdQJW5YzDnZ9h64zaFmTzZ3heTKd/jzzbI50+pSwniBisFtg8J4yGQFMOHrYWO1wjDLq0gh4z8fBMRG2bvnOBF+N7EYrw6eP/DdWaKBxQ1nNG80fcWv8zRAxh0OXG6p+Q5SMQiMOl42N5kfCNz3u9H/Avd/75cSm3YKhtTWlzDrJd5uWEJ/P7iHjEXkzb62Kp6Qcr+Mbny+mvj1/vefKIvM641/veFb6r7555lfPsqBdl20TsllP2ihmxF9Iy4VEpfU0gHqHxMQgt4zMrcwZ+RG2VrC4JjM+0zsiTir18+tX/8DvGrCyoVCmPSrYLa1GgsPWZ8JxRmSPAvCqprOIbQ1KV7GUpd7t9wsAjMobAXF6653D8ruSVZScvGQQe/XNOyp+PUraOSsKLuiYpa+50I2be+W6Wi0NSKp8f2GNnaZYo81L5/ooTKb4OxVGnAVtDuyPUVVP+3e296P6lhKFQ10pdKdYqQdhc21jLsdNy0SnGSrJbU8/BWks5gzK31KVHvNo4JPP8Rb9KOV+QAzSuy4VGwPQMGgI1EAriSyNIpicTprmitlkHUh2VeleK/aJAftxabDfqAuFEyncMWR42Jd6eB0HQuP/skcuvKND7jymfb8Nk3pR1Bz5kWZP4nRec+Va1ClmO3L1xVW/nypAgxNz4CJuMQUBnI4jaTSk3oIyg04f2EEbbx45VAXR2iNo9WNqA9pGzxVp0uo8sX3/5P/qOIkeBNyboUI4fYqsULxoQDt9DbUlx+BkiHmCoyynVfIc6b8S2avIULx5ggg6qSn7wBfH6j676LV0Jh7s/Yzr6FFDydBfjtbB+xfjgV1TFmNnkPp7fxvdj/LCL57ebuY+GdPr3MMZ93W8u/wG1rZgmT8iLCb4fM+zeZal7jy+f/3Om6VMAVC2CIStGfPHsL1gf/pDl/of8/+y9Sawl2X3m9/ufE9Od3pTv5VSVNbCKIkVJpJuttha9aLjtjeGVl17YCwOWN14Y8K5XBnrrAV4ZkOFNA1664YXRbcMLA7babaklWlKLQ7EGVmVlZeab7xjTGf5enPteZpJFiiqSlUnG+4DEvTduvJsRBxFxzvnO9/8+I5bxgJPRr3BY3mG/POSiPyXGQG4yKjsGEcb5hCbUPGke8vHqfUJ0OHpGdsLd0QNW7oJ74zcw8myiedGd8mDyDtmArtHjbsl+MWEnH/Fpc8Hc1wQNZGqwxtB6Rx8DagCBlW/oQ8ATeNTNuXAbhFTufN6tuT/eI5CIy2pAdiPjLMeKkIngVcm36Q65sewXFWObcelaVJXTrmHpekSEsc3wGjkoKtau5+1pWtA57Wrenuxeh+vc4Aa/DHxv3vAnZ2tCVETg3VnFP7ibApvWLlD7gDVCiFvPNmDeB+7/DPXlbzrOav2JwKGNg41TJvkNSfHzIs8TUWkzQSQpAp2D1QpAGY2UEAKPPg00DTin2CxycCDszAzjgSdSA4RO8WtFLESvRJ/cvdxSKEZKmD0r+e5XyuSn5HvaYelUfgIiyXMyxYk9h1KQUq4ZCMkF7RQiiH3u+hNgHeEm/XsQGM6MYCCQ/R3M5QJuHxAVmC+Tz2QI0HSJmIQUk9f0z+zXlERKXu2rulVUOrg7TAWW2b8Db/4OevmUeP4Z0rfEepmWI4ODrknl4MGj1iImg7wA16fmfPwBcvg6Mrt1Ha4zVKhGNHRINiKv9sim9yF6+sWngKG89TX6xUPC6hEa3bVCzXcXYASbTxFb0V9+QHX0jcEpXVy/oF4/4moRIcZI8JfJczKb0ncXBFcjYtBsRF7sMd19lywfs3vrJmzoCgezd3C+5nD3twihp+kvmI3us6o/o/dJhaYaqbvzVBZlK+rujFXzhLLYZVzeYtU8pvcbimzKbHRvkOTvo/oTDsrb1L7mojtBBJx2FFJyUBzx3uIvOa4f0YaaEB0q0NKw9ise1R8DkNsCVdgp9hjZCVE9QxqS+BjZy8d49XgN+BiJGunVs3A1IBQmY8dW7GcTOu1pQs9hOeVhaEl6QaEyFitmGxCTSMsh4V414aStuV2OedxuqGzG7WpEaS1H5YjKZrw5mbHyyaolvcLK9/gYycQAulUEC1EhqA7oSrzBrxprF/h/T1ds+UdU4f1ly2vjgnd3KoImu5E7VcajTc/aR1xMScz3B2y8GH5KEVcY1iPuF8buruH0LGKt0neR5VKZbENcygLaVnn8JGIzwRilKJ+Nr2/fvVGWw7YwUUFUEJt0QADRKd0cpAyU0ZKNQaJgMqHYTerLK5gc8p2btjS3M+JnDvUKm4h6xbxZEjUSNzGF64wFrEBhnqV+jw1SCBqUm1b8SagqLDq0cZAbZG+E/Jr7Gd+Mw37DIEWOvPsGxnvILNE7qBvoYyrlXtVpJmO2UvQQkpqyKmGdyh+JEcy2RLzIE9H5YJiJwHL7LdQ7pN0Q/VlSRjZL6OpEUgqpjD6ElIS+fw/KCmlr1LWwOIG+RW69hkyG5xd2he7sB4TmHCAl2F58mEhdILgagkOjw9gMYokZHeGbMwgdxuRQTBERTFYR3QZbTF/m6Xzp6NtL8mIH3y8AyLIRrutw/RpVQ9dcgAgx9PTNKZkd4Ue3mey8+ZKP/NVCkU+5d+vbzNcfc3L5Xap8FyVysvg+ggWEEHp6twZVrCnxoWW+/phReYtl/Qjnk09tzRl1e8bdg28Nruz7afOQJtTkJmeSTehCy8iO2StusfZL1m7B6trrM8OIkklOUIeifLT6AffGKd175ea8PfsahR2WjcNhOeGD9TEX3Yba9bjoaWOgVKXfltBPs4rCWOrQ0kSPQXhYX9KHgEUIRIIqk6ykMBmVLZhkw5JqZMbwrb0jXhtNOe9q+hg5LEdYY+hjYGxzcmP47uKcd6Z7dCFw0bcsfYcRYeV6DPC4WfPaaMZOXlDaG5XGz4OgKZ31RnX6s/Hppr8mKF/c3vHuTsUstxRG2MktVrbedka4U+X8aN3xzcIOMuV7rxIu2udKQFWxAtOfIS71UTGSfJNvkFBWwv6+8PizVLqtERAhy2A8ga5T2lqpRobJZKtlUcgyYTK5ubcBbAX5RGg+57KKHtSBWymhhekDAwLlvpBNBF9rIiinclOmDMjUIm+Cfr9DBWQnIz7ukw/loU1q3xbMN4oUnuNBSoEytavceFJ+LvTJGr3icQBddJg3936ticobkvI3ECbL0K++Rfz+R5BlUJbgQiImr5SUnjQSMqRlyb7fKgQ1OdkqiaDMMvR8gTqP5MO7XEQEMzsgthvM4oxYlGiooG8hdonMne6B7xJR2a4w0wN0fweaVQoeUkWqMTKavezTeSkI/ZrQnKMa8esnhG6BWz8hug6xWbruVFEikk+xtsAUM1BP6OZINsLYgmx6F1NMEDM8ZYGxBXmxQxzdpmvPyIsZrl/Qt+dsFh8RY4fNpxjJUPUEX2PzCaPp6/TdJUU57MChK0QNzNcfc3zxV9T9BT627Jj7WJOBLdgdvc7p4j3QiDUle9M3MSbDhYZNc5zCn55D79c03QXjAfl91n5N1Mgkm3HRntCFlibUuOj5tP6Ipdtj7Zds/AofHCoRIxYXe6zJ6HzDXnkLEUFVqeyY3AyLWAM4KKYEIk/aBRduQxMcAvQolcmZZBkRZe4bmuBw6unidvaoYMWCCILQBkdhDO9Ojl72ab0UXJFk6+BxMaJ9w7vTPXbytJjVhDTuMSJ8beeAP794ysJ1tN7jiRy3lrO+IT80fGtvOPfyF0VU5aP1hpO2RYH9ouDd2ZTihqz8XMzyz2+XcZYm20aEr++O+LOzNYqwV1oOyoxbVUYdIued53Y1vHHPfiXcmwjHtXJRRzYObo2FH15E3twxjJ4r+W6c8sky7ZMJ3J4I96Y31+MVmhpGI0NmlRCT3996FXFOiAF6B2WpGCuUWw6ouvGivIaIMHvL0s2V0EVir4iBbJJCha7VfpL8PtefKBrAlDA6Mtjypi1fQKPIyKRiTgVWMZV294qM03aMwbyTo6c+cROZYO5kz9r6BtfQzr9AUAIQIjpvkcNf39De4bFOPwMi8gD4J8BdIAJ/pKr/nYj8l8B/Apxud/1HqvrPXs5R/nyQssDePUQvLtH5CtoOgk090dUKo0aIZrsMdBXpJsBWZm0N9B7NbAqMGSBJCcB0H5mfELM8KaayEs1L8C6ZvWRFIitdByrEvgHXwuQAufcOkuWDJSgB1CenaL85JrQLVBW/OSV0S7JqH1POQARjZ9jRHn71iNCvkwormySlqioaPXZ8GzMwpRBAObpNs/6McnREXuzRbB4TQkdW7BBDj/YO3y/IshlZMcHYkhg6ms1jXLdgtvdVTFZSjo6SMnWgmK8/ZtOe4GMHKG03p3NLMlMR1XOw81WqYo+T5feo8l2KfAoKzq25XH9CkY2Zju++4KfoB+aE3oWW/eKQtVsQ1NPHHisZhS3ofcNTt2acT8kkp48tDkducowx+Og4bj5jp9hjamcclLfJbSrzGSIeN3MiStCYihu21isWw8jkLHxD43ucBhTwpJRLg4AGKinYsRVH1ZTfnt3nwtVE1cGpiJau44P1/Ppz7T3fW57z+/t3yYxhZDOOyhGnXUNuDGHrdDfLC5xG+hBYe0/nA5/WK76xO7w+5m+DT+ua4/bZc++y7/lgteYbuz/FiG3geG1ccFjmnHXueltphd/de5baPcstX90ZISLYH1MCdgOub74/MxgJnGxS0vSqVzqvHK8jX79luDU2WIEP55EupL/xCo/XSpUp+9WwnoWfB1Vls4l0XdKtZBlsVkrvoCghz4XxWGnqiFghOCgrODwc6Jzvp8BWwtHfzVh+7NEIfg2+UbIqJXoDRAf9ZSQbGRSlX0A/D8y+YsjHN6T5Ndxz72P6p05hHmAHGAniFLNniRNBLwNYoLppw8+Fi2gXkiDNGqiyRPW48LKP7BfCzRPoRXjgv1DV74jIDPhzEfk/tt/9t6r6X73EY/tbQ8YVcnSAhEis22fl3T6QlizkRU/Kq5JFIflT+gCZxY5HybhkoBBj4f5XMV1NzAq4eJKUlC6pJ1mdg80T2dusobZgLOL75FF59ytINazy5Odhyx0QQ+hSqbLGHtWAmAwNHZAIXGMtxd7bEHvCxfugSuxWRN8Quzlu/RST71AevDu48lpjMvYOv8Vq8SGLs7+kb8/Q6NDQkRczYuwgbJ0cTAEIrl+wnn+A6xd07Rnj2QPq1Sfs3vo9snyY1+OmTetMRTah7S9ZNydAZGf8AFWlsFNGu/uUxQ5BHb1bcbn+hN6tiAKX6w+Ybz7htcO/h92SvVWx9xLP6MvHNN9htzygjx3fnf85mcmwkjGyEy78CX3skkqSCAYyLShsSWUqarfCSM6Hi+/xuP6EB5N3+d3932evOHjZp/WlY+M7+pgUflbMNRFpjBBR1r7Dx0ivYes/CXLdYYMRQ2YMxgpH5Q77+ZguepauYa/49V05/yI465qf2OajculajsrUFl+d7TPLC46bDYWx7OUlS9/TeI/TgFflYb1EJe2bm5uSsp+Gs677iW2X/dbj80ZN+RMQEf7d13f5q8uGk8axk1u+eTBikr94je0VlvxzykF3hyoQIJGS/99x5NOl0nhN0xeBSS4YE9nZwL2pXBOUz+OyuSEpVZXT00jbKM12XaEowGRCiTIZC9VIiFF4+LESO8UYUBXml8rOjt54Uj4Hkws7b2X0C6UvFLPS5EO5baPQRvKpoCjdhRK3yerrjyPVIYxu3zwfAWQs6MX2vYXYRXS1tZq7CJAL8q5Fm4h+6rjyy4hnAfNaflPy/WOIyxY9r68/S27RwzFm/OstShluz/c5UNUnwJPt+5WIfB/49Y22nk0w+7vEk/OtKnJ7oxsDfktSXq3Qxu1nI2kfCVui0iHvPEAGPPDU+TG6PIesQKoRzA6QsiLOC7g8hr4GzLb9kq8dMaZE8L6DcozeeXuwRr9iC4r9d+jO38N3c/r5j/CbEzR4TDnFVvtgc0wxw68eor7FVvv08x+hoUVitlVaWTaP/i/G975FNrn9sk/rS4exBSIwmr6Gc2uc2xDdBkyGMSUaPRGla08xUuD7BW32BCQjxp4YWvJiF5tN2Dv85ss+nZcCI4YIjMoDLlcf0bklIXT40DEuD3l09i/pfcvl6gNUA9PyDlEis9E9ynyKqqdzS5abR+zPvsLu5EFSWw4IuSm4V73BD+Z/iYueLnaMjEU1EqLDq6OJNTkZ1mQp/VsK+tjTaUcuShtbspBx3D7iDfcOv3fwb77s0/rSYUTYyZOSqg+eoDEpJRU6PBMtsWIQ0oRnu5IICBEIGvAamNmK0uScdmv2t969Q4KqctzWfLC+JKoSNWLEkIvhdjm6JimNCPdHU24VIz5r1py1DYu+Y+17vCqFGD6pl0AiPe+NhnVfAyz6nj89v+Ck7Zhklr+zv09hDZ/VDY/rBmOEyhg+revt9ZtzWJYYSfT50K69vw1Ka/h7h5Ofuc80tzwYFzyq++u7/f64YKcY3oTcR+XTReT/+Szw6TLSeJgWQuuSJVVmlMYJ4xyON5+fsHNj/wf1Ruk7GI0F5xXvoWuVEJSyFJyDLIe2EYwVdvbk+j6eXyqHh89Cdm6QYHKhOhSqQ2jP4rNwHIFiT0AF38RrgvLqO7dSih3FDpw4h60v5V4kPnHEeUgE5Xb6rH1E18ATh574FJ5jQT3oMhBPPOaNAnOYIQP1TNVlh142aFAoDLrukb0KnaeVCHUhqfF3UlWIqhIfLog/ukRrh8xKzFcPMLenr7RP6g1J+VMgIm8Bfwf4E+DvA/+ZiPxHwJ+R1JaXn/M3fwj8IcAbb7zxpR3r50G9Jy7WhIsF2m5XvXfGsNok+a+QCEq5qjEjvV55UpptubePRB8Y5mMA9PKYePH4uS2SfMAOXkP27qHmr8H3qfS7XiR15ZXaxUd0vJNKlU8+Tgng42GWQ+XTu5S3v0n9V/+E4JoUgKMb8B1KpJjeI/gGnOI3J4RuTmjnSXEpDsFDPibUp3SXHw6SpATwbkMMPcE3aOwJoYPQIWIQkyGSE1yPZ41IhnMrrKkQEZxbEWMPCxksSTkd3WO+/hgRQ9RAZkrQiKIsN4+ou3NcqDGSSLfTbkGeVVT5HiIZRTZBEFzouL33DUbl8BSAAB+uvo/XnsPyiC40+OhoaMAIWcwxWBQlxpDIN/V0oSZowKgBEwkExmbCrfIOlR39zf/pbxgKkwHCrWLKSbsk8OJEu4ue0tjt4pYSAdkmUAMI5jqJeukalBRgspsPqy0f1itq7/AxctLVNCGwkxcclSOetjUH5YjD8lmblNYyyXJya5nYnMu+JapCZvBROe0b3lteDo6kdDHyvz85ZuFSHV4dAv/Lo894Zzpl6R0bH7jsOtoYmNiMwlr6qARV7o1G3K7KQYa7/LLxxrTkzihn4yPjzFDZYY6+P5pH3juPnDdKjFC7dK2BkBslt3JtXaXAJIfNcyWkAhyOb67Hftsmxgh7e4JzynIBkzGEKClPdZVSv4v82UKDRsUFWCwjkxtvz2uoKqEBBEwGdiSMJ6BRsCVogPWjgG9S8rdkYHPBFlulZX9DUl5Bdiyce6QSOLBb4ZQmSsIoOk+jIikMHGXomUv1roBuArGNmDcLZGAl4LruiU9XqIuJx/ER7Tzm1hiqDPoAVpDd8vp+jp/MCe+doYsWjQrLFm0d8rsg915dO7obkvJzICJT4H8G/nNVXYrIfw/8Y1Jf+I+B/xr4j3/871T1j4A/Avj93//9z1/a+xIQLxfEDx4Sjk/h4RPo/VUd6HaHmMhIBfi8w9Skv7Y2cXIPH8OtYZU0XkGXp88+uBZdnKKr80To9k0KfslytG+gMYmwRK/DYGhW6PoSLUp0sjtYktKtj2mP/wJinwKHJMNWh6i6lNhY7RFXNf38Q3xzgYYW1YAGh+RV6rRiQIrptcflEJHlExZn32V9+QNku3TgfU1VHVGMDwmhoVfFuUVaf/A9MTMQA8E3WFsmglPj4ErmAXYnDxCE5eYRIfZE7fGhg9hSdxf4kDpwF9fbNZuezoHzmy0hmRYgDmZf5Xuf/FNeP/oD7uz/3ss+rS8Va7fk+4vvcNI84bI/S2VNsUFiQ2lHiDF4dbjYYk1BIUJlKlxsCerxOFZhhVPHJJsxy4fZt5z3G94a36L2Le+vn17bNVzBEYhReVbsfaWlvNJYpcCYZWgY+ZydvOLbey93cfRl4Gm7obQZ90ZTjtsauw1kenO8gxHhabt5gaQEGNucr832aYPntE8lUpXJmGQ5lc047Ybn7flZXV8TlFdYOscnmzW5sZz3PY/rhohyq1D2RSjznI333B9VvDn52SrBG/z8KK2hHCg5CdB65bxRGg+ZEYpcKUO6rxEY5cJB9SzgZZwJb+0Jn62UZafkFu5NDNNiOPfvT0ORw+a5z8akfmS2Y3BOabdOGaNK8NvCurQ99Tr1Rjk9Cdw6NJhXWHH1ZSB0Sv00oh76VVJKlnuCZEKxB7lEhgoAACAASURBVPnEpN5a076+VkwhVM/lVtqba/IaOg+IFZhZ2Kqh44Un1qmiREuDZMC+hZFcE5RkJPWfgi7C4EjKOG/QiwZtU4PoFVG5W6Uk7ypRe1KmV41KfLKG8wbd9NtKWoiNJ+xWyO0J8or2Nzck5Y9BRHISQfk/qeo/BVDV4+e+/x+A//UlHd7fCHWe+PAxYb2BJ6fbwBwlOdOabUDO3/CQFPNMYZlnaByuafdVoJD2DfrZD9HgEmHWbtB6nrwpR7O0pBa2yashQHBpNBA84jt0dY7u3X2pp/KyoMHRX34A0SNZhdGYRkkmx5oxxex1MAWhvSR0a0K/Qn23XVXzqFUkzxBjKPbfRYphKVwAVAPN+jO823Bx8qe49gKMJa/2KLhFXsyY7H6FdvNkewlu0gqasVwtRKh6RCzV6DafvzgxDOxMXmdcHfH44jts2mNc2KCA8y0aPUEdMXqSk3dSsDlfX5fdlsUuPtRs2mM+PfmX7E7eoCp2X/JZfXk4bR+z7OZcdMd0sSMTi8nGWHIqWxE0oJR0wWLEkpHTa1KrRRRLup597PEa6MJP+gkOAVEjLgZm2YixLaiDS7bRz92bV0E5Vz32s28UT6D2HSNTUErG3WqPaoChWGFbCTKyGQdFxWqrqrzsO3bzgs+aVHM3ywrujyZsvOdRswKBr+8c0MfA0vV4jYRtfz/O8m2J/XAmlOHHuwRVGh841kAflI0P1MFjEbxGTrqOo6pklhfcqyqO25aLvqc0hnujEZPsZnrx8yCq8qRxLPpAaYX7o4JR9mpOGL8sRH1W4DUrlI0TICIiHI6Eo5EwyYVJnpK8788EI1BYpXFw3kQu6shuZdivhNsTIRsowTaeCJs6lXxDateqgiwTVMHmiVSbTIT1GhaLSLNRxMLenqEsha6D9UrZ2R1mG16hOU0EZXCK30D0SmiUbCr4RsjHQr9IHp7je4auTJ6UbgO2gnwmNyrK57GlFiQTmBni5bbsW4FcIN9OwRcR2dMUrNNF2LFoHZGxuf6NIUFX/TVBCSDWoF0guoDZ9KiPmFmFToskq/ARPdkQV12qpDXbUaWLqWy8C6ktX0HcjCKegyRd7P8IfF9V/5vntt/b+lUC/PvAX7+M4/u50LTQOTibQ90mKXC8Gn1e3c0/g6C4mhEZSUtwZYG8dudXe8yvMqYHxCcfwsVjYrMk3dg9aixSTlGNSeFXjtDs+XChLSksBvoGXP8sVX1gCN0SNJJN7yOXH6IxEH2TlHzFDBXoLn6IWx8T3RJRRWMAYzDVLnZ8CzE5s7f/HfLpXfLJ8K7Hxfl3cf2CZvOIEDpC7MnMGMFgturIojigq09T22ERyTDWYLMRJhtRVrcpJ/epxncRGZ7H1fPwoaNuz8nsCDA4v8JKRhDF+xXPPyONZFhbbUkLyO04kZbG4nzNfP0Jdw+GUz4fYqDXjjY0BA2wLUHOTYERy63yDk2oaUzNql8QpaOPPV57jEJmCgopmOY77BdH/GDxF7w9+xqjbFhKrMJYPq7PCRqxYl9QTP440hQ94fkxuSeyDi0r37GTPyvtGRKOyhHHbQ2agnKaEBgZy5N2xXcXHd/cO2Led8z7jh+uLjBbLeqi72hDoDCWqEoplpHNMQgja7EDU5q/Nh4xsoZm61M+d45eI5laTruWPkYsgtPIRdczzXIuug4rhv/z+JSdIieq4lU5aVq+dbB/Q1Ru0ce4fUa+eH/6qPz1vGbjn93VZ63nWwfjwZZ5A4xzYa8SjjfKIoAVZZYLVaa8PhO+edswLVIJc5VBJspHl/DeRWTVK2d16q2Pxspbu4ZFJ3z9lhnk81FEODoyNA14r5SF4fTUs14FmjZ9r6rEAFmu7OwYoo/kJYzHz/wpu264C9sAGpS4JXpjr1uiUlP+bCaEVqmPn+udFfKdlKeqvVLdhmI23Hv6czEWdKlJFTmx0GtSTEIKzhFJPpVjgdsZXHQwMkgEvQgQFPNgeKG+YuQnx4qzAqMK1iBFhuYGebxCX5uhjxZpdeLK0i8mexusIJVN5eGvaMDOzQjiRfx94D8E/rWI/MV22z8C/gMR+TdIM9ePgf/05Rzez4EsI5zP4fQCnH+OoPw5oSRi0wJlgf3m17F3j34VR/rKQ12Pthv06YfoxRPomqRM05iUlHmxDSFq0PEMqgnEKhGScRs12G7QdgM7t5GBDthNVhF9i988JfQrwuaEGJqU1H3rdyj33qZ+/K/Q0HPt+ZnlCBYxgmQVWTmD6Mh3XiPfefCyT+lLhesXuH6BaqTdHG8T0SH4GkSw+RhjKhaX38f1lzT1U0SEqA5jCozJcN2czeIjuuaEvjkFY9jZ/8YgB+0AZ4vvE2JH213SuRWqyZ7Bh35b0J0Gm0KGSEZmCzJT0vsNoM+RvELnli/rNF4KRIT9/IiP4vdpQ/JCtGIZ2UQyuujITE6uGWKUEB1BfbLpJRBDg2SCU8dZ+4R7o9c5aR/z5vSrL/nMvlycdmuOihl/fP5D+uh/KkF5FZnzeYiAj4G53yRfxQHi9dGU91eXvLe6YNF3bELPAsNHmwW5GI6qMbOs4Kxv+OHqgrFNZFobAxvf86TZUBhzXdr99mSXwlhUh5VqW1nLP7xzh39xdsZF19PHyO/u7nDa9ex6z3nXk5mkvlp5jzWGtfe8aQ2PmoYd79h4T/LxF6rM8u2DYXr2XqGPkR8uWhYuIMBBmfHurMIKfLTu+GTd8dGqo7KG18Y5hTV4VZ42jrem5cs+/JeKr+wZWhd5/zKpI0dZUk8GhPcvlUkOF43ilG1gVrJnaJxeq4I3PSy65F+56GCvernn9LIgIozHqYz7g/c9pyeB1So933ZmUJSGpk5OYLfvwHQnkWldC+OxYoxgs+E8Cz8XJrmgaUgkUWzj1hntWbv4Wslnglsqbq3EXvFt8qBsT5XYR6pbN0Sl9kp87KCN6DISL3xSTgrIvkVUoI1pgJML5k6OEUEfFOhlAKeJo7CSCMyBQQ5GyKpLpdsKFDaVeRdZet1CW4c+XYOPyO0J1D1cbMVruUFujWF/jOSv7jU5TNbkp0BV/5jPr+/5Z1/2sXxR6GoNXfesVuJvCyEpKCcjeHAP82CYJcoAevoJsVmhzSbF33kHrn0WlGNzKEZQVqm8uxxBs0rfK4nALCrIKzCCDizQ4AqSjwndgu7yQ9Q1SDFFXIYdHyLWIrZATEH0NRp6NEY0gorDMCIfH5HP7mLKGbbcGdTEESCGVK7oXc1m9RAxBSItxlYp1Tt4TDbFGIsxGUWxRwwNtpimERWCYjA2pxwdEkPLxfGfUZT7jCb3X+7JvQQ433Cx+og8n0J7Qm5Logacb7A2w5gZMXZEDYgk8iIzIzJb4kJD79ZkdkyuMB4dUWbDsh/YLW6xcBfkkhNNJKhHEDZhzVFxn5WfU9oRMUYsGV4dVnJKa2jDhjTyTAoEJXLRn+BeiMEcBvroeVif08dEYFggfIHfcRpYuoZPNufEGDHm1R1w/irwcb1kJy95MJ5x0bWsPPQxkIthEzx/PT/lo/Ulhc1ovacxHmMMnXcsfU8fPUfljNdHU6os5241AYSgSjawvubeqOIf3rnNo03Dw3rDOMtwClObMcsbYlRW3jPWtO9RVfG4SSrLxntG24XYPirvL9d8c2+PbGDX4/P4YNmxcOmuVuC88xSmo7SGp42j34oI2hB5VDu+MkvEZBcGWMP4Y6gywYqwXwkHlSIx8ngtnNTKazPhswCNh6Ox4CKcbpRxDsV2VhsVVr1y0cBuqbg4LPuGz8OnDwNnp5EQBBElBmW5hKM7V0QvbNYp9bt32wWybZbqbDbsthMRyj2hPVdsxfWlZKukstSYQnHKzNCvlBgi/VyJHkAJTujnii0SkTlkxCeJoAQS4ZgLMjPIriV+5tBaYWagA0qQBznUihSC3MmuXevkqsRkYIVhslchyxHMikSUG0FdvCYokyWdR3sPyy0flBnk3gytMgiKzErs4RizU76yKkq4ISl/46CnlzAeb6W9X+QHSArMrkfmK/T9h+jX30bKYUmqNXhivYKTT8A1KbXbdUlNeUVSZh66DdQ55CNQty3v7pKSshxBliPBg82QyTBDc6LbgC0I3ZIYOtT3qAZit8QtHiLlHv38Q4hK9CmtOj15M7Rf019+iFhLdfB1fH1ONh6Wsjcv94ihYzP/YVKgGoPNKmw2I8vHRN8Q/Ia+PcX1SzRGvG9ADNbmeN9is3EKigkOY3NiaGg2jwdJUjb9BUU2wUhGme8QoqdzKxDBYEEiuZ3howOUUXnA4e5v07RnKdFRcnyoEdnl7v63GFXDUgvtFQeAMsv2yENNE2tC9IQQeNo/ovZrQBnbCV3ocNoTNWJNhohFNCVTT7MZe8UtGl9T2WGVegNMs4of1aec92ta/SL0ZEJA8Rr50eaUf378r/n37n3rl3iUrzaiKhd9ClIzCBeuZel7uhggKnXwLHpDQJllObnJqIwBgXnfpnRqIj/aLMiM4ZAxS9/z2ng6SHLtveWK875HVbnoexbObUuUDfeqESuXFJRGhMMyEWpWhLXzHFYvKv9GmWXhHLfKYSoCoyrz3v/E9vPOX4fijK25LrftQqQPkcIa9subqdmTdeT9eaTz0Hp4vAIRxQi4oGRWmBZCF1JORGZTuve0SLZrG6eM8/T9p0vlK8PMZ3sBlxdK1yk+PR5xWxt955QsE4JPpbd5DmUphAi7u4bpVMjyYRNrAMWewRRJJTl9M/lShh5Co5gCYic0p5BNlfZYiF4xmRAdtKcwuQduM2ySUp1Cs816iEC3rVjoEhFs7ufEUw+twgTkwCIbhZLr4JxrJ5aReUHJOhRIbjFv7KKLFlxExjlqBH2ySgTlRYN2Hl33aGaQPsCsxBiD3p9B7TGv7WBuT5Dd6pUW/tz0hL9pyCys1unVyBczlY0KTYdezlF9HT2fI/dv/9IP9ZXGC16SNgXktJukpAwhGY3EkJZyxECzSK9K6rUgqdjyHC0niBhkPJxwjechYonNBSYriWJQm2M0EPsVISvpnn4Hk42IeUD6VfL+00iafivqW2K7BDGIGdiSGWBMjrVjVIQsn+H7JUW5j5gckVSW2DfnqAb6dpFUgKHD+w3WFIixiMkJrn7OF1UwZniTxxB65quPWTdPcG6D8xsMhhh7YvTJkvcqOAwwYhkXtxgXB6g6diYP6NwS3V6bADvj11/iGX35sJJxNLrLyi3orhK71aPaY50h4jFi6UPHZpviLSh5LLAmY5SNKO2I0o6xkjHJZ9yuhkeWz7KKCAQNSIxf2P9dgMrm5MbyqJnzsD7njfGtX+KRvroQEkl22jd8uJ7TeE/nA210xK1/YkiyfJbak4tnCXQaiTGgCKW1KMrCdRTGUpld3p0Oj9FYOsd5n8YuIsL90YhPNht6HzntO7wqhSSFqYuR71xc4mPEGsNro4rTtmWcZczynJ0sY78osK/w5OdXjWTtLtfBTlewItftYgRKAx+tOk67wHfnNZUx7JWWB+OCt2YV7+5U3K5eXaXLrwI+Kk/XihVhr4L3z1NZt/PbaY2mfQqjSGWTqE1TIvhZDZ2HSQE7k+RveTiG80a5Pby1sBcQNVLXivdJxxJTUQPrpTLdSSE7AEh6f+vQUNykUb+AbCxkY6G8JTRPI+tPAtGnNG/ZAfVKcxbxTfKutIViJwa/idRPlWwsiEB5SzADLKFXVeJlgCaiAroKyWMyrYXBjk3z6FLQeUAXEe87xIJ4QaMipUGmBvvbJRp0sESlHozQDy8JH16kjVUGuYHOp8TvfLsI1jhoHVQ5cn9G9u3byK+J98UNSfkbBrl3BN/5XoqY/0UsqmKETU1cbzB7w1MAijFINUnBOK4Bm6VRp3eJQJNtUrpKUlaKPNsOwNZPo++hXYPNX+nVil8lTD5KZSMRgnfJU1E9mBwxJeo7zOgA2nlqZ78liUyJkYwYHaFfE0NPNr33sk/npcBkBXu3fg80EsoDYuxw/RqNDpuPaDaPCaEhakDjdnk8OiIGIwbfr+jF0HeXZNmU0fQ+49mwvD0BThffI6i7yjvHhw4QRuUtOrckhDaR6tFhTc64OqKqDmj7OVVxQGYrrMnwoSXPxozK/URqDggiwjf3/4APl99DjGDEEugQgUgkaqQPHYqmcnmu2keY2KRg3c1vcau8zU5xwNd2v0lhh6XUh1Tu/c7kiIvuSsX7xcq9DQaNSrFN9r7oN8MhKUWoTMaTZkMdPLkYVCN9UDJJ1STGGAKpPswYoQuBxvcUJiMTwaliRMnEcLsa8wcH9xhnwyKFAGr/oupvnGXkxjAqLbMiY+E8l32HD5GnbYeQQnJKhS5G9suCwhgOypy71Yixtezmw2vH5zHJDJ9ueiaZwQrUIbJXWArgs01PGyLddoFCFVrVRBZ3nmUf6GIi4/J9GZS6svVp+LxbpkyHtHCdyMkq2w6tA6x6uE/kdKM8WiZicpLB5TbgxBrYL6GyQvuTotbBQYzget0+DdM0zxgoSmU0Mhgj7Owo+weWPBPs8DQBPzeMFcb3DetPArZM3pTRK/VJxK0hK5JOxTegMRIK0CAUuzb5VTpl8voAG/gk+b+rU7SPxHOfypV3BZ72xCeAAdOn17iKidA0AlODtmn0LruGeJL+VgYYngMQ3zsnPlwkcjezsGlgnCEHFeIielajl00qjR8XUKaQHJXkVyl93Pp6vrr8xHB6vYFAphPYm8KTk6T4+0XgPProGH0wPJULAHfegvf+FJDkIN23XDO/+lzWatC0vHtl4HLNDsctQZmhTz8iLM6wu4df8km8XKhG6sf/Cr96jIhC7FC/ScpSVdStiRrwl3OIPep7uBpCSSTGDvEQ6gsgYvLxSz6jl4Msm7Kef4AxOYGGZv0Y7zbkxS4hNni/3ibNxzT6VEE1oltVpTEFXXTI4iNG49vsHHyNohyWWqj3Gzq3oncrYnS0boGIpXMLqmKXSXmI8zWd32CsYTa6y87kdUbFPr1b0/kVnVsQtv6JvV9j7fDUqAD3x2/xxuS3+HD9PQyGUgoiSh8TOenZpn5rJCMnElGJ9NpzkO+yU+xyf/wW7+x8g2/sfvtln85LwSyr+O3ZPX6wesJ5nxS94QvoKT2RuW/4cHPCQTHhbjUsxX4uBtGU1r3wHU0M9NHTkZRsV02aWYsRg6pPpdwCQRWRFLQRY2Qny7lVDtM7evpjxGwbAn1UblUFT9qO0lqCPksDViATIbc2lYArHJYlISp3q4oHk/ErO/H5VcNH5bvzhpXzBFU+3nQQlYMq409P13xa94ys4UntmBWW1iuFEU47T+OVKhMeNY5744K1jxy3blAk5ShLc+dJITxZKz6mBZzCJKJSFQqbCMnHa+UyVT2ycdCF9NoHWPfK03Xk9R3lnf0BEkLPoesivoeiSNoJ7xNBmZy8BNfDdKrEKKxXAApz5eDAMB5gOMnPg+hSsrfRVPLdXkT6eRqGqwOTpami7yGrUnvXTyKjOwIIoVNsOZy2Va/oOqSpchvRU58ISAXmEI2yHToSxxbJSN6VyrbCMyKZpBTwCNQR3UTUKTIwO4I4b4jvnUGISTXZx/TgXG09KHOLnjewtRzRzCBR0aiE986g8cisQKYFFBnm9Z0XQndeFQyn1xsI9HLBteHIL4oI1A060NRQ1gtiu0pl3t6lHuZKMfkCtobcL2xPGcHEkEJ1UOKP/gLzrX97UAN3v35Ke/LXQCS4OqnONKIxInlOdBukmBL8ZSqhv/ZSjandTAbRowL1Z39Cefg7lLvDUwBmxYy2fsJm9SltfUrfHmMkBzKcuyT4bqvkzbZ+3gpcqamVGNtUKq8RY0esFz+irY+pxnde4ll9yVAlhJ4n539B21+wbp7SdHN6v6btFoxHtymzMSIZxgjT6g5GMpxraPo5TXdGjA5jSowY9qZv0XZzVOPg1JSIsFPuMmlm+NzR+DUuOvpYp5L5tBOgeFx6pwYXOxbugv3yiNcnb/Pu7BuDC3q5wiyveGtyyGvVHsftktp3uC/4W5HIvK+pQ89ROfulHuerjpXvr80XfIx4jSm3DkFIwUJBFTwUeU5lM0osPkbitise25yvzHbZL0bMXceRHd5i2DTPKIzw/mpNjMpJ23LSdTyuN2ycxwGLPnlUFsZQWHOtyALIrOHBZMzYWt6ZDStM7MfxWd2z9gER4aDM6KOy6ANR4bPaESI0RHYLm8pugSbGVH67hSpcdg6dVr9QUdSvI6wRXp8Jf/o40gZlUsCiT6G0QdNcfFok/qLr0zavQEiJ3krypTTAuk9E5lf3lXWvTAdavrxYKDFCnsu1VU2MScvinNJ1kRiFvIXpNDIaJeX5/DIyGplBzVv+JvTLyPLDQDeP+EZRr/hGUol3mu4ldXRIUxhJFvtED/1KkTyQVcMkzbWN6GUkzgPU2xvXaVqBCNskHJVEQM4MOCAHrKBeoUvz7TgOmMOBtmGIxNNNeu8CuuzSKk1jkWmRGInzOq3k9B41wKJDM4FxgVy2icq4zDBv7aXx+UWN3H71+u0bkvI3DPFiDo9PeWG084V/LIIqMsCkQW03xO//i2ceftE9W8L93D94/sOWoMSk8m+x6XcujxPZmQ9Hmu7rM9zyIaFbQejR0G+vTUFjADHEZp6UlbZM6j+xiaDUNCLVIofo6c7fZ/XBP6f8u3/4sk/rS8dm9SO69pxm9SnOrQi+JdLiXOqonil4A6rbcm/geVPaEHv65oysmAGRZvVoUCRlkU85W77PYvOQ3q1ZNZ8RYqKFer/GrTdU+QFFPoYgrNtjrCnY1CdU5QEgtG6DlZ6D3a+QZ1MW9afcDd8kz4ZFalR2xEnzhKW7IGikV0dQh8FQ2RE+ejptUSJKRJD0GoWOlqf1Q/7vJ/8btd/wB0f/FsVAFanvTm/zD279Fn8+/4TkkPjFXVquSp9r3zPLfz38hn4ZqGzGSbth4Tp8CIQQCCgWxeszsjJqxEWlshavyn5Z0oSkMnhzvMtvTffZyQuWvueIYd3PQPKfjMpRUfDHp2esvGftPFEj572jtBZQpmpZhcDdrCITuQ5WvT9KCtSjapj38vNYukAf4vVQ8LR1GIHTJtL4SGZSKMnYGuoQKZKwFzHbSZlCaYWgMM4MRwNSUV7hcGwI6tGoGOCggPMuEY9GkmLSu+Q/KcK12rIP6XsrMM5S2I7fhu8MmaRsG8V5TXoK3VrrR8jy1I7zVsmscnhkWK+Uuoa9vURO9n1K/B46NCrtZeT8Lx1+LcQYcUuSrUiWEr516/MpklytfEgqSiGRlhrArxQ1DEpFCUl1qk7Rp30KzwmkgBwPZHo9bcZu32dAKYmk9JrSvqOmz8uAFoJ+RQanoqT1iAK5oBcOared/kV0nCOthyrHHE0IT9dI7VAC0ke0rdGoz2aIFrI395Hm1fTDGF7P9xsOXa6h6355P9h0hPWGoa1XxPNH4B2SFeholpK9+/anzyBfUFFun7TGJI/F6S6U4xSokw3rltPoQBW3eUrslkTfXrdV8k6MaHCIySErkxydkGolrhSqvkGNEDTglp/i20uyav9lntaXClVls/iI1eX7NPXxtm3ic5eiYRshwdZz4PN+Bbap331zjjH5C78wBHRuSdcvabpzmu7ymqCEpPdTNTT9BUqgzHf+f/buPM6yu6z3/efZQw1dPaWT7iTdmeiQCRAQGwSZcpODA4IBhevwEpQjcsxLNKggqNyjHofD4AGPBw2Hy6hX4Xougyh4EBESQKYkJNChM3amTs8173FNz/1jrequdKq6a1ir9q69vu/Xq19Ve/qtXz299t5rPev3+z1MNx4hTkLcA2Y7h7Np3mmCvRtOMTayne1bnoJRvpGAM8Ek24a382jrQWa6xwniNpHHaVIyTrLRVcmJERsnRm4Q0U1iJoJjhDMhiUXsGD2fK7aUpyL1fLdMPMhXJ+6nHYeLvnOXokpacbmbRAxXy/Uds6U+zIZqnTBOaCTpiD8HOieekUY18oQw7LDB6+DQjWMCj6mYMdRp8KVjB7hww2Y2legi4pzEnUPtDs0o4rbJSe5rNGhFMYk7tYqlBXLMGKtWiQxGqxWCJGH78DBbh+qcOzLChRs2cN7oCLtGyzldfk43TnikGfBos8uhdshUkI7kbcUJ5wxVaUSOGWypV9k6XKEawli1RivqUrOEGCdxZ6RqmBlnD1U5p2SFcwAm2wnjbZjsGo0IOolTq6QjJNsR1LPDnQgYNfBKWlgnzsYSbBwCKukAIwOOtdNp9GUTx874eML4sYQodJoNCMJ0khecLKDj2Zqfx48nxJERx86R4YSxjRXMnO07rNTTvoOZhO64M/NgRPPhdNosnDz18yRNjs8tLn3ilNCySWIJJKFjVcAqjJxTzlh6K8Hbno6i7GZDThPS20N2In4kDtMJjJAmKrsOSVY1C8Pn6taOle/4m6E0I+PDtfTqTLpwb3plph3CdAdG69iFm6nMBmnMYk/PudshNKOTA65aIVGUUH9Wf85QLNfRbAkklUo+oyjnuMOBw/hTryjXcP8ggOFRCFrY6EY8jqA9u7w2KjXYeBa28Wwww7btKt200Nro2VCpknRnslGU2bd1ti+lSwlkoyo9waNueqnRKulPHDwiiQJqI6MQd4laZUtShnSahwmDKfCAxVMZ8yffLfJ4WgYTA2r1cpW6bHenmWk9Qr02Rrs7md07/7MyTdsGYQN3CONGVgH48ZNwu+EMtfoGploPkjxu+YfBF3nIhtqmLDVexdyoUklPsE8kJx+/n87VsA49oRHNcKh1gNuPf51LNz2ZWqVchyMHWpPcMvUgk2GLMIlYeX1viEmoV6pcunEHQyWL46b6EFvrQ/P2u8XFkF37SiusVrMLDDNRSC3oMlxpcbzTYjYMSpWsTLKK3ffPNjjeCQiShNgddyd2o1qxdNRkxdhYq7NlqMalGzdx6cYxLt+8iekg5M6ZGe6anmHnhlGeue0stg6Xc0Tl/bNd3bt2pwAAIABJREFURqsVZsOEQ+2QVpTQzmYjuTub6zU6cUIrSjjWjhiqVtg6XOPajUM80go41Eq/b7YP19i9aZTZ7Lkb+nC9sCIdbKRJydidIEpHVlVJk5HzR0vWSJdjq1VgZBi2GHTidARbHKbFc7YOG63AGatnF75LZHIiYWoyZnY2Zvw4hOHJ/IQZhAHU62ndDYBmEwzHPT0MHwqdRsOp1hKGhytUS1hJOQ6c1uF0andn3ImDbGSkZ8nJKtgQWTUiTh5W1oEIkm46a7lSS8etDG2D2nC53s8A3kygm2BVw81PHiKeqDebjZY0soRllpisVNLRlTVPp4LXDEZJR6yW7P0MaWVvtgyna1lULC2I08mW+Zvq4Nn5dTLeTpOYFcNHqjATp6Mu58qVGOmQ9AMzJM/0vhyMVq6j2RKoDA2R5JlM7IZ4p4s3O9jG8lwht7N2wOH7sU1n4902zE5libWlnkhmCaFtu7DN52DbzqOy45LiOtynamPnUhnaCFbF4/DEGoknvt2tjmXTyNyzdT19LtlWYX68bWgjNrw5LcBTIlHQSCt3z136Xszc/JLFnwA2TH1oCxs2XVyuiw5AGDaoVmrpiMhF//aE2LvEYZfTjWtLPKRKlYpVaQcT6RTxEtkytI3ZcJIgTosIUbH0YH3eiqinSxg5TtfbTHWO83DrPvbP7uPyLd+3Bj3vH/ubxzjUnuae2cO0k3AVKcr0QH3Yaly6YUdu/VsvttaHacQhVoF6pYp7Wj10oU9CB7pJTL1SISatBF6zCmEc00lialahHcdMBJ1SJSlrlQpVM5pRxEi1gntadCjBMXdCd7ok1LxC4DEzIWyu12nHMV85epw7p2c40u3gwNh0le9OTvOq3ZeUbuq3uzMVRAxVjYo5s2FMK0oIEmeoakyHCeeMGDtGhjnWDYlJ43zfbIfzNwyxqValWU8/ObcO19g6nJ42TgYRG2rl2R8hLT40XDPOGjFmOk47Smd8ZqsA0YnS6dxD1TQhWU+L1LKhDkeb6cCrkWr6eMXg3I1GIzDOKs9pDO7O4UMxk5MJx449NkGZPp4W0Om0YWQkHWE5N7IycagnUKsbnTZs3ux0Os7YWLmOGwGC6XQUZRKmw009OrFqVbp8fgI2At4lW4yS9PQl4sTYgSQEstF/YcOpDJUvYe6NGNtcwwnSRNncAuZVYIOlb/BqlrHsJCcfb4SQVfumQppY8wq+gXSUZQlVdmzERmp4NVvrwtPRkgDMdPDRGr5/Akbr2ZoYQCs6maAEThb7BQ434IL+K7qoJOWA8Yqd5gR8BZIYmi28ZFdxbcsO7LzdxHf8K4wfhOnjLD1BCScK6SQxtvlsKtt2YmfvKqq7fatSHyXuTJOETbLLXumVMGKI01Wm3Q1qQ1lOMp3K/BgeQRKQdGeoDG2kUivPemuQjns0q2VXDBdK/MzNNzlDEhOnUqnglh681of67wupSPX6GKMjO5icfZhqZYgo7izyzKUkwZ0w7jBc38xQydajBJjuTLFv6nYmukfSitTZmqiPXULgsRcZFhIkHaaDcR5tPMATNz+ZSolGmgdJxNHuDJNha9VLL8Q4kcc83Bnn2ezOqYfrQzeJCaOYiW6XdhSdKKKz6PM9IYwTKjBXXgyAdjsm8YTv23o29RIWc7pi8ya+cXyc2SjCDIIsRpEn1CvpohZRktCOjOF6lQPNJgfabQ51Okx1Aypm6b+KMR2E3HT0CK+46KJe/1lrysxIHA60AvY3A9pxOr63AkSxE+LMBhEbaxWGKhW6ccLDjYDxIMSPNxmqVthUq7B1qIq5kwDbh+vUSnZBEWC4Zmwecg7OpOfeQTZQKMqSQomnU7khzXF0E5idN+mhZrBpCGpVo2LphZxqyd7W7TbMzsYcPpRODltoSf25pGSrnSZz56Z+WyVNYIZBQqdSYXzccU+oVoyR0XLtj+Fsut5kHCTE2WSvUyfQhNOnvOjUFcCSdBSwx9lalU0Y2lxwx/uMV9K1odPp3JxY0Ys6ad5ii0HDYdrnjfabO2/MbldJDy1DsA2GbSzZmzrjM910+HM7Sj8U5xdLdtL7axUYb6UfoK1u+iF56mdADMQJcbNLPy4qUs7/3UHWaOVT2XtOthhrpd6Pu2+xfOY4dFrpepQrGecSRzB9BN+4jcquy7Fa+WLYOX4XSdimUqlj1Xp2+XHu26eSHQ0l6dFQ0OB0cbb6GEl7grB5bC263jeq1To2V3xpVSpZ4jwiChtUquVK9m7esJONI9sZqm8gjkNWexU7jrrUa6OMDm/Lp4PrRDfu8ImH3087aqb7pUOUTYmvUqHG/ArzpxcTY0mFY93DBIsmjQfTBaNbaUYdAo9yWR12Jupw18whksWKuw2oBxpTPNKeTat1n3HCdyohHeSSLSiSLvPg6QjK/Y1ptpWo8NCcrUNDnDsyzEilyki1wqZalboZ2+pVttbr7BoZZqxaZUO1SgLcOjHJ/Y0GzTCkkyTMhiHNKCKKE5pxxK3jk9lyLuXSjmNmw4iKG3HidGMn8oQou5ATJM6DjS6zYczRTkQjik5MCW+GEYfaIQ80urTihDB2jnVChks4xfasEWMmgK0j6bRvT9Jz8TBOk5ROeqqTJs/TWYztrEBOO4TA03P4kWq6rqrhbC7ZqKugmzA+/vgRlAupVeetTZlNaIoimJ2FTtcJugnuzvHxdG3LMqnUDXcnmM4mgM2fFzs32m/uy2SxU5gkTVAmDtWakQRF97oPxWANp7KlCmOVNOFYI41Z19MiOjP++NF+cwnNud8T0vPGLZWyDUYFwNshfqSBb6hni+7aY7N5UZKuS3lsFqbaMNtJCxUt9LY1wIxKM+rL72slKQdNo/n4SzyrtWmsdB8EPnUEf+i70Jha/lqUaQvp/8PUUZK7vkZ88L6+/AAoWnfiHmqj27CRLVmhnPnfPtk3j8dkZdsWbygJiZtHcTPCqQcK7XO/MatRqY1SqcwterNSIVHYIOxOUa2OEHYn8uriulCp1Ljk3BfS6U4TJx1YcELo0gVRA5IqQdTMp4PrxL0zd3Kw9TDTwThB0iUiPdqOidIiQid20TN/3lWsykh9hGqlRjtuFdfpPnTuyBZGrE6cUwGrKIk5FsyW7auag50mU1FAJRt5thJzMTMzjnbbzIQ5Fh9cJyaDgG3DwwxV0yrxI9UqBoQYXYdDnS7j3YDj3S6H2i0OdTpMBwGtOKabJESka82G2fqWVTOmgnKdiTejdCmBIHGqFU6sqRh6us6fQTq6MnFmw4jZKM4Ku1havDaGIHbaccI9Mx0Sdy7eOMx4tz8rrxbJgCRxHp6G2W46SrIVpcnHiHRm6NxoymwmLZFDmD3eDmGmDVPddM3VkboR5TmAYx3odp04WzNxUZZW+u52s3U843lTvhMIutBuOlNTzsGDCUHXabXKFcfaqFEdPjlZ6cSp3NyYi7mp32cKy9woypZjw+WKIQCtBDu3hm8yGK1kFa/I3rzAuJ/5sHzuCmMAfiTC2+VbE95nuyRTbezwbDb82U8mb510kd5WlCZ9g+zf6dQreCc8OV28jyhJOWDSdf1ybLBWgWoeo7jWF584iMcxtBurS/omMYw/SnLkQWhO5da/9cPS9SjDDkQLFX1ZamwdjzuEMwdIkscXMhlkVqmyYWwXlkdBDA8JujNE4Sxx1F59e+vMkcm9mFlaQX6VnIQjU7cRlSyOM8EEzXCWwEOSU44oQwIiD5dcBMbciZKYs4e3l2qqN6QnzrM5jh6NSBixeukuhg1XqiTutM+43MXiTs4kM2JPmAzKNap3zrFul5FqjQ3VKhUsTTg6DFeMVhQxE4Yc73aZ6gZpYtKhEycnBrlEDkEUESbOcKVSyqIGx9oBR9ohx7sRQZImicIkrWsQe1YZ3Z3pIGaiGzETxHQTTwuwkp2DJzATRBzthERJUsIoQiuIeWAqYaLjzAQnB6rNN39ezqkS4Hgbxtsw1XEqnnC0WbLPxmGjXrXTn8LNS0h6NpJyLkkJWQXlSpoHabWc6emkdINWhs5Ki7hEbSdqZ0u/zz/EmXvjLuGwx2MIAyecLte+eELkeOgwFUPTYe46lvvib+aFOFA3kvvKd0HRuzHJ8TbJVCd9Y7qf3PdWsluZnVw7o8+U66ygDEbq+Y6kjJK0slaJrkC6Z8Oi4xjCVZ6seALTx/DZ43inXCOuAKojZxHOPIyHDZb3DbSAJCJqHacyvDWXvq0XlUqdTWddTq2WlbNbFSdOOrSbB4mico1cS5KIQxO30Q1nyeNKjhPT6k7SDRur79w6MlbbSJh0F030Lm3CbSokoh01CJKATfVyva8PtCYYD/L7TkiAmahNsIpk3Xp06cYtJL6cvW5hCdCJI+pWoVrCNSm31Ou0o5ipIOBYt8tMGBAmCe0oYjYI6bifTKJxMkF06t5Wq1QYrlaYjmIaUblGAI7Vqtw72+XB2S6NIKYTO3OrbMdAO3YebQbMBhHdKEkTmDx2FuPc70HiPNLs8mAjYMdI+ZYKOthwjraciXY6MGglItJk22wAX3nUmer054l4UTZvqbB5a7Ym5woSi3FWJb3dhqDjRIEThTBcrnpYeJiORk3CtFL3qqrcAUkbZvYneInOqwGoG/H+AI7E6fTuuSHQbdLJdMsRg1UMny3XexrAa4Yfb0IrgHawulNrJy1WNFTF+rD2SP/1SFZneCjfkZQA7U46pLgkzCp4tQZTh1n1txFAHOD33pr7f8t6EXen8DCPhFi65lh9w9k5tLW+1OqbsbxGmiUBrcajJFG5RgsFUZNW9xiJ5zUF0cGM2dajObW3PgxXN1Cr5HPSnBBzpHOQGrXSjaS8a/ZIuoh8jo53Gxxpn7qC/2ALE+ec4dETK6GuRgWjnUTUSrYvQlpM7UCrxUPNFpNBl6kwJEgSOnHMZBgSZdPpTzd6DSB0p4px3ugwB9vlGmXeimJaYUzXnVaUMD8nNpfcbSQwE0F73szG+ceGCXPTSY2pIGGsllZeL5PEnXsnYyY76fTt1ejG6czHY01nsl2uo/BKxdi+3dLq0iv8qomz6t+zszA9DVEcMzRUrv0xmHHCVpq0XfUpYQxhE+JuTNgo1/7oSQKzMTTj9MNv/p+/3FBEkExG2IbyfVdbpZJW7G6Eq121KtXqwq5NOTSUv/L97w66MM5/1OPEdKlGUgJ4Zzbf6dmzE0QPfje/9taJYHI/cXcmnS+SA4vLNdV7zsTRW+h28ltDMoqazEx8L7f21oMkCU8uvp2Tum2k87iyjoNtc30reQYxSLocaj2cW3vrRSPu5J6kHA9mmV7t6P91phEFJB7lMgPRPWEqCGhF5fue+fbkFIc7bRphSCdOiP3kFNtTZzeeTuROJ46pWyUrFlMeUTY9Pkk4bQGr+bmOU591cmk7Z0PV6CZOtNIM0zrViZz7JtLTmdVKgDBKp9uXTRA4UxNJLmNM3NMK4TNTMDNTrve1J2n1bs/pq9UjiNrkMgZmXQnBu768L5TFOHAshovKN8rcgyir6p3T+zB0Kn04ihKUpBw8EwWse9juEB8vV5GNZPxgegkxtwYjOHgPXqKTH/cETwI8yVaLzqPNxAmbx3Npaz058vDniYIc39seMzN1b37trQNWqTI8tAXP8WsvTJrUq6O5tbceOM5wjpXhYyIebT1IWLJyl82gSzuHtVHn65IQlWzN3pluh7unJ+nkkDhv49SBexqTq+/YOvPlo8c40u7QjeMTlc/PNGpyIQnQiELunplh9LQVOwZP3SrUK0boyRlrFSxm7mWRp5XCp7tR2ZaEZ7ydcLSVXw4nIq0IvmWkXMm1VjPh6DGnlePKPhOTMDFetoyvEzSS/HbIGJIAamPlemPb3FD8PA71EsATrGy7IpA8OpsmKfMSO/GjKykQXDwlKQfNsQKSiXFMnHfF8H537FHynTfvaZm8ankO2s0q2NAWvJPjSLO4S6Xen8PSi9JqHKTdOki+l12rWMnOfOrVUSpWw3M5Qjpp5TWF16e6DZHkOrLHmQkn6ZasANE3p+4vpN2jQX8ebBbl65OHcn1HHw3azASdUo1ei5KEh5pNmlGcSyyDxJkOQmZLdFEW0lGQ5wzXCKLVlWZLSKc5TwcJDzW7WMlGpIZRQpDzcqbDVWiH5TrmabWc2Zl51ahzEEfQaJQsM+QQ5DxhJkmgUi/X/uhGWkw2r6/WDiSz5doXPXGSo7PpVZe8JJ5WGetDSlIOmk4Bla4cLK9hxetFEdOKkyS/dQXXieZDX2TVBXMeIyZqlGsNwKA7SZhz0SWzGpu2XJlrm/0uiFqMz9xDnhcf6tUhoqhc1QVnoimCnEc9duI2rbhchcUmg2IOCr1kpVePdtpEOb6nZ6OAg50WYYkuzAZJQpAkdHPKZsTAVBhyqGRrUoaexjHJpTBbWnB1Ooz57lS5lnCoVSq5zWScc3IafXlMTIQrXotyMWZQr5XrO2b2kYA452t/4QwkUXm+YwDiAwHkmeztQnKwXBfCiBOYyHGYOaRtjVTxPrwYVq6MSRkUNDrKw/J8EHgSw4YCRuuFHZISVfiO2lNEjcOQ50mzO92pB/CkPFVDRzfuAsv3/edJwvCGc3Jts9+NT99Doz2ea5uV6hAjQ+WqSn24+Qhxzu+/2WCSsGTTlIesVki7m2rlKr0a5HxBMQZumzjMSImmKo9WqzTC/N7TDnSiiOPdci3hECZOkPiKq1GfKgCSxDnQKlccd2w0qjmfysx24byx/jsJL9J4QSsjnXt+eT4bARoH8m8ziaAzXq4kZXIkgjyvtzgkj3bxToniWKvAbAHHyluH+3J2nZKUg2ZsQzHtluq73aA1k3+zjcncCyX0MwfiqEOul3w8Iu6Mk2vis8/VaqPpyt25Smi3DuXcZn9LPCLxfEc9JknMUH1jrm32u+logiDXI01oxg2OdQ7m2ma/Cwq60LJ9qFzLYRzp5D9a70i3RSfPNan7nAPDOZ+gODBUKdcpRs0qNMM4l4Krcw63wrLVrWSoWmHTUL5tOrB/ujzHjQCdAgbgViowOlqu93VOS+o/ts0kt3qi64cV8EF2KCrT6WCqiL/3rPzWmc9T2d4ig29DQTtavTxXzqxSgdkC1vYM2lRKlKSs1obzXQwHAMesjpVolEsUtiDX0x6AgPZMuSoq1yobyDuOrc4x3Eu2Jk4MSc7rcEaETOdYvX49mElyrGYwz3ClmBGa/apZQDIxAo50ivn/6UdhkhDn/F0dAWO18nxPA4xU4O6pfPebpsPBZrmme1fM2Jnztb8EODBTru/qkQJq+sUxTE6U5wIOgBcQRwOGtpUsuzZcwN/bAS/TIU+Y5J+5M2C8P493lKQcNI1GMe2W7Io4RSz4HkeluiIeRd30iCZntvH83NvsZ+1GESMenW63XBVsj898L/c2nZDjM+Wqkt6Icl5BPrNlZFsh7farok7z7m4cLqjl/lQpaB2lHcMFzUrpUxNBvqPMHXi0Ua4iTo+0QorI39w1Xa4kJcCxnFdGiinfCe+mTcUkwQ4fKtGJDDBUwI7jVQiLOZTqYwVcJAhyXw2rr3nNIMj5/efAoabWpJQ1cCjfNddOKGLeQD9rF5DsdaPSh2s+FMU8hjj/qzPtw9/Jvc1+FhR0JGOVkq1dFxRzpXC6+Ugh7faro50CFmgCNtXPKqTdsjnSKWCpkj5WVIGbSnm+qnF3mnlWC818d6agi+Z9ajaIch5jnpoIyjUC8Ggz5HABpxyNvE/u+9zZ5xTzITY5WZ4ZYQCFfKW2IAnLtT/ySAGfYzEkYXn2R8MgKiCO0x28D0dRKUkpS3PwWK97sGbck3RV49wbjrF6f677UIhKjSKunCXNYpIk/SouqApwtZbzok99bmxkRyHtJkm5TiBnwqlC2j1esjVSi9LslquicqugtSPvmi7P8gOtKKKIwSiPttpEeZcX7mNxQQnzWiGpz/71zUeLeU8/XLKRa91uMftNJ99B130vKKgAUWWo/5JChSro/VeqwjnuUMTf246hDy+GKUkpS9MsT1VqswqFzcuulmfxjOrQGIVUXErK9bFVHyqmMEtj6qFC2u1XI0NbCmm32y0madevttSKqQp/1/TthbRbNhNheb6rAWoFDXmslWjd45FqMX9rAH05hawoUVTMyXK7W54YAkwUdJ1lsmTJtfvuLqbdch2BU9jaLI2CkvF9q6D3XzhTojgWOcWj3n/v7P7rkfSnAqYC9bciPvQSKNN0byvoJC8o1+XwWkHVeoN2uaopF5IwBxrt8owyB5gpKCl7tK2RlHnolmxkby0p5ju1D4/XC1MpcM3xvAvy9LNmQe+9ghZx6ltFjRHolmjtOoDpglb+KGK2aRm1CxqhWTbWKk9+wgrMIVT6sNBdiQ7DZFXC8lyp8AJP8uLjjxbWdr/pHC/oMi4BSUFT/PpRZ7aoKtzlOmI/cOy2QtrtlCxpfl9jbyHtNoJyjUgtyqFWuQpizRaxGD/w3cnynEG2o+K+T7sFFM/rV0da5flbizRc0Jlppzz5cgCKOkwuqj5r2ZTsemJhvF6eN3aRMxMSTfeWdetoia7lFnilIo5LlBgaGiusaStRtfmZmXJNyy5KJygmedPulmskZZdiChC1k3KtpViUmW4x/z/9qqgxFJtr5VmaZbJV4HuvTGtSFpQwL52CzsPLsycWKyrRaUyRmuW5DlasqfLskFFQ3AXFqA8H/5TnTF9WZ7Y8l86KrHBlJfp2Hx47t7C2zcrz0dVuHe11FwZCs1VMIYy4oKRdv0oKOhHvdgso51pCjaIWfiqZHaPFrAXcj/Y3i/sMK9PotaREM46KdE+JxkRIecU65MnH4fJ87vp0gTtNu//iWJ4zfVmduDxHmnFY3EmeHy1PZerDt/9NYW0XOSW/30wcu6fXXRgIx5vf7nUX5DSOeFHLGpTLBEGvu7Bm4gKnEt94TzHLQ/SjblDciU8Ylmd//OdHynMxv0h3z/a6ByJroJjr5uUzVZ4x0kmtuLRd0oeLzSpJuQxm9qNmdreZ3Wdmb+l1f9ZUeXKUxU5PmjpSXNt9ptUqbo25pERJyqSjkZT5KNOHmMjgK3J9pk6B06r6zbGiKmwArW55kpSP6itGRGRtlWnafJELws7239UhJSmXyNJSxX8J/BjwJOBnzexJve2VFCFpFFh44MBdxbXdZzZtKKYqNQBJeabNQ7kKYYiILEWRaxO/7KInFtZ2v/nk8eKG9PzdAw8U1raIiJRcmWpXfvnBAtvuv9lMSlIu3bOA+9x9v7sHwMeA63rcJynClh3FtX3Fs4tru8+MXPDcwtq22mhhbfcbG7mi110YEMO97oCI5KhaYJLyqWefV1jb/eZl555TWNs/sfP8wtoWEZGS29XrDqyhpxf4fbqn/76rlaRcul3AI/NuH+CUt4aZvc7MbjGzW44dG6yKr8Nv/uVed2HNDA8Xl8wY/sEfK6ztfrPlvCcX1PIQlRJV977mpX9dUMvVgtrtT//nC/++kHavedo7Cmm3X/30edcX0u47nv53hbRbNl95/pt73YU1tZ2h3Ns8mxqXbt6We7v96jVPfWphbT/jwgsLa7vf3PyjV/W6CwPhbT/U6x4MhnMLus5yma6b52L363rdg8Ew+ivluaA48uSLimv7KZcU1vZKledMf/VsgfseswKNu7/P3fe4+57t27evUbceq5Bk4k9enX+bfW74N/NPDBXRZr974mu+DnmeRFbO4omvuTm/9taJPT/8mZxbHOXaV/57zm32ty0bz+Pqp/55rm0+84m/w6UXvDDXNvvdy6/8Ra4de2Wubf6ni/+Ii7Zemmub/e5rV/9u7m3+5cU/Q7VarosPn7r2p3ii5Tey/orqGJ++Nt/9ez348ouuWRdt9rv/58n5V4UvW/Lzou2j/GwBg3r+7rryzMABeNGPjGI5fx1Uh+AHn12uOH7fb+T/9275CRgbK1ccR/8o/2Si/d7W3NvsdyNvzf+co4g282BFLjw+SMzsOcAfuPuPZLd/B8Dd/+tCz9+zZ4/fcssta9hDERERERERERGR/mVmt7r7noUe00jKpfsWcJmZPcHMhoCfAT7d4z6JiIiIiIiIiIise7Ved2C9cPfIzF4PfI50MbcPuvudPe6WiIiIiIiIiIjIuqck5TK4+2eBz/a6HyIiIiIiIiIiIoNE071FRERERERERESkp5SkFBERERERERERkZ5SklJERERERERERER6SklKERERERERERER6SklKUVERERERERERKSnlKQUERERERERERGRnlKSUkRERERERERERHpKSUoRERERERERERHpKSUpRUREREREREREpKeUpBQREREREREREZGeUpJSREREREREREREekpJShEREREREREREekpc/de92Egmdkx4KFe9+MMzgGO97oTA0BxzIfimA/FMR+K4+ophvlQHPOhOOZDccyH4pgPxXH1FMN8KI75UBzzsR7ieLG7b1/oASUpS8zMbnH3Pb3ux3qnOOZDccyH4pgPxXH1FMN8KI75UBzzoTjmQ3HMh+K4eophPhTHfCiO+VjvcdR0bxEREREREREREekpJSlFRERERERERESkp5SkLLf39boDA0JxzIfimA/FMR+K4+ophvlQHPOhOOZDccyH4pgPxXH1FMN8KI75UBzzsa7jqDUpRUREREREREREpKc0klJERERERERERER6SknKPmJmF5rZF81sn5ndaWY3ZPdvM7PPm9m92c+zsvuvNLOvmVnXzN54Sls3mNnerJ03nGabHzSzo2a295T7X5m9NjGzRStDnaZv15nZd8zsdjO7xcyet5rYLMcgxXHe4880s9jMXrGSmKzEIMXRzK42s+lsf7zdzP7zamKzHIMUx+yxq7MY3mlmN600LssxSDE0szfN2w/3Zu/rbauJz1INWBy3mNk/mtkdWTuvWU1slmPA4niWmX3S0u/rb5rZU1YTm+VYp3Fc9Hlm9jtmdp+Z3W1mP7KSmKzEIMXRzM7O/pYwSzXtAAAgAElEQVSGmb1npTFZrgGL4YvM7FYz+27285qVxmW5BiyOz7KT39V3mNnLVxqX5RqkOM57/KLsff3GhV5fhEGKo5ldYmbtefvke1cal+UapDhmjz0169+dln5OjqwkLss1SHE0syEz+1AWvzvM7OoVhmVx7q5/ffIPOB94Rvb7JuAe4EnAO4C3ZPe/BXh79vsO4JnAnwBvnNfOU4C9wAagBvwrcNki23wB8Axg7yn3XwVcAXwJ2HOaPi/Wt42cXE7gqcBdiuPy45jdrgL/BnwWeIXiuKL98Wrgn9YqdgMcx63A94CL5vqqGC7/PT3vOS8F/k374or2xd+d9/t2YAIYUhyXHcd3Ar+f/X4l8AXtj6eN44LPy/p9BzAMPAG4H6gqjsuO4xjwPOBXgPdoX1xRDL8f2DmvP48qjiuK4wagNu/vOjp3W3FcehznPf5x4H/N75/iuKz98ZJT21QcVxTHGvAd4GnZ7bPRd/VK4virwIfm9fNWoJJnvDSSso+4+yF3vy37fRbYB+wCrgM+kj3tI8DLsuccdfdvAeEpTV0FfN3dW+4eATcBC14BdPebSU/uTr1/n7vfvYRuL9a3hmd7LulBpy/w2kIMUhwzv0b65X50Ce3kZgDj2BMDFsefAz7h7g/P9XUJba3agMVwvp8FPrqEtnIxYHF0YJOZGelFsQkgWkJ7qzZgcXwS8IWsrbuAS8zs3CW0t2rrMY6ned51wMfcvevuDwD3Ac86U3t5GKQ4unvT3b8CdM7URp4GLIbfdveD2c07gREzGz5Te3kYsDjObRtgBJ3HnKnPiz7PzF4G7CfdH9fMoMWxVwYsjj8MfMfd78ieN+7u8Znay8OAxXH+seNRYApYdETmSihJ2afM7BLSq6HfAM5190OQ7uCkGevT2Qu8wNJpMxuAFwMXFtTVRftmZi83s7uAzwD/saDtn9Z6j6OZ7SL94FmzYf0LWe9xzDwnG5L+z2b25IK2f1oDEMfLgbPM7EuWTiN7dUHbX9QAxBCAbPs/SnoBYs0NQBzfQ3qgdhD4LnCDuycF9WFRAxDHO4CfhHR6I3AxcEFBfVjUOorjYnYBj8y7fSC7b00NQBx7bsBi+FPAt929u9YbHoQ4mtkPmtmdpN8xvzIvabmWfbiEdRxHMxsD3gz84Vpud4F+XMI6jmPmCWb2bTO7ycye34PtD0IcLwfczD5nZreZ2W+v8faBgYjjHcB1ZlYzsycAP5B3H2p5Nib5MLONpCeub3D3mXSgyNK5+z4zezvweaBBuiOt+Reru38S+KSZvQD4I+A/rOX2BySOfw682d3j5fY/LwMSx9uAi929YWYvBj4FXLaWHRiQONZIv4iuBUaBr5nZ1939nrXY+IDEcM5Lga+6++OucBZtQOL4I8DtwDXApcDnzezL7j6zVh0YkDi+DfjvZnY76Yn4t9e6DwMSx4U6vWYjr2Bg4thTgxTD7GLs20lHDq31tgciju7+DeDJZnYV8BEz+2d3X7NRvgMSxz8E3p0df6/xplMDEsdDpEstjZvZDwCfMrMn65hn2WqkS4o8E2gBXzCzW939C2vVgQGJ4wdJBwrcAjwE/HvefdBIyj5jZnXSHfdv3f0T2d1HzOz87PG5dVFOy90/4O7PcPcXkA7zvdfSBVvnFtz9lRX270PZ6z+71L5lQ40vNbNzVrLNFfZzUOK4B/iYmT0IvAL4K0unTayJQYmju8+4eyP7/bNAXfvjY/q31P3xAPC/PZ2Sdxy4GXjaSra5gj4OSgzn/AxrONV7zgDF8TWkSw+4u98HPEC6puKaGJQ4Zp+Nr3H3pwOvJl3f84GVbHOF/VxvcVzMAR47iuAC0lG+a2KA4tgzgxRDM7sA+CTwane/fyXbW6lBiuO8vuwDmqRrwa2JAYrjDwLvyM5j3gD8rpm9fiXbXIlBiaOnS4mMZ7/fSrru8eUr2eZKDEocSb+rb3L34+7eIq338IyVbHMlBiWO7h65+2+4+9Pd/TrSmgX3rmSbi9FIyj5iZgZ8ANjn7u+a99CngV8gHfHwC8A/LKGtHe5+1MwuIp3K9Rx3nwSevpo+uvupFVQX7JuZPRG4393dzJ4BDAHjq9n2Ug1SHN39CfP68mHS4i+fWs22l2qQ4mhm5wFHsv3xWaQXaLQ/ZpYax+zne8ysRvqe/kHg3avZ9lIMWAwxsy3AC4GfX802l2vA4vgw6YjeL1u6huIVpGteFW6Q4mhmW4GWuwfAa4Gb12pkxjqN42I+Dfydmb0L2Ek6Uv+bq9n2Ug1YHHtikGKYvac/A/yOu391NdtcrgGL4xOAR9w9MrOLSb9jHlzNtpdqkOLo7iemJZvZHwANd3/Para9VIMURzPbDkx4OrNuN+l3jI55Msv4jvkc8NuWTpMOSI/FCz+PgcGKYxY/c/emmb0IiNz9e6vZ9kKd0b8++Uc6/NhJq07dnv17MWnlqS+QZqi/AGzLnn8e6RWBGdIFSw8Am7PHvkxahfcO4NrTbPOjpEPIw+z1v5Td//Lsdhc4Anxukdcv1rc3ky6QfDvwNeB5iuPy43jKcz7M2lb3Hpg4Aq/P9sc7gK8DP6Q4rmx/BN6U9WEv6VQFxXD5MfxF0iIba7IPDmIcSRNB/0I6RXkv8POK44ri+JzsvruATwBnKY6njeOizwN+j3R0y93AjymOK47jg6QjQxrZc56kGC49hsBbSUf93T7v3w7ti8uO46s4eR5zG/AyvadX9p6e95w/YG2rew9MHEnXl507j7kNeKniuOLvmJ/PYrkXeIfiuKL98RLSY519pNXFL847XpZtSERERERERERERKQntCaliIiIiIiIiIiI9JSSlCIiIiIiIiIiItJTSlKKiIiIiIiIiIhITylJKSIiIiIiIiIiIj2lJKWIiIiIiIiIiIj0lJKUIiIiIrLumFlsZreb2Z1mdoeZ/aaZnfbY1swuMbOfW6s+ioiIiMjSKUkpIiIiIutR292f7u5PBl4EvBj4/TO85hJASUoRERGRPmTu3us+iIiIiIgsi5k13H3jvNu7gW8B5wAXA38DjGUPv97d/93Mvg5cBTwAfAT4C+BtwNXAMPCX7v4/1+yPEBEREZETlKQUERERkXXn1CRldt8kcCUwCyTu3jGzy4CPuvseM7saeKO7vyR7/uuAHe7+x2Y2DHwVeKW7P7Cmf4yIiIiIUOt1B0REREREcmLZzzrwHjN7OhADly/y/B8Gnmpmr8hubwEuIx1pKSIiIiJrSElKEREREVn3suneMXCUdG3KI8DTSNdg7yz2MuDX3P1za9JJEREREVmUCueIiIiIyLpmZtuB9wLv8XQtoy3AIXdPgFcB1eyps8CmeS/9HHC9mdWzdi43szFEREREZM1pJKWIiIiIrEejZnY76dTuiLRQzruyx/4K+LiZvRL4ItDM7v8OEJnZHcCHgf9OWvH7NjMz4BjwsrX6A0RERETkJBXOERERERERERERkZ7SdG8RERERERERERHpKSUpRUREREREREREpKeUpBQREREREREREZGeUpJSREREREREREREekpJShEREREREREREekpJSlFRERERERERESkp5SkFBERERERERERkZ5SklJERERERERERER6SklKERERERERERER6SklKUVERERERERERKSnlKQUERERERERERGRnlKSUkRERERERERERHpKSUoRERERERERERHpKSUpRUREREREREREpKeUpBQREREREREREZGeUpJSREREREREREREekpJShEREREREREREekpJSlFRERERERERESkp5SkFBERERERERERkZ5SklJERERERERERER6SklKERERERERERER6SklKUVERERERERERKSnlKQUERERERERERGRnlKSUkRERERERERERHpKSUoRERER6Qkzu8LMvm1ms2b2673uTz8xs2Eza5jZzuz2x8zsrb3ul4iIiEhRlKQUERERKTEze9DMjpjZ2Lz7XmtmX1qDzf828CV33+Tuf3FKv+7MknQNM4vNrDPv9u+uQd8ws6+b2c+v4rWdLAE7Y2bfMrM3mll9Ka939667b3T3gyvZvoiIiMh6oySliIiIiNSAG3qw3YuBOxd6wN2fnCXpNgJfBl4/d9vd/3RNe7lyr3X3TcBO4HeAXwT+oac9EhEREelTSlKKiIiIyDuBN5rZ1oUeNLMfykYCTmc/f2ipDZvZT2SjIqfM7EtmdlV2/78B/wfwnmx05OXL6bCZHTazJ2e/v9bM3Mx2Z7dfb2Yfy36vmtn/ZWb7zey4mf3t/L/TzJ5vZt/I+nebmT03u/+/Ac8E3p/1779lbb3HzI5lsbjDzK44U1/dveHu/wpcB1xrZv8h28Zzs21Pm9lBM3u3mdWyx0ayv+mCBf72+8zsRfNuj2RtXLWcGIqIiIj0EyUpRUREROQW4EvAG099wMy2AZ8B/gI4G3gX8BkzO/tMjWaJx48CbwC2A58F/tHMhtz9Gh47QvKeZfb5ZuDq7PcXAPuBF867fVP2+5uAHwaeB1wAhMC7s/5dAnwK+D1gG/BW4FNmdpa7/xbwLdLRkBuz2y8BfgC4FDgL+Dlgcqkddvf7gTuA52d3hcDrs20/H3gp8NolNPXXwPxp6NcB97j7vqX2RURERKTfKEkpIiIiIgD/Gfg1M9t+yv0/Dtzr7n/j7pG7fxS4izShdiY/DXzG3T/v7iHwZ8AosOSRmKdxEyeTks8D3sbCScr/BLzF3Q+6ewf4Q+CnzcyAXwA+4e7/6u6Ju38W+B5pUnMhIbAZuBJwd7/T3Y8us98HSZOSuPs33f1b7h5nCcz3z/sbTuevgZeZ2Ybs9quAv1lmP0RERET6ipKUIiIiIoK77wX+CXjLKQ/tBB465b6HgF1LaPYxr3X3BHhkia89k5uAF5rZRUAT+ATwAjO7kvQYd1+WiLwQ+Gw2nXsK+Hb2+Nmka2L+/Nxj2eN7sn4v5J+BDwD/EzhiZn9lZhuX2e9dwASAmT3JzP45K1w0Q5ooPudMDbj7g9nfcV2WVL4G+Ngy+yEiIiLSV5SkFBEREZE5vw/8Mo9NIh4kTebNdxHw6BLae8xr5yUNl/LaM7kTqAK/Atzk7uNAA3g1cLNnsm1d4+5b5/0bcffjpAnT95/y2Ji7vzvbhs/fYNbku9z9+4GnAk9jGQWHsjUzn0o6zR3g/wZuAy51983AfwFsic19hHTK988A/7aCEZ0iIiIifUVJShEREREBwN3vA/5f4Nfn3f1Z4HIz+zkzq5nZTwNPIh11eSZ/D/y4mV1rZnXgt4Au8O859NVJ16V8PSendt90ym2A9wJvM7MLAcxsh5nNTVX/CPDKrH9VMxvNfj8ve/wIsHuuITN7tpntyYrbNIEAiM/UVzMbM7NrSNe//FJWRAdgEzDt7o2sCNAvLyME/x/pNPfrSad/i4iIiKxrSlKKiIiIyHz/BRibu5GNUHwJaYJxHPht4CXZSETM7L1m9t6FGnL3u0lH+/0P4DjpOpYvdfcgp77eRJrou3mR2wDvAP4V+DczmyVNkD4j699+4KdI16k8Tjo1/QZOHiO/G3i1mU2a2TuArcCHgSnSQj0PkRYUWsz7s20eJq2g/rc8di3P3wBea2YN4C9JE8RL4u6zwD+Sjnr99FJfJyIiItKvLL0ILSIiIiIi64mZ/Smww92XUhFcREREpK/Vet0BERERERFZnqxgzi8CL+txV0RERERyoeneIiIiIiLriJm9HngQ+F/u/s0ed0dEREQkF5ruLSIiIiIiIiIiIj2lkZQiIiIiIiIiIiLSU0pSioiIiIiIiIiISE+pcE5BzjnnHL/kkkt63Q0REREREREREZG+cOuttx539+0LPaYkZUEuueQSbrnlll53oxDXX389MzMz7Ny5s9ddERFZU7t37+b666/vdTdERERERETWJTN7aLHHlKSUZTty5AjtZotzvNrrroiIrJmHpyd63QUREREREZGBpSSlrMhwrcZbn//Dve6GiMia+eMv/0uvuyAiIiIiIjKwVDhHREREREREREREekpJShEREREREREREekpTfeWZet2uxAnve6GiIiIiIgMqBtvvBFABetEREpESUpZtiRJwL3X3RARERERkQG1f//+XndBRETWWGHTvc3sQjP7opntM7M7zeyG7P5tZvZ5M7s3+3lWdv+VZvY1M+ua2RtPaesGM9ubtfOG02zzg2Z21Mz2nnL/K7PXJma25zSvX6xv15nZd8zsdjO7xcyet5rYiIiIiIiIiIiIyElFrkkZAb/l7lcBzwZ+1cyeBLwF+IK7XwZ8IbsNMAH8OvBn8xsxs6cAvww8C3ga8BIzu2yRbX4Y+NEF7t8L/CRw8xn6vFjfvgA8zd2fDvxH4P1naEdERERERERERESWqLAkpbsfcvfbst9ngX3ALuA64CPZ0z4CvCx7zlF3/xYQntLUVcDX3b3l7hFwE/DyRbZ5M2my89T797n73Uvo9mJ9a7ifmN88Bmius4iIiIiIiIiISE7WZE1KM7sE+H7gG8C57n4I0kSmme04w8v3An9iZmcDbeDFwC0FdXXRvpnZy4H/CuwAfryg7YuISJ860pwluL/Bm970pl53RUREZODdf//9jIyM9LobIiKyhoqc7g2AmW0EPg68wd1nlvt6d98HvB34PPC/gTtIp5KvKXf/pLtfSTq68o8Weo6ZvS5bs/KWY8eOrW0HRURERERERERE1qlCR1KaWZ00Qfm37v6J7O4jZnZ+NlLxfODomdpx9w8AH8ja/FPggJldCPxj9pT3uvt7V9C/D5GO8Dzo7i9eSt/c/WYzu9TMznH346c89j7gfQB79uzRlHARkQFy7tgmhnadyzvf+c5ed0VERGTgaeaCiEj5FJakNDMjTSzuc/d3zXvo08AvAG/Lfv7DEtra4e5Hzewi0gI4z3H3SeDpq+mju7/mlLsW7JuZPRG4393dzJ4BDAHjq9m2iIiIiIiIiIiIpIocSflc4FXAd83s9uy+3yVNAP69mf0S8DDwSgAzO490rcnNQGJmbwCelE0R/3i2JmUI/GqWoHwcM/socDVwjpkdAH7f3T+QrSf5P4DtwGfM7HZ3/5EFmliwb8BPAa82s5B0XcyfnldIR0RERERERERERFahsCSlu38FsEUevnaB5x8GLlikrecvcZs/u8j9nwQ+uYTXjy/St7eTrospQKVSAU963Q0RERERERlQu3fv7nUXRERkja1JdW8ZLMPDwyTdoNfdEBERERGRAXX99df3ugsiIrLGCq/uLSIiIiIiIiIiInI6SlKKiIiIiIiIiIhIT2m6t6xIN4r44y//S6+7ISKyZh6enuCJu87tdTdEREREREQGkpKUsmznnnsuMzMzDO3UybqIlMcTd52rRfxFREREREQKoiSlLNuNN97Y6y6IiIiIiIiIiMgA0ZqUIiIiIiIiIiIi0lNKUoqIiIiIiIiIiEhPabq3LNuNN97I/v37e90NKamDBw8CsHPnzh73RMpo9+7dXH/99b3uhoiIiIiIyMBRklKWbf/+/dz7vb1ctGWk112REmpOdwDoeqvHPZGyeTjb90RERERERCR/SlLKily0ZYS3PPcJve6GlNDbvvoAgPY/WXNz+56IiIiIiIjkT2tSioiIiIiIiIiISE9pJKUs28GDB4mbQa+7ISIiIiIiA+rGG28E0FrQIiIloiSlLFu73SaJkl53Q0REREREBpQKdYqIlE9h073N7EIz+6KZ7TOzO83shuz+bWb2eTO7N/t5Vnb/lWb2NTPrmtkbT2nrBjPbm7XzhtNs84NmdtTM9p5y/yuz1yZmtuc0r1+wb/Mef6aZxWb2ipXERERERERERERERB6vyDUpI+C33P0q4NnAr5rZk4C3AF9w98uAL2S3ASaAXwf+bH4jZvYU4JeBZwFPA15iZpctss0PAz+6wP17gZ8Ebj5DnxfrG2ZWBd4OfO4MbYiIiIiIiIiIiMgyFJakdPdD7n5b9vsssA/YBVwHfCR72keAl2XPOeru3wLCU5q6Cvi6u7fcPQJuAl6+yDZvJk12nnr/Pne/ewndXrBvmV8DPg4cXUI7IiIiIiIiIiIiskRrsialmV0CfD/wDeBcdz8EaSLTzHac4eV7gT8xs7OBNvBi4JaCurpg38xsF2li9BrgmQVtW0RE+tjRZkBw//286U1v6nVXREREBt7999/PyMhIr7shIiJrqPAkpZltJB2B+AZ3nzGzZb3e3feZ2duBzwMN4A7SqeRr6c+BN7t7fLr+m9nrgNcBXHTRRWvUNRERERERERERkfWt0CSlmdVJE5R/6+6fyO4+YmbnZyMVz2cJ06fd/QPAB7I2/xQ4YGYXAv+YPeW97v7eFfTvQ6QjPA+6+4tP07c9wMeyBOU5wIvNLHL3T53Sz/cB7wPYs2ePL7c/IiLSv3aMDTG861Le+c539rorIiIiA08zF0REyqewJKWlGb0PAPvc/V3zHvo08AvA27Kf/7CEtna4+1Ezu4i0AM5z3H0SePpq+ujurznlrgX75u5PmNeXDwP/dGqCUkRERERERERERFamyOrezwVeBVxjZrdn/15MmgB8kZndC7wou42ZnWdmB4DfBN5qZgfMbHPW1sfN7HukIyd/NUtQPo6ZfRT4GnBF9vpfyu5/edb2c4DPmNliFboX7JuIiIiIiIiIiIgUp7CRlO7+FWCxBRyvXeD5h4ELFmnr+Uvc5s8ucv8ngU8u4fXjC/XtlOf84lL6IiIiIiIiIiIiIkuzJtW9ZbCMjo4Sx91ed0NERERERAbU7t27e90FERFZY0pSyrLt3LmT7qOtXndDREREREQG1PXXX9/rLoiIyBorck1KERERERERERERkTNSklJERERERERERER6StO9ZUUenu7wtq8+0OtuSAk9PN0B0P4na+7h6Q6X7ep1L0RERERERAaTkpSybFrEWnppzA4CMLxzZ497ImVz2S59/omIiIiIiBRFSUpZNi1iLSIiIiIiIiIiedKalCIiIiIiIiIiItJTGkkpy3bjjTeyf//+XndDSurgwXS6905N9xaREtm9e7dmMoiIiIjIQFOSUpZt//793L3vO5y3xXrdFSmh2SkHYJrxHvdERGRtHJ72XndBRERERKRwSlLKipy3xXjNC7X7yNr70E0RgPY/ESmNuc89EREREZFBpjUpRUREREREREREpKeUpBQREREREREREZGe0nxJWbaDBw/SbWh9LBERERERERGRvNx4440ApS2YqCSlLFu73SbS8lgiIiIiIiIiIrnZv39/r7vQU4VN9zazC83si2a2z8zuNLMbsvu3mdnnzeze7OdZ2f1XmtnXzKxrZm88pa0bzGxv1s4bTrPND5rZUTPbe8r9r8xem5j9/+zdf5Dd9X3f++eLWEY4GAJYElqQQtdWbBFuwemWwlBcrhlqRiHFomFu6JRQhgv1XvUa1Ua1k+s27b2xC5ZLbieeLiFXBtL40kmvUIHg1uVqbFR6gZo6whbeuGQ3jaNKaEHYLEKyLLHv+8f5koplV7sr6ew33vN8zOycPZ/z+Xy+L+3sSKP3fH5k4Cjjp8t2RZJXk2xvvv7R8fxsJEmSJEmSJP133TyT8jDwyapaDVwCrEtyPvBpYGtVrQK2Nu8BXgE+DnzhyEmSXADcClwMXAhck2TVNM+8H7h6ivYdwHXAthkyT5cN4D9U1UXN1/8+wzySJEmSJEmSZqlrRcqq2l1V32y+fw0YBs4BrgUeaLo9AHy06TNWVd8ADk2aajXwdFXtr6rDwBPA2mmeuY1OsXNy+3BVfXcWsafMJkmSJEmSJKl75uVMyiTnAR8EngGWVdVu6BQykyydYfgO4LNJzgIOAGuAZ7sU9WjZLk3yHLALuKOqnu9SBkmSpD/zyr7i5ZERNmzY0HYUSZIkddHIyAiLFy9uO0Zrul6kTHIqsBlYX1XjSeY0vqqGk9wFPA7sA56js5V8Pn0T+Omq2pdkDfBvgLdtOU9yG3AbwMqVK+c3oSRJkiRJkvRjqqtFyiSL6BQov1xVDzXNe5Isb1YqLgfGZpqnqjYBm5o5PwfsTLICeLTpck9V3XMM+e6js8JzV1WtmS5bVY0fkeUrSf5FkvdU1cuTct4L3AswMDBQc80jSZI02ZmnhtP73svGjRvbjiJJkqQu6vWdM10rUqazZHITMFxVdx/x0SPATcCdzevDs5hraVWNJVlJ5wKcS6vq+8BFx5Oxqm6e1DRltiRnA3uqqpJcTOcsz73H82xJkiRJkiRJHd1cSXkZcCPw7STbm7ZfpVMA/L0ktwDfA66HPysEPgucBkwkWQ+c36xi3NycSXkIWNcUKN8myYPAFcB7kuwEfq2qNiVZC/wmsAR4LMn2qvrIFFNMmQ34RWAwyWE652L+UlW5UlKSJEmSJEk6AbpWpKyqJ4HpDqC8cor+LwLnTjPX5bN85g3TtG8Btsxi/N5psn0R+OJsMvSCU045hYNvvN52DEmSJEmSpAWjv7+/7QitmpfbvbWw9PX18eoud7tLkiRJkiSdKIODg21HaNVJbQeQJEmSJEmS1NssUkqSJEmSJElqldu9dUxefLW474nDbcdQD3rxB507q/z9k9QrXny1OL2v7RSSJElSd1mk1Jz1+kGuatfr7ALg9D7/xy6pN5ze57+9kiRJWvgsUmrOev0gV0mSJEmSJJ1YnkkpSZIkSZIkqVUWKSVJkiRJkiS1yu3emrOhoSFGR0fbjqEetWtX50zKPs+klNRD+vv7PW5FkiRJC5pFSs3Z6Ogofzj8Lc76qbaTqBeNv9p5XZSX2w0iSfNk7w/aTiBJkiR1n0VKHZOzfgquuTJtx1AP+v2tBfj7J6l3vPn3niRJkrSQeSalJEmSJEmSpFa5klJztmvXLva/3nYKSZIkSZKkhWNoaAigZ88it0ipOTtw4ACHD7edQpIkSZIkaeHo9UuKu7bdO8mKJF9LMpzk+SS3N+1nJpbkq/0AACAASURBVHk8yQvN6xlN+weSPJXkYJI7Js11e5IdzTzrj/LMLyUZS7JjUvv1zdiJJANHGT9ltuazK5Jsb+Z54lh/LpIkSZIkSZLeqptnUh4GPllVq4FLgHVJzgc+DWytqlXA1uY9wCvAx4EvHDlJkguAW4GLgQuBa5KsmuaZ9wNXT9G+A7gO2DZD5imzJfkp4F8Af6Oqfha4foZ5JEmSJEmSJM1S14qUVbW7qr7ZfP8aMAycA1wLPNB0ewD4aNNnrKq+ARyaNNVq4Omq2l9Vh4EngLXTPHMbnWLn5PbhqvruLGJPmQ34W8BDVfW9N7POYi5JkiRJkiRJszAvZ1ImOQ/4IPAMsKyqdkOnkJlk6QzDdwCfTXIWcABYAzzbpajTZfsZYFGSrwPvBv55Vf1OlzJIkiT9mVf3wfjICBs2bGg7iiRJkrpoZGSExYsXtx2jNV0vUiY5FdgMrK+q8SRzGl9Vw0nuAh4H9gHP0dlKPp/eAfwl4ErgFOCpJE9X1X85slOS24DbAFauXDnPESVJkiRJkqQfT10tUiZZRKdA+eWqeqhp3pNkebNScTkw49bpqtoEbGrm/BywM8kK4NGmyz1Vdc8x5LuPzgrPXVW15ijZdgIvV9XrwOtJttE5H/MtRcqquhe4F2BgYKDmmkeSJGmy00+FJcvfy8aNG9uOIkmSpC7q9Z0z3bzdO3QKi8NVdfcRHz0C3NR8fxPw8CzmWtq8rqRzAc6DVfWnVXVR8zXnAiVAVd3cjF8zQ7aHgcuTvCPJu4C/QueMTUmSJEmSJEnHqZsrKS8DbgS+nWR70/arwJ3A7yW5BfgezU3ZSc6mc9bkacBEkvXA+VU1DmxuzqQ8BKyrqu9P9cAkDwJXAO9JshP4taralGQt8JvAEuCxJNur6iNTTDFltmbL+b8DvgVMAP9XVe04nh+OJEmSJEmSpI6uFSmr6klgugMor5yi/4vAudPMdfksn3nDNO1bgC2zGL93qmzNZxsB91lJkiRJkiRJJ9i83O6theWUU06hJl5vO4YkSZIkSdKC0d/f33aEVlmk1Jz19fXx0u6X244hSZIkSZK0YAwODrYdoVVduzhHkiRJkiRJkmbDlZQ6Jnt/AL+/tdqOoR609wedV3//JPWKvT+AJcvbTiFJkiR1l0VKzVmvn5Ggdh2qXQAsWd7XchJJmh9LlvtvryRJkhY+i5Sas14/I0GSJEmSJEknlmdSSpIkSZIkSWqVRUpJkiRJkiRJrXK7t+ZsaGiI0dHRtmOoR+3a1TmTsq/PMykl9Y7+/n6PW5EkSdKCZpFSczY6Osp3hr/FaWe2nUS9aPzVzuvET7zcbhBJmifjr7SdQJIkSeo+i5Q6JqedCZd+pO0U6kVPfbXz6u+fpF7x5t97kiRJ0kLmmZSSJEmSJEmSWmWRUpIkSZIkSVKr3O6tOdu1axev7287hSRJkiRJ0sIxNDQE0LMXJlqk1JwdOHCANw61nUKSJEmSJGnhGB0dbTtCq7q23TvJiiRfSzKc5PkktzftZyZ5PMkLzesZTfsHkjyV5GCSOybNdXuSHc0864/yzC8lGUuyY1L79c3YiSQDRxk/XbYNSbY3XzuSvJHEu60lSZIkSZKkE6CbZ1IeBj5ZVauBS4B1Sc4HPg1srapVwNbmPcArwMeBLxw5SZILgFuBi4ELgWuSrJrmmfcDV0/RvgO4Dtg2Q+Yps1XVxqq6qKouAn4FeKKqXplhLkmSJEmSJEmz0LXt3lW1G9jdfP9akmHgHOBa4Iqm2wPA14FPVdUYMJbk5ydNtRp4uqr2AyR5AlgLfH6KZ25Lct4U7cPN2JliT5ltUp8bgAdnmkiSJOlEeP01GNk3woYNG9qOIkmSpC4aGRlh8eLFbcdozbzc7t0UDj8IPAMsawqYbxYyl84wfAfwoSRnJXkXsAZY0aWoR83WPP9qYPNUg5PcluTZJM++9NJLXYooSZIkSZIkLSxdvzgnyal0inrrq2p8FqsZ36KqhpPcBTwO7AOeo7OVvA2/APzH6bZ6V9W9wL0AAwMDNZ/BJEnSwvST74Zzl72XjRs3th1FkiRJXdTrO2e6upIyySI6BcovV9VDTfOeJMubz5cDYzPNU1WbqurnqupDdM6ufKG5mOfNy2w+doz57mvGf2WW2X4Jt3pLkiRJkiRJJ1TXVlKms2RyEzBcVXcf8dEjwE3Anc3rw7OYa2lVjSVZSecCnEur6vvARceTsapuntQ0bbYkpwN/Dfjbx/NMSZIkSZIkSW/VzZWUlwE3Ah8+YsXjGjoFwKuSvABc1bwnydlJdgKfAD6TZGeS05q5Nif5DvAosK4pUL5NkgeBp4D3N+NvadrXNnNfCjyW5KvTZJ4yW2Mt8O+r6vVj/HlIkiRJkiRJmkI3b/d+EpjuAMorp+j/InDuNHNdPstn3jBN+xZgyyzG750qW/PZ/cD9s8mx0J1yyikctlYrSZIkSZJ0wvT397cdoVVdvzhHC09fXx8797zcdgxJkiRJkqQFY3BwsO0IrerqxTmSJEmSJEmSNBOLlJIkSZIkSZJa5XZvHZPxV+Cp6a4fkrpo/JXOq79/knrF+CvAsrZTSJIkSd1lkVJz1usHuapdu97YBUDfsr6Wk0jSPFnmv72SJEla+CxSas56/SBXSZIkSZIknVieSSlJkiRJkiSpVRYpJUmSJEmSJLXK7d6as6GhIUZHR9uOoR61a1dzJmWfZ1JK6h39/f0etyJJkqQFzSKl5mx0dJRv/+G3WHRW20nUiw6Nd17HF73cbhBJmieH9radQJIkSeo+i5Q6JovOgvdcm7ZjqAe9/HAB/v5J6h1v/r0nSZIkLWSeSSlJkiRJkiSpVa6k1Jzt2rWLw/vbTiFJkiRJkrRwDA0NAfTsWeQWKTVnBw4coA63nUKSJEmSJGnh6PVLiru23TvJiiRfSzKc5PkktzftZyZ5PMkLzesZTfsHkjyV5GCSOybNdXuSHc0864/yzC8lGUuyY1L79c3YiSQDRxk/XbbTkzya5LlmnpuP52cjSZIkSZIk6b/r5pmUh4FPVtVq4BJgXZLzgU8DW6tqFbC1eQ/wCvBx4AtHTpLkAuBW4GLgQuCaJKumeeb9wNVTtO8ArgO2zZB5umzrgO9U1YXAFcA/S/LOGeaSJEmSJEmSNAtdK1JW1e6q+mbz/WvAMHAOcC3wQNPtAeCjTZ+xqvoGcGjSVKuBp6tqf1UdBp4A1k7zzG10ip2T24er6ruziD1lNqCAdycJcGrzDDc8S5IkSZIkSSfAvJxJmeQ84IPAM8CyqtoNnUJmkqUzDN8BfDbJWcABYA3wbJeiTpfti8AjwC7g3cD/VFUTXcogSZL0Zw6/CiPjI2zYsKHtKJIkSeqikZERFi9e3HaM1nRzuzcASU4FNgPrq2p8ruOrahi4C3gc+HfAc8z/KsaPANuBPuAi4ItJTpvcKcltSZ5N8uxLL700zxElSZIkSZKkH09dXUmZZBGdAuWXq+qhpnlPkuXNSsXlwNhM81TVJmBTM+fngJ1JVgCPNl3uqap7jiHffXRWeO6qqjVHyXYzcGdVFfBHSf4Y+ADwnyblvBe4F2BgYKDmmkeSJGmyd5wO713yXjZu3Nh2FEmSJHVRr++c6VqRsjm/cRMwXFV3H/HRI8BNwJ3N68OzmGtpVY0lWUnnApxLq+r7dFY1HrOqmnxL93TZvgdcCfyHJMuA9wO9fS+8JEmSJEmSdIJ0cyXlZcCNwLeTbG/afpVOAfD3ktxCp/h3PUCSs+mcNXkaMJFkPXB+s0V8c3Mm5SFgXVOgfJskD9K5ffs9SXYCv1ZVm5KsBX4TWAI8lmR7VX1kiimmzAb8H8D9Sb4NBPhUVb18zD8ZSZIkSZIkSX+ma0XKqnqSTkFvKldO0f9F4Nxp5rp8ls+8YZr2LcCWWYzfO022XcBfn00GSZIkSZIkSXMzL7d7a2E55ZRTOFCvtx1DkiRJkiRpwejv7287QqssUmrO+vr6GH/J3e6SJEmSJEknyuDgYNsRWnVS2wEkSZIkSZIk9TZXUuqYHNoLLz9cbcdQDzq0t/Pq75+kXnFoL52r/yRJkqQFzCKl5qzXz0hQu3Yd2gVA35K+lpNI0jxZ4r+9kiRJWvgsUmrOev2MBEmSJEmSJJ1YnkkpSZIkSZIkqVUWKSVJkiRJkiS1yu3emrOhoSFGR0fbjqEetWtXcyZln2dSSuod/f39HrciSZKkBc0ipeZsdHSUb/3h83DWqW1HUS8a3wfAy4sOtRxEkubJ3n1tJ5AkSZK6ziKljs1Zp/IT1/7FtlOoB73x8LcA/P2T1DPe/HtPkiRJWsg8k1KSJEmSJElSqyxSSpIkSZIkSWqV2701Z7t27YL9B9qOIUmSJEmStGAMDQ0B9OyFiRYpNWcHDhyAw2+0HUOSJEmSJGnBGB0dbTtCq7q23TvJiiRfSzKc5PkktzftZyZ5PMkLzesZTfsHkjyV5GCSOybNdXuSHc0864/yzC8lGUuyY1L79c3YiSQDRxk/XbYzkmxJ8q0k/ynJBcfzs5EkSZIkSZL033XzTMrDwCerajVwCbAuyfnAp4GtVbUK2Nq8B3gF+DjwhSMnaQqCtwIXAxcC1yRZNc0z7weunqJ9B3AdsG2GzNNl+1Vge1X9ReCXgX8+wzySJEmSJEmSZqlr272rajewu/n+tSTDwDnAtcAVTbcHgK8Dn6qqMWAsyc9Pmmo18HRV7QdI8gSwFvj8FM/cluS8KdqHm7EzxZ4yG3A+8E+buf4wyXlJllXVnpkmlCRJOi6vHmBkfIQNGza0nUSSJEldNDIywuLFi9uO0Zp5ud27KRx+EHgGWNYUMN8sZC6dYfgO4ENJzkryLmANsKJLUafL9hydlZgkuRj4aeDcyYOT3Jbk2STPvvTSS12KKEmSJEmSJC0sXb84J8mpwGZgfVWNz2I141tU1XCSu4DHgX10CoaHT3jQo7sT+OdJtgPfBv5gqgxVdS9wL8DAwEDNa0JJkrQwnX4K713y02zcuLHtJJIkSeqiXt8509WVlEkW0SlQfrmqHmqa9yRZ3ny+HBibaZ6q2lRVP1dVH6JzduULzcU825uvjx1jvvua8V85WraqGq+qm6vqIjpnUi4B/vhYnilJkiRJkiTprbq2kjKdJZObgOGquvuIjx4BbqKzOvEm4OFZzLW0qsaSrKSz7frSqvo+cNHxZKyqmyc1TZktyU8B+6vqR8D/DGyrqvHjebYkSZIkSZKkjm5u974MuBH4drNNGjq3ZN8J/F6SW4DvAdcDJDkbeBY4DZhIsh44vykGbk5yFnAIWNcUKN8myYN0Lr55T5KdwK9V1aYka4HfpLMC8rEk26vqI1NMMWU2Opf3/E6SN4DvALcc809FkiRJkiRJ0lt083bvJ4HpDqC8cor+LzLFZTTNZ5fP8pk3TNO+Bdgyi/F7p8n2FLBqNhl6wSmnnMLrdajtGJIkSZIkSQtGf39/2xFa1fWLc7Tw9PX18fJLFiklSZIkSZJOlMHBwbYjtKqrF+dIkiRJkiRJ0kwsUkqSJEmSJElqldu9dWz27uONh7/Vdgr1or37APz9k9Q79u7rXP0nSZIkLWAWKTVnvX6Qq9q169AuAPqW9LWcRJLmyRL/7ZUkSdLCZ5FSc9brB7lKkiRJkiTpxPJMSkmSJEmSJEmtciWlpB8rg4ODjI+P09fndm9JvaO/v9+dDJIkSVrQZl2kTPLTwKqq+n+TnAK8o6pe6140SXq7PXv28Pr+/by8KG1HkaT5sfcHbSeQJEmSum5WRcoktwK3AWcC7wXOBe4BruxeNEmaxqJ38I5fuKLtFJI0Lw4/+vW2I0iSJEldN9szKdcBlwHjAFX1ArC0W6EkSZIkSZIk9Y7ZFikPVtWP3nyT5B1AdSeSJEmSJEmSpF4y2yLlE0l+FTglyVXAvwYe7V4s/Xk2NDTE0NBQ2zEkSZIkSTrh/D+v1I7ZXpzzaeAW4NvA3wW+UlW/3bVU+nNtdHS07QiSJEmSJHWF/+eV2jHblZT/a1X9dlVdX1W/WFW/neT2ow1IsiLJ15IMJ3n+zf5JzkzyeJIXmtczmvYPJHkqycEkd0ya6/YkO5p51h/lmV9KMpZkx6T265uxE0kGjjJ+2n5JfiXJHyX5bpKPHO3PLkmSJEmSJGn2ZlukvGmKtr8zw5jDwCerajVwCbAuyfl0VmVurapVwNbmPcArwMeBLxw5SZILgFuBi4ELgWuSrJrmmfcDV0/RvgO4Dtg2Q+Yp+zW5fwn42Wb+f5HkJ2aYS5IkSZIkSdIsHHW7d5IbgL8F/IUkjxzx0buBvUcbW1W7gd3N968lGQbOAa4Frmi6PQB8HfhUVY0BY0l+ftJUq4Gnq2p/k+kJYC3w+SmeuS3JeVO0Dzdjjxb5aP2uBf5VVR0E/jjJH9Epmj511AklSZIkSZIkzWimMyn/PzqFxvcA/+yI9teAb832IU3h8IPAM8CypoBJVe1OsnSG4TuAzyY5CzgArAGene2zT5BzgKePeL+zaetJ/+2//Td++MMfsmHDhrajqAcdOHCg7QiSNL9e3cfI+Ij/7kqSNE9GRkZYvHhx2zGknnPUImVV/QnwJ8Clx/qAJKcCm4H1VTU+02rGKTIMJ7kLeBzYBzxHZyv5fJoqdL2tU3IbcBvAypUru51JkiRJkiRJWhBmdbt3kkuA36Sz9fqdwE8Ar1fVaTOMW0SnQPnlqnqoad6TZHmzinI5MDbT86tqE7CpmfNzwM4kK4BHmy73VNU9s/mzTMp3H50Vnruqas1Ruu4EVhzx/lxg1xQ57wXuBRgYGHhbEXOhOOecziLSjRs3tpxEvei6667j9UM/ajuGJM2f00/lvUuW+++uJEnzxN0LUjtmVaQEvkjn4ph/DQwAvwy872gD0lkyuQkYrqq7j/joEToX8dzZvD4808OTLK2qsSQr6Vxsc2lVfR+4aJb5p1RVN8+y6yPA/53kbqAPWAX8p+N5tiRJkiRJkqSO2d7uTVX9EfATVfVGVd0H/I8zDLkMuBH4cJLtzdcaOsXJq5K8AFzVvCfJ2Ul2Ap8APpNkZ5I3V2puTvIdOisn1zUFyrdJ8iCdy2ze34y/pWlf28x9KfBYkq9OM37KflX1PPB7wHeAf9dkeGOmn5kkSZIkSZKkmc12JeX+JO8Etif5PJ3LdH7yaAOq6kmmPssR4Mop+r9IZxv1VHNdPpuQVXXDNO1bgC2zGD9tv6r6LPDZ2eSQJEmSJEmSNHuzLVLeSGfV5d8D/j6d8xn/ZrdC6c+3/v7+tiNIkiRJktQV/p9XasesipRV9SdJljTf/5PuRtKfd4ODg21HkCRJkiSpK/w/r9SOo55JmY5/nORl4A+B/5LkpST/aH7iSZIkSZIkSVroZlpJuZ7OBTh/uar+GCBJPzCU5O9X1W90O6Akvc2hwxx+9Ottp5Ck+bH3B7BkedspJEmSpK6aqUj5y8BVVfXymw1VNZrkbwP/HrBIKWleLVu2jPHxcfr8D7ukXrFkuWdjSZIkacGbqUi56MgC5Zuq6qUki7qUSZKmNTQ01HYESZIkSZJ0gh31TErgR8f4mSRJkiRJkiTNykwrKS9MMj5Fe4DFXcgjSZIkSZIkqccctUhZVT8xX0H042NwcLBzJmBfX9tR1KP6+/sZHBxsO4YkSZIkSTpBZlpJKb3Nnj17eH3/fvYu8lhSzb/au7ftCJIkSZIk6QSzSKljs2gRi675G22nUA869PuPtB1BkiRJkiSdYDNdnCNJkiRJkiRJXWWRUpIkSZIkSVKr3O6tOTt48CBMTLQdQ5Lm1dDQEICXNkmSJElSF1ik1JxNTExAVdsxJGlejY6Oth1BkiRJkhasrm33TrIiydeSDCd5PsntTfuZSR5P8kLzekbT/oEkTyU5mOSOSXPdnmRHM8/6ozzzS0nGkuyY1H59M3YiycBRxk/ZL8lZzZ9lX5IvHuvPRJIkSZIkSdLbdfNMysPAJ6tqNXAJsC7J+cCnga1VtQrY2rwHeAX4OPCFIydJcgFwK3AxcCFwTZJV0zzzfuDqKdp3ANcB22bIPF2/HwL/ELjjbSMkSZIkSZIkHZeubfeuqt3A7ub715IMA+cA1wJXNN0eAL4OfKqqxoCxJD8/aarVwNNVtR8gyRPAWuDzUzxzW5LzpmgfbsbOlHnKflX1OvBkkvcddQJJXVevvsrI+DgbNmxoO4p6zMjICIsXL247hiRJkiQtSPNyu3dTOPwg8AywrClgvlnIXDrD8B3Ah5ot1+8C1gArupf22CW5LcmzSZ596aWX2o4jSZIkSZIk/Vjo+sU5SU4FNgPrq2p8ptWMk1XVcJK7gMeBfcBzdLaS/7lTVfcC9wIMDAx4s4zUBTn9dN67ZAkbN25sO4p6jKt3JUmSJKl7urqSMskiOgXKL1fVQ03zniTLm8+XA2MzzVNVm6rq56rqQ3TOrnyhuZhne/P1sWPMd18z/ivHMl6SJEmSJEnS8evaSsp0lkxuAoar6u4jPnoEuAm4s3l9eBZzLa2qsSQr6Vxsc2lVfR+46HgyVtXNxzNekiRJkiRJ0vHr5krKy4AbgQ8fseJxDZ3i5FVJXgCuat6T5OwkO4FPAJ9JsjPJac1cm5N8B3gUWNcUKN8myYPAU8D7m/G3NO1rm7kvBR5L8tVpxk/bL8l/Be4G/k4z9/nH88ORJEmSJEmS1NHN272fBKY7gPLKKfq/CJw7zVyXz/KZN0zTvgXYMovx0/arqvNmk6EXnHTSSUxMTLQdQ5LmVX9/f9sRJEmSJGnB6vrFOVp4Tj75ZA4fOtR2DEmaV4ODg21HkCRJkqQFq6sX50iSJEmSJEnSTCxSSpIkSZIkSWqV2711bA4d4tDvP9J2CvWg2rsXlixpO4YkSZIkSTqBLFJqzpYtW8b4+Dh9ForUhiVLvMBEkiRJkqQFxiKl5mxoaKjtCJIkSZIkSVpAPJNSkiRJkiRJUqtcSak5Gxwc7Gz37utrO4p6VH9/P4ODg23HkCRJkiRJJ4hFSs3Znj17eH3/fl5Z9JNtR1EPmtj7YtsRJEmSJEnSCWaRUsdm0TtZ/Au/3HYK9aAfPvo7bUeQJEmSJEknmGdSSpIkSZIkSWqVRUpJkiRJkiRJrXK7t+bs4MGDMFFtx5CkeTU0NATgpU2SJEmS1AUWKTVnExMTUBYpJfWW0dHRtiNIkiRJ0oLVte3eSVYk+VqS4STPJ7m9aT8zyeNJXmhez2jaP5DkqSQHk9wxaa7bk+xo5ll/lGd+KclYkh2T2q9vxk4kGTjK+Cn7JbkqyX9O8u3m9cPH+nORJEmSJEmS9FbdPJPyMPDJqloNXAKsS3I+8Glga1WtArY27wFeAT4OfOHISZJcANwKXAxcCFyTZNU0z7wfuHqK9h3AdcC2GTJP1+9l4Beq6n8AbgL+5QzzSJIkSZIkSZqlrhUpq2p3VX2z+f41YBg4B7gWeKDp9gDw0abPWFV9Azg0aarVwNNVtb+qDgNPAGuneeY2OsXOye3DVfXdWWSesl9V/UFV7WrePg8sTnLyTPNJkiRJkiRJmtm8nEmZ5Dzgg8AzwLKq2g2dQmaSpTMM3wF8NslZwAFgDfBs99LO6G8Cf1BVB1vMIPWsiVdfYWT8ZTZs2NB2FPWYkZERFi9e3HYMSZIkSVqQul6kTHIqsBlYX1XjSeY0vqqGk9wFPA7sA56js5V83iX5WeAu4K9P8/ltwG0AK1eunMdkkiRJkiRJ0o+vrhYpkyyiU6D8clU91DTvSbK8WUW5HBibaZ6q2gRsaub8HLAzyQrg0abLPVV1zzHku4/OCs9dVbVmhr7nAluAX66qkWly3gvcCzAwMOD111IXnHT6mbx3yels3Lix7SjqMa7elSRJkqTu6VqRMp0lk5uA4aq6+4iPHqFz+cydzevDs5hraVWNJVlJ52KbS6vq+8BFx5Oxqm6eTb8kPwU8BvxKVf3H43mmJEmSJEmSpLfq5u3elwE3Ah9Osr35WkOnOHlVkheAq5r3JDk7yU7gE8BnkuxMcloz1+Yk36GzcnJdU6B8myQPAk8B72/G39K0r23mvhR4LMlXpxk/Xb+/B7wP+IdH/FlmOktTkiRJkiRJ0ix0bSVlVT0JTHcA5ZVT9H8ROHeauS6f5TNvmKZ9C52t2jONn7JfVf068OuzydALTjrpJCYm3M0uqbf09/e3HUGSJEmSFqx5ud1bC8vJJ5/M4UOt3F0kSa0ZHBxsO4IkSZIkLVjd3O4tSZIkSZIkSTOySClJkiRJkiSpVW731rE59CN++OjvtJ1CPWhi74uw5PS2Y0iSJEmSpBPIIqXmbNmyZYyPj9NnoUhtWHK6F5hIkiRJkrTAWKTUnA0NDbUdQZIkSZIkSQuIZ1JKkiRJkiRJapVFSkmSJEmSJEmtcru35mxwcLBzJmVfX9tR1KP6+/sZHBxsO4YkSZIkSTpBLFJqzvbs2cPr+w/w+qIz246iHnRw75+2HUGSJEmSJJ1gFil1TE5adDIrr/0HbcdQD/rew59vO4IkSZIkSTrBPJNSkiRJkiRJUqtcSak5O3jwIBMT1XYMSZpXQ0NDAJ6HKkmSJEldYJFSczYxMQHWKCX1mNHR0bYjSJIkSdKC5XZvSZIkSZIkSa3qWpEyyYokX0synOT5JLc37WcmeTzJC83rGU37B5I8leRgkjsmzXV7kh3NPOuP8swvJRlLsmNS+/XN2IkkA0cZP2W/JBcn2d58PZdk7bH+XCRJkiRJkiS9VTdXUh4GPllVq4FLgHVJzgc+DWytqlXA1uY9wCvAx4EvHDlJkguAW4GLgQuBa5KsmuaZ9wNXT9G+A7gO2DZD5un67QAGquqiZv7fSuJWeUmSJEmSJOkE6Fqhrap2A7ub719LMgycA1wLXNF0ewD4OvCpqhoDxpL8/KSpVgNPV9V+gCRPAGuBz0/xzG1JzpuifbgZO1PmKfu9+ezGYjyRUWrNj14dY2T8EBs2bGg7inrMUFY29gAAIABJREFUyMgIixcvbjuGJEmSJC1I83ImZVM4/CDwDLCsKWC+WchcOsPwHcCHkpyV5F3AGmBF99JOLclfSfI88G3gY1V1eIo+tyV5NsmzL7300nxHlCRJkiRJkn4sdX3LcpJTgc3A+qoan2k142RVNZzkLuBxYB/wHJ2t5POqqp4BfjbJauCBJP+2qn44qc+9wL0AAwMDrraUuuCdpy/lvUsWs3HjxrajqMe4eleSJEmSuqerKymTLKJToPxyVT3UNO9Jsrz5fDkwNtM8VbWpqn6uqj5E5+zKF5qLed68zOZjx5jvvmb8V2Y7ptkS/jpwwbE8U5IkSZIkSdJbdW0lZTpLJjcBw1V19xEfPQLcBNzZvD48i7mWVtVYkpV0Lra5tKq+D1x0PBmr6ubZ9EvyF4A/rarDSX4aeD/wX4/n2ZIkSZIkSZI6urmS8jLgRuDDR6x4XEOnOHlVkheAq5r3JDk7yU7gE8BnkuxMcloz1+Yk3wEeBdY1Bcq3SfIg8BTw/mb8LU372mbuS4HHknx1mvHT9furwHNJtgNbgP+lql4+rp+OJEmSJEmSJKC7t3s/CUx3AOWVU/R/ETh3mrkun+Uzb5imfQud4uJM46fsV1X/EviXs8nQC0466SQmJjxyU1Jv6e/vbzuCJEmSJC1YXb84RwvPySefzMShibZjSNK8GhwcbDuCJEmSJC1YXb04R5IkSZIkSZJmYpFSkiRJkiRJUqvc7q1jMnHoIN97+PNtx1APOrj3T2HJqrZjSJIkSZKkE8gipeZs2bJljI+P07dkcdtR1IuWrPICE0mSJEmSFhiLlJqzoaGhtiNIkiRJkiRpAfFMSkmSJEmSJEmtciWl5mxwcLCz3buvr+0okjSv+vv7GRwcbDuGJEmSJC04Fik1Z3v27GH//gMsWrSk7SiSNG/27v2TtiNIkiRJ0oJlkVLHZNGixfyNaz7TdgxJmjeP/P6vtx1BkiRJkhYsz6SUJEmSJEmS1CqLlJIkSZIkSZJa5XZvzdnBgweZmGg7hSRJkqSFamhoCMAL6ySph1ik1JxNTExQ1XYKSZIkSQvV6Oho2xEkSfOsa9u9k6xI8rUkw0meT3J7035mkseTvNC8ntG0fyDJU0kOJrlj0ly3J9nRzLP+KM/8UpKxJDsmtV/fjJ1IMnCU8Uftl2Rlkn2T80mSJEmSJEk6dt08k/Iw8MmqWg1cAqxLcj7waWBrVa0CtjbvAV4BPg584chJklwA3ApcDFwIXJNk1TTPvB+4eor2HcB1wLYZMs/U7zeAfzvDHJIkSZIkSZLmoGtFyqraXVXfbL5/DRgGzgGuBR5ouj0AfLTpM1ZV3wAOTZpqNfB0Ve2vqsPAE8DaaZ65jU6xc3L7cFV9dxaZp+2X5KPAKPD8TPNIkiRJkiRJmr15OZMyyXnAB4FngGVVtRs6hcwkS2cYvgP4bJKzgAPAGuDZ7qV9uyQ/CXwKuApwq7ck9aBXX32R8fEfsWHDhrajSJK04I2MjLB48eK2Y0iS5lHXi5RJTgU2A+urajzJnMZX1XCSu4DHgX3Ac3S2ks+nfwL8RlXtO1r+JLcBtwGsXLlynqJJkiRJkiRJP966WqRMsohOgfLLVfVQ07wnyfJmFeVyYGymeapqE7CpmfNzwM4kK4BHmy73VNU9x5DvPjorPHdV1ZqjdP0rwC8m+TzwU8BEkh9W1Rcn5bwXuBdgYGDA+68laQE5/fSzWbJkERs3bmw7iiRJC547FySp93StSJnOksNNwHBV3X3ER48ANwF3Nq8Pz2KupVU1lmQlnYttLq2q7wMXHU/Gqrp5lv0uPyLLPwb2TS5QSpIkSZIkSTo23bzd+zLgRuDDSbY3X2voFCevSvICnTMe7wRIcnaSncAngM8k2ZnktGauzUm+Q2fl5LqmQPk2SR4EngLe34y/pWlf28x9KfBYkq9OM35W/SRJkiRJkiSdOF1bSVlVTwLTHeB45RT9XwTOnWauy6dqn6LfDdO0bwG2zGL8jP2q6h/PJstCdtJJJzEx0XYKSZIkSQtVf39/2xEkSfNsXm731sJy8sknc+iQR25KkiRJ6o7BwcG2I0iS5lk3t3tLkiRJkiRJ0owsUkqSJEmSJElqldu9dUwOHfohj/z+r7cdQ5Lmzd69f8KSJe9rO4YkSZIkLUgWKTVny5YtY3x8nCVLFrUdRZLmzZIl7/MQf0mSJEnqEouUmrOhoaG2I0iSJEmSJGkB8UxKSZIkSZIkSa2ySClJkiRJkiSpVW731pwNDg4yPj5OX19f21EkaV719/czODjYdgxJkiRJWnAsUmrO9uzZw/79B3h33tN2FEmaN7t/8L22I0iSJEnSgmWRUsfk5HeczN+94n9rO4YkzZvf+vpn244gSZIkSQuWZ1JKkiRJkiRJapUrKTVnBw8epCbaTiFJkiRpoRoaGgLwLGhJ6iEWKTVnExMTVLWdQpIkSdJCNTo62nYESdI869p27yQrknwtyXCS55Pc3rSfmeTxJC80r2c07R9I8lSSg0numDTX7Ul2NPOsP8ozv5RkLMmOSe3XN2MnkgwcZfyU/ZKcl+RAku3N1z3H+nORJEmSJEmS9FbdPJPyMPDJqloNXAKsS3I+8Glga1WtArY27wFeAT4OfOHISZJcANwKXAxcCFyTZNU0z7wfuHqK9h3AdcC2GTIfrd9IVV3UfH1shnkkSZIkSZIkzVLXipRVtbuqvtl8/xowDJwDXAs80HR7APho02esqr4BHJo01Wrg6araX1WHgSeAtdM8cxudYufk9uGq+u4sMs+qnyRJkiRJkqQTZ17OpExyHvBB4BlgWVXthk4hM8nSGYbvAD6b5CzgALAGeLZ7aaf1F5L8ATAOfKaq/kMLGSRJLdm7bw9jIz9iw4YNbUeRJGnBGxkZYfHixW3HkCTNo64XKZOcCmwG1lfVeJI5ja+q4SR3AY8D+4Dn6Gwln0+7gZVVtTfJXwL+TZKfrarxIzsluQ24DWDlypXzHFGSJEmSJEn68dTVImWSRXQKlF+uqoea5j1JljerKJcDYzPNU1WbgE3NnJ8DdiZZATzadLmnquZ8mU2S++is8NxVVWuO8vyDwMHm+/+cZAT4GSat6Kyqe4F7AQYGBrz/WpIWkLNOXca7l7+TjRs3th1FkqQFz50LktR7ulakTGfJ5CZguKruPuKjR4CbgDub14dnMdfSqhpLspLOxTaXVtX3gYuOJ2NV3TybfkmWAK9U1RtJ+oFVwOjxPFuSJEmSJElSRzdv974MuBH4cJLtzdcaOsXJq5K8AFzVvCfJ2Ul2Ap8APpNkZ5LTmrk2J/kOnZWT65oC5dskeRB4Cnh/M/6Wpn1tM/elwGNJvjrN+On6fQj4VpLngP8H+FhVve2CHkmSJEmSJElz17WVlFX1JDDdAZRXTtH/ReDcaea6fJbPvGGa9i3AllmMn7JfVW2ms21dkiRJkiRJ0gk2L7d7a2E56aSTqIm2U0iSJElaqPr7+9uOIEmaZxYpNWcnn3wyb/zIKqUkSZKk7hgcHGw7giRpnnXzTEpJkiRJkiRJmpErKXVMDh4+yG99/bNtx5CkebP7B9/j3cvf13YMSZIkSVqQLFJqzpYtW8b4+DjvXv7OtqNI0rx59/L3eT6WJEmSJHWJRUrN2dDQUNsRJEmSJEmStIB4JqUkSZIkSZKkVlmklCRJkiRJktQqt3trzgYHBxkfH6evr6/tKJI0r/r7+xkcHGw7hiRJkiQtOBYpNWd79uzhwOsHeM8bZ7QdRZLmzfde29l2BEmSJElasCxS6pic/I6T+ZWB9W3HkKR580+f/T/bjiBJkiRJC5ZnUkqSJEmSJElqlUVKSZIkSZIkSa1yu7fm7ODBg/BGtR1DkiRJ0gI1NDQE4IV1ktRDLFJqziYmJsAapSRJkqQuGR0dbTuCJGmedW27d5IVSb6WZDjJ80lub9rPTPJ4khea1zOa9g8keSrJwSR3TJrr9iQ7mnmmva0lyZeSjCXZMan9+mbsRJKBo4yftl+Sv9jkez7Jt5MsPpafiyRJkiRJkqS36uaZlIeBT1bVauASYF2S84FPA1urahWwtXkP8ArwceALR06S5ALgVuBi4ELgmiSrpnnm/cDVU7TvAK4Dts2Qecp+Sd4B/C7wsar6WeAK4NAMc0mSJEmSJEmaha4VKatqd1V9s/n+NWAYOAe4Fnig6fYA8NGmz1hVfYO3F/9WA09X1f6qOgw8Aayd5pnb6BQ7J7cPV9V3Z5F5un5/HfhWVT3X9NtbVW/MNJ8kSZIkSZKkmc3LmZRJzgM+CDwDLKuq3dApZCZZOsPwHcBnk5wFHADWAM92L+2UfgaoJF8FlgD/qqo+P88ZJEkt2rP/JX40cogNGza0HUWSpAVvZGSExYs9YUuSeknXi5RJTgU2A+urajzJnMZX1XCSu4DHgX3Ac3S2ks+ndwB/FfjLwH5ga5L/XFVbj+yU5DbgNoCVK1fOc0RJkiRJkiTpx1NXi5RJFtEpUH65qh5qmvckWd6solwOjM00T1VtAjY1c34O2JlkBfBo0+WeqrrnGPLdR2eF566qWnOUrjuBJ6rq5WbcV4Cfo3Om5pE57wXuBRgYGPD+a0laQJa9awnvXHEKGzdubDuKJEkLnjsXJKn3dK1Imc6SyU3AcFXdfcRHjwA3AXc2rw/PYq6lVTWWZCWdi20urarvAxcdT8aqunmWXb8K/IMk7wJ+BPw14DeO59mSJEmSJEmSOrp5u/dlwI3Ah5Nsb77W0ClOXpXkBeCq5j1Jzk6yE/gE8JkkO5Oc1sy1Ocl36KycXNcUKN8myYPAU8D7m/G3NO1rm7kvBR5rzpacavyU/Zrn3Q18A9gOfLOqHjvOn48kSZIkSZIkuriSsqqeBKY7gPLKKfq/CJw7zVyXz/KZN0zTvgXYMovx0/arqt8Ffnc2ORa6k046Ccrd7JIkSZK6o7///2fv3uPsqstD/38e7jflbkIQSpNQhXKOWgJHq1J/9XR2AlEg/oKXVtqCt3gBqsETKqdypD9+aqjwa9WolUuseEMlFBCBUglYAibQIMGAMikghEwCIQlJJpO5PL8/9ppx9rD3TPYwe/Yk83m/Xvu1Zz/ru57vs9bsYHzyXWtNbnYJkqRRNipP99auZe+996ano6fZZUiSJEnaRc2ZM6fZJUiSRlkjL/eWJEmSJEmSpCHZpJQkSZIkSZLUVF7urWHp6Org/112ZbPLkKRR89SLTzOVY5tdhiRJkiTtkmxSqm4TJkxg06ZN7DVp32aXIkmjZirHehN/SZIkSWoQm5Sq24IFC5pdgiRJkiRJknYh3pNSkiRJkiRJUlPZpJQkSZIkSZLUVF7urbrNmTOHTZs2MWnSpGaXonFq8uTJzJkzp9llSJIkSZKkEWKTUnVra2ujfctWDuvZr9mlaBx6alNbs0uQJEmSJEkjzCalhmXvPfbib9/0580uQ+PQZUuua3YJkiRJkiRphHlPSkmSJEmSJElN5UpK1a2jowO6s9llSNKoWrBgAYD3Q5UkSZKkBrBJqbr19PRA2qSUNL6sWrWq2SVIkiRJ0i6rYZd7R8RREfGziFgZEY9ExPlF/JCIuCMiflO8H1zEXxsRSyKiIyLmDsh1fkSsKPJcMMicV0fE2ohYMSA+u9i3JyKmDbJ/1XERsVdEXBMRD0fEQxHxtmGeFkmSJEmSJEkDNPKelF3ApzLzOOCNwMci4nhgHnBnZh4L3Fl8BlgPnAdc3j9JRJwAfBA4GXgdMDMijq0x57XA9CrxFcAs4O4haq417oMAmfnfgD8D/iEivJ+nJEmSJEmSNAIa1mjLzGcz88Hi5xeBlcCRwOnAwmLYQuCMYszazFwKdA5IdRxwX2ZuzcwuYDFwZo0576bc7BwYX5mZj+1AzbXGHU+5oUpmrgU2ADVXZEqSJEmSJEnacaNyT8qIOAZ4A3A/MCEzn4VyIzMiXjXE7iuA/yciDgXagVOBZY2rtqqHgNMj4nvAUcCJxfsvRrkOadxr2/IC21uf58ILL2x2KRpnWltb2WeffZpdhiRJkiTtkhrepIyIA4AfARdk5qaIqGv/zFwZEV8A7gA2U24Ydo14oYO7mvKKzmXAk8C91WqIiA8BHwI4+uijR7M+SZIkSZIkaafV0CZlROxJuUF5XWb+uAi3RcQRxSrKI4C1Q+XJzKuAq4qclwFPR8RRwE3FkK9l5teGUd81lFd4rs7MUweZvwv4m3773Qv8psq4bwDfAJg2bZqPv5YaYML+B7PXqw9i/vz5zS5F44yrdyVJkiSpcRrWpIzyksmrgJWZ+aV+m/4V+Evg88X7jTuQ61WZuTYijqb8YJs3ZeYLwOtfTo2Z+dc7Mi4i9gMiM7dExJ8BXZn5q5cztyRJkiRJkqSyRq6kfDPwfuDhiFhexP6WcnPyBxFxLvAUMBsgIiZSvpz6lUBPRFwAHJ+Zm4AfFfek7AQ+VjQoXyIivgu8DTgsIp4GPpuZV0XEmcA/AYcDt0TE8swsVdm/1rhXAbdFRA/wTHFckiRJkiRJkkZAw5qUmflzoNYNKN9eZfwa4NU1cr11B+d8b434DcANO7B/1XGZ+QTwmh2pQZIkSZIkSVJ9RuXp3tq17LbbbpDeclPS+DJ58uRmlyBJkiRJuyyblKrb3nvvTU/HaD9gXZKaa86cOc0uQZIkSZJ2Wbs1uwBJkiRJkiRJ45srKTUsHV3buWzJdc0uQ+PQU5vamMpBzS5DkiRJkiSNIJuUqtuECRPYtGkTe02yUaTRN5WDvDegJEmSJEm7GJuUqtuCBQuaXYIkSZIkSZJ2Id6TUpIkSZIkSVJTRWY2u4ZdUkSsA55sdh0NdBjwXLOL0Ljl90/N4ndPzeJ3T83k90/N4ndPzeT3T82yq3/3fi8zD6+2wSalhiUilmXmtGbXofHJ75+axe+emsXvnprJ75+axe+emsnvn5plPH/3vNxbkiRJkiRJUlPZpJQkSZIkSZLUVDYpNVzfaHYBGtf8/qlZ/O6pWfzuqZn8/qlZ/O6pmfz+qVnG7XfPe1JKkiRJkiRJaipXUkqSJEmSJElqKpuUqktEXB0RayNiRbNr0fgSEUdFxM8iYmVEPBIR5ze7Jo0fEbFPRPwiIh4qvn//p9k1aXyJiN0j4j8j4uZm16LxIyKeiIiHI2J5RCxrdj0aXyLioIj4YUQ8Wvz9703Nrkm7voh4TfHfvN7Xpoi4oNl1afyIiL8p/v/Gioj4bkTs0+yaRpOXe6suEXEKsBn4Vmae0Ox6NH5ExBHAEZn5YES8AngAOCMzf9Xk0jQOREQA+2fm5ojYE/g5cH5m3tfk0jRORMQngWnAKzNzZrPr0fgQEU8A0zLzuWbXovEnIhYC92TmNyNiL2C/zNzQ7Lo0fkTE7sAzwP/IzCebXY92fRFxJOX/n3F8ZrZHxA+An2Tmtc2tbPS4klJ1ycy7gfXNrkPjT2Y+m5kPFj+/CKwEjmxuVRovsmxz8XHP4uW/8mlURMSrgdOAbza7FkkaDRHxSuAU4CqAzNxug1JN8Hag1QalRtkewL4RsQewH7C6yfWMKpuUknY6EXEM8Abg/uZWovGkuNx2ObAWuCMz/f5ptFwJfBroaXYhGncSuD0iHoiIDzW7GI0rk4F1wDXFrS6+GRH7N7sojTvvAb7b7CI0fmTmM8DlwFPAs8DGzLy9uVWNLpuUknYqEXEA8CPggszc1Ox6NH5kZndmvh54NXByRHjLCzVcRMwE1mbmA82uRePSmzPzj4AZwMeK2/5Io2EP4I+ABZn5BmALMK+5JWk8KW4x8E7g+mbXovEjIg4GTgd+H5gE7B8Rf9HcqkaXTUpJO43iXoA/Aq7LzB83ux6NT8XlZncB05tcisaHNwPvLO4N+D3gTyPi280tSeNFZq4u3tcCNwAnN7cijSNPA0/3u2rhh5SbltJomQE8mJltzS5E48r/BP4rM9dlZifwY+CPm1zTqLJJKWmnUDy45CpgZWZ+qdn1aHyJiMMj4qDi530p/wXi0eZWpfEgMy/KzFdn5jGULzv798wcV/+iruaIiP2LB9VRXGbbAqxoblUaLzJzDfDbiHhNEXo74MMSNZrei5d6a/Q9BbwxIvYr/v/v2yk/i2HcsEmpukTEd4ElwGsi4umIOLfZNWnceDPwfsqriJYXr1ObXZTGjSOAn0XEL4GllO9JeXOTa5KkRpoA/DwiHgJ+AdySmT9tck0aXz4BXFf8b+/rgcuaXI/GiYjYD/gzyqvYpFFTrB7/IfAg8DDlnt03mlrUKItMH04qSZIkSZIkqXlcSSlJkiRJkiSpqWxSSpIkSZIkSWoqm5SSJEmSJEmSmsompSRJkiRJkqSmskkpSZIkSZIkqalsUkqSJKlhIiIj4h/6fZ4bEZc0YJ75EfFIRMzvF/vriFhevLZHxMPFz59vwPyHRMRHRjqvJEnSeBGZ2ewaJEmStIuKiG3As8BJmflcRMwFDsjMS0Z4nk3A4ZnZUWP7E8C0zHxuJOftl38q8MPMfH0j8kuSJO3qXEkpSZKkRuoCvgH8zcANEfF7EXFnRPyyeD96sERRNj8iVhSrIt9dxP8V2B+4vzc2lIj4VUS8IiJ2i4gNEfG+Iv7diHhbROwREV+KiF8U9X2g377z+sX/rgh/HnhN70rNiDgyIn5efF4REX+8I3VJkiSNV3s0uwBJkiTt8r4C/DIivjgg/mXgW5m5MCLOAf4ROGOQPLOA1wOvAw4DlkbE3Zn5zojYXOcqxnuBPwbagN8AbwW+A5wMnAN8CFibmSdHxN7AfRFxO3ACcDTwP4AAflI0IOcBU3triIj/BdyUmV+IiN2BfeuoTZIkadyxSSlJkqSGysxNEfEt4Dygvd+mN1FuPAL8CzCwiTnQW4DvZmY30BYRi4GTgH8dRln3AKdQblJ+GfhIRBwDtGVme0S0AMdFxHuK8QcCxwItwAzgP4v4AcAfAGsH5F8KfD0i9gEWZeZDw6hRkiRp3PByb0mSJI2GK4FzKV+WXctQN0uPkSuHeyivnnwr8DNgA+VVnHf3m+ujmfn64vX7mXlnEf/7fvGpmXntwOSZ+e/A2yjfj/O6iPjzEaxdkiRpl2OTUpIkSQ2XmeuBH1BuVPa6F+hdqfjnwM+HSHM38O6I2D0iDqe8EvIXw6xnFTAJ+L3MfKqY+1OUm5cAtwEfjYg9ACLiNRGxbxE/NyL2L+KvjojDgBeBV/Tmj4jfA9Zk5jeAa4E3DKdOSZKk8cLLvSVJkjRa/gH4eL/P5wFXR8SFwDrgrwEi4p2Un8T9dwP2v4HyJeIPUV51+enMXPMy6lkKdBY/3wN8DviP4vPXKd97cnlEQPly7tMz8ycR8VrK96iEcnPyfZn5REQsi4iHgVuAXwOfjIhOYDPwFy+jTkmSpF1eZA51VY0kSZIkSZIkNY6Xe0uSJEmSJElqKpuUkiRJkiRJkprKJqUkSZIkSZKkprJJKUmSJEmSJKmpbFJKkiRJkiRJaiqblJIkSZIkSZKayialJEmSJEmSpKaySSlJkiRJkiSpqWxSSpIkSZIkSWoqm5SSJEmSJEmSmsompSRJkiRJkqSmskkpSZIkSZIkqalsUkqSJEmSJElqKpuUkiRJkiRJkprKJqUkSZIkSZKkprJJKUmSJEmSJKmpbFJKkiRJkiRJaiqblJIkSZIkSZKayialJEmSJEmSpKaySSlJkiRJkiSpqWxSSpIkSZIkSWoqm5SSJEmSJEmSmsompSRJkiRJkqSmskkpSZIkSZIkqalsUkqSJKkpIuI1EfGfEfFiRJzX7HrGkojYOyI2R8Sk4vP3IuLiZtclSZLUKDYpJUmSxrGIeCIi2iJi/36xD0TEXaMw/aeBuzLzFZn5jwPqeqRo0m2OiO6I2Nbv89+OQm1ExH0R8RcvY99tRQN2U0QsjYi5EbHnjuyfmR2ZeUBmrh7O/JIkSTsbm5SSJEnaAzi/CfP+HvBItQ2Z+YdFk+4A4B7g472fM/OyUa1y+D6Qma8AJgEXAX8F3NjUiiRJksYom5SSJEmaD8yNiIOqbYyIPy5WAm4s3v94RxNHxDuLVZEbIuKuiDiuiP878H8BXy5WR/5BPQVHxJqI+MPi5w9EREbE5OLzxyPie8XPu0fE/46IVRHxXERc1/84I+KtEXF/Ud+DEfHmIv4PwEnAN4v6/qHI9eWIWFeci4ci4jVD1ZqZmzPz34DTgbdHxP8s5nhzMffGiFgdEVdExB7Ftn2KY3p1lWN/PCL+rN/nfYocx9VzDiVJksYSm5SSJElaBtwFzB24ISIOAW4B/hE4FPgScEtEHDpU0qLx+F3gAuBw4CfATRGxV2b+KZUrJH9dZ813A28rfj4FWAX8Sb/Pi4ufLwRagLcArwY6gSuK+o4BFgGfAQ4BLgYWRcTBmfkpYCnl1ZAHFJ9nAicCU4CDgfcBL+xowZnZCjwEvLUIdQIfL+Z+K/AO4AM7kOpbQP/L0E8Hfp2ZK3e0FkmSpLHGJqUkSZIA/g74REQcPiB+GvCbzPyXzOzKzO8Cj1JuqA3l3cAtmXlHZnYClwP7Aju8EnMQi/ldU/ItwOep3qT8MDAvM1dn5jbg/wDvjogA/hL4cWb+W2b2ZOZPgF9RbmpW0wm8EngtkJn5SGaurbPu1ZSbkmTmLzJzaWZ2Fw3Mb/Y7hsF8CzgjIvYrPr8f+Jc665AkSRpTbFJKkiSJzFwB3AzMG7BpEvDkgNiTwJE7kLZi38zsAX67g/sOZTHwJxFxNLAF+DFwSkS8lvLfcVcWjcijgJ8Ul3NvAP6z2H4o5Xti/kXvtmL7tKLuam4FrgK+DrRFxFcj4oA66z4SWA8QEcdHxK3Fg4s2UW4UHzZUgsx8ojiO04um8p8C36uzDkmSpDHFJqUkSZJ6fRb4IJVNxNWUm3n9HQ08swP5Kvbt1zTckX2H8giwO/ARYHFmPg9sBs4G7s5CMdefZuZB/V77ZOZzlBum3xywbf/MvKKYI/v7KZ1lAAAgAElEQVRPWKT8Uma+AfjvwOuo44FDxT0z/zvly9wB/hl4EJiSma8EPgfEDqZbSPmS7/cA/z6MFZ2SJEljik1KSZIkAZCZjwPfB87rF/4J8AcR8b6I2CMi3g0cT3nV5VB+AJwWEW+PiD2BTwEdwL0jUGtSvi/lx/ndpd2LB3wG+Brw+Yg4CiAiXhURvZeqLwRmF/XtHhH7Fj9PLLa3AZN7E0XEGyNiWvFwmy3AdqB7qFojYv+I+FPK97+8q3iIDsArgI2Zubl4CNAH6zgFP6R8mfscypd/S5Ik7dRsUkqSJKm/zwH7934oVijOpNxgfB74NDCzWIlIRHwtIr5WLVFmPkZ5td8/Ac9Rvo/lOzJz+wjVuphyo+/uGp8Bvgj8G/DvEfEi5QbpHxX1rQLeRfk+lc9RvjT9fH73d+QrgLMj4oWI+CJwEHAtsIHyg3qepPxAoVq+Wcy5hvIT1K+j8l6efwN8ICI2A1+h3CDeIZn5InAT5VWv/7qj+0mSJI1VUf5HaEmSJEk7k4i4DHhVZu7IE8ElSZLGtD2aXYAkSZKk+hQPzPkr4IwmlyJJkjQivNxbkiRJ2olExMeBJ4DrM/MXTS5HkiRpRHi5tyRJkiRJkqSmciWlJEmSJEmSpKaySSlJkiRJkiSpqXxwToMcdthhecwxxzS7DEmSJEmSJGlMeOCBB57LzMOrbbNJ2SDHHHMMy5Yta3YZDTFnzhw2bdrEpEmTdmj85MmTmTNnToOrkiRJkiRJ0lgWEU/W2maTUnVra2ujfctWDsvdhxz71Mb1o1CRJEmSJEmSdmY2KTUse++xBxe/tWXIcX9/z+2jUI0kSZIkSZJ2Zj44R5IkSZIkSVJT2aSUJEmSJEmS1FRe7q26dXR0QHfPqM23YMECAB++I0mSJEmStIuySam69fT0QOaozbdq1apRm0uSJEmSJEmjr2GXe0fEURHxs4hYGRGPRMT5RfyQiLgjIn5TvB9cxF8bEUsioiMi5g7IdX5ErCjyXDDInFdHxNqIWDEgPrvYtycipg2yf63aTo+IX0bE8ohYFhFveTnnRpIkSZIkSdLvNPKelF3ApzLzOOCNwMci4nhgHnBnZh4L3Fl8BlgPnAdc3j9JRJwAfBA4GXgdMDMijq0x57XA9CrxFcAs4O4haq5V253A6zLz9cA5wDeHyCNJkiRJkiRpBzWsSZmZz2bmg8XPLwIrgSOB04GFxbCFwBnFmLWZuRToHJDqOOC+zNyamV3AYuDMGnPeTbnZOTC+MjMf24Gya9W2ObPv+ub9gdG71lk1lUqlvld/H/jAByiVSnzgAx/oi11xxRWUSiWuuOKKirHXXXcdpVKJ6667ri92xx13UCqVuOOOOyrGLl68mFKpxOLFiyvi999/P6VSifvvv78v9tBDD1EqlXjooYcqxj7yyCOUSiUeeeSRivivf/1rSqUSv/71r/tizz77LKVSiWeffbZi7AsvvMA73vEOXnjhhSHjGzdu5Mwzz2Tjxo0VYzdt2sS73vUuNm3a1BfbunUr5557Llu3bq0Yu2XLFs4++2y2bNkyZI6uri6uueYaurq6KsZ2dnZy5ZVX0tlZ+ce72pwdHR1cdNFF5Xuf9tPd3c2NN95Id3d3Rbza+Fp1VMtRK2+tmqvl7unpYcmSJeVbIQwxtl61cleTmTz55JPkgNsx1Iq/3DpGYr56jq+WeuYbiXNRb+5Gnf9GauR5Gst1jJXjrsdYrnks1yZJkqSxZ1Se7h0RxwBvAO4HJmTms1BuZAKvGmL3FcApEXFoROwHnAoc1aBSa9YWEWdGxKPALZRXU2oHtG15kdbWVi688MJhv1pbW3nmmWd2eM7f/va3Fe8AP/3pTyvee33rW9+qeAe4/PLLK957XXbZZRXvvf7u7/6u4h3g05/+dMV7r09+8pMV770+8YlPVLwD/NVf/VXFe6/3vOc9bN++nfe85z1Dxs866yy2bt3KWWedVTF29uzZbN68mdmzZ/fFZs2axdNPP8273vWuirGzZs2ira3tJfFqOc477zy+973vcf7551eMPffcc7n11lsrGse15nz3u9/Ngw8++JLj++xnP8tXv/pVLrnkkop4tfG16qiWo1beWjVXy/31r3+dSy65hH/+538ecmy9auWu5u677+ZDH/oQ99xzzw7FX24dIzFfPcdXSz3zjcS5qDd3o85/IzXyPI3lOsbKcddjLNc8lmuTJEnS2NPwJmVEHAD8CLggMzcNNX6gzFwJfAG4A/gp8BDlS8lHVWbekJmvpby68tJqYyLiQ8U9K5etW7dudAuUXqbelS61VmrtyEqY1tZWAB5//PGKeFtbGwBr1qwZcs729naAl6zoXLZsGQBLly6tiFcbX6uOajlq5a1Vc7Xct956KwC33HLLkGPrVSt3Nddff33F+1Dxl1vHSMxXz/HVUs98I3Eu6s3dqPPfSI08T2O5jrFy3PUYyzWP5dokSZI09jS0SRkRe1JuUF6XmT8uwm0RcUSx/Qhg7VB5MvOqzPyjzDyF8uXcvykezLO8eH1kmPVdU+z/kx2trbikfEpEHFZl2zcyc1pmTjv88MOHU9IuZ8L+r2DKlCnMnz9/2K8pU6Zw5JFHNvtQdnkRAcBuu1X/z0Lv9sFMmTIFgKlTp1bEJ0yYAMDEiROHnHPfffcFYL/99qsYO21a+ZlXJ510UkW82vhadVTLUStvrZqr5Z4xYwYAp5122pBj61UrdzW9q1r7r24dLP5y6xiJ+eo5vlrqmW8kzkW9uRt1/hupkedpLNcxVo67HmO55rFcmyRJksagzGzICwjgW8CVA+LzgXnFz/OALw7Yfgkwd0DsVcX70cCjwMGDzHsMsKLGtruAaYPsW7U2YCoQxc9/BDzT+7nW68QTT8xd1fTp03N6SynXfvVfhnyd997359y5c1/WfHPnzq2ao6Wlpe/V37nnnpstLS157rnn9sW+9KUvZUtLS37pS1+qGPvtb387W1pa8tvf/nZf7Pbbb8+Wlpa8/fbbK8bedddd2dLSknfddVdF/L777suWlpa87777+mLLly/PlpaWXL58ecXYFStWZEtLS65YsaIi/thjj2VLS0s+9thjfbHVq1dnS0tLrl69umLs+vXrc+bMmbl+/foh4xs2bMgzzjgjN2zYUDF248aNOWvWrNy4cWNfbMuWLXnOOefkli1bKsZu3rw53//+9+fmzZuHzNHZ2ZlXX311dnZ2Vozdvn17XnHFFbl9+/aKeLU5t23blvPmzctt27ZVjO3q6spFixZlV1dXRbza+Fp1VMtRK2+tmqvl7u7uznvvvTe7u7uHHFuvWrmr6enpySeeeCJ7enp2KP5y6xiJ+eo5vlrqmW8kzkW9uRt1/hupkedpLNcxVo67HmO55rFcmyRJkpoDWJY1emm9jbcRFxFvAe4BHgZ6r+X8W8r3pfxB0XB8CpidmesjYiKwDHhlMX4zcHxmboqIe4BDKT9U55OZeWeNOb8LvA04DGgDPpuZV0XEmcA/AYcDG4DlmVmqsv+hNWr7X8DZxfztwIWZ+fPBjn/atGnZexnprmbGjBnQk3zrjL8Ycuzf33M7ex05gfnz5w97vgsvvBDgZeWQJEmSJElSc0XEA5k5rdq2PRo1adHEq3V96NurjF8DvLpGrrfu4JzvrRG/AbhhB/Z/vkZtX6B8X0xRXJqbo/OEWYDJkyeP2lySJEmSJEkafQ1rUmrXtffee9PTsX3U5pszZ86ozSVJkiRJkqTR1/Cne0uSJEmSJEnSYGxSSpIkSZIkSWoqL/fWsHR0dfH399w+5LinNq5n6pETRqEiSZIkSZIk7axsUqpuEyZMYNOmTew1aejm49QjJ/jgG0mSJEmSJA3KJqXqtmDBgmaXIEmSJEmSpF2I96SUJEmSJEmS1FQ2KSVJkiRJkiQ1lZd7q24LFixg1apVL4mvXr0agEmTJlXEJ0+ezJw5c0alNkmSJEmSJO18bFKqbqtWreI3v1rB0QfuUxHfsnEbAB25tS/2VBGTJEmSJEmSarFJqWE5+sB9mPfm36+Iff4//gugIt4bkyRJkiRJkmrxnpSSJEmSJEmSmsompSRJkiRJkqSm8nJv1W316tV0b9k+avMtWLAAwIfvSJIkSZIk7aJsUqpu7e3t9HT1jNp81Z4kLkmSJEmSpF1Hwy73joijIuJnEbEyIh6JiPOL+CERcUdE/KZ4P7iIvzYilkRER0TMHZDr/IhYUeS5YJA5r46ItRGxYkB8drFvT0RMG2T/qrX1235SRHRHxP89nHMiSZIkSZIk6aUaeU/KLuBTmXkc8EbgYxFxPDAPuDMzjwXuLD4DrAfOAy7vnyQiTgA+CJwMvA6YGRHH1pjzWmB6lfgKYBZw9xA116qNiNgd+AJw2xA5NEoWLlxIqVRi4cKFFfGbb76ZUqnEzTff3BdbsWIFpVKJFSsq+tc89NBDlEolHnroob7YY489RqlU4rHHHqsYu2rVKkql0ktWdq5bt44ZM2awbt26vtgLL7zAO97xDl544YWKsc899xynnnoqzz33XEW8o6ODiy66iI6Ojr7Yiy++yFlnncWLL75YMbZWvL29nTlz5tDe3t4X27p1K+eeey5bt26tGNvZ2cmVV15JZ2fnoDGA7u5ubrzxRrq7uyviXV1dXHPNNXR1dQ2Zo5Zq42vNV0+8Wm21ZCZPPvkkmblDNdejVu6enh6WLFlCT8/QK5Jr5agWb+SxNEo9x9cMY6UOjaxd6fe6Kx2LRpbfDe1K/D5L0vgQo/Uf+oi4Efhy8XpbZj4bEUcAd2Xma/qNuwTYnJmXF59nA6XM/EDx+X8DHZn5xRrzHAPcnJknVNl2FzA3M5fV2PexWrUVKzg7gZOK/D8c7HinTZuWy5ZVnWanN2vWLHo62vnqqcdVxD//H/8FwLw3/35f7JO3P8b23fdmypQpw56vtbWVffbZh+985zsV8VKp1PfzbbfdNmi8UWNHKscZZ5xBe3s7++23HzfccMOwcpx66ql0d3ezxx57cMsttwAwffp0MpPddtuNW2+9tW/s2WefTVtbGxMnTuxr8laLAVx88cUsXbqUk08+mUsvvbQv/tGPfpTW1lamTp3KV77ylUFz1FJtfK356olXq62WxYsXc9lll/GZz3yGU045Zcia61Er94IFC1i0aBGzZs3iwx/+8LByVIs38lgapZ7jG0v1aee2K/1ed6Vj0cjyu6Fdid9nSdp1RMQDmVn1KudRebp30Th8A3A/MCEznwUo3l81xO4rgFMi4tCI2A84FTiqQaVWrS0ijgTOBL422M4R8aGIWBYRy/qvqpOG0rv6ceCKx3r0riTsv3qw9x8hBq7Ya2trA2DNmjWDxgB6m+1Lly6tiLe2tgLw+OOPD5mjlmrja81XT7xabbVcf/31Fe8jqVbu3oZxbzN5ODmqxRt5LI1Sz/E1w1ipQyNrV/q97krHopHld0O7Er/PkjQ+NLxJGREHAD8CLsjMTfXun5krKV9mfQfwU+AhypeSj6Yrgf+Vmd2DDcrMb2TmtMycdvjhh49SaWPbq/bfiylTpjB//vxhv6ZMmcKRRx7Z7ENpqH333ReA/fbbb9g5dt99dwD22ON3z8OKCAB2263yj/qECRMAmDhx4qAxgGnTyv/AcdJJJ1XEe1fHTp06dcgctVQbX2u+euLVaqtl9uzZFe8jqVbuGTNmAHDaaacNO0e1eCOPpVHqOb5mGCt1aGTtSr/XXelYNLL8bmhX4vdZksaJzGzYC9iT8j0cP9kv9hhwRPHzEcBjA/a5hPIl2bVyXgZ8lPJqyuXF6yP9th8DrKix713AtH6fryn2/8lgtQH/BTxRvDYDa4EzBjv2E088MXdVZ555Zp5+6vR85st/U/H6xHvPyE+894yXxObOnfuy5ps7d27VHNdee222tLTktddeWxG/6aabsqWlJW+66aa+2MMPP5wtLS358MMPV4xdvnx5trS05PLly/tijz76aLa0tOSjjz5aMba1tTVbWlqytbW1Ir527dqcPn16rl27ti+2fv36nDlzZq5fv75i7Lp163LGjBm5bt26ivi2bdty3rx5uW3btr7Ypk2bcvbs2blp06aKsbXiW7duzY985CO5devWvtiWLVvynHPOyS1btlSM3b59e15xxRW5ffv2QWOZmV1dXblo0aLs6uqqiHd2dubVV1+dnZ2dQ+aopdr4WvPVE69WWy09PT35xBNPZE9Pzw7VXI9aubu7u/Pee+/N7u7uYeeoFm/ksTRKPcfXDGOlDo2sXen3uisdi0aW3w3tSvw+S9KuA1iWNXppDbsnZZSXcC0E1mfmBf3i84HnM/PzETEPOCQzP91v+yX0uydlEXtVZq6NiKOB24E3ZWblE0l+N/YYhn9PykFrK8Zci/ek3OF7Un7+P/6LvY+cyvz584c934UXXgjwsnJIkiRJkiSpuZp1T8o3A+8H/jQilhevU4HPA38WEb8B/qz4TERMjIingU8CF0fE0xHxyiLXjyLiV8BNwMcGaVB+F1gCvKbY/9wifmaR+03ALRFR6wndVWuTJEmSJEmS1Dh7DD1keDLz50DU2Pz2KuPXAK+ukeutOzjne2vEbwBu2IH9n69W24Axf7UjtezK9t13X7q7O0ZtvsmTJ4/aXJIkSZIkSRp9DWtSatc1adIkOp4Z/lOo6zVnzpxRm0uSJEmSJEmjr+FP95YkSZIkSZKkwdiklCRJkiRJktRUXu6tYXlq47a+p3n3jwEV8ac2buPYI0e1NEmSJEmSJO1kbFKqbrUeZLN/rAZg70mT+mLHHumDbyRJkiRJkjQ4m5Sqmw+ykSRJkiRJ0kjynpSSJEmSJEmSmsqVlKrbggULWLVq1Uviq1eXL/ee1O9y78FMnjzZVZmSJEmSJEmySan6rVq1isdW/pKJB0ZF/MUNCcBGnh8yx5qN2ZDaJEmSJEmStPOxSalhmXhg8Nd/Uvn1uWZxF8BL4tX0jpUkSZIkSZK8J6UkSZIkSZKkprJJKUmSJEmSJKmpvNxbdVu9ejUdm8fuPSUXLFgA4EN5JEmSJEmSdhI2KVW39vZ2usbwLSWrPXlckiRJkiRJY1fDLveOiKMi4mcRsTIiHomI84v4IRFxR0T8png/uIi/NiKWRERHRMwdkOv8iFhR5LlgkDmvjoi1EbFiQHx2sW9PREwbZP9atb0tIjZGxPLi9Xcv59xIkiRJkiRJ+p1G3pOyC/hUZh4HvBH4WEQcD8wD7szMY4E7i88A64HzgMv7J4mIE4APAicDrwNmRsSxNea8FpheJb4CmAXcPUTNtWoDuCczX1+8PjdEHkmSJEmSJEk7qGFNysx8NjMfLH5+EVgJHAmcDiwshi0EzijGrM3MpUDngFTHAfdl5tbM7AIWA2fWmPNuys3OgfGVmfnYDpRdtTbt/B5++GFKpRIPP/xwRby1tZVSqURra2tf7Omnn6ZUKvH0009XjH3++ec57bTTeP755yvi69atY8aMGaxbt64vtnbtWqZPn87atWsrxm7bto0LLriAbdu2VcTb29uZM2cO7e3tfbGtW7dy7rnnsnXr1iHHAnR3d3PjjTfS3d095Niuri6uueYauvpdt9/Z2cmVV15JZ2flH8FqeQG2b9/OpZdeyvbt24edo6enhyVLltDT0zNobbXG1orXGpuZPPnkk2T+7p6q1Y6j3hzVYoPFq6l1jurJUc/YejUy92jW0YzjGAvnbizU0Azj9bglSZKknVGMxl/cI+IYyqsYTwCeysyD+m17ITMP7vf5EmBzZl5efD4OuBF4E9BOeYXjssz8xCBz3ZyZJ1TZdhcwNzOX1dh3Q7XaIuJtwI+Ap4HVRY5HBjvmadOm5bJlVafZ6c2aNYuuji1cdPqeFfFrFpebSn/9J0Pf6vQfbumkZ/f9mTJlyojX19rayj777MN3vvOdvlipVOr7+bbbbhs0Xs/YenPMnDmTzs5O9tprL2666aa++Kmnnkp3dzd77LEHt9xyCwDTp08nM9ltt9249dZbBx0LcPHFF7N06VJOPvlkLr300kHHfvSjH6W1tZWpU6fyla98BYCzzz6btrY2Jk6cyMKFCwfNC3DWWWexceNGDjroIL7//e8PK8eCBQtYtGgRs2bN4sMf/nDN2mqNrRWvNXbx4sVcdtllfOYzn+GUU06peRz15qgWGyxeTa1zVE+OesbWq5G5R7OOZhzHWDh3Y6GGZhivxy1JkiSNVRHxQGZWvRVjIy/37p38AMoNvgsyc1O9+2fmSuALwB3AT4GHKF9KPpoeBH4vM18H/BOwqNqgiPhQRCyLiGX9V9VJvXpXGA5ctde7eq7/6sHef0AYuJKv2liA3qb40qVLhxzbu3L08ccf74u1tbUBsGbNmiHzAmzcuBGADRs2DDtHb/O1fwO1Wm21xtaK1xp7/fXXV7zXOo56c1SLDRavptY5qidHPWPr1cjco1lHM45jLJy7sVBDM4zX45YkSZJ2Rg1tUkbEnpQblNdl5o+LcFtEHFFsPwJYW2v/Xpl5VWb+UWaeQvly7t8UD+bpfZDNR4ZZ3zXF/j8ZrLbM3JSZm4uffwLsGRGHVanzG5k5LTOnHX744cMpadw45IBgypQpzJ8/f8RfU6ZM4cgjj2z2IVa1557l1ad77bVXRXz33XcHYI89frcKNSIA2G233YYcCzBtWvkfIk466aQhx/auYJ06dWpfbMKECQBMnDhxyLwABx54IAAHHdS3+LjuHDNmzADgtNNOG7S2WmNrxWuNnT17dsV7reOoN0e12GDxamqdo3py1DO2Xo3MPZp1NOM4xsK5Gws1NMN4PW5JkiRpp5SZDXkBAXwLuHJAfD4wr/h5HvDFAdsvoXw5df/Yq4r3o4FHgYMHmfcYYEWNbXcB0wbZt2ptwER+d2n8ycBTvZ9rvU488cTcVZ155pn5jlNb8t6vn1bx+uD7SvnB95VeEq/2+uD7Sjl37tyG1Dd37tyX5P7lL3+ZLS0t+ctf/rIi/vjjj2dLS0s+/vjjfbHf/va32dLSkr/97W8rxj733HN56qmn5nPPPVcRX7t2bU6fPj3Xrl3bF2tra8tSqZRtbW0VY9vb2/P888/P9vb2ivjWrVvzIx/5SG7durUvtmXLljznnHNyy5YtQ47NzOzq6spFixZlV1fXkGM7Ozvz6quvzs7Ozr7Y9u3b84orrsjt27cPmTczs6OjIz/3uc9lR0fHsHN0d3fnvffem93d3YPWVmtsrXitsT09PfnEE09kT0/PoMdRb45qscHi1dQ6R/XkqGdsvRqZezTraMZxjIVzNxZqaIbxetySJEnSWEX5Fo5Ve2kNuydlRLwFuAd4GOi9XvVvgfuBHxQNx6eA2Zm5PiImAsuAVxbjNwPHZ+amiLgHOJTyQ3U+mZl31pjzu8DbgMOANuCzmXlVRJxJ+TLtw4ENwPLMLFXZ/9AatX0cmEP5MvP2ooZ7Bzt+70k5uGsWd3HgpP/G/PnzR7y+Cy+8EKAhuSVJkiRJkjQ8g92Tcuhu0jBl5s8pr6as5u1Vxq8BXl0j11t3cM731ojfANywA/s/X6O2LwNf3pEaxoN9992Xju4tzS6jpsmTJze7BEmSJEmSJNWhYU1K7bomTZrExtXPN7uMmubMmdPsEiRJkiRJklSHhj/dW5IkSZIkSZIGY5NSkiRJkiRJUlN5ubeGZc3G7HtQTl9sQ/khTAPjtfY/cFJDSpMkSZIkSdJOxial6lbrwTRbWA3AgZOG7j4eOMkH3EiSJEmSJKnMJqXq5oNpJEmSJEmSNJK8J6UkSZIkSZKkprJJKUmSJEmSJKmpvNxbdVuwYAGrVq16SXz16vI9KSftwD0poXxPSi8dlyRJkiRJkk1K1W3VqlU8uvKXHHpQZXzTxvL7nvHckDme39CAwiRJkiRJkrRTskmpYTn0IJj59qiI3XxnAi+NV9M7VpIkSZIkSfKelJIkSZIkSZKaypWUqtvq1avZuqXZVdS2YMECAO93KUmSJEmStJOwSam6tbe309XV7Cpqq/ZQH0mSJEmSJI1dDbvcOyKOioifRcTKiHgkIs4v4odExB0R8Zvi/eAi/tqIWBIRHRExd0Cu8yNiRZHngkHmvDoi1kbEigHx2cW+PRExbZD9q9ZWbHtbRCwv8iwe7nmRJEmSJEmSVKmR96TsAj6VmccBbwQ+FhHHA/OAOzPzWODO4jPAeuA84PL+SSLiBOCDwMnA64CZEXFsjTmvBaZXia8AZgF3D1Fz1doi4iDgq8A7M/MPgdlD5JEkSZIkSZK0gxrWpMzMZzPzweLnF4GVwJHA6cDCYthC4IxizNrMXAp0Dkh1HHBfZm7NzC5gMXBmjTnvptzsHBhfmZmP7UDZVWsD3gf8ODOf6q11B3JpDHnwwQcplUo8+OCDFfElS5ZQKpVYsmRJX6y1tZVSqURra2vF2JUrV1IqlVi5cmVFfPXq1ZRKJVavXt0Xa2tro1Qq0dbWVjF227ZtXHDBBWzbtq0ivmHDBk4//XQ2bNjQF9u4cSNnnnkmGzdurBi7detWzj33XLZu3VoR7+jo4KKLLqKjo6Mv1tXVxTXXXEPXgOvzq43t7u7mxhtvpLu7u2Jsrfj27du59NJL2b59+5BjOzs7ufLKK+nsrPzjnZk8+eSTZOaQOWrFq+np6WHJkiX09PQMObbe+arVPNbtjDWPtpE4R57n4Rvtc+fvSpIkSRp7YjT+gh4Rx1BexXgC8FRmHtRv2wuZ2f+y6kuAzZl5efH5OOBG4E1AO+UVjssy8xODzHVzZp5QZdtdwNzMXFZj3w3VaouIK4E9gT8EXgH8f5n5rcGOedq0ablsWdVpdnqzZs2ic/sW/vJdURG/+c7yd2nm26PabhWuuzGJ3fZnypQpI15fa2sr++yzD9/5znf6YqVSqe/n2267bdB4PWPrzTFz5kw6OzvZa6+9uOmmm4aVY/r06WQmu+22G7feemtf/IwzzqC9vZ399ld3hqcAACAASURBVNuPG264AYCPfvSjtLa2MnXqVL7yla8MOvbiiy9m6dKlnHzyyVx66aV9Y2vFzzrrLDZu3MhBBx3E97///UHHnn322bS1tTFx4kQWLlzYF1+8eDGXXXYZn/nMZzjllFOGVUc1CxYsYNGiRcyaNYsPf/jDg46td75qNY91O2PNo20kzpHnefhG+9z5u5IkSZKaIyIeyMyqt2Js5OXevZMfAPwIuCAzN9W7f2auBL4A3AH8FHiI8qXko2kP4ETgNKAE/O+I+IOBgyLiQxGxLCKWrVu3bpRL1M6gdyVh/9WH9er9h4WBqwTb29sBKlZY9q4Gffzxx4cc29tUX7p0acXYWvHeFZ79V3/WGtu7onTNmjUV8euvv77ifTh1VNPbvL3llluGHFvvfNVqHut2xppH20icI8/z8I32ufN3JUmSJI09DX26d0TsSblBeV1m/rgIt0XEEZn5bEQcAQx56XRmXgVcVeS8DHg6Io4CepeifS0zvzaM+q4B3gCszsxTB6ntaeC5zNwCbImIuynfH/PXA+r8BvANKK+krLee8eTAA+DwI6Ywf/78Ec994YUXjnjOkbLnnnv2raQcrojoW0nZ37777tu3OrLXlClT+lZSDjV22rRpLF26lJNOOqlibK34gQce2LeScqixEyZM6FtJ2d/s2bO57LLLmD179pA5asWrmTFjBosWLeK0004bcmy981WreazbGWsebSNxjjzPwzfa587flSRJkjQGZWZDXkAA3wKuHBCfD8wrfp4HfHHA9ksoX5LdP/aq4v1o4FHg4EHmPQZYUWPbXcC0QfatWhvl+2LeSbmpux/lB/GcMNjxn3jiibmrOvPMM3PmaS15/dWlitdf/nlL/uWfvzRe7fWXf96Sc+fObUh9c+fOfUnuBx54IFtaWvKBBx6oiN97773Z0tKS9957b1/s8ccfz5aWlnz88ccrxv7qV7/KlpaW/NWvflURf+aZZ7KlpSWfeeaZvtiaNWuypaUl16xZUzG2vb09zz///Gxvb6+Iv/DCC/nOd74zX3jhhb7Yhg0b8owzzsgNGzZUjN2yZUuec845uWXLlor4tm3bct68eblt27a+WGdnZ1599dXZ2dk55Niurq5ctGhRdnV1VYytFe/o6MjPfe5z2dHRMeTY7du35xVXXJHbt2+viPf09OQTTzyRPT09w66jmu7u7rz33nuzu7t7yLH1zlet5rFuZ6x5tI3EOfI8D99onzt/V5IkSVJzUL6FY9VeWsPuSRkRbwHuAR4Geq9L/VvgfuAHRcPxKWB2Zq6PiInAMuCVxfjNwPGZuSki7gEOpfxQnU9m5p015vwu8DbgMKAN+GxmXhURZwL/BBwObACWZ2apyv6HVqut2HYh8NdFbd/MzCsHO37vSTm4m+9MDj/ivzd0JWUjckuSJEmSJGl4BrsnZcMu987Mn1NeTVnN26uMXwO8ukaut+7gnO+tEb8BuGEH9n++Wm3FtvmUV1pKkiRJkiRJGkENvSeldk377rsv2bOl2WXUNHny5GaXIEmSJEmSpDrYpFTdJk2axLpnn2t2GTXNmTOn2SVIkiRJkiSpDrsNPUSSJEmSJEmSGscmpSRJkiRJkqSm8nJvDcvzG373NO/+MXhpvNb+hx/RiMokSZIkSZK0s7FJqbrVejBNZ64G4PAjJg2Z4/AjfMCNJEmSJEmSymxSqm4+mEaSJEmSJEkjyXtSSpIkSZIkSWoqV1KqbgsWLGDVqlUvia9eXb7ce9KkoS/3hvLl3q7KlCRJkiRJkk1K1W3VqlX8auUveeUhlfFNG8vvPbs/N2SOTesbUJgkSZIkSZJ2SjYpNSyvPATeVKqMLbmt/D4wXk3vWEmSJEmSJMl7UkqSJEmSJElqKpuUkiRJkiRJkprKy71Vt9WrV7Nla7OrqG3BggUAPpRHkiRJkiRpJ2GTUnVrb2+nu7PZVdRW7cnjkiRJkiRJGrsadrl3RBwVET+LiJUR8UhEnF/ED4mIOyLiN8X7wUX8tRGxJCI6ImLugFznR8SKIs8Fg8x5dUSsjYgVA+Kzi317ImLaIPvXqu3CiFhevFZERHdEHFIrjyRJkiRJkqQd18h7UnYBn8rM44A3Ah+LiOOBecCdmXkscGfxGWA9cB5wef8kEXEC8EHgZOB1wMyIOLbGnNcC06vEVwCzgLuHqLlqbZk5PzNfn5mvBy4CFmfm+iFySZIkSZIkSdoBDWtSZuazmflg8fOLwErgSOB0YGExbCFwRjFmbWYuBQZeSHwccF9mbs3MLmAxcGaNOe+m3OwcGF+ZmY/tQNlVaxvgvcB3dyCXxpAVK1ZQKpVYsaJikS3Lly+nVCqxfPnyvtjq1asplUqsXr26YuxTTz1FqVTiqaeeqoivWbOGUqnEmjVr+mLr1q1jxowZrFu3rmLs+vXrmTlzJuvXV35NN2zYwOmnn86GDRv6Yi+++CJnnXUWL774YsXY7u5ubrzxRrq7uyvinZ2dXHnllXR2/u6PUE9PD0uWLKGnp6di7Pbt27n00kvZvn37oPsDbNu2jQsuuIBt27ZVxDs6Orjooovo6OgYsrbM5MknnyQzhzyWWjXXilfLXauOWjmqqVVzrXg9quWo5xyNRB31nIt6a26keuZsRn0aWfX8Duv9To9lO+N3d2eseVfhuZckSbuKGI2/0ETEMZRXMZ4APJWZB/Xb9kJmHtzv8yXA5sy8vPh8HHAj8CagnfIKx2WZ+YlB5ro5M0+osu0uYG5mLqux74YhatsPeBqYOtRKymnTpuWyZVWn2enNmjWLju1bKL23Mr7ktvL7m0pD5/i3H8IesT9TpkwZ8fpaW1vZZ599+M53vtMXK5V+V9Rtt902aLyesc3IcfHFF7N06VJOPvlkLr300r742WefTVtbGxMnTmThwnKvfcGCBSxatIhZs2bx4Q9/uG/sWWedxcaNGznooIP4/ve/X3N/gJkzZ9LZ2clee+3FTTfd1Bc/44wzaG9vZ7/99vv/2bv36KrqO///z7eCCnUKaiMIteMQXFMdv3Zao9/2O8rqmq6eI0KroLHqr2BnvEZb4duG/rDan4xMqW38fXWml9Qr0os3aoFSio4/l4KW6JBarHHAYlJwKhAwgYSQ2zk5798f5yRmh73PyQk5hITXY629ds57v/f78947p2A/7AsrVqzI2tu6detYsmQJd9xxB9OmTct6LFE9R8XDakf1EVUjTFTPUfF8hNXI5xwNRh/5nIt8ey6kfMYciv5kcOXzO8z3O30kG47f3eHY80ihcy8iIiLDiZn93t1DH8VYyNu9uwc/EXgGmO/uzfnu7+6bge8BzwPPAm+QvpV8KHwB+F3UBKWZ3Whm1WZW3fcKOpHB0j35vXHjxkC8vr4eIHBF59q1awFYs2ZNILepqQkgcOVm2P5Az5WVva+6hPQLlABaWz941XtUb8uXLw+ss+VH9RwVD6sd1UdUjTBRPUfF8xFWI59zNBh95HMuosYbjHORr3zGHIr+ZHDl8zvM9zt9JBuO393h2PNIoXMvIiIiI0VBJynNbDTpCcpfuPuvMuF6Mzsts/00YHeuOu7+iLt/yt2nkb6de2vmxTzdL7O5eYD9Lc3s/9t+9nYVWW71dvcH3b3E3UuKiooG0tJR40N/BcXFxVRUVAz6UlxczOTJk4f6EAumpCT9Dw7nn39+ID5hwgQAJk6c2BObPn06ADNmzAjkjhs3DoDx43suHA7dH2D06NEAHHfccYH4mDFjABg7dmzO3kpLSwPrbPlRPUfFw2pH9RFVI0xUz1HxfITVyOccDUYf+ZyLqPEG41zkK58xh6I/GVz5/A7z/U4fyYbjd3c49jxS6NyLiIjIiOHuBVkAA34K3N8nXgEszPy8EPh+n+2LSN+S3Tt2amb9MWALcFKWcc8AaiK2vQSUZNk3sjdgHOkJ0g/15/jPO+88H6lmzZrll8yI+b/9PLhcNSe99I2HLVfNiXl5eXlB+isvLz+o9ptvvumxWMzffPPNQPwPf/iDx2Ix/8Mf/tATe++99zwWi/l7770XyN2+fbvHYjHfvn17IL5z506PxWK+c+fOntju3bv94osv9t27dwdyGxoafMaMGd7Q0BCI792717/4xS/63r17e2LNzc1eWlrqzc3NgdxkMukrV670ZDIZiHd2dvp9993nnZ2dPbGuri7fsGGDd3V1BXI7Ojr87rvv9o6Ojqz7u7u3tbX5vHnzvK2tLRBvb2/3hQsXent7e87eUqmUb9u2zVOpVM5jieo5Kh5WO6qPqBphonqOiucjrEY+52gw+sjnXOTbcyHlM+ZQ9CeDK5/fYb7f6SPZcPzuDseeRwqdexERERlOSD/CMXQurWDPpDSzC4GXgTeB7qfYfwt4DXg6M+H4LlDq7o1mNhGoBj6cyW8Bznb3ZjN7GTiF9Et1vu7uL0SM+QTwWeAjQD1wl7s/YmazgB8ARcA+YJO7H/TkRDM7Jay3zLavABe7+1X9OX49kzK7qufgoxPOpaKiYtD7W7BgAUBBaouIiIiIiIiIyMBkeyblqEIN6u6vkL6aMsznQvJ3AR+NqHVRP8e8OiK+AljRj/0bwnrLbHsMeKw/fYx0Y8aMIekHhrqNSFOmTBnqFkREREREREREJA8Fm6SUkWvSpEn8pf79oW4jUllZ2VC3ICIiIiIiIiIieSj4271FREREREREREREstEkpYiIiIiIiIiIiAwp3e4tA9Lc+MGLcnrH4OB41P5MGPS2RERERERERERkGNIkpeQt6sU0O7p2ADBpwqTcRSboBTciIiIiIiIiIpKmSUrJm15MIyIiIiIiIiIig0nPpBQREREREREREZEhpUlKERERERERERERGVK63VvyVllZSV1d3UHxHTsyz6Sc1I9nUpJ+JqVuHRcREREREREREU1SSt7q6up4c8sfGX1KMJ5oTq+bR7+fs0aioQCNiYiIiIiIiIjIsKRJShmQ0afARy61QOz9VQ4cHA/TnSsiIiIiIiIiIqJnUoqIiIiIiIiIiMiQ0pWUkrcdO3aQbB3qLqJVVlYC6HmXIiIiIiIiIiLDhCYpJW9tbW14cqi7iBb2Uh8RERERERERETlyFex2bzM73cxeNLPNZvaWmc3LxE82s+fNbGtmfVIm/nEzqzKzDjMr71NrnpnVZOrMzzLmo2a228xq+sRLM/umzKwky/5RvY0zs9Vm9kamzj8dyrkRERERERERERGRDxTymZRJ4BvufhbwaeBWMzsbWAi84O5nAi9kPgM0ArcB9/YuYmbnADcAFwCfAGaa2ZkRYz4GXBwSrwFmA+tz9BzV263Af7n7J4DPAv+vmR2Xo5aIiIiIiIiIiIj0Q8EmKd19p7u/nvl5P7AZmAxcCizLpC0DLsvk7Hb3jUCiT6mzgFfdvdXdk8A6YFbEmOtJT3b2jW9297f70XZob4ADf2VmBpyYGeMIvuFZ+nr77beJx+O8/Xbwa1BTU0M8Hqem5oOLb+vq6ojH4wfdNh4Vb2xsZObMmTQ2fvDV279/P1deeSX79+8P5DY3N3P55ZfT3NycM37gwAHmzp3LgQMHArmJRIL777+fRCL4P5X29nbmz59Pe3t7T6yzs5PFixfT2dkZyG1ra6OsrIy2tracuVHxjo4Obr/9djo6OgaUC5BMJlm6dCnJZDJnja6uLlatWkVXV1fOGlG57s727dtx96z7R+XmK6pGWDyVSlFVVUUqlepXjahjPNTeBiu/UDUKJZ/ejuTjGK50TkVERgb9eS4iIofCDsdfIGZ2BumrGM8B3nX38b227XX3k3p9XgS0uPu9mc9nAauAzwBtpK9wrHb3r2UZ6zfufk7ItpeAcnevjth3X1hvZvZXwK+BjwN/BXzJ3ddkO+aSkhKvrg4dZtibPXs2rYkDnPbPFoi/vyr9XfrIpRa2W8Cunzpj7EMUFxcPen+1tbWccMIJPP744z2xeDze8/Nzzz2XNZ5P7uGoYWY8++yzPdvnzp1LfX09EydOZNmyZT3xmTNnkkgkOO6441i9ejUAV155JU1NTYwfP56nnnqqJ/eSSy6hq6uLUaNGsWbNmqy5UfHLLruMtrY2xo4dy4oVK/LOBbjllluora1l6tSp/OhHP8pa484772Tjxo1ccMEFLF68OGuNqNx169axZMkS7rjjDqZNmxa5f1RuvqJqhMUrKytZuXIls2fP5qabbspZI+oYD7W3wcovVI1Cyae3I/k4hiudUxGRkUF/nouISC5m9nt3D30UYyFv9+4e/ETgGWC+uzfnyu/L3TcD3wOeB54F3uDwX8UYBzYBk4C/B35oZh/um2RmN5pZtZlV79mz5zC3KCNV339IqK+vB2DXrl2BePeVlb2vQGxqagJg3759gdzuq+96Xz0YlRsV774Ks7W1dUC5kJ5QBnjnnXdy1uie9N+4cWPOGlG5y5cvD6yj9o/KzVdUjbD42rVrAXomjXPViDrGQ+1tsPILVaNQ8untSD6O4UrnVERkZNCf5yIicigK+nZvMxtNeoLyF+7+q0y43sxOc/edZnYasDtXHXd/BHgkU3MJ8BczOx1YnUn5ibv/ZAD9LQU+Cexw90uy9PZPwD2eni16x8z+TPqqyv/s0+eDwIOQvpIy336OJqPGQXFRMRUVFYNee8GCBYNecyilnzLwgQkTJvRcSdnb6NGje66k7DZu3LieqxJ7O/bYY3uupMyVGxUfM2ZMz9WRA8kFKC4u7rmKMVeNkpISNm7cyPnnn5+zRlRuaWkpS5YsobS0NOv+Ubn5iqoRFp8+fTorV65kxowZ/aoRdYyH2ttg5ReqRqHk09uRfBzDlc6piMjIoD/PRUTkkLh7QRbAgJ8C9/eJVwALMz8vBL7fZ/si0rdk946dmll/DNgCnJRl3DOAmohtLwElWfYN7Q2oBBZlfp4AvAd8JNvxn3feeT5SzZo1y+MzY/6VX8UDy8xrYz7z2oPjYcvMa2NeXl5ekP7Ky8sPqr1lyxaPxWK+ZcuWQPzNN9/0WCzmb775Zk+strbWY7GY19bWBnKj4g0NDT5jxgxvaGjoiTU3N3tpaak3NzcHcpuamnz27Nne1NSUM97S0uJz5szxlpaWQG5nZ6ffd9993tnZGYi3tbX5vHnzvK2trSfW0dHhd999t3d0dARyW1tb/eabb/bW1tacuVHx9vZ2X7hwobe3tw8o1909kUj4o48+6olEImeNZDLpK1eu9GQymbNGVG4qlfJt27Z5KpXKun9Ubr6iaoTFu7q6fMOGDd7V1dWvGlHHeKi9DVZ+oWoUSj69HcnHMVzpnIqIjAz681xERHIh/QjH0Lm0gj2T0swuBF4G3gS63wTxLeA14OnMhOO7QKm7N5rZRKAa+HAmvwU4292bzexl4BTSL9X5uru/EDHmE6Tfvv0RoB64y90fMbNZwA+AImAfsMnd4yH7nxLR2yTSbw4/jfTk6z3u/vNsx69nUmb3/irn40XnFvRKykLUFhERERERERGRgcn2TMqC3e7t7q+QntAL87mQ/F3ARyNqXdTPMa+OiK8AVoRt65PXENHbDiDWnx5EREREREREREQkPwV9JqWMTGPGjKHNDwx1G5GmTJky1C2IiIiIiIiIiEgeNEkpeZs0aRLNe94f6jYilZWVDXULIiIiIiIiIiKSh2OGugERERERERERERE5uulKShmQRMMHL8rpHYOD41H7U1SAxkREREREREREZNjRJKXkLeqZjzsSOwCYVDQpd5EiPTtSRERERERERETSNEkpedMzH0VEREREREREZDDpmZQiIiIiIiIiIiIypDRJKSIiIiIiIiIiIkNKt3tL3iorK6mrqzsovmNH5pmUk/rxTErSz6TUreMiIiIiIiIiIqJJSslbXV0df9zyFpxyYnBDcwsA749O5C7S0FKAzkREREREREREZDjSJKUMzCkncuyl5wZCXav+CHBQPEx3roiIiIiIiIiIiJ5JKSIiIiIiIiIiIkNKk5QiIiIiIiIiIiIypHS7t+Rtx44d0No21G1EqqysBNBLeUREREREREREhglNUkre2traINk11G1ECnvzuIiIiIiIiIiIHLkKdru3mZ1uZi+a2WYze8vM5mXiJ5vZ82a2NbM+KRP/uJlVmVmHmZX3qTXPzGoydeZnGfNRM9ttZjV94qWZfVNmVpJl/6jeTjKzFWb2RzP7TzM751DOjYiIiIiIiIiIiHygkM+kTALfcPezgE8Dt5rZ2cBC4AV3PxN4IfMZoBG4Dbi3d5HMhOANwAXAJ4CZZnZmxJiPAReHxGuA2cD6HD1H9fYtYJO7nwvMBf4tRx05wjzwwAPE43EeeOCBQPy73/0u8Xic7373uz2x9evXE4/HWb8++HV57bXXiMfjvPbaa4H4W2+9RTwe56233uqJbdmyhXg8zpYtWwK57777LvF4nHfffTcQr6+vJx6PU19f3xPbt28fl156Kfv27Qvk7t69m4svvpjdu3cH4nv37uULX/gCe/fu7Yk1NjYyc+ZMGhsbA7nNzc1cfvnlNDc398SampqYNWsWTU1NgdwDBw4wd+5cDhw4kDPe0dHB7bffTkdHRyC3paWFa665hpaWlkC8s7OTxYsX09nZmbNGWC5AMplk6dKlJJPJnLldXV2sWrWKrq4PrgROJBLcf//9JBKJQG4qlaKqqopUKpWzRlSuu7N9+3bcPWc83xphfYTF8h0vqkY+8j2WMIORG9ZHPr+TQvYcdY4Ot8E47uFopB+fiBSG/uwQEZFCO9r/rrHDdeBmtgr4YWb5rLvvNLPTgJfc/W975S0CWtz93sznUiDu7tdnPn8b6HD370eMcwbwG3c/6GpHM3sJKHf36oh93w7rzczWAN9191cyebXA/3L3+rA6ACUlJV5dHTrMsDd79mwOJNo59p//VyDeteqPABx76bk5a3T99DU+ZKMpLi4e9P5qa2s54YQTePzxx3ti8Xi85+fnnnsuazyf3JFUI1eumfHss89mjV922WW0tbUxduxYVqxYkbPnK6+8kqamJsaPH89TTz2VtUZYLsAtt9xCbW0tU6dO5Uc/+lHW3DvvvJONGzdywQUXsHjxYgDmzp1LfX09EydOZNmyZT25lZWVrFy5ktmzZ3PTTTdlrRGVu27dOpYsWcIdd9zBtGnTssbzrRHWR1gs3/GiauQj32MJMxi5YX3k8zspZM9R5+hwG4zjHo5G+vGJSGHozw4RESm0o+HvGjP7vbuH3uV8WN7unZk4/CTwGjDB3XcCZNan5ti9BphmZqeY2VjgEuD0ArUa1dsbpK/ExMwuAP4a+Gjfnc3sRjOrNrPqPXv2FKhFkaER9Q8aveNtbekXKrW2tvarZvdVm72vFo2qEZYL6UlpgHfeeSdnbvc/HGzcuLEn1n316q5duwK5a9euBWDNmjU5a0TlLl++PLDOFs+3RlgfYbF8x4uqkY98jyXMYOSG9ZHP76SQPUedo8NtMI57OBrpxycihaE/O0REpNCO9r9rCj5JaWYnAs8A8929OVd+X+6+Gfge8DzwLOkJw2TWnQbfPcBJZrYJ+Brwh7Ae3P1Bdy9x95KioqLD3OIwM24MxcXFVFRUDPpSXFzM5MmTh/oIRxwzyxkfM2YMAGPHju1XzXHjxgEwfvz4nDXCcoGeq3GnTp2aM7ekJP2PNeeff35PbMKECQBMnDgxkDt9+nQAZsyYkbNGVG5paWlgnS2eb42wPsJi+Y4XVSMf+R5LmMHIDesjn99JIXuOOkeH22Ac93A00o9PRApDf3aIiEihHfV/17h7wRZgNPAc8PVesbeB0zI/nwa83WefRaRvyY6quQS4hfTVlJsyy829tp8B1ETs+xJQ0uvz0sz+v+1Pb5m4AduAD2c79vPOO89HqlmzZnls5nSf/qvFgSV2banHri09KB62xK4t9fLy8oL0V15eflDtn/zkJx6LxfwnP/lJIL5kyRKPxWK+ZMmSnti6des8Fov5unXrArmvvvqqx2Ixf/XVVwPxmpoaj8ViXlNT0xPbvHmzx2Ix37x5cyB3+/btHovFfPv27YH4rl27PBaL+a5du3pie/fu9S9+8Yu+d+/eQG59fb3H43Gvr68PxBsbG33mzJne2NjYE2toaPAZM2Z4Q0NDILepqclnz57tTU1NPbF9+/b5ZZdd5vv27QvktrS0+Jw5c7ylpSVnvL293RcuXOjt7e2B3P379/vVV1/t+/fvD8Q7Ojr87rvv9o6Ojpw1wnLd3ROJhD/66KOeSCRy5iaTSV+5cqUnk8meWGdnp993333e2dkZyO3q6vINGzZ4V1dXzhpRualUyrdt2+apVCpnPN8aYX2ExfIdL6pGPvI9ljCDkRvWRz6/k0L2HHWODrfBOO7haKQfn4gUhv7sEBGRQjsa/q4Bqj1iLq1gz6S09OVVy4BGd5/fK14BNLj7PWa2EDjZ3b/Za/siej2TMhM71d13m9nHgP8APuPuH7wdJDjuGQz8mZShvZnZeKDV3TvN7AbgInefm+349UzK7LpW/ZFzi/6aioqKQe9vwYIFAAWpLSIiIiIiIiIiA5PtmZSjCjjuPwBzgDczt0lD+i3Z9wBPm9l1wLtAaabJiUA18GEgZWbzgbM9fYv4M2Z2CpAAbs0yQfkE8FngI2b2F+Aud3/EzGYBPwCKgDVmtsnd4yElQnsDzgJ+amZdwH8B1w34rIiIiIiIiIiIiEhAwSYpPf0m7PCH2MHnQvJ3EfIymsy2i/o55tUR8RXAirBtffIaInqrAs7sTw9HgzFjxnDAE0PdRqQpU6YMdQsiIiIiIiIiIpKHQl5JKSPUpEmTeH/PkTtJWVZWNtQtiIiIiIiIiIhIHgr+dm8RERERERERERGRbDRJKSIiIiIiIiIiIkNKt3vLwDS09LzNu3cMODgesT9FBehLRERERERERESGHU1SSt6iXkyzI7EDgElFk3IXKdILbkREREREREREJE2TlJI3vZhGREREREREREQGZvZABwAAIABJREFUk55JKSIiIiIiIiIiIkNKk5QiIiIiIiIiIiIypPp9u7eZ/TVwprv/f2Y2Bhjl7vsL15oMN2VlZTQ3NzNpUj+eSUn6mZS6dVxERERERERERPo1SWlmNwA3AicDxcBHgZ8AnytcazLc1NfXc6C1lfdHW+7khn2Fb0hERERERERERIaF/l5JeStwAfAagLtvNbNTC9aVDF+jRzHqC5/NmZZc/VLBWxERERERERERkeGhv8+k7HD3zu4PZjYK8MK0JCIiIiIiIiIiIkeT/k5SrjOzbwFjzOzzwHJgdeHakiNZZWUllZWVQ93GEdOHiIiIiIiIiIgcmv7e7r0QuA54E7gJ+K27P1SwruSIVldXN9QtAEdOHyIiIiIiIiIicmj6eyXl19z9IXcvdfcr3P0hM5uXbQczO93MXjSzzWb2Vne+mZ1sZs+b2dbM+qRM/ONmVmVmHWZW3qfWPDOrydSZn2XMR81st5nV9ImXZvZNmVlJlv0j88zsdjN7x8zeNrN4tmMXERERERERERGR/uvvJOW1IbGv5NgnCXzD3c8CPg3camZnk74q8wV3PxN4IfMZoBG4Dbi3dxEzOwe4gfSLez4BzDSzMyPGfAy4OCReA8wG1ufoOTQv0/dVwN9l6v/YzI7NUUtERERERERERET6IeskpZldbWargb8xs1/3Wl4EGrLt6+473f31zM/7gc3AZOBSYFkmbRlwWSZnt7tvBBJ9Sp0FvOrure6eBNYBsyLGXE96srNvfLO7v52t3xx5lwJPunuHu/8ZeIf0pKkcgR5++GHi8TgPP/xwT+ytt94iHo/z1ltvBXKrqqqIx+NUVVX1xOrr64nH49TX1wdyt2/fTjweZ/v27YF4WP7OnTuJx+Ps3LkzkJtIJLj//vtJJIJf84aGBmbMmEFDwwf/s+rs7GTx4sV0dnYGcjs6Orj99tvp6OgIxMPy29vbmT9/Pu3t7Tlzk8kkS5cuJZlM0h9hx+LubN++Hffge7WiaoflR9VIpVJUVVWRSqVy1o6qkc94UfGuri5WrVpFV1dX1lg2UceST25Uf4c6Xj4KVVdGrny+tyIiIiIifem/J0c2y/aLNbO/Bv4G+C4fXPEIsB/4Y2bSMPcgZmeQvjrxHOBddx/fa9tedz+p1+dFQIu735v5fBawCvgM0Eb66stqd/9alrF+4+7nhGx7CSh39+oc/QbyzOyHpCdKf575/Aiw1t1/GVWjpKTEq6uzDjNsXXPNNbS3t1NcXByI19TUkAJGXX95zhrJn/+GD9kxB9XIR21tLSeccAKPP/54IB6Pf3A3/nPPPRcZG4zcfGvMnTuX+vp6Jk6cyLJly7LWuPLKK2lqamL8+PE89dRTPdsvu+wy2traGDt2LCtWrOiJh+XPnDmTRCLBcccdx+rVq7Pm3nLLLdTW1jJ16lR+9KMfkUvYsaxbt44lS5Zwxx13MG3atJ7cqNph+VE1KisrWblyJbNnz+amm27KWjuqRj7jRcXvvPNONm7cyAUXXMDixYsjY9lEHUs+uVH9Hep4+ShUXRm58vneioiIiIj0pf+eHP7M7PfuHvooxqxXUrr7dnd/yd0/4+7rei2v5zFBeSLwDDDf3Zvzbd7dNwPfA54HngXeIH0r+eFkIbGDZnfN7EYzqzaz6j179hyGtmS46b7acteuXTlzm5qaANi3b18g3tbWBkBra2vO/O6rHPtejRmWW1tbC8A777yTszcIP5bly5cH1rlqh+VH1Vi7di0Aa9asyVk7qkY+40XFu//xYePGjVlj2UQdSz65Uf0d6nj5KFRdGbny+d6KiIiIiPSl/54c2fr1TEoz+7SZbTSzFjPrNLMuM8s54Whmo0lPUP7C3X+VCdeb2WmZ7acBu3PVcfdH3P1T7j6N9O3cWzMv5tmUWW7uz3GE9Lc0s/9vc6T+BTi91+ePAjtC+nzQ3UvcvaSoqGggLQ0LkydPpri4mIqKisAyZswYGNXPR3WOOzG0Rj5LcXExkydPLuzBDrIJEyYAMHHixJy548aNA2D8+PGB+JgxYwAYO3ZszvzRo0cDcNxxx+XM7b6qderUqTl7g/BjKS0tDaxz1Q7Lj6oxffp0AGbMmJGzdlSNfMaLipeUpP/B5/zzz88ayybqWPLJjervUMfLR6HqysiVz/dWRERERKQv/ffkCOfuORegGpgK/AE4Fvgn4Ds59jHgp8D9feIVwMLMzwuB7/fZvoj0rda9Y6dm1h8DtgAnZRn3DKAmYttLQEk/jjeQR/qFOW8Ax5O+/b0OODZbjfPOO89HqvLyci8vLz8oPmvWLI/NnOGX/PKhnEvs2v8rtMZg9PHQQw95LBbzhx56qCdWU1PjsVjMa2pqArkbNmzwWCzmGzZs6Int2rXLY7GY79q1K5C7bds2j8Vivm3btkA8LH/Hjh0ei8V8x44dgdzOzk6/7777vLOzMxB///33/ZJLLvH333+/J9bR0eF33323d3R0BHLb29t94cKF3t7eHoiH5be1tfm8efO8ra0tZ24ikfBHH33UE4mE90fYsaRSKd+2bZunUqlAblTtsPyoGl1dXb5hwwbv6urKWTuqRj7jRcWTyaSvXLnSk8lk1lg2UceST25Uf4c6Xj4KVVdGrny+tyIiIiIifem/J4c/0o9wDJ1Ly/pMym5mVu3uJWb2R3c/NxPb4O7/K8s+FwIvA28C3W9V+BbwGvB0ZsLxXaDU3RvNbGJmMvTDmfwW4Gx3bzazl4FTSL9U5+vu/kLEmE8AnwU+AtQDd7n7I2Y2C/gBUATsAza5ezxk/8g8M7sD+GfSt5rPd/e12c7ZSH4m5YIFCwCoqKgIxGfPns2BRCejvnJZzhrJ1S9xbtFpB9UYjD5EREREREREROTIk+2ZlKP6WaPVzI4DNpnZ94GdwIey7eDurxD+LEeAz4Xk7yJ9G3VYrYv606S7Xx0RXwGsCNvW3zx3/w7wnf70ISIiIiIiIiIiIv3X30nKOaSfX/lV4H+Tfj5j7lc4y4g0ZcqUoW4BOHL6EBERERERERGRQ9OvSUp3325mRZmf/6WwLcmRrqysbKhbAI6cPkRERERERERE5NBkfbu3pS0ys/dJv7DmT2a2x8z+n8PTnoiIiIiIiIiIiIx0ua6knA/8A3C+u/8ZwMymAJVm9r/d/b5CNyjDTCJJcvVLufMa9kHRaQVvR0REREREREREjny5JinnAp939/e7A+5eZ2ZfBv4D0CSl9JgwYQLNzc1M6s/kY9FpeqakiIiIiIiIiIgAuScpR/eeoOzm7nvMbHSBepJhqrKycqhbEBERERERERGRYSjrMymBzgFuExEREREREREREemXXFdSfsLMmkPiBpxQgH5ERERERERERETkKJN1ktLdjz1cjcjwUVZWln725KRJB22bMmUKZWVlQ9CViIiIiIiIiIgMV7mupBQ5SH19PQdaW2kYHXwsqTc0DFFHIiIiIiIiIiIynGmSUgZm9GhGz/xiIJT4za+HqBkRERERERERERnOcr04R0RERERERERERKSgNEkpIiIiIiIiIiIiQ0q3e0veOjo6IJXqV25lZSWAXqYjIiIiIiIiIiKRNEkpeUulUuDer9y6uroCdyMiIiIiIiIiIsNdwW73NrPTzexFM9tsZm+Z2bxM/GQze97MtmbWJ2XiHzezKjPrMLPyPrXmmVlNps78LGM+ama7zaymT7w0s2/KzEqy7B+aZ2anZI6lxcx+ONBzIiIiIiIiIiIiIgcr5DMpk8A33P0s4NPArWZ2NrAQeMHdzwReyHwGaARuA+7tXcTMzgFuAC4APgHMNLMzI8Z8DLg4JF4DzAbW5+g5Kq8d+DZQftAeMiDxeLxnyRX/9re/TTwe59vf/nYgd/Xq1cTjcVavXh2Ir1+/nng8zvr1H/waq6qqiMfjVFVVBXK3bt1KPB5n69atPbGGhgZmzJhBQ0NDILepqYlZs2bR1NQUiLe1tVFWVkZbW1vWGEAikeD+++8nkUgE4slkkqVLl5JMJntinZ2dLF68mM7OTvojLN/d2b59O97nytdUKkVVVVX6qtgsPWSrEXUsYaJqyMCF/Q6j6PyLiIiIiIjIka5gk5TuvtPdX8/8vB/YDEwGLgWWZdKWAZdlcna7+0ag74zHWcCr7t7q7klgHTArYsz1pCc7+8Y3u/vb/eg5NM/dD7j7K6QnKyWCNzVRW1vLggULepba2lree++9Q6r7n//5n4F1tx/+8IeBdbfvfOc7gTXAokWLAutuX/3qVwNrgGuuuYZkMsk111wTyL3yyitpbW3lyiuvDMQvv/xy6urquOKKK7LGAK677jrWrl3L9ddfH4jfdtttPPnkk8ybN68n9uUvf5lXXnmFOXPm0B9h+evXr+fGG2/k5ZdfDuQ+8MADLFq0iIceeihrD9lqRB1LmKgaMnBhv8MoOv8iIiIiIiJypDssb/c2szOATwKvARPcfSekJzKBU3PsXgNMy9xyPRa4BDi9cN0OnJndaGbVZla9Z8+eoW5HDpOuri6AwBWIYTGA+vp6AHbt2hWI19bWAvDOO+/0xLqv2Ny3b1+/+gjLX758eWDdbe3atQCsWbMmaw/ZakQdS5ioGjJwYb/DKDr/IiIiIiIicqQr+CSlmZ0IPAPMd/fmfPd3983A94DngWeBN0jfSn7EcfcH3b3E3UuKioqGup3DzsaNo7i4mIqKip6luLiYyZMnD3VrBXXssccCMGrUqKwxgAkTJgAwceLEQLy4uBiAqVOn9sTGjRsHwPjx4/vVR1h+aWlpYN1t+vTpAMyYMSNrD9lqRB1LmKgaMnBhv8MoOv8iIiIiIiJypCvoJKWZjSY9QfkLd/9VJlxvZqdltp8G7M5Vx90fcfdPufs00rdzb828mGdTZrl5gP0tzez/24HsL4fHBRdcEFh3C7tVG+COO+4IrCH6du+wW8Yff/xxRo0axeOPPx7Iffrppxk7dixPP/10IP7MM88wZcoUfvnLX2aNATzyyCNMnz6dhx9+OBD/93//d6666ir+7d/+rSf285//nAsvvJCf/exn9EdY/rRp03jwwQe56KKLArk33XQTixYt4oYbbsjaQ7YaUccSJqqGDFzY7zCKzr+IiIiIiIgc6axQL1IwMyP9zMlGd5/fK14BNLj7PWa2EDjZ3b/Za/sioMXd7+0VO9Xdd5vZx4D/AD7j7nsjxj0D+I27nxOy7SWg3N2rc/QemmdmXwFK3P2rYfv1VlJS4tXVWYcZtqZPn07KneOuvzEQT/zm1/yPoiIqKip6YgsWLAAIxERERERERERE5OhjZr9395KwbYW8kvIfgDnAP/a64vES4B7g82a2Ffh85jNmNtHM/gJ8HbjTzP5iZh/O1HrGzP4LWA3cmmWC8gmgCvjbzP7XZeKzMrU/A6wxs+ci9o/MM7NtwP8BvpKpffahnBwRERERERERERFJG5U7ZWAyb8O2iM2fC8nfBXw0ola/7lF096sj4iuAFf3YPzLP3c/oTw9Hg2OOOYZUKtWv3ClTphS4GxERERERERERGe4KNkkpI9fxxx9PMpHoV25ZWVmBuxERERERERERkeGu4G/3FhEREREREREREclGk5QiIiIiIiIiIiIypHS7twxMIkHiN78OhLyhAYqKhqghEREREREREREZrjRJKXmbMGECzc3NTOo7IVlUpBfliIiIiIiIiIhI3jRJKXmrrKwc6hZERERERERERGQE0TMpRUREREREREREZEjpSkrJW1lZWfp270mTDto2ZcoUysrKhqArEREREREREREZrjRJKXmrr6/nQGsrjaM/FIinGnYNUUciIiIiIiIiIjKcaZJSBmb0cZzwhbmBUPvqnw5RMyIiIiIiIiIiMpzpmZQiIiIiIiIiIiIypDRJKSIiIiIiIiIiIkNKt3tL3jo6OiDl/cqtrKwE0Mt0REREREREREQkkiYpJW+pVAq8f5OUdXV1Be5GRERERERERESGu4Ld7m1mp5vZi2a22czeMrN5mfjJZva8mW3NrE/KxD9uZlVm1mFm5X1qzTOzmkyd+VnGfNTMdptZTZ94aWbflJmVZNk/NM/MPm9mvzezNzPrfxzoeREREREREREREZGgQj6TMgl8w93PAj4N3GpmZwMLgRfc/UzghcxngEbgNuDe3kXM7BzgBuAC4BPATDM7M2LMx4CLQ+I1wGxgfY6eo/LeB77g7v8DuBb4WY46IiIiIiIiIiIi0k8Fm6R0953u/nrm5/3AZmAycCmwLJO2DLgsk7Pb3TcCiT6lzgJedfdWd08C64BZEWOuJz3Z2Te+2d3f7kfPoXnu/gd335H5+BZwgpkdn6ueRIvH4z1LrviCBQuIx+MsWLAgkPvwww8Tj8d5+OGHA/EXX3yReDzOiy++2BP705/+RDwe509/+lMgd9euXcTjcXbt2tUTa2xsZObMmTQ2Br9K+/bt49JLL2Xfvn2BeEdHB7fffnv6WZ0ZbW1tlJWV0dbWFshNJBLcf//9JBLBr3lnZyeLFy+ms7OTXKJqRMXDpFIpqqqq0rfuZ7g727dvx/t5K/9gCBszrLds/R1q30Nx3CJR9H0UEREREZGj1WF5u7eZnQF8EngNmODuOyE9kQmcmmP3GmCamZ1iZmOBS4DTC9dtTpcDf3D3jpyZR5lUUyO1tbUsWLCgZ6mtreW99947pLp//OMfA+tuy5cvD6y73XPPPYE1wNe+9rXAutu1114bWANcffXVJBIJrr766kDul770Jdrb2/nSl750UPz111/nqquu6oldfvnl1NXVccUVVwRyr7vuOtauXcv1118fiH/5y1/mlVdeYc6cOQcdf19RNaLiYR544AEWLVrEQw891BNbv349N954Iy+//HLO/QdL2JhhvWXr71D7HorjFomi76OIiIiIiBytCj5JaWYnAs8A8929Od/93X0z8D3geeBZ4A3St5Ifdmb2d5leborYfqOZVZtZ9Z49ew5vczJkuq+WbG1t7Yl1dXUBkEwGv6r19fUAgSs3AZqamgAOukozTFSNqHiYtWvXArBmzZqeWNSkbyGFjRnWW1Rutvih9CAyVPR9FBERERGRo1VBJynNbDTpCcpfuPuvMuF6Mzsts/00YHeuOu7+iLt/yt2nkb6de2vmxTybMsvNA+xvaWb/3/Yj96PACmCuu9dG9Pmgu5e4e0lRUdFAWhrWjhl3MsXFxVRUVPQsxcXFTJ48eahbK6gxY8YAMHbs2J7YscceC8CoUaMCuRMmTABg4sSJgfi4ceMAGD9+fM7xompExcNMnz4dgBkzZvTESktLA+vDIWzMsN6icrPFD6UHkaGi76OIiIiIiBytCvl2bwMeATa7+//ptenXpF8+Q2a9qh+1Ts2sP0b6xTZPuPt/u/vfZ5afDKRHd/+nzP6X5Bh/PLAGuN3dfzeQsWTgzj333MC6W9T/mV+4cGFgDfCDH/wgsO62bNmywBrgiSeeYPTo0TzxxBOB3KeeeooTTjiBp5566qD4pz71KZ588sme2DPPPMOUKVP45S9/Gch95JFHmD59+kHP0fz5z3/OhRdeyM9+lvudTFE1ouJhbrrpJhYtWsQNN9zQE5s2bRoPPvggF110Uc79B0vYmGG9ZevvUPseiuMWiaLvo4iIiIiIHK2sUA/nN7MLgZeBN4HuN2B8i/RzKZ8GPga8C5S6e6OZTQSqgQ9n8luAs9292cxeBk4h/VKdr7v7CxFjPgF8FvgIUA/c5e6PmNks4AdAEbAP2OTu8ZD9Q/PM7E7gdmBrr/SYu0deBVpSUuLV1dU5ztLwNH36dFLujL3hzkC8ffVPOadoHBUVFT2x7pfd9I6JiIiIiIiIiMjRx8x+7+4lYdtGhQUHg7u/AljE5s+F5O8CPhpRq1+XlLj71RHxFaRv1c61f2ieu/8r8K/96UFERERERERERETyU7BJShm5jjnmGFKp/l2BO2XKlAJ3IyIiIiIiIiIiw50mKSVvxx9/PMlE/16wXlZWVuBuRERERERERERkuCvo271FREREREREREREctGVlDIwiU7aV/80EEo17IKicUPUkIiIiIiIiIiIDFeapJS8TZgwgebmZib1nZAsGqdnUIqIiIiIiIiISN40SSl5q6ysHOoWRERERERERERkBNEzKUVERERERERERGRIaZJSREREREREREREhpRu95a8lZWVpZ9JOWnSQdumTJlCWVnZEHQlIiIiIiIiIiLDlSYpJW/19fUcaG3jwOiTA/GOhv8eoo5ERERERERERGQ40ySlDMgxo4/nY5d+MxB7d9X3h6gbEREREREREREZzvRMShERERERERERERlSmqQUERERERERERGRIaXbvSVvHR0dpFLer9zKykoAvUxHREREREREREQiaZJS8pZKpaB/c5TU1dUVthkRERERERERERn2Cna7t5mdbmYvmtlmM3vLzOZl4ieb2fNmtjWzPikT/7iZVZlZh5mV96k1z8xqMnXmZxnzUTPbbWY1feKlmX1TZlaSZf/QPDO7wMw2ZZY3zGzWQM+LiIiIiIiIiIiIBBXymZRJ4BvufhbwaeBWMzsbWAi84O5nAi9kPgM0ArcB9/YuYmbnADcAFwCfAGaa2ZkRYz4GXBwSrwFmA+tz9ByVVwOUuPvfZ+o/YGa6CvUQxOPxniVX/NprryUej3PttdcGcpctW0Y8HmfZsmWB+Kuvvko8HufVV1/tib3xxhvE43HeeOONQO4777xDPB7nnXfe6Ynt3LmTeDzOzp07A7m7du0iHo+za9euQLyxsZGZM2fS2NjYEztw4ABz587lwIEDgdzOzk4WL15MZ2dnIJ5IJLj//vtJJBI5czs6Orj99tvp6OjIWSOZTLJ06VKSyWQgNywelRsllUpRVVWVvrI2o6uri1WrVtHV1ZUzN1/uzvbt23Hv52W8h1g3n3ihehsKI+lYjmQ6zyIiIiIiIkEFm6R0953u/nrm5/3AZmAycCnQPau0DLgsk7Pb3TcCiT6lzgJedfdWd08C64DQKxndfT3pyc6+8c3u/nY/eg7N6zU2wAn0+2bno0tn025qa2tZsGBBz1JbW8t77713SHW7JwX7Tg4+/vjjgXW3u+66K7AG+OY3vxlYd7v11lsDa4CvfOUrgXW37knSvpOlV199NYlEgquvvronNnv2bOrr67n88ssDuV/+8pd55ZVXmDNnTiB+3XXXsXbtWq6//vqcuV/60pd4/fXXueqqq3LWuO2223jyySeZN29eIDcsHpUb5YEHHmDRokU89NBDPbG77rqLH//4xyxatChnbr7Wr1/PjTfeyMsvvzzgGvnUzSdeqN6Gwkg6liOZzrOIiIiIiEjQYXm7t5mdAXwSeA2Y4O47IT2RCZyaY/caYJqZnWJmY4FLgNML1204M/ufZvYW8CZwc69Jy945N5pZtZlV79mz53C3KEegvldJNTU1AbBv375AvL6+HghOxEbltrW1AdDa2pqzRm1tLUDgStGoeFRulLVr1wKwZs2anlh1dTUAGzduzJmbr+XLlwfWgyWqbj7xQvU2FEbSsRzJdJ5FRERERESCCj5JaWYnAs8A8929Od/93X0z8D3geeBZ4A3St5IfVu7+mrv/HXA+cLuZnRCS86C7l7h7SVFR0eFuccgdN+5UiouLqaio6FmKi4uZPHnyULc2ZMws8HncuHEAjB8/PhCfMGECABMnTsyZO2bMGADGjh2bs0ZxcTEAU6dODeSGxaNyo0yfPh2AGTNm9MRKStKPcj3//PNz5uartLQ0sB4sUXXziReqt6Ewko7lSKbzLCIiIiIi0oe7F2wBRgPPAV/vFXsbOC3z82nA2332WQSUZ6m5BLiF9NWUmzLLzb22nwHUROz7EulnS3Z/XprZ/7fZ8kLqvJhtu7tz3nnn+Uh18cUXeyx+sX/tV+8Glkuv/aqXl5cHcsvLyw+KubvHYrGeJVd87ty5HovFfO7cuYHcxx57zGOxmD/22GOBeFVVlcdiMa+qquqJbdq0yWOxmG/atCmQu3XrVo/FYr5169ae2I4dOzwWi/mOHTsCuTt37vRYLOY7d+4MxBsaGnzGjBne0NDQE2tpafE5c+Z4S0tLILejo8Pvvvtu7+joCMQ7Ozv9vvvu887Ozpy57e3tvnDhQm9vb89ZI5FI+KOPPuqJRCKQGxaPyo3S1dXlGzZs8K6urp5YMpn0lStXejKZzJmbr1Qq5du2bfNUKjXgGvnUzSdeqN6Gwkg6liOZzrOIiIiIiByNgGqPmEszL9BD+y19CdkyoNHd5/eKVwAN7n6PmS0ETnb3b/bavghocfd7e8VOdffdZvYx4D+Az7j73ohxzwB+4+7nhGx7ifQEaHWO3gN5ZvY3wH+7e9LM/hqoAs519/ejapSUlHj3ra8jzfTp00k5/O1NDwbi7676PmcWnUBFRUVPbMGCBQCBmIiIiIiIiIiIHH3M7PfuXhK2rZC3e/8DMAf4RzPblFkuAe4BPm9mW4HPZz5jZhPN7C/A14E7zewvZvbhTK1nzOy/gNXArVkmKJ8gPYH4t5n9r8vEZ2VqfwZYY2bPRewflXch8IaZbQJWALdkm6AUERERERERERGR/htVqMLu/gpgEZs/F5K/C/hoRK2L+jnm1RHxFaQnF3PtH5rn7j8DftafHo4GxxxzDKlU/67AnTJlSoG7ERERERERERGR4a5gk5Qych1//PGkEql+5ZaVlRW4GxERERERERERGe4K/nZvERERERERERERkWw0SSkiIiIiIiIiIiJDSrd7y4CkEh28u+r7gVhHw39D0ZlD1JGIiIiIiIiIiAxXmqSUvE2YMIHm5mYmFZ0Q3FB0pl6UIyIiIiIiIiIiedMkpeStsrJyqFsQEREREREREZERRM+kFBERERERERERkSGlKyklb2VlZenbvSdN6lf+lClTKCsrK3BXIiIiIiIiIiIyXGmSUvJWX19Pa2sbo0cX5cxtaNh+GDoSERGmUxVDAAAgAElEQVQREREREZHhTJOUMiCjR5/AF2femTPv17/518PQjYiIiIiIiIiIDGd6JqWIiIiIiIiIiIgMKU1SioiIiIiIiIiIyJDS7d6St46ODlKpwzdeZWUlgF6+IyIiIiIiIiIyQmmSUvKWSqVwP3zj1dXVHb7BRERERERERETksCvY7d5mdrqZvWhmm83sLTObl4mfbGbPm9nWzPqkTPzjZlZlZh1mVt6n1jwzq8nUmZ9lzEfNbLeZ1fSJl2b2TZlZSZb9s+aZ2cfMrKVvfyIiIiIiIiIiIjJwhXwmZRL4hrufBXwauNXMzgYWAi+4+5nAC5nPAI3AbcC9vYuY2TnADcAFwCeAmWZ2ZsSYjwEXh8RrgNnA+hw958q7D1ibo4aIiIiIiIiIiIjkoWCTlO6+091fz/y8H9gMTAYuBZZl0pYBl2Vydrv7RiDRp9RZwKvu3uruSWAdMCtizPWkJzv7xje7+9v96Dkyz8wuA+qAt3LVkcNj1apVxONxVq1aFYhv2LCBeDzOhg0bemJvv/028Xict98O/nr//Oc/E4/H+fOf/5w1BrB3716+8IUvsHfv3kB8//79XHnllezfv78n1tbWRllZGW1tbYHcAwcOMHfuXA4cOBCIh+Unk0mWLl1KMpkM5HZ1dbFq1Sq6urpyxt2d7du3433uzw+rnUqlqKqqItXngaNRNcLiUb0NRo0o+Rx3WDzf4x4MYbXzHa+Q/R0J4x3pfYgMBn2fRUREREQ+cFje7m1mZwCfBF4DJrj7TkhPZAKn5ti9BphmZqeY2VjgEuD0wnV7MDP7EPB/A/9yOMcdCZqadlFbW8uCBQsGvNTW1vLee+8dVPvHP/5xYN3tX/7lXwJrgNtuuy2w7nbzzTcH1lExgKuuuorOzk6uuuqqQPyKK66gqamJK664oid2+eWXU1dXF4gBzJ49m/r6ei6//PJAPCz/tttu48knn2TevHmB3Lvuuosf//jHLFq0KGd8/fr13Hjjjbz88suB3LDaDzzwAIsWLeKhhx4K5EbVCItH9TYYNaLkc9xh8XyPezCE1c53vEL2dySMd6T3ITIY9H0WEREREflAwScpzexE4Blgvrs357u/u28Gvgc8DzwLvEH6VvLD6V+A+9y9JVuSmd1oZtVmVr1nz57D1Jocibqv6ut7FWS3vlfNhOXX1tYC8M477wRyq6urAdi4cWPO+PLlywPrbLXXrk0/yWDNmjWB3KgaYfGo3gajRpR8jjssnu9xD4aw2vmOV8j+joTxjvQ+RAaDvs8iIiIiIh8o6CSlmY0mPUH5C3f/VSZcb2anZbafBuzOVcfdH3H3T7n7NNK3c2/NvJhnU2a5OVeNiP6WZvb/bY7U/wl838y2AfOBb5nZV0P6fNDdS9y9pKioaCAtjTjjxk2kuLiYioqKAS/FxcVMnjx5qA8lL8ceeywAo0aNCt1uZjnzi4uLAZg6dWogt6Qk/U6n888/P2e8tLQ0sM5We/r06QDMmDEjkBtVIywe1dtg1IiSz3GHxfM97sEQVjvf8QrZ35Ew3pHeh8hg0PdZRERERKQXdy/IAhjwU+D+PvEKYGHm54XA9/tsXwSU94mdmll/DNgCnJRl3DOAmohtLwEl/eg9Mi+sv7DlvPPO85Hq4osv9nj8Yn9m+Z6cy7XXzvPy8vJDGq+8vDy0xsqVKz0Wi/nKlSsD8d/97ncei8X8d7/7XU9sy5YtHovFfMuWLYHcuro6j8ViXldXlzXm7t7Y2OgzZ870xsbGQLy5udlLS0u9ubm5J9ba2uo333yzt7a2BnJbWlp8zpw53tLSEoiH5ScSCX/00Uc9kUgEcpPJpK9cudKTyWTOeCqV8m3btnkqlQrkhtXu6uryDRs2eFdXVyA3qkZYPKq3wagRJZ/jDovne9yDIax2vuMVsr8jYbwjvQ+RwaDvs4iIiIgcbYBqj5hLMy/Qw9rN7ELgZeBNoPuNFN8i/VzKpzMTju8Cpe7eaGYTgWrgw5n8FuBsd282s5eBU0i/VOfr7v5CxJhPAJ8FPgLUA3e5+yNmNgv4AVAE7AM2uXs8ZP+ceWa2CGhx93v77t9bScn/3969h9lVl4ce/74QSEVRUGK4KibBC8dzRB2sbYWH5/E4QwgVIYkliHJsEBn1kBSTFm2fErGtlyEWL4cUKCA3oWJAaAEDpWrCASWBcglEG5LDJTQZIBEmIWEyl/f8sfdMs4e9Z88esmdPJt/P8+xn7f3ud/1+715ZrL3nx2+t1ZR9p6GONVOnTiUTzjrzmqq5t/7L3zBhwl60tbUNu7/58+cDvKY2JEmSJEmS1FgR8UBmNpV7r/y5qDtBZt5DYTZlOR8tk78BOLRCW8cMsc9ZFeI3AzcPYf2qeZm5YCi1jGV77LEHA26EXFeTJk0auc4kSZIkSZI04uo2SKmxa/z48XR11WcGbjmtra0j1pckSZIkSZJGXt3v7i1JkiRJkiRJg3GQUpIkSZIkSVJDebq3hqWr6xVu/Ze/qZq3ceNTTJgwZQQqkiRJkiRJ0q7KQUrVbOLEiXR0dDBhwl5VcydMmOKNbyRJkiRJkjQoBylVs0WLFjW6BEmSJEmSJI0hXpNSkiRJkiRJUkM5SClJkiRJkiSpoTzdWzVrbW2lo6ODgw8+eEj5kyZNorW1tc5VSZIkSZIkaVflIKVq1t7eztat29g3Dqiau/7Fp0egIkmSJEmSJO3KHKTUsIwfN57PH/eXVfMu+cXfjkA1kiRJkiRJ2pV5TUpJkiRJkiRJDeVMStWss7OT7B25/hYtWgTgdS0lSZIkSZLGKAcpVbPe3l4yR66/tWvXjlxnkiRJkiRJGnF1O907Ig6LiJ9HxKqIeCwi5hTjb46IuyJidXG5fzH+7oi4LyI6I2LegLbmRMTKYjtzB+nzioh4LiJWDojPLK7bGxFNg6xfNi8iDo+IbRHxUPHxD8PdLpIkSZIkSZJK1fOalN3AlzPzPcCHgS9GxJHAecDdmXkEcHfxNcAm4Bzgwh0biYj3Ap8DPgS8DzgxIo6o0OcPgePLxFcCpwBLq9Q8WN6azDyq+Di7SjuSJEmSJEmShqhug5SZuT4zHyw+3wysAg4BTgKuKqZdBXyimPNcZi4HugY09R7gV5m5NTO7gV8CJ1focymFwc6B8VWZ+dsh1DykPI0OCxcupKWlhYULF5bEr732WlpaWrj22mv7YzfddBMtLS3cdNNNJbnLly+npaWF5cuX98dWrVpFS0sLq1atKsndsGEDLS0tbNiwoSS+ZcsWTjvtNLZs2dIf27x5M5/85CfZvHlz1VyA7du38/Wvf53t27f3x1555RXmzp3LK6+8UpLb1dXFRRddRFdX6X8q3d3dXHnllXR3d/fHent7ue++++jtLb2IaLl4ZvLUU0+RA87l7+np4ZZbbqGnp4dqKrVRS7xSbrnPV2sbtaj1s7xWO6O/Sv/eo1m9tudY43aqzm0kSZIk7dpG5O7eEXE48H7g18DEzFwPhYFM4K1VVl8JHBsRb4mIfYATgMPqV21F74iIf4+IX0bEMQ3of5e0cUs7a9asYf78+cN+rFmzhmefffZVbd95550lyz7XXHNNyRLgkksuKVn2+au/+quSJcDcuXNLln3OOOOMkmWf6dOns3HjRqZPn94fmzFjBi+99BIzZsyomgtw+umnc8899/DpT3+6pI1Vq1Yxc+bMktzZs2dzxx13cOaZZ5bEzznnHG644QbmzJlT8rkXLFjAZZddVpJbLr506VLOOussli1bVpJ7/vnnc/HFF7NgwQKqqdRGLfFKueU+X61t1KLWz/Ja7Yz+Kv17j2b12p5jjdupOreRJEmStGur+yBlRLwBWAzMzcyOWtfPzFXAt4C7gJ8BD1M4lXwkrQfelpnvB84FfhQRbxyYFBFnRcSKiFjx/PPPj3CJ2pW99NJLALz44ov9sb6ZkjvOrgRob28HeNWMzjVr1gDwxBNP9MfuuOMOAG677baS3HLxG2+8sWTZZ8WKFQAls00rqdRGLfFKueU+X61t1KLWz/Ja7Yz+Kv17j2b12p5jjdupOreRJEmStGur6yBlROxFYYDyuszsO8+2PSIOKr5/EPBctXYy8/LM/EBmHkvhdO7VxRvz9N3IZljXiIyIK4vr316l/87M3Fh8/gCwBnhnmbxLM7MpM5smTJgwnJLGnLe8YSKTJ0+mra1t2I/JkydzyCGHNPqj1NWb3vQmAPbbb7/+2F577QXA3nvvXZI7ceJEAA488MCS+OTJkwGYMmVKf2zq1KkATJs2rSS3XLxvxubAmZtNTYV7SB199NFVP0elNmqJV8ot9/lqbaMWtX6W12pn9Ffp33s0q9f2HGvcTtW5jSRJkqRdXGbW5QEEcDVw0YB4G3Be8fl5wLcHvL8AmDcg9tbi8m3Ab4D9B+n3cGBlhfd+ATQNofaSPGACsGfx+STgWeDNg7XxwQ9+MMeq448/Pltajs8HL2uv+vj8p87JefPmvab+5s2bV7aNCy+8MJubm/PCCy8siV9zzTXZ3Nyc11xzTX9s8eLF2dzcnIsXLy7Jvf/++7O5uTnvv//+/tjjjz+ezc3N+fjjj5fkrl+/Ppubm3P9+vUl8c2bN+esWbNy8+bN/bGOjo6cOXNmdnR0VM3NzOzs7MwLLrggOzs7+2Pbtm3LOXPm5LZt20pyt2/fnn//93+f27dvL4l3dXXlFVdckV1dXf2xnp6evPfee7Onp6ckt1y8t7c3n3zyyezt7S3J7e7uzp/+9KfZ3d2d1VRqo5Z4pdxyn6/WNmpR62d5rXZGf5X+vUezem3PscbtVJ3bSJIkSRr9gBVZYSwtsk4XmI+IjwDLgEeBvrs4fJXCdSl/XBxwfBqYmZmbIuJAYAXwxmL+FuDIzOyIiGXAWyjcVOfczLy7Qp/XA8cBBwDtwPmZeXlEnAx8n8Jg44vAQ5nZUmb9snkRMR24gMJp5j3Fdv95sM/f1NSUfafJjjVTp04lE74x46qquZf84m/Z96C9aWtrG3Z/8+fPB3hNbUiSJEmSJKmxIuKBzGwq9964enWamfdQmE1ZzkfL5G8ADq3Q1pBuVJOZsyrEbwZuHsL6ZfMyczGF09YlSZIkSZIk7WR1G6TU2LXHHnuQvdXzdpZJkyaNXGeSJEmSJEkacQ5Sqmbjx4+nZ/vIjVK2traOWF+SJEmSJEkaeXW9u7ckSZIkSZIkVeMgpSRJkiRJkqSG8nRvDUtndyeX/OJvq+atf/Fp9j1oyghUJEmSJEmSpF2Vg5Sq2cSJE+no6GDfg/aumrvvQVO88Y0kSZIkSZIG5SClarZo0aJGlyBJkiRJkqQxxGtSSpIkSZIkSWooZ1KqZq2trXR0dHDwwQcPKX/SpEm0trbWuSpJkiRJkiTtqhykVM3a29vZ9vI2DujZv2ru05vXjUBFkiRJkiRJ2pU5SKlhGT9uPF9pmls17xsrLhqBaiRJkiRJkrQr85qUkiRJkiRJkhrKQUpJkiRJkiRJDeXp3qpZZ2cn9OSI9bdo0SIAb74jSZIkSZI0RjlIqZr19vbCyI1Rsnbt2pHrTJIkSZIkSSOubqd7R8RhEfHziFgVEY9FxJxi/M0RcVdErC4u9y/G3x0R90VEZ0TMG9DWnIhYWWyn4t1aIuKKiHguIlYOiM8srtsbEU2DrF8xLyL+R7G+xyLi0Yj4veFsF0mSJEmSJEml6nlNym7gy5n5HuDDwBcj4kjgPODuzDwCuLv4GmATcA5w4Y6NRMR7gc8BHwLeB5wYEUdU6POHwPFl4iuBU4ClVWoumxcR44BrgbMz878BxwFdVdqSJEmSJEmSNAR1G6TMzPWZ+WDx+WZgFXAIcBJwVTHtKuATxZznMnM5rx78ew/wq8zcmpndwC+Bkyv0uZTCYOfA+KrM/O0Qaq6U1ww8kpkPF/M2ZmZPtfZUXxdeeCEtLS1ceGHJuDZ33nknLS0t3Hnnnf2xRx55hJaWFh555JGS3HXr1tHS0sK6dev6Yxs3bmTatGls3LixJPd3v/sdf/zHf8zvfve7kvimTZs48cQT2bTpv3a9LVu2cNppp7Fly5aS3FdeeYW5c+fyyiuvlMS3bdtGa2sr27Zt6491d3dz5ZVX0t3dXZK7fft2vv71r7N9+/aqbWcmTz31FJml5+f39vZy3333FU7dH0YuQE9PD7fccgs9PT1VcyupJb9SfeXqqKWNSu3Wop5t7IxtVC+19jfS9Y3WGiRJkiRJrzYid/eOiMOB9wO/BiZm5nooDGQCb62y+krg2Ih4S0TsA5wAHFa/ast6J5ARsSQiHoyIPx/h/ndZ7VufZ82aNcyfP3/YjzVr1vDss8++qu277rqrZNln4cKFJUuA+fPnlyz7zJ49u2QJcNppp9Hd3c1pp51Wknvqqaeyfft2Tj311JL4rFmz6OrqYtasWf2x6dOns3HjRqZPn16SO2PGDFatWsXMmTNL4tOnT2ft2rXMmDGjP3bOOedwww03MGfOnJLc008/nXvuuYdPf/rTVdteunQpZ511FsuWLSvJveSSS1iwYAGXXXbZsHIBzj//fC6++GIWLFhQNbeSWvIr1VeujlraqNRuLerZxs7YRvVSa38jXd9orUGSJEmS9Gp1H6SMiDcAi4G5mdlR6/qZuQr4FnAX8DPgYQqnko+kccBHgE8VlydHxEcHJkXEWRGxIiJWPP/88yNconYFXV2FicIDZ0H2zQLccdbkmjVrAHjiiSdKcl966SUAXnzxxapt33jjjSXLPnfccQcAt91227ByAVasWAHA8uXLq+ZWUkt+pfrK1VFLG5XarUU929gZ26heau1vpOsbrTVIkiRJkl6troOUEbEXhQHK6zLzpmK4PSIOKr5/EPBctXYy8/LM/EBmHkvhdO7VxRvzPFR8nD3M+q4srn97ldR1wC8z84XM3ArcDnygTJ2XZmZTZjZNmDBhOCWNORP3mcDkyZNpa2sb9mPy5Mkccsghjf4oO8Vee+0FwN57710S33PPPQEYN25cf2zy5MkATJkypST3TW96EwD77bdf1bb7ZlUOnLk5depUAKZNmzasXICmpsK9pY4++uiquZXUkl+pvnJ11NJGpXZrUc82dsY2qpda+xvp+kZrDZIkSZKkMjKzLg8ggKuBiwbE24Dzis/PA7494P0FwLwBsbcWl28DfgPsP0i/hwMrK7z3C6BpCLWX5AH7Aw8C+1CYVfmvwLTB2vjgBz+YY9Xxxx+fxzcfn+vbVld9nPMnX8x58+a9pv7mzZtXto22trZsbm7Otra2kviSJUuyubk5lyxZ0h97+OGHs7m5OR9++OGS3GeeeSabm5vzmWee6Y+98MILecIJJ+QLL7xQkrtp06Y88cQTc9OmTSXxjRs35rRp03Ljxo39sc2bN+esWbNy8+bNJbnbtm3LOXPm5LZt20riW7duzbPPPju3bt3aH+vq6sorrrgiu7q6SnI7OzvzggsuyM7Ozqpt9/b25pNPPpm9vb0luT09PXnvvfdmT0/PsHIzM7u7u/OnP/1pdnd3V82tpJb8SvWVq6OWNiq1W4t6trEztlG91NrfSNc3WmuQJEmSpN0VsCIrjKVF1unmARHxEWAZ8CjQd8eHr1K4LuWPiwOOTwMzM3NTRBwIrADeWMzfAhyZmR0RsQx4C4Wb6pybmXdX6PN6CnfePgBoB87PzMsj4mTg+8AE4EXgocxsKbN+xbyIOB34CpDA7Zk56HUpm5qasu801LFm6tSp0AtXfuz7VXO/seIi9j7sdbS1tQ27v77rSL6WNiRJkiRJktRYEfFAZjaVe29cueDOkJn3UJhNWc6rrueYmRuAQyu0dcwQ+5xVIX4zcPMQ1q+Yl5nXAtcOpY6xbo899oARvDPupEmTRqwvSZIkSZIkjby6DVJq7Bo/fjy9nb3VE3eS1tbWEetLkiRJkiRJI6/ud/eWJEmSJEmSpME4SClJkiRJkiSpoTzdW8PS2d3JN1ZcVDXv6c3rmMIRI1CRJEmSJEmSdlUOUqpmEydOpKOjg70Pfl3V3Ckc4Y1vJEmSJEmSNCgHKVWzRYsWNboESZIkSZIkjSFek1KSJEmSJElSQzlIKUmSJEmSJKmhPN1bNWttbaWjo4ODDz74Ve9NmjSJ1tbWBlQlSZIkSZKkXZWDlKpZe3s7217eygG9+5TEn+5ob1BFkiRJkiRJ2pU5SKlhGT9ub776B58qif3dfdc1qBpJkiRJkiTtyrwmpSRJkiRJkqSGcialatbZ2Qk9OaTcRYsWAXidSkmSJEmSJFXkIKVq1tvbCzm0Qcq1a9fWuRpJkiRJkiTt6up2undEHBYRP4+IVRHxWETMKcbfHBF3RcTq4nL/YvzdEXFfRHRGxLwBbc2JiJXFduYO0ucVEfFcRKwcEJ9ZXLc3IpoGWb9sXkTsHRFXRsSjEfFwRBw3zM0iSZIkSZIkaYB6XpOyG/hyZr4H+DDwxYg4EjgPuDszjwDuLr4G2AScA1y4YyMR8V7gc8CHgPcBJ0bEERX6/CFwfJn4SuAUYGmVmivlfQ4gM/878DFgYUR4PU9JkiRJkiRpJ6jbQFtmrs/MB4vPNwOrgEOAk4CrimlXAZ8o5jyXmcuBrgFNvQf4VWZuzcxu4JfAyRX6XEphsHNgfFVm/nYINVfKO5LCgCqZ+RzwIlBxRqaqa2lp6X9Ui8+ZM4eWlhbmzJlTknvxxRfT0tLCxRdfXBJfsmQJLS0tLFmypD9277330tLSwr333luSu3LlSlpaWli58r8m365evZqWlhZWr15dktve3k5LSwvt7e0l8Q0bNtDS0sKGDRv6Yy+//DKf+cxnePnll0tyt27dyuzZs9m6dWtJfNu2bbS2trJt27b+WFdXFxdddBFdXaX/SVSK9/T0cMstt9DT09Mf6+3t5b777iucol9Fpdxy7QJ0d3dz5ZVX0t3d3R/LTJ566ilyiJcDqDV/dzTWt9FY/3ySJEmSpKEZkdmAEXE48H7g18DEzFwPhYFM4K1VVl8JHBsRb4mIfYATgMPqV21ZDwMnRcS4iHgH8MEG1DDqtb/8O9asWcP8+fP7H2vWrOHZZ599Te3+5je/KVn2ueWWW0qWfb7zne+ULAG+9rWvlSz7fPnLXy5ZAnzpS18qWfb5zGc+U7Lsc8YZZ5QsAU455RTa29uZPn16Se4pp5zCunXrXhWfPn06a9euZcaMGf2x2bNnc8cdd3DmmWeW5FaKn3/++Vx88cUsWLCgP3bJJZewYMECLrvsMqqplFuuXYBzzjmHG264oWTweOnSpZx11lksW7asan/Dyd8djfVtNNY/nyRJkiRpaOo+SBkRbwAWA3Mzs6PW9TNzFfAt4C7gZxQGDLsHXWnnuwJYB6wALgLuLVdDRJwVESsiYsXzzz8/wiVqNBo4O6zvdbnZikDJrMS+GZs7ztAcLL5ixQoAli9f3h+74447ALjtttuq1lopt1y7AGvWrAHgiSee6I/deOONJctqas3fHY31bTTWP58kSZIkaWjqOkgZEXtRGKC8LjNvKobbI+Kg4vsHAc9VayczL8/MD2TmsRRO515dvDHPQ8XH2cOs78ri+rdX6b87M/8sM4/KzJOA/YDVZfIuzcymzGyaMGHCcErapU18/f5MnjyZtra2/sfkyZM55JBDGl1aw0RE2dd77FH6n96ee+4JwLhx4/pjEydOBODAAw8sya0Ub2oqXIHg6KOP7o9NnToVgGnTplWttVJuuXYBJk+eDMCUKVP6YzNnzixZVlNr/u5orG+jsf75JEmSJElDU8+7ewdwObAqM7+zw1u3An3nxZ4B3DJw3TJtvbW4fBuFG9tcn5nPFAcNj8rMfxhOjZn52eL6J1Tpf5+IeH3x+ceA7sx8fDh9qnbvfve7S5Z9TjrppJJln3PPPbdkCYVTlndc9lm4cGHJEuAHP/hBybLP1VdfXbLsc9VVV5UsAW666SYmTpzI4sWLS3JvuukmDj300FfFFy9ezKRJk/jJT37SH7v88suZOnUq//iP/1iSWyn+ta99jS984Qslp2V//vOfZ8GCBXzuc5+jmkq55doF+N73vsepp57Kd7/73f7Ysccey6WXXsoxxxxTtb/h5O+Oxvo2GuufT5IkSZI0NFGvmxVExEeAZcCjQN+5rV+lcF3KHwNvA54GZmbmpog4kMLp1G8s5m8BjszMjohYBryFwk11zs3Muyv0eT1wHHAA0A6cn5mXR8TJwPeBCRRuevNQZraUWb9sXvGamkuKdT0LzM7Mpwb7/E1NTdl3muxYM3XqVOhNfjjtvJL43913HXsfuh9tbW39sfnz5wOUxCRJkiRJkrT7iYgHMrPszajHlQvuDJl5DxAV3v5omfwNwKEV2hrSFJvMnFUhfjNw8xDWL5uXmU8C7xpKDZIkSZIkSZJqU7dBSo1de+yxBwxxBu6kSZPqXI0kSZIkSZJ2dQ5Sqmbjx4+nt3NoN1hvbW2tczWSJEmSJEna1dX17t6SJEmSJEmSVI0zKTUsnd3b+bv7riuJPd3RzhT2a1BFkiRJkiRJ2lU5SKmaTZw4kY6ODvY+uHRAcgr7eQ1KSZIkSZIk1cxBStVs0aJFjS5BkiRJkiRJY4jXpJQkSZIkSZLUUJGZja5hTIqI54GnGl3HTnAA8EKji9AuxX1GtXKfUa3cZ1Qr9xnVyn1GtXKfUa3cZ1SrsbLPvD0zJ5R7w0FKDSoiVmRmU6Pr0K7DfUa1cp9RrdxnVCv3GdXKfUa1cp9RrdxnVKvdYZ/xdG9JkiRJkiRJDeUgpSRJkiRJkqSGcpBS1Vza6AK0y3GfUa3cZ1Qr9xnVyn1GtXKfUa3cZ1Qr9xnVaszvM16TUpIkSZIkSVJDOZNSkiRJkiRJUkM5SKmKIuL4iPhtRDwREec1uh6NPhFxWET8PCJWRcRjETGnGF8QEc9GxEPFxwmNrlWjR0Q8GRGPFveNFcXYmyPirohYXVzu3+g6NTpExLt2OJY8FBEdETHX41a75noAAAhUSURBVIx2FBFXRMRzEbFyh1jZ40oUfK/4++aRiPhA4ypXo1TYZ9oi4jfF/eLmiNivGD88IrbtcLz5h8ZVrkapsM9U/C6KiK8UjzO/jYiWxlStRqmwv/zTDvvKkxHxUDHuMUaD/W29W/2e8XRvlRURewL/AXwMWAcsB2Zl5uMNLUyjSkQcBByUmQ9GxL7AA8AngE8CWzLzwoYWqFEpIp4EmjLzhR1i3wY2ZeY3i/9TZP/M/ItG1ajRqfjd9Czw+8Bn8Tijoog4FtgCXJ2Z7y3Gyh5XioMI/xs4gcK+9N3M/P1G1a7GqLDPNAP/lpndEfEtgOI+czjwL3152j1V2GcWUOa7KCKOBK4HPgQcDPwr8M7M7BnRotUw5faXAe8vBF7KzAs8xggG/dv6f7Eb/Z5xJqUq+RDwRGauzcztwA3ASQ2uSaNMZq7PzAeLzzcDq4BDGluVdlEnAVcVn19F4QtZGuijwJrMfKrRhWh0ycylwKYB4UrHlZMo/NGYmfkrYL/iHwbajZTbZzLzzszsLr78FXDoiBemUavCcaaSk4AbMrMzM/8f8ASFv6+0mxhsf4mIoDCp4/oRLUqj2iB/W+9Wv2ccpFQlhwDP7PB6HQ4+aRDF/wP4fuDXxdCXitPOr/DUXQ2QwJ0R8UBEnFWMTczM9VD4ggbe2rDqNJqdSukPeo8zGkyl44q/cTQUfwrcscPrd0TEv0fELyPimEYVpVGp3HeRxxkN5higPTNX7xDzGKN+A/623q1+zzhIqUqiTMxrA6isiHgDsBiYm5kdwCJgMnAUsB5Y2MDyNPr8UWZ+AJgKfLF4Oow0qIjYG/g4cGMx5HFGw+VvHA0qIv4S6AauK4bWA2/LzPcD5wI/iog3Nqo+jSqVvos8zmgwsyj9n64eY9SvzN/WFVPLxHb544yDlKpkHXDYDq8PBf6zQbVoFIuIvSgcRK/LzJsAMrM9M3sysxe4DE9v0Q4y8z+Ly+eAmynsH+19pycUl881rkKNUlOBBzOzHTzOaEgqHVf8jaOKIuIM4ETgU1m8eH/xlN2NxecPAGuAdzauSo0Wg3wXeZxRWRExDjgF+Ke+mMcY9Sn3tzW72e8ZBylVyXLgiIh4R3H2yqnArQ2uSaNM8XoqlwOrMvM7O8R3vBbGycDKgetq9xQRry9eCJqIeD3QTGH/uBU4o5h2BnBLYyrUKFYy68DjjIag0nHlVuAzxbtifpjCjQvWN6JAjS4RcTzwF8DHM3PrDvEJxRt3ERGTgCOAtY2pUqPJIN9FtwKnRsT4iHgHhX3m/pGuT6PS/wR+k5nr+gIeYwSV/7ZmN/s9M67RBWh0Kt7V8EvAEmBP4IrMfKzBZWn0+SPg08CjEfFQMfZVYFZEHEVhuvmTwOcbU55GoYnAzYXvYMYBP8rMn0XEcuDHETEbeBqY2cAaNcpExD7Axyg9lnzb44z6RMT1wHHAARGxDjgf+Cbljyu3U7gT5hPAVgp3itdupsI+8xVgPHBX8XvqV5l5NnAscEFEdAM9wNmZOdQbqGiMqLDPHFfuuygzH4uIHwOPU7h0wBe9s/fupdz+kpmX8+rra4PHGBVU+tt6t/o9E8WzGCRJkiRJkiSpITzdW5IkSZIkSVJDOUgpSZIkSZIkqaEcpJQkSZIkSZLUUA5SSpIkSZIkSWooByklSZIkSZIkNZSDlJIkSaqbiMiIWLjD63kRsaAO/bRFxGMR0bZD7LMR8VDxsT0iHi0+/2Yd+n9zRJy9s9uVJEnaXURmNroGSZIkjVER8QqwHjg6M1+IiHnAGzJzwU7upwOYkJmdFd5/EmjKzBd2Zr87tD8F+ElmHlWP9iVJksY6Z1JKkiSpnrqBS4E/G/hGRLw9Iu6OiEeKy7cN1lAUtEXEyuKsyD8pxm8FXg/8ui9WTUQ8HhH7RsQeEfFiRJxWjF8fEcdFxLiI+E5E3F+s78wd1j1vh/hfF8PfBN7VN1MzIg6JiHuKr1dGxB8OpS5JkqTd1bhGFyBJkqQx7/8Aj0TEtwfEfwBcnZlXRcSfAt8DPjFIO6cARwHvAw4AlkfE0sz8eERsqXEW473AHwLtwGrgGOBHwIeAPwXOAp7LzA9FxHjgVxFxJ/Be4G3A7wMB3F4cgDwPmNJXQ0T8BfDPmfmtiNgTeF0NtUmSJO12HKSUJElSXWVmR0RcDZwDbNvhrT+gMPAIcA0wcBBzoI8A12dmD9AeEb8EjgZuHUZZy4BjKQxS/gA4OyIOB9ozc1tENAPviYhTi/lvAo4AmoGpwL8X428A3gk8N6D95cAlEfF7wE8z8+Fh1ChJkrTb8HRvSZIkjYSLgNkUTsuupNrF0mPnlcMyCrMnjwF+DrxIYRbn0h36+kJmHlV8vCMz7y7G/2aH+JTM/OHAxjPz34DjKFyP87qI+NROrF2SJGnMcZBSkiRJdZeZm4AfUxio7HMv0DdT8VPAPVWaWQr8SUTsGRETKMyEvH+Y9awFDgbenplPF/v+MoXBS4AlwBciYhxARLwrIl5XjM+OiNcX44dGxAHAZmDfvvYj4u3Ahsy8FPgh8P7h1ClJkrS78HRvSZIkjZSFwJd2eH0OcEVEzAeeBz4LEBEfp3An7r8esP7NFE4Rf5jCrMs/z8wNr6Ge5UBX8fky4ALg/xZfX0Lh2pMPRQQUTuc+KTNvj4h3U7hGJRQGJ0/LzCcjYkVEPArcBvwHcG5EdAFbgNNfQ52SJEljXmRWO6tGkiRJkiRJkurH070lSZIkSZIkNZSDlJIkSZIkSZIaykFKSZIkSZIkSQ3lIKUkSZIkSZKkhnKQUpIkSZIkSVJDOUgpSZIkSZIkqaEcpJQkSZIkSZLUUA5SSpIkSZIkSWqo/w9ZtL1d046vpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1584x1584 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hue = retweetcount and followers, totaltweets\n",
    "f, axes = plt.subplots(3, 1, figsize = (22,22))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.stripplot(x = 'dt_date', y = 'noTweets', data = top_user_df, jitter = True, ax = axes[0], size = 6, alpha = 0.3)\n",
    "sns.boxplot(y = 'dt_date', x = 'noTweets', data = top_user_df, orient = 'h', showfliers=False, ax = axes[1])\n",
    "sns.boxplot(y = 'dt_date', x = 'noTweets', data = top_user_df, orient = 'h', showfliers=True, fliersize = 2.0, ax = axes[2])\n",
    "\n",
    "# Axes and titles for each subplot\n",
    "axes[0].set_xlabel('Date')\n",
    "axes[0].set_ylabel('No. of Tweets')\n",
    "axes[0].set_title('No. of Tweets Daily')\n",
    "\n",
    "axes[1].set_xlabel('No. of Tweets')\n",
    "axes[1].set_ylabel('Date')\n",
    "axes[1].set_title('No. of Tweets Daily')\n",
    "\n",
    "axes[2].set_xlabel('No. of Tweets')\n",
    "axes[2].set_ylabel('Date')\n",
    "axes[2].set_title('No. of Tweets Daily')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the seaborn boxplots and stripplot, we can see that most of the users in the dataset do not tweet a lot in a day. From the stripplot, we might not be able to discern the outliers in the dataset, and might think that most of the users tweeted in the range of 1 to 30 plus tweets daily. \n",
    "\n",
    "However, boxplots tell us a different story. The first boxplot at the middle of the visualization revealed, in fact, most users tweeted between 1 to 8 tweets. Whereas there are many outliers as shown in the second boxplot, at the bottom of the visualization. These users tweeted a lot, ranging from 10 onwards. There were at least 7 users who have tweeted at least more than 100 tweets per day in the timeframe considered.\n",
    "\n",
    "#### Top 5 users with most number of tweets daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cONZaffrLdymdTOq2173adZzLuunGF+qVL32xvviNv9bqZsZ1HKda7Y7rCAAAAAAAABihi5Zd1tpta+1pa+17JF2jnZLrjKSeMebZI0uIS7LWanltU4lEzHWUiRQEO0teNZot11EwhZqttk6fW9XiPEXXJDr2yHGdOLOkX3nT65RMxF3H2ZcPvettSsbj+tM7Pq/+jC7lF41GVaowYxcAAAAAAGCWXHbPLmPM70v6A0m/v3soIek/DTMU9q9cralcqyoZn4yB2HHkGaPtMgOjGKwwDHX63Kp831MkEnEdBwfU6/X0n+68R9c+/Wl6zc0vdx1n3+bTaf3Gu9+usytruvv+77qO40QsiKpSrbmOAQAAAAAAgBG6bNkl6VckvVlSXZKsteuS5ocZCvtjrdXS2rpSCZYvvBKpVFKb+bysta6jYIpk8kWVq1XNpVKuo+AQvvGdB5QtFPWB294s3/ddxzmQm//Bjbrpxhfqc/fcq41c3nWckYtGImq2W+r2eq6jAAAAAAAAYET2U3a17U4LYCXJGJMcbiTs11aprFqjqXiMJQyvRDQSUavdVr3ZdB0FU6LeaOrs6poW2KdrIlXrdX3hG/fpxS+4QS/5uee7jnNgxhj95rtvUzQa0e13fF5hGLqO5IBh3y4AAAAAAIAZsp+y6wvGmP9H0oIx5jclfUPSx4cbC5cThqGW1jaUTjKraxB839NWqew6BqZAvx/q8eUVxWOxiZsRhB1f+Pp9arba+vW3v9l1lEM7sjCvD77jrXp86Zzu/c4DruOMnJHUbLEXIwAAAAAAwKy4bNllrf3fJX1V0p2SXiLpD621/3bYwXBpxVJZrXZbsSBwHeWKbOYLuuOur6vheFAylUgomy+ylCGu2Fo2q3qzoWSCffQm0Xo2p3u/+z295pWv0LOe8VOu41yRW17+Mr3kBc/T/8/enYdHWZ39A/+eZ/bsAUIgQMIaENlUNkFBkH1HEEG0tbVSK7ZW+1rbX7Wbdntt62vrSrW2KgiCIvu+CcgOCooQIIQECNkz+/6c3x/BrWUJyTzzzEy+n+uaKyGZOfcXzEwun3vucxauWovyqmq940SV0WSC3enWOwYREREREREREUVJfSa7AOAIgOVSyh8D2CKE4CE0OgqHVRSVnENKcnzvKHno6DE89dcXsXzjVizbsFXXLEajEYFgCC4PtzKkhnO43Cg+V4qM1FS9o1ADvbNiLSwmE6aPGaF3lEYTQuD+GVOhCAX/WPR+k2rmW80m2J1OvWMQEREREREREVGUXLXZJYT4Luqmul67+KVcAMu0DEVXVlFdDX8oCLPJpHeUBpFSYvnGrfjza28iq3kmbrz+Oqz9cCcqqmt0zWUwKqiqqdU1A8WvYCiEgtNnkJxkg6LU930EFEs+LTiJg599jskjhyXMeWstMjNw96Sx+OzEKWzZvU/vOFFjNBrhDwQQDIX0jkJERERERERERFFQnyuyPwIwEIADAKSUBQBaahmKLi8YCqHoXCnSkuNzuM7nD+Dvb76DhavW4eYbeuHXP3oQ35k2CRDA4tXrdc2WbLPhQmUVVFXVNQfFp+LzpQgEArBaLHpHoQZQVRVvL1uFrGaZGDNkkN5xImr4zf1xfZdOmL9sdZNq6EtIeH1+vWMQEREREREREVEU1KfZ5ZNSBr74gxDCgLqz30kHZZXVCKthGI1GvaNcs/Kqavz6by9jzyefYtbEsZh7z12wmM1onpmBcUNvwY4DH6Ow5Jxu+QwGA0LhEFwej24ZKD7V2B04X1aB9DRuXxivtu09gOLzFzBrwpi4nZq9HCEEvjfjDqhSxeuLP2gy2xkKCHh1Pg+SiIiIiIiIiIiioz7Nrp1CiJ8CsAohhgFYBGCltrHoUgLBIIpLL8TlVNenBSfx5F9fQGVNLX465z5MHD4EQnzVM514+1CkpSRjwfLVul6INRmNum+nSPElEAyioOgMUpOTv/EzTfHD6/Nj8eoNyG+fhwF9euodRxPZLZrhrvGj8fHnx7Fj/yG940SF2WyC3enWOwYREREREREREUVBfZpdPwXgBHAMwCMANgH4hZah6NJKKyoBKWHzFLKgAAAgAElEQVQwGPSOUm9SSqzeugN/eOWfyEhLwzOPzkXvbvn/db8kqxXTRt+OoycLcejoMR2S1km22VBeVYNwmFsZ0tVJKXG65DxUVYXZnFjTQE3Jis3bUOt04p4p4xK6YTnqlpuR3yEPb36wErUOp95xNGcxm2F3Jv7fk4iIiIiIiIiI6tfsGgzgn1LKqVLKKVLKl6WU7AREmT8QwNnSMqSlxM9UVyAQxMvz38Xby1ahb8/u+M0jP0CrrBaXvf+wm/ujdcssLFi+BuFwOIpJv6IoCsLhMJxuTgPQ1VXV1KKsqgppKSl6R6EGqqypxaqt2zHoxt7onJerdxxNKYqCOTOnIRAI4o0lyxJ+O0OjwYBAMIhgMKR3FCIiIiIiIiIi0lh9ml0PAvhMCLFDCPF7IcRYIUSa1sHom86VlUNRBBSlPv/J9FdZU4tf//0V7Dz4Ce4cOxKPfPtu2KyWKz7GaDBg1oQxOF9egS2790cp6X8zm80or6rWrT7FB58/gIIzJchITUnoaaBEt2jVOgDAzAljdE4SHTktszB9zAjsO/IZ9nzyqd5xosLr9+sdgYiIiIiIiIiINHbVzomU8m4pZWcAswGUAXgVQJXWwegrXp8P58sqkBonZ3V9fuo0nvzrCyirrMJj370XU0cNr3eT7qYe16Fbpw5YsnYjvD59LlAmWS2orKlFSKfpMop9UkqcKi6BIgSMRqPecaiBTp4pwc4DH2Pc0FvQIjND7zhRM+62W9CxXRv8671lcLgSf4rV4/XqHYGIiIiIiIiIiDR21Q6EEGKmEOJFAAsAjAbwCoDhWgejr5RcKIfRaIj5qS4pJdbv2IXfv/QakpOS8NsfP4Sbelx3TWsIITB70lg4XC6s2LxNo6RXpigKVFU2iYvA1DAXKqpQY7fH1bai9E1SSrz9wSqkp6Zg0u236R0nqgwGA+bMnA6314c3l67QO46mLGYzHC6X3jGIiIiIiIiIiEhj9emevAygL+qaXHOklL+XUm7XNhZ9we3xoryyMuanuoKhEP6x6H38673l6NUtH0//+CG0yW7ZoLU65bbDoBt7Y/XWHaiqtUc4af1YLCaUV3KAkf6bx+tDYclZpKem6h2FGmHPJ5+ioOgMZowbddUtVhNRbk4rTB05DB8d/AQHPj2qdxzNmM0m1DrZ7CIiIiIiIiIiSnT1aXY1AzAHQAaAvwghdgsh3tA2Fn2huLQUZpM5ps8EqrE78PQL87B1z35MGTkMP7n/XiTZrI1a867xo6GqKhav2RChlNcmyWpFda0dwWBIl/oUm8JhFQVFZ2A2m2AwGPSOQw0UCAbxzoo1yM1phaH9b9I7jm4m3T4UuTmt8PriD+BO0K3+jAYDAoEgAsGg3lGIiIiIiIiIiEhD9Wl2JQFoCSAbQCsAzQGYtQxFdZxuDyqra5GSnKR3lMsqKDqDX/zlBZSUluHH983GjHGjIrLdYlazTIwZMhjb9x3EmXOlEUh6bYQQkADsTmfUa1PsOl9eDqfbjWSbTe8o1AjrPvwIFdU1mD1pfMxvD6slo9GIOTOnw+FyY/6y1XrH0YwQAj6/PmdAEhERERERERFRdNTnKt9eAHcCKABwn5Syi5RytraxCADOnDsPawxvr7Vl9z48/cI/YDab8Nsf/wD9e/eI6PqTR96GZJsV85evhpQyomvXh8ViRnlVddTrUmxyuj0oOnsemWlpekehRrA7Xfhg4xbc0L0benbtrHcc3XVs1wYThg/B1j37cfhYgd5xNCFQt/0oERERERERERElrss2u4QQvwcAKeX1Uso5Uso3pZSnoxetabM7XaixO2JygiQUCuGNJcvwj0Xvo3vnjnjm0blo17pVxOsk22yYOup2fFpwUpeLsDaLBdUOB7e/IoTCYRScLkKSzdakJ4ESwZK1GxEIBHH3pLF6R4kZd4wajpyWWXjt3aXw+hJvAspsMaPWwXO7iIiIiIiIiIgS2ZWu2o6JWgr6BiklTp89h6QYbHTZnU78/uXXsWHnbkwcPgRPzLlP020WRw4egOwWzbBgxRqoqqpZnUsRQgASqHVwK8OmruT8BfgCAdhieNKSru5saRk279qLEYMHoE12S73jxAyzyYQ5s6ahqtaOhSvX6h0n4iwmExwuNruIiIiIiIiIiBLZlZpdBiFEphCi2aVuUUvYBNU4nHC6PTF3Yb2w+Cx+8dcXUVhyDg/fOxOzJo7VfMrFaDRi1oSxKCktw7a9BzStdSlJNisuVFZFvS7FjlqHE2cvlCE9JUXvKNRI85evhs1qwR2jb9c7SszJb5+HMUMGYcPO3fj8ZKHecSLKYDAgGAxxSpeIiIiIiIiIKIFdqVPRDcCBy9z2ax+taVJVFUVnz8Xc9oUf7juI3/z9VShC4NePPIhBN/aOWu1+va5Hfvs8LF6zAT5/dLfYsloscDrd8PkDUa1LsSEYDKHgdBFSkpO4fWGc++Tz4/jkWAGmjhqO1ORkvePEpBnjRiG7RTPMW/Qe/IEEe80TSMgtGomIiIiIiIiIqM6Vrt4elVJ2lFJ2uMStY9QSNjHVtXZ4vF5YLWa9owAAwuEw3lq6Eq8sWIwu7XPxzGNz0b5NTlQzCCFw9+SxqHU4sWrL9qjWrgsA1Doc0a9Lujt99hzCYRUWc2w8H6lhwuEw3l62GtktmmP0LTfrHSdmWcxmPHDXNJRVVmPxmg16x4koIQTcHq/eMYiIiIiIiIiISCMcVYgh4bCK02fPIyVGpg4cLjf++OobWPPhTowZMgg/e/C7SNNpK7f89nkY0LsnVm7Zjhp7dBtPdVsZVka1JumvqqYWZVVVSEvl9oXxbsvu/ThXVo67J46F0WjUO05M6965I0YMHoA123biRFGx3nEixmw2wc5zu4iIiIiIiIiIEtaVml3PRy0FAQAqq6vhDwRgNpn0joIz50rx1HMvouD0GXx/1nR8a+pEGA0GXTPNnDAaoXAYS9ZujGpdi9kMl9sDr88X1bqkH38ggIKiYqSlpEAIoXccagSP14clazegW6cO6Nuzu95x4sKsCWPRPCMd8xa+lzDnXFnNZjicLkgp9Y5CREREREREREQauGyzS0r5ryjmaPJC4TCKzpciNUX/qa5dhw7jV8+/jHA4jKcenoOh/W/SOxIAILtFc4waPBBb9+zH2dKyqNZWFANq7M6o1iR9SClRWHwWigBMnAKKe8s2boHT7cE9k8ezcVlPNqsF98+YinNl5Vi6frPecSJCURSEwuGEad4REREREREREdE3cRvDGFFeWY1QKKTrxXVVVfHOirX4+5vvoH3bHDzz2MPonNdOtzyXMmXUMNisFixYsSaqdZNsVpRWVES1JumjvLIKlbW1SNVpy06KnPKqaqzZthO39r0BHdu10TtOXOndLR9D+9+EFZs/xOmSc3rHiRAJr8+vdwgiIiIiIiIiItLAZZtdQog/Xfx4Z/TiNF1ny8qQquNZXS6PF//7j39hxeZtuH3QADz50PeQkZaqW57LSU1OxtSRw/Hx58dx5PjJqNU1m0zw+Pxwe7xRq0nR5/X5cKr4HNJTY+9nn67dwpVroSgKZowbpXeUuDR78nikpSTj1YVLEAqF9I7TaIqiwO3hdrRERERERERERInoSpNd44QQJgA/j1aYpkwNq1AUfQbtzpaW4annXsBnJwpx/51Tcf+dU2CM4e3bRt16M7KaZWLB8tVQVTVqdY2KghqHI2r1KLpUVUVBUTGMJqPm59N5fX48/68F2LJ7H88Q0sjxwiLs/vgIJgwfgmYZ6XrHiUspSTbcf+cUFJ+/gOWbt+kdp9HMJjPsLm5HS0RERERERESUiK7UXVkLoBJALyGEQwjh/PrHKOUjje07/Bl++fxL8PkDePKh7+H2Qf31jnRVJqMRd40fjTPnS7Fj/6Go1U1OsqG0vJLNiQR1vrwCTpcbKUk2zWu99cFK7PnkCP6x6H0898bbcLjcmtdsSlRVxdvLViEzPQ0Thg3RO05cu6lHdwy6sTeWrt+CktILesdpFIvZBLvTxddwIiIiIiIiIqIEdNlml5TycSllOoBVUso0KWXq1z9GMSNpQFVVLFmzAc+98TbaZLfE7x57GF07ttc7Vr3dfEMvdMpti3fXbIA/EIhKTaPRiEAwALeXWxkmGpfHg6Kz56Oydefew59i6579mDh8KGZPGoePjx7Hz559HoePFWheu6nYdegwThWfxV3jRsFqMesdJ+59a+pEJNuseHXhewiHw3rHaTBFUaCqKvyBoN5RiIiIiIiIiIgowq66b56UcrIQIlsIMeHiLSsawUg7Hp8Pz73xNt5fvxlD+t+Epx6eE3fbfAkhMHvSOFTX2rFm286o1VUUA6pq7VGrR9oLhcMoOH0GNqtV861Ea+wOvPbuUrRvm4M7x47A+GG34ulH5yLZZsMfX30Dby5dgUCQF+IbIxAIYuHKtWjfNge39L1B7zgJIS0lGffdMQmFxWexOoqvt1rx+f16RyAiIiIiIiIiogi76pVdIcSdAPYCuBPADAB7hRDTtQ5G2igtr8Avn3sJh44ex7fvmIjvz5wGs8mkd6wG6dapA/r27I4Vm7bB7nRFpWZykg0XyiujelYYaavkfBk8Pj9sVoumdaSUmLfwPfgDAcy9564vz8XLa9Mav3vsYYy+9Was/fAjPPXciyg+H9/bxelp9bYdqKq1497J43U7BzERDejTE/16Xo8lazbgfHmF3nEaTFEUuDwevWMQEREREREREVGE1edK4JMA+kkpvy2l/BaA/gCe0jYWaeHQZ8fw5HMvwun24P/94H6MvnUQhBB6x2qUWRPGIBAM4v11m6JSz2gwIBgKweXhVoaJwO504WzZBWSkpmhea8PO3fjkWAFmTxqHNtktv/E9s9mEb98xCT+dcx8cLjee/OsLWLNtB5uq16jW4cSyjVvRr+f1uK5zR73jJBQhBO6bNglmswnzFr4Xtz+bX5zbRUREREREREREiaU+zS5FSln+tT9X1fNxFCOklPhgwxb8+fU3kd28OZ55bC66J8iF4NYtszB8UH9s2rUX58rKr/6ACDAajaiqqY1KLdJOMBRCQWERUpKSNJ8AOldWjvnLV6N3t3yMHDzwsvfrc11X/Omnj6BXt3y89cEq/OnVN1Bjd2iaLZEsXrMBoXAYsyaO0TtKQspMT8O9Uyag4PQZbNixW+84DWI2meBwuSGl1DsKERERERERERFFUH2u8K4VQqwTQtwnhLgPwCoAq7WNRZHi8/vx/L8X4N3V63HzDb3wqx99H1nNMvWOFVHTRt8Oi8mEhSvXRqVess2KC5VVCIfjc7KB6hSdPY9QOAyL2axpnVAohBffXgSL2Yw5M6dddZoyLSUFP7n/Xtx/5xQcP30GTzz7PPYd/kzTjIngzLlSbN2zH6NvvRmtslroHSdh3dr3BvS+Lh8LV61FeVW13nGumaIoUFUVPn9A7yhERERERERERBRBV212SSkfB/AqgF4AegOYJ6V8Qutg1HhllVX41fMvY9/hzzB70jjMvecuzS/s6yEtJQWTR9yGA59+js9PFmpez2AwIBxWee5LHKuqteNCRSXSorB94ZJ1m1B09jwemHEHMtPT6vUYIQRuHzQAv/+fHyIrMxPPvfE25i18Dz6/X+O08UlKibeXrUKyzYopI4frHSehCSHwvTunQhEK/rHo/bidkOJziYiIiIiIiIgosdRr7y4p5ftSyseklI9KKZdqHYoa78jxE3jyuRdRbXfgZ9//DsYPuzXuz+e6kjFDBqN5RjrmL18dlbNkzCYjyqtqNK9DkecPBHCiqBipKcmaPyeOnTqNFZu24bYBfdGv1/XX/Piclln4zSMPYtKI27Bt7wH8/M9/x8kzJRokjW+Hjh7DZydOYdroEUhJsukdJ+E1z8zA3ZPG4bMTp7B51z6941wzRVH4ZgUiIiIiIiIiogTDs7cSjJQSK7d8iD+++gaapafhmUfnomfXLnrH0pzZbMKMcaNQWHIOuw4d1rxeks2KyupqhMJhzWtR5EgpUVhyFgISZpNJ01oerw8vzX8XWc0yce+UCQ1ex2g0Yub40Xhy7gMIh8P49d9ewfvrNyHMnz0AQCgcxvzla9C6ZRZuHzxA7zhNxvCb++H6Lp2wYPnquDvD0GI2o9bp0jsGERERERERERFFEJtdCcQfCODFtxdhwfI16NfzevzmkR8gu0VzvWNFzeCb+qB9mxwsWrUOgWBQ01qKokCVKpwut6Z1KLLKq6pRWV2L1BTtty/89/vLUVVrx0P3zIDNamn0etd16oA/PP4jDOzTE0vWbMTTL/4jLs9MirRNO/egtLwCsyeNhdFg0DtOkyGEwPdm3AFVqnht8dK42s7QbDLC5XZHZQqYiIiIiIiIiIiig82uBFFRXYPf/O1V7Dp0GDPGjcIj990Nq6XxF9jjiaIomD15HCprarFu+y7N65lMZjYb4ojX58epMyVIT0vVvNbuj49g+/5DmDJyGPLb50Vs3WSbDQ/fOxMP3TMDJaUX8PNn/4YP9x2Mq0ZDJLk8Xry3biOu79IJN3TvpnecJie7RTPcNX40Pvm8ANv3H9I7Tr0pioJwWMIfCOgdhYiIiIiIiIiIIqRBzS4hxK8jnIMa4ejJQjz51xdRVlWF/7n/W5gyclhCn891JXUXvbti2YYtcGg8dZVktaCqthbBUEjTOtR4qqrixJliGI1Gzad/qmvteH3xUnTMbYupo4ZrUuOWm27AHx9/BHltWuOVBYvx9zffgcvj1aRWLFu6fhPcXh/umTy+yb7m6W3ULTcjv0Me3ly6AjV2h95x6k0IwOtns4uIiIiIiIiIKFE0dLLrQERTUINIKbFu+0f4/cuvIzU5CU8/Ohc3XM/phlkTx8Lr92Pp+s2a1lEUBaoquZVhHCgtr4LD6UJKcpKmdVRVxavvLEEoFMLc2TM0baxlNcvEk3MfwIxxo7Dv8Gf42bPP4+jJQs3qxZrSikqs37Ebtw3oi7w2rfWO02QpioI5M6chGAzhjfeWxc2UocGgwOXmazcRERERERERUaJoULNLSrki0kHo2gSCQcxb+B7+/f4K3NC9K3776EPIaZmld6yY0LZVNoYP7IeNO3ejtKJS01pWiwUXKqs0rUGN4/Z4cfrsWaSnan9O17rtu3Ck4CTumTweraPwfFQUBVNGDsOvH3kQZpMRv3vpNbyzYg1CTWDacOGKtTAaDLhz7Ei9ozR5OS2zMH3MCOw/chR7Pj6id5x6sZjNqHU49Y5BREREREREREQRctVmlxCirRBiqRCiQghRJoR4TwjRNhrh6NKqa+14+oV52Lb3AO4YNRyPfuceJFmteseKKdPGjIDRaMSiles0rWOzWlBjdyAYTPzmQjwKh1UUFJ2B1WqBQePtC8+WlmHhyrW4oXs3DL+5v6a1/lOn3Hb4/U9+hGED+2HF5g/xy/97GefKyqOaIZo+P1mIfUc+w+QRtyEjCmew0dWNu+0WdGzXBv96fzkcLpfeca7KbDLB5fFCVVW9oxARERERERERUQTUZ7LrDQDLAbQG0AbAiotfIx0cLyzCL/76As6VlePR79yD6WNHQlEauhtl4spIS8XE24dg7+FPcbywSLM6QghISNQ6OSEQi0ouXIDH69W8GRwMhfDi24tgs1owZ+YdupwfZbWY8b0ZU/HYd+9FZW0tfvGXF7Bhx6642VauvlRVxVvLVqF5RjrGDb1F7zh0kcFgwJyZ0+H2+vDm0pV6x7kqIQSklPAHeG4XEREREREREVEiqE+XJEtK+YaUMnTx9i8A3C9PB5s+2otnXnoNVosFv33kIfTrdb3ekWLauKG3IjM9DfOXr9b0gr/NYkUZtzKMOQ6XC8XnLyA9VfvJn8WrN+DM+VI8cNe0qNS7kr49u+NPjz+Cbp3a4433luPPr/0b9gRqxu7YfwhFZ89j5oQxMJtNesehr8nNaYWpI4fho4OfYP+Ro3rHqQcJr8+vdwgiIiIiIiIiIoqA+jS7KoUQ9wghDBdv9wDglf0oCoVCeH3xB3h98VL06NIJTz86F21bZ+sdK+ZZLWbcOXYkTp4pwZ5PPtW0Tq3DyQmBGBIMhXD8dDFSkpI0n3w8erIQq7Zux/Cb++OmHtdpWqu+MtPT8NMH7sO3pk7ApwWn8MT//g2HPjumd6xG8/kDWLR6PTrltsXNN/TSOw5dwqTbhyI3pxX+ueQDuDxeveNckcFghNPt0TsGERERERERERFFQH2uAn8XwAwAFwCUAph+8WsUBbUOJ5556TVs+mgPJg4fiscf+DZSkmx6x4obQ/rdiHatW2HhyrUIhrQ5V0sIAQiBWkfiTM/Eu+LzpQgGg7BazJrWcXu9eHn+u2jZvBnumTxO01rXSlEUjBkyGM88NhcZaSl49rV/440ly+K6Kbtyy4eosTtw75QJ3L41RhmNRsyZOR0Olxvzl63SO84VWcwmvm4TERERERERESWIq14tlFIWSyknSSmzpJQtpZRTpJRnohGuqTtVXIIn//oCis6exw+/NQuzJo7hBd5rpCgKZk8ai/KqamzYsVuzOklWKy5UcOAxFlTb7Th3oQLpqSma1/rXe8tR43Bi7j0zYLVYNK/XEO1at8LTj87FuNtuwYadu/GLv9S9psSb6lo7Vm7+EAP79ER+hzy949AVdGzXBhOGD8G2vQfwybECveNcltlkgsvrhaqqekchIiIiIiIiIqJGumznRAjxyyvcnopmyKbow70H8Nu/z4PBYMBvHvkBt+xqhF7d8tGrWxcs3bBZs221rBYznB43z3/RWSAYxImiYqSlJtdN3Gnoo4OfYOeBj3HHqOHonJeraa3GMhmNuGfyePz8wfvh9fvx1P+9hBWbtsXVRf53V6+HqqqYOWGM3lGoHu4YNRw5LbPw2qL34fH59I5zSXWvERI+f/xOOxIRERERERERUZ0rjQm5L3EDgPsBPKFxriYrFA7j3++vwCvvLEF+hzw8/ehc5LVprXesuHf3xLHweH1YtmGLZjUUCNid3BJLL1JKFBafg1QlzCaTprWqamrxzyUfoHNeO0wecZumtSKpZ9fO+OPjP8KN13fDOyvX4vcvv46qmlq9Y11VYck5fLjvIMYOHYyWzZvpHYfqwWwyYc6saai2O7Bw5Vq941yelPDGaDOOiIiIiIiIiIjq77LNLinlX764AZgHwAbgOwAWAugYpXxNisPlxh9f+SfWbf8IY4cMxs++/x2kpSTrHSsh5Oa0xtD+N2Hd9o9QVlmtSY0kmw3nyys1WZuurrKmFhXVNUjTePtCVVXx8oLFCIdVPDR7BgwGg6b1Ii01ORk/vm825sychlPFZ/HEs89j16HDese6LCkl3l62CmkpyZg8Ypjecega5LfPw5ghg7Bx5x4cPVmod5xLMhqNcLjdV78jERERERERERHFtCseACWEaCaEeAbAYQBGADdKKZ+QUpY3pqgQ4lEhxGdCiE+FEO8IIaxCiA5CiD1CiBNCiEVCCHNjasSb4tIL+OX/vYQTRcV48O47ce/UCXF3ET3WTR8zAgaDgkWr12myvtlsgsfrhcfLKYFo8/r8OFFUjPRU7ZvDaz7ciaMnC/GtqRPQKquF5vW0IITAbQP64g//80PktMzC3998By/Nfzcmt5vbf+Qojp06jeljRiLJZtU7Dl2jGeNGIbtFM8xb+F5MbhdoMZnhcLr0jkFERERERERERI10pTO7ngWwD4ATQE8p5a+llDWNLSiEaAPgRwD6Sil7ADAAmAngTwCek1J2AVCDuu0Sm4Q123bi2df+DVWV+NUPv48h/W7UO1JCapaRjnG33Yrdhw7j5JliTWooQqDGzq0Mo0lVVZwqLoHBoMBoNGpaq/h8KRatXIebenTHbQP6alorGlpltcAvf/h93DFqOHYe+Bg/f/ZvOF5YpHesLwVDISxYsRptW2Vj2MD4//duiixmMx64axrKq6qxeM16veP8F5PJCJfHi3A4fs6vIyIiIiIiIiKi/3alya6fAMgB8CSA80IIx8WbUwjhaGRdIwCbEMIIIAlAKYDhAJZc/P6/AUxpZI24IKXEtr37kde6NZ5+9CF0zG2rd6SENmHYEKSnpmD+sjWQUkZ8/eTkJJRWVGiyNl3ahYoq1NgdSE3WdqorEAzixbcXITnJhgfumgohhKb1osVoMGD62JH41Q+/DwD47QvzsHjNBoTCYZ2TAet37EJZZTVmTx7HSdc41r1zR4wYPABrP/wIBUVn9I7zDV88j31+v85JiIiIiIiIiIioMa50ZpcipbRJKVOllGlfu6VKKdMaWlBKeQ7AnwEUo67JZQdwAECtlDJ08W5nAbRpaI14IoTAr374IH5832yka3zWEAE2qwXTx4zE8dNF2H/kaMTXNxmN8Pn93MowStweLwpLziIjLVXzWu+uXo+S0jLMmTkNaSmJ91zN75CHPzz+I9za9wYsXb8Zv/nbK7hQod8ZdA6XG0vXbUbvbvno3S1ftxwUGbMmjEXzjHTMe+c9BIJBveN8gwSbXURERERERERE8e6KZ3ZpQQiRCWAygA6omxxLBjD2Ene95GiMEGKOEGK/EGJ/RUWFdkGjyGa1wGjk1EK03DbgJrTJbol3Vq7RZHrFYFBQVVsb8XXpm8JhFQVFxbBaLJpP/XxacBKrt+7AyMEDcUP3bprW0lOS1YoH774TP/r23SitqMTP//x3bNm9T5dJxaXrN8EXCGD2pHFRr02RZ7NacP+MqThfXoHlG7fqHecbzEYj7Dy3i4iIiIiIiIgorkW92QVgBIDTUsoKKWUQwPsABgHIuLitIQC0BXD+Ug+WUs6TUvaVUvbNysqKTmJKKAaDAXdPHIsLFVXY9NGeiK+fbLOhrKKKWxlq7GxZGdxeD5JsVk3ruDxevLJgMVq3zMLdky7Vl088A/v0xJ8efwSdctviH4vex/+9MR9Otztq9c+VlWPDzj0YfnM/tG2dHbW6pK3e3fLRr+f1WGDvwSoAACAASURBVL9jFwKB2JnuMptNsLvY7CIiIiIiIiIiimd6NLuKAQwUQiSJusMybgdwFMAWANMv3ufbAJbpkI2aiD7du+L6Lp3w3rpNEd9y0Gg0IhAMweXxRnRd+orD5UbxuVJkpGq7faGUEv9c8gHsThfmzp4Bi9msab1Y0jwzA//vB/dj1sSxOHj0GJ743+dx5PiJqNResHwNLGYTpo0eEZV6FD2jbr0ZLo8Xuz8+rHeUL5lNJnh9vpg4p46IiIiIiIiIiBom6s0uKeUeAEsAHARw5GKGeQCeAPCYEOIkgOYAXo92Nmo6hBCYPWkc3B4vlmmwpZbBqKCqhlsZaiEUDqPg9BkkJ9mgKNq+hO08+DF2HzqMaWNGoGNuW01rxSJFUTBx+BA8/eOHkGyz4Q+v/BNvLV2p6ZlLR46fxKGjxzBlxDCeY5iAunfuiJyWWdiowVRtY0jJc7uIiIiIiIiIiOKZHpNdkFL+SkrZTUrZQ0p5r5TSL6UslFL2l1J2llLeKaXkVSfSVPu2ORh8Ux+s/XAnKiPcmEq22XChsgqqqkZ0XQKKz5ciEAzCarFoWqeiugZvLFmG/A55mHT7UE1rxbr2bXPwzGNzMfKWgVjz4U489dxLKD5/IeJ1VFXF/OWrkNUsE6OHDIr4+qQ/IQRGDB6Ak2dKcLrknN5xvsHnD+gdgYiIiIiIiIiIGkiXZhdRrJgxbhQggHdXrYvougaDAaFwCC6PJ6LrNnW1DifOXSjXfOJHVVW8vGAxpJR4aPYMzSfI4oHFbMZ3pk3G4w98G3anC0899yLWbNsZ0Ybutr0HUHz+AmZNHAuzyRSxdSm23NrvRljMppia7jIZjXA4eW4XEREREREREVG84hVcatJaZGZg7NBbsOPAxyiM8JSByWhERXVNRNdsyoLBEI6fLkJqcjLqjvvTzqot23Hs1Gl8+45JaNm8maa14s0N3bvhTz99BD3yO+OtD1biT/P+hRq7o9Hren1+vLt6PfLb52FA7x4RSEqxKtlmw6Ab+2DngY/h9sbG2YYWixm1DqfeMYiIiIiIiIiIqIHY7KImb9LwoUhNTsaC5ashpYzYusk2G8qrahAOcyvDxpJSorDkHFRVhdms7cRP0dnzeHfNBvTrdT2G9LtRszoerw/Vdrtm62spPTUF//O9b+G706fgeGERnnj2eew78lmj1ly+aSvsThfumTJe82Ym6W/EoAEIBIPYvu+g3lEA1L05wev3IRQO6x2FiIiIiIiIiIgagM0uavKSbFZMG3M7jp4sxKGjxyK2rqIoCIfDcLrdEVuzqaqqqUVZVRXSUrTdvjAQCOLFtxchNTkJ9985VbOmSygchj8QQGZaGuxxOk3yxdlLv/vJw2iRmYHn/vk2/rHoffj8137cYkV1DVZv3YHBN/VB57x2GqSlWNOhXRt0ym2LjTv3RPRNBo0hJRr080tERERERERERPpjs4sIwPCb+6N1VgssWL4G4Qi+s99sMqG8qjpi6zVFPn8ABWdKkJ6aovnEzzsr1+JcWTm+P2s60lKSNatjdzjRObct8tvnwWg0xvUF9jbZLfHbR36AicOHYuue/fj5n/+Ok2dKrmmNRavWAQK4a/xojVJSLBo5eCDOl1fg6MlCvaMAABQh4PH69I5BREREREREREQNwGYXEQCjwYCZE8fgfHkFtu7ZH7F1k2xWVNbUcmusBpJS4lRxCRQhYDIaNa11+FgB1m3/CKNvvRm9u+VrVsfhdKFFs0y0bNEcJpMR3Tp1gMfri2iTNdqMRiNmTRyDXzz0PYRCIfz6b69g6frNUNWrb+F58kwxPjr4CcbfditaZGZEIS3FioF9eiE5yYYNO3frHQUAYDSZ4HBxEpeIiIiIiIiIKB6x2UV0Ud8e3dGtY3ssXrMRXl9kJm0URYGqSl5AbaCyyipU2+2aTlkBgNPtxivvLEGb7JaYNWGsZnUCwSAgBDrltv1ySi01OQmd89qh1uGMme3cGqp75474w+OPYEDvHli8ZgN++8K8K042Sinx1gerkJGaikm3D41iUooFZrMJt/XviwNHjqLG7tA7DixmE2rjdFtRIiIiIiIiIqKmjs0uoouEELh70jg4XC6s2LwtYutaLCaUV1ZFbL2mwuP14VTxWaSnpmpaR0qJ19/9AE63B3PvuQtms0mzOk6XB/kdcmE2fbNGdovmyG7RHA6nS5Pa0ZSSZMPD987EQ7NnoKT0An7+7N+wfd/BSzby9nx8BCeKinHnuJGwWiw6pCW9DR/UH2FVxZY9+/SOApPRCH8ggGAopHcUIiIiIiIiIiK6Rmx2EX1N57x2GHRjb6zeugPVtfaIrJlktaK61o5gkBdQ60tVVZw4Uwyz2QSjwaBpre37DmLv4U9x59iRaN82R7M6dqcLbVploVl6+n99TwiBDu3awGw2weOL/zODhBC4pe8N+OPjjyA3pzVeXrAYL7y1EG6v98v7BIJBvLNyLfJyWmNo/5t0TEt6ap3VAj27dsHmj/bGzFaePn9A7whERERERERERHSN2Owi+g93jR8NVVWxeM2GiKwnhIAEYHdye6z6OldWDqfLjWSbTdM65VXV+Pf7K9CtY3tMGHarZnV8fj8sZhNyc1pf9j4moxHdOnaA3x9ImDPesppl4qmHH8CMcaOw95NP8bP/fR6fnywEAKz98CNUVNdg9uRxUBT+KmrKRgwegGq7AwePHtM7CiAlPF9ryhIRERERERERUXzgFUai/5DVLBOjhwzCh/sO4sy50oisabGYr3h2EX3F6fag6Ox5ZKRpu32hqqp4af67gAB+MHuGZg0XVVXh9niR36H9VafUkpNsCXN+1xcURcGUkcPw60cehMlkxDMvvYa3lq7Esg1bcOP116FHfme9I5LObuzeDc0y0rFxx269o8BsMcPu5BmLRERERERERETxhs0uokuYPGIYkm1WLFi+OiLr2SwWVDscCASDEVkvUYXCYRScLkKSzab5tM/yTdtQcPoMvjNtMrKaZWpWp9bhRId2bZCanFSv+2e3aI6cllmwJ8D5XV/XKbcdfv+TH+K2AX2x5sOdCASDuHvSWL1jUQwwGAwYfnM/HCk4idKKSl2zWEwmOFyJ9dwjIiIiIiIiImoK2OwiuoSUJBumjhqOIwUn8cmxgkavJ4SAQF3jgy6v5HwZfIEAbFaLpnUKS87hvbUbMbBPTwy+qY9mdVweL9JSkpHTMuuaHte+bQ5sVgs83vg/v+vrrBYLHrjrDvx0zn344bdnXfO/CyWuYQP6waAo2PTRHl1zGI1G+AJ+nrFIRERERERERBRn2OwiuoyRgwciu0UzLFi+GqqqNno9m9WKC5VVEUiWmOxOF85euID0lBRN6/gDAbz49iKkp6bgu9OnQAihSZ1QKIRQMIgu7XOveUrNaDCga4f2CAQCCIUS76J7n+u6on+vHnrHoBiSmZ6Gvj2vx7a9BxAI6DsBKyDg9ft1zUBERERERERERNeGzS6iyzAajZg5fgxKSsuwbe+BRq9ntVjgdLrh8wcikC6xBIMhHC88jZTkJM23L1ywYg1Kyyvw4N13IqWeWws2RK3DhU557WCzWhv0+CSbFfkd8mB3uhLm/C6iKxl5y0C4PV7s+viw3lHg9SXWVCURERERERERUaJjs4voCvr37oEu7XOxeM2GyDSpBGB3OBq/ToIpOncOqiphMZs1rfPx58exYcdujB06GD3yO2tWx+F0Iat5Jlo2b9aodVo0y0TbVtmo4faX1ARc16kD2mS3xMadu3XNYTGbYXfyOUdEREREREREFE/Y7CK6AiEEZk8ah1qHE6u3bm/0ekk2K0orKyOQLHFU1dSitKISqSnJmtZxuFyY9857aNc6G3eNH61ZnUAgCKEIdMptG5EtEnNzWiM1OQkujzcC6YhilxACIwYNwKnisygsOadbDrPZBLvTrVt9IiIiIiIiIiK6dmx2EV1Ffoc89O/dAys2f4gae+OmsixmM1xuD7fIusgfCKCgqBjpqamanZ0FAFJKvPbuUrg8Hjw0+y6YTSbN6jjdbuS3z4tYDYNBQX77PITCYQSC+p5lRKS1W/vdCIvZpOt0l9FgQCAYRDCYeOflERERERERERElKja7iOph5vjRCIXDWLJ2Y6PXUhQDauxNa4ssVVXh8wfgcLlQUV2D02fP4bMTp3Do6HEoQsBkNGpaf+ue/dh/5ChmjB+FvDatNatT63SibatsZKanRXRdm9WCrh3y4HS5oapqRNcmiiVJNisG3dgHHx38RPdpRq+fb0ogIiIiIiIiIooX2l5hJkoQrbJaYOTgAVi3fRfGDhmMtq2zG7xWks2K0ooK5GRnRTBhbAgGQ/AHAwgEQ/B6/XB73XC5vfAGAoCUAAABwGA0wGQ0IdlmhcFg0DRTWWUV3ly6Et07d8S4obdoVsfr88NqNqNdTitN1m+ekY52Oa1w9kIZmqWna1KDKBaMHDwQW3bvw/Z9BzF26GBdMggAHq8PaSkputQnIiIiIiIiIqJrw2ZXjLBYzLA7XXVX2OQXX5WABBSDAiEUKIoCg6JAUcTXPudwXrRMHTUcH+49iAUr1uCnc+5r8DpmkwlVbg88Xh+SbNbIBYyScFhFIBhEIBiEz++H2+OF2+eD2+1B+MupIwlFUWAymmAyGZFhSdF0m8LLZw3jxbcXwaAo+MHdd2r2fFFVFV6fD326d4VRw+Zdu1at4HC54XS7kZqs7RlnRHpp3zYHnfPaYeNHezBmyCBdXjvM5rrfya2yWkS9NhERERERERERXTs2u2JEr675CIVDUFUVobAKVVURVlWEw2GEQiEEgyEEQiGEQmEEQkGEgiH4wwEEQkHUXQb82sVAIb6aohGirjkmFCgGpe6jIr5slOlxETFepSYnY8qoYViwfA2OHD+Jnl07N3gto6Kg2m6P2WaXlBLBUAj+QBDBYBAenw8utxcujwf+QOAb9zUZjTCZjEhJToq55uuyTVtx8kwJHr73LjTPzNCsTq3DifZtc5CSlKRZDeCr87sOHT2GQCAIs1mbs8eI9DZi8EC8smAxjp4sxPVdOkW9vsVsqnsDChERERERERERxQU2u2KEwaDAYDBf8+OklAirF5tjYRVhNfy1z+uaZYFgEMFgCKFwCIFgGMFQED5/AMFQCFLKS6+LuvaZonzRHDP8R9NMxFxjIxpG3XIz1m/fhQUrVuN3XR5u8L9BUpINpeWVaJPdUteGY92UVgD+QBD+QABOtwcerxdur+/Ls6EkAIOiwGQywmwyxmyD7j+dPFOC99dtxqAbe2PQjX00q+Nye5CWmoKcltHZltJqMeO6Th1wpOAEMo1pTfJ5SIlvYJ+eePuDldiwY7cuzS6DwYBgKIRAMAiziU1lIiIiIiIiIqJYx2ZXnBNC1G2bZjAADbgeF/6PKbKvGmdhhMJhBEN1zbFgMIxQuK5pFggEEQyFEFbVr+bJmsj2i2aTCTMnjMYLby3CjgMfY0i/Gxu0jslohMvthtvr1XwaSEqJQDD45ZSW2+uFy+OF2+NFIBjA16cCTSYTzEYjUmNwSuta+PwBvDR/ETLTUvGd6ZM1qxMKhRAKhdAlr0tU/70y0lLRvk0OzpwrRbMMnt9FicdsMmHogL5Ys20nauwOZKanRT2DlBJen5/NLiIiIiIiIiKiOMBmVxNXN1GmNKRP9mWT7OuTZOrXmmahUAiBYAjBL7ZfDAYQCoXhC/kRDIe+sf2iQVGQlpoSwb+Zdgb26YXV23bi3dXrMaB3D1jM1z6RBwCKYkBVrT1iza5QOIzAxQktn98Pp9sLr69uSuvLCT5xcUrLaILFYkZyki0itWPN/GWrUFZZjf/3g/uRbNPu72h3utG1Yx5sVotmNS6nTXbLuvO7XC6kpsTHc4foWowYNACrtmzH5l17MW3MiKjXVxQFHq8X6XHyu4mIiIiIiIiIqCljs4saTGnEhNbXt18MhcI4VXwWNQ4HMtOi/+79a6UoCmZPGoenX5iHNdt2YsrIYQ1aJznJhgvllWjXKrve/46qqiIQDCEQDCAQDMHtqTtHq25KKwihCEDWTfyZTEaYjEakp6Y0qbPZDn72OTbt2ovxw27VdPszu9OJls2bIatZpmY1rkRRFHTOa4ePPz8OfyDQ4KYrUazKbtEcvbp1webd+zB55LC6KeYoMptNsDvdaB2lLUqJiIiIiIiIiKjh2OwiXXx9+0WzyYTrOnfAqTNnUVFdjcz0tJhvzlzXqQNu6tEdKzZtw7CB/Rr0zn/jxTNhXB4v0lKSv/G9YKhuu0h/MAiv1w+31w231weP1/eN+xkMBphNRtisFqQka7sdYjywO52Yt/A95Oa0woxxozSrEwgEYVAUdGzXRtefVYu57vyuTz4vgMlojOutJ4kuZeTggfjL62/h4Gefo3+vHlGtbTGZYHc6o1qTiIiIiIiIiIgahs0uiglGgwFd2reD2WTE2QtlyExPi/kL97MmjsFP//R/eH/dpgafC2U0GlFWWQmf3//VWVpuD0JqGOLiFo+KImAymmA0GpCRlhrzjUC9SCkxb+H78Pr8+MVD34PJqM3Lm6qqcLrd6Nm1C0wm/V9C01JS0LFdOxSWlKB5ZobecYgi6obu3dA8Ix0bd+6JerPLYDAgFA5zcpKIiIiIiIiIKA7EdjeBmhRFUdC+bQ7at81Bda0D4XBY70hXlNMyC7cP6o9Nu/biXFl5g9ZISbKhrLIaJ06fQXlVNYLBEFKSk9AsPR2Z6WnITE9DemoqkmxWmE0mNrquYPOufTh09BhmThiNdq1baVan1ulC21atkJGWqlmNa5WT3QItmmVwCoUSjqIouH3QAHxacBKl5RW6ZPD5A7rUJSIiIiIiIiKi+mOzi2KKEALtWrdC1w65qHE4EQyF9I50RdNG3w6LyYSFK9c16PGKoqBZRjoyM9KRmpwMq8Uc8xNtsai0vAJvL1uJHvmdMfrWQZrV8fh8SLJa0C4nW7MaDSGEQOfcXBgVA3x+v95xiCLqtgF9YVAUbPxoT9RrK4qAy+2Nel0iIiIiIiIiIro2vKpOMSk7qwWu79IJTpcb/kDsvqs+LSUFk0bchgOfHsXnJwv1jtMkhcJhvDT/XRiNRjw4a7pmzcJwOAy/L4D8Dnl1583FGJPJiG6dO8Lj9cX8VGRT5/H52JS8BhlpqejXuwe27T0Q9d8HZpMZdhcnJomIiIiIiIiIYh2bXRSzmmeko1e3LvD6/PD6YvfC8Nghg9EsIx3zl6+Gqqp6x2lyPli/GaeKz+L+O6eiWUa6ZnXsThfat22DlKQkzWo0VmpyEjrltkOtw6F3FLoMp8sFSMDn88f85GosGTloADxeH3YdOhzVuhazCQ6nC1LKqNYlIiIiIiIiIqJrw2YXxbS0lBT07paPcDgElyc2t5Iym02YMW4UCkvORf1CbFNXUHQGH2zcilv63oCBfXpqVsfpdiMjLRWtWzbXrEaktMpqjpbNW8Du4DRKrKlxOGCz2dCrWxfkd8iD3cEmSn1169QBbbJbYuPO3VGtqygKwqoKfyAY1bpERERERERERHRt2OyimJecZEOvbl1hNChwOF16x7mkW27qg7w2rbFo1ToEgrwoGg0+vx8vz38XzdLTcN8dkzSrEwqFEA6r6JyXGxfnqQkh0DG3DUwmU0xPRDYlqqqiutaOFhmZ6N65I8wmE1o0y0S71tmosXMKrz6EEBgxeAAKS87hVHFJ1Otz20mihpNSIhgKIRQOIxxWoaoqG/1EREREREQUcUa9AxDVh9ViRo/8zjh26jRq7Q5kpKfpHekbFEXBPZPH43cvvYZ123dh4vAhekdKeG99sArlVTV4au4DSLJZNakhpUSt04XunTrAajFrUkMLJqMR3Tq1x8dHj8NkMsbkGWNNRTgcRo3DiXatWyEvp9U3Gqa5Oa3hcLvh8niRkmTTMWV8uLXvjVi4ci027tyDTrntolZXUQRcHi8y0lKjVpMoUYTCYZw6cxaVNTUQACAAVQKAhICAoihQhIAiBKAoMAgBodT9WQgB8eX3FShK3de+eEzd/RTUPVT58j6KotQ9VggI1DXLcfHzuu/VBam7DyC+/vnF9eoeK4Cvff7l1+sWICIiIiIiohjDZhfFDbPJhO5dOuFEUTGqamuRmZYWUxccru/SCX2u64plG7bgtgE3ITU5We9ICWvfkc+wZfc+TLp9KLp16qBZHYfThVYtmqN5ZoZmNbSSkpSELnntUFBUgmYZsfVcaSqCoRDsThe65OWidcsW//V9g0FBfvs8HPr8OALBIMwmkw4p40eSzYrBN92AHfsPYfbk8VFrEFrMZtidTrRt1TIq9YgSRTAYwrHCIjjcLmRe4k1KUsovJ7yklKjrgdV9lFLWTYCFVQASUgISErj4UdbdCWrdJ3V//uJ+F9cUAKSou1/dn/5b3e/GL2pf+u8hLn7rYtJrbtJZLWa0bZ0Nk5H/20VERERERKQl/l8XxRWjwYCuHfJQWGJEaUUFMtPSYmprubsnjsUTzz6Ppes341tTJ+odJyHVOpx4bdFStG+Tg+ljRmhWxx8IwGBQ0KFtm7htFLVs0RwOtwcVVdUxNw2Z6Hz+ALw+H67v0gnNM9Ivez+b1YKuHfJw9MQpZKbH1utZLBoxeAA279qL7fsOYOzQW6JS02wyweFyQ0oZt68FRNHm9fnx+alCBAJBZKZd+vdPvE5JXWuTrsZuh8PtxnWdOvBNDURERERERBriVTWKO4qioFNuW7Rr3QrVdjvC4bDekb7UtnU2hg3shw07dqO0olLvOAlHSolXFy6BL+DH3HvvglGjd0mrqgqX242uHTvAZIrf9wQIIdChXRtYrVZ4vD694zQZLo8XwVAQvbrlX7HR9YXmGelol9MKtU5nFNLFt/ZtctClfS427twTtTN/FEVBWA3D5w9EpR5RvHN5PDh8vABhVUVaaorecSLuy60UFQUGgwFGgwFGoxEmoxFmkwkWsxlWixlWiwU2qwUZ6Wnw+Xz4tOAkX0eIiIiIiIg0xGYXxSUhBNq3yUHn3FzUOhwIhUJ6R/rS9DEjYDQasWjlOr2jJJyNO3fjk88LcPfEsWiTrd2WYrVOF9rltEZ6AlykMxoM6NaxPQKBYEw9TxKV3emE0aCgV9d8pCYn1ftxua1bIS0lBU63W8N0iWHEoAEorajEZydORa2mgIDP749aPaJ4VW234/DnJ2AyGpFs41mEX0hNSUEoFMaRgpPw+vjmEyIiIiIiIi2w2UVxLSc7C9d16giHy41AIKh3HABARloqJg4fgr2HP8XxwiK94ySMc2XlmL98DXp164JRt9ysWR2Pz4dkmxXtWrXSrEa0Jdms6NIhF7VOZ9SmYZoaKSWq7Q6kJiejZ34X2KyWa3q8otSd3yUlYua1LFYN6NMTKclJ2LBzd9RqKooCt9cbtXpE8aisohKfFZxCUpIVVsu1vQY2BSnJSRAAPjl2Ai6PR+84RERERERECYfNLop7LZplomfXLvB4vTHzzvtxt92KjLRUzF++ms2FCAiFQnjp7XdhNpvw/ZnTNTvjIxwOw+fzI799HgyGxHp5zGqWiTbZLblVngZUVUWN3Y7s5pm47v+zd99Rct3ZfeC/L1YOXZ1zdW5kkgMCIECCEQwTOBzNaoKo5NWuvJYs7eyRV7K8weOwliytdyVrdKTVWivL8lAacSx5yBmCM2AECRCRIEAAndABuRudKocX949qNAESJFJXvwrfzzk4neq930V3V1XX7757b0/3Hbe+dLtUDHZHkcykYVnWCkdZOVRFwSNbNuPoySEsxOKrsqZLVbGY4H2H6EZs28a5S9MYmTyHUDDAuVSfwetxQ1VknBgaRSKVcjocIiIiIiKiilJZu7lUtUIBPzau6YemGyVx9b3bpeJrn38SZ86ex8HjJ50Op+z93U/ewOSFi/jvvvYV1IRuPOh+JcQSSXS3t8HnrczWS52tLfC6PSVxH6kUhmliIZZAe0szejs77jpJGg4GEG1tQYyJlc/0+PYtsG0bbxw4vCrrqYqMVDrNixeIPsY0LZw5ex5nL11CJByELElOh1Ty3C4XvB4PTgyPYSG+Ogl7IiIiIiKiasBkF1UMv9eLTYN9ECAgmXJ+7s3O++9De3MT/uaHr0LnrKQ7NjwxhR+89hYe3vI5bNm4vmjrJNNpRMIhNDfUFW0NpxXmd3VC1w3+Tq4ATdcRTyQx0N2JzpbmFas4bG1sQE0oiCSv+v9UjXW12DjQhzcPHIZhmkVfTxRFWJaNXF4r+lpE5UI3DIxOTmFmfh6RUAiiyJcVt0pVFQT8PpwaHceV+QWnwyEiIiIiIqoIfFVKFcXjdmPDQC/cbhfiDldGiKKIn3n2GVyZX8BrqzhbppJkcjn8yXf/FvWRGvz8V75UtHV0w4Bp2ejpaC9ai8RS4XG7MdDViUSSrfLuRjaXRyaTxYaBPjTW1a7ouUVRRF+0AxAE5DUmVz7NEzu2YTGewPsnh1ZtzWyJtMolclpe03BydBzxZAqRUKjinzuLQZFlhIIBjExM4dLMrNPhEBERERERlT0mu6jiuFQVa3u7EQz4sRiPO9p2atNgPzYM9OHvfvIGUhm2jrtd/+nvf4i5xRh+5fmvweMuzrB727aRSKXQ39kOt0styhqlprYmjLamRsQ5v+uOpNIZmKaBTWsGEA4GirKGqihY09OFVDrDpOSnuHftAOpqwtizShcTSJKIVNr5qmEip2WyOZwYGoWmawgG/E6HU9ZkSUI4GMCZc+dw7tI0W6USERERERHdBSa7qCIpsozB7i401EawGE84uln8/LPPIJPN4Qd73nQshnJ06PhJ7D10FF9+4hH0d3UWbZ1EMoWmulrURWqKtkYp6mhpQsDnQyqdcTqUshJPJKEoCjYODhR9tlvQ70d3ezsW44mirlOuRFHEYw9swamxcVy6UvyqCFVREUuytSRVt0QqhQ+GRiBIIgI+n9PhVARJkhAJZfOPwgAAIABJREFUhXD24kVMXbjECxyIiIiIiIjuEJNdVLEkSURvZwdaGxuxEHMu4dXR0oyd99+HH7+zn3MZbtFiPIH/8Ld/h+72VvzUU48XbZ28pkGWJERbW4u2RqmSJBH9XVGYlgVN150Op+TZto2FeBzhYADr+3tWrQqwpbEOdZEwq/A+xaPbNkOSJLy+72DR13KpCivtqKrNLSzixPAYvG43vG630+FUFFEUEQmHcXHmCsbPnYdp8nGGiIiIiIjodjHZRRVNEARE25rR3dGGxXgChmk6EsdPP7MLoijiez/6sSPrlxPLsvCnf/19aLqBX/nZr0OWpKKtk8pkMNAThaLIRVmj1LldKga6O5FMpbiB/xksy8JCLIbm+joMdEehyKv3+yIIAno7OiCLEnKcF/UJoUAAWzauw9uHjxZ9vpkgCLBtm3PUqOrYto2L07MYGp9E0O+DqipOh1SRBEFATSiIK/MLGJ2ccuxvViIiIiIionLFZBdVPEEQ0NbUgP6uTiSSSUeqWCLhED7/yIN479gJnDl7btXXLyd73j2AD0fG8PyXP4+WhvqirRNPJtHe3ISgv7rnjURCIXS0tiCWYOXQjRiGgcV4Al3tbehub4Morv7TpqLIGOztRiabg8nNz094Ysc2ZLI5vHfsxKqsl80z2UXVw7IsTF24hPFz51ETCkJexWR/NSokvEJYTCRx+swEdN1wOiQiIiIiIqKywWQXVY3Gulqs7etBOpNFzoHNyi899jCCfj+++4PdHED+KS5Mz+CFH+7GPWsG8MT2rUVbJ5PNwevxoL2pqWhrlJP2pkaEgwEkU2mnQykpmqYjkUpjoDuKtqZGCILgWCwBnxc9He1MSt7A4NLPZ8+7B4q+liRJvJ9Q1TBMEyOTZ3Fx5gpqa0KOJPurVTgYQDqTwcmxM6wmJSIiIiIiukV81UpVJRIKYcNAH/Kahkwut6pre9wu/PQzT2BkcgpHTp5e1bXLgWEY+OP//D24VRd++RtfLVpiwTRN5PJ59Ec7IUl8CAQKs0L6oh2AAG6qLcnkcsjk89g42IeG2ojT4QAAmupr0VBbizgTXtcRBAFP7NiKyQsXMX7ufFHXcqkK56dRVdB0HafHxrEYTyASDjma7K9WoUAAuq7jw5EzyObYxpaIiIiIiOhmuNNLVSfo92HTYD9sy0YqnVnVtR/ZuhmtjQ3465d3cxbDx7y4+zWcvXgZ//3XfwrhYKBo68STSfR0tMPn9RRtjXLkUlUMdnchlc5U/fyuZCoN2wbuGewvqTaXgiCgu6MViqJw4/NjHtx8L1yqij37DhZ1HVVRkMxkq/4+QpUtm8vhw5ExZHO5oj4f0835fT7YsHFiZBTpTNbpcIiIiIiIiEoak11UlbweNzYO9kFR5FW9Sl+SJHzzS09jenYer+8v7qZsORk6M4EfvrkXj267H5s3rC3aOslUGpFQGE31tUVbo5yFAn50tbdWdau8WCIJt9uFjQO98HrcTofzCYosY7Animwux4T5NbxuNx783D1479jxol7EUKhusR1phUu0GpLpDI4Pj8GybQRKKNlfzXweD2RJxvHhUSTYRpWIiIiIiOhTMdlFVculqljf1wu/14fFRGLV1r137SDW9nbj7378BjLZ1W2lWIoy2Rz+5IUX0VAbwc8994WiraMbBkzbRndHG9sxfYaWhnrUhkNIJFNOh7KqbNvGQiyOmlAQ6/p64FJVp0P6VH6vF33RDsQTKc7/u8YTO7ZB1w28ffhocReybeTyrKyjyjMfi+PE8ChURYHPw+rnUuJxu+Bxu/DhyBgW46v3NysREREREVE5YbKLqpqiyFjT24W6cA0WYvFV2TgWBAHPP/t5JNNpvPT6W0Vfr9T9x//yAyzEE/iV578Gt8tVlDVs20Y8mcRAtANuV+kmMUqBKIro6WyHJIpVU71iWRYWYgm0NjZgoKsTsiQ5HdJNNdRG0FTP+V3X6mxtRn+0E6/vP1jUNoOSJCOeqq5kMFW+6dk5nBobh9/n5fNkiXKpKnxeD06NjWNuYdHpcIiIiIiIiEoOk11U9WRJQl+0HS0N9ViIxVdlFktXeyse/Nw92P32Ply4PINUOoNMNodcPg9N02EYBkzTrPiqjfeOncC7Rz/Ac7seRV+0o2jrJJIpNNfXo7YmXLQ1KomqKBjo6UI6k4FZ4a3ydMPAYjyOno42RNtaIIrl8bQoCAK62lvhdrtZIXqNJx7ciunZeZwaGy/aGm5VrbrKR6pctm1j6uIljE6dRU0wAEWWnQ6JPoOqKAj6fRgan8D07JzT4RAREREREZUUvqIlQqGapau9FYoiY/L8JdSEApCKXN3xtS88hYPHT+I3f+8PPvN2giBAFASIolh4XxQgCmLh7fLnxOXbiKKwdIy4/LEoXHOsKC4fL1xzzNXbf3yNj24jfmYcoiBA+FgconDN56/5WBQFQBDwvR/9GL2d7Xhu16NF+z7n8hpkWUa0raVoa1SioN+Hno52jJ8/j9pwZSYJc3kNmWwWa3t7yjIRKksSBrujOHZ6GKoiQ+YmNbZu2oC/+vsf4bV9B7FhoK8oayiKjFgyBdO0IEnlkRwluhHTtDBx/jymZ+cRCYXKJtlf7WRZRjgYxOjkORimidbGBrZnJiIiIiIiApNdRMsEQUB7cxNUWcbI1DmEAv6iXuFcVxPGP/+1f4ixqXOwbBuWbcGybNiWdd3H1tLHy5+3lj5/9fb2R7cpfM1a+tyNbvPROW3bhmla0A1j+Zz2J9a95lzXrGF/LI6P3+ZWK9J8Xg/+0fNfK1rbOMuykM5ksGlNP69WvwPNDXWIp1KIJ5IIBvxOh7Oi0tksDMPExsF+BP0+p8O5Y16PGwNdnTg9PoHacLjqNzwVWcYjWzfjh2/uxXwsjtpwaMXXEAQBsC3k8nn4vJxrROVJ1w2MTp3FYjyBSDhU9Y8d5UaSJETCQUycuwhdNxFta+bPkIiIiIiIqh53f4k+prG+DrKiYOjMBHxeD1xq8WZXdHe0obujrWjnd4pt259IiBXeXk2WFT72uN1FnQ0SSybR0dqMoL+yEjWrRRAE9Ha044PhUeTy+aLNVFttiWQKiiLjnjX98LjdTodz1+oiNWhNNeDy7DwioaDT4Tju8e1b8MM39+LN9w7hv3lmV1HWsCEw2UVlK5fXMDQ+gVwuj0gREsK0OkRRRCQcxIXpaRimgZ6ONlbnERERERFRVeMrIqIbqA2HsHGwD9lcHtlc3ulwys7V1oeyLENVFbhdLng9bvi9HgT9PoQCAdSEgkVNdKWzWfi9XrQ1NhZtjWqgKDLW9ESRyeZgVMD8rsVEAj6vFxsG+ioi0XVVtLUVPo8HqUzW6VAc11AbwabBfrxx4HDRfmcVWUIinS7KuYmKKZ3J4sTwCHTdQCgYcDocukuFhFcI03NzGJk8WxHP00RERERERHfKkWSXIAhhQRC+LwjCsCAIQ4IgPCAIQkQQhD2CIIwtva1xIjaiq4J+PzYN9sM0DW4glxnDNKFpOvqjnZypswL8Xi/6OtsRiydvuUVlqbEsCwuxOOrCNVjT2wVVUZwOaUVJkojB7k4YhgHdMJwOx3FP7NiKWCKJox+eLsr5XaqKWCJZlHMTFUs8mcLxoRFIkgy/z+t0OLRCBEFAbTiMhXgcIxNTfA4gIiIiIqKq5dQu8B8CeNW27UEAmwAMAfinAF63bbsPwOtLHxM5yuf1YMNAP2RJRCKZcjocukXxZBI9HW3weiqncsdpDXW1aG6oRbwM7wemaWIhHkdbcyP6uzqKNiPOaR63GwPdUSSSaViW5XQ4jrpnzQDqasLYs+9AUc6vyDIy2RxMs7q/z1Q+rswv4MORMXg9HnjcldGSlq5XEwwimUrj9JkJaLrudDhERERERESrbtWTXYIgBAHsBPDnAGDbtmbbdgzAlwH85dLN/hLAc6sdG9GNeNwurO/vhdfj5pX8ZSCZSqEuHEZjXa3ToVQUQRAQbWuFS1WQyeWcDueW6YaBWCKJ/mgnoq0tEATB6ZCKqjYcQltTI2LJ6n6sEkURj2/fitNnJnBx5sqKn18QBMAGcnm2uaXSZts2LkzPYGRiCsGAH6paWVWtdL1gwI9cLo8PR8aQy2tOh0NERERERLSqnKjs6gYwC+AvBEE4JgjCfxAEwQeg0bbtywCw9LbBgdiIbkhVFKzt60FNKICFeLxsW7lVOk3XYdlAd0dbxSc1nKDIMga7u5DPa2UxFySXzyOdzmJdfw+a6uucDmfVdLQ0Iej3I5XOOB2Kox7ZuhmSJOH1/QeLs4AIZJnsohJmWRYmzl/A5PmLqAkFK7aqla4X8PtgWjY+HB5FJls+F6cQERERERHdLSeSXTKA+wD8iW3b9wJI4zZaFgqC8MuCIBwRBOHI7OxssWIk+gRZkjDQFUVTXR0W4vGqbxNWamzbRiKZRn9XJ1yq6nQ4Fcvn9aAv2oF4srTnd6UyWeiGgY1r+hAJhZwOZ1VJkoj+aCdMy6rqVlahgB9bN63H3kPvF6XCQZFktrelkmWYJkYmz+LylTlEwiGIIudXVhO/1wNBEnF8eBTJKr/wgYiIiIiIqocTr3wvALhg2/bVS62/j0Lya0YQhGYAWHp7w75Dtm3/mW3bm23b3lxfX78qARNdJYoiejra0N7chMVEAmYZVLdUi3gyhdametSGqyux4YSG2ghaGupLtlVePJmELEnYONAPv9frdDiOcLtUDHZHkUylqjox/8T2rcjkcnjv2PEVP7eqKmxtSyVJ03WcGhvHYjyBSDjESucq5XW74VJVfDg8VpbzNomIiIiIiG7Xqie7bNueBnBeEISBpU89DuA0gJcA/MLS534BwA9WOzaiWyEIAqKtLehpb0cskYBhGE6HVPVy+TwURUFHS7PToVSNztYWeN1upLNZp0NZZts2FuNxBP1+bOjvhcftcjokR9WEguhobanqhMxAdxTtzY3Ys+/AilciqoqCbD5XFi09qXpkczmcGB5DLpdHOBhwOhxymNulwutx4+TIGcwvxpwOh4iIiIiIqKic6mnyawC+KwjCCQD3APg3AH4XwC5BEMYA7Fr6mKhktTTWY01PNxKpNDSteluFOc2yLKQzWQx0dUKRZafDqRqyJGGwOwpd06GXQMLXsiwsxONoqK3FYHcXFIW/CwDQ3tSIcDCAZCrtdCiOEAQBT2zfiqkLlzB+7kIxVkCOc7uoRCRSaXwwNAqgMLeJCChUofr9Xpw6M4GZuXmnwyEiIiIiIioaR5Jdtm1/sNSKcKNt28/Ztr1o2/a8bduP27bdt/R2wYnYiG5HXaQGGwb6kM5mueHpkFgyiWhbC4Lc2Ft1HrcbA91RxBMpR+d3GaaJhVgCnS0t6O1shyRxNs1VoiiiL9oBCEBeW/m5VeVgx+Z74XapeG3fgaKcP5PNFeW8RLdjPhbHieHR5UoeomspsoxwMIDRybO4OM2Zx0REREREVJm4I0h0l0IBPzat6YemGyXV0q0apLNZBHw+tDY2OB1K1aqtCaO9uRGxRMKR9TVNRzyRxEB3Jzpamjib5gZcqorB7i6k0pmqnN/ldbux43P34r0PTiCZXtkKN0VRkKjSqjkqHZevzOH0mXEE/D64VNXpcKhEyZKEmlAQ4+fPY+riJUcvUiEiIiIiIioGJruIVoDf68WmwT4IEKq2XdhqM0wTmqajP9oBUeRDmZM6Wprh9/mQyqxusjebyyOTzWLjYD8a62pXde1yEwr40dXeWrXzu3bt2ApdN7D30Psrel6XqiCRTK3oOYlulW3bmLp4CWNnzyEcCLCVL92UKIqIhII4f3kaE+cvVOUFEEREREREVLm4Q0y0QjxuNzYM9MLtdiFepRvKqymeSKC3ow0eN9s1OU2SRPRHOwsJSH115tcl02lYpolNawYQCvhXZc1y19rYgNpwqCqTMx0tzejv6sRr+w+u6OauIsvI5HIlMbeOqotpWhibOocLl2cQCQUhSZLTIVGZKCS8Qrh8ZQ5jU+dhmkx4ERERERFRZWCyi2gFuVQVa3u7EQz4sRCLs0VMkSRTKdRFatDAap6S4XG7MNjdiWQqXfQrxeOJJFRFxcY1/fB5PUVdq5IIgoCeznZIoohcvvrmd+3asQ0zc/M4OXpmRc8rCEJVfj/JObpu4PSZCVxZWERNKMjqZrptgiAgEg5hdnERwxOTTNgTEREREVFF4KtjohWmyDIGu7vQWBfBYjzBFjErTNN12BDQ3d7G+UwlJhIKob2lCbFkcSobbdvGfCyGcDCA9f09nE1zB1RFwUBPF9KZDEzTdDqcVbVl03oE/T68tv/gip5XAJDN5Vb0nESfJpvL48PRMaQyaURCQT4P0l2JhIJIJFMYOjMBXWfCi4iIiIiIyhuTXURFIEkiejs70NrYiIUYE14rxbZtJJJp9EU7mOgoUR3NTQj5A0imV3Z2nWVZWIjF0dJQj4HuKGfT3IWg34eejvaiJSVLlSLLeGTrZhw9OYT5xdjKnVdVEEtUX2tIWn2pTAYnRkZhmBZCgYDT4VCFCAUDyGRz+HD0DKtUiYiIiIiorDHZRVQkgiAg2taM7o42LMYTMKqsiqIY4okkWpvqURsOOR0KfQpRFNHf1QHbBjRtZeZ3GYaBxXgCXe2t6G5vY8uuFdDcUIf6mkjVze96fPtWAMAbBw6v2DldioJEqrq+j7T6Yokkjg+NQpFl+Nm+lVZYMOCHYRj4cPQMK1WJiIiIiKhscceQqIgEQUBbUwP6uzoRTySh6Suz+V+Ncvk8VFVFR0uz06HQTbhUFYPdUSQzdz+/K69pSKYyWNPThbamRrbsWiGCIKCnow2SLCObyzsdzqqpj9TgnjX9ePO9wyt2AYIsy8hrGmfeUNHMzM3jw5Ex+LweuF0up8OhCuX3eSEAOD48hlQm43Q4REREREREt43JLqJV0FhXi3X9PUhnsmwRcwcsy0I6k2X7ujISDgYQbW1BLHHnrfIyuRyyeQ0bBntRF6lZwegIABRFxpqeKLK5XFVVnj6xfRtiySSOfHh6Rc9bTUlDWh22beP85WmMTJxFKBiAqihOh0QVzutxQ1VknBgaZcUqERERERGVHSa7iFZJJBTChoE+5DUNGbaIuS2xRBJd7a0I+LxOh0K3obWxAZFQCMk72DBLptKADdwz2I+g31+E6AgA/F4v+jrbEYsnYdu20+Gsik1r+lEfqcFr+w6s6HnZ+otWkmVZmDh/AVMXLiISDkKWJKdDoirhdrng8bhxYngMC/G40+EQERERERHdMia7iFZR0O/DpsF+2JaNVJotYm5FKpNFwO9DS0O906HQbRJFEb3RdgiCgLx26xWNsXgCbrcLGwf74PW4ixghAUBDXS2aG2oRr5L5XaIo4vEHtuD0mQlcnLmyIudUVaVqvn9UfLphYGRiCpdn5xAJhzmnkFadS1UR8PtwanQcV+YXnA6HiIiIiIjolvDVM9Eq83rc2DDQB0WREU/eeYu3amCYJnRdR3+0g5t9ZUpVFAz2dCGdydx0fpdt21iIxVETDmFdXw9bdq0SQRAQbWuFS1Wqpur04a2bIUvSilV3uVSVyS5aEXlNw6mxccQSSURCIc4pJMcosoxQMICRiSlcmpl1OhwiIiIiIqKb4u4xkQPcLhXr+3rh9/owH4sjnc1CNwynwyo58UQSvZ3t8LhZ3VPOgn4/utraEUskPvU2pmliPhZHa2MjBro62bJrlSmyjMHuLuTzGowqeCwKBfzYsmk99h5+f0XmKMqSBE3XoeuV/72j4slkczgxPAZN0xAKBpwOh+6Apuu4PDuHk6Nn8NbBI/j+q6/h7UNHoem606HdEVmSEA4GcObcOZy7NF017W6JiIiIiKg8yU4HQFStFEXGmt4uzC0sIpFKIZnOFFobCgDsQqstVVGgKjKkKtz4TyRTqIvUoKE24nQotAJaGuuQSKcQSyQQCly/iasbBhKpFHo72tHSyHaVTvF5PeiLdmBkcqoqKkp27diG/e8fx/73P8BjD2y56/PZNpDN56AonDFHty+RSuHk2ARURYbP7XM6HLoB27aRzuYwvxjD3OIi5hZimIvFCm8XFzG/GEfsUyr2//rl3XjsgS3YtWMbakLBVY787kiShEgohLMXL8E0TXS2NrPanoiIiIiIShKTXUQOkiUJTfV1aKqvAwCYpoW8piGnachks0ik0kilM9AMHbABCAJkSVpOglXqZoOm6YAgoKejreI33KuFIAjo7WjHB0MjyOXzcLtcAIBcvvC7vra3B7XhkMNRUkNtBMl0GtNz86gJlteG7O3q7+pEe3MT9uw7gEe33X/XjzWiUKjMCfqZ7KLbM78Yw9D4JHxeD1yq6nQ4VcuyLCwmkjdMZs0vvc3m89cdo8gyamtCqKupwT1rm1BXE0ZdTRi1NWHU1dQgEg5idPIsXt27Dz947S28/MZebLtnA57euR09He0O/U9vnyiKiIRDuDhzBYZpoLu9HZJUmX+DEhERERFR+WKyi6iESJIIr8cNr8eNyDVX/uq6gbyuIZ/XkMpkkUinkUxfPwNJkWWoigJFkcs6QWTbNpLpNNb1c2ZTpVEUGYM9XTg+NAJFlpHTNJimhU1rBhDweZ0Oj5Z0trYgkUojnc3C5/E4HU7RCIKAXTu24f/7/n/FmbPn0RftuKvzqaqKRCq1fPEC0a24OD2LifPnEAoEIMv8s7yYNE3HXCy2lMxa+rewuPz+QiwO82OzJX1eD+pqwmiorcXa3m7U1dQsJ7PqI2EEfL6bXni0rq8H6/p6MDM3jx+/sx9vHzyKfUc/QH+0E0/v3I77N64riwp+QRBQEwpiZn4BhmGijy2HiYiIiIioxAjl3Ht98+bN9pEjR5wOg8gRtm1D03XkNQ3ZXB7JdAbpTAbpbG55poIgCMtVYOWyiRZLJNFUX4vu9janQ6EiuXxlDsMTUwj6vVjb2wOP2+V0SPQx2VwOx04Nw+fzQimTx447kc3l8avf/je4f8M6/KPnv3ZX5zJME7lcHls2rV+h6KiSWZaFsxencX56GpFQsGIrtVeLbdtIZTKYX4xjdqkqa/6aFoNzi3EkUqnrjrmavLlajVW3VI1VWxNCfU0NamvCRXl+yuRyePvgUfzk3f2YmVtAbTiEXQ8+gMe23Q9/mVz4EYsnEPD7MdgdhaJU7nMEUbUTBOGobdubnY6DiIiI6FYx2UVUYSzLQl67mgTLIZlOI5HKIK9dbb0jlOw8sGwuD8DGpjUDvFq4gtm2jSvzC4iEQtwkK2HzizGcGptAbU1lz+/6i+//AG8dPILvfPufIuC7u1lJi/EE7t+4jlWp9JkM08T42Qu4Mj+PSLiy718rxTRNLCaSS1VYhflY11ZlzS3GkNe0645RFWWpneAN/kVqUBMKOvq3hmVZOHZ6GK/u3Y9TY+NQFQUPbb4XT+3cjramRsfiulXxZBJulxtre7vYfpOoQjHZRUREROWGyS6iKmGYJjRN/8Q8MN0wlm8jyxIU2Zl5YJZlIZZIsqUdUQmZPH8Rl65cQU2ocuepnb88jd/6vT/Ezzz7DL746M67OtdiPIENA72c20WfStN1jEycRSKdqvi5eLcjr2nXtBa8WpV1TYvBeOK61s0A4Pd5UVcTXqrCKszNqouEURcOo26pxWC5JBLPXbqMV/fux76jH0A3DGwY6MPTO7dj02B/SVf9pdJpiKKEdX2s0iaqREx2ERERUblhsouoyum6gZymIa9pSKUzSGYySH18HthSFZgiF28e2EIsjmhbS1lczUxULUzTwsmxM8hrOvzeyp3f9S//6P/BYiKBf/fbv3FXG8vxRBLRthY0N9SvYHRUKbK5PIbGJ6BpOoKB6kmIXp3FObcYL7QUXPhobtbsUpVWMp2+7hhRFBG5psVg7VKLwbqaEOoiNagNh+F2VV41USKVwhvvHcZP3n0PsUQSzfV1eGrnduy8/z64XaWZTEpns7AtC+v6euGr4OcJomrEZBcRERGVGya7iOgTbjQPLJlOI5PLw7ZsCMLKzgNLpTNwu11Y39dT0lcwE1WjbC6PY0Mj8LpdFdueb//7H+A7f/U9/NY//AfYNNh/x+fJ5HLwe7wY7ImuXHBU9kzTQjqbwdD4JERRhM9THQmBvKbhlbfexY/eegeZbO66r7lUZSl5Ff6o1eByVVYNaoKBkmqzvNoMw8DB4yexe+8+TJy7AK/bjUe33Y8nH3oA9ZEap8P7hGwuj7yuY31fD4L+u2sHS0Slg8kuIiIiKjdMdhHRLfvseWACABuS9FErxFvZqDIMA6l0BveuW8MWOEQlaiEex6nRcdSEghWZkNYNA7/2L/4t+qLt+I1f+vk7Po9pmsjm8tiyaf0KRkflwLZt6IaBvKZD0zSks1mkszmkM1nkNQ22bcPr8VRkNdLHWZaFd44cw9++8hMsxhP43Po1WNvbc12LQb/XWzYtBp1k2zbGzp7Dq3v349Dxk7BtG5s3rMXTO3dgsDtaUt/DvKYhk81hXV8PwsGA0+EQ0QpgsouIiIjKzd2VYxBRVRFFER63Cx63C+FgAM0otOoyTBP5fKEVYjqbRTKdueE8MFVRoMjXzwOLJVIY7Iky0UVUwiKhENpbmnD+8jRqw2Gnw1lxiizjkW2b8fLrb2NuMYa6mjv7P0qStJTw0OBSKz+pUY2uzr/UdB25fB6pTBbpTAaZXA6WVbiAzAYgS+LyhR9ej9vZoFfRhyNn8MJLr+Dspcvo7mjDr/3cNzDY0+V0WGVLEAT0RzvRH+3E/GIMe/YdwBvvHcLhE6cQbW3B0zu344H7NkG5ywr7leBSVQiCgJOjZzDYHUVdCVagERERERFRZWNlFxEVzbXzwJLpNJKpNNLZLEzLggABhmGgsa4OA92dJXV1MhF9kmVZODU2gUwui4Cv8tpUzS4s4lv/+vfx5Scewdc+/+Qdn2cxnsD6/l6EqmgmU6X5qJVvIamVzmaRzhT+aboGCEIho4WlmZaB8Tk5AAAgAElEQVSyDFmWKrLq8VZduDyD7778Co4PjaI+UoOvf+EpbLtnQ1V/T4olr2l498gHeHXvPlycuYKg348ndmzFE9u3lkRFlWEYiKdS6OvsQFN9ndPhENFdYGUXERERlRsmu4hoVV07Dyyv6QgHAlAU569IJqKby2sajp0egVtVoaqVN7/r9//fv8TE+Qv4o//9t+54FmEskUS0tQUtjfUrHB2tNN0woOuFSrxcPo9kOot0NoNsLo9r/z4utOeVoSgy5CqeI3UjsUQS33/1Nbx54DA8Lheee/JRPPXQ9pKoNKp0tm3j5OgZvLp3H46dHoEsSXjg3o14eucOdLW3OhqbaZpYjCfR3dGK1sYGXtBEVKaY7CIiIqJyw2QXERER3bJ4MoUTI6OoCVbe/K4Phkbwe3/2H/HrP/9NbLt34x2dI5vLw+N2YW1v9wpHR3fCsixougFNL1xgkcnmkMpkkM5kl1rt2gAECIIARZELSS1Z5ub8TeTyGl55+x28/PpeGIaBXQ9uw1eefKwiqz7LweUrs/jxO+/h7UNHkdc0DHZH8fTOHfjc+jW3ND+1GCzLwmI8AVmW4fO44fN64Pd64VJVuFQFqqJCkirrOYSo0jDZRUREROWGyS4iIiK6LecvT2PqwiXU3uFsq1JlWRb+p//j/0RdTRj/2z/+5Ts+RyqdwdZ7NjBhsop0w4Cm6cjrOrLZPNLZNNLZHDLZ3PJtBACiJEFdSmo5lQQoZ5ZlYe/h9/Hi7j1YjCewZeN6fP2LT6GZ7epKQjqbxVsHj+An77yH2YVF1NWE8eRDD+DRbffD5/E4EpNlWYUqSsOEoeu49pWnqihLSTAPfB4PFEVZSoQpfPwkKgFMdhEREVG5YbKLiIiIbotlWRgen0IynULAX1mzqV56/W38zQ9fxe/91rfQ1tR4R+dYiMdx/4Z1cKnqCkdX3SzLWp6jldc0pLNZpDJZpNMZGJYJAYXNcVEUoMjKcttBbpqvjA9HxvDdl17BuUvT6O1sx/PPfh4D3VGnw6IbsCwLR08O4dW9+zA0PgmXqmLn/ffhqZ3b0dJQOi1WDdOErhvQDR2GaQGwISxVWvo8bng9Xvi9brhdLriW2ueylSjR6mGyi4iIiMoNk11ERER02zRdxwenh5euxK+cpE4ilcI//vbv4rEHtuAXv/rsHZ1jMZ7Aur4ehIOBFY6uOui6gbyuQdN0ZHI5ZLJZpNJZZDUNuPp3q21DVmQosgJVkSuupWYpOX95Gi+8tBvHh0dRH6nBN774NLaxcrFsTF28hFf37sf+ox/AME1sWtOPZ3buwIaBvpL9GVqWBcMwoRkGDMMozNATANg2FFlZbono83qgKjJUReXjAFERMNlFRERE5YbJLiIiIrojiVQKJ4ZHEa6w+V1//J+/h/dPDeGPv/3bcLtct318PJlER3MLWptKp4Ki1JimBU3XoOkGcvk80plsYZZWNgfLspZuZUMUpcIcLVZprbrFeALff/U1vHXwCDxuF76y6zE8+dADUGTZ6dDoDsSTSby+/xBe23cQsWQSrY0NeOqh7Xhw871wu8rnggXTNAttS3UDpmnCtoGrDwtejxs+jwc+jxcejwsuRYGqKvydJbpDTHYRERFRuWGyi4iIiO7YxelZTF44j0i4cuZ3jU6exbf//Z/il376K3h8+5bbPj6Xz8OlqljX11OE6MpTKpNBLJFEKl1IauU1bWmT2gYgLCe0FJnVGU7L5TW88tY7ePmNvTBME0/u2IbnnnwUAZ/P6dBoBeiGgQPHTuDVvfsxeeEifF4PHtt2P3Y9+ADqyngOo23bS7PBCtVglvXRa1xJFOHzeZdng7lUFerSfDA+3hB9Oia7iIiIqNzwMjciIiK6Yy2NdUikU4glEggFKqNtX1+0Ax0tTXht/wE89sD9t11NpCoKEqk0bNuu6kok07QQSyRwYfoKEqkUZFmCohTaDno9bqfDo4+xLAt7D7+Pv33lJ4glktiyaT2+8YWn0FRf53RotIIUWcZD99+HBzffi9HJs9i9dx9++OY7+NFb7+L+DevwzMM70BftKLvHLkEQoCoKVEX5xNcsy4KmG7gyvwBDN2Av3R6CAI+qwuf1wOf1wOt2Q1UVuBQVisKXyURERERE5YZ/xRMREdEdEwQBvR3t+GB4FLl8/o7a/pUaQRCwa8c2/PmL/xVjZ8+hP9p5W8eLogjLspDX9LJqD7ZS8pqG2YVFXJi+AsMw4PW4UVvGFSPV4MTwKL770m6cvzyN3s52fOsXn0d/1+393lN5EQQBA91RDHRHMbuwiD3vHsAbBw7h4PEP0d3eiqcf3oFtmzZAroAWgKIowu1SP/F4bNs2DNNEMp3BQjwByzJRGA5WOMbncS/PBnOpKlyqAlVRIUmsBiMiIiIiKkVsY0hERER3LZnO4PjQCEIBPyRJcjqcu5bL5/Gr//x38LkNa/Erz3/tto9fjCewtrcbNaFgEaIrPbZtI5nO4PKVWcwtxiAIAvw+L+QK+F2oZOcuTeOFl1/BieEx1Edq8M0vPYOtm9aXXVUPrYxcXsM7R97Hj/fux6UrswgHA9i1Yxse374FQb/f6fBWlWVZhbaIeqE1Igr1YAAA19VqsKUZYepSIkyRZd53qKKwjSERERGVGya7iIiIaEVMz85hbOocIuFQRWz4/cV/+QHefO8wvvPt30bQf3vziuLJFNqbG9HW1Fik6EqDYZpYjCdwYXoG6UwWqqLA5/VUxM+/ki3GE3hx9x68fegovB43vrLrMex6cBuUCqjiobtnWRY+HBnDq3v34/jwKBRZxvb7NuHpnTvQ2drsdHiOMwwD2tJsMMO0cPXRThCEQgLMW5gP5na5Cq0VVYWJfypLTHYRERFRueErWiIiIloRjXW1iCfTWIjFEAqW//yuXdu3Yc+7B/D2oaP40mM7b+tYl6ognkxVbLIrm8tjdmEBF2dmYZomvB4PIuGQ02HRTeTy+cJ8pjf3wjAtPL1zO76y6zH4fV6nQ6MSIooiNq0ZwKY1A7g4cwU/fmc/3jn8Pt4+dBRre7vx9M4duG/dIESxOtv5ybJ8w/aOlmXBMEwsxBOYmZuHbQOCCMC2oSpXq8EK88FURVlqi6jw4gAiIiIiohXCyi4iIiJaMbph4MTwKAABHnf5z+/6l9/5MyzE4vi//tlv3NbGrmVZSKbT2HbPxorZyCy0Kkzj0kyhVaEoCAj4fRXRtrLSWZaFtw8dxYu79yCWSGLrpg34xhefQmNdrdOhUZlIZbJ488Bh7Hn3PcwtxtBQG8FTDz2Ah7duhtftdjq8kmeY5nJLRNM0r/ua1+OGz+NFwHd9NRgrLclprOwiIiKicsNkFxEREa2odCaLY6eHEQz4y7510/73j+M7f/U3+K1f/kVsWjNwW8cuxhO4b92ask/66YaBxXgC5y9PI5vLQ1VV+DzuikniVbrjw6N44aVXcP7yDPqiHXj+2c+jv6vT6bCoTJmmiaMnh7D77X0YmZyC26Xi4S2b8dRDD6Cpvs7p8MqObduF2WBL88Esy1p+bFVkGT6vB16PG36vd6kSTIWqyFVbVUeri8kuIiIiKje8XIyIiIhWlM/rQV+0AyOTZ1Fb5vO7tmxch6Dfjz37Dt52ssu2beTy+bJNdmVzOczMLeDSlVlYtgW/x8tWhWXk3KXL+O5Lu/HhyBgaaiP4H3/hZ7Bl0/qyvj+S8yRJwpZN67Fl03pMnL+IV/fuw2v7D+In776He9cO4umd27Gur4e/Z7dIEIRCJZeiAJ7rv2aaJvKahlQmg8szs7h6iaoNwOtywe/zwOfxweNxQVVkuBQVisKX90RERERUvVjZRUREREUxNnUOswuLCJf5/K7v/ejHeOn1t/EH/+v/jPpIzS0fl0il0NJQj46W5iJGt7Js20Y8mcKlmStYSCQgiSICPh+rCMrIYjyBF3fvwduHjsLrceOnnnwMT+zYxpZoVDSxRBKv7TuA1/YfQiKVQntzI57euQM77rsHqqo4HV7FsW37mraIOkzLgoBCclESRfh8Xvjcbvi817RFVBRIEh/H6fawsouIiIjKDZNdREREVBSGaeLE8Bgsy4LXU74zXWYXFvGtf/37ePbxh/H1Lzx1y8fl8hoURcaG/t4iRrcydN3AfCyG85dnkNM0eFwueNwuVmeUkVw+jx++sRc/eusdGKaFpx56AM/tegx+r+fmBxOtAE3X8d6xE3h17z6cvXgZXrcba3q7MNjdhcGeKKKtLZzxV2SWZUHTDRhLrRGv5VJV+L1e+H0eeN1uKIoC19JsMD7W040w2UVERETlhpd4EhERUVHIkoTB7iiOnR6GqsiQy7SypD5Sg3vXDuLNA0fw1acev+X/h6rISKTSsCyrZCuj0pksrswv4PLsLCwb8Hs98DE5UlYsy8JbB4/g+7tfQyyZxLZ7NuDrX3gajXURp0OjKqMqCh7e8jnsvP8+DE9M4Z3D72N4YhJHTw4BANwuFf1dnUvJry70dLSx4nCFiaIIt0sFXOonvmYYBlLZDBYTCZimuZzgEkURPo8bXo8HAZ8XLlVdng/GajAiIiIiKid8dUFERERF4/W4MdDVidPjE6gNh8v26vFdO7bi/VNDOHTiFLbft+mWjhFFEbZtI69p8LhLp7LNsizEkylcmJlBPJGCLEsI+v0lm5CjG7NtGyeGR/HCy7tx/vIM+qOd+NZ/+zz6o51Oh0ZVThAErOnpwpqeLgCF1prDE1MYHp/E8MQk/vaVnwAAFEVGX2cHBrujGOzpQm9nRyFRQ0Uhy0sXnXzs6ciyLOiGgYV4Alfm5j+aDWbbcKkqfF5P4UIIj2e5GkxVlLJ9PiciIiKiysVkFxERERVVXaQGbelGXLoyh0go6HQ4d2TDQB8aaiN4bf/BW052XZXNl0ayS9N1zC3EcHF6Bnldh9vtQiQccjosugNnL17GCy+9gg9Hz6CxLoJv/eLzuH/jOm4+U0mqCQXxwL0b8cC9GwEAyXQaIxNnMTQ+geHxKfz9njdh/+QNSKKI7o625baHA13Rsm6BWy5EUVyq5gKA6yt7DdNENpdHIpWCYVq4+ggjCAJ8Hjc8bg8CvsJsMJeqQlUVyGxVSUREREQOYbKLiIiIiq6zpQWJVBqpTLYsZwiJoojHt2/FX7+8G+cvT6O9uemWjpMkEclU2tEkXyqTwfTsPGbm5gEB8Hu98Pm8jsVDd24hFseLu/dg7+H34fO48XPPfRG7dmwt2xahVJ0CPh82b1iLzRvWAgAyuRzGJs9iaHwSw+NTeOXtd/HyG29DEAR0tjRjTU+h7eFAdxRBv8/h6KuLLEmQJQkeuK77vG3b0HUD8VQSc4uLsG0bEADbsqEqylI1mBc+rweqIkNVVKiKzApiIiIiIioqwbbtm9+qRG3evNk+cuSI02EQERHRLcjm8vhgaASqosDjdt38gBKTSKXxa//id/HIts34B1/98i0dk9c0SKKIjYP9RY7ueqZpIZ5M4vzlGSTSaSiyDL/Xw43GMpXN5fHDN/fiR2++A8uy8NRD2/HlXY+WZeKY6GbymoYzZ89jeHwSQ+OTGDt7DrpuAABaGxuwprcLg92FNok1ZVotXMlM04RuGNB0A6ZpXvc1r8cNn8cNn8cHj8cFl6JAVRXObitRgiActW17s9NxEBEREd0q/lVJREREq8LjdmHjQB8+HD0DO2fDWwKt/W5H0O/D1ns24N3Dx/DNLz4Nt+vmCTtVURBPpmBZ1qokmvKahtmFRVycvgLdMOD1uFHLVoVlyzRNvHXwKL7/6h7Ekylsu3cjvv75p9BYF3E6NKKicakq1vX1YF1fDwDAMAxMnL+4nPzad+QDvLbvIACgsS6CNT3dy3O/6iM1bOfpMEmSIEnSJ54jbduGbhhIpDKYj8VhWYWLbm3YkEUJPp93eTaYS1WhLs0H40UaRERERHSrWNlFREREqyqTzeHk6BgEUYTPU16VKaNTZ/HtP/xT/NJPP4fHt2+9pWMW4nHct3ZN0WbP2LaNZDqD6bl5zM4vQBAE+L0etrYrY7Zt4/jwKF54aTcuTM+gv6sTzz/7efRFO5wOjchxpmni7KVpDI9PYnhiEsPjk0hlsgCASDiEwe5oIQHWE0VLQz2TX2XAsixougHd0GHoBnD1ZyYI8KgqfF4PfF4PvG43VFWBS1GhKHyOKzZWdhEREVG5YbKLiIiIVl02l8fJsXHYlgV/Gc2Psm0b/+zf/RFs28bv/JNfv6VN1MV4HGt6uhFZ4QorwzQRiydwYWYGqXR2eU4KN3bL29TFS3jhpd04OXoGjXW1+OaXnsb9G9bx50r0KSzLwsWZK4XKr4kpDJ+ZRCyZBFCoyB3s7sJgTyEB1t7cyEqhMmLbNoyltoi6bsCyTACFx0JRFOHzuJdng7lUFS5VgaqokCT+jFcCk11ERERUbpjsIiIiIkfk8hpOjZ2BYZgI+H1Oh3PLXt9/CH/+4t/j27/+P6C/q/Omt0+k0miur0Nna/OKrJ/La5hdWMCF6SswTRNejwdul7oi5ybnzMfieHH3Hrxz+H34PG781FOP44ntW1mhR3SbbNvG9Nx8ofJrqfXh3GIMQGFm1EBXoeXhmp4uRNtaIEuSswHTHbEsazkJphvGdV9zqSr8Xi+8Hhd8Hg/UpUSYIsu8cOA2MNlFRERE5YbJLiIiInJMXtNw+swE8nkNwYDf6XBuSS6fx69++3fwuXVr8Cs/+/Wb3l7TdAgCsGnNwB2vWWhVmMalmVnMLcYgCgL8fh83aStANpfHy2+8jVfeeheWZeHpnTvw5V2PlF2LT6JSNruwiJGJKQyNT2J4YgqXr8wCKCRF+qMdy8mv7o42qIricLR0twzDgG6YhbaIpoWr6S1RFOB1u+HzFuaDuV0uqIoCVVX4fHoDTHYRERFRueGlokREROQYl6pifV8vTo9PIJFMlUXCy+1y4aHN9+GN9w7hZ5/7AoL+z45ZUWTEkimYpnXbrZUM08RCLI7zl6eRzeWhqipqQkFemV4BTNPEWweP4MXdryGRSuGBezfi6194Cg21EadDoztgmuZSuzUThmHAti0AH91PC3dZAbZtQxAEiKIISRIhiWLh/aW3vG8XR32kBvWRGjy4+V4AQCyRxPDE1PLcrxd37wEAKLKMns72pblfXeiLdrJytgzJsgxZluGB67rPW5YFwzCxEE9gZm6+8EkBgG1DVZZmg3kK88FURVlqi6jwfklERERUJljZRURERI7TDQPD41NIptMIBwNOh3NTF6Zn8Jv/9g/wzS8+jS89/vBNb7+YSODeNYPwety3dP5sLoeZ+QVcnpmFaVvwe7xQVVYbVALbtvHB0AheeGk3Ls5cwUBXFM9/+Rn0dnY4HRp9BsuyYJgmDMOEYRowTeuar9pQZAUejxselws+jxsulwuKLEOWpOW5Q6ZpwrQs6LoOTTeQ13QYZuH9q63YlpNkAoBrXqZ9IkEmiBCX3udG/N1LpTMYmZzC8PgUhiYmMXXhEizLgiSK6GpvXZ77NdAdZdVlhSrcvw1ougHTNK/7mtfjhs/jRcB3fTWYUuFtZlnZRUREROWGyS4iIiIqCYZpYmRiCrFkEjXBoNPh3NS/+s6fYW4xhv/7f/knEMXPrthaiCewpjuK2prwp97Gtm3EkylcujKLhVgckiTC7/VCYmulijF18RK++4NXcGpsHI11tfiZLz2DzRvWMllRAmzbhnm1Mss0YBjXb3aLogi3S4XP416ak+eCqshQZAWKIq9ICzTbtmFaViEpZhaSa9bSx4ZpIq9p0HQDmq4vJ8w+niAT7MK7ll3Il0mSCFGUIF2tHmOC7JZkc3mMTp1dmvs1hfFz52GYJgRBQEdLEwa7C20PB7qjCJVBRTLdOdu2C7PBluaDXbt/osgyfF4PvB43/F7vUiVYYT5YJdzHmOwiIiKicsNkFxEREZUMwzQxNnUOC7EYakIhp8P5TAeOncC//09/jd/85V/EPTeZx5VMp9FQG0FXW+snvqbrBhZiMZy7PIOcpsHtUuF1uytio4wK5mNxvPjKT/DOkWPweT346pOP4/HtWyBXeFVAqfmsVoM2ALeqwutxw+N2wet2w6WqUGQZqqJAlqWSvU+apgXTMpf+f1eTZZ9MkF2tWtENA5ZlfeI8S3my5QSZKAofJcmW/lUrTdNx5tx5DI9PYmh8EmNT56DpOgCgtbEBg91RDPZ0YbCnC7Xh0n7uopVzNUGuGzpMw4SNQnKsq60NrU31Tod315jsIiIionLDV9hERERUMmRJQn+0E2fOiphdWCjp+VSbN6xFKODHnncP3DTZ5VJUxJPJ6z6XyeYwMzePy7OzsCwbfp8XPi/bY1UK3TBwemwch06cwrtHj8G2bHzx0Yfw7BOPsA1akdxKq0G3S0Ug5Cu0GlRVKIqyVKEll20yR5IK7Q2h3Hqr0xslyCzLWq5g0TR9KUmm3TBBJgjCUoVLIUUmfqxyrNISZKqqYG1vN9b2dgMADMPAxIWLGB4vzP3af+w4Xn/vEACgoTZSSHwtzf1qqI2U7PMY3R1JkiBJ0nVz3RKpFEzLcDAqIiIiourFZBcRERGVFEkS0RdthygKmJ6dQyQcKsmNQlmW8ei2+/GD197C7MIi6iM1n3pbRZERSyShGwZS6QwuzMwgnkhBkiQE/f6K2RCudtlcHseHRnD4w1P4YGgE2VwebpeKB+7ZiK8+/cRn/o7QrTEMY6k665OtBgVBgMftQtDvhdvlgtfjWU5kKYqyIq0GK8WdJMgKLRULCcVCouyjFouarn+UIDM+arNoWiZECHC73dclBMqdLMvoj3aiP9qJZx9/GJZl4dylaQyNT2B4fArvnxrC3kNHAQCRUHAp+VWY+9Xa2FCSz2lEREREROWObQyJiIioJFmWhckLF3HpyixqgsGSTAjNL8bw6//q9/Clxx/GN77w1GfediEWhyLL0AxjuU0alb94MoX3Tw3h8IlTODl6BoZpIuj34b51a3D/hnVY198D9TYSCtXuo+oiE6Z5tZroY60G3S54PO6yajVYrbK5HOLJFGbmFpBMpyEIgMvlgsflquiflWVZuHRlFsPjUxgan8DQ+CRiiUJ1b8Dnw2BPdHnuV0dLU0k+v9GdSaRSaGmoR0dLs9Oh3DW2MSQiIqJyw8ouIiIiKkmiKKK7vQ2iIOHC9DRqQqWX8KqtCeO+dYN468ARfPWpx6F8xgwmv/f/b+/Og+Q8D/vOf5++u6fnnsHgxgwA4iAIihJJiZI2sizLjiqRLbkqrlJt1tFms9Gua33EXimxV1Vru9Zbq3V27TjlJF6v7UjlyFbJV6JK2bG0shI7iiURpCjiBjgYACRB4pi7e7p7pruf/WMaEEkRFAkC/c7x/VShevqa/g2ewcyL99fP85QIqUBvpqeLCXUvXJue4djxUxw7fpKzU5eIMTI6NMj3/1eP8ejRIxyY2LPmvlfXihjj6qys1uq+Wa9cajCTzlAs5DfcUoObVbFQoFgosHV0hMbyMouVKtdn55idnyfG1aUlS8XChhvXVCrFzq1j7Nw6xvvf/Q5ijFybnuH05NStfb8ef/okAMVCnoMTq0seHto3wcSuHc5ClCRJku6AZZckSVqzQgiM79xGOp3i4vNXGFqDhdf73/0YT5w4zeNPn+Bdb3voto/L5Zzds17FGHn2hRd5/OmTHDt+iktXXgBg9/at/PAPvI9Hjx5h9/atG3qmyp1otVpUa7WXFVq3lhrscanBzSafy5EfyjEyNHhrSdcbs/PcmJ2l1W6RSacpFYsb8nsghMDYyDBjI8O89x2rE2WmZ+c4c+HirQLsqdNnAcjnsuzfs5vD+yc4vHeCfbt3+ftDkiRJeh0SK7tCCGngGPB8jPGDIYQJ4HPAEPAk8KMxxuWk8kmSpLUhhNBZ5ilw4dnnGOzrI72GToYePbCfsZEh/r+vfv01yy6tL+12m3MXL/PE8VM8fvwk16ZnCCFwYHwPf/dDf4tHHjjC2MhQ0jHXnNWCq06z1SSbzrB1ZIT+3jK5bNalBnVLNpNhsL+Pwf4+9u7aQbW2xMzcAtemZ1hprpAKKYrFwoZeAnR4cIB3P/wQ73549ffG/GKFszfLrwtT/NF/+DIxRjLpNPt27+LQvtXZX/eN76FYyCecXpIkSVp7EtuzK4TwM8AjQF+n7Po88Mcxxs+FEH4D+FaM8V+91udwzy5JkjaXK1evM3n5WQb6etdU4fXvv/KX/N4X/oxPfeKn2L19a9JxdIdWmk1Onp/k8adP8sSJ0yxUKmTSaY4c2MejR4/w8AOH6e/tTTrmmtNut6nWaqw0m2TSGcaGhxge7KdcKq25mZha22KMVGs15hYqXL1xg1qjQYpAoVCgkM8lHa+rKks1zk1d5MzkRc5cmOLCs8/TbrdJpVKM79zO4b2ryx4e3DtOuVRMOq463LNLkiQpOYmUXSGEncBngP8d+BngB4HrwNYYYzOE8E7gF2KMr7nTu2WXJEmbz9XrNzh38RL9fX1rZrmrxWqVH/+FT/HedzzC3/87H0o6jt6AWr3BU6fP8vjxk3zr1FlqjQbFfJ633H+QR48e4S2HD1AqFJKOuebcKrhWWqTTKbYMDzI6NGjBpbuqVm+wsLjI1ZlZFitVIpFCLk8hn9t032f1RoPzFy93Zn5dZPLSs6w0m4QQ2LVtjEN7Jzr7fo1byifIskuSJCk5SS1j+M+AfwzcPAofBuZijM3O9eeAHUkEkyRJa9vY6AghleLshYv095bJZJLfgrS3p4fHHjrKXx17ko988AMuMbXGzS9WePLkaR5/+iQnzj1Ds9Wir9zDY299kEeO3s8DB/aTXQPfV2tNu91mqVZneWWFdDrNluFBRgZXC650enMVD+qOYiFPsZBnbHSE5ZUVFitVrs3MMjs/T4yRbCZLqVjYFMVXIZ/n6MH7OHrwPgCWV1aYvPwcZzp7fv2nbxzji//5rwHYtmWUQ3tXlz08vG+C4U4AHncAACAASURBVMGBJKNLkiRJXdH1/8WHED4IXIsxPhFCeO/Nm1/loa865SyE8DHgYwC7d+++JxklSdLatmV4iHQqxenJKco9pTWxr8v73/0Yf3Xsm3z1iad4/7vfkXQcvcK16RkeP36SY0+f4tzFS8QYGR0a5Af+xjt55OgRDozv3hQnzN+olxVcqRQjQ0OMDg3Q29NjwaWuymWzDA8OMDw4QLPVYrFS5cbsPDdmZ2m1W2TSaUqFwpp4A0Q35LLZW2UWQLPV4uJzz3N68iJnJqf4+lPH+crXHgdgdGiQQ3vHObRvdenDrSPD7p0nSZKkDafryxiGEP4P4EeBJlAA+oA/Af4mLmMoSZLegJn5eU6dv0C5VCKXS7bwijHyyf/712m123zqEz/picSExRi5fOVFHj9+kieOn+LSlRcA2LN9G48cvZ9Hjh5h9/atjtOraLfbLNUbrKwsE0KKkaFBtgwNWnBpTWq321SWaszOL3BteobGyjIpAqViMfHfC0lqt9tcfuHFzsyv1X2/FipVAAb6ejm0b4LDnQJsx9gWy/67xGUMJUmSkpPInl23Xnx1ZtfHY4wfDCH8AfBHMcbPhRB+A3g6xvgvX+v5ll2SJGl+scLJc5MUi3nyuVyiWf7ir7/Bb33+T/j5n/gfOLh3PNEsm1G73ebc1CWOnTjF40+f5PrMLCEEDk7s4eGj9/PIA0cYGxlKOuaa1G63qTUaNBorpFKBkcEBRocG6S33rJm98aTvJsbIUq3O3EKFq9PTLNVrBALFwurvh81cbscYuXLtOmcmp1b3/ZqcYmZ+AYByT2l15tfe1Zlf4zu2WX7dIcsuSZKk5KylNR7+CfC5EMIvAd8EfjvhPJIkaR3o7y3zwMH9nDj3DDFGCvnk9st619se4rNf+FO+9NWvWXZ1yUqzyYlzz3Ds+CmeOHGahUqFTDrNAwf28+Hv/17eduQw/b3lpGOuSTFGavUG9cYyqRQMDwwwunuIPgsurVMhBHpKRXpKRXZsHaVWb7BQqXB1eobZhQUCgXwuRyGf23RlTgiBHWNb2DG2he971zuIMXJ9ZvZW8XXmwhTHjp8CoJjPc2DvHg53yq+9u3ZsmuUhJUmStH4lesQaY/yPwH/sfHwBeHuSeSRJ0vrUV+7hwUP3cfzsM8QIxUIyhVchn+M9j76NL/+XbzD/4Yolyz2yVK/z1KmzHDt+kqdOn6XeWKaYz/PQ/Qd59OgRHjx8gFKhkHTMNenbBVeDEFYLrr27d9LbUyLryWxtMMVCnmIhz9jIMMsrKyxWqlybmWV2fp4YI9lMllKxsOmKL1gtv7YMD7FleIjvefvDAMzMzXPmwtStfb8+d/rPgdX9wfbv2cXhzp5f+/fsSnwmtSRJkvRKiS5j+Ga5jKEkSXqp6lKNE+efIZ1KUyomU3Y8f/Uan/jUr/KRv/03+aH3vzeRDBvR/OIiT5w4zePHT3Ly3CTNVou+cpmHHzjMow8e4ch9+yxrbiPG2FmicJkADA70MzY8RG+5x78zbUrNVotKdYnpuTmuz8zRbDZJp9P0FAvOYHqJhUqFsxcucXryAmcmL3LpygvEGEmn0+zbtZND+1f3/bpvYo9vMOhwGUNJkqTkWHZJkqQNpVavc+LsM8QQKJeKiWT4pX/x/3J9ZpZf/eTHN+WMgbvl6o0Zjh0/yePHT3L+4mVijGwZHuLRo0d45Oj93De+27/f27hZcNUbDQKBwb5exkaG6SuXyWY9mS/d1G63qSzVmF1Y4NqNGRory6QIlIpFcrls0vHWlGqtxrmpS5yZvMjpyQtMPfs8rXabEALjO7ZzeP8Eh/ZOcHDvHnp7epKOmwjLLkmSpORYdkmSpA2nVm9w8vwk7Xabck+p66//taeO888/83t84r//KG89cqjrr79exRi5dOUFjh0/xbHjJ7l85UUA9uzYdqvg2rVtKyGEhJOuTTFG6o1l6o06ERjs7WNsZIj+3l4LLul1iDGyVKszt1Dh6vQ0S/UaIQSK+Tz5XM6fPa9QbyzzzKXLnJmc4vTkFM9cepaVZhOAXdvGOLR3gsP7Jji4d5zB/r6E03aHZZckSVJyLLskSdKG1Fhe5uT5SVZWmvSWu/sO82arxU/+4v/JxK7tfOIf/rddfe31pt1uc27qEo8fP8mx46e4PjNLCIGDE3t4pFNwbRkeSjrmmlZvNFiqNwhAf2+ZsZFh+nvL5LLOSpHejHpjmYVKhWvTM8wvVogxks/nKObzzip9FSvNJpOXn7tVfp2bukRjeRmAraPDt8qvQ/smGB0aTDjtvWHZJUmSlBzLLkmStGEtr6xw8vwFGo0Gfb3lrr72H/zZl/i3X/oKv/rJj1vWvMLyygonz03y+PGTPHnyNAuVKpl0mqMH9/PI0SO87chh+rs8XutNvbHMUr0OMdLXW2Zrp+DK53JJR5M2pJWVJovVKtdnZpmem6fdbpPNZigVCqTT6aTjrUmtVouLz1/hzORFzlyY4syFi1SXagCMDA5waO84h/atFmBbR0c2xMw5yy5JkqTkWHZJkqQNbWWlyanJC9Rq9a4WXtOzc/zk//bL/OD73sNHPviBrr3uWrVUr/PUqbMcO36Sp06fpd5YpljI89b7D/HIA/fzlsMHKRbyScdc0+qNZWqNOrSht9zD1lELLikJzVaLSnWJ6bk5rs/M0Ww2SafT9BQLZDIuGXo77Xab5168yunJqc6+X1MsVCrA6qzUQ3snOLRvnMP79rJz65Z1OXvOskuSJCk5ll2SJGnDW2k2OTN5kcVKhYEu7hvyK7/zu5y9cIlf/4WfJbsBT4De3COqsrTEYqXK4tISi9UqleoSi9WlzmWVucUK5y9eptVq0d9b5uEH7ufRo/dz/337NuTfy93UWF6mVqsTI5R7SmwdGaK/r49C3oJLWgva7TaVpRqzCwtcuzFDY2WZFIFSsUgu51KiryXGyAvXb3BmcurW0ofTc/MA9JSKqzO/OgXY+I7t62IGnWWXJElScjy7IEmSNrxsJsPh/ROcn7rE9PwCQ10qvN7/rndw7PgpvvGtE7z74Ye68pp3KsZIrd5gsVplsfqK0uolZdbN227e32y1XvXzhRDoKRXpLZXo7enhA+95F48ePcL+PbvW5bv1u+lmwdUmUi4V2bt7JwMWXNKalEql6Cv30FfuYfe2rdTqDeYWFrk2M83s3AIxQDGfp5DPbYhl+u6mEALbt4yyfcso73vn2wG4PjPbmfk1xZkLUzxx4jQAhXyOAxN7OuXXBPt27/TNEpIkSXoZjw4lSdKmkEmnOTAxzrmLl5iem2Owr++en3h84MB+xkaG+dJXv9bVsqvdbrNUq992ptWtjzslVqVTYrXa7Vf9fKlUinKpSG9PD+VSibGRIfbt3rl6vadE760/377eUyxaar0By8srLNXrtGObnuJqwdXf2+vSjtI6EkKgVCxQKhbYPjZKvbHMQqXCtekZ5hYWiTGSz+co5vP+fLyN0aFBRocGec+jbwNgdn6BMxcu3iq/Pv+nXwQgm81w357dt/b92r9nt28IkCRJ2uQsuyRJ0qaRTqc4ML6Hycsprk3PMNh/bwuvVCrF+9/1Dj77hT/l8pUX2b196xv+HDeXyHq1mVaVpZuzrL5zJtbtlqpOp1IvK6W2j42+vLQqvby0Kvf0UCp4YvZeWF5ZYWmpRptIqVBkfMd2Bvt7KRYKSUeTdBcU8jkK+SG2DA+xstJksVrl+sws03PztNttstkMpUJhXSzPl5TB/j7e+dYHeedbHwRgsVr9dvk1eZE/+dJXiF/8C9KpFHt377y17OHBiXFKRX+WSpIkbSbu2SVJkjaddrvNhWef58XrN+554bVYrfLjv/gpvuftD/P3fvgHqb6koKq8oqRafFmJtXp7tVa/bXGVzWS+XUq9oqR65UyrcqmH3nKJYj7vUloJulVwRSgWcmwdGWVowIJL2kyarRbVpRozc/Ncm56h2WyRSq/u8+XSfG/MUr3OualLt8qvyWefo9VqEUJgz45tHO4se3hw7zh95Z57nsc9uyRJkpJj2SVJkjalGCMXn7vCc1evMtjXd09nLv3G7/0Bf/n4k6/5mHwu+7LC6rVmWt0ss/I594BZD1aaTZaWarRim2I+z9aRUQb7e511IIl2u021Vmd2fp6rN2ZoLK8QApSKBfI5l+V7oxrLyzxz6dlb+36dv3SZlZUmADvGtnB4/wSH9k5weN8Eg/dg/07LLkmSpORYdkmSpE0rxsilKy9w+fkXGRq4d4XX9ZlZvvhXf02xkH9ZYVXuzL7qLZXI5bL35LWVjGazSbVWo9Vuk8/m2LZlhMG+PkrFggWlpFcVY6RWbzC/uMjV6Wmq1ToxQDGfp5D3zQ13YqXZZOrZ52+VX+emLlFrNAAYGxni8L69t/b9Gh0afNN/x5ZdkiRJybHskiRJm1qMkedevMrUc1cY6r+3M7y0sd0suNrtNrlsjq2jwwz191twSboj9cYyC5UK16ZnmF+sAJDLZSnm3UfxTrVaLS5deZEzk1Ocnpzi7IUpKks1AIYG+jm8b+LWvl/bt4y+4Z/dll2SJEnJseySJEkCnn/xOpOXn2Wwv5d0Op10HK0TN/feabVa5HM5xkaGGBrop6dYtOCSdNesrDRZXFrixsws07NztNqRTCZNT7Hg76w3od1u8/zVa7fKrzOTF5lbXASgr9xza8nDQ/sm2LVt7LuWjJZdkiRJybHskiRJ6njh2g2euXSZ/r5eMp481G3cLLjarTbZbIaxzgyucsmCS9K912q1qSwtMTM3z7XpGZrNFql0oFQsks1kko63rsUYefHG9EvKryluzM4Bq/uoHZxYXfLw8L4Jxndu/45jBcsuSZKk5HgkLEmS1LFtywipVODs1CUGestkPGmojmarxVKtRrPZIpfNsn1s1IJLUiLS6RT9vWX6e8vs2bGNaq3O3MICV2/MsFhZIpUKFAt58rlc0lHXnRAC20ZH2DY6wvc+9iiwuu/m2QsXV8uvCxf55qkzAORzOQ6M775Vfu3dvTPJ6JIkSZueM7skSZJe4cbMLKcnp+jrLfsu+U2s1WpR7RRc2UyGsZFhhgZWCy73y5G0FtXqdeYXK7x4Y5rqUg2I5PN5ivm8xfxdMrewyJkLFzkzOcWZC1NcvvIiANlMhvGd2/l7P/xBfvB935NwyjfPmV2SJGm98eyNJEnSK4wMDXJ/KsXpZy5Q7imRy2aTjqQuWS246jRbTTLpDGPDIwwPWnBJWh+KhQLFQoGtoyM0lpdZqFS5Pj3D7MICMQZyuSylQt6fZ2/CQF8vjz10lMceOgpApbrE2amLnJm8yInzz5CyVJQkSUqEM7skSZJuY25hkRPnnqFcKpHLWXhtVO12m2qtxkrzZsE11Cm4Sp4QlrQhrDSbLFaXuDEzy/TsHK12JJNJUyoW3KPyLnLPLkmSpOQ4s0uSJOk2Bvp6efDQfRw/+wztGCnk3f9ko7hVcK20SKdTbBkeZHRo0IJL0oaUzWQY6u9jqL+P1u5dVJaWmJmb59r0DM1mi1Q6UCoWXbpXkiRJ65ZHspIkSa+hr1zmwUMHOH72PBAp5PNJR9IdarfbLNXqLK+skE6n2TI8yMjgasGVTltwSdoc0ukU/b1l+nvLjO/cTrVWY25hkas3plmsVkkRKBQKvsFDkiRJ64pllyRJ0nfR21N62QyvUqGQdCS9Ti8ruFIpRoaGGB0aoLenx4JL0qYXQqBcKlEuldi5dYxavc78YoWrN2aYmZsnBMjn8xTzeYJ7UUmSJGkNs+ySJEl6HcqlEm85dIAT586zVKtTKlp4rVXtdpuleoOVlWVCSDFqwSVJr0uxUKBYKLB1dITG8jILlSrXp2eYXVggxkAul6VUyLvcqyRJktYcyy5JkqTXqVQscPTgfZw4f4HKUo1yqZh0JHW0221qjQbLy6sF18jgAKNDg/SWe8ik00nHk6R1J5/LMTqUY3RokJVmk0p1iRuz89yYnaXVbpFJpykVi/6MlSRJ0ppg2SVJkvQGFAsFjh7Yz8nzz7BYrdLb05N0pE3rZsHVaKyQSsHwwABbdu+y4JKkuyybyTDY38dgfx97d+2gWltiZm6Ba9MzrDRXSIUUxWKBXDabdFRJkiRtUpZdkiRJb1Ahn+OBA/s59cwFFisVesvlpCNtGjFGavUG9cbyrYJrdPcQfRZcktQV6XSKvnKZvnKZPTu2Ua3VmFtY5OqNaWaWlkgRKBQKFPK5pKNKkiRpE7HskiRJugP5XI4j9+3j1DMXmF9cpL+3N+lIG9a3C64GIcDQwAB7d++kt6dENuPhrCQlJYRAuVSiXCqxc+sYtXqd+cUKV2/MMDM3TwiQz+cp5vOEEJKOK0mSpA3MswOSJEl3KJfNcmT/Pk5fmGJ+YZH+PguvuyXG2FmicBmAoYF+9u7aQW+5x4JLktaoYqFAsVBg6+gIjeVlFitVrs/OMTs/T4yRbCZLqVgglUolHVWSJEkbjGcKJEmS3oRsNsPhfROcm7rE3MIiAxZed+xmwVVvNAgEBvt6mdixnb5ymWzWw1ZJWk/yuRz5oRwjQ4OsNJtUqkvcmJ3nxuwsrXaLTDpNqVh0CVpJkiTdFZ41kCRJepOymQwH945zfuoS0/MLDPX3JR1p3YgxUm8sU2/UicBQXx/j27fR39trwSVJG0Q2k2Gwv4/B/j727tpBtbbEzNwC16ZnWGmukAopisUCuWw26aiSJElapzyDIEmSdBdk0mkOTIzzzKVnuT47w2Bfn/uT3EaMkcbyMrV6HQj095bZvX0r/b1lT3RK0gaXTqfoK5fpK5fZs2Mb1VqN+YUqV6enmZmfJxAoFvIU8vmko0qSJGkdseySJEm6S9LpFPeN7yKVgqs3Zhjst/B6qXqjwVK9ATHS11vmwMS4BZckbWIhBMqlEuVSiR1bR6nVGywsLnJ1ZpbZ+QUikUIuTyGfc58vSZIkvSbLLkmSpLsolUqxb/cuUqkUV65eZ2igf0MVXu12mxgj7Rhpt9u023H1ertNO3aut9uQChC//bx4s+AaH6O/t0w+l0vui5AkrUnFQp5iIc/Y6AjLKyssVqpcm5lldn6eGCPZTJZSsWDxJUmSpO9g2SVJknSXpVIp9u7aSSqkee7FFxjs70/8xNxqGfXtgupWMdW5bLfbAARe1lHx0hsjkE6lyKTTpNNpMpk0+VyaTDpDNpsmk06TzWRIp9OkUylSnT/pVKrzWAsuSdLrk8tmGR4cYHhwgGarxWKlyo3ZeW7MztJqt8ik05QKBTIZT2tIkiTJskuSJOmeCCEwvnMbqXTg0vMvMNTf94YLr5szplYvv11Q3SyubhZVkdU+6tsvDrdujKv3Z1JpMpkM2UyaXDZLplNW3SynspnVAutmOXXzMoRAOv3y65IkdVMmnWawv4/B/j727d5BZanG7PwC16ZnWKhWSREoFYvkci6LK0mStFlZdkmSJN0jIQT2bN9GKgSmnnuefC53q7iK7XanoQqEAPE7plOtPj+TTt8qpnKZHJl05tb11RlVGVIhrJZRLyulUi+ZXRUsqSRJG0IqlaKv3ENfuYfd27eyVKszt1Dh6vQ0M/PzhBAo5vPkczl/90mSJG0ill2SJEn32K5tW8nnclRrNbKZzK3ZVDdLqpfOpkqlwsuuS5KkVxdCoKdUpKdUZMfWUWr1BguVClenZ5hdWCAQyOdyFPI5f6dKkiRtcJZdkiRJXbBleCjpCJIkbWjFQp5iIc/YyDDLKytUqktcn5llem6edrtNNpuhVCiQTqeTjipJkqS7zLJLkiRJkiRtKLlslqGBfoYG+mm2WlSqS0zPzXF9Zo5ms0k6naanWCCT8bSIJEnSRuBRnSRJkiRJ2rAy6TQDfb0M9PUysXMHlaUaswsLXLsxw0K1SopAqVgkl8smHVWSJEl3yLJLkiRJkiRtCqlUir5yD33lHnZv28pSrc7cQoWr09PMzM8TQqCYz5PP5QghJB1XkiRJr5NllyRJkiRJ2nRCCPSUivSUiuzYOkq9scxCpcK16RnmFhaJMZLP5yjm86RSqaTjSpIk6TVYdkmSJEmSpE2vkM9RyA+xZXiIlZUmi9Uq12dmmZ6bp91uk81mKBUKpNPppKNKkiTpFSy7JEmSJEmSXiKbzTA00M/QQD/NVotKdYnpuTmuz8zRbDZJp9OUigWyGU+rSJIkrQUelUmSJEmSJN1GJp1moK+Xgb5eJnbuoLJUY3ZhgWs3ZlisLBEClIqFpGNKkiRtapZdkiRJkiRJr0MqlaKv3ENfuYfd27ZSqzeYW1jk2sw0y8sr7u0lSZKUEMsuSZIkSZKkNyiEQKlYoFQssH1slHpjmbRllyRJUiIsuyRJkiRJkt6kQj6XdARJkqRNy7ccSZIkSZIkSZIkad2y7JIkSZIkSZIkSdK6ZdklSZIkSZIkSZKkdcuyS5IkSZIkSZIkSeuWZZckSZIkSZIkSZLWLcsuSZIkSZIkSZIkrVuWXZIkSZIkSZIkSVq3LLskSZIkSZIkSZK0bll2SZIkSZIkSZIkad2y7JIkSZIkSZIkSdK6ZdklSZIkSZIkSZKkdavrZVcIYVcI4SshhNMhhJMhhJ/q3D4UQvhSCOF853Kw29kkSZIkSZIkSZK0viQxs6sJ/M8xxsPAY8D/FEK4H/hZ4MsxxvuAL3euS5IkSZIkSZIkSbfV9bIrxvhCjPHJzseLwGlgB/Ah4DOdh30G+HC3s0mSJEmSJEmSJGl9SXTPrhDCOPBW4OvAWIzxBVgtxIAtySWTJEmSJEmSJEnSepBY2RVCKAN/BPyjGOPCG3jex0IIx0IIx65fv37vAkqSJEmSJEmSJGnNS6TsCiFkWS26Phtj/OPOzVdDCNs6928Drr3ac2OMvxljfCTG+Mjo6Gh3AkuSJEmSJEmSJGlN6nrZFUIIwG8Dp2OMv/KSu74AfLTz8UeBf9ftbJIkSZIkSZIkSVpfMgm85ruBHwWOhxCe6tz2vwCfAj4fQvgHwGXgRxLIJkmSJEmSJEmSpHWk62VXjPE/A+E2d39fN7NIkiRJkiRJkiRpfQsxxqQz3LEQwnXgUtI59KaNADeSDqFEOPabl2O/eTn2m5djv3k59puT4755OfYbw54YoxulS5KkdWNdl13aGEIIx2KMjySdQ93n2G9ejv3m5dhvXo795uXYb06O++bl2EuSJCkJqaQDSJIkSZIkSZIkSXfKskuSJEmSJEmSJEnrlmWX1oLfTDqAEuPYb16O/ebl2G9ejv3m5dhvTo775uXYS5Ikqevcs0uSJEmSJEmSJEnrljO7JEmSJEmSJEmStG5ZdilxIYR0COGbIYR/n3QWdU8IYSCE8IchhDMhhNMhhHcmnUndEUL46RDCyRDCiRDC74cQCkln0r0RQvidEMK1EMKJl9w2FEL4UgjhfOdyMMmMuvtuM+7/tPPz/ukQwp+EEAaSzKh749XG/iX3fTyEEEMII0lk0711u7EPIfxECOFs5/f+LyeVT/fObX7mPxRC+FoI4akQwrEQwtuTzChJkqTNwbJLa8FPAaeTDqGu+zXgP8QYDwFvwe+BTSGEsAP4SeCRGOMDQBr4SLKpdA99GvjAK277WeDLMcb7gC93rmtj+TTfOe5fAh6IMT4InAN+rtuh1BWf5jvHnhDCLuD7gcvdDqSu+TSvGPsQwvcCHwIejDEeAf6vBHLp3vs03/nv/peBX4wxPgT8r53rkiRJ0j1l2aVEhRB2An8b+K2ks6h7Qgh9wHuA3waIMS7HGOeSTaUuygDFEEIGKAFXEs6jeyTG+JfAzCtu/hDwmc7HnwE+3NVQuudebdxjjF+MMTY7V78G7Ox6MN1zt/k3D/CrwD8G3Cx4g7rN2P8Y8KkYY6PzmGtdD6Z77jZjH4G+zsf9eKwnSZKkLrDsUtL+GasnP9pJB1FX7QWuA/+6s4Tlb4UQepIOpXsvxvg8q+/svgy8AMzHGL+YbCp12ViM8QWAzuWWhPOo+/474M+SDqHuCCH8EPB8jPFbSWdR1x0A/kYI4eshhP8UQng06UDqmn8E/NMQwrOsHvc5m1eSJEn3nGWXEhNC+CBwLcb4RNJZ1HUZ4G3Av4oxvhWo4lJmm0Jnf6YPARPAdqAnhPDfJJtKUreEED4JNIHPJp1F914IoQR8ktVlzLT5ZIBB4DHgE8DnQwgh2Ujqkh8DfjrGuAv4aTqrOUiSJEn3kmWXkvRu4IdCCBeBzwHvCyH8m2QjqUueA56LMX69c/0PWS2/tPG9H5iKMV6PMa4Afwy8K+FM6q6rIYRtAJ1Ll7XaJEIIHwU+CPzdGKPL2W0O+1h9c8O3Osd7O4EnQwhbE02lbnkO+OO46husruQwknAmdcdHWT3GA/gD4O0JZpEkSdImYdmlxMQYfy7GuDPGOA58BPiLGKMzPDaBGOOLwLMhhIOdm74POJVgJHXPZeCxEEKp8+7u7wNOJ5xJ3fUFVk+C0bn8dwlmUZeEED4A/BPgh2KMS0nnUXfEGI/HGLfEGMc7x3vPAW/rHAdo4/u3wPsAQggHgBxwI9FE6pYrwPd0Pn4fcD7BLJIkSdokMkkHkLRp/QTw2RBCDrgA/P2E86gLYoxfDyH8IfAkq0uZfRP4zWRT6V4JIfw+8F5gJITwHPDzwKdYXcrqH7Bafv5Icgl1L9xm3H8OyANf6qxi9rUY4/+YWEjdE6829jFGly/bBG7z7/53gN8JIZwAloGPOqtz47nN2P9D4NdCCBmgDnwsuYSSJEnaLIL/35AkSZIkSZIkSdJ65TKGkiRJkiRJkiRJWrcsuyRJkiRJkiRJkrRuWXZJkiRJkiRJkiRp3bLskiRJkiRJkiRJ0rpl2SVJkiRJkiRJkqR1y7JLkiTpDoUQWiGEp0IIJ0MI3woh/EwI4TWPr0II4yGE/7pbGSVJkiRJkjY6yy5JkqQ7V4sxPhRjPAJ8P/C3gJ//Ls8ZByy7JEmSJEmS7pIQY0w6gyRJ0roUYGCXggAAAdRJREFUQqjEGMsvub4XeBwYAfYAvwv0dO7+8RjjfwkhfA04DEwBnwH+OfAp4L1AHvgXMcb/p2tfhCRJkiRJ0jpn2SVJknSHXll2dW6bBQ4Bi0A7xlgPIdwH/H6M8ZEQwnuBj8cYP9h5/MeALTHGXwoh5IGvAj8SY5zq6hcjSZIkSZK0TmWSDiBJkrTBhM5lFvj1EMJDQAs4cJvH/wDwYAjh73Su9wP3sTrzS5IkSZIkSd+FZZckSdJd0lnGsAVcY3XvrqvAW1jdJ7V+u6cBPxFj/POuhJQkSZIkSdpgUkkHkCRJ2ghCCKPAbwC/HlfXie4HXogxtoEfBdKdhy4CvS956p8DPxZCyHY+z4EQQg+SJEmSJEl6XZzZJUmSdOeKIYSnWF2ysAn8LvArnfv+JfBHIYQfAb4CVDu3Pw00QwjfAj4N/BowDjwZQgjAdeDD3foCJEmSJEmS1ruw+sZjSZIkSZIkSZIkaf1xGUNJkiRJkiRJkiStW5ZdkiRJkiRJkiRJWrcsuyRJkiRJkiRJkrRuWXZJkiRJkiRJkiRp3bLskiRJkiRJkiRJ0rpl2SVJkiRJkiRJkqR1y7JLkiRJkiRJkiRJ65ZllyRJkiRJkiRJktat/x+y5bUoJN2fqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To change the number of users, adjust the value in head()\n",
    "# top_user_df.set_index(['dt_date', 'username']).sort_values(by = ['dt_date','noTweets'], ascending = False)\n",
    "user_most_tweets_df = top_user_df.sort_values(by = ['dt_date', 'noTweets'], ascending = False, axis = 0).groupby('dt_date').head(5)\n",
    "\n",
    "# Extract 'days' out of dt_date so we can plot a scatterplot\n",
    "# Will return an int:\n",
    "user_most_tweets_df['dayofNov'] = user_most_tweets_df['dt_date'].apply(lambda x: x.day)\n",
    "user_most_tweets_df['noTweets'] = user_most_tweets_df['noTweets'].astype(int)\n",
    "\n",
    "# Plot 2 subplots\n",
    "# 1st subplot - show who are the users who tweeted the most\n",
    "# 2nd subplot - trend in number of tweets\n",
    "f, axes = plt.subplots(2, 1, figsize = (20,20))\n",
    "f = sns.scatterplot(x = 'dayofNov', y = 'noTweets', hue = 'username', data = user_most_tweets_df, size = 'noFollowers', sizes = (250, 1250), alpha = 0.75, ax = axes[0])\n",
    "sns.lineplot(x = 'dayofNov', y = 'noTweets', data = user_most_tweets_df, markers = True)\n",
    "\n",
    "# Axes and titles for each subplot\n",
    "# First subplot\n",
    "axes[0].set_xlabel('Day in Nov')\n",
    "axes[0].set_ylabel('No. of Tweets')\n",
    "axes[0].set_title('Most no. of tweets daily')\n",
    "\n",
    "# Legends for first subplot\n",
    "box = f.get_position()\n",
    "f.set_position([box.x0, box.y0, box.width * 1.0, box.height]) # resize position\n",
    "\n",
    "# Put a legend to the right side\n",
    "f.legend(loc='center right', bbox_to_anchor=(1.5, 0.5), ncol=4)\n",
    "\n",
    "# Second subplot\n",
    "axes[1].set_xlabel('Date')\n",
    "axes[1].set_ylabel('No. of Tweets')\n",
    "axes[1].set_title('Trend of no. of tweets by top users')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top user on 9th Nov 2019 is: belleng324\n",
      "Account description, if any: n.a\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>text</th>\n",
       "      <th>tweetcreatedts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>87618</td>\n",
       "      <td>belleng324</td>\n",
       "      <td>#HongKong friends, read this. https://t.co/lMALaNoQ14</td>\n",
       "      <td>2019-11-08 16:43:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88320</td>\n",
       "      <td>belleng324</td>\n",
       "      <td>Citywide moment of silence to mourn for the tragic death of the university student who was suspiciously heavily injured during the protest in Tseung Kwan O.\\n\\nMay you rest in peace, Mr. Chow. We’ll carry you in our spirits. \\n#RIP \\n\\n#HongKongProtests #JusticeForTszLok #JusticeForHK https://t.co/VVQg6TQpsO</td>\n",
       "      <td>2019-11-08 16:37:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88347</td>\n",
       "      <td>belleng324</td>\n",
       "      <td>11.08@Tsuen Wan\\n#HKPolice fired tear gas at press from less than 2m away, whereas there were no protesters around at all\\nSource: Apple Daily\\n#HKPoliceBrutality #HKPoliceTerrorism #HKPoliceState #HKProtest #StandWithHongKong https://t.co/ohpYBvPiVY</td>\n",
       "      <td>2019-11-08 16:37:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88440</td>\n",
       "      <td>belleng324</td>\n",
       "      <td>Jimmy Sham, activist &amp;amp; driving force behind #HongKong protests, has been brutally attacked by a group of men with hammers &amp;amp; is in hospital. We call for an urgent investigation to bring perpetrators to justice. Attacks on activists must not be tolerated!</td>\n",
       "      <td>2019-11-08 16:36:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88443</td>\n",
       "      <td>belleng324</td>\n",
       "      <td>This is a horrific and brutal attack on Jimmy Sham, one of the driving forces behind #HongKong protests. \\n\\nWe're calling for an urgent investigation to bring the perpetrators to justice. Attacks on protesters must not be tolerated. https://t.co/fT8yZyoogo</td>\n",
       "      <td>2019-11-08 16:36:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         username  \\\n",
       "87618  belleng324   \n",
       "88320  belleng324   \n",
       "88347  belleng324   \n",
       "88440  belleng324   \n",
       "88443  belleng324   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                        text  \\\n",
       "87618  #HongKong friends, read this. https://t.co/lMALaNoQ14                                                                                                                                                                                                                                                                   \n",
       "88320  Citywide moment of silence to mourn for the tragic death of the university student who was suspiciously heavily injured during the protest in Tseung Kwan O.\\n\\nMay you rest in peace, Mr. Chow. We’ll carry you in our spirits. \\n#RIP \\n\\n#HongKongProtests #JusticeForTszLok #JusticeForHK https://t.co/VVQg6TQpsO   \n",
       "88347  11.08@Tsuen Wan\\n#HKPolice fired tear gas at press from less than 2m away, whereas there were no protesters around at all\\nSource: Apple Daily\\n#HKPoliceBrutality #HKPoliceTerrorism #HKPoliceState #HKProtest #StandWithHongKong https://t.co/ohpYBvPiVY                                                              \n",
       "88440  Jimmy Sham, activist &amp; driving force behind #HongKong protests, has been brutally attacked by a group of men with hammers &amp; is in hospital. We call for an urgent investigation to bring perpetrators to justice. Attacks on activists must not be tolerated!                                                   \n",
       "88443  This is a horrific and brutal attack on Jimmy Sham, one of the driving forces behind #HongKong protests. \\n\\nWe're calling for an urgent investigation to bring the perpetrators to justice. Attacks on protesters must not be tolerated. https://t.co/fT8yZyoogo                                                       \n",
       "\n",
       "            tweetcreatedts  \n",
       "87618  2019-11-08 16:43:09  \n",
       "88320  2019-11-08 16:37:35  \n",
       "88347  2019-11-08 16:37:24  \n",
       "88440  2019-11-08 16:36:35  \n",
       "88443  2019-11-08 16:36:33  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe - Example of someone who tweeted the most:\n",
    "# obtain username of this user first - we are using 9th nov\n",
    "top_user_name = top_user_df[top_user_df['dt_date'].apply(lambda x: x.day) == 9].sort_values(by = 'noTweets', ascending=False)['username'].head(1).values[0]\n",
    "print(\"Top user on 9th Nov 2019 is:\",top_user_name)\n",
    "\n",
    "# Print account description, if any\n",
    "print('Account description, if any:',data[data['username'] == top_user_name][['username', 'acctdesc']]['acctdesc'].head(1).values[0])\n",
    "\n",
    "# Obtain this particular username and the first top tweets by this user from the main dataframe 'data'\n",
    "data[data['username'] == top_user_name][['username','text', 'tweetcreatedts']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accurate as of 13th Nov 2019:\n",
    "1. From the first subplot entitled 'Most no. of tweets daily', we can identify who are the top users who tweeted the most. We also show a particular example on the top user for 9th Nov 2019. \n",
    "2. Together with the first subplot, the second subplot entitled 'Trend of no. of tweets by top users daily' revealed that the number of tweets tweeted by top users daily is decreasing in general over time. \n",
    "3. It is interesting to note that there seems to be a correlation, albeit a weak one, between the no. of tweets tweeted by top users daily and any major incidents during the movement. Around 8-9th Nov 2019, the number of tweets tweeted by the top user hit almost 200. The major event was the death of a Hong Kong university student who fell from a multi-storey carpark during a dispersion by the police. Around 4th Nov 2019, the major event was exactly the report of this incident.  \n",
    "\n",
    "Sources:\n",
    "- https://www.channelnewsasia.com/news/asia/hong-kong-protests-timeline-key-dates-anti-government-12082516\n",
    "- https://www.channelnewsasia.com/news/asia/hong-kong-protests-prayer-vigil-alex-chow-death-lawmakers-arrest-12078718"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Demographics of Twitter Users\n",
    "#### Location of Twitter Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of distinct locations listed by twitter users is: 6215\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>locations</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>hong kong</td>\n",
       "      <td>49093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>香港</td>\n",
       "      <td>6829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>united states</td>\n",
       "      <td>1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>hk</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>hong kong</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>hongkong</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>canada</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>taiwan</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>new york, usa</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>london, england</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          locations  count\n",
       "1   hong kong        49093\n",
       "2   香港               6829 \n",
       "3   united states    1058 \n",
       "4   hk               912  \n",
       "5   hong kong        625  \n",
       "6   hongkong         439  \n",
       "7   canada           382  \n",
       "8   taiwan           349  \n",
       "9   new york, usa    341  \n",
       "10  london, england  314  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location = data['location']\n",
    "print('No. of distinct locations listed by twitter users is:', len(location.value_counts()))\n",
    "unique_locations = location.value_counts()\n",
    "\n",
    "# Remove n.a.\n",
    "unique_locations = pd.DataFrame({'locations': unique_locations.index,\n",
    "                                'count': unique_locations.values})\n",
    "unique_locations.drop(0, inplace = True)\n",
    "\n",
    "# See top few locations\n",
    "unique_locations.sort_values(by = 'count', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty natural to expect many of these users to claim to be residing in Hong Kong since these users are probably closer to the ground. Thus, they could spread news quickly from what they see in person. \n",
    "\n",
    "We will discount 'HongKong' from the visualization and focus on the distribution of the remaining locations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To remove 香港\n",
    "hk_chinese_word = unique_locations.iloc[1,0]\n",
    "\n",
    "# Obtain the row index of locations that contain hong kong:\n",
    "ind_1 = unique_locations[unique_locations['locations'] == 'hong kong'].index.values[0]\n",
    "ind_2 = unique_locations[unique_locations['locations'] == 'hk'].index.values[0]\n",
    "ind_3 = unique_locations[unique_locations['locations'] == 'hong kong '].index.values[0]\n",
    "ind_4 = unique_locations[unique_locations['locations'] == 'hongkong'].index.values[0]\n",
    "ind_5 = unique_locations[unique_locations['locations'] == hk_chinese_word].index.values[0]\n",
    "ind_6 = unique_locations[unique_locations['locations'] == 'kowloon city district'].index.values[0]\n",
    "\n",
    "list_ind = [ind_1,ind_2,ind_3,ind_4,ind_5, ind_6]\n",
    "\n",
    "# Drop these rows from unique_locations\n",
    "unique_loc_temp = unique_locations.drop(list_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 127469 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 127472 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 127469 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 127472 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1152x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Focus on top 20 locations first\n",
    "# Convert any possible str to int/numeric first\n",
    "count = pd.to_numeric(unique_loc_temp['count'])\n",
    "unique_loc_temp['count'] = count\n",
    "unique_loc_temp = unique_loc_temp.head(20)\n",
    "\n",
    "# Plot a bar plot\n",
    "plt.figure(figsize=(16,13))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.barplot(x = 'count', y = 'locations', orient = 'h',data = unique_loc_temp)\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Locations')\n",
    "plt.title('Top 20 Locations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick counting of the top 20 locations, without hong kong, shows that majority of these locations come from the western world. We see the expected ones such as the United States, Canada, UK and Australia, where some people and politicians are also watching over the protest movement and speaking out against the ruling government and police. \n",
    "\n",
    "#### Follower Count\n",
    "##### Top users with most followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>noFollowers</th>\n",
       "      <th>dt_date</th>\n",
       "      <th>username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>26193629.0</td>\n",
       "      <td>2019-11-19</td>\n",
       "      <td>BBCWorld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14177611.0</td>\n",
       "      <td>2019-11-19</td>\n",
       "      <td>CGTNOfficial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12642783.0</td>\n",
       "      <td>2019-11-09</td>\n",
       "      <td>XHNews</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7065479.0</td>\n",
       "      <td>2019-11-15</td>\n",
       "      <td>PDChina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4274353.0</td>\n",
       "      <td>2019-11-14</td>\n",
       "      <td>ChinaDaily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>gasvs_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>HK4freedom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>gb3a13a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-11-09</td>\n",
       "      <td>HK42929919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>hkppl19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51392 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       noFollowers     dt_date      username\n",
       "0      26193629.0   2019-11-19  BBCWorld    \n",
       "1      14177611.0   2019-11-19  CGTNOfficial\n",
       "2      12642783.0   2019-11-09  XHNews      \n",
       "3      7065479.0    2019-11-15  PDChina     \n",
       "4      4274353.0    2019-11-14  ChinaDaily  \n",
       "...          ...           ...         ...  \n",
       "51387  0.0          2019-11-13  gasvs_      \n",
       "51388  0.0          2019-11-13  HK4freedom  \n",
       "51389  0.0          2019-11-13  gb3a13a     \n",
       "51390  0.0          2019-11-09  HK42929919  \n",
       "51391  0.0          2019-11-13  hkppl19     \n",
       "\n",
       "[51392 rows x 3 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reuse code from top_user_df\n",
    "# Sort according to noFollowers\n",
    "top_user_df = top_user_df.sort_values(by = 'noFollowers', ascending = False)\n",
    "user_most_followers = top_user_df.groupby('username')['noFollowers', 'dt_date'].max().sort_values(by = 'noFollowers', ascending = False)\n",
    "user_most_followers['username'] = user_most_followers.index\n",
    "user_most_followers.reset_index(inplace = True, drop = True)\n",
    "user_most_followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize = (25, 8))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.barplot(x = 'noFollowers', y = 'username', orient = 'h', data = user_most_followers.head(30))\n",
    "plt.xlabel('No. of Followers')\n",
    "plt.ylabel('Usernames')\n",
    "plt.title('Top Twitter Accounts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Acitivity of Top Accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           username noTweets  noFollowers     dt_date\n",
      "284   BBCWorld       1        26193629.0   2019-11-19\n",
      "410   CGTNOfficial   1        14148818.0   2019-11-19\n",
      "2513  SPIEGELONLINE  1        2578743.0    2019-11-19\n",
      "6089  tictoc         1        857903.0     2019-11-19\n",
      "2845  TomFitton      1        716396.0     2019-11-19\n",
      "...         ...     ..             ...            ...\n",
      "829   ChinaDaily     1        4274353.0    2019-11-03\n",
      "3413  RT_com         1        2983379.0    2019-11-03\n",
      "4645  airnewsalerts  1        2169399.0    2019-11-03\n",
      "1190  Echinanews     1        597933.0     2019-11-03\n",
      "7607  nbcbayarea     1        329697.0     2019-11-03\n",
      "\n",
      "[75 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "user_most_followers_daily = top_user_df.sort_values(by = ['dt_date', 'noFollowers'], ascending = False, axis = 0).groupby('dt_date').head(5)\n",
    "print(user_most_followers_daily)\n",
    "\n",
    "# Extract 'days' out of dt_date so we can plot a scatterplot\n",
    "# Will return an int:\n",
    "user_most_followers_daily['dayofNov'] = user_most_followers_daily['dt_date'].apply(lambda x: x.day)\n",
    "user_most_followers_daily['noFollowers'] = user_most_followers_daily['noFollowers'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a5ef85190>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 1, figsize = (15,10))\n",
    "f = sns.scatterplot(x = 'dayofNov', y = 'noTweets', hue = 'username',data = user_most_followers_daily, size = 'noFollowers', sizes=(50, 1000))\n",
    "\n",
    "# Axes and titles for each subplot\n",
    "# First subplot\n",
    "axes.set_xlabel('Day in Nov')\n",
    "axes.set_ylabel('No. of Tweets')\n",
    "axes.set_title('Daily activity of users with most number of followers')\n",
    "\n",
    "# Legends for first subplot\n",
    "box = f.get_position()\n",
    "f.set_position([box.x0, box.y0, box.width * 1, box.height]) # resize position\n",
    "\n",
    "# Put a legend to the right side\n",
    "f.legend(loc='center right', bbox_to_anchor=(1.5, 0.5), ncol=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Most Mentioned Username \n",
    "\n",
    "Might there be figureheads in the protest movements that can be discovered in these tweets? Twitter users might be tagging these people to maybe inform them of events that are happening on the ground and hence, require their attentions. These figureheads or prominent figures might be lawyers, lawmakers, politicians, reporters and protest leaders who are supportive of the pro-democracy movement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_users(df):\n",
    "    # df: dataframe to look at\n",
    "    # returns a list of usernames\n",
    "    \n",
    "    # Create empty list\n",
    "    list_users = []\n",
    "    \n",
    "    for i in range(0, len(df)):\n",
    "        users_ith_text = re.findall('@[^\\s]+', df.iloc[i,:]['text'])\n",
    "        # returns a list\n",
    "        # append to list_users by going through a for-loop:\n",
    "        for j in range(0, len(users_ith_text)):\n",
    "            list_users.append(users_ith_text[j])\n",
    "    \n",
    "    return list_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>mentioned_users</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@hkgetv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@SenateMajLdr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@SolomonYue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@SenRickScott</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@seariousforhkg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mentioned_users\n",
       "0  @hkgetv        \n",
       "1  @SenateMajLdr  \n",
       "2  @SolomonYue    \n",
       "3  @SenRickScott  \n",
       "4  @seariousforhkg"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply on dataframe data['text']\n",
    "list_users = find_users(data)\n",
    "\n",
    "mentioned_users_df = pd.DataFrame({\n",
    "    'mentioned_users': list_users\n",
    "})\n",
    "\n",
    "mentioned_users_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>mentioned_users</th>\n",
       "      <th>totalcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2209</td>\n",
       "      <td>@SolomonYue</td>\n",
       "      <td>6153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3706</td>\n",
       "      <td>@joshuawongcf</td>\n",
       "      <td>3652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3912</td>\n",
       "      <td>@marcorubio</td>\n",
       "      <td>2852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3438</td>\n",
       "      <td>@hk_watch</td>\n",
       "      <td>2656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2942</td>\n",
       "      <td>@business</td>\n",
       "      <td>2607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mentioned_users  totalcount\n",
       "2209  @SolomonYue     6153      \n",
       "3706  @joshuawongcf   3652      \n",
       "3912  @marcorubio     2852      \n",
       "3438  @hk_watch       2656      \n",
       "2942  @business       2607      "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentionedusers = mentioned_users_df.groupby('mentioned_users').size().reset_index(name = 'totalcount').sort_values(by = 'totalcount', ascending = False)\n",
    "mentionedusers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 8297 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/griffin/opt/anaconda3/envs/py37/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 8297 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,8))\n",
    "sns.set_palette('PuBuGn_d')\n",
    "sns.barplot(x = 'mentioned_users', y = 'totalcount', data = mentionedusers.head(15))\n",
    "plt.xlabel('Mentioned users in tweets')\n",
    "plt.ylabel('Number of times')\n",
    "plt.title('Top users and how many times they were mentioned in tweets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the 15 most mentioned users, if not all, are directly related to Hong Kong and the protest movements. In particular, a quick google search on each of these users will return queries that show that they are either supportive of the protesters and protests and/or against the Hong Kong Administration and the police force.\n",
    "In summary:\n",
    "1. @SolomonYue - a Chinese American politician related to the Hong Kong Human Rights and Democracy Act passed in the U.S\n",
    "2. @joshuawongcf - a local protest leader who was planning to take part in the elections but was banned.\n",
    "3. @GOVUK - threatens to sanction hong kong officials over their handling of the protest\n",
    "4. @HawleyMO - a U.S politician\n",
    "5. @HeatherWheeler - Minister for Asia and the Pacific who sent a letter to Hong Kong government officials on the proposed sanctions.\n",
    "\n",
    "With the above bar chart, we have come to the end of this section where we have visualized information from tweets in the dataset. \n",
    "\n",
    "\n",
    "All in all, more than 200k tweets over a period of at least 10 days starting from 3rd Nov 2019, we want to know what is the general sentiment of tweets over a period of time. e.g. daily, weekly etc. The rudimentary sentiment analysis that we did above using the Vader Library from NLTLK revealed plenty of false positives -  upon closer inspection of these random tweets that were rated to be positive towards the government and police, they turned out to be either negative towards them or supportive towards the protesters' cause. Hence, we need to turn to deep learning to give us better results. The section on deep learning will be continued on a separate notebook."
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}