IntegrationTest_txt_gensim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dariah_topics import preprocessing as pre\n",
    "from dariah_topics import visualization as visual\n",
    "from dariah_topics import evaluation\n",
    "from dariah_topics import doclist\n",
    "from dariah_topics import meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Liste mit Dateinamen erzeugen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_txt/Doyle_AScandalinBohemia.txt',\n",
       " 'corpus_txt/Doyle_AStudyinScarlet.txt',\n",
       " 'corpus_txt/Doyle_TheHoundoftheBaskervilles.txt',\n",
       " 'corpus_txt/Doyle_TheSignoftheFour.txt',\n",
       " 'corpus_txt/Howard_GodsoftheNorth.txt',\n",
       " 'corpus_txt/Howard_SchadowsinZamboula.txt',\n",
       " 'corpus_txt/Howard_ShadowsintheMoonlight.txt',\n",
       " 'corpus_txt/Howard_TheDevilinIron.txt',\n",
       " 'corpus_txt/Kipling_TheEndofthePassage.txt',\n",
       " 'corpus_txt/Kipling_TheJungleBook.txt',\n",
       " 'corpus_txt/Kipling_ThyServantaDog.txt',\n",
       " 'corpus_txt/Lovecraft_AttheMountainofMadness.txt',\n",
       " 'corpus_txt/Lovecraft_TheShunnedHouse.txt',\n",
       " 'corpus_txt/Poe_EurekaAProsePoem.txt',\n",
       " 'corpus_txt/Poe_TheCaskofAmontillado.txt',\n",
       " 'corpus_txt/Poe_TheMasqueoftheRedDeath.txt',\n",
       " 'corpus_txt/Poe_ThePurloinedLetter.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_txt = \"corpus_txt\"\n",
    "#path_txt = \"grenzbote_plain/*/\"\n",
    "#path_txt = \"wiki/\"\n",
    "\n",
    "doclist_ = doclist.PathDocList(path_txt)\n",
    "doclist_txt = doclist_.full_paths(as_str=True)\n",
    "doclist_txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Liste mit Dokumentenlabels erzeugen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Doyle_AScandalinBohemia',\n",
       " 'Doyle_AStudyinScarlet',\n",
       " 'Doyle_TheHoundoftheBaskervilles',\n",
       " 'Doyle_TheSignoftheFour',\n",
       " 'Howard_GodsoftheNorth',\n",
       " 'Howard_SchadowsinZamboula',\n",
       " 'Howard_ShadowsintheMoonlight',\n",
       " 'Howard_TheDevilinIron',\n",
       " 'Kipling_TheEndofthePassage',\n",
       " 'Kipling_TheJungleBook',\n",
       " 'Kipling_ThyServantaDog',\n",
       " 'Lovecraft_AttheMountainofMadness',\n",
       " 'Lovecraft_TheShunnedHouse',\n",
       " 'Poe_EurekaAProsePoem',\n",
       " 'Poe_TheCaskofAmontillado',\n",
       " 'Poe_TheMasqueoftheRedDeath',\n",
       " 'Poe_ThePurloinedLetter']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_labels = doclist_.labels()\n",
    "doc_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metadaten (alternativ zu `PathDocList`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>basename</th>\n",
       "      <th>filename</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Doyle</td>\n",
       "      <td>Doyle_AScandalinBohemia</td>\n",
       "      <td>corpus_txt/Doyle_AScandalinBohemia.txt</td>\n",
       "      <td>AScandalinBohemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Doyle</td>\n",
       "      <td>Doyle_AStudyinScarlet</td>\n",
       "      <td>corpus_txt/Doyle_AStudyinScarlet.txt</td>\n",
       "      <td>AStudyinScarlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Doyle</td>\n",
       "      <td>Doyle_TheHoundoftheBaskervilles</td>\n",
       "      <td>corpus_txt/Doyle_TheHoundoftheBaskervilles.txt</td>\n",
       "      <td>TheHoundoftheBaskervilles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doyle</td>\n",
       "      <td>Doyle_TheSignoftheFour</td>\n",
       "      <td>corpus_txt/Doyle_TheSignoftheFour.txt</td>\n",
       "      <td>TheSignoftheFour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Howard</td>\n",
       "      <td>Howard_GodsoftheNorth</td>\n",
       "      <td>corpus_txt/Howard_GodsoftheNorth.txt</td>\n",
       "      <td>GodsoftheNorth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Howard</td>\n",
       "      <td>Howard_SchadowsinZamboula</td>\n",
       "      <td>corpus_txt/Howard_SchadowsinZamboula.txt</td>\n",
       "      <td>SchadowsinZamboula</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Howard</td>\n",
       "      <td>Howard_ShadowsintheMoonlight</td>\n",
       "      <td>corpus_txt/Howard_ShadowsintheMoonlight.txt</td>\n",
       "      <td>ShadowsintheMoonlight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Howard</td>\n",
       "      <td>Howard_TheDevilinIron</td>\n",
       "      <td>corpus_txt/Howard_TheDevilinIron.txt</td>\n",
       "      <td>TheDevilinIron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kipling</td>\n",
       "      <td>Kipling_TheEndofthePassage</td>\n",
       "      <td>corpus_txt/Kipling_TheEndofthePassage.txt</td>\n",
       "      <td>TheEndofthePassage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kipling</td>\n",
       "      <td>Kipling_TheJungleBook</td>\n",
       "      <td>corpus_txt/Kipling_TheJungleBook.txt</td>\n",
       "      <td>TheJungleBook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kipling</td>\n",
       "      <td>Kipling_ThyServantaDog</td>\n",
       "      <td>corpus_txt/Kipling_ThyServantaDog.txt</td>\n",
       "      <td>ThyServantaDog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lovecraft</td>\n",
       "      <td>Lovecraft_AttheMountainofMadness</td>\n",
       "      <td>corpus_txt/Lovecraft_AttheMountainofMadness.txt</td>\n",
       "      <td>AttheMountainofMadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lovecraft</td>\n",
       "      <td>Lovecraft_TheShunnedHouse</td>\n",
       "      <td>corpus_txt/Lovecraft_TheShunnedHouse.txt</td>\n",
       "      <td>TheShunnedHouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Poe</td>\n",
       "      <td>Poe_EurekaAProsePoem</td>\n",
       "      <td>corpus_txt/Poe_EurekaAProsePoem.txt</td>\n",
       "      <td>EurekaAProsePoem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Poe</td>\n",
       "      <td>Poe_TheCaskofAmontillado</td>\n",
       "      <td>corpus_txt/Poe_TheCaskofAmontillado.txt</td>\n",
       "      <td>TheCaskofAmontillado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Poe</td>\n",
       "      <td>Poe_TheMasqueoftheRedDeath</td>\n",
       "      <td>corpus_txt/Poe_TheMasqueoftheRedDeath.txt</td>\n",
       "      <td>TheMasqueoftheRedDeath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Poe</td>\n",
       "      <td>Poe_ThePurloinedLetter</td>\n",
       "      <td>corpus_txt/Poe_ThePurloinedLetter.txt</td>\n",
       "      <td>ThePurloinedLetter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       author                          basename  \\\n",
       "0       Doyle           Doyle_AScandalinBohemia   \n",
       "1       Doyle             Doyle_AStudyinScarlet   \n",
       "2       Doyle   Doyle_TheHoundoftheBaskervilles   \n",
       "3       Doyle            Doyle_TheSignoftheFour   \n",
       "4      Howard             Howard_GodsoftheNorth   \n",
       "5      Howard         Howard_SchadowsinZamboula   \n",
       "6      Howard      Howard_ShadowsintheMoonlight   \n",
       "7      Howard             Howard_TheDevilinIron   \n",
       "8     Kipling        Kipling_TheEndofthePassage   \n",
       "9     Kipling             Kipling_TheJungleBook   \n",
       "10    Kipling            Kipling_ThyServantaDog   \n",
       "11  Lovecraft  Lovecraft_AttheMountainofMadness   \n",
       "12  Lovecraft         Lovecraft_TheShunnedHouse   \n",
       "13        Poe              Poe_EurekaAProsePoem   \n",
       "14        Poe          Poe_TheCaskofAmontillado   \n",
       "15        Poe        Poe_TheMasqueoftheRedDeath   \n",
       "16        Poe            Poe_ThePurloinedLetter   \n",
       "\n",
       "                                           filename                      title  \n",
       "0            corpus_txt/Doyle_AScandalinBohemia.txt          AScandalinBohemia  \n",
       "1              corpus_txt/Doyle_AStudyinScarlet.txt            AStudyinScarlet  \n",
       "2    corpus_txt/Doyle_TheHoundoftheBaskervilles.txt  TheHoundoftheBaskervilles  \n",
       "3             corpus_txt/Doyle_TheSignoftheFour.txt           TheSignoftheFour  \n",
       "4              corpus_txt/Howard_GodsoftheNorth.txt             GodsoftheNorth  \n",
       "5          corpus_txt/Howard_SchadowsinZamboula.txt         SchadowsinZamboula  \n",
       "6       corpus_txt/Howard_ShadowsintheMoonlight.txt      ShadowsintheMoonlight  \n",
       "7              corpus_txt/Howard_TheDevilinIron.txt             TheDevilinIron  \n",
       "8         corpus_txt/Kipling_TheEndofthePassage.txt         TheEndofthePassage  \n",
       "9              corpus_txt/Kipling_TheJungleBook.txt              TheJungleBook  \n",
       "10            corpus_txt/Kipling_ThyServantaDog.txt             ThyServantaDog  \n",
       "11  corpus_txt/Lovecraft_AttheMountainofMadness.txt     AttheMountainofMadness  \n",
       "12         corpus_txt/Lovecraft_TheShunnedHouse.txt            TheShunnedHouse  \n",
       "13              corpus_txt/Poe_EurekaAProsePoem.txt           EurekaAProsePoem  \n",
       "14          corpus_txt/Poe_TheCaskofAmontillado.txt       TheCaskofAmontillado  \n",
       "15        corpus_txt/Poe_TheMasqueoftheRedDeath.txt     TheMasqueoftheRedDeath  \n",
       "16            corpus_txt/Poe_ThePurloinedLetter.txt         ThePurloinedLetter  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md = meta.fn2metadata('corpus_txt/*.txt')\n",
    "md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corpus laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus_txt = pre.read_from_txt(doclist_txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO preprocessing: Accessing TXT documents ...\n"
     ]
    }
   ],
   "source": [
    "doc_tokens = [list(pre.tokenize(txt)) for txt in list(corpus_txt)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "id_types = pre.create_dictionary(doc_tokens)\n",
    "doc_ids = pre.create_dictionary(doc_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sparse BOW Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sparse_bow = pre.create_sparse_bow(doc_labels, doc_tokens, id_types, doc_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>1202</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6804</th>\n",
       "      <td>2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4305</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>1753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19419</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "doc_id token_id      \n",
       "1      1202       270\n",
       "       6804      2750\n",
       "       4305        49\n",
       "       822       1753\n",
       "       19419       21"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_bow[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modeling with Gensim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving Sparse BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pre.save_sparse_bow(sparse_bow, \"gensim_txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Market Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO summa.preprocessing.cleaner: 'pattern' package not found; tag filters are not available for English\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import MmCorpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO gensim.matutils: initializing corpus reader from gensim_txt.mm\n",
      "INFO gensim.matutils: accepted corpus with 17 documents, 22232 features, 369906 non-zero entries\n"
     ]
    }
   ],
   "source": [
    "mm = MmCorpus(\"gensim_txt.mm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO gensim.matutils: initializing corpus reader from gensim_txt.mm\n",
      "INFO gensim.matutils: accepted corpus with 17 documents, 22232 features, 369906 non-zero entries\n"
     ]
    }
   ],
   "source": [
    "corpus = MmCorpus('gensim_txt.mm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": 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": "markdown",
   "metadata": {},
   "source": [
    "##### Remove Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "type2id = {value : key for key, value in id_types.items()}\n",
    "sparse_bow_collapsed = sparse_bow.groupby(sparse_bow.index.get_level_values('token_id')).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sparse_bow_hapax = sparse_bow_collapsed.loc[sparse_bow_collapsed[0] == 1]\n",
    "hapax = [type2id[key] for key in sparse_bow_hapax.index.get_level_values('token_id')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "len(hapax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO preprocessing: Finding hapax legomena ...\n",
      "INFO preprocessing: Finding stopwords ...\n",
      "INFO preprocessing: Removing features ...\n"
     ]
    }
   ],
   "source": [
    "hapax_from_remove = pre.find_hapax(sparse_bow, id_types)\n",
    "stopwords_from_remove = pre.find_stopwords(sparse_bow, id_types, mfw=75)\n",
    "\n",
    "#features_to_be_removed = set(hapax_from_remove + stopwords_from_remove)\n",
    "features_to_be_removed = stopwords_from_remove\n",
    "\n",
    "sparse_bow_short = pre.remove_features(sparse_bow, id_types, features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pre.save_sparse_bow(sparse_bow_short, \"gensim_txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO gensim.matutils: initializing corpus reader from gensim_txt.mm\n",
      "INFO gensim.matutils: accepted corpus with 17 documents, 22232 features, 192053 non-zero entries\n"
     ]
    }
   ],
   "source": [
    "mm = MmCorpus(\"gensim_txt.mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert sparse_bow to list of (doc, tokens) tuples (like doc2bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc2bow_list = []\n",
    "\n",
    "for doc in sparse_bow_short.index.groupby(sparse_bow_short.index.get_level_values('doc_id')):\n",
    "    temp = [(token, count) for token, count in zip(sparse_bow_short.loc[doc].index, sparse_bow_short.loc[doc][0])]\n",
    "    doc2bow_list.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(doc2bow_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO gensim.models.ldamodel: using symmetric alpha at 0.05\n",
      "INFO gensim.models.ldamodel: using symmetric eta at 4.49781855799937e-05\n",
      "INFO gensim.models.ldamodel: using serial LDA version on this node\n",
      "INFO gensim.models.ldamodel: running online LDA training, 20 topics, 10 passes over the supplied corpus of 17 documents, updating model once every 17 documents, evaluating perplexity every 17 documents, iterating 20x with a convergence threshold of 0.001000\n",
      "INFO gensim.models.ldamodel: -14.379 per-word bound, 21304.5 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 0, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #15 (0.050): 0.003*\"ex-prize\" + 0.003*\"cameras\" + 0.003*\"arduous\" + 0.003*\"chaos\" + 0.003*\"nonchalance\" + 0.003*\"locally\" + 0.003*\"slow-footed\" + 0.003*\"rajah\" + 0.003*\"im-which\" + 0.003*\"earthward\"\n",
      "INFO gensim.models.ldamodel: topic #16 (0.050): 0.003*\"ex-prize\" + 0.003*\"locally\" + 0.003*\"diagnosis\" + 0.003*\"typewriting\" + 0.003*\"slow-footed\" + 0.003*\"well-ascertained\" + 0.003*\"chaos\" + 0.003*\"bags\" + 0.003*\"indulging\" + 0.003*\"earthward\"\n",
      "INFO gensim.models.ldamodel: topic #19 (0.050): 0.003*\"chaos\" + 0.003*\"indulging\" + 0.003*\"arduous\" + 0.003*\"earthward\" + 0.003*\"slow-footed\" + 0.003*\"obliterated\" + 0.003*\"well-ascertained\" + 0.003*\"length\" + 0.003*\"diagnosis\" + 0.003*\"im-which\"\n",
      "INFO gensim.models.ldamodel: topic #2 (0.050): 0.004*\"chaos\" + 0.003*\"length\" + 0.003*\"diagnosis\" + 0.003*\"ex-prize\" + 0.003*\"panelling\" + 0.003*\"arduous\" + 0.003*\"nonchalance\" + 0.003*\"cameras\" + 0.003*\"indulging\" + 0.003*\"well-ascertained\"\n",
      "INFO gensim.models.ldamodel: topic #1 (0.050): 0.003*\"member\" + 0.003*\"chaos\" + 0.003*\"indulging\" + 0.003*\"sub-lieutenant\" + 0.003*\"locally\" + 0.003*\"footmarks\" + 0.002*\"bags\" + 0.002*\"punkah-towel\" + 0.002*\"rajah\" + 0.002*\"cameras\"\n",
      "INFO gensim.models.ldamodel: topic diff=5.870159, rho=1.000000\n",
      "INFO gensim.models.ldamodel: -10.952 per-word bound, 1980.5 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 1, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #0 (0.050): 0.005*\"length\" + 0.004*\"chaos\" + 0.004*\"obliterated\" + 0.003*\"ex-prize\" + 0.003*\"slow-footed\" + 0.003*\"footmarks\" + 0.003*\"member\" + 0.003*\"well-ascertained\" + 0.003*\"punkah-towel\" + 0.003*\"cameras\"\n",
      "INFO gensim.models.ldamodel: topic #1 (0.050): 0.003*\"member\" + 0.002*\"chaos\" + 0.002*\"indulging\" + 0.002*\"sub-lieutenant\" + 0.002*\"footmarks\" + 0.002*\"locally\" + 0.002*\"bags\" + 0.002*\"punkah-towel\" + 0.002*\"earthward\" + 0.002*\"rajah\"\n",
      "INFO gensim.models.ldamodel: topic #8 (0.050): 0.003*\"typewriting\" + 0.003*\"nonchalance\" + 0.003*\"cameras\" + 0.003*\"ex-prize\" + 0.003*\"footmarks\" + 0.002*\"obliterated\" + 0.002*\"injunction\" + 0.002*\"writhed\" + 0.002*\"diagnosis\" + 0.002*\"chaos\"\n",
      "INFO gensim.models.ldamodel: topic #15 (0.050): 0.005*\"nonchalance\" + 0.004*\"cameras\" + 0.004*\"ex-prize\" + 0.003*\"faintly\" + 0.003*\"locally\" + 0.003*\"gallop\" + 0.003*\"uncomfy\" + 0.003*\"bags\" + 0.003*\"rajah\" + 0.003*\"arduous\"\n",
      "INFO gensim.models.ldamodel: topic #2 (0.050): 0.004*\"mastodon\" + 0.004*\"chaos\" + 0.004*\"panelling\" + 0.003*\"locally\" + 0.003*\"diagnosis\" + 0.003*\"ex-prize\" + 0.003*\"typewriting\" + 0.003*\"nonchalance\" + 0.002*\"sentinels\" + 0.002*\"arduous\"\n",
      "INFO gensim.models.ldamodel: topic diff=2.633298, rho=0.577350\n",
      "INFO gensim.models.ldamodel: -9.815 per-word bound, 900.9 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 2, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #3 (0.050): 0.006*\"mastodon\" + 0.004*\"locally\" + 0.003*\"slow-footed\" + 0.003*\"bags\" + 0.003*\"earthward\" + 0.003*\"panelling\" + 0.003*\"nonchalance\" + 0.003*\"typewriting\" + 0.003*\"blue-black\" + 0.003*\"blindness\"\n",
      "INFO gensim.models.ldamodel: topic #10 (0.050): 0.002*\"well-ascertained\" + 0.002*\"ex-prize\" + 0.002*\"chaos\" + 0.002*\"locally\" + 0.002*\"earthward\" + 0.002*\"typewriting\" + 0.002*\"diagnosis\" + 0.002*\"sub-lieutenant\" + 0.002*\"im-which\" + 0.002*\"member\"\n",
      "INFO gensim.models.ldamodel: topic #5 (0.050): 0.002*\"indulging\" + 0.002*\"bags\" + 0.002*\"ex-prize\" + 0.002*\"locally\" + 0.002*\"member\" + 0.002*\"diagnosis\" + 0.002*\"slow-footed\" + 0.002*\"arduous\" + 0.002*\"lie-at\" + 0.002*\"up-and\"\n",
      "INFO gensim.models.ldamodel: topic #9 (0.050): 0.003*\"locally\" + 0.002*\"offence-at\" + 0.002*\"cameras\" + 0.002*\"member\" + 0.002*\"grain-land\" + 0.002*\"typewriting\" + 0.002*\"apply\" + 0.002*\"earthward\" + 0.002*\"indulging\" + 0.002*\"writhed\"\n",
      "INFO gensim.models.ldamodel: topic #13 (0.050): 0.005*\"nonchalance\" + 0.004*\"faintly\" + 0.003*\"bags\" + 0.003*\"uncomfy\" + 0.003*\"locally\" + 0.003*\"gallop\" + 0.003*\"sub-lieutenant\" + 0.003*\"apply\" + 0.003*\"grain-land\" + 0.002*\"cameras\"\n",
      "INFO gensim.models.ldamodel: topic diff=2.147151, rho=0.500000\n",
      "INFO gensim.models.ldamodel: -9.298 per-word bound, 629.3 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 3, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #13 (0.050): 0.004*\"nonchalance\" + 0.003*\"faintly\" + 0.003*\"bags\" + 0.003*\"uncomfy\" + 0.003*\"locally\" + 0.003*\"grain-land\" + 0.002*\"gallop\" + 0.002*\"apply\" + 0.002*\"sub-lieutenant\" + 0.002*\"typewriting\"\n",
      "INFO gensim.models.ldamodel: topic #10 (0.050): 0.002*\"well-ascertained\" + 0.002*\"ex-prize\" + 0.002*\"chaos\" + 0.002*\"locally\" + 0.002*\"earthward\" + 0.002*\"typewriting\" + 0.002*\"diagnosis\" + 0.002*\"sub-lieutenant\" + 0.002*\"im-which\" + 0.002*\"member\"\n",
      "INFO gensim.models.ldamodel: topic #5 (0.050): 0.002*\"indulging\" + 0.002*\"bags\" + 0.002*\"ex-prize\" + 0.002*\"locally\" + 0.002*\"member\" + 0.001*\"diagnosis\" + 0.001*\"slow-footed\" + 0.001*\"arduous\" + 0.001*\"lie-at\" + 0.001*\"up-and\"\n",
      "INFO gensim.models.ldamodel: topic #9 (0.050): 0.002*\"locally\" + 0.002*\"offence-at\" + 0.002*\"cameras\" + 0.002*\"member\" + 0.002*\"grain-land\" + 0.002*\"typewriting\" + 0.002*\"apply\" + 0.002*\"earthward\" + 0.002*\"indulging\" + 0.002*\"writhed\"\n",
      "INFO gensim.models.ldamodel: topic #17 (0.050): 0.002*\"ex-prize\" + 0.002*\"well-ascertained\" + 0.002*\"footmarks\" + 0.002*\"diagnosis\" + 0.002*\"nonchalance\" + 0.002*\"chaos\" + 0.002*\"arduous\" + 0.002*\"slow-footed\" + 0.002*\"obliterated\" + 0.002*\"in_ductions\"\n",
      "INFO gensim.models.ldamodel: topic diff=1.600805, rho=0.447214\n",
      "INFO gensim.models.ldamodel: -9.039 per-word bound, 525.9 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 4, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #5 (0.050): 0.001*\"indulging\" + 0.001*\"bags\" + 0.001*\"ex-prize\" + 0.001*\"locally\" + 0.001*\"member\" + 0.001*\"diagnosis\" + 0.001*\"slow-footed\" + 0.001*\"arduous\" + 0.001*\"lie-at\" + 0.001*\"up-and\"\n",
      "INFO gensim.models.ldamodel: topic #11 (0.050): 0.002*\"chaos\" + 0.001*\"earthward\" + 0.001*\"well-ascertained\" + 0.001*\"cameras\" + 0.001*\"length\" + 0.001*\"ex-prize\" + 0.001*\"slow-footed\" + 0.001*\"locally\" + 0.001*\"arduous\" + 0.001*\"lie-at\"\n",
      "INFO gensim.models.ldamodel: topic #8 (0.050): 0.001*\"typewriting\" + 0.001*\"obliterated\" + 0.001*\"cameras\" + 0.001*\"nonchalance\" + 0.001*\"footmarks\" + 0.001*\"ex-prize\" + 0.001*\"injunction\" + 0.001*\"writhed\" + 0.001*\"rajah\" + 0.001*\"diagnosis\"\n",
      "INFO gensim.models.ldamodel: topic #4 (0.050): 0.004*\"obliterated\" + 0.004*\"typewriting\" + 0.004*\"offence-at\" + 0.003*\"indulging\" + 0.003*\"cameras\" + 0.003*\"earthward\" + 0.003*\"locally\" + 0.003*\"collapsible\" + 0.003*\"bracelets\" + 0.003*\"slow-footed\"\n",
      "INFO gensim.models.ldamodel: topic #14 (0.050): 0.010*\"killing\" + 0.007*\"harry\" + 0.005*\"burglary\" + 0.005*\"sobbing\" + 0.005*\"nonchalant\" + 0.004*\"anomalous\" + 0.004*\"punkah-towel\" + 0.003*\"adjustments\" + 0.003*\"two-bang\" + 0.003*\"accredited\"\n",
      "INFO gensim.models.ldamodel: topic diff=1.195333, rho=0.408248\n",
      "INFO gensim.models.ldamodel: -8.889 per-word bound, 474.2 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 5, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #3 (0.050): 0.009*\"mastodon\" + 0.005*\"locally\" + 0.004*\"blindness\" + 0.004*\"panelling\" + 0.004*\"nonchalance\" + 0.004*\"bags\" + 0.003*\"old-maid\" + 0.003*\"necks\" + 0.003*\"inclining\" + 0.003*\"penguins\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO gensim.models.ldamodel: topic #15 (0.050): 0.007*\"nonchalance\" + 0.005*\"cameras\" + 0.004*\"faintly\" + 0.004*\"gallop\" + 0.004*\"ex-prize\" + 0.003*\"up-and\" + 0.003*\"uncomfy\" + 0.003*\"locally\" + 0.003*\"sub-lieutenant\" + 0.003*\"bags\"\n",
      "INFO gensim.models.ldamodel: topic #19 (0.050): 0.006*\"obliterated\" + 0.005*\"well-ascertained\" + 0.004*\"arduous\" + 0.004*\"im-which\" + 0.004*\"defined\" + 0.004*\"crest-fallen\" + 0.004*\"footmarks\" + 0.004*\"individualized\" + 0.004*\"negative\" + 0.003*\"revision\"\n",
      "INFO gensim.models.ldamodel: topic #4 (0.050): 0.004*\"obliterated\" + 0.004*\"typewriting\" + 0.004*\"offence-at\" + 0.003*\"indulging\" + 0.003*\"cameras\" + 0.003*\"earthward\" + 0.003*\"collapsible\" + 0.003*\"locally\" + 0.003*\"bracelets\" + 0.003*\"slow-footed\"\n",
      "INFO gensim.models.ldamodel: topic #12 (0.050): 0.001*\"diagnosis\" + 0.001*\"cameras\" + 0.001*\"locally\" + 0.001*\"chaos\" + 0.001*\"earthward\" + 0.001*\"well-ascertained\" + 0.001*\"typewriting\" + 0.001*\"nonchalance\" + 0.001*\"slow-footed\" + 0.001*\"footmarks\"\n",
      "INFO gensim.models.ldamodel: topic diff=0.899293, rho=0.377964\n",
      "INFO gensim.models.ldamodel: -8.797 per-word bound, 444.9 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 6, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #2 (0.050): 0.008*\"mastodon\" + 0.005*\"duplicate\" + 0.005*\"sentinels\" + 0.004*\"locally\" + 0.004*\"chaos\" + 0.004*\"panelling\" + 0.004*\"typewriting\" + 0.003*\"nightingale\" + 0.003*\"ex-prize\" + 0.003*\"throwing-stick\"\n",
      "INFO gensim.models.ldamodel: topic #12 (0.050): 0.001*\"diagnosis\" + 0.001*\"cameras\" + 0.001*\"locally\" + 0.001*\"chaos\" + 0.001*\"earthward\" + 0.001*\"associates\" + 0.001*\"well-ascertained\" + 0.001*\"typewriting\" + 0.001*\"nonchalance\" + 0.001*\"slow-footed\"\n",
      "INFO gensim.models.ldamodel: topic #1 (0.050): 0.001*\"member\" + 0.001*\"chaos\" + 0.001*\"indulging\" + 0.001*\"sub-lieutenant\" + 0.001*\"footmarks\" + 0.001*\"locally\" + 0.001*\"bags\" + 0.001*\"punkah-towel\" + 0.001*\"earthward\" + 0.001*\"rajah\"\n",
      "INFO gensim.models.ldamodel: topic #0 (0.050): 0.008*\"length\" + 0.005*\"chaos\" + 0.004*\"slow-footed\" + 0.004*\"hawklike\" + 0.004*\"obliterated\" + 0.004*\"in_ductions\" + 0.004*\"punkah-towel\" + 0.004*\"earthward\" + 0.004*\"well-ascertained\" + 0.004*\"injunction\"\n",
      "INFO gensim.models.ldamodel: topic #19 (0.050): 0.006*\"obliterated\" + 0.005*\"well-ascertained\" + 0.004*\"arduous\" + 0.004*\"im-which\" + 0.004*\"defined\" + 0.004*\"footmarks\" + 0.004*\"crest-fallen\" + 0.004*\"individualized\" + 0.004*\"negative\" + 0.003*\"revision\"\n",
      "INFO gensim.models.ldamodel: topic diff=0.681894, rho=0.353553\n",
      "INFO gensim.models.ldamodel: -8.739 per-word bound, 427.1 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 7, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #13 (0.050): 0.002*\"nonchalance\" + 0.002*\"bags\" + 0.002*\"faintly\" + 0.002*\"grain-land\" + 0.002*\"uncomfy\" + 0.002*\"apply\" + 0.001*\"locally\" + 0.001*\"accepted\" + 0.001*\"typewriting\" + 0.001*\"discharges\"\n",
      "INFO gensim.models.ldamodel: topic #14 (0.050): 0.011*\"killing\" + 0.008*\"harry\" + 0.006*\"burglary\" + 0.006*\"sobbing\" + 0.006*\"nonchalant\" + 0.004*\"anomalous\" + 0.004*\"punkah-towel\" + 0.004*\"adjustments\" + 0.004*\"two-bang\" + 0.004*\"accredited\"\n",
      "INFO gensim.models.ldamodel: topic #4 (0.050): 0.004*\"obliterated\" + 0.004*\"typewriting\" + 0.004*\"offence-at\" + 0.003*\"indulging\" + 0.003*\"cameras\" + 0.003*\"earthward\" + 0.003*\"bracelets\" + 0.003*\"collapsible\" + 0.003*\"locally\" + 0.003*\"slow-footed\"\n",
      "INFO gensim.models.ldamodel: topic #3 (0.050): 0.010*\"mastodon\" + 0.005*\"locally\" + 0.005*\"blindness\" + 0.004*\"panelling\" + 0.004*\"nonchalance\" + 0.004*\"bags\" + 0.004*\"old-maid\" + 0.004*\"inclining\" + 0.003*\"necks\" + 0.003*\"penguins\"\n",
      "INFO gensim.models.ldamodel: topic #10 (0.050): 0.001*\"well-ascertained\" + 0.001*\"ex-prize\" + 0.001*\"chaos\" + 0.001*\"locally\" + 0.001*\"earthward\" + 0.001*\"typewriting\" + 0.001*\"diagnosis\" + 0.001*\"sub-lieutenant\" + 0.001*\"im-which\" + 0.001*\"member\"\n",
      "INFO gensim.models.ldamodel: topic diff=0.520918, rho=0.333333\n",
      "INFO gensim.models.ldamodel: -8.700 per-word bound, 415.9 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 8, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #16 (0.050): 0.007*\"corrupted\" + 0.005*\"do-well\" + 0.005*\"wills\" + 0.005*\"diagnosis\" + 0.004*\"nonchalant\" + 0.004*\"millionaires\" + 0.004*\"interlocked\" + 0.004*\"member\" + 0.004*\"genial\" + 0.004*\"earthward\"\n",
      "INFO gensim.models.ldamodel: topic #3 (0.050): 0.010*\"mastodon\" + 0.005*\"locally\" + 0.005*\"blindness\" + 0.004*\"panelling\" + 0.004*\"nonchalance\" + 0.004*\"bags\" + 0.004*\"old-maid\" + 0.004*\"inclining\" + 0.004*\"necks\" + 0.003*\"penguins\"\n",
      "INFO gensim.models.ldamodel: topic #11 (0.050): 0.001*\"chaos\" + 0.000*\"earthward\" + 0.000*\"well-ascertained\" + 0.000*\"cameras\" + 0.000*\"length\" + 0.000*\"ex-prize\" + 0.000*\"slow-footed\" + 0.000*\"locally\" + 0.000*\"arduous\" + 0.000*\"lie-at\"\n",
      "INFO gensim.models.ldamodel: topic #8 (0.050): 0.002*\"sulfur\" + 0.001*\"shiney-feet\" + 0.001*\"newcomer\" + 0.001*\"ghost\" + 0.001*\"indicated\" + 0.001*\"teachings\" + 0.001*\"refreshing\" + 0.001*\"legal\" + 0.001*\"well-balanced\" + 0.001*\"messenger\"\n",
      "INFO gensim.models.ldamodel: topic #5 (0.050): 0.000*\"indulging\" + 0.000*\"bags\" + 0.000*\"ex-prize\" + 0.000*\"locally\" + 0.000*\"member\" + 0.000*\"diagnosis\" + 0.000*\"slow-footed\" + 0.000*\"arduous\" + 0.000*\"lie-at\" + 0.000*\"up-and\"\n",
      "INFO gensim.models.ldamodel: topic diff=0.400626, rho=0.316228\n",
      "INFO gensim.models.ldamodel: -8.674 per-word bound, 408.5 perplexity estimate based on a held-out corpus of 17 documents with 192053 words\n",
      "INFO gensim.models.ldamodel: PROGRESS: pass 9, at document #17/17\n",
      "INFO gensim.models.ldamodel: topic #5 (0.050): 0.000*\"indulging\" + 0.000*\"bags\" + 0.000*\"ex-prize\" + 0.000*\"locally\" + 0.000*\"member\" + 0.000*\"diagnosis\" + 0.000*\"slow-footed\" + 0.000*\"arduous\" + 0.000*\"lie-at\" + 0.000*\"up-and\"\n",
      "INFO gensim.models.ldamodel: topic #2 (0.050): 0.008*\"mastodon\" + 0.005*\"sentinels\" + 0.005*\"duplicate\" + 0.004*\"locally\" + 0.004*\"chaos\" + 0.004*\"panelling\" + 0.004*\"typewriting\" + 0.003*\"nightingale\" + 0.003*\"creep\" + 0.003*\"ex-prize\"\n",
      "INFO gensim.models.ldamodel: topic #19 (0.050): 0.006*\"obliterated\" + 0.005*\"well-ascertained\" + 0.005*\"arduous\" + 0.004*\"im-which\" + 0.004*\"defined\" + 0.004*\"footmarks\" + 0.004*\"crest-fallen\" + 0.004*\"individualized\" + 0.004*\"negative\" + 0.003*\"lie-at\"\n",
      "INFO gensim.models.ldamodel: topic #11 (0.050): 0.000*\"chaos\" + 0.000*\"earthward\" + 0.000*\"well-ascertained\" + 0.000*\"cameras\" + 0.000*\"length\" + 0.000*\"ex-prize\" + 0.000*\"slow-footed\" + 0.000*\"locally\" + 0.000*\"arduous\" + 0.000*\"lie-at\"\n",
      "INFO gensim.models.ldamodel: topic #1 (0.050): 0.000*\"member\" + 0.000*\"chaos\" + 0.000*\"indulging\" + 0.000*\"sub-lieutenant\" + 0.000*\"footmarks\" + 0.000*\"locally\" + 0.000*\"bags\" + 0.000*\"punkah-towel\" + 0.000*\"earthward\" + 0.000*\"rajah\"\n",
      "INFO gensim.models.ldamodel: topic diff=0.309687, rho=0.301511\n"
     ]
    }
   ],
   "source": [
    "model = LdaModel(corpus=mm, id2word=type2id, num_topics=20, passes = 10, iterations = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.21092145449352817),\n",
       " (2, 0.088421008090027883),\n",
       " (3, 0.04709042797737098),\n",
       " (4, 0.12311292619277209),\n",
       " (6, 0.059615117896932551),\n",
       " (13, 0.012075812920660041),\n",
       " (14, 0.012981899766136805),\n",
       " (15, 0.23323998434938839),\n",
       " (16, 0.10844284050321322),\n",
       " (19, 0.092282127260728292)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_document_topics(doc2bow_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['member',\n",
       " 'chaos',\n",
       " 'indulging',\n",
       " 'sub-lieutenant',\n",
       " 'footmarks',\n",
       " 'locally',\n",
       " 'bags',\n",
       " 'punkah-towel',\n",
       " 'earthward',\n",
       " 'rajah']"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>length</td>\n",
       "      <td>chaos</td>\n",
       "      <td>hawklike</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>in_ductions</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>injunction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>member</td>\n",
       "      <td>chaos</td>\n",
       "      <td>indulging</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>locally</td>\n",
       "      <td>bags</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>earthward</td>\n",
       "      <td>rajah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mastodon</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>duplicate</td>\n",
       "      <td>locally</td>\n",
       "      <td>chaos</td>\n",
       "      <td>panelling</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>nightingale</td>\n",
       "      <td>creep</td>\n",
       "      <td>ex-prize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mastodon</td>\n",
       "      <td>locally</td>\n",
       "      <td>blindness</td>\n",
       "      <td>panelling</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>bags</td>\n",
       "      <td>old-maid</td>\n",
       "      <td>inclining</td>\n",
       "      <td>necks</td>\n",
       "      <td>penguins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>indulging</td>\n",
       "      <td>cameras</td>\n",
       "      <td>earthward</td>\n",
       "      <td>bracelets</td>\n",
       "      <td>collapsible</td>\n",
       "      <td>locally</td>\n",
       "      <td>slow-footed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>indulging</td>\n",
       "      <td>bags</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>locally</td>\n",
       "      <td>member</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>arduous</td>\n",
       "      <td>lie-at</td>\n",
       "      <td>up-and</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>writhed</td>\n",
       "      <td>nightmares</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>trapdoor</td>\n",
       "      <td>rajah</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>injunction</td>\n",
       "      <td>in_ductions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>locally</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>cameras</td>\n",
       "      <td>arduous</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>accepted</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>faintly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sulfur</td>\n",
       "      <td>shiney-feet</td>\n",
       "      <td>newcomer</td>\n",
       "      <td>ghost</td>\n",
       "      <td>indicated</td>\n",
       "      <td>teachings</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>legal</td>\n",
       "      <td>well-balanced</td>\n",
       "      <td>messenger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>locally</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>cameras</td>\n",
       "      <td>member</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>apply</td>\n",
       "      <td>earthward</td>\n",
       "      <td>indulging</td>\n",
       "      <td>writhed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>chaos</td>\n",
       "      <td>locally</td>\n",
       "      <td>earthward</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>im-which</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>chaos</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>cameras</td>\n",
       "      <td>length</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>locally</td>\n",
       "      <td>arduous</td>\n",
       "      <td>lie-at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>associates</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>cameras</td>\n",
       "      <td>locally</td>\n",
       "      <td>chaos</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>slow-footed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>bags</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>faintly</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>discharges</td>\n",
       "      <td>accepted</td>\n",
       "      <td>apply</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>molecular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>killing</td>\n",
       "      <td>harry</td>\n",
       "      <td>burglary</td>\n",
       "      <td>sobbing</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>anomalous</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>adjustments</td>\n",
       "      <td>two-bang</td>\n",
       "      <td>accredited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>cameras</td>\n",
       "      <td>faintly</td>\n",
       "      <td>gallop</td>\n",
       "      <td>up-and</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>locally</td>\n",
       "      <td>bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>corrupted</td>\n",
       "      <td>do-well</td>\n",
       "      <td>wills</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>millionaires</td>\n",
       "      <td>interlocked</td>\n",
       "      <td>member</td>\n",
       "      <td>genial</td>\n",
       "      <td>earthward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ex-prize</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>chaos</td>\n",
       "      <td>arduous</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>in_ductions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>strong-minded</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>safe</td>\n",
       "      <td>squat</td>\n",
       "      <td>gaily</td>\n",
       "      <td>cameras</td>\n",
       "      <td>ringlets</td>\n",
       "      <td>vampire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>arduous</td>\n",
       "      <td>im-which</td>\n",
       "      <td>defined</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>crest-fallen</td>\n",
       "      <td>individualized</td>\n",
       "      <td>negative</td>\n",
       "      <td>lie-at</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0                 1                 2                 3  \\\n",
       "0             length             chaos          hawklike       slow-footed   \n",
       "1             member             chaos         indulging    sub-lieutenant   \n",
       "2           mastodon         sentinels         duplicate           locally   \n",
       "3           mastodon           locally         blindness         panelling   \n",
       "4        obliterated       typewriting        offence-at         indulging   \n",
       "5          indulging              bags          ex-prize           locally   \n",
       "6        obliterated           writhed        nightmares       nonchalance   \n",
       "7       punkah-towel        offence-at           locally  well-ascertained   \n",
       "8             sulfur       shiney-feet          newcomer             ghost   \n",
       "9            locally        offence-at           cameras            member   \n",
       "10  well-ascertained          ex-prize             chaos           locally   \n",
       "11             chaos         earthward  well-ascertained           cameras   \n",
       "12        associates         diagnosis           cameras           locally   \n",
       "13       nonchalance              bags        grain-land           faintly   \n",
       "14           killing             harry          burglary           sobbing   \n",
       "15       nonchalance           cameras           faintly            gallop   \n",
       "16         corrupted           do-well             wills         diagnosis   \n",
       "17          ex-prize  well-ascertained         footmarks         diagnosis   \n",
       "18       nonchalance          ex-prize     strong-minded         sentinels   \n",
       "19       obliterated  well-ascertained           arduous          im-which   \n",
       "\n",
       "              4             5                 6               7  \\\n",
       "0   obliterated   in_ductions      punkah-towel       earthward   \n",
       "1     footmarks       locally              bags    punkah-towel   \n",
       "2         chaos     panelling       typewriting     nightingale   \n",
       "3   nonchalance          bags          old-maid       inclining   \n",
       "4       cameras     earthward         bracelets     collapsible   \n",
       "5        member     diagnosis       slow-footed         arduous   \n",
       "6     footmarks      trapdoor             rajah  sub-lieutenant   \n",
       "7       cameras       arduous       slow-footed        accepted   \n",
       "8     indicated     teachings        refreshing           legal   \n",
       "9    grain-land   typewriting             apply       earthward   \n",
       "10    earthward   typewriting         diagnosis  sub-lieutenant   \n",
       "11       length      ex-prize       slow-footed         locally   \n",
       "12        chaos     earthward  well-ascertained     typewriting   \n",
       "13      uncomfy    discharges          accepted           apply   \n",
       "14   nonchalant     anomalous      punkah-towel     adjustments   \n",
       "15       up-and      ex-prize           uncomfy  sub-lieutenant   \n",
       "16   nonchalant  millionaires       interlocked          member   \n",
       "17  nonchalance         chaos           arduous     slow-footed   \n",
       "18         safe         squat             gaily         cameras   \n",
       "19      defined     footmarks      crest-fallen  individualized   \n",
       "\n",
       "                   8            9  \n",
       "0   well-ascertained   injunction  \n",
       "1          earthward        rajah  \n",
       "2              creep     ex-prize  \n",
       "3              necks     penguins  \n",
       "4            locally  slow-footed  \n",
       "5             lie-at       up-and  \n",
       "6         injunction  in_ductions  \n",
       "7        nonchalance      faintly  \n",
       "8      well-balanced    messenger  \n",
       "9          indulging      writhed  \n",
       "10          im-which       member  \n",
       "11           arduous       lie-at  \n",
       "12       nonchalance  slow-footed  \n",
       "13       typewriting    molecular  \n",
       "14          two-bang   accredited  \n",
       "15           locally         bags  \n",
       "16            genial    earthward  \n",
       "17       obliterated  in_ductions  \n",
       "18          ringlets      vampire  \n",
       "19          negative       lie-at  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Topic probabilities - zum Testen der Funktion\n",
    "topics = model.show_topics(num_topics = model.num_topics, formatted=False)\n",
    "keywords = []\n",
    "\n",
    "num_topics = model.num_topics\n",
    "topics_df = pre.pd.DataFrame(index = range(num_topics), columns= range(10))\n",
    "\n",
    "for topic, values in topics:\n",
    "    keyword = [value[0] for value in values]\n",
    "    topics_df.loc[topic] = keyword\n",
    "\n",
    "topics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>length</td>\n",
       "      <td>chaos</td>\n",
       "      <td>hawklike</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>in_ductions</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>injunction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>member</td>\n",
       "      <td>chaos</td>\n",
       "      <td>indulging</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>locally</td>\n",
       "      <td>bags</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>earthward</td>\n",
       "      <td>rajah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mastodon</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>duplicate</td>\n",
       "      <td>locally</td>\n",
       "      <td>chaos</td>\n",
       "      <td>panelling</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>nightingale</td>\n",
       "      <td>creep</td>\n",
       "      <td>ex-prize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mastodon</td>\n",
       "      <td>locally</td>\n",
       "      <td>blindness</td>\n",
       "      <td>panelling</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>bags</td>\n",
       "      <td>old-maid</td>\n",
       "      <td>inclining</td>\n",
       "      <td>necks</td>\n",
       "      <td>penguins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>indulging</td>\n",
       "      <td>cameras</td>\n",
       "      <td>earthward</td>\n",
       "      <td>bracelets</td>\n",
       "      <td>collapsible</td>\n",
       "      <td>locally</td>\n",
       "      <td>slow-footed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>indulging</td>\n",
       "      <td>bags</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>locally</td>\n",
       "      <td>member</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>arduous</td>\n",
       "      <td>lie-at</td>\n",
       "      <td>up-and</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>writhed</td>\n",
       "      <td>nightmares</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>trapdoor</td>\n",
       "      <td>rajah</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>injunction</td>\n",
       "      <td>in_ductions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>locally</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>cameras</td>\n",
       "      <td>arduous</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>accepted</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>faintly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sulfur</td>\n",
       "      <td>shiney-feet</td>\n",
       "      <td>newcomer</td>\n",
       "      <td>ghost</td>\n",
       "      <td>indicated</td>\n",
       "      <td>teachings</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>legal</td>\n",
       "      <td>well-balanced</td>\n",
       "      <td>messenger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>locally</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>cameras</td>\n",
       "      <td>member</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>apply</td>\n",
       "      <td>earthward</td>\n",
       "      <td>indulging</td>\n",
       "      <td>writhed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>chaos</td>\n",
       "      <td>locally</td>\n",
       "      <td>earthward</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>im-which</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>chaos</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>cameras</td>\n",
       "      <td>length</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>locally</td>\n",
       "      <td>arduous</td>\n",
       "      <td>lie-at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>associates</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>cameras</td>\n",
       "      <td>locally</td>\n",
       "      <td>chaos</td>\n",
       "      <td>earthward</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>slow-footed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>bags</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>faintly</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>discharges</td>\n",
       "      <td>accepted</td>\n",
       "      <td>apply</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>molecular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>killing</td>\n",
       "      <td>harry</td>\n",
       "      <td>burglary</td>\n",
       "      <td>sobbing</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>anomalous</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>adjustments</td>\n",
       "      <td>two-bang</td>\n",
       "      <td>accredited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>cameras</td>\n",
       "      <td>faintly</td>\n",
       "      <td>gallop</td>\n",
       "      <td>up-and</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>locally</td>\n",
       "      <td>bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>corrupted</td>\n",
       "      <td>do-well</td>\n",
       "      <td>wills</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>millionaires</td>\n",
       "      <td>interlocked</td>\n",
       "      <td>member</td>\n",
       "      <td>genial</td>\n",
       "      <td>earthward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ex-prize</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>chaos</td>\n",
       "      <td>arduous</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>in_ductions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nonchalance</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>strong-minded</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>safe</td>\n",
       "      <td>squat</td>\n",
       "      <td>gaily</td>\n",
       "      <td>cameras</td>\n",
       "      <td>ringlets</td>\n",
       "      <td>vampire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>arduous</td>\n",
       "      <td>im-which</td>\n",
       "      <td>defined</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>crest-fallen</td>\n",
       "      <td>individualized</td>\n",
       "      <td>negative</td>\n",
       "      <td>lie-at</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0                 1                 2                 3  \\\n",
       "0             length             chaos          hawklike       slow-footed   \n",
       "1             member             chaos         indulging    sub-lieutenant   \n",
       "2           mastodon         sentinels         duplicate           locally   \n",
       "3           mastodon           locally         blindness         panelling   \n",
       "4        obliterated       typewriting        offence-at         indulging   \n",
       "5          indulging              bags          ex-prize           locally   \n",
       "6        obliterated           writhed        nightmares       nonchalance   \n",
       "7       punkah-towel        offence-at           locally  well-ascertained   \n",
       "8             sulfur       shiney-feet          newcomer             ghost   \n",
       "9            locally        offence-at           cameras            member   \n",
       "10  well-ascertained          ex-prize             chaos           locally   \n",
       "11             chaos         earthward  well-ascertained           cameras   \n",
       "12        associates         diagnosis           cameras           locally   \n",
       "13       nonchalance              bags        grain-land           faintly   \n",
       "14           killing             harry          burglary           sobbing   \n",
       "15       nonchalance           cameras           faintly            gallop   \n",
       "16         corrupted           do-well             wills         diagnosis   \n",
       "17          ex-prize  well-ascertained         footmarks         diagnosis   \n",
       "18       nonchalance          ex-prize     strong-minded         sentinels   \n",
       "19       obliterated  well-ascertained           arduous          im-which   \n",
       "\n",
       "              4             5                 6               7  \\\n",
       "0   obliterated   in_ductions      punkah-towel       earthward   \n",
       "1     footmarks       locally              bags    punkah-towel   \n",
       "2         chaos     panelling       typewriting     nightingale   \n",
       "3   nonchalance          bags          old-maid       inclining   \n",
       "4       cameras     earthward         bracelets     collapsible   \n",
       "5        member     diagnosis       slow-footed         arduous   \n",
       "6     footmarks      trapdoor             rajah  sub-lieutenant   \n",
       "7       cameras       arduous       slow-footed        accepted   \n",
       "8     indicated     teachings        refreshing           legal   \n",
       "9    grain-land   typewriting             apply       earthward   \n",
       "10    earthward   typewriting         diagnosis  sub-lieutenant   \n",
       "11       length      ex-prize       slow-footed         locally   \n",
       "12        chaos     earthward  well-ascertained     typewriting   \n",
       "13      uncomfy    discharges          accepted           apply   \n",
       "14   nonchalant     anomalous      punkah-towel     adjustments   \n",
       "15       up-and      ex-prize           uncomfy  sub-lieutenant   \n",
       "16   nonchalant  millionaires       interlocked          member   \n",
       "17  nonchalance         chaos           arduous     slow-footed   \n",
       "18         safe         squat             gaily         cameras   \n",
       "19      defined     footmarks      crest-fallen  individualized   \n",
       "\n",
       "                   8            9  \n",
       "0   well-ascertained   injunction  \n",
       "1          earthward        rajah  \n",
       "2              creep     ex-prize  \n",
       "3              necks     penguins  \n",
       "4            locally  slow-footed  \n",
       "5             lie-at       up-and  \n",
       "6         injunction  in_ductions  \n",
       "7        nonchalance      faintly  \n",
       "8      well-balanced    messenger  \n",
       "9          indulging      writhed  \n",
       "10          im-which       member  \n",
       "11           arduous       lie-at  \n",
       "12       nonchalance  slow-footed  \n",
       "13       typewriting    molecular  \n",
       "14          two-bang   accredited  \n",
       "15           locally         bags  \n",
       "16            genial    earthward  \n",
       "17       obliterated  in_ductions  \n",
       "18          ringlets      vampire  \n",
       "19          negative       lie-at  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_df = pre.gensim2dataframe(model)\n",
    "topics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>length</td>\n",
       "      <td>member</td>\n",
       "      <td>mastodon</td>\n",
       "      <td>mastodon</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>indulging</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>sulfur</td>\n",
       "      <td>locally</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>chaos</td>\n",
       "      <td>associates</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>killing</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>corrupted</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>obliterated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chaos</td>\n",
       "      <td>chaos</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>locally</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>bags</td>\n",
       "      <td>writhed</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>shiney-feet</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>earthward</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>bags</td>\n",
       "      <td>harry</td>\n",
       "      <td>cameras</td>\n",
       "      <td>do-well</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>well-ascertained</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hawklike</td>\n",
       "      <td>indulging</td>\n",
       "      <td>duplicate</td>\n",
       "      <td>blindness</td>\n",
       "      <td>offence-at</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>nightmares</td>\n",
       "      <td>locally</td>\n",
       "      <td>newcomer</td>\n",
       "      <td>cameras</td>\n",
       "      <td>chaos</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>cameras</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>burglary</td>\n",
       "      <td>faintly</td>\n",
       "      <td>wills</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>strong-minded</td>\n",
       "      <td>arduous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>slow-footed</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>locally</td>\n",
       "      <td>panelling</td>\n",
       "      <td>indulging</td>\n",
       "      <td>locally</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>ghost</td>\n",
       "      <td>member</td>\n",
       "      <td>locally</td>\n",
       "      <td>cameras</td>\n",
       "      <td>locally</td>\n",
       "      <td>faintly</td>\n",
       "      <td>sobbing</td>\n",
       "      <td>gallop</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>sentinels</td>\n",
       "      <td>im-which</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>obliterated</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>chaos</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>cameras</td>\n",
       "      <td>member</td>\n",
       "      <td>footmarks</td>\n",
       "      <td>cameras</td>\n",
       "      <td>indicated</td>\n",
       "      <td>grain-land</td>\n",
       "      <td>earthward</td>\n",
       "      <td>length</td>\n",
       "      <td>chaos</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>up-and</td>\n",
       "      <td>nonchalant</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>safe</td>\n",
       "      <td>defined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>in_ductions</td>\n",
       "      <td>locally</td>\n",
       "      <td>panelling</td>\n",
       "      <td>bags</td>\n",
       "      <td>earthward</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>trapdoor</td>\n",
       "      <td>arduous</td>\n",
       "      <td>teachings</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>earthward</td>\n",
       "      <td>discharges</td>\n",
       "      <td>anomalous</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>millionaires</td>\n",
       "      <td>chaos</td>\n",
       "      <td>squat</td>\n",
       "      <td>footmarks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>bags</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>old-maid</td>\n",
       "      <td>bracelets</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>rajah</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>refreshing</td>\n",
       "      <td>apply</td>\n",
       "      <td>diagnosis</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>accepted</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>uncomfy</td>\n",
       "      <td>interlocked</td>\n",
       "      <td>arduous</td>\n",
       "      <td>gaily</td>\n",
       "      <td>crest-fallen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>earthward</td>\n",
       "      <td>punkah-towel</td>\n",
       "      <td>nightingale</td>\n",
       "      <td>inclining</td>\n",
       "      <td>collapsible</td>\n",
       "      <td>arduous</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>accepted</td>\n",
       "      <td>legal</td>\n",
       "      <td>earthward</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>locally</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>apply</td>\n",
       "      <td>adjustments</td>\n",
       "      <td>sub-lieutenant</td>\n",
       "      <td>member</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>cameras</td>\n",
       "      <td>individualized</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>well-ascertained</td>\n",
       "      <td>earthward</td>\n",
       "      <td>creep</td>\n",
       "      <td>necks</td>\n",
       "      <td>locally</td>\n",
       "      <td>lie-at</td>\n",
       "      <td>injunction</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>well-balanced</td>\n",
       "      <td>indulging</td>\n",
       "      <td>im-which</td>\n",
       "      <td>arduous</td>\n",
       "      <td>nonchalance</td>\n",
       "      <td>typewriting</td>\n",
       "      <td>two-bang</td>\n",
       "      <td>locally</td>\n",
       "      <td>genial</td>\n",
       "      <td>obliterated</td>\n",
       "      <td>ringlets</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>injunction</td>\n",
       "      <td>rajah</td>\n",
       "      <td>ex-prize</td>\n",
       "      <td>penguins</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>up-and</td>\n",
       "      <td>in_ductions</td>\n",
       "      <td>faintly</td>\n",
       "      <td>messenger</td>\n",
       "      <td>writhed</td>\n",
       "      <td>member</td>\n",
       "      <td>lie-at</td>\n",
       "      <td>slow-footed</td>\n",
       "      <td>molecular</td>\n",
       "      <td>accredited</td>\n",
       "      <td>bags</td>\n",
       "      <td>earthward</td>\n",
       "      <td>in_ductions</td>\n",
       "      <td>vampire</td>\n",
       "      <td>lie-at</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0               1            2            3            4   \\\n",
       "0            length          member     mastodon     mastodon  obliterated   \n",
       "1             chaos           chaos    sentinels      locally  typewriting   \n",
       "2          hawklike       indulging    duplicate    blindness   offence-at   \n",
       "3       slow-footed  sub-lieutenant      locally    panelling    indulging   \n",
       "4       obliterated       footmarks        chaos  nonchalance      cameras   \n",
       "5       in_ductions         locally    panelling         bags    earthward   \n",
       "6      punkah-towel            bags  typewriting     old-maid    bracelets   \n",
       "7         earthward    punkah-towel  nightingale    inclining  collapsible   \n",
       "8  well-ascertained       earthward        creep        necks      locally   \n",
       "9        injunction           rajah     ex-prize     penguins  slow-footed   \n",
       "\n",
       "            5               6                 7              8            9   \\\n",
       "0    indulging     obliterated      punkah-towel         sulfur      locally   \n",
       "1         bags         writhed        offence-at    shiney-feet   offence-at   \n",
       "2     ex-prize      nightmares           locally       newcomer      cameras   \n",
       "3      locally     nonchalance  well-ascertained          ghost       member   \n",
       "4       member       footmarks           cameras      indicated   grain-land   \n",
       "5    diagnosis        trapdoor           arduous      teachings  typewriting   \n",
       "6  slow-footed           rajah       slow-footed     refreshing        apply   \n",
       "7      arduous  sub-lieutenant          accepted          legal    earthward   \n",
       "8       lie-at      injunction       nonchalance  well-balanced    indulging   \n",
       "9       up-and     in_ductions           faintly      messenger      writhed   \n",
       "\n",
       "                 10                11                12           13  \\\n",
       "0  well-ascertained             chaos        associates  nonchalance   \n",
       "1          ex-prize         earthward         diagnosis         bags   \n",
       "2             chaos  well-ascertained           cameras   grain-land   \n",
       "3           locally           cameras           locally      faintly   \n",
       "4         earthward            length             chaos      uncomfy   \n",
       "5       typewriting          ex-prize         earthward   discharges   \n",
       "6         diagnosis       slow-footed  well-ascertained     accepted   \n",
       "7    sub-lieutenant           locally       typewriting        apply   \n",
       "8          im-which           arduous       nonchalance  typewriting   \n",
       "9            member            lie-at       slow-footed    molecular   \n",
       "\n",
       "             14              15            16                17  \\\n",
       "0       killing     nonchalance     corrupted          ex-prize   \n",
       "1         harry         cameras       do-well  well-ascertained   \n",
       "2      burglary         faintly         wills         footmarks   \n",
       "3       sobbing          gallop     diagnosis         diagnosis   \n",
       "4    nonchalant          up-and    nonchalant       nonchalance   \n",
       "5     anomalous        ex-prize  millionaires             chaos   \n",
       "6  punkah-towel         uncomfy   interlocked           arduous   \n",
       "7   adjustments  sub-lieutenant        member       slow-footed   \n",
       "8      two-bang         locally        genial       obliterated   \n",
       "9    accredited            bags     earthward       in_ductions   \n",
       "\n",
       "              18                19  \n",
       "0    nonchalance       obliterated  \n",
       "1       ex-prize  well-ascertained  \n",
       "2  strong-minded           arduous  \n",
       "3      sentinels          im-which  \n",
       "4           safe           defined  \n",
       "5          squat         footmarks  \n",
       "6          gaily      crest-fallen  \n",
       "7        cameras    individualized  \n",
       "8       ringlets          negative  \n",
       "9        vampire            lie-at  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topic = topics_df.T\n",
    "doc_topic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisierung "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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": 28,
     "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": {},
   "source": [
    "##### Create a document-topic matrix (that is a pandas data frame actually)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc_topic = visual.create_doc_topic(mm, model, doc_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize document-topic matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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siyTjm6EqGSejUORhZvYhMghz4hx/BKaSquNnZufGdZmGvOgGyIAVCrmtDWwfv6PLyHmV\nrwInp34jw+LBob+p5cqXKJxb6gEo46AyZKj6WCV5UCt8cHEOI4GtViRnahWuY56718/bluQ4HQO0\nc/cTwst5HnFofYCO7t4/PIOH3L2Llc5xeggJDV4Jb+O58iTQ4Yl1SfFfl6J6cr3RDfxdFAZrjQQH\nB7v7YjO7BQkbHkTig62Rl/h48FxY5AXFawZStn1kZg8DDYJjK3a89ogn7IaM6VjEf01Or9lSeVap\nY9aL67UV8JS7tzazPVBI75SY7yngH+4+KhVyBdX0O8jM/ljkeziC0nlew4Fr3H2kmTkqWPtIWde8\nuuVBZR5UhuqOnzoPqtKwXM7RhWvSOAXqpd+EkUl4ohUpLJvg18DN4SGVWVg2hQ1QqZ80nnH3hWG0\nvkAigJdRCPB7M/sv8hJaufvS2H4o8uKuD6ORxg3AIs/1bXq4nOM1jevwFBJCvIK8o4vMLJGpJ+WO\nCuFJd18WXmvCe+2BOLplqFzSVkCb4MwWpHKaDorxxb4HkGdVsg6kQgTxfek8qgwZMlQjrDED5e73\nuftm7j60MvuZ2fERTky//ln+npVCE3TjXCVJuKmbbakkXEslqZaDtMhjA8TxXIV6I22Iqp+3dfdL\nY23u7uPc/SrkYRxC5VCsOOxGMX8HZFR2IZdHVdH5LPXvcJSTNdXdW7v7XZVcZ7K+7VPrSBeGXRAG\nO0OGDNUQ1Y7hc/d7KM5ZrTRC5HEcMD0EBW+gthVfoZya5sAMM/sIeSnDkVewHeKILgDGmdkOiEv5\nwCQTP55cbbmDkRiiZvAlN6EKHJ9QxkNDeAnroSodjryZ6939UlMCcYM4zjFI/XdnrHmWKVn1IGBH\nFD5bP4QHByDFnpnZYKIGnpk9iWoGtkLezRsopLggOKMmSO04gpxXBPJ0tjWzQ5HxBnmOj8W1qxtc\n3HOIn/s3sJOZ9aJ0uSLM7EikRjQkUU/ENj8gw/QAqjFYH+WtzSWqVJjZlXGs11GDxdl5c1fbYrFZ\n2K564vT6FW8seHMlGhb+nPFzLhZbKYTQYD6q+LAJuvGBknLfRzfJTYCZrm6y7yIjsATxOb1M/Zwa\nowoT76Cb5SvI8xhPTuTRCZXz+RUyGm2Ryuy3lN0aojXKqfohLfIIg/EC8miOQC0waiEv60SkjjsC\nhej+Fsd/HBmHa5B45enU+vYEJruKuM4GrnP3F5HBM/S7+TsSNPSOMaBQ6VDkebZE1dxBRjNJuF4A\n3OnuzyPO7BCkGHyMlCIx+Mm/I3VlByQYaREiiXVivvbASSjPqk6sY1L8/ToSioyKMaXgWbHYDBmq\nPKqUgcrnMMysr5nd/BMc+gd3r5u8UEXs3cIL2BH1TtoOGYitYp8vEFHfDN0wj0SVvkcEYb8uktu3\nQwmktd09MQAveK4R4c7Aw+6+1N0/R1XSS0ryuPulkZCaXJt33H1m/J2EtdZDbdEbAY+4+y7u3hYZ\nn4eQsXjC3Y919/uRoXrf3bdCN/W9kEe2JMKE9VBZIty9JcpFmoYMlyMjnO5r1SL4qkUo56k9UiKa\nu/dFBjjh4maQ4+JeQEapJVLZJVxku7iWI939S1fzxvtRKHA75D1dHsd2lJx7DlDL3XeJdQwP4Uu6\nKWKGDBmqEaqUgaouCGHEQcgwvItuqIuRBzM6hiWJrtsiLyYtI89PUq0o3kd9pBpArkgu8thqVnKu\nHcNgzEViim9QVY1CYd+vUOjuRCTr/h55b/lY7F6wYGyZXFwYoGuRnL8iKI9bKraODBkyVCNUm/+4\nwZfcjUQCX6Kw0Wfopt0KeSxfozI5o0zFaPshI3IjCvvMR+0p3g2lXsIF1TUzI8cFLSGn4nsVhcfu\nRxzIaHIJvfOB2a5E3deRZPq3plJFuwK7mNkExFVhZnfE3/WCc3FU466Bmf0BheZ2Ah4ydb59KNY3\nDEqqb9wFvBbrXYbyrtZGHsUk4DemgrNzUCjvUZSntIMpYXVTpH57JD5/FRWdnYrEDFfHmJfNrAuS\nqyfJyCBj/Ki7vxCcz4mmFhlrA3XMbJ0QkPweaBbXpQ5SE9YGuqN6ggekvtvrkXfWAnFmICPex1QI\n9734Hq83Ff2tEfttAExARjZdnaOGmb0Wx12HXHmlEvwUHFQmB8+QRsYrVR5VzYNKEn6TIq2Xpz67\nCZUmao8UcwPiKfpdxN/0RDfohAvazN1nIC6ol+eKnv4tNWcnZDDmU5oLuoNccdEzgOMjxHUs8IcI\nzz0VY5qaKn6/ggzXqeQk4IuQEKAm4oT+icKHs4D/C++nbRyvDjIGn8dxbwRujZBWulPuBBSCSxoK\n3ov4m+cQRzYIydsPRZzR5sgYTUfG77sY/26sa2tUcXwy8vJejjUtQEbranLJyL3Qg8AF8X4gegiY\n5+7bI4PZL7XWxCCdiryvnsgod7dcA8RaqGnhNkhw0SS2X4c4qGVAD6Ceuw+jMAYiAUe3eL+M3Hc+\nmGgamUbGQWXIUPXxkxioSnBL81NhoA7kWkiAbnQPxd/3o5sdyKPZOV5XxfauSJQAuqEODTXe9cCv\ng1uC4IKCq9gZ5QPtjW7I82P8Xu6+KzJ+t7r7xwCuQqedkRE4BzXoa4Q8p1vd/TV33zJu3NeiENqv\n3X1Q7NMi1pBUY1iEDMRoV926HuTyk+7Puw5XhCx7O1TZYluU8NwLCR8eQfzOkBj/JvJCvgXGoNJF\n3dx9MTJMR0QoMuFuvkEhvOHu3s3dp5nZI6iCw24elUBQW5HWyHOajjzadvHZLOCiCLW9isQlW4Yh\n+hfQIq7Fslgn6GFgKvIKm7n7xShX7lhKhzB3pjQ6o+r4r8f7rdF3/i7ytFe2lmOGDBnWAKqaB7Ui\nGIVuzDugCgqNCC4o+JQ0F7Q/pW90+VxQTfQ0vj9q23AuuvEXhLtPd/frUViwvFyjpXl/J+HVQcDp\nYWwuo7SKryJlPr4B7nD3z9C574WuyWiUnzUvjnUo8iSmIy+nTYG5CnI3EU7siTy5rWJbDWSMlwLn\nF1l/YsiWUToXahnFw8ulzjkeBN5PbUo3XCymeLwCeWOnIFFGWcrIDBkyVFGscQNlZi3M7KUIodUx\ns+ZmVjPyjEDN6JaiUN0RwS2djRrivYZCfwejflILkOdzIXrqfhF5UHvH0/RLFD/nUYhrWguFnXoj\nVVs6N2dnM3vVzD4ys0ti/fWRmm+t8CLeQYVdMbOLTI30hiKOJsHGiLuZFn//GLLqpJHi9kgpNzNC\nnd+g8OdfkSG8wcxGRc7W7sjzAHkKl6Mb81mo0d9oFKbbAt20t0cey/qmauk7oNp8FsfubGYvo/Da\n7mbWDHmIS1GIsk8caxfkjRkwO4QjFwAHhreatMAAGcTWZvaGmfWPbU3M7O34Pj4ys+eRzP4VQkpv\nZr2CczoPcWL9kHf8SPB56QoY7VE+1YfIg/oszrsHsLGZnUWGDBmqFX4qA1VRbmkJpbmljeM1CYWl\nTkA8wx6oOGuvCAXOQN4DKJRVG9g78ozGopDQj4g3KvY0/QSqxL12HHs+4rPS16gZ8iQOA/4URu8V\nFBrrhYzapkBvM5uBDOWJ5JJ0ExyBwovtUYhuApKXL0bGsRdq1jc/1vu/+PtAFLK7HYlFXoq/E+9k\nVqx/ZxT2akROBDEbGZPdUQizDuqxNA5xV0kh25uQt3VeXNcrUT7TuiifaNcwxA8gfmcO4r8mo265\nz6DvqA3KW+oc1+uD2H4SuYK3beK8hiI5/2Hkfhs3owrlXZCoYyBqQLgHUhEeSenvcv041/3ifK6K\nbZ8An4enWwLLisVmyFDl8VOp+OaHIQFKat0lhQK7Iw8IJDJIBAGjEWfyFlLvnYRuUGe6++GmhnpD\nzawNpcNCr6Oco+SusxFqM3F3HLs5QPAfg5KdIrR1OnC6mW2HhAa/RTf/WjHsydjWBTX6axuew/Wx\nxmVxvO7ICDV295Fx3H/Gv+siz+z4mPNMYKi77xBewePIoF2KwnWjgfbunhjEZ9HN+2p3/9pUiaJz\nKAd3RIq2L1D7i4XIeL4CzA2DmPRjutDVxG8XM7sVcWLd0E3+NZSbtAh5LGPj79uQYnIu8hJHx99d\nkMJx/eCNCI5xEbkWHkPcfZ6ZLUEqzH+hEGt3d3/LzGajPKYvw5mb6e7dTIVopyDhxsvuPhZob2Zn\nAlu6ivIOQmHOB5Nr7O6bWqrRJHnwrGFhhgxVHms8xFcGinJL8Xk+t5R+ml7RPCNgOW4pbcQL1ZQ7\nGinPOocRns2Kcx7JOW+OSihtj27wyTm/hsJamwETTd18kzUfhAzII0ho0QFxN1OLHKtYE8c3kdf3\nL1ficj1kRLuhck5D0LV/wSte5Heiu99XYPsnHt13qXy+Uv5vt9B3kyFDhmqMqpAHVSjPCBR6uh81\nx1sQocFTUAgHSjfU61vG/KOAU8zsXlRYtTc5NWApBJ/UJfF6UIkd4tjNgV8hIcXk2N4C1YprDPzb\nzAYi4zIahePuM9XA24ecQGMYqmf3BgrpbYC4MlBNu/4ohPUfxD0djapa/AF9X8Pd/VEz2xsZqopg\nGcpJmoS8waS5Xz0U0ts31nc2MrbbxOe1kJeShGb3cvdLzOwqxE+NR2HPHZCX9lp4Y98j2X5DFJZ9\n0FRGakBcx0SV2NLMurha1l+J2tYfhMK6ye8gwXhgsKmvVFeUF/WKmb2EvqdOZjYuVJa1zWwA+r62\nNLNDPdXRd3k4LKtgTdkaFc+HXl25Tf7N5+UPCljjjSs89pFNtix/UODwz96r8NgMGVYUVcGDWi7P\nCCCkzJ+Qkw4nCbJJn6B/AFeZmgiWZWifQFzKW6hB32tljDWCW4ob8mXkyu+kkdx5bkL5SjNQCOof\nSCTxIXqin4SM0O2o5M66kZt1PFLX1US1/xJO5ivEIV0d/FlSDmkrFFrcCbg2jNurFPeO8rEMeVNr\nIz4rETpciHisoUgUcRUqNJu0cZ8SxyTWkjQJ7Az81VVb7wvgBlfzydeRIRmLhCPvudrLT0Fe2Ni4\nVjMLrHFt4NOQ5f8PhVhLECrFWYh7WooM4k4oD+wp9PsYkNqlGTJkU5GBLCWSyDioDBmqPtZow8Lq\nAMtrZmilGxJ+hfJ1Foe38V933yC8pm8Qj/M64s+upDR/NgCJBBxxL1vF3L9K+CkzuwGYluLPHkeN\nEYt6A4V4l+Bxerj7Z2a2I3Clu//aVOWiDhKngAzlnsiTy5+jYxx7azP7glxCMcjraouUdBe7+16m\n6hBvufsdlmpiGHxR4gWOjONMMLOFSInpZtYH5ZWdmLfvaJSfNQY9ePwK8V75138QCkEmnNRcd29A\nEXTp1MEnjHqp2MelUQkPanUh86AyVHdUtGFhVQjxVQppg2FqW34D4or2Bn4swnUk+/YlZ1xOLW98\nJTEGaGRmH6PQlqNw2l1IeTaG5fmzgyJMODI1zw+xzv+L9weHIOCoSqxlfTMbnicOSDiapUgSXxup\n4z6L9TRDhV5/NLOmSKCRRkekcoRcbb2SBNjUmhuGmGN9ZLQqiorUz2uBhA2OPLTuZcyXcVIZMlRz\nVDsDlcDMdkNeyJ7uPgsJBCqDPREHcnZq23nu/lwl5kjzZ7cg3mkY4mT2QfzZu2a2jMrzZ0NQ+O2U\nmKtM/mwF0BHVLnwRCSs+QuGwEfkDzaw9UlCeGJueR6HZhMtKFJpDkGx8c3QtVvXv6wPC44rjtqAw\nf1lJWJXwjCqKynhFlUHmFVUtZLUcqwYHVWmY2c6oft1+7v5BbLvUzM6Jv0ea2Y2Rd/WGqXlgPqYC\nD4TaLcnlucLM3jMVck0aBNY2s7fM7AkzG0vp1g0F+TPE+aT5s6UoDHafqXjqHYg/m474oGYhOGid\nt8Z8/uxd4DIze9TM3jGzB81KEmz3MrN3kIexUepaNUbG7eU4dmvk2T2AQnpHIGPZHBhmZomBqmlm\n/zOzBYjvudjdXzQlBzcAzjezH00J1UkbEZCROizOf/O49v2As8zsVeRdYmbtUGWLB+P6mZnVM7Nn\nUJuQAyPUBypPNR5xSn9Ozjmu460hwLiMXHKwAcea2fiYezlDmXFQGaoyMuMkVEcDVRvlI/3G3d8p\nY9w6YXxOQzlK5WEtd98BqeguiW2nAXdH/bi/IHHAcFe/J9x9lrvv6u7t3X23UJARn/Vy9z+n1nxY\nCABGAZ3iTfJYAAAgAElEQVTcfUsk+DjJ3WujChGHJHMjwzUZyczno86356NE4P7oZtwK6GFmddDN\nen8UVkvqEIJu3H9393ZIcXi+u/dA3tBod9/A3TdGYo1W7t47pWI8yd3rIO9089h2Y8y3PhJvLPBc\nnyvCu9kCGaL3kWikpbtvgmorenBopwK/c/Wt6oLUjHuhpNoW7r4e8G9Xf6qj3L1rSN5/IOeJXoW4\nqrpIfr84to8BxoeIoyuqOtIydU2yYrEZMlQDVMcQ32IUWutHzmMphIcBXK03GppZozLGghJkoXSD\nu57ohgzyhqZB5bkvJELYE3WtXQ+FAP+KlGrb5ByCkkZ+oMTW09OTxLhxrgTbRP4+iJyH9iryhhqi\n8kHzkAd2SFyLl8xsfTNrWGSdR5jZVnHcRSgXK7kmu8ffZa25j5n1RPzPKe7+TQhCRkQ47nNySc+v\nARea2abo2vdAFczrm6Tmb6DQJqgyx59Q64zGwJshmGjg7okq8yFyhuvPqERWUhR4XSRIScpnZciQ\noRqgOhqoZagI6otm9md3/1uRcfnyxPLkiqVEBGWM6wr8kcpxX+kkm+fI3aSXExtAiSEquk5T1YiW\nSPad9KX6EhgWApADiF5HK4Hymg8WWvNyRhUJMN5GBukaQhDi7g9FyHRflIj9ODIyFyPDdC0K/x2J\n+L0u7v5JqPoqkgh9l7v/pcJnmyFDhiqH6migksZ9+6KK5bPd/a4Cw/qgJ/eewHfu/l05N/5CGIOM\n4QjEa7RHuUO/TnNf5GTQI5Hn0dEk9z7B3cflzXk4Mi6gG/+/TAmzjYCr3P0elBO0p5m9Ra4m4e9T\n539QHPtmVMdvMDnPCVLt2JH3NsbMZqFcp69QyPEyYPvgdvqjkkXpG39tK53gejQSdExBHsxk1Obj\nQ2RkQJzTOygMNwaFIBejRowW+9cws8eQR7Mwjj0MJR0vQDUTB6OyUxvHmmoA95saFLZAZY3mmNkC\nk1R+7Tj35CHj87h+lyOD1w44zt3nJidnqYaFDTDObtCciuC6uVWg6VxFk4qhWok/MuTwc+aVKoPq\nyEEB4OpZtBdwUXgM+VgQN9HbKN1ErzK4BVXdfotcsddTy+G+1kZCgYpwX+8jeXedeF0R23dFXNMi\nVJl7B2QcCyK8mUHA/qZqEV/ER/VQ2GwsMhYDUKHYG1Gn3VdQ+O9OJK74I/CbctZ8EzIm7ZA32wmF\n52qhUlR7u3tnco0H/4FEE33QA1FjVLvwIRR6exEZujGoJuO3yCNsh2oozoltW6Nmi1NQWJU4z6ax\njhmIxwIVDP4UGarfo7Bqqd96moOqm6nQM2Sokqh2HlQ6adbdPyHnjTyVN/QBd++f3uCpArFBvidI\nyu2AjMEiM9schZr+5e6HhlLMEZc0PLVvB3RjvAY98T/m7hcApLivU8kVx30LlXECeRf93H1M5B6N\nie114thXp8ZdiyopJPLqN9LXAgkuHkyH2EzJr4Pd/WFT8vAfkBpwW2QE/xfXrSFwD7nCr6Cuxcl1\ne9SU/AoyEi+7++5xjKTQ7HhgkrsnPM/DwMnu/pqZnR/X+KLwXIYgT+5HJHU/NF4PRIjSUGuPwWa2\nGBnX95Fo5FIkvAAZrSRR+s/kmliChBpPuPvKhjozZMiwhlBtPajVAcvlVu0d/NIDwO/MbCryjI4C\nusTNsBA+RzlCaaww9+XuO4YS8XPgec91G55ZwVNK80inAa+lZPV7pebbxN3n5e1b0hjQVKUh3c+q\nUKHZiqJGzL1bsWPHmp9GHtbRsd5OcdzHU0PXBsabSj/1RAYvwXhU5b0xGTJkqJaodh5UReDuu1R2\nH8vlVu2T8Eso5DU4+KWlKFG2PvBXU3WH/yEPrqapRUcL4G9A9wgv1kWt15uT845qAceY2QkoDDbI\nVIVhZmo5JdyXmW2Dwn1jC6z5SqRcm49CZrUiL2lLVzt3kvfoZj8/dn0eqfKmBP/1IPL+miL1HCgs\ndqWp4saGMfe+RKX48KiGo5yoC5BxaGdmB7v74yhHrJOpKWIP4PvwjGYir3OEqVTUWSgZeS+kCPza\n3S8hJ68HqRMPAf4U1zypQfgxqljxHTJio2J7+ziX74H3zOx6d78y79qVcFDNN92E66allflVHAt/\nLH9MgrpFKzxlyLBKcfdG+WmcK4/MgxIqk1u1HeJUGiMCfgQwxd2TIraLwzhtibyeHZDxSJKFu6L+\nWNugm/VWBY6T5r7+ijyoRXlj6gGvp3KrfoVCgSPJiRbWAh4PYzUcOCgUgBOAHSKJdSaSa++AwpTd\nw2i9jAzrhqgJ4QJkNEGCh91i+3zgGnfviIQND5rZxNjeAIkg/ozCiD2QIVqIfntNUfjzQnKFef9o\nZu+hShez4nhnoAK7k+P9Y6ZGiGuh6hKgPLGBZtYVPSicE9d8KUqmTjeMzPKgMmSoBijXg7K8YqlV\nCaaKD7ehG/ORQEd3L7cUkKkI62HAZq6eRouRWu4i4iZsZr9BHk4SfpqL8oFw98fM7H/IMDQmx9uA\neJj+pqoNR4UH83fggFCh/Rqp1nD3riFqwN2/Ipd/tQA4xtVmZAuUgzWX0sjPU+qFqll0RJ7Gk+iG\nfk/Mf0TMdV9cqx/cvX2o4JpGTlVSQ6c58j4e8lzh2ksQD7Q14tzejtfnQL3wKB0Z+9rI2HwUOVv3\nmCpKtHD3V8zsS1QU96uY+zVkrP6LRCMXu/vgUEUS4dZdY+wwxFv1BB7xXIPEK5AysC1wk7s/HNsf\nRMm/iTeWIUOGaoIq7UGZUNYaj0bS7A7oBlduQdWY7yBUiudXsXkZqnu3Q4pfSgxUGpXNrfoMeTMJ\nT3MYyxuaQlgH9Tqaisod3cfybT/y85S2Qx7eGNRqfRegprsnITvc/QNXFYnuSGK+PvKGDkHGYTqq\n+pAUhf0hte8CJCmfgwzmzfFRKxTe2x51yTVUTqkeOe8mWWOhkkMtkWLvMlfH32cokudkZjXjPN8u\n9HmGDBl+XlghDipUZHejJ+kvUfjlO1RpoaW7L4sw0TvoBtYcqbKaoJvZSe7+TijXkjbioDbin6Nk\n1rGotNA+oQLrijidR11N805E3s6epuZ9WwBbhydwr6sjbiHsgrrGDkGeRJLj1A8Zgb+Y2rIfENt7\nRFgM4FQz64dq3S2O3CoDdop8orqIL0qwAN1Mt4/3fRCf0jqu468RH/OwqQr68a5ySTehnlCPxrgF\nwJ0R8muORAb1zOwbZGT3jmMnvM596Eb/lZm9iSTl41BC7AhkXJaiENpUxHm9jwq8tjKzb5EXtqGZ\nbR3fb2MUjlw3rktS3uh9pKRbZmZbpbZdGddyNDJWmyLjBjLS15pZNyQhB0iqqB+LqsKficKkmwMT\n4jsZjX5z98Y5/iY8qtuRkfsElYPqGx7bpjHHI5QFqwG11ylzSJXCauKVsvpvGVYGJ/zv/QqP7Vev\nvMI+wop6UDchI9AeEewD3D3JUUm8kv2A54L/GIhyWjojbuCWGDMASZa3RyqtN2N7G+AWd28X4Z0L\nXb1D2gO/MrP27n4nkkif6+5HoxvT6FCGFTNOIKP0MPJM9g2FmiNDeR26oR2OwmMvANenRBPLkMhh\nGQozgRr7LXLVfRsI7Gil674NRvk9NZFReAKoG8bmfnRT3Se5jmWsG6TEW4pk4j+gsOS6yHOZD/R2\n994xVw1kGNuj7+T0GN8FuBx5n0me1GPIwE9DuVg3IaM1mNz3OwWJIRzJ2b+PNV2OOv5ORW3h0wz+\nQlQrrxN66Dg6to9FsvLP49rUQblZDyGjPi/2+Ry4K7iyD4HZyED3Rg8zDyHBR9NY27ZxLd5FzQzn\noDDocgoIy4rFZshQ5bGiBqo7ubYP95PLPxlCrlvrEcAQU522nYCh4d3cjngGEK9wK4C7Lw0jBzDL\n3ZNK4ACHB1czGT0pb8MKwMzWRsbgybjBjkUli0rlVrl7S2Q4HnH3a1JT3BJigB3JdcFtADSOc9sf\neTdt4vwXAP+O6zMgrs9i1ExvG3JczSxKX8d8LEnlN41z909jzVMQr/MoyitK0BN5Ri+Ru2b3ugrU\nTgB+62orsgiJKE5BBuxpVw+p7sBpccz0upYCdya8VGASEmZchZJu28V1HIkeBu4wVW3fDhkSEK93\nhqsw7efIiJzp7rshj/y8GHcsMCEehD5Cv5uR8aqDuLwzEF9WG9g8uKarkGc2LtbzSv4FzUQSGTJU\nfaxqDuopYK/IPemMbpA1gDmpnJsO7r51OfOUcB/hjZyDpNNlchQVwJ6opNB0U5fZnpQuC1QeEiVd\nmk8xZHST+nr7kKtogLsvQiKGP6JQW0fgRFPi73qx3zgUMq1tKl+UzkGqQdk5SJ1DNbgxKmn0ElLj\nbQvcWM41q0iTwIpgCArhPpy3/Szk9VyHPLK1TaWhOuWN2xMZ+gSFcsMMVXtPfkPN3f3tEMUcgDzI\nZ81sVxSenIu+27XJFZHNkCFDNcKK3pDSjfpKGsW5+7zgYm5EbSmWovyXj8zsMHcfGpxNe3efip64\nfwfcEAR4IbVgQ3K5N00R3zKywLi5lL7JFcKRwIkphVc9VPV7ndg/XeW7ZL5Y8xQKk/PPofDSpe5+\nr5kdE9fk1dSYa1Eo85vwBG/3XMv4ge5+uZl9gMKAfZCB2t/M3kaht1oURg1U3247FNpKqnfXQVzT\nj+Vcs2Io+P2WgSeQd/McOSUgKPz4KQoLtkXnBwrX9TGze5GMvSIPHM8BZ5jZGe7uZtbR3SebWSvU\nGHKAmTVHIc1zgG9CBbkfuUaLBTFr8rQK8y8/Z+7l53xuGaonKmKg1jGzT1Pvr0NhlXvM7FxyIokE\nQ4ChSIyQ4GjUWO4idLMdjEJQf0C5K/3Q0/LvkJqsBO4+NTyE7VBYcUzq4/rAJWZ2MQpxrRXhpE2A\nDfLEGu2Qd9PE1NjwR+AkFP45BuXn9DS1dTgb3dAfMrNrY+6vUZO+FnHchOVzJJq4w8xuJNde/Fnk\nIeHub5Lj19KYgQzRoSgf6Z/Iq2uEwmwvIpn4D2a2B6pht6mZDY1rXgvd9L9G/NdTSFLdOwQUA2M9\ntRD3NCjmvh7J0tMFZ+cBh5qKyrZKfV+fAtPM7G4Uqm1mZu+5e8KXHQqcgDyVVkhSj5kdj5SQTZF3\nM5OcZ/xGHO8tlGyb9goboiLAS5FRSkQ5B6LvapqpaeL3iC+8AdjDzBxxTu3iGg4ytbUH9fIqBUsl\n6tbPavFlyFAlYbkIT9WGFcjHMrOnkarvXlNlhgPcPVF23eDuI0xdWXd39xPN7EVU7HWGme2IJOq7\nmtkQVAYo7cmth570d0r4MDNrHF5QTWQ8znT3aRZVFVw163ZBbcnLDSuZ8nzSLcz7ouTfjuim/S65\nigqPoxJMP5jZeUDt8LzuRCGuFxGX83AY5kFIBNEHqe+ecvfW+etLDJS7D4qw57XufpOZnYYaK54Y\nYbk9kDihQaxrI6RG/AdwsKse3i1Igv4CORXmd8ibmxxe46XoweDL1KXYBnlemyMj2g0Z1rHo4eHb\nuL7bxprPAeq7+6Vm9jlSji40s0auKud/A95y9wdMtRDHoRy5HyiAJlbTD6FeeV8XkHkZGTKsCli9\nRhND+FYmqnupo+5IBQYKR/0j/k7EGiNQqOqWPLFGsn/yhL0r8Nt4ap+Orsss4OMCYo2T4/Nm6MY6\nrSILjZv/XHK9oUYVGfpiIhYJpd/myOvZBnFMIG8ladT3V6TSG4dCW7ujsB9IDLIMeCtCfen19EfF\naNONFuug8kb9UHgunXv1jLsvRP2okiriuyEjND7WVRdVGN8RGOnuX8axhiBvJ8H1nmtPkghmngH+\ngwq8JiHdx5Gnl18IOI1pqHrFkyg5GWRMDwhDlpxXc7L8qQwZqhWqu4Eqhh+AE8ysB+I+ElXeHFdS\nbzHMTz6PG15iwNJija7u/m14KJUVa/T2qJ4Qc44sMKZQIdaaSPlXTNCx0N2vN7P7kdqtb4G5Equc\nCDCORPLrtpTmy15z9z1NfbSeN7NE0LAw1rxWal2G1IEXpBdjqsJRURyNxCNDUNjyiQJjSkQjgfR1\n3xdJ/fdHHXq3IyeoeLcS68iQIUMVQ3U3UMXEGsMizLcA5UadBmBmr5Yj1kiqFdRHN++Epzkb5SBt\ngpJAH0LCg4URCmsNbGxK6K2IWCON283slAjz1Qd+C5we4b4uiPNbgLinq5HXUgdxZBegcFiCvRAP\n1zXmusJylTGSG/wsJCT4HjgX8YX3FFjXAsQTtUbJxG1Mre4TocMFyPNsbmYfuvsdpqTeQSi0uGV4\nTs+jkN4GZrYTMvoPsjxeRVL335jKRF2FjOe/gbtQ0vCuqN1GJ+B/ZjYQeZOXIE5zC8T1vYoUfcuQ\n4XvM3SenD5bmoJpvthm3vTOdVQ3/YmaFx87Y45AKj91yysQVWE2GnyuWfVShIE4JarQs2lquyqFK\nlzrKwzpm9mnqdTZRRNSUyHksEl0kGIL4iyGpbUcD/UxJpW8i4p3YrzcKUU1EgoxfIQPUGT3Zb48S\nWq9GIadpqBhpj3i/LOafBiw1s6lmdlbeOYwwsynxyv+sENYFLnH3nVDy7AnoBu/IMPVAlcDbmBR/\nt6EuvuMRb3V5JLz2if2SHlofohBdcr3S6sX0sRuSE3dsAhzq7r+K7dsgbusM4J8RjnwecU3bokoT\nNyAeaQPUjj5Jvt25wPH2RA8FD6D8proo+fp7ZOD+ioQntZDHNRlVq3gAGfKjEH93S8z1KvL6ziPX\nV6sEWR5UhgxVH9XGg3L3YsZ01/SbPB7pUVJVBFzN9PYqMPds4MDYF1QKaDTKn/kdCju9hG7yC5BS\nbA66yU9FT/rvA61clTNKrSmFkhCfqTzT0egm/4CZPYO8hnNS4we7+9Pxd1OkPEx4ofWQN7Us1mLA\nDu7+Vnx+OnBzeFBLkbGtjbiersD57n5jCBuuN7NFyBsdZlJNLgP6u/ubEe781tXFGGSop0cawUBT\n0d6hsY67QwjxpLu3Mcm8BwHdLFeQNjkn4jq/i4xtc9Su5GjgkxCt7Ia64l6M8tDqIo+uJvBfd+8Z\nodKGiMNqCLzp7sfGdR5FLvE3Q4YM1QjVxkBVAmke6UH0JH9dZfdNkAgq3H3HeD8MGYehwMbufkEo\nDNtWdIFmti0ygvvGvxch5V4+p5VWnRlSHd6emmcXckq5j5HiLzFQSZLs9shTXhDHqBfvzwovrhEK\n5R2JQmWjiygQCyrg0nD3Uaa+Wvsimfd1SIFXwp/FteqX2q2Xu08wScmH58+Zd/5vunv3YoePf5dR\nmntbxs/zd54hw88eP/f/uKMR35LwSCfE9jvd/YbYfgxwJuIyaptZzfAM0nMMCv7HkEF4BfFWw8zs\n+pjnWsRLfQ8c6O6zzexwlIu0NjI+YyMcORe40lUwdyaSQN8ayrq6ZjYWybhrmNkV4eF9DlxnZr9H\nHlEfcrlYi1ANvX+Zko7vQ+HNdZDRegZ5HA+gAqpfo462H4SHsQVSC5ZUbzezeagM1T6x6c3wVJoT\nKkkzGwzciXKhOsSxh6JyTqA6fcsQ/9QaJczWjXDkpBgzKtaZIJGwJwXyzkIe6/1AWzPr4+5DzKxB\nrKMlCiHOTs2xsSkfzigcvlyOg1odmHvSCeUPClSGV/Jv/lv+oIA1blb+oAxVDktfK0u4Who1ux+w\n+haysBJdamrXLX9MJVGdOKhKIdRme6OyRgmPtCPibk4ys45B6vcBeqQ8p6PT87j7JBSiGof4lFHA\nVxFKuwjxLvUQn3VCfH5S7P4bYD93Xwe13tgE3VAPRS0/QGWJfhdhtQ3Q03835M3MQGV7QInPd6Cb\n7tqoondyY6+L+J5usf6SaubI6JyOPKATkXHbNYzTZkjmPQTxUr1Tp14PeMnd2yHvqxuSsB8U13Ua\naqexF+L/DkOJxtNQ88G2KD9rGxSafIycsTvPVeB3ObjaevwdlXCajjywdvH99Edhy6lxbRq6ymbN\nJNf4cX3Us2pXFApsQK6Acfo4GQeVIUMVxxr1oPL4oreB49y9Ev2sC6Kumf2AjO8SlPu0MI7RBFUx\nSCopLKN0Hs+H5Fp/JGvsi0oV7e7u/4n3h5kqFxzm7h3MbCHQJkrwbI5u5KDmhNvE3F/HmnoiI3YR\ngKuDb/vU8YagCgrNkCFK6v+NQcbhDlTg9dMI8bVAgoj9XYVXu8aNfVNkjPoizmdjpH57HXFz2yLj\n/Ii7X2hm7YG7U8lzi5CCDnJqvvGxps3jvFuj5oAvxdrfQnxRJ2T0p8X2yahB4Trx3Qwxs3eQV1k7\nrunI1GWfRIQaw4g+aGZtUBjva3ffPnixAREavJ4cd7czKjac5GCdQxSwzZAhQ/XCmg7xrQxfVNac\n9WPOvkjhNQNY391nWi5JF4rk8RTAdCQg+E+83xKJIxIUK7paA+gWXkHuoOrR1DlvjgQ3Ade5+1Nh\ngC4FcPerQ0ixD0rY3TPG/xfd6DuiMCBIYj7O3fvE8QqGuRDvtJGZJd7MxmbWxt1n5J2TA6Pcff8w\nFO+Z2pSUhfx8rvSF/8BVFZ4wWP1JFdjNwxXACHc/KIzRyLzPW1CBRpUZMmSofljTBiqNyvJFY1FL\niKUF5io0d8IjgaTZ36LQVYPgkRzl22yOvIf+ru60yf694qZcG0mw0zUBa1muYeFmSGY+BYXg5pra\nvk9CcudPEOdzuJnthcKOXRAfciTibz6LJ/+jge9MrdrvRobnR1RbcCtkmLZC1RsGmypAvIdu9vvF\nGt5CxmEUufJCNcNT2xZ4GXFm881sAMof+h55oluFh1cCV5koR0KR0aiJ45+Qx9aanFihgamVe31K\niz+W5H03jmof3gS4qXbin4Du4XktAJ4O4/QyMqiTEA94VJxDuzjOUcgLbm4qa9UcydLT6Qc/GRoO\ne2m1zLu6eKWsYWHVwWrllSqD1cArVQZVgoNaQb5oKXl8UTHk8Uj1UE5OWxRKG4d4pPeQZ3QKaoF+\nZ3oK5D3tiTyVj/IOsdjdu0b+zxRgUqzxe1R+ZwHiQ05F3NK/4xgd0U30FWTYQB7TUORVJOHOgSjB\ntjYSEHRA1cvPBD519Za6LNZcG+UPzUbfbytynuqVMV8b4BsUGpsTawFdc3M1llxIrrFkCUyVJdzd\nv4jPeyJebTEyNDfG0J2BW919uzj/BA8BrcNjTjAJGW2AC1GO0xjEiW2CDPuTse9/id8BMkrNUAhy\nXHz+HRKhbIQeNNYjJ8hIn0fWsDBDhiqONW2g6sZT/gQkk74L3fCecPcf3H0eKpLai9J136bE+1b5\nE3peQdnU9uvCgCwi1FsoKXde3LwXoxvpbSjxtqGpfl+CwSjMtwkSBaRxnJmNDe6nNblQ4BTkvfwD\n6OLup6Ib9wPuPtzd2yBPrgeRTOruw9y9FRI9vIBysXZC3uUSJHb4xlUXb0uU0wQyDIvcfWyMedbd\n27t7NyJ3ytVEsB/wkbv/zt3Pj2vQIs61PTAvru975OrkjQQ6RWhyLJKRgzzw9RAHZnFdm7n7IKSe\nS/pD9SBXg/DvwPuJSCK+L9Ofvi2qo3cg8shGxj4HIKHDpsj4DAW2dvcjgOOAie6+o0fRXVQYt10I\nPF5FXnEpZCKJDBmqPtZ0iK9o3lEBVJQvKg/l8kUp8cbryIuZ5O7jTHXefnT395J1mlkd5El0cfdP\nTEmqSUgrv07c90jdNtrU3fczdCPfDHlRs1LrTOYoafhY6mKIX2uMDPbalN8uPo2krt5MFDqsUcZx\nXkUeZlLg9QDUhn2LYvukkC6Vb2b2BjK49cxseCrfqiO5Qq7L1dGLc92OXF5Xf3KFgYueX2BlGjFm\nyJBhDWJNe1CFMBrVY1sn+IiDYtuLqF/RhqDWF6GYW1V4HpXtSVAXeSztgGPCOJ0P/Dlvv8SQfBVe\nyKGxvhrAZu4+AlUyWBeFCO9Ahm8IqmdXE/FSjurNrW+q+LAfgKs1/UdmdljMa2a2fRxzFlIY7oJu\n2OOKnNvifFFDrK8ExY7jKrOUHvcU8niPK2dtY5DHCWWEYoNbugZxUJBrTGjxeUekRNwIVY5Yhr6D\npCZghWofmmosZsiQoRqhyj1ZuvskU6Xw5GZ7p0ehT1MDvefj5roYSZpnFZyo8jgT1ZSbhp7iR7j7\nqaaur53cfbqZ7Y6qpIM8q+Gu/kOvoVDdEtTgz4jEWDNbN94PiLGXIbJ/F8QXfZxaw0SkxFuI6s5h\nas73HXBnrGU2Ku76MRI+HI+KvjpqMngpEk4kJZXeQOHJt8O4v4DCkKUY8RCmrI9KFd0QxxxsZmOQ\nAdnClKj7FQoF9jazO5DxedDM7olz/tRUTeIPKJH5DsTFpQ3kxkBjU8Lu3Lg2g4J/7BXrP87U62lu\nvG8OHBzrXA9YZmYjUEhw3VADfhvXL/m9zIzvZCAqlTQ4db4librAPKvXqFDl8w2S61gB/KLG3l6v\nUZGhVXO92dgqt46KORfunr3yXoiXAhnwYageX2cU9quHyPk3UWhqa5RnVCv2uQX4bTnz9wVujr9n\nxhfoKJcJ5A1dFH8/BPSMv5sDbxeYoxVS8jVGIotzUsd6A0mxWyAuqlvqs+TYBc8t71rsgozWpsjz\nfg3xhbUQz9MkxvVB+VSgpN2d4+//A95IzTU875qUNc9IFEItte74ewNkqOvF+/OAi1Pj/rQSv4MJ\n2dhs7C9hbFVaR/pV5TyoKoJEvAEKL95FFI315Zvp5Sf7Jk37KotF5GrRTaRwsi+UFm/0MfVtWgic\n4ur2W9YxZnnpBowJSoQpeec2OW/cOHf/NMZMQUZvDpJ6vxDHrgn819TJtpG7J40Z70dKzWJoW2ie\nsk4m0I3izRyhdDX7DBkyVCNUewNlqltXO2/zse6+Mg1+fjLxhru3iPkrm+wLMMTdT8+bsqzmfuUW\nfC0HhcQHBYu4hoGqDMorBlvWfmU1c1zZc86QIcMaQlUUSVQKLnlxh7zXqu8+V0XEG2ZWVkdgUFir\nU4zthHKUykOxc6sI3gWamFn3OGYtM2vn7nOAOeHhQfk5awXnic/yhRDp968DPUxllzCzemaWbi+/\nMoMTESwAACAASURBVBiYjc3G/kLGVqV15LCiscGf84vgXQpsPxtxOm+gShPJ9j4o52kaCs91K2f+\nvgR/VOiYSAk4KP7eAIWppqGqELcVmyO210VG7U0kIX+bHAf1Rt7YmeS4nGLnluaghqe23wz0jb87\nIB4oaQR5UmxPyjlNQbxamoOajwQlyat7GfMcggzYlDi/M+L9iPh8V1QncFq8Dsg/v+yVvbJX9XuZ\nezpVJUOGDBkyZKgaqPYhvgwZMmTI8PNEZqBWE6L00ZS813Zrel0Zqg/MbD1TG5Rinx9WkW0rcNx6\nSSK3mW1pZgfkJ3qvwJxmap2SoRIws190Ha4sxJchQyDEFbcCTd192zAOB7j7XwuMPbjAFN8B012F\ndNNjmwJ/AzZ2973NbBugu7vfVWDekaj24FqIz/wCGOPuZxcYO8ndO5W3LbavA/wRaO7uJ5naprR1\n9+EFxk5EaQbroYog41Gdx+WELjH2buAhd/+2wDVJj53uKh5cLsLQ/tvd50aCfifgr67Cz/ljewBT\n3P0HU8eDTsCN7j4rNWa565eGuy/X5sdUzutTd19oan3THrjPJQBKxhT6HaTnfbzAvNeiHL83y9o3\nxs5A3Os9wL+8jBt2RdabN77ca2xmN1G6ZFn++Z25susoC5kHlSFDDneg8lOLAVwNF48oMrYfqh5/\ndLzuQEnCY8wsv5jwIFTCaeN4/x6qJ1gI67pKSB2M/lPviHLhSmBme8eNYxMzG5B6DWL5diYJ7kFp\nAomM/zNgOcObHMLVOPRg4BZ3P4ziTR/7xHmNN7PBZranFc/JmGRmXYt8lo+/xI2zJzr/u9DDQyHc\nCvxoKrP1R9Tp+b68MQ3i1QXlNG4Sr1MJ1WsBPIZa57RGSrTNUOJ8GvvHq1+sMfk93EmuZVA+3gYG\nRpTlVFO1mWLYMo59LDDDzP5Whkq1IutNoyLXeAJ6UKqDrtOMeHVAOYerYh3FsaZVGtkre1WVFzA+\n/p2c2jalyNjnkKeVvG8a2xqzvFqyMvNOR1Xknwe6xrZpeWO2R1XcZ8W/yetgYL0i804osIapRcZO\nRobsdaBdsq5yrl0N5Pl9hspwXQY0zhvzDjKgHyC15fT8c0uvIf69Cjgqf+15YyfFvxcD/dLbCowd\nBTRIvW+AmnGWNe+5wBnlrOF5VMk/ed8MeK6ca9YWdTGYhW7gvcsZ3zuu7xxULq37iq53Ba7x68Ba\nqfe1gNdX9rqV96r2iboZMqxCfBXhCQcws0MpXs1iM3efnXr/RWz7xswW5439IbiEZN5uKBxYCJcj\nQ/eKu483s1boibUE7j4VmGpmD7l7/rGKYZGZ1U2tYQtKJ16n0R95kk+4+5uxhhHFJo5Q6PGo2/Nj\nwIOoOslL6Ek7wZ7L710Un5nZ7aiiyt9NBZSLRXzmmtkFyMvoFfxZMc6sKarakmBRbCuExWZ2JDL+\n+8e2YvNu5u7p38psVJqsIEzFi7ciVzdzKnC2mZ3iaiOTjFsfOCbObTZKsXgKXdehlM5zrMx6oXLX\neD3UeeGbeF8/thVCZddRHCti1bJX9vo5vlBNw/+gQsCfoRYoLYqMvQWVpkq8l6diWz0iPys1thPi\ncr6Lf98Dtl8F620DPIry4z5MXkXG7oGeur9EBmQmsEs5869TgTVMRMnqRwG18z57vMD4nsDx8XcT\noGWxYyOPsE28bwbsUWTsRiiPr1e8b06RepioIeZUVLPyUsTv/LnI2G1QG5sj431L4LwiY29GDxZ9\n4/Uv4KYiY69HDx23AzvkffZu3vv3gL8AmxaY57wVXe8KXOPjkac3CLgXNW09bmWvW3mvTCSRIUPA\nzGq7iN16QA1XfL6xu39TYKyhBOIesWkM8JgX+A8VT6ZLUUjHUJJxDVfTyfyx/0Dc0HzUebk9cJa7\nP1Bg7CuoQ/P16En1+Jj34iLntz6qXWgoPFOwGnVU87gLqO/uzYPbOcXdTyswtpW7f1hongJjL0Ec\nUFt339LMNgaGunuPMvbZkFS5Lnf/uMi4puSad47zPKFK3thOSAQCCu/l15xcIYRgIj3vEwXGGHAR\ncJ1H7cu8z9d19+/S4wv9plbRevt5nlDHzK52NTItNH4j1HUbYKy7/291rKvUMTMDlSGDYGbPAAe6\n+5J4vxHwjLt3Xsl5K6O2m+LuHczsINQT7Gx0s9u+wNiJ7t45rY5LthUY+yJwrbs/m9o20N1PLjB2\nLKpm8pS7d4xtb7g6HidjVkQVNwV1AJiUmneauy8npTc1xrwWCTC+QF7RO64uyfljD0fV8kci49sL\nONfdHy20thAFtHH3e0ztbOq7+0cFxrVB/Mw2lDaSy3XyrgwqomY0s6cpWz13QHq+csYWTFUws2eB\nB939wXj/T6COu/crMNaQ+KOVu19uZs2Bjdx9uR50ZvZRofWsyHXLOKgMGXJ4Ehga3NNmKGx3TqGB\n8bT8d2BDdFNM2tY3TI3ZCCnF6poaLybqtoYovFIIyf/JfZF38V1xURwLg2+ZYWano7Bk/SJjWwLn\nmVlXd78stnUpNrGrO3R609K8IeU2iSyARe7uZpbwYPXKGHsF8vb+4+4dzaw34mIK4UIkKPki5m2C\nQrXLGai0F4eUjbWAB8h5wmncQ85D7U14qIUWUJHfQwqT4nsYX+R8QD3YKor9yh9SEIcAT5nZMmAv\n1B17OeMUuAV1btgV8aRzEd9YSJWZ/l3VAQ5D4qHKY0Xigtkre/1cX6gJ5tNIYbZTGePeB7YuZ67j\nkLhgbvybvJ4CDi6yz9VI7TYZ3TyboHBKobFdkUHaFN1MH6NIHUhgEjJ+t8T5rUtxpdujwE6xTy1k\npAevgmt7DuJdPgROQm1RzigyNlEdTkVhSyiuOpye975G/rbUZ1OQ8UirGYspCSfmz59sW5HfQ2ps\nZdSMf6jItkp+D41Tr83jt3Zzsq3Y7yf+LVcFWta1rOwr86Ay/OKRF64yFE6aAnQzs25eIFwFzHb3\nt8ua193vBe41s0Pc/bGKrMXdzw8e6jt3X2pmPwIHFhmbPIHPM7MLvGxOwFyhy9PMrC8SgBRTYZ0K\n3Ii8v8+QhPr3pSYzG1DOeSyXwOnu15i6Un+PPJiL3f2FIlPMMfU9G4U6Nn9B8dYp/zaz54CH430f\noiN1AVTGi6uMh1ru7yGFyqgZj0PfRRp909vMbC6FQ3zFvLiJeeMNeez7xvZCobjFoTxMrlsT5FEt\nf1BxfAlqII9qhWxNZqAyZFg+XPV4ke1pTDCzISgsWCJ28AKVA9z9MTPbFyW7prmMy/PHmio+nIaM\n5MmIg2lLrpllMTxL8YRTgNtSxx0UvMXv8wfFTehYL1A1Ig8Ty/m8INz9heC41orjFRShIKM8HzgL\ncR/rotBSoTnPNbO0YGWgFxAoBB4JaXUjMzsJJdPeUWTsH1Ao9kwUcuyNDEYhVOb3MAuWF4CkETLt\no4CWZvZU6qMG5KTeyXyVCre6e8swvN3dfUwFdxsAPAFsaGZXIo7yoiJjr039vQQpRg+vzBoTZCKJ\nDBlWAGZ2T4HN7u7LVQ8ws9vQja43qjBwKFKaFSKjh6Cb/29d5ZbWAV71vAaaBfab7CE8yNve0N2/\nN7OCHEAh42Bm4929ohUfKgwzOwUl8C5AT9/JE36rvHE1EffUe1WvIebfHcnuDSXTLufFxRr+7u4F\nOcgC4yvze8gXgGwOvO0pAYipp1xLJNJIq+rmonDgktTYSn/HsV/B30wxmNlWwG7our1YCY9xhZEZ\nqAy/eJjZDe7ev5hyylOKqRWcf5q7t0/9Wx/VVetVYOwEd++SvnmY2VQvoOLL2+80d7+lwPbh7r5f\nSlmVVj4sZxxin+sR9zSEVFjNC9fBa4JKPOUr3XYtMHYGemovKG/PG/si4umKJTSnx3YDbgK2RuV3\nagI/FAhtVQpm9rq7d1uZOYrMOxWJDUoJQAo9sFRwvvR3nI+C33Hsdw3iAR/3IoagmNFLTV7oAef/\n2TvvsEmqauv/FkMOM6CYySggAipJkgoqBsSAEgRMgFwDKgYwKwgqYroSRCWIgqCCGAERRHLOSeAK\nY0BRUSQqmfX9sU/NW119TndVvzP38/uo9Tz9vFPVu6pPh6lTZ++115pFkEtekHadBezT5rtsok/x\n9egBR6e/Y5lTkj5k+wsqiGjmai9EqgpCL+6pwO1EU2QOXRQf6pTpQ3KU6XThEvBCF3qIMqhWa/WU\nmomLahPHEBPZK4na1VuIZuAcbiaaoNvgXuAaSacxOEnmPt+DCc3E44l6x5sJDbshdGTbXZHSa8c3\nxpATgF2Y0ONrpnFzenwP2b5d0nyS5rN9hqSvTjpe21umv23cs+t4O9HG8LCk+3PnZqpeVdVm70j/\nXpKQtMq95rcI49MqrfcmgsQzUlg3h36C6vGYh+3L0t+zWoRXaY1LO7zEiZKWJHp1Lif+wx9eiN2L\naNBdVtIxRF3lrbnAtpTpRAo4CWilJN4xtfZ420dI2j19fmdJKtGnPwqcn2pQ9TpNbtL5EVO1wDZj\nvknSDNuPAEdKuiK9XhNfAF7VMj21MHEzUZ+YXRjX0QQ772XExL4jU7+VJroQQFqPV9LR6Zzn2L5h\nXHyb2lU16Uk6jJC+OjltvwJ4beGwlW2/vrb9aUUPXGf0Kb4ej3lowkbHCV9rIaIZspjuUHvFhy6N\nr98BDvbo3pt6fFtSx4W2N0gsugOBW4Ef2l45E3sxwR68hhoDLLEdc2NYhLAHuXHMWM8m1LgPB/5K\n6Ce+NZcWlXSeRyhXTIoqJVtL4y5ATBRDKcLEHKxWLBUB5Bjbt09nvClV+Pz0WJmgj59tu8kCrB+z\nFCGZVf+ez87EDTUX5/al/RcQjdLnpu2NgS/Z3rAZOw79CqpHjwkaHQv1qruIldU3bd9fi92NuADd\n6ZBSWrRUM0pYmEilzA+sLil70aAbZfp5wI6S/kDcrVfpnNxkliV1FM77mVRz+CBRB5pJMO9yWMAZ\nX6scJL2KSLkuSDDZnkPUMXL1wDcRdOZ3p9delmhCzaE1204d/MFIFi3E6mgNYqJ8Ym4AHpQ4yk7O\nk4w3pQrPJvrjNiNSrs9imKZevb+3EUzFZUhtFURNKpfKvVXhGVVJbu1I3Izk8E6ivWIW8Tv7J4Us\nwDj0K6gePRIkvcL2Lxr73mH7G5nYA4gm2nrvzd3EpDXT9ptqsVe6wcIbwbrbP53rOqZWGc5dmCXt\nQdz9bk6wvXYmjAMPysQun3vPrpn61WJbkzq6QNLnCMrxzxm82OYK7ZcRF8ozXZBbmnAMXdh2ZxGW\nEd8cN4Z0sT+B0E48kuiX+lT9t6Pu/Updx3s6IVZ8AXAOoYg/SpPwGmIyu9Ahr7Ua8DnbQ7WiRJao\nEx/OBj6d++5qx8xMg727FDMO/QqqR48pfFLSA7Z/DUGIIO5EhyYoQmWiTsX+uRI9W1LTKXWGNCX6\nqaAwl8zeXkuIqRaJERXcofHV9h8UDZSbEBfJ85xh5SW0JnWk1OHuTm6pKWX05QI5YPv0t14bKjaG\neljmqdQYujGhTL48tWtajr1me6fcOQpY1PbFjTFkDSFtVzXFs8i/n879SumYLuO9GlgHWINYzd8p\n6QLb9xXi77d9vyQUQsk3SFq1MI5/EqutsZD0qcZ2dY5sH9so9BNUjx5TeDVBaNiT0CZbjYKKA7C4\npOUqZpxCPLNSGXiwEXsK8ANFgygEe+qUwnlnE2SHsROUpBWJOsdpaXsRSSvY/n0m9lOEJlqVGjpS\n0vGFdFUXUsdarll5275DoTs4hI4ss+sk7UBM7s8gmmXPL8QeQaT2LmNYMxCYmH051h9Mk4nm5qjb\n97jm7TXJeG2/Px27BJFSO5KwIlmoMLw/pe/5J8Bpku4gLDWGkNKdewArMHgTkEsH1lOYCxMp9Il6\npvoUX48eNSi6+39FXOx2duE/iKQtiJXVzUSKZkVCAeJMYFfbX63FzkdMSi9Ou04DDk+Ms+Z5TyAc\nc09nDNNN0qXESu7BtL0gsTIaarKVdCPhQXV/2l6EcPXN3jHXjhtJ6lD09Gxq+460/TjgrELxfAGi\nPlGlic4k0mdDpouKBuWPU2uoBfat1/ZqsRfZfl5zfyPmVbZ/LimrBJEjaiiMGg8ldAnvIDyQ3li/\nAVAwKYvwlDBv/by/J+pkdcr2XwlDwl1tXzbheN9NECTWIVKp5xA3ML8eNcZ07AsJssYp1e+p8fxV\nxO994CbAiQE75twLEQ3Rm46LHTq2n6B6PNZRqw0o/V2QSOWYco9M9R9vtbR5Y+7iOcFYulyQcrWt\nbFOvpDOArWqpuCWJBs1cQ+0Moq9pBQbvlnOrgTcDHyN6hUQQKj5r++hM7OHE6rB6L28CHrH9ttx7\nHgdNab5tSzTn/ojBSb2Uwuz6OnP8webS+Q4jmI6/TNsvJUgdRwIHjJtsR5x3D2JSusw1pYkxxyxF\nTJb17znXkJ21cenwGpfYfnrnY/sJqkeP9lA0ThZRZ1dJOs72tirQ2HMMuo5jOY1wbf1Z2n4N8F7b\nL87E/oQoiJ+WxrI5wcz7UxrLe2uxJxM06CYdfGg1kOJXJwgNJtyEf1OIG5o8R0yo6xIT3woMXjzX\nqsWckXudqdDs5NtF+WJJoum3OYZcOrB+XNbrq/Z8jrJdEVIGbjq6jLcrJO1LpAJnM0jIyX0WexOy\nTD9mPMGl/nufQZCJ9rF9cNcx9jWoHj0S1K7R8VUjTtFs4qyKymNp7KVJbM6J85PZO4hGz4OJ1cst\nxAU1hx+nR4UzRwxnmY6T5wLp9at/l/CIpJVt3wxzUmjZmhGhULEnjUmyDk+m1ddF+eJk4MJRYyig\naOCV8BdJHwa+n7a3A/6WVq7N1+ky3q7YlmiqHUrpZVCt7Pes7SsRXOq/94cJpfdWK7om+hVUjx4J\nmqDRcS6+dkUDrxTGqxTZG4m72qwNdzp2cSLo3pavtfao9JeC6n667VNbnGt3wtvpBOLCvBWhJp6j\nur+YSGPNTrHLAzvZHloJSTrX9iYt38/ngC80mIQftD2ktq0pF+I5Dc0qiOOOWwmNGM9ncq9de35p\ngrI9h1FJqE/cRTQm3zTJeCcY5wnAOz2Cit7xfJ11+8aes5+gevSYQrqLrTc63md7tUzcp5r7YJBK\nq+G+l6rGNarvZag/qnShTDWw1zOcghpJ522RgtqKaMicj2hAHTXeqwkB2H+l7cWAC0orsDTmiphx\nowt0+jSZbc8wWSTXUNvlM+uifPF+QhPwRMaktdpC3VXSW493grGsC/yU0M2rv7+6nfyLbP+6lNpu\npLTrgsRDun3urhXYp/h69Kig4UbH9UbcXY6l0nqCvpcYhjZ28umRtBEFm3Hi4nIXwawaS0uvv8aY\n578CbEg4yY67gxWDabpHSufXlKLG1Wl7KUm7OK+osRNBQFmAWn2EvA7eDEUfzwPpvItQplbnlC/e\nV4h9kKDaf5ypG41sWkstRWgdJpStVoYjxjug1JG5EZrzVG4MNXwnjXlUCvOFwK/Jp7YHvg9Ppts3\nEv0KqkePBIXNxDrExf48oh41qtGxfuxIKq0Gm2TPtX1FIW4dQg26kom5g6C755hVEykrSHqt7Z+M\neP5sgjo+tu6i6AN6C1P1rdcC33aNZl+L7aKocaPHUOBrsR8mLqCV6sJOwM9sfyETO2fyH7Uv7Z8N\nrO929iA30V7U9euEW3FRJV3S/rY/LGkb28ePO+ckmFupwsx5W+v2jT1XP0H16DEITTU67gE82Xbp\nbrx+TJFKq+Em2dcCpSbZ6phZAB4tKnsoweK7psX4drF9RG17BvCJHDNP0reJVcIvGEz9DNHMU3w1\n+UIQTEqT7zVEY29dUeNq14z6arFHAl8sMQIz8S8nBGMBTnOicGfihlJ/I9KBpwKvtT3WIkTdRF3H\nyhdVnxVBGR9ZB5u09iPpK8T3+zPG0PO7MBpTOvIcBnX7XmC7i9U90Kf4evSYAw03On6L+I+Wi81S\naQun3pHBJtnPE+KcQxNUs66k0TIxmwBvTbn/B5hK6eTqPy9W2KLvAjwO+DYhy5PD79JjQaYkmQbu\nZBsXxd+nx5znChfFX9JeUWMD4MqW7w3bp4w4F5I2JBpun6BB9YeZxPeXw7/SGM5gvD1IF1HXNvJF\npxCr58Ul1bXsxnk2Db0cBeklQgkf4rOux+co7F0YjdsTJJAfp/OdzZTMVSf0E1SPHlNYmKi/tGl0\n7EKlvTWdu2rkXQj4cyG2S13pFWOenwPbO0jajrjA/AvYIZfWSvhNM60kaZtGTPOiWE1gFREkd1H8\nEDEpvTNtn0ZZQunlhf2TYkFCimp+oF4bvJtoLs7hJ+nRBjMJM8aX1vZla2ZpBZXri9u59u89gT0l\n/dR2SW6riu1MPkjYxfbsxthKk9nCbqFEn1bFH7PdSrdv7Pn6FF+PHoNQyB3VmyL/WHtuEgvsLk2y\nnepKmnLUPVIZR91a3DOIovg1hDX6b4AP5NJXXdJgXZCYeee3qenVjil+FxO8/gzgOA+a6f2vI61k\nKyxMUPNvLazMupy38pda0fa+Cn3IJ9vOWqUUvuesYkQXRmPFPJzOe6nQr6B69EhQeBB9BXgq0TW/\nPMHMq9dIJrHA7tIke76kNVvWlVo56ib8HHi37V+lC9kHgEvq7y2xrbYAnibpwNqxMymoeHfEm4Gv\nS/onkTo9myCM3JF5b68Gvszo76IeP9bcMDHontp2sGlS349hFYeVajGTiLqe0Hid7xFGjtPFIUT6\n7UXAvsA9RH/aABFCYavxLGBWgz4+k9r7bKA1oxG4QtLPGEECaYt+gurRYwqfIfLxv3K4o25GNMrO\nwSRUWhccYwvoUlfaiuSom17n1kTwyGF9J1+eRFL4ssJ0sY5bCcPFVxMTcYV7KJsQtobttwCkSWJr\n4GvEBJS7Du3LmO+igrqZG17Z4eJ5JFFL+W+iL24nhin/FWvv0tzYWuIZFMwNO+J5ttdW2N1XyvI5\nW5dViRT1kgzSx+8hmq5z+CDw9DaMRmKSu53BWlapRWAk+gmqR48pPGT7dknzSZrP4VA6RJdO2MD2\nnP/Mtn8haYjWDAMNjANwxq+IDnUlujnqPizpk8QqY9e0OlgF+J/aeK4CrpJ0rDMK49OFpDcSJJQ1\ngX8AB1MgodDtu9gbWJ+0MrV9pcKKJIcuF89FbJ8uSQ5jx70VRopzmrRt/zz9bX0TomFx4r8Senvj\njlsKWNapjyyDh1Ias/o9PIEMocH2T4GfStrQ9gUth30TUWMbi5YkkFboJ6gePaZwp0I26GxC4+42\nBhty6+higb1u7d8LE5TzgVqWpJlphdNFMfu4xIhbUtKuhKPuYYXYI4lV0YZp+8/EKuLETOz6CnHQ\nygCwWsXlGlSPds09uLQv4auEPck3CFHZ3494b12+i5y5Yba43vHi+YDCKuW3ieH5Z6Y8vwagDqKu\n7tDALelMYkU7P/H93ZYo7TnCwoFEKvmJkj5LrFKLkkvALZJ+zFRK+BzCfPJPmdjWjEZJCxNs0Wcx\n+FnkTCxHoidJ9OiRkFYg9xMX5B2JZtljbN+eie1sgd04fqAYLelE21tqUC6mwsDkoEHVhM2peSa5\n4Kgr6VLb66rWGKuykvgNZAwAC5/DQKE93cFfY3v1wjieRXxmmxCprRtzk1n6Lu4jUmrjvosjCEmk\njxAU/fcCC9h+RyZ2FeDrwJNsryFpLeDVzvSkSVqPSOEtSaQcZxK9WRdmYk8lRF33oCbqaju7Mkq1\nn6px+xwXGqer70thKb+s7b1U0+XLxK9G+I6J0FMsNg4r1PCPZVD3cUfbm2diu9jAHA/cAOxAtF7s\nCFw/EbPPdv/oH/1jHj6AtWuPdYkL2FXTON/l6e/RHY45H1ikduzKwMWF2ItanO+jxGrvYYKqXa3+\nbgf2Kxwzk0hhfp5Q6rgR+E4hdkWC2lxtLwKsUIhdFPgsQfq4hKglLlyIPYtIB15R23ftXPiOL0t/\nr67tu6QQewhwKlHT2onoefpaIfYa4Ckpfr3mazRi9yUYoou1HPPQb5AwsRz7ex7z/BX1cRLknQsn\n+Vz7FF+PHglqqaeWYrtYYH+59u+HiabWbRvnG0nh9mB3/4IKO/SNlBHxdL7gvxdxIVxW0jFEWuet\nhTGcIemLjDAAtL0fsJ+k/Wx/dNTYazi39jjY+VRSheOJxtoKj6R9OWmedYBP2f54473kFNsXtX1x\nIx2YZSimFcY2HlRJ/77zighVze4vkl5JpHtLLQkvAp7pdPWW9B3gukLsPkSD87m2L1H0Kf22EDub\naIg9MNW5ziHU+H9aiP9Hqgt+L21vT9xgjMPhxM1WCdVncaekNYga20QkkD7F16NHgrrpqU1sgV04\nX2vzPUXv047EJPezTGw21y/p8QQzTsQd7T8az3c2AEzHLUWk6+r1hrNLJ5K0qMfIB6mbW/C/iZXT\nNk7ivs3UYy32F8C7CamptSVtTTSsDpFTlFdJL2kHbklMCMsyJer6aSczyUbsicBuDuIFCquVg22P\n8hprDUlPJn4bewBLuVDzSq97EFN1yfMIw8uRvWalz6D2/NsIevuahGLJ4sAnbX+zdEwJ/QqqR48p\n/K3N5JTwsO2vjwrQoKTOEFzTtnM3872n2H5nulAc2vYgR/3mpBHPdzYATBej3YFlCPmmDQg1+Jwr\n64bAEcQFazlJzwbebvtdmVP/XdKrPegWXKI430j06Jyl0Bw8n7zsD4Tf1qHAapL+TEg6ZenrwKOS\nlqsu2OmCPnRHn+puz7B9IqECkv0cFbR+E0oW10u6OG0/j2jczh3TmnAg6XCCpPE3YrLcmvwqsjrH\nHwgCRldknZVr563UQc6mLLPUCv0E1eMxj1qarLWeGvBzSe9itAV2dee6KpGaqu6mX8XoC9K7qBXQ\ngW846fglfJRId72DuNjOVRQm1ruIOsuVjf27E+/tQtubpSL95wqn/irwMtLnYPsqSS8oxNbdgiFU\nN3LMwHQqnyjpRkLr71uUWXyzgZckEsZ8tkexJj8OnCvpLGLCez7wX5lzPiJpe6JfahS+NOb5wwvN\nBgAAIABJREFUHI4mCAcvo0Y4KMQ+ntAVvBP4J/APj5DskrQMsYIay+JrpKD/mLbvAv4w6jWmiz7F\n1+MxD+XVpStkU2aJbZeLzVGxzwZeWV0MFc20J9keujhLOo4gG1T09R2AJW1vU4upJJPWI9NH5HyD\namtIOpYgc1SNvFsCVxP1tuNds7FQsmyQdCXRKPqApOucVyi/yPbz2jAJa8eMdQtunG9xQuT3dbaH\nbsDVwX03Pb80U2KqQ2nRWtx/E2SAHzDYAFxcwbRBjcV3te21JC1AsP6KUkKSnklMaO8HZthephDX\nhcV3IVF3upqYrNcg6mazCFfese7Lk6BfQfV4zMMTNBa6m0DnkwipmAoPpn05rOFBivYZkpqWE68k\nLhZHM0jAKELSysCf0gSyKWHlcFR1oW5gGYKpdW86di8iNfgCouZWb0j+k8KK4SfAaZLuAP5QGMYt\nCgNGpwvt7pRXA8DoiakW89zav+8FtlXo0OXwCtsfq8XfIWkLCv1CaULK9Yo1UdXL6qrzJWXwLmhN\nOEh1sOcT39OShNFgqREa4Am26zdn35ZUMm+8lajVXZdea3XivX6IINP0E1SPHv+3oQ4W2DUcBVys\naIqEZOpXeInLJW3g1Gsj6Xk0ZHRsPwhcKGkj239vs8ogitbrSno6kRb8KXH3vEUm9okMKqk/RPQN\n3SdpQGHd9lbpn3snksUsyrYX7wAOIMz6/kxc1HYbMeaR0BgdPKIfqoku7rutMUn9riUOTau8TxCp\n0cWBTxZiX05MSAfYLjWN13F7BxbfKtXkBGD7N5JWsz27YkSW/k/Ujumljnr0mMdobYE9Z6f92cQe\ne37atZMbpn6a8pdagBCM/WPaXp6oQeTwJEWD6OPiFPo78Bbb12ZiH7X9sKStCJPDg5Q02zI4BrhI\nUkVPfhVwbKrbjDIQXHUUaSOtRnYccfwc1CeREfsm0cE7Bji9ltbdiVB5nxa61O0U2oEneYxjsULF\n4m6HmO5YwoHtd9eO3TKRNkZhZ6IG9d/Eb+184vPI4TqFE/D30/Z2wG8U/mXVKq/6P/FEokXg12l7\ns3TuzhNUX4Pq0eM/AIkhVkRFSW4ccz7wcdtnpO1Ngc/Z3igTexFBUvg4QaX/nUZYe0hal6ni+Xm2\nx04CGmPJoZAD2pXh3rFcja+V5Udi0O1ve49x46sd09Z9t3VatGPd7rsEtfsE4Fu2SzcgcxRA2r63\n2nHF70IT2MmnlWZF3oGgpB9CKK8sWl+9p5umt9j+S9p+CvBtT+Co209QPXokSNqd0Ky7h2hGfC7w\nkXlVAJ4ucgSDEukg1QzeAVxg+3sKMdVtbe9fi5lp+24VPK88RsZJ4/tjzidSUM3esRNqMU8mUoDf\nJQgiFV18JsFmXC1z3gtsb9jcXxjDYsB9th+VtCrBsPyFM+K4ifixLjHJnEykRZ9leygtmogwW9Tq\ndosTdbuXE6uo1RvxM4mU2k7E6uVI4HtNVqHCffkfDJMvJv4u1MFOfhJIut72M2vb8wHX1fe1Plc/\nQfXoEagu7pJeRji/fpKQE5rr/4nnBlJN63IGWVjr1OpCXc/X1AOc8xQFhmLj+GU8Qh1CmebbTMxb\nCIWLdRlM3d1D3IXnHGq/TkxqYy00FGrkzweWIhQtLiVU4YdSj9UqRNKewP1VWjR34VfoF65ZTXQp\n9XWV7dVGHPN4gjr/PiJd+XTgQNsH1WJas0Ub517fZaPCLxIr2cUJhfJKVX2UcsrGhGp8JSBcDSTH\nWj2YaNyualvbATfZfs+oMefQ16B69JhCdbe+BTExXSep1PDZ/qSDd+2rAKtRuGvviJ2JpskfMdUz\nVVKRqGpcddxFXKA/Y/t221tCN4aipCcRfU9Ptf2KtFLb0PYRmfATJW3h5KGVg0N89DuSXu+Gsd8I\ndLHQkO1/S9oF+HoiWTR7uyo8pOhvegtT9ZUFCrGt63aKpuO3EhPSUYRX122SFk2xcyaojt/FAsA7\nSQLGiv6tbzR/Z+5gJ1/DEWQEhHOw/e5U66zaKA61/eNRx5TQr6B69EhIhfOnASsCzyaaHs903gJ7\nG+AU2/cobDfWJi70Q30vjbv28whZntJd+3uA7zrjMjsdKLyqHiGYewBvIERW/wps4obMjqSnMXy3\nPCRflMgfRxK1sGdLmp8QC10zE3sPsBjBEHyIEXfsKf6VDCso7JOLbYtEDHkXQQzYJd2EXFMY79i0\naCO+Vd1O0reJ2lPu83yx7dNr24sS7sfL2f4vhY/XqjkChEJJYgGmSB9vAh6x/bbsh9EBSj1sHeKX\nJ9Q1fpXew4xm+rIVPE0V3/7RP/5/eRDWDmsTjbEQnflrFWIrpeZNCKO8V1JQAWdKQfw9wIfSv7Oq\n0YQS903AcUT9QnPpvV0+YlzXNPbvTwjankwU/X8O/Kxw3kvS37o6+FhF7Bbj/QaxuriFELq9Bjii\nELtSGuPfCXv4nwIrFmJfQNC1P1w79sD/xd/YDMILq238D4heo2vT9qIjfjs5dfKJVfMb5/k8ISe1\nITV1/kLsrsRN2M1p+xmE9Ufn1+1TfD16JDhScMsAO6TM3llOjqkZVGmOVxIpjJMkDXkKJUihQ7cj\noasGcaHKjeETCufblxIF9IMV6hJH2L65+7uagxn1uoTC66gaQ1Oq5rXEXfoDjMe/Ui2lUubegEgd\nThcbOZQTrrb9aUlfBn5RiD2WsI+vam9vIOjQQ3f8jlXL2bXt2TT6pSQdZ3vbTFq0WvFlvZjawCGL\n9KikWbbbfE4r294upRpxpCdLaedHJK1c/U4Uyucj03EdUH2WdUZhqRF5N8LS5CIA27+VNJGaeT9B\n9eiRkBhT6xH1BID3KmyxP5YJ/7PCzXZzYP9UFJ+vcOr3Efp5P3aklFYCisrhti3pr0T67WEiNfhD\nSafZ/tBEbw7eBnwrsctE+De9LdVI9mvEziZSRW0mqA8QK5KVJZ0HPIEQKZ0u7kt//y3pqUSN6SmF\n2EVtH13b/m4iNgxBQXX/EMOpw/qFtjLW23KSgbfAvcA1CqmhOqkj11j8oILiXd0ArEz5e9mTUB6Z\nTXzHy1Pua+oEd2tEfsD2g5pq4J2fgjbiOPQ1qB49EiRdDTzHqYFS0WNzRe6OOeXVX06kx36r6PVY\n09OkpCuo7m8mqMWHAz+x/VCi6v7W9sq12AMzp7gLuNQFDyBJswBG3b1LOoGowZ3OGHvvFD8/QdcW\n4ZA7XfIHaRV5EOEO+zXiAneY7U9lYvcH7iBWTSZYY0sRKSlco2Srg/NtF3JLx9gu7rSbEyoSqxPK\nGxsDb7V9Zu4c6UZp1bR5Y24VXCDM1MeR+73PYtBB+ixgn9zvKNU77yR+x+8han6/cc2vqy36CapH\nj4Q0QW1aXdAU/UBnllI6CruISh3iHNtXNZ6v7BWycEbUVdKniQJ6rjH3ma7ZgUg6lLgQVs2Wryfs\nIx4PzLb9vsbxrUgHHS+guxFW7HXx1e1tH5KJPdoNe/fcvsxxCxEOudlJVXkqdm3YU1RoSZfZXkc1\n23QlwdvMebuQW1rFppueo3LnKEHjfbw6SQxpqim8kpmqVp87pviPZMZwAnAtgwSMZ9seeu10M7UL\nkaYWYbh4uCeYbPoJqkePhJTn/zyRfhNxt/gR2z/IxO5OFIOr//xbEbWoeg/LC0e9nu2zarEl99Uq\ndqgxU6EwvbHtR9L2/ATVfBNiZbd6LfYbRIF9M2JltjVh+b5L87xdoLyxYKnvZ0DdIF2sB8ZZeI1D\nbQ/ZXEw43gttbyDpl8CBhAjqD+sr0+Z4E7NyESdKevP9ThB7LvAih6ZimzGvxbD6xo9qz1eyTZXE\n0OnE73cz4Hyn9oHMeXOGjCWjx9z3PLavbbroa1A9eiQ4qMRnMmUr/mHbfy2E70LYS/wL5qSZLmCw\nh6U+AS1CUIVvLJzvMqZWW80iuMnrsC1FNFtWK4vFgMelQnwztdOFdNAFMySpujtOk86C9QBJHwU+\nBiwi6W6m3t+DtPOzai3102Iy+0xKV32QKefb95dP147c0jF2NnCepJ8xWIP6SjNQ4W21FmFtUWn3\nDfR4Oanxp/Tl6m5IDBXGUI15Y9vnpY2NKNdR75O0ie1zU+zGTNUJmydtNvW2avTOoZ+gevQYxP0E\nOWFhYBVJqzhvXy4GGVKPMDyxRGCIg36JuHCvKOk5RP5+TorP3ew7KnwBuDJNqtWK73OpHvKrRmwX\n0kEXnEKYBFZ23m+noWZuez9gP0n72f7oBK9xW4fYkZOZp/qHis63NXQht3SJvTk95mPK1LKEDcat\nMGtYtpqcEv4GlGxHICbSb1V1SaJulG30Jup1R9Vi7yDqdzm0buodhz7F16NHggr25Q2GVxX7AeI/\n6ICFhu2vZmIvI+i4Z3rKWG+gOVSDjqVDcMH4Lt0lr582L3HBZqEL6aALUr3h7em8AKcR9YahC1OK\n3YHoUdpX0rKEfX1Wkicds6jtf3cYzym2X94ydqS4bS2ujZ3JPIGkI4Av2x6lIl/FTiQxNI44k763\nrW0fp9AQxPbdI87Xqal35Nj6CapHj0BiN1X25c9Rsi/PFYJT/DrU7LLdsNCoxVV1j7rz65wifdou\n0s6J9EjW+E4tFR8ax2RJB5OQOrpAoZn3KFF/eWYiVJxaIChsRNTKFre9XCKkvN32uzKxC9u+v7Fv\n6SaZIHPcOHHbNYlm4ccRK9S/A292zRepFpv77CopqW/Wx6d2VPcq9oUEjf+vBKNyZC+WBiWGzvYI\niSF1kKlSB1V1RbvGDCINWWeBdnYX7lN8PXpM4X7b90tC4T10g0LxOgvbl0m6hXSRkbSc7T9mQq+T\ntANRr3kG0Rh6fuNcnQ3vUt1rO4brEzkJnYWZskswcK6krzcu7F9Kf18HPJkp2/ntiXRRbgxd6g3P\nS0SCK2COm+2CmTgIKaKXERdnbF8l6QWF2Esk7eopk8fXE71dqxTiK5w05vlvAh/woJ3JYQQRoYnZ\nRA9YffVyTxrDYQTrrcIxBNV9S2pU98IYjkjHXsPUd1xEmpDa6t59myRTlbb/J40rp6P4K0l70E5V\nvUtT70j0E1SPHlNobV8u6dWE3fpTiRrJcoSx4LMy4e8hLgIPEBewXwL7lgahsPZencG766MyoV0U\nH44iLpgViWMHgl68Te01zkqv/+XG3fLPJZX8oLrUGx5KJIqKUPEERlx0bd+iQdGE0vl3IGopZxLf\nx+NpcTG0nbV5r2GxanJK8Wem+l4OGzVWgj9Xoq9Laq64Hm/7CEm7p8/8LEmXFM77d9s/GzPOSbF0\nStt9FMBhaFn6jLdLf+sOyFnyziQ3WyX0E1SPHgnuZl++L1Gj+pXt50rajLC7yJ3338QENbZRUdJe\nwKbEBHUy8ArCFiI3QXVRfFijUWw/Q1KprrGYpJUcMkAoRFJLF+a7bLdlAx5I3N0/SdJnCap7aZK4\nJaX5rFDp3p0pB90B2L4mne9oYhJ+gRu2Hwqh2lz6cpRg7exUu6vbmcwujHfx+gpa0nIEwxKCrVhH\n1bz7F0Vv2q1EGjGHKxRmiD9nMF3W2Z02gy4yVc/MpFEXbmy/0fZ3lXcXzrIUx6GfoHr0qCHd4T+J\naHiFSHXl0nYP2b5d0nyS5rN9hqQhgkQ65yqEcsEKDNaKcnf5WxMqDlfY3inVCb6biYPw8rlSUhvF\nh8slbVBLgz2PslX6+4EzNSiZ8/ZC7BkKf6Gx9QbbxyTCyIvTeV/rWuNxA+8ADiDU5f9MqCjslgtM\nRIKVCTr2KoStx0G2v1Z77XFsuRzqdiYwws6EoK2fK+lm4r2tCLwrrbiaDc5dqO6LEJ/rS2v7SlYi\nXdFFpup8QiB21L7qJmaSzzqLniTRo0eCoslyL6LeMqemkytIS/oVkWLbD1iaSPOt57zd+lWEOnfT\nSfayTOzFttdPF/LNiBXB9c47yY5VfNCUrM0ChATOH9P28sANJQpzIlJUr3lDKY1YIHeMInVsQtgw\nHJlSfIvbHqUEMRaS3gccYM/pxZoFfMW1JmRN0Ag9wTjqn9mNzRXHvIImkC6qHTtSpkoTOBzPTfQT\nVI8eCZJuIgr5t7eIXYzomRLRnDmLkPwZOlZJXqflGA4hmlrfQNxh30vYK0wk+qkpWZssnJdUqjyI\nlre9q0Z4EHUcy15E4XxV26so+rGOt71xJvY7wO4elFD6su2SIePIRmhNuQTnetWypI6OK9/WtcM0\nMe+aOe/Qe0tptF0YZvztXIvpLF1UO3ajzDiOqj1fdzi+hKnPb5TD8dgxt0U/QfXokZBWA5vbbtpP\nTPe8exMrrB8zmAYbedcuaQVgpu2rG/tLdhDVeSe2g0jn/wGx2nuz7TXShHW+87I9XajKVwLPJXyo\nsnT7WmxOhqckoTSnEdr2iso0Qk+CjivfbO3Q9lDKTNL5RLqwed4hB2FJxxPkmx2AfYhJ53rbu2di\nW0sXpeeOJlKjV9bG4VyKWB0cjruMeSz8v2TU1T/6x3/qg1gtfIBgpJ1LKAJU+z5QOOZ1wG+JovLd\nxB3l3YXY32UeswuxWwGzattLErWaesxT0t/lc4+58Hlcmv7WTQizxneEXNK21fPEnfg1hdiL09/K\nKHExkvFjJvYqYKna9uNGnPcyYgVbH++1hVgRZIdPpu3lCMv17Hk7fGbXEMoQ1efwJOC0QmxrQ8fq\nPTFlkLkA0aeXPS+hzVhtbzTqtQjSSStDTIKkMjN9focDlwMvne6Yxz1Kuks9ejyWsER6/JFQQliw\ntq9U8P0C8Grbs2zPtL2EC9bltlfMPEq6ZHu51kDrSHHt1ThfJWfzLtt/qD+IXqfpoosH0dK2jyPV\n7ByrzxJV+TiFJNKSknYl5JgOK8R+GbhA0r4KI8jzic88h4c8rIJQoq8fQrjC7pC27yGUNXL4uaR3\nSXqKpMdVj0LsfQ6blocVagu3AcsWYk+UtEXhuSaqmtCdKYU4ixCFzWEX4BBJv5f0e+K9jkqrXUuQ\ngNpgZ4d6xEsJGv+bCGHl6Y55JHoWX4/HPGx/eoLD/uYyAw0ASS+y/WsV7BCcpwrnbhpL/083B5o+\nRq/I7OuKvQh6/bKSjiF5EBVix1KVFU3PD9j+ksLf6G6iMP8p26flTmr7qBpRBOB1Lsv9jG2ErqFL\ns3BFQqmbH5aEey9V9NAdRqzo7iXEg+egRnUX8DGFoO9DjKa6H5rqb58gGHeLA1l5Kkfq8dlq4fmV\nsDTwG0kXM5h6zqVGq9rTFoRdyHVS0dk3N+ZPjhlLFn0NqkePBIXD6TYeLMx/3/bLajHVZPNC4u7z\nJxT6UyR92vZemrJDqMPOF8W/RYh2Vnf1uxEK5W+txbyTWCmtRIiOVlgCOM/2UD+W8n1AlRTPB516\nnmrxIz2IanFrE1TpNYg78icQum1X12IqK4qx3k+Nc1eU/3oBf4jyn2pkH2eKiv1L4DPOsOgkXUSk\nvi5JY3oCIbdUlDzqilLtcF6jSz0wxWftYFxT4a/FHkmw+VYk2iBmENqSQ+QfSSu6wczM7Wv1nvoJ\nqkePgFp4GxUmmwqlSaf1f9jEDvwk8JK06zTiYvuvWswswmpjP6DO0LrHBeKFpH2BPwHHEpPOG4gC\n+eXAO21v2ogfqfEnaRvbxyuaeG9hNFX5WuLCuS+Dq5HqvDkmWJ3yXynF29MngOxIqCKsTfQnbQ18\nwvbxmdjcyvcuohY2pLCe4udISXmEDl6H8X4O+ELjpumDzqhgSPoFSbrI9rMThfwK10SJpzGO+YDn\nELXTO9MNzNNyk3COmNGFyTpwXD9B9egRSCmlrTylBrA8YZ8wVvF6zHk7/4eVtARxQR5S0JY00/bd\npXpIbpKSdJXtZzf2XekQxR14TgWNv3rqp7YqGqsIruh92pEgUzRle0qTemvKf1coRICrZuHTS6la\nSScR9aqq12tTIn23IsESPLoWewjwdAa1+G62nW0u7jDWLqaClbRSXZQ4d9N1ru1NMqvqUanGNmNd\njaCWf4HBG5GZwJ62czJgI9HXoHr0mMLHCTWAs4j/rM8HsuZ3atGnU/sPO6txNz6TWn9I47x1BW0k\n/QN4i+1ra2HHEkKjlclhvRZQqpH8W9K2wA/T9tZEH1d1TB1tNP7+qTDIW0lhvDcAD3pdnUt8rpeW\n0k0Z3EJZdmdipJXk2UQPz7/GhM9PSPz8LR37JOK7eV46x9G12Bel2KoW9x1igp8uZlQ1vHTeRYCF\nCrGtpItsb5L+zjXFh4RVid/lksCravvvIfq+OqOfoHr0SLB9SqqpbJB2va9UewHWqiandOwdkpp1\njEn+w+YUtA+lpqBte8tUoH5hriZTwI6EdNAhxAXsQuCN6YL37kZsG42/LYg02dEE424s6pOTxjvf\nzibklk5isMbXWc8tc97tgQPTCuIcwpbip5nYZavJKeG2tO+fkh5qxN5EUNarxudl074hFFa+9zRT\nownHAKfXUss7MSydVCEnXbRNIbY+nicy2FDb9jc1ANs/lXQi4UT9uUnOMTS2PsXXo0d3KJo4N7V9\nR9p+HHBWLt8vaUPbFzT3l86bScUN7Uv7r8m93qSQdBAxeT2NKIQXNf4qwoOkD9ku0b9HvdbI1KCi\n8XUIrjEua+PNwnlNwurYJxMpxz2Ifquh1URK2y0HVPWp1xN1vD2BE21vpikfqFmEl9jFaft5RN/X\nppnz/p6YwO4gVr9LEn5PfwN2daMRWNLLqdUkbf+y8J4WIup1c+qBwHyllbCGFfmXJxpqs6k4tZSp\nUpLryp2jK/oVVI8ek6Hq06kuXtsAny3E3iTpY7SQtqGbgvblktazXbJqmAO1k9epxGMvI1Mramyv\no5Aq2lHSYQymGbN1sAZG2rhXE5FGO+pW492YUHD4QdreBshS0iUdnmL/RqyetiaIIjnsRkxKlRTT\nUcAJKY1X0d+/lDtwDE4DflhNNJJeml7nSGKFO+BGa/sUyqr6dVyQJv05qUVJlzMs8lqhtSK/ajJV\naZwLEPp8QzJVwHkKd9+md1Rnw8J+BdWjx4RINN5Kl+3XLvTpqJu0zVKEgvYmadc5wN7VSq0RewNh\n8f174kJQZLp1HMPutg8YtU/Se4F3EvWuP9Oogzmvbbem7Wua+3OQtCGh7NHGUfdCYBMniSqFPcc5\ntjfIxP6YWDH8BjiLSO+VbgDmCXIrXyXJpxypocX5JhJ0TTXBdVM24Lm2Hx2xWu8iU9VJQHgU+hVU\njx41qGXvTdr/Gwp36g0sartV82yaiIqpqQZeNj6k+xiIBtUDGvveWt9n+0CijvN12+9sed5DUhrq\n24Sw7igSxFdp76i7FHExrlZti6d9Q3Dy/JL0zHT+MyTNsL1MMzaRDA4Cnkmoi8wA/jUpy62Gv0j6\nMPD9tL0d8Lf02xvrmpvBy4jvZxmgXqO7hxAeLuFOSYsThI9jJN1GbcXTwIO2LakiYJT8wXBvWNij\nx9yHCnYbhM/QdHCipC1snzzitataRhbOd/e/jbi4nN+CkdZmDNsTd+ArNph5SzB18W+O651pdfP8\ntOtsFxpUbT9fofSwM3CZQsHgSJfVJNo66n6eMPY7g1g9vICwoR+CpC3TWF9A1H5+TawscziY6Bc7\nnkhvvZnxNvJtsAPxO/tJ2j4v7ZtB1MWaYx6p1O6wV/mOOgi6JrwGuI/woqoU+UuqKk2Zqp0JTb4h\nqGPD8Cj0Kb4ePRI0j3pvEltsMcJZ9UEy/SYqdPVXcL67fyfiYrshcbdcZKTVxlCU11H0fa1IpgGY\nEP4cUnlPqb7/YspAbyvgUNsHNWNrx8wgqOwHErJHAj7mQRWOHxKrgYOJmszuwLq231A455OZqt1c\nZPuvhbiDic/pHNu3lsaYYqsU2JxUlgqK6um5kRPJJFAHpXYFxXwvas3CKTb7e5a0f3NVndtXe25z\nQq1DwC9LNxaamw3DnkBhtn/0j/8fH0RD5vwt4mYAZ/zfHm9tPE8m0oJ/JOjK/5uvfTWwWG17lEL5\nWsB/A/9DSDmtnfY/FfhDI3ZpgmL9N4JQ8V3g8YXzVgrln0rbRYXyju/tbCK1dxTRfPp+yqruryJY\nc79L288BflaIXYVoHTiVWMH9mqhh5mJzSu0lVffTCBWSFdPjEwQBovT+Ls99n4XYTxIU+/q+/yrE\nXpL+1sfcWsG9/uhTfD0e85D0gfTPVr03th+R9KikWR4vyIkiT7UjsKLtfSUtS1hmXDzNcY9lpEla\nzfYNiv6uIbjGrNJkCgNiMPVWyRLlcBCRFvqY7ftqY7hV0ifSGKo7+M1s71g4TxOHECnZFxH+Q/cA\nJxC07+ngTcTNyLuJyWlZgm2Xw97A+sCZALavVMhA5XA84TN1OOW0ZYWHbN/VSHWW0l5Psb1vbfsz\nkrZrBmlKy3FlSfV07BJEujGH9wBvkPRupx494B3ERNtEq4bhNugnqB49piw1/pgeC6YHlC8G9wLX\nKARm61TaHMGhfgHdNx37NaZ/AX08cQG9k6gR/cPDabgPECm4XDOtmWIh4skUBo4ELkrsOIjUXbbW\nYPuFVRqMWG3Un6to9VtI+gjhyTWkj1dAF4Xy1vCU2/B9lGszFbpMJA/b/nrLYXRRaj9V0huA49L2\n1oRwbhPHEj5erbUcCabma4DjJf3Q9hcp34jkGoaHjBvboK9B9eiRoCSAOm5f2v+W5j6YU7Buxla6\ndXWNtCydd8JxV4y09wNZRlqHc80ArnOBmlw4Zm1qtHjbVxTixtZTJH2R6NdaHPg3afXGiFWc5rJC\nuSZwLJZ0BNHY/BFilfVeYAHb78jE7k1Lh2V1U2qv6owVwWc+pm6eBj67rt9z9dtV2Ll/nfh+1iwd\nn+pORQHhtugnqB49EpQXdR1lmd2qKN7lAippFUKpoKkkPtRDkmGkXUhMEN/KxLZW5pb0U+A9nlDy\npgSFGO+LCJuGaqIe6AlS0p2T9FPbr2l53rEK5aXJpkJ90pH0FNt/SaSRXOwfmvs6TiQ52wm7bGI5\nT9Dle5Z0mO1da9u7EarquX633Yg2grpO5fa2D+k8xn6C6vFYh6RXENpy2zKlRgDRW7PFtA3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04X0qOA3xOf2bLAW5yxg9CgcePJhHLHgHGjpCNzY0qwG2oEaYVztO2sDUYJCsr4WwkrkSfbXqgQ\ntxrhUCzgdNvXN57vPJH8p0DSqwlllKcSLNPliN9FTv5qQaLmujmRyvyE7e83YrK28Ixu1xiJPsXX\no0cNaULKTkqNuO8oen+Wc4H+XMNY4dMaTpH0SwZZfAOCnJOkthLWYaoH6dkKhYqcBt2RBBW8St/8\nmaB65/p0vkLYPNyYxrZKGnuOvVY3bjzIGeNGd2zmdSgoLCtpQbdQ8Zb0boIgsQ4xqX6LQhOppKNt\nv4lQRWjuq15/nk5A6tBfJek7BGvvzrS9FPDl5qRew75ELe5XDrLEZgSxIodLiBuK9YClCTPC19ve\nphYzORmigH6C6vGYhwbtyweeouCZJOlVBC13QWBFSc8hyAE5Be3W+nq290wX8IoFd6iHWXy3EsSI\nVzOokH0PUT/KvcejCdbalUz1IJmMBh2wsu3tFA2k2P53olznsEB9grb9PwrTxRzGGjdqAtdZ4He0\nV/FemJhUL7M9Tn1+YCWRVmsl2niXRt0u6NKou1Y9XWr7DkmjGHkP2b5d0nyS5rN9hqSvFmJ3sV0Z\nIv4FeI2kN9UDutTY2qKfoHo85uEJ7MuBvQm77DPTOa5Mqbs5GFFPKerrSdrf9ocZlCmq9lXjvQq4\nStKxpXpPBusSTKw2Of0H0+qwIgesTFk891JJhzNFX9+RQWfXOtoYN07yXdycHvONO94hbjsDeJLC\nQbbaP6fuIumjQGXkdzfMkTx6kKjj5dBlIumCLioZ80laqqq9KezWR13j71QomJ8NHCPpNgqW9rYv\n1bBz8rn1mMaNXvWZzTHLzN3ojUNfg+rRYwJIutD2Bqr1jahhylarp2T19ZzpDynUlSYye2uc43ii\nYD6k8JCJfSnRGLs6IYy6MWXLj4WA3Zha8Z0DfK1Num1uQ9KitkcyK1OKb29CV65uLJjrQdrP9kdb\nvvY8UXxQt0bdNxMTa0WS2Ya4Gci6Nyfiw33ERLoj0WN1jDPyWerQIzg30a+gevSYDNdJ2oGwiX8G\n0ed0fj3AHfT1JL2TsL9YSYP22EsQbLPpYmngN5IuZpB1mEtJnpru0jcgJtTdbf+jcN53pFTanHSa\nwqZiqPG1C9TBskHShoRu3+LAcond93bbOTuR9xEX2bEahrY/muo4z2iMYYgAAjyQCC2/TZPgn9N4\npovdgUWJ39e+hM5eiaBzlKRLmdLie53t35RObLtaLT0q6fYcwaeGrUjOyenYWxPRJIvMamsJZxRZ\nxsLTbA7rH/3jsfggLhqfJYrHl6Z/L1yIvb6xPV9m3yyCwPA9Bhs4HzeXxvvC3KMQe3qbfWl/rkl2\nWs2Z6RzHExfkm4kL8qnAAYXYiwj2YL2h9tpC7BnA/C3H8DaimfiOdNx9wK8LsWMbdf+TH7nvsfH8\nxfU4opes1GS9F/Bz4H/S9lOB8yYZV7+C6tFjAjhSSR9Pj3E4PcPM+1XjfHcBdylEO//qWq+QpGav\nUOfeG7dgm6VVy6LA0mnlUNURZgJPa8RuT4idrpjICRWWAHJ6hDOIvpm2hnqdLBts39LgcQzYmtTI\nF7OJJuSTGFxJ5ggVuxMTz4W2N1NQzj9XeP2xjbqTQC28xObWS415/jhJ3wSWlLQr0SN4WCG202pr\nFPoJqkePCSBpXSLfvwKDF46hWobtd2tQXy/HzKtwArCupKcTBfmfEvpqW9RiKsO7rL5eY5zn2t4k\nw1TMFa7fTqTAnkqwA6uL1t3AwY1xnk+wuZZm0GX4HjIuuA46+JA+3wh0sWy4RdJGgBODcHeGDfKq\nC+Qf02PB9IDyZH+/7fslIWkh2zdIyiqkz8OJpI2X2NxASSkEaOecXEMXRZaR6EkSPXpMAEk3Ehek\nAT8oF6i2CufW9YmL4cUuyOtUJAlJHyLM9Q5SQcBTHUVd20LSe5xRz2hx3JbONPPWnv86sRIba6in\nvGXDp2w3JZxINY4DCGV3EenA3Z0v9ncRlv0xsRp6H1HXuYOg1W+Rib2KmEiaViKXNWO7YG4QLTLn\nbK3WMeH5WyuyjD1XP0H16NEd1cqkZWxTX+/5QFZfT9JFhOHfx4FXOejYWZsKSdcDr/Sgvt7JznhH\ndUVatTR7enI9U/VjxvkE5VQi7HIj6VxHgSU51ttLoRgxi/BPGmIozu2JJFHEIcgRrbzEOpy7k1rH\nhK/RSpFl7Hn6CapHj+6Q9GIipXY6gxeO3Gqgi77e6kSv0AW2v5cmnW1t75+J7aSv1+G97QVsSkxQ\nJwOvAM61vfWY4+aaT5CkzxG2EnVVhA86Y/OgFgoKmlLf2Bb4Qe3wmQTDcv1a7Ezbd9cmiQHUJ4d5\nNZEoPMSqHqLMEKZv6zKvIGkXwsLkt9M+Vz9B9ejRHZK+S+jaXcdgP02OBt1aX2+CcbQWde1wzmuA\nZxOsuGen9OR3XfAVqh23vjNmfrXnjyRT7yl8ZkOTXWmlU4gd2KcOwrJKHkaNSaLecLpSLfb/uYlE\nk6l1oJbSXpI+TWQJViBSnpVB5JVdx9qTJHr0mAzreYyleA1j9fUqqJv22pCoq6SsqGtH3Gf7UUkP\nK8wIbyNo3FkkgsIKwPyJ6VZKB9bHtTDB9rq1cNoZiZjwQHqNRQi/ohzGKijYvkrStcDLPLrfB6cG\natsrjoprGzMdKIwxj2msDre3fcjoI0eiM6NO3aS99krHLALsStRqv0r4cHVCP0H16DEZzpe0ukc0\nQlZwO329Cl0kc7qIunbBpZKWJJhjlxHU6Qtygeqg8Wf7hMax36Mhl1PDMQQ9v6qX7ERYXuTwZeAC\nhVqGCFuMz2Zev6uw7FZE39NdaXtJwqTvJ5nYeTGRAOxq+2u193BHonlPfF7bn57gsL0ZlvbKTs6K\nVomNCWLLFYRifLFFYBT6FF+PHhMgERRWJoRKH4CypYAaWnqlfWl/a8mcirGnQbmlrM9Uh/clYBnb\nt6TtFYCZtoeo4+n562mv8dc8dlXgJNtPLzz/coKZB3DaqNpaqt1VlO5fl24cJB0FPBMYKywr6Urb\nz2nsKzEqW8d2QUq3rlV9vop+squdscSY4NyrEMaZT7K9hqS1gFfb/kwmdqy0Vy32ckJV/yTgLKKe\nOlHquV9B9egxGV7eIXZzoDkZvSKzD7pJ5nQRdW0F25Z0MkHvxvbvxxxyLdGL1Ubjr9mL9Vfyn0GF\nKwi1czPCmTW975tt/0bR3PwSSbe61txcQ2thWfIr19I1c4YkNSaSBQuxXXAK8ANFkyxEv9Ipc+G8\nECvkPQnbd2xfrWiIHpqgaCHtVcHRJjGTWEVtDhwq6Ta3ZL3W0U9QPXpMALewFtBk+nqttdeItMsp\nwLKSjiGJurYZ/xhcLmk9T6kjDEFTahZLMEbjT9LGts8DnmD7/jYD0DA1/yBJWWo+g83N3yRWR83m\n5mpcXdJbl0r6ClCl2HZj0N6kjnk1kXw4neudafs04PC5cF6ARW1frEEFjpIFyXuI1ocHiM/2l+Qn\nsqpF4fmEnNa6wC30Kb4ePf6zIGkWsBRBevhI7al7JqUfZ17j8UyJul7osqhrl3PeADwd+AORBhtK\nX6qDk2wtbTm236h2TBdqfpfm5icAH2JYhHZI8UGhgPBJamlG4DOeElmtx85HTCQvrsUebnteqj9M\nC5J+AbybUCVfW9LWhO/TK6Z53hMJ5t65wCVubwkzfK5+gurRY94ipaD+5Jq+HjCgr1eLXZe4U21K\n5uRy/afbfvG4fROMd/nc/jarxsL5LiTkj14LfL/5vO33Zo5pTc1Xt+bmU4k+qD2IfrO3AH/P1QNr\nxywRw/S9Y9/sXIakjYmVcvV7GKK6T+PcKxF9dBsRKhm/A3bMfc+STgO2aZBAvm/7ZdMdxyj0Kb4e\nPeY92ujrVTiGjIRSHeog6joJbP9Bg3YJT6BQB5O0AXAQQTxYkKAS/8uDGn9bEquQl1FOkTWRo+af\nXIhtY4RY4fG2j5C0e1rlnSUpm8qUtCbBRnxc2v4H8Bbb12Zi59VEcgThkjwgoTQ34FAgeUlaKc5n\n+54R4Ut72K23pI0419BPUD16zHs8avvhRDU/qEpBFWL/bvtnhecqdBF17QzVzOkIKvsChCBtzpzu\nYOANBL19XcLOfZV6QEo7fl/S9Q434LFI1PzX116zSM1PjL331rZ/BwwpbyRU6aa/SHol0YeVVYwg\n6lkfcDJqTKvfasXRxLyaSO6yne2Zmy4k3QxcSNSHziGazkt4VNJyTs7DaZU9z9NvfYqvR495jI4p\nqC4SShOJurYY75Uku4QWlOKK6j7n+Wb9R9JBjLYHGUrxdRxvl+bmLYmL8bLEym8m8OncTUGOsl+i\n8Uu6yPbzpvM+cpD0eWJV+iMGfw+Xz4VzLwQ8jyA0bEzckFxte6tMbCWrdRbM0ZP8r1HU/7mBfgXV\no8e8R5cU1E6EdNEC1CSUiAvUANJKrLOoawt0sUv4t6QFgSslfYGgmzfp2Ze2feEMFX3OUwzbg1Ro\n3dzsKZWNu1LsKMyW9Emmvqs3ErqHOZwh6YvM/YmkmvTqCvVmqudrOniEWFE+QvzWbkuPIdg+RdLa\nBCEH4H1NQo4m8Ckbh34F1aPHfxAk3eiWEkqaUNS1xXlzdgnfs31gJnZ5woNqQSLFNQs4xPZN0xlD\nx/F2aW7u0py6FPBpphRAziZWW3dkYs/IDM05duB/CiT9m6h1foVgSA7ZkzTin8Yweefs2vMVszPr\nU2b7/Z3H2E9QPXrMW3RMQR0JfNEtJJQ0oahryzG3skuQtI4bnkcq+EKli3hOLHZaF3FJ5xOTyA+B\nXxPNzZ/PTfSSziI1p9bSl9l0a+O4GcBitu+ezli7IrUq7MWU2eVZhAbeXXPh3K8hPrf1gQeJxtuz\nbZ+eid2fIKo0xZGHVkWamz5l/l/0ve8f/eOx+CD6QV5MUK2XJ9he+xRir08XixtT/DVEXSAXe3H6\nexlRSxGhaD7d8e7fZl/afzmwRm37DcBFhdh1ao+NiTv3L8yF8a5HsAyXIdJ9JwAbFGIvSX+vqO27\nshB7bPpcFwN+A/yJ8PHKxc5K7+fS9PgyMGsuvLcTiFXcSumxF/Cjufz7XI1Y/f6B6CPLxdwILNTy\nfNcDK9W2VwSun2RsfQ2qR495j0Vsny5Jjh6TvSVdxqDtQ4UuEkqtRV07oos009bADxUyOM8nWHwv\nzZ3Uw+6y5ykUKKYFTyle3Cvpo7b/OiL8H6kvraqvbU1Zpml1hy/UjoT6/EeIz/mLmdhvEbJP26bt\nNxGT5Uj32hZY2fbra9ufTiSWaUPSCcQK/GYifflm4KJC+GyiLtpGSuv9wJmSBnzKJhljP0H16DHv\n0Vpfzy2bYSUJ2M/Rm/INSacwQtS15Tk7SzPZni3pDcBPgD8CL7V9X+H8dTr3fMRKatak4y3gZGCU\nWsVuBBttNUl/JjWnFmIXkLQA0WB8sO2HKuJIBvNqIrlP0ia2z4U5/VbZz3cC7EesJNvQ4v9NEGGa\n7NIhBqaDUPEM5oJPWT9B9egx79FFX68V7M6irm1wLLFSGCvNlOpf9Yv14wg69EWScIaSTqw+KnO/\nh4nJYZe5MO6BoY160t2aU78J/B64Cjg7EUJKNah5NZG8Azgq1aIE/BN461w4L7ZbsysJfcNx/XkA\naC76lPUkiR49/h+Fwur8YI8Qde14vi5W51k5pFrsRLJI04Wkd3n6Hkyjzj+/7SFBVYVj71HEinDO\nROKWjcktXncmgP+XSRqTQNIPiJuRNzuYkosC57thR9LqXP0E1aPHvIU66Ot1PO9YUdeO58tZndeG\nm5ftSQy3JzH43v6YidsGOMX2PQpTu7UJ8dWJeoVKE2ltDNMS5JW0O1FHuodQEH8u8BHbp444Zq5O\nJKmZ9vUkx+Jqv+195sb5O4yjCxN1rvmU9Sm+Hj3mPcbq602IuSrU6Q5W5xUkvYdglv2Nwcbi3CT5\nSdvHK3T+XkKQDb7OVDNqV9RThhWqbROst+lgZ9sHSHoZoUr/JqJpd2iCak4kShYWc2Ei+SnRVHwZ\n0/T6aiLVMXckGHf7SFoOeLLtHHHl/7R37jF2VWUUX6sKaLCUGmmQYEVJgGALFMVNGucAAAq9SURB\nVBiLWiD4iKiAgqBRVIqIEkjkDzBGQ6CpQakRopgQwsvwMEYIVv4gIVIoUpQK6SMtCGjARsQHCVIk\n8hBl+ce3T+fO7T5zzzlz7sy9c9cvmXTOud/dszOdnO/uvb9vrTpOz635lDlBGdN/qujr1UY1RF2r\nkJQCJvt5uZXO+QAOVI8mz0RxGP8JhLbenSSznkJVqJNIC2qu4orE93EAN0t6lGTZGVe/Esm+kupU\ndtbhKsSHig8CWIlYKd6OKNvvpk4l6gq05FPmBGVM/7mE5HWooK9XB9YTda3C5ZO8Viav8zTiwVyF\nZxiGfh8BsCqtOso+hfekYUKts4rbwLDneBeAbzFsN8pWwP1KJL8luVjS1j6MvVThA7UJ2KFQXuYC\nXKcS9VcpeRU+ZeeroU+ZE5Qx/aeyvl5NTkYSdQUASX9ND9FGSOqlTZfjKUTPy52YmHyvyMR+BtHn\n9QNJ20m+HbH12ZQmCbXOKu4sAIcBeErSSwxzyLKVQL8SyTIAy9O54KuY4jljF6+l88NiK24vlCfg\nypWoHPckuzNzrxZOUMb0nzFV1NerSR1R18ow/KbORTwchVD/vlp5u/Y/p69d01cpkl5CR1KW9DeU\nN8n2pGFCrbyKk/R6SgwHpN/JZPQrkUzJ3bYHVwJYDWAByUsRTdcX5QI7m6FRkqTZB58yV/EZ02dY\nQ1+v5riVRV1rjnsr4jyiEPv8PIA9JZ02lXH7RUffzUJJX00VZ9m+mxR7PICtkv6YVnGLc5V5JL+C\nWDnsC2AzYsvqQeXt4Vt1IZ4uSB6EkOEigHskPdb1emWF8lT1WPiUPQNM8Cm7VlJtrzInKGP6DMnH\nAOyPaExtdZuGFUVda475e0kH97qX7vdFALYOdftuUs/S0elyXVmvUmpGHgOwXtJh6WH+XUlTlS8a\nCEhejzDQ3Nxxb4WkFR3Xx+beW6BwJe4etzWfMm/xGdN/+lKFRXKVpG8CuDtzbypsJHmUpPVpzKUo\n93S6sOP7NyFKrXdqZO0z+0v6LMnPAbGVWFZtlz7ln43xrcZbSF5T8kB9RdIrJEFyN0mPk+zHVu1M\n8VEAR5K8XOMeYichqvAA5BNQL9SiT5kTlDF9po/bPHVEXXvCcfmiXRCH/kWz7UIAj+feoz4JwNak\nTt/NWYjqtX+n2FUIgd1cgvoLQ4z3lwDuJvk8oil6tvAsoqfplvQh5HyUSEXVbNTN+pQhlDZq4QRl\nzJDBBqKuFTmhwVymQwC2Fyuwc9/N8pJYYrySD+n77ENZ49bnK9JW5rz0c2YLVPhKnUhyBYD7UP5/\nV6dR91SM+5SdyeRT1mSCTlDGDB+VRV3r0LnSq3pOg+kRgJ2Umn03P0EI2q5O158CcH2Fn1F7q2sI\n2NE8LqlovC1zva3TqPtyqoD8L0P66VkA72gyQRdJGDNksIaoa8Pxu89pTkb0DLVy8N02JG9BOM2u\nk5TdiuyKLwwTkd6zqZ/zmw2wnmvxVQC+jTCvvABRmr5ZUm01CScoY4YMNhR1rTH+FgDv6zin2R1R\nXr1T1WHNnqm+QPI4xGrvaES15CaEdfmPJnnPAkw8S9lJ3Ha2QvIBSctIvoiJFZhFdekemfeMIZxy\n90Q06s5DuCGv74ojQlXj6XS9H6bgU+YEZYyZQFFeXSSZlIQelrQ4EzsQPVNJEWEMcT5yDmKb6aBM\n3EkIBYp9EFtPCxGGeu+ZxunOakhuzf2tNMFnUMYMGQ016OpQ55xmUVd/1FqSrTYk94Lh8ro7ohpv\nHSK5PlsS/h3EWdUaSUvS6usL0zPTwSEl9EdzSbwk/gCELFW3ZUyu320jyTG14FPmBGXM8NFEg64y\nkq4geR9i2w4AzpzknKZOz1S/2IKoHlyEEK7dTvJB5a3nX5P0HMk5JOdIWkvyh9M62wFA0v9IPkFy\nYcXtzdsAXA3gWkysgsyxFMDpJKfsU+YtPmNMY5JKxoEIPT4gtsyeQFT0tSVqWnUucxHl5RcifI12\ny8SsQawIvwfgbYhtvjFJ75+ueQ4KJO9HiA0/hEgkACbKF3XEbpB0RMVxW5N9coIyZkgZkAKFGbd+\nZ9g/HI1YRW1D/B7WSbo3E7s7gFcQn+pPRxz2/1TV/KxmFWUyRp0l9R2Vol9HJPPVmKhan60YZcan\nTNKfas/RCcqY4WRQChRmmiSauw7ABknTLbM01JDcG8B7ER9wHpb0967Xc5WiBdmKUXb4lEk6gOQ+\nAG6TVNunzAnKmCGljqirCUieAmAVgAWIh25pafVsJ6m1X4zoayKAYwGslHTDFMfdjORTJmlJurel\nyXZvYzdLY8yMs5HkUcXFDBUoDBvfB3CSpHmS9pA0dxSTU+IbAJZIWi7pDMQWaVbHkeR5SZewuJ5P\n8tyScf+jWPlM2afMCcqYIYPk1tRMewRC1HUbyW2IMusjZ3Ryg88/1OV5NMI8h9giLngx3ctxtqTt\nxYWk5xFqIzluZZhC7knybABrAFzXZILe4jNmyBiEwoRhI23tAbGNtTdCobzzsP8XuffNZkjeBGAx\ngDsQq51PIkr2twDRbtARuxXAIWllVPRRbSlrcGZLPmXugzJmyGgo6jrqnNjx/UuIh2eB0GFFP0I8\nmb4K7kj/zs3E3gXg52llBABfQ4myO1v0KfMKypghZdhEXc3gQnLv7gq+rtfnIJLSh9KtuwFcJ2mn\npl2SGyUd3nWvUZGEE5QxQ0odUVcTkLwRYcexPV3PB3C5pC/P7MxmllxSaTDGDp8yTFyZzQXwG0m1\nJaW8xWfM8FLZfM/s4JDuw36SS2ZyQgPCpH83JD+AMIYstPiK8vzOPqjWfcqcoIwZXhqZ7404c0jO\nT1VohVKCn4OhsTcZ1yPMDDegXItPkraRPK/7BZJvbZKkvMVnzBCTlM0LUVeb7/WA5JcQZnq3pVun\nAbhU0s0zN6uZgeSHJa3puneGpBszsb+TtLTHeK37lDlBGWNGCpIHY1zx/V5J02oPMigksdhHEeK6\nb0H0Kr0q6dRM7GUA3oAoyOksz5+qtcvkc3SCMsaY0SO5316AqM4DgIsl/awkdm3mtjr9oPrhU+a9\nV2OMGU3mI4RinwSwL4B3kqQyqxZJx1UYr3WfMq+gjDFmBCH5BwCXSbqB5JsRIrpH5ryxSM4DcAmA\nY9KtXyOEZV/o6xydoIwxZvTIuemSPEbS/ZnY2wE8AqAooPgigEMlnZKJbc2nzAnKGGNGkHQGdTqA\nd0taSXIhwon4oUzsZkmH9bqX7rfmU+YzKGOMGU2uAvA64mxoJSKp3A5gLBP7Msllkh4AdjTuvlwy\n7qIuT7K1JBtVSjpBGWPMaLJU0uEkNwE7VDV2LYk9B8BN6SyKAP4JYHlJ7EaSR0laD0zNp8wJyhhj\nRpPXkm1GYaGxF2JFtRNJJf9Qknuk6391xyRLDgHYBeFTVpxvLQTweJMJOkEZY8xociWA1QAWkLwU\nwKkALsoFktwNwKcB7AfgjXF8BUha2RF2QtsTdJGEMcaMKCQPQlhoEMA9ZW7DJO8C8AK6tPgkZXuf\n2vIpc4IyxhgzKSQfkbSoYmxrPmVOUMYYYyaF5DUAfixpa4XY1nzKfAZljDGmF8sALE9K5a9i3A8q\nl3Ra8ylzgjLGGNOLj9WIbc2nzFt8xhhjWqUtnzInKGOMMQPJnJmegDHGGJPDCcoYY8xA4gRljDFm\nIHGCMsYYM5D8H9A9YuT7pgFGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f56f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "visual.doc_topic_heatmap(doc_topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize topic distribution in a single document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZlhxQZmaWJQeUmZllyQFlZmZZckCZmVmWHFBmZpYlB5SZmWXJAWVmZllyQJmZWZYcUGZm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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7e7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual.plot_doc_topics(doc_topic, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('member', 0.00026884036514488509),\n",
       " ('chaos', 0.00023899736846881792),\n",
       " ('indulging', 0.00023731935589771453),\n",
       " ('sub-lieutenant', 0.000229486030764898),\n",
       " ('footmarks', 0.00022674463248052186),\n",
       " ('locally', 0.00022592295873420586),\n",
       " ('bags', 0.00022131332121631022),\n",
       " ('punkah-towel', 0.00022112263415940259),\n",
       " ('earthward', 0.00021587535404803251),\n",
       " ('rajah', 0.00021485113926602268)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = model.show_topic(1)\n",
    "keywords = [item[0] for item in items]\n",
    "keywords\n",
    "items = model.show_topic(1)\n",
    "propabilities = [item[1] for item in items]\n",
    "propabilities\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Y3lEQiDQvMTNzXFmpAuBcOdlpCKZMQVdn33TF4nY4YBk2iQk/XotzKXlTvUQ\nAPPp0HbM1vqRKSipEAiBuo8OQZaXgOTzJni9T3icgGcEh+/7CDaNCerDdc5dwwgKwtj45TGU/3Nb\n1PuNJK9uJDRvOQmb1ozJj18cdh+s3YHyf25F06YTYffhsDM4/sgXmPL0JYgZsCMKltoN+1H3UeCM\nV8HQOy8TH1npN5ZUIHrnZixhb++E8dhJED4PrNV5sK/9cScUyxZBNC4PonHOxRVrt6N7S2h/Ky2P\nvRD18UYDKgRCpPSpLWj5rgw5N89FzMTUwA3g9KCsenuPW7gAW7cJdR8d9JoaMhjsegu2r/gXEhbl\nY8KDK4LKp+qNMy//hKavj4OJ0CcgENuWPg9ZbjxybpyL+AX5IYUgdtjCD7/csbsS25Y+j4QFeci6\nepaHWsoXuvJW1H18CK3bysN+dn/segsO/d+HAICZr1wTVIym1m3lqHxzZ9DnPKHQsbsSP612Ztaa\n+dLVIc9L24+no5YreyTR+OjTHmVdG79C18avQHg8cJUKOMwWOEJUBY1kyEj4Igkhwz+IMJCPT0Lc\nrGwoJ6dBMT4JXKkQrJ0BY7LB0qFHy/dl6DxQ49WyKJpwhDwoxiVBPi4RkkwVxKkxkGXHgysRgCPg\ngnA4cFjtMLfrYVUboK9qh76iHZrjjTA2RCfNYCiIEuWIm5ODtF9MAV8hAk8mBEfIA2tlYGrqhqVD\nD2NDFww1ndCeboW+sh2sPbI4/L1IMlWIn5ODlFXFEMZLwRULwDocYEw2dB2uQ/eJJnQeqIWxTh2V\n5/lCXpCI+Pl5iClOgSRDBWGCDA6TDRa1AYaaTnQdqUfDZ0cHdQz96Z2XmEmpkGSoIMmIdc2LpV0P\nY70aVW/vGfR5oYQGy7IR6xKoEKBQKJShJojgdEDgAHXREAJUHUShUCg9EC4XwoJcCDJSwZXLQfg8\nvzGE1B9+FlL/HJkUMRcvh3zJvICxiQAMSRpKKgQoFAqlh9Q/PwhefPABFEMRAkQoQNLv1gXtZGbv\nHBpVLRUCFAqF0gMvXoWOtz+AYX/0vZ8T7/sl+MkJMB0vQ/u//gMAyHjlcVir69H67GsAAOG4HCTe\ndytsDc1ofebVqI/BG1QIjAJEqhQUXhU4bnnLwW/RcuDbIRgRxRdD+V3R38XgYDkzOElx+CnOCKXa\nLT+5ylibDUTYl23Ocroa3V/9AOVlKyFbOh+67wc/uRONHUShUCi9OByIWbUMhBv9cOdE7EzAY2/r\nsxZkLVaYcfJhAAAgAElEQVRwpO6+RYa9RwAA0lnhOwWGAt0JUCgUSg+tL7yGuJuvRtrTf4SlohpM\nlwYOs+8wKpovtwTfOeMAuE6TbVeRwQh+SpIzQF1P5FKmqxssw4AXYoC6cKFCYBRg6W7H6U9eAE8k\nhSg2CVyxFDyRFMKYBMhSw48xT4k+/b8rnlgKYWzSoH1X9HcRfWJWnweeypluVTK1OGD9UIQA09Xt\njBeUnOAKUGdv64QgIxWC7HRYq5wB6oiA7wxQJwotQF24UCEwCOQ++3cYSo6j9d13otIfy9hhbHP+\nQLR1fZmgpCk5KFhzd1SeQYkO/b+r/gzGd0V/F9FHND4f3Zu3QvP1d245BaKB5vNvEX/ndVDddBma\nHnJ6Jms+3gTJtIlIfvguaD7ZDK5KCdmCWQAA40Hvob+jDRUCFAqF0g/dz3uiLgAAwHSkFPb2TpiO\n9AVptHd0Qbd1F+TnLYTyspWuctZuR/c30Y/j5Q0qBCgUCqUH1maHbN4sdG/+Ifp92+1oevjZvuik\nPXR9/A1YOwPFysUAnNFGuzZ8AVtDS9TH4A0qBCgUCqWHzg0bobpqDaSzp8FSUwemWwfW5DthVPe3\nP4b2AG9hehwOaD7ZDP1P+4YlOB0VAhQKhdJD/E1XAwA4ySLwkxMD1g9ZCPjB3jE8wfnGvBBQXbAS\nymXLYWmoB+HyYDhRAtmUqeAnJqL9o/9Bd2A/ACD+kkuhmH8OjGWlMFdVgQiFkM+cibYPNsBc1ec8\nIs7LQ/Jtd0B/5DCsra3gCIUQ5+Whaf0rXp8vSEpCyp3rwBgNaH79NTBaZ1jgpBtugqS4GLr9+8F0\nayBITYUoNxf1zzwNh5+Vx1BCOFzIUnOhyJoISVIWxHEpIIQDu8UIi6YdhuZqNO8PPuNXMEiTcyBL\ny4csJRd8qQJckQw8kQSM1Qy72QCrthOWrjYYWmugqQw9mQlPJIU8oxBxRXMhkKvAFYrB4QvgsFlh\n0bTB1NkMY1sduquOw24eO+GCKcFRuy6w891YY8wLARcs0PivF8Da7dDu2oXMRx6BfNZslxBQzD8H\n3T9tR+fXX7maaHftRMrtd6Dh+X8AAAiHg4Qrr0bjv16AtbkvS5C3CB+snYEgJQUpd66FXd2Fljff\nAGPse6mICwpgOl2Ojk8+7mtEiPft4pBDoBo/EylzVoMvVXjc5UsU4EsUkKXmQZqSg6bdX8DYHlni\nGA6Xh7iieUhbcInX+zyR0/xRpEwEMicgAefC0FqDxh2fBnw24fIQmz8NqsJZkKbkghBPH0muQARJ\nYiYkiZmImzAH6QsvhabiKGq3vh/R5xqtZJ9/M5S57s5KR9f/Jqy+EqcsRur8vsQ+jTs/Q3vJ4HvC\njmZkS+ZBkJ4C9X8/HfRnnTUew927doC1O8OyMgY9rG1t4Me5J47X7t3jds3o9RCkpoErlQEAhJmZ\n4KlUbgLAFw6LGalrfwVrSyuaX1vvJgAAwFJXB3F+AWIW9UtXOSIEAFBw6d3IXHqNVwEwEFlqHgou\nuw9J05eF/TxJUhYmXPuwTwHgs11CJqz67oD1Jt7wKDKXXgNZar5XAeANwuEidtwMiOOCSxw01ug8\nuTtqfcWOm+H6N+tg0HUm+nF5xhqSmZMhO3dO4IpR4KzZCdg6OtyuWZsNGBDPO+Oh33tty5VJwRj0\n4McH78HHlUjBkUig3vQVHBZPj8O2jz5E/KWXIe6iX0CYmQXdnj0wVVYE3f9gIZDFQpqU7fUeYzWD\ncHngcN1/NoRwnLsGWSwaft4Y0vNU42YiY/GVINzQf4ramhOwm3QB63F4vlMosg4HHDYzCE/g8bkA\nIHf1HSj/6NmzTjWkazgDS3c7hDF9v3kOjw+H3RZSPyJVMsTxfbmVu6tPnHVzGQ6cIXIUA84iIcB6\neREPpOPzz7zaB9u1oaf3k06eDMZgQNKNN6HxX/8Eo3N/WTFaLVr/8zb4iYlIvXMdZFOmwnCiBK3/\nfXdQbJSDgcPlIffCO9zKWAeDjpO7oak4CkOLMz0mVyiGPK0AqsLZUGQVuerGT5wPq7YTbUeDOyyT\nZxQic9k1GJho2abXoLu2FPrGM7DpNbCbDU5P2NhESBIyIEsfB5EyEZ1le4N6TmfZPiRM7tlxsSwM\nbXVo2b8Zpo5GtxeSatxMJExd7Lb650sVSJy2FE17vhrY7RiHRefJPW5qHGXeFKjLD4bUi2qce/rU\nzrJ9URndWIfTE2doKDhrhEAwmCsqYG31bZtr6ww+TaTh5Al0b9+OlDvXIvmWX6Jp/SvO3cfAPtva\nUPfk41CtWo2YRedCNm0a9Ieik1A8VFLnXwxRbJLr2qJpR/Xmt2DWtLnVYywmaKqOQ1N1HJNvf8Zt\npZ0yZ1XQQiBr+XUYKAAad36GjpO7wToYt3JLdwcMrbVQnzoAABApE2Hubg/qOe3HfoIiqwjq8v1Q\nl+2HzehdqKtPH0RXxRFkLr0asQV9Koy4onlo3rsJLDs8wnm46Dy1H8lzVrl2SHFF80ITAoQgtmC6\n69Km10DXEJ2czUNJ+jOPouPtD2A+dWbInknEdCcw5Gi2/4j03z4AS309jKfK4DCbwYuJAUcoQvvG\njwAA5uoqNL/5OnKefAq6Q4dga28HRyiEICXFM0SEwwFzTTWqf/8QlMuWI+fJp6HZuhXqLd+AFxOD\n9AcegulUGaxtbSA8HqRTpoK12WCp8ww5MBRkLrkGqsJZruu6Hz9wvXD9cfyNh5C1/DrXS5NwuJBn\nFEJXf8pvO75MCZ5I6lZW+fVr0NUH95IYKJj8YdV3oWzDk0HVZR0Man94H8b2BqTN/wUA56GxasIc\ndJbuCdB6bMFYjKj88hUUXHIPAKflVto5a9C46/Og2mcuvgp8mdJ1feaLl0fMuVcocBU9GcZCIPPN\nZwZpNNGHCoEe1Ju+hqWuDopzFiBm4SIQHg+MTou2Dz5wq2cqL4fh5ElIJxaDI5WCNZth6+zw0asT\nzdYfwI+Lg3LZMqi3fAPGaILh6BGI8vIhmVgMRq+DuaoSmh+3wdYe3Oo2mvBEUigLprmurTp1SCu+\nlgPfua2cE6csCigEEooXuF13nTkUtAAYCgxN7jHleSLJMI1keDG01MDU2eRSkanGz0TT3q/BMv5z\n33K4PMQMsC6yaoPfSY8JHA6Yy8I75xNNyHdGFh0CxrwQUG/ZDPWWzR7lTS+/5FFmKDkOQ8nxgH22\nvf+e3/tVD9zvUdb+0f/Q/tH/AACszYr2jR971BkuZKl5boeimqrjIa3YLN3tMHU0ug4AZWnjwOEL\n4bD5PoeRZ4x3u+4o2RXiqAcXm0Hjds0ViH3UHPt0ntyN9EWXAwC4QgmUuZMDWvgocorBFQydXnsk\nwmj1aHv+rbDapv3jj+AqZFEekXfOGhNRim+kyTlu17r60yH30X8VTzgcSJMyfdbl8IUQxaW4rq06\nNQytNSE/czBhbFb3giFalY1E1KcPuQn0uKK5AdsMPBBmrOaoj2ukE0n4B9Y0dPN19v6yKS4kA17Y\n5q7QA1cNbDNQsLjdS8p0s9c3ttWH/DzK0OGwWdxW/rLUfAiV/s2l5RmFbteaiiODMraRDKM3ht3W\nMYRCYMyrgyiBEcUmu13b9BofNX0z0GvXn5OVSJXidq1rCH3nEQ7SpGyoCmchJneyx6E0xT/1P32M\nuKJ5ruuCNXfj5LuPeVhx9dI/e5ZZ3Yz6n0aO+nMoqLvtoYjad3/5PXjxqiiNxj9UCJztEAKuMHLd\nrcPqrv/n+jlI5Qrd7zkGWVUgkMci7Zw1iMmZNKjPOZvgiWWIySn2Gr9p4C6h89T+oRrWoNDw8OND\nHtnTdNy/YUU0oULgbIft+Y8EqhiAge39nCuTAfr1wba/H3fZfeCJ5R7ljMUEs7oZ5q422M16WLWd\ncNisYGwWcHgCZK+4cVDHNdpgLEY3AR5XNM+rEOh/HsA6GHSdHh6/l2jBdAUOTTKaoULgrIcFYzV5\nrM5DhcN3303YLb71oYzFPUrqYFre8MQyDwHAOhic+fwlGFvr4Eta9bdvpzhRnzqAhCnnuq7l6QUQ\nKOIGmH4SN3Ph7pqTsJv0QzhKSqjQg2EK7JaBoatD3xYMNAdkQhECwsETAqnzLnK7tuq7UP7RczC2\n1sLvdoXiQUfpbrjPGUHcBPcgZ9KUbAgUfbpsNQ0TMeKhOwEKus4cQvKMFa5rRWahW+LyYIjJKXa7\n1lT69rfQ1pa6XasKZwcdaiIUFNkToRrf5wWtqz+Fyq9fD6qtQB4b9fGMdiyadtR89y6yV9zkKkua\nvhzN+5w5JQiHi9yVt7rutR7eGvLvaKzDkUkRc/FyyJfMc4aOD0CkB8zBQIUABYbmardrecb4kP94\n3Zy/WBZGP3b/NqMWVm0nBApnKG9RbBJEsUkwd7WG9MxACAaodPTNVT5qeiJJyIjqWMYK3dUnYDPq\nwJd4nrHIM8a7qRXVI/xAOP6WayLuo+PtDwJX6oEIBUj63Trwk4OLRmzv9JapJPpQIUCBobnKFakT\nAJS5k9G4+4ugvYaFygQ3k1BDS3VA5yBdwxnEFfXlc4ibOB+NOz8LY/S+GXjWwJiDt9tWZE6I6ljG\nCqyDgbpsH5JmLHeV9WZmU+ZOcZXpmyphCTLA33AhnT09cKUAhCIE5IvnugRA91c/gOnWQXXdGtha\nO6D/aS9E4/MgnjIBDr0Rrc+sh61laOaPnglQ4LDb0FnaF5aZL1N66Hr9kTzzfLfrYLJGdZzY6XYd\nP/EciBPSg35mMDDW8A6gxfFpHmEtKH10lu5xWyAoMosAQqDI7gsrPtJ3AQBga233+M98uhKspZ+3\nuMMBW2s77J1dYK19UYD1u/aja2No4cXFU53zYzpehu4vvod++16wNjsc3Trovt+J9pfeQeuzr4EI\nBYj75ZUgXG6AHqMD3QlQADhfyglTznXFEEqZvQq6htOwav0nv1ZkFSG2X/A5AOiuLgn4PFNnk9s1\n4XCQs+ImlL7/RIgj983AsUtTcoAAjqscvhBZy66N2hjGIlZ9F7R1Za5cEorMQtj0GjcHvHDyPw81\nTX/+m0eZ8uILIBqXB9PJcmi/3QZLVS1YpschjhAI0lOhWL4ItrYOaLf+HNLz+CnOxPXaLT+5ylib\nDUQocF1bTlej+6sfoLxsJWRL50P3/eCn4aRCYARDOFxIEjLAEYjAFYjAFYogkMW6rgVyd4/C2Pxp\nEMYkgLGaYdN3gbFawFhNcFjNYKxm6JsqfT7LZujG8dcfRO7q26HInACeWIai6/4A1sGgs3QPuiqO\nuM4OuAIRZGkFUI2f5XEg3H78Z7BBJsWp+/F/yFxyletaoIjD1HX/gLmrFdqak9A3V8Fm1IIxG8ET\nSSCQqyBUJkCSmAlpSi64fCGOvf6gz/61dWXoqjiC2HynkFJkFWHSLx9Hw47P0HXG3XZdIFdh/BX3\nuyyVGKsZLGPz6l/gC2lSttt3xRWIwZcpA35XDqsZVr3G7buy6tSw6rzrhIfyd+GLqs1voei6RyCQ\nq6AqnAXRAA9xh93qo+XIJmblMrT+Yz3MZ7ycH7EsrPWN6Hj7A2Stfxaicbloeyn4AHFE4HzZMx19\n36tdrQE/PcUZm6rn70a7eTti1qxAzEXLqBA42xHIY1Fw6T1B1xcqEyFUJvq8H0yi8Nrv/4tJt/bF\n3iccLuKLFyC+eIEzvSSHAw5P4LVt1+lDaNrzZdDjVZ/aB75EjpQ5K9HfLLX3oDhx2lK/7QOFMwaA\nlgNbXEIAcHorZy2/DvGTzoHDagaHL4JApnTzC2AZO6q3vI2YrCI3u/hARPO76izbh/rt//N6bzh+\nFx6wLDpL9yJlzioABJJ+qrxoH/APNbbW4HTxwmzfQRK9wXR1g5cUD15yAuxqZ2gWe1snBBmpEGSn\nw1rlzCVCBHwQLhdkiFJM0jMBihuM1ewMJe0FrkDkQwCwaDv6I2q3bgh6F9BL6+EfUP3tO37DTkeC\nRdPuNb6NNCkb8oxCSJOz3QQAYzWjctMb0DeeoeaNAegs2+d1bkfDeYA/+qtnolGvF0u1M1CiqKjA\nVdb74ldc0LfY6D07YHRDE6qCCgGKBzXf/ge1P7wXVBIQQ3M1Tm98vicHb3jOV91Vx1H63uNoPbw1\npHbBxqSp+PwlWDSBV3e6+lMo//g56BudaQRDMSk9G7GbdB7nP6zDEXIe4pGGdPrkwJUQejgJ0zGn\nf4xkVl//xkMlgMMByfRiKFYuRux1axB3y5UAAEv50Pz+CDsC0r0RQoZ/EBQPCOFAnjEeMTnFkCRk\nQBSfCofNCrtRB5tBA13DGbQe/iGqz1RkFUGeVgBJUqYz5INIBo5ACMZigt2ghUXbCZO6CYamqtCi\njxKCmOxixGRPhCQpC0JlAliHA4xJD4u2E4bWGjTv3RTVz3I2IEvNR/4vfuW67q4uQfWWt4dxRJGR\ntf5ZsHY71Bs+hX7vQU8zaUIgnTMd8TddDe22Hej6OHj1J+HxkPLX38B0pBRdH33tKo+96kLIz1vo\nVpe129Hy+IuwNfgP686ybKRRv6gQoIweJsyPxb3/9owE+siy/ehs9O2XEG67geROVWDdyxPB2B34\n3bk0HAIAyNLykX9xnxCo2vwWtDUnh3FEkZH+zKPgKpzGAHa1BubTFbA1t4Lw+eDFqSAalwdeXCwY\nvQHNf3kOjC7EuEiEeAoWDgfKS86HYuViAIC1vhldG76A5Uy1Z/sBUCFAOat59dQiAKG/zC+4IwNr\nfpMTcru8aQqsfWkiLCYGf1g+uvXe0YAvkaPohkdBOE579rYj29C09+sArUY+hMeDbP4sSGZOhTAz\nDUToPKBlrTZY6xpgOHgUup92D/MonURDCFDrIAolSCqPaPHAOXuGexgjhrgJ81wCACyLjtKxMTes\n3Q7dz3ug+3mPM9+GUgHWYnVm+xoBi+ZoQ4UAhUIJGQ5fiPjJfXpsbW1pUIYEow6WpfkEKBQKZSAp\nc1a5eQi3HgnNsutshiOTQLFqCTgiUcBIoup3Ng76eKgQGAM8sGEq8qYr8N4fT2Pnxy3InxGDO/45\nARIFD9pOG0p3duHbN+rRXjcwb4DzNzhuthLTVsQjd6oC8RkicDgEHQ1mnDmowU8bmtFS5Rl47b63\nJ6NwnhJrC39G9mQ5lt6YhoKZMZDHCVB7QocDX7dhx/+awdh9b5+v+VM+JsyPhTJJCJZl0dVkwYt3\nnAhJTz/Y9J47DGRtoe+QAb1z8+FfK1BTonObG5uZwRcv1PidG4GIg0XXpGLVukyI5Tyv7wmTzo5f\nzxoevXTchLlImNS3C+iuOQlDS82wjGW0IT1nJlTXrQER8IOqT4UAJSQuujsb1z02DlVHtXjhlhII\nRBwUn6vC+bdnYMEVyXh46X6om9xfsOfdmoGL783GyZ/V2PlxMxrLDeAJOZh0rgqLr0vFkuvTYNTa\n8fDSfTDrPR2DXj6xEMZuO759sx5b/9MI1sFi7UtFuPqP+bj6j/n4/B/V2PJ6vVubc69NxTWP5uP0\nfg2+eKEG7bUm8EUcZEyQ4Ymts8E6gBdvL0HprqEJpeuP/i/7WasTcevfC4Nuu/LOTHC4xG1u0sZL\ncdNT433OzV2vFmPSYhXsVgfumbYLDoaFUMzF6rsyseK2DI8xDRbxE+cjfdHlYBm7KwSEr+xz1ZuD\nD50wVkj69VqIxuWhdt0DQbeJX3c9JDMmwWG2oGHtw2Dtnn9PwwEVAmOImEQBTh/oxgs3H4eDca4y\na0p0qD2hw12vFuMX92bh7YfK3dr89H4T9nzaAp3a5lZevlcDdZMFVz6SB4mChwnzY3Hkuw6PZxq7\n7Xjq8sNQN/d5/D595VH89ftZEIq5mHdpstuLTpkkxOUP5gIA/nGju2dy5WEtLro7C1IlH1f9IR9/\nXn0Ag5x+OCTYEJ3hYhIFeHjJPre5qSvV4+pH833OzaTFzrg/P77X5PoOLSYGnz5XjbzpMcibrojC\nJwkewuWBy/X9mmg5+O0QjmbkwBGJAlcagDAvCwDQ/fl3I0YAANRjeMyx6eVa18ujl5Ltapw50I1p\n5ydAIHL/yi0mxkMA9HLwmz4v2+Qc76vATa/Uur3kAEDbYcXhb50CIylbDKGkLyTuudekgC/iwGL0\n/kfw43vO6KJJOWJkTwo+eNtIZeDcAPA5N+nj+3Ts5fs0Hu3OHHQeUHJ5EVsFRgxjMaFu2wa0HDhL\nhYA49Lg+HLnz+zWfDMHJcQigQmAMwdhZVB7ybslQsr0TAhEHGUWyoPvTdlpdAkUo9R7b3NvuAADa\na/vOH6TKPv3nxEXOlW7VUa3Xds2VfecPWcWjXwh4w9fccLh9L3eb2XMLZLc6y/iiwf+zNalboK0t\nhUXTDsZiAutgYNNroK0tRePOz1D63l9HfXiISCBh7ARYk1MV6zB6ns0NJ1QdNIboarHAbvOusmip\ncv7wkrIlqDzs/gLmCzkoXqTC+LlKpI6TIjlHDIGIC76I43ox+TJi6G73HjK4/0q/d+VKOH2r3Qnz\nY30euvYiiw3u8GykYtB432F5mxsAbgfwWcUyj91Ar1D0djYTbQzNVaga47GTkh+8O+y2XKn3nbE/\nrA0tEBXmgZ+aBKZbF/azow0VAmMIb6vHXsx6Z9hlkcx9Rb++dBEIB9CpbfjujXpse7cRbT0rVQ6X\n4JWTCz36ChdpDN8lVA5sasOX/6z1W9/Y7f0lOlrQdwUOdd2ftloTHl66Hw9+MAWXPpCLJTekQae2\nIS5V6NoxlGz3n+RnLDDpusdQv3MjNLWBkxNFgjArHaZTZwb1Gf1pe+51xN1yBRLvvx1Nv/8b7O0j\nw6+CCoExxMAXfH8kMc6v2qR1fzERjlN4PHftUbTWDEjHyI+u7tmotYN1OJ/JF3C8mqye7aibzHj6\nyqN45LPpkCr5iEkQwKi1o2x3F/Z+3or9X7UN9xAHnZL3/zQkz2F0erS9+GZYbfvHGPJG/LrrPQsd\nDjjMFjj0RqQ+9SDsrR2wt3fCYfGdgKdj/XthjS8UqBAYQyiThBCIOLB62REk5zq3ry3VfS9ensCp\nWy7b3eUhAAAgNim6SS0cDIumCgPSxkmRPXls6vsjhS/i4FevTIRcxR8SU9CzGUYffrx+1mwB/AgB\nyQzPgIUD4SXFg5cUH/YYogUVAmMIQoDC+bE4vs1zmzlpcRwsJgYN5X1RD3sthUw672qLqcvjoj7G\nku1qpI2TQhllATNWmLU6EZkTZWg8Hd4LKmvhVRDGJKJu18co/MWv4bBb0FV1FI37v3ZL+Ug4XCRP\nXQ5VwUxwOFx0VR1D06HNrjqZC66AWJWK2p8/QPrcNZAl50LXdAYN+76Epds9N0PO0hshS84FXyJH\nb4Y4m6EbJR885uorvnAeDr/Zl8FMEp+BwjW/divLWngV4sbPAQDUbH8f6gr3FKDBjoknkiJl+vmI\nySgCX6IAYzWj8eAmdJa7R351GDydIIPFYfLv0Gitbw6776GGCoExxoV3ZaFsVxdslr7dwOSlccib\nrsDuT1vczg16X/5ZxXIQDtxs8pNzJbjgztDS5wXDtncbsfTGNAhEHCRkin16Mceli9BRP3I8h4cK\nucqp+49LFYEv4vg95/GFOC4F+RfcgfrdGyFNzEZC0QLwxQpUbf1PTw2C3OU3Q55agPaTOwECJExc\nBHF8Gs58s94VJE0cm4yCleuga65A/e5PkT7vEuSvuA2ln/zNlVGMw+VBrEpB7c8fwm42QFUwE4kT\nF6J04zMhj7th7+doPf4jiq74ne/PFsSYcpffDFFMElpLtsNm0IAvUUAgifHoq3tz+KEuHGb/v82W\nx14Iu++hhgqBMUTJdjVqjmvx4rEFHvesZgfefdjdPpllnTro5DwJ1pe6W+oYtXa8fOcJLLk+DTNX\nJ0RtjNoOK+6ZuhMzVyfgr9/N8lnv0JZ2vHFfX3rH8XOVuOyBXIhkXIhlPIjlfecfT2ydDYuRgVnP\nQN1swSvrTrj5Pjz8yXS3dr1qsP7tTHo7/n79Mbd2STli/OqVYojlzrb9TTP/dXQBzHo7TDpn23d+\nX47mivBXlr0c/aED2ZPkmLYiHi8e9fweAaf67sXbT3j4g/TC5YtQuvFvsBk06Dx9AJ2nD2D8xfcg\nadIStJb8iJwl10GeWoBj7zwMtkfyd54+gKLLH0L6nF+gYe/nAJxB4k598QLMGmfOYEt3O8ZddDdi\nc6dBXeE0D5WnFqBh35fQNpwCABg76qHMngTGFroAZ2wWMN3+zzyCGRNPJAfLOmDRdaC75oTrMw7E\nHMGhcOsLr4XddqRBhcAYQhbLw6ZX6lBxSIvbnp8AsZwHbYcVpTu7sOX1Oq9tnrj0CFbemYEpy+IQ\nmyICY3Ng18YWbHm9HtoOK7Iny6MqBHo5uKkdiZliTFykQnKOGGI5DzarAyU/qnFyh9rlUNWLPI6P\nzIm+fRyEEi6EEi5iEgWul3wv4bYTSXlIyhF7bScQcSAQCaCI76sbKfMvS8Z1jznzz25/v8ltDnh8\ngphEAWZdmIiic2Ix5+JE7PnMV0J3FnZjnxmw1eAMv8GXOlfDQkU8ODwBpt36nEdLnsjd9NGq7wvd\n4WCcO0dOPw9iXUsVClauhd1sAGM2QJU/AwKpEoNJoDFV/fA2ss+9BrnLbobNqEXnmQNoOkCzxvmC\nCoExRO9LrHyfBg/MDy62u0Fjw8ZnqrDxGe824VvfacTWdxo9yl+4xXsy+kDt+vPN+jp8s967cBrI\nwU3tOLgpcJ5gb4R7wFp7QhdW23DmJj5DhGv/XAAuj+Cd35f7fMHv/6oNL59YiIkLVX6EAEH/ZFEE\nA6y8CAd2sx51uz7xaNn/BQvA7RzBGw6bBcKYBIy/6B6wDjss3e2o+WmD3zbOIYTv8BZoTGZNK059\n8QLkKfmIL5yLpEmLYWitRnddadjPjAY8lRJclRKWihq/9YTjc0EAWGoawPqxHIrauAb9CZQhgwQI\nS0sZuUxeEgcen4Cxs9j3pW+VSG/k0V6TX18IpDGwGpzOZgJZLADA1nNt0XZArEpFd91JsExovgze\nsN+fAs0AACAASURBVJv0OPnRE2As3k1+vT1jsHcLAKBrroCuuQKCA5swbvVdYQkBwuOCn5wEh9kM\nRtMdUcwfyewpUF6+CpbyKrQ+61udFHv1xRBkpED9zifQ7xj8DHZUCFAoIwBlogCA05vYl66/P7oO\n/ytEZc5ktJ1w7mJi82cAALRNTh14V/UxxOZORWLRArSWbB/QkgAhBsqzGroAh+8DbIve08EtNm96\nSM+IBKu+C3xJaIH3xBMLIV+2EKL8HBC+87CetdlhqaqB7qfdMB4J3ZFNVORU9dkCOImZS05BkJEC\n8dQiKgQolLOF3kBzEgUP2ZPlqDnuPazAudekAoDfMNsOuw3JU5eDsZogTcxGfOE8aKqPw9TpVEFp\nqo9DU30MaXMugkiVAkNrNYSKeCizJuH0N+tdO4ZgUaSNx5SbnnJesCxsxm6c+N8TLmsdTfVxpM5Y\nhYSJC8EydsRkTYRI4X7ORDhc8CUKcAXOmDzCmASIValgbGbYTfqAKqD+TLj0AXTXl8Ki7QDLMJDE\np6Or+mhQbQmPi7jrr4B0zgzPe3weROPzIRqfj7p7HwZrDc2jnZfgjJtlKavwW89a5wyiyE+O/lmc\nN2ii+TFAb1KZhlMGPL7mUOAGlBHJpMUq3Pb8BAjFvj2/AeC38/ZA3+X9BZS18Cqoxs3Ckbd+OxhD\ndINwuJhy4xM4+h9Pk86CVeuc5qajjKz1zwIsC/WHn0G3Y69nTmFCIF80D6qrL0Hb+rdhOh68iinj\n1SdAeDy0PP4irDUNPusJstKQ/Md7wDIM6u982G+f0Ug0T6OIUigjhJLtavzpgoPY/Fodqo/p4GBY\n2K0O6DptqDqqxbb/NuK564/5FABDDeHywOF5BvnjCsSwm/ReWowOtD/udCaZ97ZAZlnofnJmdJPN\n823i7A2H3mlCzItX+a3HjXVacQ1VtFGqDhoDPHttcFtdyshH02rBF8/X4AvUDPdQAuKwWaBtKEfS\n5CUwqZtBuFyIFImIK5yD2p8+GO7hhY1h3+Gg6glzQnOmtNY1QaxUQDxtIowHfVuQiadMAADYGn1Z\nf0UXKgQoFErYVG97F4VrfgO+JAaEENiMWuhbqmBo8x8hdiTDdAV3JsKRSQNX6ofpWCnEkwshnTUZ\nnW94F5LCgmxI5znPI0zHy7zWiTZUCFAoY4jaHf9D7Y7/DdnzGKsZJz96csieNxRwJGIwusDqLNbs\nmTXOH4bdhxCzeim4KiWUV6yGYfch2JpaXWqnmDUroDj/XBAeFw69AYYdB8Iaf6hQIUChUCj9SH7o\nbrS+8Dqsdd4Pb4XZGQCArs9C80JmbXY0PvgU+KlJSPnLb6A43zOpkrW2Eer/fur34DjaUOsgCoVC\n6SH1zw+Cn5QAlmFg2H8EphNlsDU2gwgF4CfGQ1w8AdJZ02Ctb0Lz31706x/hD356CkSFeeAlqMAR\nCeGw2mDYfQjW6vqQ+omGdRAVAhQKhdIDLzEeSffeCZ7Kv0dzw0N/AaMd/hSRZ4UQEOZnIOXP61zX\njff/HbbW6KdlS/z19ZDMKApYr+GeZ2BXe0/mHi4ZLz8MIuTDfLISHa9thMM4dkIoJ//+Vogm5gEA\n9DsOo+O1jcM8IgrFPxyJGMoLV0A2fxaI0D3vhcNghG77Lmi+/m6YRudONIQAPRMYZkTjs8GNcUa5\nlMwogmLlAmg++WGYR0WhnL04jCaoP/oCXZ9+DUFmBgRpyXCYLbC1tcNa3xS2CmikQoVAD23PvwfC\n5YAjk4Irl4Ajd/4/4Z5rh3QcQxE1kEKhBIa1M7BU1cBSVRPVfjkyCRSrloAjEjkzKPlB/c7g75yp\nEOgHyzjAdOvAdPfp+gY7eoe5vAaMVg/C48G4/wS0W3YN8hMpFMpwIT1nJlTXrQEReHpae4MKgbOE\n+l+NLTtrCoXiiTA/G3E3X+5c/bMsmG4dWCb80NTRggoBCoVCGQLk5y0ACIHDbEHjfY9FlJsgmtAA\nchQKhTIECPOyAADdn383YgQAMAqFwEgwaaVQKJRQITxniHDjgWPDPBJ3Rp0QADO2zLPGOlRoUyhO\nrA0tAAB+atIwj8SdUXcmwNqd+Uq5MTJI506GZMYE8OJjwVXKYe/QwFrXDOPBUhgPl4Wc+WewUa5Z\nAuXl5/mt0/b3d2E8ciqkfrPf6ztY7nznS+i+3wsAIEIBJNMLIZ1dDF5KAvgp8XAYzGC6dbBU1sN0\n7AyMB06E/kEACLJTIZ0zCbKF08GRSeDQG2Hv0sJcWoWuDzb3VQxi2ytIT0Lq0/d6jL//Z4j5xRLw\nlHIQsdD1GTSfboPxUGnYdtuCzBSIpxVCXJzXkwRcAUZnANOlg/lUNcwlZ2A64T8LlD84EhFU118I\nfnoieHFKcMRCEB4X9i4d7O1q2Fs6nd9DaSXsrZ4pGIPpXzKtEKKJ+RBPGefq32G2wmGywFxyJqL+\nKdHFsGM/RIV5UFxwLswBsosNJaNPCFhsACFIf+FBEL778PmpCeCnJkA6dzLs7V1Qv/tVyC/U0Y4g\nNREAIJ1djLhfrgFHJnG7z1VIwVVIIchIhnzxLLQ8+RbMpZVB989VyqG6fjWkcyd7lHOVcghz0sBL\niIX6P1+A0RrAGEJLjNE7/mA+Q+K918Le3oWONz4N6TMAQNKDN0M8eZxHOU8VA54qBsK8dMSsXojW\n596B6Wh5SH1zRELE/GIxFOfP92oKyIuLAS8uBijMgWzxTABA14dboN28KyhrkUD9cyQicCQiyBbP\ndPVva2iFfucRdH/9c0ifhRI9DPuOQlRUAOk5M8FLiIM9QK7hoWL0CQGbDQn/d7WHABgILyEWifff\niJrr/adnG0pYm33Qn8FPc3o2BOvklvy7W9Dx2kbodwVOTMNPikPyH24HN9Z/0u7/b+/M49uorj3+\nmxntqyXL+xo7++4szuKELCQkpaRAm5atLYEkFFpIW6CUQqGQxyv0NVBeuqSFtjTv07J9Al1CKKEh\nK9k3Q3BWHC+xHduSrcjapdHM+0NY9miXI8tWdL//RDO6c+6ZiTW/mXvPOVdZPRGyMWVoX/8KOKs9\nLj8CfXzhv+62ZdCuWBCzvShHl9A5AIB60cywAhCOvEfvhuW9vTC/tT38SlPB/mRnIe9Hd0NcnNgr\nv2peVdw36IJn7k/Yvrg4L6E+CMlHNn6Uf2GZyXYUPv8Y2A4TWGMXuCgJoqZNfx10v9JKBHiPF1kr\nl0I5axIAgHO54bnYAp/Ff6NRVE8ExQinOdQLZ8K6OzV1uWNh2bYPtr0nQKsVYNRK0CoFRHot9Hev\nSFof4sJcqOZVCfZ5203wtpkAzgdJaQFEuf2Wt6NpZK++Fe7GNnhbO6PaDicA3o4ueFs6wDndYFQK\niEvzIdJrwWjVyHv83oSHU3r9DxYAx4mz4BxO0DLJVZ2DdsUC6G5bJtjns9rhaWwDZ3WA1ighLS8C\nrZL3HXPTdaCkYnRv3hrVNsUwyPnBXcIbNMfB09IJ1tgNzukGKArSyhKI8/SCbFHrziNRbffvI0QA\nOA6Ok2cD9hmlHKJ8A8T52YJm8fZBGBxyH14j2BblGSDKMwyRN/38GGoHEoGSiKFdsQCs0YyWH/4y\ntMFv/f9obpgD/bf9N9bsNbdCNm4EjJveTqGnkfFZ7fBZ7fDCCOCLceMkigCTpYbh/q+j+/X30fP+\nxxHb0QoZCp97EKJcPSiJGIU/X4dLDzwXsXiddFSpQAA8Da24/NyrYctcUAyNnIfugGLGBKgXJbYO\na6//AGKeQ/FLjwbEoPccmu7+adi2tFyKog2PBOo02ffXwviHLVHnE2iFDKWvPA0A0CydA+XMiWh5\neEPEuabstV+FdERRYNt+8BMYfxvHAi80Hfe8Rvbarw7IvqQwB56W1CxXSAhP85ofD7ULYUm/6CD4\nJw+j0fPhQThPXQhsK6onCp7sMoFoN08A4BwumLf8J7BNMTSkI0sitlcvru53MAfjprcj1jnifRxM\nr74bWFh7INgPn4p5Dv39BxDyFtgf1cKZAQEAANOr78S88QYLIpOlhuq66RHbi4Lektyfx1kbPoGJ\n7f59JGKfCAAhEmknAqzRHNdEneUfuwKfKbEoZCLzmibOsEzHceEappHGmSmxCMrqiYFt+9E6eNuM\nUW1zdiesuwY4DMfzML/5QcxmjuNn4ruBUhQ0y+cKu4gzWccbdPNUTBsbsS0XtNygpN9bQbLo38dg\n2CdkHmknAo7a+KJ9XOebBIXgpKPKBsulYYfrXGNc7YKf5JmgKJxeJGUFoKSSwLbjaF1c9h3HTsfV\nLhjXuUawRnPMdrzbA28c7SQl+RBl91skJIHcheCnbdmYcv/wTRxtVTVT4+5nIP6oaqYK39AIaQUl\nYiAy6CApKYDIoI99wCCRVnMCAOC9FOdrLc/DXd8aeHKTjigcRK+GF66zjQM6jpJLw+6XjigWbLsv\nNMVlz9N82X/DjVEuN5hE/I9nAR7ZmHLBdiKLAvl6hAuOU1IJGLUCPkvoQuS2PcehvXkRaNkXgklR\nKFj/XfS8/zEcR+uSUizMtud438Q2RSH73lugWjgD7c/+YVgUIyPER+4PV0M6aoQgxNd1th62nQfg\nODGw3J2BknYi4G5ojb/t+caACIgLc0FJxMMugWwwSDRmvhcK4W/Wkso+EWCNZrBd8d1EeS8LT9Nl\nSMoTE+CE/I9jOEg2aZRgW5SdJUiwSxSRQRdWBHw9NjSveQbyqWNgWH0rGJ0G0opi5Dx4u6Cd+c0P\nYNt3UvCmGi++Hhs6NmwO2AcAaUUxyjb/V6CNt80I297jJBx0GJK95nYoZ1fB226Ebc9heDtN4N0e\n0FIJ1EvmwfDdbwEAnLWnYfzN5pT4lHYikMiiK8FjtLRcBl8GiADvdMdulAB0vzeE4Gsai2gx0JFI\ntv9MsoMCYrzYOGvPoeWRF6FZNjckHBUAdLcvR9bKpbAfqIVl6x54L5sS6r6/fc2XasBoVILvxYU5\n0N2+HOLCnAHZJwwOsgmjoZxdBfA8Lj/1YsiwpHX3IagWzob+zpshnzoe8inj4PzkTARrySPtRCDm\nL1DQNLhtZtSx4ZO9/F3/65hoLaABVEtMuv8MI9hkuy1wfXohQuPY+HpiJ8DxHi8sW/fA122BatFM\n/5BUv+tIiRiorpsOZc1U9HxwQFhqIw567fd8sB/K6on+sNqgv/f+9q9s+U9KkhUJkVEtmAUAsO7Y\nH/53xPOw7ToIcW421EvnQ1kzg4hAOGhpfCvyAP70+v5cSwu4p5L+142Whp83iES8KygNJsGhqr4u\nC0x/fDclfdv218K2vxaiHB1UNVVQzqsSJHFRDAPtl+fD8vePwLkG8NbkZWHbXwvX+aao9mWjy9Dx\niz8PqA9CcugtJW0/dCJqO/vBE1AvnR9oP9ikXXRQcB2ZaDAaZeAzz/rIk9AA4Zz9REAd//UHAFo5\n9PkZviARYLLUKfeBNZpx5R870froi2FDnPWrbk6a/fbn/xTSh3RU6VX30Uv5uscxev1LKPjGt5Ni\nL1Oglf7fji9GYAJr9n+fyL3uakg7EYg73p+moexXPsF+cHjV8E4nHIdOBT7TChkU08fHdZw43wBx\n4WCv0hwb6/YDgm1Rjm6IPPHTsWEzGr/5BFof+1Ugokc1rwrqpbOTYt9VV4+ODZsF9nv7IAwdnnp/\nVJ1iTvT/B9Ucf0Ki+0LjYLsEIA1FQF41Jq6QQ9nYcjDqvjcB9/n4whoJobgb2wRvUYp+iWPRkEdJ\nrEol7obWAUXiDDbeNqMg7l+U5DeUYPuEocVx3P8wpb3p+ohtJCNKoFnh/95x+GRK/Eo7EWA0Kqjm\nx36iybplUeAz72VhP3wqSmtCNHi3R5D4pZw9KaQ4WTCUiBk+iUw8D8u2fYJdmmVzIzROLf3nTAZj\nzmo4zMkQ/Nj2HgHbYQKtkCP7nq9DMX0SJCWFEOfnQFJWhOzVtyH/J98FLZfB09gC2/7jKfEr7UQA\nAHS3fynq95rlNZCNrwxs2w+fIpPCV4l1V18FSophYHjgNkEWcTC625ZDnD/0FRJ7se44DJ+5J7Ct\nu/NGqJfEP/wizsuGav60qG1U102Lek2CkY4qhbRfDoUnRiIko1FCdV10H6LZTzrJjuK6xuFZFp0b\nXwPbfQXKmhkwPPBN5P/s+yh47lHkP7UOyjnTAJqGt6Udxl//JWXXN+2igwD/j0G9uBq2fScEwxS0\nUo6sWxcLnvI4p9tfC55wVbhOXxRsSyuLUfD0d9D25K9D2uY8dEeg3Pdwgfd40bnxdeQ/uRaUiAHF\n0Mhe9RUo50xGzwcH4DpdDy5oARxGp4Fm6WzIJo6EtKIY7vpLsO2LHNlhuG8l9N9eAefxM3B+eh7O\nunr4roQOQ9EKGVTXTUfW15YEhjZ9V6xw1UUvu03LZTDctxLyCSOj2u/tI++xewRDp5HaDhTe5wMo\nCqVr10GSkwdQNLzdJlg/q0X33h1hj6FEIqjGToRy7ATIikohztKDc7vgMXbAdrYOlqP7wXnCRzAx\nCiV0cxdAOWY8xLps0JLwkWrnn344ZJ+uZiFUYydCkpMHWiYHfCwcFy/AcfECrJ/VgrX2hLGUfNgO\nE9qfeRlFv3wi5IGBszlg3XUAPf/endKk1rQSAcu2fTC/tR25D92B7HtvQfa9t8Q8pnntszHbaL+y\nELpv3BCXD8Ubw5eDvfL3nbjyTvg/fPnEkcj90aqoVS57yX0kQsQFz8Nx4gw6fzX4i0xEovXHLyP/\niTWBapySsoKImbds1xW0P/sHiEvzkffo3al0MyLuC81oWvUUZGPKkf/UfQD8JSWCy0pcDbRMCmXN\nVCgTqBvE+zhcevD5uNsPxH73X/458IJ+EVBNmIzRU2egadNLYK0WiFQaaGfOgWHJjZCXjkDrX18N\nOcaw5MtQjZ8M22e16Dz5Djyd7RBn50Bfswg5y1YgZ9mKsDfx0u/8ELKiEnjN3Wh74zW42/2VA0pW\nPwh5WQU4lxMXN6wH5wlNNCz65lrISspg+vA9OBvr4XM5IVKqoa2ugW7eYjga6lMmAgDAOZy49L2n\nUtZfLNJqOMh9rhHgOHRufD2mUrJGMzo2xJd2TYmY2I2uxgZNxyUA0TugQImHdnzX29qJy0//Fo5j\n0QvI2Q99irYnfg222xKz2uhQ4DrXCMu/dsc/RPjFoi2RRL4X294TCYchu+rqcflnv4urrc9ig21v\n9BjzSPaTLQAAQEukMO3YBnd7K3x2G9wdbeh87x1cObQPytHjIC8tDznG9NG/0fCr52D8cCsc9efB\nWnvgbKxH6xt/hqutJWJfsiJ/mXPTh1sDAgAA7X9/w++LTA7VhClhj1WMqET3vo9gOX4Ini6j39fO\ny+h8bwsubnhWYC8TSZ83AZ6HqzfCh+fR8oNfQjlrEhQzx0OUowejVYHtssDT1OZfaP74aZIXMAiw\nXRZ0vvw3SCuKoaieCNX8KtBKBTi7A77uHrjONqD7b+/3tTeawXu8w26C0vz2h7Bs3QP5pFGQja+E\ntLIY4qJcgKHBu73g7E54202w7jwC1+mLIUNF4TC9sgXdf30PsnEVkI0ph6jAAGllCWipGJRYBN7H\ngXO54Tp9EZ6LLbAfq0toAXjO5YbplS1wHD8dsC/O0YHWqMCo5AH7nMUGT2snzG9vH9QF5nmWxZXD\n+0P2d+3dgazZ86GeWAVnc6PwGG+EZDWOg/38acgKi/1DWBEy053NDYJtb3cXfHYbGKUKEkP4cGSf\n0wlZQYSy24lmwF+DUPwwuAgURQ29E4S4oSgGs6q/D4ulCWfOvjNo/YwceSNKS+YBANo7anH6dOTV\n4Xp9OnT4pavqM1Xnls6Ur3scEkMuXG0taP59+Os9ev1LcF1qRPOrG+O2q505F3krVuLC+sfAs8IH\nuNHr/f3UP/8kfE6hIFc88jRE2ixcOfwxOreFZoIblt4E/fzFcNSfR/f+XXDUnx/Smz+tUkBz4yLQ\nMlnMcPfuzVuifs/zfGIlesOQPm8ChGFD4O82wRLRidLcvBednacgk2XBYBgXn09XSarO7VqA7Yme\n+cqotWH3i7P0UE2YAllxKeTFZaAkEtAiMShR7NuRNL8Ijoa+CXRGoYRI4+/HYwq/vnTX7u3Qz18M\nReVoKCpHg7VcQc8nx2HasS1mf8lGWTMD+rtuifvNOJYIJAMiAoSE4TjfVT9xx4PHY4PHY4PDYYwp\nAsnyKVXndi3A+6IPt9ISYfQLRdMw3LACWbPng/piYR5vdxfYbhN4rxeMSg2JITesLY+pExJDLnKW\nfwWtf/tTQIByV6wEKAqcxw1rXfiqALzXi+ZXN0JfsxDKsRMh0mZBf931kBhyYNy+FV5zV6KnPiCk\nI8uRvWplYLjLZ7EOizUgiAgQCIQBIcmOXhLEYxQ+mVc+8d+gJVI4Gj5Hy2uhk+H6BUtguP7GsLYa\nN74AWVEpStY8hIpHng5Umu355Bgaf/M/8HS2R/XFdakRbW/+pW8HRWHUT1+AavzkiP4kG8P9dwEU\nBfObW2HdEX397FRCRIAQN2VlC1BZ0Vcfv6vrHD75VBiBVVGxFM3N+1BZsQw5ORPAMFK4XN04fGQj\ngkt5jxu3EjpdJSRiFTjOA6fLjKNHf3NVPu3c9URIm/nznsTBQy8KfPq8/n20th4R+LR4UV+4a6Rz\nKyqsxsFDL2L2rIcD59bSekhgi6ZFKCtbgLzcKVAohAlzn9f/G83NwuzldKU35p5zCcfoRWr/Yjeu\n1mbB/t64fsfn4dcIlxVFrpopUqlReMcqgPPh0ubfw9k4sIWTAvA8zAf2QL9gCeQl5VEno5MF/UUZ\nG1fd+UHtJ1GICBDipq3tKMzmixCLFZg8KXIFyaqpq0FRDFrbDoPnfMjPr0JBwTRcvtyXBq/LqkBe\n7mR0dHwCu6MTDCOBLqtiUHwSi5UhPo0ZfTM4jhX4dOz4ppjn1mur/7kF2xoz+mbk5U1FQ+NHAICK\nEdfD4ehCXd2bcDhTM/SQCiiGgW7uAnTt/ECwX79gKQDAeip87ZveMfz+yMsroRoTuTBh1qz5EGmy\n0HPySEICQInFAMeFHXahZTIA/iinVEwU804XKJUSnCN2pFkqISJAiBuv1wGvt7csc+QfDc/zOH7i\nd+A4/5hxa9tRTJp0V+AmSVE0xo77Go4d+x1s9r7X+AZ8lDKfysoWoaBgukAEenp6i61FvyHwPI+G\nBr+vrW1HMXfOYwFbIpEcBQXT0Xb5KJqadgMAFAoDCvKr4PHawHHXzsp2nMeD7AVLYTvzGdieKxCp\n1NDOnIus6hrYzpwKeRNwt7dBml8I7fTZcDY3wHb2MzByJTRTpiF74Q3wOexglKqwfTEKf1llWXEZ\npLkF8HR1xjWeLs0vRNGdq9H53jtwd7bD57CDYkQQabXIqq4BAFjraq/ySsSHp6UdsrGVEBfmDauC\nhkQECEmnpfVg4GYLAF6vHXKZPrCt0RRDLtMJBCDVPjkcRoFPidrqxeu1C2wpFP7CenZ733i43d4B\ngIJUqoHHE7o2cbrS8c+3oJkyHWUPhGb4dvzjrZB9ndveRfHd94MSiVDw9W8JvnO1NKH93TdQvu7x\nsH1Zao9CM20WJDl5KHvwR31fcBx8DjucLU0wH9gT9i2BUapQcFv4rHVncwOM27dGO82kYd93BLKx\nldAsXwDXmeglQlIJEQFC0rFa20L20XTfn5pCnvrCcsE+cZxX4FOybPX0tKCr6zwqRiyFj/UnRo0o\nX4L29pNhr0s60rjxhcDnSEM+4XA2XcSF9Y9FbROuZAQAlK79PjxdRpj37YTb2B5Ya5ySSCDRG6Bf\nuBQl934PPocd9S/0lWRwXWqKaDPV+Kx2mN/4F7Q3XY/SP/4CbIcJrLEr6jrcpk2DXyaGiAAh5Thd\ng5fFOhyoO/0mpk27D2PG3Ayny4xLl/ahsWnPULuVtqjGTQLPsmh5bRPYnish37tamsDaelC86gEw\nCmUYC8OD3IfXCLZFeQaI8oa+0i4RAULKsVguwe22QKEwwOEwDbU7SSc3dxLksmwcPvK/cDiGX+2k\ndENeOgIeY0dYAeglOIt4OOK5dHmoXQgLEQFC3KiU+RCJZGBEUgAUJBIVtNpSsF4X7I7w2Zrh4Hkf\nzpx9FzNnPIT2jpNwOkxgGAlUqnyc+ux1AABNi6FQZEMkkkEq0UAm1UKvHwWWdYFlnXA4TKBpBgp5\njsAnvX4UfD53wj7RNAONukRwbnr9KLhdloTsAIBOVwkv64DPF1rRkjAQeIg0WlAME3EyOGvGHADD\nWwzan315qF0ICxEBQtxUV68TbKvVRZg+7X5wHIvde55OyFZ39wWYus4gxzAOYrESLOuGs1/4pFZT\ngqoq4evz1KwRAPyZxB/v/znkckOIT1On3AMACfsklxswbdp9gnObOuWeAZ1bW9sR5OZMRM1c/yQn\ny7pgs11G/cXtsFiaYxxNCMZ27jR0NYtQeOe96Nr9IdztbeC9/igr5ZgJyKqeC+Uof0a5+cCuoXQ1\nLSEF5AiEJDKv5ifo6WnFp6f+T7A/L3cyJky4HSdr/wSz+SoTnTIQeWk5tNNnQ1ZSDpEmC7RYDJ71\nwnmpCc6mi7B+egKerswbeiMF5AiEYQRF0ZBIVGGHgbTaUgD+kFJC4jibG0PKUhOSAxEBAiFJ8DwH\nk+kM8vImw+dzw2JphlisgF4/Gnr9SBiNdbDZUpcbQSDEAxkOIhCSCMNIUFIyD3m5kyCVZoHnWdhs\nHeg0nkJb21HwPFmcnZA8kjEcRESAQCAQ0pRkiEBarTFMIBAIhORCRIBAIBAyGCICBAKBkMEMizkB\nAoFAIAwN5E2AQCAQMhgiAgQCgZDBEBEgEAiEDIaIAIFAIGQwRAQIBAIhgyEiQCAQCBkMEQECgUDI\nYIgIEAgEQgZDRIBAIBAyGCICBAKBkMEQESAQCIQMhogAgUAgZDBEBAgEAiGDISJAIBAIGQwRAQKB\nQMhgiAgQCARCBkNEgEAgEDIYIgIEAoGQwRARIBAIhAyGiACBQCBkMEQECAQCIYMhIkAgEAgZzavS\n2QAAAA5JREFUDBEBAoFAyGD+H/pUh6gZrESBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be2f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_nr = 3\n",
    "number_of_keywords = 10\n",
    "visual.show_wordle_for_topic(model, topic_nr, number_of_keywords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": 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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}