Crawler_GME.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "civilian-administration",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from youtube_crawler import *\n",
    "import json\n",
    "import sys\n",
    "import csv\n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "from selenium import webdriver\n",
    "import json, time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "polish-router",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference: https://stackoverflow.com/a/57076690/15164646\n",
    "from selenium import webdriver\n",
    "import json, time \n",
    "\n",
    "def get_videos(ticker):\n",
    "    driver = webdriver.Chrome(\"/Users/miao/Desktop/chromedriver\") #add path to webdriver\n",
    "    driver.get('https://www.youtube.com/results?search_query={}+stock'.format(ticker))\n",
    "\n",
    "    youtube_data = []\n",
    "\n",
    "    # scrolling to the end of the page\n",
    "    while True:\n",
    "        # end_result = \"No more results\" string at the bottom of the page\n",
    "        # this will be used to break out of the while loop\n",
    "        end_result = driver.find_element_by_css_selector('#message').is_displayed()\n",
    "        driver.execute_script(\"var scrollingElement = (document.scrollingElement || document.body);scrollingElement.scrollTop = scrollingElement.scrollHeight;\")\n",
    "        #print(end_result)\n",
    "\n",
    "        if end_result == True:\n",
    "            break\n",
    "\n",
    "    print('Extracting results for {}. It might take a while...'.format(ticker))\n",
    "\n",
    "    for result in driver.find_elements_by_css_selector('.text-wrapper.style-scope.ytd-video-renderer'):\n",
    "        title = result.find_element_by_css_selector('.title-and-badge.style-scope.ytd-video-renderer').text\n",
    "        link = result.find_element_by_css_selector('.title-and-badge.style-scope.ytd-video-renderer a').get_attribute('href')\n",
    "        channel_name = result.find_element_by_css_selector('.long-byline').text\n",
    "        channel_link = result.find_element_by_css_selector('#text > a').get_attribute('href')\n",
    "        views = result.find_element_by_css_selector('.style-scope ytd-video-meta-block').text.split('\\n')[0]\n",
    "\n",
    "        try:\n",
    "            time_published = result.find_element_by_css_selector('.style-scope ytd-video-meta-block').text.split('\\n')[1]\n",
    "        except:\n",
    "            time_published = None\n",
    "\n",
    "        try:\n",
    "            snippet = result.find_element_by_css_selector('.metadata-snippet-container').text\n",
    "        except:\n",
    "            snippet = None\n",
    "\n",
    "        try:\n",
    "            if result.find_element_by_css_selector('#channel-name .ytd-badge-supported-renderer') is not None:\n",
    "                verified_badge = True\n",
    "            else:\n",
    "                verified_badge = False\n",
    "        except:\n",
    "            verified_badge = None\n",
    "\n",
    "        try:\n",
    "            extensions = result.find_element_by_css_selector('#badges .ytd-badge-supported-renderer').text\n",
    "        except:\n",
    "            extensions = None\n",
    "        #print(verified_badge)\n",
    "        \n",
    "        #threshold for views above 1000\n",
    "        #if \"K\" in views or \"M\" in views:\n",
    "        youtube_data.append({\n",
    "                'title': title,\n",
    "                'link': link,\n",
    "                'channel_name': channel_name, \n",
    "                'channel_link': channel_link,\n",
    "                'views': views,\n",
    "                'time_published': time_published,\n",
    "                'snippet': snippet,\n",
    "                'verified_badge': verified_badge,\n",
    "                'extensions': extensions,\n",
    "            })\n",
    "        \n",
    "    print(\"crawled\", len(youtube_data), \"videos\")\n",
    "    #print(json.dumps(youtube_data, indent=2, ensure_ascii=False))\n",
    "    driver.quit()\n",
    "    \n",
    "    return youtube_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "technological-candidate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting results for GME. It might take a while...\n",
      "crawled 645 videos\n",
      "CPU times: user 31.7 s, sys: 1.98 s, total: 33.7 s\n",
      "Wall time: 6min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "video_lst = get_videos('GME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adolescent-denmark",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"gme_videos.json\", \"w\") as outfile:\n",
    "    json.dump(video_lst, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "mediterranean-miracle",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "light-migration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/miao/Desktop/CA/final proj'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "rough-merit",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {'title':[], 'url': [], 'ticker': [], 'channel_name': [], 'views': [],\n",
    "      'time_published': [], 'snippet': [], 'channel_link': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "superb-bread",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(video_lst)[['title', 'link', 'channel_name', 'channel_link', 'views', 'time_published', 'snippet']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "distinguished-turtle",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>channel_name</th>\n",
       "      <th>channel_link</th>\n",
       "      <th>views</th>\n",
       "      <th>time_published</th>\n",
       "      <th>snippet</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1  AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2  GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4  BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "\n",
       "                                          link      channel_name  \\\n",
       "0  https://www.youtube.com/watch?v=QxAURd6O50g       Happy Money   \n",
       "1  https://www.youtube.com/watch?v=27pvUBtMCeQ  Simulate & Trade   \n",
       "2  https://www.youtube.com/watch?v=KMOKJirlL-k     JacksonHunter   \n",
       "3  https://www.youtube.com/watch?v=FAs1vYKXGRc          TickrBox   \n",
       "4  https://www.youtube.com/watch?v=6rn2_EoDoXU          TickrBox   \n",
       "\n",
       "                                        channel_link       views  \\\n",
       "0        https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1  https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2            https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                 https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                 https://www.youtube.com/c/TickrBox   642 views   \n",
       "\n",
       "  time_published                                            snippet  \n",
       "0      1 day ago  Thank you for your support. Sub up, Like up, C...  \n",
       "1   23 hours ago  AMC Stock and GME Stock are the best hedge aga...  \n",
       "2   21 hours ago  In this video we go over the chart, SPY, Black...  \n",
       "3     7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...  \n",
       "4    2 hours ago  Join the 100% Winner Community https://www.pat...  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "physical-student",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      GME Stock Dumping Setting Up For Earnings Anno...\n",
       "1      AMC Stock & GME Stock Hedge Funds And Big Bank...\n",
       "2      GME Short Squeeze 3-11-22 | Fox News Highlight...\n",
       "3      SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...\n",
       "4      BREAKING: Jordan Belfort (Wolf of Wall Street)...\n",
       "                             ...                        \n",
       "640    Bitcoin's \"Bart Chart\" | Benzinga Live 🚨Sal Gi...\n",
       "641    Ryan Cohen Takes Stake In $BBBY | Benzinga Liv...\n",
       "642    Ryan Cohen Takes NEW STAKE In Bed Bath & Beyon...\n",
       "643    GameStop GME - Price Prediction and Technical ...\n",
       "644    BBBY surges after big stake by Ryan Cohen reve...\n",
       "Length: 645, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data['title'] + data['snippet']"
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  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "excessive-forge",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isNaN(num):\n",
    "    return num != num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "optimum-nebraska",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['text'] = data['title'] + data['snippet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "chinese-drill",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>channel_name</th>\n",
       "      <th>channel_link</th>\n",
       "      <th>views</th>\n",
       "      <th>time_published</th>\n",
       "      <th>snippet</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>640</th>\n",
       "      <td>Bitcoin's \"Bart Chart\" | Benzinga Live 🚨</td>\n",
       "      <td>https://www.youtube.com/watch?v=Gw3JRYRScpA</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>3.1K views</td>\n",
       "      <td>Streamed 2 days ago</td>\n",
       "      <td>Sal Gilbertie, CEO of Teucrium Trading Jesse K...</td>\n",
       "      <td>Bitcoin's \"Bart Chart\" | Benzinga Live 🚨Sal Gi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Ryan Cohen Takes Stake In $BBBY | Benzinga Live 🚨</td>\n",
       "      <td>https://www.youtube.com/watch?v=bcUALXe0Uy4</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>2.7K views</td>\n",
       "      <td>Streamed 5 days ago</td>\n",
       "      <td>On today's Benzinga Live: -Ryan Cohen Takes Ne...</td>\n",
       "      <td>Ryan Cohen Takes Stake In $BBBY | Benzinga Liv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Ryan Cohen Takes NEW STAKE In Bed Bath &amp; Beyon...</td>\n",
       "      <td>https://www.youtube.com/watch?v=HjRadTv8o6c</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>6K views</td>\n",
       "      <td>Streamed 5 days ago</td>\n",
       "      <td>Benzinga's Cannabis Capital Conference Kicks o...</td>\n",
       "      <td>Ryan Cohen Takes NEW STAKE In Bed Bath &amp; Beyon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>GameStop GME - Price Prediction and Technical ...</td>\n",
       "      <td>https://www.youtube.com/watch?v=uAQ3tYnD2i0</td>\n",
       "      <td>Crypto Boss Andrew</td>\n",
       "      <td>https://www.youtube.com/channel/UCjK59q5KhEW2M...</td>\n",
       "      <td>22 views</td>\n",
       "      <td>2 days ago</td>\n",
       "      <td>Join our CBA telegram chat; https://t.me/+II4n...</td>\n",
       "      <td>GameStop GME - Price Prediction and Technical ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>BBBY surges after big stake by Ryan Cohen reve...</td>\n",
       "      <td>https://www.youtube.com/watch?v=sOARjtEpbNg</td>\n",
       "      <td>CNBC Television</td>\n",
       "      <td>https://www.youtube.com/c/CNBCtelevision</td>\n",
       "      <td>1.6K views</td>\n",
       "      <td>5 days ago</td>\n",
       "      <td>Turn to CNBC TV for the latest stock market ne...</td>\n",
       "      <td>BBBY surges after big stake by Ryan Cohen reve...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "..                                                 ...   \n",
       "640           Bitcoin's \"Bart Chart\" | Benzinga Live 🚨   \n",
       "641  Ryan Cohen Takes Stake In $BBBY | Benzinga Live 🚨   \n",
       "642  Ryan Cohen Takes NEW STAKE In Bed Bath & Beyon...   \n",
       "643  GameStop GME - Price Prediction and Technical ...   \n",
       "644  BBBY surges after big stake by Ryan Cohen reve...   \n",
       "\n",
       "                                            link        channel_name  \\\n",
       "0    https://www.youtube.com/watch?v=QxAURd6O50g         Happy Money   \n",
       "1    https://www.youtube.com/watch?v=27pvUBtMCeQ    Simulate & Trade   \n",
       "2    https://www.youtube.com/watch?v=KMOKJirlL-k       JacksonHunter   \n",
       "3    https://www.youtube.com/watch?v=FAs1vYKXGRc            TickrBox   \n",
       "4    https://www.youtube.com/watch?v=6rn2_EoDoXU            TickrBox   \n",
       "..                                           ...                 ...   \n",
       "640  https://www.youtube.com/watch?v=Gw3JRYRScpA            Benzinga   \n",
       "641  https://www.youtube.com/watch?v=bcUALXe0Uy4            Benzinga   \n",
       "642  https://www.youtube.com/watch?v=HjRadTv8o6c            Benzinga   \n",
       "643  https://www.youtube.com/watch?v=uAQ3tYnD2i0  Crypto Boss Andrew   \n",
       "644  https://www.youtube.com/watch?v=sOARjtEpbNg     CNBC Television   \n",
       "\n",
       "                                          channel_link       views  \\\n",
       "0          https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1    https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2              https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                   https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                   https://www.youtube.com/c/TickrBox   642 views   \n",
       "..                                                 ...         ...   \n",
       "640                 https://www.youtube.com/c/Benzinga  3.1K views   \n",
       "641                 https://www.youtube.com/c/Benzinga  2.7K views   \n",
       "642                 https://www.youtube.com/c/Benzinga    6K views   \n",
       "643  https://www.youtube.com/channel/UCjK59q5KhEW2M...    22 views   \n",
       "644           https://www.youtube.com/c/CNBCtelevision  1.6K views   \n",
       "\n",
       "          time_published                                            snippet  \\\n",
       "0              1 day ago  Thank you for your support. Sub up, Like up, C...   \n",
       "1           23 hours ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "2           21 hours ago  In this video we go over the chart, SPY, Black...   \n",
       "3             7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4            2 hours ago  Join the 100% Winner Community https://www.pat...   \n",
       "..                   ...                                                ...   \n",
       "640  Streamed 2 days ago  Sal Gilbertie, CEO of Teucrium Trading Jesse K...   \n",
       "641  Streamed 5 days ago  On today's Benzinga Live: -Ryan Cohen Takes Ne...   \n",
       "642  Streamed 5 days ago  Benzinga's Cannabis Capital Conference Kicks o...   \n",
       "643           2 days ago  Join our CBA telegram chat; https://t.me/+II4n...   \n",
       "644           5 days ago  Turn to CNBC TV for the latest stock market ne...   \n",
       "\n",
       "                                                  text  \n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...  \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...  \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...  \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...  \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...  \n",
       "..                                                 ...  \n",
       "640  Bitcoin's \"Bart Chart\" | Benzinga Live 🚨Sal Gi...  \n",
       "641  Ryan Cohen Takes Stake In $BBBY | Benzinga Liv...  \n",
       "642  Ryan Cohen Takes NEW STAKE In Bed Bath & Beyon...  \n",
       "643  GameStop GME - Price Prediction and Technical ...  \n",
       "644  BBBY surges after big stake by Ryan Cohen reve...  \n",
       "\n",
       "[645 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "forward-sight",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Special module written for this class\n",
    "#This provides access to data and to helper functions from previous weeks\n",
    "import lucem_illud #just in case, regularly update your lucem_illud with the following code: pip install git+git://github.com/UChicago-Computational-Content-Analysis/lucem_illud.git\n",
    "\n",
    "#All these packages need to be installed from pip\n",
    "import requests #for http requests\n",
    "import pandas #gives us DataFrames\n",
    "import matplotlib.pyplot as plt #For graphics\n",
    "import wordcloud #Makes word clouds\n",
    "import numpy as np #For divergences/distances\n",
    "import scipy #For divergences/distances\n",
    "import seaborn as sns #makes our plots look nicer\n",
    "import sklearn.manifold #For a manifold plot\n",
    "import json #For API responses\n",
    "import urllib.parse #For joining urls\n",
    "\n",
    "# comp-linguistics\n",
    "import spacy\n",
    "\n",
    "#Displays the graphs\n",
    "import graphviz #You also need to install the command line graphviz\n",
    "\n",
    "#These are from the standard library\n",
    "import os.path\n",
    "import zipfile\n",
    "import subprocess\n",
    "import io\n",
    "import tempfile\n",
    "\n",
    "#This 'magic' command makes the plots work better\n",
    "#in the notebook, don't use it outside of a notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "third-norman",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "hybrid-guitar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spacy.lang.en.English"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(nlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "together-guyana",
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_tokenize(word_list):\n",
    "    tokenized = []\n",
    "    # pass word list through language model.\n",
    "    doc = nlp(word_list)\n",
    "    for token in doc:\n",
    "        if not token.is_punct and len(token.text.strip()) > 0:\n",
    "            tokenized.append(token.text)\n",
    "    return tokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "sunrise-refund",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'ssdfd'\n",
    "type(a) == str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "opposite-things",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n",
      "138\n",
      "nan\n",
      "257\n",
      "nan\n",
      "351\n",
      "nan\n",
      "353\n",
      "nan\n",
      "544\n",
      "nan\n",
      "584\n",
      "nan\n",
      "591\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "for i in data['text']:\n",
    "    cnt+=1\n",
    "    if (type(i) != str):\n",
    "        #print(type(i))\n",
    "        print(i)\n",
    "        print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "quick-appointment",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "d = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "driving-blues",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d.drop([137, 256,350,352,543,583,590])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "suitable-harrison",
   "metadata": {},
   "outputs": [
    {
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       "      <th>title</th>\n",
       "      <th>link</th>\n",
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       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>133</th>\n",
       "      <td>Why is DRS Taking So Long (#GME Stock) - Direc...</td>\n",
       "      <td>https://www.youtube.com/watch?v=PZaDTt3o6Xc</td>\n",
       "      <td>CosmicLightningWarrior</td>\n",
       "      <td>https://www.youtube.com/c/CosmicLightningWarri...</td>\n",
       "      <td>5K views</td>\n",
       "      <td>1 month ago</td>\n",
       "      <td>The CosmicLightningWarrior explains the curren...</td>\n",
       "      <td>Why is DRS Taking So Long (#GME Stock) - Direc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>AMC Stock &amp; GME Stock - Tether, The Biggest Sc...</td>\n",
       "      <td>https://www.youtube.com/watch?v=qFY5mlYTj1A</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>15K views</td>\n",
       "      <td>3 months ago</td>\n",
       "      <td>AMC Stock and GME Stock continue to be the bes...</td>\n",
       "      <td>AMC Stock &amp; GME Stock - Tether, The Biggest Sc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>🔥 GME SHORT INTEREST ALERT! + PENSION FUND BUY...</td>\n",
       "      <td>https://www.youtube.com/watch?v=LurOMCZwaVY</td>\n",
       "      <td>Thomas James - Investing</td>\n",
       "      <td>https://www.youtube.com/c/ThomasJamesPropertyI...</td>\n",
       "      <td>11K views</td>\n",
       "      <td>3 months ago</td>\n",
       "      <td>The second largest pension fund in the US just...</td>\n",
       "      <td>🔥 GME SHORT INTEREST ALERT! + PENSION FUND BUY...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>AMC GME Stock | SEC Charges Social Media Stock...</td>\n",
       "      <td>https://www.youtube.com/watch?v=u5rP5Vky6kE</td>\n",
       "      <td>Stock Swinger</td>\n",
       "      <td>https://www.youtube.com/channel/UCjE4IxdydVIpg...</td>\n",
       "      <td>5.4K views</td>\n",
       "      <td>1 month ago</td>\n",
       "      <td>AMC ...</td>\n",
       "      <td>AMC GME Stock | SEC Charges Social Media Stock...</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>AMC Stock &amp; GME Stock - So What Happened Today...</td>\n",
       "      <td>https://www.youtube.com/watch?v=RlVpicfR6gw</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>18K views</td>\n",
       "      <td>1 month ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock - So What Happened Today...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "..                                                 ...   \n",
       "133  Why is DRS Taking So Long (#GME Stock) - Direc...   \n",
       "134  AMC Stock & GME Stock - Tether, The Biggest Sc...   \n",
       "135  🔥 GME SHORT INTEREST ALERT! + PENSION FUND BUY...   \n",
       "136  AMC GME Stock | SEC Charges Social Media Stock...   \n",
       "138  AMC Stock & GME Stock - So What Happened Today...   \n",
       "\n",
       "                                            link              channel_name  \\\n",
       "0    https://www.youtube.com/watch?v=QxAURd6O50g               Happy Money   \n",
       "1    https://www.youtube.com/watch?v=27pvUBtMCeQ          Simulate & Trade   \n",
       "2    https://www.youtube.com/watch?v=KMOKJirlL-k             JacksonHunter   \n",
       "3    https://www.youtube.com/watch?v=FAs1vYKXGRc                  TickrBox   \n",
       "4    https://www.youtube.com/watch?v=6rn2_EoDoXU                  TickrBox   \n",
       "..                                           ...                       ...   \n",
       "133  https://www.youtube.com/watch?v=PZaDTt3o6Xc    CosmicLightningWarrior   \n",
       "134  https://www.youtube.com/watch?v=qFY5mlYTj1A          Simulate & Trade   \n",
       "135  https://www.youtube.com/watch?v=LurOMCZwaVY  Thomas James - Investing   \n",
       "136  https://www.youtube.com/watch?v=u5rP5Vky6kE             Stock Swinger   \n",
       "138  https://www.youtube.com/watch?v=RlVpicfR6gw          Simulate & Trade   \n",
       "\n",
       "                                          channel_link       views  \\\n",
       "0          https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1    https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2              https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                   https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                   https://www.youtube.com/c/TickrBox   642 views   \n",
       "..                                                 ...         ...   \n",
       "133  https://www.youtube.com/c/CosmicLightningWarri...    5K views   \n",
       "134  https://www.youtube.com/channel/UCW6xUGOnJ_XOR...   15K views   \n",
       "135  https://www.youtube.com/c/ThomasJamesPropertyI...   11K views   \n",
       "136  https://www.youtube.com/channel/UCjE4IxdydVIpg...  5.4K views   \n",
       "138  https://www.youtube.com/channel/UCW6xUGOnJ_XOR...   18K views   \n",
       "\n",
       "    time_published                                            snippet  \\\n",
       "0        1 day ago  Thank you for your support. Sub up, Like up, C...   \n",
       "1     23 hours ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "2     21 hours ago  In this video we go over the chart, SPY, Black...   \n",
       "3       7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4      2 hours ago  Join the 100% Winner Community https://www.pat...   \n",
       "..             ...                                                ...   \n",
       "133    1 month ago  The CosmicLightningWarrior explains the curren...   \n",
       "134   3 months ago  AMC Stock and GME Stock continue to be the bes...   \n",
       "135   3 months ago  The second largest pension fund in the US just...   \n",
       "136    1 month ago                                            AMC ...   \n",
       "138    1 month ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "\n",
       "                                                  text  \n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...  \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...  \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...  \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...  \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...  \n",
       "..                                                 ...  \n",
       "133  Why is DRS Taking So Long (#GME Stock) - Direc...  \n",
       "134  AMC Stock & GME Stock - Tether, The Biggest Sc...  \n",
       "135  🔥 GME SHORT INTEREST ALERT! + PENSION FUND BUY...  \n",
       "136  AMC GME Stock | SEC Charges Social Media Stock...  \n",
       "138  AMC Stock & GME Stock - So What Happened Today...  \n",
       "\n",
       "[138 rows x 8 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[:138]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "parental-annex",
   "metadata": {},
   "outputs": [],
   "source": [
    "d['tokenized_text'] = d['text'].\\\n",
    "apply(lambda x: word_tokenize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "animal-evaluation",
   "metadata": {},
   "outputs": [],
   "source": [
    "d['word_counts'] = d['tokenized_text'].\\\n",
    "apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "level-arnold",
   "metadata": {},
   "outputs": [
    {
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       "    .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>title</th>\n",
       "      <th>link</th>\n",
       "      <th>channel_name</th>\n",
       "      <th>channel_link</th>\n",
       "      <th>views</th>\n",
       "      <th>time_published</th>\n",
       "      <th>snippet</th>\n",
       "      <th>text</th>\n",
       "      <th>tokenized_text</th>\n",
       "      <th>word_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>[GME, Stock, Dumping, Setting, Up, For, Earnin...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>[AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>[GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>[SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>[BREAKING, Jordan, Belfort, Wolf, of, Wall, St...</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>640</th>\n",
       "      <td>Bitcoin's \"Bart Chart\" | Benzinga Live 🚨</td>\n",
       "      <td>https://www.youtube.com/watch?v=Gw3JRYRScpA</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>3.1K views</td>\n",
       "      <td>Streamed 2 days ago</td>\n",
       "      <td>Sal Gilbertie, CEO of Teucrium Trading Jesse K...</td>\n",
       "      <td>Bitcoin's \"Bart Chart\" | Benzinga Live 🚨Sal Gi...</td>\n",
       "      <td>[Bitcoin, 's, Bart, Chart, |, Benzinga, Live, ...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Ryan Cohen Takes Stake In $BBBY | Benzinga Live 🚨</td>\n",
       "      <td>https://www.youtube.com/watch?v=bcUALXe0Uy4</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>2.7K views</td>\n",
       "      <td>Streamed 5 days ago</td>\n",
       "      <td>On today's Benzinga Live: -Ryan Cohen Takes Ne...</td>\n",
       "      <td>Ryan Cohen Takes Stake In $BBBY | Benzinga Liv...</td>\n",
       "      <td>[Ryan, Cohen, Takes, Stake, In, $, BBBY, |, Be...</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Ryan Cohen Takes NEW STAKE In Bed Bath &amp; Beyon...</td>\n",
       "      <td>https://www.youtube.com/watch?v=HjRadTv8o6c</td>\n",
       "      <td>Benzinga</td>\n",
       "      <td>https://www.youtube.com/c/Benzinga</td>\n",
       "      <td>6K views</td>\n",
       "      <td>Streamed 5 days ago</td>\n",
       "      <td>Benzinga's Cannabis Capital Conference Kicks o...</td>\n",
       "      <td>Ryan Cohen Takes NEW STAKE In Bed Bath &amp; Beyon...</td>\n",
       "      <td>[Ryan, Cohen, Takes, NEW, STAKE, In, Bed, Bath...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>GameStop GME - Price Prediction and Technical ...</td>\n",
       "      <td>https://www.youtube.com/watch?v=uAQ3tYnD2i0</td>\n",
       "      <td>Crypto Boss Andrew</td>\n",
       "      <td>https://www.youtube.com/channel/UCjK59q5KhEW2M...</td>\n",
       "      <td>22 views</td>\n",
       "      <td>2 days ago</td>\n",
       "      <td>Join our CBA telegram chat; https://t.me/+II4n...</td>\n",
       "      <td>GameStop GME - Price Prediction and Technical ...</td>\n",
       "      <td>[GameStop, GME, Price, Prediction, and, Techni...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>BBBY surges after big stake by Ryan Cohen reve...</td>\n",
       "      <td>https://www.youtube.com/watch?v=sOARjtEpbNg</td>\n",
       "      <td>CNBC Television</td>\n",
       "      <td>https://www.youtube.com/c/CNBCtelevision</td>\n",
       "      <td>1.6K views</td>\n",
       "      <td>5 days ago</td>\n",
       "      <td>Turn to CNBC TV for the latest stock market ne...</td>\n",
       "      <td>BBBY surges after big stake by Ryan Cohen reve...</td>\n",
       "      <td>[BBBY, surges, after, big, stake, by, Ryan, Co...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>638 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "..                                                 ...   \n",
       "640           Bitcoin's \"Bart Chart\" | Benzinga Live 🚨   \n",
       "641  Ryan Cohen Takes Stake In $BBBY | Benzinga Live 🚨   \n",
       "642  Ryan Cohen Takes NEW STAKE In Bed Bath & Beyon...   \n",
       "643  GameStop GME - Price Prediction and Technical ...   \n",
       "644  BBBY surges after big stake by Ryan Cohen reve...   \n",
       "\n",
       "                                            link        channel_name  \\\n",
       "0    https://www.youtube.com/watch?v=QxAURd6O50g         Happy Money   \n",
       "1    https://www.youtube.com/watch?v=27pvUBtMCeQ    Simulate & Trade   \n",
       "2    https://www.youtube.com/watch?v=KMOKJirlL-k       JacksonHunter   \n",
       "3    https://www.youtube.com/watch?v=FAs1vYKXGRc            TickrBox   \n",
       "4    https://www.youtube.com/watch?v=6rn2_EoDoXU            TickrBox   \n",
       "..                                           ...                 ...   \n",
       "640  https://www.youtube.com/watch?v=Gw3JRYRScpA            Benzinga   \n",
       "641  https://www.youtube.com/watch?v=bcUALXe0Uy4            Benzinga   \n",
       "642  https://www.youtube.com/watch?v=HjRadTv8o6c            Benzinga   \n",
       "643  https://www.youtube.com/watch?v=uAQ3tYnD2i0  Crypto Boss Andrew   \n",
       "644  https://www.youtube.com/watch?v=sOARjtEpbNg     CNBC Television   \n",
       "\n",
       "                                          channel_link       views  \\\n",
       "0          https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1    https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2              https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                   https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                   https://www.youtube.com/c/TickrBox   642 views   \n",
       "..                                                 ...         ...   \n",
       "640                 https://www.youtube.com/c/Benzinga  3.1K views   \n",
       "641                 https://www.youtube.com/c/Benzinga  2.7K views   \n",
       "642                 https://www.youtube.com/c/Benzinga    6K views   \n",
       "643  https://www.youtube.com/channel/UCjK59q5KhEW2M...    22 views   \n",
       "644           https://www.youtube.com/c/CNBCtelevision  1.6K views   \n",
       "\n",
       "          time_published                                            snippet  \\\n",
       "0              1 day ago  Thank you for your support. Sub up, Like up, C...   \n",
       "1           23 hours ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "2           21 hours ago  In this video we go over the chart, SPY, Black...   \n",
       "3             7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4            2 hours ago  Join the 100% Winner Community https://www.pat...   \n",
       "..                   ...                                                ...   \n",
       "640  Streamed 2 days ago  Sal Gilbertie, CEO of Teucrium Trading Jesse K...   \n",
       "641  Streamed 5 days ago  On today's Benzinga Live: -Ryan Cohen Takes Ne...   \n",
       "642  Streamed 5 days ago  Benzinga's Cannabis Capital Conference Kicks o...   \n",
       "643           2 days ago  Join our CBA telegram chat; https://t.me/+II4n...   \n",
       "644           5 days ago  Turn to CNBC TV for the latest stock market ne...   \n",
       "\n",
       "                                                  text  \\\n",
       "0    GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1    AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2    GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3    SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "..                                                 ...   \n",
       "640  Bitcoin's \"Bart Chart\" | Benzinga Live 🚨Sal Gi...   \n",
       "641  Ryan Cohen Takes Stake In $BBBY | Benzinga Liv...   \n",
       "642  Ryan Cohen Takes NEW STAKE In Bed Bath & Beyon...   \n",
       "643  GameStop GME - Price Prediction and Technical ...   \n",
       "644  BBBY surges after big stake by Ryan Cohen reve...   \n",
       "\n",
       "                                        tokenized_text  word_counts  \n",
       "0    [GME, Stock, Dumping, Setting, Up, For, Earnin...           28  \n",
       "1    [AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...           33  \n",
       "2    [GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...           36  \n",
       "3    [SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...           28  \n",
       "4    [BREAKING, Jordan, Belfort, Wolf, of, Wall, St...           34  \n",
       "..                                                 ...          ...  \n",
       "640  [Bitcoin, 's, Bart, Chart, |, Benzinga, Live, ...           24  \n",
       "641  [Ryan, Cohen, Takes, Stake, In, $, BBBY, |, Be...           34  \n",
       "642  [Ryan, Cohen, Takes, NEW, STAKE, In, Bed, Bath...           32  \n",
       "643  [GameStop, GME, Price, Prediction, and, Techni...           14  \n",
       "644  [BBBY, surges, after, big, stake, by, Ryan, Co...           32  \n",
       "\n",
       "[638 rows x 10 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "another-client",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordCounter(wordLst):\n",
    "    wordCounts = {}\n",
    "    for word in wordLst:\n",
    "        #We usually need to normalize the case\n",
    "        wLower = word.lower()\n",
    "        if wLower in wordCounts:\n",
    "            wordCounts[wLower] += 1\n",
    "        else:\n",
    "            wordCounts[wLower] = 1\n",
    "    #convert to DataFrame\n",
    "    countsForFrame = {'word' : [], 'count' : []}\n",
    "    for w, c in wordCounts.items():\n",
    "        countsForFrame['word'].append(w)\n",
    "        countsForFrame['count'].append(c)\n",
    "    return pandas.DataFrame(countsForFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "lightweight-postcard",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = d['tokenized_text'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "reasonable-right",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "recorded-router",
   "metadata": {},
   "outputs": [],
   "source": [
    "countedWords = wordCounter(d['tokenized_text'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "broad-comedy",
   "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>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>stock</td>\n",
       "      <td>1462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gme</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>amc</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>the</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>and</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>to</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>gamestop</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>this</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>|</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>for</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>is</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>are</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>a</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>$</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>market</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>we</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>short</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>squeeze</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>today</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>you</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word  count\n",
       "1       stock   1462\n",
       "0         gme   1066\n",
       "23        amc    685\n",
       "33        the    672\n",
       "21        and    665\n",
       "66         to    428\n",
       "44   gamestop    368\n",
       "40       this    238\n",
       "50          |    211\n",
       "5         for    209\n",
       "288        is    206\n",
       "32        are    203\n",
       "63          a    189\n",
       "61          $    188\n",
       "38     market    178\n",
       "54         we    171\n",
       "45      short    167\n",
       "46    squeeze    161\n",
       "414     today    157\n",
       "8         you    152"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countedWords[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "every-court",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "gmetext = nltk.Text(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "affected-rochester",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py:855: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n",
      "/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py:846: MatplotlibDeprecationWarning: \n",
      "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
      "  \"2.2\", name=key, obj_type=\"rcparam\", addendum=addendum)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.reset_orig() #Seaborn messes with this plot, disabling it\n",
    "gmetext.dispersion_plot(['bubble', 'bullish', 'buy', 'long', 'price', 'sell', 'short', 'squeeze', 'stock', 'gme', 'amc'])\n",
    "sns.set() #Re-enabling seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "forced-highway",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizeTokens(word_list, extra_stop=[]):\n",
    "    #We can use a generator here as we just need to iterate over it\n",
    "    normalized = []\n",
    "    if type(word_list) == list and len(word_list) == 1:\n",
    "        word_list = word_list[0]\n",
    "\n",
    "    if type(word_list) == list:\n",
    "        word_list = ' '.join([str(elem) for elem in word_list]) \n",
    "\n",
    "    doc = nlp(word_list.lower())\n",
    "    \n",
    "    # add the property of stop word to words considered as stop words\n",
    "    if len(extra_stop) > 0:\n",
    "        for stopword in extra_stop:\n",
    "            lexeme = nlp.vocab[stopword]\n",
    "            lexeme.is_stop = True\n",
    "\n",
    "    for w in doc:\n",
    "        # if it's not a stop word or punctuation mark, add it to our article\n",
    "        if w.text != '\\n' and not w.is_stop and not w.is_punct and not w.like_num and len(w.text.strip()) > 0:\n",
    "            # we add the lematized version of the word\n",
    "            normalized.append(str(w.lemma_))\n",
    "\n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "rolled-deadline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>channel_name</th>\n",
       "      <th>channel_link</th>\n",
       "      <th>views</th>\n",
       "      <th>time_published</th>\n",
       "      <th>snippet</th>\n",
       "      <th>text</th>\n",
       "      <th>tokenized_text</th>\n",
       "      <th>word_counts</th>\n",
       "      <th>normalized_tokens</th>\n",
       "      <th>normalized_tokens_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>[GME, Stock, Dumping, Setting, Up, For, Earnin...</td>\n",
       "      <td>28</td>\n",
       "      <td>[gme, stock, dumping, set, earning, announceme...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>[AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...</td>\n",
       "      <td>33</td>\n",
       "      <td>[amc, stock, gme, stock, hedge, fund, big, ban...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>[GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...</td>\n",
       "      <td>36</td>\n",
       "      <td>[gme, short, squeeze, |, fox, news, highlight,...</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>[SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...</td>\n",
       "      <td>28</td>\n",
       "      <td>[shocking, jim, cramer, talk, amc, gme, gamest...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>[BREAKING, Jordan, Belfort, Wolf, of, Wall, St...</td>\n",
       "      <td>34</td>\n",
       "      <td>[break, jordan, belfort, wolf, wall, street, t...</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1  AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2  GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4  BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "\n",
       "                                          link      channel_name  \\\n",
       "0  https://www.youtube.com/watch?v=QxAURd6O50g       Happy Money   \n",
       "1  https://www.youtube.com/watch?v=27pvUBtMCeQ  Simulate & Trade   \n",
       "2  https://www.youtube.com/watch?v=KMOKJirlL-k     JacksonHunter   \n",
       "3  https://www.youtube.com/watch?v=FAs1vYKXGRc          TickrBox   \n",
       "4  https://www.youtube.com/watch?v=6rn2_EoDoXU          TickrBox   \n",
       "\n",
       "                                        channel_link       views  \\\n",
       "0        https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1  https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2            https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                 https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                 https://www.youtube.com/c/TickrBox   642 views   \n",
       "\n",
       "  time_published                                            snippet  \\\n",
       "0      1 day ago  Thank you for your support. Sub up, Like up, C...   \n",
       "1   23 hours ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "2   21 hours ago  In this video we go over the chart, SPY, Black...   \n",
       "3     7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    2 hours ago  Join the 100% Winner Community https://www.pat...   \n",
       "\n",
       "                                                text  \\\n",
       "0  GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1  AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2  GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4  BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "\n",
       "                                      tokenized_text  word_counts  \\\n",
       "0  [GME, Stock, Dumping, Setting, Up, For, Earnin...           28   \n",
       "1  [AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...           33   \n",
       "2  [GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...           36   \n",
       "3  [SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...           28   \n",
       "4  [BREAKING, Jordan, Belfort, Wolf, of, Wall, St...           34   \n",
       "\n",
       "                                   normalized_tokens  normalized_tokens_count  \n",
       "0  [gme, stock, dumping, set, earning, announceme...                       16  \n",
       "1  [amc, stock, gme, stock, hedge, fund, big, ban...                       24  \n",
       "2  [gme, short, squeeze, |, fox, news, highlight,...                       23  \n",
       "3  [shocking, jim, cramer, talk, amc, gme, gamest...                       24  \n",
       "4  [break, jordan, belfort, wolf, wall, street, t...                       22  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['normalized_tokens'] = d['tokenized_text'].apply(lambda x: normalizeTokens(x))\n",
    "\n",
    "d['normalized_tokens_count'] = d['normalized_tokens'].apply(lambda x: len(x))\n",
    "\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "modern-receiver",
   "metadata": {},
   "outputs": [],
   "source": [
    "countedWords = wordCounter(d['normalized_tokens'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "rental-mediterranean",
   "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>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>stock</td>\n",
       "      <td>1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gme</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>amc</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gamestop</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>|</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>market</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>$</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>short</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>squeeze</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>today</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>video</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>hedge</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>good</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>live</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>explain</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>prediction</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>current</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>poor</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>condition</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>price</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           word  count\n",
       "1         stock   1538\n",
       "0           gme   1066\n",
       "15          amc    685\n",
       "31     gamestop    368\n",
       "34            |    211\n",
       "27       market    191\n",
       "42            $    188\n",
       "32        short    181\n",
       "33      squeeze    168\n",
       "305       today    158\n",
       "29        video    142\n",
       "16        hedge    131\n",
       "24         good    113\n",
       "66         live    108\n",
       "30      explain    105\n",
       "179  prediction    104\n",
       "25      current    101\n",
       "26         poor     95\n",
       "28    condition     93\n",
       "161       price     88"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countedWords.sort_values('count', ascending=False, inplace=True)\n",
    "countedWords[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "powered-dallas",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13239\n"
     ]
    }
   ],
   "source": [
    "#.sum() adds together the lists from each row into a single list\n",
    "gmedist = nltk.ConditionalFreqDist(((len(w), w) for w in d['normalized_tokens'].sum()))\n",
    "\n",
    "#print the number of words\n",
    "print(gmedist.N())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "baking-south",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'gme': 1066, 'amc': 685, 'new': 68, 'buy': 56, 'big': 38, 'sec': 33, 'get': 29, 'day': 27, 'run': 25, 'set': 24, ...})"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmedist[3] #words and frequency of length 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "objective-remedy",
   "metadata": {},
   "source": [
    "From this we can lookup the distributions of different word lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "insured-brisbane",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_common = pd.DataFrame(gmedist[3].most_common(30))\n",
    "plt.plot(all_common[0], all_common[1])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "female-conservation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def spacy_pos(word_list):\n",
    "    tags = []\n",
    "    doc = nlp(word_list.lower())\n",
    "    for w in doc:\n",
    "        tags.append((w.text, w.tag_))\n",
    "    return tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "periodic-sleeve",
   "metadata": {},
   "outputs": [],
   "source": [
    "d['normalized_tokens_POS'] = [spacy_pos(t) for t in d['text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "external-polymer",
   "metadata": {},
   "source": [
    "This gives us a new column with the part of speech as a short initialism and the word in a tuple, exactly how the `nltk.ConditionalFreqDist()` function wants them. We can now construct another conditional frequency distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "demonstrated-providence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('gme',\n",
       "  FreqDist({'NNP': 710, 'NN': 341, 'VB': 7, 'FW': 5, 'JJ': 1, 'NNS': 1, 'VBP': 1})),\n",
       " ('stock', FreqDist({'NN': 905, 'NNP': 557})),\n",
       " ('dumping', FreqDist({'NN': 1})),\n",
       " ('setting', FreqDist({'VBG': 2})),\n",
       " ('up', FreqDist({'RP': 45, 'RB': 24, 'IN': 18})),\n",
       " ('for', FreqDist({'IN': 209})),\n",
       " ('earnings', FreqDist({'NNS': 21})),\n",
       " ('announcement?thank', FreqDist({'.': 1})),\n",
       " ('you', FreqDist({'PRP': 153})),\n",
       " ('your', FreqDist({'PRP$': 48}))]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmefdist_WordtoPOS = nltk.ConditionalFreqDist(d['normalized_tokens_POS'].sum())\n",
    "list(gmefdist_WordtoPOS.items())[:10] #(w.text, w.tag_) pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excited-merit",
   "metadata": {},
   "source": [
    "This gives the frequency of each word as each part of speech...which can be uninformative and boring. What we want is the converse; the frequency of each part of speech for each word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "electric-printer",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmefdist_POStoWord = nltk.ConditionalFreqDist((p, w) for w, p in d['normalized_tokens_POS'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "color-angola",
   "metadata": {},
   "source": [
    "We can now identify and collect all of the superlative adjectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "hawaiian-anatomy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'short': 119, 'current': 101, 'poor': 95, 'gamestop': 73, 'new': 58, 'live': 55, 'amc': 47, 'bullish': 30, 'big': 29, 'free': 28, ...})"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmefdist_POStoWord['JJ']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "billion-spectacular",
   "metadata": {},
   "source": [
    "Or look at the most common nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "starting-cleanup",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('stock', 905),\n",
       " ('gme', 341),\n",
       " ('gamestop', 198),\n",
       " ('market', 173),\n",
       " ('today', 156),\n",
       " ('video', 140),\n",
       " ('hedge', 122),\n",
       " ('squeeze', 112),\n",
       " ('#', 87),\n",
       " ('price', 79)]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmefdist_POStoWord['NN'].most_common(10) #top 10 most common"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "antique-meter",
   "metadata": {},
   "source": [
    "Or plot the base form verbs against their number of occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "spanish-princess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VB_common = pd.DataFrame(gmefdist_POStoWord['JJ'].most_common(30))\n",
    "plt.plot(VB_common[0], VB_common[1])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "minor-impression",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = wordcloud.WordCloud(background_color=\"white\", max_words=200, \n",
    "                         width= 1000, height = 1000, \n",
    "                         mode ='RGBA', scale=.5).generate(' '.join(d['normalized_tokens'].sum()))\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.savefig(\"GME_word_cloud.pdf\", format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ignored-intranet",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "advanced-attraction",
   "metadata": {},
   "outputs": [],
   "source": [
    "#All these packages need to be installed from pip\n",
    "#These are all for the cluster detection\n",
    "import sklearn\n",
    "import sklearn.feature_extraction.text\n",
    "import sklearn.pipeline\n",
    "import sklearn.preprocessing\n",
    "import sklearn.datasets\n",
    "import sklearn.cluster\n",
    "import sklearn.decomposition\n",
    "import sklearn.metrics\n",
    "\n",
    "import scipy #For hierarchical clustering and some visuals\n",
    "#import scipy.cluster.hierarchy\n",
    "import gensim#For topic modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "pleasant-maria",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize\n",
    "TFVectorizer = sklearn.feature_extraction.text.TfidfVectorizer(max_df=0.5, max_features=1000, min_df=3, stop_words='english', norm='l2')\n",
    "#train\n",
    "#initialize\n",
    "gmeTFVects = TFVectorizer.fit_transform(d['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "sporting-peoples",
   "metadata": {},
   "outputs": [],
   "source": [
    "PCA = sklearn.decomposition.PCA\n",
    "pca = PCA(n_components = 2).fit(gmeTFVects.toarray())\n",
    "reduced_data = pca.transform(gmeTFVects.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "happy-context",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotSilhouette(n_clusters, X):\n",
    "    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize = (15,5))\n",
    "    \n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "    clusterer = sklearn.cluster.KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(X)\n",
    "    \n",
    "    silhouette_avg = sklearn.metrics.silhouette_score(X, cluster_labels)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = sklearn.metrics.silhouette_samples(X, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    \n",
    "    for i in range(n_clusters):\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "        cmap = plt.cm.get_cmap(\"nipy_spectral\")\n",
    "        color = cmap(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        y_lower = y_upper + 10\n",
    "    \n",
    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "    # 2nd Plot showing the actual clusters formed\n",
    "    cmap = plt.cm.get_cmap(\"nipy_spectral\")\n",
    "    colors = cmap(float(i) / n_clusters)\n",
    "    ax2.scatter(reduced_data[:, 0], reduced_data[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n",
    "                c=colors)\n",
    "\n",
    "    # Labeling the clusters\n",
    "    centers = clusterer.cluster_centers_\n",
    "    projected_centers = pca.transform(centers)\n",
    "    # Draw white circles at cluster centers\n",
    "    ax2.scatter(projected_centers[:, 0], projected_centers[:, 1],\n",
    "                marker='o', c=\"white\", alpha=1, s=200)\n",
    "\n",
    "    for i, c in enumerate(projected_centers):\n",
    "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1, s=50)\n",
    "\n",
    "    ax2.set_title(\"The visualization of the clustered data.\")\n",
    "    ax2.set_xlabel(\"PC 1\")\n",
    "    ax2.set_ylabel(\"PC 2\")\n",
    "\n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % n_clusters),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.show()\n",
    "    print(\"For n_clusters = {}, The average silhouette_score is : {:.3f}\".format(n_clusters, silhouette_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "organic-general",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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iTlu0aBH++ecfhIaGYvDgwQDKHxy5b99F4cY86tmzJ06fPo1Lly5hw4YNkJGRQUlJCS5duiRyXUtLS0RHR7PDIDU0NBAdHQ1PT08AwPLly9kGrokTJyI0NBR+fn7ssTh8+DBfHrm+fv0KBwcHXLhwAZ6envjjjz/E5gEof7hcvnw5X0X+4MGDEsXVMjExAQA8e/YMmZmZiI+PZ3tACOvJxTAMli9fjm/fvqFRo0bYvXs3Lly4gJUrV0JaWhpxcXFs74WysjL2AdnAwACnT5/GxYsX8X//938Ayh+2Kga9B8oboTkcDoKDg3H06FH2gTotLY0Npn/x4kW2gcvDwwOXLl3CqVOnYGxsDGlpaVy/fh15eXki880wDJYtW4b8/HzIy8tj7dq1uHDhAjZv3gw5OTnk5ORg69atlR6/qpC0vP2M87+4uBj79u3DmTNnsGjRIonSe/fuXTg4OCA8PBzu7u5sI865c+fYh8+QkBD2WsnhcODv74/Tp09j4MCBEvcs/fbtG1xdXZGfn4/69etj1apVCA8Ph4eHBxo2bIji4mIsWbJEaG8JSdIoTFWuH6mpqbh27RoAYOzYsbhw4QLOnj3LDu9NTU0V2vMvOTkZGzZsQFhYGPr37w+g/OVAbGwsli5dioiICIwdOxZA+Tn8zz//CGzjw4cPUFdXx4EDB3D27Fm2XHz+/BkeHh5ij+uRI0fYBq6pU6ciPDwcfn5++OOPP5Cbm1tpI/CzZ8/Y87dx48bYtWsXwsLCMGXKFPZLrbwkvf5bWlrCy8uLXW/ixImIjo6GpaVlta8ZXL1792bvTdxzJiEhge++ww2fkJ6ezn7VWFxDR3R0NNvjtXnz5nzXeV7v37/Hpk2bEB4ezhegvip1m+qWZ1EOHTrEnp+WlpY4e/YsAgIC2J5Bu3fvrtEvKZ49exbFxcWoX78+fHx8cOnSJRw/fhx//vknZGVlceHCBZSVlYktA9+/f8eKFStQXFyMtm3b4uDBg4iIiICzszO7D2G9sITdO4ODg8EwDJo1a4ajR4/i0qVLOHjwIJo2bQoZGZlKA94vXboU0dHRlf5r0aKFyG2oqKhg79696NatW6XHj9vIz60HcfGOhvg3fm2bECIe9eQi5Dc2f/58mJub4+TJk4iKiuIb5vb161csX74cKioqP/xWbd26deBwOACAKVOmYNasWQDAVuRiY2PZoUHjx49n32JqaWmhadOmGD16NBiGgb+/P4yMjKq8f95K06xZs9CoUSMAwOzZs3HlyhUUFBQgIiIC9vb2fMGeFRUV+eJGcYOAA+UPGNx53O0rKCjAxcUFMjIyUFVVxbRp0zB9+nR8/vwZN2/ehJmZmdD0qaioIDY2Funp6VBXV4eKigqys7P53ozz9hrgUldXx+bNmyEvLw8dHR0MHz4cJ0+eBFDe40JDQwM9evTA/fv3kZqaCi0tLUhJSSE9PR0aGhpITEwEwzD4+vWryLeQqqqqMDc3x/nz53Hjxg18/foVqqqqbC9AWVlZ9iGPtydKQkIC2rVrhy5dumDr1q3YuHEjlJWVhe6DV4MGDZCfn4/09HQkJCSgR48eGDNmDAYMGFDpW31u7BbuQ4iMjAz7G6WlpbEPxWZmZli6dCm7nre3N8zNzVFYWIijR49i6NChfNutX78+Fi5cCAUFBbRr167SPHCVlJRAUVER7du3x+PHj5GSkoLdu3dXGtiX28jFMAxiYmLYByJNTU2hweqfP3/O9k4ZNWoUe64NHDgQ//zzD+Li4nD69Gk4ODhAWloaFy9exIcPHyAvL4+GDRsiLy+Pr+FOWFmTlpbG9u3b2d/AwcGBfaDnXjd4f/+4uDg0aNAABgYG2LdvH6SlpfnOH2GePn3KNkxYW1vDxsYGANCuXTuUlpZCSkpKop5wVSFpeWvYsGGNn/9Dhw6FqalpldKrra3NDlFq3749zp8/j+vXr6O4uBg5OTlo3Lgx+/IAALZv3872RNu2bRsGDBiAz58/V7qf8PBwNhjz7Nmz2WFM7du3h5ycHGbPno3CwkIEBgZi/vz5VU6jMFW5frRt25bt9aKhoQEFBQV8+vQJbdq0YYch5eTkCOxjwIABbK/dcePGsQ/mvXr1YnsK29nZITAwEABEBqTetGkTunTpAgBwc3PDnTt38OHDB1y5cgUMw4j8OAu3rGhoaLDHtF27dnBwcMDatWuRmJiIpKQkkb0VuY1EQHnvQQsLCwDAwoULcffuXYGXIZJe/1u0aIEGDRqw6ykrK/Pd+6pzzeBSUFBAnz59cPHiRVy7dg3z5s3DrVu3AABKSkr49u0b4uLi+PInJSUFc3Nzkdts3ry50Ot8RX/99RcbL2nevHk4duwY8vLyqtSIVN3yLAq3QVxNTQ2bN29mrysbN25EfHw8dHR0JLpXSop7XhUWFiI+Ph5KSkrQ09PDiRMnoKCgwDbWKCoqiiwDly5dYnvZ29vbs0Pux40bh7CwMKSlpbEvDnkJu3dy05Obm4vbt2+jb9++MDExwfnz56GsrCzQ+7KiXx238L8Sp4wQUjXUyEXIb05PTw96enpgGAYvXrzArVu3cOrUKfah2dfX94cbuXjjv6ioqLD/574Fffr0KTutR48efOtyOByoq6sjOzubfSMtjrAKCe9QSe6QiIoSExMr3bYo3O0XFhYKVPJ4ty+qkQsoPxa3b9/G9evX8fDhQ4EeEhVjXgDlD0e8bxN5K6i8b5hzcnIQHR2NLVu24PHjx3xDQgFU+gns0aNH4/z58yguLkZkZCRGjx7NNnL16dOHreQbGxvD2toaISEhuHjxIi5evAgpKSloaWmhT58+sLW1haampth9rVy5EvPnz8fr16+xZs0aAOUNDCYmJhg5cmSVGwW4eGMIcb/UxdWsWTO0b98eT548EVrGWrduLfAWVxL16tWDt7c3WrZsiaFDh6K4uBi+vr6wtLQU21jTokULNsD8rVu32AdXUfG4eIfj+Pr6sjHAeCUlJaGkpASysrIoKytDQkICrl69igcPHiAtLY2vfAkra40aNeJr9BFW1oYNG4awsDDcuHEDgYGBCAwMhIyMDDp06ID+/fvDxsZG7AMh73nK7dXAJeq8lZSoB5WaKG/VPf///PNPidPPVbHxg/d3KCkpAQC2t2LDhg35hloqKChAT09Porg3vOdLxWtyr1692P8LO18kSaMwVb1+fP/+HTExMbh16xYePXrEF4cLEH5d422k5m3Y500z73Rh6ZWRkWF7JAPlPTo4HA4+fPiA3NxcZGdn8+WZF7esvH37li/+I6/ExESRjVzcHroABD6qYWBgILTH749e/wFU65rBy8LCAhcvXsTTp0/x6dMntpFrwoQJ8PHxwadPn/Dq1StERUWxeRE37FNSFWPPqaioIC8vr0o9sKpbnkVJS0sDUF4WeRvOdXV1qzScWBhh1zkHBwdcuXIFT548Ye8PcnJy0NPTg7m5OcaOHVtpoxrvtXnDhg3YsGGDwDLCYlMJu3fOnDkTt2/fRnp6Onbu3ImdO3dCUVERhoaGGDRoEKytrcV+cXnJkiU4ffq02PQC5cMrW7VqVelyleH2QqzYG5f3bxqqSMh/DzVyEfIb+vLlC3bs2IGMjAxwOBy4uLhASkoK2tra0NbWhq2tLQYPHoy3b98KBD2vKhkZGcjJybF/S0v/bxQzt0LG++ZO2Btw7nLC5lWsoBcWFgpNQ2UqVvyr4ke3n5+fDzs7Ozx58gRKSkqwsLDAtGnTUFZWhtWrV4tcr2LPGGHpePHiBezs7JCTk4OWLVvCysoKBgYGiI+PlygAL1D+kKuhoYG3b98iPDwcf/75J/uwNWrUKHY5KSkpuLm5wdbWFhcuXEBsbCyePXuGFy9e4MWLFzh+/Dj8/f3ZnkbCmJmZ4fLlywgPD8f169dx//59ZGZmIiIiAhEREXBycpJ4aBcvScuYMLwNs1Wxe/dutoFg4sSJ2L9/P0pKSrBs2TIEBQWJLTfGxsZIT0/HpUuX2F4Sohq5uLG2xCkuLsbXr1/RoEEDTJ06FdevX4ecnBwGDhwIe3t7NGvWDC4uLiLXr/h1M2Fpl5eXx4EDBxAbG4tLly4hLi4OL1++RGJiIhITE+Hv74/g4GB2mJE41Xl4rIj3wVvYdQGomfJW3fO/OuVKknOee739kR4IlZ0v4uZJkkZR25L0+vH582eMHTsW7969Q8OGDWFhYQFDQ0Okp6dj165dIvfB+8DNm3be+JPi8guU33NKS0v5zjveYy1u/R+9V/Buu+Lvy3tv5aqJ6z/DMNW6ZvAyNTWFnJwciouLERERgYcPHwIoH5IYGxuL+/fvIyoqiu3RJa4XV1VUbGDhHqOqnBvVLc+icPddnaGOFVVsXBR2nVNXV0dwcDCio6Nx9epVxMfHIzU1Fffu3cO9e/cQEBCA4OBgsdcjSe4xWVlZAtOEbVNTUxMRERGIjIxEVFQU7ty5g3fv3uHWrVu4desWzp49iyNHjvDVG2tT8+bN8fTpU4Geodz8qqurUyMXIf9B1MhFyG9IVVUV4eHhyM/Px4MHD2Bvb883VERaWpqtiEkSDPlH8X5t7tatW3y9IR4/fsxWLnh7v3ArzBVjIbx//15g+7y9GW7evMn2JsnLy8Pr16/Rrl079iFe3EOEqHlt2rTB8+fPoaSkhPj4eLZC+OXLF2RmZqJt27Zi30xGRESwgcU3btzIBsSNiIgQuY6kfH192eN36tQptjfO7du3Jd6GlJQURo0aBS8vL8TGxrLxmNTV1fl6p2RkZODFixdITk7GnDlzsHDhQjZg/bx581BQUICAgACRjVyFhYV4+fIlkpOTYWRkBHt7ezAMg5cvX2L+/PlISkqCv78/XF1dq/ygUbGM8X6J6+PHj+wwuYo9iADJKvgVycjIsPGBAGDGjBk4c+YMPn/+jMTERBw6dAhTpkwRuT43wDzvMCDuMMaKWrduzf7f1dUVU6dOZf9OTExEs2bN2DIfGxuL69evAwDmzp3LpoH70PkjPnz4gJcvX+L9+/dsbKG8vDwEBwfDzc0NmZmZOHfuHF9sHF6852nFXgEeHh548uQJtLS0sGDBApEPQLznGW/8L2Ffvqtqeavp87865UoSmpqaeP78ObKyspCamsoOe/7+/bvEMbl4z5eYmBi+HjG88ZdqcvhoVa4fJ06cYH/Tffv2sb2ahPVi/Blu377Nnt8lJSVsb2R1dXWxQ6natGmDxMRE/PHHH3zX93fv3qGgoACtW7cWWy54z/WHDx+yX3sDIDRmZVWu/6LKd1xc3A9fM5SVldGjRw9cu3YNe/fuRUlJCRo0aIAOHTqgZ8+euH//Pnx9fdlGGu4wTHG46a1rw8natGmDpKQkpKamIi8vj+1F9eDBA7i5ueGPP/7A+PHjBXrqcYm6xgHC6z9v3rzBq1ev8P37d6xfvx5AeUPq/v37cfDgQaSmpiIqKgpWVlYiywBvuduxYwffBycSEhLQpk0boXXFimWZYRgkJycjJSUF9evXZ2Msfvz4EVu3bsW5c+dw7949PHr0SGRoiqVLl2Lu3LlC5/HiDT3xI3R1dXH16lUUFBTg5cuX0NLSQllZGduLVdxLO0LIvxcFnifkNyQvL8/2wMnKysKUKVMQHR2NtLQ03LlzB7Nnz2YfIH72V26A//UUAoATJ07Ax8eHHbrAjfciJSUFe3t7dh3uUIZnz57h5s2bKC0txYULF4QGCuZ+ERAoj2Py4MEDPHnyBCtWrMCoUaNgaGjIxrHhrUBygxdzu+rzvhXmfgmJd/vfvn3DokWL8PTpUzx48ADz5s2DlZUVDA0N2UYsYXgb6i5cuIDk5GRcvXqVL4ixJENKKtt2cHAwUlNTceLECb4v9Emy7VGjRkFKSgolJSXsukOGDOE7XoGBgXB0dMTatWuxevVqPH/+HO/fv+ereIt7gPvy5QvGjBmDBQsWYN68ebh58ybS09Px6dMnNh/S0tKV9rQQhhvfBwCuXLkCd3d3PH/+HHFxcXB2dmYfrnjLWE1SUlLCggUL2L937dolNgh/9+7d+f5u3bq1yEC62trabGPDgQMHEB4ejuTkZAQGBmLMmDHo1asXZs+eDYC/PERFRSEpKQmxsbHsUD2g+r2ovLy84OTkhBUrVsDDwwOvXr3MRDsBAAAgAElEQVTCx48f+eLfiPv9O3bsyDbInD9/HkePHkVKSgrOnTsHPz8/3LhxA8+ePRP7hp93iNOJEyeQn5+Pz58/Y9OmTQLLVrW8/azzv6bxxpRzdXVlHxrnzZsnUTwuoPyjG9zeCZ6enjh58iRevXqFsLAwrF27FkD58eAGaa8JVbl+8Jbj0NBQpKamIiwsjK+Rq7rXTEmsWrWKPX9WrVrF3i8HDhwo9vrELSvcAPgvXrxAfHw8pk2bBktLS3Tr1k1oAHkuc3NztjeSu7s7IiIi8PLlS7i7uwsdqliV6z9v+U5KSsKrV6/w/v37GrtmcBuuuD3VunfvDikpKXb4OHd6p06dKh3Wzpve7OxsPH369JeeYz+C20DEvV48efIEDx8+xIYNG/DgwQOcOXNGbPxJdXV19r578+ZNPHv2DMXFxfD398eDBw8Ell+1ahWmTZuGBQsWwM/PD6mpqfj06RPftYB7XokqAz179mQbjbZt24aoqCi8evUK3t7eGDt2LExMTPi+JCyOs7MzZs6ciblz5+L06dN4/fo1Pn78yNcTTNx9Qk1NDc2bN6/034/2uOOysLBgz2kPDw8kJydj9+7dbCxK3g+SEEL+O6gnFyG/qfnz5yMhIQGPHz/Go0eP+Hp/cHXq1AkzZ8786WmRkZHB33//jSlTpiAnJwceHh58DTxSUlJYsmQJXxyUYcOGYe/evSgtLcXkyZMhJSUFhmFgYGAgUNHT1dXF+PHjceLECcTExPB9Kh4ojyvF7ZGkrq6OFi1a4P3793jw4AEsLS1ha2uL1atX8/VaWLhwIYDyXg7m5ubo168foqOjERYWJvCFvtGjR4v9LHzfvn3h4eGBgoICXLhwARcuXBBYRtKH04osLCwQGRkJAALHlXfbvL1ohGnVqhVMTEzYr2AB/EMVgfLYH5cvX8ajR48QEhLC9yAFlPcgnDBhgsh9aGhoYM6cOdi+fTtSU1MxefJkgWVmzpwpdFiOJNzd3WFvb48PHz7g4MGDOHjwIN98R0dHvjfUNW3EiBE4ceIEHjx4gIKCAqxYsQKHDx8W+lDcvHlzaGpqsvGVRA1VBMrPj+XLl2PKlCnIzs4WCGyvrq7OfuyhS5cuaNSoETIyMnD79m2+BmAu3g9QVMWsWbMQGxuLN2/ewMfHBz4+PnzzW7ZsyX6JUxhpaWls2rQJTk5O+P79u0DcFxUVFb4PBgjTt29fqKqq4uvXr7h27RqMjIzAMAyaNm3KntdcVS1vP+v8r2mDBw/G8ePHcfv2bTx+/Bjjx48HUN77lbdMiaOmpoZt27Zh7ty5yM/PFxg2LScnB3d3d6EfQqiuqlw/zM3NceTIEZSVleHo0aM4evSowPaqe82sDLexv+LXiZs3b17pRyXGjx+Pc+fOITExUWi6Z8yYwfaUFaZ169ZwdnaGl5cXsrOz+Xq06OrqCsSWrMr1v23btlBUVMT3798RGRmJyMhILFy4EH/99VeNXDMGDBiA1atXs41q3MYtAwMDNgA9N82S6NChA5KSkpCfn4+RI0dCS0tL6NdxfzeTJ09GVFQUHjx4gMuXL/N9KAIov+aIiyUlLy8PCwsLnD9/Ht++fcOIESPY+k/nzp2RkJDAt/ySJUvg6OiIrKwsuLm5wc3NjW9+p06dMGDAAAAQWQamTJmCpUuXYsGCBXj79q1A2W/VqhUmTZpUad6lpKSwZs0aTJ8+HQUFBViyZInAMqampiJ7sdUGHR0dODg44MiRI7hy5QpfTENDQ0MMHz68FlNHCKkt1JOLkN+UsrIyTpw4gaVLl8LQ0BAqKiqQlZWFuro6DA0NsWTJEgQEBNToV37E0dfXx/nz5+Ho6MgO71FTU4OpqSkOHz7MfvmKy8XFBTNmzECLFi0gLy+Pjh07wt3dXeRQqNWrV2PDhg1shVpRURHa2tpYuHAh9uzZwz7ISklJYd26ddDV1YWCggIaNmzIfo3RysoK1tbWaNKkCRQUFKCtrY2CggJISUnBy8sLixcvRseOHaGoqAhlZWVwOBysX7+e7fkgStu2beHr64suXbpASUkJ6urq6Nq1K/bv38/2bJEkWLQww4cPx/r166GlpQUFBQU0a9YM5ubm8PPzYxtXJN029ytVAPDHH38IVESVlZXh5+cHV1dXdOzYEaqqqpCTk0PLli0xatQoBAcHV/p1wmnTpsHHxwd9+vRBs2bNICsrC1VVVZiYmLANodWlqamJ8+fPw9nZGX/++Sfq1asHJSUlmJiYwMvLC8uWLav2tiUhJSWFFStWsMc9Li4OQUFBIpfnHZ4orpGLOz8wMBCWlpZo3Lgx5OTk0KJFC1hbWyMwMJANcq6uro6DBw+id+/eUFVVhYqKCvT09LBlyxY2oHh0dHS1esE0a9YMgYGBmDZtGrS0tKCkpAQ5OTm0bt0a9vb2CAoKqvQLmV26dEFwcDCGDh3K5kNDQwMjRoxAYGBgpcPjGjZsiIMHD8LY2BiKiopQV1eHtbU1goODhe67KuXtZ53/NU1aWhr79u2Do6Mjm1ZDQ0P4+fmxjW3ihk9z9e/fH2fPnsXYsWOhoaEBOTk5NGzYEJaWlggKCmKHVdeUqlw/unbtip07d0JXVxeKiopo3LgxevfujWPHjrE90Kp7zayMtLQ0AgICYG5ujvr160NVVRXDhg1DQEBApV/aq1evHo4cOQJnZ2e0b98eCgoKUFNTY/Mj7GVTRbNmzcLGjRuhpaUFOTk5tG3bFitXrsScOXMElq3K9V9ZWRlr1qyBlpYW5OXl0bRpU6iqqtbYNaNhw4Z8Q9C48QplZWX5rm+SNnLNmjULvXr1grKyMpSVlWu0wfVnUlBQwOHDhzFr1iy0b98e8vLyaNCgAbp06YIdO3ZIFOds3bp1sLOzQ6NGjVCvXj0YGBjAx8dHaIOLjo4OgoODYW9vzzZiKSgoQEtLC9OnT8fRo0fZ64GoMgCU9xA9fPgwTE1Noa6uDjk5ObRq1QoODg44efKk2MZZXj169EBQUBBGjhwJTU1NyMvLQ1FRER07dsSCBQvExtSrLcuWLcPKlSvZr8s2adIE9vb22L9/f431GCOE1C1STF0bLE8IIUSo8+fPw9XVFUD559hFNSgSQmrP06dPkZmZiebNm6Nly5Z8Hw1wcnLCjRs30KRJE9y4caMWU1n3ODg4ID4+HjIyMnVmaBwhhBBCah4NVySEkDosLy8P+fn5SE9PZ4e6yMrKYsSIEbWcMkKIMDExMWx8HD09PWzYsAGKioq4e/cu+/U6CpZMSM3IzMxEUVGRRMuqqakJfKmWEEJI3UONXIQQUoc9fPhQINbG+PHjRQZBJ4TULisrK/j4+CArKwuPHj0SaJCWk5MTiKlDCKmeOXPmID4+XqJl3dzcxMYlJIQQUjdQTC5CCKnDNDU10bhxY8jLy0NTUxMzZ86sNPg3IaT2NGnSBEFBQbC2tkarVq0gLy8PWVlZNGnSBBYWFjh+/DgMDQ1rO5mEEEIIIXUSxeQihBBCCCGEEEIIIXUe9eQihBBCCCGEEEIIIXUeNXIRQgghhBBCCCGEkDqPGrkIIYQQQgghhBBCSJ1HjVyEEEIIIYQQQgghpM6jRi5CCCGEEEIIIYQQUudRIxchhBBCCCGEEEIIqfOokYsQQgghhBBCCCGE1HnUyEUIIYQQQgghhBBC6jxq5CKEEEIIIYQQQgghdR41chFCCCGEEEIIIYSQOo8auQj5DTAMI9G0383vkMbfIQ2k5tDvSQghhFTPv+Ue+m/Jhzj/hTxW9F/MM6kd1MhFJLJkyRLo6OiI/de/f392We7/f7W4uDjo6OggLi4OALBr1y7o6Oiw8x0cHODg4FAraRPl5cuXGD9+PN+0oKAguLu7/5L9VzxmkigqKoKbmxvOnTsndrmkpCSMGjUKHA4HQ4YM+dGkCrh8+TIWL17M/l2dvPwu+vfvjyVLltR2MiQSEhICHR0dpKen1+h2f2W5J4QQ8uPqSv0wPT0dOjo6CAkJ+eX7/hV10a9fv2Lx4sW4c+fOT93PjygpKcHSpUthZGQEIyMjxMbGCizzu+Xj317fqe6xrc56wp53CPlZZGs7AaRucHZ2ho2NDfv3nj178OTJE3h5ebHT5OXlayNpfHR1dREQEAAtLa3aTorEIiIicP/+fb5p3t7eMDY2rqUUVe7Tp0/w8/ODm5ub2OW8vLzw9u1beHl5oVGjRjWeDj8/vxrfZm3x8vKCsrJybSejVv3u5Z4QQgi/ulI/bNq0KQICAtC6devaTgpWr15d49t8+vQpzpw5A2tr65+6nx9x/fp1hISEwNnZGT179kSnTp0ElhGWj3+j/2J9R9jzDiE/CzVyEYm0bt2ar2LQsGFDyMvLw8DAoBZTJUhZWfm3S9N/WVZWFrS1tWFqalrbSfntCavsEUIIIb+zulI//J3S9KtexP5uL3yzs7MBANbW1tDU1Kzl1BBC/s1ouCL5aUJCQjBo0CDo6elh+PDhuHbtGt/8d+/eYf78+TA2Nkbnzp3h6OiIJ0+eiN1mYWEh1q5di759+4LD4WDw4ME4ePAgO1+S4WoMw2D//v0wNTWFvr4+xo0bh0ePHvEt8+jRIzg5OcHExARGRkaYPn06Xrx4Uel+hHXfDQoKwtChQ8HhcGBqaopdu3ahpKQEQHkXdu7bTh0dHezatQv9+/fH27dvcfr0ab4u0tU5XtztXb16FYMHD0bnzp0xZswYxMTEiF1PXP7T09MxYMAAAMDSpUtFDj3Q0dFBfHw8bt++zTdEIDU1FbNnz0avXr1gYGAABwcH3L17l12PO6Tg0KFDsLS0hLGxsdDhBQ4ODoiPj0d8fLzAb5GcnAwnJyd07twZvXr1wrZt29hjDgBlZWXw8fGBubk5OBwOBg0ahKNHj4o9JoMGDcLMmTMFpo8ZMwZTp04FAJSWlsLHxwfDhg2Dvr4+DAwMYGNjw3e8d+3aBXNzc3h5ecHExAQDBw5EVlaWwHDF3NxcuLm5YeDAgdDT08OwYcMQHBzMt29hQxwrdq2v7JwR5ebNm7Czs4OhoSF69+6NVatWIScnR+iywoagCBsacvToUQwePBh6enro06cP1qxZg7y8PDYv1Sn3ospLdfNNCCHk56rp+uG9e/ego6ODS5cu8U1/9eoVdHR0EBERIXBPKisrg6enJ/r37w8Oh4P+/ftj+/btKC4uBiB6eGPF+50k9/2KeOuK3KGMwv7x3t+DgoJgbW0NAwMD6OvrY8SIEQgPDwdQXiedMGECAGDChAnstivWSQsLC7F79272PmxhYQEfHx+UlZXxpW358uXw8fGBqakp9PT0YGNjg4SEBJH54R4Hf39/WFlZQV9fH6ampti2bRsKCwvZ48bNz8CBA4UOdROVD0CyuntSUhKmTZvGDoecOXMm3rx5IzbdQN2v70ia93fv3sHFxQVdunRBr169cOjQoUqPjaTrFRQUwMPDAxYWFuBwODAyMsKkSZPw9OlTAMKfdwAgMzMTa9euhZmZGTgcDoyNjTFz5swaHx5K/nuokYv8FO/fv4ePjw/mzJmDnTt3gmEYzJo1CxkZGQDKL2o2NjZITEzEypUr4eHhgbKyMtjZ2eHVq1cit7tx40ZER0dj8eLFOHDgAAYMGAB3d/cqxVi4e/cuIiMjsXLlSri7u+Pjx4+YPn062wgSGxuL8ePHo6ysDBs3bsSGDRvw/v172NjYiE2bMPv27cPKlSvRo0cP7N27F3Z2dti/fz9WrVoFoLyB5K+//gIABAQEYMyYMfDy8kKTJk3Qr18/BAQEoGnTptU+XkD5sV68eDFsbW3h6ekJRUVF/N///R8eP34sdPnK8t+0aVP2RjVjxgy+IQm8AgIC0KlTJ3Tq1AkBAQEwNTXFy5cvYW1tjTdv3mDFihXYtm0bpKSk4OjoiPj4eL71d+zYAScnJ2zYsAHdu3cX2P7q1av5tq+rq8vOc3NzQ5cuXbB3715YWFhg//79OHnyJDt/zZo12LlzJ4YPH469e/di8ODB2LRpE3bv3i3yOI4YMQLXrl1jKykA8Pr1azx8+BAjRowAAGzbtg27d+/GuHHj4Ovri3Xr1iErKwtz5sxBfn4+u967d+8QGRmJ7du3Y+7cuWjQoAHfvgoKCmBra4vQ0FBMnjwZe/bsQZcuXbB8+XLs3btXZBqFqc45Ex0djSlTpkBdXR07duzAwoULceXKFcyePbtK++YVFhYGd3d32NnZ4cCBA5g5cybOnj2LDRs2AMAPl/uK5aUmrhWEEEJq1s+oHxoZGaFNmzZsow/XuXPnoKKiIvRl3P79++Hv74+ZM2fi4MGDGD9+PHx9fat8j5X0vi/KmDFjEBAQwPdvwIABkJWVxahRowAA/v7+WLVqFQYMGIB9+/Zh69atkJOTw8KFC/Hu3Tvo6uqy9cpVq1YJHabIMAymT58OX19f/PXXX2zd5++//xZY/uLFi7h8+TJWrFiB7du348uXL5g9ezZKS0tF5mPVqlXYtGkT+vfvD29vb9jZ2eHYsWNwdnYGwzBwdnbGjBkzAJTf74WlUVw+Kqu7p6SkwMbGBhkZGdi8eTM2btyIN2/eYPz48WzZEubfUN+RJO/5+fmwt7fHs2fPsG7dOqxatQpBQUGVDh+UdL1FixYhODgYU6dOxcGDB7FkyRIkJSVh3rx5YBhG6PMOwzCYNm0abt68CVdXVxw4cADOzs64desWWw4IqS4arkh+irKyMuzevRvt27cHACgoKGDSpEl48OABBgwYgMOHDyM7OxsnTpyAhoYGAKBv374YMmQIPD09sXPnTqHbjY+PR8+ePTF06FAAgImJCerXry/QSCCOvLw8fHx8oK6uDgDIy8vDihUr8PLlS3To0AEeHh7Q1NSEr68vZGRkAAC9e/eGubk5du3ahb///lui/eTm5sLb2xvjxo3DihUr2O2oq6tjxYoVmDRpEv788080b94cANhu9M2bN4e8vDwaNmzITqvu8QKA79+/Y82aNRg5ciQAoHv37hg4cCB8fHyEridJ/jt27AigfJiCqGF2BgYGbIwpbj7WrVsHOTk5HDlyBCoqKgAAU1NTDBs2DFu3bkVQUBC7voWFBXtDFEZLS0tg+1wTJkyAs7Mzm9+rV68iNjYW9vb2SElJQWBgIObPn8/2wOrduzekpKSwb98+2NraCi1Pw4cPx86dOxEZGclWPM+dOwclJSW2Z9unT58wb948vreP9erVw6xZs/D8+XMYGhoCKA++unjxYvTs2VNo3kJCQpCUlITjx4+jS5cuAIA+ffqgpKQEe/bsgY2NDVt+K1Odc2bnzp3o0KEDX6NfvXr1sH37dnz8+FGi/VYUFxcHDQ0N2NnZQVpaGsbGxqhfvz6ysrIAlA/X/JFyX7G81MS1ghBCSM36WfXD4cOH48CBA/j+/TsUFRUBlDc2DB48GAoKCgLLx8fHQ1dXF6NHjwYAGBsbQ1FRscqxMSW974vSvHlzth4IAOHh4bh8+TJWr14NExMTAMCbN28wefJkvt7krVq1grW1Ne7du4dhw4axQxO1tLSEDlO8du0abt26ha1bt2L48OEAgF69eqFevXrw9PSEo6Mju15JSQkOHDjAHotv375h8eLFePr0KTgcjsC2X758ieDgYMydO5dtyOrVqxeaNm2KRYsW4dq1a+jXrx87rLVjx45o1aqVwHaUlZVF5qOyuruXlxfq1asHPz8/Nt09evTAwIED4evry/eRIl7/hvqOq6trpXk/ffo03r17h7Nnz7IfQdDX14e5ubnYvEiyXlFREb59+4aVK1eyH5kyNjbGt2/fsHnzZnz+/JmvnHPz/PHjRygqKmLx4sXo2rUrgPK6Wnp6Ot+LaUKqgxq5yE/RoEEDtgIDgB17n5ubCwCIiYlBx44d0axZM/YtjLS0NPr27YvQ0FCR2zUxMcHJkyfx8eNHmJmZoV+/fkKHkImjpaXF10DAvdHm5uYiPz8fjx49wsyZM9kGHgBQVVWFmZkZoqOjJd7P/fv38f37d/Tv359vqBz3jeLNmzfx559/SrSt6h4vAJCRkWEf9IHym3ffvn2F5qUm8y9MfHw8zMzM2AYuAJCVlcXQoUOxe/dufPv2jZ2ura1d7f1wb5YAICUlBQ0NDXz9+hVAeU81hmGE/i7e3t64e/cuBg4cKLDNVq1aoUuXLggLC2MbucLCwjBo0CDUq1cPQHkDIVD+JjotLQ0pKSm4cuUKALBDICTJX3x8PDQ0NNgGLq7hw4cjODgYCQkJ6Nevn0THoqrnTEFBARITEzFr1iy+6YMGDcKgQYMk2qcw3bt3R0BAAKytrWFhYQFTU1NYWVlBSkpK5DpVKfcVj2dNXCsIIYTUrJ9VPxwxYgR27dqFq1evYsiQIXj48CFev36NTZs2CV3exMQEHh4esLW1hbm5Ofr27Qt7e/sq56cq9/3KJCYmYunSpRg3bhxsbW3Z6dxhfrm5uUhNTUVqaio7HFLSfcTHx0NGRkbgS9fDhw+Hp6cn4uLi+BqYeBv7mjVrBqD8pamobQOAlZUV3/ShQ4di6dKliIuLk7jOIoq4ujtQXrczMTFBvXr12HKjrKyMrl274tatW0K3+W+p70iS9zt37kBTU5PvK58tWrSoNE6dJOvJy8vjwIEDAMobfdPS0pCcnIyrV68CEF1GmzVrhiNHjgAoH+GQlpaGV69e4d69e1U+dwipiBq5yE9Rv359vr+5F3buuP/s7GykpaXxDTHjxfsmjtfy5cvRvHlzhIaGYu3atQAAQ0NDrFq1SuLA3RXTJi0tzaYtNzcXDMOgcePGAus1btyYvZlKghtgk9tbqKJPnz5VaVvVOV5AeRBYOTk5vmmNGjUSGm+gJvMvTE5OjshtMwzDNxRQ2HKSqngspKWlwTAMgP/9LrwNf7zEvbkbOXIk1qxZg6ysLHz48AGvXr3i61L96NEjrF27Fo8ePUK9evWgpaXFvpHj7p9LXP7EHScAbIOdJKp6zuTk5IBhmBr/GuaQIUNQVlaG48ePw8vLC56entDQ0ICrq6vI30KScs9V8XjVxLWCEEJIzfpZ9UNNTU0YGRkhLCwMQ4YMwblz56ChocH30ovXlClToKSkhFOnTsHd3R2bN2+GtrY2li1bhh49ekicn6rc98X5/PkznJ2dweFwsHLlSr55r1+/xqpVqxAbGwtZWVn88ccfbKODpPvIyclBgwYNICvL/+jXpEkTAOCr3wmrQwHgi91Vcdu82+KSlZVFgwYNfrjuCIivuwPl5SY8PFxgyCpQXg8Wle5/Q31Hkrzn5OQIPQ5NmjTBly9fROZF0vWuX7+OTZs2ITk5GUpKStDR0YGSkhIA8WU0NDQU27dvx/v376Guro4OHTqwL44J+RHUyEVqhYqKCoyNjbFo0SKh80V9blpeXh4zZszAjBkz8O7dO1y9ehV79uyBq6srIiIiaiRdUlJSQi/4nz9/Zt8iVayUcX379o29qKuqqgIoj9fQtm1bge1VpRGnuscLKL/5MQzD9wbpy5cvQm/qkua/utTU1ERuGyh/w1uVxr/q4P4uhw8fZn8rXi1bthS57uDBg7F+/XpERkYiLS0NLVq0YD8BnZeXhylTpkBHRwfnz59H+/btIS0tjejoaFy8eLFKaVRTU0NaWprAdN7jxFUxRkbFGCBVPWeUlZUhJSWFzMxMvulFRUWIiYmBvr6+wDpSUlKVpgMAhg0bhmHDhiE3Nxc3btzA/v37sXDhQnTt2pV9U8zrR8r9z75WEEIIqXk/ct0fMWIENm7ciNzcXERERGD06NEie89IS0vDzs4OdnZ2yMjIQHR0NPbu3YtZs2bh1q1b7Hri7m01dd8vKiqCi4sLpKWlsWvXLr4Xk2VlZZg6dSrk5OQQGBiITp06QVZWFi9fvqy0Jz8vNTU1ZGVloaSkhK+hi1vn+pGh/GpqagDK6yi8wxCLi4uRlZX1S8IEqKiooGfPnpg0aZLAvIoNe1z/lvqOJHlv0KCB0Hol98WvKJKs9/r1a8ycOZONGccdlurv74/r16+L3PadO3ewePFi2Nvbw8nJiR3OuGXLFr4PUhFSHRR4ntQKY2NjpKSkoF27dtDT02P/hYaGIigoiG+oHFdBQQEGDRrEfiGtZcuWsLOzw9ChQ/Hhw4caSVf9+vXB4XAQHh7OdxPLzc1FVFQUO3yM2437/fv37DI5OTl8wSE7d+4MOTk5fPz4kS+PcnJy8PDwYL8cwn0bxavitOocL67i4mK+m0xBQQGuXbsm9E2lpPkXtz9xunXrhqtXr/K91SstLUVYWBj09PTE3sSFEXbsJEkDAGRlZfEdy+zsbPz9999ib/gqKiowMzPD5cuXceHCBVhZWbFpSE5ORnZ2NiZMmIA///yTnc79apSoN6Ci0vj27VuBm3xoaCjk5OTYipeysrJA2b937x77/+qcM0pKSujYsSMuX77MN/3GjRuYOnWq0PWUlJSQlZXFfkWpYjoAYO7cuXBxcQFQfhwtLS3h7OyM0tJStpJdU+X+V1wrCCGE1Lwfqe9YWloCADw9PfH582c29pQwNjY2bCDwRo0awdraGnZ2dsjNzUVeXh5bz+O9ZxQXF+Phw4fs3zV131+5ciWeP3+OPXv2CPSaycrKQkpKCv766y/o6+uzjRYV91FZvczY2BilpaUCvX24DWUVwyNUBfdl37lz5/imh4WFobS0tErbrm790tjYGC9fvkTHjh3ZMsPhcODn54fIyEih6/wb6juS5r179+5IT0/n+yJlZmYmHjx4IHK7kq73+PFjFBYWYtq0aWwDFwD22YPbk6tinu/fv4+ysjLMnj2bbeAqLS1lh1hWpd5MSEXUk4vUiokTJ+Ls2bOYOHEiJk+ejAYNGiA8PByBgYFYunSp0HXq1asHXV1deHl5QU5ODjo6OkhJScHp06d/aOx8Ra6urnBycsKUKVNgb2+P4uJi+Pj4sG/agPLP37Zo0QJeXl5QUVGBtLQ0fHx8+Lp4N1hk3Z4AACAASURBVGjQAFOmTIGnpyfy8vJgYmKCjx8/wtPTE1JSUujQoQOA//UsOn/+PDp37gxNTU2oqqriyZMniI+Ph76+frWOF69ly5Zh7ty5aNSoEQ4cOID8/Hw2OGh18s+NqRUTE4P27dujc+fOEh1bFxcXXLt2DRMmTMDUqVMhLy+PY8eO4c2bN/D19ZVoG7xUVVVx//59xMTESDwETVtbG8OHD8fKlSvx9u1bcDgcpKSkYMeOHWjVqpXQXne8Ro4ciZkzZ6K0tJSvAt2uXTsoKytj7969kJWVhaysLC5evIjg4GAAomNZCGNtbY3jx4/DxcUFs2fPhqamJq5cuYJTp07BxcWFLTNmZmbYt28f9u7dCwMDA0RFRfF9try658zs2bMxY8YMzJ07F9bW1sjMzISHhwfMzMzQsWNH9pPQXGZmZjh69CiWLVuGMWPG4MWLFzh48CBfpax79+5YvXo13N3d0bdvX3z9+hVeXl5o27Yt37lQE+Ve0nw/efIE8vLyQoP0EkII+fV+pL6jpqYGMzMzHD9+HHp6enyxvyrq1q0bDh48iMaNG8PQ0BAfP37EoUOHYGxszDY0GRoa4tixY2jTpg0aNGiAo0ePoqCggB06VxP3/UOHDuHMmTOYP38+SktL+RoP5OXl0alTJ2hoaMDf3x/NmzeHqqoqbty4gcOHD/Ptg1svi4qKgpqaGntf5erbty9MTEywevVqfPr0CZ06dUJ8fDz279+PUaNG/dB9UEtLC6NGjYKXlxcKCgpgYmKCp0+fwsvLCyYmJujTp4/E26osH6I4OzvDxsYG06ZNw/jx46GgoICAgABcunRJ7MeZ6np9R9K8jxgxAkeOHIGLiwvmzZsHZWVleHt7V9qQJMl6urq6kJWVxdatWzF58mQUFRUhJCQEUVFRAP7X063i8w73he26deswevRofP36FceOHcOzZ8/Y9ZSVlZGZmYnXr18LxIojRCyGkGpYvHgxY2ZmJvG8N2/eMNra2sypU6fYaWlpaczs2bOZbt26Mfr6+szw4cOZoKAgsfvNzc1l1q9fz5iamjK6urpM3759mc2bNzPfv39nGIZhYmNjGW1tbSY2NpZhGIbZuXMno62tza5vb2/P2Nvb822z4jrcaba2toy+vj7TtWtXZvr06UxSUhLfegkJCcy4ceMYDofDmJqaMocOHWJWrlwpsP1jx44xQ4YMYXR1dZmePXsyrq6uzNu3b9n5Hz58YEaPHs3o6uoyq1evZhiGYc6dO8f06NGD4XA4zO3bt6t9vLj5j4yMZMzMzJjOnTszkyZNYp4+ffrD+Xdzc2MMDAyYrl27MoWFhUL3L+x4P3nyhJkyZQpjYGDAGBoaMo6OjmweGUZ4WRElJiaGLQuhoaFC8yIsHcXFxYyXlxczYMAAthytXr2aycrKqnSfxcXFTPfu3ZmRI0cKzIuNjWWsra0ZfX19pkePHszkyZOZO3fuMIaGhoy7uzvDMIJlksvMzIxZvHgx+3dGRgazbNkypnv37gyHwxH6e3/79o1ZsWIF061bN8bAwICZO3cuc/nyZUZbW5t58+YNwzCVnzOiREVFMaNHj2Y4HA7Tp08fZuPGjUxeXh7DMAxz6tQpvn0wDMMcOHCAMTU1ZTgcDjNu3Djm8ePHDIfD4fsdjxw5wgwZMoTR19dnjI2NmTlz5jDp6ens/OqUe1HlRZJ8m5mZCZRPQggh1Vdb9UOuyMhIRltbmzl8+LDY/RQXFzM7d+5kBg4cyHA4HKZHjx7M8uXLmczMTHadlJQUZvLkyYy+vj7Ts2dPZvv27cyePXv48lCd+z5vncTe3p7R1tYW+o+7n6dPnzL29vaMgYEBY2xszNja2jLXrl1jBg8ezMyePZthGIYpLS1l5s+fz+jp6TFDhw4V2A/DMEx+fj6zefNmpk+fPoyuri4zaNAgZv/+/UxJSYnQtPHmUVjdildJSQmzZ88etl5lZmbGeHh4MAUFBewywuoOFUmSD1Fpevz4MePk5MQYGhoyBgYGzNixY5lLly6J3BdXXa/vSJr3jIwMxtXVlenatSvTrVs3ZuvWrcycOXMqrQdJsl5ERAQzdOhQRk9Pj+nduzfj4uLCxMfHMzo6OsyxY8cYhhH+vHPs2DFmwIAB7LPU4sWL2XM4KiqK7zcQV/4IqUiKYaoQFZEQUqfs2rULXl5eeP78eW0nhRBCCCGEEEII+akoJhchhBBCCCGEEEIIqfOokYsQQgghhBBCCCGE1Hk0XJEQQgghhBBCCCGE1HnUk4sQQgghhBBCCCGE1HnUyEUIIYQQQgghhBBC6jxq5CKEEEIIIYQQQsj/s3fn8VGVh/7HP+ecmclMNkhYEnYQCQGLS0XEBQGVamttqbV2o1pb6aJeV1CsWJdS4QpeqqWK3orWeiuttba12l9rtfXaWpVa17L1KgoREsAEsk5mOc/vjwnRmESyzMyZmXzfvvLKyZkzk28e1NfwzfM8RyTr+bz85sYYXDd7twSzbSur82c7jb+3NP7e0vh7S+PvHdu2sCzL6xjSA6l8n6f/BtND45w+Guv00Dinj8Y6PXJtnJPxPs/TksuyLOrrm4nFXC9j9InPZ1NSUpDZ+V0Xu2pH4nD0GLBzZ+JeVox/DtP4e0vj7y2Nv7dKSwtwHJVc2cB1DbW1TUl/Xf03mB4a5/TRWKeHxjl9NNbpkYvjnIz3eZ6WXJJiLS0MmT4NgD3bdkFBgceBRERERERERERSI3em9oiIiIiIiIiIyIClkktERERERERERLKeliv2kWlspPXNzcTqW4jHM3P9q9XS0n7svvYKJhTyME2SOTatxaGMHv9cYxUVYx8y0esYIiIiIiIiIl1SydVHrb/8BU0/uYdYJAoZejMDy32v/GlZdBkmhzaex4IWx04UXBk6/rnGKi0l/8GHsXKpLBUREREREZGcoZKrr6IR3IYG7HETMCYzWxYrHoetmxPHZWXgOB4nSh7LsvD5HUw0nrHjn3WMaftwwTUdvjZ1dZjWVojHvE4pIiIiIiIi0iWVXP1gBQIQCCSKgAxkxd4rJCy/H3y588dtWRaW38Gy4hk7/qlmXBeiUYhFIRrDxGKJ41gs8bXrgmWBlRiv9ucZk/jamLahM1jGgG23XX/gwwa77TiQh1VQAD6/Zz+viIiIiIiIyIfJndZDOjG2TcOYse3HkvlMLAYHZkzF4ph4vP048TmWKKPayin8/sSHz58oMocOwy4pxRo2DGvQIHB84Pdh+Q5c5wOf773S0+cHn5N43Odrv8Zqf6zt+Y4Pa9AgrGDQ6yESERERERER6ZJKrlxm29ROmep1CukBE49jqnZA3IVQCHwOOL7E/leFRYmCqaQEa9Bg7NIhWKWlWCWlic+DS7AGD05cpzJTREREREREBiiVXCJpZlpboTWMCYch3IqJRcF1sYeXEfj6N3COOCpRWBUWQl5eh6WGIiIiPeG6LmvWrOGhhx6ivr6eo48+muuvv55x48Z1ef2ePXtYvnw5f/vb3wCYOXMm11xzDeXl5emMLSIiItIvKrlymTHY0SgArt+f2FtJPGPCLbg7dyaWAoZCWEXF2FPHYU+ejD3+EJzDPoI9tuu/fIiIiPTGHXfcwfr161m+fDllZWWsXLmShQsX8rvf/Y5AINDp+ssvv5x4PM69994LwI033siFF17Ir371q3RHFxEREekzlVw5zIrHGfOXpwDYfvKpmBzaeD4bmLpazL79GAwWgAHn8CMJfPUC7ImHYg0dqllaIiKSdJFIhHXr1rF48WJmz54NwOrVq5k1axZPPPEEZ5xxRofr6+vr2bBhA3feeSdTpya2OfjGN77BhRdeSF1dHSUlJWn/GURERET6Qq2HSBKZaBSamzCNjZjmZuxDJuKfczL20GFYw4bjHHEk1qDBXscUEZEctnnzZpqampg5c2b7ueLiYqZOncqGDRs6lVx5eXnk5+fz61//mhkzZgDwm9/8hvHjxzNo0KC0ZhcRERHpD5VcIv1kXBezZzc0NibuRphfgD1qDM68jxH41GewSkq9jigiIgNIdXU1ACNGjOhwfvjw4ezatavT9Xl5eXz/+9/npptuYvr06ViWxbBhw3jggQew+3lDE58v+TdEcRy7w2dJDY1z+mis00PjnD4a6/TQOHdNJZdIP5l3qrCKivF9+rM404/BnjhJSxFFRMQzLS0tAJ323srLy2P//v2drjfGsGXLFo466iguuOAC4vE4q1ev5qKLLuLBBx+ksLCwTzls26KkpKBPz+2J4uJQyl5b3qNxTh+NdXponNNHY50eGueOVHKJ9IExBurrE/tuxWIELvgSga981etYIiIiBINBILE314FjgNbWVkKhzm+EH3vsMX72s5/x5z//ub3QWrt2LXPnzuXhhx/mvPPO61MO1zXU1zf36bkfxnFsiotD1Ne3EI+7SX/9XORWVxPfuhmnohK7h3fM1Dinj8Y6PTTO6aOxTo9cHOfi4lC/Z6blfMnlui7r1/8Pv/nNw+zZs5vy8hHMn382n/vcFzTTRvrE1Nbi1tVi5RfgzJiJM2Mm/jPnex1LREQEeG+Z4u7duxk7dmz7+d27d1NZWdnp+hdffJEJEyZ0mLE1aNAgJkyYwFtvvdWvLLFY6t50x+NuSl8/V7g11YSXLoGWZgjlE1y2ArusZ0UXaJzTSWOdHhrn9NFYp4fGuaOcL7nWrFnNL37xIPPnf5aTTprLzp1V/PjHa6mu3skll1zpdTzJEiYWg8ZGTH09xKL4ph9L4OsLsY84SmWpiIhklMrKSgoLC3n++efbS676+no2btzIggULOl0/YsQIHn/8cVpbW8nLywMSSx6rqqo488wz05pdks/duiVRcAG0NONu3dKrkktERCSb5HTJtW/fPh5++BeceeZnWLTomvbzZWXlXH31FXz6059l3Ljx3gVMMWPbNI4c2X4svWficcyOHWAMFBZgT52K7/hZ+OefhVWsO06JiEjmCQQCLFiwgFWrVlFaWsqoUaNYuXIl5eXlzJs3j3g8Tm1tLUVFRQSDQebPn88999zDZZddxqWXXgrAD37wAwKBAGeddZbHP430l10xGUL57TO57IrOs/lERERyRU6XXDt2vE08HueEE2Z1OH/kkUfjui7PPfe3nC65sG3e/cjhXqfIWqa1FfNOFQwaTN6iq3GmHYk1bJhmbomISMa75JJLiMViLF26lHA4zDHHHMM999xDIBCgqqqKU045heXLl3PWWWcxfPhwfvazn7Fy5UrOO+88bNtm+vTpPPjggxQXF3v9o0g/2WXlBJetSMzgqqjELivzOpKIiEjK5HTJNXhwCQDV1Ts7nH/nnSoAdu3a2ek5IiYaxWx/G/x+rPIR+M89H/8pH/M6loiISI85jsPixYtZvHhxp8dGjx7Nli1bOpybOHEia9euTVc8STO7rFxLFEVEZEDI6ZJrzJixTJt2BOvW/TfDhpVx9NHHsHNnFbfccjOBQKD9Fts5yxiseDxx6DigGUg9Yvbsxho6lLwrl+AccyxWQepufy4iIiIiIiIiyZHTJRfA979/CytX3sy11yZ+k1lYWMSFF17Cfff9uMvbaOcSKx5n7FN/AmD7yadifDn/x91nJhbD1NZiGhuw8vLwnfMlfHNO9jqWiIiIiIiIiPRQzrcepaVDWL78VhoaGti7dw+jRo3Gtm1uvXUFRUXaZ0Lalie+/TbW8OH4Tj4V38nzcI6e7nUsEREREREREemFnC+5/vSnPzB+/CEceugkioqKANi8eSPxeJzJk3V3mYEuUXC9hT3lMIIrVmm/ChEREck5bk1128bzk/VeR0REcprtdYBU+8lP7uGnP723w7mf//xnFBYWcdRRR3uUSjKBCYdx39qGPbmS4Pf/U2/6REREJOe4NdWEly4hctePCC9dgltT7XUkERGRlMn5mVxnn/0FVq1azoQJh3D44Ufypz/9gSee+H8sWrSEgoJCr+OJB4zrwr59uLt3Y0+aRN6VV2OPHOV1LBEREZGkc7dugZbmxBctzYkZXfrFnoiI5KicL7k+/emzaG1t5eGHf84DD9zH2LHjuP76Zcybd7rX0SSNjDHQ2oppasTs24dVVIxvzlzyvvNd7NIhXscTERERSQm7YjKE8hNFVygfu0LbdYiISO7K+ZIL4Jxzvsg553zR6xjiEVNbi6mrhUAAq6gYZ9ZsAhd8C6distfRRERERFLKLisnuGxF255cldhlZV5HEhERSZkBUXINVMayaGp7I2Msy+M06WdcF7NjO/gD+D5zNr5T5uFUTsUqKPA6moiIiEja2GXlWqIoIiIDgkquXOY47D3iKK9TpJ1xXUz1LkxLC3b5CPIuX4zvpDlexxIRERERERGRFFLJJTnFxOOYne9AXpDA176C/5R52IdM9DqWiIiIiIiIiKSYSi7JCcZ1MTU1mOZmrOJifMedQN7Cb3kdS0RERERERETSRCVXDrNiMcY+9ScAtp98KsaXe3/cpr4e99294LpYQ4cRWPgtfHNP0b4TIiIiIiIiIgNM7rUektOMMRCJQKSVWHMTbksY5+hjcE6YhW/2ybpjkIiIiAw4bk11290TJ+sXfSIiMqCp5JKsYGprcffVAWD5AxDMwykvx3fGp3HO/gJWDs5SExERETkYt6aa8NIl0NIMoXyCy1ao6BIRkQFLzYBkPNMaxuzfh++kuYmliCNH4h81ktJJ49m3v4VYzPU6ooiIiIgn3K1bEgUXQEtzYkaXSi4RERmgVHJJRjOui9m+A2vUaIJXfwerpBQA22dj2bbH6URERES8ZVdMhlB++0wuu6LS60giIiKeUcklGc3s2AFDhpD3nevaCy4RERERSbDLygkuW9G2J1el9icVEZEBTSWXZCzT0AB+H8GrvoNv+gyv44iIiIhkJLusXEsURUREUMmV04xl0Tx0WPtxNjDNzZjmJgiHMY2NOB85HGf2XK9jiYiIiIiIiEiGU8mVyxyHPR892usUPWbq9+NW12ANH4592KHYkyYTmP9ZrCwp6ERERERERETEOyq5JO2MMRCLQSQCkQgm0pqYuRWL4TvueILfW45VPMjrmCIiIiIiIiKSRVRySUqZcAumvgGamjDGBcsCY7AcH/j9kJeHVTIEe+Qo7MM+QuCrX8fy+72OLSIiIiIiIiJZRiVXDrNiMUb/5c8AVM2Zi/Gl74/bxGKYne8kcpQOwZ4xE3vUaKySEqwhQxLnSkqxhg+HgkItSRQRERERERGRflHJleNsN56W72OiUUxdHTQ2YLCwLLBGjibv2xfhnHASVl5eWnKIiIiIiIiIyMCkkkv6xTQ3Y3bXgAFr6FCck+ZgTzgEe8xYnGmHY5WUeh1RRERERERERAaAAVFy/fa3j/CLXzxIdfVOysrKOeusczjrrM9piVw/mbpazL592Ed+FP8nP43vxFlYRcVexxIRERERERGRASjnS65HH/01t9zyfc4++/OceOJsXn75n/zgBytpbW3lS1/6itfxso4xBlpbMY0NmH378X9xAXkXX4rlOF5HExEREREREZEBLOdLrsce+y3Tph3BZZctBmD69Bns2PE2jzzykEquXjKtYdwdO7CCIcjPxzf3ZPK+8W0VXCIiIiIiIiLiuZwvuSKRCEOGDOlwbtCgwezfv9+jRJnNGAPhMLS0YFrDEI1iojEsx8bEYjjHHEveNy7EnnAIVmGh13FFRERERERERIABUHJ9/vNf4uabb+QPf3icE044iX/96zV+//vHOP30M/r3wpYFrkv8zTeSEzQFLNelJRQCwH37LYxtH/Q5xjVYwTwIhbBGjcYuH4FVPgJ7yFCs0lKcE2Zhlw456OuIiIiIiIiIiKRTzpdcc+eeyosvbuB73/tu+7kZM47j0kuv7NfrBj5xJkWHjKOpMUw8bvobM2Xq2j4HevqEQAB7zFjsseM0U0tEREREREREskbOl1xLllzJa6+9woUXXsKUKYfxxhv/Zt26/+a6667m5ptX9fkOi/bIkRQeNoloXROxmJvk1CIiIiIiyefWVONu3YJdMRm7rLzbcyIiItkop0uu1157hRde+DtXX72UM8+cD8BRRx3NyJGjueqqy3j22b9ywgmzPE4pIiIiIpJ6bk014aVLoKUZQvkEl60AILzoUty9e7GHDiW46jYVXSIikrUOvklTFquu3gXAtGlHdDh/5JEfBWDbtszdTyspmpoYMmUCQ6ZMgKYmr9OIiIiIiIfcrVsSBRdASzPu1i3Env878Zdfwrz9FvGXXyL2/N+9DSkiItIPOT2Ta9y48QC88spLjB8/of38a6+9AsCIEaP6/uKxJtj7b5yGFkymLldsbsF+910AnP2vQzTkcaDkcXw2xEOZPf45TOPvrYE6/paJYsWa2j4aiJSdigmUeh1LRCQt+ruk0K2pxtTVJm6eZAyE8rErKjHPP9vhOou+beUhIiKSCXK65KqoqGTOnJNZs2Y1DQ31TJ36EbZte5N7772biopKZs+e2+fXDmz/Oby1jvxoNImJkyz83l9+i1+6BII5NnHPscmPD5y/4Gccjb+3BsD4G2ywHbAcjOUHK3GM7cNYPpzGbTRPudrrmCIiKdfVMsPeFF3vf76xLPznfBHfscdjl5XBscdhH3kUZu9erKHDcI49LoU/iYiISGrldMkFcP313+cnP7mH3/zmV9xzz12UlZXziU+cyVe/uhCfr+8/vuVGIFKPGxqPMRl6d0UTx8cmAOJ5wyDP8ThQ8liWBX4HNxrP3PHPYRp/b2XD+NvR/diRWsAABmP5wPK1FVTvFVbGst8rrywfxleA6ysEXwHGycf4CjC+4rbPhR0+YsVTvf4xRUTSoqtlhr0qud73fMsY7JLSRMEF2GXlhFbd1jZLrLL9vIiISDbK+ZLL7/dzwQXf4oILvpX8F7cD4OQlpnxnIif2vuMAODn0x21Z4DjgxjN3/HOZxt9bmTb+xsWKN4MbwYq3YsUawQnQOuLjxAsPxfiLE0WVvwjjK8L1F2OcAnBCGCcEdg79v0lEJAXsiskQym+fyWVXVCb1+XZZuTabFxGRnKC/WfRHPJz4yIS/ZHYlHn/fcSvEY91fm20sCywb4m7mjn8u0/h7KwPG3zJxrHhzotAyLsbJBycPN1iOmz+KWPFhhCdckMgqIiL9YpeVE1y2os+zrfr7fBERkWyhkquPjJ0HeaXYrXsSq3EyUet7+/U4re8m/lKcKywg5mDH45k7/rlM4++tDBh/Y9kYXxGxoUcSLT2WaOkM3GA5+PK9CSQikuP6O9tKs7VERGQgUMnVR5FxX6Bgwlya6puJxzP0b/nhMMXTEss064+5E4JBjwMlj+NYDCrOz+zxz2Eaf29lxPjbfuL5YxPLtkVERERERDKASq6+ckJQchiu1UQ8lqF3OCuGuief9zpFSlg+G0oKMnv8c5jG31safxERERERkc5yaP2aiIiIiIj0lFtTTeyZp3Frqr2OIiIikhSaySUiIiIiMsC4NdWEly5pv+NicNkK7dklIiJZTyVXX5kdRFpfBhPGsjJ0T6LmVkpOXApA3V+XQX6ex4GSyFhEWoMpGX+LvUAVAK65HBiS1NcXERER8Zq7dUui4AJoaU7ceVEll4iIZDmVXH32OOGWtYCNbXmdpTsuzo53AbD5r9y6uyIQbrEB9yDjb7CIfMjjdtuHD7AxOEAeEAKCYN5BJZeIiIikgltTnSiXKianv2AqKcFtbMTy+7AGl2JXVKb3+4uIiKSASq4+M4ADTGg7zkQx4N9tx+PIrT9uC9txiLtxEuMfByJtH61AMxZxAAwFQBAoBDMYQxG0fxQC+RhCQD6YfGA4hrHAUCBjG0wRERHpgqfFUS94uVzQrakmctt/Yfn9EI0SuPRK7LKytHxvERGRVMql1qOTf/7zH1xyybe6ffxrX/sGX/vaN9KYSJIvjMUOErOw8oAAkI8xh2OoxJjxGEYDo4BSVFqJiIjkrmzaZ8rL5YIHvreVlwd5eVBXm5bvKyIikmo5XXJNnlzJ2rX3djr/3/99J5s3/4tTTz3Ng1SSLMa0ADsw5khccx6GchKzr1RmiYiIDETZtM+UXTEZQvnthVw6lwt6+b1FRERSKadLroKCQj7ykWkdzj3zzF948cUX+N73VjB27DiPkknvNQHNQJTEksQ4xo0BRxI3t5NYdigiIiIDWTaVN3ZZOcFlK9qWVlamdbmgl99bREQklXK65Pqg1tYwq1ev5PjjT2Tu3FO9jiPdigH1QBhoxcJtW4pYDGYUhuFY1kjygiNobj4eFVwiIiIC2Vfe2GXlns008/J7i4iIpMqAKrl+/vOf8e67e7n99rVeR0kPyyI2ubD9ODvUYVGLYTCY0RgmYhiLMdMxHElis3+wLZu8YAHNLU2A62liERERyRwqb7yTLZv+i4hI7howJVc0GuWXv/w5p5zyMUaPHuN1nPTId6j/24lep+iBOLAPaMDCxTWfwTXfBoZ4nEtEREREeiKbNv0XEZHcNWBKrj//+U/U1r7LF7/4Fa+jSDsXaMbiHQxDMWYGLidgzGdJ3CVRRERERJItFTOusmnTfxERyV0DqOR6kgkTDmHSpAqvowxQLolN41tIzNiKABaGIMYcTdz8B3CUpwlFREREct37Z1wZy8L/uS/gO/a4fhdS2bTpv4iI5K4BUXLFYjE2bHiOL3/5PK+jpFdznOJ5fweg/onjIN9Jwzc1JMqs/UA9FhYGsACDHwiCmYzLRzFmEoZDgEM5sNeWiIiIiCTfgdlbpq42UXC1hnE3byaydy+xR3/T7+WF2bbpv4iI5KYBUXK98cb/EQ6HmTbtCK+jpJcx+LY0th/388VI7J3ltn2OfuAjgoXd9rgPQzHGfBLDaAylYIYkPjMCGNbPLCIiIvJhXNdlzZo1PPTQQ9TX13P00Udz/fXXM27cuC6vj0aj3H777fz617+moaGBj3zkI1x77bVMmTIlzcklFTrsl2VZGMvChqfj4QAAIABJREFUNDYBYBUWJm15oTb9FxERrw2IkuvNN/8PgPHjD/E4SSYywG6gGYi3FVWQWEpo2o8SnLavfIA/8WHKMAwHyjGMxJhyoAzDRKAknT+IiIiItLnjjjtYv349y5cvp6ysjJUrV7Jw4UJ+97vfEQh03vfyhhtu4KmnnmL58uWMGTOG1atXs3DhQn7/+99TVFTkwU8gydRhvyxj8J/zRSwg+tDPwbhaXigiIjljQJRctbXvAuhNWpe2A4UYcxpQhCEI5GEIAXlgEl9DEAi2PZ5PosAajDaIFxERySyRSIR169axePFiZs+eDcDq1auZNWsWTzzxBGeccUaH63fs2MEvf/lL7rrrLubMmQPAzTffzPz583n99dc57rjj0v0jSJJ9cL8s37HHY5eV4Rx7nJYXiohIThkQJdeXv3zewNuPq5Mm3ltmGMEigsECgrjuf2CY7208ERERSYrNmzfT1NTEzJkz288VFxczdepUNmzY0Knk+utf/0pxcTEnnXRSh+ufeuqptGWW1OpuvywtLxQRkVwzIEqugceQuIth/fvONZKYjTUYY4ZjOBTDWDBjMRzjSUoRERFJvurqagBGjBjR4fzw4cPZtWtXp+vfeustxowZwx//+EfuvvtuampqmDp1KkuWLGHixIn9yuLz2Qe/qJccx+7wWXpo1Ehcxya+dTO2Y2GXl+NWVxPfuhmnohK7vGPZpXFOH411emic00djnR4a566p5MoJLhAmUWw1kpitFQQK2q+Iu+vAHU1iqaHlQUYRERFJh5aWFoBOe2/l5eWxf//+Ttc3Njayfft27rjjDq666iqKi4u58847+dKXvsTjjz/OkCFD+pTDti1KSgoOfmEfFReHUvbafRHbtYvIpk0EpkzB94GCMRPEdu1i7w3fwTQ34+bnM/iaa9i3fHn710N/8IMuc2faOOcyjXV6aJzTR2OdHhrnjlRyZTUXqGpbehgC8jHmSAyzMOZIMCOJj2lbemCNIVFwiYiISC4LBoNAYm+uA8cAra2thEKd3wj7/X4aGhpYvXp1+8yt1atXM3v2bB555BEuuOCCPuVwXUN9fXOfnvthHMemuDhEfX0L8bib9NfvC7e6mubvXIVpacEKhci/+ZZOM6O8Fn3hn0T2NyS+2N9A7aOPE3v/1y/8E/9Jc9qvz8RxzlUa6/TQOKePxjo9cnGci4tD/Z6ZppIrq+0ACombyxOlFh8osvKh9sXXPcomIiIiXjiwTHH37t2MHTu2/fzu3buprOx8B73y8nJ8Pl+HpYnBYJAxY8ZQVVXVryyxWOredMfjbkpfvzdimzbhNicKPdPcTGTTJnxDh3ucqiMzsQITDLVvPm/NmoN58cX2r83EyV2OZyaNc67TWKeHxjl9NNbpoXHuSCVX1mrBwhA352PM570OIyIiIhmisrKSwsJCnn/++faSq76+no0bN7JgwYJO10+fPp1YLMZrr73GtGnTAAiHw+zYsaPTJvXStQ/evdCu6Fwmeq2rzeftLjajFxERyWYqubJSExbvYJiAMad5HUZEREQySCAQYMGCBaxatYrS0lJGjRrFypUrKS8vZ968ecTjcWpraykqKiIYDDJ9+nSOP/54rr76am666SYGDx7M7bffjuM4fPrTn/b6x8kK3d29MNN88G6KuruiiIjkGm3Dn1XqgR1YVGHMMcTde4Fh3V/e0sLgj81m8MdmQ9smtCIiIpL7LrnkEs4++2yWLl3KF7/4RRzH4Z577iEQCLBr1y5OPPFEHn/88fbrf/jDHzJjxgwuvvhizj77bBobG7n//vspLS318KfILnZZOb5ZszO24BIRERkILGOM8TJAXV1TVq4f9Tl349g/Ju5OILVDaEjcLXEn4AczGcN0XDMfGPnhT21qYtiExL4ce7btgoLU3eEo3Xw+m5KSgqz99yfbafy9pfH3lsbfW6WlBbpVdpaIx11qa5uS/rr6bzA9NM7po7FOD41z+mis0yMXxzkZ7/O0XDEjGSAC1ABRLHwYinDNRRjzGcDyNp6IiIiIiIiISIZRyeUZA8QB932fw1jsa3vcj6EYY87GNR/FMB4Y6k1UEREREREREZEMp5LroGLA/5EooCIklg5GgScxpgHYRaKgMm2f339s3jfnysJ0OrIBB9P2GXy45lMYMx3DKGAMMCTFP5+IiIiIiIiISPZTyfWhYjj2F0gsG4xjfaDAcpxK3JgPTAAIYggAeW0fwfedS5w3hIAQmLbPhDAE246DQAGJ2VpajigiIiIi3ort2kX0hX9iJlboLowiIpIVVHJ9KAdj5mLxEoaytsLKDwSwLR95oZlEG48nbnJjkzcREREREQC3upq9N3yHyP4GTDBEcNkKFV0iIpLxVHJ9KAvXXNTlI7Zl4/cXAMm/a1AyuUO03FFEREREeie+dTOmuTnxRUsz7tYtKrlERCTjqeTKZQUFvLtpm9cpRERERCTLOBWVuPn5sL8BQvnYFZVeRxIRETkolVwiIiIiItKBXV7O0B/8gNoX/omZOBm7rMzrSCIiIgelkquPDIamtn9ivLcnV5AgDo6HyURERERE+s83YgT+k+YQi2n/WRERyQ4qufroYf9D3MePiRRFMea98ydEZrG0+QbPcnXQ0sKgL34WgP0PPgyhkMeBRERERCTXuDXViT27KiZr3y4REfGUSq4+qrNreZu3GWXGYIyhxWpmj72HIEGvo73HdQk8+9f2YxERERGRZHJrqgkvXQItzRDK110YRUTEU7bXAbJZEUUMNoOptd/FAFPjh3Fa6ye8jiUiIiIikhbu1i2Jggva78IoIiLiFc3k6qcqu4pBZhBfbbmAs1s/j09DKiIiIiIDhF0xGUL57TO5dBdGERHxkhqZfjAYWq0wC1rO5QutX/Y6joiIiIhIWtll5QSXrWjbk6tSd2EUERFPqeTqh0YaGWbK+Fjk415HERERERHxhF1Wrn24REQkI2hPrn6wsBjvjmesO87rKCIiIiIiIiIiA5pmcvWDHz8nxE70OsaHMvn5XkcQEREREREREUk5lVx9tNPeSTHFjI1n8CyuggL2vlXtdQoRERERERERkZTTcsU+qrJ3UEwxM+LHeh1FRERERERERGTAU8nVR4004sdPsRnkdRQRERERERERkQFPJVc/BAliYXkdo3vhMMVfOpviL50N4bDXaUREREQkzdyaamLPPI1boy0sREQk92lPrj4wGFqtVvLwex3lw8Xj5P3pj+3HIiIiIjJwuDXVhJcugZZmCOUTXLYCu6zc61giIiIpo5lcfbDL3sl+a5/XMUREREREuuVu3ZIouABamhNfi4iI5DCVXH3wsu8l6q16RjDC6ygiIiIiIl2yKyZDKD/xRSgfu6LS20AiIiIppuWKfbDTrqKFZkKEvI4iIiIiItIlu6yc4LIVuFu3YFdUYpeVeR1JREQkpVRy9ZLB8JfAn4laUa+jiIiIiIh8KLusXPtwiYjIgKHlir1UZe9gt11DwAS8jiIiIiIiIiIiIm1UcvXSE4E/0GDVU2iKvI4iIiIiIiIiIiJttFyxl571P0OeCeJartdRDq6ggD27671OISIiIpJybk11295Tk7U8T0REZIBSydULL/ie5y1nG0WmkP2WyiMRERGRTODWVBNeugRamiGUT3DZChVdIiIiA5CWK/bCE4H/R71VT7EZ5HUUEREREWnjbt2SKLgAWpoTX4uIiMiAo5KrFzb5NlJoirCwvI7SM+EwxV8/l+KvnwvhsNdpRERERFLCrpgMofzEF6F87IpKbwOJiIiIJ7RcsYfqrf28a+8laIJeR+m5eJy8R3+dOL79Tm+ziIiIiKSIXVZOcNmKtj25KrHLyryOJCIiIh5QydVD+6x9RIlSRKHXUURERETkA+yy8h7vw6VN6kVERHKTSq4eqrarabVaGeIO8TqKiIiIiPSRNqkXERHJXdqTq4eqnO3EieNTLygiIiKStbRJvYiISO5SY9NDO+ztGEz2bDovIiIiIp20b1LfNpMrEzap9/lsgkE/Pp+Dz2djWRbGGGIxl1gsTjgcJRZzvY6ZNFouKiIiqaKSq4e2+rYQMH6vY4iIiIhIP2TSJvW2bVFUFCQQ8GGMwbLe+2WqZVn4/YnSKxQKEInEaGgI47rGs7zJoOWiIiKSSiq5eiBGjHfsKoJk0Z0VRURERKRLvdmkPlXy8nwUFb333vL9Bdf7HTjv9zuUlhbQ0BCmtTWWloyp0NVyUa//LEREJHeo5OqBKnsHjVYDhabI6yi9k5/Pnm272o9FRERExHvvL7i6K7c+6MASxsTzsrfoysTloiIikjtUcvVAxIoQx8VnHK+j9I5lQUGB1ylEREREpI3jWF0WXLFYjJaWli6fEwwG8fv9HYquaLQpK5cuZtJyURERyT0quXrgn75/0GqFcTRcIiIiIlkjEzc4LyzsegbXI488wvbt27t8TmlpKeeff3778w4UXfv3d12KZbpMWC4qIiK5Sa1NDzwdeAoHBz9ZtvF8aytFiy4FoGHVbZCX53EgERERkfTIxA3OfT6bQKDrt9/hcLjb5xnTccaWZVkEAj58Pjun7rooIiLSXyq5eqDO2keeycJN52Mxgj//GQANK25VySUiIiIDRiZucB4M+jvdRfGAj33sY1RXV7d//cYbb7Bt2zYAjjjiiE7XG2MIBv00NramLrCIiEiWUcl1EDvtd6i13yWYjSWXiIiIyACViRuc+3xOtxvNl5WVUda2P5UxhhdffBFIzNqaMmVKp+sty8Lny7L9YkVERFJMJddBvGu9S8SKMMgt9jqKiIiIiPRQJm5w7vPZPbquurqauro6AA499FDyu7lLdk9fT0REZKBQyXUQ//C/QJgW/AS8jiIiIiIivZBpG5x3N4vrg1577bX242nTpvX79URERAYK/frnILY5b+LDh62hEhEREZF++OAG8l2JRqNs2rQJgPz8fMaNG9ev10sGt6aa2DNP49ZUH/xiERERD2km10HssXfjN1l2V0URERERyTixmIvf/+H7aG3dupVYLAbA4Ycfjm13/4vWdNxZ0a3OvLtUioiIdEcl14cwmETJhUouEREREemfWCyOz2d/6DLDV199tf34sMMO6/Y6YwyxWDxp2dya6sQdKUtKoK4Oe8oUKJlIfOvmjLtLpYiISHdUcn2IOquWRquRgMnzOkrf5Oezd+Ob7cciIiIi4p1wOEoo1P0+r3V1dezcuROA0aNHM3jw4G6vtSyLcDialFxuTWK2ltlXh/vvrdiTJhErKWXQHWtwKioz7i6VIiIi3VHJ9SHecrbRYjUz3PX+bjx9YlmYoUO9TiEiIiIiJJYXRiIx/H6ny9lce/fubT8+8sgju30dYwzRaDxpyxXdrVugpRnT2AiRCKaxCRMMEdm0CfuoYzPuLpUiIiLdUcn1IV7yvUiEKAHdWVFEREREkqChIUxpaQHGmE5F16RJk/jWt76FZVnkdzML/8Bm8w0N4aRlsismQygfq7AVEwhgFRZihUIEpk6llcy7S6WIiEh3VHJ9iK3OVhxsLLL09sytrRR+9xoAGm9aDnlZuuxSREREJEe4rqGhIUxRUbDLoqugoKDb576/4HLd5N1Z0S4rb5+tRWkp1NYSmDoVX3k51DUl7fuIiIikmkqubjTRxOu+Vykw3b/RyHixGKF7fwxA43e/p5JLREREJAO0tsaA7ouurry/4Eo8P7k+OFvL9nV/V0cREZFMpZKrG/vsOiJWhHyjDdtFREREJLlaW2NEo00UFQUJBHzdll0Hzkej8aTP4BIREck1A6Lkev3117jrrjVs2vQvQqF8jj32OC666FJKSkq7fU69VU/MiuJ3/WlMKiIiIiIDhesa9u9vweezCQb9+HwOPp+NZVkYY4jFXGKxOOFwNGmbzIuIiOSyHpVc5557bo9f0LIsfvKTn/Q5ULJt3ryJSy75FtOnH8PNN69i79493HXXGq65Zgdr167r9nm77HdoJYIflVwiIiKSXVzXZc2aNTz00EPU19dz9NFHc/311zNu3LiDPvfRRx9l0aJFPPnkk4wePToNaSUWc2lsbPU6hoiISNbrUcl1YA+AZF+bDnfccRuTJlWwfPmtOI4DJDb0vO22W9m58x1GjhzV5fP+6X8RMDg4aUwrIiIi0n933HEH69evZ/ny5ZSVlbFy5UoWLlzI7373OwKB7u8a/c4773DjjTemMamIiIhI8vSo5PrpT3+a6hwpsX//Pl566UWuvfaG9oILYPbsk5k9++QPfe425w0Cpvs3gSIiIiKZKBKJsG7dOhYvXszs2bMBWL16NbNmzeKJJ57gjDPO6PJ5ruuyePFiDjvsMJ577rl0RhYRERFJij7fNmX//v08+eSTPPjgg9TW1vLmm29m3CyuN974P4wxlJSUcuONS5k37yTmzZvFTTddR319fbfPMxhq7N1aqigiIiJZZ/PmzTQ1NTFz5sz2c8XFxUydOpUNGzZ0+7y1a9cSjUb55je/mY6YIiIiIknXp43n77zzTu666y7C4TCWZXH44YezevVq9u3bx7p16yguLk52zj6pq6sDYPnym5g583iWL19FVdV21q79Ee+8U8Wdd96DbXfu+RqsepqsRgImL92RkysU4t1/vNZ+LCIiIrmvuroagBEjRnQ4P3z4cHbt2tXlc1599VXWrVvHL3/5S2pqapKWxefr8+9Tu+U4dofPkhoa5/TRWKeHxjl9NNbpoXHuWq9LrgceeIAf/vCHfPOb32Tu3Lmcc845AJx33nksWrSI2267jeuuuy7pQfsiFosCMHlyJUuWJDJNnz6DwsIibrjhWjZseJ5jjz2u0/O229sJWy0McYekNW/S2Tbu2INvMCsiIiK5o6WlBaDT3lt5eXns37+/0/XNzc0sWrSIRYsWMX78+KSVXLZtUVJSkJTX6kpxsX6Blw4a5/TRWKeHxjl9NNbpoXHuqNcl109/+lO+8Y1vcOmllxKPx9vPz5o1i8suu4y77747Y0qu/Px8AI4/flaH88ceezwA//73li5Lrh3OdlppJY9g6kOKiIiIJFEwmHj/EolE2o8BWltbCXUxs3vZsmWMHz+eL3zhC0nN4bqG+vrmpL4mJH5jXVwcor6+hXjcTfrrS4LGOX001umhcU4fjXV65OI4FxeH+j0zrdcl186dO5kxY0aXjx1yyCHs3bu3X4GSafTosQBEo5EO52OxGAB5eV2XWHV2LVbbP1ktEqHg5psAaPrOd+FD7qYkIiIiueHAMsXdu3czduzY9vO7d++msrKy0/UPP/wwgUCAo446CqD9l5if/OQn+dSnPsVNN93U5yyxWOredMfjbkpfP9XcmmrcrVuwKyZjl5V7Hadb2T7O2URjnR4a5/TRWKeHxrmjXpdcI0aM4KWXXuL444/v9Njrr7/eaf8HL40fP4ERI0by5JN/5Oyz3/vt5N/+9jQARxxxZJfPe833CmR7wQUQjZJ/x+0ANC2+RiWXiIjIAFBZWUlhYSHPP/98e8lVX1/Pxo0bWbBgQafr//jHP3b4+pVXXmHx4sXcfffdTJw4MS2ZBxq3pprw0iXQ0gyhfILLVmR00SUiIpItel1ynX322fzwhz8kGAwyZ84cILGXwx/+8Afuuusuzj///GRn7DPLsrjwwkv47nev4bvfvYYzz/w0b7/9FnfddQdz5pxMRUXn32ZGiPCWvY2g0VJFERERyT6BQIAFCxawatUqSktLGTVqFCtXrqS8vJx58+YRj8epra2lqKiIYDDIuHEd9+88sHH9yJEjGTIky/cnzVDu1i2JggugpTkxo0sll4iISL/1uuRauHAhVVVVrFq1ilWrVgFw7rnnAnDmmWdm3G2n5849lRUr8rjvvv/m6quvoKiomPnzP8vChd/u8vp/O1updnYx2C1Jc1IRERGR5LjkkkuIxWIsXbqUcDjMMcccwz333EMgEKCqqopTTjmF5cuXc9ZZZ3kddUCyKyZDKL99JpfdxS9eRUREpPcsY4zpyxO3bdvG888/z759+ygqKmLGjBlMmjSp169TV9eUUetH/+r/X64uvJKx8bE4ON1et93ZzlzfbL5ftzKj8nfQ1MSwCYnlo3u27YKC1N3hKN18PpuSkoKM+/dnoND4e0vj7y2Nv7dKSwt0q+wsEY+71NY2Jf11P/jfYLbsbfVB7+WuxC4r8zpOJ/p/XfporNND45w+Guv0yMVxTsb7vF7P5DpgwoQJuK5LfX09Q4YM6bCxaTZ7zfcKBvdDCy4RERER8V42721ll5VnTVYREZFs0aeS66GHHuKOO+5o37MBEhvSX3HFFXzyk59MWjgvbHW24DN97v5EREREJE20t5WIiIi8X6/bnAceeIBly5Zx6qmnctlllzFkyBD27NnDY489xuLFi3Ech49//OOpyJpy//C9wCu+lxlkBnkdRUREREQOQntbiYiIyPv1uuS6//77WbBgAUuXLu1w/jOf+QzXXnsta9asydqS6zXfq4StFka6I72OkhyhELX/+3z7sYiIiEguscvKCS5bkdF7W4mIiEj69Lrkqq6uZu7cuV0+9slPfpJHH32036G8YDA8438av/F7HSV5bJt45RSvU4iIiIikjPa2EhERkQN6vW39tGnT+Pvf/97lYxs3bmTy5Mn9DuWFN53/Y7vzFoNNiddRRERERERERESkl3o0k2vDhg3tx2eccQbLly+nqamJj3/84wwdOpT6+nqeeeYZfvrTn3LjjTemLGwqVdlVtFgtDHOHex0leSIR8n+wCoDmyxZBIOBxIBEREZHs4NZUty2DnKyZYiIiIlmiRyXXV77yFSzLav/aGMODDz7I+vXrO5wDuOKKK7JyT65d9k4AHByPkyRRNErBqhUANF90qUouERERkR5wa6oJL13SvqF9cNkKFV0iIiJZoEcl1/3335/qHJ5ycflt3iNYvV+9KSIiIiI5xt26JVFwAbQ0J2Z0qeQSERHJeD0quWbMmJHqHJ7aZe9kj72HIW6p11FERERExGN2xWQI5bfP5LIrKr2OJCIiIj3Q67srArz88su88MILRKPR9mWKxhiam5t58cUX+cUvfpHUkKlWa9UStSIE3EFeRxERERERj7x/H67gshVtx5XYZWVeRxMREZEe6HXJ9T//8z8sW7asvdx6P9u2OfHEE5MSLJ12Ou8QIUKAPK+jiIiIiIgHutqHyzdrttexREREpBd6vQnVAw88wIknnsjzzz/P17/+dc455xxefvllbrvtNvLy8vjUpz6VipwpVWXvAMDCOsiVIiIiIpKLutqHS0RERLJLr0uuqqoqFixYwKBBg5g2bRovvvgiwWCQ0047jW9+85tZt0m9wfCXwJP4jd/rKCIiIiLikfZ9uED7cImIiGSpXi9X9Pv9BINBAMaPH8/bb79NNBrF7/fz0Y9+lHXr1iU9ZCrtsndSY9dQbHJwP65gkLo//Ln9WERERES6ZpeVax8uERGRLNfrmVxTpkzhz39OFCfjxo3DdV1efvllAKqrq5ObLg2q7V2ErRbyTcjrKMnnOMSOOprYUUeD43idRkREZMCpq6vj1VdfpaGhocvHGxsb2bBhQ5pTSXfssnJ8s2ar4BIREclSvZ7Jdf7553PxxRezf/9+li9fzimnnMJVV13FaaedxqOPPsrRRx+dipwp83f/s0SI4PTtRpMiIiIinUSjUW688UYefvhhAHw+HwsWLOCKK67A739vi4Q33niDc889l02bNnkVVURERCRn9Hom16mnnsratWs59NBDAbjpppuYMGEC69ev55BDDuG6665LeshU+pfvNfJMHnbvhyLzRSKE1txGaM1tEIl4nUZERGTAuPvuu3nsscdYtGgRa9as4ROf+AT33XcfCxcupKWlxet4IiIiIjmpT9OX5syZw5w5cwAoKSnJun24Dtht7eYt501CJt/rKKkRjVJ4U6J0bDn/AggEPA4kIiIyMDz66KP8x3/8B1/72tcAOOWUUzj55JNZtGgRF110EXfddVeHGV0iIiIi0n89Krl6u1fEMccc06cw6faG79/UWw2Mdkd7HUVERERySE1NDVOnTu1w7rTTTsMYwxVXXMGSJUu49dZbPUonIiIikpt6VHJ95StfwbKsDueMMe3nDhwf+Jwt+0psdjbh4uLTflwiIiKSROXl5bz++uvMnDmzw/nTTz+dXbt28Z//+Z8MHz6c008/3aOEIiIiIrmnR+3O/fffn+ocaefi8orvJZxc3ItLREREPDV//nx+9KMf4TgOc+fOZfz48e2PnX/++VRVVXHvvffyj3/8w7uQIiIiIjmmRyXXjBkzUp0j7Z72/5mX/C9S4pZ6HUVERERyzNe+9jV27drFLbfcwo4dO/jud7/b4fHrrruO/Px8fvzjH3uUUERERCT3DNh1ek8E/h9RYhSbYq+jiIiISI7x+/3ccMMNXH755TQ0NHR5zZVXXsknPvEJnnjiiTSnk0zh1lTjbt2CXTEZu6zc6zgiIiJZb0CWXAbD285bhEzI6ygiIiKSwwYNGsSgQYO6fXzKlClMmTIljYkkU7g11YSXLoGWZgjlE1y2QkWXiIhIPw3IkqvRaqDWriVogl5HSa1gkH2PPNZ+LCIiIiKZwd26JVFwAbQ0J2Z0qeQSERHpl16XXPv37//Q30hmg0arkThxAvi9jpJajkP0hFlepxARERGRD7ArJkMov30ml11R6XUkERGRrNfrWwt+7nOf4/HHH09FlrT5m/8ZGq1GQibf6ygiIiIikiPcmmpizzyNW1N90GvtsnKCy1YQ+OZFBJf9J3ZZWRoSioiI5LY+zeQqKSlJRZa02ea8icHFl+urNaNRgvffC0D43PPBn+Mz10REREQ80pc9tuyyci1RFBERSaJez+Q699xzueWWW3juueeora1NRaaUMhjecrblfsEFEIlQdM0iiq5ZBJGI12lEREQGnEgkwt69ezudf/zxxwmHwx4kklTpao8tERHjDFbOAAAgAElEQVQRSa9eNz2/+c1v2LlzJ+eff36Xj1uWxcaNG/sdLFWq7B3829nKIDe79xUTERGRzPbMM89wzTXX8NnPfpbLL7+8/fyePXu44oorGDx4MLfffjszZszwMKUki/bYEhER8V6vS65PfepTqciRNn8IPE6DVc94d4LXUURERCRHbd68mYsuuohDDz2UE088scNjpaWl3Hnnndx+++1ccMEFPPLII0ycONGjpJIsB/bYcrduwa6o1B5bIiIiHuh1yXXxxRenIkda1Frv8qvgLykyRTg4XscRERGRHHX33XdTWVnJAw88QCAQ6PCY4zjMnTuXmTNn8rnPfY61a9eycuVKj5JKb7g11W0l1uQu99LSHlsiIiLe6vPGVE8//TTPPvsse/bs4fLLL2fTpk0cdthhjBo1Kpn5kuqv/v+lwapnZHyk11FEREQkh7300ktcccUVnQqu9wuFQpx33nmsXbs2jcmkr/qysbyIiIikV69LrpaWFi666CKeffZZCgsLaWpq4utf/zoPPvggGzdu5IEHHmDSpEmpyNov2+23uTd0DwB+un/DKSIiItJftbW1lJcfvAAZN25clxvTS+bpamN5lVwiIiKZpdd3V/yv//ov/vWvf3Hffffx3HPPYYwB4JZbbqGsrIzbbrst6SGT4aHgz9nhvM2o+Givo4iIiEiOGz58OFVVVQe9bufOnQwZMiQNiaSvYq+/Susdt2PceGJjedDG8iIiIhmq1zO5fv/733PFFVcwc+ZM4vF4+/lhw4bx7W9/m5tuuimpAZPBYPiH7wWK3WJ8fV+hmX3y8tj/P79oPxYREZH0OOGEE1i/fj3z58/Hsqwur3Fdl/Xr13PEEUekOZ30VOz1V2lZ8HmIRCAQIHjbj7BsJ6s3lj/YvmIiIiLZrNczuerr67vdd2vQoEE0Nzf3O1SybXT+xV57D4Wm0Oso6eXzEZl3OpF5p4NvAJV7IiIiHvvqV7/K1q1bueyyy7pcjvjuu+9y5ZVX8vrrr3Peeed5kFB6Iv6/f0kUXACRCO6/Xsc3a3ZWF1zhpUuI3PUjwkuX4NZUex1JREQkqXrdfEyaNIlHH3200+2wAZ566qmM3I/r93m/Y7+1j2Fu5mUTERGR3DN+/HhuueUWrrrqKubMmcNhhx3G6NGjicfj7Ny5k40bN+Lz+Vi2bBlHHnmk13GlG85Jc+DuOzHhMJZlYU873OtI/aJ9xUREJNf1uuT69re/zcUXX8y+ffuYO3culmWxYcMGfvWrX7F+/XpuvfXWVOTsl03ORgpMAXbvJ65lt2iUvIcTyxVbP3sO+P0eBxIRERk45s2bx29/+1vuv/9+/vrXv/LUU09h2zajRo3i3HPP5ctf/nJG35VawPeRwwne9iPCNyzFKi4mev99OBWVSSuG0r100K6YnNhXrO0OkdpXTEREck2vS65TTz2VlStXcuutt/L0008DsGLFCoYMGcINN9zA6aefnvSQ/REmTLWzk5AJeR0l/SIRii/5NgB7zpyvkktERCTNxowZw7XX/n/27jw+jrp+/PjrMzN759okbdIzpaUnN6UXV5F+OZRLEVQQKUi98ECuSkGkInLLoQWRn1yifDm+Kghf/CIqFpRakFOkF1cvkrZp7mSvmfn8/tgkzbFJN8kmu0neT80ju7Mzs5+dspOd977f789V2R6GGABlmJgTWicuymD2U1vpYFvAyX/djYMe6DLKyvFfd2NrYG349hUTQgghetKvRk2nnHIKp5xyCh988AF1dXUUFBQwdepUDCP3MqU+Mj8kQoQiHc72UIQQQggxyrz99tts376diooK5syZk+3hiH4YrOynbJUOGmXlUqIohBBixOpzkOvcc8/lmmuuYdq0aUydOrXTY+vXr+fyyy/n6aefztgAB+pfnleIqhgBRmEmlxBCCCGyoqGhga997Wu8+eabaK1RSnHwwQdz2223MW7cuGwPT/TBYGU/SemgEEIIkXlpBbn+9a9/obUG4JVXXuHVV1+lpqam23ovvPACW7duzewIB+Df5ts84n8Yn/aiSD19txBCCCFEpt1xxx28++67fPvb32b//ffngw8+4J577uHqq6/ml7/8ZbaHJ9LUsWeWddTijO5bSgeFEEKIzEsryPU///M/PPnkkyilUErxwx/+sNs6bUGwk08+ObMj7Kd6VccNoWupV3VMcfbJ9nCEEEIIMYq88MILXHLJJSxduhSAo48+mrKyMi677DJaWloIBoNZHqGA7o3fO94HBr1nlpQOCiGEEJmVVpDrqquu4vTTT0drzdKlS/nBD37Avvvu22kdwzAoKChg+vTpgzLQdGk0L3lW82v/g3xgvk+FUyFZXEIIIYQYUrt27WK//fbrtGzBggU4jkNlZSXTpk3L0shEm66N370XXUL8ztva71unnJaVnllCCCGE6L+0glz5+fnMnz8fgF/96lfst99+hEKhQR1Yfz3n/SM3B39MVMUY447FgzfbQxJCCCHEKGPbNl5v588ghYWFAMRisWwMSXTRtfG78+LfOt1XID2zhBBCiGGmz43n58+fz6uvvoppmhx66KFs27aNa6+9lsrKSk488US++c1vDsY40/aS529EVJRpjnxDis9H/S8far8thBBCiOxra/Egsqtr43dz8Sdw3nh9z/0Fh2MuWNSvnlldyyCFEEIIMTT6HOR66qmnuOKKKzj//PM59NBDWblyJa+99hpHHHEE99xzDx6Ph69+9auDMda9ihHjffN9Qlr6XABgWcRP/Uy2RyGEEEKIDpSSNgq5IFXjdyNFI/i2IFW6gauuZZCD0ctLCCGEEKn1Ocj1wAMP8JnPfIbly5eze/duXn75ZS699FIuuOAC7r//fh577LGsBbl+HljFFvMjxjsTsvL8QgghhBBtVq5cSV5eXvv9tgyuq6++ulPbB6UUDz300JCPT3Rv/N52391Rhf3S6k4N6dMNXHUtg5ReXkIIIcTQMfq6wQcffMBpp50GwIsvvojWmiVLlgBwwAEHUFlZmdkRpuk/5jv8r+8PFLlF+PFnZQw5x7bx/uH3eP/we7DtbI9GCCGEGDXmzZtHKBRCa93+07Y8GAx2Wu66bpZHKzpqC2jFf3EX0e9f0Z7BpetqcHfvTv7euKHH7dvLIKHXXl5tgTR3R9VgvAwhhBBiVOpzJldBQQHNzc0ArF69mvHjxzNlyhQAtmzZQjgczugA07Xa+wLNqol93KlZef6cFItRuCw5dfmuDyvB6vM/txBCCCH64eGHH872EEQ/tWVi6VgMvXs39to1GFOn4W7aBPE42uuFcHGP26cqg+z2HDuqiF52EW51NUZpKf5b75RsLyGEECID+hz1WLhwIatWrWLTpk08//zzfPnLXwbgueee48477+TII4/M+CB700wzf/H+iWe9TxPQARTS50IIIYQQQvSPMWMmKIW7fh0AiScew3vm5zGmz0A3NaHy8qC2pvd9dCmD7MpeuwbnzTfAcXC2bcVeuwav9FEVQgghBqzP5YpXXXUV4XCYu+66i8MPP5yvfe1rANxwww2MHz+eSy+9NOODTCVOnO3GNi7Lv4ibQtdTb9RR6o4ZkucWQgghhBAjk1FWjufML6AmTsKYNRulXTSgisIYJSXJ3z2UIKar61ey8iWtEEIIkRl9zuQKh8Pcd9993ZY/8sgjjB8/vs8DcHB4z3yPGlVDXMWIqghRkr9jKkaUKFEVJaaiNNFEs9HEDrWDXcYu4ipKo2qk1C2lSGenTFIIIYQQQows5oJFGE8/1d5o3lpwONaCRb2WIPZ5/wcfgq6uRpWOwVywKEMjF0IIIUa3jDVp6k+Aayc7+WzeGdRTT1zFcXBxcXCV23orucTFARRG6/882oMPH17to9gtwcSiUTVm6qWkpUk1otFD+pxCCCGEEGLw9dRXKxN9s9oa2fu+dxXU1mYkaCaEEEKIpD4HuWbNmoVSvadUr1u3Lq19JUjQrJoxtUVAdxhKH2JHtrKxGfqZA8foMYSR7DEhhBBCiJFob321+qNt5sa2DDH/dTf2KcDVFiAzZsyURvVCCCFECn0Ocn3zm9/sFuRqbm7m9ddfZ8uWLVx22WVp76uMMn7T9DiOM/ymzjZNg6lFE2nJQoBNCCGEEKI3ruuyatUqnnjiCRoaGpg7dy7XXHMNFRUVKdfftGkTt9xyC2+99RaGYTBv3jyuuOKKfmXqi561zdwIQKQlGbBKM1iVOkAmgS4hhBCioz4Hub797W/3+Nj3vvc93nnnHT772c+m+eQW5boc2x1+QS7LMPDhy+0gl9dLw09/3n5bCCGEEKPD3XffzaOPPsoNN9xAWVkZt9xyC1/5yld45pln8Hb5TFBbW8v555/PvHnz+PWvf00sFuOmm25i2bJl/P73v8fn82XpVYw8xoyZEAi2B6r60sC+twCZZHgJIYQQSRnryQXw6U9/mu9+97tcc801mdztgESjUY4//mjcLoE0r9fLX//6cpZGNUQ8HmJf+GK2RyGEEEKIIRSPx7n//vu5/PLLWbx4MQC33347Rx11FM8//zwnnXRSp/X//Oc/E4lEuPHGG9sDWrfccguLFy/m9ddfZ9EiaYqeKT31+kpr2x4CZJLhJYQQQuyR0SDXRx99hG3nVmbTe+9twnVdVq78MeXle1LuDUOmahZCCCHEyLN+/Xqam5tZuHBh+7KCggLmzJnDq6++2i3ItWjRIu66666UGVv19fWDPt7Rpr+9vnoKkA2kBFIIIYQYafoc5Fq1alW3Za7rUllZybPPPsuxxx6bkYFlynvvbcDj8XDMMUuwrMzF9DZGN/Datn/S0hLDcXJzlkVlO+zzjw0AfHjETLRl9ntfGk2z20yDU0+dU0eDU0eLm/xAtaTgeM4vWZaRMQshhBBiYKqqqgAYN25cp+Vjx46lsrKy2/oTJ05k4sSJnZb94he/wOfzMW/evAGNxbKMAW2fimkanX5ng1tVhbNxPeaMWRjlQxhQmjA++dOBMXs2djCIjkRQgQDeOXMwMnDcc+E4jxZyrIeGHOehI8d6aMhxTi0jQS6AvLw8jjvuOFasWDHgQWXSpk0bmTJln4wGuABWN7zAz6tX4dW+vkwGOaQCEZe3vrEJgIP+NJ1IIL3/+G2dIKqjACgUXuXFq7x4lBeP8uBRFpby4MGDpSzeankDSgbtZQghhBCiDyKRCEC33ls+ny+tzKxf/epXPPLII6xYsYKSkv7/gTcMRTgc6vf2e1NQEBi0fffGrqykeuWV6JYW3GCQ0jvuwOoSUBxS4WkU3r2K+Lp1eOfMwcpw0C1bx3k0kmM9NOQ4Dx051kNDjnNnfY78rF+/fjDGMWg2bdqIUgbf/e6FvPPO23g8Xj7xiSV861vfJRgc2Acvv+GnwtoHrXMzzOVzbCAZ5KrwVhDz9v7PndAJ6p06dtm7mOCZwEzfbCZ5J1NqjaHUGkOxVULYDFNoFlJgFhJQgW4zbQohhBAiu/x+P5DszdV2GyAWixEI9PxBWGvNnXfeyc9//nO+9rWvcd555w1oHK6raWhoGdA+UjFNg4KCAA0NkazM0J145XXi9Y3JO/WN1LzyOp6jjxnycXTiL4BDFhADqG3OyC6zfZxHEznWQ0OO89CRYz00RuJxLigIDDgzLbPpTTnGdV0++OA9DMPkG9/4Nuedt4x1697lgQf+Hx999CGrVt2LYUhqn6tddto7aHKbKDKLmBucx4ryq9nHNzXbQxNCCCFEH7WVKe7cuZPJkye3L9+5cyezZqWezS+RSLBixQqeeeYZli9fzgUXXJCRsdj24H3odhx3UPffEz1tBtofaG/0rqfNzMo40pGJWRezdZxHIznWQ0OO89CRYz005Dh3llaQ69xzz017h0opHnrooX4PKJO01tx88x2UlJRSUTEFgIMPPpSSkhKuvfZq1q5dw6JFR2R3kFlU79RTY+9GowmZeRyTfyyXl11JiSW1h0IIIcRwNWvWLPLy8li7dm17kKuhoYF3332Xc845J+U2y5cv5/nnn+cnP/lJt8b0orOBzJA4lGTWRSGEEKNRWkGuvpTj5VLpnmmaHHroYd2WL1p0JJCceXG0Bbm01lQ71TQ4DfgMH0flL2ZecAGHheYz2VMh5YdCCCHEMOf1ejnnnHO49dZbKS4uZsKECdxyyy2Ul5dz3HHH4TgONTU15Ofn4/f7+d3vfsezzz7L8uXLmT9/Prt27WrfV9s6orOOMyRmIluqN/3dv8y6KIQQYjRKK8j18MMPD/Y4BsWuXTtZs+YfLFx4OGPH7vmWLRaLAVBUVJStoWVFQifYntiGX/k5ufBUFuUdwSfylkhgSwghhBhhvvOd72DbNt///veJRqPMmzeP++67D6/Xy7Zt21iyZAk33HADp59+Os888wwAN998MzfffHOn/bStI1LLRLZUb0GsgezfmDETAsH2bY0ZqUtVhRBCiJGkzz25WlpaCAaDnZa99dZbHHTQQRkbVKbE43FuvvnHnHfeMpYt+3r78r/+9U8YhsFBBx2cxdENLVvbbI5vZV/fdM4vWcaSguOzPSQhhBBCDBLTNLn88su5/PLLuz02ceJENmzY0H7//vvvH8qhjSgDzZbaWxBrIPvP5bLKwc5+E0IIMXqlHeRat24dK1as4Pjjj+fCCy9sX15fX89ZZ53FPvvsw09/+lOmTZs2KAPtjwkTJnLCCZ/iN795CI/Hw377HcDbb7/Jww8/wGc+cwaTJ0/J9hAHle0xWHXZbAA+VruY6J3EnZN+Lj23hBBCCCEyYKDZUl2DWM7aNbjh4vbgz0D337GsMldIrzAhhBCDKa0g19atWznvvPMIBoPsu+++nR7zer1ceeWV3HfffZx99tk89dRTlJfnzh+q5cuvYtKkyfzxj//LQw/dR2npGL785a9x9tlfyvbQBp1jGfzujPFsS2zDpyw+U3iGBLiEEEIIITJkoNlSHYNYWikSTzwG2u0U/MnVbKz+kl5hQgghBlNaQa57772XcDjMo48+2q2PVSAQ4JxzzuGTn/wkZ5xxBvfccw8rV64cjLH2i8/n47zzlnHeecuyPZQh42qXBreBGns3hjIot8q5rOwKFoYOz/bQhBBCCCFGlIFkS3UMYrm1tdiPP5J8oEPwJ9X+h3O5n/QKE0IIMZjSCnKtWbOGr3/96702ai8pKeH888/nN7/5TcYGJ9LnaIeojrLT3glogkaIJaElfHHDZKb69sU7ZSFIg3khhBBCiJzSFsRyd1RhP/3kXoM/fS33y7WA2EjMThNCCJE70gpy7dq1i4qKir2uN2PGDKqqqgY8KNE7V7vUO/U0uY0kSAAKBfiUjyneKZxb8mXm+PdjYryYsWePB2DXh5UQCmV13EIIIYQQIrV0gz99KffL1f5XudgrTAghxMiQVpCruLiYnTt37nW9mpqaXrO9xMAldIIt8c0UmoXMCezPgYGDGecZxxhrLGOtMsZ7JxAyWoNZiebsDlYIIYQQYgQarOyodII/fSn3k/5XQgghRpu0glzz5s3jd7/7HSeddFKv6z355JPMnj07IwMTe2itqXPqqHVqUEox2VvBDRNuZaovd2ayFEIIIYQYDbKdHdWXcj/pfyWEEGK0SSvI9aUvfYmzzjqLG2+8kYsvvhifz9fp8Xg8zu23385LL73EvffeOygDHa2q7WrqnXoCRoBTiz7DwtDhHBA4SGZJFEIIIYTIglzIjkq33E/6XwkhhBht0gpyHXDAAaxYsYLrr7+ep556ikWLFjFx4kQcx+Hjjz9m7dq11NbWctFFF3HUUUcN9phHPK01ER2hxW1ml72LI/OO5oKSr3JA4CCUNI8XQgghhMiadLOj+lvSmOlSSOl/JYQQYjRJK8gF8MUvfpFZs2Zx33338Ze//IVYLAZAKBTiyCOP5Mtf/jIHHXTQoA10JHO1i4NDxG2h2W2h2W0mYATIN/M5Nv+/+PbYi5ns3XvjfyGEEEIIMbjSyY7qb0ljtkshhRBCiOEu7SAXwNy5c5k7dy4AtbW1GIZBYWHhoAxsOGh0GtnqbkFr3et6Go2Di6sdDGW0zoUIGtDaRSmFiYnfCFDmKWNecCHHFZzAdN8MfIav130LIYQQQoihtbfsqP6WNOZCKaQQQggxnPUpyNVROBzO5DiGnTmB/Tix+ETicRu39xgXJgb5RgF5Zj5BI4jf8BNQAYJGkIARIND6e4JnImVWeeZKEj0emn7wo/bbQgghhBBi8KUqaUynDFEaxQshhBAD0+8g12h3RP5RnDz5RGprm7FtN9vDSc3rJfKti7I9CiGEEEKIYa2vfbI6ljQSLsZZ+zKJJx4FrXstQ5RG8UIIIcTASJBLCCGEEEKIHvS3T1bbOtHvX4G7bSt621aMWbNR0GsZYsdSyEw3oRdCCCFGOglyjWSOg/X2mwDYBx4MppnlAQkhhBBCDC8D6ZPVtq3KCyV7sTY1oYrCaZUh9hRck8CXEEII0bNRF+S68srL2bhxPf/zP09neyiDLxolfMInANj1YSWEQlkekBBCCCHE8DKQPllt2yrAOPgQPGeehbVgUVpliKmCa4DMviiEEEL0YlQFuZ577llefPEFysvHDXhfz1d/xK/+8y5OwsXdy+yK2eKLRPlD6+0L/vN/xAL+rI4nkwyl8HhMEgknZ46/ozWfHTudM8pnZnsoQgghhMiAtqwpzznn4v7nHcyjP9GnPlkD6bGVsnn9xvUy+6IQQgjRi1ET5Kqu3sUdd9zK2LGZaeD5UaSBN2t3UuoJoHMkyNJVIB5rv70z3kLEcLI4msxSSmHaBo7j5szx/zjWzNx8aRArhBBCjARt5YK6rhZ300aM6dNx3ngdo49lgx17bPX2XF33lTpApmX2RSGEEKIXoybIdeONP2L+/AV4vT7eeOO1jOwz3+Oj3BfKmSBLVz53Tw+usd4gMd/IyeRSHTK5cuX4tziJbA9BCCGEEBnSVi6om5ogHkc3NaN8/oyXDfbW2L5rgExmXxRCCCF6Z2R7AEPh6aefZMOG9Vx88feyPRQhhBBCCDEMtPfTyssDrzf5u71sMHW/rFTcHVXYL63G3VGV+vG2YFosirttK87aNb2Pq6wc66jFEuASQgghUhjxmVxVVZX87Ge3c+WVP6CoqCjbwxGDKOLYxLWD1qDRtOV3aXTrsrZ7dFmn87LkktYf3fpY2/IO6ygUKFAaUNDs2EP1UoUQQggxyDpmTVFcDDU1fS4b7FjyqJub8Xzhi3hO+GTn7KwZM9FK4a5fD0DiiccwFyySXltCCCB1ObMQomcjOsilteaGG65l0aLDOeaYJdkejhhkW6ONFHv8KBQqGX8CFEbrfVMZmEphKIWpDCwUljJa7ydvW63rmG3rtP8k73uU0bpcJfuCoTrtd16B/OERQgghRoqe+mmlWzborF2D++EHuNu2QFMzsfc34Tz/f/hvvbNTSaLnzC8Qr6wEQEcj2GvXYISL5aJWiFGut3JmIURqIzrI9bvfPc7772/ioYcexbaTWTZt/Zts28YwDAxj5FZsOpbFw+d+tv12f9naJeo4nbKcOmY/td9uXV+TTJvSXdaF7tlSnffTPZMK3TEjq+OewDAMbNdt3TgZmPryhAP4VOk+HQJQySCUItnHSwghhBAiE/bWUN7dUUXiiUdxt26GxiaS38AZuNXV3WZFNKZOQ1fvSvb+2llF/NcPYXi9clErxCiXqjRazgdC9G5EB7leeOEv1NXVcdppJ3Z77JhjFnL++V/hggu+loWRDQ3bY/HrpWcMeD9boo2EDA+Q/HzWWqhHW8hIKbXnNq25U60L2jKcDFozqFp/G0phYbTfNpXCbLvfHpzaE6TqvMzAYxjkBX3EozZKJ8dgKYMjiyaQZ3kH/JqFEEIIMXJkstwn3X25GzeA1hgzZ+Oueze50DCSH6bCxZ1Xrq3FmD4d3dSMTiSgsRFKSjr1+3I3boBwOLmuZHgJMSq09QaUGVWFSN+IDnItX34lLS0tnZbdf///Y8OGddx0022Ulo7J0shyh9aamOuQ0C6O1jjaxdYah+R9W7soFJ8tm8Gnx+6LQmEo9gStVFs5YIcAVmtAajAzpyzLIBwOUVvbjG27g/Y8QgghhBjeMlnu03Vf3osu6THo1N64PhbDnD4D4+jFOC+uRoVCxO/8CUbHWRRnzEQVFaN8flAKjQLtJi9uw8Wtfb1qcDesR40txxg3Dv+tdybHJMEvIUYsmVFViL4b0UGuyZOndFtWWFiIx+Nh1qw5Qz+gIaZcl8lbtgOwZfIEdIrSzAYnzu54lJDpae9BlW95KLC8FFpeCi0f++eN4aQxU/EZ5lC/BCGEEEKIAclUuY+7o4rEbx9H19WgfH50XS3R71+B0TrrYtfgmVFWjveiS4h+/wpUKITzzzWoUAjl83UbR9cLWdDtt92N65OzL9bWoWtq0ZEIekcVief+iPPS6mTwa9MmjOkzUEVhKW8UYoTZW2m0EKKzER3kGu28sTj3XrAcgFOfeYBYwN/pcVu71CSihD0+7pr9X5R4AgRNC1ON3D5lQgghhBhdMlHu03GWxGRAaTq6uQUdj6M9HhSkDp7V1mLk5aFjUWhqRHt9ySBXinF0vZDdczs5k6NGJ0sdzeTHd135cTL41dSc7OXV1ITy+aRnjxBCiFFt1AW5rrpqZbaHkBURx6bJjpHQLrabLEOMuDbFHj9HFE2gwl8gjdmFEEIIMeJkotzH3bgBXVebDCRNrsA4bAHOa6/CundxK7djVOwDxcXdtjNmzEQrhbt+ffL+7Dl4PncW5oLD0x5H2/jttWuI//ohaGxElY7BOvlU4u9tQuVF0V4vqjWjTHr2CCGEGM1GXZBrtHC0Zlusqf1+gxMn4ZpYyiDs8RD2+JgZLOa8CftT7PH3sichhBBCiOFtwOU+4TDupo0Qj4PXi3nm59H/9uLuMxX33f/g7N5N7MbrMG69s1s2lufMLxCvrkbl5aG8XlS4GKOsrE/N8MxTcmgAACAASURBVI2ycrynfgZrwaJOwTqjNXhHuBhqa6RnjxBCiFFPglwjiKs1cdchph1qEzGKOpQdrpr1X+QXFJFvefFKby0hhBBCiPR1mP0Qrwdn40Z0TQ26uRliMaivw33zDZy1azBO/UynTa0Fi7CffqpTuWR/m+GnKmmU0kQhhBBiDwlyDXNaa+LapS4RpdaOkW8mg1glHj/7ewPt600JFECH+0IIIYQQIj1tsx/qWBx3/XrcDRvAMFAFBZAXQrV+sajp3vohVbmk/dLqjDTDF0IIIURnEuQaxuKuw9ZoE17DIGh6mFdQzoWTDmG8L0TY48doacn2EIUQQgghhr32mRK//XWwExCLQ14I8vIwiktAa4zSMVgLFvW4facMrC7N8AkXY7+0Orkc0i5jzCV9Kb8UQgghBosEuYaRmOtQk4gSdx0crYm5DhWBAq6aupDpwSIKLF+2hyiEEEIIMSK5H7yfnNnQ600GubTGGDcB3/eu6nM/rI7ZXYSLid/5k2TASyk0oLTuUxljprk7qki8vxF7/qHgL0hr/f6UXwohhBCZJkGuYSLi2Hwca2JKoJApgQLGe/MY4wuyX6iUA/JLU2/k8dBy4XfabwshhBBCiL5zd1SR+PWDuFs3g2FgzJ6D99zzsY7/ZL8bvbdld3UsXXSrqwFQJSVZK2NsC1ipaITqR/LxrLweSsf2vs3GDVJ+KYQQIidIkGuY2BlvYVaohDtnfSL9jC2vl+aV1w3uwIQQQgghRjh77Rqc/7wDtg2Ggefc8/B+6byM7Ltj6aJRWprs66Xd9ib1Q609YKUUuqUFZ+N61F6CXF3LL7MxbiGEEAIkyJVztNbU2zFiroutXRLaxdWaFjfBlECBlCQKIYQQQgy1ujpoaATXTTacT3Mz+523cV78G+bRx2Dtf2DKdbo2pgfdqUn9UGsPWEUjqGAQc+Zs3L1tk6K5vhBCCJENEuQaIK01jta42sVB47bdb73tao0Lrb+T9zWdlwPJD0sKXA35loewx0+Jx88Yb5BST4Aij4/DCvqY9u26GNu2Jm9OnASGkcmXLoQQQggxInVrol5UBPn5EI8ne3IVhve6rXYdot/+OjoSQd31Uzzf+Bbm5ArMBYu6lfJ1a0yfxVK/toCVen8jxQvm0ujLx7X3Fubq/hqEEEKIbJAgVx89UbWB3+/chFYAmg8i9SgUJgpDKUylMGj9rRSmMvAbBj5l4jcsfGbyd8Cwkr9b73sNE69h4lMm8wrLmRkqHvhgIxFKDjsAgF0fVkIoNPB9CiGEEEKMYKmaqFsLFmHNPQy3urrXWRQ7but89BF6dw1ojW5oIH7LDaiCQoyDDyFw6505HRAyysqxJozHCoegtjnbwxFCjEIDnbFVZnwdvSTI1UcVgQIMpVBKcer46RwWGIsPE7/RGsTqELjyGSY+w8RUkkElhBBCCDEcpGqibh21GP+td+61HK/jtso00WiwE637iqADAXR19YAbs8vFmxBiJBvojK0y4+voJkGuPppfOI5fH3ASlmUQDoeorW3GTiOFWwghhBBC5L6emqinU47XqYn85Mm4TY2wdcueFWIxVOmYATVml4s3IcRwlk6QfqAztsqMr6PbiA9yOY7DI488zDPPPMmuXbuYNGkyZ5/9JU444VPZHpoQQgghhMgxA2mi3nXbyIrLcFr7owIYBx7cWqqY/j67XhDKxZsQYrhKN0g/0BlbZcbX0W3EB7l+8Yu7ePzxR1i27OvMmjWHNWv+wY9+9AOUMjj++BOzPTwhhBBCCJFj2rK23B1V2C+t7mdZoMa79Hwif34ObBssC99lVwA67X2mvCCUizchxDCVbpB+oDO2yoyvo9uIDnK1tLTw298+xuc+dzbnnHMeAIcdNp8NG9bx298+NqAgl+Nqtjfb1LU4OE5uliuqqMOY1ttVUQdtOFkdTyaZpqbFm9vHP5WYCxFbU+RVjAuY2R6OEEIIIXrQn7LAVNsEHn4U++mnsE47HXPGzF73mU7WlnXUYrl4E0IMS30J0g90xlaZ8XX0GtFBLq/Xyz33PEBJSUmn5R6Ph5aWgc0U8+jmKL/6qJ6E7aD1gHY1aPzRFv7ZevsraxuJ+kdOkEspMM0GHMfN2eNvKDDbf1SH28nHfrmgkICpsj1MIYQQQqTQl7LAtuCUrq3pto1n8bF4Fh8LgP3S6h732Zesrf5kmkmzeiFEtkmG1fAWe/CXJB57BM/nz8Z33rJsD6dHIzrIZVkW06fPAEBrTU3Nbp599mn+9a9X+N73rhrQvltsTWWLTUVA5WyQxeM1+e0JXwLA7zUxR1DikFJgWQY2QxfkanGgOuZS6FGYxp7AlZEiiBWyFCFTEbAUQVMRMBVBK/k7ZClmF5gS4BJCCCFy2N4yDtqCRoTDxO+8LbmeUmilUFqn3Ka3ffY1a6svmWbSrF4IkSskw2p4ij34S2LLLwU0sddeA8jZQNeIDnJ19Pzz/8e1114NwKJFR3DssccPeJ95HoM8T+4GufAEuPcb1wPga/0ZKZQCj8cgoYxBO/62q4m7kHA1cQ07oi5zCk1+sH8eIWtP0CpoglISsBJCCCFGgo4ZT20BJu+BBxGcUIZlmViWgVIKXRQgETCIvvEmekwJ8U016KZmPGedg7nP1NYAVuf+W71lMXQMgGmlcGtrcXdUdRhZ5w88fco0k2b1QowqkrkpMi3x2CPs+TukSTz2iAS5sm3OnP1ZtepetmzZzH333cM3vvFl7r33IXy+kRT6EQPlak1DQlMT1ygFPgO8hsJvwmHFFp8c72V24ah52wghhBAjVqqLwK4ZT6Gbf0L45BPxei201p2+1FIeC++0aXgqKig447NE1qxh18WX4Dz/HJ5b7wB0yuypnrIY2gJgzto1JJ54DPvxR0g88d8ogNbMsI4ZWH3qbSPN6oUYNSRzUwwGz+fPbs3g0oDCc/a52R5Sj0bN1frEiZOYOHESBx98KBMmTOSii77B6tV/5fjjP5ntoQ0erSlsqAGgvqA4mf4kOkm4mtq4ptnRkPw/+R7FwlIPp0zwUREyCHsNCj0KQ46fEEIIMSL0dBHYMeMpuHA+JdMmgWEAPWdtKyv5cdo/bx6TXlxN9U9uw964IflgH7OnjLJy3HAx6OSkOrq6Gg0YJSXd9tGX3jbSB0eI0UMyNzMrl7Pi3KoqWt7YjDuuAkrHDupztWVtJR57BM/Z5+L70nmD+nwDMaKDXLW1Nfzzny+zcOHhhMPF7ctnz54DwI4dO7I1tCHhi0V4fOnBAJz66AZi/mCWR5Rbmm1NZcShLGAyv8TDtHyTSUGD/QotmflQCCGEGMF6ughsy3gKLpxP6feWA+m3JFCWhXZdSq/4Hg3bdhCtb0w7e6rTRVSHrCtVWopCJYNeSqFbSxg7BrrSveiSPjhCjA6SuZk5uZwV5+6oInr1FcTtOLblxfujwR+b77xlOVui2NGIDnK1tLTw4x+v5KtfvZBzz/1y+/J//nMNAPvuOz1bQxNZoLUmoSHhQszRVEVdjhrr4UcH5lHgMbI9PCGEEEIMka4XgYSL23tnhW7+STKDi54DXE6kEZ2IY+YVoYw9X4wpw0BrTcH4MSQi0W7ZUx2b1VNbmxwHdLuI6rgdaOzWEsbE44+QePrJPZlnOZxhIITIDsncTC2x+q/YTz+Fdcpp7TPe7k06WXHZOg+7GzegIxHwmOhIRDL2OhjRQa4JEyZy4okn8eCDv8QwDGbP3o/169/loYfuZ/78RSxceHi2hygGWVtgqzLiEnM1flPhNcCjFDMLTL41IygBLiGEEGKU6XgRSLiY+J0/aQ8yjfvNw2AY3QJcibqd7H7+AWLbN+A01wOQf9ASxpx8Yaf1lFJorcmLNVKv/FhHLQb2ZATouhrcTZswps9A+f0Y+x+IrqtB+fydZlXseLFihItRrSWMbetA9+CYXOAIIWB4ZG4OZXAosfqvRL50FjgOiccfhYf/O61AVzqz7GbrPGzMmIkKBMCOowIBydjrYEQHuQCWL7+KSZMm87//+wfuv/9eSkpKOfPML7B06QUyI94I4urkTIhRRxNtzdRyWid/sAwIWYrpAZNvzwhS7DMo8RmEZFZEIYQQYsTr6UKq7SLQfml1+zf13gnj8YZSt3eIbn6Hlo2vdFrmRBpSrqssC/8BB+B+8CGeomByRsYx09FPPEr8vfeI/v3vNP35r8RW/w23srK1BLEMNW58yguVVBda7sb10ndHCDEsZSo4lG6gzH76KXCc5B3HwX76qfSCXHvJihuM/mfpviajrJzg9TcTqNxMZPwU3JIxA3rekWTEB7m8Xi9Ll17A0qUXZHsoYhDEHM32FhdNciZEn6ko8SmmhCwqggbjAiYVIZMpIZOwV0lQSwghhBhF0rmQ6hhACn3qk91mUWxjhcswggV4iscT27Y+recP7DOly4yMHnyzZ+Pdd18Kv/IVImvWUH3b7SQqP4ZYLNl/q32K9g5jTHmhpaXvjhBiWMpEcKgvgTLrlNOSGVyOA6aJddrpaT9Pqqy4TqXnGTwP9zX4Z5SXE5w9jVhtM67tDui5R5IRH+QSI1djwmV7i8sBRRZnTvYxPmAyPpCcCVGCWUIIIYRI50KqYwApcOySHj9DBCbvx5SLH0C7Dh/e8Lm0nr/HGRk9HgD8CxYw8bFHwXWTjesTCezmFmyfj2g0gd3hoqXrhZb03RFCDFd9bY6fKrvJWbsGd9tWVF4IBb0GyjyLj4WH/zvZk+u00/G0lpH3R9dAlPeiS1p7LA78PCwzY2aGBLnEsOHqZAmi7UKLq6mzXeaXerjpoDz8pgS1hBBCCNFZbw3mU5UuWn7vkI5PGa19QVt/K48HT1EhltYEAl7icZvGxiiu2z27C/bedyeXGtPn0liEGM2G+r2Y6vmMsnK8F12C8+LfMI/+RK/BoVTZTQCJJx5Fb9uKBoyDD9lroMyz+Ni0G873+nq6BKKorW3vvThQMjNmZkiQawRzTZM/feKM9tu5TGuNrSHW2k8r7mpiLugOn+k0yf5algKPodg/7OWGg0P4pW+8EEIIIVLorcF8qjKQwcgEb2msx3U7l5F4fD58/tS9vzqOw+MxKS4O0dgYJRaz+/S82WyInMtjEWKk6SlolWr5UL8X3arUz+fuqCJ+520QacF543WMXmaMTZXdBIDWGLNmo5ua8Jx51pBls/YnENWXPluSoTtwEuQagWxXE3U0Lh6uufAnaFqDRXG3vcuD1ns6PnT83RZU6vSY1snHOmybznYp94WmQ2eK9tttASyvofAZMCloMjlkMiFoUOgxyLcU+R7V/rvIbzK1LJ+WhpZOqfxCCCGEEB2lajDfUxlIT/24+uv9/7zGH3+9qvuYTIsLrroTX6DnQBfsmakxP98PJANd6V7Q5lLZSy6NRYiRpKegVY/Lh/i96PQwQUZPgatUY04dVEr2JFSAKgpjLVg0aK+hq74GovrcZ2sYzIyZ60Z8kEtrzR/+8Ht+97vH+fjj7RQVFXPkkUezbNnXCIXysj28jGibWXBn1MXWYCgImAqlSL7xO6yrILlc7VmuFBgktzOUar9ttt43Oz0OllLtj5ttj7ev37oNbY8ng1dm+zbJwFbbvgwgaCnG+AzG+o3233srP7QsA5+paMn40RRCCCGGP9d1WbVqFU888QQNDQ3MnTuXa665hoqKipTr19bWct111/Hiiy8CcOKJJ7JixQqCwd6DMMNJOt++27aLx5O57Pdoc1PK5a5j49iJTsu0q3EiCQyvidFhDO2BrqCH6LvvEr3x+vQuaHOo7CWXxiLESNJT0KqnIJKurUle/Gk9KO/F9mD77NkQnoY5Y1bK935fZoztKaiUzYynvgSiJMg/9EZ8kOuRR37FvffezVlnfYm5c+exfftWfvnLe/jgg/e54467hm2D8oitqYo6oBRag9eASSGTUyb4qAgZTAya+BWY0RYMBSoQxDCSDdkNOgakkoGm4XochBBCCNHd3XffzaOPPsoNN9xAWVkZt9xyC1/5yld45pln8Hq79536zne+QywW48EHH6ShoYGrrrqKH/7wh9x0001ZGH3/7K0cJJ1v323bwbKMlJ+LtHZp/s8/cKLNe9av20nD63/CO7YC/8SZvY5v3pLTyC8qBiAQzCeYX5gcd9yh9pVt1L3+MW48OcW9b2yIcZ+ejafAD3QIdFnQUleL8vn2ekFrHbU4Z8pepARHiL73wkpn/Z4CyN36ERYXtwfCtVJ4PncW1oLDM/pe7Bhst4NBCu9ehVGe+r3f1xljUwWVBprxNFS9ySTIP/RGdJDLdV0efvhBTj31dL7+9W8BMG/eAgoKivjBD65gw4Z1zJo1p9/7j9qaiEOqmZ4zytbJPlUxVxNzkk9nKJiSZ3HKBB9l/mT2U0XIJM/q8KGsuZkxMycAsOvDSgiFBnegQgghhMi6eDzO/fffz+WXX87ixclmuLfffjtHHXUUzz//PCeddFKn9d944w1eeeUVnn32WaZNmwbAtddey7Jly7jkkksoGwYBiXTLQfZ2URSNJggEUjefT+zaxs6n7ui0LL5zM9V//AVWURmTv3l3r2N89S9PMa5iOlP3m8u+B8wDktlb23/7DtHtjZ3Wje1sJraruT3IBcmm9IHD5uIZU0pi1y5UUfGei6VwGLepCeWxOi3PpbKXXBqLEIPBfuft1kbqx2CMGdu5fLjDOUorhefML2AtWNTje6LTOa11fTPF+j0FkLsu75glpbTGCBdjlJXtNdCTTiCobR1dW9P+HDoSIb5uHRyyoMf3fjZnjB3K3mQS5B96IzrI1dzczAknfJIlS47vtHzSpMkAbN++rd9BLp8JxX6DmriL1oMb5bKUwmvAlJBJRchkWp7JtHyT/QotCjzSdV0IIYQQe6xfv57m5mYWLlzYvqygoIA5c+bw6quvdgty/etf/2LMmDHtAS6A+fPno5Titdde41Of+lS/x2JZmf+cYppGp98Aifc3oqKRZBlONIJ6fyPWhPFp7c+tqsLZuD5ZVlNeTiKROptL+fx7yny6MPzpfZFYuXkTlZs38e6rqznzwquJvtfQHuDyleVReGAZdnOCyNZ6fCXd96kTCfLPOIOa667Df9GleCeMw62qwv7pbRheDyRsgpdcjjVhXFrj6fr6jfI9F3mpjrMYHHKsh8ZgHmf7328T/dLn0fE43LMKc/Z+KK8XFQgQvP5mdOs5SsdjuOvXkdhdjfvMUwSvv7nT+65NosP6zrp16N27MXtaf8L45E9XHZa7psIOBtGRCCoQwDtnDlTvJHr1Fe3Luu7brarq9fGO67h1tejmFlQoiPL5MYJBvHPmYPf1WPf0Wro8Z6pzVl8M5G9Gv6TxuvpDzh2pjeggV35+PhdfvLzb8hdffAGAffaZ1u2xdH2uIsAJUwupb4jgOIPb+DxgKsb6DbyGlBQKIYQQondVVVUAjBvXOdAxduxYKisru62/Y8eObut6vV6KiopSrp8uw1CEw4OXRV5QEGi/bc8/lOpH8tEtySyJQCJCINqANa73YI9dWUn1yivRLS24wSCld9yB1cOYPYVjqbj4QXQi1u0xI5ifcptAKLm8bNJUgnmFfPzRBmKRFmp3VfLOK3+jrHpy+7pmwKLp/RoCEwsZd+oszICn2/6Ux4NvvznonTuw77yVkoVziVduJtbUgIpGMPLyCNktBMMh7MpK4uvW4Z09u8fjkPL1d1m343EWg0uO9dDI5HFue5+5q1dDIpEsLY5GUVUf45k2Dd3UgPHsk4SWLMEtzMfeUotWCm+4CGXHCVRuJji7+zVp2znN3lKLNhTeooJO66fz/u4kPI3Cu1clt5kzB6u8nJa//pW4HQePCSn2HX/vPVTVx5h5edCUwHj2SfLPPrvT87W8kTz/OBvWox0Ha9o0Ci/8BoGjjsIqL6cgY0e69bikcc5Kaz8d/maoYJDiBXN7PPcPB3Lu6GxEB7lS+fe/3+I3v3mIo446hqlT+x/k8puKcWEvtSRkdj8hhBBC5IxIJALQrfeWz+ejvr4+5fqp+nT5fD5ise4BnXS5rqahIfNTxJimQUFBgIaOXzT6C/CsvJ7EP18m8fij1Nx1N9zxU4LX34R1wIE97ivxyuvE61tLBesbqXnldTxHH4PHYxIK+YDOfUvNQB4E0p+4aJ85h/D1a+/F8iQDVs2N9Tx4wyVo7fL+f16jIF7Yvm7LR3XJ3x/UUvfadiqWHooZ7B7o8s6YgTYMbGVS88rrqHAxsXXr0PE4yuul2QoSWfc+LVcu7zUDo7fXDz0cZzEo5FgPjUwfZ7eqqv19puNxtGGA44Dfjy4fT7ypGXfTRhK2S9PfX8Z/8WW4r/0L/vs3JBI2RiifyPgpxGqbu++89ZzGP9fgPv7f2FqjLC+R8VPSfn+3vWav18KyDMzycoLjxqG1xrYd1MFzUTNmEv/Pu6hAoNO+3bpanPXr0E6yV46yrPbXEbz+ZiA5c6IKF5OIxHDjcXA1NoqIN4gOFlIAGf9vurdzVp+0Hl9n43rMmbNp9OVDqn+HHmQimywTRuK5o6AgMODMtFEV5HrrrTf43vcuZvz4iaxYcXW2hyOEEEIIkXF+f7KPUzweb78NEIvFCAS6f9vr9/uJx+PdlsdisQHPrjiYXwQ6jtt5/6Vj0YVh3EgEd/06cByaViwn+Iv7e+y1oqfNQPsD7X1Z9LSZ2HZyv66ryc/3o7Xu9wQ9Sqn2ABeAPxDCtCzsRBwnHkcn9ozfyvehPAaJmghOc4LdL29h7H91/0JWeb0Ys+ZAYRFOQRjnhb/CpMmohI3Ky8OurobqatyW1t44LS3E163DKh2b9uvvqNtxFoNGjvXQyNRxtteta3+f4fHgvWwFNDViLj4Wo7SUxG8fT1Y3e724LS3EN23CXv03CAZw4wm8F12KWzIGt6exlI7FPPk0fPMW7OmtVTIG+6XVe31/G4YiP9+P12t1O4cppbAsE7Mon8BPbiW2azeNtU244eL2fbs1tehIBMaMhUgEQkHcxka01kRf/gf200+1nzespedjr1gOCtzNm5PnpdaAS6b/m07nnJW20rGo0rG40PO/QQpD2c8rXXLu6GzUBLn+/OfnuP76HzJpUgW33fYzCgoK976REEIIIcQw01Z6uHPnTiZP3lMOt3PnTmbN6j6rU3l5OX/+8587LYvH49TV1Q2LpvMdGTNmQiKRzKYwTZTH0+t07b01BI7FbBKJ5h4vFNv0FgR77tF7qPxoE/vNX4zPH+TDdW9iJ5IBxfFTZ6LeU2hHg4LJSw/B8Bh8eM8rOBGblq11KfepXY33nKW4dfXEb7oOHY2it2zBmD4dVRRubTrf8yxl6b5+IUTvus6a5znhU53eQ+bRx2D/6f/QgCoKo1AQaUH5/CgfUFOT3vN0bdC+l9n6fD6L/PwOk1b0cH5SRjJbxltaTElpMY2NUdwZM0EpdOV2iERhyxYIBdG7q9HBEMrvh/q6TrO56o8/xtz/AHRTEyovD2qTr8uurCTxyuvoaTMyFgTKhXNWqtlssx3kEp2NiiDXI4/8ip///GccdNAh3HjjbeTlpZ9m3pMtWxyefbaR5uYYjjPI0yv2kxWL8pXW208+GcX2mVkdTyaZpiIUcnP6+I9kcvyzKxvHP5GAlhZNc7OmpaX7z7JlAY48MvWMZEKIoTVr1izy8vJYu3Zte5CroaGBd999l3POOafb+vPmzePWW29l8+bNVFRUALB27VoADj300KEbeAYYZeX4rruR6PevQHk8HYI+vW/T46xhrqa+PoJlGfj9HizLbG9Knyz5cXEcF7+/e1khwPYP1tPSWM/a53/fabnl8XLgov+iob6SWFUTyjQwfCZKKYyABydi40bslPu0YzESTz+Fu20rettWjFmzMabPwPrEEjyf/Xz7RV+6F4Iy66EQ/dNbwMXdUUX8ztvAY6ETCXzfvRSjdAyJp5/ca/B5IM/bMcCVbgZq2/ksP98PkyfinPkF3OrqZABr+zZUuBitalElpRjjx0NhUacgm3n0J3DeeB3l87W/LreqiuqVVxKvb0T7AxnNdhroOWugM0ruLcgosm/EB7mefPK33H33Tzn22OO4+upr8XhSfwjpqz/9Kca990YwjNy9wPe6MSblJWdE+uldMeIjrHG9aY6c2uPhSI5/dg3k+MfjGreHTZUC0wTDSKa6t902zeRty9pz37KS65omvPhiXIJcQuQIr9fLOeecw6233kpxcTETJkzglltuoby8nOOOOw7HcaipqSE/Px+/389BBx3EoYceysUXX8zKlStpaWnhmmuu4dOf/vSwy+QCsPY/kOAv7s/oN/227dLU1HN/MtXYgDdciLI6f7Q+9OhP8sZLz9HcUAuAYVpMnr4fC48/naLSMuyKCLGqJrTtUr36I8ygh0RNsqeaf3z3ZvZaa+IfVyazQfJCaEA3NWFMnNQpwAUSvBJiKPT0PmvL9umYtWXsd0DGspBSPa9pql4DXFprmuI2XsvE16XnUcdAV/yoo0k8/RS6rga3rg5VVg7RKMb48aiiMNaCw7EWLOr0Oowur8t5+UV0S+5lO+2t1DCdUsRcyCYTvRvRQa7du6v52c9uo7x8HJ/97OfZsGF9p8cnTJhIOBzu1761BstSVFQYqWaSzhEhbp/6CwAGcULUrFAKPB6LRMLO4eM/csnxz650jn8spqmt1di2JpHYU06jNXi9imBQUVSkKCw0CIUUeXmQl6cIBg38fvD7FX6/IhBQ+P0QCChCoeR2HX/8/vS/KRRCDJ3vfOc72LbN97//faLRKPPmzeO+++7D6/Wybds2lixZwg033MDpp5+OUopVq1bxwx/+kKVLl+Lz+TjxxBNZsWJFtl9Gr9yqKux161J+2z7UAZ7G2kZKCgvQrtteAgRw8JEncPCRJxCPRUnEY/gDQUxrzxeuRYeMp/7NStyYQ92/tnfaZ3juhG7Po5Qiqk0IBFGAcfAheM48C2vBok4XWvY7QIBh3gAAIABJREFUb+O8+DfMo4/B2r/nxvtCiMHRU7bPYJ6b8vJ6DnC9tLWaZzZVURNNlkvPGxfmjFkTKA7s+YKyLdBVMGUiTmsQh3BxsvywuDgZqOsQ1OlUQtnldZkzZuEGg1DfmFPZTnsrNUy3FFG+RMhtSuvsXqLW1jYPWpO0Z555ihtv/FGPj1955TV86lOn9GvfDzwQ5YEHolRUKLnIzwIJsmSXHP/sSnX8XVdTU5MsHbTtZLbV+PEGZWUGY8YYFBcbFBYmg1pjxyr22cdk7FhDAlT9YFkG4XBoUP9+iZ4VF4cGPOuOGBqO41JTk/5sVemyLIP8aAOVF34r2Xw5Rxr/eqNNFEwsB607Bbr2JrarmV1/+5DI5mQPLm9pkJIjK8jbt6TTetq2idfW04C3QzlN9ywC+523iZzzeYjHwevFf+ddKMPssfSmJ5k41+2t7Eckyd+VoTHUx7m392mmtb22VF7etpsH3t7cbXlpwMu1R8/Bk+Jv6kCPUdt5uuaV19HTZg5JtlM655vumVo3dSsz7e3xXDMSzx2Z+Jw3ojO5Tj75NE4++bRsD0MIIQaFbWvq6jRNTcnAVnGx4rDDPOy7r8GUKRbHHuvF75cglhBi5ImvW5ec+QtyphQmWt+E+97fKTpiAdowupUu9sQ3JsTEM/fHjdtoR2MGurfW0K4LrqaxtimZWbHnkW7r2v/7NLqpCSwTohFiK7+PMWHikAcDc3EGMiGG0lBm+/j9nh4nwfi/D3YAYBmKz8+eyPu1Tfzz41qqI3FeqazliIldAupa4/d7ei3RToc1bhyeo48ZsoBiOuebvZUaSiniyDCig1yDSSlIJDQffujmbCZLwG3hrffnAHDQtHeJGAObBjyXJPsGaRwnd4//SCbHP7vajr9tuxQVKebP93DAAcmg1uTJI2eCCSGE6Il39mxUIJDs+ZIDpTBtF1jRSAtNBfmULr+cwLx56EQClWY/WMOb+mO5dl1QioYdu9Hh4l4v5ux33sb+858gFoUoEApC24ziQxwMlBnIhBg6lmWmDHAlHJfKpigA+48p4JiKMcwtL+KfHyf7BG7Y3dgtyKWUwrKG1+fJvpxv9hZ8lFLE4U+CXP103HE+ysv9OT27nBXTcGny9oUXBrB9IyfIlZxdzpfTx38kk+OfXW3H37ISzJ1rEg5L6ZYQYnSxxo0jeP3NxNet6/e37ZkspWu7wNKxGImX3qRy40a8s2aTf+rJ+A88EO/MmSivNxmwapVOSaO2bXAc6t7fjD1mHADO2jW427ai8kKo1uc2ysrbg196+zbIy0MVFuH50nm4b77R6yxgg1VSKDOQidEgV0pyLSv1+cQyFB5DkXA1ddEEWmvqY3tmbv2orqVP+8sFqY65nG9ERxLk6qeKCpODDw5RW2vkbv1rs9Me5Dr9dD+E/NkdTwbtqT/O4eM/gsnxz66RWH8vhBB9ZZSXY5WO7de2bQEhd0clurER/8rr8Cw+tv9jab3A0rt3Jxd4LGLPP0f8H39H19ejZszEHDMmOUvZuv8w5o7bCRx+eI+ZXm1lR7Ft22nYUYMO5eG+tBrCYRJPPIrethVNsvF828Wcu3EDymOhTRPlgFE+Du8ZX4AzPt9j6c1glhRK2Y8Y6XKpJLenHqtKKaYX5/FudSMf1bfw439sYFdkTxlizHH6tL9s6+mYy/lGdCRBLiGEEEIIMaq4Gzfg7qjEfeMNcF2iX1+GeuLJfs9E2HaBZa9dQ/zXD+G+9QY0NaObk4339Yfv4xbkY02ZSuJPf+Tjk07Gu+80CpYuxX/EEXj32y+Z6eU42PEENgYt26uIv/1vCIeJ33BdsgSnqQnl8WDMmo1uasJz5ll7ZjqbMRNVVIwxaxY6YeP78c0pZ0HrehwGs6RQyn7ESJZLJbk99eMC+MKcSfz4H+uJOS6bGzpnbgU9PZRJ52g/kt6OuZxvRBsJcgkhhBBCiFHFmDET3dgIrf2utNY4L/6t30EuSF5geU/9DACxmt3ozZuhuTnZAL4oDE3NuP/8B2qfaeitW4iv30D1d76T3NiyMObNx3/pFVhHLe6UraCbmsBjoXx+lMeCRAKVl4cqCmMtWNTp+fuaySAlPkL0Xy69f2zbxeNJ3UdrXJ6fa4+ew9+37WZLfQtNCZv3a5MB+Nml+T3uLxfl0jEXuUuCXP3kxB1qNtZRXx/BcXLzJKAiLYxpvd3wYSM6kDoddbiJ1cUpmVUEPUyTK4QQQgjRG6OsHP/K64h+fVkyA8Lvx+xSrtifXjvujip0XR14fTCmFBrqQWvYthVtedChEMTjkJ8PzU2QlweOg5o9B2NMWfsFm93edysPPJ5kWaMPVFEx3osuhdqa1nU19kurO41R19Zgr30Za8GivY5bSnyE6L9cev/YtoNlGSmzud6raeLJTR9zwJhCZpfm85ePdrY/dkhZUbf1tdbYdm5eN+bSMRe5a1QFuXbsqGLp0i9w/fW3cuihhw1oXxsf/YAND7+PHXdydnY5y40yrfX26m+twTaGd08upcD0m/x/9u47vsry/v/46z4zkywy2EsT9lAgYst00bpaq3XiQsVVqfy0xRYFFS1acaEi6NfWalW0Ku7WKgJWcQEKiAwFISAJI4OsM+/798chB0ISSCA5SU7ez8cjD07uc5/7/uQKSa68cw1HnIOkHgmc/beTmrskERERaaWco8dhvLKQ4NLF2EePw9Gvf/i5w621U1sAZhbk47llMsGvV2J5vVBRDhihkCtgYuVtwSwuxDgmG2P7NizDAJebmFkPYEtNC//CZhbk7193ywxi69YD99Q/Yxi2ar/UHVyja/IUvPfdE1poHggMHkLMA4/UK+iq7RwzP5+KlVswO3SDI1z7TCTatZQpch6Pn9hYV63PbSouZ/2eMtbvKat2fESnVI5NTahxvmEYeDz+JqmzMbSUNpeWq82EXPn5O5gy5UbKysoOf3I9BL0mlbs9JHSLb7Fzlm2mxbaU4wBwprix2+q3hXUkWEELT6EXf+m+3T0sMBwGNoeBYbdhsxsYDgO7y47NZcPuthOXGUu77gkkdk+g2ymdmvcDEBERkVbP0X9grVMU61r3xeGw4Tb9OOIdOM88DcPpxDItAkET314HgaQkAmVloddaAFZoSiRAIIDl9WK0b49hWRj7ph3aUtNwjBwdvnfw82VYu3dDp86htbx8PvzPPbsvaMuss8bg0sWh1+1bSNrcvfuI1wgyC/Lx3D4VX8BHwOHCdXfzLagtIocXCJj4fAGcTnuN0VzZqQlkxLnZWRFacL6dy8H4XpmM61YzvLYsC78/2GKnK4rUR9SHXKZp8t57b/P44480+rWdsQ4csY4WG3KBgyXDZgBg2/fW3MygRUV+JaY3SEz7GLqN74w7xY0z3okzwYEz3oGj6nGCk9g0N65kV41v1i15W1sRERFp3Q5e98XZfwDt4uy44uOwTBMjbf8UH8Nm4LTZcfToRvyCF6lcsoSdEycS2LwZ3DEQCIRGc1kWVFZifv89RkkxtmOzMZJTqq0p41v4L7x3Tccq3/dHWbsDW0oKVnER/ldfxvmb3wL7Aq6UlGo12kePJbDsE6xteaGPoX36Ea9XY25Yj1VZCU47VmVlsy6oLSL1U1rqITU1vsYi9N2T47lnTD88gSD+oEm8y4GtlmmNVb/TlpZ6IlazSFOI+pDrhx82Mnv2LH7963MZOnQ4t976++YuqU2yrFC4FawIEt8pnmPP70GXkzviTnE3d2kiIiIiYYE1qwguXYzzkksxbHbihg2lXacMqtanMGy1/6Gt6njMiSfSZc0adl13HWX/eR9iYyBvG5hBwMCWlIyRkY5t6HAcffqxb8gX/iWL8Nx4Lfj9oTUaunXHltYeAHPjBgJAYNknGBCqZd8URYqKwtMYbQ88EhoJhrFvUfqaa3bVhy07ByM2FgI+jNjYamFZVfvYR405qoX6RaRxmaZFaamHxMSYWndbjHHYiXHUvjj9gQGXaTbdAA6zIB//f97D2vETjtPP1PcQaRJRH3JlZmbx0kuvk5GRyYoVXzV3OVHLDJgEfSbmvregP/QvFmAAloU9xkHX0zoz8Hd9iElr3euDiYiISPQJrFlF5UXnhkYxORy0u+8B2nUOTRGsK9w6mOF0gt1Oxv/9H1wzibJXXganA7KyYO9eqCjHSOqAuWY1/tXf4H9rITEzZxF46w2o+uXStMDvJ+avDxFc8hEBwHC7MXfvxgJsaWmhEVxFRdWmOtoys7Dt2+Gx2g6NhoHzvAvqtRh91XXi7r2f2B1bqOzYHTMtfX/7XHJ+aPH8+XOJfX6BfkkVibBDbYrh9QaAuoOu2hwYcIVe3zTMgnwqf3ctwWWfgmXhe/5Z4l78l76HSKOL+pCrXbsk2rVLau4ymoU94OHcxRMA+NeY5wg6GjdYCnqDePZ4CXqCGDYDm8uO3WXDmeCkXUYM8R3iiE2PwZ3iwp3iJibVTdrAVBwxtf8FQURERKQ5+d95C6uwCMwgzu7dSbv4ArCsOgOuYMAD2LA7qi/4bNhsWKZJ+mNz8PzvYwLffw8/bYf4eCyPB1vuiZiffRI6ed+aX/afj8T//LPhEWOkpoWmHP7mtwRXroDKitB6XhhgmRAbV2M64oG//Fat2WV5PZjr1uHbvZvAW2/UWES/LrasLFzJcZR9sQKrVxBbZhbBpYtDAReAz0dw6WL9gioSQYfbFANCQZffX05iYgwul6POsKvquN8fbPIRXLBvGvRP2/evU1hZqe8h0iSiPuRq6xymt9GvaQZMPLu9+MsDJHZNoNOYLNoPTiMuM4bY9jE4E531+quBiIiISEti79ARv2GAaZH+9NMYdnutAdfubV/w0w//we/dC4A7No3OOWeQnLF/h0bDZguN6Jo3j59OPjk0OquyEmtnPv6X/omtY4fQiHe/H1JTQ2un9jwGtm0Flxub04m5YT2OkaOJmTlrX3jVG7DCj6stRF/LbovExmHt2ROqJyGh2iL6h2Pm57N7xp/wlZRixcQSM3MW9lFjYP7cUNDlcmEfPe4oWltEGqquTTFqnGdalJRU4nDYiIlx4nDYcThsGIaBZVkEAiaBQBCPxx+xReZt2TkYHTvBli37plzH6nuINAmFXHJYVtDCV+rHX+oPTUG0GbgSnWSdkMGJs4Zid2tkloiIiLR+jtN+ge3thTji4ogdPbrWc4ryV7Fl7SvVjnkr9/DD1/8gZ/j1JCR3Dx83nE5iR4/GNXw4vi+/DIVa5eVYu3dhxcZiOZ3Y4uPxPTwb1+QpOHr1IlgaCs6M9umQkhpeU8sxcvRB05Qyq9Vw8C+/FBURM3MWwc+X4X9lQZ2jv+oS3LAOq6L6L9OOkaOJfX5BaE2u0eNw9Ot/6IuISKM6eFOMw309BwImZWWNP+jhSNgys4id8+T+NbnOOFvfQ6RJKOSSQwp4gpRvq8CV5CS1bwrtB6WQnJNMWv8UYtO1rpaIiIhED1tmFnFz5pEQ6wjtoljLKK49O5aHH3fp/SsC/nJ2/PBfwKIo/5tqIReA5ffTbtIkCr1+zI3rwWYPrdtlWRiWheF27w+lHniEwOfLMDAwevbC98hsrJIizLJynL88g+DnyzD2LTp/8DSl2n75tWVmQu6IUB2AI/fEGuFYXezZvTHj4qCktNov047+AzW9SKSZ2DKzqo3srO/Xc0thy8zCfekVzV2GRDmFXFInM2BSWVBJbEYMY+aeSEKn+OYuSURERKRJ2TJDa1HVtQ6XaYYWZjYMO8kZ/Q8IucA0/TXON5xOYsf/gpiEVCzLxDfrHqzy8tAOiomJWF4Plj8AKamhkO2c34SmF3krcbzyEobTieXz4Vu3Dk+XDpR//gX+rXk1pinV9svvwVMYHfsCr3q1Q1YW7R9+mMIvVmD1qjlyTESahy0zq0G7pYq0NQq5pFaVuzz49vpxtXPSaUwHBVwiIiLSZjgcde+kmNbhOEr3bMCygqxeOrPac6lZQ2p9jbN9+/B0QxLiMX/YCIYNw+8DpwsjORlzwT9JGv5XXPFxoQWhY/cvZm+4XLgHDsTVpw9JV12FZ823lLkSOHiZ6IN/+a3v+j11tkOHDjhHjYnYmj3Sehxqhz8RkeakkEuwTAtvkZegz8T0mVgBi0BlkNR+yfz8wVxiMzQtUURERNqOQ22g445NrfM5pzux9uvZQtczN6zHKi0LjeIqK8Pa8iOWw0nitdeSfted4HAc8v6G0xmqoV9f3EBpqQevN1BnPQ1dv6elUqDSstRnhz8RkebSpkKu444byv/+91VzlxE5hkFBSv/w4wNZloXpM/GXB/Ds9hKT6iaxSwJxWbHEZcYSkxZD+8GpxGXGNkPhIiIiIs3Hsqw6g6at6xaGH2d2H4sZ9LIr71MAtm98j16DL6v1erBvd7HEBPB4IBiE2BgSzj6L9HvvAahziuTBqnZIS0yMAeoOuppi/Z5IB04KVCKnvp/box0hKCLSlNpUyNXWBO1uPhh2b/VjviAVOzxYQROb04Yj3knXUztx7Pk9SRuQ0kyVioiIiLQcgYCJ01n77tGVZfkAJKT0pHP2LwEo2fUdPk8RFXu313m9KpbXC6YJlomzWzfS588H6g64yir92AyIi3FWO35g0OX3l2OaB09eDGms9XvMgvx9OzW+BHUsft8UFKhERkPCxJY6QjCwZlVo589RY7Q5gkgbppCrDfEUevEV+UjOSaLHmV1JOqYd7Xok4GrnOvyLRURERNqIQCCIw2GrdTSX05WI31tCeclWduUtIxj04vMUA+CKSa5xvhUI4CvYCe5Egp8vw1y7Frw+wCJ9zhwMm63WgGvD1iLmL1zN+i1FAAzrm8lVZ/enY/uE8DkHBl1FGzaHAqGUFCgqqtdIq3qP3NkXgJjb8rC25WHr3QcDIhI4tdRAJdo0JExsiTv8BdasovKS88Hng/lziX1+gYIukTZKIVcUq5qSGKgM4i3yYrPb6HF2Vwbc0BdXovPwFxARERFpY8yCfMpW7yB23Khan8/o+nO2b3wHywyw9bvXqj2X3vVnNc43HA7iMtNx7tyFN6cXwX598S1bhuu444gdVfs9CgoruO2JT/D5g+FjX64t4IdtJTw59SRi3fu78IZh4HI5MJ+Zh2fZMqyNGzCOzQ4HEXUFFQ0ZuVMVgBgJ8ViAVVaGkZwSkcCpJQYq0aihYWJL2+EvuHRxKOAC8PkILl2skEukjVLIFSXMoIW/zE+gIkCwMgiGgSNYySVbfg8GvDP+WdJHdqX3pcdgs9dvvQcRERGRtuTA4MeTloS7Xz8MR/Xucmb3URiGQf6Piwn4yoDQCK6Ox5xGatagWq9rOJ24OnXEmZFO4iefULl0KYFt27D8/vBi8gd68+MfwgHXb8YeQ1Gpl0Vf5VG418Oir/I4/Wc9qp1vmSZxI06g4ql54PVirf8Ow+0+5GicBo3c2ReAGIBt8BCc512II3dExAKnlhaoRKPWHibaR42B+XNDQZfLhX30uOYuSUSaiUKuVswyLXx7/XiLfGCBM9FBfFYcqX2TSeyWQEKyReyVpQCMnjMC4uObuWIRERGRlqG2qXpVwY+zaxf8332He8CAGovQG4aNzO6jyew+mmDAg2HYsNnrt/RDVaAVM2IE2O11rsP1zYZdAKQkurns9L5UegMsWbGNoGmx5ofdNUIuw2YjJicbPF4w943+8vsPORqnISN3WnsAIvXTmsNER/+BxD6/ILQm1+hxOPr1b+6SRKSZKORqwSzTIugJYgYszICJte9fM2BhBUzAwJngIOuEDDqNySJzeDqxGTH7O2Ll5c1av4iIiEhLVNdUPWf//qQ8cD8xAwaERlkdZrdDuyPmiO5vOJ3hHRcPZlkWWwtCf6Ts0TEJwzCIi3GSlhTDzqJKNuYV1/o6V+/e4HKCIw6jYyfc99x/yDCqocFVawlAIr37o7Qcjv4DNUVRRBRytTSWZeEt9OEr8QEGdrcNm9OGzWHDkeDAnezCneIiNj2G1L4ppB+fRkInjdASERERqa/apurFdu1M4jFdYd8uhbVNIzxS5WVegkGThAR3eNmI2ha1BwgETaryr7iY/V310O6KlXh9wVpfZ7hcYBgYTicxd95br5EsrSW4qq+GrDMmIiLRSSFXC2BZFkGviX+vD39pAGeik56/7kbagFSSeibiSnLhaufEHmOvs0MkIiIiIvVz8FS9uGFDSUwMjcoyHI27dukX/9vMv1//FoBBQztz9oWDD3m+w27DMMCyoMITCB+v8PgBcLvstb7O8vmwdeuOkZ6BVVxE4OMlhx3NFFiziuDSxdj69cew2Vv96KeGrDPW3A4ccUanjs1djohI1FDI1UQs0wq9BUNvZnD/4/3HTEy/BZaFzWUnJsVN53Ed6XF2N1L71NyCWkRERESO3oFT9Zz9B9Cuc2iqXm1/TDRNk1JfGfGueBy22gOmuhTuLg8HXAAV5b7DvsYwDLpmJrIlv5TNP5VgWRaV3gB7SjwAHNOl9j6ib9MmjPQMMAx8Tz6OER+PkZxS52imwJpVVF5yPpbHAxUV2AYPxpbZoc7zW8M0wIbuENhcDh5x5vjL/ZDSq7nLEhGJCgq5GsgMmCyftYri9SV4S/0EftgLB3aILEJ/ejMMDHtoIVDDbmCzGxj73hxxDpwJDlwJTmIzY0ntk0xyThIpOUnY3Q3rPImIiIhIw1VN1WuXFAvUDLg27N7Ea2vfY/XO9fiDfgwMjk3rwWVDzuXYtB61XbIay7R486Vvjqi2QdnpbMkvpajUy7PvrKVwr4fgvmmUA3q1r3mvQADv2rVY5eVYfh9s345lt2Pr3afO0UzBpYtDO9EFAxAMYu0phHZJtZ7fmNMAmzIsay0L5B884iy4YR30UcglItIYFHI1kBW0KPquGCzIHJxG6pAUbG47jhg79hg7jlhH6N+q9+McOOJC/zrjHDjiHNgaeRh8nWw2/IOHhB+LiIiIyH4Ohw2Xq/bu8AurFrJ218bw+xYWG/ZsYvqi2Tww/nY6Jh46QFn+2Va2bi48orrO/HlP/rNsC15/kFc/+j58PKWdm7HHd65xvuFwUP7hotAfWi0L7HYIBg+5w6J91BiYPxdMM7TTY/u0Okc/NdY0wEismdUa1hk7eMSZPadPc5ckIhI1FHI1kN1t59R/jsHhsJGSEk9RUTmBgNncZdUuNpbi95c0dxUiIiIiLVJMTGiXw9qmKbZzJwAwsttwjk3rwdpdG/ksbwUBM8j73y/l8iHn1XndkqJK3nttNQBduqeQ92NRg+rKSovn3ut/xvyFq1m/JfTa4X0zmXh2/30L0O9nWRbeNWvwfP4FRmIihjsGUlKw/IFD7rDo6D+Q2OcXhNbkGjAQA6PO0U+NNQ2wNa2Z1ZRay4gzEZHWSCGXiIiIiLRJDkfdm/pMGnYJVw29KBx2/azrUD7LWwFAYUVxnde0LIs3F3wT3iHxzN8O4on7Fze4tuyuKTxw0yjKKv3YDGqEW1X3wrLY/eDDoQPuGNx//BMUFUFKKhTuwSzIrzNIcvQfiKP/wMPW0lihTGtZMysSGjLirDWshyYi0lIo5BIRERGRNslxiCUk4l1x1d5///ul4cfdU2pOGayyfk0BmzfuDl0jwcX7b+5feH771mLeePFrzvztQGz26veua0RZQmzNcAvAMk0wDIq/+prgTz9hS0vD8lQQXLoY+6gx+B6Z3ajTAhtjGqBGMDVcJKZ4iohEE4Vc0ayigtSRwwEo/PgLiIs7zAtERERE2o66RnEd7NOty1mw5i0AEt0JnNprVJ3nFhdWhB+Xl/n4ft2u8PsV5T6++Wob437Zm8SkmOovtCysYBDDcfjuuWWGlsooLfXgi0uE2Dis4kLMjRsJYOB//98YTieG292gaYFVI4ZIScHaW0xg+HEQ0+6wr2uI1rBmVkuiKZ4iIg2jkOsIffDBf3nuuWcwTSs8HD0SSkpKwo/tdjsul5unnvo78fHxNU+2LOx5W8OPRURERGS/ukZPHeiLbV/z8LKnAXDY7Nz6s2tJcNfS79qnU7cUOnRJwgzu73sV/LQXALfbQefuKcTFu6rXYZqUL/oIR0YGMQMHYJkmRi2bBlXV6w+YlJZ6ME0rPDrK/+rLBDBCwRYW+P1YAH4/pKYeti2qRgxVhWX27Gx2Z6TjnHEvtM847OulaWiKp4hIwyjkOkKbN//AihUrSEtrH7H8qKysFNM06dixEzExMTgcToYOHYbb7Y5MASIiIiJRwOGw7Vt0Hg6VcX21/Rse+GQeADbDxtSRN9A7vdchr92lewpX/35k+H3TtJh56zsAdO2ZyoVXDa/xGsNmw9v9WPYuWYxj01YSRv4MZ7tEHA4bhmFgWRaBgEkgEMTj8Yc3PTpwrSbnb35LcOUKqKzASE7FOeEyvA/+FcPpxPfwbGyHmeZWNWLIKisHnw+rrAwrIZ7ghnUYCrmajaZ4iog0jEKuo5CYmEiHDh1Di342Eb/fT3FxMXv37iUmJoYxY05i6tRpxMbGNtk9RURERKKRzWaQmBiDy+U47Ciur3es5YFP5offz+08hN0VRSza9CnZaT3onNShXvc0gJhYB57KQI0RXBAaneX3Bwl4vPjfegN/ZQWVf38G1+QpUFRU52Ljta3VdGAYYm5Yhy0htGh+faa5VY0YMhI8WC4XRkIiRlwc9pw+tNB9xNsMTfEUEak/hVwtVDAY5KefthMMBklJSeWMM85i3LiTGT78BOx2e3OXJyIiItKquN0OEhP3r4N1uGmKr377Dqa1P95ZlrecZXnLARiQ0Zvbx06u130Nm8HkaSfj9fpJSKg++r7qD6WlpZ5qay9ZxYV4p03FSEiottj4gSO3aluryTFy9AFhiNWgaW4HjhgiJRX73iJSc4+n1J2IGVDMJdVpx0cRaakUcrUwgUCAbdu2YZpBUlPTuPLKqznllNNITGzcRT9FRERE2ooDA676LjZ/8O6KB4pzVR9RXxVW1XVtd4wDd0z1bnf3dTMhAAAgAElEQVS1gMu0QiOpDANz924wDIyq9Vb3BVhAtZFbrslTDhliHck0twNHDDkcNhwp8VBUftjXSduiHR9FpCVTyNXC5OfvICMjg6uvvo7jjjueTP3AEBERETlidrtxyIDLDAQIlJbiSEjA5nSGj9/ys0ns9ZbVes12MYnhxwcuW1GfhezhgN0Ry7x4vYH9x6seJCSEFpC3rHCAZW5Yh1mwA2tPIUZaKhQV1RliHTjKxjFy9GHrEWkI7fgoIi2ZQq4WJBAI4PV6OeWU0/jFL04/+gsaBoGc3uHHIiIiIm1NQkLtAVfJmm/ZNP9pKrbmhXehju/Vkx4TryCpX18cdgepccmHvLZlmmAYlJZ68PuDh13vq+q4Z+VK9jz2BM7/NzU8DdH/6svgqcSWlobl9WDrPxBbn744ck/ElplJcN23mF9/DcEgVt5WLNMMBwvmhnWAFb6WRtlIU9KOjyLSkinkaiG2b9+Oz+elZ89j+M1vzm+ci8bFUfTxF41zLREREZFWxuGw4XLV3t0tWr6Cii1bqx0r/2ET306/i8EPP0Bc586HvLbl92MFgxQv+5Jg/8EAlJRUhndudDjsNXZH9G7YSPHvb8L39ddgt2P7+WjIHYFn2lSs4iLMjRsxunbF2roVMDC/34gjdwQA5rdrIC4OggGwOzC/XY3Zu0+NQKuljLLRmk3RSzs+ikhLppCrBdi+fRtOp4uJE6/hF784g7S0tOYuSURERKTVi4lx1jmqql3fPpSu30DSgP6409tTsmYtuz5ajOX3s+fTz4j77bm1XtMKBDAcDjzffkvJhh+xjz2p2vOBgElZmTf8/oFhT+DzL/CtXr3/WhjhUMpwu7Edm42tW3dMd0xouuIBIZV91BiM+XPB5wOXC/vocbUHWvUYZXNwANXYgZRGk0U/7fgoIi2VQq5mFggE8Pn8XH/9TZx33gXNXY6IiIhI1HA47HWukZU6bCipw4Zi+nyYPh82dwy7PloMgOn11voa0+Ol7D//ofz99+Gk8TgOCrhqnH9Q2OOaPAX74CGYu3dja5+OrWcvzE3fYxkGhmVhJKfgvOIqfA/PrhFSOfoPJPb5BQSXLsY+ehyOfv0xC/JrBFq2zMxDjrKprSbfIw82aiDVUkaTiYhI26OQq5mVlZWSkJDAqFFjG//iFRWknDYGgKL/LA4NcRcRERFpIxwO2yGf9+7Zw/Krr8MKBqsdbz/y57Web9jtFD0xNxQG1WMdooPDHoqKiHngkdDxlFR8j4TCLMMwcP72Quy5JwIWjjPPxgDs+9bjqmJLz4ABg7C1bx96v45pY4caZXNwTcGlixs9kNKaTdFNU1FFpCVTyNXMiouL6ddvAJlNMZfdsnCsXxd+LCIiItKWHG6nw2B5RY2AC6i2y2K16zkduCbdAKmp1RZ7r0ttYY8tMxNbZhaBj5fsD5csCyMlFbCqjbKy71uPC+qeAtjQaWMH12QfNZbgyhWNGkhpzabWo6GBlaaiikhL1yZCrs8++5SnnprLjz9uIjk5hV/96jdccsnl9driuSmVlpZiGDbOOuvXzVqHiIiISDSqaz2uKrFdOpPzh/9HoKyM0o3fs/O/HwKQ9/K/yL75plqvZ8vOqfcv+YcKe2oLwMwN66CyAsvrxdqzh8Dny3Dt6yc21hTA2mqyNUEgpTWbWr6qwMoqLgK/H/fMWTj6Dzzka4KfL8PcloeREI8BrWYqqlmQj/+HDQSGHwcx7Zq7HBFpQlEfcq1e/Q1Tp07hpJNO4eqrr2PVqq+ZP/8JTNPksssmNktNgUCA7du3ATB+/OmcdtovmqUOERERkWgWCJg4nfY6nzcMg/Y/OxGArNNOZc//PiVYWUlFXl6d12to2FRX2FN7AGaBYWCu+w4A/ysLcOSOCF2jEacAHlyTAqm2ydywPrSr57rvIBjEM20qcfOeqXuqa0E+/ldewtqWhwXYBg9pFVNRq8I8w1PJ7hcScc64F9pnNHdZItJEoj7k+tvfnuLYY7O5/fa7ATjhhBMJBAI8//yzXHDBxbjdMRGtx+/3s2XLjxxzzLGcf/5FnHbaL3E4ov7TICIiIhJxgUAQh8NW62iu9Q88RNmmTWSefBKO+DhK128gWFkJQFyXzjXOtyyLQCDY5GGT87wLMHfvxkhIwLDMcIimKYDS2GzZOeD3QzAIdjuG03nI0NbcsB4sC1vvPlhlZTjPu7BV/D8MB9OGgVVRQXDDOgyFXCJRK6rTFZ/Px8qVy7nyyknVjo8dexIvvPAPvvnma4YPPyGiNe3cWUDPnr2YNWs2nTt3iei9RURERNoSj8dPbKyr1ueKV35NoKyMLc8+V/0Jm42OZ59V43zDMPB4/E0eNtlzR2B7643QtEXDwCwqwizIr9f6Wy19QfCWXl9bcPDnwD1zVmiUk9OJkZxyyNC2KuA1ACM5BccBa8a1ZOFg2lOJEReHPacPZnMXJSJNJqpDrp9+2o7f76dr167VjnfqFAqX8vK2RDzk8nq99O7dVwGXiIiISBMLBEy8u/bgSknCOGjkfLcJF7PttYV4CwqA0M6J7fr2oduEi0no2aPauZZl4fcHCQRCvxo35fS+qhAt+Pky/K8sIPDyCwTeWnjYBb5b+oLgLb2+tqDWz0F6Bu4rr8YCHAft5nmwpgx4mzIArarb+GEDqbnHU+pOxAwo5hKJVlEdcpWVlQIQFxdf7XhcXBwA5eXlEa3HNE2CwSDdu/c4/MmNwTAIdukafiwiIiLS1pQWlZKW1A7LNDFstvDxrPGnkjX+VIIeD0GPF2e7xGrPV7H27VBdWuqJWM22zCzMlFSw9v0iXo+1vxprYfrGUFtg0ZLqa6sO/hwEPl9GYN+oQWLj6jUyqykC3kgEoLbMLBydOuJIiYeiyP4OKCKRFdUhl2mGOgZ17apjGDU7Mk2ppKSE5OQUfv7zkZG5YVwchcvXROZeIiIiIi2QlZLG3oLdtOucVSPoArDHxGCPqX2N1gMDLtO0mrzWAzV07a/GXCvsaNQVWLSU+tqygz8HBkaLCB4VgIpIY4rqkCshIRGoOWKroqJi3/MJEa2nuLiIwYOPo0ePXhG9r4iIiEhb5otJoLTUQ2KCu9agqzYHBlxeb6CpS6yhoVPDWsrC9HUFFi2lvrbs4M8BWPjfWlhn8BipNdQUgIpIY4rqkKtTp87Y7Xa2b6++DXTV+xGbNkhoEXzTNDn55FPrHFkmIiIiIk2jcus2KteuIXnkCNwdsrACgRrrdEEo3DIMA78/2CwjuA7U0KlhTblWWL1rOERg0RLqa+sO/hzUFTxGcg01BaAi0piiOuRyu90MGjSEJUs+4sILJ4TDpY8++pCEhET69u0XkToCgQBbt26lT58+jBkzLiL3BKCykuSzxwNQ/Ma/ITY2cvcWERERaSHMgnwqb5mM+fVKyoGYk08h4ZxfETNwEM5uXTGcTizLIhAwCQSCeDz+8CLz0jAKLFqXuoLHo51C2NBRYApARaSxRHXIBXDZZRP5/e+v5/bbp3L66WexZs0qXnzxOa677ne43bWvv9DYSktLSU5O5vbb7yIlJTUi9wTANHF+vTL8WERERKKf1+tl1qxZ/Pvf/8bj8TBy5EimT59OWlpana9ZsWIFDz30EGvXriUuLo5Ro0Zx6623kpycHMHKm465YT3W7t0QDALgXb0a7+bN2BIS9o1SuU9hTCNSYNH61WcKYV1BlnbSFJHmFNmV15vB8ccPY+bM+8nL28Kf/nQL77//b66/fjIXXXRpxGooLd1L167d6NlTa3GJiIhI05oxYwaffPIJc+bM4dlnnyUvL4/JkyfXef7mzZuZOHEivXv35pVXXuGhhx5i1apV3HTTTRGsumnZsnMgMQErGMAywNahIzH33Idr0g2NEnCZBfkEPl6CWZDfSBWLNK+qEXl1fY1UBVm+eY/jmTa12v/92kaBiYhEStSP5AIYPXoso0ePbZZ7l5aW4vP5OPHEnzfL/UVERKTtKCgoYOHChcybN4+hQ4cC8OCDDzJ+/Hi+/vprBg8eXOM1CxcuJCMjgz/96U8YhkHPnj2ZPn06F198MXl5eXTp0iXSH0aTsLljsLp0xXC5cU39M45+Axrluhq1ItHqUCPyDjWdsTEWko/UovciEn3aRMjVnHbv3sWQIcdz/vkXNXcpIiIiEuWWL18OQG5ubvhYjx49yMzM5Msvv6w15DrrrLMYO3ZsrRvjFBcXH1XI5XA0/qQBu91W7d9DCaxehX/JRxiJiRiAo0PH0GtLihqtNv8PGzA8lWAY4KnE+GEDjk4dG+Xazakh7SxHpzW2ta1PH/w2A3P3Hmzt03D17Ytt39eUabfhPvvXgIVzxM8aPFLSzM/Hc/tUrMpKjNhY4u69H1vW0QddrbGdWyu1dWSonWunkKuJWJZFQUEBAGPHnkSsFn0XERGRJlZQUEBKSgput7va8YyMDHbs2FHra3r1qrmcwlNPPUV6ejq9ezd8BEYVm80gJSX+iF9/OO3aHbpvVf7f/1J84YVYfj9GbCzuQYMwXC6MuDhSc4/H0Ui1BYYfx+4XErEqKhr92i3B4dpZGk9rauuAJw6f00HQYcPucpKUFIsjJZ7Ajh3snvGn8NdD0viTG/z1ULFyC76AD5x2CPiI3bGFuD6Nt+xLa2rn1k5tHRlq5+oUcjWR3bt3YVkWN9wwmbPO+nVzlyMiIiJRYNu2bZx00kl1Pj958mRcLleN4263G6/XW697zJo1iyVLlvDoo4/idDqPuFbTtNi7t+KIX18Xu91Gu3ax7N1bSTBY+8Y6Zn4+e393E2ZhERhgVXowe2XjOm089pw+lLoToai8Ueoxiytg/BkYGDhHnNio125O9WlnaRytsa39X6zA7wtAUgqm10/hFytwjhqD/4sV+EpKQyeVlIaPN4TZoRsBhys8kquyY3e8jfA11RrbubVSW0dGNLZzu3axRz0yTSFXI7Msi127duHxVDJx4iQuumhCs9ZjHmInJREREWldMjMzeffdd+t8fsmSJfh8vhrHvV7vYUeV+/1+7rjjDl5//XWmT5/OqaeeetT1BgJN1+kOBs06rx/47juw2cEALAuwMI7JxjhxFCZgNlJdB6/HZQzLxdaEH3NzOFQ7S+NqTW1t9crGiokN/9+3euUQCJh1Hm+Q9hm47p61b02u3php6Y32NQutq51bO7V1ZKidq1PI1ch++uknnE4nEydO4tJLr2jeYuLj2fPd5uatQURERBqN0+msdXphlfXr11NcXIzP56s2omvnzp1kHWJNm7KyMm688Ua++uorZs+ezemnn96odUeaLTsHW9eumDvzwevDGDAIx6m/aPT7HGrxbZFoVrX7YlUQVbXuVl3Hj+T6jf21ZObnU7FyC2aHbtA+o1GvLSIth0KuRmRZFh5PJVdeeTUTJlze3OWIiIhIG3P88cdjmibLly9nxIgRAGzatImCgoLwbosH8/l8TJo0iXXr1vH0009zwgknRLLkJmHLzCLmgUcIfL4MAwN77ogj/mX7kPdphF3kRFqruoKopgiojpZZEFrM3hfwEXC4cN2tXVBFopVCrkZUWLiHxMR2jBw5urlLERERkTYoMzOT008/nWnTpnHvvfcSGxvL9OnTGT58eHhnRZ/PR0lJCUlJSbhcLubNm8fy5cuZPXs2vXr1YteuXeHrVZ3TmgTWrCK4dDH2UWNwNfG6qI01akVEmpa5YT1WZSU47ViVlRp1KRLFFHI1ksLCPZSWlnLmmb+ie/cezV1OSGUlSRf+BoCSF18F7fAoIiIS9e6++27uvfdebrzxRgBGjRrFtGnTws+vXLmSSy+9lH/84x/k5uby9ttvY1kWU6ZMqXGtqnNai8CaVVRecj74fDB/LrHPL8DRf2CT3rMljloRkeps2TkYsbEQ8GHExmrUpUgUU8jVCAKBAIWFRVx66eVMmnRDc5ezn2ni+vR/4cciIiIS/eLi4pg5cyYzZ86s9fnc3FzWr18ffv8///lPpEprcsGli0MBF4DPR3Dp4iYPuUSk5bNlZhF37/3E7thCZcfumGnpzV2SiDSRo9ubUQD46aftdOjQgXPPvQC73d7c5YiIiIi0SfZRY6BqeqXLhX30uGatR0RaDltWFnHjxmlasUiU00iuo1RcXIxpWlxxxVVkZGiXDhEREZHm4ug/kNjnF4TW5Bo9Dke//s1dkoiIiESQQq4jZJompaWl+Hx+fvWr3/CLX5zR3CWJiIiItHmO/gM1RVFERKSNUsh1BCzLYtGiD+natStjx57MVVddi9PpbO6yRERERERERETaLK3J1UDr1n3HzTffyNatW2nXrh233PJHEhISm7ssEREREREREZE2TSO56snr9XLffffw8cdLKC8vw+v10rNnz+Yu67CsuLjmLkFEREREREREpMkp5Kqnd999i/fff4+kpGQyMnqSl5fX3CUdXnw8u3/Mb+4qRERERERERESanEKueggGg3z99QqCQZOUlJTmLkdERERERERERA6iNbnqYcWKr1i6dIkCLhERERERERGRFkoh1yEEg0FWr17Ff/7zHh5PZesLuTwe2l10Lu0uOhc8nuauRkRERERERESkybT56YqWZbF3bwmFhYXs3VtCSUkJJSXFlJSUsHLlclasWI7H4yE1NRXDMJq73IYJBnF/8H74sYiIiIiIiIhItGqTIde6dd/x8ssvsHXrVnbt2onX68Xv9xMI+PH7AwQC/n1nGmRkpJORkQGA3+8PXyMQ8GNZVjNULyIiIiIiIiIiB2uTIddrr73MokUfYpo1Rzc5HHYcDnv4/b1797J3794a5zmdDuLi4pq0ThERERERERERqZ82GXJdffX1/OIXZxzVNex2G/375zRSRSIiIiIiIiIicjTaZMiVnp5Oenr6UV3D4bCRkhJPUVF5I1UlIiIiIiIiIiJHSrsrioiIiIiIiIhIq2dYzbx6ejBoNuftj4rdbmvZ9Zsm9rytAAS7dAVbdGWaLb79o5zav3mp/ZuX2r/52GxG69vtuI2yLAvTbJpupr4GI0PtHDlq68hQO0eO2joyoq2dG6Of1+whl4iIiIiIiIiIyNGKrqE9IiIiIiIiIiLSJinkEhERERERERGRVk8hl4iIiIiIiIiItHoKuUREREREREREpNVTyCUiIiIiIiIiIq2eQi4REREREREREWn1FHKJiIiIiIiIiEirp5BLRERERERERERaPYVcIiIiIiIiIiLS6inkEhERERERERGRVk8hl4iIiIiIiIiItHoKuUREREREREREpNVTyCUiIiIiIiIiIq1eREIur9fLnXfeyYgRIxgyZAg33XQTe/bsqddrPR4PZ555Jq+99loTV1mdaZo8+uijjBw5kkGDBnHllVeyZcuWOs8vKiri//2//8ewYcMYNmwYt99+OxUVFRGsOLo0tP03btzINddcQ25uLiNGjOCmm27ip59+imDF0aWh7X+gt956i5ycHLZt29bEVUavhra/3+9n9uzZjBw5ksGDB3PJJZfw3XffRbDi6NLQ9t+1axdTpkwhNzeX3NxcJk+eTH5+fgQrjl5PPPEEEyZMOOQ5+vkb3Y6kD7lixQomTJjA8ccfz8iRI/nzn/9McXFxhCpuPdTXjRz1ayND/dfIUV81MtQnPTIRCblmzJjBJ598wpw5c3j22WfJy8tj8uTJh31dcXExkyZNYsOGDRGosronnniCl156iZkzZ7JgwQIMw+Dqq6/G5/PVev5NN91EXl4ef//733n00Uf55JNPuPPOOyNcdfRoSPsXFRVxxRVXEB8fz/PPP89TTz1FUVERV111FV6vtxmqb/0a+v+/yvbt2/X/vhE0tP1nzJjBv/71L+6++25effVVkpOTufrqqyktLY1w5dGhoe1/8803s2PHDv72t7/xt7/9jfz8fK6//voIVx19qn6eHo5+/ka3hvYhN2/ezMSJE+nduzevvPIKDz30EKtWreKmm26KYNWtg/q6kaN+bWSo/xo56qtGhvqkR8hqYvn5+Vbv3r2tJUuWhI9t2rTJys7OtlauXFnn6z744ANr1KhR1q9//WsrOzvbevXVV5u61DCv12sNGTLEeuGFF8LHSkpKrIEDB1pvv/12jfNXrFhhZWdnW99//3342Mcff2zl5ORY+fn5Eak5mjS0/V9++WXruOOOszweT/jYjh07rOzsbOvTTz+NSM3RpKHtXyUYDFoXXnihdemll1rZ2dlWXl5eJMqNOg1t/61bt1rZ2dnWRx99VO38sWPH6v//EWho+5eUlFjZ2dnWhx9+GD72wQcfWNnZ2VZhYWFEao42+fn51sSJE63Bgwdb48ePty655JI6z9XP3+h2JH3IBx980Dr11FMt0zTDx7788ksrOzvb2rp1a5PX3Fqorxs56tdGhvqvkaO+amSoT3rkmnwk1/LlywHIzc0NH+vRoweZmZl8+eWXdb5u8eLFTJgwgZdeeqmpS6xh3bp1lJeXc8IJJ4SPtWvXjr59+9Za81dffUV6ejq9evUKHxs+fDiGYYQ/fqm/hrb/iBEjePzxx3G73TWeKykpadJao1FD27/Kk08+id/vZ9KkSZEoM2o1tP3/97//0a5dO0aNGlXt/EWLFjFixIiI1BxNGtr+brebuLg4Fi5cSFlZGWVlZbzxxht0796dpKSkSJYeNb799luSkpJ48803GTRo0CHP1c/f6HYkfcizzjqL++67D8MwajynKYv7qa8bOerXRob6r5GjvmpkqE965BxNfYOCggJSUlJqfKPOyMhgx44ddb7u7rvvburS6lQ1b7VDhw7VjtdVc0FBQY1zXS4XycnJh/wYpXYNbf/OnTvTuXPnasfmzZuH2+1m2LBhTVdolGpo+wOsWrWKZ555hn/9618UFBQ0eY3RrKHt/+OPP9KlSxfef/995s+fT0FBAX379mXq1KnVfhmR+mlo+7vdbu655x7uuusuhg4dimEYpKen8/zzz2OzaW+XIzFu3DjGjRtXr3P18ze6HUkfsrbve0899RTp6en07t27SepsjdTXjRz1ayND/dfIUV81MtQnPXJH/dFu27aNnJycOt8qKytxuVw1Xud2u1vsvPLKykqAGnXXVXNr/Bhbsoa2/8H+8Y9/8MILLzBlyhTS0tKapMZo1tD2r6io4JZbbuGWW26he/fukSgxqjW0/cvKyti6dStPPPEEU6ZMYe7cuTgcDi666KJ6b/Ah+zW0/S3LYv369QwZMoR//vOfPPvss3Tq1IkbbriBsrKyiNTclunnb+sWiT7krFmzWLJkCXfccQdOp7OxP4RWS33dyFG/NjLUf40c9VUjQ33SI3fUI7kyMzN5991363x+yZIltS6M5vV6iY2NPdrbN4mYmBgAfD5f+DHUXXNMTEydH2NcXFzTFRqlGtr+VSzL4pFHHmHu3LlMmjSJyy+/vKlLjUoNbf+ZM2fSvXt3LrjggojVGM0a2v5Op5PS0lIeeuih8F/DHnroIUaPHs3rr7/OVVddFZnCo0RD2/+dd97hhRde4KOPPiIhIQEITX0YO3Ysr776KpdddllkCm+j9PO3dWvKPqTf7+eOO+7g9ddfZ/r06Zx66qlHXW80UV83ctSvjQz1XyNHfdXIUJ/0yB11yOV0Og85zHD9+vUUFxfj8/mqpZA7d+4kKyvraG/fJKqGBO7cuZOuXbuGj+/cubPWoe5ZWVl88MEH1Y75fD6Ki4vJzMxs2mKjUEPbH0Kd2dtuu423336bP/zhD0ycODEitUajhrb/q6++isvlYsiQIQAEg0EAzjjjDM466yzuuuuuCFQdPY7k+4/D4aj2fTgmJoYuXbpoG+wj0ND2X758OT169Ah3JgCSkpLo0aMHP/74Y5PX29bp52/r1lR9yLKyMm688Ua++uorZs+ezemnn96odUcD9XUjR/3ayFD/NXLUV40M9UmPXJNPzjz++OMxTbPaopSbNm2ioKCAoUOHNvXtj0jv3r1JSEjg888/Dx/bu3cva9eurbXmYcOGkZ+fz5YtW8LHql573HHHNX3BUaah7Q/whz/8gX//+9/Mnj1bHYGj1ND2f//993n77bdZuHAhCxcuZObMmQDMnz//kNu8S+0a2v5Dhw4lEAiwevXq8DGPx0NeXh7dunWLSM3RpKHt36FDB7Zs2VJt2HhlZSXbtm1T+0eAfv5GtyPpQ/p8PiZNmsTq1at5+umnFXDVQX3dyFG/NjLUf40c9VUjQ33SI2efMWPGjKa8QUJCAps3b+aFF16gd+/eFBYWctttt9GjRw+uv/56INQhKSwsxOl0Yrfba1zjscce4+STT6ZPnz5NWWqY3W6noqKC+fPn07NnT3w+H3fccQemaTJt2jQA9uzZg91ux+FwkJmZyaeffso777xDnz592Lx5M7fffjsnn3wyZ599dkRqjiYNbf/XXnuNJ598kltuuYVx48ZRUVERfqs6R+qvoe2fnJxc7a24uJjXX3+dG2+8scWO1mzJGtr+HTt2ZMWKFSxcuJCcnBwqKiqYOXMm+fn53HXXXS12WnhL1dD279KlCy+++CLffPMNxxxzDLt37+bOO++ksLCQmTNn1ro7ltTfBx98QGlpKeeccw4Q+ku7fv62HUfSh3ziiSd44403mDVrFgMGDKjWJ6irn9kWqa8bOerXRob6r5GjvmpkqE96FKwIKC8vt/785z9bQ4cOtYYOHWpNmTLFKiwsDD//2WefWdnZ2dZnn31W6+uzs7OtV199NRKlhgUCAev++++3TjjhBGvw4MHW1VdfbeXl5VmWZVl5eXk1atq9e7f1u9/9zho8eLCVm5trTZ8+3fJ4PBGtOZo0pP2vuOIKKzs7u9a3SP+/iRYN/f9/oKqv56rzpeEa2v6lpaXW9OnTrdzcXGvQoEHWFVdcYW3cuLG5ym/1Gtr+33//vTVp0iRr+PDh1gknnGDdeOON+v/fSP74xz9al1xySfh9/fxtexrahzz11FPr7BPU1c9sqxKzNSIAABPCSURBVNTXjRz1ayND/dfIUV81MtQnPTKGZVlWcwdtIiIiIiIiIiIiR6PJ1+QSERERERERERFpagq5RERERERERESk1VPIJSIiIiIiIiIirZ5CLhERERERERERafUUcomIiIiIiIiISKunkEtERERERERERFo9hVwiIiIiIiIiItLqKeQSkTpZllWvY9K41MYiIiIiIiINp5BLWrWpU6eSk5NzyLdx48aFz616HGmff/45OTk5fP755wDMmTOHnJyc8PMTJkxgwoQJzVJbXb7//nsuvPDCasdeeeUV7rvvvmaq6MiUlZVx3XXXMWjQIIYNG8aPP/7I559/zmmnnUb//v2ZOHFijc/H4TT0/Pry+Xz85S9/4a233mr0ax/stddeIycnh23btjX5vURERCQ6TJgwoUZfu3///owZM4Y777yTkpKSGq/ZvHkzM2bM4OSTT2bgwIGMGTOGm2++mXXr1jXo3mvWrKFfv37qu4jIITmauwCRo3H99ddzwQUXhN9/4oknWLt2LY899lj4mMvlao7SqunXrx8LFizgmGOOae5S6u29995j5cqV1Y7NnTuX4cOHN1NFR2bhwoUsWrSIO+64g2OPPZbOnTszZcoUTNNk/vz5pKWlkZSUxMiRI+t9zfPOO69B59fXzp07+fvf/85f/vKXRr+2iIiISGPo27cv06dPD7/v9/v59ttvefDBB/nuu+948cUXMQwDgP/+97/ceuutHHvssVx33XV07tyZ/Px8nnvuOc477zwef/xxRo0addh7rlu3jkmTJhEIBJrs4xKR6KCQS1q1rl270rVr1/D7qampuFwuBg8e3IxV1ZSQkNDiamoriouLAbjooovCHa7i4mKGDRvGiSeeGD4vKyur3tfMyspq0PkiIiIi0aK2fu2wYcMoLy/n0Ucf5ZtvvmHw4MFs3bqVP/zhD4wcOZKHH34Yu90ePv+0007joosuYurUqSxatIiYmJha7+Xz+Xj++ed55JFH6jxHRORAmq4obc5rr73GaaedxoABAzjrrLNYunRpted/+uknpkyZwvDhwxk0aBCXXXYZa9euPeQ1vV4vd955J6NGjaJ///6MHz+eZ555Jvz8wdMVa2NZFk899RRjxoxh4MCBnH/++axevbraOatXr2bixInk5uZy3HHHce2117Jx48bD3qe26ZCvvPIKp59+eniI+Zw5c8J/HZszZ054NFxOTg5z5sxh3LhxbN++nddff73aNLcjaS8IDTm/6qqrOP744znhhBO4+eab2bFjR/j5nTt3cttttzF69GgGDhzIueeey4cffljtGlWjsU455RT69+/PaaedxnPPPVft454zZw4AvXv3Dk9v3b59OwsXLgy3VW3TD9955x3OOeccBg0axJgxY/jrX/+Kz+cLt8/B53/wwQecc845DBgwgJ/97GfMnDmTioqK8PNz5szhlFNOYfHixZx55pnhel9//XUAtm3bxkknnQTAbbfdVufU2iuvvJJf/epXNY7//ve/5/TTTw+//8orr3DOOecwePBgBg4cyNlnn827775bx2ej9um827ZtIycnh9deey18rLi4mDvuuIMTTzyRAQMG8Nvf/pZly5ZVe92nn37K+eefz5AhQxg2bBjXX389mzZtqvPeIiIi0vr1798fCPUNAZ577jl8Ph/Tpk2rFnABxMTE8Mc//pFzzz2XvXv31nnNpUuX8thjj3Httddyyy23NF3xIhI1FHJJm7Jjxw7mz5/P5MmTefTRR7Esi9/97nfs2bMHgMLCQi644AK+/fZbbr/9dmbPno1pmlx88cX88MMPdV73nnvuYcmSJfzxj3/k//7v/zjppJO47777qoUDh7N8+XL++9//cvvtt3PfffdRUFDAtddeGw6ePvvsMy688EJM0+See+5h5syZ7NixgwsuuOCQtdVm3rx53H777YwYMYInn3ySiy++mKeeeoo77rgDCE3HO/fccwFYsGAB5513Ho899hjp6emMHj2aBQsWkJGRccTttW7dOi688EIqKyuZNWsWd911F2vXruXKK6/E7/eze/duzj33XL744gtuvvlm5syZQ6dOnbjhhht48803w9eZMWMGjz76KGeddRZPPvkk48eP59577+Xxxx8HYPr06dU+jmuuuYYFCxZU+zj69etXo76XXnqJKVOm0KdPHx577DEmTZrECy+8wIwZM2r9eN566y1uuOEGevbsyeOPP86NN97Im2++yfXXX19tEfldu3Zx1113cemllzJ//nw6d+7M1KlT+eGHH8jIyAgHi9ddd121KbcHOvvss/nuu++qhUbl5eV89NFHnH322QD885//5I477uCkk05i3rx5/PWvf8XpdHLrrbeGO55Hwuv1ctlll/Hhhx9y880389hjj5GVlcVVV10VDrry8vK47rrr6NevH3PnzmXmzJls2rSJa665BtM0j/jeIiIi0rJt3rwZgC5dugDw8ccf07dvXzIzM2s9Pzc3lylTppCRkVHnNQcMGMCiRYu47rrragRlIiK10XRFaVNM0+Txxx+nV69eALjdbq644gq+/vprTjrpJJ599lmKi4t58cUX6dSpEwCjRo3il7/8JY888giPPvpordf94osvOPHEE8MjaXJzc4mLiyMlJaXetblcLubPn09ycjIQWjB92rRpfP/99/Tu3ZvZs2fTpUsXnn766fAP+Z///OeccsopzJkzh4cffrhe9yktLWXu3Lmcf/75TJs2LXyd5ORkpk2bxhVXXMGxxx4bno5XNRw9KysLl8tFampq+NiRttcTTzxBUlISzzzzDG63O3z93//+96xfv5733nuPwsJC3nvvvXBHafTo0Vx++eXcf//9nHHGGWzZsoWXX36ZKVOmcM0114Q/DsMwmDdvHhdddBHHHHNMjY+jqq0P/DgOZJpmeNTVPffcEz7u9Xp5/fXXw6O5qliWxQMPPMDIkSN54IEHwse7d+/O5ZdfzpIlSxgzZgwAlZWV3HPPPYwYMSJ8ztixY1myZAlXXnklffr0AULTcPv27Vtr251yyinExcXx7rvvcuONNwKh9S68Xi9nnnkmEAqarrzySm644Ybw6zp37sw555zDihUr6NixY63XPpw33niDdevW8fLLLzNo0CAg9PmeMGECDzzwAK+++iqrVq3C4/EwadKkcKe2Q4cOfPjhh1RUVJCQkHBE9xYREZGWwbKsamtjlZSU8MUXXzB37lwGDx4cHtFVUFAQ7tscqboCMhGRuijkkjYlJSUlHHDB/r80lZaWArBs2TL69OlDZmZm+Ie3zWZj1KhR1UYQHSw3N5eXXnqJgoICxo4dy+jRo6sFDPVxzDHHhAMuCIUSVbVVVFSwevVqbrjhhmp/xWrXrl04JKmvlStXUllZybhx46p1UKqmqn3yyScce+yx9brWkbbX8uXLGT16dDjgAhg4cCCLFi0C4M4772TIkCHhz0+V/9/e/cdUXf1xHH9yL/daF4HR5QY3bBIxIUGYBTeZLUdmbtScteuyoAn4Y9qPqdBMczGkoFXcAkZgqAhDC6itpNLW1rKZP5ZZf7TaWg3QfkGYXKRWQs7vH+x+vvd6QRHdjHo9Nv7g8+Pcc86Hjbv3533eZ9GiRWzatInOzk6OHj3KuXPnRh1HfX09x44d4+677x7XOPx1dXVx8uTJoHvz8/PJz88Pur6zs5Oenp6gYqiZmZlMnTqVgwcPGkEuCAy2+QJw/ssaL8Zms7FgwYKAINf777+Py+XC6XQCI0sPYeRvp7u7m+7ubiPTanh4eNyfdb7Dhw/jcDhISUkJGGt2djYvvvgiAwMDpKenM2XKFNxuNzk5OcybN4+MjAzS0tIm/LkiIiLyz3H06NGgTHiTyURWVhbPPvusUQM1JCSEs2fPXo0uish/mIJc8p9is9kCfvf9E/Yto/J6vRw/fnzUJWwwkolz7bXXBh3fvHkzsbGxdHR0sGXLFgBmz55NSUnJmBk5F+ubyWQy+jY4OMi5c+eIjo4Oui86OtoI0o2HrxC7L/vpfL/++usltTWR+fJ6vdjt9jHbHRgYMIJ8/nzjP336tDEO/zpU/np7ey/a/9H42r1Q/0a7fsuWLcaz93f+fPrPh+8Z+y9pHI/FixcbWVXXX389hw4doqyszDh/4sQJSkpKOHLkCKGhoSQkJBg1xC71s/x5vV76+vrGfN59fX0kJiaya9cuGhoaaG9vp6mpiYiICB5++GHWrl1rjFlEREQmp5SUFOM7T0hICFOmTMHpdAZla8fFxV2wTMLff//NqVOnLrhcUUTkUinIJeInPDwcl8vFhg0bRj1vtVrHPL5mzRrWrFnDzz//zMcff0xdXR3FxcXs27fvivQrJCSEkydPBp3r6+szMsDOD9r5/PHHH4SFhQEj2V8AlZWVxMfHB7U3WiDtQv2ayHyFh4dz6tSpoOOffPIJycnJREZGjjlWGMnI842jubnZGJu/iS7J87V7fv+8Xi9ff/110BJH3/UbNmzA5XIFtRcZGTmhflzInDlziImJYd++fcTExBAaGsrChQuBkWe/atUqLBYL7e3tzJw5k9DQUL7//vsLZteN9rb1/Ayz8PBw4uPjA5Zl+vMFJtPS0qitrWVoaIhjx47R1tbG1q1bSUpKIicn53KGLiIiIldZWFgYs2bNuuh1d9xxB83NzfT19eFwOILOHzhwgNWrV/Pyyy+P+dJSRORS6ZW6iB+Xy0VXVxc33XQTs2bNMn46Ojp48803Ry14+ddff7Fw4UJjN8UbbriB3Nxc7r33Xnp6eq5Iv2w2G6mpqezduzcgEDE4OMj+/fu57bbbAIw3aP67FA4MDAQUgU9PT8disdDb2xswRovFgsfjMXZNHC3j5vxjE5kvgIyMDA4cOBBQ3+rbb79l1apVfPXVV2RmZvLll1/yww8/BNzX0dGBw+Fg+vTpZGZmAtDf3x/w2V6vl6qqKiPD6lIlJCQQFRUVtJPju+++y8qVKzlz5kzQ9Xa7nR9//DGgH7GxsXg8nnHtNOkz3oKqJpOJ++67j48++ogPPviA+fPnG8++v7+frq4u3G43aWlphIaOvMvw7SI6VvH3sLAw+vv7A8b3xRdfBFzjcrn45ZdfsNvtAWM9fPiwUSuuqamJu+66i6GhIaxWq7F0AQL/LkVEROTfLTc3F4vFwnPPPRf0Iu3PP/+kpqaGyMhIsrOzr1IPReTfSJlcIn7y8/PZs2cP+fn5FBYWEhUVxd69e2lvb2fTpk2j3nPNNdeQkpJCbW0tFouFpKQkurq6ePvtt43smiuhuLiY5cuXs2LFCvLy8hgeHqahoYGhoSGjNlNSUhJOp5Pa2lrCw8MxmUw0NDQELJGLiopixYoVVFdX8/vvv3P77bfT29tLdXU1ISEhJCcnA//PUHrvvfdIT0/nxhtvJCIigm+++YbPPvuMtLS0Cc0XwKOPPsqDDz7IypUrWbZsGUNDQ1RXV5OSksKdd95Jeno6HR0dFBQU8PjjjxMVFcU777zDkSNHqKiowGQyMWPGDBYtWsQzzzzDTz/9RGpqKl1dXbzyyitMmzZt1Cy18TCbzTzxxBOUlZVRWlrKggUL6O7upqqqioceeojrrrsu6Pr169dTUlKC2WwmOzub06dPU1dXR29v75hL+0YTHh4OjNS+uvnmm43i7qNZvHgxO3bswGw2U19fbxy32+3ExcWxe/duYmNjiYiI4NNPP6W5uRkY+VI5muzsbFpaWnj66adZsmQJ3333HY2NjQGBtwceeIBdu3ZRUFDA6tWrcTqdHDp0iG3btpGXl4fFYmHOnDlUVlby2GOPkZeXh9lsprW1FavVqi+xIiIi/yHTpk2jtLSUzZs3k5uby9KlS3E6nZw4cYKmpiaOHz/Otm3bgkp2iIhcDgW5RPzExMTQ2tqKx+OhtLSUM2fOEB8fT3l5OW63e8z7ysrKqKqqorGxkb6+Pux2O263m7Vr116xvmVlZbFz505qamooKirCarWSkZHBCy+8YBSKN5vN1NTUUFFRQVFREdHR0SxbtozOzk5jW2eAdevW4XA4eP3119m+fTuRkZFkZWVRVFRkBFruuece9uzZw8aNG3G73ZSWllJYWEhFRQXLly9n586dZGRkTGi+Zs6cSUtLCx6Ph/Xr1xMWFsa8efN48sknsVqtOBwO3njjDTweD+Xl5QwPD5OcnExdXR3z58832nn++ed57bXXaG1tpaenB7vdTk5ODuvWrbusbaZzc3Ox2Wzs2LGDt956i5iYGAoLC8esY7ZkyRLCwsLYvn07bW1t2Gw2br31ViorK4OK51/I1KlTKSgooK2tjf3793Pw4MExl3zOmDGDW265hd7eXubOnRtwrq6ujvLycjZu3IjVaiUxMZH6+noqKir4/PPPeeSRR4Lamzt3Lk899RQtLS18+OGHRuB26dKlxjU2m43du3fj8Xh46aWXGBwcJC4ujuLiYgoLCwFITk5m69atvPrqqxQVFXH27FlSU1NpbGwkISFh3HMhIiIik9/999/P9OnTaW5upqqqit9++w2Hw8Hs2bOprq4mMTHxandRRP5lQs5dThViERERERERERGRfwDV5BIRERERERERkUlPQS4REREREREREZn0FOQSEREREREREZFJT0EuERERERERERGZ9BTkEhERERERERGRSU9BLhERERERERERmfQU5BIRERERERERkUlPQS4REREREREREZn0FOQSEREREREREZFJT0EuERERERERERGZ9BTkEhERERERERGRSe9/VsndfJc6H0MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 10, The average silhouette_score is : 0.089\n"
     ]
    }
   ],
   "source": [
    "X = gmeTFVects.toarray()\n",
    "plotSilhouette(10, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "normal-hormone",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 813 ms, sys: 19.8 ms, total: 833 ms\n",
      "Wall time: 846 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "km = sklearn.cluster.KMeans(n_clusters=10, init='k-means++')\n",
    "\n",
    "km.fit(DescriptionTFVects)\n",
    "\n",
    "d['10_kmeans_predictions'] = km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "governing-business",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " best\n",
      " conditions\n",
      " poor\n",
      " current\n",
      " hedge\n",
      " explains\n",
      " video\n",
      " market\n",
      " gamestop\n",
      " bullish\n",
      "\n",
      "\n",
      "Cluster 1:\n",
      " live\n",
      " like\n",
      " dashboard\n",
      " channel\n",
      " tomorrow\n",
      " subscribe\n",
      " blessed\n",
      " stay\n",
      " profitable\n",
      " predictions\n",
      "\n",
      "\n",
      "Cluster 2:\n",
      " prediction\n",
      " price\n",
      " analysis\n",
      " gamestop\n",
      " march\n",
      " technical\n",
      " used\n",
      " join\n",
      " predictions\n",
      " going\n",
      "\n",
      "\n",
      "Cluster 3:\n",
      " short\n",
      " squeeze\n",
      " gamestop\n",
      " cosmiclightningwarrior\n",
      " situation\n",
      " info\n",
      " current\n",
      " explains\n",
      " new\n",
      " 2021\n",
      "\n",
      "\n",
      "Cluster 4:\n",
      " po\n",
      " box\n",
      " level\n",
      " spy\n",
      " lrc\n",
      " san\n",
      " jose\n",
      " 610545\n",
      " ca\n",
      " 95161\n",
      "\n",
      "\n",
      "Cluster 5:\n",
      " gamestop\n",
      " squeeze\n",
      " stocks\n",
      " short\n",
      " live\n",
      " com\n",
      " news\n",
      " moass\n",
      " time\n",
      " buy\n",
      "\n",
      "\n",
      "Cluster 6:\n",
      " today\n",
      " wall\n",
      " continue\n",
      " moass\n",
      " street\n",
      " market\n",
      " talk\n",
      " hedge\n",
      " set\n",
      " need\n",
      "\n",
      "\n",
      "Cluster 7:\n",
      " talks\n",
      " dave\n",
      " portnoy\n",
      " payne\n",
      " amcshortsqueeze\n",
      " shocking\n",
      " urgent\n",
      " charles\n",
      " amcstock\n",
      " amcapes\n",
      "\n",
      "\n",
      "Cluster 8:\n",
      " gamestop\n",
      " week\n",
      " sec\n",
      " meme\n",
      " know\n",
      " video\n",
      " report\n",
      " let\n",
      " looking\n",
      " earnings\n",
      "\n",
      "\n",
      "Cluster 9:\n",
      " minute\n",
      " twitter\n",
      " follow\n",
      " updates\n",
      " comment\n",
      " sub\n",
      " like\n",
      " news\n",
      " support\n",
      " happymoneyyt\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "terms = ngTFVectorizer.get_feature_names()\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]#why\n",
    "for i in range(10):\n",
    "    print(\"Cluster %d:\" % i)\n",
    "    for ind in order_centroids[i, :10]:\n",
    "        print(' %s' % terms[ind])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "durable-rover",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 780 ms, sys: 6.01 ms, total: 786 ms\n",
      "Wall time: 809 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "km = sklearn.cluster.KMeans(n_clusters=4, init='k-means++')\n",
    "\n",
    "km.fit(DescriptionTFVects)\n",
    "\n",
    "d['4_kmeans_predictions'] = km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "legislative-capture",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " short\n",
      " squeeze\n",
      " gamestop\n",
      " today\n",
      " moass\n",
      " stocks\n",
      " new\n",
      " live\n",
      " week\n",
      " citadel\n",
      "\n",
      "\n",
      "Cluster 1:\n",
      " prediction\n",
      " gamestop\n",
      " price\n",
      " analysis\n",
      " going\n",
      " live\n",
      " predictions\n",
      " tomorrow\n",
      " march\n",
      " action\n",
      "\n",
      "\n",
      "Cluster 2:\n",
      " like\n",
      " minute\n",
      " comment\n",
      " support\n",
      " twitter\n",
      " blessed\n",
      " profitable\n",
      " stay\n",
      " subscribe\n",
      " thoughts\n",
      "\n",
      "\n",
      "Cluster 3:\n",
      " conditions\n",
      " best\n",
      " poor\n",
      " hedge\n",
      " market\n",
      " current\n",
      " explains\n",
      " video\n",
      " gamestop\n",
      " continue\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "terms = ngTFVectorizer.get_feature_names()\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]#why\n",
    "for i in range(4):\n",
    "    print(\"Cluster %d:\" % i)\n",
    "    for ind in order_centroids[i, :10]:\n",
    "        print(' %s' % terms[ind])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "unexpected-convert",
   "metadata": {},
   "outputs": [],
   "source": [
    "d['tokenized_text'] = d['title'].apply(lambda x: lucem_illud.word_tokenize(x))\n",
    "d['normalized_tokens'] = d['title'].apply(lambda x: lucem_illud.normalizeTokens(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "authentic-yugoslavia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>channel_name</th>\n",
       "      <th>channel_link</th>\n",
       "      <th>views</th>\n",
       "      <th>time_published</th>\n",
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       "      <th>normalized_tokens_count</th>\n",
       "      <th>normalized_tokens_POS</th>\n",
       "      <th>4_kmeans_predictions</th>\n",
       "      <th>10_kmeans_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>https://www.youtube.com/watch?v=QxAURd6O50g</td>\n",
       "      <td>Happy Money</td>\n",
       "      <td>https://www.youtube.com/c/HappyMoneyFinance</td>\n",
       "      <td>989 views</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Thank you for your support. Sub up, Like up, C...</td>\n",
       "      <td>GME Stock Dumping Setting Up For Earnings Anno...</td>\n",
       "      <td>[GME, Stock, Dumping, Setting, Up, For, Earnin...</td>\n",
       "      <td>28</td>\n",
       "      <td>[gme, stock, dump, set, earnings, announcement]</td>\n",
       "      <td>16</td>\n",
       "      <td>[(gme, NNP), (stock, NNP), (dumping, NN), (set...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>https://www.youtube.com/watch?v=27pvUBtMCeQ</td>\n",
       "      <td>Simulate &amp; Trade</td>\n",
       "      <td>https://www.youtube.com/channel/UCW6xUGOnJ_XOR...</td>\n",
       "      <td>9.8K views</td>\n",
       "      <td>23 hours ago</td>\n",
       "      <td>AMC Stock and GME Stock are the best hedge aga...</td>\n",
       "      <td>AMC Stock &amp; GME Stock Hedge Funds And Big Bank...</td>\n",
       "      <td>[AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...</td>\n",
       "      <td>33</td>\n",
       "      <td>[amc, stock, gme, stock, hedge, fund, big, ban...</td>\n",
       "      <td>24</td>\n",
       "      <td>[(amc, NNP), (stock, NNP), (&amp;, CC), (gme, NNP)...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>https://www.youtube.com/watch?v=KMOKJirlL-k</td>\n",
       "      <td>JacksonHunter</td>\n",
       "      <td>https://www.youtube.com/c/JacksonHunter</td>\n",
       "      <td>1.2K views</td>\n",
       "      <td>21 hours ago</td>\n",
       "      <td>In this video we go over the chart, SPY, Black...</td>\n",
       "      <td>GME Short Squeeze 3-11-22 | Fox News Highlight...</td>\n",
       "      <td>[GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...</td>\n",
       "      <td>36</td>\n",
       "      <td>[gme, short, squeeze, |, fox, news, highlight,...</td>\n",
       "      <td>23</td>\n",
       "      <td>[(gme, NNP), (short, NNP), (squeeze, NNP), (3,...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>https://www.youtube.com/watch?v=FAs1vYKXGRc</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>5.2K views</td>\n",
       "      <td>7 days ago</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>SHOCKING: Jim Cramer TALKS AMC &amp; GME (GameStop...</td>\n",
       "      <td>[SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...</td>\n",
       "      <td>28</td>\n",
       "      <td>[shock, jim, cramer, talk, amc, gme, gamestop,...</td>\n",
       "      <td>24</td>\n",
       "      <td>[(shocking, NN), (:, :), (jim, NNP), (cramer, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>https://www.youtube.com/watch?v=6rn2_EoDoXU</td>\n",
       "      <td>TickrBox</td>\n",
       "      <td>https://www.youtube.com/c/TickrBox</td>\n",
       "      <td>642 views</td>\n",
       "      <td>2 hours ago</td>\n",
       "      <td>Join the 100% Winner Community https://www.pat...</td>\n",
       "      <td>BREAKING: Jordan Belfort (Wolf of Wall Street)...</td>\n",
       "      <td>[BREAKING, Jordan, Belfort, Wolf, of, Wall, St...</td>\n",
       "      <td>34</td>\n",
       "      <td>[break, jordan, belfort, wolf, wall, street, t...</td>\n",
       "      <td>22</td>\n",
       "      <td>[(breaking, NN), (:, :), (jordan, NNP), (belfo...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1  AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2  GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4  BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "\n",
       "                                          link      channel_name  \\\n",
       "0  https://www.youtube.com/watch?v=QxAURd6O50g       Happy Money   \n",
       "1  https://www.youtube.com/watch?v=27pvUBtMCeQ  Simulate & Trade   \n",
       "2  https://www.youtube.com/watch?v=KMOKJirlL-k     JacksonHunter   \n",
       "3  https://www.youtube.com/watch?v=FAs1vYKXGRc          TickrBox   \n",
       "4  https://www.youtube.com/watch?v=6rn2_EoDoXU          TickrBox   \n",
       "\n",
       "                                        channel_link       views  \\\n",
       "0        https://www.youtube.com/c/HappyMoneyFinance   989 views   \n",
       "1  https://www.youtube.com/channel/UCW6xUGOnJ_XOR...  9.8K views   \n",
       "2            https://www.youtube.com/c/JacksonHunter  1.2K views   \n",
       "3                 https://www.youtube.com/c/TickrBox  5.2K views   \n",
       "4                 https://www.youtube.com/c/TickrBox   642 views   \n",
       "\n",
       "  time_published                                            snippet  \\\n",
       "0      1 day ago  Thank you for your support. Sub up, Like up, C...   \n",
       "1   23 hours ago  AMC Stock and GME Stock are the best hedge aga...   \n",
       "2   21 hours ago  In this video we go over the chart, SPY, Black...   \n",
       "3     7 days ago  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4    2 hours ago  Join the 100% Winner Community https://www.pat...   \n",
       "\n",
       "                                                text  \\\n",
       "0  GME Stock Dumping Setting Up For Earnings Anno...   \n",
       "1  AMC Stock & GME Stock Hedge Funds And Big Bank...   \n",
       "2  GME Short Squeeze 3-11-22 | Fox News Highlight...   \n",
       "3  SHOCKING: Jim Cramer TALKS AMC & GME (GameStop...   \n",
       "4  BREAKING: Jordan Belfort (Wolf of Wall Street)...   \n",
       "\n",
       "                                      tokenized_text  word_counts  \\\n",
       "0  [GME, Stock, Dumping, Setting, Up, For, Earnin...           28   \n",
       "1  [AMC, Stock, GME, Stock, Hedge, Funds, And, Bi...           33   \n",
       "2  [GME, Short, Squeeze, 3, 11, 22, |, Fox, News,...           36   \n",
       "3  [SHOCKING, Jim, Cramer, TALKS, AMC, GME, GameS...           28   \n",
       "4  [BREAKING, Jordan, Belfort, Wolf, of, Wall, St...           34   \n",
       "\n",
       "                                   normalized_tokens  normalized_tokens_count  \\\n",
       "0    [gme, stock, dump, set, earnings, announcement]                       16   \n",
       "1  [amc, stock, gme, stock, hedge, fund, big, ban...                       24   \n",
       "2  [gme, short, squeeze, |, fox, news, highlight,...                       23   \n",
       "3  [shock, jim, cramer, talk, amc, gme, gamestop,...                       24   \n",
       "4  [break, jordan, belfort, wolf, wall, street, t...                       22   \n",
       "\n",
       "                               normalized_tokens_POS  4_kmeans_predictions  \\\n",
       "0  [(gme, NNP), (stock, NNP), (dumping, NN), (set...                     2   \n",
       "1  [(amc, NNP), (stock, NNP), (&, CC), (gme, NNP)...                     3   \n",
       "2  [(gme, NNP), (short, NNP), (squeeze, NNP), (3,...                     0   \n",
       "3  [(shocking, NN), (:, :), (jim, NNP), (cramer, ...                     0   \n",
       "4  [(breaking, NN), (:, :), (jordan, NNP), (belfo...                     0   \n",
       "\n",
       "   10_kmeans_predictions  \n",
       "0                      9  \n",
       "1                      0  \n",
       "2                      5  \n",
       "3                      7  \n",
       "4                      5  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "stone-bermuda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropMissing(wordLst, vocab):\n",
    "    return [w for w in wordLst if w in vocab]\n",
    "\n",
    "d['reduced_tokens'] = d['normalized_tokens'].apply(lambda x: dropMissing(x, TFVectorizer.vocabulary_.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "worldwide-defeat",
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = gensim.corpora.Dictionary(d['reduced_tokens'])\n",
    "corpus = [dictionary.doc2bow(text) for text in d['reduced_tokens']]\n",
    "gensim.corpora.MmCorpus.serialize('senate.mm', corpus)\n",
    "senmm = gensim.corpora.MmCorpus('senate.mm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "spanish-carolina",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 696 ms, sys: 8.46 ms, total: 704 ms\n",
      "Wall time: 800 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "newslda = gensim.models.ldamodel.LdaModel(corpus=senmm, id2word=dictionary, num_topics=10, alpha='auto', eta='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "thermal-dating",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>moass</td>\n",
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       "      <th>1</th>\n",
       "      <td>gamestop</td>\n",
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       "      <td>short</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>need</td>\n",
       "      <td>price</td>\n",
       "      <td>talk</td>\n",
       "      <td>talk</td>\n",
       "      <td>market</td>\n",
       "      <td>moass</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>moass</td>\n",
       "      <td>gamestop</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>know</td>\n",
       "      <td>market</td>\n",
       "      <td>big</td>\n",
       "      <td>price</td>\n",
       "      <td>know</td>\n",
       "      <td>short</td>\n",
       "      <td>price</td>\n",
       "      <td>market</td>\n",
       "      <td>new</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>citadel</td>\n",
       "      <td>big</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>week</td>\n",
       "      <td>prediction</td>\n",
       "      <td>new</td>\n",
       "      <td>info</td>\n",
       "      <td>fund</td>\n",
       "      <td>analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>squeeze</td>\n",
       "      <td>gamestop</td>\n",
       "      <td>hedge</td>\n",
       "      <td>live</td>\n",
       "      <td>update</td>\n",
       "      <td>analysis</td>\n",
       "      <td>live</td>\n",
       "      <td>new</td>\n",
       "      <td>hedge</td>\n",
       "      <td>earnings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>news</td>\n",
       "      <td>sec</td>\n",
       "      <td>long</td>\n",
       "      <td>prediction</td>\n",
       "      <td>moass</td>\n",
       "      <td>shiba</td>\n",
       "      <td>high</td>\n",
       "      <td>update</td>\n",
       "      <td>moass</td>\n",
       "      <td>moass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>street</td>\n",
       "      <td>tomorrow</td>\n",
       "      <td>prediction</td>\n",
       "      <td>moass</td>\n",
       "      <td>new</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>moass</td>\n",
       "      <td>trade</td>\n",
       "      <td>need</td>\n",
       "      <td>buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bullish</td>\n",
       "      <td>happen</td>\n",
       "      <td>price</td>\n",
       "      <td>know</td>\n",
       "      <td>stonk</td>\n",
       "      <td>update</td>\n",
       "      <td>short</td>\n",
       "      <td>need</td>\n",
       "      <td>portnoy</td>\n",
       "      <td>week</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_0   Topic_1     Topic_2     Topic_3   Topic_4     Topic_5  \\\n",
       "0     moass     today      market    gamestop  gamestop         buy   \n",
       "1  gamestop      news     citadel       short      live    gamestop   \n",
       "2      wall      meme        live         buy      need        live   \n",
       "3      need     price        talk        talk    market       moass   \n",
       "4      know    market         big       price      know       short   \n",
       "5   citadel       big     squeeze     squeeze      week  prediction   \n",
       "6   squeeze  gamestop       hedge        live    update    analysis   \n",
       "7      news       sec        long  prediction     moass       shiba   \n",
       "8    street  tomorrow  prediction       moass       new     squeeze   \n",
       "9   bullish    happen       price        know     stonk      update   \n",
       "\n",
       "     Topic_6   Topic_7   Topic_8     Topic_9  \n",
       "0   gamestop     short     short    gamestop  \n",
       "1  dashboard   squeeze   squeeze     squeeze  \n",
       "2   analysis  gamestop    update  prediction  \n",
       "3    squeeze     moass  gamestop       short  \n",
       "4      price    market       new       price  \n",
       "5        new      info      fund    analysis  \n",
       "6       live       new     hedge    earnings  \n",
       "7       high    update     moass       moass  \n",
       "8      moass     trade      need         buy  \n",
       "9      short      need   portnoy        week  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topicsDict = {}\n",
    "for topicNum in range(newslda.num_topics):\n",
    "    topicWords = [w for w, p in newslda.show_topic(topicNum)]\n",
    "    topicsDict['Topic_{}'.format(topicNum)] = topicWords\n",
    "\n",
    "wordRanksDF = pd.DataFrame(topicsDict)\n",
    "wordRanksDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "serious-trace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.09 s, sys: 43.2 ms, total: 1.14 s\n",
      "Wall time: 1.32 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "newslda = gensim.models.ldamodel.LdaModel(corpus=senmm, id2word=dictionary, num_topics=5, alpha='auto', eta='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "alive-jerusalem",
   "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>Topic_0</th>\n",
       "      <th>Topic_1</th>\n",
       "      <th>Topic_2</th>\n",
       "      <th>Topic_3</th>\n",
       "      <th>Topic_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>squeeze</td>\n",
       "      <td>gamestop</td>\n",
       "      <td>gamestop</td>\n",
       "      <td>gamestop</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gamestop</td>\n",
       "      <td>need</td>\n",
       "      <td>short</td>\n",
       "      <td>need</td>\n",
       "      <td>prediction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>short</td>\n",
       "      <td>big</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>moass</td>\n",
       "      <td>market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>update</td>\n",
       "      <td>know</td>\n",
       "      <td>prediction</td>\n",
       "      <td>know</td>\n",
       "      <td>gamestop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>new</td>\n",
       "      <td>news</td>\n",
       "      <td>live</td>\n",
       "      <td>update</td>\n",
       "      <td>moass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>moass</td>\n",
       "      <td>buy</td>\n",
       "      <td>analysis</td>\n",
       "      <td>market</td>\n",
       "      <td>analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>live</td>\n",
       "      <td>moass</td>\n",
       "      <td>citadel</td>\n",
       "      <td>bullish</td>\n",
       "      <td>know</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>buy</td>\n",
       "      <td>bullish</td>\n",
       "      <td>trade</td>\n",
       "      <td>watch</td>\n",
       "      <td>inflation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>info</td>\n",
       "      <td>important</td>\n",
       "      <td>talk</td>\n",
       "      <td>short</td>\n",
       "      <td>dip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>market</td>\n",
       "      <td>right</td>\n",
       "      <td>moass</td>\n",
       "      <td>squeeze</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_0    Topic_1     Topic_2   Topic_3     Topic_4\n",
       "0   squeeze   gamestop    gamestop  gamestop       price\n",
       "1  gamestop       need       short      need  prediction\n",
       "2     short        big     squeeze     moass      market\n",
       "3    update       know  prediction      know    gamestop\n",
       "4       new       news        live    update       moass\n",
       "5     moass        buy    analysis    market    analysis\n",
       "6      live      moass     citadel   bullish        know\n",
       "7       buy    bullish       trade     watch   inflation\n",
       "8      info  important        talk     short         dip\n",
       "9    market      right       moass   squeeze        live"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topicsDict = {}\n",
    "for topicNum in range(newslda.num_topics):\n",
    "    topicWords = [w for w, p in newslda.show_topic(topicNum)]\n",
    "    topicsDict['Topic_{}'.format(topicNum)] = topicWords\n",
    "\n",
    "wordRanksDF = pd.DataFrame(topicsDict)\n",
    "wordRanksDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "olympic-poster",
   "metadata": {},
   "outputs": [],
   "source": [
    "d['tokenized_texts'] = d['tokenized_text'].apply(lambda x: [x])\n",
    "d['normalized_texts'] = d['normalized_tokens'].apply(lambda x: [x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "affecting-upset",
   "metadata": {},
   "outputs": [],
   "source": [
    "#########WEEK 4\n",
    "W2V = gensim.models.word2vec.Word2Vec(d['normalized_texts'].sum(), sg=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "found-negative",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "638"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d['normalized_texts'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "celtic-linux",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gme': <gensim.models.keyedvectors.Vocab at 0x1330185c0>,\n",
       " 'stock': <gensim.models.keyedvectors.Vocab at 0x133018780>,\n",
       " 'set': <gensim.models.keyedvectors.Vocab at 0x134185ba8>,\n",
       " 'earnings': <gensim.models.keyedvectors.Vocab at 0x133bd3898>,\n",
       " 'amc': <gensim.models.keyedvectors.Vocab at 0x133bd3a90>,\n",
       " 'hedge': <gensim.models.keyedvectors.Vocab at 0x133bd3a58>,\n",
       " 'fund': <gensim.models.keyedvectors.Vocab at 0x133bd3fd0>,\n",
       " 'big': <gensim.models.keyedvectors.Vocab at 0x133bd3908>,\n",
       " 'bank': <gensim.models.keyedvectors.Vocab at 0x133bd38d0>,\n",
       " 'get': <gensim.models.keyedvectors.Vocab at 0x133bd3940>,\n",
       " 'catch': <gensim.models.keyedvectors.Vocab at 0x133bd3e10>,\n",
       " 'short': <gensim.models.keyedvectors.Vocab at 0x133bd3828>,\n",
       " 'squeeze': <gensim.models.keyedvectors.Vocab at 0x133bd3208>,\n",
       " '|': <gensim.models.keyedvectors.Vocab at 0x133bd37b8>,\n",
       " 'news': <gensim.models.keyedvectors.Vocab at 0x133bd39e8>,\n",
       " 'market': <gensim.models.keyedvectors.Vocab at 0x133bd3b70>,\n",
       " 'shock': <gensim.models.keyedvectors.Vocab at 0x133bd3780>,\n",
       " 'cramer': <gensim.models.keyedvectors.Vocab at 0x133bd3ef0>,\n",
       " 'talk': <gensim.models.keyedvectors.Vocab at 0x133bd3c88>,\n",
       " 'gamestop': <gensim.models.keyedvectors.Vocab at 0x133bd36d8>,\n",
       " 'say': <gensim.models.keyedvectors.Vocab at 0x133bd3748>,\n",
       " 'buy': <gensim.models.keyedvectors.Vocab at 0x133bd3860>,\n",
       " 'break': <gensim.models.keyedvectors.Vocab at 0x133bd3a20>,\n",
       " 'wall': <gensim.models.keyedvectors.Vocab at 0x133bd3710>,\n",
       " 'street': <gensim.models.keyedvectors.Vocab at 0x133bd3358>,\n",
       " 'seller': <gensim.models.keyedvectors.Vocab at 0x133bd31d0>,\n",
       " 'moass': <gensim.models.keyedvectors.Vocab at 0x133bd3198>,\n",
       " 'real': <gensim.models.keyedvectors.Vocab at 0x133bd3160>,\n",
       " 'happen': <gensim.models.keyedvectors.Vocab at 0x133bd30f0>,\n",
       " 'ryan': <gensim.models.keyedvectors.Vocab at 0x133bd3128>,\n",
       " 'cohen': <gensim.models.keyedvectors.Vocab at 0x133bd30b8>,\n",
       " '$': <gensim.models.keyedvectors.Vocab at 0x133bd3080>,\n",
       " 'bbby': <gensim.models.keyedvectors.Vocab at 0x133bd32e8>,\n",
       " 'info': <gensim.models.keyedvectors.Vocab at 0x133bd32b0>,\n",
       " 'urgent': <gensim.models.keyedvectors.Vocab at 0x133bd3390>,\n",
       " 'dave': <gensim.models.keyedvectors.Vocab at 0x133bd3240>,\n",
       " 'portnoy': <gensim.models.keyedvectors.Vocab at 0x133bd3588>,\n",
       " 'manipulation': <gensim.models.keyedvectors.Vocab at 0x133bd3550>,\n",
       " 'citadel': <gensim.models.keyedvectors.Vocab at 0x133bd3518>,\n",
       " 'charles': <gensim.models.keyedvectors.Vocab at 0x133bd34a8>,\n",
       " 'payne': <gensim.models.keyedvectors.Vocab at 0x133bd34e0>,\n",
       " 'share': <gensim.models.keyedvectors.Vocab at 0x133bd3470>,\n",
       " '🔥': <gensim.models.keyedvectors.Vocab at 0x133bd3438>,\n",
       " 'go': <gensim.models.keyedvectors.Vocab at 0x133bd33c8>,\n",
       " 'right': <gensim.models.keyedvectors.Vocab at 0x133bd3b38>,\n",
       " 'update': <gensim.models.keyedvectors.Vocab at 0x133bd3ac8>,\n",
       " 'house': <gensim.models.keyedvectors.Vocab at 0x133bd3f28>,\n",
       " 'crash': <gensim.models.keyedvectors.Vocab at 0x133bd3320>,\n",
       " 'reason': <gensim.models.keyedvectors.Vocab at 0x133bd3f60>,\n",
       " 'end': <gensim.models.keyedvectors.Vocab at 0x133bd35f8>,\n",
       " 'expect': <gensim.models.keyedvectors.Vocab at 0x133bd3e48>,\n",
       " 'explain': <gensim.models.keyedvectors.Vocab at 0x133bd3668>,\n",
       " 'hold': <gensim.models.keyedvectors.Vocab at 0x133bd3278>,\n",
       " 'inflation': <gensim.models.keyedvectors.Vocab at 0x133bd3630>,\n",
       " 'think': <gensim.models.keyedvectors.Vocab at 0x133bd3cc0>,\n",
       " 'know': <gensim.models.keyedvectors.Vocab at 0x133bd3be0>,\n",
       " 'wrong': <gensim.models.keyedvectors.Vocab at 0x133bd3cf8>,\n",
       " 'sec': <gensim.models.keyedvectors.Vocab at 0x133bd3d30>,\n",
       " 'low': <gensim.models.keyedvectors.Vocab at 0x133bd3c50>,\n",
       " 'new': <gensim.models.keyedvectors.Vocab at 0x133bd3d68>,\n",
       " 'economy': <gensim.models.keyedvectors.Vocab at 0x133bd3c18>,\n",
       " 'rate': <gensim.models.keyedvectors.Vocab at 0x133bd3da0>,\n",
       " 'stock|': <gensim.models.keyedvectors.Vocab at 0x133bd3dd8>,\n",
       " 'option': <gensim.models.keyedvectors.Vocab at 0x133bd35c0>,\n",
       " 'interest': <gensim.models.keyedvectors.Vocab at 0x133bd39b0>,\n",
       " 'technical': <gensim.models.keyedvectors.Vocab at 0x133bd3ba8>,\n",
       " 'analysis': <gensim.models.keyedvectors.Vocab at 0x133bd3b00>,\n",
       " 'change': <gensim.models.keyedvectors.Vocab at 0x133bd3978>,\n",
       " 'price': <gensim.models.keyedvectors.Vocab at 0x13335c860>,\n",
       " 'action': <gensim.models.keyedvectors.Vocab at 0x13335cd30>,\n",
       " '+': <gensim.models.keyedvectors.Vocab at 0x13335c978>,\n",
       " 'ape': <gensim.models.keyedvectors.Vocab at 0x13335c908>,\n",
       " 'important': <gensim.models.keyedvectors.Vocab at 0x13335c1d0>,\n",
       " 'thing': <gensim.models.keyedvectors.Vocab at 0x13335cbe0>,\n",
       " 'need': <gensim.models.keyedvectors.Vocab at 0x13335cef0>,\n",
       " 'today': <gensim.models.keyedvectors.Vocab at 0x13335c048>,\n",
       " 'doj': <gensim.models.keyedvectors.Vocab at 0x13335c160>,\n",
       " '💎': <gensim.models.keyedvectors.Vocab at 0x13335c128>,\n",
       " 'come': <gensim.models.keyedvectors.Vocab at 0x13335c208>,\n",
       " 'vectorvest': <gensim.models.keyedvectors.Vocab at 0x13335c0b8>,\n",
       " 'want': <gensim.models.keyedvectors.Vocab at 0x13335c3c8>,\n",
       " 'fall': <gensim.models.keyedvectors.Vocab at 0x13335c390>,\n",
       " 'cause': <gensim.models.keyedvectors.Vocab at 0x13335c358>,\n",
       " 'loopring': <gensim.models.keyedvectors.Vocab at 0x13335c2e8>,\n",
       " 'hype': <gensim.models.keyedvectors.Vocab at 0x13335c320>,\n",
       " 'video': <gensim.models.keyedvectors.Vocab at 0x13335c2b0>,\n",
       " 'prediction': <gensim.models.keyedvectors.Vocab at 0x13335c278>,\n",
       " 'friday': <gensim.models.keyedvectors.Vocab at 0x13335c240>,\n",
       " 'sell': <gensim.models.keyedvectors.Vocab at 0x13335c080>,\n",
       " 'run': <gensim.models.keyedvectors.Vocab at 0x13335c4e0>,\n",
       " 'near': <gensim.models.keyedvectors.Vocab at 0x13335c588>,\n",
       " 'nft': <gensim.models.keyedvectors.Vocab at 0x13335c470>,\n",
       " 'delay': <gensim.models.keyedvectors.Vocab at 0x13335c748>,\n",
       " 'lrc': <gensim.models.keyedvectors.Vocab at 0x13335c710>,\n",
       " 'fud': <gensim.models.keyedvectors.Vocab at 0x13335c6d8>,\n",
       " 'bearish': <gensim.models.keyedvectors.Vocab at 0x13335c668>,\n",
       " 'plan': <gensim.models.keyedvectors.Vocab at 0x13335c6a0>,\n",
       " 'hour': <gensim.models.keyedvectors.Vocab at 0x13335c630>,\n",
       " 'day': <gensim.models.keyedvectors.Vocab at 0x13335c5f8>,\n",
       " 'meme': <gensim.models.keyedvectors.Vocab at 0x13335c5c0>,\n",
       " 'week': <gensim.models.keyedvectors.Vocab at 0x13335c400>,\n",
       " 'time': <gensim.models.keyedvectors.Vocab at 0x13335c828>,\n",
       " 'dip': <gensim.models.keyedvectors.Vocab at 0x13335c7b8>,\n",
       " 'massive': <gensim.models.keyedvectors.Vocab at 0x13335c780>,\n",
       " 'watch': <gensim.models.keyedvectors.Vocab at 0x13335c8d0>,\n",
       " 'ken': <gensim.models.keyedvectors.Vocab at 0x13335c898>,\n",
       " 'griffin': <gensim.models.keyedvectors.Vocab at 0x13335c198>,\n",
       " 'ceo': <gensim.models.keyedvectors.Vocab at 0x13335c7f0>,\n",
       " 'huge': <gensim.models.keyedvectors.Vocab at 0x13335cb00>,\n",
       " 'win': <gensim.models.keyedvectors.Vocab at 0x13335cac8>,\n",
       " 'crypto': <gensim.models.keyedvectors.Vocab at 0x13335ca90>,\n",
       " 'investor': <gensim.models.keyedvectors.Vocab at 0x13335ca20>,\n",
       " 'rally': <gensim.models.keyedvectors.Vocab at 0x13335ca58>,\n",
       " 'dd': <gensim.models.keyedvectors.Vocab at 0x13335c9e8>,\n",
       " 'start': <gensim.models.keyedvectors.Vocab at 0x13335c9b0>,\n",
       " 'like': <gensim.models.keyedvectors.Vocab at 0x13335c940>,\n",
       " 'ready': <gensim.models.keyedvectors.Vocab at 0x13335c438>,\n",
       " 'datum': <gensim.models.keyedvectors.Vocab at 0x13335cc18>,\n",
       " 'blackrock': <gensim.models.keyedvectors.Vocab at 0x13335ccc0>,\n",
       " 'trade': <gensim.models.keyedvectors.Vocab at 0x13335cba8>,\n",
       " 'feed': <gensim.models.keyedvectors.Vocab at 0x13335ceb8>,\n",
       " 'long': <gensim.models.keyedvectors.Vocab at 0x13335ce80>,\n",
       " 'report': <gensim.models.keyedvectors.Vocab at 0x13335ce48>,\n",
       " 'super': <gensim.models.keyedvectors.Vocab at 0x13335cdd8>,\n",
       " 'bullish': <gensim.models.keyedvectors.Vocab at 0x13335ce10>,\n",
       " 'ultimate': <gensim.models.keyedvectors.Vocab at 0x13335cda0>,\n",
       " 'guide': <gensim.models.keyedvectors.Vocab at 0x13335cd68>,\n",
       " 'explode': <gensim.models.keyedvectors.Vocab at 0x13335ccf8>,\n",
       " 'soon': <gensim.models.keyedvectors.Vocab at 0x13335cb38>,\n",
       " 'tomorrow': <gensim.models.keyedvectors.Vocab at 0x13335cfd0>,\n",
       " 'cover': <gensim.models.keyedvectors.Vocab at 0x13335cf60>,\n",
       " 'truth': <gensim.models.keyedvectors.Vocab at 0x13335cb70>,\n",
       " 'march': <gensim.models.keyedvectors.Vocab at 0x13335cf98>,\n",
       " 'take': <gensim.models.keyedvectors.Vocab at 0x13335cc50>,\n",
       " 'st': <gensim.models.keyedvectors.Vocab at 0x13335cf28>,\n",
       " 'asap': <gensim.models.keyedvectors.Vocab at 0x13335cc88>,\n",
       " 'look': <gensim.models.keyedvectors.Vocab at 0x1342dc668>,\n",
       " 'retail': <gensim.models.keyedvectors.Vocab at 0x1342dc438>,\n",
       " 'surge': <gensim.models.keyedvectors.Vocab at 0x1342dc6a0>,\n",
       " 'buckle': <gensim.models.keyedvectors.Vocab at 0x1342dc5c0>,\n",
       " 'live': <gensim.models.keyedvectors.Vocab at 0x1342dc4e0>,\n",
       " 'high': <gensim.models.keyedvectors.Vocab at 0x1342dc518>,\n",
       " 'level': <gensim.models.keyedvectors.Vocab at 0x1342dc400>,\n",
       " 'spy': <gensim.models.keyedvectors.Vocab at 0x1342dc3c8>,\n",
       " 'tsla': <gensim.models.keyedvectors.Vocab at 0x1342dc390>,\n",
       " 'mar': <gensim.models.keyedvectors.Vocab at 0x1342dc320>,\n",
       " '🚀': <gensim.models.keyedvectors.Vocab at 0x1342dc358>,\n",
       " 'moon': <gensim.models.keyedvectors.Vocab at 0x1342dc2e8>,\n",
       " 'show': <gensim.models.keyedvectors.Vocab at 0x1342dc2b0>,\n",
       " 'margin': <gensim.models.keyedvectors.Vocab at 0x1342dc278>,\n",
       " 'call': <gensim.models.keyedvectors.Vocab at 0x1342dc240>,\n",
       " 'gensler': <gensim.models.keyedvectors.Vocab at 0x1342dc208>,\n",
       " '️': <gensim.models.keyedvectors.Vocab at 0x1342dc198>,\n",
       " 'shib': <gensim.models.keyedvectors.Vocab at 0x1342dc1d0>,\n",
       " 'gary': <gensim.models.keyedvectors.Vocab at 0x1342dc160>,\n",
       " 'shiba': <gensim.models.keyedvectors.Vocab at 0x1342dc128>,\n",
       " 'finally': <gensim.models.keyedvectors.Vocab at 0x1342dc0f0>,\n",
       " '🔴': <gensim.models.keyedvectors.Vocab at 0x1342dc0b8>,\n",
       " 'robinhood': <gensim.models.keyedvectors.Vocab at 0x1342dc080>,\n",
       " '||': <gensim.models.keyedvectors.Vocab at 0x1342dc048>,\n",
       " 'monday': <gensim.models.keyedvectors.Vocab at 0x1342dc470>,\n",
       " 'well': <gensim.models.keyedvectors.Vocab at 0x1342dc6d8>,\n",
       " 'drop': <gensim.models.keyedvectors.Vocab at 0x1342dc710>,\n",
       " '🚨': <gensim.models.keyedvectors.Vocab at 0x1342dc550>,\n",
       " 'forecast': <gensim.models.keyedvectors.Vocab at 0x1342dc588>,\n",
       " 'hot': <gensim.models.keyedvectors.Vocab at 0x1342dc860>,\n",
       " 'level2': <gensim.models.keyedvectors.Vocab at 0x1342dc828>,\n",
       " 'deepbook': <gensim.models.keyedvectors.Vocab at 0x1342dc7f0>,\n",
       " '🏴': <gensim.models.keyedvectors.Vocab at 0x1342dc780>,\n",
       " '\\u200d': <gensim.models.keyedvectors.Vocab at 0x1342dc7b8>,\n",
       " '☠': <gensim.models.keyedvectors.Vocab at 0x1342dc630>,\n",
       " 'chart': <gensim.models.keyedvectors.Vocab at 0x1342dc5f8>,\n",
       " 'stonk': <gensim.models.keyedvectors.Vocab at 0x1342dcb00>,\n",
       " 'daily': <gensim.models.keyedvectors.Vocab at 0x1342dcba8>,\n",
       " 'mover': <gensim.models.keyedvectors.Vocab at 0x1342dcb38>,\n",
       " 'btc': <gensim.models.keyedvectors.Vocab at 0x1342dcac8>,\n",
       " 'eth': <gensim.models.keyedvectors.Vocab at 0x1342dccf8>,\n",
       " 'doge': <gensim.models.keyedvectors.Vocab at 0x1342dccc0>,\n",
       " 'bitcoin': <gensim.models.keyedvectors.Vocab at 0x1342dcc88>,\n",
       " 'sndl': <gensim.models.keyedvectors.Vocab at 0x1342dcc18>,\n",
       " 'dashboard': <gensim.models.keyedvectors.Vocab at 0x1342dcc50>,\n",
       " 'stake': <gensim.models.keyedvectors.Vocab at 0x1342dcbe0>,\n",
       " 'benzinga': <gensim.models.keyedvectors.Vocab at 0x1342dcb70>}"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2V.wv.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "neither-framework",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('stock', 0.9998062252998352),\n",
       " ('amc', 0.9997835159301758),\n",
       " ('$', 0.9997791051864624),\n",
       " ('|', 0.999762773513794),\n",
       " ('live', 0.9997110962867737),\n",
       " ('gamestop', 0.9996991157531738),\n",
       " ('bbby', 0.9996843934059143),\n",
       " ('moass', 0.9996776580810547),\n",
       " ('talk', 0.9996538162231445),\n",
       " ('short', 0.9996510744094849)]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2V.most_similar('gme')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "charitable-biodiversity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('btc', 0.99913090467453),\n",
       " ('gme', 0.999069333076477),\n",
       " ('$', 0.9990635514259338),\n",
       " ('|', 0.9990558624267578),\n",
       " ('stock', 0.9990365505218506),\n",
       " ('amc', 0.9990243911743164),\n",
       " ('🚀', 0.9990054368972778),\n",
       " ('live', 0.9990031123161316),\n",
       " ('mover', 0.998986542224884),\n",
       " ('squeeze', 0.9989818334579468)]"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2V.most_similar('bullish')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "wrong-drove",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('moass', 0.9996432065963745),\n",
       " ('stock', 0.9996041655540466),\n",
       " ('$', 0.9995770454406738),\n",
       " ('|', 0.9995646476745605),\n",
       " ('gme', 0.9995555877685547),\n",
       " ('gamestop', 0.9995529055595398),\n",
       " ('amc', 0.9995481371879578),\n",
       " ('btc', 0.9995073676109314),\n",
       " ('bbby', 0.9994943141937256),\n",
       " ('live', 0.9994703531265259)]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2V.most_similar('citadel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "valuable-diploma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('market', 0.9973212480545044),\n",
       " ('crypto', 0.9971807599067688),\n",
       " ('stock', 0.9970856308937073),\n",
       " ('bbby', 0.9970109462738037),\n",
       " ('amc', 0.9969691038131714),\n",
       " ('|', 0.9969620704650879),\n",
       " ('live', 0.9968990087509155),\n",
       " ('🚀', 0.996881365776062),\n",
       " ('prediction', 0.9968746900558472),\n",
       " ('trade', 0.9968708753585815)]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2V.most_similar(positive=['gme', 'hedge'], negative = ['bank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "involved-opinion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.16062663, -0.23212145, -0.03260498, ...,  0.00136783,\n",
       "         0.05346525,  0.4363671 ],\n",
       "       [-0.14450172, -0.1987903 , -0.0330665 , ...,  0.00441647,\n",
       "         0.04828367,  0.3799808 ],\n",
       "       [-0.1383642 , -0.1980534 , -0.03278654, ..., -0.00123822,\n",
       "         0.0450214 ,  0.37463954],\n",
       "       ...,\n",
       "       [-0.03762012, -0.0569005 , -0.00912174, ...,  0.00044975,\n",
       "         0.01639073,  0.09617153],\n",
       "       [-0.05218557, -0.07685625, -0.01216075, ...,  0.00297205,\n",
       "         0.01716377,  0.14791018],\n",
       "       [-0.08139051, -0.11066982, -0.01639156, ..., -0.0032721 ,\n",
       "         0.02870957,  0.22088397]], dtype=float32)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numWords = 50\n",
    "targetWords = W2V.wv.index2word[:numWords]\n",
    "\n",
    "wordsSubMatrix = []\n",
    "for word in targetWords:\n",
    "    wordsSubMatrix.append(W2V[word])\n",
    "wordsSubMatrix = np.array(wordsSubMatrix)\n",
    "wordsSubMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "single-pocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaWords = sklearn.decomposition.PCA(n_components = 50).fit(wordsSubMatrix)\n",
    "reducedPCA_data = pcaWords.transform(wordsSubMatrix)\n",
    "#T-SNE is theoretically better, but you should experiment\n",
    "tsneWords = sklearn.manifold.TSNE(n_components = 2).fit_transform(reducedPCA_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "liberal-northern",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_frame_on(False)\n",
    "plt.scatter(tsneWords[:, 0], tsneWords[:, 1], alpha = 0)#Making the points invisible \n",
    "for i, word in enumerate(targetWords):\n",
    "    ax.annotate(word, (tsneWords[:, 0][i],tsneWords[:, 1][i]), size =  20 * (numWords - i) / numWords)\n",
    "plt.title('Vector Visualization by T-SNE', size = 18, color = \"blue\")\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()\n",
    "plt.savefig('w2v_tsne.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "excess-daily",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaWords1 = sklearn.decomposition.PCA(n_components = 2).fit(wordsSubMatrix)\n",
    "reducedPCA_data1 = pcaWords1.transform(wordsSubMatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "sacred-mayor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_frame_on(False)\n",
    "plt.scatter(reducedPCA_data1[:, 0], reducedPCA_data1[:, 1], alpha = 0)#Making the points invisible \n",
    "for i, word in enumerate(targetWords):\n",
    "    ax.annotate(word, (reducedPCA_data1[:, 0][i],reducedPCA_data1[:, 1][i]), size =  20 * (numWords - i) / numWords)\n",
    "plt.title('Vector Visualization by PCA', size = 18, color = \"blue\")\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()\n",
    "plt.savefig('w2v_pca.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dominican-shore",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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