IntegrationTest_v01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n"
     ]
    }
   ],
   "source": [
    "from dariah_topics import preprocessing as pre\n",
    "from dariah_topics import visualization as visual\n",
    "from dariah_topics import mallet as mal\n",
    "from dariah_topics import evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Liste mit Dateinamen erzeugen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_txt/Poe_ThePurloinedLetter.txt',\n",
       " 'corpus_txt/Lovecraft_TheShunnedHouse.txt',\n",
       " 'corpus_txt/Kipling_ThyServantaDog.txt',\n",
       " 'corpus_txt/Howard_ShadowsintheMoonlight.txt',\n",
       " 'corpus_txt/Howard_SchadowsinZamboula.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_txt = \"corpus_txt\"\n",
    "#path_txt = \"grenzbote_plain/*/\"\n",
    "\n",
    "doclist_txt = pre.create_document_list(path_txt)\n",
    "assert doclist_txt, \"No documents found\"\n",
    "doclist_txt[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_csv/Howard_ShadowsintheMoonlight.txt.csv',\n",
       " 'corpus_csv/Lovecraft_AttheMountainofMadness.txt.csv',\n",
       " 'corpus_csv/Poe_TheMasqueoftheRedDeath.txt.csv',\n",
       " 'corpus_csv/Doyle_AStudyinScarlet.txt.csv',\n",
       " 'corpus_csv/Poe_TheCaskofAmontillado.txt.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_csv = \"corpus_csv\"\n",
    "\n",
    "doclist_csv = pre.create_document_list(path_csv, 'csv')\n",
    "doclist_csv[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_tei/Schnitzler_DerAndere.xml',\n",
       " 'corpus_tei/Schnitzler_GeschichteeinesGenies.xml',\n",
       " 'corpus_tei/Schnitzler_DieFremde.xml',\n",
       " 'corpus_tei/Schnitzler_Amerika.xml',\n",
       " 'corpus_tei/Schnitzler_DerWitwer.xml']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_tei = \"corpus_tei\"\n",
    "\n",
    "doclist_tei = pre.create_document_list(path_tei, 'xml')\n",
    "doclist_tei[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#####  Liste mit Dokumentenlabels erzeugen - (Funktion wird durch Thorsten's generischere Funktion ersetzt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_txt/Poe_ThePurloinedLetter.txt',\n",
       " 'corpus_txt/Lovecraft_TheShunnedHouse.txt',\n",
       " 'corpus_txt/Kipling_ThyServantaDog.txt',\n",
       " 'corpus_txt/Howard_ShadowsintheMoonlight.txt',\n",
       " 'corpus_txt/Howard_SchadowsinZamboula.txt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_labels = list(pre.get_labels(doclist_txt))\n",
    "doc_labels[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Corpus laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus_txt = pre.read_from_txt(doclist_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus_csv = pre.read_from_csv(doclist_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus_tei = pre.read_from_tei(doclist_tei)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Tokenisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24    extensive\n",
       "44        swift\n",
       "50         tall\n",
       "54        heavy\n",
       "81         dark\n",
       "Name: Lemma, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_tokens = pre.filter_POS_tags(corpus_csv)\n",
    "list(doc_tokens)[0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the', 'purloined', 'letter', 'edgar', 'allan']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_tokens = [list(pre.tokenize(txt)) for txt in list(corpus_txt)]\n",
    "doc_tokens[0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['arthur', 'schnitzler', 'der', 'andere', 'aus']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_tokens = [list(pre.tokenize(tei)) for tei in list(corpus_tei)]\n",
    "doc_tokens[0][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Create Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "id_types, doc_ids = pre.create_dictionaries(doc_labels, doc_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Sparse BOW Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sparse_bow = pre.create_mm(doc_labels, doc_tokens, id_types, doc_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc_id</th>\n",
       "      <th>token_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>409</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "doc_id token_id   \n",
       "1      409       1\n",
       "       2         1\n",
       "       3         1\n",
       "       4         1\n",
       "       2053      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_bow[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Convert to Doc-Topic Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'import pandas as pd\\n\\ndoc2id = {value : key for key, value in doc_ids.items()}\\ntype2id = {value : key for key, value in id_types.items()}\\n\\ncols = [doc2id[key] for key in set(sparse_bow.index.get_level_values(\"doc_id\"))]\\n#idx = [type2id[key] for key in set(sparse_bow.index.get_level_values(\"token_id\"))]\\n\\nset(sparse_bow.index.get_level_values(\"token_id\"))\\n\\n#doctopic_matrix = pd.DataFrame(columns=cols, index=idx)'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''import pandas as pd\n",
    "\n",
    "doc2id = {value : key for key, value in doc_ids.items()}\n",
    "type2id = {value : key for key, value in id_types.items()}\n",
    "\n",
    "cols = [doc2id[key] for key in set(sparse_bow.index.get_level_values(\"doc_id\"))]\n",
    "#idx = [type2id[key] for key in set(sparse_bow.index.get_level_values(\"token_id\"))]\n",
    "\n",
    "set(sparse_bow.index.get_level_values(\"token_id\"))\n",
    "\n",
    "#doctopic_matrix = pd.DataFrame(columns=cols, index=idx)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Remove Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "basepath = os.path.abspath('.')\n",
    "\n",
    "with open(os.path.join(basepath, \"tutorial_supplementals\", \"stopwords\", \"en.txt\"), 'r', encoding = 'utf-8') as f: \n",
    "    stopword_list = f.read().split('\\n')\n",
    "    \n",
    "stopword_list = set(stopword_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sparse_df_stopwords_removed = pre.remove_features(sparse_bow, id_types, stopword_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2989\n",
      "2948\n"
     ]
    }
   ],
   "source": [
    "print(len(sparse_bow))\n",
    "print(len(sparse_df_stopwords_removed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Create Feature Remove Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopword_list = pre.find_stopwords(sparse_bow, id_types, 100)\n",
    "len(stopword_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1877"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hapax_list = pre.find_hapax(sparse_bow, id_types)\n",
    "len(hapax_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Remove Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "feature_list = set(stopword_list).union(hapax_list)\n",
    "clean_term_frequency = pre.remove_features(sparse_bow, id_types, feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "344"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clean_term_frequency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Topic Modeling with Gensim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Saving Sparse BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#num_docs = max(sparse_bow.index.get_level_values(\"doc_id\"))\n",
    "#num_types = max(sparse_bow.index.get_level_values(\"token_id\"))\n",
    "#sum_counts = sum(sparse_bow[0])\n",
    "\n",
    "#header_string = str(num_docs) + \" \" + str(num_types) + \" \" + str(sum_counts) + \"\\n\"\n",
    "\n",
    "#with open(\"gb_plain.mm\", 'w', encoding = \"utf-8\") as f:\n",
    "#    pass\n",
    "\n",
    "#with open(\"gb_plain.mm\", 'a', encoding = \"utf-8\") as f:\n",
    "#    f.write(\"%%MatrixMarket matrix coordinate real general\\n\")\n",
    "#    f.write(header_string)\n",
    "#    sparse_bow.to_csv( f, sep = ' ', header = None)\n",
    "\n",
    "pre.save_bow_mm(sparse_bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Import Market Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import MmCorpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mm = MmCorpus(\"gb_plain.mm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#in case you're only loading the corpus - build dict first\n",
    "\n",
    "#import pickle\n",
    "\n",
    "#pickle.dump( id_types, open( \"gb_plain.dictionary\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#id_types = pickle.load(open(\"gb_plain.dictionary\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#mm = gensim.corpora.MmCorpus(\"gb_all.mm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#id_types = pickle.load(open(\"gb_all.dictionary\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "doc2id = {value : key for key, value in doc_ids.items()}\n",
    "type2id = {value : key for key, value in id_types.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING gensim.models.ldamodel: too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n"
     ]
    }
   ],
   "source": [
    "#model = LdaModel(corpus=mm, id2word=type2id, num_topics=60, alpha = \"symmetric\", passes = 10) #import momentan in visual \n",
    "# -> da ich mir noch nicht sicher bin, welche Funktionen in das tm_gensim.py sollen\n",
    "model = LdaModel(corpus=mm, id2word=type2id, num_topics=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jemanden',\n",
       " 'völlig',\n",
       " 'entgegen',\n",
       " 'fällt',\n",
       " 'new',\n",
       " 'traurige',\n",
       " 'italienische',\n",
       " 'groß',\n",
       " 'gefestigte',\n",
       " 'begleitete']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Anzeige der keywords für topic n\n",
    "n = 1\n",
    "topic_nr_x = model.get_topic_terms(n)\n",
    "\n",
    "topicTerms = [type2id[i[0]] for i in topic_nr_x]\n",
    "topicTerms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n = 0\\nfor key, value in type2id.items():\\n    if value == topicTerms[n]:\\n        print(key)'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Anzeige der id des ersten keywords aus dem topic\n",
    "'''n = 0\n",
    "for key, value in type2id.items():\n",
    "    if value == topicTerms[n]:\n",
    "        print(key)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n = 0\\ntopicN = model.get_topic_terms(n)\\ntopicN'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gensim-Funktion -> Anzeige des word_id-probability-mappings eines topics n\n",
    "'''n = 0\n",
    "topicN = model.get_topic_terms(n)\n",
    "topicN'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.020*\"traurige\" + 0.020*\"völlig\" + 0.020*\"entgegen\" + 0.019*\"new\" + 0.017*\"begleitete\" + 0.013*\"fällt\" + 0.013*\"jemanden\" + 0.011*\"gefestigte\" + 0.010*\"groß\" + 0.010*\"später\"'),\n",
       " (1,\n",
       "  '0.012*\"jemanden\" + 0.012*\"völlig\" + 0.011*\"entgegen\" + 0.010*\"fällt\" + 0.010*\"new\" + 0.009*\"traurige\" + 0.008*\"italienische\" + 0.008*\"groß\" + 0.008*\"gefestigte\" + 0.008*\"begleitete\"')]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Topic probabilities\n",
    "topics = model.show_topics(num_topics = 10)\n",
    "topics[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2975</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2985</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2989 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "5      1\n",
       "6      1\n",
       "7      1\n",
       "8      1\n",
       "9      5\n",
       "10     1\n",
       "11     1\n",
       "12     1\n",
       "13     3\n",
       "14    28\n",
       "15     1\n",
       "16     2\n",
       "17     1\n",
       "18     1\n",
       "19     6\n",
       "20     1\n",
       "21     3\n",
       "22     1\n",
       "23     5\n",
       "24    23\n",
       "25     6\n",
       "26     1\n",
       "27     1\n",
       "28     1\n",
       "29     2\n",
       "...   ..\n",
       "2959   1\n",
       "2960   1\n",
       "2961   1\n",
       "2962   1\n",
       "2963   1\n",
       "2964   1\n",
       "2965   1\n",
       "2966   1\n",
       "2967   1\n",
       "2968   1\n",
       "2969   2\n",
       "2970   1\n",
       "2971   1\n",
       "2972   2\n",
       "2973   1\n",
       "2974   1\n",
       "2975   1\n",
       "2976   1\n",
       "2977   1\n",
       "2978   1\n",
       "2979   1\n",
       "2980   3\n",
       "2981   1\n",
       "2982   1\n",
       "2983   1\n",
       "2984   4\n",
       "2985   1\n",
       "2986   1\n",
       "2987   5\n",
       "2988   8\n",
       "\n",
       "[2989 rows x 1 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.io import mmread\n",
    "test = mmread(\"gb_plain.mm\")\n",
    "import pandas as pd\n",
    "pd.DataFrame(test.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualisierung "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lda_model = 'out_easy/corpus.lda'\\ncorpus = 'out_easy/corpus.mm'\\ndictionary = 'out_easy/corpus.dict'\\ndoc_labels = 'out_easy/corpus_doclabels.txt'\\ninteractive  = False\\n\\nvis = visual.Visualization(lda_model, corpus, dictionary, doc_labels, interactive)\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''lda_model = 'out_easy/corpus.lda'\n",
    "corpus = 'out_easy/corpus.mm'\n",
    "dictionary = 'out_easy/corpus.dict'\n",
    "doc_labels = 'out_easy/corpus_doclabels.txt'\n",
    "interactive  = False\n",
    "\n",
    "vis = visual.Visualization(lda_model, corpus, dictionary, doc_labels, interactive)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Create a document-topic matrix (that is a pandas data frame actually)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "doc_topic = visual.create_doc_topic(mm, model, doc_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Visualize document-topic matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAGFCAYAAADkXBchAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzsnXe4XUX1ht8PAQERUKSpgFSDgChVehUURKoIKgQBBZQq\nHZEmPwSliIKKSCeAKL0jkoSmdOktQKiht4RAKFm/P77Zufvse+ptuSHzPc997j27zMyePfvuddas\n9Y4igqysrKysrKysrPY0zeRuQFZWVlZWVlbWlKRsPGVlZWVlZWVldaBsPGVlZWVlZWVldaBsPGVl\nZWVlZWVldaBsPGVlZWVlZWVldaBsPGVlZWVlZWVldaBsPGVlZWVlZWVldaBpJ3cDsrKysrLal6TZ\ngfWA0cB7k7c1WVkfK80AfAm4NiJea3ZgNp6ysrKypiytBwyb3I3IyvoY64fAuc0OyMZTVlZW1pSl\n0QDnnPpXFvvyoi0P3nO/Azn+6CP7tAH9UWZ/lZvb+vFs6/+tsn7bZd7KBFbiky2Pe4OJDLczd3Sr\nY7PxlJWVlTVl6T2Axb68KEt//WstD5511lnaOq4T9UeZ/VVubuvHs61z8Im2y/xkh8fTxnR4DhjP\nysrKysrKyupA2XjKyupjSRoq6Y3J3Y5GkrS6pImSZungnKck7daf7Ur1DJd0XH/Xk5WVldUbZeMp\nq08laf70Yv5qh+c1NTjSS3W7UvnFz6uSrpXU9/7j7m0o6v6o0oaJpe3bpMOjD+orl/2mpJslrdnb\ncpM6bd+ywF/7qO621VtjStLpki6qbOvRGO2gzk+k8tsPyvB5a6fzZuqPdmVlZfWdsvH0MZKk6SZ3\nGwDRM8Oh4XmSPgOsCFyeNgWwFjA3sC4wM3BVJ56UHuqZVOc86fexwAPAXKXtf+/jOoemslcCXgWu\nkPSlnhYmqUdxjhHxWkR8XNLiezpGuxdUvz/V0+Jwu3p6fl1t9b3N+7K4fiuzv8rNbc1tXZi+fzVm\n42kyS9a+kh6X9J6k0ZIOSPuWlPRvSeOTh+VkSZ8qnXu6pIslHSjpeeCRtP0pSQdJOlfSOEnPSfpZ\n6bxu37wlzZq2rZY+zyZpmKSXU/2PShraxiU9mX7/L5V3g6RPSnpA0sml+haS9HbyOK0OnAYUbfhI\n0sGlMjcA7o6IV4rTgdcj4uWIuBvYGxswK5Tafpak1yW9I+kqSQtX+n0VSTema3ta0gmtvvGH9XLx\nA4wDPoyIV0rbJ5TqWFfSQ5LGSrpa0lyVNuyQ9r+bfu9cp9q3UrkPATsDMwLfTOd3m0qTdE+571J/\n7iTpUkljgQPrXZukzdI9ei+V+4vK/pq6UrnbS7oo9fFjkjasnLNE6vuxkl5M92T20v6Z0raxkp6v\n1tmOJH1R0t8lvZGekUskzZ/2HYKNz41K42p16ozRUnkN70npudlC0ghJ44Ef1GnWU9gIuiId/1g6\n/wZJV5TKmz1d9y8lLQRcl3aNTW3tE0/fVlv0w4uzH8rsr3JzW3Nbs/H08dRRwL7AYcBi+J/xS5Jm\nBK4GXgOWATYH1gH+WDl/bWDRtO87pe17A/cAX0t1nCBp7dL+Vt+8jwCGYKbMEPzifrWN61keGzeF\nZ2jTZFD8EBgq6buSpgHOxiCyM4FbgT2At7ERNA9wTKnM7wKXNqnzvVTn9OnzmcDSuD++kfZdKekT\nYMMN9+0/gCWA7wMr071ve6NPAXvh614VmI/SNUn6IXAocADu3wOBwyVt3aTMwvPT6X+CQ4CLgCWx\nkVojSctgj9m5uD8OAX6trinIRjoYOD+VexUwTNJsqcxZgX8Dd+F7sR4wJ3BB6fxjcN9siD2Ia+Cx\n3pZkr8+1wFv4/q0MjAWuSfuOSfVdQ9e4uoU6YzSV1+49+Q1wPH5er63TtOVS+T9M5X8jbd8GWLFk\nkJ2CDbkjscG1Rdq+QGprx8ZkVlbWwCijCiajJM0M7Ab8LCLOSZufAm6V9BNMO90mTZc8LGkX4HJJ\n+5W8MOOAHSLiw0rxt0TE79LfJ0paGdgTv9Cg9dTAvMA9EXFP+vxMm5dVtOv15J0BICLulfRL4G/4\nhTsf9igRER9Iest/TrouN1KaHr94D6lXWXpZ/wq/NG9PHqYNgRUj4rZ0zA+BZ4GNgQuB/YFzIqIw\nlp6UtAcwQtLOEfF+m9faTNMCO0bE6NSGE1M7Cx0K7BURhVH4tKTFgZ2wYVm9zpmwQfshMLLDtgxL\nRmpR1kKV/XsC10dEAVcZldqyD3BWk3JPj4gLUpkHArtiw+Q6YBfsLZx0zZJ2AJ5J92gMsB3wg4gY\nkfYPBZ7r4Lq+DygiflqqY3vgDWCNiLhe0rvA9OVxJanuGKX9e3J86Zh6Ksp/q/IMPCfp58CpkubD\nX3yWiogAQl0xf69ExPi2eiArK2uyKBtPk1eLYW/JDXX2DQHurcSZ3IK9hV+m6x/0/XUMJ4D/1Pm8\newdt+zNwYfJKXAdcEhHVMjvVcdiA+TnwrYhoJyNtLeDliHi4sv1WSYE9PE8AW0TEK5K+AXwA3F4c\nGBGvS3oU9zfAUsCSkn5UKq8wJhcAHu3wuuppfGE4JY3BnpfCEFoIv0T/VjrmE8CblXLOkzQRT9e9\nDGwXEQ922Ja7WuxfDLiksu0WYHdJSi/3erq/+CMixqdpwTnTpqWAtdK2sgJf+0zYg1a+T2+k+9Su\nlgIWqVPHJ1Md17dbUIf3pFV/NlREnC9pE2A/YPvKGOlIe+53ILPOWhvmt9X3Nu+36ZSsrI+TRvEB\no/igZtuEBsfWUzaeJq/ebbKvWVBrefs7HdRXnDexVEehmqmgiLgmfTveAE8J/lvSiRGxbwf1VTUX\nNvw+wlON/2rjnO8Cl9XZvgXwMPBaRLxd2t7Io1buz5mBk4ET6hzfroetlT6ofC4HAs+cfu9AyXhI\n+qjyeQ/sLXyrzlpLE+ne/npTeq3GSL2x1k7Qcr1rLEIBZsb3bd86ZY3B9784p6eaGbgTT3VX63il\n++Ety4L27kknz1yNkpH2dexBbI0Hb6Ljjz6yXyCFWVlTgxZmum6xUK/wERfRntM3xzxNXj2O41jW\nrrPvIeBrKfap0Cr4H/ljbZT9jTqfH0l/Fy+WeUr7v07lRZYyrM6KiG3wS/yntFYx5VUP53oa9lYM\nBX4naUjlvHrnbEj3eKcAnouIpyqGE7jfpiUFj8OkhVQXTfsA7gYWT+c/Wfmp58XrU6WpnOeBherU\n/3Tl8JfS9nqLVL5C6R7K2YYL9KBJD+GxVdbKwGNNvE6tdDewOPB0nWt8FxiFDYhJ41TOquzEoLgb\nWARPc1XrKLxR9cZVtzHawT1p2R8R8RF+TuuN5xPwM78BsJekVZu1Kysra3AqG0+TUSmQ+mjgt5K2\nlrSgpBUkbYcX/pwAnClpcZnv8wfgrGpcUAOtLGlvSYukOIvNgd+net8D/gvsJ2lIykD6dflkSYel\n4O6FUtzHd+gyPprpZexR+5akOdMLndSGFYCtI+I84GLgXHWleo8GZpa0VspCmjFNGc4I3Fypo6FX\nJCJGYY/HKZJWlrQUcA6OeSo8WEfjwN0/SlpK0sKSNpLUlwHjrXQocICkXdM9WkLStpL27KCMG4Ct\n5czBJYEzsEHSjsp9eCywtpyhuUiKPfo58Lv6p7alk4DPAudLWjaN7fUknZamAt8BTsVG9JqSlgBO\np7uXB2COdJ/KP3PiZ+RV4NLUB1+StIacOfn5dO5o4KuSFk3j6hM0GKM0vid7NOg3b5DmlfSwallj\nzwDrSJpLXUH0GwE/wnFe/8JB52dL+nQ6pzDSNpT0OZUya7OysgaXsvE0mRURh+OX12HYODkfmCN9\nO18Xv4Bux1lD/8JBue3oWAw2vAdnDe0ZEeUYkO1wvNWdOBbpl5Xz38dZQPcCI/BLeas2ruej1MYd\n8Tf5SyR9GRssO0fEC+nQnwGfIRltKZ7qLzjr62UcrLwRcFVETKRWrb79b4vjUi7HsTsTgQ1S24iI\n+4HVcVbU3dgDeGZqb59KieaNjcBJiohT8RTRj4H7cB8PpSuNHlpf52+AG/F1XofjyZ6qHNNy6jcl\nBWyBA7Dvx0bEQRFxdr3jm5RbLnMM9l5NgzPS7sPj7I2SN2sf4CZs1F6HM/3eqlPuD/B9Kv/skJ6R\n1bChciF+fk7BMU+FR/IUHMN2Jx5XK9cbo6nNje5JuU/rXff02GNWRl3sCXwrte32ZOz9FfhlRDyQ\njjkIZ9P+KdX/DP4/cCzwIjausrKyBqHUc6981uSWzLN5CvhaRNxX2v4Uzgj6Q4PzhgK/j4jPNNg/\nHGcX/bte+QMlSfcCv46If/ZT+f/FU5kH4DiWrwPDgdnqTAfWO38ijaGGgV+EI7GH6DPtlNmkrq9i\nQ/MbwCz45fpfYNeIeDV5D3tdz+RUGnf3RMQv6n0uHdd0/A4GpfvR9lgqnXcIsHFEfL3JMUsDd911\n84gc85Q11WqnT83b52WWYp6WCTMEGyp7nnooTV007wFX6t9/Yh5Tp+e1q4WA4RExJr3gOiU8l2nj\ne2CvSZk2fkzjU9uXpM9hQ/ZV7I0cgr1rL+Bsw6lRg/1bX29o4YP92rKypnpNVcaTrI89zRv4Ap6C\nKerrLc273IcHyETo4ue9VE6kzz8qHXuupPMq508r6RWZvVTckwMkPZmu/R5Jm0XEBxHx64h4R9JX\nJF0u6a10HSMlLdDivvxQ0h3p+DGpf+co3xM8JXp66oOhdCEj3kzX80HlWms8CFFLG5/EqSptL6dt\nLJva846kWyQtUumXjSTdJZOtR0k6WIaJgqe/ZgF+EhH3RsTTETEyIvaqE2BerWfRUh311nk7Pnl4\nis/D5ZihoyW9lvrukMo5g4IuXqlv59RvE+T4o/I47PUzqCYk8zptmZ+usfRGGl+nyXFMYyTtXzp2\nxdTmNVN9hwBLqftaiVlZWYNIU5XxxNRD814OWEb9Q/P+M+brLIVjVoThj2vhAOPTZQMNHNC7oWqX\nPfkWjv8pXuIH4iDanwJfoSuIdlUAOfD3RhzguwamVZ9GLWaj3n2ZDseUfBXHTs2PA5Kha426sRhS\nOg+OKdss7V8T9+3SpWtdCt/fnkj4Hu+Jx9eHlEjfklbBMVfH4/u/I461KeLQXkzXu2kP6jm1jfZV\nx+c2GL66PH5eDq6MZ5i8dPGqQb8JTob4Hc7w+yu147DeNVbV8BlUa5J5Vc/QNZYWweNr94h4Fcca\nHiZpafnL2dnAHyJiOI73OxZ4kK5ns6/XSszKyuoDTTWcJ2Wad69p3un8N0nQwPTt/rSI+L+0e7ic\nmbc3jvW5FhgPbIINKXDQ+aUR8W6q7wBg7Ug0cGB0Mpx2xMHEu6T6tioCvnGae1nd7ktEnFHaP1rO\nmLpN0kzJI/SyDNl8u+g7Sa+n4+/u47ihAA6MiJtTPUfhdc+mD9PMDwF+UxqXT8vewN/imK/bJB2J\nDZS/4ASCG3Dm5csd1NOu7ouIIvvyifQsrE3XeIaBo4v/PD2fZU1LLSNtLzwOi7UTj5dhqcU4hN49\ng01J5lRgnBERpbH0SnksRcTV8pp15+Ig9nGk9QYj4j1Jk9ZLbNHerKysyaipxngi07x7Q/NupMUw\nbLKsW7CRSkR8KOkfeI2vYckDtRHwvXTswjhD6V+SqsDOIlhvKeCmkuFUT93uS+rLQ9L5n6HLyzof\nXbyrgdT9pb/HpN9zYoNhKWAlSQeVjvkEML2kGSLivYj4laTj8D1aAS8ZcqCkVaOWON6snnZVTQ6Y\nREevV0/0L138HOD/Kts2w0Z3oabjsE01ewb7jGSetA/wAPZwLx0RVdhoW9pj992YdeaZa7Zt+e21\n2Gr9eti41ppmoYYx6oNOb2+0Vr+UO8ul9V4PWYNRJ93YjlO9sc6//mbOv/6Wmm1vvTMe7m3v9Tc1\nGU+Z5t1ajWjezVSPTF3eNgyvGfc57NUaT9fq8cV//vVx8HNZBSm/2X0rVHNfkpF2DZ6K/QE2fudP\n26bvdvbAqPyCLPqnTOM+mK6pzK4DSwZ9MoAvxC/5A4H/Ye/Kj9usp10ieTNyeDvH9CVd/K2IKOMb\nkPRyneOajcPePoN9STIHG1yfx/21AO3x07rpuH1+ztJf6RWkPCtrqtWW66zCluvUsoHvfvRJlv/J\n/g3OqNXUFPOUad6153VC826kh+lOpl4pbffJEbdiQOWW+OVzQcmL9BA2kuaP7lTngrl0H7CqDDds\nV0NwMPgBEXFLRDyGjclWmlyE57uBL9fpgycbnZA8bU/QWbZdDZE8qT9y3QeCLl5Wq3HY22ewHZJ5\nVXXHkpwNeg6eTv8VcJpSIkPpvEwYz8oa5JpqPE8RMUFSQfP+ALv158D/5IfhIPIzJR2Gpx86pnlj\nw2Nd7I5fP9X7nswT2k/SaPwS70bzxsG1D+LYq57QvJ8H3ouIt9VF814yIl6Q9B1M814+vXRHk2je\nGII5HgdrN6J5D6lMq5Ha+jvg75LuwfEw38XxTVUD9Tw8zbQIjhEh9c04ScfgGJVPpLpnxQG5b4Uh\njSfiGJq/S/oNDtr9BnBbRDzeoF+ewS+h3VKM0JI4eLyVnsYv1A0lXQW8GyZh91b14m3K2w7H8XXP\nYjzDRDxVtESartsAG5/nY2NeuK+/jZEF7dZzA7C3pK3x1PKPMJiyKc+kBzoJwybPl/Rb4HV877+P\nF8N9R1JBF38dGzdHUJ8u3o6ajsM+eAaHYQ/fpXLm4XPAl1IdR6dnbGMct1YsPt1oLB2JMyd3xc/d\n+viLTpGtOBpYQCbjPweM7TBeLSsrawA0NXmeMs275zTv8+hOeJ4zIi7FsV174xiOnwDbRsRNlTKG\n4biU56qxXCmo+HBgf3xPrsYvlKfS/tdxnM+nUt/ciV/MDeNEUlbTttiIfRBPH+1V79DKeS/gOKmj\ncIZbXy3X0orGfR1+WX8Tj7//YM/H6HTIQ3hq8hg8xv6Dr237iDi3w3p+jcfH7Xg66sw22trp9fSE\nLn4TNl46bQttjsMeP4PRHsl8Vkqes3pjKWX/7Qb8KCLeSX2xDbCKpB3TqRfi6eXh+Nncsp0+yMrK\nGlhlwngvpRY07ylF6mead39JPSBrq0JxlnQ6MGtEtEIBDJgGY5uyBoeUCON3nH9yn8Y85YDxHDA+\nJemju65rfVCHKsU8ZcJ4Vmup5zTvbvDBNs8bKqlh9p8MatyuXvmSZpY0QtIDkubB06/z9AAtUP7W\nsBu101/9pnTtBQBxYp2fj1Lgcl/UNaOk38jwyHdlAORwVYCWg1mpT77bg/NGlPr0PRlee5nMhOp3\ntRrjTc7rBjLNysoafMrGU+/Vb647dad5l3+u6qt6okTz7rSJDODyMCmwdgSeLlklvKzKh1HLOupY\nETG2nvElad7U12/X6f+3JX2xB9WdT+2yLv/BUMfysi6dYAWa6WS6cBVfxtmO/wRmb3ZSM0mapk78\n22BU0NWvC2HA6IM4DusvA1B/T5+NrKysKUDZeOqlImLBdqfsZLW9PAyORyno1tdgOvhfcPzLV9J5\ng255mOQVmsTdUR8sDyNpXkwafx1DNQtQ5+qpnFnS56HyEhobycuGvCvpmmaGTvXbfvLOnIDjyT7E\nWZqnUksb3wS/iN9N17t2O16SiJgQtcu6vA+Mj9plXSbFnUnaS9ILaUycqJR1KOlXkrot1izpf5IO\nTR83BI6MiGsj4pmIuCciTooSQFTS9JKOSeNmnKT/qETmLvXnhpIeTH3xk3Tds1Tq/oOkf6W/P5vG\n5LPyEi73SdqycnzTpWDkKfHAsXwTJT2Zti8oL4/yYjJkb1d3Ajqlfn0+Im6PiANwfOBP5WSJop4l\nqs+eapdm+kS6tjfkpYWOknSGpIvr3eNGY1zSl1NfbFk6dotU75B07UOBjUrnrVavjqysrMmrbDwN\nrDpdHuaIUsr6OOyRmQMvH/LtUrmDbXmYHwJD1TfLw0Rq083Yc7BB1K4ZV+/6ZqJr2ZeVgNlw0Hsn\n2gYvwbEsDjbfFVgg3YuncEbZWLwUzk8xyLGvPQ1rAQviDMVt8NTitmnfacBiMtQRAElfx9lzxTI0\nLwLry3T9RjoJZ2ZugbMS/wFcLWmh0jEz4XG7Pc5OPQfTtYslSEj3+XtpHzhj7U4c/L849oKdJWm5\nSv3NloJZDo+voXh8FefODFyZ+udr+Nm5rJmBXNKZqe2bpnbPiL+YNFuaaX8cPD6UrnUGN6bx/b6F\nOmM8Ih7Fz+qfZa/mFzGcc5+IeAQ/Bxek9hTn3drGNWVlZQ2wphpUweSW8vIwPV0eRsBZ2Hj6Xilb\nq5mmBX4eEXemcofiPl222NaGmi1Rsh6GG65aXEO63nZApJ3odWCXdM2PSboyteHUiHhe0nUYkFlk\nqf0YGBldiwX/FBszr8kJATcD/wyztwpv3rbAvBHxYjrnOEnfTmUVeIdpcfbmA0XDJF2Ajf/CUFsH\nZ5xdBJOyzY4rXctJkr6FDaw7Stsb9nNEvCrPEL5VGV/3UUtBP0TSptjw/lPj7py0dMpjGDUANrBb\nPXu7YA/eZenadyGhSBrU8WGjMR4Rf079ew72Ot4WESelfe9IeheYvnpePe31xzOZddYa5x9bfW9z\nttpi81anTvHKgd1ZP19t+16dP4oPGFVJ2p7Q4Nh6ysbTwCkvD9NajZaHuQRPk22GY3Za6UNKae8R\n8aikN/E9aNt4qnwuL1GyKPBs5QV3O32vByvG4hjsWSp0CnCqpF9gL8hWlO57RNwkaUHMxVoJGyU3\nSTo4vB7hkhjI+JhUE8c0PbWex/fLhlPSMGz4z50Mrx8AV0SCRiZP1C+xsfSFVOb0dKf0t7MUTI3S\nlNph2ICZB/8fmwEb6e2oHI/U9NmTNAF7gSYZfBExUdJdtP5S0kjbY17XR9gr1yMdf/SRLP31/mCc\nZmV9/LUw07FwZYGFV/iIi6hObNRXNp4GTnl5mNaqtzxMYP7OAxj0qYj4R6uCGnioOplWa7b8yEAF\nA7daJuVy/GVpk3TstFSWeAmzwG5JP79LHrJfycDYmbGhuTRd46TQuNLf3cZuRNyRYpC2lAOwNwG2\nLh2yL57q3B3fu3eAE+i+PE47S8FUdSw2BPfClPV3MR+p5dI7yahbBCgWom732au3/EtP9TXMLZuI\njb+XelFWVlbWZFCOeRo45eVhas9rd3kYpfYdARwKnCNpixbtmlbSspMKMDh0NkrLxvRSjwDzqXZZ\njeX7qOy2lQyjszAA8sfA+RUPSj09TJen5h58H+aK7suOtJPBeC6e9toQG2Fl1MVKwKURcV5E3I+n\nqBfp4PIKfUD3sbIScEZEXBZeFPlluqbhWmlbPBYuTJ+bPXuPpizMlyjd32SAtYIi1R3jcjbp6TjO\n8HT8heCTrc7LysoaXMqepwFS5OVhero8zCRFxJGSPgKGSZomIs4vLqFy6IeY6Lx78Tdwa0RUCdY9\n1b9wpuFZkvbFAcRHYIN0oNPT/4YNosDBzJMkaTgOlL8TB0QvjgPbb4iIccDjks7F11EkHcyJp0/v\njYhW3K9hOD7tlziWquxFehzYTNKKwJs4Bm9uOl8EdzSwtqRbgQkpy/JxYFNJV6RjDqe+J2gmSXPh\n/3NfwNO+ewB/iogbS9dwKPWfvWLq8o/AgZKewIbzrtgAK2M0fg5sEhHrlNpdM8bDpPKT8dItR+Bn\n7W7sSduldN66khbF9+ytBlP1WVlZk1HZ8zSAirw8TKPlYW7BAbSNlocp13l0usazSinfVYOliC07\nFxtjY+lsmYtGBtAqki5KbdwIT73cjnlCv8Yv8Faen3brqlEyhLqlrUfEKJyR9WhE3FHZfQ3OZrsW\nj7cTsEelHBe5LfZeHYMNg4vxWGqZNJDqvgPHTp1b2X0ENgyuwfdiTCq7pohWdeCpuW+m9hTE31/g\njLlb8BeGa9K+dVULmNwx1TsKT2cOwUkHk56r9OytR9ezdwP29pafvaPT9RXZomNxbGD5Xn8OZ0YW\n5XZbAkleU/BbwNYRMTFljW4N7JCC6cFxbI/iZ/Vl7GXLysoaZMrLs/SjJM2Ppyu+ljKE2j1vKPD7\niPhMg/3Dcfr/v/E31aMjYv86x9wTEb/oYfMHTPJSJJvgTMKmAeGSPocNlfWxF+0N4H/A4RHxn9R3\nZwAbFdlRfdzOukumpAzHG4GFI+Kpvqw3lT8b8EHUAZlKehw4MSJOKG2rO/aaXUMHbVkdr732Bqa7\nv1/atxyOJ4qIGNDpp+q1NeuzJmVMxEv31B07pWsfBZwXEYc0OK7pM9zuNTQ4ZmngrrtuHpEDxrOm\nWu30qXn7vMxSwHjL5Vk+ttN2kqarTCNMlmbQfwTuLTB/Z4pQihOJBoHcAl6gveVhLsLjdmtsHMyF\n48h6TM3uiSRtjIOqH8exPL8Hbu4PwwkgTVdV2/A57CGcCxuMNbvp/ynEsdjo/Xtp23Z4WqpPlpjp\njer1WU+UkinWBUbS5V2aj+7etprTyITxrKyPrQbVtJ2stgncqqUAny7pYkkHpvibR9L2jzuBu92+\nnU3SWZJelynHV0lauLT/ZZXW/ZJJ1W+pazmS8ZKi9PkhmRo9TtIzkk6q3I96ZOp55eU9jlMXrfno\ndMojrTwEkmbFwbz7RcSNEfFsRNwZEUdHxBWVw+eQdFG61sdUWs8tteFvkp5M1/WIpN0qddVrZzmu\n5tOYKTQKT4sNAaaXtKy6ltX5KI3jos8+SPd0plTHF9LnBdLnn6mLiv6izFIq2jNc0nGlz0/haZ3f\nYmPyPpkXVqjb2KtcX11yedrXlDpe0pk47b44bwY8PXpm9UBJm6Vx/156Jn9R2d9qfBbjad009sZK\nulqOaaqrOn02t6Qr0z1/QtJWqS27VU6tGTs4BmxbPJV2Gh4H0wMPSTqtTr2ZMJ6V9THXoDKe6JzA\n/cfK+WvjtPh1cNBzoY87gbvZN+BCZ+KU9O/gbDwBV5VemjdiknUx3TEEp1JvhJcjOQnHtxTLk5yL\n40IWx3E1a+LYkLKqZOpX8L0oaNmr4FiTdhdrHZd+NpaBmt2U+jCAg3FM2ZLAVTjIfLZ02DTAs3gc\nLYbH2/9JKtMFm7YzIs7GY/JFTHtfAhvs1+K+WQq/QG+iq8/G4jFcBHavATwXEU/J2YEnYDDlonis\nFUHNjfS7NIC1AAAgAElEQVQaDsReAhtyf5YDjaHO2Cud14xcDu1RxwOP21XVRfbeHHsD7ykdh8wP\n+3vqlyVwkPmvJW1TOqzV+ASPp73w87Mq9v6Un41WOhv3xWo4ePynOGmjqurY+UNq12dwP07E3sZ5\nqM9Ty4TxrKyPuQbNtJ0ygbunBG5wH1UNwCIVHUmL4HTyFSPitrTth9iA2BinbY8Edkjnroaz714G\nFoqIGyQtBVwTXp4EbFAWelrSr/CLYJfS9npk6t0xrfnS9HmndE0tFREfyR6/U4CdJd2d2n1+Socv\n6/SIuCDVcSA29JYHrkvj47BK+1fCxkIRc9W0ncl7tBMek9elbT/Bwc2bR8Sxki4DzoyIJ2XP5nvY\niFgDJwSsjgP0wWNsHHBl8sA9iwP4m+nKiCgWuT1a0p6p7MdoMPaKbTQgl8tTVNvSmjoOHh9Xp+OP\nSPu7eWLws3Z9RByZPo+StDiwDw78b2d8gsfTjhExOh1zIvCrFn1EOnZIusZliudY0g542rWqumMn\nIq6T9Ho65pWos5g0DBxhPMa9Rrz5YqvDsrI+lvrz89X8mN7r7vsf5KJvbdb6QAaR8UQmcPeGwL0F\nXVynQmVv1BDMy5lEwY6I1yU9ivsd/BI/XtJn6XqpvwysIQexrkjJsyRpHbzm1xCcqj8t8ElJM6YM\nJqiQqeWFZOeptOMjSe1Sv4mIi9PLflXsofg2sK+k7SPirNKh95fOGS9pLCVytZxa/mNsuM6Ix17x\nUm2nnQula761dMyHkm6nq09vBD4trzm3Cg40HoG9ceB+/m36+184VugpSddg78PFpb6sp6rB+CIt\n6NxJzcjlS9AedbzQacDvJQ3D92NzumcFLoYp8WXdAuye6liM1uMTnO4/utLudq4X7M37oPQFiIh4\nQlK9567p2OkD9QlhfM9DfsNss3y6ZtuWG2/AVht/p8EZWVlZhc675ArOv+TKmm1vvj227fMHk/GU\nCdytVY/ADZ76ebK8IX2DnfSxQXmT+jUi7k/fqtfAL/UDsFG6L05dn45kKMiZXJfj6Z0DsSdjVexJ\nm46ue9nonvYqkDZld/07/fyfpFOwJ6lsPDUkV6eYk99hj8h/8XTavnQHXTZrpxocU+7TtyTdh6c0\nV8JTejcC56d4nkVInqeIGCdnUa2Bg5MPAw6V1+Or6+Fodo0t1Oy8dqnjha7C7KJTgcsj4o1amwuo\n//y2Q+iunlev3e2Svps9A1X1tF/bVZ8Qxo8/7ACWXrLHtldW1lStrTb+TrcvGnff/yDLtul5Gkwx\nT5nAXXteuwTudgyRh7ChvEKxQdLs2Ggre7FuxjFOX8GegXvx9N+OwJ0lL8gywDQRsXdE3B7m/Xyh\nVSOSETCG0v1IMS3LtHENzfQwfhm1q5XwVO7JEXFvMjwnxfO02c5R+CW7SumYabGhWe7Tkdh4WhUY\nkTyMj2LW1gsR8USp3okRcUMYO7EUpmav1cF1ldVs7DVTR9TxMPPqbGxwn9qgzIco9VPSysBjyQPW\nbHx2CtVspEcweX4SGTwZsLM1PqWu2u3XTBjPyvoYa9AYTymQuiBwby1pQUkrSNoOU4AnYArw4pLW\npAcEbkmLpOmazXFqOWkqsCBwD5EzZboRuFNw90IpVqMnBO4503RQMWW0AoblnYfhgeemly+U6MSS\nZpc0Y5oyrEfgbvnNOxk3lwGnSFo5xS+dg2NKysbYSBykf09EjE8vtpvwEhwjSseNwi+i3SQtIMP/\ndmyjP8BB0ftL2kgGav6JNl9gkj4rZ1z+UM6+/JKk7+HYmeq0UDM9DiwrZ24tIulwYLlO2hkGHP4Z\nG77rSfoK9rzNSK0RMRKDET+MiCK+ZgSVPpW0gaRdJS2VvJxD8b2tTse2q7pjr5VSGwvq+Capj5eX\ntH+K1ZnU5NLfB2HYayPv6bGYEn5Q6u+heLr6d6nOZuOzT1hdKVj736mO5ZIRdTKm23fiCX06Hb+h\npM8pZZhK+rmkMph2NJVnOG0vE8b3wv+Dj62c91VJi6bzBtPsQFZWVtKgMZ4gE7hpTODeB3uEWhK4\nm2zfFgeBX469ShOBDVIbC43AY2J4advwtG1k6bruw5TnfbH3bCsc/9SOjsWeijPwNODbVBazbaJx\n2NDdA9+rx/FYOZnasdBqUeCTU53np/I+i6cgG7XzLmzMVNu5Pw5mPiu1Z0Fg3Yh4q3TMjdjQqNen\nI+SU9O/iJUw2xS/4h7Bnc8uUhVXvmppeY72xVz5QdRAdJW1La+p4ua4PI+J1GijFGW0BfB+Pl0OB\ng8IZi+U6W43PWdUdK9BM1T7aGseFjcT37a94TL1XOecIlRAH1F7rCzhh46hUVpHxmwnjWVlTkT72\nhHGZh3N8RPxhcrelHakxGfpe4NfRgMCtzqjkdannmvKo5G2RstWCSt7G+UPxGPps71pdt+ymROv+\nUgrUngN4tY5B3uiciXSPMyo+R/QzUbyvn2UZFfAMsHZEDC9t7/Vz0JO2Nnr26xy3NHDXnddcmGOe\nsrL6UKWYp6mXMN4TaZBSySVNh1PomxG4O6GSTxEWs5pTyTvRoKCSDyalPu0Ww9RCc9fZtgD2Ap/e\n60b1s9J0/8zY+/V5nOn4JK15WgOlTCXPyppCNKim7XoiWQ2p5Pgf/jHqHyr5CeqiR4+VCdzj09/X\nqo+o5Pg+fR9PKRZt6A2VvK0MJfWMSv5c6fMq6Z58Mn3eU82p5HuW+vAjnBBQ9O0ESW+qPu272TW0\nRSWXqfIny2Tvd1M716+U1ZBuLZPFr0vte1PSCJWCk9MxC0u6MZX/gIx7qLZ3CTUg6ad9H8k4ieL+\nTJQxAcX5B0kaWdpfdwyqMm0nafX0eS1Jd6T7fYvMYAIgIl4u/+AsxZPxVPqelfvYDn1+A5nu/o6k\nC+TYvqHp+Xs9PV/V+zxLo+cylT2vpEvTPXpL0t8lFZiB6TCMcxSetnsRB/MfI3ub6kqOsbsjPW9j\nUp/Wg2sWxw8H5sfoj4lpLCPpVEn3yl+GkDSdpHtkLyq0oMJnZWUNHk3xxhOtqeSX48yl/qCSX0AX\nPXqVtO3H6fPzDF4qebvqCZV8ptILdzXg9tR+sDHUjEr+Bo4vexD4Hr43y+F4pHfoJyp5ekFfg71z\nP8DjaP/U3kKfojnd+tM4PmplnAzwGO6rwvARjh16L13TTunaJ3ka0pi9hgYk/cTMehVntoH7t/y5\n2DYi/d2KjF/Py3EENoSWwbF99aCXhc7AjK8tKlN/re4z2AO6K/aGrpeOuRjHBH0bB9TviPugrFbP\n5aU4sH9V3HcL4dg2wjDTS4HLImKeiNg8Ip5t0heFpsNB8V/FsYfz09zTtinwHAZ4zk1XJu9u6bqP\nSp+PxP1XgGWbUeGzsrIGkaboaTtNfir52IKvlDwcAGPCROnZmXqp5GvgYO41KGWUVeI/nlZ3Knng\nMfnjqIVrbg0cERGXpM99TSX/Jg6IHlJCB4yuFNWUbl2OmSm18fvYsLkq1bEosE5EvJSOOZDaqdgf\n0XrM3oz79eL0+1TgJ+l+PYUDjH+TymtFxq96dQI4MCJuTu07CrhC0vRhtlb5+g7AMWQrVoPF27jP\n4P7cqdSf/0zXP2dKEHkkeXDWxMvDFGr4XEr6JoZ8fqlIxkhj50FJy0TEXfRAEXFG6eNoSXsAt0ma\nKQV9V49/I3mbxlWe4XdSe0ZIGof/d60RXWs6NqPCd5Nmnh3NVm8mNSsrqyfSzO0T+6do44lMJc9U\n8jYUrankS2HQ6BNNimlKt05TQ/+X+mFOzOqZERu5pGt+tjCckqrjoZ0xO4Iug3V1uvpzDZzxNW2p\n3J6MwTK1fEz6PSf2pBTXuj5wOPD98r0q7W/nPlf78yVgdNQS1V+iO9m72XNZ9HGRxUpEPCzpTTxm\ne2Q8pf47BI+Tz9DlsZ+PDlESEfFfScdiw/uo3vxP2HO/A5l11loCxVbf25yttqg667Kysqo674J/\nct4/avOv3nqrEY+4u6Z04ylTyVsrU8lpSSVvNo4KtaJbn4VfrLtiD88EjEEopgrrjceGdPJ6l5B+\nj8QG60LYILgFT4+tiQPg7yiMrx6OwfJ1FnVOmt6XFx4ehtf964aY6OA+1+vPnpK9i3Y26r/y9ol0\nH9vT0UDyGobXYA/hD/D4nj9tqzsN3Exp+nYVPCW6SIvDm+r4o49k6a9/rTdFZGVNtdpqi+5fNO6+\n538ss8oabZ0/pcc8ZSp57XmZSt6+ylTy+4AvqhQM3wOtBPwhIq5NXr4PsCeo0EPAfCoFmdOd4dNy\nzKYU9jdxDM49adpoBDZe16DE40rH92QM1pWkT2Ne1IiIqE4DF+rRfe5AzZ7Loo8n1SfDS2elC2r7\nCrXPLTh+qpGG4Bi7AyLiloh4DH+JaaVGz+O++AvQ6hhgOrRyDg3Oy8rKGkSaoo2nyFTyTCVvIbVB\nJY+IG1ObL5S0TjrmW5LWbbN9YEN+6zQeVsB9VY6HuT4dc5akr0paFQdnlw3ZYfjLQKsxW+3fwmBd\ni1pqeadjsN64KG87Fwc87yNprjo/09C7+9yOmj2X1+MvF8MkfV3S8jjpYXgp7usGTJffWs5+PJSu\nRZHr6Rls1BTX811suNZIzhrcqLRpNLCapM+nLx1I+hr2dm6Xput+AfxB0pfSOT2iwmdlZQ28pmjj\nCTKVnEwlb6UylXxkqvuwtO17peM2Be7ABsKDuL878QBsh+/H3fiFfQIljlIyKjfGRs5tmG59YLmA\nNGbXo/uYnU5S+XpHkAjlpbJvxPfn1tJx5TE4EhvfS6WpVuiAWp6m/9bHQeiPAi+Ufsak31/s5X1u\npcCJCr+l67k8n9qA8hHYkzQSx3mNAracVIAz7n6N7+/tmPt0Zp169pT03Yh4FT8Hm+NxsS/Ouqxq\nEezhKnQwXpvwCeBlOXHjHOC0iLgqteUUbMydLUn1nv22eiUrK2vA9bEnjPdEmsKo5I2kBlRytUky\nrlNepxTzQq9jI2y/iPhfZ1fRmUrXVo1JKlTgJEQfUcOTh2hvbJzMmOq/BjiuHLzci/Lbpqk3KeMo\nbPxsDLydjIJi34E4AHzfiDiuQREDLtUhfSdP62eLbDRV6O+SDgE2ioile1l3r8jvyRs9HJgtTT23\ne94hqd6vNzlmaeCuu24ekWOesrL6UKWYp5aE8Sne8zSlSQmQN0D1NKKS95Rk3A7F/PK0Keji1ayL\nv+FfNQBTEc/QxdaZG3utHsBxKsX2v/dVZZJ2xN6hF7D3ajHMcJoFe2AGixYC7oqIJ8uGU9K22BOz\n/YC3qkOF19HrlIw+OVQ8K23BXCvK32izsga5svFUX9XlUaQmFHM1IEKn/VWK+Ysy/XiiTM3+QKZq\nT5RUTtHvtnCrTMGeKGm19LkhQToiPoiIX5cYMmV1IxlL+qRMvT65VF9vKeavh2nUd2PPzFykAHS1\noJenY1aRidzjJT0tE6dnqhwzb+rPtyWNxVN6T6SfUXiMfxgRr0QXHXtC6fyG1PC0f4e0/930e+fS\nvi/g6bnfR8RPwgTzZyLi5oj4KfbmFHFX50p6Nl3rfZK2rNSzedpejKPrVBs4Xj52uXTP9ylt21nS\nqDSmHpb0o9K+p7BhNzTdt9NK+1bHU4kHp3tbE5At6RCZgv3jdA/GSjpR0jTpmRgj6SXZe1W9L41I\n3+VyfyQTxd+UdJ66oKKn46Dq3UvjbT51kdDbMsI1AOT3OsfOTxc+5Y2izyV9LvXX/qVjV0z3bM30\n7B6Cp1aLa96mnevMysoaWE3pqIJ+UUQsWNl0FP5WvgeO/ZkHGKIuivmtOMtoLgwt/COOgSm0NvAW\nThf/NKY8j8QxEifh2IzVgIMkrR0RBYiz1TfQMkH6NWBhPG3USsvjeI+1cJzY+xExQYZg3iYzka6g\nRDGXPVl74HihRbFxNK5UZiuK+XvpnCK9+0zsDfkOXubjt8CVkr6SOE4L4b49EHtG5gROxH1b9pC8\ngPk7jdTs5VOmhgcO2D4Gr4FXQEEPxVyt/+GMylMkjYuIszErazrgd91KZlKmINg4uRPDK8diQ/Ms\nSU9ExB2S5saxVnvjOJdP4/T+bl4LSWvhuJ99IuJvadsmOGh6NzxluiFwuqRnI2Ikjt07G4/B3fC9\nKLQdcF7q8/MwQ+q/lWoXwuTv9dLfF6bfj+JxuzJwmqR/RcQd6ZxL07Wumvrozzg+aa1KuRvh6cTP\n4til/TEDaXc8zu5Pn4Uz5RagM89MQX7fJZWxF/aALhyGVhbk9zGY/D4bNojrkd9bPeeFngE2w57f\nRVI/vBsRY+VklkskXZf672ycpTlc0gw4eH09/D9D+J5lZWUNMmXjqYXUTxRzSR8CN0XEAWnTcBk9\nUKWYN1MrgnQjDRTFvNg/G34BjgVulz1Mrejl+wPnRESxnM6TMtl5hKSdE7epCLCv4VVV6m72om1K\nDceG014RURiFT8tZazvil97COH6oDL7sphT3VI4lOknSt3DA+h3YGP8EcHF0LRfyYJ1r2SjVu31E\nlIOk98KByIXX8PjkQdobGBkRr0magF/gr5TK+zR+yRfepnOAGyXtFrXkbGHq+3i6yN+LRsS30/7H\nJe2HWVN3qH3St4ChRV2SzsZGw68i4m1J72OYZrnNdfu4kaJ/ye+XVZ7zos5QV1D+K+WYp4i4WtJf\nsbF8J/7fcGDa955MHv+wzYzgrKysyaRsPLVWppi3ViOKebEEzKfwNNoWEfFKerG3opcvBSyp0vQT\nXcbkAvhbe2/VkBouTw8uBJwq6W+lY6bFa/AV7WnpBZFT+H+JjaUv4PE0PV2A1nuxwfyApGvxvfxn\nRLxZKuYb2ODcrE4Q82J4gd6ybsFGfzP9EHgiEiU8Gc9PY4/aGaXjRleMqZdw9iiVbcW0XLuk72q5\nNdT2vpD6l/z+CWqf83a1D47D2xxYOiKqcNC2lAnjWVk919ROGB8IZYp5azWimG+BYZSvVTKOWtLL\ncYD5yXgKpXp8ux62VmpGDZ85/d6BkpGXVKAaHsNxQnO18D7ti1PQd8cvzXfwdU0PEEZJrCtpRRxc\nvyumoC8fEU+nMkbh6d4dJF1d54XbCa280I+BxSWVyxKeFj2jtK1TGng7pO9G5fZ1HOZAkd870ULA\n5/G1LkB7/LduyoTxrKyea2onjA+EMsW89rxOKObPRcRTdVK1m9HLixfJ3cDi6fwnKz/1vHh9qjSd\n+TywUJ36C4Pmn9gAqGusqmux6JWASyPivPBixE9RZ2mOiPhPRByG7/P7wCal3a9iD99CwN9lynqh\nh/G4K2slaknw1bYtieN3VsdevuJnTWBFmS3WU7VD+m5HjcZbJxoQ8nsd1X3GUuzgOXha/Fc4VmyO\nynmZMJ6VNciVPU8tlAKpC4r5B9hdPwdeT2wYDqA+U9JheFqgY4o5NjzWxW789VO970kqKOajsUeo\nG8UcT4E8iGMyekIxfx54L8WYFBTzJSPiBUnfwRTz5ZPBMppEMcdTTePxsiwdUcwjYpSkgl6+E477\nOArHPBUerKOB/0j6I47Begf3+ToR0S7otLc6FDhB0ts4YPiTOPj6MxFxfEQ8J2lP4I/JUDoL99EX\ncaD6WDxF8ziwWfIsvYnj2uYm3SuZhL02nq57GRvRn6NyLyPi1dT3w4HzJW2ZYr5+hw2qe/D033ex\n4VXP4C+0PXB7RNxS3SEvurwdsF/7XVXTzuslFaTvPbHH9CRqSd/taDSwgpy9Ng7zwqCz9P+C/H4X\nNt5+S2Py+z7pmHrk90Np8pxL2hj4TUQU085PpzI2lHQVjjd7B0NLZ8GesPH4eT8NfwEprnkBmej/\nHDC2iO/LysoaPMqepzYUmWLekmIOrCqnVhdBGK2mM7ZN7R1JF738ARItOnloVscemhuxJ+rQ1N4B\nUUScivv+cLz+3QjskXuydMyf8Rj4PKaePwycgrOkjkmHHYHbfw2OnRuDM7wKvY2z1q7EsVyHA78I\n07CrbXoJe6CWAM6RMQTz4ynBvXEf/gTYNiJuqnddMm5hV2yY1IuLuhDYRgZFtuuBqt7vjXBsWF3S\nd5s6Bnt3HsLjbd4GdTVTf5Lfy8/5rNhzWpzzAk6gOAp4ERvYq+M4tB9FxDup7m2AVWReGLjvr8EG\n8st03mdZWVkDoEwYn0xSH1LMNXDE8EnlpyzEK7CnalccIzOJ/Nxm/TU06JT9pTrTfH2udO2n05xE\nvgA2mHtL9y5o09W6Apinkz6rU3ZH4yj18at4ivdCbLjtRx2qtXpJ2U5lnI4NzsDG8jPYiDkyui8b\n9LFQfz2PpeMyYTwrqx/UCWE8T9v1UpKm62m2TF82g/4jhm+BF4Mtc2/mwKncH2KP2HlpWq9X5OeI\nGNub8zvU+dSmo19MLVMIbGT0lQJ7JmqusTeGUw81P37ur4quJU6K9vWXrsaexhmAb+OFnT/Ans6P\no/r8eczKyhpcmiqn7WT1FTH8kbT9KUkHySTpcZKek/Sz0nlVYngAM6hNYngLVYnhT8hk548kvZ/+\nHpva9YF6TgxH0rx4Gu11YK2I+GUYNlhDfk51vCFpI0mPyfTmayR9scl9OV2lBXAlDZep4kdLek2m\nMx8ik8cLqvg4SR/KlPaPZIL3REnfbdZhETEhuojjL+Mp0PFRSyKf5BmRtJekF9KYOFGlgG1J00s6\nJt3zcZL+k/q3qnLZL5cNp9K4albPHJIuT2PjCUk/qNOHDcneaSwVnpCnUn+1olrPIemi1K+PSdqw\nUt8SMh1+rKQXZWr87JVmTUj9+mxE/JWuuKxe09clrSHpttTvb0i6KY1RJC0o6ZLUrrGSbpe0dqXs\nuSVdWerTreRnebfSMbNK+lt6Lt+SdL1K5P866g+Cf1ZW1iDSVGk84TiEffGUzGLAD4CX1EUMfw1n\nIm2OEQB/rJy/Ngmsh4O0C+2N45e+luo4ofLPetK3yjDFvMrmKRPDhwA70573Y3ls3BRrya2FM6c2\nTHX+IrXpUeCaiDgT05L3wNM2c+GsvmNKZVaJ4ZHadDMOUN+gwuipub6kmXD8yI9wBtNswHltXE9Z\n2+Bg4eXxPTsYYwSWStf0UmrTtzH88LE67eit1gIWBNZI7dk2/RQ6CU9fbgEsieO2rpYp6WW1CnRe\ns0U9Z2JO1Op4bP4MJy+UdSnu51Xx+FwIe9lIv4ulR5bF9/wCHHv3IF3joLz238HpvCVxbNswGXpa\nZBP+GyctLI3H7ZypzGZ6ly5UQEFfXx8nBJyMg7eXS3UU9PW/4fG3Oo4tUzIsL8ZTokvgQPu/Uou7\nuBLfv6/hZ/uyigF/Nn5mVsPA0J/SvU//Ccyerm9pHD91fdEPdVR9HjcNLwn0Q7xEzndl9tckgj+t\nn8esrKxBpKlu2k79RAxPuiUiiqU6TpS0MpORGI6p3AdiSOD5+J/yOtAjYrhwNtnNwPeivWC5aYGf\nR8SdqdyhuE+XLba1ofsiosgyfCLdj9XCjKtv4cy2b5Synt6gPTZVJ3od2CVd82Py8jVrY4DmfNjA\nmTciXkzHHyfp25ijdFDaJuBZqQaRPToilmyznkXxEinLFnPxkranhCNQG2RvSa+lw18tTds1o1qf\nHhEXpOMOxPFty+Mg8F2wd3ISlV3SDsAz8vIno6qFyevGrYcDt3tFX5enlmcBrowu2OkkeGqKNyrH\nHB0iaVP8xeBPMoZjbRzfcE+p/Y+X2rsKNjTnLE3P7ysvibM5Nuqq6lOCf1ZW1uDTVGc8kYnhvSGG\nX4JT4It1u1rpQ7po0kTEo+qiTLdtPFU+lynUi2I6dPllUwVa9oUerBiLY7CRQvr9CWzslA2j6an1\nGgZmA5XXA6zGyjWrZwjwQTmIsdSflI5ph+zdie4vlTVeXny56P+lgLXStrICe7wK42nDdMx02Ig8\nF3t9e0Vfj4g3JJ0JXCfpXxg7cEFhxMrT7Ydhr9Y8+P/dDHTRxRfFfToJnxARTyQDvNBX8fp4r9fe\nXmZI19ipevI81lUmjGdl9VyZMN65MjG8teoRwwNjER7A7CdF7fpqddXAQ9XJtFpPSNZ9rWZtmBkb\niUvTdY8Ljat8Ht0ik7DVtbZSu2TvTtTq2i/D06nV9o0p/X0DsFMq64VKll1P6OtHSFohIp6OiO0k\nnYC9ct9P+9aJiNvxdOTaeO2/J/CzfyG1dPF6Km+fGS8+vXqd49+kc/XkeayrTBjPyuq5MmG8c2Vi\neO157RLDldp3BOYtnSNpixbtmlbSspMKMEtqNpqQrzvUI5gOXY5RWb6Pym5X9+A+nCu6k8j7MpPu\nYdyfyxQbSv1ZqBnZu1mf95RqfTeOU3q6zrWXv6S8EybFP1cHT9AT+voHlOjrEXFvRBwdEStjA6wI\npF8JOCMiLouIB3E26JdKxT6C+3QSokFetLrcp3fjuKWP6lzj69RXXz+PWVlZg0xTnfGUAjcLYvjW\nKSNnBUnbYZLwBEwSXlzSmvSAGC5pEZnWvTnw+1Tve3hNrf0kDUnZNd2I4SmYdCFJi9MzYvic6sp4\nK4jhW0fEeTi49lxJhcdxNIkYLml2STOml3M9YvgkRcSROJB4mGozo6rfzD/EcMDlZTbNacCtEdGT\n6aN6+hfObDpLzpJcmS469ICkfEfE43ga6ixJm0j6Urre/VPcUyEBc0mq/rTl/Y2Ix4Brgb+m8pfB\nMM7xpWOuxy/mYZK+LpPLz8Rk7zKzpHqfRpOo1mkcTE97OgmDI8+XtGx6ltaTdFplCrOZHge+KWlF\nScUCx3NPaqiv9QBJy8hZdJth+vrDqa+PlPQNSfNJWhcbXg+Vyt40XddS+Pme1K6IeBRPCZ4iablk\nRJ2M+zTSMdfj6fdLJH1TzppdSdIRaUyTzn1YUvHFqM+exzb7MCsra4A11RlPkInhtEEMr+MhqHrI\njk7XeFbJgKoaLO+kNpyLjbGxdEZMbmoApTZuBHwK36+/pmsT9i52ot4YW9viYPpjsDfjYjwOygH/\nkfa9kH7GpN9Lp/2rUFrrr0k9z+Ox8U/8oq96tzbCz/XdNCZ7V6+1EdW66ZRrRIwBVk71XYvj044D\n3sARx+kAACAASURBVGgzoQC66OvD0/kFfX1pGVtR0NdvxYZyQV+/Fhs5Q3BfPIrH8h/DOARwlukb\nOG7x0nSNVfDd1pgAPjL1w1/xdGt5/KyP8RynpXrOxXFTxWLQM+EpuOlSv/Tl85iVlTUIlQnjfST1\nITF8ckrSvcCvI6KdgPBm5QwFjsfTLANGP0/epxuBhfHL7p6I+EVvrmUgJJO4W5LMZep3Myr6YdgQ\nuAGvwddjWru8pmIRXP0eNhZuB/4SEcN7Wm6DumYCPlkEUFf7Q8YCfBBeH67dMjsmpMsYg2eAtXtz\njWl8djz22mmzMmE8K6tf1AlhfKr0PE0tkldw7/T4f1JL3u51M+g/+vnl2FMYwDxyGvzJwM0R8VTP\nmtt/kjRNB9NZjTQ3jpubG8fEvYWDkIvtfckGCoxamBt7VrbGQdLXK0Fl+6yiiPHNMs9Sdl0niRot\nJWlaSWtK2jBNAa6EvdBPYgM8Kysrq66y8dR36hMXnqwq/fwamZD8jrpo2hNlevg1pXN7RT8HFouI\nX4eJ4QXpuK/p531BWy7Tzz+Fx/El6ZzbcCo42Guyi7oI62NlwnqkfrxKDhAu2vCyzO8pPv9P0nOl\nz6uke/LJ9HlPmXw9TtIzkk5SLY2+oKxvKOlB7L2ZNxlRx6V9r0g6mvay6YhaOvkkLlBpexlcuqyk\nO9K4uUVSTSC2TH+/S6a/j5J0sEpE86RxqdznIuLmiNgRTzMdXi5PTUjjkn5a7sfSOZdJOiX9faik\ne6rHlI4dLum40uen5FioU9O4eVrmtDU6vyD8byFphKTxOLB8OuBEnI13C/aUnpum3npa1+k4O2/3\n0vidT9KvJD0vG//FsVfKKxpI9l4HnuabKOnJRnVkZWVNXmXjqY8UEQv20ZRdPfr55TiL7G0cW7Iu\nphWPwTEdZfWaft5AHdHPI+LMiPgs/UNbLtPPL05tXyEi5ouI7UsejBdSuUuln/9iY+57wDdTu64q\nGQw3Yrp3MU00BJipZCSsBtye2g/OwtwVZ5xtgwnh1fXaZsL3c/t03Cv4XhQE8VVwjN0m9K2E79me\nmJb/ITYuvdPwxzPx1OoQHJ8zFMextdIJ+H/HRqmsVqTxfwCzywkYRf2z4XFcgGp7EuT/CwzS/Bpe\nL+/PMky0mX6DkzgWw8/Sa5gv9SscbL4zsJdql6jptK7dcZD5KXSN32cxrPYpElhTDiBfEUN5A1gO\n37eh+FlZrmUPZGVlTRZNjZynQSu1pp9PB2yWMveQ4YeXS9otpjD6efSQtqz69HNwH1VfvjMAd0TE\nk8kAWhtYMSJuS2Xdi19qG+Ng4ZHADunc1bAx8DI2qB5Pv0eUrqFsLD8t6VcYdLpLafu0OEj4gdI1\n7A4cGRGXps87pWvqSwVwYETcnOo4CrhC0vQR8T7uv9+UxtnTybv3WypZoN0KNpyynPbfkjQuAy5/\ngAPDwUvZvBIRI3txjVdGxF/S30dL2hPfo2ZYkeMj4pJSO48Frk8ZpACj5EzXfXASQMd1RcTbkiat\nlVjeJRPf75H0G/ysbxcRz6fzXpVndd+KgV8wOisrqwNl42lwKdPPW6sR/XwLuphahc4t/T0E84Em\nEcgj4nVJj+J+BxtGx0v6LJ52GUEyntJUzIqUPEtyjNX+qexZ8PP0SUkzRhfn6P2K4TQL9kSU2/GR\npHaJ653o/tLfBbRyTuA57IlbSdJBpWM+AUwvaYbKOKunckxaO6TxYcDJkn4WXubkB3S+zmFV91c+\nv0gX/byRqpiMxfCUb1m34Ck3lbIGe1JXN0XEU5L2wbF550fE31ud00iZMJ6V1XNlwvjHS5l+3lr1\n6OcAz0VETYyIpHJ/NqNJF0yf+yW9jj0KqwMHYKN0X4wemA5PKSJpfjydehKe6nodL8b7t3RcUXej\nezrQZPSivjId/GC8yG6NWhlOybicg654tnZI45fjvtkgGYqrYs9Lb9SMft5I1eej3nNVb6z0pK5G\nWh1Po35J0jR1sCBtKRPGs7J6rkwY/3gp089rz2uXft6OIfIQ/rIwiaWUApoXpZa+fTOO5fkK9kDc\ni6f/dgTuLHmUlgGmiYi9I+L28CK4X6CFEjpgDKX7kWKulml4Uv/obuDL0Z2a3U6Q8h543BUem5ak\n8WSQXQT8CLPLHukEXdFHqjdOHsLPUVkrA4+VvE49Ud3xK+n72Nu6BjA/NmDL+qDeeVlZWYNL2fM0\niBQRE+TMq99K+gC/vOfAL6ZhOIj8TEmH4SmDjunn2PBYF9PP10/1viepoJ+Pxh6hbvRzPOXxIDYm\nekI/fx54L8WEFLTlJSPiBUnfwbTl5dO042gSbRkbMOOxQVOPft4yUy3F3VyGadI74diwo3DMU9kY\nG4mD028vMtck3YRf+uVg8FF4aY/dsFdlFWxgtaMTgP0ljcIG7C+oXRKkL1SvT8rbDsfxcs9iPMVE\nPP22RDl2Cfi0pLmwN20BjCvYDti/hIM4CceKnS/pt9gLtwhea277khEyDPfV4tTGEw2U6vXJscDt\nafry73hJl5/jtfjaL1j6N3BhRPwpbRoNrJA8lOMi4jWZIfUnYN+IuFXStsCVkq4u4vDSeWtLuhWY\nEBE9WT8vKyurn5U9T4NMMYXRz+WlKu6TsQn1poAGnH4uaXUZvVAd39tiA/BybJhOBDYop6Wna5uG\nrsBm0t/TYMOquK77sNGzL/aebYXjn9rRsTgL8Aw8Dfg2dabPeqlGCwT/GCAirsMG8DfxePoP9iiN\nrpxzOM5afBwbPJ8G1oqIcvbjYcAncRbj9TQmjd9Al2FVjkfryfV8Avi9pK82OabVZ1ICxBbY0Lsf\nr9t4UESc3ey8OtsWwMvGFDoGe+ceAl5ORtTpwH8LAysi/oWNqbNlSCh4EeNv4oSMppC+rKysyaep\nnjCe/qkNGAG7D5rcsdSP9PPksXoExwe9g6f7hgOzRYd0a0krAjdhA+m7lX1FAPc25Reb6pCc07F9\nSdjeMiIuqOx7EAcbbxsRA+pFUQ/I2em82fEivZ0uXdOszG/h6bvVcQzU68CpGMXwl4j4WeX4P2Gv\nzhkRsV0v6p0/1ff1yfVcNVOr/w9NzmtJmlcmjGdl9YumGMK4OiRg95P6m4BND8ufbOrwviyEF54d\nkwyVol96QtLeDk9Frq6uRVYLTZvKvb7bWf2nwB6AH5c3SloBT22OG8C29FopZq3PDKekhYExEXFb\nGNb5EV39tqUSUBQg/b0l8HQf1d1bWnt/qqf/V7KysqYAdWw8yaoSsA9I+5aUabnjJb0q6WTVEpd7\nRcAuu+g15RCwocU/eUk7y4TnCfLq7D8q7TtX0nmV46eVydQ/TJ8lE5CfTNd+j6TNSqcEMJekyyW9\nla5jpKQF0vmN7ssPZUL125LGpP6dI+0rqOSzA2fI1O536ZriejNtO402lKYttsBIhCtxEHmxb348\nRRnA86m/T1MDknOp2L4gbA/Dxlw5GHw7DHesQUJImlfSpTJl+y1Jf5c0Z2n/6apMbUo6XvaeFZ9v\nladAJ6iLJD8hlfm2pGeoQ6GWtKCkS2S691hJt6sWglo8Z7uVPk+UtL2ki1IfPSZpw8o5q0u6TX7W\nX5D0GxlqWnhJ/gDMp+5E7HuwAVX2oGyattWQxCWtJ+kmmbj+ahqnC1aOWV7S3ele3U4loSG1c6Kk\ntXpzz2XS+dPpep+T9PvSvp+lPno39XONN7LcFur8f5D05dSuLUvHbiE/s0MkHYLH/Ual81arV0dW\nVtbkVU88T/UI2C/JWWBXY2LvMjggeR3gj5XzBwUBu6T+JmA3lbwcyO+B3+FA2r8Cp6d/wOCX94bq\niokA+BYOnC5exAfigOaf4qDq43EcxappfxHM/C7O8lka/3MvJwzUuy/T4bXNvorjjYq4DfBLcG5g\nLI4TWT6VuzOOJVoTWJL2WVJb4gysx9M1b1/a9wxQGIOL4P7encYkZ+g7wvZLmEQ9NJ03I46POY3u\nRvGlOPB7VdyPC+GYtVYqj+0J+D6dhPtwbxJoEz8by1CfQj0zNjrXSsddDVwmByk308GpjUsCVwHD\nZPo3kj6fyrwNj4Gd8H0p2FC7pfOfw/1fJmIHHivlqbntqN9vn8JxYMuk9n+EyfGkdsyEvbgP4DF2\nKI3X8OvxPZe0OX6uf4I9ahuT+E6SlsWB/gfh52Q9Gq9/dwt1/j9ExKP4fv45GdpfxF8W9omIR9I1\nXQBcUzrv1gZ1ZGVlTUZ1lG2n1gTsGXBMynvAw5J2wRk9+0UmYDfSXsBpEVF4uY6X9A38T3YkfnGP\nx8t3DEvHbAVcGhHvpvoOwKvAT8rYSYbTjjiGaBe8oOtWpeDoUZV2dLsvEXFGaf9oSXsAt0maKWWi\nvSx7n56KiDvS9Reelrs7jDfaDhuo4JfHLJJWi4gbIyJk/hKYSj2pXNUhOcuU5r4kbJ+OX+5H4qDo\nURFxn0pr/Er6JrAE8KUiCF6mST8oaZmIqMIZm+l/pRiuEXJ6+1eKtqoOhTrF/ZRjfw6RtCk25P9E\nY51exHNJOhAH9y+PQag/B56JiMJb9VjyjhwFHB4RY2Uw5kcNMj7PAY6SvYHCmWzfx0bhJEVE1Rv3\nE/yF7CsR8RD+YiA8Pt/H/1vmrXNdvb3n82KMxL/Tc/IcTqAg7RuHSePvYCP93nodGhEfNvr/EBF/\nlvTt1DfvA7dFxElp3zuy93b6NjNos7KyJpM6RRVkAnZrNSJgN9JimDZc1i0kgGD6R/wP7Akblr6F\nb4Rf4uBvyDMB/1L5bW6vURHwthRwUyWrrKpu9yX15SHp/M/Q5amcj+407x5Lzr5bnrS+W5i4fQE2\nqHqzun1fEbavBP6SplB+jAOiqxoCPFvKHiQiHpaX0FmM7mTrZqoGQI+hBc1anh4/DOMn5sHP9gz4\nXjXTpD6KiPHJGCrqGkL35/IWjJD4YkR0W+y3rJSefwXOchQ2PF6vHaYgL858OEZXfA6Ps0htfyi1\n475kABVq9Gz3+J7jNfj2AJ6SF9y+Crg8PTf/wrFaxb5rgIuji/vVibbHbLaPsLe5R8qE8aysnmug\nCeOZgN1ajQjYzVSPcFzeNgx7ID6HvVrjsYEInq4BvzRfoFbFArbt/IOvuS/JSLsGT//8ABu/86dt\n07dRXifaHr/EXqi8WCdI2jUiqst+tKs+IWwnY+4cbJwsT1oQt6JG47+8fSLdPaj1gvN7QrM+Fk+9\n7gU8ge/5hbS+V83qakbfbjcY+nTgxHT8zxoccwX2YO+Ax/A0mCdWtL2T4Ove3PPn5MV+v4n/h5wE\n7C1p9fh/9s4zzJKqatv3QxZJL4iKkiQ5ZCQpOQqKAoKIIo4g8AImQOBTQMkoGV5ERCRJFkQlSBIJ\nEgUFJA0ZhpzDDENmWN+PZ1d3dXWd2GF6hv1c17lmTp2qXbt27dO1ztpr3StigpzltiZGhuwP7Cdp\n+S4yOpfBS5UfYEP3+Q6PBzJhPCtrIBpuwngmYPc9bjAI2PfRn3C8MiXqdUTchJcJvoUNmfNKXqQx\n2EiaL/rTnZ9O+9wFrKb+wdDNNAozpfaMiBsj4kFsTLZSs/Hsp9Sn0ZiZtHTl9QxeomzWbqP70Eqd\nErZPwcWCL2jwsByDA6d7AsslLQbMSi9M9EX6zmHwg7RT1VGoV8bp/xdFxL2YjTV/F22XNSa1W9Yq\nwOuludVKhbE9Lb0Gf4/kUi+LAAdFxDUpLmiOmn4snZaoC63U5vnLannPI+KdiPhbROyClxdXxvFg\nRMQHEXF1ROyB5+f82NNcp0aE8f/BBuVB6d+zVcpIbHRcVlbWyFJHnqfIBOyBELBHVZbVSH09HDhX\n0h04vmsjvHxVNVDPwQG7C+Nfv6SxmSDpCBwrNXU696z4ITcuzET6DY57Oleu5j4OG6e3hAO06/QE\n/kO+k6Tf4QfILxrsW9bj2FjcUNKlwFspRqSRNsRB1qdUPUxyZtp2OIi+UbtjKZGcMWcIBo+wDUBE\n3J88f2/WXURE/EPS3Xhp9SfYWDgOYxyKOLyrsSdjNF52+g6Ok+oUhjiW/hTqh4BN0zJZcX0DTeX/\nLc5kPBbPoVE4WPvIdhuIiA+KHx0RtVC5V3GSyfaSnsPezYPp+4PjbGxsnJTm72ewh62qAd1zOTt3\nahwg/yY26t/EsVFfARbAy8ivYg+3gAcAUnzn1yJi3XSusVT+PqQlvhPwXD4I/526HY/nj0rHrZc8\nYC/j73BdmENWVtYkVMfZdjGZEbDbuJ7hImCfg/9Qll8fj4gLcWzX7jib6H8xePH6Shtn4diZp6qx\nXOlhfwAmXI/BS20b4KUQIuIV/Av5o2ls/oONkuqSTbnNl3CsymbYyPsp9Q+sqvfvGRwndQiuPF/N\ntqxqG+DKBktzfwaWk7REk3b7kJxxYG+/flW3RXuE7eq1vRrOxKz9HN//V3Gg/99xUH5PWno654F4\nbt2Kl5FOa9THJqqjUO+azn0j/gFyOf2NspbEbfqO0TN4Hq0A/BcbUycCv2yjj70NRkyIiFomVjKo\nvomz4+7Gfwd2r+zzBjayC0PzQDwfG/a9wfW0uuev4e/fDfjvyNrAV1Os42sYtXAVnmvbY3hq4SGe\nAxtXxbn6/X1IRvOXgNHJi1UYaNvJsFHw+D6Av6Mv0N/zl5WVNQI0IgjjGkIC9nBK0p3AgRFxfsud\nsyZ7qYZuPqWp0+/m5DQm6pICPqmlTBjPyhoSTTaE8SlJMpX7fOz1yaqRamCnbR63laSGmY6SrpG0\nTan94jVehp3+JmV0TRaSAa9vyQV5q5/NL8NKn077PCkDThdJn3c1xoOoTYB+y57NJAMur5L0snph\nnadKGo7C5R39ekxju1GLfYZknmdlZY0cjRTjacjcXzJ5+/UGr0sG6zwR8V5EHNgivmeSSZOoFI4M\nA3xd0ni8/CfgZvUSs1tBHKHzUjgF8HQpzMBaFLhT0lp1bYwkyXyz6bAhvlXls2nwUvgs2EhZBFPZ\n78ZxYzCJy4JExGudfAckLYp/cNyK4aJL4PifIQ2cHmLDbNBLPmVlZY0sjQjjKSIWGMIlu+Ppn8VV\nvLYbonMOimRN7qVwnsFjvQzmUX2Ag+o/imO8XtLgl8J5JSJeiIixEXFxRKyDg4BPlnqD9tW8LM7J\nki4unacoi/OCpK0b3K/pJB2RxnSCpJvVS4pvV9viAOkz6UvnBidmLIghtbdGxJMRcXNE7BMRt6Z9\n+pUbSn27RtJRlf7+VaXyOZLmlEujvCnpEUnfruzfckyq50nzbc907Hi59Mn/lppYD9fG2zMixkTE\nYxHx94jYoRxfJmmV1PYbkl6RdJmkWdNnTcu7lOb05pKulfQmzlrtJ9WXbynK0TxGTWmcGg1Fyaes\nrKwRpBFhPA2l0i/hfqnJ6fVs6xYmqSb7UjgRMbGUDl4thbNBmK8zHKVwjsGZXMtBW2VxTgLWV9+l\nsw1xhtS5Dc5xHM7Q3BxnJ/4JuEzSgm30ryD4fwNf/5X4QbpKaZcXcXD8N4oHeo36lRtq59xJpwGf\nxvUCN8NJEnOWPu9mTMDB7P/G8+23uDzJIumz54C51FtKqJ8kLYMLQt+Ds0RXwZ7GwjPVtLxLSQfj\ne74oJvdXz9OofEuRnLIC9aVxqhqKkk9ZWVkjSMMRU5DVhZRL4Qx2KZz78XXNjzOZmpbFiYibJT2I\ns6GKh9jWwJ+ihiotA1q3BuaJiOfS5qPkUhzfoz3MwxbAg+E6Z8gFobfFGXQkZMZOuJzIvpL+gwsx\nnxURj6U2ase4lZIx8yVg+SJQUtK29OWNdTQmJV0SEb9L/z9URjmsiflvf8Lep2slPQ/8C8/D00sZ\nmP8P+HdElDN3y/1qVd6l0NERcUFpv2o/m5ZviYiXVFMap0aDOs+zsrJGnrLxNHKVS+G0VielcKpk\n7KZlcZJOwqnrRyRvy5cpMbYqWgJ7Qh5U36fydLRXoBq8THdm6f3ZwDUyZf0N6KmNdnrqxxewh2gv\nSRtGxFXVBjvQKOC9coZJRDwgl5cpq5MxKXR35f1zpBIwCeuxrVwyZW3sudsLM91WiIjnscfqvEaN\nq3V5l0KtSuR0UrKnW3Uzz2uVy7NkZXWv4S7PkjV8yqVwWquTUjiL4Wssx6m0KotzOnCwpM9jCvyj\nYdp7nWbCbLFl6R3DQrWMoz4nduD054HlJR1W+mgqzIrqqaeXDKlL0mtvSVdgz1Yz46lVaZh2gZqd\njEmhluVm0hL6WRgyujeGfu6Il6xblRdqVd6lUKvvQ9slewagbuZ5rXJ5lqys7jXc5Vmyhk+5FE7f\n47ouhZM8QTvhB+x/0+Z2yuK8AlyAPUJb4XIajXRH6uMnamLr2lk+2xbDNZeib1LD0emzZrofx/1A\n4zHuUxomxdwsUfr8PmCa5E0s9vksvVl8QMdj0pUiYhwu6ltc013Ufw/aLe8C7WWxtVOyp640TlWD\nPc+zsrJGmLLnaYQqcimcgZTC+VhaUpoRGwi7YHr9BhE9VNh2y+KcjD0bU9GfBt6jiHhI0tnA6Wls\n78D3ZW28xNqQ/yWnzY8GflFdgpR0ErBr8kxNh+/7GXi838VLZtvgYGhoMMZ4+fdISRvgwsG7UjKM\nIuLB5MH6vaTvY0P8aOrL0bQ1Ju1I0vZ4We6vqV8zYMNiMby0Rbq2uyQdh6nd76XrPo/2yrtAe561\ndkr2jKVSGkfSCtgjt3byoA3aPG8RS5aVlTWJlD1PI1iRS+F0WwrnSrx8cxd+kI4BloqI60p9qSuL\n8xA2oMp9/gf2glxeCgQvn6usrfFD9AjsDforHufagHo5jX8nbLjNjj06fU/g4PEx2Pv0JPae7YMD\nq2/D47l3RPwq7V83xo9hQ/K09LoWGyrVeLqt0zHXYuPhBDze1T61NSbJMzonDhKfKGmW6j54/i6F\ny8nck869IrBxRNyQzvcQnu9LYeTEjWnM3k/GcNPyLjXnrFW0V7KnrjTOjNj7NW1qZzDneVZW1gjU\niCjPkjV80iCWwpGL8T4GLBMRd3VwXNOyGHKJjzOwR+ix0kcT8EPrWuwh2jsGsRSOakqLSJoRG2Jb\nJYOrnXZOxd6ToN7jMTYiFhiMe5EMlH0w4uDTwOvYI3hURFyc9pkDeGOw4nbaHRNJO+JCwmsBL0XE\ni3XXnObD0REx+wD7VTWkAW6IiNUH0m6XfbkeuLmTOEC5sPd7uJ7epU32y+VZsrKGQJ2UZ8nLdpOp\nJE0bEQ0L+w5XNxhkkrJ6ieGb41/0gQ2lMen9khirsCCdBcR31kHHSc2JPQ2v0kswb0c7AT8rvX8O\nG1MFW2jiYPQx6QTMG/ohjluaA8du9cT9RMTLg3GiLsZkQeC+NrMhB0vlcYbe+KMh0SB/D9sN2s/K\nyprEyst2wyhZk5oYHsAM6p4YXlaVpPyIXHZloqR31VsGZ4Kk9zR4xPC1cazTcemBXlzrYBLD58VG\nz444C2u82iSGR8Trqb8vlILFd8ZLZY8AYyW9juNzDpP0b9XTt5E0t6Rz1UvPviB5/AptCPwqIq6I\niCci4o6IOC4i/lBqo1geLN5/VtINMkX7HknrqFSzrTRnNpHp2G9I+i9eKn0OZ/99D9gkHf92OkfZ\nY3cNNrLWSPf46rRtPszU+kBSHyNS0nqSxqQ5c5kqtf0kbZc+fyv9+/2a4R9XHvuIeK10/BItvmPt\nUNiL79tpMsahirtA0hkY5LlbaY5/StL+kp6QNFtp3ysk/T29LQjmf0vHtZP8kZWVNQmUjafh1SQn\nhkfEAvT/g982MbyiKkl5bRxgu2E6566pTw/g+JjJhhgeEY/jwOi7sNHQMTG8omPpXx7oaew9O5sa\n+rYcSH4FMA4/jFfBy3KXq7c223PABjJUtaWSsXlhamcFnCX5S+o9gQdhQOTSOIvzKGCaiJgvHX9u\n6vsSGDB5oKTvpmM3AU7E97sgnW8KPIULB3+SvhmdH8XG1pa4xt28lOaFpC3xEuCeeI7uBRwgaXSb\n1/0RHFfV6jvWjnbDWZufo5JMkfRDHDN1POk6U5zTAfien5D6tHPqSzFmBcF8y3RcNSs2KytrhCgv\n2w2T9CEihgOPStoLP5T/iI2kdeFDRwwv69VKyjuS3gcujoij06YqfftbOC5x+9Ix2+IlszVxyZLt\nMVjzZUl3Yo/c+dGYvbQ+8BlgtWL8Zep1HW/o8Ii4PO2zLw7oXij17SfAP4pAdeBhSYvjIOfTUxba\nm8C75fucvE0TavAN0wA7RMTYtN9vsJFVaD9gt1KM1ePpfDvi+LhC56g39imA70TERcB3aO871o6u\nKt2zfkqZde/hbLkyYXxiMvZul/Qr/CNidGl+FX1oRTDPysqaxMrG0/ApE8Nba0ojhrejhvRtnF22\ncFriK2t6HE/0j4i4Xi6C+wUc67QOcL2kfSLilzXnWwR4smIs3FqzX7Vvz+Ix/zg2nhalf3bgjcDO\nkhSdZ6K8WRhOpfN9HHoC1BfEhZ1PKu0zNVAloO9CX1hoUb+y3e9YO2pFKm+oiHhY0s9wHcQzI+LP\n3baVCeNZWd0rE8YnH2VieGtNMcTwDtSMvj0T9qp9m/7ew56HfUqNvzG9Dk+epL0lHVpjbHcS5F/u\nW3FM0be6dgYS8Fw3DkV7xZLkdvQ39KrB989XPXylvrX6jrWisBcaaKLC6nhufaZLQxPIhPGsrIEo\nE8YnH2VieN/jpnRi+GDodmBh4MWaPlS9UWXdh38YzVDz2f3AvJLmLG1bsWa/Vg/0MfQf71VwYeNm\nx3ZM0U7j/TSwYM04PN5mM+18x1pR2DtR7XWm2K2vYo/nQjh2C+gxgifWHZeVlTWylI2nYVJEvIMh\neYdJGi1pAUmfl7QNJoa/g4nhi0taiy6I4ZIWlinGm+Hg6aImV0EMH5UCqPsRwyVtJGnBFEfSDTH8\n40oQRPWSlEdHxDkYFnl2Kch5LImkLGkOSR9JS4ZNieGSPiNpQxzrszywTelBfTiwtaQdJC0kZ35t\nkraXdTI2nEbRghiOg6FPl7PO5pe0oqQ9UtzTcOgsvER4oaRVUx/WlHSMpE9BT4bY9pKWlbPkS6yN\nPwAAIABJREFUNsCxZldHRJ2H7ErsrTtdzvBcBQeGB30NplZepCMxafsXad5thZdoq+Nd1VhgdTn7\nrK6MSiPtB+wp6cfpfEtI2jrFiLWjs/CPl2bfsauBr0jaQIZaHk+lPE0jSbpWpqUXGgt8QdK8xXUm\n7+5vcOzWTdiI30fS8qXjngDWTfO9rXNnZWUNv7LxNIyKEUgMl9PH12GYiOEyJHEFhocYvnVEXF/p\n85ARw8uStIacbj5LTbu150tekfmBvVJg9bR4iecJ4M/puk/EmX/3psMux9laV6TPj8GZm9+sO08a\n341xdtutOCvxQGwsvV13TIN27sAsrm9iD+N+uLzMGZVjPqe+6f/7pGt8hBp6eSNFxMl42e57eB5c\ni43g2mVb9Ud0rAh8CnOqbsUeznno+x07hdYU9kb3cgHgY6X3h+ExvQ94QdLcqd3rI+L36Zouw/fz\nzJJH7CfAl/A9bxSLlpWVNYmVCeMdSpOWqv1/EfFw5bgBUapVQ9Xusp0BUbXlTLEDow1iuJz59bWI\n+Fxle8t7oy6I4d0oefiuBv4nXFuunWMmJZFb2AA+F9gjBgn8OFjzq4vzFkDPlyLig+r9GKwxbHL+\n2jnaxnEtx0uZMJ6VNSTqhDA+WXmeJNUFbw63hpKqXUAcg1520lKYbbMocGdabhiJ2oleds8n07at\n0v8/ib1NtUr39XzsMWlXbXlzSueQpI9jDEKnxPDhUg+Ru8PU+Y4k6WuS1k1vD8Mcrn/hOLfRdI5h\nGFaVln8bKqwXajyZw6n8yzQrawrVkBtP6aE1qanaSCqI1oNN1b5a0vQyabknVV6OHxqvwaNqr4OL\nop6cflUXmglnVg0GVRtJ00k6Io3pO6mvb6qXFl68LikfF/VU7TLtuVwi5KOpb+MlPY6X1w6MiDdS\nH1pRtZupj9crLZ/dgmOznsdcoG2Kh6oq9O207Y7y/Un3bFtJf5Fp2w9K+k9lPN5M+4Wkh/HSVHVs\nv66RQeSeGQM5hQsO3wRsEK6ndhHOMCzaWCCN/3PpPLeqL4AVST9IY/JW2u+8hjdH+oqkcZK2KG1r\nSA4vfZc3l+OK3gR+kMZ7vUrbm6Y5NUPd34BWkrSxpNtSPx6WtI9cb674fD+ZAv92+n78X4N2tsJG\n+tKl7/t301x8R44zK/b9WRqzOWXv7RoY91AcN2+7/c/Kyho+DYfnaZJTtRtosKjam6Zg8C2BreTA\n66nwEtwVMfRU7Vmwp2DAVO2k43Cw9+bYW3Rwut4N6UvH3q6N/jbSrsC/6Z6q3UhVw+lTwCXY6FwK\nx/q8gwnWnWofHKO2JHApxjCsisdiffxdOhHP14PxnCz3ZTlGCJE7Is6IiEXwd2TbiNg2Il5N92At\n7IUqNBMew7Xx/boMuEiO4Smu6xg8BxdJY3EdNZL0bRy4vUVKJOiEHH4wToJYFMcE/i1de1lbAH8u\nsZza9vxIWhXHJB2d+rED9pzulT7fDH+H/xdnyX2N/oyuQufiOMR76f2+nxsR/0ztnylpZknL4L+L\n26YfTjtjRtuJpeOebPcasrKyhk9DynnSh4iqHRF3ynydk/BDtuAmTe5U7btkLtK6ETFYyzmXRMTv\n0v+7oWoDLKX+8Mjq0ugPgSciovAsPSjHohyCS2V0olMj4rzUn71woPEn0n3fDngoInYo9Xkh/KOh\n0Egnck+D4ZsXUzL8UuxYOX5sX0mbYoP/t3ieT8D39A38sL+zOniyZ/ggYMNKEH+7/Tw6InqgnJLO\nxplzM0TE25Jmxt+3jcqnrfajifYFDi79nXpc9j4ehgPq58GJBlelRImn8Hewn1J/JgDv1yy/7o1/\nCJ6IjehTI+KSdNx4Se9iYOiQLdtmZWUNXEMNycxU7dbKVO0Oqdrp/f3YG1bu59zANaX3o+g/T27E\nmIS5I+KpbvocEW+m/hV9HoW9W2VVzzvSidxTY4/K0bjcyxaprY9i78gG2BNS8KOK5aQrgceBxyRd\njrP//loxzjdLfVslInro3B32s0r1vgQzkTbCnqjNsLfyKrrT0sDKkso/EKYGppM0A65ruAu913kp\nLq1ThXQ2VfohNRobpGOxF7YrZcJ4Vlb3GumE8UzVbq1M1e6Cqo09NOWsxMJLUz6mGQW7U6p0sz63\nk0QwORC5H5LRCmdL+nnafiReitwNp+6/hbEJ0wFExAQ5+2tNjNvYH9hP0vKlLMM78Jzalr5GUCf9\n7PN3IBkh5+N5ch429v7YhRFa7ss+wF+qH6QfeE+lZc0v4r8XxwG7S1qjUwMKL0WD0SSz4yzHjpUJ\n41lZ3WukE8YzVbvvcZmq3VrdUrXrNAaPR1mrAK9HRPHAqlKlZ8GFczs9z+cr21aq2WdyIHIXRnPx\nvVwZ+ENEXBQR92I20/yV830QEVdHxB7YgzM/9qgWegTHUm0s6djSce32s9H4nIUBrYul9s9ssF87\nuh34bE0/en6oRMQ7EfG3iNglnW9lHANXp0aE8QWxh3o77K2scrE6vt9ZWVnDryH1PEXEO5IKqvZ7\neJliThzcfBb+lXqapP2xW79jqjY2PNbDbvsN0nnfllRQtcdij1A/qjb+FXwvXobohqr9NPB2ilUo\nqNpLRsQzkr6Kf8GvmJYdx5Ko2jgm5E3sSWpK1QZmxMtpu2BA4walh+3hwLmS7sDLFRvhwOOqsXoy\nDrCdihZU7RRLcnoa2zvwfVkbL7F2ghLoVmfhmK0LU3zSU/hhvAlwaPSCN9vRb/Gy2LGY7DwKx9gc\nWdrnahzo/ze87LM/9r51ot8Bu0o6DC+TLo8N1bKOBG5Ny0Ln4gfvD3FsTzONxUTuc4F3om/WYjPt\nh5MoxuOltOlTv/4nIo4u7TdbmmdTYW/p3hhdUBjgDwGbpvEBx4r1eMwkfQUDIq/DcWlfSZ8XP2SA\nnoK4awHXSHo/IgoyeKN+zhYRRTZbrYcuIv4p6QU8Zx4tLwk2UDNP3wE43vJJjM34ABuCS0TE3nIG\n3dTY4HkTxxG+iZcskfQr4NMRUdz3sbh23dJ4Dr+O59UZGNB6mqQrcEzh7hFxROm4z8vZpRNwbGVG\nHmRljTANebZdjECqdtK7DBNVOx13M1M4VbtFu822l8nVb1FP1Z4eZyu2fd50LzbAWYP/xcbUicD1\nchr4LHhsr8OB0hfj632kps1vJSO10Iyk2LKIeBL4Or6f/8UezD37NNA+kbuqTojckvSXdL52idyn\n4nn2JDZC7sYGejEnd8VG0Y34h8rl2EtT6DWcFXgVvlfbA9+KiMJ4Kt+PB7Fh/y1Jh9f080GcbbgV\nfUGxzYyHc3Cc3Fk1n1WPa9hORPwd/4D6Iv57dDP+wTI27XIqTgC4Af/NWBv4aimucS4cVF7oz3is\nrsH37Vv4b9C8+G8H6bu4A866LJAKR2Dv+5h0XLnNrKysEaLJkjCuAVK1R4rUAVV7gOcZFqp2m30Z\nLkJ7T/ty1uffcPmML+Klutk7WYpMXrCNI2LZ9H5m/P1pP8KwS6VrP5XG9PbAS437A7NGxKYDONca\n+IE/23BcW+XcH2Aqd1sxgOme7EvvuIzHhuIvyj8yJmHfMmE8K2sy0hRLGJ+SpO6o2p2eQ+pL1R6O\nZbdWGi5Ce/HZnNjrMj2wakQ8GxHvDzSGKwwFHS7j4o/0pbffjJlenyht7yRzsJUmp19U99BLsf8C\nXmb8WzJuJ7Ump3HMysrqQJOr8TRkf5Qk7an+NO1aqvZAFBHvRYmq3aQ/UpeEdrxE+DxeSpmJFMei\n7gntob608ffUS9V+USOL0I6kefCS3CvAOhHxWtpeLtpLOserMmG6IGVfrgSCbHBfTi2WyNL7ayQd\nI+lQSS9LejZ5H8rHfFbSDan9eyStk/qxUf8z9CoFKpfp7T0soNL2nqVfSbtJeibNid+oLyW7TJCf\nIOlm9UJV665z38pyJZJ2Tt7f8lj8tcV5PynpkjR/HpG0hWro7pXztEOaf780DvfjHwoz4fitop15\nJF2Y5uy41ObHK+f6vkwV70fqb9C3/dO1LlHzWSaMZ2VN4RpqVMGQKCIWGMLmj6cxfbsZemGodAhO\n8d4Fx53MBYxSL6H9Jkwc/wQODD8Wx3uB07tfx7Eqh1ba3R34JY6p+RIO2n0gIgpOTqPYpO9hOvh+\nOINxTxz38j/0xxvUaUUcU7I2jut4NyUWbAncIhuof6NEaJe9dLvgZalFsHFURidUCe2Bg8MPT33d\nIvoXuq1e34w4du47GANwPI6n6YRG/l0cX7ciKUtN0g0RcZUkpT4+hmOwZkn7DvYPgbVxfNuamNt0\nHo4LPDl9fhwem83TfpsAl0laMiKqsV6FmsapJa2Fl4YbnfcMHN+4Oo4vPBonj9RKvaT5G3FW4kRM\nMb889bVfUL8MnP0e9rI+UProQvw9WA1jKI7H3ry103GbYHr5TnjZd0NM6n8yTAWvnudYbLCvEhVc\nRtK5OMljfRzjJVyq6B1JBWF8Kcy42h/4ergI9M54ft+NA/dFXzxHVlbWCNFkaTwNpZJ3ogromyTS\nyCS0PxsRj0qaFbglIv7WYL9GGg5Cu3DQ+w3AN9rMVpoG+GFE/Ce1uxUe0+WLbW3orogosjofSfdj\nHTym6+O4pNWKa0jX2w4LrBO9AvwoXfODyRhdB4Mo6wjyR0n6MjY6BkKQb3beUen/y6XAeZSo7E3a\n64Q0Px7f8xlx3NM3I2JCOuaL2JCZv0jmkCGV90paLmXoNSX1l/o0raQzcZmalWuSL4BMGM/K+jAo\nG08jW5nQ3lqNCO0XYK/K13FsWSu9TwngGBEPSHoN34O2jafK+x7qN/YoPFl5KFbBkIOheyvG4rP4\nIQ1DS5Bvdt5FgPeitxQSEfGIpGb3txvS/Mw4m/F8SWumgM9ReNx7EBcRcV/p3t5Ge6R+sLfsbeAL\nYX5ax4pMGM/KGhEa6YTxrIEpE9pbq47QHhhDcQ9mbSki/tSqoQYeqk6W1QZKIR8MtaK3d0qQHywC\ne52acZe6Jc3fmZbhdsHLqI3Gvbq9Fakf/CNhC7zMfXaTvrdSJoxnZU1ijXTCeNbAlAntfY9rl9Cu\n1L+DcGzWmZI2b9GvaSQt39OA2V2zUaK1D1D3A/PK2X+FVhyktttVNwT5F3EmW1kdpd/ja59GUs9x\ncuHk2ZocMxDS/ER6Celj8Lh/unTuxYBZ6YXitiT1J12EjbmTJH2zRR8yYTwrawpWNp5GsCLiHRzo\nfZik0ZIWkPR5SdtgKOA7mNC+uExv7pjQLmlhmY6+GQ6aLWp5FYT2UXI2Vj9Cu6SN5Ky4xemO0P5x\n9Wa8FYT20RFxDoZVnp0Ch6FEaJc0h6SPpCXDOkJ7jyLiVzgo/ixJ3ypfQmXX94FjJa0oc3ROAW6K\n1tTqdnUlzjQ8Xc6SXAU4CBukw5LSHhEPYY/J6ZI2kTR/ut49UtxTna4F5pQzPhdI9+lLHZ73ARz3\ndaKkFZIRdQImdDcrvfISJs2vmvq6ppzR+KnSftNI+kR6LSQT3HuKMIcBsXfj+/85SStiyv41pWXE\nw4GtJe2Q2tgVL/keTkVhTtpo4BRJXy+2S/qVpDK9fyyJMJ7m63SSpqJEGMeJHUvINP/ycZ+XM17n\nqCyvZmVljRBl42mEKz4chPaf4fIpg0loL5/z0HSNp5cMqOpD+w1sqJ6NjbHXcdByu2pqAKU+bgx8\nFN+v36drE/YudqK62oe/x+O/iUqIico+awBb4jFsRJAv/ia8n/p9P74XP8D09OWpMSra0GjgORyA\n/Wd8/RPoe+2BDaydon3S/OI4y+8ZPJc3A3aMiDJxfGMcaP5PvPT2MKV7Gy1I/TJCQKX9/0wi8Uv6\nWtqcCeNZWR8iTZaE8SlJGj7idqFXcFbSsRHxs6473l4fi2trRsb+Xvrs6IiYvcP2+xDaZQo0OKD3\n1tJ+0+EA5v8B1owKfTqNZcfnH6iS9+k6YKEGKe/ttvMl7GlZA3u3XsEB1X1I1cl4uhrXt6uNjEzG\n5e8jYpa6z9M+Y7ERAPYePQocEy610m6f58aG0ToRcU1pe5/qAZVzvY25ZbcCvysfNxhq9F1MxlNH\n5HZlwnhW1mSnTBhvUzI/aFJruIjba+PYlfXwfd+xWDIbQj1BXzL2kfiXfZmM3Yip1VRqTGh/Ahtk\nZW2CPUmT9JeCpK9JWjctyayLl65uGIjhlLQQRkjcEgZGTuyib9OlWKAf0jpQPzDW4JM4o+4M7DFa\nv0n7a0naMC2/rYw9qI9i47Hdcy2CPVivAf9QgsUOogY7qD//Ms3KmkI12RlPsroibquXhLyXpKdJ\nAdLqnrj9gaTV0/vZJJ0l6YV0/gc0sojbr4QpzLcDL+O07s+X+n66pFckvSHpUjmgtzzuq0q6Ll3b\n43LsyYw196eH0I6XVx4pvTamLxH6hRTXVRy7nqQx6fjLJH2i0vZ26fO38HLhS9Gf0H4aLjw7fWnb\nNsAfavp6iKQH8BLSrJIOUF8q9lLpfoyXydT/Tr/6i8+3TmMxQdKfJe2qUvq9KhTyNOZ/wvf8FBww\n/LU0Zq/IpPaJMoG8TLa/WdItab4/I+lgOX6m8Ir8GgdFfyDpUbUmVS+fruUNSTdKWhj4Mo5zex24\nRNJtqR8PS9qnPC5JE9L9Gxvmhb2C6waWx7dnzmBj6VRsPP8ZL+F9HS/bFtTxb1fvUeVcT0XEDRGx\nA17yPCD1vTjfEmnuvi5Tu0+XNEfp8/UlXa9eYvnFksrA3X7fxcr1NCSoV/bLhPGsrClck53xhInb\nP8UxQIvi7Jfn1UvcfhkTtzfDKfTHVo5fB/+CXRcHORfaHcdMLJPOcYykcpZbq1+RB2GmzPrp3+/T\nHjtnRWzcFJ6hTZNBsSWwlRyUXQSaXpECTW/C2W3jsRdnLhwrUahK3K6quO7p0r+n4fT1r+KsO+EH\n6NTQkyF0GX7wL4FZOqvQf2zB/KelG7ya9emjGFa4JSZBz1u+JplAvh8mmo/CMUwHyMycsm7DSy9f\nT8fNk9o7g/5Lh+NxOvvCeMy2w6DQQmcBT+L5tCyeF++ldj+PgZ6/xnPmGuwhaThPIuIMbMT9MyLm\njYhtcSzRd7C3bHU8t8FU+aWxQbIUNrSWAnZMnxVAy51wQPxTeC6skLbdjGOEivnxZNpfeK7+JF3X\n+xgQeWFaqvsVjnE7Go/zDjj7ca+6a5L1dbwk+m5pe3XObIyDoc+NiLkiYjPgYODT2GDYLI1FQ+p4\nRcfgv18bp/PNipeob8P3an3M2DqvdMxHsfdzOfx9m4hjvgr1+y6WPlsbWAADOr+LY562btC3c9N5\n7qV3/M8N08oLwvjMkpbBf8e2TT90dqbxfcvKyhpBmqw4TxqZxO1C8+BYhSKD54lmO5c0HMTt8uez\nYcrx68CtsodpQ2CliLgl7bMl/qP9Newl2AM4MyIKY+lRSbsA10r6fkT0PDSjCaFdpi430jTADhEx\nNu37m9TPQvsBu6XgXoDH5Sy/Hemf7v0H7G06Gxsll1JjyKZMvEJPSDoSG4aF0TYvcFjKUgN7zwrt\nBFwWEUem98Wcabh0VVW6V3viuJ9b0ubrZUTDRhFxnkzVfjwiCmDjg3I8zSHAARFRePkmlueCakjV\ncuJWAHtFxA1p2yG4kO506T7uCxxc+n49Lns1D6NvxuWhkn6JA7inweN7UunzpnMGmB9n7S1fxBak\na20LDRERr0p6IbUD8CPsbe2ZMzLF/AlJC0XEwxFR9gKS/mY8L2mxiBhDg+9isY0GBPWavmXCeFbW\nFK7JyngiE7cHQty+SVLgX9+PAJuH62l9AXtTegKsI+IVeTlr0bRpaWBJ9S2WWhiTn6FvHbFu9WZh\nOCX10Lnl5cEFcamP8gN6auoNtTOBgyV9BntNflR3QpnV8+PU9kz4+zCutMtR6ZzfxQHYf4qIYmln\nUaDPwxjPmbaNJxyrNCNwpdQnJX1azDkCz+vqPLoRYxvmjoinOjhfobtL/382/ftx7L1aGlhZTvkv\nNDUwnaQZSt+vw7GROlf6/29LYwOt58xnMXW8Jygzeqnu7aoco7Q0sLb6E8kD39+H0w+FA/By9cfw\n34bARnIrzEYzgnrbikwYz8oaEfqwEcYzcbu16ojb4EKw9wEvVzKtmtGfi+ufCQc3H1Ozf7setlaq\nI1QX55op/bsd/Uua9AuOTsbfJdgrMD1OGe/zlJG0Ejay9sbG7jic6r9rqZ39JZ2F7+kGwP6Svpm8\nX+0EF7eicxfXtQFOtS+riAWrO0/RZrcByeWxLtooU8j3ob9hSOWHyUvJWHpUBpDeLek/YbRB0U6z\nOTOKAUjS7HiJrzDYZsLz/qc15ysMxL9hT/V2eLynwktr09FazQjqnSoTxrOyJrEGShif3IynMnH7\nlMpnY4DvSvpImBEDQ0PcvjP9v5a4jQvSni7pBrzU0cp4aoe4fRL2gFxVejg1I25vWdkWwFMNsrrG\n4HnweRwwTAqyXYTeX+O3A4sPQlZYV4qIF+QA/wUj4o9tHnYKcAlwSESE+rMGVwLGRsQhxQZJ89ec\n+2FsABwjqVgGvBCPTXXOrFR5/yLmEJW1DL33fAw2kuYrltFqNIa+sTfgh+/rEdHsodstqfp24LMV\nL1JTRcRTks7FS4kF96jpnJF0HwZcFsV5y1T3drQL/m5fUDrfpniJs8r9KoytRXB80Y1pW5Uq3uy7\n2KnaIYx/Ey85r93quKysrJGlycp4ioh3JBXE7ffw8sWc+AF1Fg6+PE3S/ngZomPiNn4wrocDWDdI\n531bUkHcHos9Qv2I2zhY9V4ce9UNcftp4O0U+1AQt5eMiGckfRUTt1dMy45jScRtbNC9CSxGPXG7\nYbxWRDws6SKcar4jjgk7BMc8FR6sQ4GbJR2LDbk38JivGxHtQjkHqv2wATMee5Kmx8DG/4mIo6s7\nJ0/gnPQFKpb1EM5Q+ybwb3y/igc/kmbAy1HnY2/FPDgYu6iR92vgBkm74TnzJfov2V0N7J6WaW7G\ngeFLkJbkImKCpCOAo1Nw/g24bMgqwLgUYP5bnH11LAaJjkpjcSTNNZZEqsb3tChkWzcXytsOwHGC\nT6Zr/wAviS1Rjieq0f8B90paNi3FNZ0zEfGgpCuA36cYqIk4mPrNmrZnljMvp8VLfqNxTNseJePs\nOGyQ/FHSYel6F8YGyrYYkvkysL2k54D5cMB6+QdQ7XexyTX3SNKvgE9HRJFhO5ZEGMfLoa/j4Pwe\nwni6/rsk7R4RR5SO63PfKsuFWVlZI0CTXbZdfDiI25/FD58hIW7XaGts+F2MDdIPgK+kvhERd+OM\nqIUxl+d2/ADvarmhG4UBjNthz89deIy3onfZBvp7Al+pxLdF6bOL8cP6WHzPv4ANh0ITgTlwJuID\neJ5dgq+bFOD9vzhw/L94qbaPQR0Rf8eew9/jOTlTaq+8z97pvHvg+XwZNtofS58/k96vkM7zWxxs\n/Mv6kepRHal6KxrDSst9/irO8rsVG3274Id6n/3lkihXpuDom4Ar0rVU58wteAz3o++c2Tq9vxYb\naiekvlZ1AF5mewh7dmcG1i4ZHETEs9jonCr14y78PX01krAhtRy+J0fiDFtKbdR9F+dL/WzFRMuE\n8aysD5EyYZz+VOPJVaoQt4f4XMNFRu/XvtqgME8KqYZUrg7p1MnDshc2mObGAfEPYc/q6aUl6W76\nty+wcUQs23Ln9to7JPXza9hT9rJMef9aRFxU2m/A5y3Ntxfw8u0bpc/uAP6aflh1rbp7lc77KPC5\nDud5v3Fo45g1sLE1WzOPlzJhPCtrSJQJ41O4VENGV2Pi9pB1g+Eho494SZqqki3XbTufodeLtQeO\nj1oJLx9+Fcf6jSQtCNwWEY+meL/h0MxUPEYDVRv3b7iK8xbfjVwMOCtrhCsbT9aQPaRVIm7LhOgP\nZJL0B5IKwvmAyeg4pqMAGhbHDRcZPSS9n65xoqR30//Ha+Bk9HbGuCkhPV3bJqX3/5X0VOn9qune\nTJ/e/0TSXTI5/AlJx1Xux1YypXpDSffiJIZ5Un8/kj57UY7P6+RBeDxe/l0uIv4cEQ+ECd4XR8SG\nEfG3Uh/mkXRhGudxks6V9PHKuOwh06vHyYiHGSqfrymTyyekPl8vQ0WLz78vE8bfkXSfStiB5K3d\nFINcJ0o6JW0DL3d9IOnRyvm+k+bsa5LOqYyp0nfl0TQP75Dhm1UdC+wq6WONBrGN+VB3/07B352N\nS3Nz9VKzC8q0+TfS/KkmC5TPX9Rz7DMO8hLnZaX9ZpP0pKR9ZQ9XgWB5tRjTRufIysqatMrGExAR\nCwzhkl1B3D4LB8PuhmNBNgdO0ORPRl8//X+5dJ0bpnPuijMSB5OM3kh1hPRL1Vs+4zpMhi4goaOA\nGdVb2mN14NboLRVTxL4sjmnSa+EYtLJmxJmU26b9XsRJCm/gGJlVcfzdJrQhORvsi8BvKkiARroQ\nZ6athufEgjguq2hvcwy83APH8j2L4+aKz6fGdO1rcBD7F3BsVhHPtAkOAj88Xd/vgVOTAUxq8woc\nc/dJzERbIX22VdpWvAczrTbGy3xfwd+BPUqf74UD6rfHiQ9HA2dIWq20TwDnAA/TAAKb1Go+QP/7\n92McJ3k5vXPzptL+B+Hs2aVx9u7ZSiVyarRCOmd1HLYCVpBUxGH+HidmHJj+LYzFhdP5O+HMZWVl\nDaMmq2y7yVER8Zqk9/Efzh9ExKnlzzX5k9Gfr8SCPCppLxzQPBhk9ALuWdYM2GAkGUCtCOn/xMHm\nYEPpNhw7syaOJ1oTBy2T+lk2pB+XtDc2gsuwzWlwQP89pWvYGfhV4kAhZy+2C81cCN+rPlgNSS/S\n6zH6TUTsKemL2OCZv0gokDP67lVv6v/OwIkR8Yd07N5yMeKi5t8s6XVJ9MJJy7DT3XDZlqK+4tHJ\n27I7Li/zsqR3gLdK9/J1efVrXPQndAvYKiLeTP09A/8g2Fv1pPWxyXDaAbi+1EakfS+WdFQVhdDm\nfID6+/cWMF30p7IDHB4Rl6dt++IafQtRg0GJiJfqxiFlze6IUSZz4QzNZYoED0lFRuTaetyNAAAg\nAElEQVSL7Wb5ZWVlTRpl42l4lMnordWIjL45vbytQmeX/j+K1oT0a/HDf3bs8biWZDzJQcIrUfIs\nJSNjj9T2LPh7Mr36MsTerTx4Z8HegnI/Jkr6TxvXXlbVUFwBz4Wz6TV8RgFPljIxiYj7ZDr3otg4\nXBTf27JuJnngwuVNTgP+LulKTFA/L2WAkY4/oXL8jTi7sBuNLQynpB6CPM1J63dQUUT8XeaoHYi9\nVWW1Mx+gcv/aUJXKrtT/dhhy5b6fn7x6e+ByRG3ztKrKhPGsrO71YSOMT67KZPTWakRGf6r6gEke\ngp63DdrrGdeIuDv9ql8TG097YqP0p3j5aVrSEk2KPbkYc4P2wryg1TCraFp672Wje9pt/NzD6dhR\nlMYhemv9Va+57jzV7U37EhHbSDoGe0C+CRwkad2IKAyPOrL5YFDNi7bLVHNoTlqvag/slTyisr0d\nYj40/07WqRmVvW2lZfqiIPMinR5fViaMZ2V1r4ESxnPM0/CoTEavagywTPqjWmgoyOiFasnoEXF6\nRHwXxyRt38Z52yGjbwUcLhe6LR/XiIxejXdq50FdJqQDfQjpZS/WDTjmZjHsQbkTL4ftAPyn5FFa\nDpgqInaPiFvDhPFPt+pEWmZ5ltL9SDE2y7VxDUTEK9jI/FFlLtRpDAZ89vRL0mIYsFmAWe+jfm5U\nz3tnRBwaEavgpahvl46vErhXpnXh3vfonJBdJq0/WnmVuVBlHtW/cQmZQ+g7T5rNh1bQ2kZzsxuD\nsdE4HIW/21/G8NM1K+enwXFZWVkjSNnzNAyKTEYfdDJ6oWhOSC8bY//Ewem3luJursfLPuVg8Idx\n2ZCdsAdqVRLUsA0dA+wh6WFswO5K++VGwAHdNwD/SfflLuw9XBF7pP6Trvkfku4GzpL0E+wROw64\nphS7dgwO8L4Nz7fv4Pn2SLr2+bGRfBH29ozCgcp/SMcfDpwrM5Suwp7BTWiNSxgLrCPpJuCdiGhZ\n6DfaI61D//nwCzxv3yu11Q4xv1nf15O0CE7gKIpEN52Hkj6Fx2h0RBTLtGOpjIOkr+Bkgi9ExJ2S\nDsfxT0tGxDjgcWyobSjpUhxP1onHOSsra5iUPU/DpMhk9MEko1e3b00vIf02vGTYQ0hPuhbP92tK\n265J2/5Zuq67sNHzU+w924K+WWHNdCTOLvwDXgYcT02B3UZKy5Ofw/FHv8LMp3/j2LHDcBHjQhtj\nPMU/cazaw5hiXbR1Hs7YPBUT4efBdPJCb2KD6XwcKP67tP9Kkv6Sgt53xllv92Oa+tYRcT3NtRvO\nGnwynbfda68jrR8LfKq8W+WYh7CXsw+CgRbE/CY6EY/FHTjjdN2689ZsmxZ7tmYsbeszDjJa4SRg\n34go6mPuCzyHx76gye+Ljb3n6J9xm5WVNUKUCeOTsTQIZHQNHym80ASc0XdtOv5hDSIZXUNEH08B\nx/PiDLfnK5/Nj7ML18RG8EvYWP1ZRHQUUDyYUhd0bFUo2zLa4b1OPCAyXbvQROzZOh/XoqvGPrVq\npyNK92BI0jTA7DUZg5200fT70eS4lkR6ZcJ4VtaQKBPGJxOphhQ+CTRcpPC1MfNmKRywvShwZ8ps\nG04yesdK+IfpcD+3qnw2DfYUzoKXtRbBGYJ309mS3VBpQLTqiHity6WjgnE0P2aHjcbLbCNeEfH+\nQAynpIEE12dlZY1wZeOpQ8n6qaSHZCr1WEl7ps8GTAqXac9PSSoDDWtJ4fjBtGB6Pxik8A9kivL0\nMpm6IIW/nvoVkt6SdDOdk8JfiYgXopeYvQ4uGPt74KDiAa3mVOuTJV1cOg+SpknXvHWD+zWdpCPS\nmE6QdLN6QY/taluMCjgTL4OWtTi+Bz9IAeZPRsTNEbFPKWsNSctLek+9hPmJMq26GN/L05i/LulW\n9QWdFnNkzzQG4yU9LjPCyvusKOn2dI9upZIcIJchOUm9FO/75diuhpJ0jaSjOulH0rh0v5+OiEtx\nvFGf2nbN7nVJc0r6SxqrByVtWGljCZkg/noav9PlAPFy/38t6WiZOP6cpG0lzShT0cen7/KXSses\nke7TLOn97Om7+WTqx12SvkUDqQFJX9Jn0/HfKu27eboXo2R+VDPKeVZW1ghRNp461yE4HmZ/7D35\nNvC8Jg0pPOiF/g2UFF54hjYNk7Y3o5cUvgyOBbkMGwubMzik8GOA+UgZaWpNtT4JWF8unltoQxzz\ncm6DcxyHA9g3B5YE/gRcJmnBNvqHpJmAb+AlyCvxA3GV0i4v4qWpb6gBcVo2oM/FcTjfwMt7P8ZB\n3Eunvv0F34Nl8DhfJGnuSlO74hioZXD80vFycDOSZsSevnuwkbIffe8J+Pv+JL63i+I5/EtJnYKB\nGvajwfUvgint/ypta3WvC+2D4wOXBC7FQfKzpTZmxcvCt6VrXh8nXJxXaeO7+D6tgJMxfofnwY3Y\nwPw7Dtwux06Vv28z4GXYDVJfT0j7lwnqZd1IzfcjIh7A3/Pj5fI6c2MW1/+LiPvx/WpGOc/Kyhoh\nytl2HSg9SHfCXoYz0+bHMG9mcieFvxJ9acg3qC8p/BPAugXwUp2Twut0P76u+fHDqRXV+mZJD+Il\noMIw2Br4Uwk1UO7LvOnzeUrwx6MkfRn4Hu0tI20BPJgebkg6B3uiboQeavROOKB7XxmKeQ1wVvTS\nr7cE5gCWTVlVUApSp9f7V2hfSZtiI7Qc5H1JRPwu/f9QOdNuTYy0+A4ey+0i4l08//oEiac5t3+p\nvcclrYyNt07izZr1o9A5cszSNBjueTH+UVCo6b0u7XdqCn4nzccfY4P/75j4fnsKNiftsx3whKSF\nEmYCDKH9Vfr8EBLnKyJOTtsOwD82lqIE1yyUArmPKm06LnmqvoGNyOr+7zf6fkTE8Wn+nYmTNW6J\niOPSZ2+ohnKelZU18pSNp86USeGt1YgUXqfCICx+5bdDtT4JZ34dkTxQXyZRs2u0BGbmPCj1IVdP\nR3teOfAy3Zml92cD10j6cbHUmB6Ip6d+fAF7dvaStGFEXIW9S3eUDKc+Sp6p/bFnYy78vZwBB6iX\ndXfl/XP0UrpHAXclw6lQv/svoyS+l9r+CB6LfhTvFmrWj0K7YMN/akwQPxqPY5HJ2S7BvOdcEfGm\npNdL51oaWDttKyvwUmphPPUEy0fEB5JerrT7fJoe1WsAvNyJs1S/gZlf06VXtxiBbbGhORF7srpS\nJoxnZXWvTBgfXmVSeGs1IoXXaTF8jWXPSyuq9enAwZI+jxlMj0ZEo6WNmTB6YVl6x7DQhFadk7Qo\nXvJbXtJhpY+mwliAk3s6bUPqkvTaW9IV2LN1Fa1p1kfi5dzdMIfpLbwcO11lv2aU7pYByinW5nDs\n0fwX8Dpegl6xRf+qataPQs9HLxn+oRQ/dLakn5e2t0Mwb0UmvwhfQ9Uz+2yLNuqy/hqFMfwUe7x2\nxsuib+Al5+r9aVfLAB/Fc3Iu4Pnmu9crE8azsrpXJowPrzIpvO9xXZPCkydoJ7zs+d+0uSXVOkzi\nvgB7hLbCHKNGuiP18RPRn1zdTjbVtngJaSns5SheR6fPmul+/IAEez6WKWJ1arQy8IeIuCgi7sX8\nq/nb6F9ZY4Cl07JpoZVqznNjRJwQJos/Sko4GAYVxmvx/eiWYF7W7dhz83jN/e20/EozrQxcGBHn\nRMTdeM4u3OKY2u+HnIl6Ko5RPBUblNO3Oi4rK2tkKRtPHSgFUhek8NGSFpD0eUnbYFL4O5gUvrik\nteiCFC5p4bS0shkOqCUtBRak8FEpqLYfKVzSRpIWlLQ43ZHCP17KMCpI4aMj4hzgr/gPfeGtHEsi\nhUuaQ9JH0pJhI1L4xyR9QtJn5Iypf2C45zbRCxs7HNha0g6SFpK0K07/P7zS3snYcBoFnNbowhJE\n8Wwc3LuJpPnljLQ9UtxJQ6XrHA2cHRH3RcSY4oWXDr8gaVFJS0u6QNLX0/sFJW2LjbsLUnPnYO/C\nBZJWTmOwafKegY3yTVNbS+O51Cli4GxspJ6U+rEB9mSV9RD2oq2X5tkBOIh6KDRbut9zpfm6N046\nKIyjdu91Mx2HuVp/lLMZF5C0vpxFNyBEA33H/yHgi5JWSt7IE3ByRe/O0g8lleG0Y6l8P9L2EzBJ\n/CB8f6bCnsfycUtJWiQdl1cHsrJGoLLx1KEik8K7JYVfiWGJdwEH47FbKiKuK/WloFrvjpdHaqnW\naVyeBS4vBYKXz1XW1nip7wjsDforHudWAfUb4Xt5QfWDFDw+BnufnsSeiH2wgXsbHs+9iyDlMBjy\ni3icLklj8DPslQRnr72KY34uxNlWVUBbU8p1WjbcEMd53Y7vU3XJ9gSc1ffH1NfZsQHSTNXztqJt\nF+9Pxff7SWwM3g1sUMyNNu91q2t+FpdwmQq4Ao/rUcCrJYO8nf42uoZCB+ExvRzHOz6L51FZHwMW\nKPWt3/dD0mhchHl0RHwQLhM0GthOvaiEgnL+n3TcyjV9zcrKmsTKhPERIA0CKXwkSINICm9xnhnx\ng3mr9BCe4qUuSfBZU56UCeNZWUOiTBjPGnbJ3KQlsaehk+O2ktQwi0+GHG6jXlDoBzhgd2bgB5KG\n7ekh6dqiD6XXREm/bX30oGjQfunIENW31JeZVXxWvs63JN0r6fuDde5uJWnf0pi/JwM7j1IJRDtS\n1GpeNznuVElt10PMysqaNMrG08jQgB6KalLmRaZBv97gdclAzlvRRHwdnQbqdlrmBeApYEecbXVp\nEafVqdRLpq6+xkuqKwYcGOb4ydJrLvovj3XSh6k6iM8ZaBxPcc6G5WaSiuv8BEYKnIfZRt9s0N5w\nxuXcg8d9Pjzu29MfBjoSlMuzZGVNwcrG0ySSrJ9KeghXjt9VQ1DmBcdCFVliX8YZYJun99tJKkpI\nrJ7aGOwyL/dI6uH5yAHV49Mv89oyFqU268q8LBMR80TEiThe5hM4sL3o++lyGY43knG0UGXcV5V0\nnaQ3cRbd+en4cjbdMqRK9zV6M1x2pPyakNruU9YjbVs6bZs3vd9K0quSNpR0L87enCd9tp2kMcnb\nM6aZtycZXaek/T6dtv1ELh0yQdITko6TlziralZupnydL4bL6eyPg6Y3SufZV9IdcpmTR9M1FKVw\nfi3p+XQN10tavtTnpnNL0tySzk3j85IciD9fpV/vp349ExF/wvFUG5XaWEPSLXLppGckHawS+T19\n7/ZUb4maOyR9vXL8B3JQ/e1pn39ImlPSl9N4j0vXMQM1ajSvlcuzZGVNMcqZHJNOh+CH2C44UHgu\nYJR6y7zchMuWfAJnlx1L3wfdOsA4zHQqa3dMBd8HB6ceDfw3Iq6SVHiHni5YO3KJi7LKZV5exoDD\nj9BaK+JA+bVxMPW7EfGOpC2BW2Qv199wmZMrIuI02WO2Cw6+XwQbR2X+UqsyL2+nY4r0/NNw6v1X\nMcPoMOASSYtFxER5afEyHJC/NYYi/gbYLSJaoQfaVTvByDNir8m2eIxfSOO0HwaS/hejKE6UNCEi\nzigfLOMI/ohBl6smfAPY+/djnLH1GUwXPwyTuItji3IzK2CExqySVomIG1tc11v05RotBGyKM+SK\nwPfD0/vROCD/Z8AVkhaMiNdoMrdk79UV+LuwSmrzF8DlkpZsAJYFz4HpUhufxgH5p6Q+jMKZkW8B\nB6T998IllbbHEM3VgTMkvVAJVt8XJ0m8hUu5nJfO9S28ZHwBHuu67MCiPEufeZ0gn0V5lhvxvOgp\nzyLpCOzpmxnPTwGv1LSflZU1iZWNp0kgfbjKvNwp6ef4IVY88L+SPntPXZZ5kZlJe2Mj6dbkYdoQ\nWCkibkn7bIljsL6GoZN7AGdGRFFv8FFJuwDXSvp+hc7dSD9U30K4AeyQcA7tahqcyXhP6Xr2w0Zc\nYSw+LiMndsQGZ3GumbGBMC2wVkT00LUrCQePS9obP5x/VNretNxMVclr820cz1b2xk2Ls8ZeSfvN\nmPr63Yj4e9r2vzjLcFucTdpsbn0TJ7D0sMlk5MOrmNxezjwtPl82XU8xt38APBERBaX8weTNOQQ4\nIM2pPYF1ijkCjJW0Gs42LYynAH4eEf9K5zkZZ7IuEBGPp23n43p9/YynyOVZsrKmeGXjadIol3lp\nrUZlXm6SFHj58RFg84h4Ua6L9h6l2mQR8YqkB/B4g5fklpT0nVJ7hTH5GZwi3kpnYs9eWZ0Sot+t\nGE4zYo/ZyZJOKu03NfBapa/nYINw7cQd6/1QWhcbiKOAWUh15SR9pASNbFluJqkwEqfD2Iujoree\nHRhMWfaKLJjO10N7T0bErfSOf7O5tTSwsPqXWpk+tV0YT0tJGp/ONS32ZhY4kFH0n/83Yt7S3GlM\nZgSulPrEmU1LfzREuQTN83gZ8/HKtm4ZWYNTnmWvfZh11r6O4y222IIttmhJKMnK+tDrnHPO4Zxz\n+v7mHTeutoJWrbLxNGmUy7y0VqMyL5tj0OLLEVEuRNTIo1Yez5kw6+iYmv3b9bCNi97yIlW1HN+k\n6v2fKf27Hf0L006svL8EFwFeGRcg9gkdG3Qx5jbthZd7VsMev2mBt9RBuRl6jcS3Ek+pqur8q9Yp\nLG8PaDm3ZsJso2/T/96UvTD3Yw/jROCZyg+Iuu9OuV/FOG+AURdlvVN5Xy7fUlfOpa4kTbsanPIs\nRx/Nsssu22UXsrI+3Kr7oXH77bez3HLLtXV8DhifNMplXvoe10mZl6ci4rGK4QQet2lIweMAkubA\nxlpBWr8dWDwdXy3n0SimphO9iB/W1fFtqrTM+TSwYE2/yt6OIkZmT+CiSjDxcsBUEbF7RNwaEQ/j\nIrZldVJuZlw6f53hVKeHsYHRU3IlxTEtT9/yOo3m1u245MmLNWNQ9ka9m+7fEzX3bAz9oZKrAK9H\nxNPp83eA+WrO8XSb19mucnmWrKwpWNnzNAmUAqmLMi/v4aWFObEL/ywcaHqapP1xUHPHZV6w4bEe\nLvOyQTrv25KKMi9jsUeoX5kXTMm+F8dedVPm5Wng7YgYr94yL0tGxDOSvoofGCumh99YUhkLTEd/\nExcMblTmpVYR8bCki3CQ9Y44JuwQvMRVeLAOBW6WdCz2yLyBx3zdiGiXBD+j+rOR3kkB0Q+n8+0n\n6RfY27Zrm+3uBxyTlqQux8tVywOzRcT/pX2UrvU3kqbGcXAbpGDvh4FpJO2EPVCr4jgeH+jg/NHA\nL6pLoWmpcFdJi9Ysk7alFAx9PDaOX8Xj8FN8H09O52k2t87CyQ4Xpjilp3B9v02AQ6OXdN9MvwV2\nTvf3N3gZbz9S+ZOImJCCso9O43cDMCs2sMaVAvM7RkKkeb5JRBQJHGOpzOu0dFouzzIDNhqPpDcu\nbSywnqRFcFD9uEEy7LOysgZTEZFfk+iFPQhFqvdjwM/S9sVxjMcb2JtxPDBj6bhTgb/UtPcYzlD6\nIzYengZ+WNlnFDbWJuAH2TrYq7V6+vznmKUzIZ37L/iXejvXsw3+4/8ejuf6bGpn89I+s6Z+Hlza\ndlw610ScJXgANhbLbc+XPl+qyflnBf6Al6wm4CWuBSv7LIeNk3HAeFwOZ482r++a1Ifq69LSPivh\nbLk3cJmcTdM+86bPt8JB9XXtfws/TN8CXkrn27jR9eNEgNeAL6T3O2OjYwJwKbBlOmaW1I/3cCmh\nunPfAxxRus6jmozDvhghUd0+Pa7H+Dw2gq8Dli193nRu4R8Kp5aOfwgHqc/U7LyVPqyGS8+8hef/\nL7FHrrzPj7DR9jbwXBqrVdNnaxRjVtq/3z2r9iW9f7SyT3Vej05zboHSPiukfnwpvf9Ymp/jKX0v\nK+0uC8Rtt90W7ejss89ua79ONBRtDlW7ua+5r+22edtttwX28Pf83Wr0muQGRH4N3gsbJTtN6n4M\nwnXcCWw2qfuRX/k1El+dGk8bbrhhW/t1oqFoc6jazX3NfW23zU6MpxzzlDWilJaXzsc8pqysrKys\nrBGnbDxNWRqychAapjIvEfFeRBwYfdPmh1wyebwozdKvXMtw9iUrKysra2QrB4xPQYqIBYaw+eOB\ncxt81mk9u5Gof+PMs6ysrKysrKbKxlNWWwpnk73WcsfJVGHgZCN+U1bWSNIMAPfd115i5Lhx47j9\n9ioDdGAaijaHqt3c19zXdtssfadq61aWpYhc+DsrKytrcpGkb2O0Q1ZW1tBoy4g4u9kO2XjKysrK\nmoyU4K/rYyzI2833zsrK6kAzYL7cFRHxcrMds/GUlZWVlZWVldWBcrZdVlZWVlZWVlYHysZTVlZW\nVlZWVlYHysZTVlZWVlZWVlYHysZTVlZWVlZWVlYHysZTVlZWVlbWJJKk6SR9VtKgcBclXS1ptprt\ns0i6uss2PzPwnjVsexpJ60raQdLMadunJM00ktrsd46cbZeVlZU1ZUjS1MDWwDrAx6n8QI6Itbts\nd50mbW7TTZtD0e4QXv9EYK6IeKGyfQ7ghYiYuos2ZwSOBbZKmxaJiEclHQs8HRGHdNnXD4BP1vT1\n46ndabts8yngWuCfwLUR8Ug3/au0Ox9wOTAvMD29Y3AMMH1E7DgS2qxTJoxnZWVlTTk6BhsPlwD3\nMAj1LiXtC+wD/Ad4djDaHMJ2B/36k9Rg+/TAu122eTAuCbUmftgX+gewH9CR8SRpqdLbxSR9svR+\nauBLwNPddBSYB/dzDeCnwImSnsHG1LURcVKX7R6D7//SQJmr9FfgxBHUZj9lz1NWVlbWFCJJLwHf\njYhLB7HNZ4GfRsQZg9XmULU72Ncvaaf036OBvYEJpY+nBlYH5o+Iz3XR9uPANyPiX5JeB5ZOHpKF\ngNsjYpYO2/uAXmOxzth7C/hxRJzSaV9rzrUQ8HPgO8BU3XjeUjsvAatExAOVMZgfGBMRM46ENuuU\nPU9ZWVlZU47eBR4e5DanA24a5DaHqt3Bvv6fpH8F7AhMrJxrbNrejeYEXqjZ/lG685h9BvfzUWBF\n4MXSZ+/i5cWJdQe2UlpiXAV7n9YElgUeAI4DrummzaSp06uquYHXR1Cb/ZQ9T1lZWVlTiCTtBiwA\n/CgG6Y+7pEOBCRFx4GC0N5TtDsX1p3avATaNiFcHsc3rgD9FxLHJQ7JURDyWYp4WjogvDda5BipJ\n7wKvAmfjpbrrBmMsJJ0LjIuI7YsxwEbfhcATEfG9kdBm7Xmy8ZSVlZU1ZUjSX4G1gFeAe4H3yp9H\nxKZdtHkM8F3grvSqtrlrl30d9HaH4vqHSpJWBS4DzsRxWicAiwMrAWtExG0dtLVRu/tGxEWd9RQk\nXQCsij1Y19Ab6/RQp21V2p0buAJ7zBbGsUoLAy8Bq1eD3idVm7XnycZTVlZW1pQhSac2+7zLX/LN\nlmViABlsg97uYF6/pKPa3XcABuSCwB44uHkm4Hbg0Ii4u8N2Pmhz1+g2PimdZykcNL4GsBrwPnBN\nRHxnAG1OA3wLe4iKMTgrIt4aSW32O0c2nrKysrKysvqqhXFXVtcG5OQoSZ/D3r21cAafIuJDFz+d\njaesrKysKUjpV/eawILA2RHxuqRPAeMjYkLTg5u3u1Bq87qIeEuSBiOuaLDbHarrH2xJapRNF8A7\nEdEtAmHQJekneExXA2bGy6z/TK+u45+aLDcG8DbwcEQ8NqnbrD1PNp6ysrKypgwNEXRwDuA87GkI\nHMz8qKRTgFcjYrcu+zro7Q4XIHEwVEEL1Okp4A/A/hHRdFmuhFRoqYj4dbv7ltr/D72AzOsiYlyn\nbTRotxiDKlqh2BbADcDX2jXQhqLNOn3oXG1ZWVlZU7CGAhB4NA68nhe4r7T9XOAooCvjaYjaHbTr\nl/SXdvftMhB9a+CX2EC6FT/YV8DE8YMwymB34B3gVy3a+kmLz3u6CnRsPEXE8p0e06a+iMfg53gM\nwJiFg4ADgXE4kP4IYNtJ2GY/ZeMpKysra8rRqhgQ+K7U54f3WODTXba5HrB+RDxVafMhYL4u2xyq\ndgfz+gfFu9JEWwG7RcR5pW0XSbob2CEi1pH0BDYCmhpPETFktecKSVoN2AEvh24WEU9LGg08FhE3\ndNnsMcD2EVHmfV0l6W3g9xGxuKRdgE7AnkPRZj9l4ykrKytrytFQAAI/CrxZs3127BXpVkPR7qBd\n/2DxgJpoJeoBm3ekz8DLS/MOcT9aStLXgTOAs4DP4SVRgFmBvYANumx6QWB8zfbxmNcFNqY/Nonb\n7KepWu+SlZWVlTWZ6O/ALqX3kSrJ7w90W7LkesxjKrc5Fa5xNhC69FC0OxTXP1R6ivplo22BJ9P/\n58Bwyo4kaW5JP5B0iKSjyq8u+/oLYMeI+F/6srNuxLTxbnUbcLikOYsN6f+HAf9OmxbGYzUp2+yn\n7HnKysrKmnK0G3CFpDHADJgIXQACt+iyzZ/iZY/lcUmVwzDMcXZcsqNbDUW7Q3H9AEjaDNgce4Km\nK38WEd0YELsDf5L0ZfxQDxzzNArYLO2zAo4Ba9W3jYDLIuI9SesAF+EyLaNwgeT5cUzV7V30E+Cz\nwHU128cBs3XZJthQvBB4StKTeAzmxX3fOO0zE45VmpRt9lPOtsvKysqagjRE0MFZgR/RF+Z4XEQ8\nO8C+Dnq7g3X9yUuzd0S8kbLZiuDu7YFT8fLQCqm/P++yr/PjpbtFsHFzP3BCRIztsJ0PgE9GxAuS\nbgGuiIh9isK4uIbeWcDlEXF8F/18FMcR/aNSbPe7wB4RsVinbZbaFrA+fcfgylYZhsPdZr9zZOMp\nKysrKyurrxIkc5OIeE3S/RgZcE7FeDgAmD0ifjRpe9ur1L/PRcTDkl4FVo2IeyUtDVwYEfN30eae\nwHeAbYArcYzTfDhj8oCI+M2gXcBkorxsl5WVlTWFaIigg0u1aPOJiGgrwLtJW/0bj7ir3X1L7Q/a\n9UfEWqW38wJF9tZbGBQJDqL+F/aetdO/pfj/7Z13mGRllf8/hyEziMK6RIkOgkqSoMAQh6BIHFxA\nUWAlukvGn6AkmTETRsAAjCQFFpclr0sGVwGXjOMwgIQZokoeMiPw/f1x3pquvqGt9ZkAACAASURB\nVH2ruu6te3u6y/N5nnqm+lbd732ruh/u4Zzzfg9MlfT+YN9Fkc+fvLEOkfQa8AYwT3rpL3iG7IH0\nc9km6R/gPdI3AQviJbx3gJOKBk4pi3eWpLcH86fq1JOqDs1BrxmZpyAIgt6gZtNBmnSbbxx/x/ty\n9pf0doH1NWsM0C0zg63Kz59KVetKetHMpgPjJd2XDCMnSzrTzLYCLpa0aIH1NcprrdYKBWfQmQ9E\n3lPSq2Z2FZ5hOtvMTsL7fM4DxuPmo1t0qptznXmBj+Ll0GllHNvTd7lO0/faCklasc3rtWoORuy2\nC4Ig6B22xJuPt8S3kS+Snt8JbAtsjO/gOqmA5k741u798P6ZNdPzh4Ev4Q26m+MmhIOxAr5dfAVg\nZ2A68G9Jc830/LH0Whmq/Pyn4lkc8N1/26bn5wKTzOwGPGi8vMD6VgCeb3re+C6yj0I3eEk7SWps\nz/9/wP3p+fF4tmhX3OuqtClkus4sSdMk3Vl21I2kFSS92PS81aPj76AOzU4uGo94xCMe8eiBB76z\naoOc4xsCD6TnW+Cltk4178TNLLPHtwbuTM93BB4ruNY7gW1yjm8D3DNcPn86Z15gwaafd8Odug8C\n5i2xznlwk8YVKvq9HwzMn54vS6oqVfh3tRC+O+124FF859rsR0nNefBAedUK11m5ZqtH9DwFQRD0\nDnUYBK4GPJFz/In0GnimY8kCmg3dvBLLdKDs7q3KPn8az7KXPKOzG02WAZIuBi4uuUbklgLj6XK7\nfBOnpPW8jX9/S+I77KriF8AmeI/XX2g/k68j0ncwf7c6dWu2Isp2QRAEvUMdBoEPAUelfpeG5jzA\nUek18NEnfyu41geBb2Z05wW+Sf9Zd0Wo8vNvi2dcwEt1i5RcUyuuxDN2VfAssHMajGzAMma2bN6j\npP7ngH+RdKSkH0s6tfnRxbp/ChyZ7CWqog7NAUTmKQiCoHeowyDw33HTxafNbErSXB0fg9LoA1oR\n+FnBtR4AXJ3RXSP9u11BrQZVfv6HgO8nywIDdjGzvKwWkn5ZYq2PAMeZ2YZ40PdG84sqtivsO8Dp\nwE/wz3xXznsaDfOFG/Fxl/OXSpw3GOsC44Ct0ky/7HdQZuByHZoDiN12QRAEPURNpoOjcZ+fZs2L\n5Fvju1nrgkl3laQ7Lem+0fbE9pqVfH4z2wAvh62Eu56/Rn65Supwt11Gv9JdYWa2MO69NAXv63qx\nhfAfi+gm7S/jweeekvLmEZbCzM5t97pKzBesQzP3OhE8BUEQ9B6p9+Md/YP+R77Kz99sMdD9yurF\nzPbE7RO6Gdqc1bwPDyIN37XXPN8OlRtPM6KJsl0QBEGPkAbrHo2XxBbHsy+Pm9lEYIaks0vqfgXY\nHy/PrS/pCTM7DN9pdWUX610Z2BT4ZzI9uJImlNCr5fPT32JgWCPp/Bpkr6hBc0QTwVMQBEHvcAyw\nJz50d3LT8anAoUDh4MHMvgZMAH6c9Bs9My8nzVLBk5ntC/wcH9r7V/qXxJSuWZTKPz+ApLzdhl2R\nXMHbXfOrVV+zLJJOqEM3lS5bZgaLli7r0swjgqcgCILeYQ98gOtNZnZG0/E/4n1FZTgI2FfSFWZ2\nVNPxuylmtpnlGOBoST/sQiNLHZ+/Lj6U+Xke4JPAB4Gbh345c4QfZ36eB1gL+Cxw4jDSHEAET0EQ\nBL3D0riJYZa56Jt3VpQVgPtyjr9D31b+MnwIuKSL8/Oo4/PXgqSdssdS2fHnuNFjz9PK5sDM/h1Y\nZ7ho5hE+T0EQBL3DNGCjnONfID8A6oTp+OiULJ+lvB8TeOC0VRfn51HH5x8y0o7AU4DD5vRa5jDX\nUH5Ez5BoRuYpCIKgd5gAnG9mS+P/czzezD6Gl7O2bXtma04Bfpp2rxmwnpl9ETez3KeLtT4KTDSz\nzwB/YuAOriI+Rw3q+PxDzUp0cW82s1HAXrjXUV4j/ubdLG6I+ALV+0pVqhlWBUEQBD2EmY3FB8Ku\ngRtC3gtMkHR9F5q7A9/Gb+wAzwDf7mL3WuU+R026dXz++2jh8YSPRHkUOE/SLQU0T8kewseqfB44\nX9KBJdf6Ezx4+g05o1QkDZusVs73asASwIeBf5N01nDQzL1OBE9BEARBJyRTy9Ejwe+oSszs+8DX\n8AzZnfgNeR3caf08fBbfOGB8p9YNybm8mfdxO4SbgXMkvVtyrS8Ae0j6nzLnt9CsJZtlZsdnDjW+\ng99KeijnlDmimXudCJ6CIAiCoDVmNhl4UtLEzPFjgOUk7WtmJwCfl1RZU3IZzOxZYFNJf65Qc8Rk\ns4aKCJ6CIAh6BDN7mc7KS21HWGQ0Ky9ZNWkvA2yPz5+bt/k1SYeX0Kv88yfdmcDakh7NHP8ocI+k\nRcxsFeAuSQsXXXeVmNkRuJnpgVW5y9eRzcq5xgJkdkRKyp0lOCc1G0TDeBAEQe8wAXfYvoa+8tK6\n+M64n+K2Az83s7klTW6p0p9rGbxkdaOZdVyyAjCzcfjA4cdxD6apwPJJ/95OdTLU8fnBA68NGGiD\nsEF6DbyUVWgkipl9AdiF/OCx7MiTscBmwOfM7AEGNuKXGYw7i3wLiK4ws4WAH+LfwWI5byk8xLgO\nzTwieAqCIOgdxgLHSGo2iMTM9ge2krSzmU0BDqa/A3c7/gk4uU3JaqtUsjqWYm7j3wdOknS8mb2G\nbyN/DrgQD9jKUMfnBzgdOMPM1gbuwjNZ6+G7Db+X3rM1BewQzOxg4Lt4ALoDcC7ekL8uHuiV5RXg\n8i7Oz+Nk4BAzqyyblfgRHuh9DfgV8O+4V9f+wFFtzhtqzQFE2S4IgqBHMLPXgTVblJfulzTazFYC\npkjqyOCyrpJVCpjWlPRYKreNlfSAma0BXClp+U61mjQr//xNGrsDBwIfS4ceBk6XdFF6fQF8l+Db\nLSSyeg8BJ0j6j/RdrCHpcTObACxadrddHZjZ5XhA8hJQVTYLM3sSLwf+1sxeBT4l6dE0S/GLkrYZ\nDpp5hElmEARB7/ASsF3O8e3o87hZCHitgGajZJWlq5IV8AYwX3r+F/psEMCzXWWo4/MDIOlCSetL\nWjQ91m8ETun1tzoNnBLLAren528BjcDzV8AXi66vGTOb28y2MLP9zWzhdGwpMxtdUrKRzfpffBbh\nzMyjLIviJqwAr6afAW4FNh5GmgOIsl0QBEHvMBHv6dkM7/lplJe2AQ5I79kSvwl2SuUlq8T/ARvi\nruD/A5xsZqsB49NrZajj89fFX/GenCeAJ4HP4DP4VsB7tUphZsvhZc9l8eD0BjxYPDL9fEDrs/OR\n9K9l1zMIj+N9bk8AD+F9Snfiwe4rw0hzAFG2C4Ig6CHMbEP6ykuG30BOl3R72xPba1ZaskrnrIh7\nRk1JTb4n49msR4DDJT1Rcq2VfH4zewlYWdILbXbxASBp0VavtdH/BfCUpBPS3LUTgdvwZvzLJO1d\nVDPpXoEHS3sDL9JXDtwUmCxpTEnduYFN8QzhRZJeM7OlgFclvV5S8zDgPUmnmdkWwNV4FnNu/G8g\nd07dUGvmXieCpyAIgiDoj5ntCVws6Z30vCWSzi+hPxcwV8MM08x2oy94PFPSrBLLbtgKbCjp4Uwv\n1fLANEkLltDMZrNWTpqnAvNJKpzNanOdtYFHJU0ZrpoQwVMQBEHPYGbvAUtmHcDNbDHgOUlltn4/\nDqwr6cXM8Q8C95Ydo5LRGs1A1+rCfjx1fP46SFmcb+FO4k9XrP0yHjxNywRPY4FLJS1eQrPybJaZ\nzYMHZAdIeqTo+UOl2YroeQqCIOgdWvXKzId79ZRhefK9cebDt4CXwsxWAH6Cl4Lmb34JL5GVCXQq\n+/xm9oFO31s00JP0rpl9A/hlkfM65HrgUGC/xuVScHoC3ltWhrF4QDbLrN9XPIOSfwOS/m5mq5dc\nz5BptiKCpyAIghFO8gwCDzr2SVv2G4zCdxkVmutlZts3/bh1sixo1hyH3zzLcgEe7HwV+BtteooG\no47PjzcXD7ambgK9m4BN6O47zOMI4Dozm4YHpRcBY/BdcmV38Y0i/zMuQ4mdi01cgGezKvNfqklz\nAFG2C4IgGOGYWWNr9nLA08B7TS/Pwm/Qx0m6o4Dm++mpGJjR+XvSPELSf5dYcsOTaW1JD5c5P6NV\nx+ffpNP3Siq8e8/MDgCOx01B78GtG5o1ryqq2aQ9N7Ab7gI/Gndsv1DSWyX1fg3MlLRfKgWujg/b\nvRKf+VdqN56ZnQ7sgbuX383A76DMiJ7KNXOvE8FTEARBb2BmtwDjJb1coeZ0vOfphao0k+4twHcl\n3VixZqWfvy6agtM8NFz6s2D2DMLr8CB6DB6UNLJZG2d7zArotp2HKGmz4aCZe50InoIgCIKhJjl9\nn4GXWaYy0LW6sp1RZSjSOzOn19pMptzazOzhyJKmt3hPO91Ks1kjnQiegiAIegQzGwXshfcj/TMD\nd7BtXlJ3XBvNr5bU/Azej7N8sxypj6jkzsDKPn/KDOWVLLOUXesewK8lvZM5Pi+wm6RSzeRt1j37\nu8Xdtnec0xk6MzsHOETSa5njC+HeXIX/turQzL1OBE9BEAS9gZn9BA8efoOPPOn3H3hJh5XQPB44\nDi/V5GnuVHKt04AH8UGuAxrGy5hkVvn5kz9QR5Rcay22CinQ/S5wNO6sDe6y/h3cgX0mcCZwR6dG\nnDVms1p9B/8E/FVS4U1tdWjmEbvtgiAIeofdgF0kld2SnscBwF6SflWhJnhz9/bKDPHtkso+f1mH\n8wI0skBZlqG7eXGnAvtlHNVvMrO3gbMkfcLMDgXOKaB5BYNks8ys42xWsoGw9Fg4ra3BKHycTqE+\nqjo02xHBUxAEQe8wC99lVCXz0jfAtkpuBtag2vXW8fmB2T1ahwKr4kHDg8Cpkh4rqHNfOl94UPNu\n08uj8Nl213ax1JXwgbhZXgUahqaPUGz48pZ0ls06CbcJGIyGDYSAP+e8LnwnYhHq0GxJBE9BEAS9\nw8nAIWZ2oKrryfgF8CX8JlklVwOT0jDgPzGwYbzMVv3KPn8qVV2TjBe3Bq4C7sfnzxk+SuUBM9tO\n0g0FpK9I/66J72Br9qRq2Cpc2sXS7wFONLM9JD2fPsuH8fLoXek9Y3BLh06pOpu1Gf4d3gzsDLzU\n9Nos4AlJzxZYX12aLYmepyAIgh7BzC7HbyIvAQ8wMCAZX0LzVNw3Z0p6ZDVL+ebUsVW/ys+f1reE\npOdStug6SUdl3vMDYCtJnyqx1j3xhvGOhyl3qPsx3H9pBeApPOOyLPA4sIOkP5vZjsDCnZZizewt\n3K5iaub4asCdkhZIPWIPFpmdl855ssJAvxbN3OtE8BQEQdAbmNm57V4vY2Y4iG+Oyu7gq4M6Pn/S\nfRtYLTsvzcxWBqZImj//zDmD+QyVrYGV8WzMQ8ANktoFrO30bsWdxLPZrF8CC0na2My2AH4maeUq\nPsNwJ4KnIAiCYI5iZvNXnYHpFjO7DG+Uf9XMngIOl3RJ5j27ACdJWnaOLHIQzGx+4J0KSpiVZ7NG\nOtHzFARB0EMkM8NN8cbhiyS9ZmZLAa9Ker3tye11P5o0fyfpLTOzbm7KyZPpW/huvsXNbGVJj5vZ\nRGCGpLNL6lb1+WfStxtuMnCWma2IN88LH5Z7JN5nNWwws7nwxu4DgMXx7FNX36ukh81sVdpksyRd\n0Uai54jMUxAEQY+Q+j2uxbMC8wGNgORUYD5JB5TQXAz4T7yXSMCYpHkO8LKkI0qu9ThgT9xDajLw\nyaS7K3CopPVLaFb++ZOu4TvtjgCWSoefBU4ETqu7v6YIdXyvGf1KslkjnbkGf0sQBEEwQjgVN7P8\nENA8NuNy3HW7DJPwxutlgTebjv8a+GxJTfAm9P0kXUj/Qb5/BFYpqVnH50fOJEnLAIsAi0haRtKp\nwzCIqPx7NbO5zOxYM3sG3x24Qjo+0cw6MtrsNaJsFwRB0DuMBTaUNMuTJbOZASxdUnMrYGtJT2c0\nH8GNLsuyNPmeTHMB85TUrOPzY2ZH43PcZmTHfnShWcsoHer5Xo/Bs1nfwLNZDabiGbmyJdaG51WW\n2c7lwHmS2g77zWgujvtNNb7Xfn8IZZ3bs0TwFARB0DuMSo8sy+C7pcqwEP0zTg0WBd7JOd4p04CN\ngKyT9xeA+0pq1vH5AXYFJpjZ/+GDjC+R9EIXeuBZsr3wUTJTyQ8iylDH99rIZt1kZmc0He8mSwhe\nYv0a7vN1Jx7orIMPHz4P+Dhwo5mNl3Rlh5rn4VnSieSM6KmKCJ6CIAh6h+vxTMB+6WeZ2WjgBKDs\nyJLf4zfPY5s058KzEB1nBHKYAJxvZkvjWZHxaVfXHsC2JTXr+PxIWt3MPgHsDvw/4MdmdgNwIXCF\npLfaCuRTxygdqOd7rSObBe5yfrKkfgasZnYMsJykrczsBPxvr9PgaSywkaT7u1jXoETDeBAEQY9g\nZsvgrtWGu0jfnf59Adg4Oyy1Q81PAjcB9wKb407bn8AzTxsWHU+S0R6Lj8xYAxidrjFB0vUl9Sr/\n/C2usyHuuv4FYAFJHyih8SywqaS8USLdrq/q7/UeYJKkC8zsNWCN1IR+HLClpI1K6s4E1s7ON0w7\nO++RtIiZrQLcJWnhDjWnAbtLKptl64jIPAVBEPQIqS9pDbzM1Lhxno3365TJjiBpajKDPBAvfY0G\nLgN+KukvXa73VnxuWiXU8flb8AbekP53oHDglKhjlA5Q/fdKPdks8L6mDRiY1dogvUa6XpHy8KHA\nD8xsf0kzulhbWyLzFARBEASDYGYr4Nmm3XGvo98BF+H9TzNL6FU+SqdOqs5mJc1jcK+vyfjcPeED\nh/cBvifpu2Z2GLCNpI6CQTN7GVgQTw69ycDvddGy6+13nQiegiAIgqHAzF7CvZdeSDe5djeg1/Gg\n4khJU4ZkgS0wsz/gN/U/4X1OF0l6pkvNukbJtPpeszvY2l5/qDCz3fGs5sfSoYeB0yVdlF5fAHeL\n6MiBPs0MbImk87tYbt91IngKgiAIhoJ0Y7tY0juD3eRwk8ttgI9IWrv+1bXGzL6Hl/4emJPr6ISU\nqTkauIa+HWzr4p5ck3CPpq8AB0ma3EonaE8ET0EQBMGwxMw+gjcO//OcXksd1DFKx8wuxcemnJE5\nvj+wlaSdzewg3HpgtQ41R1o2ayXgX/Hv9RBJz5nZ54AnqwqAI3gKgiAIgiGmxlEyrwNrttjBdr+k\n0Sm4mCJpoQ41K8tmFSndlulPMrNN0jpvAzYGVk3f61HAOpK+UFQzj9htFwRBEPQMZvZB3EJgJeBE\nSS+Z2aeAv3Xbp1QxjVEyawAvNh2/nP4u3kV5CdgOD2qa2S69Bm58WsQ0dCxwzCDZrCnAwQy+9sOa\nrn1ogTV0yg/wtZ6SbBUa3AwcVNVFIvMUBEHQI9RRXqljhEZdmNnqwI3ATGB54GMp6/AdYFlJe8zJ\n9TVjZi/gPlkPZ7yTlgemSVqwpO6+wM9xU9A76dvBtg1wgKSzzewIYD1Ju3aoWXk2qy7SWleTND3n\ne31I0vxVXCcGAwdBEPQOE4D38ZEfxwPfTs/fB34K/Bn4ebrBdsq1wIq4t9EtwG/xnXAr4dvLl8RH\naOxQySfojlPwQG4MfT5B4IHExnNmSS2pZZRMKpttgv++xuNZuDeBTSSdnd5zcqeBU6KRzcpSOJtl\nZh/o9FFgfc28gv9NZlkLqCzzGGW7IAiC3qHK8kqDOkZo1MW6wP45x58BluhGOJUD1yN/iO8vS0jW\nMkomrec2vOenKibiQfdm5GSz0nu2BP63A61XGHzenKX3lBniezHwQzP7l6QxV3KEPwko83vKX2CU\n7YIgCHqDmpqFKx+hkc4fhQ/GHUd+QLJ5p1pNms8BW0u6L1Oy2RI4R9JHimom3e1wf6dGdqX5xqmS\njc21jJIxs/eAJbPnm9liwHOSygQkjZE0DT8mAx7C/ZhuL6izSafvldRJMJbVnxfPsu6FB1/vpn8v\nAvaS9F5RzTwi8xQEQdA71NEsXMcIDfCG6b3wsuJUBs9GdMJVwHFmtkv6WWa2LPBD4NIudE8GzgG+\nJenNLtfoC+sbJbMbsDrVjZKxFsfnA2aVFa0qm1UmICqoPwvY18wmAp/Ev9f7JD1S5XUieAqCIOgd\nqiyvNDgdOMPM1iZnhEZ6z9ZA0UGsuwG7SOqqRJXhCOC/gOeABfDPuQTwB3yrfVmWBk6rKnBqIOld\n4IIqtMzs4IYssE/KQjYYhfd8PVRSu7JsVmrq74hunOUlPQk8Wfb8wYiyXRAEQQ9RVXklo1npCI10\nzrPAppL+XHZdbbTH0pfNuVfSjV3qXYY7o/9nFetLmtu3eGn2LkZJ0wvoNd67HPA00FyemgXMAI6T\ndEeJtb4PLJETPC0FPCZpgYJaonWGrIE6DcrM7JROry/p8E7f2/aaETwFQRAEQ03aLr8icKCG+Y3I\nzPYGjgPOxefbZYfNXlVCs1UQ0Tgm4FZgR0kvF9C9BRhf5Jw2Wo1s1iR8Q0BeNmt5SWsV0Fyu0/dK\neqJDzaxNxtppfQ+nn1fGg8l7yvTS5V5zmP/NBkEQBG0wsw9IerXxvN17G+8bDpjZ5cBmeC/WAwwM\nSMaX0Dy4xUvNnlS/K9o0nAKdVnScIclojgO+i5cT70yH1wO+g5dfZwJnAndI2ruofhXUmc2qCzM7\nHB95s2cjgDSzD+GB7+8lnVzJdSJ4CoIgGLk096M0ZTMGvI1iZZBaR2ika7Q16pT0ryU0pwMfBhYE\nXsY/9wdxn6PX8V19jwObSXqqqH6VmNlUfL7c7ZnjGwJnSfqEmW2B7xJctoBuHbsYK8tm5WivhFs2\nrIr/nT0InCrpsZJ6z+C2HA9kjn8SuF7SUl0uGYiG8SAIgpHO5vTtpNusIs26R2iUCo464Fu4b9I+\njZtvslQ4EzgL3y12MV6GKjXjzMzmL9Lb1YaVgLxM4Kt4ORPgEdxnqwiV72KUVNXfVaPX6xpJfzez\nrfEdkvfjvxvDd3E+YGbbSbqhxCU+gAfQWT4MdGylMRiReQqCIAjmCGY2N15iWQm4SNJrqQn5VUmv\ntz05X+8xYGdJ92eOrwVcKmlFM9sgPc9zoW6lOwoPzA4AFqdviO9EYEbDubvgWm/FA9Q9JD2fjn0Y\nN3JcSNLGKfP0M0krF9B9IWlWtouxymxWc/N5Gv1znaSjMu/5AZ49+lSJtf4S2AjfednYcfoZ4ES8\nbLdnUc08IvMUBEEwgqlj63eR0Rhl+6hS4/C1wLK4B9ENeDBxZPr5gNZnt2RJ8u9rc9PnMP4sxTMQ\nRwN7At+gvzP7VDwzVzh4AvbGHdmfNrOn8Jv8snhZsTHqZjTe/1SEWQz05OqWyrJZkpoDr1WBXXLe\ndg7lM54H4G7iFwHzpGPv4r+j/1dScwCReQqCIBjB1LT1u1XvVL+3FdHMucYVeLC0N/AifW7gmwKT\n5fPpimr+Bg+S9pF0Xzq2Fh7w/FXStskt/HuSViug+yiwv6SbMs7lqwB/kPShomtNuoZ7ZK1Mn63E\nDZLaNagPpln5LsYqs1nJ9mEvSa+moPFwSZdk3rMLcFKRXq+c6yyEZzQNt314o5t1Z4nMUxAEwchm\nhRo0K+txacNYYENJszyGmM0M3JSyDHsDvwLuMbPG7r25gZvSa+CN40cU1F2a/GzOXPRlNwqTgptr\nzey3wDsVBTtj8d/f58yskl2MVJvNmklfYD4ZOMvMVgRuT8fH4tnHrnbFpWCptMnmYETwFARBMILp\n1AunoGatIzQSo8gf/LoMxcbHzEbSX4EtU0ZodjZH0sNN78l6AnXCNLyPJvtdf4HizuoAmNlceDlw\ndh8V0FUfVeIV4PKS57biZOAQM+s6m5XZKDAR/10fAXw/HXsW+DZwWhn9lHE6itb9WSvmnVeUCJ6C\nIAhGMG2cqgfQqZnjEI3QuB7va9mvIWVmo4ETgK7KQ5IeouQokhZMAM43s6Xxm/F4M/sYsAewbUnN\nY6i+j6quXYx1ZLMambdJwCQzWzgdKxU4N/ELYBM8A/kXqpmZOIDoeQqCIBjBDGLg2EyZnqfK+qhy\nrrEMcF26xhjg7vTvC8DG2VEgbXROAY6V9MZgYzq6Gc2RRr4cD6xBGvsCTJB0fUm9WvqoknbVuxgr\n9+RKukfjg5BnlDm/heYrwOflg4xrIzJPQRAEI5jM7qWqqKOPqh+SnjazNfABwY05dGfjN9O3Ckit\nRV/f0adonWnottx0Kz5UuSpq6aOqYxdjTdksgF2BCWb2f/iA5EskvdCl5sv0+Z7VRmSegiAIgmCI\nMbN7gEmSLshkno4DtpS0UUndyncxJt1Ks1lNup8AdseD6KXxYO9C4IqCQXRD78u41cOekt4su65B\nrxPBUxAEQe9gZpsAX6f/uIsTJf2+C81KR2gkzVa9WrPn0Ema3uI9eXpzp/PWlDS17Lqa9NqOpWmm\nzIgaM9sBOB9vlD4OLwnO7qMq6a7dsBXYUNLDmaBseWCapAVLaGazWQ2T0FOB+SSV8eTKu86GwJfw\nRvwFJHXsN9akcR99FgUzGNifVdh4M48o2wVBEIxg0jDcsyS9nf6v+1zgMny3UmPcxU1mtpekizrU\nrHuEBsAV5PdVNY4puXDv2MlMNUnvmtmT5O/gK0OzSeNieIP3dcAf0rH1cY+moiaWAEi60sy2xYOm\nN/Cm9HuBbr5TqGEXI26SeTfe7/Vi0/HL6d/s3i1vAG/hAU/hwClxRXXLaU1knoIgCEYwaRjuOpJe\nNLOHgDMlTcq853BgX0mrdqhZ6wiNdP444Lv4dv070+H1gO/gAclMfCbdHZL2zhUZqLk3MB74iqTK\n+l7M7FLgFkk/yRw/ENhC0o5VXatbzOzXwExJ+6XM0+rA87ib+ZNl+pfqyGY1aa+AZ5t2x+0afoe7\ng18iaWZZ3bqJ4CkIgqBHMLN3gE9IejRz/KPAVEnzl9B8G1hN0iOZ4ysDU8popvOnAvtJuj1zfEM8\nk/aJNNvtnE6dplOg91G84foJPJMxmy4CvdfxcmDe93q/pNFldOugql2Mru/d9AAAHLNJREFUGc2X\n8eBpWiZ4GovPCVy85Fr/gAfMf8L7nC6S9EwZraEmynZBEAQjGDO7FxiXSltP4eaA2V1c49JrnWrO\nHqGBZy3WBB7JvG1NoPCNuImVgLy5eK/i40VI1/ynApp1lWxexJuQs67XO9C/jNUxbXqqZvd8AedJ\namsTMODkvl2Mu9Jnq1BmF2MzdXly3YKP0nmgC41+mA8xPgyfmbcsMG/z62X60/KI4CkIgmBkcyXw\nTnp+MnCama1J/3EXewGHFNAcihEa9wAnmtkekp4HMLMPAz8C7krvGQM83amgpBO6WE87jgd+kXas\n3YF/B58BPgvsW1JzAl6yvAYvWxqwbtL8KW4X8XMzm1tSob4iSe/imZwLS64tyxHAdWY2DZgfL6s1\nsllfLCsq6VvVLK8fxwP74H+b38FLw8sDO+LfeSVE2S4IgqCHMLOd8Jtdo7+psdvuypJ6hmcdjgCW\nSoefBU4ETis7riM5dF+JBwlP4QHJssDjwA6S/mxmOwILS/pVmWtUiZl9GjgY/14NH9lymqQ7Supd\nig8BPiNzfH+8l2xnMzsIL212PMS4LtJuxuZs1r10l82qBTN7DDhY0m9SiXFNSY+ljRWfkfSlSq4T\nwVMQBEHQCRWO0GjoGb5jbfYcOjyg6NQ1PavXcEbPpawbeh100keVLCKmSFpojixyBGJmbwCrSnrS\nzP6Cu43fmzKn90lapIrrRNkuCIKgRzCzycAFqnCwb/MIjaqCpgYpa3Wtmf0WeKdsFquJnTI/z4M7\nkO+Jl3NKYz7I96PkD5v9XQnJl4Dt8NluzWxHn0P2QpS3F/hH5WlgSeBJ4DFgKzxLti595e2uieAp\nCIKgd1gc7015HvgPPOj5Y5eadYzQaAQjR+PjQhbHs0+Pm9lEYIakwoNxW5Qm/ysNs92VksN2zewz\neJ/PcuT7UpXJaE3Ee5o2w3uehO8824a+ESpbApUFwv8gXI5vkLgDOB24IFlYLMvAQLU0UbYLgiDo\nIczsg/hOoy8BG+GlsAvwbeBPlNSsdIRG0jwOzwgdhzelfzJtf98VOFTS+mV0W1xrReBPZctfZnY/\n8Gc8e/UXMqXBsn5EyZbhQNxZvFG2PD1r39DLpL/X9cjP6P2yAv31cUPTRyRd3a3ebN0InoIgCHqT\n5PnzReCrwBhJXVcbqhihkXQeBfaXdFPGO2gV4A+SPtTtWtN1FsBHoHxO0sdKaryR1pc3yHdYUZcF\nQh2Y2XZ4EN4oTzavW1XZCtRBlO2CIAh6EDObB1gH+DS+VftvFUlXMUIDPIOVF4zMhfcqFSYncDBg\nYeBN4MtlNBN34P1OlQVPZvYesGTWtNLMFgOe66K5vTYLhBo4GTgH+JYqHOKbBhaPJT+bdVoV14jg\nKQiCoIdIPTRfAnbGe3Euw5uQb+5CM2+ExreBS7pY6jS8rJgtJX4BuK+k5qGZn9/HTT7v6GQ+XhtO\nB042syVwN+zssNkpJTSzvVMN5gNmldBrMBY4ZhALhCm47UJHwVON2aylcbuHKgOnvfCxPrNwA9N+\n2Sx85mP314myXRAEQW9gZk/jQ2yvw8shV0t6u0vNWkZomNkOwPl4Se04vJ/oY8AewLZdDsetlGSB\nkGX2AOMiWaLkNwTevHws8HrTy6OAjYHlJa1Vcq2VWyCY2WG0zmZNwrNZXwEOKpLNSk72F0v6z07P\n6UDzKeAM4PtlLS86ITJPQRAEvcMEfDdcN1mWLJWP0ADfGWdm2+JB0xv42u8Ftus2cDKzBckfzVEm\nQwQeHFTFYelfw3fVvdf02ixgBn277cpQhwVC5dmsxG9wl/mPk5/Ru6qAVoMF8YCstsAJIvMUBEEQ\n9AhpvMt5eEZkAMPMJPMWYHzFgS5mti/wc3zm3AALBElnm9kRwHqSdu1QsxZDzxYZvQaFMnpNmj8C\nXpL0g6LnFiEyT0EQBEGv8GNgEbxJ/re4aebiwDH4eJmuSBmSvIxW4QyJpM26XU8L3clpBt2BwHj6\nLBA2aVggSCo6k7AWQ09Jcw3+rsJ8E/hvM/ss+dmsw6u4SARPQRAEwZBTUxPy5vhcvLtTVuMJSTeY\n2av4TfU3Jde6Im6+uBp9vU6NtUIJk0wzG4UPbB5H/q6wzcusNZ17G3Bb2fNzqN3Q08zm77Y/L/FN\nfOTPw+nnbMN4JUTwFARBEMwJ6thSvxDQ2Pr/MvBh3NzyT8CnuljrqcB0YAt8cPF6eGP+ycDXu9Dc\nCw/optLFjd3MPiDp1cbzdu9tvK8INWWzGgHkt0gu82a2cvL6Ku0yj2cYvyrpvBLndkwET0EQBMGc\noI4m5IfxHXszgD8C+5vZDPzm/Jcu1ro+sLmk51NG631Jt5rZN/Gt72V2xu0G7CLpf7pYV4OXzazh\nGfUK+YGYUX6UTB3ZLPDgeU/gG/T/HU/FbSfKBE/vUP06BxDBUxAEQQ9hZl/Bg4UVgPUlPWFmhwLT\nW8x+60SzjhEaWwNH5hy/Cc/ogDc9F2n8/TE+FBbgBOBa3JtqFp7lKcso+iwFXgCWwgO1J/BgrQyz\nqM50c3P6eo8q6aWqO5uV2APYL7nMNwfRfwRWKal5KnCQmR1SwaDplkTwFARB0COY2dfwctiP8f+r\nb2QZXsH/T75w8DTYCA2gbPBUeROypAubnt9jZsvhN+EnuxxmPBVYHS/Z3QF8w8xmAfulY2U4GTjE\nzA7s9iYv6X/znndJ7dksanCZx4P8zYFt00DobMP4+JK6/YjgKQiCoHc4CNhX0hVmdlTT8buBk0pq\n1jJCg4qbkNM4modwg80HAdJ6761grd/BAzlwQ8//Bn6PO1h3tN0/h7F4luhz3d7kzWz1Tt9bwOuq\n8mxWDnW4zL+Cu+rXSgRPQRAEvcMK5N903qHv5l+UykdoQPVNyJL+bmbzV7nGJu3rmp4/CqxiZosC\nL3eRNXoF38FXBffTfxdgKzrOEtWUzcoyATjfzJbGs03jzWy2y3wZQUn/WuH6WhLBUxAEQe8wHViT\ngf8n/1ngwZKa1+EDhsuWp1pSQxPyT4EjzWwfSe9WqDsASS8N/q6251d5k6/SAR2oLZuVPa8Wl3kz\nmxvYFFgJHyf0WhoW/Kqk19ue3CERPAVBEPQOpwA/TRkYA9Yzsy/i3jf7lNSsY4QGZvYe0OipaT6+\nGPBcSTfwdXHfpK3M7E/4Dbl5raX6XdL3eRBevsprmi9lg1DVTV5SNliugsqzWbknS7fi5dlKSH1u\n1+JmpvMBN+B9c0emn7sZfTObCJ6CIAh6BEm/MLO38B6dBYGLgGeAQyRdXFK2sYX8uLxLUv7G2eqm\nPB++E60MrwCXljy3HWcDWwH/RV9/VldUeZM3s+07fW+BYLfybNYQcSre47cG3pPW4HKKzd1rS8y2\nC4Ig6EHScNzR2czOnMbMDk5PJwHH0mcBAB6IbQwsL6mMd1ItmNlMYJtUZqxK8wo8WNobv8mvkQwi\nNwUmSxpTQKvTIbil5sVVSRtn+QFIWrSE/gvAhpIeNrPX6PtelwemSVqwqGYekXkKgiDoQVKDd6VN\n3hWN0DisIYdnV95rem0WbnBZqrRiZjfjw3ZfyRz/AHBFFyNPnqHg3LYOGIvf5GeZ9UvCzcCb9Dum\njhlxNWWzwC0zGiyGzx28DvhDOrY+7gE2sYBmM6PIz4YuQ4W/w8g8BUEQ9Ahmdh+dzYu7pYBmvxEa\nQBUjNDCzW/BA5+Uy57fQfB9YIqeP6p+BZySV8g4ys8/hTucHVNVflDIwG0qalsmQjAUulbR4Fdfp\nYn21Z7PM7FLgFkk/yRw/ENhC0o4lNH8NzJS0X/peVweexz3OnqyqUb+OicZBEATBnOFaYEW8UfoW\n4Ld4WWwl4C7cfftGM9uhgObRuDv3N+jfizSV8k3oSNqsqsDJzFZv2h328cbP6bEWXhp7potL3A3M\nDzxuZq+Z2UvNj5Ka19M/CyMzG407o3c1ssXMNjGzq83sUTN7xMyuMrONimhImqvDRzdlwK3xv9ks\n1+JzBMtwBLBhssGYH+/7m4Fn8/Ic7UsRmacgCIIewcwm4/93PTFz/BhgOUn7mtkJwOclrdOh5qPA\n/mmERnOGZBXgD5I+VHKto/CgbBz5O9g6LrGlLEnjZpbXiP4WcJCkc0qu9Ua8sfts4G9ksnuSzi+h\nuQxerjJgDB6gjcHHv2xcpFct9ZGdJeltM/sycC5uFHlb0t8A2AnYS9JFRddaF2b2BO4hdnLm+BHA\nwZKWK6k7Nz47cHVgNG5/cKGkt7pcct81IngKgiDoDVJj89rJyLH5+EeBeyQtkoKeuyQt3KHmW8Aq\n8hl5zcHTx4E7JY0uudaf4MHTb/ChvdmA5LCc01ppLYcHCY/jLuXPN708C7c+eC/v3A7138TnBP6x\nrEYL3blxh/I16OImb2bTgXUkvWhmDwFnSpqUec/huPv8qiXXugnwdWBV/Hf1IHCipN+X0UuaewG/\nAK7Bx94I+AzuS7avpPPKatdNNIwHQRD0Dm/jWYbsvLAN0mvgGZ53CmjWMUIDPDOwi6SuSlTQz+eo\nrlaUh4AFqhZNRp4Xpkc3Os22AisAV+e87Srge51qtslmnUZfNusmMyudzZJ0npk9iPeTNVzmpwFj\nJd1RYK3bA9ckl/nBGt1fBx6S9GyZNTeI4CkIgqB3OB04w8zWxnucGvPi9qHvxrk1xYKeykdoJGaR\nPxR2OHIUcLKZHU2+Ueirc2RVCTO7FxiXesiewkuh2e92XHqtUw7Dg7q38R1x38hks05N2axj8b6i\nUqQgafey5yeuAJYAnkvPB+M9M8t+nkJE2S4IgqCHMLPd8XlxH0uHHgZOb2QHzGwBfIdUx5YDaQfY\n8fQvL02QdH0X6zwCb24/UMP8RtS08yy7TmN4eCcdj5fQ3jSzrwE/xoc5346veSxeIj1E0pkl9N8B\nPtGiHDxVUumZgmY2F/BR8vvefldWt8315gW+BHxf0pKldYb532wQBEHQg5jZ5fi4k5eABxiYzSk1\nSqUOUr9PS1Tf4NxSmNlO+K6zRn9Toz/pygIas7NZadPAidnAy8z2B75exNAzc/5n8KxVo2etmdqC\nUjNbGDhB0uFlNaJsFwRBEMwJXsFHZgx7hltwNBiSLqf77/ZK+nrjTgZOM7M1yclmdXGNM/Bdhp8n\nZ9NAXUh6DSgdOEFknoIgCEY0yWdoZUkvDDb6otNxF3WP0BiJJJ+k/fFS479IesbMvgJMlw+3HRYk\nu4oLqg74qshm5Wi+ge/eHCm9b7OJzFMQBMHI5jD6xk4c2u6NBah7hAYwe6v+priJ50WSXjOzpYBX\nJb3e9uR8vVZBX9Zh/dyCujsDv8IbqD+FD+4FWAR3X99muKwVd4G/zsyeB/4Dtz7o2mKhomxWljvw\nfqcRFzxF5ikIgqAHSIHIl4DrJP2tQt3KR2ik85fDnaSXxYORxtiXU4H5JBWeb2dmh+GO6NcAd+J9\nNOvivkGT8G38X8ENMycX0L0PmCTplxmvq7XwLfJLDJe1Ju0PArvgfw8b4VYLF+ABauHxMjVns74D\nnEj+LsYpVV6vSiJ4CoIg6BGSmeOqZW6QbTRfB9ZssdPq/i5MMq/AM2Z7Ay/SF5BsCkwu04ScAr0b\nJJ2ROb4/sJWknc3sIGA/SasV0H0T+LikGZngaUVgWpndZnWtNec6ywBfBL4KjJFUuOJkZlcBW+Hm\no5Vlsyx/fp4YJrsY2xGz7YIgCHqHO4G1KtZ8EcibhbdDeq0sY4HvSJqVOT4Dn0NWhq2BG3OO35Re\nA58bt2JB3b/i5aUsY3FX8zLUtdbZmNk8wDrAp4Hl8dEyhZG0Pe6jNBH3DbvXzB4ws2+mDGJZVsh5\nrNj0b2HM7OXs7MH0eNHMnjGz/zWzrocDR89TEARB7/Az3MxxGeAefEDwbEqWQY4HfpEyQgNGaHSx\n1lHpkWUZ+nq4ivISsB1e9mpmu/QawEIl9CfjppBfxT//Uma2PnASbiI6nNaKmW2Gl+x2xr/jy5Lu\nzSXXiqRXgLOAszLZrImUjCWqzJA2MYHW5dCf4oHZz81s7qLl0GYieAqCIOgdLk7/ntZ0bHYZhPxg\npS1VjdDI4Xq8MX2/xqXMbDRwAp5xKcNE/Ma4GX7jbDisbwM0eqi2BIr27vwAr9TcBCwI/A7fxn9S\nthdsTq/VzJ7Gm/yvw3cHXl3EELUD/UqyWRnNj+O9b/M2H5d0VQm5scAxg5RDp+B/z6WDp+h5CoIg\n6BEGK6HU9H/6pUjZi+vwYGwM7vczBngB2FjScyV1N6TPYd3wZunTJd1ewZrnxct3o/Fep8I7Aute\nq5ntB1ySRrVURots1oXAzZLyepc60VwR38G3Gn1BPuk5ZXqeOunRM7OVgCmSFiqzbojgKQiCIBiE\nukZopB2Cu9J/7MuFkt4qv9pqMLPLgL0kvZqet+N13CX9DEkz61/d0JLJZl1IRdksM7saeA8v/z6O\nZ94Ww005vy7p9yU0n8R3Rk7KHD8MOEzSsma2OnB9mV2SDaJsFwRB0COY2R7tXpf0yxKabUdoUKIU\n2LSed/Gb8YVlNczsA43BvGb2gUGuV2SA70z6fJgGC4jmw0ttGwLbt3pTjWutmwnUkM3C/cI2l/R8\n2nn3vqRbzeybeOm5zOaHukq3/YjMUxAEQY+QjBebmQfv0ZkFvFnGDdzM7gf+jDeODxihMaczLWb2\nHrCkpOfSDTjvplb71vfUt3NXu1LQcFnrcCH9va6dbB8eA/aRdEsqq/1J0oIldWsr3TaIzFMQBEGP\nIOlD2WNmNgb4OW5EWIYxwBeG8QiNzenbnbbZHFzHw8AGg7xnuKx1uDAVWB0v2d0BfMPMZuGbCMpa\nQCDpNuC2SlbYgsg8BUEQ9Dhmtg7uEL1KiXNvBn4k6drqVxb8I2NmWwMLSbosNXT/N7Ay7h+2q6SO\nrBXmRDk0Mk9BEAS9z7vAUiXPPR33jlqCYThCIzX/dkSsdXgh6bqm548Cq5jZosDLKpbZednMlkw7\nNF+hTTmULnr0+olF5ikIgqA3MLNss7IBS+L9H09J+lwJzWE9QqOpdyjbzJ4l1tqjmNkmwG2S3k3P\nW1LVfL4InoIgCHqEnEBH+Dyym4EjJP2lhGYt3lGpWTjvBiTgbeBR4DxJ53azvn7Cc9jnaiStdSgw\ns/mBg/D+rzwbjE/NiXV1QpTtgiAIegRJlc8rrfEmXskYjZEUZIyktQ4RZ+MDh/+LPluBwsyJcmhk\nnoIgCIJBqXiEBmZ2KXDDIGM0DgL2k7RaG52WvkpZyq61KkbSWocCM5sJbJN2x3WjM+Tl0AiegiAI\neog09mR78gOdw0voVT5CI+lWMkajRU9WHnO8j2gkrXUoMLNpwG7dZoPmRDk0ynZBEAQ9gpmNA67C\nPXJWwX10lscDnntLyp4KTAe2IGeERhfLfQnYDpiUOb4dfV5ICwGvtROpo1RZFyNprUPEEcAPzeyA\nboKaOVEOjeApCIKgd/g+cJKk483sNXyI63P4+JOyPk11jNCAIRqjEQxr7gbmBx43szcZaIPRkSP+\nnCiHRtkuCIKgR0gB05qSHku72cZKesDM1gCulLR8Cc1aRmgk7crHaKSt6l8HVsUDsgeBE8sMma2b\nkbTWOjCzG/Hy8tnA3xg4+uf8DnWGvBwamacgCILe4Q18SC34HLqVgAfSz/9UUrOWERpQzRgNMzsY\nOEvS22b2ZeBc4DI8K2b4yJSbzGwvSRd1c61uGUlrHSI2ANaX9MduROZEOTQyT0EQBD2CmV0B/EbS\nZDM7CdgBOA8Yj7s2b1FCs5IRGkmr8jEaZjYdWEfSi2b2EHCmpEmZ9xwO7Ctp1U7XWgcjaa1DgZnd\nC/ybpP+b02spSgRPQRAEPULaGTda0hQzWwhv6t4AeAQ4vKrG2pIjNDCz94AlJT3XtL18wNsoWV4x\ns3eAT7TYwTdV0vxFNetiJK21LsxsK+B43O8rb/RPqTl0Q1EOjbJdEARBD2Bmo4BlgCkAkt6gr/G6\nUiS9NPi7ctmcvp10m1WxlpS9GCfpZeApYBzuTt7MuPTaHGUkrXWIaGxiuClzvNAcujlRDo3gKQiC\noAeQ9J6ZXY//3/YrVelWOUKjea5YVTPGgCuBd9Lzk4HTzGxN4Hb8BjwW2As4pKLrdcNIWutQUEkA\nDRyG7yh9GzgG+EamHHpqKoceC1QSPEXZLgiCoEcws7uBIyVl/0++G80L6Ruhkbcj6oQCWrWP0TCz\nnXD/oEbPUKNkc2UZvToZSWsdKQxVOTSCpyAIgh7BzD6Lez0dC9yD776bTZkekqpGaCStIR+jEQxv\nzGwjYH9gReBfJD1jZl8Bpku6tUON2eVQM3sUD0DPzLxnf+DrksZUse4o2wVBEPQO/5P+vYr+GaJC\nPSQZnmEQl+8CrFCRTi5mNhm4oMKSYG2MpLXWhZntDPwKL7l9ij6bjUWAb+GGqZ0w5OXQyDwFQRD0\nCGmXUUvK3KjN7HPAwUBXIzSGAjO7Ci8xPg/8B3Bhtx5CdTGS1loXZnYfMEnSL5PB6xrJjHUt4BpJ\nS5TUrb0cGsFTEARBj2BmywJPZS0EzMyAj0h6soTmh4H/BDYGSo/QSFq1j9Ewsw8CuwBfAjbCXcsv\nAC4absHfSFprHaSRLB+XNCMTPK0ITBvOdg0RPAVBEPQIzT5KmeOLAc+V9E6qZIRG0hrSMRpmtgzw\nReCrwBhJw7ZVZSSttSrM7HFgP0k3ZoKnPYCjJH28hOaQlEN7/pcTBEHwD0SjtynLaHwbdxkqGaEB\nQztGw8zmAdYBPg0sjwd+w5KRtNaKmYzbCHwV/7tdyszWB04CJpTUXBy4zsxqLYdG8BQEQTDCMbNT\n0lMBE1M5pMEo/KZ8f0n5h4AFuljekGJmm+FlsJ3xz34ZsB3Q8RiZoWIkrbUmfoD7ht0ELAj8Dm/8\nPknST8oISto+Uw49Io3CqbQcGmW7IAiCEY6Z3ZKebgL8AZjV9PIsYAZ+Q3qkhHYtIzSSdqVjNMzs\naWAx4Dp8B9fVkspm3GplJK21bsxsXuCjeIZ0mqTXK9SupRwawVMQBEGPYGbnAod0E9DkaDb6lLI3\ni8Iz6NqM0biNvjEaOwGlxmiY2X7AJWn8ybBmJK21SszsMvz3+2p63o7XgQeAMyTNLHGteYDPA19O\n/74kaemiOrnaETwFQRAErajS/sDMpgPrSHoxlVLOzIzRII3R2FfSqrkiwYgmBfgHS3otPW/HfMD6\nwJ8kdbxTs0U59ELgZkmdblpof40InoIgCIKhZqjGaAQjGzP7OHCXpIU6fP+QlEOHbOdDEARBMDIx\ns43M7AIzu93Mlk7HvmJmYwvq3GtmH0o/PgWMy3nbuPRaEAA8jJdzO2UCsJSkHSVdUlcfWey2C4Ig\nCFpS4QgNmANjNIKRjaT3gI6tBiSdVeNyZhNluyAIgqAldY3QSNq1j9EIgjqI4CkIgiBoyUgeoREE\ndRE9T0EQBEE7/op78GQZCzxeVtTMJg+2ky8IhisRPAVBEATtaIzQ+DR9IzR2x0do/KwL3cYYjafM\n7EdmtkYFaw2CISHKdkEQBEFLzMzwxvBv4iM0oG+ExrFdajeP0dgIHwVT6RiNIKiDCJ6CIAiCQalz\nhEbSr2WMRhDUQfxxBkEQBP3oZISGJ6SALkdoJK15gHXwAcbLA38roxMEQ0X0PAVBEARZZtI3y27m\nII+5gQNwL6hCmNlmZjYZD5bOB14DtgM+0uX6g6BWomwXBEEQdEXRERrpnCEZoxEEdRDBUxAEQdAV\nZjYK+KSkjp2gzWw/4BJJL9e3siCohwiegiAIgiAIChA9T0EQBEEQBAWI4CkIgiAIgqAAETwFQRAE\nQRAUIIKnIAiCIAiCAkTwFARBEARBUIAInoIgCIIgCAoQwVMQBEEQBEEB/j/iwL7AkD8iVQAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5780b2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "visual.doc_topic_heatmap(doc_topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##### Visualize topic distribution in a single document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGGCAYAAACNCg6xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nipw4mZmZmVX0/5aGv3gXNQSeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5c1fb0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual.plot_doc_topics(doc_topic, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#heatmap = visual.make_heatmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#visual.save_heatmap(\"./visualizations/heatmap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#vis = collection.Visualization(lda_model, corpus, dictionary, doc_labels, interactive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#vis.make_interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#vis.save_interactive(\"./visualizations/interactive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('traurige', 'völlig'),\n",
       " ('traurige', 'entgegen'),\n",
       " ('traurige', 'new'),\n",
       " ('traurige', 'begleitete'),\n",
       " ('traurige', 'fällt')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_topics = evaluation.topic_segmenter(model, type2id, 10)\n",
    "segmented_topics[0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "traurige                     {4}\n",
       "später                 {1, 2, 4}\n",
       "linien                       {4}\n",
       "derselbe                     {3}\n",
       "jemanden                     {2}\n",
       "fällt                        {4}\n",
       "italienische                 {2}\n",
       "new                          {3}\n",
       "gefestigte                   {0}\n",
       "daß              {0, 1, 2, 3, 4}\n",
       "fingerspitzen                {0}\n",
       "schwankt                     {3}\n",
       "völlig                    {2, 4}\n",
       "begleitete                   {2}\n",
       "entgegen               {1, 2, 4}\n",
       "groß                      {1, 4}\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "umass_probability = evaluation.token_probability(doc_tokens, segmented_topics)\n",
    "umass_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.038163048300435064"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "umass = evaluation.calculate_umass(segmented_topics, umass_probability, doc_tokens, 10)\n",
    "umass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/bs4/builder/_lxml.py:250: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  self.parser.feed(markup)\n"
     ]
    }
   ],
   "source": [
    "wiki_list = evaluation.wikipedia_table_crawler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wiki_corpus = evaluation.wikipedia_crawler(wiki_list, size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "uci_probability = evaluation.token_probability(wiki_corpus, segmented_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pmi = evaluation.calculate_pointwise_mutual_information(segmented_topics, wiki_corpus, uci_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uci = evaluation.calculate_uci(pmi, wiki_corpus, 10)\n",
    "uci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "![success](http://cdn2.hubspot.net/hub/128506/file-446943132-jpg/images/computer_woman_success.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}