Insights_potus_kld_plots_and_leaders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# %%\n",
    "#  Plots on selected model \n",
    "\n",
    "* model: (50t,600,10)\n",
    "* scale: 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing helper functions\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from helpers import potus\n",
    "import glob\n",
    "import re\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'helpers.potus' from '/mnt/batch/tasks/shared/LS_root/mounts/clusters/ilab2-aj/code/Users/12823819/ilab2/helpers/potus.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import imp\n",
    "imp.reload(potus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (50t,600,10)\n",
    "\n",
    "Load results for scales 125 and 25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load lda model from /home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/lda.model\n",
      "load bow from  /home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/bow.pkl\n",
      "File exists and is readable\n",
      "/home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/speeches_dfr_potus_50t_600_10_klds_100_100.pkl\n",
      "/home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/speeches_dfr_potus_50t_600_10_klds_125_125.pkl\n",
      "/home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/speeches_dfr_potus_50t_600_10_klds_25_25.pkl\n",
      "t: 50, Nw: 25, Tw:25\n",
      "962  before trim\n",
      "/home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/speeches_dfr_potus_50t_600_10_klds_50_50.pkl\n",
      "/home/azureuser/cloudfiles/code/data/processing/potus/experiment/potus_50t_600_10/speeches_dfr_potus_50t_600_10_klds_75_75.pkl\n",
      "962\n",
      "1\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>kld_filename</th>\n",
       "      <th>topics</th>\n",
       "      <th>Nw</th>\n",
       "      <th>Tw</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>speeches_dfr_potus_50t_600_10_klds_25_25.pkl</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>/home/azureuser/cloudfiles/code/data/processin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   kld_filename  topics  Nw  Tw  \\\n",
       "0  speeches_dfr_potus_50t_600_10_klds_25_25.pkl      50  25  25   \n",
       "\n",
       "                                                path  \n",
       "0  /home/azureuser/cloudfiles/code/data/processin...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Nw\n",
       "25    962\n",
       "Name: speech, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp = 'potus_50t_600_10'\n",
    "scales = [25]\n",
    "result = potus.gridExpResult(exp, scales)\n",
    "\n",
    "print(len(result.df))\n",
    "print(len(result.settings))\n",
    "display(result.settings)\n",
    "display(result.df.groupby('Nw').speech.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare ts object\n",
    "\n",
    "\n",
    "a pandas data frame with a date column set as index. date is converted using `pd.to_datetime`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.df.date = pd.to_datetime(result.df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
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       "      <th>file_name</th>\n",
       "      <th>speaker</th>\n",
       "      <th>title</th>\n",
       "      <th>speech</th>\n",
       "      <th>novelty</th>\n",
       "      <th>transience</th>\n",
       "      <th>resonance</th>\n",
       "      <th>speech_len</th>\n",
       "      <th>probs</th>\n",
       "      <th>token_count</th>\n",
       "      <th>raw_tuple</th>\n",
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       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1789-04-30</th>\n",
       "      <td>washington_speeches_000.txt</td>\n",
       "      <td>washington</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Fellow Citizens of the Senate and the House of...</td>\n",
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       "      <td>8687</td>\n",
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       "      <td>1256</td>\n",
       "      <td>(First Inaugural Address, 1789-04-30, Fellow C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789-10-03</th>\n",
       "      <td>washington_speeches_004.txt</td>\n",
       "      <td>washington</td>\n",
       "      <td>Thanksgiving Proclamation</td>\n",
       "      <td>Whereas it is the duty of all Nations to ackno...</td>\n",
       "      <td>0.347742</td>\n",
       "      <td>1.387929</td>\n",
       "      <td>-1.040186</td>\n",
       "      <td>2541</td>\n",
       "      <td>[0.00055468, 3.893e-05, 0.00082558, 0.00052866...</td>\n",
       "      <td>960</td>\n",
       "      <td>(Thanksgiving Proclamation, 1789-10-03, Wherea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790-01-08</th>\n",
       "      <td>washington_speeches_003.txt</td>\n",
       "      <td>washington</td>\n",
       "      <td>First Annual Message to Congress</td>\n",
       "      <td>Fellow Citizens of the Senate and House of Rep...</td>\n",
       "      <td>0.789467</td>\n",
       "      <td>1.202466</td>\n",
       "      <td>-0.412999</td>\n",
       "      <td>5278</td>\n",
       "      <td>[0.00029236, 2.052e-05, 0.19161724, 0.01777297...</td>\n",
       "      <td>900</td>\n",
       "      <td>(First Annual Message to Congress, 1790-01-08,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790-12-08</th>\n",
       "      <td>washington_speeches_001.txt</td>\n",
       "      <td>washington</td>\n",
       "      <td>Second Annual Message to Congress</td>\n",
       "      <td>Fellow citizens of the Senate and House of Rep...</td>\n",
       "      <td>1.163939</td>\n",
       "      <td>1.712157</td>\n",
       "      <td>-0.548218</td>\n",
       "      <td>8383</td>\n",
       "      <td>[0.00017585, 1.234e-05, 0.3299131, 0.01151088,...</td>\n",
       "      <td>271</td>\n",
       "      <td>(Second Annual Message to Congress, 1790-12-08...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790-12-29</th>\n",
       "      <td>washington_speeches_002.txt</td>\n",
       "      <td>washington</td>\n",
       "      <td>Talk to the Chiefs and Counselors of the Senec...</td>\n",
       "      <td>I the President of the United States, by my ow...</td>\n",
       "      <td>1.920160</td>\n",
       "      <td>2.423603</td>\n",
       "      <td>-0.503444</td>\n",
       "      <td>7898</td>\n",
       "      <td>[0.00021747, 1.526e-05, 0.05494575, 0.00020727...</td>\n",
       "      <td>607</td>\n",
       "      <td>(Talk to the Chiefs and Counselors of the Sene...</td>\n",
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      "text/plain": [
       "                              file_name     speaker  \\\n",
       "date                                                  \n",
       "1789-04-30  washington_speeches_000.txt  washington   \n",
       "1789-10-03  washington_speeches_004.txt  washington   \n",
       "1790-01-08  washington_speeches_003.txt  washington   \n",
       "1790-12-08  washington_speeches_001.txt  washington   \n",
       "1790-12-29  washington_speeches_002.txt  washington   \n",
       "\n",
       "                                                        title  \\\n",
       "date                                                            \n",
       "1789-04-30                            First Inaugural Address   \n",
       "1789-10-03                          Thanksgiving Proclamation   \n",
       "1790-01-08                   First Annual Message to Congress   \n",
       "1790-12-08                  Second Annual Message to Congress   \n",
       "1790-12-29  Talk to the Chiefs and Counselors of the Senec...   \n",
       "\n",
       "                                                       speech   novelty  \\\n",
       "date                                                                      \n",
       "1789-04-30  Fellow Citizens of the Senate and the House of...  0.000000   \n",
       "1789-10-03  Whereas it is the duty of all Nations to ackno...  0.347742   \n",
       "1790-01-08  Fellow Citizens of the Senate and House of Rep...  0.789467   \n",
       "1790-12-08  Fellow citizens of the Senate and House of Rep...  1.163939   \n",
       "1790-12-29  I the President of the United States, by my ow...  1.920160   \n",
       "\n",
       "            transience  resonance  speech_len  \\\n",
       "date                                            \n",
       "1789-04-30    1.331562  -1.331562        8687   \n",
       "1789-10-03    1.387929  -1.040186        2541   \n",
       "1790-01-08    1.202466  -0.412999        5278   \n",
       "1790-12-08    1.712157  -0.548218        8383   \n",
       "1790-12-29    2.423603  -0.503444        7898   \n",
       "\n",
       "                                                        probs  token_count  \\\n",
       "date                                                                         \n",
       "1789-04-30  [0.00018299, 1.284e-05, 0.01475338, 0.00017441...         1256   \n",
       "1789-10-03  [0.00055468, 3.893e-05, 0.00082558, 0.00052866...          960   \n",
       "1790-01-08  [0.00029236, 2.052e-05, 0.19161724, 0.01777297...          900   \n",
       "1790-12-08  [0.00017585, 1.234e-05, 0.3299131, 0.01151088,...          271   \n",
       "1790-12-29  [0.00021747, 1.526e-05, 0.05494575, 0.00020727...          607   \n",
       "\n",
       "                                                    raw_tuple  \n",
       "date                                                           \n",
       "1789-04-30  (First Inaugural Address, 1789-04-30, Fellow C...  \n",
       "1789-10-03  (Thanksgiving Proclamation, 1789-10-03, Wherea...  \n",
       "1790-01-08  (First Annual Message to Congress, 1790-01-08,...  \n",
       "1790-12-08  (Second Annual Message to Congress, 1790-12-08...  \n",
       "1790-12-29  (Talk to the Chiefs and Counselors of the Sene...  "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = result.df[['date', 'file_name', 'speaker', 'title', 'speech', 'novelty', 'transience', 'resonance',  'speech_len', 'probs', 'token_count', 'raw_tuple']].set_index('date')\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>speaker</th>\n",
       "      <th>title</th>\n",
       "      <th>speech</th>\n",
       "      <th>novelty</th>\n",
       "      <th>transience</th>\n",
       "      <th>resonance</th>\n",
       "      <th>speech_len</th>\n",
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       "      <th>date</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-28</th>\n",
       "      <td>gwbush_speeches_032.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>State of the Union Address</td>\n",
       "      <td>Madam Speaker, Vice President Cheney, members ...</td>\n",
       "      <td>1.748165</td>\n",
       "      <td>2.051343</td>\n",
       "      <td>-0.303178</td>\n",
       "      <td>33649</td>\n",
       "      <td>[4.038e-05, 2.83e-06, 6.005e-05, 3.848e-05, 9....</td>\n",
       "      <td>3746</td>\n",
       "      <td>(State of the Union Address, 2008-01-28, Madam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-19</th>\n",
       "      <td>gwbush_speeches_034.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Remarks on the War on Terror</td>\n",
       "      <td>Thank you all. Deputy Secretary England, thank...</td>\n",
       "      <td>1.433824</td>\n",
       "      <td>2.953103</td>\n",
       "      <td>-1.519279</td>\n",
       "      <td>18532</td>\n",
       "      <td>[6.925e-05, 4.86e-06, 0.00010303, 0.00375672, ...</td>\n",
       "      <td>345</td>\n",
       "      <td>(Remarks on the War on Terror, 2008-03-19, Tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-28</th>\n",
       "      <td>obama_speeches_002.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Acceptance Speech at the Democratic National C...</td>\n",
       "      <td>To Chairman Dean and my great friend Dick Durb...</td>\n",
       "      <td>2.642174</td>\n",
       "      <td>1.928734</td>\n",
       "      <td>0.713440</td>\n",
       "      <td>25901</td>\n",
       "      <td>[5.656e-05, 3.97e-06, 8.407e-05, 5.391e-05, 0....</td>\n",
       "      <td>595</td>\n",
       "      <td>(Acceptance Speech at the Democratic National ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-03</th>\n",
       "      <td>gwbush_speeches_033.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Remarks on Emergency Economic Stabilization Ac...</td>\n",
       "      <td>A short time ago, the House of Representatives...</td>\n",
       "      <td>3.339841</td>\n",
       "      <td>2.308997</td>\n",
       "      <td>1.030843</td>\n",
       "      <td>4198</td>\n",
       "      <td>[0.00029651, 2.081e-05, 0.00044073, 0.0002826,...</td>\n",
       "      <td>561</td>\n",
       "      <td>(Remarks on Emergency Economic Stabilization A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-11-04</th>\n",
       "      <td>obama_speeches_000.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Election Night</td>\n",
       "      <td>If there is anyone out there who still doubts ...</td>\n",
       "      <td>2.445935</td>\n",
       "      <td>2.056777</td>\n",
       "      <td>0.389158</td>\n",
       "      <td>10517</td>\n",
       "      <td>[0.00015364, 1.078e-05, 0.00022836, 0.00014643...</td>\n",
       "      <td>547</td>\n",
       "      <td>(Remarks on Election Night, 2008-11-04, If the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-11-13</th>\n",
       "      <td>gwbush_speeches_035.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Speech on Financial Markets and the World Economy</td>\n",
       "      <td>Thank you very much. Please be seated. Thank y...</td>\n",
       "      <td>2.464317</td>\n",
       "      <td>1.763558</td>\n",
       "      <td>0.700760</td>\n",
       "      <td>18279</td>\n",
       "      <td>[0.04399939, 5.26e-06, 0.00724698, 7.137e-05, ...</td>\n",
       "      <td>354</td>\n",
       "      <td>(Speech on Financial Markets and the World Eco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-19</th>\n",
       "      <td>gwbush_speeches_036.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Remarks on Plan to Assist Automakers</td>\n",
       "      <td>Good morning. For years, America's automakers ...</td>\n",
       "      <td>3.146432</td>\n",
       "      <td>2.309540</td>\n",
       "      <td>0.836892</td>\n",
       "      <td>6446</td>\n",
       "      <td>[0.0002141, 1.503e-05, 0.00031847, 0.00020406,...</td>\n",
       "      <td>181</td>\n",
       "      <td>(Remarks on Plan to Assist Automakers, 2008-12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-12</th>\n",
       "      <td>gwbush_speeches_037.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Final Press Conference</td>\n",
       "      <td>\\n&lt;THE PRESIDENT:&gt; Thank you. Tapper. We have ...</td>\n",
       "      <td>3.645072</td>\n",
       "      <td>3.182389</td>\n",
       "      <td>0.462683</td>\n",
       "      <td>39634</td>\n",
       "      <td>[0.00600836, 3.06e-06, 6.489e-05, 4.16e-05, 0....</td>\n",
       "      <td>971</td>\n",
       "      <td>(Final Press Conference, 2009-01-12, \\n&lt;THE PR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-15</th>\n",
       "      <td>gwbush_speeches_038.txt</td>\n",
       "      <td>gwbush</td>\n",
       "      <td>Farewell Address to the Nation</td>\n",
       "      <td>Fellow citizens:\\nFor eight years, it has been...</td>\n",
       "      <td>1.613090</td>\n",
       "      <td>1.937509</td>\n",
       "      <td>-0.324420</td>\n",
       "      <td>10668</td>\n",
       "      <td>[0.00013051, 9.16e-06, 0.00019405, 0.00012439,...</td>\n",
       "      <td>69</td>\n",
       "      <td>(Farewell Address to the Nation, 2009-01-15, F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-20</th>\n",
       "      <td>obama_speeches_001.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>I stand here today humbled by the task before ...</td>\n",
       "      <td>1.920067</td>\n",
       "      <td>1.738715</td>\n",
       "      <td>0.181352</td>\n",
       "      <td>13384</td>\n",
       "      <td>[0.01489551, 7.57e-06, 0.00016036, 0.0001028, ...</td>\n",
       "      <td>940</td>\n",
       "      <td>(Inaugural Address, 2009-01-20, I stand here t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-29</th>\n",
       "      <td>obama_speeches_003.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on the Lilly Ledbetter Fair Pay Restor...</td>\n",
       "      <td>It is fitting that with the very first bill I ...</td>\n",
       "      <td>2.770151</td>\n",
       "      <td>2.039948</td>\n",
       "      <td>0.730203</td>\n",
       "      <td>4935</td>\n",
       "      <td>[0.00031491, 2.21e-05, 0.0004681, 0.00030014, ...</td>\n",
       "      <td>88</td>\n",
       "      <td>(Remarks on the Lilly Ledbetter Fair Pay Resto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-07</th>\n",
       "      <td>obama_speeches_005.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on the American Recovery and Reinvestm...</td>\n",
       "      <td>Thank you, everybody. Please have a seat. You ...</td>\n",
       "      <td>2.099385</td>\n",
       "      <td>1.957616</td>\n",
       "      <td>0.141769</td>\n",
       "      <td>14758</td>\n",
       "      <td>[9.447e-05, 6.63e-06, 0.00014046, 9.004e-05, 0...</td>\n",
       "      <td>52</td>\n",
       "      <td>(Remarks on the American Recovery and Reinvest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-24</th>\n",
       "      <td>obama_speeches_006.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address Before a Joint Session of Congress</td>\n",
       "      <td>Madam Speaker, Mr. Vice President, Members of ...</td>\n",
       "      <td>1.908518</td>\n",
       "      <td>1.905891</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>34335</td>\n",
       "      <td>[4.168e-05, 2.92e-06, 0.00106819, 3.972e-05, 9...</td>\n",
       "      <td>803</td>\n",
       "      <td>(Address Before a Joint Session of Congress, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-26</th>\n",
       "      <td>obama_speeches_004.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Nominating Judge Sonia Sotomayor to...</td>\n",
       "      <td>Thank you. Well, I'm excited, too. &lt;Laughter.&gt;...</td>\n",
       "      <td>2.707594</td>\n",
       "      <td>2.709706</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>8803</td>\n",
       "      <td>[0.00016456, 1.155e-05, 0.00024465, 0.01125762...</td>\n",
       "      <td>95</td>\n",
       "      <td>(Remarks on Nominating Judge Sonia Sotomayor t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-04</th>\n",
       "      <td>obama_speeches_007.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address at Cairo University</td>\n",
       "      <td>Thank you so much. Good afternoon. I am honore...</td>\n",
       "      <td>2.114990</td>\n",
       "      <td>1.913108</td>\n",
       "      <td>0.201882</td>\n",
       "      <td>34589</td>\n",
       "      <td>[0.0828473, 2.97e-06, 6.292e-05, 4.034e-05, 0....</td>\n",
       "      <td>554</td>\n",
       "      <td>(Address at Cairo University, 2009-06-04, Than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-09</th>\n",
       "      <td>obama_speeches_011.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to Congress on Health Care</td>\n",
       "      <td>Madam Speaker, Vice President Biden, Members o...</td>\n",
       "      <td>3.008327</td>\n",
       "      <td>3.262299</td>\n",
       "      <td>-0.253973</td>\n",
       "      <td>32058</td>\n",
       "      <td>[4.604e-05, 3.23e-06, 6.846e-05, 4.388e-05, 0....</td>\n",
       "      <td>281</td>\n",
       "      <td>(Address to Congress on Health Care, 2009-09-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-01</th>\n",
       "      <td>obama_speeches_008.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on Strategy in Afghanistan and Pakistan</td>\n",
       "      <td>Good evening. To the United States Corps of Ca...</td>\n",
       "      <td>1.916320</td>\n",
       "      <td>1.971783</td>\n",
       "      <td>-0.055463</td>\n",
       "      <td>27068</td>\n",
       "      <td>[0.02631954, 3.88e-06, 8.23e-05, 5.275e-05, 0....</td>\n",
       "      <td>1226</td>\n",
       "      <td>(Speech on Strategy in Afghanistan and Pakista...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-10</th>\n",
       "      <td>obama_speeches_010.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Acceptance of Nobel Peace Prize</td>\n",
       "      <td>Your Majesties, Your Royal Highnesses, disting...</td>\n",
       "      <td>2.101170</td>\n",
       "      <td>2.123856</td>\n",
       "      <td>-0.022686</td>\n",
       "      <td>24598</td>\n",
       "      <td>[0.06828414, 4.21e-06, 8.918e-05, 5.716e-05, 0...</td>\n",
       "      <td>174</td>\n",
       "      <td>(Acceptance of Nobel Peace Prize, 2009-12-10, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>obama_speeches_009.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2010 State of the Union Address</td>\n",
       "      <td>Madam Speaker, Vice President Biden, Members o...</td>\n",
       "      <td>1.964747</td>\n",
       "      <td>1.816679</td>\n",
       "      <td>0.148068</td>\n",
       "      <td>41860</td>\n",
       "      <td>[0.00850936, 2.42e-06, 5.12e-05, 3.282e-05, 7....</td>\n",
       "      <td>846</td>\n",
       "      <td>(2010 State of the Union Address, 2010-01-27, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>obama_speeches_013.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>News Conference on Congressional Gridlock</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Hello, everybody. I am glad t...</td>\n",
       "      <td>2.997956</td>\n",
       "      <td>2.648344</td>\n",
       "      <td>0.349612</td>\n",
       "      <td>29052</td>\n",
       "      <td>[0.00977934, 3.81e-06, 8.079e-05, 5.178e-05, 0...</td>\n",
       "      <td>2695</td>\n",
       "      <td>(News Conference on Congressional Gridlock, 20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-15</th>\n",
       "      <td>obama_speeches_015.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on Health Care Reform</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Hello, Ohio! &lt;Applause.&gt; It i...</td>\n",
       "      <td>4.073154</td>\n",
       "      <td>4.138498</td>\n",
       "      <td>-0.065344</td>\n",
       "      <td>24134</td>\n",
       "      <td>[6.667e-05, 4.68e-06, 9.927e-05, 6.354e-05, 0....</td>\n",
       "      <td>411</td>\n",
       "      <td>(Speech on Health Care Reform, 2010-03-15, &lt;TH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-15</th>\n",
       "      <td>obama_speeches_014.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Space Exploration in the 21st Century</td>\n",
       "      <td>Thank you, everybody. Thank you. &lt;Applause.&gt; T...</td>\n",
       "      <td>2.138417</td>\n",
       "      <td>2.178462</td>\n",
       "      <td>-0.040045</td>\n",
       "      <td>20505</td>\n",
       "      <td>[7.202e-05, 5.05e-06, 0.00010707, 6.865e-05, 0...</td>\n",
       "      <td>624</td>\n",
       "      <td>(Remarks on Space Exploration in the 21st Cent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-28</th>\n",
       "      <td>obama_speeches_012.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Wall Street Reform</td>\n",
       "      <td>Thank you very much. Everybody, please have a ...</td>\n",
       "      <td>1.931615</td>\n",
       "      <td>2.232449</td>\n",
       "      <td>-0.300834</td>\n",
       "      <td>20082</td>\n",
       "      <td>[0.00226557, 5.02e-06, 0.00010648, 6.824e-05, ...</td>\n",
       "      <td>1217</td>\n",
       "      <td>(Remarks on Wall Street Reform, 2010-04-28, Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-15</th>\n",
       "      <td>obama_speeches_017.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on the BP Oil Spill</td>\n",
       "      <td>Good evening. As we speak, our nation faces a ...</td>\n",
       "      <td>1.701138</td>\n",
       "      <td>1.712087</td>\n",
       "      <td>-0.010949</td>\n",
       "      <td>15327</td>\n",
       "      <td>[9.315e-05, 6.54e-06, 0.00013854, 8.878e-05, 0...</td>\n",
       "      <td>714</td>\n",
       "      <td>(Speech on the BP Oil Spill, 2010-06-15, Good ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31</th>\n",
       "      <td>obama_speeches_016.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address on the End of the Combat Mission in Iraq</td>\n",
       "      <td>Good evening. Tonight, I'd like to talk to you...</td>\n",
       "      <td>2.315469</td>\n",
       "      <td>1.995833</td>\n",
       "      <td>0.319636</td>\n",
       "      <td>14913</td>\n",
       "      <td>[0.00549059, 6.67e-06, 0.00014136, 9.059e-05, ...</td>\n",
       "      <td>480</td>\n",
       "      <td>(Address on the End of the Combat Mission in I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-23</th>\n",
       "      <td>obama_speeches_018.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to the United Nations</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Mr. President, Mr. Secretary-...</td>\n",
       "      <td>1.875054</td>\n",
       "      <td>1.788741</td>\n",
       "      <td>0.086313</td>\n",
       "      <td>23771</td>\n",
       "      <td>[0.06801354, 4.43e-06, 9.393e-05, 6.021e-05, 0...</td>\n",
       "      <td>443</td>\n",
       "      <td>(Address to the United Nations, 2010-09-23, &lt;T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-03</th>\n",
       "      <td>obama_speeches_019.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Press Conference After 2010 Midterm Elections</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Good afternoon, everybody. La...</td>\n",
       "      <td>2.711334</td>\n",
       "      <td>2.683977</td>\n",
       "      <td>0.027357</td>\n",
       "      <td>47096</td>\n",
       "      <td>[0.0049988, 2.45e-06, 5.187e-05, 3.324e-05, 0....</td>\n",
       "      <td>521</td>\n",
       "      <td>(Press Conference After 2010 Midterm Elections...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>obama_speeches_020.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks at Memorial for Victims of the Tucson,...</td>\n",
       "      <td>Thank you. &lt;Applause.&gt; Thank you very much. Pl...</td>\n",
       "      <td>2.279645</td>\n",
       "      <td>1.528152</td>\n",
       "      <td>0.751493</td>\n",
       "      <td>15757</td>\n",
       "      <td>[0.00908183, 6.92e-06, 0.00014657, 9.396e-05, ...</td>\n",
       "      <td>1430</td>\n",
       "      <td>(Remarks at Memorial for Victims of the Tucson...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-25</th>\n",
       "      <td>obama_speeches_023.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2011 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, members of Co...</td>\n",
       "      <td>1.897771</td>\n",
       "      <td>1.837164</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>40559</td>\n",
       "      <td>[3.632e-05, 2.55e-06, 5.4e-05, 3.461e-05, 8.19...</td>\n",
       "      <td>727</td>\n",
       "      <td>(2011 State of the Union Address, 2011-01-25, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>obama_speeches_021.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on the Death of Osama Bin Laden</td>\n",
       "      <td>Good evening. Tonight, I can report to the Ame...</td>\n",
       "      <td>2.160135</td>\n",
       "      <td>2.001310</td>\n",
       "      <td>0.158825</td>\n",
       "      <td>7885</td>\n",
       "      <td>[0.00019376, 1.36e-05, 0.00028809, 0.00018467,...</td>\n",
       "      <td>505</td>\n",
       "      <td>(Remarks on the Death of Osama Bin Laden, 2011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-19</th>\n",
       "      <td>obama_speeches_022.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on American Diplomacy in the Middle Eas...</td>\n",
       "      <td>Thank you. Thank you. &lt;Applause.&gt; Thank you ve...</td>\n",
       "      <td>1.937459</td>\n",
       "      <td>1.972939</td>\n",
       "      <td>-0.035480</td>\n",
       "      <td>33299</td>\n",
       "      <td>[0.06347695, 3.07e-06, 6.507e-05, 4.17e-05, 0....</td>\n",
       "      <td>350</td>\n",
       "      <td>(Speech on American Diplomacy in the Middle Ea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-25</th>\n",
       "      <td>obama_speeches_027.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to the British Parliament</td>\n",
       "      <td>Thank you very much. Thank you. &lt;Applause.&gt;\\nM...</td>\n",
       "      <td>1.881103</td>\n",
       "      <td>1.857862</td>\n",
       "      <td>0.023241</td>\n",
       "      <td>24541</td>\n",
       "      <td>[0.02297016, 4.36e-06, 9.248e-05, 5.926e-05, 0...</td>\n",
       "      <td>1175</td>\n",
       "      <td>(Address to the British Parliament, 2011-05-25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-21</th>\n",
       "      <td>obama_speeches_026.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on the End of the War in Iraq</td>\n",
       "      <td>Good afternoon, everybody. As a candidate for ...</td>\n",
       "      <td>2.544364</td>\n",
       "      <td>3.034250</td>\n",
       "      <td>-0.489886</td>\n",
       "      <td>5492</td>\n",
       "      <td>[0.05534188, 1.87e-05, 0.00039612, 0.00025402,...</td>\n",
       "      <td>1057</td>\n",
       "      <td>(Remarks on the End of the War in Iraq, 2011-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24</th>\n",
       "      <td>obama_speeches_029.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2012 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, members of Co...</td>\n",
       "      <td>1.913629</td>\n",
       "      <td>1.840703</td>\n",
       "      <td>0.072925</td>\n",
       "      <td>41607</td>\n",
       "      <td>[3.432e-05, 2.41e-06, 5.103e-05, 3.271e-05, 7....</td>\n",
       "      <td>341</td>\n",
       "      <td>(2012 State of the Union Address, 2012-01-24, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-06</th>\n",
       "      <td>obama_speeches_028.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Nominee Acceptance Speech at 2012 Democratic N...</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Thank you. &lt;Applause.&gt; Thank ...</td>\n",
       "      <td>1.738005</td>\n",
       "      <td>1.571403</td>\n",
       "      <td>0.166603</td>\n",
       "      <td>26311</td>\n",
       "      <td>[5.617e-05, 3.94e-06, 8.349e-05, 5.354e-05, 0....</td>\n",
       "      <td>387</td>\n",
       "      <td>(Nominee Acceptance Speech at 2012 Democratic ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-06</th>\n",
       "      <td>obama_speeches_031.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2012 Election Night Victory Speech</td>\n",
       "      <td>Tonight, more than 200 years after a former co...</td>\n",
       "      <td>1.703439</td>\n",
       "      <td>1.153935</td>\n",
       "      <td>0.549505</td>\n",
       "      <td>12354</td>\n",
       "      <td>[0.00012569, 8.82e-06, 0.00018682, 0.00011979,...</td>\n",
       "      <td>1794</td>\n",
       "      <td>(2012 Election Night Victory Speech, 2012-11-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-16</th>\n",
       "      <td>obama_speeches_030.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Sandy Hook Elementary Shootings</td>\n",
       "      <td>Thank you. &lt;Applause.&gt; Thank you, Governor. To...</td>\n",
       "      <td>1.549324</td>\n",
       "      <td>1.242497</td>\n",
       "      <td>0.306826</td>\n",
       "      <td>9342</td>\n",
       "      <td>[0.00017554, 1.232e-05, 0.00026108, 0.00016731...</td>\n",
       "      <td>264</td>\n",
       "      <td>(Remarks on Sandy Hook Elementary Shootings, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-21</th>\n",
       "      <td>obama_speeches_032.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Vice President Biden, Mr. Chief Justice,\\nmemb...</td>\n",
       "      <td>1.983780</td>\n",
       "      <td>1.899733</td>\n",
       "      <td>0.084047</td>\n",
       "      <td>12253</td>\n",
       "      <td>[0.00011815, 8.29e-06, 0.00017562, 0.0001126, ...</td>\n",
       "      <td>495</td>\n",
       "      <td>(Second Inaugural Address, 2013-01-21, Vice Pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>obama_speeches_033.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Immigration Reform</td>\n",
       "      <td>Thank you! &lt;Applause.&gt; Thank you! Thank you so...</td>\n",
       "      <td>1.896743</td>\n",
       "      <td>1.309411</td>\n",
       "      <td>0.587332</td>\n",
       "      <td>17516</td>\n",
       "      <td>[0.00334907, 6.41e-06, 0.00013571, 8.7e-05, 0....</td>\n",
       "      <td>2303</td>\n",
       "      <td>(Remarks on Immigration Reform, 2013-01-29, Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-13</th>\n",
       "      <td>obama_speeches_034.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2013 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, members of Co...</td>\n",
       "      <td>1.839322</td>\n",
       "      <td>1.888182</td>\n",
       "      <td>-0.048860</td>\n",
       "      <td>40609</td>\n",
       "      <td>[3.464e-05, 2.43e-06, 5.151e-05, 3.302e-05, 7....</td>\n",
       "      <td>859</td>\n",
       "      <td>(2013 State of the Union Address, 2013-02-13, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-01</th>\n",
       "      <td>obama_speeches_035.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Statement on the Government Sequester</td>\n",
       "      <td>Good morning, everybody. As you know, I just m...</td>\n",
       "      <td>2.601175</td>\n",
       "      <td>2.051339</td>\n",
       "      <td>0.549836</td>\n",
       "      <td>29581</td>\n",
       "      <td>[5.418e-05, 3.8e-06, 8.058e-05, 5.164e-05, 0.0...</td>\n",
       "      <td>3222</td>\n",
       "      <td>(Statement on the Government Sequester, 2013-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-21</th>\n",
       "      <td>obama_speeches_036.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to the People of Israel</td>\n",
       "      <td>Thank you. &lt;Applause.&gt; Thank you so much. Well...</td>\n",
       "      <td>1.745539</td>\n",
       "      <td>1.720239</td>\n",
       "      <td>0.025300</td>\n",
       "      <td>31798</td>\n",
       "      <td>[0.01345588, 3.42e-06, 7.246e-05, 4.638e-05, 0...</td>\n",
       "      <td>655</td>\n",
       "      <td>(Address to the People of Israel, 2013-03-21, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-08</th>\n",
       "      <td>obama_speeches_037.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on Gun Violence</td>\n",
       "      <td>Hello, Connecticut. &lt;Applause.&gt; Thank you. Wel...</td>\n",
       "      <td>2.239898</td>\n",
       "      <td>1.633872</td>\n",
       "      <td>0.606026</td>\n",
       "      <td>18347</td>\n",
       "      <td>[8.89e-05, 6.24e-06, 0.00013214, 8.473e-05, 0....</td>\n",
       "      <td>622</td>\n",
       "      <td>(Speech on Gun Violence, 2013-04-08, Hello, Co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-13</th>\n",
       "      <td>obama_speeches_038.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Trayvon Martin</td>\n",
       "      <td>I wanted to come out here, first of all, to te...</td>\n",
       "      <td>2.017945</td>\n",
       "      <td>1.294482</td>\n",
       "      <td>0.723463</td>\n",
       "      <td>11935</td>\n",
       "      <td>[0.02367926, 9.24e-06, 0.00019582, 0.00012547,...</td>\n",
       "      <td>892</td>\n",
       "      <td>(Remarks on Trayvon Martin, 2013-07-13, I want...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-24</th>\n",
       "      <td>obama_speeches_039.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks on Education and the Economy</td>\n",
       "      <td>Hello, Warrensburg! &lt;Applause.&gt; Hello, Mules! ...</td>\n",
       "      <td>2.209701</td>\n",
       "      <td>2.170142</td>\n",
       "      <td>0.039560</td>\n",
       "      <td>24707</td>\n",
       "      <td>[0.00141263, 4.28e-06, 9.062e-05, 5.809e-05, 0...</td>\n",
       "      <td>1090</td>\n",
       "      <td>(Remarks on Education and the Economy, 2013-07...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-10</th>\n",
       "      <td>obama_speeches_040.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to the Nation on Syria</td>\n",
       "      <td>My fellow Americans, tonight I want to talk to...</td>\n",
       "      <td>2.528655</td>\n",
       "      <td>2.816757</td>\n",
       "      <td>-0.288102</td>\n",
       "      <td>12873</td>\n",
       "      <td>[0.04937784, 7.99e-06, 0.0001692, 0.00010848, ...</td>\n",
       "      <td>696</td>\n",
       "      <td>(Address to the Nation on Syria, 2013-09-10, M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-04</th>\n",
       "      <td>obama_speeches_041.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Speech on Economic Mobility</td>\n",
       "      <td>Thank you. &lt;Applause.&gt; Thank you, everybody. T...</td>\n",
       "      <td>2.012682</td>\n",
       "      <td>1.486710</td>\n",
       "      <td>0.525971</td>\n",
       "      <td>38033</td>\n",
       "      <td>[3.688e-05, 2.59e-06, 5.484e-05, 3.515e-05, 0....</td>\n",
       "      <td>597</td>\n",
       "      <td>(Speech on Economic Mobility, 2013-12-04, Than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-28</th>\n",
       "      <td>obama_speeches_042.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2014 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, Members of Co...</td>\n",
       "      <td>1.712051</td>\n",
       "      <td>1.658727</td>\n",
       "      <td>0.053324</td>\n",
       "      <td>39771</td>\n",
       "      <td>[3.585e-05, 2.52e-06, 0.00293729, 3.417e-05, 8...</td>\n",
       "      <td>582</td>\n",
       "      <td>(2014 State of the Union Address, 2014-01-28, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-20</th>\n",
       "      <td>obama_speeches_043.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Address to the Nation on Immigration</td>\n",
       "      <td>My fellow Americans, tonight, I'd like to talk...</td>\n",
       "      <td>1.573868</td>\n",
       "      <td>1.417770</td>\n",
       "      <td>0.156099</td>\n",
       "      <td>12638</td>\n",
       "      <td>[0.00012591, 8.84e-06, 0.0001872, 0.00012, 0.0...</td>\n",
       "      <td>1471</td>\n",
       "      <td>(Address to the Nation on Immigration, 2014-11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>obama_speeches_044.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2015 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, Members of Co...</td>\n",
       "      <td>1.592861</td>\n",
       "      <td>1.842766</td>\n",
       "      <td>-0.249905</td>\n",
       "      <td>39641</td>\n",
       "      <td>[3.665e-05, 2.57e-06, 5.45e-05, 3.493e-05, 8.2...</td>\n",
       "      <td>732</td>\n",
       "      <td>(2015 State of the Union Address, 2015-01-20, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-07</th>\n",
       "      <td>obama_speeches_046.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks at the 50th Anniversary of the Selma M...</td>\n",
       "      <td>&lt;AUDIENCE MEMBER: We love you, President Obama...</td>\n",
       "      <td>1.623872</td>\n",
       "      <td>0.844560</td>\n",
       "      <td>0.779312</td>\n",
       "      <td>21381</td>\n",
       "      <td>[7.046e-05, 4.95e-06, 0.00010477, 6.716e-05, 0...</td>\n",
       "      <td>1055</td>\n",
       "      <td>(Remarks at the 50th Anniversary of the Selma ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>obama_speeches_045.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks in Eulogy for the Honorable Reverend C...</td>\n",
       "      <td>&lt;THE PRESIDENT:&gt; Giving all praise and honor t...</td>\n",
       "      <td>1.754457</td>\n",
       "      <td>1.079524</td>\n",
       "      <td>0.674934</td>\n",
       "      <td>17575</td>\n",
       "      <td>[8.147e-05, 5.72e-06, 0.00012116, 7.765e-05, 0...</td>\n",
       "      <td>2178</td>\n",
       "      <td>(Remarks in Eulogy for the Honorable Reverend ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>obama_speeches_048.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>2016 State of the Union Address</td>\n",
       "      <td>Mr. Speaker, Mr. Vice President, Members of Co...</td>\n",
       "      <td>1.415708</td>\n",
       "      <td>0.863662</td>\n",
       "      <td>0.552046</td>\n",
       "      <td>35096</td>\n",
       "      <td>[4.255e-05, 2.99e-06, 6.328e-05, 4.055e-05, 0....</td>\n",
       "      <td>1530</td>\n",
       "      <td>(2016 State of the Union Address, 2016-01-12, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-22</th>\n",
       "      <td>obama_speeches_047.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Remarks to the People of Cuba</td>\n",
       "      <td>Thank you. &lt;Applause.&gt; Muchas gracias. Thank y...</td>\n",
       "      <td>1.832376</td>\n",
       "      <td>1.189345</td>\n",
       "      <td>0.643031</td>\n",
       "      <td>23524</td>\n",
       "      <td>[0.0477215, 4.75e-06, 0.01421468, 6.457e-05, 0...</td>\n",
       "      <td>2058</td>\n",
       "      <td>(Remarks to the People of Cuba, 2016-03-22, Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-15</th>\n",
       "      <td>obama_speeches_049.txt</td>\n",
       "      <td>obama</td>\n",
       "      <td>Commencement Address at Rutgers University</td>\n",
       "      <td>Hello Rutgers! &lt;Applause.&gt; R-U rah-rah! &lt;Appla...</td>\n",
       "      <td>1.386135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386135</td>\n",
       "      <td>28940</td>\n",
       "      <td>[0.02182939, 3.58e-06, 7.593e-05, 4.866e-05, 0...</td>\n",
       "      <td>1136</td>\n",
       "      <td>(Commencement Address at Rutgers University, 2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          file_name speaker  \\\n",
       "date                                          \n",
       "2008-01-28  gwbush_speeches_032.txt  gwbush   \n",
       "2008-03-19  gwbush_speeches_034.txt  gwbush   \n",
       "2008-08-28   obama_speeches_002.txt   obama   \n",
       "2008-10-03  gwbush_speeches_033.txt  gwbush   \n",
       "2008-11-04   obama_speeches_000.txt   obama   \n",
       "2008-11-13  gwbush_speeches_035.txt  gwbush   \n",
       "2008-12-19  gwbush_speeches_036.txt  gwbush   \n",
       "2009-01-12  gwbush_speeches_037.txt  gwbush   \n",
       "2009-01-15  gwbush_speeches_038.txt  gwbush   \n",
       "2009-01-20   obama_speeches_001.txt   obama   \n",
       "2009-01-29   obama_speeches_003.txt   obama   \n",
       "2009-02-07   obama_speeches_005.txt   obama   \n",
       "2009-02-24   obama_speeches_006.txt   obama   \n",
       "2009-05-26   obama_speeches_004.txt   obama   \n",
       "2009-06-04   obama_speeches_007.txt   obama   \n",
       "2009-09-09   obama_speeches_011.txt   obama   \n",
       "2009-12-01   obama_speeches_008.txt   obama   \n",
       "2009-12-10   obama_speeches_010.txt   obama   \n",
       "2010-01-27   obama_speeches_009.txt   obama   \n",
       "2010-02-09   obama_speeches_013.txt   obama   \n",
       "2010-03-15   obama_speeches_015.txt   obama   \n",
       "2010-04-15   obama_speeches_014.txt   obama   \n",
       "2010-04-28   obama_speeches_012.txt   obama   \n",
       "2010-06-15   obama_speeches_017.txt   obama   \n",
       "2010-08-31   obama_speeches_016.txt   obama   \n",
       "2010-09-23   obama_speeches_018.txt   obama   \n",
       "2010-11-03   obama_speeches_019.txt   obama   \n",
       "2011-01-12   obama_speeches_020.txt   obama   \n",
       "2011-01-25   obama_speeches_023.txt   obama   \n",
       "2011-05-01   obama_speeches_021.txt   obama   \n",
       "2011-05-19   obama_speeches_022.txt   obama   \n",
       "2011-05-25   obama_speeches_027.txt   obama   \n",
       "2011-10-21   obama_speeches_026.txt   obama   \n",
       "2012-01-24   obama_speeches_029.txt   obama   \n",
       "2012-09-06   obama_speeches_028.txt   obama   \n",
       "2012-11-06   obama_speeches_031.txt   obama   \n",
       "2012-12-16   obama_speeches_030.txt   obama   \n",
       "2013-01-21   obama_speeches_032.txt   obama   \n",
       "2013-01-29   obama_speeches_033.txt   obama   \n",
       "2013-02-13   obama_speeches_034.txt   obama   \n",
       "2013-03-01   obama_speeches_035.txt   obama   \n",
       "2013-03-21   obama_speeches_036.txt   obama   \n",
       "2013-04-08   obama_speeches_037.txt   obama   \n",
       "2013-07-13   obama_speeches_038.txt   obama   \n",
       "2013-07-24   obama_speeches_039.txt   obama   \n",
       "2013-09-10   obama_speeches_040.txt   obama   \n",
       "2013-12-04   obama_speeches_041.txt   obama   \n",
       "2014-01-28   obama_speeches_042.txt   obama   \n",
       "2014-11-20   obama_speeches_043.txt   obama   \n",
       "2015-01-20   obama_speeches_044.txt   obama   \n",
       "2015-03-07   obama_speeches_046.txt   obama   \n",
       "2015-06-26   obama_speeches_045.txt   obama   \n",
       "2016-01-12   obama_speeches_048.txt   obama   \n",
       "2016-03-22   obama_speeches_047.txt   obama   \n",
       "2016-05-15   obama_speeches_049.txt   obama   \n",
       "\n",
       "                                                        title  \\\n",
       "date                                                            \n",
       "2008-01-28                         State of the Union Address   \n",
       "2008-03-19                       Remarks on the War on Terror   \n",
       "2008-08-28  Acceptance Speech at the Democratic National C...   \n",
       "2008-10-03  Remarks on Emergency Economic Stabilization Ac...   \n",
       "2008-11-04                          Remarks on Election Night   \n",
       "2008-11-13  Speech on Financial Markets and the World Economy   \n",
       "2008-12-19               Remarks on Plan to Assist Automakers   \n",
       "2009-01-12                             Final Press Conference   \n",
       "2009-01-15                     Farewell Address to the Nation   \n",
       "2009-01-20                                  Inaugural Address   \n",
       "2009-01-29  Remarks on the Lilly Ledbetter Fair Pay Restor...   \n",
       "2009-02-07  Remarks on the American Recovery and Reinvestm...   \n",
       "2009-02-24         Address Before a Joint Session of Congress   \n",
       "2009-05-26  Remarks on Nominating Judge Sonia Sotomayor to...   \n",
       "2009-06-04                        Address at Cairo University   \n",
       "2009-09-09                 Address to Congress on Health Care   \n",
       "2009-12-01     Speech on Strategy in Afghanistan and Pakistan   \n",
       "2009-12-10                    Acceptance of Nobel Peace Prize   \n",
       "2010-01-27                    2010 State of the Union Address   \n",
       "2010-02-09          News Conference on Congressional Gridlock   \n",
       "2010-03-15                       Speech on Health Care Reform   \n",
       "2010-04-15   Remarks on Space Exploration in the 21st Century   \n",
       "2010-04-28                      Remarks on Wall Street Reform   \n",
       "2010-06-15                         Speech on the BP Oil Spill   \n",
       "2010-08-31   Address on the End of the Combat Mission in Iraq   \n",
       "2010-09-23                      Address to the United Nations   \n",
       "2010-11-03      Press Conference After 2010 Midterm Elections   \n",
       "2011-01-12  Remarks at Memorial for Victims of the Tucson,...   \n",
       "2011-01-25                    2011 State of the Union Address   \n",
       "2011-05-01            Remarks on the Death of Osama Bin Laden   \n",
       "2011-05-19  Speech on American Diplomacy in the Middle Eas...   \n",
       "2011-05-25                  Address to the British Parliament   \n",
       "2011-10-21              Remarks on the End of the War in Iraq   \n",
       "2012-01-24                    2012 State of the Union Address   \n",
       "2012-09-06  Nominee Acceptance Speech at 2012 Democratic N...   \n",
       "2012-11-06                 2012 Election Night Victory Speech   \n",
       "2012-12-16         Remarks on Sandy Hook Elementary Shootings   \n",
       "2013-01-21                           Second Inaugural Address   \n",
       "2013-01-29                      Remarks on Immigration Reform   \n",
       "2013-02-13                    2013 State of the Union Address   \n",
       "2013-03-01              Statement on the Government Sequester   \n",
       "2013-03-21                    Address to the People of Israel   \n",
       "2013-04-08                             Speech on Gun Violence   \n",
       "2013-07-13                          Remarks on Trayvon Martin   \n",
       "2013-07-24               Remarks on Education and the Economy   \n",
       "2013-09-10                     Address to the Nation on Syria   \n",
       "2013-12-04                        Speech on Economic Mobility   \n",
       "2014-01-28                    2014 State of the Union Address   \n",
       "2014-11-20               Address to the Nation on Immigration   \n",
       "2015-01-20                    2015 State of the Union Address   \n",
       "2015-03-07  Remarks at the 50th Anniversary of the Selma M...   \n",
       "2015-06-26  Remarks in Eulogy for the Honorable Reverend C...   \n",
       "2016-01-12                    2016 State of the Union Address   \n",
       "2016-03-22                      Remarks to the People of Cuba   \n",
       "2016-05-15         Commencement Address at Rutgers University   \n",
       "\n",
       "                                                       speech   novelty  \\\n",
       "date                                                                      \n",
       "2008-01-28  Madam Speaker, Vice President Cheney, members ...  1.748165   \n",
       "2008-03-19  Thank you all. Deputy Secretary England, thank...  1.433824   \n",
       "2008-08-28  To Chairman Dean and my great friend Dick Durb...  2.642174   \n",
       "2008-10-03  A short time ago, the House of Representatives...  3.339841   \n",
       "2008-11-04  If there is anyone out there who still doubts ...  2.445935   \n",
       "2008-11-13  Thank you very much. Please be seated. Thank y...  2.464317   \n",
       "2008-12-19  Good morning. For years, America's automakers ...  3.146432   \n",
       "2009-01-12  \\n<THE PRESIDENT:> Thank you. Tapper. We have ...  3.645072   \n",
       "2009-01-15  Fellow citizens:\\nFor eight years, it has been...  1.613090   \n",
       "2009-01-20  I stand here today humbled by the task before ...  1.920067   \n",
       "2009-01-29  It is fitting that with the very first bill I ...  2.770151   \n",
       "2009-02-07  Thank you, everybody. Please have a seat. You ...  2.099385   \n",
       "2009-02-24  Madam Speaker, Mr. Vice President, Members of ...  1.908518   \n",
       "2009-05-26  Thank you. Well, I'm excited, too. <Laughter.>...  2.707594   \n",
       "2009-06-04  Thank you so much. Good afternoon. I am honore...  2.114990   \n",
       "2009-09-09  Madam Speaker, Vice President Biden, Members o...  3.008327   \n",
       "2009-12-01  Good evening. To the United States Corps of Ca...  1.916320   \n",
       "2009-12-10  Your Majesties, Your Royal Highnesses, disting...  2.101170   \n",
       "2010-01-27  Madam Speaker, Vice President Biden, Members o...  1.964747   \n",
       "2010-02-09  <THE PRESIDENT:> Hello, everybody. I am glad t...  2.997956   \n",
       "2010-03-15  <THE PRESIDENT:> Hello, Ohio! <Applause.> It i...  4.073154   \n",
       "2010-04-15  Thank you, everybody. Thank you. <Applause.> T...  2.138417   \n",
       "2010-04-28  Thank you very much. Everybody, please have a ...  1.931615   \n",
       "2010-06-15  Good evening. As we speak, our nation faces a ...  1.701138   \n",
       "2010-08-31  Good evening. Tonight, I'd like to talk to you...  2.315469   \n",
       "2010-09-23  <THE PRESIDENT:> Mr. President, Mr. Secretary-...  1.875054   \n",
       "2010-11-03  <THE PRESIDENT:> Good afternoon, everybody. La...  2.711334   \n",
       "2011-01-12  Thank you. <Applause.> Thank you very much. Pl...  2.279645   \n",
       "2011-01-25  Mr. Speaker, Mr. Vice President, members of Co...  1.897771   \n",
       "2011-05-01  Good evening. Tonight, I can report to the Ame...  2.160135   \n",
       "2011-05-19  Thank you. Thank you. <Applause.> Thank you ve...  1.937459   \n",
       "2011-05-25  Thank you very much. Thank you. <Applause.>\\nM...  1.881103   \n",
       "2011-10-21  Good afternoon, everybody. As a candidate for ...  2.544364   \n",
       "2012-01-24  Mr. Speaker, Mr. Vice President, members of Co...  1.913629   \n",
       "2012-09-06  <THE PRESIDENT:> Thank you. <Applause.> Thank ...  1.738005   \n",
       "2012-11-06  Tonight, more than 200 years after a former co...  1.703439   \n",
       "2012-12-16  Thank you. <Applause.> Thank you, Governor. To...  1.549324   \n",
       "2013-01-21  Vice President Biden, Mr. Chief Justice,\\nmemb...  1.983780   \n",
       "2013-01-29  Thank you! <Applause.> Thank you! Thank you so...  1.896743   \n",
       "2013-02-13  Mr. Speaker, Mr. Vice President, members of Co...  1.839322   \n",
       "2013-03-01  Good morning, everybody. As you know, I just m...  2.601175   \n",
       "2013-03-21  Thank you. <Applause.> Thank you so much. Well...  1.745539   \n",
       "2013-04-08  Hello, Connecticut. <Applause.> Thank you. Wel...  2.239898   \n",
       "2013-07-13  I wanted to come out here, first of all, to te...  2.017945   \n",
       "2013-07-24  Hello, Warrensburg! <Applause.> Hello, Mules! ...  2.209701   \n",
       "2013-09-10  My fellow Americans, tonight I want to talk to...  2.528655   \n",
       "2013-12-04  Thank you. <Applause.> Thank you, everybody. T...  2.012682   \n",
       "2014-01-28  Mr. Speaker, Mr. Vice President, Members of Co...  1.712051   \n",
       "2014-11-20  My fellow Americans, tonight, I'd like to talk...  1.573868   \n",
       "2015-01-20  Mr. Speaker, Mr. Vice President, Members of Co...  1.592861   \n",
       "2015-03-07  <AUDIENCE MEMBER: We love you, President Obama...  1.623872   \n",
       "2015-06-26  <THE PRESIDENT:> Giving all praise and honor t...  1.754457   \n",
       "2016-01-12  Mr. Speaker, Mr. Vice President, Members of Co...  1.415708   \n",
       "2016-03-22  Thank you. <Applause.> Muchas gracias. Thank y...  1.832376   \n",
       "2016-05-15  Hello Rutgers! <Applause.> R-U rah-rah! <Appla...  1.386135   \n",
       "\n",
       "            transience  resonance  speech_len  \\\n",
       "date                                            \n",
       "2008-01-28    2.051343  -0.303178       33649   \n",
       "2008-03-19    2.953103  -1.519279       18532   \n",
       "2008-08-28    1.928734   0.713440       25901   \n",
       "2008-10-03    2.308997   1.030843        4198   \n",
       "2008-11-04    2.056777   0.389158       10517   \n",
       "2008-11-13    1.763558   0.700760       18279   \n",
       "2008-12-19    2.309540   0.836892        6446   \n",
       "2009-01-12    3.182389   0.462683       39634   \n",
       "2009-01-15    1.937509  -0.324420       10668   \n",
       "2009-01-20    1.738715   0.181352       13384   \n",
       "2009-01-29    2.039948   0.730203        4935   \n",
       "2009-02-07    1.957616   0.141769       14758   \n",
       "2009-02-24    1.905891   0.002627       34335   \n",
       "2009-05-26    2.709706  -0.002113        8803   \n",
       "2009-06-04    1.913108   0.201882       34589   \n",
       "2009-09-09    3.262299  -0.253973       32058   \n",
       "2009-12-01    1.971783  -0.055463       27068   \n",
       "2009-12-10    2.123856  -0.022686       24598   \n",
       "2010-01-27    1.816679   0.148068       41860   \n",
       "2010-02-09    2.648344   0.349612       29052   \n",
       "2010-03-15    4.138498  -0.065344       24134   \n",
       "2010-04-15    2.178462  -0.040045       20505   \n",
       "2010-04-28    2.232449  -0.300834       20082   \n",
       "2010-06-15    1.712087  -0.010949       15327   \n",
       "2010-08-31    1.995833   0.319636       14913   \n",
       "2010-09-23    1.788741   0.086313       23771   \n",
       "2010-11-03    2.683977   0.027357       47096   \n",
       "2011-01-12    1.528152   0.751493       15757   \n",
       "2011-01-25    1.837164   0.060606       40559   \n",
       "2011-05-01    2.001310   0.158825        7885   \n",
       "2011-05-19    1.972939  -0.035480       33299   \n",
       "2011-05-25    1.857862   0.023241       24541   \n",
       "2011-10-21    3.034250  -0.489886        5492   \n",
       "2012-01-24    1.840703   0.072925       41607   \n",
       "2012-09-06    1.571403   0.166603       26311   \n",
       "2012-11-06    1.153935   0.549505       12354   \n",
       "2012-12-16    1.242497   0.306826        9342   \n",
       "2013-01-21    1.899733   0.084047       12253   \n",
       "2013-01-29    1.309411   0.587332       17516   \n",
       "2013-02-13    1.888182  -0.048860       40609   \n",
       "2013-03-01    2.051339   0.549836       29581   \n",
       "2013-03-21    1.720239   0.025300       31798   \n",
       "2013-04-08    1.633872   0.606026       18347   \n",
       "2013-07-13    1.294482   0.723463       11935   \n",
       "2013-07-24    2.170142   0.039560       24707   \n",
       "2013-09-10    2.816757  -0.288102       12873   \n",
       "2013-12-04    1.486710   0.525971       38033   \n",
       "2014-01-28    1.658727   0.053324       39771   \n",
       "2014-11-20    1.417770   0.156099       12638   \n",
       "2015-01-20    1.842766  -0.249905       39641   \n",
       "2015-03-07    0.844560   0.779312       21381   \n",
       "2015-06-26    1.079524   0.674934       17575   \n",
       "2016-01-12    0.863662   0.552046       35096   \n",
       "2016-03-22    1.189345   0.643031       23524   \n",
       "2016-05-15    0.000000   1.386135       28940   \n",
       "\n",
       "                                                        probs  token_count  \\\n",
       "date                                                                         \n",
       "2008-01-28  [4.038e-05, 2.83e-06, 6.005e-05, 3.848e-05, 9....         3746   \n",
       "2008-03-19  [6.925e-05, 4.86e-06, 0.00010303, 0.00375672, ...          345   \n",
       "2008-08-28  [5.656e-05, 3.97e-06, 8.407e-05, 5.391e-05, 0....          595   \n",
       "2008-10-03  [0.00029651, 2.081e-05, 0.00044073, 0.0002826,...          561   \n",
       "2008-11-04  [0.00015364, 1.078e-05, 0.00022836, 0.00014643...          547   \n",
       "2008-11-13  [0.04399939, 5.26e-06, 0.00724698, 7.137e-05, ...          354   \n",
       "2008-12-19  [0.0002141, 1.503e-05, 0.00031847, 0.00020406,...          181   \n",
       "2009-01-12  [0.00600836, 3.06e-06, 6.489e-05, 4.16e-05, 0....          971   \n",
       "2009-01-15  [0.00013051, 9.16e-06, 0.00019405, 0.00012439,...           69   \n",
       "2009-01-20  [0.01489551, 7.57e-06, 0.00016036, 0.0001028, ...          940   \n",
       "2009-01-29  [0.00031491, 2.21e-05, 0.0004681, 0.00030014, ...           88   \n",
       "2009-02-07  [9.447e-05, 6.63e-06, 0.00014046, 9.004e-05, 0...           52   \n",
       "2009-02-24  [4.168e-05, 2.92e-06, 0.00106819, 3.972e-05, 9...          803   \n",
       "2009-05-26  [0.00016456, 1.155e-05, 0.00024465, 0.01125762...           95   \n",
       "2009-06-04  [0.0828473, 2.97e-06, 6.292e-05, 4.034e-05, 0....          554   \n",
       "2009-09-09  [4.604e-05, 3.23e-06, 6.846e-05, 4.388e-05, 0....          281   \n",
       "2009-12-01  [0.02631954, 3.88e-06, 8.23e-05, 5.275e-05, 0....         1226   \n",
       "2009-12-10  [0.06828414, 4.21e-06, 8.918e-05, 5.716e-05, 0...          174   \n",
       "2010-01-27  [0.00850936, 2.42e-06, 5.12e-05, 3.282e-05, 7....          846   \n",
       "2010-02-09  [0.00977934, 3.81e-06, 8.079e-05, 5.178e-05, 0...         2695   \n",
       "2010-03-15  [6.667e-05, 4.68e-06, 9.927e-05, 6.354e-05, 0....          411   \n",
       "2010-04-15  [7.202e-05, 5.05e-06, 0.00010707, 6.865e-05, 0...          624   \n",
       "2010-04-28  [0.00226557, 5.02e-06, 0.00010648, 6.824e-05, ...         1217   \n",
       "2010-06-15  [9.315e-05, 6.54e-06, 0.00013854, 8.878e-05, 0...          714   \n",
       "2010-08-31  [0.00549059, 6.67e-06, 0.00014136, 9.059e-05, ...          480   \n",
       "2010-09-23  [0.06801354, 4.43e-06, 9.393e-05, 6.021e-05, 0...          443   \n",
       "2010-11-03  [0.0049988, 2.45e-06, 5.187e-05, 3.324e-05, 0....          521   \n",
       "2011-01-12  [0.00908183, 6.92e-06, 0.00014657, 9.396e-05, ...         1430   \n",
       "2011-01-25  [3.632e-05, 2.55e-06, 5.4e-05, 3.461e-05, 8.19...          727   \n",
       "2011-05-01  [0.00019376, 1.36e-05, 0.00028809, 0.00018467,...          505   \n",
       "2011-05-19  [0.06347695, 3.07e-06, 6.507e-05, 4.17e-05, 0....          350   \n",
       "2011-05-25  [0.02297016, 4.36e-06, 9.248e-05, 5.926e-05, 0...         1175   \n",
       "2011-10-21  [0.05534188, 1.87e-05, 0.00039612, 0.00025402,...         1057   \n",
       "2012-01-24  [3.432e-05, 2.41e-06, 5.103e-05, 3.271e-05, 7....          341   \n",
       "2012-09-06  [5.617e-05, 3.94e-06, 8.349e-05, 5.354e-05, 0....          387   \n",
       "2012-11-06  [0.00012569, 8.82e-06, 0.00018682, 0.00011979,...         1794   \n",
       "2012-12-16  [0.00017554, 1.232e-05, 0.00026108, 0.00016731...          264   \n",
       "2013-01-21  [0.00011815, 8.29e-06, 0.00017562, 0.0001126, ...          495   \n",
       "2013-01-29  [0.00334907, 6.41e-06, 0.00013571, 8.7e-05, 0....         2303   \n",
       "2013-02-13  [3.464e-05, 2.43e-06, 5.151e-05, 3.302e-05, 7....          859   \n",
       "2013-03-01  [5.418e-05, 3.8e-06, 8.058e-05, 5.164e-05, 0.0...         3222   \n",
       "2013-03-21  [0.01345588, 3.42e-06, 7.246e-05, 4.638e-05, 0...          655   \n",
       "2013-04-08  [8.89e-05, 6.24e-06, 0.00013214, 8.473e-05, 0....          622   \n",
       "2013-07-13  [0.02367926, 9.24e-06, 0.00019582, 0.00012547,...          892   \n",
       "2013-07-24  [0.00141263, 4.28e-06, 9.062e-05, 5.809e-05, 0...         1090   \n",
       "2013-09-10  [0.04937784, 7.99e-06, 0.0001692, 0.00010848, ...          696   \n",
       "2013-12-04  [3.688e-05, 2.59e-06, 5.484e-05, 3.515e-05, 0....          597   \n",
       "2014-01-28  [3.585e-05, 2.52e-06, 0.00293729, 3.417e-05, 8...          582   \n",
       "2014-11-20  [0.00012591, 8.84e-06, 0.0001872, 0.00012, 0.0...         1471   \n",
       "2015-01-20  [3.665e-05, 2.57e-06, 5.45e-05, 3.493e-05, 8.2...          732   \n",
       "2015-03-07  [7.046e-05, 4.95e-06, 0.00010477, 6.716e-05, 0...         1055   \n",
       "2015-06-26  [8.147e-05, 5.72e-06, 0.00012116, 7.765e-05, 0...         2178   \n",
       "2016-01-12  [4.255e-05, 2.99e-06, 6.328e-05, 4.055e-05, 0....         1530   \n",
       "2016-03-22  [0.0477215, 4.75e-06, 0.01421468, 6.457e-05, 0...         2058   \n",
       "2016-05-15  [0.02182939, 3.58e-06, 7.593e-05, 4.866e-05, 0...         1136   \n",
       "\n",
       "                                                    raw_tuple  \n",
       "date                                                           \n",
       "2008-01-28  (State of the Union Address, 2008-01-28, Madam...  \n",
       "2008-03-19  (Remarks on the War on Terror, 2008-03-19, Tha...  \n",
       "2008-08-28  (Acceptance Speech at the Democratic National ...  \n",
       "2008-10-03  (Remarks on Emergency Economic Stabilization A...  \n",
       "2008-11-04  (Remarks on Election Night, 2008-11-04, If the...  \n",
       "2008-11-13  (Speech on Financial Markets and the World Eco...  \n",
       "2008-12-19  (Remarks on Plan to Assist Automakers, 2008-12...  \n",
       "2009-01-12  (Final Press Conference, 2009-01-12, \\n<THE PR...  \n",
       "2009-01-15  (Farewell Address to the Nation, 2009-01-15, F...  \n",
       "2009-01-20  (Inaugural Address, 2009-01-20, I stand here t...  \n",
       "2009-01-29  (Remarks on the Lilly Ledbetter Fair Pay Resto...  \n",
       "2009-02-07  (Remarks on the American Recovery and Reinvest...  \n",
       "2009-02-24  (Address Before a Joint Session of Congress, 2...  \n",
       "2009-05-26  (Remarks on Nominating Judge Sonia Sotomayor t...  \n",
       "2009-06-04  (Address at Cairo University, 2009-06-04, Than...  \n",
       "2009-09-09  (Address to Congress on Health Care, 2009-09-0...  \n",
       "2009-12-01  (Speech on Strategy in Afghanistan and Pakista...  \n",
       "2009-12-10  (Acceptance of Nobel Peace Prize, 2009-12-10, ...  \n",
       "2010-01-27  (2010 State of the Union Address, 2010-01-27, ...  \n",
       "2010-02-09  (News Conference on Congressional Gridlock, 20...  \n",
       "2010-03-15  (Speech on Health Care Reform, 2010-03-15, <TH...  \n",
       "2010-04-15  (Remarks on Space Exploration in the 21st Cent...  \n",
       "2010-04-28  (Remarks on Wall Street Reform, 2010-04-28, Th...  \n",
       "2010-06-15  (Speech on the BP Oil Spill, 2010-06-15, Good ...  \n",
       "2010-08-31  (Address on the End of the Combat Mission in I...  \n",
       "2010-09-23  (Address to the United Nations, 2010-09-23, <T...  \n",
       "2010-11-03  (Press Conference After 2010 Midterm Elections...  \n",
       "2011-01-12  (Remarks at Memorial for Victims of the Tucson...  \n",
       "2011-01-25  (2011 State of the Union Address, 2011-01-25, ...  \n",
       "2011-05-01  (Remarks on the Death of Osama Bin Laden, 2011...  \n",
       "2011-05-19  (Speech on American Diplomacy in the Middle Ea...  \n",
       "2011-05-25  (Address to the British Parliament, 2011-05-25...  \n",
       "2011-10-21  (Remarks on the End of the War in Iraq, 2011-1...  \n",
       "2012-01-24  (2012 State of the Union Address, 2012-01-24, ...  \n",
       "2012-09-06  (Nominee Acceptance Speech at 2012 Democratic ...  \n",
       "2012-11-06  (2012 Election Night Victory Speech, 2012-11-0...  \n",
       "2012-12-16  (Remarks on Sandy Hook Elementary Shootings, 2...  \n",
       "2013-01-21  (Second Inaugural Address, 2013-01-21, Vice Pr...  \n",
       "2013-01-29  (Remarks on Immigration Reform, 2013-01-29, Th...  \n",
       "2013-02-13  (2013 State of the Union Address, 2013-02-13, ...  \n",
       "2013-03-01  (Statement on the Government Sequester, 2013-0...  \n",
       "2013-03-21  (Address to the People of Israel, 2013-03-21, ...  \n",
       "2013-04-08  (Speech on Gun Violence, 2013-04-08, Hello, Co...  \n",
       "2013-07-13  (Remarks on Trayvon Martin, 2013-07-13, I want...  \n",
       "2013-07-24  (Remarks on Education and the Economy, 2013-07...  \n",
       "2013-09-10  (Address to the Nation on Syria, 2013-09-10, M...  \n",
       "2013-12-04  (Speech on Economic Mobility, 2013-12-04, Than...  \n",
       "2014-01-28  (2014 State of the Union Address, 2014-01-28, ...  \n",
       "2014-11-20  (Address to the Nation on Immigration, 2014-11...  \n",
       "2015-01-20  (2015 State of the Union Address, 2015-01-20, ...  \n",
       "2015-03-07  (Remarks at the 50th Anniversary of the Selma ...  \n",
       "2015-06-26  (Remarks in Eulogy for the Honorable Reverend ...  \n",
       "2016-01-12  (2016 State of the Union Address, 2016-01-12, ...  \n",
       "2016-03-22  (Remarks to the People of Cuba, 2016-03-22, Th...  \n",
       "2016-05-15  (Commencement Address at Rutgers University, 2...  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.loc['2008-01-01':'2018-12-31']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## now average across all scales to arrive at mean metrics for each speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              file_name     speaker  \\\n",
      "date                                                  \n",
      "1789-04-30  washington_speeches_000.txt  washington   \n",
      "1789-10-03  washington_speeches_004.txt  washington   \n",
      "1790-01-08  washington_speeches_003.txt  washington   \n",
      "1790-12-08  washington_speeches_001.txt  washington   \n",
      "1790-12-29  washington_speeches_002.txt  washington   \n",
      "\n",
      "                                                        title  \\\n",
      "date                                                            \n",
      "1789-04-30                            First Inaugural Address   \n",
      "1789-10-03                          Thanksgiving Proclamation   \n",
      "1790-01-08                   First Annual Message to Congress   \n",
      "1790-12-08                  Second Annual Message to Congress   \n",
      "1790-12-29  Talk to the Chiefs and Counselors of the Senec...   \n",
      "\n",
      "                                                       speech  speech_len  \\\n",
      "date                                                                        \n",
      "1789-04-30  Fellow Citizens of the Senate and the House of...        8687   \n",
      "1789-10-03  Whereas it is the duty of all Nations to ackno...        2541   \n",
      "1790-01-08  Fellow Citizens of the Senate and House of Rep...        5278   \n",
      "1790-12-08  Fellow citizens of the Senate and House of Rep...        8383   \n",
      "1790-12-29  I the President of the United States, by my ow...        7898   \n",
      "\n",
      "                                                        probs  token_count  \\\n",
      "date                                                                         \n",
      "1789-04-30  [0.00018299, 1.284e-05, 0.01475338, 0.00017441...         1256   \n",
      "1789-10-03  [0.00055468, 3.893e-05, 0.00082558, 0.00052866...          960   \n",
      "1790-01-08  [0.00029236, 2.052e-05, 0.19161724, 0.01777297...          900   \n",
      "1790-12-08  [0.00017585, 1.234e-05, 0.3299131, 0.01151088,...          271   \n",
      "1790-12-29  [0.00021747, 1.526e-05, 0.05494575, 0.00020727...          607   \n",
      "\n",
      "                                                    raw_tuple   novelty  \\\n",
      "date                                                                      \n",
      "1789-04-30  (First Inaugural Address, 1789-04-30, Fellow C...  0.000000   \n",
      "1789-10-03  (Thanksgiving Proclamation, 1789-10-03, Wherea...  0.347742   \n",
      "1790-01-08  (First Annual Message to Congress, 1790-01-08,...  0.789467   \n",
      "1790-12-08  (Second Annual Message to Congress, 1790-12-08...  1.163939   \n",
      "1790-12-29  (Talk to the Chiefs and Counselors of the Sene...  1.920160   \n",
      "\n",
      "            resonance  transience  \n",
      "date                               \n",
      "1789-04-30  -1.331562    1.331562  \n",
      "1789-10-03  -1.040186    1.387929  \n",
      "1790-01-08  -0.412999    1.202466  \n",
      "1790-12-08  -0.548218    1.712157  \n",
      "1790-12-29  -0.503444    2.423603  \n"
     ]
    }
   ],
   "source": [
    "many_window_values = ts.pivot_table(index='file_name', values = ['novelty', 'transience', 'resonance'], aggfunc='mean')\n",
    "\n",
    "ts = ts.drop_duplicates(subset=['file_name'])\n",
    "ts = ts[['file_name', 'speaker', 'title', 'speech', 'speech_len', 'probs', 'token_count', 'raw_tuple']]\n",
    "ts['date'] = ts.index\n",
    "\n",
    "ts = pd.merge(ts,\n",
    "                 many_window_values,\n",
    "                 on='file_name', \n",
    "                 how='left')\n",
    "ts = ts.set_index('date')\n",
    "\n",
    "print(ts.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best that we remove the n speeches at each end, since they do not have complete metrics\n",
    "\n",
    "n = 25\n",
    "e = 962-n\n",
    "\n",
    "df = ts[n:e]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(11, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f06e9306940>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06e92c2ac8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06e9271fd0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cols = ['novelty', 'transience', 'resonance']\n",
    "df[plot_cols].plot(\n",
    "     figsize=(11, 9),\n",
    "    color=['red', 'blue', 'green'],alpha=0.7,\n",
    "    subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06e642b2b0>]"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cmap = plt.cm.get_cmap('OrRd')\n",
    "\n",
    "# between 0 and 1, 0 for the leftmost color of the range, 1 for the rightmost, upper > lower\n",
    "upper_color = 1.0\n",
    "lower_color = 0.2\n",
    "num_colors =  15 # len(self.data.columns)\n",
    "colors = cmap(np.linspace(lower_color, upper_color, num_colors))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "b = sns.kdeplot(x=df['novelty'], y=df['transience'], fill=True, colors=colors, thresh=0.015, alpha=1)\n",
    "b.set_ylabel(\"Transience\",fontsize=20)\n",
    "b.set_xlabel(\"Novelty\",fontsize=20)\n",
    "b.tick_params(labelsize=12)\n",
    "y_lim = plt.ylim()\n",
    "x_lim = plt.xlim()\n",
    "plt.plot(x_lim, y_lim, color = '#dadada', alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06e6334470>]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "b = sns.kdeplot(x=df['novelty'], y=df['resonance'], fill=True, colors=colors, thresh=0.015, alpha=1)\n",
    "b.set_ylabel(\"Resonance\",fontsize=20)\n",
    "b.set_xlabel(\"Novelty\",fontsize=20)\n",
    "b.tick_params(labelsize=12)\n",
    "plt.ylim(-4,4)\n",
    "y_lim = [0,0]\n",
    "x_lim = plt.xlim()\n",
    "plt.plot(x_lim, y_lim, color = '#dadada', alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06e9128908>]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cmap = plt.cm.get_cmap('Oranges')\n",
    "\n",
    "# between 0 and 1, 0 for the leftmost color of the range, 1 for the rightmost, upper > lower\n",
    "upper_color = 1\n",
    "lower_color = 0.4\n",
    "num_colors =  15 # len(self.data.columns)\n",
    "colors = cmap(np.linspace(lower_color, upper_color, num_colors))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.relplot(x='novelty', y='transience', data=df, alpha=0.8, color = 'orange')\n",
    "b = sns.kdeplot(x=df['novelty'], y=df['transience'], fill=True, colors=colors, thresh=0.3, alpha=0.4)\n",
    "b.set_ylabel(\"Transience\",fontsize=20)\n",
    "b.set_xlabel(\"Novelty\",fontsize=20)\n",
    "b.tick_params(labelsize=12)\n",
    "y_lim = plt.ylim()\n",
    "x_lim = plt.xlim()\n",
    "plt.plot(x_lim, y_lim, color = '#dadada', alpha = 0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06e917e6d8>]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DYPgQdR9BdLyMY2dOygEfOi6fw81liAXWntZOnV+u0Zw/I94UDYVCdHd3n/GY5uZmiouLz9kozegxtOf7T0TsNwintmF4AwjDg+n2YySPInL9kGzG6N6Cf+tNRPp/S9jZi9h7H+T6AJHv3fIviJJVGAYn9VcxZRyj5nrwvuXzH0ae+Ol6y+j8co1m+IxY0JcvX84f/vAH4vH4kPe3t7fz/PPPs2bNmvM2TnP+nOr5Sky7E/P4k5g7NyH2fRtjwYcQmS6wQpDrh7r/DY2PI3I9mPvvw0w1gpOGbB/4ouAvV90Xw3WIZXeTMaoH+6uw6x5E8+OY5euUqA8zT/x0vWV0frlGM3xGHHL54Ac/yC233MJHPvIRvvSlL510X0NDA5///OfJZDJ84AMfGDUjNefOicrKDAZZhJtBSBdpBiA0B7PmasglYMUXQUro2w2Nj0O6HXxRhB1HeiJgBkB2gvDArJtUS11fKdJTQuHCFO7hnwzGzR38GMf+C1b+H7KeqmFlq+j8co3m/BmxoF9xxRXcdtttPPDAA7z73e/GstQp1q1bR39/P1JKNm3axOrVq0fdWM3IGaistPbeA8kmhDBh/u2IcB2Wrxje+JLqipjrh2V3w/H/hkxX/tESIXO4EuTSzyH2fAUqr4H6h8AbAWEiXYmx/+sYM96N2/JbddGQNtK1cFxjRBuaelCFZqyYLimx51Qpetttt7F27VoeeeQRdu7cSW9vL0IIrrrqKv76r/+a9evXj7admrMgkBCrJ5xqOvULG6qF+R8DI4DM9iIy7YiO5+HoY2DHwVeuwiN7vwar7oMdfw9WEMwQcvbNyH3/THrVP2Gt/ylWfD+i9RlwbWSmE8MH0vADEtPtH+y+KMwAFvHBYRYazXgxnVJiz7n0/9JLL+XSSy8dTVs058jAF5Yd38LMpE/qyeLvfVF9kZPNCF8JcsWX4NCDytN20oCETAf4K8AKIV0XLvsxHH0NWRQEJ4URXYMv14KLB/oPILP9J6o/021QsABRug4avq+6JRo+mHcLcv+DBFbOO+e0w+niVWnGlumUEqubc00BBr+wpq1ucDNYe+/BWvR3sOMzCCsEnqAS9Y4XwEmAFVA9zN354LrgFIDrQL+De/DfABsjOxt6dkLRYuj6OWZBHfS/iqj7MPLQD5So+6oQy+7CdV2MVV9DJlrATeN2vIKRbcOX2AMhRizG08mr0owt0ykldsRZLi+99BKf+9znaGtrG/L+trY2Pve5z/HKK6+ct3Fv5dFHH+U973kPy5Yt4+///u9H9dyTnbd/YQ2yqrd4Lpb3pF3I9qh/pYOUc5FHtiHL/hcILxgWWGGYeRO2CEHlVYjIQuh+DYqXQccL0P48HP0x+Ncgj+6EuluQi+5ELPgI7P4Cxhufh9fvRLgp1Wo3fRSRbkX07jinqUIne1USw41h7fsSEfsNPZ1IMyKmU0rsiD30Rx55hMOHD1NRUTHk/RUVFbz++uvE43HWrVt33gYOUF5ezic+8QleeOEFMpnMqJ13KjCYyYLy0IW0VTaKJ6zCH9JVMW9jCTQfgIqr4dh/QewgsnwjBCpVHLz1OQy7H4SJqLwKSlYpMXdtVP65DZ1b1OObXkGE66D/XyHTDUIgvEXIhn/BnPdhaNgJ827B7dh6TkvcExepk2Pz5vEniRQ2a09dM2ymU8vlEQv63r17z7rpuWbNGjZv3nzORg3FQHXq7t27T7s6mK4MTgdq+BaQRppBxPyP4zT9F2bdR5GHnwdRrES+ZA1u/5uI8o3I0BxsTxSrfR9G9/8DNaMIiUS2H0ZIB2RezIVQFwYnqVIao+tBeJBdFQhPBplLILO9EKiCwuXIeR9RYp7tUUa+ZYn79tg4cvEpr2ngImW4sRNj7gwfIMYk/qnj9VOX6ZQSO2JB7+rqorz8zEuV0tJSurq6znjMhaa0tGBEx0ej4TGyZIwofQ8UzsKTaILQTEi1Ippeh+R+iF4GniLIdELiCEbCAdODMCXe7tcg04mwDwESrAKEmwU7iwxdDPES1YVRuoiArTwc0wfpDtXISwjIRjH8vvwwDA8ULoCD38K0E+p30weGF09xDf6CEDT9EvZ/Y9BbYtEmorU3nDx0Wi6G5XfC7nuVXaYf5n8Uul7FNG08optQdJS6eUr3FJtCizbB220agkn3PclzXnZLF+INkGqFQCUUzDvr+zQanP97vTT/A6Hztmb4XMjvyIgFPRwO09raesZjWltbCQQC52zUWNDVFcd1hxd7jUbDdHRMnl40Bi6FqeewurfguAZy52PIiitwa/8KT+pNSDbCsb3gLwPvbOySMjJFK/DKbjytL0DhYmS/UMKdi6lQTboDt3Y+RjAIrb8FJ4XMFEJkJWBB+zPgjULpOuh+DZlyEEYXcs5fQ+8+qH6PEsh8B0d7yZ30pysIJPfh3f31t8T803j2f4OEb+HgEOgBLzkdeAfh1ZWYx58EBG5b3uM3vGRlCclR+oyCbuMpNrH762R9C8+4ChjqezIZPP3z+X6/fbN6IHwx1iGwyfY3OcBY2X26i8SIBX3FihX87ne/o6Ojg2g0esr9bW1t/O53v9OFRaPM6YRCICnqexKx49PgphEsQESvxG17AbP6Ojj830qoId9Yy4uouxUJmLkO6E9D8iAE16g/UCmVVx2tQmQ7cDtfxai8Vl0UzCB4S6H7KPhWgWhTRUmRhWCYUBhAtm9BHPo+csEnoe5j+R7rXnLh5QB4c8cxKq8cnFNKtkeFY7LdRLJ7TslqiUfeSaSsE9G5BSO6DrdzB3Le34xq/HO0siCmQ2bOdEoBnIyMWND/6q/+imeffZabb76Zz372s1xxxRV4vV6y2SzPP/88X/va10gmk7r0fxQ5k1CoP7CvgzNDiYYVADeNYcyAzjfVctjnQO8eSKiFpjj+BpbVjXDawewBj4BwGJwU4KowSE8P0AOJGDK+BVG1UBmTPg4+CRkDxCx1W8eL4ClAdmQQwYw6j53APfa7wddgll1HJLsbY/99iHg9CA/mwjuQuCBTWKaDbHj41CZiy/3I3V9GuHGEMDAW3Ulv5LpRFcjRGjw9HcRuOqUATkZGLOgbNmzgE5/4BN/97ne57bbbEEIQiUQGy/6llNx6661ceeWVo2qobds4joPrujiOQyaTwTTNwdYDU5kzCYWZaUNmyxB0gVUA1iJor88f1wqepdDXDuFVYCagpBrhKcHwRJEyiDCvAGGqIqOOF5QXXrwCiqoQvjJkezci1gM9vfmQzC5w02qDNXwZpAtU2uOAwOYqwOxS5xzA8GJYJmLXN9XeZqASDD84fYjGX4BhYDQ/BTOux+nYNriRarhx6NyCdHO4+NSe7YHv4F+7+rwF8q0rHtdXMeRAj5GuAqaD2I3WxU8zNpyTGn7yk59k9erVPProo+zcuZNYLEZhYSErV67kAx/4AJdffvlo28mDDz7IAw88MPj7k08+yW233cbtt98+6s810RgUCm/xiVFxwsLT+BKuFUIKH8ItR1q1kDwCdEBCABbkWqB6NbQ9qzIPD+2F0FzMdDeEZ+M6+U3Orm1gRCA4Exp3gNyOUVyAEb0CiY0oqoQjfwC84Muq2HhsM5T8MVT/GRz7LSCVd1/1adzOLcr4vDi6uRSmm8Fw+5HpVsTsm+Hgd9SmmhlQw6MbHsKY9zHclv8GQAjjVE98FARyqBWPXHQH2Ysfxky3nnMWxHQQu+mUAjgZOWf3dsOGDWzYsGE0bTkjt99++7QQ76FwfBWYvgrMspWqE2LVRogJrEg5SVGN5VuOm96LWRRFdrymMkLIgC+La82E40dA1qhKUbMV7AxUXobseQOqr0fYMURfL1T8D5WpElmBTFm4dgqOH8KYuQbsBJjdYBdCOgT+BCAhHIGeDihdC/4KpJ3GNcNkV33zJHEMuE14DCDZBlKqBEk3p8IzTgZycfCEEDKjzmv4kAv/VnVxfCujIJCnW/Gw9odnHMJxNqaD2E2nFMDJyNSPV0wBUkYtvkUfV2X8NX+EPPQHwIXWfQRKLibrqcEMezH64xCug4IAbss+kLXQtxdhtKm0shkboDsJ2V7czn7IFSGPvQmAVfVOld3S/CQYFsJJQ/kG5PE3cY8XIb2lmN4SkB3glKhQS9iCbDeYXsgYSDOu0hvbXsD0VhCrefdJr8G/4FbY8RmQGRWyCVar1DdfMUgb6dgQWQJlG8n655I2agjP8426QI5VaGS6iN1YdsWcDFlCE5nzEvRkMkksFsNxnCHvnzFjxvmcXpNHIrAJ4Q1fh2x4HpCQKwIcRHwLnsBliFwMkdmJ05OArhyUrEHEnwd/AU5X/mPufxkidSAloqwSUiAsF7q24aSqIabGy+GkMcM2dGxBzHkvUtqIWA7XWIbh3anE0CkCsw6MAIRMOPYqojsD3gQieiWGkyIom/FmW8BJkvPPI1N4Ob75t6rHWxHE7Jth/7cBkW/o9SGcw4/hLP78YIz8XAXyTMIwlqGR6dQCeLTF90yb/5rhcU6C/stf/pIf/OAHNDQ0nPYYIQR79+49Z8M0b6OrXaUpSgdyhagdwg6Il2BYvbjHN+PmC3CEN4UTa4NkvnzC9CM8XYAB5XOg7f9BS4PauCxZDcJAFBWqsEf+eCfmA8OHmW5FtD2rQiCBdbietRil+arT/l7oOg72ftXAK2tCgQ96d2IV1GHteQg6/g1kFitQg7v0HyCyEHf/Ayrd0VeEWPNtRPII0nFUZamdOElYz0Ugz5Y+OB1CI2PNWKRonmnzf6AgSHNmRizov/jFL7jrrrswTZO1a9dSWVk5LTJNLjQnvJ9WzM5GpOsiCubAsWLAgXQCrFIQrbipmYBE+LM4/TakDAjYiGD2RB8WQti9FhzdD8mcut30QOdmKJiHFXEBI5+zLhAFEpmwcVpbMYUEaSNSryDdi3CPdWGYjWDHoGADlF0Crb8DWQoxC6ovAicO7c+BzCBwINGAsfvzuHM/glh+NzYF5HzlWMl6vG0/ws2kR01Yz5Y+OF1CI2PJWKRonjEUpgV9WIxYiX/0ox9RWFjIT37yE+bNmzcWNk17Tng//4SZ9oCdhOgVuL39GGXroO8oeCTIJtzcMnCyiICL0628TeHtAuFA5TXYu3+pSrXNABQuwBSHwVQ9WxBALokjc9iHtkHBfIg1YEV9IB3EzNXQtRUn5sMM54AcosBCxvNteg2/2tTsk6pitG8X2GGVlz7zz/N9YOaCuw+QKjUy14vc/RWctT8kacxEFMykbP0ynJ7mYQvr2Zb6w4mRT6fQyFgwFvsQ0yFLaKwZsaAfPXqUG2+8UYv5KPNWkTK8AUTDwxiZIOS6IBeG1r0YlSvhyLMgvLjpGBQtQ7APJ10C1CKoRwQy4JrY6QpoPAQFczBLClX1pzCgvUP1dcn1gKcQrAhmUQn0HAVxFKdkFXYmhbVgA7T8JzgphN/GiYUwI/kqUiFxk36MqmVKvGUZpPthxh9Bf1/+BXlAWCq9EQkYKvtGmCf94UsEhOcTS1cO+30621JfC8PYMxbv8ZlCYRey98pkZsSCXlhYiNfrPfuBmmFzikjZRVC4HMyDSswRUFoLR7cAAjeTUo9LvYrDEnB7EVYrYuG7ybU0g8fErCxUG40t/wVd+8ATIdspIbAA+logB1g23oWXqmlFvjIwPJiZTuirx379Z1iLr4HjfZDrBkwcFmAKH6LQj5SOCtcMdGPMFqpc9LL10N0Ibh1UvhNan0aKhYhACjn7A7htW87rD384S30dIx97xuI91qGw82fEgr5x40a2bt2KlBIh9Bs9GrxVpESuVBXydLyQ75cSB7M/X6mZw032qvmfFYtw2trAYyJmb4DEIey9T4O/HFPuhYxfNbHKzoH4EeWRGwHM2avAcyWk23Dau8ke3gP+TujdibfEVl51xUZMZzt2SxNW6UrwFiMMC9l6FHpfh3AdImTgtloYkQCIDKRROewDueVHn8Vd9H4IzcGwY7iBDG7rH8BODP7hD6xKaO8mKEuG9cc73HCKFoaxZazeYx0KOz9GLOh33HEHf/mXf8k999zDZz/7WUIhvRgaKQJJQLYMpvQB4GYQuTIAJCbCKVFZLZ4MxAwoKcbt2gtmAFE+B+fQFsBFlJRiH8lCtgez7gro2AKhi8m2dNjk0zEAACAASURBVIBhYYY6ofodkG4FTwTanoOKK8GOYVZVA1XQ8p84QLY3gLfMA13boexS6Ilj12/GKrTV8zoJnHgJRnk+HdU4DDIvrtJUXjoA7WCXY+R6SHiXgAUetwcWXz74hw+cWJWYNl7HGlaWxHCW+gYuYXsPRqoZN1hLwqjRYj4GaPGdeIxY0D/1qU8RCAT4+c9/zq9//Wtmz55NOHxqK0chBP/2b/82KkZOJQSSSPz3WL3boP5BhMwgox+CXFh55jjITAApTETlMmj1gtGH210P4QVQ4FVibvoRtWuxW9sg9rratGxvI5usgJZtmOGUKsMvuHKw4yG5GJSvV9WmhqU2KWtvBFzMkiBOV5xsWwZvqQRfCaZ1GCe8AMRBFX8XHtXs6/jT6sUEF+AmmzEKXLD6IbgW+t7ItyYQ4GQIZA/i4sP1RIgH1gwKa9BtPLFcN41hZ0mcbalv4FLc/RjsulsNuDYDFK/4Mj0lf4k78omLGs2kYsSCvnXr1sH/p1Ip9u3bN+RxOhxzMgPhBW/uOGbvVkT998BNIt250L0NSi5GdG+HlECIFMy4GA78Sg1cNsvAzSCNbmRPCXgiiOol2C2HId6AGUwCRWT7wxDfhVlWBOGLUGmINunGVnBTqjVu7DXIpcHw46/0A0JtVmY6MQslTr9FttvEW9EK5Rswm3+FnZuNVb0Mcv1w/BBuLotZbIHRiixcAmXlarO0cTOYfSpsU7kSOhsxki8jPEUYwk9h+hh9dbcgEeecJXG2pX7Y3jMo5oD6d9fdhDesoM9aPiafrUYzURixoO/fv38s7JjSDG56NjyMOfsG8BaB6UW6M4D8mLfje6BwDYTTKhZ9ZDNEZ+E27gQzBeXzkU17IVSMqF6KfeBZKJiNGUwAkG2LQUElZokPipaSfuNZwFXiWrQMy/OmCrnMfKcaISdM0seOw65n8K+4Bpp+AdLBjBg4LIT+AxCsgeKV0NGm+q2E5iBmmMjuLrC6ITwXju0CCqBopZpg5FZAUQH4SqH1D2CmEZatVh+tvyMw910kjZm4vgo8hkQ4SXC8gAWG75TN0renKKaNGvxu82lTFo1U8wkxH8BJYaRaIKwFXTO10RVBF4CA26TEvGwlYu99yNA1yFylyiyJLIBYEiwJXhta9ihB8qZwY7OhYDaEQ7i9DrJoNUZJEXbjfpA2ZkkEujxkOyVmoY0or0DKUtJ7XgDAKkH1TfEcVnni0obGn6vsF8ODVXkRdiukD+3DX71eefACOLKTbNzAW9iivPdcErulETPSgCioVcfNuA7aXkBYXbjdOYSxD1FxNbTuBgQkW9Tz2UXgB4MMUtp4jr5IaNbl+JJvIObcDHu+BjKFGajFXvS5k7IkTk1R9BCo+xuco7+CTNuQKYtusBbDDJws6mYAN1B9oT5ujWbc0EHFMUYgVZhl9g2Ixp8iMyUQf1N1NowsgGO7VQ+V9B7o6VZFQEETlzroP4CgGbezF4SBUTUX+83X1WZnUQDiDWSzCzCLDJAuqQM7SB9Pg7SxinOAACusOiV6I/nKTUeJtpuDjs1YdWshfoh0czt0vQydL2GGs1C4GFC55MbCa8EKIaLroGsHxA7i7HwMCpeALwrCg7BjyHS7uli0HlQv3uhTFwRHbdqKXB+WjON75W7E1g/DwYdg1Zdh4aeRNe8lF14+KMwCScR+A2vfl9SgaKTqj77rboyyVer8A3F3t2nw/Y6ZS2DFl1UhFah/V3yZmKUrDTVTn/Py0FtbW2lrayObzQ55/8UXX3w+p5/0DHiYxv77oPIaZOgaCHuVmAK0HVCdCulQLWmdg1CxALe7H1ItCG8cClfB4W2IkIRDL4Kco6o9K64iu+spzJIElG4g3dwNgQRWRaEKrUipNialC9ENSmijG6Bv34nMFGFAtg+rMIvd1wjVl6jCH8MD7T2ABDuBOPyvKtPGPKCafslXkHEHOrfkVxi7wU4j/BXgK1KeueGFGe9SWTe5EJgORC+H5l8hildCcg6kG2DHZ2HxJnDi+JL78fh6cHMpLBKInp0Qb0AIA9NfocJHTurESD04Je7uYtBT8peEN6zASLXgBqqJWUv1hqhmWnBOgv7iiy9y3333cfjw4TMed7oN0+nCQH65tIoRSUt5wNIFu1jFnJ1G5UEmBJgueNPgrwILhPcgFK3AOfQyImJCthe7z8IM7YHQOrJvPIO57EYQBunD9dB/EE+kH9nZBBVXw7H/JNluQWgOHHtFVWy6NoTrCJbGVbqk4YNslxLa7i3QtRUQUH09yHZwYtD5cv7VeFUue9c2KFoO8V1KuEOzoNjCNQMYbhYafwZ2AbgCyi6DymsgkVDnTR7NTzsyGZxw5ImA6UEc/gUCF6PhBwhfMTLTDUs+B/5qSLdAuk3F6M3AKdOQ3h53dzHUBugkipnrtrGa0WDEbsvOnTv52Mc+Rn9/PzfffDNSStauXctNN93E3LlzkVKyceNGbr311rGwd1JhZtrACmKEN0K6E6JXqE1D6YAVVwOXk5bypK0YuDnc429CgQp1OO3dCH8/SInda6oKeinJtrQDLjhJ0ge2Q+8bWHUXqbRCO0by4AGS8bn4VvwpvoJ2fMUpfKUOvsoAPt8hkp0BQED5lRA7rEI+4XmkU/NU/rmdBtch29yWrwRlUH8HK0NNv/KYU20IfzbvOWeVd294waiD1meg8adqslG2G4IzVKqkrwwCVUCdungcfBBqrodD/zI4j1Q4Kdj7dVj6GXXhka56fSu+jNv5urJlilSADqzkvK9+CHPnJryvfkjtHSDH2zTNJGPEHvr3vvc9vF4vjz/+OBUVFTz66KOsW7eO2267DSkl999/Pw8//DB/+7d/Oxb2TipcfxW+6Frk4f9UczINC0IXg90AsXowFwFvgD8HjotrLEWafYjQCpzuFHgdEEXYPS4AZihDttuEsBdz3qWk3/gDBKqxirLQf4BkZrmSAH8IX7kXlUGTy/8ArgNWCKwIyUQJQTsB5ZdDshkr9zp2twCjGrpewaxai9N6LC+mjnq8MPNi7QdMqLxWCXXHq6o/u7sXSvJhttbdapNXmOrxsUPgWQ3lV8Hxp5HFq8F1EE6NGjCNiXAzJ948YSFMC+lk4KKvQqIJp+KP6beW4i+6YkpVgE6H4dKaC8OIPfTXX3+dq6++moqKisHbpFSehBCCT37yk8ydO5f7779/9KycpFhuL/Lwi2/pd1IA8ZdUGCTugieCXPx+3Nr34wauRNaux679n9jN9dC/F+EewM7UAAZmcZBstwXhOsxAN+mWfpAuVpGEme8j2VsO/YfwVZXiK3UAVzXfMjxqYxQTcMBJ4asuVvFzJw6p48rzrboOCuYqwXaSkGwCTzFU/ZF6vOFVK4qqd6kVRahWhUE6X8q36EW10+3dAf6o8qhBhVQAzBgIC7ejCSlMZHAmudkfIuefg2QBIjIf4StDeCNqcpGvGIQPgYvofg0RWYTMxQarE2OBtYOtcCc7Z24bq7mQCCRBt5Fwapsqfptkq6QRe+ixWOykSUQej4dkMnnSMatXr+Y3v/nN+Vs3yTGObAEMKL0E+uPKU03vgr4kFC7BKZ4J2Rgc/C+1iZneiSHnQ99eZNBUF4J0B8as1cjQLKR9AKtmLjg5SDVgzb8cQtUk92wHO4lv8QakE4fevVC8XHnFVX+suiZaQuW3Ry9Xcencdug8mA9lGBC9UnnyPbtg5p/jHGvBO2c2dL4CxavA6YHqS6FjK6QaIRUAJwy5XrAKwM4p8XZVmwD8LtiVYPequLnhh4IwxABhIiwvlhtDmn4oXAZ7/gOWfhZ6diKsMLS/CDPfA7vuAScBR36MtfweAv6ZJEXN+H6wo4zuDjkxGIuhHReaEQt6aWkpfX19J/3e1NR00jG2bZNOp8/fukmO9JWpiUAt25VwGt1KOJMGlNXhmhGMfY8jpA3+NBgRRDaGCCQAC7vXUJ0QZY5swxtY0RKwU6QPH8TyHwMWknz5UQjOxFdToQqBWn8PJRdBx/P50ngf1PwZZPuUVx0/orz2/v0QssDKp/f1vAaeZVBUC8efgd4s8DKUb4Du7RDLQdNxKL4I4ocAv4qjG958q1yvqiTFVHNCq66DxufzGS9+dSE59lsM/zKgBJqeRETqVEim+zVl887Pq/NZBbD8Htj+aSXmoHrdHPwO3shFJH1nF/TJtMmou0NODKZC6GvEgj579uyTBHzlypU8//zzvPnmm8yZM4eOjg6efvppZs+ePZp2Tjr8LZsRdr8SSukCraqB1bE9MPNKSB7F6MsiAlEo8ILhw23vALcfnH7sPo/qZS73kN2XQ5gFUHk16YMqD5z5F0P3DgjW4vPVQ2c99O2FqmtV1aebAUzlHR9/GoqWqfx3X5m6DcD0gR0HJHaPif/iedCWH0AtpbK7Y7PKVundmg8dSfCEIIsS+ugG6H0jnwopIHqZaqvrK4OiVRCSICxkrAEhgKIoHNsOlYvUBqm/AirfAa1PK29fWOr5O7eoPYeBTdmB6KBzdkdhrDytt18kkIvP+VxvRXeHnBiM1fDwC8mIBf2KK67gW9/6Fr29vRQVFfHBD36QZ555ZnDoxdGjR0kkEnzmM58ZC3snDR7Zi5HpgEwAzBwYRZCywE1A8k0omItwmhCZ1yCVw5FLIHUMEchCxVVgN2K6u1AZJUHMIgnNvwRzGVZRBoSfZHwWvmofyHyxkt2vYt+GBS6AgxL1nPLUC+ZA439AySqwgoNiDuQzVlogNBu6XwFRALjgOjgdnfnqUj8Eq6Fvj4qz2/lJSBVXQ9tBKCtTIm/H1PH0QlMD+BMIT0R58dl8frtVAO3PnsiWKV4FfW9Cphuy/eq8xkDffaEuELkYeEvO+t6Phac11EWC5XciAteMivDqzoXjz1QIfY14U/Qv/uIv+PGPfzw4R3TNmjV8+9vfpqamhvr6eqLRKPfeey833HDDqBs7WfC3bMaTalCbhjiqn3muV/VDMXvVxKHGnUoYjXyDLCTC0w6Ggd2wXQme4SfbGwQkuFnS7TbWzHngKSbZ2KFi5AMx8IqNYA1MJvLkLRH5IiNbiXn8Tcj1QfwoRBbmjzOU0Ibnq6IjIXD6fcoT9oTVfYBV5Kg0x/YXkZ6LMOZegiy7VOWpZzog8abKUbdjaiKSk4HyBUqwTb86X+W1yFQnRJZA86v52zPKho4XYcb16rX4o9C+GRZ/BgI1+VWFjZx3C47tDPWWn8RYbDIOdZFg/zdOqlLVTG5SRi1y0R0nHIlJGPoasYdeUFDAypUrT7rt2muv5dprrx01o4ait7eXu+++m82bN1NcXMwdd9zBn/zJn4zpc46UgSW5L3sQkWlToZHwIkhuV7Hi/PAIOrcBtaoS1E7hJMMQGkgHBJwsZsSArizgxyw2wbEBU+Wzz3gXvPokvkXroO1ZtZkpvDDjj6H7dah6pwqzDLTNrbpOib0ZAk+ETLcBRgZqLgdc7OM9gK3K/SMLwarHG+lRJfvV18OxI1C6Vs02jSyAnhh07TkRgunfr16n2axEGqBqoxocHZwJhUEIzsT2VmMV2ZBqUh6+R6g0SlywIupCEKhR7+Ts98Hhn6ixdgjwFOJ07sSp/sBZP4ex8LSmwnJcc2bOJ/Q1UfZsJk1zri9+8Yt4PB42b97Mvn37+OhHP8qiRYuYP3/+eJsGnLwkN9Ie5ZEHLoGCIii5AZp2QLEX+vbh9mchbCiRtwrU4939UHYZ9uHXwDAg0UTWXQUcAEzSXT6sNX8O8SMkD+xXoY/2F8kniCtRb39eiX3nKypmHqxRqwRhQvPjKt4dvQoS9QTr5qpYeLoDTC/+4HHoacd58zXlQZdHABun4UWsEg/0HYfam5CHfoeUwKwr1fOZfqSsUb1iyt+hNkT9ZarkP5EGs0nlqhvbMeZ9FLd9M4ZvPgSAbD7kY3jAW4wsXKny0QWq0nTGO6H+u+AtwrHKkIv+blje0lhsMl7o5fhEEYjpxrmEvs60Z3OhmRSCnkwmefrpp/n1r39NKBRi7dq1XH311fzqV79i06ZN420ecGJJLjIFILKAC4kjUFSRHygxT20w+ivVCLfy+Ug5S42ac3pUKqKvDAIzMEsK1Ji5VA/mkj9SceVEKxz9aT5MEcVXWw3t9fm+JvnI2UDhT+XVquJSWNBfr+5LHIHkMTLkZ4jacTj+39j2fEg0gietzmVbeL31EPgTaPx3IKSae5VdpipZwwswjcPI2BFE5TtVlszRFzHkXjgeUqX+hg/CC9XeQSSgIkrJVgyZhEgdOAVQcJFqFubmi4+KliETTbjHfg9IDAPcVf8XWfFHuLn0iERtLDYZh7pIsGjTmCzHp0L63HTiTHs2cGGbwp2ToG/dupUf/vCH7Nq1i/7+flzXPeUYIQR79+49bwMBjhw5gmEYzJkzZ/C2RYsWsW3btmGfI5tN4Tin2jkU/f2STCZ19gPfgpGLkQlfihHIx58NC2w/mMVQtBacCMy4HKwAsn0fwkkjhQcZvRmqHRWv7t2DW7YYo3QGeMuwZSOGbIXSleTSfozgLBCCbCSArFkGBSWq+hNO9DgpWgLdr4Kwlfe/5GOQaIJgCbm4BH853tolJDtexC27HoIz8ZiNJAA3ZUFUki2QEKnDrf4wRvlMdWHI9SF725CVl0PJ/0S4aYRhQtdWRNWVCG8+GyUTh8hMiJSBaAMSIAVU/Znq5SJNCMwAumHWLaoi1BMBK4YM1OCWpAdTFWU2QMozW12vcgAj+0zSlIFRhsiBV7Zg2H24ViFZUXJO5SIZ31V4VyzHsPtxrQi+SBXp2MhsGg4+2U382DYIrz9x47FtSO9yMuLsm8Jn41y+3+PNRLZZ/e2vP+V2mYzj9PePkd2nTomDcxD0Z599lltvvRXHcZgxYwZz5szBNM2zP/A8SCaTp4y5C4fDJBKJYZ+joMA/WNE6HCKRwLCPBSBVCoahwhEFcwGQGRchBNIOgJAIT1BtHLoeMDIIM4C00whvVFVR2qWIAIDA7jyuxsxhke3qxQzmPXHpIJw0xBqgZK0qBJIOICAyX6UQuvnKVCcJffshWIXd04swwDtjNqolgDqft7wSuptxksrzswpcECayvx+R60WIOrXpmWwE14tZWgb+IvWa022QEwi/UFk1ThqVwRJAdu4HJCK/eU7vLiiYg8yhbrdjSp+D1dD9KsJtQfiKMcouVbnybgaCJXgCI/wc3o50VZ778d+qmL+w8FW9S9UHiHPpwFid/1GM+HsyHPoTYOQ/00EcLJHAFxmdvu5jYvcYM2FtTpWC5XlLii3KCQqqi++FtHvEgn7//fdjWRYPPfQQGzZsGAubTiEYDBKPx0+6LR6Pj2hAdTYrGGIhMSTRaJiOjtiwzy2QBIRNoGI5NG6Do9+HbFht+EXroGMHlM2EjmboeBE3GUQEXSi5GOfN3yC83YCBbVyMWWRB0wtkezyYxV4oWonRuBPL06CWdFYQedzBV1kAhcvzQ6YFBKsRzc8i259D5ZHnjbMCZBIz8KRaCS69BHbdB9ErsQ9uJlySgmMxwMU5vEXNEu0TUH4FTufvsSoroO330PUKUsyFdA9O+SJIdmHY/RjdLYiulzGCSbVCCM9TIpluUuElT0++9F+oPYX05epCFA5AZBHQD0d+jcFBpL8C4lno+AVy3oeRkeX0GuW4mfMLMQTdFrx77zo59t3zPNm1PzzvYpGRfk+GS9ATwNu75ZR4fbbuQyTP8/2AsbN7LJnINgujgkjF2lP2bPqNCsoikQtq94gFvb6+nuuvv/6CiTmoYibHcThy5MhgwdL+/fupq6u7YDacjpPinQs/rgqJzIAq2nGbobMHCurAyDepEj71wMJlKjTipFDqm++/0r1dPV6mIZdSv4uZKqWv/bkT1ZjlV6mNx3SrerxYDwgVdhm8ckkyXSYUlhOsmwOxI1C2Dru5SaUpepuhby9OtkoNoC4rBcPC6cuvBnpUSwGil0HLXowVN0O6BdH6DEJmIFuAMfcy6HpVrQ7an1O2dx5V+exu3st07XyKpICAVPnkfQdUvr0VAlEIqTb1OgKVEKzFPfwTwvN8Z40bn23z0My0YrgxhLSRwsLFO+GzU3Tl6ORiIhWGjVjQg8EghYWFY2HLGZ/z2muv5Z//+Z/5x3/8R/bt28fvf/97fvrTn15QO4birRsiMlaPkC7KY64BX60S9pQHCqKqpL7yGmhvUhuThhf6doBMqLQ9KVWJPg44Bhj5pZoVUOGMolXgLYTYTjKH9uALdKvYu5tT3RtLLobyK/KpjDaZ3jAUzCK4YAG0PAXFq7D3KQ/eP6MIqt+D07gP3ATe2hpwMzjt7ZBqxZqzDDp7AQPZ3YtZ6CCdGEamF4SEWBZEvwoh1fyJ2vj1FkHKAFLgNkDpGujart6D8GUQOwD0qHmj0XUACHtnvqoVQEK2Vx2faVMbSxcvB+kOKdhn2zwUSDUoI90KTmpwUIZjlZ11dul4ZpVMJIHQDI+JUhg2YkFfv349r7/++ljYckbuuece7rrrLi677DKKioq49957J0TK4kn5yYZX5VJHFsCxnRA/rjxm/3rcnr0YFf8D3BRgKiHMdEKoTpXiFy2Dti6Vwjew0emmVYdDaWO3dmJFA9DyK4IFNsmuYjLMxOc7pLJKMl3KEw7VkXH71MWhIENwxTXQvxeKVmDvz4t5VEKuF2f341C0HK9nH3Q24sRUqwCrKJ8KCchcVMUCiysQhkcNjw7PQ8b3YRR6VZilb58alOEUq/cgkAYc1a2x+o+h64iaMRqdB8wFw4d0XUR5FLy3wKEfqNdqhmDu3yATx9Trt0L4+7bgHvz+kIJ9torQgNuE3P8gzLsFDj2kLhzZXoyl955ldun5Z5Wc7wViogiEZnIxYkHftGkTN910E9/97nf5+Mc/jhAXxmsoKiriu9/97gV5rpEwmJ9shRCmX3nIjf8Obki1mTUDEDuEEckPfMh0QDIANavVBmbnYaheoYQ/3Q6zNqrqSzMLHh8ULcfvvES6zYHq6wbb0garBMnmo2TkbOXd2zHEgZeRhTGCM/zqwtC9HbId2PVboHA5/qjLQN66kygAJ413ziI4sk2JucxhlQVU9abhQebKIbIA030Nen3Qo4qWZPPrGDWLAaHa50Y3AKa6iFjtIMMqlJQ6pjoyxg6B1Qu9x9W5vcXIGX+GE1mJeeRnmAs+gZSuekzzf8LMPwfAKF8PBx9QOelwimCfrdjHzLRBpg2nYxtG3ccQMgtC4HqjJz1ktFsF6LRDzXgxYkF/4IEHqKur4/777+c//uM/WLx48SkZKKDSFr/yla+MipETmYF4p5k6imj4HrLg2vwQCB+IajDSULMSWnaBlZ8ligvtLyDLr1Ax9/BCpBUBox6nswezZDXeyiKyDW9g9u9TcWxHYB89gOUvVDnkwiRYngHRqNrz9h1BFK9B9r0BnV35lrhXYLccB9fGP7cOjh0EaeP0AtLGW6Zmjjq+y0DEsKoqoWcnzLhGefull2ByACLrVNUoEnnkeSheDcGoCgOVXQ6JZgheAtnX8tcLS3Vp7GqEwhLwxvO9ZQBPBFcEyMy5jrRRQ9Hcv4ED/0d57Dgw6ybc9lfUexhZgNv81Mlv+FsE+2zFPoP3Z7sRTf+eL7LyYAg/kUj/oMCOdhXoVOjap5mcjFjQn3jiicH/t7S00NLSMuRx00XQB+KdJeZvIXEUgjm1tDcSICtVepy3WN3mZgcHHEvpgBRI4SPnqwVh4l3x59g7HlE55IVLVVzckOApwl+VJd1+BNtaiuX9/+3deXzU1b34/9f5fGbLZCEkhCQQIsgSQHaQxQ2h7sVeBJdWhbpcS11obcVq6U9t/V5rW1uvV7F0xfZC9VqrUC0iWq0CdQFEKlV2hEAIWcmezPL5nN8fJ4lESEgwMMn4fj4ePGpmPpl5zxTec+ac93mffWY068+E1FHmrNKeY6Dy342Lpq45fahqE4EzpkLW+VC8FscdAhVb8KU5ZgTf70qcwk+g6mM8aR6oLIXsLwEedNHH2P3HQINjFjt1BB1Ng+QhWOk9TZteHYFIDSROMsky61zzQRDMgYL15n/D26H3+aZXix3EVQm4o26iwcohuWo17p6nsfvOANfFzTiHsP90rPQLcfy9UYDX8reasI+3eNj8YbvtYagzyZxB83CL30Ed/Htzgu3sXaDSJkDESocT+uuvv34y4uj+mppQVX5kpiAObwbPaVD2nknObgRTo51s6lUjPcxBD8O+jD60H39KnZmiiFZDuHEnop2EQ3/sfhMgWksgN0DD3n1EIwPw9B9hFhD9qWaB1ZNAZO8OcPyADzwWgfQG093QieDoYWiPg2/ybejIYZQTwjmwFaq34+kVBCyThIr+gfaNxw4UoT1BVMk6k8wjPQAHS30CoZ6fnvFJlplSyRlrPliyLja19T3HoiM7UNF6dM1+9ICbAIXuM4yIr2/LUeyhV3EjLpT+C8/I+3Abay7rrRw8bSTs4y0eNt3fY3QaVtGroGzckvWNHR9pTrCdXVUSD137PqsrLRqL1nU4offt2zkbG+JF03wptTtg0Ddhz1qzmafPpbBvnSlXrN5pygSrd4KvHquHD9ceTaTnJMKk4tevm7nj9AmgbJxaH3blVnyjvkJ471aczUuxUyLg7UEgYzyUb6Nh80emlrvyZTP/DpCYSyC10nwwKAV2Ck61HxwXDq8zR9MddqE2H6ehJ3iCeNIbOyF6U9DVUbBc7OF9oHALqnafabMbNlUoVjCE2flkOjy6kWRTKZms0IkD0Knj0NrCqjyATuuJ8gzCdRrAk4KO1kDJn6AojM/yYQ2/0zTw0g7YXijfhp06GN6fh6O9zfPO1SkXE2ij2uN4i4caRdibje/QmlYTbGdXlcRb2aGsCXQf3aKXS1fWNNJ0EvrgyT4f0s9s7NltgbfWlCzqg+a/k4ZAWh8IlaIbUrGKdxLo2Rvl1qLr/aiqXXgGn0V05z9Nu1scocCSrgAAIABJREFUfPYWwtrBqQliJx6G4n9AxjkEeoRNpUx2kpnmsLwoitApYyBShVPlNdU2WPiszZDugnZwymqAHmajUtoEKGswDbZqAMuLnZ5svmn0ucx8SyjfhNZ9sFLCpoxSY5JwvcayI7iJHrTyo7090a6Dp3wb1L0DVYDTgOo9FV2Tj9XvDBxPovn24UlE1R8waw5OCOwA9pifwL/+P7S/ccGycd450DTv/DmmKtqTYDuzqiTeyg5lTaD7OOGEvnnzZp577jm2bt1KVVUVycnJnHHGGcyaNYtx48Z1ZoxdWtN8qZWaBzueRPW/Bl1caJJptBZIBJUFkUOgPoSaHhBMxUpKgx2vQW0ElXoGbrnf9DApfx8SB+BUaeyECOgwvnQzu+JUecxIHWjusNjUziBSZVrMHo4AwyGhGp9vO2ReAAccnLqAuTYtiMfeCT2ngD8D+s1E734TlI2dlmA+kD5ZasoIE3LQvhFYvl2Az5x/mjkVc+C0Am8pSmWhep+NW7AS2+ptWgYnWGbh1o1C8ZuoIRfDzsVYg76JW7AaK2MifPRTc4i1W2rWFw5vRvlScPB9+uZ20rxzLBJsPJUdyppA93FCCf2///u/+c1vfnNUb5StW7fy/PPPc8stt/Dd7363UwLs6prnS7UDOV82B0iUvWMWHTOmmN2ckSQz2s04D2o1bsgDh/8B2kXVK7B3QOJp6Lr9qEAtnuwsooddnNJKbCsAbgO+XjZENeFyP+hSMw9fGwDb9HdBB0A5+MaPgP3Pg1UBERcsL05dAqDxpLrQOw2YCGXr0dURsIPQ4wzsnCGmjcCBv4Jbb+bMI9VY6fnQ+wLzYmv3Qdl6iCRCn+HmW0GPM3AL/wEhbXaB2qUQsc1mKLcG5X6Edi9o/PBprK/XDrj1uHYmBBMBBzzJaCvBNPJq0sq884nM58ZTgj3V4nFNIF51OKGvWrWKX//61/Tp04fbbruNyZMn07t3b4qLi3n33Xf55S9/yW9/+1uGDh3KZZdddjJi7lKavs5TtQUd7If64G6UG0K7k8wCafqZUNu4lb7mE9ycmehDe1BuFDspjFsZgroqLG8P3LQJ6GgNquojPM5hojWpOEkTsKMbIVoPvp74hjfuvsSCwbOg4CWTiJUP+n2lsd9LEk5tgmnHW7gPz9g5sH85RMrNTtNDr5lkjsYOVkD0PYj2gVCx2fEa6QmAFWwwH1D1ByFxgDmzNBIEb7k5N9SbCv50rFAYFw+kpECD99NmYd4k8PQxB2ekjj7iJBi/2d6PaQdgB9PRdjJq2F1YVbtxi9+GaO0x551lPvfUi7c1gXimdEdaEALXXXcd+/bt48UXXyQt7ehWnuXl5Vx++eUMGDCAZcuWdVqgn1dZWQ2u276XekLNuXQBCTUbUe/dCNpFe8ebOeqStWZqod6GlGHoYAKqNmq6D+oC0A5unR989ZB7Ne62VRCpQiXUm01J3h5EncHghrCzBmB2cEbNZqLC1yHQ0yyAKi8c3oLj9DOJU0fw2Nsaz/bU0PcrJq7ycqj40CTypsMxsCD3anTJPjNloiNYwXpzn783pI03VTo1dRD+V2OfmojpL1MfMTtFAxG0JwmVNMC8Zk8iKvIBDLoZXbAahn6X+rTzsesLcQJZeKu3mAThScSbORl3/wu4BEwDxCF30NDjLOpV36OSdNDNx7fx5qMbV3VCs62O6soNo9pyInF/+q0oNmsCX6T3ur2PeywdHqFv27aNmTNnHjOZA6SlpXHJJZewYsWKjj50t6VR1KkcVKJDQuJAqMtHOVvQh73Qexr40tHROlTVYdShV02iThgMtWGgGLAgMN4k7aR6nMMaHeqB8pVBtAZPzkQ4uJLo1n9B4mkm6SYNgtpCsIpNqwA3ZHqXDE4zSbx8I0SioAIQbUDvfAWSB4HrYPdQEPU07zrF8plk7uuBddqZULLGTH14U83IOuSDiu3grzHfOJRtTjUq2w01eyB7BJRtQNXtNb1o+l2JSkuF0HR04d/Rlhd311Mw4VyqEyYAoFL6kjDhDHyRQthyDw7mbFXtAjt+CxMmo4+xC1nmc2NDpqy6hw4ndMdxCAQCbV4TCARwnOMf5htv6q1++MY8glW6FgBd+j4EstCl683ItX4j6J4QLQJvoTlv0zsSKzcVt6QEKirAE8ROqcepAd2QZEbqVmOPldPPhtKN0CMCvdKgbA9gqkSIRlHKi1a+5mZXOpwOEa85pzq5DtJTGk9RmtzYFqAcHUk1m4X6DjPlgzX7oN8ss6AZrYbiXeaDwC4EkqB6m9k16qY2jszD5oMhdYR5E5QXVf5/kL8bBsxBNRSi7aSjkm5TgrDdIrxK0aL3dxsJWuZzhWhdhxN6bm4ub775JnfddReWdfQBAa7rsmbNGvr1+2LNryk0yVWr0dt+gXZrUL5eqKF34igvVqjMXKSj4K2ESIYZVaf2gIItUB7CSlC41lC0ZzjK3YzdE5wyhfaOgkN7UMrT2OM8ClimD0zmeVC0BlMM7jMVKJUfmx4s9T4gaqZWPEFwNKabYRm6Ngr2EEjyY/UZAvVFuKXrsZIHQMUmiNSC08Ncjw3pWVCrTfOs1JFQ1/ghkuQ154Jqx/R0sRNQzhbTddFtTPR1B7ACGShvKpHGksQjFzUtX0Lj3HrDp29mY4I+1uKnzOcK0boOJ/QZM2bw6KOPctttt3Hvvfc29ycHyM/P52c/+xm7du3iO9/5TmfG2eUl6ALsqg8hayp4eph+5uv/EyuQhcKF9OtNL3TdAFYAvP3M1EZiMtT5ob4ey/4nrj0SHTgTlZaJne1A4augQziMhqoQNCSb6pgUG8r/hfaONu1oozVQXo6ut7AHj4BIjtmlGjH15/S5DF12CIJnQfVuLG8JpE6EvX8yj+dLx63+BMs3AsJ7QddBQsiM1u3kxrp0Dxz6EHNyUj70GGWmf5QNykLZ+aate6QGEvo2ngjkmg+bnCvQyj5qURN/JgyYCzueMvPyjQm6wcppdfEznmq8hehMHU7oN9xwA2vXruXNN99kzZo19O7dm4yMDEpLSykqKsJ1XcaPH88NN9xwEsLtmhSaQOU/UTufNNMdp9+AOvgy2o2gdBRHBbFqXkf1u8J0UgxmQ70HrBxwy00nQicRoj2w0hOgbB1u2RSo2orKGm3m1u1kc/hDZCAU/QOcOpy6JOzBp0OB2S2qPF70kIuhYhs663xIGYKqL0RXHIaCDyBSgXXaBIhWAo2LpdocV6fCfohEzDmgw2aZuXA7CGWbILm/eV2FH5jfCSrTAbF8o+khU/YuauD5cHCVKZhXjYusuDBoHvQch7Pn/7DTLyTB77bcpBIqggMvosf9N24k1Jygj7eZReZzhThahxO6z+djyZIlLFmyhOeff578/HwOHToEmOmY2bNnc9NNN+H1ejs92K4qwd0PO56k8cBMkyTrDqACGbjKAyjcUAXO8Gl4lY0qfNn0d6kImUOkqTELjqFkqI2CnYCVmYurHXTRLojWoLKHwYF3ABdSR6EtP5Y/w7TkdRsPoXUUFK5Gp02AQ9vQVTtB12P5K6FvY3+ZYD8oXW+mYVDgpGN6zIBK9pkdqvueNf1OlMf0My/6CAK9wFNuNidFPGZhFwW+FFS/MWYX6WnXgi8FGorQe/9sjp0bNA9nz/9BtBbH3/vYi5qhYtyo03x/gh/scJksfgrRQSe0scjr9TJv3jzmzZtHbW0tNTU1JCUldeiMz3hih4pwXbADmabrIKqxuVbAHHkGYPnM2mb5ChRJqNr9qL7j4NBHYOdCWo6Zfig5APYYOPQali8NBk3HLS5Ah21o8KPsInMuZ+qIxg+QxiZZ2kE7vdHRsBn9V21HJVlYWKZhV/lm6HOROc0oOAHqDkJ5IfQbb1r4EoaMaabmPHUEHP7A1Jwf+tB8CCR6zAEWPfIAy2ycqtqJ8pZAwYvmsA5lgz8NPf5JdI9RULkVt/idFjXlCX6OXtT098Yb2ov94d3N0ytW3u24/kwzgm8ii59CtOlz93JJTEz8wibyJqbywo9DClYwgDr8ITrvW+iDq83UROO8sBupx3bDaG85RNLRNftQOZNQ1dVQ/jG470GvKVC4HUiAHtmQ/ywWGny94LTxuAd3AGlwYBMkDUKHAuZaFHZPHzp5HCTmonv40NWfQDQEWGaapWQf1LnQwwfDv2YO23DDkHOFadBV8jYE+0KDhpTzzelE3mpI9EPNTrMIW/CSqYCx/ahRN0D+X00ybzqVOlwJZRsI9Z2DThyDnf6lFvPcx1rUZPBtuP/6f40dKQE3jLt9EWrkD9BbfiyLn0K0U4cTemVlJSUlJeTm5uLzfdp34/nnn+fvf/87wWCQr3/964waNapTA+3KjkxSrqvA0ejUSUT7XI8VKmkxL2z7M7F6jTELlZVhcKPo8DZQHpRt+qLjKTL16/vfB1LBaux3Ur4Ra8DFZmt/ShL0yjaj5pK14EmCHmPh8GZ05ceoaCUq4xwoiYLjA3pA9mAIpUHwNHSoBJX/HOCajU9p48HuD6XbTWy+WtPKoORdSMwxI/KDL5tKloRSUA58/FMY+zM4/G7jAdgeGHwruuQdrF6XmJrzz0yPHKuvipeyT5N5EzdClCScCb+XxU8h2qnDCf3RRx/lxRdf5J133mm+benSpfz4xz9u7u3y97//neeff55BgwZ1XqRdWGvNnwAS/Kp5Xjhk9SXhtP+AD39gDqhQQ6HfbFytwe+iQ16setckRu2Ar8LMq7u9IJpgdpYqyxwM3XOcOWTC9kOvSZCQA4WvgNbm7E83Aw5+CIm54GyDPpc0HkAN2tsD9v8FcEBlQ1RDTZWZhvEcNuWGLqYkMvdKHG8vLDsRVfEh2leOQyoAls+HsryoIXeYo+UU6OJ1uK5qc2rks5tUEgM+M1I/qrY8UxY/heiAowvJj2PTpk1MmTKlxeaiJUuWkJmZybJly3jssccAeOqppzovym6gaTqheWFPF5BS9Qq+jTdj/2sBvo03k1K/BnfXU+hAFgT7oYMhdKiM5g2R/oiZBkma0lgK6AF/NQQ1Zt76NHNotPKaBKobTIfGkn9CwyHTdyWaBA2N/7cmeyB7iGkSVrQOit4wo/loJTqaCE3TF95aSPKDt6rx4ArLlChaPly86Eg9zmln4QTCuPgxm4AUrpVMQ8JI3JTR6KI3cQvfwHEVeuhdHZsaSRqIHvrdI3q9mOkVlEVy/QaCbj6KDnWoEOILqcMj9OLiYqZMmdL8865duygsLGTBggVMmGC2db/yyits3Lix86LsBj5bX+3NuQx14PnmLe24YVTp2+DWmKSo/OYXw//CypxuEq6OmqkOK8vMY/f9slnEdBog6JhzOg99BN7hpocKuRDINsk/HECTA4E6s8PTEzUHU3gSoXgdEDbX2bnoOgewcQMR84keDpvaeKvxr4PWpokXLlbtbnTNW1CRhT3oRpxdLevF66xc6nrkknDmuBOfGlFWi284rj8DT91OfBtuNIuk/kz8Q28lSiKOP0umXoRoRYcTekNDA36/v/nnTZs2oZTirLPOar6taTfpF8mx6qap248VPK1xVAtgoZRFi8FmtJaGQbOwE07HilSjbQ92wXKoqYE6H6RPAm8KhMugeA2EyyFzuikbzBwAB1408+tRP+r0y9GFb5jHVTb0PhsOvATZF0D+GvAk4/YYSX3vyQSSk1GH/o6rMdU0niTInGZq3LHMnP5p49D7X8RjKXRDHXrfn2Hcf+McUS/elFg/79TIkdMwQTcf9fFPzXvo64ndazS8dyPeQBa2lSzdFYVoRYcTemZmJnv27Gn+ed26dSQlJTF06NDm2yorK1sk/S+Co+qrlcf0NdHR5tG4W/oB1tDvwfYnW1ZuqL7onBz8BW+j0AQzzkHVPwvhBKithdDGxlOJPKYlbfkGyL4EDr1qRuAEzej+4CrTAVFHzQdA+fumd3lNHTo5D50zkXpPfzSKhoQz8PbvYz5EvF7shmIzb9//Wkh0zOEXWx9BuXVQX4IK9IEeg7HrdhBJOrPdo+QT6V1+5HtpZUyEXb8yzcd0VE7LEaINHU7okyZNYvny5Sxbtgy/388bb7zBRRdd1KKvS35+PtnZ2Z0aaFf32aZRbsl72INvRR9cZRYYLR964I1UpFxMYELb0xORhP74BlwPNZ9ArQt1KeDuh2hjwzNPspnndh2z5V/ZZnHUTjD/HSqF8k2mjlzZ0LMXqmgLrjqn+Tk0irBKQ/l6Eqx7H4r/gMq5FHb9ziy69ptl+qyHD5vWAv1monb9HgpexO/PwjP0+8cdJZ9o7/IW76U2nSRRFlo1/nWVDUZCHFOHF0W/8Y1vkJiYyEMPPcR9992Hz+fjjjvuaL6/rKyMDRs2MHbs2E4NtKurt/q1XNiL1hJNPZPQhKU4o39BeMLvqUq5BBeLOiuX6oQJ1Fm5zYlNobH75BKsex/vJ7/FPfQWWnkhe6gZcTed9gPNhzrjHtHQCm2OoWsoNl0Yk6aANw36n2fqy3uMRBWsJFD/UYsFRiu7Hxz+C2RPh12/NY8ZKoGit0x9ur8X9JkBu34PvhR0pBbl1JlRsru/zfek1e37x/m9Fu+latyVGshssUlLNhgJcbQOj9D79evH3/72N1avXg3A9OnT6dOnT/P9BQUFXHvttcyYMaNTAly2bBkvvPACO3bsYMaMGfzkJz/plMftbG2eW5lwGmCSdtDNP2r6oWkka1d9iNq/ErSD8qbgHq41c9l9xkCBaw6bVh6zOBqugoxzoXgt2D5T9dL7XCjLN+1t696DgXPQ4SpIzkPXF6GS+qFCRST4kolmDMTxZ2GHy8wuVzuh5ZSRU2vaCAy/1xz23JjMwTUj5VZGyUdOsdi6prGVrwJfTzN9oh180UPU+1qfemnxXobL8aYMw92+qMVirGwwEuJoJ7RTNCMjg+uvv/6Y940aNapTNxX17t2b2267jbVr1xIKhTrtcU+Gtg4BaGv6oXkkm3Ue6O3AQHNqkc9rkmzNHtNjhQHg143b+DOhZi866wKULwVq96FLdprEnhAFxzVTNtjomk+wkvqbOfWeI7B2fw/rQDqOpxdW3h24/iy0lYDyJDUeZ9d0TmkEEvpA6DDaiQLupyPlY4ySP/sarZwvo2jA8WVjZ5xp5sJ1BOvQa6QMv59ocDBWqBiqcghqB7uhsMUHXZ2VC4FcVGA0CceZpooHJ7LeIMSRPtfW/7q6Ovbu3UtdXV1zyWJnu+iiiwDYsmULRUVFx7m662qre6BZBAyhlEJZXnTjUXkKB7cmH9Vritk0VOtAjQW9x4EVxM2YilW/z4zKaz5B2aVoDxABfD0xM2q68Vi4t6D/mahP/ghoaCjCCgaat9iz7TE4/QYzh+7rYVreZl+E3vqY+ZYw6gH0riXmGD/Lf8xR8mdfo1v8NnbOFdho2GmSOYFMXE9PPBUbsD9+ENcFVIiEvjNxit/DjtYeNc/+RTgtR85KFZ2hw3PoAIcOHWL+/PlMnDiR2bNnM3fu3Ob7Nm7cyGWXXcZ7773XaUHGg7aOTnP9mdg0wL4/w+lfp2njDsqL1Wss2gmjMy+CoVeZMkadTrTBxm3QEPaaRdC+Yz6tI7c85rCLmj3o5EFg2SjvIZTlw6zQKtMDXUebt9i7wxfietLQ4x9DD1+I7nMJOn85KlRkOkXuW4kzbhHO6Eeb1wM+m2iOeo3hwzjF76FTx0CgNzp4Go6VgpUxCXYuRjl1WIShLh92LjZTMu2cZ483J7reIMSRTmhj0VVXXUVZWRnTp0+nrKyMzZs3N98/evRoysrKePnll5k0aVKnBvt5pKcndej61g5hPWHV/cAfOGp7u7dnDqBMVcnOxbD/RdTgK9GhFFNzXvoOKlJj5sf3vWSuVR68fb9sDpGorIaEAaa2vd9VqIZDZq45UgPJp2PjogZNhXeWNHZnNIdOKMsDtg/LG8Dbs5953LK3TBOupEGmx3qkDHwDsGwL3BKwHcieBsAx27Ed6zWqekgZAIF08wEGpg8METP370ZAa5PYlYPttYAoXlVOYsYZnfv/wUnQaX9PisvBjoJ95Bjr5L0Pnf73+xTojjHDqY27wwl90aJFlJeX89RTTzFp0iQWLVrUIqF7vV4mTJjApk2bjvtYc+bMYf369ce8b9y4cTzzzDMdDa9VZWU1ZrqgHU7GSd2KTFIG3tny6LTBd1LVkElS/UbsonexBs4DK4DqORx2/t1cg4VKyYODKxvPDq02Oznz/wKn3wRKQeUWCJWhLA8641xI6AXhw+jq3VC3BvxXoQbOg/znUYP+E3b/HnwZRAmiB95JdUNvkqtW48n/C9QfRAWy4LQroXgNTiSCdl2wfIR1GnVtvC+tvcZqN4/kI2638KMS+uG4HixcbKVMiwFt40ba91xdQWf+PQnqNHyO56gP/JPxPpysk+hPpu4YM5y8uFv7kOhwQl+zZg3Tp09vc/SdnZ3drq3/S5cu7ejTd1ufrdywPDZupJ4Edz9OIBs7Wotbsh4740zUpgXo+iDYAVTGeWYGxvKYmnO3sXzRaYBoFZT+02wi8iSY2ys+NI27qndCv3EoPRgCvXAOvI+VeyXaCqDGPYbr6Uk4OKzF6UAuAWxfmumJnv8cus+XsQpX43h6tauypK1KnyNvDwey8KaMbOxOCXYwF/rOxC1+7wtbxSJnpYrO0OGEXlpaymmnndbmNV6vl/r6+hMO6kjRaBTHcXBdF8dxCIVC2LaNx/O5W7mfck0NvFLCH6E+fBS7cfFLD/0uevg92BXvw84nTXK2bFN/XrIG+l1pFil1XWN73YTGnai2mZZxHXBrzO9EasHbE/qNQe36FUqHwN8bO/erOCXrzUYhy0dkwu8AzLeDxvJCRRgdKjENukKHIXkI2pOCzv4KVZ4R7Vqca20B87O3q5S+zQnem9qX+gYHO/3CuK5iaUubZa9CtFOHF0VTU1MpLCxs85pPPvmEXr16nXBQR1q8eDGjRo3iN7/5DS+++CKjRo1i8eLFnfLYsdDa4lc0aSiq5yiUP9VsGvKmYs78jJhzOvtcApbfHB2nvNDnUkzPFfvTB3cdsLyonKGoxhJBHcg2deO7f2sWHRs/QDx1O5s7QVrlG7BpMIuk2jWPpWx0fRFuwau4kVCnJ5amBF+dMAFShlCnco7abPVFc+R78kV+H8SJ6/Awd9y4cbzxxhuUlJSQkZFx1P179+5l3bp1XH755Z0S4Pz585k/f36nPFZXcFQliK8nVp+LCFSsNQuEDWXg1KESatF2tqkJD2bj2slYA+aYmnRPIuQ/DxnnQa+zzE5Q2wepo1F9h6LtACo5DyIVaOVD40UF/OjUcYT7Xg/Kau5kCJ+WF3JwJSiFUl4YdAu6ZB1Y3nbtyox1DXWsn1+IrqDDCf3mm2/m9ddf5/rrr2fhwoXNUyt1dXVs2LCBhx9+GKUUN910U6cHGw9MnxIvlluD8qZAxgTUJ0ugdjcE+sCIhfDvh8zZpP4BJmEXrcPqNRHQEMiAg6+a6ZjDGyF9MvQ+D5XZB7XjSfSO11B2ojk/NH85KnwQFcjE8fQinDicOiuX5PoNxywvZMxj2HW70Yc3wb5nUdEa7FEP0WDltPmaYl1DHevnF6Kr6PCUy+jRo3nwwQcpKCjgm9/8JkuWLAFg/PjxfPOb3+TAgQM89NBDDB48uNODjQcNVg72oBtNeWHPkaidi8z2eO1C/QHYsRjG/AQGzDFtcsveh9Q8KHwZClfBwdXQc5Q5Ni5aC2Xrod8I2PMH8ATRdhBdXwD7V5h+6tqFcAVW3h3NC2yOP/PTnjNNorVoy4/zyfNoTw907jXogd/A2fdXAu6BNl9TrGuoY/38QnQVJ7SyOHv2bMaPH8/TTz/Nv/71LyoqKkhKSmLMmDFcd911nH766Z0dZ9wIuAdw9v0Va+A3UIHeZr5cqcbt9q6ZM9dRUDYqoze6YZA5mNmTbLble5LNCUXpE6H8/8Cbia7+BI0H25sA4WpwI+hQuVnUHHwHKJuIv3/zaLW1igo3Uo8dKsItWN0i5uN1Nmxr09Sp2N0Z6+cXoqs44VKR/v37s3DhwlbvLy8vJy0t7UQfPm7ZoSJoTJpW30sg2gDRBlRCHzOHnnsVbH3E/Pe+P6NG3AelfwQrFR2tADeACqSg01LRDafhWkmoXmehC/+BjYNWHnOIRtOi5kFTz+70/bT3TmsVFQnu/hYtgIF2dTb8bOvg9v5eZ4n18wvRVZzQ1v+2VFdX8+ijj3LBBRd09kPHhSOnO9yS92DQPJQCbQWh/1xzSIXWpuY8WgXV28GXBpbXlCq6UbQnGZ11Ee6on6DHP0bUSsfKu6PxDFAfBPvB4FtxS9a3Ws98ZEVFczIPHUKNXAj+THNRO2uhj2odfIprqGP9/EJ0FR0aoRcUFPDRRx/h8XgYNWpUi9LEUCjEH/7wB5YsWUJlZSUJCQmdHmx39mkVhkmaettiCB2CkrXoMY+ga/ZCQgZojbL9oBNNiWLxWhh2N2z5Ecqfbkbefb+M+++fYIcLIecKnOL3cJUPe/hCHDdIxN8brex21XV/dkFRW16svDuI+Pvj+NLaVS0S6xrqWD+/EF1FuxP6f/3Xf/H000+jtdk+7/V6ueeee7juuut47733uPfeezl06BA+n4+5c+cyb968kxZ0d9Na0nR9Geiydbh7noHwYay+l6AilWbjUKTSzKlHG0xN+oDr0D1Gog5/AIfewIrWmvucEPbAa00NeuW/cTL+gzrVWJWSkNMihmOV9R29oBjB3f4kzoTfd+iIt1h3RIz18wvRFbQroTcdOWdZFgMHDkRrzSeffMJDDz1EMBjk/vvvx3VdrrnmGm699VYyMzNPdtzdSmtJk9H/hXvg5ebr3JL3sIfdDR89ZJptBTJh8G1mO78KmEqUPX8EZaFShqP6XAr5f4adRWZRdfA8ArVbUEEHK1TcnLiBVsv6ZEFRiPjRroT+wgsv4PV6+d///d/mo+U2bNjAjTefTEFnAAAYwElEQVTeyA9+8AOysrJYvHgxeXl5JzXY7qq1pIlTb+Z9m+4LH0ZHGyDv2yhfKlRthR3/A+FKGPNTdDSMsvymzDHzPNj3f1Bf2Hi+aATyl2Ml9MX/8cO4rmpO3JHkka32Yj/RBUXZyCNE19OuRdEdO3Zw4YUXtjgn9Mwzz+SCCy5Aa81DDz0kybwNx6z7tnxEAgNaLub5M6HXJEjJQ+so2ImmhwvaHEDh1MGgeeaMTa1NEm8qd0SZzUTbF6GcOvN4jYnbFy5odRTeYOVg5d2OrSJYhMDyHndBsWkKqal1gG/jzeYbAO3rZimEODnaNUKvrq4mN/for99NTbq+aAdCd1Rrdd91Vi6k5H56dmbkIKr4TbPZKFpj5sBzr4GiN9A1uyFai1O2BTXpKbQdxD70d1S0+tOErjygw2h1RO/3Y30TAFMR488guWo17p6nsfrMADSq11lUJUxtc7Td1ulLHZl3F0J0rnYldNd1j9nd0Ov1AhAIBDo3qjhzvCqMeqsfKZ5KVMkm1K5fm5E3QH0B7H8ON/daKHwFN/Miwjlfp8HKIaAPEhjyLdTWn6HrD4DyodLGo4veMqWLTRq/CXiO8YGild18W/NmosI3CUwY2GZilnl3Ibqmdle5KCXzo59Ha1UYTdMXduUmiBw2tefK0zjajpi+52icod+nyj8R+HSB0/UkwmlzUMmn4/h6Y/UYQFSltP1N4IgPlKT6jSeUmGUjjxBdU7sT+qJFi1i0aNEx7xs2bNhRtyml+Pjjj088sjh25IKi5Q2gtv0Csr+EUp7GqZGQaZFr+cDbA511MVXe8WgUQTf/04QdDuPmv2BOtpnwe7zJA6lqyGj1m8BnP1BONDHLYQxCdE3tTuhN9ecn6/ovis/WpFt9L0JFS3FK3sXKmgqDvgG7fmMWPQPZ6NxriDoetNck5TanOzijQ/XYJ5qYZSOPEF1TuxL6tm3bTnYcXwgKTUr032Z6JXu62fqPZbohWgHcQ69j9T4bxj6K9qagawtwC9/Eyf7qp6N6XYOV82Xc4rfN6UNwwtMdnycxy0YeIbqe7neOWzfVNDL3bP1/ULMbLD/2oHk4FduxB96COrgSGgpg52I4/etw4GXUaVehB3+DBivniFF9CEUDduOWf6K1zaPqxHbEcKzacUnMQsQHSeinSFOpn9au6YbohmDXr7EGfROndDNq3OOoQ6vN6LhkHcqy0AdXEe1zLQH3wBFlggqHANbBVTD6EcLe7HaNquUQCCHinyT0U6Rp7tvFhx3INCcSuSFAowfeiOPY2AWvfvoLKhFcsEIlgP7MvLnCdcFx7XbXfUvtuBDxTxL6KXJkRYljpWAFAyhl4WR/hSrPiOP2Iv+8ZYJSOy5E/Ov0fuji2Fr27Fa4VjLRYfdR5RmBRrXZ07sz+n231n5AaseFiB8yQj9FjldRcrz7P2+ZoNSOCxH/JKGfQserKGnr/s9bjSK140LEP0noXyBSoihEfJM5dCGEiBNdOqGHw2EWLlzItGnTGDt2LDNnzuStt96KdVinjEITdPNJrt9gerhIv3EhRBu69JRLNBolOzubpUuX0qdPH9566y3uvPNOXnrpJXJyco7/AN1Mi52cgWy81VtkI5AQot26dEIPBoPMnz+/+edp06aRk5PDRx99FHcJ/bM7Ob05l6EOPI9DAFCyEUgIcVxdesrls0pLS9m7dy+DBg2KdSid7lg7Oanbj8URm4GauyoKIcTRuvQI/UiRSIQFCxZwxRVXMHDgwA7/fnp60vEvOkJGRnKHn+NzKS4HOwp242es3dgPHQfb23ib5cPbM4dAcuuxnfK4O4nEfWp1x7i7Y8xwauOOaUKfM2cO69evP+Z948aN45lnngHMEXjf+9738Hq93HfffSf0XGVlNbhu+xYVMzKSKSmpPqHnOVFBnYbP8Xw6Qi96F3vQN9EHV+FGXLMRaPCdVDVkohuOHVss4u4MEvep1R3j7o4xw8mLu7UPiZgm9KVLlx73Gq01P/jBDygtLeW3v/1t8zmm8eaonZzRWqKpZxLtcy1WqEQ2AgkhjqvLT7k88MAD7N69m6eeeiquD6Nu3sl55kh84QJw6ogGBlJn9UMnnBbr8IQQ3UCXTugFBQU8++yz+Hw+zjnnnObbf/SjH/GVr3wlhpGdPN7qLdBU6WL5SJFSRSFEO3XphN63b1+2b98e6zBOGelZLoT4PLpV2WK8a/sA6JNDdqMKET+69Aj9i+bIQzCancSe5XIsnRDxRUboXUhnHGTREa1O8bj7T8rzCSFOLhmhdyGnume5HEsnRHyRhN7FnMqe5ad6ikcIcXLJlMsX2Kme4hFCnFwyQv8Ck2PphIgvktC/4ORYOiHih0y5CCFEnJCELoQQcUISuhBCxAlJ6EIIESckoQshRJyQhC6EEHFCEroQQsQJSehCCBEnJKELIUSckIQuhBBxQhK6EELECUnoQggRJyShCyFEnJCELoQQcUISuhBCxAlJ6EIIESckoQshRJzo8gl9wYIFnHPOOYwbN46LL76Y5557LtYhCSFEl9Tlj6CbN28eP/7xj/H5fOzevZu5c+cybNgwRowYEevQhBCiS+nyI/TBgwfj85lT6ZVSKKXIz8+PcVRCCNH1dPkROsAPf/hDli9fTkNDA8OHD2fq1KmxDkkIIbocpbXWsQ6iPRzH4YMPPmD9+vXccssteL3eWIckhBBdSkwT+pw5c1i/fv0x7xs3bhzPPPPMUbfff//9DBo0iLlz53boucrKanDd9r3UjIxkSkqqO/T4XYHEfWpJ3KdOd4wZTl7cGRnJx7w9plMuS5cu7fDvOI4jc+hCCHEMXXpRtKysjJUrV1JbW4vjOKxdu5aVK1cyefLkWIcmhBBdTpdeFFVK8cwzz/DAAw/gui59+/Zl4cKFXHDBBbEOTQghupwundDT0tJYtmxZrMMQQohuoUtPuQghhGg/SehCCBEnJKELIUSckIQuhBBxQhK6EELECUnoQggRJyShCyFEnJCELoQQcUISuhBCxAlJ6EIIESckoQshRJyQhC6EEHFCEroQQsQJSehCCBEnJKELIUSckIQuhBBxQhK6EELECUnoQggRJyShCyFEnJCELoQQcUISuhBCxAlJ6EIIESckoQshRJyQhC6EEHFCEroQQsSJbpPQ9+7dy8iRI1mwYEGsQxFCiC6p2yT0Bx98kJEjR8Y6DCGE6LK6RUJfuXIlycnJTJkyJdahCCFEl9XlE3pNTQ2PP/449957b6xDESdAoQm6+STXbyDo5qPQsQ5JiLjliXUAx/PYY48xe/ZssrOzP9fjpKcndej6jIzkz/V8sdKl4tYu7F8B234ObhgsH4lDF0C/maBajiW6VNwdIHGfOt0xZji1ccc0oc+ZM4f169cf875x48Zx//33884777B8+fLP/VxlZTW4bvtGhxkZyZSUVH/u5zzVulrcQTcf35afmWQOQANs+Rlhfx51Vm7zdV0t7vaSuE+d7hgznLy4W/uQiGlCX7p0aZv3/+EPf6CgoIBp06YBUFdXh+M4XHHFFZ2S5MXJZYeKjkjmjdwwdqgYEnKP/UtCiBPWpadcrrnmGr785S83/7xkyRIKCgr44Q9/GLugRLs5/kxsy9cyqVs+HH/v2AUlRBzr0gk9ISGBhISE5p+DwSA+n4+0tLQYRiXaq97qh3fod1HbHm2eQ9dDv0u91S/WoQkRl7p0Qv+s+fPnxzoE0QEaRVXKJSRMOAM7VIzj70291Q+NinVoQsSlbpXQRfejUWYBVObMhTjpunwduhBCiPaRhC6EEHFCEroQQsQJSehCCBEnJKELIUSckIQuhBBxQhK6EELECUnoQggRJyShCyFEnPjC7BS1rI5tN+/o9V2FxH1qSdynTneMGU5t3EprLUfICCFEHJApFyGEiBOS0IUQIk5IQhdCiDghCV0IIeKEJHQhhIgTktCFECJOSEIXQog4IQldCCHihCR0IYSIE5LQhRAiTkhCP0JFRQW33347Y8aMYdq0abz00kuxDum4wuEwCxcuZNq0aYwdO5aZM2fy1ltvxTqsDtm7dy8jR45kwYIFsQ6l3VauXMmll17KmDFjuOCCC9i4cWOsQzquAwcOcMstt3DmmWdy9tln8+CDDxKNRmMdVgvLli1j1qxZjBgxgnvvvbfFfe+88w6XXHIJo0ePZs6cORQUFMQoyqO1FvfmzZu58cYbmThxIpMnT+Zb3/oWxcXFJy0OSehHePDBB/F6vfzzn//kkUce4Yc//CE7d+6MdVhtikajZGdns3TpUt5//32+/e1vc+edd3LgwIFYh9ZuDz74ICNHjox1GO32z3/+k5///Oc8/PDDbNq0iT/96U/069cv1mEd149+9CPS09NZt24dK1asYMOGDTz99NOxDquF3r17c9tttzF79uwWt5eXl3PHHXfw7W9/m/Xr1zNixAi+853vxCjKo7UWd2VlJVdffTVvvPEG//jHP0hMTOT73//+SYtDEnqjuro6Xn31Vb797W+TmJjIhAkTmD59On/9619jHVqbgsEg8+fPJycnB8uymDZtGjk5OXz00UexDq1dVq5cSXJyMlOmTIl1KO32xBNPcNtttzFmzBgsyyIzM5PMzMxYh3VcBw4c4NJLL8Xv95ORkcE555zDrl27Yh1WCxdddBEXXHABqampLW5/7bXXGDx4cHP88+fPZ9u2bezevTtGkbbUWtxTp07l0ksvJSkpiYSEBK6//no2bdp00uKQhN5o7969WJbFgAEDmm8bOnRol/sLfzylpaXs3buXQYMGxTqU46qpqeHxxx8/6qt1V+Y4Dv/+9785fPgwF154Ieeddx4PPvggDQ0NsQ7tuObOncvKlSupr6+nqKiItWvXcu6558Y6rHbZuXMneXl5zT8Hg0Fyc3O73b/PDRs2MHjw4JP2+JLQG9XV1ZGcnNzituTkZGpra2MUUcdFIhEWLFjAFVdcwcCBA2MdznE99thjzJ49m+zs7FiH0m6lpaVEIhFeeeUV/vSnP7FixQo+/vhjFi9eHOvQjmvixIns2rWL8ePHc9555zFixAguuOCCWIfVLsf695mUlNSt/n1u27aNX/7yl3zve987ac8hCb1RMBikpqamxW01NTUkJibGKKKOcV2X733ve3i9Xu67775Yh3NcW7du5Z133uGGG26IdSgdEggEAJgzZw69e/cmLS2NG2+8scsvRLuuy80338yFF17I5s2beffdd6msrOSRRx6JdWjtcqx/n7W1td3m3+e+ffu45ZZbWLhwIRMmTDhpzyMJvVH//v1xHIe9e/c237Zt27ZuMXWhteYHP/gBpaWlPPHEE3i93liHdFzvvfceBQUFTJs2jbPPPpslS5bw6quvcsUVV8Q6tDb16NGDrKwslOpep+dUVFRQWFjI9ddfj8/no2fPnsyePZs1a9bEOrR2GTx4MNu2bWv+ua6ujvz8/G7x77OgoIAbb7yR2267jZkzZ57U55KE3igYDHLhhRfy+OOPU1dXx/vvv8/rr7/Of/zHf8Q6tON64IEH2L17N7/61a+aR5Bd3TXXXMNrr73GihUrWLFiBV/96lc5//zz+f3vfx/r0I5r1qxZLF26lLKyMiorK/njH//I+eefH+uw2pSWlkZOTg7PPPMM0WiUqqoqli9f3mJeuiuIRqOEQiFc18VxHEKhENFolAsvvJCdO3eyevVqQqEQTz75JHl5eV1marG1uIuKivj617/Otddey9e+9rWTHoccQXeEiooKFi5cyNtvv01qaip33XUXl19+eazDalNBQQHTp0/H5/Ph8Xx6ROyPfvQjvvKVr8Qwso554okn2LdvHz//+c9jHcpxRSIRHnroIf72t7/h9/u59NJLufvuu/H7/bEOrU1bt27lxz/+Mdu2bcOyLCZNmsQDDzxAenp6rENr9sQTT7Bo0aIWt91xxx3Mnz+ft99+mwcffJCDBw8yevRoHn74YXJycmIUaUutxa2U4oknniAYDLa474MPPjgpcUhCF0KIOCFTLkIIESckoQshRJyQhC6EEHFCEroQQsQJSehCCBEnJKELIUSckIQuRBdw7733kpeX163aHouuRxK66Lby8vLIy8tj2rRphEKhY14zffp08vLyutxBDu1x4MAB8vLyulU3ShFbktBFt3fw4EH++Mc/xjoMIWJOErro1nr06EFqaiq//vWvKS8vj3U4QsSUJHTRrQUCAW699VZqamp48sknO/S7L7/8Mtdddx3jx49n1KhRXH755fz6178mHA43XxMKhZgwYQJTpkxpddrm/vvvJy8vjzfffLPF7bt37+bee+9l6tSpjBgxgrPOOou77rqLPXv2HDe2J554gi996UsAzU20mv688MILrFmzhry8vFaPMwuHw0yaNIlJkya1eD0ivklCF93eddddR25uLs8++yyffPJJu37n0Ucf5Tvf+Q67d+9mxowZXHfddWitefTRR7n55puJRCIA+P1+LrvsMsrLy4/ZajYcDvPKK6/Qq1cvzjnnnObb16xZw6xZs3jppZcYOXIkc+fOZcqUKbz66qtcddVVxz0icOLEicydOxcwJ2fdcccdzX+GDRvGueeeS25uLqtWraK6uvqo31+9ejUVFRXMmjULn8/XrvdExAEtRDc1ZMgQfe6552qttV61apUeMmSIvv3221tcM23aND1kyBAdiUSab9u0aZMeMmSInjp1qi4uLm6+PRKJ6Hnz5ukhQ4boxYsXH3X9/Pnzj4rh5Zdf1kOGDNEPP/xw820VFRV6woQJeuLEiXrnzp0trt+xY4ceM2aMnjlzZovb77nnHj1kyBC9f//+5tv279+vhwwZou+5555jvv7f/e53esiQIXrp0qVH3Xf99dfrIUOG6D179hzzd0V8khG6iAuXXHIJY8eO5bXXXmPjxo1tXvv8888DcOutt5KRkdF8u8fj4Z577sGyLJ577rnm28eOHUv//v154403qKioaPFYK1asAGhxcMGKFSuoqqriW9/61lEHMAwePJirrrqKjz/++HOfhzlr1iz8fj/PPvtsi9v37NnD+vXrmTRpUoszckX8k4Qu4sY999wDwE9/+lN0G12hP/74YwAmT5581H0DBgwgKyuLAwcOUFVV1Xz7FVdcQSQSYeXKlc23lZaWsm7dOoYPH87QoUObb9+8eTNgTrx64oknjvrTdCrW5z2xvmfPnlx66aXs2LGjxUnyf/7znwH46le/+rkeX3Q/nuNfIkT3MHbsWC6++GJWr17NqlWruOyyy455XdOc85Gj8yNlZGRw8OBBqqurSUlJAcwI/H/+539YsWIF1113HQAvvfQS0Wj0qGPFmkbxTYm1NXV1de1/ca249tprWbFiBc8++yzjxo0jHA6zfPly0tPTu80B0KLzyAhdxJUFCxbg9Xr5xS9+0Wp1R9Pp8aWlpce8v6SkpMV1AFlZWUyePJkPP/yweWS9fPlyvF7vUadaNf3eX//6V7Zv397qn844P3X06NGcccYZrFq1isrKSlkM/YKThC7iSm5uLl/72tc4cOAAy5YtO+Y1w4YNA8xB1Z+1b98+Dh06RE5OTvPovElTAl6xYgVbt25l+/btnHvuuaSlpbW4bvTo0QC8//77n+u12LYNgOM4bV73ta99jVAoxIoVK/jzn/+MUoqrr776cz236J4koYu4c/vtt5OSksKvfvUramtrj7p/9uzZACxevLjFZiTHcfjpT3+K67pceeWVR/3eRRddRFJSEi+++CIvvPACYBYmP2vWrFmkpKSwaNEiPvzww6Pud133mB8mn5WSkoJSisLCwjavmzFjBsnJyfzud79j/fr1nH322eTm5h738UX8kTl0EXdSU1OZN28ejzzyyDHvHzduHP/5n//J7373O2bMmMHFF19MQkICa9euZceOHYwfP56bb775qN8LBAJccskl/OUvf+GZZ54hNTWVqVOnHnVdz549efzxx7n99tu5+uqrmTJlCoMGDcKyLAoLC/nggw+oqKhgy5Ytbb6OxMRERo8ezcaNG7nrrrsYMGAAlmUxffr0FouwCQkJzJw5k6VLlwJwzTXXdOTtEnFEErqIS3PnzuXpp5+moKDgmPfffffdDB8+nGXLlrFixQqi0Si5ubnceeed3HTTTa3OP19xxRX85S9/IRKJMGPGjFavmzJlCi+++CJLlixh3bp1bNy4Ea/XS+/evZk8eTIXX3xxu17Hz372Mx5++GHWrVvHypUr0VqTlZXVIqEDXHnllSxdupSMjAymT5/erscW8Ufptuq7hBDdwgsvvMD3v/99br31Vu68885YhyNiRObQhejmotEoTz31FB6PR2rPv+BkykWIbmrjxo1s2LCB9evXs2PHDq6//nqysrJiHZaIIUnoQnRT77zzDosWLSI1NZWrr76au+++O9YhiRiTOXQhhIgTMocuhBBxQhK6EELECUnoQggRJyShCyFEnJCELoQQcUISuhBCxIn/H1NFM/Coo96tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cmap = plt.cm.get_cmap('Oranges')\n",
    "\n",
    "# between 0 and 1, 0 for the leftmost color of the range, 1 for the rightmost, upper > lower\n",
    "upper_color = 1\n",
    "lower_color = 0.4\n",
    "num_colors =  15 # len(self.data.columns)\n",
    "colors = cmap(np.linspace(lower_color, upper_color, num_colors))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.relplot(x='novelty', y='resonance', data=df, alpha=0.8, color = 'orange')\n",
    "b = sns.kdeplot(x=df['novelty'], y=df['resonance'], fill=True, colors=colors, thresh=0.3, alpha=0.4)\n",
    "b.set_ylabel(\"Resonance\",fontsize=20)\n",
    "b.set_xlabel(\"Novelty\",fontsize=20)\n",
    "b.tick_params(labelsize=12)\n",
    "plt.ylim(-4.5,4.5)\n",
    "y_lim = [0,0]\n",
    "x_lim = plt.xlim()\n",
    "plt.plot(x_lim, y_lim, color = '#dadada', alpha = 0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                speaker                                              title  \\\n",
      "date                                                                         \n",
      "1913-07-04       wilson                              Address at Gettysburg   \n",
      "1939-09-03  fdroosevelt              Fireside Chat 14: On the European War   \n",
      "1940-12-29  fdroosevelt      Fireside Chat 16: On the Arsenal of Democracy   \n",
      "1941-12-08  fdroosevelt  Address to Congress Requesting a Declaration o...   \n",
      "1929-10-25       hoover                 Message Regarding “Black Thursday”   \n",
      "1913-03-04       wilson                            First Inaugural Address   \n",
      "1865-05-02      johnson  Establishing Rewards for the Arrest of Certain...   \n",
      "1937-10-05  fdroosevelt                                  Quarantine Speech   \n",
      "1914-05-30       wilson                               Memorial Day Address   \n",
      "1863-01-01      lincoln                          Emancipation Proclamation   \n",
      "\n",
      "            resonance   novelty  \n",
      "date                             \n",
      "1913-07-04   2.351272  4.517970  \n",
      "1939-09-03   2.231600  3.366204  \n",
      "1940-12-29   2.112122  3.105796  \n",
      "1941-12-08   2.051148  4.667633  \n",
      "1929-10-25   1.999156  3.678252  \n",
      "1913-03-04   1.846531  3.706513  \n",
      "1865-05-02   1.803011  3.685109  \n",
      "1937-10-05   1.793473  4.810320  \n",
      "1914-05-30   1.768136  3.946460  \n",
      "1863-01-01   1.728853  3.659949  \n"
     ]
    }
   ],
   "source": [
    "print(df.sort_values('resonance', ascending=False)[:10][['speaker', 'title', 'resonance', 'novelty']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              speaker                                              title  \\\n",
      "date                                                                       \n",
      "1991-07-31       bush            Press Conference with Mikhail Gorbachev   \n",
      "1977-03-09     carter     Remarks at President Carter's Press Conference   \n",
      "1879-05-12      hayes             Message Regarding Election Regulations   \n",
      "1904-02-11  roosevelt               Proclamation Declaring US Neutrality   \n",
      "1877-07-18      hayes                  Message Regarding Railroad Strike   \n",
      "1903-10-20  roosevelt    Message Convening a Special Session of Congress   \n",
      "1880-06-15      hayes        Veto Message Regarding Military Legislation   \n",
      "1859-12-19   buchanan                               Third Annual Message   \n",
      "1856-08-21     pierce  Special Session Message Regarding Military Sup...   \n",
      "1930-04-28     hoover                  Message Regarding Law Enforcement   \n",
      "\n",
      "            resonance   novelty  \n",
      "date                             \n",
      "1991-07-31  -2.555009  4.735530  \n",
      "1977-03-09  -1.747105  5.365519  \n",
      "1879-05-12  -1.657300  2.311075  \n",
      "1904-02-11  -1.585016  3.095731  \n",
      "1877-07-18  -1.474844  4.008101  \n",
      "1903-10-20  -1.445847  4.325960  \n",
      "1880-06-15  -1.440864  2.983855  \n",
      "1859-12-19  -1.430170  1.378904  \n",
      "1856-08-21  -1.422308  1.163411  \n",
      "1930-04-28  -1.408528  3.514217  \n"
     ]
    }
   ],
   "source": [
    "print(df.sort_values('resonance', ascending=True)[:10][['speaker', 'title', 'resonance', 'novelty']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.4854400e-03, 1.1360000e-05, 2.4073001e-04, 1.5433000e-04,\n",
       "       3.6696001e-04, 6.5720000e-05, 4.6720001e-05, 8.9100004e-06,\n",
       "       2.1845480e-02, 1.9319999e-05, 1.4773000e-04, 1.7680000e-05,\n",
       "       5.7268541e-02, 1.9490000e-05, 3.5402961e-02, 4.2660002e-05,\n",
       "       5.3979998e-05, 6.2259998e-05, 4.7080001e-05, 7.9840000e-05,\n",
       "       8.4680003e-05, 3.8083971e-01, 5.2470001e-05, 1.0230000e-04,\n",
       "       9.8531038e-02, 8.6299997e-06, 5.1660001e-05, 1.0169355e-01,\n",
       "       8.3569998e-05, 9.4188511e-02, 1.2640000e-05, 1.2158000e-04,\n",
       "       4.2719999e-05, 1.5506000e-04, 9.3922596e-03, 3.4810000e-05,\n",
       "       5.9589998e-05, 9.4099996e-06, 1.1894000e-04, 1.5065140e-02,\n",
       "       2.4418000e-04, 8.8899997e-06, 7.7040000e-05, 4.2420001e-05,\n",
       "       1.7933939e-01, 4.4760000e-05, 3.5820001e-05, 1.6059999e-05,\n",
       "       7.0700000e-05, 8.5270003e-05], dtype=float32)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['1913-03-04']['probs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# That's enough; didn't use the rest from here onwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f06e9987ef0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06e98e8358>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06e9910978>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts[plot_cols].plot(\n",
    "    marker='.', \n",
    "    alpha=.5,\n",
    "    linestyle='None',\n",
    "    figsize=(11, 9),\n",
    "    subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f06e9997f60>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.query('speaker == \"obama\"').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-279-8b120ae93dc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# monthly resampling,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mts_monthly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#mean or other aggregate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mg\u001b[0;34m(self, _method, *args, **kwargs)\u001b[0m\n\u001b[1;32m    934\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m         \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_resampler_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_downsample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGroupBy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_downsample\u001b[0;34m(self, how, **kwargs)\u001b[0m\n\u001b[1;32m   1040\u001b[0m         \u001b[0;31m# we are downsampling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m         \u001b[0;31m# we want to call the actual grouper method here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_loffset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    947\u001b[0m             )\n\u001b[1;32m    948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    950\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_aggregate_string_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_try_aggregate_string_function\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m             \u001b[0;31m# people may try to aggregate on a non-callable attribute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmax\u001b[0;34m(self, numeric_only, min_count)\u001b[0m\n\u001b[1;32m   1556\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1557\u001b[0m         return self._agg_general(\n\u001b[0;32m-> 1558\u001b[0;31m             \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"max\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnpfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1559\u001b[0m         )\n\u001b[1;32m   1560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_agg_general\u001b[0;34m(self, numeric_only, min_count, alias, npfunc)\u001b[0m\n\u001b[1;32m    998\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m             return self._cython_agg_general(\n\u001b[0;32m-> 1000\u001b[0;31m                 \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnpfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1001\u001b[0m             )\n\u001b[1;32m   1002\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mDataError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m   1018\u001b[0m     ) -> DataFrame:\n\u001b[1;32m   1019\u001b[0m         agg_blocks, agg_items = self._cython_agg_blocks(\n\u001b[0;32m-> 1020\u001b[0;31m             \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m         )\n\u001b[1;32m   1022\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magg_blocks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magg_items\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m_cython_agg_blocks\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m   1131\u001b[0m             \u001b[0;31m# Clean up the mess left over from split blocks.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mlocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m                 \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m                     \u001b[0mnew_items\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# monthly resampling, \n",
    "\n",
    "ts_monthly = ts.resample('M').max() #mean or other aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7bde26ef28>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f7bde219400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f7bde23f908>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_monthly.plot(\n",
    "    #marker='.',\n",
    "    #linestyle='None',\n",
    "    figsize=(11, 9),\n",
    "    subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6 - AzureML",
   "language": "python",
   "name": "python3-azureml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}