TextPreprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "string = '''\n",
    "At Waterloo we were fortunate in catching a train for Leatherhead, where we hired a trap at the station inn and drove for four or five miles through the lovely Surrey lanes. \n",
    "It was a perfect day, with a bright sun and a few fleecy clouds in the heavens. \n",
    "The trees and wayside hedges were just throwing out their first green shoots, and the air was full of the pleasant smell of the moist earth. To me at least there was a strange contrast between the sweet promise of the spring and this sinister quest upon which we were engaged. \n",
    "My companion sat in the front of the trap, his arms folded, his hat pulled down over his eyes, and his chin sunk upon his breast, buried in the deepest thought. \n",
    "Suddenly, however, he started, tapped me on the shoulder, and pointed over the meadows.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "785"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['At', 'Waterloo', 'we', 'were', 'fortunate', 'in', 'catching', 'a', 'train', 'for', 'Leatherhead', ',', 'where', 'we', 'hired', 'a', 'trap', 'at', 'the', 'station', 'inn', 'and', 'drove', 'for', 'four', 'or', 'five', 'miles', 'through', 'the', 'lovely', 'Surrey', 'lanes', '.', 'It', 'was', 'a', 'perfect', 'day', ',', 'with', 'a', 'bright', 'sun', 'and', 'a', 'few', 'fleecy', 'clouds', 'in', 'the', 'heavens', '.', 'The', 'trees', 'and', 'wayside', 'hedges', 'were', 'just', 'throwing', 'out', 'their', 'first', 'green', 'shoots', ',', 'and', 'the', 'air', 'was', 'full', 'of', 'the', 'pleasant', 'smell', 'of', 'the', 'moist', 'earth', '.', 'To', 'me', 'at', 'least', 'there', 'was', 'a', 'strange', 'contrast', 'between', 'the', 'sweet', 'promise', 'of', 'the', 'spring', 'and', 'this', 'sinister', 'quest', 'upon', 'which', 'we', 'were', 'engaged', '.', 'My', 'companion', 'sat', 'in', 'the', 'front', 'of', 'the', 'trap', ',', 'his', 'arms', 'folded', ',', 'his', 'hat', 'pulled', 'down', 'over', 'his', 'eyes', ',', 'and', 'his', 'chin', 'sunk', 'upon', 'his', 'breast', ',', 'buried', 'in', 'the', 'deepest', 'thought', '.', 'Suddenly', ',', 'however', ',', 'he', 'started', ',', 'tapped', 'me', 'on', 'the', 'shoulder', ',', 'and', 'pointed', 'over', 'the', 'meadows', '.']\n"
     ]
    }
   ],
   "source": [
    "tokens = nltk.tokenize.word_tokenize(string)\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['At', 'Waterloo', 'we', 'were', 'fortunate', 'in', 'catching', 'a', 'train', 'for', 'Leatherhead', 'where', 'we', 'hired', 'a', 'trap', 'at', 'the', 'station', 'inn']\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import RegexpTokenizer as regextoken\n",
    "tokenizer = regextoken(r'\\w+')\n",
    "tokens = tokenizer.tokenize(string)\n",
    "print(tokens[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1983', '2011']\n"
     ]
    }
   ],
   "source": [
    "x=''' In the year 1983 and in 2011, India world the cricket world cup'''\n",
    "#In which year India won the world cup\n",
    "t=regextoken(r'\\d+')\n",
    "t1 = t.tokenize(x)\n",
    "print(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tokens = [token.lower() for token in tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['waterloo', 'fortunate', 'catching', 'train', 'leatherhead', 'hired', 'trap', 'station', 'inn', 'drove', 'four', 'five', 'miles', 'lovely', 'surrey', 'lanes', 'perfect', 'day', 'bright', 'sun']\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "tokens = [token for token in tokens if token not in stop]\n",
    "print(tokens[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['waterloo', 'fortunate', 'catching', 'train', 'leatherhead', 'hired', 'trap', 'station', 'inn', 'drove', 'four', 'five', 'mile', 'lovely', 'surrey', 'lane', 'perfect', 'day', 'bright', 'sun']\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "lmtzr = WordNetLemmatizer()\n",
    "tokens = [lmtzr.lemmatize(token) for token in tokens]\n",
    "print(tokens[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computer\n",
      "comput\n",
      "comput\n"
     ]
    }
   ],
   "source": [
    "print(WordNetLemmatizer().lemmatize('computer'))\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "porter=PorterStemmer()\n",
    "snowball=SnowballStemmer('english')\n",
    "print(porter.stem('computer'))\n",
    "print(snowball.stem('computer'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('waterloo',)\n",
      "('fortunate',)\n",
      "('catching',)\n",
      "('train',)\n",
      "('leatherhead',)\n",
      "('hired',)\n",
      "('trap',)\n",
      "('station',)\n",
      "('inn',)\n",
      "('drove',)\n",
      "('four',)\n",
      "('five',)\n",
      "('mile',)\n",
      "('lovely',)\n",
      "('surrey',)\n",
      "('lane',)\n",
      "('perfect',)\n",
      "('day',)\n",
      "('bright',)\n",
      "('sun',)\n",
      "('fleecy',)\n",
      "('cloud',)\n",
      "('heaven',)\n",
      "('tree',)\n",
      "('wayside',)\n",
      "('hedge',)\n",
      "('throwing',)\n",
      "('first',)\n",
      "('green',)\n",
      "('shoot',)\n",
      "('air',)\n",
      "('full',)\n",
      "('pleasant',)\n",
      "('smell',)\n",
      "('moist',)\n",
      "('earth',)\n",
      "('least',)\n",
      "('strange',)\n",
      "('contrast',)\n",
      "('sweet',)\n",
      "('promise',)\n",
      "('spring',)\n",
      "('sinister',)\n",
      "('quest',)\n",
      "('upon',)\n",
      "('engaged',)\n",
      "('companion',)\n",
      "('sat',)\n",
      "('front',)\n",
      "('trap',)\n",
      "('arm',)\n",
      "('folded',)\n",
      "('hat',)\n",
      "('pulled',)\n",
      "('eye',)\n",
      "('chin',)\n",
      "('sunk',)\n",
      "('upon',)\n",
      "('breast',)\n",
      "('buried',)\n",
      "('deepest',)\n",
      "('thought',)\n",
      "('suddenly',)\n",
      "('however',)\n",
      "('started',)\n",
      "('tapped',)\n",
      "('shoulder',)\n",
      "('pointed',)\n",
      "('meadow',)\n",
      "69\n"
     ]
    }
   ],
   "source": [
    "tmp = 0\n",
    "for ngram in nltk.ngrams(tokens, 2):\n",
    "    print (ngram,)\n",
    "    tmp += 1\n",
    "#     print '\\n'\n",
    "# print '\\n'\n",
    "# print len(tokens)\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ngram_freq = nltk.FreqDist() #WE INITIALIZED A FREQUENCY COUNTER\n",
    "for ngram in nltk.ngrams(tokens, 1):\n",
    "    ngram_freq[ngram] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('trap',), 2),\n",
       " (('upon',), 2),\n",
       " (('waterloo',), 1),\n",
       " (('fortunate',), 1),\n",
       " (('catching',), 1),\n",
       " (('train',), 1),\n",
       " (('leatherhead',), 1),\n",
       " (('hired',), 1),\n",
       " (('station',), 1),\n",
       " (('inn',), 1)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngram_freq.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine all the above functions into a single function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_text(text):\n",
    "    sentences = nltk.tokenize.sent_tokenize(text)\n",
    "    sentence_tokens = [tokenizer.tokenize(sentence) for sentence in sentences]\n",
    "    tokens = []\n",
    "    for sentence in sentence_tokens:\n",
    "        sent = []\n",
    "        for word in sentence:\n",
    "            if word.lower() not in stop:\n",
    "                sent.append(word.lower())\n",
    "        tokens.append(sent)\n",
    "    ##THE SAME FOR LOOP CAN BE WRITTEN AS FOLLOWS\n",
    "    ##tokens = [[word.lower() for word in sent if word not in stop] for sent in sentence_tokens]\n",
    "    tokens = [[lmtzr.lemmatize(word) for word in sent] for sent in tokens]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['waterloo', 'fortunate', 'catching', 'train', 'leatherhead', 'hired', 'trap', 'station', 'inn', 'drove', 'four', 'five', 'mile', 'lovely', 'surrey', 'lane'], ['perfect', 'day', 'bright', 'sun', 'fleecy', 'cloud', 'heaven'], ['tree', 'wayside', 'hedge', 'throwing', 'first', 'green', 'shoot', 'air', 'full', 'pleasant', 'smell', 'moist', 'earth'], ['least', 'strange', 'contrast', 'sweet', 'promise', 'spring', 'sinister', 'quest', 'upon', 'engaged'], ['companion', 'sat', 'front', 'trap', 'arm', 'folded', 'hat', 'pulled', 'eye', 'chin', 'sunk', 'upon', 'breast', 'buried', 'deepest', 'thought'], ['suddenly', 'however', 'started', 'tapped', 'shoulder', 'pointed', 'meadow']]\n"
     ]
    }
   ],
   "source": [
    "sentence_tokens = process_text(string)\n",
    "print(sentence_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_ngrams(input_sentence_tokens):\n",
    "    ngram_list = []\n",
    "    for sentence in input_sentence_tokens:\n",
    "        ngram_sent = nltk.ngrams(sentence, 2, pad_right = True, right_pad_symbol='<e>', pad_left=True, left_pad_symbol='<s>')\n",
    "        ngram_list = ngram_list + list(ngram_sent)\n",
    "    return ngram_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('<s>', 'waterloo'), ('waterloo', 'fortunate'), ('fortunate', 'catching'), ('catching', 'train'), ('train', 'leatherhead'), ('leatherhead', 'hired'), ('hired', 'trap'), ('trap', 'station'), ('station', 'inn'), ('inn', 'drove'), ('drove', 'four'), ('four', 'five'), ('five', 'mile'), ('mile', 'lovely'), ('lovely', 'surrey'), ('surrey', 'lane'), ('lane', '<e>'), ('<s>', 'perfect'), ('perfect', 'day'), ('day', 'bright'), ('bright', 'sun'), ('sun', 'fleecy'), ('fleecy', 'cloud'), ('cloud', 'heaven'), ('heaven', '<e>'), ('<s>', 'tree'), ('tree', 'wayside'), ('wayside', 'hedge'), ('hedge', 'throwing'), ('throwing', 'first'), ('first', 'green'), ('green', 'shoot'), ('shoot', 'air'), ('air', 'full'), ('full', 'pleasant'), ('pleasant', 'smell'), ('smell', 'moist'), ('moist', 'earth'), ('earth', '<e>'), ('<s>', 'least'), ('least', 'strange'), ('strange', 'contrast'), ('contrast', 'sweet'), ('sweet', 'promise'), ('promise', 'spring'), ('spring', 'sinister'), ('sinister', 'quest'), ('quest', 'upon'), ('upon', 'engaged'), ('engaged', '<e>'), ('<s>', 'companion'), ('companion', 'sat'), ('sat', 'front'), ('front', 'trap'), ('trap', 'arm'), ('arm', 'folded'), ('folded', 'hat'), ('hat', 'pulled'), ('pulled', 'eye'), ('eye', 'chin'), ('chin', 'sunk'), ('sunk', 'upon'), ('upon', 'breast'), ('breast', 'buried'), ('buried', 'deepest'), ('deepest', 'thought'), ('thought', '<e>'), ('<s>', 'suddenly'), ('suddenly', 'however'), ('however', 'started'), ('started', 'tapped'), ('tapped', 'shoulder'), ('shoulder', 'pointed'), ('pointed', 'meadow'), ('meadow', '<e>')]\n"
     ]
    }
   ],
   "source": [
    "string_ngrams = process_ngrams(sentence_tokens)\n",
    "print(string_ngrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(('<s>', 'waterloo'), 1), (('waterloo', 'fortunate'), 1), (('fortunate', 'catching'), 1), (('catching', 'train'), 1), (('train', 'leatherhead'), 1), (('leatherhead', 'hired'), 1), (('hired', 'trap'), 1), (('trap', 'station'), 1), (('station', 'inn'), 1), (('inn', 'drove'), 1), (('drove', 'four'), 1), (('four', 'five'), 1), (('five', 'mile'), 1), (('mile', 'lovely'), 1), (('lovely', 'surrey'), 1), (('surrey', 'lane'), 1), (('lane', '<e>'), 1), (('<s>', 'perfect'), 1), (('perfect', 'day'), 1), (('day', 'bright'), 1), (('bright', 'sun'), 1), (('sun', 'fleecy'), 1), (('fleecy', 'cloud'), 1), (('cloud', 'heaven'), 1), (('heaven', '<e>'), 1), (('<s>', 'tree'), 1), (('tree', 'wayside'), 1), (('wayside', 'hedge'), 1), (('hedge', 'throwing'), 1), (('throwing', 'first'), 1), (('first', 'green'), 1), (('green', 'shoot'), 1), (('shoot', 'air'), 1), (('air', 'full'), 1), (('full', 'pleasant'), 1), (('pleasant', 'smell'), 1), (('smell', 'moist'), 1), (('moist', 'earth'), 1), (('earth', '<e>'), 1), (('<s>', 'least'), 1), (('least', 'strange'), 1), (('strange', 'contrast'), 1), (('contrast', 'sweet'), 1), (('sweet', 'promise'), 1), (('promise', 'spring'), 1), (('spring', 'sinister'), 1), (('sinister', 'quest'), 1), (('quest', 'upon'), 1), (('upon', 'engaged'), 1), (('engaged', '<e>'), 1), (('<s>', 'companion'), 1), (('companion', 'sat'), 1), (('sat', 'front'), 1), (('front', 'trap'), 1), (('trap', 'arm'), 1), (('arm', 'folded'), 1), (('folded', 'hat'), 1), (('hat', 'pulled'), 1), (('pulled', 'eye'), 1), (('eye', 'chin'), 1), (('chin', 'sunk'), 1), (('sunk', 'upon'), 1), (('upon', 'breast'), 1), (('breast', 'buried'), 1), (('buried', 'deepest'), 1), (('deepest', 'thought'), 1), (('thought', '<e>'), 1), (('<s>', 'suddenly'), 1), (('suddenly', 'however'), 1), (('however', 'started'), 1), (('started', 'tapped'), 1), (('tapped', 'shoulder'), 1), (('shoulder', 'pointed'), 1), (('pointed', 'meadow'), 1), (('meadow', '<e>'), 1)]\n"
     ]
    }
   ],
   "source": [
    "ngram_freq = nltk.FreqDist() #WE INITIALIZED A FREQUENCY COUNTER\n",
    "for ngram in string_ngrams:\n",
    "    ngram_freq[ngram] += 1\n",
    "print(ngram_freq.most_common())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# %matplotlib inline \n",
    "\n",
    "With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.\n",
    "\n",
    "stopwords : set of strings or None\n",
    "        The words that will be eliminated. If None, the build-in STOPWORDS\n",
    "        list will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mayank/anaconda3/envs/dl/lib/python3.6/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f29c8105828>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sMtC+ajvtK7fjbAqfd2ywDFgIiKJYD9Qfet0pCMJevLWFhw5BQBYbXu8/XHjs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\nVsxkEtQ51HbuYU3VS7hFJ0e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Od5EwK4/CqxegTIzCY3Vi3N9Exavr6dhV6+sn+ZixaLPj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GN19+LBTZTMyEzDiazv+Yo2l//mVGnbil4ejVLQEK9MJxx0smjGRc0lQ11Et7sVjaDDII+lUDsT\ngzyWrebAGhL9vOkIahWWtVtw1jVhXDSf3pUbERRyjMccijIpHtuOCuy7KiP6LQ4EKKJ1ZFx9FDGH\nBXfpmbcFuhTcDqmewOMevQ2532VizAluHRtzpePBsoP2BfqZP+UaBS6z/72pSxsZu23C9HQUWiVO\ns8NPAACoYwfvMxIpdGkm5BplUCtkf2I0AsOXAQPLU3MEQdgIdAN3iaIYlBw/VI/h5s1Dp439HYFo\ndzXQ7pKCxTGKZGYYTmCTOTJGwqn6Y+hxtbHa+ikOj7SZyAQ5nr2ivS3Oatb3fkWbs86rlSsFNUdE\nX0iediptPf5CoN7h6/I1SRc+jqGTm4iSGymzrKDGvg2AbM0ECrUzSVXlBxUC1q07sO+uIvbCk2l7\n+UN6fliJbs5Uuj79AWdtI11f/BTSEjBMzAq7pmBQROuGPTckBAFtdgL64gyMM8YgUwV/VB0t3ZjL\nQlc/JxbNo2796NTPNK2oRLxxPtFFiegzo+mt9o8LZCwqBKDxl8iUjVDw2CXBpTSoJJdLiHyE/poE\nY04s7SW+jLmoFCOmgoTgkyJEf81EfybTQKQuCN2CNdK1Wxp7iEo2kHVCEXs+3D8MtKEwIiEgCMKd\ngAt4u+9QA5ApimKbIAjTgKWCIBSLohhQ4RKqx/CQPl8hR5sRi0KnRq5To4zVET09B7fFjqvXjrV6\n/xVc/C9BQGCzeRkiAzI5gqT7iIi0Ov37PTtFKUhpUozsIexHjX2bVwCAZE2kqQrQy4MXErq7eqQG\nM4KAbvYU5CZDxLQH+Q+fP6w1GiZmjb4QiBCtn2/wK3BKn3EindWlpE45BtHjQmNKHDUhYG3ppeK/\nW8k9cxLT/3YMa++UMoRkKjnjrplNzPhkbK1mKpZEzrUfDL01nXhcHuQaJVknFlP1SWnQce6+DXfc\ntXNYc/sX3nqGaX89GmGE7AHdfdbMQItCkAlknTiO/AunjXjt255bybT7jqbomtk4euzUfbvT+3c0\n5MaSfmQBFf/diq3VHHT+aGLYQkAQhIuRAsYLxX4+aFG005ecJoriekEQyoECYJ90SNamxzD5ef/u\nleP+LnW59DjdrDo+NJna7wiNWvt2PwEwHMiQI0OOh5H5oxscgd3ietztIYWAH9xuFElxuDskHcRR\nWUvM2cdhWbsV++6DPzPJ2Wmm9Ut/V2Htmk8xphVSt/ZTiUhu2nGj+pllz64kKsVI8rwcFr5zPj3V\nHWgT9Si0ShydVtbc/gXOnpHlp7osTiqXSMJm0l8WMObcKYhuDwq9mm9O8Wfy9bg8xE5I4aiPLsZc\n0yll2OnVlL+3ibxzhh2apKOkkZZ1NSRMz2Duk6ficbox5MSiidex8/V1FFwcPDU/0rXXfb8LbaKe\nomtmM/WuIxl/wzwcHVY0fb8lQPUX24J+xmgjomKxvpjAZwOyg44B/g0cJopiy4BxCUC7KIpuQRBy\ngeXABFEUB3USDtcSCIXEFAWvfZvF2XMrMPd4hh0HzC9Ws6v0t6E6GE30u4P2DgIHw+Gm89lk/p4O\nV/iCtARlJlP1R+MU7XS6mrF5enGJDnI0kwD4tvPVoBYESO6gbnfboIFhgG86XgkQSMVR80lXF0b0\nfYaCvYvADliIUPvS9zQvWR1yiFypQaWXWmhaO/z/ljqDjKtuS2DWETqi4+Ss/N7Mv/5fI73dB2fA\n/P849n2xWIgm87cDauBbQWrq3Z8KeihwvyAILsANXBNOAOwLJKYpaWt20dM1spt67lH6oEJg1rmB\nrJ0D0bR7JRWRZggBsRkTKZh3kff9xk/+jt3cZ47GphOfNRlDYh4qjRG5Uo3LbsbcUUdX025aK9bj\ndu1/QaUSNEzSHU61vYwdllV4BmzU/ULgYMO261/COCUHw5Rc9OMzQvrhf2uU/+1DulbtDHleY0ok\nc84ZmFska6dunT9f/5/uTyQlS8UDNzTQ1e7m8BMNvwuA/8OIJDsoWJP5QN5haexHQOS73wCYYuQ8\nsyQDj1tEqRJ4+dE2vl0qlUz/84000rOVuN3w4SsdLH2zi7OvjEFnkJGaqWTyTC2CDC48ooq/v5RK\nUpqC+GQFb36fxZ/Pr6O10UValpIb/5ZIeraSxS9K1wCI0sm48tY4Dj1Gj7nHw3Wn16CNknHLQ0kU\nT9Ww8ESpld/FR1cNSoMwmtAaE7Gb2ymYdzGxGYHFanKlBrU+jtiMiWRMPJZ1H929fxY2AHHKNOSC\nku2WlUFTNQ9GWPc0Y93TTNNHqxGUcvTjMki7/Aii8pIkLozfGG6LnZbPNgwqAAA00Uk0bP6Wnvpd\nQc8XTdHy1lNtbF0rBf1ff9wXO/twbR5P39/Msk+lZ++TLWP4xy2NrPimlzd/zOH2S+v4yyNJFE7U\n0NHq5vpTq4mJl3PvM6l+586fX0Fbs+SzP/uqGE69JIbGWifP/K2FnVttTJqp5eHX0zl2rG+Ntz8m\nFUc99OfRoUX5HZHhgFJ10rKULMjZRXa+imeWZLBljZWWRhcv/rOVnVvtGEwyXvo8ix1bJc335AtM\n3HxBHQ/c2IgpRo7F7OHGc2uZMF3Lrf9I5MKFkiYkk8Ffn0nhkdua2LnVzuJfctix1c62TTauvSue\nKL2M02dWkJSqoKfLQ0+Xh1svqeO2fyXx8C2B2UpOazcKtQ5BNvyKwcGgNSVh7qwPKgD2hkKlJa34\nSOpKvws5xi2Oft62XJD8lv8rAmBviE43PZsr2X7DKygMWgyTszFMycE4NQdVon/6oejy4LGPcpqf\nKGJv7MTVZcGyu5HerVX0ltUGUEQEg6Wtlqw5Z6DUSApM+x7/uEH5NjtHnmJkxTe9Q7IA4pMVXHNn\nAs//vYWaCif5xWraml3ExMsDzvV0ShrTsWebOOZME3ddWceMw3T84/U0LllYyebVVprrfd9FqRKY\nc6See66uD/Xxv2Mf4YASAv2o3OVgd5md4qlatm+x8fcXUv3OZ+RIG9CGX63sLpMEQldHaDU9OV1J\n7li133UycpRs22Rj9hE6br1EuvGa6iPbLNcvvR+QNHKFWodSrWP80TdE/gXDQB+XSWzGRO97S1cj\nPS0VOG29aAzxmJLzUap9zcYzJiyiu7mcnpbgqXkWTxciHtLVY6m1j06Tjt6+orFoRSKdLqkCU0Ag\nXV04KtcfbciVauKyp2FMykMXk4paH4fH48Jtt+C0m7F2NlC79VvsvYEZZa4eKx3Lt9GxfBvq1FiK\nX/Ivgu/8dXsAgdxIMG3cJeys+ooec2iNWKHQcPj0O/h21T2BJ0UP7RWbEOTBH++n/9rMvc+m8u6K\nXH74pIfH7owsLVulFvjolQ7KNkpZOBtWWEKe68c5V8fw+uNt7C61s7vUzllXxjDzCB3ffNTNl+/7\nkgYPOVRHd6ebTSsDUzJ/x77FASkEsvNVjBmnpnSDZAksebOLt5+RNp28IjXV5Q5OuzgaS29kWkxj\nrZOq3Q6++7iHt59p914DYO3PFs69OoYHbmwkPllBV7sbp0PSbs09g1/f7bThdtqCbhwjQVym5FNf\n88Edg7YGzDnkdJLGzAZBoPjI61j333sl8rC94BKdfNvxKnOMp1EUNRsAh8dGl7uFTb2hLYjB0Olq\n5tvOV5ltOJUomQEPbnrc7VTZS6m17wgQBlP0R5Go9E+jTCaXAu0h3vc19u2UWX4Z1noGw8zzg2eJ\nyWVy5Ao1Kl0Muth0qjd+FvZa9vp2zNvq0BWljfYyvVhf9lrAsbz0w3G5HVQ1rAg732Huom3XWoCg\n2UEtjS6uP9XXevOrHfncfVU9a38KvHfUGn832PZNgf0CQp1TqQXSslX0dntQqaXrnDVrj5eQ8L3n\n2rnoT3G880w7/+/RZK4+oeq3ZFj/P4sDSgjYbSJvLctGoYDH7mr2aubjpmh4/9ccFAqB6nIHt10+\nNJPR44E7r6znursTeP/XHOqrnNx2eT1Oh8iT97dw7Z3xfLgqh+5ONzeeU4ezj93w47e6eP/XHLo7\n3FxxfOh+tfsCHrcrbG/YupJvScybiSBIefCG+Gw66oLnJYuIrOgOH65Z1vV22DHeNYpuVnQHtp9s\nclRQavGvEdzY+23E17V6egbN/Cm1LA+4fiiodIGppOa2GjrqShFkchSqKFRaI1Gx6ThtkXG1WCua\nRiQEZDIFCrmaw6bdysbtb9HauQu1yohRl0Ju+gL02iTWb3udzp4qNCoT43JPwmTIwONxk5Y4jZVb\nngJAFN1kpcwlLXEaze1l7K75blh1Av0uHQCL2YM2SrqfkjOUKJTDj4U47CL1VQ7yitSs+TF4vvvh\nJxrYscXG7lIbjTWRudTkClhTloJGI3Dxma2sWx34nDz3ehzzD/cxrhZnBu4Zd/3NxLkXS/0y3n3D\nzAN3BWctLp6g5KjjtMyepyYpRUZ0jIyqCjfr19j5/msbK346uDMIDyghoNYIXHB4JQBpF85n0luT\nwC1y58VPEzt/LMlnziYqJxFZ8mssfjG0Cbt1ndUbD+hHfbWTO68MvBHMPR7+eVsgoRRAdbmDs+aM\nrPpxuGir3hR2jMPajbWriajoFADUQTa8/+tIyp/jfe3xuNj106t01o/MJWbZM7KqdkNUMmqVkbbO\ncqINWbR37cHu6KbF0U1Lxw5mT7reO9bm6GLD9jcpzjuNXkuTnyUgCHLkMgW/bn6Cww+5k4bWTWHr\nBBQKgQtuiGXDLxaa612oNAKmWDmbV0lB4u2bbBx3jokNKyxcd08CbvfIVPM3n2jnunsSqNxpp2q3\ng6lzo/huaQ82i2Rla3UyTrogmq8+COyYFgpuF+woczJpqorCccoAISAIMGlqeO6gscW+MaVbggug\npGQ5738eWPg4pkDBmAIFZ1+gY9N6B3fd0klFub87WZuXh7VcokrXZGYRfdgCGt98Pey69jcOOCpp\nAFWCkaRTZ1B6zUtsvlhq8tC+fDtlN7yKsz1sk4X/CbRVB+bPB0N/KimAQhU16FiNQcFJdxRx4m1F\npI0zoo9Vcfpfi0kuMJBWbOS6d2dxxFW5I1r3gQZTSoH3ddOOFSMWACBlEY0EBl0KscYcqhtXEm3I\nRB81fMrg6kapVsBia0etksjluut2YG6VqCRatvtzcYki5I5Vc/dTKbz+QzaPv5/BrRfW0lQnbYIv\nPNxCb7ebJz7KYMMvlkHdP5Hg2yXdvPTPVq65M4EnPsxk2jydX4XzVx90MXGGlp+/Gtpz3b9pF4wN\n3Ozz8hUYTeG3toFzSzYHWhP5hQre/Tg+7HUmT1Px7sfxTJ+p8jseu+jYsHMPBBxQlkA/VAlGnO29\nuHpHdgMezDC314YfBLidPlNUJh9c+4kyKdHoFXz1+E66m6V5e9Z2UHxEIt8/V07Trl5+fPm3sXz2\nBQRB5rWSADpqRkZn0A9r5ciEgD4qEUNUCjurviIv4whM+jS6zYEkcJHA5e5/RkSCcRvv3V3M7Ra5\n56rQ7tTWRhe3Xexby5LXffMX5gRPTd1dZg95DuCzd7r47J3grhalUuCHT3uwW4dmcZT0CYGx4wLv\n+SnTfZtxXa2btHQ5er1Ab6//Z+j00u9ls4rs2e2vxcfGyXjpnXjiEyRh8v3XNj56z0LZVic93R6m\nzVRx5rk6jjpOavZkMMr4zwuxzJ3UiCo5mZjDF6JOSyfl0itoeE3KqJfr9SRdeDEKg4G6Z58GUcQ4\ncxb6yVNAkFH/nKTwplx6BdbyXWiyc2he/C4euz3ouNHCAWMJdHW4WZDTlzMsEFkPXkGg+OnL0OVL\nmtSk169FV5jqd1yh1/iOA9l/OhaZWrpxVIlG7+f1X0NQypmy+Ebfud8ADksnTtvoWzwqrZz2WisL\nr86jYG48k45NIS7TZz3UlnZx3C0HZnbPcKDSRful8UYqWMMhkjTNwRBjyKG9ew8e0U1Vw0oKsiWN\nsTjvNGZO+ANR6ljG552KSe9jWq2s/5mUhEnMn3pz2OvH5k7BkDIGbUwy6YecgEyhCjtnf+OQw3R8\nuT2fPdvtPHbH0N1rn/7XgsUsMn6Skuxcf132smukzLlN6x3cf4ckxC64XB9wjX68/44F94DkwuIJ\nSpZvTPYKgOLMem64sp2fvrfR0uzGZhNZ8ZOdG69ppziznl9/lhSq6BgZ+YUKHI2NNC9+F3dvLw2v\nvm9bPd4AACAASURBVOTdy9wWC01vvk7dM0+hychEN66YmCOPls573GjH+IjpLLt20vjGa3js9kHH\njQYOSEsgUqiTTETlJDLmXh+TtSY9FleXJehx8456omeMofI/XwLgaO7zQ4qQeMJUKh77gpjZBZh3\n1vvO/QZwu/YNtWzjrl5aKvcgekQ8QXy9qxbXoFAdMHrBiNEfMPfhwEg96Q/sAjS2bqGxVWKRLC0P\nzS1vtrayassz3vcul80vPXT11ue8ry1ttWTMOpVdX7+AXKUdtX4Cw8Xp5+k4/TwdcfFy2lrdvP+G\nmaXvm/0KxYYKjwe2lzmZeoiKwiIllXt8mnxmtrStbStxet1Gk6aEtpJLt/i7gs67ROd97Y7gp/vg\nHQtzDpWC0AsXadm1owdRFBGUSilA0a/Q7sVf42hqxNXZQf1LL0jnBhYkDlCCBx03CjiohQCCgOjy\nsOWip/38jOqUmKDH++cEg3FKDgq9hvijJ9LyxdD5+w8WuJ2Dp726HL7zf7raxMP3xHp/sq3bHByy\n0N9t0d+s/oCDIKBQ68KPGyYa3vZlJ1mrAhvo/JbYV01lhouP3jHz0TtmLr7KwOsvjJ6FW7JFEgIF\nRQq+/jzwfFmJk452D3W1biZNDW0N7R0Unj3Pl1W0dVP4RlNbB8QTps3o+xxRpHfLJtL/eCO1TwTv\nNeJsa6N71SpSr7wGRA8Nr76C6Az8vEjHDRcHtRCwN3Ziq2sn+cxZNCxeSVRuItaatpDHRaebrnXl\nyFQKPA4XqjgDzm4LotNN6/clJJ40HW12Ap2rA5kr/y/iP8938fTL3dx8rYnzztAHCIDUZDnNrcPn\nnEktPgJTcgEqfSxKtQ5BpsDcXkNXw046akuxdETmJ9eakohJL0alNaGLz0SlNaHUGgIsgUPO+UfI\na3hcDtYuvj3itQ8UAgci+pvK2Hv2O3VXxLjyj0ZmzVfz83e2YQkHSYPXUVgUXMvfViJt7iWbHSw6\nXkt2rsJrMZiipXujt1f0syJS0uQkpfhciLt3hC8gbR/wDKSk+ea2Ll3ifW2rrvLLDLJVS9mLPevX\n0rN+rd/1Gl4NTI8ONm60cNAIgbw7TkWTGoMy1kDeHadQ/uASLBXN7Prrh2RefSST3rgOe0MnO+99\nH9Hp9h6f/M4N2GravMern/+OCS9ejSCX4eqxsv3Wt3E5rTR/so7xz19JyxebDpiG2AcCXC4Ri9Xj\nlyqoUQss+ySVsflKVAPyyI3ZFRGZz1pjIhmTjyMmCC2GISEHQ0IO6RMX0VFXSs3Gz7F2De4zNibn\nkzH5+Mi/VAhEmZKxdP3OW7M/MHm6isnTVVxxdgtPvRbPhrV2tm4cmnbbr8EXBgkOO50iu/o28NIt\nThYdr2XSFJV3wy/qSw8t2+LwCz8mp/hTwZxxXhRnnDd41t1A9AuXgwnDbTR/H3Al0G8H3yGK4hd9\n524HLkdiEb1BFMWvh7qo3tJatl75gt8xT3MbltZ21GoP5o3V5F27gK1/eR97Yye7/hpYsBTquNts\n96adDkTiCVPpLaml9rUfh7rcAxYCAnnKScgFOe3uJmJkScgFBU3uKjrdzeQoJ1Du3EyyPButTI9G\n0NHkrqLdPfhGaLOLzF5Ux4xpajZsdkTsDorLnsqYOef5ueTsve10N+3GYe0GUSQ2cyJak9SoPCat\nmJi0YhBFVr9zS8jrdtSWYu9pDTiu1BrJnXW29/3OH19BDEFtLXo8KDUGZL1t5Ew/Dbulk5aKdaSN\nW4jL3ovTbkEmVyCTKXDazdh6WkjIOYSm3b+i0cdh7qjDkJCLKHqw97aRkHMIlRuWBgT4ZXKB+zdL\nufsvXbKSynWBmvpFz86gYL6Um77kni2s/29g57AT7xzPzHOlCuzV71Xx6QMlAWPGLUwmb3Y8KUVG\nYtOj0BiU2M0uuptsNO7sZsdPzWz/sQmXPXKlRxWloPjIZPLnJ5Ccb8CQqEGplmO3uOist9JS0UvV\nhnbWvj94caXH7bsNZLLhubn78/JT0+RMnqZi03oHk6dJ7pj33rR478t3Xjdz0+1GLrxCx8cfSbQU\nJ52h9Y4biLj4kW3iygMvBh8Ww200D/CYKIp+nMqCIIwDzgGKgVTgO0EQCsRQT94QUPHSz8TOzqNz\nQxUd6yrJuXoBgkKO6Bq5vzN2wThM03LZ/cBv3/R5NBEvT8Ui9lDvlApWPLjpcDdTpJpJu7uRRncl\nABpZFN2eNqrc27znRhtaUxK5M8/0e9p3LHsxIG+/dstX6OOzyJlxOlExfVW5gkBUTCqWjuCpjQ5z\nBw5zR8BxjcE/x7urccegPXejTMl43E5AQC5X4XHakcnkuF0OhL4An+hxI1dq8Lid1G/7HnNHAwk5\n09EY4nG7nDjMHd5zwTAwIJ9caAwQAoIAGZOive8zJ8cEFQLJY33Za3Wl/umXSfkG5lyYw7TTMgLm\nKVQqdDEqUsYamXJSOp0NVv511A8hf5OBmHJyOsfcUoQuJnCnU6il66YVm5h8QhqTT0zn9avX4LAE\n/723bHSwfrWdl99PYMWPNrZsGJmPOy9fwab1DoonShr+pnW+61kt0m9eUKhErRaw20VvjcDeQeHO\nDn+B+OzjPTz7xBBcVQdgeCwcwoo9URR/BiJ1LJ4MvCeKol0UxQpgNzBjBOvzg62+k5STJhM3Lx9d\ndtyoCACA9h/LKLvxdRyt/1uFaHKUOEVfrYVdtOLBHSSbfPBzo4GcmWf6pSraettCFm71tlZR9s3T\n9LZWeo+NmXM+wfLg9xWa96whY+IxtNVslrihRBFbTyvIZLRWrsOQkINSYwSk4zK5itbKdRiT8gac\nGxzJBYFjEvIMaI0+90bGxOiAMYIAyQUG7/u6Ev9agMLDEoMKgGCITtGSNSWySvPTH5wUVAAEgyAj\nQADs7fd/+ekeLjuzhZefHvlzlztG0mf76wY2rgsUKnKFVCUsCJCTp6Czw0NtzV59s5v9hUBcggy3\ni8j//bYx+GFhJDGB6wVBuAipdeTNoih2AGnAqgFjavuOBSBUo/nBYKlqo/LFn4melsX2vwdJB/gd\nfmj11FGknIlJFk+Xpw2r6OPGiRIMpMnH0CEMX+t3u0Auk5pMh4MhIcfvfcXKxWGubad8xTtMOOFW\nZHIF2uhkYtKL6agNdHuMFvrjAeWr3wNgz9qPCFDtaqRK7vpty7xZN3VlPq2/fPViBJk8ooyclEJD\nwLG9N+SEXD1qvQJ7r+9Xjs3QodZJj67T5qal3J/zaM17VRx2xRisPU62L2uiYl07jTu6MbfZ0cer\nGTM3gcOuGIMhQcqCOeGO8Txz1vJBS3NiM3x+cUuHg+Wv7mH3yhY6aq3I5AKmZA3JhUZyZ8ZRMC+R\ntYv3L9dWvxAoGq+koc5NU2Pw3794opLWZjcajcD6IJxD1ZUu2ts8xMZJ+vGkKQehf2eIGK4QeBb4\nG9IT8jfgUeAygqtqQW+t4Taad7SbMVe0jrhgZ2QQkCvVff+0KJQav7PqqGgM8dk4LJ24nDap89dv\nQI/oEp1sdfyCgMyvRWOJQ6IS2OlcD0CLuy7gXD/kcojSypDLA/+0FVVOzjhZx8dfWDAZZdQ1REbF\n3dNSQXdzedhxtt422io3kJAnGZPJhfP2qRAIROi/2WCbfKQpmYljDAgywS+NOXOyJAScNjdKjRxB\nJpAxIZrdK30xj4GuoPpt3Xj2SoO29br49MESNn9WF3Db2XpdtFaaKfm6gWsXz8OYpCGlyEjiGANN\nu0Jr5OOPTun7biIvXbqK5t3+Yy2dDhq2d7Px41pkMgHZCIjnhoKuTg+maBkZWQoUCoExBQq+/TI0\n00BRsa+moGRLoBAQRfj1ZzsnnCrFDIIFnUeCmPRitMYk6ssic8HtDwxLCIii6E3XEAThRaCfg7cW\nGGiHpgOj1iVCFasj/+ZFKE1aLJVt7Hz06/26uSaNmU1a8UJUWtOgkazo1CKiU4sGHBFxuxy4nTY2\nLP3bvl/oXhhu0/i96wTWfp/mlyba3unh/ttiePLheMornQEppKHQVhWeHG/g2H4hoE/IQSZT4PGM\nfpOc/QmHxYUqSoFSIycuM4rWSh/DZmafJdCwo5vMSdLrjEkxfkIgpXBAPGAvV1A/Nn06+N+it9XO\nsud2cfK9E7yfO5gQMKVIm6K1yxkgAPaGxyPise+f57KsxMnseWrSMuTkFypQKoWgrqB+YTGmQEFW\njrTtlWwOrki+8XKvVwiApAiNlpuno7aUDoIz/e6NqIRMLK01yOTKsIzCI8GwQuGCIKQMeHsq0K+e\nfQKcIwiCWhCEHCAfWDOyJQ74XIUct9mBq9uGTKvc79q1Qq1DFRU9jFQGQeKt15rCDz2A8J/nu9Cm\nVaBJlf4F2+TzptUQm1c5qADQx2f7vW/dsy7iNXQ17PBSPMvkCmKzJ0c8dzAsfmnopG1vP5fE3Bma\n8APDYN1HvkDv5BN91BDjFiYTlykVuK3/sIaOOinXf86F/q60qaf45qxZ7M+WOxRs/NhHo9H/uaFQ\n8lUDAFExKv7y3RGMmRPIrPlbYPFbkgBVqQT+/m8pfrL4zUDa6jdelo5NnKLirPMl19YP3wS3GEq3\nOJlV3OjNPtpSkcqashTufcjE8adomTZDxfSZKo48RsPlf9Dz6DMxLN+YTGm1r2lVP12JVKvi2y/i\ns6f5pTMXLrjcj+m2HwUnXIelpRpEkbyjL4/49xgOhttofoEgCJOR7OVK4GoAURRLBUF4HyhDchVf\nNxqZQf2wN3dT+cpyYmfl0fjlltBrlss44ZsrkKsVLL9+KW2bAo2R2f86gaRZmd73S+c9EzAG4JRf\nrgWgYkkJmx/9LmQbx3FXzyJhejraRD0qowaXxUHdsnIallfQvHpw/2h7zRZWvSulQM559ARyZmaS\nc/u1fmvSZ0STdUIRGccWojJocHRasbWZaS+pYc1T7+Fxhdf2owsTSF2Q57dOc20nrZsbIlrncKFQ\nawe8E/1I7yKB22nztkoMx5R6MGBgNs/AAG/mgHhA/fYu6ko6iUnTojUpic/WeS0GY5IkiOy9Ltqq\ngvP0R4Jg1CGhULHO1zjJlKzlkhdm0LC9m7UfVI9IEI0UpQO0+YK+orFgWvvA7KNgzKN7o6fHwxXn\nt/HEi7EUT1Ci0wucdb6Os84PX4UukyvImnYKcoUaj9tJxZoPEQdRWIN1BJTGC1JWmmzfutZGtdF8\n3/gHgQdHsqhQUCcacZnt1C/dgDxKRezMXLo21+DeKz4guj107W4jtjgJ05i4QCEgQGxx0pA+u2N7\ncOZITYKOiTfMI/XwPL/jKpOGnFOKyTmlmPaSRjY+tIyeqsA0xnAQ5DIm3DCX3NMm+EVcNAk6NAk6\noscmUvb8qrBCQJOgY8HLZwYcN+TEYsiJHfE6B4Nc4SvD97jdDDWPzuPyCQ25cuSaOEBivJz3Xkwi\nJUlBda2Li65r4vLzjZx9ih65HH5eaeO+R9q5++YYFh0eRW2Di6QE3+Py3otJ5GQqiYuR86c7W/j8\nWwvffpjqNxfg2w9TkclgT6WLK2+S7qGBLpzkAa6dfiHgdnpo3tVDXWkX4xdJRnfGxBg/txFAXVnX\noMawRq+Q6gTGmkjI0aGLVROdqkWpkaNQy1BqhtYje9v3jRQt9FlQKWONnHT3ePLnxLN+SS07f24O\niE/sa9TXuf0CuaGwZaMDj0eqSYgUjfVuLjytleUbk72Mo4Ohp68ToTEpH4Uqil2/vBGR0hKsT3dH\n+UYKTrgWQSandfvKyBc9DBw0FcMAutwEMs6ZgdviwNbUjaWqlZhDcih/KjAnu3N7syQE8uICzhmy\nY1Ea1AHHAzDg7965LVAIGHNjmf2vE9AmhmYoBIgdn8yhz5/Gqtu+DGqVhPx4mcCMBxaRMj9n0HEu\n6+BB8v51hkP/Oj8/JqSMHxbcTp/ZLZMr/Em1IoBsgBAZeK2RICNNwTFn1WN3iCxbmkZRgYpzT9Nz\n5On1iCJ8/X4q0yerOf4oHXOOq0UQYMtPkuWoUgqcc2UTMSYZ336UyuffWsjNVnL0mf5z122y85f7\n2thUYvfbfNqqzNh7Xaj1CqJTtWj0ClwOD2njJHdh445u3C6R2i0+YZExOZqNn/izoIaKB4CUfvqH\n9+chV4yeFvn2n9ZTeFgiR1xbQFqxz7VZtDCZooXJdDfZWPFGBWsWV+G07b9cydItTr8uYsHQ2yvR\nRY8pGNqWZ7eLHDm7iUXHa5g1T83YcUpi42QoFAJdnR6qq1zsKHOyaoWD1SskZaW3rZrU8QspXHA5\nTmsPFWs+RBBk5M4+G60xCblSQ8ueNdi6Q3NOtW5fSU+9RM9t7x7d9rV746ASAnK1gspXlmNv7iHr\nsnnUL91IzhWHBh3b2ae5G8cENoWIm+DTZiwNPUSlGFDoVLjM/sEXXZp0o7ttLnoq/bVjdYyWOY+f\nhCZWkvQNyyuo+nQbnTuacfY6UEQpmXTzoaQukCwEpV7NzAeP4YvjX4n4+xZfO9srALp2tVL/YznN\na2uQKeXo06OJm5xK8tzsQa8Rbp1xk1PJPrHIb53aRD3W5shaLUYCl92/KlOh1ODq47aJBAqVz50U\nrIfycLCpxI69r5d0c6sbg15GXo6Srxb7/LoGvYySbQ4v+WPpDun+cDhF3n9ZuoeeeFFy7YwrUPHs\nIwl+cwGuv9xEdqaCfz/byRffSb+DKEL9ti5yDpEUlMQ8AwggV0pzqjdJ91pdaScej4hMJpAxITCX\nf+8isX7MOi+b424bh2yAG8Fpd9NWaaZpVw8Oixun3Y3HLTLvkqE1EdrxUzM7fmomZ3oc087IoPio\nZJRqyaIwJmk49i9FzL4gO+ICtFCQG3Wk3Hc1jop6mp98b9Cx11wc2SZ58pHD6wPR3eXhg3csfPCO\nJfxgpHu07JunAo6X//puwLEdPwZXuEyZ4+iqLgNAbUpAn5RNZ8WWIbtSI8FBJQRaftqBJtmEoJCx\n85EvybxwNu1r9gQdW/P1TibedCgxRYnoM6LprfFpTfnnTQGgvaSRHa+uY/ajJ5B35kR2vOYfsMw8\nVuLWr/yk1C+NL7owwc+1Uv7BFrb+x79ButvuYs1dEmPGnH+fSOKMDFQmDcbcWLr3RFZ7N+acyay5\n8yvqfwr8jm2bG6j6fNug8/deZ7C4R/Pqam8soH+diz66iB8ufi/idYZDb5t/rCE+9xAat/8c0dzo\n1LFeNlDR46Y9wo5r4RDMb3zPQ+1cdLYBt1tyG6xeb+OIeVpWfJ5GRbULRZ/3xKCXERstw+2Gqy40\n8utaG599Y+bGq01+c2UyyMpQIAgwZYLaKwQA1iyu9gqBcUclozX5/NTLX5H+3g6rm3UfVjPjrCxS\nioxkTIr2WgeWTgclXzcEfIcbP1tAfHZf39ybNlD6TeCYfkTFqIYsBPpRsa6NinVtfHib9D5zUgxH\n/bmQnOlxRKdouWvVIh49+ges3cNL5Y45ZxHKpDjcXaOnjBxMSJl2DL1NFbjtVjLnnsGuL55lzDFX\nsvurF0f9sw4qIQBga+xCkAkgCFS/GdpXJnpEuna1EjcxBdOYOD8hoEuXNPzOna1eX3+wGIEpX7Ii\nOrf7m225p/uIz0SPyM43Nwy65spPy0icIWXOpszPjXhzbfy1MqgAiBR7rzMcvOsUhrbOoSIua3LE\nQiAuy5cN1NtaNSjtw1Bw9hWNAa9Xr7fxxvv+6Y93P9zO3Q/7z/3TVdEcebrk1nvg9ljmTNewe4/T\ne2wgjjojuPuvrtR3Pybm6jEkSLGOzgYr3c0+l1fN5k5mnCVxBKUVR9Pbau+bH2gFxGfrvAIAGFQA\nABjiI3CJRojqzR28cukqznhoMpNOSEOjVzB2QVKACytSaMePbuOUgw2OnjbcdisaU6I3MCyK+4bY\n8qCivFMnGJj81PlMe/lSJj0eLF7tD59LKDAuANC1owVHlw1LQw8xxYEpg6Y+V9LeQeGE6b4UvY6y\nJuztg5uJHWU+Fsy4SSmDjPRH1aeDa/rhsPc6w2G464wIA2IA+vgsTMkFgwyWoDHEE5s9xfu+aeev\ng4zef/jiOzNLXk/m07dTSE9TsOSLobuo2mssWLskLTk2U0fiGCmu1O8K6kfNZt/7lCIjcVnSuGBC\nwJSsDTg2GLKnxw5pfDiIInz/lK/NZFzW8Po5qDKTUcQdXOnUow17TzvjzryN1EOOw223kjBurl9s\nbDRxUFkCxuI0qt9cib4wGblGGTbA2K/Bm/KCN4vu3CWd79jeTNrheX5uI5VR8o27zA4/KwLwCwRH\noi07Onz+b23S4EHkgWjbMrgmNxi0Sfr9ts5I0Fa92U+rz5l1FpuWPhByvFyhJm/Oechk0i1q720f\nNVfQSLFrj5NTLx45yV59WRd5s+OJTtX64gEb/YVAa6UZa5cTrUlJUp6B+ixp8w8WFA7XMGggFGoZ\ncy8enitoMDgGBIQd1sitNk1hFpriPNRZKURN8xVaagqyyHk7eLJh5SX3IjoDPyPn7QdxtXdR88dH\npAOCQNLNF6DKSEZu0uOx2nG1ddL6wn9xVAf/O6pz04maXoQmPxN1QRaIIh6zFWd9C9ayPXQuWRZ0\nnv7QqSRcfToAtTf9G2dTGzFnH03U1LEo4qU6BldrJ40PvYq7M3TRXd3qT6lf+0UfYaGauLGzqP7l\ng5DjR4KDSgjYW7qJyorD3thFwsJxyBQyPM7QWQgdg1gCHqeHnr6NsbNPCMQUJ3k3fFO+FOTr3NEy\naEZj9knjyD5pXMTfQWWKPMXR0TX8TJioRH9emn25zkhQtW4JxsRclFopJVKtiyEmrZiOusDqSX1c\nJtkzz0AX46OdKl/57j4zh38r1JVKQmBgS8+9LQGAmi2dFMxPICFXT1yGpF3XlQRaAq2VZkTRV8uo\n0spxWAOfD6VGztn/nEJseuQ1F4dfk8+y58K3gzz0cl+qdH1Z5C1aTcfP99v8Rwq5SQ+CgEytIvHG\n89BO8LmX5EoFcqMOVUZSgBBQ56QRf/XpqDIC3cPyaAPyaAOacbk461owrxmcwkSZlogiIZrokw7z\nO65KTyL9nzfS/NRirJt3hpgtoDYlIFdK2n/z1p8i+NbDw0EmBHqxVLfj6rHR+ssuoqdmBa0T6Edv\ntbShRyUbiB2fTHtJI7F9mUEVS0u8ufV7Piqh+A+zGXPWJGq+2gH4gsIVS0aXq0amGFpu9nChihma\na2BvjPY6nbZeNvz3r+jiMihccAVKjZ6CBZeFnSeKHirXfERP8/BjIwcq1rxfxaFX+DbNPavbqC8L\n3Nx/fWMPBfMTUOsVzLkoh+0/NvnFDfrR22bn+fNXcMVrs1GoZNyz9hhAahna22ZHHaXwBqCddjev\nXrGaKaekM/mEoByPfph7SS4Lrw/vwuvH02csp2F75EKg6d9veV9rx+eRfLt0b9h2VtHw1xdCTQsJ\nQS4n6cbziJo+DsvGHVScf6ffeXm0Iagmbq+ow7KuDPOaEnp/2YSr2d+CVqYlkHLH5ST+6Vx6flxH\n64tLAq7Rj6SbL0B0uwM/2xBF5nN3knzrxTiqGqi765mA/sMFJ13Pzk+eHOrXHhYOKiEwlDqBvWHI\njqG9pJHoQknDb9/q0wD6hYgxLxa5So7b4caYK1kPHdsH7x+7/dV1AVlFg2I/UV04uv03iQNlnea2\nGrZ+8S/SJx5L4piZg47tbtxN9abPMLcF8un/L6Cz3oq5w+GlZw5mBQDUbO1E9IhSQgShU0MBard0\n8u6N6znjocneDV+hkhGd4lMKOuosLL5lI7VbO4nP1kUkBJp393jJ7cJhz+q2IQmAfYWo6eOwrCuj\n6fF3As4N5orp+DA4KwCAs66F9re/JOG6s9AW54Uc14+uTwPbkLp7LHhsdmQaNaqsFLQTxgRYBC5r\nD4JcgThKiRCD4aASAkOpE9gbhmzpBu7P+GnbGuhvF+QyTPnxtJc1oc+S/HeWhsFvZk2s9oBsR2lv\n9Q9W7o91TpmlZeOq8Pn/TmsPFavfx9bdTHTqWNT6OC8thLmjju7GXXTUlf3Pbv4DUVfS5e0iVr0p\neNzG3uuiZU8viWMkF99gQgBgx8/NPHbCj5z3+DQS8/RoDEocVjfmdjvLnt1FydcNuBzSvVC9ObLq\n8Bcu+JUFV40hbXw0sRlRGBLUKLVyFEoZPa12WivM1JZ0Uvpt46BFbPsTotNF6ysfj7pCY9kg9cGQ\nx4bpGSGKdH+3Ouip3uUbMR41CwD97IkBQkAQ5BSfdTuWFukZ2PPdayNb9CA4qISApaqNrMvm4eq1\no89NQF+QhFwfWcTckCUJgej8BKxNvdhagmd0RI9NxNZuQa4O/dPYO6yo+9wtMUOkn9hf6K3r2u/r\nvPzmWK4/MzImUYCGbT/SsO3HfbYeW08rq9++eZ9dfzTwxh8i41d84pTIUmr7Yelw8NLFvhTqWx9L\nZdHJJhb9xwhIm9dZ03bSsK2bu8ZH1pvjxxd2D2kNvzWsJbv3SZ2Bxyal6QpyOYJSETQ4DeBs7sDd\nEVyJtG7e5RUC6rz0gPONm0JbI6ONgypF1FzZyp5nllHzzio2XPMGsTNzaflh8DRKR7f0B9OlmZAp\nZBhyYoNaAf0wFcSjzwjs5jQQzWt9GqppTDzGINQUvznEwHUOBxddH8OTi1N5sq+StniKhr8940un\nLZ6iIadAxT2PJ1E4Xs0/Xk4ZEj/LaOPd1fl8taeIibPCBz3/8mgqX+0p4uKbDgxGzH2J9T+b+WFp\nF9W7R7/i9ECFo2LUWOyHBVdT6EpmZ70v7VyZEh9AamRursLcXI2ltQZL6761iIfbaH4xUNg3JBro\nFEVxsiAI2cA2YEffuVWiKF4zWovVZsSSf+NR9GyTNvGKl8JrR5WflFJwwVT0mdGMOW8KMqWM3e8E\n8tk3raomaVYmaQvycPUOzt29/v7vkKvkXqqFI14/m8+PfRlnzyAPmABxE1No2zz8tM+hYu91znzo\nWDb8/Yew68w5ZTwVS0o48RwjKrXAH8+WHqYTzzGyZ0fgb1Ox08GDNzfz7vRM/t/l++/7BcP64r7h\nlQAAIABJREFU5WaOPM3ElDk6tqwavH4jf4KUAbU5zLhIkTdOw9Of5fDqI80sfm7f8r0MFd8v6eL7\nJV1MnBXFI+9k/dbL2S9wDeL3HwzGI2cSfdoRUobRCODuDX1fudoHWAiCgCLagKvd5+YrPOVGdn78\nBHKlhrSZJ1L18+Cd+EaCYTWaF0Xx7P7XgiA8Cgx0UpaLojg6pO97ISorjuq3VtK5MXLK44HVvukL\npTSxrt2tAePaSxpJmpWJQqfyFlnZBikC2/jwMoy5cegzJath0UcXUfvtTlo31WNt7sVtc6FNMqDP\nMBFdmED81DTU0dqQlNX7CgPXmTI/J2CdCAIqo8Zvnd3lbVQsKWHPDgeHHavzphzu2enAYvYQEy9l\nDikGkJOJoohaIyCTBSQ67Fc01khCKikjPF1wfLI0pql2dLrUTZ0/vOKo33HgIO6SkzAe5UtYcNQ2\n0fXxT7g6uvGYbYh2Bx6Hk8yn/l/Ya8lUoe9BQem/9fa7mLzvnQ5E0YPLbkERFb5f9UgQCZX0z30a\nfgAEQRCAs4AjRndZwdG7s4n8Px+FOtGIx+GiZVnwRuUD0Tmg2rffbROMQqGjtClgXDDm0H44ex2s\nuPETZj50LNGFCSiilGSfXEz2ycURf5/9gZGss3SjjU2rbTzxXiqCIHD9WXUIAjQ3uHj6gzTaWlws\n7iNQ83hg2ee9PL80nStPGh5VwGigvkra0JMzBu8Nq4mSEaWXIXqgpX50hMC0Q38XAgc7vAJAFGl9\ncQk9P60f9rVkutBp2n5WhseDx+ZvYdu7WhBkcnKOuMCPQHFfYKSB4flAkyiKA6tIcgRB2Ah0A3eJ\nohiYI8XwGs0jirT8KHmaZKrIlm5p7MHeaUUdPfgP2V7W5JeGB/4CJBiszb0s/8N/KbxkOgUXTQu7\nFmcYN9O+Qv86j/3sMhRR4TXklrW+TfytZzp46xlfBokowv1/Ck5B8fi9gRbW/kZDtfQbp4SxBOKS\npPunrdmJy+VTClIylRx+sokpc3Vk5atRqgWaapxsXGHmo5faaW30FxjxyUrOuS6O3CIN46ZK99il\ntyZy6a2JfuNOKtqOI0jLRUO0nNMuj+XUS2P71u/kl6+6+e/L7VjNwU2qr/YUcdelNaz7qZexU7Sc\neVUcM4/Q09XuZtdWK28+3kp52ehQbmflqznt8lgmzdahN8mo3u1g2cddfPlup9/vtjf+/HAKk2br\niEtSYLd5qN7t4NZzqgadcyChd8XmkAJgby0+FJQpoeNwyjTf/eGobw0wn/urg9t2rMHcsm+aPfVj\npELgXGAgP2oDkCmKYpsgCNOApYIgFIuiGBAiH06jeXtLD22/7EI3JpGessiDPp3bW/y6iAWDy+yg\nt6oDQ46PTyVcjQCA2+Gm7IXVWBp7SJiejik/HnW0FoVOha3FTG9tF927W2lZX0fL+t9OQ3Y73Hx9\n+hukHZ7nt05BIcPZbfdbZ+Ovlb/ZOkeKhj5LICZBgUoj4LCJnHJpLNfcnYQownmzdtHR4vIKgYGu\noIQUJS9/P4a+zoDe6tvsQjXZhWqOPM3En8+opHaPT5ibYuXezd/pEFGqBNqbXXS2+WeMBHORFUzU\n8rdXMjDFyrFbPQgygZyxanLGJnDkaSYuXVAe8nvGJSk44hQTNz2S4nXLxSUpiEsy8PI/hkeZvDdO\nOD+Ga+9L9v4ebrfIuKlaxk3VcvSZ0dxzWQ0drYGZMSecH8OisyQ3qdXiQauTMW6qlsf+mx1yzkCI\nLl+Vs0w9uEW3r2DbGbpbmjp/8L2kH/JoA8rUBJz1gftI1MR872t7eWDgV65U43ba6aoZGX9YJBi2\nEBAEQQGcBnhVYFEU7YC97/V6QRDKgQJgCFVKoTHcRvMrb/ksout/f+HgvOWDoa3WjuGKmTT9uo3q\nhwOLUyLFrzdHttZgMEzNI/u+8+j6dRvVD38YcN7ZY6fykzIqPykb9mcMF/Gnzibl0iNpfvdnmt7d\ndyXwnW0uaeOJkpGcoaJ6l50pcyU3jSDA1Hk6vl/SRXxSYDygpcHJj5920dXu5tdveti51YZMgIWn\nmrjm3iQM0XIuuSWRB671CfPyMhvXHi+1B3zpuzzSc1V8/Fp72MBwTILCKwA2rjBz+4XVCAIUTdFy\n40MpZOarUSiEkJrz7KMMTJuvY8WXPSx5rZ2GKgdZBWomzdJRUz5yi3PG4Xquuz8ZQYDP3u7g3adb\n6WhxMWWOjuvuTyZ/vIZ7n0/nlrP9tfv+ef1z2hpdyOQwZY6OB1/PDDpnb7i7fenbiqRYBIXcTzDs\nD4QMCgsC0ScviPg6xkWzaXv1k4Djurm+sKl5ZWCr3DHHXs2OT56I+HNGgpFYAkcC20VR9D4RgiAk\nAO2iKLoFQchFajQ/onr/pHMPA7mMpreWeRvNC4Kw3xvNCwo54979CzWPLqF71Y7wE37Hb4aNy83M\nWWRgwowoqnfZmXmEnrf+08qshXquuD2RH5Z2UTgpeGbQIzcFWpifvd1Be6uLe55NZ94xhkE350gg\nk8PL3+cRpZfxzH2NfPKG5G4TRSjbYOWqRXt49P0s7ngqjfuvCW49TpodxVlTd2K1+EyMLassYTOi\nIsX9L0vU5384bg8V231By/XLzVx2eDl//kcKi86M5pH3srjpjEoAxk7Weuc9dbevIt/jluZ9/UFn\nwJxgcNa30Ll0GdGnHI5Moyb79fvp/nqlt8pWbtIj06ppfOT1UfmuAyE6XQhKBTGnL8R4xCH0LN+I\nu7MHVUYSukPGI9Nrse+KzD1jLSnHcNg0tMV5WEvL8ZitKOJMRE0tQqZRITqcNNz/IvaKwNqajorN\nJIybS2+DtH1aO/Zd1t2wGs2LovgycA7+riCAQ4H7BUFwAW7gGlEUR42UPtJG8/sCUUUZyNTh/em/\n47dHfV9cICHFd3uXrrNgMMk4+ZJYcos0xA0xM6irbYCLQg6MoJo/K19NVF/nse2bgvvu6yudjJsW\nOo61a4vNTwDsC1gtHip3BE8nLltnZdGZ0RRM9BENFk7WeueFmyOXC7gHaXTfuWQZ6pw0tJMkviLj\notl+5z2W0Yl57I2WZ94n4dqzEJQK5DHGAPI3a0k5zf95h6wX7w57LUdVPd1f/0rSzRcGxAc8ZivN\nTy4OKgAADKmSu8iYIZHqlX/90nC+TkQYbqN5RFG8JMixj4CPRr6s0LA1dlG/dPAmLvsChqnheUJ+\nx4GBxmppY49LlDZ6l1OkdL0FvVHOyZdIqZzxQWICAHK5wOyj9Ew/TE92gZr0XBUqtQylavR69cYO\naFj/xNLskOO62kO7QLra9z2nTGerO6Sx3d4iff7ANOHoWLl3Xrg5hmh5QNxkIESXm8Z/voF+7mT0\ncyehKc4Dlwt3rxVXUxu23fumgMq8phRHzZOYjp2LpjgPRZwJd7cZR0UdvSu3YF61NeJryfRRWDZs\np+vTn4maOhZ53AAq6b+/PGg1877c9PfGQUUbEQ6yKDUJp83BNGcsqqQYPA4n1Q99QO+WSr9xCpOO\nmIWTMBySjyYrEZlWhbm0mrbP19K90j/tNPu+89DmJKGIkXyEWXec5Xe+5NQH/Tl53B5UKbEkX3Q4\nxlljcbZ2071yO03v/IhnL7bT/vUmnDobj8OJZUcdrf/9NWC96vR4Cp75A41v/IC75/+3d+bxUVVn\nH/+eWTLZJ8kkITtJWISwL66AC1jZqqAirQq2Vktr9bW+1grYWpdiXVpX9KWt1rpSUVoraLWWRcQF\n6kJYQoBAFshO9kwyk2RmzvvHvZPMZGaSgCET5H4/n3yYu8398czMee55znmeY8MybyohqRZkuwN7\nSTWFK7t1i50uMpZfTcS4TPQRoZ0aKl78j4/Nhiy5xMterQfLKL7fd0zDYI4gY8WiTnu5WtuwFVVR\n9OtXfc4NzU4iacnFhOdkIHQ6bEcqaPxk4MYh3DOEYhP0hEXoyPvSRrtdsuuzFlxOmDIjgvhkg8/0\n0IzhJu77UxqpWcpgZEOtgz07W7HbXESZ9Zx9cf+sseA5A625IXBDa20M7AQGIhdD9OD3TuZYT9f4\nRUqsn+zC+skurlwQxtvv9G1d6u5VO0+UjooapeZQN/Jzk7nnPkVHX+4h9IpTrHvj39S98e8TE9HL\nWin9ybfKCQx//CZMqRbaK+po+uIQBnMEWb9dStma96j7oKv3YJk/lcTvX4izxY69uBpHvRXz9Bwi\nx2dS+tQ71G/pCjXZi6qwF1UpDiMjgabPD9BW3hXh6p5zYIiJZPgffoR0urDmFhIxJoP4hecRmp3k\n1WAaYiPJ/t0NmFItNH6Wj8EcQeSELKImDfPR68YYG8mQay+iZf9R7KU1mJLjCB+d7nOeISYSU3o8\nLXtL0IUaOzV0dwKG2EgSF0/3slfkhCzi5kz2ub9l/lRCMxM77RWSHEvk+ExiZ473shfAsEd/iM5k\nxFZQTltFHabkuAHtSblzBWLjDYw7J5xdnyoDjdZGJwd228iZEo7e4Ds91O0Ajha08fjdFRzc3dXo\nTJ4e0W9OwPMJ+I6riikrDs7U4d6IsegDtkWxam/G03611Y7O6/zheU1zQ2AHZzCA49R3dE49J+z1\nuhh22U2dvYHsS3+oFZDrC6b0BBCCfQtXeTXMKT+dS+rP5mO0RFP1+kcAVK3dRtVa7xkq4vG3GbN+\nJan/c7lXo1b5slKm2miJIjQjgfqte3ocGA7PSefAjU/haOia4ZB663ziZk/GMv9sat/7AlN6AiOf\n+ylt5XV90uvGcvk57L3it73aIjwnnX1Xeq/GlHrr/M77u+018rmf+twfYNyGe33u79dmeh1j1q/0\nslfY8GQcDS0cXPasV+sx/ImbA+qd9OhC9q16v+dyFn6InZhGfa7vwGnF0XYqj3WQPszENcssLF/S\nNd1vzf2VrN6QBeAziOruATx0WxklBV1ahIDFt/ReH8rapDRsSRk9T2s8vM/Ov9bWM++6WO5dk8bt\nVxbRbh9c8+eP5NkZNiaUFU+l8vDPvePWialGbvmNUpBwzQNdOSPvvlbP3O/FMGyM/wWJPK/pPh5g\nMgnefyeBkSMMGI1djWdKVhlOJwzNMPCvdxKYON5IeYWTVQ838c+NNsaOMfLS83F8b0ktzzwRy6QJ\nRiadW0VVtROjQfDrldEsvjqcvXnt3H1PI8UlinfJz01m9MSuwdbCA8ncdkc9m7e28fCDZmbNDCU2\nRkd4uOD5F62svFdJinTrSEzQdWroT6KSh5M8dS7h8WmMvfZedHoj9YW+ZW76k9OqgFxPRE7IpGVv\nsU+DZi9R5kyHDet5zVzpdOFobEHodQj9yZvFXljp5QAAOo4rXyB9hKlTK3DCel2tfWsk7YW+S+Z1\nHG/svL+nhkAL0PdmL1Bs1t1e4SNTadlb7PP42JIfOIa7a/k/T9gBAAz70QUBj1UcbSckVDB8bCgu\nj4fOw3n2znnq3ccD3OctWmYhJFRpiFKzQrh3TRpjp/ZekO7ALqVBmLXQzNSLIhE6ZRA5LtH3WevV\np2uoq3aQeZaJJ9dnEhuv5DUkphgZNSmMRT+2dDqlYPB/D1ThcsJFl0dz+0PJxCcZETqYeEEED7+a\nQWi4jiP77Xz4VoPf69zXAJ3XBboGoK1NMnNONQuvqSE1u4whGcqfU/1Mbv1pJE+tbmbs5Epefq2F\nZ5+KxWJRvnfJSXoe/I2Z+x5sZMykSqqqlYtW/DKKS2eGsnhJDYcKHLy11kKIseen81uWRTJhfAjn\nX1TF8JwKtm5r63QAnjq6a+gvmisOc2jjahqK95K37iH2vH4fxz77R7/eozvfmp6AMd5M3OzJxM2e\n7Pe4PtL76SRq8jDM03MIzVTi/QZzOKIfVtPqqPMtWtXZ0KrdQ2O8soj2iegFaK/uW532QBo8G2u3\nhnEb/M9y8Hf/tNsv77SXLizEb9a2ISaCjnrfAS9ng2/p7szrziZqRCKxE9P4+s71WItqGf/Ad9lz\nn5IrMf6B71Ky7isa91cw5YmrQSewlTey/7H/EJllIXpkIhMfXgDA7ns2ID0cT0VJO5OmRRAW4f0j\nlVKpqHnpVWYquzmBv79QyzU/sfCdq83MmBvF8QoH6cNCaLW6WH79UR56KZ3Q8MA/+r+/UMfMhWai\nY/Ws+ms6He0Sg0HQZnexcKx377H+uIOVS49y//NpDMsJ5W//HeHzfju3nFwBtO5cckU0k6dHEhGl\nI31414PA717OoLnRyfGKDnZssvLZh133y/uylUfvLOOu36cw79oY5l0bg8MhOweCiw+1cf+PS+lo\n93b27utWPp3KvGtjsLW6MIYIDAYR8Jq+8MZbrXy4SZkR9NwfrdyzPJqcUUbqG1yYTII/Pm/ly6+7\nwmohRsFPbo7k5lvq2Luvg/wDjVy1IIyFC8J4c33gabSTJhj59PM2WlsVjdu2e89CcuvYtIVODds/\n7f/KrFW5m5EDVITrW+MEhE5gK6zEXuy/pEF7RVfpg4zlV2OeloPTaqf5q8M07TxIx/EmkpfNRh/e\nt/UJAtGXhVvcA4N91dv53o6+fSlOREP9Fv+Lt3vdXwgy7r6KyAnZnfZyWm24bB2k3XGFHwF901S8\nVglNTXrsyl71HlqzneaCaoTqSK1FtbQ3tJK70ncAD6D8aOCpn19ss3LpVWafnsBfHq3m0B47C2+M\nJS3bRFK6kQ/WNbB2dQ3V5R0U7LUz7tzAPYLjFR3cvrCI629PYNK0cGITDFSWtnNwt//pjCUFbfxk\nTiFzFsew5OcJhEfpaG5wUlflIH+XjdKi/hkrmHhBBN9ZZPbZP3xsl6NvbnB5OQGAbRubOLTbzjXL\n4pg8I5KoGD0FBXa2vdfEe6/XB2zMt21sYsK54UyeEUl8koE2m6SgwM7d15WclAMAOHCg67NyucBm\nk0RFCerV56K8/d6fZXq6ntBQwf58Zb/DAQcOORh9Vs/TvA8fcXD+eSZCQpQprOefa+LZNV0PNW4d\nnhpOBacyL6A73xon0FHThO1gGWVr/tXrueZpObRX1nPkly/iaOx6KkhZNvtUSuyko0apotFXvadS\nQ+lTvtmM3TFfMBrztBzylz7uZS/Axwk4Glv8hpIM5r4vau7GcxZN+pUTCEs2U7LuK2p2FPV67fo/\n17L+z/6zdrdtbGLbRv+LfWx/v4nt7/s/9strA5cScFN5rIPHf9l7SRNTtIW2plra7ZINr9R3Joz1\nhTnZ3qUEMqZcztGvNgY8/8kVFTy54sQaFXPOFIwxcRzfvYO/75zFM79+neTZ11Cf+zlhKaOJmxZH\n3defkjh9DmXv+c4me+bXviHJb0KrrWfn0dbNuUjvzjfgU7Lfi1A1/Pfk6mZmTDeRtyuJpkYXX+/2\ndi696egvhl74PUq2v8m46++nZv+nVHz94Sm717fGCdRs2Mm4Dfcigcq/fIirXYn7CoOeqCnDadlX\ngrOl64msrbTGq0HL+u0SdD30AtyxevP5o75xxnDNhp1EjBtK3NwpSKDcwxEE0tvfuDWk3DLPy14A\n0eee5XV/d2jI0176cBMZK6/xed/a975k3IZ7sZdUU/NO19J68Vee73OuP1wdTqauXkx7bQuoTkAI\nQVhSNAiIGpnY6QSOf1bEOX+6DntlU2cIqS9Ex+q5/Foz6VlG1vyuhutuUVadKz/awZH8NtKzQ2io\ndRIWrmPieWFsfa+ZXZ93DQCmTbuKkMhYqnZtYsjEWRR++CKjrr6L6r3bcHW0EZ6QTkiUBZ0xhMIP\nXiDlnPmd2+X/fY/kyZcRGpdMa3UJjSV5NBTtQR8SxvAZS6nI24I+JIz6o3sZfuFSjhfsJHnsLA5t\neQGXs4NxV9xN+b7N1BXtQkoXEZZ0wi3KDLH4YWdjyZxIRd5HpIz/DkWfvUHG1AVU5m8jbeI8Cj56\nEUdb7xnF0aMnEhIb39mSdjQpMXHp6EC6nEhHOyDQhZhob6glMmsU1qLeK/r2hSOFDq5eGM6779sw\nR+sorzi5chGFRQ4e+X0Tf3slnp/cVsfiReFkpBtYfL3yYPDxJ3bS05TZT797MAad6i3iLXqGZRsw\nR+uIjdERH68nIkLQ0tK3xt/68ddYP/7meUwhURbC41LY+9p9jJj/M+AMdwKxM8cTOc53IYzK17bS\nsrfr6cx2uALL3CmYzx+FrbASl62NyEnD0IebOHjz6s5GzdHYQtTUEWQ//ANshZUYYiLQmYy05B0l\nYoz/4lD1W/aQcM10Yi4ZD0KgMxnRRYT6nSffF0qf2UjWA9FY5k4hZEgMLlsbRks0powEH72nitJn\nNpLz+l1e9jJaogkfleZ1/6YdBxiy5GIve0VOyKK93H8yuKvdQfJNl2GeMZaOqnpCUi3Ub8ol9lL/\ny0wYo0w4bcoT175V7/s956s7fXMQDz6z9WT+28y+Kop/vtrALx5K5KofxvDOa41UlTm46RcWQsN0\nhJgEKRlGrE0unlt1nDseTPRwAoKolOHkv/UYANLlRB8SSntzLWFxyTQU7UFnNFG8+RVM5gQihmR6\nbaeeezlVuZuJHTGF0k+7Bvyc7XY67FbMKWdRdeATAOqP5dFYcYioxGzCY1Ow1pTgsFupLeyqbtlS\ne8xrMfLa4lyaKgswp4xE6PQ42220tzTg6mjrkwMAaMr3no1SvU1xsJWb/wlA2/GuXkVNXTXS2X91\nfeobXPxqRTR/eCSGwmIHF1168sXwnlzdTGio4K3X48nL72Dx9TW0qz2G+1c1sePjIVhbXDy12kpc\nnNJN2PiPeO5a3sC/N9lxOiHeouPmGyN5+tn+GZ/pK81lh8ictZT89b9Hpz+1zfRp4QSM8dEY430X\nVjBEe4cYjiz/K3FzphBz4VgiRqcjDHqsu4to2nHAa7C08J5XSFo6k/BRaYSPTKWjtomC2/9EwpUX\nBHQCjnorhStfJmnpJZhnjMHZbMN25OTjds5mW6fepCWXIAx6HPVWWvcf89F7qnA22yh//t9e9nLU\nWyl79l2v+zsaWym85xWyH7qh0171m3KpfuNjxry5wud9C1e8xJDrLyZidDqhQxOwHa7g+D8+93EC\nU564GmHQ07i/Elul/xDMqSA0TIet1YXTCaFhglarC6dTYmt1odMpD8CODqWxaLNLhFcYQXJ0+1sM\nvfg6Sj9/m7amGmKyJ1BfuBtzRg6hMUOITMoic9YNADSXHvTatjf4HwMCSeGnawmLSWL4hTew/4PV\n6I1KD0xnNOFyKOMDLmcvFTg9jtubjoPQEZM2hqIdb564ofpAfzoAN+On+oaSuieKZY/q+u3Fp/ov\nveB0wqpHmlj1iO93q6LSydCRXWG7519U4v5PPiZ4e0PXvcrKncTGKF8Azyml3TX0N5W5mzrXGT64\ncfUpuw+AkANYhC2giD6WktbQ6A+GDg/hormRJKYYeOsvDSz6UQz2VsnBvXbihxioLO0g6ywTrVYX\n656vZ8UfhvDIXd6Nd3zOBeiMJtqb67CMOpdj29eTNOk71B7ciWXUeRzdplSkjRiS6bUNEB6fRsLY\nGZR81FV6yxQZR/LYWTjbbRhCIyj6bB0jL/kR9uZaQsKiObxd6XGeNWsZBzf/GYCQiBgSR16AJWsy\n+99/BnPKWUing9riXaRPnk9p7geMnX8nrfXlODvsHPv6XZwdZ84awydDeXEKN/64js1b7YSFCS67\nNIyqKieffDZo7faVlHLqN3kDzQlonJF4ZsIKocxld/XhodYQGsHQi68DoGzHO7icDkZcfht5ax8k\n5Zz5Ssx/ymxCY5MAKNr0std207F86g5/zbDZN+Nst1F36AsajyolNYROj9DpO5/64zLGU1+23+vp\n/kRIHX8ZDWX7aaktZchZ02mz1tJQdurr05/OLPhuGL/43yiyMw3Y7JKCww7mLeh9XZEgcuqdgBAi\nHWV94STABfxZSvm0ECIOWAdkAsXAYillvbrk5NPAPKAV+KGUsseREs0JaGhoaJwU39gJ9CXdzQH8\nQko5GjgPuFUIkQOsADZLKUcAm9VtgLko6wiMQFk+cs03EaihoaGhcero1QlIKSvcT/JSymYgH0gF\nFgDu8pUvAwvV1wuAV6TCDiBGCNF7DQINDQ0NjQHnhGYHCSEygUnATmCIlLICFEchhHCvnJwKeBaK\nKVX3eQ2ld1to3grUAsFfqbx34hn8Ok8HjaDp7G80nf3L6aBzqBBimbpm+0nRZycghIhEWTDmDill\nkwhcJtXfAZ+Yv+dC8+r7f/lNY1sDwemg83TQCJrO/kbT2b+cTjrxaEtPlD6VwBNCGFEcwOtSSneG\nS5U7zKP+687qKAU8i9ynAb3n0WtoaGhoDDi9OgF1ts9fgHwp5RMehzYAP1Bf/wB4x2P/DULhPKDR\nHTbS0NDQ0Bhc9CUcNA1YCuwVQrjzye8BHgHeFELcBBwF3IVk/oUyPfQwyhTRG/uo5aS7MwPM6aDz\ndNAIms7+RtPZv5wROgdFspiGhoaGRnD41qwspqGhoaFx4gTdCQgh5gghDgohDgshfKuRBREhRLEQ\nYq8QIlcdgUcIESeE+I8QokD9NzYIul4UQlQLIfZ57POrSx2beUa17x4hhP+lzAZO5/1CiDLVprlC\niHkex1aqOg8KIQZkcQchRLoQYqsQIl8IkSeE+Lm6f1DZswedg82eoUKI/wohdqs6H1D3Zwkhdqr2\nXCeECFH3m9Ttw+rxzCDrfEkIUeRhz4nq/qD9jtT764UQu4QQ76rb/WdPKWXQ/gA9cATIBkKA3UBO\nMDV101cMxHfb9xiwQn29Ang0CLouBCYD+3rThTI+8z7K1N3zgJ1B1nk/cJefc3PUz98EZKnfC/0A\naEwGJquvo4BDqpZBZc8edA42ewogUn1tRMkpOg94E/i+uv+PwC3q658Bf1Rffx9YN0D2DKTzJWCR\nn/OD9jtS738nsBZ4V93uN3sGuydwDnBYSlkopWwH3kDJOB7MBMqUHjCklB8D3Yv5D7oM7gA6A7EA\neENK2SalLEKZWHDOKROnIk+TjPgedAYiWPaUUkr3eoxG9U8CM4H16v7u9nTbeT0wS4gihLVcAAAC\nw0lEQVTASUgDoDMQQfsdCSHSgPnAC+q2oB/tGWwnECi7eLAggQ+FEF8JJcMZumVKA4kBrx5YAuka\njDa+Te1Sv+gRTgu6TtFDRjyDyJ7ddMIgs6caushFyR36D0ovpEFK6S6H6qmlU6d6vBGwBEOnlNJt\nz4dUez4phHAvNxjMz/0p4G6UAp6g2Kff7BlsJ9Cn7OIgMk1KORmlKN6tQogLgy3oJBhsNl4DDAMm\nopQSeVzdH1SdoltGfE+n+tkXTJ2Dzp5SSqeUciJKoug5wOgetAwanUKIscBKYBRwNhAHLA+mTiHE\nd4FqKeVXnrt70HLCOoPtBAZ1drGUslz9txp4G+ULHShTOticFhncUsoq9cfnAp6nK0QRNJ3iNMmI\n96dzMNrTjZSyAfgIJYYeI4Rw5yV5aunUqR430/cQYn/rnKOG3aSUsg34K8G35zTgCiFEMUq4fCZK\nz6Df7BlsJ/AFMEId6Q5BGcjYEGRNAAghIoQQUe7XwGXAPgJnSgeb0yKDu1sc9UoUm4Ki8/vq7IYs\nlFLk/x0APadFRnwgnYPQnglCiBj1dRhwKcr4xVZgkXpad3u67bwI2CLVUc0g6Dzg4fgFSpzd054D\n/rlLKVdKKdOklJko7eMWKeX19Kc9B3KE298fyqj7IZS44a+CrcdDVzbK7IrdQJ5bG0p8bTNQoP4b\nFwRtf0Pp+negeP6bAulC6R4+p9p3LzA1yDpfVXXsUb+wyR7n/0rVeRCYO0Aap6N0l/cAuerfvMFm\nzx50DjZ7jgd2qXr2Ab9R92ejOKHDwFuASd0fqm4fVo9nB1nnFtWe+4DX6JpBFLTfkYfmi+maHdRv\n9tQyhjU0NDTOYIIdDtLQ0NDQCCKaE9DQ0NA4g9GcgIaGhsYZjOYENDQ0NM5gNCegoaGhcQajOQEN\nDQ2NMxjNCWhoaGicwWhOQENDQ+MM5v8BjtP7lwnne/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29d52d4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "wordcloud = WordCloud(stopwords=[]).generate(string)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}