NLP TFIDF Workshop Instructor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Natural Language Processing!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is Natural Language Processing?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plug NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It'll bring up another window. From here, download: \n",
    "\n",
    "\n",
    "corpora -> stopwords\n",
    "\n",
    "all packages -> punkt\n",
    "\n",
    "corpora -> wordnet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import everything we need, explain as we use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk.classify.util\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import wordnet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Break apart a sentence(tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This',\n",
       " 'is',\n",
       " 'a',\n",
       " 'test',\n",
       " 'sentence.',\n",
       " 'It',\n",
       " 'will',\n",
       " 'break',\n",
       " 'everything',\n",
       " 'apart!']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"This is a test sentence. It will break everything apart!\"\n",
    "sentence.split(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it misses punctuation. Luckily, NLTK has a solution!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This',\n",
       " 'is',\n",
       " 'a',\n",
       " 'test',\n",
       " 'sentence',\n",
       " '.',\n",
       " 'It',\n",
       " 'will',\n",
       " 'break',\n",
       " 'everything',\n",
       " 'apart',\n",
       " '!']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"This is a test sentence. It will break everything apart!\"\n",
    "word_tokenize(sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also have NLTK determine the part of speech (POS) for each token in our sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\Allison\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('This', 'DT'),\n",
       " ('is', 'VBZ'),\n",
       " ('a', 'DT'),\n",
       " ('test', 'NN'),\n",
       " ('sentence', 'NN'),\n",
       " ('.', '.'),\n",
       " ('It', 'PRP'),\n",
       " ('will', 'MD'),\n",
       " ('tag', 'VB'),\n",
       " ('everything', 'NN'),\n",
       " ('!', '.')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('averaged_perceptron_tagger')\n",
    "sentence = \"This is a test sentence. It will tag everything!\"\n",
    "w = word_tokenize(sentence)\n",
    "nltk.pos_tag(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NN means singular noun, JJ means adjective etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use NTLK to find the definition of words, synonyms, antonyms, etc.\n",
    "\n",
    "Some words have multiple definitions and multiple parts of speech depending on the usage. This can get very complicated very fast, so for this purpose, we're just going to assume that NLTK knows what its doing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step we have are removing stopwords. Stop words are words that are used for grammaical purposes but carry little meaning (the, a, I, is, etc). \n",
    "\n",
    "So, let's remove them. Issue is, there are over 100 English words that are considered stopwords. So unless we want to create a list of stopwords and iterate every word over the list by hand everytime we write a program, we need a new solution.\n",
    "\n",
    "So, lets just let NLTK remove them for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Our', 'symposium', 'will', 'also', 'include', 'two', 'rounds', 'of', 'workshops', 'with', 'several', 'choices', 'in', 'each', 'round-', 'so', 'you', 'can', 'brush', 'up', 'on', 'your', 'Python', ',', 'learn', 'about', 'data', 'visualization', ',', 'or', 'deepen', 'your', 'knowledge', 'of', 'machine', 'learning', '.']\n"
     ]
    }
   ],
   "source": [
    "para = \"Our symposium will also include two rounds of workshops with several choices in each round- so you can brush up on your Python, learn about data visualization, or deepen your knowledge of machine learning. \"\n",
    "words = word_tokenize(para)\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Our', 'symposium', 'also', 'include', 'two', 'rounds', 'workshops', 'several', 'choices', 'round-', 'brush', 'Python', ',', 'learn', 'data', 'visualization', ',', 'deepen', 'knowledge', 'machine', 'learning', '.']\n"
     ]
    }
   ],
   "source": [
    "words = word_tokenize(para)\n",
    "useful_words = [word for word in words if word not in stopwords.words('english')]\n",
    "print(useful_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that \"Our\" was not removed even though it is a stopword. Thats because NLTK's list is only in lowercase. So let's move our paragraph to lowercase first so it doesn't miss any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['symposium', 'also', 'include', 'two', 'rounds', 'workshops', 'several', 'choices', 'round-', 'brush', 'python', ',', 'learn', 'data', 'visualization', ',', 'deepen', 'knowledge', 'machine', 'learning', '.']\n"
     ]
    }
   ],
   "source": [
    "para = para.lower()\n",
    "words = word_tokenize(para)\n",
    "useful_words = [word for word in words if word not in stopwords.words('english')]\n",
    "print(useful_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can look at simplifying our data. There are two types we can use, stemming and lemmatization. \n",
    "\n",
    "The purpose of both of these is to bring more complex words down to their root so that we can compare them more easily.\n",
    "\n",
    "For example: \n",
    "\n",
    "jumped -> jump\n",
    "\n",
    "rolled -> roll\n",
    "\n",
    "However, English has some curveballs. \n",
    "\n",
    "Happily -> happy\n",
    "\n",
    "writing -> write\n",
    "\n",
    "Stemming basically just looks for common suffixes and cuts them off. Sometimes this results in stems that aren't real words (happi, writ)\n",
    "\n",
    "Lemmatization uses a full morphological analysis to find the lemma of the word and thus usually returns English words.\n",
    "\n",
    "Lemmatization is much more computationally complex but returns cleaner results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "snowed -> snow\n",
      "charged -> charg\n",
      "bottled -> bottl\n",
      "wolves -> wolv\n",
      "ate -> ate\n",
      "owed -> owe\n",
      "provision -> provis\n",
      "crying -> cri\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "corpus = ['snowed', 'charged', 'bottled', 'wolves', 'ate', 'owed', 'provision', 'crying']\n",
    "porter_stemmer = PorterStemmer()\n",
    "for word in corpus:\n",
    "    print(word, \"->\", porter_stemmer.stem(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "snowed -> snow\n",
      "charged -> charg\n",
      "bottled -> bottl\n",
      "wolves -> wolv\n",
      "ate -> at\n",
      "owed -> ow\n",
      "provision -> provid\n",
      "crying -> cry\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem.lancaster import LancasterStemmer\n",
    "lancaster_stemmer = LancasterStemmer()\n",
    "for word in corpus:\n",
    "    print(word, \"->\", lancaster_stemmer.stem(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "snowed -> snowed\n",
      "charged -> charged\n",
      "bottled -> bottled\n",
      "wolves -> wolf\n",
      "ate -> ate\n",
      "owed -> owed\n",
      "provision -> provision\n",
      "crying -> cry\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "for word in corpus:\n",
    "    print(word, \"->\", wordnet_lemmatizer.lemmatize(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can change the default part of speech if you want to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets get started on our tool!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is topic mining and why do I care?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is tf-idf?"
   ]
  },
  {
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czLX78Hz6uqZwwxB/d/J78IbjX437N83CD2wZm225cKhg5yUBLN06eA+pkz+epSKT9Xpdcp9+9rOfxWAwwNKlS4Vwvve978W73vUuIa10r6DaevDBB2Pt2rWyTcJKInvjjTfiqquukiGwT86fBNYsBgGDgEHAIGAQMAhQlGKMjGchj6Zx7TVXwg9rQNDCHXetx5133Y3fe8lLECUF0lihFjpYsmQCt9x0IzavvwtB3Ec6O4NTzvgC+lTjKDqSBFDuyh0J8kFB07CO3SfhoWQr/FUIKClYJj6D5AuiKJKPMrhFiGwmhIREVRYrBnJGRtN0uz3RFbqhOrCKvnBjhlFF7CWLAZqnMwWORLriDyGPvApJbUlIK7JHM78ofvoiNBVT69Kx25xfBMQJQFws6brsmO2p1ObCXzO6Pgh7TBAghU9yVTjQPpAFLJr8hUhyCCSTAWwrkHAouiYwfIfBYbRts5uKg5LPi0gtZJ0UnKZ+TVJ5baHT4o+qgVNpgl/98gZYXgtTgxjLRobQqAeCk2V1AdVD4PK+6JKdHJOYrOkHK5FpWjX06e+ZFfCDGnr9SFwJLKf0H1UFXFvTvhwpCsrqGcfswrELMfeTWPLFQ9yWswIMfuLLgzgPUHlUOYq4h4BkvLCR5nSC8CVAzEUBT5HoOyjsAH6tjtl2r1TqA+x74CFoOl0ceuCB2Lw1Al+8LNfCII7hOVTccxR0s6CLhuj5DKp77CSVqaNosmd0/tFHHy3+p4cccgguuugiyYH6zne+U4gm1VG+4NG/lGsS0l6vh9tvv12ILXF92cteJo81g6hIUtmvWQwCBgGDgEHAIGAQIH9wAJrwHTfHRz/8QWRZgZPfeyqW77wcO63aWUJYgsCD45EIKWRZDG+oieGxCfzr5z8LJ0nw3e9+G/0YGLD846CjBU/bxpe++EU0m0MYarVQrw+jwYCdblv0UFpA+Uc6T+hewOh+gKpUs1aTJOfLV67Cpk0b5Y92HCkougxYOfbcbTfU6xN4znOO0QSLFDfjMQWkPfzBq49Bq7UGW2dnSZOE0O678xoMBzXEOfDaP3wjPK+F5UuXYsuGDUCWQOU5okGC44//QwRhA0HQ0ME2JGelfyD9bDW5LRD32zhkzz2xpN5Au08zNrB61W5ohnX4bg2+54Lci+qkEO2CxDqRqHgyzbM++zn4tRCu20DND1CvjyDOqNNSECRhLuk5Tb+Wg5e9/OjSROwJCfzTP/1TcadIkhz9mMSGlYpIWnVAUaXyCrksCowsWYJzzzoL/lCIVbvvjcCTbsGgI4vpmuh+YAFzM1PSf2mdlnvCPhgE5roekkGM4eFhtNttUVjlxUH8VF287jVHoVFfiuHWnpLBICs6ck/inoV+2sbSnUYka0Cr0cRQq4kozvHa447FaKOObgwkpOOeCzuLRE12wzo838N733sGHHsUoecgdHzcecc9oo56no2hViDnoDYC2D7W7rUCnblpnHPOOUL8oyhFGATynNmOo0k3VeHKHflx/g9AwrlhwwZceuml8qxed911opIyOT8VUn648H6Q6BMzLjy+xx574Oabb8Z+++0npn4qrJVPq7wUSEvzwyBgEDAIGAQMAk9vBMSG7TgWBu1p/OpXt8FxXdAHVVMk/qxkR/7BzeGGrvirMn/Ss/ffF/vtuQLr774D69Y/gE4Uw2vSZqyw7rbb8Oa3nIQ4BvqDRPwOHdfG8tGlSJJCskNlaQwnCHDLbbdj5c67IIoUolj7rs62Z7HrbrvgrLM/gzCk0sWx0CeRhJABUj4K8R/Vxm49TuqmVCUDOEENqaijVFFzNLwAf3bCW3DBd76DWi3A9NQUliwdRxoNcNZZZ2F4eBTfu/AiIQskvGEQolkL8LnPnC1kMNVxXEJYg3odg/YcmmGAL33lK/CDcdz/wAMyp9GRgKIgTvqrv0a7q9XVwHbg2TnS7hyazRGceeaZyJNC5pVnGdKoj1NP/XuNtfwsUKQxXM+D44a48uqrENRCITuFKnD++efjW9/8LsLAge958H2tsG4nCJbEWthYEePGG25AEuU46uWvRDTQDE0nHyAt1j6jbKvN+9pnlkNhtD8XtvL5gtHro15vyL7Ki4MHmdqLjFaC1yzADyjzJrj40ivRGp3AttmeKNA0hydJhDf98RuwdGwUWQrMdmPt3GG52GXVzqJA8h6s2mV3fPSj/yT+u43AkuC0A/Y7AFGkPUiY0kr8Hmwf8Gs4+KC9obIYP7jw+0J6+WJFbbwynYvfMv2FOZn56DSZyqP+QaWz1WqBpn2qpVdeeeW8L+lDdUKCzw/JqrwwANh1112FpPK549i4f3HZ1Ifqx+wzCBgEDAIGAYPA0woBlSml+lPqW198j6pZUJ4fqi25UtuUUh2VqiKPFNsUOVd91c+nVBzPKNXuKTX7S/WVT71boTGmTjj94+qeQapUeo86/mUvUDTYwnHVlVddplSRqFQp9bd/9046/6mXHnWMmp3uqN7slDrqhYcqx2qqWmuFuv76a1Ue9dTc7LR69+mnqomlDeV7UHGqVJ73lFKT6hmrxpXt7KrWHvwG1VOFKlSs0qSnVBEpNbhWveGYtSqoPUutSwu1QbVV975b1eFLlqgJOMraaY36/mU/lbEUUaRmN92t1t92vQosS9mWr4465lUqVkoVhKTXUU2HmVJt9eIXH6vuU0p141wplajBttvUroB6RhAqtJapvQ88Uj3wwKxKexvV+juvUcvHbOX5DfUHf/Je1eEpaqOKpm9Rf/jy5ysPK9TEkhXq0p9eogZxV03Pzqi/OumvFVxb7bxqVzZWKppWU/ffqeqepcLGmNrvwEPU5ExbFZlS2zZvUr//6uMULF8d/vyj1KBQqkvYFT8aD/0tF9yzZFqpZFINA2p0t7Xq5slIZblSaaqUTEfdrVT/OvXtc9+n4CxRcJoK9SHlWL5yJCzJpgxIp1bVCC1VEw9QKMuvq02JUjGfn7Sj/vhVz1AuRpUbPlNN54ma3LJORdP3KcBVjeW7qiOOOlaptK0yFakt2zYqZkblmCaGRtTNU7GaVkr14km1Z41ZX2vKC1eqfY94sVq/cYtSKlebNtyhdl7SUCtX7KxecsyJqlso1VcDFReJ6saZSpK2+vV1X1ND9ZoCmipSSg0ypeKExxKVFEpFBe8sx5upPM2Ufir1rsfyc3Z2VpoXRaGqbe7gd37OPPNMzfrpuWHb6k1vepO0j+NYxpJlBE2pmZkZNT3NmZvFIGAQMAgYBAwCBoHFCDCCB/A8bNv8AOp1zc2Zz1JEJlEty/RKcoh+hlTsGkBMGSvF8Ehd0hfR5NkIXExtuA/XXHUtYAWYzlIcevjh4j/ZH+T44AfOwP9511tx+WWX4b571qPeGsLVV16H5tCw+D8edPChYt4dGh7GaaedgqOOeomM4+KLdTAJFJO7UzF1UVg0o1JV1XHsIl+qFG4Z5T/b7iMqUjTGxhA6AYaDEXz+/52PI194hA4Aci0MLx3H+ed/SSKvTz75ZHzrW9/SCfxpjq2HeNtfvhlNv4VLLrlMxiH5TnNytQL77bpSihoc/vJX4IqrLsXy5cNw6yHW7LILrrv2asHmxpt/iXs2UBEuMOjM4WdXXY16cwy/+MUteP4Rz5NAtNHhIXz6kx+HxXyks7P48Q9/BPge/vkzn5ZE9O95z9/iip9eiUazKUn/x5dO4N+/+Q3JqXn1FVfiu9/9IWhR5v3iUq3LryhUDNg5Gr6HVTvvDi8IoGzWhgc8m/hR8WbKKzpnKrhDLVFuqfiVvhrSleUBg0TBp28nr5PmEnEvXyTa3oZDRZMf5WFi6U742te+Bstz8ZwjXoyvf+MCcKBRkmDp+Cj+7PXHibrZbg/QaunIdxYS2GftWnE7SKM5XPCDi7Byp6WC38joOH556y2Y2bYR09PbMBC3Yg+W5ZIFwnId7Lb7Xgh8F2FIv1WqupWZnSm/Kn/gcl8F0ONYU+2kWX7z5s1ivn8k31HiKC4o8mvmiVJKUz4rSo2MjGB0dBRbt26VNpXC+jiGZE4xCBgEDAIGAYPAUwoBO2fEimPBY84huj8qJYEsnKV4RW6X0J57y1j7WgCylWazLoEqWzdvQRoV+OEPLkISK2TKx8aptph9GRxCMzRNu+8/4zR02rN45tq9gSRBGDqY63Rx8t/+HZK8kGCRuNeXP9hfPv98xHEfL3rRcyVFEEmomPilMICObpeAIBJVRnspHbvPwKLmSEOM/QyAmpqcRhZn2G3PvdCOIkkAL+wlinDFTy6VOb3rXe+WMWqfQO1w+o53/A1adfqJNpCywpJH3pkiaLakmMFMv4u/eNvbmL5TgqIkGMsFxseGsGR0BPfedz9qdRftmc247JIfIokzvO3t7xCSkhUZHNdHlifo9uaQRBHm5qbxwhe+EPHsDK699ho0mjW89cS/hOP5YnqXRPtJIuPvMvem7+Ocz52LfqSDynSK//K+yWOq4LEsVDaAY4eYGF8mY00z5rxN5JMznRYj9iU/a4GsPQu6XvDeu4uqLDFD1MhIHfTA8FwXludJeih9F/iwMIRM5/QUQt7t4q571kNlCqedfjpqQ4yHshD4HrZs3Yh3veMkGSFzE/TEB5UFzQLcdddd8F0Xju+g3gzkZaDf6yJsDCHwbRx39Etxw3VXI04KxIpBVDoXLbMyuI0hrFi6DEWaoN3uimsAx2o7FjKa2Jl1V3xQ6QCtCyDIIB7DD5JN+krTNL9ixQoJgKJbxMMtjNhn28UmfrYnMWVeVC5VBoDq+8P1ZfYbBAwCBgGDgEHg6YKAzWo7eW+AAw58jpAPpn3K+jrnpM3SmRLhLq6XumSpctHr9oFsRpz5Lr78KsANceQLDoeHGJvW38d4ILhuC3azhVhlaDZqSNMMSdRGEDjwWIXKc1FkGbIsx8jwUrzy2NdKsJbfcJDnPUmS34+6yPNYdFImjGIkEBXUKpm/1E0qxydqKmUzpmoC0IsihEz+HkVYsWwcBTIsXbFM/DgtO0fSnsO999yFO++8U9SwJUuWoVEfQ7PRFALmOB722n0/dNtdCRxjvBL9aR3Pg4q6GAx6GPbq2P/Zh0qhAvqoSgYDFikIHai5OUyMLWWgP4ZGR/GrW3/BGC8c8+rjJaDHtj3xPczzDL7nIIr7QqJYqWtuZhI3/vzn6PUG2HnVaowx/2yjCdsORDmlD6RL/PIU9963Xsi8TJ0vF/JiQTTIxEjcY9x7x6+RpJZcjySbNL7IIvElzjOWtrV1jlHPxXevuBxTvQ7i3gBRPEARx4jzHHEeYWpqEv3ZTXLfyPTKkvTiG+yoVJRTFmtQiOA7Gb74hS/iFa/6E6zaawl6WR9WygwFLobrTazZ/5l4+RGHY6hWQxYItUUSDdBwHfRgY+3hz0NNeF8uJFsxz6ifwEln0Qha6PRzJGXgWpZHyJEhzRzsu3atLoigckT9SJ4FyQhGHZYgER/mr51XiPW+R/uTAVCLFVMS1ioo6qH6YKUpXmsxieVLEH1ZWVmKSioXRvLzYxaDgEHAIGAQMAgYBMgL7AhOo4aDnvtq7LfvM5AnEbasuw21HKhbNGX686mBHEsn7m+EDP0vgKjAl752IeA18MpjjpVIcGYBcJwYRdEBS5/6YopPETgOgoCMQieEF+ErrCGzgF43Qa8dQ6UxUMyiPuSiGORohA10+nPSppDKQyGKPEGWzkAVPfRSDkHB9jx0e13AG4NjDSPN+ihUDzHaQOCil8xAWUwYDzR8H57twG+GQoIZOCSlRMsAISCAXwUBAegXA9QaPgqVIAzIbjJYDR/KLTAoMmaNkqxaEqzD8qGkf5aFidxFd3JaovmBuqRNogqZM50TSaFisiNNbny/hpofsigSBnGEgjXr6cjAklUS1KPzv7o+c7/STM6UULkECNUaIRoNj80kJQMJIA32/MjJzKvq6NK0hcXRAYFDM38DeRHCI8GnyZqpmFIbRX0IDHfyAtZF1Yp1gkQIb15EmNt8L5YPh0uh5lcAACAASURBVHBVDiYckBzzVTUkm0RxAM+qw85jjNQdybSQMtepa8OiDJ0yYMwHLJYi9ZHAxjS5LUkaXQxyB25rDD2pFkYy7SFnPlqbEWdb4GKApN+SFFV8DLnkVoFEJZJWikFdqcokhVRQZz5WCvx0ZVBSSEC05rKkrq2jxB7z/wNVMn6eSFK5mHw+VGdUUbnQtE+yyjXdBLhQSeVClZUfsxgEDAIGAYOAQcAgQO9EkkJ6A9otHHDgIeJjePEPvo2azZLriaSX6vcjUTrjKBFyQ1WoaHfw40uuxLbZAvsedgRWr14Nqlxxqg3NQ0M+mEedFCMvMvT7XUm/RG2PvICJ1uF4EvE+MbEUrBjgesx3VKA3sxmtWg1JGmNkaFiGR5JFNrjT8hVgUiKVDsTJ0HVC3HHXneLHOtjWxfR0H41GTSo96XpC9KHNoGy6ANAPkcWMHKgowtjYhERkMx8l5zQ9PYU46WLTpk1SxagXD9CLE/zq17ejGfroMZcWTdmOQm7TI0IJOdWesDIz0EuWOq9fKE6lXDxE/QGaDZ1ydWxCO42yJf0O0yyVHJlU5uhRy7lqV4MCc91pzM1MY+vmzZLeaRAnyKjAZTG2TU3h6quvQJqwpKj2DRWw5BsvTVO2gzCsozeYQ70eigqc8h6RWStfJ9GXzFY0lTvILEfXapK8VXpmmt4VIqs7Slfmsotc0scyTZmk4mIjSsRSzhTIskQU3vvW3yt5SAMEoM29HyegOq22TOLX69dhLk3keZDCtVQabRdF6kBluu4WZ8EqWzqf7gCWSsAwKqqWOvEZ/aJtkGw6roXNk1tRbw6Vx7Xyz/0sgasLH7DHQvyi9bzKW2RWBgGDgEHAIGAQMAg8aRCwHb+uq5K6rqRbet3r/hCnnvb38DwfF/3gAhloksSi5kn1JWWjFo5jl30Owmvf8FdIizou+s43sbxlI+528Y53vw+uD/R7Uyj6WrV07ECq56SJi6985btwnAAXXHABmGKJeVKnpzbgVce+CAPxyfPQGFkmitoXz/s67GAU53xW57Xsd/tYufMa2Ehx5x03IWfNddjYY/dnCtn6yEc/iat/dgsc24arXDhUXZVPnVJUOBr/fYum+hRWGKI5OopXvOIV+PHF/4l3vPMkhCEwiAqMDjWQRCnuvuNOrF6+M3bffXepttUIAyQRCRKpr4XMUmCJ++3JoYfC9sCisblFNVW0O7z75PfC8S285EXPxdbJSFImkZ2zQpPr1jE8PI6x4WHBZWLlGvzJG98k5P2fPvYJDHo9jI00EQTa45MKnB/U8Yw9d8fvv+o1qFHtnCeoi58tVstqYHz1bgjrOR64dysC8lIng6sycZWI6QaRObASBknZ8JSSAgEy8NK1ksROK7Ks/KQJuGJVJ86Nl6byVzhwc0+CrRI7g9ccxnv//gzc86tf4KQ3n4hkMABCC6puA0GIt//ZSbh5/UYG3MObo25NguyjX7dgdWPUewW0c4cuTYrcE1/p1Cmg3BxJnkH8eJGiYdfg5y62btyCa667CZ1+iiCsS0EA3hzmdXUlQT/pvza3U/UsxHlkMV5m2yBgEDAIGAQMAgaBJwMCdsFklsqCSlNYrosvnHcefJ9VjXK8/vXH40c/vhgjI0NgrlQGuLDYzarVu2OqmyAufDHxNmtavRtuNdBcuhwvOuoI2bHnqhXYcO8G3H3PegSBj9NO/yDe/OfvEpPocccdJ0npDzvsMMRJB3Nzm7B+/d0SVd7txELo/uJt/xuO6+DEt54gCmd9aBgHH/pshK6FIu5g3a9uR78fI00Ujn3FK/HJT34NtmNLNDwrEdkkqCzbKeZiTbFo7XXJoJMMeRzjA//wDxjEGb761a9gv/2eBd+1WXQK9AXdd78DsHVmUtTA4bAuRIfm3c50G7nF2ka2BOKUIiIsXo/arUUKTWNyKuogb3RtaBjPPuxwZJ0ZHPSs/XDllVeLStpsDOHPT3iL+NeOjIziwAMPRJGmeN8pp8D2HHzoH96PI498IaZnZiUZPysUsf471eqpbdvwN+84Sch8FaZD2lUtiolRrRpAf9cMuOe+BzA1RZLuSGQ/yaXLKH7LRy5SaAFHKQrEC4tFcqoXBjTpeTOQTonfqs5ay1l7umoU81FRZbZ9/PFbT4Rr57jgy+fjD37/Nej3ewhtD1seeAA/uuJK3W8Uoz4k4rYovrwWy8emJP+SrlXBZkUBKr5UlllRwKZ2XWWXSOEwP6+ysPmBLRJoR6CY2YD3mmjQT5QKqoRUkVWTnBKwef/lhemaLYOAQcAgYBAwCBgEfvcIuOIL6VJVctGbm0FjpIWw5kty/fHRMbzmNa/BoNMFK/H4boCIJZV07LTs+/Vd64XQeBbgMZw9S3DK6WfgsquPw+Zejn322QeB5yKKLQw3bPFJ/Oa3vimKquda+JfPfwF77n2g+F4+c/9DhLTstPM4Ntzfhd+oS3S7bVsIpSxTgWfufxCSbAA3aOHI5x6ORhCg3+6I2f+Pjn4+4h7wo5/34CgSUp1dnzqrYjUkiEiIPGGKVkci2+uNukTaT09PY7rdEZ9CJSUnC4QSy67w9pPeLubkJFLwlYVWa0xqyNOZgWSOBNXmpVgiVUhxjoScypYwLjH5W0EdH/vUJ3DevsdhctNGvOD5R4hPJIPQwnoTblDD2WefjT123w122qPXKj73uc/hT088Cb/85S0YHx8XYt9qNcTUP7JkOV74wiPxkt87EnlaiHtERSQXHiuyMEqmFlwP6LRn8LOrf4H9j9sfhZOL76ZFlkrfYov+miwlSr25XEhaxTSuX0C4VyfZ0nSYrhoSa2TZKOhXKguVVYU4ShG4IVLeELvAz664AjvttBPa/TbqrEevC1hhaGgUfMHRZWR1JFPh2igclmLgjCoKTJADTZCdDD6T8FPBzVOAZWkthVtvvhUJ/VzrI3IWE/LzaeV4FrCh5G3pZP3liM3KIGAQMAgYBAwCBoEnFwI2iRv9/mhhbQwNod/pYOvWzUjiGJs2349Bry3sy6OyyMpPdob3/d17MDXoo5N1sXqXCdQtII9YrV5J2dC9DzgE9923AV/9wufgWx763UQqI+31jAMx0+7h2GOPhePYiNMEI+MT2LjxTsTRtLAJpkV64IFZ7LV2b0xNdzBIUtE/WYGISuCLj34l+tEsbrvlGiHDvfacBJ1sm5zGV797IcZWrIDjDOAxOr6wJUdmYrtISkWOVZRYirSgKkfa4jiY7bXRH7Rx/GuOQxiUOTkB3H7nOsRZgg984Ey4eQLXsSSHapa7iCwPkVUgKFIhqAye0lXedZWrvkOVrg/HTkhjpZ78xKo1iAeb8PF/+qiQJkaX25aL1at2RTTo49XHHqOJlBfAaw7hdX/yJ8iijpBZVkWiMimpiGwLWzY/gG9+46sS+OV7VIu5LKin/CavEvxh+3j9m46Dp9o462NnyktCzCAn8Zglhx1D6owAWRdhkYnbgkRdkYdKFieqqDT0u8gsH6lFLVIxbSr4YgJ4iOwGYsTIFfuwxO+UwMTJALOdGSxdMoZepwM3Z57SOu6fnsSanVYhb8eLOKgjAWRunWJpIs+Vqxw4ksaJz+gYYquOwp6F5xaw8gK+48CyYiDu42/fcybcRgsnnqRTWJGUS+Q+3REYTCdsmuMl8WYZAimkxh1mMQgYBAwCBgGDgEHgSYSAxapCmocwL6ZWR+lfSE9EOcB6QsJWShVNfPcU+qIpMuWTgzrPzBkexNKTDMkuTbJiQ2bkOfslYyqVt3lli8IXM24mkuDdskgOdXRzyrr05WmsmmkVmZjYadq2bI6UB309MvZXsK+2mIITq4HII/1M0CwYOU43BgedMEBIGinCarKgzlV+lWIEnq+fpBmeDckiECNBkFhwSXL8GEgZYe9i2mOfBXw6EiiFmH6n/B776Ds+g9bRtHpwUhJ4D7k3UhLanF6qMgfSSlquiY5Of0QVkt84bg5WR3fzTnE/Z043hBIe8QXVqiezgrI3Tb6FjxEvFeG+u2/Eoc97FbZOxphsd9Bq5nCsAnliIVAukm4f+XhNGCnPDhZx3VQKSTE4KoPN2qQ2swD46FoWfOaHZU5Vi+S0JeNy7QJOOiOTUmBEvk731FRUarVPaN6ZxR5rn4X1D2zDfb0ultUAv2BC1BypU0fuakzkaeBEox5Q60qSsT4m2AyM4g/SGTgM9LMsDA+tBMZX4eqbbsGq5U15kpi3tkYLgdxz/qAXqn62Sbn1E/nE/Ebqewd84AMfwKmnnjrf6QknnIBzzz23vLd692+K/J8/2WwYBAwCBgGDgEHgaYgA054KM1r8B5MGUfleEkrioo+TMFEpJLHimh8SW1IiMXSL5yUVQ+3ft+jPP/NmllH0PEGuW3JWkiqpj17RhYp5lTdEq4P8YqOQ1ECaiM33Lv1JQXgx3zoMihEaUnbAcyzqfyUJlN161GWL8khpqq+4NE3IEuTEwP2yYpXYtcl+dQooXofEnLSHyhxx4D+xPzMwR4gnFTv6w9oyTxIZtuaZ4gvJa8gOhl4xuMoGnQMkCT2Z90MsFX5cVy8WQk6lI57A8xSSNAJcB6v32g/7PWMNfDcVH09lOZicnoTP0lC8dQ4TpMblXZyHQ+ZPpDgnuoFQxZa5yT7iTGgdKKYk440iAeWlHRdvedObsMtua/CTyy+F71CBp2QMTE1P4dxz/gUbtmyV6lJDQflaQvLosCKVEuLLWWz3KKgACoHQb9aV6PX6cLxQD6LIkBQO/vJtb8OSiabMg1AEnodi0XNMzOe/btf5Q4BsdhkEDAIGAYOAQcAg8DtBwBWCJJ6F5BaaYWgyqtU6MgpNgDg+ynaSMl8UP/oryjmKeR61oijmbBIxi8nfNbW0GIRVFBCrukxTMwP6M1IJZICLVmmZfF54nKSJEuWQZIcqIAmbskW5bUh4Ug4UaamIVYSKJnsqgwArspOUKvpQMnWRxUhxkmmaiksmteBtKeOXuQjhK++FVSqVFhVSVfIykm8SMI1KDYyGL3EodI5VJshXrhIFT4ZBumxTnSSELAeqqbUQu3LoDOVCEYvKlro6YTvzswaK4VYcLwmu7qJcyXhkKKVnKGnvwr3S5DoItSLNbPjfv+Df8TfvPBkn/K8T8aWvfg6FU4Ni9BRTNjU91Fwd4CTZGkpyzitKGVReiIFKQsypPCq4os4WYkZPqapyboWCxegkq8CHP/KP+JdV++B1rz0eMdNj9XmWguUUQmKHxlfirW97O5oU3Jlfls8NK1QVVLf5RPnIFAO3yIkZzEUMmQk2YbZaxFEGsFJWnuI1x/8Bzv7iV/C6Nx6P7iCVkryM8i8YLWXxBYMOsxpsBnixd6byFSarYTU/DQIGAYOAQcAgYBB4kiBQVnHnX+pqITMh2aSsxj/kOyz02xM1kuqp1taEt9J6Om8qZ1J0CqwlKZCeWL3HKQOntQm7kPKTpXrIQJuSQNDsL4RNaBAzrStQ8SOZZWonUmSt2Opx85uMVIgfx0TCqOchiYVkMNzSY95+ViR/mkjvMFPps1LbSBbnF0XCyyh9ujhwNFIjdsEFlAnZJWpch/7I+MQ0rxPfC+WzdJZWSXsk88xhFamcxytxjKKzllWPOA4xU1fsVFpsb6ImbjsuvSSG77jwCgfByDjO/pdz0ZjYB3esuxe77LoKg6gDqyylakUKrk8iSKcMqrh6kSeB7gUa5flLiKosXF/0Ve3XSfmUeW63dTC+0yrU6AeapFBJgTBsotEMMdueQe77eNZ+++LDp50s12HkPrtibgT92GiCn4OuEHx9cXXO1jJdFItktWoNwInw059chR9f8jOc9bXjRDf2nEJ8Yy2qtkL8C7gMYBOll7iSiBOreTDn52Q2DAIGAYOAQcAgYBD43SNA7UpGsUBtWO2m/MPNNdXP6rs0JaclWeEXrjW54997MUmXiivJVXUaL8AgFVLLHTmUmLvlOqRBixZVQFE9lBrv3G9ppU5ILa+pr7uYM80b/kkmOTwKeWLeJ6kWxqx3lnPWfSyMn2PTH6XLYQo54rU1RvOjY5J+4iKjymRe823kujrDJjGyyxr1Qvgtr3Rl2JH6c+Sl20ShPVPZzTzuJVnVarceRTn7ksqWvqrzA9Qb7CPwmR5LB4MVgxR5mqPf3oz3ve/vkcRCgeF7CiGrODH9FidVkBbqYxWNk4h4uU8L2HN+pLLiG8s7QiVcTsgxPLFMcsn24y4+dMb75YVikCbYtm0rsjTFzGwHP/rRRaBL66DfA1NYcdGzr1Rb7adbkDAvmrw4P+RA3I3Q77bxv//mHfqdynMliK3BiluSWkqjpJNSLSBW3U0+k4ufnx3gM18NAgYBg4BBwCBgEPgdIWALkRSViQSQpniSU5JUPSK9rhSnMvipKIlWrhO0C6ehZ2pB4kbljWbphRygNNEL2SqoIDL8aWFhJSVek4ZbLppGkGBKjUpYtPmXNEzGIkSX5JDkWSJf9HEZuybXUpG+JL2Vn6e0J48r/1VklgRLlFvphTS4nHj5nfH3olOKqjzfSCpAKcVeqO4tIvWUeUVQ5fh3II4cU1lmkz1VGHP2ougpS+cXLXEg2SNuxEfwoxPvooXqK83vWqHlAd67hXtFwphl9JlwkUc92M6wkNIo6+LbXzkfY4ED5TOZUwLkzJIgWqkMrPKTXQyHuH4wt1O58Hvl0ynCKW9VXiDnC4LtIeN9HUR4+1vfDlYh6yZdRDnL4Oawc4txa0jtDM2gBhZr5Rxj5FKByy6YTzYDMeYzwqkTTk3kHVFkh1oNId/X3fBLTM12JFevZ/GlgD7BBbI0kudHMBTfU+LLFFPsjMR6ezyreT3x64oSs2du7/i98ueuji0+Xo2mOsa21XZ17Mm3nkdWNnSYpN5Xjb1al7/cZSiltOGP8t2Bs9Xn/XfNUY+Vj/LCdTnWhUUPb9H4Fw6ZLYOAQcAgYBB4ghBwWbudf/AsqmzlskBBSs4j++l/OM8g4VihpIqqKCXLxnPRhlnxWtR8iV0w8MX2WUhIlqp/+orWPe19qo+U12NqITdAXXbqwCK2ZQS+B1aIKrtm+dOyDXmTJOYvswBIBn0Zrh6Ltv6zBGp1vYX5bm/pnR9dSfd4ASaJDxYSu1t0fazAIEH0F7m18nxLB0cxMKpcZK90zdHrMegAKu60Ydsh4Psy+mpktuPBcqgtugI9t6ulmoX+Xn4rD3OOXIhJwFxLHFFD11mt+cPyh9cnmLIQkzJQit8lLypfF8pRVH1qgbM8hyudnoxhYbJwJW09OKJW26g3WppMsU8Addmv7wdzn3JxvNLLhOZ9yVVbYimoO5LYX67Fh0H61wOxWJEKLE41LmvS7FG5u7oEKqtiMfsDyQQLcJXTKF+D6JTKYhRVn9LFf8kPeUyoogvboWuLaM763YouB2WwIV+c+ByJH68MK5xXjW3WzFWLlXreb2LKVxT9Aqf9xqsplPek+rodIZ7fuWhjob2QLxmrPly6Sy9qqze1vr7Dbr5gMYkHcXcSuIUvhR8KJ0KGQHIRe1YuKjtfDkTZZuVgvmTZfFnRL1dBwvxlHlLLQkflaNqO+Duz/C8LhlQveVbp4867K/dXxs2X1kWEUp65Hcb5sF/ldVXGFSupQycoW/SttnlvaFwokFp8+VYI6VTv6Gdbd7mA48NewhwwCBgEDAIGgUeFADmS/KF7VK2rv/LSePF/zAtna3L0EP9RLw4+Wmg+TxwW7SrZxCIKVl6Xq4WrPsQ1KrLEzhaNdWFz4eztrvcIXxbOXcRytut+UZ8LjcvLP/IYF5pX7fR6ey6o+19o+3CDrfrY/vjCedX4F+E633SBSOuBP1Sb+caLNna45vzFqv1cV9fd7pYsvj3zB/Tp1bn6MvNdzm+Ux+e/L+C/gNtDX5M9zp/2oC+LpvWEby5ifEIW+X1htBXJ3P6yfGl0SpV9EeHiHMgaRf3V5FQTsoX+tu/nv/ebeNRYAN0yuGjPDBK/XLaphnPuMnxJ98BgTI2PJsOagJLQSzo5W+irzFlcVbZ/PBYmtxhi7n04Zr1wxiNvsb/yYSHh1q8VGnfy0mrMj9yJOWoQMAgYBAwCjxeBhb/uj7cHc55BwCDwsAgIuZlX+krCLoF1ZG+s4MZTNetiVS9x2rCoCNK4rQ3NVRYH1taiU4V2LclRqBiWzcIQFWsr+3nQaKrj2xPd6rqLm8twKv67wNEWN5HtUrPUCnl5lIGBkmSYDjtFAubPZdYHT/nwJYCSrjs+Ms6PlefgoXAKnWmD2RrkZaYAmEOM1c9oYSnzFrMUheVSMS7JLFXT+bcN0kf9Rbiu4F0enG/zoCk85A42p3sIr03h2rZsZIJx2T+zgkjFOKaI4+t4he1Ddmd2GgQMAgYBg8DjRMAQ1McJnDnNIPBoENDJH0riKKocqR1N2fk8wdICYtlG/A526FnpXA9UI+lXrK0UJdkU32ieu5iJLSaiiwkUt6tji/dvfz32RPK3oyi50KrqY2GP3mLQHH3M6UdsQbmZ+Ja7oHsMS9LSP9uVfLnsn+nDcpcBjLSMcF6lIiz+wvrqrvgTL1L4GYg3P9VqHHSd0PMRVwliIhLtfMPt4dlx2Dt+501TGRy6pnCMtnZk4Tip4tIxQ14KFgayYw/mu0HAIGAQMAj8lgg8/F+p37Jjc7pBwCBABKgGPtx7YEWw+GuofxVJVkmEdJo3XRyBqirJKSmbrEveVRXH2C5dxm8CfV7N/U0NH89xjjCFpZh9Qc+ZydiY9I0LU7VZdr6IYOo5aSrKal9aHSYqZWiilCyW5MhSAuSRxsSzqgpsup1w1Ec65aGOUfKWu8bAQU2qdwzU0sU49EuDHvtDdWT2GQQMAgYBg8Bvg4AhqL8NeuZcg8AjIkASQ71tR/9Q+vgyTJ1KHZmnJ6V42ZWyqCJyP3OZ6SwZmrBpg3+BRNKv6ctSmWQOiAVd9MHD4dFFi/CvR/drX/LghZPJxh7EyKqr67wYfq7gFR5y1GRe7CNlJgoGPuUFLGeAwI4Q2qlkcWCSCVYiziR4LIYqEgxyZgPRGSGstANYMZRlSXlbgW3RGBhspUOb9FqyTxBS+rxX06Q/7KJzFiZUbvGYfHSWEhYWcVQPyCMgTRExOIpNeL+sTAo80H2WWvEO6D6oa7PDIGAQMAgYBB4fAtV/4Y/vbHOWQcAg8IgIMAdDVZlrviFJFdU58qGM0fk27ll/P2zHhW278H0fvlfD6Mg4Nm+elvRaSa69LLOsQDQY6JxuaYI8B2bnYsSxpkraR1OTxpyFH0oZMV+UoiyOYxnKli1bZM3oeC4MYMoy6oVAp9NBtZ/X5JLlHKuS/LY0oSdlP4NOVwjev3/93zAUjGDp6FJsmiQtD6EyhThJUDD9Ghc7AqJZrBxvwfV8DA+vwM03rxOiF8UDHaUvYyEjLPDnrz8ensN0Yg65oqjL3ba+HruTbACCDQOvJGFbWZZZXy4hVuWloyiSee2Y/a3f6+nGkg4P6Ha7sFSM5+y7DyZGRtGONBGlPyprQ7PwBDlvUmRIs3w+m9zcXEf6qfDSnZqfBgGDgEHAIPB4EDAE9fGgZs4xCDwqBPjrtehXbD79EfPe5mjPTOGSn1yGMKxj773XwrJcSclmux6yPEGSDKTa1/XX3wDXsdAdxMhzC47tgWnULrv4RwjCBj74wQ/DDxjaQ1dLrXt2e104tiNkq9/vC/HrdiL0+xGCgKWJgaVLlwoJJcnr9Xq6QpzrynaDackAac/jSZLBcVxpQwJNpihR9TSHO1oh3ucZz8DYUIh+p4+pKRZfgOThdZgujZXT0hR51sHN1/0MKo0BK0UWD/D9Cy4UNwDXK1OiMYCqhO7mG28SJXRsp51kTTfWVquBmelpDAYDGQPHkeWFBDSRZJOAJ4kul+vX9Fw5lzAI0Wox9ZkmurOzbdmW+ZDIs9SuDbSGRtDtTiPqdKREcy+pXFiZkzeGwxRYnQE824XnOkLs6ZYxPNwCyWmFi3RufhgEDAIGAYPA40Jg0V/Px3W+OckgYBB4VAhoUqrlvAL9QQebt23Cq179GhTKRlhvISlixEmO2Zku8rSLG274KbK0jyOe+2yEbhOWCtBrxwiDuvhHvvKYYyUfJ1y/EgmRFswuWqApBFOhXqsJIU0TBT8MESdU/bQiSqWwIqaeRxJpCWElOZVgIMdBXhRwPUeKeJQ1MxCnWhF1XZI/G36tjjzPsM/+B+CDp5+KocDHme8/HYNCIQibOuuv5cAJXaT9Wbz5z09EGrt4wYtfAEdF+Mg/vB+9glXAfKSpgutkUEUEqBhbNndg14bwhjf/JWwXmJneBlg5WkMNIb1K2ei0B6I+syRumjL4TMFxKZsyKWuO9tQ0pWq5S2nM4g9aUh0ZGUISxSAxnpmaZFwU0oR3yEY0aGO4HiJ0AvTKwnR8BbBVAqQZglpNcM4LJcSY94zKrOvaQlgf1SNhGhkEDAIGAYPAwyJgCOrDQmMOGASeSARICqU0mMhxQT3A9ddfi0Ec4ZTTTsXWqS3o9jMkCcsBK0mptOdeuyPLOhL9bvs+LrjwAkxMDInqyJq5rRBgsvpGvYWZ6QHSTIlqSvM8TfCFJLZ3ROF0XYu57zEy0hQS1etHoiYODQ0tqKKAKI9pEqHbmUO73UazWceGjVuRqQK9wUCIsB8EyMhWmR+U7gGipjLivcChzz5IFMwrfvIfiPMUrOnFlE2SlN+18Kubb8E9906im9l4/R/9EVwSvjwW073tNuB5IRzEcJwU6M/BcwOkuY+XveLVKJTC+PgoJy03JgxDOa8x1MLk5Czoz1oLa3BZfoQJ9Cnh5jmGRodkjEWuQDO/VK7LgbnZHjiXjffdh9GJCXFv6PYA8veJ5eMgDiovUGsBvRjoDbpyD9PJrUA0QC/pCRmlukxcJahfUTXWZPjBT49+MXjwfrPHIGAQMAgYBHZEwBDUHREx3w0CTzgCOoxnoVsF3/NwxZWXC3l8+1//byQpUKu7mzlvXAAAIABJREFUCEJWUVMo0hx5kkvJWMd1USQRvvOdf2cBLMRRH+j3RO2jXyhJl+MH8FwLg2gAKqBR3IftWPNqHk3Q9FOdmelgMIhRr4dChGkOT9NUSBXN4lROPd8X1ZXkdTBIsXLlUtRCD41GDVGUIY5T0GzPpVJVpRKdyrF6l1VYvWoYnekHsHHr/RgghctCZfyRp7joexchLVjGbBQv+72XYMlYS1I6zXYkZarEz9uIgaKLyU33CWn3G6PYc++1KIoUWR5DJVr15LwHUSbEcMmSEbiMKVPaxJ4XGfJc2+bp50smS4W4NUSCTt6qMDzcAP1PV65eLUTWq7lojQAzcz15CfDFpcFBpwc0AiAIOOcc3vCwRGEFvicmfr5QUJTt9WLwUmFYVbhbuONmyyBgEDAIGAQeGwKGoD42vExrg8BjQICKGSO/udbR9oxG136pFnbfdTdkaY6X/t6RUpmXZC8vEiGaUZLD8ekvGaAXxehEXZx33jnIsgHuXnc7JkaWSRIA13Lwfz/0QUwMN3D+v38XbljDdZdfieHaOGpeC17gww0suIGHWtjE//3Ep2DVAgwkOMtC4AdYu9suINl64xvfiCUjExiuj6BWq4nf61AjhGsxKh9oRwW80IXrerAdhSyP4HoKGaOX6Gvr2AjGGjj/X89Fveji3X93KqYyG0kfsFzazyP82xe/hSxYghPefQZ23XkN/vGMv0XDjvHzm+5FOwbagxyq6AL5LD5/zmdg2cvw8Y9/FvUGELopbrj+JtQbYxhbugy1WgPjI8MILA81r4EPfOBjUk6V5NOxLczObsMeq1ZhojWC33/tH8IPPalO1RgZQ7vbRRbluPj734fjBLD8ugRtBd4Ydt5pBfp9G9NzfVhBAHLxJAM8y8cXzjkH++2+O0bGhjFca4H4r1i+FCxLPLF0KS79yU/m3S30g2JU08fwC2OaGgQMAgaBeQQMQZ2HwmwYBJ5oBOh3SvM1E9h7UHYAxZRSYFopD+9+x3sx0Qxw203XohXa+PiHPwo7d5DECrVGC3FhSz0pBu54to2a68G1bTHDD8o0R74q4NFf007QdoHJPMMfHfVKjHkr4OUjLNwE5QNBLUPotfChj30GX/rOhUhdJamYkrkItUEE34Yoq3lkoT9I4Du+JMca8Qq0XAtf+NoPoFwbcQ7044Ek5Lcdhre3wRgpasRMFKAcB6t3WYOG1cBd90SY6rio1UlfBxKElLQVYqfA2ufuL2A/Z+2eGLOBs8/5NPrMalDzYGehuDV888tfRYIhvOj5B2OJD1xx0UU49sXHI8pamBnEgOshj/tY4uWC3+mnnoZpZocaWBjMziGsOWjkGVpw8b1LLkfQDCUt7bOfdwSGmi24WY4TX/cGhK0JYHgpcos5XGcQWAMceOixyBoT2Br14Idg0iygn+KU952BDbN99K0mNk92kCUpfnLxD+VFJC1svOXtJ2GgCnE3IFFWzKRQLPi9PtFPmOnPIGAQMAg8VREwBPWpemfNvJ4kCOj8oBwMPSd1bXobTOqZdgd0WBQl1LMVzjj9VAy3hsSn1LYDXHb5TyW1UqediMI6uW1WouIPfvah6OVzsEk+nQDv/D/vQy/t4tVHvxh5dwbIPXTTDF44iqn2Vsy078fM7FZ4eYLAdnDy37ybRnRMdfvwmyFangvf8nDRRT8Gwhq2Tc2hH8W4965bMdIMGXqEz3zmnyXCndmigloIycfKnKzMX8qPRMG7KGwfQ+NLkGddbLh3G7ZuTpBkfVhWAkqpeerDG2pgv0P3lQCmXVavxm6rJnDdNT/F5rkUbQbfZzbuuuZa+F4dhx35Muyx6zCKaBJn/v0p6Azo39nEXZNb0I17iOM+VoyPoN/tshIA7n0gh+sz6KwOxy3QCBh4FSFotPC9C76P3qCD//j+BVBFjt7cLFMQoB8nWLPbXuhHbWSqg9tv/TlmZxPcs2ULUqtAb7aHOMqlbdTNYDea+MaFF2GoGcJxbTxjz91w6mmniYK8cdNmXHnVNToPK9Vya3HxAPPf7ZPkl9IMwyBgEPgfgID5H/N/wE0yQ/yfi4A26OvipNzWSyFkh+b3yV4HcZFg45ZNyK0ccd6HH+aoewqvfNmRaAUhli1ZgWOO/mM0WyOYmRsgoUm9iEGH1EGxBM6KZ6FoP4DV2Vac946/wGx9HP9x3dX45eSv4blLEFgtFIM+NtzyQ6zubMPKbox24sFvBsjzKURxG2negDOyEr/oboQzFkiA0fLx1Vi3cT1+ds2P8curLsZnzj4fA8XE+jnswoGjasjtIRSWA8XIefhwrDHAb2FiWYZsy50454MfgHJc5GoKX//G/8MUhvGJT30aq1e0gLwPTIzh058/G/GWdfjHj3xGSDd8C9+88D9x4/1dvOcfz0TS76PuZFi3bp0Q6x9eeQWWh1pNTi0bN2xYj7t+8RMsCRK8690noWcDketAZX0kDvmuhw3r7sILnvcCeKqBIEvgW7N42zvfjE12E/956X/g1usuhdOlBJxj59XL8PXzviAlWeEUWDXSgFNzJPeVXzBgqo1Xv+qloKdpkfQR1mo4/fRTsW3bVszObsFznnM4bFv71FpSUat09ahuv1kbBAwCBgGDwG9EwBDU3wiRaWAQeOIRYMJ7UVMtIEtijE8sRac3QJommJycxFDDRzNwsWS4hjyNccklP8Z7Tj4d40vGdYBSniMeKLjDY5icmtNBSFsnMb11Cj2vhd322RWhr+BlKUPzJY3V8G5r0KzZTG6KfIZmaweWk8AbrSFtDuG1J/wlHGXRAQGdziylUonMXzIximbNw3e+822klIEtRxLvF5mFRNkS4Z8XOmF9wbKulosVKxrwszZuvPwSJFkuwV3fvvA76DsNvPzol+jM957FyCPsc+jBGPEULr/0CnC4+fQ2nP/1byMcbWCP/YZQsKJTHiOJc9RGx7H73qsBDNDpzekyqnmBnVYtx5IhH5NbN6PLKSsbNc9BkqUSlc//6GZmIszOzgGDPqKpjbj+hiux6oCDcPjhB8PKU/j1ISTdNnNF4SVHvxw7T4yJwt1hIBZD+1WBVbsvRRr34LiW+OhOTIwjDBuYnm6LS4FrM4iNPrlcFvmfzufALQ+ZlUHAIGAQMAg8IgKGoD4iPOagQeDxI1CFQ+kU+mWs1KLumDpqdnIabhCgHw3geq7UsG/Um7jl5psw25/Fr267GY6dg1Wh/u0bXxM3Aeb6pGPkkvEWFHMrgRkAdAL66679OZat2lX8QWuehdAtJAAq7ffAVAHPfNa+iOlD2kvBQqwMdrJ9B6oW4D8vuxwNKo8qkjyjrMwE38XYxDCQxYj6XbRnGfHPXKNMK8Uyrhyz/m+EVv5Y4qUCHHTw/nDzLtqb74Zt6eT/t/7y19j74MMw3AAcNqbzKpP+BwFU1MbmX9+N22+axYYNG3DPphizcYHMYgYA1hpNYefALrvtgaAB+LDRagwjYT/0gO3M4fBDnoV77lmHuW6MXHJMWeh1umJub/dzDI+GGBkZATwfYctHLXQwsfMu4g7hey7UXBs+o7GyGPfefRcOP/BAjDWbsF0XISXRKMHZ//I57LHrLuKz6/sOOt1I8J8YH8fo6Dg2T06h2WBeWibW4r0hNhyjWQwCBgGDgEHgsSBgCOpjQcu0NQg8LgQWKWlSLFTh02f9M4YaDVz+0yskGT6z0DOrFG3rvW6OiSXjQHcaw6MefnL5hVCqgyhtS9AUrADoZeh3Ion6rwc+kFvAaBMHv+RQdO5fDzbsMqe8GMXZqwcFDzfcfTe2qRy1lidVqZBYQBTBi3PsNrocZLa2lSPK2khZ7SlJMTc1i9rwMFYum0DTYyUrF5YXIHNrOmcrUzjxfxKbVZYY3+TgI+f8G9YMAfV0E6iqfuSDn8ft6xJ8+PNnIVHAmM/r5kDhSNnW4bBAS1k46/T3432nvR9928Ezn/MinQAhsCQ5/ohvY9PGe3H/A13EaYY4YxEBuiPYsDwPN17/czxz333QaAZwHRu00S+tNeArB9awgz4ULD9D1O+LP2zWmcV9t9+J0HMxYA2uZk2Cs+CGWLPLrrj+6p/B4stAThFaZ+s/+NmH4Y5f34H23AziQRdxfxY333SDTmNlK+yyYjmeue+hKFQmabGYc1X8cw1JfVy/OeYkg4BB4OmLgCGoT997b2b+O0TgkEMOkapJn/rUpyS5PpPLU6TjwnKd8D0oJsHPUtx6661CgPqdDuJE6UTwQQ1pXsBqNNCO+wzBBwKFpSubcOY24vabHhBqulUBCXxROec2bcGWbbMYODayESBlhVI7RLNwMDzXw51XXInYAjYNpkBLvU+FMy2w/p6NmOlGOOiAA7Bk2JU8+Yzmpy7oMQhI1Er2xQinWBPugYeDDtodrQbQnuvjM5/8MnyvhTV7jiAk34wV4NeRMujJD/Dc5z8P6M2gs+FeXP+LWwG/gaXLVqBuK6j/z96dwFlS1Wfjf2qvuluv07MwLCqgLIq4ixGICgYERTbja4wxEhPNa5Y3kTdqYgwuf2NeE+OaNyS+Lq+oCCFRcUsC7kncEowLKuswM8z03nervc7/8zt1a6YZGF9E4lTPfYpPc7vvrVt1zvdcmqdPnTrHtICJaThKIe6vor+2BNtpwLYsZDLmoDAQDiPsWYn1wgITAWBL6XIDxTCEbzpYHMoN/EYZ2BsNyB5POuUxWLnjNnzn+zvK4G/lgGMjS3L09y6jF8dYC4eYkGlbGybQ8HSv8TCJ9EIKySCE6fs49thj9YpcZ5xxBvyJCdx2882H8JPFU1OAAhQ4PAQYUA+PdmQtai2w/j8ziXMGHnniSXqieAmfr/nD1+hQKtMSLSyuoNX2ocIIhsxv5Ab4k9e9AabjwQ2akBWhGq2mnjD+iO1b4DgyaDOC9Popx8WWhz4EbWOIl//6y/UE9sOsQKQKuI02LnvJr8MMWkhVLjMmlX16to8ijBEggxUNdficDqYxTAflpfNU4RW//Ur9/qee9mQ9PtWVYCurSJlKjy21YY9Cao40G8KRVZzcaVx40XP0LATXf+J6TE8cBaUCvVypaPiugaQfwWm2kXaHeNrZZ8HEKn7wn/+KXfPLgOXj0kt/ES1btBxkca5DbRb38er/+UosrXX1pPgNGccK4KUvfwUs38STnvQkRHEBSybC6g8xMzEJ33ZgedKpHCMpQhSeD9gezjz9TLRVgXOeeS5W1iIoW2EYJTAsF5dc8jx9kb7RaMrkALq+cZxgrrMZTzvrbKx2B3Al6KaZXmxBloo98cQTEXW7mNq8WS/DahjWAXfx1/pDysJRgAIUqJeA4kYBCjwoAkVRKPm64oorJNLoLwNQL73s11ShlEpHX/JdGq0plfeVZ0E5JpQpA0INU4Z0ylLw0oGpZjq+avuOMoxAPfmMZ6lQKbU6iPVRBkt3qGM3u6rVailYDWXZvnrPxz+q1tRQHdv21IS1VVn2VoUGFDyoVquhbKupjE1z6s1XXaV2ZoXqZ3KoUD1la1u14aktMw9XFpqq1fZU0NYdl6pjmyqwLHXrQl9FuVJSkSxVaphlKsxjlRWxSopcJSpWabFXhdG8isKBUpEc+3Z18VnHKtOZVW3rePXIE89SdxUrqpfm+jhRf6BUPFRK9dXq2p3qlKkZdTQcWedVnfP8l6rdfWmWVO0JV1U/7amvXftRNRtMKJieQiD7BardnFMNQH8dd9wRak+q1Hx3oOKkq9RwtzrzyM3q2MaM+kGk1HzcVT21onbHA5X0dyq1fKfajJYyvE0KwaSCDB2V9mi46lk//zT1mC1HqCYsdZtSKpQGzHJ11LaOctyGTLYl08uqSdfWj57fUsHUrILTUO/82w8ooVVK6pnor0IlD8pnjAehAAUoMC4C67t26pWcWRoKHMYCpnRB5gU+8qH3odNqlHOhujY8Rya2L1duWu1H6EU53vSWP8PnP/9JLC6GaDVcROEQjakpPP6xp0L1+9jkK+RWgO98dy92rOZ49ZvfCMOcR5DfjSkDCAwL/X6EzDDxst9+BX7t+c9HI8vgyBjTIkEoN0nNdfDk856Orc1JZL0YaQ8oXAOxaSLMFTbPNtEbxIjTAlk2hGfmcE0DSvcSlr2YluHDd1rwpIdS5j21Apz82KfqNbT6+QCPfcyj4GRys1Yoq57C8QK9olMUZ/AaTbzo156HGCka07O4+ILnwtRr2quyN9L28Pjzz8d73vE2eEaKyekOpLu3P+iVnxILeOa55+g79j1Z0ECGJzgWdg/6uHu4jJYsruq2YeYyPRUwkPG+nWl8+rOfgl3IYN1Q/0lh+i7SKME73vlubJudQ8f10E0KpGEua8XifR/5ENJEhjKU9+hHssSUTN2aJQhXe/ib930Al/3qC/Vz5Q1So28Vf9WOJPhAAQpQ4H4J8Lfm/WLiThR44AKyypLcJiVdqtUmd/BLQD3r6c/ArrvuRG91SQefOMqQZ4DlOsgLU4fT3/yt39T3gydxX08wr9d6zwtcdfW1OP/s05BnEZDmuPXbP8JRky288OW/jpXebXjG6SehkJWVohx+q43ltIs/uvz3kCZ9tD0JcTLGNUKvSNA3Mty6sBP/8o2v4eFHnoCm3YAsbv+3H/gQMpXr2TxbbQ+ZkcPzLZh6LlRZIUvu5Je16BWM3IaKZTomAK7M1Wrj0Y95KvI0xabJKZx08vGYdBwErgM9h71toD01C8drIlE5Hv+UU9GaAoarfTzi+GMx0bSRS5A2LVhwIGM+L770QvSW57G6dwmWbWGyM4nn/+KlGGTL+Iu3vU3PXmAYBkwZt2q7iCwL7elNGOxZgZEBvtlEw/bQ6swiTVOcevpTEA1X8dyLLoDp2SjiBBf84gswOTONW277EdIihW0pPV0VfAdP/rkno1ApXvn7r9SLJ8i44VbDw3Of+1zERYQLL7wQgygpu891g1etzl+11WefjxSgAAXuj4AhXcX3Z0fuQwEK/HiB6j+lN7zhDXjta1+7b+fLLrsMf3XllfsCqkzMhCxBkcZ6qUy31SlndYetb5xyHANhVsCQ9CMTzecZHEtCmoE8GeiwaDmuvKDvOh+kKSL4mMpdmaIU3dYAXrYTvhUA0VEyJxMGscyFmqPoDnXPZeQ1kPT3IkCKU089Fd9fBX7+vAvw0fe8C82oDKBFx4CyHGSZLKeaI3cdFEWGpurDSGREgovYbcI0ZKKpAmYGGJkEwxyhsReBPYckMeDatraQJVQbzTXkUQOW1UTm6KGdSIsMBQZAdxdcr4mhfTTkxv08TuE3FFJYiLNcr3hlRENkyQDoNKGyBozchO1kUOYQKRyspAEmDAXfzqAQYpC7cEwP7iCFkovxPpA4McI0QtNpwollRIF0h2bIHA9rcQzfDdCUm/Z7S8gcB71mB54MQ036aNoF+pkPZdhoWwUgN7IZQDdK0Gi1IVOgug70lFiGjNaQnmRpRMOCBGduFKAABShw/wTK/3Pcv325FwUo8AAFpP+s+lNQyS1FtgvTdvU679VN8HJoR6ZfkpuInP09bp7cdDTabFevCl/+5Fi6t7LpBZD70vVc8AbQRADb3l7OwamTFdBs6HWPYLVl9agMdhHDtjt67lWz8OEUEXoL82h5DizLgJLgZ2b6Jig4MiDB1L24sGyYcsFc3yhl6tWU5FK2XitLiqmLaiMwZnUwc91RPRTQaEoZOrB8R5dbDiGbZ8qbmjAmH6LLLLeAybsMW5YMKPScp64j8Vx2bsD2fB32DblWr88n52/oG7hmnPJu/fLfAZoy75Ucq+nuY/bgQG5q0ueQ8gmc/gPAxKQsTiCbrAQ1MSWLWkFmmNUTHriiXKDp6pOO9ivr12l5ukzN8nT6D4UyfssTI4PyHfw3BShAAQrcD4F1v2nvx97chQIUeEACZexcf7FiFFoO0ql2kKfvHXZGO+qH0SElQkrg09s9spGJQgGmhDaVwpCwG8borQ70ZPqdZgOGTLhkOnqlpFz34u6vbhUodSpcf95ql3sUWpLxum3fa6PnZZn6dS+XSbP8dbT/PLKDHq27f89ywtUD3iuVLPfb/wtt/3P6zetOJmF63zn08yWSfHtfz0usLrcDjnlA8Fx3iuoN926vda/wWwpQgAIUOLjA/t/nB9+Hr1CAAoeJQHWVOcsyWWYepu9AyXyiUYKZqQmYhqUXeEpTBWd0af4wqTqrQQEKUIACG0iAAXUDNRaLSoGfVkACqixV6uibtAw9b+cdu3ehW5gYpLG+q77tt2DbJsdM/rTYfD8FKEABCjxggfLa1gN+O99IAQpsNAG5mSuJY8hM/v5EB3EUouHaaAcNtFplOE3TTC/XudHqxvJSgAIUoMDhIcAe1MOjHVkLCtwvAVlFVW4QqqYUiOIBYDtIErl7vbyjKs5SyEwCeZ7rlZ/u14G5EwUoQAEKUOBBFGBAfRAxeSgK1F3AUDJ/ktzOI3ev53B9D4XMTaU3mdEUsG1ZurQolyyte4VYPgpQgAIUOCwFeIn/sGxWVooC90dA4qgsIqBgyET7SpZXkvAqywrIFzcKUIACFKDAoRFgQD007jwrBQ6JwPrJ4hVyKKNAohRUlsKSSed1ZM1Ha1/JTwyqh6SheFIKUIACYy7AgDrmHwBWf0wFRqsGSBTNiwJFkaNQ8lO5KelNreakGlMiVpsCFKAABQ6dAAPqobPnmSlwyATUaFZ5CaSZKqCXaS3kcr+seFXF1ENWPJ6YAhSgAAXGXIABdcw/AKz+mAlUvaLKLHtL5YYpubJflCG1+oWgqgQ7ZjysLgUoQAEK1EOAd/HXox1YCgr8bASk59QwUP7j6bXmJz1Z+rNcglQHVMNZd3W/iqw/m+LxLBSgAAUoQAERYEDl54ACYytQrl9fTjJVBtHyyj9D6dh+JFhxClCAAjUR4P+JatIQLAYFKEABClCAAhSgQCnAgMpPAgUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIUoAAFKEABCtRKgAG1Vs3BwlCAAhSgAAUoQAEKMKDyM0ABClCAAhSgAAUoUCsBBtRaNQcLQwEKUIACFKAABSjAgMrPAAUoQAEKUIACFKBArQQYUGvVHCwMBShAAQpQgAIUoAADKj8DFKAABShAAQpQgAK1EmBArVVzsDAUoAAFKEABClCAAgyo/AxQgAIU2BACBYBsVFL5Xr5kq76vfh49fb8fqveve4Na9/3P9FupX1XH6sQH1qva58Dnq/35SAEKHA4C9uFQCdaBAhSoh0CBMtmYMH5MgQ4WLPj38oFoSpWehmGgUDFgFDqPGmYG/ZryYJg5MHIH5Ff6AY4KMHRzVO77X1eQFsvL1ioKGLDKIigD8qQ8KKMsg5zPqI5tyPdK/yMvG8bofQdW4Cf8OUcMKAWVu/qYhpzHVFCZAZgGTFPKm5Tnhbu/PD/hebg7BShQf4H9v6nqX1aWkAIUoMD4ChjSc1iUIVV6GSWs6k0C5Ppex+r5+0MlobTcJATvP2b1nOTUcg8lIdmA/qqeqx73nanMsvt+/Mm/kfopmIYxCqiG/AgpRfU/KwMSyNeH8p/8LHwHBShQfwH2oNa/jVhCCmwYAUONwtH97lGrYseGqeIhK6j0bkqfZbkXysllAAAgAElEQVRJH7W576fytapo9+y/lneU7xLr/YFU9q6OoSNolVRl5wOaRYKonK3apTqTfiy7Z+/x1AP9oayHBGULpi6DpQtfnUJ6cZVRFk7Buu/yPNCT830UoECtBBhQa9UcLAwFNrZAGWAkpMp3B6ScfVWrnv9Jevr2vXnsv5Geziqs6l5PbS3eVXgdEe37Ub6pWsbUrVL+JPtJmL1nOyijQHnc6vX9x9sXFHXUrdoR1cCAcsf9pxu98Sd5GIVrc1TCUR2q8xpKYrKpA+xPclTuSwEKbDyB/b9hNl7ZWWIKUKBWAhJ07hl2alW8DV8YCW/VWE/51X3/fn2PhpDeo2V07lMy3FO+u/dxyufvA0x2lzGnB7w0ypEHPPtAfizrpc9ffZSUBNL9xyp7UOWJe5d7/178jgIU2OgC7EHd6C3I8lOgVgJVVKnShRTu4EGi2vtgVViXSw62y5g8L57rHcvL2+JXhtbSu0DZQ3rPPxRkLxkeUG77jqRvuCoAJaFXbkIqb5mSYQTSg6oflYz1HJ23kGRajnWVXkxlWHokqLxa7jFq81GjyT4/afsZsJFD6XGulpxaDqC/5NgKKBQK5ei7vsphB2PS/KwmBcZQYP1vvDGsPqtMAQo86AISavIyrKhi9Kh76/QN2jpkSNDQ4wkVsNYf6N69KEp0UUZvedCLtREPKEExTdORy/676MVoOIz08+KWZTK21ECcxNoyDEMgS4A8BVSObDQ2OCmAOEl1+EziuLwBSd+GL/HVhBxX7siX8Z1JVuwbsSptlRdyLLk/KUeelUE1U+XtSvtspUdWxonK175IvO/V+/GNhFoDSZ5BPjsqy3UYlZkG0jSGylOEYYwkH32W7scRuQsFKLAxBRhQN2a7sdQUqKVAPgoRsAyoIoNhmkjTXE9zJOMI9VjC0TcShORb13V1XRzH0Y96n1rW7mdfKAmnrisXugrEcYwsz3RgNU0TpmmjkKmhDAO2LYYmLKscAhAEHiRt5nFcTs9kmIjyDP3hAK7r6AAp+0p7DdZWdJgdDoewTAtpLr2Uo6BqmHpfme7JNi3kwyFglTcwJaqQmZ+QV12zmkf+ICn/KHkgWmWslVGx0o8qXajllmUS0uVECo1GgFwpJPH+16v9+EgBChw+Agyoh09bsiYUOMQCBSwZLmhb+Kt3vQOdVhO2ZWF2dhaG6eovCVWWaeovCVlB0MT09Cy6vSEMS3rOJHAd4mrU6PS2bSOXHssih2356HVDHUiTWCEKUx1Wy3B670L3esv42NVXoem4uPiSi7GyvIZWqwnplDQNCbM2smEPVp4AKsPWLXM66E50pvUldmWZel9JoVIGGQJgeRYuOe9Z8Gwff/W/r0Sc5vK3yCiSVuF0f2Ld/929y3dfz6hCwql8yeX8HEZRdpWatsL8wk485Ogj8finPBWWbQJmNR73vo7E5yhAgY0uwIC60VuQ5adAjQT6a10gSRAEge55k8dut1tej1136Vdf/lUKcZogDkP4QYA0U7q3tTcMD1KjB94zd5AD1v5p6R2VcFhIWDMMTE1NwbEd+L6Pt771L/SjZLjhING9qTKxvmxxFKE9NY3TTjsN7XYDf3/ttdh1927dB7lvBjCVw2u48Cfb+Jcvf0m3V5ErZHr4RTn0M5NL/jII1Ch0GaQH9Vvf+Jr+Q+Skk07Wf4zEOoVKpNwfRyVi/jStVeyb41WPOUAYDbDtiK3YNDOtP08yGmTUWVz7NmQBKUCBBybAgPrA3PguClDgPgRa09OAbSGJYsRxDhkLqS/d77usL3Fn/z960ONotSTHNvS+1SX/+zj82D0ll/jlUryEUqUMyHjTT3zyE7AsB9IDLXSmacD3Xf2zAElUlNeQZTjqIUfjCU94HLxGC3fccSfStAyRUZpg0OsB0hMZ9XDtNR9Fvz+E47p6WMDi8lBbS7PpoCk3TRnAzp07sHdhTZ/zhBNOgFMu8DSKpnJsuelqf1D9aRps/SV+z/N0AJc/duSmsFyGIfAK/0/Dy/dSoPYCDKi1byIWkAIbRcAs18YsCky2XbgSbuRmniRCWiT6q8iy0SXrFFlRYBinSItU95DJPeSulcGXFYvWjWSUuCNZpMwjEoCqV6vHTAck2W99NCq/l7GL8Sg47RvGqHeU1zM92nH9+0bHHN1UVB2zPJa8lunL4VKGqkz7ctK+naU85evScvue1rWSm4vkS44lNx2VZV//8/r3wHYgmbJQBVzXhGnkaHqeDmlFbuqbnDIbCCWPFgoyT6jcZmQ7nv5DYdhfxRvf+GoYaYrXvub1iAwDa70IvuOi0W4B8SJuvunf8L7/8yH80evfhL9817vgqAI33vgvWI0Byxa9FBlSWCrGH/zPy2G5FsLcwpbZaSRxpG9k0vMA6HVRy8vu8u1oAdVRXSU2S1uk+2+g0xWtZgUYrYWlFMwihlGEUEp6bcu8K/+jMlSKPM0Q5RYSZUAPu4X0tkv7jrYRtjxIu8hjta3/vnqOjxSgQH0FGFDr2zYsGQU2mIAMQPX1lEbZcK8OF53WDLqpjiVlWNABIodSub4RR8ZCyg3/zaaHKFuBgSGwsorhMIdcOk56cuka+vu1dAiVJkAqd60P9RhFxBGQR0hSYK0Hffe39KzJMYeF3NHeA7AClQx1WkkGEZAoXaA0B1aLLvoZsNTL0ZMeRel/TIaIF+d1ZpS7xZd6EtHk7QXSwRKQh8BgoIPjUM4zmrEgHshYTiAfrGBQRDqGytDNOAWGaYpEIlMmgSpFkksP5QDx2hKiGEjSAVDIV1QeA8DycKCjVzdK9bhclYXI4wF8s5Ab6TEcSNgDFgHMZ5kujyETIUjmkzvxLReN6TYecXwHbpJhxw924Vs/2gPDlTaSm/FzILwbH//YBwE1hbOf9yKce8GzcYQFvPf9V+nQGxVyOIXMSFB09+Lr3/gGuqmC99ATMVhdwKSTwxquIu72gcIBMle6dPU40lCVRYmTUO7Bx8JwFyCBcnWtbDYpw3BZMrr2XZImTULYZgw77cEObF3/NAMsuaEuTnQvsts5AvClrYEilWMu61kMxD6R2QtUgcXVnmboZ7Fux7CfI87kxituFKDARhFgQN0oLcVyUmADCAwHMWDbCFwTncBCt9+HKblFcpOkA+k41P8q57qU8ZBpoWAbBkw7xcr8Dhx7xJH41Kc/izADJiYmYVtzaLaPgu84yOIu4u4iLr7oUgRegOnZWQStNp59wfPgt2Rcoo3+IEEcpzAQIYkXgSJGrxvh2KOPxRGbj4Dvmzj/WReiyAq84AUvwGRzBk9/+nlotZsIoxCGY+GUkx8Bx23h8U86C+22p+WHSQin6WFw1234jRf/MgJrC3avDJFbJvJcwmUXSFdhBRa2zczCtyx0Wh1ceOGlUKaDDA6y3EAWRTAtG/O3/hCnPeFxaLc3IcsyLO7dieO2zembyVyvjW3btujL6q1WA2u9Ndz4+X/E9iO34Jxzz0enOYW3/eXb0fGn8LdXfxSRAziWBdc2y5mlpNdVTyBawAkMNKRXNTfxha9+DbYnw4QVbNvSYft73/kOCrOF2S3b0G4FOOupj8d/fvcHuOX2PlwT6BchbMPR9yTtmu8B7Qkc84iTMDXRAdaW0Ww28Zb/783wbROu72Fu0yY87LiHod+PdAlct6lb3PEN7Nx9Gx6+fRs+ds3fY355DS9+0a/AcVrwzFmkBWA7kmojNB0Pht1AY7KDo7Y/BMPVZRhBgKnWJFb6MSQ4y0iCPItQxH1E3VU8+/zn4MhjjoRhWTjxhBPg2x6+eOMX9R8AXmBBVsgafQA3wH9JLCIFKMCAys8ABSjwoAkMwr6+u0amP8oyucNb5tNcd/jRTTz6ajCAxAAivW8MM8kw0fD0jT+fvfFGzG4/GqmSmQEUTn/sI9HAGt7/N3+Ohz7sSPzDJ74ENGfRVQ6S1MHnPn01Aq+Fm27uw2y5MFzAGu6GjxzXX3M9JjY9CmEaoBsn+pyf/qfrsG2mg3BnCCQzWO656KddBH4TKkzQ9F3kcBAXdnkxvojhuT6itRDNwEIWrsGcmEHcbGARGZK1W3DDp/8KU+0p2M4MstUMm90AiHv49Cc/hrYziYnpJyL3WoA/hX6qMDc5jbjbhZUZaHamccKjn4A75ssbxFQSoej20elsxVXXfBZBYxZe0NbLfGaGBMwMgdnUl/QtW+50T1FIl7AtgV/CsnT5yrKgPjJjEjNOgRk3wt+++936pVzmPi16+Mwnv4rrb/ghpO/6yDYwZRd43Z+9GmuLK3jDq/8Eoe6xnUAUG8jXuohMYHJuG67+mysR796F2773IxhOB3/8p3+KYHICyjSwvLiGnbfvwJETAd7/wet076gBD3kcwbcNzM5M4VP/+EUcc+wjcc0/fAYGUlzwnGdiSwv4wIffByfYjKnOSYDfhiTk1YU7sHn70XjUyY/C3XfsgdU0UMjQgyRCFgforkR4xNFb8InPfBzbTj4ei0WBj7z3Ssw5Ji4++2w86fSLsCbX+y1B2TcgY92Hkt9SgAJ1FGBArWOrsEwU2KACmzbN6B5CCaiRXIFWOTZPb0HHa6DhenAcG47twbZ9eLaLM88+F8vdNXi2Dd+Vrr0Mg8EAH7jqKiBMkBUR4mgBX7jheqzs2oHXvv5PsRbbOPr4R+P7P/oBlpbnEfV245xnnIx2MMAzzz4f/VguqVvwWrO44z9vwR/8jz8ErCa2HrkdO3bfibjoYn7vHqSDAW766ldgN6cxO3ckbMdHkuUwHB/Tkx2owobrt5DkcmlYLqHH8JstwLVhFgkc08Xa4gBN2HDiGC/5lT/SwwWM2e3YubeLPat99KIhXvziM9BuriFfuQ0798Z6yIPnBIBhwpOVn5RC0JzA1OatGMowhiLB4p5d2DblAmGEL33+S1jMMpz2lDOwPL+Ir3zqHzAsMrzsVZdjOVzCf7/wv2GLEZST8RsZHFffdz+6ichBUgQ44ymPgauGGCwul8MV5OaoZIhrrv0sEpjwZ6Z1djOyBHNHziALB/jqjZ/H7t17dC+orSxEwwhWaxKPe8oZeNiWJrxmA88+7zzYjQ6c6Snc9P3vYueuXcjyCCcefwy2Tzfwst/4Hdx2R4g4A5qBuLYQhkN85GPXQi7d33zrDoTDIa65+oPoLu/AH7/qNcgzoBtb2LGyhj0ri4hUD14ErOycx1o+QGqF6IYFGo6PwEzxzj9/Mxb7KX5412584YavwjUMPP3sZ2Lv8hJ+5+Uvw23f+zq+fOO/oZvJbVecmmqD/mphscdQgAF1DBudVabAf5WA3F1tOxb8oJx835LbwPVk8aastqnn3xzd0K9n7b/l9tswNT2NPJGxlxnMRqDvWjcdF3997bV6aEAh4yrjBG9/y59hZQ0YJC6+/Z0vYW7TBPI8huPk+NT1H8XvvvyXsbQ4jxtu/Hekhol4JcL3//0W7N21ihe/4hX47A2fwuzcNFI4mJ7ZjOX5JQRGpier/+Ftt0O6Hy3bAdIUS0sLkGvhEqykO9I1y1WhVWEBYQ/xsKt7L7fNNpEPI7zjzX+OJLWg3Bn87h+9Cc0Zv+ypRIC//us34VWXXwQXi/jGN7+JTO4lkyEPva5egNRAjtOf9gx8/Vs3wbAD9HoRptoNfPvrX4WdKdxwwxegPBexDMaVu5b6Q7iTbYS2qY8VdVcg/b16An55dCwYeqooObIDw+rgkoufA0sNEXdXdbkSvUpThM/e8DUkVgePfeITy3EYtgG77cMuIqSri7j9zt0wswItx8EPbr4VueXjuBNPlsWp8Ikrr0Qmq1hFKW7ftRPTmzdhbsuMDt5fuuEG/NzjT0Gambju45/VwwOKLNGPmTzaPt7+1/8HjfY0bM+CqSJMT3m4e9caFDycc+kLILM6yOIA0XCAxf48Tj3pJL1oQRoPMRmY5UT9TgHPc9BoTeI7N92MIsnQkCaTCXl9H69/yxuxd+dtOPfsJ+p5ZKtFWf+rPv88LgUo8OAJMKA+eJY8EgXGXkCmPJIJKmVVIrmgGjQb2NNfQS/sI5Spp9IESS43OcUIsxg3/+hm3WtpOTIeMgV6PRiGiUc99gk46dRT9XABR7KhVeC6D12Nwmzj4hf9FpJQ50g90T9sA3ms8Hu/+/tAuAd/9NpX6V4/rzGLKy5/CxqYwOVX/BacVogMXaRy4T8GHNPDkZtkAaY+Nm3bhBS2ZFPA8/WwAhgOPL+jewR135shY0jl0nmORuAiCge6N7AT5Pj0dTegn8/gzGf9Cl700hfKPUow9NyhsnBBE7/xsl/DE084Br//B3+A1CzvO7cDmdJpoPv03n/Vh5HYHvTipWYB084x0bFkQiUsL/Vx++pQj02VHuYpuYvfcxDbQG4Cm9ttWOEQUZIg0UeQu46kJ9jAIJOxqVM489lPx/SkgUa4htUQKCxT33DWwyyifALveffbYBpy276BxHBx1FQMp1jC/7j8TzFhyh1ZC3j+i16GueMfhZe94mVomsA/ffw6ZEkOc3oOlucjN8qFFookwcREGx++5iqg8HD13/2jdnNsGTQao+G7ePijTsVpZz4drakGMhkvbOVAdycS08PJT3omPvTBtyPIgaYdoHA6KIoIH7/xejSsDNtaExj2MvgtC7Fh4yW//XuwHB+/dM65ODLwMWWasP0A73nfB1E0p2AGNmQtAjOK2X869r+hCLCRBBhQN1JrsawU2AgCSun5NOWXi1yul7Cmb94flV2mD5K12mWTxU0DV8LpaPqoIECcplheXdE33AxD6T6VG99jSIZN0xDXXfMhbJrwMdmexuTkFlj+Zvidk7H5iFMAI4SZh8jlfelAr0kkQwc8r5wBK9bTEukbzXUgftITHqcPn+Vyh7heZl73+Eq5m80A3d4qPMeFJSMl81jPvwnHQxTG+maetXQRqZFi1/wiXE/hnz93FU6esDDnGZgNHMx2Ogjaj8bWo34B3/z+XrheU9/Vr3/xZhEcV9a9Lxf2lDQu+TdoeIDcfW5msAwXwyhFYMrUUhLUDRSuhTyM0TItyPDTwlBQno2G4+oZZvUEV9KDnMprpkx6ALQaeN4vXQovDbHzrl0YZlG5upfXwePOeBqOnrNhyR1Rcre82cJLX3QxbPRx165VFMNV3Pytf8XetRCXXPI8ND3oOVR/8P3v6snyTzzxRAwzmbCrbG9TeqGlPft92K6Hm3/4I9x6ywJcuFDyF4B0v8rfAQEQyaxdsoDDsK/Hz6LdxpEPOw6+BGtZH0BGjcYG4oaFlXgJl5zzTGBowHdt9GUOf38b+omD737rX+Eg0tNw5bYBzzDw8l/9VQStKdyyUGCogEkZVaEHOOjT818UoEDNBRhQa95ALB4FDk8BiTNlANG/hKTnFRKQZEL4cv13xzLhOk55VVavqAQ0XB+2UaDZcuA7NpLeAIYTwPYmEWUuTE+hFdi6h1MVOYKWg0GyppfjHAwieGjqeTN7/SHgWegPUjQDudlIQnQG01SA5+obteJoiKmO9N7Jawqu5cNvyAVkE7YTYGLrVszNzWHX8i4Uto0wk4nuZRH7AkYqWdKAaxtIQ1nZydY3OslUVtIjnMpB8xxBozGa+kgUyl/Hhp6DNQOMvLwT37D0pe7qcyBLyWbDgXQcw3XKqbWytJx6qhx9OroTTYZUlCMrADfA2eedh7zo4Ts3fQNREuOb//4ddKMcp5/587pnUc6WSMy0Gjj2mM1oOgpJmKC7PI9v/8c3kBsezjzjqXqsarMVwHBsyFKsy0uLaNi2njpsotPWQzXiQRdwXWRJoqez2rx5EzKZ39QCZNJ9WYBARk3IlxxD7tCXhQVkvrA4HOr20jM85HKflI+1aBlTnTb23n03MuWikJVOZYaBOMRR247G3OY5LC7vwRFHb0OYFjCKBJZMGRanOPmER+J9H/6kDtCVIR8pQIH6CzCg1r+NWEIKbBgB3S86ykf3WejRa3IXv9yuInGxKRFN5u0cJRaZdzMahmgH0sNqoNCX3Ruwpzuw3SZmp+bQ7/YwXJuHyodIenuwtjKPfm8NSdjFV7/8z7rHNPUDPPbpp8BuRNh98wp8Yxp6wqV0CabVAzzgxq/dgjS29ThS30hhuwlgJDjyqGMQuAo3ff3LsA25lcnVwxbkErXMqflPX/o6estddHIfD51+CJxOgKOOPAa+1cDCUh9LucJ3dy9j19IqkqyLQbQTiyu34utf+By8HGiYJoZFilgCsS/duyYsmLpHWbvJGErpXlYGjLxAIZfAJWJlBXxlA1aG1XgVYaqgIgXPkBvPJEgbyDMDqlCwCgUHBSyjgPKmcMKZZ+PSs07DX7z+lQhMB7/84lfhhJMfiVde/kJIMeRWsAQ2DHMKF/3KL+Fppz8GxnAefiPAq9/0Z+gbTZx+ynFwwy4K08bjzjwThamw+/v/ATst0BCnAkgGQ3ib5nD7bbvg+jZ+7smPRqcli1ZJPS2kUQRVxLr3VT4DMkwBloMosvQQhO98/tP4l+99G/OJQpQDngFssQL86Btfxzfv2okFONi7DHhxgmlbJk+V3tcA8cRm/NuPdmF1GKMfRsiSEOGu78Ma7MbrXn057pQpcPcL3+fHk09SgAL1EWBArU9bsCQU2PgC0hX5Y7f9rxvSKym3Jo1Wbcql51SW7zRMfTOMpcr11nVfaw78wgUXo9tfhcoLLMlASgm7RYh8sAAbawi8VN8Ic8nzflnfIW66AR79xMfDDgz8zm/+ulzlxtrqAG2noXvt4PjY0+sjHvZw9Pat6MU9FDJrvKmwadMcht1VuLaCdO7K2NNApo0yLbzuijciKWyozMDSnWvwMh8XX3oRdtxxG3oLq3jXX30AyynQnuvocxfZPH74va9g21yAP3715WjK+FQUMF0P3ShBYUgSlQU89alHPcnSRWjDlYieyZRdo6EOtovJoIXGzCRkvXrDMuAaHhyU02FJkJZlUA3XhCtLoBoKllMgs1tIhxkufO45WNp5Oz7zyetxx11LOP+8X0Ag07zqPxwUZNSrhEikNi658BK4ySLef9XV2N3LAL+BSQcIECMscjztvPOxMMjRbvpwldzaBISDBG6rid7CEq54y58jGa7h6Wc+eXRVP9fdnoZh6F7k6n8+ellWZcFvzWBCAmt/BZc87yK0XbmUL8NpU+R3LuKdb343btm9jE3bN2FqWoYAmMDeHTh+8xyanTnkRrnIWDtw0V9b1SsWeBMBGgixtucu3QNc9tv/2A8oX6QABWoiUP2OqElxWAwKUGDjC0iPaBVEq0ep1T3jgfzykeUr9SVtWRZVgqAhqxABvuuWYydl3nYFhHGG//bCX0LLybCy5zZccMEvYn7vkr4q7jgervv7j8L22oDRwlve8hZ92Xy1u4BLX/B82L6Hb3/rS5ie3A7btPV69n5jSk9zlUr6dBR27bgVjuXDlLCcRTj24cfD9ywkgx6+8sWvwbbkBqoUFzzrXHzgqr9DIr2UeQ7LlQnlTbz4pS9Bx8vgGgO87jWvwVovwTCRYZgK13z4OjzjaRdhNcxx2Yt/VfcILi4uwJ+ZwTBJEMmlbZlIX88jMKLTd1nZehUnkfMdWcC00KtnxXmO4eIC+t1lJHGolzRN9JDPWC9zKjeZFaYBo8hhFHJsWcPKR2o4eNJppyIbZHjHW9+GAi2c96yz900NauTS41oGXfiTOOussxFggCvf/2GEqoX21qOQD9cw02nooP7zF16Ehxw1pcsxNzGFqJ9CSWNlGU455RR85NpPYvtxx+AZzzgdgQ99Q5nU0/N8yE1nsuJXOXhCqm8BzQkcd/xDESZrGCytYmFhSQ96MB2FZ55/Ca75h88jsC3M77kT/W6GQsmkrDP41ctejGEkd/ZvQSgrSMUZWhMSdQ1c8SevR27ZOP1pT8PCnnLKrI3/3xdrQIExEVDcKECBB0WgKAolX1dccYWksn1fl112mT5+9bo8Hq5brgqlioG6+n1vVIFcuDZctZQpNVBKhSpXKi9UXsQqLyJtpdJYqTRSqojVUC2rYbKgptyWevgTnqruGmaqiDK9i4gV+R6VLn5fXffedyqYM0oGnPq+r9qOqWBCtWem1O5VpcJCqSgP9f4q3KlUuCBXvpXfCJTvuyqwPNU0Z1TbmVSbj5xQdvNIddzJZ6t+oVSSD5VSaype2KFe95o/VrbdUYbdVtLPK+eaafoqXphX557xTIXmw9S/3zWv5otM9dduUyr6nrruva9TciFbUm/gtaQvUs34cwqYVuf/0u+pKCmrG2WpWpu/WZ107HYFZ5NaGCgVSyVzpYpMqSLtqyy6VZnWJuU0j1N3DyI1DLtK5ZFSK7uUZfvKdBrKN231hre/Sy3GSkWhvLn8ZGVKqWw4VNlgRcXJolrLChWL8+p3VWBCeX5LGThahVIepdRynqhCTqxylRRKpd2BUoM19Ye/dpHyWpPKmTtevfWDn1EqXFbx2k61kCYq7c+rfGWnuv7aq5VMFNXy2sqCpyZcQwYhKMsxVaiU6iZK9Qfy3R4VLX9XPXZuWp34uNPVjmGuYpWrOJHzKhX2l5RSPfXlL39ObbIb2i5oOLrt2ttm1Hd/+D112sNOUkc/6rHqrkyplUypoUpVlMfqc//wKTVlQk17hvZ3Tf0Xki77R6//puoWSt29uEuVtT1c/+tjvShweAmwB3VM/hBhNSnwsxDI0wxZZiDGtB5bKHepd+TGGLlrW/rDTAOmYcM0HMilXhk4GcmM7YYNOw/QX0vRTfqYdE0YvVX0owHkpvAkTpHlDeTeHC74lZfieZeeKwuxo9FooJ8WmJmewL995auYaQEqzmAqE0UmPX0tdAc5dsyv4mW/8RI4TgrlKJxw8gm4e+edmHakJ9TQN1lZhlyBDpAmFtyZrbj8VZfj2zd9AyqT1Z0KOI6Dxf4Ahe9j2zHbgeHdmHIMdAwLzc52KEjZfh93z98Nx1KI4r7uDc6cDHv33IKPvv9/6ZuaFlfm4Vo2wtSHkgn701XYeemffx4AACAASURBVASlHYDV7pqeDzVMW1BFH749RLi0BNfxy57GoI2znnE6LKRIkeGO730PMuuqcJaX6svbrSw/gNXowHUm0JChALLGaeMISG+rXHNXmIfnlGOBO6a0hx5koG++slsN9OMEz3nRbyLud9E2Qpy0fQLw2nA7c5i0HdgyhdPEHM698CKcc+45GMYytViKfqLw6y9/Keb3yGhRudcrhSNLsOYtDLIWwkYbHd8A+itwZHSvYyGOY3hBR39KTn3ME3HHwi5tFw7lvRZWdi1isj2J2LMx5TmYAtC2AFemBotSnHX+OXjxy/87lBtABkPI8rmyPfvcX8CF5z4GDUOGaJQ3uP0s/jvgOShAgZ9ewJC8/dMfhkegAAWq/5Te8IY34LWvfe0+kMsuuwxXXnnlvqmV5AUdzvbtcXh9E4YhgiBAt9uF67o62FmWhJ97b2ImFjJvqoTNapO73dvtNmRFKhmjuLKygqkpiSUyVWpPPyevyT6yLS8vY3p6ugw6nlc+ujZUFKIXpnCnp+AUQyThKlKzCdV3MTEV4BFH+/jhwmYcceyj8J1vf0IW5IRvW/pyvgTS9VtVVjmvlGFiYgKSg/TCAzJ2Ms/185OTErSAleVlTE3PYseOHTjqqKMwDGMdxuSu9WoTKzESHzl+FEX77Dqdju6Gn59fxua5aSRJAkcm4R8NnyjyHKblYHW1DzlnFOd6WEJ17PWPcuzV1VVtKOWU81Vm6/ervpd9pF30+FDJs6N2ql7/cY8SNmV/ea8YHvhZv/vuu9FqtXTbldN5Nfe1b56nup7y+ak+R1lWoN+XOk5iOIzQaPj69N1uH+12U9dL2kKGYMiXHFs2+V7qIfXesmXLjysyX6MABWoowB7UGjYKi0SBjSogQVPChWw6YI2CysHqI2FPtiqcShCRkCbhpgoYEmIknErwkderUJplWXlnvfSmtduQnyX8yf4SwCTgyHs6ExOQaUtN04bjB2jKV1MGIMgNUQZUNsT0ZFMvPiTBSkJNFU7l56qMUs4qeFXlzWW+Txnlmcg8qkqHKPk5TZJyhaw8xxFHHKH3CQJP10321fukqS6v1LUKhOvtpD693gBzc9N6/lUJslKf4WCg329alv4jQMKphDXPu+8/AmTnKpyKn5xfbCXQH2wTOynT+k3Kc7BN6l5tYidf0hbr7eR1Of/WrVv3tW+z2dTtJe0rx5By+b6v6xUEEj67+jhVqJdwmiRlOTqdlg62Ek7FUKavknC6/pxiJuFU6iOfTW4UoMDGEWBA3ThtxZJSoPYCErAkREnQkKAgP68PDAdWQIKFBJ+qp0tel4AiwUVCiYQW+V62KoDI9/KchJoqSFaBqNpf3ivP+Z0J5BJc9Z3zJizT1udKsxhqFBTbE03kRQRZQMB1bB12pDzSa7u2tqbLIKFXtqo3UI4tz9mypj1kyk9Xl1fCXxLH++osdat6j9M01wFK9pWtCtJyTNlHemVlEy85v9RBegglkMkKXeIqNr7fQKpDmqn/CJCMXwVbfYD7+JdYVceUfaX8EhYPtknQq8ot+0gZq5B4sPeIlbS71EvaQdq2al95j4Rk2da3kfxcta+8LvWTczWbZc+49JrKVvWKim+3uzr6zCgdsuUcct5qk3rKz1JHOVf1ffVHRbUfHylAgXoLMKDWu31YOgpsKIEqyEg4kGAggUJCwsE22V+Cl4QM6XGVICLvkx4vCTxynPWbhI8qAMnz8j7Z5LKxvE+OJ5uc03Yc5EkZliVHxokER8CxHQQtD0kSIUxS9LrLCIdd2JaBLJUA1NXvl1AnAUmOJeGmOrY+wajXd9QBvC+QSvnk/Z5fXoauwlMVOuW90pMnx6zClzwnr1fHl9cqAwmnZeDP9p1Dek4dPcuBLBmb6XrIpf8oGk1FVRVw3aMcU/aVACh+cvz/V+CsbKVHW8KsPB5sk+NLT6aEWunNlmOLRdW+8r6qfeX5qn3l81Ft8h6paxXqJbBKOeVn2eR9cvzZ2Vl9fEsGDY+26o8geay+5CXZXz5D/6+6VsfhIwUoUB8BBtT6tAVLQoENL1CFCwkW0uMpweDHhQMJJNKDJgGnCnMSKqSXsepZExR5vepxlNclhMhz1febN2/W4UZ+lk0ClYzRtGSVIttAnpaLAViWj0IViOMI3mQbt915J7K8i2//xw2QRUfl/RKkqsAjoUjKX4Wk9Q0kdZT59KWHVsouYUveP7tps57LVHpMpYyyyeu+3JE06iFe/7ycq/KS1+UYEuy0m1LotJsoimzUS2rqcCf7SUiVUCqhU84dyKShB9nkHNUKTvIo55D3HWyT8lVllHJI+eV9B9vkdTmHPIqVvFfOUbWvPCevST2lblX7iq9ssr/8ceJ5AWxb6iVjTRu6XtXnR95fhXt5z/rALPuIgewj5ZTjV+WvylP9fLA68HkKUKBeAvtH69erXCwNBSiwAQWqMFH1fEpg+HFbFXqq/ar3yXsk4FSbhJoqsB4YrKr3VpeB5T3lOFWZN1SGBth6jXoLrp6IX47lBS4gqxm5PrJcwZJ5Q2WyfFPmGy0v2a8vw/pyVWWqLtVXda5+rl4/8D1yXtmq8lb7yc+VQ/XcgT+vr/P6y/lVnQ88d3Wc6rGyPLBM1esHPlZlleelflUdD9xv/c9VvdbvW9Wjem79+aVM1c/rzyfHrOq7vl6yTzX+eP0+8r0cq6qj/Lz+eNW55XluFKDAxhH48f/32Dj1YEkpQAEK3FtgdBVYPygJnxJ6R7/2dGA0YZh5ufb7vd/NZyhAAQpQ4BAJsAf1EMHztBSgwM9AQIdQBVk2VbpGZTHPMrNmeklVmUlT5nSXZVdlG+XZn6Bg5V38+0KvXK7Wl6z3Tz+1/2Cy70/YJ1AW6x4Fk1UJqu0e5R3VsXrt8H98AJ6HPwprSIHDRmD/b7rDpkqsCAUoQIFSQKH8R3KqfCkdUqvUJ4m0HBdqSqyUFx+ErbonrHr8qQ55kINIrcZ3k2Ba/WEwvgqsOQUOdwEG1MO9hVk/Coy9gPSQlpvOe0r6S8uAV74i4VRuaPrpoQ68EefAnx/QGQ4o2P6yH1DgA/Z7QOfimyhAAQrURIABtSYNwWJQgAIPvkCh70Yv7xKX/Ca/8ORRFUb5KCu+V1sNO+UOFnAP9nxVlcP3sYaNdPhis2YUOKQCHIN6SPl5cgpQ4L9OwIRlVL/i5B790aa/kecl7EhktfUYT6vatdrvfj3e8298uXt833n0+9f/dM9978/hDZnH6oBNzzOgx9Ye+ML6cx3w2mHz4709DpuqsSIUoMA9BB7Qr+R7HIE/UIACFKitwI8LNNVr1WNtK8GCUYACFBg7Af5mHrsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKEABCtRbgAG13u3D0lGAAhSgAAUoQIGxE2BAHbsmZ4UpQAEKUIACFKBAvQUYUOvdPiwdBShAAQpQgAIUGDsBBtSxa3JWmAIUoAAFKHBvAaUUlMqhkKJAoh9R5IiTLoAuPv1/34HWzBEwgmlYtgvTNmAYFnyrAdsw0PZbMI02vvzFbyIMFTIAq1GKQRpDYYBCJchVgd6gCxUByAGVryFHFxlSpAUQF3LeEEAPfRRYSzPkear3yTFAnEZAsYJCraBXyPdANhxirbuky5tkIaJE3g8UhRwf5b9UAuz7SvVzw6yLREXI0wxfvvp/4wjXgG34uGsArMj7CiBFDwOsIspy5MkPgPQHeMTWObjuUWhMn4C/+9y/IkSMYdwFUoVMARkU8mQBv/P856JjujAmH475DFhLE0RxDyrNINb9uIfFdIhYipgU+oRrvVVh0eUOB0PI0Yo8BrIYyEMoAHEudAXSpCyjHE9eED/NuvcmPP6YCTScFgYZECqgl2dSPAxi8QXCONLnyXKlf17r9pFlGaJoGcAagCVI7Q/lxoB6KPV5bgpQgAIUoECtBNbHAokyJjzXQRZ2MVxdQJxIqnR0+HMdC7ZrAaaNpu/DQAF59wUXXIjZzVuwdzlE4DswDIXV3grSNIJpAK1mB0qSXAEYZnm+7nBVKwzDGMs9CcQKUZrAtC1YlqWP2x2uwXVchL0uTMOAY7oYDgewXRcT7TYGYQ+ObevyKgUofXw5rIQ/AMboUb5XJmzbQirBD8Dxx2zBdBtwXBs/umsvhjo1ShBMkasUpmnCsiN8/V9uwNLiKtI0R9gf4itfvwkmTDQ8D0VRQLKiBQMqG+CjH/l7bJvbDpguJEsOkgi+56LIcqAwEHgBbMfDMJdIaiKLxMvVgVECrGvbUIUE4xRK6mABw2GCWP+si63/ZVhAmioMowSpymHlCawsQpGbyCWky16mhUIBvuMiTmJdDqmXYSmEUYhOpwVlAL4fIMszJCOX/Wf52X+3/pP4sz87z0gBClCAAhSgQE0EDElxOrzpR/29PCdhzoPjeMgGIfypaXiejyzKYUhvq20izTPEcYipwMLSyl4MBwOcdfY5GPYjmEWOyfYMHLdAXgx0XU1PziOn8xGmMSYaDWRZD57bxGR7MwpY8GwLrmGgt7QGJUHZb+qyBJ1ZRMMEwyTRwTHPYmRZjiAIsLSyhDRLYRgS2jJ0e339Hn1Sdc/IY8LWTxtKYe6hD8MjTzkBvmvia1/9GiTglpsFw7DhmGWgvuZjH0c/M+CbQyBdxseu+zjitOxuTfIEWZQCRQaVRjBsA/MrPWzdthktG5hstrC4tKx7n7vdLpZWViCp07MsycuwPUcHcJVJnjRg2UCSpHCChu6plvJ4DReNwMFwGMKxFbIkRG95DwwzR7PhwpG0asgfEA4KODqky/ssw4ClpCWBfrcHIMHayjyUihAmAxT6H+lZlV70Blx7RpdthHBIHu7ZWoekCDwpBShAAQpQgAKHXEBCWdndVoY6VQa4wTAEHA8GLFhBC9d/8rMIh11k+SqSuIe7d9+CYRJjx647ML98B17ykgukvxA77tyJoOVDmU30u6HuaYSER7kMHg3RXd2DPE/gOQ2JwAjcJmzHxDABIrkenXSxcvcOtNtTSBILaeGVRHkTnj2LjuvDa9iIkgi2YerL4jNT05jfs6D3k57Xdqulw62CBfnS3ZA6pgGSK32nXR4zCPCWv3wrhv0e3vrmv4BjAXEsMd0HcgdZNsS/f+mb+OB7P4PM2IIP/PXr0fL7uOvO2+E7PvJYwbALBJ4JIx7C8T0k/hFYyQL83UfeiwAZwu4SZme2oshzBK6HTrOD1ZV+2WMq1EaOeNjTPbFIV1FEXXh2C3lhIs4UijTXvaDDIWBK25gJ+v15tGdbsMumQhgngN0CrBYsq10G1AJwANhmhr27bsfsTBuLC3sxOz2Dfi9Epz2LxcUB+n1p5gaGiYUEnnY7lJ9JBtRDqc9zU4ACFKAABWonIL2m+zfXdfWAziwrdE+eaZZpSHot8zzH9NQEBoMQm7cdAcOx8cY3vAZbtk5isLSCKJdxjwXajTaQ5LAtG3v27oHdaKAzNQlLLk9nMtZRvjHR7RaQ01mOg7bnYMvmGWBxCaYdwLN9LC70yqEBhqnDtJTUdW3IgWzThgET27dvR3dtCNs2kGUKhlGOlZXxsrprVc41ei4vlA7MEkW3P/Rh2LrVwtLCIm6/NYQ+RWFBBhiYKsUX/vmrSFIgM2085/yzIZ2qEirloTA9bWEoGRuQob97F5YHCm57BjOTTSzM78B0p4M0UTAtG27g6UA9N9VClAB7F5Yx6HbhNRpwXVMnSjOwUORKd2YnqQRgC3v3LqPRAHxXwrqJyakpRGsrQJZjdfX/Z+874C6pyvOf6XPr17/tsAtIEaUIiCAriCLVgBEVEFD/gthpkhA1RJEYjTEaSyyAMaKxRwENqICAWEBQITRdYGF7+dr9bps77fx/zzt3vrIsEXWX3WXP7N7vzp1y5pznTHnmPe/7vHUUPFcs4ElqAGwvt5IiFIJGDXMXzcX4yHoMDg7LC0dvdQBhACGtlbLNLkLWoun+31ZzmqBuK+T1cTUCGgGNgEZAI7C9ISDcVMyoUzVzbNrfyFFTCe4JggAhDXVuCUrR49LIhv9jYHyyhuWP/QFjoxMYnL8AbfI1Mj2SJfqdGhYGhyrYfdGu8N0CFu26EAMDfXBsF//wvo+iWDaxYWxCrJ1R2EB7ZDUOPXB/fO2r38TakUm8528vhef1ouD3YWzDGEbG1sHxXSF3juPCNByQUF98yXswXqvDdo3MQpkbh2k97fJv+pUyMEj8YG0PKHh4zal/BRWFuOUnN8OSZpuwTQeWqfCz2+5GEts4+NAXwO3twVFH7A+0xvHoyjpAdwTHh2UlgK3wwH0PwPB68PLjX4lF8wawcHgQURzgPe+5BL7ro+QVxIK5Yf0kii5QqFZQqlZRGxtBux1hbNWj2GPBXPT3DGL9hgDnvPWtKBbKOOTQQ7Fq1bg4J7TrbcDysO9z94fvFzF/zjycdeY5SJIUYZIiUXnb6SDRgV8pIm6HuPCCi9HXOwDDcGEYDq677kY88XgNdAemS3Gl6KFWY5DUNp6UnjQCGoEtgkCapoqfyy+/nHf3qc8555wj5efr+a0njYBGQCOwvSEwdY9SoUpUqFIVKhUrFcUNpdS4uvZzH1ZmcY76wc8eVHIba7VVGkYqipSKQqVUqlRncp3qGTCzEfXiLqoeSxEqDDpKRSvVe89/vXLckipYlvItKLfkkT0py4Qa8G118mvepSZTpdY3WOBKpSb/oA5wPXXuuy5VKA6ocs+w8sxhddIrTlWq01Jf/cYX5FjVoqcsQHmurUzhZaZyXF/B9lSnWwfeeeM4u/+yPh2lVIP16kQqjUaUUmvVusd+omx7odp97+PURCuru1KhisaXqyp8VUCf+vr3b1CdxoT6zc9vUoxpeuGJZ6sxxeokSqk1SoWPqv3mFpU3dIR6aINS9fpG1T/Y9TQwS8qGoxxYyjNdRTZre1X1hWu+o+IkUCpuq3YQK9V8QC3pg+pxFyi48xTsgurtK6tTzzpDhWxCLVCu3aeAguoplaVMCyUFu0cduKBX7d5bVm7xOWrFaF3FqqWawaNqaGBYASVVLPSpIGAfp+qWW3+qYLgKZkE9b/8XqjiRblSj48Rj207agrqNXxD04TUCGgGNgEZAI7D9IJBHunetqIaCbZlAHCNOQqRhiFNOOQWOW0S1SktmBa5bFqugbRro75+LWj2FVe7B0S97KTwLmBgfgeO5GFu1HF/60tcA5eLq/7gK7U4dG0fGQKmjSy6+AHEU4wfX/wgrVwHVkoNGfQIwU+yycC6u/MznUVmwC/77+9/DRG09vvfdbyMNGzj/ogvRM1hEGIaIkwSrV6xEkiZ405vfyEB5mSYm62i2I0xMtiGh6lxqpDAUXRbIES0k4p/qoNrbA0sFWPHYI/j9o49DcYg97MCmTyl8GH4ZRxxxMJxCD/bb/4XYdcjB75f9Ab9/PESqTISdBlauXonJZoylS18Mz6EXQIgxxkOZQG//IKIkRNAORA6raKRIO02cd+5bkZJiW7YM5VNWqr+3gIA+BbaD2351J9av24irv/R5OEaMjatWIIotFKpzsWiXPRCFITqdBu6997cYG5vA5GRDIvHLZR9h2kbRszA+XkN/zxxc/4Mb4bgORkdH8ZIjj8J5b38LimUfyx/9A+IoQppE6O/tmVY/2EYnpyao2wh4fViNgEZAI6AR0AhsXwhQl4kEleQ0/wZaQQuMwuFwuMto+zqjwIFWnML1SzAUg34M9FV74Xg9gOXjpUe9HNd9/8sIOwH6+3qlvF/96leIOoyuT3Hsca9AnHRge5ms0gf+4YM4+iUHIwljrFq9UTRUy5Uq4qCJDevXwO7tw2WXX4GXHnW4DEXbPmAmbYxubKE20cJHP/YJRO0OBoaH5Vif/OS/4qKLLpD23HzrT1EoOKhWC6hLVD8k+p/klP6p4pdq0KfTQqHSCzttIQrH8Ys774HBmqQhJtdvANwyjjz2OAwNVNGgnFShgjNPOQG1NStx730PgkVQ/fXuu+/GeEvhhS84AJ4J3PDD68St4PmHHIoHHnoYlDWlu0QcdbD0sAMwUPEZnw8lA/dm5jqqUhSLRTpP4DOf+3ccfNDz4bo+VNpC0BnHnXfcjnLPIHbbZz/c98Bvpcvoz7vvc5fguONeJjJgjpNJdFFHlhVg0NhYbQS9PX0g7+4fGESrHeCTH/8kJsfHsHb1GriOgU7QREqN2W09bVsDrj66RuDZg0A+PKaH+J89fapbohHYmRDI72Ec2k9UZ2qIf3xinQzx3/6Nz6nywO7qpl8/rmIO/XOIvB2r9th65QNqqL+i3n3xh1UjUTJ8HiRKTbYnVK01rlJu3dqogrWPqrF6SwWtjgradRWoSP3rpz+h+gtltVd5gQLmqVvvXamaSqmxxsNKhY+pQyqW2u+Y16nHZWSbfgRKJfVJNfnYLxR53Smvf0PmcsDh6bCtwlZd8ctAhAAAIABJREFUjrexNqoWLFminnfI4Yqj7+P1mF4IMrXrE9K+IGnLvkmqVJq2lIqeUAftOqwGi/2qf4/DVaczqlS4Wp129MGqMHdf9ds1gWqrRNU5mk+/huU3Kbc4pPZ96enqCXpCBI+qg/YcUMCAqodKNVuBeuXRByh7sE89OFpXE4lS9clAjY/SKSBQat296oSD9lXArmp0QimO7teSSKmJO9Vzh2lr3kWtrNPBgvXjyP5DasXyW9Uxuy5Ui597jFreVlKXuEX3gFglaUelq+9V+y8YVPB3VWN0vyDK0UpVKTqq6Jcod0rBKWXZviqXB9XqtQ1Fz4cgzIb3V697QkWqxgZu09NfW1C39RuCPr5GQCOgEdAIaAS2CwS6Y+KiF5rroQK9Pf1ojE2gVqtLtD4zNDUaCdohg8cp+m7izNOOQm2ijq985WtoBxKwD9sEyr6NSsETeSQEAbzBQaSpQqlSxtDQHPieg4suugjNdhMbG2vFQttoMIMSBf17EYeh6JuuXr8Ba8doyDXEFcAs+KhUSqiUe/D97/8QpllB0XXhMvBql4UwTQdDg4PYMLoBY2NjYg8uly2xLEZRBMdhBFQKywAYyZ85NlCtwMTJJx2DoDWGsZFR2KZCPLERDzzwIA46/BD0DnuIYSJhwJdF/SYHSWsSD957D+558EEsf2gZVj42CqcwINHztmpgxePLsWDX3VHuLaPRjFGqeBJ932rVAaMD24gxODgHdaZ9EoOuQRMvCn4BNgWiuIzKpWGEomdjwdwB2EaKJXvuBtcHQlalQJmtNkwjgeHa2G+/58E0bCQxkCgbtl3Fgw/ci8H+sohs9fWWkcQBGo0J7L//frDNishqMfvX8JyFMFDIfBK24XmpCeo2BF8fWiOgEdAIaAQ0AtsXAqRCpAb5B2g0mygUSqC8lEqVkMJqxRKt0HLJRZiGuPLLn8cuC0qY2LgOr37dRQjSzEmg0xpBFEwiJgM0mvjgJRdgzpw9Qd/IOlN5WgX85398A0Ec4k2nvRIFvxdGmmmTMg1Aqx2hFbThekUUy+IuCtu1kbYaWPHIMjQagUSyozs83t9XxehoDT19JRkqJxldsmQJVq5c1yWnSZYVis6hSGSIX4bbyczYZtPBa151EsolR/xB6fJwz113ol5r4UVLD6cSFloJxKWBqUfpFkA/UlUbxQ2334If3XATnBTwK31Iow5cIxKS3eqkgkG1QlcJYO261eAQPKxEouZZZ0pwESfxnVVKhuRt2xVZqUa7Dddx0OrUYRoRVq9cI/pRzFClSKI7TdFhXbdmBdgxVCigSwCTVqmU4WMuBvsG8cTjj6PTHMfkxGQm52XHGBldBRgRenor6DDPAEyM1ZkSddtSxK1+dMWMCil9OLoO17l3C5MyTC3qJsxlDuAklQ9XMtUXT5zZn25a3W6e3D/5wuZBpw8su+eLNln8pO2yy41XGc9kfuef6Vqwrqz39LpU8g+nxEGSAuf7ZO2a3vNpzrEMKafrJdRtTt6GrJTuMRT9TvLj/bHyWe9M/WyqW6Z2ycvIy83qwO3DKEGQdBCnNSRpR9oaMY9yt0s7SYwkDiWXcCIZilnG05+I5Z/y2XzJqeRoZn5p+bCdec5pVUeimI+526sKSJjQOG0jTutopMwKrSSHcUyBOD1pBDQCGoFnKQLKSBAbCpNpjDQIodoNpEYDBbcKy+rFZKcNxzTRnmTmIcAVembCcnoBexDLlj+Ga7/3cdz+w09hfrUXD/zmEXj+rlDOAFIzwBtfeyau+eJXsNvCOdgwNimW1MmxSZx9+mvRHqth7WgNqdVB/1A5e3JFNVTLLpCYUOVQcso3RxIkVoC6VccuBxyAcqeDw56zEOs3rkUrCrB+ZAxRorB6zRjCdopWs4Nrr/seluw6F/VGC55ngaSVllJqpspfclGnGz9l9WDvw4/Cy45eCm/iIXTCIk58499jBQbx3gvOxRwD6LEAJ+1kO/Tvjlf99QlwzQnc9pWr8e9f+zHWoYilR74Arh2DJs6jjzsB8aoRLP/Z/SjzAWsEqPZWYNl9WDHp4/bla6HUGIb6ApTsJrwkRBJS2qqAKJ5Eu52lIG0mgOHORwdVHHzM/rjtv7+GNfetgB0ZMOwS6AQ72NOPtSMmrrvtdwjaE7ANwDH4SKuLfmtiu+jARZwm6AQR6qNjSFoNHHnEYSgXHcluRe1awy1KH2zLU32rE9RMILfrhNxtqRIHbHbSDCpER2UuEvHcbPt8Pndkzn53/Zm7IxHPLHgz4cpJ2+wasI6bTlJvUcqdTc42t+2m+/7lv/N6zj72X15uVoLJNMwUT8t0Pbo9SzJOUjijP+XFZEZ/b6kKPM1yqNPHnpHv7jkm+HM4hNGcXMnczfTMYTfz3DQMKL4Zc5WMuMzs/6d5YL2ZRkAjoBHYoRBIkVC3VO6XDJriPdKEilOYji2i+FzLLEum3DcpmO9KylJYLvbddwlcK4EKG/jQBz6MZhtotVOQ/N772/sRBQn2fM5uMkROVwEapWg2vOWWW3DbHXei3hjHipWPCjli3nj6CfT2D8D2TFERqBYtpFSxKvqIFCkm8MA9d+Lb3/k6JOOVYaEm1kEXDy17FKVSBeeffz5WrlmHnirTqTIRAIOSWO/Z9/TsKUmdVA/HHnssylaKa77yTcRWGW7fHEkV6koSVtp2iQ0fgD4OOfRg9Jd9rHjwf/G/D/4eiTJxyqtOzIT8TRsHHnQwgtok/u6CC7DmibVwLAOe56He7ODdl34AKzeMo9zXA8uKEIV1pFEMy3TRDAKYFi2pDCyTYH6YRhm2V8HZ5/w/eGaK8848WzwN2J5mvQHb8XHu2y/AOC3LZgqXxBsJ2u0m5i9cgOfsta/UuxOQBKcolHtgmhaOWvoSNOp19PX3iBZqKMkTtu2JO7t3tkZdpiLkMqJEq5tIO3QlwTIL34wDc3ue9cLzMgmIjKAy2o6fGSMPW6r2QkZISJ4ugeKBZ36m6y9kdJNymB4u/0xvSf7zdI83c6/Z84LH7EUzfv0pAGXbEl6BfkYpT5rtHjTrikzujpGGsWnJ27dFc66VyLXrmBZSy0Fq2ZJo7o+WvcnBpvuemUBmfvLzIYvCzNdtsvsmP598dMqCcIgma0uXlzL5RlfA2ODNuYuJQWclPWkENAIagZ0EAb6Yc4qZntSxUCz5aDYmUSp6dL0UQXiul1HDbvL63XbbTV70KfV04403ynaFggsbNo5++cvQToGf3PQjuZ/TGFAquTjhhBPwmtNei1a7DZOR57YhA52dMAY6CTZsHEU7aMFjxighxxEceCL11N/rg2OF73zbebBtuiCk6Omt4gtfuBL77b03VBjirLPOwrx5c7v7drpGCPKM7Lk3+0lpSFaqo48+Wnxsr7rqKrTbbQQb14s1klml+DyfmgwDp512mshcRXECg8D4Ll7x8qNl9JcZDd705nPlmA///n4ccsgh4vNar9dRLnu4/rof0kkUqYphWaS/fAnwAccW9QRG4PORyw8ZQ5zECIIILznqZYjSCE+sWI5KgWlNs2F9OC5u/PGN6BscAIKO9AVHdkulkrRj+SN/kExbnu/DYsouw8QF51+Iyz98hTSJJJeDhb0l2se37TS7X7ZCXVJQMTc/TJdw0u9DzRwmzw6cGmZm1qcla1Zd+IvDyrEM0aZIkH24/I9bCLnVrPKexOqyMrKcC7MOvJkfbMvMz6ab5PXh8pnbzZzfdJ8/73dOnHJiPd2s7FhZm/PjdkGYBUR23BwfJRdrXv+Z33kZM767KeNMM4ZFHTflIUKCiN4rkY0YMSKE7DKEKdCByvTk/rym/h975fX8PzaRVTPqLr8zy2hq0OHdgJm25EJml6UqQjOx0U5dFGQwJHtL3wx0f+yger1GQCOgEdhxEGDaT76yiz5obljhvZOmzlietjRqmlaMqdEzI4VFM52MkdP66KDTmcDQ4BBSI8KZZ5wjvqr1Zh0f/+IXsHTpixBGAXoqRfgkrqaFH9/8E+z5nD3xiX/7VzBV6K9+fpvYqcLIA+xeGIUq0iBAwQLoTlmvt2DBB2ILj60fwZf/42o4noFyuQxLMjp5eO9734tKbx9OPvVU7LHb7lO2sFKpIL6oCVNhifEhfzZkvzKNJ2DuvAU49/+dgV/f/UuEjRqO++uT5Yk+ZaaQZ2CmW0oL71vf/k6ps4oCfPub/4U5QwWE1NSybCC1cdXVn0OUNFBvTcK2CxgYGIJtGUjDGO97/wcwPrpcjCWAjyCMRVPVdB100JB6EmK61PlWCbZdhmH24rZbbke9NorBwT4h/IODgxjq68O69RvAbF/V4T502h10aO52PNx91110SMVkbRxDw3NgmJZ8PvuFLwpP+/wXvoChvqpITlH/lchsy+kZOX42pE8iwan7mKf1UOXLsqUks/Ty4yd7GetWjz/oM5gPtyoarDPfxG6hT+tLjixjErM3z4Z/p0676ZXTjG962Rade7rkavqg8qY6/VPmsmHrTRbywuMFtMkQxpO3+suWSH0kl7EkA5F3SxjZWy57lZ4NfO0wWA/W50865XN8ps+T2bV9quWzt/q/fmW3YxNmGsEUqso3Wb7QZr7EvEVnr035Ofl/labXaQQ0AhqBHR+BfERK0cRpZBZN3rrjVKFYdDE+tl4aKbf0ruVU/KBkgYUwTnH77beLEP13vvsNfOOb30FvqU8e9N/94fX41re+jiimdU+JFW/N2jX43UMP4bTTT4dlGrjpx/+DsB2hWOrDxEQAw/XRV+2BkaaIIoXhwbmYoBarXRDi+drTT0cnaIPWRo856k0D9YkaHnrwQVz1hS9i0YK5YvOk/ipz27NZYj2U59GTaVDS6cCqlHHe298mLmxO0cerX33yVMdme+QWWFMst0uXLpVyFy1ejKGBPkQxUO6pIAoCJInCq049CZP1URx++IsEB5JTxzExNlHHB/7h/WLM5XYcw1TKwGS9jl0W7wrXtUTsP2FKVgPoRKEEWcEp4vClhyFNG9j/gOdjoL8qFm1afmktpcLB5MhGVCoeyqUyYNpYvHixxJqcf/67MDIykumsmjZOfOVJaIUdvPkt/w+Tk03MG+yHvx2MGBoUuZpCfSvMZHYqQh53DX2M6qEHbt65mYWVQwnM5JDXhjwyM6KThEh4ndSOhIKdl0W5meL8O13tJ59oXDezgbNp6FMRnM2XM32cp5pLxbLLtabUvlvOzMPIomwBrcCztnuqYmcsp6k+v3lMvRI+LdI3sxJZDfNic3z4ImHOQuvJOOTbcl/64KiUgyvE2EJsdiT6z0kLSM0wQyB15L5FKyW7XEbP8wP/0W/WOa93TnrznWauYz3pTySVyjeYsS8XzWxLViaFRQJpcwKPb7omzysbsVkEg7sMU6HMFyEZWjGQKEeGeGafQ9OHyy+lK664ApdddtnUinPOOQdXXnllNgzWXco+1JNGQCOgEdieEKALXmoYaCUpinSxVJFkwTTEWhkCyRjaahiWb8vomGdT+iiQHPRx3GbCI4AC72YR42N19FQHxS7R7EQw7TaKIde5aKWu7NfbW0UrTFB0PbGQRkEdNaOMck8B69ZtQMGOMW9gGJjo4OECUPJL6KtHjFCC73uIaI0MYzjVioyrMlqdFtmVq1dh4aJFaLQDFAq+GB1GR8cxPNiHiL6XSYSC58NkBJE8v0wxjGXPJwbJBgB9MG0GEzlIbFvW8ynCbfg0IjZiZFMp4jCGXaD7QxNOsQfNVgC/VIDRmYBrkhAXxZeWD5MoiuGID2yKIGrBccoYG6uLlBbb39vXhyhVcKJViKMUhr8Ik2KQS+GZhpBgSSkgljwbzShC0afVug7bpgMChQiKUt92BCG3Kk0Q1MdRLZfFohtHCeIk8xtwRM0gOwuDTgyfnaqyIG6H1t9tOM18am+VavDkpV20VquJc2NKfQXTljRf1197LQzLhWXZkk3C9Tx84LLLqfollje+KZFYdEIFZvvKTgtbtL2IvmUYYqqn0zM/nJjpIp9o4iZpoE8KT0O+geQEmNtxHb8Z0ZeTi5n758vq9RqCgEPAGT1rtzsIw1iOmSQJNm7k22SKVovbGGi1AjSbzcyszsp0uUh+7Ol6yqkk1WU59EnJp7weXD4xMTG1TUq2ZJAs5WoBXJVK/fidtSeLNqcDukT300Va3sym8WEqs5z8sXrtFvuJcybYPn4TFxLiMKTcxGyiz6OSzqYmHb2bMOMG3LCFn/3kxzj+zDdDpS5Msd428K3PfRJzXQe33r1MLpq8jTO/c/WDvN1cJ8MSdOqIQ+m3iKH2mTFd+iLoMNNF5rvD7Xm+sF/YTxEJp0zpVD9QW4/H4YdtJ0GlhRfKAf12JtYtxyH77YH1Yw0kzRpKBhAkDoL6BFTYQjvIhoS6BesvjYBGQCPw7EKADymVwrOYzciGYZEKkbDwgQvA8eE6SoxNVEgioaWeKI0blEMCU3WaTKdkoK+vL3PxVEDRc+Az9VOhLFHtftFDpVpEkpKsMc0o45IceJUy+qqeHGrR3GEMDs5HmtpApYQBz4eHBH7RkQAj3roLxSqcSrmrbMM60OJrYNGiRdIvxYKfuSwYEHLKJ67vu2JhNBl5NGPqPqa7S8TEKs8by8o8TrmedG2KNNFaTD8Hy4HtMU0BUCxVRPu1Ui6IMYOBUCbbzW3TNlI0RceVB+FzynM4/A/091ZQLBjo6+2TZ50YoVwfTrkCw4pRsC0UHQueSaMc681a2PK/WJRcqvALPmzHgelkmbl4DD+TUIVtmij39EHc+NJE/FVdzxXXDFKDnA14nt11yjTgbGa0eQZcz8jsFNZb52gpfNdBvT6J3p5eyRVr2jYuuehiSbV18qteC8/NLGxIUnnz+dhHPwLTdLFs2XI4Dt82FBzXg+u4Mk8SNTY+JnphjP4jgaNjtESSdwkqiRXJje/78s11+XZiIqfQFy8Iw5J9eYFl5DILuBEJClnPUzKVC5BlkTyRrBUKdBDP3ixY9tDQkAgBMy2ZaZgoFUsoFstyEZEkhx36gIRysbJenU4kx+V3o9EQXxE6OFcqmfbb5CR9VGzwm8t7e3uFVHGeFwsnlsP6s12c8jZwP2qrkbDz/Ipj2pwNIcw8qbmeU0622R5OxaIvxJR6c4VCQUgxLxK2z6Uj9WYm0rxGq4NKpZc559AZH8eFF16MN5z3NrlYsystgq8ieMpAbPpT9tBNi2N9eDy2kfizXry4wyiUZew3ijuzuZzntp7nIooj6TviyPPFdW1ZF3RIKJty4/Q8Cg7TGb+IKEq6WCgkaQTbcED5qHajgVK1B5/+9Gfxjre9FT3VPkyMT4qPkO0UhODbzla+XDYFRf/WCGgENALPMAL0vpuK3xV1lhlEzjBlZEncnrppQnkvnp443MRnTHcZv3i/RvYcEkZFUkfjgOyfLc/MBTwwZZ9SCajNy5RALRNwlAFPmd37/9QRplzHZtcj25t37D/1ri2el4zH4APUsLIRyy45ndnSWUeQbbtBvFOmHtbRyupnWEgF1DyVLPfetHaZkY2b0XCiDBJ/E4rBwYIJn3vEKxtFFVxkdI+4ZftmSGdOaSTwHBHNbMOCeFZlldWTnTyr67K10veiULA5q1R3m2fq60/tuz+5Xoy8KxeL2LB+nVhKad7++Cc/DbdQFKscMyNwIhFKaSmLI3iOg8MPPxy33/5LdKIYrSAW8djRiZpsS0doniiWkREuOg7zw4g/1/OFrJHckHQySwUdggk4yRwtiYZpCulhYePjNbGwFnxfTkQKEYvvJuiI3RSixjcuTjxxSJSb9RYikky+ZbITAfT39wrhqU3SCmqKNZSE1nc9iUj0PAdxFAnpYt2iJJV5toXklxMtqPxUq1X5TaLIaXx8XKLvqOFWb3XQaIewXR+UgaAllWSMxJUXPE/Dem1STkwxFFqZX69fKGF0dFSILfP/Wha7nnIbGWHlcUm8fa8glsKe3l6Ylo31G0bQoE5Id+reb/LbD9yiJxGJaE4KuXvwoUdxxEsOlr6VsXFE8PnWS2u3zVzDm594c2GkJIkqyTZJMcm969Bing3f01+HvkO08NJ/iZNjW+JfQxzDUEmf0UJaKVe6LwEd6Xd6laxfNyJ9zG058XxoBxF8z4XnO3AqQzj8pa/A9d/5Gq760tfgFQeQhB04fgWmU4ZrZzZm2Vn/0QhoBDQCz0IEeC/m6CRv8qRDQqS6v8USyKX5g6/bfiGHtOwZlhCqjEVme8szg2RImeI+kFGp7FnFdZyEdtEIIYelTHwWXs114s7HTPWmAZduYjRudimwPE+kipmiqRTWLTQvm8tyKjhz2dRDTHbKtunOZtH90p6MIuX752Vl5WTrsn1mbjF9PInHmIrJyAwrs1mh0SWEhJvWl64Bxswd7rLnsyWxHDJmOU3epX94dCLaHVqUGB6hl9mytCuHBbofkpiyj7LaZ0+zjKTOeAWRZ2S+zRQe22hmJsJbpQoFx0YUNMTcf+yxx2NoeD5st4hPf/ZKhBGHb7Po7lajKdu1G6O47+6fY3J8I4468ggce/xxMF36BQLVvn6po5jFGR6uIiFTlmOhHUYwun4iJDm0wlGSIo1CMbWbpoGonaVPW79+owzDk8DJMIRBC6wjVwKXkXh2gkQIZBSSnCVQLIfWS5Wg4DlwHQthu41WYzLzVaEXpmnCtuizkpn65RqmXhv9XIwElpFZO9kIZozI6U5uBaUFleQsn2hNpKsA60jCRuLl+R68AqUhDFi2IySMxDMKaTH2ELaaqFTL6FCug2QuyCIvDduC7XhT1tBEhsCz4RwO6RR8V65X3gxWrl2Hdphg49gEBocHwajH6YkXQ6bCz5tDJwXCoI13nnE6Pvivn8F5l7wXvbzRWAyw5NAQbyZKyHOSTg89TJeXzZGMsr18UZlp3aVUSZxkQ/xr1myUjfPtMtWF7OLMLbAZqU8RJ5GQcPrkcPt2m5bvsriH5G/a7B/PdkTbzrRctBMX9SDBUS96Lt5yztvxsU98Xm6q9DSph5SU47kw7UKyaRv0b42ARkAjsCMjkFvoKKREHiNWOvnDVpHGOJnlk0o8mzU3cE9x0stgMCLAyO+ZFtrKQiDmEyr5dBOmiEonA59JYE2YlKJMGbMitkxQ6o/eYn6iUOhyMbEi0utUxVnANCmoWG5nUxpSsZmfp+qbnNLl6xVsKEX7I/dmm7LWZ8Q4ewby+ScWzq6dMqtttm12TG7XJa5dCyv3IW6Zu58BIyWeNNZ2iWS2gQTspqDkFI1jNhxlwk6zpALcn1bRxGTaGUayEMdE+iqzRGdGKSIKhDDSMFNA4mObBJVch99dS7Xd1XWlo0VmbWW9WdGZtDVH5pn9nt2bW/zYKcJOG16xgF/feSd++ctfSi7fX955F8448wyEMVOOZcPNDEjhCev4DvbcZw94Ppl+il/+7Hbp4ygRqTCp4cjGjRJoFYdtIXSdSKFaKUpZHNpmJ2UXGAOqFIocOjcUfOYDToCFC+eiVKTUBH03Q3SCAGvXrBHSJ1a1NsmNJdbFzNqWiq+MZMTisDdJJIe+CwXQMkqnk7jTxsT4uAyPs00kxByi5z5R0ALSOJOc6GLME5RtEpcfpTLLZkwdNEvcBTiET7LGaDxaYklcq9UyJiczks2TuhNGaAZtLJg/H47rYv3atVKnsfUbUChRkBgoFW10uom8/FKRcmvip2Ixf3AYotPmi0ELtni3048zxW5LdpHIwcEBWoUhZH7WqZFdXXJ5umZmifz5bXeipUz81etO42i/3LqEjqd8F84yguUX+qyyuj/oHpH7n5JAsm/Ydn4LmTfYb0Pik55Zwll6ivHauAzjcx96L+Tkky8odITnvpxI8Ht6fHlxYT3CKBD3EroskW/TVYEvJpTM+uQnP4VCwcK/f/oTdPAAXU/pw6PEb3dztdfLNAIaAY3AswABEqTMVJHZ5eQ3b/VdC11XFUaML1PPgW67uW3+EWKWLyczyvanFY/Pg9wIwS04nxNB2UO00jO99Dxro9j/GFBB649sn2UzJDnLysiCp+XHFvjDGmWyi9MUKatzl7w93WNM4UFUs38sW4DiF5msTBnplaF6LpeJBDkLesp2yU2m3JbtpvGJaM6e8hqStAp+3ReBmVtN79M9rvQ2LddTFco23+TnzDKeiflp9LfS0SjTk7Tb+NSnPpVFpJk2dn/OnkJ8hISyjwwj86m0TbTHR4TtX3HFh9Db1wOvp4RmECGMY8mIUG/UMTg0gKTTRn9vrxCPUqUM28wsgDyJSGpIdugELVISKsWXr7wKvdUq6Bjc1zeMDRvGxKGafowUrJ03fz6WP/aYWCtPOeUUjI2Nix9noVhEyiChJMVZZ54JCs/XRkYRtwM5x57/vOfBMVzcctPNuPD88+G6dI4uYtWqtegb6JV6iN+oCZx33rlyTNvyJUsEiWyr1RYraEawbCGor3jFK4ToHnnkkWJB3WWXXQQjOqEPDw5i2WOrJT8x/XeLPh3NE6RRhDnz5+KbX/ua5B12hGA76B9ahMceW4mJCfpTmrDE/4fpPCNYjo2LL7wIPT094v/LOpx66qkIYoXRMcYNAuMTNfjFzAVh+hTJTmqePK0ozcSTU2DXfZ6HRbvvBTfKbit8u+NrXUJpEFpSxedgupSZc+yzfKJLAwkmP4yIN0wfhunAMIt40YuOFEsxCXuiEvT0VOTcYt1HRuizS7/lCAW/AMd2sGLlCsFu4cKF+MUv7kGhkFmoebz7778XlQL9hXsx2YxhqjaKnon9DzkCSxb0Y+OaR8CAUUJWdHnpbvs3yhwj/a0R0AhoBLY4At3hXyGB0yxm6jBTxtTuEm6Sf6Y2mjXTva9TUpLqO0JQu7SjWz7v3ZyycmZTkozO0gKbZZuSqN+p8kl0uxbEGUSqW+zUVlMV3GTF9M/pZ8/UTt2Gzmyv1KVLjjNSLTRWDCT5ftNlZks25fCZRZWyi11uyh1m1F2IvNiEKaTJFbQYD0LBAAAgAElEQVTgdYkpt5UCqQdP8vnk43N7bpIXSSs0F8gLRZfWblpHbkurbGYRz/trdj/k7Xumv7dyLWzA7IXlVfCLG65F6u2OoT2PQ2ymMOMEqhOjJWb6NsoFB2liwqvMRZT4uODt5+Kj73oD+ps1fPDj16DNYf40QfL7hzFcnIvCwG6oM72a1w8jVJjnR5hXqaLkVbAhsQBu3xzHwsF+2F4P3vS2v0GNxkffwmQwgt3nD8N1hvDVb9+EVkJJiXVYMsxhhDYeqtVR7+8TBcwmh7AdF47qwKuNYshzsNr2MClBRS30TI5IZN/1N92GL3/3JjGW+z1V9Jc8oLMRTrQORb8HbmkA//mt65FQwzUFeooFzPMM9Ff6cec9D6PlmGgqSJYOY8MKFJTCXb/6NcoDA1hbC+D3DaOMCOWkjRc//3n4xo3fZew9rLQNK/YxOroR1UIZbz3nUkyGgDs4DIcX9cQqHHHgvvj+T36XvdPSZSFNcN3Vn8fiSj8++183o5O6Qr1oa7z9hu9huGBixYpVGG+mKPb3gGFUvFwkklOuCzpvM39agEGsw69+ch0e7CzAlVd/A4NVF6kVo6IAyabsJGiZzP3rwFYOM6/JnYi3nI5SCPlWzJtMQn09H2isQp9Tw8N33YzeRXvj8n/5FFAooYAyhhBh48N3wSzZ+Mg3v4/HkjLM1MTrX3YY9hyo4Mw3vw/NkMoFLhCtBcIxfOZD75chpw1jHq7/6UNYF7cRIoTvWvjQe/8eUWUeevY4CPOHhmCmZSSoIkwi/PO5p+PAkolfPzEiMsk8dUxGqc60DDzTV6s+nkZAI6AR2JoI0N9fPlaWNamb2FGGew26pfEpQRezLFaDnp/Zp0u0MrbTrSHpBYeTyQM46giUDaDEYF3DB6wyTNPnkwF0IqMZhPkGYRUBywfjQUwOb8MDB7clJJ0SSCYtkRZMw4FnenAMT+JR8hHpnJxNwZTXaZMV2U/WkW3KbIf5pjLsPt2yrCgJmKLJzRFfzrzl/M73m30IBpRNE1ADBWSfYhaAxXX5eql8N9qfxwEzbxEZxpbM9C+ggcUDZb8s2CB6rrhdcBnxsuAb0kNwDAeGVQLsEujCxtFOtnS6rlkLsrbnfdutVL7RFIjbZoa12aqTRVSoEUZg7BJ23+cgeD4liDKJH/IdmRiFZlgyfO4waMhI0evZKNMfNGE+XiAKA/z9ey5Avd1CFFv4zUMPodXagInxUVzyN+9Eq1GXDg8oIYUQdsERfTGnOgBYHlqNAI1WS1QF6G9B0vVvn/0CqCwAowMYLcmgERoeOnKdMn8wXVP5BpPCSTsw0giB4YosQ6Hko8czMVCp4DOfuhL/c8cdaMQNrFrzKMolHwgbeOx//xdpaiBJPBx06JEIEoVYBRgbWwufChMqwbvfdSEUL2IDEslvxZ3MVmdYGFq8GLXmGP7wyDIse/g+7L3rLohbNfzolpuwbnS9BHgF9Rre/IY3Sr5kChffff9DePSJFWg26zj/Heeh7Dv4xL99ErVaU/LOL/vtvXj/375Phu6XPGcfjIyNoRU0EcUpTjz+WDF/XnLJJQiiCLVmgjAhUjOiD+VNjtVNgeYIHrjvt7D7dkVf3wDoZWo7VveNLRvMkDdk+vMwejDvb37PeD2lO4PIbBUdqPoo3nneuehMtAC/hF/e/Ru0WuPYsHEEFc8Q34grPvovWLWxhvZYDf3VCo5/2dEYGRlDs0UlFIsmYiBs456f3QbHrQCJhf/50U1ILAOUp1JBgN/dfQ8YpHfgYYcL944VM15lwXCHvmA/qDDGjT/+iZx71OpjZg09aQQ0AhqBZy8COWvKWjh9v86Xz/z+Yyhssm1XBzsbh5q9LudD2fHydSw/mycJnmJWcth8m4xY/rGabB/ryYIyMvzk+rA9XJdPWbum8J8FUFZG/mIg2HRx4t7Tm+Zl5tvnZc/8nsYxx3rm2m09z9pt1SmhqcykLBKBS7D/fs+dkkbKo/OyCmRyDmGY+VSoOMD8hbuiE6ZYtmwZihK1n+C+Bx8QMXhauHdfshgeg5qMGO+49DK85a1vRtm3sfyxZYijGtYtfwie7cIcGMBdq1ag4AOeyFiYeO/73440buGe22+TQC3QmduMsrejxBfCYpoBTCMTnJdGGIyUoTmx606ABJP1Ghp12tR7cOCBe1GNEyUqHDPcEA5+/KPbkaKE9176Ydz8kxsRRilCMiEzxvkXvwGOZeKuX/5CDPa1RlOIsGubsE0PQSfBHx5+GOP1NqqVEoYXLsTXr/kyliyah/+97wH0DiyAYbuYqK3E8kcehwEPN//8Fuy5z2JUix5s38eHPvIvOPW1r8HvH7hLUrZxaOBLV1+JVfUOWO0Hf3MDJUBhedSwM/CNb34bl773Itx288249ze/RV8pCzLa/EmSDQf8+s67MH/RQswZGpSs9ry0lJENP/DiEamLfIyBHC8fUTHoK8wfqQzNiwRWlODmm3+KBx9ag8NeejRG16zGgfvsKS/ivH7ve+gBzCkDrd8vw43X/QB+bx/Mag8OP/LFuP+un+L++/+AZsIArSLaq1Zj+cNrccprTxNn8fvu+bm4OPieD8MroDE2IQFux5/wCqlTamV1dmBicM89RB71jp/+LBtMkfeHrX65bB5mvVQjoBHQCGgENAI7GQJb/YnL9Lxke+UeE53GRix76NfwbOZ/zXQvaaqnI3A+rOB4JuKkDcNOEKXMQ1vCbrsugGROSANMtGuI4eK/b/gpjCiCaUWgRq7pD+CfPvsZPLr6cSzde1eUk3X4xEf+Bp3CAH5wyy2wijbCTlMsk0knxN9cdjla7QkEagNTAQBGCUCvRO6ZaGdmdRrNlQVHnDoipEYsQTpmnJnQ261xuKUSlDGABYv2l9hFujR7KCCcbDLiCN/93nfwVye9An/7d28FdYEpJMzsFbQmX3DRZSi4Hgz6Sq4eRX+5BBV3JGlBYpSxz34vFpmrqmugTFVk28MuC3uw/96LsOzex/H4WIiRJMZ/XvkhjKxaA8cqo2/3eXIKtzjWTau07ePTn/0sOmN/wFEvOwJB3MEPr/0+QnsAh73sFMSNdXD9RGLT22EqosMXXHARStU+XHzxRRIg1KxPZm9lMw2IFCZQCsFYDbfefBvmLdkFvSXIiwRBig2WScuzAwcRQ40yZ/j89Y5vel0dPPrI0Ec1pbNnlOKr//VNUHr1O1//FgpxCA8dbAxDxL1lYLAHBw6WYccmvvOlb2E8VVA9gzj5jFMxxx3Bhz96OZoW0LF78J5Lr0BqAld96bPYZ48SnGgN4thB2hgHxjai0/FwwqtPxknHLhWLb1M1EPFciAww6fPeBz8f6x5Zh9GJAK5NVWq6NWz1S2YnuwXp5moENAIaAY2ARuDJCDwzT1vLRK1GS1mER35/H9KIOWUpY5EzHjoCZ1WhvJHDIXfHwo0/vhWhsrBk112FIJUKPtbXgaGheSiXelDwPCQiQZSg0ayjHYciau9J5HaEwYEetNsxVjz+KAZKdBNwECdxlpc2MkQ2at36jSI1BKMAqILINZhGR7TW6OthUS+JBNVkUBTrOW1CLxRdbBxtiqDu3PkLRbOVWpz0BXFsD61mG0+sWoHrf/DfGBioolIqo+i48J0SLLuIefN2FykrktKh4QGxJRqOJQFjhUoPRsebEghW8lxMjG8AggjuQFWG7EvVefDLJfHjVHFDaD4VCRLPEYtfqeAiokXWNNFu1pHEgcT8FQpFtOs1lHv6cevPfondFy+C52TyVlQsoNTVol2XSHBWsVBAGAToq1TFcCzm400IWtBm/mNCw4xd2SAFu5U9m/mtZhZUvoZMm055IhInslXOUYaqq9JguvjDsuWg5Z0R9rRmcj8mJwl4oDjFHgsWyovDysfXILFdhPRS6ulFEkT43W9/TUlWNCcbuPWeBzGWOqC+/snHHgbHaIll3LdSjK55AqFRwcknnwxR0SJBTmnuJ7E3AGbZ8F2sXbMOVBdrx42sUU++hvQSjYBGQCOgEdAIaAS2MAJbnaAymw+9rRftOgDLirBh7XKoJBZJKJLUrAL8m5EVto/D0BNrVuGnv7gTqeFin733QEQfAcToqQK1Rh1FkrMOI/Vd2F4JRduVwBcqg6EVU/sokyjyfAz3ldAO6jBdVwgog4TaTRJhB/OGh5AohgFxiNuBQd9YI6QKGpBQQ8nJ0mFKnnkPShVFN5PuCrXxjZi7YAno8bpubC1Kjo0ihe+pt+v7YFam0THJ7IowrMP2FEzfkWF5lRpodoDRiXH4xRKiEKg3mLozFXI4MdnGns/dT/L2pkmAgb4+8YFUY+vgOQaaayewfM2EWCmpQ9qiDizF7rtOvULuisVMt7VcQoFD2q0s8zzTnDaZZSmJMVmPUe6vAI4nLwztTpwJ78exuGJwuJ6pXp88ZS8XTGdaLNowrUwFbroX2Y8k9LQpZ2LAxiYaohLT2X1JaQXMJ8xUeEXUJuqSjo4uGbZfxNjGUXBfcVk1Mz8dzy0jqnfgmcyRnJ0/w4PA6MYNuPH66/GbX92JZeMJ/EXPh28kePVfvQQDZQO/+/XDQNzCj268Hv683XD4i5dKMABb41ANWTqPrVCiaMJ28wWBPtOZf8eTkdBLNAIaAY2ARkAjoBHYsghsZYKadnN2AVd/+Rok4RjaI4/i+9/6jkSicEQ3ZAA9A1BEgkIMfqLsdfAhL8KDj69BAAdHHb47XAqYAtgwSZ6Q4H9++H0UJY+tg2Yzhus6WHHn7dhveACvfP07gdICLNh1b/S3J3DzD74L17NAJSPX9JCEMR5b/RgGBheh6M+HrSpQRorEbMH1gA2Pr0djJBDOTIfuJEqx/IlH8fO7foNWEiPpBLAQiQzWyEQbhufC8Qsip8TI9lRNSBKBwfm7Y2iwD3vudgDGN0ZojDYRNppotsbRimtodEbRSTpYt3610PPeMoPDqNtahbILGJuoC+l1zBT0yTXKJRhVH61WE3CGsMuiXrEKH3jgUtBAHKuGZF0S6piEkHHzqIl1jz8KwxnAeW/7W0TKxLwFvXBVA0t2W4CNkxFWr5tEsxUgaE8ijAM0gg4i1cIdt9+KctETGSkJiKKNl9wtZ6GGgerAXJRKVahOTdwB2Eu0hlJAWWyngUKbsk9FA2U/s5lmqhmZULNIYDA4zqYjACcTrz39dLFiPrp8Ag0qElSGULVsFEy+RBRw068fRtsoY+nxpyANInh0TlUOLv/o5ShFk/jaRy7HNV+8GlHfYpz4jr8DwkkcfOi+OOolB+P9bz4PG1avxcX/9HFc8R/fwNDcEop0V/CAiuGiRGu5kwCNBI8/shq7PG9POEmMQacgyQem2i511X80AhoBjYBGQCOgEdgaCGxlggoZwmY6zv0OOhjve/9FEhz0xjecjWu/999YtWoVGOxOTUqmH03jVNJdPvLII1i7gcY3By9+yRGIQ6DguFj1yBNYvPsAwriOj/3z5Wg2EhmN9TkUXa9j44qVGN/YxAknvxqxWcRxJ56KwUoBn//0J1CfGAcTSQXtFJbj494H70cQJrAtT4ZuJ5pNWEUf9VaCoD6JybHxqRFp6oX+5zXfwthkKPqanuMLoaY2KqxM47NUrMAxmSIuzVQBqJcWA2e94RwwkcCVX/wSmpMkZTRqxqg3alizbi36enrR29ODaglodwKA4vmJEj1UivUTO4uyE7YtkecI6Xdro1guY+PaFuhPe/TRJ6HNhB1OgoIMi2fZsMQEGEf4+c9/Bvg9+OtTXyP1Xrr0xQhakxjfsBaxYaMTgsWKy0MYxmCmLcfwcdYZZ1KtS1wpBKRZZ2A30qlYxu7P2QvB5AhoAJ1oJVBxKLIfiYph+EUYjouJpkIYUEUgG0EnVc3cPLjAkgQCLLHdbOLAgw5E0OnghJOOFx9SZsjiSTA+wqAmA/XURthu4eiXHw2fygBkxamBXXbbXSydK35/H6679gap8tHHHpO5FlR78MIXvQjrH3kIn7v6a2haBey2z+Islo0vIRzi78QizaHSNjq1Ntau2oh5u8xFX4lCHrNEB2YhoX9oBDQCGgGNgEZAI7BlEdiqBJWWPPE0pOXLL+DCC96BvZ6zEOVCAWeffSb23ntP1GsxCpKisy2BUy9/+cux9177wWDmUdPEe/7uElhJKsFFCxctwX4vOARp3ETEgKtlj4pnYxwB//xPH8ZfvfY8cAT4xJNPQpR66BtcgPHaBjTXTeCw/fdDqQR4PtM8KZz5hreIjyjhpGtjpdSP1WvWg3E4thPjyKUvRBDQEkj/UxtXfPgaOIweD2vgyLlFLTLLg192EHUi1OptiUhXijZXBwyNJzE7841vwCPLH8Df/O27cdZZr0EU0L3BRF/PAHbZ9XloBTHKlRIma02UPV+G92v1JsK4I5JQtmGg0WwhaLbFbQCuj1ApxGELvSUfVa8kGqvHnPRihFGE3fsW4Y7bb8sMfZ6Hv7/073DOOW+BU6jg8MNeDNu08f4PfBCL5/cjatfxwsNeBkq2eqy2Cdxwww2YO28BBoaH8S8f+yg67RidgCk+Z05io82YZqGCAw8+BPfdeQd+v3wEqZuRaXENmKBrgAnlOKCebNAYF51lWrJTlcqLCV9OxLLtAOO1DgrlMo455hjstdcSjIxsQP/wLnhi1QbJljfU34vDXnwkArOAgd0X48VLD6KsbeaCYdjY98BDsO9uc7L0tsUqKnMGcMDzesi80WwpvO71b4Sd1nHFJ76AutmD+XOyYDckHfHbTWOahk3EaYB1a0cRJzaOPuYlEliXMjVs5oI8Ewg9rxHQCGgENAIaAY3A1kBAbcUpVUpNBg3VSUM1tn6l6nSeUGm8Wr33ggvVQNnORnpRVp5lM4uu8Fm/WJCUBhtHR9RYq6NaSqmIdUyUUnGiVNhQ6zc8zCxkyjKqyjTLyrQ82Z8x4xe+530qkM07amTdo6q+fp2qlqFMOpUajurvKaqCayirOqje/M5LpOwwbqvRIFSdJFUfPP/1qm+eoVCG8sxhelCqefPmqMdWrlEvf8kBcpxla5VqdZRSaUvtu6CsYC1Suxz416qRxCpghROl4jRRYXNSqU5Nffbz/0ShAqmzJ5FhtjKdqurvWaxOPPF1qhmmqq6aqhnVlIom1NEHHaAcf4Ha55AT1WTUUo1WXXVYcNJS6dhD6q+POVgNLDhALV/VVvUmwQlUJ9ygJmorlecVlG2bqlotq6Ltqx5bMhSrNQG3UqoT15WKRpRqjqqvfunLCqVhBdtWlk1vUSjLchQMW4WJUlGkFPswiltKpR2lklCpmO1LlUpDxVqnaUN1xteo+cWSevsV/6Z+N5mqtMNKhSpRsWpM1NTVn/lHVaVAKsrKgKsMw2BKYMXsoZZJ11JXwexVvbvsoxR7PFip4pFHFNweBbOoegfnZP1rFFSlMkf940f+Ta2LlRpn0+NAqTBSYaupwnhc/ea2a1WF55JZVVd+5yZVY/07G1RbtVSSNNUQoLzKIoXSArW+E6hIxSqMWnIepLFSadRQKnlUveX4E1UPetWySKkgZXsDFSQdweOpLpk0TRU/l19+efZu1j2nzznnHNklX89vPWkENAIaAY2ARkAj8NQIbFULKgk1tS1TZaBveI5YQQ3LwD9+5KMYGR/Fnb+8VWysFrVJDaCvt4wL3v0uyQdf7RtAoeCiHQYwVYiw1UKn3hYd0eGhfqQJU6IyPCaCSjpYss/eaMQKH/7YFRINH8UdDMyZA9P3MV7v4D+/cjU8I0Kj1oJh2RirbcRnPv3PGG/UYVP/yTJhmgYu+9CH8Lvf3I1C2UGYtmFaJl53+hkYnjsPu+62DyrVAsoliGQUo/oLpQqssolKlVktLMkHT3bClMG240mCgHPPPRdpPI7TXnOCRKYzY1LBdXDXXXfhG1//BlyHwWIhCrYD2C4efuRRySNfKfvwbA9eoSTD4OKvWazCK5VQq61FONlA2QKaQQBlu7BsG1+/6jNwDGBysiGMf5/nPh9RqsRKPNkYh8uQ9NQB3DJOP/10HHroAehh3S2g2jMoZWwYHRXhAoohNBqtqXz20y9ImRMq28kgNrdQgmo1cd0P/wdOhRJafN1QSKIYpWoVTrEi4f1D/X3S39Pl8CdPQRNubz/qzbbkFkanDavkiAzYia98BSZG1iMxmGs5xXEnvRIXXfxuFMmm0w4iCeCK4RR8pIaH/V90BPrnVWA7Jo4/aikoXWu7ligsxKmFwaE56LQ6eOGLDkPFCmGlsbgj0FBOC2kKC2seW4Yf33oHEjA4CrDol5AmEkyV+eLOaoH+oRHQCGgENAIaAY3AFkbAIHfdwmVOFceCw66ckBEy+9MquKYBsz0HRoH+iSWEAVOVdWjcxOjYBCrVXjgcxgYw1m7DdVLYjRZ6qkOI2wkslxmgVgr5U+2FMNImIiPBRLEK1QI8h2JWDVScLCEYUAAHmlv1GuZRF9TrFfmj1Z0RDJfnIkkjOGmKju1CxU1UGxvR8H0or4xSWhG3g4lGE16ljHiihmpPD1bVYgxXbFitGpKgjmBoIUaaCRZ4FlxLIVUhlOnAjA0hvczvGUeRZCZiMBWzNvGbqcqo2T9Rr6PS24RnMHUn7cAeOsrGhnqCwb4YZkICaQLtMUkvCtfCWFxFvyqgVQPcuREmg0n00wk0aCJNC0jtomRm6i2X0IpSpGUPLhqwVAg7KUEFCobrom52ULFaSDsJlD0sRJI+s2msUCqYiMIYjk1fX9aLxDTLZc/EBikJf5zCb7ex9Dl74y5nDr7+45tx2h696CQxPL+AqJMiRT1TGJh0UB1gQJoYySWRHMf8GSC3vg0JzlrU04YXjwohj/3FUGELcdiB4VcxUQ9QqpQQtzowSiEcK4HfoPNsAZZfxGQSIU1aKLs+zKQg/q4bNoxhaLgFZc5FbbIjWrPjE0C5THa9Fo7bS+ddyeTVjunqEOOrn7oQ77jg37HvC47Ht3/1A8yn/GncgG0znVzXF2LqLJ+eyS+lK664ApdddtnUinPOOQdXXnml6MDmCwXP/If+1ghoBDQCGgGNgEZgFgJblaDySDn7zWxulIriQg/iVCjyQF3HPpJHmTKjblctSZZYtInm+ptS0IxyupHloWShFSOYWOFYSnZMU8SNOG8xaokR39TclKXMXZttJ9HnSGEoakQZIpGUi2CxDawdqQknbpvNs15AQn+D7rLcwkaJpez43Z34NQsMlpi1lYsNiWHnHsRDxr3lmFmmpbysOCuDXJFZeruQKUlwzzJYHwq1PplEZXVm+zl1HSpYJSOVDF/Z8ox8ZvXJlmR/Z/fN9BpKUiWiGbv+8RXY49CXoDE2jnptFEVafaVdLI3HoONpZqme3n96Lu9v6etuPL+SnMPZsTMRq+ntsx5huZyIY9YHpMNZn+bnFdfzfPEEftl+qh+oG2AgiWO0WxHK1QoajUnMH+6BaVTxwLJHMDR/CHEYwWOiBPaxJFeW2Sf90QT1SZDoBRoBjYBGQCOgEfizEMgY0p+169PbiYQgIxHcnvmgSE45n+WHlVLkd0Yy8lJJB/KPEJBZBc0oRwiDSRVT2Z6bUXkzI5dZ83gkoRfdfPc8Bq2XXCaHntqX+X5J0uzu/lltsjLzmnXLkp/c3pRyeIysrKwdebnTe3U3mFoxDX22KDtu3lYuy+qXk1OWRHLNI7F+U7xM6pq1l+tZznTZ+fGl/YI595+uS7Yf9+Enm6aqmC/oEsCpn1MzJkyJeEpRGhrCu895I3zXRKFYYlC9fCRIThIc2EKGp0j6VBnZDOuX1ZF1z/o3q8c0nvw9/SEuGRZ5e7luuu+nOqRbXt4/MwrhEakCYBgoV0toNibBZAX1NvDxT/0rFswfElQc0xL5rq7S2SY11z81AhoBjYBGQCOgEdjSCDyZyWzpI+jyntUIJJRAoERYmoJD2wMDA7j9Z3fsOG02DFFOSJMExVIJ1157LagkcfbZZ0sbMpLcTSZm6stlx+lYXVONgEZAI6AR2JER6JrTduQm6LpvUwSYbYFBTq4Lw7BADVvPZcatrFZ0Xc1tlyLTnzO+bVrp/OAmOkELLn1WLUt8RClxdfLJr0LczaTV6URgUBun3NEh31t/awQ0AhoBjYBGQCOwdRDQJqGtg+tOU6rtONi4YYO0N4qiWeSUC2fw00w3dTtDhioTnu8jZIKETgdFitzSVcCy5EPLcD5lZDv/pb81AhoBjYBGQCOgEdhaCGiCurWQ3UnKZYBRf/8AfM+H4zh44vHHJXqezZ9F6LYry+l051CaK6BMl1LwfZJTU4b8aUHlskKB/rBAEIRdH+PpffWcRkAjoBHQCGgENAJbBwE9xL91cN1JSjVh2QxPm54WL16cRctvjpAyWGpzy6d33yZzPuW5ZgSWlZhyrDvRksrJ92e3M1+vvzUCGgGNgEZAI6AR2PIIaAvqlsdUl6gR0AhoBDQCGgGNgEZAI/AXIKAtqH8BeHrXzSPwlEbSp1yx+XKeiaW5vurTqtqTBWKfiSrqY2gENAIaAY2ARmCnQ0AT1J2uy7d0gzdnhJ8OLMqOtrlttnQ9/rzy1JQwq2gMbL4QEtPZsgSb304v1QhoBDQCGgGNgEZgiyCw/TKHLdI8XYhG4OkhME1UN7N9Tk43s0ov0ghoBDQCGgGNgEZgyyOgCeqWx1SXuIMiIIbSTeve1XPddLH+rRHQCGgENAIaAY3A1kNAD/FvPWx34pJ3nPeemYlkN9thdE6dpZe12a0kkxZTpk5MTMA0TfT09MiyRqOB3t7eqZ24DbVV+a0njYBGQCOgEdAIaAQ2j4ChKPaoJ42ARuAvRoBpXylLxUuKov+cJ0Ht6+uT30wGQEkrEthcvuovPqguQCOgEdAIaAQ0As9CBDRBfRZ2qm7SM48AraIknq1WSwgqtVQp9s9MVZxIWnOrab7tM19LfUSNgEZAI6AR0AjsGAhogrpj9JOu5Q6AAC2kJKQkqjMnklOSVW05nYmKnjsx4HEAACAASURBVNcIaAQ0AhoBjcBTIzD7SfrU2+k1GgGNwP+BQD6kT+toFEXiZ8rNuZyWU6aBzYf1OeyvJ42ARkAjoBHQCGgEnhoBHST11NjoNRqBp40ASWhOQElSOTWbTbGo5m7etKJyu3K5/LTL1RtqBDQCGgGNgEZgZ0RAD/HvjL2u26wR0AhoBDQCGgGNgEZgO0ZAD/Fvx52jq6YR0AhoBDQCGgGNgEZgZ0RAE9Sdsdd1mzUCGgGNgEZAI6AR0Ahsxwhogrodd46umkZAI6AR0AhoBDQCGoGdEQFNUHfGXtdt1ghoBDQCGgGNgEZAI7AdI6AJ6nbcObpqGgGNgEZAI6AR0AhoBHZGBDRB3Rl7XbdZI6AR0AhoBDQCGoFthsBMPWxKE+ZyhEyZnUsVclm+nIlg2u32VH253bN90gT12d7Dun0aAY2ARkAjoBHQCGw3CDAldq6HzWQuzDI4MTExVb88GyGTvuQk1XVdFAoFSf7C5bnu9tROz8IZTVCfhZ2qm6QR0AhoBDQCGgGNwPaJQLFYBElqEATwPA833njjlKWUxJPWUVpMSUo5X6/XhZjSssrtmZkwt6xuny3cMrXSBHXL4KhL0QhoBDQCGgGNgEZAI/BHESA5JUnNraDHHXccTjvtNPT29uLoo4/GVVddJeSUbgCTk5OSgXDmsD+trsxM+GyfdCapZ3sP6/ZpBDQCGgGNgEZAI7BdIZATThJNEk6mxp43b56QVhLXiy66CG95y1uwZMmSqXpzn3z4n5bVnOBObfAsm9EW1GdZh+rmaAQ0AhoBjYBGQCOw/SIwPj4uVlGSTdu2UalU5Hd/f//U8P5HPvIR7LnnnjjjjDPA7XNySqsqh/ef7eSUvactqNvvOaxrphF4EgIz/ZJ4g9oZ/JCeBIJeoBHQCGgEnoUI/OAHP8CrX/1qsZLS/5Q+qiSmnGcE//DwMG644QY8//nPFz9ULmPgVO4yQGssCW9OZvl8MAxDkMrX7UiwaYK6I/WWrutOiwBvLjkhzYd48pvTTguKbrhGQCOgEXgWIcBh+ze96U245pprplpFcppH83Oh7/s46KCDZPj/7LPPFosr98vdBObMmSNBVVQJ4LOCzw4SVhJVBlftSJMmqDtSb+m67rQI8AbFmwtvNCSmjOTkm7KeNAIaAY2ARuDZg8D999+Pk046CU888YTc50k8SS5JNmmk4CgaJ1pO3/e+9+Hkk0/GXnvtJc8HPif4XNic1TR/huxISGmCuiP1lq7rTosAZUZKpRJqtRre85734Ctf+YoM+3BoR08aAY2ARkAjsOMjQGLJz9DQEEZGRqRBjPbn/Z+ElEYKRvXPnHIpqsMOOwxnnXUWjj32WMydO1fI7cwh/nzYf+a+2/u8NsFs7z2k66cRAMSJnkM1HLbhjYpvyZqc6lNDI6AR0Ag8exDI/UlJTjlsz4nR/Zw4csYPLakkpSSftK7mJPSOO+4AP/Pnz8cvfvELUQTgdtyf5eauYVLYDvJHR/HvIB2lq6kRICnlEA/foOk8zzdrPWkENAIaAY3AswMBGh0Y0U9ySnJJgwQtn5zPBfr5m/d/DvXzGZAP63Mb/l6zZg0WL16MSy+9VNwEOPJGqyy329EmbUHd0XpM13enRSAnprzh7GjO7jttp+1EDc+HJ2nR4efPnRgEwrJoLeIL2Uy/uz+3TL2fRmB7Q4CSUmNjY1It3s9zAsnznhNHzEhCafnM1Vt4LXA7fnMioaW4P10ABgYGRJKKgVMHHnigrM//8HraEZ8Z2gc170H9rRHYzhGg/2lPTw8eeugh/P/2vgPcsrI89119r93POTNnKh2kCNLBgIiKAipdxQIWsJFEE2Mhhngt1yjKFUQTjYkmSvQajSUWLCgqAorGBjYEZGgzTDl9t9XXus/7rb1nDsbcG69Pnszs8615zuy29lr///77//73/+r9998vZn4u5nooAv/dCFC7Q3MjF1dqcuhDx6TjXIR/14ML89zcHH7xi1/sjD7mNfVQBMYFAWpBeYz8QvnIP84XKiJuuOEGXHnllTKXqE2lUoJklHNj+UGiys8POeQQ/PjHPxZTPrWr47IuKEFdPtr6XBHYjRHgzpm7YAokCiZqqUaCbjdutjZtBSAwWmip7aG2ZnT8rprU5d8daY14Lf7m9VAExgUBym8qHEgkSTpHVgP2jyb6U089FQ8++KB8Ntr88TPOj5GfKl+zPOrLX/5yPOEJT5CgqomJiZ3a1XHASgnqOIyi9mHsEaB2ioJqdCwnqaP39FER+O9CgAvnSAtEUsoFmGT1dyWoy4kov88/Hr/rdf67cND7KgL/GQRGm6/Rhoy/b8r473//+7jwwgvFgkBlxCjWgOeTyHI+fPCDH8TZZ58tVgp+zmvw++OiNV2OnxLU5Wjoc0VgD0GAAosCbCTg9pBmazPHFIHR4jkilHzN3yi1Pb/LMfo9k+zyGjz43p7oP/e79FvPXVkIkFDydz2yPNA1hnPlzDPPxNe+9jUhnNSGssQpiSfPv/jii3HZZZfhuOOOk7nB95bPC1rY6B7AQKnfdd7trugrQd1dR0bbpQgsQ2CkWaJmisdIwC07RZ8qAv/tCPB3yb8RUf1dG0QtEr870sD+rt/X8xWBPQUBzhNqQEkySVTpZ83n3Jgx4Il+2HvttZfMJ/qXMqKfVjR+b7QOkNjyvf/f+ba7Y6Vppnb3EdL2KQKACKSRUCIgI02TgqMI7E4I8Hf5+yyWXGy5SP8+19id8NC2KAK/DQESS5JSajp7vZ48v+qqq+RUBhdecMEFQkRZTequu+4Scz7PHW3eRtccvTd6PW6PqkEdtxHV/igCioAioAgoAorAbo0Ataej3Nb33nsvDjroIPEvPemkk3DYYYdJgn0qJcbRt/Q/OzBKUP+zSOl5ioAioAgoAoqAIqAI/J4IbN++HWvWrJG0bNSCsnQ1zfosU0rSymOUteX3vNUe/XUlqHv08GnjFQFFQBFQBBQBRWBPQ4ABUEyyz2T9JKdMO8UAp+WBT4w9+H3dZvY0XJa3VwnqcjT0uSKgCCgCioAioAgoAv+FCIyi90ePLLyy9957iy8qb8tAKGpSV3qsgRLU/8IfoV5aEVAEFAFFQBFQBBSB5QiQgNKET4LKx0ajsfNjfkZiyvcZTEWNKt0AVuKhBHUljrr2WRFQBBQBRUARUAQUgd0YAU0ztRsPjjZNEVAEFAFFQBFQBBSBlYiAEtSVOOraZ0VAEVAEFAFFQBFQBHZjBJSg7saDo01TBBQBRUARUAQUAUVgJSKgBHUljrr2WRFQBBQBRUARUAQUgd0YASWou/HgaNMUAUVAEVAEFAFFQBFYiQgoQV2Jo659VgQUAUVAEVAEFAFFYDdGQAnqbjw42jRFQBFQBBQBRUARUARWIgJKUFfiqGufFQFFQBFQBBQBRUAR2I0RUIK6Gw+ONk0RUAQUAUVAEVAEFIGViIAS1JU46tpnRUARUAQUAUVAEVAEdmMElKDuxoOjTVMEFAFFQBFQBBQBRWAlIqAEdSWOuvZZEVAEFAFFQBFQBBSB3RgBJai78eBo0xQBRUARUAQUAUVAEViJCChBXYmjrn1WBBQBRUARUAQUAUVgN0ZACepuPDjaNEVAEVAEFAFFQBFQBFYiAkpQV+Koa58VAUVAEVAEFAFFQBHYjRFQgrobD442TRFQBBQBRUARUAQUgZWIgBLUlTjq2mdFQBFQBBQBRUARUAR2YwSUoO7Gg6NNUwQUAUVAEVAEFAFFYCUioAR1JY669lkRUAQUAUVAEVAEFIHdGAElqLvx4GjTFAFFQBFQBBQBRUARWIkIKEFdiaOufVYEFAFFQBFQBBQBRWA3RkAJ6m48ONo0RUARUAQUAUVAEVAEViIC1pvf/OY3r8SOa58Vgd0PgRQFQqCIEUc2urmBsAB8M0ARbIGBBN20hsSI0U+WYGY2bMsCkCMyIqSwkRUF7Hgbiu4CDNPBwwMHAwPoZ0DdSssuFwYKg98CwjRFXuSwjAIGMkD+EsBIgSwE+otAGGCbUYWTAQXvZQJOACDJkToZ0hwwDRNJP4Jp2TAM3iZFlC4iQYg4s+GaFoo0hZEuAdwWGwaS1JRzMzbELJBnOUwTiKMQluUM21pI/3hn/kvZhoL3S9GZ3wHPt5EkMRYyF4McqFqbkQ16QG7h4S5Q8y0Y/E6SITMLpEaKQbIEm/1NM6SFDaKSoYBVJDByC1lcILdDRHFPcEEABFEMw3XQS2KYpgETOQqjQDcF+kmOilUgSxZh5CEMGEAUod/rowcHFdNEFoYwHBtGAsRxjm7SR8WcQdybg+lMoNvNYboGlnLAKULkcYRenMF1HF5ND0WgRCB+CEg5+epIAJnHQZxhgAHiPIIZJQgLC4nB33MMs7CALEbR34o0t9BPDUSOhRwhiixEERcyZ+MkLuWLaaMbx3DiCFYRAmmMrHBgWvchDGZhYgpdzlcLWFzowfFdRHEGxzABo4s46cEyqzJHKQfirIM0DGGbNuI0Rp7byGX+9mEYXaRxAGQW0rR8P7dSpMkiLDNHFFKeJCiKGEXGeWyIrOpTfvQXkYcD5KaHgdWDjQjhtgeRuE30MgOmYcBONgNZH0lSx1w/hVOxEIcpTNtEVuQosgSmaSHPEoT9HXDyGP0oRseuwDSArBQMyHpbYdlA3/AQJhlsyxQRBvQAxABcFHEOwywQhIswDF/GyjD6FHTo9btwkCLc8hBs08GM5YmcS6MQpmFhkC7AsYAiSmDEDpIM2JH04PUegEUJmvvoRAUsx0RuAGnahW1yaDh2lP/je6gGdXzHVnu2ByJgoIBB8kTpztkpMzTDn7/2lWhWpvHWv7oWhuXAcX24ngNhbEI1TQRRiDSNyd5gNBy8/11XYsPaA3HFG/8XTAvodbsYdLoYDLqISM5ywLZtWI8Qclx9Rn8ZXnje2dhn3TpZDLkopWmKgAsNpahlCXkahIHQWtf3wHWq3xsg4aLkmLAsA7BMZBlgUMp7Foo8RZImfBtJQgJKMW4giiK5vut5u0auZLvla4MEtrxm0FtAc6KJtLuE1aun8aJL/xBbZ2aBIsQnPvaPmGw3sOmBbSivnsMoSlGXk+aaOQz20Rh+anChJ0EvUAQpLNNANwpguQ4SkmrTQsW3keUxSKYNw5R2xnEsRJsdIQY8MnaUfTYM1BoNeS/sD+T9NOPiCwRBBL/qo780i+OOOVww4uLDTcPM4jzSNIJlm6j45UJXdl7/VwSIQIyr3vZmVJwqXv/6tyHkvLItFGRU3DiZpvxRfhSctdxo5jE+/I8fxMTUGlx1zXswsziQLZljGbCtQqa767ooDANBkcFwnFImRCEnJixO1LyLi597HmqVCdx17yaZ7xMTdZn/RVEgjRLZWCdRX3aQRUZZkcO1THi+jzzLRKZxGuZ5CoPtIs81TJiWi1zmFcUZt4qxyMCKVxnKwkLInzSGG10YsnF0bAdxklEsodPfjurqSTSabbzwkhdjEKZI4i6++pXPyf0f3j6DiFDILB/9kkyZi6ZlwK87OPTA/bBh3TTecc215aY1o7TI8OIXXgzPaWK//Q+A61qIkxT9fh9pFqDTXZJ+EoN+ZxG+Xx3KNSBOuMkN4FGeGSlOf/zJmGq18cQnny8StuK5KLIUtusg4ubCcWQstmzegapfx6C/gOMOPxitZh21miXdf3j7LEwbSLkRNkv5NerNOD4qQR3HUdU+7aEIcDraAByYtgF7yFGpPXzZy16GSgX46D99GN1ujCwbkbgcEPJlwrVSeDaFVgX5/A5881tfgd+cwOlPOhXUR9YbLVTrdRGYjm0JmaR2jqTztx7UdJgW+kGKOKLmLxNtg22YMCxqTEtSVqvWdn29ACqOC8evwqReI42oHJUjCWNKVlCYcwEl95SFdPi5X63Csm3kXK3+g8M0c8wvzMIn+cv7sOtVhKGJV7/6pdh3/SrkaR1Tk22kcQ6v0tp1FXNI+kmFyaJFiWsIR2X7qBGl1tdwSTBzVLw2TFRg2R5gctEMkaQD0QSxef1+gIrriOaU2lPf92C7DdiVhmiI5ufnZaGueiYc14JX9+FYJgwbcCse8ixFbXIVPvgPH8bG9VNwbGBxCVgzOSmbibRIRYuzqwP6TBHghrWCo44+GlUnx+f+9UvYvj2QeewaFhzTAVwXjm3ANUjiTAzmtgpx/PbNt8Fw63jK6U9Hu1kFdahJMkCRDQAjRxxSH+vJ3LMNoyRLlSpFD5IkBYoMC/NzSPIU++yzDwZJKMNBLWPVs2FXHO5AUauXc44bYts2keYZkJswbQ+O48nezaRJAyny2ESWUjIZSLNYtJSuZcGkNYKbwZhz1YFhWEJUyYYpM2zaO0jIbQcwHdGe1l0bvYdnUavV8ZznPAe1qi0kcm5uDpZhYGFhAWlawHEssTJxM0gZxnmbULgZJmbnuuj1qLEtRC/geSaMIkeSRmg2gZlt2xAlmSgBajUPtuWj6jekn6ZrCcmmJOn2Ug4DbMuB61ZFyxnFA4RBILKxs9BDGNDSksnGNWePcrvsXAXYe79phP0ltNevx99+6O9hG7FopGfnE6yZXiUyuKDVy+ZaMd6HEtTxHl/t3R6FgIk859JhA1YBzwSqYt/NsddeB+Ov//qd2Pbgz/HHf/yXiBMTsdjGRW2HhITHNmAEPcDy8Lor3ohb/+3H+OiHP4jzTz8B1aG2kOzHMG0RwFwa4mAAI6e2hZoJG4VhoTDMnZrHSsWD55hYWCjN3RTMNYum7gym7yMtYnR7S6UmJYkACk7XRphS92DBs30xNdJcR3NgkmXo9AeyEJKX9npdEbgcpkEUi2aX95cLsu/GULUy1KTyLSLU72zD0lwHVbuFuzZtxknHHAaH7hHGGvTn+5iqWdixoyu0ExZJJykoTXMmHJPkvhR9ppnANtKSoJpcqOYBLArpX+jGyOEhLlIUVgjTjtCwTDhseEHSasIiAY86otVJ4WChR9O/A7OxCgv9CBUzQZZRk8XvpKJ5Ml1uJtiGFk489cn49Cc+hOnpA0BFy9KOUkMCO0cQUNOlhyKwDAG7hdPPewauvfq1ePiB7Xj5S14jc9XNTNiZhZDMKytg5YmY7KtOH3/56j/BF7/2Q/z9xz6HQx5zKHwTiPp9uA6JX4g06or2NTOBsBfCpA8NN722C7g+LMtGkaTYZ+NecAwPcwvz4npC1yDPAvKMhJJtdNDpDRBFCSL6FKWAbTgoDMoViPVgMFgAFYXcRJu2C7tS4VRClqXoURuZdOCYDdl0ZoWJouDJnKu0fJSWDhcDWJ6LiCS8YsDNuvjFbd/FkSc9Gd/+znfxjLPPQG9pgHargWjQR1FkYinyPINGHyRxBjrp8I8qS8flhTN6Qsg0TZJIPsnSBLZr4J8/9QnMzGzH7MIC6iS4YV+0vOyDbTURBSGWluZRb7fQ7+VoNmwsdbqIchcZbKQF4FVMtBpVpHGMNROr0fAcDDqzcE0DSUKFRB1znR4yG4jyFNO1GmD6OO4Jj8d3b/kqNkwfiFrVwbaZLkjiHctCb0AXgvE+lKCO9/hq7/Y4BCiUaYDOyFFhFaV202tN4mlPOwsVD/jsv3wWPVrSYCLlymBb4pNETQXVd0XQw/U3fAdB4uGoIw5E2IlJPUUY0yxNMyD5UjbUnLpuuRMvjdwUCbvEQhiGyLICrVYdPC+PAmRJjEbTQcxbG6WLQBwNkKeR3Bt5hqSgx2yBftiHa9uo1jwM+gFM10O7PQXXqcjITE40dmpMHcdBnGaIU/ri/vvDgIWgH6JNk1fDxwtecBnqzXVYt66FNE9kyaFGJ48TBL0M6zas33kdLpDlYZZ+eaJ1ptKigMm/ksqiH3aFzCYDYLJRZVeQWy7YIt4jCQoYeYp2u4UMpvixcZHu9zui5eh0OnIbt16BV6uLedWrVZFzlSoSWZwHVCQXBZbml8Rv94RTTsbS4g5c9fb3YMOaVaKxMg0blUpFxmnUcn1UBOKALMrEM555Hhr1Cr5xww14aOuiWAKMPEM+/KFzy0nylSURvvSlL6EfGjj62OMFQJrEPcdFkRaIkgT20KWmPwAmGnU4hjHUBlrIshxpSkJXoNfpisWjPbkKcRYjjHoYdObFTE07exDH8P0aPN+FV6VTJdtgIUyBpW4BKvwaJF5yuIjjAYq8j8LM0GxW4XtVuNSKooJKpS0+l3KqMNNCSC77ZNLSQSuO4yKmMrbXx3vfdQ02bd6Gww45AGlWoFGj9jdHbXg/akIzejPkdFkoXZN4bc5XkuMsijA56QqmlHe87GDADSKFsIVwEKFWcZEEKdoT7RKPPrXOELLYajdF00stMfvdbNVh2hV06YMxtFJlCTfCBrpLJOEGqhUfhu0gon87t6utNnr0/XVsDAY96pgxiBMccvDBmJ/dhre/7WpMTzdkTHhNy/F2yje5yRj+t2slGsPOaZcUgT0NgZGik3yGu3pTCGqGPAQaq9bDMXMJALjxhu8jiCjCuPsv6BSAInNgVeq46RvX46FtIQZZC/turKPZcJFENNOHoK/ZUqcnwjeOQ9T8kijS+V7+eB0RlxQNOapVBjwUpQbVAkyXAU82Ht7Rh1MrSXLdryFJElj0QaWtmtpSh36ZMXzXR9RfEh80mvv6gxhRWJoHuX4tzC3Cr3iiZaD9LisMWI67a9gewVRNeF4V1ALf9I0b8I2bb8diR1STqJg+5hY7MBDDtyuoVxzcfe89IsAZXLWL6ZmwjV2BBVx4DbocyFJkotZoYcfMdviOKLEx6PVkA5DAhWvX4ThUBVFzkckiRvzZZ9u0ZBOw9/o1mJnbjrnFgtZHzM3NCOHvd7tA0EWnn6FaBfphgEZjAk61ie78Ig7YdwPe+bb/ibmZAKbhil8bXSn0UASWI+B6vkRG1dZMo7u4FRQMX7vhpuHPOx+6zgxt4UWOn//851jq00fcw/S0Dd8FOos74Lo+er0YXn1KOJjD3zB9G+k7zl8s/cO5OeMrsas7MBjdVJhirSEhrVY8VBs10a7OLaYYxJloW9neqD/0z05T0HvArxsIGfGZZOjMLSKJCsT5AFHWg2kXYlqnb7Zh0DSfCRlNaHmQg7OTAUIM0czE7E5rj8w9A/jZ936Em7/xY0xMbcDcYgTXMkRmsY1FnqHBjSKl2dBziEGOPNitRr0ubbYqPuIox6AfiWaVEqJeL79HFlxpTWBhKUbY6wrxTYoEtZqPbVuXxKeWIN5z76+pK0CWBDCRYRBRc0rlgS1Kgc7iPOouZZNXKgfyAkm/j1a9igValUhkDRNxkSAOI8R5DterwmtOYv8Na/Gud7wNswsFstSUWFa7VEVLX8b1P5WA4zqy2q89EgFyJ/HPZNRqHgFZT0zgptNG3Ctw2qnHwio6eNkLny2m9LTiIKGpKzcQU9laFHjJCy9FUNmAU856Aax4AXkewnELnH/2OeAC125PoNmooV1roGJZsEwbzdZqbNm2iDin9tYpd+Y0P8WhCNdatY6M7YmXMFnxccY5F2CrWJh4fo5GvQokA1z09NMx3W7iwdkQXqUtWgXPZuCGgY9+7GNYu2Ff+H4TDb8qvplrp1fBMU1EcSoEmdoc+nkN1xIh6VwmeUj0rwkES0s4/9wXIShW4a3XXAdkAeICaLamQJdRg1qfJMGqNS1ZuBmAlZLsD316jcJGEITCQBOaN4tEtK6mPQHDWYv1+x2CuldF2/OwdqqFK97yNwiLNiK0kfVm8Kvbb8Xauo9PffoWmF4di91QtCFFZwd+euuNWL96Pfbe71G45YebUFu9XoisRedThGi3JtGePhi/fuBBFFGBrFeg3t4Hzzrv8Wj7AY455mTYdgMGbIQJgzT0UAR2IcAAw9Ko0sbznv14+NYcXv/qN4k5Pso4V8XlUzSnCAY449znY/N8hrMueA4qJtAwAky6EdIgxYtf+hoY5hSm1+4v83PNpIfpWhWNiotmYxUy+pDaDN4zhFg6pgUTLqJUtnXM54EjNqzD2slJPPGMp6NSn0BcGGLu9+iTGgV49rnnYs3eB2LTlgiuy2jEQkjaxz/+CUxN7otadSOcioNG08X0qkms9idRgNHyouYtOy5C0UGCmLlBZCdNLSlb4aCHZ5x/CaLYxMv/7HVot72SYBc5kKSiqaQ2kpYgHsRHMoWkKZKkDNyiV2sR5eI76lhNmPlww0yZk2W46NnPQcNpYN99DkbNtegHIcFWpt3Gxo37oFlvoNKo4VGPOgi12iTOO/tM1OpVHHrkYxEJyU9gWX6ptS4YINrHxvX7YP2avdFstVCpOli/tg3PdFCYDsLCQaPdRtWsw0EDeezhomeehqYb4ahjHwfHbCGODIQZCft4H0pQx3t8tXd7FAIMeMrEZ4okVYiZRPSTtdpway2ce9bTkUVLcBxqT0X5gYiqgTCFR+cyh+SMHxh48R9dJtqNLA9gWjlu/tZNZfomaimH8p8BQtSQBp0+Xv8XbxCTWUItBaldMNgZ4CQwSr4melUZyAwDKRU14iZaaiRE40o1DN0MrJLkijEtTfDPH7sOr3jFKxH0+mjU6WMWochzZDl9OQv84Ac/xEAi6Gm6oj8sj98ufn2/ImlZzEoF+x9yCBzLFczEMJdz4Uof2e4SDrki+0stda1ew8LCPByPqWcG+NUv70KzNQG3WUEWAe0qtcIxfMfEu6+6Ch/6h3+V71uNKqZXT6DiWfjkJz4l5NNrtBANQtEuX/ehD6BRpZNfgK/f8DUsJhkY7F+l5ovmO9tDe9UaTK1eDct2YZkODNPFoY/aAOQBgkFpEuSQMguCHorAcgQkwJCOlKaPJz7+OFiIkMcpukEGjwF6OePUM5lDTJ0xiG2kuYsLn/scZHGAJOnAsjO8+c1vwec/fz1a7TXodPpi9k7iGM2aKRH5zKhBCzYJFrNaUC6JOzh91Q3qBzlDU3FBoisS/c35l9M9gCeSIDLrhiSmM0H/VhFpYQ/f/uY3cMmlLwFd1nkZGhnwywAAIABJREFUznKSxiJPxH3ovvsekmDCzQ9vW951kRMkpSSUEv2PBHYRYRACYVHDsceVLgz8El13SulYyre169aUqZnSUmaNsh3wXKa/Y8aBjev2kg11RsH2iIONY3csUNNcpDH++n0fgO35yLjxJVUPEnENipM+bvz6LXAsujU1RKPKdFYZzfhMtZXFeOChB9AfRJjv9NBuTIKaBaaNqng2rv/SN9EfJMgkU0iBgmYty8EhB22EjWB4H+JtSNDlI5o5hi9UAo7hoGqX9lwERr6STCFCoU21H4Nu6LPIN8455zwcuM8qWEUPMVMWUQ4zfRPVi1kXSPoYRGU6p+Mfuz+MShO2Y+GnP/shKm5Foke/dfOt2L5tK7qdDgaD0meSQUSf+MQnxf3UhCWBQTlTVi0jiSR+XJToz0oBmnDdGhoXJY2TBE0wdr8Qs7icnea472d34JIXXorW5CT+4AlPwsLCkvhd0eXgWRdcIJrNt7zlLRiEEbbsWJDUMRxBdm2UCkpGlCY+5oB0TMme41Rt7H/oRhQJCS29RGPRrtBHTbAbXYNLm/jDlb8LPqeGdWKiLf2lKf3cc89DZ36AODaxfXERm7c8gH7EBcQHwiW8++oP4PY7aK7PMLl6GieddBLu+OEPMLvAaFwHHu32SYKvXX89+n1qb0J88XOfQWqVLhQSWRWngtsRjzkK7ckpGTMS0SQBjj76YMGh2wklc1ic/XY/3LIH+v9KRUB+x4WNLLNx2pNPwV7rG8iCLmbmlxgiCPEx57aJJNFy0Y0M1Fatx0mPOxG+W8ClC45l4D3vuRbMfbq01MPc/OIwbVoXF1/4LFSpOc0KdAaJEFTZzQ43dmJqlzlP6pnBzklLucVkbP1ozlIwlLKDGddyw0TO1HcmsOPh+/CcZz0DreoUNq5bjzgKEIeFRMqfd/7TxBpz6Ytfjpm5AuvXry3nLTe84qVD4kiyyNzAOaw8hs1UT4UL+Ktw3InHD6WRnFUyYkMSy+GIRz0K9VpToug9rwLfdlDxHJimjbVr12KytRZzs0uwLU+CvMrfFwn2aJM8ujcZdYF3Xf0+pMEAhx9+OHohM3wkuOe+e8VSRZHR61Ap4Ej2FNuie0OIgNkCmHfVANat34igG2DrzAzuvf9O1DxTch9/9jOfR24wI4KDPMpLGAE85qiDJLVWuNiX9IB0ARu1bJznghLUcR5d7dsehgCFrylBO46RgDm2C4tJoxswXVrZQkyu3w+fvO69aNhL+Is3vFW0mH0yQYdMZw7/43WvRVDfH1/50r+gZtME5NGxCY854kjMzG+VqPsjjzoG7YkJ2q+ESL72NZcDmYFaexWYllSIZVbArFXKPIpMSs+lo2CUfgyXfmjU3YiioWxzCXSOtN9BzbJBhQIJZsXx8aY/fw0mGj6W5ufx9Ru/LkFgTF/FlC8f/9g/YXZmO37w4x/h+ZdcilXTE0KoKXzFd5Qr05BckoCabh9p52EYRQ1HHHswVu9VknhGwzpclPNcCgowgyjzvFKRU+TU+JQxu6UGtUA29O3lXV71qldh+7YF3PydO7A4u4R2rYXEthAsDfDA9s3YsCbFzKbNeONfXI2BU0Nem8J7/+Z9WHzgJ/jIR65DtwAG4QBf/KdPglUMjvuD49A0FnH/T2/D9V/7LmwMgLSDN/3Vu+C1W3jXe/6XpBCjGtj0bDA/6uFH7ou9923ASC0s0HmtYiJgTlU9FIFlCNBUTOdm5hJdt+9B+NhH/wmTfgcf+IdPopPacCs+8mRWEtS/5fVvglFfi49+5l/RapO8cmZ7gNXCls2/RhHPI13cjiJNhJRxo3XtVVfikoufK249P737Xta7KDWfeZl6qcz2QXrI1wU8+r/TrG+6ZeEACbDk7CVdjmBRjjlAQVeBPMbrXvky2atVnTV44P4tkgqKcoK+p5/41L/g29/8NH69aTsuesEr0ePPXxzjc9CqU+bg8JGZVRhFBNMIgIUZhLV9cMRTzketXlqGlsH1yKdmAcu2kDNtFvJhQBYQhsxTDEQswlGYkg6LClj2kjthBliVClkbiLu49pp3YaFb4Ovf+y5u/9lt6C8tcUuMdRvWotPdQo8jtPxqmS+aLcgL1CbqqDabYChnbxDjZ3f/FJ2I/u0GpidaWHh4C1504Tm44UtfFSI/FydwiirMhCItwqFH7od99pmEHaaYi3IkVUuKCjyyg+P3Sgnq+I2p9mgPRoA5/8ojFoJHzYQEJiSM7qRN3cRjDj0IT3zcMfj0pz8tgQwDLjxMkdTt4YYvfgXHnPwknPLYR2OiYiCR/M4uaGMbxF0xLTUatkStUpPy0b//AK65+moYfgX9HTNi/2ISf4/OnEO/rV1wku0lpYlftKjkjmSpRmmKKiAVZVybAVIkgeU3H/j1JnS6Ac45/3zRcEjeVZqwkkSSjM/MzGCwtAjmDp1bKIMFdt2zfFZqQHn/AAuLOySzACPpWVmKiavtwh4m46dGhwFLTEZliKl/ufZUrpYX8JgcOw0wP7sdnQ79fIHHHHEwah4rUwUwXFdyyNIO6WHADJG4b9NDCFnbxa5i1YaN8K0IX/vqDUI2Hd/DV758I5Z6IR77uFNx0MZpeEWE//2xTyFPerKwffpfv4zDjzkK69d4CBndywToTHZbRFSYYHrdtOSozWgaZS7KRxRQ+E1E9PVKRCBlNSUqR+0ccVDgmMc+Do899mB88O8/gu0zUocOtpUAYQdf+ML1eMpTn4qTH38EfIoRw0YQAUu9BM12FbBiWEaEesOF57GCUYG9NuyPL1//BXEV8PyqaOlkf5gzOIlEciSfSB1I3EryRi0pp3upcySx4w4zYf4o2QRTC8vcpr++5x7J13r0UcfJ9ZjGjt+bm58TleCRx5+EbifAL+78laSwkrmbF8hJdEWVy3nNGyWM6EK4tIDMraG6ai1Cpgv4D/SKd/zqV5ILNQwD0dZGcYhB0EdexBD5k/Wwfno9gihAk0lPZfM91AlLx4fCLEvwt+97P+KFAU444dGI0hS1iQn0B4Foe7M8xNo1vgSCMsiKaIklKs/R7fckan+/Qw+XzAatqbZU+aJml8UWOrM7BHfWPBAfWwo2k+GvESzPxPTaNag59MVNRCvNrCfjTuDGvX8rUYZpn/dgBIZiUCrGMFRIPJwY/MrE1TSbMem1Y+LZzzoPnc1b8MXrbxX/VBghvvX567F98xzOv/j5okWkQ+lCEtHqDwwKuBULjz78cDhmFdV6C+1qG3/6p6+RSH1Jj1LyX0nOLxCStcnyUQJaEj1qTuh2UC5I5XlDwJela6L2kgdTWXU7A5H3X/jsv6JmV7F29TRavo+106vR9Kt47PEniPtCp9dDq11FIikMhtd8xEMuJv5wsCi+XGTAXDwZbWzkLszURW7EMDILFs1+bL2YJh8p5tgPRggz80Cr1cIt375ZSDZ9RbmgOLaJMGdGAS4as3jyqY8GMxp2F7sglKSsRZaj7gB3/OhHuPfBBdGWfP97P0YGB39wyhPxzPPOgIcU3//+D2EUMTb96g5s2rIDz3rec0S3JBiycUUqfoRMVF6t1ZAluZSsZOlKWSMf0X99sdIRKHJ7uBmMYFkNwK7heRc+Hd3FHm6+9UdiUkbUxxf+5eO498HtOPusc4XQDpKeJI13vDoMu4Ek6CGZ24yTjz0MrCjFzBFerQ2mQFtaKNNJcYNECSBzWVhqif5IRtHETxO4FNsQd5/yc/FfF6JIq0vK/P0ysZgP2MxdBHGCb998E7xKBfV6A65n4YADDoBn17H/2v2lstJBhxyIDoOYitI9J2cZYpZozulkw4OlmGPkQShp4BajWPI1i6azbMZOywvbs7S0JASSfrCWaYkPa5m8NQcr4LGkK+UBtbQMqpKDFpnfJLx5Ct/3YTbaknmEOVYZtFmt1sTKVPFMnHjc8WCaOPq5UpTRX5aWJ1aZiyQ3nyteWfNLs+KuRA1rb9sO1CqWVJXjBsT1mQZs2AwjEp9fr+rDSDIEeYAegtI3eNTXMX18pOQe005qtxSBPQUBBg6JmR8N2KjChiu+iVWnCsf0S1/T6mpccMlLUbF7uPySV2Nx0wLmtt2Niy5/J7bmk3jBhSdJUAId8VebIaz0+7j3l1+EXz8ejz7mZOwY9DBIAiyGPcx3FnDZZS9B3GflI0b3lgUCclK12moUiyGmmDPRNhGZq5FjWszoq9Me3M4APSNHGJFM5UAUI/DqmEsNxBlrbbMrNnrskgWcef5ZmIkH2DY/i6UwwMxsD50kx70PPYw8DvHrH9+GCQNosgSjEGNGELvDAgKSYh+m1YRrT8G364jSEJ7LexQIliI4rBnumMhqJmbTQBLoM2aLC2SF/eEia6Zgve88deBaqzC/UODYk09BVPSRZAPMhzmiwsOEl8OyItQrNXz7tq3oZtvRXtuHldNpghWnCpx82vEY9Jfw1j97Jd51xeW4w1iDx1x0BZ7ypNPxkpe+CLVWHVk3ArOovv6KKxA1NuAPLz4fE0WOdiWCwXQxiQtUGrA7Hsx+gbSVoO220c6npDb4v1sg95QfsrbzvwSBzAyRO0A/8ZAxotzKcP7558PPNuPyl5yDqDfApru7+NM//2sE1Wm86PlPgz0YYIL5OYMZ2Oih6SWotA+At+YI7HPsSQgLWjtihJ0OlroP448uuwy+1cLWmc1DNyMAQQ7fZNGNHDu68xgUOfJ8EkGSoFEDmnQRioF4Ni5JspsKCbaMKoqlRIqBWJ6Dexe6YoU45xmnIMhCxEmB/vwiZrduwSDs4+65Lpa6d+Arn/1bTFqpWEdoXXBMF5bBqlmlZUnMM8xy0ZwAurOopCFcVjahSZ46xqALVDcgzGlWz6VcMYONEjMX31uK2Yw+7QVdHwJwg18MtsI1usirZRU3q/CQoAc3yVCRalg2YrqUOzmssIcGPYrCFP5EE166iIbBFFU2brn9QURWC8FgCdU8Ry8aYJuVYK/2flhTNOBmlJcFJloT6KVMAeagPrkGXl6g0doHc3MpnN4cIqeLjF5W9gTMqA4ktOWEmHTbmEANXhqUcve/5Je2e1xUCeruMQ7aCkVgGQKclkxYPSxlR1Uaqz1xQy+pn3Ix/Uw1PeSDAPfeeRf++ZOfwNalAIlZgcXKhDkw0a4i7nVgVgu84U2vxWP/4DR8/J8/gkrFRKfbl4CKJMvxy1/+EjUmtvZsVDxDUj6RDrLQd73aEBXlQ1u2wTQsxLkLVpC544ffxb6rq+gnZQRwd8cMZuZmcdsPf4JummLtmrpoOqit8GqOZAd4cPMDQlqZIzVPqcHMEIQRmm0K8wFyJg1nF3NqZspaLwzCkkCsgmVJDeSZjek1e8EscgyCHtdN0XxUW/StA/qdAfpZiGrdKKvcSHLunEHIEumfM0uCkcPzfCSxgdXTGzAxNSlJwe+5+074FabEogvfErKFGZitSWzZ3odTt3DUcQfLAk3SmCYRznz6WfAsE1vuuwd3/PB2MTWuPvBQGcdV++6H4048AfnSDO6/+27cs+khOLUJUK/L6mCjPAWlKdRG3Ivw0P0PIbfEqQN06GMyb9WiLpsW+hQWS2oW9AinqZu6xAxOcwJZ2EHc2Y7rP/sZfO/ffon5Tgqz2pT5VHXLxPSOmSLtzyPqz4tr59EnnoAr3/03kp6u0w/F950Wk82bN0uJ4oMP2g/9qFtqLN0qBoMQ1GROrmojThOYZgWnPvFELC4FuPOnt8O3gFUTVfEdTwYD0bze+PWbZI4tzi2Kb/uq9WuRmTm+98NbENNaEOWoug4cx4XlOqyPik4/QrNqoMpEwoZ4osOk9lSukIB+OyHLJvt1+K02bCtHPlgSb4KyTHIGlwn6I5Y39uC4tKyU+1MiZ1tumc3EZEqsQnLCMozes3M4do4wixAG9FAYaoe5KbU8KS6S5QYec/hhcIsAt3znx/AbNanqR2s8ooHkgN4yM4eYvrmuR/s+GvUm+mmKhR0LqDkeWP6Y6atYJIGmfZP9ZAXBLMPs1m0Szd/wPVR8+vKH6HYj9Hsp7r9vMxKkIkPsnKGs438oQR3/MdYe7kEILHd3GjWbJjXDogCTJ+LEz1Xl2GOPRRDN4d1XX4n//ekvojqxGqc97algCkKWK2TtactvAIaP2/5tK+765U/Q70Tit0nTtut5Yp775je/CUkkH8UMkEfFtRExB0zFQy8MRBtbrfoIkhyVelWEJIXjT+64E75TkXrWjek1uObqa7EgyfJZQjVHGrHISw8vfelLhfTdeeedSMJYSKrpsMa2IT6XW2neqjfxghe8QMxgrPQyOiR3KfOXDpkaNRRGvYk472N2+6y4otm2jbkd25HFEWrNKdA3K4wKTE1NjS5TBnoQPoP1vS0hx9Wqh8XFeRx//PFijrv88ldj+45FDAYxrGodVq2GYGZGzGtJr4djjzmqNNdJFRcXJ598CpLBIh649278/NcPwKvaOPP0U+HXHLDM1gXnngskS/jKDd/Er+7rlJqbYYuKgtHU5WLLxbefJHhwGysCmfAqpAmsLkMcVETvHER9IsSUKZ5YnZPzJKN/i+fj/LPOxFTTx4f/4YO48upr0YsSPPb448QOwflA8gOXwVW0DJTzi1WU1q5ZBQb2I0vBxO/Xf/az+Oznrwcr3D9w/32Y8hqlkTtlxTV+j8FFNFlnsqlc6vfgSsGOSLJRRFGBIAjg+HW85X++FUkODLo9rF01JdXlnnz6U4R0bt32EAaDvqS/kwoBZllB795ND2L92g14/BNOQyzBTOWcLedBmWSKjgfcYFK9a7pVpFEHWx68R2SBZboiQ+jXEPRZ5pTZUFg9TpIXlPvBodsA/edZLYp5nIskKf0+Wb5YZGB5X5PmecmPBdkQ+5UJPPHkU1BBF1e84+2YK4DZfoacZbgooxYWYdgJ4BWICgv9IEEi2VDIp2sI40gCw1zLlrFgJb5BMEDY76PXD7BqzWo06jIcSPMUNeaIrVXR7Q3w0MMPC5llsa2iSMr8tGM+J1T6jfkAa/f2LATK/KePbLO4KlJpKvZqG6hUkWUm3v/+98I15nHHHTfhjrsWkfsNXHXtu+DmEZy0CyPto5d7QOVAXPv+92Jx5k7svbqFSb+G6dWr4Tk1tNuTssun+ZsRwMyhGvRCVFgrnuW0PR9dVjSRFDWmlPR0ueAhwSknHIP16zfAMD20G2388p4HcORxx2JisoWkv4S6B9SqFfzJa1+Ha699J4pBiHWrV6FWrcI2DSmJyMIBhx3xGJx55pn4yHUflcCgkY/bLhSGvmAGNUE10Sa7fob7fnEnvnfTHRLRXG2YsjAMOikiaiNM4Pijj4TnOKJBoYbGtuku4cG2fDTqLdTrLfFBfcmLL8XnPvcZ/ODfbsX+66cw3fRhWi722vcgtDfsj3pzEn//4Y/g1X/0MlRMaj8KGGYd+xx0GF7xkufCt1M8OB/ilBMPwynHrpEAiNyu4byzn4pD1wKvuuKdiBwX11xz1U6tB33+ZEXLBrDTLr70zR9gIQLSiGFYgOdZKIblWHfhoM9WOgJBypKfJryiLwUu4PjohQbe996rUTVT/Oh7t+Lnd22C05rEpz75sdIOw5RwUgPYkspEtluVPML3b7oPjVoTXqWGdqslG7fnX/JK0VPSVL7j4S2yGRU7ju2AeYcZnMV8vsyaQf/TP778tfAaNtJoFtNT0/BrJtbutQFudRqbHtqOJ512GuJuD9yzsXLo1e9+N6677oOoVx3ss8802q0aGjUf01OrJC3TMcccg5NOehy+8PmvIknlzlJVihWtWM2NQVjM/Wqw/DATBdRWwTd7uPuOm/Htm26Vz03Lk/Klfq0p/q6sXMecxpI+lhWo6PdtGeKL2mq00V3qIYwzdMICAUmh58m5TGLKatCM0C/IWukLmlRx6WveiM9+/kP4/tevx1p7EhtWr8Xqqb2xuroXJtYfiUEWAkYfa1vT8Nyq2ILadgvdOEZhW5BYVwC+54uliCS5wg1xpYo0mEURFlIm1WT6gzRCFs3iqzf+G5bCClImnS0K+NyckDyP+QZWCepKl3ja/90WgeXm3bLqCTmNiUE3gOXXsWGfvXDFFa9ClnQRdBO84k9ehVWr2lKu0DIKRFEIg75nqOHkU54M205hebaUEp2fX5RFikbkq6++ugyGKjJJGN2sV8T8T+mcWQ5yEmMJ5gFqtTqe9exnosLcq2mKmJoDuqiFMa77p49j/0cdjIXFRew1PSFEi3kZqSH5wz/8Q/zLZz4FRKFoWPgdajdYK3txbg5f/vL1ovnYvn1GTIm/dVBI2OiQlmRYtZpZBgLcdsstkknAr/sSkcvSi47TACNhk96CaHxEASIBDyNxZ4r2wvO8nW054/QzUGc1LJY9NU1Mr1mNLbPzzJaPF7zgRXjes58j5lLSStfyELDUjVfHxRc/DwuLPcH8omefhwlWsqLGt9aE5Th4yQsvEhOpYfs4/7yzBRP6GZebDkbxs6ZqjM995RYEqYNHH3KAmD7pIyc+vL8VCH1zpSIgVZ0YIpTGovmjG5BTqWJq9RQuveSFSFhSzbbwij95JdoNbnJKpLhBEzJj2YijFGeccaaUPWZgELWrNINzPpz4B0fi3ddehdxI8Yuf/RRGTkpIb6MKdszMoZ/2JX+okaWoVWs46klPxslPPFWKY/QHC2IpidIEhmPiqmvew0L1sI0c27Z24ddqGAwCXHDRc/DGN74B4SAUawWDh2YXOqhUqhh0Orjxhs+h1XJgWPRBL9tfKjEZmMnSxCnywoBdn0AWFdi4bhJGNsC3vv41hClLEBtImN7DraDq10ELCw+STcnYapYa5PLK3Ax68P0aHN9CVuTIkpTu9DIBmaIuiGIM4r4ET5qug7jbx+NPPwO+kcEqEklvlxe2BD/2DGD1upa4GzGA0nFZdtXAIApgeg6CNESv1xH5w/sz0X+rNQE60zenprA48zDqPoNQOVq2FPEwEeCrX78ZUWrh4EMPBBP/c3PLIh//fjM/6tV4PI4k9nj0RnuhCIwbAsMVRoQ1+2aUlVzKxcbC6WecKn5UKDyc9oQnol4DXLdMxs1k/ySBO+Z7WL1hf7z3b94pqV+o+WBamcI0sbi4iHPPPRcbN2xEHATY9OtNgqBfYfCOj8Sy0EsS+B6FOknvAFe+/69x++0/xtqNa0GVK01o73jHO+E3Grh7030oTANL84uysLEiDYMTSFLPPfdsnHbm6eISMDnRlmjYztISZubnxRTo+x7WrFktbd41jMN0AHxjtNqaFi545jPAVAW3fvsbonlhXsaQbgkZ0O0zUb9VuvFKMEdJholFqXEwkaaZ1NquVqsSKDG/MI8HH9iEs89+qpD0HbNzkr/q3Auehbe940pUXBMJa40Pr8DFgeVujjr+BKyebmFqwwYc++hHwaSCKUwQDgI4E5M47bTTxLb4lKeegdVNmjGZeYdG2hS5lNdxgXgBt37/TsCcwEUXPUP8axntu6znu+DQZysaAVtM4fwdl3NbftG2K2TsSU8+Dc2mg9pEC2ed9TRa7XdOmdIyQ7bH/MMOPv/5z+Mdb3+7pDMb1ZznXP3qjd/AsSeegKmpFm744pdQYaUzIm45UgHNMQxsvv9e1L1aOQ4Z8MGPfhxbtjyIimOiUnGR9CL0uxE27r1vGanvO2jXaZIHas02E47iFX/6pzjnnLOE0IZxgVarIVks7n/wIczN96TdLgMgR3NeeszQSQMGfdRdiVaSsqsXPuNs1F3gpm/dKPOeeUZcvyrBV/1+IAn6pyYnmVUPaVIW8eAcZHJ90V5WalLUwKvVYNqWaIcpQjkB6Q7UnJxigVXQJagXDuC2mwhTA1sfeACH77cXCioI0gK9zMR37rgDOZNJBxGedvppiGPOdRNVrwq/UUcHA3Gr4DjySOhWZTvYtmUz+mGKVVMtzM88DNu0kccmikEAw4jwndt+Rp0rnv+i54r2N+UOdgV4qBvF8l9A+ZPT/xUBReC/CwGGlfIYkkt5TlI6fLt8zRc0PEWkZTDgIcoazBEvMotxq0VRViJKUIGdMY0MkycuIUNTfBslm6A4tfKKzLdX+ndR7A0vI2SW0VbMP7ho2vCRocLonW5AaY3MtJAaliTqpu8sAxBMliukPsV0xZTFlP7MA2q6dVlceC3JnSipvmluY2nEodZx2EdqS3Zpj4c0bQhARr9au0AWbMNZz70UX/3yzRj0I3huhiTrwGMi8p4rAV+BG8AzywARCUemQc0YBpux23JfXp/OvTmyIoGY1dh+g8Fg5QLPvnHBYpsoLiVwaxh85Vj8fo6YpWhlcDgy7CWzsDJUuEBmscpOGfZlZpksMAzwqnpNGce8+wu4Gy7AFW9+O1512Rlo+HWYlo0ozuAPA1yGl9aHFY4AMw9JBSf5zfI5f5X0saRWjekyHESgC06ZwWikK5TfoqTS5znMvVZmx/hNOA0wOog+ph4CPki4ZgEn2g4YNA9MYN5hOqYEjYKR9bSzmyiyKow8ZL0PRJYrE9rKE3hpiMJsih875VOVJVOlesZwstO53Ng5W0T2ZJxjIgBMOJRvBbfU7rAwSIRK3oVhtsGUW8y1bCdb8dxnXYjPfON2LAy6QkJrBp0CCknbZng+QnaZlaAlQJFYEUgWbWb0FH3BmXyUssjGkuWgwetKO0IYzLNlOAhMF/7gYbz9bW/Fm97xAXznp3fgkEMehZpJ+VyIJto1TDjVKRSVGi67/HK893+8RhLThUaG6kAqFiCsGHCtstABUSAclJsMMkjNCrhDHiQhmjGtXywicC/cVWfgzW+9Fi99xTMx4WeSS5Y82KVP/W8O4hi9Vg3qGA2mdmUlIUBzl2QhHFZ1gVRo4cZfCJSIelOizsvcgDy/NHXJpBf/xv/X9C8XD96H35TFjt9jkAQTyRtl6uydqEtJw7L0DM8vBSf9t0rqxteyOIm5fee3/nNPShufRNszuMOqtHHdP/wd6i0X3/3uT4QSil8OFM2TAAAMmUlEQVQnfbRsG8mgKJs5Et/De3JtFq4rjePywKWcL0ryWra5JMxiYjelOuEj2shzCAOTk1OLyvrjFhd/rjQ56T6vyYNYMBE5EczkfWqbH3poi5geUzr8GjF+cvuvUG+38PSzn4qJOsmpiaXuAJ6S0yGO+rALgZKSil8kSZf8eqlt51ymVp+V6MoaELu+M3rGc/hXygH5rQ954ugM+VwumoNTiWfLeZI03pI3SPy43aJcIQ2kXWH5lGZAEr8l5GuYiUIsD5x74jfJ+/PKPMrgqNGM4RVZqnhUVllKKPM0Y0jFhaza4gfOS4gisgD+6sq3Iw37uPGbt8JzbFg065vDOV2CVN5OLiUt2/la2iJBi5ZYf0ZWEjmB+fFknpdBVtyFHrDvvljVnMaJx5+Aex64FzFdo2wTtlngile/GmnmwLbreO5Fz0TOdhvl5rQcGGIn3ZHLC7YiT5gyjJvpFPfff59kDEjpZGsW+N5tP0Kj1cK5zzgH9Zol2tVOZxHWUCYu68jYPR39SsauY9ohRWDPREDUeiUDGnZgJF9HwmzUr1LU0Q+pjPem2GdyKtEHUlNIPybuzodf4KK28+DFRHtRks3R+yI8Ry9EMHMVMEQ7SDErjpESrFWeRJEq3xk2jjW3eb6UFOUpbIf4fJUnjPpQakh2LUvloja68fLH5W0uF70mfbZQQa3q4nnPPg9/93d/h/nFAUzTQy5CnRZJxvsPF1dZYrko7bpfqRsavR4WHRDNURnlLzSfC/2wy8tbNFoXCE+UQ5JxA3HJfCVCOJU7SQp+JvyXEUnAZ8Rj4157ScEF2x4gjTt4x1UfwOv+4lXY/8AmV3BkXKAdtlcPReCRCHBLyg0PKVwhZUDpk8nfILdITEHF+TgieMu/O5rZyx/LzzknR/OSev+SsNG/EkOSSiFC7SlJH+ntro0pLQOZbHY5Y0qfUban3BRLATu5eHlVlh6hXBreY9i8UhbwBecjz+BVHzlfSdzYckmuxCT4DBLKmKmEQq+CAw4/Ape/7s/woQ99CIuDaOdML7N2lJvrkTa5ZNPlvcoTS0zYLkJa0ne2hySc8q+0MEmzXQ+HHH00giCU5P7HHXo4Gq06TMNBvVrDNe97P2A2cMlLXo7DD9hLxoK7WZtXZYoU2yxlinRPEkWLVUfkqmnCsg3sv+9G6avN8tXxAO997z/iL9/wOhx4oC97YLaM6cZKVcQQxDF9UBP/mA6sdmsPRYByc7RalFRzuGCMAgb4YUleCoPSmXSURJQ+WeUnsdmTUp8UrmbileuNxcRFuaRJIqksb8FgHe7wd2bllMWP1+EZNAuWCxYvsYswlfWtRo0sl43RcsIWUXTafFJ+hBAFSKNlgchL8x1Ngfy4bFx5LXLb/9tBF4IgieA7vqwxvcX74VU9uPX9cMGFF+MNV7wWRx++EWEvk5KCaR6iVm2LSVQSi5K+F3bp12YkMEQr40q0vFg9TSYUpy2fRFjKU0mvy4yD1PQMMZCViiiUulEuqrakhfLKjYWRDkeOVX+oyGHKKOqZHNGjclEkFrMz38Mppz4Z99wV4/6gK5VkakEPhd8UrYzLkrNOOa7/N1z0s5WDQJGn4FYnYiR/msM2mYTeREBNH7M/SJAfNfOcTLZoOvl++csdbsh4zohU8jkj1EeyQ36nOeyclZp8IWwsbjF0mZT5mohHpgkndxGa5TbZTWgpKd2KSgrLPRhfZ3AzOpMayK1YsnhKy1hhSmYQ3WlsiUi3kMIqMpgMgmIpZMOCy37RzYiJ9AtfrEC5wbzGEVzJt2QjTQewHQNFmsKsb8CrXv/nuPxPLsW6iTXI4hRxnkgGApLVUivcF02uUVRg5CVunJB0mOJcZg5om6yZln2+xRhRYbIZFuMAtumgCgvXf/w6vPCSl6Gou+h2YpheBYcdfyK+e8tNsAugYvTF5YLSjwdlHqXqSA7Km/zvEe5VgaTjijMP0da7Ja7gV5u2YyaNkOYZXNOFZxmIwy4824FlMZP/ziuN3ZNdm4Wx65p2SBHYAxEYCRvazIZ8aNQLCstd2oaRXBJjW7m8SDJrPs1EOSrfG36H36NoLETYl4JyJ+Ea3oB0a/nBpYdLmpBTMi1SSpHbI2r5m+eLTJdLlGbuktbSJ7UMtSgXyEf2QRq2/Lb/4XNqih3bw8z8AlZVG6i3V0sJwSQNMYiAqseqMPOo1GlWZ8WnpkTQjtrNC5OUEthywRkGmgzXbVk6ZGFnN3NIVS+Ox/A96byYLkufVfaupPAl/RQN81AHVBJ8qpL4jHiR9DIiOIXrWOBizJyHd/7qHiBfh193ljBRqcG0MrBWDimvaZb3kfb/h6joBysJAf5Gy1k57LWYvMvdnsgH8WHnL5H2d9roRwKF5/N3OPyxcxYMN1zyOPyNc0PKf3KebK74rJzvI5xLr+7RdYdygR8yT6pBP3T+fjlbOYvK3z6bQ1JcDBPvs1gG3X2kZPLoUoUJo6yoUQo6tl2sGpnMW5YV5TaX36H1giZw26yisCsyY4LBIrYtzGKx10d7soqw10fFr8ETslvOe2LA/sr8pre8mPDLnrHNnKfinz9qN5Eop3E5swsXrl0B8gHOPvdsLC7NIPcqMJ06umGK1LWFgPa68zArJlzJnsC+U3NMuVMMi30sw7S8rYxMlEYi41zbQH3dWtx+N+VDDVv78/BrLlym18pZQMCCxV2D+FYsu9ZokMbkcXx7NiYDpN1Y2QiMFpERClyERn/yHteHYVAB1xsGd1oitBgoxfJJ5Tdl2eF6NdSciKHQ4IIxMhoOTxzdaNmjcDpeWPw3WaO+/JDpU2QBEdFaLnw7r8Incn65JO58f7hA7no9on3LbrjsKReHnX9c8gwTjVZTTHyLW2elolaGEI4DhCEzGFQRZ110+7OiLRViKAuxALXzyqIhfcRivfMj8VEl7nKOYFj2jWewLeWYsAcMqiq12FnBKlsFYqapEYzLxVaCH/gd2OIKwM9M00DEUob9Pga9AXqDFGvaLQziAGnWxyBMJLp6lFB9V8v0mSLAeUUd39D8LHRV6r6VbjXMEiFuIiPW95uIcckfkbRy7j3yjJL+yiaSP3GJqSLhLNMl8Q1eubz66B5UNZbzQuaGbJBLzar8P5x6Qli5wRX5NNp8lXfnFJU5J0+GtIR+ocM7MS8rN4S8I+WPadqI4xBZHCAvTOxYWEK12ZAMBSxCwi1ghYGcaTpM6TSyBfGSow3qsp6LP21JIEekaOe+VOxUpTxokQyHMUwzR96qInQ8me+dfioFTnx6+sTb4fkURlUUTIvHVtMVSGp/LZN8BJkCZXgITPQhpmNAkSPs9RAtdeXrk81JpEmILAsRxqnkswXTTe36+ugyY/WoJv6xGk7tjCKgCCgCioAioAgoAns+AqPNwp7fE+2BIqAIKAKKgCKgCCgCisBYIKAEdSyGUTuhCCgCioAioAgoAorA+CCgBHV8xlJ7oggoAoqAIqAIKAKKwFggoAR1LIZRO6EIKAKKgCKgCCgCisD4IKAEdXzGUnuiCCgCioAioAgoAorAWCCgBHUshlE7oQgoAoqAIqAIKAKKwPggoAR1fMZSe6IIKAKKgCKgCCgCisBYIKAEdSyGUTuhCCgCioAioAgoAorA+CCgBHV8xlJ7oggoAoqAIqAIKAKKwFggoAR1LIZRO6EIKAKKgCKgCCgCisD4IKAEdXzGUnuiCCgCioAioAgoAorAWCCgBHUshlE7oQgoAoqAIqAIKAKKwPggoAR1fMZSe6IIKAKKgCKgCCgCisBYIKAEdSyGUTuhCCgCioAioAgoAorA+CCgBHV8xlJ7oggoAoqAIqAIKAKKwFggoAR1LIZRO6EIKAKKgCKgCCgCisD4IKAEdXzGUnuiCCgCioAioAgoAorAWCCgBHUshlE7oQgoAoqAIqAIKAKKwPggoAR1fMZSe6IIKAKKgCKgCCgCisBYIKAEdSyGUTuhCCgCioAioAgoAorA+CCgBHV8xlJ7oggoAoqAIqAIKAKKwFggoAR1LIZRO6EIKAKKgCKgCCgCisD4IKAEdXzGUnuiCCgCioAioAgoAorAWCCgBHUshlE7oQgoAoqAIqAIKAKKwPggoAR1fMZSe6IIKAKKgCKgCCgCisBYIKAEdSyGUTuhCCgCioAioAgoAorA+CCgBHV8xlJ7oggoAoqAIqAIKAKKwFggoAR1LIZRO6EIKAKKgCKgCCgCisD4IKAEdXzGUnuiCCgCioAioAgoAorAWCCgBHUshlE7oQgoAoqAIqAIKAKKwPggoAR1fMZSe6IIKAKKgCKgCCgCisBYIPB/AL/WeFQhE9dUAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF (term frequency- inverse document frequency)\n",
    "\n",
    "TF(word) = (Number of times term word appears in a document) / (Total number of terms in the document)\n",
    "IDF(word) = log_e(Total number of documents / Number of documents with term word in it)\n",
    "\n",
    "TFIDF = TF * IDF\n",
    "\n",
    "\n",
    "Basically, it is a measure of how frequently a word appear inside of a document versus inside of a corpus.\n",
    "\n",
    "Lets see a simple example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider a document containing 100 words wherein the word cat appears 3 times. The term frequency (i.e., tf) for cat is then (3 / 100) = 0.03. Now, assume we have 10 million documents and the word cat appears in one thousand of these. Then, the inverse document frequency (i.e., idf) is calculated as log(10,000,000 / 1,000) = 4. Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12.\n",
    "\n",
    "The higher the TF is in proportion to the IDF, the higher the score and the more important it is.\n",
    "\n",
    "Do we care about order?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So what are we working with? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to be using a transcript of Seinfeld dialogue!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEID</th>\n",
       "      <th>Dialogue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>Do you know what this is all about? Do you kno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>(pointing at Georges shirt) See, to me, that b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>Are you through?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>You do of course try on, when you buy?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>Yes, it was purple, I liked it, I dont actuall...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEID                                           Dialogue\n",
       "0  S01E01  Do you know what this is all about? Do you kno...\n",
       "1  S01E01  (pointing at Georges shirt) See, to me, that b...\n",
       "2  S01E01                                   Are you through?\n",
       "3  S01E01             You do of course try on, when you buy?\n",
       "4  S01E01  Yes, it was purple, I liked it, I dont actuall..."
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'https://raw.githubusercontent.com/amanthedorkknight/the-seinfeld-chronicles/master/scripts.csv'\n",
    "df = pd.read_csv(url, error_bad_lines=False)# converters={'Dialogue': i: str for i in range(0, 100000)})\n",
    "df = df[['SEID', 'Dialogue']]\n",
    "df.loc[:, \"Dialogue\"] = df.loc[:, \"Dialogue\"].map(lambda x: str(x))\n",
    "df.loc[:, \"Dialogue\"] = df.loc[:, \"Dialogue\"].map(lambda x: x.replace('\\'', ''))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe has each line spoken as a new document. We're going to be looking at each episode as a document so we need to combine these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEID</th>\n",
       "      <th>Dialogue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S01E01</td>\n",
       "      <td>Do you know what this is all about? Do you kno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S01E02</td>\n",
       "      <td>So I move into the centre lane, now I get ahea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S01E03</td>\n",
       "      <td>Most men like working on things. Tools, object...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S01E04</td>\n",
       "      <td>Went out to dinner the other night. Check came...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S02E01</td>\n",
       "      <td>Im always in traffic with the lane expert. You...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEID                                           Dialogue\n",
       "0  S01E01  Do you know what this is all about? Do you kno...\n",
       "1  S01E02  So I move into the centre lane, now I get ahea...\n",
       "2  S01E03  Most men like working on things. Tools, object...\n",
       "3  S01E04  Went out to dinner the other night. Check came...\n",
       "4  S02E01  Im always in traffic with the lane expert. You..."
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby(['SEID'],as_index=False).agg(lambda x :' '.join(x))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That worked well. This brings us from 50,000+ documents to..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome. Let's get started with sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "v = TfidfVectorizer(ngram_range=(2,4))\n",
    "x = v.fit_transform(df['Dialogue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1072387\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['00 00',\n",
       " '00 00 so',\n",
       " '00 00 so what',\n",
       " '00 again',\n",
       " '00 again im',\n",
       " '00 again im really',\n",
       " '00 charge',\n",
       " '00 charge for',\n",
       " '00 charge for not',\n",
       " '00 how',\n",
       " '00 how could',\n",
       " '00 how could that',\n",
       " '00 oh',\n",
       " '00 oh thats',\n",
       " '00 oh thats not',\n",
       " '00 so',\n",
       " '00 so was',\n",
       " '00 so was it',\n",
       " '00 so what',\n",
       " '00 so what did',\n",
       " '00 thats',\n",
       " '00 thats what',\n",
       " '00 thats what said',\n",
       " '00 what',\n",
       " '00 what 98']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = v.get_feature_names()\n",
    "print(len(feature_names))\n",
    "feature_names[:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us over a million features! Let's try to bring that number down a little."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = TfidfVectorizer(analyzer='word', token_pattern=u'(?ui)\\\\b\\\\w*[a-z]+\\\\w*\\\\b', ngram_range=(2,4), max_df = 10, min_df = 1, stop_words = 'english', max_features=100)\n",
    "x = v.fit_transform(df['Dialogue'].values.astype('U'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many will this give us? And what do they look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['air conditioner',\n",
       " 'apartment building',\n",
       " 'b o',\n",
       " 'best friend',\n",
       " 'big salad',\n",
       " 'black white',\n",
       " 'bubble boy',\n",
       " 'bum bum',\n",
       " 'candy bar',\n",
       " 'coffee table book',\n",
       " 'coffee tables',\n",
       " 'coffee yeah',\n",
       " 'doing voice',\n",
       " 'door jerry',\n",
       " 'door open',\n",
       " 'dressing room',\n",
       " 'drop dead',\n",
       " 'dry cleaning',\n",
       " 'elaines apartment',\n",
       " 'friday night']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = v.get_feature_names()\n",
    "print(len(feature_names))\n",
    "feature_names[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool! Can we rank them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>youre better</td>\n",
       "      <td>0.033863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>piece cake</td>\n",
       "      <td>0.032083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ok im</td>\n",
       "      <td>0.030049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>george doing</td>\n",
       "      <td>0.027180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>mr wilhelm</td>\n",
       "      <td>0.026706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>getting married</td>\n",
       "      <td>0.026679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>walk away</td>\n",
       "      <td>0.026528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>long island</td>\n",
       "      <td>0.026252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>funny guy</td>\n",
       "      <td>0.026234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>just forget</td>\n",
       "      <td>0.025437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>air conditioner</td>\n",
       "      <td>0.025373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>youre thinking</td>\n",
       "      <td>0.025368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>oh god oh</td>\n",
       "      <td>0.024582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>thirty minutes</td>\n",
       "      <td>0.024552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>just curious</td>\n",
       "      <td>0.023771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>yes sir</td>\n",
       "      <td>0.023471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>night club</td>\n",
       "      <td>0.023018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>getting little</td>\n",
       "      <td>0.021742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>best friend</td>\n",
       "      <td>0.021095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>jerry nods</td>\n",
       "      <td>0.021089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               term    weight\n",
       "98     youre better  0.033863\n",
       "66       piece cake  0.032083\n",
       "64            ok im  0.030049\n",
       "22     george doing  0.027180\n",
       "58       mr wilhelm  0.026706\n",
       "25  getting married  0.026679\n",
       "92        walk away  0.026528\n",
       "55      long island  0.026252\n",
       "21        funny guy  0.026234\n",
       "47      just forget  0.025437\n",
       "0   air conditioner  0.025373\n",
       "99   youre thinking  0.025368\n",
       "63        oh god oh  0.024582\n",
       "86   thirty minutes  0.024552\n",
       "46     just curious  0.023771\n",
       "97          yes sir  0.023471\n",
       "60       night club  0.023018\n",
       "24   getting little  0.021742\n",
       "3       best friend  0.021095\n",
       "42       jerry nods  0.021089"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "weights = np.asarray(x.mean(axis=0)).ravel().tolist()\n",
    "weights_df = pd.DataFrame({'term': v.get_feature_names(), 'weight': weights})\n",
    "weights_df.sort_values(by='weight', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait, I thought this was a numerical representation? What happened to that? And what even are v and x?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('god bless', 'S01E01') 0.10136535458235003\n",
      "('drop dead', 'S01E01') 0.09386876208397262\n",
      "('t shirt', 'S01E01') 0.08592205380295506\n",
      "('sits couch', 'S01E01') 0.1675970910549163\n",
      "('bum bum', 'S01E01') 0.9570328828292984\n",
      "('ho ho ho', 'S01E01') 0.17184410760591012\n",
      "('sits couch', 'S01E02') 0.15670051989776576\n",
      "('door open', 'S01E02') 0.8253051312693338\n",
      "('door jerry', 'S01E02') 0.15670051989776576\n",
      "('youre thinking', 'S01E02') 0.15670051989776576\n",
      "('jerry jerry jerry', 'S01E02') 0.49518307876160034\n",
      "('best friend', 'S01E03') 0.7559289460184544\n",
      "('just forget', 'S01E03') 0.3779644730092272\n",
      "('funny guy', 'S01E03') 0.3779644730092272\n",
      "('hey hey hey hey', 'S01E03') 0.3779644730092272\n",
      "('thousand thousand', 'S01E04') 0.47075894873971746\n",
      "('dry cleaning', 'S01E04') 0.7271315277892294\n",
      "('parking lot', 'S01E04') 0.22345726816390515\n",
      "('just curious', 'S01E04') 0.4469145363278103\n",
      "('t shirt', 'S02E01') 0.5869810054358336\n",
      "('youre thinking', 'S02E01') 0.572474147563686\n",
      "('leave message', 'S02E01') 0.572474147563686\n",
      "('getting married', 'S02E02') 0.6966750618598654\n",
      "('air conditioner', 'S02E02') 0.7173868260447447\n",
      "('funny guy', 'S02E03') 0.5157568515410913\n",
      "('jerry nods', 'S02E03') 0.8149113538523385\n",
      "('thirty minutes', 'S02E03') 0.2644132285854279\n",
      "('youre thinking', 'S02E04') 0.31347335692170714\n",
      "('leave message', 'S02E04') 0.6269467138434143\n",
      "('funny funny', 'S02E04') 0.34001483864990956\n",
      "('walk away', 'S02E04') 0.6269467138434143\n",
      "('thousand thousand', 'S02E05') 0.2359285737341668\n",
      "('apartment building', 'S02E05') 0.9717703988575501\n",
      "('just forget', 'S02E06') 0.8556696337208148\n",
      "('tape recorder', 'S02E06') 0.5175224419559857\n",
      "('god bless', 'S02E07') 1.0\n",
      "('night club', 'S02E08') 0.15505910308360119\n",
      "('heart attack', 'S02E08') 0.9002395132648976\n",
      "('tv guide', 'S02E08') 0.08684641221809429\n",
      "('ice cream', 'S02E08') 0.3974709976744647\n",
      "('just forget', 'S02E09') 1.0\n",
      "('night club', 'S02E10') 1.0\n",
      "('leave message', 'S02E11') 0.40237272589918793\n",
      "('walk away', 'S02E11') 0.8047454517983759\n",
      "('long island', 'S02E11') 0.43644123002104734\n",
      "('door open', 'S02E12') 0.6695998552669262\n",
      "('apartment building', 'S02E12') 0.22983556284598014\n",
      "('night club', 'S02E12') 0.6356839517186632\n",
      "('shuts door', 'S02E12') 0.21189465057288773\n",
      "('elaines apartment', 'S02E12') 0.22319995175564206\n",
      "('hot dog', 'S03E01') 0.055404200098244843\n",
      "('hu hu', 'S03E01') 0.055404200098244843\n",
      "('joe dimaggio', 'S03E01') 0.9907483271087778\n",
      "('turns walks', 'S03E01') 0.11080840019648969\n",
      "('ho ho ho', 'S03E03') 0.24172278888282092\n",
      "('air conditioner', 'S03E03') 0.7671281474795587\n",
      "('night club', 'S03E03') 0.4714975313712664\n",
      "('mrs seinfeld', 'S03E03') 0.25570938249318625\n",
      "('son bitch', 'S03E03') 0.25570938249318625\n",
      "('ho ho ho', 'S03E04') 0.06337911609754213\n",
      "('prognosis negative', 'S03E04') 0.9979895228120855\n",
      "('youre thinking', 'S03E05') 0.6160159603549691\n",
      "('ice cream', 'S03E05') 0.31581309768530175\n",
      "('mr lippman', 'S03E05') 0.31581309768530175\n",
      "('coffee yeah', 'S03E05') 0.6488825095876549\n",
      "('parking lot', 'S03E06') 0.7209825114295935\n",
      "('air conditioner', 'S03E06') 0.5213515957686679\n",
      "('funny funny', 'S03E06') 0.2606757978843339\n",
      "('son bitch', 'S03E06') 0.2606757978843339\n",
      "('way way', 'S03E06') 0.2692080767675286\n",
      "('door open', 'S03E07') 0.36028631501015546\n",
      "('coffee yeah', 'S03E07') 0.7205726300203109\n",
      "('piece cake', 'S03E07') 0.34203745218032044\n",
      "('ok im', 'S03E07') 0.34203745218032044\n",
      "('oh god oh', 'S03E07') 0.34203745218032044\n",
      "('funny guy', 'S03E08') 0.08258276986136186\n",
      "('jerry nods', 'S03E08') 0.08698884185640561\n",
      "('tape recorder', 'S03E08') 0.899052427107099\n",
      "('oh god oh', 'S03E08') 0.41291384930680924\n",
      "('right right right', 'S03E08') 0.08258276986136186\n",
      "('long island', 'S03E09') 0.7352198324619238\n",
      "('getting little', 'S03E09') 0.6778287379232756\n",
      "('youre thinking', 'S03E10') 0.18582708809307968\n",
      "('funny guy', 'S03E10') 0.18582708809307968\n",
      "('thousand thousand', 'S03E10') 0.39148319209691107\n",
      "('long island', 'S03E10') 0.8062435416548883\n",
      "('george going', 'S03E10') 0.18582708809307968\n",
      "('table book', 'S03E10') 0.21577435185621102\n",
      "('coffee table book', 'S03E10') 0.21577435185621102\n",
      "('thirty minutes', 'S03E11') 1.0\n",
      "('walk away', 'S03E12') 0.43830596600100225\n",
      "('george doing', 'S03E12') 0.8988258341680707\n",
      "('getting married', 'S03E13') 1.0\n",
      "('ok im', 'S03E14') 1.0\n",
      "('just curious', 'S03E15') 0.4280452447657971\n",
      "('night club', 'S03E15') 0.4280452447657971\n",
      "('heart attack', 'S03E15') 0.4970275657992871\n",
      "('hot dog', 'S03E15') 0.45088291621639626\n",
      "('george going', 'S03E15') 0.4280452447657971\n",
      "('funny guy', 'S03E16') 0.7071067811865476\n",
      "('piece cake', 'S03E16') 0.7071067811865476\n",
      "('ho ho ho', 'S03E17') 0.15749204274764722\n",
      "('parking lot', 'S03E17') 0.07679986412258691\n",
      "('funny funny', 'S03E17') 0.08330243330535535\n",
      "('night club', 'S03E17') 0.07679986412258691\n",
      "('way way', 'S03E17') 0.17205807399228273\n",
      "('keith hernandez', 'S03E17') 0.9299716034213539\n",
      "('holds hand', 'S03E17') 0.07874602137382361\n",
      "('friday night', 'S03E17') 0.23623806412147086\n",
      "('parking lot', 'S03E18') 0.12107251123216223\n",
      "('thirty minutes', 'S03E18') 0.12414056543189111\n",
      "('night club', 'S03E18') 0.12107251123216223\n",
      "('keith hernandez', 'S03E18') 0.9773801788772505\n",
      "('yes sir', 'S03E19') 1.0\n",
      "('god bless', 'S03E20') 0.9619056050451001\n",
      "('right right right', 'S03E20') 0.24850172850479388\n",
      "('friday night', 'S03E20') 0.10191956794629534\n",
      "('wants talk', 'S03E20') 0.05095978397314767\n",
      "('funny funny', 'S03E21') 0.39290914609870375\n",
      "('mr lippman', 'S03E21') 0.3714181061583736\n",
      "('security guard', 'S03E21') 0.8412318309061507\n",
      "('just curious', 'S03E22') 0.40310882304807855\n",
      "('thirty minutes', 'S03E22') 0.4133238562122035\n",
      "('ice cream', 'S03E22') 0.4133238562122035\n",
      "('oh god oh', 'S03E22') 0.40310882304807855\n",
      "('george going', 'S03E22') 0.40310882304807855\n",
      "('wants talk', 'S03E22') 0.4133238562122035\n",
      "('door open', 'S03E23') 0.37228627297943784\n",
      "('jerry nods', 'S03E23') 0.37228627297943784\n",
      "('elaines apartment', 'S03E23') 0.7445725459588757\n",
      "('la la', 'S03E23') 0.4103871168865769\n",
      "('yes sir', 'S04E01') 0.8771866321347397\n",
      "('la la', 'S04E01') 0.33951701901680353\n",
      "('toilet paper', 'S04E01') 0.33951701901680353\n",
      "('george doing', 'S04E02') 0.6751936282776038\n",
      "('black white', 'S04E02') 0.7376405386997961\n",
      "('toilet paper', 'S04E03') 1.0\n",
      "('t shirt', 'S04E04') 0.18696011786940045\n",
      "('dry cleaning', 'S04E04') 0.3955560542073106\n",
      "('air conditioner', 'S04E04') 0.09888901355182765\n",
      "('heart attack', 'S04E04') 0.10586236888693995\n",
      "('coffee yeah', 'S04E04') 0.09603397655532579\n",
      "('ted danson', 'S04E04') 0.8822544528114132\n",
      "('hu hu', 'S04E05') 0.4860878321835309\n",
      "('mrs seinfeld', 'S04E05') 0.2502694772640337\n",
      "('ted danson', 'S04E05') 0.83730747527602\n",
      "('drop dead', 'S04E06') 0.22589542002311241\n",
      "('tv guide', 'S04E06') 0.22589542002311241\n",
      "('mrs seinfeld', 'S04E06') 0.4374717843535683\n",
      "('wants talk', 'S04E06') 0.413543291765238\n",
      "('ted danson', 'S04E06') 0.7318079680870109\n",
      "('dry cleaning', 'S04E07') 0.024833221219236718\n",
      "('leave message', 'S04E07') 0.06868423669111161\n",
      "('tv guide', 'S04E07') 0.02564604684681804\n",
      "('hu hu', 'S04E07') 0.43409264767321365\n",
      "('george going', 'S04E07') 0.02289474556370387\n",
      "('bubble boy', 'S04E07') 0.6682077791351414\n",
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      "('hu hu hu hu', 'S04E07') 0.43125146522167823\n",
      "('door jerry', 'S04E08') 0.39410986865770004\n",
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      "('george doing', 'S04E11') 0.6758951662060412\n",
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      "('door jerry', 'S04E13') 0.6778287379232756\n",
      "('apartment building', 'S04E13') 0.7352198324619238\n",
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      "('rochelle rochelle', 'S04E14') 0.9035090775088661\n",
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      "('oh god oh', 'S04E16') 0.1756477465901503\n",
      "('mrs seinfeld', 'S04E17') 0.6037287934799052\n",
      "('oh god oh', 'S04E17') 0.5566018598302968\n",
      "('youre better', 'S04E17') 0.5707065038675769\n",
      "('drop dead', 'S04E18') 0.7459883905046657\n",
      "('hey hey hey hey', 'S04E18') 0.6659589486088902\n",
      "('funny guy', 'S04E19') 1.0\n",
      "('turns walks', 'S04E20') 0.364517755468029\n",
      "('prognosis negative', 'S04E20') 0.9311964378951146\n",
      "('rochelle rochelle', 'S04E21') 0.18396200330292517\n",
      "('b o', 'S04E21') 0.9752523182022674\n",
      "('o o', 'S04E21') 0.12264133553528345\n",
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      "('night club', 'S04E23') 0.39964335000772866\n",
      "('heart attack', 'S04E23') 0.4640485179338819\n",
      "('joe dimaggio', 'S04E23') 0.5376991095975767\n",
      "('wants talk', 'S04E23') 0.4097705658877543\n",
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      "('funny guy', 'S04E24') 0.7071067811865476\n",
      "('just curious', 'S04E24') 0.7071067811865476\n",
      "('youre thinking', 'S05E01') 0.9486832980505138\n",
      "('piece cake', 'S05E01') 0.31622776601683794\n",
      "('sits couch', 'S05E02') 0.057656678430164865\n",
      "('george going', 'S05E02') 0.11531335686032973\n",
      "('holds hand', 'S05E02') 0.1773532056550925\n",
      "('puffy shirt', 'S05E02') 0.9511321052781091\n",
      "('post office', 'S05E02') 0.05911773521836416\n",
      "('dressing room', 'S05E02') 0.2092299913916467\n",
      "('door open', 'S05E03') 0.1037098609796367\n",
      "('best friend', 'S05E03') 0.09845685261303998\n",
      "('air conditioner', 'S05E03') 0.9611378799636126\n",
      "('son bitch', 'S05E03') 0.10679309777373472\n",
      "('right right right', 'S05E03') 0.09845685261303998\n",
      "('friday night', 'S05E03') 0.10095181168407379\n",
      "('toilet paper', 'S05E03') 0.11432382531732926\n",
      "('candy bar', 'S05E03') 0.10679309777373472\n",
      "('just curious', 'S05E04') 0.8130267097045827\n",
      "('mr lippman', 'S05E04') 0.41681466101095255\n",
      "('ok im', 'S05E04') 0.40651335485229134\n",
      "('heart attack', 'S05E05') 0.036206218674358435\n",
      "('piece cake', 'S05E05') 0.031181167405850068\n",
      "('ok im', 'S05E05') 0.062362334811700136\n",
      "('pig man', 'S05E05') 0.9969091150778722\n",
      "('ho ho ho', 'S05E06') 0.15584582085090534\n",
      "('thousand thousand', 'S05E06') 0.3202071987630234\n",
      "('ice cream', 'S05E06') 0.7792291042545267\n",
      "('right right right', 'S05E06') 0.15199419166339254\n",
      "('friday night', 'S05E06') 0.4675374625527161\n",
      "('post office', 'S05E06') 0.15584582085090534\n",
      "('just forget', 'S05E07') 0.1886039632735731\n",
      "('security guard', 'S05E07') 0.21899873883017276\n",
      "('lloyd braun', 'S05E07') 0.9573234027371808\n",
      "('thousand thousand', 'S05E08') 1.0\n",
      "('walk away', 'S05E09') 0.33864637328082897\n",
      "('night club', 'S05E09') 0.33864637328082897\n",
      "('elaines apartment', 'S05E09') 0.3567143105035777\n",
      "('getting little', 'S05E09') 0.6772927465616579\n",
      "('o o', 'S05E09') 0.42972863980005543\n",
      "('hey hey hey hey', 'S05E10') 0.034881588464907264\n",
      "('tv guide', 'S05E10') 0.8986874973380348\n",
      "('mr lippman', 'S05E10') 0.03576551003301416\n",
      "('getting little', 'S05E10') 0.034881588464907264\n",
      "('table book', 'S05E10') 0.24301792230507685\n",
      "('coffee table book', 'S05E10') 0.24301792230507685\n",
      "('coffee tables', 'S05E10') 0.2655800046497378\n",
      "('george doing', 'S05E11') 1.0\n",
      "('shuts door', 'S05E12') 0.1697425533650196\n",
      "('toilet paper', 'S05E12') 0.9854884401032432\n",
      "('just forget', 'S05E13') 0.13838166847620048\n",
      "('turns walks', 'S05E13') 0.29152960345385154\n",
      "('black white', 'S05E13') 0.9300674015197065\n",
      "('bubble boy', 'S05E13') 0.17560077667870388\n",
      "('t shirt', 'S05E14') 0.474346302757234\n",
      "('tape recorder', 'S05E14') 0.4197026174800315\n",
      "('hu hu', 'S05E14') 0.609132073690639\n",
      "('mr lippman', 'S05E14') 0.1185865756893085\n",
      "('ok im', 'S05E14') 0.11565578477250167\n",
      "('hu hu hu', 'S05E14') 0.4197026174800315\n",
      "('hu hu hu hu', 'S05E14') 0.1556087759017646\n",
      "('jerry jerry jerry', 'S05E15') 0.7252351456514581\n",
      "('ok im', 'S05E15') 0.688501258903648\n",
      "('jerry nods', 'S05E16') 0.5824674500467342\n",
      "('elaines apartment', 'S05E16') 0.5824674500467342\n",
      "('george doing', 'S05E16') 0.5669773710406003\n",
      "('dry cleaning', 'S05E17') 0.9190403201659468\n",
      "('mrs seinfeld', 'S05E17') 0.2297600800414867\n",
      "('son bitch', 'S05E17') 0.2297600800414867\n",
      "('coffee yeah', 'S05E17') 0.22312664822457534\n",
      "('door jerry', 'S05E18') 0.3113228640494019\n",
      "('hey hey hey hey', 'S05E18') 0.46698429607410286\n",
      "('thousand thousand', 'S05E18') 0.16396649950431977\n",
      "('shuts door', 'S05E18') 0.15566143202470095\n",
      "('mrs seinfeld', 'S05E18') 0.5065233984824261\n",
      "('son bitch', 'S05E18') 0.1688411328274754\n",
      "('getting little', 'S05E18') 0.15566143202470095\n",
      "('george doing', 'S05E18') 0.1596059914081552\n",
      "('holds hand', 'S05E18') 0.1596059914081552\n",
      "('black white', 'S05E18') 0.523102608872465\n",
      "('just forget', 'S05E20') 0.25643752732499253\n",
      "('table book', 'S05E20') 0.5955282603625999\n",
      "('coffee table book', 'S05E20') 0.5955282603625999\n",
      "('b o', 'S05E20') 0.3450232065850368\n",
      "('coffee tables', 'S05E20') 0.32540891769620206\n",
      "('t shirt', 'S05E21') 1.0\n",
      "('mr lippman', 'S05E22') 0.4946565706820786\n",
      "('coffee yeah', 'S05E22') 0.07259583037307611\n",
      "('table book', 'S05E22') 0.4801529739954585\n",
      "('coffee table book', 'S05E22') 0.4801529739954585\n",
      "('black white', 'S05E22') 0.07720086801515325\n",
      "('puffy shirt', 'S05E22') 0.08745516095874337\n",
      "('coffee tables', 'S05E22') 0.5247309657524601\n",
      "('hot dog', 'S06E01') 1.0\n",
      "('hey hey hey hey', 'S06E02') 0.030936361409406976\n",
      "('dry cleaning', 'S06E02') 0.033555712792720606\n",
      "('big salad', 'S06E02') 0.9989579348910137\n",
      "('drop dead', 'S06E03') 0.4212730461635828\n",
      "('jerry jerry jerry', 'S06E03') 0.39614407695568454\n",
      "('candy bar', 'S06E03') 0.8158424424292872\n",
      "('sits couch', 'S06E04') 0.22459658547128036\n",
      "('long island', 'S06E04') 0.9744518324651261\n",
      "('sits couch', 'S06E05') 0.8942370481474833\n",
      "('just curious', 'S06E05') 0.2980790160491611\n",
      "('tv guide', 'S06E05') 0.33389968839703893\n",
      "('bum bum', 'S06E06') 0.5723025959847601\n",
      "('shuts door', 'S06E06') 0.4510013543421676\n",
      "('way way', 'S06E06') 0.505198969311735\n",
      "('holds hand', 'S06E06') 0.46243001461518035\n",
      "('sits couch', 'S06E07') 0.0829909450163802\n",
      "('just curious', 'S06E07') 0.0829909450163802\n",
      "('hot dog', 'S06E07') 0.349675178183688\n",
      "('hu hu', 'S06E07') 0.174837589091844\n",
      "('turns walks', 'S06E07') 0.6119315618214539\n",
      "('hu hu hu', 'S06E07') 0.10038845562007057\n",
      "('big salad', 'S06E07') 0.6699597114142813\n",
      "('door jerry', 'S06E08') 0.03378227331978328\n",
      "('jon voight', 'S06E08') 0.9859719893518141\n",
      "('tim whatley', 'S06E08') 0.1634563985378788\n",
      "('door jerry', 'S06E09') 0.19163408659578207\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dry cleaning', 'S06E09') 0.2771460757134842\n",
      "('jerry nods', 'S06E09') 0.13457227846407913\n",
      "('turns walks', 'S06E09') 0.06728613923203956\n",
      "('piece cake', 'S06E09') 0.06387802886526069\n",
      "('dressing room', 'S06E09') 0.9272264579571368\n",
      "('ho ho ho', 'S06E10') 0.9581897099701452\n",
      "('oh god oh', 'S06E10') 0.11681358347621573\n",
      "('right right right', 'S06E10') 0.11681358347621573\n",
      "('yes sir', 'S06E10') 0.23362716695243146\n",
      "('post office', 'S06E12') 0.03152026242047284\n",
      "('tim whatley', 'S06E12') 0.4090415299566631\n",
      "('super bowl', 'S06E12') 0.7031327128581927\n",
      "('label maker', 'S06E12') 0.5807718036684764\n",
      "('parking lot', 'S06E13') 0.5225954113872587\n",
      "('mr lippman', 'S06E13') 0.5358383104594621\n",
      "('jon voight', 'S06E13') 0.6631525774970123\n",
      "('youre thinking', 'S06E16') 0.572474147563686\n",
      "('best friend', 'S06E16') 0.572474147563686\n",
      "('thirty minutes', 'S06E16') 0.5869810054358336\n",
      "('hey hey hey hey', 'S06E17') 0.572474147563686\n",
      "('leave message', 'S06E17') 0.572474147563686\n",
      "('holds hand', 'S06E17') 0.5869810054358336\n",
      "('youre thinking', 'S06E18') 0.43830596600100225\n",
      "('youre better', 'S06E18') 0.8988258341680707\n",
      "('tim whatley', 'S06E19') 0.7628242890068205\n",
      "('mr wilhelm', 'S06E19') 0.6466058336430616\n",
      "('walk away', 'S06E21') 0.33347995215373644\n",
      "('apartment building', 'S06E21') 0.3617153726811047\n",
      "('turns walks', 'S06E21') 0.35127224321590106\n",
      "('oh god oh', 'S06E21') 0.33347995215373644\n",
      "('candy bar', 'S06E21') 0.7234307453622094\n",
      "('post office', 'S06E22') 1.0\n",
      "('friday night', 'S06E23') 0.1251558110631891\n",
      "('toilet paper', 'S06E23') 0.9921370988714793\n",
      "('hot dog', 'S06E24') 0.4217660109073737\n",
      "('george going', 'S06E24') 0.20020156994160196\n",
      "('black white', 'S06E24') 0.448520279663524\n",
      "('rochelle rochelle', 'S06E24') 0.7621432425513942\n",
      "('just curious', 'S07E01') 0.16736926726824972\n",
      "('getting married', 'S07E01') 0.705195950509387\n",
      "('wants talk', 'S07E01') 0.057203503540013155\n",
      "('la la', 'S07E01') 0.5182451498117612\n",
      "('la la la', 'S07E01') 0.4503730938217605\n",
      "('getting married', 'S07E02') 0.9870942162251166\n",
      "('friday night', 'S07E02') 0.16014058915503757\n",
      "('youre thinking', 'S07E03') 0.09149040168550512\n",
      "('jerry jerry jerry', 'S07E03') 0.1927434523438782\n",
      "('coffee yeah', 'S07E03') 0.4818586308596955\n",
      "('security guard', 'S07E03') 0.8498774781367063\n",
      "('way way', 'S07E04') 0.5398115146755957\n",
      "('oh god oh', 'S07E04') 0.4819006747774366\n",
      "('yes sir', 'S07E04') 0.4819006747774366\n",
      "('mr wilhelm', 'S07E04') 0.4941123434217584\n",
      "('door jerry', 'S07E05') 0.02257720473127365\n",
      "('leave message', 'S07E05') 0.02257720473127365\n",
      "('shuts door', 'S07E05') 0.02257720473127365\n",
      "('son bitch', 'S07E05') 0.22039915064324125\n",
      "('yes sir', 'S07E05') 0.02257720473127365\n",
      "('mr wilhelm', 'S07E05') 0.13889595248441552\n",
      "('jean paul', 'S07E05') 0.9514997626646289\n",
      "('hot tub', 'S07E05') 0.15729402319995694\n",
      "('jerry jerry jerry', 'S07E06') 0.0629743709198364\n",
      "('just forget', 'S07E06') 0.0597846559380757\n",
      "('hey hey hey hey', 'S07E06') 0.2391386237523028\n",
      "('walk away', 'S07E06') 0.0597846559380757\n",
      "('soup nazi', 'S07E06') 0.9652453248091064\n",
      "('door jerry', 'S07E08') 0.2583196344430393\n",
      "('turns walks', 'S07E08') 0.8163055998252572\n",
      "('george going', 'S07E08') 0.5166392688860786\n",
      "('t shirt', 'S07E10') 0.052671279152937844\n",
      "('jerry jerry jerry', 'S07E10') 0.16233084718674523\n",
      "('best friend', 'S07E10') 0.1027390847571923\n",
      "('lloyd braun', 'S07E10') 0.9777882762913577\n",
      "('jon voight', 'S07E10') 0.06518588508609051\n",
      "('piece cake', 'S07E11') 0.8898717255965677\n",
      "('friday night', 'S07E11') 0.45621081966979576\n",
      "('parking lot', 'S07E12') 0.22973170156113695\n",
      "('leave message', 'S07E12') 0.07657723385371232\n",
      "('yes sir', 'S07E12') 0.6126178708296985\n",
      "('candy bar', 'S07E12') 0.4153047657496689\n",
      "('mr wilhelm', 'S07E12') 0.07851774951277872\n",
      "('sue ellen', 'S07E12') 0.6224271294298527\n",
      "('oh god oh', 'S07E13') 0.9368239414697007\n",
      "('black white', 'S07E13') 0.34980123311557215\n",
      "('hu hu', 'S07E14') 0.09116572213756502\n",
      "('la la', 'S07E14') 0.05024794168140669\n",
      "('marisa tomei', 'S07E14') 0.9945672201836928\n",
      "('just forget', 'S07E15') 0.332009052396365\n",
      "('hu hu', 'S07E15') 0.34972286594755064\n",
      "('coffee yeah', 'S07E15') 0.34972286594755064\n",
      "('marisa tomei', 'S07E15') 0.8032171706292955\n",
      "('mrs seinfeld', 'S07E16') 0.8504718811201784\n",
      "('dressing room', 'S07E16') 0.47422646937665597\n",
      "('hot tub', 'S07E16') 0.22761114903812812\n",
      "('night club', 'S07E17') 0.3556904808209076\n",
      "('holds hand', 'S07E17') 0.3647039031277564\n",
      "('dressing room', 'S07E17') 0.8605087709586531\n",
      "('best friend', 'S07E18') 0.4792972970686673\n",
      "('funny guy', 'S07E18') 0.4792972970686673\n",
      "('funny funny', 'S07E18') 0.5198789291966637\n",
      "('apartment building', 'S07E18') 0.5198789291966637\n",
      "('t shirt', 'S07E19') 0.45621081966979576\n",
      "('sits couch', 'S07E19') 0.44493586279828384\n",
      "('parking lot', 'S07E19') 0.44493586279828384\n",
      "('just curious', 'S07E19') 0.44493586279828384\n",
      "('right right right', 'S07E19') 0.44493586279828384\n",
      "('thousand thousand', 'S07E21') 0.5418255543811619\n",
      "('yes sir', 'S07E21') 0.10287630943823024\n",
      "('post office', 'S07E21') 0.10548326034733739\n",
      "('candy bar', 'S07E21') 0.22317349134889616\n",
      "('mr wilhelm', 'S07E21') 0.527416301736687\n",
      "('sue ellen', 'S07E21') 0.5972775341363111\n",
      "('yes sir', 'S07E22') 0.3626212044219759\n",
      "('wants talk', 'S07E22') 0.3718102556592484\n",
      "('mr wilhelm', 'S07E22') 0.7436205113184968\n",
      "('sue ellen', 'S07E22') 0.421060008830781\n",
      "('jerry nods', 'S07E23') 0.5824674500467342\n",
      "('elaines apartment', 'S07E23') 0.5824674500467342\n",
      "('wants talk', 'S07E23') 0.5669773710406003\n",
      "('door jerry', 'S07E24') 0.27267916496203987\n",
      "('getting married', 'S07E24') 0.8616825809393526\n",
      "('getting little', 'S07E24') 0.27267916496203987\n",
      "('marisa tomei', 'S07E24') 0.3298412886478973\n",
      "('getting married', 'S08E01') 0.2444805188162461\n",
      "('jerry nods', 'S08E01') 0.2444805188162461\n",
      "('ice cream', 'S08E01') 0.2379788292340722\n",
      "('way way', 'S08E01') 0.7799666248769482\n",
      "('george going', 'S08E01') 0.46419467118130564\n",
      "('sits couch', 'S08E02') 0.3673643755081497\n",
      "('tape recorder', 'S08E02') 0.888750990841612\n",
      "('long island', 'S08E02') 0.19923437846637404\n",
      "('youre better', 'S08E02') 0.18833681085405873\n",
      "('just forget', 'S08E03') 1.0\n",
      "('air conditioner', 'S08E04') 1.0\n",
      "('t shirt', 'S08E05') 0.1327868116670686\n",
      "('jerry jerry jerry', 'S08E05') 0.9549022532366714\n",
      "('post office', 'S08E05') 0.2655736233341372\n",
      "('dry cleaning', 'S08E06') 1.0\n",
      "('mr wilhelm', 'S08E07') 0.6619085787947349\n",
      "('hot tub', 'S08E07') 0.7495845738260187\n",
      "('sits couch', 'S08E08') 0.4216713182197207\n",
      "('ice cream', 'S08E08') 0.43235673678090414\n",
      "('shuts door', 'S08E08') 0.4216713182197207\n",
      "('turns walks', 'S08E08') 0.4441689189836588\n",
      "('marisa tomei', 'S08E08') 0.5100668802723247\n",
      "('best friend', 'S08E09') 0.2655044319695569\n",
      "('holds hand', 'S08E09') 0.2722324826167334\n",
      "('sue ellen', 'S08E09') 0.9248766793539843\n",
      "('apartment building', 'S08E10') 0.6104989018847334\n",
      "('george going', 'S08E10') 0.18761449836592714\n",
      "('post office', 'S08E10') 0.7694750748402681\n",
      "('hot tub', 'S08E11') 0.041557069366736636\n",
      "('little jerry', 'S08E11') 0.9991361318587414\n",
      "('sits couch', 'S08E12') 0.49329042551474267\n",
      "('youre thinking', 'S08E12') 0.49329042551474267\n",
      "('thousand thousand', 'S08E12') 0.5196091495407411\n",
      "('walk away', 'S08E12') 0.49329042551474267\n",
      "('youre better', 'S08E13') 1.0\n",
      "('piece cake', 'S08E14') 1.0\n",
      "('best friend', 'S08E15') 0.1631364534686901\n",
      "('shuts door', 'S08E15') 0.1631364534686901\n",
      "('way way', 'S08E15') 0.9137042201321869\n",
      "('wants talk', 'S08E15') 0.33454086927003124\n",
      "('bum bum', 'S08E16') 0.8810701352280664\n",
      "('parking lot', 'S08E16') 0.23144156433221105\n",
      "('son bitch', 'S08E16') 0.25103749462495417\n",
      "('piece cake', 'S08E16') 0.23144156433221105\n",
      "('getting little', 'S08E16') 0.23144156433221105\n",
      "('hey hey hey hey', 'S08E17') 0.13799360045978704\n",
      "('youre better', 'S08E17') 0.9904331205246241\n",
      "('mr wilhelm', 'S08E18') 1.0\n",
      "('door open', 'S08E19') 0.038748185085855515\n",
      "('getting married', 'S08E19') 0.019374092542927757\n",
      "('tim whatley', 'S08E19') 0.044496958337789674\n",
      "('hot tub', 'S08E19') 0.04271378537464902\n",
      "('yada yada', 'S08E19') 0.8820672982130061\n",
      "('raquel welch', 'S08E19') 0.09898604769065615\n",
      "('yada yada yada', 'S08E19') 0.4543983051400334\n",
      "('parking lot', 'S08E20') 0.641734527137725\n",
      "('just curious', 'S08E20') 0.3208672635688625\n",
      "('walk away', 'S08E20') 0.3208672635688625\n",
      "('holds hand', 'S08E20') 0.3289982434711482\n",
      "('mr wilhelm', 'S08E20') 0.3289982434711482\n",
      "('little jerry', 'S08E20') 0.4071676639970058\n",
      "('hey hey hey hey', 'S08E21') 0.5227366244549025\n",
      "('mr lippman', 'S08E21') 0.8039746529336625\n",
      "('candy bar', 'S08E21') 0.28349811091732396\n",
      "('god bless', 'S08E22') 0.10796406233937686\n",
      "('hey hey hey hey', 'S08E22') 0.08925368465934341\n",
      "('leave message', 'S08E22') 0.17850736931868683\n",
      "('coffee yeah', 'S08E22') 0.09401567267563535\n",
      "('george doing', 'S08E22') 0.09151542961923183\n",
      "('friday night', 'S08E22') 0.09151542961923183\n",
      "('raquel welch', 'S08E22') 0.9606891098016018\n",
      "('elaines apartment', 'S09E02') 0.07364820214314514\n",
      "('getting little', 'S09E02') 0.013983569383140479\n",
      "('la la', 'S09E02') 0.22731959649935468\n",
      "('o o', 'S09E02') 0.5678268045666353\n",
      "('la la la', 'S09E02') 0.13169910031821158\n",
      "('doing voice', 'S09E02') 0.46739750829316734\n",
      "('hello o', 'S09E02') 0.30482446193032653\n",
      "('hello o o', 'S09E02') 0.30482446193032653\n",
      "('o o o', 'S09E02') 0.3251460927256816\n",
      "('hello o o o', 'S09E02') 0.30482446193032653\n",
      "('mr lippman', 'S09E03') 0.431705005201725\n",
      "('friday night', 'S09E03') 0.143901668400575\n",
      "('lloyd braun', 'S09E03') 0.8904622947184847\n",
      "('air conditioner', 'S09E04') 0.3237049316568319\n",
      "('getting little', 'S09E04') 0.8953097927857674\n",
      "('george doing', 'S09E04') 0.3059991701997769\n",
      "('t shirt', 'S09E05') 0.27811444664446006\n",
      "('door open', 'S09E05') 0.5714253190064515\n",
      "('thirty minutes', 'S09E05') 0.27811444664446006\n",
      "('walk away', 'S09E05') 0.5424820540818609\n",
      "('george going', 'S09E05') 0.27124102704093045\n",
      "('yes sir', 'S09E05') 0.27124102704093045\n",
      "('post office', 'S09E05') 0.27811444664446006\n",
      "('hu hu', 'S09E06') 0.6742377031032748\n",
      "('right right right', 'S09E06') 0.3200434439576966\n",
      "('george doing', 'S09E06') 0.32815354774869876\n",
      "('hu hu hu', 'S09E06') 0.387134609250448\n",
      "('super bowl', 'S09E06') 0.43060162228463816\n",
      "('ok im', 'S09E07') 1.0\n",
      "('drop dead', 'S09E08') 0.7418129376062472\n",
      "('best friend', 'S09E08') 0.15282263935827262\n",
      "('funny guy', 'S09E08') 0.2037635191443635\n",
      "('thirty minutes', 'S09E08') 0.2611587698927411\n",
      "('apartment building', 'S09E08') 0.1657619823697947\n",
      "('elaines apartment', 'S09E08') 0.05365874919402168\n",
      "('hot dog', 'S09E08') 0.05365874919402168\n",
      "('sue ellen', 'S09E08') 0.5323530540386503\n",
      "('ho ho ho', 'S09E09') 0.6387224968280555\n",
      "('thirty minutes', 'S09E09') 0.31936124841402774\n",
      "('ice cream', 'S09E09') 0.31936124841402774\n",
      "('ok im', 'S09E09') 0.6229368812878251\n",
      "('youre better', 'S09E10') 1.0\n",
      "('ho ho ho', 'S09E11') 0.12100724138406152\n",
      "('elaines apartment', 'S09E11') 0.12431321411788196\n",
      "('right right right', 'S09E11') 0.11801662527212134\n",
      "('holds hand', 'S09E11') 0.6050362069203076\n",
      "('candy bar', 'S09E11') 0.7680538632173968\n",
      "('joe mayo', 'S09E12') 1.0\n",
      "('best friend', 'S09E13') 0.816496580927726\n",
      "('leave message', 'S09E13') 0.408248290463863\n",
      "('getting little', 'S09E13') 0.408248290463863\n",
      "('best friend', 'S09E14') 0.24253562503633297\n",
      "('shuts door', 'S09E14') 0.9701425001453319\n",
      "('long island', 'S09E15') 0.4859235336474821\n",
      "('hot dog', 'S09E15') 0.47189437493504965\n",
      "('table book', 'S09E15') 0.5201893974083879\n",
      "('coffee table book', 'S09E15') 0.5201893974083879\n",
      "('piece cake', 'S09E16') 1.0\n",
      "('security guard', 'S09E17') 0.6250613266328898\n",
      "('youre better', 'S09E17') 0.551950331981027\n",
      "('post office', 'S09E17') 0.551950331981027\n",
      "('piece cake', 'S09E18') 0.9686813657458406\n",
      "('youre better', 'S09E18') 0.24830709143472515\n",
      "('ho ho ho', 'S09E19') 0.024322105108393905\n",
      "('t bone', 'S09E19') 0.999704173844991\n",
      "('walk away', 'S09E20') 0.6308387638209675\n",
      "('ice cream', 'S09E20') 0.3234123090373581\n",
      "('ok im', 'S09E20') 0.6308387638209675\n",
      "('right right right', 'S09E20') 0.31541938191048374\n",
      "('jerry jerry jerry', 'S09E23') 0.10096576330945779\n",
      "('just forget', 'S09E23') 0.19170349247839072\n",
      "('parking lot', 'S09E23') 0.2875552387175861\n",
      "('getting married', 'S09E23') 0.10096576330945779\n",
      "('yes sir', 'S09E23') 0.09585174623919536\n",
      "('wants talk', 'S09E23') 0.2948420707889041\n",
      "('security guard', 'S09E23') 0.22259777765756872\n",
      "('ted danson', 'S09E23') 0.34783585503962583\n",
      "('bubble boy', 'S09E23') 0.6081600717405544\n",
      "('puffy shirt', 'S09E23') 0.24326402869622174\n",
      "('soup nazi', 'S09E23') 0.3868904507370748\n",
      "('little jerry', 'S09E23') 0.12163201434811087\n"
     ]
    }
   ],
   "source": [
    "feature_names = v.get_feature_names()\n",
    "corpus_index = [n for n in df['SEID']]\n",
    "rows, cols = x.nonzero()\n",
    "for row, col in zip(rows, cols):\n",
    "    print((feature_names[col], corpus_index[row]), x[row, col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finishing up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLP is always a work in progress, but it's fun to see a simple example.\n",
    "\n",
    "Thanks for coming out and I hope to see you at DSI events in the future!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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