EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer \n",
    "from nltk.tokenize import TweetTokenizer \n",
    "\n",
    "lem = WordNetLemmatizer()\n",
    "tokenizer=TweetTokenizer()\n",
    "\n",
    "import nltk\n",
    "import string\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "from wordcloud import WordCloud ,STOPWORDS\n",
    "import re\n",
    "import time\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, HashingVectorizer\n",
    "\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "APPO = {\n",
    "\"aren't\" : \"are not\",\n",
    "\"can't\" : \"cannot\",\n",
    "\"couldn't\" : \"could not\",\n",
    "\"didn't\" : \"did not\",\n",
    "\"doesn't\" : \"does not\",\n",
    "\"don't\" : \"do not\",\n",
    "\"hadn't\" : \"had not\",\n",
    "\"hasn't\" : \"has not\",\n",
    "\"haven't\" : \"have not\",\n",
    "\"he'd\" : \"he would\",\n",
    "\"he'll\" : \"he will\",\n",
    "\"he's\" : \"he is\",\n",
    "\"i'd\" : \"I would\",\n",
    "\"i'd\" : \"I had\",\n",
    "\"i'll\" : \"I will\",\n",
    "\"i'm\" : \"I am\",\n",
    "\"isn't\" : \"is not\",\n",
    "\"it's\" : \"it is\",\n",
    "\"it'll\":\"it will\",\n",
    "\"i've\" : \"I have\",\n",
    "\"let's\" : \"let us\",\n",
    "\"mightn't\" : \"might not\",\n",
    "\"mustn't\" : \"must not\",\n",
    "\"shan't\" : \"shall not\",\n",
    "\"she'd\" : \"she would\",\n",
    "\"she'll\" : \"she will\",\n",
    "\"she's\" : \"she is\",\n",
    "\"shouldn't\" : \"should not\",\n",
    "\"that's\" : \"that is\",\n",
    "\"there's\" : \"there is\",\n",
    "\"they'd\" : \"they would\",\n",
    "\"they'll\" : \"they will\",\n",
    "\"they're\" : \"they are\",\n",
    "\"they've\" : \"they have\",\n",
    "\"we'd\" : \"we would\",\n",
    "\"we're\" : \"we are\",\n",
    "\"weren't\" : \"were not\",\n",
    "\"we've\" : \"we have\",\n",
    "\"what'll\" : \"what will\",\n",
    "\"what're\" : \"what are\",\n",
    "\"what's\" : \"what is\",\n",
    "\"what've\" : \"what have\",\n",
    "\"where's\" : \"where is\",\n",
    "\"who'd\" : \"who would\",\n",
    "\"who'll\" : \"who will\",\n",
    "\"who're\" : \"who are\",\n",
    "\"who's\" : \"who is\",\n",
    "\"who've\" : \"who have\",\n",
    "\"won't\" : \"will not\",\n",
    "\"wouldn't\" : \"would not\",\n",
    "\"you'd\" : \"you would\",\n",
    "\"you'll\" : \"you will\",\n",
    "\"you're\" : \"you are\",\n",
    "\"you've\" : \"you have\",\n",
    "\"'re\": \" are\",\n",
    "\"wasn't\": \"was not\",\n",
    "\"we'll\":\" will\",\n",
    "\"didn't\": \"did not\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Nonsense?  kiss off, geek. what I said is true...</td>\n",
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       "      <td>\"\\n\\n Please do not vandalize pages, as you di...</td>\n",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22256635</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27450690</td>\n",
       "      <td>\"\\n\\n Please do not vandalize pages, as you di...</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54037174</td>\n",
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       "      <th>3</th>\n",
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       "</table>\n",
       "</div>"
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "data = pd.read_csv('inputs/train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 75720.,  12184.,   3919.,   1571.,    714.,    460.,    343.,\n",
       "           281.,    250.,    409.]),\n",
       " array([    6. ,   505.4,  1004.8,  1504.2,  2003.6,  2503. ,  3002.4,\n",
       "         3501.8,  4001.2,  4500.6,  5000. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comment lengths\n",
    "data['length'] = data['comment_text'].map(lambda x: len(x))\n",
    "\n",
    "plt.hist(data['length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [id, comment_text, toxic, severe_toxic, obscene, threat, insult, identity_hate, length]\n",
      "Index: []\n",
      "id               0\n",
      "comment_text     0\n",
      "toxic            0\n",
      "severe_toxic     0\n",
      "obscene          0\n",
      "threat           0\n",
      "insult           0\n",
      "identity_hate    0\n",
      "length           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Empty comments?\n",
    "data['comment_text'] = data['comment_text'].map(lambda x: x.strip(''))\n",
    "missing = data.loc[data['comment_text'] ==\"\"]\n",
    "print(missing) # None\n",
    "\n",
    "null_check=data.isnull().sum()\n",
    "print(null_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Please', 'vandalize', 'pages', 'edit', 'Merwin', 'If', 'continue', 'blocked']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tokenize, remove stop words, convert to frequency matrix\n",
    "stop_words = stopwords.words('english')\n",
    "tokenized = data['comment_text'].head().apply(word_tokenize)\n",
    "word_list = tokenized[1]\n",
    "filtered = [word for word in word_list if word not in stop_words and word.isalpha()]\n",
    "filtered\n",
    "# Apply to all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "      <th>length</th>\n",
       "      <th>exc_density</th>\n",
       "      <th>clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rowsums=data.iloc[:,2:8].sum(axis=1)\n",
    "data['clean']=(rowsums==0)\n",
    "\n",
    "subset=data[data.clean==False]\n",
    "\n",
    "subset.iloc[:,2:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total comments =  95851\n",
      "Total clean comments =  86099\n",
      "Total tags = toxic           9237\n",
      "severe_toxic     965\n",
      "obscene         5109\n",
      "threat           305\n",
      "insult          4765\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Look at multi tag correlation matrix and other data, prevalence of multi tags\n",
    "\n",
    "#marking comments without any tags as \"clean\"\n",
    "\n",
    "print(\"Total comments = \",len(data))\n",
    "print(\"Total clean comments = \",data['clean'].sum())\n",
    "print(\"Total tags =\",data.iloc[:,2:7].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>insult</th>\n",
       "      <td>-0.045008</td>\n",
       "      <td>0.249083</td>\n",
       "      <td>0.512352</td>\n",
       "      <td>0.060638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identity_hate</th>\n",
       "      <td>-0.027275</td>\n",
       "      <td>0.121335</td>\n",
       "      <td>0.138648</td>\n",
       "      <td>0.082282</td>\n",
       "      <td>0.206365</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>toxic</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080910</td>\n",
       "      <td>-0.020737</td>\n",
       "      <td>0.003128</td>\n",
       "      <td>-0.045008</td>\n",
       "      <td>-0.027275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severe_toxic</th>\n",
       "      <td>0.080910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286341</td>\n",
       "      <td>0.088632</td>\n",
       "      <td>0.249083</td>\n",
       "      <td>0.121335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obscene</th>\n",
       "      <td>-0.020737</td>\n",
       "      <td>0.286341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045706</td>\n",
       "      <td>0.512352</td>\n",
       "      <td>0.138648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threat</th>\n",
       "      <td>0.003128</td>\n",
       "      <td>0.088632</td>\n",
       "      <td>0.045706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060638</td>\n",
       "      <td>0.082282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insult</th>\n",
       "      <td>-0.045008</td>\n",
       "      <td>0.249083</td>\n",
       "      <td>0.512352</td>\n",
       "      <td>0.060638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identity_hate</th>\n",
       "      <td>-0.027275</td>\n",
       "      <td>0.121335</td>\n",
       "      <td>0.138648</td>\n",
       "      <td>0.082282</td>\n",
       "      <td>0.206365</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HeatMap Saved to Plots Folder\n",
    "\n",
    "corr=subset.iloc[:,2:8].corr()\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "fig = plt.figure()\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values, annot=True)\n",
    "fig.savefig('Plots/heatmap_categories.png')\n",
    "plt.show()\n",
    "plt.close(fig) \n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7tnJPSMuQe0hkRBQlpSUAHnWUldd9n2ouXEHVY6r0p/1/RZWXA1oAxtzCYDAYDAaDwWCo\nQYvVJO/KEO1BRWUVFZXyihEaUi3TR+qlkaLSCmdbdITLo47I8BCP8pr7NNTGwdRPg3eUHkZXbAhW\nhbzTxvWLBqAsu5yEQaLFrygQDXJlsXyGxIZQkVfhsS1hUCxKa65i+0Y7Zd7qCImVeWHXYTh4KK/U\n2ho9ufq1H8Efu0VzVFZZ0qRtdUvsD8CCvz6iV5uhQLUm+UAmwhVEmV6SHtpJfkvZJZWOBnlzloxj\neEgQw7vGAPDTX7nO8R1iZWXo1O5xAPyZXky51ky7WokOq7zSanZNcrSStiJUEOlVh/YSe9d40exa\nWLSNDvUoW5RSwABtjlFcLs+47JIKhnWRe+f3m3MAMYVYlyGrMYVldY9n1/gwLK2jdG9rkV51cG8r\nu0Tuwe5t2eYdvrQFEBEuK5JBSnHS0L8BkJmdAUDfXkeRmy/9r6iQtk4dfgYAW1P+olM7Mclsk9QW\ngCqrionjrwCgQxvRCq9Yu4yConwAunft6ZQ999YT0r8iOa9TTzyTuBiZ80tXLwbgnFMnsHbjagAK\nCkVDvnjlr/Wej7+MPRxcYknCwh3V2/O1sjpGX4KQIDgpWb7P3Saf9v/TV1cfFxtWfVyGLDbQSoaY\nvFKI0eVF+ufbLhq2Zld/B9hefTtoFEbSMhgMBoPBYDAYatBiNcmd28jrgCskiBBtX1ZaXv02V1As\nrxCRYdWnUFTiqZUrKC73KK+5T0NtHEz9NHinYKO8phZsKnKMo5S2UbOqLI/vdZG5IMc5ztnPzeDK\nlzoMBw4JEe08nOLc+WbNf8ktEUegyFCxT0+IbE+4SzRThaU5PrcBeG3H3a443CX3hvySTMJCIn08\ng5ZPcXkVv+4UzdnfDhPNWL82kezOE9VUW61RrqiynN+tbYdcWllF39YR+rsUpuSVMbijjNWSXaJ1\nG9wxmkUp+c16HuMi5Zpsq6ggvfTAu2fHt2oDwPirxOG0e78B3H3JGbX2cS8Hau0D1dr/Hbmljr25\nzdq0Italy73YLrEsWLDDMzqsbdfcEJuzStihbYzd21qrj3dvy0467N6Wr+3YBOkVg8KiQoK1jfHR\n/Y4FYPuurY5WuVWSjKfdaHBQEInx4quyap044x/bfyh708WeeMu2jQBk5mTSp2c/AI+y4mLPflZW\nVVBeITLHgD7HAFBSWuLYOB939HAA5i7+2et59Lw1WeopFm1+ZUkl5dkii5SmlenPUud7eY6UZRRB\n90SpY3xv+eyeCO//Id+v0nEJrptV7TS5S//0vD0WrxHTbXJLoLXcOkmW2ylbsyFTu3ZM0/Uf3R4G\nyC0TW3RqKk1yixWSs/Nlgr9+6yhHSHz43SVO+T1vLATg4/+cCYhMcvebCz3quOeNhR7l1NinoTYO\n5H6e1OtwACYMktl2fPfuJLeSH2NEqKx95BQV8efu3QB8sVx+oG/Mm0dpRWAmAC7tuXvZ8cdz3jHy\nAz2ykywXJUZFOY4M6fnykNqQmsrcjRsA+GTpMgC2pKUF1B7Aeccc49EeiPOEe3sAczduqN2e2w/V\nXZj1R7D12LeO+vxlf11H9zEFuY7uYwot9Do2ObWvXUOOe7nF0pdW0bKMWlSWR6d4eXLkaQG6IRpy\n3EuM7ABA+zhZeh3T70bCQ2SsbGe+rMJdPrXVEvllW7XAMlubUShVLdDo906vD1iAmRtl7dW9eFuO\nCGl2Hfb/zUl8kMzt1kEH5gtyTobM5bcfuwuAR6bN8rpPfeU1qSkg2zS1DqGudpqjrSmfvF5rm21u\nVWXV75FmC8eWnphf//i5c4x7HWs2rKpVf826g4KCnXp+mCfXIjc/hyED5N66eIWn7FGTTU9vq7Ut\nJFrmcEwfkT06nt+OsFby7PnjpvUABAdBWy3MbtFmDxYwvIt8t80ijmgFfVtXf4fq/0OCqp33QrXp\nRkIE7NTC7mrtoxgTBqnyKKCDWFrRuxV8tk6+n9VLPmesr/dUfcaYWxgMBoPBYDAYDDVQ9lvHfu2E\nl4x7S167CIDB//dBs7XbFG20xH5Ov/oqR/PoL79v3caoZ54BoKjMtxAxrWPkde7bm2RpuH/nzn63\nuzpFnI2OfvAhn9vcl+3tD/bndQxkTOHguY5BSlQZ4/vfzqc14iR7i53sTpvorgAMSR4HiFPdyJ6X\nAfDbVgkPtSdvc73t2yHb6mpnUBdZeVqzZy4AJeUFhGlN8pEdRgCwdMdMn+vzlYuHDAGgsFRWt84a\nOIDr358GwL1jxwJw1+efA3D1SSfy/Zq10r5ekfhg8WIu0nV8sFgci/bkNtG6aBPz7rOfAHDZzRMC\nruOROFkjLrYsHs7b9+d53cMv8NI9Erf7pe8lPv91owdx3SP/A+CnT94FYMzlk9EWA8z9UkKP/j7n\nu1r1PTJtlldTCvdy8G5uYTjwaHNKEuEdxEuuPFdWJrOX5FKyu7TWvjVDutW1zRcuEssSPlpbrfVv\naPXIxtf9TMY9g8FgMBgMBoMhQFqsTbIhcOas38DY/hIa6utVYsf03eo1rNF2q+U6tFKvtm359+mn\nAzCgi2jxju2WzE2njALg0ZkN25cBPDnhPMBTE/jKnF8AmLFC7K1S8/IIC5Hp1rOtOC+cePjhnDVA\nHD3eWrDAr3N8csJ5tTSPr8z5xaM9gLCQEI/2AM4aMMDv9vYHLe06uo8pHJzX8awj/wVASJBoT1bu\n+qnWPg057uWUiB1n62gxyMsu2kNsuBjg5fpok9wQkaHizFZSXuBsK60o9ChrDv7cI3PvkiESbm7V\nzp0Ul4vBoVVDXxQcVK2Dee+33wCYOHQorWPEtrGlapCbkulFck0Gh4YFrFVrDJl7dzvfs9PFqFMp\nRWaq2KtfcP2/AXjs2kuo0A5fD079CvCuSTYcONhh1ArKArfBTv85C1ec3O8ju0oils6XtCeyizjG\nrrphnbOvtyYCnesfrKm9zddzaGp78xZrbtHUuHvp1ueBezAQ4XIRHa4zC+XX770dFyGTfc1DDwLQ\nLjaWlTt2AnDsww/71F76888BEK89uT/6/XcmvvGmT8faD1L7s8xHZ7P055/zaA/wq01/29sf7M/r\nGMiYun+a67j/Ua4wgkJlXlSViTt4ZI/BlOywn0By77cqywmOksxelfmSHVKFRlBZkFWrzmO7JQMw\necRIAH7ftpW358uLyjkDBwLQt6M4FXZv3cYxvdieKfU+OeE8ftuyBah2Mm0Opj4rJgO7UsUcJzM7\ng5hoWV21naF+Wz6fEcfJi6Sd9WzLjk28/K6kDHc3txjQR9zzj+otn+9+9qazbeL4fwCytPz17C8A\n+OU3ebm6N1ZeWLZVVvBeYWFznGq9nHr+5Sz6ScxuTr9Y+vn9B28zeJSY64y/WqJSrP3dLW6ulgn+\nd+d1teoz5hYHDm+eJZ9fb4CvJUhGgwJkj5vFVMwq19kri6soTddRLfaKiUXJ3jJKU8t0+YEXscXG\nmFsYDAaDwWAwGAwBcsiYW7iHsvElTM2BTHF5ubME2hC5xaJhmrNe4qVcNHgw3du0blT7dlZAX7DD\nidmfzd2e3VZj2ttX7M/rGMiYun/uqzYPhOu4v3AldiS6/2gAytL+AkCFhBF5+HEAhCS0B6Bwzc+4\nWokGKaTXCQBUFedRuPYX+a610ADjjxbt6bXvvw+II1+fDqI5tlcCXMvF6dE2BwII0Rr/8spKvln1\nR1Oepldio0WDe9XzlwLwycszOftK0RrbGuL7nr2db3/52uO4d5/71NEkV1TK6kTvHv3or2POTv30\nDWffyZeKFvaG+6+S/cvLefvpD4FqTfI2PQZpfs7tpmLHpnWcf+2tAHw7/S0ALr31AX7UDnupO7YC\n8OZDt1NcWOC9EkOjiB16HnmLPm2y+pRe9Ug85Royv3uxzv226ZDs8eG+myBsfnZ7Y7t30GE0yQaD\nwWAwGAwGQw0OGk3yQ+99w46NfwLQtpNoRb6d/ibL5v7o0/G9Bkh2nDGXTwbwCIdjOzAMO2M8AMee\nfBrB2nlp52ZJovDRi08w4drbPNoPcbkoKhBb0tf/c6vTTn1t7C9S3ZxoYrQdrK/8tkW0VKcfKXFb\nLhoymJU7JYG77fgVaGKL+tp0bw9g5c4dzdbegUJTXUf3MQW5js0xpuY6Nh9hnfpiletQTVWi0QwK\nj6Zca5Vtm2QVEoarVScAyjPFoauyMBvl0vPHTZP86i8Scu7S40QbvT0zkxU7dni0665BthnVpw8A\nUxYs9FpeH9cOr07UMHezhJtbq0Pf1UV+gbi5lJXJ+efmZdfa59zTL6Rfr6OkPF9+N22S2jrl7dqI\nhvyBfz3GbY/eUOv45E6HAXD/jY8621JSPcfivf2snf3rzz+46t4nAXjz4TsA6HHk0bz+oDyr3n9W\nQide/+iLlJZI9rbdW8Vm/NNXn6H/8SMAOGbEqQC07tCJSXeIj8Ocz6cD4u/jXg4w6Y6HnfLt+rnc\n1AQyL2z+GSPmqJ2Dg1mi50ieVrf2crnorBMc/VEutrery8tZ6WMozZokjJiEVSr26Pkrvg2oDoDg\naElp13qchHMM69i7Xk3yD3IZWeJHTqGetyQDsOmZbX5vawqC9bhX+nmPaE4OGiG5TcfOPDDpHABH\ngL3/7c99FpLdvXwBD09fW4BdMOtzj0+AR6aL6cZHLz7BsSNPA+CpGy8HIH13itd26mujqYgOE9fW\ncUeLM82IXr04or0sr7aOlni4MRHhROqsbREuV8Bt3f2FOKsM69lD6g0P56kJElv0ltGy3Pv2/AVM\nWSjZfrZlZATclnub7u0BPDVhgkd7AFMWLmyS9vYX++s6uo8pyHV0H1NoudfxnnvEaejhh+W3edNN\nf6NdO1l+X7NGnhhJSdHs2SOC0Y4d4qDWpUsiRx/dpdZ+mZki6CxaJEvTmzc3V3a/piV/2TfU8i93\nT1unmTTpeN55532PbRMnDiExUebcCy9ICttbbjmFrCx52L8xZZ6z73HHibDYvbunec/69akcf3z3\nOuuYMuVX9icXn305514jUWFcIfK7Oe2kMU55RpZEIrnloWu578ZHALj1ERGWS8tK2LlHlqYffel+\nAAqLWp65QllpCbeMH+Gx7cYxxznfN69eDsD/br+GyAiJs11QKIqd9m06sE479KVtFs+vvYuWsGSl\nbMvTLyKF6ens3iBRDnYtlLI5v/5AXGxCc5xSk7BHm9KsLy8nQ79A9neFOmXrtZlbtNZkBSog2ySe\nei0AVaXyIlL4p2/CvE1YhyNoPV6yGtrCslVZvwLhaHlMcHhSdXro5iRUj19SgkTyycrJIiZKnlHu\nmQHt+WXPj+hIiXYTGx1Hp/Zy/7XnWElpMUkJcl9Jy5ToLFERUeTol972bSW7aGrabsrKG3eN6sKY\nWxgMBoPBYDAYDDU4aDTJWWmpVOo3K/szxA+tWoduos265sHnPLanpVQbso+aIE4gPfoNpCBPrOIT\nW7d3yu2QOeOuEoeO+KTWfDtdwlmtXjTfaae+NpqCswcM4JVLJwLVWdTcKdDZsjLy89mTI+fROlaW\nn9rF+hQVxQM769ngh0Xb8vxFFzK6b1+P+u468wzuOEO0Nt+uXg3AIzNn8vvWbX63Z7fp3h7A6L59\nPdoDuOOM0z3aAwJusy5sp6SKqipcernIDq1Y0QjHsv15Hd3H1K7PfUyh5V7HgoLa2aBWrZJwePHx\nEm7u11+3sHq1aItnzBDzp3POeYXgYFVrv1692up6SwI6R/dl4cbw8vyr/TzCi7eOl5CfsbHhnHee\nOOStXClzoFevdo7G/NxzpSwzs5C8PDG9aNNG5mNaWj7BwTL/27aVa1ZaKvff0aP7kpKS7VMd+4Pf\nVy3i8TvESc/Wbu3e67b6p8cqPSuNNz98BYB/XSkmC4+//ADPvyVmDA/f+hQAxSXFbEsRU5bXp9e9\nDG6z/+ZFbbp0TOacU2XVaOXaZQCUlpcS5gpzvgO0adWW4UMk/F9IkIgPRSVFLFwqmlFL3+8mjr+C\nDm1Ey/fcW08ALUvT/kmRaHSDAPsOvdxNW2xrD5vC3TL75zdJOPlKAFqNuVnq1SZMxZuX1Hus7Xib\neOpkx2HPDs2Y/sWjdR4HcKREvaWoHN6vd8+mYcRQcYy150rbpHYUaROesFCZR53adyZf/9bs+fHj\nfDFBqbIu5Q14AAAgAElEQVSqaNNK7rX2HPvul2/o1V1MtUYefwoAufk5lOpnX1yMrBB+9E3znaHR\nJBsMBoPBYDAYDDU4aJKJvD5nNZNPERu6YP3Gdd9bn3HPxDNr7est4PkDU8Qe84nrRVvsLRzOszPE\nDu/mc050tNT/myVvgnbb7oRHRvHQe98AcNu5I5126mujMZzQQ7Ths2+9xUmyYGcse+jrr5mpQy/t\n0lpHd1646CIAJo8c4WxzXf1/AfdlUHIyANedLOd93jHHEF5Ds29ZFi/Mng3A7Z9IiJyqRszHQcnJ\nHu0BHm3ac/2F2bN9ai82PNyxk83WmoekaLHby8gvcJJgnKKdkmavq84+ZGeF+3z5clpFi82Vu1Z5\nb17dU76lXkdvYwot/zq6Y2s9KxsIyeXrfr7QkjSGgeDrWChtv2lfn+DgIOeYxoxnYxy0WjItaV6c\nP+YS2rUWR8Xv5kpYvBMHn8y8JT873wF2pe4kM1t8Azq1l0yZ4eGR/LpUno22TffOPTsoKJR71vwl\nvwCedqlNwYE0L2KHnAtAwsi/A2BViNY67eP73JL7CCoohIRT5J4dM/B0Z3vpLnm+pH/xGIDXZD/u\nDGgnn8O7wItaYd3QXXLAK70BKNldrVUP7xBa57aVk6ufeYcfdgRQPVeycjIID5dn5K5UWclLSmjl\naIHt+bFpm9i7H9t/qONoa8+xvRmpnD5iLADbd211yhITkgCIiZLVq09nfYC/sqyvyUQOGiH5ua8W\nsGGFzISkdvrHPv0tx3HP3Ut36CnyQ/71e0m/Oefz6bjCRBgad+U/ATw8fT999RkArrhLljdiE5Io\nypcud+ouwtB9l5/NHS/JD7VYLydgWWzTnr1fvvU/QDyL62ujMXxzo9Q7um9fx4N84H8kA9uG1NR6\nj31loizrX3nicGdbY4SrmiRGRXHlcKn7ltGnOttsrp8mntCvzW2am5xd95XDhwfc3q2jRzvxh+1I\nJLYn/7HJ3Rxzh/ZxsuSTVVjI9MWLARhzlHjNd2/TmuQkcWRYtn27s+393xYB8EdKbefOlnod3ccU\nDpzruL9xf5ivSPkegPV7F/pdT3ZR/df+YOVAEob8oSXNC6VULSEjSAU5gq2dpdCboOu+X83tdR3T\nFByI8yJuqJi0xI8Q5/6qsmL2TheHvMp8EQxbj7uTsE59PI7LX/Et2T++BoBV5VvEH1tI7hYPX0kQ\nLir3gbjnft0bmjfuZXXNI2+ccfLZAERqIfzH+d+Sm19baVQfJuOewWAwGAwGg8EQIAeN415Rfh6v\n3n9zneWrfv3F+Xz70bvq3O+pGyfVWVbfcQCPX3dJveUgIXfqa6MxDOnWzfn+62YJktiQ5tGmfXxc\ns/TJJquwkCe/kzB30xaJFvXXu+6kQ3w8UG2W0VQawaxCCTP15HffebQH0CE+3qf2QkOCSdAmFbty\nxAHJdhYb0LkLiVGRHvsnREbSKUHC2thZyLakpbFmlziIKfRyNBYROmSbN1rqdXQfUzhwrmNLorhc\nVpkOVa2wwTv7e154W1F21+rVp+Grq6y5NMgHMrmLJNsj2owu/sRLaXuBrBJalRJ2Ljg60fme9YM4\njBas+sHvtgbJI4jSin2jQbYJdN74M19m/fyl/x0LEKNJNhgMBoPBYDAYanDQaJL9ISRItHihIWKH\nXFpRRKRLNHBF5ZJgwP4/vzTTOe7wNkMBqKwqZ0e2GNtb+u2n0qokNkyMydvEJAOwNXMVVZbYlNqf\nzUmUTjwBkFdSXM+etY8Z1rNns/TJG7bD2YwVK7h2pGgCu7VqtU/aA7h25Eif2vsrPZ2Pf18K1HYM\ne2PePIK0obJdFqSU8/3+L6vfdGvu1xDmOjbcHvh+HQ0Gg6ElkfvrR/JFBRE/3HMFurIgi/TPJSxm\n6e4NAbfx5nL/jzni3u71lOrnl1Ls/V7sp7MX59az/8HBQSMku0eqaIj4CInF16e9OCDtyd1ERZUs\nb4QESfYo+393ITk0WASR4qpyuiVJNIuiMpkkseFJpOWLY1aI3q9n62NZF4AjRqCkZIlJQLfWrTiq\nk6QHtaMjVNYRr/fBs8UAPi4iIuB2k7Wg4mtGNFtoHNC5i7PNPZ2yr202d3sfLvm93vKaQm9dQrC/\nkR7MdWy+9g5WGuPE1Lf9SZzUw/NB7U/EhFZRMkd7t5P7afs4eVGLCUvEpe+FpRXipFxclkdawTYA\ndmSLU/Pm9Pp/Z4HQv6PEVD3hsAnOtt+2SqbUFSlNm920JWPmhSctbV7kLvwAq1ziryecfIVstKoo\nz9hRz1HNx/qHtjS4jwpW9LpTTAIbIyR3TxQl5Zk9xUTxy/VZVOhn5VFtxZTxr+xSDk+S/RbuEJOk\njrGhFJbJc/CIVvK825pTwrp03xRK/mLMLQwGg8FgMBgMhhocNJpkf7DfaCsqJe5fdlEqyUn9AdiW\nuQrA+X9r5krnuLJKCfm1JWMZR7Q9HoAOcRICLrd4L7HhoomrqrJNKyzHtKOiqnnyirszc7XEz73+\n5JPpmiSmH6/qjG2Pf/sd6TrW7uHtJDbMjaNGceHgY4Fq7WFyAMvXGx55GIBfNsjS0LerVzuh0vbm\nydtfhMvlLI3/Y/gwAI7vUb208752yvKnTff2QMKzubcHsvzfFO3tS1rCdXQfU5Dr6D6mYK7joUyQ\nkkfHsO4X0K/9SQ3uH+GKcT4Tozrq7xKBqak1hgM7jea4bhKX1tJLxPM2T2/x4cEOBg6FeeEeu9hv\n9KKi7ZhXVVZMefo2APKXSSbRmGPGkHTGjQCUbF9Vb3X5K74NvC8BYlVaVJY23ilzW7bIU9ty5HN7\nbik9tXY5q1jC3PVpHUG59jo8r488C19ftpfL+rcGYEeuHNsjMdxokg0Gg8FgMBgMhn3FIalJXr17\njv5WbSuaWZiit1ge/7uzMa1aa7V+76+AZ1gvpYNjW/sp9M2jMyWT4Nj+/R0N5KQTTvD4rMlHv8vb\n+gs/Sca0hXfe4Xe7tq3oyUcc4fHpCzP/EK3p47P8eyMOUiqg9uw2/W1vX9ISrmMgYwrmOh4qjOwp\nWUN7tT3O2ZaWLxmxVu/5BYD0/O2UVYq9ZaTWGCZEdSA5URLtrEtdEEDLddv3H935NACGJo93wknN\n3vA2ANnFqfTrIM6lmQU7dU0WSdpuNjJUtJd/7plPt6QBAKzR59E6uitH6mNX7pLkVK6gMJKiJMZW\nhq4vJDiMuPDWur09AKTm/RXAOR64HGjzYlP6Er9bShx9XQD984/II4Z5fNbF/tAkA2x6aluj66jU\n9sfx4cEAdIoNpXdrsUUuLLcDHlQ/l77dLI7bI5JjHe1zVKjIXOubSYsMh6iQ7O0HZdXYVvP/umuq\n3m9/Ccc26fmyRH38o49x71jJKniGzvzWPi6O/BK5Ma3bIzfwKQsXMnWhCPuuYJmoJeXltdION8TJ\nTz0NwEVDBgOSja5rK502Uqd1rqqqIr1A0nAv15nnpi1axGfLAnDB1W26twfQtVWSR3sA6QUFHu0B\nAbe5r2gJ19F9TEGuo/uYgrmOhyK2eZm7ELRh728A/LxpKuD9PlhQKil00wq2O/sHQqWXbGPHdBan\n7SHJ5+h9yvl+nWQn25YlL2/HdRtPZuFuAFrHdAUgWLkcZ62BnUSQKizLITjI87GYWbjTUZpkFUrM\n8/4dT2GVFpgHdhqtj81ld94mAHKL0wI+xwORA3VeGPY/7/2RDoBlQUqezAc7w603n/ed2sQCIEjv\nV9WMcaCNuYXBYDAYDAaDwVCDQ1STfHCTlp/PDdM/AHA+G6K8UpY3Yq673u/25m/a5PG5L5i/adM+\nbW9/4M91HNDnaABef/x9ANa5lU2eKE4gr7z/33rryNdLn4POv9tZH9mmP6d8/FqDxweCt+u45Ks/\nmfKxaHyaok1XiIuPX/4GgE9mTgdg+pdTG11vIBzf7TyPT3/wJ/xWc9G7nefyb2lFEfO2yJjui5W0\ncr1MbzOoyxgGdz3Lo2zm2hfZnbvRY7+MghTCQiRcVGqehLlKjOzoaD5DgmXVJSGyHa2iOgPQOlpC\nDaYX7HDMMRIixVm2qCyXXjpufmFZjm5F1eqfr5h50TgCnReBsP3xMY2uw1CNN23xpUPld/nTunVk\n6pXLVtHRTvmJh8vKxbyNG53/P1jiv+mMLxhNssFgMBgMBoPBUINDRpO85Ks/6ywrKS1hV6o4X/yy\n6CcApnwiQdjLyqrtXxbNkBBV02a8w//eeabO+m684jYALjnn7ww+q0+dfZk2YwoA/337KZ/Pw1sd\n/h6vtMHP6JPOZNzo8wFI7iRJVKKjYsjNF81Iyh4ZkwW//wLAu5+96Xf7vo5FQzz9mmQg+njmNGfb\nzVfdCcCFYy/1qQ4bb/1oDIHMLfd51RSsWidZ6E65REITxscm8MkrMwOuIz5WArz7W0dLJygocL1A\nz269AAgPi2D1+pUN7H1w0j7WMyPXjuy1lFc27VyuD9vha0AnSQoxuOtZlJQXAvDNmucBsW+tyab0\nJbUcq9Pyt3k4XtvM3jil1vGLtn7hsV92UarXYw9VDtR5YWiZdEyQ58+Y/kc5GuTkJAk9euvHHzsJ\nuuwsrP4m7PIHo0k2GAwGg8FgMBhqcMhokm3mLv6Zb2Z/4bEtLjqOE46VwOf/uGAyAO1bS3ifB56/\n09kvKChYfx7Y7xbXXnoTAJefdxWzF/4AwE8LxMu7vKKcNkmStntg30EAdO3Yrdn6csF1YwEczeUl\n4/7OMH0tbnzg/wDIzc8hZXftNJ2z5nwFwLpNa5xtp4+Q+gYPFI3qf567s9ZxzYU/c8t9XjUFln6T\ntlcB7M9A6wjk+JZKeUU5464e3eh6LtArFplZ6QFrklekfA/A+n2Yrr4piQyN8/g/ryR9n7afnChJ\nno7sMMLZVmlJYoaCsux6j/VmGxtIFCN/j/UFMy8aR2PmhaHlsSMrE4DU3DxKyuQ6rtklkWXax8fT\np4PIZ710Qq0+HToQouWyiqqmtYE/5ITklD3bmbtodq3tX/0k+dvffvpDAEYcN0oKmliYaQmMP/1C\nAOYums2dT9y0X/uydadnrviRx5/iWPIvXlH/A2P95rUenwC9e/YDYPAAMfz/9pevm6yvDeHX3DoI\n59XBSpBeph8yQF68Zv38ZcB1FZdLeL/sotTGd6yJCFLBPu9rmxjYWM24zOkNO15xQakIPkopokLj\nARjd+xoAvvzjGaqsSu8VtFDMvGgcB+q8mDLlcn7+WTKO/utfInN8+ukyduyQUGgLFmwG4NFHx/Hm\nmxI/+pZbxKTk4ovfpLRUQt9NnDgEgD17crnvPnEsHDLkMQAmTTqeCy4QhdfTT4tS7J57zuTss18C\nIC+v2umx5y3JAGx6ZlsTn6l/TFu0uNY2O15ylWVx/5ee9+Ca/zclB7ZK1GAwGAwGg8FgaAYOOU1y\nQ+TmyTJzaRM7VrUkKipk+aKisnYAdkPzUd/c8jXs2eSJN/L388UMpakdEJuS9m06Ok6bR/c7FoCI\n8Aj2poumbM5vkojhpXef83q8PTftcz37lHNpldgGgIwsSdQwe+EPvPHBi4A4SNr06yVLr28/VTts\nnr+h5V55ZAq9e8jqRGRElNMnu181acnXpC4idOg/X7A1ntFhiQDEhic1S5/qYkvGMgB+3igh/BIj\n23POUTLP2sf2AGB49wuZu3ma9wpqMOo8MaHZul6cso8aeiKtO0gIuO8/egeAk8ddzPoVotkq17/d\nxDbtKSkWx7DE1rLkW5Cbw4JvxdyqpLAgwDNsORzK82Jf4XIFM326hC479VS5d8yatYa//U2yj44d\nK0mk4uMjGD9+IACtWokj28CBnZk3T8JnlusMdYMGdSUuTkIdBgdX60A/+mgpALNnrwfglFP60Lmz\nXKu1a3c309lVM2Hg3QB8suKRgOtoTue8+jCaZIPBYDAYDAaDoQZGkwxERUZzwjEnAtW2rK++/8L+\n7FKz8uN8yfd+3pkXcen4K4DqJAvuGjlD4znU5pbNo7c/S1SkaF6ffPUhAIpLiunVvTcAsVGx9R4/\n7jQJTWhr39/+6FUyczKAas30xHFXEOoKBeCZNx51jl27UVLONiYcns1zbz3hfH//ebEt//KHT/lk\n1vSA6msuKqvKCQ6ShBh24gtfaROT7PO+diKOHq1FC9U5oa/TbmVVuV/tBkJKjmjC7AQRe/O3Okkr\nRva8DIC+7U8iXYf7+jN1Qb31lRSJNnjLGnHE7NitJzs2SxtHDJR06aUlxezeJud9zEliD5qbmUF0\nrNi8ZuwRh6KczHTCw0WL11I0yWZeBDYvmpL44RMJbS/a7IJVYhNctOFXp7yyssrjE6rDtBYUyMrF\nhx/+zrvvLqpV99//Lve4mJhwAP7zn2848cSeuo7q/UpKal8DpWptIqqHzN8j7u1eu9AL6x/a0vBO\nBziHnJB8yTl/55Jz/l5re3auGMrf/+y/gepoDwcjdozn8LBwrrv8ZqB6WXvOrz85ERpWrF3aYF11\njeehiJlb1RzWpQdfzxah0v18Fy6d69PxIcFya7ry35cAUFhULXQsXDoPgM7tuzoOtu5CclNE+rDZ\n+Nf6WtuycjK9bt+f5JVkkBDZHoDO8X0B+H17/U6rSVEdAegU39vndtbp6As9WmsTGlcMw7tfAMDc\nTbKUva/jBq/TAk+b6K6ACEMn9rgYgMxCWUrem/+X12MXzPrc4/95X3+CsqMX6Xnk7oS2Y5PksrSq\nqpz9rCb2pm9KzLzwfV6Ehck9x3aGAzFfAFi6NPAYyxGHHUNoexFcy9K2Ap5Ccn18+OHvALzzziQG\nD5YoU5GRohiYPHkaW7ZIFJF77jkTgE6dEoiKCgu4r4Wbi4HmddyzzV/a6c8VKfJ8OG/g3WQVygun\nbeqTVrDNma8d48UEZUDHU5y6lutj9+RuYlSvfwDw04a3ALh8yJNMXXw7AKf0uhKAHzdU53rwFWNu\nYTAYDAaDwWAw1OCQ0yR7i2UbH5vA6BPlTeyefz4MQH6hOCI0FIbsQKS0TJakHv7fvU5mwTNGSp77\n00eexdhR4wBYvkbeYu9+6hYAMrMzatW1cOk8fphX91L2qXpcTxh0YhP1vuXiz9w6GOeVO7MXfsd5\nOtRgeJgs4X3w5VS2bN/k0/G/am2xuwa5Jtt2/cVJQ//WyJ4eHGzNXOVoDNvFSvbMET0vY8VOMa3K\nK5W4o2EhEXRJOBKA47udC0CVVUGQCvWpnZ3Zkl1yc7rcG3q0PpY+7eS3nRgpGsg1e2S1IL1gu7P8\nHR4ipjeRoXGOBqlzgjgqfbbyMf9P2Avzt0iIxaSoTrTTGeBO6yPhvz5Z8QhFZbk+1VOfZti9rCVr\nkG3MvPB9Xjz6qDz3XnppDpdcIiHVMjPFHKcxmuSQ+HbO95KtnvHVJ0582/k+adI7zveVK3d67HfB\nBW84GmTbdKKqynIc98aOFQfm8vJKqqo8NfbvvFNba33HHZ/X2tac2KH32kR3pX2cXOflOz1XVOPC\nWzNjlWTvragqA+D8o+9zNMlDk88B4Ms/nnWOOevIfwHw+aonnPB/NoVluaBDE+aXZgXcd6NJNhgM\nBoPBYDAYanDIaZLrSvhgawA/elHeWm66QmxZLrrh7Fr72pn36iI4+MAZ1l2p8sb6xgcSWPzND19m\n1LDTALj/RrHzvOPaBwC47ZHrax2/LWVLvQk7Dj9M7IgOBU2yP3PL27zyBRXkxduiBfLw/+5jw19i\nvzlxnDiHnjVqPPOXzAHgsZceACAj23tmrrSsvQ22YVVZjoPLoc7ylG/pliSh72zNYZ92w+jTblid\nxxRo7cr3617jzL43+NXe7I3vAFBeVUbvticAOFo6+3NfY2urvlv3KucPvAfASShxWu9rmPHHM3q/\nQyf0pZkXvs8LW+P68ssX88YbYs/cFOHRVGi4870it+H7Wl0UFZXVWeZuR90Y6rNFjugk5xF/dAyu\nOHHK3PGeb+MTEyYhAf/W6wq+/fNlr/vkl2Q4GmQbyy35S0iQaNLd93EFV9tf25kVbXvmlOw/iQyV\n74WNyLp44EhzzUxlpVyMv3ZIhpvjvQh19tJvq4TW9dbVtpUsr9gmGwcSlmU50S+GDpQb6d+GNT6l\n76GML3PLsizv7sY1aK1jBbd0qqoq+ejr9wH4dJbEKz7tpLHcfJVkGnzoVllWm3z3JK/HW1X7Jybm\ngUpZRbGzPD2gk/xeuyUNIC5C7lXKbdlxa6Ys+S7XS+4VVeVOymalfFtctCMWzNk4lTW7fwGgtxa8\nbMecmLBEQvRDrLRClq1LygtI0xEGtmf9EcCZNkxRWS7frXsFgHOOuhUQAe3EHmL+88um95ul3ZaI\nmRfVNDQvko8RJ74PP1zqRIsYOlRMVNavDzwTYlVRLsExreSfA+SlPqKzFogHxhIcKdc+upeYxmx+\nehsVhf5lLizUJi2z1r7IyYdPAuDbP0UxV6HnTEOOneWVEunDFpbl2GqBObMwBYAhyaKAWrVrNicc\nJs6ja/f84ld/3THmFgaDwWAwGAwGQw2MJlljh5yyzQPSMmsvi6zfshaAoUcPIyZK1Pju2uLYmDgA\nhgyQ2IV/rFvRfB3eB3Rs1wmAvHzfHF4M3vFlbmVkpZHcqVuddYSGiuZlcP/jmqGHzYutSZ/58wy6\nd5VQSONPv2B/dikg7BjidixcX3l5/tXO9+7d61+Fqo/27eX+cu+9ZzJ4cDIAgwZVh777dfHNHtuW\nbP/S57pfWXBNwP2yY8+mbw7cucnmn+IvxQuLYaIkG2PJFhm//m3hbFFGUqqTVg7tBO//UbuOFxaL\nVrAg71oA1mfI8QDHD5fPj9fK8VC7jn2B+7xoTsq0k5w9H/bFvLiwXyuyS+S5sT1HnMTm6fBi445I\n5Iv1YtLRq5X8lrrEhbE9W/pZVC7a6zGHJ7A+Q47p3Vr2W7gjn46xokn8bs0/ATiiVQRVFSXOd4Dk\n+DASIuS++8Hqaofz1DyZF68uuLZWnzsWiQnGrl3VISN79Gj8yl3prnVEHiGTztWqCwAV2XsaXW9z\n0P2fXTz+3zk9lbIM0db2vCUZwG8tsiBa4sKyHJbu+AbA0fLO3ezbys6ibWK2OLr3/zlmdvY2gPT8\nbQCM7ClZNH/Z9D7tYg7T398NoM+C0SQbDAaDwWAwGAw1OOQ0yZ3ad60VNiouJp7TR4wBoENbUS08\n9drDtY59/4t3AHjuvld46ymxs/zqx88AySt+zqnnAdWapnc+faPeviR3EkeG00eMrXe/+hzjkjt1\nb/D4mnV8+eZPAMz/fQ7bdsqbdU6eGLbHxsQxcqgkaLAzmz3xyoMN1h8oyZ3lTS8+NgHQ46/fEm2N\nfE5eNrtSxd6ooKjl2nk3Zm7N+e0nJpwpge6vu0zC2qxYu5T4OMlgZYdTqyt9fZh2DomOipbPyBin\nLD5OxrZ9m47O+BXqTGNVVZVe63A/3q6jfRsJ5VRfHTavPjrVsW3ftVeuXauE1pw6/AwA/vhzufcT\naQJsrbu92hPIWHhjzYZVgPxe12+WVaWsHAmhlRgvjin1/VYBHn98PAATJrzm49lUs2ePaOauvXY6\nS5fe5ffxBwK5OuHnub0hXvs7jdb+XhsyISnStzrO1Xkw3OvYIJfKpzoMjaOs0uL7zaKRPbeP/DZs\nrbA7tub3y/VZjO8t+9lmu5/9mVlrv/P6JPH6MlmJu6y/rMrsyC2lR6Jc6NDgIOdYu11f6dChtiZ5\n3LgBADz//GzKywPRoELesm+IPELssqOPlEQYxZsWB1RXc7PlhR1AtZNe0nFxBEeLmBgSLQELQmKC\nqcj3byw+WfGI831P3maPT2/7eNu2O3ejx2dNbNvm6Uvvc7a99/udfvXTG4eckHzSkJM5acjJHtuK\nS4rZniLC4j1PiUH/D/Nn1TrWzhZ2+2P/5O8TZKnsmktkycfCYt1miRX5zOtSZscZrgs74kNDkR/q\ne/CeMOhEnyJHuNex4PdfADj2qKGcfpII2BER8uTIys5k83aZhDfcJ1lqFq/0LTtQIHz80jd1lv3v\nwersOE+/Jj+Wj2dOa7a+NJbGzK0Xpz5DWbksa516ogiSF58ziYysNABmfP8JANtStvLEnf+tdfwH\nL8oSaqd2nWuVjRt9vscnwHNvPi7HfVW9DNVQHe7H11WHTcqeHUzSv5EELegXFOazZOVvADz/9hO1\njmkqpv1Xlne7dqxtvuLrWHjjkRfl5vvva+7l9mvuBcAVIl7e9otAXb/Vp56S2LMjR/YCJIOWjR0f\ndeLEIYwbN1DqdckDac2aXdx114x6+1UXw4b14IQTRMJ84onvfTpmyRJ5qKxaleKYhjz/vERs+eqr\nVUycKPYQ7v1cs0ayZNn9/Pe/xUGsZ8+2JCTIfcXuxzXXTCMjQxygb7vtVEBS8E6ZIveYqV+IiVqw\ngkr9QhishaZKLy+Im72EP526qvYx7vU1dHygTBh4t9cHfU1sJ7jTel9Tp6f/gU5osOJvh4l5UE6J\nRF7oEicvr10TwuiWIEJYdrGUjUyOI7NYhJySCjG3OPuIRP5MLwIgSEvO327OYUSypNfeliM2N1Gh\nQaxPFwG8S7y0cXK3OEKD/XOSGztW7HtWrtzpxCS2zS0CFZABSneuJWe+PLfih08EIHawxPzNWxLY\nb7smN8fImJRYFuF6rJ7Nzwu4vuKUEo9PgIiOMratRybhihPR0dfoFgcyxtzCYDAYDAaDwWCogbLq\nWr/dl51QqtYrzxVnSJ75+Ogwnv1Ylman3CGah+c+Xs5feunxvbslpu+4e2prcKIjXA2W23V4Kwd4\n6B/HMfM3ybe+6E8JA/N/Zx1Japa84S7fKJq+d+8ezVl3fgVAvo5nGBEWQvcO8jZ9+WmSRejjORuZ\npL9f9/wcr23W5FAfix4hIVwUKeFnHspruU6EYUrxSoLWmrr9rp7Xb/R/VYjWZExEBKNrOH/9Oyeb\nIn3MmAgpc9/n3znZuiySYWHyRh+tNVIFVhUfaJOB32yPJmCCHrNSXe9XxUX19v/eWLk+3sY4PiiI\nGLMzI1cAACAASURBVN3ezsraMTknaTMPu+/nZXiPf9xcuJ9rQ+fpC/WNxaSo6IDP0zaTcHe4q49l\ny+7mmGNqaye91fPbb/8G4IYbJMPYqFG9efzx72odWx/p6RIztn372xxt9vz5t9Xb52XL7gZw+rlw\nocSYP+GEJ519Fi8WDfWQIY8xd66sqIweLSsiZWUVLFp0BwCDBzdNlrX9ga+a5EMF22zCFxFDqdr7\nBSnwJRKkt/281dcQw4eLU/G4cQOcuMNvvinxkrdsacT9LCiY4Cgx80o46TIAovrJimPZ3i0Ub5EV\n58oCHcvX8j2TY/4KMWm7NEruf+8VFjIhUlZvPiuS+2DLzwu5f7AsK9aX/Ywm2WAwGAwGg8FgqEGL\ntUk+rq9kB3ryg6XOtpmLRIt5wpEdHO2pK0Tk/Pjo6swruYXV2rSGyu193Mu97VMXZwxNBmDKrLWO\n1tSm2C0LTut40Tzdd/kQzrtvpk9125ixqE2vEBcZ2tEqVKssgoAjXWJLtqlC7Nu2VFS32zVEpnvf\nEBe/lck5ZVfJe3Zfl4u15XLMYO34taSslN4u0bCv02X1EQJOOPR/Ztc2duyj6xobEcnkLHFKCdN9\nL7Ysj3KAyVmZHuUAHxcV8rHWGn+QJDajV2TVb1gZpes4LTzC67icFCb2gT+UlNQ6NiFI5sykqGjn\njXqVtp2erfe3gHcKxc50VHh4zSocOgYH019fn9/LpI50N2e5I/T5A3TWYfPm6LBrFT6qhaKU4jSt\ngfd2rh2DRTva3xXqtQ/1jYXNO4UF9Z5noEyefBJDhogddVaWXOOOHeN9Pr5rV3FUmjr17wCMG/eK\n332wnZYqKqqo0LahoaHVj4nJk08C8OhnzT7++KNkWnz33b+Tmyu2ol9/vcop791bki2522U3SlPX\nQggNjnASJYSFyG94/V6xtd6auZLIUFFcDeoifiBtY5L91jyfN1C09nnF6RSV53m0pVCs27sQgAEd\nT3GOWZ4iqwl7cjcBMLbfTWQUioNWpEtWTCyq+HnjVAA6xh9Rbx12H7IKxRY9whVDWsE2AH7fXr0K\n6Y8m19u+vuYT8rZfIAvk9qr655+vcGyQbWfZxtD1thl1JhEJbdud0LaBZyK0Ncn28+na6Bgy9DMt\nEA2yHebNPfOet22HEi1WSA4NkYdZmZvBvP091FWdFvqILrK8/dA/quPH3vWG3Jgsy2qw3K7Dvdxz\nH+/9s4+NCpcHe3pObc9dd3p2kuWWzNxiWsXJQ3yXdmBpCDMW1cKnbWowKjyCB3O197Reaj8rIoLX\nCiRiwWM6isEVWZl00eM3OVqiHHxcVMQz8TIWN2SLsHpFVDQv62OfipdjT05L5dJIMSO4K9e3tJYh\nOoNVbFD1Ik2+vmmN0ALYR0WFzvmU67FTNcrtc3Yvdx8Hf7BNA14pyPcYFxBTjRzdv7u1icH5mdUC\nS4S+uceoILK0MFno5xOokxZ474iNdcxCntZjfH1OljM+tqnKe4WFTp/8NQcbExHBK17mQLy+HnfE\niqDyQVGhRx9ArlN9Y9EUhIW56iz7179Gcfjh4hBoC6YXXzzY57rtB/rYsZLJasqUyznnHHEMKy5u\n+CUPoHNnGROXK5gQ/VLtnvL2X/+SyDfu/azZRzuG8xVXTCU1tbbz0KZNYpZ19dXvAZCXV/cLyYFE\nuCuaeZunA9WZwM4+UmJXb81cSVGZjMW8zeLENUELm361oQXiLzZO4eJBEnXoPe1sOWHg3QxNFoew\nL/941jnmrCMlWs7nq8RZtm3sYfywXiIv2Rnv3GmojrhweTmfseop51zPP1qcWt2F5AONVq2ine+2\nqdHVV0t843/8I/A4u/siy16V22dckDEQaErMaBoMBoPBYDAYDDVosZrk9TtEu3NU99Zs35uvv0v+\nc9tpDGD1X5JN54b//lKrjugIV4Pldh3eygGy80tpmxjlsa1DUjRrt4omblmhaEXOH9GTz+dtrnW8\nza9rJFTK61+v5tnrJGTbBf+pHQrMG2YscMweeuiQW5OyMmrt81VxMQu049rwMNHkdAgO5lS9/D5V\nmwQsLSvjcG16YZtWlFpwjP4+s0Q04aeER7DVi5NaffR0Sb23x1T7BDyunb/C3UwnBum2HogTjeVT\neXke5QCDQsM8ygF+KfVf6zajWM5nQWmpx7gAbKyocswnyrzoqXfrbHlbK8vZo7+7Owf6winaNCE2\nKIjT9bVI1NqOfi6XU5+9TPh2oW8rLN6YUcccOEGvQNga/tPDIzz6AHJe9Y1FU/DDDxImctasGxwt\n6xVXyDL37Nnr+fTT/wMgR6/GbN1aPc9PO00ceM8+ewDJyfL7f/HFiwB44435jtZ9925ZYXnwwW94\n7jkJdXfNNb6FTszOLtL1XepolR96qNokavbs9QAe/XTvI0CivkdMm/YP0tLkflVWVumczy23fArA\n9OkSYrKwsJT16+U+dv/9B64mMr8kw9Eg24SG+JedsSFKK+T6VFaVU1Je+3cSEiTmTO79cAXXMJ8r\nTvOqQfa1jvySjFrllhV4iLSWwowZK2ttGzhQwmG6XMEBh4Hb/viYRvXLF/pqM7a/KiocTbKtAfVm\ndrHkqz+d73c+cRMAsxf+0JxdDIjQJBcdz5VUmcGR1avmm59vfHZPXzGaZIPBYDAYDAaDoQYtVpP8\n1kzJaDX1rlM5Z5gYtgfr4OCPvLfEsX9tbj6es5H37pbg+GOOE2eV+OgwHpy6CIBK7TVw9gmH8emD\nZwKQXyT2f+989yeZuZ72uZtSclimQ6Vdcoo4SEz7cX29fTBjUa1lzNDazFHh4fxUw7mq1Iv9qgIK\n9LF2CDOAmCA7fJocs62ygmND5W3ctmu+KzaO1wr802raDhT35ObUKvtTlw10hTr2z18VF3stB3i5\nIN+jPFDqGhd/qLLEMSgQCt1C0H1aVHd4Nm/99Je6ztW9D0C9/WhObrnlkzrLJk+uX9v73XdrnU9v\n+9YM0bZw4RYWLtziV/9ycmRc7AQn/vYRoLJSfm9/+9tzzjalV0nmz7+NBx4QbfGYMS/61beWjtVM\nqw/+UF4pqyi2Nhiopd1uyLOhoTpawnk2B08/fV6tbYsWSSKoxiQT2Rf8qlcYo1UQXbUPTiCOe1E9\nZOXjiHurnQnDO4TW2maz/iH/7i/+EprkImO++ANFdJYVSRWkUFr+sbxlCGpiWqyQnKZv1qffPoMw\nbURf6jZRC7QjSl0xfe19GipvqI5dGQWc/C9JPR0RJsPlHqnB5uaX5hGiU2LaUSTc97vtlfnOd/co\nFb5gxqJaOH5Emy78NyGRlErfblwfa8Ho8Tjxwj8pPJxo/dBeqqNcBAHHaCHZjoZwlCuUNeU1HzCB\n86M243guIZFntdNYsjb7WF5WyvzSMqcc4Nn4BI/y5qBDcDCX6hjH3bUpy+2xcU60ijQ9xqvKy7g7\nVsbPHifbjKRzcAgX6zid7nW85bwIFOnzSWSAfgGwHQLvzM2hbB/EanfvA8AAV6hHHwBaBQXVOxZh\n+iXh4qgoj3LAOdfMKhOV9PvvZSn3/fevcISLmBh5wL366tz91q/9TZcEMZfpliSpjmPCWnFidzGX\n+TNV7okZhSmNamPRti8AGN1bzGGUUs62QOqwX258rWNQF1GObEhbRI9WgwBYkeJbtsf9zT33SMbR\niopKx0ndfuFr6dimY0e5QllSGvgza+XkdU3VpSah8K9iXLHyDAyOENknrLVrnwjHNsbcwmAwGAwG\ng8FgqEGLzbhnMDQXYUo1ydJ+Y7Cd9OwQb9504uFK1Vu+r3HVCGVUHsAY2tpbe/z3l54mwm0OHBi6\nIoOh5aO0SdvInpeyYMtHAJRVHhgh/gYN6gpAu3ZxpKTIEv/KlTv3Z5d85nK9ApZfVUWU1ipP1ytg\n3p4dB4rjXnBkMFaFvk+XyZ1ahSgiOsrKVNH2wM0RTcY9g8FgMBgMBoMhQFqsTbLB0Fzsby0yQIkP\nffBln31JIJrjmhS3kHNqKf0wGA4m+neUZDN5JRn0aS/hPVemtDwNpTcGDxZn9ISESCeLpJ1g5Kef\nWpatbk2WaZ+VsRGRjgN4S1h9bCzh7cNoPVL8SIp2iNbYqrQc7XJIrNgpR28rIylSxNleSeJ82Dba\nxUtLUmtW6TdGSDYYDAaDwdBoVu/+GYDY8FaEh0Q3sHfL4q+/amfXtLNOtnRsM7b0ykpaH0wZ9xTk\nLBdr3OjDJdtkSEwIO96RlOhJw8QBPnRnOcnxEsu7TDv17cgtJVRHwShrhKPfQTSaBoPBYDAYDAZD\n09DiHfe63vGN1+0FK78DIPO7xsfaTBgxidihtWMkgn/ZcoLCJQxW55s+anSfGqI8S0IF7X79miav\nOzhSwlpF9DhWPrsPBsCV2IGgKFmGCg6PAaCqrISqYgkFVpYuWXBKU/6kaONvAFTkNH65w59+uhI7\nAHj0s6pMHEfc+1maIo4LTdHPuuZoc+NvJie/+mmJk0RVqSxxVZUVUZG9B4Dy9G0AFG9doT+XQ9XB\nsLjnG/WNY8HK75rsngR4vS8divckkN/7gXhP2h/93F/3pNSnJcNjp/jehLtEk7x2T91h//zup1Xl\ncU8CqMje0+LvSa7ETgDEDb8YgPAuRwJ6vqjAdZX2veCSSJ3lsqjuTIruuDvurVy7DIDU9D0B96Mx\n3Pfs7fWWJx4nv5uS3fIcV8GK+EHyO0ufLdl+yzLLGdhOxmBNmsyLrvFhbM6q22nUOO4ZDAaDwWAw\nGAwBcsDaJEf1OxmAnHnvA1BZVDvDmcF3gsLkLSxu2EXEHjNWbwyu5wi9S3iUo60KSRAtbuThx5Fw\n8j8A/UYPZP/8/+ydd2Ab5dnAf5IsD3k7tuPsPUhCNiSMEMIKm4RZZumgjLILpaWFthTaQvlYZZSW\nUjaUvVcIhJ29QwLZw4ljx9uyNSzp++N57yRZki3b8kre3z86373v3XPnu+fee95nPGl+7Xe2nIZ8\noXI6Rh4GECZn5adPAiREzh6Psm6EXrukrAIAUgeNByBz6ukA+Oqrqf7qBQBqV7wv/Tt5hmpEgVju\nGv1eMlLET82oHOYP+Kn3iMUuO60QgLK6HeSn9wOgtFaseIN7jSczpRcAX295GWh9+qr0ccdonZQg\nQp91QJ73/UQndYWcncFBRUcCba/O2SIWa6Q+zyroljrJICm7kKJL/g8IypwIQmcaCm1yv12bmUW9\nKmj0hDO+arETx05JmExtoTlLss1ho2qZOBqEpoALqEkCT7nXbLu5UnR1Vopciy2ViUk92GMHyZYk\nqdyVOVWmG4wXU1cSaJR/WO0yqVpnTc3AmpphLgfXyXSbTU2/he9EbgRfXSV+l9zkob8+l1T2aqwo\nTojMyX1GAFB4zh9EJkcUmdpB2pDJAFTZnm3XfjpDzvbKeKBic2STd8KVAKQNk8Fq2et3EfB5m+sG\nhJfHbs8rrLJeXhiDe02gwStKNSVJXkjuRid9s0cCUNWwF4DS2q3kpvUGJMgIoNZdQblTnitjgN1a\nLEnJWiclgOQ+I7ROSpCcncnG0sUAeH0uxvY5usvkaI9OSjRZ086KGBw3bF4CgGvHWvz18gHf65Tr\nAPCUbKJulWQEsaXLB79jzFEkZYieKnnxt9Juz0Zzf4tUdotv3G7OTHN01Kl0OvFmt7BvdnHuWHV9\n6qTioAULb2wQd4z2fB9pdwuNRqPRaDQajaYJPdaSbJA5SWrFV3/7CgFv26w/iSLQKF8wFfMej6t9\ntKAFI4Bjz5PXJE6wGKT0H0PhOX8EwJoS++vTVytfY/UbF5pTPIYlyZqWRXJvyS+ZOmgiEG6Ncu9c\nB4CnJPjV25Fy1m9cCBAmpzVN/PND5WxqMXPvXNcuGZvSWCPphKo+fyZh++wIGqv2UP7hIxHrLVZR\nDZZkyTmZlNOblD5ijU0bNlW2qdmcUIxtebN/Sfn7D7R4/FNGgvrwZ8G24Pq/Hiu/f1ggv54WYnD2\nOaUyVrlzFwFlkzamfAMEwpYNvi+Ve8WoEhYI+KO2ay2hOgnoUr3UE3USQOE5f9xvdFJXyhlKZ+ik\ngbljAchxFLF0R9uCBxurJIAsll4K1UkAKX1GJlQnJZrUQRPM5epvJIC26ovI2YG82VcB0FhbTu2K\nD8K2VX/7Cr3PvwuAwjN/B8DuJ6/F3yCzZpsbGwH4c3YOzzjjC94z6M4V9+JNAZe5q5F1KmAvJ1Xe\nXZkpVnOmsj2zlNqSrNFoNBqNRqPRNKHHW5KtaeJLlzH+eGqXdU3am56GLUN8fArPvj2qFcTvli/R\nyk/+BUDdWkkQ36JjjwpWcQw7hOzDzwOgZsmbnStnPM5HVhsO5aeWCDmjYViLnOs+S+h+E43f48K1\nbWWr+hjWrrwTrwbAMWJ6RJuM8cdSu+I9INx3zmCIMpgd0hdcjaqPMgJZLXBIv1aJZBJqAY61HNFH\n+dy21C5eQnUSoPVSHIQ+6xDdOut3O3umTuoCOaPRGToppf8JALi8dYztMxNofcU9I2Vna/RSInTS\n1KmDYu5/6dLtccvSFCPgGUKCCKNgzPpY7WmR23xeKuc/AUDRj+8DIGfGBVR8/E8ATk2VPlsbG5mZ\nIkU1NjZ2vv91onFuqjdTwFV8K4HQFpuFPnNlFsFIAVdW6WVgtrxACtJlWLtwVx3+BMRq9shBcsPW\nFaQNmRS2LuuQOdQuN6JY/VF6aQx6zf4lEAzcCcXfUEPJsxJtauQ9jRuVl7J+40LT7YF2RDl3pJyG\nfImQ80DD5xRlVfbGXwHoc8l9JBcNa9LKQsbYWQBURHkhVSsPBH8AClRMS/+s4Lqb1Xu1JTeL7oKR\nnzVUL2UdMgdA9JLWSc3S0rMOUPLsr/drnZRIObuK0AHxwNxxnXbcROik4cMl883cuRN5443wAXp7\nBsnYgplOfHWVMZsZH1hWlWu7Ke6STQAEfGJVkIBEGSQv8cgAe7XXw8TkSJeTnowxODawpVkpfjky\nh/jiYvkI/K6sIaHH1+4WGo1Go9FoNBpNE3qkJblu1UdmxRqLTU4hKacIx6jDAajf8FWXydadSR0s\nwSFpI6ZF2SrzEqWv3dl6K0iztH6+o6fIecCjrF81i98g//SbIjaHBqw0pUJ97D+8GBx2Wd4Zs+5m\n96du1UeAVNIK1UkAjlGHa53UDKmDJ7b4rEMbrLMx0Tqpo7DbZKrf63OT65D7f2elBCAmwpWpRdqh\nk156SdKyDR9eYC4nRCRnFbZMSU+WlCl52I0gylB8dRUAJPcehsUmSjEsZZ2ajQp4xR3FcP0BmKKs\nx6u9Hg5Jlv/BGmVd7iGTcWHYHGJ9D3j9JGXbw7blTcum5L3w69cvK5m5o+V6hFbZ+3BT+3PVa0uy\nRqPRaDQajUbThB5pSQ40eszgAyM4BiB72lmAtiTHInPyKTG3OdfK9XTv+i5mm86ip8ipEdy7N0Rd\nb8vMi7o+lPIG8Cp33bEqvsUS4oq5trS90nUORtCNc91nYToJRC9pnRSbWM97e551iy22P2/A13qL\nptZJ8XFwX/H5tWClrE78eDvFgtyEtuike+89G4CUlCRz2eCmm15tuywlm3AoS7JRGbBuzfyIdp69\nW+T4/Q4iY4IEQNYuf8/cbs8fCAT94Q3LM8ACt1hPr8rIZJNKB9cTLcgGqb3FMm5NsZI1Ts7XW6V8\nsQemRrT3B2BFifh0f7NDihsl6q7rkYNka7KDmkWvA5Ax/ji11mJWQEodIAEDrp1ru0K8boktPQfH\n8ENjbq/+5uVOlCY2PUVOTRAjar4pRj7T5hjVC/4pBepYFKVg228+ab08/X8k07y7XpLgjrxp2bhK\nZRBbv1X8PByDUmmsldeIp0KmNJN72c0yp7ZUNclms+Bztvy6sSZLpoOaRa+H6SSQymxaJ0ViZCSI\n9bzH86ynFqWQM0UiPkvelynY3EOzqftBcqZmjpKo0NrvneZyxcLWTcFqndR6AgTMSpZdQVt00v33\ni7IpLg7eH0YwX3to+OFbM9tG+sGiG6INkut/+BaQD7K84y8HZMAMMiBOH3t0WHtP6VZzeasaGD9a\nV8vB9nD3hJ6IU+lpa4qVuk3yLBtV9lLXRf5vjx8aDHYclhscRD+zKtKtpbVodwuNRqPRaDQajaYJ\nPdSSnIa3XCpsNWxeCgTrswNkTTsT0FabUFIHHmzm4gzFs3czkMigmPbRU+TUBLGmpEdd73e1XPlp\nTAHc+YUsz9/afNt4sSSJBbfP6eK/kdY/lZIP9gGQd7hK0OyHjKPF+rv79b2AWCCNdEOhuTnjmba0\nKguVt3yn1klxYgRfx3re43nWXSVuPJUquEnNr9asrsXXoHx4VH5ib6U3vhzqseTUOikulu/80Fye\n0E+spqEVLTuLtuikvn3lmQ+1JM+dKwGbDzwwH6+3bQ4MzvVfkjHpJAAaNhsBgZG14Iy80K5tK81A\n0abW49A+1V+/aK65JF1cEpKBMXZxVbixqiKip8Ghp49p3Ul0EX63H1uaPHu2HPn1eyOf42dXl5mX\n0thqsyYmhaK2JGs0Go1Go9FoNE3okZZki0pxApi+yaFWm7ThsmzvNcC0OB/oJPcZFXV9w5blnSxJ\n8/QUOTVBkouGR13vLdsWs8+PVJ2BjGS4YqosD1BuZUYFPoCX2mB4NXzX9rwt/mh504L+ao5BYvH1\nlHtwKz9lS5LYCsq/qqLXEWJNSsoU1WgEi7REa3QSoPUSsZ91iP95T+mdTGpRStg6X4Of1D6yLrWf\n+Cem9nGZyxab5BqMN4BP66T4magq7lmw4PVJMFlnWpAN2qKTTjtNgupWrtyJwyHWWMMnua1WZJCg\n3pJnItPRxaLsjb+QO+unADhGHQGIL7Uhe9VXLwDgLg4GJ37sEh/eEp+P4Uk9clgXldSiFHqfLL7t\nRjwJQNln4Vby4XmpOJLE0rxqr8wWnDYyl7e/l3btqbzXI6+mJSmoFF071gDgKdlIctEIowUAWYfO\npfyDhzpbvG5JTKVRmqA57gTRU+TUBMmcfHLU9fUbF8Xsk6o0T6MfXlwbfVtHULVcBkhZYzPwu+TF\n11gtA+GAP4A9Rw5et7G+VfuNpZMApZeCOgnQeonYzzrE/7y793pMd5lQXHukpGPotmjt4kHrpPgx\nKu4Ny59CSlJk2e7Ooi06ad689QDcffeZuN2iE+6556PEC9cCfnc95R8+DGD+tsQFDnEvcQUCpKr0\nQPfVJibx/IzTxPXD6xa3pkEjiyjsnwtA6S6pIFjYP5dNayXyus9AyQX92RvL2Lp+T7uOHQgEqFkj\ngXqVS6vVysh2dR4/BxfKNdihyrkWpNvb6mEVhna30Gg0Go1Go9FomtBDLcmRKU5qFr1O/hm3hK1L\nHzeLqi+eBcDnjF0z/UDAlpEbdX13CzrpKXIe0KhAnNyjfwxEr2Llq6vAuSZ2/ranJEaFgdlQJ14P\nZhU+gEyZ7eRkNTn0+TZwhhSfag4j9ZtBxaLqiDbOjfUEAuGRHhabxZyCr15ZG9/BFLF0EhCml9LH\nSR7Zqi+e1TopxrMO3et51zopftLsmQCkJKVhzJ5YLSrgKtCBmXsToJNWr5b/55dfbuwAATuWMlVp\n8Fmnk3McYsE3LKDtdXYp3izJ6g89ToL9Sosr2fa96Nj0LHFh2vZ9STC/vdKryantT0Xnd/txqLzI\njsHB1G7Fr4TPCu2p9bBJvUBmDRH3uo83VSUkV7K2JGs0Go1Go9FoNE3okZZkLJHpeJwbviZnpnxd\nJOX0Vs3sZE49DYCqz5/pPPm6IdbUtqfp6kx6ipwtkVw4BIBBv3k34fve/rdTE77PCFTKKyO1WVJO\nH1IHiCUhY8JsIFgBKpSAX3z59r17H353y36943vDLRKbQq2yKP/iHfjdDFlep3LBnzQcrvmgTWcS\nlUCUSI6ssRns+0Ksu62uyhZDJwHkzNwbppMAMqeepnVSjGcdutfzrnVSyxg6aWShFM1Yu2cB/XNG\nAwm2IFttYToJIHXAmITopKlTBwGwalXkDMG+fdGLk3QX1ntlmu2qjEz2+cV23JwF+Zn7XmHjth8A\n+PNDv2t231u+2w3A1g3iXxzwB7Co9GqGHrVYLVF1ajRuu/YuAEYMHgnAJTeeE7Ott6qR4tfiiyVY\nvbc+7DdR9MhBssUSJf9dwE/N0rcAyDvuF+bqzEniwG9URQp4XR0vYDckZt7IOAYyoeSfJlG60fM3\nNk88g7ueIuf+QnLhkIS8NI0Bw763/w4Ec362hMMOv/tUlncor4gzD4IsFQf38GL5/dtxkX0TTfXq\n1rlYhBJLJwHULH0rTCeB6CWtk5oZJLfiedc6qfuwqUzyANssNvIc/QDYXrGmVftI1GC+tTppzhwJ\nUDvkkMER2/72tw8j1iUKi13cCHJmXASAY/SR2DIky46/XoLvXDvXUbNISmN7SjZH7GOpxxP22xKj\nh48lNbXliqihhA6Cmw6I4x0gAxw8SlxhBg8Y2qrjN8eo/DQ8yrCxtVL06ezhOczfIi+Vxnakt9Du\nFhqNRqPRaDQaTRN6pCU5FnWrJP1MzpHnA2BNzcSaKpVoMiZI/sbapW93jXBdTqzqM4lwbU8kPUVO\nTcAnU3zOtZ9S9cVzQOsDZL8rgzuPkeUqZVC1WmBCkSxPkRlVclIj+/YU6lZ9HKaT5DdD66SYzzp0\nr+dd66R4GZg7FoAcRxFLdyTeraMl2qOTHnxQprQ2bSpt9XEtyaKgsg87l/TRRwJgyypQx6/GtV2s\n2IaLla9O5fm1WOl97p8ASBkwNmK/tow8ANIPmkH6qMMBcRsBcH73eavlDCUrI7vlRh1AZkZWwvd5\nxIBMMpLF5a1KJdpPslraZUE20JZkjUaj0Wg0Go2mCfuVJdnw7atdIRE+2Yeda27LOuQM2bbsXdNX\n0Ox3AFgE/G4nNkfkl6NVfQH7Xd0jMKGnyNkSjTUScbY/B2eVvfFXABo2LW7zPtaWwvXK3U8ZAvih\nHOzq8/23KoDvhda5NXYrAl5XyzoJwvTSgaKTgJjPe3d51rVOip8Uuypq4a1jbJ+ZQLDASGfQUoO9\nggAAIABJREFUHp3UFguykfes9zmxrcFJWflkHCxBFUZquj1PXgtIJU6jT8AnFlDXtpX4G8QXOSmv\nr+y37ygzmLrXydcB4N7zA42VbS/W0REW3a467mvry0lSwYTl9fFVSY2X/WqQbFC79B1AqlsZ0eRJ\n2RJdnj76CJzrvwzv4O/8spmdTcBTD1EUvSW5dVWRape/B0DDlmUx2+TMvIQkNdXUWjpLzpyZlwC0\nWc6WMF6cznWftW9HSjHi75gco41Veyj/8JGY241qY7lHXxqxLefICwFo2LyE9pQ22lYVuS5f/bvv\naN+MYrchVCeBZLkI1UlAuF46UHQStPt53190ErRPzpZImE5qhtAB8cDccW3aR2OVDPxi6aWO0knj\nxsmAtKQkWKnunHOmAPDYY9EVUfpB8iEQOjj2OUWhuXd9Z65L6SulzY3/bfZhktHBXjgEf4MEDJc8\nL/nUvft2RBwndcA4Cs75AxDMOJQ19XQq5j0OwI2ZMvBsTcU9u8rt/udf3QO0S4XHjcUCyfbkuNtb\nU6z4PUoXKvnsOUl4q8IHwtWuyPejw26l3tt+PardLTQajUaj0Wg0mibsl5Zkw1HfufYzMzjGIGva\nWRGW5EAHWem6Ez5nlZlXMpSk7EIg+tdrNNzF68N+o5E17UxoozWks+TMmnamWugYq000LHbJa2ZN\nTiPglfryFmUVCHgaCDRK0IkxxWZNzcCeK9eiYesKadfoxpYuFcB8teXmPszpOWWdbKwuNYNYmsPv\ncTWbGsnY5hg5nZS+o8O2JRcNAyBz4ommO0GiuPpQ+TXSw/V0QnUSEKaXsqadJdtC9NKBopOAmM/7\ngaaToH1ydgfsNtFxXp+bXIdE3+6sXAfE70Lk94jbZCy9FKqTgDC91B6ddNFFsr+qqmBqv0MPHQzE\ntiQ71CyQgadkE3tf+r2cR4gbjpHmrWDub6TfKOlndWRR/c3/gObvI9fOtdQsfgOAnCMvACB18ERz\ne3sq7s2e2X3TC9pSrRQe1wuAmrVicc89JJviV8NzJyfbLOSkhg9nZwzK4rXvytstg7YkazQajUaj\n0Wg0TdgvLckGNYtfJ2PC8eov8dNJLhpO6qDxALi2rwbEOre/4yndSkq/gyLW23sNAKBh89LOFikq\nPUXOtmDPk+T6GRNmmymADAtwoNFjJr/3e4370Y8tMx8Ax4hpADjXLSC5t/jkJSlrhL+hhkCjJJE3\nUozVLHsnobJXfvIERZf8Xf0VnhIrZ+YlODd8pWSJryDHvcqQ+vb38KejZdkdYjwdLafdoiXZZhOf\nbZ+vZ1heaxa/DqD0UlAnAaQOGn/A6SQg5vPeXZ71/VknJZqD+84CwIKVsrrtQMcFoVZ+8gSA0kvt\n10l//KPoTJcrOAM3eHCvZvskFw4O+7t64StRAzmNpALVX74gMv/4PnOba9uquORzqdlElCU5KavQ\n3NbainvdnaQsGZraHDYaiuXa9TlDzrf4lcgKfCN7pTGpjwSNVjSIv/KQnJSEyKItyRqNRqPRaDQa\nTRP2a0uyt3wXDZukTGba8EPN9VmHij9q0GrTsu9mTydaKUuAlH6jo67vKnqKnG0hpb9K9eN101gt\n6YYMX1VfXQVpQyWSuv6HbwBIHTQxop0toxf2/P4AeMuLzW22dCljaqQRSnR2BPfuDWby+vQxR4dt\ns6ZmkjvzxwCUf/hwXPu7SQXBzx4Gv54ny0t2B7f/5djYfa0q40ffwr5MGCPXbPFKuWa1zhocaSoN\nlVssEOlp6VTVyPXr01us+SWlu8nOEt/usvJIy0RH4S3fBUDDpiVhOglEL2mdJHSn531/1kmJxhJi\nd0uzd2yKMffuDYAU1UiETioqEnl3765m0iSZJSgujpJ6JwQjPsTAW7qt2faessjt8RY78dWWhf1t\nxLhA0GrsB7Kt8dk+/Srl5OFzxof93ZFYLBa+fVPyeVotseVMyZfMG6l9g1WkqpbLjED6MAeuPeEz\nbZsqXHy/rwEAryogsqrEmRCZ9+tBMkDNIpneDH0hpQ2TF6s9fyCAGUS1P+PeuS7q+rQhci0sSZKW\nxZi27yp6ipxtwcyHG2X6MXXwJDM4r7FKBm111R9HzctjVJIKJX3cMWH7tqZlxD3NGC9VC54CwDFS\nKj8Z/wsQFxKA2pWS9NhTsimufX4UY5z02JLYfU47TtKord+0lsJ8CVScMU2medd+v4o5J0h6pbIK\n+cCorq3C7ZZnPDtTUnmlpqbRt1AGzPf/524AnPWdl++2ZtHrEYPktGFTtE5SpA2Z0m2e9f1ZJyWa\nZTvfN5eNPMkW5QrRUW4XVQueSohO+vWvpb3P52fDhhIAfvnLowG45JL/Ru0TOlAF8Lvro7YziHqP\nxBmkG2jGpWysSqu2pbHRHCS3FLjndIq+64zBsUEgEKCuTt5LWZmxK/45t8iA17m1IaSz/FhskRUw\nXY1+HCq5fp9MuRa7axPzPGp3C41Go9FoNBqNpgn7vSXZtXMtINVpAFL6jMRw8jfdLrat6BLZOhNv\nxS48JRsBSC4aYa43as6njz0agLpVnVcdKRo9Rc62EduSEvUebEV2d+fajs+V1lizD4CaRa8BkH3E\n+cGNKoF93glXAFDyzM00d74Gdiuo2TF8Ic13NpMH3+sVC0Hf3v0pKRUfjfJKkW3imCmmm0Xotrxc\nCcDxKjeG6rJqNm8TndDQ0Lz1pyNw7VzbRCcBWA44nQTgKdkY9qyDPO/d5Vnfv3VSYpk6UNKJBQJ+\nGrwqKLmDq0c21uxLiE76178kBeMJJ4zhkUcWAJCZmRqzvTpIm2RONN8onZdhsTIoSdzRWrIP19RV\nd7BUMY7rlPuiOUuyQfqQNKypcj6134nlu/D4XpR+LKndAurlUeCwc+aYPAA2V8i1OH1ULo8slhmB\n9tyB2pKs0Wg0Go1Go9E0Yb+3JBsYvskFc35jrjMsAJ4933eFSJ1O3Zr5AOQ1sdpAsJync/0XBFQy\n966ip8h5oFK9UKw2GeMlj5stM5gmyUjsnzH+OOpWz2txXyeNgCzl1vfc6viO//5nbwMSwOdv4s+3\nZPVCAlEs8CcfcwaAue2rJQuorROLRmf65IWidZJQt2Z+i8860KXPu9ZJ8bF0x7vmclvLUreFROik\nH//4MACSkqzce+/Z0jelZwyR8pQf8nh7Movd8fniGvqvs6k1LdgDWmzb6PSRO1oFYqtUcMm97BE6\n3m6zsKpEZgSX7BaLc2ayzZhMaFfJ7Z5xBySA+u8l+r2xqoSkHKkEZLHJ6WdMPKnL5OpMjOnArEMk\n8Ckpp7e5zVAqBXN+Q+mrf5aVXVT1q6fIeaBi5Pys/PxpAPJPvTGiTc7RPzazdBj5n6OxtRJmDWmb\nHE0HyEDUATLA+5++1baDdCChOgkgKafogNNJIM97S886IM+71kndmkRU3GsLidBJN9zwcrvlyJx8\nMn5PQ8sNm/QBWuxnVdVZozFGBe5V+v2MT5bltcotLdqdeP41Z9Dgap2cieK3d8v/Ji019vkYuPd6\nzOC9XkdIBqd9n1dG+E8cOzToujGyl7jIeHwB05WvPWh3C41Go9FoNBqNpgkHjCUZNaVas+RN8o6/\nImxTcu+hXSFRp2Okn6n45HEACs++PaJN2tCpFF34NwDK338IAG/5zk6SUOgpch7oONd+BkDWlNNI\n7hM+DW1z5JAz4yIAKuY93ux+rpgqv2eqomauxuC2EyOz3e0/hOgkIEwvHSg6CeR5b+lZByi68G9a\nJ3VzOrPiXjQSpZPaSljgYAf2acoyj6SMPC3NwXeq+l5zcxmbt29s9zHbyu69u+Jua0my4C6T8zF+\no/HsqrKY29qLtiRrNBqNRqPRaDRNOHAsyYq61fPImSGBFtbUzC6Wpmto2LQYgOpvXyH7sHMitqf0\nE5Ne38seBcBdLJWNXNtX4autACDgF3OfNTXT9M9LUV/uyQWDukxO1/ZVAGFyGv/nUDkTJWMsrKkZ\nAKSPndVhx3Cu+6zD9h0fYiGqmP9vii66J2Jr5uRTAKhb9REAnijVqNaUwphHOk7CnoARTJQz40Kt\nk759BSDm894TdVJXyBmNztBJy9d92GH7jo/266SeSJqKUCvz+SiIs+JeT8DmsJF9sNy3hkk3c2Q6\nWx7rvBmaA26QHPC6qV3+HgDZh/8oofs2qv3kHn0pIErJUEyhv8ZyNAwl2f+XT+F3SZSmT/36XXX4\nG2S5sUJKElcvfKXN8lZ9/jRWVTEoc+rp0c4ICCp+47ezaY2cXSVjU5KyCgDIP+1XHXaMrh8kC+5d\n31G/QXKMOkbPCG5QZUfzjr8SgJLnb+l02XoCRnW92uXvaZ2kAq+s9pT9RieF/nYlWieRcJ20/W+n\nxtXuued+CsBFFz0JQHGxVPns1+8Wnn/+ZwB88IHUdJg7dxJ2u+QGXrtWnqlbb32T++8/FwgPMHz4\nYXHV+PbXkvHnCWfnVQ3tDBprGin7rCJsnT3HblbdC/g63pVn//nk0Gg0Go1Go9FoEsQBZ0kGqF0q\nuRyzpp0FgMVmT8h+LUmyn+iWhXh3It8ttsx8bJn5AESTzqhW1R6rDUDFJ/8CoGHrcgDyZv/StDgk\nAiOtjTFN2VY6Q872ynggU/nZfwFIGz4NCFowAVIGjAUkB7Bz3YJOl62nULv0Xa2TFBWf/CvsWQcS\n9rz3JJ0E7ZfzQKU76aSdOyvD/t69W3IFWywWduwQS+lzzy0K+wVYtux3gFiSN2+W4LQ+fSTdmc/n\nZ+9eyXVcaBPL87WZWdT7JSB4f7AqJ+fZKTg2L2xdww5Xp1iQDbQlWaPRaDQajUajacIBaUn21VcB\n4FzzKQAZE2d3pTjdgobNSwHY/fhlpA6ZDED66CMBsBdKOipbRi62NAksCjRKOha/twFfjdRRb6za\nA4Bn7xbcuyVgxb1rvbT3xU7fkkg5bRm5ImuInH6vWGRC5fTs3SLyhciZKBkPRBqrS4FgOrPsw86N\naJM766c0bBQrSWsT7h8I+OqrtE4KIfRZB0gdMrlH6qTuImdnMHWgBMZ9X7qQ4fmSum/Fro+6RJbu\npJN27RJLcmGh3APz5n0HQO/emRQXy3jkyitnAjBt2hAqKqTYSb9+OeY+XnppCQBnnz3ZXPf66ysA\nyFYp4L5xuzkzzdGiPIvf/q7tJ5NgDj19TMS6pCwZmnqrGtn9mvwfA81UBklNsuJuFAu60So3LYnK\nhsaYfeLFEqtCVWdisVgi6iMe9MdhAKz/4+b27dsqDt5Dr5YSiJsf2mFus+fIP6LP6YWkD01r0/Hi\nlTNUjlAZDFIKZSrIXRpZUjIRcnZ3+hfJ/+fan9zMxLFTAHCkSTnK8sp9fLNMStPe/dgdZp+Fb64B\n4OIbpIToxq3BUr4jh0op0uceeD3qQzhxjCiaqy+VIJbRQ8dQ75Kylp8vlBK0dz0czIVqKJWLrj8T\ngB+2bDC3nXWSBFsdd+SJXPenywHwKKXVFvS1SCwpSUkUZcsUZXWDvAh7Z2VhVRHhuenyUlm2bTv5\nGRLAVlxV1elyTup/It+XSkWw0b0PV3L8QK/0vgDsq5OI7tK67UzsL+V3V+762OzfdF3vzKERfd2+\nBrNc8N4a0SFF2cPZXLYMgByHZFvwNrrIdUjfvbXyQVnVsDdC5kMGnka9V6aOy50SZGS8U3ql9w2T\n2ZAxVOam63pnDo3o6/bJ/2xg7rgwmQE2ly0Lkxkg19G3WZmjMXPmSACGDStg/XoZsObny73wzjur\nOe208eYywE03ncC994rMkybJ83r44cNYuVLJ7JaX85gxfQBYuHArP/wQKcsZZ0wA4K23JCvP3LmT\n+O673QAcc4w8tytW7GTIEKnql5kp1cTWrdttltodP76fXJOibF58cXFE34ULt5gyA9x778fm4OuM\nMyYC8OijC5g6dVCEzDNmyHXOyxP9U1xcxQsvxHb/sCjXnFkjLuarzf8DwOPrvqW6f3XeZJ77WPTX\n3sr6DjvOjBmSveSii8T148EHRa/edtspPPbY5wA88cQlAIwceRvJyfLO37HjrwAUFd1s7uvqqyU7\nydCh+dx4o7g29VbuFldnZPKMUwbYGxtjf1x190Fy0SnitlT7vZOARwa/9Tti30e5qUkco6rurdgj\n53/EwMxm8ycHAoGseOTT7hYajUaj0Wg0Gk0T9nt3C8NEH816662Sr/0dz+w2LcJdIQdAv3Okzv2W\nR7pWzq7ir795AIC136/i4usleMmjas8P6jeYvJz8iD5Wq3w9W5RFMF5GDT2Ih+/4DwDPviEpeW67\n99ekJEtKpwF948tVetSh8kV/yrFzALj6tp8lxGqqr0VimTNpEi5VherTDWI1mjxwIGV1tQD41bP5\nq9knMLiXXNubXpY0SzWuzrOCpSSl0T9HrCoNHpGtKGsYq4olj/Kk/uKCYVhlWyJaX7CwYteHEe0m\nDzgJgC83vwhAQcZAUuxiYff6Yv8fG/0e1u35Qh3jRAD8Aan1tap4XptkbtrXSKcWKrfRbvKAk8Jk\nBkixO5qVORoDBoib1hdfbGTTJpnevf76Y83tw4cXxux73nmHAJKua+rUwQB8+aVUNMvNFQtsXV10\neVJSJARy+nSxoNvtNrKyZLZw926ZzZgxYzgpKfKqvvPO903ZjHV33y3uDLfcMjtqX8OSHIoxxZ+c\nbDPXGc9BqMyGfDU18hykp6fEvA4AE/odJ+1d+xjT5yiAiJmD7sRVcybw2uebOvw4S5duA+Df/74Y\ngMsvl1Ki06cP5ec/fxaA+fNFN7366uVUVcnsydat+yL2tW6dzDQkJQVtnKemyv99a2MjM1Pkf9Sc\nJfnDBe+2+VxOPDqY9m7lOpmBKinb0+b9RaN6tdJ/J+cTaJT70lsTdJ0ofiV8ViY92cqOannGzhsn\nsy6JqsKnLckajUaj0Wg0Gk0Tuq0l2ZYmX7iDf94Pm0OWy7+Sr9+q5TVkjBQrR++TxHfFYoF9X4pz\nfNUycXG2ZyXRZ45YADrKl7clOe3KAT1UjqYy9D+viMyD0tV++pvrtz0RX43zeK7FiJsHU79Nvk7t\n2SqBUyDAtv8Ux3uqHcqAPmIFeui/f2fvvpKwbZXVFdG6tJmfnncFny38BIDHn/9HxPZtuyItL00Z\nN3I8P//RVQBc+8dfAFDf4EyIfPpaJJYNJSWcNkF8P9eXiMVjdJ8+fPu1nNsxo8V/c1dFJWuL5Xmo\nc3e+FdxisZJsEyuQ3Zaq5KhkVOF0AJwe0St5jr70Shc9kZ8u/rD+gC9iXb2nOqKv1+dmQr/jAdhT\ns8k87to9CwAYqdpXNezF5ZUUUn2zxZ9yY9mSCJmTrMmM7n0EAA1esf74/GLBGlU4PUxmgF7p/cNk\nbrqu3lMd0dewCk/od3yYzABr9ywIkxnA5a1rVubmqKsLzhwYwVM//vFhZGfL+2PsWDmP8eP7MXGi\nyLxqlfgh5+Q4+OYb0e2GlW/fPrmGRx01wgy8CuXtt8UX+c03pbjFnDmPmQFclcpH1ufzR7VEl5bW\nmvIBpKUlc+SRwyP6hsoMMHHiAFwur7kMMHnywKgy+1U6MZ/PH+uShbFmtwSeZqXmk5oUu0BNcxwz\nWWS68+eHm37XBw8V66Db6+OZDyWg8VePygxG71wH/7lF7umDBkm6sPIaF1f+n/j9Lvuh1Nz3S384\nGYChfcV/dXBRFvPuk9gKrwr8GnPJM22SuzkaGuR6jx59e9j6IUNuNZevvPL5uPZVUCDBfx9/HPQr\nXuKRmcbVXg8Tk5Oj9gvl9vt+HdexohFqSf7fu8oK/nViZwsadspzuOvFEtOUa8yoR2NUfpq5vLi4\nzly3qyYyxqu1dNtBclKmDDh3PLMHv3LcHnnLEEAGn/3OkmCNjfduA8DfGGD07TIFZwwMvTWN7HhG\npiY6yk2hJTmNKYLm5Nj1vxJzkBzvwDiUeK5F+lAHWx8VZd7o9LX6GB3NBwveAeCWK27nH0/dC8BX\nSyWgwedLrLyTxk4NC0RrDX0K5UXzs3Ov4Ka/XA1AVU1lc11ajb4WiWXVzp2s2SXPlV+9df/w1lvm\n9qe+/trcZgTz+bsgoPnbra9FXW9R7gYBgjLN//7JiHZN1+1z7oza1xhgBgKir0prt5rbKuuD06b7\nlIuEPxB7gLRkxztRj2HI3XRdS3Lvc+6M2ddisbYosyF3czJHIzQ3rcEzzywEwGq1mK4IBpdc8l9z\n2QjWs9msEYPJ5cvFfa6xMbo8xmD1xBMfMtfdf/8n5nGBiGMDPPDAfHM5Wrto60JlNrj00qci1rUk\nc3MkWWWAlufoR6pdBsnGh028GAPec25/j6Xfy4fP1FHyjnv1jlPMwbHBA9fM5IVPJFD5hU/EZWH2\noYN4Qu1n0s+Cg88f/en9sL6+z67j+BtfB2BbSUT+gG7Fiy/+HAjmXH755aXmtilqYLza6+EQ5Sq3\nRg2cu9/bPn5CXSya49Ot1WZaC+OOt1lb53oYC+1uodFoNBqNRqPRNKHbWpLdZSqXpCf4NWtLC47p\nU/vK11Koe0K09GkdTUtydgbxXAt3qbtbWpAN7vnnnwGYO/scrrrkBgBuvVpSnL350Ss89aqqbuVq\nfy7LjPRMyiva5tR/4RmXApCakpZwq66BvhaJpznLcOi2rrAgt0RTi2p7+wbitLLGa42NJV93lzte\nollyoxHNJaEt1tjWHjdau3j7RqM9Mh9UJPmhjdmFtuBQAYkN7qAV0VhOskW+W4+bOpCjJsi772+X\nH2Gu97XjGnRHzj//iZjbFrjFPeGqjEw2Ncq16t4aObEMz0vFkSSz+qv2ipvUaSNzeft7cU9sz62g\nLckajUaj0Wg0Gk0Tuq0lmRYsOq69Yind/pQE2vgaEms9iJsEWZ4sSW3/8o7rWnTzj2qjAMHrH77M\n6x9K+q1xIyWB/42/uJXhgyXZ/6/u/GVEn2hWC6vpdxl54mXle8nL6dUmOR9/QYLb8nML+MN1fwEw\ni2YkqjCPvhaa7sisueez5TsppjFqoqQ9czU4SU6RIMPe/QcDsHfXNvPv+loJxCvoK8FYpcU7qKuu\nCmsX8AdYu/irzjgFTQezsVQKjXh9Lsb2ObpN+/jlA58BMP/+s1izRVKgpSnr8k/vnhfR3oKFU26R\nqnorNyUm7VdrsaZI8LwlOQ1/vfg2Wx1Sq8LvqjOrLfqc8jwYlWF9ziqsqeK7HVAV/yzJafjqWheg\nvVVZjx+tq+Vgu709p9IjqfP4ObhQ4rqMVHAF6faEDM+67yC5BXa9KFH/Q65QkdJuP649cnF2vyHR\nrFkHZ5AzWW7U5HxxbB94cV/KFsgNaFSyy5mcFbYdMNsYUZZtJevgDPMYhhzRjlGzViIyh98oeWkb\nqxvNzBOh+4gmZzzXIhFkJsNlUgCOOZIQgLEFkCPvSHMcnhQsBIdNjdkm9QmuW67ibFqaAln7g7yQ\n//Hfe7n/D/+M2F5RJSVde+dLjunvt6w3txUVqkpjlZFKc/2mdcycLvk8v1j8WfNCNKHWKQpw2ZrF\nHHOEVLC6QLkdPP9mZGBMotDXQtPVNHo8FKrBrk+9lDOycigtVrnd1fNcVV5q/u1R08A7NklAVZoj\ng6pyuQ+NQfK4Q4/sBOk1ncHA3LEA5DiKWLqjbbl4b73oUABO/c1bZuBec3yybAdXniFGhCvuk4BG\nm9XKyP5SVfC77bEHnE6Xl8F95N3cnsC9jEmSZzzgqsfqkMwZSdmS0cpTshFPiWQ9SeknedADPjFs\n2TLzCXhcalmMFf6GGpzrFshynKWyL0mXMUIyMMYuY4QbqxKbCak7s6fWw6YKuVazhsj1/3hTVUJs\ng9rdQqPRaDQajUajaUK3tSRHy2ccus65WfJAbro/diWnmjV11KwRC+2Op3dHbG/YGWwXbXsi5DSO\n35wcALteKom6vuk+mpOzuWvRnvzQE8U4yZs/gkHZsdv5ony2GeteOAtGSApLjlVpKD8NZnHizJPO\nA2DJym8pqxBLVE6WTEnNnX0u675fHbHvhStkivYn514BwLbi4A5/co7k6/1y8YKIfv99+XGevPcl\nAErK5Hp+uOAd6l1yT40eJl/7Xy/9IqJvKH979E8APPeApO5aunphmAW3rRzo12Jn32AA6p01Mj35\nuKqO1xKXZ2Ty+6zwm3TA7tanVbwsQ6ZHbw/Z199rxdL0UG3rLE6h55Nomju3RB3XOMaX771mVnb0\n+yUsyGK1EvC37OpmsSqXnyhtF7z1v7jk0PdF/HTmfRFKil2mvF3eOsb2kXzPra24t0VVC/z2sfPY\nWyF6qFEFRdY2eLnzGUnX979PfwDguocW8OgNxwCw4xVJk+bz+Xnw1ZVA85bke15cymt3SN7fPeUS\n8DXu0mdbJS+AxSouDpbUDHy1MlPiLdsm65LTcO8RWQN+mYFJGz4NAL+zCmuaWIEbq0XX+5yVWOxq\nejZOS/LHKoi7xOdjeFK3HdZ1GHarhZI6SaJg/CYKbUnWaDQajUaj0WiacOB9cmjiRvnB89FFwb9d\nKivPexvld2sl3HR4y/t6aS3cdpQsnydua2GW5BmHzALg8guuITNDfMSqa8SisGjlN1ErBD345N8B\n+PUVtwHw5D0v4lMWrq9V8Y2Hn/q/iH7fb1nPTSro7fILrwXgx2dfRqNPTm7Ttu/VPpq3nhrV7+7/\nz90A3HnzvVxyw9lA+9Kz6WvR9cxNS4u5rrUWw/0Jw4JsEI8VuaV28e6jO6Dvi+YJtRoPzB3Xpn0c\ncbDEUMy4+mUWfhc+wzqwMJPlT1wIBC3JeyvrOev2tvk/3/nMYu58ZnGb+obSWCWBNs71XwSD+VVh\notDoMc9eqfLpKd0a3BalXWu5wKEs+IEAqWp/9x1A96Mj2crkPnINjIJQYwvT+PvXbfMQCEUPkjUx\nuUWlnDQGy2tK4QQ1E1VSF2wXzyD5253B5WlRZvtuuEPcBNLtYKTCNNKO5qVBvcy60UdVOq3zgCMg\nlYf++H83AjA4B7bLWBJ3C0kiv13+Vdjv6RMn8PZKKRE7Z9IkAEYW9WbWKIlQvOZ6mZKM6YD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wHYV+3ijS83RbQ9JFksPd3gErSKxqrdpA4S9whLL5mqCXjd2HtJujx7gazzVuw2+1jsKWa7puQc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\n9Dv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G9hjeTp1SvNqWN5lkACgpIfuvsWM/AwDs2vUcBg8m++SPP74LADBjhn078aqqWowe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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12aaece80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Word Clouds by Categories: All Toxic Cat\n",
    "\n",
    "\n",
    "subset=data[data.clean==False]\n",
    "text=subset.comment_text.values\n",
    "\n",
    "\n",
    "wc= WordCloud(background_color=\"black\",max_words=2000,stopwords=stop_words,collocate=False)\n",
    "wc.generate(text.sum())\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Words frequented in All Toxic Categories\", fontsize=12)\n",
    "plt.imshow(wc.recolor(colormap= 'Paired_r' , random_state=20), alpha=0.95)\n",
    "fig.savefig('Plots/toxic_wordcloud.png')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset=data[data.clean==True]\n",
    "text=subset.comment_text.values\n",
    "\n",
    "wc= WordCloud(background_color=\"black\",max_words=2000,stopwords=stop_words)\n",
    "wc.generate(\" \".join(text))\n",
    "plt.figure(figsize=(20, 10))\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Words frequented in Clean Comments\", fontsize=20)\n",
    "plt.imshow(wc.recolor(colormap= 'plasma' , random_state=20), alpha=0.95)\n",
    "fig.savefig('Plots/Clean_wordcloud.png')\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}