fedickinson/Scraping_The_Partisan_Divide.ipynb

## Scraping_The_Partisan_Divide.ipynb
{
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   "cell_type": "markdown",
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   "source": [
    "# Scraping the Partisan Divide: Sentiment, Text, & Network Analysis of an online political discussion forum.\n",
    "\n",
    "### In this notebook I  apply data science techniques to investigate partisan division on scraped user post data from an online political discussion forum.\n",
    "\n",
    "## My process:\n",
    " - Scrape text and user political ideology of each post in the forum in my given timeframe.\n",
    " - Consolidate partisanship into two groups along liberal to conservative political axis.\n",
    " - Distinguish top phrases for each group.\n",
    " - Analyze sentiment between partisan identities on posts matching political keywords.\n",
    " - Identify communities via network analysis to examine partisan division.\n",
    "\n",
    "\n",
    "## Demonstrated Python Skills:\n",
    " - Web-scraping with Scrapy\n",
    " - Text Analysis with NLTK and Sklearn.CountVectorizer\n",
    " - Sentiment Analysis with TextBlob\n",
    " - Network Analysis with NetworkX and Community\n",
    " - Visualization with Seaborrn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Scraping Data with a Scrapy Spyder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  See my github gist for the code of my spyder:\n",
    "\n",
    "https://gist.github.com/fedickinson/322d2f801667666d219a774886552ccd\n",
    "\n",
    "\n",
    "## Structure of Forum to Scrape:\n",
    "\n",
    "### Defined Number of Threads -> Multiple Pages per Thread -> Multiple posts per page\n",
    "\n",
    "## Process:\n",
    "\n",
    "- load start page for for each thread\n",
    "\n",
    "- load each page of each thread\n",
    "\n",
    "- parse components of each post of each page in each thread\n",
    "\n",
    "\n",
    "### Output: csv file 'forum.csv' with collected data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Import Scraped Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import packages\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(251125, 8)"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forum = pd.read_csv(\"forum.csv\")\n",
    "forum.drop_duplicates(inplace=True)\n",
    "forum.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Clean & Format Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum = forum.dropna(subset=['userName'], how='all')\n",
    "forum.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert to numeric: threadReplies, userMoney, userPosts\n",
    "\n",
    "#take commas out of strings\n",
    "forum['threadReplies'] = forum['threadReplies'].str.replace(',', '')\n",
    "forum['userMoney'] = forum['userMoney'].str.replace(',', '')\n",
    "forum['userPosts'] = forum['userPosts'].str.replace(',', '')\n",
    "\n",
    "#convert to numeric\n",
    "forum['threadReplies'] = forum['threadReplies'].replace(' replies to this topic','', regex=True)\n",
    "forum['threadReplies'] = pd.to_numeric(forum['threadReplies'])\n",
    "forum['userMoney'] = pd.to_numeric(forum['userMoney'])\n",
    "forum['userPosts'] = pd.to_numeric(forum['userPosts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/franklindickinson/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  import sys\n",
      "/Users/franklindickinson/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# clean and convert dateTime data\n",
    "forum['postDateTime'] = forum['postDateTime'].str.strip()\n",
    "filter1 = forum['postDateTime'].str.contains(\"minutes ago\")\n",
    "filter2 = forum['postDateTime'].str.contains(\"Yesterday\")\n",
    "filter3 = forum['postDateTime'].str.contains(\"Today\")\n",
    "forum = forum[~filter1]\n",
    "forum = forum[~filter2]\n",
    "forum = forum[~filter3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum['postDateTime'] = pd.to_datetime(forum['postDateTime'], format='%d %b %Y, %I:%M %p')\n",
    "forum = forum[forum['postDateTime'] >= pd.datetime(2016, 1, 1)]\n",
    "\n",
    "forum['YearMonth'] = forum['postDateTime'].map(lambda x: 1000*x.year + x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/franklindickinson/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>postDateTime</th>\n",
       "      <th>postText</th>\n",
       "      <th>threadReplies</th>\n",
       "      <th>threadTitle</th>\n",
       "      <th>userMoney</th>\n",
       "      <th>userName</th>\n",
       "      <th>userPolitics</th>\n",
       "      <th>userPosts</th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>sLeftRight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-01-02 18:24:00</td>\n",
       "      <td>\\n\\n                        \\n                ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Do you think Trump will ever mention the horri...</td>\n",
       "      <td>325.98</td>\n",
       "      <td>xxxxxxx</td>\n",
       "      <td>Revolutionary</td>\n",
       "      <td>16928</td>\n",
       "      <td>2016001</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-01-02 18:49:00</td>\n",
       "      <td>\\n\\n                        \\n                ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Do you think Trump will ever mention the horri...</td>\n",
       "      <td>7036.96</td>\n",
       "      <td>TotallyRad</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>9903</td>\n",
       "      <td>2016001</td>\n",
       "      <td>Liberal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-01-02 19:48:00</td>\n",
       "      <td>\\n\\n                        \\n                ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Do you think Trump will ever mention the horri...</td>\n",
       "      <td>325.98</td>\n",
       "      <td>xxxxxxx</td>\n",
       "      <td>Revolutionary</td>\n",
       "      <td>16928</td>\n",
       "      <td>2016001</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-01-02 20:29:00</td>\n",
       "      <td>\\n\\n                        \\n                ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Do you think Trump will ever mention the horri...</td>\n",
       "      <td>7036.96</td>\n",
       "      <td>TotallyRad</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>9903</td>\n",
       "      <td>2016001</td>\n",
       "      <td>Liberal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-01-02 22:23:00</td>\n",
       "      <td>\\n\\n                        \\n                ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Do you think Trump will ever mention the horri...</td>\n",
       "      <td>325.98</td>\n",
       "      <td>xxxxxxx</td>\n",
       "      <td>Revolutionary</td>\n",
       "      <td>16928</td>\n",
       "      <td>2016001</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          postDateTime                                           postText  \\\n",
       "15 2016-01-02 18:24:00  \\n\\n                        \\n                ...   \n",
       "16 2016-01-02 18:49:00  \\n\\n                        \\n                ...   \n",
       "17 2016-01-02 19:48:00  \\n\\n                        \\n                ...   \n",
       "18 2016-01-02 20:29:00  \\n\\n                        \\n                ...   \n",
       "19 2016-01-02 22:23:00  \\n\\n                        \\n                ...   \n",
       "\n",
       "    threadReplies                                        threadTitle  \\\n",
       "15            5.0  Do you think Trump will ever mention the horri...   \n",
       "16            5.0  Do you think Trump will ever mention the horri...   \n",
       "17            5.0  Do you think Trump will ever mention the horri...   \n",
       "18            5.0  Do you think Trump will ever mention the horri...   \n",
       "19            5.0  Do you think Trump will ever mention the horri...   \n",
       "\n",
       "    userMoney    userName   userPolitics  userPosts  YearMonth sLeftRight  \n",
       "15     325.98     xxxxxxx  Revolutionary      16928    2016001             \n",
       "16    7036.96  TotallyRad        Liberal       9903    2016001    Liberal  \n",
       "17     325.98     xxxxxxx  Revolutionary      16928    2016001             \n",
       "18    7036.96  TotallyRad        Liberal       9903    2016001    Liberal  \n",
       "19     325.98     xxxxxxx  Revolutionary      16928    2016001             "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean userPolitics field\n",
    "forum['userPolitics'] = forum['userPolitics'].replace(\"<bound method SelectorList.extract of \\[<Selector xpath='div\\[3\\]\\/div\\[2\\]\\/a\\[2\\]\\/text\\(\\)' data='\",'', regex=True)\n",
    "\n",
    "forum['userPolitics'] = forum['userPolitics'].str.extract(r\"data=' (\\w+)\\\\n\")\n",
    "\n",
    "#sLeftRight\n",
    "forum['sLeftRight'] = ''\n",
    "\n",
    "forum.loc[forum['userPolitics'].isin(['Conservative','Republican','Libertarian','Capitalist']), 'sLeftRight'] = 'Conservative'\n",
    "\n",
    "forum.loc[forum['userPolitics'].isin(['Liberal','Progressive','Socialist','Democratic','Anarchist','Green']), 'sLeftRight'] = 'Liberal' \n",
    "\n",
    "forum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#clean post text data\n",
    "forum['postText'] = forum['postText'].str.strip()\n",
    "forum['postText'] = forum['postText'].replace('\\n','', regex=True)\n",
    "forum['postText'] = forum['postText'].replace('^,','', regex=True)\n",
    "forum['postText'] = forum['postText'].str.strip()\n",
    "forum['postText'] = forum['postText'].replace('^,','', regex=True)\n",
    "forum['postText'] = forum['postText'].replace('^\\s+','', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum['postText'] = forum['postText'].str.replace(r\"[\\.\\,\\?\\!\\<\\>\\=\\+\\-\\{\\}\\[\\]\\\"\\']\",'')\n",
    "forum['threadTitle'] = forum['threadTitle'].str.replace(r\"[\\.\\,\\?\\!\\<\\>\\=\\+\\-\\{\\}\\[\\]\\\"\\']\",'')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Apply NLP with NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "#apply stopwords\n",
    "\n",
    "stop = set(stopwords.words('english'))\n",
    "\n",
    "forum[\"postText_split\"] = forum[\"postText\"].str.lower().str.split()\n",
    "\n",
    "forum['postText_split'] = forum['postText_split'].apply(lambda x: [item for item in x if item not in stop])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#reset index due to error at index 1469\n",
    "forum.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "def stemify(text):\n",
    "    s= [stemmer.stem(i) for i in text]\n",
    "    return s\n",
    "\n",
    "#stem text\n",
    "\n",
    "forum['postText_stemmed'] = [stemify(forum['postText_split'][i]) for i in range(0,len(forum['postText_split']))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def joinify(i):\n",
    "    s= [' '.join(i)]\n",
    "    return s\n",
    "\n",
    "#join text\n",
    "\n",
    "forum['postText_joined'] = [joinify(forum['postText_stemmed'][i]) for i in range(0,len(forum['postText_stemmed']))]\n",
    "\n",
    "#https://stackoverflow.com/questions/38147447/how-to-remove-square-bracket-from-pandas-dataframe\n",
    "forum['postText_value'] = forum['postText_joined'].str[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4)  Apply Setiment Score with TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum['postText_sentiment'] = forum['postText_value'].apply(lambda post: TextBlob(post).sentiment)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum[['postText_polarity', 'postText_subjectivity']] = forum['postText_sentiment'].apply(pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forum['postText_tb'] = forum['postText'].apply(lambda row: TextBlob(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Distribution of # of Posts / Thread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1abcbe4dd8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac9aca160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot distribution of # of threadReplies \n",
    "forum.hist(column=\"threadReplies\",bins =500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1abc9e8a58>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rz0XEE/j7UzYemChpPHA4sJsR/P1xaBSq/bmSqU3al6ZJp8LvADYDLRGxG4pg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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abcab0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we data is very right skewed... let's replot with the most replied to threads removed\n",
    "forum_threads_max_replies_no_outliers = forum.iloc[500:]\n",
    "forum_threads_max_replies_no_outliers.hist(column=\"threadReplies\",bins = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When were there the most posts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#posts over time `\n",
    "forum['postDate'] = forum['postDateTime'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>postDate</th>\n",
       "      <th>postTextcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>2017-12-03</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>2017-12-02</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       postDate  postTextcount\n",
       "593  2017-08-16            800\n",
       "695  2017-12-01            782\n",
       "709  2017-12-15            779\n",
       "717  2017-12-23            743\n",
       "589  2017-08-12            740\n",
       "697  2017-12-03            726\n",
       "640  2017-10-02            726\n",
       "592  2017-08-15            708\n",
       "733  2018-01-08            688\n",
       "696  2017-12-02            680"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What dates had the most posts?\n",
    "\n",
    "posts_over_time = forum[['postDate','postText']].groupby(['postDate']).agg(['count'])\n",
    "\n",
    "posts_over_time.add_suffix('_Count').reset_index()\n",
    "\n",
    "posts_over_time.columns = [\"\".join(x) for x in posts_over_time.columns.ravel()]\n",
    "posts_over_time = posts_over_time.reset_index()\n",
    "posts_over_time.sort_values(by=['postTextcount'],axis=0,ascending=False).head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What was happening in the news on the dates that had the most posts? \n",
    "\n",
    "\n",
    "- 2017-08-16\n",
    "    - after Charlottesville riot (August 11, 2017)\n",
    "\n",
    "\n",
    "- 2017-12-01\n",
    "    - General Flynn indicted\n",
    "\n",
    "\n",
    "- 2017-12-15\t\n",
    "    - GOP loses Alabama special elecion\n",
    "    \n",
    "    \n",
    "- 2017-12-23\n",
    "    - right after GOP passed tax bill\n",
    "    \n",
    "\n",
    "- 2017-08-12\n",
    "    -  day  after Charlottesville riot (August 11, 2017)\n",
    "\n",
    "\n",
    "##### Interesting two days coming from same week... might make sense to look at posts in larger intervals such as week.\n",
    "\n",
    "##### Also would be interesting to follow an event from the start over time to see how threads develop regarding a specific topic... look at Charlottesville riot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abbd4bf98>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I8miXFSxviuR0mR5o8bfSLZxPc+yR6ig/WZ9DugiFCDbdMx5Xn9IzrccVq7Rd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb993eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posts_over_time.plot(x='postDate',y='postTextcount',kind=\"line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Analyzing Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userName</th>\n",
       "      <th>userMoney</th>\n",
       "      <th>userPolitics</th>\n",
       "      <th>userPosts</th>\n",
       "      <th>sLeftRight</th>\n",
       "      <th>postText_polarity</th>\n",
       "      <th>postText_subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75024</td>\n",
       "      <td></td>\n",
       "      <td>-0.002348</td>\n",
       "      <td>0.497610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>golfboy</td>\n",
       "      <td>48014.29</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>51295</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>-0.021701</td>\n",
       "      <td>0.522087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Huey</td>\n",
       "      <td>12317.42</td>\n",
       "      <td>Liberacon</td>\n",
       "      <td>40696</td>\n",
       "      <td></td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>0.489628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Str8tEdge</td>\n",
       "      <td>41657.45</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>34823</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>-0.051177</td>\n",
       "      <td>0.523102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>11798.70</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>33656</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>0.058102</td>\n",
       "      <td>0.481287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userName  userMoney  userPolitics  userPosts    sLeftRight  \\\n",
       "0  Cannonpointer        NaN           NaN      75024                 \n",
       "1        golfboy   48014.29  Conservative      51295  Conservative   \n",
       "2           Huey   12317.42     Liberacon      40696                 \n",
       "3      Str8tEdge   41657.45   Libertarian      34823  Conservative   \n",
       "4       roadkill   11798.70  Conservative      33656  Conservative   \n",
       "\n",
       "   postText_polarity  postText_subjectivity  \n",
       "0          -0.002348               0.497610  \n",
       "1          -0.021701               0.522087  \n",
       "2          -0.001258               0.489628  \n",
       "3          -0.051177               0.523102  \n",
       "4           0.058102               0.481287  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Users dataframe\n",
    "users_df = forum[['userName','userMoney','userPolitics','userPosts','postText_polarity','postText_subjectivity','sLeftRight']].drop_duplicates()\n",
    "\n",
    "\n",
    "#get mean sentiment values for eahc user\n",
    "users_df_g = users_df[['userName','postText_polarity','postText_subjectivity']].groupby(['userName']).mean()\n",
    "users_df_g.reset_index(inplace=True)\n",
    "users_df_g.head()\n",
    "\n",
    "\n",
    "#get most recent post values for each user\n",
    "users_df_sorted = users_df[['userName','userMoney','userPolitics','userPosts','sLeftRight']].sort_values(by='userPosts',ascending=False)\n",
    "users_df_sorted.drop_duplicates('userName', keep='first',inplace=True)\n",
    "users_df_sorted.reset_index(inplace=True,drop=True)\n",
    "users_df_sorted.head()\n",
    "\n",
    "#merge to get user dataframe with latest Uservalues for each User combined with their average sentiment score\n",
    "user_df_sentiment = pd.merge(users_df_sorted,users_df_g,on=\"userName\")\n",
    "\n",
    "#to show numPosts, we want to remove the few outliers who have posted an extraordinary number of times\n",
    "user_df_sentiment_outliers_removed = user_df_sentiment.iloc[18:]\n",
    "user_df_sentiment_outliers_removed.head()\n",
    "\n",
    "\n",
    "\n",
    "user_df_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1abb9270b8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb964dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of # of posts / user\n",
    "\n",
    "user_df_sentiment.hist(column=\"userPosts\", bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 3727.8260869565215\n",
      "std = 8872.24047269501\n",
      "median = 49.0\n",
      "iqr = 2141.0\n"
     ]
    }
   ],
   "source": [
    "# standard metrics\n",
    "userPosts_series = user_df_sentiment['userPosts'].values\n",
    "userPosts_series = userPosts_series[~np.isnan(userPosts_series)]\n",
    "\n",
    "print('mean = ' + str(np.nanmean(userPosts_series)))\n",
    "print('std = ' + str(np.nanstd(userPosts_series)))\n",
    "\n",
    "print('median = ' + str(np.nanmedian(userPosts_series)))\n",
    "print('iqr = ' + str(sp.stats.iqr(userPosts_series)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Barplot of identified partisanship of each user\n",
    "user_barplot_df = user_df_sentiment.groupby('userPolitics').agg({'userPolitics' : 'count'})\n",
    "user_barplot_df.columns = ['count']\n",
    "user_barplot_df.reset_index(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_barplot_df.sort_values(by='count',ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1abab2fdd8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZOT0iokOG4zO9zYG3AY8D9wM/sr21pC8AnwcOq9tNAHYENgCulvRm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\nL6r9NP4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abad87828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"count\", y='userPolitics', data=user_barplot_df,color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aba8b9978>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SSEm35e2Bkv4vZ35Ol7RvLv+5pEmSZpXaUzjv6ZIezfuUx6KZmVkD1ZvI8g4w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\n3OmZmVmf4U7PzMz6DHd6ZmbWZ/x/gISbL50wcF4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba85c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "post_barplot_df = forum.groupby('userPolitics').agg({'userPolitics' : 'count'})\n",
    "post_barplot_df.columns = ['count']\n",
    "post_barplot_df.reset_index(inplace=True)\n",
    "post_barplot_df.sort_values(by='count',ascending=False,inplace=True)\n",
    "sns.barplot(x=\"count\", y='userPolitics', data=post_barplot_df,color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The % of Posts with userPolitics filled in is: 0.6212757608639073\n"
     ]
    }
   ],
   "source": [
    "print('The % of Posts with userPolitics filled in is: ' + str((forum['userPolitics'].count()/forum['postText'].count())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userPolitics</th>\n",
       "      <th>sLeftRight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Conservative</td>\n",
       "      <td>Conservative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liberacon</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Libertarian</td>\n",
       "      <td>Conservative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liberal</td>\n",
       "      <td>Liberal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userPolitics    sLeftRight\n",
       "0           NaN              \n",
       "1  Conservative  Conservative\n",
       "2     Liberacon              \n",
       "3   Libertarian  Conservative\n",
       "5       Liberal       Liberal"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userPolitics_unique = user_df_sentiment[[\"userPolitics\",\"sLeftRight\"]].drop_duplicates()\n",
    "clrs = ['red' if (x == 'Conservative') else 'blue' for x in userPolitics_unique[\"sLeftRight\"] ]\n",
    "userPolitics_unique.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usersLeftRight = user_df_sentiment[user_df_sentiment['sLeftRight'] != \"\"]\n",
    "forumLeftRight = forum[forum['sLeftRight'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of Users')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rkbtasc/gbxQTE2P9eenSpfbsSkREfkcfdBIRMSgFvIiIQSngRUQMSgEvImJQCngREYNS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\niBjU/wPgIamPBQvR5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba39da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x = usersLeftRight[\"sLeftRight\"], palette = ['royalblue','indianred'], order=['Liberal','Conservative'], saturation=1)\n",
    "plt.title('User breakdown by Ideology')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('# of Users')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of Posts')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kzGOey4LD29ubFi1aAHD55ZdTU1NDYGAg+fn5hIaGsnbtWu644w7at29Pamoqw4cPp7i4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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba68e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x = forumLeftRight[\"sLeftRight\"], palette =['royalblue','indianred'] , order=['Liberal','Conservative'], saturation=1)\n",
    "plt.title('User breakdown by Ideology')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('# of Posts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Apply Sentiment Analysis to Political Keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13731\n"
     ]
    }
   ],
   "source": [
    "list_post_trump = []\n",
    "list_post_trump.append(forum['postText_tb'].apply(lambda row: row.word_counts['trump']))\n",
    "forum['postText_trump'] = list_post_trump[0]\n",
    "\n",
    "\n",
    "forum_pt_trump = forum[(forum['postText_trump'] > 0)]\n",
    "print(forum_pt_trump.shape[0])\n",
    "#Users dataframe\n",
    "users_df_pt_trump = forum_pt_trump[['userName','userMoney','userPolitics','userPosts','postText_polarity','postText_subjectivity','sLeftRight']].drop_duplicates()\n",
    "\n",
    "\n",
    "#get mean sentiment values for each user\n",
    "users_df_pt_trump_g = users_df_pt_trump[['userName','postText_polarity','postText_subjectivity']].groupby(['userName']).mean()\n",
    "users_df_pt_trump_g.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "#get most recent post values for each user\n",
    "users_df_pt_trump_sorted = users_df_pt_trump[['userName','userMoney','userPolitics','userPosts','sLeftRight']].sort_values(by='userPosts',ascending=False)\n",
    "users_df_pt_trump_sorted.drop_duplicates('userName', keep='first',inplace=True)\n",
    "users_df_pt_trump_sorted.reset_index(inplace=True,drop=True)\n",
    "users_df_pt_trump_sorted.head()\n",
    "\n",
    "#merge to get user dataframe with latest Uservalues for each User combined with their average sentiment score\n",
    "user_df_pt_trump_sentiment = pd.merge(users_df_pt_trump_g,users_df_pt_trump_sorted,on=\"userName\")\n",
    "\n",
    "usersLeftRight_pt_trump = user_df_pt_trump_sentiment[user_df_pt_trump_sentiment['sLeftRight'] != \"\"]\n",
    "forumLeftRight_pt_trump = forum_pt_trump[forum_pt_trump['sLeftRight'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b67788f28>,\n",
       "  <matplotlib.axis.YTick at 0x1b67788630>,\n",
       "  <matplotlib.axis.YTick at 0x1b6778ea90>,\n",
       "  <matplotlib.axis.YTick at 0x1b68901518>,\n",
       "  <matplotlib.axis.YTick at 0x1b68901c18>,\n",
       "  <matplotlib.axis.YTick at 0x1b68906358>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ExMTg5XWpgry8vIiNjXVzovpNZS8ibtGkSRMiIiIwmUxEREQQFBTk7kj1miH3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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b42657f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subjectivity - trump\n",
    "sns.set_style('whitegrid')\n",
    "s_leftright_pt_trump_subjectiivity = sns.boxplot(x = usersLeftRight_pt_trump[\"sLeftRight\"], y= usersLeftRight_pt_trump[\"postText_subjectivity\"], palette = ['cornflowerblue','lightcoral'])\n",
    "s_leftright_pt_trump_subjectiivity.set(title= 'Subjectivity - \"Trump\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Subjectivity')\n",
    "plt.yticks([0,.2,.4,.6,.8,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b6893e0b8>,\n",
       "  <matplotlib.axis.YTick at 0x1b6892a320>,\n",
       "  <matplotlib.axis.YTick at 0x1b68955048>,\n",
       "  <matplotlib.axis.YTick at 0x1b689f1b00>,\n",
       "  <matplotlib.axis.YTick at 0x1b68909240>,\n",
       "  <matplotlib.axis.YTick at 0x1b68909940>,\n",
       "  <matplotlib.axis.YTick at 0x1b689fa080>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBUhtugodRXB6fdPtJUuuXhc4ifB0NVVCRyASjMkSuHV3r+vXO8eHhdSmK6wGjRM6BnUg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba20feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# polarity - trump\n",
    "sns.set_style('darkgrid')\n",
    "s_leftright_polarity_pt_trump = sns.boxplot(x = usersLeftRight_pt_trump[\"sLeftRight\"], y= usersLeftRight_pt_trump[\"postText_polarity\"], palette = ['steelblue','tomato'])\n",
    "s_leftright_polarity_pt_trump.set(title= 'Polarity - \"Trump\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Polarity')\n",
    "plt.yticks([-.6,-.4,-.2,0,.2,.4,.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hilary / Clinton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3425\n"
     ]
    }
   ],
   "source": [
    "list_post_clinton = []\n",
    "list_post_clinton.append(forum['postText_tb'].apply(lambda row: row.word_counts['clinton']))\n",
    "forum['postText_clinton'] = list_post_clinton[0]\n",
    "\n",
    "list_post_hilary = []\n",
    "list_post_hilary.append(forum['postText_tb'].apply(lambda row: row.word_counts['hilary']))\n",
    "forum['postText_hilary'] = list_post_hilary[0]\n",
    "\n",
    "forum_pt_hilary = forum[(forum['postText_hilary'] > 0) | (forum['postText_clinton'] > 0)]\n",
    "print(forum_pt_hilary.shape[0])\n",
    "#Users dataframe\n",
    "users_df_pt_hilary = forum_pt_hilary[['userName','userMoney','userPolitics','userPosts','postText_polarity','postText_subjectivity','sLeftRight']].drop_duplicates()\n",
    "\n",
    "\n",
    "#get mean sentiment values for eahc user\n",
    "users_df_pt_hilary_g = users_df_pt_hilary[['userName','postText_polarity','postText_subjectivity']].groupby(['userName']).mean()\n",
    "users_df_pt_hilary_g.reset_index(inplace=True)\n",
    "users_df_pt_hilary_g.head()\n",
    "\n",
    "\n",
    "#get most recent post values for each user\n",
    "users_df_pt_hilary_sorted = users_df_pt_hilary[['userName','userMoney','userPolitics','userPosts','sLeftRight']].sort_values(by='userPosts',ascending=False)\n",
    "users_df_pt_hilary_sorted.drop_duplicates('userName', keep='first',inplace=True)\n",
    "users_df_pt_hilary_sorted.reset_index(inplace=True,drop=True)\n",
    "users_df_pt_hilary_sorted.head()\n",
    "\n",
    "#merge to get user dataframe with latest Uservalues for each User combined with their average sentiment score\n",
    "user_df_pt_hilary_sentiment = pd.merge(users_df_pt_hilary_g,users_df_pt_hilary_sorted,on=\"userName\")\n",
    "\n",
    "\n",
    "usersLeftRight_pt_hilary = user_df_pt_hilary_sentiment[user_df_pt_hilary_sentiment['sLeftRight'] != \"\"]\n",
    "forumLeftRight_pt_hilary = forum_pt_hilary[forum_pt_hilary['sLeftRight'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b68a1b5f8>,\n",
       "  <matplotlib.axis.YTick at 0x1b68a21f28>,\n",
       "  <matplotlib.axis.YTick at 0x1b68a288d0>,\n",
       "  <matplotlib.axis.YTick at 0x1a3d730668>,\n",
       "  <matplotlib.axis.YTick at 0x1a3d730d68>,\n",
       "  <matplotlib.axis.YTick at 0x1a3d7334a8>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27CMjI+vcbtGiBbp16waNRiN5KCIialxmy37gwIHWzEFERBKS/vqCREQkO5Y9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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aba1d0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subjectivity - hilary\n",
    "sns.set_style('whitegrid')\n",
    "s_leftright_pt_hilary_subjectiivity = sns.boxplot(x = usersLeftRight_pt_hilary[\"sLeftRight\"], y= usersLeftRight_pt_hilary[\"postText_subjectivity\"], palette = ['cornflowerblue','lightcoral'])\n",
    "s_leftright_pt_hilary_subjectiivity.set(title= 'Subjectivity - \"Hilary\"/\"Clinton\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Subjectivity')\n",
    "plt.yticks([0,.2,.4,.6,.8,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a3d74f6a0>,\n",
       "  <matplotlib.axis.YTick at 0x1a3d7473c8>,\n",
       "  <matplotlib.axis.YTick at 0x1a3d749940>,\n",
       "  <matplotlib.axis.YTick at 0x1b679ad780>,\n",
       "  <matplotlib.axis.YTick at 0x1b679ade80>,\n",
       "  <matplotlib.axis.YTick at 0x1b679b15c0>,\n",
       "  <matplotlib.axis.YTick at 0x1b679b1cc0>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hjh0hhMDSpUvx0EMPwd3d/bZtKJW2UCismtQv1e/W5+jsbGfhSogMOTvbwd/fH1999RX8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\nRBLGECAikjCGABGRhDEEiIgk7P8BzQxYQdmGnMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b689336a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# polarity - hilary\n",
    "sns.set_style('darkgrid')\n",
    "s_leftright_polarity_pt_hilary = sns.boxplot(x = usersLeftRight_pt_hilary[\"sLeftRight\"], y= usersLeftRight_pt_hilary[\"postText_polarity\"], palette = ['steelblue','tomato'])\n",
    "s_leftright_polarity_pt_hilary.set(title= 'Polarity - \"Hilary\"/\"Clinton\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Polarity')\n",
    "plt.yticks([-.6,-.4,-.2,0,.2,.4,.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6680\n"
     ]
    }
   ],
   "source": [
    "list_post_obama = []\n",
    "list_post_obama.append(forum['postText_tb'].apply(lambda row: row.word_counts['obama']))\n",
    "forum['postText_obama'] = list_post_obama[0]\n",
    "\n",
    "forum_pt_obama = forum[(forum['postText_obama'] > 0)]\n",
    "print(forum_pt_obama.shape[0])\n",
    "\n",
    "#Users dataframe\n",
    "users_df_pt_obama = forum_pt_obama[['userName','userMoney','userPolitics','userPosts','postText_polarity','postText_subjectivity','sLeftRight']].drop_duplicates()\n",
    "\n",
    "\n",
    "#get mean sentiment values for eahc user\n",
    "users_df_pt_obama_g = users_df_pt_obama[['userName','postText_polarity','postText_subjectivity']].groupby(['userName']).mean()\n",
    "users_df_pt_obama_g.reset_index(inplace=True)\n",
    "users_df_pt_obama_g.head()\n",
    "\n",
    "\n",
    "#get most recent post values for each user\n",
    "users_df_pt_obama_sorted = users_df_pt_obama[['userName','userMoney','userPolitics','userPosts','sLeftRight']].sort_values(by='userPosts',ascending=False)\n",
    "users_df_pt_obama_sorted.drop_duplicates('userName', keep='first',inplace=True)\n",
    "users_df_pt_obama_sorted.reset_index(inplace=True,drop=True)\n",
    "users_df_pt_obama_sorted.head()\n",
    "\n",
    "#merge to get user dataframe with latest Uservalues for each User combined with their average sentiment score\n",
    "user_df_pt_obama_sentiment = pd.merge(users_df_pt_obama_g,users_df_pt_obama_sorted,on=\"userName\")\n",
    "\n",
    "usersLeftRight_pt_obama = user_df_pt_obama_sentiment[user_df_pt_obama_sentiment['sLeftRight'] != \"\"]\n",
    "forumLeftRight_pt_obama = forum_pt_obama[forum_pt_obama['sLeftRight'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b67a32e10>,\n",
       "  <matplotlib.axis.YTick at 0x1b67a32e80>,\n",
       "  <matplotlib.axis.YTick at 0x1b67a43b70>,\n",
       "  <matplotlib.axis.YTick at 0x1b67a66eb8>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b285f8>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b28cf8>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0axrqTZ5URUQkAyx7IiIZYNkTEckAy56ISAZY9kREMsCyJyKSAZY9EZEMsOyJ\niGSAZU9EJAMseyIiGWDZExHJAMueiEgGWPZERDLAsicikgGWPRGRDLDsiYhkgGVPRCQDLHsiIhlg\n2RMRyQDLnohIBlj2REQywLInIpIBlj0RkQyw7ImIZIBlT0QkAyx7IiIZYNk3c6WlpZg3bx7Kysps\nHYWIbIhl38xlZmaioKAAGRkZto5CRDbEsm/GSktLsWvXLgghsGvXLo7uiWSMZd+MZWZmwmg0AgCM\nRiNH90QyxrJvxnJzc2EwGAAABoMBubm5Nk5ERLbCsm/GevXqBaVSCQBQKpXo1auXjRMRka2w7Jux\n/v37w86u9q/Yzs4OERERNk5ERLbCsm/GWrRogYcffhgKhQIPP/wwPD09bR2JqA5ODbYeScreaDTi\nnXfewdChQxETE4MTJ07UWb9+/XpERkYiKioKP/zwgxQR6Ir+/fujU6dOHNVTo8SpwdYjSdlnZWVB\np9MhPT0dsbGxSExMNK0rKSnBmjVrkJaWhpSUFLz//vvQ6XRSxCDUju5ff/11juqp0eHUYOuSpOw1\nGg169+4NAPD398fBgwdN6/bv34+AgAA4OjrC3d0d3t7eOHLkiBQxiKgR49Rg67KXYqNarRYqlcq0\nrFQqodfrYW9vD61WC3d3d9M6Nzc3aLXaG27n8OHDUsQjokYgJyenztTgnJwcBAYG2jhV8yVJ2atU\nKlRUVJiWjUYj7O3tb7iuoqKiTvlfq1u3blLEI6JGQK1WY+fOnTAYDFAqlVCr1fw3f5s0Gk296yTZ\njRMYGIgdO3YAAPLy8uDr62ta5+fnB41Gg+rqaly6dAmFhYV11hORPHBqsHVJMrIPDw9HdnY2oqOj\nIYRAQkICVqxYAW9vb4SFhSEmJgbDhw+HEAKTJ0+Gk5OTFDGIqBG7OjV4x44dnBpsBQohhLB1iBvR\naDQICgqydQwiklBpaSmSk5Mxbtw4lr0FNNSbkozsiYhuxtWpwSQ9nkFLRCQDjXpk39CRZSIiunmN\ndp89ERFZDnfjEBHJAMueiEgGWPZNSG5uLiZPnlznscmTJ0On0yEuLs50IpslWHp71HQcO3YM48aN\nQ0xMDAYNGoTFixejse3tra6uxmeffQYA2LBhA7Zu3WrjRI0fy76JW7BgARwdHW0dg5qJ8vJyTJky\nBW+99RbWrFmD9evXIz8/H2lpabaOVkdJSYmp7CMjIxEWFmbjRI1fo56NQ+aFhobi22+/BQCkpqYi\nJSUFBoMBs2bNQvv27bFmzRpkZGRAoVDgySefxMiRIxEXF4fS0lKUlpYiKSkJ7733Hs6cOYOLFy+i\nT58+ePXVV238qchWtm7dil41SK+7AAAE0ElEQVS9euGf//wngNqLGM6ZMwcODg5ITEw0zZCLiIjA\nqFGjEBcXB0dHR/z5558oLi5GYmIiunfvjri4OJw8eRLV1dUYM2YMnnzySfzyyy9YsGABlEol2rVr\nhxkzZuDrr7/GF198AaPRiDFjxmDr1q2YPXs2AGDAgAFISUnBt99+iy1btkCv18Pd3R1LlizBBx98\ngIKCAixduhRCCNxxxx34/fff0bVrVwwcOBAlJSUYP348NmzYgPnz52P37t0QQuDZZ5/FE088Yauv\n16Y4sm9GAgMDsWrVKowdOxbz5s1DQUEBvvnmG6SmpiI1NRVZWVn47bffAAAPPfQQ0tLSUFFRAX9/\nf6SkpGDdunVYt26djT8F2VJxcTHatWtX5zE3NzdkZ2fjjz/+wPr165GamoqMjAwcPXoUAHDXXXch\nJSUFMTExSE9Ph1arRW5uLpYuXYrk5GQYDAYIIRAfH4+lS5di7dq1aNOmDb788ksAgIeHB9atW4eQ\nkBDs3bsXlZWV2L9/P7y9vdGyZUuUlpZi5cqVSE1NhV6vx4EDB/DCCy+gU6dOePnll005o6KiTNv8\n6quvEBkZie3bt+OPP/5AWloaVq9ejQ8++ADl5eVW+jYbF47sm5EePXoAAAICAjB37lzk5+fj1KlT\nePbZZwEAZWVlOHnyJADAx8cHQO0ZjAcOHMDPP/8MlUrFG8nI3F133YX//ve/dR4rKirCoUOH0KNH\nDygUCjg4OOCBBx5AYWEhgL+uTtu2bVv8+uuvUKlUiI+PR3x8PLRaLZ5++mlcuHABxcXFpt8aL1++\njODgYHh7e5t+FpVKJfr27YstW7YgLy8PQ4YMgZ2dHRwcHDBlyhS4urrizJkz0Ov1N8zesWNHGAwG\n/Pnnn/jmm2+wcuVKpKen49ChQ4iJiQEA6PV6nDp1Ch4eHpJ8f40ZR/bNyP79+wEAe/bsQefOndGh\nQwd06tQJq1evxpo1axAZGWm6wqhCoQBQe3DL3d0d8+fPx3PPPYfLly83uoNxZD0hISH46aefTIOC\nmpoaJCYmwsPDw7QLp6amBnv37kX79u0B/PWzdFVxcTEOHTqEZcuW4aOPPsK8efPg7u6Otm3bYvny\n5VizZg1eeOEF9OrVCwBMV74EgMGDB2PTpk3Yt28fgoODceTIEWRlZWHhwoWIj4+H0WiEEAJ2dnam\nG59ca/DgwZg3bx46deoEDw8PdOjQAb169cKaNWuwatUqPPHEE7jnnnsk+e4aO47sm5js7GxERkaa\nlq8die/btw8jR46EQqFAQkIC7r77bqjVagwbNgw6nQ5+fn5o06ZNne2p1WpMmTIFGo0GLi4uaN++\nPYqLi632eahxUalUSExMxNtvvw0hBCoqKhASEoKYmBicPn0aQ4cORU1NDfr164fu3bvfcButWrVC\nSUkJBgwYAFdXVzz33HNwdHTEf/7zH4wbNw5CCLi5uWHu3Lk4ffp0ndde3YUUFhYGOzs7tG/fHi4u\nLoiMjISjoyNatWqF4uJiBAQEoKamBvPmzYOzs7Pp9f369cOsWbOQlJQEoPaY1i+//ILhw4ejsrIS\njz/+eJ0bK8kJz6AlIpIB7sYhIpIBlj0RkQyw7ImIZIBlT0QkAyx7IiIZYNkTEckAy56ISAZY9kRE\nMvD/cm4r3u2v2QkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3d74f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subjectivity - obama\n",
    "sns.set_style('whitegrid')\n",
    "s_leftright_pt_obama_subjectiivity = sns.boxplot(x = usersLeftRight_pt_obama[\"sLeftRight\"], y= usersLeftRight_pt_obama[\"postText_subjectivity\"], palette = ['cornflowerblue','lightcoral'])\n",
    "s_leftright_pt_obama_subjectiivity.set(title= 'Subjectivity - \"obama\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Subjectivity')\n",
    "\n",
    "plt.yticks([0,.2,.4,.6,.8,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b42657278>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b3ef98>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b517f0>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b78390>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b78a90>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b7b1d0>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b7b8d0>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XSURE96fGEnjttdcAAAkJCWjUqJFkgYiISDo1lkCLFi0AADNnzqzzmUBERPRwsHiKqLOz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\n400zlhOUmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3d730f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# polarity - obama\n",
    "sns.set_style('darkgrid')\n",
    "s_leftright_polarity_pt_obama = sns.boxplot(x = usersLeftRight_pt_obama[\"sLeftRight\"], y= usersLeftRight_pt_obama[\"postText_polarity\"], palette = ['steelblue','tomato'])\n",
    "s_leftright_polarity_pt_obama.set(title= 'Polarity - \"obama\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Polarity')\n",
    "plt.yticks([-.6,-.4,-.2,0,.2,.4,.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Russia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1240\n"
     ]
    }
   ],
   "source": [
    "list_post_russia = []\n",
    "list_post_russia.append(forum['postText_tb'].apply(lambda row: row.word_counts['russia']))\n",
    "forum['postText_russia'] = list_post_russia[0]\n",
    "\n",
    "\n",
    "forum_pt_russia = forum[(forum['postText_russia'] > 0)]\n",
    "print(forum_pt_russia.shape[0])\n",
    "\n",
    "#Users dataframe\n",
    "users_df_pt_russia = forum_pt_russia[['userName','userMoney','userPolitics','userPosts','postText_polarity','postText_subjectivity','sLeftRight']].drop_duplicates()\n",
    "\n",
    "\n",
    "#get mean sentiment values for eahc user\n",
    "users_df_pt_russia_g = users_df_pt_russia[['userName','postText_polarity','postText_subjectivity']].groupby(['userName']).mean()\n",
    "users_df_pt_russia_g.reset_index(inplace=True)\n",
    "users_df_pt_russia_g.head()\n",
    "\n",
    "\n",
    "#get most recent post values for each user\n",
    "users_df_pt_russia_sorted = users_df_pt_russia[['userName','userMoney','userPolitics','userPosts','sLeftRight']].sort_values(by='userPosts',ascending=False)\n",
    "users_df_pt_russia_sorted.drop_duplicates('userName', keep='first',inplace=True)\n",
    "users_df_pt_russia_sorted.reset_index(inplace=True,drop=True)\n",
    "users_df_pt_russia_sorted.head()\n",
    "\n",
    "#merge to get user dataframe with latest Uservalues for each User combined with their average sentiment score\n",
    "user_df_pt_russia_sentiment = pd.merge(users_df_pt_russia_g,users_df_pt_russia_sorted,on=\"userName\")\n",
    "\n",
    "\n",
    "usersLeftRight_pt_russia = user_df_pt_russia_sentiment[user_df_pt_russia_sentiment['sLeftRight'] != \"\"]\n",
    "forumLeftRight_pt_russia = forum_pt_russia[forum_pt_russia['sLeftRight'] != \"\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b67b90a58>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b90b70>,\n",
       "  <matplotlib.axis.YTick at 0x1b67b99630>,\n",
       "  <matplotlib.axis.YTick at 0x1b67c67b70>,\n",
       "  <matplotlib.axis.YTick at 0x1b67c6c2b0>,\n",
       "  <matplotlib.axis.YTick at 0x1b67c6c9b0>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wYcMIDw/H29vbiCgiIoJBI3uLxULPnj0BCA0NZf/+/Y51e/fupUuXLnh7e+Pn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\ngMpeRMQEVPYiIiagshcRMYH/AzOATUWCDte8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b42657e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subjectivity - russia\n",
    "sns.set_style('whitegrid')\n",
    "s_leftright_pt_russia_subjectiivity = sns.boxplot(x = usersLeftRight_pt_russia[\"sLeftRight\"], y= usersLeftRight_pt_russia[\"postText_subjectivity\"], palette = ['cornflowerblue','lightcoral'])\n",
    "s_leftright_pt_russia_subjectiivity.set(title= 'Subjectivity - \"Russia\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Subjectivity')\n",
    "plt.yticks([0,.2,.4,.6,.8,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1b67f09be0>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f09978>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f143c8>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f38f28>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f3d668>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f3dd68>,\n",
       "  <matplotlib.axis.YTick at 0x1b67f414a8>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PefvttwHIzs5m2bJlNDY2MmfOHEJDQ8nKymLjxo14eHgwYsQI7r33XpKTkykv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\nphAQETGw/wNkq7GPGA7J5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b67b99860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# polarity - russia\n",
    "sns.set_style('darkgrid')\n",
    "s_leftright_polarity_pt_russia = sns.boxplot(x = usersLeftRight_pt_russia[\"sLeftRight\"], y= usersLeftRight_pt_russia[\"postText_polarity\"], palette = ['steelblue','tomato'])\n",
    "s_leftright_polarity_pt_russia.set(title= 'Polarity - \"Russia\"', xticklabels=['Liberal','Conservative'], xlabel = '',ylabel='User Mean Polarity')\n",
    "plt.yticks([-.6,-.4,-.2,0,.2,.4,.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7) Top Words Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect_total = CountVectorizer(ngram_range=(2,2),min_df=500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>look like</th>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont know</th>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit state</th>\n",
       "      <td>1564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>donald trump</th>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont think</th>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fake news</th>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hillari clinton</th>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white hous</th>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont want</th>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im sure</th>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year ago</th>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answer question</th>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sound like</th>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax cut</th>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year old</th>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk point</th>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont like</th>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wall street</th>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tell us</th>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>democrat parti</th>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont care</th>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last year</th>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peopl like</th>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont get</th>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go back</th>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>piec shit</th>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>would like</th>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mani time</th>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everi time</th>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feder govern</th>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>act like</th>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill clinton</th>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>even though</th>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont need</th>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont even</th>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>someon els</th>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make sens</th>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one thing</th>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dont see</th>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count\n",
       "look like         2280\n",
       "dont know         1972\n",
       "unit state        1564\n",
       "donald trump      1431\n",
       "dont think        1307\n",
       "fake news         1111\n",
       "hillari clinton   1104\n",
       "white hous         979\n",
       "dont want          977\n",
       "im sure            953\n",
       "year ago           921\n",
       "new york           889\n",
       "answer question    880\n",
       "sound like         879\n",
       "tax cut            871\n",
       "year old           831\n",
       "talk point         775\n",
       "dont like          769\n",
       "wall street        753\n",
       "tell us            736\n",
       "democrat parti     713\n",
       "dont care          713\n",
       "last year          706\n",
       "peopl like         706\n",
       "dont get           682\n",
       "go back            674\n",
       "piec shit          636\n",
       "would like         628\n",
       "mani time          626\n",
       "everi time         610\n",
       "feder govern       608\n",
       "act like           567\n",
       "bill clinton       565\n",
       "even though        564\n",
       "dont need          561\n",
       "dont even          559\n",
       "someon els         558\n",
       "make sens          539\n",
       "one thing          535\n",
       "dont see           533"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort n_grams of two by count descending. \n",
    "\n",
    "corpus_total = forum['postText_value']\n",
    "\n",
    "corpus_total_fit = count_vect_total.fit_transform(corpus_total)\n",
    "\n",
    "total_counts = pd.DataFrame(corpus_total_fit.toarray(),columns=count_vect_total.get_feature_names()).sum()\n",
    "ngram_total_df = pd.DataFrame(total_counts,columns=['count'])\n",
    "print(ngram_total_df.shape)\n",
    "ngram_total_df.sort_values(by=['count'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer(ngram_range=(2,5),min_df=20)\n",
    "\n",
    "# Create separate df for Liberal and Conservative posts\n",
    "\n",
    "topwords_liberal_df = forum[forum['sLeftRight'] =='Liberal']\n",
    "\n",
    "topwords_conservative_df = forum[forum['sLeftRight'] == 'Conservative']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(271, 1)\n"
     ]
    }
   ],
   "source": [
    "# apply for liberals\n",
    "corpus_liberal = topwords_liberal_df['postText_value']\n",
    "\n",
    "corpus_liberal_fit = count_vect.fit_transform(corpus_liberal)\n",
    "\n",
    "liberal_counts = pd.DataFrame(corpus_liberal_fit.toarray(),columns=count_vect.get_feature_names()).sum()\n",
    "ngram_liberal_df = pd.DataFrame(liberal_counts,columns=['count'])\n",
    "print(ngram_liberal_df.shape)\n",
    "ngram_liberal_df = ngram_liberal_df.sort_values(by=['count'],ascending=False)\n",
    "ngram_liberal_df_20 = ngram_liberal_df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ngram_liberal_df_20.reset_index(inplace=True)\n",
    "ngram_liberal_df_20.columns = ['Top 20 Phrases', 'count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Top 20 Phrases</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>donald trump</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unit state</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>heh heh</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dont know</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hillari clinton</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dont think</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>look like</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>new york</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>democrat parti</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>white hous</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tax cut</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fake news</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>im sure</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>year ago</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>health care</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>wall street</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>year old</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>dont want</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>foreign polici</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>berni sander</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Top 20 Phrases  count\n",
       "0      donald trump    307\n",
       "1        unit state    212\n",
       "2           heh heh    199\n",
       "3         dont know    195\n",
       "4   hillari clinton    182\n",
       "5        dont think    173\n",
       "6         look like    165\n",
       "7          new york    152\n",
       "8    democrat parti    150\n",
       "9        white hous    126\n",
       "10          tax cut    103\n",
       "11        fake news    102\n",
       "12          im sure     95\n",
       "13         year ago     94\n",
       "14      health care     90\n",
       "15      wall street     90\n",
       "16         year old     88\n",
       "17        dont want     88\n",
       "18   foreign polici     88\n",
       "19     berni sander     83"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngram_liberal_df_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Top 20 Phrases - Liberals')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YIRIAAACGOXWITE9PV1RUlCRp7969OnLkyB0dt3r1auXk5BT6+c8//6ytW7fejRIBAABK\nJKcOkb6+vtYQuX79eqWlpd3RcYsWLVJeXl6hn+/atUv79rG2GwAAuHeVuMXGN2zYoFOnTmn48OHK\nyspSp06dtHXrVvXp00f16tXT8ePHlZGRoblz58pisej111/X+PHjtX37dh06dEgPPvigqlWrJkm6\nePGihg0bJovFopycHE2cOFEHDx5Uenq6IiIiNG/ePI0fP17nzp3TpUuX1Lp1aw0dOlTvvvuuMjMz\n1bhxY1WvXl2TJ0+WJJUvX15Tp06Vj4+PPVsEAABgc041iWzUqJGWLVumFi1a6JNPPrFuf+SRR9Sq\nVSuNGDHCGiAl6eDBg/Lx8dF7772ncePGKSMjQyEhIfL19dXs2bN19uxZ/c///I+WLFmixMREJSYm\nytXVVQMHDtRTTz2l9u3bKzIyUhMmTFB8fLxat26txYsX2+PWAQAAilWJm0T+kcViyff3ww8/LEny\n8/PTL7/8UuTxrVu31unTp/Xyyy/Lzc1NgwcPzvd5+fLl9c0332jXrl3y9vZWdnZ2gXOcPHlSEydO\nlCTl5OSoVq1af/V2AAAASowSFyI9PT2Vnp4uSTp06NAdH2cymQqEzt27d6ty5cp6//33tX//fs2a\nNUvx8fEymUzKy8vThg0b5OPjo0mTJumHH37QmjVrZLFY5OLiYv3OZK1atRQTE6Nq1arJbDZbawMA\nAHBmJS5EtmrVSomJiQoPD1eDBg1UpkyZOzouMDBQsbGxql69umrXri1JqlevniIiIrR8+XK5uLjo\nlVdekSQ1bdpUAwcO1Pjx4/X666/LbDardOnSqlmzptLS0lS3bl0tXLhQDRo0UFRUlEaOHKkbN25I\nkqZMmWKbGwcAAHAgJsut4znYnNlsVkyiyd5lAAAMWBPbxN4l2J3ZbFZQUJC9y3BKjtzbwmpzqhdr\nAAAAUDwIkQAAADCMEAkAAADDCJEAAAAwjBAJAAAAwwiRAAAAMIwQCQAAAMNK3GLjzoL1xu4+R15j\nq6Sjt7ZBX22H3gK2xyQSAAAAhhEiAQAAYBghEgAAAIYRIgEAAGAYL9bYSejwffYuwQmZpET6ahv0\n1jbo693GS4tA8WESCQAAAMMIkQAAADCMEAkAAADDCJEAAAAwjBAJAAAAwwiRAAAAMIwQCQAAAMOc\nJkRu2LBBsbGxd2Xf1NRUhYaG3tG5jOwLAADgLJwmRAIAAKD4ONUv1nz99dcaMGCALl68qPDwcIWF\nhWnPnj2aPXu2XF1dVaNGDU2aNKnQff/o4sWLevnll5Wenq6HHnpIkydP1tmzZxUZGamsrCx5enoq\nOjq60H0BAACcmVOFSDc3Ny1ZskQ//fSTBg4cqNDQUEVGRmrVqlWqVKmS5syZo40bN8rNza3AvreG\nyIyMDE2bNk0+Pj564okndOHCBcXExKhPnz5q06aNdu7cqdjYWEVERNx230qVKtmpCwAAALbnVCHy\n4Ycflslkkq+vrzIzM3Xx4kWlpaVp2LBhkqTMzEy1aNFC/v7+Bfa9VY0aNVSuXDlJUqVKlXT9+nUd\nO3ZMixYt0uLFi2WxWOTu7l7ovgAAAM7MqUKkyWTK93eFChXk5+enBQsWyMfHR1u2bJGXl5fOnj1b\nYN+iziVJAQEBGjBggJo0aaKTJ09q7969he4LAADgzJwqRN7KxcVFY8eO1cCBA2WxWFSmTBnNmDFD\nZ8+e/UvnGzlypKKiopSVlaXMzEyNHTv2LlcMAABQMpgsFovF3kXca8xms2ISmV4CwN22JraJpJv/\nOxsUFGTnapwPfbUdR+5tYbWxxA8AAAAMI0QCAADAMEIkAAAADCNEAgAAwDBCJAAAAAwjRAIAAMAw\np14n0pH9vgwF7h5HXh6hpKO3tkFfAZRkTCIBAABgGCESAAAAhhEiAQAAYBghEgAAAIbxYo2dhA7f\nZ+8SnJBJSqSvtkFvbYO+/hW8mAg4BiaRAAAAMIwQCQAAAMMIkQAAADCMEAkAAADDCJEAAAAwjBAJ\nAAAAwwiRAAAAMKxEhcisrCy1a9fO8HGXL1/Wpk2bCmxv166dsrKy7kZpAAAA95QSFSL/qqNHj2rr\n1q32LgMAAMBpOPwv1ly9elXDhw/XlStX5O/vb91++PBhRUdHy9XVVZ6enoqOjlZeXp7eeOMN+fn5\n6cyZM2rYsKEmTpyouLg4HTlyRKtXr1ZYWFiBayQmJmrHjh2aNWuWnn32Wf2///f/dPToUZlMJi1Y\nsEA+Pj6aPn26zGazJOmpp57S008/rX79+unDDz/U/v379dJLL2nnzp1KT0/X2LFjtWTJkmLrEQAA\nQHFz+Enkxo0bVbduXSUkJKhnz57W7ePGjdP48eO1cuVKhYeHa/r06ZKk06dPa8qUKVq7dq1SUlKU\nnp6uQYMGqXnz5rcNkPHx8frqq680d+5ceXh46OrVq/rnP/+plStXqnLlykpJSdG2bduUmpqqNWvW\naNWqVfr444+Vlpam8uXL6+zZs9q+fbv8/Px06NAhbdmyRcHBwcXWHwAAAHtw+BB5/PhxNWzYUJIU\nGBgoN7ebw9O0tDTVr19fktSsWTMdP35ckuTv7y9vb2+5urrK19e3yO887ty5U7/99ptcXV2t2x5+\n+GFJUtWqVZWVlaWTJ0+qadOmMplMcnd3V2BgoE6ePKknnnhCX375pfbv368XX3xRO3bs0JdffkmI\nBAAATs/hQ2RAQIAOHDgg6eYj7NzcXElS5cqVdeTIEUnS3r179cADD0iSTCZTgXO4uLgoLy/vtudf\nsGCBypYtq8TEROu2W89Ru3Zt66PsnJwc7d+/XzVr1lRwcLA+/vhjeXt7q3Xr1kpOTlZ2drZ8fX3/\n3k0DAAA4OIf/TmTv3r01evRohYeHKyAgQO7u7pKkyZMnKzo6WhaLRa6urpo6dWqh5/D399exY8e0\nbNky9evXr8Dn48aNU0hIiB577LHbHv/4449rz549CgsLU05Ojjp27KgGDRpIuvnGePPmzVWuXDm5\nubmpbdu2f/ueAQAAHJ3JYrFY7F3EvcZsNismseDEFABQtDWxTYrcx2w2KygoqBiqubfQV9tx5N4W\nVpvDP84GAACA4yFEAgAAwDBCJAAAAAwjRAIAAMAwQiQAAAAMI0QCAADAMIdfJ9JZ3ckSFTDGkZdH\nKOnorW3QVwAlGZNIAAAAGEaIBAAAgGGESAAAABhGiAQAAIBhhEgAAAAYxtvZdhI6fJ+9S3BCJimR\nvtoGvbUN5+4rq1AAzo1JJAAAAAwjRAIAAMAwQiQAAAAMI0QCAADAMEIkAAAADCNEAgAAwDC7hcgN\nGzYoNja2wPaIiAhlZ2dr1KhRSklJKXS/20lPT1dUVFSR+6Wmpio0NDTf9Qrz+eef6/z583d0fQAA\ngHuFw00iZ8+eLQ8Pj790rK+v7x2FSCPXW7FihTIyMv5SPQAAAM7KrouNf/311xowYIAuXryo8PBw\nhYWFqV27dtq8efNt93/rrbf07bff6urVq6pdu7amTZumefPmaf/+/bp27ZqmTJmi0aNHa82aNfmO\nW7BggZKTk3Xjxg2Fh4erZcuW1s9+v96ECRPk4eGhn376SWlpaZo+fbrS09P13XffaeTIkVq1apVW\nrlypTz75RG5ubmratKlGjBihefPmKTU1VRcuXNDPP/+s0aNHq1WrVjbtGwAAgL3ZdRLp5uamJUuW\naP78+Vq+fPmf7puRkaGyZctq6dKlSkpK0oEDB6yPmQMCApSUlCRPT88Cxx0+fFgpKSlau3atkpKS\ndOLECVksltteo1q1alqyZIn69Omj1atXq23btqpfv75iYmL0/fffa/PmzUpKSlJSUpJ++OEHbdu2\nTZLk4eGhxYsXa+zYsVq2bNnfawoAAEAJYNdJ5MMPPyyTySRfX19lZmb+6b6enp66ePGiXn/9dXl5\neenatWvKycmRJNWqVavQ477//ns1atRIrq6uKl26tMaNG6fU1NTb7lu/fn1Jkp+fn/bty/9TZKdO\nnVJgYKDc3d0lSU2bNtXx48cLHPdn368EAABwFnadRJpMpjveNyUlRWfPntWsWbP0+uuvKzMz0zpR\ndHEp/DYCAgJ0+PBh5eXlKScnR/379y806N2uHpPJJIvFooCAAB08eFC5ubmyWCzau3evNbwauQ8A\nAABnYNdJpBGNGjXSggULFBoaKg8PD9WoUUNpaWlFHle/fn21atVK4eHhysvLU3h4uKEXdxo3bqw3\n33xT77//vjp16mQ9T1BQkIKDg3XkyJG/c1sAAAAlkslS2BcEYTNms1kxiUwvATi3NbFN7HZts9ms\noKAgu13fWdFX23Hk3hZWm8Mt8QMAAADHR4gEAACAYYRIAAAAGEaIBAAAgGGESAAAABhGiAQAAIBh\nJWadSGdjz6UvnJUjL49Q0tFb26CvAEoyJpEAAAAwjBAJAAAAwwiRAAAAMIwQCQAAAMMIkQAAADCM\nt7PtJHT4PnuX4IRMUiJ9tQ16axuO3VdWkQDwZ5hEAgAAwDBCJAAAAAwjRAIAAMAwQiQAAAAMI0QC\nAADAMEIkAAAADCuRITIrK0vt2rUzfNzly5e1adOmAtv37t2rI0eOSJJatGhR4PMNGzZoy5YthZ53\n1KhRSklJMVwPAABASVUiQ+RfdfToUW3durXA9vXr1ystLa3Q47p376727dvbsjQAAIASpcQsNn71\n6lUNHz5cV65ckb+/v3X74cOHFR0dLVdXV3l6eio6Olp5eXl644035OfnpzNnzqhhw4aaOHGi4uLi\ndOTIEa1evVphYWGSpG+//Vbbt2/XoUOH9OCDDyo7O1tvvPGGfv75Z5UvX15vv/224uLidN999ykg\nIEDvvfee3N3dlZqaqs6dO2vw4MHWWr7++mtNnjxZb7/9tqpWrVrsPQIAACguJSZEbty4UXXr1lVE\nRIS+/vpr7d69W5I0btw4TZkyRfXr11dycrKmT5+uN998U6dPn9aSJUtUunRpBQcHKz09XYMGDVJS\nUpI1QErSI488olatWqlz586qVq2arl27poiICFWvXl19+vTRd999l6+On3/+WR999JGys7PVqlUr\na4jcv3+/du7cqbi4OFWqVKn4GgMAAGAHJeZx9vHjx9WwYUNJUmBgoNzcbubftLQ01a9fX5LUrFkz\nHT9+XJLk7+8vb29vubq6ytfXV1lZWXd0nXLlyql69eqSpPvuu0/Xr1/P93ndunXl5uYmLy8vlSpV\nyrp9x44d+u2336x1AQAAOLMSEyIDAgJ04MABSTcfYefm5kqSKleubH0pZu/evXrggQckSSaTqcA5\nXFxclJeXV2C7yWSSxWIp9Lhb972dIUOGqF+/foqKirqj+wEAACjJSkyI7N27t86fP6/w8HAlJCTI\n3d1dkjR58mRFR0erV69eWr58ucaMGVPoOfz9/XXs2DEtW7Ys3/bAwEDFxsbq5MmTf6vGkJAQXbly\n5bZvgAMAADgTk+X3ERyKjdlsVkzin088AcDe1sQ2sXcJf5nZbFZQUJC9y3A69NV2HLm3hdVWYiaR\nAAAAcByESAAAABhGiAQAAIBhhEgAAAAYRogEAACAYYRIAAAAGEaIBAAAgGH8Rp+dlOT11xyVI6+x\nVdLRW9ugrwBKMiaRAAAAMIwQCQAAAMMIkQAAADCMEAkAAADDeLHGTkKH77N3CU7IJCXSV9ugt7ZR\nPH3lRT4AtsAkEgAAAIYRIgEAAGAYIRIAAACGESIBAABgGCESAAAAhhEiAQAAYJhThMgNGzYoNjbW\n8HHt2rVTVlbWbT9LTU1VaGioJCkiIkLZ2dkaNWqUUlJS/latAAAAzoB1Iu/A7Nmz7V0CAACAQ3GK\nSeQfvf/++3r22WcVFhammTNnSpKuXLmil156Sb1791bPnj21c+fOfMckJiZqyJAhys7Ovu05b51Y\nfv311woJCdHZs2d19uxZvfDCC+rTp49eeOEFnT171nY3BwAA4CCcahJ59OhRbd68WUlJSXJzc9PQ\noUO1bds27dmzR//4xz/0r3/9S+fPn1d4eLiSk5MlSfHx8fruu+80d+5cubq6FnmN/fv3a+fOnYqL\ni1OlSpU0bNgw9enTR23atNHOnTsVGxurt956y9a3CgAAYFdOFSJPnTqlwMBAubu7S5KaNm2q48eP\n6+TJk+rSpYskqUqVKvL29tbFixclSTt37pSrq+sdBUhJ2rFjh65evSo3t5utO3bsmBYtWqTFixfL\nYrFYrw0AAODMnOpxdkBAgA6KW/EVAAAgAElEQVQePKjc3FxZLBbt3btXtWrVUu3atfXVV19Jks6f\nP68rV66ofPnykqQFCxaobNmySkxMvKNrDBkyRP369VNUVJT1msOHD1d8fLwmTpyoJ5980ib3BgAA\n4EicahL50EMPqVOnTgoPD1deXp6CgoIUHBysZs2aacyYMfr3v/+tzMxMTZo0yTpJlKRx48YpJCRE\njz32mB544IEirxMSEqLPPvtMmzZt0siRIxUVFaWsrCxlZmZq7NixNrxDAAAAx2CyWCwWexdxrzGb\nzYpJNNm7DAD3iDWxTexdQrEzm80KCgqydxlOh77ajiP3trDanOpxNgAAAIoHIRIAAACGESIBAABg\nGCESAAAAhhEiAQAAYBghEgAAAIYRIgEAAGCYUy02XpLci+u22Zojr7FV0tFb26CvAEoyJpEAAAAw\njBAJAAAAwwiRAAAAMIwQCQAAAMN4scZOQofvs3cJTsgkJdJX26C3tpG/r7xwB6AkYRIJAAAAwwiR\nAAAAMIwQCQAAAMMIkQAAADCMEAkAAADDCJEAAAAwjBAJAAAAwwiRf9Pu3bsVERFh7zIAAACKFSES\nAAAAhpXIELlhwwa99tpreumll9SpUydt2LBBknT06FH16dNHffr00dChQ/Xbb7/p5Zdf1jfffCNJ\nevLJJ/X5559LkgYMGKDz589bzzlr1iwlJCRIkn799Vd1795dkjR9+nSFhIQoJCREy5cvlySNGjVK\ngwYNUs+ePXXlyhVJ0vXr1/X888/ro48+Kp4mAAAA2FGJDJGSlJGRoUWLFmnhwoV69913JUmRkZGa\nMGGC4uPj1bp1ay1evFgdOnRQSkqKzpw5I09PT+3YsUO//fabsrKyVKVKFev5QkJC9MEHH0iSPv74\nY3Xp0kXbtm1Tamqq1qxZo1WrVunjjz/W0aNHJUnNmzdXUlKSypYtq2vXrmnQoEHq1auXnn766eJv\nBgAAQDErsSGyXr16kqSqVasqOztbknTy5ElNnDhRffr00fr165WWlqbHH39c//3vf7V9+3a9+OKL\nOnjwoFJSUvT444/nO1+NGjVUpkwZnThxQps2bVLXrl118uRJNW3aVCaTSe7u7goMDNTJkyclSbVq\n1bIeu2fPHmVlZVnrAAAAcHYlNkSaTKYC22rVqqWYmBjFx8drxIgRatOmjcqVK6dSpUpp8+bNatWq\nlapVq6bly5erQ4cOBY4PDQ3VwoULVaVKFVWsWFG1a9eW2WyWJOXk5Gj//v2qWbNmgeu3bdtW8+fP\n15w5c/I9IgcAAHBWJTZE3k5UVJRGjhypXr166a233tJDDz0kSWrfvr2uX7+u8uXLq2XLlsrMzJS/\nv3+B44ODg7Vjxw716NFDkvT444+revXqCgsLU1hYmJ588kk1aNDgtte+7777NHToUI0ZM0YWi8V2\nNwkAAOAATBYSj9X169f13HPPae3atXJxsV2+NpvNikksOEkFcG9bE9vE3iU4DbPZrKCgIHuX4XTo\nq+04cm8Lq82pJpF/x759+xQaGqqXX37ZpgESAADAGbjZuwBH0aRJE23atMneZQAAAJQIjNwAAABg\nGCESAAAAhhEiAQAAYBghEgAAAIbxYo2dsJTH3efIyyOUdPTWNugrgJKMSSQAAAAMI0QCAADAMEIk\nAAAADCNEAgAAwDBerLGT0OH77F2CEzJJifTVNuitLYwMt3cFAPDXMYkEAACAYYRIAAAAGEaIBAAA\ngGGESAAAABhGiAQAAIBhhEgAAAAYRogEAACAYTYPkVlZWWrXrp2tL2PIypUr7+r5Vq9erZycHH33\n3XeaP3/+XT03AACAI7onJ5ELFy68q+dbtGiR8vLyVL9+fQ0ZMuSunhsAAMAR2eQXa65evarhw4fr\nypUr8vf3t24/evSoJk+eLEkqX768pk6dqsOHD+vdd9+Vu7u7zp07p549e2rXrl06cuSI+vbtq169\nemnHjh2aM2eOPD09rcd5e3tr8uTJOnjwoHJycjR06FD5+PgoNjZW7u7uCg0NValSpZSQkGC9/ty5\nc7V69Wr9+uuvioqKUlRUlPWzUaNGyWKx6OzZs7p27ZpiYmJUu3ZtvfXWW/r222919epV1a5dW9Om\nTdO8efO0f/9+Xbt2TV26dFF6eroiIiL0r3/9S0lJSZo9e7Yt2goAAOAwbDKJ3Lhxo+rWrauEhAT1\n7NnTuj0yMlITJkxQfHy8WrdurcWLF0uSzp07p3nz5ikqKkoLFy7UjBkz9N5772n16tWyWCyKjIzU\n/PnztXLlSjVr1kwLFy7Uli1bdOnSJa1bt06LFy/WN998I+nm4/NVq1bpmWee0enTp/Xuu+8qPj5e\ntWrV0n/+8x8NHjxY5cqVyxcgf1ejRg2tWLFCQ4cO1cyZM5WRkaGyZctq6dKlSkpK0oEDB3T+/HlJ\nUkBAgJKSktS7d2/5+voSHAEAwD3FJpPI48ePq1WrVpKkwMBAubndvMzJkyc1ceJESVJOTo5q1aol\nSapTp47c3d3l4+Mjf39/eXh4qFy5csrKytKlS5fk7e2tKlWqSJKaNWumWbNmqUKFCvqf//kfSZKv\nr68iIiK0e/du6zklqVKlSho5cqTKlCmjU6dOWfcvTPPmzSVJjRs31tSpU+Xp6amLFy/q9ddfl5eX\nl65du6acnBxJyncdAACAe41NQmRAQIAOHDig4OBgHT58WLm5uZJuBq+YmBhVq1ZNZrNZ6enpkiST\nyVTouSpUqKCMjAylpaWpcuXK2rNnjx544AEFBATos88+kyT99ttvGjZsmAYOHCgXFxfrtrfffltf\nfPGFJKl///6yWCySZP3PWx06dEhNmzbVvn37VKdOHaWkpOjs2bOaM2eOLl68qM8//9x67O/X+b3+\nvLy8v9ExAACAksVwiLx27Zq8vLz+dJ/evXtr9OjRCg8PV0BAgNzd3SVJUVFRGjlypG7cuCFJmjJl\nitLS0v70XCaTSZMnT9bQoUNlMplUrlw5TZs2TRUqVNDOnTsVHh6uGzdu6JVXXsl3nLe3t5o0aaJu\n3brJy8tLZcuWtV6rdu3aGj58uGJjY/Mdk5KSoi1btigvL0/Tpk1TqVKltGDBAoWGhsrDw0M1atS4\nbb1NmzbVwIEDC9QAAADgrEyWwsZy/78vv/xSZrNZL730ksLCwpSWlqYxY8bomWeeKa4ai8WoUaPU\nuXNntW7d2ubXMpvNikksfPoK4N4wMtyioKAge5fhlMxmM721AfpqO47c28JqK/LFmrfffltPPvmk\nPv30U9WvX19bt27VihUrbFIkAAAASoY7epzdoEEDxcXFqXPnzvL29ra+XOJMpk+fbu8SAAAASowi\nJ5EVK1bU1KlT9fXXX6tNmzaaOXOm/Pz8iqM2AAAAOKgiQ+SsWbNUt25dLV++XF5eXqpSpYpmzZpV\nHLUBAADAQRUZIn18fOTl5aVNmzbp+vXrqlChgnx8fIqjNgAAADioIkPk7NmzlZycrE8//VS5ublK\nSkrSjBkziqM2AAAAOKgiX6z54osv9MEHH6hbt27y8fHRsmXL1LVrV7355pvFUZ/TWhPbxN4lOB1H\nXh6hpKO3tmE2m+1dAgD8ZUVOIn//ZZbff1UmNzc336+1AAAA4N5T5CTyySef1PDhw/Xrr79q5cqV\n2rBhgzp16lQctQEAAMBBFRkiBw0apC+++EKVKlXS6dOn9fLLLys4OLg4agMAAICDKvK5dG5uru6/\n/36NGTNGTZo00ddff61Lly4VR20AAABwUEWGyBEjRmjDhg365ptvNGfOHLm7u2v06NHFURsAAAAc\nVJGPs3/88UfNnj1bsbGx6tGjhwYOHKhnn322OGpzaqHD99m7BCdkkhLpq22UvN6yAgIA2FaRk8gb\nN27oypUr+vzzz9W6dWtduHBBmZmZxVEbAAAAHFSRk8j+/fura9euateunerVq6cOHTpo6NChxVEb\nAAAAHFSRIbJr167q2rWr9e9PPvlEFovFpkUBAADAsd3RL9a8/fbbunr1qqSbj7czMjK0a9cumxcH\nAAAAx1RkiJw6daomTJig5cuXa+DAgdqyZYuys7OLozYAAAA4qCJfrPH29laLFi0UGBio69eva+TI\nkdq5c2dx1AYAAAAHVWSI9PT01I8//qjatWtr7969ysnJUW5ubnHUVqjU1FSFhoYW2P7uu+/q4MGD\nysrK0tq1a+/oXLt371ZERMTdLhEAAMCpFRkiX3vtNc2cOVPt2rXTf/7zH7Vs2VJt27YthtKMGzhw\noBo1aqT09PQ7DpEAAAAwrsgQeebMGc2bN08eHh7asGGDNm/erDFjxti8sG7duunChQvKyclRkyZN\ndPjwYev27OxsXbx4US+//LJCQkI0btw4SdKoUaOUkpKiuLg4nThxQvPnz9dvv/2mV199VX369FGf\nPn109OjRAtf64Ycf9MILL6h79+6aN2+eJOnw4cMKDw/Xc889p+eff14///xzgQloaGioUlNTZTab\nFRoaql69emnQoEHKyMiweX8AAADsqcgQuXz58nx/V6xY0WbF/FH79u21fft2mc1mVa9eXTt27NCJ\nEyf0wAMPyMPDQxkZGZo2bZpWr16tnTt36sKFC9ZjBw0apAcffFBDhgxRXFycmjdvrvj4eEVHRysq\nKqrAtbKysrRgwQIlJCRo5cqVkqRx48Zp/PjxWrlypcLDwzV9+vRCa01OTtYTTzyhlStXqkePHrpy\n5cpd7wcAAIAjKfLt7KpVq2rAgAFq1KiRSpUqZd0+aNAgmxbWoUMHxcXFqWrVqoqIiFB8fLwsFos6\ndOggSapRo4bKlSsnSapUqZKuX79+2/McO3ZMu3bt0ubNmyXptgGvTp068vDwkCS5ud1sSVpamurX\nry9Jatasmd56660Cx/2+XuagQYMUFxenf/3rX6pSpYoaNWr0d24dAADA4RU5iWzQoIE1FGVmZlr/\nz9bq1q2r1NRUHTx4UG3atNG1a9e0ZcsWtW7dWpJkMpkKPdbFxUV5eXmSpICAAPXr10/x8fGaM2eO\nunTpUmD/252rcuXKOnLkiCRp7969euCBB+Tp6akLFy5YfwoyNTVVkrRp0yZ169ZN8fHxqlOnjtas\nWfO37x8AAMCRFTmJHDZsWHHUcVvNmjVTamqqXFxc1KxZM504cUJlypTRpUuX/vS4SpUqKScnRzNn\nztSgQYM0duxYrVmzRhkZGRoyZMgdXXvy5MmKjo6WxWKRq6urpk6dKl9fX7Vo0UI9evSQv7+/atas\nKUlq2LChRo0aJS8vL7m7u2vSpEl/+94BAAAcmclSxG8Yrl+/XrNnz9bly5cl3XyEazKZ9O233xZL\ngc7IbDYrJrHwSSqAv29NbBN7l1Aks9msoKAge5fhlOitbdBX23Hk3hZWW5GTyHfeeUeLFy/Wgw8+\n+KePkAEAAHDvKDJEVqxYUfXq1SuOWgAAAFBCFBoiN23aJEmqXr26hg4dqvbt28vV1dX6+e1eUAEA\nAMC9odAQmZKSIklyd3eXu7u7duzYYf3MZDIRIgEAAO5hhYbImTNnSpIyMjLk7e1dbAUBAADA8RW6\nTuRXX32ltm3bqlmzZnrqqad0/Pjx4qwLAAAADqzQSeT06dM1btw4Pfroo/roo480c+ZMvfvuu8VZ\nm1MrCcuPlDSOvDxCSUdvAQC3KnQSmZOTo+DgYPn4+Kh37976+eefi7MuAAAAOLBCQ6SLS/6P3N3d\nbV4MAAAASoZCH2fn5uYqLS1Nv/+gza1/V6lSpXgqBAAAgMMpNET++uuvCg0N1R9/FTEkJETSzSV+\nvvjiC5sXBwAAAMdU5DqRAAAAwK2K/NlD2Ebo8H32LsEJmaRE+mobBXvLCgMAcG8r9MUaAAAAoDCE\nSAAAABj2p4+zd+7cqc2bN+v8+fMymUyqXLmyWrdureDg4OKqDwAAAA6o0BA5f/58mc1mdenSRb6+\nvrJYLPrll1+0atUq7d+/XyNGjCjOOgEAAOBACg2RmzZt0ubNmwssOv7000/rqaeeIkQCAADcwwr9\nTqSHh4fS0tIKbD937pw8PDxsWhQAAAAcW6GTyDfffFPh4eGqXbu2fH19ZTKZlJaWphMnTmjq1KnF\nWePfkpWVpY8++si6ULotXb58Wdu3b1eXLl1sfi0AAAB7KjREtmrVSp9++qkOHDhg/bnDKlWqqHHj\nxipVqlRx1vi3pKena+3atcUSIo8ePaqtW7cSIgEAgNP70yV+du/ere+//15NmjTRM888o8cee0yl\nSpXSunXriqu+vy0uLk4nTpzQ/Pnzde7cOQ0aNEj9+/dXt27dlJycrIyMDHXu3FlHjx7ViRMn1KVL\nF2VkZOQ7x4IFC9S9e3d17dpVSUlJSk1NVWhoqPXz0NBQpaamKi4uTrt27dLq1auL+zYBAACKVaEh\ncvbs2VqyZImOHj2qsLAwffzxx9bPEhISiqW4u2HQoEF68MEHNWTIEJ06dUr9+/fX0qVLFRkZqYSE\nBHl7e2v69OmKjIzU6NGjNWPGDHl7e1uPP3z4sFJSUrR27VolJSXpxIkT+X5P/NZrNW/eXGFhYcV1\newAAAHZR6OPsrVu3auPGjXJzc1Pfvn31wgsvyMPDQx06dCg0RDk6X19fLVy4UOvWrZPJZFJubq4k\nqVGjRvLx8ZG7u7vq16+f75jvv/9ejRo1kqurq0qXLq1x48YpNTU13z4ltR8AAAB/1Z8+zjaZTJKk\n2rVrKy4uTpMmTdJXX31l3V4SuLi4KC8vT5I0d+5cde3aVTNnztSjjz5qDX+fffaZypQpIzc3N332\n2Wf5jg8ICNDhw4eVl5ennJwc9e/fXyaTSRcuXNCNGzd05coVa6j847UAAACcWaGTyODgYPXr108j\nRoxQo0aN9NBDD2n27NkaOnSocnJyirPGv6VSpUrKycnRzJkz1bFjR02ZMkWLFi1S1apVdenSJf30\n00+aO3euEhISZLFY1KtXLzVs2FD333+/JKl+/fpq1aqVwsPDlZeXp/DwcN1///1q0aKFevToIX9/\nf9WsWVOS5O/vr2PHjmnZsmXq16+fHe8aAADAtkyWP3kWu337dvn5+alOnTrWbampqXr//fc1fvz4\nYinQGZnNZsUklpxpLnA7a2Kb2LuEEs9sNisoKMjeZTglemsb9NV2HLm3hdX2p7+d3apVqwLbqlev\nToAEAAC4x/3pdyIBAACA2yFEAgAAwLA/fZz9u23btmnXrl1ydXVV69at1bx5c1vXBQAAAAdW5CRy\n5syZWrhwoapWrar77rtPM2fO1HvvvVcctQEAAMBBFTmJTE5O1scffyx3d3dJUq9evdStWze9+OKL\nNi8OAAAAjqnIEHnffffp6tWrKl++vCQpLy/P+m/8dSyPcvc58vIIJR29BQDcqsgQ6evrq65duyo4\nOFiurq768ssvVaFCBUVGRkqSoqOjbV4kAAAAHEuRIbJFixZq0aKF9e8/LjwOAACAe1ORITIkJEQn\nT57Unj17dOPGDTVr1kwPPfRQcdQGAAAAB1Xk29mbNm3Siy++qJMnT+r777/X4MGDtWHDhuKoDQAA\nAA6qyEnk4sWLtW7dOlWsWFGS9Morr6hv377q3r27zYsDAACAYyoyRObl5VkDpCRVrFhRJpPJpkXd\nC0KH77N3CU7IJCXSV9v4v96ysgAAQLqDEFm3bl3FxMSoR48ekqR169apbt26Ni8MAAAAjqvI70RG\nR0fLYrHojTfeUEREhPLy8jRx4sTiqA0AAAAOqtBJ5MaNG9WtWzd5eXlp1KhRxVkTAAAAHFyhk8gV\nK1YUZx0AAAAoQYp8nA0AAADcqtDH2cePH1f79u0LbLdYLDKZTNqyZYtNCwMAAIDjKjRE1qxZU+++\n+25x1nJbN27c0MCBA3Xt2jXFxcWpXLlyBfZp166dNm/eLE9PTztUCAAAcO8pNES6u7vr/vvvL85a\nbis9PV2XLl3iV3IAAAAcSKHfiWzSxDEWFI6MjNTp06c1fvx4nTt3ToMGDVL//v3VrVs3JScn59s3\nMTFRQ4YMUXZ2tvbs2aPw8HA999xzGj16tHJycvLtO2rUKI0fP17PP/+8unTpokOHDkmSNm/erLCw\nMIWHhys2NlY3btxQhw4dlJubq7S0NNWvX1+XLl1Sdna2unXrposXL6pv377q06ePevbsqaNHjxZb\nbwAAAOyl0BA5fvz44qyjUBMmTNCDDz6oSZMm6dSpU+rfv7+WLl2qyMhIJSQkWPeLj4/XV199pblz\n58rd3V2RkZGaP3++Vq5cqSpVqmjjxo0Fzl2tWjUtWbJEffr00erVq3X58mXNmzdPy5YtU2Jios6f\nP69du3YpKChIBw4c0Pbt21WnTh3t3LlTO3fuVIsWLXTw4EH5+Pjovffe07hx45SRkVGc7QEAALCL\nIn+xxpH4+vpq4cKFWrdunUwmk3Jzc62f7dy5U66urnJ1ddWFCxeUlpamYcOGSZIyMzPVokWLAuer\nX7++JMnPz0/79u3Tjz/+qIsXL2rgwIGSpKtXr+rMmTPq0KGDvvzyS6WmpioiIkJbtmyRi4uLevTo\noUceeUSnT5/Wyy+/LDc3Nw0ePLgYOgEAAGBfJWqJn7lz56pr166aOXOmHn30UVksFutnCxYsUNmy\nZZWYmKgKFSrIz89PCxYsUHx8vAYNGqRHH320wPlu/Q3w6tWrq2rVqnr//fcVHx+v5557ToGBgWrR\nooX27t2rS5cuqU2bNjp06JCOHDmiRo0aaffu3apcubLef/99DR48WLNmzbJ5HwAAAOytRE0iO3bs\nqClTpmjRokWqWrWqLl26lO/zcePGKSQkRI899pjGjh2rgQMHymKxqEyZMpoxY0aR569YsaL69eun\nPn366MaNG7r//vvVqVMneXh4yM/PT9WqVZOLi4tq1aqlihUrSpLq1auniIgILV++XC4uLnrllVds\ncu8AAACOxGT54zgPxcJsNism0VT0joADWhPrGC/dOQOz2aygoCB7l+GU6K1t0FfbceTeFlZbiXqc\nDQAAAMdAiAQAAIBhhEgAAAAYRogEAACAYYRIAAAAGEaIBAAAgGGESAAAABhWohYbdyastXf3OfIa\nWyUdvQUA3IpJJAAAAAwjRAIAAMAwQiQAAAAMI0QCAADAMF6ssZPQ4fvsXYITMkmJ9NUoXvICAPwV\nTCIBAABgGCESAAAAhhEiAQAAYBghEgAAAIYRIgEAAGAYIRIAAACGOWWITElJ0erVq+1dBgAAgNNy\nynUiW7dube8SAAAAnJpThsgNGzbo1KlT6tmzpyIiIlS1alWlpqbqn//8p44fP67Dhw+rbdu2ev31\n1/MdN2rUKP3444/KysrS888/r86dO6tdu3bavHmzPD09FRsbq4CAAN1///2KjY2Vu7u7QkNDVa1a\nNc2ePVuurq6qUaOGJk2aJHd3dzvdPQAAgO05ZYj8ozNnzuj9999XZmam2rdvr5SUFJUuXVqPP/54\nvhCZkZGh3bt3a/369ZKkHTt2/Ol5s7KytHbtWlksFnXs2FGrVq1SpUqVNGfOHG3cuFGhoaE2vS8A\nAAB7cvoQWaNGDfn4+MjDw0P33XefypcvL0kymUz59vP29lZkZKQiIyOVkZGhp59+usC5LBaL9d+1\natWSJF28eFFpaWkaNmyYJCkzM1MtWrSw1e0AAAA4BKcPkbeGxcKkpaXp0KFDeuedd5SVlaU2bdqo\na9eu8vDwUFpamqpXr64jR46odu3akiQXl5vvJFWoUEF+fn5asGCBfHx8tGXLFnl5ednsfgAAAByB\n04fIO+Xr66v09HQ988wz8vLy0oABA+Tm5qYXXnhBAwcO1P3336+yZcsWOM7FxUVjx47VwIEDZbFY\nVKZMGc2YMcMOdwAAAFB8nDJEdu/e3frvNWvWSJI8PT21dev/1969R1VZ53sc/2xuxk1FtNBRE1A7\naoMu0KEJdJwZb03RpEsUCOtYHSOXKd5G5eIFxdQobDC8TDV1DAkoPS2PpyxtGiYdzbYSmccryiF1\ngAkcReO6n/NHKxpTm54ENnv7fv3FfvZ+vs93f9ez9LN+ez/P/qB5+3e/82ixWJSWlnZNrYkTJ2ri\nxInXbA8PD2/+OzIyUpGRkTfdNwAAgKNwyvtEAgAAoHURIgEAAGAaIRIAAACmESIBAABgGiESAAAA\nphEiAQAAYBohEgAAAKY55X0iHUF+Rqi9W3A6VqtVYWFh9m4DAIBbAiuRAAAAMI0QCQAAANMIkQAA\nADCNEAkAAADTuLDGTibNO2jvFpyQRcplrj8UF3cBAG4GK5EAAAAwjRAJAAAA0wiRAAAAMI0QCQAA\nANMIkQAAADCNEAkAAADTCJEAAAAwjRAJAAAA0wiRAAAAMM1hf7Fm7ty5ioqK0siRI3Xq1CmtXr1a\nL774opYsWaLS0lLZbDYlJiYqPDxc7777rnJycpr3feGFF3TixAllZGTI3d1dkyZN0kMPPdT8/HPP\nPafDhw/r8uXLCg4O1jPPPKOqqirNmzdP9fX1CgwM1L59+/T+++9rz549Wrt2rTp06KDOnTtr5cqV\n6tixoz1GAgAA0GYcdiUyOjpa27ZtkyS9+eabmjhxogoKCuTn56ecnBxlZ2crLS1NknTmzBlt2rRJ\nmzdvVmBgoD766CNJUl1dnbZs2XJVgKypqVHHjh31xz/+UW+88YaKiopUXl6uDRs26Ne//rVef/11\njRs3Tk1NTTIMQ6mpqVq3bp1ef/11DRs2TOvXr2/7YQAAALQxh12JDA8PV3p6ur788kvt2bNHc+bM\nUXp6uqxWq4qLiyVJjY2Nqq6ulr+/vxYsWCBvb2+VlJRoyJAhkqTAwMBr6nbo0EFVVVWaM2eOvLy8\ndOXKFTU0NOjUqVMaP368JGno0KGSpOrqavn4+OiOO+6QJA0bNkzPP/98W7x9AAAAu3LYEGmxWBQV\nFaX09HRFRETI3d1dQUFBCggIUEJCgmpra7V+/Xq5ubnp97//vT788ENJ0tSpU2UYhiTJxeXahdjC\nwkKdP39ea9euVVVVlQ9YfeUAACAASURBVN5//30ZhqH+/fvr0KFDGjBggIqKiiRJfn5+qqmpUUVF\nhW6//XZ9/PHH6tOnT1uNAAAAwG4cNkRK0oQJEzRy5Ei9/fbbkqSYmBilpKQoPj5eNTU1iouLk4+P\nj0JDQzV+/Hh5eXmpY8eOqqioUM+ePa9bMyQkRNnZ2Zo0aZI8PDzUq1cvVVRU6D/+4z/0u9/9Tu+8\n845uv/12ubm5yWKxaMWKFXr66adlsVjUqVMnPfPMM205AgAAALtw6BDZ1NSksLAwBQcHS5I8PDy0\nZs2aa173wgsvXHf/8PDwa7Z169ZNb7311jXb//znP2vmzJkKCQnR3r17VVlZKUm69957de+9997M\n2wAAAHA4Dhsid+7cqXXr1ik9Pb1NjtezZ08lJSXJ1dVVNptNycnJbXJcAACA9shhQ+TYsWM1duzY\nNjtecHCw8vLy2ux4AAAA7ZnD3uIHAAAA9kOIBAAAgGmESAAAAJhGiAQAAIBpDnthjaPLzwi1dwtO\nx2q1KiwszN5tAABwS2AlEgAAAKYRIgEAAGAaIRIAAACmESIBAABgGhfW2MmkeQft3YITski5zPWf\ncQEXAKC1sBIJAAAA0wiRAAAAMI0QCQAAANMIkQAAADCNEAkAAADTCJEAAAAwjRAJAAAA09pdiNy6\ndasyMjJuqsaUKVN06tQpXbhwQdu3b5ckLVy4UIWFhS3RIgAAwC2v3YXIlnTs2DF98MEH9m4DAADA\n6bTLX6z59NNP9dhjj6mqqkqxsbGaPHmyPv74Y2VmZsrV1VW9evVSWlqa6urqlJycrEuXLqm6ulrR\n0dGKi4trrrNhwwYdPXpUeXl5kqS8vDy99NJLqqmp0dKlSxUSEtL82traWi1atEjnzp1TQ0ODUlNT\n1a9fv+vWnzJlivz8/HTx4kVt2rRJS5cuVWlpqWw2mxITExUeHt7mMwMAAGhL7TJEurm56eWXX9bZ\ns2c1bdo0TZo0SampqdqyZYv8/f21du1abdu2TYMGDdL999+vMWPGqLy8XFOmTLkqRCYkJOiNN97Q\n5MmTdejQIQ0aNEjTp0/X1q1btXXr1qtC5BtvvKGf/OQnyszM1PHjx7V37155eHjcsH5UVJRGjx6t\nLVu2yM/PTytXrlR1dbXi4+O1Y8eONp8ZAABAW2qXIXLgwIGyWCzq1q2bamtrVVVVpYqKCiUmJkr6\netUwIiJCv/jFL/Taa6/pvffek4+PjxobG7+37qBBgyRJXbt2VW1t7VXPlZSUaMSIEZKk/v37q3//\n/iovL79h/cDAQEnS8ePHZbVaVVxcLElqbGxUdXW1/Pz8WmYYAAAA7VC7DJEWi+Wqx35+fgoICFB2\ndrZ8fX21e/dueXl56ZVXXtGQIUMUFxenffv26c9//vNV+7m4uMhms92w7j8LDg7WZ599plGjRqms\nrExr165V165db1j/m1pBQUEKCAhQQkKCamtrtX79enXq1KklxgAAANButcsQ+V0uLi5KTk7WtGnT\nZBiGvL29tWbNGlksFi1dulTbt29X586d5erqqvr6+ub9evfurePHj+vVV1/9l8eIiYlRUlKS4uPj\n1dTUpKSkJF2+fPl763+zX0pKiuLj41VTU6O4uDi5uDj19UoAAACyGIZh2LuJW43VatXq3BuvigIt\nJT8jtEXqWK1WhYWFtUgtfIu5th5m2zqYa+tpz7O9UW8smQEAAMA0QiQAAABMI0QCAADANEIkAAAA\nTCNEAgAAwDRCJAAAAExziPtEOqOWuvUKvtWeb48AAICzYSUSAAAAphEiAQAAYBohEgAAAKYRIgEA\nAGAaIRIAAACmcXW2nUyad9DeLTghi5TLXP8ZdwEAALQWViIBAABgGiESAAAAphEiAQAAYBohEgAA\nAKYRIgEAAGAaIRIAAACmOWWIzMrKUm5urvbv36/Zs2f/oH3y8vLU0NBwU8c9cOCAjh49elM1AAAA\nHIFThsgfY+PGjbLZbDdV46233lJFRUULdQQAANB+tfubjY8fP14vvfSSOnbsqPDwcL3++usaOHCg\nxo8fr7y8PGVlZenw4cO6fPmygoOD9cwzz3xvvaqqKiUmJsowDDU0NGjZsmUqLi5WZWWlZs+erUcf\nfVQZGRlyd3fXpEmT1KNHD2VmZsrV1VW9evVSWlqaJGnJkiUqLS2VzWZTYmKivL299Ze//EWff/65\n+vbtqx49erTFeAAAAOyi3YfIX//61/rLX/6igIAA9ezZU3v27JGHh4f69Omj+vp6dezYUX/84x9l\ns9l0//33q7y8/HvrFRcXy9fXV88995xOnjypmpoaRUdHa/369crMzFRRUZHq6upUUFAgwzA0btw4\nbdmyRf7+/lq7dq22bdumxsZG+fn5aeXKlaqurlZ8fLx27Nih4cOH6ze/+Q0BEgAAOL12HyLHjBmj\nDRs2qHv37po9e7Y2b94swzA0ZswYdejQQVVVVZozZ468vLx05cqVf/m9xhEjRujMmTOaPn263Nzc\n9NRTT13zmsDAQElfr1pWVFQoMTFRklRbW6uIiAhduHBBVqtVxcXFkqTGxkZVV1e38DsHAABov9p9\niOzfv7+++OILVVZWau7cudq4caN2796tV155RYWFhTp//rzWrl2rqqoqvf/++zIM43vr7d+/X7ff\nfrteeeUVHTp0SM8//7w2b94si8XS/J1IF5evvyrq5+engIAAZWdny9fXV7t375aXl5dOnDihgIAA\nJSQkqLa2VuvXr1enTp1ksVj+5fEBAACcgUNcWDNs2DB16dJFLi4uzX97e3srJCREZWVlmjRpkmbO\nnKlevXr9ywtb/u3f/k35+fmaPHmy1qxZoyeffFKSNHToUE2bNu2qEOji4qLk5GRNmzZNMTEx2rJl\ni/r376+YmBiVlJQoPj5eMTEx+slPfiIXFxcNHjxYGRkZOnXqVKvOAwAAwN4sBktnbc5qtWp1rsXe\nbeAWkJ8R2iJ1rFarwsLCWqQWvsVcWw+zbR3MtfW059neqDeHWIkEAABA+0KIBAAAgGmESAAAAJhG\niAQAAIBphEgAAACYRogEAACAae3+ZuPOqqVuvYJvtefbIwAA4GxYiQQAAIBphEgAAACYRogEAACA\naYRIAAAAmEaIBAAAgGlcnW0nk+YdtHcLTsgi5TJXrvwHALQFViIBAABgGiESAAAAphEiAQAAYBoh\nEgAAAKYRIgEAAGAaIRIAAACmESJ/pKysLOXm5l6zPSIiwg7dAAAAtC1CJAAAAExzmpuNz507V1FR\nURo5cqROnTql1atX68UXX9SSJUtUWloqm82mxMREhYeH691331VOTk7zvi+88IJOnDihjIwMubu7\na9KkSXrooYean3/llVe0Y8cOubm5aejQoZo/f37zc01NTUpNTdXJkyfVq1cv1dfXt+n7BgAAsAen\nCZHR0dHKzc3VyJEj9eabb2rixIkqKCiQn5+fVq5cqerqasXHx2vHjh06c+aMNm3aJE9PTy1evFgf\nffSR7rjjDtXV1amgoOCquseOHdM777yjN954Q25ubnr66af1pz/9qfn5wsJC1dXVKT8/X+fOndPO\nnTvb+q0DAAC0OacJkeHh4UpPT9eXX36pPXv2aM6cOUpPT5fValVxcbEkqbGxUdXV1fL399eCBQvk\n7e2tkpISDRkyRJIUGBh4Td2SkhINHjxY7u7ukqShQ4fqxIkTzc+fOHFCISEhkqQePXqoe/furf1W\nAQAA7M5pvhNpsVgUFRWl9PR0RUREyN3dXUFBQbr//vu1efNm/eEPf9C4cePk5uam3//+98rMzNSK\nFSvUoUMHGYYhSXJxuXYcQUFBKi4uVmNjowzD0IEDB64Km0FBQSoqKpIklZeXq7y8vG3eMAAAgB05\nzUqkJE2YMEEjR47U22+/LUmKiYlRSkqK4uPjVVNTo7i4OPn4+Cg0NFTjx4+Xl5eXOnbsqIqKCvXs\n2fO6Ne+66y7dd999io2Nlc1mU1hYmEaNGqWjR49KkkaNGiWr1aro6Gj16NFDfn5+bfZ+AQAA7MWp\nQmRTU5PCwsIUHBwsSfLw8NCaNWuued0LL7xw3f3Dw8Ovu33q1KmaOnXqVduefvrp5r8XLFjwY1sG\nAABwSE7zcfbOnTv1xBNPaO7cufZuBQAAwOk5zUrk2LFjNXbsWHu3AQAAcEtwmpVIAAAAtB1CJAAA\nAEwjRAIAAMA0QiQAAABMI0QCAADANKe5OtvR5GeE2rsFp2O1WhUWFmbvNgAAuCWwEgkAAADTCJEA\nAAAwjRAJAAAA0wiRAAAAMI0La+xk0ryD9m7BCVmkXObKRVsAgLbASiQAAABMI0QCAADANEIkAAAA\nTCNEAgAAwDRCJAAAAEwjRAIAAMA0hwyRdXV1+tWvfmV6vwsXLmj79u2t0NHX6urqVFBQ0Gr1AQAA\n2guHDJE/1rFjx/TBBx+0Wv3KykpCJAAAuCU4zM3GL1++rHnz5unixYvq3bt38/YjR45o+fLlcnV1\nVYcOHbR8+XLZbDbNnTtXAQEBKisr009/+lMtW7ZMGzZs0NGjR5WXl6fJkydLknbt2qW9e/dq8eLF\n2rhxo4qKirR+/Xq9/fbbOn/+vH71q19p1apVstlsunjxolJSUhQaGqoxY8YoNDRUp0+flr+/v7Ky\nsrRhwwadPHlS69at04wZM+w1KgAAgFbnMCuR27ZtU//+/ZWTk6OYmJjm7SkpKVq8eLFef/11xcbG\natWqVZKkM2fOKD09XQUFBSosLFRlZaUSEhJ0zz33NAdISYqMjNSBAwckSZ988on+9re/qbGxUX/6\n0580evRonTx5UgsWLNCrr76qqVOnauvWrZKksrIyzZo1S3l5eaqqqtJnn32mhIQE9e3blwAJAACc\nnsOsRJ44cULDhw+XJA0ePFhubl+3XlFRoQEDBkiShg0bpueee06S1Lt3b/n4+EiSunXrprq6uuvW\nve222xQYGKji4mK5ublpyJAhOnDggM6fP6/g4GBVV1crOztbt912my5fvtxc08/PT927d5ckde/e\n/Yb1AQAAnJHDrEQGBQWpqKhI0tcfYTc2NkqSbr/9dh09elSSdODAAfXp00eSZLFYrqnh4uIim812\nzfZRo0bp2WefVXh4uCIjI5WZmamf//znkqT09HTNnDlTq1evVv/+/WUYhun6AAAAzsZhQuTDDz+s\n8vJyxcbGKicnR+7u7pKkFStWaPny5YqLi9Nrr72mpKSkG9bo3bu3jh8/rldfffWq7b/85S916NAh\nRUZGKjw8XEeOHNGYMWMkSQ8++KCmT5+uuLg4nTlzRhUVFTes7+/vr4aGBj377LM3/4YBAADaMYvx\nzdIa2ozVatXq3GtXMoGWkJ8R2uI1rVarwsLCWrzurY65th5m2zqYa+tpz7O9UW8OsxIJAACA9oMQ\nCQAAANMIkQAAADCNEAkAAADTCJEAAAAwjRAJAAAA0wiRAAAAMM1hfvbQ2bTGvfxude35HlsAADgb\nViIBAABgGiESAAAAphEiAQAAYBohEgAAAKZxYY2dTJp30N4tOCGLlMtcuWgLANAWWIkEAACAaYRI\nAAAAmEaIBAAAgGmESAAAAJhGiAQAAIBphEgAAACYRogEAACAaXYJkU1NTXr88ccVGxurf/zjHz+6\nzowZM1qwq++3cOFCFRYWqrCwUHl5edd9TWVlpZYuXdpmPQEAANiLXW42XllZqerqam3duvWm6qxb\nt66FOvrhRowYccPnunXrRogEAAC3BLuEyNTUVJ05c0aLFy/WvHnzNH/+fNXU1KipqUmzZs3Sz3/+\ncz3wwAPq06ePPDw8tGzZMiUnJ6u6ulqSlJKSorvuuksRERHas2ePiouLtWzZMnl7e8vf318dOnTQ\njBkzNHfuXAUEBKisrEw//elPtWzZsqv6mDJligIDA3X69GkZhqHMzEx169ZNq1atktVqlSQ98MAD\nevTRR5v32bp1q0pKSjRv3jxlZ2dr165dampqUmxsrCIjIzVnzhzl5+e33TABAADswC4hcsmSJZoz\nZ47S0tK0evVq3XvvvXr00UdVXl6u2NhY7dq1S1euXNH06dM1cOBAPfvss7rnnnsUFxenM2fOaNGi\nRcrNzb2q3po1a9SvXz9lZmaqvLxcknTmzBm9/PLL8vT01KhRo1RZWalu3bpd1UtoaKjS0tKUk5Oj\njRs3KiIiQl988YXy8/PV2NiouLg43XPPPde8hyNHjqiwsFAFBQWqr6/Xc889p4iIiNYdHAAAQDth\n99/OPnXqlKKioiRJd9xxh3x8fFRVVSVJCgwMlCQdP35c+/bt0zvvvCNJunjx4lU1Kioq1K9fP0lS\nWFiY/ud//keS1Lt3b/n4+Ej6+qPmurq6a47/TUAMDQ3VBx98oICAAA0dOlQWi0Xu7u4aPHiwTp06\ndc1+p0+fVkhIiFxdXeXp6amUlBR98cUXNz0PAAAAR2D3q7ODg4P1ySefSJLKy8t18eJFde7cWZLk\n4vJ1e0FBQfr3f/93bd68WWvXrm0Ond8ICAjQyZMnJUmffvpp83aLxfIvj3/48GFJ0sGDB9W3b18F\nBwc3f5Td0NCgQ4cO6c4777xmv6CgIB05ckQ2m00NDQ2aOnWq6uvrzb59AAAAh2T3lcgnn3xSSUlJ\n2rlzp2pra5WWliY3t6vbSkhIUHJysvLz81VTU3PNVdlLlixRUlKSvLy85O7urjvuuOMHH3/btm16\n9dVX5enpqTVr1sjPz08ff/yxJk+erIaGBo0bN06DBg26Zr8BAwZo+PDhio2Nlc1mU2xsrDw8PH7c\nEAAAAByMxTAMw95N3KycnBzdd9996tKlizIzM+Xu7v6Dbv8zZcoULV26VMHBwW3Q5besVqtW5/7r\nVVLgx8jPCG3xmlarVWFhYS1e91bHXFsPs20dzLX1tOfZ3qg3u69EtgR/f3899thj8vLykq+vr1at\nWmXvlgAAAJyaU4TIcePGady4cab327x5cyt0AwAA4PzsfmENAAAAHA8hEgAAAKYRIgEAAGAaIRIA\nAACmOcWFNY6oNW7Dcqtrz7dHAADA2bASCQAAANMIkQAAADCNEAkAAADTCJEAAAAwjQtr7GTSvIP2\nbsEJWaRc554rF2QBANoLViIBAABgGiESAAAAphEiAQAAYBohEgAAAKYRIgEAAGAaIRIAAACmESIB\nAABgmt1C5NatW5WRkdFi9QoLC5WXl9di9a5n9uzZ2r9/f6seAwAAwBE4zc3GR4wYYe8WAAAAbhl2\nDZFFRUV69NFHVVNTo6efflojR47Uxx9/rMzMTLm6uqpXr15KS0vT9u3b9dZbb8lms2nmzJlasmSJ\nQkNDdfr0afn7+ysrK0tvv/22SkpKNG/evOb6p0+f1qJFi+Tm5iZXV1etWbNGXbt21eLFi/W3v/1N\n1dXVGjFihBITE7Vw4UJ5eHjo7Nmzqqio0KpVqzRo0CDl5OSooKBA3bp105dffilJamho0JIlS1Ra\nWiqbzabExESFh4frgQceUJ8+feTh4aHnn3/eXmMFAABodXYNkZ6entq0aZOqqqoUHR2t4cOHKzU1\nVVu2bJG/v7/Wrl2rbdu2yc3NTR07dtT69eslSWVlZXrttdfUvXt3xcTE6LPPPrtu/b1792rQoEFa\nuHChPvnkE/3jH/9QQ0ODhgwZoujoaNXV1TWHSEnq0aOH0tLSlJ+fr7y8PM2fP1//+Z//qe3bt8ti\nsWjChAmSpIKCAvn5+WnlypWqrq5WfHy8duzYoStXrmj69OkaOHBg2wwQAADATuwaIsPCwmSxWOTv\n7y9fX19VV1eroqKiOdTV1tYqIiJCvXv3VmBgYPN+fn5+6t69uySpe/fuqquru279iRMn6g9/+IOe\neOIJ+fr6avbs2ercubM+++wz7du3Tz4+Pqqvr29+/YABAyRJAQEBOnjwoEpKStS3b195eHhIkkJC\nQiRJx48fl9VqVXFxsSSpsbFR1dXVknRVnwAAAM7KriHymxXEyspKXblyRX5+fgoICFB2drZ8fX21\ne/dueXl56fz583Jx+fYaIIvF8oPq7969W2FhYZoxY4b++7//Wy+99JIGDBggX19fpaWlqbS0VPn5\n+TIM47p1e/XqpZMnT6q2tlbu7u763//9Xz344IMKCgpSQECAEhISVFtbq/Xr16tTp06SdFWfAAAA\nzsquIbK2tlaPPPKIrly5orS0NLm6uio5OVnTpk2TYRjy9vbWmjVrdP78+R9V/+6779b8+fOVlZUl\nFxcXLVq0SB4eHpozZ46sVqs8PT115513qqKi4rr7d+nSRbNmzVJMTIy6dOkiT09PSVJMTIxSUlIU\nHx+vmpoaxcXFER4BAMAtxWJ8swyHNmO1WrU694etpgL/LD8j1C7HtVqtCgsLs8uxnRlzbT3MtnUw\n19bTnmd7o95YPgMAAIBphEgAAACYRogEAACAaYRIAAAAmEaIBAAAgGmESAAAAJhm1/tE3srsdasW\nZ9aeb48AAICzYSUSAAAAphEiAQAAYBq/WGMHVqvV3i0AAAD8YNf7uhghEgAAAKbxcTYAAABMI0QC\nAADANEIkAAAATCNEAgAAwDRCJAAAAEzjF2vaiM1m09KlS3Xs2DF5eHhoxYoVuvPOO+3dlkN76KGH\n5OvrK0nq2bOnJk+erPT0dLm6uioyMlIzZsywc4eO5dNPP1VGRoY2b96s0tJSLVy4UBaLRf369dOS\nJUvk4uKidevW6cMPP5Sbm5uSkpIUEhJi77Ydwj/P9vPPP1dCQoL69OkjSYqNjdVvfvMbZmtSQ0OD\nkpKSdPbsWdXX1+upp55S3759OW9v0vXmGhAQwDnbApqampSSkqLTp0/L1dVVzzzzjAzDcOxz1kCb\n2Llzp7FgwQLDMAzj0KFDRkJCgp07cmy1tbXGb3/726u2Pfjgg0Zpaalhs9mMJ554wjh8+LCdunM8\nmzZtMh544AEjOjraMAzDePLJJ419+/YZhmEYqampxnvvvWccPnzYmDJlimGz2YyzZ88aEyZMsGfL\nDuO7s83Pzzdefvnlq17DbM178803jRUrVhiGYRhVVVXGL37xC87bFnC9uXLOtoz333/fWLhwoWEY\nhrFv3z4jISHB4c9ZPs5uI1arVcOHD5ckDRkyRIcPH7ZzR47t6NGj+uqrr/TYY4/pkUce0YEDB1Rf\nX6/evXvLYrEoMjJSf/3rX+3dpsPo3bu3srKymh9//vnn+tnPfiZJGjFihPbu3Sur1arIyEhZLBb1\n6NFDTU1NqqqqslfLDuO7sz18+LA+/PBDPfzww0pKSlJNTQ2z/RHGjRunWbNmNT92dXXlvG0B15sr\n52zLGDVqlJYvXy5JOnfunLp27erw5ywhso3U1NTIx8en+bGrq6saGxvt2JFju+222/T444/r5Zdf\n1rJly7Ro0SJ5eno2P+/t7a1Lly7ZsUPHMnbsWLm5ffvtFsMwZLFYJH07y++ew8z4h/nubENCQvS7\n3/1OOTk56tWrl1588UVm+yN4e3vLx8dHNTU1mjlzphITEzlvW8D15so523Lc3Ny0YMECLV++XGPH\njnX4c5YQ2UZ8fHx0+fLl5sc2m+2q/1hgTmBgoB588EFZLBYFBgbK19dXFy5caH7+8uXL6tixox07\ndGwuLt/+0/DNLL97Dl++fLn5O6n44UaPHq277767+e8jR44w2x/p/PnzeuSRR/Tb3/5WUVFRnLct\n5Ltz5ZxtWatXr9bOnTuVmpqqurq65u2OeM4SIttIaGioCgsLJUlFRUXq37+/nTtybG+++aZWrVol\nSSovL9dXX30lLy8v/d///Z8Mw9BHH32koUOH2rlLxzVw4EDt379fklRYWKihQ4cqNDRUH330kWw2\nm86dOyebzaYuXbrYuVPH8/jjj6u4uFiS9Ne//lWDBg1itj/C3//+dz322GOaP3++Jk6cKInztiVc\nb66csy3jv/7rv7Rx40ZJkqenpywWi+6++26HPmdZCmsjo0eP1p49exQTEyPDMLRy5Up7t+TQJk6c\nqEWLFik2NlYWi0UrV66Ui4uL5s2bp6amJkVGRmrw4MH2btNhLViwQKmpqXr++ecVFBSksWPHytXV\nVUOHDtXkyZNls9m0ePFie7fpkJYuXarly5fL3d1dXbt21fLly+Xj48NsTdqwYYMuXryo7OxsZWdn\nS5KSk5O1YsUKztubcL25Lly4UCtXruScvUljxozRokWL9PDDD6uxsVFJSUkKDg526H9rLYZhGPZu\nAgAAAI6Fj7MBAABgGiESAAAAphEiAQAAYBohEgAAAKYRIgEAAGAaIRIAbiFlZWVKSkqydxsAnAAh\nEgBuIefOnVNZWZm92wDgBLhPJAC0M4ZhKCMjQ7t27ZKrq6smT56sESNGaPHixbpw4YK8vLyUnJys\nkJAQLVy4UD/72c80YcIESdJdd92lY8eOKSsrS+Xl5SotLdXZs2cVHR2tp556SlFRUfriiy/00EMP\nacmSJXZ+pwAcGb9YAwDtzLvvvquDBw9q+/btamhoUFxcnLZs2aK5c+dqzJgxKioq0qxZs7Rz587v\nrXPs2DHl5OTo0qVLGjVqlB5++GGlpKRo3bp1BEgAN42PswGgnTlw4IDuu+8+eXh4yNvbW1u2bFF1\ndbXGjBkjSRoyZIg6deqkkpKS760THh4uDw8P+fv7q3Pnzrp06VJbtA/gFkGIBIB2xs3NTRaLpflx\nWVmZvvvNI8Mw1NTUJIvF0vxcQ0PDVa/p0KFD89///DoAaAmESABoZ4YNG6b33ntPDQ0N+uqrr5SY\nmCiLxaL33ntPklRUVKS///3v6tevnzp37qyTJ09Kknbt2vUva7u6uqqxsbFV+wdwayBEAkA7M3r0\naIWGhmrChAmaOHGiHnnkEeXm5mrz5s2KiopSWlqasrKy5OHhodjYWO3fv19RUVE6ePCgunXr9r21\ng4ODdenSJc2fP7+N3g0AZ8XV2QAAADCNlUgAAACYRogEAACAaYRIAAAAmEaIBAAAgGmESAAAAJhG\niAQAAIBphEgALpCTiwAAABBJREFUAACYRogEAACAaf8PPV9/ediNlpEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b8461eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.barplot(x=\"count\", y=\"Top 20 Phrases\", data=ngram_liberal_df_20,palette=[\"royalblue\"]).set_title('Top 20 Phrases - Liberals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3992, 1)\n"
     ]
    }
   ],
   "source": [
    "# apply for conservatives\n",
    "corpus_conservative = topwords_conservative_df['postText_value']\n",
    "\n",
    "corpus_conservative_fit = count_vect.fit_transform(corpus_conservative)\n",
    "\n",
    "conservative_counts = pd.DataFrame(corpus_conservative_fit.toarray(),columns=count_vect.get_feature_names()).sum()\n",
    "ngram_conservative_df = pd.DataFrame(conservative_counts,columns=['count'])\n",
    "print(ngram_conservative_df.shape)\n",
    "ngram_conservative_df = ngram_conservative_df.sort_values(by=['count'],ascending=False)\n",
    "ngram_conservative_df_20 = ngram_conservative_df.head(20)\n",
    "ngram_conservative_df_20\n",
    "ngram_conservative_df_20.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Top 20 Phrases - Conservatives')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bmxuXLl0CYOfOnTg6Ot5REAXYsWMHaWlpODn9PgQ//PADCxYsYNGiRRiGYTu3iIiISGGm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b764c49b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ngram_conservative_df_20.columns = ['Top 20 Phrases', 'count']\n",
    "sns.set(style=\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(10,10))\n",
    "sns.barplot(x=\"count\", y='Top 20 Phrases', data=ngram_conservative_df_20,palette=[\"indianred\"]).set_title('Top 20 Phrases - Conservatives')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8) Social Network Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15795\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threadTitle</th>\n",
       "      <th>postDateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2018-01-13 06:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birthright Citizenship</td>\n",
       "      <td>2017-06-07 23:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Does Primary Voter Turnout Mean Anything</td>\n",
       "      <td>2016-02-24 11:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘Russian Troops’ Kept Hillary From Going to W...</td>\n",
       "      <td>2016-12-21 08:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td># 1 Halloween costume</td>\n",
       "      <td>2017-10-30 12:39:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         threadTitle        postDateTime\n",
       "0                                                    2018-01-13 06:18:00\n",
       "1                             Birthright Citizenship 2017-06-07 23:19:00\n",
       "2          Does Primary Voter Turnout Mean Anything  2016-02-24 11:37:00\n",
       "3   ‘Russian Troops’ Kept Hillary From Going to W... 2016-12-21 08:05:00\n",
       "4                              # 1 Halloween costume 2017-10-30 12:39:00"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get first postDateTime for each thread\n",
    "\n",
    "firstpost_df = forum.groupby('threadTitle').agg({'postDateTime' : 'min'})\n",
    "firstpost_df.reset_index(inplace=True)\n",
    "print(firstpost_df.shape[0])\n",
    "firstpost_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/franklindickinson/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#merge userName and political view onto firstThreadPost df\n",
    "\n",
    "forum_user_merge = forum[['threadTitle','postDateTime','userName','userPolitics','sLeftRight']]\n",
    "forum_user_merge.drop_duplicates(inplace=True)\n",
    "forum_firstpost_complete_df = firstpost_df.merge(forum_user_merge,how='left',on=['threadTitle','postDateTime'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16012\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threadTitle</th>\n",
       "      <th>firstPostDateTime</th>\n",
       "      <th>threadOriginator</th>\n",
       "      <th>originatorPolitics</th>\n",
       "      <th>originatorLeftRight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2018-01-13 06:18:00</td>\n",
       "      <td>xxxxxxx</td>\n",
       "      <td>Revolutionary</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birthright Citizenship</td>\n",
       "      <td>2017-06-07 23:19:00</td>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Does Primary Voter Turnout Mean Anything</td>\n",
       "      <td>2016-02-24 11:37:00</td>\n",
       "      <td>Fuelman</td>\n",
       "      <td>Capitalist</td>\n",
       "      <td>Conservative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘Russian Troops’ Kept Hillary From Going to W...</td>\n",
       "      <td>2016-12-21 08:05:00</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Conservative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td># 1 Halloween costume</td>\n",
       "      <td>2017-10-30 12:39:00</td>\n",
       "      <td>Hypocrisy</td>\n",
       "      <td>Capitalist</td>\n",
       "      <td>Conservative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         threadTitle   firstPostDateTime  \\\n",
       "0                                                    2018-01-13 06:18:00   \n",
       "1                             Birthright Citizenship 2017-06-07 23:19:00   \n",
       "2          Does Primary Voter Turnout Mean Anything  2016-02-24 11:37:00   \n",
       "3   ‘Russian Troops’ Kept Hillary From Going to W... 2016-12-21 08:05:00   \n",
       "4                              # 1 Halloween costume 2017-10-30 12:39:00   \n",
       "\n",
       "  threadOriginator originatorPolitics originatorLeftRight  \n",
       "0          xxxxxxx      Revolutionary                      \n",
       "1    Cannonpointer                NaN                      \n",
       "2          Fuelman         Capitalist        Conservative  \n",
       "3         roadkill       Conservative        Conservative  \n",
       "4        Hypocrisy         Capitalist        Conservative  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns to reflect threadOriginator and firstpost\n",
    "forum_firstpost_complete_df.rename(columns={'postDateTime':'firstPostDateTime'}, inplace=True)\n",
    "forum_firstpost_complete_df.rename(columns={'userName':'threadOriginator'}, inplace=True)\n",
    "forum_firstpost_complete_df.rename(columns={'userPolitics':'originatorPolitics'}, inplace=True)\n",
    "forum_firstpost_complete_df.rename(columns={'sLeftRight':'originatorLeftRight'}, inplace=True)\n",
    "\n",
    "\n",
    "print(forum_firstpost_complete_df.shape[0])\n",
    "forum_firstpost_complete_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#insert new columns into main df\n",
    "\n",
    "forum = forum.merge(forum_firstpost_complete_df,how='left',on='threadTitle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5241\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userName</th>\n",
       "      <th>threadOriginator</th>\n",
       "      <th>postText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>nefarious101</td>\n",
       "      <td>1523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Huey</td>\n",
       "      <td>Blackvegetable</td>\n",
       "      <td>1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Huey</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nuckin futz</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>JoeyBone</td>\n",
       "      <td>1186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>1178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bigsky</td>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Termin8tor</td>\n",
       "      <td>Blackvegetable</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>Blackvegetable</td>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Huey</td>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>Bigsky</td>\n",
       "      <td>1069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Blackvegetable</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Blackvegetable</td>\n",
       "      <td>Termin8tor</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bigsky</td>\n",
       "      <td>roadkill</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Huey</td>\n",
       "      <td>JoeyBone</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cannonpointer</td>\n",
       "      <td>Huey</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>nuckin futz</td>\n",
       "      <td>nefarious101</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>Bigsky</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>roadkill</td>\n",
       "      <td>nuckin futz</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          userName threadOriginator  postText\n",
       "0         roadkill     nefarious101      1523\n",
       "1             Huey   Blackvegetable      1486\n",
       "2             Huey         roadkill      1241\n",
       "3      nuckin futz         roadkill      1194\n",
       "4         roadkill         JoeyBone      1186\n",
       "5    Cannonpointer         roadkill      1182\n",
       "6         roadkill    Cannonpointer      1178\n",
       "7           Bigsky    Cannonpointer      1174\n",
       "8       Termin8tor   Blackvegetable      1150\n",
       "9         roadkill   Blackvegetable      1148\n",
       "10            Huey    Cannonpointer      1134\n",
       "11   Cannonpointer           Bigsky      1069\n",
       "12  Blackvegetable         roadkill      1047\n",
       "13  Blackvegetable       Termin8tor      1030\n",
       "14          Bigsky         roadkill      1002\n",
       "15            Huey         JoeyBone       947\n",
       "16   Cannonpointer             Huey       924\n",
       "17     nuckin futz     nefarious101       896\n",
       "18        roadkill           Bigsky       886\n",
       "19        roadkill      nuckin futz       840"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get aggregate for each user posting on thread by other user\n",
    "\n",
    "forum_userMatrix = forum[['userName','threadOriginator','postText']].groupby(['userName','threadOriginator']).agg('count')\n",
    "forum_userMatrix.sort_values(by=['postText'],ascending=False)\n",
    "forum_userMatrix.reset_index(inplace=True)\n",
    "#forum_userMatrix.head()\n",
    "\n",
    "forum_userMatrix_2 = forum_userMatrix.drop(forum_userMatrix[forum_userMatrix['userName'] == forum_userMatrix['threadOriginator']].index)\n",
    "forum_userMatrix_2.sort_values(by=['postText'],ascending=False,inplace=True)\n",
    "\n",
    "forum_userMatrix_2.reset_index(inplace=True,drop=True)\n",
    "print(forum_userMatrix_2.shape[0])\n",
    "forum_userMatrix_2.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge dataframes to get posts for all users\n",
    "\n",
    "forum_userMatrix_3 = forum_userMatrix_2.merge(user_df_sentiment,how='left',on='userName')\n",
    "forum_userMatrix_4 = forum_userMatrix_3.merge(user_df_sentiment,how='left',left_on='threadOriginator',right_on='userName',suffixes=['_pU','_tO'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkX version: 1.11\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "print('NetworkX version: {}'.format(nx.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.convert_matrix.from_pandas_dataframe.html\n",
    "\n",
    "G=nx.from_pandas_dataframe(forum_userMatrix_4, source= 'userName_pU', target= 'threadOriginator',edge_attr= ['postText','userPolitics_pU','sLeftRight_pU','userPolitics_tO','sLeftRight_tO'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_username_list = []\n",
    "node_sLeftRight_list = []\n",
    "\n",
    "for node in G:\n",
    "    node_username_list.append(node)\n",
    "    node_sLeftRight_list.append(user_df_sentiment.loc[user_df_sentiment['userName'] == node, 'sLeftRight'].iloc[0])\n",
    "    \n",
    "node_dict = dict(zip(node_username_list, node_sLeftRight_list))\n",
    "\n",
    "colorlist = []\n",
    "for value in node_sLeftRight_list:\n",
    "    if value == 'Liberal':\n",
    "            colorlist.append('blue')\n",
    "    elif value == 'Conservative':\n",
    "            colorlist.append('red')\n",
    "    else:\n",
    "            colorlist.append('grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qOsdyV65cUZkyZRQdHa18+fKZzgEAAADwktxGjhw50nQEIEk2m021atWSh4eH\ndu/erTVr1ihLliwqX7686TQ4mIwZM8rb21sffPCBOnXqpHTp0plOslSmTJl09+5drV+/Xm+//bbp\nHAAAAAAviZMtcEhLly5Vr169lD59enXs2FFjxoyRzWYznQUH07NnT8XFxWnx4sWp7ufj7t278vb2\n1tatW1W6dGnTOQAAAABeAmMLHNbmzZsVFBSknDlzqmLFigoLC+NxuPgPjx49kp+fn0JDQ9W+fXvT\nOZabMWOGIiMjtWnTJtMpAAAAAF4CYwsc2oEDB9S0aVPlzZtX2bJlU0REBE9owX84duyY6tSpo/37\n96tIkSKmcyz19OlTlSxZUrNnz1a9evVM5wAAAABIIJ5GBIdWqVIlff/99/rll1/06NEjVa9eXVev\nXjWdBQdSunRpDR06VMHBwXr27JnpHEt5eHho3LhxGjhwoOLj403nAAAAAEggxhY4vBIlSmjv3r36\n9ddflS1bNvn7++vEiROms+BAevfuraxZs2r06NGmUyzXsmVLeXp6asmSJaZTAAAAACQQXyOC07h1\n65YaNWqkDBky6OTJk1q+fLlq1aplOgsO4saNG/L19dXy5ctVvXp10zmW2r17t9q1a6ezZ8+muicv\nAQAAAKkRJ1vgNHLkyKEdO3bI3d1d3t7eatOmjRYvXmw6Cw4iV65cCgsLU/v27RUXF2c6x1IBAQEq\nW7asZsyYYToFAAAAQAJwsgVO5+nTp+rQoYNiY2N1/fp19ezZU4MGDUp1j/5F4vzjH//QzZs3tWzZ\nslT1M3HmzBkFBATozJkzyp49u+kcAAAAAH+BsQVO6cWLF+rTp4+ioqIkSTVq1ND06dOVNm1aw2Uw\n7dGjR6pYsaL69++vTp06mc6xVI8ePeTl5aXJkyebTgEAAADwFxhb4LTsdrvGjBmjr7/+Wnnz5lXW\nrFm1dOlSZciQwXQaDDtx4oRq1aqlffv2qWjRoqZzLHP9+nW98cYbOnTokAoVKmQ6BwAAAMCf4J4t\ncFo2m00ff/yxQkJCFBsbK0mqXbu2bt68abgMppUqVUoff/yxgoKCUtXjoHPnzq0+ffpo6NChplMA\nAAAA/AVOtiBVWLFihXr27KnAwEDt2bNHGzduVLFixUxnwSC73a7GjRurTJky+vTTT03nWOb+/fvy\n9vbWunXr5OfnZzoHAAAAwB9gbEGqsW3bNgUFBendd9/VqlWrtHr1avn7+5vOgkE3b96Ur6+vlixZ\nopo1a5rOscyXX36pJUuWKCoqKlXdBBgAAABILfgaEVKNunXrKjIyUhEREWrbtq2aNm2qNWvWmM6C\nQa+++qrmz5+vDh066Pbt26YXj9VeAAAgAElEQVRzLNO5c2fdvHlTGzZsMJ0CAAAA4A9wsgWpzpkz\nZ9SgQQM1a9ZMq1at0uDBg9WrVy/TWTCob9++unLlilauXJlqToKsX79eoaGh+uc//8lTuAAAAAAH\nw8kWpDrFixfXnj17tG3bNjVq1EgzZ85USEiI4uPjTafBkPHjx+vcuXOaP3++6RTLNG7cWDly5NDX\nX39tOgUAAADA/8HJFqRat2/fVmBgoAoWLKhLly4pX758WrhwoTw9PU2nwYBTp06pRo0a2rNnj3x8\nfEznWOLHH39U8+bNFRMTwyPPAQAAAAfCyRakWtmyZdO2bdt0584dZc6cWU+fPlX9+vUVFxdnOg0G\nlCxZUqNGjVJwcLCePn1qOscSFStWVEBAgKZMmWI6BQAAAMC/4WQLUr1nz56pU6dOunjxosqUKaOo\nqCht3LhRBQoUMJ2GFGa329WsWTOVKFFCn332mekcS8TGxqpixYo6deqUcuXKZToHAAAAgDjZAhfg\n7u6u8PBwlS9fXrt371abNm1UtWpVHT161HQaUpjNZtO8efO0aNEi7dixw3SOJYoUKaL27dtr9OjR\nplMAAAAA/C9OtsBl2O12jRs3TmFhYerXr59Gjx6t8PBwvfXWW6bTkMK2bt2qzp07Kzo6WtmzZzed\nk2S//vqrfHx8tHfv3lRzPxoAAADAmbmNHDlypOkIICXYbDYFBATIy8tLY8aM0bhx49SrVy9lz55d\nZcuWNZ2HFFSkSBFdvXpVCxcuVOvWrZ3+cdDp06eX3W7/158HAAAAgFmcbIFLioiIUI8ePTRp0iSN\nHDlSHTt21Mcff+z0H7qRcE+ePFHlypXVo0cPde3a1XROkj169Eg+Pj5aunSpqlatajoHAAAAcGmM\nLXBZUVFRat26tcaPH69Zs2apTJkymjNnjtzd3U2nIYWcOXNGAQEB2r17t4oXL246J8kWLlyouXPn\nau/evQyHAAAAgEHcIBcuq1atWtq0aZOGDh2qTp066fr162rSpInu3btnOg0ppHjx4vrkk08UFBSk\nJ0+emM5Jsnbt2unhw4davXq16RQAAADApXGyBS7v3Llzql+/vrp06aKLFy/q8OHDioyMVJ48eUyn\nIQXY7XY1b95cRYsW1aRJk0znJNmWLVvUq1cvnTx5klNaAAAAgCGcbIHLK1asmPbu3avly5frlVde\n0TvvvKMqVaro9OnTptOQAmw2m8LCwrR8+XJt3brVdE6S1a9fXwULFtSXX35pOgUAAABwWZxsAf5X\nXFycmjRpokKFCqlWrVoaPHiwVq1apYCAANNpSAHbt29Xx44ddfToUeXMmdN0TpJER0erQYMGiomJ\nUaZMmUznAAAAAC6Hky3A/8qaNau2bNmiuLg4RUREKCwsTC1atNCKFStMpyEF1KlTR0FBQerSpYuc\nfYP29fXVW2+9pQkTJphOAQAAAFwSJ1uA/+PZs2d6//339dNPP2ncuHEKDg5W37591a9fP57wkso9\nffpU/v7+ev/999WjRw/TOUly6dIllS1bVseOHVPevHlN5wAAAAAuhbEF+APx8fEaOHCgNm3apAUL\nFui9995TnTp1NHnyZLm5uZnOQzI6e/asqlWrpp07d6pkyZKmc5IkNDRUt27dUlhYmOkUAAAAwKUw\ntgB/wm63a+LEiZo1a5ZWrVqlgQMHKkuWLFq8eLG8vLxM5yEZhYWFaebMmdq/f7/SpUtnOifR7ty5\nI29vb+3YsUOlSpUynQMAAAC4DMYW4G/Mnz9fQ4cOVUREhGbNmqX/+Z//0bp165QjRw7TaUgmdrtd\nLVu2VP78+TVlyhTTOUkydepUbd26VZGRkaZTAAAAAJfB2AIkwNq1a/XBBx9o8eLF2rFjhyIiIrRp\n0yYVLlzYdBqSye3bt+Xr66svv/xSDRo0MJ2TaE+fPlWJEiX01VdfqXbt2qZzAAAAAJfA04iABGjW\nrJlWrlyp4OBglS9fXh999JGqVaumgwcPmk5DMsmWLZu++eYbde7cWTdv3jSdk2geHh769NNPFRIS\novj4eNM5AAAAgEtgbAESqEaNGtqyZYv69Okjm82mOXPmqFGjRvruu+9MpyGZ1KxZUx07dtR7773n\n1I+DbtWqldzc3LRs2TLTKQAAAIBL4GtEwEuKjY1V/fr11alTJ9WrV0/NmzfXyJEj1a1bN9NpSAZP\nnz5V1apV1bFjR/Xq1ct0TqLt3LlTnTp10pkzZ+Tp6Wk6BwAAAEjVGFuARPj555/VsGFDBQQEqHfv\n3goMDFTLli01duxY2Ww203mw2Llz51SlShVFRUU59VN9mjZtqpo1a6pfv36mUwAAAIBUjbEFSKQ7\nd+6oWbNmyps3ryZOnKiWLVuqaNGimjdvnjw8PEznwWILFizQ5MmT9eOPPzrto79PnTqlGjVqKCYm\nRlmzZjWdAwAAAKRajC1AEjx69Eht2rTR48ePFR4erq5du+r+/fuKiIhQ5syZTefBQna7Xa1bt1bu\n3Lk1ffp00zmJ1rVrV2XOnFkTJ040nQIAAACkWowtQBI9f/5cXbt21alTp7Ru3TqNGTNGO3fu1IYN\nG/T666+bzoOF4uLi5Ovrq9mzZ6tRo0amcxLl559/VqlSpXT48GEVLFjQdA4AAACQKvE0IiCJ0qZN\nq3nz5qlGjRqqWbOmQkJC1KFDB1WpUkXHjx83nQcLZc2aVeHh4erSpYtu3LhhOidR8uTJo169emnY\nsGGmUwAAAIBUi5MtgIUmTZqkGTNmaNOmTfrnP/+p3r17a+nSpapTp47pNFho2LBhOnz4sCIjI5Um\njfNt1vfu3ZO3t7ciIyNVrlw50zkAAABAquN8nxIABzZgwACNHj1atWrVUuHChbVy5UoFBQUpPDzc\ndBosNGLECMXFxWnGjBmmUxIlY8aMGjFihEJCQsTeDgAAAFiPky1AMli/fr06d+6sxYsX6/XXX1ej\nRo3UrVs3hYaG8mjoVCI2NlaVK1fW9u3bVbp0adM5L+3Zs2d68803NWXKFDVs2NB0DgAAAJCqcLIF\nSAZNmjTR6tWr1a5dOx07dkw//PCDVqxYoR49euj58+em82CBIkWK6PPPP1fbtm316NEj0zkvzd3d\nXePHj9fAgQP14sUL0zkAAABAqsLYAiSTatWqadu2bRowYIBWr16tXbt26fz582revLkePHhgOg8W\naN++vUqXLq0BAwaYTkmUZs2aKXPmzPrmm29MpwAAAACpCl8jApLZ+fPnVb9+fQUFBWno0KHq3r27\njh8/ru+++065cuUynYckunPnjnx9fTVjxgw1adLEdM5L279/v1q2bKmYmBilT5/edA4AAACQKnCy\nBUhmhQoV0p49e7R+/Xr17dtXX375pQIDA1WlShXFxMSYzkMSZcmSRYsWLVLXrl31888/m855aZUr\nV5a/v7+mTp1qOgUAAABINTjZAqSQ3377TW+//bZy5sypb775RosWLdLQoUP17bffqkqVKqbzkEQj\nRozQ/v37tXHjRqd7HPS5c+fk7++v06dPK2fOnKZzAAAAAKfnXJ8IACeWKVMmbdiwQc+fP1fjxo31\n7rvvasGCBXr77be1evVq03lIouHDh+vevXtOeUKkWLFiCgoK0pgxY0ynAAAAAKkCJ1uAFPbixQv1\n6NFDR48e1YYNG3T58mU1adJEgwYNUu/evU3nIQnOnz+vihUrauvWrfL19TWd81J++eUXlShRQvv2\n7VOxYsVM5wAAAABOjZMtQApzc3PT3Llz9dZbbykgIEDZs2fX3r17NXv2bA0YMEDx8fGmE5FIhQoV\n0tSpU9W2bVs9fPjQdM5LyZkzp/r166chQ4aYTgEAAACcHidbAIOmTZumSZMmadOmTcqTJ4/efvtt\n5cmTRwsXLlS6dOlM5yGR2rVrp4wZM2r27NmmU17Kw4cP5ePjo5UrV6py5cqmcwAAAACnxckWwKA+\nffpo/Pjxql27ts6ePastW7ZIkurXr6/bt28brkNiffHFF9q8ebPWrFljOuWlpE+fXqNHj9aAAQPE\nDg8AAAAkHmMLYFhwcLC+/vprNWvWTFFRUVq6dKkqVqyoqlWr6sKFC6bzkAiZM2fW4sWL1b17d127\nds10zkvp0KGD7t69q7Vr15pOAQAAAJwWYwvgABo2bKi1a9eqU6dOWrp0qSZNmqQePXqoWrVqOnLk\niOk8JIK/v78+/PBDdejQwanuw+Pm5qYJEyZo0KBBevbsmekcAAAAwCm5jRw5cqTpCABSvnz51KhR\nI3Xq1Elubm7q06ePChYsqDZt2ujNN99U0aJFTSfiJVWtWlVhYWG6ffu2qlatajonwYoWLaq1a9fq\n8ePHqlChgukcAAAAwOlwg1zAwVy8eFH169dXq1atNGbMGO3bt0/vvPOOxo4dqy5dupjOw0u6cOGC\nKlasqE2bNqlcuXKmcxLsyJEjCgwMVExMjDJmzGg6BwAAAHAqfI0IcDAFChTQnj17tHnzZnXr1k2V\nKlXSzp079emnn2rkyJHcuNTJFCxYUNOnT1fbtm314MED0zkJVq5cOdWpU0eTJk0ynQIAAAA4HU62\nAA7q3r17at68+b9utnr37l01btxYb775pubOnSt3d3fTiXgJHTt2lKenp7788kvTKQl28eJFlStX\nTidOnFCePHlM5wAAAABOg5MtgIPKmDGjIiMjlSZNGjVq1EheXl76/vvv9csvv6hx48a6d++e6US8\nhJkzZ2r79u2KiIgwnZJgBQoUUOfOnTVixAjTKQAAAIBTYWwBHJinp6eWLVsmHx8f1axZU/fv39fq\n1atVqFAhVa9e3ekeK+zKMmbMqCVLlujDDz/UlStXTOck2JAhQ7RmzRqdOnXKdAoAAADgNBhbAAfn\n5uamWbNmqWnTpqpWrZouX76s2bNn691331WVKlX4EOxEKlWqpN69e6tDhw568eKF6ZwEyZo1q0JD\nQxUaGmo6BQAAAHAajC2AE7DZbBo5cqT69OmjgIAAnThxQoMHD9aYMWNUq1Yt7dy503QiEig0NFQv\nXrxwqhvP9uzZU8ePH+fnDAAAAEggbpALOJlly5apT58+ioiIULVq1bR9+3a1bdtW06dPV5s2bUzn\nIQEuXbokPz8/RUZGqkKFCqZzEmTJkiWaMmWKDhw4oDRp2OkBAACAv8L/MQNOpk2bNgoPD1fz5s31\n3XffqU6dOtq2bZtCQkI0adIkHg3tBPLnz6+ZM2cqODhY9+/fN52TIG3atJHdbtfKlStNpwAAAAAO\nj5MtgJM6cOCAmjVrpgkTJqhDhw66cuWKGjZsqJo1a2rq1Klyc3MznYi/0blzZ9lsNs2bN890SoJE\nRUWpS5cuOn36tDw9PU3nAAAAAA6Lky2Ak6pUqZKioqI0bNgwff7553r99de1Z88enTp1Si1bttTD\nhw9NJ+JvTJ8+Xbt27XKa0yK1atVSiRIlNHv2bNMpAAAAgEPjZAvg5C5fvqz69eurWbNmGjdunJ49\ne6bOnTsrNjZW69evV44cOUwn4i8cPHhQgYGBOnTokPLnz28652+dOHFCtWvXVkxMjLJkyWI6BwAA\nAHBIjC1AKvDrr7+qUaNGKlWqlObOnSs3NzcNHTpUq1at0saNG1WkSBHTifgL48eP18aNG7Vjxw6n\n+PrX+++/rxw5cmj8+PGmUwAgxcXHx+v48eM6f/68njx5IpvNpnTp0qlo0aIqUaKEbDab6UQAgANg\nbAFSifv376tFixby8vLS0qVL5eXlpblz52rkyJFau3atKlasaDoRf+LFixeqW7eu6tWrpyFDhpjO\n+VtXr15V6dKldfToUac4jQMAVnj27Jl27typn376STdu3PjD17z22mvy9vZWQEAAT24DABfH2AKk\nIk+fPlXHjh117do1rVu3TpkzZ9Z3332n9957T/PmzVPTpk1NJ+JPXLlyReXLl9e6detUqVIl0zl/\na9iwYbp8+bIWLlxoOgUAkt39+/e1cuVKXbp0KUGvL1asmFq0aMHNxAHAhTG5A6mIh4eHFi9erNKl\nS6tGjRq6fv26GjdurA0bNqh79+7c2NSBvf7665o1a5aCg4N179490zl/a+DAgdq8ebOio6NNpwBA\nsnr69KlWrFiR4KFFks6dO6dVq1bpxYsXyVgGAHBkjC1AKpMmTRpNnz5dLVq0UNWqVRUbG6sKFSpo\n9+7dmjJligYPHqz4+HjTmfgDLVq0UK1atfSPf/zDdMrfypQpk4YPH66BAweaTgGAZLVlyxZdvnz5\npa/76aeftGvXrmQoAgA4A8YWIBWy2WwaPny4QkJCVL16dUVHR6tIkSL64YcftHPnTrVv315Pnjwx\nnYk/MHXqVO3bt0/Lli0znfK3unbtqgsXLmjLli2mUwAgWTx9+lSxsbGJvj4mJoZfcACAi2JsAVKx\n7t27a+rUqapfv7527typHDlyaPv27Xr06JEaNmyoO3fumE7E/5EhQwYtWbJEvXv31sWLF03n/CV3\nd3eNHz9eAwcO5Kg8gFTp4MGDSfpv5fXr13XixAkLiwAAzoKxBUjlWrVqpSVLlqhVq1Zau3atvLy8\ntHLlSpUqVUoBAQGJOhqN5FW+fHmFhISoXbt2ev78uemcv9S8eXOlT59eixYtMp0CAJa7cOFCkt/j\np59+SnoIAMDpMLYALqBu3br/uknu/Pnz5ebmpmnTpqlTp06qUqWKjh07ZjoR/0f//v3l6empcePG\nmU75SzabTRMnTtTw4cP16NEj0zkAYKnHjx87xHsAAJwPYwvgIvz8/LRz506NHj1aEyZMkPT7B/pJ\nkyapbt262rZtm+FC/Ls0adJo4cKFmjlzpvbt22c65y9VrVpVfn5+mj59uukUALCU3W53iPcAADgf\nxhbAhXh7e2vv3r365ptvFBISovj4eLVu3VqrVq1ScHCwwsPDTSfi3+TNm1dz585VcHCwfvvtN9M5\nf2n8+PGaOHGibt26ZToFACzj4eHhEO8BAHA+jC2Ai8mbN6927dqlH374QZ07d9azZ89UvXp1RUVF\nafjw4Ro7diy/hXMgb7/9turXr6+ePXuaTvlL3t7eat26tT755BPTKQBgmTx58iT5PfLmzWtBCQDA\n2TC2AC4oW7Zs2rZtm27evKl33nlHDx8+VMmSJbVv3z5FRESoW7duDn9jVlcyefJkHTp0SIsXLzad\n8pc+/vhjhYeHJ+kxqQDgSCpVqqT06dMn+vps2bKpQoUKFhYBAJwFYwvgotKnT6+1a9cqS5Ysql+/\nvuLi4pQnTx7t3LlTly5dUrNmzXT//n3TmdDvf6+WLFmivn376vz586Zz/lSuXLn00UcfaejQoaZT\nAMASmTJlUqFChRJ9feHCheXu7m5hEQDAWTC2AC7M3d1dCxcuVIUKFVSjRg1du3ZNGTNm1Pr165U7\nd27VrFlTN27cMJ0JSWXLltXgwYMVHBzs0KeOPvroI+3evVs//vij6RQAsETt2rWVPXv2l77utdde\nU82aNa0PAgA4BcYWwMWlSZNGkydPVtu2bVWtWjWdO3dO7u7uCgsLU5MmTeTv76+zZ8+azoSkvn37\nKmPGjA59X5QMGTJo1KhRCgkJ4d4/AFKFbNmyqVmzZi81uOTOnVvNmzdXhgwZkrEMAODI3EaOHDnS\ndAQAs2w2mwICApQhQwZ17NhRtWrV+tdv5DJlyqR27drJ399f+fPnN53q0mw2m+rWrasPPvhAlStX\ndti/H6VLl9aUKVOUP39++fj4mM4BgCTLnDmzsmbNqtWrVytXrlyKj4//w9dlyJBBPj4+at68ubJk\nyZLClQAAR2Kz86tHAP/m22+/Vffu3bV8+XLVqlVLkrR582a1b99es2fPVosWLQwXYt26derdu7ei\no6Md9n/mIyMjNWDAAB0/flxp06Y1nQMASdatWzd5enpq1KhR+vHHH3Xt2jU9efJENptNnp6eypcv\nnypVqqRXXnnFdCoAwAEwtgD4L1FRUWrdurXmzJmjd955R5J09OhRNWnSRAMGDFDfvn0NF6Jnz56K\ni4vT4sWLZbPZTOf8F7vdrtq1a6tt27bq2rWr6RwASJLDhw8rMDBQp0+fVtasWU3nAACcAGMLgD90\n9OhRBQYGatSoUfrggw8kSRcvXlTDhg3VoEEDTZo0SWnScNsnUx49eiQ/Pz+Fhoaqffv2pnP+0KFD\nh9S0aVPFxMTwm14ATis+Pl7VqlVT586d9f7775vOAQA4CT4pAfhDZcuW1c6dOzVu3Dh9+umnstvt\nKlCggPbu3avDhw+rTZs2evz4selMl+Xl5aWlS5eqX79+io2NNZ3zh/z8/FSzZk19/vnnplMAINHC\nw8P1/Plzde7c2XQKAMCJcLIFwF+6du2aGjRooNq1a2vy5MlKkyaNnjx5oo4dO+rq1atau3atsmXL\nZjrTZU2dOlXLli3T7t275e7ubjrnv5w/f15+fn46efKkcufObToHAF7K3bt3VaJECa1evVqVKlUy\nnQMAcCKMLQD+1p07d9SkSRMVKFBACxYskLu7u+Lj4xUaGqr169dr48aNKliwoOlMlxQfH6/AwED5\n+flpzJgxpnP+UL9+/fTo0SPNnj3bdAoAvJT+/fsrLi5O8+fPN50CAHAyjC0AEuThw4dq3bq1Xrx4\noZUrVypDhgySpJkzZ2rcuHFat26dypcvb7jSNd24cUO+vr5avny5qlevbjrnv9y+fVs+Pj7avXu3\nihcvbjoHABLk1KlTqlGjhk6cOKFcuXKZzgEAOBnu2QIgQdKnT69vv/1Wr776qurVq6fbt29Lknr1\n6qWZM2eqQYMG2rhxo+FK15QrVy6FhYWpffv2iouLM53zX7Jly6aBAwcqNDTUdAoAJIjdblfv3r01\nbNgwhhYAQKIwtgBIMHd3dy1YsEBVq1ZVQECArly5Iklq3ry51q1bp/fee09hYWGGK11TYGCgmjVr\npu7du8sRDyz+4x//0NGjR7V7927TKQDwt7799ltdv35dH374oekU4P+xd59RUV3t38e/M0MVLNiN\niqjYjRoVRUGiWBBQrDGIWLCLXaMpahQ1sSe2aKwRUVFjV0qsEZCYBCP22IgNewPpAzPPC//OE268\n72gEDuX6rJW1wpw95/xmYhzmOnvvSwiRT8kyIiHEv7JgwQK+++47fvrpJ2rVqgXAlStXcHNzw8vL\nCz8/P1QqlcIpC5eUlBTs7OyYOHEiAwYMUDpOFps2bWL58uX88ssv8mdDCJFnJSUlUadOHTZs2ECb\nNm2UjiOEECKfkpktQoh/ZdKkSUyfPp3WrVsTFRUFQM2aNYmMjCQ0NBQfHx+0Wq3CKQsXMzMzAgMD\nmTRpElevXlU6ThZeXl6kpqayY8cOpaMIIcR/NXfuXOzt7aXQIoQQ4p3IzBYhxDvZt28fgwcPZsuW\nLbRr1w6AxMREevfuTUpKCjt27KBYsWIKpyxcli1bRkBAACdOnMhz7aAPHz7M8OHDuXjxIiYmJkrH\nEUKITGJiYrCzsyM6OprKlSsrHUcIIUQ+JjNbhBDvxMPDgx07duDl5cX27dsBsLCwYNeuXdja2uLk\n5ERsbKzCKQuXUaNGUaZMGaZPn650lCzatWtHjRo1WLVqldJRhBAiiwkTJjBhwgQptAghhHhnMrNF\nCJEtzpw5g5ubG1OnTmXEiBHAy24O8+bNY+XKlQQHB1OvXj2FUxYeDx8+pFGjRmzZsoXWrVsrHSeT\ns2fP0r59e65cuULx4sWVjiOEEACEhoYyatQozp8/j5mZmdJxhBBC5HOaGTNmzFA6hBAi/ytfvjzd\nunXD19eX58+f4+TkhEqlwtHRkbJly+Ll5YWdnR02NjZKRy0ULCwsqFevHgMHDqR///6Ym5srHcmg\nXLlyXLx4kTNnztC2bVul4wghBGlpaXh4ePDtt99St25dpeMIIYQoAGRmixAiW92/f5+OHTvi6OjI\n0qVLUatfrlY8evQonp6eLFmyhN69eyucsvAYN24cd+7c4ccff8xTHYDu3LlDw4YNOXPmDJUqVVI6\njhCikJs/fz7Hjx8nKChI6ShCCCEKCCm2CCGyXVxcHB4eHlSoUIGNGzcaNkI9d+4c7u7ujBo1ikmT\nJuWpL/8FVUpKCs2bN2fMmDEMGjRI6TiZfPHFF9y/f5/169crHUUIUYjFxsbSoEEDTp48SY0aNZSO\nI4QQooCQYosQIkekpKTg6elJcnIyO3fuxNLSEng5o8Hd3d0w80Wj0SictOC7ePEiH374IREREdSq\nVUvpOAZxcXHUrFmTQ4cO0aBBg9cPunkTli6FCxcgMRGMjKBUKejUCfr2BfnzI4R4R3369KFKlSp8\n/fXXSkcRQghRgEixRQiRY9LT0xk2bBjnz58nKCiI0qVLAy+/ZPfo0QNLS0u2bNlCkSJFFE5a8K1Y\nsYL169cTGRmZp1ouL126lJCQEEJCQjIf+OMPmDMHjh2DJ09e/+QGDaBLF5g+XYouQoh/JTw8HC8v\nLy5dumS4KSCEEEJkB2n9LITIMUZGRqxduxZnZ2datWrFrVu3AChevDjBwcEUK1YMZ2dnHj16pHDS\ngm/EiBG89957TJs2TekomQwfPpyrV69y+PDh//9gUBD06AE7dvz3QgvA2bMwaxZ89BGkpOR8WCFE\ngZKens6oUaNYsGCBFFqEEEJkOym2CCFylEqlYs6cOQwZMgRHR0cuXboEgImJCf7+/rRr146WLVty\n7do1hZMWbCqVinXr1rFp0yaOHDmidBwDExMT5syZw+TJk9HpdBAZCSNGwI0bb36S3buhf3/Q6XIs\npxCi4Fm1ahVWVlZ8/PHHSkcRQghRAEnrZyFErmjRogWlSpXC29ubVq1aUalSJVQqFc7OzpiZmdGv\nXz/D4yJnWFhY0KBBAwYMGED//v3zzPKtunXrsmHDBszNzWmwYMHLGStv68IFqFgRmjTJ/oBCiALn\n8ePH9OrVi61bt1KuXDml4wghhCiAZM8WIUSuCgoKYsCAAWzatAkXF5dMj/v4+LBmzRq6dOmiYMKC\nb+LEicTExLBr16480xOZc30AACAASURBVBEqPDyc73r0IDAuDlVa2r87iYsLhIZmbzAhRIE0bNgw\nTE1NWbp0qdJRhBBCFFBSbBFC5LoTJ07QvXt3Fi9eTO/evQ2PR0VF4eHhwZQpUxg5cqSCCQu21NRU\n7O3tGTFiBEOHDlU6jsGxypVpc+fOvz9BkSJw/Dg0bZp9oYQQBU5UVBSdOnXi0qVLWFlZKR1HCCFE\nASV7tgghcp2DgwOHDx9m0qRJLF++3PB406ZNiYiIYOnSpXz22Wcv9/AQ2c7U1JTAwECmTJnCn3/+\nqXQcg5ZGRu92gqQk2Lkze8IIIQoknU7H6NGj+eqrr6TQIoQQIkdJsUUIoYj333/fUFiZPn06rybZ\nVatWjcjISMLDw/H29iY1NVXhpAVT7dq1mT17Nr17984b73F6OqbZ0VEoPv7dzyGEKLACAgLIyMjA\nx8dH6ShCCCEKOCm2CCEUY2NjQ0REBAcOHMDX15eMjAwASpUqxeHDh0lLS6Njx448f/5c4aQF09Ch\nQ7GxsWHKlClKRwGV6uU/70otH2tCiNeLi4vjs88+Y9myZajl7wohhBA5TD5phBCKKlu2LMeOHePy\n5ct4enoaZlmYm5uzbds2GjZsiKOjI7du3VI4acGjUqlYu3Yt27Zt49ChQ8qG0WigWLF3P4+l5buf\nQwhRIPn5+eHm5kbz5s2VjiKEEKIQkGKLEEJxxYoVIzg4GL1ej7u7Oy9evABAo9GwePFiBg0ahIOD\nA9HR0QonLXhKlSrFhg0b8PHx4dGjR8qGsbd/t+dbWoKnZ/ZkEUIUKBcvXiQgIIA5c+YoHUUIIUQh\nIcUWIUSeYGZmxrZt26hevTpt2rTJ9MV//PjxfPPNN3To0EH5GRgFUNu2bfHy8mLQoEEo2qBu0CAw\nM/v3z2/VCho2zL48QogCQa/XM2bMGKZOnUrZsmWVjiOEEKKQkGKLECLP0Gg0fP/997i6uuLo6MjN\nmzcNxz766CN27tyJt7c3/v7+CqYsmGbPnk1sbCzff/+9ciFatYKWLf/VU/UqFXz0UTYHEkIUBDt3\n7uTBgweMHDlS6ShCCCEKEZVe0duYQgjxekuXLmXBggWEhoZSr149w+OXLl3Czc2NgQMHMnXqVFTZ\nsamqAODy5cs4Ojpy/Phx6tatq0yIP/6Anj3hr7/e6mmhZcpgd/EipUqXzqFgQoj8KCkpiTp16uDv\n70/r1q2VjiOEEKIQkZktQog8acyYMcybNw9nZ2ciIyMNj9epU4dffvmFPXv2MHToULRarYIpC5Za\ntWoxZ84cevfuTUp2tGH+Nxo3hjVrwNb2jYbrgEBga/v2tHRwICYmJkfjCSHyl7lz52Jvby+FFiGE\nELlOZrYIIfK00NBQ+vbti7+/P25ubobHExIS+Oijj1CpVGzfvh1L6UKTLfR6PT179sTa2ppvv/1W\nuSDnz8O8eaQFB2Py9GmWw+nAKWA3MB8wMjZm6tSpfP/99+zZs4dmzZrlcmAhRF4TExODnZ0d0dHR\nVK5cWek4QgghChkptggh8ryTJ0/StWtXFi5ciLe3t+FxrVbLiBEjOH36NEFBQZQvX17BlAXH06dP\nadSoEatXr6Zjx47Khnn0iF1OTtQDalWsCMbGULIkO/V6egYGZhpaunRpFi9ezPjx41m7di0eHh7K\nZBZC5AldunShefPmfPHFF0pHEUIIUQhJsUUIkS9cvHiRjh07MmHCBMaNG2d4XK/XM3v2bNavX09I\nSAi1a9dWMGXB8fPPP+Pl5UV0dLTi3TuuX79O8+bNuXjxoiHL06dPKVWqVJax77//PitWrKBXr15M\nmTIlX2yIGRMTw5kzZ3jw4AFpaWmo1WrMzc2pUqUK9vb2MmtLiH8hNDSUUaNGceHCBUxNTZWOI4QQ\nohDSzJgxY4bSIYQQ4p+UKVOGHj16MHbsWO7evYuzszMqlQqVSsWHH35IiRIl8Pb2xt7eHmtra6Xj\n5ns2NjY8fvyY1atX4+XlpehGxCVLluTu3bscPXoUd3d3AMzNzfntt9+4du1aprGPHj3i8ePHbNu2\njbFjx3Lr1i3atm2bJzdSvnbtGvv27ePEiRPcv3+fxMREUlJSSE5OJj4+ntu3b3P27Fnu379P1apV\nMTIyUjqyEPlCamoqHh4efPvtt8pt9i2EEKLQk5ktQoh85fHjx7i5udGwYUNWrlyZ6QvowYMH8fb2\n5rvvvuMjaQP8zrRaLQ4ODvTr149Ro0YpmuXx48fUrl2bEydOUKtWLeDl7BtnZ2f+82PMxMSEkSNH\nMmXKFLp27cp7772Hv78/ZmZmSkR/rTNnznDo0CESExPfaHzFihX5+OOPKVq0aA4nEyL/mzdvHuHh\n4Rw4cEDpKEIIIQoxKbYIIfKdFy9e0L17d4oWLcqWLVsyfYmOjo6mU6dOTJw4kfHjxyuYsmC4du0a\nLVq04NixY9SvX1/RLHPnzuX3339n586dwMslZBUqVODBgweGMSqVCr1ej6WlJV999RVDhw6lf//+\n3L17l71791KyZEml4htcvXqVvXv3vnGh5RVra2u8vb0xNjbOoWRC5H+xsbE0bNiQkydPYvuGXc2E\nEEKInCCtn4UQ+U7RokU5cOAAxsbGuLq6Eh8fbzjWqFEjIiMjWbt2LePHj0en0ymYNP+ztbVl/vz5\n9O7dm+TkZEWzjB07lt9//93QClylUjFu3LhMS4T0ej0qlYqEhASmTZtGcHAwgYGBtGjRgpYtW/LX\nX38pFd+QLyIi4q0LLQC3bt0iIiIiB1IJUXBMnjyZoUOHSqFFCCGE4qTYIoTIl0xNTdmyZQt16tSh\ndevWmWY3WFtbExERwenTp+nVq5fiRYL8bsCAAdSpU4dPP/1U0Rzm5ubMmjWLTz75xLB0aMCAAZiY\nmGQap9fr0Wg0xMfH4+Pjw6+//sr8+fMZPXo0Dg4O/P7770rEB15u9nvnzp1//fxr165lWTYlhHgp\nLCyMsLAw6T4khBAiT5BiixAi39JoNHz33Xd4eHjg6OhITEyM4ZiVlRU//fQTxsbGtG/fnidPniiY\nNH9TqVSsWrWKvXv3EhwcrGgWb29vEhMT2b17NwDly5d/bXtqlUqFWq0mOTmZLl26cPXqVUaOHMn3\n33+Pu7s7+/fvz+3owMu9Wt5lttXdu3e5dOlSNiYSomBIT09n9OjRLFy4UDp4CSGEyBOk2CKEyNdU\nKhUzZsxg3LhxODk5cfbsWcMxU1NTNm/ejIODAw4ODoovIcnPrKysCAgIYPDgwZlmEeU2jUbDggUL\n+Oyzz9BqtQAMHTqU0qVLZxqXnp6ORqNBr9ej1+vp2LEjDx8+xMPDgwMHDjBs2DBWrFiR6/mz4727\ncePGuwcRooBZtWoVJUuWpFevXkpHEUIIIQAptgghCoiRI0eyaNEi2rdvT3h4uOFxtVrNvHnzDEtI\noqKiFEyZvzk5OTFw4EAGDBig6F44HTp0wMbGhtWrVwPg4uLy2k1jtVotRkZGPH36FGNjYzp37kxS\nUhLNmjUjIiKCJUuW8Omnn+bqa0lLS8sT5xCiIHn06BF+fn4sXbo0T7Z5F0IIUThJsUUIUWB8/PHH\nBAQE0L179yzLREaOHMnKlStxdXVVfClMfjZ9+nSePXvGsmXLFM0xf/58Zs2aRXx8PBqNhiFDhlCp\nUqXXjjU1NeXKlStkZGTg5eVFRkYG1apVIzIykhMnTuDl5UVKSkqu5M6OL4IajSYbkghRcEyZMoXe\nvXvz/vvvKx1FCCGEMJBiixCiQOnQoQNBQUEMGTKEDRs2ZDrWpUsX9u/fz6BBg1izZo0yAfM5Y2Nj\nNm/ezOzZszMt2cptjRo1wsXFhQULFgAwcOBAEhISsoxLSUlBo9FgbGzM6dOnuX37NmPHjkWv11Oq\nVCkOHz6MTqejQ4cOPH36NMdzm5ubv/M5/t7qXIjCLioqin379uHn56d0FCGEECITKbYIIQqcZs2a\n8fPPPzN9+nQWLlyY6Zi9vT3h4eHMnz+fqVOnSmeXf6F69eosWrRI8XbQs2bNYsWKFcTGxlKlShWa\nN29OuXLlsoxLTEzEyMgIMzMzLl68yJEjR1i0aBHwsnCxdetWmjdvniutoa2trd/p+aampjRs2DCb\n0giRv+l0OkaPHs1XX31FiRIllI4jhBBCZCLFFiFEgVS7dm0iIiJYv349kydPzlRUsbW1JTIykkOH\nDjFgwADZA+Nf6Nu3Lw0aNOCTTz5RLIO1tTWDBw9m+vTpAAwePJiyZctmGfeqFbS5uTkqlYqkpCSW\nLFnCtm3bgJf7+ixYsIBRo0bl+L4+zZs3f6fZLdbW1q99jUIURgEBAWRkZODj46N0FCGEECILlV5u\n6wohCrAnT57g7u5O3bp1Wb16NUZGRoZjSUlJ9O7dm6SkJHbs2EHx4sUVTJr/PH/+nA8++IClS5fS\nuXNnxTLUrFmTo0ePUrNmTSpVqkRqairx8fGZxqlUKt577z2SkpJ48eIFrVq14vz58+zYsQMnJyfD\nuL179zJ48GB++OEHOnXqlCOZf/zxRy5evPjWz1Or1XTp0oUGDRrkQCoh8pe4uDhq167N3r17adas\nmdJxhBBCiCxkZosQokArVaoUR44c4e7du/To0SPTspciRYqwa9cuatSogZOTE7GxsQomzX9KlCjB\npk2bGDp0KPfu3VMswxdffMGnn36KiYkJ/fv3p0WLFlnG6fV67t27h06nw9TUlOPHj9OlSxc++ugj\nLl26ZBjXpUsXDhw4wJAhQ1i5cmWOZHZ1daVChQpv/bwmTZpIoUWI/+Pn54ebm5sUWoQQQuRZMrNF\nCFEopKWlMWDAAO7cucO+ffsyre/X6/XMnz+fFStWEBQURP369RVMmv9Mnz6dkydPEhISglqd+zX8\n1NRU6tSpw9q1a6lYsSJOTk7ExcWRmpqaaZyJiQlFixZFpVKh1+t5/vw5kyZNYuvWrfzyyy+UL1/e\nMPb69eu4ubnRtWtX5syZk+2v6+nTp+zateuNC3x2dna4urpKW1shgAsXLtC6dWsuXLggy+qEyEV6\nvZ7r169z48YNtFotGo2GIkWK0LhxY4oUKaJ0PCHyHCm2CCEKDZ1Ox7hx4zh+/DihoaFZZhds2bKF\ncePGsW3bNtq0aaNQyvwnPT0dJycnevbsyYQJExTJsHXrVhYuXMhvv/1G69atKV26NLt3784yTqPR\nULt2bZKSkgxLinx9fTl69CjHjx/H0tLSMPbJkyd4eHhgbW3Nhg0bMDU1zdbMKSkphIeHc/36dR48\neJDleGpqKjdv3uTixYucOnVKkUKWEHmNXq+nXbt2dOnShTFjxigdR4hCQavV8uuvv3LlyhViY2PR\n6XSZjhctWpRq1arRuHHjd94IXoiCRIotQohCRa/X89VXX/HDDz9w8OBBqlevnun4sWPH+Pjjj1m8\neDFeXl4Kpcx//vrrL5o1a8ahQ4do1KhRrl9fp9Nhb2/P+PHj0Wq1+Pv7c/z4cTIyMjKN02g0AFSs\nWBGtVktiYiKlSpXiww8/5OHDh+zduzfTvj7Jycn069ePhw8fsnv3bkqWLJkj2c+dO8ft27dJS0tD\no9Gwfft2jhw5YijC+Pr68t1332X7tYXIb3bs2IGfnx+nT5/O9P+qECJnxMfHs3PnTm7duvWPY01N\nTXFycqJly5a5kEyIvE+KLUKIQmnVqlX4+fkRHBycpThw/vx53N3dGTFiBJ9++qks3XhDmzdvZvbs\n2Zw6dUqR6cQ///wzPj4+/PHHH9ja2tKiRQuCgoKyjCtSpAiWlpZotVp0Oh2JiYm4uLiQnp6OtbU1\nq1atyvTfXKfTMXnyZIKCgggJCcHGxibHX8v+/fvx8PAAXhaIMjIyiI+Pp2jRojl+bSHyqqSkJOrU\nqYO/vz+tW7dWOo4QBV5ycjKbNm3i7t27b/wcjUZDmzZtcHBwyMFkQuQPMidZCFEoDRs2jKVLl9Kh\nQweOHz+e6Vj9+vWJjIwkMDCQkSNHkp6erlDK/KVPnz40adJEsaVErVu3pn79+vzwww94enpibW39\n2jvfycnJPH36lPr161OmTBnKlStHaGgoLVq0ICoqijlz5mQar1arWbhwIb6+vjneGvoVFxcXw7+/\nmp2jVMcnIfKKOXPm0KJFCym0CJFLDhw48FaFFnj5mRUeHs7NmzdzKJUQ+YfMbBFCFGpHjx7F09OT\n1atX07Vr10zH4uPj6dGjB+bm5gQGBmJhYaFQyvwjLi6ODz74gG+++SbL+5kbLl68SOvWrdmxYwd9\n+/alfPny/Pbbb1nGFSlShLS0NJo0aUJKSgparZbLly8TGBjIpEmTmD17Nt7e3lmet2fPHoYMGZKj\nraFfsbCwICkpCYBixYoRHx/PxYsXqVOnTo5eV4i8KCYmBjs7O86cOUOlSpWUjiNEgff06VNWr16d\nZbP5N9WgQQO6deuWzamEyF9kZosQolBzdnYmJCSEESNGsG7dukzHihUrRlBQEFZWVjg7O/Pw4UOF\nUuYfxYsXZ/PmzQwfPvyt74Zlh7p169K1a1f2799PmTJlcHd3f+3slqSkJIoUKcKtW7e4d+8eSUlJ\n2NjYMGDAANavX8/EiRM5evRolud17drV0Bp61apVOfpaunTpYvj3V7/surm55eg1hcirxo8fz8SJ\nE6XQIkQu+fXXX/91oQVeFkhf3TAQorCSYosQotBr0qQJx48fZ/bs2cybN4+/T/gzMTFhw4YNuLi4\n0LJlS65evapg0vyhRYsW+Pr60q9fvywdC3KDn58f69evp1u3bpw5cyZTS2fAsB9LcnIyjx8/xt7e\nHq1Wy7NnzzAzM2PgwIEEBATg6enJuXPnspy/efPmREREsGjRIj7//PMce41/77SSmpqKjY0NN27c\neG2XJSEKspCQEC5cuMDEiROVjiJEofGuy4ASEhJyZdmtEHmZFFuEEAKoWbMmERERBAQE8Mknn2T6\nAq1SqZg5cyafffYZTk5OnDx5UsGk+cMXX3xBSkoKixYtyvVrV6hQgVGjRnHu3DmOHDnCmDFjMDY2\nNhx/VUzTarWUKFGC0NBQmjdvTq1atShWrBj37t1jyZIlLF68GHd3d+7cuZPlGtWrVycyMpKwsDC8\nvb3f6e7ff9O0adNMPxcvXhyVSkXfvn2RFcCisEhNTWXs2LEsWbIk29uvCyFe79Xm8e8qOTk5G9II\nkX9JsUUIIf5PxYoVCQsL4+TJkwwYMACtVpvp+ODBg1m3bh2dO3dmz549CqXMH4yMjNi8eTMLFizg\njz/+yPXrf/LJJxw/fhwnJyfS0tIwMTHJdPzV7Jb4+HiKFCnCr7/+ytOnT6lSpQp2dnYcPHiQmJgY\nRo4cibu7O/Hx8VmuUbp0aQ4fPkxaWhouLi48e/YsW1+DkZER1tbWhp/PnDlDo0aNSExMZMaMGdl6\nLSHyqsWLF1OzZk3c3d2VjiJEoZGRkZEtszZfbfAuRGElxRYhhPibkiVLcujQIZ48eUK3bt2yrDd2\nc3MjNDSUkSNHsnz5coVS5g9VqlRh6dKl9O7dO1vukL2NokWL8uWXX3L37l38/f0ZM2YMGo3GcPzV\nzJDU1FRSU1N59OgRTZs25fz58zx+/JhWrVoxc+ZMGjVqhIODAz179sxSfAMwNzdn+/btNGnSBAcH\nB27cuJGtr6N3796Zfm7bti0ajYbZs2fLWnhR4MXGxjJ//nwWL16sdBQhChUjI6NMM0L/rew4hxD5\nmRRbhBDiPxQpUoQ9e/ZgZWVF+/bts8xYaNKkCRERESxfvpzJkycrsi9JfuHp6Ym9vT3jxo3L9WsP\nHjyYFy9ekJKSQtOmTQ0b5arVmT/6UlNTee+999ixYwfe3t6Ym5tz7tw5atWqRffu3Rk/fjxmZmYM\nGTLktct31Go1ixYtYvjw4Tg4OHDq1Klsew3/WWxZuXIlDg4O6HQ6vLy8su06QuRFkyZNYvjw4dja\n2iodRYhCRaVSUbp06Xc+x99nZwpRGEmxRQghXsPY2Bh/f3+aN2+Ok5MTsbGxmY5XrVqVEydOEBkZ\nSZ8+fXJkz46CYvny5Rw9epSdO3fm6nWNjY2ZN28e6enp7Nq1i549e6JSqTIVx4yMjNDpdDx+/Jgi\nRYqwc+dOSpUqhZOTE0lJSZiamtKuXTvWrl3LxYsX/+fynTFjxrB8+XI6duxIcHBwtryG999/P9OM\nnMTERCZOnIixsTF79+7l2rVr2XIdIfKasLAwIiIi+OKLL5SOIkShVLdu3Xd6fsWKFalZs2Y2pREi\nf5JiixBC/BevZiz06dMHR0dHrly5kul4qVKlOHz4MFqtNkf27CgoihYtypYtW/D19X3tZrM5qUuX\nLlSuXJldu3YxePBgzM3NAQyzXNLT04GXXRPUajWPHj2iWrVqnDhxgvr162NnZ8f9+/fp27cv+/bt\nY9OmTVlahP9dt27d2L9/P4MGDWL16tXvnF+tVvP+++9neuzrr7+mXbt2qFQqunXr9s7XECKvSU9P\nZ/To0SxYsAALCwul4whRKDVs2PCdlgDb2toa9kcTorCSYosQQvwPKpWKzz77jClTpvDhhx9mWSJi\nZmbG9u3bady4MY6Oju/cKrGgat68OWPGjKFfv365umGeSqUy7Pdw+vRpGjduDPz/IgtgKMBotVps\nbW0NHamio6O5e/cunp6e/Pzzz6xcuZLg4GCmTJnCTz/99F+vaW9vT3h4OAsWLOCLL75452Vm/fr1\ny/Tzr7/+yldffYWpqSnnz5/Ptlk0QuQVq1atomTJkvTq1UvpKEIUStevX8fCwoIjR468dr+yf1Kp\nUiVatGiRA8mEyF9UeukfKYQQb2TPnj0MHTqUrVu34uzsnOX44sWLWbhwIQcOHKBRo0YKJMzbMjIy\ncHZ2xtXVlc8++yxXr+3k5MTVq1dZuXIlPj4+PH/+HFNT0yzLv4oWLYqRkRGWlpa4urpy9+5dTp48\niZOTE0FBQezcuZMSJUrQrVs3fvrpJz744IP/es3Hjx/j4eFB1apVWb9+/b9uW3vp0iXq16+fqWjj\n5+fHuXPn2LVrF1ZWVjx69EjuIIoC4dGjR9StW5ejR49mmdUlhMhZer2esWPHsmzZMgBmzpyJk5MT\nJ06ceOOiS4UKFejVqxclSpTIyahC5AtSbBFCiLfw888/06tXL1auXEmPHj2yHN+xYwe+vr5s2rSJ\nDh06KJAwb7t16xZNmzYlKCgIOzu7XLvu5cuXqVu3LkFBQYwYMSJL1yALCwsSExMxNjamcuXK3Lt3\nDx8fH44dO4azszOhoaFYWlpy9epVoqOjOXv2LGPHjiUyMvJ/bgCYnJyMt7c3T548Yffu3VhZWb11\ndr1ej7m5eabCkJmZGWfPnqVhw4YkJyczZ86cXC9gCZEThg4dirm5OUuWLFE6ihCFysOHD6lXrx6P\nHz/G3NycK1euUKlSJQCioqL4/fffefjw4X99vqmpKTY2NnTq1AlLS8vcii1EnibFFiGEeEunT5/G\n3d2d6dOnM2zYsCzHIyIi6NmzJ3PnzmXAgAG5HzCP2759O1OnTuWPP/7I1V/IWrRoQUJCAkOHDsXP\nz48nT55gaWlJQkIC8HIfl/T0dCpUqEDFihW5cOECq1atYsKECbi4uBAXF8eJEyewsLDg4sWLrFu3\njjVr1hAREfE/iygZGRl88skn/PTTT4SEhFClSpW3zt6+fXsOHz6c6bGIiAhWr17Nli1bUKlUxMXF\nGZZECZEfRUVF0alTJ/7880+5Ky5ELvruu+8YNWoU8PLz5uDBg1nGZGRkEB0dzaVLl3j48CFarRaN\nRkORIkWoUqUKdnZ2lC1bNrejC5GnSbFFCCH+hWvXruHi4oKPjw9TpkzJsoTj8uXLuLq60r9/f778\n8ktZ4vEfBg4ciEql+p+bzWa3s2fP0qhRI06cOIGbmxvPnz/PdPzvy4qKFy+OWq3GwsKC0aNHs2vX\nLlJTU2nXrh3Lly/HwcGBgwcPMmHCBKKjo/npp5/+cZnQkiVLmD9/Pvv37zfsHfOmVq9enaWw16BB\nA/bt20e9evVITk7m448/ZsuWLW91XiHyCp1OR8uWLRk6dCgDBw5UOo4QhcLz589xdHTkwoULqFQq\n9u7dS+fOnf/xeXq9nrS0NIyMjDJ1zBNCZCYb5AohxL9ga2tLREQEP/74I+PGjcuyCWqtWrWIjIxk\n//79DB48+F9tMFeQLV26lLCwMH788cdcu2aDBg2oVasWY8aMYfDgwZQqVQrAcAc9NTWV4sWLAy/b\nRtva2vLs2TPu3LlD8eLFcXR0xN/fnwkTJnDixAmmTJnCokWLKFWqFD4+Pv+4Ee6rdfAuLi5vvalt\n27ZtDR2UXjl79iwWFhZ4e3tjbm7O1q1bZYNmkW9t3LgRvV4vswGFyCV79uyhVKlSXLhwgffee4+E\nhIQ3KrTAy83nTU1NpdAixD+QYosQQvxLFSpU4Pjx45w+fZq+ffuSlpaW6Xj58uX5+eefuX//Ph4e\nHrx48UKhpHmPpaUlW7ZsYeTIkdy6dSvXrjtz5kzOnj2Lg4ODoQD29xkur9pcPn78mDt37tCgQQPW\nrVuHr68vO3bsYPTo0QQGBtKtWzcWL15saAd98+ZNpkyZ8o/X7969O/v27Xvr1tDVqlWjSJEiWR6f\nNWsW06ZNQ6VSYWRkRM+ePd/4nELkFXFxcXz++ecsW7YMtVp+NRUiJyUkJODi4kK3bt3Q6XSMHz+e\n2NjY137GCCHejSwjEkKId/RqCYdWq2XHjh1YWFhkOp6eno6vry9RUVEEBQVRoUIFhZLmPXPnziUk\nJISjR4/myh2y9PR0SpcuTfXq1bG1teXgwYM8f/6cMmXK8OjRIwAqV67M7du3MTExwcrKitTUVCwt\nLVm8eDETJ07ExcWFBw8eEBMTQ0xMDL/99htly5bFwcGB8ePHM3z48H/Mce3aNVxdXenVqxezZ89+\no2Vmnp6ebNu2LdNjxsbGJCcnM3HiRAICAnj27BmHDh2ibdu2/+4NEkIBEyZMIC4uLleXFQpRGIWF\nhdGhQwdSU1MxU6dzgwAAIABJREFUMTEhKipKun4JkYPk9oEQQrwjc3Nzdu3aRfny5Wnbti1PnjzJ\ndNzIyIhVq1bRo0cPWrRowaVLlxRKmvdMmjQJtVrN3Llzc+V6RkZGjBgxgps3b9K0aVNMTEwADIUW\ngDt37qBWq0lLS6NEiRLY29sTFxfHzz//jLu7O8+ePSM2NpaePXtibGxM+/btMTIyIjg4GD8/P/bv\n3/+POWxtbYmMjOTo0aP07ds3Swvq13F1dc2ylEir1bJnzx4+//xz0tPTMTExwdvbG7mPIvKLCxcu\nEBAQwJw5c5SOIkSBlZKSgo+PDx9++CGpqak0b96cpKQkKbQIkcOk2CKEENnAyMiI9evX4+TkRKtW\nrbhz506m4yqViilTpjBz5kxat25NeHi4QknzFo1GQ0BAAEuXLuXXX3/NlWsOGTIErVbL2rVrqVq1\nqqEjUsWKFYGXG//Z2NgAcOXKFaKiomjUqBE//PAD3bp148KFC3h6erJ8+XLmzZvHs2fP6Nq1KzY2\nNuzdu5eBAwfy+++//2OOMmXKcPToUZKSkujYsWOWDXv/U5s2bQxLLP6+1OLTTz+lXLlyjBgxgjJl\nyvDw4UNpmyvyBb1ez5gxY5g2bZp0MREih5w6dYqKFSuyYcMG4OWG6ydPnpT9VoTIBVJsEUKIbKJS\nqZg/fz4+Pj44Ojry559/ZhnTr18/Nm3aRI8ePdi+fbsCKfOeSpUqsWLFCvr06ZMr+9pUq1aNpk2b\nYmxszPvvv0+ZMmUAiI2NNRQx/vrrLywsLNDr9RgbG/Po0SOMjIwYMGAA69evZ+7cufj5+fH111/z\n5ZdfEhUVxeTJk2nWrBnr1q2jS5cuxMTE/GMWc3NzfvzxRxo2bIiDg8P/3ODW2tqakiVLAmTajPf6\n9etcv36dSZMmkZCQgJGREZ9//vkbzZYRQkk7d+7k4cOH+Pr6Kh1FiAJHq9UyefJk7OzsePr0KSVL\nluTu3bsMGTJE6WhCFBqaGTNmzFA6hBBCFCQODg6UKFECb29vnJycDDMmXqlevTodOnQwdN2wt7cv\n9K2h69aty7lz5zhw4ABdu3bN8euZmZkRFRVFVFQUKpWK1NRUMjIysLGxMcwwKVeuHPHx8SQkJFC2\nbFkcHR25dOkSOp2Orl27snnzZtq2bcu5c+eoV68egYGBVK9enR49emBubs7o0aPx8vL6x00H1Wo1\nHTt2RKvVMnjwYFq3bv1f9/X5888/iY6OBsj0Z+bp06f06dOHpKQkbt++zfPnz7l582auvJdC/BuJ\niYl4eHiwZs0aqlWrpnQcIQqUCxcuYG9vb+h85+XlxcmTJylatKjCyYQoXGSDXCGEyCGvus5s2bKF\n9u3bZzl++/ZtXF1dadu2Ld98802hn9KbmJhI48aN8fPzw9PTM0evlZqaSqVKlXBwcCApKYnY2FjD\nXjoajYb09HRUKhVVqlThxo0bmJqaYm5uToMGDTh9+jR79+5l3rx5NG7cmCNHjtCtWzc2btzIrVu3\nOHHiBA0bNuTTTz/lxIkTHDp0CHNz8zfKtXPnTkaMGIG/vz+urq5ZjgcGBjJkyBASExMxMTExdMBS\nq9UkJCSQmppK9erVSUpKIi0tjVu3bmUp9gmRF0ybNo2rV6+ydetWpaMIUWBkZGSwcOFCpk6dSnp6\nOkZGRoSEhNCuXTulowlRKEmxRQghclB4eDg9e/Zk2bJl9OrVK8vx58+f061bN0qWLMmmTZve+Et5\nQXXq1ClcXV35/fffqVKlSo5ea/z48aSlpREYGIhOp+PFixfo9Xpq1qzJ5cuXAbCysuL58+fo9Xqa\nNGmCpaUl0dHRWFpacvz4cRwdHVm6dCmjRo3i+++/x8fHB3Nzc86dO0fJkiXp06cP6enpbNu27Y1b\n2kZGRtK9e3dmzZqVZbr3/fv3sbGxQavVotPpUKlUhs1wV65cyfDhw/nqq6/YvHkzt2/fpkGDBpw4\ncSJ73zgh3lFMTAx2dnacOXOGSpUqKR1HiALh+vXr9OzZk7Nnz6LT6ahVqxZRUVGGfcmEELlP9mwR\nQogc1KpVKw4dOsT48eNZsWJFluMlSpQgNDQUMzMz2rVrx+PHjxVImXc0adKESZMm4e3tTXp6eo5e\na/DgwezevRsfHx8qV65M3bp1UalUXLlyBTMzM+BlMczOzg6A6OhoYmNj6dWrFykpKSxatIi1a9cy\nadIkvv32W8aNG8cPP/xAfHw8Xbp0QafTsWHDBh4+fMikSZPeOFfLli0JDw9n3rx5TJ06NVNnofLl\ny1O5cmXDni1/nw01c+ZMAMaMGWP4c/TLL78QFhb2bm+UENls/PjxfPLJJ1JoESIb6PV6VqxYQb16\n9YiOjkan0zFjxgz+/PNPKbQIoTAptgghRA5r0KAB4eHhfPPNN8yYMSNLW15TU1MCAgJwcnLCwcHh\njTZWLcgmTpyIqalpjreCrVevHjY2NjRp0oS7d+9y+/ZtQxGjRo0awMtfYs+ePYuJiQkZGRmkpaWx\nd+9eateuzebNmzE3N6dz587s27ePnj17snbtWr788kvOnz/P+PHjMTU1Zc+ePYSEhLB06dI3zlaj\nRg1++eUXDh8+TL9+/QzLhQBcXFwMLastLCwMj9+7d4/ffvuNokWLMnnyZKpWrWqYXSOTWEVeERIS\nwsWLF5kwYYLSUYTI927fvk3r1q0ZN24cqampWFhYcO7cOaZPn650NCEEUmwRQohcUa1aNU6cOMHe\nvXsZNWoUGRkZmY6r1WrmzJnD2LFjcXR0fKPWwQWVWq3G39+f5cuX88svv+TotYYMGcLWrVv54osv\nMDc3p169eqjVas6fP28oZKSkpGBvbw/ArVu3qF27tuGOfN++fZk2bRoXLlygbt26PHv2DL1eT7t2\n7fD398ff3x8rKytCQkKYP38+u3bteuNsr1pDJyYmZmoN7ezsjKWlJRqNhri4uEzLk179gu3r68uj\nR49Qq9XcvXuXlStXZsv7JcS7SE1NZezYsSxevBhTU1Ol4wiRb+n1ejZu3EjdunUJCwtDq9Xi7OzM\n8+fPqV+/vtLxhBD/R/ZsEUKIXBQXF0eXLl0oV64cGzdufO0Xjn379jF48GDWr19Pp06dFEiZN+zZ\ns4cJEyYQHR1NsWLFcuQaiYmJVK5cmaioKBwcHICX+6KoVCqaNm1qKHoZGxtTsmRJHjx4gJmZGSVK\nlMDT05MtW7bQtWtXRo0ahbOzM7t27eKjjz4iICCAkSNHcu/ePY4dO0bTpk35448/cHFxYe/evbRs\n2fKNM2ZkZDBhwgSOHDlCcHAwFhYWVKxY0bBvi7m5OcnJycDLQtWDBw8oXbo0y5YtY/369dy7d48X\nL17w7Nkzw4wYIZQwb948wsPDOXDggNJRhMi3Hj58yKBBgzh69ChJSUmo1WrWr19P//79lY4mhPgP\nMrNFCCFyUfHixQkNDSUtLY1OnTqRkJCQZYyHhwf79+9nyJAhrFq1SoGUeUPXrl3p0KEDI0eOzLFr\nWFhY0KtXLwIDA1m4cCHx8fHUqFEDjUbDqVOnDEUerVZLtWrVUKvVpKSkYGtrS2hoKNWqVWP79u3c\nv3+fadOm8cknn7B69WoGDhzIpk2bUKvVuLu78+DBAxo3bszGjRvp3r07V65ceeOMGo2GJUuWMGjQ\nIFq2bMmtW7eoVauWYclT8eLFDWN1Op1hb6AhQ4bw5MkTihQpQkZGBmPGjMnGd06ItxMbG8v8+fNZ\nvHix0lGEyLd27dpFnTp1CAkJISkpiQoVKnDr1i0ptAiRR8nMFiGEUEB6ejrDhw/n7NmzBAcHU7p0\n6Sxjrl27hqurK7169WL27NmoVCoFkiorKSmJJk2aMHXqVPr06ZMj14iKiqJXr15cuXKFGjVqoFar\niYmJQaVS4eDgQEREBAAmJiY0bNiQ33//HWNjY5o1a0bTpk354YcfsLCw4NKlS/Tu3ZvGjRuTnp5O\ndHQ0o0aNonfv3tStW5fw8HBMTExYu3Ytc+fOJTIykrJly75V1h07duDr64ujoyNHjhzJVKzT6/Xo\n9XqKFy/OkydP0Gg0rFq1itWrVxMbG8vjx4+5c+cO5cuXz9b3T4g34eXlRdWqVfnqq6+UjiIUotfr\nuX//Pk+ePCE9PZ1ixYphbW2NkZGR0tHyvGfPnjFq1Cj279/PixcvABg0aBBr1qwplL8bCJFfSLFF\nCCEUotfrmTJlCrt27eLgwYNYW1tnGfPo0SM8PDywtbVl3bp1hXIZyOnTp+nQoQO//fYbVatWzfbz\n6/V6PvjgAxYuXIhOp8PNzY2KFSty79499Ho9JUqUMHT3qVu3LpcvXyYjIwMbGxvi4+MZMmQIGzdu\nxN3dndmzZ/PBBx+wadMmvvzyS9zc3NBqtSxZsoSePXuyevVqAKZNm8bBgwc5duwYRYoUeau8kZGR\nuLu7o9PpSElJIS0tjRIlShj2dFGpVOzbt49OnTqRlpZG7dq1KV68ODdu3KBhw4b8/PPP2fr+CfFP\nwsLC8Pb25tKlS5k2dRaFQ1paGr/99htXrlwhNjbWMCsPoGTJklSvXp1mzZq99qaDgNDQUHx8fIiP\njycpKQkTExMOHDhA+/btlY4mhPgHUmwRQgiFffvtt3z77beEhoZSt27dLMeTkpLw8vIiISGBnTt3\nZlo2Ulh888037Nixg7CwsBy5C/rdd98RHh7O1q1bqV27Nnq9nitXrqBSqXB2dubYsWPodDpMTExw\nd3dn9+7dqNVqOnfuTFxcHC9evCAmJobAwEAyMjIYMWIEQUFBtGvXjsDAQBYsWMDJkyeZO3cuQ4cO\nRa/XM2DAAOLi4ti5c2emFs5v4tSpUzRt2tTws62tLdeuXTP83Lx5c06ePAnAhg0bWLZsGTdu3ODZ\ns2dERkYaNvwVIqelp6fTpEkTpkyZQq9evZSOI3LZn3/+ycGDB3n27Nn/HGdqakrjxo1p3769zNT4\nPy9evGDSpEls376d58+fo9frDd0Nc2ofMyFE9pJiixBC5AEBAQFMmjSJPXv2vPaLcEZGBmPHjiUs\nLIzg4GBDN5zCQqfT4erqSosWLZgxY0a2n//Zs2dUrVqVa9eucfnyZVq1akXp0qUNs0XKlCnD3bt3\nAShfvjwJCQkkJCRgbm6Ora0tnp6ezJ8/nyJFinDp0iWmTZvG/fv38fHxYciQIRw7dowOHTrw5MkT\nQkNDadmyJWlpabi5uVG7dm2WLVv21l8wPvjgA6KjowEMBaiMjAz0ej1qtZorV65QvXp10tPTqVev\nHhUqVCAmJgaNRsNff/2Vje+eEP/d8uXL2bVrF0eOHJEv0YXM+fPnCQ4ONmzg/SYaN25Mp06dCv2f\nlbCwMPr160diYiKPHz9GpVIxffp0aeksRD4jG+QKIUQe0LdvX9atW0fnzp0JDQ3Nclyj0bBs2TL6\n9u1Ly5YtOXfunAIplaNWq9mwYQPff/+9YQ+V7GRlZYWHhwcBAQE4ODhQq1YtihcvTnp6Ounp6TRu\n3Ngw++Tp06d07doVtVpNcnIy5cqVY8mSJfj6+mJkZMT48eOZP38+ly5d4v79+/Tv3x9fX1/279+P\nSqWia9euxMbGYmJiws6dOwkLC2PhwoVvndnFxYXy5cujVqtJT0/HysqKV/dPdDqdYSNSIyMjZsyY\nQXx8PPHx8dy6dYu1a9dm35snxH/x6NEj/Pz8WLp0aaH/8lzY3Lt3j4MHD75VoQVeLhvNib/j84vk\n5GQmTpxI9+7duXv3Lo8fP6Z48eKcPn1aCi1C5EMys0UIIfKQyMhIunXrxrfffouXl9drx2zdupUx\nY8YQGBhI27Ztczmhsvbv38+YMWM4ffo0JUqUyNZzh4WFMXz4cC5cuEB4eDitW7fGwsKClJQUNBoN\n5cuX5+bNmwBYWlry3nvvceXKFUxNTencuTNmZmacPXuWu3fv4u/vj7W1NW3atCEsLIwRI0bQtm1b\n6tWrx8CBA6lWrRqRkZGYmZlx584dWrZsyYIFC/j444/fOO/BgwcZNmwYsbGxaLVaTE1NSUtLA17u\nQ2Nubs6TJ08wNzdHp9PRoEEDbGxsuH79Onfu3OHp06cYGxtn63soxN8NHToUc3NzlixZonQUkcv2\n7NnDmTNn/tVzy5Qpw7Bhw956eWV+9/vvv9O/f38SExO5desWAB07dmTfvn3yd7UQ+ZTMbBFCiDyk\nZcuWHDlyhE8//ZSlS5e+doynpyc//vgjXl5ebNq0KZcTKqtz5864ubnh6+tLdt8raNWqFRkZGfzy\nyy84OTlRvXp1rKys0Ol0pKWlYWdnZ/iFNyUlBTs7O4yMjEhNTSUqKorjx48zYsQI0tPTGTx4MBUr\nVuTLL7+kf//++Pv7s2LFCqysrBgxYgR37txh2LBh6PV6KlWqxIEDBxg9ejRhYWFvnNfBwYGHDx+i\n1WqBl7NZXnUkAkhNTWXr1q3Ay5lBfn5+3Llzh3v37qHVapk4cWK2vn9C/F1UVBT79u3Dz89P6Sgi\nl6WkpBATE/Ovn//o0SNOnz6djYnytrS0NL788ks6duxIbGwst2/fRqPRsH79ekJCQqTQIkQ+ppmR\nE4vfhRBC/Gtly5ale/fujB49mocPH9KmTZssU/BtbGxwc3Nj8ODBJCcn4+joWGim6Ts7OzNr1iyK\nFStGw4YNs+28KpWKlJQUDh48SJcuXahatSpr165FrVajVqu5fv06lStX5unTp+h0Oq5du0b79u25\nevUqiYmJeHh4sG3bNvr27cvly5e5dOkSX3/9NXv37uXOnTtMmDCBfv36sWbNGn755RciIiIwNzen\nefPmlCtXjsaNG+Pp6UmnTp0oU6bMP+Y1MTEhNDSU58+fo1Kp0Gq1lC1blsTERODl7JZLly4xevRo\nAGrXrs2aNWto3rw5arWaoKAgfH1937obkhD/RKfT0aNHDyZPnkyLFi2UjiNyWWRkJJcvX36nc7ya\njVfQnT9/Hnd3d6Kjo4mNjSUtLY1KlSpx5swZWrdurXQ8IcQ7kpktQgiRB9nY2BAREUFISAgjRowg\nIyMjy5h69eoRGRnJ9u3b8fX1JT09XYGkuc/c3JzAwEAmTJjA9evXs/Xc/fv3Z/fu3cTHx9OpUyfK\nlSuHlZWVoRDTokULzMzMgJczR1QqFaampmi1Wn788Udq1KiBSqUyFEKCgoL44Ycf2LBhA8bGxgwa\nNIi+ffsSGBiImZkZ06ZNM7Ribtu2LQsWLMDNzY179+69Ud42bdpQrlw5TExM0Ov11KhRA7X6/3+0\n37hxg99++w14Obtl5syZnDt3jtjYWIoXL07v3r2z9f0TAmDjxo2Gjlui8Hm1sfi7iI+Pz4YkeVdG\nRgbz58/nww8/5PHjx1y+fBm9Xo+Pjw83b97E2tpa6YhCiGwgxRYhhMijypYty7Fjx7h69Soff/wx\nqampWca89957hIWFERMTQ7du3QyzGgq6Bg0aMHXqVPr06WNYRpMdypYtS9u2bQkMDESlUuHn50dC\nQgIZGRloNBp27dpFjRo1gJd3Xo8ePcrAgQNRqVQkJyej0+lYu3YtM2bMID09nSFDhmBkZMS6devo\n16+fYZbJ8uXLOXDgAHq9no8++siwF0y/fv0YOHAgnTp1IiEh4R/zOjs7o1arDWMvX76cqTV2Wloa\nixYtMvzs7u5O0aJF6dSpE9bW1hw5coQ//vgj294/IeLi4vj8889Zvnx5psKfKDyyo/BfkG8eXLt2\nDScnJwICAnjx4gWxsbGYmpoSHBzMunXrCs0sVSEKA/kUFEKIPKxo0aIEBwejUqlwc3PjxYsXrx1z\n4MABSpcuTZs2bXjw4IECSXPfmDFjsLKyYubMmdl63sGDB7NmzRrgZZcoU1NTLCws0Gg0JCUl0aJF\nC8zNzYGXexOcOnWK0qVLo9frCQ8Pp1evXixfvpwBAwZQrFgxxo4di6urK927d8fX15fNmzezZs0a\nHj16xIoVK9DpdHTu3JmkpCQApk6dSuPGjenVq9c/fuGwt7fn9u3bhtk0T58+pWbNmpnG7Nixw9C2\nWqVSMWvWLCIiInjx4gW2trZvtSmvEP9kxowZuLu7Y2dnp3QUoZC/F3yVPEdeo9Pp+O6772jRogWJ\niYmcP3+e9PR0GjRowIMHD3B1dVU6ohAim0mxRQgh8jhTU1O2bt1KjRo1aNOmDQ8fPswyxtjYmPXr\n1+Pm5kbLli25cuWKAklzl0qlYsOGDaxdu/atNpb9J+3btzds0Ghqasq4ceMwNTUlNTUVjUbDtm3b\nqFu3LvDyl+eLFy8yaNAgjI2NSUpKYv/+/aSlpVG1alUyMjI4fPgwe/fuZe7cuVy5coWQkBA2bdpE\nv379cHZ2xtvbmydPnjBo0CD0ej0qlYoVK1ag1+v/cSNgMzMz7O3tqVChAsWKFUOn01GpUiVMTEww\nNjZGpVKh0+lwcnIiLi4OgHbt2lGuXDk6duyIkZERMTExBAQEZNv7JwqvCxcusGnTJr7++mulowgF\nFS9e/J3PUaxYsWxIknfcvn0bFxcXvv/+e7RaLefOnUOlUjFt2jROnz6dLe+ZECLvkWKLEELkAxqN\nhpUrV+Lm5oajoyM3btzIMkalUjFjxgy++OILnJyciIyMzP2guaxcuXKsXbuWvn378uzZs2w5p0aj\nYeDAgaxduxYAX19fUlJSMDU1xdTUlPj4eBwcHAwbyyYlJREYGEijRo2Al500mjZtysyZM1mwYAHp\n6ekMGzaMxMREtmzZwqeffkrlypUZPnw4vXv3Zu7cuVSrVo3jx4+zcOFC4GXxbPv27URFRf3jF9c2\nbdpQvnx5kpOTAfjzzz8NG+a+ujt89+5dHB0dDbNgZs2aRXBwMBYWFjRr1ozRo0cX6Gn7Iufp9XrG\njBnDtGnTKFu2rNJxhIKaNWtG0aJF3+kctWrVyqY0ytLr9fj7+9OkSRO0Wi0XLlzgxYsXFC1alKio\nKOnWJUQBJ8UWIYTIJ1QqFTNnzmT06NE4Ojpy/vz5144bNGgQP/zwA127dmX37t25nDL3ubu706VL\nF4YPH55t7aAHDhzI1q1bSUpKokyZMnh6elKxYkUSE/8fe/cdV2X9/g/8dSYcNmew9xBkuMDBUBQ3\nKJmK4krTxJ0jMUvNlTnqY6Y5cm8LLffepWaamXtvxYmIoOzz+v3h1/sRPxcqgtb7+XjweNThfd/n\net/QiXOd676uB1AoFFi4cKE0CcloNCItLQ1169aFiYkJsrOzMX/+fDRt2hTJyclo2bIldDodevXq\nhcDAQAwbNgytW7dGUlISVCoVRo0ahWXLlgEARo8ejY0bNwJ4dHvY2rVrMWPGjOdWnkRHR+Phw4d4\n+PAhZDIZLl++XCi2x6pUqYLw8HAcOnQIUVFR8PLyQs2aNZGSkoKsrCx89tlnxXLthP+mZcuW4dat\nW+jevXtphyKUMo1GA2dn51c+Xq/Xo1KlSsUYUem4ceMGmjRpgrFjx8LCwkKqwKxTpw5u3br1r9ij\nIAjPJ5ItgiAI75hevXrh66+/Ru3atbF79+6nrmnYsCE2bNiAnj17YtKkSSUcYckbN24cjh8/jnnz\n5hXL+VxdXVG1alX8/PPPAIA+ffrg/v37UCgU0Gg0SEtLQ82aNWFubg4AyMzMxNSpU9GyZUupWe75\n8+exd+9eREdHIzMzEzt37sQvv/yC7t27w8HBASNGjMDChQsxd+5c/P3331ixYgWMRiPatGmDs2fP\nAgAcHR2xbt069O/fH1u3bn1qrJUrV8aVK1dgZmYGOzs7kISHhwfMzMwgk8mkeC5evIjx48ejbt26\n2LRpE0aOHInk5GSULVsWtWvXxoQJE3D37t1iuX7Cf8uDBw/wySef4Pvvv/9X9toQXs6yZcuQlJT0\nyhOFgoKCoFAoijmqkrVs2TKp2vHMmTO4cuUK5HI5fvjhB2zcuBFqtbqUIxQEoSSIZIsgCMI7qFWr\nVpg/fz6aNGmCtWvXPnVNpUqVsHv3bkyZMgX9+/cvVOXwb2NqaoolS5YgKSkJZ86cKZZzdu7cWWqU\nGxgYiEqVKqF8+fLIyMiAQqHArFmzpCagRqMReXl50Gq1MDMzg9FoxB9//IH27dvjk08+waRJk2A0\nGtGtWzekpqZi1qxZmD9/Po4fP45Fixahffv2cHJywjfffAOlUom4uDipGXJAQACSk5PRqlUrHDly\n5Ik4VSoVIiIi4OXlJU1/OXHiBIxGI/Lz86FSqQAAe/bsQUhICH755Rd88MEHOH78OIKCghASEoI/\n//wT1tbWaNu2bbFcO+G/ZcyYMQgPD0dUVFRphyKUooyMDNSvXx/x8fG4cuUKNm3aJDX+LqqKFSui\nRo0abyjCN+/u3bto06YNPv/8c3h5eWHVqlUwGo1wdHTE2bNn0blz59IOURCEEiSSLYIgCO+o+vXr\nY82aNejUqdMzbzPx8PDA7t27sW/fPrRq1QrZ2dklHGXJCQoKwtChQ4ttHHSjRo1w+vRpnDp1CgDQ\nt29fZGVlQS6Xw8LCArdv30Z0dDQsLCwAPOrdMmPGDPTt2xcqlQr379/HzJkzUblyZezevRsxMTFw\ndHREz549YWdnh9mzZ6N9+/YIDg5Gr1690KpVK3To0AFxcXHIysrCBx98ICXIoqKiMHHiRMTGxuLq\n1atPxFqrVi3Y29vj9u3bIInjx4+jatWqkMlkUCgUUg+XyZMnIzIyEjt37sSoUaPg5OSEuXPnon79\n+qhVqxY2btyIQ4cOvfa1E/47zp07hylTpkj9hoT/pk2bNsHR0RGbNm2SHjt69ChWrVpVpIo5tVqN\natWqoXHjxu/s6ON169ahXLlyyMvLw82bN7Fv3z7IZDK0a9cOFy9ehIeHR2mHKAhCCRPJFkEQhHdY\n1apVsX37dgwaNAjffvvtU9dotVps2rQJJFGvXr1/9a0iPXr0gJ2dHYYOHfra51KpVGjfvr3UKLd+\n/foAgBo1auDevXtQKpWYNm0awsPDATyqbjEajTh//jwcHR0BAOnp6XBwcMCcOXPQoUMH3L59G3v2\n7MHSpUufDmnmAAAgAElEQVRRv359NGvWDJ07d8bAgQNhbm6OIUOGYNKkSbCzs8Nff/2FUaNGSfEk\nJCSgZ8+eiI2NfaI8Pzo6GteuXYPRaIRWq4XRaISvry+srKyQk5MDpVKJgoICzJgxA1lZWfDz88Oe\nPXtw6NAhKJVKuLq6Ytu2bShTpgwSEhJe+9oJ/x19+/ZF//794eLiUtqhCKXgwYMHaN26NRo2bPjU\nZP7Jkyfh4eGBWrVqwdXV9YlEio2NDUJDQ9GpUyfUr1//nUy0ZGRkIDExET169ECVKlWwbNkyZGdn\nQ6lUYtWqVZg7d65UdSgIwn8MBUEQhHfepUuX6O/vz4EDB9JoND51TUFBAfv168eyZcvy4sWLJRxh\nybl58yYdHR25ffv21z7X6dOnaWdnx5ycHJLk9OnT2aBBA8rlclpbW1Mmk/Hrr7+mpaUlAVAmk9HC\nwoITJ06kqakpAdDS0pIjRoxglSpVuHz5cjo7O9POzo43b95kVlYWy5UrxxkzZvDWrVt0cXHh2rVr\nefXqVdrZ2VGv13PlypVSPEajkd26dWOdOnWkmEgyPz+fNjY2dHJyoq+vL+VyOcPDw6nRaAiAKpVK\nimXOnDnScZmZmaxZsybVajW7du3K+Ph4yuVyLlmy5LWvnfDvt27dOvr4+DA7O7u0QxFKwc6dO6nV\naimTyejg4EAAT3yFhoayoKCA5KPXrytXrvDvv//mgQMHGBMTw507d5byLl7Pjh076OnpyRYtWtDD\nw4NyuZxyuZzlypXj3bt3Szs8QRBKmUi2CIIg/Evcvn2blStXZqdOnZiXl/fMdd999x2dnZ154MCB\nEoyuZK1fv56urq5MTU197XNFRUVx6dKlJMmHDx/SYDAwNjZWSmK4u7uzUaNGlMvlBEC1Ws0aNWqw\nTp06BECFQsGaNWsyMjKSkyZNYuvWrRkaGspmzZrRaDTy2LFj1Ol0PHnyJH/99Vfa29vz8uXL/PXX\nX2lra0utVssTJ05I8eTl5bFx48Zs3759ocRaXFwca9WqRb1eL8XWoEEDKhQKWlpaUi6XUyaTsVy5\ncoX2l5+fT19fXxoMBmq1WtauXZs2NjbP/R0ShOzsbPr6+nLt2rWlHYpQwh4+fMguXbpQpVJJid2n\nJVo0Gg3Pnz//zPN07NiR06ZNK8HIi8/Dhw/Zp08fOjk5sWvXrlQoFDQxMaFcLuegQYNKOzxBEN4S\noqZNEAThX0Kv12Pbtm24fPky4uPjn9mf5eOPP8bEiRNRv359bNiwoYSjLBkNGjRAs2bNkJiY+Nrj\noDt37izdSqTRaNClSxc4OztDLpfDzMwMly9fRsOGDaXJRLm5uThy5AiaN28OjUaDgoICHD58GO+/\n/z6GDRuGgQMH4urVqzhw4ACSk5MREBCAESNGoE2bNqhatSr69OmDli1bolq1ahg+fDjMzMwQFxeH\ne/fuAQCUSiWWLFmC48ePY9iwYVKctWrVgrW1NVJTU2Fubg6j0Qh/f3/Y2toiMzMTZmZmIIkLFy5g\n//790nEKhQKrVq3Cw4cPkZ+fj5ycHDx48KBYbsUS/r2+/fZb+Pn5ISYmprRDEUrQ3r174ePjg5kz\nZ8LExAQRERHYs2fPE+vMzc3xv//9D56ens88V3Bw8FObfr/t9u3bh4oVK+Lq1avw8PDADz/8AIVC\nAVNTU+zduxdffvllaYcoCMLborSzPYIgCELxys7OZsuWLRkVFcV79+49c93u3btpb2/PWbNmlWB0\nJefxLTozZ858rfM8fPiQOp2OFy5cIEmmpKTQxsaGTZo0kSpZfHx82Lx5cyqVSgKgUqlkmTJl2LNn\nT+kxBwcHDhgwgE2bNmVycjI9PDxoMBh448YNGo1GNm7cmAMGDGBBQQFjYmLYv39/Go1Gtm3blmXK\nlGFMTAzz8/OluG7evEkvLy9pf4cOHaKPjw9NTEwYGBhIuVzOmJgYmpubE4D0qatSqWS7du2e2Gf7\n9u35/vvvUy6Xs3bt2lSpVExLS3utayf8O129epVarZZnzpwp7VCEEpKVlcV+/fpRo9FQoVAwMjKS\nfn5+T61oUavVrFmzpnT70LNs3ryZUVFRJbOBYpCTk8PBgwfT3t6egwcPprm5OdVqNWUyGWvXrs2s\nrKzSDlEQhLeMqGwRBEH4lzExMcGiRYsQFBSEmjVr4ubNm09dFx4eLk2lGTZs2GtXgLxtHo+D/vTT\nT6WJQq9Co9GgdevWmDNnDgDA0dERcXFxqFixImQyGdRqNc6fP4/Y2FhoNBoAQH5+PtLT0+Hj4yNN\nK8rOzkZWVhaOHj0KtVqNSpUqwdfXF926dQMAzJo1CwsXLsSOHTswb948/PTTT1izZg1++OEHaDQa\nnDp1Cl988YUUl52dHdavX49BgwZhw4YNCAoKwr179+Dv74+CggIYjUbs2LEDdevWhVqthkqlglwu\nR0FBAZYtW4Y7d+4U2ucXX3yBX3/9FR9//DF27twJlUqFDh06vPJ1E94xJLBmDZCQAFSpAgQFASEh\nQGwsMHUqkJsrLU1KSkLXrl3h4+NTigELJWX//v0ICAjA1KlTkZ+fj+HDh+PIkSNPfV2VyWTQaDRF\nagr7uLLlXfh/z+HDh1GlShX89ddfqFWrltS83Gg0YvLkydiyZQtMTU1LOUpBEN46pZ3tEQRBEN4M\no9HIYcOG0dvbm+fOnXvmuhs3bjA0NJQffvghc3NzSzDCkjF58mSGhIQUaij7sg4dOkQXFxepsuTg\nwYN0dnZmo0aNKJPJaGJiwrJly7JNmzaFmtLq9XqOHz+eZmZmBEBra2vOnDmTrq6uPHPmDO3s7Ojl\n5cVFixaRJDdu3EgXFxfeuXOHu3fvpp2dHS9evMgLFy7QYDDQ3t6eycnJhWLbtWsX9Xo9//rrLzZv\n3pwJCQk0NTWlQqGgWq3mkCFD6OjoSABSlYuNjQ3HjRv3xD47d+7MTz/9lL6+vrSysqJMJuPRo0df\n+boJ74j588mwMFKpJB+lXZ788vcnBw/mjm3b6OrqyszMzNKOWnjDsrOz+fnnn9PCwoIqlYp2dnbc\nsmULFQrFUytaZDIZnZ2di9yHxWg0Uq/XMyUl5Q3v5NXl5eVx9OjRNBgM/Oqrr+js7EyFQkGlUkln\nZ2eePXu2tEMUBOEtJipbBEEQ/qVkMhmGDh2Kfv36oXr16jh06NBT19nb22PHjh24desWGjdujIyM\njBKO9M3q1q0bnJycMGTIkFc+R7ly5eDk5ISNGzcCACpUqIAyZcqgcePGIAm5XI5Tp06hUaNGUCqV\nAIC8vDwolUqkpaXB3d0dAJCVlYXZs2cjOjoa33//Pb7++msoFAr06dMH169fR7169RAfH4/OnTsj\nLCwMSUlJaNmyJZycnLBo0SLk5+eja9euOHz4sBRbREQEpk2bhsaNG6N8+fIAgJycHJQpUwb5+fk4\nd+6c9DNVq9VQKBRIT0/HpEmTUFBQUGifgwcPxowZMzB06FDY29tDrVYjMjISuf+oahD+Zb76Cuje\nHfj9dyA//9nrTp4Ev/wSD5o0wf/GjpV6FAn/TgcPHkSFChUwbdo05OXlISYmBitWrECdOnWeeN14\nzMPDA/7+/khMTCzSc8hksre6b8vp06dRvXp1bN68GR07dsQXX3yB9PR0GI1GJCQk4NKlS/D29i7t\nMAVBeIuJZIsgCMK/XPfu3fHtt9+ibt26+O233566xtzcHCtWrIC7uztq1KiB69evl3CUb45MJpNu\n0dm6desrn+ejjz6SGuUCQN++fTF9+nRER0cjOzsbarUaY8eORbNmzaRbh1JTUzFhwgQMGzYMFhYW\nyM3Nxblz5xAaGooff/wRZcuWhY+PDwICAtClSxeQxOjRo3H+/HnMnDkT/fr1g8FgwMCBA1G3bl0k\nJSXBxsYGTZo0QWpqqhRLs2bN0L9/f8ydOxe7du2CTqeDVqsFSaxduxaxsbEwNzdHdna21Cg3KytL\nSh495ubmhtatW+PAgQNwcnJC3759ce/ePVSuXBn3799/5WsnvKWmTgVGjQIyM4u0XAYg5v59NN+8\n+VG9i/Cvk5eXh+HDh6NmzZq4dOkSsrKyMGXKFHTo0AHh4eHPPM7Kygrp6emYNWsWZDJZkZ8vKCgI\nR48eLY7Qi43RaMSkSZMQERGB9957D/fv38fXX38NU1NT5OXlYfny5ViwYAEUCkVphyoIwtuudAtr\nBEEQhJKyefNm6vV6rly58plrjEYjR40aRXd3dx47dqwEo3vzNm3aRGdnZ965c+eVjr9//z5tbGx4\n/fp1kmRBQQF9fX2ZnJwsNYWUy+VcuXIlrayspNJ6g8HATp06MS4ujgqFgjKZjHZ2dpwyZQorVqzI\n8+fPU6/X09fXl/PnzydJHjt2jHq9nidPnmRqaird3d25fPlyGo1GNm/enMHBwaxTp84T45l79+5N\ntVrNevXq0c3NTbrFady4cXR1dS3UKPfxaOj/X0pKCrVaLdesWUNnZ2fWqVOHpqamDA4O5pUrV17p\n2glvocxM0tPz2bcNPe9LpSI3bCjtHQjF7PDhwyxfvjxdXFyo0Wjo7e3NkydPctSoUU+9bQj/1wxc\nqVTS39+f06dPf+nnnD59Ojt06PAGdvNqLl68yOjoaIaFhfH777+nRqORmgIHBga+8v8/BEH4bxKV\nLYIgCP8RderUwbp165CYmCg1e/3/yWQyfP755xg5ciRq1aqFnTt3lnCUb07dunWRkJCAjz766JUa\nMlpaWqJZs2aYN28eAEAul6N3795YsmQJQkNDkZeXB5VKhVGjRiEhIQE2NjYAgPT0dCxduhSJiYnQ\naDQgCZI4cuQItFotli9fjlGjRkGtVqNfv35ISUlBQEAARo4cidatW8PCwgI//vgjEhMTcfHiRcye\nPRv5+flISUnBwIEDC8U4fvx42Nvb4+LFi7h69SqcnZ2Rl5eHS5cuIT09HTKZDFqtFjKZDPn5+fjt\nt99w/vz5QudwdHTEhx9+iA0bNiAsLAxVq1ZFfn4+HBwcEB4eXugWJuEd9v33wIULr3ZsXh7wf/8d\nCO++/Px8fPXVV6hRowauXr2KtLQ0tG7dGkeOHMHw4cMxaNCgpx73uEqubt26cHNzw0cfffTSz/22\nVLaQxJw5cxAaGopatWrB2dkZvXr1gpmZGXJyctC/f38cOXIEOp2utEMVBOFdUqqpHkEQBKHEnThx\ngu7u7k9tkPpPW7ZsocFg4I8//lhCkb152dnZrFChAn/44YdXOv7333+nj48PjUYjSTIjI4M6nY6r\nV6+WPuVVKBTcuHEjra2tpU9/raysWK9ePQ4YMEBqlmtra8uff/5ZGisdHR3NOnXqMDY2lkajkUaj\nke+99x6TkpJIkuPHj2flypWZk5PDU6dOUafT0dnZmQsXLiwU4+TJk6nT6SiTyVivXj0qlUo6ODiw\nXbt21Gq1VKlU0vd1Op10/n+6desWtVotd+zYQZ1Ox27dulGtVnP27Nk0GAzctGnTK10/4S1hNJKR\nka9W1fL4y96evHGjtHcivKZjx44xNDSUvr6+NDMzo6WlJZctW8b8/HyGhIQ8s6LF3NycMpmM1apV\no06n4+XLl1/p+dPT02lmZlZorH1JS0lJYaNGjVihQgX+9NNPtLOzo0qlopmZGa2trfn777+XWmyC\nILzbRGWLIAjCf4y/vz927dqFuXPnIikp6ZlVHrVr18aWLVuQlJSEb7755p0Yz/kiJiYmWLJkCT7/\n/HOcOHHipY+vWrUqTE1NpYofCwsLdOrUCVu2bIGfnx9IQqFQYMSIEWjXrh3s7OwAPBr7/NdffyEs\nLKzQKOjRo0fj448/Rs+ePTF9+nQcPHgQ586dw/z58yGTyTBz5kwsXrwYW7duRZ8+feDs7IykpCSU\nKVMGc+fORV5eHj7++GMcOHBAirFBgwZQKpVQKpU4e/Ys8vPzkZaWhoiICFhZWSEvLw85OTlS896Z\nM2ciKyur0D4NBgO6dOmCRYsWoUWLFlCpVDAxMcH69evx888/o23btpg7d+4r/hSEUnfjBnDw4Oud\n4+ZN4KefiiceocQVFBTg66+/RmRkJO7du4c7d+6gTJkyOHz4MOrVqwdnZ+dCryv/pFar8fDhQ2i1\nWjx48ABjx46Fq6vrK8VhZWUFg8GAC69aZfWakpOTUbFiRVSoUAENGjRA69atUVBQgIKCAlSpUgXX\nr19HtWrVSiU2QRD+BUo52SMIgiCUkjt37rBatWps3779E70//unKlSsMCgpiz549S/XTx+I0bdo0\nVqhQgdnZ2S997IQJE9i6dWvp369cuSJVqQCgXC6nXC7nzp07C1W3mJmZMTAwkDNmzKCNjQ0B0MXF\nhd9//z0DAgK4dOlSfvfdd6xYsSL1er3UH+WfvWbu3r1LDw8PLlu2jCQ5dOhQ+vv7083NjTdv3iT5\nqO+Om5sbW7ZsSZlMRo1GQ7lczsGDB1Or1UrjWS0sLKR/njNnzhP7TE1NpU6n4969e6nVajlu3Dgq\nFAqePHmSJ06coKenJ4cNGyZV+QjvkEOHXq+q5fHXl1+W9k6EV3Dq1CmGhYUxMDCQVlZWtLS0ZFJS\nEnNzc3n+/Hmq1epnVrTg/8bam5iYMDExkQ0aNHjt14DY2Fj+8ssvxbS7orlz5w4TEhLo7+/P1atX\nMygoiHK5nAaDgQqFghMnTizReARB+HcSlS2CIAj/UTqdDlu2bMHNmzfRtGnTJ6obHnNxccGuXbtw\n7NgxNG/e/Jnr3iWJiYnw8PB4Zi+C52nXrh3Wrl2Lu3fvAnh0fRo0aIALFy7AyckJSqVSqm7p2LEj\nnJycAAC5ublIT0+HXC6Hl5cXZDIZUlJSMHToUIwZMwa9e/dGu3btYGZmhipVqqBz585SP4THvWZs\nbGyQnJyMbt264dy5c/jiiy/g4+MDe3t7NG/eHHl5eZDJZIiOjkbZsmUBPJouIpfLsWTJEjRt2hRO\nTk64fv06FAoFZDIZbt26he+///6JfWq1WvTq1QtTpkxBr169cOjQIbi7uyMhIQH+/v74/fffsWbN\nGnTs2BF5eXmv8dMQSpxKBbzExJhnkos/I98lRqMREyZMQFhYGEgiJSUFSqUSS5cuxbhx47Bv3z54\neXk9d9S7TqeDXC5Hjx498Msvv2DGjBkvNX3oaYKDg0u0b8vatWtRrlw5ODo64pNPPkF8fDwuXboE\npVIJlUqFEydOoFevXiUWjyAI/2Klne0RBEEQSldubi5bt27NyMhIpqWlPXNdTk4O27Rpw2rVqvH2\n7dslGOGbcefOHbq4uLxS/5FWrVoV+uRz3759dHNz48yZMymTySiTySiXy7l3795C1S0mJiZ0cHDg\n5s2bpeoWZ2dnduzYkYmJiezWrRtPnjxJvV7PgIAAzpo1i+STvWa+++47VqpUiVlZWUxLS6O3tzcr\nVKjAHj16kCTnzZvH5s2b09LSkpGRkdK0pEWLFtHLy0t6XqVSSblcTq1Wy3379j2xz3v37tFgMPDP\nP/+kg4MD58+fT7lcztWrV5MkMzMz2ahRI9atW5fp6ekvfR2FUnL3LqnVvn5ly4wZpb0ToYjOnj3L\n6tWrS9OGHBwcWKNGDaakpJAk586d+9xqFgAMDAykUqlkdHQ0y5cvz9mzZxdLbAsXLmR8fHyxnOt5\n0tPT2bFjR3p6enLdunWMiYmhQqGgq6sr5XI5ExIS/jXVm4IgvB3ERxKCIAj/cSqVCgsWLEClSpVQ\no0YNXL9+/anr1Go1FixYgFq1aiE8PBznzp0r4UiLl06nw9y5c9GhQwfcvn37pY7t3LkzZsyYIfWx\nqVy5MlxdXWFhYQFra2uYmZlBLpdj+PDh6Nq1Kzw8PAA8qjLRaDTYu3cvGjRoAI1Gg2vXrmHVqlV4\n//33sWLFCty9exdJSUmwsrLCgAEDcPnyZanXzKBBg3Dy5En06tULnp6e6N+/P2xsbLBixQpcvnwZ\na9aswaxZs1CrVi3s2LEDVatWRWpqKuRyOXJzc3H27FlkZGRAqVQiPz8fer0eJCGXyzF58uQn9mlt\nbY1+/frhm2++weDBg7Fo0SJERUWhU6dOKCgogLm5OZYvXw4fHx9Ur14d165de+2fi1ACbG2B8PDX\nO4ePD9CmTfHEI7wxRqMRkydPRpUqVWBhYYGLFy8iIyMDPXr0wLZt2+Do6IjPPvsMHTp0eO55IiMj\nceLECdja2iI0NBTOzs4vPKaoSqKyZfv27ShXrhyUSiWmTZuGNm3aYOvWrbC2tsatW7eQnJyMJUuW\nQKFQvNE4BEH4jyntbI8gCILwdjAajRw1ahS9vLx45syZ566dOnUqHRwc+Mcff5RQdG9OUlISGzdu\n/FJ9BwoKCujt7V1o/8uWLWNYWBhHjx5NhUIh9W85ePAgbW1tpU+HTU1NaW1tzf3790tVLxYWFgwO\nDubChQsZFBTEhw8fMiQkhE2aNGHdunWl2P7Za+bevXv09vbmTz/9RJL86aef6OzsTL1ezz179tDH\nx4ejRo2iQqFgaGgo5XI5zczMmJiYSF9fX6mfi4mJCVUqFS0sLHjnzp0n9pqRkUF7e3seOHBAej6l\nUsmvvvpKWmM0Gjl27Fi6urry8OHDr/qjEErSvHmvV9XSvXtp70B4gcdTzipWrMhKlSrR1dWVjo6O\n/PXXX0k+eh1r0qTJCytaKleuTIVCQXNzc2ki2dWrV4stzuzsbJqamr5SD60XefDgAT/++GM6Oztz\n9erV7N27N1UqlVTZV7ZsWd66davYn1cQBIEkFcOGDRtWKlkeQRAE4a0ik8lQvXp1aDQatG/fHtHR\n0XB0dHzq2tDQUPj5+Un9O/z8/Eo42uITFRWFb7/9FkajEZUrVy7SMTKZDA8ePMDWrVsRFxcHAPDz\n88NXX32FPn36YNGiRbCyskJ2djZu3LiBxo0bIyUlBXfv3kVBQQGcnJyQnp6OOnXq4OjRo0hPT4el\npSWCgoJw+/Zt3LhxA0lJSRg5ciRkMhlUKhVCQkIQEhKCDRs24O+//0ZcXBwiIyPRunVrNGnSBDVq\n1MCNGzdw584dzJ07F5GRkXBwcJBiPHDgAPLz86FSqZCRkYG0tDR4e3vj/v37yM3NhYuLCxQKBSIi\nIgrtVa1WQy6XY+HChejduzfGjh2LuLg4fPfdd+jduzdMTEwgk8kQEREBR0dHtGrVChUrVoSXl1ex\n/6yEYhQUBKxZAzyjku25bG2BKVOA/5u2JbxdSGLGjBlISEhAuXLlcODAAeTm5qJChQrYtGkT/P39\nkZ2djUqVKuHXX3997rk8PT1x4cIFmJqaYsCAAZgzZw6GDBmCGjVqFFu8SqUSS5YsQVRUFBwcHIrt\nvI8rCG1tbTFhwgR89NFH2Lx5M1xdXXH16lX06dMHK1asgLm5ebE9pyAIQiGlne0RBEEQ3j7Lli2j\nwWDg9u3bn7tu3759dHR05JQpU0omsDfkcZ+UY8eOFfmYlJQU2tjY8P79+9Jj3377LVu2bMnevXvT\nxMREqm45duwYtVot5XK5NJnI2tqaf/75J11cXAiACoVCmv6j0+l49uxZDhs2jFFRUdTpdLx48SJJ\n8vbt23R2dpZ6zUyePJkVKlRgVlYW8/LyWLt2bVavXp2+vr6MjY2lu7s74+LiKJPJaGpqSl9fX5qb\nm1Oj0dDGxobOzs6Uy+VUqVT08PB4as+Chw8f0snJifv372flypU5b948mpubF5rK9NjOnTtpZ2fH\nuXPnvuyPQShpO3aQTk4vV9FiYkL+73+lHbnwDJcvX2bdunVZoUIFNm7cmE5OTrSxseGECROkCrmb\nN2/SYDC8sKLFysqKXl5eNDMzY3R0NL/44gvGxsa+kQlkLVq04IIFC4rlXNnZ2fz8889pb2/P5ORk\nTp48mSYmJrS1taWlpSUtLS3522+/FctzCYIgPI9ItgiCIAhPtW3bNhoMhheO5Dx79ix9fX05cOBA\nFhQUlFB0xW/GjBksV64cs7KyinzMe++9xxn/aBKanp5OrVbLY8eOUalU0sHBgXK5nC1btuSIESPo\n5+cnvZFxdXVlbGwsFy9eTDs7OwKgj48PW7duzXHjxrFu3brMzs5mcHAwW7Rowdq1a0tvcjZv3kxn\nZ2fevn2bRqOR8fHx7NKlC8lHyRg3NzdWqlSJarWanTt3pl6vp6enJ1UqFcPDw6nVauno6EiZTEZr\na2uq1WoqFAq6ublx7dq1T93r999/z5iYGG7bto1eXl6cOHEiFQrFU285O378OD08PDh8+HAxGvpt\nt3o16eFRtESLpSU5alRpRyw8hdFo5OzZs6nX69m5c2e6u7uzbNmy9PT05J9//imtO3LkyAtHOwOg\nUqlko0aNqFQqaTAYuGXLFhoMBl67du2NxD9y5Eh++umnr32ev//+m+XKlWNcXBxPnDjBqKgo6XYh\nuVzO6tWrMyMjoxgiFgRBeDGRbBEEQRCe6cCBA3RwcCiUUHia27dvMywsjG3atGFOTk4JRVe8jEYj\nmzVrxj59+hT5mDVr1rBq1aqFHuvbty/79+/PhIQEWlpaSlUrp0+fpl6vl/q5WFpa0s7Ojlu2bGF4\neDhNTEwok8no4ODATZs2sXz58ly4cCH//PNP2tnZsWLFipw6dar0PP3792dcXByNRiPT09Pp4+PD\nxYsXk3z0c9PpdFSpVGzbti3lcjk7d+5MlUolTSV6XGUTFBQkJYUMBgNjYmKeutfs7Gy6ublxz549\nbNiwIb/77ju6uroyNDT0qeuvX7/OkJAQduzYkbm5uUW+pkIpOHyYSy0teelZSRZra/K998iVK0s7\nUuEprl27xtjYWJYvX55dunShTqejm5sbW7VqVWhK2Jo1a16YZHn81b9/fyqVSlpZWXHjxo0sV64c\n582b98b2sHz58me+9hRFXl4eR40aRYPBwHnz5nH9+vW0tLSkqakp3d3dqVAo+D9RkSUIQgkTyRZB\nEAThuU6fPk0PDw9+9dVXz61SePjwId9//33WqlXruSOk32apqal0dXXl+vXri7Q+Ly+Pzs7OhZrC\nXpg6PxQAACAASURBVLhwgVqtlmfOnJHGispkMrZv355jxoxhQECA9IbGzs6OFSpU4B9//EGtVksA\n1Gq1LFu2LHft2kUHBwempqZy4MCBrFevHnU6Hc+fP0/y0SjufyZgDh48SL1ez5MnT5J8NP7ZwsKC\nZmZmVKlUHDBgAGUyGS0sLLh48WI6OTlRJpNJ5fWmpqY0MTGhtbU1z50799T9zpgxg7Vr1+ahQ4do\nb2/PdevWUS6Xc8OGDU9dn5GRwUaNGrFevXpiNPRbbP/+/QRAvUrFHXXqkB98QDZrRrZpQ/brR/7f\n75TwdjEajVywYAENBgN79erFqlWrMigoiFqtljNnziz0ej1hwoQiJ1qGDx9OtVpNe3t7Dh06lEOG\nDHnpJuIv6+zZs3Rzc3ulY0+ePMmqVauyTp06PHPmDDt27EiVSkUfHx+amprSwcFBel0UBEEoSSLZ\nIgiCILzQtWvXGBQUxL59+z73VqH8/Hz26tWLQUFBvHz5cglGWHy2b99OR0dH3rx5s0jrhwwZwo8/\n/rjQY82aNePEiRNZu3Zt2tnZUSaTUaFQ8MKFC7S3t6dKpZKqWzw8PDh//nx26NCBOp2OABgQEMCx\nY8eyZ8+e7NSpE7Oysujn58e2bduyVq1a0s/gxIkT1Ov1PH78OMlH04rKlSvHhw8fkiQbNmxIa2tr\nqlQqBgcH08bGhgqFgo0bN+ann35Kf39/AqCbm5tUWePn58ekpKSn7jU3N5fe3t7csWMHP/jgAw4e\nPJgRERF0cHB45u9FXl4eu3btyvLlyxfrBBOh+ISEhDxKtuj1b2QijFD8bty4wffee4+BgYEcOnQo\ndTodQ0JCGBAQwKNHj0rrjEYju3TpUuRES8+ePWlra0udTscaNWrwjz/+oMFgYEpKyhvdT0FBAc3N\nzXnv3r2XOmbChAnU6/WcPHkyDx06RFdXV6pUKlasWJFyuZzx8fGisk4QhFIjki2CIAhCkdy9e5cR\nERFs27btc/94NRqN/Oabb+ji4sJDhw6VYITFZ+DAgYyJiSnSJ7kXLlygTqcr1Otl9+7d9Pb25pEj\nRyiXy+nl5UUA7Ny5M8ePH8+goCDpzY21tTWdnZ157tw5qbpFqVRSq9Xy6NGjdHFx4c6dO7l79246\nOjoyJCSE33//vfRc06dPZ/ny5ZmdnU2j0chWrVrxo48+Ivmoia9CoaCvry8BsHHjxtRoNNRoNPz9\n99+l5rxKpZLu7u6Uy+VUq9XU6XRSwub/N3/+fFavXp0XL16kVqvlvn37qFAo+M033zzzGhmNRo4Z\nM4Zubm5iNPRb5siRI5TJZATA1atXl3Y4QhH8+OOPtLe3Z9++fdmsWTN6e3vT1dWViYmJfPDggbQu\nNzeX0dHRRU60NGzYkCEhITQ1NaXBYOD58+cZFBRUbI1rX6Ry5crcvXt3kdZeuHCBNWvWZEREBE+f\nPs0xY8ZI1TgODg5Uq9X88ccf33DEgiAIzyeSLYIgCEKRPXjwgLGxsYyJiSn0R/3T/Pjjj1JjxXdN\nbm4uK1euzEmTJhVpfd26dblo0SLp341GI6tUqcIVK1awYsWK9PDwoEKhoEKh4NWrV+nk5EQzMzMC\noIWFBf39/Tl69GiOHj2arq6uBMDAwEA2bdqUv/zyC/38/Jidnc0+ffowLi6Oer1eutXHaDTy/fff\nZ79+/UiS9+/fZ5kyZaQ3SIGBgVIS53EFg42NDVetWkVfX186OztTJpNRJpNJtxyVLVv2mdOE8vPz\n6e/vz40bN/KTTz5hly5d2LFjR2o0mhc2nly8ePE7+zvxbxUZGUmZTEZ7e/vSDkV4gVu3bjE+Pp5l\ny5bllClT6Orqyho1alCv1z+RWLh37x59fHyKnGjx9/dnt27dqFKpqNPpuGHDBg4aNEjqC1USPvzw\nQ06bNu25a4xGI2fOnEm9Xs9x48bx6tWrrFy5MtVqNatWrUqlUskyZcrw+vXrJRKzIAjC84hkiyAI\ngvBScnNz+cEHHzA8PJypqanPXft4DPD8+fNLKLric+bMGer1eh45cuSFa5OTk1mrVq1Cjy1ZsoQ1\natTgr7/+SrlcTm9vbwJg9+7dOWnSJAYHB0tvdMzNzWlra8vLly/Ty8uLKpWKMpmMLi4uXLduHePi\n4jhs2DBmZmbSy8uLnTp1Yo0aNaRbd+7cuUMXFxdu3LiRJHno0CHp9qJ+/foxMTGRMpmMNjY2lMvl\nVCqVbNeuHYcMGcJatWoRAHU6nTSVyM7OjpUrV37mfn/88UdWqVKFd+7coV6v54EDB6jRaNihQ4cX\nXqsdO3bQzs7ujTbbFIrm1KlTUsPmbdu2lXY4wnP8/PPPdHBwYL9+/fjJJ5/QwcGB1apVY2ho6BM9\nli5cuEBbW9siJ1psbGw4c+ZMqtVqent7c+DAgdy/fz/t7OxKNGkxfvx49uzZ85nfv3btGmNiYlix\nYkUeOXKEy5Yto7m5Oc3NzVmhQgXK5XL27t1bTEATBOGtIZItgiAIwksrKChgv379GBgY+MI+HMeO\nHaO7uzu//PLLd+6P4NmzZzMoKOiZt9Q8lp2dTYPBUGgMcm5uLl1cXHjgwAF6eXkxICCAKpWKCoWC\nN2/epKurK62srAiAGo2GwcHB7NGjB3/55Rc6OzsTAB0cHOjt7c3Tp09Tp9Px5MmT3LZtG11cXFi1\nalV+99130vNt3bqVTk5OvHXrFslHzWwDAwO5dOlSRkdHMyQkhAqFgiqViqamprS0tOTff/9NR0dH\n6VYnU1NTKhQKajQaOjg4cN++fU/db0FBAYODg7l69WqOGTOGTZs25f/+9z8qlUpeuHDhhdf18Wjo\nESNGvHO/E/8m9evXl5Jwwtvpzp07bNWqFX19fbl48WJWqlSJERERdHJyYr9+/Z6Y/rZr164ijXZ+\n/GViYsJ169ZRo9HQ09OTYWFhzMjIYGBgYKFqvZKwadMmRkVFPfV7S5YsoZ2dHYcOHcq0tDS2aNGC\narWawcHBtLa2poWFBXfs2FGi8QqCILyISLYIgiAIr8RoNHLs2LF0d3fnqVOnnrs2JSWFFStWZGJi\nIvPy8koowtdnNBoZHx/PXr16vXDtJ598woEDBxZ6bOzYsWzbti2XLVtWqHdLz549+cMPP7BcuXKF\nqltsbGx44sQJRkdHS59MV6xYkcOHD+eECRNYs2ZNqeFlixYtqNPpCiV4BgwYIE0NMRqNbNu2Ldu2\nbUsLCwuuWLGCMplMisHKyorbt29nUFAQ/fz8KJfLqdVqpf4dQUFBz61UWb58OStUqMDMzEy6uLhI\n05OqVatWpGt7/fp1VqpUiZ06dRINLEvBuXPnpKqWot4uJ5SsVatW0cnJib179+akSZOo1+sZFxdH\nOzs7rlmz5on1CxYskEa6F+VLoVBIz2Fra0u9Xs9Lly7xs88+Y5MmTUo8EXr9+nXqdLpCz3v79m22\naNGCZcuW5f79+/nHH3/Qzs6OarWaderUoUKhYHh4uJh2JgjCW0kkWwRBEITXMmvWLDo4OHD//v3P\nXXf//n3Wr1+fsbGxL+zt8Ta5e/cu3dzcuHbt2ueuO378OB0cHAolDu7evUsbGxtevXqVBoOBlStX\nlqpH7t69S09PT2kCkYmJCYOCgtikSRMePnxY6rOiVqup1Wp56tQphoaGcvbs2UxPT6erqyu7du3K\niIgI5ufnk3w0DjokJIRTpkwh+Wj0sr+/Pz09Pblx40ZpFPXjN1udO3fmyJEjWb9+fQJgVFQUPT09\npaa5VlZWvHPnzlP3azQaGRISwmXLlnH27NmsXr06169fT4VCUeRbUjIyMhgTE8P69euLN0sl7L33\n3iMAqlSq504YE0peWloa27dvTy8vL65cuVKaOlStWjXWqFGDV65ceeKYwYMHS4nSoiZapk6dyjp1\n6tDExISOjo5cuXIl9+3bV+K3Dz1mNBqp0+mkyUerVq2io6MjP/nkE2ZkZPDzzz+nWq2mi4sL/f39\nqVAoOHbs2BKPUxAEoahEskUQBEF4bcuXLy9S49Pc3Fx27NiRISEhvHHjRglF9/p27txJR0fHF8Yc\nGRnJ5cuXF3qsZ8+e/Pzzzzlp0iQqFAq6ublJ1S1z5swp1LvF2tqaDg4O3LlzJ7t160YPDw8CYGho\nKGNiYnjgwAHa2dnx1q1bXLduHT08PBgeHs7x48dLz3fy5Enq9XoeO3aM5KNpMxqNhl27dpXesMnl\ncioUCqrVap48eZJ2dnZUKpV0dHSkXq+nubk5AdDX15fjxo175n7XrVvHwMBA5uTkMDAwkKtWrWJo\naCidnZ2L/AY+Ly+PXbp0Yfny5Xnt2rUiHSO8nosXL1KhUFAulzMhIaG0wxH+Yd26dXRxcWGPHj24\ncuVKOjk5sXnz5nRwcODQoUOlxOpj+fn5jI+PL3KSBQDlcjk//vhjDh8+nCYmJqxQoQL79u3LrKws\nBgQEcMmSJaW0e7JmzZpcvnw5O3ToQC8vL/7666+8cOECg4KCqFar2aBBA2o0GtrZ2RUacS0IgvA2\nEskWQRAEoVjs2LGDBoOBS5cufe46o9HIYcOG0dPTkydPniyh6F7foEGD2KBBg+cmEebOncvY2NhC\nj505c4YGg4Hp6em0sLBgVFQUzc3NqVAomJ6ezjJlykg9Wh5P0ggNDeXNmzep1+spl8ulW5CWL1/O\nfv36sW3btiTJ9u3bs127dtTr9YVu5ZoxYwbLly8vjaPu168fNRoNBwwYQI1Gw5YtW0pvurp3785K\nlSqxcuXKlMlkDAwMpLW1NRUKhdSk91l7NhqNDAsL46JFi7h69WoGBATwxIkTVCgUnDhxYpGvrdFo\n5OjRo+nm5lakhsTC62nWrJn0xvtFPZeEkpGens5OnTrR3d2d69evZ58+feji4sKEhAQ6Oztz+/bt\nTxyTmZnJ0NDQl0q0AGD9+vW5YcMGmpqaMjQ0lKGhoczJyeGnn37Kpk2blmofpffee482Njbs2rUr\nMzIyOGfOHGo0GlpZWbFu3bqUy+V8//33n+hVIwiC8DYSyRZBEASh2Bw8eJBOTk6cOnXqC9fOmjWL\n9vb23LVrVwlE9vpyc3NZtWpVTpgw4ZlrHjx4QK1W+0SZf1xcHKdOncpBgwZRpVLR0dGRANitWzcu\nWrSIQUFB0hshvV5Pb29vLl68mBMmTGCZMmUIgO7u7nR3d+eNGzfo7u7OzZs3MzU1VerpEBYWJn3q\nbTQa2bRpU/bp04fkozdlSqWSsbGxlMvlXL16tXTLgYWFBTt06MDY2FjKZDJGRUUxMDCQpqamVKvV\nVCqV0hjpp9myZQt9fX2Zm5vL6tWrc9asWWzdujXNzMyYmZn5Utd40aJFNBgM3Lp160sdJxTdlStX\nKJfLaW5uzrJly5Z2OALJzZs3083NjYmJifz9998ZHBzMhg0bMjQ0lA0bNpSaXv9TSkqKlKR9mS8/\nPz8eP36cFhYWdHd3l8bI7927l/b29qVWcZiZmcmePXvSxsaG9erVY1paGmNiYmhiYsKqVavSxcWF\narWaCxcuLJX4BEEQXoVItgiCIAjF6uzZs/Ty8irSpJkNGzbQYDBw2bJlJRTd6zl79iz1ej0PHTr0\nzDXdunXjiBEjCj22fft2+vn58f79+1IpvJWVFRUKBTMyMhgQEEAfHx+p2sTV1ZVubm68f/8+/f39\naWlpSZlMxrCwMH722Wdcs2YNvb29+fDhQy5fvpy+vr6MjIzkN998Iz1namoqXVxcuH79epJkWFgY\n3dzcaGJiwn79+tHW1pYKhYKmpqbU6XS0trammZkZraysaGFhQTMzM8rlcjo4ONDKyuqZfXaMRiNr\n1qzJOXPm8Pfff6eLiwtTUlJoamrKzp07v/Q1fjwa+l0cF/4uiIuLk37PFi9eXNrh/KdlZGSwa9eu\ndHV15YYNGzhx4kTq9Xp2796der2eX3/99VOryg4dOkQLC4uX6tECgFqtlmfOnGGZMmVoYWFBNzc3\nJicnMysri/7+/vzpp59K4SqQe/bsoa+vL9u1a8f169fTz8+Ptra2NDU1ZUJCAlUqFX18fEQVliAI\n7xyRbBEEQRCKXUpKCsuXL89evXq9sHfHX3/9RWdn5+dWjLxN5s2bx4CAgGeOgz5w4ADd3d0L7dto\nNLJChQpcu3YtO3XqRHNzc+r1egJgx44dmZyczICAAOnNk729PcuXL89x48Zx/fr1dHFxkUZE63Q6\nnjhxgvHx8fz8889Jki1btmTnzp2p1+t54sQJ6Xm3bdtGR0dH3rx5k1988QU7depElUpFV1dXxsfH\n09zcnHK5nDExMTQ1NWVUVBRlMhmjo6Pp7e1NhUJBExMTmpiYMCoq6pmTpH777Td6eHgwJyeHzZo1\n45gxYzhq1CgqlUpevHjxpa/x43HhI0eOFKOhi9HVq1elW8PMzc1LO5z/tO3bt9PT05MffvghT548\nKVWytGnThp6enty7d+9Tj1u9ejVVKlWRpw49fk3RaDQ8cOAAW7RoQVNTU1avXp3dunUjSSYlJbF5\n8+YluX2SZHZ2NgcOHEgHBwf+/PPPzMnJYZcuXQiAHh4ejIyMlG51FE2cBUF4F4lkiyAIgvBGpKWl\nsXr16mzVqtUL76+/ePEiy5Yty759+771f1QbjUYmJCSwe/fuz1xTqVIlbty4sdBj8+bNY506dXj7\n9m0qlUo2adKEtra2lMvlzMjIYLly5RgYGCi9STIYDNTpdLxz5w5jYmLo7u5OAIyMjGTt2rV57do1\n6vV6HjlyhLdu3aK9vT0HDBjAqlWrFmqi+emnn7JRo0bctm0bq1Spwq5duxIAk5OTKZfLaWVlRXNz\ncwYHB9Pe3p5yuZwRERH09fWVPl0ODw+np6cnP/roo2cmP+rVq8dp06ZJDXpv3LhBg8HAGjVqvNJ1\nTklJYaVKlfjRRx+J0dD/UFBQwIMHD3LJkiWcMWMGp02bxlmzZnHZsmU8e/bsc5NTtWvXpkwmo1ar\nZadOnUowauGxzMxM9urVi87OzlyzZg1Xr15NBwcHduvWjeXKlWN8fDzT0tKeeuy3334r9VJ6mUSL\niYkJV65cyUmTJtHMzIwNGzaUejr9/vvvtLe3582bN0v0Ovz111/S9LWbN2/yxIkT9PHxoYmJCTUa\nDa2trWlubv7CpuuCIAhvMxlJQhAEQRDegKysLCQkJCAnJwc///wzzM3Nn7k2LS0NTZo0gb29PebP\nnw9TU9MSjPTl3Lt3DxUrVsTEiRPRuHHjJ74/depUbNu2DUuXLpUey83NhYeHBzZu3IjBgwdj9+7d\nKCgowL1799C6dWu0aNECAwcOxKlTp0ASdnZ28PDwQFhYGLp164bw8HDcvXsXCoUCPj4+GDZsGNLS\n0rBw4UL89ttvSE5OxpdffgmDwYCGDRtiwIAB0vOGh4ejbdu2GDx4MI4fPw4PDw+Eh4dj3759UCgU\ncHBwwMOHD5Gamgq1Wo2CggJYW1vj4cOHyM3NhbW1NUjCxcUF8fHxGDRo0BN73rdvH5o1a4YzZ86g\nb9++MDc3R/Xq1dGsWTNs27YNNWrUeOnrnJmZiZYtW6KgoABLly6FpaXlS5/j34Ikdu3ahWPHjuHm\nzZtPXSOXy+Hi4oLQ0FAEBwcX+t6ZM2dQpkwZBAcH4+jRo7h16xb0en1JhC78n127duHDDz9EtWrV\nMGbMGHz11VdYu3Yt2rRpg+nTp+PLL79EYmIiZDJZoeNIonv37pg+fTqMRmORnksmk4EkTExMMGrU\nKISFhaFOnToIDAzEhQsXsGfPHri6uqJixYoYOXIk4uPj38SWn5Cfn48xY8Zg4sSJGD9+PFq3bo3J\nkydjwIABMDU1RePGjbFgwQL4+vpi3759sLGxKZG4BEEQ3gSRbBEEQRDeqPz8fCQmJuL48eNYu3Yt\ndDrdM9dmZ2ejffv2SElJwcqVK6HVaksw0peze/duNGvWDAcPHoSjo2Oh76Wnp8Pd3R2nT5+GnZ2d\n9PioUaNw7tw5DBkyBL6+vnj//fexfft23L17F6mpqahXrx5I4sCBAwAAa2tryOVy/PHHH5g6dSq2\nbNmCI0eOwM/PD5mZmTh69ChiYmLQvn17JCYmokmTJvDw8MDixYuxc+dOBAQEAABOnz6NiIgIeHl5\nYciQIejRowfS0tJgMBiQlpaGnJwcdO/eHVOnToXRaERWVhaaNGmCS5cu4cSJE1Cr1fD19UW7du0w\nYcIEjBgxAu3atXvimsTFxaFOnTqIj49HUFAQ/vrrL8TFxeHevXu4cOEC5HL5S1/n/Px89OjRA/v2\n7cPatWvh5OT00ud415HEqlWr8PfffxdpvVqtRvXq1REZGSk9FhISgsOHDyMwMBAymQwHDx58U+EK\n/5+srCwMHjwYS5YswZQpU+Dh4YHWrVsjODgYcrkcf//9N5KTk59IkAFATk4OGjVqhG3btr10okWl\nUqFDhw4YPnw4goKCoFQqYWVlhWHDhqFNmzZISkrC5cuX8dNPPxX3lp/q5MmT+OCDD2Bra4tZs2ZB\nrVYjPj4e+/btQ7Vq1ZCamorjx48jPDwcdevWxZAhQ0okLkEQhDfl5f/qEQRBEISXoFQqMWvWLERF\nRaF69eq4cuXKM9eamppiyZIlqFatGiIiInDx4sWSC/QlRUREoEuXLmjfvv0Tb4Ksra3RpEkTLFiw\noNDjXbp0wfLly2FmZoaqVati3759yM/PBwB8+OGHGDFiBO7fvy8lJVQqFfz8/PDZZ5/hiy++wK1b\nt6DRaKQqhREjRmD69OkYMmQIbty4galTp2LJkiXo2rUrOnToIJ27TJkyGDNmDC5duoRNmzahUaNG\nIIlbt27h3r17sLS0hLOzM3x8fGBtbQ3g0Rujq1evwsTEBJmZmSCJRYsWYd26dejfvz+2bt36xDUZ\nMWIExowZA2tra/To0QNDhgzB4sWLkZKSgunTp7/SdVYqlZg2bRpatGiBsLAwHDt27JXO8//Yu/Ow\nmL//f/z32Zpp35ehRSRSaVEiLYoiWZM1tMi+r1miyL5vyR6RXVkSIRGFooXsL2uRtdI6bfP4/uFn\nfq9edsry/jxv19V1uWbOnHOeZ8xc1/Mxj/M4f7MTJ058c6AFeJ/NlJiYiNTUVADAhQsXkJaWBjMz\nM9y4cQMhISF1NVXGf1y5cgUWFhZ49uwZMjIy8M8//8DFxQUDBgxAZmYmpKWlcfXq1U8GWt6+fQsz\nM7PvCrQA74NzHA4HdnZ2WLVqFbp06QKRSARra2s4ODjAy8sLycnJ2LVrF9atW1ebl/tJYrEYK1eu\nhL29Pfz8/HDy5ElkZmaicePGSE1Nhb+/P1JSUpCbm4u0tDQMGzYMWVlZdT4vBoPBqGtMZguDwWAw\nfplly5Zh7dq1iIuLQ9OmTb/Ydu3atVi0aBGOHj2KFi1a/KIZfp+qqio4ODjA09MTEydOrPFcUlIS\nBg8ejNu3b9fYFjBs2DAIhUJ07twZrVq1Qq9evXDmzBm8ffsWOTk56NmzJzgcDpKSkgC8D9xIS0vj\n4MGDyMrKwtq1a3Hz5k3Iy8uDz+fj7Nmz2LNnDx48eIB9+/YhPDwca9euhbKyMtq3b4/p06cDeH8D\n5uzsjKysLMTExMDOzg5z5szBzJkzwePx0LJlS+zYsQOGhoaQkZFBcXExjI2NUVRUhGfPnoHH40FN\nTQ1RUVEoKyuDp6cn4uPjP7pJ7NWrF2xsbDB06FAYGhoiLi4OISEhiIuLw4sXL764lexrIiMjMWHC\nBOzduxfOzs4/3M/f5PHjx4iMjJQEzr6HoqIiRowYgSZNmuDVq1dwc3NDfHw8CgoK6mCmjH8TiUQI\nDg7G9u3bsXbtWtja2sLb2xsikQguLi5Yt24dVq1aBS8vr0++/v79+7C1tUV+fj6qq6u/e/xGjRrh\n6tWrmDFjBnbu3ImePXsiNTUVKSkpYLPZMDc3x4IFC9CzZ8+fvdQvevToEXx8fCAWi7F9+3YIhUKM\nGjUKe/fuhZ6eHkxNTREVFQV3d3fs378fAoEAmZmZ6N+////JwCqDwfjfwmS2MBgMBuOXmTx5MubO\nnYu2bdsiJSXli23HjBmDdevWwc3NDSdOnPhFM/w+XC4XkZGRWLRo0UeZB7a2tmCxWJKgyQfjx4/H\nhg0bYGJiAkNDQ9y4cQMVFRVgsVgYNGgQQkJCkJubCw6HA+B9HQ5dXV1MnjwZgwcPBofDgba2NoqK\nimBubo6RI0ciMDAQV69eRWxsLHx8fKCurg4rKyusWLFC8gsxi8XC3r178fbtWzx69AgcDgdisRgK\nCgpgs9m4du0aFBQUYGNjg8rKSgDvf1lXVVWFkpIS2Gw2mjRpgvXr18PBwQFr1qyBu7s7cnJyalxf\ncHAwli5dChaLhZkzZ2LatGnYuHEjKioqEBAQ8FPr7eXlhX379qFfv37YtWvXT/X1t8jIyPihQAvw\nfjtbWFgYsrOzoa+vj4SEBHh7e9fyDBn/dfXqVbRo0QL379/H9evXweFwYGlpiZYtW0JNTQ1HjhxB\ncnLyZwMtiYmJaN68Od69e/dDgRYVFRWcOnUKx44dw86dO9G2bVscP34c+/fvh6ysLAIDA2FhYVGn\ngRYiwubNm9GyZUt07doV586dQ2FhIZo0aYK9e/fCy8sLIpEIR48exdatW3H06FFJna6mTZvi4cOH\nKC8vr7P5MRgMxq/ABFsYDAaD8Ut5e3tj8+bNcHd3x6lTp77YtkePHjhy5Ah8fX2xZcuWXzTD76Ov\nr4+VK1eiX79+KC0tlTzOYrHg7++PzZs312hvZGQECwsL7N69G8uXL8edO3fg5uYGNTU1nD17FvXr\n14e2tjbatm0L4H3h4H/++Qd5eXmIjo7G6tWrJX2dO3cO7969w4EDB7BhwwaMHDkSpaWl2LRpE7Zt\n24axY8fCx8dHEjzR1NSEtbU1Ro4cCVNTUxw4cABDhgxBRUUF+Hw+jh07Bj8/P0m2SkFBAbKyslBW\nVoaqqipkZGQgOjoab9++Rd++fTF69Gh06tQJ7969k8zJ2NgY7du3x5o1azBs2DDcu3cPmZmZZ/4F\n8QAAIABJREFUmDp1KjZt2vRRcOZ7OTk54ezZswgMDMT8+fPxv5ygW1ZWhocPH/5UH7du3YKCggK6\ndeuGd+/eYf78+bU0O8Z/VVRUYNasWXB3d0dgYCC2b9+OGTNmYOrUqZg3bx52794NPT09XLp0CY0b\nN/5kHzt37kT79u0hFosln9tvqXX0oY2cnByOHTuGwsJCjBgxAkKhEA8fPsTixYthbGyMpKQk7Nmz\np063Dz179gydOnXCxo0bce7cOUyYMAGLFi2Cra0tysrKMGbMGERERIDNZuOff/6Bj49Pjdfz+Xzo\n6+vj7t27dTZHBoPB+BWYYAuDwWAwfrkuXbogOjoaAwcO/GpxxtatWyMxMRELFy5EUFDQH3lz7eXl\nhRYtWny0lWjQoEE4cuTIR9s2Jk6ciJUrV6JDhw7Q1NTE06dPIRKJwGKx4OPjg5CQEDx48ABcLhfA\n+5s4aWlpBAQEoHXr1rCxsYG5uTmqqqrA4XAQEBAAKysr2NnZITg4GHp6epg7dy5iY2OhqqqKxYsX\nS8b29PSEvr4+SktLcevWLfj5+YHFYuHdu3fYunUrPDw8cO/ePWhqakIkEoHL5UJTUxMsFgtisRgW\nFhYIDw8HAEyZMgX29vbw9PRERUWFZIygoCCsWrUKpaWlmD9/PgICAjBz5kzIycnVSmaFsbExLl26\nhEOHDmHo0KGSm9L/NVlZWSgqKvqpPjQ0NKClpYW4uDi0bNkScnJytTQ7xr9lZGTA2toamZmZyMjI\ngIGBASwtLVFdXY2BAwciMDAQq1evxurVq8Hn8z96PREhMDAQfn5+ACD5PH3IQPuUD9sTuVwuxGIx\n5OXlsXHjRjRt2hTu7u6SjBoLCwv4+vqitLQUPj4+CA0NrZOTqIgIu3fvhqWlJVq3bo1Lly5JsuUW\nLFgAJycnWFhYYMWKFfD19cX9+/eho6Pzyb5MTU1x48aNWp8jg8Fg/EpMsIXBYDAYv4WdnR1Onz6N\nSZMmITQ09IttDQ0NcenSJZw4cQJ+fn5/5M11aGgoTp06hcOHD0seU1dXh6urK3bv3l2jbfv27QEA\n8fHxmDdvHlJSUuDi4gJVVVVcu3ZNcryzi4sLgPdHIL969QqqqqpYv349li5diqdPn0JKSgo3btyA\npaUlZs6ciRUrViAiIgIZGRkYNmwY+Hw+WrVqhdWrV+P69esA3meGlJWVgcfjQSwW49mzZ1BSUoKi\noiIuXbqE0tJS2NnZoUePHpJrePr0KaSlpVFUVITi4mLJqUUsFgtr1qyBtLQ0hgwZIgmEGRoaokuX\nLlixYgV69+4NIsKRI0ewfv16JCYmfrS16kcIhUIkJibi2bNn6Nq1608HJf5EtbGNgsfjwdfXF+np\n6Vi0aFEtzIrxb5WVlQgJCYGrqysmTZqEqKgohIeHo3Pnzpg6dSqeP3+O06dPIzU1Fd26dftsH717\n98bixYvBYrEk329cLvez24g+nDgkJSWFqqoqKCkpYdKkSejTpw88PT1RWFiIoUOHIi0tDWFhYZJt\nfdbW1pLPdm16/fo1evXqhfnz5yM2NhazZ8/GwYMHYWRkhFu3bmHKlCm4cuUKkpOTcfz4cWzcuFGy\nVfJTTExMmCK5DAbjr8cEWxgMBoPx2zRv3hwXLlzAqlWrvpq1oqGhgYSEBLx9+xbu7u4oLCz8hTP9\nOkVFRURGRmL48OF49uyZ5PEhQ4Z8tAWKxWJh/PjxWLlyJQYNGgR5eXmUlJRIbrL8/f0xZ84cZGVl\nQUpKCsD77UQFBQVYsGABlJSUMHz4cLRo0QLV1dW4fPkyoqOj8fTpUyxcuFAS+NiyZQtCQ0MxadIk\neHt7o7KyEhYWFsjNzUVYWBiICKtWrYKLiwvEYjFkZGTg6emJ3r174+nTp+ByucjJyQGPx5Os961b\ntyAjI4O4uDgA739537NnD27fvo2goCDJNc6aNQuhoaHIy8vDkiVLMHPmTPTo0QMGBgYYOHBgrWQo\nycnJ4ejRo9DW1oajoyOeP3/+033+Sf5dWPlnPHz4EKqqqnB0dKyV/hjvZWVloVWrVkhOTkZaWhoc\nHR3h7OyM06dPY9WqVQgODoaVlRXOnTsHXV3dT/ZRWFgIW1tbHD58GERUI9DyuVo9bDYbRARpaWlU\nVFRATk4OnTp1wuzZszF79mykp6fDy8sLO3bswP79+yEvL48LFy5g3759WLt2ba2vw5EjR2BmZoaG\nDRvi2rVraNy4MTw9PeHv7w99fX34+/tj3rx50NfXR3Z2Njp06PDVPpnMFgaD8b+ACbYwGAwG47fS\n19fHxYsXcezYMYwaNeqLBSFlZWURFRWFRo0awcHB4Y+7uW7dujVGjhxZ4zjodu3aIT8/H9euXavR\n1svLC9euXcO9e/cQEBCA+Ph42NvbQ1lZGQ8fPkRubi5MTEzQsWNHAO9PN6mqqoKxsTHmz5+P6dOn\n48mTJ9DQ0EBBQYGkFsugQYMgIyOD0NBQGBgYYObMmTh58iSEQiEWLFgADocDBwcHPHnyBC1btkRc\nXBx8fX1RUlICIsLdu3fx6tUrXLx4Efb29gDebw9QV1cH8D4AoKurWyMbSVZWFjExMYiMjMTWrVsB\nvH9fe/fujaVLl8LZ2RkGBgbYvHkzIiMjkZ2dLWn3s7hcLjZt2gRPT0/Y2tr+T51g8jMnN/1bdHQ0\nhgwZUit9Md6fQrZw4UI4OTlh5MiRiI2NxYULF2BtbY1OnTqhdevWmDRpEnbs2IH58+dLtgP+V3Z2\nNoyNjZGRkQEiknz3fchW+RQ2my0JjJaVlUEgEMDExERSZHbdunUwMTHBlStXMGfOHJibm6OkpAS+\nvr4ICwuDqqpqra1DQUEBvL29MWnSJBw4cABLliyRBFtiY2Ph7+8PKSkphIaGYtasWUhNTYWKiso3\n9c1ktjAYjP8FzNHPDAaDwfgjFBYWolu3btDQ0EBERMQn6xp8QERYvHgxwsLCEBsbC2Nj41840y+r\nqqpC27Zt0a1bN0yZMgUAMG/ePDx79gxhYWE12gYFBeHly5dYtWoVVFRU0KVLF8THx+Pdu3fQ0NDA\noUOH4OHhIdm+w+Vyoa6uDpFIhKtXr+LixYtYvnw5rl+/Dh6PBzMzM/j7+8PR0RF2dnbIyMiAUCiE\nnZ0dunXrhhUrVuDUqVM4f/48bt68CQ8PD3Tq1Ak+Pj7YvXs3FBUVUVRUBDk5OTRr1gwtWrTAqlWr\nYGFhgbS0NEkRzg8nGKWmpkJfX19yPffu3YODgwO2b9+Ojh07IicnB2ZmZrh16xZevHiBDh064P79\n++jXrx8SExPx4sULyMjI1Nra79q1C5MmTcLevXvh5ORUa/3+LlVVVQgLC0NeXt4P9yEQCDBz5kwU\nFxdLTnth/Ljbt2/Dx8cH8vLy2Lp1K5SUlDB69GikpqZi+fLlWLhwIWRkZLBz505oamp+tp+0tDQ4\nOTmhvLwcVVVVkkDL1zJaxGIxpKWlUVZWBg6Hg/r16+Pq1avIy8uDjY0N+Hw+unTpgnfv3mH//v1g\nsVgYN24c3r59W6sneJ05cwZ+fn7o0qULFi9eDD6fj1mzZmHNmjWQl5fHlClTEBQUBGlpaZw+fRoW\nFhbf1f+Hk9KeP38OBQWFWps3g8Fg/EpMZguDwWAw/ggKCgo4ceIEKisr0blz5y/W4GCxWJg2bRoW\nLFgAZ2dnnDt37tdN9Cs+HAe9dOlSpKWlAQB8fHywb98+lJSU1Gg7cuRIyeNDhw7F0aNHYWFhAUVF\nRRQUFODatWto2bIlOnbsCBaLhaqqKsjIyKBFixaYMWMGBgwYAIFAgObNm6OyshI8Hg+zZs2Cqqoq\nRo8ejTFjxoDD4WDr1q1Yvnw5pk+fDm9vb9jZ2SEhIQFt27YFh8PB4cOH0bBhQxARVFRUMGTIENy+\nfRvp6eng8/m4ffs21NXVIS0tjerqalRWVqJ169bYsGFDjesxNDREVFQUBg0ahPT0dGhra2PgwIFY\ntGgRzMzM4OLigmXLlmHLli0oLy/HzJkza3XtBwwYgL1796Jv376IjIys1b5/By6Xi0aNGv1UHx8y\nlJhAy8+prq7GsmXL4ODgAF9fX5w+fRo5OTkwNzeHrKws5s6dCz8/P3Tu3BknT578YqDlyJEjsLW1\nhVgsrhFo4XA4Xw20SElJoaysDCwWS/KdKRAI0LlzZ1RXV2PChAk4e/YsNm/eDBaLhcTERBw8eBBr\n1qyplXUoKSnBqFGj4OfnJ9mmmJubCwsLC6xbtw4dOnRAx44dMW3aNDg6OiI7O/u7Ay0frtfIyOh/\nKlONwWD838MEWxgMBoPxxxAIBDhw4AD09fXh7OyM169ff7G9l5cX9uzZg969e2PPnj2/aJZfp6en\nhzVr1qBfv34oKSmBtrY22rRpgwMHDtRop6mpiR49emDjxo0ICgpCdXU1dHV1weVyUVlZiVmzZmHq\n1Km4ePEilJSUAACPHj1CWloazp8/j5SUFKxevRqvX78Gm83G1atXYWdnh4CAAEyfPh23bt3C4cOH\n0axZM0yYMAFxcXHQ0dHB4cOHUVBQgNevX6Np06aoV68enj9/jjdv3kAkEuHFixfw8PDAhQsX4O7u\nDpFIhJ49e4LNZkNGRgbFxcV48OABtm3bBpFIVOOabG1tERYWhi5duuDJkyeYNm0aIiIikJOTg5CQ\nEMmRs2PGjMH69etr1LepDR+Ohp45cyYWLFjwR55e9T2sra1/OFAiLy+PEydOYMmSJbU8q/9b7t+/\nDwcHB8TExODKlSvw9/dHcHAwevbsiaVLl4LP52Pq1KmIjo7GtGnTvnhU8/Lly9G7d2/w+XyUlpbW\n2Db5uS2UHwItPB5PUtNFUVER+/btg5GREby9vZGXl4fJkydjxYoV2LdvH5SUlFBSUgI/Pz+EhYV9\n8/adL0lKSpJsS7p+/TpcXFywadMmmJub4/HjxwgJCUFmZib27NmDDRs2IDY2FtLS0j88HlO3hcFg\n/O2YYAuDwWAw/igcDgcbN26Eq6sr7O3t8eTJky+2d3Z2Rnx8PAICArBkyZI/5ua6b9++aNWqFcaP\nHw/gfdHb/xbKBYAJEyZg3bp1kJWVhaenJw4ePAgDAwPIycmBiHDixAnY29vD1dVVcgyssrIyTE1N\nMXnyZNjY2KBdu3ZwdHREZWUlkpKScOrUKVy9ehUbN27EmDFjUFhYiClTpuDVq1do164dNm3aBDMz\nMyQkJKBnz574559/4OfnByJCVVUVoqOjsXz5csjLyyM/Px8sFgu3bt2CWCxGWVkZ2Gw27ty5g0aN\nGn3y6O6ePXti8uTJcHNzA5/Px+DBg7FgwQI0aNAAPj4+mDt3LkJCQiAQCDB48OBaX3tjY2MkJyfj\nwIEDGDZs2GezBf4G6urqsLOz++LJLZ9SVVWFO3fuQENDA9bW1nU0u/9tYrEYq1evRuvWrdG3b1+c\nPXsWRAR7e3tcvnwZhw4dwsKFC/H06VOkpaXB1tb2i30NHToUM2bMAJ/PR1FR0WePdP63D0eufziZ\n6EP9pEWLFsHFxQVLly5FYmIi2rVrh+PHj2P69OmS93vatGmwtbVF165df2odRCIRAgIC4OnpiaVL\nl2L79u2oqqqCm5sbJk2ahCZNmmD27NmYNm0axGIx7t27B39//58aE2DqtjAYjP8BxGAwGAzGH2rl\nypWkra1NWVlZX22bnZ1NpqamNGrUKKqqqvoFs/u6wsJCatSoER08eJAqKipIKBTSzZs3P2rXrl07\nioiIoOzsbOJyuTRmzBjS1tYmKSkpUlBQoISEBNLQ0CAtLS0CQABIWVmZmjZtSocOHaKcnBxSUVEh\nJSUlYrPZ1KdPH2revDlVVlaSr68vjR07loiI0tPTSV1dndasWUNCoZAGDBhAN2/eJB6PR4mJicTn\n80lRUZGEQiGdP3+ewsLCiMfjkYKCAnG5XOrRowe1aNGCABCPxyMVFRWysrL67PWPHz+eHB0dKScn\nh1RVVenRo0f05s0bUlNTo7t379LWrVuJx+PRlStX6mz9O3bsSG5ublRUVFQnY/wq586do/nz51Nw\ncPBX/yZOnEgnT54kaWlpmj9//u+e+l/pwYMH5ODgQG3atKH79++TWCym7du3k5qaGq1atYp27txJ\nampqtHbtWhKLxV/sq6SkhJydnUlKSopkZWUln2EWiyX595f+OBwOcblcAkD16tWjiRMnEhHRmTNn\nSEFBgQwNDWns2LHUpUsXyVwSEhKofv36lJeX91PrcO3aNTI2NiYPDw969eoVERHFxcWRsrIyycjI\n0PTp08nNzY04HA75+flRZWXlT433b6dOnaK2bdvWWn8MBoPxqzEFchkMBoPxR4uMjMTEiRNx+PBh\ntG7d+ott3717h549e0JOTg67d++u1eKrP+rKlSvo2rUrrl27hvXr16O8vBzLly+v0eb48eOYNWsW\nrl27BldXV9y4cQNCoRCPHj2CQCBAly5dUFZWBrFYjP3796O6uhoNGjSAvr4+cnJykJWVhSVLliA+\nPh7nzp0Dn8+HlZUVevXqhQEDBsDY2BjHjh2DtbU1Zs+ejczMTBQVFeHatWvIz8+HkpISBg0aBLFY\njLCwMGhoaKBfv35YtGgRVFVVwWazUVJSgnHjxiE+Ph4PHz5ESUkJuFwu+Hw+EhISPpk9IRaL0bt3\nb0hJSaFhw4bIzc3F1q1bsWjRIly7dg379++HgYEBOBwO7t69W2tHHf9bZWUlRo4cibS0NMTExEAo\nFNb6GL/KgQMHEBcXB01NTcmR4P9WXV2N7OxslJeXo3379hg2bBhKS0vB4/F+w2z/TmKxGBs2bMDs\n2bMxY8YMjBs3DoWFhRg+fDhu3ryJrVu3YtOmTZKjlL9Wj+Tly5dwdHTEo0ePwGazJdvuvlQI9wMW\niwU2mw0ejweRSAShUAhra2tERUUhJycHlpaWqKqqwuLFi7Fw4UKkp6dDRUUFxcXFaN68OdasWYPO\nnTv/0DpUVlZi4cKFCA0NxcqVK9GvXz+Ul5dj4sSJ2LFjBxQVFbFw4UJMmjQJpaWlOHDgANzd3X9o\nrM/Jzc2FqakpXr9+XSffDQwGg1Hnfne0h8FgMBiMr4mNjSU1NTWKjY39atvy8nIaOHAg2djYSH6J\n/d3mzZtHTk5OdPfuXVJXVyeRSFTj+erqamrSpAmdO3eOsrKyiMvl0tSpU0lfX5+kpKRIWVmZYmJi\nSE1NjfT19SW/eGtoaFCrVq1ozZo1VFpaSnp6emRkZEQAyNnZmdTU1Oj58+e0c+dOMjc3p8rKShKJ\nRGRsbEzr168nNptNUVFR5ObmRnp6enT//n1is9nEYrFIKBSSWCymAQMGkI2NDQEgU1NT0tLSIiMj\nI2Kz2ZLMm1atWn322ktLS8nW1pbGjRtHampqdP/+fSopKaH69evT5cuXKSkpibhcLu3YsaPO1l8s\nFtO8efNIT0/vk5lFfwOxWEwtW7akbdu2kYaGBvn7+9P+/ftp9+7dNH/+fPLx8SFDQ0MSCoWUnJxM\nBgYG5Obm9run/Vd5/PgxtWvXjlq2bEm3b98movcZIjo6OjR27FhKSUkhIyMjGjhwIBUWFn61v1u3\nbpGqqirx+XySkpKSfG5lZGS+ms3CYrGIy+WSoqIisVgsUlFRIXNzcyoqKqLS0lIyMzMjZWVl2rhx\nI2lqatLFixcl444cOZK8vb1/eB1u3rxJVlZW1LFjR8rJySEiouvXr1PDhg1JTk6O+vTpQ9OnTycu\nl0vm5uZ19j0rFotJVVWVcnNz66R/BoPBqGtMsIXBYDAYf4Xk5GTS0NCgXbt2fbWtWCymmTNnkoGB\nAd2/f/8XzO7LqqqqyMHBgRYuXEjOzs60b9++j9qsX7+eunXrRkREFhYWZGBgQIaGhiQvL096enrk\n7u5OPj4+5OXlJdlSUL9+fWrZsiWpq6tTfn4+7d+/n0xMTCTbfPr160f9+/cnsVhM7du3p6VLlxIR\n0ZUrV0hTU5Osra1JKBTS9u3bicPh0Js3b0hFRYVkZWVJIBDQ1atXKSYmhlq3bk1SUlLEYrFoxIgR\n5OrqSrq6usRisUhDQ4NYLBYdO3bss9f/5s0bMjQ0pC5dutCAAQOIiGjLli3k4OBAYrGY2rVrR4qK\nilRaWloHq///27lzJ2loaFBCQkKdjlMXDh06RGZmZrRhwwZisVgUERFBRESvXr0iNTU1Cg4OJlNT\nU7K3t6eHDx8SgL82sPSricVi2rRpE6mpqdGiRYuosrKSysvLKSAggIRCIR0/fpw2bNhAampqtH37\n9m/qMz4+nmRkZEhOTo74fL4kiKKkpPTVQAubzSYul0tqamrEYrFIWlqa6tWrR9nZ2SQWi8nb25u0\ntLRowoQJZGdnRwsXLqwxrra2NuXn53/3OlRVVdGyZctITU2NNm7cSGKxmKqrq2nZsmUkIyND8vLy\ntHnzZmrRogVxOBwKDAz86haqn+Xo6EinT5+u0zEYDAajrjDBFgaDwWD8NbKyskhbW5tWrVr1Te03\nbtxIWlpadPny5Tqe2dc9efKE1NXVKSQkhFxcXD56vri4WJL5ce7cOeJyuTRz5kwyNDQkPp9P9erV\no8jISFJVVSVjY2PJjZmuri516NCBpk6dSmKxmOzt7al9+/YEgLS1tUlXV5fi4+Pp/v37kropRERT\npkwha2tr0tbWpvHjxxOXy6WIiAjq378/ycvLE4fDod69e1N5eTmpqKjQiBEjCAB5enqSlpYWycnJ\nkUAgIBaLRYaGhqSgoEBPnz797PX/888/pKmpSYqKinTz5k2qrKwkIyMjiomJoSdPnhCPx6OpU6fW\n1fJLxMfHk7q6OkVGRtb5WLWlsrKSmjRpQidOnCBLS0vicDj04sULIiLy9/enMWPGkL6+PhkYGFBM\nTAx5eHiQnp7e7530XyI7O5s6dOhALVq0kNSGunPnDllaWlLnzp3p/v371KtXL2revLkk2+Vrtm7d\nSgKBgFRUVEggEEg+q0Kh8Jvqs3A4HNLS0pJkt6ioqNDVq1eJiCgsLIw0NDSoTZs2NH36dHJ1daXq\n6moiIioqKqIGDRrQ8ePHv3sdHjx4QPb29uTg4EAPHjwgIqJnz56Rvb09KSgokLW1NUVERJCMjAwp\nKytTSkrKd4/xI0aPHk0rVqz4JWMxGAxGbWOCLQwGg8H4qzx+/JgMDQ1p5syZ3/SrakxMDKmrq9Ph\nw4d/wey+bP/+/dSoUSNSVVWlhw8ffvT89OnTafTo0UREpK+vTy1atCAdHR2SlZUlIyMjatGiBQ0Z\nMqRGdou6ujoZGRmRsrIyPX78mNLS0khTU5NkZGSIw+GQj48PNW3alMrLy2n+/PnUqVMnEovFVFpa\nSg0aNCBFRUXS0NAgoVBIrq6udPbsWeJwOCQlJUUcDodyc3Np8ODBFBQURGw2mzgcDllYWJCTkxOp\nqqqSnJwcSUlJkaKiIrVo0eKL2SlXrlwhGRkZateuHRERHTlyhExMTKiqqopGjhxJUlJS9Pz587pZ\n/H+5ceMG6erq0oIFC+r8l/nasGnTJnJycqKSkhLicDikra1NREQpKSmkpaVFoaGhZG5uLllLKSkp\nWrNmzW+e9Z9NLBZTeHi4JABaUVFRI8Nl/fr1dPnyZdLX16cRI0Z8U9aVWCymgIAAEggEpKmpSdLS\n0pIgSoMGDb4p0MJms0koFEqK52poaFB0dDQRvc/u+/B53bNnD9WvX59evnwpGX/EiBHk6+v73evw\nIWtnxYoVksDNoUOHSEFBgWRkZGju3Lnk7+9PHA6HXFxcqLi4+LvG+BkbNmwgPz+/XzYeg8Fg1CYm\n2MJgMBiMv86rV6/IysqKhg4d+k0nD6WmppJQKKTQ0NBfMLsv8/X1pWbNmlFgYOBHz+Xk5JCysjLl\n5+fTvn37iMvlUmBgIDVr1oykpKTI2NiY1q5dSyoqKtSyZUvJTZqRkRF1796dvLy8iOh9tkP37t0J\nAAkEAmrXrh0tWrSIysvLydjYWLKN6fz588ThcCgkJERyukhFRQXx+Xxq3LgxCQQCsre3p5MnT5K1\ntTVZWFgQi8UiAwMDcnR0pHr16pG0tDTx+Xzi8/lkZ2dHAwYM+GIAY//+/cRmsykmJobEYjHZ2dlR\neHg4FRUVkZycHHXu3LluFv4/nj17Rubm5jR06NBaPUGltpWUlFC9evUoJSWFIiIiiMVi0ZgxY6i6\nuppatmxJmzdvJgMDAzI3N6eIiAhavXo1SUlJSW6aGR97/vw5de7cmczMzCgjI4OIiF6/fk3dunUj\nc3NzysrKomXLlpG6ujodPHjwm/oUiUTUo0cPEggEpKWlVWPrUNOmTb8aaOHxeDW25QEgPT09yda/\n3NxcEgqFpKSkRNHR0SQUCmtshztz5sx3bx/KycmhDh06kJWVFd26dYuI3mfHDBw4kOTl5UlHR4cO\nHz5M+vr6xOPxKCws7Jv7ri1JSUlkbW39y8dlMBiM2sAEWxgMBoPxVyosLKR27dqRh4cHlZWVfbX9\ngwcPyNDQkAICAn7rjWhRURHp6uqSiorKJ2/yvby8aMmSJVRdXU1qamrk4uIi+ZXcysqKGjRoQCNG\njKiR3aKgoEDa2tqkpaVFqamp9OLFC1JVVaWGDRsSi8Uid3d3UlVVpSdPnlBSUhLVq1dPclPWtGlT\natWqFTk7OxObzaaMjAxycHAgTU1NatSoEeno6NDy5ctJXV2dFi1aRCwWi9TV1YnP55Ouri5JSUlR\nvXr1qFGjRqSlpUXm5ua0cuXKL66Bp6cnycrK0ps3bygpKYl0dHSotLSUQkNDicfjUWpqap2s/X8V\nFhZShw4dqFOnTn/s0dALFy4kT09PIiJq3bo1ycrKUmxsLG3bto1sbGwoIiKCzM3NSVdXlyoqKkhX\nV5c8PDx+86z/TGKxmHbt2kUaGho0e/ZsKi8vJ6L3RxnXq1ePpkyZQtnZ2dSpUyeysbGRbLn7mry8\nPLK0tCQ+n08aGhrE4/EkQZQPNZS+9PchMKOsrExsNpsAUJMmTcjf35/EYjFVVFRQmzYMZ/yEAAAg\nAElEQVRtSCgU0tKlS8nZ2ZmCg4Ml4xcWFpKent43FRD/sA47d+4kdXV1mjt3ruR76PLly1S/fn2S\nl5cnb29vCg0NJSkpKdLR0fltta8KCgpIVlaWCR4yGIy/EhNsYTAYDEatyc7OpuPHj9OBAwdo7969\nFBUVRUlJSVRRUVEn44lEIvL09CQnJyd69+7dV9u/efOGbG1tqV+/fh+dCPQrpaSkEJfLpa1bt370\n3NWrV0lHR4cqKytpzZo1xOVyacaMGWRqakp8Pp8cHR1p9uzZpKysTM7OzpIbNnNzc+rZsyc5OjqS\nWCyW3JSxWCySkpIiPz8/yU348OHDadiwYUT0fouKjIwMRUZGEgDy9vam8PBw4nA4JCsrSyYmJqSm\npka9e/emoKAg4vF4JCMjQ3w+n2xtbalhw4YkJSVFZmZmxOFwaNSoUaSlpUVnzpz57PWXlZWRvLw8\nmZqaUmlpKfXo0YMWL15M1dXVpKOjQ02bNv1l23sqKipo8ODBZGlp+cedevLmzRtSVVWlu3fvUmlp\nKXG5XJKSkqKcnBzS1NSky5cvU9OmTcnOzo5WrVpFWVlZBOCbgwT/l7x48YJ69OhBxsbGkvonZWVl\nNGHCBNLW1qYzZ85QQkIC1a9fnwICAr75O+vBgwdUv3594vP5pKSkRBwO57sCLR9OJpKTkyMOh0Ms\nFouMjIzI2dlZModx48aRrq4u9ezZk+bMmUNt27atkdE3bNiwb95q8/LlS/Lw8CATExNKS0sjovc1\ngYKCgkhWVpYUFRUpMjKS3N3dicPh0MCBA+vs+/tb6erq0j///PNb58BgMBg/ggm2MBgMBuOnpaen\nU0REBM2fP5+Cg4M/+luzZg3FxMRQQUFBrY9dVVVFw4cPJ0tLyxr1Cz6ntLSUPDw8qG3btj90Ykdt\n8fDwIFVV1U9ug7K3t6e9e/dSeXk5ycrKUp8+fUhVVZUEAgE5OjqSurp6jewWFotFAoGA1NTUqEmT\nJnTkyBEqLy+nxo0bSwIyjRo1okaNGlFsbCzl5+dTvXr16OLFi5SdnU3y8vKkq6tLBgYGxOPx6Pnz\n58ThcKhhw4YkLy9PK1eupAYNGpCJiQm1b9+euFwueXt7E4/HI1lZWZKVlSVlZWXq3r07ycjI0IoV\nK0hTU/OTdWk+CA0NJaFQSD179qSsrCxSU1Ojt2/fUnx8PPF4vF9awFYsFlNISAg1aNCgzk7wqays\npCtXrtDRo0fp0KFDFB0dTadOnaK3b99+9jWTJk2SBMX27dtHbDZbcoy2v78/7d27lywsLEhNTY2K\ni4vJzc2NGjduXCfz/5vt27ePNDU1afr06ZIga1ZWFjVv3px69uxJL1++pKCgINLS0qKTJ09+c7+X\nL1+W1DWRk5OTbP/51kCLvLw8ASBpaWmSkpIiNptN+vr61KRJE8rLyyMikmTiGBgY0PHjx0lLS4ue\nPXsmmcOpU6dIR0fnm75bo6KiSEtLiwICAiTr8ODBA7K0tCRFRUVq06YNxcbGkpqaGklLS0tqxfxu\nnTp1+iNqbjEYDMb3YhERgcFgMBiMH0BEOHnyJK5evQqxWPzV9urq6ujatSu0tbVrfR5z5szB7t27\nERcXB319/S+2r66uxqRJk3DmzBnExsZCV1e3VufzLd69ewc1NTVMnDgRixcvrvHc4cOHsXDhQly+\nfBmzZs3C0qVLMW7cOJw6dQp37txBt27doK6ujj179qBNmzY4duwYAKBFixbQ09PDzZs3cePGDZw8\neRJTpkzBo0ePQEQYPHgwTp8+jaysLBw9ehRz585FWloaTExMYGJiglevXiE5ORmjRo3CqVOnUFVV\nBS6Xi9GjRyMpKQnHjh3DsmXLMGbMGLi5uSEhIQFSUlIAABaLBRcXF5w8eRICgQAjRoxAVFQUkpOT\nISsr+9H1V1RUwNDQEMrKynByckJJSQkUFBSwdOlS2Nvb4+bNm3j+/DkEAkHdvxn/n4iICEyZMgX7\n9++Ho6NjrfT57t07JCUl4eHDh3j79u1HzwsEAjRo0ABmZmZo2rSp5PGnT5/CwsICWVlZEAqFcHJy\nQnp6OgYNGoQ9e/YgKysL7dq1g46ODlq2bIlZs2ZBWloamzdvho+PT63M/W/35s0bjBo1CtevX8f2\n7dthY2MDIsK6deswd+5cLF68GK6urhgwYAA4HA527doFoVD4TX0fOHAA3t7eEAgEKC8vR2lpqeS5\nJk2a4O7du198vbKyMvLz8yEQCMDj8VBaWgpVVVWwWCwkJSWhUaNGyMzMhJOTE4gIR48eRf/+/bFl\nyxZ06NABAFBYWAhTU1Ns2rRJ8tinFBQUYOzYsbh06RJ27NgBW1tbEBEiIiIwZswYEBECAwMhEokw\nb948GBkZ4cyZM9DQ0Pimtahr06ZNg5ycHAIDA3/3VBgMBuO7sH/3BBgMBoPx94qLi0NKSso3BVoA\n4PXr1zh8+DDevHlTq/NgsVgIDg7G2LFjYW9vjxs3bnyxPYfDwapVqzB48GC0adMGGRkZtTqfb6Go\nqIhevXohNDQUV65cqfFcly5d8ObNG1y6dAlTp04Fi8VCSUkJnjx5Ivn3nj170Lt3b8jLy4PH44HF\nYiEzMxPnzp2DmpoaNm/ejM6dO0NPTw8eHh6orKzEzp070bRpUyxevBi9evWCnp4eli1bBicnJ1hb\nW+PBgwdgs9nYtWsXbG1tkZ2djTdv3iAqKgobNmwAh8PB+fPnwePxcOrUKQwePBgsFgsAUF5ejtjY\nWFhZWaFt27Y4duwYzMzM4Ovri0/9riMlJYXZs2dDXl4ecXFxqF+/PrZt24anT58iPDwcxcXFCAkJ\n+SXvxQeDBg3C7t270atXL+zevfun+8vJyUFkZCRSU1M/GWgBAJFIhDt37iAqKgrnzp2TPB4UFITh\nw4dDKBRCJBIhOTkZfD4fSUlJCAoKQlJSErhcLi5fvowxY8ZgyZIl4PF48Pb2/ul5/y+Ijo6Gqakp\ndHV1kZaWBhsbG7x8+RLu7u7YuXMnkpOToaWlBSsrK7Rv3x6nTp36pkALEWHRokXw9vaGsrIyKisr\nJYEWFouFhg0bfjXQoqamhvz8fEhLS0NBQQGlpaUQCAQQi8U4ePAgGjVqhLy8PHTv3h08Hg/r1q3D\nggULMHDgwBpBlcmTJ8PV1fWLgZZTp06hefPmUFRUREZGBmxtbZGXlwcPDw+MGzcOqqqqOHLkCA4e\nPIh58+Zh8uTJyMjI+GMCLQBgYmKCrKys3z0NBoPB+G5MsIXBYDAYP+TOnTu4evXqd7/u7du3OHHi\nRB3MCBg9ejSWLVuG9u3b4+LFi19tP2HCBKxYsQKurq44ffp0nczpa+PLycnBy8sLRUVFksc5HA7G\njRuHlStXQkFBAQMHDkR4eDgGDRok+dW5f//+ePv2LeLi4uDh4QEAqKqqgr6+PpSUlDB37lwUFRVh\n5cqVOHPmDOrVq4eysjIIBAKsXbsWDx8+RGhoKFasWIFmzZrh0qVL2Lx5M4gIhoaGOHv2LIgI0tLS\nSE1NRXV1NZYsWYKDBw/C1dUV1dXVqF+/PjgcDgoKClBeXg5lZWU0adIEjx49QqNGjQAAT548+Shz\n54NBgwYhNzcXgYGB2LRpE5ycnDB79mwYGBhg4MCBWL58OV68eFH3b8S/tGvXDvHx8Zg+fToWLVr0\nyUDRt3j16hWio6Px+vXrb2pfWVmJCxcuIDExEVlZWTh+/DimTp0KADhx4gTEYjGKiopQUVGBYcOG\nYe7cuTAwMMCAAQOgpqaGdevWoWfPnpLg1/9VeXl5GDBgAKZOnYqDBw9i6dKlkJaWxvHjx2Fubg5L\nS0skJCRg48aNGD58OA4cOIDAwEBwOJyv9l1VVYXBgwcjJCQEWlpaKCkpQXFxMQCAy+WiXr16ePjw\n4Rf70NDQwNu3byEjIwMNDQ1J4FlZWRkrV66EnZ0dqqur0b9/f7DZbPTp0wfPnj1DUVER5s6dK+nn\n1KlTiIuLw/Llyz85TnFxMUaMGIEhQ4Zg27ZtWLt2LWRlZXH27FkYGRnh7Nmz6NWrF1atWoWuXbvi\nn3/+QWJiIhYuXAg2+8+6PTA1Nf1qAJ3BYDD+RH/WtymDwWAw/hrXr19HdXX1D702OzsbOTk5tTyj\n9/r27YudO3eiR48eiImJ+Wr7Xr164dChQxgwYAB27NhRJ3P6HCsrK2hqaqJJkyYYM2ZMjed8fX1x\n9uxZPH78GCEhIaiqqoK8vDwePXoEAHj58iUuXLiAHj16AHifKcJisZCWlobU1FS0bNkSixcvRrNm\nzdCvXz/Y29tDLBYjJiYGffv2xdixY6Gnp4dp06bh4MGDSExMRMeOHaGnp4fMzEzY2dmBy+VCUVER\nOjo6OHbsGIYNGwZZWVk8ePAALBYLBw8eRMOGDdGqVSsQEV68eIHExES8fPkSo0aNQnp6Orp37461\na9ciNjb2o+vncrkIDg7GunXrcPToUSQkJODo0aO4fv06li9fDg6Hg5EjR9b9G/EfpqamSE5Oxt69\nezFy5EhUVVV9dx+xsbHIy8v7rteIxWIkJSVh/vz5mDZtGhQVFQEAGzZskGzFWrdunST4cvbsWUya\nNAmpqal48eIFli5d+t3z/F8SExMDU1NTqKmpITMzE23atEFpaSlGjRqFUaNGYf/+/fDz84OTkxPu\n3buH9PR02Nvbf1PfRUVFcHFxwb59+1CvXj0UFBTg3bt3AAAZGRkoKyvj2bNnX+xDU1MTb968gbS0\nNHR0dJCdnQ2xWIwmTZrA19cXAwYMAADMmTMH9+7dg5qaGnr27Inly5djz5494HK5AN5vTfP398eW\nLVugoKDw0TgXLlyAmZkZRCIRrl+/jvbt26O8vByTJk1Ct27dUFlZifDwcHA4HHh6esLGxgZPnz5F\n69atv2e5f5mmTZvi4cOHKC8v/91TYTAYjO/CBFsYDAaD8d3y8/Px+PHjH359ZWUl0tLSam9C/+Hq\n6oqYmBj4+/sjIiLiq+3t7e1x7tw5BAcHY968eT+czfC9WCwWhgwZAhkZGVy6dAl79+6VPCcvLw9f\nX1+sXbsWWlpacHNzw/r169GrVy8YGxvj6NGjGD16NO7fv4/Tp0/Dy8sLLBYLRAQtLS28e/cOGzZs\nQHZ2NoKDg3H27Fm0adMG5eXlSEhIwKNHj3DkyBGMHz8excXFkJOTQ3p6OhYsWIDy8nI4OTmBxWLh\n8ePHKC4uRlRUlGS++fn54PF4SE9Ph4eHBxQUFCAlJYXq6mrk5ubC3d0d27dvR3R0NFauXIlZs2bB\nx8cH9+7d+2gN+vbti4KCArx69QqRkZGoqqrC2LFjJdk5x48fr9P/K59Tv359JCYm4uHDh+jevbsk\ng+FbPHz4ENnZ2T80bkVFBVgsliTIJBKJcP78eXC5XBgbG8PBwQFz586Fubk53Nzc0KBBA0ydOhXG\nxsbQ0tL6oTH/dgUFBfD19cW4ceOwe/durFq1CjIyMsjIyICVlRXy8/ORkZGB3NxctGrVCl5eXjhy\n5AhUVVW/qf9nz57BysoKV65cgVAoxNu3b5Gfnw/g/ZYgKSmpjzKY/pth9CHQIhAI0LBhQ9y/fx9i\nsRg2NjZo3rw55syZAwA4evQoNmzYgOLiYmzZsgXe3t7YsmULdHR0JH1NmjQJbm5ucHFxqTGGSCTC\n5MmT0adPH6xcuRLh4eFQVFTErVu3YGFhge3bt8PKykpSy2nbtm1YsWIF4uPjIS8v/93r/qvw+Xzo\n6+t/dXsWg8Fg/GmYYAuDwWAwvltmZibKysp+qo+nT5/W0mw+zcbGBgkJCQgMDPxsqv2/GRkZITk5\nGdHR0Rg6dOgPZTP8CC8vL5w+fRrr16/H2LFj8eTJE8lzY8aMwfbt21FUVIRly5ahqKgIjRs3lhS8\nvX//viS4UVRUBD6fD+B91tHTp0/RsWNHBAYGQkVFBUFBQeByuWCz2Xjw4AHc3Nwwbtw4lJeXY9Om\nTcjLy0NMTAy6du0KDoeDGTNmICAgAKWlpSgtLcXZs2dRXFyMfv36gcPhgIhQXV2NsrIyXL58GUZG\nRgDe3/Dl5+cjKioKysrKCA8Px7x58zBlyhR0794dhYWFNa6fw+Fgzpw5mDVrFjp06IAlS5YgKSkJ\n0dHRkpoS3t7evywA9m8KCgqIiYmBpqYm2rZt+81bmjIzM7+5jtGnfFhL4H1dJOD99pgVK1bg5MmT\nEIlEOH36NKZOnQqRSITExEQEBQX98Hh/s5MnT8LU1BQyMjLIzMyEo6MjxGIxli9fDhcXF8ycORNb\nt25FQEAAZsyYgRMnTmDcuHHfvN0qMzMTZmZmyM7OhoqKCl69eiUJtOjp6UEkEqGgoKDGaz4EPT9Q\nV1fH27dvwePx0LBhQ9y6dQtisRh2dnZgsVgIDw8Hi8XCvXv34Ofnh+rqauzevRuzZs2Ch4cHunTp\nUuN6z5w581EW09WrV2FpaYknT57g+vXr6Nq1K4gIa9euhY2NDbKzszFz5kwMGDAA9vb2KC8vx40b\nNz7KqPtTMXVbGAzG34gJtjAYDAbju4lEolrp40e3IX0rIyMjJCUlYcuWLZg2bdpXb9iFQiHOnz+P\nnJwcdO3a9buyGX6UsrIyOnfujBs3bmDKlCnw8vKSBHr09PTQvn17bNu2DY0bN4a1tTXWrFkDd3d3\nmJiYYN++fZg6dSrS0tKQkJAAX19fSe0JgUCAmzdvIi4uDunp6Rg2bBjevHmD/v37o7y8HFu2bIG1\ntTXmzZsHa2trODs7Y+vWrZCRkUGzZs1QWVmJsrIysNlssNlsaGtr48SJE7C0tASfz8fgwYNBRAgP\nD0fHjh3RtGlT6Ovro7S0FNHR0ejQoQPCw8Ph7u6OoUOH4vDhw7Czs8PAgQM/CkR8KOB79OhRDB8+\nHF26dMHAgQMhEomwefNm3Lt3DwcOHKjz9+JTeDwetmzZgm7duqF169a4ffv2F9uLxeIaAbMfIRaL\nkZ6eDgDYunUrqqqqoKioiDZt2mDOnDlo06YNzM3NYWZmhrlz50JWVhaenp4/NebfprCwEEOHDsXw\n4cMRHh6O0NBQyMnJ4dmzZ3B1dUVUVBRSUlJgYWGBli1boqioCGlpaWjRosU3j3HixAnY2dmhsrIS\ncnJyyMvLk9RWMjU1xYsXLz76jvhvoEVFRQX5+flgs9nQ09OT/P+xtrZGTk4ODh8+DIFAgOLiYnTv\n3h2KioqYPHmy5DSuRYsWSfoqKCjA0KFDa2wfqqysRHBwMNzd3TFr1izs378fampqePHiBVxdXREc\nHAwtLS2cPn0a586dw/Dhw+Hp6YkHDx6gSZMmP7z+vxpTt4XBYPyNmGALg8FgML5bbWQZENFP/fr/\nrXR0dHDx4kUkJCTA39//qxkrcnJyOHr0KOrVqwdHR8dfUqB1yJAh2LJlCyZOnAiBQIAFCxZInpsw\nYQJWr16N6upqrFy5Ei9fvpScFCQWi5GamgolJSW0bdsWubm5kJaWBvC+gLFYLEanTp0wefJkyQlM\nycnJUFFRQUlJCaSlpbFlyxbcuXMHoaGheP78OU6fPg1PT08UFxcjPDwcpqamkpvM6OhosFgs9OnT\nBwKBADIyMnj69CmcnZ1x584dvHr1ClwuF5WVlVBSUkJYWBjEYjECAwOhqqoKDoeDvLy8GoU+AYDN\nZiMkJASzZ8+GWCzGgQMHwOfz4eTkhI4dO8LMzAwjR478bTUbWCwWZs2aheDgYLRt2xaJiYmfbSsS\niX466wsASktLIRKJEBcXBy6Xi+7duyM+Ph6FhYVISEhAQEAAAGDz5s3o16/fT4/3N4mPj0fz5s0B\nQFKTBACioqJgaWkJR0dHnDt3DgkJCXB0dMT48eMRGRn5yfomnxMWFgZPT0/IyMiAzWajoKBA8r62\nadMGd+7c+ej/44dAy4esGQUFBUlwRigUSornNmrUCI8fP5ZkTRER/Pz8wGKx0KxZM7Rr1w7z58/H\nvn37JEerA8DEiRPh7u4uud6bN2+iVatWSElJQXp6Ovr16wcWi4Vjx47B2NgYKSkpkhpWXbp0wenT\np7Fnzx5ERkZKsuD+FkxmC4PB+BsxwRYGg8FgfLd/3wD8KB6PJyn4WNdUVVURHx+PnJwceHp6fvVm\nmMfjYfPmzejWrRtsbW1x586dOp2fg4MDKioqkJKSgh07diA0NBSXLl0CALRq1Qqampo4cuQIbGxs\nYGBggHXr1qFNmzYwMTHBjh07MH36dFy6dAnJyckYMmQIeDweAEhuzHNycnDixAm0b98eJiYm6NWr\nF6qrq3Ho0CF4e3tj1KhR0NXVhb6+PgYPHoxOnTqBw+HAx8cHeXl5ICLk5uYiJiYG5eXl6NOnD/bv\n34/x48eDiBATE4MXL17AyckJDRo0AADs2rULCgoKiIuLA5vNxs6dOxEfH49evXph27ZtiI6OrrEG\nnTt3hkAgwMGDB8HhcLBr1y7cvHkTo0aNQnh4OIqKimoEoX4Hb29v7N69G56enjXq6/ybWCyutWDk\n8ePHUVlZiQb/j703D8gp/f//H/dddyuJNCiVIkJSISkUk4RsWaKoGDuDKMt7LFkbu6yTJTsha5G9\nMiOkbI01S9lS0ibt3f3+8HG+0xsjNQvv33n8Nc51rnOuc44a53ler+ezXj06d+7M3LlzcXR0pHr1\n6tjZ2REREcHr16/x9/ev9Pm+BXJychgzZgxeXl4EBgayYcMGNDQ0yMnJYdiwYfj6+nLkyBEmTJiA\nl5cXy5cvJzIyskxE+eeQy+V4e3szZcoU6tSpQ15eHtnZ2RQVFQHQtWtXLl++LPz5PX8UWkpLS1FX\nVxeq9963H8G7Spc3b96wY8cOmjZtCsCyZcuIi4sjLy+PNWvWMHDgQNatW4ehoaFw/OPHjxMREcHi\nxYspKSlhyZIl2NvbM3r0aI4dO4aOjg5v375l+PDhDB48GIBdu3ZRp04d2rdvz3fffcejR4++2Qoo\nsbJFRETkW0QUW0REREREvhhjY+NKCyU6Ojr/aExtlSpVCA0NRVVVFScnJyFJ5FNIJBJmzZrFrFmz\nsLe3L1eUdEWRSCRCuoiuri6BgYG4u7sL/ibe3t6sWLECgMWLF5OQkEDPnj1JTk5GLpdz+vRprK2t\nadGiBQ8ePKBKlSrAu9hlHR0d2rVrh6+vL8XFxSxbtoyQkBAsLCzIy8vj3LlzvH79mr1799K7d2/h\nPikpKZGWlkbjxo2RSCTUqFEDiUTCuXPnMDU1pWrVqlhZWaGoqEhoaCi9e/dGTU2NvLw8qlSpQnFx\nMerq6qxbtw6AatWqcejQIebNm8f8+fMZMWIEt27dKnMP5s6di5+fHyUlJXTp0gVra2tCQ0M5fvw4\nffv2ZfHixaSkpPxtz6E8vI+GnjJlCosWLfpAWFFRUfnLxMj3PizJycmoqKiQkpJCdHQ0U6dORSKR\nMH36dCwtLalRo0alz/e1ExUVRfPmzcnLyyM+Pp7OnTsDcOXKFSwtLSkpKeH69evIZDJatGhBlSpV\niImJEQSN8pCXl0evXr3YvHkzhoaGZGRkkJOTI1Tgubq6cuLEiQ+q4/5baFFRUaG4uJiioiKqVatG\nUVERxcXFyGQyNDU1mTVrlrD+c+fOsWjRIjIyMjhw4ABTpkzBycmpjCiSmZnJyJEj2bx5MykpKdjZ\n2XHs2DGuXLnCsGHDkEgkxMXFYWpqyqFDh7CysuK3335j3rx5zJkzh/HjxxMfH0+dOnUq+xj+NQwN\nDXn16tUHnk8iIiIiXzOi2CIiIiIi8sXo6+uXSceoCF/yEvRXoaSkxK5duzAzMyt3i5CXlxfbt2/H\nxcWFkJCQv21tnp6eHDx4kOzsbHr16oWjoyNjx44F3nmaPHnyhNjYWLp164a2tjabNm3C1NSUpk2b\nsmHDBnx9fbl8+TIxMTGMHDlSiIJ++PAhR48epXr16gQFBdGgQQOGDh2KiYkJEomEu3fv4uzsjI+P\nD61bt6Z69eqsW7cOS0tLwsPD2bx5MxKJBFVVVXJzc1mzZo3QSnT27FmaNGmCVColMjKSs2fPoqqq\nilwuR1NTk6tXrxIVFSXEVTdt2pT169cza9YsZs+eTa9evQSzUXiXIqWlpcWePXuAd1/8i4uLCQgI\nwM7ODuCrMPR8Hw29e/duxo4dW+blW1FRkVq1alXq+EpKSqipqXHr1i1atGhB/fr1WbVqFS4uLmRl\nZdGzZ0+ys7O5cuUK8+fPr+zlfNXk5uYyceJE3NzcCAgIYMuWLWhqalJSUoK/vz/Ozs4sWLCAoKAg\ngoKC6NKlC/PnzycwMBA1NbVyn+fVq1fY2NgQERGBsbExqampQlUXwNChQ9m3b98HrY//LbTIZDIk\nEgkFBQVUrVqVKlWqCIJNs2bN6NatG6NHjwbemYS7ubmhoqLC8uXLiYmJ4d69ex8Yent7e9O9e3fu\n3btHmzZt6NevH+fOnaNevXqUlJSwcOFCOnTowOvXr5k9ezaTJ0/GysqKe/fuce7cOZYuXYpU+m3/\nk19BQYEmTZqUEWhFREREvna+7d+8IiIiIiL/GiYmJhWeW1BQUGmxpqJIpVJWrVpFnz59sLW15eHD\nh5+d4+joyKlTp5g4caJQYfJXU6tWLTp27Ci0pyxfvpzY2Fh27dqFoqIiP/74IytWrBAqQGJiYhg8\neDDp6enI5XIOHDjAwIEDMTEx4caNG1SvXp3S0lJSUlJo1qwZDRo0wM/Pjzdv3jBjxgwiIiIYMGAA\neXl5rF27Fnt7e6Kiorh58ybTp0/n5cuXpKWlAe9aJ54/f45UKuXEiRNkZmbi6urK/v37GTVqFBKJ\nhJcvX6KqqoqtrS1GRkbk5uYKiUVLly4VrrNv374MGDCAQ4cO0bVrV9zc3ASjZIlEwrx58/Dz86Oo\nqAgLCws6depEjx49mDFjBm5ubhw5coQbN278Lc/gS6hbty6//vorDx48oHfv3rx9+1YYq8zPBrwT\nMydPnoyKigr16tXDxMSEpKQkrl27hq+vLwoKCsycOZNq1arh5ORU2Uv5arlw4XXlesEAACAASURB\nVALm5uakpaURHx+Ps7Mz8K5iq2PHjpw6dYrY2Fg6duxIr1692LlzJ5cuXcLV1fWLznPv3j3Mzc1J\nSEjA2NiYV69elamgGjFiBEFBQR9UMUml0jJCi4KCAsrKyuTl5aGhoYGenh7JyckUFRXRqVMntLW1\nhZ+F/Px8+vTpw3fffUeXLl2wsLAQDG5VVFSEcxw7doyzZ89y9+5dtm7dyq+//sqECROQSqUkJSXR\nrl07Vq5cKUSV3759G2dnZywtLUlMTKRdu3YVvf1fHaJvi4iIyLeGKLaIiIiIiFSIli1bYmxs/MXz\n5HI52dnZNG/enHPnzv0NK/s87w1PfXx8aN++PdevX//sHHNzc6Kjo9m0aRPe3t5/i7nv+1YiADU1\nNXbv3s3EiRN5/Pgxw4YNIzw8nGfPnuHp6YmamhohISHo6upiYmLC2rVrhXaB9+lDysrKSCQSYmNj\nCQ0NxdramqVLl6KhocH8+fNJSkpCTU1NMMvdt28fhoaGmJmZCRUqx44dE14QtbS0UFNTw9PTk0aN\nGlG7dm309PSQSqW8efOG169f8/DhQx4/fkxxcTF6enpYWlqycePGMqktCxYsQEFBAUVFRQoLC/np\np5+EMXt7ewwMDNi+fTsA8+bNY+/evaxbt47Q0FDh/P9GFPR/o6GhwbFjx9DW1i5TKWVpaUnNmjUr\nfFypVEpcXBxyuZwXL15QXFzM4MGD0dfXRyaTsXfvXm7cuIGXl9dfdCVfF3l5efj4+NC3b18WLVrE\nzp07hVap4OBgWrVqRbdu3Thz5gxJSUlYWFjQoEEDLly4gJGR0RedKyoqCisrK7KysjAwMCAtLY2n\nT58C735PjBgxgg0bNnwwTyqVIpfLywgu7/1jNDU1adKkCXfv3qW4uJjevXvz4sULdu/eLcSmjxkz\nRmgtWrhwIf3792flypU0bNhQOEd6ejqDBw8mOzub77//ngsXLghC3u7du2nevDm3b99m4MCBHDp0\niL59+7J582YWLVpEZGQkmpqaFX0EXyWib4uIiMi3hii2iIiIiIhUCKlUSt++fb/o5UZFRUXwVVi7\ndi2enp6MGzeuTFXAP8no0aNZuXIljo6OREVFfXZ/fX19fvvtN65du0b//v3/ktSZP+Lo6MjLly+F\nyg0LCwumT5+Ou7s7VapUYfDgwaxZswZFRUWmTp1KeHg4o0aNIi8vD7lczo4dO5g0aRK6urpER0dT\np04dSktLyc7OxtLSEgUFBdasWcPz58/x8vIiPz+fkSNHUlRUxK5duxg1ahQZGRlERUWxZcsWSktL\n2bZtGw0bNkRTU5PU1FTq1KlDREQEp06dwtXVlbCwMGxsbAAYOHAgFy5coHXr1ujp6fH06VNevHhB\n1apVcXNzE65TQUGBPXv2cPDgQQYMGMDevXvZu3evMD5v3jzmzZtHQUEBhoaGeHh4EBERwdKlS1FU\nVOTOnTsfGOz+W8hkMjZv3kyPHj0EM2UFBQVsbGwEo+IvwdjYmAULFiCTyfDw8KBVq1Y0bdoUiURC\n3bp1SUxM5O7du4IZcXh4OIWFhX/Dlf07XL58GUtLS548ecLNmzfp3bs38M7s2cPDg9mzZxMeHs7k\nyZP5+eef6du3L+vXr2fZsmVf7JWzY8cOunXrhoKCAjVr1iQzM1MQWt4bRH9OaHkvutaqVYvMzExq\n1KiBpaUlV65cQS6X4+zsTExMDGFhYVStWhWADRs2EBkZybNnzwgJCcHb25t27dqV+RlJSUnB3Nxc\naNH76aefUFRUJDMzkwEDBvDjjz+ipKREcHAwFhYWmJubk5uby7Vr15g0adI/6of1TyFWtoiIiHxr\nSEq/hk9DIiIiIiLfLCUlJZw8eZIHDx6U8d/4I4qKitStWxcbGxtq1qyJk5MTLVq0YN68eXh7exMd\nHc3WrVtp27btP7z6d5w5cwY3NzchgehzFBQU4OXlxdOnTzly5AhaWlp/2Vr8/Px4/fo1q1evBt5V\nAr03i/Xw8KB169YkJSUBoK2tjZubG7Gxscjlch4/fkxCQgItW7ZELpczZswYFixYQGFhIUpKSmho\naAitGJs3b+bXX3/F3d0dVVVVEhISsLa25vXr18jlchISEmjRogXXr1+noKCAYcOGERwcTHFxMdWr\nV0dNTY2jR4/SqVMnVq9ezeDBg7G2tubJkyeoqakhl8tJS0tDVVUVNzc3VqxYQWhoqGAMCnDt2jUc\nHR1Zv349o0eP5vTp05ibmwPvWpecnZ0ZM2YMaWlpmJiYcPHiRYKDg1m4cCFVqlTh2bNnX1WE7bZt\n25gyZQr79++nffv2REdHExUVVW4xxMjIiIyMDIKDg2nevDm1a9cu1zx9fX369u0rvMx/ixQUFDBn\nzhyCgoJYtWoV/fv3F8aio6MZNGgQjo6OLFu2jOzsbAYPHiyIhHXr1v2ic5WWljJ37lyWLVtGtWrV\nKCgooLS0VGibU1NTo2fPnoJ30HskEgkSieQDocXQ0JDExES0tLSwtLTk7NmzwLsksXv37hEeHk7L\nli0BuHTpEs7OzigqKrJlyxZSUlJYsmQJV65cETxmDhw4wLBhwwB4+PChUNVz/vx5Bg4cSFFRES1b\ntmTdunWMHz+e8PBwevfuzfbt28u0IP2vkZycjJmZGampqf+TYpKIiMj/Hgp+fn5+//YiRERERES+\nXaRSKcbGxlhYWAj/0FdWVkZVVZXq1atjZGSEk5MTdnZ2aGlpoaqqiqurK8uXL+fatWtC8oeXlxcv\nXrygffv2FaoIqAxGRkZ06NABd3d3atSogYWFxZ/ur6ioiIuLC/fv32fKlCl069aN6tWr/yVrMTQ0\nZNy4cYwfP14w23RwcGD48OGCd8qbN2+wtbXl1atXBAUFMX/+fC5evMjr169RV1enR48exMTEkJqa\nipKSEq9evaK4uBgbGxvevHnD+fPn6dy5M1ZWVly+fBlzc3NiYmJIT09nxIgRHD58mB9++IHmzZuz\nc+dOqlevjpOTE9u3bxeeTdu2bYmPj6ewsBAHBwdCQkJ4+vQpS5cuZceOHRQWFlJYWIiKigpVqlTh\n1atXhISE4O7ujoaGBgB16tRBR0eHGTNmMGfOHMaOHcugQYNQU1OjUaNGjB07ltGjR1OtWjWKi4vZ\nvXs3q1evJi4ujmvXriGTyQTj3L+C4uJiYmNjuX37Nvfv3ycpKYnc3Fy0tbXL9XJnbm6OhYUFrq6u\n6Onp0aVLF6pVq8abN2/KtFH9N9WqVcPMzAxbW1u8vLzo2rXrFyUMZWVl8fz5c0xNTVFQUCj3vK+F\nuLg4unTpIrSttW7dGnj3PObMmcOUKVNYs2YNPj4+RERE0LVrV3r37k1QUNAXt8oUFhYyZMgQtm/f\njp6eHunp6RQVFQlCsZaWFh07dvzADPtTQkuTJk0EQaR58+ZERkaioKCAkZERaWlprF27lu+//x54\nV63SqVMnatasiYeHB7a2tri5uXHs2DF0dXXJyMhg+PDhbNmyhaKiIg4ePEjjxo2FVrtJkyZRWFjI\nggUL8PDwwM7Ojjt37rBt2zbmzJlT6YS4r50qVaqwaNEivLy8hMQ1ERERka8ZsbJFRERERORfIScn\nhx49eqCjo8PWrVvJzMxk3LhxXL9+nW3btgkvXP8k9+7do3PnzowZM4YpU6aUa87atWtZsGABR48e\nFb5eV5YuXbrg7u7OoEGDhG2hoaGMHz+eNWvW4O3tzd27d0lPT0dXV5dJkyZx4MABZDIZT58+5dmz\nZ9jY2JCens748eOZM2cOhYWFSCQS9PT0hGSTkydP8uTJEywsLGjXrh1HjhxBW1sbqVRKq1atOHjw\nIOrq6qipqfHkyRO+++47mjZtyo0bN/jhhx84efIkjo6OFBcX8+bNGw4dOsTSpUtZsGABb9++pUGD\nBjx+/BiJRMKPP/7IiRMnUFdX59y5c2VeDCdMmMCDBw9o1qwZMTExnDx5EplMRu/evbGzs2PixInk\n5ubSoEETPDwiePlSnz17wigszMPTswceHmp07Fjx+52Wlsbly5d59OgR6enpH4zXqVOH+vXr06ZN\nm3Il3Ny8eRNnZ2fGjRuHr68vAPfv3+fmzZs8fvxYOIe6ujp2dna0bt0aZWVlhg0bhra2doWrE1q3\nbv1NGea+Fw7Wr1/PihUrcHNzE0StR48eCcLc1q1bqVmzJjNnzmTnzp3s3LkTe3v7Lz5fZmYm3bt3\n5/fff8fExIT79++Tm5tLfn4+AAYGBjRq1IhTp06VmfcpoeV95ZeGhgaNGzcmNjYWqVSKuro6urq6\nuLm5MXXqVACKiopwcHCgqKgIDQ0N9u/fT5s2bZg0aRJDhw7lxIkTDB8+HBcXF1JTU9HW1mbVqlXc\nu3cPV1dXXr58iba2Nnv37uXgwYPMnTsXIyMjzpw588WVPd8y9vb2zJgxAwcHh397KSIiIiKfRaxs\nERERERH5V1BSUqJ///5s3bqV0NBQ3N3dcXV1pVatWnh4ePD69WvatWv3j36trVmzJv369cPX15fH\njx/j4ODw2YoGKysr6tevj6urK6amphUyDf5vVFVVWb9+PUOGDBG2NWrUiISEBC5fvsybN2+oW7cu\n5ubm3L59mx07duDn50d8fDyvXr2iSpUqDBo0iKNHjwovac+fP6e0tJTmzZvz8OFDXr58SaNGjWjR\nogW5ublkZGTw4MEDCgsLadiwIVeuXKFLly7Ex8fz4sULCgsLhQQUgOvXr7N7926WLFlCfHw8Cxcu\nZN++fTx48IBBgwaRkZHBnTt3kEgkGBgYYGJiwokTJ9DT0+P+/ftlXpYcHBzYuHEjhoaGpKamcvXq\nVZycnGjSpAmjRo3C1XU0Cxaoc+uWJydO1OHGDSklJSaAKTduyAgOhrNn4c0bsLKCL+kwuHXrFgcP\nHiQxMfGTHjw5OTk8efKEhw8foqur+9l2nVq1atGvXz+mTZtGfHw8Tk5OglD17NkzQkNDiYyMZM+e\nPTRo0ABFRUWuXLmCv78/NjY2FTZfLigooEWLFt9EzO+NGzfo1q0bubm5HDt2DFtbWyHVZ8eOHfTr\n14/Ro0ezevVqMjIycHZ2JisrixMnTlQoNj4xMRFbW1seP36MpaUl9+/fJzMzU2jxatasGdra2kRG\nRpaZJ5FIUFBQoKSkpIzQYmtry9WrV1FXV0dfX587d+4I423atMHExIRFixYJvz98fHxISEggJSWF\nU6dO4evri7a2Nr6+vowfP56AgAC2bt2Krq4uW7ZsYf/+/QQFBdG/f3+ys7Px9PRk7dq1uLm5ERwc\nzJgxYzh69Oj/nAnu54iNjaW4uBhra+t/eykiIiIin+Xr/7+xiIiIiMj/LKqqqhw+fJiCggL69u1L\nQUEB/fr148aNG9y7d48WLVoQFxf3j67pfYTqhQsXGDp0KEVFRZ+d06tXL0JDQ/nhhx/YuHFjpdfw\nvvLk/v37ZbYvXbqUmzdvYmVlJURQ+/v7U1hYSE5ODikpKdStW5cFCxZgZ2eHiYkJiYmJuLq6oqqq\nilQq5fz58xQXF9OjRw98fX0pKSlhypQpXLx4kQkTJlBQUMDNmzepXr06o0ePpn///hQUFLBx40Yc\nHBxITU1FQ0ODwsJCLl++TM+ePVFSUiI/Px8VFRUeP35M+/btSU5ORkVFBTU1NRITE9m5cyfdu3en\nffv27Ny5k+PHjwvXJZPJ2LdvH9u2bWPw4MEcP36cbdu2YWZmhrl5f9q0yWLlSkhO/nirVmEhREXB\nhAng4QHleGQA3L59m7CwMN68eVOu/VNSUggJCSE1NfWz+/4xGtrFxUUwgc7MzOT27dv4+fmhqqoK\nvPPlGTduHJMmTaK4uLh8i/8IaWlpXL16tcLz/wmKioqYN28enTp1wtvbm6NHj6KjowNARkYGAwYM\nYPHixZw9e5aJEydy+PBhWrVqhYuLC2FhYWhra3/xOWNiYmjZsiWpqam0aNGCR48ekZaWJtzr9u3b\no6CgwIULF8rMey+0FBcXlxFaOnbsyJUrV1BSUqJmzZokJycjl8uF319FRUWsX79eEFp2797NwYMH\nSUpKIiQkhJMnT3L+/Hnc3d0xNzenuLiYmzdv0rx5c0aPHs2KFStwdXVl1qxZqKmpsX//fhwdHTEx\nMeHOnTucPHmSgICAb7JlrLKIiUQiIiLfEqLYIiIiIiLyr6KsrExISAhKSkr07NmTvLw8atWqxYED\nB5g+fTpdunRh9uzZ/2jiSo0aNTh9+jSpqam4uLiQm5v72TnW1tacP3+eRYsWMXPmzEpFEyspKeHh\n4cHmzZvLbFdVVWXPnj0cOHCAW7ducf36derVq0f79u3x9/dn8uTJaGlpUVJSwsaNG1myZAn5+fns\n2rWLNm3aCGtSUVHh8OHDVK1alW3btqGurs6iRYs4e/Ysurq6FBUV8eLFCxQVFcnOzkYqldKzZ08O\nHz4MvEvMadSoET///DPjx4+npKSElStX4ubmRnFxMZGRkRgZGdG3b18yMzNRUlJCKpViaWnJjh07\n2LlzJ0OHDhWSXwBq167N/v37mThxIitWrMDX15fjx6+SmPgzz5/rlOu+lZbCzp0wbNi7//4z3r59\ny6lTp4QWkvKSnp5OWFhYuZ7v+2hoLS0t7O3tSUlJISYmhsLCQjw9PYX9tm7dilQq/SKflk+RmJhY\n6WP8Xdy6dYs2bdpw4cIFrl69iqenpyBIREZGCqbAV65coWHDhowbNw4fHx9CQ0Px8fGpUMXO4cOH\nhfYdMzMzkpOTefLkiSCcuLi4kJKS8pH4d2dgG8XFYUAkcvlRYCVt2/5AdHQ0UqkUDQ0NJBIJWVlZ\n5OXlMWLECGJiYoTfZ/CupWz8+PEoKSmxYMECNDU1mTBhAq1ateKHH35g1apVbN68GQ0NDcaPH4+V\nlRVDhw4lNjaWNm3acP36dQ4cOED37t0xMzMjMTGRjpXpmfvGEROJREREviVEsUVERERE5F9HSUmJ\nPXv2oKWlRbdu3Xj79i0SiQR3d3euX79ObGwsrVu35ubNm//YmtTV1Tly5Aiampp07tz5k0lLf8TY\n2Jjo6GhOnTqFl5dXpQSiYcOGsW3btg+OYWZmxowZM1BSUmL58uUALFmyhOzsbDQ0NHj48CG1atVi\n7ty5mJqaCqa6zs7OqKioIJFIiImJQU9Pj9atWzNz5kzevn3LwIEDBd+QP5p/rlq1Cg0NDVJSUsp8\nyS8sLKR69epMnjyZX375haioKHr16iVEIXt6epKeno6ysjJZWVmUlJQQGRmJhoYGeXl5TJw4kQED\nBpSpHLK2tmb+/Pn4+voSEBBA377PuXtX9Yvv3c6dEBj45/tcunSJrKysLz42wNOnT7l792659pXJ\nZAQFBeHs7EybNm04fPgwjo6OQntcZmYm//nPf1i9enW5KmY+x9cYA11cXMyiRYuwt7dn5MiRhIeH\nCz4jhYWFTJ8+HTc3NwIDAwkICODJkydYW1sLLWUV8W8qLS1lxYoVeHh4oKKigoGBAVlZWdy9e1cQ\nyoYPH05sbCz37t37w8xxwG/AAUpLBwOdATugGzCB335bQn5+MEpKndDV1SUxMZHCwkKGDx/OwYMH\nOXbsmCCaZWRk4OLigomJCTY2Nnh4eODs7CxUy7z39gHYu3cvx44dE9Kr5s+fz4oVK2jbti2bN29m\n/vz5/Pbbb3+JIPctY2pqyq1btyrcaiciIiLyTyKKLSIiIiIiXwWKiops376devXq4eTkRHZ2NgA6\nOjqEhYUxfvx4vv/+exYsWFCpVosvQSaTsW3bNlq2bImdnR0vXrz47JzvvvuOiIgIMjIy6Natm3Ad\nX0rDhg1p1KgRYWFhH4yNHz+ehg0bsm/fPl6+fImFhQWmpqbMmTOHCRMmoKenR1FREUFBQSxYsIC8\nvDw2bdpEp06dhBfN9PR0oeJl2bJlSCQSAgIC2LBhAx07dkQul7N3715cXV2pVq0aUVFRrF+/nuzs\nbB48eMDr16/Jzs4WvE50dXVZsGABNWvWJCcnBwMDAyIiIujduzc1atQgOTmZqKgo3NzcWLt2LVOm\nTEFTU5OffvqpzLWNGDECW1tbtm+PBCr2BV8uhwMH/mxczoMHDyp07Pd8SSuDRCJh9uzZODg4kJmZ\nSc2aNYWxdT/+yG4tLVqOHo3nwoWMXbWKYYGBdAkLQzslpVJr/Bq4e/cubdu25dSpU8TGxjJ8+HCh\nmuXevXvY2Njw+++/c/36dbp06cL27dtp27Yto0ePZu/evRXyJCkuLmbcuHHMmzeP7777DnV1deRy\nObdu3RL2mTZtGkePHuXJkyf/t0UCrAOWAbaA0ieOXh3oSX7+Fq5ebYpcLqdPnz4cPnyYffv20aBB\nA+Dd3zF3d3f09fV58+YNAQEB2NnZkZSUxMqVKwkODhYi4yMiInB3d0dRUZF69eoRExODTCajSZMm\n5OTkcOXKFaZMmSLGHfMuuatGjRpfdQWXiIiIyHtEsUVERERE5KtBQUGBTZs2YWpqSqdOnYRqEolE\nwpAhQ4iLiyMqKgobGxvu3Lnzj6xJKpWyfPlyBg4cSNu2bUlISPjsHDU1NQ4dOoSxsTHt2rXj+fPn\nFTr3sGHDPuoBI5FI2LVrFwoKCkLayeLFi0lOTqZx48bEx8dTo0YN/Pz80NHRYfjw4aSmptKhQweh\nuiU+Pp7WrVtTu3ZtAgICePnyJa1atcLR0REzMzOkUim5ubmoqKiQkZFBUVERJSUlDB48GLlcTvPm\nzTExMaF79+74+PgwatQo4uLiaNeuHYWFhezevZtu3bpRq1YtVFVVKSkpQVNTk4yMDC5evEhSUhLb\ntm0jODj4A0FpzZo1XL/enrw89QrdN4ALF+DixY+P3b9/n5cvX1b42MCfGup+jIKCAk6dOoWlpSVb\nt27l+IoVZNvbM3bnTjrevg2xsWhnZFAzPR3d5GSsYmMZunkzA3btQusLKl7et6/825SUlLBs2TLa\ntm2Lh4cHp0+fxsDAAHhXdbJx40batm3LDz/8wNGjR1FTU8PT0xN/f3/OnTvHyJEjKyQu5OTk0L17\nd/bt20f9+vXJyclBJpOVEVr8/f0JDAwkpYyYtRwYyadFlrIUFmohly+hYcPJXLt2jaVLl9K+fXth\nfM6cObx8+VIwj7a0tOTWrVvcuHGDAQMGAO8EmaVLlwqVTsOGDePcuXNMnTqVESNG0LVrVx49ekTz\n5s2/+D78LyP6toiIiHwriNHPIiIiIiJfHaWlpXh7e3P+/HlOnz4tfAF+P7ZhwwZmzJjB1KlT8fb2\n/seMIjdt2sSsWbMICwvD0tLys/uXlpayePFi1q1bx7FjxzA1Nf2i8+Xl5VG3bl2uXbuGvr7+B+Mb\nNmxg9OjRPH36lDp16mBkZES1atVwdnYmJiaGy5cvs2zZMlxcXDA0NERbW5vWrVuzd+9e5HI59erV\nIysri379+lFaWkpgYCDJyck0a9YMNzc3Vq9ejZqaGrNmzWLatGnMnDmT6dOnU7VqVerXr4+Kiopw\nzBs3bnDu3DmqVKlCVlYWCgoK7N+/n1mzZpGXl0dmZibJycnUqlWLAQMGoKKiws8//0x0dDS9e/cm\nJiZGeBkHsLQs4No15S+6X//NhAmwcuWH26Ojozl9+nSljg0watQoatWqVa59V65cydmzZ5HJZPSr\nW5dW69fToJwVWmk1anCkZ0+e/eH+fIouXbpgZWVVruP+XSQkJDBkyBAUFBTYsmULRkZGwlhaWhrD\nhw8nMTGR3bt307hxY27cuIGrqys2NjasXr0adfWKiWwvXrygc+fOJCcnY2lpSVxcHGpqajx79gxA\nqN766aef/ssUuSMQBnx5y5qaWgITJuxg4cK5wrbQ0FBGjRqFRCKhY8eOhIWFUVxczJkzZ4Rn8/Tp\nUwYNGkR8fDzZ2dmEhoZSq1Ytoapvw4YNeHh4VOg+/K8zdepUNDQ0PqiKExEREfnaECtbRERERES+\nOiQSCStWrMDR0ZEOHTqU+QItkUgYOXIkMTExhIWF0b59+3JVm/wVDBs2jDVr1uDk5ERERMRn95dI\nJEydOhV/f386duxYrjl/RFVVlYEDBxIUFPTR8REjRqCvr0/Pnj0BWLhwIXfu3MHe3p7Y2FiqVq3K\n7Nmz0dDQYObMmaSnp2NlZYVMJgMQ4q1zc3M5dOgQt27dok6dOkyePJknT54gk8nIz88nIiICdXV1\nNm7ciKqqKp06dSIhIYFHjx4RExPDmDFjePToEY0aNcLS0hIVFRVKS0vJy8sjNTWVrl27YmBggLq6\nOtnZ2RgbGxMUFER+fj42Njb4+vri6upaxm/k7dvKCS0AGRkf/55UUlJS6WND+f1RsrKy8Pf3x9/f\nH9XkZJyPHCm30AJQMz2dnkePUiMt7c/3q1mzXCLg34VcLmf16tXY2NjQv39/IiIiyggtp0+fxtzc\nnAYNGnDp0iVMTExYt24dDg4OzJw5k6CgoAoLLfHx8bRs2ZJnz57Rrl07rl69ikQiEYQWRUVFfvnl\nF6ZNm/aR9CkvKiK0AOTmGmNk5Cf8OSEhgR9++AEtLS1KS0t58uQJ+vr6zJ07VxBa9u3bR/Pmzblz\n5w55eXmEhoZy9epVrK2tqVatGnfv3hWFlj9BrGwRERH5VhDFFhERERGRrxKJRIK/vz8uLi7Y29t/\n4JdiaGjIuXPncHV1pU2bNqxateofMU10cXERvEwOHjxYrjlubm7s3buXAQMGsHv37i863/DhwwkK\nCvqkQLBu3Tri4+PZsmUL/fv3p2rVqixcuBBPT0/MzMx4+/Yte/bsYdy4cSgpKbF06VI8PDxQVFRE\nIpEQGRnJiRMnGDp0KFOmTAHA29ubmzdv4ujoiFwu5+LFi9jb2/Py5Uvu37/P1KlTkUgkaGho0KhR\nI86cOcPmzZt5/PgxcrkcJSUlioqKCAwMxMPDg9zcXO7cuUNJSYnwQm5hYcG+ffsAmDRpEtra2kyf\nPl24rr/iUV67duOj2/+KVhuJREKVKlXKte/SpUvp0qULpqamDLx/n6qCfXiIfgAAIABJREFUT0j5\nqfn6NXZRUX+6j5GRkWC8+0/z6NEjOnbsSHBwMBcuXGD8+PFCelB+fj6TJk1i6NChbN26lSVLlpCb\nm0vfvn3ZtGkT0dHRuLu7V/jcp06dol27drx9+1aIZc7Ly+P169fAO9Fy3bp1TJw48SPJYjWB7yt8\nboBDh95dZ05ODr1790ZbW5u7d+/i6+tLixYtMDAwYMKECWRnZ+Ph4cG4ceMoLS2lXr16DBkyhDlz\n5jBnzhyGDRvG7du3qVevXqXW87+OmEgkIiLyrSCKLSIiIiIiXy0SiQQ/Pz88PDyws7P7g5nlO6RS\nKePHjyc6Opq9e/fSsWNHHj9+/Levq0OHDpw8eZJx48Z91FPlU3POnj3L9OnT+fnnn8sdDd28eXNq\n1arFqVOnPjru5OSEgYEBkyZN4tGjR8ycOZMLFy7g4uLCpUuXUFFRYcaMGchkMpYuXUpWVhaNGzdG\nJpNRWlpKamoqDg4O/P7779y7d48zZ86goqLC0qVLuXPnDsrKymRnZxMbGyv4Stja2iKTyXj16hV5\neXkcOnSINm3a4O7uTmRkJKtXr6akpISoqCh69uzJwYMHsbe3x8LCAkVFRe7fv0+rVq1Yt24d8O45\nbtu2jQMHDnDkyBEAqlYt1+35UxITf2fTpk0fbG/YsCEqKiqVOvZ3331HtWrVPrtfcnIy69atY+7c\nuZCeTssKJiABGD56hGpOzkfH9PX1+f77yokGFaG0tJRffvmF1q1b4+zszPnz52nYsKEwfuvWLVq3\nbk1SUhLXr1/HwcGBixcvYmFhQd26dbl48SLGxsYVPv/GjRvp168fUqmUtm3b8vvvv/Pq1StBVNHU\n1GTFihX8+OOPH3jsSKVSpFJPoHzR4p8iJgayskoZOHAgycnJJCQkEBkZSYMGDQgJCWHLli1cvHiR\nZs2aERERgYGBAXPnziU5OZnt27dz69YtQkNDWbdunVB1JvJpTExMePjw4VeZvCUiIiLyR0SxRURE\nRETkq2f69OmMHTsWOzu7j4opDRs25Pz58zg7O2NlZcUvv/xSbjGjolhYWBAVFYW/vz8LFy4s1/lM\nTU2Jjo5mz549jB07ttztLMOHD/+oaADvBKlp06ZRp04d3N3dGTJkCDKZjNWrV9OnTx9atWpFVlYW\n+/fvx83NjTp16uDv78/IkSNRVlZGKpUSFhbG77//jqenJz4+PpSUlNC7d2/09fWRSCRIJBIhjjsu\nLo5ff/2Vdu3aoaamxp07dzh79ixv375lyZIlyGQyLl26hI6ODsXFxcTFxVG/fn3MzMzIzs5GSUkJ\nAwMD1q9fz/Pnz4mLiwOgRo0aBAcHM2LECB4/foy5efmfxcfvi5x58zoyY8YMwsPDy4xVr1690tUD\n9evXRyqVkpWVxe3bt4mNjeX27dsfxEnPmzcPLy+vd547AQHUrkQLU9W3b7G+fPmD7XXr1sXFxeUf\nN8d98uQJjo6OBAUFcf78eXx8fAT/pNLSUtasWYO9vT0TJkwgJCSE6tWrs2jRInr16kVAQAABAQEo\nK1esXUwulzNt2jSmTZuGuro6zZo1Izk5ucxLuI6ODnPnzmXChAkUFBQIc9+JLFIUFRWRy8tXnfRn\nZGaWMmjQeMLDw8nPz+fMmTPo6ekxbNgwduzYwcqVK+nWrRtv3rzB09OTkJAQpk2bRnJyMk2bNuXR\no0d07ty50uv4/wsqKirUq1fvvyK7RURERL4+RINcEREREZFvhvXr1+Pv78+ZM2fKfD3/I7dv38bL\nywtNTU02b96Mnp7e37qmFy9e4OTkRMeOHVm+fLnQOvFnZGdn06dPH9TU1NizZw9qamqf3d/AwIC7\nd+9+1JA1Pz8fAwMDGjdujI2NDQDLli0jMjKSHj16IJfL0dTUFL64Ozs7M2fOHObOnUtBQQHm5uZY\nWVmRnZ1NSUkJdnZ2jBgxgps3b9KqVStsbW2JiIhAQUEBZWVl9PX1mTJlCiNHjkRFRQUNDQ0CAgLo\n06cPM2fOZMmSJSxdupTx48ejp6fHjBkzOH78ODExMSgoKJCRkUFxcTGmpqY0a9asjCfNihUr2LNn\nD0uXXsDJScYXBP6UQSa7xps3psTFXaFnz56cPHmyjJ/J7du32b9/f4WOraqqSocOHXj06BGJiYnk\n5+eXGatXrx6mpqYoKChga2vLvXv33pk89+wJR49W7IL+j/sNGrBn0CDgnWhUv359HBwcKixaVITS\n0lKCgoKYNm0akydPxsfHp0z7UkpKCkOHDuXVq1fs2rULY2NjUlNT8fDwICcnh927d3/U8Lm85Ofn\n4+HhQVRUFFpaWigpKaGurs6lS5eEVkITExOGDBnCrFmzyggt78VDRUVFiouLkct/AuZ+4kzlpRAF\nhSbUq/culn3MmDHY29vTpk0boqKiePnyJaWlpezatQs9PT3MzMzIzc1lzpw5/PTTT2KkcwXo378/\nvXr1ws3N7d9eioiIiMgnEStbRERERES+GUaPHo2fnx8dOnTg9u3bH92nSZMmREdHY29vj6WlJUFB\nQX9rlYuOjg5RUVHExsbi4eFBUVHRZ+doaGhw7NgxNDU16dChA6mfifbV0NDAxcWFbdu2fXRcRUWF\nsWPHoqenx9atW7G2thbioTt16kS7du14/fo1hw8fpmPHjpibm7Nx40a8vb0ZO3Ys3bp1Q1tbm/r1\n69OwYUOSkpLYvn07JSUlWFlZkZKSIhiX5ufnU6tWLRITEwFo2bIlKSkp7Nq1C3iXFCKXy/ntt9+Q\nyWQ8efKEBg0aEBERQf/+/TEyMqJq1aro6+uTlJTE3r17SU9PF65l4sSJ6OjocOCAD7a25XkCH0cu\nP86aNauwsbEhMDCQHj16kJSUJIw3btz4k4Ld55DJZBw/fpy7d++WEVrgXYLUnTt3CAkJYdOmTUyc\nOFFI00r9v3tWGTSkUho1akSnTp0YPXo03bp1+0eFlmfPntG1a1fWrVtHREQE06ZNKyO0HDt2DHNz\ncywsLLhw4QLGxsacPXsWCwsLWrRoQWRkZKWElrS0NOzs7Pj1119p1KgReXl5aGhocPHiRUFosba2\npn///h8ILVKpFIlEgkwm+z+hRY5E8vG2rC8jC3v7ZrRs2ZIff/yRWbNmkZmZycaNG3n69Ck2NjbE\nx8cLJtJ5eXmcP3+eGTNmiEJLBRF9W0RERL4FxMoWEREREZFvjl27duHj48OJEydo3rz5J/e7efMm\nnp6e6OrqsmHDBnR0KufN8Gfk5ubi6upKSUkJ+/fvL1eqSmlpKbNnz2b37t2Eh4f/qXfFxYsX8fT0\n5N69ex99QUtNTaVRo0b88ssv+Pr6YmdnR0hICOfOncPFxYX8/Hxq167N7du3OXz4MJcvX0ZV9fMJ\nLDVq1GDKlCksXLiQSZMmAeDq6ipEcstkMpKSkigoKCAnJwdlZWV69erFr7/+iomJCRcvXqRx48ZY\nWFhgZGTE+vXrefv2LXp6elStWpVbt27h6+v7ztPk/8jIyMDS0pJhw4IIDOzA06efXWYZ7OygtNSJ\nuLjfSEpKQktLi4CAAAIDA7lw4QLVq1cH3qUJBQcHf5HPj6qq6gfeH39GrVq1GDRoEDKZjMuamrT/\nL3Hmi+nYEc6erdwxKkBpaSk7duzAx8eHcePGMX369DL+Inl5efj6+hIWFsb27dtp3749xcXF+Pn5\nERQUxPbt23FwcKjUGhISEnB0dOTt27fY2toSHR1N/fr1uXTpkiCoOjs706BBA9avX//RihaZTEZR\nURFyufz/qls0gHgq49tSu3Yc1asPJiYmhhMnTjB48GA0NDQoKioiICCA3r17M2jQII4dO4aCggLh\n4eF06NChUvfi/+8cOnSIoKAgQkND/+2liIiIiHwSBT8/P79/exEiIiIiIiJfgpmZGQYGBgwcOJAO\nHTp8UkSpVasWQ4cO5eHDhwwbNgwdHR2aNWv2t3xNlslk9OvXj3PnzrFy5Up69+79WTFDIpHQoUMH\nVFVV8fDwoG3bttStW/ej+9atW5fAwEBMTU0/6jeirq7OgwcPKCkpwdDQkGfPnpGQkICuri5SqRQj\nIyPi4+PR1tYmMTFR8Nb4HHl5eejp6XH58mVkMhnp6ekkJCQwceJE7t27x4MHDzAwMBBakDp27IhM\nJuPGjRskJSUhl8t59eoV3bp1Izw8nLp166Ktrc2jR4/Izs7GycmJrVu3MnXqVKEFS1VVFVtbW8aP\n783s2YO5fr0K2dnlWi6NGqURFqaGk1Nr1q5dS2pqKj169MDa2pqHDx+ycuVKBg4ciKKiIgoKCjRt\n2pTs7GzevHnzp1VJ1apVQ0VFhZxPGNR+irdv3/Ly5UtOnjyJ7rVrGH6QhvOFWFpC//6VO8YXkpyc\njLu7O+Hh4Rw8eBBXV9cyf3+uX79O586d0dDQICwsjEaNGvH06VN69OhBamoqJ0+exMzMrFJr+O23\n33BwcCA/Px9nZ2dOnz6Nrq4usbGxwj7Dhg1DTU2NTZs2lTFPfS+0vE/J+n9CSzHKynJq13bizRvD\nCq+ttPRnzp5dyvnz5xk4cCA1atTAyMiIkydPoqGhgZWVFb///jtmZma4ubkxfPjwSt0LkXdR3suW\nLWPixIn/9lJEREREPonYRiQiIiIi8k3Sr18/Nm7cSNeuXbl48eIn91NSUsLPz4/w8HD8/f3p06cP\nKSkpf8uaZDIZQUFB2NjY0K5dO549e1auecOHD2fz5s306NFDSOP5byQSyZ8a5cK7Fpx169bh5+fH\ns2fPaNq0KcuWLWPy5MnExsaiq6vL48ePKS4u/qLrMjAw4LvvvsPHxwd4VxEilUoF4aFJkyY0adKE\n5cuXk52dTdeuXXn8+DH29vYUFxejoKDA+vXrSUlJoVOnTpSUlKCiokL9+vXR0dFBKpUycuTIMuds\n1aoVM2bMYONGZ3bsKKRLF/izpGVd3VIcHZ9TXOyAllYRxsbGDB06lF27dnHnzh3gXQRzzZo18fLy\nElpOFBUV6dWrFyNHjsTW1pbatWtTpUoVCgoKUFVVFVJ+7O3tPzC/LS+PHz/m0KFDVBs4kEonWv+D\nRqqlpaXs3r0bc3NzzM3NuXLlCuZ/cC6Wy+UsX76cTp068Z///Ifdu3ejqanJ0aNHadmyJd26dePE\niRMf9Rn6Evbs2YOzszMlJSX06dOH8PBwqlevzo0b/y/a+6effiIzM5MdO3aUEc3+KLQUFhYil8uF\nNiIFBQX69etHevpSoGLmQDLZbdaubc0vv/zC4MGDUVJSYtSoUZw/f549e/bQpk0b1NXVWbx4MXl5\neYjfOP8ajIyMSE1N5c2bN//2UkREREQ+idhGJCIiIiLyTfO+bD8kJAQ7O7s/3begoAA/Pz+2bNnC\n6tWr6dev39+2riVLlrB27VpOnjxJo0aNyjUnLi6OHj168J///IexY8d+MJ6WlkaDBg14/Pix0Arz\n3zg6OuLu7i4Y2759+5aVK1dy4MABmjVr9sl5n6OgoICjR49Ss2ZNoqKiUFNT45dffsHLy4vWrVuT\nmprKixcvGDJkCGvXrmXQoEE0b94cPz8/8vPzMTU1pbCwEAcHB3bt2oWqqio5OTnIZDK8vb2ZO3cu\n0dHRtGjRQjhnaWkp/fr1o3bt2qxZs4Zr1yAwEG7cgNevC3j06B4lJa+pVu0q3t4KzJ49kU6dOtGn\nTx9GjRpFZmYmenp6mJub8+uvvwLvKnUcHBxo164dP//880evtaSkBEtLS3bt2oWpqSkAwcHBlUo/\nkcvldGjfHt0ePTCuYHVLkYkJsps34R+IB05NTWX06NHcvXuXbdu20bJlyzLjz58/x8vLi9zcXHbu\n3ImhoSEFBQVMnTqVw4cPs3v3bsGsuaKUlpbi7+/PsmXLgHctQlFRUeTn5wuCqUQiYeXKlRw/fpyz\nZ8+WERLfCy3KysoUFhZSUlIitBFJpVJGjRrFzp07yc3NRSZbQ17ecL7kO6SiYgZt2wbx6tUWnj9/\nTl5eHqdPn8bExIQePXoQFxeHl5cXM2fOpGXLloSGhmJlZVWpeyLy/2jZsiVr1qzB2tr6316KiIiI\nyEcRK1tERERERL5pnJycCA4Opm/fvpw5c+ZP91VWVsbf35/Dhw8zc+ZMBgwYQFpa2t+yLl9fX2bP\nno29vX2ZVoc/o0WLFvz222+sXr2aKVOmCNUX76lZsyZdunQRzGg/hre3NytWrKBx48YsXLhQqOyZ\nNGkSKioqFb4eZWVl2rRpI/hu5OfnExYWhr6+PleuXCE3NxcdHR2Cg4O5fPkyrq6uhIaGMnv2bEpL\nS3nw4AGFhYVs376dfv360bRpU6pUqYKuri61a9dGSUmJvn37lmnTkUgkbN68mRMnTrBv3z4sLOCX\nX+DiRbh/X5mDBxNRVu6CpuZqAgMXc+LECX7++Wfmzp1LTk4OmpqazJ8/n7i4OI4fPw68a1E6evQo\nhw4dYv369R+9VgUFBRQUFIRWlMzMTMEQuKKoq6uTlpFBXCV8g4KSk7n36FGl1lEeQkJCMDMzw9jY\nmLi4uA+ElkOHDmFpaUn79u2JiorC0NCQBw8eYGNjQ1JSElevXq200FJUVMQPP/zA6tWrUVdXp337\n9ly9epX09PQyQsu2bdvYt28fp0+fLiO0vDfD/ZjQIpFI8PHxYdeuXeTk5FCnTh1cXWNQUNgMFH5i\nRWVRUUlHU3Mu167N48WLFxQUFHDjxg3y8vKoX78+8fHxHDp0iMDAQCZMmMDQoUNFoeUvplmzZsTH\nx//byxARERH5JKLYIiIiIiLyzfP9999z8OBB3NzchJfqP8Pa2ppr166hq6uLmZnZJ1t3KsuQIUMI\nDAykS5cunxWC3mNoaMiFCxeIjo7G3d29jMknvPOl2Lhx4ycTljp37kxhYSGRkZGMGjUKc3Nz0tPT\nSUxMFDxk8vOVOH++LaGhXTl8uAdhYV25cKENhYV/XjFhYmJCQEAABgYGyOVyTp48yeDBg4UYZ0ND\nQ1q3bs2IESPo0KEDv//+OwMHDhReeMeMGUNeXh61a9fm3r17QgT0li1bGD58OFWqVPnAg6FatWrs\n3buXsWPHkpCQUGasR48e9OzZkydPnjBnzhw8PT2pVq0a9vb2LF++HICxY8eipaXFqFGjhPYSLS0t\nwsPDmTdvHkc/EcWsrKwsJA09e/bsg+fwpeTl5ZGWlsaxli0pcHT88gN07ozS4sXY2dlx4cKFSq3l\nU6SlpTFgwABmzJjB4cOH+fnnn8sIdDk5OQwfPhwfHx+OHDnCzJkzUVRUFNplhgwZwsGDB6lRo0al\n1pGVlYWTkxOnT5+mdu3a1K9fn9TUVB48eCC0jSgqKnLo0CFWrFhBdHS0IExKJBLB+0dFRUUQWt77\ntUgkEmbPnk1gYCDZ2dnUr1+f9u3bs3//XmrU+AmYhL7+Mz4luigoZFOnziWKi/ujpLRPqJ4JCQkh\nICCA7t27Y2xszIMHD+jWrRvBwcHcv39fbB/6GxATiURERL52RINcEREREZH/CQwMDGjXrh0DBgzA\n2NgYExOTP91fJpPRuXNnrKysGDNmDLGxsdjb25croedLaNSoEdbW1ri6umJgYEDTpk0/O0dNTY2B\nAwcSEhLChg0b6Nmzp7CuevXqsWzZMmxsbNDV1f1g7vvElW3btuHm5kb37t1Zvnw5qqqqFBebcfZs\nB06dcuTu3SYkJ+vy8mUdXrzQ5dGj+sTHNyU1VZvq1TNQV/+w1aWkpARjY2Oys7N59uwZRUVFJCcn\nk5WVRVZWFlKplBcvXqCvr8/r16+pWbMmr1+/pn79+ly8eJGcnByaNm1KWFgYNWrUwMDAgISEBLKz\ns/H29mbHjh0kJydTt25dmjRpIpxXR0cHdXV1pk+fjpeXV5mo4U6dOrFo0SLi4+OZMWMG3t7erFy5\nktGjRzNkyBCq/n/s3XlcjXn/+PHXOac6LaeFCJGkJg1CaTQUE7Jlq6iYuRXGloylMWPfxr4PY8ta\ndoVQtixZKnsRRYYoKZQWldbT+f3hdu5pwrS4fzOP+3s9/2rOda7P+VxX5zEz17v3oq2NhYUFAQEB\naGtrK0sOatWqRadOnfDw8MDBwaHCvdy9ezf29vaYmJiQnJxcIdBTHSKRiPyCAu40aULJ7ds0rcSY\ncAD69IHAQKzat8fS0hIPDw+aNGlSqe9SZR05coQ+ffrQoUMHAgMDMTEp3zD2xo0b9OjRg0aNGnH0\n6FFMTU3Jz8/H29ubgIAAjh07Rv/+/WvcfDopKQkHBwfS0tJo3ry5MkMpIiJCmWkklUo5fvw4vr6+\n5R623wc+oHyg5X2wD2DRokUsX76c3Nxcmjdvjrm5OWFhYUgkErKzsxk79ivu3p1Eaek1OnSwxcJC\nj0aNoGVL0NK6hJbWJJKTp2JgUICJiQmGhoZ07tyZ9evXc+HCBaZOncqePXvQ1tbmxYsXODs7ExQU\nVKNx14IPy8vL4/Dhw3h5ef3dWxEIBIIPEnq2CAQCgeB/yq1bt+jduzdr1qzBw8OjUufk5+czdepU\ngoOD2bJlC7169frs+7pz5w5OTk7MnDkTb2/vSp1TVlbG5MmTOX36NCdOnMDY2Bh498CYlJSEn5/f\nB88rKCjA2NiYiIgIzM3NmTt3LseOyXj0aBS5uTp/+bm6uln06nUSC4vyAYaSkhJ8fX0xNzfnzZs3\nlJSUoK+vT+3atUlMTKS1igoTVFXpV68eGU+eULduXZ7n5dGgf386BQZyr7RUOZXF1dWVjIwMHjx4\ngImJCW3btuXhw4e0b9+ejRs3cvPmTYyMjJSfrVAo8PDwQF9fv0L5T4sWLUhISGDGjBmkpaWRnp6O\nkZERCoWC3377DQA7OztiY2NJSkoql3kREhLCqFGjiIiIwNTUVPl6z549mTBhAr169eLevXscOnTo\nL+/bX5FIJGRkZLBr1y6QyxmZl8dYIyMMnj7lzwVeRYBK+/ZI+veHH3+EPwSYbt++Td++fZk4cSK+\nvr41CnBkZWUxfvx4rl69yo4dO7C3ty93XC6Xs2zZMlavXs26detw//ckpHv37uHu7o6NjQ3r169H\nW1u72nt479atWzg5OSEWi+natSuRkZG0bt2a0NBQ5HI5ADKZjJCQELy8vEhOTlae+75sSKFQKAMt\npaWlqKurKzOUlixZwsKFC8nLy6NVq1bo6+tz+/ZtiouLKSwsxM7OjpiYGHR0dLh+/ToNGjRQrr9j\nxw4mTJjA27dvUVNTY/r06UgkEnbs2EFycjIymYzjx49ja2sLvPu+uri40KJFCxYuXFjjeyOoKDU1\nlTZt2vDq1au/eysCgUDwQUKwRSAQCAT/c2JjY+nZsydLly5lyJAhlT7v/PnzDB8+HEdHR1atWoWO\nzl8HJqoiMTGR7t274+npyaxZsyr9kLx69WpWrlxJaGgobdq0ITU1lZYtWyof8j5k5syZZGVlsX79\neg4eLOPbb/MoKan89Whp5eLicgQzs//0CFF7/ZqpRUWEyeX0CwhAS1ub7OxsWqmqsqC4mI6A7kfW\neyMScUGhIPSbb0jR1OTSpUtIJBKkUqmyvGPTpk2sWrWKvn37cvbsWc6ePVtuxPCbN29o27Ytv/zy\nC4MHD1a+Pm7cOB4+fMiFCxe4fPkyvr6+2Nvbs23bNq5du4apqSlxcXHY2Njg5eXFpk2byu1t48aN\nynKUOnXqANC/f3+GDRuGs7Mz2dnZ+Pn5KR/aq0NDQ4PMzExu3ryJSCTi6dOnPHnyhMePHjHa1JTf\nunUjJyWF3xMSaGVvT3BJCTMvX4aPfEeePXuGk5MTnTt3ZvXq1ZUe5f1Hx48fZ/To0QwYMIBFixah\npaVV7nhycjJDhgxBJBKxa9cuZQBry5YtzJgxgxUrVny2rIJjx47h5eWFWCzmu+++Y//+/XTo0IGQ\nkBBliVDdunU5ePAgHh4evHjxQnnuH69dXV2doqIiSktL0dDQoKDg3ZShJUuWsGjRInJzc2ndujUq\nKio8f/6cvLw85HI5Ojo6ZGdnY2xszJ07d5BKpco19+zZg6enJ5qampSVlXHu3Dny8vLo06cPZWVl\ndOnShcDAwHL/vtizZw9Llizh5s2b5dYSfD4KhYI6deoQHx9f44lXAoFA8N8g9GwRCAQCwf+cVq1a\nce7cOaZNm/bJUcl/1qVLF2JjY5FIJMo1PqemTZsSERHB4cOHGT9+fIUGuB/zvjSme/fuhIWFYWho\nSMeOHQkMDPzoOT4+Puzdu5enT7P4+WdxlQItAPn52pw61YOSkv88yBpnZCDavJnuO3ZwRaHANzub\nLgoFQSUl9OHjgRYAHYWCfoDvxYuMaNkSbW1ttLS0sLGxQSaT0bRpU968eUNaWhrdunWjrKyM5cuX\nl19DR4egoCDGjx9fbjKQnZ0dGhoa1KpVC2dnZzZv3szevXvp0aMHM2bMAN5lvwwaNIiAgAAePHhQ\nbl1vb29cXV3p37+/8uH8/UM7gJ6enjKrqLpMTExIT0/n6tWr6OnpkZOTQ/v27Tl+4gTnxGLq7t9P\nj7Q0Lo8cia+aGubjx3800AJgZGTE5cuXiYuLY8CAAbytwoSjnJwchg8fzg8//MDu3btZs2ZNhUDL\ngQMHsLGxwcnJiXPnzmFkZEROTg6DBg1i/fr1XL58+bMFWtauXcvQoUMBGD58OPv27cPKyqpcoMXE\nxISgoCBcXFwqFWjR1NRU/i4XLFjAkiVLyMvLw9LSkoKCAjIzMykoKEAikVBSUoKOjg4NGjQgMjJS\nGRwpLS1l+vTpeHp6IpVKEYvFJCQk8PbtW3r06IFcLmft2rWcPHmyXKAlLS0NX19f/P39hUDLf5FI\nJBL6tggEgn80IdgiEAgEgv9JX375JRcuXGD+/PmsX7++0ufp6Ojg5+eHn58fQ4cOZezYseUm5NRU\n/fr1uXjxIrGxsXz33XfKXhJ/ZeDAgRw+fBhPT0/8/f2VjXI/pkGDBvTt25eRI+/y5En19pqRYcCt\nW/8exaxQ0Dw+HgBRWRnWcjkzFQpCJRK+qEKSrAXQas0aTHn3sHSY1OTrAAAgAElEQVT37l2ysrIo\nKSlh8+bNjBkzBj8/P3bt2sWqVau4ceNGufPbtGnDggULcHd3Vz5M29vbc+XKFZYuXYqKigqTJk0i\nMDCQsLAwzp8/r1xj2bJlypG/f7Zo0SKMjY0ZMmSIss/HHzNZ3o+Arg6RSESrVq24desWrVq14syZ\nM+Tk5DBx4kT27NmDsbExN2/eJCsri8mTJ3P16lV69+79l+vq6ekpH/Q7d+5cqXKKsLAwLC0tkUql\nxMbG4uDgUO74mzdv8PT0ZPbs2Zw8eZIpU6YgkUi4ceMG1tbW6Ovrc/Xq1b/siVQZcrmciRMnsnjx\nYiQSCcOHD2fPnj2YmZlx5swZZaClTZs2bN26lf79+5OZmak8/4+9ezQ0NJSlQ1paWsrg06xZs/j1\n11/Jy8ujWbNmZGZmIpVKlY12s7OzGTJkCNnZ2Zw6dQp9fX0Anjx5QqdOnVizZg0qKiqoqKhw+fJl\n/P39cXR0REtLi3v37jFmzJhyGWoKhYLRo0czatSocmPMBf8dwkQigUDwTyYEWwQCgUDwP8vMzIyL\nFy+ycuVKVq5cWaVze/Towd27d3n79i2tW7fm0qVLn21furq6nD59moKCAvr27VvpYI69vT0XLlzg\nl19+4datWyQlJX3yr7oTJ07i0iWtjx6vjAcPmgFQ78ULWsXGVjiu/u9eGlVhVlzMTIWCTp06kZqa\nirm5OU+ePOH58+d89dVXHD58GJlMxrp16/juu+8q3J9Ro0bRokULxo8fD7zL8lBXV8fW1hZNTU3S\n09M5duwYy5YtQyQS4evri0KhoG7dusyaNYsbN25w+vTpcmuKxWJ27NhBRkYGkydPRiqVlptA1KJF\nC5o0aVLla4V3TY0bNGjA/fv3sba2RiwWIxaL6dy5Mzdv3qR///5Mnz4dGxsbwsPD6d69e4VMk49R\nU1MjICCAHj160KFDBx4+fPjB9+Xm5jJ69GhGjhzJtm3b2LhxY4UStKioKNq0aYOmpibR0dG0bduW\nsrIyVq5cSe/evVm2bBkbNmyocRPpt2/fcuPGDUaNGkV8fDyNGzfGxcWFo0ePoqenx9WrV5Xv7dKl\nC4sWLcLFxYWcnBzl66qqqsqJXBoaGhQVFVFSUoJMJlMGWiZPnsz27dt58+YNRkZGvH79GiMjI16+\nfElubi7Z2dmsX7+e0NBQDhw4gLm5OQqFgp07d9K2bVvu37+PiooKhoaGLFmyhNGjRzN37lz09fV5\n/vw5zZo1q3Btu3fv5unTp8yaNatG90hQOUJmi0Ag+CcTgi0CgUAg+J/WpEkTLl68iJ+fX5UbVerp\n6eHv78/q1asZPHgwkyZNqlK5xqeoq6tz8OBBGjVqRNeuXcnIyKjUeRYWFkRFRREaGoqBgcFHm+QC\n5OZaUVzcukb7TEkxIjtVm69u3ED8Gdu8tXz1iqsnTmBnZ0dKSgo6OjqYmpoSFBRE79698ff3x93d\nHXt7eyZMmFDuXJFIhJ+fH5cuXWL37t3Au1Kia9euMW/ePCQSCXv37kVHRwc3NzdiYmKUI8EnTZqE\njo4Oo0ePprS0tNy6UqmU4OBgwsLCePDgQblgi0gkwtXVlfr161fpOhs0aICrqyubNm1CV1eXe/fu\nkZ+fj6OjI1evXkVNTY0uXbpw8+ZNFi1aRGBgIG5ublX6DJFIxC+//MK0adPo1KlThdHQ58+fp1Wr\nVpSVlREbG0u3bt3KHS8tLWXu3Lm4urqyevVqNm3ahJaWFhkZGfTt25egoCCuX7/OgAEDqrSvP3v6\n9CnBwcGsX7+eEydO0LhxY+zs7OjZsyfq6uo0a9aMx48fIxaLlQ2Rx40bh7u7O2/evFGuo6ampsx6\n0dTUVAZatLW1yc/PR6FQ4O3tzZEjR8jMzKROnTq8efMGS0tLEhISePXqFYWFhezcuZNVq1axZMkS\nHBwcyMrKYtCgQUyfPp3i4mIUCgUdOnSgZcuWTJ06lZiYGLS0tLh+/foHGwKnpqby448/4u/vj5qa\nWo3ulaByhMwWgUDwTyYEWwQCgUDwP8/IyIiLFy+yZ88eZs2aRVV7w/fr14/Y2FhevnyJlZUVV65c\n+Sz7UlFRYevWrXTp0oWOHTuWm67yKfXr1+fChQvUqlULPz8/0tPTP/i+d21NVD54rLJKS1UxiHpF\n2+joGq3zZ40UCkYVFuLh4UFmZiZZWVnExcVx4MABhg4dyoYNGygrK2Pt2rVcunSJgwcPljtfW1ub\noKAgJk2axP3797GzsyMyMhJ3d3eKiorw9fVlzJgxjBw5EhMTE4YNG0ZZWRlSqZR169aRkZFRoVEu\nvBsJfeLECW7fvl2hhElbW5tvv/22UhkuIpEIExMTvv32W8RiMatWrUJPT48rV67QqFEjBg4cyMGD\nBykpKSEkJIRatWphZWVV6RKiD/n+++8JCAjAxcWFQ4cOkZeXx7hx4/Dy8mLDhg1s2bIFXd3ynXUS\nExPp1KkTUVFRREdH079/fwAuXryIlZUVLVu25PLly9XO6oF35UKHDx9m9+7dxMbGVghYisVi9PX1\nadeuHaNGjcLExIQJEybQp08fvLy8ymU2SaVS5WSiPwZadHR0lIEWT09PoqKiePHiBRoaGpSUlNCm\nTRuioqKU5UPLli1j8+bNuLm5MWzYMMLDw7G0tFT2bFJTU2Pw4MFER0dz+vRpjI2NqV+/Ptu3b68w\nFhvelQ+NGjUKb29vrK2tq32vBFXTokUL4uPjK93/SiAQCP5/EoItAoFAIPg/oUGDBly4cIFjx44x\nZcqUKgdc9PX12bt3L4sWLcLV1ZUpU6bUaDrNeyKRiMWLFzNy5Ejs7e25f/9+pc6TyWScOXMGAwMD\n2rVrR1paWoX3lJTUeHsAmN57/HkW+pN2qqps2rQJGxsbSktLkUqlWFhY8PDhQ3R0dAgLC0Mmk7F3\n7158fHx49uxZufNbtWrF4sWLcXd3x9ramoiICMRiMfPmzWPnzp0sW7YMNzc3goODefPmjbLsyNXV\nFQsLC6ZPn05WVlaFfRkbG+Pu7s6hQ4eIiooqd0xbWxtPT0/c3NzIyMio8JCnrq5O8+bNcXd3Z8iQ\nIchkMvz8/LC3tyc7O5vS0lLS09Pp3bs3x44do127duzbt4/vv/+eI0eO0K1bt0qXEH1Ijx49OH36\nNGPGjKFJkybk5uYSGxtbYZz5+3IZW1tb3N3dOXXqFIaGhsjlcubNm8egQYPYsmULS5cuRVVVtdr7\nKSsrIzAwkLt37yqDJJ9Sp04dhgwZQpMmTRg7dqwyOALvyoXeZyNpaWmVC7Tk5eVRVlaGs7MziYmJ\nJCUlUVZWhoaGBsbGxoSHhyORSJDL5YwaNYrY2Fjq1avH7Nmz+fnnn3F3d0cul2Nra4tMJsPZ2Zkt\nW7aQlZXFjz/+SLNmzejTp89Hs3t27txJSkqKsiGz4P8PPT09atWqxdOnT//urQgEAkEFQrBFIBAI\nBP9nGBgYcP78ec6fP8+ECROqHHABGDBgAHfu3OHx48e0bduWmzdvfpa9+fr6smDBAjp37sy1a9cq\ndY6KigorV65EJBLRoUOHCoGazzUNtREpn2ehP/nqyy+Ji4vDycmJevXq8erVK4qLi9m8eTPe3t5s\n2LDh3fu++oqJEycqm9f+0ffff0+bNm3YvHkzqampvH79GhcXFxQKBXp6enTr1g1fX1/WrVvHxo0b\nuX79OiKRiM2bN1NaWvrR3hpNmjShX79+uLq6VuiDIhKJSEtL4+TJkxgYGPDixQsSEhLQ1tbG29sb\nNzc3LCwsEIlEFBQUsHz5cmbOnElmZiampqZYW1vz/PlziouLMTU1JS8vjzlz5hAUFFTlEqI/e/v2\nLTt37kQikaCpqYmenl6FEeZZWVkMHjyYZcuWce7cOSZOnIhYLCY1NRVHR0cuXrzIrVu36NmzZ432\nAnDy5MmP9pH5lKSkpHKThjQ1NZXNpLW0tCgsLCyX0VJWVkbXrl0pLi7m/v37vH37Vlnydfv2bbS1\ntSkuLqZz5840bNiQhIQEpk+fTvv27Tl27BgA69ato7CwEJlMxo4dO9DQ0CA8PBxjY2MeP37MihUr\nPrjX58+f89NPPwnlQ38ToW+LQCD4pxKCLQKBQCD4P0VfX5+zZ89y48YNxowZU630cwMDA4KCgpg5\ncya9e/dm1qxZlZ4q9Cmenp5s27aNPn36VGjg+jHOzs7k5ubi4+ODg4MDly9fVh7r0QNqUP0BwJfE\nYcOtmi3yEUWlpbRt25aAgADy8/NRU1Pj/v37ZGdn88UXXxAZGan8i/XPP/8MvCv/+CORSMTGjRu5\ndu0aRkZGREVFIRKJmD9/PrNnz2blypVkZmaSmppKmzZt6NWrF69fv8ba2pp+/fqxffv2DwYDpFIp\njRo1YuHChfTq1avcpB+FQsHUqVNZuHAhT58+VZZAjR8/vkJgY9u2bXz11VcYGRkhl8sxMzPD2dmZ\n0NBQxGIxERER2NnZUVhYyJUrVz5aQpSbC6tWwbRp4OsLs2dDeHj597xvcPvq1Svi4uKIjY3l7t27\nDBw4UFm6c/HiRdq0aYOBgQE3btygVatWwLugSNu2benSpQtnzpzB0NCw8r/Ij8jPz68wZruy9PT0\nsLW1Bf6TxfLHn/8YaJHL5bRr14569epx48YNsrOzadiwIc+fPycrKwstLS0UCgUmJiYMHz6crVu3\n4uzsTNeuXcnLy6NRo0bcvn2bhw8fcurUKa5fv07Tpk1JTk5GJpMxa9YsDhw4gLq6eoV9vi8f8vHx\noU2bNtW/WYJqE/q2CASCfyoh2CIQCASC/3P09PQICwvj/v37DB8+vFLlDX8mEokYPHgwt2/f5vbt\n27Rr1447d+7UeG+9e/fmyJEjeHp6sm/fvr98v1QqZciQIWRmZrJ7924GDBhAUFAQANra8KdeqFXW\nnTDU+Ez1SH+SkJ7OokWLePHiBZaWlrRv357i4mIMDQ3x9/fH09NT2VdFIpGwa9cufv311wq9VGQy\nGUFBQTx58oQjR44A4OTkhJaWFkeOHCEoKIjNmzczYsQICgoKGDBgAKWlpaxatQqAsWPHVtiburo6\nRUVFfP/993z33Xf06dOH/Px8AA4ePEhZWRlubm7Ex8dz5swZfvvtt3KjiAGKiopYunQps2bNYvLk\nyQDcunWL/v37K39Hjx49YunSpcoSItmDBzBlCowdC97epA35iUV9o7C0hB9/hCVLYPVqmD8fevaE\nzp1h6dISfH2nMWDAABYvXsyePXvQ19dHT0+PU6dOIZPJcHBwYOLEiQwePJiNGzeydu1a5bjkyZMn\nM3r0aAIDA5k1a1a5jJKaiIqKqtHYdDMzM3R1dZXlejKZTBlo0dXV5e3bt5SWltK8eXNsbGw4d+4c\nGRkZ1K5dm9TUVKRSKRoaGmhqaqKqqsq6desYP348jRs3ZuvWrYhEIry9vQkLCyM0NJSZM2eSm5tL\n8+bNefjwIaqqqri7u7NmzRrMzc0/uEd/f39SU1OZPn16ta9TUDNCZotAIPinEimqk0MtEAgEAsH/\ngPz8fPr370/dunXZuXNntXtTvO9/8dNPP/HDDz8wderUGvW5ALh79y69evVi6tSpjBs37pPvjY+P\nx9HRkeTkZOLi4ujTpw+TJk1i0qRJ3Lol4ptvoDpDlGrzmut8hSlPqnkVH1cGjFJRYUV6Os2bNycv\nLw+ZTMbr169RKBSoq6tz/vx5nJycSE5OVmYVHDx4kGnTphETE1NhdPHkyZPZsGED6enpaGlpcfbs\nWXx8fIiLi+P69es4OzvTqVMnoqOjcXZ2ZtWqVcyZM4eVK1dy5MgRHB0dlWv5+flx69YtNm/ejEKh\nYNiwYWRmZhIYGIilpSUbN27E0dERfX19bG1tldOO/mjTpk2EhIQQHBysbExrbm5OWFgYJiYm1KtX\nj4KCAl6kpfFry5a4q6pi+PvvFX5Z+WgQRQeO4MxGxqL4wN/K6tSJIzy8Hi1b1qlw7MGDB3Tp0oWc\nnBzOnTvH119/Dbxrjjt48GAMDAzw9/dHX1+/ar/ET3j9+jULFy6s0JC3qkJDQ5W/66KiIoqLi9HR\n0aGgoICioiJlE+KtW7fy8uVLpFIpOjo6yOVyNDU10dTUJDk5mbNnz9KvXz/kcjlGRkaUlJSwb98+\nWrduzZQpU1ixYgXa2tro6upy584datWqhaenJ+rq6mzZsuWDe0tJScHKyoqzZ8/SunXNpn4Jqu/2\n7dv861//EgIuAoHgH0fIbBEIBALB/1laWlqEhISQk5PDoEGDql0KJBKJ8PLyIjo6moiICNq3b09c\nXFyN9mZpaUlERARr165lzpw5n+wv07x5c0xMTDh+/DitW7cmKiqK7du3M2nSJKys5Hh6Vv3zRcj5\nnq3/lUALwF3gkIYGwcHBjBgxAktLS/Ly8ujevTsikYh69eoRGRmJlZWVMgsEYODAgXTq1EnZ7PaP\n5syZQ3FxMaNHj0ahUNC1a1fq16/Pnj176NChA/PmzePevXtkZmZy+PBhdu3axZQpU1BXV68wCloq\nlSpLV973eCkoKKBHjx40adIER0dH7t+/T2ZmJr/99luFvRQXF7N48WJmzZpFQECAsgGws7MzJ0+e\nRF9fn9TUVHxGjqTIzY0f4uMxvHPng1ExLQroxjl+4wcCcUOdggrvychowYgRdcjO/s9rCoWCLVu2\n0LFjR2bNmsWvv/6Ki4sLUVFRBAUF8fXXXzN48GCOHTv2WQMtjx49on379jUOOALUrl0bbW3tcqVD\nhYWFFBUVUb9+fXx8fNi8ebMy0FK/fn0UCgV169ZFX1+fxMREgoKC6Nu3LyUlJWhoaNCxY0eio6Mx\nNDSkffv2rFy5EgsLC6RSKUePHqV27dr4+/sTExPDmjVrPrgvhULByJEj+eGHH4RAy9/MwsKCx48f\nf5ZSToFAIPichGCLQCAQCP5P0/j3A79cLsfV1bVGE4YaNWrEqVOnGD16NA4ODixbtqxaJUrvNWnS\nhIiICEJDQxk7duwn1xo5ciRbt24F3o26joiI4M6dO7i7u7N8eQHffQciUeWSWSWUMIKtLGVqtff+\nV8LU1MgrKGDLli14eXnx8OFD9PT0ePLkCerq6jx9+pS1a9fi7e3N+vXry527Zs0aIiIiCAwMLPe6\ntrY2LVu2JDIykh07dih7t8ybN4+SkhLGjBlD+/btadCgAVZWVvj6+hIfH8+vv/5Kenp6uQwGdXX1\nct8FNTU1du7cSVRUFF9++SUA3t7e1K5dG1NT0wrXt2vXLpo1a0a7du2YP38+DRs2pLCwEGdnZ44d\nO0Z6ejrykhKm37+P9NAhKlO4IwYGcpj9DEJMxe/CtWswatS7nzMyMnB1dWX9+vVcvHgRb29vRo4c\nyaZNm+jatSs//PADJ06cYOLEiYhEokp8euVERUVhZ2enHJ9cUxoaGspmuO+DLoWFhdSqVYv58+cz\nf/580tPTqV+/PvXr16ekpARjY2O0tLSIjY1lzpw5eHh4IBKJUFdXx8/Pjw0bNnD58mXMzc25desW\nbdu2BWDJkiVYW1sTHx/Pzz//TGBgIJqamh/c144dO3j58iXTpk2r8TUKakZdXZ0mTZqQ8G7WvUAg\nEPxjCMEWgUAgEPyfJ5VKCQoKQktLi/79+yubiVaHSCRi5MiR3Lhxg1OnTmFvb1+taSzvGRgYEB4e\nTkJCAoMGDVJmW/yZm5sbkZGRPH/+HEDZr0NdXZ0ePRxZvTqD6dNFSKXPPnj+e18Szzzm4McYPt8j\neHn3mjRBungxcrmcmJgY1NTU+PLLLxkzZgzx8fE0atQIiURCamoq2trapKWlcevWf5r0ymQy9uzZ\ng4/POM6eTSUkBIKDIToaOnb8hgEDBjBlyhTu3r1Lp06dMDMzUwZfNm7ciFQq5cyZM8yYMQNXV1e6\ndu1K48aNmTZtGjk5OUD5zJb3tm/fjpOTE0eOHOHnn3/m4cOH2NjYVLi+kpISFi5cyOzZswkJCaGg\noED5vi+//JKwsDDKysrwa9oUyaFDVb5//TnGL8z+4LGzZyEg4DJt2rTBzMyMa9eu0bx5cwDu37/P\nrFmz+Oabb5BIJERERFT5sz8lMDCQPn36oKKiQo8ePcplClXX+0CLTCajuLiYgoICZDIZa9eu5Ycf\nfiAnJ0d5fSKRiKZNm1JSUkJMTAxdu3ZlwYIFAFhbW3Pnzh169uzJuHHj6N+/PzKZDBMTE4yNjenQ\noQPff/89b9++xd3dnWXLltG4cWPOnj3L7t272bFjB/7+/uzfv5/Tp08zbdo0AgICPkv2jqDmhL4t\nAoHgn0gItggEAoFAAKiqqrJnzx7q1atH7969a9TYE95lpZw9e5bvvvuODh068Ouvv1Zr8hGAjo4O\nJ06coKysjN69e5Obm1vhPVpaWnh4eLBjxw7la1KplF27dtGpUyfs7e0YPjyRbdtuYGy8hq5doVkz\n0NfPQyR6gI1NJh0sN7JP+jUzWFylQEtV7lSoRELnly8ZPGQIGhoayOVy9uzZw7Bhw7h+/TotWrQg\nOzubBg0aoKamxtSpUxkzZoxyDDRAXh6cOfMVWlq36NatDv36gasr2NjA8eNzOHbMljlz1uPm5kZu\nbi7z589nwYIFFBUVoa6uzrFjxxCLxQQGBjJ06FDc3d1Zv349paWlzJkzR3nv/hhsycjIYPXq1axc\nuZLDhw+zcuVKrKyslFkuf7R3716aNGmCnZ0dixcvVvYaadiwIZcuXUJXV5fSkhIGfWC6TWX15RgS\nKgYzsrLAxycBf39/li9fjlQqRaFQsH37dmX51cmTJ4mKimLLli1MnDixRtlX8K6kZunSpfzwww9I\nJBJ8fHw4e/ZstUar/1l6ejoymYzS0lLevn2LVCpl4cKFeHl5UVhYiJWVFS9fvqRWrVqYmpry4sUL\n4uLiqFOnDrGxscrx3qdPn+bt27dYWlqydetWBg4cSHFxMe7u7iQmJrJu3ToAJkyYQLt27dDR0WHT\npk1ERkby+PFjkpOTSUpKIiEhgatXrzJ27FieP3/+WQJKgpoTJhIJBIJ/IiHYIhAIBALBv6moqLBj\nxw7MzMzo0aOHMsuhusRiMePGjePq1ascOnQIBwcHHj9+XK211NXVCQwMxNTUlM6dO5Oenl7hPSNG\njGDbtm3lgjpisZjFixczYcIE7O3tadq0ASLRryxceI0HDyAtTUqtWnaoqvZhw64OjJepEVuFfSWp\nqDBIVZUpQJRYjOIDf+kvBC4Btzw8GK6ri1xdnWbNmtGxY0dKSkrYsGEDAwYM4MKFC0yfPp3Xr1+T\nlpaGiooKN2/epG3bthw+fJjMzEz274fWrWHGDEhKMgL+U6qiUMCTJ7X5/Xc3Fi1yQ1d3LmPGjKFd\nu3a0bt1aWSbUuHFjAgMDuXbtGi1atKBWrVocOHAAR0dHNm/ezKNHjyqUES1atAgPDw/MzMwICwvD\n1taW8+fPVxj1LJfLWbhwIbNmzSIiIoJnz57RrFkz7t69yxdffEFoaCg5OTkMlcnQun+/Cne6vFbc\nw5OADx5r0sRL2ew3NzeXf/3rX6xatYoLFy4wYsQIRCIRxsbGREZGcufOHdzc3CgoqNgHpjJKSkoY\nPXo0mzZtorS0lLlz57Jx40Y6d+7MzZs3q319AC9fviQxMRG5XE5+fj6qqqoMHjxYWfpkaWnJs2fP\nMDIyomHDhsTHx5OUlIRIJEJTU5PMzExCQ0OZOnUqu3btwtLSklevXrF3714uXbrE5MmT2bp1KwcP\nHkRDQ4O9e/cSHx9PmzZtuHfvnnL61Mdcu3aNvXv3VvveCT4fIbNFIBD8EwnBFoFAIBAI/kAikeDn\n54eVlRWOjo5kZmbWeE0zMzMuXLiAs7Mztra2bNiwoVpZLhKJhE2bNtGrVy/s7e1JSkoqd9za2ho9\nPT3OnTtX4dyxY8eyadMm+vfvT9euXVm9ejXwLqNn+vTpxMTEIBKJ0P76a7a6uBAIVAzn/EcOEAIM\n0dTkqo4Oq1RUsCsr4/KPP4KPD+GNG+MPBNWrR1/gG+Cb0FBc3dwwMTHB0dGRjIwMRCIRKSkp/P77\n7/Tv35/U1FRls1ZNTU1MTU3x9vbGycmJMWNuMmYMJCb+9b1KSxNx544HZ87YsXXrVn755RcWLVqk\nLBHr2bMnHh4efP/992zevJnw8HDllJ5x48aVy2xJSkoiICCAWbNm8ezZM1auXMnu3bsxNjbGz8+P\ntLQ05eceOHCAevXq4eDgwNKlS2natCk9e/bk1atXmJmZcezYMfLz85lkagrVzHR6z4mKE5AAMjNV\nKS2F6OhorK2t0dLSUmYN/dH7UjNNTU26dOnywQDep7x584Y+ffpw+fJlioqKWLhwIfPmzcPDw4Mt\nW7YQERFRo4BlcnIycrmcvLw8xGIxJiYm7NmzBzU1NYyNjUlPT8fU1JRatWoRHR1NRkYGZWVl6Ojo\n8PLlS86ePYudnR2urq7KoNuDBw9Yu3YtAwcOZN26dWzbto2mTZvy8OFD5s6di4uLC1lZWZXe45Mn\nTzh48GCNs4MENSNktggEgn8iIdgiEAgEAsGfiMVifvvtNzp16kTXrl2r/BD6IRKJBF9fXyIiIggI\nCKB79+4VgiWV8b7pq4+PD/b29uWmHr3vF/O+Ue6f9evXj5CQEEJDQwkJCSE5ORmAMWPGIJFImDlz\nJjNmzCAkJoZJhoZYA8GmpmQ1b06CigrxwHWRiPVAB6AfcLWgAB0dHXr37g1At1WruDt6NIodOxip\nosLSRo2Irl0beDdqOygoiMTERM6cOcOVK1do3LgxCoWCvn374uzsTEBAAKNHj1Y+TNetW5fnz59T\nVNSBQ4esqcqze1GRiNzc0fj63kEsFtO+fXs2btyoPL5r1y5ltkRwcDCrVq3CxcWFyMhI7t69qwy2\nzJ49Gx8fH+rXr89PP/3E2LFjadq0KVlZWQwdOlRZ2iWXy1mwYAGzZ88mLi6Omzdv8uDBA6RSKWZm\nZsjlcmXwrkX9+pW/kI/Q58OBwJISBatXb6Bnz54sWLCAzYIsirAAACAASURBVJs3f7TR6/tSs65d\nu9KhQwd+//33Sn32s2fPsLOzIy0tDbFYzJQpU5g9ezZeXl789ttvFBUVIZfLiYuLq1Y50evXr7l5\n86ayZE5LS4uXL1+irq5OrVq1kMvlmJqaIhaLiY6OJicnh7KyMnR1dZFIJAQEBCCTyfjiiy84ceIE\nCxYsIDw8nGXLlqGurk5MTAxeXl706dOHwsJCPDw8GDZs2F9ms3xIYmIily9frvJ5gs+nadOmvHr1\n6oMllgKBQPB3EYItAoFAIBB8gEgkYsWKFTg5OdG5c2devHjxWda1sLAgMjISR0dHbGxs2Lp1a7Ue\nRsePH8+SJUvo0qULUVFRyte//fZbTp8+/dEAka2tLREREaipqeHh4YFCoUBLS4tx48YRFhZGgwYN\naNKkCQMHDuSFigqujx/zcPt2uhsa0lpFha+BcUD8v9crKSkhKyuLc+fOoaGhQXFxMZ07d6Z+/fqo\nqKiUC1qoqKgA74JZeXl5nD59milTpqClpUVaWhojRozg+fPnWFtbk56ejkKh4Pbt2zRq1IijRxtR\nVlanyvepsFCCoeFsBg5046effmL58uXKfjwSiQR/f3+ioqIICwtjx44dnD9/HolEwuLFiykoKCA2\nNpZTp04xefJkLly4wJUrV5g2bRrZ2dnk5+ezePFi2rZti7u7OwcOHEBXVxdHR0eWLVuGk5MTJiYm\nXL58mcaNG5OcnExRURHm5uaIP0MmhColH3w9Pz+dAwd2cOzYMVq0aEFcXBypqakf/Z6JRCIWLFjA\nTz/9RMeOHbly5conPzc6OhpbW1sA9PX18fDwYMWKFbi5ubFmzRqKi4uRSN7NV+rVqxeGhoZVuq6c\nnBzCwsJ4+fIl8G60+fsgikQioU6dOhgZGZGXl8e9e/fIy8tDLpfTvn17jIyMGD9+PI8ePcLe3h6J\nRMK1a9f48ccfOXDgAEeOHMHCwgIVFRXmzZsHwOTJk2nRokWNpjL9/vvvn6VHjaB6JBIJX375Zbng\ns0AgEPzdRArhvwwCgUAgEHzS/Pnz2b17N+fPn6dhw4afbd179+7h6elJ/fr12bJlS7XWPnXqFEOG\nDCEgIAAnJycAvLy8aN26Nb6+vh897+bNm7Rv356BAwcSEBDAmzdvaNSoEd9++y2DBw9mwoQJFBcX\n8/jxY3R0dJg0aRLBwcHExsYikUjKlU1oaGgA0LBhQx4/foyqqiq1a9fGwsKCa9euoaGhQf369YmP\nfxeiWbZsmfJB99y5czg4OKBQKLC0tCQ9PZ38/HxMTU3R09Pj7Nmz2NoO4tq1dcjlelW+PwCqqtC1\n61p0daMQi8W0bNmS6dOnA++au9rZ2XHv3j1OnDjBpUuX2Lx5M69fv0ZdXR1bW1u6d+/O2LFjsbKy\nYs6cOQwcOJBr167h7e1NdHQ0paWl9O3bl2vXril7g1hZWdG3b19MTU1Zvnw5rs7O3AwP51FKCjv2\n7mXwgQNw9Gi1rue9U3SnF6fLvaapmU+PHsfp3j1TGax4r2HDhpiZmWFra6v8nf3ZyZMn8fLyYtOm\nTXzzzTdcv36d9PR0ZQAlJyeH4OBgXr58iY2NDbq6uly8eJHWrVsTGBhIaWkpIpFImV1iaGjIgAED\n+Prrr7G0tPzo57734sULzp07p8ywGTlyJFFRUWRkZJCbm0vr1q0RiUSkpaWRlpaGSCSioKCAcePG\nkZqaikQiISUlhZiYGPr168f27dvR0tLi7t27dOnShRkzZrBq1Spu3bpF3bp1OXToED///DMrV67k\nzp07NfhtgLu7+wcbJgv+/xg6dCj29vaMGDHi796KQCAQAEKwRSAQCASCSlm2bBl+fn6cP38eY2Pj\nz7ZuSUkJixcvZt26daxcuZJ//etfVf4L+5UrV3B2dlaef/nyZUaNGkV8fPwn1+rfvz9PnjzBwMCA\nQ4cOMWnSJPbt20dSUhJ9+vShY8eObNu2jZycHNzd3QkLC1Om6f8x2CIWi7GxseHWrVuo/3vCjkKh\nQF1dnbdv36Kurs6XX37JtWvXUCgUaGpqsn//fvr27Uvt2rVp0KABCQkJSKVS1q5dy8SJE1FVVUVF\nRYXMzEwUiuXI5ROrcXf/w8OjlAcPbHB2dmb9+vU8evQIXV1d4F3gqUePHqipqXHz5k0mTJjA6dOn\nyc/Px8jIiIcPH+Ln58fRo0c5e/YsIpGInTt3cvLkSfbt2we8m0A0cuRIpk6dyuvXr5FIJOwNCGB3\nly7ohYfTOC8PcWEhpUBdCwvkamqoxlalFXFFK/FlMiuV/2xrexV7+wi0tT9dCqOjo4ODgwNWVlYf\nPH7u3Dl27dqFiYnJR9eQy+VkZGQQFxeHVCrl/Pnzyu+EmpoaR48eRS6X869//Yvs7GwA6tSpw1df\nfUWzZs3Q0/tP4Ky4uJhnz57x8OFD4uPjld+x9evXs3PnTlJSUnj16hUODg5kZWXx+PFj3rx5g7q6\nOvn5+fzyyy/k5+dz/PhxkpKSkMvlbNmyhW+//RaA7OxsbGxs8Pb2ZunSpYSEhGBra0tiYiJff/01\nJ06c4Pbt28qx6dXVtm1b+vTpU6M1BNW3YsUKnj17xpo1a/7urQgEAgEAkrlz5879uzchEAgEAsE/\nnZ2dHSKRiNGjRyuDBJ+DRCLhm2++wdHRkZ9++omwsDAcHByQyWSVXsPIyAgnJyeGDx+OSCTC3d2d\ndevWYW1tTePGjT96XuPGjQkKCsLW1pa5c+cye/ZsAgICUCgUDB48mF27dqFQKMjNzeXevXtYW1tT\np04dUlNTy62jUChIT0+nTp06ZGdnY2Zmhp2dHfHx8RQWFmJvb09kZCS1a9fm7du3lJaWUqdOHVJS\nUjAyMiI9PZ2CggLKysqIjo6mWbNmTJs2jeDgYMrKyigt9QJafPgiKqlWLTF79nRlzJgxtG/fnqSk\nJBwcHAAwNDQkJiYGmUzG3r172bdvHzt37iQnJ4cOHTrQq1cvPDw8CAwMxMDAAED58/usHC8vL+bP\nn8/KlSuJiIhguZkZ46KjaRsbi+Hbt2iVlqIJyABRRgaSly8pgyqN2P6jdPQZznbe8C5o0anTRTp3\nvoCGRtFfnAlFRUUkJiaipqZGo0aNyh1LSEggMjLyLzNQxGIxMpkMTU1NgoODlYEWdXV1wsPDefXq\nFcOHD1cGWgAKCgrIy8vj8uXLPHr0iJcvXxITE0NkZCQxMTG8ePFC2cB479697N+/n6SkJJ4/f06/\nfv14/PgxCQkJlJWVoVAoKCws5Mcff8TY2JhFixbx6tUrGjduTGRkJJ06dQKgrKwMNzc3rK2tCQ0N\nZfLkybi4uFBcXIyTkxM+Pj64urpy5coV5WdXl76+Ps2bN6/RGoLqy83NJTg4GC8vr797KwKBQAAI\nPVsEAoFAIKi08ePHM23aNBwcHEhISPisa1tZWXHjxg1atGhBmzZtOHDgQJXOb9GiBREREWzatImZ\nM2fy/ffff7RR7nsdOnSgdu3adOvWjSFDhjBo0CBsbW1Zt24dXbp0obCwkIEDB6Kvr4+qqqqyzEJN\nTQ2xuPz/Qsjlcjp06IBCoSA1NZVz586xefNmRCIRFy5cQF1dHSMjI2UvlytXruDp6YlMJmPevHmI\nxWJKSkrQ0tJCW1ubw4cPM2rUKHr27Al8uLlrVbx9C1988QXr1q3jzp07/Pbbb7x+/Vp5fOHChdy+\nfRtdXV1mzpzJhAkTADh//jw+Pj4MGTKk3IN0QkICzZo1AyA0NBSFQoGnpyf9+vXj56IiLP39Mf3D\n6OgPqcn/hIXTmWe8y7CytIzFzi4SVdXK94EpKSnh4sWL5RriJicnc+LEiSpNEKpXrx6urq4AaGtr\nc/PmTeLi4hg7dmy5QItYLKZ+/fqkp6dTVlZGYWEh9+7d48mTJ+Tk5FBSUqIctb19+3YOHTrEo0eP\nSElJwcPDg6tXr5KQkIBMJqO4uJiysjJcXV1p06YNY8eOpbCwkNGjRxMbG1suI2f+/Pm8efOG3Nxc\nLC0t8fb2BmDatGk0aNCA8ePHA9SoX4vgn6Fly5bCRCKBQPCPIpQRCQQCgUBQRf7+/syYMYPTp0/T\nsmXLz77+9evX8fLywtLSkvXr11O3bt1Kn5ueno6TkxPNmjXj+PHjPH36VFku8yH79u3Dz8+PCxcu\nsH//fsaOHUteXh6LFi2iQYMG+Pn5kZiYSE5ODnl5ecrJOh9qRKmiooK5uTnx8fGMGzeO169fo6mp\nybZt29DT00NHRwd7e3vq1KlTITOoqKiIu3fvEh0dTXZ2NmKxmJCQkH+XoWwmP9+p8jfwA+zsICLi\n3c9jx47l1KlTuLu7s2TJEuV7xo8fT3FxMeHh4eTk5PDq1SsUCgVqamq8evWq3H20tLQkICAAKysr\n2rVrx9SpU+nVqxez6tVjSXExqsXFNdrvpyTShAEc4jbWAHh5+WNiUvXJVgDm5uYMHjwYePe9rs6E\nLIBLly6xbds2goODWbRoERkZGcpjIpGIBg0a8OrVK0QiEbVr1yY/Px+FQkFxcTGlpaXK5rKrV6/m\n7t27hIeHk5SUhIeHB8eOHePt27fo6OhQUvKuKbCFhQUDBgxgxowZaGlpERwcTLdu3crtKTQ0lDFj\nxjBhwgR27tzJ1atX0dLSIiQkhHHjxhETE6P8Hm7bto2UlJRqXft7QhnR30uhUKCvr8+DBw+UGWgC\ngUDwdxIyWwQCgUAgqKKhQ4eyYsUKunXrRkxMzGdfv127dsTExGBsbEyrVq0IDg6u9Ll169bl/Pnz\nvHz5Ek1NTXbu3PnJ9w8cOJDHjx8TExPDoEGDCA4ORqFQMHfuXFxcXEhJSeHbb79FJpPRqlUrioqK\nMDIyolGjRjRu3Bh9fX3llCFA+fB68eJFLl68SLdu3VBTU6Nt27Z89913mJubf7AESyqVYmNjg6en\nJ927d6eoqIhz585haGhI48aqlb7+j/lj7+FVq1ahqanJb7/9xqtXr5Svz5w5k4MHD+Lk5MTr16+V\nk5pKSkrKTeiRy+U8evQIc3NzTp8+TUFBAS4uLuzYsoUfRKL/aqDlKY3xYb0y0NKkyROMjKofJEhK\nSiI9PZ3ExMQaBRsGDRqEv78/S5Ys+WSgRV9fn/z8fMrKyigoKKC0tFT53rlz55KSksL58+dJSkqi\ne/fuHDhwgKKiIqRSKQqFAm1tbXR1dTEwMGDmzJmYmZmRmJhYIdDy6NEjhg8fzrx581i+fDmHDh1C\nS0uLZ8+eMWLECPbt21fue/jncqqqEolEmJub12gNQc2IRCIhu0UgEPyjCJktAoFAIBBU0+HDh/H2\n9iYkJIR27dr9Vz4jMjKSoUOHYmtry9q1ayvdK6aoqIju3bsTHR3N8+fP0dHR+eh7ly5dSlxcnDIw\ns337dkaMGIGrqyuOjo4cPXqUmJgY6tati4WFBUZGRujo6CASiSgrKyMzM5PExERu3LhBeno6ampq\nFBcXM2HCBOXUF0tLywqlR58SHR1NaGjovxv9Krh0aSnw8QydTxGLISgI/l3tAsDjx4+xtLTExcWF\nPXv2KF+fPXs2K1asYM6cOUydOpWWLVvy4MEDNDU1ef36NSoqKjx58oSOHTvy7NkzOnTowMSJExkw\nYADT69VjWWZmtfYIIAckHzlWgCo31drzU/E0bksdqF1bSvPmIrp2PUJxcc2m6LRr146CgoIaPaQq\nFAqOHj3K7du3y71uaGhYLtDyfkxzQUEBqqqqyqyWiRMnUrduXTZt2kRqaioWFhbExcWhqqpKrVq1\nKC0tpW7duiQnJyOTycjIyKBjx46Eh4dX+F7l5+fz9ddf4+npyfr161m1ahWurq6Ulpbi4OBA3759\nmTJlSrlzbt++zaFDh8oFDqvCyMiIYcOGCeVIfzMfHx/Mzc2VZYACgUDwdxIyWwQCgUAgqCZXV1e2\nbdtGnz59iIyM/K98hp2dHXfu3EFfX59WrVpx/PhxZXPQ/Pz8clOB/kgqlSon57Rr167CGOA/GjVq\nFCEhIaSlpQEwbNgwmjRpwrFjx7h16xYJCQmMHDkSZ2dnWrZsia6uLsXFUq5csSU8vAsxMR4UFPxI\n796zcHFxQVtbG5FIREBAALq6ulUOtADY2NjQqlUrDh8+zNWrOzAwiK/S+X9kYpKBi0v510xNTVm7\ndi379+/n/v37ytdVVFRQKBTY2toC7xoYL1y4kPz8fGW5zcOHD2nWrBnnzp0jKyuLgQMHEhgYiEsN\n/35VBpwTi8lu1AgaNaK4Xj0eSiTc++orhhrWxdPwGUvC9Xn+XJ3kZBFnz4KhYeX7q3xMTk6O8ndf\nXSKRiDp16pR7zcDAgPT09HKBlqKiIgoKCtDQ0EAul6NQKBg2bBjm5uasW7eO1NRUdHR0uH//PhKJ\nhFatWiEWi2nWrBm///47paWl5Obm4uDg8MFAi0KhYMSIEbRt25azZ8/i5uam7Ckze/ZstLW1+emn\nn5Tvf/HiBT4+Pjg6OlY70ALv+gEJgZa/n5DZIhAI/kmEaUQCgUAgENSAubk5rVu3xt3dna+++uqT\n43KrS1VVlV69emFqasr+/fuJiYkhOjqaa9eucePGDR49ekRxcTH16tUr9/ApkUgoLi4mKSmJDRs2\n0LdvX2rVqlVhfQ0NDVJSUrh79y5dunRRln6EhIQA0LlzZ2QyGWKxmJSUBoSHdyYsrDtxcS1JTm7C\ns2eNefrUhLi4NhQWtqNRIzOysi4hl5fg6OiIpmbVG9wqFAoMDAxITExEW1ub7OwUSkq6AepVXKmI\n4uJFfPedWblxwwDW1tYcPXqUHTt2MH78eLKyshgyZAi+vr7Mnj2boqIivvjiC7S0tEhOTub69eu0\natWKxMREAA4ePMikSZNo3bo1np6ezCgoQDX/02OXP0UCSAcPxiAiAiZNwuXiRdIGDGDU8eNIv/iC\ny5cvY2Fhiqbmu2wdeDe2+v2o5OrS1dXlzZs3FNew/Only5fKhrt6enrk5OQogzA5OTnKiVPa2tr/\nj707D4/xehs4/p2ZTNbJIhIh9iBNQqQiCLFTlErs+xJbqKWKlpaiat+32lrUllpKbRFSIdYgQYLE\nnhBCFrLIPpnMzPtHat6qJJLor9o6n+tyXck85znnPCMtc7vPfZOTk4NGo6Fr16507tyZr776iufP\nnyORSHR1Wdq3b09sbCw1a9bk7NmzyOVyatWqRfny5Tl+/DgGBgav7WHFihUEBQXh6upKdHQ027Zt\nQyqVEhAQwOzZswkICEChUJCWlsacOXMYMmQI9evXZ/fu3TRu3JjQ0FBKmvRdq1YtOnbsKIIt/wBK\npZIdO3YwfPjwd70VQRAEkdkiCIIgCG+rXbt2/PLLL/Tq1YuAgIC/fP7U1FR27NhBREQE9vb2yOVy\nlEolubm5ZGRk8ODBA/z9/dmwYQOXL19+5d6hQ4cSFRXF6NGjadasGdevXy9wjfHjx7NhwwZd+9vu\n3btjZmZG/fr1KVu2LADnzzdm+/aBhIW58uKFxWtzqFQGREXV4tKlEZiYBOLi0qzI4rxvYmVlhUQi\nYciQIZiZnQHmABklmEGFXL6ZGTMsGTBgwCv1QV46dOgQMTExfPvttyxYsIBu3brRuXNnnj59ikKh\nYMWKFRw6dIh+/fphbGxM//79CQkJQSaT8fTpU/r06UNAQAB6eXkYaTSlftaXKv4eEDofHMz5CxdY\nv349arWawMDA14JFkB+Ie1tSqZTktzj+9EcSiQQDAwMyMzOxt7enZcuWVK9enZo1a6Kvr4+pqSnZ\n2dmo1Wpat27NsGHDGDt2rC7QAvnto728vEhMTMTa2pqgoCDkcjnDhw8nPT0dPz8/TExMXlv71KlT\nLFy4kHHjxrFlyxZ2796Nnp4ecXFxeHt74+vri5mZGcuXL6dWrVo8evSIq1evsmzZMqysrEhOTmbH\njh0l+pmtWbMmPXr0KHHmlvC/Ubt2bSIjI9H8Bf8tCoIgvC2R2SIIgiAIf4Fq1arh4eFB7969cXR0\n/MuKZSYmJrJnzx5iY2Pf+AEiKyuLhw8fotVqqVo1vy2wubk5Z86cwc3NjU6dOtGvXz/c3d11118q\nW7Ys586dIzs7Gzc3N6RSKRKJBD09PSQSCefONSEoqBUq1evZBAXJzLQlL8+VevXuIpOV7niNVqvF\nwsKC7du3Y2pqSps2Rty9GwK4otUqirxXTy+D1q1vEB3dg8mTv+TixYs8evSIFi1avDLOzMyMhIQE\nVq9eTXh4OHv27GHkyJG0bt2a0NBQxo4dS48ePZgwYQI1atQgISGBO3fukJaWxpQpU3B1dWXEiBGM\nHjuWukFBSN4yywQPD9I9PPDw8EAul+Pj44ONjY3uCNOfRUdHv1LktzSuX7+OTCYrMFOkJJpGR9Pu\n+XPMPTxwb9uWRo0aUaVKFWrUqIGTkxPOzs6YmZmRkpKCk5MT8+fPp3v37qSmpiKXy1Gr1djY2NCo\nUSNyc3N58OABN2/exNzcnK1bt7Jo0SKOHj1KrVq1Xlv78ePHdOjQgcWLFzN58mT27NlD7dq1UavV\ndOnShe7duyORSOjevTtZWVls3bqVkSNH6gJYL1tJ9+vXj2HDhpGTk0N2djZKpbLAZ7WxscHV1ZXO\nnTu/1fEj4a9lZGTEunXr6NatW4FZfIIgCH8nEYYXBEEQhL+Ih4cHfn5+DBs2jH379r31fNnZ2Rw4\ncKBEH6ZVKhXnzp3j6tWrutdGjBjBxo0b6d27N9u3b6dbt266I0J/NGHCBFasWKEL6pQrVw6JRML9\n+3acPdsUtbpkWRTx8U4cPfpxie75M0dHR4yMjLCxsUFfXx9Dw58wNPQAFiKV3iC/0sn/q1ULRo3K\nY8yYHVy9+jHlylkzcOBAVqxYwZo1a17pKvTSnDlz0Gg0aDQajh07RkpKCuvWrUMmk3HgwAFcXFxY\ntWoViYmJ6OnpoVQqiYqKol+/foSEhPDgwQNat23L/dTUt3pWgKcqFU5OTiiVSm7dusX58+fp1atX\noeNf1jQpLZVKxePHj9+6ZotcqaSiRkOGjw92Hh7Y2Ni8NsbCwoL69eszfPhwxo4dS4cOHUhLS0Mm\nk6Gvr4+dnR12dnYYGxtz6tQpHj9+TOXKlQkJCWH8+PFs3ryZDz/88LV5c3Jy6N69O2PGjGHlypV8\n9dVXNGvWDMj/vU1KSmLv3r388MMP7Nixg8OHD+Ps7PzKHGvWrCEvL4/PP/8cc3NzvLy8+PTTT2nV\nqhUODg7Y2dlRs2ZNateuTZcuXfDx8aF169Yio+UfSNRtEQThn0J0IxIEQRCEv1hYWBgff/wxy5cv\nLzQjoTgCAwNLXXjXxsYGHx8fpFIpKpWKKlWqEBQUhIODAyEhIXh6erJw4UIGDx6su0er1VKvXj3m\nz5+Pq6srmzdvJjc3l19+6U5kZJ1S7cPU9AWffroeY+OcUt1fuXJlJkyYgEwmQ61W07hxY+7cuYOt\nrS2XL4cjk3XAzMwZU1MLtNqnhIfPQ6HIzzR49OgRXbt25caNG9SsWZMZM2YwdepUwsPDX+nOdPPm\nTRo0aICtrS2xsbEEBATQvHlzPvjgA9LT03nw4AEGBgZMmDCBPXv28PTpUypUqMDo0aMJCwvD3Nyc\no0ePsiQvj/5/aHtcUhlmZtSTyVCZmbF06VI8PDxwdHQkLi4OQ8OCa9VotVq2bNnCo0ePSrXms2fP\nCAwMpGbNmri4uKCvr1+qecomJpJmbo6qmNkxGo2GsLAwDh8+rMsC09fXp3r16vj5+aHVaqlcuTLh\n4eG0aNGCYcOG8fnnnxc4l4+PD8nJyVhaWpKSksKePXuQSCR8//33TJo0iapVq7JkyRI6d+5cYF2V\n+/fv4+7uTnBwsGjf/B8wefJkLCwsmDp16rveiiAI7zkRjhcEQRCEv1i9evUIDAxk0qRJbNmypVRz\naDQa7t+/X+o9JCQk6P51Vy6X4+3tzcaNG4H8Vr+nTp3StTl+SSKRMGHCBJYvX87Dhw/Jzc0lLU3B\ngwfVSr2P9HRzLl50L/X9crmcbt260a5dOyC/RoZMJsPY2BiFwhBj49MkJMzCySkIO7twVq1apLu3\nSpUqbNmyBWtra2Jj0xg7dimOjh/j4zPplTWmTp3KtGnTePr0KQYGBoSGhgL5NWOqV6/O2rVrAVi0\naBFGRkYAKBQKVq5cyaFDhzh37hx79uxhhUZDnqlpqZ/1lEzGmBkzsLa2plu3buzdu5dPPvmk0EAL\n5P+eubi4lOooS25uLqGhoSQmJhIUFFTkOkXSakkqW7bYgRbIrxPz4Ycf4u3tTV5eHkqlEqlUip+f\nH3p6epQtW5Zr164xYMAAmjZtWmgr3x9//JGzZ8/Spk0bzpw5w+bNm7l58yYdOnTg888/Z9y4cdy6\ndQtPT88CAy0ajYahQ4cybdo0EWj5jxCZLYIg/FOIYIsgCIIg/A/UqVOHkydPMn36dH744YcS33/j\nxo0i2zUXd46Xhg4dyrZt23QdZxwcHDh37hybN29m8uTJug4sffr0ISIigtjYWAAuX3YjK6vo+ihv\nEh1d+g5NkZGR9OvXj8jISKpUqYK/vz8ZGRmEhITg6OjIt99+i1Qq5ciRI3h6erJ8+XLdc6tUEBxc\nm8TErejpxZCUFIy//zJ2756Hi8sjtm6FM2fOExYWhpeXF5DfcnrRokVcuHABQ0NDhgwZwvz583nx\n4gVyuRwjIyMkEgmPHz8m9fdjQ3v27EGlUqGpVo2YUn5gTwFi2rZlzZo1zJs3D4lEwp49e+jdu/cb\n73V1dcXd3b1ER1pyc3O5cOECNWvWRC6Xs2TJEjZt2kRSUlLJNy+RgExW4ttkMhkKhYIXL16QkpLC\n9evXsbGxQU9Pj/DwcGbMmIFarWbVqlUFBkpCQkKYOnUq8+bNY8aMGaxZs4bx48fTsmVLHj58yKRJ\nk1iyZAmyIva2evVqNBoNn332WYn3L/wzOTs7ExER8a63IQiCII4RCYIgCML/UlRUFG3atGHixIkl\n+kB39OhRQkJC3mrttLQ0evfuTcOGDYH8Fs6jR4+mCZ/CNwAAIABJREFUZ8+eujFJSUl06tQJJycn\nfvjhB/T09JgzZw7Pnj3D0tKSw4c7cuVKg7fah6VlEp999n2J78vOzmbLli3k5OSQl5fHqFGjWLZs\nGX379uXGjRvUrl2b+Ph4zMzMiIuLo3bt2igUCvLy8qhVqxWnT1fjyJH6qNWFfdjWYmx8j1GjYrhz\nZyWNGjVi7dq1fP311yxdupRatWoxYcIE9u3bR/ny5enevTstW7bEysqKhw8fArBw4UI2bdqEh4cH\n9vb2nNi3j7lXrtCwBH+9ygHypk+nuZ8fd+/e5fz581hZWeHs7ExcXFyxC9f++uuvhISE6LJvCpOR\nkUFaWhrW1tbs3LmTCRMm8OWXX5KTk0PlypXx9PTE2tq62Pt/GxqNhkOHDnHt2jXc3NwICwsjODiY\nixcvsn79eoKDgwvsDpSYmIibmxvz5s1j+vTpODk5cfHiRUaNGoVcLicwMJBTp04VmfFz7949Gjdu\nzIULFwosuiv8O2VnZ2NpacmLFy9KfSxOEAThryAyWwRBEAThf6hGjRqcPn2aVatWsXjx4mLfV1Cb\n4pKytLSkc+fOTJs2DaVSqSuU+0dly5YlMDCQp0+f0r17d7Kzsxk1ahQnTpxAKpWiVr99p5W8vJJn\nPQDExMSQkJBAtWrVcHd3Z9euXUilUg4cOEB0dDSXLl3C0tKSevXq0alTJ6pVq4aVlRXly5cnPf0W\nrq5HGTVqPe3bH0Muzy1gBQlZWfb8+GMTrlypweTJk5kyZQpBQUH07NmTiIgIsrOzmTVrFhs2bMDH\nxwelUkmNGjWoU6cOEomExMRE2rZti6+vLyYmJpy4coVxlStzvpjHelKAmVIpwU2b8uzZMyZMmEDb\ntm359NNP8fT0LHagRaPRsHz5clQqFVevXtVlML2k1WpJSEggJCQEBwcH1Go1x44dw8fHhy+++IKc\nnBxdxs6uXbuoWrXqW3cnKg6pVIqDgwNeXl5cvXqVnTt38uzZM+bPn4+fn1+BgZa8vDx69+5N7969\nmTt3LvHx8VSuXJkbN27QqVMn1q9fz86dO4sMtKjVaoYMGcL06dNFoOU/xsjIiKpVq3L37t13vRVB\nEN5zItgiCIIgCP9jVatW5fTp02zatInZs2dTnKTSv6LLiUKh4Nq1a0RERNCgQQPs7Oy4cuWKLivj\nj+MOHTqEiYkJ7du3R09PjyZNmqDRaAoJUpSMnl7Ji+NmZGQQGhqKRCLh2rVrnDx5koSEBDZt2kRO\nTg4mJia0bNmSunXrFnjE5CVr6+c0bnyJAQN2oFCkFTgmPd2E1NQZnD2bnz0TGhpKjx49yMvLY9++\nfZiZmVG2bFnCwsKoVq0aX331FU+ePKFMmTKsWLGCRo0aIZFI+PLLL3FxcSHk0SPaaTSsrlKF0+Rn\nrvzZY+BE5cpMq1uXI46OfPLJJ3zwwQfMnj2b0NBQTp8+TWhoKA8ePCjW+7VhwwbUajUxMTGEhobS\nvHlzzM3NOXfuHMeOHePcuXP8+uuvjBs3jn379vE0NpaJtrbUmDoVf6WSc8AxrZbVwIH586lQoQIr\nV67k3LlzkFO64sbFVbVqVQ4fPszs2bOxt7fH29ubffv2Ub16wcfPJk+eTGJiIuvXr+fZs2eEhoay\nfv16DAwM6Nu3Lxs3bqRy5cpFrrlq1SqkUinjxo37XzyS8I7VqVNHHCUSBOGdE8eIBEEQBOFvEh8f\nT9u2bfHy8mLOnDlFBglOnTrF6dOn32o9W1tbRowYgVarZceOHUyaNAk7OzvatGnD3LlzXxuv0Wj4\n/PPPdZk48+fPR19/NIcPe77VPiwsTjFqVECxC7BmZWURGBjI1atXkUql6OnpUblyZaKiopBIJJib\nm9O9e/c3fqD+s5iYKuzY0R+VquCjBYaGh9m8OYPk5GSOHDmCiYkJ/v7+KBQKjIyMSE1N5cMPP6Rl\ny5ZkZmZSrVo1Tp48SUxMDHfv3iU3NxeJREJeXh4WFhZkZmaSnZ1NW6CLuTnlzcxISE7GvmlTegUE\nILG0ZMmSJbRq1YoaNWpgYWHBvXv3yMzMpF69ekyZMoVFixaxYMEChg4dWujPS2xsLPXq1WP79u30\n7dsXrVaLkZER9vb23Lp1CxcXF+7fv8+2bduY8PnnfKXVUv/RI6okJVFQzlGukRGBKhXLJRJefPgh\nnTp1KtH7XFLx8fHk5OQwf/58GjVqxIIFCwrs4qXVapkwYQJr1qzB0dGRp0+fEh4eTqVKldBqtXh5\neWFvb/9K0eeC3L17Fw8PD13NGuG/Z9asWeTm5hb4/zlBEIS/i8hsEQRBEIS/Sfny5Tl16hT+/v58\n8cUXRWa4uLm5YWJi8lbrvcwMkEgkDBw4kLCwMPT09Fi8eDHXrl17bbxUKmXlypX07NmToUOHkpOT\nQ4MGN7G2fptCvWpSUzdw5MiRYhVfTU5Oxs/Pj6tXr2JiYoJGowHy6898+eWXGBsb06RJkxIHWgCq\nVn1EmzYnCr1uYNCeOXO2snv3bsLCwggNDUUqlSKTyXBzc2P8+PG0atUKiUSCQqHg+fPn1K1bl06d\nOjFixAiaNGlCbm4u9evXp379+mRnZwMQCHylVvOtuTnd7t9nsVZLzQYNSE5OpkGDBmzduhVPT0+U\nSiXNmzdn586ddOnShS+//JKgoCC+//57vLy8CiyYrNVqGT16NEOHDmXGjBlkZGRQsWJFvvnmGyIj\nI3FyciI1NZUdO3YwbPBgflQq6RoWRvVCAi0A+tnZdMzLY6tKRUsHhxK/zyVlaGioe8Zhw4YVGGg5\nceIEzs7OrFmzhlmzZvHs2TN2795NpUqVAFi5ciUJCQnMmzevyLXUajXe3t7MmDFDBFr+w0RmiyAI\n/wQi2CIIgiAIfyMrKytOnDjB2bNnGTt2rC6Y8GcKhaLQYxTFYWJigrv7qy2XK1asyNmzZ6lcuTLN\nmzdn/vz5r9WGkUgkfPPNN0yePJkbN25w5MgBateOLfU+9AgDdnPjxg3Wr19PREQET548QaVS6cbk\n5uYSHR3N0aNHuXTpEjExMSgUCrKysnTXDQwMyMrKwsDA4LXnKomaNaPQ0yu4Hs6LF/p06HAEV1dX\nEhISePLkCRMnTqRHjx44OzsXerRLKpViZmZGixYt6NOnDzdv3nztg55CoWD58uUkJCQQERGBs7Mz\nRkZGbN26ldWrV7N06VICAwO5d+8e8+bNo1evXkD+h8ZLly5Rt25dXFxc+PXXX1+Z92UR2Q0bNnDt\n2jXMzc3p0aMHs2bNonr16piZmbFo0SJ6dO/Ofmtr6kVGFhpk+TMbqRSrcuWKObr03N3d8fb2xt7e\nnunTp79y7erVq7Rr147hw4eTlJTEDz/8gJ+fH5999hlt2rQBIDQ0lHnz5rFr1643FkRdsWIF+vr6\njBkz5n/2PMK75+zsLNo/C4LwzolgiyAIgiD8zSwtLTl+/Djh4eGMHDkStVpd4Lh69eqVukhp1apV\nUSheb9kskUj4+uuvcXd358SJEzRt2pTbt2+/Nm7kyJGsX7+ey5cv07TpBSpWfF7iPZiQzmLWsAst\nbuRnFezduxdjY2NWr17Nw4cP2bBhA6tXr2b79u2EhoYSFhaGiYkJmZmZALrnj42NZevWrXTr1q3Q\nAFVxWFkl4ep6udDr+/dHsHfvXrZu3YpCoSAxMZGyZcsWe357e3s6d+5MXFyc7jUjIyMMDAywtbVl\n6dKlfPbZZ7p6NGvXrqVnz57Y2dnh7u7O8uXLefHiBf7+/rr79fX1mTNnDvv372fKlCkMHjyY8PBw\nBg8ezJgxY2jbti3Dhw+nZcuW6OnpsX37dsqUKUOjRo0YMmQIvXr1wrdHD+xL2N0qx9AQtVxeontK\nSiKRcOPGDeLi4ti4caPuqNT9+/fp06cPnTp1wtPTEycnJ3r27ElYWBjW1tZMmTIFgBcvXtCnTx/W\nrVv3xuDknTt3mD9/Pps3b/5LaiIJ/1w1atQgPj6ejIyMd70VQRDeY+JPGkEQBEF4B8zNzQkICOD+\n/fsMGTKkwO5DdnZ2NGvWDJmsZN18cnJyWLBgAY8ePSrweu/evQkJCWHr1q0MGjSIZs2asWzZsteC\nPj179mT8+PEsWjSJ8ePDqVo1q9h7MCaDr1jA52yhN7AX8Pw9SLJz504yMjK4cOECcXFxmJubU7t2\nbZydnalYsSItW7ZEq9Xi6OiIUqlEJpMhk8nIzMx8JSOmtKpViyn0Wnq6hhs3bjBw4EAGDRpE+fLl\nSzy/vb09TZs21X1fpkwZkpKSUKvV+Pn50aRJE168eEHbtm3Jzs5+ZWxGRgaNGjVizZo1bNiw4ZV5\nGzdujK+vL8HBwdSvX59z584xfPhwtmzZwvbt27lx4wZ6enoolUp8fHxwcnJizJgxrFq1iowff6Sk\nYROZWo3sLQJbxSGXy9m5cyf79+/HwMCA+Ph4xowZg7u7O3Xq1OHevXskJyeTlpZGgwYN8Pf3Z+vW\nrUilUrRaLcOHD+fjjz+me/fuRa7z8vjQrFmzsLOz+58+k/DuyWQyHB0diYyMfNdbEQThPSaCLYIg\nCILwjigUCo4cOUJ8fDz9+/cvMJDg4eFB69ati11c1s7OjunTp9O/f3/c3d25cOHCa2NMTU3p0aMH\n27dvZ/To0Vy6dImDBw/SsmVL7t+//8rYOXPmoFAoWLSoLyNGHKF1ay16ekUHPD7gNkv5gm/4//oZ\nVYHVQGuplMTERKpUqYKNjQ3Dhw9nyJAh9OjRg65duzJ48GCsra3p0aMH8fHxGBgYoFarUavV2Nra\nFquT05sYGCgLvZaamoCRkREZGRkFth0uLnt7e93XSqUSpVLJtm3bGDx4MPv372fgwIFkZWVhaWn5\nytGgPXv2MG/ePAYOHMjYsWMJCgoC4NKlS3Tp0gVPT0+GDx/O1KlTiYmJQV9fnxUrVqBUKklMTCQn\nJ4fly5eTnJzMihUrWLJkCXOHD6dJbsm7ShkolRj+Xnfmf0GlUnHgwAEOHz6MgYEB06dPp3bt2hgY\nGHD79m2++eYbTp8+zQ8//MDs2bOZOHEie/fuxcLCAsg/QhUVFfXGgrgAy5Ytw9DQkE8//fR/9jzC\nP4uo2yIIwrsmuhEJgiAIwjuWk5NDjx490NPTY/fu3QUeHYqNjeXKlStER0eTlvZqC+OX3XocHByo\nX7++LhPGz8+PoUOHsnz5cvr37//KPZcuXaJ///7cvXsXqVSKRqNh1apVzJkzh2+//ZbRo0frjlrM\nmDGDe/fucfHiRUaNGkW97EZsm/WQ8zQnhTLkIceYTOpynS4cZAQ/YkDBH+4vAcNr16ZN27aUKVOm\nyPclMzOTpKQk3bEPiUTC6NGjS3SspyBRUdXZvn1QIVc3YWY2kZkzZ5Kenl7qNTQaDXv27OHOnTvI\nZDJsbGzIzs7m4sWLNG3alO3bt9OvXz9yfw+C3Lt3j/T0dJo2bcqTJ0+QSCS0atWKCxcu4ObmxpMn\nT5g8eTJDhw5Fo9FQp04dFi1axKZNmwgICKBSpUokJiYSEBDATz/9xJ07d/Dx8eGzzz7js8xMii4b\nW7iDXl6E16tX6vehMFKplEuXLvHw4UP09PR4+vQpH3/8Md999x1Vq1YF8o8SeXh4sGPHDsaNG8fX\nX3/N4MGDAQgPD+ejjz4iODiYWrVqFbnWrVu3aNasGaGhoW9VB0n4d1myZAmxsbGsWLHiXW9FEIT3\nlAi2CIIgCMI/QG5uLn369CEnJ4d9+/ZhZGRU4DilUsmVK1fIzs5GrVajr69PjRo1Cu3OExERgaen\nJ3379mX27Nm6AIpWq8XFxYWVK1fSqlUr3fg7d+7g7e2NsbExmzZtolq1asTHx+Pk5MSZM2fo06cP\nO1QqPrx7FzVSXmCOEgPKkIIhhWeMvJQH7PTyIrqYH+DVajURERHs378fiUTCjBkzimyZXRy3bn3A\nnj29cXC4Sc2aUejrK5FIIC/PgBYtjJg0qQMjRoygQoUKb7XOlStXSEtL4/Tp01SoUIEWLVrQr18/\nFixYQOXKlalbty6XL1/m2bNndO7cGbVaTWxsLKtXr8bPz4+5c+cSFhaGXC7nwYMHlPu9WO3EiRN5\n/vw5w4YN092XlZVF27Zt0Wq1GBsb06pVK7755huysrJYIpczqZTHrx5VqsT2QYPIe0Ph2ZIwMTHh\n7NmzWFpacvLkSfLy8rCxseH48eO6QFpmZiaNGzdm5MiRnDp1CktLS92xqvT0dNzc3Pj2228L7Fz0\nR3l5eXh4eODt7S2yWt4zx44dY8mSJQQGBr7rrQiC8J4SwRZBEARB+IdQqVQMGjSI58+fc+DAgbdu\n/fzSs2fP6NatG9bW1mzfvl0376pVq7h48SI///zzK+PVajVLly5l8eLFzJ07lxEjRjB06FDs7e0Z\n9ckn4OpKmQJqzBTXTUdHfundu9jj1Wo1x44dIzQ0lNatW9O8efNSrw35mS0GBkpsbZ9SUJ1UfX19\n0tPTS12c+KWYmBjatWvH4MGDUalUBAcHs2TJEhwcHNi0aRP379/nwIEDrFu3jsTERIyMjPD09OTA\ngQPI5XKmTZtGy5Ytsbe3x9LSksjISMLDw/H09GTmzJnMmDGDvLw8LCwseP48v4Dxy25Nv/32GyqV\nCqlUymyNhqlv8Rzb+/cn+g3ZI2+i0WjIycmhbdu2fPXVVzx69Ah7e3sWLlxIkyZNmDp1Kvv378ff\n3x87Ozv69++Pvr4+devW5eeff+bcuXMYGhqi1WoZOHAgRkZG/Pjjj29cd+HChfz2228cP35cFMV9\nzzx58kTXWUwQBOFdEMEWQRAEQfgHUavVDB8+nOjoaPz8/DA1Nf1L5lUqlYwaNYrw8HAOHTpE5cqV\nSU5Oxs7OjqioqAKP5kRGRjJ48GCsrKyYOHEiQ4YMIWbUKPRmzHirvbwwNWXl55+jLUHh3+fPn7Nm\nzRrkcjlTp5Y+dKBS6SGXlz5QVBLJyck8fvyYwMBAcnNzGThwIL/88gsNGjTAy8uLsWPHkpqaSpUq\nVdDX1yclJYUmTZowbdo02rdvr8vgefDgAU5OTjRq1IikpCQcHBy4ePEixsbGxMTE0KBBAy5cuICp\nqSlpaWm6bk0KhYKMjAwmAwvf5jksLNjety+pNjbFvkcikRAbG4tKpSIkJIRy5coxa9YsevbsSXZ2\nNlu3bqVLly6vZCmtW7eO7777jj59+nD69GkWLVrEgAEDuHjxItWqVQPgp59+YtmyZVy6dAljY+Mi\n93Dz5k1atGhBaGio7n7h/aHVarG0tOTOnTu6rDBBEIS/kwjxC4IgCMI/iEwmY9OmTTg4ONCuXTtS\nU1MLHavVQkwMnD8PwcEQG1v4vAYGBmzevJl+/frh7u7OpUuXsLS0pFOnTvj6+hZ4T+3atblw4QJN\nmzZlwIABmJubc7uE7YML3ItSiWFOTonusbS0xNHRkdzcXMLDw0u99t8VaIH8uiTh4eFkZmbSsWNH\nDh8+jK2tLffv38fHx4eMjAw2b95Mbm4uubm5VKpUibNnz9KhQwckEgmhoTByJIwcWZ0aNWI5fXoq\nkZGT8ffPIz4+gXv37lG2bFnOnz9P27ZtcXFxQU9PD8iv45ORkYFEIsF+1ixS3+IYkGVqKpb79r3S\nzrooMpmM1NRUHjx4gL+/P8nJyZiYmODp6YmhoSGxsbF07dr1teNgn376KZ999hkrV66kX79+DB48\nmC1btugCJZGRkUyePJndu3e/MdCSl5eHt7c3s2fPFoGW95REIsHZ2VkUyRUE4Z0RwRZBEARB+IeR\nSqWsX7+ehg0b0rZtW5KSkl65npEB8+ZBs2Zgbw9Nm4KHR/7XrVvDypVQUCxDIpHw5Zdfsn79ej75\n5BN27tzJiBEj+PHHHwvt8iOXy/nmm284fvw42dnZHP/tt7d+Psnvv0pCKpVS7/c6L2fOnEGpfHN9\nmHctKyuLLl26oNFoqF69OtbW1ty9e5cePXqwYMEC7OzsuHjxIlOmTAEgKSmJ1NRUfH0zcHNLwsMj\nlx9+gOPHITKyLNAOrXYgWVm7sLKKRF9/DFlZWbrW0ufPnyc3N5eyZcui0WiQSqWYmJhw+OpVAt6i\nZXYesD8xkW3bthEcHEx8fHyBPy+ZmZlYWlqi1WoJCgoiJiaGlJQU3Z4UCgXBwcGYmZkVuM7LejXL\nly9n2rRpuLi40KFDB9172atXLxYvXoyTk9Mb97xkyRLMzc0ZOXJkqZ9b+PcTHYkEQXiXxDEiQRAE\nQfiH0mq1TJkyhYCAAI4fP065cuXYuBHmzoWHD4u+194+f1yPHgVfv379Op6engwYMIBdu3bh6+tL\no0aNipwzJyeHxWXKML2EWSl/9sLUlFWff46mBMeIAJ4+faoLDFWvXp1hw4aRV8zaMbm5MvT11aXZ\nbqmFh4dz9OhRJBKJrv6LWq0mMzOTXr16MWvWLMqVK8fWrVuZMGECtra2pKZ6k5X1BVB0p6Z82dSq\ndZzExMGkp+cfHzIzMyMrKwu5XM7EiRORSCTMmTOHTyQS9mq1lKYKzUWgMfnBOq1Wi0wmo1u3bqSn\np1OxYkXS09NJS0ujb9++aLVaZsyYgVar5fHjx4wYMYK+ffvSu3dvAgICdAGzP1MqlTRv3pyuXbuS\nlJREaGgocXFxeHp6snDhQnx8fMjNzWXr1q1vLJAcERFBq1atuHz5sq6zkfB+WrduHVevXi1WfR9B\nEIS/mt673oAgCIIgCAWTSCQsXLgQQ0NDWrZsSa9eF1iyxJzMzDffe/cu+PhAejoMGfL69bp16xIS\nEkLXrl0xMjJi7dq1bwy2aDQabCZMIH3pUkxzC27tXBxPK1YscaAF8o9CvfzAn5qaip+fH3Z2dpQv\nX77QbInU1FRevMiialXbUu+3NOLiyjH40VOWKJUogcScHH4DTtWpg9zSksDAQEJDQ0lMTKRMmTIo\nFArKl/+auDhvQFHMVYy4d+9jYDwwC2NjY3JyclAoFAwYMIBGjRrh6emJTCbDT63mB2AMJUtrjgWm\n/P71y0DL+PHjWb16NTVq1ODatWsolUq+++47LCws6NevHyqVCpVKha+vL02bNqVx48Zs3Lix0EAL\nwGeffUbFihWpVasW69ev58qVK0gkEry8vGjcuDEpKSlcvXr1jYGWvLw8hgwZwty5c0WgRaBOnTps\n27btXW9DEIT3lMhsEQRBEIR/gb5997J790doteYluq9cOdi9G1q2LPi6Uqlk4MCB/Prrr0RERODg\n4PDamEePHnH58mUePHhARkYGPXfvxunWrVI8BWiAX7t3J9LZucT3xsbGsnHjRt33FhYW2NrakpWV\nRfXq1bG3tyf39yCQUqnk8ePHhIWF0aFDB+rUqVOq/ZbGgwdV8fXtT8u8IHbTG0v+v+7OQyDQ2Jh5\nlpY41q3LgQMHqFu3Lu3adWfTps/IzCxNIc8U9PRaY2n5lKZNm2JnZ0elSpUIDg4mPT2dK1eukJiY\niARYBwwBilPBJQYYBxz+/XszMzNWrFjByJEj6dixI+fPn0epVDJ+/Hg0Go3uaFR0dDSrV69m4MCB\nNG3alEGDBjFp0qRC19m4cSNLly7F19eXDh06cOTIERo0aADAjRs3aNCgAY6OjgQGBhZYyPmP5s2b\nx6lTpwgICHjrFuHCv19KSgpVq1YlNTVVdKMSBOFvJ4ItgiAIgvAv0KYNnDxZunu7dYN9+wq/rtVq\nqVOnDk+fPiUgIICGDRvqrl26dIlTp06R84ejQw6RkfTYuxdZKf4K8cTWlo3Dh1Ngz+U3uHfvHv7+\n/qSmpqLVapHL5UilUl2ApaC/0kgkEgYMGECNGjVKvF5pPHxYhV9+6UlmZn52igdnOcbHKHg1HSms\nQgU84uOx/D1Y1Lt3OOvXVyn1ujVr/kaPHkEYGhq+di0nJ4dHjx5x9epVVLm5dIiOpg/QAAo8VpQ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mUKt27domfPnuzdu5fQ0FCmTJnCkCFD8PT0xMXFhV27dmFoaEhYWBijR4/mxIkTDBw4sMhA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\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EVqtl1apVhISECM9IbGwsw4YNw8vLi/Dw8EobID/Pzp07mTJlCjt37qR9+/Zvf/n/C+nU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b69788ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.spring_layout(G, k=0.3*1/np.sqrt(len(G.nodes())), iterations=20)\n",
    "plt.figure(3, figsize=(15, 15))\n",
    "nx.draw(G, pos=pos, node_color = colorlist)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 234\n",
      "Number of edges: 3385\n",
      "Average degree:  28.9316\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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63lGjRqG8vFzSajjgO+DrdDpJ57qHKuCrVCpJAb/rBlu9Xo/4+HioVKqA5+CN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\nuhVd13E6nfh8PqCh0k9FRQU2m43Y2FgMw8DlcgUvtNL1pmU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Vqfs5o314JSNTY6MIrJ4P\nNBy01RSNHvFdWhXcn9fNwu+viWbcRQkMzksjp42TVGcUmfExfFtUx+oDkQX3AKpi47y0AeSVp7N1\n9joqVStlqkqFqnJIVdmsaXyqacxUVfaFMF7SBdkoEQT3APr2VWH1e++993jmmWeYM2eOBPfHiQT4\nQgghhDjtjB8/nrfeeuuIy6P69u3LeckmMeHF9wBkGjWYXjd5eXm0LY0jSrO33OkHw/rZuOpsB+lt\njryEyzRNZmw4hBFh7oRV0bgouSupvljwNF91J6Ao7FVV5moa21sYM75HcsRBoVFRjHmUg8TH8vXX\nX3P33Xcze/ZscnJyIlyBaCQBvhBCCCFOOaYewDSaryqTk5PDkCFDmDJlSpPXHQ4HQ7pGFihmOB1U\nLv4Mz7464vSYkPud38PKRb1s2K3KUd+fvaWUVfurI1obwKDEfDLsCSG3dykKizWNPc28H9uvK+S0\niXhd+H3gc4fcfM2aNVx33XV8+OGHwbsExPEhJxiEEEIIcdKZpomxcx2BjUswigsxfV5QQLFHoWbm\nYek1CC0zv0mfiRMnMmrUKO66667goUzT0ElzRgMR3PoEFG5ax8YNTrQQD8FaNRjUzYqtmeAe4JMN\nh9Aj3L2P0xxkOhJb3c+tKKxQVdoZBoevMKp7Rzq8OJEV//o7BZEtDTQNLKF9dbJ7926GDRvGSy+9\nxPnnnx/pzOInJMAXQgghxEmlF27Ev+gTjIO7wGwamJv11egVxeibl6Nm5mO98Dq01HZAw8VXGRkZ\nfPLJJ4wcObKhQ8CP3apBK1NFjrB6J1EL/KimiaE0H7Q3GtjNSlrikWk5hyt3RX7zq0U59hzHckhR\n2APkAEq0nbize9D+mbuwJiewcOVqCs5sF9HalCgniqXlC7dKS0sZOnQof/jDH378dxPHlaToCCGE\nEOKkCWz5Du9nr2Mc2HFEcN+0oR+jaBPeGS+g790WfHnixIlNLr7CascSFXpaTXOSag16ef2EOlL3\nnJb3TH2RJt9DyN8oHI2pKOxwRpN8/cV0fv8v5E99GGtyAmvWrOGdVbshLrIDrlp2lxbb1NfXc8UV\nVzBy5EjuuuuuiOYTzZMAXwghhBAnhb53K75570F9K/LSq0rwzXkTo7IEgKuuuori4mKWLVsGgKIo\nWJIzIlqXaZh4DjXUlY8NoZq4RYW2iS2HVFGWyMMuawQ7+ABVbZNo/9RdxBZ0Cr42depURl5/A1r7\nbuEPbI9C63PxMZv4/X6uvfZazjjjDP7v//4v/LlEiyTAF0IIIcRJ4V82G+oqW274E2ZFMf6lnwFH\nv/hKy+8d0brcJQFqdzUE+O1CCPCj7EqzB2sP1zkl8m8W4iyOiPp7XV4M/cfDy36/n3fffZebbroJ\nS+/BEBUb1rhazhloSenNvm+aJr/5zW8AeO2111BCSHsS4ZMAXwghhBAnnF6yD2PftpYbNte/cCOm\nt6Fiy7hx45g7dy6FhYUAWHoNpkY79o2zx1JX9OOtsD1Ns8Vd/IBuooeQfnPbWdkkRbeco94cDYUu\nsW3D7g+gqKCoP4Z/c+bMIS8vj/z8fLT0DljP+yVYW1djVGnbAevQW47Z5uGHH2bTpk188MEHWK3h\n/w1EaCTAF0IIIcQJp6/+Gnye8AeorcC/8ivgyIuvFIsVpftAvIHmy2w2x3UoQMXqH9dlAzq0EOB7\nfFDvaTnAb5cYxXkdWl8Bp1GsxU6cJfwPLgC1FbVMf/rf+L1+oCE9Z8yYMcH3rQUXskRpS6Un0NwQ\nTSjtOmO/+m7UY+z8v/DCC3z00UfMmjWLmJjIv8UQLZMAXwghhBAnnFG+P/IxSn8c46cXX6Veej3v\nrN2D2YpDqZ6yAPu/rMfwN339XMOg/TGq8pjAjoOhfZi4e2AHsuJDvzgrOIdpYLXp+Akt8G5OwOtn\n1ovT+ee4v7JnVxFz587l2muvDb6/atUqRv/tZeqHjOXddXvwR8UdOYhmQW3XBcuFo3Bcex9qbPM1\n+T/66CP+9re/MWfOHJKTkyNauwidBPhCCCGEOOFMf+QlI/F7gz/+9OIrRVFYorZlfWz7FqvDGH6T\n2iIfe2bW4a86MpBXgUsNg3zDQGlmN3/JJj9eX8u7+H2z4nni8i60dbYmDcYAoxoPVahtIrii9zCb\nvl3PM7f8haGXXEpCQkOAXlNTw6hRo3j++edZsHEnH1fYiLv971gvGo1lwDAs/S7Fcs5w7NdMxDH6\nd9j6DT1mWcz58+dz5513MmvWLDp06HBc1i1CI3XwhRBCCHHCKapKxEUj1aYVZSZMmMB1110XvPhq\n4MCBTFmzjldfeJ7pf7qbfM1HG9VHvM0KARPdY1K/P0DVRi/u4mPvwGvAxYZBPrBNUdijqvgOOyha\nWGKw46BOtxDKZQ4/I41Eh4VHv9zO6gM1NJe+b5omihLAZvER1yaOkpISel/Sl7X/XoYaQbnMRrV7\nquiV3y04129+8xsuuugiRo4cSdeuXXn99ddRbHasfYe0eux169Zx7bXX8t5779G7d2SHnkXryQ6+\nEEIIIU48hzPiIRRH03zus846K3jxFcC5557L4sWLUWx2UofeyPUff0ef52Yx/Z197Hi7hh3/quHg\nPFeLwX1wPqA9cIlpcol+ZJ93F3jYW+o/4vWjOa9jEl/fdhaTR/akS1I0phnANPUf/gtg6G6slnps\nlnp8vjrS09MxTZPUHpkcUitCmiMUtYVVBHx+Xn/9dTZv3sykSZOYMWMGSUlJDBo0KKwxi4qKGDZs\nGM8//zwXXnjhcVurCJ0E+EIIIYQ44bSO3SMbQNWwdO13xMsTJ04Mlszs0aMH+/fvp6ysjHPOOYeK\nigo0RzRVbh3dY0IEl93+dJ/eME12VlRw+/vf8f2e0pDGqPeY6NUJ9Lf34rKkbjhxYQRKMAIlmEYl\nNquJ1+vBZrPh8XiIjo5m+vTp+DtaSDgjteETR4SKd+7n3Wfe5qGHHuKDDz7A4XDwxBNP8Pvf/z6s\nUpbl5eUMHTqU+++/n1GjRkW+QBEWCfCFEEIIccJZep2P0ib8ko9qZi5q+yM/JFx11VUcPHiQZcuW\nYbFYGDBgAEuWLEHTNEaMGEF8fDxah8hKTQKUoOPSPdT46yiq38/Csu+YWfw18/dt54o3vuDJeevY\nUew9avnMylqdjZu9rJpZR5s1bq4OBPi1JY5nUwYxKLpdsJ3H48EwDPx+P2VlZaSnpzN37lxMTAbc\nMphlFWtw6xFUIvrBR6+/x6RJk+jcuTPz5s3D5XLxi1/8otXjNPYbPnw499xzT8TrEuHTHnvsscdO\n9iKEEEII8b9FUTXMmnKM/TvC6Yyl36VoGblHvKWqKqZp8uGHH3LNNddQWFjIjh07GDJkCNHR0Uyb\nNo0OFwwgYf0B4tTw6rF7FJMHDy3g26pNbKzdwW7XPqr8NcH3/YaBVt2erTujOVihU1ZrsL/MYEdx\ngNLdfg7OdeHY7iem3sQJxAJxQJqicqGjLX3tSfhNg+3eKgA6d+7M/v37yc7Opk+fPmzcuJEtW7aw\ned820ixJOK2RlZ6MTojhkZf+AsBtt93GHXfcQUFBQavGCAQCXHPNNWRkZPDCCy/IRVYnmRyyFUII\nIcRJYT1/JEbZAYxd61rVT+t2DpYzL2r2/XHjxvGXv/yFoqIiBg4cyEMPPQTAoEGDcLvdfLd+DXGq\nl0yiw1r3dquH7YEfA3pVUbimVweu7tmeTinxxNpsWBUb9R7Ytj/AN+v81NQbDDIMUs2GoL45VkWh\nrz2FrrYEUjUHH/r3kZ+fz+bNm6mqquKaa65h5syZFBUVYbMdn4o6+fn5AKxYsYItW7YwevToVvU3\nTZM77rgDn8/H5MmTUVVJEDnZJMAXQgghxEmhqBr2q+7EN/M19O2rWmzvC+jonfrT5rKxx9whjouL\nC1589ac//Ym1a9fidruJiopi2LBhzJw5k6yzB5O/ppaO1tYd9i3VPfzbW4iiKJimyU198rj9nK70\nTG+DqjZdU3Ic5KRqnNXZStneAJ5v6iG0M7jEKFZ+5eyMp87ER0PZz0OHDpGbm4tpmmRnZ1NaWkrA\njKwuPkC0s+EbgCeffJL77ruv1R8cHnvsMdasWcM333wjt9SeIuQjlhBCCCFOGsVqx3bVb7EOuQm1\nfTewHiW4tEeh5vXmkYXbGfjoixBC+sf48eN588030XWd7t278/333wNwyy234HK5yO7bg/+rWk1t\nXOjBrMuu8oJrK4sq9mCxWLjv/B48Nbw/vTOTjgjuDxcXrdKxs42c4bGojtBTV2JUKzdH51JStA+b\nzUb//v2ZNGkSgUCAyspKPB4P5f6qkMdrTvueeWzfvp358+fzq1/9qlV9X3nlFd59911mzZpFbGzz\nt9mKE0t28IUQQghxUimqivXMC7GeeSH6gZ3o21b9cImVAo4otDPORktK597eV/By5848/fTTPPDA\nA8ccMycnh4svvpgpU6YwcOBAFi9ezKBBgxg8eDCKolBbW8sGfxUf5St0+fYQBfZkHIp21LH8psE2\no5Yp5TtZXLcfwzD49TndeODCHjjtoX9AiMmwkjU0mr3/qccMsYJPuiWaggOHWGWa9O/fn6effpp2\n7drh8/lwuVzo6Rp1NfXEWsLLw2+TkcyQsZdz7/0TuPPOO1sVpM+YMYM///nPLFq0iNTU1LDmFz8P\nCfCFEEIIccrQMnKPengWIDc3l/vuu4+HHnqIG2+8kfT09GOONXHiRK677jqeeuqp4A23VquVLl26\nsHr1ahRF4V/z5xAIBOhYa+dXOX3JqA4Qo1pRUHAbATb7q5jt2ottwBls3uTBarWi+33cNiC/VcF9\no9h2NhJ6BKhc62258Q/6mHFMBr744gssFgtVVVXU19c3pClZFEr1qrAD/DPO7UFVXTUfffQR27Zt\nC7nf4sWLue222/j888/JzT36v5c4eSRFRwghhBCnjb/97W+kpaVx2WWXYZrHvgu38eKrmpoalixZ\ngv7D5VSXXXYZGzZsQFEUzj77bLp06cIGXyUxD17LtSXfMLz4K4YXf8nVJfN4uHIlS7wllJaVUldX\nh6Zp3Ngnj66pCWE/g7ND6/LUu9oS6JaSydq1a4mLi6NNmzYoioKiKGzbto26FD+Vh1XxCVW7rjlc\ndd91/OMf/+DGG28kOTk5pH4bN27k6quvZtq0afTp06fV84qfnwT4QgghhDhtqKrKZ599xoYNG3jp\npZdabD9hwgQmT55MWloaGzduBGD06NFUV1djGAYDBw7E5XIBsGTJEhRFwVDA+ElO/aZNm/D5fBQU\nFHBl95yIniG6rYWojNCTKGyKhlZZT5cuXSgvL6esrAyLxUJGRgZpaWkUHixiQel3rQrys7rkcOsz\n41EdGpMnT+a+++4Lqd/evXu57LLLePbZZxkyZEjI84kTSwJ8IYQQQpxWevXqxa233srEiRMpLi4+\nZturrrqKAwcO0KlTJxYvXgxA9+7d0TQNTdNwOBx4vQ3pMh9//DH5+fmYpomm/ZiPb7fbMU0TwzCw\nWa10b9smovWrVoXYdq3LkvYGAvTo0QNVVfH5fKiqitvtpri4mEAgQLVey1b7Hqrs9bgCzV9+5UyK\np/8vzuWeKX8g+4z2vPzyywwbNoycnJY/tFRWVjJ06FDuuecebrjhhlatX5xYitnS91tCCCGEEKcY\nn89HZmYmHTt2ZNmyZccsm/mPf/yDf//73+Tl5TFt2jQAOnXqxK5du/jNb35DZmYmjz/+OG63m0cf\nfZQ///nPpCclMKZXDue0TyU+yoaiQJ3Xz363yeW5ySRE2SNaf/lqD4cWu0Nq6zEC3G3dyq7qUnw+\nHxaLBbfbHXxmXdcZMGAA69atQ9d12sQm0l5NJ1GLo0/vPlRWVrCzaDd5Z3bisTf+SkJawwcUt9tN\nhw4dmDt3Lt27H3kr8OHcbjeXXnop/fr145lnnono2cXPT3bwhRBCCHHasdlsfPzxx6xcuZLJkycf\ns+24cePYtm0b8+fPD742aNAgDMNg69atjB07NpifP/js/rx09bksuO0SHrmkgIs6ZdK3XQp9slI4\nPzeD0d0zwzpc+1OmHvr+6gZ/NfUOlZiYGLxeLz6fD13X6datG5qmoSgKubm5qKqK1+ulz9l98edo\nLPdvYPSkXzNt26csqlvJA688EgzuAd566y369evXYnCv6zo33HADWVlZPPXUU2E/szhxJMAXQggh\nxGlp0KBBjBw5kvHjx1NSUtJsu8aLryorK9mzZw8Affv2xW63s3XrVjIyMrjwwgvJSoghfv6/GNMv\nn8y45m+51Y5R8z5UAU/oAf5eu5WioiIMo6G2ptfrxWKxcPDgQfLy8khISGDFihUYhoHT6WT37t1k\nZWVhGAajRo3iyiuvZMCAAWRmZv44fyDA008/ze9///tjzm2aJnfddRc1NTW8+eabckvtaUL+lYQQ\nQghx2jF0ne9nLeHa/lfS29mFXw25me0rNjfb/p577kHXdebOnQtAQUEBFouF0tJSAO6+7ddMvW4Q\nXRIdP//aAyZVG30hta0EiojGbrdTXFwcPB/Qpk0bLBYLmZmZJCcns3XrVjweD2PHjqWkpAS73U5t\nbS15eXkcOHCAMWPGNBn3o48+IiMjg3PPPfeY8z/++OMsW7aM6dOnY7dHlpYkThzJwRdCCCHEaaO6\ntIovp3zGxgVr2LOpsMl7ikWlS/9uFAzpy/mjL8Fqb1qOsqCggJiYGBYvXozL5SI+Pp5AIEBxcTHx\n6+ZirJrL5kN1bCurx+XXSXfaOSs7gSjr0S/ACpdpmOydVUddYeCY7dzAfFVlhwKzqxdSVl2O1Wol\nEAjQr18/duzYgdVqRVEUSkpKsFgs/PnPf2bXrl0sXbqU9evXs2nTJs455xz2799PdHTDtxKmaVJQ\nUMDjjz/OsGHDmp3/jTfe4K9//StLliyhbdu2x/NPIH5mctGVEEIIIU4L6+avYtojb1C69+jpOGbA\nYPOS9Wxesp7vZi3htn/eS1JmSvD98ePHc/vtt6PrOtHR0aSkpHDw4EHemzaNwMqFzFqxg+V7q/AG\nftz77JgUxUW5yfzqrHZ0TQ39ltdjUVSF+M62Ywb49cBSVWW3qqIB2UoaZZRjsVgwTZNXXnmFyy67\njLi4OHbs2IGqqowbN45PP/2UX/3qV0yePBmr1cr06dMZMWJEMLgH+PLLL9F1ncsvv7zZ+WfOnMkj\njzzCggULJLg/DckOvhBCCCFOeevmr2LKAy9SU1odcp/sM9pz75sPkZCWCIDf78fhcPDmm29y8803\nc+GFF7Jw4VKS49Mpqz32zbJOu4Xre6fz98u7oB6HHHy/y2Dnv2owvE3DMDewH1inqhQflu9e4i3n\n85KFGIbBxIkTgzvwDoeDDRs2oOs627Zto1+/frRv356NGzeSnJyM0+nk9ddfZ9CgQcGxLrjgAm69\n9VZuvPHGo65t6dKlDB8+nFmzZtG/f/+In1WceJKDL4QQQohTWk15NdMendyq4B5gz6ZCpjz4YvB3\nq9VK165deeKJJwBISEwDJb7F4B6g1hvgteV7uW36hhZv0A2FNVplT5zKTqAQ2AmsVhQ+VFW+tFia\nBPcAdtWGYRgoisIf//hHli9fTklJCYWFhQQCAQYMGMDcuXNJTU0lIyMDp9NJTEwMdXV1DBw4MDjO\nsmXL2L17N6NGjTrqurZs2cIvf/lL3n77bQnuT2MS4AshhBDilDZ3yixK9xwKq+/WpRvZunxj8PfG\ni68+/HAGy5ZtAKV1+fUfrD3IX+buCGstP7UlWuMLi4XZFgtfWCws1TTqmqlSo6FitVrp3bs3cXFx\nLFiwgNraWi6++GIAnn32WV599VUqKyuZOHEiOTk5uN1uLr744iaVb5588knuv/9+rFbrEXMcOHCA\noUOH8uSTT3LZZZcdl2cUJ4cE+EIIIYQ4ZRmGwfoFq8Pu7/f5WfTBN8HfBw0aRFJSEg8//DglpRWt\nHs8EPl5fjMunh70mAMMwqa4P/ZsAv6nj9/t58MEH8Xg8bN68GYfDwcKFC1EUBY/Hw9q1a3n//fep\nrKykXbt2lJaWNtmF37x5M0uWLGHcuHFHjF9VVcXQoUO5/fbbj6i4I04/EuALIYQQ4pS16ovlFG0s\njGiMzUs24HU3pOEMGDCAQKmXA0WlYY+3u9LNG9/tiWhNJdUG5TWhB/i6qaNpGqNGjWLlypXous7V\nV4+kpKQSVYvm2lFjyMnJ4/zzzw/WzE9MTGyyU//UU09x1113NTlwC+DxeLjqqqsYPHgwv/vd7yJ6\nLnFqkCo6QgghhDhlHSoshghz3mtKK6ktq2bnnkPMfO5D2tqy2e8pimjML7aVcffADmH337I3QMAI\nvb3TEsO5fc5GURQef/yvoMTy4cfz0CzJoChUVAaoqAww4JzLwfRRW1NMQUEBFRUN31Ls3buXTz75\nhB07dmCaJgFfAIvNgmEY3HTTTaSmpjJp0iQUJfIDxOLkkwBfCCGEEKcs3XfsWvEhjRHQ+f7zpXz5\nxmdUl1RSr7d8qLYle6s8Yff1BwwWrve3qo9ds3FR50E8M+llvpy7ElVz4vEE4CcB+apV63/4SSc3\nrzvl5eUAPPvUM1x39gheu+MflO8rwe/1Y7FZqXJV46eON7+ahqYd33r/4uSRMplCCCGEOGV9/tqn\nfPjXdyIaw8TEFmPHX99we+zSyp1sdx+9ln6o0mLtbHlgEFoYJTNrXAb/b2p9q/vttlSz9MBWAoHQ\n8v8tikrPhHb0b5tPTVUNlmMcKG6TnsSZQ8/iuofHoEqgf9qTHHwhhBBCnLK6nt0dW5Q9ojECRiAY\n3ANozVSqaY0oqxpWcA9Q72n93upudxnL9mwJObgHCJgG6yv3srl4zzGDe4CKg+XMfXM2L935LAF/\n5N+aiJNLAnwhhBBCnLLa98glv1+XiMbQfhLc9ozNol9ce5yaI+wxM+PC/9BhhJE7sb3+EH5akbT/\nAz8G2+tDLzG66ovlvPW7l1s9jzi1SIAvhBBCiFNa36EDIuqvKk3DHYdmpWtsOpcnd2dQQj7WVtbC\nB7ikU3LY63G1cge/xFtDqa827PlKfLWU+epCbv/9Z0vYtnxT2POJk08CfCGEEEKc0s695gI6n9Xt\nuI9r16y0j07m4qSu2JXQ6460T4ziNwOyw553x8HW1dDf7ipBJ/wjkwEMtrtC38VvuDtgXtjziZNP\nAnwhhBBCnNIsVgu3PXcv7Xt0/FnGT7E5GdQmH5XQcuqv6pZGjC28QoS6YZLsVOiQFloIZpom+72V\nYc11OFfA13Kjw2z8dgP11aHv+rdEr3fjKTyIa+NufAfLMY3WpxuJ0EkVHSGEEEKcFmora5jywEts\n+nYdfnfrAtZQLK/axdYWdrqv7p7G5Gt6ooZ5wLaRz2+ydrefad94j1kPv9BVxsKq7RHNBZBmc3Jp\ncvdW9fnNP+5mwFWDwp7TNE2q566gfMYC6pZtwF9eA7qB4rARlZdF3OAC0n71C6zJCWHPIY5O6uAL\nIYQQ4rTgTIzjnjd+z661O1jw7pdsWbKBqtIq/F4/9ig7jhgH1aVVYY/fztGm2QA/xqpxTa+2TPrF\nGREH9wA2q0K/Tjai7Aqvz/GgNxPkF3rKI54LwELrzxnUVoaf9+/ZuZ/CB1+kbuVW+EnlH9Pjw7Vh\nF64Nuyj74GuSR11E5oM3yCVbx5EE+EIIIYQ4rXTslUfHXnkEfH5qK2vxubw4YqN48fanIgrwU+1O\nUqyxlPp/TE2J1exc0imJe85vR0Fm/PFYfhPdc6xcN8hk2vwjL9+qCbgp9lYfl3miLLZW97FYwwsT\nXZsL2XnHU3h37G+xbaCkkuIXPyZQWUvO326XIP84kQBfCCGEEKcli81KYlobADz1bop3HYhsPEXj\nvIROHPK4G9QAAAAgAElEQVRVE8AgSrVxXvs2PDDCeTyW26we7S0kxvqorGuaNb3XXYnPbN2B3KPR\nUMiLSmlVH9WikZ6X2eq5AtV17L7nnyEF90GGSdm/v8KalkjmhOtaPac4khyyFUIIIcRpz1XjwuPy\nRDxOrNVObkwqOY42uA0fewOH+Ofi3Xyw9gC+YyXLRzJnlMrgHtYjXvcfh+AeINUWR6o9rlV92vfo\nGFblokOvfop70+5W90M3KP/ga/Tj8G8oZAdfCCGEEP8FrHYrFquFgNcf0Tg1ATdra/Zx0FeNx/Cz\nfD2wvuG9v8/fzYV5SdwxIJsOSdGRL/ow+VkqtQE3AdPArfuJ1myoxyFdxYJKXkxqq/s5k+JbnS5j\nGgbV81e1eq5Gvr0llLw1m/Q7R4Q9hmggO/hCCCGEOO1Fx8XgTGrdLvXRbK47wG5PGR7jyA8K28rq\neWXZHi56bTlvfLc34rkOFx8DC2o3MrN0HStqiojRbDjUI3f1W0NFobszkw5Rrb+Uy1vf+p30illL\ncK3f1ep+h6uetyKi/qKBBPhCCCGEOO1pFo0uAyK7DKva72K7q7TFdmUuPw/N2coLSwojmu9wUVaN\nWHtDpZsUayxW1UJudApOzR7WeBZU+sRl09OZFVb/nWu2sXNN68pzutfvggirr3sKizH145Oa9L9M\nAnwhhBBC/FcYdN3FWKNaXy2m0QFvNUaIN8a6/QZPfrOLedvLwp6v6Xg6dd6GwFZTG8IzTVHJsIdX\nI35gQj5dYzPCXo/f7eO7/yxuVZ+Ayx32fI1Mjxe9LvJx/tdJDr4QQggh/ivkFnSi64BurPtmdav7\nugI+ttYXt6pPtSfA1JX7uCi/9SkwP7W/xkOVpyEtKHDYLa+dY9pS6C7D28oDt3FWR8RrctXUA+D3\n+ynZvonqXZupLS/hYHU9G0rr2H2gmIMHD3LgwAEOHjzIdd5UborNjWhOxWZFjY587f/rJMAXQggh\nxH+NW564k0m3PM7ezYUh9/EZAVbV7qFGb33e+cLdleytdNMuMarVfQ/3zY5yjB++PDjkq8FnBLCp\nFhKs0fRytmNlTRF6iN8uAGjHIUnjs0//w4oVkxnSIZlzO7Slvb0hbOwBDIjWKO2eROUl5xGb35P0\n9HSYvYL9f5wc0ZzWlATUMOvvix9Jio4QQggh/mskpCXy21cfoGPv/JDae/UA31cXssvdcu790VS4\n/Lwe4YHb8nofryzfE/y9VvdwyFsT/L1LbDoFcdnYlNBuo43TorCqkYV4bRMVnr2xI09f1puhXbJw\n2psG3XGqTq6vlL77ltBt73LapiSTNvoS7B3SI5rXeW6viPqLBhLgCyGEEOK/Smp2Gg+8+0dGPDCa\nvD6dUS3NB8ZLqnayM8zgvlFJ3ZG30LbGnK2l/5+9+w6PqkofOP69d1omk0oSQk2A0Hvv0qQXaYqK\nKLCooKKuyuJaVlHXVdGfBRcbKqIsdhAFBKTXSC+hJQQSIKQR0svU+/sjEo2ZJJNMwPZ+nsdHcu85\n554J/PHeM+95D4mZpb89SLFnwi9W7Fv71aNfcAsa+YRgUt2vcPvrfGjhW4f+wc1It2ZUez71ainc\nNdxMuI+VSgtlOuw4T+7B+vUCFINKQN/qB+i6YH/C7xxd7f7iZ/IdiBBCCCH+dEy+Poy+bwKj7h3P\nkU37iT8Yx55VO0lLKJ1nn+0o8PpZhT8dgGV3ukjJtdIwyPN0nQ1xl3jw2+MlP/voVV4Y0YLrm4aw\nYhvEJ/+ce1/PJ5B6PoHkO6zEFqRS6LTjwoVe0RGoN9PcEo5OUUl2pHNWu0gTUwT2Kp4LYNTD7YPM\nhAd59m3BFa6EGGzrllB72kiy1u/Bnnq5Sv0BAgd1wVS/aifuCvdkBV8IIYQQf1qKotDh+q5MmHMr\ntSPCy9zXK96HQhZjcTCsUxW+O57KoYs5lfSAQpuTr4+kcOuyQ9icxSv1QWY9X93eiRndG9Koli/T\nrjfRIKTsGrpFb6JTQAS9g6PoG9yMnkFNaOVXF52iUrdVA2Jc8bTu1Y6mXVtW+bP0b2ekYVjVgvsr\nnHEHMdY20eBf09AF+lWpr3+vtjR68Z5qPVeUJSv4QgghhPhLUNzkpfvpfbjs5Sp+45822KqKwm2d\n6rM2Np095zJpEmKhV2QQFuPP4dbF7EI2x19m2cEktidklVw36BQW39Sefk1CSq4F++uYNcrMss1W\nwoJU2kXqCPZXMerB7oCsfI1j5xxsi7FTaHdCsA5jtyDC0+vQo0cPxlw/jpTTSWRWYTW9TWT1gnsA\nrAU4920gZNxtKDqV888uxp5cSaqQohAwqAtNFj6Maq5ezX9RlgT4QgghhPhL8A2wlLnWyCeEc0VV\nTye5okGgiZk9I0t+DjQbuLlDPWwOFwcuZvP2rkSOp+Xhb9KTmmdjd2ImmYWOMuPc2yvCbbnNIIuO\ne0aZUZSyK/m1g6B5fT0D2hnYke7gYFBD1v6wDqvVSs+ePWnZsw097hjA1y8sxaL3rfSztGygo3G4\nd6GhM/EYmstJrTF98evaktQPV5Gz9RCFxxNKtdMFWPDv2YbgMX2pNbav25cvUX0S4AshhBDiL6Ft\nvw78+KvDmyLNIYTkXyTDnl+tMQdGhRDgUzacMupVekYE07leIEsPJPHQqhNlDnltE+5H78hgWoT5\nMqpV7XKf4S64/6VgPx2jLTqaK0W8c/wYDqeLbt26AfC/9Z9zIOsgE7uOIed8Jjjdl9oMqh1Mn4Fh\n6NTkSj5xxbTsy1CQB36BGOuG0vCJaWiPOslcswvrhXQ0mx3V4kPgwC6Ym1bvlF1ROUXTvDxTWAgh\nhBDiD8DldPLM6LmcP5FY6npsfip7s89Wqc48QGSQD5/d1ok2dfwrfq6m8fXRFOZvOYOPXqVugA+j\nW4YxuVM99LrilWtN0yoN5D2x+lwO87ac5NixYwAYjUYiIiI4ceIEHRq34a5R08i5mEVBTj6aAnqz\ngeBGodTu0oBmubF0tHkX4AP43PUCaq06Xo8jqk9W8IUQQgjxl6DqdHQY1KVMgN/cEk6B00pMXhKu\ncvr+Wh1/I/NHtao0uIfi3Pyb2tflpvZla8TbC5w48lyYaxs8fHLFBta18HXtAO6//3727t2L3W4n\nICCApk2bkpyWzBOf/BuHw0FwcDBBQUHF/9eCCM4KxjfCj461vJyA3oRilJNof2uygi+EEEKIvwyH\n3cGCO18kZuuhMveO5V3kRN5FClwVl5ZsV8ePF0e04LpfbIitrpx4G45CjVpta26D6caUQuIadufx\nxx/HYrGwcuVKnn32WQYNGsQDDzyA2ew+p99x+hC25W+C5ulrTllKrTr4/O05FJ2sIf+WZEeDEEII\nIf4y9AY99yx8mHYDO5e518avHjfU7kgH/4aEGvxKBUn+Rh1Dm4WycFwbts7qWSPBPYCqV6hiZlCl\nrmtUm8upydjtdubNm0fLli3Zs2cP99xzD76+vuWmAumiOqDWbezVs3WRrSW4/x2QvwEhhBBCVFta\nYgpHNu2nqMCK3qAnLCKcTkO7of6Oq6KY/XyZdUdzVucf41iCnXNprpIY26jq6eDfgPZ+9QkIsBMa\n4mRgOxMRwWZCLcYan4tLA5e1+ivm7hiLcjmzZQMA99xzDwsXLmTMmDEEBgZW2E9RFNQm7XFdjK/e\ng01mdF0GV6+vqFES4AshhBCiSlwuF/u/j+bH73ZycncMBdm/qECjKES2aUzb/h0ZPH0kgaFBv91E\ny+E4thvt4A+M7GJkRGcDh844OHHOQYGt+L6PEZrX09O1mT+q6v3G14qctWlExzq4o50Ls6nmXoqa\n1A2jUydfdDodH3zwAQsWLPCon6HrEDKifyDQkVflZ+qadEAXUnafgbj2JMAXQgghhMesBUW8+8Dr\nHN60H83lJrdE00iMOUNizBmiv9nOrU9Np/Ow7td+ohVwHN0B9uJoXlEUOkUZ6BRVM5tcq8KlaRy9\n4ORoNpy+6KRd45oL8JOSU/j73//F/v37yc/Pp1+/fpX20TSNZ154iT2rN7Ps9sEYC7M9fp4a2Qrj\nyL95M2VRgyTAF0IIIYRH7EU23rjzRU7uivGofUZSOh/9822cTifdRva6yrPzjDMpDldS3G89DQAu\nZrjYE1t86NX+eActI/QYdN5/Y+AE4i7l8sHkycyePZvp06dXmjJVVFTE9OnTSUhI4JtVG/HXirCt\nXYyWmlhhP1QduqYdMI6+G0V/7V+ShHsS4AshhBDCI0ueeM/j4P6KvMxcPnvuIxq2jKROk3pXaWae\ncxzdCY6Kq+RcK8fPObjyJci+OAdBwSmM6+J9isvBpGxsIRFYrVY+//xzDh0qWzHol1JTUxk3bhyN\nGjVi06ZNmM1mAHxufxLH4a044w7iuhAHDtvPnSyB6CJboW/bB7VRmxqp4S9qjgT4QgghhKhURlI6\nRzbtr1bfzOQMNi75ntuemVHDs6o6LS/rt54CUBzcr977c8CcY8smZ3MBF43BhLc2ofNiJX/N8TTS\n0u28+NKrdOvWjYYNG5bbNiYmhjFjxjB16lSefvrpUoG6otNj6Hw9hs7X40y/gCvtHFiLUHz9URs2\nR7VUvGlX/HYkwBdCCCFEpTZ8tIa8zNxq94/Zdgi71Y7B9BuncfwOVu81TcNm10oq9xQ4imjospNi\nDmf+dhs9MzXG9zRhMlY9Jz8+o4B3os+RZ3Pyf68uYt7Tfy+37Zo1a5g2bRqvv/46kydPrnBcXVgD\ndGENqjwf8dv4/dawEkIIIcTvxvEdR73qn3o2me1fbKyh2VTObrdz8WIKp06d5uLFFOz2nwJ7Q82X\nu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wghhBA1xpl+AeuH//J6HH3f8ZwLb0vP3qPJyqreAVRt27bk+eceZtWqVaxcuZLAwEBG\njx7Ne++9R35+PrVq1WLXrl0s/L//cvSrvTT0rVMmfcWlaWQ7CkgoyCCuMBWXpqFTVIL0vjT0CaaZ\nJRydUlzzZlxPIwfiHZxLL365ad9Ix/heJsKCKv72IavQxitbzrJgl/sUpBDVxNchbfFRSp9P6tI0\n7s86xX57bpV/N8OHDeS7bz9BURRyc3P58ssvWbx4MadOneK2225j2rRpdOjQocrjlic9PYPYuDNk\nZmZRv14dWrZsitlsrrHxRWnyXYgQQgghaoxq9geDCezenWqqGIwsXPhRtYN7gJiYEzz40D+5+87b\n2bx5My1atADAaDTy8ccfk5yczIf/eRdi8omwuK/aoyoKwQYLwYEWOgQ0wK65MCg6VDd57L8M7rs3\n13NTXxNmk1qm3a8FmY08M6w5jULMPPzdyTL3M1xW1hamM843vNT1TdbLHKhGcA+wcdMO5r/8Osdi\nDvLtt98ycOBA5syZw4gRIzAayx7S5a2wsBDCwkJqfFzhXuX/6oQQQgghPGUJQA1r4N0YZj90zbuw\n/+ARLyej0L//YObOnVsS3APcc8895OTk0NQvknMbYslOc1OVxw1VUTGperfBPcCFS8XBfbN6Om7s\n41lwf4VOVZjetSH/6N/Y7f3vCi5w3JqJ8xeJFz8UXa72Vma73c5L8xfQuXNn4uLiWLFiBWPHjr0q\nwb249iTAF0IIIUSNURQFNapsdZUqqd8cNSiM3FzPD64qT15u2Wo4DRo0YGS/YXQJaotRrXzDqKeu\nHJA1qIMBX5+qh1g6VeHh6xrjayib0nPElsn0jO08mbmPzYXJbC1MY6/Nu43MPj4BTJlyR7Xy68Xv\nmwT4QgghhKhRhi5DwL96ddcdLo2H313GmjVrUFXvw5TyxugU3hYTNRfcX1E7SKFF/eqfxGox6Xls\nYBM3d1y4NI0NhReZe3kPczIPUeBhyc3ypKVd4mA16tWL3z8J8IUQQghRoxSTGX2XwaCr+lY/U6vu\n3PDgkzz00EOcS0zwei7ZWZnk5ZX+JsBaaCUjNtXrsd3p19aA0eBdeHVT+9L7ATSXHbTCUi8rPj41\ns0E1LT2jRsYRvy8S4AshhBCixhl7jEDffTjoPV8lV6M6YBx9FyNHjeLo0aN06tTGqzkoisKFC2eo\nW7cu/fv357nnniM6Opot/1tP+rmrE+BHhHgfWoX7m2gd/nPde00r3rDscrmKU6BUlcAAf6+fAxBW\nQ1sWfW0AACAASURBVCfcit8XqaIjhBBCiKvC2G8iil8wjsNb0NLOl98wIARds04YB92CohantxiN\nRpZ+soiOna8nLe1StZ7fvXsndmz7loKCArZv386GDRuYOXMmfqkGWvg0qtaYlTFXI/f+13SqQlQt\nC8dT89E0J5rr530EV6qbB9XyIyc3F5u98sOyyhMaWosOHbx7iRK/TxLgCyGEEOKqMXQehL7jABzH\nd+M8uRctKx3NbkPR61EsgaiN22DoPBjFVDblpHbtUK6//jo+/XRFtZ49etQQFEXBYrEwfPhwhg8f\nDsAH/1zIzs82e/W5ylNTxwv56FXQNFyufMBZ6p5eryfhzGlUvfuyk/VNQTQ2h2DWGdGh4sBFnsNK\nXEEqGfafXxYG9O9DeLhssP0zkgBfCCGEEFeVoqoY2vbB0LZPlfu++sozxJ6KZ/+BqpXM7NWrM3P/\ncZ/be8EhZU9wra5gP8gvAttPC+m5BRrUQLn32Eu5OF02QAHVH9DAVQA/baxt1qwZ1w8ezX8XflzS\np7lvOE3MoYQY/UoO3yphgsbmUNJsOZzKTyXZkcPkyeO9n6j4XZIcfCGEEEL8boWFhbDsf2/RrVtH\nj/v07tWFk8d/ZPNm96v0jTo0RdV5HwK1bKBj2mAfWkf8XDXns32XcFVzFV/TNJbHpDBuyT4OX8xF\npzOh0/n/9F8Aqj4MVReMovrg6+vLyBGD6Nq1+LTZbgGN6BbYiNqmgLLB/U8Mqo76PsH0CYpiVLse\njBk9tFrzFL9/EuALIYQQ4nctKqoxa1b9j9n3/Y0WLaLctlEUha5dO/DMvH+wedMKli//msmTJ/PD\nDz+UadtpSDfqNq/v1Zz6t9Uze4wvUXUNTBts5t5RZiwm+OjIGVLzqn6Kr93pYtbyGP72xRE2nb4M\nboJ0RdGhqGYcTj8SEjNISUkm8exh+tZpQQtLnXID+18z6QwEZyrs/GpLlecp/hgUraaSxYQQQggh\nrjKr1coHHyxj94/7ycvLR1VV7DYrp04dwd/PyIIFC+jduzcAO3bsYMKECSxdupShQ39erT558iR3\nD51GU7V6J+6GBig8PsmC0VD6RNu4lCKGL4nm7u4NmDsgCqWcE29/zeXSmPHVUb4+mlKFWWjodQ4W\n/PtZ9r+/DXuhrQp9i5lrWXh1x7uYfH2q3Ff8vkmAL4QQQog/PJfLxaeffsqjjz5Kv379eOmll2jY\nsCE7d+5k/PjxfPLJJwwbNoyDBw8ycuRI/v3Mv7nw9SESTyVX6TmqAqO6GRnWxeT2/tnLBXR6Ywdb\nZ/akfV1/j4L8lzbH8/ym+ArbBCl6bvENp5PBn0BFh6qoFGpODphNZFL9AL3ZsDY89u4z1e4vfp90\n8+bNm/dbT0IIIYQQwhuKotC+fXtmzpzJiRMnmDFjBlarlYkTJzJgwABuvvlmdDod99xzDwsXLuS2\n22+jnjWO+KMXyCvybK1TVaBvGwM39DCVG7gH+egZ2CSEoe/vZVTLMGr7GSsM8p0ujX9+f5K0PPcr\n8L6KymP+jbjfryG9TUHU0ZkI0hkIVPUE6owcUQzYPPymwJ0Tx05wKucM/fv3dztPTXPhOPEjjh/X\n4DiyHcexXThOH8SVfQk1PAKlGoeZiatPVvCFEEII8aeTmJjI3LlziY6O5qWXXiItLY2///3vzJs3\nj6eeegqAtXPvoI0Rlm2xEp/sxOEsf7wgi0LvVgZGdK04YL/ifweSWPhdAgvHtqZtywAMPjq37ZYd\nvMis5TFu74Uoel4IbEp7o/tDrY4rClt07sf1mAqrLm2hc++uLF26lNDQUAA0lxP7ru9wnj6Elpro\nvm9QGPpGbdD3GYfqF+jdPESNkgBfCCGEEH9a27ZtY/r06Zw7d445c+bwwQcfsHjxYoYOHco7twxg\nRvfmAMQmOYg+aSfuopO8Qg2HC3wMUDtIpU2EnoHtjZhNnq+Ua5pG5mkraZuLcNk1gtsaCW5rQuej\noKigOaHokpPJ3xxma0Zmmf4mVF4LakZnY0C5zzioKOz2NsAHnB192BkTjdVqZdmyZfTt2R3ryndw\nxR/yqL9SOwLj6LvQhVVvT4OoefK9ihBCCCH+tJKSksjLy2Pu3Ll88MEH9OzZk6lTpzJixAja2X4+\nBbZ5fT3N6+uxOzXyizQcTjAbFXxNeLxZ9pcURaFWMx/86hi4tKeQzCM2Mo+UTcPJzra77T/Ft06F\nwX1Neuzxx5j9+INYLBZuuXkS398/gaZqgcf9tbRz2L57D9NND6H619wZA6L6pEymEEIIIf6UFi1a\nxJw5c9i4cSPPP/88p06dokWLFjgcDpYuXYrJYCjTx6BTCLKohAaoWHyUagX3v2T01xF+nYXg9ka3\n9+24T6ToZao85aXs2b9VZzAZqd2wDl999RWnT5/m/XtvqVJwf4WWfh77ps9qYEaiJkiAL4QQQog/\nnVdffZXnn3+erVu30rZtWwACAwN5+eWX+c9//gOASb02Wco6o0JYdzOWiLKJE2Y3tev7G4NoqbdU\nOm5TTSPAy0zrPF0hX6z8kszMTNasXo1f+tlqj+VMPIEr57JX8xE1QwJ8IYQQQvxpaJrGvHnzePfd\nd9m+fTuhoWGcPXuOc+eSyM8vXplesGABTZo0wdfkflX9atCbVYLblS2tGakrW+KyszEAvQffHOiB\nhi6XV/M6nhrLiy++SKdOnZg3fRId6gRVf7DCXBz7yx4sJq49ycEXQgghxJ+CpmnMmTOH9et/YOq0\ne/jbnY9w6NAxcnPz0OlUgoODaNOmObGxifj7m6gV1vWazs+3nh5jsIot8+egfIJvOGuLMsjn52u+\nVVh/badpxGlatUplhjeuy5qlb7Ji5Qo++eQTetbxQa96l5LkSjrtVX9RM6SKjhBCCCH+8JxOJ7Nm\nzWLz5t3oDYGcjk+ooLWGTtV4ekJ3/t6h1rWaIgCXjxaRsq2QX8TzPJIVy05bdsnP//RrxDjfMI/H\n3Kco7FVVtCoE+b6BFqb+ZybdRvUuuXZp6Xx8k054PIY7Smh9zDP+7fae5nTgOL4bLfsyOB1gNKKL\nbI2uXpRXzxRlyQq+EEIIIf7Q7HY7d9xxB/sOHCc9w0Z+fkKp+23C/bi9c31CLAaMOpUCu5NTafks\n+iGG9pbWDGoaes3mGtTahH8TA7bLLnLO2smKsTLZtw4n7flkaMVVfXI1RyWjlNZV03C4XBxWVZwe\nBPn+IQFM/MfkUsE9gJ+/P94l/LjnzEzDuX8DzoQYtIzSJwc7DKtQ6zdD16wz+g795OCsGiK/RSGE\nEEL8YRUVFTFp0iSSki6RkWElPz+/5N6ENuHc0qkevSODCPApWzFnercG7E/K5sylfJqEVr6ptSao\nOgXVosNg0WFpaKBWOxPBR42kR9t4Pe8cWZqT74suMc4chr/qeZjWU9MIdjo5paokK4rbQN83wEKL\nnm24/o7htO7bvsx9xVh2P0BV/XoMx/FobJs/h7ws9x3sNlwJx3AlHMN5ah/GG2ahWq5NedA/Mwnw\nhRBCCPGHlJeXx9ixYwkNDcXiF0ZOzs8nrj49uCmzekZgMZUf6jSu5UvjWr6k5BZdi+m6ZQrWUbu3\nL7f7NiBgi55385OIdRRw2J5LX1PVasq3AFq4XKT6+5JyfTe279pNkH8QTZo1IbRhbQZOGU7dqHrl\n9lcjW+E8ss2rz6PWaVTyZ8ex3dh++ASshR71dZ07gXX5Akw3PYzq4+vVPP7qJMAXQgghxB9OZmYm\nI0eOpE2bNkyYeCsTb5xRcu+xgVE80LcRBp1nm1Xr+PvgdGnovNxgWl2qXiGkgw+j82vT83AgG62X\nOe+wYjO6MLopo1mZFv06Muq/D2FaGsby5ct5/f33POqnb9Udx561aKmJlTd2w6U3oet8PQDOjGRs\nWz73OLi/QrsYj33th5jGza7WHEQxKZMphBBCiD+UtLQ0Bg4cSM+ePVm0aBGffroCu734RNg+kUHM\n7hPhcXB/xW8V3F+hGhSC2/mg0ykM8QnBjsbqwnRcVayF4tu2CZHP3w3AkCFD2Lx5Mw6HZzn9iqKi\na1I2dcdTG04kMmjCLbz//vsU7FoNedmVd3LDmXAc56WL1Z6HkABfCCGEEH8g58+fp1+/fowdO5ZX\nX30VRVE4ffrnw5lu71Iff1PZfPs/AlMtHUFtTGwszGBJ/kXm551jeWEaNs2zra8nXAXE39gDQ2hx\nLfvw8HAaNWrEnj17PJ6DofcY1IhWVZ67EhzOsGff4u9//zs/fL+GhO1rqzxGCWshzgMbq99fSIAv\nhBBCiD+G+Ph4+vXrx5133skzzzyD8tNG0vyC4gOsgnz0DGhybcte1jR9Y5Vvii6RjwsNeCXvHC/k\nJLDTmkWBq+xKvEvTOGXP5+P8ZO7LOME7K78rdX/o0KGsX7/e4+cregPGG2ahNGzueZ/gOhhHTMNc\nrzHjx49n6ROziQrx97i/O85zJ5BK7tUnOfhCCCGE+N07duwYw4YN46mnnuLuu+8udc9oLD6Rdkrn\netQLNP8W06sxe/OzOWDPKXXte2sG31szaK4zM8InFH9Vhw6FIpwcseezriijpLzljh0/cvDgUTp1\nagcUB/hPPvkk8+bN83gOqiUAn5sexrb5C5xnj0JWuvuGPn7oGrVC32csutD6JZe13MtV+chuafk5\nYCsC0x/77/O3IgG+EEIIIX7X9u3bx+jRo3n11VeZPHlymftOpxOAIPMfMzXnl745noaznHuxzkJi\n889X2D8/v4BF7/+Ptxa+CECfPn2IiYkhMzOT4GDPq/IoBhOmobej2a3YD2zCdf4kFBWAywVGE2v2\nHuX7pBTevf8NVPVXCSHOqtXxd8vpAIdNAvxqkgBfCCGEENecpmnYCq3oDHr0hvLDke3btzNx4kQW\nLVrE2LFjy9zPyckl6ULx4Un633ijbE24kO19yc7z55NK/uzj40Pfvn3ZtGkTEydOrPJYisGEsccI\n6DGi1PVR4628MXQojz76KC+//HLpTgZTteZdegwjmKRUZnVJgC+EEEKIa8LpcLLr6y3s+z6apNjz\n2AqtqDoVv2B/WvRozcDbh9GgRWRJ+3Xr1jFlyhQ+/fRTBg8e7HbMBW++z+XM4kOUcq3lrX3/ceRa\nvV/9Liws/ZIwbNgw1q9fX60Avzwmk4kVK1bQu3dvmjRpwj333FNyT1cvCoeqA1f1/z7U4HAU/R//\nG5nfigT4QgghhLjqtn+xifUfrCLp1Lky93IuZXMx7gK7v9lOy55tuOM/M9m0YzOzZs1i5cqV9O7d\n2+2Ymqaxdu2mkp+/O57Gg30b/aFTdQpsNq/HyMy8TG5uLv7+xRtdhw4dyuuvv46maSUbk2tCrVq1\nWL16NX379iUyMpKRI0cCoDZph1q3Ma6k09UeW43qUFPT/EuSKjpCCCGEuKrWvPMNy5750G1w/0tF\neYUc2rCPp254hDn3Pcy6devKDe4B9u07xN59h0t+jr2Uz86EzBqb97WWVWQlOSfP63HSL6VRv359\nRo0axfvvv09ISAgOh4O4uLgamGVpUVFRLF++nKlTp3Lw4EEAFEXxLkAPCMHQxf03NsIzEuALIYQQ\n4qrZ8dVmvnvzK6z5nueW56XkcGv7cbRo2qLCdrFxZ0s22F6xIiYFp+uPWV4xv8hBdkEWmod1790x\nm3344rPFnD9/nilTprB+/XpatGiBw+HgscceIyEhoeYm/JNevXrx9ttvc8MNN3DhwgUADF2GoNRr\nUvXBFBV9u+tQjD41PMu/FgnwhRBCCHFVuJxONny4ukrB/RXJpy6w7r1vK2xjtVrLXPvyaAorj6VW\n+Xk14Xy6k+z86uedmw06IoKMaJq92mO0b9eSnj27EhgYyK233soXX3xBSkoKd9xxB3v37qVbt250\n7tyZ5557jpiYmBqrNX/jjTfywAMPMGrUKHJyclCMJkyjZ6KENfR8EEVF17E/hj431Mic/sokwBdC\nCCHEVRH97Q7OHU+odv+jWw7ichWvZrtcLk6ePMnSpUt58MEH6dOnD/fdO6tMH02DmctjWHMizePn\nFNirH5RrmsbO4zbeWl3AlqM2/M3VD61qWXy4p08rNFdBtVbxdTqVRo3qlLnu4+PD3Llzyc7OJjEx\nkddee42MjAxGjRpF8+bNmTt3Lrt37y75XVfXnDlz6N27N5MmTcJms+FKvwABwWD0oNSlfzD6XqMx\nDrm9RvcJ/FUpmhwTJoQQQoir4I07X+Dwhv3VH0ABvx5hHEk5wYEDBwgNDaVbt2507dqVrl27kpSU\nxN/u/AcuV9mgWqcqzBvSlFEta9M01OJ2eKvdyd4LWTQZOAK/+L0E6MoPiZwujT2xDmISHOQWuLC7\nwKQHpwvOphYHxrcOMNGnlbH6nxfYeTaVoe9+D6o/qurncbBrMOjx9dFo3z6KzZs3u23TrVs3Xnnl\nFfr37w8Uv5wcPHiQFStWsGLFCi5fvszYsWMZP348AwYMKDlArCocDgdv3D2J4RGBNPZVoaIXFZ0B\ntXZD1CbtMHQbiiJlMWuMBPhCCCGEuCoe7X8f6YnepcsEtqvNiPvH0aVLF0JCQoDiwPT111/nlVde\nod+A0Xz55apy+/voVaZ3bcDg5qGEWYwYdQr5NifxGQUsPZDEgfQcsi6f49jXiwk8vJ5altK535qm\n8f1+G4fOOLiYUfEK95BOBsb29C53/MjFDPotWI1OMWBVLCiqEUWp+FuBsLBazP3HbJZ/vZS9e/eS\nnp5eUkHnl5544gkAnn/+ebfjxMXFsWLFCpYvX05sbCyjRo1i/PjxDBs2DIvF/UvSr9m2fol97w8o\nTg/SjBQVtdNATINvk1X7GiYBvhBCCCFqnMvp5JFes8hO866qTf/Jg5n6n59Tcex2O/fffz+7du3i\nu+++w+nU6NFrFJk/1cKvEs3FXaNuYNiAQby54E1C9TYeGtaQ9g0CgOJV+483FrH/tGe16X0MML63\niT6tq7eK73RpfLAtkc+2XeSYvYBCKn6haNy4IePHjeT+2TNo2LA+hw8fplu3bixevJjbbrutTPut\nW7cyZ84c9u7dW+lckpKSWLlyJStWrGDPnj0MHDiQ8ePHM2bMGGrVquW2j233Khw7vqla/XtFRd9z\nFMZ+EzzvIyolAb4QQgghapymafyjzz1cvnjJq3GunzqC256ZAUBWVhaTJk1Cr9fz2WefERBQHIiP\nnzCF71a5T0upSAvf2vQIiip1rdBZSKC/jaGdA0lP1/PjqaodPOVrglv7+9Apqmq1+AtsTmZ8eYTV\nJ9MrbauqCs2bRzJmVD9eeOGFUvd69epFXl4eR48eLdPPZrMRFhZGfHw8oaGhHs8tMzOTVatWsWLF\nCjZu3EjXrl0ZP34848aNo0GDBgC4cjMpWvw0FOZ6PG4JkxnT7f9CF1K36n2FW7LJVgghhBA1TlEU\n/ILLpolU1ZUxzp49S+/evWnRogXffvttSXAfHR3Nju3ruW3yeI9zxhWghaUO3QPLlnE068zYCgJZ\ntUOrcnAPUGCF7/fZqlSq0+50cftnhzwK7gFcLo3Y2HMs+fjrMvdefvlljh8/7jbANxqN9O/fn40b\nN3o8N4Dg4GBuv/12li9fTnJyMvfffz979+6lQ4cOdO/enRdeeIHUdZ9VL7gHsBbiPFC1OYmKSYAv\nhBBCiKuiZc82XvUPCA2k/62D2bVrF7179+bee+/lzTffRK/XA3DhwgUmTpzI4sUfsuSj//LiC0/Q\nqVO7CvO5a+l96RIQSY/AxpXkfVc/J/ziZRc/nvK81OWTa2P5IS6jSs9wuVykpefx/vuflLrep08f\nAgICmDlzptsSmEOHDmXdunVVetYv+fr6Mm7cOJYsWUJKSgovvPACFy+c52K0dwG682wMmqP65UFF\naZKiI4QQQoir4tKFNJ4Z8yj5mdVb2e1+Q18Ce4XzwAMPsGTJEkaOHFlyr6CggH79+nHTTTfx6KOP\nllx3uVx8/vlK/v2fV7h4MZWIiIaYfcz4aDq0Mzk0MYdekw2dbSJ03DOq8qow+TYHvf+7m7OZhdV6\nTof2Ldm/r3RwPWPGDNasWcNbb73F+PHjS92LjY1l0KBBnD9/vsZ+D86keKxL/+31OMYJD6Bv1qkG\nZiRkBV8IIYQQV0Vog9q06duuWn19LGYSii7w2GOPsWnTplLBvaZpzJgxg5YtWzJ37txS/VRVpXnz\nCNJT44g5spXDBzcTvXsNwyM7EeUbds2qtSSmObHaK19Dfe/H89UO7gGOHY8jJuZEqWsjR46kQYMG\nPPLIIxQVlT5krFmzZuj1ek6cKN3HG64c7/ZZXKHlVWOjtHBLAnwhhBBCXDV3PD+TJp2aVamPTq8j\nL8TG5sPbiY6Opl270i8JL7zwAvHx8SxatKhMwF5UVMQdd9zB66+/Tv369QE4sfso8QdOefdBqqjQ\nBgXWygP8zaerlprzaw6Hkw8Wf1rq2vXXX8/Jkydp27Ytr776aql7iqIwbNgw1q9f79VzS42p6mpo\nICmVWVMkwBdCCCHEVeMbYGH2O3Np0aO1R+1NviZSzFkU1dHYsmULdeqUPpl15cqVvPXWW6xYsQKz\nuewJqf/6179o1aoVt956a8m16BXbcXpxWm11OF2w9aiNC+lOXOVkQ9uynaRmWL1+Vlzs6VI/BwUF\n0bFjRyZMmMCrr75KUlJSqfve5uH/mhIcDpXU6veEGlS7BmYjQHLwhRBCCHEN2ItsbPhoDYc27uPM\nodM47aUr1ATWDqZe2wg+3fwlIyaP4bnnnkNVSweNR48eZdCgQaxevZru3buXecaOHTuYNGkShw8f\nJiwsrOT6G397gcObvDhR1wuKAl2b6mkdocdsAlUBhw1qp9jJPGplUupREp1FlQ9UgTZtmnL44NZS\n155//nnS09OxWCwkJiaydOnSknuZmZlERESQnp6Oj493B3NBccqUdenzuC7GV3sMJTwCn6lPV3qo\nl/CM/reegBBCCCH+/Aw+RkbMGsfwmWM5tv0wJ3cfw1pQiM6gxy/IH+oZmXHPncyfP5+pU6eW6X/p\n0iXGjh3L66+/7ja4z8vLY+rUqbzzzjvkXcxm9Stfcf5kItYCKxkXPSs/eTVoGuyNc7A37ucXmvpO\nJ2N/Wl8110BAm5aaUubaiBEjmDx5Mvv27aNly5YllYiguOxl27Zt2blzJ9dff73Xz1cUBTWqg1cB\nvq5xOwnua5Cs4AshhBDiN/Xee+/x1FNP8fnnn9O/f/8y9+12O0OHDqVHjx68+OKLbse49957KUjK\npV1gc+L2nsRW6H3qS7VpGkZNw6a6D1jbulz0cxWfUvt0djzrrJe9epxeV0hK8hmCgoJKrrlcLurW\nrUt0dDS7du3itddeY8+ePSXfisybN4/CwkJeeuklr559hWYtpHDxU5Bd9Q23TrM/lhnPoVoCa2Qu\nQnLwhRBCCPEbcTqdzJkzh1deeYXt27e7De4BHnzwQSwWC88//7zb++vXr2fP6p0EJ5s4tu3wbxvc\nA7U1jX4VrJ+eUhRyfvrzCJ9QqnbmbWlNmzam33Xdyhxepaoqw4YNY926dUyePBmTycTixYtL7g8d\nOrRmN9qazBj7TQRT2X0RFbFqCvO3HadAq6GNugKQAF8IIYQQv4H8/HwmTpzI/v37iY6Oplkz95V2\n3n77bbZs2cKyZcvQ6coGgVlZWTwy40F6BHcgO+23L7No0TR6uVw00DR8f1ql1/2qOIxdUThL8abf\nHsYAWuv9qv28wdf3Y9SokW43zQ4fPpy1a9eiKAoLFizgySefJDs7G4Du3buTkJBAampqtZ/9a/rW\nPTEMvBnMHp5g7GPBb/CtXAyMZPLkyTid13Yj9J+ZpOgIIYQQ4ppKSkpizJgxdOzYkXfeeQej0ei2\n3ebNm7nlllvYuXMnTZs2ddvmjjvuwHjMgSvDdjWn7BF/TeM6p5NGP/183KhS0NRI07o6lm0t/a1C\nYcElZugsBOkMbC66zAs5CeRQtQC3ZctmrPr2Y6zWQoYMGUJiYmKpsqHp6ek0bdqU9PR0jEYjd955\nJ4GBgfzf//0fABMmTGDChAlMmTLFm49dhjPxBPaDm3GdOwGFeWUbmHxRI1ui7zAAfZN22O12RowY\nQatWrViwYAGKolCUkEza4tXk7TuJIzsfXC5Uiw++bZsQevNgArw8JfnPTgJ8IYQQQlwzBw4cYOzY\nsdx33308+uij5R48debMGXr37s3//ve/cjeCrlixgufmzKOrrjUOm/2qzNfPB7o105N0WeNsihN3\n1TZ9NY0GLhcdNI2wX1z3qaOjyU0BJKY5efnrgpLr6bZcNmScYKwphPv8GmBWdXxRkMK7eUnk4/Jo\nXlFRjfjwg9fo07s7mqbRpEkTVq9eTevWpcuRdu/enfnz5zNgwADS0tJo06YN27dvp2XLlrzzzjvs\n2rWLjz/+uDq/mkq5cjJw7N+AK+dycekgvRHVLwhd50HogsNLtc3KyqJv377ce/MUhp6D3N0xOLPc\nvBwAismAX5eW1H34Fgn0yyEBvhBCCCGuiZUrV3LnnXfyzjvvMHHixHLb5ebm0rt3b2bOnMns2bPd\ntklLS6NDhw48MOQu4rYdvyrz9TXBuF4mercq/obh9EUHe2Lt5BVqOF1gUMGYYqdNngt3mefGWipN\nbwvkaIKdd78vLoWZbstl2+VY8l02NM3Bw807M+yyQpDOyNrCS3xSkEx8RWUzNY0ePTrzztsv065d\nq5LLs2bNonnz5jz88MOlmj/11FNYrdaSzbSvvvoqGzZsYM2aNZw9e5ZevXqRnJx8zU74rUj81h/Z\nP/lJmigWj9ob6oQQ8dxdBI/oeZVn9scjOfhCCCGEuKo0TeP//u//uPfee1mzZk2Fwb3L5WLKlCn0\n6tWL++67r9zxZs2axdSpUylMy78qc64TrHJrfx96tTQQk2hn8Q+FLNtSxLFEB+fSXWTmaujyXDQq\nJ7j/paMJTrLtuZzITWRd2h5y7JdwOrJwOdJJ7hTCQ5k/sjfQTkejhSW12vK4fyN6GQMIUfX4tyHX\negAAIABJREFUomJRdVgAxVmIr9nGgP4dSgX38HO+/a+NGDGi1PXZs2dz5swZVq9eTePGjfH39+fI\nkSM18Bvzjj0jC+tLX3gc3APYUzI499QicvdcnRe8PzJZwRdCCCFElZz68RhHtxzCWlCIqtPhG2Ch\n76RBhNQLLdPWbrdz3333ER0dzapVq4iIiKhw7CeeeILt27ezYcOGcnPzly5dyksvvcS+ffv41+CH\nuHQ+zavPo6GhAD5GhcbhOjo01tOrpYFTSQ7W7reRkOrCVU60ZNA06mka17lcBPzqnrmODmWAkbu/\niCX6wiGcWtn0G1VVadOmDefOnaNeUAidLynU0hkxKDqi2rZm8d4ttJo0nE+WFqfRREZGUlRUxLlz\n50r9fnJycqhfvz6pqan4+vqWXHc4HNSuXZuYmBjq1asHwPfff8+DDz5ITEwMDz30EI0aNeIf//iH\nV79Db517+n3SPlhVrb4BAzrRfOnTNTyjPzY56EoIIYQQlXLYHWz6ZC2HfthL/P5Y7L/Ked+45Hta\n9mpL7wn96Ti4K1CcV33jjTfi4+PDzp078fevuLrKp59+yrJly9izZ0+5wf2FCxd4+OGHWbduHSaT\niZrILHHoXWQ1tpMVd4A+ES1o3agO++PtrNhlJbew4r52RSFRUchRFAY7naVy8E1hOj44eJwOY/qy\n860Dbvu7XC4KCgqIiooiMjKS/61YUXKvWZqVOFsaXf2KA3ZFUcjIyKBr166sWLGCm2++uaRtQEAA\nXbp0YevWrYwYMaLkul6vZ8iQIaxbt47p06cDxav6Cxcu5I033mDo0KH897///U0DfJfNTs62Q9Xu\nn7fnBPkxZ7C0bVKDs/pjkxQdIYQQQlSoICefBTNe4LNnP+Lk7mNlgnuAvMxc9q3Zzdv3vcrn//mY\n+Ph4evXqRdu2bVm5cmWlwf2+fft44IEHWLlyJWFhYW7baJrGjBkzuP/+++nUqRMATodnm1IrYtGb\nSc1MZU9yBlOWbWHUWztZtrWg0uD+lzIVhU2qypWEIdUE0cZ0Xtx0hOuuu85tnyt578nJyeTn57N1\n69ZSpUDj4uKA4j0JAHXq1CEvL4/p06fz9ttvlxmvvDQdd9dfe+01XnrpJVq3bk10dDQFBQVl+l0r\nlz7dQFHchWr3dxUUkf5J2c/9VyYBvhBCCCHKZSuy8ubd84nZdtij9narjXXvfcud/acwe/ZsXn/9\ndbf1638pOTmZ8ePH895779G+ffty27377rtkZmby2GOPlVwLqh3s2QepgKZppFxIKTnltY4uEoej\n6gcvZagqB34K2i/5Wrlz7S6gOH2mvOfWqVMHq9XKqVOnmDZtGn5+pWviq6rKuXPniudVpw4Ae/bs\n4dSpUxw/Xjr3fNiwYW4D/GHDhvHDDz/w/+zdZ2BUVfr48e+9d2YyM5nJTHojQXoNVaQFlC4oKNZd\nUcTG2hZR2XVtCLZVV9ey+tfdVbGxFlz7giCIAgoRUHpNKAGSkIT0ZOq99/9izIUhPQH0t3s+r8id\nc849M/riuec+5znBYNC41qVLF66//noef/xx+vfvz+rVq1v8fU+V6s172zyGZ+eBtk/kv4gI8AVB\nEARBaNC7D7/B7nXbW9yvkzWNge0aDtZreb1epk6dysyZM5k6dWqD7XJycnjwwQd56623MJmOZxin\n9+rQ4rmdTNd1qiurCAQCxFmiSYiIbfVYh2SZfDzctGsN/kDoTUdjm1ijoqLQdR273c63335LIBAI\nq2ijaRr79u0DQoeDORwOXn31Va677jpeeeWVsLH69u1LeXm50b5WSkoKaWlprF+/Puz6Aw88wNKl\nS+nZs2e9B2WdKWp1C16VNECrbqTy0P8gEeALgiAIglAvT2UNW1fWnzveFC2ose7TxleFdV1n5syZ\npKen88ADDzTYTlVVZsyYwX333Uf37t3DPoupZ2NvS0XYrVR6q1FVlW6ODpjl1m9RLJMknq/OJzdY\nRW0dk3Xr1jXYPjc3F13XCQQCFBQU0KtXLyRJCgvy8/PzgVCA379/fwKBACaTiYULF1JdfbyKkCzL\nTJgwocFTbZcsWRJ2LSoqiscff5w1a9b8ogG+1MQbnmYxiZD2ROLXEARBEAShXl8t+A8l+cda3X9P\n1g4O7TzY4OfPPPMM27ZtY8GCBY3WYX/22WdRFIU77rijzmf9xg7EYqt/Q25zJXVOIaCFVtvdlpNr\n4bScV7FgNpux2UIFNLdt22ak/9SnU6dOuFwuAHbv3g2EHn5qU5s0LbTPoKamhj59+uB0Onn55ZcZ\nPnw47777bthY559/fr3B+snlMmtNnz4du93OwYMHOXLkSCu+bduZXI6mG52BMf6biABfEARBEIR6\n7fx+a5v6e6u9fPfhyno/W7x4MX/961/55JNPiIxsuPb59u3befLJJ1mwYEG9QXK7bu3pPrR3i+Yl\nAf07mbhunJVZk21M61/BZ9eP49mLBtMhztqisepjlkJvAGrz6du3bx+WVnQin89H9+7dqaysJD8/\nnxtuuMFY+Q+vZK5QXe0nMTEZVdUoKSmhV69evPzyy2Htxo0bx8qVK/H7/WH3GTZsGLt376aoqCjs\nuizL/O1vf0PTND777LO2fvVWiZ48HCzmNo3hbOH/A//tRIAvCIIgCEK9qkoq2z5GWd0xdu7cyYwZ\nM/jwww8brYsfCASYPn06jz/+OB06NJxrf/bEoc2ez9h+ZuZcaue6sVYGdjbTtZ2JBFM1Y7qmMnNo\nDx66PJ7fTbTSJaX1aSOarqHruhHUd+nSpcGyn7quk52djc/nw263s3nzZiIiIn7+1IRijkY2JSCb\nElB1F4/9+e/Ipnhi4zvw1lsLKS0tDcutj4uLo3v37nz//fdh97FYLIwaNYqvvvqqzhwGDx5Mv379\n+Nvf/tbq79wWUcMycAzs2ur+5pQ4Em6YfApn9H+fCPAFQRAEQahXMBBsulET1IAa9ndpaSlTpkzh\nySefZNiwYY32feyxx0hMTOTGG29stN2wS0bS9+fa+w2RJLh6VASTB0fQPkFBlutPCbJHKGScZWbG\nWCvndG1dLr5P8xMfH4/XG9r4mZ6eHnb41Ml27dpFeno6LpeLLVu20KNHTyQlGuQYwIYkmYwUpmBQ\npbraS0mJh4JCPxHWeF588aWw8RqqptNQGU2A559/nl27drFnz55Wfee2ir5gGDTw36QpUSP7YYpq\n/gm4/wtEgC8IgiAIQr0i7G1PV4mwRRj/DgaDXHHFFUyePNk4dKkhGzdu5OWXX+bVV19tND8fQFYU\nbn5hNj0zG67ac+WICIZ0t6A0M4h0RcpMHRZBxlktW8kP6ir7qnPp0KGDsQHW7XYbOfb1sVgsxiFW\nXbt2Y+fuAmTZhiQ1HqZJksLe7MO8/8GXFBQcNa43FMjXbsCtzek/0aBBg4iLi+Omm25q7lc9pRKu\nnUTspaNa3M8xuCfpj/wyc/41EwG+IAiCIAj1SunSrs1jpPc6y/j3nDlzUBSFp556qtE+Xq+X6dOn\n89xzz5GSktKs+0TYrdzx2r2ce9U43EkxYZ/1aq8wuHvLc7ydNpkLBrXstNwyvYoifylJSUlG3fnS\n0lJiY+svvakoCna7HY/Hg9frZfOWAwTqniPWKFUzMeXiacbf55xzDrm5uUb1nVodOnQgOjqaTZvq\nPzX28ssvZ9u2bSxbtqxlEzgFJEnirL/cRuzlo5u9ku8cnkGnv/8R5YSHSCFEmTdv3rxfehKCIAiC\nIPz6WB02fvj8e/R6VnybI7lzKjP+fDOyovDaa6/x5ptvsnTp0kbTVQDuvfdezGYz8+fPb3L1/kSK\nSaHfmLMZftl5mCMsqEEVU4SZCX1kkt2t+go4bBIlFRpHjjX9G6i6xs6aHIp9pWRkZLBnzx5UVSU6\nOprY2Fh27dpVp098fDwVFRUMHTqUAwfyCaoRtOiJ4mcFBcVMvnA8ycmJyLLMxo0bURSFfv36hbXL\nzs4mLy+PkSNH1hlDkiSysrJYsmQJM2fObPKAslNNkmXcE87BkhSL7g/gLyyFYHiKF4qMvW9n4qaN\np/1jN4vUnAa0vtCrIAiCIAj/1XoO70On/l3Z88OOBts4bRKZvcw4rBKKAv6AzsFCjR9zgvQe2Q+T\nxcyaNWu49957Wb16NW5345H26tWrWbhwIVu2bGlRcH8ih9vJxXdeycV3XolWUYL3jXngad2GYVmS\n6NfJRNaepvcjHPQdYdux3ZjNZhRFMarbHDp0iPPPP7/ePomJiRQWFrJ7925s9hg83tY9TOk6PDTv\nST779B3geJrOtddeG9Zu4sSJPProo9x///11xjj33HPZv38/Q4YM4aWXXmL27NmtmktbSJJE/FXj\niL9qHFWb91LyySrUihrQNGS7lajMvrjPH9zq/zf+V4gAXxAEQRCEekmSxISbJpO3N5eq0qqwz7qm\nKgztbqZbO4Uoe3jGr6bpTBxiIm6QmUN7d3HFFVfw9ttv061bt0bvV1VVxYwZM3jllVeIi2v7AVYA\nwR3rWh3c12ofr2A2QYN7jiXI8R5ii28PZrPZOKhKVUOrz0ePHqV9+/ZGc1mW0XUdXddRVRVZliko\nKCLClgLUtHqe33zzPTU1Hux2G+PHj+ePf/wjqqqGrcSPHDmSTZs2UVZWVudhKzIyknPOOYdLL72U\nuXPnMm3aNOLj41s9n7Zy9O2Co2+XX+z+/5eJHHxBEARBEBrUf9wgLplzFZHu4wcJjetv4cYJNgZ1\nNdcJ7gFkWSLJqWLasoLCf87l0btuZ8KECU3e6w9/+AMjR45kypQpp+4L+L1tHsJigQhL3ZV1n+rn\nsLeAI64SyhK8+Hw+ZFk2gvvazazl5eUkJCQY/RRFMWr6FxYWYrFYUBQr1dWtD+4Bajw+1nwXOjW3\nXbt2JCcns2HDhrA2NpuNzMxMVqxYUe8Y48ePZ+fOnVx99dX1rvIL/zeIFXxBEARBEBp13rTxOGKc\nLP5/n9BJyWXi2RYspualSPSIjaRndA1axTHkqPo3mgIsXbqUxYsXs2XLllM17ZBTkMmh6hqritZj\nUaMxoaADfi3AAd8RyvwVJClJJCYm4vf7kSQJRVHCDpSyWCxUVh5/i2A2m/H5fACUlZX9nMrjB7nt\nVYvefusdxo8LVaOpTdMZPHhwWJva65deemmd/uPHj+eqq65i3bp1dO/enZtvvpkBAwa0eV7CmSVW\n8AVBEARBaNLZE4dy70s3MHm4s9nBfS29+Aj+pW82+HlpaSk33ngjr7/+eqPlJFvF0viG3uao9PrZ\nXZbP+tKtrC3dxLrSTfxYvp3yQCVms5lZs2Zx5MgR4uLijMo5eXl5xoFVZrOZgwcPHp+SxWKk7wSD\nQaKjozGZ23aSa60vl35pzKGhcpm118NPyg3p168fJSUllJeX88gjjzBr1qx62wm/biLAFwRBEASh\nWbStqzFpLazhWNv30G7U/P31fjZr1iwuvvhixowZ05bp1cvUeyhERrVpjPW5RXhPquYiyzJut5tg\nMMhNN91ESUkJaWlpmM1mZFmmuLgYt9ttbAY9sYKOxWIxTrkFQiU09UBop2xb6CqypPHFF18AkJmZ\nyY4dOygpKQlr1rVrV0wmEzt21N08Lcsy48aNY9myZVx//fXU1NTw3nvvtW1ewhknAnxBEARBEJqk\neapRD25v/QABP8HN39S5/NFHH5GVlcUTTzzR+rEbIUe6UNr3bHV/VdP4cMsB4+/aDau6riPLMunp\n6WRnZ6OqKikpKZh/XomvqKggLS0NCK3S79271xjDYrFgs9mA0EbmmpoaNM0HUusenmqZzHDsWBEv\nvvgiABEREYwcOZLly5eHtZMkqdFTbcePH8+yZctQFIUXXniBP/7xj8ahXcL/DSLAFwRBEAShScEt\n30JlaZvG0HJ3h6V7FBYWctttt/HGG28QGXn66pmbemcSbGUy/ua8Ev699fibh9qVd1mWKSkpYdiw\nYSxYsAAAu91uBPg+n4+ePUMPFh6PhwMHDhhjWK1WHI7QpmVd1zl69CgOhwNZbroUZ2Mef/RBVFVl\nzZo1ZGdnA6HTa+sL5CdOnMiSJUvqHWfcuHGsWLECVVXJzMwkMzOTJ598sk1zE84sEeALgiAIgtA0\nT9tXcHVPNQT8oX/rOr/73e+49tprGTZsWJvHbozSoRc/+iPRWpgBc7TSw7xlP4VlztTmzlssFtxu\nN926deODDz4gKirKqH+vqiqSJNG1a1cgFNDX9qv9OyoqlDakKAoJCQn4fD7ap8VjbuH+hlq65qN/\n/55kZmbi8/mM04IbyrcfNWoUWVlZVFVV1RkrJSWFdu3aGRV4nnrqKV566aWwhxTh100E+IIgCIIg\nNE1Tm27TnDHUUBrKO++8Q05ODvPnz2/7uM3wzsEaluXXEGjmOVIF1T7u/GQdPxaFl66s3cDq9/tJ\nTEykuLiYTp06hTbKmkwoioKmaUb6DoDT6SQ6OtoYw2azGX8rikJMTAwejweXKwq/r4j09HYt+m4S\nKn37dODSSy/lb3/7G7Is8+qrr1JRUUHnzp2x2+1s3bo1rI/T6WTQoEF888039Y45fvx4li5dCkBa\nWhqzZ89mzpw5LZqX8MsRAb4gCIIgCE0zR7R9DEsERNg5dOgQd999N2+99ZZRaeZ0O3gwl2tfX0xx\nj5F8f7AIX7D+B5biah+rj9bwu0/W8/nOQ0ZAb7FYwtqZTCZkWWbjxo2cf/75uN1uFEVBkiQ0TcPh\ncJCcnBzqG2HD4zUhyW4kOYYDuWUcya8ETEiShNfrRZIkcnNzgSCzZ80gOrp5G4MHDx5A9+7J/OlP\nf6CmpoannnrKeCNy0UUXAaFV/Npg/UTNycOvNWfOHDZu3MjKlSubNS/hlyUCfEEQBEEQmqSc1Su0\ni7MN5Ph2IEnccMMNzJo1i379+p2i2TVt69atjB07lvy4Llz14Q9c9a9VvLV+L77kLsjteyJ36c/2\niGSu+c82livprNyxH03TjPr1tTnztVwuF8eOHWPTpk0MGjQIl8uFLMvIsoymacTHx5ObWwCSm8Ki\nIBWVQWTFjqxYKSmpJi+vFNkUR1BzcCSvmOTkZEpLS0lOTmb9+u/5aul7qGoFsqzWSa8xm02MGjWc\nZ//6MCu+WsQFkyawfft27rzzTj777DMuuOACzGYza9as4fXXX2+yXGZ9RowYwebNmykvLwdCbx2e\nfvppZs2aZTz0CL9eIsAXBEEQBKFJSlpX5JTObRujywBeeeUVysrK+NOf/nSKZta04uJiCgsLuffe\ne9m1axcVFRVsLvVz1+IfiZ5+H9bf/AHLxbcz45VPcDvSsVdZaGdLIlKxGWUua6vn1JIkiaKiIs4/\n/3yCwaAR4NcG45Js574HnkBWbGgNpAVJkgxE4PFaiIlNw2Qy0bt3b7799lv69u2DWfFhtXjQ1BJU\ntRxVrcAWEcTl1Fj8xUJ+f/sNWK1WRo8ezYoVK3jwwQeJjIzkqaeeIi0tDU3TuOuuu3A6nfzwww91\n8u0zMjKoqakxNuSeyGazMXTo0LAV+0suuYT4+Hj+/ve/t+G/hnAmiABfEARBEIRmUbr0b3VfKTaF\nXEcqc+fO5a233gqrA3+6Pfnkk0iSxIABA1izZg0AkZGRdO/eHYDcHft5fPqD9PV3IfaglUNLdjM6\ndggXp4xjTNxQOkem4/f5jfFkWaaiogJN07jhhhsoLy/H7XYjyzKBQABJspF7qJTi4uZVHZIkmb3Z\nefj8CmlpaRw+fBhN0+jTpw9VVZVYIyTQqkGrIjU1mnbtEvnnP/9p9M/MzGTTpk1omsbzzz+PxWLB\n4XBgMplo374906dPp1+/fnXSa5oqlzlhwoSwNB1Jknj++eeZP38+x44da96PL/wiRIAvCIIgCEKz\nmPqPQm7fo+UdzRbkAWO59rrruf/++43A+kwoLCzk1VdfJS4uDpPJRFZWFpqmUVhYyLix43j9jy/x\n58seZN+a3Vi18BQki2wmzZ7MiLhBjIw8mxhz6JRds9n8cyAvMW7cOMrKyowTeH0+P5LiJNhAjn9D\nAoEgsuxg/YafMJlMfPTRR0ybNg1d18Py/w8dOsT48eN55JFHjBV5u93OwIEDWb16NVdeeSWdOnWi\nrKwMi8XC1q1bueyyy8jLy2Px4sV17ttYucwTN9rWysjI4IorrmDu3Lkt+n7CmSUCfEEQBEEQmkVS\nTFguugUptQWpOuYITEMu5IUVGzCZTMyaNev0TbAeTz31FOPGjSM1NRWAffv2kZCQQHVVFc6DJtZ8\nsBJfjddor+k6e2sK+aZkN0uLt/Nl8TaWF+/ggKeMwTEDiLNEo+s6JpOJdu3aYTKZjBV8Xdfx+WUk\nqZVvJySFHTv306VLF95++21GjhyJ2Ww2DplSFIXIyEi+//57xowZwzPPPGN0HTNmDF9//TWSJPHC\nCy9QU1ODqqooikJVVRVJSUm8++67dW45duxYVq9ejdfrrfNZ7969qampIScnJ+z6ww8/zIcffsiW\nLVta9z2F004E+IIgCIIgNJtsc2K9Yg5Kz6FgczbaVkpIxzz2Kva6OvLUU0+xYMECZPnMhR4FBQW8\n/vrrTJo0iZSUFPLz8/H7/fTp04dB0X3Yl7XbaKvpOj9V5PJF0WbWluWQ6y3hqL+CQn8lef5ydlTn\n821pDlZzIpJmIhAI0L9/KGWpdgVfVVU0rW0bkdWgwtChQ1m7di19+/ZFkiSjhr6u63To0IG1a9dy\n880388ILL3D06FEAIw8fYODAgUyePJkhQ4agqipvvvkmn3zyCVVVVTz77LNh94uOjiYjI4PVq1fX\nmYskSXWq6QDExMTw0EMPMXv27DobgIVfBxHgC4IgCILQIpIlgojJM7FeNx/T4EnISWdBVBw43EjR\nicid+mK+cCbWax+CHkOZPn06f/7zn+nQocMZneeTTz7J9OnT8fl8JCcn8/bbbwNQXlRGJ0e60S6o\nq3xTsputVUcoC3oaHM+nB8n3VyBJ0UiylYyMjNB4P6/gHz6cD5Klwf7NIclmDh0p4tixY5SVlTF8\n+PDQ9Z/LbwYCAeLi4njuuee45pprePTRRwE455xzyM7ONnLjH3vsMbZs2UJqaip+v5+3336byZMn\n8+CDD9ZZeW9JucxaM2fOpLi4mI8++qhN31c4PUSALwiCIAhCq8jOaCznXY712oew3/IXbLc+g23m\nE1gvm42511AkWeaxxx4jMTGRG2644YzOLS8vjzfffJN77rmH/Px8kpOTeeedd7BarQQP1GAhtNKu\n6zprSvdy2Ne8DbEAAXSQnKhqqMJO7Qr+ocP5RtWdtti2dTsWi4WFCxdy7rnnYrFYjJXykpISvF4v\nP/30E8OGDePdd98lJycHi8XC8OHDjYOrkpKSuOeee+jYsSO6rjN//nx++9vf0qVLF6ZOnUpJSYlx\nv8YC/HHjxrFy5UoCgUDYdZPJxPPPP8+cOXPweBp+KBJ+GSLAFwRBEAThlAiVfTxuw4YNvPzyy7z6\n6qunJPBtiSeeeILrrruO5ORk8vLySExMZMeOHXQ4qwPJlnij3e7qAnK9zQ/ua+m6zJKloYo8tSv4\nZaUtH6c+RwsL6d69Ox988AGZmZnY7Xbjs6KiInw+H4899hgPPvggt99+Ow888ABwPA+/1qxZs8jL\ny6NHjx5UVFRQXl5OTk4OkyZNYtq0aUbqz8CBA8kvKGTe/KeYN/9pHpz7JE89/RIHDh4iISGBjh07\nkpWVVWeeo0aNYuDAgTz99NOn5HsLp86Zq1ElCIIgCML/DK/Xy/Tp03nuuedISUk5o/c+fPgwCxcu\nZMeOHQDk5+fTvn17NE0jNTkFW5XVaJvrLWlomCbt2LGX7777wVjBL68oAyxA2x5mzCaFTp068Z//\n/Iezzz47bANsTU0NGRkZdOvWja5duyLLMqtWrWLDhg2MHj2aq666ymgbERHBM888w+zZs5Ekibvu\nuouMjAwuuOACnnjiCR566CEmTpzCa6//i0DQwaOPPR82j6effpnzzh3KWR26sXTpUjIzM+vM9emn\nn2bgwIHMmDGDtLS0Nn1v4dSRdLE7QhAEQRCEU2zOnDnk5uby/vvvn/HV+9tuu8048AlCK9Tx8fF8\n/fXXdO/YlX6ezpgkE4W+Cr46tgOV1odCffv2orx0H5988kloU6wSC7R+o62igBosZOiQIaxfv56V\nK1dy22238dNPP9W24JzBQ/ntb6/kvHNHMHbsGO6++26WL1/OsmXLSEhIMHLvIZSCNGHCBHbs2MGR\nI0cYPXo0AwYMYM6cOXTrPpBA0IzvhBr/9ZFlGbfbypFDOzCb6363uXPnsnfv3nqr9Ai/DBHgC4Ig\nCIJwSq1atYrf/OY3bNmyhbi4uDN679zcXPr378+uXbuIjw+l4qSkpODxePD5fKDBFamTkFXYUH6A\nHdX5bb6nNSLA++8t4Oqrr6aiQkVSGq8u1Jj2afGoahnFxcUEAgFuv/12KiqqWPDG++i6GSSz8cCU\nkBBHbIwTh9PMsaIjvPjii/zjH//goosu4pprrjHG3L59O8OHD6eqqgpd1+nSpQsXT72Gvz77ClpD\nx+zWY8rk8Xy46LU6lZCqq6vp0aMHCxcuZMSIEa3+7sKpI3LwBUEQBEE4ZaqqqpgxYwZ///vfz3hw\nD/D4448zc+ZMI7hXVZWioiLKysrQNA13jBt7dCinPaC37DCqhni9Mhs2/EhUVBSaVoXeynGtERbG\njh1ObGwsHTp0IDU1lQVvLOKjT74BKRJJtoS9DSksLGbnrv2sX78Hf9DBHbPv5rzzzgvLwwfo1asX\nV199NREREQBk5xzmby++1qLgHuCzz5cx/+Fn6lyvfVsya9YsI69f+GWJAF8QBEEQhFNmzpw5nHfe\neUyePPmM3/vAgQMsWrSIOXPmGNcKCwuxWCw4nU6ioqKQJInEHqk/f3qKUockhUX/XozD4UBRZNCq\niIhoablMna5d0xg9emToQcTtprRMpaIySHl5VZO9Dx8+Su6hCvbtz2XFihV16tM//PClfvahAAAg\nAElEQVTDyLIcui5ZQ28zWuHjT5bUqagDcOWVV+J0OnnttddaNa5waokAXxAEQRCEU2Lp0qUsWbKk\nzmFKZ8pjjz3GLbfcQmxsrHEtPz+fQCBAjx498Hq9lJSUcEgqRI5QMEvKKbt3Ts4hIiIikCQJSfJy\n6y3X4nBENquvrutE2mXsNo2MjAyKi4vZszePyqoALXkI8fmCvPzyQny+INnZ2WGfxcTE8MgjjyBJ\nZtBbv0dgx47dvPHm+3WuS5LE888/z9y5cyk9RdWEhNYTAb4gCIIgCG1WWlrKjTfeyIIFC3C5XGf8\n/vv27ePjjz/mrrvuqnM9EAjQvn17/H4/PXr04N+ff0S3zN6cZY1BOUWr+IGAhtcbNPLT75h1I//v\npScwmTRodBNvAE0rp3u3VA4ePEiXLl0oLj7GsZJqWvOGIRAEk9lZJ00H4Pbbb8dsdoPUtvDvi/98\nVe/1/v37c9FFFzF//vw2jS+0nQjwBUEQBEFos1mzZjF16lRGjx79i9z/0Ucf5bbbbiMmJibs+nvv\nvYeiKOzbt4+kpCSsVisXXXQR68u30K5jMgmWqFM0A4nKqhrjL0VRuOq3lxDjlogw12C1Qu/e3XC5\nIunerSPRbhuDzu6Cwx4ArQaXy8XRo0dRFIXomFSg9W8XiorKWbJkSdi1YDDI7b+/D422nbQLUFR4\nrMHPHn300bASpcIvQwT4giAIgiA0Sa8qQ83bh3poL1p5cViO90cffURWVhZPPPHELzK37OxsPvvs\nM+688846n61evZr09HSys7OxWq3s2rWLIUOGsOHHjTyx+AXOHTzolM0j4Pcbv4uihAJ0SZJQFI34\nWAv333sLUy4czp2zZ9C7VxopybHY7TYgFICbzWYKCgrQ2pBCA6DpCku+XGlsolVVld9Ou4VXX1vY\n4o219fH5Gy6rGR8fz/3338+dd95ZZx+AcOaIg64EQRAEQaiXrgYJbv4Wdc+PaEdyIPjzxkxZQU46\nC7ljH0rT+3Lrrbfy8ccfh524eiY98sgjzJo1C7fbHXY9GAxSVFTElClTePXVV9E0jZ49e/Lss8/y\nwgsvEOWK4u3lb5A9aCIbtmxt0xwURSEQ8KFpGrquGwF+7cZWRVGMA7EqKiqw2+1IkoTNZkOSJCor\nK7Farezdm43PF2zTXACCAfj6668ZO3Ysd989j48/XtzmMWvZbdZGP7/tttv4xz/+weeff86UKVNO\n2X2F5hMBviAIgiAIdQSzNxP45gP0Y3l1P9RUtLwctLwcvN4PefF3VzB06NAzP0lgz549LF682NhU\n6vf7WbDgPVavyWLnrl1IcjTrsnaimBzUeDx069aN0tJSLrzwQiC0wv6vD16ma/dhtCWxYeCADLZs\nXoWqqj+v2ocCfEVRjFXz0tJSoqKiqKioMAJ7my20gl9eXg7Azp070bW2r3zbIyOZO3cuGRl9WfTv\nz9s83omczsY3D5vNZp577jluvfVWJkyYYJTnFM4cEeALgiAIghAmuDML/1cLwVPZZNt4q8Ikkw//\n959jGXbmS2M+/PDDzJ49G5PJzJw581j61Tfs3LnX+FySrezctR8kJ2Bn0b+X8OPG1WFj5OfnER/v\npKioutXzuPjiSWSt+xKz2YyqqmEpOsFgkGAwSFlZGTExMeTn52Oz2fB6vcZbj/Lycnw+H3l5R7Da\nrFRV1zR2uyYF/H42bNjA3HlPcvRoUZvGOpHJpBAb2/SbmvHjx9OrVy+effZZ/vSnP52y+wvNI3Lw\nBUEQBEEwqHk5+L9+v1nBvSHoJ7juPwS3fX/6JlaPnTt3smzZMi659HImXXAVz73wz7DgPpwEmPD7\nzdwx+yFKSo6XcszJyWHwoN4kJLTuYC6zSWbmTVcDYLFY6qToBAIB/H4/ZWVlxgq+3W5H0zQjwK+q\nqsLn87Fv3z5SUpJaNY8TZY4YhqqqLFr0aZvHOlFGRnd2bN/UrLZ//etfefrpp8nLq+ctkHBaiQBf\nEARBEARDcMNyqGpFHfOAj+Dmb87oxsqHH36YW2+9jZtumsN3369vdr8VK1bz26tuNg57ysnJoU+f\n3tx//2xsTeSXn0yWISOjI7IsYbFYsFqtYQG+JEnouk5VVRWlpaW4XC7Ky8ux2Wzouo7dbkfXdbxe\nLwkJCezatYuxo0e0aA4n69KlIx+8/yaSrFBR0fQhWU25pHcinWPtREe7ufdPd5CdnU1RUdNvBTp1\n6sRNN90kVvB/ASLAFwRBEAQBAK2qDPVg68sbann70Pa3bbNqc23fvp2vv/6aI3kVrMva2OL+K75e\nw0Pz/gKEAvxOnTpx2y3XMffBu43KNk2Jj4/FYvYz8fzz8Hg8mEwmrNbQA0JtgA+hVf2amhpKSkrC\ncvA1TcNiCZWt1DSNdu3acfDgQX7/+xtacRLucePGjiQmJppRo0ZzKk7rTXNb+eCaAfy/B2ZyydQL\nOO+881i+fHmz+t53332sWLGCdevWtXkeQvOJAF8QBEEQBACCG5dDTUXrB9BUgtvXnroJNWL+/PnM\nmnUH33zb+rSgL5euJBgMnfrauXNnAP4w51YuvmgU6F6g/pKSFouJqVMn8cbrz+GpKSEjI4OamhpM\nJpORclMb4Ou6jsViwe12U1RUVCfAD518GwrCExISKCsrIz4+ln59u7fqO6WmJnH7bdcDcMP117dq\njJPZTAqdY+1caC1APVbA+PHjWbZsWbP6Op1OnnjiCWbNmnVKSnQKzSMCfEEQBEEQANDK2r4ZUy8r\nPgUzadyWLVtYtWoVOlb2789t9Tjbtu3irbcXGSv4tQL+Kuy2AHarj8suu4CpUyeB7kORA+haNTdc\nN5VF7/8Tp9OKzWYjMTERj8cTVhWnNmjXdR2z2UxiYmLYCr7dbjcq7tQym804nU4OHDjAo4/cQ2Rk\ny+rhRzsdPP2XeXTtGvouEydOANRW/z61Ov68qVYvKSD43adMmDCBZcuWNTsda9q0aSiKwptvvtnm\nuQjNIwJ8QRAEQRBCgg0fYNRcem2t/NNo/vz5/PGPf+SHH5q32bMxX365Ao/HQ2JionFt79692Gw2\nFEXnsUfu4d2FL6MGj+GKAk0tZ9CgAQBkZWURERGBy+XC4/Gg6zoOhyNsfF3XMZlMJCcnU15eHpaD\nr6qqUTdflmW8Xi9Wq5X9+/czcOAAAr4ioq2mZgVrSbKFP9nSyczzGtdcLhcJCe5GejUtI8nB5X2S\njb/VgzvokBRHREQE27dvb9YYsizzwgsvcP/991NR0YY3REKziQBfEARBEIQQpW0nqAJIp2CMxmza\ntIm1a9dy8803U17Rgko/DSgoKKRjx45hK+kHDx7EZDLh9/uJjo4mLy8Ps9mMoihIkkT79u2BUIAP\n4Ha78Xg8YVVxatUG+KmpqVRVVYWl6Kiqis/nM+rE19bC37dvH1FRUfSJb8eCuD7Mj+rIuRY3TpSw\nsSWgq8nOtfZkXovuwblEcuS598l/6d9Gm0suntCmfP4xneNQ5BPy+GsqUDcsb1GaDsCgQYOYMGEC\njz76aKvnIjSfCPAFQRAEQQBAdrjaPsipGKMR8+bN45577glVnzkFOd1VVdVG/j2EDsoqLS3F7/fj\n9XpxuVzk5uZiNoceXCRJIjU1FQgF+LVtampqCAaDOByOsIeF2hSddu3aGcF+VVUVERERBAIBfD4f\nkZGR6LpOSUkJXq+X/fv3A3CnrTtJfoVx1liedHdhQUxP7nSkc3NkKr+PbMfjUZ1YEN2TWxztiFV+\nDuL9QfJf/DeVG3cBcMklFxMZ2bpjjzrG2rhtWPs617WjB1oc4AP8+c9/5vXXX2fPnj2tmo/QfCLA\nFwRBEAQBAKXfeWBpWZnIOmN0GXhqJlOPjRs3sn79embOnAmA0+lookfTVDUQln9/4MABIiMjKSsr\nw+FwoCgKubm5WCwWNE1D0zRSU1MpLCyktLQUr9eL0+nE4/Hg9/txOp0njR869Co5ORmLxUJlZeXP\n6T+KEeC7XC50XaeoqIjKykpycnKozNpO+5rwCjjtTFautCcyIzKFaZHJjLLGoEh1q+RolTUU/+sr\nAIYNG0ZNVQEjRwxp0e+S7LTw3OSeJDrrOYXW72P06NF89913eL3eup83ICkpiXvuuYe77rqrRXMR\nWk4E+IIgCIIgAKDEpSKndWt1fykhHVOvoadwRuHmzZvHvffea2xkHTCgT5vHtNvNYQF+dnY2JpMJ\np9NJdHQ0AIcOHcJsNhMIBFAUBZvNRlZWFgMGDMDpdCLLMmVlZWiaZpTJrKVpGrIsk5SUhKIolJWV\n4XK5UFXVCPBjY2NRFMWok79nzx6K3luBqQ0vKCpWb0atrMFqtTJkyGBuveUqxo7JRGlG1cyu8XZe\nvSyD8zrF1t9AknC73fTp04fVq1fX36YBd9xxB3v27GHJkiVUbdzFgTkvsvvyB9g5+Q/suvQ+sm/+\nC8c+XX1K3s78L2vdOxtBEARBEP4rmfqMxJ+7GwLNX5kFQJJRepyDJJ+etcMffviBTZs2sWjRIuPa\n7Dtu4o033yM/v7BVY3bu1AGzKRiWopOdnU0wGCQ9Pd0odZmbm4vJZMLj8RgPF1lZWfTs2ZN9+/YB\ncPToUWw2m5HKU0vTNEwmE0lJodNpS0tLiYqKQtM0AoEAwWCQ1NRUJEnCbDaTkpLC/n37qMpq3gbW\nhgTyiil69yuSZl7082r7GpYsfo9/TJ/C19uP8O2+EoprAkZ7swxnt3Nzfrd4Zg5JI9LSSIhoDe0z\nqE3TGTduXLPnZbFYeGnGHRz63TPstkSjewN12pT953uOvvIJ0RdlkvS7i8NSnoTmEQG+IAiCIAgG\nU9cBaEMvIPjdZ6DWDb4aovQejnnwpNM2r3nz5nHfffeFrZDHxsYwetQIFv7r3430bNjYcSP47JOF\ndVbwfT4faWlpeDweIBTgK4pCMBgkJiYGCAX4F154IS5XaM/B0aNHiYyMRD7pAefEFXxVVSkrKyMq\nKsrYYKuqKsnJoSo1JpOJ2NhYju47SKC07RuIgyWhijVjxozhpptuIrDqQ67OSOCa3nHkV3hZkX2M\nCm8Qq0mmR4KDIe3dzQqm5fY9gFCAf/PNN/OXv/yl2XM6uuA/xP1rLdGao97gHgBdp2ZrDjU79uPL\nyaP9k7ectgfH/1YiwBcEQRAEIYxl6IWgmAmu/Ry81Y03NplR+ozEMvaq07bSunbtWrZv387HH39c\n57NHH7mHzVu2s23brhaNOejsftx/72xe/cezpKWlGdf37NmD3+8nJSWFkpIS1OI8LowJcsl53fF4\nPFidUQR2bWD9+vXcdtttuN2hMpTFxcU4nc6wE2wBgsEgiqKQlJRkbOCNiooy0nNUVSU1NRVd19F1\nnYiICGJdbrRAsM1n0OpqKM1l4MCBTEmJIJD1JZIeupYcZeXqAaktHrNUN5HSfzQQqoyTm5tLfn6+\n8ZDSmOJ/f8ORJxeiVdU072aqRvG7XyHbLKQ/fFOL5/q/TAT4giAIgiDUYTlnAkrnvqg/rkDdvw29\npCC8gTMG5ayemPqOREntclrnMm/ePO6//36jnOSJ0tJSeeP157nyNzPJ2XewWeMNHNiXd95+kfLy\nUtLS0jCZjodDu3fvxmQyMSLRRrckF753HuWanklh/f2f/j+WXD+ayOJdxEWHVvBrD7E6OcCvPczK\n4XAgyzL5+fm4XC78fr8R4Ldv3x5N0/D7/aHUHYsJ1SS1OUiTbaHfS64p57bhPYzgvi0+3bSXQT9t\nYtCgQZhMJsaMGcPy5cu55pprGu2nef3kP/9B84P7ExQtXIZ70jCihvRq7bT/54gAXxAEQRCEeikx\nSShjp6EHAwR3b0CvKQdNR4qwYeoxGCnCdtrn8N1337Fnzx5mzJjRYJt+/Xpz7sg+5B7aj9MRQ0lp\nWb3tUlOTGDtmJE89+SCxsTF88cUXYfn3wWCQvLwj/HniAKY6qzBJgM9TZxwJnb6JLji2mwf6RKNV\nlVFWVobbXTfFRVVVI23H4XBw+PBhoqKi8Pl8dVbwA4EAVVVVqJJOeXQEsVWtPzRMskfgGh2qaBTc\n8BUui9JEj2aMmdaNhPbjufrqq/nxxx+JjIxk/PjxfPTxp5jMDkpKynA6HZwzqB/du4c/9BW+/SW+\nfXmtuq/uC1D83nIR4LeACPAFQRAEQWiUZDJjPo3VcRrz0EMP8cADD2CxNHxYk67rLF78BbYIH5s3\nfc2LL73O+g2b+GblN7Rr145u3boydOjZzPr9jbjdx+v05+TkhOXf5+bm8sxFQ5nRvwNyM/NjukXK\n+D5+EX9VBYmduiDLMrquG58Hg0EjwHe5XBQUFJCUlITP5zNOv42Pj0eWZUwmE8XFxXg8HnY6NTJb\n+FudyDmoB45+XdA1FXX/tjaMFCKldydiyi1cFhnFZ4uXcPfddzNx0sV89sUqvvzyez7/Yq3RNjLS\nzojMwUydegHXTr8ck8lE2Vc/tOn+lWu2ECitxBztbLqxIAJ8QRAEQRB+nVatWsX+/fuZPn16o+02\nbNhAIBDgwgsvJDk5kccevZfKykpcLheffrKcjIyMevudHOCXZ33FtL7tkZsb3f9Mz8thdv9kvrXE\n1VnBDwaDxrXY2FgKCwvp2rUrHo8Hj8eDJEnExYX6WSwW8vLyCAQCfKkdZUznrviyjzR8YxmcHc2Y\nHTKSBKpfpyI7gObTiT4/VPdeO5KNXtzIGM1kGjQeOTIKgEceeZRevYfx+htfoGk6nLRboLq6hi+X\nruTLpSt57fWFvPqXR/BuzmnT/QMFxyj9dBUJMy5o0zj/K0SALwiCIAjCr9JDDz3Egw8+WKf05MkW\nLVpEVFQUkyYdr+Lz/fffA9C9e/cG++Xk5DB27Fjjb/uRnUSYWpfKMjQ1mv1EUxEIrd7XHoqlqqrR\nJj4+nry8PFwuF0eOHDHaud1uo9pOMBjEYrGwOyebpEf/QO7819BrwlN1TE6J2L4RRKabscaGh3Jx\n56gEAi6iM0Mbh7Xy4lZ9nzqqQxV5yssruPqa3+P16U10CPnhh5+44/o7+XO1u81TUCtanr//v0rU\nHBIEQRAE4Vdn5cqVHD58mKuvvrrRdrqu8/7775Ofn8+ECROM60uWLMHtdjf6cJCdnW3k4KuFucQH\nyls93wSHlfPcOtrPBzR5PB7Ky8sxmUzGteTkZMrLy4mKiqKiogK73U5ERAQWiwVdD/WNjY0lMTGR\nI0eOEH3laJJ/fzlejm+Odfey0OFyJ7H9bXWCewCLQyEyugr/v5/Ht2QB+qmqbCSFQsYZ193B2nUb\nWtR1/4FDaDTvgaBRLXyz8r9MBPiCIAiCIPyq6LrOQw89xNy5c8Mq3NRn48aNBAIBBgwYYJw8C6HN\nuR07dmywn6qqHDx4kA4dOoT+3vY91jZGRemK36iaU1NTQ3l5OWaz2Qjw27VrR2VlZViAb7FY8Pv9\nWK1WAoEATqcTt9uNy+Xi8OHDpPz+Mn7oF0NprA13HwuJmXbMkc14y6AGULesQt22lpNTaFpDdiew\nZk0WXy3/tsV9S7QAVZradMMmKG6Rf99cIsAXBEEQBOFXZcWKFRQWFvLb3/62ybaLFi0iOTmZCy44\nnput6zq7du1i4MCBDfY7fPgwcXFxxsm0uqeJev/NYEUNC/DLysqwWCxGgN++fXs8Ho8R4NeefOvz\n+XA6nQQCASwWC2azGZfLZZySa79wCBszrTgHWVAsLdwfcGAb/Jw731pSYnvktC4seOM9vN6WV/ap\n0FW2BaraNAdLWgKxU0e2aYz/JSLAFwRBEAThF1FeVEbOT7vZ8d0W8nOOoKlqi1bvdV3ngw8+oKCg\nICzAP3jwIKqqcvbZZzfY98T0HABdDbb5+yiSXm+AX5uH36FDB/x+Py6Xi/Ly8rAA3+12G2k6qqoS\nERHB/v37AcjIyCBDKsdpb3wvQoPauHqudMygutrD11+vafUY3/hK2zSHqJH9UCJPf1nW/xZik60g\nCIIgCGeMGlRZ+/Eq1i9ey971O/FWherMS7JEu27p2NNcVB6r4Morr2xyrB9//BFVVVEUhV69jtdI\nX7duHVartckNtidW0KkOBGlr+KibIggGq5AkCY/HU2cFv0uXLqiqisPhMFJ0qqqq8Pv9xMXFsX//\nfmpqalB/ftCpXcHvfVYq5THW1k/MUwURtnpr+jfJ4cZ09nj27Mvl0OHW1bEH+I/3GFfYEuhsjmxx\nX9lhI37a+Fbf+3+RCPAFQRAEQTgj9m7cxcKHXiN32/46n+mazqGdB2EnDIvM4NNnP2Dq3b+pU3by\nRIsWLaJz585069YtrN3atWvxer1069atwb4nB/hHVDMddR25DZtSVXcCqlqOLMtGDn5ERIQR4Kek\npIS+q65TUVFBdHQ0JpMJn89HQkICkiRRUVGBJElYrVYjwLftXY85su4pvi0hOWPQ9RLwtyDIN0dg\nHjEV2e7kaGHbqvGo6LxUfZgnknsT4Qk0v6NJIWnmRUT26dx0W8EgUnQEQRAEQTjtdny3lb/f/my9\nwf3J/NV+Fr/8MQsfeq3BNrqus2jRIioqKsLScwDWrFmDoijEx8c32P/kAD+rVGf3sdbniXsDKoEu\nZ9dJ0bFarUaArygKsixz7NgxIwe/NsBPTk5G13VKSkooLS2ltLTUSNHRq+o/mbdFJBnzeZeDzdG8\n9tZIzCMvxdwnlPeemNjwb9lca/0V5I7LwJwU3XRjQIqwkDRzCsl3Nv02RwgnAnxBEARBEE6rwtyj\nvPXAPyjJP9bsPpqq8c2/vmLxK5/U+/lPP/2Epmns3r2b0aNHG9d9Ph/bt2+nR48eja7+n5yDvzcn\nh9UHipo9v5NtPHIM81m9UFXVWME/OcAHsFgsFBQUUFVVhdVqRVEUfD4fqampaJqGyWQiNTUVj8dD\nTk7ocKhTsT8ANYC5/ygiJt/MlhoFb0MhYIQduUt/LJN/h/nsccblDmel0z69XZumYLfb6XbjxXR6\n5Y9EXzgcJab+zb+S1YJzRB/aP3Ur7e67ttH/jkL9RIqOIAiCIAin1bJXP6dwf36L+2lBle8+XMn4\n6y/AZAnfYPrhhx/St29ffD4fdrvduL5p0yYSEhIazb/Xdb3OCn52djbvrNrKhIxOtI9oWc32igA8\nu2obH9jtTQb4NpuNQ4cOGSUyZVnG5/ORnp6Opmk4nU6Sk5Px+XyUlJRQVVWF2WRp0Xzq9fMYSode\n5Pe7gJff/Cf//P01aJWlEPSDyYLscKMMGIMSk1ine2SkndFjRrBgwbutnkLm8EEMGNAHAMfZPfDn\nFXP0tS/wHSxA8/iQrWZMcW7irhiNY2DD//2EpokAXxAEQRCE0ybg9bN99eZW98/PPsLqD75m1NXH\nD7GqTc/p3r17nfScdevWERMT02j+fVFRERaLBbf7+Omqe/fu5WhpOT/F9yXat58oXzMPvbI5eWPn\nIZbsyMVmsxkBfu1BV1arFV0//sDgcDg4fPgwUVFRRspObQ6+LMtEREQQGxvLsWPHMJlM7N+/n27R\nCc38tRomueKMf5977rlce+21/PODT7Bamv/wcOMNV/H++59QU9OKzbrAxRdNDPvbkhJH2oMzWjWW\n0DiRoiMIgiAIwmnz7XvLOdqK1fsT/bj0h7C/N2/eTCAQ4Icffqg3wJckqUUbbGtX9M1mMwldevGN\nqxcbyzWqglqDY2hIbC2uxjLpBt7ffACTyYSiKGiaFraCb7PZwlbwXS4X+fn5REVFIcsykiTh8/mI\njY1FlmVkWcbhcGAymYxa+OaBY8HZvLz1hph6nGP8OyYmhq5du5KVldWiMQafM4DzJ4xq1f2HDBnI\nddf9plV9hZYTAb4gCIIgCKfN0X2tL61Yq+hQYdjfixYtYsSIEcTGxhon0dZat24d5eXljQb4J+ff\n5+fnY7FYUBSFdu3acbSihkVVLmYuz6YoNYM9ZV6OelSqMCPFJqP0GMyODiN4cHMZps59KSgoMA7M\nqi3bWRvg2+32sAA/JiaGo0eP4nK5kCTJCPDj4kIr7LquG7XzLRYL+/btQ4qwobTvRWtJie1Rug8K\nuzZ69GhWrFjR4rEWvP48I0cObVGfXr268fabf8NsbmUdf6HFRIAvCIIgCMJp42/FyacnC3h9RppL\nbXqO1Wqts3p/9OhRSktLycvLCwvgT1Zf/r3L5cLv95OamkppaSnR0dFk7dqHadSVXPPFNia+9wP7\nRl6H7cbHiZhyM/t1Oy6XC1VVKS4uNvYBnLyCf3KAHx8fT0lJCVFRUXUCfE3TCAQC6LpOZWUlgFFJ\nxzRoPEX+ht8oNPjbIWHqdy6SFB7yjRkzhq+//rrF40VG2vn807e4aMr5WJpI75EkiRGZg/n04zfo\n0KF9i+8ltJ4I8AVBEARBOG1MEW3fIGq2WoxKKlu2bCEQCLBp0yYmTZoU1i4rK4uMjAySk5ONFfX6\nnBzg5+TkIMsyZrMZp9NJWVkZDoeDkpISkpKSKCkp4eDBg/Tr39/oU15ejtvt5tixYzidTiIjQwc4\nnbiCX15ejt1uD8vBT05OpqysjKioKOO6z+fD4XCg6zo+nw+fz8exY8eorq42auErCWmsNbejXG1+\nRRkViX9nl2DuVzetJjMzkx9//JHq6upmj1crMtLOvz98jcVfvMO0aZeSnBy+KTc62sVFF53Pv955\nmRXLP+Sss9JbfA+hbcQmW0EQBEEQTpvoxJg2j+FOOJ5/vmjRIiZOnMi7775LZmZmWLt169aRmppq\nBNsNyc7O5ne/+13Y3x6Ph8TEUKBaWlpKSkoKKSkpKIpCSUkJPXv2xGo9fppsWVkZLpeLo0ePEhMT\nY3x2copOZGRk2Ap+amoqlZWVREVFUV1dbQT1kiRhs9nw+XwUFxcTGRlJcXExsnx8LdaZMYy/vPUy\nt/ZOJMXSxGq+w43SdxR3Pn4Nkx8vqnMmQGRkJAMGDGDNmjVMmDChgUEad955wznvvOGUlJTy009b\nKSgoIjY2moyMHqSmJrdqTOHUECv4giAIgiCcNqOnn09Mcmybxug9sh9wPD0nNv5mBWkAACAASURB\nVDaWsWPH1snpXrduHQ6Ho9H8e6g/Rae0tJSzzjoLCAX4fr+ftLQ0/H4/gUCAoUPD885rV/ALCgqI\niYmpNwe/oqKiToCfnp5OTU0NLpcLTdPQdR2/3w+A0+nE5/NRUFBAp06djMOualf6MzIyeHfVRtam\nj+CN3AByhwywHH/oCKganqhETMOnYL3+EeyZUxg/fjyfffZZvb9Da/PwTxYTE82YMSOZNu1Szj9/\ntAjufwVEgC8IgiAIwmljj4qkZ2afVvePbRfPmBmhVJytW7fi8/nYuXNnnfx7VVXZsGEDPp+v0QC/\nsrKS6upqkpKSjGs7d+5EURRjw25ZWRk1NTWkp6dTWlqKyWRiyJAhYePUruAXFBTgcrmMAF/TNBRF\nMYL7iIiIsBSdlJQUdF3HarUSDAbRNA2fL7RPISYmBk3TjD0EUVFR2O12CgoKAEhLS8Pj8XBWx048\nv3w91ivuwnbTE7xWFcfbNXH8YUsVK+MGYsmcivzzibWXXHIJH3/8cb2/RWvz8IVfPxHgC4IgCIJw\nWo26egJR8e6mG9ZjwITB2Byh4PnDDz9k6tSpLF++nIkTw2uq79ixg+TkZHJzc5sskdmxY0cjp1/X\ndfbt20dMTAzt2oVOai0tLaWystII8FVVZfDgwWHjnLiCXxuIQ+hBw2QyUVFRgcvlQlGUsAA/Ojoa\nkymUIR0IBMIC/Li4OBRFMe7tcDhISEgwNtpKkkRGRgY+n4/Dhw9TUVGB5HDRZfgY3v12Pc7Us9j3\nc9takyZNYtWqVcam3RMNHjyYPXv2UFJS0sR/BeH/GhHgC4IgCIJwWnXo25nL7pmGLcredOMT9B8/\niCvvuwY4np7TpUsXunTpYuTL11q3bh1Dhgxh9+7dLSqRWVxcjK7rOBwOUlNTgVCAX1JSQlpaGjk5\nOei6Xqcqz4kr+JGRkWEr+CaTicrKStxuNyaTKSxFp/ZwLVVV8fv9YQF+7VuFmJgYI9ivrYVfKyMj\ng+3bt5ORkcHmzaEDxIYNG8aGDRtIT08PawuhuvuZmZksXry4zm9hsVgYNmwY3377bYO/l/B/kwjw\nBUEQBEE47TIvG8U1j9xEdFLTm24Vs4mhU0dyy0t3IysKANu3b6e6upqcnJw66TkQCvD79OlDVVWV\nEajXp778e6fTiSRJYSv4RUVFpKenk5WVRXR0tLHiX+vEFfwTA/zaFfyqqiojwD95Bb+2HKbP5yMY\nDBoBfkpKCpqm4XK5cDgcBAIBoxZ+rYyMDLZu3cqAAQP48ccfAYiKiqJr166oqlonwAeYOnVqg2k6\npyoPX/h1EQG+IAiCIAhnxJCLRjD386eYdMtU0nudVefzqHg3Qy4ewewF93LjX3+PyXy82N+iRYu4\n7LLLWLx4cYMBfnx8PF27dq0TjJ+ovgAfwOv10q5dO/x+P36/nyNHjpCens7mzZvD8vVrnVhFx2q1\n1lnBr6mpqTfAdzgcqKqKx+PB6/WiqqoR4Kenp6NpGg6HA7PZTGVlJbquGyk6cDzAHzhwoBHgQ6js\nZX5+fr0B/pQpU/jyyy/x/n/27ju6rfJ84Pj3Xg1Lsi3Ltrwznb33wEmYZZSwkgIto1BWCy2ru7/S\nwfp1sAsU2gKFMn8FymzZAUI2IXvZSZzleNvxkGVt3d8frhUrkmxZnoHncw7nkHvv++qVc3L86NHz\nPq/bHXFP6vC/nKRNphBCCCH6TVqWjQt/fhlLf/ItNi/bQH15LQFfAFOKmZlnzsWamRZ13CuvvMKd\nd97JCy+8wKxZs8LuNTY2cvDgQQKBQFwddC666KLQn/fu3YvT6cTlclFQUBAK3A8dOsSwYcMoLi5m\nzJgxEfN0zODPnj077KArg8GAy+UiLS0tIsBXVZWkpCQaGxtxuVxhGfzs7GxUVUWv1+PxeHC73Tgc\njrCgffLkyezYsYPp06fz4IMPhq4vWrSIp59+mrKyMvx+f6jOHyAnJ4epU6eybNmyiA9H06dPp6qq\nioqKCvLz8zv92Ynjh2TwhRBCCNHvVJ2OmWfM5fSrFnPWd8/j5EtPjxnc79ixA4fDQWVlJV//+tfD\nesMDrF+/npkzZ1JaWtplgL93796wDP6uXbtCh0tlZmbS0NBAWloaiqKQkpLCwYMHGTt2bMQ8HWvw\nDQZDRImO2+2OWoMPhA7RcrlcoVIdaNtk2/7eqqurGTJkCLW1tWEBvs1mIyMjA4vFwr59+2htbQXa\nAvy1a9eSk5PD4cOHI9a7ZMkSXnvttYjrOp2Ok08+mU8++aTTn5s4vkiAL4QQQohBravynHXr1sW1\nwba9x/ywYUdPVt25cycFBQUMGTIERVFobGzEYrGEsvdmszkisx0IBHA6nZhMJpqbm1FVNaxEx2Aw\n4PF4sNlsGAyGsAw+tG18PXLkCE6nM2qA7/P5qKqqYty4cVRXV1NTUxN6BtrKdEpKSpgwYQJbt24F\n2jboZmZmkpOTQ2lpacR7X7JkCW+//TZ+vz/intThf/lIgC+EEEKIQe2VV17hnHPOYeXKlZxxxhkR\n99euXcu8efO6DPAPHDjA0KFDw8pXDhw4wJAhQ8I66BgMhtAG24yMDNLT08PmaW5uJiUlhbq6Oux2\nOy6XKyLA93q9UWvwoW2jbXsrzo4BfmZmZuhk28rKSsaMGYPRaCQ/P5+DBw+GxkfbaAttdfgGgyFq\nHf6IESMYMmQIq1atirh32mmnsWzZsoh1iuOXBPhCCCGEGLR27txJU1MTLS0tzJ49m7S08DIeTdNY\nu3Ytc+fOZe/evVHLadod2yLzyJEj+P1+srOzwzroqKrK0KFDWbduHSkpKaHWlu061t/n5ubicrki\navADgQCpqakRp+1CW619Q0MDra2teDye0Em2drs9tAG3/TRbi8US1gsfYm+0XbRoES0tLVEDfGg7\n9Cpamc748ePx+Xwxx4njjwT4QgghhBi0Xn31VS688ELee++9qOU5paWlmM1mAoEAGRkZpKSkxJzr\n2A46paWlmEwmTCZTWAY/GAyGMvh6vT4ig9/U1BTqoNMe4B/bRUen05GcnIxOp4uowc/JyaGpqQmz\n2RyWwU9JSUHTNFpbW6msrKSwsBCdTofVag0LvqdOnRrK4G/YsCF0fdGiRZSXl8cM1NvbZR6bqVcU\nhVNPPVW66XyJSIAvhBBCiEHD6Wzlnnv/zNmLL2X+CWfzx3ueYPmKrbzw4luMHTsx4vl4D7iC6C0y\ng8EgwWAwlMFvbGzE6/WSk5PDnj178Pv9EQF+xw220QJ8nU6HoiiYTKaoGfyMjAzMZjMpKSno9Xpc\nLhfQFmgnJyfjdDqprq6msLAQj8cT0Qt/3LhxHDhwgNGjR1NSUhL6gDB69Gg0TaO4uDjq+584cSIm\nkynsQ0G79jId8eUgAb4QQgghBpzL5eLGm/6H6TNO5Ze3/Y4PPlzOFxu24PEG2bathFYXXHnVDznv\ngitYu/aL0LiOG2w7K8+ByAC/pKSE1tZWnE5nWIlO+7UpU6bQ2NgYNYPfsUSntbU1aoCflJQUtQbf\nZrNhsViwWCwYjcZQgA9th1a53W4sFgtWq5WWlhYCgUBYiY7RaGT06NGhIH/79u1A2weEBQsWxMzg\nK4rC0qVLox561Z7Blzr8LwcJ8IUQQggxoI4caeDc867gL399lv0HymI+53C08M47y7jk0ht4++0P\ngPg32EJkDf7mzZvJzMykoqIirESnubmZyspK5s6dS0NDQ1wZ/I41+Lr/nr5rMBiidtFJT08nKSkJ\no9GIwWAIO4AqMzOTYDBIdnY2TU1NWK1WGhoaIoL2WBttTzvtNDweD42NjVF/BrHaZQ4fPhyr1Rr6\nsCCObxLgCyGEEGLAeL1eLrv8+3y6fHXcY8oOV3DzLbfx8Scr2LFjBzNnzuwywA8EAhw4cIDCwsLQ\nteLiYkaPHk15eXlYBr+pqYmSkhJmz55Na2srqampYXN1zODn5ORELdFp32wbLcBv766j1+sxGo1h\nLTDbD7uy2+1UVlYyfPhwqqur4w7wTzzxRFRVjZnFnzNnDg6HI2oZj7TL/PKQAF8IIYQQA+b+B/7C\nhx991u1xZYcr+MX/3M3EiROxWCxdBvjl5eVkZmaGAnGAw4cPM2XKFOrr68nNzQXaDpiyWq188cUX\njB8/nrS0tIiDtbqqwVdVlUAgEDqV9lg2mw1FUVAUJXRqbbu8vDw0TcNqtYZ64dfU1KBpGg0NDaHn\nYnXSmTp1KoFAgM2bN0f9OaiqygUXXBC1TOe0006TjbZfEhLgCyGEEGJAaJrGO+98lPD4rVt3MXzE\nGJxOJ3V1dQwfPjzms6WlpWHlOc3NzXg8HgoLC8nOzg6V1dTW1pKVlYXL5SI9PT2iPAeOZvDbu+gc\nW4MPbYG03++PWaLTvrlXp9OFBfgFBQUEg0GSk5OprKxk4sSJ+Hw+RowYEZaVbw/wp02bxvbt2/H5\nfADo9XoKCgpYvnx5zJ9FrHaZp5xyCp999lnUw7DE8UUCfCGEEEIMiHfe+Yj1X2xJeLzfH6D+iJPd\nu3czatSoUJAezd69eyNaZBoMBmw2W6g8B9pKdCwWC3PnzqWxsTGiBz50XYPfHti3trZG7aJjs9nw\n+Xz4/X50Ol2oDz60ZfAVRUGn04V64ZvNZrKyssIC/GHDhuF0OvF4PAwbNoxdu3aF7k2ZMiVmBh/a\nynj279/PoUOHwq5nZ2czbNiwqF12xPFFAnwhhBBCDIgPP+p5trimpiGhFpl79uzB6/ViMBjCAnyH\nwwHAvHnzom6whbYMvslkwufzYbVaI0p0/H4/RqOR1tbWmF10XC4XbrcbVVXDAny73Y5Op8Pv91NZ\nWcmoUaNQFAWr1RrWSUdRFCZPnhy1Dn/RokUcOHAg5s9Cr9dzzjnn8MYbb0Tckzr8LwcJ8IUQQ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J61nbKQCth62rhBBCiIGi0+k48/STEx5vMiVx9VWXAOEbbCF6gG82JSX8Wh3n0Ov1UQP8YDCI\nw+EgLc3GLbf+mrvufpAWZ88+VHh9fhQ1OSLAz83NRVEULBZLKIM/e/ZsHA4HhYWFEZ10MjIysFqt\nHDx4MKJV5r59+5g/fz6bN2/GHeVDkH7SfHRTF3V77UpmPsavXd7tcaLvSYDfl3qlfl5q8IUQQhy/\nbrnlOkaMGJrQ2BNPPIGFC+ehaRpr1qzpMsAfMaLnmeSCIfkAUQN8h8OB2WxGp7fy1N9fDLXv7ClF\nTcLhCC/JzcvLQ9M0VFUNZfCnTZtGMBgkPz+/04227SfawtEMfkpKChMnTmT9+vVR12A8/dvopp3U\n1hEnnjVnDcV4zndR07O7flj0Ownw+1Kv7CiXvyIhhBDHr5ycLB568C6ysyM3rHZm+vTJ/P3JBwEo\nKysjGAwyYsQIoO1U2AMHDjB69OiwMdddezmjR43o0XrPPLOtnjxagN92MJWFZoePYC/2fVcUPTt2\nloZdy87ORlVVnE5nKINfUFAAtHXH6SzAHzlyJA6Hg9raWgoLCyktbZu7swOvFFXFeOaVGE6/HIaM\nw+WNXm6kpOegn3EqSRf9EF1uz/dYiL4h0WMfUtT4Ntp0OodOOpkKIYQ4vp2z+HT++pf7GT58SFzP\nF50wm3+9+hS5uW3Z4XXr1jF//vxQx5z9+/eTl5cX1n8ewGIxc9pp3S81aTdi+FBu+sHVQPQAv6mp\nCX/QiM/X++WzTc3hGXy73Y5Op6Ompgav14vT6URVVcxmC/X1R9i7tzRmJx1FUZgxYwYbN27Ebrfj\n8/loaGjotA4f2rr3GGacguWyX3BfqYc9pjzU8XNRx85CN6kIw2mXYLr6LoxnfBs1Vdp4D2YS4Pch\nxZwW91ddsahJ1l5ajRBCCDFwzj3ndFYsf5Of/OgGpkyZEHE/KcnIKScv4P777+CD9//J8GFHPwzE\ns8G23U03XsOwoQUJrfEb31hMSkoymqZF7aJTX3+EYw6k7TXH1sZnZmai0+koLy8nNzeXp595iQsv\nuhZfII3nXniPtev2Mn7iQq665lY+W7EGOBrgA6E6fEVRQjX7CxcuZM2aNXGVFk054wJ+9dE2TOff\ngGnJjSSdcx2G2Weg6HuevBR9T9LDfUjV6QkazAl3wlEMJtQe9qYVQgghDc0BtQAAIABJREFUBov8\n/Fz+8Idfcffdv+CVV96mdN8BPB4vlmQLC4pmc+KiE6KOW7t2LXfddVfoz50F+OPHj+HBB+7kmmt/\nSGNTc9xru+jC8/j9724DoKWlBZPJhMEQHszu3buPBBrlxMXv84X92W63o6oqGzfu4Eijwo9+dDvB\n/2bsg8G2RZSWHqC09AAvv/wmRSfM4Q+/v419+/bh8XiYOXMmr7/+OnC0Dn/mzJnk5eWxbds2pk+f\n3ul6Fi9ezPXXX4/D4SA1NbUP3rHoSxLg9zHVnEbA506go46KakrrkzUJIYQQA0mv13PJJUvietbr\n9bJ582bmzJkTutbxwKtozj//LD759BP+8exrOBydn6ZrsVi45FsX8Nif/4Cqtn3rHqtFZllZeVxr\n7i5NC6Iq4V8N2O12/EEDn39RjN/feb2/x+Plk09Xcenl3yc3dwjFxcXMmjWLX//618DRAB+O1uF3\nFeCnpaVRVFTEu+++y8UXX9yDdycGgpTo9DHVYEKXkglKd/rLKqjJ6ahJyX22LiGEEOJ4sGXLFkaN\nGhWWRd69e3fMDH47s0nlph9cyv/e/T8sKJoTkY1PTbVwzdWXsOzDV/jrX+5Fpzv6ezpWgO/z9VF9\njubF7/eEXdpVXEogYOoyuO+otPQADU0aHy37hDFjxlBbW0tDQ0OoVSbQZR1+R0uXLg19CyCOLxLg\n9wM1KQVdahbo4jiyW9WjS7GjM0v2XgghhDi2/z10XqLTrrS0lAkTxvPzn93Ip5+8zvJPXuepJx7g\n0Yd/x5lnzOMXP7uOv/7lPubMicxkxwrwFVWjL9pXq6oXjyc8wH/22VeB7h8+1dLi4rnnX0NVVaZN\nm8amTZvCMvjtAf6xG3SjOf/883n33Xcj1iYGPwnw+4lqtKC3FaCmZKEYLGGbb52tLsoqa1GTM9Gn\nD0U1Sa2bEEKIry7HkWaq9lVQc6iadavCN9g2NTXR0tISahkZS2lpaaiNpqIozJ07gyuv/CbXX38l\nRoPG6NGjYo6NmcH3elAVX5QRiTMlgdGg4etQg79+/WbWrtuY8Jx79xyipcUZ2mjbsVXmiBEj0Ol0\nUdtsHisnJ4cpU6awbNmyhNciBobU4PcjRVHQmVLBlIoWDIbq8t95/5+8/e//8NJLLw3wCoUQQoiB\n4XF5+PjZ99jy8ReU7TyAq8WNqlPB76aK/azLWsmcc4ooKSlh7NixoZaZ0Wiaxt69exk1KnoQf/jw\n4U4/IMQK8Ovr6wn4naj6pLgy4F05cdE8vli/DIMpiaamo110/vbk87S2uhKe1+3x8cif/87MmTN5\n//33uemmmzh8+DB+vx+9Xh+qw4/18+loyZIlvP7665x99tkJr0f0P8ngDxBFVVF0BhSdgdlz5rJq\n1aqBXpIQQggxID598UN+c+aPeOX3z7F73S5cDhdoGkF/ACMGStcW89db/sTd5/8Pn3+4usvynPr6\nelRVJSMjI+r98vJyhgyJ3ZM/VoBfU1OD1Wpi7tzYG3zjkZ+fwzVXX8J//v0Cra1OLBZL2MFZJcV7\nejQ/wOZN20IZ/KSkJHJycigrKwO6V4e/ZMkS3nzzzV47tVf0DwnwB4ExY8bgcrlC//CEEEKIr4p3\nHn+d/7v7GWoPVXf+oKZxYFspm59fy9CU/E4fLS0tjZmd9vl81NXVkZubG3N8tB74ALW1tdjtmTz2\n6B8YVdi9U1xVRWHMmEJu++WtbNrwEX/9y32YzWasVisWiwVN00LfCjh7kL1vV1VVxYQJEygrK8Ph\ncETU4a9cuTKueUaOHElBQYEkIo8zEuAPAoqiUFRUxOrVqwd6KUIIIUS/WfP6Z/z70dfwtsa/iTPo\n8tOyoY6KPbGTYh3r749VWVlJVlYWen3sKuVYGfyGhgays7OZNm0Sz/7jEUaNii/Iz8/L4ZlnHmbX\njhXccftPycw8+s2CzWYLncjb0NAAgNHQ88OknE4ner2eKVOmsHnz5rAAf9KkSdTU1FBd3cWHqv9a\nsmQJr732Wo/XJPqPBPiDxIIFCyTAF0II8ZWhaRofP/cebmf3s9XuI07ef/LtmPc7q7/vqjwHYgf4\nzc3Nocz/vHmzeORPd5JltzB1auTJvAA5OXby82z8+98vcOklS6M+Y7PZSEpKAtqy7gB2e/TSou5w\nu9v6/7eX6XRslanT6SgqKoo7i9/eLrM39h2I/iEB/iBRVFQkX38JIYT4yti6bAP7t5YmPH7nyq24\nWqJ/OOisROfw4cMJB/gtLS3k5x8tD7JYTBSOzObzte/x1JMP8uMfXs/111/Brbdcx733/pann7qP\nYUPTmTol+gcAgPT0dIzGtjba7QH+6aef1On6umI0GFCUtm9FOnbS6dg5pzt1+JMmTcJoNLJxY+Kd\nfUT/kgB/kJg9eza7du3C6XQO9FKEEEKIPrfmzRUE/Ylv3Kwvr2PZM+9EvddZiU5XHXQgdoDvdrsZ\nPvxoWU5raytmsxm9Xs+VV1zMH//4ax59+Pfcd+/t/PCW75KZmYHL1fk3FDabLRTgV1ZWAvDd6y5n\nzJjCTsd1Zu68GRw6WIqmaVFbZUL36vAVRZFDr44zEuAPEiaTialTp/L5558P9FKEEEKIPld9oLLn\nc+yviHq9L0p0PB4PwWCQoUOHhq65XC4sFkvMeSwWC62trZ2+ls1mC+0HqKhoez8mk4lzzzm903Gx\nGAwGLr/sQpKTkzl06BCTJ0+mtLSU3NzcsAz+7NmzKS4uxuFwxDVve7tMcXyQAH8QkTp8IYQQXxVe\nV89PR/VEmaOlpYXm5mby8vKijkm0RKepqQmdThc2r8vlCm2QjcZsNscV4Ot0bSfWlpeXh67/7n9/\nyZzZUzsdG82111zKtddcxtSpU9m2bRtGo5EJEyZQUVGBz+cLbeRNSkpi5syZrFmzJq55586dS2Nj\nIyUlJd1ek+h/EuAPIlKHL4QQ4qtCb+x5pxh9UuQc+/btY+TIkahq9BCnvLy80xIdTdPC2mQePHSY\nh/70N37/h4fRFAtr1m7G6/UCXQf48WTwO36QaC/RAdDr9dx376/jPjk3KSmJW26+lj89dDcAU6ZM\nYevWrUBbHf6mTZsoLCxk//79oTHdqcNXVZULLrhAsvjHCQnwB5GioiLWrFkTdtiFEEII8WWUlhXZ\nZ767rBlpEdc6q7+HrjP4LpcLRVF5771P+Oa3vsfceWfxk5/ewSOPPg2kcNuv/siMWV/j1lt/xcGD\nh3sU4GuaRnVNAyUl5SiqlVWrt3LX3Q9QUdG22XbIkAJ0agtZ9iSmThmHThctbAuydMnZvPjCY9x/\n3x2hDzZTpkxh27ZtQOcbbeOtwwdpl3k8id0EVvS73NxcMjIyKC4uZuLEiQO9HCGEEKLPTDl5BtuX\nb054vNmazEmXnhFxvbP6+2AwSEVFRVgnnGNVVVWj06fzzUu+F/P01pKSUkpKSklONjNvzqTYazSb\ncblcaJqGoiih6x6Ph0f+/Hfe+c9HrFq9nkAggKpLoaq6mTvuvJ/HHn+GU05ZwMUXnYfP50XBzYMP\n/JZbb/0J5553ES0tTnSqijUtlUcfuZ/77/tN2N4AaAvw77nnHqAtwH/iiSc49dRTwwL8E044gfXr\n1+P1ekMbfTtz0kknUVpaSllZWcTricFFMviDjJTpCCGE+Co4+dIzyC3s/ETazoybN5G8UZHjO2uR\nWVdXR2pqasysu9/v59rv/hiPV4kZ3HfkdLpYsWozf37s6aj3dTodRqMRj+foXoHa2nrOPe8KfvGL\nu/lsxdqor1NbW8/LL7/FFVfeBCTjcLSQl5dHS0sjd97xMx64/w7uvfe3/PpXP2LMmJFhZTftJkyY\nQGlpKV6vl6lTp1JcXMywYcPCAvy0tDTGjBnDhg0bunyv0LaB95xzzuGNN96I63kxcCTAH2Rko60Q\nQoivAkOSgZlnzktobFKyiYUXnhz1XlctMjsrz/npz+7ks8/WdWstPp+fO+68n88+i75ZteNGW6ez\nlQsvuoaPP4mvLKa11QVqMi6PSm5ubliNfrtjy27amUwmRowYQXFxMWazmdGjR6MoSlirTOheHT4g\n7TKPExLgDzKSwRdCCPFVsfSnlzD76/O7NUZn0HP29RfE/HDQ1SFXsTbYOhwtvPXW+91aS7sjRxp4\n8qkXo97rWId/482/ZNXq9d2cXUHTzHy0bCWaptHS0hJ2N1aAD5F1+E1NTRHPdrcOf8aMmaxeu5UT\nihYzeerJjJ+4kFlzzuA7V93C8uWSoBwsJMAfZCZNmkR1dTW1tbUDvRQhhBCiT6mqyncfvpUTlp6I\nGnUD6TEMKkt+/C3OvenCqLe9Xi/l5eVhh1F11FkP/EcefYqDhw7HvfZjLft4BTU1dRHX2wP8mpo6\nPvjg04TmVhSVZ597OWoWf+TI6CU6EBngHzp0iMOHD+P3+0PPLFy4kFWrVnXZ4KO52cF3rrqFExac\ni9+fxPovNlNcvIe9e/ezZcsOnn/hVb6++DJO+9qFCb9P0XskwB9kdDod8+fPlzIdIYQQXwl6g55r\n77+JH/z1p8xZXERqRmrEM1lDs/HnqMy6ZiFnX39BzLkOHjxIQUEBBkP0Fpydleh89NHyxN7Af1VX\n1/LY489EXLdYLLhcLv708BNUVyeevPvsszXYbHaqqqrCrncng79lyxZycnIoKysLPZOXl0d6ejo7\nd+6M+dqVldWcvfgynn/h1U7fg9frZflna7jqmlt47vlXu/P2RC+TLjqDUHsd/vnnnz/QSxFCCCH6\nnKIozPjaHGZ8bQ71FXWs/89qPE43qk4lJcNK0ZITKVq0gO+f/KNO54mnReZJJ50U9V5lVc+/Oa+u\niZyjPYO/fHl8B0rF0tzcgtOVGpHB7yzAbz/sCmD69Ols376d2bNnh84KaNdehz958uSIOVwuF5dd\n/n3WrotvIy5AdXUdP//FnWTZMzjrrFPjHid6j2TwByGpwxdCCPFVlZlv56zrzuP8Wy/m3Jsu5JTL\nzsBoTmL37t2MGzeu07GdtciEzkt0vB5vj9YN4HZHnqzbHuDXH2no8fwGfVJEBj8vL4/Gxsao/fZH\njBhBQ0MDjY2NpKSkMGzYMDIzM7tVh3/fA3/hsxVru73Wmpp6fv/HR9A0rdtjRc9JgD8IzZs3j02b\nNoW11RJCCCG+qioqKjCbzWGnvkbT2QZb6LxEx2yJfWBVvJKTI+do76Lj88V3Im1nkpKSIjL4qqoy\nfPhwDhw4EPG8qqpMmjQprExHUZSIAH/hwoVRO+lomsa77yxLeL2ff76JDz/sWemTSIwE+INQamoq\nY8eOZePGjQO9FCGEEGLAxZO9h84DfE3TOu2iU1gYfWNud4wfNzbiWnsG39ILHyDSbGkRGXzo3kZb\np9MZ0SpzzJgxeDweDh48GHb9zTffY8PGrQmv1+fz8exzryQ8XiROAvxBSvrhCyGEEG1KSkriDvBj\n1eA3NzejKApWqzXq/aVLzu7RGseMKeS6ay+NuN4e4E+cEBn8x2Oi3sLlllyus+Rzic5O9q5qNH/4\n4Vjd2WhbWVkZ8ayiKFHLdJZ/tiauA786s2Vr7M27ou9IgD9IFRUVSYAvhBDiKynYXI/34//D/a+H\ncf/ffcyp3sD3xqQSOLgr9phgkH379lFYWBj1fnt5jqIoUe9/+/ILmTp1YsJrPuP0k0hKSoq43t5F\n59JLl6LT6eKaS4/CUnMWD6eN5c+28dyYMpRrUgqYvr2Wiw4b2XHmDzl059N4K9racsYb4M+YMYPS\n0tKoz0Yr02lpcca13s44W5xShz8AJMAfpIqKithdsouAx0nA7SDoaSUY8Hc9UAghhDhOBcr34n7j\nMdzP3I5//fsE924ieHAHE0x+JtGI55UHcL/4B3xbIuu6KyoqsNlsJCcnR527s/IcaGtTfe01lyW0\n7nHjRvHDH34v6r32DP6555zB7FlTu5wrU9HzsG0sP0sdwdykNMxq5IcCd8khav72Jrsu+AUN76zp\nskRn+/btaJqGzWYjJycHj8dDQ0P4pt9oJ9rG+4GkM3q9LuaHKtF3JMAfZDRNI+huoSDDxLLXXyDo\nqCbYUkvAUUWg8TD+5mqC3lb5NCyEEOJLxV/8OZ43HyNYsh5cLdEfCvgJlpXg+/B5PB+9GPa7sKsW\nmZ110Gn3/Ru+g6q4SEoyxr3uoUPzefzPf2TE8KFR77dvslUUhVtv/R42W/QSIQCrouPetDHMNMZ+\npiNfRR0Hf/4YIyrdMTP4drsds9kc6n0/a9Ys7HZ7xAeCadOmcfjwYerr60PX0tPT4lpHZ2y9MIfo\nPgnwB5FgwE+guZJASw14WyP/YWlBNK+TQHMVgeYqgj2sixNCCCEGA/++bXg/fAEccbaSDPgJbPgI\n3/Kjhyn1pINOO4/HA1oLd9z+M4YOze/0WVVVUfDz7D8e4cQTT4j5XHsGH+CiC8/l9t/+lLS06AH8\nbakjmWhM6fR1j+VvcKA8/REt+8tjJv+OrcM3Go0RHwj0ej3z588Pa9N9+WXfwGrt3nqOtbBobo/G\ni8RIgD9IBAM+As1VaD53XM9rPhcBhwT5Qgghjm9aMNgWqLc2d3ck/o0fESgrAeLrgd9ZiQ5AQ0MD\n6enp/OTHN7Bl08fccftPmT9vZqgDjqqqZGVlcsEFX+eF5x4j4K+l6IQ5nc7ZMcAHuPEHV/PYo79n\nxozJYaUr4/QW5hgjT/GNR6DqCBdZRlJXVxf1/rEBvtsdPeN/bB3+pEnjWbRofkJrAkhPt3HjjVcn\nPF4kTgL8QUDTNAKOWgh085ANv4dgS89P3hNCCCEGin/HarSaQ4kN9nnxb20LSHsjg98e4ANYranc\n9stbWfHZW5TsWsXmjR+xc/tnFO9cyasvP8m5556OwWDosk69fZNtR9/85gWsW/Mu/3j6Yc495wyy\n7BksMWdhUfXxvOuoisw57Nu9J+q9jifazpw5k7q6Ovbu3RvxXLQ6/G9efD5GY/wlSx2dcvICCgtH\nJDRW9IwE+INA0NMC/vgy98fSfK0Eva6uHxRCCCEGoUDJFz0a79j6Oc///CFcWxpo2XUET2v036dd\nbbKF8AC/naIo5OXlMHnyBEaPHhkqr3G5XJjNXfe2PzaD305VVS69dCmvv/Y0/3jqXuaae1arXuA3\nUPvap1Hvdczg2+12UlNT2bkzsn3lvHnz2LZtG07n0e45l16ylGuvjmz/2ZXp0yfzyMP/2+1xonck\n/lFR9BrN07M2VEG3A9XY8wM0hBBCiP6keVwEK0q7frATFtVHYOc67G4ra5/7lNJPdzJx4TROu+ps\nhow9uvE1nk220QL8WOIN8Ns32XbGpkvCremgh81mWvaVR70+YcIE9uzZg9frZfv2YkzmbDZsLOX0\nMy/GYjYzZEge1117OdOnT2batGl8/vnnnHLKKaHxDz10FxoaTz71Ylwn8s6ZPZ3nnn2EnJysnr0h\nkTAJ8AdY0O9F8/UsA6/5WtGCAZQorbSEEEKIwUpzO8HT82+hzYaj/19bVsPylz5k2/JNXPKbq5h1\n1jxcLhcOhwO73d7pPI2Njdhstrhes7W1tUcZ/I7SzMkEeqGooqn+SNTrZrOZzMx8vnbGRWzZshOn\nsxVQ+eSToxtqn3/hXyxaOI/c3OGsWLEiLMBXVZVHHv4dc+ZM5+VX3mbFirX/nSPc1KkTOOP0k/n5\nz24kPT2+n6PoGxLgDzDN7wZ62PJSC6IFvCiqZPGFEEIcRzSt7b8eitZn/UhFHc/e9jcMJgOWIVby\n8/NR1c6D6O5m8C0WS5fPxRXg52dTrvkxKInVureraYreheje+x6j7oiPqtWxy6Gczlbee/8TzGYz\n23cW85vf/CbimSu+fTFXfPtiNm/eznPPv0pjYzM+v49ki4X582dx2aVL0esltBwM5G9hoGnB3pkn\nKN10hBBCHF8UczIkmcHds1JVty/6hwRHfRMv3f40p/3y/C7Lc6BvSnSibbI9VsbQfMr8TiYluJkV\nAKOeNY2HIy4//pd/cNfdD+D3xxcnuFwu9u1r5bHHn+b7N1wV9Znp0yczffrkxNcq+pxssh1oSi/9\nFUh5jhBCiOOMkmRBzRvRozmanEHW74590nv1gUrW/WvFgAb4XWXwjUYj63z1nT7TleRZ41lWtSes\nRr6mpo7f/f4hWlu7WwalcOed91Nb27M1iYEjAf4AU/RJ9HhXjaKi6Hr2tZ4QQggxEHRjZvVo/O7y\nAC3uzst8yrccJD+/84OroHsBfrw1+PFssgX4j1pLkz7xb/UzzppHXl5e6MRagIce/huVlTUJzVdX\n38CfHnki4fWIgSUB/gBT9UkoBlOP5lAMZtlgK4QQ4rikn7oIJbPr4Dsan19jXUnXXV3c1S1kGrre\n9DlQNfgASqqZzy1uiLKfoCspc8aTddmZFBYWhg6wCgQCfPDB8m7P1dEHH3xKMNhLpcSiX0mAPwgo\nxuQejVdNiZ18J4QQQgw0RafHsPACMHUdLHcU1DTWFPsoPhxHbbkGkxvL8a5+C62Trj0DVaIDYLVa\n+aehhsyLTiHYjeYb5smFjHz0R6gmY1iAv3r1ejZv3h73PNFs2rSdtes29GgOMTAkwB8EVFMq6JMS\nGqsYzCgG6Z4jhBDi+KUfPwfDyReDOSWu5wNBjXXFPl5Z4Yn7NUytDvwrXsf11K/wvPt3gu7IoLux\nsbFP+uC7XC60LroFWa1WWl2tjLj/JlbaAwSsnc+tGfRYT53F2Od+TVJBNgAjR45k//79AOzbdzCu\n99Hpa2ga+/cleMqwGFAS4A8CiqKgS8kC1dD1wx3pklBTsqK2BxNCCCGOJ4ZpJ2E857uohVPAED3p\nFQxqHKwJ8NZaDy986ulWk+m05P/+rnQcIbB1BZ5XHiDYHN43vqGhodf74Ov1evR6PV6vt9PnbDYb\nra2tKIrC9pnZ7Lp+EXk3X4Rl6ijUVAt+LYhiSsI4NJut2SqHrz+Zsc/+GkPW0Q8kHTP4PW8+2ibY\nC21MRf+TNpmDhKo3gjWHQEst+LvOSCh6E2pqNqpO/gqFEEJ8OegLp6AvnEKg6iCBzZ8SbKwh6HFz\nYMcB6hs8bC71sXlfoNvBa6ZVYWxB+O9LraIUz1uPY7r4xyjGtr1wfVGDD0fLdJKSYn9bn5GRgdvt\nBiA3N5dyRwMFt32f/J9cQqCphROmzODll99g+OTxPPXTnzDCGLn3oGOAP3x4112D4jF8WEGvzCP6\nl2TwBxFVb0Sflt+WlTdY8EX0rFVRjBZ0Kdno0vIkuBdCCPGlpMsdjvGsKzF966dYrvw1G4zzefpD\nN5sSCO4Bxg/RkWSI/LZbK9+Lb+UbAPh8PlwuF6mp8e1ri7dEB+LrpJORkRHK8ufm5lJZWQmAoqro\n06347Kk04EM1GsjLy6Oqqipijo4lOosWzmPKlAlxrS8Wg0FhwYK5PZpDDAwJ8AcZRVHQmVLRp+Xy\n8juf8ty/3kFNzkRNyUaXXoDemotqSpGyHCGEEF8Zp15xJqaUxPabJelh0aTYraQD+7ejBfw0NjZi\ns9ni/v3anQA/no22drs9LMA/NoC32+3U19eH7rd/AOgoKysLt9tNU1MTer2eM04/Ka71xRLw+zmh\n6GxmzPoaJyxYzNILr+bZZ18mEJDDNQc7CfAHsXXrN+L0aujMaehMKai6btboCyGEEF8CBWOHseAb\niQWrc8YZGGKP3UpaqyvHv21Vt8pzIP4afIjvNNusrKzQIVXRMvR2u526urqY96EtSVhYWBjK4t9y\n83VkZWXGtcZogpqOjZu2s23bLtav38xbb73P1df+kLnzv8499/65y43DYuBIgD+IlZSUMG7cuIFe\nhhBCCDHgLvnt1RQtPalbfeJnj9Fz8aKuu9QFy0q6HeAnUoPfmaysLILBIMFgMGYGvz3Aj5XBh/Ay\nnfz8XH7ykxs6rf1PxJYtO/j1b/7Id6//qfTJH6SkiHsQKykpYfz48QO9DCGEEGLAqarKNfffSEaB\nnXVvraT2YHXMZzOtCrNGGTh3njGukhvN6+5Wi0zo/RKd9PR0FEXB5XKFAnhN00Lrz8zMDJXoxMrg\nQ/hGW4Af//AGHA4nDz74V5zOrvvxxysQCPD00y+RbDHz0IN39dq8ondIBn+Qam5uprGxkSFDemcX\nvBBCCHG8UxSFpT++hLvef4Bv3nYF40+YhD3DiC1ZwW5VGJOvcsF8I7ddnMx585Pi36+mKt1qkQm9\nv8k2PT0dVVVxuVykpLSdB9DS0hK63zGDb7fbaWpqitp6s2OJTrvbf/MT/vzoHzj5pCKMht4t9/37\n0y/JYViDkGTwB6ndu3czZswYVFU+gwkhhBAdGU1JnHndeZx53Xm4X7oH/8GdqD1oPqEYLTRU9W0N\nfqwA3+Fo4dHHnmb58lWg2Ljw4uvIzs4iOSWLQ4fKmDRpItAW1K9atQpo+zYjKyuLmpqaiETgyJEj\neffddyNe5/LLvsHll32D997/mPPOu5iTTz4Nt9uN2WKmrvYIW7buiPu9d9Ta6uKpp15k/rxZCY0X\nfUMC/EFKynOEEEKIrqkFo1EP7erRHLpRU2jY8lG/1uBXVdXw29vv4aNlKzh48HDbRSWJlSs/Dz1z\n3vnfYenSxdx158/DMvhwtA7/2AD/2BKdY5115qmMHJHJgw/8hkmTJqFpGkULz43rfcTy0bLPaG52\nYLXG12JU9D1JDw9SxcXFssFWCCGE6IJ+zhmQEn9pzbHU3BHoxs1OaJNtol10tu8o5pzzvs1Tf3/p\naHAfxcFD5Tz40N8497wr0OkMoRp8iF2HP2LECA4ePNjp5tcpU6awdetWAPbvP8SmTdvieh+xlJVV\n8Oqrb/doDtG7JMAfpKSDjhBCCNE11ZyCbsTExMePmoaiqH0e4Ldn8CsqqrjiypvYvHl73K/1yaer\nuP3OB6mtrQ1di9VJx2KxYLPZYnbZgbYAf9u2tqD+8OEK/H5/3GuJpbGpucdziN4jAf4gJSU6Qggh\nRHwMp34LJXdEt8ephVMwFLWVp/RlH/yOm2x//j93s3Xrzm6vdfWJahoUAAAXRElEQVTqL6ipPVrm\n051OOsfqGOAbemnTrU4X+6wB0f8kwB+EgsEge/bsYezYsQO9FCGEEGLQU82pJJ13A0peYfxjCqdg\nPO8GFLUtMO2PPvg1NXV8/PHKuF/jWEFNj8PhAOLvhR9NxwB/2LACLJbETgnuKDcnq8dziN4jAf4g\ndOjQITIyMkJtsoQQQgjROTU9m6SLf4R+5tdQMnJjPqfkDEdfdB5JS29GTToa2DY2NvZZm8z2AP+h\nh/9GdXVt1wNiMvD3p18EepbBLywspLa2lubmZgoK8jhh/uwerAnGjx/DkiVn92gO0buki84gJOU5\nQgghRPeppmSMp1+G5vfh3/IZwfI9aF43KApKkhm1cBr6CXNQlMj8Zn9sst25a3Pc88eybNlKbrn5\ne51m8AsLC/n4449jzqHT6Zg4cSLbt2+nqKiI8887k2Ufr0h4TWecfhJGozHh8aL3SYA/CEkHHSGE\nECJxit6AYdZpMOu0uMd0J8APBAJ4PB5MJlNcz7dn8Jv/W17TEw0NjUDnGfyuSnTgaJlOUVER11xz\nKY//9Vl27drd7fVkZmZw4w+u6vY40bekRGcQkgy+EEII0X8CgQAtLS2kpaXF9bzb7cZsNsd9Um77\nJltN03qyTAA8Xg/QVoNfVVUVdc6uSnQgvA4/KSmJP/zul9jtmd1ai8mUxG233UJh4YhujRN9TwL8\nQUgy+EIIIUT/aWpqIjU1Ne7T47tTngNHM/jW1J4fBNV+Yq/ZbMZkMtHY2BjxTH5+PvX19bjd7pjz\nTJ06NRTgAyxefDp/evBOCgpi71/oKC0tld/85sfcfOO13XwHoj9IgD8ISQ98IYQQov/0Zf09HA3w\n586ZkcjywuTlZoT+P1Ydvk6nY9iwYRw4cCDmPO0Z/I7fAHzzmxfwyj+f4JJLlpAToytOWpqVc889\nk2eefpif/eQHib8R0aekBn+QcTgcNDY2MnTo0IFeihBCCPGV0Jc98OHoJtubb76Wfzz3csKddPLz\ns8jLOxrgt9fhT5wYedBXe5lOrJLf7OxsDAYD5eXlDBkyJHR97tyZPDd3JtXVtTzy56c4sP8wra5W\nzCYTOTlZfO+732bcuNEJrV/0HwnwB5mSkhLGjBkT99eEQgghhOiZRFpkxtsDH45m8HNzszn55CL+\n+c83E1kmM6dP5siR+tCfu+qkE+9G244BfrucnCzuvvMXCa1TDDyJIgcZKc8RQggh+ldfl+h0PMn2\nD7+7jcmTu99I4+STirjuukuoq6sLXeuqk053NtqKLxcJ8AcZ6aAjhBBC9K/+qsEHGDq0gGefeYQp\nUybEPf7ERfN58YXHyc/PDwvwu8rgS4D/1SUB/iAjHXSEEEKI/tUfNfjtAT7A1KkTefvNZ7nyiosZ\nUpAXc1xubhajCvP4z7+fJzvbTmZmJvX1R0t0eqsXvvjykRr8QUZKdIQQQoj+lUgGvzs1+GazGZfL\nhaZpod75Q4bk89STD9LU1MzDjz7FF19sxuFwolMVUq2pnHrKQsaOGcrdd98V+jBht9upq6sLzRNP\nBr/jax5r0qRJlJSU4PP5MBgMcb8fMfhJgD+IBINB9uzZIwG+EEII0Y8aGhoYMWJE3M93t0THYDCg\nqio+nw+j0Rh2Ly3Nyq9v+2HUccXFxWEZeovFgqIotLa2kpyc3GkG32azodfrqa+vx263R33GYrEw\nZMgQ9uzZE7UTjzh+SYnOIHLo0CEyMjJISUkZ6KUIIYQQXxl9XYMPkWU68YgWwHcs0+ksgw9SpvNV\nJgH+ICLlOUIIIUT/626bzO7W4EN4J514Wa1WfD4fTqczdK29TAcgIyODlpYWPB5P1PHxbLQ99kRb\n8eUgAf4gIh10hBBCiP4RqDqAd9lLeN75O98bZWFi7Tb8xZ+jacEux3a3Bh8Sy+C319l3zOJ3DPBV\nVSUnJydmmU68nXS2bt3arXWJwU9q8AeR4uJiCfCFEEKIPqJpGv5tKwkUf06wbA/42zLfXx+RDuU7\n8JbvRM0djlo4BcP8xSiGpKjzJFqi43K54n4+0FBDYOMyHjt3JpZl/8C9NQs1JZ0pBVlRe+EPHz48\nYo6RI0eycePGTl9HSnS+nCTAH0RKSkq44IILBnoZQgghxJeOFgziff8fBLathJhZeo1g1QGCVQcI\nHCoh6bzrUVMja/NdLhdWq7Vbrx9vBj9wcCe+jcsIHioGdysnFVihoYxgQxlB4LaxSdSUr8O/dwL6\n0TO67KTz6quvdvp6o0aNoqamBofDQWpqarfekxi8pERnEJEe+EIIIUTv0zQN7/vPENj6WSfB/TFj\nDu/G88afCbojg/JEavDjCfB9mz/F8+ZfCO7eCFFeF8Csg+HBZrz/fhLvunc77aRTWFjY5SZbnU7H\nhAkT2L59e3xvRBwXJMAfIJqmEXS34G+uwd9Uibu+jPvu+iUF9tS46v+EEEIIER//luUEtq3q9jit\nohTfRy9EXE+kBr+rTbb+nWvxffoyuBzxTehpxb/yDc4qSI6ZwR82bBiHDx/G7/d3OpWU6Xz5SIDf\nzzRNI9DagL+pnEBLDZq3Bc3nQqf5uPC8r6O5GvE3HCbQUksw0Pk/SCGEEEJ0LVC8Pu7MfcTYAzsI\ntoYH3b3dJlPzuvF+9hp44q/RB8Dv5cQkB621FVFvG41GcnNzKSsr63QaCfC/fCTA70eaFiTgqCbY\n2gB+b+wHg36CbgeB5iqCvuitr4QQQgjRtUD5XoLlexKfwNmE/4sPwi71doDv27AMmmoTWp4ZP7N0\nsbP+8ZTptAf4mqYltAYx+EiA3080TSPgqEHzdqNFVsBLoKWGYMDXdwsTQgghvsQCO9eCv2e/R4OH\nSsL+nGgNfrQuOpqmESzd0qP1TUwBLRiIem/kyJExW2VqmsaHH33G3574J6vXljBq9DxGj5nH7Lln\ncMstv2L37tIerUsMHOmi00+CrqbuBfftAj6CLfWoabm9vyghhBDiS07zJPC7t4s5erMPfrC+gmBl\n573qu1KYZiKwfwf6UVMj78Xohb9s2Qru/t8HWff5JrxeL6ByqKy87ebBw2zevIPnX/wXJ510Ag8/\ndDdDhuT3aI2if0kGvx9omkYwkeC+fbzPTbCH2QchhBDiKykQPbPdLcdkxxMp0Ym1yVZrqImYv7tU\nRSHYGL3EJ1qJzssvv8l3rr6ZFSvX/Te4j66pqZm33nqfc8+/gh07S2I+JwYfCfD7geZzgd/dgxmC\naO6mXluPEEII8ZVhNPX6HL1ag99LnfOcLdHr8I8t0fn009X8+Kd3UFlZE/fc27bt4jtX3UxtbX2P\n1yn6hwT4/UDzOns8R7BHHxCEEEKIryY1f1TP58gaGvbn3uyDr1jtPVpbuwZP9A8Kx5bo/PGeR6is\nrO72/Js2bef3v/9TwusT/UsC/H6gBXvh03lvzCGEEEJ8xeinLEDJGtKDCQzop50YdinRGvxom2zV\nnKEo2cMTXx9w2OljnxZ9PdnZ2bS2tuJwOFj3+UZWrV6f8Ot88NHyTkt6xOAhAb4QQgghvrQUVYdu\nxOSEx6sFY9AVjA671pslOoqioitMfH0Au10qlTX/3969RldZ3Xkc/z2Xc3K/hwhWkRBYlSmogNwi\nARUdbUsBpxKjYgsuo11tncqM09aZzuqsmdHpsrbTem2rAl21BbpEsN5La6lasQWWFYVmIUJKraUQ\nbrkn55zn6QtCDeR2znNyDsnm+3mX8zz/vffhBeuXnX3pfQ2+ZVkqLy/X3r179djjP1Fra4Jn7XdT\nV7dbj69YHbge6UPATwdrEP6ZB6MNAADOQG7lfFmjxiZemFOo0Kz5PT4ezE22kuRefKWUU5D4+CQp\nI0vvWMV93mYrfbjRdsc7dcH66Gbr1t8n3QZSj9SYBnY4sf8Eem0jlDEIIwEA4MxjZ+YoPP9WWWWj\n4y/KKVR4Xo2c8yac9HEkEpHneQqFQgmNob+LruycArnTPy45ibUpy5Y75Qq5Zedo//79fb52YqNt\nc0vyewJbWpI/dhSpR8BPAyucI7nhZFqQlZk/aOMBAOBM4xSfpYzqf5Xz0WlSVm4/L4Zkjz5fGZ+q\nlTthRo/HJ9bfW5aVUP/9BXxJCk+/Su7MT0qh+PJC1JecqfMUqrpGo0aNGnAGf8+ePXLd5K8/ct0E\nfwnBacFFV2lgWZbsULa8aLCNKVYoU7bLDD4AAMmwc/KVsejz8hoPKbp1o7z3d8lvb5Xvx2SFsmSX\nnSt30uwes/bdBVmeI/W9yba78OyFsgtKFN3+irwP9vR+Pr5l64CVqdcaIloy7wZJ0siRI/udwR87\ndqw2btyo4uLChMd9qqLigEuJkFYE/DSxs4vkR9vlRxI87tJ2ZWcXp2ZQAACcgez8EoUvrwlUm0zA\n728G/wR30mw5Ey+RV/+Oou+8Lr/xsPxopywnJCuvSM6E6Xpj2y49s3WDlnTVDDSDf2KJzvU31mrT\nptcTHvsJmZkZuv66RYHrkT4E/DSxLEt2bpm85gPxh3zblZNbyvp7AACGiCBn4Ev9b7I9lWVZcson\nySmf1Ovz0n1HdOjQh5dODTSDX15ervr6et3+hZu14vGf6E/vf5DY4LtUVk5TZeW0QLVILwJ+GtmO\nKyt/pGLNh47fbutF+3pTVjhTdnYRS3MAABhCgpyBL8U/gx+PkpISNTQ0KBKJaOXKNXru+V+ouTWs\nyVPnKTc3V+Mqxmjp0hrNnTNL77//ge5/4DH5KtCCRZ+R5/uB+nQcR9f+U88ThTA0EfDTzLJsuXkj\n5PuevLZG+ZE2+b4nyZdky3bDsjILZLOJBQCAISfVS3TiUVxcrL31BzV12j9q585dXZ+G9Pbbx4/B\n3Lx5q9as3aCCgnx1dETU1NQkydbrr28N3OfSpdeptnbJwC9iSCDgnyaWZcvJLpSU/IYXAACQHqnc\nZBuPSCSif/vy/6ql1e8W7nt7L6qGhsNJ9+e6rpYtrdGDD9yT8MlBOH0I+AAAAHEKugb/xLn5kUgk\n4TP0T/B9X7W33amn1j8vKbVhOy8vV1VVM3Ttp+frpiWLCffDDAEfAAAgTkHX4EsfLtMpKAh21OTT\nP3tRq1evD1Q7EMuydOmllaqoGKPC/HzdcOOndcGkvo8LxdBGwAcAAIhDW1ub/rL/gGzbUSwWk+M4\nCdWfOEknaMBfvWaDYrFezsYfBL7vq6iwQN97+N6UtI/0IuADAAD0ob29Xd9/9Ak988xL2rGjTkeP\nNkqSPnr+JaqaM1PLll6nOVWzBmzHj0Y0urRQ7YcPyB9RKivBwzT27fuzNm36TaDvEK9Nv96svfX7\nVD5mdEr7QepZvh/wvCQAAACDPfTISj3wwOPavXtvn+9kZIQ1+5IZevih/1NFRflJz3zPU/QPbyhW\nt0XeX/aq9eghZWRmycnKlj1yrJwJM+ROmCbLsgccy33felhfvevupL/TQP7r63fqa/+xPOX9ILWY\nwQcAADjFf//Pt/XN+x5SW1v/l1N2dHTqly+/qoXXLNPqHz+iSV3r1qPvbVfk1afk//WPf383O+RK\nsYjUfEze7jfl7X5TsS0vKjTnWjnlH+u3n8am5uS/VBya0tQPUmvgXxkBAADOII8++oTu+9bDA4b7\n7urq3tXSm7+kgwcPKbrzDXU+/9hJ4b4v3v56dTz3A0XrtvT7XqLr/YNiXYcZCPgAAABdYrGYHvn+\nD9XamviZ9W+9tUNP3v//6nx5rdTaFH9hS6M6X16t2J939/lKUWGwjbmJysvLSUs/SC0CPgAAQJcn\nfrxO27fvDFw/5thuqeVo4oVNRxTZ8lKfjz/7mWqde87ZgccVj8LCfF1fsyilfSA9CPgAAABd1j31\nXODaipIsXVAUfHujt69O3tGDvT4rKMjX5fOqArcdj7lzKjVu3NiU9oH0IOADAAB02bXrvcC1tdPP\nVVF2sFtqJUltzYpu+0Wfj2+5+Qbl5AS7ZGsgjuOounpBStpG+hHwAQAAJHmep9aW1sD140qTX7/u\nHflrn88OHzmq0aPPSbqP3ty05FpVLybgm4JjMgEAACTZtq1wRjhwfU54EE66iXT0+MjzPN3+z/+u\nFSvXKBKJJN9HN5Zlqea6RfreI/fKsqxBbRunDwEfAACgy4gRpaqv/1Og2s7oIJwx6Zy8xMf3fd36\nuTu1atXahJrJzAhrRFmpjh1rVGNjz7PtLcvS5MkTdc2iT+irX7mdcG8YAj4AAECXyy6r1JYtbwaq\nPdjSc/Y9UVZ27kk/f+e7P9CPfvRkwu10dEa0/I7b9MlPzNODD63UrnffU0tzq8LhsEpKirRw4dWq\nXrxAts1qbRNZvs+VBgAAAJJ04ECDplx8hfbv7/00m/4s/Icyraq5SE7QyXDLVviaL8odP1nS8dn7\nytmfCvwLx8wZU/TqKz9jdv4MxK9tAAAAXcrKSnXVVZcFqn3lg3a15ZYG7tseVS5n3EV//3nDhhe0\nbdtbgdvbum27nn12Y+B6DF8EfAAAgG4evP8ezZ0zK6GajIwM3XXXl1Q47XJJAWbMLVvO+dNPmm1f\nt/45eZ6XeFtdotGonlz3bOB6DF8EfAAAgG6ysrL01LoVcc/kFxYW6J6779K/LP+cQtOvljOxMuE+\nnUmz5V585UmfHTx4KOF2TtXQkHwbGH7YZAsAAHCKgoJ8Pb1+lVauWqP1G17Qa6/9Vq2tbSe9M+a8\nczXviirdVnuTpky5QNLx02nCH79ZnY6r2NuvSV6s/45sR86FcxW+8sYea+U7OzqT/h4dg9AGhh8C\nPgAAQC9c11XtLUtUe8sSbdv2ll586VdqaW6RGw5pREmJli2rUW5uz8utLNtWxtVLFa24ULGdmxX7\n4x+ktlOOqszKk3PeBLkTK+VUXNhr/9nZWUl/h1TdfIuhjYAPAAAwgKlTL9TUqb0H8b644yfLHT9Z\nXuNhRXdsljq7/gIQzpI7sVJ2XlG/9ePHj9VLP98UcMTHVVSMSaoewxPHZAIAAAxB7767R7Muma+j\nR48Fqi8qKtTvfvuCyseMHuSRYahjky0AAMAQNH78WM2pmhm4/tK5lYT7MxQBHwAAYIhafsetOvvs\nkQnXfeQjI7X8jltTMCIMBwR8AACAIaqqaqbu/cZ/qqysJO6as84aoW/e+3VVVk5L4cgwlBHwAQAA\nhrCamkVa8dh3dNFFE/t9z7IsTZk8SatWfFfVixekaXQYithkCwAAMAzEYjGtXfu0fvrkM9qy5U01\nNTXLsizl5eVq+rTJql68QNXVC2TbzN+e6Qj4AAAAw0x7e7uOHDl+uk5RUYEyMzNP84gwlBDwAQAA\nAIPwNxwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAA\nwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADA\nIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAg\nBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAE\nfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8\nAAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwA\nAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAA\nAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAA\nwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADAIAR8AAAAwCAEfAAAAMAgBHwAAADA\nIAR8AAAAwCAEfAAAAMAgfwMaRN5sYK7O+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6b0310b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import community\n",
    "\n",
    "parts_g = community.best_partition(G, weight=\"postText\")\n",
    "values_g = [parts_g.get(node) for node in G.nodes()]\n",
    "\n",
    "pos = nx.spring_layout(G, k=0.3*1/np.sqrt(len(G.nodes())), iterations=20)\n",
    "plt.figure(3, figsize=(10, 10))\n",
    "nx.draw(G, pos=pos, node_color = values_g)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I7v4FW99CRozg/O++o8HmzdT+7juyGzfmRNeufDliBPlB1tfq9Nh5E+Qngahi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\nxOUU5CIiLqcgFxFxOQW5iIjL/X9V0IXG4AIXAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b96e1f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parts_i = community.induced_graph(parts_g,G,weight='postText')\n",
    "nx.draw_networkx(parts_i, node_size = 50, with_labels = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bt = nx.betweenness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">userPosts</th>\n",
       "      <th>userName</th>\n",
       "      <th>LeftRight_binary</th>\n",
       "      <th>conservative_binary</th>\n",
       "      <th colspan=\"2\" halign=\"left\">postText_polarity</th>\n",
       "      <th colspan=\"2\" halign=\"left\">postText_subjectivity</th>\n",
       "      <th>parallel_betweenness_centrality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>230430</td>\n",
       "      <td>3245.492958</td>\n",
       "      <td>7814.731563</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>0.049938</td>\n",
       "      <td>0.155669</td>\n",
       "      <td>0.388177</td>\n",
       "      <td>0.186992</td>\n",
       "      <td>0.393454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>266312</td>\n",
       "      <td>4365.770492</td>\n",
       "      <td>12653.131178</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>0.038596</td>\n",
       "      <td>0.111253</td>\n",
       "      <td>0.362314</td>\n",
       "      <td>0.196508</td>\n",
       "      <td>0.352871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>89096</td>\n",
       "      <td>3563.840000</td>\n",
       "      <td>6651.951516</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>0.127681</td>\n",
       "      <td>0.410619</td>\n",
       "      <td>0.186935</td>\n",
       "      <td>0.114770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>357252</td>\n",
       "      <td>4827.729730</td>\n",
       "      <td>7882.223515</td>\n",
       "      <td>74</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>0.032100</td>\n",
       "      <td>0.193955</td>\n",
       "      <td>0.445022</td>\n",
       "      <td>0.169413</td>\n",
       "      <td>0.246090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>7</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046591</td>\n",
       "      <td>0.046591</td>\n",
       "      <td>0.221480</td>\n",
       "      <td>0.221480</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userPosts                            userName LeftRight_binary  \\\n",
       "                 sum         mean           std    count              sum   \n",
       "node_group                                                                  \n",
       "0.0           230430  3245.492958   7814.731563       71               32   \n",
       "1.0           266312  4365.770492  12653.131178       61               19   \n",
       "2.0            89096  3563.840000   6651.951516       25               12   \n",
       "3.0           357252  4827.729730   7882.223515       74               30   \n",
       "4.0                7     2.333333      0.577350        3                2   \n",
       "\n",
       "           conservative_binary postText_polarity            \\\n",
       "                           sum              mean       std   \n",
       "node_group                                                   \n",
       "0.0                         20          0.049938  0.155669   \n",
       "1.0                          7          0.038596  0.111253   \n",
       "2.0                          7          0.009426  0.127681   \n",
       "3.0                         15          0.032100  0.193955   \n",
       "4.0                          0          0.046591  0.046591   \n",
       "\n",
       "           postText_subjectivity           parallel_betweenness_centrality  \n",
       "                            mean       std                             sum  \n",
       "node_group                                                                  \n",
       "0.0                     0.388177  0.186992                        0.393454  \n",
       "1.0                     0.362314  0.196508                        0.352871  \n",
       "2.0                     0.410619  0.186935                        0.114770  \n",
       "3.0                     0.445022  0.169413                        0.246090  \n",
       "4.0                     0.221480  0.221480                        0.000037  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listkey = []\n",
    "listvalue = []\n",
    "\n",
    "for k, v, in parts_g.items():\n",
    "    listkey.append(k)\n",
    "    listvalue.append(v)\n",
    "    \n",
    "community_df = pd.DataFrame(\n",
    " {'userName': listkey,'node_group': listvalue})\n",
    "\n",
    "node_importance= sorted(bt.items(), key = lambda v: -v[1])\n",
    "parallel_betweenness_centrality_df = pd.DataFrame(node_importance, columns=['userName','parallel_betweenness_centrality'])\n",
    "\n",
    "parallel_betweenness_centrality_df.sort_values(by='parallel_betweenness_centrality',ascending=False,inplace=True)\n",
    "\n",
    "parallel_betweenness_centrality_df['parallel_betweenness_centrality_rank'] = range(1, len(parallel_betweenness_centrality_df) + 1)\n",
    "\n",
    "community_merged_df = community_df.merge(parallel_betweenness_centrality_df, how='left',on='userName')\n",
    "\n",
    "# get aggregate counts for each node group\n",
    "forum_userMatrix_5 = user_df_sentiment.merge(community_merged_df, how='left', on='userName')\n",
    "\n",
    "forum_userMatrix_6 = forum_userMatrix_5[['node_group','userName','userPosts','sLeftRight','postText_polarity','postText_subjectivity','parallel_betweenness_centrality','parallel_betweenness_centrality_rank']].copy()\n",
    "forum_userMatrix_6.drop_duplicates(inplace=True)\n",
    "\n",
    "usernames_distinct = forum_userMatrix_6['userName'].drop_duplicates()\n",
    "\n",
    "forum_userMatrix_6['LeftRight_binary'] = np.where(forum_userMatrix_6['sLeftRight']!= '', 1,0)\n",
    "\n",
    "forum_userMatrix_6['conservative_binary'] = np.where(forum_userMatrix_6['sLeftRight']=='Conservative', 1,0)\n",
    "\n",
    "\n",
    "\n",
    "# forum_userMatrix_6.groupby('node_group').agg({'userPosts' : ['sum','mean','std'],'userName' : 'count','LeftRight_binary' : 'sum','conservative_binary' : 'sum','postText_polarity' : ['mean','std'],'postText_subjectivity' : ['mean','std'], 'parallel_betweenness_centrality' : ['mean','std'], 'parallel_betweenness_centrality_rank' : ['mean','std']})\n",
    "forum_userMatrix_6.groupby('node_group').agg({'userPosts' : ['sum','mean','std'],'userName' : 'count','LeftRight_binary' : 'sum','conservative_binary' : 'sum','postText_polarity' : ['mean','std'],'postText_subjectivity' : ['mean','std'], 'parallel_betweenness_centrality' : 'sum'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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