0102_GENSIM_LDA_FINE_TUNING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>publication</th>\n",
       "      <th>author</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>url</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17283</td>\n",
       "      <td>House Republicans Fret About Winning Their Hea...</td>\n",
       "      <td>New York Times</td>\n",
       "      <td>Carl Hulse</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON  —   Congressional Republicans have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17284</td>\n",
       "      <td>Rift Between Officers and Residents as Killing...</td>\n",
       "      <td>New York Times</td>\n",
       "      <td>Benjamin Mueller and Al Baker</td>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>After the bullet shells get counted, the blood...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>17285</td>\n",
       "      <td>Tyrus Wong, ‘Bambi’ Artist Thwarted by Racial ...</td>\n",
       "      <td>New York Times</td>\n",
       "      <td>Margalit Fox</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>When Walt Disney’s “Bambi” opened in 1942, cri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17286</td>\n",
       "      <td>Among Deaths in 2016, a Heavy Toll in Pop Musi...</td>\n",
       "      <td>New York Times</td>\n",
       "      <td>William McDonald</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Death may be the great equalizer, but it isn’t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>17287</td>\n",
       "      <td>Kim Jong-un Says North Korea Is Preparing to T...</td>\n",
       "      <td>New York Times</td>\n",
       "      <td>Choe Sang-Hun</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SEOUL, South Korea  —   North Korea’s leader, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>53287</td>\n",
       "      <td>73465</td>\n",
       "      <td>Rex Tillerson Says Climate Change Is Real, but …</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Robinson Meyer</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As chairman and CEO of ExxonMobil, Rex Tillers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>53288</td>\n",
       "      <td>73466</td>\n",
       "      <td>The Biggest Intelligence Questions Raised by t...</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Amy Zegart</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I’ve spent nearly 20 years looking at intellig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>53289</td>\n",
       "      <td>73467</td>\n",
       "      <td>Trump Announces Plan That Does Little to Resol...</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Jeremy Venook</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Donald Trump will not be taking necessary st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>53290</td>\n",
       "      <td>73468</td>\n",
       "      <td>Dozens of For-Profit Colleges Could Soon Close</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Emily DeRuy</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dozens of   colleges could be forced to close ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>53291</td>\n",
       "      <td>73469</td>\n",
       "      <td>The Milky Way’s Stolen Stars</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Marina Koren</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The force of gravity can be described using a ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     id                                              title  \\\n",
       "0               0  17283  House Republicans Fret About Winning Their Hea...   \n",
       "1               1  17284  Rift Between Officers and Residents as Killing...   \n",
       "2               2  17285  Tyrus Wong, ‘Bambi’ Artist Thwarted by Racial ...   \n",
       "3               3  17286  Among Deaths in 2016, a Heavy Toll in Pop Musi...   \n",
       "4               4  17287  Kim Jong-un Says North Korea Is Preparing to T...   \n",
       "...           ...    ...                                                ...   \n",
       "49995       53287  73465   Rex Tillerson Says Climate Change Is Real, but …   \n",
       "49996       53288  73466  The Biggest Intelligence Questions Raised by t...   \n",
       "49997       53289  73467  Trump Announces Plan That Does Little to Resol...   \n",
       "49998       53290  73468    Dozens of For-Profit Colleges Could Soon Close    \n",
       "49999       53291  73469                       The Milky Way’s Stolen Stars   \n",
       "\n",
       "          publication                         author        date    year  \\\n",
       "0      New York Times                     Carl Hulse  2016-12-31  2016.0   \n",
       "1      New York Times  Benjamin Mueller and Al Baker  2017-06-19  2017.0   \n",
       "2      New York Times                   Margalit Fox  2017-01-06  2017.0   \n",
       "3      New York Times               William McDonald  2017-04-10  2017.0   \n",
       "4      New York Times                  Choe Sang-Hun  2017-01-02  2017.0   \n",
       "...               ...                            ...         ...     ...   \n",
       "49995        Atlantic                 Robinson Meyer  2017-01-11  2017.0   \n",
       "49996        Atlantic                     Amy Zegart  2017-01-11  2017.0   \n",
       "49997        Atlantic                  Jeremy Venook  2017-01-11  2017.0   \n",
       "49998        Atlantic                    Emily DeRuy  2017-01-11  2017.0   \n",
       "49999        Atlantic                   Marina Koren  2017-01-11  2017.0   \n",
       "\n",
       "       month  url                                            content  \n",
       "0       12.0  NaN  WASHINGTON  —   Congressional Republicans have...  \n",
       "1        6.0  NaN  After the bullet shells get counted, the blood...  \n",
       "2        1.0  NaN  When Walt Disney’s “Bambi” opened in 1942, cri...  \n",
       "3        4.0  NaN  Death may be the great equalizer, but it isn’t...  \n",
       "4        1.0  NaN  SEOUL, South Korea  —   North Korea’s leader, ...  \n",
       "...      ...  ...                                                ...  \n",
       "49995    1.0  NaN  As chairman and CEO of ExxonMobil, Rex Tillers...  \n",
       "49996    1.0  NaN  I’ve spent nearly 20 years looking at intellig...  \n",
       "49997    1.0  NaN    Donald Trump will not be taking necessary st...  \n",
       "49998    1.0  NaN  Dozens of   colleges could be forced to close ...  \n",
       "49999    1.0  NaN  The force of gravity can be described using a ...  \n",
       "\n",
       "[50000 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = pd.read_csv('data/articles1.csv')\n",
    "df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOAD DICTIONARY AND ALL LISTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"lists/bow_corpus.txt\", \"rb\") as fp:   # Unpickling\n",
    "    bow_corpus = pickle.load(fp)\n",
    "\n",
    "with open(\"lists/norm_corpus_bigrams.txt\", \"rb\") as fp:   # Unpickling\n",
    "    norm_corpus_bigrams = pickle.load(fp)\n",
    "\n",
    "with open(\"lists/norm_papers.txt\", \"rb\") as fp:   # Unpickling\n",
    "    norm_papers = pickle.load(fp)\n",
    "\n",
    "with open(\"lists/pre_papers.txt\", \"rb\") as fp:   # Unpickling\n",
    "    pre_papers = pickle.load(fp)\n",
    "\n",
    "with open(\"lists/pre_titles.txt\", \"rb\") as fp:   # Unpickling\n",
    "    pre_titles = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import gensim\n",
    "\n",
    "dictionary = gensim.corpora.Dictionary.load('models/dictionary.gensim')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA TUNING; FINDING THE OPTIMAL NUMBER OF TOPICS (SAVE ALL MODELS & COHERENCE SCORES)\n",
    "\n",
    "Finding the optimal number of topics in a topic model is tough, given that it is like a model hyperparameter that you always have to set before training the model. We can use an iterative approach and build several models with differing numbers of topics and select the one that has the highest coherence score. To implement this method, we build the following function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def topic_model_coherence_generator(corpus, texts, dictionary,\n",
    "                          start_topic_count=2, end_topic_count=10, step=1,\n",
    "                                    cpus=1):\n",
    "    models = []\n",
    "    coherence_scores = []\n",
    "    for topic_nums in tqdm(range(start_topic_count, end_topic_count+1, step)):\n",
    "        gensim_lda_model = gensim.models.LdaModel(\n",
    "                                              corpus=corpus,\n",
    "                                              num_topics=topic_nums,\n",
    "                                              id2word=dictionary,\n",
    "                                              chunksize=1740,\n",
    "                                              iterations=500,\n",
    "                                              alpha=\"auto\",\n",
    "                                              eta=\"auto\",\n",
    "                                              passes=20\n",
    "                                                )\n",
    "\n",
    "        cv_coherence_model_gensim_lda = gensim.models.CoherenceModel(model=gensim_lda_model,\n",
    "                                                    corpus=corpus,\n",
    "                                                    texts=texts,\n",
    "                                                    dictionary=dictionary,\n",
    "                                                    coherence='c_v')\n",
    "\n",
    "        coherence_score = cv_coherence_model_gensim_lda.get_coherence()\n",
    "        coherence_scores.append(coherence_score)\n",
    "        models.append(gensim_lda_model)\n",
    "        \n",
    "        ### saving each model\n",
    "        gensim_lda_model.save('models/gensim/model_'+str(topic_nums)+'.gensim')\n",
    "\n",
    "    return models, coherence_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [10:56:14<00:00, 1357.75s/it] \n"
     ]
    }
   ],
   "source": [
    "lda_models, coherence_scores = topic_model_coherence_generator(corpus=bow_corpus,\n",
    "                                                texts=norm_corpus_bigrams,\n",
    "                                                dictionary=dictionary,\n",
    "                                                start_topic_count=2,\n",
    "                                                end_topic_count=30, step=1,\n",
    "                                                cpus=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Topics</th>\n",
       "      <th>Coherence Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>0.5502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>0.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>0.5335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>0.5251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>0.5222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>0.5207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>0.5203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>0.5192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>0.5140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>0.5106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Number of Topics  Coherence Score\n",
       "27                29           0.5502\n",
       "26                28           0.5410\n",
       "21                23           0.5335\n",
       "23                25           0.5251\n",
       "28                30           0.5222\n",
       "17                19           0.5207\n",
       "15                17           0.5203\n",
       "24                26           0.5192\n",
       "22                24           0.5140\n",
       "19                21           0.5106"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence_df = pd.DataFrame({'Number of Topics': range(2, 31, 1),\n",
    "                        'Coherence Score': np.round(coherence_scores, 4)})\n",
    "\n",
    "coherence_df.sort_values(by=['Coherence Score'], ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save coherence score df and coherence score list \n",
    "coherence_df.to_csv('models/gensim_scores/coherence_df.csv', index=False)\n",
    "\n",
    "with open(\"models/gensim_scores/coherence_scores.txt\", \"wb\") as fp:   #Pickling\n",
    "    pickle.dump(coherence_scores, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VISUALIZE COHERENCE SCORES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAygAAAFzCAYAAADVIi3sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3hUZdoG8Ps50ycFZAFRkbKIBfXDsrKiLFhpKggWsCAooYgIgmVFQVcQlKJgAQQUKYpIURHFxqrYFVBURGkuShFESiCZPuf9/pgwzJm0STI1uX/XlcucZ065x0HMk3Pe9xWlFIiIiIiIiNKBluoARERERERER7BBISIiIiKitMEGhYiIiIiI0gYbFCIiIiIiShtsUIiIiIiIKG2YUx0gHvLz8zkVGRERERFRBqpVq5ZEbvMOChERERERpQ02KERERERElDbYoFDSbd68OdURKAn4OdcM/JxrBn7ONQM/55ohEz5nNihERERERJQ22KAQEREREVHaYINCRERERERpgw0KERERERGlDTYoRERERESUNtigEBERERFR2mCDQkREREREaYMNChERERERpY2kNSgi0lFENorIFhG5v4TX+4jIXhFZV/SVF/FaMKL+ZrIyExERERFRcpmTcRERMQGYCuByADsArBaRN5VSG6J2fVUpNbiEU7iVUmclOicRERERUcooFfrSavZDTsl6960AbFFK/aqU8gFYCKBrkq5NRERERJS2ZN8+2B59FDmnnoqcZs1gWbQo1ZFSKlkNygkAtkds7yiqRbtGRH4QkSUicmJE3S4ia0TkKxG5OqFJiYiIiIiSQHbuhH3ECOSceSbskyZB27MH2oEDcAwaBO3771MdL2VEKZX4i4hcB6CDUiqvaLsXgFZKqTsj9vkbgAKllFdEBgK4Xil1SdFrxyuldonI3wF8COBSpdTWI8fm5+eH38TmzZsT/n6IiIiIiCrLtn07Gsydi7+9/Ta0QKDEfdxNm2LD/PlQNluS0yVH8+bNw9/XqlVLIl9LyhgUhO6YRN4RaQhgV+QOSql9EZuzAIyPeG1X0T9/FZGPAZwNYCtKEPlmKT1t3ryZn1MNwM+5ZuDnXDPwc64Z+DknnrZ+PWyTJ8Py+usQXS9zX8f//oczFiyAZ9y4uGbIhM85WY94rQbQXESaiogVQE8Ahtm4ROS4iM0uAH4uqh8jIrai7+sCuBBA9OB6IiIiIqK0ZPrmGzh79EBOmzawLl1aYnOiN2iAQOvWhppt2jSYPvkkWTHTRlLuoCilAiIyGMB7AEwAZiulfhKR0QDWKKXeBDBERLoACADYD6BP0eGnAZghIjpCDdXjJcz+RURERESUPpSC+eOPYXviCZg/+6zU3YJNmsB7113w33AD4PUi58ILoW0/OnTbOWgQDn/+OVCrVjJSp4VkPeIFpdQKACuiag9FfD8CwIgSjvsCwJkJD0hEREREVFW6DvPbb8P25JMwf/ddqbsFTzsN3uHD4e/WDTAX/Uhus8E1fTqyrroKUjROXNuxA4777oN7xoxkpE8LNXuSZSIiIiKieAgEYFm4ENkXXICsXr1KbU4C556LwgULUPD55/Bfd93R5qRIsE0b+AYblwW0vvoqzMuWJSx6umGDQkRERERUWR4PrLNnI+ecc+AcOBCmX34pcbdAu3YoWLYMhStXItC5c5mLMXoefBDBFi0MNcewYZDdu+MaPV2xQSEiIiIiqqjDh2F95hnktGwJx/Dh0H7/vcTd/J07o2DlShQuW4Zgu3aASIn7GdjtcM2YAWWxhEva/v1w3HlnaKX5ao4NChERERFRjGT/ftgeeww5Z54Jx6hR0PbsKbaP0jT4rr8eh7/4Aq4FCxD8xz8qfB39zDPhefBBQ83ywQewzplT2egZI2mD5ImIiIiIMpXs2wfb5MmwvvgipLCwxH2U1QrfjTfCN3Qo9KZNq3xN3513wvLeezB/+WW4Zn/wQQTatoXerFmVz5+u2KAQEREREZVBDhxA1pVXwvTzzyW+rpxO+G69Fd477oA6/vj4Xdhkgmv6dOS0aQMpKAhlcbngGDgQhe+8U2yAfXXBR7yIiIiIiMpgnTq1xOZE1aoFz3334fD69fCMHRvf5uTINZo0gTtqNXnz6tWwTZkS92ulCzYoRERERESlOXwYtlmzDCW9fn24R4/GofXr4X3gAag6dRIawd+rF/wdOxpqtscfh7ZuXUKvmypsUIiIiIiISmGdMweSnx/e1o85BofXrIFvyBAgJyc5IUTgfvpp6HXrHi0FAnAOGAC43cnJkERsUIiIiIiISuL1wjZ1qqHk698fyM1NehRVvz7cTz1lqJk2boR99OikZ0k0NihERERERCWwvPoqtIjFEZXTCd+AASnLE7jiCvhuvtlQs02fDtOqVSlKlBhsUIiIiIiIogWDsEXdsfD17p3w8SblcY8bB71RI0PNOWgQcPBgihLFHxsUIiIiIqIo5uXLYdq6NbytzGZ477gjhYmK5ObC9dxzUBEr0ms7d8Jx330pDBVnSqmM/zp48KA68gWg2NeUKVPCr0+ZMqXEfY58RZ6rZcuWpe7Xu3fv8H4ff/xxmef8+OOPw/v27t271P1atmypynsvfE98T3xPfE98T3xPyX5Pp556arV7T9Xxc+J7iv976gcoVfT1ZceOaf2eFKAK5swp9z1dffXVafM5HfmK/tmed1CIiIiI4kT++CPVESgBlAh8PXumOka5HMOGQSLGzGQqUUqlOkOV5efnZ/6bqEE2b96M5s2bpzoGJRg/55qBn3PNwM+5dLZHH4V90qTwdvCkk1D41ltQDRqkMFXl8HM+Kuuqq2D+9NPwtv/KK+F66aUUJiqZtn49si+5BOLzhWv+yy6Da/FiIOIRsEjp+DnXqlXLEJZ3UIiIiIgqwbxihaE5AQDTli3IuuoqyJ49KUpFVWVas8bQnACAd9iwFKUpm37GGfCMHGmoWVauhHX27BQlig82KEREREQVpP36K5wDB5b4mmnzZjYpGcw2ZYphO9C2LYLnnpuiNOXz3XEHAq1bG2r2UaOgbdmSokRVxwaFiIiIqCJcLjh79YIcOlTqLqZNm5DVpQvkzz+TGIyqStu4EZa33jLU0vXuSZjJFJrVK2JVe3G54BgwAAgEUhis8tigEBEREcVKKTjuugumn34ylN3/+Q/8HToYaqaNG0N3UtikZIzodU+CLVsicNFFqQlTAapxY7gfe8xQM69dC9uTT6YoUdWwQSEiIiKKkfWFF2BdtMhQ83XvDt/QoXDNm1dyk9KlC2Tv3mTGpEqQHTtgifpsPcOHlzrYPN34b7oJ/s6dDTXbhAkwffddihJVHhsUIiIiohiYVq+GfcQIQy14yilwP/106IdYmy3UpLRvbzzul1/YpGQA27PPQiIeiQo2a4bAlVemMFEFicD91FPQ69U7WgoEQo96ud0pDFZxbFCIiIiIyiF798LZuzfE7w/XVHY2XPPnA9nZR3c80qRcfrnheNPPPyOra1fIX38lKzJVgOzbB+u8eYaad+hQwGRKUaLKUfXqhRrmCKZNm2D/z39SE6iS2KAQERERlSUQgPO226Dt2mUou6ZOhX7yycX3t9vhmj8f/ssuM5RNGzaE7qSwSUk71pkzIS5XeFs/7jj4e/RIYaLKC3TqBN8ttxhqthkzYPr449QEqgQ2KERERERlsD36aPF1MYYMQaBr19IPstvheukl+C+91FAONyn79iUiaqVp69ej7muvwbR2LaDrqY6TXAUFsM6YYSh5Bw0CbLYUBao699ix0Bs3NtScgwYBBw+mKFHFsEEhIiIiKoV5+XLYo9fFaNMGnoceKv9gux2ul1+G/5JLDOV0alJk5044+vdHTps2aPLYY8i+9FLknHYaHEOHwvzuuxk3dqEyrHPnQov4wV3VqgVfnz6pCxQPOTmhqYcjBvhru3bBce+9KQwVOzYoRERERCXQtmwJ/dY5gn7ccXDNng2YzbGd5EiTcvHFhrLpp59CY1L2749X3IpxuWB7/HHk/OMfxWYl0/bsgXXuXGT17Incv/8dzhtvhGX+/Oo5XbLPB9vUqYaSt18/IGJNkUwVbN0a3rvuMtSsixfjmPffT1Gi2LFBISIiIopWUBBajPHw4XBJmc1wzZkDVb9+xc7lcMC1YAH8UetpmNavT36TohQsixcj57zzYH/8cUg5d0jE7YZlxQo477wTOaecgqz27WGbPBnaL78ASiUpdOJYXn3VMLZIORzwDRyYwkTx5R0xAsEzzjDUGo8fD4kaT5Vu2KAQERERRVIKjqFDYfr5Z0PZM24cgv/8Z+XOWdSkBNq1M5RNP/6IrKuvhhw4UNm0MTOtXYusDh3g7NcP2s6dxV73nHACVOSMZFFEKZi/+Qb2Rx5BzvnnI/ucc2B/4AGYPv00M1csDwZhi5rxyterF1TduikKlABWK1wzZ0JZreGS+dAhOO68M60bTDYoRERERBGsM2bAunSpoea7/nr4+vWr2omdThS+8goCbdsayqYffgjdSUlQkyK7dsExYACyL70U5m++Kfa6XrcuXFOmYP3SpTi0dSsKly6FNy8P+gknlHle0//+B9u0aci+6irknHQSHP37w/L668ChQwl5H/FmfvttmDZvDm8rsxnewYNTmCgx9BYt4Bk16ui22YxgmzZpPRkCGxQiIqqx5PffYVq9Oq3/R10W7ccfkXXFFcjq2BGmr75KdZxqwfTVV7CPHGmoBVu0gHvy5PisKO50onDhQgT+9S/jdX/4If53Utxu2CZMCI0zefXVYi8riwXeO+/E4bVr4e/TJ7Tmh82GwKWXwjNpEg6vX4/Dq1bBc//9CLZsWealtIMHYV20CM5bb0Vus2ZwdusWmrr399/j937iSSnYJk82lPzXXgvVqFGKAiWW7447ELjwQgRPOw0/z5kD77Bhab3Gi6g0vr0Tq/z8/Mx/EzXI5s2b0bx581THoATj51wzZPLnbHntNTjy8iC6Dn+HDnC98gqgZdDv7Xw+5JxzDrQdOwAAym5H4dKlCF54Ydwvlcmfc0XInj3IbtcO2u7d4ZrKzUXBRx9Bb9YsvhcrLERWjx4wf/aZoRw46ywUvvEGULt25c+tFCyvvw77Qw+F/3xE83fuDM+YMYb3Vd7nLDt3wvLuuzC/8w7Mn3wC8fliihM84wz4O3VCoHPnUKOTBv+dmVatQnbUNNGHv/wS+mmnpShR4snevVA5Odi8fXva/fdcq1YtQ/ef+j8hRERESSYHDsA+fDik6M6J5b33YP7vf1OcqmIsb79t+OFTPB5k9ewJ03ffpTBVBvP74bz1VkNzAgCu6dPj35wAQFYWCl99FYGohtK8bh2yunWr9HoVpu++Q1anTqGFJUtoToItWqBg2TK4Fiyo8PtSJ5wAX9++cC1ZEnoUbO5c+Hr2hF6nTtmZ1q+HfeJEZF98MbL/+U+Y1qyp0HUTodjdk06dqnVzAoRWmYfdnuoYMWGDQkRENY5twgTDugcAYH3++RSlqRzrCy8Uq8nhw3Bec01ohiWqEPsjj8D8xReGmmf4cASuuCJxF83KQuGiRQhccIGhbP7uuwo3KfLHH3AMGoTsiy+GuYTH/fS//Q3uJ59EwSefIBg1UL9ScnIQ6NoV7ueew+FNm1CwYgW8d96JYDlNj2nzZmRdfTVMX39d9QyVZPruO1iiVlX3Dh+emjBUIjYoRERUo2hbt5bYjJjffx+ybVvyA1WCtnFjsUeDwq/t3x8ay5Ah7yUdmN94A7ZnnzXUAu3awfvgg4m/+JEmpXVrY6bvvkNW9+5Afn7Zx7vdsD3xRGicyYIFxV5WZjO8gwbh8Nq18N12W+zrt1SE2YzgBRfAM2YMCtauxeHVq+EePRqB1q2hSnicSwoKkHXttTCVMGA/GaLvngQuvBDB885LSRYqGRsUIiKqUewPPwzx+4vVRSlY58xJfqBKiL57oqIWldN270Z2165pv9ZBOtA2boTzjjsMNf2EE+B64YXkDSLOzkbh4sXFm5Rvvy29SVEK5jfeQE6rVrCPGQMpLCy2i79DBxR89RU848ZVbUxLBenNm8M3ZAgK33kHhzdtgmvaNPg7djTsI4cPI+uaa0KTVCSRtnkzzMuXG2reYcOSmoHKxwaFiIhqDNPnn8Py1lulvm6dNw/weJKYqBIKC2FduNBQcj/5ZGj16wjab78hq1s3yL59yUyXWQ4fDi3GGPHDvbJa4Zo3L/lrYWRnh+6knH++oWxeuxZZ11xjaFK0deuQ1bkzsvr0gbZ9e7FTBU89FYWvvQbXq69CP+mkhEcvi6pbF/4bb4Rr4UJ4RowwvBZuUpI4JsX29NOQiAmigmeeicCllybt+hQbNihERFQz6Hrx6WNbtoSqVSu8re3fD8sbbyQ7WYVYliyBRKwzodetC3+XLvCMHw9fjx6GfU0bN8IZ9cMtFVEKzsGDYdq0yVD2PP44gueem5pMOTmhOynRTcqaNci69lpoW7bAMXhwaJzJl18WO1w/5hi4J05EwWefIXDJJclKHTPvv/8Nz/33G2py6BCyuneHae3ahF9fdu6EJaq59w4bFp/poymu2KAQEVGNYFm8GOaoGa7cjz8O3003GWppPVheKdii8vluuQWw2QBNg3vqVPijBnWb161DVs+egMuVzKRpzzp1KizLlhlqvhtugO/WW1OUqMiRJiVqxXrz6tWhcSYvvWS4AwAUjTMZOBAF334bWkwyEeNM4sR7//3w/PvfhpocOoSsbt1g+vbbhF7bNm2a4fHOYNOm8HfpktBrUuWwQSEiourP5YJ99GhDyd+lC4KtW8PXt6+hbl6zBtq6dclMFzPTmjUw/fhjeFuJwNe799EdzGa4Zs+G/6KLDMeZv/wSzltuAWJct6K6M33+OewPP2yoBc84A+4nnkiP36YfaVJatSp3V3/79ij44gt4Hn8c6phjkhCu6rz33w/PvfcaanLoUGh2rwQ1KXLgQLExZt6hQ9O6mavJ2KAQEVG1Z5s2DdrOneFtZbHA88gjAAC9WTP4ox6Hib5LkS6iB8cH2reHatzYuJPNBtfLLxf74dayciWc/foBgUCiY6Y1+eMPOG+9FRIMhmuqVi245s8HnM4UJouSm4vCJUsQKGV2qeDJJ6NwyRK4Fi2CfvLJSQ5XRSLwPvAAPPfcYywfaVISsJaPdeZMw1gj/dhj4e/ZM+7Xofhgg0JERNWa7NkD25Qphpqvf3/oTZse3c7LM7xuWbKk0gvlJYrs3w/L668batF3f8KKpq4NnnGGoWxZtgyOoUOBogUqaxyfD84+faD9+aeh7Jo50/DnIW2U0KTotWvD/fjjKPj8cwQuuyyF4apIBN4HHyy1SYnrXczCQlhnzDCUvIMGZcyihTURGxQiIqrWbI89BikoCG/rxxxT7PGSQIcO0Bs2DG+LxwPrSy8lLWMsLC+/DPF6w9t648Zlzz5UuzYKX38dwahZnKwvvwz7Aw8AUeMYUio/PzQlcoLv7thHjYI5aoFAz733ItChQ0KvWyW1aqHw7bfhmjYNrqlTQ+NMBg4ELJZUJ6u6I01K1CKJkp8f1ybFOn8+tP37w9sqNzf1Y42oTHzwjoiIqi1tw4bQ1MERvP/+d/E1IUwm+G67zTBOxTp7NnyDBgElLDSXdLoO6+zZhpL31lvLXadD1auHwjfeQHbHjtB27AjXbc89B5WbC+8DDyQkbqy0detgHz0alg8/BBAaU6Pq14dq0AB6gwbQjzsu9P2RfzZoAHXccaEpgCv4uViWLIEt6rfo/ksvhTdqVqm0ZLXCf+ONqU6RGCLwjhoFKAV7xAKK2sGDyLr6ahQuWwa9ZcvKn9/nK7YIp7dfPyA3t/LnpIRjg0JERNWWfdQoSMTjTMFmzUKraZfA16sXbI8/DikaSG769VeYP/ooLdZIMH/0EUz/+194W1mt8N98c0zHqoYNUbhsGbI6dTI82mSfMCH0m+TBg+OetzyybRvsjz4K65IlxrpSkD17gD17YPr++1KPVyYT1LHHhhqWEhqYcCNTpw4gAm3DBjiGDDGcQz/xRLhnzUreYoxUOhF4H3oo1KREPI5paFL+7/8qdWrLkiWG5lzZ7fANGFDlyJRYbFCIiKhaMq9cCct//2uoeUaPBqzWEvdX9erBf/XVsC5aFK5ZZ81KiwYlenC8/+qrK7SQoN6sGQpfew3ZV1wBiVgTxTFyJFRuLvy33BK3rGWRvXthmzgR1hdfNEz3WuHzBIOQXbug7dpV5n7KaoU69ligsBASMc2ystlCizHWqVPpDBRnIvA+/DBE12F7+ulwWTtwAFldu1auSdF12J56ylDy3XwzVP368UhMCZQG962JiIjiLBCAfdQoY+nCCxHo3LnMw4pNOfzee5Dffot7vIqQ7dthfvddQ620u0Bl0c84A4VLlkBlZRnqjqFDYXnttSplLFdBAWzjxyPn7LNhmzmzxOZEJeCRG/H5oG3fbhh/AADuiRMRPPvsuF+PqkgEnkcegffOOw3lI02KFjHFdizMK1bAtHFjeFuZTPCm4I4hVRzvoBARUbVjeeklmH7+2VBzjx1b7hoXwVatEDzzzPBaI6IUrHPmwBu1ZkYyWefONT6mdvrpCEYt4her4HnnoXDBAmRdf314wL0oBUf//lBZWfEfLO73wzp3LmwTJhSbOSuc6fTT4XnkkdCdKp8PsmcPtD17IH/8AW33bsju3dD++CP0z927Q/UqzLDm69UraXeMqBJEQnc6lTKMHQnfSXnzTehRs9OVSCnYIsa0AID/mmugmjSJc2BKBDYoRERUvRw6BPvYsYaSr2dP6GedVf6xIvDm5cE5dGi4ZJ03LzSQ2maLd9Ly+XzFBvn7+vat0mKCwXbt4HrxRTh79QqvBSKBAJy9e6NwyRIE27SpUmQAgFKwvPEGbGPGwPTrryXuop94IjwjR8J/3XVHB7zbbFCNGiHYqFHZ53e7Q43MkSYmspmJ/OehQ4bDAm3awD1xYtXfHyWWCDxjxoQe0Zo2LVzW9u8/2qScfnqZpzB99hnMa9caat6I/64pvbFBISKiasX21FPQ9u4NbyuHA56ox73K4r/2WqhRo8I/3Gr79sHyxhvw9+gR96zlsbz9tuHOg8rJge+666p83kDnznA/9xwc/ftDiqYbFo8HWT17ovDNNxE855xKn9v0ySewP/wwzKUstqfXqQPvPfeEGq3KNn0OB1STJgg2aYJgWfsVFITvxsBsRvDcc6vH9Lw1gQg8Y8eG7oRMnx4ua/v2IatLFxQuXw69RYtSDy9296RDh3KbGkofHINCRETVhmzfDtvUqYaad/BgqBNOiP0kWVnwRU3pGj1IPVmsUSva+3r0AHJy4nJu/3XXwfPkk4aaFBTAec010DZsqPD5tB9/hPPaa5HdpUuJzYlyOOC55x4c/u670PTNybgjlZ0NvVkzBNu0QfD889mcZBoReMaNg3fgQEP5SJNS2p9Tbd268NTVR3iHDUtYTIo/NihERFRt2MeMgXg84W392GMr9VhHscHy33wDrYxpbxNB++UXmD//3FCrzOD4svhuvRXuRx4xXvfAAWR16wYtYlrjsshvv8HRvz+y27aFZeXKYq8rkwneW2/F4W+/hXfkSKBWrbhkpxpCBJ7HHoM3ampg7a+/Qk1K1FgzAMVm7gq0bh1qUCljsEEhIqJqwfTtt4YpggHA8+CDQHZ2hc+lN28O/0UXGWq2JN9FiV6YMdC6dZmPtFSWb+hQeO6+21DT9uxBVteukJ07Sz1O9u2DfcQI5Jx3HqyLFoUfFYvk79IFBV99Bc/kyVDHHRf37FRDiMDz+OPw9u9vKIeblF9+OVrbuhWWZcsM+/HuSeZhg0JERJlPKdgffNBQCrZoAf9NN1X6lL68PMO2ZfFioAqzR1VIQQGsCxeWmSeevCNHhlbXjqD9/juyunWD/PWXcefCQtgmTQpNGTx9enhhy0iBCy5AwQcfwDVvHvTmzROWm2oQEXjGjy/+53Tv3lCTUjSdsO3pp42z3rVogcDllyc1KlVd0hoUEekoIhtFZIuI3F/C631EZK+IrCv6yot4rbeIbC766p2szERElBnMy5fD/OWXhppn7NgqrRIe6NgResOG4W1xu2FdsKDS56sIy9Klhhmo9Hr14L/qqsRdsOiHP1/PnoayadMmZHXvDlNBQWjK4BdfRM4558D+6KPFZsgCQj8MFi5ahMK330bwvPMSl5dqJhF4JkyAN6pZ1/78E1lXXQXTqlWwvPKK4TXvsGFVmvWOUiMps3iJiAnAVACXA9gBYLWIvKmUih7d9KpSanDUsXUAPAzgHwAUgLVFxx5IQnQiIkp3Ph/sUeuU+Nu3R+Dii6t2XrMZvj59YH/00XDJ+sIL8A0ceHRa3ERQCrbowfG9egFWa+KuCQCaBvezz0IKCmB5661w2fTDDzh50CDY/X6Ytmwp8VC9YUN4HnwQ/uuvr1JTSFQuEXgmTgz9dxLx2KX255/I7trVsKveuDH83bolOyHFQbLuoLQCsEUp9atSygdgIYCu5RxzRAcAHyil9hc1JR8A6JignERElGGss2bBFDGgW5lMoYXe4sB3yy1QETM/mbZuhXnVqricuzSmNWvCC0UCgBKBr0+fhF4zzGyG64UX4I9q7rJ+/rnE5kSvXRvuMWNweM0a+G+4gc0JJUdRk+ItZ9II75AhgJkramSiZDUoJwDYHrG9o6gW7RoR+UFElojIiRU8loiIahg5cAC2qIX3fL17Qz/11LicX9WvD3/Ub2Wts2bF5dyliZ5aONC+PVR5CxfGk80G10svIVDGavXKbodn2DAcXrcOvjvvBOz25OUjAgBNg2fSJHhLad71evWKTRdOmSNZbWVJD/9FT/exHMArSimviAwEMBfAJTEeG7Z58+ZKh6Tk4edUM/BzrhlS+Tmf+MQTyI0YuB7MysLPPXogEMdM2R074tQlS8Lb5nffxW+ffgpfgwZxu8YRpoMH0fL11w21/3XqhEMp+HdseuwxnDJwIJybNoVrStPw11VXYVf//vDXrw/s3Rv6omoj4/7evv12ND54EPXeeMNQ3nX99di9Y0eKQqW/dPicm5cxgUayGpQdAE6M2G4IYFfkDkqpfRGbswCMjzj2oqhjPy7tQmW9WZK0MEYAACAASURBVEoPmzdv5udUA/BzrhlS+TlrW7ciO6JxAAD/PfegaRm/+a+Uk05CcPJkmH76CQAguo6TP/4Y3gqsTh8r6zPPQIuYFUtv3BjH3nILjk3kmJcyBN5+G7777oOsWgXVti28I0bAesopaJKSNJRoGfv39uzZ8NWqBevcuQCAYLNmyLnvPuRwzZ0SZcLnnKy/8VYDaC4iTUXECqAngDcjdxCRyAnSuwA4svLOewDai8gxInIMgPZFNSIiqsHsDz8MCQTC2/qJJ8J7++3xv5BIsalNrfPmAV5vfK+j68XWPvHedltiB+SXQ9WrB/eLL+L7996De84c6KeckrIsRKXSNLinTEHh0qVwP/EECt96iwuCZrik/K2nlAoAGIxQY/EzgEVKqZ9EZLSIdCnabYiI/CQi3wMYAqBP0bH7AYxBqMlZDWB0UY2IiEqjFLT160tcZbk6MH32mWGmKQDwPPxwwsZC+K+9Fio3N7yt7d0Ly5tvlnFExZk/+sg42N9qrdI6LkQ1iggCl14KX9++XBS0Gkjar2WUUiuUUicrpZoppcYW1R5SSr1Z9P0IpdTpSqmWSqmLlVK/RBw7Wyl1UtHXi8nKTESUiWTbNji7d0dOmzbIad0atohpcqsFXYd95EhDKXDuufBfc03irpmdDd8NNxhK1jivLB89ON5/9dVQdevG9RpERJmAK8kTEVUXwSCs06cj54ILYPnoo3DZPmkSzCtWpDBYfFkWLYJ53TpDzTN2bMIXY/P17WvYNn/1FbSI6YCrQrZvh/k949PL0dcjIqop2KAQEVUD2i+/IKtjRzhGjIC4XMVedwweDPnjjxQkizOXC/YxYwwlf9euCJ5/fsIvrZ98MgLt2hlq8bqLYp07F6Lr4e3g6acj2KpVXM5NRJRp2KAQEWUynw+2CROQ3bYtzKtXl7qbtn8/HLffDkT8EJyJbNOmQdu5M7ytrFZ4/vOfpF3fG3VXw7poERAxzXGl+HyhQfeRpby8hN8RIiJKV2xQiIgylOm775B98cWwjxsHiZiaFgBUbi78V11lqFk+/hjWqVOTGTGuZM8e2CZPNtR8/ftDb9o0aRkCnTtDP/74o5lcLlgXLqzSOS1vvQXtzz/D2yonB77rrqvSOYmIMhkbFCKiTONywT5qFLIuvTS8Nkckf6dOOPzVV3DNmYNAmzaG1+yjR0OLGr+RKezjxkEKC8Pb+jHHwHPPPckNYTbDF7VytfWFFwBV6vrB5Yp+TMzXsyeQnV3p8xERZTo2KEREGcT06afIbtMGtmeeMYxZAAC9bl24XnwRrgULoI4/HjCZ4JoxA3rt2uF9xO+HMy8PiPhBPxNoP/0Ey/z5hpr3/vuBiPeWLL7evaHMR9c5Nm3eDNMnn1TqXNovv8D8+efG8992W5XyERFlOjYoRESZID8f9mHDkH3VVTD9+muxl309eqDgm2/g79bNMHZBnXAC3E8/bdjXtGULHCNGJDxyPNlHjTIOIj/ppJT9IK+OPRb+Ll0MNdusWZU6V/Tdk8AFF0A/7bRKZyMiqg7YoBARpTnzO++E1jN5sfgyUHrDhihcvBjuGTOg6tQp8fhAly7w9e5tqFnnzYN52bKE5I0388qVsHz4oaHmGT0asFhSlKhoEHsE84oVkIjB+zEpKID11VeN5+XUwkREbFCIiNKV/PUXHHl5yLrhBmi7dhV73duvHw5/+SUCl19e7rnc48Yh2Ly5oeYYOhSyY0fc8iZEIFB8UcY2bRDo1ClFgUKCrVsj2KJFeFt0HdYSGsiyWJYsgRw6FN7W69UrNrEBEVFNxAaFiCjdKAXL4sXIbtUK1iVLir0cPOkkFKxYAc/EiUBOTmznzMqC6/nnoSLuOmgHD8I5YAAQDMYredxZ58+H6ZdfwttKBO5HH039FLwixe6iWOfNA6JmUyuVUrBFD46/5RbAao1XQiKijMUGhYgojciOHXD27Alnv37Q9u83vKZMJniGD0fBZ58heMEFFT633rIlPA89ZKiZP/8ctilTqpQ5YQ4dgm3sWEPJ37Mn9LPOSlEgI9/110NFNIjan3/Csnx5TMeaVq+GKWIVeqVpxR7DIyKqqdigEBGlA12HdfZs5LRuDct77xV7OXjmmSj48EN4H3oIsNsrfRnfHXfAf/HFhppt3DiY1qyp9DkTxTZlCrS//gpvK4cDnlGjUpgoSnZ2aErgCNbnn4/p0GKD49u3h2rUKG7RiIgyGRsUIqIU07ZuRdaVV8IxfDjk8GHDa8pmg+ehh1Dw4YfQW7aMw8U0uKdPh/63v4VLEgzCkZcHRF07lSxz5hS7s+MdPDg0fXIaKTZY/ssvoa1fX+Yxsm8fLK+/bjwPB8cTEYWxQSEiSpVAANannkL2hRfC/MUXxV8+/3wUfPopvMOHx3XGKtWgAdxRK8qbtm2D495743aNSlMKtjFj4LzrLsO0wvqxx8I7dGgKg5VMP+UUBP71L0Mt+u5INMvLL0MixqrojRsjcOmlCclHRJSJ2KAQEaWA9tNPyLrsMjgefhji8RheU9nZcE+ahMIVK6CffHJCrh/o2BHefv0MNevChbCUMCg/aXw+OAYMgP2JJwxlpWlwT5qUtqure6MHyy9aBOTnl7xz0aN8huNvuw3Q+L9jIqIj+DciEVGSaVu3IrtzZ5jXrSv2mv+yy3D4iy9Cjw4l+IdWz+jRCEYtCugYPhzy228JvW6J8vORde21oR/uIyiHA66XXkIgjaffDXTuDP2448LbUlgI68KFJe5r/vBDmLZtC28rmw3+m29OdEQioozCBoWIKMnso0dDon7Drh9zDFzPPQfX4sXJGyztcISmHrbZwiU5dAjO/v2BQCA5GRCauSy7UyeYP/nEUNfr1kXh8uUIdO6ctCyVYrHA16ePoWR94QVAqWK7Rj/+5e/aFSpiPBAREbFBISJKKu3772GJWsHd37UrCr7+Gv6ePZO+vod++umhVdkjmL/+GraJE5Nyfe3HH5F9+eUwbdhgqAebNUPhBx8g+I9/JCVHVfl694Yym8Pbpk2bYIpquGT7dpijZmiLHmRPRERsUIiIkso+bpxhO9iyJVxz5kDVr5+iRICvf3/427c31GwTJ8L01VcJva75o4+Q3bkztD/+MNQDrVqh8P33oTdtmtDrx5Nq0KDYKvDRCzFa5841DPwPnnEGguedl5R8RESZhA0KEVGSmL75ptgaJ56RI9NiVXT31KnQI5ok0XU4+/UDDh5MyCUtCxbAed11xaZV9l95JQqXLcvIx56ipwo2v/02ZOfOohd9sM6da3jdm5eX+s+eiCgNsUEhIkoS+6OPGrYD//wnApddlqI0RqpePbinTzfUtO3b4bj77hLHUlT+Qgq2CRPgHDQIEjXOxTtgAFxz5wIOR/yul0TBCy80TDogwWC4KbG89Ra0vXvDr6mcHPivvTbpGYmIMgEbFCKiJDCtWlVsEHha3D2JELj0UngHDTLUrEuXwlLKjFQV5vfDMWRIscfcAMA9diw848cDJlN8rpUKIsXuoljnzg3dPYl63MvXs2faTptMRJRqbFCIiBJNKdjHjjWUAu3aIRi1wF868Dz8MIJnnGGoOe69F9qvv1btxIcPw3nDDbDOn28oK5sNhXPmwHfHHVU7f5rw9egBFdF4aHv2wDZpEsyff27c77bbkh2NiChjsEEhIkow8wcfwPzNN4aaZ+TIFKUph80G1wsvQEU8ZiUFBXDk5QF+f6VOKbt3I/uKK2BZudJQ12vXRuEbbyBw9dVVipxWcnJCd0ci2CdMMGwHLrgAetT6M0REdBQbFCKiRNL1YmNP/B06pPXsTfopp8AT9RiW+dtvYXvssQqfS/vll9A0wj/8YLxGo0YofP99BFu3rlLWdBT9mFex1zm1MBFRmdigEBElkHn58mI/nHsefDBFaWLn69MH/iuuMNRskyfD9OmnMZ/D9NlnyO7QAdr27YZ64KyzUPDBB9BPPjkuWdONftppCLRpU/Jr9evDf+WVSU5ERJRZ2KAQESVKMFhsQLjv6quh/9//pShQBYjA/cwz0I877mhJKTgHDIAcOFDu4ZalS5HVvTskP99Q97dvj8K33oI69ti4R04n3lLukvhuuQWwWpOchogos7BBISJKEMuSJTBt3BjeVpoG74gRKUxUMapOHbieew4qYqYxbdcuOIYMKX3qYaVgffppOPv2hfh8hpe8ffrAtWBBjZi9KnDFFdAbNDDUlKbB17t3ihIREWUONihERIng9xcbs+G//nrop5ySokCVE2zXDt6hQw01y/LlsETNxhXaOQj7fffB8dBDxV7yjBoFz+TJgNmcqKjpxWIp1owEOnSAOvHEFAUiIsocbFCIiBLAsmABTNu2hbeV2QzP/fenLlAVeB94AIGzzzbUHPffD23TpqMFlwvOXr1gmzXLsJ+yWOCaMQPeu+9OqzVfksF7++0INmsGAFDZ2fCU0LgREVFxMTcoImIRkX+JSI+i7SwRyUpcNCKiDOXxFJta1terF1STJqnJU1VWK9zPPw+VdfSvfHG54MzLg/h8kL/+QlaXLrCsWGE4TOXmonDJEvh79Eh24vRQuzYKPv0UBW++icM//MCphYmIYhRTgyIiZwLYBGAWgCPL4bYDMDtBuYiIMpZ1zhxoO3eGt5XNBu8996QwUdXpzZrBPX68oWb64Qc0HjcOWZdfDvOaNcb9TzgBBe+8g2C7dsmMmX6cTgTbtoWqUyfVSYiIMkasd1CmA3hIKXUqgCMrda0CUPI8ikRENVVhIWxPPGEo+W67DeqEE1IUKH78N90EX7duhlrdt9+G6X//M9SCp5+Ogvffh3766cmMR0RE1USsDcrpAF4q+l4BgFKqEICj1COIiGog66xZ0PbuDW8rpxPe4cNTmCiOROCePBl6w4al7uK/6CIUrFhRLRoyIiJKjVgblG0Azo0siEgrAFviHYiIKGPl58M2ZYqh5B04EKpevRQFSoDateGaORNKK/6/D1/PnnAtWgTUqpWCYEREVF3E2qCMAvC2iDwCwCoiIwAsBjAyYcmIiDKMbdo0aAcPhrdVbi68Q4akMFFiBC+4oNiYGs+998I9fToXISQioiqLaUJ6pdRbItIJQB5CY08aA+iulFqbyHBERJlC9u+Hbdo0Q807eDBQu3aKEiWWd8QIqAYNUPjf/8LRuzcC7dunOhIREVUT5TYoImJCaLau/kqpQYmPRESUeWxPPQU5fDi8rdepA+/tt6cwUYKJwHfbbdj2r3+hefPmqU5DRETVSLmPeCmlggDaA9ATH4eIKPPI7t2wzpxpqHmHDQNyclKUiIiIKHPF9IgXgMkAHhGRh5VS/nL3JqIaTdu8GX976y1YTjgByukEnE4oh+Po9xE12GwZv8K47cknIW53eFtv0AC+vn1TmIiIiChzxdqg3AmgAYDhIrIXRVMNA4BSqlEighFRZjKvWAFn797I8cf2uwylaUeblaLmxfC90wlENjdHvs/KQuCii6Cn+PEi2b4d1jlzDDXv3XcDTmdqAhEREWW4WBuUmxOagoiqBdm2Dc6BAyExNicAILoOFBRACgoqdU3v4MHwjB4NlDDtbTLYJ06E+Hzhbf3EE+G75ZaUZCEiIqoOYp3Fa1WigxBRhvN64ezTB3LoUFIva3v2WWi//QbXjBlJv2uhbd0Ky8svG2qe++4LPbZGRERElRJTgyIiFoTWPOkF4HgAuwDMBzBWKeUr61giqhnsI0fCvG6doea/5JLQD+tuN8Tlgrhcxb/3Vf2vEMvy5cjatQuuV16Bql+/yueLlW38eEgwGN4ONmsG/w03JO36RERE1VGsj3hNANAKwEAAvyG0DsooALkAhiUmGhFlCstrr8E2a5ahduDii6EtXVr+AHi//2jT4nYDR5qXou3w91HNjWXxYmi7d4dPY167FtmXXYbCxYuhn3JKIt6mgbZhAyyLFxtq3hEjAHOsf60SERFRSWL9P+l1AFoqpfYVbW8UkW8BfA82KEQ1mrZ1KxxDhxpqwSZNsG3UKPw9ltm5LBbAYoHKzT06+0YMvAMGIOv662HasOFolt9/R3b79iicPx/Btm0rcLaKsz/2GEQdTRxs0QL+7t0Tek0iIqKaINZRpaX9lJHZc4MSUdW43XD27m1YoFBZrXDNmYNggtcAUQ0bouDdd0OPkUWQ/HxkXXMNLK+8krBra+vWwbJ8uaHmeeCBlA3UJyIiqk5i/b/pYgDLRaSDiJwmIh0BvAFgUeKiEVG6s48YAdP69YaaZ9w46GedlZwAublwvfoqfL17G8ri98N5++2wPfYYoCpyXyY29rFjDduBs89G4Ior4n4dIiKimijWBuU+ACsBTAWwFsAzAD4C8O8E5SKiNGdZtAi2qPU/fN27J3+BQosF7ilT4P7Pf4q9ZB8/Ho6BAwGvN26XM331FSwffGCoeUeOzPjFJomIiNJFTA2KUsqnlHpIKXWSUsqplGqulBqllIrf//WJKGNomzbBMcw4/CzYrBncU6ak5gd1EfjuuguuF1+Eipri1/rqq8jq3h04eLDq11EK9jFjDKVA69YIRD1mRkRERJUXU4MiIveLyHlRtVYicl9iYhFR2nK5QuudFBaGS8pmg2vOHCA3N3W5APi7dUPhm29Cr1PHUDd//jmyL78csm1blc5vWrUK5s8/N9Q8vHtCREQUV7E+4jUUwIao2gYAd8U3DhGlO8e99xpmzgIA94QJ0M88M0WJjIL//CcKV65EsFkzQ920eTOyL7sMptWrK3dipWB/9FFDyX/JJQheeGFloxIREVEJYm1QrAD8UTUfAHt84xBROrO8/DKsUSun+66/Hv5bbklRopLpf/87Cj/4AIHWrQ117a+/kHXVVTAvW1bhc5rffRfmNWsMNe+DD1YpJxERERUXa4OyFsCgqNpAAN/GNw4RpSttwwY47rnHUAuefDLcTz6Zlo84qTp1UPjGG/Bdd52hLh4PnH36wPrMM7HP8KXrxWbu8nfujOC558YrLhERERWJtUEZBuA+EVkrIouKFmn8N4AhsV5IRDqKyEYR2SIi95ex37UiokTkH0XbTUTELSLrir6ei/WaRBQnBQWhcSdud7ikHI7QuJPs7NTlKo/NBvfMmfBENVaiFByjRsF+991AIFDuaSzLlhmmU1YioXVPiIiIKO5iWkleKfWTiJwM4EoAJwJ4DcBbSqmCWI4XERNCUxRfDmAHgNUi8qZSakPUfjkINT1fR51iq1IqSQsrEJGBUnAMHw7Tpk2GsnvSJOgtWqQoVAWIwDtyJPQmTeC46y5IRENimz0b2vbtcM2eDZS2sGQgANu4cYaSv3t36GeckcjURERENVbMyx4rpQqUUgsBzAKwEYBegeu0ArBFKfWrUsoHYCGAriXsNwbABACeCpybiBLIMn8+rIuMa7L6brwR/ptuSlGiyvHffDMKly6FipppzPLBB8ju1Amyc2eJx1kWLYJp8+bwttI0eO8v9SYwERERVVGZDYqI3Csi3SO2OwL4HaExKdtF5J8xXucEANsjtncU1SKvdTaAE5VSb5VwfFMR+U5EVonIv2K8JhFVkfbjj3Dce6+hFjztNLgnTUpRoqoJtmuHgvfeg96woaFuWr8e2ZddBu2HH4wH+Hywjx9vKPlvuAF68+aJjkpERFRjiSpjkKiI/AKgi1JqU9H2JgCLAYxDaIrhS5VS5a5QJiLXAeiglMor2u4FoJVS6s6ibQ3AhwD6KKW2icjHAO5RSq0RERuAbKXUPhE5F8AbAE5XSh06cv78/Pzwm9gc8ZtOIqo8raAALXr3hv3338O1oMOBn+fOhadp0xQmqzrzX3+h+fDhyPr5Z0M96HTi13HjkF80dXC9pUvR+PHHw6/rZjPWL10K3/HHJzUvERFRddM84pd9tWrVMsy2U16Dkq+UqlX0/UkAfgLwN6VUQVHjsFMpVbe8ACLSGsB/lFIdirZHAIBS6rGi7VoAtgI4MqalAYD9CDVHa6LO9TGKmpcjtcgGhdLf5s2bDX8oKQ0pBUffvrC+9pqh7JoxA/4ePWI6Rdp/zoWFcPbrB8uKFYay0jR4Jk6E78YbkXPOOdD++CP8mjcvD54MvXuUKGn/OVNc8HOuGfg51wzp+DlHNyjljUFxiciRB7bbAPghYmC8jhgH2QNYDaC5iDQVESuAngDePPKiUipfKVVXKdVEKdUEwFcoak5EpF7RIHuIyN8BNAfwa4zXJaJKsM6eXaw58fXuHXNzkhGysuCaPx/e2283lEXX4bj7bmRdcYWhOVF2O7x3353slERERDVOeQ3KCgAzRaQLgHsALI14rSWM40pKpZQKABgM4D0APwNYVDQz2Oiic5elLYAfROR7AEsADFRK7Y/lukRUcdq6dbCPGGGoBc84A+6IR52qDZMJnsceg3v8eCjN+Neh+VvjMk++vDyo445LZjoiIqIaqbw7IMMBTAYwFsCXRd8f0RGh2bhiopRagVDDE1l7qJR9L4r4fimMjRERJUp+fmi9E58vXFLZ2aH1ThyO1OVKMN+AAdAbNYKzb1+Iy1XsdZWdDe9dd6UgGRERUc1TZoOilMoHcFsprz2akERElBpKwTl4MEzbthnK7qefhn7SSanJlESBTp1QsGIFsnr0gLZnj+E178CBUHXLHW5HREREcRDzOihEVL1ZZ8yAZflyQ82blwd/9+6lHFH96GedhYKVKxGMWIBSb9AA3sGDU5iKiIioZol1kDsRVWOmtWthHzXKUAu2bAnP2LEpSpQ66sQTUfDuu7A9/zxk7174+vYFatdOdSwiIqIagw0KUQ0nBw6Exp34/eGays0NjTux2VIXLJVyc+EdPjzVKYiIiGokPuJFVJMpBcegQdC2Gyfkcz3zDPQMX4yRiIiIMlNMDYqE9BORD0Xkh6JaWxG5PrHxiCiRrM8+C8s77xhq3gEDEOjaNUWJiIiIqKaL9Q7KaAB9AcwE0KiotgPAvxMRiogSz/T117A/8oihFjj3XHjGjElRIiIiIqLYG5Q+AK5USi0EoIpq/wPw90SEIqLEkn374LztNkggEK6pWrXgmj0bsFpTmIyIiIhqulgbFBOAgqLvjzQo2RE1IsoUug7HwIHQdu40lF3Tp0M1bpyiUEREREQhsc7itQLAkyIyDAiNSQEwBsDyMo8iovQQCMD03Xcwr1oF8wcfwPz114aXvYMHI9C5c4rCERERER0Va4MyHMA8APkALAjdOXkfwC0JykVEVaEUtE2bYP7441BT8tlnkEOHStw10KoVPA8/nOSARERERCWLqUFRSh0CcLWI1AfQGMB2pdTuhCYjogqRXbtCzUjRl/bHH+Ueo9epExp3YrEkISERERFR+WJqUESkPYBtSqlNAP4sqp0CoJFS6oME5iOi0uTnw/zZZ+GGxLRxY4UOD/7f/8H1zDNQDRsmKCARERFRxcX6iNdUAG2jaoeL6ifHNRERlczrhembb442JGvXQnQ95sP1unURaNcu9NW2LVSTJonLSkRERFRJsTYo9ZVS0c+L/AGgQZzzENERug7txx9h/uST0FiSL76AuN0xH66cTgQuuCDUkFx0EfTTTwe0WCfuIyIiIkqNWBuUX0XkEqXUhxG1ixBaC4WI4kj7/nvYpkwJjSPZvz/m45TJhOC554YbkuB553FNEyIiIso4sTYo/wHwmoi8AGArgGYAbi36IqI4MX/4IZzXX29YQLEswVNPDTckgQsvBHJzE5yQiIiIKLFincVrWdFA+dsAXAFgO4AOSqnViQxHVJPI3r1wDBxYZnOiH3/80YakbVuo445LYkIiIiKixIv1DgqUUt8A+CaBWYhqLqXgGDQI2p9/Gsu5uQj861+hhqRdO+jNmwMiKQpJRERElHixTjNsBdAHwFkAsiNfU0pxsUaiKrLOmAHLB8YZuz3Dh8P7wAOAOebfIxARERFlvFh/8pkLoCWA5QD2JC4OUc2j/fgj7A89ZKgFzj+fzQkRERHVSLH+9NMRQFOl1MFEhiGqcVwuOPPyID5fuKRyc+GaOZPNCREREdVIsS6K8DsAWyKDENVE9pEji60A754yBapRoxQlIiIiIkqtWH9FOw/AMhF5ClGPeEWtjUJEMTIvXw7b7NmGmu+mm+Dv3j1FiYiIiIhSL9YGZXDRP8dF1RWAv8cvDlHNIDt3wjFkiKEWbNYM7vHjU5SIiIiIKD3Eug5K00QHIaoxgkE4BwyAduBAuKQsFriffx7Izi7jQCIiIqLqL9YxKBARi4j8S0R6FG1niUhW4qIRVU+2p56C+bPPDDXPqFEInn12ihIRERERpY+YGhQRORPAJgCzALxQVG4HYHapBxFRMaY1a2AbO9ZQC7RrB9/gwaUcQURERFSzxHoHZTqAh5RSpwLwF9VWAWiTkFRE1dGhQ3Dk5UGCwXBJr1MHrueeA7SYb2YSERERVWux/lR0OoCXir5XAKCUKgTgSEQoourIce+9MG3bZqi5p06FOu641AQiIiIiSkOxNijbAJwbWRCRVgC2xDsQUXVkWbQI1ldfNdS8/foh0KlTihIRERERpadYpxkeBeBtEXkOgFVERgAYCKBfwpIRVROybRscd99tqAVbtIBn9OgUJSIiIiJKXzHdQVFKvQWgE4B6CI09aQygu1Lq/QRmI8p8fj+ceXmQw4fDJWWzwfX884CDT0gSERERRSv3DoqImBCarau/UmpQ4iMRVR+28eNhXrPGUPM8+ij0Fi1SlIiIiIgovZV7B0UpFQTQHoCe+DhE1Yfps89ge+IJQ83fsSN8eXkpSkRERESU/mIdJD8ZwCMiYklkGKLqQg4cgHPAAIhS4ZreoAHcU6cCIilMRkRERJTeYh0kfyeABgCGi8heFE01DABKqUaJCEaUsZSCY8gQaDt3Gsqu556D+tvfUhSKiIiIKDPE2qDcnNAURNWIZd48WJYvN9S8Q4YgeNFFqQlERERElEFialCUUqsSHYSoOtA2bYLj/vsNtcBZZ8Ezuq5w4gAAHOtJREFUcmSKEhERERFllpjGoIiITUTGisivIpJfVGsvIoMTG48og3i9cPbtC3G7wyWVlQX3888DVmsKgxERERFljooMkj8DwE04Ov7kJwC3JyIUUSayP/IITD/+aKi5x4+HftJJKUpERERElHliHYPSDcBJSqlCEdEBQCm1U0ROSFw0osxhXrkStmnTDDVf9+7w33RTihIRERERZaZY76D4ENXMiEg9APvinogow8iff8Jxu/Fmot6wIdxPPskphYmIiIgqKNYGZTGAuSLSFABE5DgAzwJYmKhgRBlB1+EYNAja3r3hktI0uJ5/HqhdO4XBiIiIiDJTrA3KAwC2AfgRQG0AmwHsAjA6MbGIMoN1xgxYVq401Lz33Yfg+eenKBERERFRZot1mmEfgLsA3FX0aNdfSkUskU1UA2k//AD7ww8baoHzz4f3nntSlIiIiIgo88U6SB4iUgvAKQCyi7YBAEqpDxOSjCiduVxw5uVBfL5wSeXmwjVzJmCO+T8rIiIiIooS009SItIHwFQABQBcES8pAH+Pfyyi9GZ/8EGYNm0y1NxPPQXVqFGKEhERERFVD7H+qncsgGuVUu8kMgxRJjC/+SZsL75oqPluugn+bt1SlIiIiIio+oh1kLwZwPuJDEKUCbQtW+AYMsRQCzZrBvf48SlKRERERFS9xNqgjAcwUkRi3Z+oWpHdu2G/5x5kt24N7eDBcF1ZLHC98AKQnZ3CdERERETVR6mPeInIdoTGmACAAGgA4D4RMSzOqJTiQ/dUfR08CNtTT8H23HMQt7vYy55Ro6CfdVYKghERERFVT2WNQbk5aSmI0k1hIWwzZsD21FOQ/PwSd/F37Qrf4MFJDkZERERUvZXaoCilViUzCFFa8PlgnTsXtkmToO3ZU+IueqNG8DzwAPzXXQdofOqRiIiIKJ5i+ulKRCwi8oiI/CoinqJ/PiIi1lgvJCIdRWSjiGwRkfvL2O9aEVEi8o+I2oii4zaKSIdYr0kUs2AQloULkXPeeXDce2+JzYlevz7cEyfi8Jo18PfsCZhMKQhKREREVL3FOs3wBACtAAwE8BuAxgBGAcgFMKy8g0XEhNA6KpcD2AFgtYi8qZTaELVfDoAhAL6OqLUA0BPA6QCOB7BSRE5WSgVjzE5UOqVgXrEC9rFjYdqwoeRd/r+9ew+Pqr7zOP75ZpJMEq6LyioCYrexWrqtdutlq9tau7XWWmittuoqUCoUUUtVFOsFFQRpVdSlKlu8gFShuLSi1fXyPN7qKlarsFRtCyoVpIiCcktmcpnv/jEncU5IQkKSOXN5v54nT+Z8zzkz38l5zsN8+J3fnH79lJw0Sckf/Ujq1SvLDQIAABSXjgaUUyV9zt2bJsj/xcxekbRSHQgoSoebNe7+liSZ2WJJIyW1/EQ4XekwNDmjNlLSYndPSnrbzNYEz/dCB3sHWhV79llVTJ+u0pdeanW9V1YqOWGCkpMmSf37Z7k7AACA4tTRgGKdrLe0v6R1GcvrJR0ZeiKzwyQNcfffmdnkFvsub7Hv/h18XWAXsVdfVXzaNJU99VSr6720VHVjxig5ebJ8332z3B0AAEBx62hAuV/SQ2Z2jaR3lL7E6wpJSzq4f2tBxptXpu+vcpOkMZ3dt6XVq1d3sCVEKYrjVPH22xo0d676Pflkq+vdTFtOOEHvjh+vusGDpe3b0z/YY5yPxYHjXBw4zsWB41wccuE4V1dXt7muowHlEqUDya1KzwN5V9JiSdd2cP/1koZkLA+WtCFjuY+kz0h62syk9D1XHjSzER3YN6S9N4vcsHr16qweJ1u3ThWzZqls0SJZKtXqNvXf+IYSV1yh0uHDdUDWOits2T7OiAbHuThwnIsDx7k45MNx7lBAcfc6SVODnz3xkqRqMztQ6XBzmqQzMp5/q6S9m5bN7GlJk939ZTOrlXSfmc1WOhxVS/rDHvaBImLvv6/4jTeq/K67ZHV1rW7TcMwxSkydqsYjjshydwAAAGhNuwHFzI6WNMLdp7SybpakB9x9+a57hrl7g5mdJ+kxSTFJd7n7a2Y2TdLL7v5gO/u+ZmZLlJ5Q3yDpXL7BK3+VPvaY9n/4YcX32ksqL5cqKuRt/Y7HpXi83W1UXi5Zi6sAt25V/Be/UPy222Q7d7baR8Ohhyo5daoavvKVXfcHAABAZHY3gnKZpNvaWPeMpMslfasjL+Tuj0h6pEWt1REZdz+2xfIMSTM68jrIXWULF6rq/PPV3V/U2xRkmn7b1q2yNuaONFZXK3HFFWoYMYJgAgAAkIN2F1AOlfRoG+uekHRn97aDgrV9uyquuaZHntqSSSmZbPcr5VKDBysxZYrqTz9dKu3o1CsAAABk2+4+qfWVVC6ptpV1ZUpPbgd2Kz53rko++CDrr5vaay8lL7pIdWPHpi8JAwAAQE7bXUD5s6TjJS1rZd3xwXqgXfbhh4rPmROq1X/zm2ocPrx59EPJ5McjIU2/Ewmprq75txKJ9GT3zN/19a2+pvftq+S55yo5caLUhxwNAACQL3YXUG6S9F9mFlN6QnwquGfJt5X+yuELe7pB5L/y//xP2bZtzcsNffqo5tZbu+fu7KlUOOAkErLGRqWGDuVSLgAAgDzU7ic4d7/PzPaVtEBS3Mw+UPrrgBOSrnL3RVnoEXnM3ntP8blzQ7WNo0apT3eEE0kqKZEqK6XKyua7d7Z5F08AAADkvN3+F7O7zzazOyT9q6S9JG2W9IK7b2t/T0CK33CDrPbjKUypgQO16fvfZ/ISAAAAWtXRGzVuU/oeJkCH2TvvqHz+/FAtOXmyUpWV0TQEAACAnFcSdQMoXBU/+1loEntq8GDVjR4dYUcAAADIdQQU9IiSv/5VZYvCU5QSl16avjM8AAAA0AYCCnpEfOZMWSrVvNxYXa36006LsCMAAADkAwIKul3JypUqf+CBUC1x+eV87S8AAAB2i4CCblcxY0ZoufGf/1kNI0ZE1A0AAADyCQEF3Sq2fLnKHn88VEtceWX6fiUAAADAbvCpEd3HXRXTpoVKDUcdpYavfS2ihgAAAJBvCCjoNqVPPaXS558P1RJXXimZRdQRAAAA8g0BBd3DXfEWoyf1xx2nxqOPjqghAAAA5CMCCrpF6UMPqXTFilAteeWVEXUDAACAfEVAQdc1Nqpi5sxQqf5b31LjYYdF1BAAAADyFQEFXVZ2//2K/fnPzctulr7vCQAAANBJBBR0TV2dKq67LlSq//73lTr44IgaAgAAQD4joKBLyhcuVMnf/ta87KWlSlx6aYQdAQAAIJ8RULDnamsVv/76UKlu9Gj5sGHR9AMAAIC8R0DBHiu/4w6VbNzYvOwVFUpOnhxhRwAAAMh3BBTsmW3bFJ89O1SqGz9evt9+ETUEAACAQkBAwR6J33qrSj78sHnZ+/RR8ic/ibAjAAAAFAICCjrNNm9W/LbbQrXkuefKBwyIqCMAAAAUCgIKOi1+882y7dubl1MDBig5cWKEHQEAAKBQEFDQKbZhg8rnzQvVkhdcIPXtG1FHAAAAKCQEFHRK/IYbZIlE83Jqv/1Ud/bZEXYEAACAQkJAQYfZ2rUqv+eeUC158cVSZWVEHQEAAKDQEFDQYRXXXSdraGhebhw2THVnnhlhRwAAACg0BBR0SMkbb6hsyZJQLfnTn0rl5RF1BAAAgEJEQEGHVMyYIXNvXm485BDVn3JKhB0BAACgEBFQsFuxV15R2e9+F6olLrtMisUi6ggAAACFioCC3Ypfe21oueGww9Rw0kkRdQMAAIBCRkBBu2K//73KnnwyVEtOnSqZRdQRAAAAChkBBW1zV0XL0ZNjjlHDscdG0w8AAAAKHgEFbSp94gmVvvhiqJa48kpGTwAAANBjCChoXSqliunTQ6X6r39djUceGVFDAAAAKAYEFLSqbNkyxVatCtUSl18eUTcAAAAoFgQU7KqhQfEZM0KlupNPVuqzn42oIQAAABQLAgp2UbZ4sWJr1jQve0lJ+q7xAAAAQA8joCAsmVTFrFmhUv0ZZyhVXR1RQwAAACgmBBSElM+fr5L165uXvbxciUsuibAjAAAAFBMCCj62c6fiN9wQKtX94AfyoUMjaggAAADFhoCCZvFf/lIl77/fvOxVVUpedFGEHQEAAKDYEFAgSSpZvVrxG28M1ZITJsgHDoyoIwAAABQjAgqknTtVNWqUbMeO5pL37avkj38cYVMAAAAoRgSUYueuygsuUOyNN0LlxNVXS/37R9MTAAAAihYBpciVz5+v8iVLQrW6U09V3Q9+EFFHAAAAKGYElCIWe/VVVUyZEqo1Hnywam+6STKLqCsAAAAUMwJKkbIPP0zPO6mra655r16qWbBA6t07ws4AAABQzAgoxSiVUuWECSpZty5Urp0zR6lPfSqipgAAAAACSlGK33STyh57LFRLjh+v+pNPjqgjAAAAIC1rAcXMTjCzv5jZGjO7tJX1E8xslZmtMLPnzOzTQX2YmdUG9RVmNjdbPRei2DPPKD5jRqjW8IUvKHHttRF1BAAAAHysNBsvYmYxSbdK+pqk9ZJeMrMH3f31jM3uc/e5wfYjJM2WdEKw7k13PzQbvRYy27BBVWefLUulmmupAQNUc/fdUnl5hJ0BAAAAadkaQTlC0hp3f8vd6yQtljQycwN335ax2EuSZ6m34lBfr6qxY1Xy/vvNJTdT7bx58iFDImwMAAAA+Fi2Asr+kjJnZK8PaiFmdq6ZvSnp55Iyb2N+oJm9ambPmNm/9Wyrhani6qtVunx5qJa85BI1fPWrEXUEAAAA7Mrce36gwsxOlfR1dz87WD5L0hHufn4b258RbD/azOKServ7ZjP7F0kPSBqeOeKydevW5jexevXqnnwrean/k0/qky3ud7L1qKO0+uabpVgsoq4AAABQrKqrq5sf9+vXL3QDvqzMQVF6xCTzOqLBkja0s/1iSbdLkrsnJSWDx38MRlgOkvRyaztmvllIJW++qd4tJsWn9t9fdu+9qt5rr0h6Wr16NcepCHCciwPHuThwnIsDx7k45MNxztYlXi9JqjazA82sXNJpkh7M3MDMMv9S35S0OqjvE0yyl5l9QlK1pLey0nW+q6lR1VlnybZ9PL3Hy8pUM3++PKJwAgAAALQnKyMo7t5gZudJekxSTNJd7v6amU2T9LK7PyjpPDP7d0n1kj6UNDrY/UuSpplZg6RGSRPcfUs2+s5r7qq86CLFXn89VE5ce60aDz88oqYAAACA9mXrEi+5+yOSHmlRm5rxeFIb+y2VtLRnuys8ZQsXqnzRolCt7uSTVTd+fEQdAQAAALvHneQLUMnKlaq8+OJQrfGgg1R7yy2SWRt7AQAAANEjoBSajz5S1ejRsmSyueRVVapZsEDq0yfCxgAAAIDdI6AUklRKVeeco9jataFy7S23KHXIIdH0BAAAAHQCAaWAlM+Zo7L/+Z9QLfnDH6r+1FMj6ggAAADoHAJKgYg995wqrrkmVGv4/OeVmDkzoo4AAACAziOgFADbuFFVY8fKUqnmWqp/f9XMny/F49E1BgAAAHQSASXfNTSoauxYlWzaFCrX/vKX8qFDI2oKAAAA2DMElDxXMX26Sp9/PlRLTJ6shuOPj6gjAAAAYM8RUPJY6cMPK37LLaFaw5e/rORPfxpRRwAAAEDXEFDyVMnbb6vqnHNCtdSgQaq54w4pFouoKwAAAKBrCCj5qLZWVaNGybZtay55aalq7r5bvs8+ETYGAAAAdA0BJQ9VXnKJYqtWhWqJadPUeOSREXUEAAAAdA8CSp4p+9WvVL5wYahWP3Kk6lpc7gUAAADkIwJKHilZtUqVkyeHao2f/KRq5syRzCLqCgAAAOg+BJQ8YR98oKrRo2WJRHPNKytVs2CB1LdvhJ0BAAAA3YeAkgds40b1Oukkxd56K1SvnT1bqeHDI+oKAAAA6H6lUTeA9tn69eo1YsQu4SQ5ZozqTz89oq4AAACAnkFAyWG2dq16jxihknfeCdXrjztOiVmzIuoKAAAA6Dlc4pWjStasUe8TT9w1nJxwgmruu0+qqIioMwAAAKDnEFByUMnrr6vXiSeqZMOGUL1+5EjV3HMP4QQAAAAFi4CSY0pWrlSvk05SyaZNoXrd976nmjvvlMrLI+oMAAAA6HkElBwSe/nl9JyTLVtC9bpRo1R7++1SKVOGAAAAUNgIKDki9sIL6vWd78i2bg3Vk+PGqfbmm6VYLKLOAAAAgOwhoOSA2DPPqNd3vyvbvj1UT55/vhI//7lUwmECAABAceCaoYiVPv64qs46S5ZMhuqJiy9W8rLLJLOIOgMAAACyj4ASodKHHlLV2LGy+vpQPTF1qpIXXhhRVwAAAEB0CCgRKVu6VJXjx8saG0P12pkzVTdxYkRdAQAAANFickMEyu69V5Xjxu0aTm68kXACAACAosYISpaV33WXKltcvuVmqp0zR/VnnhlRVwAAAEBuYAQli8pvu23XcBKLqXbePMIJAAAAIEZQsiY+e7Yqpk0L1bysTDV33qmGESMi6goAAADILQSUnuau+MyZqrj++nA5HlfNwoVqOP74iBoDAAAAcg8BpSe5q2LqVMXnzAmXKyu1c9EiNR57bDR9AQAAADmKgNJTUilVTJmi+Lx5obL37q2dS5ao8YtfjKgxAAAAIHcRUHpCY6MqL7hA5ffcEyp7377auXSpGg8/PKLGAAAAgNxGQOluDQ2qnDhR5UuWhMqpAQO08ze/UerQQyNqDAAAAMh9BJTuVFenqnHjVLZsWaicGjhQOx94QKlPfzqixgAAAID8QEDpLomEqsaMUdmjj4bKqUGDtHPZMqWqqyNqDAAAAMgfBJTuUl8v27w5VEoNGaIdDz0kHzYsmp4AAACAPMOd5LtLnz7aef/9avzc5yRJjZ/4hHY88gjhBAAAAOgEAkp36t9fO3/7W9V9+9va+fDD8iFDou4IAAAAyCtc4tXNfMAA1c6fH3UbAAAAQF5iBAUAAABAziCgAAAAAMgZBBQAAAAAOYOAAgAAACBnEFAAAAAA5AwCCgAAAICcQUABAAAAkDMIKAAAAAByBgEFAAAAQM4goAAAAADIGQQUAAAAADnD3D3qHrps69at+f8mAAAAgCLUr18/y1xmBAUAAABAziCgAAAAAMgZBXGJFwAAAIDCwAgKAAAAgJxBQEFWmdlaM1tlZivM7OWo+0H3MLO7zGyTmf0pozbAzJ4ws9XB73+Iskd0XRvH+Wozezc4p1eY2YlR9oiuM7MhZvaUmb1hZq+Z2aSgzjldQNo5zpzTBcTMKszsD2a2MjjO1wT1A83sxeB8/rWZlUfdayYu8UJWmdlaSV9w9w+i7gXdx8y+JGmHpHvc/TNB7eeStrj7LDO7VNI/uPuUKPtE17RxnK+WtMPdb4iyN3QfM9tP0n7u/oqZ9ZH0R0nfljRGnNMFo53j/D1xThcMMzNJvdx9h5mVSXpO0iRJF0r6jbsvNrO5kla6++1R9pqJERQAXebuz0ra0qI8UtKC4PECpf/hQx5r4zijwLj73939leDxdklvSNpfnNMFpZ3jjALiaTuCxbLgxyUdJ+m/g3rOnc8EFGSbS3rczP5oZuOjbgY96h/d/e9S+h9CSQMj7gc95zwz+7/gEjAu+ykgZjZM0mGSXhTndMFqcZwlzumCYmYxM1shaZOkJyS9Kekjd28INlmvHAunBBRk29Hu/nlJ35B0bnDJCID8dbukf5J0qKS/S7ox2nbQXcyst6Slkn7i7tui7gc9o5XjzDldYNy90d0PlTRY0hGSDmlts+x21T4CCrLK3TcEvzdJ+q3SJwoK03vBNc5N1zpvirgf9AB3fy/4xy8laZ44pwtCcK36Ukn3uvtvgjLndIFp7ThzThcud/9I0tOSjpLU38xKg1WDJW2Iqq/WEFCQNWbWK5iIJzPrJel4SX9qfy/ksQcljQ4ej5a0LMJe0EOaPrAGviPO6bwXTKq9U9Ib7j47YxXndAFp6zhzThcWM9vHzPoHjysl/bvS842eknRKsFnOnc98ixeyxsw+ofSoiSSVSrrP3WdE2BK6iZktknSspL0lvSfpKkkPSFoiaaikdySd6u5MsM5jbRznY5W+FMQlrZX0o6Z5CshPZnaMpN9LWiUpFZQvU3p+Aud0gWjnOJ8uzumCYWafVXoSfEzpgYkl7j4t+Ey2WNIASa9KOtPdk9F1GkZAAQAAAJAzuMQLAAAAQM4goAAAAADIGQQUAAAAADmDgAIAAAAgZxBQAAAAAOQMAgoAoEvMbL6ZXRvRa5uZ3W1mH5rZH7L4ugeZ2UfZej0AKCYEFAAoMGa21szeC26I2lQ728yejrCtnnKMpK9JGuzuoTtem9llZrYj+EmYWWPG8mtdeVF3/6u79+/KcwAAWkdAAYDCVCppUtRNdJaZxTq5ywGS1rr7zpYr3H2mu/d2996SJkh6oWnZ3Yd3R78AgO5HQAGAwnS9pMlmtsv/8pvZMDNzMyvNqD1tZmcHj8eY2f+a2U1m9pGZvWVmXwzq68xsk5mNbvG0e5vZE2a23cyeMbMDMp774GDdFjP7i5l9L2PdfDO73cweMbOdkr7SSr+DzOzBYP81ZjYuqP9Q0h2S/jUYFbmms38kM/uymb1iZlvNbLmZHZ6xbrmZTTezPwbrl5pZv4z31JCx7d5mdo+ZbQwuN/t1UN/XzB4N/o6bzezJzvYIAMWGgAIAhellSU9LmryH+x8p6f8k7SXpPkmLJR0u6ZOSzpT0CzPrnbH9f0iaLmlvSSsk3StJwWVmTwTPMVDS6ZJuM7PMEYwzJM2Q1EfSc630skjSekmDJJ0iaaaZfdXd71R4ZOSqzrxBMxso6SFJs4L3OVfSI00hJDAqeG/7SyqXdGMbT/drSSbpYEn/KOnWoD5F0l+U/rvsJ+nqzvQIAMWIgAIAhWuqpPPNbJ892Pdtd7/b3RuV/vA9RNI0d0+6++OS6pQOK00edvdn3T0p6XKlRzWGSDpJ6Uuw7nb3Bnd/RdJSpYNGk2Xu/r/unnL3RGYTwXMcI2mKuyfcfYXSoyZn7cF7ammkpBXuviTobb7SQegbGdvc7e5/dvcdkq5SOmCFmNmBkv5N0kR3/8jd69z92WB1vdLBamiLOgCgDQQUAChQ7v4nSb+TdOke7P5exuPa4Pla1jJHUNZlvO4OSVuU/mB+gKQjg0ucPgq++eo/JO3b2r6tGCRpi7tvz6j9TekRja4aFDxXppbPva7FuqoWIyxSOrxtatFjkxmSNkh6Krg87cIu9gwABY+AAgCF7SpJ4xT+0N00obwqo5YZGPbEkKYHwaVfA5T+YL5O0jPu3j/jp7e7n5Oxr7fzvBskDTCzPhm1oZLe7WK/Tc99QItay+ce0mJdjbtvbbHPOkkDW1zyJkly963uPsndD5D0XUlXmNnRXW8dAAoXAQUACpi7r1H6Eq0fZ9TeV/pD+JlmFjOzsZL+qYsvdaKZHWNm5UrPRXnR3dcpPYJzkJmdZWZlwc/hZnZIB/tfJ+l5SdeZWYWZfVbSDxXMcemiByUdZmanmFmpmY1SOoQ8mrHNmOCeJ72Vnj/y61Z6fFvSs0rPy+lnZuVm9iVJMrMRZnagmZmkrZIagx8AQBsIKABQ+KZJ6tWiNk7SxZI2SxqudAjoivuUHq3ZIulflL6MS8FlT8dLOk3pEYuNkn4mKd6J5z5d0rBg/99Kusrdn+hiv02XrI1Qes7MZknnSTrJ3TNvwLhQ6Un670pKSbqonR7LJK1W+j02jRAdovSXFWxXOsTc4O7Lu9o7ABQyc29vZB0AgOJkZssl/cLdfxV1LwBQTBhBAQAAAJAzCCgAAAAAcgaXeAEAAADIGYygAAAAAMgZBBQAAAAAOYOAAgAAACBnEFAAAAAA5AwCCgAAAICcQUABAAAAkDP+H4AA/vVoJHapAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline\n",
    "x_ax = range(2, 31, 1)\n",
    "y_ax = coherence_scores\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x_ax, y_ax, c=\"r\")\n",
    "plt.axhline(y=0.535, c=\"k\", linestyle=\"--\", linewidth=2)\n",
    "plt.rcParams['figure.facecolor'] = 'white'\n",
    "xl = plt.xlabel('Number of Topics')\n",
    "yl = plt.ylabel('Coherence Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}