Comparative analysis among topic modeling algorithms lda, lsi, hdp, and nmf.ipynb

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     "name": "stdout",
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     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\", 'from', 're', 'edu', 'use', 'one', 'not', 'would', 'say', 'could', '_', 'be', 'know', 'go', 'get', 'do', 'done', 'try', 'many', 'some', 'think', 'see', 'rather', 'make', 'seem', 'run', 'need', 'even', 'also', 'may', 'take', 'come', 'doha', 'istanbul', 'dubai', 'sydney', 'manchester', 'turkish', 'cape_town', 'Airline', 'airline', 'etihad', 'qatar_airway', 'qatar', 'emirate', 'moscow', 'amsterdam', 'tashkent', 'amman', 'abu', 'dhabi', 'air', 'gulf', 'london', 'singapore', 'kuwait', 'kuala', 'lumpur', 'oman', 'airway', 'airways', 'emirates', 'via', 'sharjah', 'told', 'bangkok', 'son', 'happen', 'afghanistan', 'albania', 'algeria', 'andorra', 'angola', 'argentina', 'austria', 'australia', 'azerbaijan', 'bahrain', 'bangladesh', 'barbodas', 'belarus', 'armenia', 'anguilla', 'belgium', 'benin', 'brazil', 'brunei', 'cameroon', 'canada', 'bulgaria', 'chad', 'chile', 'bolivia', 'china', 'colombia', 'cuba', 'cyprus', 'denmark', 'dominica', 'ecuador', 'egypt', 'el salvador', 'eritrea', 'estonia', 'fiji', 'finland', 'france', 'gabon', 'gambia', 'georgia', 'ghana', 'greece', 'grenada', 'guam', 'guatemala', 'guinea', 'haiti', 'honduras', 'iceland', 'india', 'indonesia', 'iran', 'iraq', 'israel', 'italy', 'jamaica', 'japan', 'jersey', 'jordan', 'kazakhstan', 'kenya', 'kiribati', 'kosovo', 'kyrgyzstan', 'laos', 'latvia', 'lebanon', 'liberia', 'luxembourg', 'macedonia', 'malawi', 'malysia', 'maldives', 'mali', 'malta', 'mexico', 'moldova', 'monaco', 'mongolia', 'myanmar', 'namibia', 'netherlands', 'new zealand', 'nicaragua', 'niger', 'niue', 'north korea', 'united kingdom', 'oman', 'pakistan', 'palau', 'palestine', 'panama', 'paraguay', 'peru', 'ploland', 'portugal', 'romania', 'russia', 'rwanda', 'philippines', 'serbia', 'senegal', 'singapore', 'slovakia', 'somalia', 'south africa', 'spain', 'sudan', 'sweden', 'switzerland', 'syria', 'tanzania', 'thailand', 'togo', 'tonga', 'tunisia', 'turkmenistan', 'uganda', 'ukraine', 'uruguay', 'united states', 'vietnam', 'wales', 'yemen', 'zambia', 'zimbabwe', 'kabul', 'tirana', 'algires', 'luanda', 'yerevan', 'canberra', 'baku', 'manama', 'dhaka', 'minsk', 'manama', 'vienna', 'bridgetown', 'brussels', 'hamilton', 'gaborone', 'sofia', 'ottawa', 'torento', 'bangui', 'beijing', 'zagreb', 'roseau', 'dili', 'santiago', 'cairo', 'paris', 'suva', 'addis ababa', 'berlin', 'germany', 'georgia', 'gabon', 'ghana', 'athens', 'libreville', 'cayenne', 'helsinki', 'conakry', 'tehran', 'douglas', 'rome', 'tokyo', 'jakarta', 'amman', 'nairobi', 'pristina', 'riga,kula lumpur', 'tarawa', 'windhoek', 'abuja', 'niamey', 'amesterdam', 'kathmandu', 'nepal', 'nicosia', 'oslo', 'jerusalem', 'lima', 'warsaw', 'lisbon', 'taipei', 'islamabad', 'belfast', 'bucharest', 'marigot', 'san marino', 'belgrade', 'freetown', 'bratislava', 'hamburg', 'seoul', 'stockholm', 'damascus', 'khartoum', 'tunis', 'kiev', 'carcas', 'lusaka', 'harare', 'london', 'washington', 'los angeles', 'lusaka', 'tashkent', 'ashgabat', 'johannesburg', 'however', 'flydubai', 'saudi', 'arabia', 'arabian', 'jfk', 'dublin', 'december', 'februry', 'january', 'march', 'november', 'april', 'september', 'october', 'august', 'july', 'june', 'may', 'bad', 'nightmare', 'able', 'pre', 'new_york', 'copenhagen', 'beirut', 'auh', 'usa', 'europe', 'euro', 'next', 'never', 'ever', 'new', 'find', 'pm', 'wrong', 'still', 'la', 'bad', 'good', 'well', 'terrible', 'awful', 'chicago', 'wife', 'give', 'almost', 'especially', 'always', 'lot', 'end', 'special', 'usd', 'mess', 'sure', 'superb', 'flight', 'excellent', 'enough', 'thank', 'really', 'already', 'royal_jordanian', 'instead', 'montreal', 'ataturk', 'manila', 'everything', 'finally', 'due', 'must', 'else', 'yet', 'didna', 'guyboston', 'turkey', 'york', 'mumbai', 'tashkand', 'gatwick', 'qsuite', 'houston', 'heathrow', 'horrible', 'great', 'due', 'muscat', 'munich', 'ba', 'european', 'melbourne', 'lufthansa', 'birmangham', 'boston', 'colombo', 'indian', 'cana', 'madrid', 'saudia', 'jeddah', 'asian', 'rude', 'pleasant', 'shame', 'polite', 'positive', 'dec', 'fly', 'suppose', 'rd', 'izmir', 'perfect', 'et', 'pas', 'annoying', 'vol', 'flying', 'mostly', 'delhi', 'ist', 'lhr', 'perth', 'medina', 'mohamm', 'hk', 'divya', 'aqaba', 'downhill', 'hurghada', 'catania', 'kigali', 'tk', 'mar', 'venice', 'trabzon', 'milano', 'prishtina', 'algier', 'khi', 'basel', 'hanoi', 'miami', 'cappadocia', 'valencia', 'odessa', 'dalaman', 'seychelle', 'atltnta', 'havana', 'aqaba', 'sharm', 'sheikh', 'malaga', 'dubrovnik', 'caracas', 'san_francisco', 'bidv', 'bcn', 'odessa', 'kinshasa', 'salzburg', 'ita', 'auckland', 'ey', 'fa', 'gf', 'utter_chaos', 'dona', 'budap', 'mnl', 'bhr', 'rj', 'uae', 'atyrau', 'sao_paulo', 'qanta', 'lin', 'detroit', 'bangalore', 'ljubljana', 'irov', 'prague', 'karachi', 'doesna', 'lahore', 'kilaminjaro', 'tbilisi', 'alexandria', 'long_haul', 'antalya', 'ercan', 'almasria', 'toronto', 'liam', 'madinah', 'frankfurt', 'abidjan', 'australian', 'skopje', 'bkk', 'hkg', 'erbil', 'kochi', 'skopje', 'hong_kong', 'newcastle', 'glasgow', 'kayseri', 'eihada', 'sulaymaniyah', 'american', 'dusseldorf', 'adis_ababa', 'los_angele', 'mauritius', 'birmingham', 'durban', 'brisbane', 'klm', 'cochin', 'geneva', 'ihe', 'riyadh', 'cdg', 'leipzig', 'orlando', 'prague', 'almaty', 'bogota', 'merhaba', 'sa', 'bishkek', 'aira', 'tif', 'vilnius', 'belgrad', 'hamad', 'dohaa', 'txl', 'qr', 'odessa', 'entebbe', 'ordu', 'giresun', 'british', 'guangzho', 'mct', 'nov', 'gla', 'rio', 'marseille', 'istambul', 'jqb', 'ho_chi_minh', 'osaka', 'cluj', 'napoca', 'cebu', 'kyiv', 'paulo', 'pisa', 'february', 'lefkosa', 'chennai', 'adelaide', 'kolkata', 'sabiha', 'cph', 'sjj', 'juba', 'shiraz', 'italian', 'bhx', 'casblanca', 'narita', 'amm', 'lgw', 'gokcan', 'tblisi', 'sarajevo', 'dxb', 'cai', 'ad', 'cgk', 'bcn', 'ruh', 'fz', 'chi-minh', 'milan', 'fco', 'cph', 'han', 'bru', 'kansai', 'stuttgart', 'ahd', 'kathamandu', 'yyz', 'jff', 'jfd', 'jnb', 'africa', 'larnaca', 'cdg', 'ey', 'ek', 'cpt', 'ica', 'phl', 'zurich', 'kul', 'ath', 'malysian', 'bwl', 'aqj', 'vnukovo', 'doh', 'south_african', 'bilbao', 'algier', 'shanghai', 'hbe', 'yrs', 'panama', 'klia', 'vie', 'bkk', 'xma', 'etihad', 'hnd', 'alitalia', 'mashad', 'cluj', 'lr', 'edinburgh', 'uae', 'bangalore', 'jan', 'syd', 'bne', 'sao', 'lhe', 'addic', 'rostov', 'mu', 'cm', 'icn', 'guangzhou', 'goa', 'khalid', 'pgs', 'sri_lanka', 'rjs', 'addis', 'dc', 'ankara', 'aug', 'lh', 'dfw', 'ryanair', 'ice', 'atlanta', 'ababa', 'asia', 'bahourain', 'cathay', 'damman', 'che', 'mon', 'thy', 'couldna', 'thata', 'Å“tva', 'vent', 'panamaÆ’a', 'pvg', 'daÂ¼sseldorf', 'Å“fly', 'meddle_east', 'av', 'er', 'cx', 'sgn', 'krt', 'kwi', 'gcc', 'fr', 'sia', 'lb', 'bud', 'fs', 'ams', 'lcc', 'sfo', 'aÂ½', 'mombasa', 'eur', 'lax', 'ia', 'iah', 'saa', 'saas', 'nrt', 'spa', 'changi', 'nbo', 'ovo', 'jed', 'qa', 'eve', 'eva', 'rep', 'sn', 'eg', 'vce', 'blr', 'boe', 'cip', 'pjs', 'int', 'gamba', 'whiz', 'boeing', 'saturday', 'rob', 'biz', 'dh', 'mb', 'intl', 'pe', 'fra', 'mile', 'year', 'generally', 'nyc', 'dus', 'st', 'co', 'ab', 'isb', 'hajj', 'barely', 'anymore', 'nice', 'complete', 'rgn', 'rng', 'ed', 'cgd', 'bbc', 'akl', 'andando', 'volrhona', 'anything', 'nz', 'kin', 'iffy', 'back', 'drop', 'ssss', 'tgs', 'miss', 'extremely']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "# !{sys.executable} -m spacy download en\n",
    "import re, numpy as np, pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "# Gensim\n",
    "import gensim, spacy, logging, warnings\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import lemmatize, simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NLTK Stop words\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 're', 'edu', 'use', 'one', 'not', 'would', 'say', 'could', '_', 'be', 'know', 'go', 'get', 'do', 'done', 'try', 'many', 'some', 'think', 'see', 'rather', 'make', 'seem', 'run', 'need', 'even', 'also', 'may', 'take', 'come','doha','istanbul','dubai','sydney','manchester','turkish','cape_town','Airline','airline','etihad','qatar_airway','qatar','emirate','moscow','amsterdam','tashkent','amman','abu','dhabi','air','gulf','london','singapore','kuwait','kuala','lumpur','oman','airway','airways','emirates','via','sharjah','told','bangkok','son','happen','afghanistan', 'albania','algeria','andorra','angola','argentina','austria','australia','azerbaijan','bahrain','bangladesh','barbodas','belarus','armenia','anguilla','belgium','benin','brazil','brunei','cameroon','canada','bulgaria','chad','chile','bolivia','china','colombia','cuba','cyprus','denmark','dominica','ecuador','egypt','el salvador','eritrea','estonia','fiji','finland','france','gabon','gambia','georgia','ghana','greece','grenada','guam','guatemala','guinea','haiti','honduras','iceland','india','indonesia','iran','iraq','israel','italy','jamaica','japan','jersey','jordan','kazakhstan','kenya','kiribati','kosovo','kyrgyzstan','laos','latvia','lebanon','liberia','luxembourg','macedonia','malawi','malysia','maldives','mali','malta','mexico','moldova','monaco','mongolia','myanmar','namibia','netherlands','new zealand','nicaragua','niger','niue','north korea','united kingdom','oman','pakistan','palau','palestine','panama','paraguay','peru','ploland','portugal','romania','russia','rwanda','philippines','serbia','senegal','singapore','slovakia','somalia','south africa','spain','sudan','sweden','switzerland','syria','tanzania','thailand','togo','tonga','tunisia','turkmenistan','uganda','ukraine','uruguay','united states','vietnam','wales','yemen','zambia','zimbabwe','kabul','tirana','algires','luanda','yerevan','canberra','baku','manama','dhaka','minsk','manama','vienna','bridgetown','brussels','hamilton','gaborone','sofia','ottawa','torento','bangui','beijing','zagreb','roseau','dili','santiago','cairo','paris','suva','addis ababa','berlin','germany','georgia','gabon','ghana','athens','libreville','cayenne','helsinki','conakry','tehran','douglas','rome','tokyo','jakarta','amman','nairobi','pristina','riga,''kula lumpur','tarawa','windhoek','abuja','niamey','amesterdam','kathmandu','nepal','nicosia','oslo','jerusalem','lima','warsaw','lisbon','taipei','islamabad','belfast','bucharest','marigot','san marino','belgrade','freetown','bratislava','hamburg','seoul','stockholm','damascus','khartoum','tunis','kiev','carcas','lusaka','harare','london','washington','los angeles','lusaka','tashkent','ashgabat','johannesburg','however','flydubai','saudi','arabia','arabian','jfk','dublin','december','februry','january','march','november','april','september','october','august','july','june','may','bad','nightmare','able','pre','new_york','copenhagen','beirut','auh','usa','europe','euro','next','never','ever','new','find','pm','wrong','still','la','bad','good','well','terrible','awful','chicago','wife','give','almost','especially','always','lot','end','special','usd','mess','sure','superb','flight','excellent','enough','thank','really','already','royal_jordanian','instead','montreal','ataturk','manila','everything','finally','due','must','else','yet','didna','guy''boston','turkey','york','mumbai','tashkand','gatwick','qsuite','houston','heathrow','horrible','great','due','muscat','munich','ba','european','melbourne','lufthansa','birmangham','boston','colombo','indian','cana','madrid','saudia','jeddah','asian','rude','pleasant','shame','polite','positive','dec','fly','suppose','rd','izmir','perfect','et','pas','annoying','vol','flying','mostly','delhi','ist','lhr','perth','medina','mohamm','hk','divya','aqaba','downhill','hurghada','catania','kigali','tk','mar','venice','trabzon','milano','prishtina','algier','khi','basel','hanoi','miami','cappadocia','valencia','odessa','dalaman','seychelle','atltnta','havana','aqaba','sharm','sheikh','malaga','dubrovnik','caracas','san_francisco','bidv','bcn','odessa','kinshasa','salzburg','ita','auckland','ey','fa','gf','utter_chaos','dona','budap','mnl','bhr','rj','uae','atyrau','sao_paulo','qanta','lin','detroit','bangalore','ljubljana','irov','prague','karachi','doesna','lahore','kilaminjaro','tbilisi','alexandria','long_haul','antalya','ercan','almasria','toronto','liam','madinah','frankfurt','abidjan','australian','skopje','bkk','hkg','erbil','kochi','skopje','hong_kong','newcastle','glasgow','kayseri','eihada','sulaymaniyah','american','dusseldorf','adis_ababa','los_angele','mauritius','birmingham','durban','brisbane','klm','cochin','geneva','ihe','riyadh','cdg','leipzig','orlando','prague','almaty','bogota','merhaba','sa','bishkek','aira','tif','vilnius','belgrad','hamad','dohaa','txl','qr','odessa','entebbe','ordu','giresun','british','guangzho','mct','nov','gla','rio','marseille','istambul','jqb','ho_chi_minh','osaka','cluj','napoca','cebu','kyiv','paulo','pisa','february','lefkosa','chennai','adelaide','kolkata','sabiha','cph','sjj','juba','shiraz','italian','bhx','casblanca','narita','amm','lgw','gokcan','tblisi','sarajevo','dxb','cai','ad','cgk','bcn','ruh','fz','chi-minh','milan','fco','cph','han','bru','kansai','stuttgart','ahd','kathamandu','yyz','jff','jfd','jnb','africa','larnaca','cdg','ey','ek','cpt','ica','phl','zurich','kul','ath','malysian','bwl','aqj','vnukovo','doh','south_african','bilbao','algier','shanghai','hbe','yrs','panama','klia','vie','bkk','xma','etihad','hnd','alitalia','mashad','cluj','lr','edinburgh','uae','bangalore','jan','syd','bne','sao','lhe','addic','rostov','mu','cm','icn','guangzhou','goa','khalid','pgs','sri_lanka','rjs','addis','dc','ankara','aug','lh','dfw','ryanair','ice','atlanta','ababa','asia','bahourain','cathay','damman','che','mon','thy','couldna','thata','Å“tva','vent','panamaÆ’a','pvg','daÂ¼sseldorf','Å“fly','meddle_east','av','er','cx','sgn','krt','kwi','gcc','fr','sia','lb','bud','fs','ams','lcc','sfo','aÂ½','mombasa','eur','lax','ia','iah','saa','saas','nrt','spa','changi','nbo','ovo','jed','qa','eve','eva','rep','sn','eg','vce','blr','boe','cip','pjs','int','gamba','whiz','boeing','saturday','rob','biz','dh','mb','intl','pe','fra','mile','year','generally','nyc','dus','st','co','ab','isb','hajj','barely','anymore','nice','complete','rgn','rng','ed','cgd','bbc','akl','andando','volrhona','anything','nz','kin','iffy','back','drop','ssss','tgs','miss','extremely'])\n",
    "print(stop_words)\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>review_date</th>\n",
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       "      <th>Recommended</th>\n",
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       "      <th>title</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2015</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
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       "      <td>6</td>\n",
       "      <td>poor inflight meal</td>\n",
       "      <td>Saudi Arabian Airlines flight from Medina to R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Business</td>\n",
       "      <td>Economy Class</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Couple Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>really surprised us</td>\n",
       "      <td>Istanbul to Johannesburg via Jeddah, and Saudi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>food good but not much choice</td>\n",
       "      <td>Kuala Lumpur to Jeddah with Saudi Arabian Airl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>no in-flight entertainment</td>\n",
       "      <td>Saudia Arabian Airlines from Cochin to Paris v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>crew helpful and friendly</td>\n",
       "      <td>Geneva to Riyadh to our final destination Jedd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Business</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>friendly crew</td>\n",
       "      <td>My flight with Saudi Arabian Airlines from Jed...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Business Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>cabin crew service very good</td>\n",
       "      <td>My flight was from Jeddah to Paris on Saudi Ar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>2015</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Saudi Arabian Airlines customer review</td>\n",
       "      <td>The worst airline I have been on. Boarding man...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         airline  review_date Type_Of_Traveller       seat_type  Seat_Comfort  \\\n",
       "0  Saudi Arabian         2015    Family Leisure   Economy Class           4.0   \n",
       "1  Saudi Arabian         2015          Business   Economy Class           4.0   \n",
       "2  Saudi Arabian         2015      Solo Leisure   Economy Class           3.0   \n",
       "3  Saudi Arabian         2015    Couple Leisure   Economy Class           4.0   \n",
       "4  Saudi Arabian         2015    Family Leisure   Economy Class           2.0   \n",
       "5  Saudi Arabian         2015      Solo Leisure   Economy Class           2.0   \n",
       "6  Saudi Arabian         2015    Family Leisure   Economy Class           3.0   \n",
       "7  Saudi Arabian         2015          Business   Economy Class           4.0   \n",
       "8  Saudi Arabian         2015    Family Leisure  Business Class           4.0   \n",
       "9  Saudi Arabian         2015    Family Leisure   Economy Class           2.0   \n",
       "\n",
       "   Cabin_Staff  Food_Beverages  Inflight_Entertainment  Ground_Service  \\\n",
       "0          3.0             1.0                     NaN             4.0   \n",
       "1          5.0             4.0                     NaN             4.0   \n",
       "2          4.0             5.0                     2.0             2.0   \n",
       "3          5.0             4.0                     3.0             4.0   \n",
       "4          3.0             3.0                     2.0             4.0   \n",
       "5          3.0             3.0                     2.0             1.0   \n",
       "6          5.0             5.0                     NaN             4.0   \n",
       "7          5.0             4.0                     NaN             4.0   \n",
       "8          5.0             5.0                     5.0             4.0   \n",
       "9          1.0             1.0                     1.0             1.0   \n",
       "\n",
       "   Wifi_Connectivity  Value_Money airline_sentiment  Recommended  \\\n",
       "0                NaN            4          Negative            1   \n",
       "1                NaN            4          Negative            1   \n",
       "2                NaN            4          Negative            0   \n",
       "3                NaN            4          Positive            1   \n",
       "4                NaN            3          Negative            0   \n",
       "5                NaN            4          Negative            0   \n",
       "6                NaN            4           Neutral            1   \n",
       "7                NaN            4           Neutral            1   \n",
       "8                NaN            4          Positive            1   \n",
       "9                2.0            2          Negative            0   \n",
       "\n",
       "   overall_rating                                   title  \\\n",
       "0               6                      poor inflight meal   \n",
       "1               8               would certainly fly again   \n",
       "2               6            On board service recommended   \n",
       "3               9                     really surprised us   \n",
       "4               6           food good but not much choice   \n",
       "5               4              no in-flight entertainment   \n",
       "6               7               crew helpful and friendly   \n",
       "7               7                           friendly crew   \n",
       "8               8            cabin crew service very good   \n",
       "9               2  Saudi Arabian Airlines customer review   \n",
       "\n",
       "                                              review  \n",
       "0  Saudi Arabian Airlines flight from Medina to R...  \n",
       "1  I fly regularly between Dubai and Riyadh but t...  \n",
       "2  Damman to Dubai in economy class with Saudi Ar...  \n",
       "3  Istanbul to Johannesburg via Jeddah, and Saudi...  \n",
       "4  Kuala Lumpur to Jeddah with Saudi Arabian Airl...  \n",
       "5  Saudia Arabian Airlines from Cochin to Paris v...  \n",
       "6  Geneva to Riyadh to our final destination Jedd...  \n",
       "7  My flight with Saudi Arabian Airlines from Jed...  \n",
       "8  My flight was from Jeddah to Paris on Saudi Ar...  \n",
       "9  The worst airline I have been on. Boarding man...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('C:\\\\Users\\\\Win 1809 UEFI\\\\Desktop\\\\data\\\\datanew\\\\199.csv')\n",
    "#print(df.review.unique())\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review'] = df['review'].str.replace('IFE',\"inflight entertainment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review'] = df['review'].str.replace('hrs',\"hours\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def sent_to_words(sentences):\n",
    "    for sent in sentences:\n",
    "        sent = re.sub('\\S*@\\S*\\s?', '', sent)  # remove emails\n",
    "        sent = re.sub('\\s+', ' ', sent)  # remove newline chars\n",
    "        sent = re.sub(\"\\'\", \"\", sent)  # remove single quotes\n",
    "        sent = gensim.utils.simple_preprocess(str(sent), deacc=True) \n",
    "        yield(sent)  \n",
    "\n",
    "# Convert to list\n",
    "data = df.review.values.tolist()\n",
    "data_words = list(sent_to_words(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "# !python3 -m spacy download en  # run in terminal once\n",
    "def process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"Remove Stopwords, Form Bigrams, Trigrams and Lemmatization\"\"\"\n",
    "    texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "    texts = [bigram_mod[doc] for doc in texts]\n",
    "    texts = [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "    texts_out = []\n",
    "    nlp = en_core_web_sm.load(disable=['parser', 'ner'])\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    # remove stopwords once more after lemmatization\n",
    "    texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out]    \n",
    "    return texts_out\n",
    "\n",
    "data_ready = process_words(data_words)  # processed Text Data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_ready)\n",
    "\n",
    "# Create Corpus: Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in data_ready]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from gensim.models.nmf import Nmf\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "\n",
    "coh_list = []\n",
    "for n_topics in range(3,50+1):\n",
    "    # Train the model on the corpus\n",
    "    nmf = Nmf(corpus, num_topics=n_topics, id2word=id2word, random_state=42)\n",
    "    # Estimate coherence\n",
    "    cm = CoherenceModel(model=nmf, texts=data_ready, dictionary=id2word, coherence='u_mass')\n",
    "    coherence = cm.get_coherence_per_topic() # get coherence value\n",
    "    coh_list.append(coherence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (15,) (48,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-2885a823cae6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoh_means\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoh_means\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mcoh_stds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoh_means\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mcoh_stds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m23\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dashed'\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.645\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dotted'\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mfill_between\u001b[1;34m(x, y1, y2, where, interpolate, step, data, **kwargs)\u001b[0m\n\u001b[0;32m   2590\u001b[0m     return gca().fill_between(\n\u001b[0;32m   2591\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2592\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2593\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2594\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1597\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1598\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1599\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mfill_between\u001b[1;34m(self, x, y1, y2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[0;32m   5242\u001b[0m             \u001b[0mwhere\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5243\u001b[0m         where = where & ~functools.reduce(np.logical_or,\n\u001b[1;32m-> 5244\u001b[1;33m                                           map(np.ma.getmask, [x, y1, y2]))\n\u001b[0m\u001b[0;32m   5245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5246\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbroadcast_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (15,) (48,) "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coherence scores:\n",
    "coh_means = np.array([np.mean(l) for l in coh_list])\n",
    "coh_stds = np.array([np.std(l) for l in coh_list])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.xticks(np.arange(5, 20, 1.0));\n",
    "plt.plot(range(3,50+1), coh_means);\n",
    "plt.fill_between(range(5,20), coh_means-coh_stds, coh_means+coh_stds, color='g', alpha=0.05);\n",
    "plt.vlines([6, 12, 23], -1.1, 0, color='red', linestyles='dashed',  linewidth=1);\n",
    "plt.hlines([-0.645], 5, 20, color='black', linestyles='dotted',  linewidth=0.5);\n",
    "plt.ylim(0,0.4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'review'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'review'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "np.random.seed(42)\n",
    "n_features=2000\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, max_features=n_features, ngram_range=(1,2), stop_words='english')\n",
    "tfidf = tfidf_vectorizer.fit_transform(data.review.map(lambda x: ' '.join(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tfidf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'tfidf' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "n_topics=10\n",
    "n_top_words = 15\n",
    "nmf = NMF(n_components=n_topics, random_state=42, alpha=.1, l1_ratio=.5).fit(tfidf)\n",
    "nmf_embedding = nmf.transform(tfidf)\n",
    "feature_names = tfidf_vectorizer.get_feature_names()\n",
    "print(\"Topics found via NMF:\")\n",
    "for topic_idx, topic in enumerate(nmf.components_):\n",
    "    print(\"\\nTopic {}:\".format(topic_idx))\n",
    "    print(\" \".join(['[{}]'.format(feature_names[i]) for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.078*\"hour\" + 0.046*\"delay\" + 0.041*\"airport\" + 0.027*\"day\" + 0.026*\"wait\" '\n",
      "  '+ 0.024*\"staff\" + 0.022*\"hotel\" + 0.022*\"arrive\" + 0.019*\"time\" + '\n",
      "  '0.016*\"connect\"'),\n",
      " (1,\n",
      "  '0.033*\"seat\" + 0.018*\"food\" + 0.017*\"definitely\" + 0.017*\"service\" + '\n",
      "  '0.016*\"experience\" + 0.016*\"comfortable\" + 0.016*\"beverage\" + '\n",
      "  '0.016*\"amazing\" + 0.014*\"efficient\" + 0.012*\"fantastic\"'),\n",
      " (2,\n",
      "  '0.132*\"class\" + 0.122*\"business\" + 0.067*\"lounge\" + 0.026*\"first\" + '\n",
      "  '0.022*\"service\" + 0.020*\"upgrade\" + 0.017*\"offer\" + 0.012*\"wine\" + '\n",
      "  '0.009*\"product\" + 0.009*\"experience\"'),\n",
      " (3,\n",
      "  '0.067*\"vegetarian\" + 0.056*\"kind\" + 0.055*\"question\" + 0.039*\"unhelpful\" + '\n",
      "  '0.031*\"variety\" + 0.029*\"train\" + 0.027*\"order\" + 0.023*\"disappointment\" + '\n",
      "  '0.021*\"visa\" + 0.019*\"parent\"'),\n",
      " (4,\n",
      "  '0.033*\"ticket\" + 0.032*\"customer\" + 0.031*\"check\" + 0.023*\"luggage\" + '\n",
      "  '0.022*\"pay\" + 0.022*\"service\" + 0.019*\"call\" + 0.018*\"bag\" + '\n",
      "  '0.013*\"baggage\" + 0.013*\"ask\"'),\n",
      " (5,\n",
      "  '0.045*\"food\" + 0.042*\"crew\" + 0.031*\"cabin\" + 0.029*\"meal\" + '\n",
      "  '0.028*\"service\" + 0.028*\"seat\" + 0.027*\"entertainment\" + 0.024*\"time\" + '\n",
      "  '0.018*\"serve\" + 0.016*\"drink\"'),\n",
      " (6,\n",
      "  '0.079*\"free\" + 0.056*\"phone\" + 0.029*\"deliver\" + 0.028*\"voucher\" + '\n",
      "  '0.024*\"catch\" + 0.023*\"passport\" + 0.022*\"airplane\" + 0.022*\"wifi\" + '\n",
      "  '0.019*\"star\" + 0.018*\"overbooke\"'),\n",
      " (7,\n",
      "  '0.047*\"pack\" + 0.037*\"state\" + 0.027*\"hungry\" + 0.026*\"juice\" + '\n",
      "  '0.025*\"account\" + 0.023*\"dry\" + 0.022*\"internet\" + 0.016*\"non\" + '\n",
      "  '0.016*\"fruit\" + 0.014*\"dish\"'),\n",
      " (8,\n",
      "  '0.025*\"hour\" + 0.020*\"plane\" + 0.020*\"minute\" + 0.020*\"time\" + '\n",
      "  '0.017*\"board\" + 0.016*\"passenger\" + 0.014*\"gate\" + 0.012*\"ask\" + '\n",
      "  '0.011*\"boarding\" + 0.011*\"buy\"'),\n",
      " (9,\n",
      "  '0.060*\"seat\" + 0.017*\"service\" + 0.014*\"staff\" + 0.013*\"travel\" + '\n",
      "  '0.010*\"change\" + 0.010*\"online\" + 0.010*\"experience\" + 0.010*\"first\" + '\n",
      "  '0.010*\"request\" + 0.010*\"passenger\"')]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Build LDA model\n",
    "lda_model = gensim.models.LdaMulticore(corpus=corpus,\n",
    "                                       id2word=id2word,\n",
    "                                       num_topics=10, \n",
    "                                       random_state=100,\n",
    "                                       chunksize=10,\n",
    "                                       passes=10,\n",
    "                                       alpha='symmetric',\n",
    "                                       iterations=400,\n",
    "                                       per_word_topics=True)\n",
    "\n",
    "pprint(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['saudi', 'arabian', 'airlines', 'flight', 'from', 'medina', 'to', 'riyadh', 'was', 'more', 'than', 'an', 'hour', 'late', 'poor', 'inflight', 'meal', 'on', 'all', 'domestic', 'flights', 'seats', 'comfortable', 'for', 'sitting', 'and', 'was', 'even', 'able', 'to', 'take', 'nap', 'crew', 'were', 'helpful', 'and', 'aircraft', 'seemed', 'to', 'be', 'new', 'no', 'inflight', 'entertainment', 'system', 'at', 'all', 'on', 'the', 'flight', 'from', 'medina', 'to', 'riyadh']]\n"
     ]
    }
   ],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sentence Coloring of N Sentences\n",
    "def topics_per_document(model, corpus, start=0, end=1):\n",
    "    corpus_sel = corpus[start:end]\n",
    "    dominant_topics = []\n",
    "    topic_percentages = []\n",
    "    for i, corp in enumerate(corpus_sel):\n",
    "        topic_percs, wordid_topics, wordid_phivalues = model[corp]\n",
    "        dominant_topic = sorted(topic_percs, key = lambda x: x[1], reverse=True)[0][0]\n",
    "        dominant_topics.append((i, dominant_topic))\n",
    "        topic_percentages.append(topic_percs)\n",
    "    return(dominant_topics, topic_percentages)\n",
    "\n",
    "dominant_topics, topic_percentages = topics_per_document(model=lda_model, corpus=corpus, end=-1)            \n",
    "\n",
    "# Distribution of Dominant Topics in Each Document\n",
    "df = pd.DataFrame(dominant_topics, columns=['Document_Id', 'Dominant_Topic'])\n",
    "dominant_topic_in_each_doc = df.groupby('Dominant_Topic').size()\n",
    "df_dominant_topic_in_each_doc = dominant_topic_in_each_doc.to_frame(name='count').reset_index()\n",
    "\n",
    "# Total Topic Distribution by actual weight\n",
    "topic_weightage_by_doc = pd.DataFrame([dict(t) for t in topic_percentages])\n",
    "df_topic_weightage_by_doc = topic_weightage_by_doc.sum().to_frame(name='count').reset_index()\n",
    "\n",
    "# Top 3 Keywords for each Topic\n",
    "topic_top3words = [(i, topic) for i, topics in lda_model.show_topics(formatted=False) \n",
    "                                 for j, (topic, wt) in enumerate(topics) if j < 3]\n",
    "\n",
    "df_top3words_stacked = pd.DataFrame(topic_top3words, columns=['topic_id', 'words'])\n",
    "df_top3words = df_top3words_stacked.groupby('topic_id').agg(', \\n'.join)\n",
    "df_top3words.reset_index(level=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XWpSNFX5eBXfs1u8pC01fSHf901sBbwAuiIhn9JBPUxHxYuDnwEG0bve2uveFlD6Kg2gerBFKm/nplDbzofMo6kyezwUupASYbBmssbKUEkzyvIjYY755S5Lam5Yx1xZ5rhERnwBOofkC1kafqh0fUm1402ueW1I2wW20osM9SyLivZTf4UW0DtYI5ft/KHBiRJxR9fNKkqbMtNTP9j9LklqZlrquRZ4T0RbtR9XPezGlz/6BtAnWWLk35bv/VZfpD30uliRpdCJie8pY7XtoEayxch/gsxFxTET0FL8jIraLiO8DJwJ70z7+x6aUceHzIuLwXvIZtx6fLyRJUyAingacS6m72q0/ehoDmtuzWIyjr0CSVBiwUZIkdRQRS4FvM3sXnQSuAW5vcst2wCnVrnMzwRpPoAQ+nLGquv+uJvfvy9xB20H5T+ClLd67HbgKuL5FudqKiDdSBtubDZLeSfm811A+uyRJVAGOvwXco8UlNwBXArfNI481IuJY4F9p3rF9B3B19d+6+wHfj4gHdpHVGrSeVHQHpY69scX7WwGnRcSyLvKRJI1JROwMfIO5E46uo3k9sgR4a0QcNeyy9Soi1qK0c1tN+LmZUgffNLJC1VSTg/+bEuh/xl2UdmWz73td4KSIuG8PeewCnAUc0OKSm4FrKX0As26l7DL/pYhYvUMem1E2TWhVrhsp3/UtXRa7Me0F/ztKku4WEZsAXwdeS+uNdK6ntIWbJtEmedukkqSRGHJ91iy/w4H3Mnv88U5Kn26zMcfNga9HxBZdZrFxi/MzeVzP3DYhlI34Ph8RB3eZzxzVJOuPMTtQY1LaoXd2uPclwDHMbjPPmBmHbiz3msAnI+KgPssaEfFOSuD/zVrkeTXN+/LvCZweEY/tJ29JUmdTNubazDGU4MqNmtV3AF+k9NnP2AFY1keez2F2Hf2DzPx1q4sjYgPgK5SNgJsFYb6d1s8vj6R8P9v1UU5J0gI1ZfWz/c+SpDmmrK5rZsG3RXtV9fO+j9LP22rjgFtovc6pmzyGPhdLkjQ6EXEPSgDeB9XeuoHW81JfBLythzzuC3yP1huWX0/z9btrAu+IiE/2GiByjHp5vpAkTbgqWOPnaD43qdm//2tQ5vY8cwTFm1hj7iuQJGHARkmS1J3Pcveu5/8L7AeslZmbAmsDewBfqN2zE/CGiLgPcBxlEdHNlN2EHgis0XD/k4Ff1O4/KCIeP8gPERF7Ay+unf4Z8HzK7gFrZ+bmmbkRsDqwlLIrw9GUHfTapb09sLx2+gLg5ZQd9dbOzE2r1+qUoJZPouzKN7BBZEnSxDma2QGNbwM+SAk6tF5mbpiZW2Tm2pQFsHsB/wR8jeadps18EDikdu6XlDpxp8xcMzM3y8w1KbvlLGf2xKktKQtvN6A7twPfpOzO80hgkyqPzTNzg+pzPBL4D+DWhvvWAI6vgjo1cxXwsupV3wnw6ob3Wr0kSfP3aWCT6u8fAc8E1s/MjSm7fe8CvIO5gfdeFxHPGlkpu/Mq4AG1c1+kTJbdLDPXq+rg9SmTmu5PmRT835RJs8N2f8rEpKC0pY8CdufutvRawEOB42v3rQV8tJsMImJTSgDOxh3yVlH6AJ4MbFR9D5tQFiA9DvhSLZmnAO/skNW7gW0bju8CjgUeU+WxQfVdr0sJtLE7ZcLaFyifvZ2F/jtKkioRsYSyC/xjam9dB7yLMul4rczcKDM3pNRpuwOvAE6j+wUxw2qTSpI0yvpsxhMom8VB6QN9M6UPd83M3KxK/1GUtl2jzYH39ZDPKuC7wBspn22LzFyj6jfeiNJWexhwJLP7jgP4WLXBQ6/uBXy4If9PUOrotap26FqUMdYj6jdGxEOrexuDX94BfAB4MOX72ZTSDtyXMlad1fXHMHcjim68hvL9N7qQ0i7djdJe36zqy19anb+s4dr1gP81EJUkDc00jrnOOIgyr4kqvbdyd90z01f8MMpiJzLzFkofb6N6ubvx/Npxy413IyIofdVPqr11JnAwcI/MXKt6flmTMs/sg8xul98b+KxBKSRpqkxj/eycKElSo2ms62Ys+LZon94GvLp2bhWwAtgf2DAz160+4+rAzpR27Ql0sVnsCOdiSZJG5wRg++rvbwBPpdQXG1bzUrehjAteV7vvjdWa2rYiYj3gJMoGcI2+D/wtZY72RpT1tztRxivrGwe+AHhD159ofHp6vpAkTbaI2BH4H2YH5b8L+DiwJ3fHqFiLMlfpizO3UuYQ9TO3Z6EZVr/vuPoKJEmVyDTgvCRJmi0imj0g3AYcnJmfb3PfMZSgCjOuAn5FWSh0CfCkzPxNi3vXB75DWUQz46TMPLBDWVcwe2D20Mxc0eLajwAvbTj1ReBZmXlnuzyqe4MySPqzzPxzk/dfTwlGOeMs4HGZ2XFgtrp/X+CqzDy3m+slSZMvIrYBLuXuRaW3A8sy8/s93L9HZn6lzTXPpCwgnpHAW4AjM7Pl4uCIWAr8H7Brw+kPZ+Yr2tyzCWUy2Ucz8y9dfoadga9SBo9nLM/Mt3e4bwfgooZTl2TmDt3kKUnqXou2IZSgCP+cmata3Hdf4NvMHiT9C3DfzLy6xT07MM9/2yNiJfDohlP7ZebKFteey+x67jWZ+f4u81kXeEZm/neba5Yze4fct2fm8jbXH0IJYlj3W2D/zPxdD3kBPCgzf9nqnuq+L1Mm+c74I3BQZp7V4b7nVGWdWTibwMMz84dNrl0TuALYsOH0M9v1LdTu3wR4bGY2nYA17N9RkjQ4EfFO5gY5+hqlz7np80Ht/p0pdULTwMQjbpMuowTdmnF6Zi7rcM+s56rMjFbXSpIWrhHUZ8uZ276DMu74tMy8osV9QVm4ekjD6dspwZGu7FCmdwMfz8yLO5W/un4r4GTKYpkZKzLz0A73LWN2/TnjOuApmXlml/kvAX5OCcg/4xrg8Zn5kzb3PZWyuGfNJm+37D+o7n04ZRy7MYDUh4FXZ+Ztbe7bnLLYdp+G06dk5lNa3CJJ6sMUjrkup/nzwDnAE5rNWWqSxl6U54cZNwNbZ2Z9IW+r+/cAftxw6hZgm8ysLzqeuf41wHsbTt0OvCwz2wbWiIjdga9QFjXPeG1m9hJ4WpK0AE1h/eycKEnSLFNY1y1n8tqiF3N3AC2Ape36uSPiCZT+/MZx2guBp2fmOV2UbxPgFZn5jjbXDH0uliRpuFrMm74TeEm7/s6IeCBlA5vGwEcfyMxXdcjvo8BLaqePBA5vVd9HxD2Bb1ECCzeW8WGZ+bM2eV1MD3Vnk/uX09u86Pr1M7p+vpAkTa6I+BazN8S9hTL36dQ297yQshFrs/m1neb2zGuObi+xKqrrl9NbvbgDA+j3HWVfgSSptdXGXQBJkjQx/qWLgAqvBq5tON6MEqzxNuCprYI1AmTmjcC/1E7vHxHr9FPYFnavHb+xm2CNAFn8X5vO4Hray7sN1lilf6bBGiVp0XkwszuQv9DtZC2AzPxzh8laS5i9MAZK3feudh2wVdoXAQcwe/ecF0bEFm3uuSYzj+h2YnJ1z/lVPo2LWl8WEfZXSNLCdQrwT62CNQJUbZsDKDuEz9iSsmPb2EXE2sweaPwT8IFu78/Mm0cU5O86ysYHLYM1Vo6g7IjX6KB2N0TEY5k9Qfg6yqYDbScIA2TmZ4DXNyYHvK7F5TsxO1jjWd0Ga6zyuqZNsMZJ+R0ladGLiK0pfceNvkbpM+4Y3ApK+7FVcKvqfdukkqShGkV91sIllED+TYM1VukmJXDE5Q2n1wSe3kWZ3tTLQpzMvJxSf17VcPrZ1WLVfjy722CNlQOYHawR4G/bBWsEyMwvA/+v18JV3svsYI0fy8xXtAvWWOV5JfA0ym8444CIeECf5ZAkNTdVY64tXEMPC1gz8wfArxtOrUuHPuOaQ2rHX2gTIGNj4K2108/vFKyxKudPgWdQFhPPOKzaCEiSNNmmqn62/1mS1MRU1XUtLNi2aJ+OYvZvdinwiG6CNcJfnwfaBWsc1VwsSdLova5Tf2eWDdaPqJ3uNI93W+AFtdPHVeO3Lev7zPwD8Djg+obTqwNvapffAtHT84UkaTJFxCOYHawR4EXtgjUCZOYngeXDKtekWyB9BZIkDNgoSZK688tuFg9l5vXAl5u89ZHMPLuL+08Hft9wag3ggV2XsrP6QqGLml618NKWJE2nYdcdzwJ2aDj+KXM7ZVvKzAuBDzacWht47kBKNjuf84CTGk5tBew26HwkSQNxJyVYY7MdZGepFlrW25EvWSALUDatHV/SaYByTN5dDYy2VZX9E7XTD+1wW31S7/KqTu7W0UBjIMlntBisHebzzqT8jpIkOIzSppxxDSV4Q1eb6QyTbVJJUg/GVZ+9LjOv6XRRtTlePSh9p7ZhX6pAhJ9sOLUWsE8fSZ2cmV/t8Z76ZhAnZOZpXd77SeDHvWQWEXsD+zacupS5gTtbqoJ5Lq+d/sdeyiBJ6mgxjLm+o48FrCtqx4d2c1MVLPEfaqfbLUZ+GbBBw/FJmfnZbvKCvwb0aHyG2ZbZAS4kSZNpMdTP3eRj/7MkTa/FUNct5LZoTyLiyZQgm40OrTYoGpRRzcWSJI3Wr5ldp7bzKWZvTrNdRGzV5vqXUdbOzrgGeFU3GWXm75m7kc4zIuKe3dw/Rv08X0iSJs9LasenZ+bxXd77HuDCAZdnWiyEvgJJEgZslCRJ3akHfmjnR/O8v75I5r493NvJtbXjQS5SGmbakqTpNOy6oz556cN9BDGqL6Z59DzK0059B9m9hpSPJGl+vt5NAMEG/1k73gG43+CK07f67uu7RsR6YylJa82CMLbz3drxrq0ujIjNgcc3nLqlx7yoApJ8ruHUaswOYDGj/rzzkAEG7ZyE31GSVDyzdnxMZv5lLCVpzjapJKkb46jPrgI+38P1XbcNB2AQ9ecxvVwcEasDj6md/ni391cbUHR9fWXOIuHMvKnHND7P7MVZw+pnl6TFatrHXO8AjusxP4D/Ynb9s29E3LuL+57G7M1yLgHaBUeufz9Hd1e8WUY1Ji1JGp1pr597Yf+zJE2naa/rFnpbtFf1/v0fZuY3B5X4iOdiSZJG6+PdbHIPf93I7dza6XbjtfvXjv+7SqNbnwQaxy2XAE/o4f5R6/f5QpI0eeqbs3242xsz83Z6nE+0iIy7r0CSVFl93AWQJEkT4Ywerv197fhqym5C/d6/cQ/3dvJDYM+G4xURcVBm/mJAaR/UcPyBiPhTZq4cQNqSpOn0IyCBqI4fHxHvoOwad/t8Eq4CIdUn6ny913Qy8zcRcQuwTnVq7z7Ksg0lAPMmwAbAWtz9mWfUd669V6/5SJJG4qReLs7MX0fEBUDjxNu9gLMHWqoeZeZNEfEr7g4euQnw2Yh44QIKHnV2jxOv6rsItmtLP4rZdfF3M/PGHvKa8dPa8d7AF2vnzqMEVtyoOt4V+HhEHJaZN/SR519NyO8oSYteRGwH7Fg7/ZkR5m+bVJI0b2Osz87MzFU9XN9L27ClanHpda8PZwAAIABJREFUbsBmlPpzHebWn/epHfdafyZweo/3PIi7+6qhLHpd2WMaX+3x+vrE5H762W+IiPO5e6PC+0fE+n22xSVJc037mOsveuwrnsnz8oj4KvDUhtOHAId3uPXQ2vGKVguSI2IzZm/SdCu91+/QvK9ZkjTZpr1+nimL/c+StHhNe123YNuifVpWOx50//4o52JJkkar1/7OC4EHNBw3Ha+tNid/YO30F3rJKDNvjIivAX/bcPoRlECOC1FfzxeSpMkSEfdhdkD+VcApPSbzJeDIgRVqCiyQvgJJUsWAjZIkqRuX9HBtfWDx9z0Oltbv36CHezv5OPBy7h4M3Rn4WUScBnwe+FZmntdn2p8G3g6sWx1vCZwWET8ETgS+SQm40etuBZKkKZWZV0TEF5g9QHo48OKI+Czwf/Q/aWcXZg/u3gk8NaI+J7grt3N3J+wWEbF6tZNrSxHxaOB5lF1vN+8jz0EGbJYkDU59Umi39zQGbHzQgMoyXx8F/r3h+CnAJRHxReBk4LTMvHwsJSsu7vH6evDDDdtcu1fteK2IeGmP+cHdgSZmbFO/IDNXRcQngFc3nH4hcFBEnAh8GTgjM6/pI39Y+L+jJAn2qB1fD/xqmBnaJpUkDcHI67PKxT1e30vbcJaI2J2ycPYZwHY95gu9158X9xHIf9fa8Tk9BrQkM/8YEVfSxTNCtVDq/rXT+0TEA5pd38FaDX+vBmzF3HFxSVIfpnnMtTKfDZCOZXaQjOdFxFtbzV2qgk49seFUAivapP8wZgekuA74xz6+nyW14zl9zZKkyTLN9bP9z5IkmO66rrKQ26I9iYiNmLsh0/cGlX5lZHOxJEkjd3GP13c7Xrsbs2N8JP3N0f4xs59H6kEgF5L5PF9IkiZHfb3QbzLz5h7T+C1lTs36gynSVFgIfQWSpIoBGyVJUjeu6+Ha+qKY63vMq35/fVJu3zLzlxHxZuDdDacDeEz1IiKuAL4PnAF8OzN/1mXal0fEi4H/oiyymfGw6gVwbRXA8QzgNOAsAzhK0qL3CuChwPYN57YEXlm97oyIs4HvAispwYWv7SLdrWvHqwMfmXdpi02AK5q9ERH3AD4GHDDPPAYZsFmSNDi9BPNvdc9mgyjIAHwE2B94csO5tYFnVy8i4neUCbqnA6dmZj+fv1+9tMNnAiM2nlqt1bXMfU54ZPWar01bnH8bsB+we8O5DSmBG18IZEScS/muV1K+626DLC7031GSBFvUji8aVp+obVJJ0hCNrD6r6altSB/jrNVi1aOB5zI74FKveq0/r+4jj01qx3/uIw2Ay+guqMaWzP1Ojuozz7pNgd8NKC1J0pSNudb0U2fO+EqVx8yzzD2Bx1I2nW3mucx+fjgtMy9uk379+9mKwXw/rfqaJUmTZarqZ/ufJUlNTFVdV7OQ26K9qvfvw+D7Zkc9F0uSNDrDGq+t/xt/TWb2mhfARR3SXUjm83whSZoc9fVCPa/dyMy7IuIPzA1qv5gthL4CSVKl3aJNSZIkADIz53P7wAoyAJl5JPAcoFXwhy0oO9++D/hpRFwQEW+KiFY7GjWm/d+UQBGtBnA3Bp4AvJMy8P6HiHhPRGzZ48eQJE2JzLyMEtj3iy0uWR14CGVi1+eAyyPiixGxb4ekhznQum6zkxFxL+A7zH9iMthfIUkLVa8B+WHuZKV6cIWxyMxVwNOBI4FbW1y2E2VS8CeAiyPi+xHxnIgY2MYC7Yo4xLSH9ZzQ9BkhM28CHg0cw9zJaFCCX+wGvAj4DHBpRHwzIp7SKcMJ+B0lSXMnX3WzCKlntkklSUM2kvqsiaGOs0bExsC3gOcxv2CN0Hv9eWMfeWxUO76hjzSg+/6NkfezS5L6M01jrk30U2cCkJl3AP9dO31Im1ueXzs+tkMWI+1rliRNlmmqn+1/liQ1M011XRMLuS3aq3r/ftLfHLh2bB9L0pSa55radupzqfutm+pztBdywMa+ny8kSRNl49rxoOq4xW4h9BVIkioO9kmSpEWnCqy4I/AC4OvATW0u3wl4F3BBRDy5i7S/Ttm14VmUwfd2C7a2BV4PXBgRh3RVeEnS1MnMv2Tm3wAPBj4InN/m8jWBA4HvRMTxEdFq1/U1B1zMRq0W7h4LLK2dOw94B/AU4P6UiU/rAksyM2ZewKHDKqwkaezmG/BhaDLzjsx8E6Xd90bghzQPKDjj4cCngR9FxM4jKOKwDOs5oeVvnZk3ZuaLgV0pbeyzaR14ZAnwOODLVeDG+m6A9bQX6+8oSZNqWBOZbZNKkkZpQW1YNw//BuxRO/cH4L3ATJ/1FsB6zK0/9xtpSYvbasf9tm+7vW8c/eySpD5N0ZjroNUDXTwjIupBkImIvSgb68y4Hvh8h7SH+f1IkqbAFNXP9j9Lkpqaorpu0IbZFp2vYfTvj3wuliRp6gyqfpqWcWxJ0vTot26yPTTbNPQVSNLUWH3cBZAkSRqHzLyZMhB8bESsAewOPBLYt/pvfbeBLYCTI+KAzPxGh7TvAE4EToyI1YAHVunuCzwK2KZ2y3pVOVbLzE/N75NJkiZVZv4COAw4LCK24e56aV/KZK565+ezga0j4vGZWQ9KdHXt+HeZee8hFBuAiHgM8Jja6cMy84NdJrH+gIskSRqODYEr+7in0TUDKkszfW1QlJl/At4DvKeaDP0ISv27T/X3WrVbHgKsjIi9MvOP8yjvuNSfE96Sme8cRcaZeQFwOHB4RGxKed7Zh/J978ncMYvHAadGxN6ZeUOHtBfb7yhJk+Kq2nF9h/h5s00qSRqBoddnoxYR9wYOqZ1+P/CGzLyziyTGUX/WN8qbs8C3S93eV28/3w6sk5l39ZmvJGkEJnnMdRgy85cR8RPuDtK8DvB3wMdrlx5SO/5sZt7SIfn69/OtzHxcXwWVJE21Sa6f7X+WJHVjkuu6YRhyW7RX9f791Sjz2er9zfMxtrlYkqSJVZ9LPahxzwU3R1uStOgMam5PfR3SsC30em7i+wokaZos9EpDkiRp6DLzjsz8QWa+LzMPBLYEHg0cz+zdG1YHPhoRXQe9zsy7MvPnmfkfmfn3wD2AhwIfAe6oXf6BKmCFJGmRy8w/Z+YJmfnKzNydEuz3lcAltUv3Y+6EJYArasf36qX+6sPf1I6P62FiMsDmgyyMJGloth/APfVJsDPqwQ6W9JHXxn3cM0tm3pCZX8/Mt2TmY4DNgGcB36tdui1w5HzzG5P6c8KO4yhEZl6dmSdl5usy8xGUjRIOBc6pXXo/4HU9pr0YfkdJmhT1emdptcnNINkmlSQN2yjqs1E7kNmLhE/PzNd0GawRxlN/XlY73qXXBCJiLWCHLi+v/+5rAtv1mqckaXwmcMx1WI6tHR/aeBARawN/3+GeZhZEX7MkabJMYP1s/7MkqScTWNcNy7Daor2qf58Agw5qYftYktSrORsGRkQ/Aa2W1o7rwZwazXee9rznaEuSFoV6HdfzGqRqTtY9e7ytMRYEETFt9dy09BVI0lSY9MnDkiRJA5eZqzLzjMw8mLKTX6OlwD7zSDsz8yeZ+XJgGbODNm4IPLXftCVJ0yszL8/Mf6cELPph7e3nNLnlXOC2huM1gIcPqXgAD6wdf6bH+/ccVEEkSUO1+wDu+WWL626oHa/fSyYREfQ+KNtRZt6UmSdm5j7Ah2tv/201eXjS/Lx2/KixlKImM6/NzBXAQ4CTa283e97pJe1p/B0laVL8uHa8IXD/Aedhm1SSNGyjqM9GbRLrzx/VjrePiK17TGN3Sn95R5l5NfD72ukF0YaWJPVnAsZch+V4Zn+Oh0fErg3HBzJ7EdK5mXlWF+n+rHa8NCIG3k8vSZpuE1A/T2L7WZK0gExAXTcsw2qL9iQzrwN+Vzv9iAFnsyDnYkmSFrRzgcaN9IL+5mg/tHbcao42zHOeNn0E3JIkLUr1umjXiFi3xzTuQ+/11LTXc9PSVyBJU8GAjZIkSW1k5onAD2qn6xOw+k37e8AXhpG2JGk6ZeZNwNtqp+fUHZl5C/Dd2ulnDatcwJa14/oi1pYiYkN6n5x0Z+24112PJEn9eXovF0fEbszdkbzevppxPbN3tds4IjbpIbsHMfxd7d7E7KD76wA7DznPYfhm7XiniOhnotdQZOadwOtrp3eIiA0GlMW0/I6SNBEy81LmLoCZVyDeJkbdJpUkLTIjqs9GbT7152rA/oMtTmeZeTlwUe303/eYzD/0eP2pteNh9rNLkkZkAY+5DkVmXgOcVDt9SMPfh9beO7bLdC8CLqydPqinwkmSVFnA9bNzoiRJA7GA67qhGFZbtE8ra8cHDzj9BT0XS5K08FTPBfWAVs/oJY2IWA94Yu3099rccm3teGkPeS0B9un2eknSovZb4JqG4yXAAT2mcWAf+c6nntuU4W/cO69+32npK5CkaWHARkmSpM7qC3/Wm5C0JUnTqdu643O143+MiHsOoTwAt9eOewmY9TLmv+vRhj3eL0nqzxMjouuBS+DlteNLgHOaXZiZq4Dza6d72c38JT1c25fMvB64qnZ64tpwmfkH4Ie100eMoyxt1J93YEDf9bT8jpI0YU6oHb8oIuqLXOdj1G1SSdLiNOz6bNTmU38eBOwwuKL05Pja8auqxUgdVf3jh/SYX72f/akRsWePaUiSFqaFOOY6TPXAF8+NiCURsR3wuIbzdwKf7iHd+vfzuoiwnS1J6tdCrJ+dEyVJGqSFWNcN07Daor2q9+8/LCIeP6jEJ2QuliRp4Tmldnxwjxvdv4DZbc5VwNfbXP+b2nEvARifCWzWw/WSpEUqM5O5dVx9XVFLEbEm8KI+sp5PPfePDD/21iD6faelr0CSJp4BGyVJ0qIQRb/PPrvWji9rkv7qw0q7lk/WXiv6zFeSNGYjqDs+Bfyx4Xht4H8jYu0+8yUiosVbf6wdd7XzUUTcj7k7BnfjBuCWhuMNI2LbPtIhIlbU69d+0pGkRWJ14N/b1Ad/Ve0S/tLa6Y9n5l1tbqtPXH1hN4WKiL26vba6vq86OCI2A7aonW7bhlvA6pOCD4iIf+43sVb/TwzweWcVcMUg0u71d4yIQ5q0xZf1k7ckLWIfAm5tON4EOG4e9UTdqNukI2F7VZIWnGHXZ6PWb/25NXD04IvTtY9T2ogz7gl8uFNfRdUvfhw9BsrIzK8CP6qdPr5qW/ali7Lu0KQdurzf/CRp2k3ZmOswfYPZn2Nb4AnA85g9l/yrmdlLv/f7gZsajreiPCMN7TPaZytJC9+U1c/OiZIkzTFldd0wDast2pPM/Abws9rpYyNiqwFmM5K5WLVrrOslabJ9FLij4XhT4H3d3FgFP67XPV/IzHobtlF9jvY/RMQ6XeS1CXBUN+UaJtfUStJE+VjteFlE/EOX974B2KmPPOv13Au6bFctBd7YR369GkS/78j7CiJiWZM6+JB+85OkaWHARkmStFhsBJwfEa+MiK53HoiIlwAPbjiVwOlNLj07Ig6PiC17SHt/4Km106d1e78kaeIdGRHHV0GmulItAn1X7XTTuiMzbwNeXzu9N/DtiNihhzyXRMTTIuI0YPcWl62sHb+qCtTVLt09gFOBjoO8dVWwr1/UTr+s13QkSX05ADg6Ipa0uiAidgW+AjRecyVzB17r6ju+PSMint3uhojYEzgJWKND2o0OiIjvRMQzup1AXX3eDzL7M10EXNJDvgtGZp5CmRTd6N8i4p29TCqPiM0i4o3A11pc8vKIOCUiHtftxO9qAti/1U5/JzNX1c4t+t9RkiZFZl4OvLd2+knAydHlzvARsXNE1INBz1hZOx5qm1SStDiNoD4btZW144Mj4sntboiIHav7uh4PHbTM/D1zFyo9nzIBuWm5ImInSrt1v+rUrc2ua+M1wJ0Nx/cGzoyIB3abQBT7RcRJwIE95i9Jam+axlyHphpf/a/a6UMp9WijY3tM9y/Au2un/wY4KXoIcBwRa0XEcyLipxGxeS9lkCQtSNNUP6+sHTsnSpIE01XXDc2w2qJ9eh3QuNHwPYDvVkGWO4qITSLiza3eH+FcLEnSlMjMP1ECLzV6QUQc0W6+bRWs8VRg44bTdwJHdsjyZOD2huNtmTtXt57XFsD/Adt3SFuSpL/KzDOZ26/6iYh4bLv7IuIFwPI+s62vRdqdue3qen4z84k26jPPrg2i33da+gokaRoYsFGSJC0mOwIfAq6IiK9ExMsjYq+IWL/xoojYKCKeGBEnUnYranRyZl7UJO1tgHcAf4qIb0fEYRHxyIho7PwmItaLiEdFxDGUju7GIBE/A86Y52eUJE2ONYBnA2dFxO8i4r0RcWBEbB8Rf22vV52gu0TEq4FfAo2Tg1YBH26VQWYez9xB1L2B30TEJyPiyfXFxNVimAdUC2I+BVxOCYS1DGg18PtfwM0Nx+sD34mIt0TZaWgm7dUj4hER8VHgLGDr6q1mwZA7+XLt+PCIOL0aoH5FRLy08dVH+pKkuX5Q/fcVwPeqQHnrwl8DD+wcEUcAP6W0kRq9MjOv6pD+KZTgeY0+HRH/FhG7zJyo6pO9I+IjwHeBrYCrgfO6/BwB7At8Abg8Io6t6r37RcSasy6M2DYiDq4++3Nq6RydmZO8M/mzgQsajlcD3gxcEBGvj4g9ImJWIMyI2DQiHl21eU8FLqMsxr1HizxWA/YHvglcGhEfjohnVf+v/HUycvX/zw5RNk34OfCYWjofapK2v6MkTZa3A9+qnXsycGFEvCMi9mysdyJijYh4cNWH+y3gN8Dft0h7HG1SSdLiNMz6bNROBv7UcLyEEnzy/RGx28wioIhYLSIeEhFHAecAM+3zcdafb6P0lTc6CLg4Ir4QEW+NiH+KiLdHxNeBc4FHV9d9lbv7N2a0bRNm5hnAq2qndwV+GhEnVv0js4JFVr/9LlUb+D+APwDfBp7G7PFhSdL8TdOY67CtqB0/E7hPw/EVlM2YenUk8PnauacCF0XEByLiMTF3bta6VR/0CyLis1XenwYe0kf+kqSFZ5rqZ+dESZKamaa6bthW1I4H1RbtSWaeSlnv02gn4BcR8ako64f+2nat5jLtFBHPrtqtfwDe2SGbUczFkiRNl1cD59fOvYXS7jwwqjnaABGxNEpQ319x95jtjLdm5s/aZZSZVwAn1E6/NMoa20dE2RB9pg7cJSLeRBnffjhlPPWsXj+cJGlR+0fglobjdYBvRMRHI2L3mbZzNb9mWUR8Hvgkpe16Ld2vDQIgM38BnFk7fWRErKjmbjXOg3pwRLyHEkDxPsBtlDVQwzbvft8p6iuQpInW9e4skiRJU2RN4IDqBUBE3AbcAKxNmVDVzCVAp8lNS4D9qtdM2ncA11X5btjivmuA5/YRJOKuzpdIkibAjsBrqhdARsT1lJ3uNqRM7mrmLZn54w5pv47S/n9lw7m1gBdUr8Z6cH1KXdiTzLwsIt4GvLfh9LrAEcAREXEzcCuwCXM7co+j7Jr0aHpzDPDPQONC2EdVr2bqQZhbMWCTJLX2XMqEm02Bh1EC5RER11L+3V+zxX3vz8z/6ZR4Zt4ZEf9ICe43U18sAQ4DDouIWymDthszuz65Hfg74HDmTkLqZFPgkOoFQETcVOXTrl78GnB0j3ktKJl5dUQ8EfgS8ICGt7YH3jNzEBE3cPczyXyCSmwDvLx6NaZ9W5V2q/9/Pp6ZX+qQ9qh/R9viktSjzFwVEQdRnh+WNby1MaUOPxwgIq6j1POt+lGbpT2ONuk42F6VpDEbZn02apl5a0T8C7MX5axOCUz4KuC2iLiRUn/WNwQ+FfhXxlR/ZuZtEfF4SgDExgXO6wDPqF7NnA0cTGkHN7q1izz/vVoQ9S7ubhsvoSwufib8dUz4+qoc6zZLZx5sh0pSdyZ6zHXYMvP8iDiTshEOzG0jfyYz7+gj3YyI51Hqq4Ma3toA+JfqRUTcQgl4tQGt+4P7ZV0pSQvXRNfPzomSJHVhouu6YRtWW7RPRwAbUbVTK0uAQ6sXVd1+W3VdvW+8rTHMxZpThAGmJUkagcy8KSKeDnwD2K7hrX2q18zY89qUZ4BmPgUc1WWWrwaeBGzecG5mje2d1TPMhsyNP7KcUi8+vMt8RsE+YUlawDLzgog4mDIvaaZeWQ14SfVaVdU79bVBSQn2+Ap6Xxv0YuBnzK4zn1+97qjaYhsxux2WVXn2A3bvMb9eDarfd9x9BdbBkha9njoNJUmSJlinwce1KJ3NrYI1ngk8IjMv6yP9Naq0Wy3KOgfYJzN/1a6AEbFbk9P1HeolSZOjXd0RlA7gzWg+Wesm4OWZeWTHTDJXZeY/Uxai/qXFZTP1YLsO2N8DV7fJ533A+1q8vS4lkFJ9otXHgBe1ybOlaoe/p1N2rZ2Pev1q3SpJLWTm+cATKDt5N9qY5osr7wLelZmvafJeqzy+RRkkXNXk7bWZu9DlOuBp1S7oXWfT4f31aF0vJvBx4OmZOfEDjZl5IWUC1Sdp/p1DWTy7Ca0nCCdld8FW77WzAeW7bvb/zx2Une1bbZwwqt+x/qxwKfCDDvdIkprIzGsozxJHUxagNLMRrftRW/6bPeo26YjYXpWkBWiY9dmoZeaJlIWpzdqDa1H6p+tzy04CDqS02cYmM/8CPJLSnu1mEeoJwCOr36/+21zbZZ5HURYwXdTikjUo31m7YI1XUNqV7dSfAZJq0wxJUlNTNeY6Asf2+V5bmXlzZj6LEvj5+haXrUP5LdoFa/wNnYMp22crSQvfVNXPzomSJDUxVXXdCAylLdqrzLwrMw+j1NHXtLhsXZpvZARd9EWPYC5WI+t6SZoCmXkusDdwVotLNqJ5sMbbgbdm5gu7nc9cjbE+kTJmWbc6pX3bGKzxLuDNmXlEN+kPi2tqJWkyZeYXKZu9Xdfk7SXMXRt0O/CCzPxcn/mdS+lXvbnJ22tQ6rnGdthtwKGZeVw/+fVqUP2+I+4rqNfBNwNf66ackjTNDNgoSZIWhcy8DtgB+Cfgy8BVXdy2irJD0UGZ+cjM/FOba3cFXgicCLS77q9FAr5LGex9cNUR0Mmy2vGPMvOULu6TJC1Mr6cMdh5NmVjTamJOo0uB9wP3ycyP9JJZZh5PqQtfCfyE7hYGnwd8GHgssEM1kahdHq+l7K7XadffM4EnZuZLM7PVouaOMvMs4L6UnW1PoCzguZYuFwtHxAbM3v0ogbf3Wx5JWgwy8yfA/Sn1w00tLrsL+Bbw8Mw8vI88VlAmrp5K64mutwPHAffLzK/3mP5JwB6UHV+/A9zSxW03AJ8BHpaZL8nM23vJcyGrFtK+iLID4UfpHDgCyuD0SuCNwI6ZeXCL6/6DEkDjKOCHdFdHX0VZwHT/zHxrZjb9f2CEv+Oy2vG7M/O2Lu6TJDWRmXdUE4XuS1mk0mwCcKNVwPcou8r+TYe0R9omHSbbq5K0sA2zPhu1zPwQpd327Q6X/hx4dmYemJmt+gNGKjOvqdqzDwKOpPR7X04JpHk9pcz/Djw0M/+uGi+G2TvVQ+uFuc3yPBW4D/B8yjNFN+3cS4BPUSZdb5uZ3+9w/bLa8ecy85xuyyhJi9DUjbkO2Qk079v/SWaePd/EM/MDwPbAW4C2G9fO3EL53d5HGVO4b2be2OGeZbVj+2wlaeGZuvrZOVGSpJqpq+uGbKht0V5l5ieBpcARwPld3PIb4D3AvbtMf5hzsQDrekmaNpn5x8zcG3gW8H3a1/XXUAIe75qZ7+gjr58CDwY+Qal/Wvk2sE9mvrvXPIZgWe3YNbWSNCEy80uUfs7jaL/+6MvAHtVaovnk93XgIZTAvq3a6quAL1FiO4wkWOOM+fb71tIaRV/Bstrxh6sA0JK0qEWLtY6SJElTLyJ2BHamTBSe2W3oZkrj9jzgl5l5Q59pb0dZrLMU2Jiy094tlJ0gLgB+kZk97VIYESdQdpOYcUBm/l8/5ZMkLTwRsT6lw3UnYCtgPcoEmuuBPwO/BH7XKmhRH/ltRAmGtTVlJ991gBsp9eDvgHMzs5sAx63SX0rZ6W9rSj14I3AR8IPMvGx+pR+MiNgfaByoPTEznzWu8kjSpImIdYBHUALYb0wJhncpcGZmXj6gPLYEHgVsC2xIqU/Oq/Loq73WJI81KBNkdwK2o+xivqTK60rKotJfT1OQxk4iYmfggZRnhM0og7c3UHbgOw/4bT8LYCNibe5+3tma8l1HlfblwDnAeZnZzUT2etoD/x2ryc3XcPdOin8E7u3iX0kanIgISpClXYAtKM8Ut1D+/f0tcHZDcKVe0l3wbdJ2bK9K0mQZVn02ahGxDbAvcA9gfcq46e8pC14uGWfZBqX6jI2b712VmZvPI711gb0o7dDNKN/bTZQx4Yso/ew9PXtExA+BPavDBB5owEZJ6t60jblOuqqPf09KwOTNgTUo38/VlKAY52bm9T2kZ5+tJE2gaaufJ6H/2T5mSRqtaavrFpuI2J6yaewWlO9zZlOgC4FzMvNPbW7vNo+BzsWyrpek6RYRm1LmZ29N6Ve9jbKJ4AWUsdue59i2yGddyhztpcCmwO2UzejOHET9NyiuqZWk6VDVO48G7kWp326mtLu+m5lX1q5dWV07Y7/MXNljfhtT6rl7cvdcrt9R6rmpazMPo68gIi6j9HNAmQ+1NDM7bWwsSVPPgI2SJEkTotaw/WFm7jXO8kiSNOki4ijgddVhAg/IzF+NsUiSJGkBiYgnA42Tul6emR8ZV3kkSYuH7VVJkoYjIl4G/GfDqa9l5pPHVZ66JkGoTsjMvxtjkSRJWlDss5UkqTv2MUuSNN2s6yVJi4lraiVp8RlEwEbNT0TcF/h1w6l/zczXj6s8krSQrDbuAkiSJKmzqmG7VcOp5WMqiiRJ02RZw98nOllJkiTVLGv4+w/AJ8dUDknS4rOs4W/bq5IkDUBErAO8qnb61HGUpY19uTtY413AEWMsiyRJC9Gyhr/ts5UkqbVlDX/bxyxJ0vRZ1vDJfKn7AAAgAElEQVS3db0kaWq5plaSpLFZ1vD3TcB7x1QOSVpwDNgoSZI0GZY1/P2DzPzquAoiSdI0iIj1gd2rQxe+SpKkZpY1/P3uzLx9XAWRJC0etlclSeosItbs8folwDHAvRtO3wqsGGCxBmFZw98uspUkaa5lDX/bZytJUhP2MUuSNN2s6yVJi8yyhr9dUytJ0ugsa/j7PzLzynEVRJIWGgM2SpIkTYZHN/y9fFyFkCRpiuwLrF797cJXSZI0S21y8++BT42xOJKkxcX2qiRJnR0ZEZ+PiMdHxBrtLoyIPYBvAQfX3vpoZl41tBL2Z2ZM2EW2kiTV2GcrSVLX7GOWJGm6WddLkhYT19RKkjQej6r+eyPwvnEWRJIWmsjMcZdBkiRJkiRJkiRJkiRJkqSOIuKDwD9Xh9cCZwHnAH8BbgY2BO4F7AM8oEkSvwIempm3Dr+0kiRJkiRJkiRJkiRJkroRESuZHbR3v8xcOZ7SSJI02+qdL5EkSZIkSZIkSZIkSZIkacHZGHhS9erGz4GnGaxRkiRJkiRJkiRJkiRJkiRJ3Vpt3AWQJEmSJEmSJEmSJEmSJKlLFwK39XjPDcBRwL6Z+YfBF0mSJEmSJEmSJEmSJEmSJEnTavVxF0CSJEmSJEmSJEmSJEmSpG5k5tERsQJ4IrAP8ABgB2BzYF3gTuAa4ErgJ8DpwEmZec04yitJkiRJkiRJkiRJkiRJkqTJFpk57jJIkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiQtKKuNuwCSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkLjQEbJUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSagzYKEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSVGPARkmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpBoDNkqSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJNUYsFGSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJKnGgI2SJEmSJC0AEfGQiDg6In4cEX+JiDsiIhtey8ddxkkQEStq39sh4y6TJGmyRcQhtbplxbjLJEnSqNXqwhx3eSaVzxWSpFGIiItr9c0O4y6TJEkav4hYWXtGWDbuMkmSFq+IWO68qP75/UnS4uE47WA4TitJ6od9qpIkDZ9ragfDNbWSNDqrj7sAkiRJkiQtZhGxNvCfwKHjLoskSZIkSZIkSZIkSZIkSZIkSZIkSZI0DK6plSRNKgM2SpIkSZI0XscAzxl3ISRJkiRJkiRJWiwiYnnjcWYub36lJEmSJEmSJEmSJGkhiYhlwLKGUyszc+VYCiNJ6odraiVJE8mAjZIkSZIkjUlEPJK5Hcs/A04BLgNWNZz/8ajKJUmSJEmSJEnSlHtb7Xj5OAohSZIkSZIkSZIkSerZMuaO+a4cfTEkSb1yTa0kaZIZsFGSJEmSpPF5Ue34v4BDMjPHURhJkiRJkiRJkiRJkiRJkiRJkiRJkiRpCFxTK0maWAZslCRJkiRpfPatHb/DjmVJkiRJkiRJkiRJkiRJkiRJkiRpPDJz2bjLIEnSlHJNrSRpYq027gJIkiRJkrQYRcSawI4Np27IzAvGVR5JkiRJkiRJkiRJkiRJkiRJkiRJkiRp0FxTK0madAZslCRJkiRpPDaqHV87llJIkiRJkiRJkiRJkiRJkiRJkiRJkiRJw+OaWknSRDNgoyRJkiRJ47Fu7fiusZRCkiRJkiRJkiRJkiRJkiRJkiRJkiRJGh7X1EqSJtrq4y6AJEmTJiLWBh4B3BfYGLgOuBT4bmb+ZQDprwnsUqW/FbABcBtwNfB74AeZeeN886ny2hJ4ELAjZUeCNYGbKbsRXAL8NjP/0GfaGwMPBnau0l4buAW4nvI5LsjM3w3gM2wC7E35rrYA7gSuAM4HfpSZq+abR5M816jyvB+wKXATcDnwvcy8ZEB5bAA8CtgO2LzK43dVHlcNIo8W+e4APATYEtgMuAH4C/DzzDxvSHluBjyc8v/hhsDtwK8z85Rh5CdJC0gMPMGIoPw7vgulXlwPuJLyb/n3B/GsUstvKbA7pd7YhPJc9Bfgl4OsN6q6d19gKbA15dnoEspnunRQ+UiSpk9E7Aw8kNKu2pTSZrwG+C2lvhrJbnwRsSmwG6WNvAmwDqXevBo4GzgnM3MA+awG3IfymWf6FO6itPUvBy6itLf66leIiB0p/QjbUtpvQWmvXglcDJybmVfP71NIkibRsPvNG/LZlNI3uzWl3XsrpT/4AuDHg+wPjoi1qrzuVeW1WpXXzOe6eVB5SZI06vZWRDyI0re7FbCKUsf9gjImOO/2aZXH+pTng3tQ6tJVlP7jS4CzMvP2QeTTJN9tgYcBOwDrU8aof5SZZwwjP0nS4hMR61Dav7tS6rj1KfX2NZS5PT/KzNsGlNd2lGeEe1GeEVan9PdeTXlG+E1mXj6IvCRJ6taoxmAjYitKu3IpZQ7yTP33ncy8aRB5VPmsT+kL3pZStwelnXwRZW7SQNuvC2UMW5I0HRynlSQtdqPsrx2Ealx4D+CelIBVNwJnZOZPe0xnO8p6zO2BtYDLKO3YMzPzjgGWdzfg/pTvdiNK2/wKyvf6+0HlU8tzdeChlLW7m1P6xa8A/jczrxtGnpKkkXJNbff5uKZWkhYgAzZKklQTEbMWoGRmVOc3A5YDh1IaanWrIuI04A2Z+ZMe81wKPAt4AmVgcZ02l98ZEWcC7+snoF3V6Hwu8DJKp2yn6/8MrAQ+m5knd3H9U4FXAo+hDIy2u/Zq4Azgc8D/ZGZXuyBExBLgOcBLgT2BJS0uvToiTgCOyMw/d5n2DpTO6RmXZOYO1XsbAG+q8t24xf0/Ad6Ymd/sJr8m9+8IvAd4KmWCWd2qiPgK8ObM/FV1z0rg0Q3X7JeZK3vIc0PgMOAfKIE9Wl13EfAx4OjMvKXLtA8Bjm04dVxmHlK9tzfwNuDxzP1/5ReAARslTZ36c0bN9m3ePzQzV7RJdxvgcOCZlI7eZjIifgr8O/DpbuvdJnmtA/wT8AJKJ3ar6y4CPk15Zrmhz7w2BN4OPI8yQbnZNacDh2fmmf3kIUmaPhFxT+C1wIGUCU2t3FW14T4HrBjwJOXVKO20v6G0j3frcMu1EfE/wHsz86IO1zbLb3Pg9cDBwDYdLr8rIs6htLlWZOZvO6S9PqXNeAgl0H47GRHnA1+jtP96mkAmSZo8w+w3r+XzTOBVwF607ne+OiJOovQHXzyPvPagtLGfwNydfGfcVn2ud42qPRoRuwL/R5l4NeOPwP6ZefYoyiBJGqxRt7eqtuqLKO3HVvldGhHvAo7JzDt7zaPK57HAGyib063Z4rIbI+JrlHq763osIpZTxhdnvD0zl1fv7Q+8mbI4ue4k4Ix2ffQd+u8Bls7nGUOSNNki4n6UuV2Po8xVWqPN5bdFxDeAf+2nzVgtvHkZpd5+QBfXXwJ8E/hMZp7ea35dlunlwNHMnqN1AvC8hbTYWZI0PKMcg42I3YF3AE+ieX/wbRFxPPCW+SxKjYinA/8M7EP79uvJwPLMPH8eeY19DLuhLPtQ2smbNZw+h9LX/IdB5ydJGg7HaR2nlaTFbFT9tb2u2YyIFfx/9u47SraqTNj488LlkjMoKipBERFwwIAg4MVMUEHFMOKIypjFOGYBRUVG5xtFURFxAJ0ZRxwRExhAEEUERESCjoFLFJCcJL/fH/u099TpqupT1dVd3fc+v7V60WfXOXvvqr6LXTu9G15RS/r7PqCI2IfSL9y6y6OfBv4+/zvFfswdgY806lV3Q0QcCxyYmbf0qms/EbEW8C7KmuSH9bnvIuBI4HODHHbQZ//2OpT53lfRfe/uWRGxJ53zxXUHRkSv16D2OUqSZpd7agcuyz21kjSH9Q2iJEmSioh4PGUxypvoPpkJZUHq04FfRsSHBsj79cCfKUH6nkr/YI1QAi4vAr4bEd+NiDUHKGs94GfAMbQI1lh5EPBS4Ngp8l4lIo4Hvk35HNp8z1iHsvjoq5RT4KdU/S3OB46mvIdewRon8n8d8MeIeGWb/PuU+1jgt5TNRV2DNVYeB/wwIj44RBmvpvw725vuwRqhvN/nAedO9z1VZb6c8u/vIPoEa6xsTPl3+vtqMnw65X4Y+DnwLPxOKknTEhFvppxQ+wZ6DyxDOX3ocZQ29NdRTmsftKynUE5zP5Q+A8uVjYEDgD9FxB5DlPUk4CLgrfQYWK48hbLRduC2V5K0dImIBRHxr5R28c303+gCpS/yBEq79j8jrs4ZwCmUcYSpgjVC6We+ntLfev0gBUXE7pT2+Z1MHawRyvveGngv5ftDv7yfAPwO+DBTBw+B8n1jM8pBDge0uF+SNI/N5Lh5rYwNI+IXwHGUw476jSWuQ9mQ9PuI+MAQZS2MiKOAsynj1r02AUE5mf7ZwOkRcVxE9Hr/I1H1x8+gcxPQb4AnuQlIkuan2e5vVZtbfkQ5nK1feQ8BPgd8q1poPEgZa0bEd4AfU9r/XsEuAFajLJY+LyI+GxFDHzocEStExJcoBxN0C9YoSdK0RMTHKf3fAyhtTb/Nv1D6jM+h9BmPiogVByhrU0p/79O0CNZYeTgluOOn25YzQH0iIj4JHE7nGq1PAC8xWKMkLf1mew42It4JnAXsRu/x4BUpY8HnVcEdBy1j02rc+VvALkzdf/1H4KKIeP8QZc2lOWwi4kXAyXQGazwZ2NFgjZI0fzhP6zytJC3LZnO8dhQiYvWI+C4lcFO3YI2D5HUA8FN6B2uE0i6/ldKP3X6IMvYG/kRZY9wzWGNlC+DfKd8Bthu0rEa521P2D72d/nt3JUkC3FOLe2olaWwMjiNJ0hSqjtcPgQ0aL90M3NPlkeWBAyLi0JZF9Au4eAdwPdDrhJndgdPabJapBpNPofcmlTuA64Dbp8qrh29SJke7ubPK+1ag3ykIfUU5zfY0uge7SMrfpFv9VwG+HBHvGbLcLYGfUBY4190M/K3HYx8eJKBiRLyWcqJQt7/lfcCNdH52C4GjqkHwgVULqj9CCcS5bpdb7gNuALotrH4o5d/d04Ys+0PABymDHBPup7zH+4bJU5KWVRHxCeAwui9Ouge4qcejWwM/rzYFty3rBcAPgA27vJyU/493O2Fofcrm3lcPUNZ2VVkPaVlWUNred7QtQ5K0dImItSltx7/Qe0PNLZR+adcsRlylXouF7qf0JW+ie7u5AvC5iHhfm0IiYmfgeGDtLi8n5T33G1fol/ejKJtzurXHUD7L6+jeb5QkLeVmYdyciHg0ZfNLr8OHbqF7G7cQOLhaZN1qPjwiVgdOopx82+17wd+A23o8/kLg1OrApJGLiH0on3W9vf8BsFNmXjkTZUqSZtYY+lurU4IoPrWRfgelPe1md+ALbQuIcmL96UCvhca30X1edTngjcAJgwaIrDkSaI4/d5tflSRpWP02h95GWV/TrS8MpZ95Qpv+aUSsS9ls++g+ZV1H77VKI1W1zccB9TnY+4A3ZOa7MtN2VpKWcrM9B1sFePoEnUGC76W0td3Wlq4H/CAi1h+gjB2AM+k97nwb3fvKC4CPRMQgfeU5NYcdEe8CvkYJVjLhGGDXzLx5lGVJkmaO87QdnKeVpGXTrIzXjsgCyhrf3Rvp/fb6dFUdcPAhOtvL+6p8uo3VPgQ4aZBAihGxP+XwgG7BofrNv24EnBIRu7Utq1Hu1pQ29oGNl25llsbDJUnzi3tqlzyCe2oladYZsFGSpKl9hSUTbGdTJvVWy8y1KItWHgUczOTBv3dVJ5G2dQ3wH5STWLcEFmbmqpm5XmauSAmS91Lg1MZzj6XdCelvZ/LJ6xMDvutWZa2fmatRJkq3BPYB/pMpBoAj4qXAsxrJpwJ7Axtk5spV3mtQgk88qnrtS9X7nlJ1Ss436Ow83wx8knJi30qZuVZV/3Upn9V5jWw+NsTA78osCX6RlMVKTwdWrspbBdgE+DAlMGXd/6sWXE313h5POYm+PmB+D+WEoX+g/FtYh/J32ZGyQCqr+49k8mR7G+8Emqf9/pny72QLYIXMXDczV6Kc5vB24OravasC/xMR3QYY+nkiJVgjlM/rk5STKVao3uOKlH97rRe1SdI88/raT/MU2Rsar9d/ftHMKCJeQ/n/ed3fgI9QNvGsmJlrU9rOPSgbgOsmBn2nXKgUEVtRvhM0TxP8KrAznW3VE4Ev0jn4uzxwRETs1KKstSht7xqNl44DFlXva6Ks7YCjWDLpewiTv+9IkpZyEbE8pZ1oBp64GfgoZfHwipm5ZtUvXRHYlnLK/E/oPjk6Kr+i9BefTZk0XVD1Jdeu6vFYyneCvzSe+/BU7WZEBHAEnacD30wZo9iW0m9dszausDawE/AuymEIU73vwyhBPSbcBXyKchDEqpm5RtXXXwlYjdIuv5myiLrXYjdJ0tJjRsfNI2JV4ATKuHjdL4AXVGWtCawEbEoZ62xuan0V0PYQnyOAXRppV1D65A/KzFUyc3XKBuBXUk6Sr3s88NWqfR6Z6uTbr9C5mfdLwB6Z2WsTryRp7pvt/tYXgG2q388CXsyS+eE1KRtuXs3kvuk/RUSzrz1J1S//OpPHZi8GXg6sk5mrV/OqD6neS3OOeDfazXk37Q28ovr9ZsoJ9RNznRNzj0+kzDND57h7U6/x+Ymf64eonyRp6bKYsr7nBcBmlPHe1av1NQsp/dP9gHMbzz2Ldv3TjwEPrl3fT1lL9lRgzaqs9as2dWXKOPB+lEN27xj6XXVRBb46hfJeJ9wOPC8zPz/KsiRJc9MY5mCfSZlXhbKG6v2U/t3CzFy3yn9nStCkuvUo61DbvKdNgROrZybcSVnntIjSJ1+96iuvDjyXMq9a99qIeF2LsubMHHZELF8FmjyUznXKH8rMfTPTuV1Jml+cp+3kPK0kLbsWM7PjtaPwNuBp1e9XA28FNs7MhdU64pUp7eBPpshnK8peGSj9xc9T9mNO7BlaidKv/n7juTWA46s9On1FxLMoc9bNgJCfrZU1Mf+6C6XPW7cK8PUquPSg/psl8+enAM8DVq/mzVcBHgC8hjJf+12WzN9+r5HP9+g/33vMEHWTJI2Ge2qnLss9tZI0T4QHnEqS1CkiejWOhwNvycxup7ROnCJ3Cp3B864FHp2ZN/Qp7zmUjtrxvfLu8sx+lI01E6fI3g88IjMv6fPMxcDmtaR3Zua/tSxvFWCvzPzPHq+fSAk+MeEzmbl/y7xXoAyMn5CZXU+8qYIe/obOSd+fAi/NzKv65L08ZWPPG2vJ11IGtrsuVI6IjYBun+NtwIszszlwXX/26ZSNUvXTfd+SmYdNUcfzKEEKJ9wIPCMzf9XnuedQNhZ1O3V3l8w8tc+zTwJOp5zSNOFw4B2ZeVef59YDvgU8uZb8vczco88z+1IWjzddATwzMy/u9awkLe26tDmXZuZGLZ/dFDifzkDGlwNPy8w/9Hnu3cDHG8nfzMwXdLu/emYBpa16TC35bmDvzPx2n+eeCnynUcfLgC0y8/Y+zx1FWaw14X7glZl5bJ9ndqUMSDcHv6mePbrXs5Kk+S8iPsLkgPQnAS/r1x+vPf9IShvaNXB8l37NMZm57xR5vo/Sz71wqvKr+9egTOTW+1enZmZzQXL9me2AM2tJNwLb9fsu0Hh+U+DhmXlKl9ceBFzJkoVXdwOLMnPShHePvB8EPC4zv9vmfknS3DeGcfMvAK9tJB8CfCAzu25UjYiHUhZW1Rf+3gs8MTN/3aesl1AW/tb9CHhBr802EbEype3eq/HS/pn5mT5l7UuL7xXVuPkRlE1HExL4YGZ+tFf+kqS5bzb6WxGxGHh4l5cOzMwPd0mfeO6hwBl0ngh/fGY+f4o6vYclG4QmfAXYLzPv7vHM2pQNM9s3XnpuZn6nT1kHAQd2eekCytxjM+hkv3p3fL/JzJFu6JUkLT0i4p+AqzOzGSCq1/3LUQ4zPaiWfAuwYZ9+5kLgr3RuwHlhZv5vyzLXpoxzf6PPPacCT6kldV1fFBGbUYJZbVJLvpoSlKLnWiZJ0tJlFuZgD6J7/+5MSt/wrz2eC+DLwL615LuBh2TmdX3qs5CyuXfbWvKFlPb2d73fSdd+753AZpl5eZ9nZvvz+1BmHtTlvtUohyzsWku+B/jnzDRQhSTNA87TTnrGeVpJWsbNxnht7dlTaTGmWrv/aJYcNlf3E2DPzLylZZ33pft+zDso47Q9AzxGxFuBf28kH5WZ+/V5Zi3KYXz17w03A8/OzDO7PwUR8TJKEMT6XtpzKN8BegYw6fH9JoG39tuH2yWfg2jRN5YkzS3uqe35nHtqJWmeWG7cFZAkaZ74HvDmfgEVq8Bzu1NOjpkwcXpLT5n5ncz8RttgjdUzX6JE+p+wHOWkgK4iYiU6gzVexeSB137l3dErWGOlvoDpHiYvMuqX9z2Z+bVewRorb6czWOOvgN36BWus8r4PeDNQ7/g+gM4Oa1uv6BessSrvx5RAmnV7T5Hv7nQGa4Qywdx3gXO1UemN/e7p4xN0Bms8IjPf1C9YY1XmdZQTgy+tJe9enRAxiHuA3Q3WKEnT8g46B23vpGxG7RugKTMPpZx6V/f8iHhMt/snXqdzYBngtf0GlquyTgH+sZH8MDoXTHeoFm01J6g/2G9guSrrRKb4ziVJWjpFxAaUdrHuJOA5bTa6AGTmH3ptdBlWZn6sbbDG6v5bKCfe/76WvCgitujz2LaN6yPaBmusyvxTt2CNlX+g85Tcb7YNHlLl/ReDNUrSMmFGxs0j4sFMHsM9JjPf12sTUFXW5cDTKYuqJywA3tfzHRQfbFxfRFkk3XNRdjWe/RLgrMZL76k28QwtItaknHhf3wR0N/ByNwFJ0lJhXP2tw/oFa6zyvhx4WyN5j2quuatqc2zz1PpTKIt+uwZrrMq6kRIsYnHjpWa73MaNDBisUZKkQWTmsW03/1b335+ZH6JsVJ2wBvDyPo9tSmewxjPbBmusyryxX7DGtiJiJ0owq3qwxouAJxmsUZKWHWOcg72Usja4a7DGKt+krAu+ppa8EHjeFHm/nM751Uspm3j7Bmusyvw4UA8AtRLwll73z5U57Ih4COVw93qwxpuBXQ3WKEnznvO0ztNK0jJrlsZrR+nPlP5gq2CNU3hlv2CNAJn5KSbvG943Ijbsdn/lNXQGa0xgr37BGquy/pPJ/d/HA7v1e66Hfx0kWKMkaZnlntrOstxTK0ljYMBGSZKmdi9lMrPnqS4TMvNcJgfse211Es+o/Ttl8m/CU/vcu07j+tJ+E6ZDWLv2+3VTnS40iIhYFXhDLSkpJ7v2PEWgrvq7vYPOieZBO58nZuY3W957RON6myn+/s26fH2qgfOaoyinDrUWEdsDO9aSrmTywHhP1WKxgxrJ/zxIHYDPZ+b5Az4jSapExBrAPzWSD22zgLjyAcr//+v273P/mxvXp7U9XSczTwC+NUBZ+9F5ut4fKIGG25R1LPCzNvdKkpYqb6NsiJlwIyXg/r1jqs/QqiD6n24k9+vrr924vqTrXcOZybwlSUuHmRw3fz1Q30xzI+VQnyll5mXAAY3kvarFTJNExNOAZoDkN2bmHS3Kuht4HWXMesKDKUGYhxIRD6P0bZ9eS54IQtXvUCVJ0vwxjv7W9cB7W957PHBt7XoF4LF97v9HYN3a9b3A69ocVpiZNwNvbSQ/oZrPHMTBBmuUJM1RhzauxzXe20pEvBT4EZ3rzH4CPDkzL+3+lCRpKTWuOdh3VQH++8rM24DmeOnje90fEcHkwwbempnXdLu/hwMowQ4nvDoilu9x79jnsCNia+BMysEREy4HdszMk2erHpKkGeE8rfO0kqThDDJeO0rvbLsPdgqnZObXW957AJ0HHSxPj/2f1feCNzaSjx1gf+tngHMbaf32DHVzLZP3q0qS1ME9tT3Lck+tJM0yAzZKkjS1H2TmIItgP9e43ojJEfSnrdrAUu9EPjYiVupx+82N682rQIijclPt9wdOceLOoJ5N50Lgn2XmrwfJIDP/SGdgwy0jYt1e93fRDMLYzwV0ngy4KtBrknkBkwf3v9i2oGqSvfX9leapDF8eYtD/fykT/ROeMuDzRw54vySp01Mo7cuEe4HPt324+v/+lxrJXU+wq06L3aGR/Jlu9/bRDDy1WURs2uPe5zauj8zMewYo6/AB7pUkLR2aC22PzMxru945PzRPo92uz703Na57bkIawkzmLUlaOszkuHmzj/qf1UEybR0F1Mc8lwee2bKsCzLz1LYFVWPVP50iz1YiYlvKd4Eta8mLKYExThsmT0nSnDSO/tZX2mxyBagCLf6ykbx5n0ea7d5JU51a3/BtJgekGqQtvQc4ZoD7JUmaNZl5MZ1riAYZ753qgNiRioj3UgJfrVhL/irw7Mxs1k2StPQbxxzs9ZS1qW39vHHdr++6beP1y4ATBiiLqj08qZa0FrB1j9vHOocdEc+kbNCtr+X+NbBdZl4wW/WQJM0Y52lxnlaSNLgBx2tH5RrgOyPKa5A9Q7cBxzaS9+hx+2OAhzXSWu8Zysz7u9y/S0Ss3DYPynz2nQPcL0laNrmntjf31ErSLFow7gpIkjQPDLoo56KI+CPwiFrydsBv2+ZRBfLbjDIZugawOp2R8SfUBy4XAA+iywnrmXl7RFzIkonVtYGvRcSrR7QI6Cxg9+r35YBvRMRLB5wI7qUZDPAHQ+ZzLksG0qP6/fstnksmT+T2vjkzI+IS4LG15LWAbifNP5bOv+HfgFPbllU5ccD7p/15ZuatEfEH4NFV0pYRsVo1mD+V61zwJUnT1hzsPWPAE98BvgEcWLveMCIempmXN+57Ep2HPdwNfHfAsk4DrgPWq6XtAPypflNErELnYicY8HsYZXPvfXT/3iRJWspUhwVs0kj+6jjq0kY1aWnRecwAACAASURBVPsYYH1KX38VSv+0rnm4QHMRVN1ZjetXRcR5wBeqBVDTcTalPzxRv2dExMHAwdUp9ZIkzci4eXXQUHOT6zcHLOu2iDgJeEEteQfKBqGmZh97oLIq36Bz3LWZ55QiYnfgf+hcTHYOsMcQfX5J0tw2jv7WoBtK/9y4XqvPvds3rgdttzMivgm8o5Y8SFv6mwE3DEuSNBIRsSIl8NNDKeu6VqP7HGX9QNSHRMRyPcZvf085kHfN6npz4IsR8bbMvHV0Ne9UrVH7PLBf46WPZOYHZ6pcSdLcNcY52J9Vhwi0NUjftblu9ofVgemDOhd4ce16e0ogxL8b9xx2ROxHadvre8VOBF7Uco2vJGnuc552CedpJUnAjIzXjsrpmXnv1LdN6V7gewM+cwLwL7XrrSNi5cz8W+O+Zvt5WWb+asCyvgl8mSVz4CtQDi48veXzPxmwPEnSssk9tb25p1aSZpEBGyVJmtq5Qz5Tn9B8bK8bJ1QdqpcBLwWeDCwcotx+C46+QGf0/j2ASyPieEpH7CfTmFD8AksCNkKZwP2/iPg+8C3glMzsFrCwjeZpRRtHxOuGyGfjxvWDWj53S2beOGBZzYXSa/S4r3mi7wUDLjgjM6+IiGaHvatqEr3ZaX9yRGw1SJmVFWu/Lwc8EGizmKt14FJJUk/NBVHnDJHHxcAdlEBR9Xybg8vNsi7IzLsGKajacPsr4Fl98oUSwKo+TnEb8IcBy7ojIv6PJUGFJUlLt8c1rm8BLhxHRXqJiM2AVwIvpHOcoK2e/fzMPCcizqEsaoIyuXo48C8R8TXKxptfDtp2V3n/tQqYUV9A/QHgNVXe3wd+7qYeSVqmzdS4+RZ09g1zyLLOobMd69YP7ZY+TB+7+cwmEbFqdRrvlCLiDcBhdC6U+g7w0rZ5SJLmjzH1txYPeH+ruc6IWJ/Jc66jaEt7tdvdOPcoSZo1EbEO8ApKoKbHMfga7KAEZJy0Dikz74uIL9EZxPjVwN4RcRyln/jTIdYw9bM6ZZPvM2tp9wKvzcwvj7AcSdL8Mq452MUD3t92nS5MXoe83pDrkJv91W7rkMf1+UVEfAx4byP9i8AbRxQgRJI0NzhP2/sZ52klaRkyk+O1IzSquczfdQm0OJXzgPtZEmxqAaW9bwZjnHabnJm3VPt4HtXIt23ARud8JUltuKe2d1nuqZWkWWTARkmSpjZMoMHmM+v2uzkinkMJqvDQIcqqW73Pa58HdgN2raWtRAkQ+dKqHn8CzqBE7f9x2yCLmfndiPgi8Jpa8gLgudUPEXEF8Isq75Mz83dt8gY2aFy/uvqZrnVa3nfzEHk3gy72OpFg7cb1X4YoC+BqWgRsBB7AkpOKJhw6ZJlN69A41aGHG0ZUniQty5pt2OJBM6g2/FxGZ/Dgbm3jtMuqXDJFvjD5+9JlQ55mvxgHlyVpWbF+4/qSGT7ptrWIWAh8HHgz0xuH79fPh7LY7Gd09i83At5T/dwVEb+u7jmVcljDHS3LfhMlGOTDa2kPAPavfu6NiN8CP6/yPjkzb2qZtyRp/pupcfNmf/HGzBxmjHbKfmhErEjnoisYrt/bLGuivDabeJ5Lac/rDgfeMujhPpKkeWW2+1uDtqVt5zq7jfMuHrAsmNyWrh0R0XJ82LlHSdKsiIhXAZ+g/XqjXlan9wbgA4FdgG1raWuwZK1URsTFlLVdp1LWdg17OC/AMXSOLd8CvDAzfzSNPCVJ89+45mBnqu8Kk9ch71n9TFe37wXj+vzeTGe7nsD7M/OQWShbkjS7nKftXdZEec7TStJSbpbGa0dhVHOZA7f/mXl7RFxPZz+1zXeAxYOWVbmEzoCNg/xtnPOVJLXhntr+FuOeWkmaFctNfYskScu8W4Z4pjkx2QzM93cR8QrgW0w/WCP0adurScPnAYcAd/a4bVPg5cCXgMUR8YuI2Cci+i1kmvA64O30/rw2BPYGPgtcHBHnR8Qbq8nWfqY7cN5Lc4K3l2E6tW2t2bhunvjbVtt/ozP1WUL7z/O2GayDJC0rmt8rhvmuApO/r3RrJ2azrLVmqCxJ0tKrOTE5J4IFVsEavwW8jekfmtR3DD8zLwKeSDkcoZsVgScB7wS+C1wbEV+JiK2mKjgzr67yPr7HLQuAbSiBRr4BXBMRx0fEjlPlLUlaKszUuPk4+7zDltetH9p2LLbb+/20m4Akaek2hv7WTM13Ntux+xluLrDZli5PCU7VhnOPkqQZFxEfAI5iNOtu+q3tuh14CnAkk4NQQTmkdQtgP+CrwJUR8aOI2GPIujTb8tOAU4bMS5K09BjXHOxMrtWdzXXI4/r8mu36ZZTvL5KkpY/ztL3L6lVeN87TStI8NVvjtSMyqrnMUbXL4/4O0FVmOucrSWrDPbWDlSVJmiEGbJQkaWZEq5siHgEcQWebfB8liML+wM7AJpQNKStmZtR/6B2QoavMvCcz30cJzPhe4Cy6L/Cd8CTgK8DZEfHIKfLOzPx3YCPKSa0/Be7q88hWlOCNF0XEE/vct7BfudPQ6m80w5qfz7Dvte1zM/VZwtz4PCVpWTWqBctt8pkPZdkmSdKyayY38QziPcCujbTrKH3glwCPAzYAVgMWNPr5Gw9aWGb+MTMXATtRNvJe3uf2VYF9gN9ExL9HxApT5H1tZj4f+AfgU8Af+ty+ENgTOD0i/isiVh/gbUiSlg3D9Ndmsx86bHndnmmbz6+AG2vXawA/jYgth6iHJGkesb81pbnSx5ckLeMiYmfg4EbyXcDXgNcC2wMPo/TnFnZZ23XpIOVl5m2Z+Rpgc+CjwG/p3S4uDzwd+E4VuHGDQcoCftS4fg7w9epQIkmSJiwN/bNxrkOerc/vdDrXJD+cMta84SyVL0ma25ynncx5Wkmah2Z7vHYpMB++A0iSNKz5sM91NstyT60kzZIF466AJEnzwBqUwAqDPlN3Y9e74ABgxdr1ZcBzM/M3LctZbcB6AZCZVwEfBz5ebejZAdgReHL1+4qNR7YBTo2I7TLziinyvpEShOKzEbESsF2V98RPs86bACdHxI493vcNlEAWE3bKzJ+1eJvzQfPk3DWHzKftczc0ru8GVs7M+4csV5I0Hs3vFaNqP7p9X5nNskbVLja/h0mSll7XN667nfw6qyJiNUrAxrr/BvbLzDtaZDFUPx+g6iv/rKrHRpQAjjtW/310s6rAW4F1gX9qkfdvgLcBb4uIB9Xy3pESXKQ5uftSYIOIeIYnz0vSUmumxs3H2ecdtrzm6ba98u7mAuBVlCAZD6jSNgBOi4hnZeY5Q9RHkjSPzPP+VrO9W47Sr711wHya7e99Q+QhSdJM+Ujj+nzK2q62G3uHXdv1R+ADwAciYh3Kd4QnU74jPIHJ67+fDvw4IrbPzLbt6MeAU4BDamnPB74dEXtl5t+Gqbskad6bc3OwI9BcO/vyzPzqDJU1rs/vFOBDwAmUQ/wAHkU5/OFpmfnnWaqHJGnmOU+7hPO0krTsGct47Rww7B6ZufYdQJKk6XBPbX/uqZWkWbLcuCsgSdI88PARPNNcgENELKCcTF73igGCNQKsN2jFmjLz1sz8QWZ+MDOfSgnW8CLgjMatD6ZzgW6bvO/MzNMy86OZuWuV927A9xu3rkYJ8tjNXxvXmwxShznu6sb1owbNICJWBDZqeXvzs1wIeHquJM0/ze8VGw2aQUQsTzk5sK65OHkkZVU2HqKsh0XEMCf7bDTEM5Kk+anZx9k4IsY95v1sYOXa9R+AfVsGa4QR9PMBMnNxZn4lM1+bmVtQ2sf3M3nB9ssj4mkD5v2XzPx6Zu6fmdsCDwL2Z/LJw7sA+w71BiRJ88GMjJt3SVs7IoZZfDRlPzQz7wJubyRvNIKyupbXS2aeD+wM1A9LWody0NFOQ9RHkjRPzcP+Vre2fKMh8mm2pTdm5qhOppckaWgR8QBKkMQJ9wHPb7v5t1ob1i14xEAy84bMPCEz35WZOwDrA6+kBJeoewzwrgHz/jjwJqDe9j4LOKk6BFiStOyZi3Ow0zWb65DH9vll5snAM4Gba8kbUYI2Ng/4kyTNX87T9i6ra3m9OE8rSfPLXBmvHZOB2/+IWJWyl7euzXeAjQYtq9Jmz5AkSdPhntr+NhriGUnSEOb7xKkkSbNh2xE8c36Xex5G5yDvFZl5atsCqkHmYSZb+8rM2zPzuMx8MnB44+UXRMRK08j77sw8MTN3Z/IC4R0jotv7Oa9xvfOw5c9BZzeuHx4RGwyYx7bACm1uzMwbgMsayUvT5ylJy4rfNq4fP0QejwZWaaR1+77SLGvLKlhwa9UAcZvvRhcC99auVwMeOWBZqwCbDfKMJGlea55kvgaw5TgqUrN14/rrmXn3AM8/YZSVmZCZl2bmxyifzyWNl/eZZt7XZOZnKJuBzxpl3pKkOW2mxs0vprNv2K1P2Uazr9ytLBhNH7v5zJ8zs7nBqK/M/D2wI/CnWvIalAAZzxqiTpKkpcBc729l5nXAXxrJo2hLe7XbkiTNti3pXGd9Zmb+qdfNXWwDLD/aKkFm3pSZR1f5f7vx8sDfETLzcEoAyPtqyTtTAlSsM2w9JUnz1lycg52u2VyHPNbPLzPPoBz0UD/I78HATyNim9mqhyRpRjlP2/sZ52klaek2J8drZ8nmEbHy1Ld1+Ac6P697gYu63DftNrk6/Ke598c5X0nSqLmntndZ7qmVpFlkwEZJkqb2vEFujogtgEc0kn/Z5dYHNK4vH6QcYI8B7x/G+4B7atcrM2Anr49PAlc30pqBLQB+1Lh+7nSCRs4lmXkNkwNlvGTAbP5xwPt/3Lh+0YDPS5LG74zG9Q5VIOdBvKBxfUVmdvsuciZwf+16IbD7gGXtDKzfSGu+BzLzDuCCRvJA38OA5zJ/J9ElSQPKzCvpXCwL4w9Y0WyTm0Hzp/KcUVWkm6of+q+N5G598WHyvh04cCbyliTNSTMybl61J80FSXsNWNaqQHPzzKR+aI/0gcqqNPvYvcrqKzMvBXaic3H0KsC3I2KYekmSlhJzvL/1i8b1oO12dHlmqLZ0CPWgVESEY8uSpKa5Pt57L/DuRvJG1ebYQfM6hrJmqb5O7AnAqRHxwOFrKUmab+boHOx0Ndch7zxT7dtc+Pwy89eU9VpX1ZLXA34SETvMZl0kSTPCedolnKeVpGXLnB6vnWELGHwfT/M7w28z829d7mu2nw+LiEGDNj+fzpgl9wBnD5jHMO5tXDvfK0lLN/fU9uaeWkmaRQZslCRpas+KiI0HuP8NjetLmdxRAri7cb1W2wKqzSJvG6BOQ8nMW4DrG8mrjijvBBa3yPtE4I7a9frAm0ZRhznivxrXb68mqqcUEQ8F9h2wvG80rp8TEU8YMA9J0nidBtRPgF0AvL7tw9WJOfs1kr/X7d7MvBn4eSN50HZ4/8b17/ucZPjdxvU/R8QKA5TV/B4mSVr6fb1xvd8Qk66jNJ2+/naUxb8zrXlwwEj6+bOQtyRpbpmpcXOY3Ed9WUSsPUBZr6KcMDvhPuAHLcvaKiKe0ragiNgaaN7ftY/dRmb+pcrv3FryQuDrEfGyYfOVJC0V5mp/q9nu7RoRmw7w/B7AJlPkOVNubVyvMUvlSpLmj+mM964OvG601emq+R0BhvyekJnfAPYE6pt2twJOr9YoSZKWHXNtDna6zgSurF2vAHxwBssb++eXmRdT5p7r3xXWBH4YEU+bzbpIkkbOeVqcp5WkZdR8GK+dSa3rHxGrAS9vJDf36ky4kPL9oO7NA5QVTN5jdEpm3tk2j2lwvleSli3uqe3NPbWSNIsM2ChJ0tQWAJ+pBg/7qk6PaQ5+fjEz7+9y+xWN680jorkZpZcDgS1b3ktELGh7b+O5dZkcvf/qxj1DRdyv6tTcrHN1877MvA74XCP54Ih40jDlVmVP+becRV+kTERPeChw+FR1jIiVgGPonNCeUmaeyOQTiv6r+lsPZTY/z4hYHBFZ+zl1tsqWpLmiCqh8TCP53RGxWcssPgxs2Ej7TJ/7m6/tEhHNyduuImIPyml5dYf1eeRIOk8feiTwLy3L+ieGDHIVEfs22peMiEXD5CVJmnWfBuqLetYGjhm2HzwCzb5+q1P0qsD9Rw9S0DTe4+aN60l98ZnMu1HO0c02eMhyJUmzb6bGzQG+QDlpfcI6wCfbVCoiNqT0e+u+mZnNNhqAzDyFsvC47vCIWLlFWSsARwD1z+Aq4H/b1LWXakz8qXQu9loAHBsRr22bj31dSZqbZqu/NYv+m84DABcAn4+IKdekRcQalH593dmZeeYI69fPNY3rRw+TSUQc1KXN3WjatZMkzQXNvuSOVfvVxuFMXnPV0wi/I9wH/HXIvMjM7wO7AbfVkh8J/CwiHtE2n4g4tdE2Lh62TpKksZhrc7DTkpn3AIc0kl8fEXsNm+cUY+Nz4vPLzD9T1lL9rpa8KvC9iHhO23zs90rSnOM8rfO0krSsmrXx2jnqaRHxopb3fhjYoHZ9H2WvziTV94LDG8mvGCCQ8huBxzfS+u0ZGqWRzPcCdGm7j55e1SRJo+ae2p5luadWkmaZARslSWpnd+CwfsEJI2JzSgT7+j3XUSYCJ8nMa4GL61kAX4qIFfuUERHxPgY/2XX3iDg9IvZqu+Cneq+fovP9XMLkE3O2iojzIuIVbSZHaw6mc6D7diYHEpxwKGUCdcJKwEkDDDIDEBGPiojDgY8P8txMyszLmDyJ/Qrgf6LHiboRsSlwErBLlTToiUPvBO6tXT+CsrB667YZVP8Wd4mIE4A9ByxfkjR9/wbcUbtemXICejMYcoeIeAfwjkby/2Zmc9FT3fFMXhR1ZET0DUBVDc5+rZF8GZMHxv+uahePbSQfHBH7TFHWsylBkCVJy5jMvAb4RCP52cC3o+UJ7xHxyIgY1cm5pzaud46I10xR/nrAj5i8sXYqx0bEFyJii7YPVBtn3t1I/kmXWw+JiP+KiO0GyHtd4KMt8pYkLT1GPm4OkJlXAV9uJL8qIj7cb+NRtQnox8BateR7mbwRt+kjjevHAN+MElC5V1krAf8FNA8WOqTa/Dst1em8z6S8nwnLAV+o+vaSpPlrqepvZebfmDzX+QzKvHfPk94jYi3KKfUbN15qbuidSb9uXL+uzSZnSdIy5Vzgltr16sDn+gUmjogFEfE5oNVmnZo3RMT3IuLpbdujap3W/2skn56Z93W7v63MPBV4OnBjLflhwOkR8Zjp5C1Jmh/m4BzsKBwJXFC7Xg7474h40yB9wYjYMCIOof/6pznz+WXmlcDOwHm15BUpY+AvmW7+kqSxcZ7WeVpJWhbN5njtXPUfUwVRioi3AG9rJB+bmZf3eexIOg8MDOD4iHjCFGW9hLL/uO5s4MR+z41Qc773KYOsqZYkzUvuqe0syz21kjQGBmyUJGlqv6z++ybgjCro4Srw96B1j4yID1MGfR/UeHb/zLy+T95faFzvApwdES+M2gk/EbF2RLy4qsvEZpzrgItavocAdgS+CVwTEf8REftExGMiYmHHjREPjoiXVWU1O3KHZWZ2yf+xwNHAXyPifyLi1RGxTTUZWs97verz+zHwnkYeX8rM27tVvjqpbk86AxOuSQlq+IuIeFVEPKI+CRwRy1ULo3aLiA9FxHmUU2LfQDkhdi45EDi/kbY3sDgivhkRB0TEm6v38QNKoM+JU4pOZMm/0Qnd/kZLXsz8KfD2RvLmwLkRcVz1N+oIFhkRK0QJePmiiPgscDlwCvBcOifxJUmzoDoBvTmJ+nDg/Kq92GyiXYyIlSJi14j4IZM3zl4F9D31NTPvBV4K3FVLXhH4TkQcHRE7RBUQOiKWj4jHRcTngZPpbHPvA/bp1d7XvIPOk+6WA75SfcfYqVHWEyLiSOD7VZ3uoXwnkyQtWz5EaXfqdgX+HBEHV+3F34NEVP2bf4iIN0TEyZS+4qg2o5zN5LboiKof/riJhdLVeMLmEfF+4P+A7at7TxugrFUo7fiFEfHbanH0blW/vt4/XiEito6IAymLk+pjF7cxeaE1wAqU9v/MiPhTRHwiIvaMiIfXF7dV7fGjqgns8ykLpyfcx+RTdyVJS4+ZHDeH0jf8QyPtg5TgEHtOlFWVt3FEvJeyMOpRjWcOyMzm4twOmfk14L8byc8GLoqI19THSiNinYh4BaXde2HjmR8wwrYvM+8A9gBOaLz0yYg4aFTlSJJm3dLY3/oE8LNG2ispc48vixKcEYCIeFBEvJEy37lj45kvZuZ3Z7aqHb7TuH458KuIOCQi9o+I1zV+Vp/FukmS5oBqnvSoRvLLgFMj4tlRO9g2IjaIiFdRAkG9vkr+Pzo3ufazHLAb5XCfKyPi8GqNziOjdjhv1efeKCJeSwm89NRGPp9u+/76ycxfAouAa2vJGwCnRcTjRlGGJGnOm0tzsNOWmXdT1rteV0teEfgMcEGUwI2PiVrgq6rdfWCUgMrvjYgzKBtr3wOsM0WRc+bzy8y/Utann1lLXgD8Z/X9RZI0vzhP6zytJC2TZnm8dq45lxIMeRXg5Gr8eJuJ+eWIWFj1Xb/L5ACKVzM5QFWHzLwJ2JfOPalrU75rfKrqr06UtUJE7BwRE+14fV/p7cDLeuw/HrnM/D3l7zphIfDLiPhKRLw7Il7fmO/dZTbqJUmaOe6pdU+tJM0FC6a+RZKkZd7LKYtU1gGeSAl6SETcRBnkXNjjuX/LzObkYdMRwCuAbWtpWwHHARkRN1M27TQDDN5DGVB+X/u38XfrUAZQ951IiIjbgb8BqwErdX0KTgIOmyLvVYEXVT8Tef+NMti6SvXTzXlM8V4y8+yIeB7lVIH6CbNPYsnpePdXf5cFlFOSWp96O06ZeVdEPIMSALG+2WplYK/qp5vfUv4dfKuRfmeXe5tlfqaaLP8oSwbGl6dMXr8QICLuoZw8tTK9/3bjdv+4KyBJ45KZX4yIzeicPF0FOKD6uScibqOz3ay7DtizxeIrMvO3UU7k+SplEBdKO/uK6uf+6nvLGnQP5Hsf8LrMPL1FWTdExPMp3z3qG2AnvmP0K+s9wNZ0frcalm2MJM0TmXlfROxN6a8vqr20FvCB6oeq/QhKGzJTdcmIeDPwEzrHC/atfu6JiFsohxA0x+d/C+wP/GaIoresfiZMtJfQu32+H3j9FKfmAmwCvLP6gTJecQtl8dcalHGLbj6Ymee0qXzNrCzSkiSNxEyOm5OZt1fjwT8ENqy99OTqZ6JtX4kl/dSmLwOHTvlOitdSNiwtqqU9jDKGf0RE3EFpO1fr8fw5lAVVI23LqrHjF1JOzn1p7aUDI2K1zHxnj0d7sa8rSXPLbPa3ZkzVL38Rpd2u9023pIwpExG3UvrBK0/OASiH1L11JuvZxf9SxtLrG4m3qX66OQm4tWXetrmStPT4KPB8ykafCTtR2q77a/3g5nqrWyhzm83gDm08iHIg7RsmEqq29C7Kd4Refe4vZmZzDdHQMvP8iNgZ+DFL+ubrAqdExO6Z2QzY3I9toyTNM3NpDnZUMvOSiHgWcDxl/HfCFpTAjbBk7TaU97QcQ5hrn19m3lStU/42JXgjlPf2pYhYPTMHDfps2y5J4+M8bSfnaSVp2TKO8dq54LfAf1GCTS3HkvHje6ux47Xovo/2VuAFmXnjVAVk5g8i4q2UgI8TeS0A3lL93FfNZfcq6w7gxZnZDPw80z4GHF27Xg3Yp8e9x1DWd7dh2y1Jc5R7at1TK0njNtTkoSRJy5JqkPCZTD5BZy26T2beD3y0zQRcZt4FPIdyyltTVGU0gzXeAuyVmT+cKv96UVO8viqwHt2DNSbwReB5mdmtkzVV3itXefcK+Pct4CnVKXR9Ve/5ccBPe9yyHGXieQ16B2u8k3Ly7JySmddSJgiOol2Aiq8DO1UD5s2FWje1LPNQygmEl/S4ZQXKQut+wRr/ClzZprzpiojV6Zz0h7KRSpKWWdX3jf0pk5tNK9B7YPl8YIfMPHuAsr4BPAu4osvLy1VldRtY/itlEPtLA5R1BqWN+kvLspKySfn/tS2jYYvG9ZUsOYVYkjQPVH2jZ1IOGri3x21r0nujy8gmFat2bB/KwQhNE/2sZrDGM4CnU/r8rYvq89pEe9mrfb4OeGFmfnWIvIPyWa5L9+AhtwNvyMxD+uQxodkG28eTpHliJsfNa2VcDGxP2XDUzZp03wR0N3BAZr66x5h2t7JupfR5v0z3dnAVem8C+gawKDOva1PWoKpTevcBjmy89I6IOGLiBPsu7OtK0tw0W/2tWZWZfwF2BL7b45bV6R6s8X7gc8BzM7NbP3rGVHP1zwMunGZWzTb37My8bJp5SpLmiGqjzu7ApV1enlin1Fxv9RfgGZk5yOE8U60VWp2y/qpbn/se4GDgdQOU165Smb+nrGf6Uy15DeAHVdCnXh7duHbsV5Lmobk0BzsqmXkuZR1yryDHE2u316L3fqt7gQtalDWnPr/MvA3YDfheLTmAT0XE+/s8ar9XkuYQ52k7OE8rScuYWRyvnXMy89+AD9LZXi6grBPuto/2L8Cu1ZrmtmUcBrwYuKHLy8v3KWsx8NTM/F6X12ZUZh4DHEQJiDWUiGi23eCYtiTNae6pXfII7qmVpFlnwEZJklrIzF8BWwKHUzbCdHM/cDLwpMz8wAB5XwU8CfgQ0C8a/+2UwImPHnTwMjNPoCwwOgg4ne5BI5pupUT8f2JmvjYz7+6R928oHbJ3AT+iXXCJuygn1D49M/fKzNYBKTLzksx8CuUEvW+1LO/Gqrx/BjbIzM+2LW82ZeaNmbkf8FjgEOBXwDWUhVq3AOdRTvF9fGa+ODMnTvF9QCOrKU89qpX5Y2AzykkOP6Ms4p7KpZQJ8ecBD87MX7Qtb5p2onMg4Qqg9UCFJC2tMvMzwCMpm1qv7XcrcC7wSmCbYU6uy8zTKO3Ge4D/m+L2xcCHgU0zs9cm3X5lnUHZyPNp+rdtpwE7lmL53AAAIABJREFUZ+ZHBi2jZlHj+mPVZl1J0jySmfdk5lso7cdRlAnOfu6jBEp8C+XE3VHW5Tjg8ZS+aL8FyH8E3khpy/q14928DNiTsij4d7QL/v8HytjAIzPz+D73vZsyqXwY8BvaLWS6Evg3YLPM/PxUN1dB+esn+CVlbESSNE/M5Lh5rYwrMnN7yumwv6B/u3oj8B/A5pl58BBl3Z2ZrwaeAJxA94VcE+4CfkBpw/fOzF7vfyQy8/7MfA3w742XXgMcGxHNYNBgX1eS5qoZ72+NS2benJnPoRxIcDL95x1vp2xy2SYz31htfJ11VRCqbYG9ga9QFmbfQNlY3NbOjeuDRlI5SdKckZkXUtqLT9O7/wulDfkEsEVmnjVgMZ+lrIs5FDiLdut3rgeOALbMzAMys80Y8cAyc3FVt4tqyasA34mIPZv3V5tb62uZbqd8LpKkeWguzcGOSmZel5l7AdtQ1km3CfJ0O3Ai5X1tmJnvaVnWnPr8MvNOYC/KofF1H4mIj/d4zH6vJM0xztM6TytJy7JZGq+dk6o9MztT9tD0ciNlLvrRmfnzIco4DtgU+DgwVbD+i4G3A4/KzLEFd8rMDwGbU9Yg/5iy5/Q22q2rhslt99njCD4pSRqMe2rdUytJ4xIztD5HkqR5KyI6GsfMjMbrKwM7UAbx1qIENrwS+FlmXjPNslegBHTYinKiT1AW114MnDWqTk5VzqMog6cbUk5hX54yEHkdcCFwUa8gjVPkvRylg/sI4KGUk18XVnnfSFm8e0Fmtgka2aa85SmLph4BrEv5m9xF+btcQQlacUnbE/rmm4h4EHBVLen6zFxvGvmtAmxH+XexLuU0wtuBm4FLgIszs3ka46yIiH8F/qWW9MbM/Nw46iJJc1VEBGXy+VHA+pSNMtdTAgCfOd3vKl3K25gSFPoBlDb4FsoA9/mZ+bsRlrMCZRPQJlVZd1Emf8/IzCunmffqlO8oE0GBrwAe4eCyJM1/Vbv4WJa0i2tRDjC4kTJJ+ttaIPyZrMc6lHbs4ZQ+8p2UcYRfj7i9XJsyMbsx5f2uSjkA4FZKu3l+Zk61eKpX3qtVeW8KPLDKOylt/18oAS3+NMiG4IjYDagvaDouM180TP0kSeM3k+PmjXLWqcrZAFiP0j/8KyUI8tmZOfRp6V3KWrEq62GUtnW5qqwrgJ9nZr+NQmNlX1eS5o+Z6G/NFdV7ezLwEEpbeh9l/PhS4BfDzEXPNVVAqgtrSWdn5hPHVR9J0syLiJWA7Sn937UpASuupbQH54yqX1qVM/EdYQPK2q6g9LevAS4Afj/KfvCoRMQbKEFDJnwiM981rvpIkkZrrszBjlL1nraktO/rUdr4iXnWq4DfA38cxWED8+3zs98rSXOf87Rzi/O0kjS7Zmu8drZFxL6UYMgTjsnMfRv3bEh57w+n7N29FvgzcHpmtjkQqG1dtqDsdV6fsgb6xqqsczLz0lGVM04R8XXKAX8Tds/M74+rPpKkwbmndqi87b9K0pAM2ChJUsNUARuluSQiXk85/WHCSZm567jqM5Mi4pfAxEKvyykd/3m/kUuSNF4RsStQn0x9Q2Z+flz1kSRpWRERhwITm3QT2Ko6+ViSJE2TfV1JkmZHl7na3TLzxHHVR5KkuSAi/geYOJzndmDjzPzrGKskSZKGZL9XkqTBOE8rSRqFNgEbNToRcTXlcEWAszJzu3HWR5Kk2WD/VZKGt9y4KyBJkqThVKchvr2R/ONx1GWmRcRqlNMtJhxisEZJ0ogsqv1+OXDUmOohSdKyZlHt9+MM1ihJ0kgtqv1uX1eSpJmzqPb7WQatkCQJgKfUfj/cYI2SJM1ri2q/2++VJGlqi2q/O08rSdIcFxGPZkmwRoCDxlQVSZJm26La7/ZfJWkABmyUJEmaAyJi4YD3Lw8cCTyilnwncPQIqzWX7AQsqH634y9JGqVFtd8/ZkBgSZJmXiMo//3Ah8dYHUmSlkaLar/b15UkaebUA1IdOLZaSJI0RzQ2t94GfGKM1ZEkSdNnv1eSpMEsqv3uPK0kSXPfotrvv/SgAknSMmRR7Xf7r5I0AAM2SpIkzQ2HRMT/RsQzImKFfjdGxOOAk4GXNV76QmZeP2M1HK/6oi87/pKkkWgEi7oM+PIYqyNJ0rJkR5YE5T8uMy8cZ2UkSVqa2NeVJGl2RMTmLAlI9cvMPGmc9ZEkaY6or+85PDOvG1tNJEnStNjvlSRpMM7TSpI0L9XHtA8aVyUkSZpN9l8laXoiM8ddB0mS5pSI6GgcMzPGVRctOyLiU8BbqsubgDOBC4BrgTuANYCHAU8GtuqSxYXA4zPzzpmvrSRJkiRJkiRJkiRJkiRJkiRJkiRJkkYlIvYF/qOWdExm7jue2kiSJEmS6haMuwKSJEmaZC3g2dVPG+cBzzVYoyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJo7PcuCsgSZIkAP4M3DXgM7cChwI7Zublo6+SJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEnLrgXjroAkSZIgMw+LiKOBZwFPBrYCNgLWA1YB7gVuBK4DfgWcBpyQmTeOo76SJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJC3tIjPHXQdJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqQ5ZblxV0CSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJGmuMWCjJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSgwEbJUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSGgzYKEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS1GDARkmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpAYDNkqSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJDUYsFGSJEmSJEmSNOdFxOKIyNrPRuOukyRJGp+IeHBEHBgRp0TEVRHxt8Z3hVOneH5RRHwxIs6PiOsj4t7G8/vW7t238drRM/z2JEnSLKi+D7T+/iBJkmZPRGzUaKcXj7tOkqS5ISK2iYjDIuKciLg2Iu5ptBkHjbuOkiRJU3EOWpKWbnPx//MRcWqjTovGXSdJkiRJmm8WjLsCkiRJKiJiLeCttaSbMvNT46qPJEmSJEmSJM1FEfFm4JPAwiGeXRv4KrDbqOslSZIkSZIkSZoZEbES8DngleOuiyRJkiRJkiRJkqRljwEbJUmS5o61gANr15cCBmyUJEmSJEmSpEpEvBo4bMhnlwOOB54y0kpJkiRJkiRJkmbakcA+466EJEnjFBH7AhvVko7OzMVjqYwkSUsx29z5ISK2BZ5bSzo3M789rvpIkiRJWvoZsFGSJEmSJEmSJEmSNOdFxGrAvzWSrwK+DiwG7mqkN72MycEafwqcDFwH3F9L/8V06ipJkiRJkiRJGo2I2InJwRp/DXwPuBq4r5Z+zmzVS5KkMdiXzvnOUynzpJIkabT2xTZ3PtgWOLB2fRRgwEZJkiRJM8aAjZIkSZIkSZIkSZKk+WBvYM3a9YXADpl5S8vn92tcfyQzPziSmkmSJEmSJEmSZkpzbPdYYN/MzHFURpIkSZIkSZIkSdKyx4CNkiRJkiRJkiRJkqT5YMfG9afaBmuMiAXAE2tJdwGHtnk2M48Gjm5zryRJkiRJkiRp5JpjwwcbrFGSJEmSNFe51kiSJEmSlk7LjbsCkiRJkiRJkiRJkiS1sHnj+tcDPLsxsFLt+g+Zedv0qyRJkiRJkiRJmikRsRDYpJZ0a2b+cVz1kSRJkiRJkiRJkrRsMmCjJEmSJEmSJEmSJGk+WKtxfeMsPStJkiRJkiRJGo81G9c3jaUWkiRJkiRJkiRJkpZpC8ZdAUmStOyIiE2AxwIPBtYAArgduA5YDFycmTdMs4xHA48B1gfWpizMuhY4KzMvm07etTIeCmwBbERZCLY8cAPlfZw9qnIkSWojIh4JbA2sB6wD3EsJOvF/wPmZOe1FyhHxYOCJlLZvNeBvlDbvpy2fXxHYHtgQeACwAvBX4HLg55l5x3TrKEmSJElaJqzSuL5/lp6VJEmSJEmSJI2HY7uSJEmSJEmSJEmSxs6AjZIkaUZFxGrA24B9gU2muD0j4g/AScAxmXluyzIeCLwbeAHwsD73XQR8GjgqM+9rk3f13CrAc6qfXSgBJ/vdfynwBeDzmXnzFPduBFzS4+WHR0T2ez4zo9/rkqSlUxU8+F+APYGH9rn1/oj4FfAN4OjMvLZLXgcBB9aSPpSZB1Wv7Qa8H9ihS94nAH0DNkbEIuCdwFOBlXvcdmdE/BA4KDN/3S+/Ks/L6HzPD8nMq6Z4ZlvgV43kMzLzyS3K+1fKZz3hXZn5iamekyTNTVUfdQfgIZRA//dRgvxfCpyZmXePsXojExFrATsCjwBWpQRzvhI4bRTBnP8/e/cdLVlRtXH4904gM+QoIhkUlCBRsoKAShQQRGVQVEDMiuinSBAQsyACgiAGlGAABck5iAhIkKQEUXLOzMCwvz/qtHO6bp9Ot2/f9D5r3QXn9Al1B9ZUV9WuvYt3vI6UkHkxYG5SwYTbSX3stF68o8E7BawGrED67zeFVEDh4eK9A77rmJnZ8JG0GLAOKXH/AsDzpOT9dwA3RUTTuc+qxw6mSYO4t2ckTQTWAJYlFV+YB3iRVBDoduCWwfalxbzz6sz8s3+O9J3n7xFx52CebWZmo4+k2Uhj4TcC8wLPkMaIV/ViHCVpFmDF4vmLkMaI00jjtfuBayPi+cG+p8F7FyYVGlqY1KdOAJ4lrb3eEhH/7fU7zczMaor+bz1Swdf5gOmkPujyiHisjfunkPrnFUh951PA3cClvZxf7cf4sIjbWpk0zp0HmIU0X/x48Z67evEeMzMb14Z0blfSAsC6pBjnKaR+/baIOLvN+ycBawFLk9Yw5yD1gw8CV7aKI+6ivUuT+veFqO/fb3S/a2ZmvdBtkXdJ85PGycuTxsqzk+ajnwRuAW7tco14RJD0RtL4dyHS7/c0qQ/+a0TcP5xtMzOzsa0fsUb9UMQAr076rrAQaW78SeAR0tp1y7l1MzPrDUnLA28h9SvzA6+S1ivvAm7uds9JMVe6NmmudGFgVlLc8EOk/R492cvS4L3zAeuT9s/MRZozvY00P/tSG/cvQZojXrLU5ptJY+GejmPHahxX6f0rkubLFyfN7T9O+m/x105ye5iZjWRO2GhmZmZDRtJawO9JiTDauoUUiLwC8AZSEqpW7/giKcnUnG08/03AccCnJG0TEfe08fwFSAPQvEJvM28ADgc+I+m9EXFVB/eamZlVKiatDwM+Tdro0soE0gTnWsBWpMTD7bxnMnAM8JEu27kwcDKwZRuXzwZsA2wt6Sjgcy0mXy8iJYKu2Qz4eYt3bNbg3NqSpkTEsx3ee1GL683MbASS9A5gf2AjqvvQ5yWdCxwcEbd08OxLgY1LpzaNiEs7uH8qcFLp1MkRMbWb6yUtC3yDVNBgcoPbZ0g6C/hyt5txJW0KHEJaJG60Oep5SSeSkkA/WdxTt0jdafEBScsAXyZ9Z1i44rIoElUfERFndPJ8MzPrnSJA+MPAPqQku1UeknQacGizYNsG/Wzu3hTLO8DJwIFUF8sB2LhJwZy6/rzT/roRSeuQihpsTgqcrvKSpMuBXwGnR8TLbT5/Cql40vtJc+xV191Lmic/sp1ANDMzG/mqxlzFOueBwB40XkudIekSYP+IyAvetHrn0sDOwDtJyaqqCvYAvCrpSuA77Sa7aPLeOYFPALsBb6ZJ0g5J/wL+CJwQEbcN5r0Vz18M+BNpc1TN08AOEXFJr99nZmb91aR/nQJ8DfgYKaFT7hVJp5CKwDUqprcUcDCpH521wf3PS/ou8M12x4MN3jGk48Nik887SXFdm9KigK+kR0hj6h9ExCPtvsfMzMa3FsXOmxVD3yMiflZ6zlSq11bXI8Ufb06KsSq7CWg6hpW0OmkNeguq53xfLeZ7vzGYsaKkeYDPAbuSEltUXXcPqeD8Ud1+lzAzs5GnxZrpJRXrpTV533ggPSryLmlC0a4dSIXd39SsIcDTkn4NfDsimq3j1p5/AvVxzD+LiD1a3VfxrB8CnyqdOj0idm7jvkWAL5FisZZsct1twA+BnzoBhZnZ6NXjPncqIzzWqMO27AMcCUwsnT4N+FCrZJFFbPNXgK1JiRobCUnXkWKAf9fieXsCx1d8/BFJzfZBXRQRjfYXmZmNeZJeD3yRtMb3+iaXvlbsyziDNA5rmUSwSAB5APAeUhLCRmZIupoUP3RWB+3eBCjPrV4WEZsUny1H2mu7PY3zZz0t6Xuk/mV6g2dvSFq73ZjGMUj/kbR/RJzSQXvHbBxXs71TknYkraO/peL2J4t18O83W5uWtCRwDzO/c7wGLNfOPEKDZy0B3Fd6VgArRMS/On2WmVlZvqhoZmZm1hNFBvyLqE7W+BwpK35X1XskTZZ0EvAtGg9MXyFl+3+lwWcrA9dIqhr0lU2mOlnjK8ATQFUlgUVIk/GbtPEeMzOzpopKP+eRJsarEk09S+pjGz6ig9cdz8BkjTNIlZKaVgUqKsheS3WyxhdJm1Ybte9TwG+LBB9VLsyO21ksbXTNJGCTZjcVE+HlBCNPADe28T4zMxshJM0j6Y+k/mMzmic8ngvYEfi7pB8ViZJHDUm7kqrR70LjZI2QFhq3B26QtEWHz58g6RjgYlL1warvFnOR+vRbJa3ZyTsavHOipG8DdwB7Up2skaI9awKnS7pc0oKDebeZmXVO0srArcBPaJ6sEWAxUjGCu1sEyY56khYqEib/hfRdo1kANaRAqS1IxQm+1OY7PkgKUDqQJsk4CksD3wTulPTWdp5vZmajTzEeuxXYl+rCdxNJY+VrJR3UwbP3JvU73yRtwm0W5Asz52L/JOlPRZKJjhX93X3AEaTg3lZz3suRklX9o5v3tWjLKqR58HKyxn8D6ztZo5nZ2FWMe28mbZBtlKwR0tzs7sD1RexU+f4divs/SONkjZDmV78OXCBpri7a2I/x4f2kpMgfoUWyxsIipGRW/5K0fQfvMTMzGzKSDgauIs3FdrSvStKckn4BXE/aBNtsnDuJNHa+WNKpklqNoRu9byqpfz+AJskaC8uQ4qrvLBJKmpmZtVTszTmBlKy4UbLGKleT4oj2pXWyRkhJO/Ym9VN7t3H9idnxTl2OlSeTihqUndTo2uy+LwJ3k+aZK5M1Ft5EKopwk1JhWjMzs671I9aog7ZI0neAo6lP1vhtYJdmyRolTSqSZN1BKgBclawR0trv2qT9RJcUe3nMzKwHir+PvwX8C/gkzZM1QpovXYsUn3Nqi2dL0mHAbcAHqE7WCKkf2RA4s/i7vtnekJYk7URae92JxskaKdpzMHCeUpHY8v2HApeRYpqqYpBeD/xK0vcH2dYxF8dVev/sRXGG06lO1ggwP3AocFmzfj4iamvRNROAj3fZvI9S//3lQidrNLNecMJGMzMzGypHAnOXjqcBPyAt4M4ZEVMiYqGImI0UbLwOaaB/Lo2TLOZ+AEzNzt1MqmC/bETMEhELRMQspMXPA4FnStcuTJrAnZv2TAcuIC22bgjMV7xjwYiYu/gdNgR+BJSrEE0GTmkyeHyCtOi8N/DV7LMnS59V/ZiZ2ThQJDA8nTRpWvYMaaJyXWDWiJgnIqaQNvisQZrEvYRUSaZdO5E2EdWefwCpL50cEfMXz16bVCUpb+eCpP5yqdLpV4FTSAkc54mIOSNiPtLk7+ZAXhFpW6DZpPJF2fE7mv0ykmYFNqj4uFWyx3dQP+F+cUQ0TVhpZmYjh6TFgCtIVfoaeR5oVJltAvAJ0kJwxxtmhkOxCfdX1C+uvkZKttxojD0H6fd7Y5vPn0AK5Nqr4pJppMTRZYsB5xfVEjtWjNf/RNr83CgB5XTSuLlRVfoNSYUalujm3WZm1jlJGwBXAitVXPI0aXyYmxs4QdKBQ9S0YVUUDbqOVCG+kddI/VlVtdimiaiKYLdvkPrpRnPQM4rnNwqOfj0p8KnpuNrMzEafYhx2PrBo9tEzNB4jTgQOkHREm69oFqj7Imn9c0BV+sK7Sf1P2+Pt0iagnwNVyflfIo2BO5kL74qkzUhJPcoB7DcA60bEbUP9fjMzGx6SliYloXhD6XSQ+p9G/d4SwNm1jT9FssbTqI+lqhXMa9R/bcDAxBTN2tfP8WHVRqtXi3c8S+MigHORYrV2a/M9ZmZmQ6LY7Po16udfa2urjdYey/cuTlqD/gCN529fprp/3xm4qN245aJ//yYpodT8DS5p1r8vCVwuF5w3M7P2dFvkvWp8+BppPvppGveJk4EfS/pKs0ZFxNXAnaVTc5JinDu1NfVzyw+S5tAbKhJYnkRKgtwokcYrpD640Xz7yqSYpWZJKszMzCoNdaxRh22ZnbSP6fOl0zOAfSJiv2Z7ayRNAc4h7cVtlERrGtUxwJsAVxdjcDMzGwRJ8wHnAV8EZqm47FnguapHNHn2JNKe0S9T/Xd9vsekZhPS2GnZquc3I2kb4NfU75+prVU2GoduApxQuv/7wFeo//1eJo1jG/mMpH26bOuYiuPKTAJ+B+ySnZ9G9Z/lWqQ142bfWY7Ojj8sqer/34aK/z/zuY5jOnmGmVmVqizBZmZmZl0rkmJsXjo1Hdg0Iq5pdH1EvAD8tfj5UXF/ZeV2STsC5YFtkIKnDo+IAQPpiLgdOEjSz0kTvbVNw8sBh5OSWVWZRqpcf2xEPFp1UfE7XAlcKelI4M9AbaJgseIdA5JPRcRzwLHF77UU8I3Sx89FxLFN2mZmZuPHQQxMTHgusFtEPJlfHBHTgRuLn6OLid12N9jUqtzeCrwzIh7Knv0KaQH4ugb3/gJ4Xen4fmCniPhrgza+DFwIXFhUgj+BmRVrvizpdxFxQ4P7Hpb0D1JQE8DiklaOiH9U/D7rUz/5fj8zK922StiYf35hi+vNzGyEKJIdnwa8OfvoduAw4OyIeKq4dnFgB1IS/UVK174L+CGpMMBItgppgVOkBdWjSJUMb4qI14qFzDWAz1FfLX5W0nh04zbe8Wkg30T7OOnP8rdFFTuKzcebk4KzNgDmI30/6EjR5lrC57IrSYukl0bEg8W1E4DVgA+SEkrOVly7HPAbSZtERKMEYWZm1iNFX/oHBm7IOYfUL10SEdOKv7PfCHwI+Az1wV9fl3RnRPw6e8b3gN+Ujg+lfmPqV0nBRLk7mVksp2YFUhBwzT+L5zdyV8X5thXz3OeS5ofL/kUqeHQecHdEzCiunxtYnRQYthOpj2/lC8D/ZefuIRUWOhe4oxYcXcw/bw/sx8zArzmBUyWtFhH/bf+3MzOzEe4XpPEYpHncI4BzI+KFYry1PCmxxBeonzvdT9L1EXFam+95hNTfX0AqrHdXMX8MQJFEfwNSlfNNSvetSmfj7a9TvwkI0sadX5DG/lcWa6618eSypKJD25ICi6sq03dM0h7AcdQXFjgbeF+xXmxmZmPXb0jFYSHNvx4LXBURrxT9z+qk8dkOpXuWBfaX9AvgZNJa6IukMeEpwK0REZImk9Zyv0nqJ2t2krR5RFzQRvv6PT6cAfyFVHTnWuCWiHi89mFRVO8txXv2YeZGIQHHSfprRPyzjfeYmdn4VZ7bXYD62NonGdjv1TSMUy5ZmzRHDWkD7o9IG3v/XqytTiTFGG+Y3yhpNtI4eNXsoz8BPwWuiIgnimsnF+/6GGkMPqG4dj3SeucHWrQTYH/gS9m5u0nz7ucBd5b696VJ30P2Y+Z3lrmA0yStmsd/mZnZqFNeM/0caY635vs0X9ts1TfuxMy44WeA75IKut9RGrOuBqzY5BnXk+ZJrwZuAR4q9VGTiudvTSqgW147PVjSFRFxRZNn/4y056dmKimZcSemZscn19ZoK/ygwT03k743XBQR99ROFsVydyatQ9fGvguTkk+sUZu7NjOzUWMo+9yW+hRr1G5bFgLOAtYtnX6BtC56dot7RZpH3zz76ArS3HoeA7w6M2OAZy2uXQH4taS3N+i3r2TmvMH61I+xrwJ+2aR5jo8ys3GjmOs8HXh79tEzpPHNH4Ebi32oFAnxVgbeBryX1vtMDmZgor6nSGO40yPivuK5cwNbkeYuy7kjlgF+L2mtiGhUlKbKYsxce32JFINUXnudhbT2ejj1c7m7SDqBtGfoM8W5B4rrzoqI/xTtnYfUrx4GLFS6/3BJp5XXRNs01uK4yg4i/f8CcAep8MG5tfloSXOR/tt/g9S312xMk/F9RFwo6Q5m5gNZiPT/ZB5j3sw21O9zfpD0/7yZ2aCpSfJ6MzMzs65I2oo0qKv5TUTs2qNnTyRNMi9VOr1/RLRVKUDSMsANzFwMfRlYMiIe60X7Su9ZEbiJmZPEjwCLN0ooWbpnKeDe0ql/R8RSvWyXmZmNPpIWJfUPs5VOnwts3YvkQ5IOJG14LXsKWLmTYF1JWxTtKj9jnXY32kjajzThXHNqROST9rVrf0BKHFXzmYj4YcW1h5EqNUHaALUfaVGh5nW1xd4G994DLF06tWw50MrMzPpL0n3AG0qnlq4t4ja4dn/qg3UhLXTuWVtQbnDPfKTg4fWyj7aJiMqFOUmXUr8YvWlEXFp1fYP7p1K/0HhyREzt4Pqau4B3RcTdTe49kIH9/qoRcXOTe5YC/gHMUTp9Iymxc8PF5mLh+BukqoMDRETTCr6SvgB8u3RqOrB3RJzY4r41SBuiysFqX4yI7zS7z8zMBkfSucAWpVMB7BsRP25yz1tIQUELl04/C7wpIh5oct99tPl9oMG9mwCXlE5dFhGbtHnvVDrrrwVcTgpuKjsM+Ho743lJmwJzVX0PkbQuKZC5XKTxaODzzYLXJC1ISrC5fun02RHxnlZtMjOzkUlSVfDX0cCnqzaeFhtJL6a+gvujwBsbFQqtq5GsAAAgAElEQVQq3bc1aQ309y02tZbv2ZO0+aZWtOc1YLmIuLf6LpD0TtK8c3kceQ+wbUTc2sZ75yN9Lzmk4vNNaPP7gaRDSMmiy44BPtnun4OZmY0eFf3rNFJBvd82ue94YM/SqSdI86sbAf8GtoyIOyrunYs0zlutdPrMiNiuRVv7Oj4s1l9/0sF4fBHSxt61S6d/FhF7tLhvKRxHZWZmDK5PaLK2+l/SeuftHbTjWNJm1pqnSN8N/tzivi1Jia/KBQW2j4g/NLlnA+BSZo6jISXn+GLVendx30LAmdSvebf8PmFmZqNHD+KUDmRg7BBUFHlv8ayvkPqZqoLr+fVTgF8B5XHnpRGxaZN7FicVaq/1iQEs3yw+Krt/EVK/Xx4zrxgRDRNuSdqRlNCkJoCvAYe32Au0NGkP1Uql00dHxL5N7plKB2vQZmbWX8MQG9yPWKNLaeN3krQC8GdSIq2ah4H3RMT1bbQjj+GeBnw8Ik5ucd+apBjgRUqnPxsRP2hyz57A8aVTP42IPauuNzMbTyR9g4GFb84lzWlWxgWV7l8eeEdEHNvgs7eR1icnlE7fTBpXPlLxvImkxICfyD76XkTkhVzL921CfWxPzX+ALarmeCvWXq8gFZ5fELgQ2KEq0X4RV3UtMHfpdNN+qbhvLMdxXUrjRJ4/AT5R9X1F0ryk+e5yAs0bI2KNJu/6JGlOvObyiGiVRLR8//nUJ48+KCIObPd+M7NmJrS+xMzMzKxj82XHTQdoHdqZ+mSNN1CfxKGpIslSeTA8G6kCT09FxJ2koKeaRZhZedDMzKwTn6U+WeNTwO69SNbYxCFdVFbfLzs+oN1kjYXvA/eVjneUNH/FtRdlx5s1eW75s8tJibha3lsET5WTNd7nZI1mZqODpNlJ1eXKLgb2aLZ5JSKeIlVvuy/76Gs9beDQeIa00bdVMPLBpIXwsp1a3PMp6pM1Pk5a2K6sDBjJ/5GCrDtSLMYekJ3evVWyxuK9NwDbA+XvSZ8tqiSamdkQkLQW9ckaAQ5ulqwRoEgW/C7q/86ewsyqtaPddgwMoP5qRPxfu+P5iLikWdJo0rx4eWPRcRGxb6tKw0Ufvg0pUUjNuyW9uZ12mZnZqHE2LRIJFgHL7wbK1yxMi4rpEfHHiDijkySFEXECKbF/zQRg7zZuPYL6ZI0PAG9rJ1lj8d6nqpI1tkvSLJJ+SX2yxgC+FBH7OFmjmdm48plmyRoLnweeLh0vQErWOI1UkK9hskaAiHiegePidxVz3s30dXwYEV9pN1ljcf0jpO8cT5RO71okVjYzMxsOrwDv7jBZ40rUj5drz2iarBEgIs4FPpyd3r/Fbd+iPlnj0RHx6Wbr3cW7HgO2JiWmqtmm2OxrZmZW5Sk6TNYIEBGHtZussbj+WWBH4M7S6U0kVe6ziVSQ/fzSKQG7d9DMD1A/Zr6qSbLGiQzcm/TliDi0WbLGop33ksa+z5ROf6RIpmxmZtaOfsQatSRpQ+Aa6pM13gas22ayxvkZWATvA62SNQJExN+AHahfv/68pMktG25mZnUkLUpatyw7l7Re2TJZI0BE/LNRssbCV6nPV/UosHlVssbieTOATwK/zz7apyg014nppN+lco63WHv9dHZ6Q1KyxruA7aqSNRb33w58Nzu9Y4ftrBkrcVyNnBURH2/2fSUinqa+6CHA6pKWbfLck4HnS8cbNZs/KCueW94zPIP6BM9mZoPihI1mZmY2FJ7Ojtfs4bPfnx0f3Wrxs4HfZMdtZ9Tv0F+y43WG6D1mZja25RO5x0fEo0P4vldIE5ptKyrQvr106nmgZVKlsoh4BShvcJoIrF9x+aXUJ/XYpNEibJHwqVxp58Ji81A5mVVVssfNs+MLK64zM7OR5/2kTbA1rwJ7tbMAGRHPMHBD7FqS1uth+4bCYa2q2QEU4+cTstOVY3ZJszIwyPmAYqNPOz4HvNTmtTV7U1+F8MyIyMfxlSLiWuoTRS4OvKfDNpiZWfs+lR3/k1TZvaUiiPdH2ek9Jc3R6PpRJt9seyX1leMHpfhuUg7SfoCBwXWVioC7A7PTHx18y8zMbIR4lRTkW1Wx/X+KxPd5cPXHJQ1FTNn3SQHTNW+vuhBA0lbUV7mHVIyhMri714pEUucDu5VOTwN2iYhv9asdZmY2ItzcZEPS/xTJJxptiD0mIm5p4/7LgPtLpyYDb6m6frSMD4vkkD8tnZqV6rVgMzOzoXZMUVSoE1+kvqDAkRFxTbs3R8RpwFWlU+tIatjHF8kxyuvT/yne3+67ngAOKj8Sz/+amVlz3RR570pRXOCH2emmc8XASdnx7pLU8MqBpmbHzWKbdwaWKh3fwMAEjpWKovA/KJ2aDfhgu/ebmdm4N6SxRu2QtCtwATB/6fQlwPoR8e/Gdw3wCWDO0vFvI+KMdtsQEVdTv/d3CWCrdu83M7P/+SxpTFLzFLB7u0mAm5G0HLBldvpL7ex3LWKZ9gVeLJ2ejc7nL4+NiJvaeN/l1K+91uwXES+08Z5fZMerdxFTNSbiuCq8Qur7WyoSM9+Qna7cy1Ssu+d//nu12a69qJ/PPysiHmjzXjOzlpyw0czMzIbCdUB54Li5pEMkzTKYhxYDyrxS0HmdPqeoVl9OGNFx4g1Ji0l6u6T3Spoq6eOS9ir/MHAD0ZKdvsfMzMY3SUtQX5kO4JdD/Nqb2q2UVJInP74yIl5seGVz+aRrwz66qF7019KpuWicGPnt1Febv6D4Zzn54jsq2pIncnTCRjOz0eNd2fG5EfHPDu4/C8iTH+bPHEkaJWFs5qrseKUm165JfeDVi3TwXaRYdM8rILaSF2o4ssP7oX+FGszMbGBA7HERMb3hlY0dSf1c8rx0MV87khRj+bWz04d1UXiomby/PLHN4LGy31JfDMH9pZnZ2HFeO0n9S36cHS8FrNy75iRFkYQ7SqdWlTRb1fUMLGb014i4oOGVQ0DS0sDV1PeRTwDvKBJtmJnZ+NLJHOx1g7z/b9nxG5tcO5rGhy58a2ZmI8XxnVwsaRIpgVNNAEd18d521zDz/v2EiOi0SN7pQLmgoed/zcysSsdF3nug0/HhmaS52ZolaSORhKQ1gVVKp14g9ZFV8j746C7WeB2zZGZmHetTrFGrNnyZVCx91tLpXwJbRsTTHTzKMcBmZiNDHnNzfDsJFdu0FfXJ8J4ATmn35oh4kIFjs0736wxm7fVRGhfgG6BIzF/eZzsHneeKGCtxXI2cHRH/7eD6TvYyARydHX9I0hzNbpA0KwOLN7QszGhm1olJw90AMzMzG3si4jFJvwPeWzr9VeBjkn4DnANcFRHPd/joFUkbdmteBbZuvzhenenA7MW/LyRpUqvKEJI2Bj4EbAMs2MU75219iZmZWZ23ZsfPAv8Y4nfe0sU9ebDU7EXy4k7lk8eLNbn2IuBtpePNSFUEy8rJGB9l5u92IfDx4t8Xl/SmiLitdmFReXfT0r1RvM/MzEaHPMnT7zq5OSKiGNN+vnT6bVXXjwC3dJhs+Z7suNlYdd3s+KoicXIn/szAAKyGJC1A/feBl4HLOnwftJkE2szMBkfSisAC2elO+917Jd1A/fj3bYzuMdim2fETwLk9fkceiNxNYaPnJP2TmYk/VpE0Vxfz9mZmNvKc2cnFEXGbpH8By5VOr0MHc8VF8ooVSEHCU4C5qS+mUzN76d8nkeaAq4KSN8mOh7qY0f9IWpsUnL1w6fTdwLsi4q5+tcPMzEaUyzu49v7s+EngtkYXtnl/szncETE+lLQg8CbSPMHcpD4/D+haITt24VszMxsOj0fErR3eswapmGzNnRHx7y7e3WgNs1Hix170789IugdYvji1qqTZu0j8aGZmY183Rd4rSZqHFPuzEGmueA4Gjg/zNeam48OImC7pFOCTpdN70HpNeY/s+IyquCdJE4ANstPd9MF3SHqJmXPhjlkyM7N29CPWqKFinfcYYM/so29ExNc6fNbC1CdeepGBe3za4RhgM7NBKBIBL5Od7mXMTb635uwOC70DnAHsXjpeU9IsbT7nKaCTOd587fWqDpMi3w/MXzruNFfEWInjaqTTvT6d7GUiIv4h6VJmxpDNA+wCnNjktp2ozwFyN9C3AsFmNj44YaOZmZkNlX2BNYE3lM4tDHyq+HlV0i2kbPiXAhe1UW1n0ey4NiHcC/MBjzX6QNLrgOOAdw/yHXMP8n4zMxt/FsqO7+1DlbxuAq/yPnpjelPFbv4mn10IlBeANwcOzK7ZrPTvF0VEFP9+MfAaMKF0b3mT1GrUT8zeFBGPt9lmMzMbRpIWYmDC37wiXjvye97SXYv64r4Or88Dj6c0uTavWPf3Dt8FcGMH165NfZD2M8BHuyjUkC8mN0sCbWZm3cv7xyc7rAJb8zfqEzaO5H63HWtkx9eUxqODJmlOYJXs9PqS3tzF42Yt/fsEYBHACRvNzEa/fANLu/eUA31XbXVDUbF8N2BXYH1gli7e2zDwttjUmwePX93F8zsmaTvgFOqDkv8CbBMRDdeTzcxsXOgkKVM+rrq/w3Fhfn/DeKPhHh9KWgOYCmwPLNHFO1341szMhkMvCtm+2mUh2zy+asAapqQpzEyiXLOhpNW7eN/k0r9PJMVwd5No0szMxrZu+sY6klYgJUfckfp55na1Mz48ifqEjTtImhIRz1a0aVZSEoeyZgkdVsza8SqwdRcxSwDTmTm/vJCkSRHxajcPMjOzcWNIY42amBs4G3hn6dyrwMcjolm/WSUfPz8DfKyL/nRyduwYYDOzzrw1O34W+EcPn5/H+PZiv85spOIz7bRzsGuvnc6RtrV228Soj+Nq4r4Or+9kL1PN0dQX/d2b5uP7fO7+2D59rzKzccQJG83MzGxIRMTDktYGjiUF5uYmAasXP/sC0yWdA3w3Iqoq5zRL2jRYczQ6KWlJUkLJpXvwjgmtLzEzM6uTV5Ftldy4F7pJzDBUfXTD/rlwDfACMGdxvLakuWvVbyW9nlQJqOZ/lXAi4klJNzJzAWIz4Iela8uJHiElhzQzs9GhUZ90XxfPyZNNzSdJI3Sh7plOLo6IGVnwU7Ox6nzZ8UOdvKvwcAfX5puUFqE3hRqGcj7BzGw8y/9+va/L5+T97mj/ezsvvnB3j5+/MPUJjgGO6NGz56f37TUzs/7rJvlCfk8+N11H0takgNjXd/Gusqog5rw/hf70UasBv6V+rPx7YLeIeKkP7zczs5Grk3nYGdlxw+QRHdyfF6ipGZbxYZFY+Ujggw3e3wkXvjUzs+HQi0K2qzB0a5iLNDj37R68q/Y+J2w0M7NcN30jAJJmAb5JSqQ4mH3KLceHEXGjpJuYmaRiduB9wPEVt2xLfV97N3BFk1fk/f0ketPfQ4q/cjEgMzNrZqhjjaqcTH2c8LPAjhFxQcX1rTQqVOAYYDOz/sv7lXsj4rUePn/QscNFDoqXSYkaq55bpaP9M/Rv7bbKWIjjqjLY/xbt/Fn+AXgAeF1xvKakt0bE9fmFklYhJausmUYqAGFm1lNOGmRmZmZDJiIejYgdSBtbfgD8s8nlswDbAVdIOkVSo0FdN9n821UVQHwSA5M13gkcAryHFHi1ACmh1MSIUO2HVCXQzMysl0ZikigYuj66coNPRLxCffDUJOqr5bRKulg+3lhSOVjMCRvNzEavPMHga3SXjDhfOJxIe9XbhsNQfj+YJzvOK9q1o5MF7eFIAm1mZt3L+91Og5hq8n53tAfZDnXxhb4XNjIzs1Gnmz4574/zfv5/JO1OCoYdbJAvVMeu5f1p0P13jU7MQ32bZgDfcbJGMzMbZDGfoZrDHY7Ct/MCFwEfYnDJGsEx7GZmNjxGeiFbz/+amVm/ddM31pI1/gH4LINL1gjtjw/zJAtTm1ybf/azFmN798FmZjachjrWqEq+JnwZcPEgnucYYDOzkWGo+5Xhjh0e7Nprv/fnjoU4ripD/mcZEa8Cx2Wn9664PD9/ekQ80ftWmdl452AXMzMzG3IRcVNEfDYiVgAWJ1WyOwq4kcaDsV2BMyXlmfHz6n13lxMkDvLnvrwRkt4OvD07/dmIWCkiDoiIsyPiHxHxZES81KDCxFxt/hGZmZlVyScEKydXh1neR3+5R/1znjgxlydS3Lz07+V774qI/zS5d25gHfhfENkGpc+m07yqrpmZjR8jNXHyUJqWHXeTpLmTe4ayUIOZmQ29XvWVY63P7fXvMxyFjczMbOxrqw+QtBwpCDZPavgn4FPARsAypKIHs+ZzvqRNPt3o1/eDu4D7S8cTgfMkbdqn95uZmXViOMaH3wPemp37D/BtoFbUdyFgTgYWvnV/amZmo1U/C9l6/tfMzEaL/YGtsnOPAz8CdiGNHRcl7amZlI0Pl+7ynb8ixfTWvE3SCvlFkhYD3lk69Rpwcotnuw82M7ORpF9roxdkx1sDpxV7arrRz/GzmZm1b6j7FccO995Ij+Pqt59QPx+wq6R5yhdImhP4QHbfMUPdMDMbnwZbvcbMzMysIxHxEHBa8YOkRYCdgc8Dbyhduimpqt1PS+ceyx63pKRJRXb8obBDdnxyRPygg/sX7GVjzMxsXMr7vqUlTWiQJHi45e1cpk/vzRM2lpM0lpMu5wvJAFcCLwOzle69Clif+gp810TEi4Nsp5mZ9c9T2fEEUuDvcx0+Z57seEYXz2jXSC6slFdTzP9c2tHJPXkS6IvaSOBsZmbDJ+93u+knGt2XP3e0GeriC3l/OR2YfQTOFZiZ2fCZQtoY2+k9ZVX98QHArKXj+4FtIuKmNt/TbsG7vD+dQGpjPk7ttYeADwIXAcsX5+YCzpH03og4Z4jfb2Zm1om+jg+LDT9Ts9PfBfZvM3bLhW/NzGy0yvvc4yJirz6962VgjojwZmUzMxsxJM1FSthY9mtgzzbjbbsaH0bE45L+CLy3dHoq8JXs0g+RivHUXNig6Hsu74PvjojlummnmZlZF4Y61qjKYcDFwOGlczsAZ0naPiJe6vB5eX96XkRsOZgGmplZV4a6X3mKlKC/xrHDzY2FOK5hFRGPSPotsGtxag5SfNePSpe9n/o/t5sj4uo+NdHMxpmRvBHUzMzMxoGIeCQijgJWBv6afZxnsr8dmFY6ngysO4TNe0t2/MsO71+rVw0xM7Nx62/Z8RRgleFoSAt/z4436tN7b6Y+WeQbJb1O0pupn/jPEzsSES+TkjbWbJb9s/JeMzMb0fLFZYClunhOXsn9qSabYPLNrxMbXlVt3g6v76eHs+MVu3jGSh1cO1xJoM3MrDt5v7tUl8/J+908eHe0yfuzXm/kyZ8/C7BEj99hZmaj2xtaX9LyngHja0mTgK2z07t3EOQL7Re8y/s76H2f2lCxcXdD4JbS6dmAP0jasR9tMDMza1O/x4fbASodXxYRX+ig0K4L35qZ2WjVzzXM/F2zAYsN4fvMzMy6sSUwe+n4n8DUDoqjD2Z8eFJ2/CFJ+R7pqS3uaSTvg5cs5sTNzMz6YahjjSpFxDeBfYFyjPQWwLmS5u7wcY4BNjMbGfK/j5duMG4ajEHHDktalDT3WTbaY4erjIU4rpHg6Ow4L6qUHx87hG0xs3HOCRvNzMxsRIiIF4CvZ6ffkl3zEnBVds3OQ9ishbPj+9u9UdIUOk9WlQcwd5rkw8zMxpiIeAC4OzudJzQeCS7IjleUlCc+7rkicdbF2enNqE+6OAO4pOIR5WSM6xRVf52w0cxsFIuIx4GHstNrdvGo/J6bm1z7XHbcaZW5bhZg+yVPHt1N0YR1Orj2xux4aUmv7+KdZmbWH7dkx/NLypMvtqOTfnc0uD47XleSGl7ZhYh4koFz1f0qnGBmZqPDGj24p1F/vCT1RQf+GxGXtvsCSQvT5hg4Ip5h4Nz429p912BFxCPAJsB1pdOTgd9ImtqvdpiZmTUzDONDF741M7PxKi9ku66kWYbiRRHxKPBgdtrzv2ZmNtLk48PTImJ6B/cPZnx4LvWxYa8DNq8dSFqX+uKyTwN/aOO5twPTSseT6S5OyszMrBtDGmvUSkQcDexB2ntTsxFwkaT5O3hUPn5eTtLig22fmZl1LN8DMgVYpYfPz2OHe7Ff52Xgru6aM+KN+jiukSAirqL+u8bKkjYEkLQ29X9mz9P5WraZWducsNHMzMxGknuz4zkbXHNGdvzRIUzekC8az9vwqsb2pvMkHXmSjykd3m9mZmPTadnxnsWE6IgREfcCN2SnD+7T6/OEinnCxuuKzb2t7p0MbAO8tXTuGeo35JqZ2ehwTXa8fSc3F0FO+T1XN7nl6ey400RVG3d4fT/lv/eKklbr8Bnvb/fC4jvFPdnpnTp8n5mZ9UlE3Ak8np3utN9dioGBRc363dEgLxqwALBVj9+Rj4WHsrCRmZmNPtt2crGkNwHLZaevbXBpPi/9n07eA7ynw+svzY536/D+QSmSYL0DuLx0eiJwoqRP9LMtZmZmTfRzfDiYwrcTgHf1tjlmZmZ9cwX1CZzmBrYcwvd5/tfMzJp5NTueOAxt6Hp8WNi62xdHxAzgF9npPSr+HeCUiHi5jee+BFyVnXYfbGY2vvWzz+1HrFFTEXEysAvwSun0WsClkhZp8xl3Uf+9QMCOPWtkYyPhu5GZ2YgSEQ8wsEjqB3r4ijzG991dFLh5b3b8t4h4peGVo99YieMaCY7Ojvcq/rl3dv6XEZHn7DAz6xknbDQzM7OekzSpy1tXyo4fbnDNicB/S8ezAadKmq3Ld9Kk4tB/s+N3t/m8lYGvd9GU54CXSsdTXEXIzMyAH5KqBNXMB5w8iP52qOQJGrcdzKbVDioCNkrYuFGTz8tuBJ4oHR9E/QLtpUVwl5mZjS5nZ8dbSVq2g/vfAyzT4plld2TH67f7IknrAKu2e32/RcT9DAxG/lq790vans6rMeaFGvaT1GlBBDMz658/Z8cflzS5g/s/SQrOrXmaUZ6wsQh2yxNIf7lITtEreX+5taS1evh8MzMb3baQ1EkxgX2y438Dtza4rutid5ImAp/toE0wsJjR2pI27/AZg1IE724JnFs6LeBHkvbvZ1vMzMwq9HN8OJjCtzsBS/WuKWZmZv1TJHDK14sP7PGcb1nev28nKS98ZGZm41eecGDKMLRhMHPF6wAbDvL9J2XH20qat9hT9L4W1zaT98EflfT6jltnZmZjRd/63D7FGrXTjjOA7ajf3/pm4IoO+sS8P/2SpDl60b4KI+G7kZnZSJTH3OwpKU/w160/A1E6XgDYtd2bJS1GWjssa7ZfZ7QbK3FcI8GvgKdKxztKWp6BcwHH9q9JZjYeOWGjmZmZDYXDJZ1SLKa2RdICwKHZ6bw6EBExDfhSdno94GJJS3XwvomStpF0CVAVyHRpdvy5VkFPkt5KSgw1e7ttqYmI14CbstN5Vn8zMxtnIuIR4NvZ6S2BsyTN184zJC0vaa/WV3YvIs4ELs5OHynpoGISty2SFpT0FdqcaI+I+4B7SqcWBcpJnSoTNhZ9b/n7Rl596KJ22mBmZiPOr6lPyDsJOKadwCVJU0jJksuui4i/NLntr9nxu9upJltUEcwrvI1EP86Od5D04VY3FWP0H3Xxvu8CL5SOFyElq243mbOZmfXXUdnxCkBbyYskrQ58Kjt9fLH5dbT7Zna8AfDlXj08Iv4MXJedPqWYZ++K+1ozszFlEnBUO3+3F2uf+dzxT4q501xe7G4lSXnBgypfp8OE/hFxPqnoTtlJ7Yy5e6n4brIt8Lvso8Ml5evbZmZmfdXn8WG3hW8XBY7stj1mZmYjxCHUb0JeHTii24c1G7NHxB+BG8qXA7+WNP9QvM/MzEadR7LjNw5DG7odH84J/GywL4+IO4ByLNdspOQgOwDzlM7fGhF/6+DRJ1L/u80GnFokguyK+2Azs1Gt333ukMYatSsizgHeBTxfOr08cKWkfL9NI9+mPuHj4qQ13qHqE0fCdyMzs5Hoh8DLpeP5SHsyJg32wRHxLwYWez9C0oJtPuJIYM7S8UvA8YNt1wg2JuK4RoIihqtcmGEW4Czqc3pcExF5rg4zs55ywkYzMzMbCpNJC55/kXS3pG9L2k7SG8oJMoqkiStK+jxwM7By6RkzqEhcERGnAN/LTq8H3CHpp5K2yhNYSZpV0pslfUDSiaTJ2DOBTUjBTI38HHixdDwXqSLQ18rVDCRNkvQ2SceSFn4XLT66rOK5zfwxO/6qpMskHSxpX0l7lX+6eL6ZmY1OBzEweeBWwD2SDpG0lqTJtQ8kTZa0mqR9JF0E3AHs0od27kx98sQJwAHAvyR9UdIa5XYWbZ1f0saSPle09SFSEufFO3hvVVLGFxhYabDde1t9ZmZmI1SxCPed7PTmwAl5P1QmaV5SwuC8et3BLV55OfBo6Xj24l2Vi9lFAPIZwFtbPHsk+A0D+9PjJX21SDo5gKR3kMbEi1O/0N9SRDwKHJad3gE4s5NNxsU8wAck3dDB4r+ZmXUoIq4DzstOHyTpo83uk7QKKWCr3F8+w8DEyaPVHxk4P/yNYgzfVsCbpE0kbd3kki8Ar5aOlyMFR7+l3UYq2VTSmcB27d5nZmajwrtJBXUqi+lIWgn4E1C+5nHguEbXF+O128uPII1/Z23yDhUFer7WQdvL9gPKQcevA66StHLF9fn755P0f12++38iYjpp/vsX2UdfkfRDb7o1M7Nh1q/x4aXZ8W6Stmrx3GWK+xZuty1mZmYjUUT8nYEJpr4g6ThJbRd4lzS3pH0ZWBQw9wVSHHXNCqT45bY30Rb9+9sl/RFoNtdsZmajS17k5kNFHFI/XZodbyTpY81uKGJ3LgBW6lEbTsyOpxY/za5pKiKmAV/KTq8HXKxUuLYtxT6pbSRdAqzRSRvMzGxE6Xef249Yo7ZExKXAZsBTpdNLksalTddpI+JhBiaf3Bn4vTooRFDEAH9I0o1FfHeVm6gvsPBGSRu3+x4zs7EqIh4hJdEt2xI4K8+/UGpZrlEAACAASURBVEXS8k3yGBxKfTzPIsD5khZq8ryJkn4I7Jh99OOIeKKdNo1iYyWOayT4MfV9fz7PcEwf22Jm45QTNpqZmdlQW4YUOPR74D7gVUlPS3qcVPXgDlIijTwp09daVLPbj4HV12cFPgycAzwp6WVJj0l6iZQg4mbSJpo9gJZJHooJ4q9np+cgJeu4R9ILkp4ApgNXAR9n5gbjk+mu+t/x1Cf6ANiINPg9ijRQLP+Ymdk4EBEzgJ0YGOQ0L/BVUhDv9KKPfYbUN91ISn78dvo0/i8mx7cA/pF9tBTwLeD6op3PSHpS0qvAE6Tf67tFW7up1JQns6y5vNhI20xVUsYHIuL2is/MzGzk+zZwZXZuD+AGSbuVg3ckLSbpE6TFyg2ye34SEX9q9qKIeAX4SXb6PcDlkt5ZTmqoVMhg3+JdtaCoq9v9pYZDUY1vD+C50ukJwCHAfySdIGl/SZ+R9C1JN5D61yWLaw/o4rWHA7/Nzm0N3Cvp+8XGornKH0qaQ9JbJX1Y0m+Ax0hzAKt38X4zM+vMh0ljuxoBP5F0lqTNa31hEejzJkmHA38jBWiV7R0RD/SnyUMrIoJUOOHh7KOvArcrFedZXvUFjuaWtKGkAyTdAlxCk+TOEXE58Lns9Eqk7zunS9peUl1CDKUCDytK2lnSj4D/ABcD21Af5GVmZqPbtcU/9wWuLvqEOeB//fHykg4GbgAWy+79VIsg6GOz402B6yTtKGlK7WSRKPF9RVsOLU4/DtzWyS8SEReSxp9lywI3STpR0hbl8WHx+y0raddibPgf4BudvLNJW2YAuzNwjfZTpIBnx+GZmdmw6OP48CzgwdLxRNKmru8W430Vz54gaXVJRwC3AisW13dT+NbMzGwk2YeBiRY/Btwn6SBJ6+WbYSXNo1QQfh+lxImPkWKBl2/2ooi4BPhidvpNwN8lnSppu4r+fSVJ75N0NPBfUkzVe/DeMTOzseQc6pNirAjcIelHkj4raa/sZ8WK5wzGdaT55bLjJJ1UxO5MhP/N166kVFTnLlLyQ+jN+PBU4MXS8dqkxFI1rwC/7PShEXEK8L3s9HqkP+OfStpKWXITpYRSb1YqLHsi8AhwJrAJae3czMxGp772uf2INeqwPdeS+rLyPtdFgcsktXrHIcAfsnPbkmKAv6dUPKgu+aWkOSWtKekjkk4lrS2fDKzWop3PMXCf1fnF3PhXivF4+b/Te1q03cxsLDmIgXsutyLlRzhE0lqSJtc+KOYXVyv+7ryIlP9hl0YPjoirgSOy06uT+qzPS6rtJUHSXJJ2BP5CirEpuwUYdCHWEW7MxHGNBBFxN3BuxcdPAKf3sTlmNk51kwTBzMzMrJVo8pmAeZp8/gLwxYhomoyw2BDzaUnXAt+ncSX2WYufZu4Hnmzynu9IWoSUdDI3R/GTO440cP5Ai3c3et9jkrYFTgNe3+n9ZmY2dkXEU5LeSUp0vA+Nx/TN+tjXmnzWMxHxL0nrkIKLP0TjDT1TGpyrewyp0l27LiruyQObqpIxznxRxN2S7gWWbvBMMzMbpSJihqSdgfOBVUofrUIRjCvpOVJ/OnvFY/4MfKbNVx5GSq5cDrhaDzgPeE3S08BcwCzZfScCVwBva/M9wyIi7pS0BWlhs9yPLwx8pMmtvyAlZf5W6dzLbbwvJH2I9P1lp9JHc5P+m3wGQKlAw4vF+fzP1szM+iQiHpS0PSlxQ7mi+dbFTxR94dxUr08fHBG/HtqW9ldEPFz0n3+ifq53OdKYGWZ+T5iNxnPNrd5xVBG4dSgzx98TSRWIdwSQ9ArwLOk7T8fvMDOzUemDpCDn+UmbVH8HUPQ5c1A9fvpuG/3xcaSkhWuUzr2ZFOwaSgWFJgNzZve9AuwGfKX9X+N/DibNfZfH6BNJxQX2AJD0IjCtuG7IklAUG6X2kfQ89YkzPgzMKemDRWEHMzOzvurH+DAiXpb0GVJMU80kUrLIzwHTij5yPgb2xxeS5ok37vS9ZmZmI0XRF24NnAFsWPpoYVIRuwMAiv5wOmldtes9WxHx/aJ/P5iZfetEYOfiB0nTSf17VTyzmZmNMRHxH0k/B6aWTi8BfKLilj2AO3vchpD0SVJSqPJ889Ti5xVJz5Lma/O+8BZSgo5OYoQbteFZSb+jfs9OOYb4TxHxWJeP34/U7nIikVlJ88AfBpA0jVT8di7SWq+ZmY0xw9Hn9iPWqMP23CxpI9L87hLF6QWAiyW9OyKurLgvJH0A+DmwQ+mjKcBni59exgAfRn2i5FkozY1nLiL9+ZqZjXnFnpqdSHFDm5Q+mpeUEPirAEWsj2i93zN3ALAM8L7SuQVIe1+/U4ybXqZ6v+s9wPYRMa3D9442Yy2OayQ4mpR8NHdSRLTcr2RmNliukmZmZmZD4UvAFsCRpIXUGW3c8wApicMKrZI1lhUV7JYiLYZeT3vJqO4kDcbeASwVEfe0eMcXgXcDf2vx3CuBLSJir4h4tY12VL3vL8AbSRP1p5GqUDxNGgCbmdk4FhGvRMSnSf3ET0lV15uZAVwNfJr6hc4hFREvRMSHSe38CfBgG7dNIwVv7U/qn3fv4H1PAH9v8FHLhI1Nrmv3XjMzG6Ei4iFgA6oDa+amcbLG14AfA9tExEttvuslYEvgXw0+nkBaXM0XUo8GPtbO80eCiLgGWBe4vI3Lp5MW4Hcn/TmXPd3m+16MiJ1JG32frbhsdtKifrNArTtoI0mkmZkNTkRcQdqgekeDj0VK1tBoY+pzwEcj4utD2LxhExE3kwKszq+4pPY9oSqAuuV8d0QcQfoecm/FJZNJ/WWzIO3HSHP0ZmY2BkTEP4F3Ag9nH81L4/HTa8ChEdGogF3+7GmkhMw3N/hYxTvyIN9nSQHWVf1hq3e+FhGfBfYEnqq4bA4aJ4eC5sUGuxIR+1Ek4ih5H/A7Sd6ca2Zmw6If48OIOJ2URLlRLNisxfPz/vhMYDsc92RmZmNARDwKvB34NinWqZG5SPO+zZI1NopzavS+Q4F3AfdVXDILsCDN+/dHaS92y8zMRo99SWOtYRMRV5OSJTaKraqNP/O+8GpgM6rjgDp1UpefNRURM4pY7d1I/Wgjs5L64GbzwfcDT3bbDjMzGxH63uf2I9aow/bcSYoJu7t0egpwnqTNm9z3QkS8l1QE77mKy9qJAb6NFJPcrI0XAvvgWGEzswEi4ilSDNGRQFXeg3moTtZY2a8UeRR2BQ6vePasVCdrvAxYLyLurvh8zBhrcVwjxJ9JCT/LgpTA0sxsyHVdrc3MzMysSkRMJ00Knw8gaS5SwqZlgUVIg7sgDeoeIg0E746IrjbLFIkxjgKOkjQPKYHEoqQJ29mB50kJIe4Gbi+SOnX6jnOAcyQtDaxXPH+O4tn3AtdGxMPZPT8Dftbl7/RCcW9X95uZ2dgWEf8C9pQkYFVgRWAh0iTqS6RNq3cBt0TEMy2edSBw4BC185/AxwEkrUCq0LMgaYH4NdLC6yOkZMr/HExFpIhYo/VVlfd+jFGUMMvMzNpX9INbS3oH8GVgI1JQcCMvAOcCBxcBT52+6z5Ja5GqzO1N2ojTyN+AAyLizwCpOx8dIuJ2SZsAmwM7AW8DFif9rk+TknRdAJwYEf8FkLRw9piq5BpV7/y+pJNIgW+7ACu3uoU0z3ABcEZEXNvJ+8zMrHsRcaukVYCPkIJgV21y+cPAqcBhxQbXMauYN96i6EO/AGxK882zLwAXA78A/tDmOy4sxt3vBz4KrEP1d56af5Oqxp8JnDOYIkRmZjbyRMT1Rb98EDCVgcG3kOZoLwG+HBHXdfDsByWtSyrity9pTbaRF4BfAQdFxKATQ0TETyWdQUrsvyuwfItb7iD1pccP9t0V7TlE0nPA90hBzgDvAf4kadtivdfMzKyv+jE+jIgfSvor8A1SwqoqfweOiIjfwOiaCzczM2um6Cv3k/QD0hh1e2CZFre9ClxHiqs+NSJu7+B950lanpQUa09S4o5W/ft9zOzf/+z5XzOzsaWYe9xO0gakQjJvJe3VqSpeO1TtOF3SP0jjw21pXFAHUgHc7wPHRcQMSUv1qAmXkPq8/HmPkJI3DEpEnCLp96T+d3dgdap/x5o7SQXjfwdc0u0eKTMzGxmGq8/tR6xRh+25T9KGpD7uTcXpOYA/StolIirfGRHfkfRTZsYAv6nq2totwE2kGODT213HjohjJf2RNHbeuHjP/KR18lb9t5nZmBYRrwCflnQUsD+wDWkfapUZwLWkGN+ft3h2AF+R9DPga6TkgVVJGmcA1wDfiYhhLULQb2Mxjms4RcRrks4H9iqdvrDYc21mNuTkOT8zMzMzMzMzMzMzGw+KggLrA68jLTLPIFVC/zdwTVGAoBfvmYWUzHAF0oLna8B/SMn+x3wVwDJJ7yct7Nb8rqia2+3zFgbWAhYmJYKeTCqm8CTwT1Khhme7b7GZmfWKpMVIiSEWIfWHzwOPAbcDN43XzSmSZiX9ubyB1JfNSfqzqRU0uHWw30kkzVG8YwnSn/1cpGCrZ0gFiG7PCxCZmdnoJamuT40IZZ/PThqjrkQq+vMc8ABwZUQ8Msh3TwbWJBXrmZ+UuPAJUn//18EU6Wnj3W8gbYpaiNTfvUoqGHgPqT8d1cHFZmZmvTDU48Ni7L8Bac59LuBF4H7guoj49+Bab2ZmNnpIWhJYgzTnuwApGcRzpDnxu4A7I+LFHr1rTlL//joG9u/3AHd4/tfMzPpN0vzAhqQ10CnAy6R56Bsj4o7hbFuvSJoHWBdYlNQHz05a530auJs0xn5i+FpoZmZjVT9ijfqliAFemxQDvAAzY4CfIMUA3+EYYDOzoaVUZW1VYEVSzM28wEvAU6S5zFsi4pkunz2J9Pf8MqS/62cBHgceBK6OiKcH/QuMcOM1jqufiv/P7gcWK53eISJ+P0xNMrNxxgkbzczMzMzMzMzMzMxsSEg6Fdi5dGr/iDhiuNpjZmZmZmY21rQK9DUzMzMzMzMzMzMzMzMzMzMzM7Oh5TiuoSdpR+D00qkHgTdExKvD1CQzG2cmDHcDzMzMzMzMzMzMzMxs7JH0RuC92ekLh6MtZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZjZqfTI7Ps7JGs2sn5yw0czMzMzMzMzMzMzMmpI0S4fXL0iqWjexdPq6iLi+pw0zMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzszFL0qbARqVT04Hjhqk5ZjZOOWGjmZmZmZmZmZmZmZm1cpGkH0hardlFkiZI2g64Hlg5+/jQIWudmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmY0pkhYBfpKdPjEiHhmO9pjZ+KWIGO42mJmZmZmZmZmZmZnZCCbp78CqxeH9wHXA7cCTwAxgXmBFUrW6JRo84qcRsWcfmmpmZmZmZjauSKoL/ooIDVdbzMzMzMzMzMzMzMzMzMzMzMzMxiPHcfWGpLmB3YrDOYDlgfcDU0qXvQCsEBEP9rl5ZjbOTRruBpiZmZmZmZmZmZmZ2aiyZPHTrl8CnxiitpiZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZnZ6LcAcEyLa77gZI1mNhwmDHcDzMzMzMzMzMzMzMxsxLsDiJZX1fs38LGI+GBETBuCNpmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZnZ+HBERBw73I0ws/FJEZ3urzQzMzMzMzMzMzMzs/FG0uuBLYD1gJWAJYH5gNmAl4EngYeAa4BLgLMj4tXhaa2ZmZmZmdn4IKku+CsiNFxtMTMzMzMzMzMzMzMzMzMzMzMzG48cx9UbkpYC7i2dmg48DFwJHBMRVw5Ds8zMACdsNDMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxtgwnA3wMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzGykccJGMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzs4wTNpqZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZllnLDRzMzMzMzMzMzM/p+9+46SrCr3Pv59YGCAgWGIIpKRICJJJSlBRUXwgt5rRi+YBbOiGFHMXtM1oCKoiLxmr4JgVnJSMgqCCKOAEiTHAYbn/eOcZk7vrqquqq7u6un5ftbq5exdO/UJ+5xaq/0hSZIkSZIkSZIkSZIkSZIkSZIkSZIkSSoY2ChJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJklQwsFGSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJKlgYKMkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVLBwEZJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqSCgY2SJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkFAxslSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIKBjZKkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVDGyUJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqGNgoSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUMLBRkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSpYGCjJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSwcBGSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZKkgoGNkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJBQMbJUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCgY2SpIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkFQxslCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJKhjYKEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSVDCwUZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkqWBgoyRJkiRJkiRJkiRJkqRJFREHREQ2fo4e9po0GFHZJyK+FRGXRcStEbGwON+7D3udkpYcETG/2IM2GPaaNDNExJXFtfXoYa9JgxcRxxbn+aXDXlNTROxRrO+3w16TJEmSJEmSJEmSJEnSTGdgoyRJkiRJkiRJkiRJkhZ7EXF0EVwy8vOhPsd7XjHO/AEvWVrsRcS6wNnAccB/A5sD85iEv0uLiA3a3OPlzwMRcVdEXB8RF0XEzyPisxGxf0RsNOh1SdJ0ExEf7HK/HMTP/GH/vupeRBxWnL8b+hznly2uhbf1Mc42LcZ5Wj9rkiRJkiRJkiRJkiRJkibTrGEvQJIkSZIkSZIkSZIkSZpEb42IL2bmTcNeiDSTRMQc4NdUIY3Tyaz6Zw7wCGAr4FkjH0bEFcD3gCMy859DWaE0DUTENsBzGlUXZuZPh7UeSVPilKK8ZkQ8JjMv63aAiFga2LnFR7sBn+1xPbsW5QeAs3ocQ1OgDuSc26j6bGbeMaz1SJIkSZIkSZIkSZIkTTUDGyVJkiRJkiRJkiRJkjSTrQi8G3jbsBcizTBvY3RYYwK/AM4Abq3LI66YwnWNZ1PgUOBdEXEE8P7MvH3Ia5KGYRvgA43ytwADG6WZ7SzgfmDZRt1uQNeBjcC2wEot6p8cEZGZ2eKzdsrAxj9m5j099NfUeRvwqEb5KMDARkmSJEmSJEmSJEmStMQwsFGSJEmSJEmSJEmSJEkz3YER8ZnMvG7YC5FmkFcV5Vdn5teneA2fY2wYZAArA/OAValC6bZmdDgVdfmNwHMi4nmZ+YdJXqskTZUTgOt7aP+VonwMVbBfN+7sYR4NWWbeGxF/BJ7UqN4V+GoPw+zWpn5V4HHAxT2MtUtRPqWHvpIkSZIkSZIkSZIkSdKUMbBRkiRJkiRJkiRJkiRJM91ywKHAa4e9EGkmiIh1gfUaVdcB3xjCUo7PzJPHaxQRs6kCqd4E7E0V6jhiXeDkiNirm7EkabrLzHOBc7ttHxFlYONJmXn0QBc1Q2Tmo4e9hgE4hdGBje0CGNvZtfHv64G1irG6CmyMiMcAa7ZY29Bl5kuBlw57HZIkSZIkSZIkSZIkSZo+lhr2AiRJkiRJkiRJkiRJkqRJcH1RfkVEbDyUlUgzz+ZF+YLMzKGspAuZuSAzf5OZ/wFsDVxWNFkeOC4iNpr61UmSNKXKUMS1u31HjogAntyo+jmjn6m70r2y7YPAGT30lyRJkiRJkiRJkiRJkqaMgY2SJEmSJEmSJEmSJEmaiY4HLmmUZwGHDWkt0kwzryjfOpRV9CEzLwG2pwqZapoLHDP1K5IkaUqdQRWO2LRbl30fB6zaKJ8KnNYo9xLYWM55Xmbe1UN/SZIkSZIkSZIkSZIkacoY2ChJkiRJkiRJkiRJkqSZKIH3FXUvjogth7EYaYZZoSg/NJRV9KkOhHoxcGXx0ZMi4r+GsCRJkqZEZt4NnFdUdxu0WLY7jdGBjWtGxOZdjrVLUT6ly36SJEmSJEmSJEmSJEnSlJs17AVIkiRJkiRJkiRJkiRJkyEzj4+Ic4Ad6qqlgI8AzxneqqZWHZrzBGBtqr8VugH4Y2Ze3EXfAB4PbA2sAdwP/As4JTP/OQlrXQfYEVgfmA1cD1wNnJ6ZDwxwntn1POtS/V7LAjcB19Zz3TOouRpzBrAN8DhgLWAZ4BbguMk4lo15ZwHbAxsCa1Id15uozuOZmXlbv0MPZoXDk5l3RMTLgLOKj94G/LifMSNiC2BLqutqZapzfBPVPfePCSy33XybAFsBqwOrAg8CtwJXABdP4PwOVUSsBewMbAAsD9wOXEx1zT7YRf9HU133zX3v3My8ZIBrXBp4IovurRWAfwP/BM6YrGMfEZvV865NdR/+G7gU+ENmLpyMOXsREUsBm1Jdl48AVqIKdL2H6jxcDVxah6YOTb037kB1v65Gtb7rqI7j34e5tpmq8RzcjOqemQPcTHVdnJ2ZN0zSvCsBT6K6Llei2pevBU4b9h4ZEWtQ7VVrUu3js6j2u/nAJZl5zSROfwqL3o8BduuyX7PddZl5VUSUe89uwF86DRIRGwPrtFhTz+r7eWQ/XoPR+/HpmXl7P+MOQkRsTbXPPJJFz6OLgAsyMyd57jlU1/5mwFzgDqr3v1Mz88bJnLtb9b6wEbAF1Xv5XKrQ/VuovgecM+i1DuOc1N89dqK65tek+h5wE3AN1TvDwL97SJIkSZIkSZIkSZKkwTOwUZIkSZIkSZIkSZIkSTPZe4HfNsr7RsT2mfmHQU8UEaNCPjKzp1C7iDga2L9R9fLMPLqf9hHxQuD9wGPb9L0IeFtm/r7FZ8sAbwDeDjyqRfeMiF8Bb8nMy9v/Rt2JiCdTBWm2Cwu6JSKOAT6QmXdMYJ5dgXcAT6UK82llQUT8BvhgZp7Xw9jzqYImR2yYmfMjYgXgYOBAqqDG0r+An3Y7Tw/r2QQ4FHg2MK9Ns4URcSbw6cw8fpzxdgdO6tBk/4jYv81nG2bm/M4rHo7MPDsiTgee3KjeOSI2zsy/dTNGRMwD3gnsB6zXod2lwJHAlzPz/n7XHBHrUl3Hz6EKN2rnoYg4D/gRcHS7wKOIOJnR995TMvPkHtZzAPDNRtW3MvOAftpHxOOBjwFPp3Uo6PUR8SHgq61ClSJiH+ADwHZt5r6Uat/7VfvfqLM66OndwJ5UoZytLIyI04CPZObvehj7ZNqci4h4HtWevlWb7rdExGeAz2XmvR3mOIDRx7+p030MVVjv7m3GXR04hOo+eGSHMaC6Nv8EnEh1bV4xTvuBqffkdwMHUYWctmpzNnBoZv5mnLGeBJzeqLoTeFRm3tnHunYCzmxU3Q2sPZFn3nRRB7C+F3g+VYhnKxkRFwBfotoTHupy7HWoAs9GXJeZ69SfrUv1bvEiqmDm0v0R8XPgkF6uwYi4Eti4UbVJZl7ZQ/8VqK6/l1Ldz23fFSPib8AJwFGZ+adu5+jSqVTPrhEbRMS6XYRE7tL492kAmfn3iPgHi56BuwJHjDPOrkV5IaPvp3FFxLbAu4Bn0n4/fjAiTqXajzu9x5RjH0u1n414WWYe22XfpYDXUQVAb9ym2TUR8Wng8MxcGBF7AM0953eZuUe36y3mXws4DHgZVehxKSPi91TXfsd33Yi4ltbfBaD6HTp13yUzx5zTiFgVeC6wN9V1sNo4a7gU+ALV82JBp7YdxhjKOanfnw+m+u7R6lwA3BcRv6b67nFBL+NLkiRJkiRJkiRJkqSptdSwFyBJkiRJkiRJkiRJkiRNljosqwwl/Ogw1jIVImLZOmTme7QJa6xtDfwmIt5Y9F+bKjTqs7QPaAmqsLJz67DFiaz3UKrQoHZhjVCFar0FuLQOtep1jjUi4gTgFKoAw3ZhjQCz6zZ/jIgvRcTSvc7XmHcz4EKq0JxWYY0DF5WPAZdShUG1C2sEWJoqeOm4iDgpItacijVOQ4e3qNu7m44R8Xzgb1QBcG3DGmtbAJ8DLo+IHXpaYTXXrIj4H+BK4I10DmuE6m8Dnwh8Evh+r/NNtYh4M3AO8AzaB5itBXwZ+E7z3oyIZSLim8BxtAlrrG0B/DIi3tLH+laoQ3IvAF5I+3AwqO6t3YHfRsSP6oC2vkTE8hHxXeCHtA9rhGqf/ChwSkR0DL8atIjYG7iCKpRqvLBGqK7NrVgUnDglImJjqvP3PtqENdZ2BH4dEV/r9AzIzDOo9vgRKzE64K0XryvK35khYY0HUe1Zb6B9WCNU9/x2wDeAi+rn50TmfQbwJ+C/aR3WSF3/HODiiDhwIvP1sK6XAFcDn6J6Dxsv2Htj4M3AJRN5H2njdKqQxKZO72Ij7zXN83hq49+ndTtOrQxsvLDbaz4i5kTEt4HzgBfQeT+eRRWW9/uI+H5EtAvNG4j6XepMqneLs5hxiQAAIABJREFUdsGAUD3DPw+cPMg9OyKeTvUO+BraBwQG8DTg7Ih42aDm7kZEbAdcDxxFFdrYze++BfBV+twbhnFOImLNiPgFVdj53rQ/FwDLAfsA50XE5yfhXpckSZIkSZIkSZIkSQNiYKMkSZIkSZIkSZIkSZJmuvcU5T0i4ilDWcnkCuDbjA2Muge4s0X7pYDPR8SzAOpwklOAJxTtbgfubdF/ReCEOuSx98VGHEwVZtgMLFoI3AZkiy6Pogpb6zrsrg52OZv2AXz3tJkvgNcDP42IWd3OV6z1d8AmLea7q4/xxlWv8ztUIWit1rwAaBeGtDtwVh1otqQ5qUXdjuN1iog3UQUhtgp+WwjcSuvreAOq4Ki9ul1gRKwC/Ap4B+3Dx+6g9X0O44eCDVUd6va/VEGHI+6n/TF8EfCRum9QBdQeULS5h/bX++fqkMFu17cWVTDZ/rQ+lvfVa32oxWf/BZwUEXO7na9hFvB/VL9v0wKqfauVJwI/ro/LpIuIXYGfAKu0+DipzsHNVOdzmNYAfgtsWtTfSXX+Wnk18O1xwrPKwNcyeHFcEbEqVehc01d7HWe6iYhPUB2fOS0+fpD21/CWwBn9BNvW8+5IFd5a3nO31fOWZgNfLgOsB6kOU/4k8P+AduHI99J+H4EB7+OZeTtwUVFdhiiWyiDGdoGNj4qIjXoc65Rx2gMPB4ufRhVK3et+/ALgdxGxUjdz9Soi1qAKiW937d7J2PfpJwMnUl2HE53/6cAJjN6Pk+p4tNqDZwHfiohuAjYHZQVgmTafLQD+TfX8bmUz4JyIKPfxtoZxTiLiMVQB1Hu2aTLy3WNMV+BNVM9wQxslSZIkSZIkSZIkSZqGDGyUJEmSJEmSJEmSJEnSjJaZ5wA/K6o/Ooy1TLLXsSj06QqqALM1M3NOZs4F1gLeCdzd6BPA4RGxDFXY36Pr+l8DewFzMnNeZq4AbM7YEKmVgU/1sdbHAR+v//0Q8BXg8cDszFwFWA54BvDzot9c4CcRMW+8CeoQrF8DzdCghVThbs8CVq6PzSpUATJ7AD8thnk2dTBcj75GFdoIcD7wYmC1er6VgHnAS4Cr+xi7nQ8xNtjtVqpzvmFmLpeZK1MdwxcC5xVtN6I6tq0Caq4ADmz8HFN8fnbxefPn5n5/oamQmTcA84vqrTv1iYhnUgUMlmGjX2LRdbwqVdjPU4AfFkOsAPwgIspAz1ZzLV33f2rx0e1U+9iO9Xwr1/f5bGA74A1UYZTtwr+mi62ojiVU1+t7gE0yc+QYrkh1vc4v+h1cB7K+G/jPuu5yRu97KwOPoAq6LINSR/a9jur74USq89p0Yj3vGpm5fON8Pwn4JtX1MGJ7qj2hV4exKPDpL8ArgLXre3kVYCWqPf+Kot9ujA2wHHEW/d3HBwKfbTauQyGPYHT41u3Ah6muweXr63L1zJxNFSK2C9WedApTe21+kSosFarj9d/Aqpk5NzOXB9ajuk5uLfq9GHh7h3H/X9Fn64jYqce1HUD1zBvxh8w8v8cxppWIeAVwSFF9H/AxYAtg2foaXp7qXeM3RdvVqAKT1+hx6tnAsSw6nr+txx+5Z2ZT7e+fZ2x44+cj4sk9ztet91Jd900Lqe7BvaneR1ao95FZVGHPL6EKBb6byVOGJI4X3NcMdLwZuLRRPq1o23asiFgH2HCctbTqtxzVe+G2xUcnAM8FVm/sx8tRBe8dw+i9Zieqd87J8E3gsUXdhVTvZvPq/WYFYG2qd/Zr6jY7AO+f4NxrU10vywIPAF8Gdqa611at9+At6/pmEHIAR0ZEu/8/wbtY9AwoQwbfTednxpUd1ns3VbDqa6mekSvWz7Y1MnMOVRj2M6nu5+b5W5nq/Wnc53dtSs9JRKxOtZ9t0Kh+kOo71p6M/u6xPPB04PhimH2pnv+SJEmSJEmSJEmSJGmaicxW/wFqSZIkSZIkSZIkSZIkafEREUcD+zeqjsjM1zU+fxxwEaMD1vbJzDLIcaT98xgdsvb3zNxgnDWM+kOczIx2bdv0P5rRv8PLM/PoHtqP+C6wf2Y+0KbfbsDvgKUb1T+hCrt5CHhTZh7eYd73MjrE8AHgUZl5U4c+B1CFppTuAZ6dmSd16PsW4HNF9dcz81Xt+tT9fkYVuDjiWuD5mXn2OP1eWq91Vl2VwI6Z+YcOfeYD67f46NPAIZk5qcFkEbEzVVhSM3DnYuAZdSBhqz5LU4VWvb746LOZ2SmgrNX5/FZmHtDjsnsSERswNuDyKZl58gDGPg7Yp1F1d2au2KbtPOAyqgDUEbcDe3a6tiJiP+BbjL7vzgW2zw5/xBcRH6EK+2r6JbBfZt7Srl+j/ybA0zKzDFsd+fxkRgdr9XRMe70WOuwFl1Adw3+26bcW8EdgnUb1z4GnsSik7RU97nvPzcwypLXs9yVG3yO3Ay/NzBPG6fd04MdUoYojnp+ZP+rQ52Rah5x9DXh9ZpYBcyP95gEnMzpo9ILM3G6cNR7ABO7jiNiBKuRxxK3ADpn51y77bwysn5m/73bOHtY2n9Z78k+Al2TmfW36PZLqOnlMo/o+4HGZ2TJ8LCI+A7ytUXVMZrZ6Nrdb61+AzRpVHZ/9k6F8f5nIGuq9+k/AnEb1dVT70OUd+h3M2ADo4zNz3w591mFRwFrpfZnZNpy7fm7+ktH36BXAVpm5oEO/K4GNG1WbtLs26vZPowqPbj6f5wP7ZubF7fo1+s+j2oM+Puh3iYjYl7FB1Y/MzOvbtP8HsG5dPC4zn9P4LICbqMI2AY7OzJe3GWc/qj17xENUYYtlYGrZ76tUAX8jbqV6Fv5inH57Aj9i9DXZcf+PiGOB/RpVL8vMYzu0fwlVgGvTeHv3ilShhWUgM8DvMnOPDvPtwdigU4B/Ub1Xtw19jYhXAUcW1XtnZhmSXva7lkVh5ADrZua1nfq0GOMxVCGF38zMO7vssyNVKOdqjeqXZmZ5vMt+U3pO6v6/YFHQMsA/qJ79bb9D1P0OAI5i0TvKQ8ATF/fwXkmSJEmSJEmSJEmSZpp2/0VMSZIkSZIkSZIkSZIkacbIzEuA7xXVH64DZmaSM6lCZVqGlgFk5inAMUX1c+v//USnsMaRNkAznGgZoG2g0jhe3imsESAz/5exgY0H1EFNLdUBSc2wxtuBPcYLa6znOxY4pDkc8M7x+rXw/cx8x2SHNdbex+i/BbsReHq7sEaAzFwIvJEqwKzpoIhYffBLnNbKkKg5dWhPK69hdFhjUgU/dby26mChMgjzCcBe7frUIYVln18C/9FNWGM971/bhTVOI7cAz2oX1ghQB4iVwZV7UYU1nkaHkNq6/ynAt4vq53VaVB12eWCj6kGqIKyOYY31fL8BDiiq3zVevxaOz8zXtguXque6DSgDbLetAxEnUxkIeUS3YY0Amfm3yQhr7OBC4MXtwhrrNf0LeCZwR6N6OeDgDuN+mWofGPGCiFi1mwVFxFMZHdZ4K/D9bvpOY29jdDDeAuCZncIaATLz01Qhx037RMTWrdqP48hOYY31fGcCLyqqNwWe38d8nXyS0c/nfwE7dxPWCNX9nZkfnaR3idMYfe0C7NqqYURsyKKwxpG+D6uDh09vVLUKn203x8VdhDVuTvX8HfEAVchgx7DGem2/BF5RVPezH3dySFH+LfC6cfbuu6jCoq8a0Brupwqj7xjyl5lHAb8qqgd93beb+7LM/EK3YY11n7MZe6+WYeOtTOk5iYhnMjqs8Vaq7x4dwxrreY8G3tOoWor+vntIkiRJkiRJkiRJkqRJZGCjJEmSJEmSJEmSJEmSlhQfoArcGrE18MIhrWWyvLEO4hvPsS3qbgA+NF7HevzvFtWP72LO0u8z8wddtj2Uan0jlgZe3aF9GXLywfHCogpfAP7WKD83Itboof/9wJt7aN+3iHg0owNiAA7JzBvH61sHLL0BuKdRvRydj+1MdFuLujllRUQsxdiQoGPGCx1t+CJQBim9qUP7t1KdjxG3UgUTtg0bWkx9JDOv66Ldj4BWYXtv6jLMrAxsHG/fOpjRf2N5eGae3q5xKTP/DzilOV9ElCGHnTxAd6FUZOa5jL22ntDDXP1YpShfPcnzTdQbM3PBeI0y8xrgw0X1fhGxUpv2f6MKUh2xHGPDOts5sCgfnZn3dtl32qmDbg8oqj+dmX/ucohDgX8UdZ32yFZuY2xQW0uZ+XPguKK6PCd9i4inM3afeWUdDDp0dfDvJUV1y8DGFvWntmjTDHHcsEOwdjnWKS1bjfYOqgDtEV/IzLO66AdA/b55RqNqh4jYqtv+nUTEjkBzrATeUL9jjbeuuxkbzNyvI+pnQVdti/JkPy8mJDN/y+hn3PYRMeY9bcSQzkn53ePQXkKMqcLp5zfKz+s2/FeSJEmSJEmSJEmSJE0NAxslSZIkSZIkSZIkSZK0RKhDM44uqg+LiFlDWM5kOD8zy8Cudv7You7b3YRZten/mC77NX2l24aZeRdwTFH97FZtI2J14OmNqnuBo3pZWB2I96NG1VLAk3sY4meZecP4zQbiWYwOMboZ+E63nTPzn8APi+q9BrCuxcldLeqWbVH3WGC9ou6L3U5ShwqW7Z8SEcu36fK8onxkN0Gci5mFwDe7aZiZ9wCXFtXnZuaFXc5VBlltWodwjlHXl4G+XZ/rhu8V5d166HtiZl7bQ/szivLmPfTtRxl0Op0Dv/7cS9gm8HWg+TxcEdi9Q/vDi/JrIyJatqxFxFrAvkV1GaK2uNkFaAZbLgS+3G3nOqzyyKK61+fR9zPz1h7al+vbeYAhaeUefn5m/mJAYw9KGZbYbo9q1t8FXNCiTRniOGasiFiTsXtTx8DG+j39BY2qZOr3406eWZRP6TEk/GfAIEI8e9k/pvp5MQhnN/69NJ2fOVN6TiLiEcBTG1V3Ad/oYT4y8wHgx42qpYEn9TKGJEmSJEmSJEmSJEmaXAY2SpIkSZIkSZIkSZIkaUlyGKNDmDYF9h/SWgatDMppKzPvZGzY1mk9zPWPojyvh74ADwIn9tjnuKK8VZugu10ZHWB4Rh342Ksy/HKnHvqe1Md8/dq5KJ+Ymff3OMaPivITIqJVYOFMtVKLulbhpeWx/kdmntfjXP9HFTY1YhlahA5FxDrARkX1sT3OtTi4JDPLvaiTcu/pet/KzDsYve8tBcxt03wbYOVG+crM/Fu3czVMZB/pGGLWwlVFudd9uVd/KMqviIiD2oVgDln5/OioDvwrr60dOnT5BaOP/6aMDu9q5VVU9/+I3/cYKDYdlXvkOXUocC/K59FaEbFhD/17OtfA7xgb2rt9j2O0s3tRno57eLnPPDYiVmvRbtfGv8+qg61LFwB3t+nTri4Zfx/fjio0dcTlmfn3cfq0MpH9uJMdi3JPoZyZuRD49QTX8O/M/HMPc97I6HM1KyLmTHANfYuI9SPi6RHx/Ih4RUS8rvxh7DtRGaDdNNXnpAz/PL0Ome7VZF2jkiRJkiRJkiRJkiRpAGYNewGSJEmSJEmSJEmSJEnSVMnMayPiK8BbGtWHRsSxmdkqoG1x0mt4zV2MDvTqpX8ZbtQq8K6Tv2TmvT32uRB4iEX/kdpZwBZAGZhXhmrNroNeevWYovzIHvpe0sd8/dqqKJ/bxxhln+WATYCuw38Wcyu3qGsVtDPhY52Zd0TEFcBmxbhlYNXji/IdzMzz0c++NdH+zX1vJcaG18LYfeT+PveRNYtyL/vI/B7nurMotwujHIjMPDcizmVR4OjSwOHAOyLie1QBWedMk2drGYLVbZ89GuWt2zXMzIfqd4tPNapfRxUGOEYdavnqovorfaxxuhnE8+hyqmu5+V6xFXB1l/17OteZuTAiLmZ02OTWwC97GacUEStSBXc2nTmRMSdJGfYdwC7ATx+uiFgb2LhDHwAy88GIOItF900ZYteq7s+Z+e9x1ljuxw/2uR+vVZR72Y872bwoX9jHGBcwsQD5fgIs7wSaIY1zGR3iOGkiIoB9gJcAe9Lf86pTKPFUn5PyGl2+z2v0sUV5UNeoJEmSJEmSJEmSJEkaAAMbJUmSJEmSJEmSJEmStKT5GPAqYMW6vB5VsNLnh7aiwbi9x/YLi/IdE+i7dI9z9xwsk5l3R8TNwBqN6tVaNC0DeXapfyZq1R7a3jKA+bpVrmt+rwNk5vURcR9VUGO7cWey8ne9KzPLYMBW7eb3Od/VjA5sbHWs1yjKV2fmQ33ON51N5b7Vqn+7vavcR7ZgMIF6vdxXEz02ve7L/dgfOB1YpVG3AfCu+mdBRFxQtzkZOCkzW4WhTrZ+wszKPq2eN01fBz4ELF+X942ItTLz+hZt96J69xhxPXBcH2ucbgbxPMqI+DuwZYdx21mQmTf0OifVuW4GNo53rruxeou6vw1g3IHKzJsi4lKqPW7EbjQCGxkbslgGDJefjQQ2bhYRjyjOya5F+1O6WGa5H2/J1O/HnaxSlP/Vxxit9ole9Pq8gOE8M4iIx1Ltl2XIYa86hcVP9Tkpr9HdaB1Y2qsl6V1ckiRJkiRJkiRJkqRpb6nxm0iSJEmSJEmSJEmSJEkzR2bexNhwxvdExJxhrGeAcsj9e9FryNqIMpCmDGSByQs3WaGHtq3C/iZLeQwGdWyXpJCYrYtyu3C3qTzWZWDYbX3ONd1N131rOuwjU7kn9yUzLwW2p33o2mxgR+Bg4ATgxoj4dkQ8boqWOKKfe7Wb583DMvNW4LuNqmWowqFbObAoH5WZD/S2vGlp2M+jyXy36FWr0Mfpuo+X928Zqtgs3w+c02GsMszx4b4RsQqjgzihCnIdz3TYj1uKiGBscOCdfQzV77U7Yto/LwAiYjuqa2SiYY3Q5v8DMaRzMm2vUUmSJEmSJEmSJEmSNDgGNkqSJEmSJEmSJEmSJGlJ9Cng1kZ5TeDNQ1qLuhddtFl2iHNPB4MK7Vkswn8mKiLWAtYrqi/ssvtUHusl4nxMI0v6PtK1zLwyM3cHdgGOBK7p0HwO8FLgooj4XEQsMwVL7Fc/5+pLRfnVETHq73QjYn1gz0bVQuBrfcy1OFhcnkcz7r7s0alFeZuImNso79b49x8z874OY50NNMNHm313YezfrZdztzJt9+PMTEb/vtDfeifrd5w2ImI28D3GBqL+EXgv8Axg8/rz5YGlMjNGfoCPdjPPkM7JtL1GJUmSJEmSJEmSJEnS4BjYKEmSJEmSJEmSJEmSpCVOZt4O/E9R/Y6ImDeM9dSWpL/lmTt+k6763dqizS1F+f3NwJcJ/Oze55onW3kMVu5znLJfq2M7Ez21Rd2ZbdpO5bG+uSiXAUfTxUzdt8p95OsD2kc2GMYvMxUy8/TMfE1mrgdsCPw3VRDhZS2aB/AW4OtTtLx+njndPG9GycwLgLMaVesBexXNXsPo++bEzOwUcrk4GfbzaDLfLXpV7uEAw3zH7OSUorwU8GSAiFidKkRvRMeAxcy8Fzi3UbVrm38DXJaZN3axvnI/PmJA+/Gju5i7G7cV5X6u+37vlcXJ/sAmjfIC4PmZuX1mfiwzf5OZl2fmbZl5Xx282LRiD3NN9Tkpr9F3D+ga3aOPdUuSJEmSJEmSJEmSpEkyU/9YUpIkSZIkSZIkSZIkSRrPF4DrG+V5wDsnMN6oYJGIWLrH/tM1yGcyrN9rh4iYA6xWVLcKRLqpKG/U61yLmfIYbNDrABGxFrBcUV2Gz8xUr29Rd2KbthM+1rUNi3KrY11exxtGxGT8vd9DRdl9q7Kk7SMDlZnzM/PbmfnazNyC6l55L/DvounLIuJpU7Cknp85Lfq0et60cnhRPnDkHxGxDPDK4vOv9riu6WwQz6Ng7LHv9nk0OyIe0eucLebr9lx3Ul7rAIMKCByozPwX8Neierf6f3elClgd0TGwsXZa499bRsSqjbGayqDIdqb7fnx9Ud6sjzE2H7/JYu8/i/JHMvNHPfRfvYe2U31Opvs1KkmSJEmSJEmSJEmSBsDARkmSJEmSJEmSJEmSJC2RMvMe4GNF9Zv6DPsBuLMor9hj/34CpRZXm0fE8j322YbRf+/0IHBpi3YXFuUyIGimuaQoP6GPMco+9wFX9LecxUdE7ATsXFSfkpl/b9Nlwsc6IlYCNimqL27R9NyiPBfYstf5uuC+1Vq5j2wfEWWoqbqUmX/PzI9RXcNXFx+/dAqWsN0A+rS6T1v5IXBjo7xnRIzcJ88Fmu8YVwO/6mNt09UgnkebASsVdd0ee+jxXNfh2ltNYL6WMvMu4PKiunzeTCdleOLIu9NujbqFwJldjNUMbAxgl4hYEdh2nDnbKffjHSNi2S77ToXyeb1DH2P002dxU95nx/bY/4k9tJ3qc7KkffeQJEmSJEmSJEmSJGmJZGCjJEmSJEmSJEmSJEmSlmRHAM1wtjnAe/oc67aivGG3HSNiVSYnjG26mgXs3WOffYvyJZl5b4t2vynKG0dEP2Fdi4syPGnvPoKM/qson5uZD0xgTdNeRMwFjmnx0Wc7dCuP9Xp9XFv/yei/23sA+GPZKDOvA/5WVE9GsF3f+1Ztt/GbLJbOAO5plOcAew1pLZPlwaK89GRPmJk3AP9TVJchXpNhn14aR8QqwC5F9Tnd9M3M+4EjG1VLAa+p/31g0fyIzHyol7VNc+UeuUNEPLLHMcrn0fWZWYZ8dlK+K4znaYwNqv1Dj2O0c3JR3m9A406GMjzxCRExh9HBcxdl5h1djHUGkI3ybsCTqN79Os3ZzmnAgkZ5JWDPLvtOhfK6f34dBNqViFibxeNZOtFnxhpF+ZpuO0bEpsCmPcw11eek/O6xWURMxbNNkiRJkiRJkiRJkiRNIQMbJUmSJEmSJEmSJEmStMSqg5U+VFS/Flivj+H+UpSf1EPfV7Pk/S3P67ptGBErAi8rqk9o1TYzr2Fs2FJ5jmeSXzA6GGk14MXddq7DtJ5fVJ84gHVNW/X19F3g0cVHJ2Xm8R26/pnRAa8Ab+xh3gDeUFT/PjPva9PlB0X5VRGxZrfzdanvfSsidgC2HuxypofMXMDYPeYDETGT9uk7i/LcKZq3DN+bMwVzbhkRT+6h/SuB2Y3y3YwN3+vkq8DC5ngR8Thg90bd/cA3ehhzcXAao6+rpYGDuu0cEctRvQ819fo8emEduNmtcn1nZebNPc7ZTrmHbxcRzxrQ2INWhifOAp7F6EDV07oZKDNvBf7UqNqVseF3V2Tmv7oc717GXgcfnEb78U+AZoD4I1gU0tqN9zIFgbkDMNFnRhkEvnIPfd/R41xTek7qUNnzi+qZ/N1DkiRJkiRJkiRJkqQl0nT5YxVJkiRJkiRJkiRJkiRpWL4FXN4oz6b3YBAYGxL4ijqgraOI2BB4dx/zLe6eFhEv6LLth4C1GuWFwJHjtG/aOyLe3Mvimro5j8OSmVdShTY2fTIiVu9yiC8wOjDtXjof28VaRGwJnAPsVXx0C3BAp76Z+RBweFG9f0SUQVTtvB54QlH3hQ7tPw80wxxXAb4VEbO6nK8b5b61d0Q8YrxOEbEsY4/FTPNhRoehbgV8pt/BpuE+ckNRfkwvnSdwHW5elK/vc5xefTEiZo/XKCLWAd5fVH8nM+/odqLMvBY4rlH1CMaG9/04M2/qdszFQWbeBXyzqH5HRHR7bX0QWL+o+2KPy5gHfLKbhhGxF7BvUf3VHudrKzN/D/yxqP56RKzVqv0w1WHXZZjqexj9d+an9jBkM9xxG2Dv4vMyIHI85X68LV2e51YGuR/XAZXfK6o/Xoe0jreO/6CHAPMhm9AzA7i2KJfXREsR8UyqEN2uDemclN899o2I1/cxzsg6pts7gyRJkiRJkiRJkiRJSzwDGyVJkiRJkiRJkiRJkrREy8yFwKFFdT9hOj8qytsBh3TqEBEbA78EVu5jvpngmxGxe6cGddDiW4vqY+pwoZYy80Tg10X1ZyPiI72EjEXEahHxbqpzNJ19FHioUX4E8OuIWKNdh4hYOiI+Dzyv+OjLmXnzJKxxaCJidkTsERHHARcBWxRN7gKenZn/6GK4IxkdMBfATyLiieOs4UXA/xbVf2Rs2ObDMvMG4FNF9Z7A8RGxShdrJSI2iYhOwUOnAjc2yssDR3W6TyJiDtV+9/hu1rC4ysw/AUcV1W+JiKMiYoVux4mIFSPiIOC8gS5w4i5m9L6xSUQ8rYf+x0TEVyOivJ/aiogNGPtcPKmHOSdiG+C7EbFcuwYR8UiqZ8fcRvUC4NN9zFcGmpZBlV/pY8zFweeAuxvl2cCvImKTTp0i4i2MvTaOy8yL+ljDq+tnd6f5dmJsoNtfge/3MV8nhzD6PnskcGY3wXEAETEvIt4TEVPx995liOK2Rfk0utcMd1yaKvC201wdZeaFwNFF9cERcURELN/tOBGxUkS8gbFhxRP1fuD2Rnll4HcR8dw261iqXscPqf6W/75W7aaZC4ryq3sM7j25KH88Itbt1KEOVf0x1btWr6b0nGTmccDvi+ovRMRhEbF0t+NExOoR8R7gxF7mlyRJkiRJkiRJkiRJk8/ARkmSJEmSJEmSJEmSJKkK57hwIgPUoUKnF9Ufj4ijI2KbiAh4OBBkm4j4BFV43KZUgVDnT2T+xcz5wIPAClThKYdHxLYjgUQRsWwdsHcCY0Purgfe3sUcLwaubJSXAt4LXBkRh0TE4yNimWaHiFg1InaLiLdGxG/ruT4GPKqfX3KqZOaZwCeL6m2ByyLi7RGx3khlHR73POBs4E1Fn0uojtHiYp+IeF3xc2B9fj9eBzmdDdwB/AbYh7F/M3cV8OTMPKubCTPzNuAAIBvVq1AFcP1vfW+PXMfLRMSuEfE94LtUoVUj7gb2y8zmOK0cBvyuqHsWcFVEfDginti8jus5t4mIgyLid8BfgBd1+H0eAL5WVD8bODUinhERyzbGXr8OM7oM+I+6+sxx1r+4eyNQXhuvBOZHxIciYucyADAi5kbETvW1eDxwE1V436ZTs+TuZOY9jL22fh4RP6o/eaaTAAAgAElEQVQD4g4q7q19irYrAK8F/hwRl9THY6+IWHvkeQcPX5NbRcQHqAK/HtkY4y7gG5Pw65XOqf/3ucBFEfHSiJjXWOM6EfF24M/AY4q+h2XmFb1OmJm/By5t8/GfM7OX8LvFRmbOZ+yzZV2q4/7hiNis8T40OyKeGRG/pAp6bLoBeHWP0/+bak8H+FhE/CYi9oyI2fV8S0XE4yLic1SBgis1lw68KjMX9DhnR5l5EvCBonpD4IL6/XDPiHh4HVHZOCJeHBHfBa6hCmUeRmBj018y86Yexhrv+u4psLF2EGODFl9DtR8fVu+7s5sfRsTK9T59UET8jGo//iLQMUC0V5l5HWOv+zWA/4uIP0fEZyPibRHxzoj4CvD3eh2zqd6HP1wOOcj1DcjPivIewJ8i4tMR8eYW72Nl+P0RRflRwPkR8YaownIBiIjlIuLpEfEDqtDCOVTHo6c9c0jn5AUs2oOgum8Ppfru8Y6I2K7Dd4+31e9t/6K659fu8leVJEmSJEmSJEmSJElTpJf/sqUkSZIkSZIkSZIkSZI0I2VmRsT7gBMmONRrqAKpmoEx+9c/D0TEncDKjA5uS6rQq6cA201w/sXFJcB3gE9ThZkcVP88WB+jeUC06Hcn8F+Zeet4E2TmLRHxTOCnwOMaH60PfGKkUM/3IDCX0edlcXMosBHwwkbdalTH+NMRsQC4j+r6a+Uq4LmDDquaZG+dQN/7gC9RhbHd1UvHzPxVRLyFKkx05DqdBby5/lkYEXfQ/jq+B3hhZv61i7kWRsTzgf8Ddm98NA94X/1DRNxezzW3l9+l9jHg+cBmjbqdgF8BD0XEbcCKwLJFv29QBSjt3Meci4XMXFAHFf4I2K3x0RrA++sfIuJuqmtqLrBMOc409nHgaSwKg1sW+K/6p3QKcHybcbasf0Y8VF+T0H5vfQg4MDOv6XXRfXgD8AOqoLxNgW8D1PfpMsDybfr9APifCcz7Zap9pvSVCYw57WXmNyJiU+CQRvXyLNqzHqjvmXmt+gM3A/v2GBAIVfj1fsBJwHJUgXJ7AFnvYyvR/u+m356Zp/Y4X7c+SvW7NsOml2bR+yERcQ+LntHDehfpFKLY07HJzH9GxFVU7yWlqzLz2p5WVo15X0T8B9V+vEvjozWp3oEOBYiIu4D7qfaeKfs7+cw8JiJWYWzQ+Bb1TysPUX13KI/HfQNe3oRl5qkRcRqjj/1mjH53aPoTVfD5SP/zIuIIqu88I1anCkn8Yv0+vpDW+8KhVPf0Li0+67TmKT0nmXlz47vHYxsfbUDjWVI/exay+H/3kCRJkiRJkiRJkiRpiTIV/8VVSZIkSZIkSZIkSZIkadrLzBOBMyY4xmXAvlSBbKVlgFUZHcyxAHh5Zn5rIvMujjLzM1RhZ9mongWsQuuQu38Bz8rMM3uY4ypgR+DrVMEoraxUz9kuMCWBi7qdc1gy80HgxVQBbA+2aDKb9mGNpwA7ZebfJml508llVME/62fmO3oNaxyRmV+gCse8pcXHS9P+Op4PPLXeb7qd61bgGcAXaH1uoTq37cIaHxpn/HuBPYErW3y8FNW+VYY1Hk4VaDTjZea/qULfPkG1Z7cyhyogtVNY4wUDXtqEZeZJwOuAe/vp3uGzpajugXZ767+B52XmsX3M24+Rc3hFUT+X9mGN3wD2y8x2z45uHEMVNNx0N3Vg5EyWme8CXk/1+5aWoX1Y45+AJ2XmOX3OezawD3BHozqorsVW4X0LgDdm5uf6ma/LNWVmHgy8nNbPDIAVGPuO+PAQdL7fBiIzrwbaBaie1seQ7fp0CobsKDNvBJ4KfIr2+/GKVMeyU1jjhf2uoZPM/DzV9fePLppfTxVM+k3GPr9vG/TaBuSFwFkT6P9G4IdtPluJsfvCQ8B7M/Mj/U441eckM68EdgC+SfvvHnOZAd89JEmSJEmSJEmSJEla0hjYKEmSJEmSJEmSJEmSJC3y3okOkJm/ArYFfkz7oI6FwE+BbZbEsMYRdQDLrnQO77mVKqjuMZnZc6BmZt6Tma8CNgO+ClzXRbcFwMnAu4GNMnO/XucdhjoU6j3AY4Fjgds7NF8InA48JzN3r0OQZoKFVIGpNwCXAD8HPgfsT3Uut8jMDw/i983MHwIbUwX5jRcEdBnwNmCzfoLIMvOBzHwz8BiqANKbxumyEDgTeDPwn12MPx94IlUIVqcQy3OBvTLzDRMMslusZOaDmfluYEOqY9RNuOlC4Gzgw8CWmbnLJC6xb5l5JPBo4BCq+2U+Vchgx6BPYD/gOcCRwF/oLlDur8AHgU0y8yf9rbg/dYDvtsBHqZ4r7fwBeGZmvrIOwp3InHcyNgj6O5l5R6v2M01mfpnq2voS1Z7ctilVoOkrga0z8/IJzvsbYEuqwMz72zR7ADi+nu9LE5mvh3UdTbWHfJCx4aGtXA78D9X9MlX7bbv3sVP7GGvggY3w8H78TmAj4DPAVV10e5AqaPAwYIvM3H0iaxhnfT+jelbvDxxH9by4h+pavBY4EXgtsHFmnlB3W7MYptMeNTSZ+S/gycBeVO8i51MF4rYLzyz7P5CZL6AKL+10DywETgB2yMyPTWjRTP05ycy7M/MV9ZxfA/7ZRbcFwEnAu4ANMnP/bueTJEmSJEmSJEmSJElTIzIn/T+6KkmSJEmSJEmSJEmSJC2RImIeVSDhusA84F6qkJDTM/PmYa5tuomIdYCdgPWBZYEbqUJ4TsvMBwY81ybAVsBq9c9DVAFlN1IFJF2RmV2Fz0xnETEL2J4q1GhNquP6b6rgmDMz87YhLm/GiYgtgMcBawBzqcJ9bgTOzcy/D3iuALamCiJdg0X7y61UIUiXZGanwM5OYy8L7AxsyqL74xrgnMzsJqhwiRAR6wLbUR3/1aj+A9p3Ut1jVwB/ycx7hrfCqRURq1C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      "text/plain": [
       "<Figure size 6400x4480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Plot\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(20,14), dpi=320, sharey=True)\n",
    "\n",
    "# Topic Distribution by Dominant Topics\n",
    "ax1.bar(x='Dominant_Topic', height='count', data=df_dominant_topic_in_each_doc, width=.5, color='firebrick')\n",
    "ax1.set_xticks(range(df_dominant_topic_in_each_doc.Dominant_Topic.unique().__len__()))\n",
    "tick_formatter = FuncFormatter(lambda x, pos: 'Topic ' + str(x)+ '\\n' + df_top3words.loc[df_top3words.topic_id==x, 'words'].values[0])\n",
    "ax1.xaxis.set_major_formatter(tick_formatter)\n",
    "ax1.set_title('Number of Documents by Dominant Topic', fontdict=dict(size=10))\n",
    "ax1.set_ylabel('Number of Documents')\n",
    "ax1.set_ylim(0, 1000)\n",
    "\n",
    "# Topic Distribution by Topic Weights\n",
    "ax2.bar(x='index', height='count', data=df_topic_weightage_by_doc, width=.5, color='steelblue')\n",
    "ax2.set_xticks(range(df_topic_weightage_by_doc.index.unique().__len__()))\n",
    "ax2.xaxis.set_major_formatter(tick_formatter)\n",
    "ax2.set_title('Number of Documents by Topic Weightage', fontdict=dict(size=10))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 4510 samples in 0.011s...\n",
      "[t-SNE] Computed neighbors for 4510 samples in 0.274s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 4510\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 4510\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 4510\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 4510\n",
      "[t-SNE] Computed conditional probabilities for sample 4510 / 4510\n",
      "[t-SNE] Mean sigma: 0.066061\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 73.404739\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.188233\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"d65e33e2-0c16-49d4-b88a-8be7d80f9cab\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\",\"dtype\":\"float32\",\"shape\":[4510]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"shape\":[4510]}},\"selected\":{\"id\":\"1046\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1047\",\"type\":\"UnionRenderers\"}},\"id\":\"1036\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1048\",\"type\":\"BoxAnnotation\"}},\"id\":\"1025\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1023\",\"type\":\"PanTool\"},{\"id\":\"1024\",\"type\":\"WheelZoomTool\"},{\"id\":\"1025\",\"type\":\"BoxZoomTool\"},{\"id\":\"1026\",\"type\":\"SaveTool\"},{\"id\":\"1027\",\"type\":\"ResetTool\"},{\"id\":\"1028\",\"type\":\"HelpTool\"}]},\"id\":\"1029\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"1005\",\"type\":\"DataRange1d\"},{\"attributes\":{\"formatter\":{\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1019\",\"type\":\"BasicTicker\"}},\"id\":\"1018\",\"type\":\"LinearAxis\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1048\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1019\",\"type\":\"BasicTicker\"},{\"attributes\":{\"source\":{\"id\":\"1036\",\"type\":\"ColumnDataSource\"}},\"id\":\"1040\",\"type\":\"CDSView\"},{\"attributes\":{\"formatter\":{\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1014\",\"type\":\"BasicTicker\"}},\"id\":\"1013\",\"type\":\"LinearAxis\"},{\"attributes\":{\"text\":\"t-SNE Clustering of 10 LDA Topics\"},\"id\":\"1003\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"data_source\":{\"id\":\"1036\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1037\",\"type\":\"Scatter\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1038\",\"type\":\"Scatter\"},\"selection_glyph\":null,\"view\":{\"id\":\"1040\",\"type\":\"CDSView\"}},\"id\":\"1039\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"fdd01f0d-c9d1-477e-b0fd-b631e9567c7c\",\"roots\":{\"1002\":\"d65e33e2-0c16-49d4-b88a-8be7d80f9cab\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get topic weights and dominant topics ------------\n",
    "from sklearn.manifold import TSNE\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.models import Label\n",
    "from bokeh.io import output_notebook\n",
    "import matplotlib.colors as mcolors\n",
    "# Get topic weights\n",
    "topic_weights = []\n",
    "for i, row_list in enumerate(lda_model[corpus]):\n",
    "    topic_weights.append([w for i, w in row_list[0]])\n",
    "\n",
    "# Array of topic weights    \n",
    "arr = pd.DataFrame(topic_weights).fillna(0).values\n",
    "\n",
    "# Keep the well separated points (optional)\n",
    "arr = arr[np.amax(arr, axis=1) > 0.27]\n",
    "\n",
    "# Dominant topic number in each doc\n",
    "topic_num = np.argmax(arr, axis=1)\n",
    "\n",
    "# tSNE Dimension Reduction\n",
    "tsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.9)\n",
    "tsne_lda = tsne_model.fit_transform(arr)\n",
    "\n",
    "# Plot the Topic Clusters using Bokeh\n",
    "output_notebook()\n",
    "n_topics = 10\n",
    "mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])\n",
    "plot = figure(title=\"t-SNE Clustering of {} LDA Topics\".format(n_topics), \n",
    "              plot_width=900, plot_height=700)\n",
    "plot.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[topic_num])\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el436422807623673047601997256\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el436422807623673047601997256_data = {\"mdsDat\": {\"x\": [0.14943518521780508, 0.2240750175387788, -0.012753866383418884, 0.12428905075194703, 0.04603966186774056, -0.001827880249523933, 0.07570458305907163, -0.03649702174617428, -0.292296351661333, -0.27616837839489294], \"y\": [0.16103028589272536, -0.06491842624043671, 0.08669124007348934, -0.03390255526770385, -0.12669271153181588, 0.2928231824101919, -0.19137627220930303, -0.07125827232085444, 0.02557017375585964, -0.0779666445621528], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [27.11109161376953, 25.25186538696289, 8.722138404846191, 8.191967010498047, 7.893429756164551, 6.0713300704956055, 5.857797145843506, 5.730267524719238, 2.89399790763855, 2.2761147022247314]}, \"tinfo\": {\"Term\": [\"seat\", \"class\", \"business\", \"food\", \"lounge\", \"meal\", \"call\", \"crew\", \"ask\", \"entertainment\", \"cabin\", \"serve\", \"ticket\", \"check\", \"miss\", \"hour\", \"airport\", \"book\", \"delay\", \"passenger\", \"drink\", \"customer\", \"day\", \"economy\", \"water\", \"charge\", \"luggage\", \"buy\", \"boarding\", \"comfortable\", \"ticket\", \"hotel\", \"luggage\", \"baggage\", \"desk\", \"cancel\", \"lose\", \"agent\", \"information\", \"email\", \"stay\", \"contact\", \"compensation\", \"manager\", \"day\", \"simply\", \"refund\", \"voucher\", \"cause\", \"policy\", \"assistance\", \"deny\", \"accommodation\", \"airplane\", \"chaotic\", \"explanation\", \"destination\", \"message\", \"reservation\", \"unhelpful\", \"bag\", \"purchase\", \"refuse\", \"send\", \"customer\", \"counter\", \"carry\", \"cost\", \"spend\", \"delay\", \"help\", \"airport\", \"wait\", \"pay\", \"arrive\", \"hour\", \"connect\", \"later\", \"extra\", \"connection\", \"staff\", \"change\", \"check\", \"leave\", \"time\", \"service\", \"travel\", \"ask\", \"gate\", \"return\", \"experience\", \"first\", \"way\", \"selection\", \"movie\", 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       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "8      0.149435  0.161030       1        1  27.111092\n",
       "1      0.224075 -0.064918       2        1  25.251865\n",
       "6     -0.012754  0.086691       3        1   8.722138\n",
       "2      0.124289 -0.033903       4        1   8.191967\n",
       "7      0.046040 -0.126693       5        1   7.893430\n",
       "0     -0.001828  0.292823       6        1   6.071330\n",
       "9      0.075705 -0.191376       7        1   5.857797\n",
       "5     -0.036497 -0.071258       8        1   5.730268\n",
       "3     -0.292296  0.025570       9        1   2.893998\n",
       "4     -0.276168 -0.077967      10        1   2.276115, topic_info=            Term         Freq        Total Category  logprob  loglift\n",
       "12          seat  4993.000000  4993.000000  Default  30.0000  30.0000\n",
       "26         class  2165.000000  2165.000000  Default  29.0000  29.0000\n",
       "139     business  1996.000000  1996.000000  Default  28.0000  28.0000\n",
       "112         food  3066.000000  3066.000000  Default  27.0000  27.0000\n",
       "418       lounge  1080.000000  1080.000000  Default  26.0000  26.0000\n",
       "..           ...          ...          ...      ...      ...      ...\n",
       "691  exceptional    34.944588    66.122299  Topic10  -5.0571   3.1450\n",
       "693         fact    40.994335   191.772858  Topic10  -4.8974   2.2398\n",
       "58        smooth    40.240784   210.575424  Topic10  -4.9160   2.1277\n",
       "539          big    40.529434   328.812805  Topic10  -4.9088   1.6892\n",
       "177        happy    34.514034   167.612869  Topic10  -5.0695   2.2024\n",
       "\n",
       "[594 rows x 6 columns], token_table=      Topic      Freq           Term\n",
       "term                                \n",
       "753       2  0.990981     acceptable\n",
       "580       4  0.957078         access\n",
       "580       5  0.036811         access\n",
       "2578      4  0.986076    accommodate\n",
       "1646      1  0.989022  accommodation\n",
       "...     ...       ...            ...\n",
       "169       9  0.022315           work\n",
       "219       9  0.987413          worst\n",
       "219      10  0.009776          worst\n",
       "769       9  0.127197          young\n",
       "769      10  0.839500          young\n",
       "\n",
       "[986 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[9, 2, 7, 3, 8, 1, 10, 6, 4, 5])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.019*seat + 0.014*service + 0.013*time + 0.012*hour + 0.012*food + 0.010*crew + 0.010*class + 0.009*staff + 0.009*business + 0.008*cabin'),\n",
       " (1,\n",
       "  '0.015*seat + 0.014*hour + 0.012*service + 0.012*time + 0.010*staff + 0.009*check + 0.009*airport + 0.008*food + 0.007*crew + 0.006*passenger'),\n",
       " (2,\n",
       "  '0.017*seat + 0.012*service + 0.012*hour + 0.012*time + 0.010*food + 0.009*crew + 0.009*staff + 0.008*check + 0.007*class + 0.007*passenger'),\n",
       " (3,\n",
       "  '0.014*seat + 0.011*time + 0.010*service + 0.009*hour + 0.009*food + 0.007*crew + 0.007*check + 0.006*staff + 0.006*cabin + 0.006*experience'),\n",
       " (4,\n",
       "  '0.010*seat + 0.007*service + 0.007*time + 0.007*food + 0.006*hour + 0.006*staff + 0.005*crew + 0.005*class + 0.004*business + 0.004*passenger'),\n",
       " (5,\n",
       "  '0.008*seat + 0.007*service + 0.007*food + 0.006*crew + 0.006*time + 0.004*cabin + 0.004*hour + 0.004*class + 0.004*business + 0.003*entertainment'),\n",
       " (6,\n",
       "  '0.007*seat + 0.005*service + 0.005*food + 0.004*time + 0.004*crew + 0.003*class + 0.003*entertainment + 0.003*business + 0.003*staff + 0.003*cabin'),\n",
       " (7,\n",
       "  '0.004*seat + 0.003*time + 0.003*service + 0.002*hour + 0.002*crew + 0.002*passenger + 0.002*food + 0.002*experience + 0.002*cabin + 0.002*staff'),\n",
       " (8,\n",
       "  '0.003*seat + 0.002*service + 0.002*check + 0.002*lounge + 0.002*food + 0.002*time + 0.002*hour + 0.002*bag + 0.002*staff + 0.002*class'),\n",
       " (9,\n",
       "  '0.002*service + 0.001*seat + 0.001*food + 0.001*hour + 0.001*meal + 0.001*serve + 0.001*crew + 0.001*time + 0.001*landing + 0.001*revisit'),\n",
       " (10,\n",
       "  '0.002*crew + 0.002*seat + 0.002*service + 0.001*food + 0.001*cabin + 0.001*staff + 0.001*entertainment + 0.001*verification + 0.001*passenger + 0.001*hour'),\n",
       " (11,\n",
       "  '0.001*seat + 0.001*hour + 0.001*staff + 0.001*service + 0.001*honourable + 0.001*shirt + 0.001*time + 0.001*plate + 0.001*guard + 0.001*hear'),\n",
       " (12,\n",
       "  '0.002*seat + 0.001*service + 0.001*class + 0.001*hour + 0.001*time + 0.001*business + 0.001*vgml + 0.001*aiircraft + 0.001*drink + 0.001*staff'),\n",
       " (13,\n",
       "  '0.001*service + 0.001*food + 0.001*seat + 0.001*little + 0.001*alternate + 0.001*signal + 0.001*degraded + 0.001*time + 0.001*class + 0.001*ergonomic'),\n",
       " (14,\n",
       "  '0.001*seat + 0.001*service + 0.001*time + 0.001*robot + 0.001*passenger + 0.001*combination + 0.001*prominent + 0.001*happy + 0.001*first + 0.001*banging'),\n",
       " (15,\n",
       "  '0.001*dislike + 0.001*sit + 0.001*cohesive + 0.001*stopover + 0.001*stressful + 0.001*experience + 0.001*work + 0.001*brief + 0.001*service + 0.001*passenger'),\n",
       " (16,\n",
       "  '0.001*service + 0.001*globally + 0.001*customer + 0.001*fluke + 0.001*guide + 0.001*seater + 0.001*crouwde + 0.001*defective + 0.001*audiovisual + 0.001*food'),\n",
       " (17,\n",
       "  '0.001*occasionally + 0.001*sinus + 0.001*cleaness + 0.001*lock + 0.001*whole + 0.001*overflowing + 0.001*normally + 0.001*aircraft + 0.001*nibble + 0.001*tight'),\n",
       " (18,\n",
       "  '0.002*service + 0.001*crew + 0.001*seat + 0.001*food + 0.001*time + 0.001*staff + 0.001*class + 0.001*business + 0.001*dedication + 0.001*passenger'),\n",
       " (19,\n",
       "  '0.001*staff + 0.001*dearer + 0.001*seat + 0.001*personification + 0.001*specified + 0.001*minus + 0.001*generation + 0.001*cart + 0.001*check + 0.001*astonished')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import HdpModel\n",
    "hdp_model = HdpModel(corpus=corpus,id2word=id2word)\n",
    "hdp_model.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.nmf import Nmf\n",
    "from gensim.models.coherencemodel import CoherenceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.059*\"service\" + 0.032*\"meal\" + 0.031*\"time\" + 0.023*\"staff\" + 0.023*\"plane\" + 0.014*\"business\" + 0.013*\"cabin\" + 0.012*\"food\" + 0.011*\"serve\" + 0.011*\"entertainment\"'),\n",
       " (1,\n",
       "  '0.043*\"time\" + 0.038*\"ticket\" + 0.025*\"travel\" + 0.020*\"change\" + 0.013*\"experience\" + 0.011*\"book\" + 0.011*\"last\" + 0.010*\"day\" + 0.009*\"pay\" + 0.009*\"trip\"'),\n",
       " (2,\n",
       "  '0.053*\"hour\" + 0.047*\"airport\" + 0.026*\"food\" + 0.023*\"staff\" + 0.022*\"plane\" + 0.016*\"leave\" + 0.013*\"arrive\" + 0.012*\"aircraft\" + 0.012*\"ask\" + 0.011*\"delay\"'),\n",
       " (3,\n",
       "  '0.076*\"crew\" + 0.043*\"food\" + 0.031*\"cabin\" + 0.030*\"ask\" + 0.025*\"service\" + 0.020*\"serve\" + 0.010*\"minute\" + 0.010*\"drink\" + 0.009*\"order\" + 0.009*\"time\"'),\n",
       " (4,\n",
       "  '0.071*\"hour\" + 0.031*\"service\" + 0.021*\"delay\" + 0.019*\"customer\" + 0.019*\"passenger\" + 0.015*\"offer\" + 0.015*\"wait\" + 0.014*\"hotel\" + 0.012*\"day\" + 0.011*\"arrive\"'),\n",
       " (5,\n",
       "  '0.100*\"seat\" + 0.024*\"cabin\" + 0.022*\"crew\" + 0.017*\"meal\" + 0.012*\"entertainment\" + 0.010*\"pay\" + 0.009*\"leg\" + 0.008*\"comfortable\" + 0.007*\"board\" + 0.007*\"plane\"'),\n",
       " (6,\n",
       "  '0.072*\"class\" + 0.060*\"business\" + 0.049*\"seat\" + 0.019*\"first\" + 0.018*\"passenger\" + 0.017*\"economy\" + 0.013*\"lounge\" + 0.013*\"experience\" + 0.012*\"service\" + 0.012*\"travel\"'),\n",
       " (7,\n",
       "  '0.032*\"lounge\" + 0.025*\"time\" + 0.023*\"food\" + 0.017*\"check\" + 0.014*\"aircraft\" + 0.013*\"first\" + 0.013*\"offer\" + 0.012*\"passenger\" + 0.012*\"luggage\" + 0.012*\"bag\"'),\n",
       " (8,\n",
       "  '0.091*\"check\" + 0.016*\"seat\" + 0.015*\"airport\" + 0.015*\"online\" + 0.014*\"ticket\" + 0.013*\"pay\" + 0.013*\"luggage\" + 0.011*\"call\" + 0.011*\"bag\" + 0.010*\"ask\"'),\n",
       " (9,\n",
       "  '0.047*\"staff\" + 0.020*\"ticket\" + 0.018*\"time\" + 0.014*\"passenger\" + 0.013*\"gate\" + 0.013*\"first\" + 0.013*\"airport\" + 0.012*\"wait\" + 0.011*\"arrive\" + 0.011*\"board\"')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "nmf_model = Nmf(corpus=corpus, num_topics=10, id2word=id2word)\n",
    "nmf_model.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.405*\"seat\" + 0.258*\"hour\" + 0.252*\"service\" + 0.250*\"time\" + 0.191*\"food\" + 0.188*\"staff\" + 0.176*\"check\" + 0.175*\"class\" + 0.169*\"crew\" + 0.163*\"business\"'),\n",
       " (1,\n",
       "  '0.549*\"seat\" + -0.398*\"hour\" + -0.208*\"airport\" + -0.185*\"staff\" + -0.180*\"delay\" + 0.176*\"class\" + 0.151*\"business\" + -0.143*\"ticket\" + -0.141*\"wait\" + -0.135*\"day\"'),\n",
       " (2,\n",
       "  '0.644*\"seat\" + -0.326*\"class\" + -0.292*\"service\" + -0.285*\"business\" + -0.203*\"food\" + -0.172*\"crew\" + -0.144*\"lounge\" + 0.141*\"check\" + -0.128*\"cabin\" + 0.107*\"pay\"'),\n",
       " (3,\n",
       "  '0.455*\"class\" + 0.424*\"business\" + -0.361*\"crew\" + -0.225*\"meal\" + 0.214*\"ticket\" + -0.194*\"cabin\" + -0.168*\"hour\" + -0.152*\"time\" + -0.152*\"food\" + -0.145*\"entertainment\"'),\n",
       " (4,\n",
       "  '-0.561*\"hour\" + 0.394*\"service\" + -0.262*\"class\" + -0.254*\"business\" + 0.243*\"check\" + 0.183*\"customer\" + -0.174*\"delay\" + -0.165*\"passenger\" + 0.157*\"ticket\" + 0.153*\"staff\"'),\n",
       " (5,\n",
       "  '0.513*\"service\" + -0.496*\"check\" + -0.278*\"staff\" + 0.275*\"hour\" + 0.211*\"customer\" + -0.205*\"passenger\" + -0.117*\"bag\" + -0.110*\"luggage\" + -0.109*\"board\" + 0.108*\"ticket\"'),\n",
       " (6,\n",
       "  '0.697*\"time\" + -0.493*\"staff\" + -0.198*\"service\" + -0.189*\"passenger\" + 0.158*\"ticket\" + -0.120*\"crew\" + -0.111*\"cabin\" + -0.110*\"customer\" + 0.091*\"first\" + -0.088*\"ground\"'),\n",
       " (7,\n",
       "  '0.443*\"crew\" + -0.429*\"staff\" + -0.318*\"food\" + 0.315*\"passenger\" + 0.245*\"ticket\" + 0.236*\"cabin\" + -0.216*\"time\" + 0.171*\"meal\" + -0.123*\"lounge\" + 0.122*\"ask\"'),\n",
       " (8,\n",
       "  '-0.571*\"check\" + 0.398*\"staff\" + -0.290*\"service\" + 0.255*\"time\" + 0.248*\"ticket\" + -0.234*\"hour\" + -0.155*\"luggage\" + -0.152*\"bag\" + 0.137*\"crew\" + 0.110*\"cabin\"'),\n",
       " (9,\n",
       "  '-0.599*\"meal\" + 0.244*\"crew\" + 0.242*\"service\" + -0.232*\"ask\" + -0.231*\"serve\" + -0.222*\"ticket\" + 0.204*\"time\" + 0.184*\"passenger\" + -0.160*\"staff\" + 0.155*\"cabin\"')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the LSI model\n",
    "from gensim.models import LsiModel\n",
    "lsi_model = LsiModel(corpus=corpus, num_topics=10, id2word=id2word)\n",
    "lsi_model.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gensim.models import CoherenceModel\n",
    "lda_topics = [[word for word, prob in topic] for topicid, topic in lda_model.show_topics(formatted=False)]\n",
    "lsi_topics = [[word for word, prob in topic] for topicid, topic in lsi_model.show_topics(formatted=False)]\n",
    "hdp_topics = [[word for word, prob in topic] for topicid, topic in hdp_model.show_topics(formatted=False)]\n",
    "nmf_topics = [[word for word, prob in topic] for topicid, topic in nmf_model.show_topics(formatted=False)]\n",
    "lsi_coherence = CoherenceModel(topics=lsi_topics, texts=data_ready, dictionary=id2word, window_size=10).get_coherence()\n",
    "hdp_coherence = CoherenceModel(topics=hdp_topics, texts=data_ready, dictionary=id2word, window_size=10).get_coherence()\n",
    "lda_coherence = CoherenceModel(topics=lda_topics, texts=data_ready, dictionary=id2word, window_size=10).get_coherence()\n",
    "nmf_coherence = CoherenceModel(topics=nmf_topics, texts=data_ready, dictionary=id2word, window_size=10).get_coherence()\n",
    "import numpy as np\n",
    "def topic_model_graph(coherences, indices):\n",
    "    \"\"\"\n",
    "    Function to plot bar graph.\n",
    "    \n",
    "    coherences: list of coherence values\n",
    "    indices: Indices to be used to mark bars. Length of this and coherences should be equal.\n",
    "    \"\"\"\n",
    "    assert len(coherences) == len(indices)\n",
    "    n = len(coherences)\n",
    "    x = np.arange(n)\n",
    "    plt.bar(x, coherences, width=0.4, tick_label=indices, align='center')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Coherence Value')\n",
    "topic_model_graph([lsi_coherence, hdp_coherence, lda_coherence, nmf_coherence],\n",
    "                   ['LSI', 'HDP', 'LDA', 'NMF'])\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}