DataScrum-Hackathon-SimilarWeb-Price-Team1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price Data vs SimilarWeb Web Traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_df = pd.read_csv('ESTIMATESACTUALS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_df = consensus_df.loc[consensus_df['Instrument']=='EBAY.O']\n",
    "consensus_df['Date'] = pd.to_datetime(consensus_df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "consensus_df.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "consensus_df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-24 00:00:00+00:00</th>\n",
       "      <td>1.082637e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-18 00:00:00+00:00</th>\n",
       "      <td>1.084034e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-17 00:00:00+00:00</th>\n",
       "      <td>1.090162e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-07-16 00:00:00+00:00</th>\n",
       "      <td>1.092368e+10</td>\n",
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       "    <tr>\n",
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       "      <th>2019-04-29 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2019-04-26 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2019-04-24 00:00:00+00:00</th>\n",
       "      <td>1.091717e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-04-23 00:00:00+00:00</th>\n",
       "      <td>1.086988e+10</td>\n",
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       "      <th>2019-04-22 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2019-04-18 00:00:00+00:00</th>\n",
       "      <td>1.085734e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-04-17 00:00:00+00:00</th>\n",
       "      <td>1.085704e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-16 00:00:00+00:00</th>\n",
       "      <td>1.085480e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-15 00:00:00+00:00</th>\n",
       "      <td>1.085589e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-26 00:00:00+00:00</th>\n",
       "      <td>1.085795e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-06 00:00:00+00:00</th>\n",
       "      <td>1.085795e+10</td>\n",
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       "    <tr>\n",
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       "      <th>2019-02-04 00:00:00+00:00</th>\n",
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       "      <th>2019-01-31 00:00:00+00:00</th>\n",
       "      <td>1.086848e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-30 00:00:00+00:00</th>\n",
       "      <td>1.088307e+10</td>\n",
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       "      <th>2019-01-28 00:00:00+00:00</th>\n",
       "      <td>1.073283e+10</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-25 00:00:00+00:00</th>\n",
       "      <td>1.073365e+10</td>\n",
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       "      <th>2019-02-06 00:00:00+00:00</th>\n",
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       "      <th>2016-12-16 00:00:00+00:00</th>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
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      "text/plain": [
       "                                   Mean\n",
       "Date                                   \n",
       "2019-07-24 00:00:00+00:00  1.082637e+10\n",
       "2019-07-18 00:00:00+00:00  1.084034e+10\n",
       "2019-07-17 00:00:00+00:00  1.090162e+10\n",
       "2019-07-16 00:00:00+00:00  1.092368e+10\n",
       "2019-07-15 00:00:00+00:00  1.092436e+10\n",
       "2019-07-12 00:00:00+00:00  1.092710e+10\n",
       "2019-07-11 00:00:00+00:00  1.092865e+10\n",
       "2019-06-03 00:00:00+00:00  1.092953e+10\n",
       "2019-05-22 00:00:00+00:00  1.093125e+10\n",
       "2019-05-14 00:00:00+00:00  1.092741e+10\n",
       "2019-04-30 00:00:00+00:00  1.092507e+10\n",
       "2019-04-29 00:00:00+00:00  1.092095e+10\n",
       "2019-04-26 00:00:00+00:00  1.091980e+10\n",
       "2019-04-24 00:00:00+00:00  1.091717e+10\n",
       "2019-04-23 00:00:00+00:00  1.086988e+10\n",
       "2019-04-22 00:00:00+00:00  1.085396e+10\n",
       "2019-04-18 00:00:00+00:00  1.085734e+10\n",
       "2019-04-17 00:00:00+00:00  1.085704e+10\n",
       "2019-04-16 00:00:00+00:00  1.085480e+10\n",
       "2019-04-15 00:00:00+00:00  1.085589e+10\n",
       "2019-02-26 00:00:00+00:00  1.085795e+10\n",
       "2019-02-06 00:00:00+00:00  1.085795e+10\n",
       "2019-02-05 00:00:00+00:00  1.085763e+10\n",
       "2019-02-04 00:00:00+00:00  1.086913e+10\n",
       "2019-01-31 00:00:00+00:00  1.086848e+10\n",
       "2019-01-30 00:00:00+00:00  1.088307e+10\n",
       "2019-01-29 00:00:00+00:00  1.109804e+10\n",
       "2019-01-28 00:00:00+00:00  1.073283e+10\n",
       "2019-01-25 00:00:00+00:00  1.073365e+10\n",
       "2019-01-22 00:00:00+00:00  1.073550e+10\n",
       "...                                 ...\n",
       "2019-02-06 00:00:00+00:00  4.666700e-01\n",
       "2019-01-30 00:00:00+00:00  4.480000e-01\n",
       "2019-01-29 00:00:00+00:00  2.800000e-01\n",
       "2018-12-10 00:00:00+00:00  0.000000e+00\n",
       "2018-10-31 00:00:00+00:00  0.000000e+00\n",
       "2018-10-04 00:00:00+00:00  0.000000e+00\n",
       "2018-04-20 00:00:00+00:00  0.000000e+00\n",
       "2018-04-17 00:00:00+00:00  0.000000e+00\n",
       "2018-02-15 00:00:00+00:00  0.000000e+00\n",
       "2017-11-23 00:00:00+00:00  0.000000e+00\n",
       "2017-11-23 00:00:00+00:00  0.000000e+00\n",
       "2017-04-24 00:00:00+00:00  0.000000e+00\n",
       "2017-03-30 00:00:00+00:00  0.000000e+00\n",
       "2016-12-16 00:00:00+00:00  0.000000e+00\n",
       "2016-12-16 00:00:00+00:00  0.000000e+00\n",
       "2016-08-26 00:00:00+00:00  0.000000e+00\n",
       "2016-05-19 00:00:00+00:00  0.000000e+00\n",
       "2016-05-06 00:00:00+00:00  0.000000e+00\n",
       "2016-03-01 00:00:00+00:00  0.000000e+00\n",
       "2015-10-14 00:00:00+00:00  0.000000e+00\n",
       "2015-10-14 00:00:00+00:00  0.000000e+00\n",
       "2015-09-01 00:00:00+00:00  0.000000e+00\n",
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       "2015-01-22 00:00:00+00:00  0.000000e+00\n",
       "2014-12-23 00:00:00+00:00  0.000000e+00\n",
       "2014-12-23 00:00:00+00:00  0.000000e+00\n",
       "2014-06-25 00:00:00+00:00  0.000000e+00\n",
       "2013-09-03 00:00:00+00:00  0.000000e+00\n",
       "2013-09-03 00:00:00+00:00  0.000000e+00\n",
       "2013-07-15 00:00:00+00:00  0.000000e+00\n",
       "\n",
       "[1383 rows x 1 columns]"
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     "execution_count": 101,
     "metadata": {},
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   "execution_count": 91,
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       "Name: Number of Included Estimates, Length: 1383, dtype: int64"
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     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "consensus_df.sort_index(inplace=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>desktop_visits</th>\n",
       "      <th>desktop_pages_per_visit</th>\n",
       "      <th>desktop_visit_duration</th>\n",
       "      <th>desktop_bounce_rate</th>\n",
       "      <th>desktop_page_views</th>\n",
       "      <th>...</th>\n",
       "      <th>mobile_pages_per_visit</th>\n",
       "      <th>mobile_visit_duration</th>\n",
       "      <th>mobile_bounce_rate</th>\n",
       "      <th>mobile_page_views</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>total_pages_per_visits</th>\n",
       "      <th>total_visit_duration</th>\n",
       "      <th>total_bounce_rate</th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foxbusiness.com</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>116450.0</td>\n",
       "      <td>2.74</td>\n",
       "      <td>430.0</td>\n",
       "      <td>0.5444</td>\n",
       "      <td>319138.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.93</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.6450</td>\n",
       "      <td>282507.0</td>\n",
       "      <td>263032.0</td>\n",
       "      <td>2.29</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.6005</td>\n",
       "      <td>601645.0</td>\n",
       "      <td>2017-08-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>papajohns.pl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oxo.ua</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4949.0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>417.0</td>\n",
       "      <td>0.3669</td>\n",
       "      <td>34451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4526</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>5935.0</td>\n",
       "      <td>6.04</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.3811</td>\n",
       "      <td>35854.0</td>\n",
       "      <td>2017-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>workday.com</td>\n",
       "      <td>356.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>2061.0</td>\n",
       "      <td>38.43</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>79221.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.18</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.7409</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>37.27</td>\n",
       "      <td>2409.0</td>\n",
       "      <td>0.1485</td>\n",
       "      <td>79369.0</td>\n",
       "      <td>2017-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>canadagoose.com</td>\n",
       "      <td>250.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>248.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.59</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0.2422</td>\n",
       "      <td>2342.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>2.88</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.4509</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>2017-08-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              site  country  year  month  day  desktop_visits  \\\n",
       "0  foxbusiness.com    999.0  2017      8   26        116450.0   \n",
       "1     papajohns.pl      NaN  2017      8   31             0.0   \n",
       "2           oxo.ua    999.0  2017      8    8          4949.0   \n",
       "3      workday.com    356.0  2017      8   22          2061.0   \n",
       "4  canadagoose.com    250.0  2017      8   16           248.0   \n",
       "\n",
       "   desktop_pages_per_visit  desktop_visit_duration  desktop_bounce_rate  \\\n",
       "0                     2.74                   430.0               0.5444   \n",
       "1                      NaN                     NaN                  NaN   \n",
       "2                     6.96                   417.0               0.3669   \n",
       "3                    38.43                  2486.0               0.1290   \n",
       "4                     1.00                     0.0               1.0000   \n",
       "\n",
       "   desktop_page_views  ...  mobile_pages_per_visit  mobile_visit_duration  \\\n",
       "0            319138.0  ...                    1.93                   93.0   \n",
       "1                 0.0  ...                     NaN                    NaN   \n",
       "2             34451.0  ...                    1.42                    0.0   \n",
       "3             79221.0  ...                    2.18                   63.0   \n",
       "4               248.0  ...                    3.59                  198.0   \n",
       "\n",
       "   mobile_bounce_rate  mobile_page_views  total_visits  \\\n",
       "0              0.6450           282507.0      263032.0   \n",
       "1                 NaN                0.0           0.0   \n",
       "2              0.4526             1403.0        5935.0   \n",
       "3              0.7409              148.0        2129.0   \n",
       "4              0.2422             2342.0         900.0   \n",
       "\n",
       "   total_pages_per_visits  total_visit_duration  total_bounce_rate  \\\n",
       "0                    2.29                 242.0             0.6005   \n",
       "1                     NaN                   NaN                NaN   \n",
       "2                    6.04                 348.0             0.3811   \n",
       "3                   37.27                2409.0             0.1485   \n",
       "4                    2.88                 143.0             0.4509   \n",
       "\n",
       "   total_page_views       date  \n",
       "0          601645.0 2017-08-26  \n",
       "1               0.0 2017-08-31  \n",
       "2           35854.0 2017-08-08  \n",
       "3           79369.0 2017-08-22  \n",
       "4            2589.0 2017-08-16  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"similarweb-webtraffic.csv\")\n",
    "df.head()\n",
    "yr = df['year'] \n",
    "df['year'] = np.where(yr <= 20, 2000 + yr, 1900 + yr)\n",
    "df['date'] = pd.to_datetime(df[['year', 'month', 'day']],format='%Y-%m-%d') \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Date  Adjusted_close\n",
      "5267  2019-08-30           40.29\n",
      "5268  2019-09-03           39.74\n",
      "5269  2019-09-04           39.64\n",
      "5270  2019-09-05           40.80\n",
      "5271      268797             NaN             Date  Adjusted_close\n",
      "4472  2016-07-05         23.4983\n",
      "4473  2016-07-06         23.5674\n",
      "4474  2016-07-07         23.6664\n",
      "4475  2016-07-08         24.3388\n",
      "4476  2016-07-11         24.5861\n"
     ]
    }
   ],
   "source": [
    "df_price = pd.read_csv('EBAY.csv')\n",
    "df_price.head()\n",
    "df_price=df_price[df_price['Date']>'2016-07-01'][['Date','Adjusted_close']]\n",
    "df_price.plot()\n",
    "plt.show()\n",
    "print(df_price.tail(),df_price.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMERICAN AIRLINES GROUP INC</td>\n",
       "      <td>AAL US</td>\n",
       "      <td>aa.com</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMERICAN AIRLINES GROUP INC</td>\n",
       "      <td>AAL US</td>\n",
       "      <td>americanairlines.de</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMERICAN AIRLINES GROUP INC</td>\n",
       "      <td>AAL US</td>\n",
       "      <td>americanairlines.es</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMERICAN AIRLINES GROUP INC</td>\n",
       "      <td>AAL US</td>\n",
       "      <td>americanairlines.com</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMERICAN AIRLINES GROUP INC</td>\n",
       "      <td>AAL US</td>\n",
       "      <td>americanairlines.fr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Company  Ticker                Domain Unnamed: 3\n",
       "0  AMERICAN AIRLINES GROUP INC  AAL US                aa.com        NaN\n",
       "1  AMERICAN AIRLINES GROUP INC  AAL US   americanairlines.de        NaN\n",
       "2  AMERICAN AIRLINES GROUP INC  AAL US   americanairlines.es        NaN\n",
       "3  AMERICAN AIRLINES GROUP INC  AAL US  americanairlines.com        NaN\n",
       "4  AMERICAN AIRLINES GROUP INC  AAL US   americanairlines.fr        NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tickers=pd.read_excel(\"SimilarWeb_Mapped_Tickers.xlsx\")\n",
    "df_tickers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "company_name = \"EBAY\"\n",
    "df_tickers.Company.str.contains(company_name)\n",
    "list_sites=[]\n",
    "for items in df_tickers[df_tickers.Company.str.contains(company_name,na=False)]['Domain']:\n",
    "    list_sites.append(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2dehands.be',\n",
       " '2ememain.be',\n",
       " 'alamaula.com',\n",
       " 'automobile.fr',\n",
       " 'automobile.de']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_sites[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sites = '2dehands.be'\n",
    "df_site=df[df.site.str.contains(sites,na=False)][['site','date','total_page_views']]\n",
    "df_site=df_site.sort_values(['site','date'])#(['site','year','month','day'])\n",
    "df_sum=df_site.groupby(['site','date']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sum = df_sum.reset_index().set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8070534, 21)\n",
      "(177239, 21)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "new_df = df.loc[df['site'].isin(list_sites)]\n",
    "print(new_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = new_df[['date', 'total_page_views']].set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.resample('1D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Adjusted_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-05</td>\n",
       "      <td>23.4983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-06</td>\n",
       "      <td>23.5674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-07</td>\n",
       "      <td>23.6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-08</td>\n",
       "      <td>24.3388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>24.5861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Adjusted_close\n",
       "0  2016-07-05         23.4983\n",
       "1  2016-07-06         23.5674\n",
       "2  2016-07-07         23.6664\n",
       "3  2016-07-08         24.3388\n",
       "4  2016-07-11         24.5861"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2016-07-05\n",
       "1     2016-07-06\n",
       "2     2016-07-07\n",
       "3     2016-07-08\n",
       "4     2016-07-11\n",
       "5     2016-07-12\n",
       "6     2016-07-13\n",
       "7     2016-07-14\n",
       "8     2016-07-15\n",
       "9     2016-07-18\n",
       "10    2016-07-19\n",
       "11    2016-07-20\n",
       "12    2016-07-21\n",
       "13    2016-07-22\n",
       "14    2016-07-25\n",
       "15    2016-07-26\n",
       "16    2016-07-27\n",
       "17    2016-07-28\n",
       "18    2016-07-29\n",
       "19    2016-08-01\n",
       "20    2016-08-02\n",
       "21    2016-08-03\n",
       "22    2016-08-04\n",
       "23    2016-08-05\n",
       "24    2016-08-08\n",
       "25    2016-08-09\n",
       "26    2016-08-10\n",
       "27    2016-08-11\n",
       "28    2016-08-12\n",
       "29    2016-08-15\n",
       "         ...    \n",
       "770   2019-07-26\n",
       "771   2019-07-29\n",
       "772   2019-07-30\n",
       "773   2019-07-31\n",
       "774   2019-08-01\n",
       "775   2019-08-02\n",
       "776   2019-08-05\n",
       "777   2019-08-06\n",
       "778   2019-08-07\n",
       "779   2019-08-08\n",
       "780   2019-08-09\n",
       "781   2019-08-12\n",
       "782   2019-08-13\n",
       "783   2019-08-14\n",
       "784   2019-08-15\n",
       "785   2019-08-16\n",
       "786   2019-08-19\n",
       "787   2019-08-20\n",
       "788   2019-08-21\n",
       "789   2019-08-22\n",
       "790   2019-08-23\n",
       "791   2019-08-26\n",
       "792   2019-08-27\n",
       "793   2019-08-28\n",
       "794   2019-08-29\n",
       "795   2019-08-30\n",
       "796   2019-09-03\n",
       "797   2019-09-04\n",
       "798   2019-09-05\n",
       "799          NaT\n",
       "Name: Date, Length: 800, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df_price['Date'], format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price['Date'] = pd.to_datetime(df_price['Date'], format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "month must be in 1..12",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1860\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1861\u001b[1;33m             \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1862\u001b[0m             \u001b[1;31m# If tzaware, these values represent unix timestamps, so we\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-64-0858e21d0056>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdf_price\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf_price\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_price\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m             \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, box, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    300\u001b[0m             \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdayfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myearfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m             \u001b[0mutc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mutc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m             allow_object=True)\n\u001b[0m\u001b[0;32m    303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1864\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz_parsed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1865\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1866\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1867\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1868\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1855\u001b[0m             \u001b[0mdayfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1856\u001b[0m             \u001b[0myearfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1857\u001b[1;33m             \u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1858\u001b[0m         )\n\u001b[0;32m   1859\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dateutil\\parser\\_parser.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[0;32m   1356\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparserinfo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1357\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mDEFAULTPARSER\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimestr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dateutil\\parser\\_parser.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[0;32m    652\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"String does not contain a date:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimestr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    653\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 654\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_build_naive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    655\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    656\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mignoretz\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\dateutil\\parser\\_parser.py\u001b[0m in \u001b[0;36m_build_naive\u001b[1;34m(self, res, default)\u001b[0m\n\u001b[0;32m   1227\u001b[0m                 \u001b[0mrepl\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmonthrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcyear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmonth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1229\u001b[1;33m         \u001b[0mnaive\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mrepl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1231\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweekday\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mday\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: month must be in 1..12"
     ]
    }
   ],
   "source": [
    "df_price.reset_index(inplace=True)\n",
    "df_price['Date'] = pd.to_datetime(df_price['Date'], format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 =df2.join(df_price)\n",
    "df3.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_page_views</th>\n",
       "      <th>Adjusted_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>1.105985e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-02</th>\n",
       "      <td>1.141334e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-03</th>\n",
       "      <td>1.229590e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-04</th>\n",
       "      <td>1.240688e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-05</th>\n",
       "      <td>1.184691e+09</td>\n",
       "      <td>23.4983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_page_views  Adjusted_close\n",
       "date                                        \n",
       "2016-07-01      1.105985e+09             NaN\n",
       "2016-07-02      1.141334e+09             NaN\n",
       "2016-07-03      1.229590e+09             NaN\n",
       "2016-07-04      1.240688e+09             NaN\n",
       "2016-07-05      1.184691e+09         23.4983"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instrument</th>\n",
       "      <th>Number of Included Estimates</th>\n",
       "      <th>Mean</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Date Actual</th>\n",
       "      <th>Actual Value</th>\n",
       "      <th>Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-07-15</td>\n",
       "      <td>EBAY.O</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2014-01-22T16:15:00Z</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>DPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-07-22</td>\n",
       "      <td>EBAY.O</td>\n",
       "      <td>18</td>\n",
       "      <td>4.073406e+09</td>\n",
       "      <td>4.537100e+09</td>\n",
       "      <td>3.108000e+09</td>\n",
       "      <td>2014-01-22T16:15:00Z</td>\n",
       "      <td>4.414000e+09</td>\n",
       "      <td>PTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-07-29</td>\n",
       "      <td>EBAY.O</td>\n",
       "      <td>39</td>\n",
       "      <td>1.616870e+10</td>\n",
       "      <td>1.635320e+10</td>\n",
       "      <td>1.608000e+10</td>\n",
       "      <td>2014-01-22T16:15:00Z</td>\n",
       "      <td>1.604700e+10</td>\n",
       "      <td>REV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-07-29</td>\n",
       "      <td>EBAY.O</td>\n",
       "      <td>33</td>\n",
       "      <td>3.567008e+09</td>\n",
       "      <td>3.633900e+09</td>\n",
       "      <td>3.542573e+09</td>\n",
       "      <td>2014-01-22T16:15:00Z</td>\n",
       "      <td>3.556000e+09</td>\n",
       "      <td>NPR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>EBAY.O</td>\n",
       "      <td>40</td>\n",
       "      <td>2.711550e+00</td>\n",
       "      <td>2.750000e+00</td>\n",
       "      <td>2.700000e+00</td>\n",
       "      <td>2014-01-22T16:15:00Z</td>\n",
       "      <td>2.710000e+00</td>\n",
       "      <td>EPS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Instrument  Number of Included Estimates          Mean  \\\n",
       "0  2013-07-15     EBAY.O                             9  0.000000e+00   \n",
       "1  2013-07-22     EBAY.O                            18  4.073406e+09   \n",
       "2  2013-07-29     EBAY.O                            39  1.616870e+10   \n",
       "3  2013-07-29     EBAY.O                            33  3.567008e+09   \n",
       "4  2013-08-01     EBAY.O                            40  2.711550e+00   \n",
       "\n",
       "           High           Low           Date Actual  Actual Value Estimate  \n",
       "0  0.000000e+00  0.000000e+00  2014-01-22T16:15:00Z  0.000000e+00      DPS  \n",
       "1  4.537100e+09  3.108000e+09  2014-01-22T16:15:00Z  4.414000e+09      PTP  \n",
       "2  1.635320e+10  1.608000e+10  2014-01-22T16:15:00Z  1.604700e+10      REV  \n",
       "3  3.633900e+09  3.542573e+09  2014-01-22T16:15:00Z  3.556000e+09      NPR  \n",
       "4  2.750000e+00  2.700000e+00  2014-01-22T16:15:00Z  2.710000e+00      EPS  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consensus_df.head()\n",
    "consensus_df['Date'] = consensus_df['Date'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_df=consensus_df[consensus_df['Date']>datetime.date(2016,7,1)][['Date','Mean']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_df.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.join(consensus_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2391445ba90>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['total_page_views'].plot(figsize=[15,10])\n",
    "df4['Adjusted_close'].plot(figsize=[15,10], secondary_y=True)\n",
    "# df4['Mean'].plot(figsize=[15,10], secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Line2D' object has no property 'secondary_y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-1c18932ccdae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_price\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2787\u001b[0m     return gca().plot(\n\u001b[0;32m   2788\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m-> 2789\u001b[1;33m         is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2791\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1664\u001b[0m         \"\"\"\n\u001b[0;32m   1665\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1666\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1667\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    403\u001b[0m                 \"non-matching shapes is deprecated.\")\n\u001b[0;32m    404\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 405\u001b[1;33m             \u001b[0mseg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    406\u001b[0m             \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    407\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[1;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    310\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mseg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    402\u001b[0m         \u001b[1;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m         \u001b[1;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    405\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    406\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    956\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 957\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    958\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    959\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    956\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 957\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    958\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    959\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m    951\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    952\u001b[0m                     raise AttributeError('{!r} object has no property {!r}'\n\u001b[1;32m--> 953\u001b[1;33m                                          .format(type(self).__name__, k))\n\u001b[0m\u001b[0;32m    954\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Line2D' object has no property 'secondary_y'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df2)\n",
    "plt.plot(df_price, secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jdvrp02a6ZP6sW2emS+6PW/ZF5w8hpLPQ4s3kpIeJCQ/9/ebnvj7T1atcxsJ083yzgc8ey74IIYQQQggBxZ+uoFq1A5aREZv5k077deePiD/z80YUGhw00yUvQ6YPDLjijxv4vKK7QQghS6ZWs8+/0VHjfBSRR17Hxz1MTNgOYDbwmc4fQgghhBBCAIo/XUGj88cMhrTzxy37ArZsMdNPnmwMfAaAsTGzPP1NODN/CCGdhhZvhofNM8qKP2b6yIiHSsWKQjbzzM38Yat3QgghhBCyVqH40wVUKkAmYx0+wW5fgBV/KhUT7Bx0/pjpfn2wNDZmpwOmfGJmxkOt1o49IoSQ5tDi94kT5k/WwID5WcSev/1bkwAtmT/ZbFSrd1fgrlSAoSGK3oQQQgghpPeh+NMF1GrWyXP6tBZ/fEf88X2T+ZNM+hgcNCrOyZNG0KnVvIVyMDNdi0KALQdj6RchpJPQ4o8goo+IPV/8Ymphuvm97nboZv64y7nhhgz27u3D/v0UgAghhBBCSG9D8acLqFRsVy9d9mW6fRkx59Qprz5IisetWFQqxeoDnkQC2L7dTB8aMqdefiflYAx9JoR0EouJP/F4+PR83j7PdNnXyZNuAPS3vmUcQw88EFgQIYQQQgghPQbFny6gUvGQSpn27ZOTprMN0Jj5Iy6eRALo6zOlYnqwk0wCO3aY+Z991g18lkHTzEx79okQQpqhUjHPKnH5APZ5FQv8BbOt3s3PExPW3XjeeTXMzXnYv7/xzx5Fb0IIIYQQ0us0Jf4UCoUXFwqFf13k918oFAo3tmyrSB3J4EkkTAcbXcZgApxNlk+pFKsPcpJJwPNM7k+ppEUhHzt2mAVa548Z9KxbZ+bhIIgQ0kmIQL1+vRZ/zOu73z2H17zGhvqI+LN1q3nODQ9b5+PevWZBjz0WJv60eqsJIYQQQgjpLM4q/hQKhQ8A+CKATMTvbwBweYu3iywgwk0sZr7Nnp726qGl6bSPWAxIp03Zwnvfa05RPG4GQFu2GPFHBJ1czjiC+vv9uvNHli/fpHMQRAjpJET82brVij+SUdbfD3zsY7P16X195nXbNilvteL3859vBKHHH28s8aLoTQghhBBCep1mnD8HALwp7BeFQuGXAVwF4POt3ChikYFPImFyLIz4Y6YlTVwFXv96M7q5//5EfV4A2L27hvl5D//2b2awIzkY27fX6s6fxsBnDoIIIZ2DPAOf8xzbilDEasB2NtTTs1lg48Yahodt5o84fx5/3P7ZS6f53COEEEIIIWuDxNlmKBaLdxQKhecEpxcKhe0A/hTArwP4zWZXuGFDDokEwzWbZWLCvGazCaxbBxw4ADz1VBYAcOmlGQwOZvC1rwEXXAAcOSLZGEkMDiZxww3AP/4j8Fd/ZeYfHExhcDCF888HnnoKyOf764Gp27aZbjmpVG5h3v427iVZDjxX3QHP07khIvfllydxxx3m/e7dOQwOmvfyCgA7d2brP+/aBRw6hPrfmwsvzCGfByYnE/Vzkc8D5TLg++bZqOH56h54rjofnqPuguere+C56nx4jrqLXj9fZxV/FuE/AdgM4J8BbAOQKxQK+4rF4pcW+9DoKOuKlsLYGAD0o1qdRzLpYW4ugdtvr2H9eg8vfOEkSiUzXz6fQ61mBjmVyjxKpVm84AXAeefl8cQT5pvuWKyMUmkOmzdnACTx859PYmoqAyCBRKIMII2hoRkAWZRKE+3fWbJkBgf7ea66AJ6nc2dqKg0ghS1bzLMJACqVKZRKNTWX+UNdq02jVDIOn8HBLH7+8wSefXYeQBITE1PIZLKYmPBRKpm/Q9lsHkAMIyPmmSnwfHUPPFedD89Rd8Hz1T3wXHU+PEfdRa+cr8UErHMWf4rF4qcBfBoACoXC7wDYczbhhywd6XQjZV8AMDISw3XXzSGdtvNJ2ZbMC5jQ5127ajh61Ig/Eoa6ZYu0h7dhqLbsa8V2hRBClow8Ay+4wIo9+nmnyeXs9O3bzfxHj5rPJ5M+MhlgdtZrmH9ysrXbTAghhBBCSKex5FbvhULhrYVC4d0rsTGkEcnkicdt+2LAHQgBwLp1WvzxQ6fn8+ZVuuaMjjLzhxDS2Ujmz5Ytjdk+QTz1+JLQ54MHzZ+5TAbIZn1H4JZn6vg4n3uEEEIIIaS3acr5UywWD8MEO6NYLH415PdfaulWkTrS6t2IP3bAs369O1+/cncl1FkdGLDvxfmzcaN5PXPGdsKRaadPcxBECOkcdOi9IEK28M//PIXvfjeBK66wovj27eaZdvKkEX/6+xudP8LYGJ97hBBCCCGkt1lO5g9pA1HOH+3oAcLLvoLzifgjwtHoqIdKxUMi4eOii8ygaf/+JZvBCCFkxRDxJx4Hbr99BseOeYgFHlNXXlnDlVfOOdM2bHCfkX19WBB/7DQpez1zhuIPIYQQQgjpbSj+dDjBVu+ClG4JUeKPni7flsugyIg/Zv6dO33kcj6eeoriDyGkc9AC+OtfX2n6c/rZl8v5iMdN2Vel4mF+3nQRE/GHZV+EEEIIIaTX4Ui/w7HfevuO8yco/ugMDHH4AO4ASKZr8Wd+3og/ngdcckkNBw7E6oMtQghZbawAHp7zE0XYsy9rmoXV3T/z80b0GRuzJbaEEEIIIYT0IhR/OhzpdBPM/Gks+7Lvzz+/FjqfLfuy4k+1ap1CF19cQ7ns4dChlu4CIYScM9WqfQYuBVf8Ma/ZrJk2M2OWKc4f3/fY8YsQQgghhPQ0FH86HF32tZjzRw90nvMc3RLZzhNW9mWcP+bnSy4xn9u3r1VbTwghy0OXfS0FHYIvzshMxvw8M2NeRfwBgIkJln4RQgghhJDeheJPh6PDTrXzR77JFrT4c/754Q4hcf6kUiY/yHT78pBMuvPyG3BCSKegn4FLIazsK5Mxr9LxS5e4UvwhhBBCCCG9DAOfOxw98NGBz8FuN3qgs2VLuPijnUMbNvgYHTWDHSn7ksEVsy8IIZ1CWKv3Zkin7Xtx/kjmjzh/5uas4DM+fq5bSAghhBBCSOdD50+HY0se3MDnILq8y/P0dCv+6G/ORfyRwGfACkoy2CKEkNWmWgU8z28QvJeClLxK5k+Y82dyks4fQgghhBDSu1D86XAk7NRk/kR3u9EdvjTBYGhh/XofU1MeZma8euZPPO4vrHM5W0wIIa2jUvGWXPIVJCrzZ27OzsN274QQQgghpJeh+NPhiBATiy1e9rBjh48vfGEGP/iBG9ijQ081Evo8Pu7VyyPkm3WWfRFCOoVabeklX0Fsq3fb7atWA2o1K/gw84cQQgghhPQyzPzpcKQsIZE4+wDojW+sNEyL+ox2BEnnMJZ9EUI6jUqlMeNsqaRS5lWcP7OzbqcvgJk/hBBCCCGkt6H40+HosNNLL63hwx8u4+UvbxR5FuP735+qf+Mt6IwgyQWS0gqKP4SQTqFaXb7zx194/Em3r5kZry7+9PX5mJz06PwhhBBCCCE9DcWfDkeXfXke8L73zS3+gRAuuaSxjkvcPgCwbp15ZbcvQkinUa0uvc274Hk+fN+rP9MkNP+225L4xV80D9eNG434w8BnQgghhBDSyzDzp8Oxzp/osOdzQXcBk/fSJYzOH0JIp2DEn3N7/km5WND589hjcVx3nen7vmmTzT8jhBBCCCGkV6H40+FUKmZAstxuN0F05o+8Z9kXIaTTWE63r1e9ypTI7tljrD+S+QMAzzxj/vxt3GiefxMT576NhBBCCCGEdDos++pwpFxhuZkXQcLFH99ZJyGErDbLyfy55ZZZ/OAH83jVq4yirbPPNm6s4fTpmNP5kBBCCCGEkF6Fzp8OR7p9LbfbTZCwsi92+yKdxA9/GMfY2GpvBVltlpP509cHvPrV1XpJq3b+bN1q279nsz4zfwghhBBCSE9D8afD0a3eW8n69fY9y75Ip3HkiIf/8B9y+PSnU6u9KWSVWY74EySXs6K3OBxTKdPxi84fQgghhBDSy1D86XDaEfgsQpA4f1j21T3cey/wrndlMLf0JnAdzZkzZiB+6hQfUWudSuXcA5+DaBFdWrsnEkB/P+gyI4QQQgghPQ1HVh1OtWoGKK0u+9KZPyz76l6uuQb45jeTuPvu3orvmp83rzMzq7sdZHXxfSNGt8r5uHu3j/e9rwwAmJoyz9ZUysf559dw6lQMZ860Zj2EEEIIIYR0GhR/Ohzr/GntcnX2Bcu+up/Z2dXegtYyP28G5jMzLMVZq4yOArt29eH06VjLxG/PA/7kT4xNbnLSTEsmgcsuMw+9J55ocVtFQgghhBBCOgSKPx2OZP60utW7Jij+sOwrHD+i8mRkxFv1YybXSa8g+zM9vbrbQVaP738/URcBWyl+x2JAMunXXZVG/DE38OOP808iIYQQQgjpTfifbocjosJKij/5vHmVjjh0/jTys5/FsHVrP779bXcUOjzs4XnPy+Mzn1ndYGIZJPcKtuyrt/aLNI92+7T6+ZdO2/cUfwghhBBCyFqA/+l2OCvV7QsAfvu35/Dyl1fqoo+EqlL8aeTWW42482d/lnamDw15qFY9HDy4urdSrwU+y3XPzJ/e58wZ4A/+II2DB12hT4c8t1r8yWTsshMJHxddVEM67ePxx1n2RQghhBBCepPeSontQWzZV2u7fQHA//yfZednln1FIwJZsPSrXDa/GB9v/Mxdd8Xx3Of6uPjilT+gvVb2xcyftcMtt6Twd3+XwgMPxPG979k6P+38aXW3Q+38SaWMuF4o1LBvX6zn7iVCCCGEEEIAOn86nlqt9ZkXUbDbVzRybILCmAQtj4+7IsXUFHDddTm89KX5NmwdMDe3PJHk3nvjOHCgc4QWOn/WDiKgHjoU/eeo1c6flKrSlGfrZZfVUC57OHCAfxYJIYQQQkjvwf9yOxwZBLe61XsYa0n88X1gbKxx+hNPxPDOd2YwOupOj3b+mNeJCVc4kcyadtGMW2FyEnjd63INuUXT08Cb35zDL/9y3wpt3dKx4k/nCFJkZejrMzfV7Kx7rrWguZJlX6mUeS8dv5j7QwghhBBCehH+l9vhrFSr9zDWUtnXDTdkcPHF/Rgacgecv/3bWdx5ZxJ/+ZdugLMIY1FlX2Nj7nIqlfaKFkGxKaz1+913J/Czn8XxjndkF/3sSrBvXwzXXZfF6dPNzW8Dn1dum0hnIOJPEJ1jtZKBz9r5A1D8IYQQQgghvQn/y+1wRPxZyW5fwlpy/vzTPyUBNA70JifN69SUK95EiT8iskxMuNMXE1RGR8NdR+eCDFy18+eRR2K48MI+3HVXPHTeIO0Qqq6/PoO77krgE59In31m2G0ql72G6/HECa9+nkj3E/Vs0+JPq8XvdNreyEnzKMBFFxnxZ7HyM0IIIYQQQroV/pfb4axk4HMQGYStBfFHCO6rHIOgeON55vgHXVE28Ln5sq9CoR8XX9y/5G0NQwauumRm//4Y5uc9PP20e3tHiz8t2ZRFkbK5kRH3OB0+7OGaa3J44AF3W/Xx0+6f+Xng8sv7cPXVjVlKP/lJDD/7GR9p3YbcQ4tNb/XzL9jq3Uwz62DgMyGEEEII6UU4UupwqtX2BT7LAKsTyr4OHvTqeToriQRqC1EC2NmcP3NznlNq1a7MHwmu1euWnJxgXk4yGT6Abse2DgyY12B53P/6Xyk8+mgc11/vlqLpAfj0tP2M7OfRo42Prje8IY/Xva49AdukdWiHT9T0lSz7kvvC3uPMmSKEEEIIIb0HxZ8Oxzp/Vn5d4s5ol/NndBR4z3syOHrUHWw9+WQMV13Vh//yX7IRn2wdwX0VkU1EN0GOTWO3Lzufdv9Iq/KVRlwLWugRgSSY++NFbFIrxZ/9+z3cemuyQSRbt85MCDqk5HgHBYAo50+US2R8/Jw2l3QAWuSdng6f3nrxx16g2YXHzFoqeyWEEEIIIWsPij8djogN7RB/2l329alPpfGNbyTxtre5Io+UK91118rbnYJijgwAg6UfUeKPHqDq3J9mBJWgQHIu2LIvO02cMkHnT1Q7+FZm/nzucyl86EMZHDrkLlPEn6DzRxwYQVFHi2d6P/Tx1sfv8GE+yroVfe51WaC+Xlvd7VA7fzZudJ0/neB8JIQQQgghpNVwxNThtNP50+5uX1I4faIAACAASURBVPLt+1NPuZdhVPefxfD9cxNTgu4YcaJEiUJBtBihhQ0t/kQdz1Z0sgpz/shyg8uPEqRa6fyRoOxgYLYc16BDR8rWgs4fLb65zh+9LvueIb3diz73p097odNbncOjW71v2mTer8XMM0IIIYQQsnbgiKnDaWer93aXPWzbJhlD4SVWYTzwQAzDw40zvOY1ObzxjUsvEwu6YxKJ8NDXpZZ9adeCFi/0sZ2cXL7jxoo/dlpU5k9UtkorB9ayjmBek0xvdP74C/NHB2br/dDH9dQp+/7gQfsoa4WjirSe8XHgK19JNjxf9HWpnT/6mmh1GaWIjgCwYYMr/tD5QwghhBBCehGKPx2ObfW+8iPados/UYJWlEhRLgNvfGMOH/pQY7vwhx+O44c/XLpCFnTHyAAwKIjIgDAoVLllX/Z3+vNaINKihnaunCsiSoVl/pyL82e5QpCIM3qf9TZVq54zuI4S+qKcP/ra0OKPdv4E3VykM/jIR9J4//sz+OQnU850fa0cOWLPoz7XrQ4l12VfmYx5ZdkXIYQQQgjpZSj+dDiSx9KLZV9RpVFRob7T0+Z3w8PNX7b/7/8l8OlPpyJ/HxQp7DEId6IEXSV6W93AZ72OxuUAjaVR54KIJM10+9KuGb0f2lWxXOFExLCg80cfp9HR8Awfjd4m3e1Lz69dIkeOnH2ZYTz4YAz/8A9tsNWReoe273zHPd5a5NEloPo8tlr80WVfAsUfQgghhBDSy1D86XBk0CPlPStJLGYGRO1y/uhBX6kUPnh3XR9mnsU6OwUHbv/5P2fx8Y+nm87dETdS0AEjYsRigc96u6K6VWkBphVlX7KepWb+ROWpBMWwpSLLDQpP+jidPBle2qOJdv7osi/7+HLbwTe/D699bR7vfW+2JflLncbYGPCZz6SWJIatJBdfbG4eCXQX9PYVi9r5Ey6mtoJUhB4ci/nM/CGEEEIIIT0JxZ8ORwbB7cj8aXfgqe4ypdu9n80lEsyN0S6WycnwdUUNgLVoAEQfAzkPQeePdspEtXrXQogexOq21udKmPPHtnqPzvzR69bbtFyhQM7dYs4f7diJchpFZf5EOX/09HNxL0WVGnYzb31rDh/7WBp/+7dtUI6bQASX2VnPuY/k2ti8uYZ9+6LKvlqb+RP1jIvHG11/hBBCCCGE9AIUfzoc6/xZ+cyfYNnX8LCHEydWbiCkB3cnT4aXe4QFwGqRJbgcnbujiRI1GjN/zHEOOg1kHY3On6WVfeltPZeyrzvvTOBnP7PHSsQfvR+21bv7We2k0KKXdtksX/wJX44+Brqjkxao9Geayfxx3WLn5vwJ+3yv8NOfmht6tQKwG8PR7fvjxxu7el1xRQ2lUgynT5ufV7LsS+69XM49OLEYy74IIYQQQkhvQvGnwxF3TDvKviR8V74Vv+KKPlx+ed+KrS/KcXI2d8f0tBdZwhQt/oRPD5YnVavhgcVyHhozf+z7aPGnNYHPx497eOc7s3jd6/JquxrX0Uy3Ly3+RLmUzoVmyr50UPNSnVP6PJ45szznjxaVmhUXRkeB225LdnyotHZ2xVbhKf+e92Swa1dfpLCoReVy2UMi4dfLwp55xmzwSpZ9yb2Xz7s3tHH+tHZdhBBCCCGEdAIUfzocGfSsZtnXSjkH9ODOFX/sdO0S0eKFFgr0/BMT4evSy9f70+iOMa/BkqyowGe31Xvj/MF1nGvmz9e+lsALXtAoxMngenraltI00+1L719rnT/hZV96v6PaeTdz/PRydYmfK/40d1yjcqYW4/d+L4M//uMMPv/56BDxTuDBB21CfCuCxZfKN76RRKXiOQKne6+493UqZVuuS1lnO5w/+bw73fPaV/ZKCCGEEEJIO6H40+HIwLwdzp+obl+joyuzvqigYT3oc8Uf+35sDGq6fd+M8yfKlaN/F3SuyPSwwGfpHKTX7XbQCi+xWsqg/AtfsGKDLlUJKy+zgc/R3b5c549eRmucP1Gt3gFX/NHCjs5ycsu+wq8BLZ7p9TXrytHuE73cxZBSKp1RdTZOn26/m0RnZS3VYdZK9H3gCnfu9EzGx8CAex/p+7rZ89Msv/qr5gK77jpXVaLzhxBCCCGE9CoUfzqcuTnA8/y2tHqX8pDgN9/Hj6/MZaIHd1HOHy0I6Hm00NKM+BOVuxOVixMUEEQgCXYBK5eBvj4fuZwfWfYVlVmzlEG5Lk/x1C7q7ZEBvy37CrZ0t++jAp+bFU7GxxGaByX7Fxb4vGmTGVVrQa+Zsjm9rYsJCGHvF0PnTDUb+CzHNpttbv6jRz3s2dOPt72tuflbhVteuHp5Ru49Hu78KZc9pFJAf7/v/G4ly75+4zcq+OlPJ/Ff/6t74pn5QwghhBBCehWKP8tkchL46EfTKxaMXKl4bXH9ANHiz7PPrsy+uZk/Zy/v0tO1KKQHiVHizx13JPHww7GGdUU5fBqnm59rNa/BsZROm4FrtPgT7ghayqC8WjVh1C95SQVTUx6qVTNI1QNVydIRsalW8wLuCfveDXwOL8NajKuuyuPyy/siA7DDMn+2bzeDe7fbV7ggINlLwW11hT7zWqtFO60Ww20539RH6uJYOt1cLeRDDxnV9u//vrnlt4pzFRlbTZQop0v8pOxrYMD8LK6+lSz78jzg/PN9R0gFzD1G8YcQQgghhPQiFH+WyQc/mMFf/VUKH/lIekWWPz/fnpIvwAyIYjEz+NEC0Mo5f8JLdfR7neHTjCNIDyo1t9ySwqtfnV9Yr50elYsT5fzRn//Qh9I4ciSGdBoYGPCdbXXFiMbPAkvL/KlWPcTjqJfGTE01inQi/kSVQLnCk56O0PkX49SpWMNygPBuX75vtqm/35T26MDnqAyfpWQmLdZZbDHOpexL2oBnMs2tY7WCoc9VZGwFUS3atSCoRdpy2Yhpcm1b549dzp497VFkmPlDCCGEEEJ6FYo/y+See8w3+yvVUadSaZ/4A5j9qFbdgddKOX+igoajnDxhro/G6Xb+qKBqvS7tKgGs6FAue84gMEz8ufVWk8NjBq5GkJJ1trrbl3H+AH0Lmc8TE15DCVqpZNav98nNy7HztqrVuz7e2oHjnk/zmkoBGzf6ka3etRgW5ZwKK/1rFH+au16jRKhmSDWZ97zcDKVzRR8/fVzHxoBf+IU8br995R4qUcJsVNlmuezVBVT9u3LZw+BgDR//+Cw+//n2qGgm82f1yuQIIYQQQghZKSj+LINaDTh92hzCzZubKwP55jcTuPvu5gN85ueBRGKF2m2FEI8boUELAs06f4aHvboY1gxReSBROTB6elTZlx7oRuW46PmjMn+Cv3MFElcYOn3aw8CAj0rFq38myk1zrt2+KhVzbiQXZWLCC3X+BJ0mOi/HLQVqTeBzlDgXFuCdyQCbNhnxJ9iZDHCPhxxvz/NDM5NiMR+Tk0bgk2snlfKd9Z0NvY3NZv4IzZYhLbd72rkSJTLef38Cp07F8MEPNmldWoRvfzuBG29sVMGiwthd8cedR4s/2vnT1we8+93zGBxszzNQxG9CCCGEEEJ6DYo/y+DgwaV31HnXu7J4+9tzTa9jfr59mT+A7XajRQrdEnsxXvOaHN7ylhwefbS5y8otCbHTo8WfZsq+zj6g1/NHZf4AQXHAXa4WVU6ciDW4FtzA4rMHQZ8Ncf5Y8ccua+tWUxIzMtIo/riuI+2osvPozJ+llilFObD0cuS8pdM+BgdrmJ/36h3k9Dl1y748JBI+stmg88e837TJh++bVuJyPuUcNCtgLaeb1FIDottNVOBzIrH0Zd17bxz33tso6r7jHVl86lNp514A3HszmMUkAp3cp/KsSaV02ZeZ34hC7RO+AXb7IoQQQgghvQvFn2Vw5kx0rsbo6NIG91G0u+xLMi/cUOPmPjs0ZC6nw4ebu6yinT/mfTbrR4oLUSKPO3/4wDtKpAj+Lsr5MzfXeL6DroUoYSuq9Ep+F5VZVKsZB1h/v/nZlH2Zz2/bZtZ96lSsQWyIWrc7QLfTmwl8Xqx8Rwhz/qTTNvT52WdjC/PZ1vVB508yaX4XJgyK025y0quvd/16v77MZnAFquY+Y+dvTtSJegbceGMKN93UZO3YORCV+XMuLsI3vzmHN785WrAO3guLlX2JgyeY62NC0+H8TrqAtRN2+yKEEEIIIb0KxZ9lEJVXMjsLFAr9eMMbmnf4RGHKvpa9mKaRsq8oYaYZtIMDMKVuW7b048kn3cstOvPHvA4O+pHOH1f8se+l3bleTnCdbgtpry5+mA5a9ndRjp1y2WtwetmBa+P8zTp/rr46j4su6g/NKqpUPMRipq08YK43KU+x4o/XsEy305idHiX+BD8vXcU0WvCJKrMLy3rJZHzs3Cnij1cPghYhRy93ft5ci0Hnj6xj40Y5DlbEWbfOvAbFn299KxEqtLhuLvcav+eeON72tqyzrKAA2AyyT0EB91OfSuPmm1cmJB6ILvuKN1+V2TTB+11fWz/8YRz791sxZ2DARyrl4/77TfmrFn8SCSP2WfGn+WylVsGyL0IIIYQQ0qtQ/FkGUZ2KZED86KPRI61mnQbGAdHezB8T3GunBQfTX/lKEh/6UPTANZhl88d/bOa97TZ3BBwlFszOmpKfdev8yEyZYKtooVi0l3TYMdZlQsH5ggN63ZJcOym082fHjhruv38qxPljlxMsZxKCLp0DB6QVfeN2V6tmcKwzf0SMGBjwkc36C+KPu8yozB8tngXzjDS/+ZtZvP3tWWeadldFZTK5Hcek7MscL8DkSAVdPFqkEOdPNht0/tiyL7Mt3lnLvq6/Poubb0472w1EX38A8Ja35PCd7yTw4x/be1hfc83evyKEZLNnmbHFRJV9BVubt4Lg/a4dkZ/6VBoveYlJKS+XTe6TnKe3vz3XkNc0MGDEn1rNCJ7tLvtaC86fchl4+9uz+M53VkAJJIQQQgghHQvFn2UQ5fyJcgXoQUWzYb8m++ScNu+ciMV8VKuNWR2a978/g1tvTUUOgPXgD7CODC0UAGYdsVjjgN20fjYDwakp63CJKlvS00+ditUzivQ+CJOTXsP5kQFoMMRXZx01On/M79785nns2VPDunVmP2Tf9bqjulgFs1IWm24zf8zPExNWtEkmfWzebMQfWdeuXeZik/Iq2aZMxkc87kfmsgTP6RNPxPDzn7uPCb0/WlBxy770dPOaSsFx/sh0EXLc42Qzf7RzSs6d/oy4duQcRF2XUpYYtr1R96y+99xypubuXxGMolrDr1QreDkmmYyPqSnb+S7YIW4pRIVcL1b2Jfi+ETvTad85n3K/iMNnYMCUeuprpp3E437Piz/f+14cd9+dwNve5jpTfR9461uz+Iu/aPNBJ4QQQgghbYHizzJwnT/hA1/tWtCDp6AL4Q//MI0XvSjfUPKzWq3eXbEjfF5dYgXYPJFgQLR80x8cFM7NAfl84zok6FWHG5t57OfDBuK7d5tR27590Q6aqSmvYeAuA/9g6Y/ej8bMH/Netn/LFrOtJ082Ckl6cOxmCoULCGHTGwOfrfMnHjfrP3HCq3da2rvXKGYHD7pOqFTKiCTaxaL3LSg8zcx4DaKd/jnKmeWWfVkhQjt/RPDL531kMn5o5o84f+S+kHOknT+27Muvb3MYQ0Ph510vF3C7VWnXVjNh4kHkM1r80WVFYUJJK5Bzum6dCcaW87rUYGv9PIoSKxcr+xJ0eZcWf5591l4bgBE3x8e9+jySB9Uu1oLzJ2r/ymXgu99N4LvfpSOIEEIIIaQXaUr8KRQKLy4UCv8aMv0thULhx4VC4QeFQuFzhUJhTYlJUWVfWlzQA05X/HEHSH/zNyk880ys3gVJf6bdmT+NZV/hA0ZdFgVYIeTUqXDxJygiVCpG5Mlk/Iaw4PAAWPvZMNFBBA/JFgoboE9ONk4X8UD2eds2MzrS4o8rFFhBJ583+yYdt06caL7sa3raC833CQZBA+Gt3iXwOZEw5VTz8169dOwFLzDbo8Wf+XksdFWKzvzR6zZuDXM+otp0u4HPdrpb9mVe3cBnK9pkMibLyD1OZr+yWcD37fqDbqGvfz1RPxdnd/4sJv7Y948/bge/WpCKCjJeDPmMFti0ILxS4o/sz4YNUlIX7m47G64wGL6tQRdjWGi5zX1yp4v4o50/lYpXv/f6+pa2vcvFiN8rc046hajSP7kuV6tDHSGEEEIIWVnOKtYUCoUPAPgigExgehbAxwG8slgsvgTAOgBvWImN7FT0AFe7O/TA8Phxd/AtRJV9HT1q5/d9k3vRzsyfpTh/Tp9290G+vQ86f2RQrh0VgHEhJBJGFAh2pUql0NA+3boH/NCB+N69RvCQ3J8wl4Mu+xJXgQhPMl1Kk2Q/arXGIGjr/DHzivPnxAmzbjl+fX1+qPNHMk7Cyn7COkTZbl826FgG5kb8MdP37TPCxe7dNWzcWMPBg3rdHpJJcf6Eu5qC17Hvm5+j3D5asNHHW++XPm+pFDA4WFvI/PHq0/P5xvI4cf7oYxIMfL7zziS+/nWjjg4MyLqjnD/u407Pp8UfHUzulri5x7IZwoRLvd4zZ5pazJLRzh/AOtUWcyz9t/+Wxt//vas06/mDIedC8FkWdvxFUEinfbz2tfbhIp0BJchcRL1Dh9zp7WItOH+ixR/zi5UqRSSEEEIIIatLM06dAwDeFDK9DOAlxWJRigESANbUv416QDc15dUHDXrwc/x4+IAxWPYlHDliT4ke3LcL6fYVDCYOc6gExR8RPE6dci+r9EI2dNDlYJwoaHD+mGBYv0H8kXk2bza5IHK85bhecIGZMDy8mPPHln2JkCLnUbZfSpPEwRR0S0xMaOePmbZ1a7Dsy7xu2OCHZv7IvoW5KcKdPx7iceuEGBuzWUjxuN1mEb4GBnxccIGPw4dj9etIBJWBAZO7ElbupsUcLUJFhTxHl301luXJdbBtm49SyXX+9Pe74d4iekpQsgxMy2UzXRwtAPDAA0bwEqEjavC6mPNHb6++rl1xK/z9YoSLP42/bzVyTuU4yTUYloMFmJKuL385hfe9L4uZGeD225OYm3P3M9r54/4cJhbLfqbTwK23zuJP/9QchP37zfUquWBSuvnEE2a6iKvtQp5/vUws4q8+nT+EEEIIIb3NWcWfYrF4B4CGYoFisVgrFosnAKBQKPx/APoAfKflW9jByIAxHpeBvEy387iBu3Z6sOxLOHq0UShoZ+aP5zWWfQHhg92g+CODhqDzRz4bHOjOzZl9S6cbM2JSKSvOiGNIt4D3fa8+6JTpmzf7yOV8DA+7Th6NLvsKdoeSgfHmzaYddankuniEM2e8hrKvbNYsL5j5Y8SfxswaGeyGuXwWC3y2LirPCXwWt5LkHQ0M+LjwwhoqFQ9HjtjjkU779WXI+Yhy/uhBoD53UW6YqMBn2Ucp+Vm/3ohPkhmVz/t1h5QIelL2Je4sWYbkFr3sZVX8wR+Yi+bkSesSSSb9SJFCrgu7vfa963AJ39fFuoOF4fu2BErPr49rMBy9Vcg5FUeY3JN6H7TDRU//7/89jQ9+MIM/+7O0IwyWSh6uvz7TEAA+NubhPe/J4FvfMip1WBi2lIhmMj7SaeCii8zKpUxRrsnnPMdMf/xxOafN73MriMUQKnT3Emdz/oQ9kwghhBBCSPezLE/JQsbPnwO4BMBvFIvFs/7bvGFDDolEbwRKyjeog4MehoeBdLofg4NursWZM2kMDhrLg1v2lMXgYOMyT53KYHAw48yfzycwONjf+h0IIZUy5R25nNsJpq+vH+vXu/OWy3ZbJR8GMELBunX9DZ16ymUPmzf31wcf1aoRTVIp4NQp1PexXAb6+uK47DJznQwN5ZxjtXNnHA8/DCQS5niLM2rr1hy2bwdKpTgGB/tD22t7XrY+/8aNZvm5nFn+0aNm+vr1KWzZAoyMmOXEFy7XTZuAkRGgUsnUB4i7dtlt27EDOHnSfEb2cXAwjmoV6O832yPr3rw5hgMHgGy2D4OD7oAzmcw1XBvVKpBOx7FrV/9CB6wE+vul3CmNyy4z88kA7jnPyeNFLwK+9jXg6NE+vPjFRhDIZoGtW80OJRJm3doJMDsbr58HnT/lefn6NmlnxOysvTbF2QMYIW3jRnPsRLzcssVcL1u3mp8nJsw1tnVrGhs3YuFc9KO/X7Y1Xj9HmYzZ1mrV3F/bt/fjk58EPvc5K5Zt2ZJFPg/Mzdl90Me1VEpicNAqqfPzVniMxVIYHEw17F+tZu9ffV/Xau49OTZmhIq4erRNT8MRszZt6kcsBhw7ZuepVsOfA8tFrr/LLjP7ND6ea3g29fX1151rWgA8eNB8plhMoa/P3sRf/GIO99wD3H130ikBe/DBNO67D/jGN5Lw/fCyqUrFnOsNG8xxvvhiWZeUKZprY+9eLKzbnKdt2+zxbwfptD3/7Xrmtpt+tVt6H+XaKJdjTe/71JQpXdy5s5VbuHR69Vz1EjxH3QXPV/fAc9X58Bx1F71+vpZbUPR5mPKvNxaLxaaSEkZHI1rGdCGnT6cBpLBhQxXDw3E888wkkkkfJ08mABjlYWhoHqWSsUGYPBgz2jp+vIxSyX7dHov1oVbz8NRTFZRKRkUx35b3oVazy1h5cqhW4zh1aqa+DwBw7Ngk5udlJG1uiqNH51AqGUuDEX7szbJ//yQGB6VsKwu51A4enKhns5TLffC8GhIJYGYmhlJpEtUqUKn0Ix6v4MILZwH04fvfn8fv/M5sfTnr1s0BSOHgwSnkcjWcOWPOw9TUFAYH0zh0KI6hoUmUSvY8CEND5QWXQxqZTAVAAidOTKNUqtbPT6VSxsaNCTz9dAwnT04uuCb6sHFjFSMjcQwNzS0MrlOYm5tCqWQu/c2bs9i3L4HjxycwNZVFLBZfWEcShw9PYvNmH2NjZltzObPuY8emsGlTbcEZYo7f8PAMSiW3J3e12gffr6FUmsbAQB4jI1i4JnIol8vI5eZhzHeGSmUSF14YA5DD975XxtVXz2Furg+xWA2pVBVACocOTWH9+homJzMAktiwwcf4uI9SyYzsTV6VuV6PHLHbdOKE2QcAGB2tolQy93TweB89OoF83oguQAZzc2YZ2az5/BNPlAGkAcwilYrXj9O2bT7m5/sA1ABUAKRx7Ng0du6sYno6j2QS9W284IJcPaB5dnYa2WwG4+P29/q4HjtWq0+X66+/30e5HMPYmL3HTp0yx8Psk73GR0bMfgDA1JTd7yNHPFx5ZR+uu24ON99sLT5GPLP3xPHjE8hkxA2YX9gm9znQKqamzL2yaZO5j4tFs57Tp+0+HDlirkmzbeYaN5hrc3q6iqGhmfr00dEqgDjKZeDYsYn6vh04UIOYSEulCYyP2/tdOHhwFkAGtZrZjljMrE8cYp5n7sH16830p582032/8V5YSWq1HGq1GAAPpVJEbW6Xc+qUvU/1Pj77bBxADtPTPkqlyfAPB3jBC/I4fjyGY8cmGsT+djE42N+z56pX4DnqLni+ugeeq86H56i76JXztZiAteTuXIVC4a2FQuHdhULhhQCuB3A5gHsLhcK/FgqFXz/3zew+pJRDBlBSIqJLXnQ7dF1aEczJkG9dpUQHgCrrac32NoMEngZLncJyVHS3r2CpgHY56f3WobuS+aPLvuQ1lQLOO8/H5s01PPRQ3FmOHO9gnkoqZbJ3ajUPp055oaU5U1P2PMlygmVfyaTpSjUz4+H0aa9+LGT+sbHGsi/Ahj6XSt5CZo0tW5EsnmAQr82ysdu4WLcvwJRNjY25mVBbtvj18kPAlMxdcYWxMDzySLy+Dgl8lv0wy5ZSND9Q9mXXrzN/5PjF475T+hcMQZZ9kuMr4c2yfglDl7Ivs2wbsO1m/sg6XIfRhRdazTmdBnI5t2wueC+KE8j3zfaJCyIq2FiXtbkh6Ha6HN8vf9kd/QbLn2Qduuxrpbt9nX++OT5Sfupmedn5w0q1KhV3unYH6eOlc8qiliXXiZw7uZeE9eula56PdNr+rv1lX37d+TM/b7v39RJRHd/keqhWvch5gsg9HFaqSgghhBBCOoumnD/FYvEwgKsW3n9V/WpNtXYPEmw7LQNnt5tP+OAxmPkjg6nDh2P1fBeZf3UCn+VnH9WqhCT7TkmHHvgHQ0LNfrviCgA89FAMhYJZiMn88ZFMmkFptarbgvvwPNOy/DvfSeDkSW+hPMevd3qSPBURHVIpvx68fOKEFxpuOzXl1Qc5MgCV82g7cQGXXlrFXXcl8MQTMZx3ntleOc/j4169s5mUzQC645dXzzMSUePXfz2H226bUZk/bk6UPkZhmRvVqlcXdwYGgP37PUf8iceNWHb4sIdEwkcuZ0p/LrywhkceiaNSMctIpWzmj1ybcq7Xr/dx5IgRSDwvWqSQ7duzp4bHH49jctIM0uX4JZM+5ue9hX3y6/PLwF/KByUMva/PhEADps36eedV6vtlu33Ze2tgwF6Ekh0jy8/lfEdgdDt6eZieNudsft50MuvrM58PhrcLbuZPuJAbj6hiDZ5HuYe0ILVS4o+cUyv+NGb+mGPq3gOAFR/n5935de5TmMAjywkTXW3mj/k5nzfnStYl+VuxmAl9fvrp+MJ87Q989n0PxSJw1VV9GB8HHnxwqp6d1AtECTvBZ9BSvnSQe50QQgghhHQua1q8WS7yz7KIAuLm0YMfLf643b7se1PqZAe3EvpsB9Mt3/RIgq3exRkhA1Y9GNSupkbnT/hA+Wc/M4O6atUMsiTwWdYhg0ERVS691HwNf/CgaQ2eTtuW3rIOOd7pNBzxJ6rrUND5E+z2lUwCz3ueGTQ/+qjtlrVhg49YzLhuwpw/W7fWFtZtPqPFHwC4885EfR1Lcf6I4CYi4Lp1RpCT/U8kzLK0C0YyXy69tIrxca8++E8mUS/Hk25msn/r1xvXlO364x43Qa77K65wQ3tlH4Jdt2R+EczE5aGdP9deazbiH/4hA6HXMgAAIABJREFU6TjeZF4RGkVoEvbu1eKPCfyembFZP0HHmlyzck1KqLi+N6emjEjmeb7j0HOdP/Z90MVn5wkXeNvR6n1+3gilfX3Axo21+vnX+6DPrz5OIkhWKu5xkTDz4HI0w8NeaLco6/yx98POnfbcyXkG4AgtWlxtB5J/9c1vmmd3reY1BNh3O2dz/pj3S9tnOn8IIYQQQjofij/LQAaA4viQwXhU2Zcum9BlNEGRQtofiyAkg/t2YMu+zLplcBwm/rhlX+7gLij+5PM+slm/Lv5oYUs+Uy5r8ceWgQCmlGpuzrhygmVL2rEjAszwcKyh25fnGWeLCG9W/JHliEDi43nPM6LTY4/FlbPICE9jY8ZB4nm+Eyqthae5OVO2pAev69b5qtOYeQ3rEBcU0uS6kYGp7L8MqEUUEvFHrhvAXptS9pNK2Wm6M1ks5tfdFyJs6QGgFivlWrj8cnOMHn44jve/P42HHzbnVgRDOa66pTtg249b54+PPXtq2Lu3invuiddFqXTax3nnmXmPHDFi3vy85whqL3yhTWeWsi/ftwKWFloAez/KtoWVfRmByZy7qG5fWhSJ6tgVvK9t2ZedtlKt3ufnvbpovH27j+PHY/B9d7v1+dVClRXIvMhW71Gt7oeGYiiXG59Z0mlNC3fnn2/nke53gL2PzPztdZPIPaZL/6L2tVsJc0QC7vldascvtocnhBBCCOl8KP4sAxkwbdtmBt0yCNQDqYkJm58QVUIRHFw89ZTbYrydzp9g2ZcMvmSf9L7pQa8MFmTgpn9XLnvI500Gzb59MczM2OWnUn7d+VMue/VBlzQbE5eKKfsyjgxpB/3TnxqxQQYz6bRfLx8aGvIaMmjOO8/H00/HMDFhhBsRIcKcP895jnGRPPZYzCmvWrfOx9iYEZCktErQokqY82d+3m5r0Pnjlly42y0ZJNr5A1jnjpQdPfe5jZnrcvxEUFy3zseWLWa+b34ziZtvTtXziUSoknMQVZ40M2POwyWXmOX84R9m8JWvpPBP/5R0tk8+L8c3mPkj+yzrfdnLqqhWvfp5zeVMCRAAPPNMrC7E6GOqhYJ02q+LhjKQDTp/RDCTey6fNw6fYNlXX59ZT7T4Y99rgdfNGwo6fxpFtai29Mtlft4+N3buNOVV4+OuAK23Ve+/CT4X50/48uXaCzI0ZEQ6ERKFY8fMMrXDR+5jwC2dk+cp0P6yLxF/tPgRJZZ0K804f4LX7tkIE4v89p46QgghhBByFij+LIOg4GFdBWa6DLJl4Kz/6Q6KIwCwZ48Z5Vvnj/n9aoo/QeeP3ofJSStsyYBWBm5B5086bb7pr9W8ujMGMPsm5UB33ploKKfSIcpzc8Y98rzn1XDRRSaTZ2IiGBJt1n/kSKPz55JLaiiVYjh2LIb+frteOf4yGM7lfMRiwAUX1HDsWKw+QM/l/Lr4MzTk1YUmwXX+mH3T5+7kSbtNVvzBwjbY+YIlFCL+6MBnwDqvRBTSg2lB3E0SOL1hg18/pocOxXDTTWkcOeIhkbDHXM6BFiYkX0m2OZMBLr44vMGf7JscV7k2xPmjBQDAijnitvrxj0X88bFjh49Ewsczz8TqbrlgCHAy6S/Mb0XDYJaSiBHBe1QCx7VQODnp1UOotQNERIBs1hWL9L2sS4SCwpMt+7LTVqpcRos/mzeb8zQy4jp5opw/etuisn10rpI73WQ9Bc+RuLxEcAVsHlEQfV+1O/BZ7jF9XnrP+RM+XV8PwWv3r/86iS9+MfoPUVCw/ru/S+KKK/IrVtZICCGEEEKWDsWfZVAuGweJuCts2ZcIIe6AUw8wtTgiA8k9e2qIxfy6+GOdKO37CtXzTNmXbKvN/GnMpgEaS922bw9z/rglWZKJA8DpiPWhD2XqeUdW/DGfkcDnVMps43/8jxXMznq4446kU/a1a5fJ5TlyxA50t2+v4dpr5+vhwMePxzAw4DtZQ4AtbZJ93rDBOD+kc1k+b4SN6WkPIyOxuitFkP07eTK2MPj2nZyUUsmKZSJGiMCiB9lBN4gVf3znsyL+yPURNpiWa/Ppp634k89bFw5gBm7G+eN2rXO7femBoYds1risgscAsOKOLb0yr8HMH0HWe/nlZlk/+YkZgWezRtjaudOczzDnD2ACee+4Yxrbthm3FtDo/NmxwyxbnD9yvFMpH6mUHeD7vnE+5fPmOghz/uTzRsSUazhK/JF7xfNch1l7nD9e/bqQTLJTp9wuTu94R7Z+XYTlY42NeZHCh4g5r3pVBTfcMIf3v79cX8fsrBUABXme6HMfJf50QtmXvvYr7es03xainExa8AmKOX/0Rxn8yZ9kIpcZdP489FAMJ07E8Mwz/BeDEEIIIaRT4H9my6Bc9pDJ2G+zg64CK/6Yn/Ugwg2CNq/9/T62b/dx9Kib+dNe54+/EEBttwnQzh93UCDCiHX++M50wAz8UilbkiXOGMAINu98px1hSlt3KQWyZV/GgSOixdveNo902sctt6QwPS0BveZY7djh48iRWH2A/zd/M4MvfnHWcar099uW0rItVvxxy5MkLyef951yluDgdd06U3p04oSH8XEPAwPAq19tT3qpZErXEgmbBRQUSIBGN4hcB+JKkHwUETJk+oUX+rjllhl873vWrjI4aLZRSgk3bDDHSZfTTE2ZbRIRTtwui3X7ymSMCPeXfxmwCEA7f2TfFnf+yLG48MIaslkfTz7pdnravds4tkRYCQoCW7f6ePnLjUIWdP7INogoGQx8TqeNACQ/z82Z4y3On9lZK/LYUkjZL/Oq7+WTJ+0jVa4/yXcSAaTVzp977onjt34r6yxrft7cW4AVf0ZGYg2lkN/+dmJhWxuXOz/vReYZifNn794qPvaxMn7t18xBmpgwIcnBsi9BO3927gyfR0RUwIbBtwsRWF3nT++WfenSLH2/N5P5E/VZwB6zlRI3CSGEEELI0qH4swzKZTM4kcFssPtUMAvIDZW138JbF4IZ6D77rBFHVqvVuwmGNT+LEGIzf9z5ZTAtg4XFyr7cNuzmd8mkj+c+18enP20W8Mgj7sB/YMAIKkNDHqamvLqwsHWrjze9qYLDh2N4+OG4M9jcvbuG4WGbHySDYN0WvK8PTtYQYHOYZJ9tVyrrRtKOhqDrxfPMdh06FMP8vNnWvXtrOHx4Ai98YRWlksld6e9H3aFiu30tPfMnGPgMGEfUpZfa7ZKyL8lc2bjRLe0StPPHBj7b3we7fYkId9VVVdx9t6qNgj1uUYHP/f12kC3OG8Bce7pjmQg5Us7285+7gdJhNDp/zOv27WYZQYE2nfadsi9x+uTzvnJCmXmtG84VDbVAcuJEY+lMUAyT8+t5ftOD4xMnPNx9d3hP+be8JYf77kvgrrvshTA3Z68LOeenT3sNJT8ixkRlvGjnmmZoyEyXe0iOuxwLOXdB9P0jGVXXXutulC778po7PC1D1udm/rR3G1YavT/6C4mldvtaLKRe1sEuYIQQQgghnQPFn2UwO+sthMya7jYysIwq+2p0zbiikMms8eH7Ho4f1wLJSu+JRcoeZOAedDkEy0BkH2SwIAKPTPd9Kfvy678bHnYzfwCbH/Pww2YDZPDoeSb3R9qJa5FHizm6W9Du3eYYHjxoPiMOHy3W9Pf7dTFC9k0G+bIOWaZtSW6CoAXdrUgYHPTr+y4iSC5nytfm5jwcOxbDunW2PCkoGAKLlX3JvrqBz4uJg+KcEmSwHxzcueKPmaYFAd2dbnbWCjlAY9C0HDfdcSuV8uvXlohkQKOLR7uC5Bi99KXmAHz2s+ZiWSwEWD5j3UvmVZw/NvDZCq6plJ1PPmcCn817EYSCOViyDB34LF2tZL/d+d1t2rTJx8yMh1p49ZPDZz6TwtvfnsOhQ82pIZWKuecAKwAGM38AczwOH/ZCnT9AtPgj162Ulsn1IMdCSvw0qZTviEL5PHD06AQ+/3nXPabLvtpNWOZPL4s/Uc+dYOaPoN0+roPNvU5kuXT+EEIIIYR0DhR/lkG5bMtf1q/366VOwVITcV3ozBAAan75Ft2vBxYfPaq7TLVvMGTFH/MaVfYlIbJB509/vymXEQdApWJab6fT1hUUzPwBrPjj+27mD2AEDBlEaJFHCwVhjhwpdZJ1aEdBf79fHxzL8Q9m/gSdP319Pt74RjtyCsu70SUrevtEhJme9rBhg4/du81xknBjN2/DXaaIP3Ju5NiIGycebghZmNfN9xEXiIgkggl8Nu+Dzp9Nm2oYG/Pg+2bwNzPjOcsMluY0Bj67YhHQeL4Fud70Nl57bQW7d9dw+rQ5AIvlwMg+BLOUgmVf1vkD7NplysomJlznj6xHpsn9K6KQLGNszIodIhTq34uYaMu+5B5qLDGKQvKdhoejH9luK3avLgq6mT9mnttuMyf3E59I45d+qa/+jAqiM4w0Iv7IuZfrQaYHzzdg7oegkyedbrx+213qpQnv9rU627JS6C8htBgYlfkTJfi4HQrddci1PuWaAjuSyUngwQf5rxAhhBBCeh/+x7MMjPhjS4SCrd6vvrqCXM7Hl7+cXMgSMZ8TIcDOb6abjli2W5X8k97ubl+A/Sffij/utgZDrnVHJyOENe6bdv7I8sWVMzDgCida/JHQZ8AVebS4oh1Bu3aZ+Usl1y2kB5na+SPbKGKKDPrDMn+08yesu5Z2LYSJP7LcZBJ4xSsqOHQohoMHvUUDn3WrecBeD9Y9FS2GeJ67bnH+fOMbruKQTluxQ46DbMfWrT6qVQ/T040lXICEcDfut8w7O+s1OEGkTbx2zQBuSVc2a/f7mmtsfcpiHaCiAp83bjRuq2efDbrtfDzveWZbHn88rpw/WvwxA2C5H2X63Jxx7YyOetizxyxDlq/XLZk/NgPJbpPe1sWQz4hzKQzdft04f8x7m/lj3YQ6ewcADh8O/1Mg5+eii6rOdLm/ZR2LOX8uuMAcm2DW02Lcf/8UfvSjyabnbxXh3b7a6165/fYkbrghs2Kt0nWplxaCorp9afHLFX/s++A1LPdXNzh/3vzmLF772jweeYT/DhFCCCGkt+F/O8tgZsarf0u9fr0Rc3zf/lO8bZuP666bx7PPxvDd7ybq/xAHhZNg2RcAHD3qNQz624F88x0sWZGBq2xrsL29uEX6+nxs2uTj1CnPyQ5Kpfy6MHTypFcfaMgAH3ADlMXBAbjihRZ59GBSv9cDW8/z64Nv2T6zPVZ4kn2bmPAQj9vSFFnmsWPiBjHTH354Ev/3/047LiShGfFHpv/Kr5gB9X33JQKtw91lSlmQ5OQExcDFnD+Ae1zl2LzwhTV84hN29JbLWXeMOJ3k23zZp/FxD8HOXYARmGTwb8KsXedPsEwMiG4Tr8+vdifpcODFnD9Rrd6zWR+XXFLDgQPGdWbddsDll5vz8Oijsfp1nM9bkWl01MMFF/Th7rsTC7+T0jmznlrNw+Cgj8HBmuP8CbaZt2VfZroVf9x9EIeVRj4TFH90yZgWf+bm7HUi4o/O/AkGMkcF/IpArUssAWB83LyKe07Or8yv3TuFgjm+UV2mwtizp4bnPrf95V9hmT/t7vb1wQ9m8I//mIwsuQsyNOTh4x9P1ctWhclJ4H/8j3S9g6Kg3T5RuT1aCHLdQY1ljcHP6s90Q+bPT35i7mvpfNdOfvSjOL7//bM8wAkhhBBCWgTFn2Uggc+AGdBXKiZkeHbWdE9KJICXv9yMHPbvt04e6cBkS1Aay76M88csezWcP7bsy7zKP/ryLfiWLa6AJQOPfN6Us8zMmGMh88tx2rGjhiNHYvVSGl0+pMuygmVfQjPOHy3KDAxYQUt/Znzcawh8npw0+ysDQFlXreY6Pnbs8HHVVa4TQpDjEtw+PV2We+mlZhnHj3vOQOr06ZjzrXqw25cMuAVxX0TxkpfYbdWZK/rY53L22pMSINkmV/xpFO0AO/hPp+37mRngmWc8jI66ZWJAuGsKCJZ92eni5grOE0Sum6DzJ5MxgsLcnJtxk0qh7vx57LF4aNlXsRhzHAx795r5//VfE0os8rFrl3EWiXATLPsKBmCHOX9OnwZ+5VdyeOtb3QMsg+ug+KN/ltIw3zfXjDjCcjkj1pnMHzM9WPYn93FQWJPl794dPr/cQ55nridxjWlxUK592b5OJtz5szrbol1ki/He92bw6U+n8alPuQ+Cz342hc99LoV3vMO9llznj30f1e3LFXwQ+r4Xun1Vwx/pK8q11+bwpjdFpKMTQgghhLQYij/nSKUCVKueU/YFmG++tSgkYbgHD3oNzp9gF7BUygggnudjeNhTuTjt+wY8FnMHqjIYlMGQbKuUYomAJYPmvj6/vn+mtbmZXwSK5z+/iulpDw89ZC49LSKI8wRwnT9aOHFFnvD32vmjpwPuebLij3kdH/ccYSGqJflinC3zR09fv978PDZmr42dO83npZsS0Bj4HBQDzyb+iAAJuN2TtBvHiB3m2IlTYGbGOKdErBwft4PCoJgjLqps1q+/v/nmNF70or6Fsi93m6QjWbCcKCzzBwg6f6L3tdH5I/vq1x0oTz4ZV6Kkj4suqiGV8lEsxur7l8vZa1+yoADjbLr22nmkUj6+/vVEfT3ZrBE2y2Wv7sAJuuf+/M9TmJgwA+Jk0q9fy1po+PCHM3j88Tjuuce1+8myggKKzuSR3wVFY88z7h8p+0omG8U7EXm0yAbYZ9QrXmGuoZe9zLyKIKmvxWAO1PbtNXieX8820h3jOhURit3A57Nvt+8Dn/xkCo8/3ro/qdpFBhhh8L77Gl0iR46Y+YaG3PnleB865E7XZWxul0E7jxZ2luf86fxzLqxUmR0hhBBCSKdA8eccsZk15lW+xRfBQ0Sh887zEYuZ9t/BzJ/GXBzT9nrzZh/PPmudQqtT9mVeL7ywhlzOx49+FHe2Nej80VkpIhaUSrarl7hVrrzS/O7++81O6QGjFk70wN91/thtdUUeOz2qHAwAfu3XzEm46qoqYjEjrMkgZmLCc5wPjeLP2UcHbtmXnR6WWySvo6NWJJN8FB3sG2z1HhR/ziYOvuAF4S4bLciIaHLeeTUcOxaD7xtBL5+3mTXj456T7RS2rHQ6PLA3mPmzdauP731vCnfe6daF6MyfKOfP4mVfQeePdahILk+xGHOyqBIJc/+ePq3LEf26yKQFllTKnNdrrqlg3744Hn88Xl+vCFTi2Ai650ZGYvja15KYmjIClhWq7PKlQ10s5juD0SjnT5j4IwNvfZ2I+DM3Z/YhKN7JZ4Md7OT+Pe88H4cOTeAzn3HbQInQB7jXRCYDPPDAFI4cmcSVV5oLWISjTkZKK5fq/Pm3f4vjk59M45WvbEIhbhIpvxTe+MYcfuu3cg3hxPJcCHaNk3MT3H7t/IkSdqLyf6KC6RudP43zLAffN2VZKynQVKvdI1QRQgghhJwLFH/OERmwySBKMjGKxdhCC3gzn+kmZMQfGUjJN+E33pjGY4/FnPwRwJQVDQ2tTqv3xrIvH1dfXcHTT8exf7/tFrRpk+mWJYNPnZVinT/uIBtAfSD40EMyaLbr1s4f7UwIK5kCXAdIVDlY0Pnze783h298Yxrvfe9cfbvKZTO4mJiIzhSKx/3QDkZBojJ/3HIw9/djY7acSsQfXfIR7Pa1VOdPMgncd19jgK4WZETY2rWrhpkZ416ZnDRimBwTN/PHXYfcB+l0o7AQNj9g3D8bN7rTojJ/9HFdvNuXuHXMz3L9ZbN+/R49eDDmuO0AIySOjnrK2WTXc/KkfUzKsX/FKySvyYo/IlA98oh0cDPn8IorrLvpwQfjmJrykM/7Sqiy2y9ZOrWa50yX500wIDtM/AlzDK5fbzrmTU2ZktSg80eW/+EPl/HKV1bw0Y82ijz5fKMAqq89V/wxZa/pNPCrv1rF//7f0/UOY51MmPOnmcyflSgZCnZg27fPXFfi9BGkG2RwO+XcaHcP4Ao++v30tF2W6/wJd/tE5QXpz7TK+fOFLyTx0pfmcfvtK/fHMCietRO6jgghhBDSDij+nCPyDbw4fqSM5ckn407ZF2DyTYaHY/XW7tLpCAC+/vVkw0B0+/YaZme9euBnO50/ttuXLev49//ejCruvdeGVqfTphRHSoQmJz2k06aLlS77Cu7bJZfUHNEhKvNHlydp14wWB/Q8WuTRxz7Y1SgWA172smp9PzMZH3Nzxrnk+57jPNFuonweDW2qw9i82a+XzmnxR7dcl21NpYxoIKWCgHVehJV9yXUQzPxpRhy87LLGAF237Mu86sDxyUk0iD/W+RMs+0J9epjzp9lrWJd9aYFCf36x8rudO42o8sAD5gRrp5Lsx+QknLIvwFwnY2NevcQrm/Xr69HBuyKo/PIvm5Mi5VnZLPC611WQyfi46SYTvivntFCooVIxx/Lhh2N1UU2ETxFOAbc0SpebRTl/dMjzyIiErIvbzs4n98GpUx5SKXM8dIc2YevWGv7P/5lxcqL0soIinl5HsOxLc801VWzY0LC6jkPEHz0Y1wLJ/v0edu/uw3e/65ZftUqg1xk8QeePEBQK5FkWFH/C7kMznw5zdt0+8szSTh4t8kS1eo/u9hW+DUtFwtbvuKM3xR993gkhhBBCVgqKP+eIDMKkk46E9z75ZKyhtbW4OaSbyJYtPh54wLgwdJtvGYju2GFe5Rve9mb+mFf5hz+ZNINXwHwTbTN8TEBwqWRyUkwpi9lON/PH3bdYzBVw9GBy27bw/76jAp81we5FZ5tfSKXMIEYG2lp8iMeBzZvNNjVT8iWfkWtCr1u3XA+WpYU5f3R+hwzqpCQlKKQExaBm0deouFB06LMRKeyxPXQohhtvNKP9xsBnf+E13OUj2TFnwy37cvdr794qEgl/0e5mmYwR94rFOI4cse6ZXM53yqx0Vg9gz8nwsLcwv3b+2G2XAfWePTVs2GDKM2X5u3eb7n6lUgyPPhpXpaFmm5///Cr2749hdNSU0wVL1ABX8NHvozJ/RDhKp0354vg4QrsEyv6ZvCFzPYYdR9m/XCCDVsSNdNoVjaLLvrrTyhAL+YuoM3+eeCKO2VmvXrYqzM4GP3Vu6I5dcm0FCYo/ss0STC9EPRe00CAije8bgVG+zNBOHlf8CS8HC5Z9yWda5fyR57I441aCdgc+a7FJH2NCCCGEkJWC4s85IoMw+Wd5YMCUzDz5ZAxTU+Gtyk+cMIc7kTCdgfr7fRw+HGsIRbbiT2Oo6koTbPWeTFoxwwTGWldBUCiQfV4s8BlwB/j/P3tnHiZJVab7NyL3zFp6qerqpXqHLmhoaUAEBWlEEBFxHFnGK4r6DC5XpVGccfCKjnpBZdRxGdRBZXy4uAAKXkBGBFyAq+wKDTRU0/S+1dJr7ZVZee4fp7843zkZUZXVtWVVfb/n6SezoyIjY8vMOG+83/vZmT/hgxWdj1IqqHCipru5PS6plB7EhIk/gHF0DYfFixVmzCgtrQkTf2prtfOHBn3UAp2XfdGgzjh/7OUe6fkR5vzh515vrw7Apsyf738/GbRFdku7aJ102/fSfc7LkwYjqtsXANx/fze2b3f6WYdw9tnGqWYyfPQ6xmLqsPhjl23S55hEN535Uyr+0DHwfbv9Oc9MAijHyS7nfM1rilCKyiNVSTh1Pm8PovlAN8r5Q9OpY9mGDaUlbYDtgCNRIMzpQOvqip0k8uiuXmY6P/e4WFdOiWQlEiaIcbGEss22bnXFjuGLHEqVCjkUnA9Ed/tyjxudk67zJ8ptFyb+5PP6e4Y+B3Z519CCj1v2Rb8To+X8IRcmZcyNBa74s3Wrh09/OoX9+0e+7Mcfj2HTpnCBDLD3tyAIgiAIwlgh4s8R4oo/gHbItLT4KBQ8a/BEzgl6TTKpB1FLlxaxZYsPNzx63jzT7h2YqLIvPVD2fSP+tLfbYo5dIuSFOn/CBqI8s4UPJAfr4jRnju6CFjVPVO4NF5rCyGa1GEBZKu78K1eWOnGG4jvf6cVtt5WOeqh8jYs/M2fq9tj0/suWFRGLKStnhgZ1JMzFYqYrGzB05k8UYc4fOvdefVW/WS6nQl1V7uCeOyb432iby23zzd/LdWHEYuGDc5fVq/UobtMmP+hYph0rWqTp6YHV1Qswx4RKbdJpcz7yMhk+WOfrSiISiSz795fmI/HyRTvzRy/f7YZF/8/nzTocOuRZg3caNJ5wgt7m5uYYc/7YDjOCBJswpwjtc9d1VU55Fz/uUSVHlQ4/5+g73BZ/9D5zc3e4KFJuCc+552Zx1lm2whnl/OKUW/YVlVXE149EGvo8zJihv2e5mGOHQoc/jwp8Hi3nDy89HStc983998dx663JEpfXcCkWdUv3006zf7z4+4nzRxAEQRCE8UDEnyPELftyn3OBw2SN2E6eZct0ts/Wrfow0N11yr6hjk/llhyNBjT46eoy7bmpRKW9nYs5KnA5bNtmu51mzlSIx9XhMjG77Atwc13sbXvmmU48/3ypu+O88wo499yBEkFgzRo9wqEW6cMll1Po7tZB3YDt5gBMOd9wOProIk46qXR9PvrRPK6+ug+NjaVlbNu3+8hmdbe36mq7/MPN/AFsx8WRlgVy4Y3OMQrdphLFqiqdWXXSSQN4//v72Wuj35OvJ5UM8lybwXCdV0cCBarv3asdPtmsyWsisY8GpSRyUB5NmPOHwwfO3G1GIhKJwfv2aecPF1S4+8bu9qUf3ZIW+r5wXRU89JleS4LX1Ven8YlP6AMb7fwp2SwAtmDjuq6iRB4uEvFzYjy/s0YT/v1Cx5/n4tB5vHWr3XmKCyFuKHcU69bF8NJLtprJP/fd3eHuLNehQuKPO29Ui3o+nUQHEm+yWX18o1q6RzmCXIdPVOZPZyfwq1/Fh51xE1YiOVy+850k3v72TGS2j+veom0dqdsoqlscfz8RfwRBEARBGA/G0VMytQgTf/jAlTtUXDcJlV0sW6avQl9+2T88Xf/dLVUayr0ymlDfZKtMAAAgAElEQVSuTEeHZ7XXrqujVtGmlIWcPxs3+hgYMM6fWAw4+eQBPPVUzHJSEHxQ7Q4yaZku//t/h18d//SnPejqQknXKGKoLirZrHZAPP20HkGtWmWPrNyQ5JHw+tcPBEHBBHX+2rbNDzKPqquVdYebBnvc9ZJIILSkbjhwQY6EuzlzdGD1K6/oN6uq0uVr99+vR3G33KLfbLCyHh6M/ZnP9KNQAK6+uox+2Rjc/VUuvEyxp8cWJbJZPSA1WUD6kUQbGrhnMvocTySUNVjmg1a7RM0u6du/XwsCXDShYw1ocYQEEhrQktODOo/ROeC6Kvbt84Luccb5Yz6rf/1r7PA2cOePeX2Uk5CfD8mk2fZMxs5Z4svl33/8nIgq4ax0+HbSZyKs7Kuz08P+/eZ7hx8jfnzK4Q9/iOGYY4qYP19ZZV9K6fPXDTjnYpReZ/1erigUJbCElX3R5yGdVshkhu/8aWvTjrREQq8HtU13xZpf/SqBz3wmjdrabpx7bvnCejkd14bi+uv1h/HAgfDfC1eAof+PnfhjnuvP8eT8zAiCIAiCMHkQ588RElb2xQeD/M63m0dDrg1ygGzebDt/XPdDVJjxWMDvfHMRqq7Odv4kEtoRAiDorMRFnbPPHkCx6OFHP9JiwfLlZnDKB/iDOUjKIZUKv5CnoOahnCQ0aH/iiRjicRU4VYjXvnYAF12Uxw9/ODZtqsPa0keJP/zY8DDX0cn80cuLx3XZHokgUW3Vyw1eXbasiLvv7sGaNeUN9Mop6xoK6qzW3q67k3GBMcr54wqudF66YhQXguy29PqRi0i6659d4sfX0Yg/ehodc3Kx0T52nT8894f+xkPRzfvx56WZPy6uoEfbRG4wd75cTlmvsfO7JrB10gjg5ZR07oeVfQF26RcXQtxcpjC4KP3ud2exZo1WeLj4E/Z/970A4/hxnSvliT/e4WUa0TOdHqzbFz/39PMVKwZQKHjYscMrmd8Vf0hI4V3qysEVvEZCVL6OO522o9zvOqWA//qvBF59tbw8KC4KifNHGG96e4G3vCWLX/xC7gELgiBMJ0T8OULCxB8+UAwr+yJosE7ChNua2Z0/agA+FnCBgQ8YZ89W6O/3gu1OpRTmztWlXyT+8DvUFLq7fbte4PHHm8F/VDvv0eSee7rxoQ/14/LLB68voAHu5s0+mpqKJVkl8Tjwgx/04p3vHIVbzyFw0YGClWtqFDo7zaCO8l54hktUCdhwsMt3zHM+2HfFj3e+U+/PJUvKOyf556NcvvKVXvzgByMT28ip1t0d7fzxPCNeuOtJ56X72bPLvsxzUz5myr501z8+vy0s0OtJaCPnz4IFdr6J6/zh+Ukm0FrhJz+x9xn//IZl/ri420rlhPPn20IO7Rvu+gHgbGv4e1Q6XHwMF3/McyrXBWzhoJx8K3ewT6IIlX3RvufvF/Ze/P9uiRVfb+4KCnP+kIiYyWinn93tK0oI0o8Uik83MfjyXeGSXjPc7B6+zJEKJVEh1O5y6Xe5XOfP00/7uOaaNM44w7ZqcZGHl5xFldANxlNP+fjkJ1ORbiJBKJdnnonh2WdjuOqqMboIEwRBECoSEX+OkH37PKt9NGCLNnzQHCX+uLkYNKjiZV6+r0ps/2MJH/zwwSPlqOzcaZeonXaaGVXwweOqVcXAwdDQULQGg1z8GatOZkcdpXD99X1DlkTxY+C6fsYDLgjQ85oaXfJBA7+wsi/arng8vD11OYSF2wIIys+AUkHgxht78dBDXXjDG8bOyXPFFXlcdNHIxLbZs5WV+UNkswoDAx4OHvSQyZgSNS6OUGt2oPSzywddYSJmba0Wlfbv99DV5VmZOLbzR2HmTF1iRy4IchjQ54ZcHzRgJjfN/v0e/vAHXVJJf8tkgAsuKOCtbzWjZDdYnIhyEh5zjH3+kxhV6vyh89SezrfVGz2jxrhiZ/7ox7DMH8AVf8zruPNHKeCyyzL4+tftL6IoAYL2OeW+hWVluc4fEi3ceblLjb8fn27EH+78UY4wYV7LhcjmZh+ep3Dqqfq7gMQfuzuYZwlPxk0zXOePeV5ueHwUUfljrvgzXKGK5qOSt7Dl8kwnvk2uSBbFBRfk8POfJ3H//eLWEEbGaLhsBUEQhMmHiD9HyL59XolbIKrsi4s58bgJgHVdFTSYSiTMQKq6enwHUlHiz+zZppU0/1uU+OP75m9um2++3RM9SOTCAN/e8YJno9Dxp/1oBhP6727mD3DkeT8ufOBuO3/sfZJM6pblLu5xvPfebtx11yj1eT4C6ur0ALa313OcP/p5e7stzPDPMnej0SCc4J2/7LIv/TwW0wLQrl0+9u/3rONrO38QdNJrb9efqaGcPzT9xRd9vPvdWZx2Wg49PR5SKfOdwrN9+PnM3/vcc8OFNWoXT9Ag3nX+kHvBFa9H61ycSMrt9gXY7d7dzB8zP/Dgg3F8/espJ8w5/IuPBD86b8KEilKRItz5w3NyePkY3x4651znDxeYwlq9d3QATz4Zw+rVOgweALZs0TvPdaWEuYiGL/6Y+d3fk+ESVkoHlDqqhuv8ifot42IbXxbfpnKdP0SUeCgI5cJLXAVBEITpg4g/I2DRIntQZAc+h99p52IDHzwlk8oqOaJljWfeD2BfwIY5fw4d8lBdbVqun3WWGWG4YtYnPqFHATfcYI9WRqOj02jBBQA3m2k84OeQcf7oRxqYhYs/ep7RGnBzd9nSpWadjjRs/NRTB3DGGcPvlDZa8JIkLubQ56+93bc+i1z8IWcLUCp8cOxuX7bLhvJgSLBx14M++5SlBQyd+UPrsnGjcVj09NjlVtztw5/zec4/P0r8CT9ergBm8pLs+cipFpUpNBng4k82q9ueR5V9RWX+dHTw+c0X6r33GrdGlPhDn3lyeZVT9hXt/Alf73zelJDu2qW3gTt/Mhkd9E3iUVjmzyOPxFEoeDj77ELwfREl/vD1Go2yr+eeG5llIWyfAiMPfI5yUvD9wZdlC2z2a37wgwQuuSS6MxkXoQXhSJjoG2+CIAjCxCDizxFy553duOkm23/PB8pcCEmlTPArhSQDtvjjOixcF8h4wS9g+eCRDwB5WRDvztXaal9NHHdcEdu2dZTk7oz3Ng1GVNneeLFwYbT4Q4P/wVq9j574Y7Z95crwc3QywcUfLszw53zbEgkjdnKRxi154vDPO38NF5KixCP6DNTV6XDvvj4zIJ4/3xb/SCggIYmH5fb02M4mLti6Tra1a/vw2c/2RebxuM6fqOWYTmn2dBrYTsTnaLRwSysTCdu5QaV8dXXFyMwfOm56frO8xx7j4k/4+5M7iJw/5QQ+k2ukp8eLzPaxnT8eFi0i8afU+UNCIb1PWDDxiy/qbT/11AHMnKnPBVqWG87Mt9U4f+xtyOeBO+6I4+DBks0tWYc//Wmk4o+9fp6nrHUz64rQdY3C7bbmLgewz42oLmoA8K//msbDD0fvj6j3EgSXYhF48MGY5TwESssTBUEQhOmBiD9HyNKlqqSdcVTZF2AuLLmrgrstXNcMDSzH2yUzb55ZPz6I5evtOgG+8Q195frmN5c6CtLp0jtMo9HOe7SwnT/j//78PWnQTMeeBmzG+cOdYvbjSOEiGBd/KkmoGw7U7Q0Id/640/lruJgymPOHixxR7hsSclzos09duvbu9QLHSF1dETNnqiBfiwaG5Ajas8d2nJTj/AGAa6/tx6c+ZUac11yjR6WXXdaPSy7JR7Ynd5cT5fwh8Wqyhj0Dbkc9En/MtK4uD7mcwuLFCjt3GrElaoDPhYZyyr5oflP2VTpPlPMHsEusuGjlln1VVSnMnl2MdP7wdbQ7fNnbSMJgdbVpU+92GePbGpX585e/xPCJT2Rw223hIXC0LbNmFQ+7jvS+WbUqh69+NfQlkbjiDx3zUufP8Mq+ort68bKv8PnLeS3HFX+efNLHKafk8Morckkn2Nx1VxyXXZbFxz9ut3OU0HBBEITpiVwpjCK2+BM+jy3+8Fwge4BFd6Dd7lNjDc9z4YO+wcSfyy/P46WXOnHWWeXdjqyksi9+DCbCscCFMer2RfvHDRDlrgRyAY2W+MOXTSV+QPlC3etfr4/96aePTVe04cK3ISzzx33OX8P36WDOH172xY/j4sXmsxIlHtE60Xu2t3vB8a6u1u2zN2/Wgc40MJ89WyGVUkF3MKDU+cM/s0N10rv66n7s2NGBb32rD9/7Xm/J3//1X3tRV1fEKafYn2sSJFyB+7Of7cPChUV873sj69Q2kXDxJ5VSSCZtMaO7W3+3L15cRD7vBW4Xu+wrXHThokOU84emkyhIrwkLTQZ0oDR/b/4ePPOHi0iFgha15s/Xbh2lynf+kBBEQhZ9V1VVmWmuiGI7f/Qj30eA+a6LCnOmdTj99AEcOuRh504Pr7zio6XFx//6X6EviYTvC6VMhlV0q/dyxZ+hp/Nl8elRr3UdQYQr/qxdm8HWrX5JsLggNDfrL7UHHrBDwl2RVhAEQZgeiPgzikRl/nB42Re/c+4KImRFV+OsR/Dcj5kzzXQeJMvdQcTs2arsGvJKcpNwkW4iMn84dKxN2ZfeoTSIs0tS9DzxETZ9+f3vu3DHHdHpoTz/ZjA+/vF+3H57Nz7zmcq4ncgFyijnj+tcIRcDH3BFOXeAaLHwzDPNZyjq9TR4Cxd/FI45pohi0cPGjX4wMM9mS0uwenvt7ePiTzmfx8HEw49/PI/167sic5/c76xlyxSeeaYLJ500/l3zRgsegqqdP6qk21c2q4L25g8+qD+APPDZzvwJfx7l/KHl1NfbHd/CcncAPYArFsPLzLhos3ZtBnPmVKOzU09PJBTmz1fo6fFw4EC484emhTmLuFAJ6O90Ep5of9FvGN9WWqYrqNC6uqIQ307AlHN2dXmW4204cIFsYEB3VgSiM396ejzrb93dwA03JAPhj4gSavhx4NsdJqq5RE13B+6U4VSoDO1dmCDCwtCpRLy0C52UfQmCIExHRPwZRfggKSorhQ8G+V1md4A1UWF8fD3ccg+C7kofKWPV3v1ImOjAZ8AMkmgwQINqGkQOlvkz0vNk1apiqGPrnnu6cfXVfVi6tLx9kkgAb3rTQMV0fOLiT5Tbx3X+kMuJDw65c2fVqgH85CfG1RLl7jvjDDMCc50/V1/dh6oqFbRVp8HsE0/EcOiQh3hcd1uivzc3+8EAMJNRJeJPX1905s9Y8Ytf9OBNbyrgyisrQ+gbTbjAmk7bZV9KaXEllwPe/e48kkmFm25KoljUA3/PU6iuVkfs/BkY0NN93xxnEnOi8mFcwYKLCzwUmNxi69f7UMo77PzR59jOnX6wzHQaJc4fPkikTma0jfRdVV2t0N2ty+BoXemGQXjZl73eUY4gor9ffzbo/To7j9y5wMvv+DLcfclFP75ff/SjJL75zRQ+9CHbWlde2RcPfA53AXGiWsBzsREwvw0i/kx9/vpXH5/8ZKqkbOvmmxM47rgq3H23fUco6npLnD+CIAjTExF/RhH+I+uWy1Bex/LlgwfAEjSoH2/nDwA0NelHuvvsMtKwyUoKEZ7owGcA+N3vunH++Xn8j/+RP7weerrb7YuLhaMl/kRx2mkDuOaa/knbEYS706LcPq74Q+clH7BzMfT3v+/GBReY0VXUvqmp0Q4/31cl+TfXXNOPTZs6g+8Hcv5861spPPNMDDU12kHX1KTX/+WXjfMnnQ4/R7kDYjzO4RNPLOL223ssN+BUwc78UZb409en757ncjrv7bzzCti82cf27R56ez1kMnr/lyP+hLVw7+nRQkk2WypEhrVb18/t5WzfbjYgbHBHIdXJpAkQ37XLC8SEbFaVOH9okFlXV0RLi359R4eHRMJ0qKTfL3IWAebmQVird1fkiQqC5tuSTNr7hQsdUSIJwX+z+L7n+2iwLCXu5mpp0fO5+TpRAk45zp/osq/wLxlXPDTizyT9whbK5q1vzeHnP0+WiDw//am+KPjlL221hzqDukjmjyAIwvRExJ8xwhVz7r+/G/fd12V1xxps/onkySeBxx7rxKxZ9vSrr9ZXqGvWjEz9WbZM4V/+pQ/33BNdbjRe2Jk/E7MOq1cXccstvcHAxjh/bPHHdv5UzvlSifBjGZ35Y7+GzgU3ZPeZZzrxwgtOq5TDPPBAFx59tDSV909/6sKGDZ1DimcnnWR/lkhsOvpoLf68+qofDIxTKRV6jvLto5D22bMnb+nVRMKdP6mUFoBIICDRgM4TEvJ37fIPB29rZ8q2bT7+5V/0nfnhlH11dXmHxR/FxBRbgAHcoGD9SPNv3RoubBAvvKA3sLZWoalJn3tPPBELxIRMRgVlhCSo0PstWqRw8KCHnh4t8lRXm1JfcsF1dnqB04XcS3xbuZuIi1i0fWHdzejviYQt0HJHze7d5vnTT/t44gm7IxgXV7j4w5cRFfgM2I4dEgjdNux8fi5M2eJP+PxRrqFynT90M0CcP9MHt3TSdewR5Th/3HNZEARBmLqI+DNGuEHNDQ0Kp5wS/QsbVfY1Ec6fmhpg+fLSN/7MZ/qxYUMHjjtu5FcKn/50P047beL71XIxoFJaVNN60IDDZP6Y9SMhaLI6c8Yavl+i3D5cNAHMANbtsLNwoYrshLV6dTFw6XCy2fLExIYGhd/8xqgCdOzr6xViMYWWFj8YlKbT4WHpdrcv4I9/7MKjj068sDoZ4edNJqMQj5vzwQRd60cq4d2xQztn0mnjTPnJT5K48874sMq+tPNHnzv0HmFlX2FBwXQO8vbzYeLP88/rv9fUKJx55gCyWYXf/CZhZf5Qzhe5Tug9Fi3S79HSovOp+G8WF6toXamMNqzVO+CGH4c7gvi2JBJGFOvqsrePupYBwNvelsOFF2ZLXk+44ddh66b/b55z8YfOkVLxxzwPczvp9w4Xm6LyglyRh3DPH/ptkBbw0wfXuUOfW1fEjMoF5L9zQznnBEEQhKmDiD+jzHHHDSCRKD/8mHBLoS6/XF+tvve9lVOY7fuYcqUeXBiolCwiKgWi8EYKdOWuhIkQBScrXDQbLPCZunTxznbjAbl1ACP++L4WgFpaPJbHokJzqVwR67jjila3M6F8+Gcsm9WlRiQQuM6fxkbX+WMLyJ5nhIZUSmfikGBAbpgf/agHf/d3+WAaOX/oPX7zmwSef96H3W69NDdmxQq94C1buPjjlTgEn39eb+CMGdrh8+Y369K1X/1Kf/mFOX/o/KPtbWnx0dHhWUIkzylzy77CMn9oXnf6YJk/ySQXxTxH/Bn8BzdKgBms9CqqS1c54g/fZv4eUfuCr59dioZQXOcYDfAlx2X6kM/b5wDdcHRvXkRRTuC4IAiCMPUQ8WeU+f3vu7FlS3iJyGDEbJc6LrqogPXrO3HxxeLjHkvc3JdKIJvVgynKlgjr9iWUD3df0CAZKBX7LrqogK98pRd33jm+rhku/vABdUODQmuryWNJpcLdaUO1dBfKh2f+pNM684cGSW6Le8rM2bFDZ/5Q2RehlDn3GhpsFww9NjUV0dhosnHI+cOdo9/8ZjJSpCBxYNYshbq6ouX8oZbuHHKw0Hm0dm2/FeyvA5/trne0bfPm6em7d3vo7PSc7pb6kZdjGfEnfN3D8m+4IMTRzh8w548r/gx+KWM7f8LLs3p7bVE9quyLcAV4W6ALXw7fF1ElZ1zwcZ0/1I3OFX/ot0Eyf6YPrliZSpV2qwSis32iQuQFQRCEqY2IP6OM7x+Zg8QPORJy937scd0flcKcOXrgDxgrf5j4I2Vf0cyaVergaWhQeN3r9IiPO4IAvS+vuCIfDMbHCx4KzUvFGhoUenu9wAGmy75KX+86f4Qjh3/GMhmd+VMoeIc7fZHzR/+dQvypW1YmY4t3Bw54wWuoQyL9nwbvPGD54EEPhYLu3uZ5wK23aqVg1y4fduBzablUKqWweLHCjh0mCJlycsKgc+6EE4q44w6jSKTT5juRhAdyNZGAtWkTlY6Z5YWVfYU5f/i6D6fsq79fHwuTLWSLJ9u3l76OO3P4QJl3++LLKBa9yKyegwfNcxoou+JPOc6fcsq+bGeX/R50frplOqadN4QpDv1uuS4vKv91yxddh1DYdCn7EgRBmD6I+FMhyCB+YqDW5NRuvVJoaChi7149GAkLfJayr6G5775ufPCD/Xjf++yr5Ntu68F11/UGpZUTDRccuLOnoUGPXrdt01/T6bQastuXMDLIWQHo/c3Ladyyr5oaLfZs367dLum0KnGLkNBAx5LcQ+QAyWaN+3DvXnv55503gAULimhv94Z0/qTTusNcoeAFJVDa+RP+RcHLB1evLuLf/q0XV17ZB983zh8aEOo8IxWIP9TlijcpINGrudkP9tNwnD80vbfXC3Uq9Pd7VuCz6/whxxMXfLhwwge6Ud2+AOM8UkoPommgzdeVXj9Y4DMfTNtlX+HzR3Vz48tRyvwWRJV9SeDz1CeqvIumu46gqHOCn5dR2VKCIAjC1CMiCk4Yb0T8mTieeqozNEh3IpkzR0EpD+3tXmird2Foli9XuOGG0h7KVVXAhz9cGcKPCxd3KGSaOjil0ygr80c4cvhnTDt/9POurtLAZ0Dn4DQ3+8H8Bw644o/+Px1LLRwox/mj5yfxh7sR58xRePFFH3aYsHYieZ4RDZJJFWRWbd3qY9GiAeTzXmTYqysifuAD5vNA20fbQq6muXP18jds0NsbVvZ1ww2mXo3cTrStAwO2CMNLvPhAtLMTJZ0mqdU77/bFhRsSSN39RMIaXz53F7niz6ZNPmbNKgbz19cr7NnjWWVfdB4MHvgc3lEsKvOHCz5R+U6Fgsl/CwsMp3mEqQ25vFyRlK4hXfEnquyrnGwpQRAEYeohw8kJ5iMf0b/MZ54pV20TxeLFqmSwMdHQXfaWFo85f0oH+SIaTi34wJ/OAaW0AyEeD8/8oYG3MHK4+JPNKqxapT98X/pSqsT5A2hxhgbkdXXKEjQOHtQBzsmkCtqe0zK6urTLKJMxy2tvN4IQ0dBQRH+/h9ZW+6eaBnjkLKqu1s4fwLhgCgUtmFx8sR7lnXOO+Y0JExGJ447T2/zXv2pLWm+vLkVbsEAhmVRBaDQXf9yGBbRvACNUuANMLqhw50tY6ReVsNG5rrt9mfl27NDfk7aIYr+e2L/fCEckzNTV6X1HribavyRg2eJPuPOHv5+d7RM+3S5FC3f+8OdRZWX8PUT8mfokk+FdvegccMOb+Tlhl0KGi4yCIAjC1EbEnwnmy1/uw4svdmL16vHtMCRUNjToaGnxghBPCXyeXpD4AxhLf5hDjUqKhJHDP2PpNPCpT/Xj2GMHcNttiSBXhgsd9DkFtIDw1a/2BVlAhw7psq+qKlXSuv3QIQ81NVq8Hcr5A5Rm2pDAQ+VItbU68wcAtmzR0/r7dTnQt77Vi7/8pRPnn1+e+DNjBnDssQN45pkY8nmT+ROPA0cfbc41nvkTdl4a8UevDw1WqRwsrOzLnQ5ox9DAgHc486c0WwhAUO4WFbrs5p7QvqaB8cqVrvhjO7b4OpFQUyza7qOowTRtWy6nnCwgUx63f3/4a+3lhAdH62WFb6cw9SA3oivY0LlYKgpFfSbMc3H+CIIgTB9E/JlgPM8eQAgCYAb0ra1+cLdOWr1PffgFeX29GWhTaRcPhybmzpWTYbSwy74UUing4osLKBY93H23rrcIE2cA/T1+3HFF/PWvXfB9hQMHtFCQy9lZNYB2kpCLi5w+7e2+9X/ACIBU1nTCCdqV87e/+cFyAO0I42VfgMn8SaWAo45SQdkWEH4ecU49dQA9PR6ee06HTVMOUFOTWQbP/Alzn82cqZBIKCb+UPi1XgZ3+PABKnfBAOYzkUzqfe/7Cl1dRrhpatKP27b5kcHJ7oCYQtRJNDHiT8yaf8YMhVhMWc4fLrzw8Gi77Ms8p22bOVOhp8cLvs9pXefNK2LfvqGdP1Et4/l7yCB+6sNLUTlRAiB3/vDzxhZ/RDQUBEGYLoj4IwgVCA0qtfNHT5NuX1MXcmLw7ky8sxc5f8LKvkT8GT3cbl8A8Na36g8gCTC288eIIdSd0fO0uHLokIeODt0S3Yg/et6DB73AAUOuoEcfjVnvC5Q6f049VYs/Tz4Zw5o1WXzta/rEqK1VmDtXIZVSgfjjZv7w8yTsPOLQ+zzySNxap2OOMdt70kmmtVRY7lRVlUI2W1r2RTc7opw/brt3Lv54nt5f3PlD4s/27bbzxw581o/kUCLxh6Y3NBQxe3YxyDOiZafTCrW1djkfD4zmAhZ/77BuX/QZp/3R0+MhHleYM0ehq8sL1pcLR1ECVk+PfQOA3sMVhYSpB5V98fMQsAVU3vUtquzQzqIa3XUUBEEQKhcRfwShAqGB4cGDXmi3L2Fqcffd3XjXu/L4x380V+q8lGawsi8SHYSRw8VU2udHHVUMMmEAO/CZuzb589pahX37PHR06Oem7EuXCnV3e4EYcNJJA1i5csAKgSbIAbh9ux/MG48r3HVXAi+9FLPez/eBhQuLTPwxLgG9LLPcqBbwBIk/Dz+s34OcP0uXmv1w4onmeWOjwpo1xmLg+yroZFbq/CHxx7yfLf6ED2qpc1kupw7vRz19yRI9X0vLYM4f/ZxK8lpbSfyhwGyd/bZjhw7TJiEnldLlbWGBz+662pk8pdtGuU+0P7q6tJNp9mw9nUq/okrXeNlXsehZ70cilgzipw+u+MOdPNyRFuX84aKQOH8EQRCmDyL+CEIFQoP8ri5zF4/aDgNS9jXVWLmyiP/8z15LWOBCDw2++d8JyYIaPfi+JCHILc11A58JLsLV1iq0tvpQSmf70Gs6OrzA8ULum3Qa+N73jGoRFvq9a5cfvGblymKJw4PydxoaVBBonM/bgvFwRMLGRoUFC4p47LF4sI4A8Ja3FPDe9/bj97+3a07iceCXv+zBvHlaXKmqMnlGJEi4IcpRZV+u+MOdP3rZCp2dZjnewT8AACAASURBVPqiRfqxtdWLbJlO8y5YQM4f35qeSABz5hSRz3s4cMAMjJNJfSzDAp/ddY0KZCaBiQR92h/d3R5yORMGTqVfUZ2/3K5N9ntThpEX2d1JmBrQ+eQKfVECapTDxxZ/RnUVBUEQhApGxB9BqECo5Kejw2N3vkvnk7KvqQsXemjw7XnAtdf24V3vqsxW9ZMdLrByuHASFfjsOn+ImhqF+fP1/x94II73vS9TMg8vpwrL/OHrsXr1AFxISCIXyb59HgYGvMAtA+g8o+pqhaOOKn19GOT+AUxZVyYD/Pu/92HVqvCQcRIsKQ9o/vwi2to8HDxohAxdDqbKDnymQSp9/+VyWjgJE3+4U4YLJ7T8+fP1ertlX4mEOX5tbX7w2nRaoaZGBe9XKNjL5Q4Le3rpe7vOn+5uLfTNmmWLP9yxFJX/A9jOKT6Q5/tAmHrQOVvq/DH/55+h6Myf8JJFQRAEYWoj4o8gVCDcKUAX9lSGAojzZzrg++Y8SKXMAV+7th9ve5v0dB4L/IhfRFv8MdO54EPCC1Aq/ixdWoTvKzzzTAxPPx07PN0shzuO+ODfdes0NKiSzpCplApcMbQOe/aEC8bNzZ149NHyaoOOPda8D4mPQ0GiNYk/r3vdAJTy8OSTsUC8SKW0SGS3ejfL4IIKwJ0/epkkxtDgd8ECwPMUWls9J3eHL0NPb2x0M39MSRkdy9ZWz3L+kGNn/36vJGS3vT3cmcPni8r86erSzh9X/IkqXeMODsAuRRssDFqYWhjxx57OP0P83ODlYFHOH95tThAEQZjaiPgjCBVILqcHNJ2dYIMmMxBcskQ/P/bYiVg7Ybygci938C3ZHmNDlPjDhR2eo8On8xIrVwhKpYBFi2whx223ftllejS2fLkRXZJJYPZs/X/P0wKF6/zh4a5DiT/xePllgnz9yhd/VLDegHEPPfFELBicplLaTcNDlHm5VnTmj/4/bWNLC2Uk6WktLb41AC7H+cNFHuP88di6GvdVS4sRnKi8beNGc8L09XmYNUtP586f/n5qU6//392tO3719GiXV6n4E+784S3jAeDAgaFLe4SpR7Tzxzzn3eO4w4e/hs9P3xeCIAjC1EfEH0GoQDxP30XnZV980PnP/9yHL36xFz/4wQStoDAu0ICXRCDiwgsLOPvsAu66S0Z6o0k5zh9OlJDCs4BIEFm0yHbsuOLPN77Rh4cf7sLrX2+LO7SsujqFeFyXiPFuXYWCGbgZ8UdvSDx+5BZBKlMCSs+/KGg+ciaecsoAYjGFBx6IB4JIJqNdT4cOecF8/f1mXaMyf1zxZ/duP5g+Zw45f8zrePmT6falxTTq3EYlMfG42c/cQZROq6BL2u7dRvw58UR9jKg7GKDFGVo3t+xLt6k3zh/drctDLlda9sW3gbt4SCCj9Yxy/tx33xBp3sKkhsSc3l7PKuniAqot/iB0Os3v+wq7d4v4IwiCMF0Q8UcQKhQdbBpe9lVVBXzsY3nMmjUx6yaMD0b8safncsBtt/XgjDPKy28RyiNKzOFOHpf16zvR3Gz3J+fiT22tfhxwDpXbbj0Ws0utCHKekFgUjwP/9/9246KLSnOfhnL+DAcuTvH28+VAok5VFfCudxXw8ssx/Md/JA9PU6iuVsjnjVjT32/WnWfZ0N8AU/blOn+SSb2/Ozs9a3BrhyVT9y6FY48tYutWD52dpqwqlVKW84dKaqqqVODy2bPHD6YvWqQwa1YRGzbEgu3t7TWCGXc19ffrdacg7+5uz+rsRuLPrl2lmT9cwKL9MGeOXh9X/MnlFOrqirj++iT27pXB/FQlqqtXtPPHTKcudzQ9m1WYM0cFQqogCIIw9ZFvfEGoUKqrddlXX5+HWExJV6dpCJUSSbD3+OAKNMRgnbLq6hRmzrSn8SwgEnnWrLEX7jp/oiDHCD8Hjj++iOOOK11ZVxgZifhDWTdA+c4fgmeSfeELul7pxRf1F1hVldknFEzb2+uFumaA6LIvEriSSSOQbd9uLmm4g4aHRq9cWYRSHpqb/WA/NTSoQFRpa/MDh08uh1DnT1WVwooVRWzZogWsfF63X0+ntQhjO388y/nT1eUFpVnZrBEKb701iYceMtlI1dUK+/aZYGua7jp/ikXt/jrhhAFceGEBSnnBdglTDy7m3HlnAo8/HiuZzsW/KPFHi5LAvHkKe/Z4kiMoCIIwTRDxRxAqlKoqXQLR12e7foTpA7kd8tLca1yIapM9nDbpgHFnAEbouPLKfvz856ZMjxxB5eIKgLwMlHDLvkbL+VNu5k+YSDlnjoLvm2VVValgn5BDpr9fCyHZrArJ/NGPtL10LLgoRPubyrkAt7ORWcbKlVo0W78+Frht5s1TVtkXiTe288c4gnI5Lf4Uix42bvQDh042qwLHJl9/Lf7QeoGJSwoNDQqf+pQWyB56KB6s98qVAygUPOzYQflEdtkXZf7w/UP7hgdRC1MHEvqIz342jXe8Q59YfX1e8Jm1xR/zvLXVZ9N10PncuUX09dmuOUEQBGHqIuKPIFQoVVW6NKKjwxPxZ5pCzp+CNPcaF3huBofn35QDL/sioSMWA845x7h1ys3jibojH1aKVVr2deS384/E+RMm/lB+GVFdrYJOZ4cOeRgY0APaVEqXg5Wb+UPojlz6OQ+utcOSySWksHKlFnPWr/exe7ePZFKhrk4HMmezyhJ5slnj/Nmzxw9EnVwOwXJeeMFnZVxatOflOD09uquXyfzhzh897Yor9EbydvXHHaeXv3mzf3gb9HQ6t6g8jgtbvHRNmHoMdhMgnwfmztXnjFv2Recfd4TpIHJg/ny77FAQBEGY2oj4IwgVCgXF7t3rBQ4QYXpBbge3zbMwNkQ7f/SgqlwxhZd9ueVd73mPfpNly0rzfcI4/3yt/F18sa0AXnRRHuedV8Cddxo3EeXHkAjCO5ANF96KvtzMHxJ/XMGK5xu5ZV8kaiSTOCz+2K81gfd25g9BrwNsxwvP/OEC0lFH6f3+6qs+du70MG+egufpdV+6tIhNm/xAgMrlFKqqtBDPy75yOYVVq7SQ98ILMUvMqapSVlel7m67vGvHDs8qKwP0cYvFdGg1CUmu+PPqq/px8WI9nZw/XNgS58/UJkr8UUqLgzNmKKRSqkT8oVwst+wrkdCuN0A6fgmCIEwXRPwRhAqlqko/Hjokzp/pCokNUvY1PkSJP7NmAffd14Wnnuoqazm8TIq7XgDg3/+9Dzt2dJQd1v4P/1DAH/7QhbVr7ZXLZoFbb+3BG99o3ESJhHYptbfrn/bBgqqHgmeMlev8Oe00vS5uvhGJM4Bd9nXoEA+0V0GHQ85Qzp9EwiyfO154WDJv3V5VBcyaVcTmzT5aW72g/TsArFhRRE+PF3TxqqrSy503r+iUfWnnj+8rrFvnOn8Uenp0J6ZCQYtXmYzC0UcXkUgo/O1vMTz4YPzw/MYVVlen0NrqB0LSscfqfbhliw6avuOOBObOLeK887QISOKPOH+mD3SsMxn7M1Ao6O5xyaQWEv/2txi+9a3k4dd4iMd1plV7uxfkmmlRSKGhQZ//LS0yHBAEQZgOlPVt39TUdGpTU9OfQqZf2NTU9FRTU9NjTU1NHxr1tROEaQwfMKVS4vyZjtCAV8q+xoeTT9YDoQ+F/JqdckoxKJEYDm5XL98Pz+uJwvN0wHNUG3qXhQuLoc9HQrmZP//4j3nccUc3rr22z5puO39U8N2mnT/UcUv/rbfXs0Q4t9vhzJl2hhB3/vA8FO78IXGGBs2NjQpbt/pQygucDwBw9NF6f/31r1r5ImfO3LkK+/b52L/fOH9yOe0iev75WODk0c4f/ZquLrAsIL2eK1YU8cILMfzwh0lr+YBxZvT0aLFo+XLj/Hn44Tg6Ojy85z155HJ6Oygsmzun6uv1a8T5MzWh/B4qRSQo/DuZNJ+Tr341BaVs58/AgMn26e/3kEiY8s6DB8dpIwRBEIQJZcjLyaamps8A+DGAtDM9AeBbAN4CYA2ADzc1Nc0di5UUhOkIF3+GM1gUpg4k/kjZ1/iwYkURzc0duOmmkS/rta/Vt9jLFU5GCyoLArTIMRqU6/yJx4GzzhoocSrycOtcDizzByVlX4DbwtrOL4rFTHmb7+suiK67CrCdP+SmyeVI/DH7aMEC2/kD2IHMgBlsU+kVTT/22CK6urxgejZr/tbZ6ZWITk1NthhHzh9AD867uz20tXnIZnUHucbGIh59NIZnn/Wt19fWKub8MfuHnD/k/BKmFuT84W41wGT8JJMKW7aYY9/VpV8Tj5uOeJT7k89roYg+myQmCoIgCFObcq4QXgXwrpDpxwLY2NzcvL+5ubkfwP8D8MbRXDlBmM7wu8JS9jU9Oftsbfl505vE+jNezJwZHlw8XO69txu7d3cMPeMoY4s/o+P8Kdd1FAUXsmMxhJZ9pdMmCJqXfrndvgBT+kXiKJVncXjHLePM0f9fuNDMv2CBeU7iDwB4ngrmp45fGzeS+KOnU47P9u3G+WMELLulO5+foOn8bzt3+kHG0j/9Ux96ejx8+9v6B4AyfWbMUCzzR89LJW2plJKyrykKiT8k5BBG/AEuvtjUCHd2ekFXLzcPSmf+mBJMcg8JgiAIU5shL+mam5vvBBCWOFEDgBtFOwAMs3mtIAhRSNmX8KEP5XHffV24+uqIMBqhYonF7Nyc8WLx4nBhYySM1Hnmlr6ZVu9eIGLkcrDKwQg38wcoFX/4dyWgxRHe7rq720MspgIRnYtia9YYYXXJEjM9lzMiIJWGkcOHxCZaD2oxT92+AO1e6ukxohAAXHVVvzU4J5cQYNrV8/kvvrgAzzPz0PvV1yscPKjDsvn+8Tz9Nyn7mpqQy8sVO7n4841v9OKcc/Q53dHhIZ/3giwwANi/X583xaLOEjRlX3LOCIIgTAdG0AsEhwBws3U1gANDvWjmzCzi8Qm4IhaGRX19iI9eGFfmzzfPq6rikcdEjtXk4EiP09veNsorIpTFZP1cnXCCed7YOLJt+O53gW9/GzjnnKzlUhkuc1lBeH19NZYu1c/7+5PYt09beo4/PhmE0W7enMPZZ+vn5Pipr8+gvl4/p+/GVEoPWJcurQqWr7t2eXj5ZQ91ddXwPO2OqaoC5szR+2PZMrM+p51mXgsAs2cDe/cC1dVecA6sWKH/RvlEixdXYfZsYMkSPX3PnsTh7UwHJWvxeC4Qm2bPTqK+Pon6euCXvzSi0oIF2WCb+DrV1MSC954zB2hp0dOPOSaH+npg4UL9/2KxOjguM2fq95g/H3j2WWDWrOoJER+jmKyfp0rguuuA00/X5yYA1NTYdeCFQubw9AQWL05g9WrgoYf0OVgoAJlMDEuW6JMhn88E52V9fRzLl+vzv7c3gfp6o7DK8Zo8yLGqfOQYTS6m+vEaifjzEoCjm5qaZgHoBHAmgG8M9aL9+7uHmkWYYOrrq9HWNv7lCoKNUjEA+sre8wpoa+spmUeO1eRAjtPkYjIfr5oaD4Ae0I10G979bv2vqwtBp6sjIRZLAkgF66TdKtVobc1j3boigBTq6roPl2PlcM89ebzjHb0AgP379Wu7u7vR1qbVoaqqFIAkYrEiAB+9vR2ge1H19UXMnl1ET08cW7Z0oKoKOHQoh0wGaGvTG7F8ud5HH/hAP9ra7HDqhoYs9u6NIZstBvNnsz4AU4fb09OBtjYgmYwDyGDTJr0e+XwPPM8DkMaOHT2HHU5ZAH1oa+PuPb2unZ1daGvTjp9MRi8LABIJ830/d24WLS164F4s6vetqdHb/9JLXYfL27IoFPR7LFmSxpNPJvDUU51YvrwyHKOT+fM00bS3e/j85/Xn+cEHuwDkUCj049prFa67Tn+mtmzpA5BCsajPZ/q8bdvWjf7+DDyviFisD0AW27b1YdOmPIAqJJN5dHf3IharQmtrEW1t+vpcjtfkQY5V5SPHaHIxVY7XYALWsCv5m5qa3tPU1PTh5ubmPICrAfwOwGMA/qu5uXnnEa+lIAgWPMQ0mayMi3hBECqbRYsU1qwp4Mtf7p3oVQlwy1Tou+3QIS8IqF2ypIijjiqisbGIRx6JBy6gwTJ/aFoqZb4j5841+SaUfdPVZWeoLVum8Pzznfja12zhBzAlXkqVTgOAeFwF70vvs2sXlX2Zbl8dHSZo2m3N/cY36rIcnt1CIdZ6Ofy9tTjkeQrxw7frqD13a6tntXoHTCj0yy9XkO1HOGKKLLbLfBYU1q7tx7/9m/6M87IvwC6f1GVfKji/9u/3gkytmhoFz9MB4ocOjcPGCIIgCBNOWc6f5ubmLQBOO/z852z6vQDuHZM1E4RpDh8wSeCzIAjlEI8Dv/xlqUtwInEDo+NxLZTowSkQiyk0NuqB6CmnDODXv05gzx4PCxYolmljvg9JdOE5QDRInjdPBS3P29o8LF2qu2jNnWuHX7uhuQTN18Fu/NXXK3ieglKelQVEIhShxR89ravLCzq9uSVzP/tZD/butdvM2+KPeW7EKJPJQuHQra0+E8L04zHHaNWsudnHBReEbqIwiSARFDBB6CQC0rm2f7/p9gUY8Yem88yfffuM+EOvr6mRzB9BEITpgvQDFYQKxRZ/xPkjCMLkJKxbWE2NCpw/CxYYN40bQEth0+HOH/O9WCjo+RoaikHL87Y2H0ppBw4PVx6MsI5jiQQwf75+PRdmZs92W7eb723e7ct1/qTTpWHcXEgK6wLGoWktLV6QMURuKnL+NDcPfnlXKAA/+lEiaP0tVCZc/NmwQR9T+izQuUbOHxOArh/DxJ/9+70gUJ2C17XzR84DQRCE6YCIP4JQodhlXxO3HoIgCCMhSvzZutVHa6uPpUuNiFJba3f8olKXsG5f8RDv8rx5dlvrnh7tmik3sJoGxL299mD4xBP1KJy6kwHAjBnatURksyooL+PdvnjJWRQ0OAdssSidjhZ/Wlu9oKU9uT0aGxWyWYVXXvHx6qseliypwm9/W7qjbrklgc99Lo0Pfzg99MoJE0bBNKMLBD3j/NGP1NmOHMJ0LnBRKJvV55J2/uj5SOisrdXuuH5pKikIwhTittviuPHGxNAzTjNE/BGECkXKvgRBmApceGEexx03gJtvNuVoZ51lLA0f+IBpf27ygPSjybQpbXkeJorPmaMC58+ttyawaxcJMOU5f9y28cTJJ+v1JUEH0KIWF21c509Xl57Xdf6EkWYaDBeqwoQjnvnjlvD4vt6Gri4PP/lJEt3dHq68slTg2bhRX/6tXy/ZQJUMd/40N+tj5ZZ3kfjjTjfijy6pnDlTWWVfNJ8ruAqCIEwF1q7N4MtflhscLiPp9iUIwhiSTutw0ULBk7IvQRAmLTU1wB//aHf6/Pzn+7B3r4f6eoULLjD2hlLnj13SAhjxh+cAEdXVRvxZty6Gr39dK+flOn9OO02Ptv/u7/LW9BNPLIbNjro6hfZ2/TyXU+g9nLPd2emhp4dKxcp7b4KXll16aR6PPBLDhz9s1mfmTL3tLS0+Ojr0epFjCdCimM5S0v/n7hGC1rMcYUqYOKicEQC2brU/CyT47dlDYg6sR172BWjxZ/t2P/hsueLPwYNAXd3YbIcgCMJEUSyGO5CnKyL+CEKF4nna1n3ggJR9CYIwtUilgB/8oLQjmRmI6gGq280K0OJPNqtQW1u63NpahUWLjFDzyCNaASnX+fOa1xTx2GOdQcYPccIJWhS65BJbFFq4UOHll/XzTMaU4txzTzxwCQ1XYMlkzPNsFviv/7L3k+cBy5YVsWGDj+OOswf+gN5Xhw6ZkrRiiG5l1m1YqyaMM1y4I4ePEX/0Ix1LEgBdRxDNP2uWwvr1XiAK0XxU/rVvn4fly0UMFARhatHdbb4vBSn7EoSKhi7OpOxLEITpAA1gB8v8iceBX/+6GzfcYESRu+7qxvvf348zzxxAVRWwdWsH5swpYu9e04a9XJYvVyWiSC4HbN/egRtvtIWY1atNXU4sZtwYvDxsuM4fniMUxapVRXR2eli3TotbvFwtmVTI571BnT89hyvwxPlT2XDhrli0y7t4aThgPjs03Yg/+v/UUe6mm5KH59evow5x11+fChUKBUEQJjNUgi1oRPwRhAqGLuKk7EsQhOmAK/7k8xRma38HnnhiEY2NZtoZZwzg61/vCwSPTMZ0vgLKC10eilTKtHknKAuIz+OWow1XYHHDpsMgJ9Jzz5WKP4mEFs1oXwwMlLxcnD+ThDDhjgKfs1k7C4vOgVwO8H2FLVv0JT6JPu97Xx6zZpnPBH3WLr20gDPPLOAvf4lj0yYZJAmCMLXo6proNagsRPwRhAqGbIpS9iUIwnTAZP7o/4c5f8rlmGPMQHc4zp/hwJ0/hGsvL9f54/vUaWzoeU84wbZocHHLzfxRqnRAT86fsG5iQuUQJv7Q9QCFOBNUBul5dhngggV6njVrBnDZZaZskYeE03nc3i7DAkEQphbUFVPQyLe8IFQwUvYlCMJ0gkpR3MyfIxF/Rtv5E8asWcDSpUW86U1mlO6W45Tr/Fm5Uq8vCWCDcfzxRnSqqlJWmCWVfeXzIS88DDl/5MZCZTMwUDpo4W4fcvUAduj33Lnm3G9sNM9Xrw7/TJCIRHlAgiAIUwUp+7KRwGdBqGBI/OEXe4IgCFOVsMyfWEwFLpbhcNZZRpApN/D5SHj88S6rHMx105RbWvWLX/TgllsSVmevKKqqgNmzdaaR256ehDJq6R0GuYvCnCVhtLR4GBhASRC2MLaEHR/qdgfYzh9+HqxYUQxaw5PzBzDlgoBdwkjL3L9/xKssCIIwrvzqV3HU1yusWWO+33i5s5R92Yj4IwgVDN1BTqcneEUEQRDGgXRa5/vwzJ8jdacsWmQGvX19o7F24bg5QNSefsGCIr70pb6yW8w2NCh85jP9Zb/vwoUKe/eWOo1of9E+DIOEoXL3y6pVupattbWj7PUTRk5YXlNdXbj4w8sLV6wId/4sXBgu3tFyKCBdEASh0ti61cPs2aqktPpjH9N3WPjvE/9tk7IvG/mWF4QKxmT+yN1WQRCmBzU1Kij76u8/spIv4sYbe1BdrXD66SGj6DGCXDVr1hTwjneUaa05AmhQ7wZE0+9FxyA6De3fvj65KK5kwsSf+vpw8YeLkLzkkbKAaJ7f/rYLv/udfSucysfE+SMIQiXS0QGsWZPD9dfbORgqYnjUz+6jSNmXjTh/BKGCoewH6cgiCMJ0oabGBD7n8yMTvy+9tIBLL+0cpTUrDxJUxjqrraEhPKeFnD8k8Lj09QHd3cNz/ggTQ1jZV1hWjwt3/ricfHLp30j82bdPBkmCIFQe+/Z56O72sGGD7VuJKl0mBy4gZV8uIv4IQgXzD/+QR6EAvO5143fXWhAEYSKprVXYsUNf4PX3eyNy/kwEJKiMtfgzY4YesLuWdtpfvOyrv9+IQgcOmOnltJXn5PMjc2IJw6NQKD0+3OETJf4sX64FntNPL895JuKPIAiVDP1Wtbba31H8BsbAgOlyKc6faET8EYQKZuFChWuuKT8DQhAEYbKTyyn09eluVZNRbKCyr3K7fB0pJP64pFJU9mXf+STxh5eDDdf5091tlxEJYwuVfSWTyrqTTUQdi1QK2Lixo2wBsrYW8H0l4o8gCBUJ/Va1tvrOdPOddfCg7sAJ2OJP5/iafyseyfwRBEEQBKFioADjzk59wTfZMs/e/GY9Yj/++OjSm9HgjW/U7/Oe99g3CMK6ffE7n9TmHbDFH6WAZcuqcPnl0R0G+GuFsYfEn/jhW7VuJ7lEIvqzUVNTvvvM97WLSFq9C4JQidBNlf37PUvY4b9h3NXKRSFx/tiI80cQBEEQhIqBgu47Oz3k896gA9xK5KabevDkk7FABBorVq4s4tlnO63uT4ARfw4eNNP0xa+ej18s8wvkvj69z++/PwGgN5jOAzW7u0dr7YVyoDwLenQ731GJw2gwc6Y4fwRBqEz4b1V7u4f580t/z3TOnZ4uZV/RiPNHEARBEISKwTh/vMOBzxO8QsOkuhpjLvwQ8+erkv1DZV/Forng5bZ3nvPDL5yjhB1+EU1B0cL44Io/dGyJU07R59nll4+8PHzmTH1XvTi2hjVBEIRh02vuR1i5P1wU4s6fwcq+vvSlFObPr5q2QdAi/giCIAiCUDHkcqbsa6St3qcjYfuL3/nkF9GFghcIC1ElXfm8eT4c509UC16hfAYG9DFZs0aLPBdfbAc4L1misHFjB264YeRt2zIZhWLRi+yeIwiCMFHwmxZc/OEiD+9waXf7sn/bvve9JAoFD+vXT08ZZHputSAIgiAIFQmVfXV0eCgUvEmX+TPRhDmlojJ/AOP+4a6eAWZc4hfX7msffjiG73639A1/+MMEGhqq0dIiTqGRQELMpZfmcf/9XfjCF0pFnpqa0Sn/ItFQxB9BECoN7lLloc/8Zoad+WOmR5V9TdcyVxF/BEEQBEGoGKjsi8JnJ1vZ10QTlpG0d2/4RTH/P3f18LygfN681i37uuSSLK67LoV9++xlXnutDo1+6CGJlhwJPPD5pJOKo5rx40Kh0tzpJQiCUAnw8q62tnCHj+38Ma89dCh8mW1t01MGmZ5bLQiCIAhCRSLiz8gI6/C0ZQsv+3KdP/r/XNiJuoMaVfYVlQXE78oKw4fEn7EUfYhYTH/uxPkjCEKlEZ35Y6bbmT/m+Z494XLHzp3i/BEEQRAEQZhQqOxr61Z9iTLZun1NNDzzZ8kSnd67eXOpTZ5EtjDnD7+I5k6QqFygzs7w6a7LSBgeJMTE42P/GZCyL0EQKpVyAp+5Y9XNAuLhzvR9umvX9JRBpudWC4IgCIJQkZAocdNN2vIjzp/hwTOSFi0qIpNR2LLFXO6RgDNjBok/gzt/+B3UKOdPR0f4dP5aYfgUCnr/xceheo7cRVL2JQhCpcFFnijnD7mFASP+kNCzZ4/528yZJP5Mz98nEX8EQRAEQagYcjn7iXHg8wAAIABJREFU/+QAEsqDi2XZrMKSJUVs3uwH3bfoDmpNTXnOn8ECn4mODin7Gguo7Msfh48ACUzi/CmP4XS+EwRhZHCRh2f18Ont7aWOoMWLS10+9H0q4o8gCIIgCMIEQ63eiWJxglZkksLFn3QaWLq0iM5OD+3tHtauTePrX9ehQLW1ej+TQBPt/DHLixrwumVf6bRe9t69Hg4cMNM3bfLw0Y+mrTu3QjQ88HmsofJKEX+GZuNGD0cdVYU77pBAc0EYD+jGg+epyLIvLgrR79bixfoCYvfu0tw7KfsSBEEQBEGYYKjsCwBWrBjAf/yH2EeGA8/8SaeBJUv0/tyyxcNtt5k/kvhjyr7M63jXlMG6fRGu84dCp2+5JYmZM830q65K4667Erj++pBUaqEEk/kz9u8lZV/ls3mzj0LBQ3OzDKMEYTwgh8/cuQqdnSbDh9+cCBOFKPdu9+7S3LuuLi8Q2KcT8q0lCIIgCELFQIHPAHDddX1oahLrz3BIpYx4lskoLFyo99/27fYl34wZ+tGUfZkLZ56dUE63Lzfzh+cOAQhKzsghtG+fOH/KYTy7fUngc/mQCyGq3FEQhNGFfofo94zavfPfp85ODz09+jmJ2K7zRynbLdTZOYYrXaGI+CMIgiAIQsXAnT90oSeUD3f+1NUpNDbqfbhhg33JV+r8CS/74k6Qcp0/6bT9d7o7S44gyQIqj/Hs9iWZP+VDA8xDh0T8EYTxgEq1Fi7U34VG/NGPDQ22KES/OUuX6unbtvmHl2MvN6pT5VRGxB9BEARBECqGFKsIWrBA2rwPFy7+1NerYB++/HK4+EMXwzSgBWxnDu/YxefhuOKP26GNLrDJEcSt+uWgpulpQN2+xsP5I2Vf5UPOn+k4cBSEiYB+pxYt0mJOa6v+PaPfksZGdXi6Kwpp9+szz8RQLJaKP9PRvSfijyAIgiAIFYPHrsVcB4kwNLzkijt/XnrJVhCOPlpPf/ZZPZ27evbujQp8NtO5IOOWfbnuEcpnIGGP2+6H4oUXfDQ0VOO++6ZfuC6FnY9P2ZcEPrt0dgI/+1miRPSkAaR73guCMDbQb8aKFfpL8dvfTqK7mzuCyPlji0KJBHDqqQPYv9/Dhg1+yW/PdPwMi/gjCIIgCEJFcc01ffjiF6U26Ejgrpv6eoXaWl1Kt2WLfdF77rkFzJihcPfdcQwMmDyfWEwNIv4gdLp799R19pBDgsSf4Th/vvpV/aIvf7m8kOjvfjeJG25Ilkz/+c/j+MhH0pPKRTQRgc8i/hiuvTaFT30qjW99yz6faMA5HV0DgjARULbPhRcW8Pa35/HcczE8/HA8+C0hh6ubBZRKAaedpsPTHn88ViLkTkf3nog/giAIgiBUFFdf3Y+PfUzqT44EW/wpwvOAxsYilLIvcrNZ4IIL8tizx8ezz/qBq2fhQlv84d2+eMbJcMQf4/wZftnX009rVeKkk8pry3LddSl885ulQtEnP5nBr3+dQEvL5LnYH0/xh95jKpd97dzp4c9/Lt9G9fzzet716+3XSOaPIIwvvb0eEgmFZBK44AL9xdjS4gUiDzlcqeyLypWTSYXXvlb/dqxb5wfCLSHijyAIgiAIgjBpofIdAJgzRz+Pyk466SR9wfzKK37g6mlsLKKjw1xUc6HG7gJmnrvij2utpwtsyiNyL8Cj6O8371ldPTzLTpTDh3eHqXRMt6+xtytNh25fJ55Yhb//+ywOHChv/ig3lMn8GcWVE6AU8Je/xCK7CgrTl95e4xyl37XWVi/4raFg5/Xrfdx0UyI4h1Ip0/Fr+3Y/+P6fPVtPk7IvQRAEQRAEYdLCA7NzOf1Id0Vdli3T0zdv1s6fdFqhrk5fWFPoMxd/DhzwAlHFbrFrL7fU+WMvq1znz44dRiQqVzAiokQeWpfJwPgGPk+fzJ+DB8s7B2i/DzimM3L+dHR4k6qMsNL57W/jeOc7s7jySgl7E2z6+oB0Wn/YGhq4+KP/vmJFEb6v8JvfJPD5z6dx771azU4mFXI5Lfbs2GGcP/Q7J84fQRAEQRAEYdLCu31ReDZ1QnGhu6WbNvno6QGyWYXZs/W87e36xbzsq6/PC+6ocnGFO3+UsjuEAUYcounlBj5zwWe47eGj8lgqKafl299O4vrrS/OJCCn7Ghvcbbz99jiOPrqqpCSQBLGio53SeZnPe8M+L4VoXnhBD0tp4C4IRF+fx5w/psSLbiRUV6ugDTwnm9WPjY0KO3aY36/6ej1vJf0ejBci/giCIAiCIEwReNkXsWBBuPNn7lyFTEZh82YfnZ3e4Tuk+vWU+0MiT0ODXsaBA3amAqAvwmlATRfja9YU8OMfa4sEuW1ooFxu6RV3CIUNsru67PIu7tCIKskpt1Snrw949NFYietjNPnKV1L4zneig6xJdBgP8Wc6lH0RVLZFXHllBgcPerjnHntH034vLfsyz6fj4HGsSAyi+QwnJ0yYevT0mO6ftbXa0dPaapw8qRRw1FH271w2q4LXNDYW0dfnYccOLX2Q82c6fn5F/BEEQRAEQZgi0J3OU081I9Yo54/vA0uWFLFpk4/2dg91dapE/CFRh3IWKIOHD8byeQ9bt9rT02kgl9Ovccu+enujM3k43CHkln3dcksCS5dW42tfS7L5zd+j7PzlXux/9aspXHRRFjffPPYuhCiBiUSH8Sn70o833wycf34Wd945DorTBBFV+keh54TJ/LGn83ORi4nFIvDpT6fw8MPjcMCmIFEiZ1cX8JrX5PCNb0S75ISpjXb+6B8Nz9O/R9z5k0waQYeYOdP8n1xBGze64s9Yr3nlIeKPIAiCIAjCFCGZBLZu7cBddxl7QlTmD6DFn85OHZw5Z47J/CHxhxw+JP6Q84eEFt/X0zdsiB2ebrqsUOYQdfuivxWLXjAN0BfgH/xgGi+/bF+WRjl/lAL++Z/1Ld3nnouFzhMl/pSb8fDoo3q5jzxSngiiFPD5z6fw+OPDH/gfPBg+3ZR9jV/g8/33A888E8N///fUFX/cQGHP0/vXbQNN4o9b9sXn4x2//vY3H7femsQll2RL3rO/v3Q5gk2YaxHQXZ327fNLvh+E6QMPfAaM+ENdwHy/9Htyxgwu/pjmBoAp+5pMGXCjhXyKBEEQBEEQphCZjF1CMXeuCkQal8WLeXewYiD+tLXZbh0q+yLnDwk5xx9vX1TzO7Hk/CHBJSon6PvfT+K++xJ45zsz1rrZ4k94m3nPC58n6o5uuXd6qVzAFQSi2LzZw003JfHjHw/fKUSCGnHffXF8/vOpCQl8JtzcpsnEunU+nnwyeojjDvjILedOjw58Du90N1iJYGNjNd761lJRSDBEO3/0PnbL9YSpT3u7hxde8NHf76G21v6t6u/30NZmsoA+/en+QOQBgFmzzPzkfn31VVv8mY5lX1NX1hcEQRAEQRAQjwPz5ins3Onhqqv6cPLJZpS6aJG5WK6vV5g3T/9/1y5bzKEOK27Z16pVA1i3LobmZj0/uW9SKcXKvvQ0XsZ16JCHefP032mQvW+f6/wJD3zmgb3cxVGO86fci33qLFNulzHatt27y7uvyrdB71MzUPngB7UIRoHc4yH+uAPvyRxkfM452nLW2hqu9HHXGQBkMgpdXV6JI2i4mT9+xKEnx8+zz0o52GBEhY0b8WccV0aoCN7whlwgjpNgw5/v2uUF5V2NjQqPPdaFxsZqAHbZV11dMZgf0MKQ5ykp+xIEQRAEQRCmHiecMICFC4v43Of68da3GvGH3ymdM0dh3jx9UUwXydTtq7TsSz8uX15EKqWY84fKvoCqKr1cEmK4Y4dEJACoqgp3JfH5+V1/LiLx6fz51q0+zjgji9/+1lY1yhV/MhlaZlmzB+u6Z0+5nczM86jW49TqfjwDn4nJLP4MRZTzx838Ibij5/HHY1b5ER88RuVYTeV9OZpEdQEkUU6cP9MP7orkmT50M2JgwLNEoSSLheLiD2XZ0e9TOq1QVWWXbU4XxPkjCIIgCIIwxbnxxt7Qjjk8DHrOHIVUSt9Vpa4oVKoVFficyWgB6JVXfBSLZnoqVRr4zAfBvK12KqLhFS8T46/l2xHl/PnxjxNoa/Px/vdnLAdIud2+jPOnvPnJIbRnj4diMdoFQnChgQthHBLexkP8cd1FUQPxycTAQLhryhV/6Dx1y+/o/CPnTj4PvOMddukWFxOjSuXK7W433Yn6rNFnxXVmCdMLLv7Q75H7nMPLvvhzQP9uHX10EevW+Th4UHcQmy6I80cQBEEQBGGKU1UFzJpVOp07fyjXZ8EChd27tYhR2u1L/58GtKkUsGJFEd3dHnbt8oLpyaRCNqszeUhw4YLCnj0eTj45h7e9LRs5OOaDaf5aPj8XUXiJFhdU7Bbw5YkaJEiVW/ZFglQ+76G9fejXcEeRKzpwPE8NKSSNBm5YqntMWlq8wA3GufnmBC69NFORYcZcLODrVxr4rB/37QsXf0iEc8vFAFv8iTqPp4KQNh5EfdbE+SMAQH297VINe87hzp+aGjvXLJ1WOO+8AgoFDw8+OL28MCL+CIIgCIIgTFNqasxzuohesMCEaZL4s2CBvvBuaXHLuxSOPlr/bcMG3yr78jygutoMkLljZ9cuH9u3+3j66VjkoC/a+cNLvcLn4e25+fuWW/ZF212u84e/h1v6dc898ZIAYj6QdZ0/5DoCxifvBygt+3IH2qtWVWH16qqS1z3wQBx/+lM8CAgfC6LKqYaCO3z4ueQ6f0icccUfOs/oHAsrC+NlX1EiDz9Hj3RbjpSBAeC22+JB975Khh8jvp8k80cA7MyfOXPChSBOTY2Z7vu2GDRvnsL55+swLxF/BEEQBEEQhGkHXVwvWGDCNGlAO3u2Qk2NCnJoXOcPoMUfPh0A5szRncOU0nf2yWlEAdGALbBElXf19nrBgNAu+wp3/hC+rwYVf4pFhA6MaT3KdRvwgf/u3eZ5sQhccUUGb397zpqfD2TdzB8+SBmPki8grOwrfD5XvKD5XOFktOjoABoaqnH99cmSvz3+eAwbN9rva4sG5rntFrOXQ8c6yvlD50BY2RHPDAkrq9TLCQ8uH02KReC55/yS43PHHXGsXZvBFVekS15z6FB4+/mHH47hxRfLHyL++c8xfPObpcdnuNgCnXkuzh8BGKzsK9x26Dom6fWplMKcOQpNTUUkEgrbtk0vOWR6ba0gCIIgCIJgcfPNPfjsZ/uC4Nv58/XF9I4dPjo7Pfi+zgJqbCxi+3Y9wDQijwrEn1de8Vmrd3V4WbpdLw3gSPxZt85cgvJBHRdi3AwVGji7QdA0gA0bWCeT9uDbzfz5/vcTOPbYKjz6qK1+kJBUrtuAD1ypU5q7TnxZgzl/eNej8XL+lHb7GrwEx50vKrdoOGzY4Jd01nrxRb0DvvOd0mCod7wjize8wXYj2Z3gwssGXecPHaOODs86t+g5bXOY84eXEUYJO3x6VKj0SPnOd5I499wcbrnFtnBt3qzPxb/8xT6R9u71cNRR1fjgB0tFoUsuyeJNb8qVTI/i7/8+ixtuSGHnzpFtGz/n+DGifSbOn+kH5XEB4d2+gGjnj/udRrk/c+fqUlrP04JQOWW6UwkRfwRBEARBEKYxF15YwKc+ZUa91GL81Vd9tLXpbiq+r9vCd3d72L/f7upF82/ebMq+yPkzfz6glBcESFdV6ba77e3hHZP4hTgJKmTfN+KPfbFOg8IwwSKRsMUiN/Pnzjv1YPkjH7EHwfRexaJXlluDiz+87Iu/9qWXwkUhN/OHC0PjVSbkDpSinD/uuka5ZoBwV8m6dT5OPz2LTZvs+R9+OIYzzsjhc5+zRZ5UKnwHRLUFjyrvsgUY+zVRQhydT/39HgqFqLKvcIGJb7stativ/+AH07jgAjtE+ki49159AH//e/tAkvtBKXvdN2/W///tb22xKGq/lsNIg63567lIS/s9n/dGtH7C5IMHNVPHLkAHNtPvgiv+3HFHN849t4ALLrCVZPpNqq7mLeDVmJasViIi/giCIAiCIAgBxx+vR67PP++jtdULLq6pM9iOHXZ5VzoNzJ1bxLZtZjq13J0/Xz9u20aikAra9BJc8OHPSbQx4o9dckbQ4DDc+WOXfe3d61mCyrHHFg+/r+8IT+Y5L8vq7AQ++tE0NmywL6G5ILV7d7ir6bnnjPuCCwlR5UbufGOJG/jc02OEJy5kuA4fOibuNnz3u0nMnVsdiAzEY4/F8MorMfzxj7ZI8fTTet/85Cd2+dBw26fbDp+o6dH7O6qMq6cnLChaWcKlO3/YurrH8777EnjqqZHbu2g/eZ69w7yI0yeqnHAkZWkjDba2HXrc+WPmGauyOaHycbtCUrmXK/6cddYAfvazHmQy9vz0fVBVZYs/3d1eaJj7VEXEH0EQBEEQBCFgwQKFmTMVHn88hu5uLv7oi+3t20vLuxYtKmLHDi8Y3PKyLwDYutU/PF3b7jncBcRbwNNg0HX+uHf/aaAdVhainT92CQkXefhr+PQoZ85Pf5rAXXclcP75tlvDLvsKX84TT5hBPn9fXupWKNhh1eOFG/islHFZ8MF3lPPHFYWuu06P1O65x14wiWHbt9tDEH43nhPVPr2ckPCowGcuwOTzwMCA+f+BA/y97dfw5a1ePYDqalssskv8osKmQ1d7xA6vqNdHiT9R+UQjERtdcezuu+NYtSpnfaYHg+8/vq/DSsDGix07PFx5ZbrsbRBGFyoD/dKXSlU/uokQlfnjQudRjlU0Ug7QZAhEHy1E/BEEQRAEQRACPA84/vgBtLXpy0QSfxYuJOePCYKmu7GLFysUix42bfKt6a74k04rHHss670OW3ShbmKAEXlqa13nj32hbpw/pRfwnlc60N26NTwk2hZ/wnOIqqv1oxscHVX2xQer994bD8KguTjAyw4mytkQli3kBh4DpeJPVOAziTk8/FovSz9u325Pz0VEzJSTo8PFwKhg56iyL3f5XMxxXUT0uve9rx+3396N6mplOVSiu9BFu46i1mOsiRJRBsvV+dKXUli7tjQjiHC37UMfyqClxccvf+m6vHyceGIOL7xgD0P5PrDLvspbv7Hg059O4/bbE/jXfy3NnBLGnnweOOqoAfzP/1la73fVVf249to+zJxZ3rJOPVX/7rz+9eb3h8Sf6ZT7I+KPIAiCIAiCYLFqFW+lq58vW6Yf//a3WDBQI5Fn0SIT+gyUln2R4JJMAmecES3+cOGERBsj/tjTKQyUBof09+XLzbr39HiBIECCxJYtvCwrfD34QJSLM75vLBbcbRFd9qUf6+qKyOc93HZbouR9Ozs9tg0TMwgJKwMK63IVlU/kij/z5ulj4La9J9HBdf6E5QMBg3XQMs+j2q3bzp/w6W4HKb59/D16eoyr7YwzBjBzpnak2Zk/Zn67Cx2fHr49I3W0mLIve3pUDk9U5yw+3T0m3/teMjh/w3C3LRbTK+V2s7vmmjR27vTxla/Ygkp02Vf08Rpr6LzmoqAwfgwMeCWuROKsswawdm3EF0QIX/xiH26+uQef+IR5TX29PsmnU+6PiD+CIAiCIAiCxUknGYGGnD8rVxaxaFERDzwQx549+hKS2pIvXmxyggAj2JjMH+MIet3rbPGHD/TCy770/93MnxkzjGX/gQdiwd9XrDDL7+nRQbEAcPTReh3JhaT/Hu7w4SJMa2v4QJRP54JUR4cXOBdoOW9+s16njRt9633dwQdtG+2/8SKRMO9HuTFhzh+3vIvmcadTSQadJwSJXpQBRZST4TPAThu+TrZbx8wTVfbFS6/ofdNpvb4k/hSLdvldR4cR6LJZPW9VlW6XTsILX1cu7kUJT3ZnMowIWge3/Kschw8XeeyOdOHvNTAQPt11/tA57AqG5DJzlxOVvTSY8+ef/imF//zPaEGqXA4eBP7wh1jJ/huvwHUhnHx+9DoeptO6uQFvAW+cP9NHEpk+WyoIgiAIgiCUBRdoaCDvefriubPTwwMPaKsIdWNZtEg/UtkWCTMk/pDbJpVSqKoCmprskV8mo5BIKKvsy3X+0MCPXDb0HpdfnsF735vF7bfrQWBTkxnN9vV5weuoJX2U84ff/eUD0SjxZ/163r1LTycRjNw/tPzly4vwfRWUO5HoQPuN3pumU77SeMEHWLW1OLwuYc4f85zn5bjOn+zhSCTX+WOcQr5V2sPFNi4K2Dkw5rnt/AkX7bq79eB940bPOm4dHV6JYEMCJ4kOrmOmrc0sg7atulphYMCcX65TKGwbbBcL37Zo58G+feazUCiUlsxxXGdMtPhjl7SFTY96bVRuUan4ox9d5w8Nvgt2Mybr2HHBKMqppRTwf/5PEl/4QmkpWnOzj3Xryh/mfuADGbz73Vn86U+jpDQIo0KhUJpHNppQy3gp+xIEQRAEQRCmLTyUme6OAsAb3mBGbDNmqKBcaO5cW6wgR1AuB8yaVQwGklQm9tBD3fja18yoOJNRmDtXWWJBaat3/TdyTJD4QwIEDTJJUCFoILl8eRGep3D77QnceKMeUfABLg0AlNID87CSAD5gb272A6cSDc6XLCHxh8Qcci8pzJungnInel8ql6N8JZp/4cLxFX942ZcbsB2V+cNFDVf8ob/t2eNFOkvOOy+LM87QSgoXTrgDi7tm7HBl85yLC67z584743jDG6rwta+ZLmL9/V6wfvRI5y9tn1tutmePEX8yGb1/aD+R+BTd7ascoQWh9PUBJ51UhTPPzEEp4BOfSOPkk6sCh537HtxFN9hy+XS+//h683n4MeRiG5/uvhd9Pl3xh8rBXFcNP3bcSRYlmA3W9v2qq9K47LJM9AwOf/6z/gA0N9v7NaqcThgfCoXRc/6EQb9tUvbFaGpq8puamv6zqanpsaampj81NTUd5fz9n5qamp5pamp6qqmp6e/HblUFQRAEQRCE8YJcLFzY4aLQ7NnhAhFgxB/AtIgHTBZQKmW/Jp3WDqOdO318//tamCGHjytGDFUa5QZK04A+l1NBcOj/+396sNfTY5ePAXpQqZQXiEitreZymQ9Ev/CFNFatqsLu3SZXiHKRHnssFiwf0ILBwoVF7N7tob/fTDfij+38oXDt8YLfXaf9bcq+zN/4oJyLGm7ZFwkhxaJniTlc8HjllRg2bIhhYMAe+PO78FEOn3Las3d1AQ89FA/ei0OvoW0jd9vBg7R8/XfKLmpp4WVf+pEypNwSPyA688fuXIXQ+TkHD2rRadMmH//933HcdZc+UPffH8cVVyAoeaLcI+6mcpfLxRZ+HGzxZ2jRhQtMUdsGGJGsVPzRj2FlX6mUfRz0eoSv92Dhz+3tHlpa/MjMqCioRFSYeJTSpZe8JHW0ody48Q4Sn0jKcf68E0C6ubn59QCuAfBN+kNTU9MMAGsBvB7AWwB8eyxWUhAEQRAEQRhffve7Ltx9dzeWLTMX3/PmcfHHiELV1aa9ezqtkGE33bmLpaHBPK+qMsvKZEwJ2Re/mMb27V4wcCORyLgy9CMXmDhHH13Effd14ZxzCtbrEgn8f/buPEqOu7z3/6e6Z+sZjUYjabRaxpaXtrzIxrsxEAPXSUwwEDsEQu7NxQmLCThciH8mBHIJXAcuceAXEpYQDje/hJOEJTnm2jixgcNiY2y8gG15K1uyLcmWZI21zz7dXb8/vvNUfau6WxrZs9a8X+f4zExPTXV1VY+s+uh5nq8+/vFRtbZG8Q3pyEig1atdRZCFDnZDu3x5pM7OKNP2Vf98991XjMOCK66oaOXKmr7whTY995xfLeIqkmq1QDt2BPFNbLbty/ZjwcJMtX+l277SlVZ+CODfyPs3/vv3p2/m/Yodv1qo0U3W4ODhVl1LtvNDnvTMH3nbp9uFsi0jFmz511/yw59025eFc7t2Fby2r2Tmj39c6aXhk8+brUDmn9dmrVT+67/xxqQ864Yb2vW1r7kAcnw8Ccayq9Cln6/x5+nz2ngbP9jxB2wfbpi1vQeyVRXNwp+RkSC+Ds0qf5oFQVnNVqEbG0u3cWZlW9HMfK/8ueeeglas6NZ9982fph97fzQaRj9VrAVxIc12msw74JWSbpWkMAzvlnSu971BSVsldU38N7M1qgAAAJgWS5eml8WV0tU+/udBkIQ0Vklj/MoffxUxq6CQXGDk39j197vl5IvFKP55awlLBj43Pu5SSTrvvFp8427hQ3t7pCBw4YZVFgwPu5v4ZcuiuELFAoGOjkjLl0eZtq/6u8BNm5IKg76+SO9857hGRwM99FCyKlpHRxSHYNu3F+KbbKuushtS275Ukp566pDuuqtJKjDF/H9dt+DJzrN/XZpV5URR0HSlrH37ks8bnb+BgUDNVldLrwCVbNMsFMquuJWtGrDw0WYX2X6y4Y9dTwvn7rijqB/9yKUW9r61tsBnny3UPXd65k+z+TXpY23E/9lf/rJx/8u99xbjQDQb/vjP51fsNAvPmlX++OGU/xzp2UHNW//8m2u74bZ2TTM66n5/giBKvZeaBT7N2tLcc7vtsrNcLr+8U6efvij1mn3ZVrKpbPvatKkQr3o402wu0mc+M3+WrLdrMZ3hj13XZqsN5tFkTudiSV7xnarlcrklDEPLRrdLelRSUdKnj7Sz3t5OtbQwTGuu6+vrnu1DwCRxreYHrtP8wvWaP7hWs+eYY1rV15eUVqxcKe3YIS1fXkhdlw0bknkrF1/cFf9l/thjk311dxd1ww3SRRe5ryuVLtVqrj3s9NPd3fbeve3q62uPqzmOOSbZr+nqktascc+9fLl7bHS0beLrkvr6XKh14IC0ZEm3KhWpp6eolSul7dul5cu74xvdnp5WrV0r3XuvtGxZtwqFxisdPfxwe1wFsmbNIp16qvt8aKgU3+iuXt0ZVxXs2dMZ7+ecczonHmtTX19bPBNp2bJ2HX/8zN2o+ZVaK1e6E9zW1qm+vnRV0N69xfjaPvdceh9BsEh9fe7zdAVFV/z42JhofdAbAAAgAElEQVS7+fdvttrbF6VuriuVUrx9et5HZ/x4m3fpq9UO9fV11D0+Ntai7swfD2vXFvXkk1IQuGOy7VetctdwcLBFfX3d2rUrORe9vckAb0l62csWadEi6VWvcl9v3uyO1w84isXkmPzXVqu56+xet7998pp927f7nzf+N/vbb09S1IGBIH6vSulAqqMjuT7+sUZRZ8Pz3dqaPL5jR/J4oZA8vndv49cmJTfv4+OBisVuLVvmvrb3WrGYvJcqFfe7tWRJUT090qFD7jqMjaUDGf+87tyZPF4qdcerAkpJ8FSpdKXO6y9/acfUnXq8WHTP397u/owx9mdVW1vLi/5/jf3c617nvp6NKhOr1lqx4sW/jplmx9zVNX3HbIFya2vy3p0v5+fFmkz4c1CSfxYKXvBzmaTVko6f+Pq2crl8ZxiG9zTb2b59L3EtQ0y7vr5u9fcfOvKGmHVcq/mB6zS/cL3mD67VbHF/LezoGFV/f9Lr0t1dktSiYrGq/n73972+vm51dQ1Lcnd8+/Yl18tVK7jUpKWlohNOGNbnP9+iD3ygpC1bhjU01Ka2toLa2gYkdevppyvq7x/Wvn3tktq0fv2QJK98SNKSJTX197v0JoraJLVr165xSa0aGRlWf39FixZ1auvWgrZtc/ttaRnX+vXSI4+06oEHBiYqHrokjam3N1Cl0qpHHx3QypWR9u93r9F3zz2RzjuvKqlFBw8eUmdnUVKnnnxydCLkadfo6KDWrZOkLv3sZ2Pas6egQqGoUmlAS5d2adMmqb9/ULt3t0gqaXx8RP39h5loO8XczbK7rm1tY5LatHu3O1+7d7dKcjfc/f2Rnn9+QIWCtGNHQe48OU8+OaSlS12qNTzcJWsweOYZtx9JOnSoS6tWubYeq+LYtm1Q+/e3SWqd+Dp5X+3d6661JD33XHJO+vuTY9q5c0z9/S7leOGF5PG9e6uqVKrxz0tSb697L2zbZq/Nne9KZUQ9PW3as8ddB7dEfZeq1TEdONAqya9wOaThYQsvu3X33e59eehQ8t7o709ew759HfFr2717XP39IxPH7d4nkvT8842v944dyTZZra0uGPnud6ty/wbvgoWtWw/FYeShQ8l12LZtUD09tYlzkxzTs8/659X9zrjjS67b9u3JcezYkTz+3HPJe2DPnuS1ufOUPPdTTw2oVrP5Ku48jY4mf064m/BuFQoV9fQU4uvgKrS61dkZaWgoSJ1X/7m3bh2I55HVatLoqHsvb9kyrLPO8pNI9/iuXYOpttXW1kWqVgMdOJD+M61S6ZRU1NiYu8a+8XHpppta9MY3VlLthd/8ZovOPbeqE06I4v9HuT8HuifO8cz/P2vfPnctOjqS35W5zlUMdqtaTb+vpvY53P+Dhobcc+Tl7xSHC7Am0/Z1p6TXS1K5XL5Q0ibve/skDUsaDcNwRNJ+SU2KcAEAADDfNVutxyoZstUx1tr08penv9GV5AbqmFit2VrJ+vsLGh0N1NYWTQyHrk2EDUlLjj982vgtZzabxVpIbCbR4sWRRkeDeK5IR0fSjvbQQ8W4WqJUSpaNf+wxW6Wr7il18GAQtx61tycDsp9/Pgk4SiXplFNqamuL9OCDRe3fH2jJkkiFgnuOZ55xA4WTtq/ZH/icnfnT0+NmFtl5s5YsG5rqt3el5wGlW506O6PUHKhs21ez9rH0bJ/kc38GTbNVw4wt6Z6d+dPREWnx4qTdyB5vb3ev2WdVNb29bibQpk0FRVG6Rc2fWeMfU/pcNG+ZSrZxH7O/O5L0spe5c/nEE+mOCr8ty3+/ptu+mh1T8vjk2r7kbdO47Sv7M/a4PyPJP9+9vcl1sGOw65bez5FbApst4Z39PbaKp2z7nf0Z16ji7+abW/Te95Z0881JGPz884Guuaakz30uXbU32wOF7f3uV0fNdZVKMqttuth1X0htX5MJf26UNFIul38m6f+V9MFyufyhcrn8xjAM75B0r6S7y+XyXZKekPT96TtcAAAAzCYLebIr41golL1ROuusmm68cUjf+lb6jstCBsndfEvJ3KA9ewKNjyfPtWZNpJ073ewQu2lsb490xRXpaol0+OM+2o2PtfjYQGNX3eGCmY0b3UG/4x0lffKT7fH+Tz/d3RVs2uTuEoaGgvhYfXv2uPlELS3J/Jhdu5LZPqWSC7E2bKjp0UcL6u8vxDOLTjmlpigKtHlzwbsJrnuKaVXw7giSgc/u6yOtTLZmjXvcDzxsBk328eHhQKVSejWzgYEgFZz4Q3r9gKDZUu8WCkrpAGb37qDupruvLx3+WAjQ0eEChoMH3c8k7zHpwx9uXimxcWNVL7xQ0DPPBKmQZ+fO+mMNgihzLpLtmy3Jnj3HvuyKeSYd/jQOcPzgyT/fzWbqpAc+H3n77PfSK4QFdfu0c9fe7n6Hh4fddbB9WliYPtbGx3ekADB7PFISMGRnJln4M9Kg8OT55+vnClkQmV39rtlMp5lif1bPp8HG1jrKwOepdcTwJwzDWhiGV4dh+IowDC8Kw/DxMAw/F4bhTRPf/3gYhhdMfO//CcNwAZ0+AACAheVDH3J3xr/xG+mlcZqt4iNJF19cVU9P+rH29iR0efxx91dSu5l94QV3M22BzZo1NQ0NBTpwILnBb2uTPvvZEX30o8ndnh/+WPVMMvDZPW6hkw1ZLpWSkEeSfvITd7fhKoLc8T38sDs+q1z59reHdN55Vb3+9dYuE8T7X7TIrWS2a1dS+WOVTWeeWY2rjuxYTzklqS5KBkTXn8Pp5M+lsfAnGfjsvmnhT7IqmvtoK8D5N9qjo8kQaTv/UeTOX6kU6T3vGYtXexsYaF4d44c82f2bLVsKdY93d0caG3PLfftOOMG9Bgt/7Ll6e6P4dezcmQQ5bW3SH//xmLZvb9wK8trXuvfHLbe0aHTU7adYjFKBlF3T1avTg4zTy74ffuCzBYq+9vakIkZKVs+zCqlqNVsd0zioabaaXbPgaDJLvUdRdih38rmFNn61jh+S2u+FLXMvJdWDftBypHApu70vG/60tCTvRZ8FEI0Gldt7qNEg7exA6cMNp55JjUKsuWomwp+FOPB5/qz3BgAAgFl3zTVjeuyxAV14YTrlsb+kW7n+ZFx9tQuSbEUva/vasyfQ2FgQt2rZjfn27YV4AGxbm2sd+8AHkvIQP/yxYb92A2hhhG1j1RmlkruJPvPM9Otpb5de9rJI3d2RNm2yti9XufIrv1LVLbcMxatBHToUpIYNr1wZTbR9KX4OSTr55OQuw45jwwb32OOPJ0vGt7fP3r+l2nmzm+hmy9InVSnp5bldC1QSWFjgMT7uVngqlaRLLqnqr/7K7cC1fQVqb4/U1hY1bZlqtOraokWRtm8P4ptr2/6YY9w53bYt+ZliMdJJJ7nH7ebcnqu3N4qra3buLMQ39LbymVUAfeIT6bvnyy6rqFiM9N3vtmp0NFCpFGnVqihV+TM6KhUKkfr6oqYtVkda6r15+JO8n9zcqWQ4dbYSxw/PhocDtbS4lbWarWY3maXem60ONj6uVLtco2qhRiuRLVqU/F7s3x/E26xYEam9PUqFOemWNv+Yks+bhz/pr+3PrmwoZL8Dow2Kv5LwJ3nMzkG2guhwFVIvxq23FnXtte2TCi3885ENf/7zP1tmbQWyI0lW+5q+PwuTleem7SnmHMIfAAAATFoQpJd5N9deO6bFiyN97nOT/+flK6+s6GtfG9bnP+9+pqvLVda88EKgsbGk8uess9zfzv/3/273WkQazfxJPrcqImt5sMocq0CypeOtQui224biCgP38zUFgXvuzZuLcZhjs4SkdOuafzyrVtX0wguFuFXJnsNCLHes7vNyOal+8pd6ny1Ll6ZDHrtZbVb5k237qlTcjX82/PGrO6SkUmVw0N2UdnS4c5IOf/wqjvpqmjPOqCqKAj31VCG1vbWVbduW/Exfn5snIyU37n74Y9dmx44gvnm3Y5RcBdB735tuM1y2LNI551T1i18UNTDg3mOrV7uqL7uhHBkJ1NHhnsPamdz5OHLlj21jS9T72tuTNjZJuugi94Tbt9t1cx8tCPNXCxsediHfsmWRdu9OP97omJovGS9vm+TzbMjQaObP6GgQ3+D759uu0Z49SajX2el+n5tV/vjHl678aXyrmw1nrMokG/7Y62tUMdO48sd99FsUpeazl16s3/u9Tv3TP7Wlws1m/Mou/9xs3Rrov//3ks47b9FLPp7pUK26Y52Jti8qfwAAAICjUC7XtHnzgF7xisn/M2oQSJdfXkmFIsuWRdqzJ9329da3VnTBBRV9//stcZtPo0GgduMoSX196b/RW+hggY0/80dyNwL+zbR9/rrXuTvDH/6wqKGhIJ4l5O9LSs/pseDDwgdr4/Jv4i386e11j7vwZ/Yrf84+u6qenkjf/W6LqtXkJtuCMQuFLISzwdtWWWKP9/a6Sp4k/HEf7fzZwG+b+ZMd9islN93LltVSN/4W8li73ubNhdT2Fnj4AUFPTxS3tNlzpMMfv/LH/YxV/hyOvXf37i2ovd1VEFUqQXy8o6NuppW9N7NhmHS48EcTx1f/vWzbl1XiWchjYYxVmz37bDpsKpUirVgRpcKB5pU/yeeN2pykxtU9Ngz8SHOI/Eora8177LFCvG1nZ6Tly6NM9VLjY5rMwOds+GO/d9mKIHvcD02MBTyNzsfhKn+y32vmzjuLuuGGtrp5NM88k/y8BVCHkw5/5H0+Nyt+TFL5M33PYXPqCH8AAACAWbB8ubshrVaDOAQpFKSzz3Z/Q7d5Ko2GItvNve3HZyGFbWMDW/1KHr+iyW6sf/VXXfjzgQ+U4ptm44c/1qLmP9fWrQV1dETxvzD7K5T5LWqnnFLTc88V4mBlpgc++zo6pDe/eVzPP1/QHXcUdeiQG1ZsbV92Q2033ytXRioUorpVwNrbo1Qlj4UJ9ZU/QVz548KfpA1jdNQ998qV6Rt/v/JHSmZG2aBpC398ixa5MLGzM9K2bQVVqy786eyM4oodybUDJpUoRz5f/nvG38+OHUl7U6mUXG87H5NpBbIb9FLJtWj5suHPKadkwx/3s+vXu1Xmnn02XeFj7Y6HDvnVSMn+/Wqa5m1f6e0tzLHrYwGqhSo298lYcGL77O6WzjwzWXnP9tfV5cKfoSH/OfxqpGSffrXYZAc+W2BZHwqlP/oOHKh/bntthw65lsM//VOlBldntz+c3/zNTt1wQ7seeCB9u/797ydpiD8Au5lmK6T5w9TnIvszYDpX+2LgMwAAADCLli+P4lYtv5LHPh8cdCtrFYv1P+uHMd3dyZyfxYuj+EY+We3LPYc/XNlanqTkxvWEEyKde25SzeS3ZNl8HEmpmT+23Ht2e392i//abOjz3XcX6743Gyzwuv9+tyx9T0/S3rV1a7rFqrPThTP2uL9Slh/++MveS6ob+NzR4cKiKArimTyjo65lqq/PhRTZm/ELL6wqCCLddVdxYnv3uM2Q8tnzXX55RVu3FvT1r7dq//4gPtc2u2jHjqBu5s/h+O+ZtrbkPFlIuXeve47DVf40awXyB4BnKyCyq3319CQzkKTkpn/x4khr1iSPS5qoYIvi97iFjjYLSEpXjPiVP41WB7OwzQJVCxls/3YsY2NSFNWHSsnMn0gnnlhTqRTpoYf8yp8kZMuGj/5+so8fOpQM765U/MeTz6vVpDXUvw6VSvL40Vb+RFGgt72tpE9/WvrKV9oOW/lzxx1F/cd/NC9vefTR9B90fiujXyU3MCB98YutdUGVX2nlf+9wy8/v2BE0Dc5milX+NPpzfqokbV9zuwpqKhH+AAAAYM7wKyn8z/1ApFlljP+vxEGQ3Bz7S2VbQGSDcf1KHv857GeDQLrlliFdcIG7e7QbXH9fUjpE8it8/KXh/ePOVv5ISdVGo5lKM+nEE93xbNlSiFcmW7RIOu64mh55pJBazamjQ9q4saadOwt6/vkkoGlvd2HXvn1udkv9zB/3dTLwOQlSkioitx9/ELiU3IyvWhVp48aa7rqrRWee2RW3BDaq/LEg52MfG1WhEOnf/q0lDmYk99zt7W6lLqvO8Gf+NONfqyVLIq1f75774YcLGh11wcTSpUn4Y+GJ3ZRnl4D3JYFZVFcB0d6eXqUtCNyS6Nu3u+vjv4Z162rq73czpaz6xpa3l5KgZ3jYhaN9fa4SzSSDl5P9S8l74LjjLPxJt99Z66UFHtnAwUIYP2wrFqXTTqspDAvxubK2L8kPf/zgo3F1i+S/Z9Rw++zcImsBSrdIqc7hZv5I0ubNLrXYsSM47MyfK6/s1DveUaprPbKWpEceSd+u+/vyg7iPf7xdn/hEhz760fQfjumBz41b9rKv7w1v6NR73/vSlxwcGEgC7aNlwZsF+NOB1b4AAACAWbR8uT90OfmLf7bCopHsyjA2X8afKWQ34XYT5rd9+c+RDZIuuMBV//j/Et+s7cuv8GlUhSKlw59165LXvGhRNKttX5Jb2autLdKWLYVUdczpp1e1d29Bu3YF8Y1kR0eyUtpDDxXi1qu2tigOBbZtK9RV/tg8mMHBIA55bGC33dTaKmBWQeLP0ZHcz9ism507C9q0yV2bl72s/pxbwLZyZaSTT67pgQeKGhhIXlsQuNArDAtxYDCZti//PbpuXU0XXlhVoRDppz8txqHO0qVRahUrKbn5Xrcuajqbxg/YshUQ7e3Sq19d0YYNVX3+88PxvgYGAu3dm25ds/fXc88FEwFHoMWLo3i1sP7+pFWss1Nau9atWGYhj4U/GzbUNDQUpCqFJOn4491+rJrOHk8qf5R63Nh5tqDK5kCde25V1WoQtzi5gc+11M+kK6fqz5m1jGYHlLvtk8/9NrEoSoIa/1grlSBVOSQl17HRal++sbH0437Vkc9fIU5SHCL+8pfpC+9XOfmVPxZmZ7dvVvnjP+6v+FarSc8+W9DDD9fHBP39QdzO6KtUGq9Y9973lvTGN3bqhz88+gDI2r5movKH1b4AAACAWeDfTPuf+2FJ9l+Dr73WpQHnnZf+J1xrHfB/1g94pPQw3cNV3LzlLe7u713vSoZl+G1B/s/6g51POqnxnUWjAdFHOobp9MQTh7Rnj/u8WHQ39DaE2s7faaclVS12E7toURSvxvbAA8U4mOnoSEKYZ55JZrVY2GbB3IEDgcbHA3V0RN4S7dZalm5v8m/kOzoiBYH01remV+Cydia7NsceW9P//J8j+uAHk+t2xhm1OKTyq70uuKCqkZFAd93VEr+2I/HfT8ccU1NPj5tb8/Oft+hd7+qIt8lWNVlocOyxtYnqp/p9+wFb9j3f1uaCnZ/8ZEi/8zvuvblhQxLC+dU069e7n73vvmLcrtTTE8UVanZDPzAQaNEiN/x6ZCSIQzi7dhag2fLg9hwWVFj4Uz/zJx3YWDBjQ9ctqLJrdvnl7ppakOFX/iRL2Ter/HEf165tHBi67VW3vbEgJPu4//X4ePL8zSp/zOho0PRYfU8/nb4tt+Dt0UcLqZk0fuWPXzGWbStsdEzpyp/k8fTQb/fxhRcKdbOoTjttkc46qz4R/f3f79Dxx3fXBVu33eZ+j+6//+gTHPuzezpn/liwROUPAAAAMAv88MMPf/yb9OxS6NddN6bduw/VBSeNbiB6eqRCwX+OdNVNM+VyTY8+OqA///PkLnLx4uT7tvKUlA5zTjghvU8LUvxAyp8RlB1UPVOWLJGWLk2+Xr++Flc/2Lk/7TQXLtx+e0tcLdLX51qvJOnBB5Pwp60tiitCnnmmELfJ9PS47xcKrvrHKjk6OhSv9GSrd42NueDD2odsaLENiJbcef/Xf03uUi1ksVa/tWtrev/7x1PvgY0bk0AuG/5ILpAqFqO691kj/nvOhmK/5jUujPn5z1vi57AWK2vjOXDABS32eKMZK3Yj3mzmT5bNprrvvmIqnHvDG9wvwre+1RqHP4sXJy1qW7YkrWJdXUlw4g+t7uxMtrf5ThYKWZughTkWlCxaFKlUiry2L/cxaROrn/njXkct1brX1ZUEjzYA2Q8v/FZMCzjWrnXbJ4Fhsr0/d8cPhexc+PvJ7ldKL+XebPUz4yp/kq/9yh0/dMiGP/Z8w8NBKpzxgyQ//MkOFG+0fXrmT/L47t2Fho/7rX++bKXMrbe6X65nnklvby2ek13hrNFzsNT71CL8AQAAwJzhL7ferO3Llh0/Egt//JasQiF9w+/v90jl/8uXp2ev+G1ftvKUlLSvSPWVPz/60aD+/u+HddZZ/hyi5PvZyqTZYjf0UnJj+YpXVLVmTU1f+Uqbvv/9FnV0ROrudrNj1q6t6YEHCt5qX8l1euaZQip0MD09UXzj3t4epWYNSdb2lbR2WRvQ8HCQmqXUaDC4hTKVSv2Np4VVUrp6y8IfyVXVBJO4Z/XDHwssrrlmLDUkfOnSSGecUVO5XNVNN7Vox45ABw+61qtsS5vPAoDOzmhS4c8557jnv//+olf5I61fH+n88yv66U+LeuIJd257epJl1Z980s0DqlSs8icb/rhjsOuZhD/u+7af7MDnjg4X6GRn/hx3XJTaPlntK2nBO//85Px1dkY69dSauroi3XtvcWJfSTDpr2RmAYe1W2bnRLnnqw9srFrvySfTAVZ2v1Ky0pedG6vMaVT5Mz6eXeo9/bPmqafSt+X+vrZtO3L4Y0GGHy5l99Ns4LPf9uUHVf6QcF+2ushknztp7Wy4eZ3duwOdf36Xbr65xVvqffr+PGS1LwAAAGAWTWbgs91sHsnb3ubuIF73unQAY/sqFqO4EkWSLr7YbfeRj2TKAZrwZw/5lT++k05KP752baQ3vzk9QMQPGWZ72LNpFP4sXiz95V8md5B9fVF87GeeWdXu3YU4GGhvTyo8tm71K3+S17d6dRTfMLa3u3NTKkVx5c/oqDvH69dHOvXUqn7846L273c3q9lBy8bCs6TNqv61nX9+Vaef7q71unXp47FwajIrffnP4++rq0t6//vHUtsUCtJ73jOuSiXQt7/dqkOHXPiTHWbtO9rKn+XL3Zyl++8vejN/rJWqoigK9J3vuB0tXuwCrlWratqypZCqvrGqGav8GBwM1NWVXE+r8BgYcAOrjz3WLUWfbfsqlVw4aIGHBTDZAdHJcyevxarGJBf+FIvSOedU9cQTRe3b554jCFw10o4dQRzc2nNYENeo8qdR25f9/tp7LxvkjI66kOAP/qBDn/pUcvKjKGlpbFT549q+/OduHEI9/XQ2tEm+9lf4GhpyAWprqxsU7kK75qt32X6KxSgVgPkhkl855T9uA+izGv1OSfVLz9tqiNlWt0pF+slPinUVN//5ny165pmC/uAPSnFoO51tXwx8BgAAAGZRs5k/fgvOZMOfP/3TMf3oR4P6jd9Ihy0256e3N4r/9Vdy7Vq7dx9KzYeZLH+otJTMX7FWoCOx1X38IGM2+efYPyY/zLKWJUlxJdPPf+4qM9rbIy1e7FZ8euSRZuFPsq9SyV2L4493YcToqFvxx2YEXXFFRWNjgS67rEuDg0Gqtc4PYLLhT/aGVHKzPm67bUjf+MZQHBBK7mbQggmrWjgS/+bUf78ee2zy2ixsvOyyioIg0g9+UNTBg+7m2H7Gr74wIyOu/ay1VanjlJqveHfuuVUdOBDE83Is/HnNa9z78bvfdQdsIdeJJ9b07LOF+PkXLUqCkx/9qEVRZOFPpHXr3CDwMEyHQm1tLgi0sMgCh44Od7337XNLh1vYsXq1W87dwqKBATfY2w9TrcVMSuZDWTXVgw8WNTwcqFRyw6wrlaQ1Kqn8SQ+I9oOPffuSAc5WqbZhQ1VBkASP2bav4eFAw8PSzTe36qab0olEdqaRb3Q0W/nT+PMwTObi2IpsJh3+uN+J3t5I/f2Bjj22W295Symz+pm87d3H3t4oXu0te6x+yOM//uyzjSt8Gv1OSfUBpr337HfffP3rrXrLWzr1iU+k38T+749dn+kc+BwELkBk4DMAAAAwC6wSolCIUtU+vsmGPy0tyayQ9HO4x6biX5X/+Z9diJBtEbrlliE98sjApJ/DtpsrLQj+rCI//LF5MFKylLekeMWvn/7U3a3ZjfyrX13Vrl0F3XWXe9xv+1qzxm+Zcp+ffLJbUeqWW1yFioU4733vmC69tBK3hPlzkhq1zSXhT+PbndZW6bWvrdbN9bHwJ9vCcji//uvjeu1rK6n3gN+aaO/pZcsinX22GwZdrQbq6Ukqfxq3fSWzjT70oTHdfnvSP3O48Edyc3+kpPripJPSc3QshLPfpYceKkxsH+mcc2q66KKKbrutRf/xHy0aHHTVTK2tbvbVY48VJlZ4CuKQbN26SDt2BKrVkjCkqyvSlVeOa3w80F/+ZVvcLrVkiZt15Ld9ZSut/PDHXqsFajt3BhPL1Ufx+9GCCgtBbNi4BRv+bJ9qNYi3t7Cop8cFtRb+2GuwQdsjI82HNVvY0qjyZ2AgXfnjh3x+C9iWLckqc67KKIgrsPy2L9eC58IcC4XuvLMlFdpYqCYllTxLl0aq1YK4nco/1i1bCnrqqUD/8A+tTSt//ICkWdtX9j1svw/ZYNOO+8tfTi/b6FdnWZvodFb+SC5cqtWOfibRfEX4AwAAgDnD5oRYq0wj/o3hi2FhwWRmuhzJpZdW9drX1v/T8aJF6flFR2JtPdklpWfLsmVJ+OaHcH51hv/6zj+/qiVLkvkrNpPn0kvdC3roIRdGNKv8sTazd71rTIVCpKuvdqmMhTitrdIllyQnx6/88d8ntr2FOP5zTIZVcDVbfr2Rf/qnEX3jG+myDwtdpPT581/D4sVR3RLmvpGRZGhuoeCCF9Ms/DnvvPR70aovgiAJ6Oy5JbfymST98IfJCmfFohuiLrlKrmo1iPdzxhlVjY4G+upXW7VnTxC3aq1bV9P4eKDnn0/aoLq7pauuGteqVTXddFNLauj3qlU19fe7dq2BgSYghL0AACAASURBVCA1J0tK/47b76mtTrZzZ2FiZbKkwseqjizkWbbMzTR68EHXXmRhh71uG7CcVCm5mVP9/QXt3y8dPOj2Y+HSgQNBqqJGSobFH67yZ//+ZLWvrq4oNdvHKn8sYLr33nTLmVXZNar8yc4G8yuVbEU0t737aO9BC1j8Y33qqUAXX9ylD3+4Qz/+cVJq06wiaLKVP/bcfluZlA6T/d8zP3h68EH33NM580dyv1e0fQEAAACz5Morx3XFFfUpiA1PnmwrVTN2M9ksXJoN1tZz0UVzpwfBqkKWLGn8ff8mrqtLuuqqpF3OQiJb+cr4VTp+5Y8913nn1XTJJelhycYCHSndcuaz7d/61oquu25UN9441HC7Zqz1JNv281L4r8GfZdPdHXnL2Ne/Ga21yfhhZbPwZ8OGWtwq19ISxZVDUnq+kYVwr32tuz7f+Y4rsbAQxsIXW53MKnxsNs7HP96hgweTyh8LYbZtK6TmDbW2SmedVdWePYV4mPKSJW6odK0WaPNmt3228sdaM30WzO3c6QZmd3cn84ksqLDr1t4e6ayzqjp4MNDTTwdxKHTKKe69ZXOL7PGOjiSA3Lw5GVB+8snJSmbZajCrVrPBy354YQ4eDOLg5NRTa9q+PdDYxK+JhUave527BvfcU0y9hqVL3Up3Fv5EkQtUOjujumDZD2f8ECa7Yp99bR9PPtldm2o1qPtZv+0r2zbXSDYwtfP1wgtBqnLIr4R6+OHGAdMjj7hzMZ2rfUnu/wFzpdpyJsyh/+UBAAAA0g03jOr66+uHLt92m2uleqk3BPaX/amo/Jkqn/jEqG69dVBvfOMcKf1RMrfIVkIy7e3uBI6NpU/g1Vcn4Y+FCL29SYVEV1d61Sp/TpJf6WE39FJ6DoitEiUlVSBZyTBv6dprx7R+/dHd2fnP91J97WvD+uAHR1ODjP3Aq6cnuYnPVkdI1vbV+HiahT8tLcnKcy0t6fe4P4fIwp9jjoni6ywllUIrV7rh2w8/7G7C7Xr61UPu8aTtS3KBgYU/Fuiceqp7Xmv96+mJ4jlcX/5yqwYHk+f1/c3fDOuv/zpJBKyKa8eOggYGXBXP8ce7n8uu0lUqSWef7Y71F78oxo+fckp6aLU97q8254c/9tjzzwd1bV8bN6aDpOFhN0PGd/CgC0FKpUgnnVRVrRbEQ9Gt7etVr3L7scDDQpBSyQ3Tfu45F564mT2BOjvrqwr9Ni4/hBkedi20Ntjewi57juygej/k2bUrCar8YMZv+/KrZrLhj4Vhtdrklqv3X4O1rs1E+EPlDwAAADDHHG0rVTP2l/25FP60tkpnnz237kKuu25MX//6UCp0kZKb+exw6t5eadOmAV1//UiqvckqtbLVHWvW1A/1ldLBjl8144cX2UDKvNQZIW9847jOOquqv//7JssnHYXLL6/oIx9JDw/329Bsxa2eniheVt3nlrRvvO9m4Y8knXmme45s9dK6dY3nJL3iFfXhT6Hg5hbZzbmFPOeeW9P/+B+j3vbpfW/fXvCWbncfbe7Wk0+6cGPJkkhveENFxx1X07/8S5uiKKhbFU+S3va2it7+9uR91NOjeDW4KArU3e3Cma6uSL/4RTrM6eiI4qBq06ZiHCxs2OCex1bXssdLpaTNyg9/ymW3j1276tu+zjnHfc/mUA0PB3Wr9dVqgXbtcuGPBZEXX9yln/40WZFtzRq3ypwFHhaOlEruPV+pBNqxI2kfO1LlT7aVqrMzCREfeyzd7padiWY/WypFiqJAzz2XPk9Suu2rWcWRe+7kcxsSnn3c35f/uJ2L6Z75EwRi4DMAAACQV7/2a+6GMruCEtJWroz0a79Wf2f0j/84rPe9b0x/+If1q6KtXBnp3e8eT7UrWTCQDSMs5PFbofzHpXT44wce/swfX1vbSwsHu7ul731vSG9+8/RUYPmvzcKwNWtq2rEjuS2rVBQvZ/5iwh+rSMmyNiUpPXjbH07tVyn5bXYW/gSB9L73jdU9bpU/27cHdcvMn3Za+niWLHFzha68Mvn9u/jiI9+BB4G77lu3JpVFxaL08pdX9eSTBR086A+bToY+P/dc0vZlQcujjxYVRUnF1YoVUdx66FrR3PYWCO3aFaRW55KSFe4s/BkZcefP2u7M888XdOyxUep9/p3vtMTH2t0dafXqWjyrJ6leiuLAc9u2QhyONKr88atp/AHLrnUwaf/7b/+tU7t3+61o6fNuP2uv22Z4+SGPX63jP68fOo2NudX6zOOPJ+9vv33OD4z8fVkb2nSu9iVR+QMAAADk2pveVNEddwzqQx86+iXd4QKMj398tG6lrGYs/LHVnkxbm/TQQwP6wQ8GU4/7K3llB9ua7Myff//3Ib3lLeMNh2/PJf5gYwtg1q6NdOhQEAcOl1/eqXK5W9Vq0LTtq62t4cOSksqfLL/yp9ksIL/9ylqq6o9bddtbq96OHW42TltbFAdUL3tZFIdyxWIU7+uyy5KAza8+OpzVq2vx6kwWnp19dlVR5Ja3P3AgiNsL+/oitbZG2rGjEAePHR2R/st/qWjbtoLuv78QBy6rVtW0YoULhsKwGA+nPvZYt4/duwt1bV/r1tW0fHktXiHMgpZshZvk2s1sJTbJBSV+e9zq1dHEcGh/CHVSNbd9e7byJ32NDx1KKvHSM39cWGStaf6+WlujuuH5Fv7YrKPsKmpSNvxJfnb37iD+HbfHLVDzw59mw6OHGoznsmHY08Wt9jWtTzGnEP4AAABgwSmXa3Nq4HOeWYATRfWtTatWRamVsewxk22jefDBAf3wh4N1lS+velVVX/ziyLS3iUwlm7tjrWC2YtX99yflDn47nO9wlT8nnljTccfVdM016blZfmjj89vp0uFPfeWPlG6XtCCnq8tVXe3fH9Qt3V4sJtVFPT1R/PNnnOGGU69aVWs6wynLnxNl4ZlV4GzaVNCBA0F8XgsFt/3OnUnlT6kk/fZvu4qjf/u31rjdbvXqKF4RbcuWQlzx0tMTadWqqK7ty0KsE05wQ5xHR12oUSqlq6pMuVydOJZD6u6OtGWL3x4Xxa9r166gYeXP1q2Hr/zZu7eg1atrKhSiBm1fUVztKLlwyI7VrwaTkvDHVpazYdNHGigtud9vm+tkQdXpp1fV1hbp8ceT97Rf4ZNuIav/82H6K38iBj4DAAAAwFSwYbOT5bd0ZSt/Vq+O6obUzld2Y2vLiTea+3O0A59tv/fcM6g/+7P6yrbrrx/R9dePpB7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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    fig = plt.figure(figsize=[20,10])\n",
    "    ax1 = fig.add_subplot(1,1,1)\n",
    "    ax1.plot(df2,'b')\n",
    "#     ax1.plot(df_price,'r')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "#     plt.plot(df_sum['total_page_views'],'b')\n",
    "#     df_sum.total_page_views.plot()\n",
    "#     df_price.plot()\n",
    "#     plt.plot(df_price,'r')\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                  | 0/108 [00:00<?, ?it/s]\n",
      "  1%|▍                                         | 1/108 [00:06<11:46,  6.60s/it]\n",
      "  2%|▊                                         | 2/108 [00:12<11:11,  6.34s/it]\n",
      "  3%|█▏                                        | 3/108 [00:18<10:53,  6.22s/it]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-4d3db956fe0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mnew_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msites\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_sites\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdf_site\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msites\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'site'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'total_page_views'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mdf_site\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf_site\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'site'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#(['site','year','month','day'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#     df_sum=df_site.groupby(['site','year','month','day']).sum()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\strings.py\u001b[0m in \u001b[0;36mcontains\u001b[1;34m(self, pat, case, flags, na, regex)\u001b[0m\n\u001b[0;32m   2521\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcontains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2522\u001b[0m         result = str_contains(self._parent, pat, case=case, flags=flags, na=na,\n\u001b[1;32m-> 2523\u001b[1;33m                               regex=regex)\n\u001b[0m\u001b[0;32m   2524\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\strings.py\u001b[0m in \u001b[0;36mstr_contains\u001b[1;34m(arr, pat, case, flags, na, regex)\u001b[0m\n\u001b[0;32m    313\u001b[0m             \u001b[0muppered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_na_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_na_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muppered\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 315\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_na_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\strings.py\u001b[0m in \u001b[0;36m_na_map\u001b[1;34m(f, arr, na_result, dtype)\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_na_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_result\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m# should really _check_ for NA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\strings.py\u001b[0m in \u001b[0;36m_map\u001b[1;34m(f, arr, na_mask, na_value, dtype)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mconvert\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m             \u001b[1;31m# Reraise the exception if callable `f` got wrong number of args.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer_mask\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\strings.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    304\u001b[0m                           stacklevel=3)\n\u001b[0;32m    305\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    307\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "new_df = pd.DataFrame()\n",
    "for sites in tqdm(list_sites):\n",
    "    df_site=df[df.site.str.contains(sites,na=False)][['site','date','total_page_views']]\n",
    "    df_site=df_site.sort_values(['site','date'])#(['site','year','month','day'])\n",
    "#     df_sum=df_site.groupby(['site','year','month','day']).sum()\n",
    "    df_sum=df_site.groupby(['site','date']).sum()\n",
    "    df_sum = df_sum.reset_index().set_index('date')\n",
    "    new_df.append(df_sum)\n",
    "#     df_sum['date']=pd.join([df_sum['year'],df_sum['month'],df_sum['day']],axis=1)\n",
    "#     df_sum['date'] = datetime.date(df_sum['year'],df_sum['month'],df_sum['day'])\n",
    "#     fig = plt.figure()\n",
    "#     ax1 = fig.add_subplot(2,1,1)\n",
    "#     ax2 = fig.add_subplot(2,1,2)\n",
    "#     ax1.plot(df_sum.total_page_views,'b')\n",
    "#     ax2.plot(df_price,'r')\n",
    "# #     plt.plot(df_sum['total_page_views'],'b')\n",
    "# #     df_sum.total_page_views.plot()\n",
    "# #     df_price.plot()\n",
    "# #     plt.plot(df_price,'r')\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sites in list_sites:\n",
    "    df_site=df[df.site.str.contains(sites,na=False)][['site','date','total_page_views']]#[['site','year','month','day','total_page_views']]\n",
    "    df_site=df_site.sort_values(['site','date'])#''year','month','day'])\n",
    "    df_sum=df_site.groupby(['site','date']).sum()#'year','month','day']).sum()\n",
    "    print(df_sum.head())\n",
    "    df_diff=df_sum[1:]-df_sum[:-1]\n",
    "    print(df_diff.head())\n",
    "#     df_diff.plot()\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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