EGC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.width\", 380)\n",
    "pd.set_option('max_colwidth', 200)\n",
    "import numpy as np\n",
    "\n",
    "from langdetect import detect\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pickle\n",
    "\n",
    "#To handle warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./dataLemmatized.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of articles over the years\n",
    "With an average of 73 articles per year for a standard deviation of 18, there is a difference between years, but it is reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand total of 1269 articles\n"
     ]
    }
   ],
   "source": [
    "invYr = df.groupby(['year']).count()\n",
    "sns.barplot(x=invYr.index, y=invYr.series)\n",
    "plt.title(\"Distribution of the number of articles over year\", fontsize= 24, color='gray')\n",
    "plt.ylabel(\"Number of articles\", fontsize=12, color='gray')\n",
    "plt.xlabel(\"Years\", fontsize=12, color='gray')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(20,8)\n",
    "\n",
    "## PlotSwagg ##\n",
    "plt.yticks(fontsize=14, rotation=0, color='gray')\n",
    "plt.xticks(fontsize=14, rotation=0, color='gray')\n",
    "\n",
    "# Less border\n",
    "plt.gca().yaxis.grid(True)\n",
    "plt.gca().spines['left'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_edgecolor('gray')\n",
    "\n",
    "# line under the bar\n",
    "plt.gca().set_axisbelow(True)\n",
    "\n",
    "plt.show()\n",
    "print(\"Grand total of %d articles\" % len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization and tokenization was performed beforehand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lem_prepro_title</th>\n",
       "      <th>lem_prepro_abstract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>plateforme citoyen dédié analyser tweets événement politique</td>\n",
       "      <td>plateforme objectif permettre citoyen analyserpar même tweets politique événement spécifique france élection présidentiel analyser quasitemps message candidat fournir principal caractéristique usa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>algorithme clustering niveau grand ensemble donnée</td>\n",
       "      <td>classification croisé clustering technique permettre extraire structuresous jacent existant entre ligne colonne tabler donnée forme bloc plusieurs application utiliser technique cependant nombreux...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>algeospf modèle factorisation clustering géographique recommandation</td>\n",
       "      <td>recommandation point intérêt devenir caractéristiqueessentielle réseau social localiser accompagnél émergence échange massif donnée digital cependantles faible densités point intérêt visiter utili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>analyser sentiment partir commentaire facebook publier arabe standard dialectal marocain approche apprentissage automatique</td>\n",
       "      <td>analyser sentiment processus pendant lequel polarité positif négatif neutre texte donner déterminé intéressonsdans travail analyser sentiment partir commentairesfacebook réel partager arabe standa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>analyser rôle sémantique résumer automatique</td>\n",
       "      <td>article présent approcher viser extraire information expriméesdans corpus texte produire résumer plusieurs variante deméthodes extractif résumer texte implémenter évaluer leurprincipale originalit...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                              lem_prepro_title                                                                                                                                                                                      lem_prepro_abstract\n",
       "0                                                                 plateforme citoyen dédié analyser tweets événement politique  plateforme objectif permettre citoyen analyserpar même tweets politique événement spécifique france élection présidentiel analyser quasitemps message candidat fournir principal caractéristique usa...\n",
       "1                                                                           algorithme clustering niveau grand ensemble donnée  classification croisé clustering technique permettre extraire structuresous jacent existant entre ligne colonne tabler donnée forme bloc plusieurs application utiliser technique cependant nombreux...\n",
       "2                                                         algeospf modèle factorisation clustering géographique recommandation  recommandation point intérêt devenir caractéristiqueessentielle réseau social localiser accompagnél émergence échange massif donnée digital cependantles faible densités point intérêt visiter utili...\n",
       "3  analyser sentiment partir commentaire facebook publier arabe standard dialectal marocain approche apprentissage automatique  analyser sentiment processus pendant lequel polarité positif négatif neutre texte donner déterminé intéressonsdans travail analyser sentiment partir commentairesfacebook réel partager arabe standa...\n",
       "4                                                                                 analyser rôle sémantique résumer automatique  article présent approcher viser extraire information expriméesdans corpus texte produire résumer plusieurs variante deméthodes extractif résumer texte implémenter évaluer leurprincipale originalit..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the French Lem and Prepros\n",
    "df['lem_prepro_title'] = df['titleLemma']\n",
    "df['lem_prepro_abstract'] =  df['abstractLemma']\n",
    "\n",
    "# Display the result\n",
    "display(df[['lem_prepro_title' ,'lem_prepro_abstract']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom Stop words\n",
    "custom_stop_words = ['algorithme', 'étude', 'méthode', 'approche', 'nouvelle', 'algorithme', 'automatique', 'système'\n",
    "                    'source', 'exemple', 'concevoir', 'ensuite', 'définir', 'effectuer', 'permettre', \"ainsi\", \"entre\", 'point', 'base'\n",
    "                    'souvent', 'utiliser', 'variable', 'donner', 'technique', 'masse']\n",
    "\n",
    "stopList = custom_stop_words + open('stoplist.txt','r',encoding='utf-8').read().split('\\n')\n",
    "\n",
    "language = open('langues.txt','r',encoding='utf-8').read().lower().split('\\n')\n",
    "\n",
    "def retraitement(txt):\n",
    "    tmp = []\n",
    "    if(type(txt) == str):\n",
    "        for w in txt.split(' '):\n",
    "            #regroupement langues\n",
    "            if w in language:\n",
    "                tmp.append('[langue]')\n",
    "            elif w not in stopList:\n",
    "                tmp.append(w)\n",
    "    return \" \".join(tmp)\n",
    "\n",
    "#les changements se font bien mais pas possible de les stocker dans la colonne correspondante\n",
    "df['lem_prepro_abstract'] = df.abstractLemma.apply(lambda x: retraitement(x))\n",
    "df['lem_prepro_title'] = df.titleLemma.apply(lambda x: retraitement(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection of the feature on which the analysis will be performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [plateforme, citoyen, dédié, analyser, tweets, événement, politique, plateforme, objectif, citoyen, analyserpar, même, tweets, politique, événement, spécifique, france, élection, présidentiel, ana...\n",
       "1    [clustering, donnée, classification, croisé, clustering, extraire, structuresous, jacent, ligne, colonne, tabler, donnée, forme, bloc, clustering, actuel, passer, échelle, succès, optimiser, critè...\n",
       "2    [algeospf, modèle, factorisation, clustering, géographique, recommandation, recommandation, intérêt, devenir, caractéristiqueessentielle, réseau, social, localiser, accompagnél, émergence, échange...\n",
       "3    [analyser, sentiment, commentaire, facebook, publier, [langue], standard, dialectal, [langue], apprentissage, analyser, sentiment, processus, pendant, lequel, polarité, positif, négatif, neutre, t...\n",
       "4    [analyser, rôle, sémantique, résumer, article, présent, approcher, viser, extraire, information, expriméesdans, corpus, texte, produire, résumer, variante, deméthodes, extractif, résumer, texte, i...\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with ou without the abstract\n",
    "WITH_ABST = True\n",
    "\n",
    "def join_with_nan(subset_df):\n",
    "    res = []\n",
    "    if subset_df['lem_prepro_title'] is not pd.np.nan:\n",
    "        res += subset_df['lem_prepro_title'].split()\n",
    "    if WITH_ABST and subset_df['lem_prepro_abstract'] is not pd.np.nan:\n",
    "        res += subset_df['lem_prepro_abstract'].split()\n",
    "    return res\n",
    "\n",
    "# Join the lematized title and abstract\n",
    "df_all_lem = df.apply(join_with_nan, axis=1)\n",
    "display(df_all_lem.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Dictionary and Corpus needed for Topic Modeling\n",
    "[source](https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 analyser\n",
      "1 analyserpar\n",
      "2 candidat\n",
      "3 caractéristique\n",
      "4 citoyen\n",
      "5 comparaison\n",
      "6 dédié\n",
      "7 fournir\n",
      "8 france\n",
      "9 lexique\n",
      "10 message\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "\n",
    "# Create Dictionary\n",
    "id2word = gensim.corpora.Dictionary(df_all_lem)\n",
    "\n",
    "count = 0\n",
    "for k, v in id2word.iteritems():\n",
    "    print(k, v)\n",
    "    count += 1\n",
    "    if count > 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove tokens from the dictionary before Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out tokens that appear in \n",
    "id2word.filter_extremes(no_below=1,   # - less than X documents (absolute number)\n",
    "                        no_above=0.92, # - more than X documents (fraction of total corpus size, not absolute number)\n",
    "                        keep_n=1000)  # - after the above two steps, keep only the first X most frequent tokens.\n",
    "\n",
    "bow_corpus = [id2word.doc2bow(doc) for doc in df_all_lem]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'plateforme, citoyen, dédié, analyser, tweets, événement, politique, plateforme, objectif, citoyen, analyserpar, même, tweets, politique, événement, spécifique, france, élection, présidentiel, analyser, quasitemps, message, candidat, fournir, principal, caractéristique, usage, lexique, politique, comparaison, candidat'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word 0 (\"analyser\") appears 2 time.\n",
      "Word 1 (\"candidat\") appears 2 time.\n",
      "Word 2 (\"caractéristique\") appears 1 time.\n",
      "Word 3 (\"comparaison\") appears 1 time.\n",
      "Word 4 (\"dédié\") appears 1 time.\n",
      "Word 5 (\"fournir\") appears 1 time.\n",
      "Word 6 (\"france\") appears 1 time.\n",
      "Word 7 (\"message\") appears 1 time.\n",
      "Word 8 (\"même\") appears 1 time.\n",
      "Word 9 (\"objectif\") appears 1 time.\n",
      "Word 10 (\"plateforme\") appears 2 time.\n",
      "Word 11 (\"politique\") appears 3 time.\n",
      "Word 12 (\"principal\") appears 1 time.\n",
      "Word 13 (\"spécifique\") appears 1 time.\n",
      "Word 14 (\"tweets\") appears 2 time.\n",
      "Word 15 (\"usage\") appears 1 time.\n",
      "Word 16 (\"événement\") appears 2 time.\n"
     ]
    }
   ],
   "source": [
    "first_bow_doc = bow_corpus[0]\n",
    "\n",
    "# Pandas df\n",
    "display(\", \".join(df_all_lem[0]))\n",
    "\n",
    "# bow representation\n",
    "for i in range(len(first_bow_doc)):\n",
    "    print(\"Word {} (\\\"{}\\\") appears {} time.\".format(first_bow_doc[i][0],\n",
    "                                               id2word[first_bow_doc[i][0]],\n",
    "                                               first_bow_doc[i][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf-idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.11743871273103482),\n",
       " (1, 0.344473385045115),\n",
       " (2, 0.12064790358704418),\n",
       " (3, 0.12064790358704418),\n",
       " (4, 0.18912176955372884),\n",
       " (5, 0.1056273910910164),\n",
       " (6, 0.1722366925225575),\n",
       " (7, 0.16713365785861597),\n",
       " (8, 0.16960006148140025),\n",
       " (9, 0.08857208664147208),\n",
       " (10, 0.3099272650846593),\n",
       " (11, 0.5673653086611865),\n",
       " (12, 0.10965384773948775),\n",
       " (13, 0.13117304567136392),\n",
       " (14, 0.37824353910745767),\n",
       " (15, 0.14857949493967418),\n",
       " (16, 0.29149477050228767)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfidf = gensim.models.TfidfModel(bow_corpus)\n",
    "corpus_tfidf = tfidf[bow_corpus]\n",
    "for doc in corpus_tfidf:\n",
    "    display(doc)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Topic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_TF_IDF = True\n",
    "\n",
    "corpus_for_lda = bow_corpus\n",
    "if USE_TF_IDF:\n",
    "    corpus_for_lda = corpus_tfidf\n",
    "\n",
    "lda_model = gensim.models.LdaMulticore(corpus_tfidf, num_topics=50, id2word=id2word, passes=10, iterations=100,  workers=4, eval_every=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic: 0 \n",
      "Words: 0.064*\"thématique\" + 0.029*\"industrie\" + 0.028*\"[langue]\" + 0.027*\"conférence\" + 0.026*\"action\" + 0.023*\"collaboratif\" + 0.019*\"collection\" + 0.019*\"auteur\" + 0.018*\"publication\" + 0.017*\"industriel\"\n",
      "\n",
      "Topic: 1 \n",
      "Words: 0.028*\"service\" + 0.025*\"relatif\" + 0.022*\"quantitatif\" + 0.020*\"validation\" + 0.018*\"médical\" + 0.018*\"protocole\" + 0.016*\"impact\" + 0.016*\"ontologie\" + 0.015*\"conceptuel\" + 0.014*\"recommandation\"\n",
      "\n",
      "Topic: 2 \n",
      "Words: 0.041*\"agent\" + 0.027*\"simulation\" + 0.026*\"mining\" + 0.024*\"humain\" + 0.018*\"stratégie\" + 0.018*\"rappel\" + 0.013*\"définition\" + 0.012*\"outil\" + 0.011*\"charge\" + 0.011*\"distinct\"\n",
      "\n",
      "Topic: 3 \n",
      "Words: 0.021*\"assister\" + 0.020*\"lequel\" + 0.019*\"durant\" + 0.019*\"affiner\" + 0.012*\"croissance\" + 0.012*\"social\" + 0.012*\"diagnostic\" + 0.011*\"conjointement\" + 0.011*\"sommet\" + 0.010*\"média\"\n",
      "\n",
      "Topic: 4 \n",
      "Words: 0.062*\"recommandation\" + 0.026*\"contingence\" + 0.024*\"client\" + 0.021*\"tableau\" + 0.017*\"voisinage\" + 0.016*\"populaire\" + 0.016*\"variante\" + 0.015*\"produit\" + 0.014*\"continu\" + 0.013*\"corpus\"\n"
     ]
    }
   ],
   "source": [
    "for idx, topic in lda_model.print_topics(-1)[:5]:\n",
    "    print('\\nTopic: {} \\nWords: {}'.format(idx, topic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the topics-keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim\n",
    "from metakernel.display import display\n",
    "\n",
    "try:\n",
    "    lda_display = pyLDAvis.gensim.prepare(lda_model, bow_corpus, id2word, sort_topics=False)\n",
    "    pyLDAvis.display(lda_display)\n",
    "except:\n",
    "    print(\"Couldn't plot the LDA viewer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate LDA\n",
    "The Coherence score is used for evaluation of topic models.  \n",
    "[paper](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  0.3599440985265805\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=df_all_lem, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the optimal number of topics for LDA\n",
    "Choosing a ‘k’ that marks the end of a rapid growth of topic coherence usually offers meaningful and interpretable topics. Picking an even higher value can sometimes provide more granular sub-topics.\n",
    "\n",
    "If you see the same keywords being repeated in multiple topics, it’s probably a sign that the ‘k’ is too large.  \n",
    "[source](https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model = gensim.models.LdaMulticore(corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values\n",
    "\n",
    "if False:\n",
    "    \n",
    "    for keep_first_most_frz in np.logspace(1, 4, 4):\n",
    "        keep_first_most_frz = int(keep_first_most_frz)\n",
    "        display(keep_first_most_frz)\n",
    "        \n",
    "        id2word = gensim.corpora.Dictionary(df_all_lem)\n",
    "        \n",
    "        # Filter out tokens that appear in \n",
    "        id2word.filter_extremes(no_below=10,   # - less than X documents (absolute number)\n",
    "                                no_above=0.90, # - more than X documents (fraction of total corpus size, not absolute number)\n",
    "                                keep_n=keep_first_most_frz)  # - after the above two steps, keep only the first X most frequent tokens.\n",
    "\n",
    "        bow_corpus = [id2word.doc2bow(doc) for doc in df_all_lem]\n",
    "\n",
    "        tfidf = gensim.models.TfidfModel(bow_corpus)\n",
    "        corpus_tfidf = tfidf[bow_corpus]\n",
    "        \n",
    "\n",
    "        keep_first_most_frz = str(keep_first_most_frz)\n",
    "        for corpus_type, file in [(corpus_tfidf, \"coherence_values_per_topics_tfidf_\"+keep_first_most_frz+\".pickle\"),\n",
    "                                  (bow_corpus,\"coherence_values_per_topics_bow\"+keep_first_most_frz+\".pickle\")]:\n",
    "            # Can take a long time to run.\n",
    "            limit=900; start=10; step=35;\n",
    "            model_list, coherence_values = compute_coherence_values(\n",
    "                dictionary=id2word,\n",
    "                corpus=corpus_type,\n",
    "                texts=df_all_lem,\n",
    "                start=start,\n",
    "                limit=limit,\n",
    "                step=step)\n",
    "\n",
    "            x = range(start, limit, step)\n",
    "            pickle.dump((x, coherence_values), open(\"./pickle/\" + file, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for corpus_type, _file in [(\"Tf-Idf\", \"coherence_values_per_topics_tfidf_\"),\n",
    "                          (\"Bag of wrods\",\"coherence_values_per_topics_bow\")]:\n",
    "        \n",
    "    legend = []\n",
    "    for keep_first_most_frz in np.logspace(1, 4, 4):\n",
    "        keep_first_most_frz = int(keep_first_most_frz)\n",
    "        keep_first_most_frz = str(keep_first_most_frz)\n",
    "        \n",
    "        # From pickle\n",
    "        file = open(\"./pickle/\" + _file + keep_first_most_frz+\".pickle\",'rb')\n",
    "        (x, coherence_values) = pickle.load(file)\n",
    "\n",
    "        # Show graph\n",
    "        fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "        fig_size[0] = 20\n",
    "        fig_size[1] = 9\n",
    "        plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "\n",
    "        _leg, = plt.plot(x, coherence_values, label='Vocabulary size ' + keep_first_most_frz)\n",
    "        legend.append(_leg)\n",
    "        plt.suptitle('Coherence score / Num of Topics in LDA', fontsize= 24, color='gray')\n",
    "        plt.title(('Using a ' + corpus_type + ' as input feature for the LDA'),\n",
    "                     fontsize=14, fontweight='bold', color='gray')\n",
    "        plt.xlabel(\"Num Topics\", color='gray', fontsize=14)\n",
    "        plt.ylabel(\"Coherence score\", color='gray', fontsize=14)\n",
    "        plt.legend((\"coherence_values\"), loc='best')\n",
    "\n",
    "        ## Plot Swagg ##\n",
    "        plt.yticks(fontsize=14, rotation=0, color='gray')\n",
    "        plt.xticks(fontsize=14, rotation=0, color='gray')\n",
    "        # plt.gca().get_legend().remove()\n",
    "\n",
    "        # Less border\n",
    "        plt.gca().xaxis.grid(True)\n",
    "        plt.gca().yaxis.grid(True)\n",
    "        plt.gca().spines['top'].set_visible(False)\n",
    "        plt.gca().spines['right'].set_visible(False)\n",
    "        plt.gca().spines['left'].set_visible(False)\n",
    "        plt.gca().spines['bottom'].set_visible(False)\n",
    "        \n",
    "    plt.legend(handles=legend,  prop={'size': 14})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With the LDA's topics, perform a clustering algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_lda = lda_model[corpus_tfidf]\n",
    "\n",
    "# gensim sparse matrix to scipy sparse matrix\n",
    "lda_csc_matrix = gensim.matutils.corpus2csc(corpus_lda).transpose() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans takes the topics and reduce them to  ideas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA matrix shape (input to KMeans): (1269, 50)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <th>1259</th>\n",
       "      <th>1260</th>\n",
       "      <th>1261</th>\n",
       "      <th>1262</th>\n",
       "      <th>1263</th>\n",
       "      <th>1264</th>\n",
       "      <th>1265</th>\n",
       "      <th>1266</th>\n",
       "      <th>1267</th>\n",
       "      <th>1268</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 1269 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0     1     2     3     4     5     6     7     8     9     ...   1259  1260  1261  1262  1263  1264  1265  1266  1267  1268\n",
       "0     8     9     9     3     9     0     5     3     5     3  ...      4     0     3     3     3     2     5     9     2     2\n",
       "\n",
       "[1 rows x 1269 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AffinityPropagation\n",
    "\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "\n",
    "print('LDA matrix shape (input to KMeans):', lda_csc_matrix.shape)\n",
    "\n",
    "km = KMeans(n_clusters=10, init='k-means++', max_iter=100, n_init=100, verbose=False, random_state=1)\n",
    "km.fit(lda_csc_matrix)\n",
    "\n",
    "display(pd.DataFrame(km.labels_).T)\n",
    "\n",
    "df['pred'] = km.labels_\n",
    "n_clusters = km.n_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate KMeans\n",
    "Silhouette refers to a method of interpretation and validation of consistency within clusters of data.  \n",
    "[how-to-interpret-mean-of-silhouette-plot](https://stats.stackexchange.com/q/12923)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Coefficient: 0.13586578021664408\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAJfCAYAAACNCkuzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XmcHFW5//FPd89MZjLZQwJhC3vYdwwgSBAFZBFZREVEEHDBy6I/XK6iDwdQUFEURK+iAsplEVRAwAXRIO6KiBcEBCEIyJKEQPZMprt+fzynpms63T379CTzfc+rXzXdXcvpqlOnnjp16lQuSRJERERERKQs3+gEiIiIiIiMNAqSRUREREQqKEgWEREREamgIFlEREREpIKCZBERERGRCgqSRUREREQqNDU6AYMlhDAXOAC418zmxM/S/u2CmZ0fQpgD/Cp+dqCZzR3mZI5qIYTzAQMws9wgzncOg7RdQwhbAF8H9gYmAA+a2a4DTaMMTAjhGuDdwNNmtlljU9N7Q5XnpfcqjwNDMP9rWAvzZqoveTSEsBnwVHx7ipldM4RJ65eh3t4yuoz4IDmE0Az8P+AkYCZQBOYDDwGfMbM/xVH/AbTG4VophDAP/43XmtnJmc/noOC+nsXAHzP/D8SXgIOBTuAvwCMDnF9NmYNr18EphDAG+D7w5piGE83spkzeALjbzA7OzGdT4EmgkH6kg0PvDfH+9SzlvCnSZQQFnGvkUZ3cVbe2nxBVE0I4BvgAsAcwOX68Rn4MIYwHLgDeCkzH88138Ths9bAleJiN+CAZ+DxwTvz/CWAFHiy8GfgR8CcAMzujIamThjOzv+I1v4Nhhzi81czeOtCZhRByQMHMOnsxbhtwKx6kdwBvM7Nbq4z6xhDCdmaWBvAfpBwgywgRQmgxs28B32p0WkRqUR5trFhOdDQwCa+Lr3mUg+RuQgh54Mf41frVeKXM1viJ1BZ4JeY6aW0Ikt8Rhxea2aehK/CYTabWsFpzix5sFEL4ER6QvARcZGbfzsxvU+Ci+P3UOM6dwHlm9lIc5xoqzipDCCcDV8fZbG5m8+LnBwMfB/YEmoG/x2X+uKJGAeDdIYR3p0khntFHvwohQKxtjuviDOC9wDZ4cHUv8HEzq1mrHkLYAz8B2R6Ygtda/gP4ipldlxkvvXT1eaAd3x5F4Abg/6XBXwjhC8BhwEZxvPnA3TEdz9dIw8VxnfwH2NTMivHz7wLvAv5gZvuEELYBPgfsC0wCFsa0fsHMflatJjCEsD5wKXAQvv1eBR4DvpH9fZm0bEb3bXBc/O3pep6Cn0W/GZgBLALuwfPDv+I8zqe8rQ4Dvohvkz2Bv1VbB5nljwPuwPPwSuAYM/tJlVFX4/nnTOCMGFiflvm8cr7j8Tx0NL5tFgK34dvllTjOocAngVn4+l0Z0/tZM/tplfVzLrAbcBS+D37dzC7KLPNDwOmUr/w8A/zJzE6p8/tzwPvidNsBJXx7nWlmv6sxzTwqrrzU2CcPBT6F5/VW4IX4+z4cxx3Q/lWxzx8PfAzYGTg+hLArFTVy2bIKuBn4CJ5H7wVOM7MX4ngteB56V1wf1wFLgf/Ozq/GuinglQvvAbYCVuFXRj5jZvfEceZQ3m9OAY4DXg+8SEV5WGX+7XiZcCReq7QMr8S4ycy+FMfpsUyo2GcOAb6Cb9M7gFPxvP0RPG/fCHwoU+akZdPn8KZR7wBycT19uF7gEULYAC/f3wRMA57HyzQzs1V1ppuIN8d6M74trozLrBzvXOBEYFNgPF5e/Cb+9n9W5BmAq0MIVxOPXyGEd+H7+BbAxLisPwGfylw9rZa+efj6+7SZXRhC2AG/6gow08z+HbfLucBDZrZTZa1xJn+m80zX8ynA3MziJsY0HwssoaIcqJK2OfQzv1XMZ328TDsM2AB4BfijmR3Zi+V2XSmq0hyzEOf7djzPrgSeBn5qZh+vuJo3MzN9eszpMU9V7Ps/Ac4G2oDJ9copM8semwbbxcBH8fLt/2qM8xbKeeIYM7sjhHAmcDnwrhDCl2Nl1TpnbbhxL03jG0MIR4YQNjCzxMz+UC8I7IWrgF3x4HAz4JshhG0BQgjTgd/jB6dJwD/xTH868JsY0PRaCOE44KfAgXiw9izwGuC2+N0q/HJXWqgviO//GMfNXvJ/JH7+r/j+cuCr+EH5Kbym/Ujgd7F9bS2bA3Pish+Owz2B74UQDq8y/ofwg9AKfF2chRd0qTfhBcsz+MFyA/zs8rY6afgf/OC/IXAodDWvSQu7a+PwBnwnbcYL/BIe/O5VZ95fww9SE+I0y/Ege06N8Wttg3+FEFrxQu2DMa3/xA98bwf+EELYuMr8bsULuv/USWPWz/FCaBlweI0AGTwwfwU4KR6w34mf5NxSOWIMsubi225DPO+Mx4PRe+K6BtgRP+lcgq+rHLA/8OMQwi5V0nAxfnBbGed7YQjhjXGZR+JNVrbDDzDP4PvXu3r4/Zfjwcfucb7z8Fr9bXqYrq4Qwnp4HtwX/32P4XniLXh+Hez96zr8JOppIKnyfda++IlcBzAOOBwPilMXAf+FB0mL8f3vrB7mmfpGnPf2+DbowMufn4cQ3lRl/G/i63s1FeVhDRfgJw/r4yesr+Db7rDMOH0tE27B814bfkn3D8Bn8HW+Hr4uqp1onRPHfwVfVx/E111VIYSpcd6nxvEfiWn7GFX2owpX4duhHd8m/w8PEivNwU9OXgAexffRo4FfxPJkPt1PnJ/E8116TJsN7ISf1D6Mr5OD4/Qb1Enf3Dh8bRzul/luv4rhr6juH8BzmffpsWh+xXgX4yc2q6goB3qhr/kN6Np2f8TLsE3w/WwZcEQvl1vPGXhlwWZ4Gf8SsC0ezAM8gB8XwPendL0s7kee2gfP268Ai3pRTlUVQpgTQkh6eJ1f70eb2Yu9qMlOy4wVwF3x/x9kvj+kh+nXWmtDkPy1ONwbuB14PoTwWAjhghDC2AHM9y78LH3/+D5POYBKg6EE2N/MdsBrDsAvMdSsEavh83jhfz1eY7o1fnkrB1xsZs+b2d74mSfAnWa2d3x9C995U2fEzy+MtXsfjJ+/z8y2x890H8Z31P+uk6bfAhua2WZmtnv8vU/E795eZfxn8fW1FeXA76DM9ycAU8xsJzPbDq95A9grhLBltQSY2dN4jRF44QLwBvzEZBVecwTlQOkoM9vDzDbGC8ib6/y+dJoPxGk2ww/oV9RIS61tcCF+UNwxfv72mB/2wmtJ18OD0EqXmdkWZrYptc/Os/aJwyPN7Jd1xlsGfBs/SL8Hr22ixu96Ox64dAK7m9ku+IGpGD8/Po53CzDdzLaMeWFTvKBuonyAyLofP5Bshx/koJwX0vV+j5ltH9fVRDyorqoiH9+O58sd8WDz3lrT9dJMoAX/Pdua2a5mNhXYBXhsCPavW4BN4j5+R5XvswrAPma2Dd50DOJ6jGVbum1vx/e9zenFSVcM3t8T315pZlvFaR/Hy7lqAWS6jGrlYTXpdr7IzHY3sy3x2vDsOulrmfABM9sWL5vAA/w34mXu0/Gzg6pMNw/Pj1tQXo9n1jk+fBDfji8DW8f9Is2fR4QQXlttorhe0yZYX47bbRu8rKr0MWBy3Ad2IlYC4OXWa83sTjxoTl0Y812aF68ApprZLPMbh9PyZzx+MlVLGvjuEy+R74dXEHQC+8UrT3vEceZWm0FMw7cy79Nj0Z0Voz5I7XKgJ33Nb6l02wG808y2NrPN8QqegUrz9LVmtouZzcKbH5wEYGZH41eTAZ7PrJe/0vc81QIcEcuUremhnKqT5vR+nHqvZ/u+KtawSRwuNLNS/P/FzPebDsIyRqQR39wiXgZ5EL8seiB+drUN5csS1Q7ivXGdmSUhhGxt9PpxmNZQPmFmf47p+GkIYRG+0/R6hwwhTMMPUOAHjRPi5dzUViGEqWa2sB+/YS/Kl/q+EUL4RsX39drploAvhhBej18uzbZp3bDK+Leb2asAIYSn4jjrZ77fBb9kOAsP4LI2pFwzV+lK/ATkiFiDn27P29LmAHhbqHfgtZ9P4Wfpc6nfju7H+IHlmngm/Rh+8P1mnWlqSfNDB/Hs2cweCiH8HW92UC0/fDn9x2Izkl76UAjhd/Uu+eI1m+cA5+P7w1yqB+Kz47AJeKgi34Hnj//FC+drQgj74oFO9uS5Wl64KdY8LAghvITXdKR54WfAZ4GDQggL8BqZvwHfq/N7svn4S2a2EsDMFuGXqQfiYbyWbgvgpRDC4/GzO+j55KU/+9cV6UHEzIpV1nnW/5lZWpv4Dzxomh7fb4VfiQBf3yVgaQjhDqqflGXtmUn39TEtSzLT7hovLWfVKw+r+TFee3dBCOF0fDv/ge77ZF/LhB/H4Ty8JnSRmf0WIITwNB5IVEvTnWa2LI53M74eW4Etqb9fTAH+U2O/+G3lh5QDVfCbazGzl+Il9GMqxt0UzzM741cJsk0yqu1TlSYCXw3eLG5SH6ZPg+QJeE30fvhV0Unx/73xK3IJAz8BrVcO9KSv+S2Vbrt5ZnZ9+qGZ3d/L5dZzBx7svieEcBiep+8HvtOHdPU2Tz1msSlbLCf6VU7Z4N6PU0+1pl2j4obOtaEmGTP7kZm9Bd/R9wTSti9HxLPl/nglzjt7Q1XlRu/pcmn6ffaAM7FinOw8n6L6md4abUl7KTvvB6vM9/E6016HX6rfAA8g/4ifxUL1m8BeyfyfrrO0jeV+eNOI9FL5n+l+CbveTWV3x3Q24zX0R8XPr8mMcxIeJH8HvwR2CHAZHuDV8kn8IH4lXgu1H16D9os60/Skp/zQxWK70j5IL7MfiTfDaasz73l4QDEhfnR5jVHT/LGa6vkurQm4A1/vk/ECOdvspE95wcwewmurDbgPz18fAO4LIQxGbU9Wj/tfDLj3wJsp3BqnOQFvwnMO9fVn/+rLdq+5Hiv0Ot8NYNrelIddzOybePOgL+L7+q74PvfbEEJ7f8oEM0vvMUnTkO2pJv0dPR2Ye3PgTsdZSvX94pUa0/VqmbHG+VbKTR7up3vTiro32cbmfD/Dr6q14Zf5s71P1JzezP5N+d6B4/Ga3t/E1w6Um7L9Xz8rZrJ6m39rTtvb/DZA2X2gAF1ty7sxs5/h+fWz+DbbCj+p/H0IYZPK8Sv0NU91Kyf6W06FEHYPIfyhh9dpPaS9N/4dh+tlYq7pme+fGYRljEgjPkgOIXwhhDAbILZFvh9v4wWwLFP1P5j+HIdbhxD2iuk4lPKdn3+Jw5ficHoIYXzMPEeRYX6T37z49iG8+cbe8dL+8Xhzi3SHWR6HlbUuyzP/Z7/7M+UC4IbM5Z+98TPiS+v8xvTs86p4SfwwfAfvj9mUC4mdzOw1eNcwPTKzBG+LCn51YCre5OHnmdH2B35kZu83s/3xQgzqXMLHD1D3mtlZZvZ6ypfNd47tx/oizQ9jiO0PQwg74u1UoZwf+s3MzsXbqIGfBNzRQ3Oir8Th0/ily2rSG3yagHMyeWM//AaV6+K62DqO9+l4afft9DMwCyFsDSRmdkG8PLk9nn8LZG4GqpDNx+cE7waPEMLE2OShlnT/2zKOP6VyGSGECXi7wq+a2YmxOUla25bmn6HYvwbqCTy4BDg2hJCLwVNv2l7eTznd74SuGzjTaf/Wx6sbawghvAZ42MzONbNDKDcd2BBf3/0uE/rhsBDC2OA3WaY1uiupffUq3S8SvIvFdJseiG/TH9SY7uHM/2+FriuFlfl6N/zqDMAhZrYXfnNhpVr5Lr2BFuA9ZrYHPZ/QZc2Nw7TM+w1+wprH76uB2u2R10hb8Js0R4r0ZGGzEELaXIwa906kXsr8nzbzObpypFjr/5KZfdLMjqB8jByH30ME5fWS5rdUf/NUuuzelFPVTMD3tXqvavfM9NVP47CV8n0Hx1b5fp0z4ptb4Df8nBtCWIqfIU+k3P7l+ppTDcyVeGEyA68Be4Jye6UnKN+VfA9+V2gLXru9gnIXYlkfx9vXHom3qX4Gv7S0AfBryjeyPIq37zomhHA/8HfzHgH+Rbn3gu/GSzGXmtktIYT/wWvqLgkhnIGfsW6CB/QBqNozAN67xr7AabHWJ22D3R9/z/z/fyGE+XQ/y+zJ1Xgtb1oYX1dxEP8eMCWut+WU13F2uZUuwds+PoPfLLld/PxZvN1YX9yA94SwI3BjCOExvAlNAb+R47I+zq8qMzsvhLAKvynq9cBPQwiHm9mSKuPOjQfoVXUu69+A3z29K36j2SP4gXJTYCxegN+Lr5ONgRBCOAG/bNrfIOoA4KoQwvN4bcn6cVlQY3uZ2bwQwpX4jVlvwS9XPofX5JxB96sKWffgB699Qgh/jOmurCFKb8JdFEJ4Ft9XZ1WkZyj2rwExs+UhhCvwnh2OxS/FjqO8LutN+68Qwnfwdv4fjCf4k/AT0BJw3iAk8SzgbXGdvoxvK/A28//CLzun+lMm9MUmeEXEMrzmFDzYWF5j/K/ibbY3Af4RQngUr7HdBD8R3pwqtclxvf4A3x4fCn6D8/qUm8WkHsb3nwK+D/8bL+srzcdvzJuK56+T8Ct818Xf0g58O4Tw3/Rt3f0Kvyo3MabjD5TL1vSm87k9zOPRzP8PhxBewGs2h6JSqi+uxLfdTOCmEMJn8DJtc2pX+j2O14RuCnwphPAOqjdROB74RMzT8ynHGUXKJ0jpepkGPBq8CeaB9DNPZfSmnFqDeU8dA6qBDyGche/PLZmPPxdCOA/4gZl9DK/d/g1ewfLDEMK/KMdE19s62rMFrAU1yXiBfhueabfEC5vH8drEc4digbH2d288OHsFz6zz8fZ2rzWzpXG8n8f0/QcPqB+lygHIzG7C7w79JZ4Rt8NrOm6me23UeXiB1oFf9tkpTr8Qz8TP4Afn2ZQL3f+K3z2I72ib4zWxX6f+2evJeGG6Ej/wnkP9oLMmM7sbv1HlP3jB8CgeWPR2+lfofsJzbcUo38GbAUzFA+T5eFOLajcYpm7Cz+7H4+txCZ6P3hRrr3stXgo7AC+gn8cLh2VxGXub2WDcGJEu60L8xAu8Bv3n1S4NxnEXVAugM9+vwm+G+RIeRGyNF+4P4yclD8V1cSxea5oe2N/Jmney99YDwA/xm5m2w9f/A8CpMZ/UchYeED+A58ct8Mvz9ZoMXYwHFK/gwdH/Ur7ZM7UQPwl7Po4zEw/iPocHuUO1fw2G8/CD76sxXTdTbh+5stZE0fvwAPsflA/UvwIOtto9p/TFnfgJ1hh8/1qNN2V6k5m9MtAyoY8ux08I015Avk6dEwEzW4CX79/Caxm3w2vk/gx8gu43JFU6Dc9jy/ETga8T2ydn5v8oHjA9hZf3Cyh3ZZodL8ErY57A19Fr8G7aFuE11f/Aj9EdlJtJ9Ea2lvhBM1tqZi9S3pcSvHKmnjvwnjwW4vvMbHpxgjbU4r66N957S9pzzgTKPS5Um6YTeBtethTw7VatR5J743xyeIVIE34SfGzcpuD73w/wfXIbfL0UBpinoBfl1BCagsdW2SYl0+Nn60PXfTWH4/taGov9G7gQjyXWWbkkGUhzN5HBEUL4MN6+8c/x0qzIqBa8P9iVVr5htg0/6O4A/N7M9m1k+hot6PHDIjLE1obmFrIOC/5IzHdQ7ofx8w1MjshIsg/ebvzP+JWQvfAa7k7g041MmMhQiE1YPlXj6zvjlTaRYaMgWRptZ7zbtwXA58ysp878RUaLp/Cb8HbGmxK8jN+keYmZ/b6RCRMZItMod6dW6dEan4sMGTW3EBERERGpsDbcuCciIiIiMqwUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhUUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhUUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhUUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhUUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhUUJIuIiIiIVFCQLCIiIiJSQUGyiIiIiEgFBckiIiIiIhWaGp0AGXwhhM2ApwDMLNfQxAxQCCGJ/25uZvMyn58MXA3ca2ZzKqaZB8wEDjSzucORThk69bb1SBBC2AC4CDgE2AAvV0dkWmvRPtM3Iz1PNkoI4XzAgGvN7OTGpkZk4BQkj0CZwLCvVGCPQvHABPBlM3ulxjhzgDnA38zs1uFJ2cjWm/XWi3k0Ab8EtosfLQI6gJcHnMBBEE+YTwZeMbMvNzQxIsNE+V4Gi4LkkenFGp9PAZqBlcCrVb4fEQfmQfZYHK5uaCpGNovDa4Bawd6cON61gIJk15v11pND8AD5ZWBvM3t8ENI1mDbDf+fTgIKFwfMqXjb9u9EJkao2Q/leBoGC5BHIzDao9nkIYS5wAHDTaLmUZWbbNjoNInXsEIe/GoEBsgwRM/sR8KNGp0NEhpZu3BMR6b+2OFza0FSIiMigU03yKBBC2BE4D7/kPgmYB/wv8Dkz66gz3X7AfwH7AdPwQOAB4NvAjWbW57bTIYSjgPcBe+DNR5YA84G/Area2U0V41e9ca8Py5uC//ajgQ2BBcBdwKfN7Pk60x0InAnsG9P5MvB74Aoz+2WV8Tejh5slY7vgXwFPm9lmNcbZEfgwcCAwA29a8zDwPeDbZrY6M+41wLszkz8VQsjO7lrg/DRd0btDCNlpoMq6DSEcCZwGzMZ//yvAH4GvmtnPqqW9nsxVkFOA24BPA0fRfZtYvW3Sw/yPAd6L56sJeJ66F/iimf21Ytxr6GG99XSlpso8Ktdr5Y2muwH/D18H0/F8fz/wTTP7QY1lzCPeTAc8DnwSOBTYCHjEzHbtIY3p9AAzq9zrcIqZXVNluv7uM5vF33gwsAlQBP4JfB/PN8vqpbdiXt8CTgV+YGbH1Rnvv4HPAg+Y2e6Zz7eI6T8M2ILyvvQocDPwNTNbUWV+J5O5IS+E8E48X+2I7wdHm9mtvblxry95Mo4/h57Lh5rLDSGMB84B3gJsA4zBt91/4nyvNbOHqs23nhDCdsDZwOvxvLcaeCYzz/t7MY/NGED5GEJoAT4AvA3YHmjHy+QXgN8A15nZ7+O48+hjvu9LuVvt94QQ9gbOBV6L799XmNk5cdzNgY8CB+H7RYLnhaeAnwFXmdmCautEGk81yeu4EMLBwJ/wwqUVb9M8C7gAP3jVmu5zwH1xuo2AVXiAfRBwPXB9CKFP+SeE8Bm8Peyb8IJkBV4Ttw3wduArfZlfL2yMB98fistL8IP+acDvQgiTa6TzIvxmrKPjdMvi8C3APSGEiwc5nely/wt4EA8kNwM6gXF4oP514OchhLGZSV6le/v1BfF9+noVD1RejL8BvPB/seJVzKShOYRwHXA78GZgfXw7TQOOAH4aQvj8AH7mVODP+MF8g/gbNwJOBx6MB+ReCyHkQwjXAj/A2wdPBpbHeZ4A/DmE8IGKyXqz3nqSzqPWes2u0/cCfwHeiefJ5fi+dDBwSwjheyGEQp1lbQP8DQ8S1qf37fPn4zcSApRYc7uvESTS/33mGOAR/KR6m/hxC7A7cAnw+xDC+r1MN3gZA3B4CGFCnfHeUTF+6vvApXhgNxP/rRPwk75LgV/HoLKmEMLlwHV4JUEOX4c96meeHJAQwkTgD3i5vjswFq/UWB8P0s8FTuzHfM8E/g+v2Ngazw8t+EnDmcAXByH5PaWhCfg53rZ4H3w7LsXLkp2BM/AgPtWnfN+Pcrcyfcfjx8pj8eNZdt/fPc77/fj6K+DH0k3xE+bPAnv2emXIsFOQvO67CfgxXrM1CS9g/hsv7I4KIRxWOUEI4Wz8zHc+XgBNNrMJ+Nn78cDzeFD7sd4mIp55fzy+vRiYZmYTzKwNL8iPA+7sx++r5wq8sNzXzNrxgu8ovFZ0M3w9VKbz7XiNHcBXgelmNhkPEq+In388hNDnA049sYb9CrwA/wSwvpmNwwvdg/GbhOYAl6XTmNnZFe3X9zKzDTKvs83smTjOpXGcmyrG2cDMnsnM4/N4MDcPP6CPN7OJwHj8QLkY+EgI4R30z6fivI4ExsXfOAevVZkG3BxCaO7D/D4KnITn50/heXUyHuzdjJdxXw0hvC6doDfrraeFZuZRa70+AxBCSA+0eeAWYJOYvkl4Pkvw4GWNvJjxRXyfe62Ztcd1VrN2NZPGvYBj4ttnqmz3m6pM1p99Zi/gRvwE/HN4UNqOB2p741cgdgK+21OaM+biNaCt+MnqGkIIO8T5JnH5WQ/gJ2JbAa1xnbfhJ37/xAOTS+osfw884DdgqplNwYPd3/Ui7X3Ok4PgbLyGdT5+MjsmprkVP2n5OPCvvswwhPBW4HI8sLsF2D7mvXb8xOlE/GrIUDsBDyiXA+8Cxsb1OQbPa2mQC/Qt3/en3K3i2/jVsfQYO5byzYKX4uXdH4Hdzawlpr0d2CuO15uTcmkQNbdY9/0ZeHvaNCJe8rwkhPBavDA9Dr+UCkAIYRLe52sncISZ/Sn9zsxW4kHMM/jB4iMhhC/Wa7KR8Rr84PComX0i+4WZvYTXulS97DwAq4A3mNnCuJxO4PZYU3wp/ts/mo4cQsgBF8a3N5rZmZk0LgTOCiGsh9deXRRCuN7MelW7VE+sRUxr0d8VbwpKl7sauDuE8Ca8Ruc9IYTz+9ssoYd0bA2chQdEB5nZk5l0LAW+GUJ4BT/x+iRwQz8WMwE4wMzuy8z73vj7/o7fCPc2vAavp/S2Uw7aPmdmF2Xm+VwM5GfgNYEXAYMZlPTWhXi+/y2+HxZj+pYCnw0hjMN/w8dCCJeb2eIq8+gE3mhmXbXfZvbEEKW3T/tMdBkeIH/YzLLBRBH4Y9y2DwEHhxD2NLO/9JQIMyuFEG7Ca7RPwJsOVUpP1H5tZs9WTH96lXmuAn4cQngYD35ODiF8xMyWV5n3OOBiM7sgM/1i/CSxpgbmyb3j8Itm1lXZEMuPx/GTl16LJ6pfim9vMLMTMvNM8JO2/42voZb+tu+aWVe5EPelfwNX9memg1juPggcnx4L4j4zryLtZ5vZA5n5L8evLvW4L0hjqSZ53XeJVW87nHYDtmPF58fiB4jfZAPkLDP7A/AkXrOyRy/TkR5cJta7dDXIvpkqpgiNAAAgAElEQVQe7Cukv33zeFBL7YrXPIEfwKpJG6/OxAP/wTAnzm9etqDOMrOn8MupTXH8oXASXibcmg2QK/wQD6R2CCHM6Mcy7ssGyCkzewyvrYJe1JJGB+NBdwdeA145zyLlk579gz/0Y9jEtr0HxrcXpwFyhc/hTTXG4e1nq/luNkAeYn3aZ0IIW+LtMFcA/1Nthma2CPhJfPvGPqQlbUJxUAhhepXv314xXq/EvP0wXuNXq113kXKQ2BeNypNp+dqffbKag/Ca7yLwkUGaZ38N9m9LzWFwyt0v1qksGaq0yzBRTfK67881Pn8uDivbGO4bh7NDCC/Ume+UONwEv6GtJ3/Eb7SYgbdPvBK4OxZCQ6Wn3w5+2TttV5re+DPfzB6uNqGZPRZCeA5vX7g7XoAOVLrON+xhnU+Mw00GYZn10nFcrEGpJW0OsQleo9QXc+t8dy9ea7h7nXGy0vEejIFYNb/Ga2Kb4vh31RhvKOyGt2VN8N+2BjN7NYRwPx5o7s6azQagd/vXYOnrPpPmmRbWvAEya1wc9jrvmtlfQgj/xJsLHI83fwIghDAb2BJvn31LtelDCG8E3oOfzM6g3BNJ1oY1Fv+E9e9mqkblybvwKzBnhRCm4icOvzGzJf2cX1oD+qCZPVd3zKH3E7xp31EhhNvxfs3vrXEy1xeDVe7W2z/vwts6fzeE8DX8ZPN+q7gRUEYu1SSv4+oUkivjsLL9Z3rGm7YVrvVKp+tVrXA8YLwLv5S/M/AN4MkQwvMhhGtDCAf0Zj59VPW3x2YjqezvnxaHPR0U0ku70+qO1XvpOm+h/jpvjeMNVU18mo5xPaQjLTf6k4566zb9rrfrtcftFbd1ejAdrO3VW+nyXo3NK2rpKT/NH7wk9aiv+0yaZwrUzzNp7XNf80zapKeyDXz6/qdmtsZDlOJNdz/Ha5u3wAPSlynfvJUGKe2V00b9XecNyZNm9l3gm/hJ2Yl4cPZKCOGBEMIF/bjqk95k2fCHpZjZvXhvOJ34vQw/ABaEEB4JIVwam4n1x2CVu/Xyykfwponj8UD/98DiEMIvQwgfCCFUO3GTEUQ1yVIpDYAuM7MPD+aMzeyueAPf8cAb8HZ5G+KX+U8KIVxlZu8dzGX205hhXl66zn9kZsfUHXN40nG2mV3egOVX7RqqF4Z7e/XVQNNXrZnGSJHmmW5dsA2i/8VvntsnhLCZmc2LveocH79fo6lFvApyJr7eLsTbtz+ZbXYWQriPcq8V1Qx0nQ97njSz98WTg+Pxts6z8eYkuwIfDiEcbWZ393J2/d0Xh4SZXRh73Xkb3uxhH2Db+Do7hHBqPFHoi0Epd2s0o0q/Wxi8K9WD8AB/f2AXvBnWgcC5IYQDKtvUy8ihmmSplLZ93H4oZm5mr5rZVWb2NjPbCL9R66r49ekhhMOHYrm9lNYIbNrDeBtXjA9eywFACKGV6ibW+HxI13kfDEc6al3ehnLNTm9r8dLxZtYaIW6LqX2c72BJl9cWQqhXY1gtP60t0jyzdeyqa1CZP8XwfjxoS9sgz8HzyjK8q8JKb43Db5lZMLN/Vbkvoy/d0fXFQPJkWobUKj+gdhkCgJk9bO5AvFnMkfiNZ+3AtX3oOSZtflDzd/TRQMpHwNsHm9klZnYo3tzvQLzpShPwtRrt1usZlnLXzBIz+4V5jzi7A+vhPQW9jF/lqNdzhjSYgmSplLavOiC2bRtSZvaPWHuctu0dimYXvZV28N8eQqh6U14IYRu8PXJ2fPBmJKmNqW6vGp+n63xW7Naqr9IAoF7tT6kX46TpOLKP3bD1Rb3tm363xoMWakjH2zqEsFGNcV5H+YpZ5Xx7s94G4oHMMg6sNkLs3za9+bW3v7sverPdByLNM+Pwm9aGQlpbnPawkDa1uLVGzxTp/vdAle8IIcykfIPuYBtInkzLkOnBH55RTa0yZA1m1mFmd1A+aZiB99XbG2l5vHOd39EXAykf12BmRTObi/fQtBo/Ccj2N9yX8q6/5W6/mNkiM/sm3uUcNPaYJz1QkCyVbsZraFqBL9QbMdR4sECNcWsV+qm0g/dGXjr/G5B2rfWJGuOcH4fz8Ie0AF1des2Lb4+qnCiecJxWY573UG77d1mo82CJGus8vYN6Uq3pejnOtfjBZUPq99vbp21f4YDYd3Dl/Lam3KvFzb2c18/x39VMlTvw43r8VHx7n5lV3pzTm3XSb7Gt7K/i24+F6g/f+Ri+ry1laG4q7OpVZgjmjZk9Sjmg+lxFbzHdhBDaQgj92b9vxPPlTsGfXHhs/LxWrxZpv7M71fj+swzdScNA8uQ/8Z5jcngNcOW0W1H+7ZXf1Stfsw/P6O36vwdvV12gh+NAbwykfOzht3VQbhqT/W29yfcDLXfrCv5QmXpXV0bCMU96oCBZuol3DKcB0ikhhO8Hf2Qn4JfKQgj7xd4pftuHWX8ghPCzEMIJ2ZtIQgiTQgifoNy9Tp8feTxY4iXZ8+Lbo0IIV6S16SGEqbG9X1qLdV6Vbn/SJxieF0J4c1pABn9k6S/wG0SqLXc13oYywbvI+nkIYXbwfpsJITSFEPYIIVyCd71XKe2J46Q6BX06zn61bnQxs0cod4IfQghXBn+8b/rBuBDCG0MI36P3gWylxcAPQwiHZX7f/vgd7GNiOms+CbIivcvwgAf8rv5PBu93mFj7dQPe7rREebtm9Wa9DdSn4vJ3B24MIWwc0zcu5vv0ATuXWPU+kgfqcbymbWIIoWqANQjOxIO7HYH7QghvyOT9fAhhhxDCefjDLPrcFZaZ/Ydy7yDfwnvkWYAHpNWk7W7fF0J4TxpkhRA2Df4kvHdQfiLboBpInjTvb/62+PayWM7m4+vg+LuqPSUR4BchhMtDCK/L3gwWa0iviW+fx5te9OZ3rMYfMQ7wjngc2DYz3xkhhNNjmdhb/Sof8Z4hrg4hHBIyT0kMfn/LtfhJ5gr8qXepHvP9IJS7PZkAPBHzwE5pGRO350HAZ+J43Y55IYQ5IYQkvub0Y7kyiHTjnqzBzK6Il4EvwC/VvTWEsBw/EE6kfHI1rw+zzeGXYw8GCCEswwuxbC3eN81sOLvoWoOZ3RRC2Al/WMZ/AWeEEF6l++++xMyqdaJ/CV4bugV+sFsVQujELwX+G39Qx/dqLPf2EMKpeF+zr8dr51bG9TQJr9Gp5Vt4d0bnAO8PIbyEF/y3mNm5cZy5eJCyJfBYCGEB/gQrgP0yN458FO/Z5AP40xbPCCEswWtrJlKugZtbJz31XBjnfSewIoRQpNw92Hy8U/6+dI90Kd6m8CS8b+sQQliMr7P0UcJnmtmvq0zbm/U2IGb2uxDCGcDX8H3puOAPZJlAeZv+L/Wf/jaQ5S8LIdyAr59bYl5OL32fa2ZVu0/r4zL+EkI4Gg8Ad8ODuY6YbybQvTeMan2298b1eJOV9ObAm+NDG6q5Bu92a2/8aWjfjGlJy5pP4zdSDdVl7oHkyf+OadsED/qW4+VOK36l6zLKD8DImoAHfGcCpbid2yi3b16OPzCj1jpbQywLN8JrktPjwFI836aBeNWuDWvob/nYit+wdzKQxN/WQrm3iSLwvmyXfb3N9wMsd3tjJp4HLgJWx3w4MTPfJ4FBvUFeBpdqkqUq8ydF7YJ3K/Q4Xri347URP8EDndl9mOX1wOn409oewQPkcXF+twNHmdn7Biv9A2Fm5+EHqtvwGqtxeJdNt+NPI6vaFCF2c7cvvs7+g+9fC/HHnu5OuauvWsu9GpiF1+Y+jN/sMjHO41fAufijgatNdzre/KMTP8DOxG8QScdZHX/T9/DLqJPjODPJnCzHtn5n4LVd1wFP4wekNvxA9iPg3cBb6v2WOhZSfhzri3He/8Fv3tzVzP7Rl5nF9L4bP/j+HD8QpvnqBuA1Zva1GtP2uN4Gg5l9A//N18d0jcObBNwNvNXMTqx3h/wgeD/+KPjHKD/Kdyblk5MBM7Of4P0ZX4S3s12JBxmL8S6wPg1sZ2ZP93MRt+An6amaDxCJNbJvwIOyJ/GgtBNf30ea2YW1ph0MA8yTT+Ll6g34SWMBLzc+g/elXetqw2l4LyC/wvfTNIh9FO9fekczu6cfv+VL+InP1XilSDO+bf+OB+sf6sO8+ls+fhw/ef8pvj1b8PXyr5iu3c2sWnDdq3zf33K3Fxbjbaa/jJcx8/Gu4Jbh/ZF/Ei/z1LPFCJZLkv6e2IuI9E4IYS5ec3eKmV3T2NSIiIj0TDXJIiIiIiIVFCSLiIiIiFRQkCwiIiIiUkFBsoiIiIhIBd24JyIiIiJSYVj6ST700EOTBQsW9DyiSLRs2TLa22s+vGvEW9vTPygee8yHs2YN+6K1/htP26CxtP4bS+u/se6///6fJUly6EDnM1w1yaqulj6ZO3cuc+bMaXQy+m1tT/+gSH//3LnDvmit/8bTNmgsrf/G0vpvuEF59LzaJIuIiIiIVFCQLCIiIiJSYVjaJIvIKHTXXY1OgYiISL8pSBaRoTF2bKNTICIi0m9qbiEiQ+NrX/OXiIjIWkhBsogMje9/318iIiJrIQXJIiIiIiIVFCSLiIiIiFRQkCwiIiIiUkFBsoiIiIhIBXUBJyJDowGPoxYRERksqkkWEREREamgIFlEhsall/pLRERkLaQgWUSGxh13+EtERGQtpCBZRERERKSCgmQRERERkQoKkkVEREREKqgLOBEZGm1tjU6BiIhIvylIFpGh8ZOfNDoFIiIi/aYgWWQolEp0PP54o1MxaiWrVq0z678wZQqFqVMbnQwRkVFHQbLIECjOn88LH/gA+SlTGp2Uhhn/7LMALNl442Ffdue7381L558/7MsdqKSzk2TpUnLNzdDcTK65mTF77MG0K69sdNJEREYdBckiQyFJyE+eTGG99RqdkoZp/ec/AVjeiHXQ1LRWrfukWKS4YAE5oP3442l93esoTJrkeWjDDRudPBGRUUlBsohIgxQXLoRSCUolxr7hDUw84wyaGlDzLiIia1KQLCIyzJLVqynNn09hxgwmf+ITNG+7LYVR3DRHRGQkUpAsIjJMSsuWUVq8mFxLC+3HHcfEs84i397e6GSJiEgVCpJFZEiUWloanYQRIUkSkjQ4bm5m0jnnMPbIIylMnNjopImISB0KkkVkSLyy556NTkLDJatXU3zhBZo22YRxxx9P2+teR8sOOzQ6WSIi0gsKkkVEBiApFqGzk6Sz04erV8Pq1VAoQFMTra97HVMuvFA1xyIiaxkFySIyJMY/8ggAS7bbrsEp6VlSKnmAWyzC6tU+LBahVPLv0lc+Ty6fJ8nlfLhqFQCF6dMprLcehcmTyU+fzpjddqNlxx1p3nJLcoVCg3+diIj0h4JkERkSzYsWNToJPUpWrqS4aBEUi+QnTSI/cSL5SZMoTJlCfsIEcmPHkhs7lnxbG7nWVnJjxpBraSHX0gJx2LzlljTNmNHonyIiIoNMQbKIjApJsUhp4UKS1avJFQokSQJJwvgTT6TtoIMYs/POjU6iiIiMIAqSRWSdVJw/P/OmSFIq0brPPox5zWu8WcTEiTRvvjlNm2zSuESKiMiIpSBZRNYZaXdrrF5N81ZbMeH976dp443JT57szSdyuUYnUURE1hIKkkVkSJTa2oZ0/kmSkCxfTmnxYr+hrlCAzk7yU6dSmDGDaV//Ovnx44c0DSIisu5SkCwiQ+KV3XYbkvkmSUJp/nxIEgrTptF+9NG0bL01hRkzaNp0U/KTJvHPe+9VgCwiIgOiIFlERrQkSUhWrCBZtYqkowOKRVp22IFJH/oQLbvsQi6fb3QSRURkHaQgWUSGxISHHwZgcT+fMJckCcmSJZSWLKFp441p3nFHCjNn0rLjjrTttx/5sWMHM7kiIiLdKEgWkSHR9Oqr/ZouKRYpLV5Msnw5LbNmMe6EExh7xBG66U5ERIaVgmQRGRGSzk6K8+eTKxRo2XVXD44POKDRyRIRkVFKQbKINEzS2Ulp4UIoFKBYZNwJJzDxjDPIt7Y2OmkiIjLKKUgWkWGXdHRQevllEqD98MNpP/pomjbbjMLEiY1OmoiICKAgWUSGSHHcuKqfl5YupbRkCeNPPJH2I46gacst1d5YRERGHAXJIjIkXt15527vSytWkCxeTNLZydTLLlN7YxERGdEUJIvIkElKJZKVK0mWLiXp7GTiWWcx9pBDaNpgg0YnTUREpC4FySIyqJJikWTZMiY+/DDkcix57WsZc8ABjD3sMFr33LPRyRMREekVBckiMiBJklB6+WXo6IAmL1Kat9mG1hdfpLDeeoy79dYGp1BERKTvFCSLSL8kpRKlV18lWbmSllmzaD/uOFq2247mrbYi19QEc+Y0OokiIiL9piBZRPosWbmS4sKFtGy7LePf8x7aXv96cvl8o5MlIiIyaHRUk1HtK1/5CjvuuCM77LADX/7ylxudnBEvSRKKL79M8eWXmXjWWax//fWMfcMbFCCLiMg6RzXJMmo99NBDXHXVVfzpT3+ipaWFQw89lMMPP5ytt9660UkbUZIkIVmyhGTFCpJSiTE77cSE009nzD771J9w112HJ4EiIiJDQEGyjFqPPPIIe++9N2PHjgXggAMO4Ec/+hEf/ehHG5yykSHp6KD48ssANG20ERPPP5/WvfYiX+MhIWtQzbyIiKzFFCTLqLXjjjvyyU9+koULF9LW1sZdd93FnuqijGT1ag+Ok4RJZ57J2KOO0uOiRURk1FGQLKPWdtttx8c+9jHe+MY3Mm7cOHbZZReamkbnLpGUSpQWLYLOTmhupv2II2h/y1sYU/HUvD458UQfXnfd4CRSRERkGI3OiEAkOvXUUzn11FMB+MQnPsHGG2/c4BQNn2TlSg+Mm5qgVGLMnnsy/pRTGLPbbt6F20A9++zA5yEiItIgCpJlVHvppZeYPn06//73v/nhD3/I73//+0YnaVgUX3wRCgXGn346bXPm0Lz55uRaWhqdLBERkRFDQbKMasceeywLFy6kubmZK6+8ksmTJzc6SUMqKRYpzZ9Pfvp0Nrj5ZvLxpkURERHpTkGyjGr33Xdfo5Mw5JIkobRgQfqGtsMOY9KZZypAFhERqUNBssg6LOnspPjSS7Rsuy0TP/hBmnfYYfh6quipH2UREZERTEGyyDooSRKKL7xArlCg/c1vZtKHP0x+/PjhTcTFFw/v8kRERAaRgmSRdUhSLFJcsIBcktA8cybTvvMd9XEsIiLSDwqSRdYRpaVLSZYsof3wwxl34ok0b7UVuVyucQk69lgf/uAHjUuDiIhIPylIFlkHJElCafFiplx0Ee1velOjk+MWLmx0CkRERPpNQbLIWi5ZtYriwoU0zZzJ2EMPbXRyRERE1gkKkkXWYkmSUFy4kEnnnEP7Mcc0tnmFiIjIOkRBsshaKikWKb74Ik0bbMC4E09UgCwiIjKIFCSLrIXSLt7GHnooE9773pEZIB90UKNTICIi0m8KkkXWQqX582nZZRemhECuubnRyanuU59qdApERET6Ld/oBIhI7yXFIp3PP09h+nTW+8IXRm6ALCIispZTkCyyFkhKJYovv0zxxRdpP/xwNvjhDymst16jk1Xfm97kLxERkbWQmluIjHBJqUTxuecY85rXMP5d76J1v/1GZhvkSitWNDoFIiIi/aYgWWQEKy5cSLJyJa3778+0K65odHJERERGDQXJIiNUcdEici0tTL3sMtpmz250ckREREYVBckiI0BSKlF88UXI58kVClAqQaHA1C98gda99mp08kREREYdBckiDZQkCaX586FUYuwhh9C2//4Upk+nsOGGFNZff+1oe1zLEUc0OgUiIiL9piBZpIFK8+fTPGsWkz/5SVpmzWp0cgbXuec2OgUiIiL9piBZpAHSJ+blJ01ivS9+kcK0aY1OkoiIiGSon2SRBii99BItO+/M+tdfv+4GyHPm+EtERGQtpJpkkWGUJAmlhQvJT5nCtCuuIN/e3ugkiYiISBWqSRYZJklHB8VnnyU/fjxTP/tZBcgiIiIjmGqSRYZBsno1xRdfZPwppzDp7LMbnRwRERHpgYJkkSGUdHZSfOEFKBQY9853MvH97290kkRERKQXFCSLDJFk1SqKL7xA+7HHMv7EE2nebLNGJ2l4HX98o1MgIiLSbwqSRQZR0tHBivvuo7RkCbnmZiacdRYTTjyx0clqjDPOaHQKRERE+k1BssggWfGb37DoM5+htHAhyUknMfn882k/7LBGJ6txli/34dixjU2HiIhIPyhIFhmgZPVqlt15J4suvJB8e7s/TrqpiVxzc6OT1ljpCcLcuQ1NhoiISH8oSBbpp6RYZPHXv87SG28k6eggP2kSedWaioiIrBMUJIv00ep581j529+y/Gc/o+OhhyhMnUp+8uRGJ0tEREQGkYJkkR6s/N3vWHbbbax+4gmKL7xAaeVKAHKtrRRmzCCX1zN5RERE1jUKkkWiZNUqVv3973Q+/TSlRYsozp9PccEClt99N/lx48iNHUtuwgQKkyeTy+UanVwREREZQgqSZZ2VlEqUliwhWb6cZOVKf61a5a/4vrR8OR0PPsiqv/2N4nPPQaFAsno1gN9419REYfp0tTXuj5NPbnQKRERE+k1BsqzVSitW0DlvHh2PPsrqRx/1JhHz51N65RVKy5aRKxQgn4fKmt8kgSQhKRa9J4r2dvLTp6vpxGBSkCwiImsxBckyYiRJwqo//YnSokWUVqxg8Xe+Q2npUq8NfvVVSkuW+GvZMpL4f7Jqldf85nLkCgVyra3Q3EyuqalXN9NlQ+dk1SqSwfoxpdJgzWnttWCBD9dbr7HpEBER6QcFyTJirH78cRZdcAFJsUjxmGNYcv310NlJUiPgzLW1UZg+fc1a4pGguZnC+us3OhWNddxxPlQ/ySIishZSkCwjRss22zDjzjsB+OfcuWw0DMHVZZddxre+9S1yuRw77bQTV199Na2trQOe72Nz5zJm550HIYUiIiLSCGqAKaPWc889x+WXX85f/vIXHnroIYrFIjfeeGOjkyUiIiIjgGqSZVTr7OxkxYoVNDc3s3z5cjbccMNBmvEK+Mf/0L3V8yiz/D8+/Mc3hn/ZK6c2ZrlSpm3QWCNm/ScwcRZsdGCjEyLSZwqSZdTaaKONOPfcc9l0001pa2vj4IMP5uCDDx6cmXe8Ag9+AcaM4ifxLX/eh49eNfzLzp8Kj357+JcrZdoGjdXI9V9c6RUFpQ5ISrD50QqSZa2kIFlGrUWLFnHbbbfx1FNPMWnSJN761rdy3XXXceKJJw585rkctE6DtmkDn9fa6sjYV8jYGcO/7I5maG3AcqVM26CxBrr+kwQ6l0HS6YFuUopdZ5aA+D6XA3KQy0Ou4NOVOmHcTJi4jQ/HbQIbHTQYv0hk2ClIllHrF7/4BZtvvjnTpnkge8wxx/C73/1ucIJkgQMGqemKiPRPUswEthXD9FVcAfkmujUNS0o+7biZfpJbaIWmVmgaC4U2Hza1+eeFMf7/mCn+ap0GLeMb9INFBpeCZBm1Nt10U/7whz+wfPly2trauOeee9hzzz0bnax1x/wVPpzW1th0iKxLkpLX1iadPuxcHoPcJAbERR+nsBpWLYJ8M+RbfNjUlnnf4oHvjAOgfaNM8BsD4OZx/hIZxRQky6g1e/ZsjjvuOHbffXeamprYbbfdeO9739voZK07vvCADz+/b2PTITKUkoSu5gdpDWxS+b7ThxCbJaTNFNLa2xxUPsooSTKfJeXlkIeWCf4aMxWmz4apu/j9D2OmQFO7B8K/vg/m/H7If77IukxBsoxqIQRCCI1Ohsi6JYnBXanDnz6ZFMuvroAPugeGSXxb8V21z6p+F6WBZ0L3IDSX+azbd5npkvLoXYHsGmlMug9JvFa20Oqv5vbuTRKaJ8CYSdAyyf9vbvfv8s1eA5xr8mG3/5urf55r8uYNI/EBSiLrIAXJIiKjSVfNZ422qtlay3ScbA1pDqDggVp645bPODN+EQqdUOyIQWF8NY/zQDJfAOLNXrl8fOViLWs+81k6Tq48LpWfFbqPl95I1nVDWZXP0lrcbuPF/8mVA9V8S/y/BQqxyULXK/NeQavIOklBsojIumz5Cx6UZoPiymCv0Or/F1ogP6YcEBbGxBuzxkHzeL8hq6m9fNNWU1v3WtOuG7va4Ne/gTm/avSvFxHpNwXJIiLDJa2V9TcVzQ6qva/xP3Rvs5q99A9+Q1e+4MtqGQ/7Xu5tVlsmepCrmk8RkR4pSBaRgakV6B0904el1ZSDPDLjVvxfrX1p1TardcZL55EUoWPxwOZRNQ3V5pHpUSDX1P0yftdosQkCOa+l7dbMoFDxvqni8wLk08/jePkmoOLzfDp+E4xdH6btBeM29S65FBSLiPSZgmSRodKxaC0KTjLtSrvJ1m5m31fUYHZrHxoDuT3G++fFVXE9ZNqHkmnP2m2YX/Pzmt/Veb+0FcZvk2k3m112vsrnFW1Xe/0d3rygeXz5hqz0Bq6m1sx3sbmCbroSEVlrKEiWUeuxxx7jbW97W9f7J598kgsuuIBzzjln4DNvnQav+f7A5zNcssFtvok1b4hKazQLrBEMd90wVeGxx3w4a9bw/haAuXPhgHcP/3JFRGSdoSBZRq1Zs2bxt7/9DYBischGG23E0UcfPTgzzxW879LR7H3v8+HcuQ1NhoiISH8oSBYB7rnnHrbccktmzpw5SHNMKJWeH6R5rZ1ydACQNGQ9rB7167/xRtM2KAFjyOfXa3RCRGQQKUgWAW688Ube8Y53DNr8kuQlli7+L3K58YM2z7VNW+c/AFixZJBq5/ugVDyZZUs+NezLlbK1dxskJMlKoDO+T/tmznX9nyO9MTMPFMjlWiHXyrjxdzQmySIyJBQky6jX0dHB7bffzsUXXzyIc00gN55cftogznPtkss1+7AR6yDXNKrX/YgwDNsg6bqJtJR5JZlh5vtMN3nJGuOk0/Kb72gAACAASURBVDYDRXL5STQ17Uu+sDO53BhyNENuDNBMjjGQayZHC+Ra8BrkDT1QFpF1ioJkGfV+8pOfsPvuu7P++us3OikiI0ayxqOj+zoskSQrur33YLUEFEm6AlN/VHWOPAm5TC1t5c2g2fmkgS1Acwxax8SgtYVcbgx+eGuJAW5z5n8PbHNd47YArUAzTU27ki9so4BXRAAFySLccMMNg9rUQlzHR7ZudBJGhSR9hDRFoEiSLCbX1RQg011feYo1hul/ucz4CWTmU25qUO4DOt/1XY5Cle8K5HLjYtDrvaDkaIdcO7ncOMiNI58bD4wnl2uHXEusnR1TJeAtfwfNXQEuNJFTl3oiMkQUJMuotnz5cu6++26+8Y1vNDop65zigWruUE2SZJsGFOOwkyRZTo5crE3N1qTWqlHNBsetMdAcT1PTa2lq3p9cbkp8tZINaLva1OayAXAB1giIcwMKQPP5uYwbf2u/pxcRaTQFyTKqjR07loULFzY6Geuk/N9fBaC088QGp6Q2r4UtAqv91a0pgAewOSAhH6fIx5C1VgDbbe6ZYbbtK5BrJUebD3NjyTGJQtPO5PObQGwSkKMZupoKNHdvNpB+lmsDxpLLFQZhbYiISJaCZBEZEmM+/jAAK+7ad1iXmyRFSIqx+7HKNq4e8na7WSs3llxuCvncFMiN8x5Jcu3kcxOAceTy4/xSP03+yGcK5Gjq9h6avMlBvfe5MUDaXEBNBERERjoFySIyKLwZQSdpzWxCpz/FurSI7r0KZF9UvPfPcpmgttyiNl+lGUK2vW2p/D7XxpgxJ5PPb+ptYLtqbVvxpgnpcHxXLxwiIiJZCpJFpEfeLKET6ICkwwNgVuPhbBMeGBNrYieRz00ixxOQy1Fo2jsGol6j6j0RNOE1qj6E8jCXi4/FplC+IayrVja9gSvbBVf5M2+K0EY+/0fGtM4ZxjUkIiLrGgXJIoMoSVbSseo7JEkzSfISpVJ6Y1ZCjgJJV5tW6N6eNft/UvE+O05S533l9EmVcaqNn/08O01Frwe5CeTz08nl1ieX34B8bgPy+an+f35rcrnJ3ZsRFOYAMLb90hrLFBERGbkUJMuo9sorr3Daaafx0EMPkcvl+M53vsM+++zT7/klpfmsXn0X8Baamw+hUNgRaPebs9I2qbkWuj+1K9OrQK58g1jld2u0rc1VmT7zvqv7rlx2nOy882uOn6ucX7n3A6/1FRERGR101JNR7eyzz+bQQw/llltuoaOjg+XLlw9ofvnCJowbfwf5/FzGtp82SKlcS332s41OgYiISL8pSJZRa/Hixfz617/mmmuuAaClpYWWlpbGJmpdsu/w9mohIiIymBQky6j15JNPMm3aNE455RQefPBB9thjD77yla/Q3t4+4HkvYQkf5IMUGL39127+u+cBeGrfGcO+7L3Yi7M4a9iXK2XaBo01EtZ/kSIzmMF5nNfQdIj0l4JkGbU6Ozv561//yhVXXMHs2bM5++yzueSSS7jwwgsHPO+lLOWX/JL1WG8QUrp2OuUTfwfgh3N3HvZl78AOPMADw75cKdM2aKxGr//VrOZ5nmd3dm9YGkQGSkGyjFobb7wxG2+8MbNnzwbguOOO45JLLhmUeefIMZGJTGLSoMxvbdQUi5dGrIMChVG97kcCbYPGGq71X6RIR/xbxSqKFGmiiSJFPsWnOImThjwNIkNFQbKMWhtssAGbbLIJjz32GLNmzeKee+5h++23b3SyRETWkJBQokSRYnwsT9LVSWNS5a9IkVd5teZ41d6XKHUtp0CBfOwRJ9/VW47LkaNEiU46aaKJaUxjK7ZiUzZle7Znq/jXSutwriKRQacgWUa1K664gne+8510dHSwxRZbcPXVVzc6SSLrpMogrkQJoGvYUyBX7f9a4/dmuspx0yCwMhjM/l/5Pp0uISFPvtt8suNUm1+9cSrTWox/rbQylrE00USBAk00kSdPIf6lnxco0EorO7BD1/v0+2aau/7P/qXTpH8ttNBEEy3xrzn+pf9PZCJTmMJYxlb9XSLrAgXJMqrtuuuu/OUvf2l0MkT6JBtEpbV/1YLQeu8rP0v/h3JAmNYkZgPIyoCoMshL59dJJy/yYrdlZAOyNGDLBnbZgC/9Px1WBoTZz6sFielfGhRmp82R6/os+5f93dlh9q/yu8ra1nrT1xqn1vQFCrTTzjjG0U57VxOm3pjLXN7Nu/uZw0QEFCSLyBC56ss7NDoJVWsbawWUnXRSpEhn/Eu/ywaG9YaV46b6W8vWU9pz5LrVCqb/Z2v7Kmv/ssPs3xjG0ExztxrEarWN6efVlpsNepto4u/8nXu4p1sgrBpHEVmbKEgWkQGrFsQ9tmtbrEVc0RWIAjVrPqtdhs8GhE00dQtCs7WX2Uv2CQmrWc1/+E+3aSsvO2eDwDbamMQkJse/qUylldauwK6yxrHaX63xsrWW9cap/KxagFp5mX0kK1BgPOMbnQwRkX5TkCwyBBISlrOcZSwb0mVUXiqvN6y89Au1a0Yrl1NtudA9kK2seZz9iyXkyfPwG2Z01TKmf+n7bM1mtv1j9vMxjKGVVsZV/I1hTFfQmAaVaRD8x/in2ksREekvBckiQ6CNNrZgC8YwZsiWkSPXFUSmw/SvhZauG3DSILPyMnq9Ya0a1/RSe/o+e6l9jWD0ojn/n707j5OrrPO+/zlb7V29L+l09hAS1kCAICAGBQYdlwdlBBEiiuC43C+RUedW5ClqFBVFZdzBEW9HHUWZwVFQH/DG3KPoqIzCPDjIosCQkKWz91pV55zr/uNUd0KA0Gm6+lR1fd951evU0l3nV92vVH1z5XddV3Q8819r9jM42M/mUPo3RUREDqRPEZEaaKON7/P9uMsQERGRaarvpjYRERERkRhoJFmkBkaDUT75xCebuh/2/PEnAbjlietn/dwLygu4PobzNopSWOKPI39ksDLI1Uuu5tS2U+MuSUSk7igki9TAnmAPN266kQ6vI+5SYnNGZRCA72777qyf+y3+W2I5b+wMVEwF3/j4xqcUliibcrRcnOXhWR6u7ZK0kuTdPB1eB+1ue9xVi4jUJYVkkRqwsOjyuuhOdMddSmy+8v9mAehN5Gb93K7l0pvonfXzxiU0IZtLm7GwaHfbaffa6fA6GEgOsCS1hN5kL+1uO61uK21uG+1eOwk7EXfZIiJ1TSFZRGpi0+LZD8fNauP4Rl7U9iI+ufyTtLham1hEZCYoJItITZz4f7YC8NuXNM+I7mwLTchgeZD+ZD9fOvxLWFbz9sCLiMw0hWQRqYlz//FPgEJyLQ2WB1nXvo6rllylgCwiMsO0BJyISAPaVdlFwk5w1ZKraHVb4y5HRGTO0UiyiEgdC01IxVSiVStCn5IpAdDj9fCpwz6lgCwiUiMKySIidWSvv5fhYDjaGNxy8I1Ph9dBX6KPLq+L/mQ/Z7SfwQktJ+DaegsXEakVvcOKiNSJwdIgrV4r6/vW05/spzfRy+HZw8k62bhLExFpOgrJIlITn772uLhLaAg7Kzsph2Vsy6Y/1c+XD/8yvUlNdhQRiZtCsojUxPa+dNwl1L1t5W10e9389cBfsyS1hJXZlTiWE3dZIiKCQrKI1Mhp/99TAPziL/pjrqT+lMMyOys7aXVb+eaR36TNa4u7JBEROYBCsojUxCu++zigkLy/UlhiZ2UnWSfLeT3ncdn8yxSQRUTqlEKyiEiNGWPYUdlBKSxx5cIruaD3Aq1MISJS5/QuLSJSQ+PhODvLO1mVXcX7Fr2PY1uOjbskERGZAoVkEZEaqIQVdlR2kLAT/M/F/5PXdL+GhJ2IuywREZkihWQRkRlWCStsK2/j4nkX8+Z5b1bfsYhIA1JIFpGa+Pj1J8RdwqwLTchTpafwbI839r2RKxZcgWVZcZclIiLToJAsIjWxt725WgsmNgU5te1Url58tTYEERFpcArJIlITL/vXJwH4369ZEHMltTWxcoVvfG5adROrc6s1eiwiMgfYcRcgInPTy37wJC/7wZNxl1FTO8o72FreyrL0Mv7pqH/iuJbjFJBFROYIjSSLiByiiU1Burwu/m7p33FS60lxlyQiIjNMIVlE5BDtKO/g3J5z+eDiD2Jb+g85EZG5SO/uIiKHYCwYw7Ed3tr/VgVkEZE5TO/wIiJTVApLbK9s54oFV9CX7Iu7HBERqSG1W4hITRQ/vzbuEmbUsD/Mbn835/Wcx+t7Xh93OSIiUmMKySJSE6W0E3cJMyYwAXv8PVy37DrO7jo77nJERGQWqN1CRGriFbc8zitueTzuMl4wYwxPlZ7iiOwRCsgiIk1EIVlEauK0O5/itDufiruMF2yXv4vDM4fz8eUfj7sUERGZRWq3EBGpCk3IHn8Po8EoruViWza+8XnnwDsZSA3EXZ6IiMwihWQRaXojwQhD/hA2Nse2HMsZ7WfQ7rXT7rYzLzmPhamFcZcoIiKzTCFZRJra7spuRsNRCksKvKLzFbi23hZFREQhWUSalDGGwcogxhi+cPgXOLn15LhLEhGROqKQLE1t8eLFtLS04DgOruty7733xl3SnPHBr54SdwnPadgfZm+wl8WpxXx8+cc5LHNY3CWJiEidUUiWpvezn/2Mrq6uuMuQWbK9vJ0Wt4UPLfgQZ3eeTdbJxl2SiIjUIYVkEamJc7/+JwBue9OymCvZxw99yqbMNUuu4ZS2+h3pFhGR+GmdZGlqlmVx9tlns2bNGm666aa4y5lTTvy3rZz4b1vjLmPS9vJ2dvo7Ob/nfE7KnxR3OSIiUuc0kixN7Z577qG/v59t27Zx1llnsXLlSk4//fS4y5IZNhaMMRqO8p2jvsOq7Kq4yxERkQagkWRpav39/QD09PRw7rnn8pvf/CbmimQmjQajbCltoRSWuHrx1azMrIy7JBERaRAKydK0RkZGGBoamrx+5513ctRRR8VclcyULaUtWFi8d+F7uX317ZzXex6WZcVdloiINAi1W0jT2rp1K+eeey4Avu9z4YUXcs4558Rc1dxRTjmxnds3PisyK/jyyi/T4rbEVoeIiDQuhWRpWkuXLuX++++Pu4w565ovrI3lvDsrOzEYrj/segVkERGZNrVbiMicUQ7LACxLL2Necl7M1YiISCNTSBaRmjj/poc5/6aHZ/WcOyo7OKvjLBJWYlbPKyIic49CsojUxLG/3s6xv94+K+faXdnN5tJmElaCN/a9cVbOKSIic5t6kkWkYY2H4+yq7CLv5Lli0RUcnz+eJeklbGJT3KWJiEiDU0gWkYYTmIBt5W3Yls37F72f13a/FtfW25mIiMwcfaqISEPxjc+W0hZe3vlyLu2/lGWZZXGXJCIic5BCsojUxFDbzE6eGw1GGfKHMBgu6ruIKxdeqc1BRESkZhSSRaQmPvapE2bsuXaUd2BbNlctvoozOs4g7+Zn7LlFRESejUKyiNS1neWdOJbDd476DvNT8+MuR0REmoSWgBORmlj/2QdZ/9kHp/39oQnZUtqCbdl8eeWXFZBFRGRWaSRZRGpi5f27pv294+E4g+VBzmg/gw8s/gA9iZ4ZrExEROT5KSSLSF0ZC8bYWt7KuwbexcXzLibjZOIuSUREmpBCsojUlZ2VnVy58EoumXeJVq8QEZHYKCSLSF0wxrCtso2+ZJ8CsoiIxE4hWURqYntv+pC+frAyyJLUEr648osKyCIiEjuFZBGpiU9/9LgpfZ0xhi3lLTiWw8eXf5xOr7PGlYmIiDw/hWQRiUVoQgbLgxgMJ+ZP5N0L3s2S9JK4yxIREQEUkkWkRt76iT8A8A/vP/IZjw37w+zx97CufR3vWfgeBpIDarEQEZG6opAsIjWx9KE9z3r/sD/M3mAvH1j8Ac7tPhfX1tuQiIjUH306icis8UOfoWCIL6z4Aie3nRx3OSIiIs9JIVlEZsXOyk7GgjEumneRArKIiNQ9hWQRqanhYJi9/l4A/umof+LwzOExVyQiIvL8njMkF4vF1071SQqFwr/MTDkiMlc8tjBBEAZk7AwXD1zMifkTWZldGXdZIiIiU3KwkeRbp/gcBnBmoBYRmSNGghE+ddUKrlt+HT/Mn4Rt2XGXJCIickieMyQXCgV9qonIIRkJRtjr78WxHIpLipzcqt5jERFpTArCIjIjBkuDlMMyb+t/G9856juc88F/gcsvj7ssERGRaZnyxL1isfhy4F3AUuDsQqHwZLFYfCvwWKFQ+N+1KlBE6psxhm3lbbR77dy08iYWpRdFDzz8cLyFiYiIvABTGkkuFotvBL4LPAwsBrzqQw7w/ppUJiJ1rRJW2F7ezubyZk7Mn8itR9+6LyCLiIg0uKm2W7wfuKxQKLwH8Pe7/9+B1TNelYjUte3l7ezx93B62+l8dsVn+fzhn6fFbYm7LBERkRkz1XaLw4BfPcv9w0B+5soRkXo3Ho7jG59vHfUtVmRWxF2OiIhITUw1JD8FrACeOOD+04E/zWhFIlKXjDHs8fdQCku8b9H7nj8gr9Z/MomISOOaaki+CfhsdaIewIJisfhi4BPANbUoTETqQ2ACtpe3ExLSk+jhusXXcUrbKc//jTfcUPviREREamRKIblQKHyiWCy2AncBKeBnQAm4vlAofKGG9YlIjLaXtxMQ8KquV3HZ/MvoS/bFXZKIiMismPI6yYVC4SqgCzgJOBnoLhQKV9eqMBGJhzGGIX+IjeMbyTgZfnjMD7l66dWHHpAvuii6iIiINKApr5NcZYDx6vVghmsRkRgZY9jt72YsGGNpZikX9F7AGe1n0Jvsnd4Tbtw4swWKiIjMoimF5GKxmASuA94GJAALKBWLxZuAvy0UCuMH+34RqW9D/hDDwTCHpQ/j0v5LeVnHy7AsK+6yREREYjPVkeQvAWcDb2XfUnAvAj4GtABvmfnSRKQWjDGMh+OMBCMEJsCyLDJ2ho8u+ygvbX8prn2o/8EkIiIy90z10/CvgNcWCoW79rvvz8VicRvwzygki9QdYwy+8amYSnQJK5TDMhVToTvRzUvaX8K6tnWsyK5gIDlAwk7EXbKIiEjdmGpIHgE2Pcv9m4CxmStHRA6FMSY6Vv9UTIVSWGLYH8a2bHJOjg6vgy6vi95EL4elD2NxejGntZ2GbU153u70vOhFtX1+ERGRGppqSP4cUCgWi5cUCoUxgGKxmAaurj4mIvsZCoYYLA9i8ex9vQaDhYXBPON+DBgrOk7ex74gDPvCMYBlWTg42JZNu9vOovQiVmVXcfn8y+PdKvpjH4vv3CIiIi/Qc4bkYrH4gwPuWgdsKhaL/1m9fXT1+7O1KU2kcQ0kB/j6kV8n5+SwsbEte99x/+sv4OhYjibXiYiI1MjBRpJ3HHD7nw+4/dgM1yIyZ6TtNKe1nRZ3GfF63eui4z8f+NYhIiJS/54zJBcKhTfPZiEic4kBtpdG4y4jVq2DgwDsieHn4Juw6X/+cav330HG8ci4XtxliEgd01pPIjWwrTTKug3fJus074fwF/dEIfkdP7911s/95rEsV8VwXtknzt+BwTAa+E+7z7EsXMvGtWw82+aIfBc3nXBOLPWJSGOYckguFotvBt4ALCTaUGRSoVBYOsN1iTQ0Yww5z6M7mYm7lNgk7Gj1jJ7U7P8M3HE7lvPKPrP9OxgLfMaCCqUgWvt7YaaFBZk8CzN55qdaaE+kyLkeeS9J3ktwWK5j1moTkcY01R333gd8ALgROB34IrC8ev36mlUnIiJygNAYdlfGKYUBrmVjDOS9BCd3zue4tl5e3reUzmQ67jJFpMFNdST5MuDyQqFwa7FYfBfw+UKh8OdisXg1sKh25YlIo7p/zdFxlyBzSLQ5TsiIX2EkqLC2o58zexfTk8zQlUyzPNeOZztxlykic8hUQ/IA8Jvq9TEgX73+7er9l81wXSLS4G655Ly4S5AGYIxhJKgQGkNgTHQkOoYmWk/ctqLR4xYvwUAmzxWHncDazv64SxeROW6qIXkL0AX8N/AE8CLgPqKWC3OQ7xMREQGiQBwYQzkMKFUvlTBkWa6N9kSKrOORcz2yboK8myDnJuhLZ1nb0U/eS+DUepdIEZH9TDUk3w28Gvgd8FXgM8Vi8fXA8cB3a1SbiDSwa957bXS8/qqYK5GZZIwhBAITEhgzeQwPGAWumBybxoZxLAvHsgAL3wQkbZe+VIYF6TxLsq2sbuvlJT0L435ZIiLPMNWQfDlgAxQKhS8Xi8VdwKlEG4zcWKPaRKSBJUrluEuQQxAaw3jgUzHh04JvUN0C3a7u7hgag2vbZB2PFjdRHfn1yDjRCHDWjUaDex4b5G9XHkHG8ehOpulJZulJZcg6nnaKFJGGMKWQXCgUQiDc7/YtwC21KkpERKZvoq2hYgLKYTgZdgMTXQ8B2wIHG9uyCI0BCwbSOXqSWVrcBC1eteXBS0S33QRr2vtoT6SmNEFuw6YNrFuwqvYvVkSkRp4zJBeLxeOn+iSFQuF3M1OOyOwLgoATTjiB+fPnc/vtt8ddjsiUGWOomJDxwKccRoHYYACLjOPSlczQnUyT95LkHI+W/QJvpjoC3JvM0pvK0pVMq+dXRGQ/BxtJvheq77YHZwCtuyMN6+///u9ZtWoVe/fujbsUkQN6fkP8MBoB9vfr/4Wo/aESBnQm0yzNtbEgnWd+OkdfKsea9l4GMvmDn0hERA7qYCF5yaxVIRKTjRs3cscdd3DVVVfx6U9/Ou5y5pTfnrIm7hLq2t5KiVIYEFTX/3UtG4t9Pb85N0GLm6TFS9DqJmlNJGl1k7QnUlEvsJdgcSbPka3dcb8UEZE56TlDcqFQeGI2CxGJwxVXXMEnPvEJhoaG4i5lzrntDa+Ou4RZN9ELHGL2jQJjCMIw2h3OtrGBwBj607mox9dL0TYRfF2PY9p6mno7cxGRejHV1S1E5pzbb7+dnp4e1qxZw4YNG+IuRxrEUKXMWODjm2gus2NZkys/+CYkZbtkXY8WN0nO9ch7SVq9JMtz7SzK5JmXztGXytLupbTKg4hIHVNIlqZ1zz338IMf/IAf/ehHjI+Ps3fvXi666CK++c1vxl3anPDR/1EA4IOfK8ZcyaGLRoFDKtVjOQzAAmOgN5XlFfOW0pFIk/eiDS9yboK+VJZFmTxJR2+rIiJzgd7NpWl97GMf42Mf+xgAGzZs4Prrr1dAbjJ+GLKjPFbd+tgixOCHIZ5t0+ol6Uvm6Eqk6UtnObmjnyPyXfSlshoBFhFpAlMKycViMQOMV9dLFhFpGBPLpJWCgPHQn1yyJzAGx7I4p28pL+1ZxLx0llYvSZuXImk7CsIiIk3ueUNysVh0gD3AscB/1bwikRisW7eOdevWxV2GTFO0RnC0TnAlDKiYHNvGR7AtG9+E5FyPhdk8izOtvKR7Acty7XQmUuTchMKwiIg8q+cNyYVCISgWi08AiVmoR0RkysphwI7yGGnbY2Gmhd5UjoFMjr7Hd/LRo9fQmUyzMJPXJDkRETlkU+1J/jDw8WKxeFGhUNhey4JEZG74xUtPmbHnmmiZqITVDTaqm2uM+hXetPho3rn8ONKON/n1G57awLrexTN2fhERaT5TDcnvJdpcZFOxWNwIjOz/YKFQOGamCxORxvajc/9iyl8bGkOp2jJRqbZNPG1ptTCkPZGiO5kh7yVo81K0J1L0p3NcsGAVKa0oISIiM2yqnyy31rQKEZlzkuMlAEqp5NPur4QBY4FPOQwZD308y8YAPckMS7KtDKRbGMi00JfK0ZFI0ZFIMZBuIeuq40tERGbPlEJyoVBovIVORSRWhfd9FNi3TrIxht2VEnsqJVa2dHJ4voOF6Twv7V3EkmwrjmXHWa6IiMjTTPn/KIvFYgp4JbAMuLFQKOwuFovLgF2FQmFnrQoUkcZXCnwGS2Mc3tLB1atO4ey+JZpIJyIidW2q6yQvB34K5IA24HvAbuDt1dtvrVWBItJ4JibXhcawdXwEz3b4m8NP4q8GDteOdCIi0hCm+ml1A3AnUSjevd/9PwC+NtNFiUj9C42hEkZhuBQGlAJ/crKdAbKOR9b1uO6YdZzQ3keLl3ze5xQREakXUw3JpwAnV9dM3v/+/wb6Z7wqEYlVaAyjQYVKGI0GByYkMIbQQEC08oRnO7R5SboTGfrTOY7Id7I420p/KsfCTJ6WlpsBmN+zKOZXIyIicugO5f89vWe5byHRbnwiUscCYyiHPqUwxN9vrWHHsrEBe79JcyGG0BiWZFuZl8rR4iVodZPkvQR5L0nOTfCizn46E+mD9xVfcknNX5eIiEitTDUk3wlcCVxavW2KxWIeKAJ31KIwETk0gQkpBdW1hqsbb9iWhQVYlsW8VI7D0zk6Eyk6Emk6k2nybpKcG7VFZByPnJsg47q0uIkXvuSaQrKIiDSwqYbkK4GfFYvFh4AUcAuwHNgKvL5GtYnIczDGMOxXGA0qBMZgW+BaDn2pLPPTLcxLZ5mXytGdzHBYrp3DWzpmfzWJ7dXNObu6Zve8IiIiM2Cq6yQ/VSwWVwNvAI4HbOAm4FuFQmGshvWJSFVoDGOBz5BfxgIWZvKs7VjGwkye5S3tHN/WW1/Lqp13XnTcsCHWMkRERKZjqkvAnQ78slAo3AzcvN/9brFYPL1QKPxbrQoUmSuMMVRMyGjg41cnxBkMQfVoVf/YFtG16tFgMNUJcwvTeS5YsJK/7F9OfypXX6FYRERkDplqu8XPgHnAtgPub60+5sxkUSL1wA9DvvLY/Tw2spuxwGcs8Bn2y4z6FcYCn9AYwmqANRhCoiAcYnhTJcuwVSEwIxhjMERfk/eSHJHvpC+ZJe14pB2HtOORcV2StkvCdvBsm6Tt4NkOLW6CVi9JWyJJ3k3i2tqVTkREZDZMNSRbgHmW+zuBkZkrR6R+lMOAOzb/iXC/5c/2/2uQclw828axbDzbxrWqF9smPV7h6yf9Jctz7Xj7Pa6RXxERkcZw0JBcLBZ/UL1qgG8Wi8XSu51BmQAAIABJREFUfg87wFHAL2tUm0isMq7H7aedN63v3bBhA2va+2a4IhEREZktzzeSvKN6tIBdwP6T9MrAL4Cv1KAuEWl0b3973BWIiIhM20FDcqFQeDNAsVh8HLi+UCiotUJkCvaUQy6+eysJu4nbK3rPiI4bDpzKUHunD/tcGsN5X4hKCJ9+USddaU3xEBGpB1PtSf7w/jeKxWIf8ErgvwqFgtotRA4w6ht+t6tMV6p5A0/34CYABrvnz/q5T8Lw6B5/1s87VdFyfoYx31AOo4mdfWmHpNPE/6gSEakzUw3JdwA/Af6+WCzmgHuBLJArFouXFgqFf6xVgSKNyAJaPJt8onlXoyh89t0AfPC6f5n1czsVi7xXXz/7XaWAcmiwLMBYLGv1OKLN46iOBMvyHke0J0goJIuI1I2phuQ1wPur118L7AWWAG8E3gsoJIuIPIddpQDbgr87oYPD2zwW5FzcZm7FERFpAFMdamkBdlevnw3cVigUKsDdwLJaFCYiMhdsG/NJuxY3nt7NOQszLMl7CsgiIg1gqiH5v4FTi8ViFvgL4K7q/R3AaC0KExFpdNEui/D1M3pY1Z6IuxwRETkEU223+DTwDWAYeAKY2Ib6dOD/r0FdIiINxRhDYKASGiohlIJoUl6LZ9OfnepbrYiI1IspvXMXCoUbi8XifwALgLsKhUJYfehPwNW1Kk5EGtdtr/3ruEuoiUpo2FMOqYQG17awgKAakLOuRXvSoTvtsDjnsjTv8uJ56bhLFhGRaZjy8EahULiXaFWL/e+7Y8YrEpE54bdrz467hIMa9UPGfEMlNIREvWe2BZYVBV8AywJT3Yl8YkPyUmA4c36ak3qSzMu4dKdtulIOrQlbvcYiInPIlEJysVi88mCPFwqFT89MOSIyV8zf+CgAmwaWx1zJPsYYdpdDKiG0JixePC/FQNalL+OQ9WwSNni2hWdbJBwLz4ZE9fbE9bRr0ZZs3vWvRUSaxVRHkv/HAbc9YB7RNtXbiHqWRUQmvfNz0aqRcayTDPvaIsqhwbPBwiIwhv6My5XHtLK2N6XNO0RE5DlNtSd5yYH3FYvFXuBrwFdmuigRkUNljGE8MIz4hopl2F4JeGl/mtVdCQayLj3pqFe4I2ljWQrHIiJycNOecl0oFLYWi8WrgO8Ct81cSSIiz25iBYnoYvBDGPFDXNvCD6NR4pN6EvTtdrj1lF4Wt3hxlywiIg3qha5LZAO9M1GIiAhAaPYtoTbih3i2hSEKxaGBjGuRdW3yCYcWz6Ij5XD+sizHdCRIudHS7xs22ArIIiLygkx14t5rD7jLIupJfifw85kuSkSaix8adpVDSkFIOYSBrMviFpdjOhOcNZCmLeHQnrTJeZZaJUREZFZMdST51gNuG2CQaFvqv5nRikRkTrjlgiue92uMMewohYQGzhxIc2xngmM7Exzept3pREQkXlOduDfV7atFRAC4/7jTJ69PLL02Fhhcy8K2oraKwMBRHQk+dHw7y1vVHiEiIvVDe6WKSE0s+dMDADy4+Ei2j/sc1prgLxdmmJ91opUmUg5dKQdPy7CJiEgdes6Q/HwbiOxPm4mIyAQ/jJZhe9OXrgYL/ufH/5lXLcpyxdGtdKS0CYeIiDSGg40kH7iByHMxaDMRkaY2sXGHb6J2iiM7EszLOuRcmx+/Yp62axYRkYbznCH52TYQEREJjWHHeBSIHcvCssCx4OTeJH+1NMtJPakoFGeqby8KyCIi0oDUkywiBzVcCRnxDbYFQWiwrGiJtjcsz9GVcuhI2Rze6k2uUSwiIjIXHDQkF4vFlwNfAo4tFAp7DnisFbgfuLxQKNxZuxJFZDaN+SFjvmE8jFonyqHhQ8e3cUJ3ko6ko7WKRUSkKTzfSPK7gE8eGJABCoXCnmKxeB3wbkAhWWQO2FUKGPUNL52fZlWbx+IWjyV5l6X5aSzP9tGPznyBIiIis+T5QvIxwMFWubgbuGrmyhGRuITGUAoMnzuti1P7Ui/8CU855YU/h4iISEyer4mwGwgP8rgBOmeuHBGJQ2AMm0cDXjo/PTMBGeCXv4wuIiIiDej5RpI3Eo0mP/Icjx8DbJrRikRmyfj4OKeffjqlUgnf9znvvPMoFotxlzWrQmMYCwy7SiHnLEjzoePbZ+7JP/jB6Lhhw8w9p4iIyCx5vpHkO4APF4vF9IEPFIvFDPB31a8RaTjJZJK7776b+++/n/vuu4+f/OQn/Pu//3vcZdVcaAzbxwM2jfgMjofkXJt3HZnnwyd2kPW0QoWIiAg8/0jytcB5wCPFYvFzwB+r968imtRnAZqdIw3JsixyuRwAlUqFSqUy51dtCI3hqdGAtT1JLj4sxyl9qTn/mkVERKbjoMNGhUJhG3AK8J9EYfi26uXa6n2nFgqFrbUuUqRWgiBg9erV9PT0cNZZZ7F27dq4S6qJSmjYMurz1GjAyb1JPntqF6fOSysgi4iIPIfn3UykUCg8AbyiWCy2A8uJRo8fKRQKu2pdnEitOY7Dfffdx+7duzn33HN54IEHOOqoo+Iua8YMV0KGKiEGOGdBhlcuzLCmO0nCUTgWERE5mCnvuFcNxb+tYS0isWlra2PdunX85Cc/mRMhuRwYdpZDwLB+RY61PSlO6knO7sjxDTfM3rlERERmmLallqY1ODiI53m0tbUxNjbGT3/6U/72b/827rJmxPZSwPnLsvz1Ea3kEzFNxlu9Op7zioiIzACFZGlamzdv5k1vehNBEBCGIa9//et55StfGXdZL0hgDDvHQ3rSDu8+uo1knG0VP/1pdDzzzPhqEBERmSaFZGlaxxxzDL///e/jLmPGjPohu0ohR3UkeMeR+XgDMsBHPhIdFZJFRKQBKSSLNLhKGK177NoWH1/bwdkLMnGXJCIi0vAUkkUakDGG8cAw4ht8Y1i/ooULlufoSTtxlyYiIjInKCSLNJDQGEZ9w+5ySFfK5kW9SS5cnuOEnlTcpYmIiMwpCskiDWDMD9lTDsGCxTmPc5dkeevKFjLaRlpERKQmFJJF6lgQGgbHA0LgrStbePXiLPOzDfLX9sYb465ARERk2hrk01ak+YTGsHHU55ULs7z32FY6Ug3Wb3z44XFXICIiMm0KySJ1ZswP2V2OtpJe15/mquPbyDZiW8UPfxgdX/WqeOsQERGZBoVkkToRGMPW0YCOlM1lq1o4vivJsZ1JEnGvdzxdn/pUdFRIFhGRBqSQLFIHdpYCyoHhguU53ntsK5bVoMFYRERkjlBIFolROTBsGw/oTTt87owuDmv14i5JREREUEgWicWuUkDFQNKG1y3JctmqFnoz+usoIiJSL/SpLDLLKmG0W96Np3dzbGcC11ZrhYiISL1RSBaZBftvIz04HnDZyhbWdCfjLqu2vvGNuCsQERGZNoVkkRozxvDUaEBnyubM+WlWtXucOZCJu6zaW7Ag7gpERESmTSFZpMa2jgWc2JPkc6d2Ne5ybtNxyy3R8fzz461DRERkGhSSRWrIDw2+gWvWtDdXQAb40peio0KyiIg0oAbcxkukMZRCw+axgPUrcszL6t+jIiIijUSf3CIzaLQSctemMYb9kKRj8cqFGd62Kh93WSIiInKIFJJFZkgQGm5+aIjPP7CXdyYMHzy+jVcvzsZdloiIiEyDQrLIDNhVCvj8A3v53p+HWZhz8XyLtNtkPcgiIiJziEKyyAuwczzgtsdGuPmhISqhoT/j4tkW+HFXVgduvTXuCkRERKZNIVlkmraM+rzzF9t5dI9Pd8qmI+nEXVJ96eqKuwIREZFpU0gWOUR3PTnKbY+P8tvBcfwQFmQdLEutFc/wv/5XdLzkkjirEBERmRaFZJFDcMcTI1x97y4yjkVX0sG1FY6fk0KyiIg0MIVkkYPYMurz4/8e5c97fR4f9rl/e4mulEPW0xLjIiIic5lCskjVzvGAp0YD9pZD9pZDtpcCvvXwMJtGfbKuTdK2WJBzsdVaISIiMucpJEvTCkLDnnLIfw/73LVxjO/+aRin2j4RGkNgIOvaLMy66jkWERFpMgrJ0lSMMXz897u5c9MYQ+UQ2wILixBDa8Im7aqNQkRERBSSZY4b90N2lEJ+tmmM/9he4uHdFTaN+vSmXPrSWpWipn70o7grEBERmTaFZGlYo5WQR/dWGKkYRn3DiB8y4ht2jgf8x2CJR/ZWGPMNrm0RGEPStkg5FgMZtU/Mikwm7gpERESmTSFZGkY5MNGkukrIw7srfPT3uymHBgsIDYQYghBsCzKuTc61afVQII7LF78YHd/xjnjrEBERmQaFZKlLoYFfbB5ny5jPfdtL3P3UOKXA4FgWjhU9nnYtulPa5a5uffe70VEhWUREGpBCstSNcT/kdXdupRQYzgsqfOb/bGM8MIQGulMO0dLE0W2AMd8w5odxlvycfEzcJYiIiMgLoJAsdcO1LV6zOEspNPQ95fD5E7riLmnaxh90Oak7FXcZIiIiMk0KyVI3XNvi8iPyAGzYbrNuoHEnfm141KY1qeXkREREGpVCskiNhGFzt1xMTJc0Mf0cmv3nXw/q5XdgWZrAKyKHTiFZpAYGBwPWrNlEc38ufzM6nLBp1s982WUVrrxy9s8r+9TD78AYcF247rpOXvrSdKy1iEjjUUgWqYEggGTSortbq2/EwfMs5s/X21ucav07MMbg+xCG0Yh1EOy7HoZQqUTXTzghyYknJmtWh4jMXfoUEZGaOPdPXwLgtmVvj7kSmSt83zA0FDI2FrVxpNMW2axNNmuTTltkMtbkffm8zdlnpznxxKRaLURkWhSSRaQmTtx6F6CQLNNXLht27gxwHIsgMLiuxXHHJTj11BSrViU47riEArCI1IxCsoiI1J09e0JGR0P+4i8yXHBBjkWLXFpaLIViEZk1CskiIhIbY6Ie4iCIRo6Hh0MsK5p097Wv9XD00Ym4SxSRJqWQLCIih6RcNuzYERBWN7y07Yll1iZuW/i+Yfv2YPJ7JoLvxGVikh1Ek1xTKYu+PoeTTsqwalWCI45IsHChPqJEJD56BxKRmig72nGwXhkThdMw3BdYo/BqKJehVDKUywbbpnqxJgNwGEbLql1wQY7eXodEwsLzLFyXyaPrWoyMuNxwQyeua+F5E49FYXgiFKdS0WNqoRCReqSQLCI1cc3af4q7hKZijGFszDAyYggCg+NY1fuZPBpjJkOxbfO0cOt5FomExcCAw7JlHgsXuuRy9uSKEdHqETbZrMWCBS6ue/Bgu2GDxdq1+oeSiDQuhWQRkTo30bcb9e5G6wOPj0ejvY5DdfUH6OmxOf30FEcfnWDpUo/OTudpIXj/0V7b1uitiMjBKCRL03ryySdZv349W7ZswbZtLr/8ct797nfHXdaccf7DnwbglhVXxlxJYwkCw65dYbXfd9+ktkRiYg1gi54em6VLPVas8OjtdejsdOjqslm40FXrgojIDFFIlqblui6f+tSnOP744xkaGmLNmjWcddZZHHHEEXGXNiccu/0XgELy8zHGsGdPSKkUtUAYA6tXJ7j44haWL3cnN8p4vvYGERGZWQrJ0rTmzZvHvHnzAGhpaWHVqlVs2rRJIVlm1P5LnE20SlQqhlLJTE6GW7bM481vbmHpUo9Fi1wSCQViEZG4KSSLAI8//ji///3vWbt2bdylSB0yxlR7gKNly/ZfFWL/2xPLoE2sCBEEhiCAVCpqk2hpscnlbPr6HJYscVmyxGPNmiQ9PU7cL1FERA6gkCxNb3h4mNe97nXccMMN5PP5uMuRWTSx2oPv7xvl9X1DpRIdLSua6BaG0N5us2KFW13CzCaZpLrqg00qFfULJ5PWZO/w0qUe/f0O6bSlSXIiIg1IIVmaWqVS4XWvex1vfOMbee1rXxt3OXPKUKI97hIOavfukN27A9LpaIS3pcUmn7dpb7fp7XXo63Po6oqOCxa49PU5mhQnItJEFJKlaRljuPTSS1m1ahVXXqnJZTPtYyd8Ne4SniEMDYOD0bbHrgtf+1o3a9YkFX5FROQZ7LgLEInLPffcwze+8Q3uvvtuVq9ezerVq/nRj34Ud1lSI5WK4fHHffr6HK67roMf/3geJ5yQUkAWEZFnpZFkaVqnnXba5Pa8MvPWP3gtAP+46qqanicMTXXliP2vG3bsCCiXTbWn2GBZFuvX57jwwhYWLNBbn4iIHJw+KUSkJlbu+o8Zf07fjzbaqFSiSXVBEK0k0dISbZ+czTpks9HEuRe/OMUxxyQ44ogEfX0O3d2O1hoWEZEpU0gWkbqye3dAqRSNBhsDjjOxrJrF3r0hL395miOOSNDV5dDaanPccUlaW5/eObZhg8v69Z0xvQIREZkLFJJFJDbGGIaHDcPDIbZtYVmwYIHDKaekyOdtslmLTGbfEmvptMVxxyU1IiwiIjWnkCwis2bnzqhP2HWtyf7hJUtcXv/6LIsWecyf73D00QkcRyFYRETipZAsIjWxPT1v8noQGAYHAxYscFm/Psfixd7kOsQaFRYRkXqkkCwiM84Yw7UrP8fu3SHuYIAxcMYZaa65pp1MRitPiohI/VNIFpFnCAJDqWQIw6cvrRbdju637YlL1EsMYEx0CQJDe7vNu9/dyotfnGJgwNWIsYiINBSFZJEmNxGAy2XDnj0hnmcRBDAw4JDNRpPnstlo6+ZcziaXs8jn7ckJdROT6iaut7fbdHQ4eO97D/wHcPENcb9EERGRQ6aQLDLHGWMolWBoKCQIzOSkuDCMRoQtC7JZm3ze4qKL8qxYkWDJEpf581/g28N9981A9SIiIvFQSBaZY4wxVCowNmYYHQ0B6Ox0OOOMFCefnKK/36Gnx5ncgCOZtLQ1s4iIyAEUkkXmgKGhkLGxKBAHQbQD3eLFLhdemOWMMzIkkwrBIiIih0IhWaRBGGOqk+ImLtGIcbQRB7zrXa2cc06ari4Hz1MoFhEReSEUkkXqgDFRf7Dvg+8bKhXD2JiZXD0iCKLJdZ5nkclEl2zWobPT5qijEpx5ZprDD0/E/TKebsWKuCsQERGZNoVkkVkQBIbxccPQUIhlMTl5zph9y6p5nkVrq01np0NPj82qVQlWrPBYtsyjp8chnbYaaxm1m26KuwIREZFpU0gWeYEqFcP27QHbtgUMDUWT5YaGQjZt8vF9g2VFo8ELFjicc06a445Lks/bZLM2LS0WuZxNS4tNItFAAVhERGSOU0gWIQq6g4MTQTdkdNQwOmoYGQnZuzdkz56QvXsNe/cGjIwYRkYMw8Ph5Nc4joVtT2ymYbjoIsOZZ6Z5zWuy9PU5nHhicnL0uGlcfnl01IiyiIg0IIVkmZOMiXp69+wJ2b07CrlRuI2C7a5dAbt2hTz1lM+f/uQzOBjgutHOcRP9wWEYhd6JvmDXjY6OQ/US9Qa3tDjPWELN8ywuvDDHWWdlYvoJ1IGHH467AhERkWlTSJY56T3v2cFDD1Wqk+CiyXC+z+QEOGOe+T1h+Cx38vSVJODZv+ZAxkQbdIiIiEhjUkiWOekzn+kkDOM7/89//iinnJKKrwARERF5QRSSZU6yrKgtQkRERGQ6FJJFaiAohzx218a4y4hVR8dyAHbG8HMoj1ea/ucfN/0OnltQDhjZOsbY9nFaF+VYdf7yuEsSkWehkCxSA+M7x7nz6p+T6kjGXUqMXhEdPvSbWT9z7s02//a12T+v7KPfQcSEhqAUEJRDgnJI6IdkutMk8h5OwqFlfkYhWaROKSSL1EiqM0mmOx13GU3Jditkepr5Hyjxa9bfgTGGse0lwkqI7VqEviHblyXbmybdlaSlP8uK1y6hfVlr3KWKyPNQSBaRmjjp958E4DfHvS/mSkRqw4SGoBISjAf4oz62axNUQjpWtHLY/7OYTFeKbF+GriPan7FMpIjUP4VkEamJ9Nj2uEsQmREmNJSHK/ijAZZjgTGYwGDZFsn2JJ0r2+hY2cZhr1pMfkEWN6WPVpG5QH+TRUREnkNQDhjdNk7rohYGXt3H4pfNJ9uXIdmawE09cyMhEZk7FJKlab3lLW/h9ttvp6enhwceeCDuckSkxowxhBVD6IeElZCgEh0xYNnRjpuWbcEBuff4dx7J0etX4CS0rqRIM1FIlqZ1ySWX8K53vYv169fHXYqIzBBjDCY0+GMBlaEKlmdHG2WGhjA0JFsSpDoSZHrSZHsz5OZVR4XTLm7Kmbw4SQc37ZLuTJLp0gRckWakkCxN6/TTT+fxxx+Pu4w5a0f7yrhLkDpnqr29YWAwvpkMuJioD9hUw60x+18n2vfdgOXYWA6TLQ8mNIS+wUnYpDqSHHHBMrqP7oxWluhIkch72I62ixeRqVFIFpGaeGDVm+MuQepE1OYQ4o8H+CM+lmtjgigQuymHRItHojOBm3JxEjZ2wsZNRKO5dsLGTe4b3XWSTx/pTeY9Ei0JEi0eybxHqj2piXMiMiP0TiIiItMWjf4SrfgQTlzACQ2j28cJxgNsJ1oFon1pnoXr+pn/ol6yfRm8rKuRXRGpWwrJIlITL7r3IwD86oQPxVyJzIQwMITlgPLeCrBfOAZsz8ZN2tHobtKNRny9Egtf0suyly9k4NQ+bFdhWEQai0KyiNREojwUdwlyEFH/bji52sP+Kz3YrgVWtNoDBsIgxHJssr1pFpzez9FvWkG6K4Xj2die/azLoG3YsIF1bz559l+YiMgMUUiWpvWGN7yBDRs2sH37dgYGBigWi1x66aVxlyXyDBMT2vZfvmxywlsQAhaWY2HbFgY4MLMaM3kNwuj5AJL5BOnOJOmuFNmeNLl5GdJdKVJtSRL5iT7fBIm8RyLnaU1gEWkqCsnStL797W/HXYLIcyoPlfHHQywgqIQ4SYdka4Jcb5pke5JUe5JkW4J0exRovUy1zSFhT47wOgknOnrRZLj97/eyrkKviMhBKCSLiNSJsBJSHq4QlkMs2+JFHziOnmM7yfWltWKDiMgs07uuiNTEtq5j4y6hYZjQMLJlFNtz6Du+i+WvXsT8tb2k2pNxlyYi0rQUkkWkJh5ccWHcJcTOhIagFOCPB4T+vj5iy7Kwner2x5aFCQydq9o5rXACHYe1xl22iIigkCwi8pzCIJooZ/yQ0DeEQYjxo6BLaKId32z2rQQBk7vFRWEYsvMydK5qJ9eXiSbCtSbwsh5exsVNOyTzCTpWtpHIenG+VBEROYBCsojUxGm/vhqAX6z98Kycz5hoS2JCgxMaKiP+5KoQJqyu6jCxxXH1OtXr0aiuVQ28YGFNfl26M0myO02yNUG6M0WqLUGyLTm54kMUdt3J4DsRficm0mlynIhIY1JIFpGacILyjD/n05ZCq67rG5QDLDtqWfCyHk7KwbIt0h3JaPvilIObioKsm462NHbT1TCbim7bCQfHs6MtkfdbAaLjsFb1BYuINCmFZBGJTRhUe3bHfIJyCMZguzaWXe3XNdU1fkNDGBow4KYcEnmPXF+ObF+G/IIsnava6T6qndy8LFDdyOLWdXG+NBERaXAKySIyY4wxkz25JjSYwDC0cQTbsbBsa7J/1xgw1V3cWvoztJ/QTdvSPKmOJKnWRLSRRbWVwUk5eNVRYLUviIjIbFFIFmlSEzu3Rbu27du9zQQTfbwH9OpCNMILk6O8sG+kNwq+Bsu1cJMOlgWprhRr/+ZYvJZ9/bpe1o12eutKkWjRLm4iIlKfFJJFmkBQDintKWGCqJ0hDAyOa5PuTpHIeSTyHsmWBMnWBInWBMmch520cVNu1KubrO7cloh6daNjdL+bikZ4naSDm3Sw3Wqivv4RAFovOizGVy4iIjI9CskiDWL/VRmi1Rqi1RyCSkBYjkaE7WdZksyEBifhsPhlA8w/pZf8ghwtA1lS7cnajuK+9721e24REZEaU0gWOQSb7x1kdNsYfikgLAf4pZCgFESTz0oBwXhAUA4oD/gMbxrFBIAddSdAFFwN8LzZ1OzX31vt8QX2jeSmHNyMQ7ItSa4vQ8tAlnR3Ci+zb/1dL+3iZlwSOY+W+dl9rRIiIiLyvBSSRaaoNFTmF8V7Jyek7T+qu/8FIHuxxeq3HUHv6k4sx4pWbHAsbDeawGa7djSZ7WmP2dXHqo97+1oanIQdPd5I/bvr1kXHDRvirEJERGRaFJJFpijZkuCvfvjyKX3thg0bOPVtx9e4IhEREakVO+4CRERERETqjUaSpWk9/vjj/OxnP2P37t0z/tzz589n48aNDAwMzPhzi4iISO0pJEtT2L17N//5n//J5s2beeSRR/jjH//IQw89BFCTPt/169fz4IMPKiSLiIg0KIVkaQr/8A//wFe/+lUymQyu65JOp+nu7sZxnJqcz3X1V4vXvz7uCkRERKZNn+Qyp5XLZe666y6+973vsXDhQhKJRNwlNY93vCPuCkRERKZNIVnmpAcffJCf//zn3HrrrezatYtcLqeAPNtGR6NjJhNvHSIiItOgkCxzzje/+U0+8YlP4HkebW1t9PX1xV1Sc3rFK6Kj1kkWEZEGpJAsc0YQBHzpS1/i5ptvpqenh3Q6HXdJIiIi0qAUkmXO+P73v8/NN99Mb2+vJs6JiIjIC6LNRGRO2LZtG5/5zGfo6OhQQBYREZEXTCFZ5oQvfvGLjI+Pq8VCREREZoSG3KThPfHEE9xxxx309PTEXYrs75JL4q5ARERk2hSSpeHdcccdADXbGESmSSFZREQamNotpKH967/+KzfffDMdHR1xlyIH2r49uoiIiDQgjSRLw/rxj3/Mhz/8Ydra2rRRSD0677zoqHWSRUSkAWkkWRrWN77xDVKplCbriYiIyIxTSJaG9L3vfY8HHniA1tbWuEsRERGROUjtFtJQjDHcc889XHfddfT19WHb+neeiIiIzDwlDGkoGzZs4B3veAeJRIJkMhl3OSIiIjJHaSRZGsb4+DhXX3017e3t5PP5uMuR5/P2t8ddgYiIyLQpJEvD+O1vf0ulUqG9vT3uUmQqzj8/7gqcP812AAAZMUlEQVRERESmTe0W0hBKpRJf+MIX1IPcSJ58MrqIiIg0II0kS0P41a9+xaOPPkpfX1/cpchUXXxxdNQ6ySIi0oA0LCcN4Xvf+x7JZBLLsuIuRURERJqAQrI0hCeffFKbhoiIiMisUUiWurd582Y2b95MKpWKuxQRERFpEgrJUvfuv/9+LMtSq4WIiIjMGk3ck7r3y1/+smarWmzbto0//OEPGGNYuHAhy5cvr8l5mtLf/E3cFYiIiEybQrLUtTvvvJMf/vCHdHZ2zvhzG2N44IEHWLt2Lel0mp///Of09vbS0tIy4+dqSq96VdwViIiITJvaLaRujY+P85GPfIS2traaTNrbvXs32WyWbDaLbdvMnz+frVu3zvh5mtZDD0UXERGRBqSRZKlbt9xyC8PDw8yfP78mzz82Nva0yYCpVIpdu3bV5FxN6W1vi45aJ1lERBqQRpKlbn3nO9+htbV1Vs+pyYEiIiICCslSh3zfZ+PGjQwODpLL5Wp2nnQ6zfj4+OTt8fFxLTMnIiIigEKy1KGvf/3rDA0N1XwL6tbWVkZGRhgdHSUMQzZt2kRvb29NzykiIiKNQSFZ6srQ0BBf//rXcV235q0Ptm1z5JFH8utf/5oNGzbQ39+vlS1EREQE0MQ9qSPj4+NceOGFlMvlWesN7u3t1ehxrXzoQ3FXICIiMm0KyVI37r77bp566in6+/vjLkVmwplnxl2BiIjItKndQurCyMgIX/nKV2qyHrLE5L77oouIiEgD0kiyxG7Pnj18+9vfZuPGjWp9mEuuuCI6ap1kERFpQArJEqsNGzbwnve8B8/zyOfzWqdYRERE6oJCssTmoYce4tprryWfz5PP5+MuR0RERGSSepJl1gVBwO2338769evZu3evArKIiIjUHY0ky6wwxvCb3/yGb3/72/zhD39gx44dtLa21nRHPREREZHpUkiWWXHttddy2223kUwmyWaz9Pf3q/94rvvoR+OuQEREZNoUkqWmyuUy11xzDT/5yU/o6+vDcZy4S5LZcsopcVcgIiIybQrJUhPGGL7//e9z0003sW3bNubNm4dtqwW+qfzyl9FRYVlERBqQQrLMmKGhIX73u9/xyCOP8Ktf/Yr77ruPfD7PvHnz1FrRjD74weiodZJFRKQBKSTLC2KM4Ve/+hU33HADjz32GLZtEwQBqVSKvr4+jR6LiIhIQ1JIlmm57bbb+Na3vsWmTZsol8tks1l6enoUikVERGROUEiWgyqXy2zatIktW7awZcsW/vznP/Nf//Vf3HfffbS0tNDe3q7JeCIiIjLnKCTL0xhj2Lp1K3/84x+59dZbuffeewGwLAvf97Esi2QyqVYKERERmdMUkpuc7/s88sgj3HHHHfz617/mySefxBiDZVlYlkVHR4dGimV6brgh7gpERESmTSG5iezdu5eHHnqIhx9+mNtvv53NmzczPDyMZVkEQUBra6tCscyc1avjrkBERGTaFJKbwODgID/+8Y+5+eabGR8fx/d9crkcqVSKXC6n5dmkNn760+h45pnx1iEiIjINCslz0COPPMIPfvADHn30UR577DF27NgBQGtrK93d3TFXJ03jIx+JjgrJIiLSgBSS54hyuTw5YnzjjTcShv+3vfuPk6qu9zj+WpZdluVHCigulmGaJmbaFS3T0q7S7Wpcf6fpIzF7lGkmZUqZ2adPZl4Tf6SYmnVdf5E/0JuiKKKEUFloGKbo1UQQEWT5ET9cApaZ+8f3OzAcZnZnBmZnd3w/H495zM4535nzOd9zduZzvud7vidFY2MjDQ0NDB48WK3FIiIiIkVQktzNpNNpZs6cyfTp0zcNzbZkyRJWrlxJXV0dGzduZMCAAdTX11c6VBEREZFuS0lyNzJ37lxuvPFGpk2bRm1tLfX19dTV1VFfX69bP4uIiIhsR0qSu5h0Os2yZctYvHgx8+fP55VXXuH1119nwYIFLF68GEBjFIuIiIiUmZLkLuTpp5/m4osvpq2tjdraWjZu3AhAr1696NWrl277LN3LLbdUOgIREZGSKUnuZBs3buTZZ5+lpaWFJUuWsGDBAubNm8fChQtZsWIF/fr1Y+DAgZUOU2Tb7b13pSMQEREpmZLkTrJy5UpmzZrFTTfdxOzZs+nfvz+pVIr6+nrq6+s33epZ/YqlakycGJ5HjqxsHCIiIiVQklwm6XSaNWvW0NLSwuTJk7n11lupr6+nZ8+e7Lnnnuo2IdXv6qvDs5JkERHphpQkFymVSrF27VrWrl1La2vrFn8vX76c+fPnM2fOHF566SXeffddevbsSSqVYuDAgTQ0NFQ6fBEREREpgJLkDqxbt44JEybw1FNPsWjRIpYtW0YqlaK2tnZTa3BNTQ3pdJq2tjbS6TQNDQ306dOHfv36VTh6ERERESmFkuQ8Vq9ezQMPPMA999zD0qVL6dOnD/X19ey0007qKiEiIiJS5ZQkA8uWLWPWrFlMnTqVuXPnsnTpUlasWEEqlWLAgAE0NTVVOkQRERER6URVmSSvXr2a1tZW1q9fv+mxYcOGrV7PnTuXhx56iJaWFmpqaqitraV3797U1dUxZMgQjTQhsi3uvLPSEYiIiJSsKpLk5cuXM2bMGBYuXMj69etZtWoVra2tpFIpUqkU6XQ659+pVIoddtiBxsbGTX2KV69eXenVEWDDhg2b7jDYHbW1tVU6hMr7wAcqHYGIiEjJqiJJXrduHYsWLaJHjx4MGTKE4cOH8wH9QHdrgwYNYtSoUZUOo2SDBw9m2LBhlQ6jsu69Nzyfckpl4xARESlBVSTJTU1NPProo5UOQ7ajadOmcfLJJ1c6jJJNmzaNXXfdtdJhVNZNN4VnJckiItINaZgGEREREZEEJckiIiIiIglKkkVEREREEpQki4iIiIgkVMWFeyLSBU2YUOkIRERESqYkWUTKY9CgSkcgIiJSMnW3EJHyaG4ODxERkW5ISbKIlIeSZBER6caUJIuIiIiIJChJFhERERFJUJIsIiIiIpKgJFlEREREJEFDwIlIeUyaVOkIRERESqYkWUTKo7Gx0hGIiIiUTN0tRKQ8fvnL8BAREemGlCSLSHncd194iIiIdENKkkVEREREEpQki4iIiIgkKEkWEREREUlQkiwiIiIiklCTTqfLv5CamseBQWVfkFSTQcDSSgexDbp7/N2d6r/ytA0qS/VfWar/ympIp9Mf3dYP6ZQkWaRYNTU1z6XT6eGVjqNU3T3+7k71X3naBpWl+q8s1X9lba/6V3cLEREREZEEJckiIiIiIglKkqWr+lWlA9hG3T3+7k71X3naBpWl+q8s1X9lbZf6V59kEREREZEEtSSLiIiIiCQoSRYRERERSehZ6QBEANy9FzAW+BLQG3gKONfM3mrnPZ8BLgQOBIYAXzGz5vJHC+5+LnAR0AS8BHzbzGa0U/5w4BpgX+Bt4OdmdnNnxFqNiql/dz8B+AbwcaABmANcbmYPd1K4VafY/T/rfYcB04BXzGybxzB9ryrh+6ce+CHwZcJ35TvAWDO7vhPCrUolbIPTgDHAXsAq4EngQjNb3AnhVo1SfvfdfT9gHHAwsBy4BbjMzDrsb6yWZOkqrgNOJCTJnwb6A4+4e2077+kLvAiMBtaWPcLI3U8BfgH8jJB4/Ql4zN13y1N+d2BSLPdx4ArgBnc/sXMiri7F1j9wODAVOCaWnwT8r7t/uhPCrTol1H/mfTsCdxAOgKVEJdb/b4HPA18H9gZOBl4oc6hVq4TfgEOBO4HbCQ0lxwHDgLs7JeDqUtTvvrv3B6YQDgwPAs4nHNxcUMjC1JIsFefu7wO+SjginBKnfRmYDxwFTM71PjObREh4cPfmTgk2uABoNrNb4+tvufvngXOAi3OU/wbwtpl9K75+2d0/QTgafqDs0VafourfzEYnJrm7H0P4oeqw9VO2Uuz+n/EbQpJQA5xU3hCrWlH17+6fI3yP7mFmmTvAzeuMQKtYsf8DhwBvmdm18fUb7n4DcEP5Q60uJfzunw40AqPMbC3worvvA1zg7td01JqslmTpCg4E6oAnMhPMbAHwMvCpSgWVSzxteSBZsUZPkD/WQ3KUnwwMd/e67RthdSux/nPpB6zYXnG9V5Ra//HU9C7AT8sXXfUrsf6PA54lJAVvuftr7n69u/ctY6hVq8Rt8Eegyd1HunuNuw8CTiUme1JWhwAzYoKcMZnQVWNoR29WkixdwS7ARra+z/07cV5XMgioJcSWrb1Yd8lTvmf8PClcKfW/BXf/JvB+wulPKU7R9R/7AxpwupltLG94Va+U/f9DwGHA/oQubecRul40lyfEqlf0NjCzZwhdCe8G1gMthDMqo8oXpkT5fn8z89ql7hZSNu7+U+CSDop9tp15NUBXHcg7GVdHseYqn2u6FKbY+gcg9gO/CjjVzOaXI7D3iILqP16Qew/hAqU3OiOw94hi9v8ecd5pZrYSwN3PAya7+2AzSyYQUpiCt4G7DwOuBy4jtGI2Eb6HbgHOKGOMEpT8+6skWcrpOuCuDsq8CXyScGQ+iHCEnbEzML08oZVsKaHVO3kEujNbH61mLM5Tvg1Ytl2jq36l1D+wKUG+EzhDI1uUrNj6byJcoHSbu98Wp/UAaty9DTjazJKnrSW/Uvb/RcDCTIIcvRyfd2vnfZJbKdvgYmCmmV0VX7/g7u8CM9z9kti9UMoj3+8vFLDvK0mWsokXiSS7UGzF3f8KbABGAOPjtPcD+xCuGu4yzGx9jHcEcH/WrBHkvwjvGUK/wGwjgOfMbMP2j7J6lVj/uPsXCReNjTKzCeWNsnqVUP8Lgf0S086N5Y9HF5AVpcT9/4/Aye7e18zWxGl7xWedTSlSidugkZBYZ8u8rkHK6RngSndvMLN/xWkjCEOxzuvozUqSpeLMbKW7/wa4yt2XEFpXryEMUfRkppy7vwKMM7Nx8XVfYM84uwewm7sfACw3szfLGPI1wJ3uPpPwA/QNwkUAN8e47ojrlTmNdjNwnrtfRzi9dihwJqGPmhSvqPp391MJLcgXAtPdPdOqsN7Mlndy7NWg4PqPB4EvZr85/o+vM7MtpkvBiv3+GQ9cSmjN/zGwA2H4sglmtqRzQ68axW6DicCt7n4Om7tbXAfMKvNvVdXp6Hff3a8ADjazI2OZ8YRrIppjF9C9gO8DrnGSpTv5DvAgcC/hS2cNMDJxoc/ebHmh23Dg+fjoDXj8+yflDNTM7gW+TRic/2+Ei2KOzurjult8ZMq/ARwNfCaWvwQ438w0/FsJiq1/wg9YT8KP0qKsx4OdFXM1KaH+ZTsq4ftnDWEIuPcRRrm4D3gaOKsTw64qJWyDZsKwcecRDhonAK8Bx3Ze1FWjo9/9JmCPTOHYzWgE4SDmOeBG4GrCgU6HatJpXTckIiIiIpJNLckiIiIiIglKkkVEREREEpQki4iIiIgkKEkWEREREUlQkiwiIiIikqAkWUREREQkQTcTEXmPcvehwBvAQWb2XCcutxkYZGZfyPN6GvCimZ3XWTFVM3dvBO4gjBXaH9gdWJJjWjMF1nul9p3tSfuZiHRESbJIFXL3jgZAvx34cSeEkstousmtWHMlUu5+BPB7YKd46/Wu7izCjWwOA1ri45wc004g3B6+EAsIg/Zv1/VX4ioiXYmSZJHq1JT19xeAWxPT1gI7dmpEUbwDknSePYGXzezvmQnuvtU0oOBbdMc7YS7efiGKiHQ9uuOeSJVz95OA+82sJjF9KOGU+UmEWzcfCswDRpvZlKxyw4CrCC2Pa4GngO+YWd4kyd1/BHwV2AVYATxhZmfEec103N1iDvBP4OtAitA1YIyZpWKZHQm3mf4voIFwK/PRZvZSnH8mMM7M+mbFdASJFmB3/xRwBXBQjPNh4HtmtirGNSqxarvHOst2u5md6e41wEXA2YRboP4DuNLM7spXTzGGUcCFwF5xnR8zszPjvN2AXxBuKwwwhXBL87ey3j+ScFZgX8LttscDbmbrY10enrW4p+PzFtPM7IhkK66718fPPZ2wHRcC15nZ9bm6W3S0n2S2c1yHMUAj8Dvgm2bWmq++zWxeor7OJtyCdlcza8uaPh7oY2bHuvsehNvOfgLoB/wf8CMzeySrfHJ95xH2mbHtlKkHLot1siNhP/2hmU2O8+sIt7w9CRhI6NZyt5l9HxHpdnThnohcDlwP7A88C9zj7n0B3L0JmA68CBxMSNb6Ag+7e87vD3c/kZD0nQt8mNCSPbPImE4H2oBPAecB3wZOyZrfTEiAjo1xtQKPu3vvQhfg7vsBTxAS4/0J3Q0OAP4nFhkNPAPcRmiFbyJ0Mzgxzt83ThsdX/+UcGDwTWAYIfm+xd2PaSeGs4Fb4jI+BhwNZBL9GkISORj4d+CzhOT7d3Ee7v4fwN3AuBjPWYQE7WdxESfEz34mxnpCnmm53A6cAVwA7BPX7Z951qPQ/eTTwEfj/FOA49lcf/nqO+k+YAc2Hzjg7n0I+0LmgKQv8Bihz/X+wAPAg+7+kTzrWqjbCAcYpwH7EepoorvvH+efH9fpVMK+fwohQReRbkjdLUTkWjObCODuPyAkRgcAfyD0XZ1tZt/LFHb3Mwin5oeTO/n9IKFF8wkz2wC8CRR7cdccM/tR/PtVd/8acCTwW3f/MKEF+XAzmx5j+nJczunArwtcxkXAvWZ2dda6nQM87+47m9kSd18PtGa3mrt7plvCkqwW6T6EZPJzZjYjzn/D3Q8mJM2P5onhUkLr7DVZ0/4an48iJHh7ZFpT3f00Qgv1kcCTwCXAVWZ2W3zP6+7+PeAud7/IzJa7eyuwPrEOW03LFuv4VOA/zezxOHlunnWAwveTVcA5sQX4ZXe/P67LFWa2Mld9J5nZCnefRNjWmdiOJxxUTYxlZgOzs952eWxxP4lwMFO02Dr9JWComb0ZJ49z96MIZw/OJez7rwIzzCxN2Cf/VMryRKTylCSLyAtZf78dn3eOzwcCn3H3NTnetwe5k+T7Ca2Cb7j7ZEIi87CZrSsxpkxcmZj2IXTBeCYzMyZYfye04BbqQGBPd89uoc50SdmDcKq8UMMI3T4eT1w0WUfowrIVd98Z2JXQLSGXfYC3s7sbmNlcd387Lu/JuA4Hx8Q4owfQm9BFYlER65Dt44Q6/n2B5QvdT+Zkd5EgbNdPlBDfXUCzuzeaWSshYZ5gZv+CTQctRjiL0UTYDg1svV8V498I+8ccd8+e3guYGv9uJnQnedXdnwAmEbrPpLZhuSJSIepuISKbRjSIrV+w+buhB6EV9IDE48PAI+RgZguAvQmta6sIfTT/GhOXomOK0lkxtTcyRib+VI5ydYnXPQitztnrtT9h3f5WRKyZzwIYmfi8fYHP5XlPRyN81LB5fZKyt5Mnlvkxwjq0FBJ4ibElFbqftLddi/EIoeX42HiwcRSbu1oAjAVOJrTUHx5jmQnUt/OZHe0zPWK8B7HlOu5D6OaCmc0ChgI/iOVvB6bk65okIl2bWpJFpD2zgC8C82PXiYLEFr1HgUfd/b8JIyEcSugDvK3mEBKQQwj9YHH3/oQ+opluBy1Ao7v3N7NVcdoBic+ZBexrZv9oZ1nrgdoc00hMnwOsAz5oZlMpgJm94+4LCd0NpuQoMgfY1d2HZnW3+BChX/KcrHX4SAfrUIpZhDr+LJu7NHRUvuj9JIdc9b0VM1vn7hMILciDCPvX01lFDgPuMLMHANy9gdCi/Wo7H9tC1ggw8T0fAZ6Pk54nJNG7mFneFnYzW004m3J/vBjxz4QRRtpbtoh0QUqSRaQ9NwJfA+519ysJicSHCAnRd2NCsIU4skRP4C/AGsLFSxuA17ZHQGb2mrs/RLgo7uuEi8kuJ7Raj4/F/gK8C1zh7tcSWojPTXzUlcCf3f1mwsVzqwlJ0UgzOzuWmUfozjA0rstyYD6hRfEYd58IrDWz1e4+FhgbL6qbTrh47JNAysx+lWd1Lgeudfd3CAcVjcCRsZ/0k4R+tXe7+/mEBO0GQkKaScR/Ajzi7vMJF7S1ES6MO9jMxhRSn7nEOr4P+LW7j47LfD+hP+6dOd5S9H6SxzwS9d1OV4W7CHW0OzA+Ue5V4Pi4n2wgdL1o6GDZU4Gz3P3hGP8lZLUkm9mr7n43oZvHdwl1MgA4AphrZg+6+wWELi5/i8s9jbBfvoWIdDs6BSQieZnZ24QW4BShRfElQkK0Lj5y+SdhJIQZhNEOTgROMLPk0Gnb4iuE0+cPx+dG4PNmtjbGvZzQyjgC+DthKLlLE+v2AmG4sqGEVsjZhBEp3skqNpbQujmHkDjtZmYLCUnX5bHsuFj2UsKQaRcS6mkKYd3zrreZ3US4sO9rhLp6nNBFI9P15bi43GmE/sGLgeMy3WLi0GPHEFp8Z8bH9wkXjG2rMwgHHdcDrxD6274vz3qUsp/kslV9t1N2OmFYumFs2dUCwkWUSwj74GOE1twZtO8KQqL8EOGMxx8IiXC2rxDOVvycUCePEPah+XH+asIFoTPjew8gXPzY2sGyRaQL0jjJIiIiIiIJakkWEREREUlQkiwiIiIikqAkWUREREQkQUmyiIiIiEiCkmQRERERkQQlySIiIiIiCUqSRUREREQSlCSLiIiIiCQoSRYRERERSfh/YSBLTdjrZ1UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the SILHOUETTE score\n",
    "silhouette_avg = metrics.silhouette_score(lda_csc_matrix, km.labels_)\n",
    "print('Silhouette Coefficient:', silhouette_avg)\n",
    "\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "# Compute the silhouette scores for each sample\n",
    "sample_silhouette_values = metrics.silhouette_samples(lda_csc_matrix, km.labels_)\n",
    "\n",
    "y_lower = 10\n",
    "for i in range(n_clusters):\n",
    "    fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "    fig_size[0] = 12\n",
    "    fig_size[1] = 9\n",
    "    plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "    # Aggregate the silhouette scores for samples belonging to\n",
    "    # cluster i, and sort them\n",
    "    ith_cluster_silhouette_values = \\\n",
    "        sample_silhouette_values[km.labels_ == i]\n",
    "\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "\n",
    "    color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "    plt.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                      0, ith_cluster_silhouette_values,\n",
    "                      facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "    # Label the silhouette plots with their cluster numbers at the middle\n",
    "    plt.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "    # Compute the new y_lower for next plot\n",
    "    y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "plt.title(\"The silhouette plot for the various clusters.\", fontsize= 24, color='gray')\n",
    "plt.xlabel(\"The silhouette coefficient values\", color='gray', fontsize=14)\n",
    "plt.ylabel(\"Cluster label\", color='gray', fontsize=14)\n",
    "## Plot Swagg ##\n",
    "plt.yticks(fontsize=14, rotation=0, color='gray')\n",
    "plt.xticks(fontsize=14, rotation=0, color='gray')\n",
    "# plt.gca().get_legend().remove()\n",
    "\n",
    "# Less border\n",
    "plt.gca().xaxis.grid(True)\n",
    "plt.gca().yaxis.grid(True)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['left'].set_visible(False)\n",
    "\n",
    "# The vertical line for average silhouette score of all the values\n",
    "plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "plt.yticks([])  # Clear the yaxis labels / ticks\n",
    "plt.xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "\n",
    "plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "              \"with n_clusters = %d\" % n_clusters),\n",
    "             fontsize=14, fontweight='bold', color='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Labels the KMeans\n",
    "Take the first 5 LDA's toplic from each cluster, then nomalize the words weight of those topics by the topic weight.  \n",
    "Then take the fist 6 most relevents words in a cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class topic_label():\n",
    "    def __init__(self, topic_id, value, lda_model_words):\n",
    "        self.topic_id = topic_id\n",
    "        self.value = value\n",
    "        self.words = lda_model_words[topic_id]\n",
    "    def __repr__(self):\n",
    "        #return \"{}\".format(\" \".join([word[0] for word in self.words[1]]))\n",
    "        return '{}: {} {} {}'.format(self.__class__.__name__,\n",
    "                                  self.topic_id,\n",
    "                                  self.value,\n",
    "                                  self.words\n",
    "                                    )\n",
    "    def __cmp__(self, other):\n",
    "        if hasattr(other, 'topic'):\n",
    "            return self.topic_id.__cmp__(other.topic_id)\n",
    "\n",
    "labels = {}\n",
    "lda_words = lda_model.show_topics(-1, formatted=False)\n",
    "for cluster in range(n_clusters):\n",
    "    values_dence = lda_csc_matrix[np.where(df['pred'] == cluster)]\n",
    "    coo = values_dence.tocoo()\n",
    "    \n",
    "    maximize = []\n",
    "    for row, col in zip(coo.row, coo.col):\n",
    "        label = topic_label(col, values_dence.toarray()[row][col], lda_words)\n",
    "\n",
    "        _filter = True\n",
    "\n",
    "        for la in maximize:\n",
    "            if la.topic_id == col and la.value <= label.value:\n",
    "                _filter = True\n",
    "                maximize.remove(la)\n",
    "                break\n",
    "\n",
    "            if la.topic_id == col and la.value > label.value:\n",
    "                _filter = False\n",
    "                \n",
    "        if _filter:\n",
    "            maximize.append(label)\n",
    "\n",
    "    first5_label = (sorted(maximize, key=lambda x: x.value, reverse=True))[:5]\n",
    "    \n",
    "    df_label = pd.DataFrame([[label.value, label.topic_id, *[word for word in label.words[1]]] for label in first5_label])\n",
    "    label_weight = df_label[0]\n",
    "    label_sorted = []\n",
    "    for row_index, row in df_label.iterrows():\n",
    "        for col_index in range(10):\n",
    "            col_index += 2\n",
    "            label_sorted.append((row[col_index][0], label_weight[row_index] * row[col_index][1]))\n",
    "    \n",
    "    sorted_words = sorted(label_sorted, key=lambda x: x[1], reverse=True)[:6]\n",
    "    labels[cluster] = [w for (w, weight) in sorted_words]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of idea over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year sampling, (-> ['2004 to 2006', '2007 to 2009', .. ])\n",
    "NUM_OF_SEP = 5 # number of partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th colspan=\"9\" halign=\"left\">2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2006</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>pred</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>8</th>\n",
       "      <th>9</th>\n",
       "      <th>0</th>\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>pred</th>\n",
       "      <th>count</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year       2004                            2005 ... 2006                               \n",
       "pred          0  1   2   3  4   5  6  8  9    0 ...    0  1   2   3  4   5  6  7  8   9\n",
       "pred count   18  2  13  15  8  15  2  1  8   17 ...   23  5  11  18  6  14  4  3  2  17\n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([2004, 2007, 2011, 2014, 2018], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2004 - 2007</th>\n",
       "      <th>2007 - 2011</th>\n",
       "      <th>2011 - 2014</th>\n",
       "      <th>2014 - 2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.264228</td>\n",
       "      <td>0.382114</td>\n",
       "      <td>0.191057</td>\n",
       "      <td>0.162602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183486</td>\n",
       "      <td>0.339450</td>\n",
       "      <td>0.275229</td>\n",
       "      <td>0.201835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.340741</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.140741</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242678</td>\n",
       "      <td>0.313808</td>\n",
       "      <td>0.171548</td>\n",
       "      <td>0.271967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.280992</td>\n",
       "      <td>0.371901</td>\n",
       "      <td>0.140496</td>\n",
       "      <td>0.206612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.245955</td>\n",
       "      <td>0.320388</td>\n",
       "      <td>0.197411</td>\n",
       "      <td>0.236246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.292683</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.292683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.154676</td>\n",
       "      <td>0.255396</td>\n",
       "      <td>0.258993</td>\n",
       "      <td>0.330935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2004 - 2007  2007 - 2011  2011 - 2014  2014 - 2018\n",
       "0     0.264228     0.382114     0.191057     0.162602\n",
       "1     0.183486     0.339450     0.275229     0.201835\n",
       "2     0.340741     0.296296     0.140741     0.222222\n",
       "3     0.242678     0.313808     0.171548     0.271967\n",
       "4     0.280992     0.371901     0.140496     0.206612\n",
       "5     0.245955     0.320388     0.197411     0.236246\n",
       "6     0.433333     0.300000     0.066667     0.200000\n",
       "7     0.219512     0.292683     0.195122     0.292683\n",
       "8     0.075000     0.250000     0.275000     0.400000\n",
       "9     0.154676     0.255396     0.258993     0.330935"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the year, while keeping the prediction dissimilarity AND count the number of article for onw prediction\n",
    "A = df.groupby(['year', 'pred']).agg({'pred': ['count']})\n",
    "\n",
    "# Result is MultiIndex dataframe\n",
    "display(A.query('year <= 2006').T)\n",
    "\n",
    "df_label_count_sampled_year = pd.DataFrame(index=[x for x in range(n_clusters)])\n",
    "\n",
    "start = pd.Timestamp('2004')\n",
    "end = pd.Timestamp('2018')\n",
    "ye_sampling = np.linspace(start.value, end.value, NUM_OF_SEP)\n",
    "ye_sampling = pd.to_datetime(ye_sampling).year\n",
    "display(ye_sampling)\n",
    "\n",
    "# For each year's partitions\n",
    "for start, end in zip(ye_sampling[:-1], ye_sampling[1:]):\n",
    "    \n",
    "    # query the current partition into the df\n",
    "    _splited_df = A.query('year >= '+ str(start) + ' & year < ' + str(end+1)) \n",
    "\n",
    "    # label conter for each year of the partition\n",
    "    from collections import defaultdict\n",
    "    class_count = {}\n",
    "    class_count = defaultdict(lambda:0, class_count)\n",
    "\n",
    "    for ((year, label_id, a), count_obj) in _splited_df.stack().iterrows():\n",
    "        class_count[label_id] += count_obj[0]\n",
    "        # VERBOSE\n",
    "        # print(\"Year {} has a label {:3} documents related to the label id: {:3}\".format(year, count_obj[0], label_id ))\n",
    "\n",
    "    # total of document for each label of the current partition\n",
    "    class_count = dict(class_count)\n",
    "    # VERBOSE\n",
    "    # display(class_count)\n",
    "\n",
    "    df_label_count_sampled_year[\"{} - {}\".format(start, end)] = pd.Series(class_count)\n",
    "\n",
    "# To percent\n",
    "df_label_count_sampled_year = df_label_count_sampled_year.apply(lambda x: x / x.sum(), axis=1)\n",
    "\n",
    "display(df_label_count_sampled_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Plot\n",
    "df_label_count_sampled_year.plot.barh(figsize=(20, 30), width=0.9)\n",
    "plt.title('Time / Cluster (Topic) influence', fontsize= 24, color='gray')\n",
    "\n",
    "## PlotSwagg ##\n",
    "plt.yticks(fontsize=14, rotation=0, color='gray')\n",
    "plt.xticks(fontsize=14, rotation=0, color='gray')\n",
    "\n",
    "# Cleanest Percent\n",
    "plt.gca().xaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:.0%}'.format(x))) \n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda y, _: 'Label:{}\\n\\n {}'.format(str(y), \"\\n\".join(labels[y]))))\n",
    "\n",
    "# Less border\n",
    "plt.gca().xaxis.grid(True)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.gca().spines['left'].set_edgecolor('gray')\n",
    "plt.gca().spines['right'].set_edgecolor('gray')\n",
    "plt.legend(loc=2, prop={'size': 20}, bbox_to_anchor=(0.5, -0.1))\n",
    "\n",
    "# Percent line under the barH\n",
    "plt.gca().set_axisbelow(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dump \n",
    "if False:\n",
    "    lda_model.save('LDA_model.gensim')\n",
    "    id2word.save('dictionary.gensim')\n",
    "    pickle.dump(bow_corpus, open('corpus.pkl', 'wb'))"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}