{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# %config InlineBackend.figure_format='retina'\n",
    "import dask.dataframe as dd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import seaborn as sgn\n",
    "import geopandas as gpd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['savefig.dpi'] = 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df = dd.read_parquet('/bigdata/citibike.parquet')\n",
    "# df.set_index('start_time', npartitions='auto').to_parquet('/bigdata/citibike_repartitioned.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = dd.read_parquet('/bigdata/citibike_repartitioned.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_is_bus_day(x):\n",
    "    return np.is_busday(x.index.values.astype('<M8[D]'))\n",
    "\n",
    "def get_day(x):\n",
    "    return pd.to_datetime(x.index.values.astype('<M8[D]').astype(str), infer_datetime_format=True)\n",
    "\n",
    "def get_week(x):\n",
    "    return pd.to_datetime(x.index.values.astype('<M8[W]').astype(str), infer_datetime_format=True)\n",
    "\n",
    "\n",
    "df['year_month_day'] = (df.map_partitions(get_day, meta=('year_month_day', '<M8[ns]')))\n",
    "df['year_month_week'] = (df.map_partitions(get_week, meta=('year_month_week', '<M8[ns]')))\n",
    "df['is_bus_day'] = df.map_partitions(get_is_bus_day, meta=('is_bus_day', np.bool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_taxizone_id</th>\n",
       "      <th>end_taxizone_id</th>\n",
       "      <th>year_month_day</th>\n",
       "      <th>year_month_week</th>\n",
       "      <th>is_bus_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-06 00:00:43</th>\n",
       "      <td>1593</td>\n",
       "      <td>2013-07-06 00:27:16</td>\n",
       "      <td>224</td>\n",
       "      <td>Spruce St &amp; Nassau St</td>\n",
       "      <td>40.711464</td>\n",
       "      <td>-74.005524</td>\n",
       "      <td>232</td>\n",
       "      <td>Cadman Plaza E &amp; Tillary St</td>\n",
       "      <td>40.695977</td>\n",
       "      <td>-73.990149</td>\n",
       "      <td>15355</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-06 00:00:49</th>\n",
       "      <td>173</td>\n",
       "      <td>2013-07-06 00:03:42</td>\n",
       "      <td>482</td>\n",
       "      <td>W 15 St &amp; 7 Ave</td>\n",
       "      <td>40.739355</td>\n",
       "      <td>-73.999318</td>\n",
       "      <td>482</td>\n",
       "      <td>W 15 St &amp; 7 Ave</td>\n",
       "      <td>40.739355</td>\n",
       "      <td>-73.999318</td>\n",
       "      <td>14850</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-06 00:00:56</th>\n",
       "      <td>1494</td>\n",
       "      <td>2013-07-06 00:25:50</td>\n",
       "      <td>282</td>\n",
       "      <td>Kent Ave &amp; S 11 St</td>\n",
       "      <td>40.708273</td>\n",
       "      <td>-73.968341</td>\n",
       "      <td>539</td>\n",
       "      <td>Metropolitan Ave &amp; Bedford Ave</td>\n",
       "      <td>40.715348</td>\n",
       "      <td>-73.960241</td>\n",
       "      <td>16233</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-06 00:01:08</th>\n",
       "      <td>938</td>\n",
       "      <td>2013-07-06 00:16:46</td>\n",
       "      <td>164</td>\n",
       "      <td>E 47 St &amp; 2 Ave</td>\n",
       "      <td>40.753231</td>\n",
       "      <td>-73.970325</td>\n",
       "      <td>2022</td>\n",
       "      <td>E 59 St &amp; Sutton Pl</td>\n",
       "      <td>40.758491</td>\n",
       "      <td>-73.959206</td>\n",
       "      <td>20186</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-06 00:01:10</th>\n",
       "      <td>349</td>\n",
       "      <td>2013-07-06 00:06:59</td>\n",
       "      <td>229</td>\n",
       "      <td>Great Jones St</td>\n",
       "      <td>40.727434</td>\n",
       "      <td>-73.993790</td>\n",
       "      <td>428</td>\n",
       "      <td>E 3 St &amp; 1 Ave</td>\n",
       "      <td>40.724677</td>\n",
       "      <td>-73.987834</td>\n",
       "      <td>15735</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1</td>\n",
       "      <td>114.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2013-07-06</td>\n",
       "      <td>2013-07-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     trip_duration           stop_time  start_station_id  \\\n",
       "start_time                                                                 \n",
       "2013-07-06 00:00:43           1593 2013-07-06 00:27:16               224   \n",
       "2013-07-06 00:00:49            173 2013-07-06 00:03:42               482   \n",
       "2013-07-06 00:00:56           1494 2013-07-06 00:25:50               282   \n",
       "2013-07-06 00:01:08            938 2013-07-06 00:16:46               164   \n",
       "2013-07-06 00:01:10            349 2013-07-06 00:06:59               229   \n",
       "\n",
       "                        start_station_name  start_station_latitude  \\\n",
       "start_time                                                           \n",
       "2013-07-06 00:00:43  Spruce St & Nassau St               40.711464   \n",
       "2013-07-06 00:00:49        W 15 St & 7 Ave               40.739355   \n",
       "2013-07-06 00:00:56     Kent Ave & S 11 St               40.708273   \n",
       "2013-07-06 00:01:08        E 47 St & 2 Ave               40.753231   \n",
       "2013-07-06 00:01:10         Great Jones St               40.727434   \n",
       "\n",
       "                     start_station_longitude  end_station_id  \\\n",
       "start_time                                                     \n",
       "2013-07-06 00:00:43               -74.005524             232   \n",
       "2013-07-06 00:00:49               -73.999318             482   \n",
       "2013-07-06 00:00:56               -73.968341             539   \n",
       "2013-07-06 00:01:08               -73.970325            2022   \n",
       "2013-07-06 00:01:10               -73.993790             428   \n",
       "\n",
       "                                   end_station_name  end_station_latitude  \\\n",
       "start_time                                                                  \n",
       "2013-07-06 00:00:43     Cadman Plaza E & Tillary St             40.695977   \n",
       "2013-07-06 00:00:49                 W 15 St & 7 Ave             40.739355   \n",
       "2013-07-06 00:00:56  Metropolitan Ave & Bedford Ave             40.715348   \n",
       "2013-07-06 00:01:08             E 59 St & Sutton Pl             40.758491   \n",
       "2013-07-06 00:01:10                  E 3 St & 1 Ave             40.724677   \n",
       "\n",
       "                     end_station_longitude  bike_id   user_type  birth_year  \\\n",
       "start_time                                                                    \n",
       "2013-07-06 00:00:43             -73.990149    15355    Customer         NaN   \n",
       "2013-07-06 00:00:49             -73.999318    14850  Subscriber      1958.0   \n",
       "2013-07-06 00:00:56             -73.960241    16233    Customer         NaN   \n",
       "2013-07-06 00:01:08             -73.959206    20186    Customer         NaN   \n",
       "2013-07-06 00:01:10             -73.987834    15735  Subscriber      1986.0   \n",
       "\n",
       "                     gender  start_taxizone_id  end_taxizone_id  \\\n",
       "start_time                                                        \n",
       "2013-07-06 00:00:43       0              209.0             65.0   \n",
       "2013-07-06 00:00:49       1               90.0             90.0   \n",
       "2013-07-06 00:00:56       0              256.0            255.0   \n",
       "2013-07-06 00:01:08       0              233.0            140.0   \n",
       "2013-07-06 00:01:10       1              114.0             79.0   \n",
       "\n",
       "                    year_month_day year_month_week is_bus_day  \n",
       "start_time                                                     \n",
       "2013-07-06 00:00:43     2013-07-06      2013-07-04      False  \n",
       "2013-07-06 00:00:49     2013-07-06      2013-07-04      False  \n",
       "2013-07-06 00:00:56     2013-07-06      2013-07-04      False  \n",
       "2013-07-06 00:01:08     2013-07-06      2013-07-04      False  \n",
       "2013-07-06 00:01:10     2013-07-06      2013-07-04      False  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.is_bus_day == False].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timedf = df['trip_duration year_month_day'.split()].groupby('year_month_day').count().compute()\n",
    "timedf_wk = df['trip_duration year_month_week'.split()].groupby('year_month_week').count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timedf2 = df['trip_duration year_month_day'.split()][df.user_type == 'Customer'].groupby('year_month_day').count().compute()\n",
    "timedf2_wk = df['trip_duration year_month_week'.split()][df.user_type=='Customer'].groupby('year_month_week').count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timedf3 = df['trip_duration year_month_day'.split()][df.user_type == 'Subscriber'].groupby('year_month_day').count().compute()\n",
    "timedf3_wk = df['trip_duration year_month_week'.split()][df.user_type=='Subscriber'].groupby('year_month_week').count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CDAGWKKWO2VKsEEXR6tWrmTVrFpUqVWL16tWULVu2sKckhBBCCCFEsZGcZsg7SDwePUE0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\n6tSpvPfeezRnSUfr6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CbPYcvvMGZJiNHvk2xiTD3ElSD7G5HbHrgIjfwYBCSBV4EngUVZ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\nF8TDSMYrHZtgjNqHBNGgw9TMQPs4DEJQkFCYnjH6ExuRErkDRvlhYkUtk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zqg3IY2dAof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4EtJhDDc1ixASqITkkZEeCbLRsgAi/lYXJAkRviLrPf/cmsgecFPDBgvatRjviCRPkKTqMOp/\nVV8QDzl9OFLJxuszDp5CQz1c7PH77RvzbmQbH1wJcsskSNx4YomNBBJkMyVJSJWjHs/77sLEhKm5\nzwPNhs0Grw7jUBYgPThYQHHsbryj2C4J8vuEEMv7B8DTL72gbnf/pXkHuwjCf8N/3bEpGt2tPcTq\nHX+ysknqXeBmv960pIQ9DIdydG2KL5+YByAkvM8+tQ+EEOmM/djeEgj80nnexUxDBXS8QwZIEHeg\n/VJ/UqTYTfjm2ZXA662s3lXbFl48v4zLK02s1Lublq5VSQpPlTWIzDn8APOCr3BAtVO56mo1moxL\nbEZX0TY3Rg1XtUmRYrdjUE+QnkODlSIGJkGSr1FD80kQO4YEAZKVJNW2Jf2EhrIG9o8WwDj1FRoh\nJUgc2rQmghQN0OAGXwHigEjD05uds9jormKufRqAT25oitRdKkHgz2FYyBNELY87kpmQ6QcqCCHQ\nNV3K+SlRlXHi+9CMnvyso1mROvPMvmewd2gvAOBa4zyWlJKmYe+RDUvMiUySA3Gn7FV7SXoDaETD\nVCknvVQqbQvfnnsFFXtB+qH42Ho6DGVB5UM4FaRH7y8ShCqr16qfTM+hePd6FTcrHbxySSh1It5U\nDo3dHmcu27KovEazGYa59vuY71xAIaODSCWIWCD00JPzW4UEcaGW0xVcB5GpMAAwnjm4pTQPQ9Oj\nAbXsXH05mH+G6gsCAFmtCJ0Y8jyP9Jmo5AgrQeJJhKSUmDiPnmgXWyA2ttB0My8Tv8vBiB0gqgRJ\nTCVKUIIQEIRv7YZbepsQGw5lYKCyFHk/nFusB+6xnDM4oc5pjEH17YSxeZMI/hip4uOeRXii0+pR\nvD9f33Z/E0PhUlRBhBludWU7PK43rqyj5U7ePvPoFEZdJ3hdIzB1ggcmi8iaojr13LpPgoQjHu/B\n4a1M6Zp/I1CDM9U8LUWK3Y/Bb7svX1xFq0exVPNXPJ99ai8Kmc1v+R5RGGHoY5BEdoTT3rYimeyH\nRs+fcOuaJvKz41TI4cln6L5X79pSYpwixb2AYHWY671ZlwAAIABJREFU+DatnoOvnloMqLiSLuOt\nkCBqIOEkVIhJSol56YJQgWgAHp0pgRDhoeAF1rpUglB0aAOL3UsYNWcwmplRtjehEREkMXgEij9+\nzU2H6bKWVFK0qJsuxx14C9yxXgFeOgxYoM+GLUiQrFZEVs8lBnMa0TCcGUbDbsCBQoK4K/DQfDWG\n5x+iEQ3PHn0Wv3v6d2EzG5ebxzGdO+avRrvBkMMs1F0/kPHsAXRoHTV7GRVrURyd+KveH95XxnK9\nC4dxnFmoY6acD1QDAYSqZCp7ZEuEukVZUKVzl6s53G0w7nuCaITI9Bg1pXu9acm2Krp0cCWIpwIB\ngI55AjCHcWQ8h7wpKoxIY1RFbe0pJb3eNtqWfFUMPPsJCCfSFHXYGEdOL4oUl0HTYYgWmZHEGob2\nIVbU7WESpKCX5f5x5GOSYiOcKkKIJhdP+8EzG41NhxlYjZHQ74AYSIWChM+fdJhBf9CQIsQ7tigr\nHvylpTJQE+kwFxqvAwAeLH4cwEHZjlKO16+sgXPgmQdFZaGgqoxHPEH4HS7huWUShHP+N2/HQFLc\nGey0e/hm+Wb9JLIqWj0Hr19eg5ZdRLHQwNOHjsj3DF0oQQxNw9GpAi7WgdVmDx13LsbB4TALa9YN\nlIwJFLnI0fUuXE2ZKKhISZAUdxpvXFnDRsvCpx6axHBuaxZK/Sau78/X0Og6+OihUWQMTfpmqPvO\nVzo4tmc4dn9dg2T7vVXkpDxlFVEliHgdLq3dt/KE5eDkjRrGihk8PB0/Pg9NRbFiUxbbb2w6TJgE\n6djYU8r1PVaKFLsJgyhB3p+vRdLYBk2H6WcYrK7exaXDAIhVlG60LLx9XahASjkTe121gk9k+Mao\nlNtY6FxA3VlFzV5G2ZyCRnR0aTPGiDKoGhHEigNKHVkFRo7dNREFonGCuqLKOFU8M7hMhxkyxnxJ\nfkyABOIGcC3AYmpqriBVqOablaomqpOFSXz+8OfxlatfQYtWUHdWUDb3uEGbQMVelN/9WGYfWk4B\nNXsZPdZClzb8MQDIZXQ8OlOSVWLOLtTw9CH/eB7ONV4F+LHI9iS8cGYJ9W4Pr1xexkghg48fmgy8\nf/+lw/jVYXRNg8MoOGexVXLCl573XYWDyjhfK+ntorWhGzYIIRjKKWoOV/GQM9V0GPe5745PKEE4\nCBAg/QkBVrtL0vh3IusGrkSQBoPA0IwYpVGC/0fcdYPgdZpEgiBOgbUJqaI2D6fHAH4VmfC+cW2T\nCJctmbUOiFtJnBmEaIo7Rtw9cbN+TakEYbAUJdjV1rsAPiJfrzZ7Uo20UO3g6DAPKkEQvW7iSpXf\nTqRR4H2Ark1lnv9Orch62ExWSRnHRsvCHx2f61s+6Y0r6+jYPej5OTyyn2O+d0q+Z2iaLPN3bE9B\nbr+4JFZdODgWuhew2L2IC8035Ji8SZ6hx0vnklzzU6S4Hah1bMyutVHvOHj98tb9aJKutPlqB6du\n1nBtrdXX7DdnJisfVDKzY3spLQyzrZO43DwOyuMnuuH7iUeCRJUg8Q82xjieP7WIa2stvDtX2TRd\nraVUpOo5NJao4YhOPsOBYX2LKYApUtxt8AAJktAmZltS2zhPkKRFC/XYySRINJ3lxfMr8vieCkT0\nR/00FaU6TN3xjZkbbjpKlwklhfoIp9wOVYdxlSC0KYJDTW1L3dVhLdabwGuqKkEs1oHNhbx72BiX\nYUDSKncpU/K/G+J+D4QDBLDhkyCqiSoA/NChH5J/z3fOi+9DWflWS+OOZfb9/+y9d7Bk130m9p0b\nOr3ul9PkPMAAIAhQAkGAWSQlKsursK4tu9a1VV7LtbWuddmuVZW95ZL1hzaUtzZod8tW7ZZlSyuK\nlKjlkigxgUgEEQZhZjDxzZuXc+ocbjrHf5x7zj039euZB4xITH+sIV7fvqm77wm/73y/74dhc1q+\n3rPXY6vkp8aLGPTJ9ZVKO5U47zUdhhPNDDc2Kqh1XCzttdCww32nS98fv6efFHiKEkRMISm82FjY\ntNzY92z7SpBoO0saHittv4yz0YSueWg44fkz8RNFVN89UeQg5AkCoJCJG57ert6Uf49nOAkSpwbS\nkWRMCsSD/kQFVYL/RMmMkCCGULwkEAkkmVxIIixIirol6Z40kmz3muYHpCJ6/2n73S1ItGRvAoHU\n7Tq9psOI3zMpHSaKkEcUDcaDqK+Spcznyi3eZ0cJbddLtjO4X+hHgR9yuB7Fc1fW8a3L69iuW++7\nEmS/59VjDD+4uYUrK1V89eIyZjbrsX0sx8MrtzehFxaQN3WcmSii5XEjsbqzi7XWLBYbt2B5LRwf\nzcmVo2trIuBjIfOwjssnL2KSxycV8Xsz9fjGfvnMPj4oqIHEfh4dyccnb1crNHUr/dwt31QdGEXp\n6z1rA2VnDXV3B+ud2cTjol4brsfAGItJ49UJ4nq1jSsrFViuh+2GJc/B2P5eHWr3ZTk01bw1atQV\nLf+5Um7Lletax+m3+z5+rHF5uRJ69tPIiqQxLc0TxPEotq0FzNRfQ8vjKbFpKS3RiWvU0R8IyFOB\nnbqFd5d4SsqR4TwODQXKK8r46jlB4KvgMTc0uRZpIIKAVYOTuBKEgML1CRO+n+FP5ilcEAKYuhkL\nhjJKNQymeIKofiBCCdJNgl/KBgGQMEcloCBgsCmf8+hEj612nxs5h9HsJABgvT0DyrxQhYpdi/uB\nZLUCBvRhDGdUEmQDBETOhwBeNWTa/549ylJJ5V7jDHH8Zi0g163IOZ0HjAShLAj2RCpXkrfBXtNO\nUFsFKkuHWtjqLKDjNRNTT0V5XE2vYc79S3xj8T/g0tYl+b4gF9SUJytSeGDXV4IMZKMLIAS3Kzf8\nz2BgJHNYbO6dBNHipGLUzyY45/4qhcHsYOi9vMZfU0ZjhEtXA9PIZq6AiRIBsU0KudD9PtO2DeeG\n8djYY/GD7wK9ECZ3S6rESJC7PX/CAQYJbBCS1EACtjLvKzcdsIRS0g6NqpbvLwvSJ0E+5Fgpt+XE\n5s35vfd9sr/fA0spD3oAPvB+9a3lUK4jALw2twtLW4eW2cVjR4Zg+PR61dnCbPNNLDVvYb52C0ut\nKzB0YKrEB/k7W23YTtyZuO3yoJAyn6UkXmLHER2gXprZxtffWcHyXiu2bx99HBQHZbiTHLhtl2J5\nL6hIUMqlZzh2W+VV+wUx8e0oVZfabu++QS5lMZNEMUGklOGFm9u4ulrDxflyTEK/X6lN9TPQFNLk\n9mYj5j0ULQPetDx88/I6VittfOvyOp57bw3bdQurlTb66OPHDdfWwu0vbSVfLQ0rkOQ3AHCvh5X2\nDTS9CmYbbwKIK7gEon1PkolpdG7x/Zub8qgvXpgMTaZ5NQ0vlKrqMTdUGlYY7blChdZFCcK3eXBo\nJ1jwIAwec+AxDxoBTM2MzQMyejZIh1FWMetKZZiiMQbIqhMJShASKEEAgOh+H0IosoaGNuW/3WRh\nMiah14iGCyNPAODBxJY1D+Fq4FBLKmNGM0dBCEFeLyGncRPWsrUeI0EAoKgEvA0rmaBwaXeyWaDj\nUJSbNhpWQNrbkZXbboa2H0ZQGhij6lLFFE/NrLT4s6fC8XgpXY8xLLQuY7VzAzP1HyUqGsU8Wcut\nw2YNMDB8e/7bqHT42CaeRV0jsvph4AnCYLkemv7vPxApj9vxmrhTvQWAK4xEpRdhLNoLDBKfa6T6\ndPSgWhg0IySIrwSxPZZsYrqPEuTh6RImS1k8engoMfBPU4KkkiPKNsaQuN+FsQuJhE2vYHfBb/To\nobqvJYjsLiMVe4L3k9JhArKawg5XLlOeZVsZT6ptO9Z3AIAbKbGdlBr2QaJPgvyYghCiE0JM3Jt5\nrYQ6WeLeGXcfidkuxQ9ubiWqOPYj7WzXw3Y9YApbtoevXFySnb7levjh7A607Aayho6HpkuycVf9\nlSCN8I6+7dVBmYdDw2KlQ8NGjU84dBL4K7Rcfp9Np45rtRfxXvlFOLQNl4YDLvW7cT2K1XIblAGv\n3N6R28tNG6/c3u4HR30cGL0YjXZDUlu7tlYNve7m4+F4FM/f2MSLt7ZCA1WUeGjbNE5u3kVeq+ux\nWEAkSBG1/1naa6ER8S5JC8IEosFf03Zh0zYWm1d8s8BkpJXmfukWDzLaNsX3rm/ipVvbkrTto4+/\nLizuNvHnb6/gvZVq4vtp7dxIUIKkdQktJa1BqC3S0mWjhENSSozap2zUOvLej48WcHayENrXYy4o\nPFnmld+nGxAeCNQnnj9uh9NhXKh6BuIfTxX/D10jcKgFylwQQjgJEvl6TN1QqsMEfU9YCTIiJflp\nPgIhhYcuqiQw5EwNLb887mRhElFoRMPDQ0/I8662b3BJPgHK9pr8jKOZI/IYoQapONuwPAt6pFTp\nQDaYC6WRIPulw7geRbXtoON4uL1ZBy/3yxEzw6YPVmqhp5QMFiQIYx7siA9WuRVXgoi0GUqDZ8yD\n25UEIUag9LSpjW/c+QYYYyAgYH7ULMxRxfjJGMVOow0tuwHNrEZMUYHF5nuSoDmce1huF6ReL9A1\nPR4eJ6g+ulacUTbHlCB60KaSPEHSfDrE9sG8iZPjAyhkzNj8pds9JaWPEEKknxHASaU074y0csD3\niniJ297uO3pfwfHJ1xH7qd+1eM6iUJUghLghFZS6AKbOAxmArVp8gTmuJOsrQfrg+EcAbABvHOQk\nUUf5e2HZfji7je/f2MQfv76IlhXN3+r+wG7XLRn4iAFjcbeF713fAAC8PreHlu2BuUWcmyzC1AMp\nqJiYEY2vdnhw4TAL04M53hEwHWsVPuHIasEkq+WvYG+110DhwYODS+Uf4GrtB9LxHQgHZGkr0N+5\ntoHlvbYMlvro414RK4d+l7K/pL2jqqpu7fHKShWbNQtrlQ4WdgOVR1S1wUsAhq93N8O4Q2mMBBET\n5+j9NSI+At2UIIyxWEDXsjxsdeax56xioXVJ5qdS5mGm/houV76Dldb1uyqJu7jbV4L18deLV2d3\nYbsU761WE9Wbaa08qgoAgn5mu26FStQnGVqmErVsfxJEVZw8fyNQgXzpkSnISinydF6gBJHHuyHv\nIXHfch4QupYTuiVN423eQ9CfEBC4zOZkC3wSJHLPpmbIbVRJ8RGmqHm9BEPLyKArLfBRSRBNE0oQ\nhpzpSm+RqcIUklDKDEpjyh1rCW2vCUKILI0LREgQ3xeEgeJW+VaM+Coqq/7NBJ8WIDmdKXiP4XvX\nN/HclXXc3Kjj9naYiIs+jw+aEkT1BBGBJwWNtR1OgoS/G8a4YiSqzkoatystMf8Nk/KLtUVc3LgI\nQohsA8IXRKTDUFAs1Geg5xdB9FYoHYYyD/MNXj46qw1gKndavteruoDvqydPDBIUEmnEQC/pMEN5\no6fqMN28MqKBbjeyR08pnXtoKI+TYwVcmC4hZyaQIAmf6a7Rw5QwUKbEPlXi/jEVTXQ3Ej5viAQB\nS/ye1JLpokKMgBMiRMIfaLZ2I3auKAnS9wTpQ+D3AGQAPH2w0wRPVKPj4s358l2f4eYGV1a4lGF+\nN2y8uF8ct1wOOvBfeGwag75c/+XbO7iyUsErfj31nJnBqfGByESHNw4CInMvbdqGaWg4NpIHmIat\nuoWO40GDUivda/kMJr+WcItnoCg7wWqxF5JtpRg33ucG2ceHF9GJT1rA73oUf/VeXNWQRJpENy3v\ntdFxPCR5/rYUIkAlPqJ5xIyJdV+G9UoHCzvN1DKbyfcfV4KkeRFFVyrFvbyzVMafv70SUmAlrZa1\nbBfb9qJ8XXU2AXBTxaZXAQXFtr2IlpOetxrFUP7uqvb00ccHie0kn580Y9SE7ZQxVNsOvnd9E9+/\nvoWdhgXbpXIlWFcmuVGPn+ByERKEJpAgfvtsWC6u++k7p8cHcGaiCMooHj00iLEinzxTeFIJIlY9\nHNoJnU8qQcQ8QLlPToyEaVpPKEHEZF7jcwH4lzA1MxYAGJoRS4cJV4bhZR1N3Q+6IseL9AE1HcYw\nxe9FYWSDVfwkEkQQK0fyF/zPzDBfvxoiQXJaUamUgZA56nvb78WCtqyhwfCj2XQlSOJmeUzZD8A3\nax3c2YmQINFUxwfNE4SyoLyzSIdhXmyMa1oerIjUX6TNRFNTk9qdVILowRgoAtTnl57HWmNNbhcV\nYjquB53w+1lr3ZHvFxRibKMzC4vydnG88JhMhQF4YB1N58joBGcnirH7SzYQTVAepKTI8P0VEkRp\nQzrR8fTJaUwP5XB8dCC5Okxq2kqSkmP/UFdcI+6h4SeoacDkYA4lf37QqzHp3ZEiySk+93YkR9Ln\n6fUElFFoREM+YqpvhpQgTqjCi0qI2BFlb7kVHzei6TAfiuowhJBpQsiFD+LcDwoYYx5jzAFwoBHm\noB4zLcvFqkJkzEWqT6inb1guOo6H3YYlA7YV5dhT40X8zaeOS7b5KxeXZWD2+NFB6DqJyWOBIB0G\nAGy/8z4/VQSYAcoYLs6XQ7mXDBSUBZ+dKKtNagegsu/7eRH00cdB4SW4x6vYrlv487dX8NW3VuQk\ndD9EV5BatocXb23te1xOcZSPTmop4+amuw0Lb8zv4tJyBS/ObPdsquxSGjunODaWzhIlQfxB8+Z6\nHbZLQwqspIn7ne0minpQBrLscIVZdKXzblYr9yv73Ucf9xPrCamYaYqvpO2UIlQ16tpajVdW8tuE\noTCmaW28p3QY/9rzO02590+fHPGPp9JXg+/r+RPswLTci0x1xDWFEiSjB6uPHvPC1WEIkWV3RfPV\nCIHN2v7f3Bg12rYzukKC+HOIDm3Iz1c0eDWXjMG9ApIqYhAQZPQMsjoPDDTDJ3MIg2YGKcRTA3ES\nRJAok9lTMrCYqV5Bx20rfiBHwsGiOSEXfa7uXI2rfwhQ9FNi7iUdpqaoR1bLbXTc8DmiJtNR34sP\nO1QjR9UTJJoOAyQEeMyD7dGEykzx71B6gugBOfjFE1/078HBP7n4T+TCStYPUj3KcHaqBENnaCqL\nHgNKedzFFq+8aBADR/OPhq6pevQIPHF8BKPFDKIghEBHODjupvpI2qbOx03NlETIUHYIA1kTx0cL\nyGf0ZE+QSNSetI/YL95EuqXDJKTe9FDx5v1AT8ao4l6in6nHNJlYek2X71HEceemihgvBsRH70qQ\niDqqGZ/XxpQgP0meIISQ/5YQ8v9Etv1zAKsArhJC3iSEDB/kGn0cDAdVMsxuN0LTn4UICSIG0+26\nhf98aQ1ff2cV37m2KY3c1qrBJGS8yNUeX3pkOnSOgYyOC9NcTkoiRmkAb+tisBErO48cHsJjh/kE\na7dp4dJyYGTGGB9khDxR7QQ0ooMyD5bXCrHv/eoQfXzQiCpBohOh713f7PocJgY4Ce17r+kkltxT\noU6co27/lHJyc6UStPXlvTb+6LWF2L4Ab6dbnQXs2Wv++ah0thcQnyt6v1Hy0XLjqTTyvrpM3Ify\nXNbecHfhUrsnI8d7RbXt4IUUj6Q++ng/MJgPVm5VTy2BtJaQpgRRy8HbfhsTQX9GV1ez08yTI6v/\nPjGhtivR59xRqlWdHi/6xzOuyJT3RMEUwiL5mix0rZyuBmIMUU8Q6TPib9NIoC4RniBRyxRDi3uC\nqH4gJV8JkjH05BVthdgRAVyQvkABI+gjpgemo4fL4zWi41D+PACgbG/hheXvyH3UVBj+uXQMmhMA\ngPd23kskbQdyPDhtWl7KM5E+QKh99+2tOogenvPFlSAPlieISmzIcZTF02GAeHoZA4XrsfhcIIFI\nEiSI7pNqGtHx9PTTOD/Cn5OrO1dxaec1AEDOCNq363HPErHAkDF0pdjAplRLfmzyaWT1sFdPkroj\nDYQQ6FqUMOgxkE9QbWhEw9nhswC4akp9RmNmo106jl7K2WoJ6TCpSo4k/xCScB0S7N/rvUZxN+Fa\nr4s1vd5PWjoMAORNA6cnBmTalRFRgqieIOqjbbsMxKiAmDwDoWm7sfmdy6JKkPuLgypB/jtw3woA\nACHkGQD/AMB/BvD7AD4C4B8e8Bp9HAAHLYl7e6sRer1R7cgSmkDwwP/ozk5ovyu+KZowGZws5WD6\nHfWnz43joakgh/Yz5yeg+4QygTpR4o1DU2ZPYrXI1HT8zz/7MHI+A359vYa1iqhCw0AZ442LcZO5\nH93ZRbXlgDGKmfpruF5/CXvWhryH/QwZ++jjoIiZpLH093rFfp48aVAPixIR1PcE2W6EvTHmtpv4\nw9fewV47SKljjOJW/VWsdm5gsXUZHa+JrXonRnYI/5397tdyvVgll9s+2ZD2HXnMxWCOB0gMTE7y\nwp/Jlfe7H5Kq8Kh48dYW1qsdvLVQvu/l3Pp48BANOgHefh2PYnarHiqLndS+GPOVDD4cj8JSSBDT\nD5AcasGlFDVnB9drL2GrMx+cI0EJstq+iSvV72GzMwcgaJ+iZPdEMYtBXzouUlvE/Junw/ASuWlz\n+SAdhgeDWSOrvBe+IwLApfx7EHGZqWuywgwBX2k2InmChhaQGyIdpmIH/UfJGAcAZAziG5amrxIL\nXxAmSuQSCqoFqSTThTgJwvfjxx/JBwaV31l6Tv49mjkaO0akxOx2dlF34ua5wheEgYXSIAW6KkFC\nJEgDxsAsTF2Tc60H3RNErawjSDUGljjXjlbhEZ4g0XSYJK++mkyH4c91QR8AIQS/ePoXkdN5cYDX\nt55Hw91DVmnf9Y4DhoAEUasFLTavyL8/e+RL8Q9H0oPryVI2vIGRmCkvP0WP6RcIq7tsz8bvPvu7\n+Pyxz+NnT/5sKC0i0ecjwSckCUn7Jt1bmqeI/+a+SE3H2efgc5OcKM6besjPRyBNuZFk4Jp4X7Ey\nxt33C6vlaWwbEK4Ow5UgqgEqE3/AYRVkJ15AZuxFEINXNYqmxCS1kfuJg5IgpwBcVV7/BoANAL/B\nGPvfAPw7AH/jgNfo4wC4l2owAowxzEZIEAZgYacV2ZImw2XYrPIO/PBwTuapaoTgNz52GI8cGsRH\njw7hE6fH5INPSDCJSTJEEzA0HaMDWTxzZkxOYd5ZLKPRcf1VJqDWsvH63C5+cHMLd7YbuLxSgUVb\naFMeVM023pFBjBoIGnfjDtVHHz0iGsSr0/jdpLz/CJJUgvsF4UdH8onbu6WCUcZDjO06b+f5jI7R\nARPE3MO2dxn/75Vvotzi71m0JfOL+eumNCsGgAl/4sQYL70blVJHYTk05lFycaGMlu0GqjNrEcut\na7J/8OD6vj/iHloygBLwmIcdawmXq9/Deud213vYj9doKhVt+hxIHx8EvB5Mu6+uVvHmfBnfvbYp\nVUlJw31UCWK5HleC+EG/IEg85sD1GO40L8KiLax2bspjou3JpTa2LE6SrHVuyetU2w52GnySe3pi\ngB/LGBzaiVR3ccBAu1ZpCNJheDAnAj8AaLplrHdm5GtCAEem6BD5uYQSxGMMhmZIwkceB4KMbvj3\n75Mgvm+YQTIyHUYcl1giF2EShJIO+HomgwtOUBSMYojEEVCDs0FjEgN+ap9QBuS0EgrGYOw4USEG\nAJbr87H31WCqYcWVGt0M8kU6TMfxsLzHFb3TgzkU/JSKaLD/oPmmqR4omqaSi/EvIvZdgfrVZcL7\nRVPBAKAiyCifVMsbvD2VMiX8wulf4McxF7frr0uCCuApUJRBLlaKyjCW15Jj32hmGidKZ2LX5CVi\nk9vjyfGBkH8QYyz0+YHuqS9JUH1Amm4TJ4dO4jNHP4Oh7FBobpNGYsTuHQmkQ4JqI9HEVKbR7Z96\n083nJLZ9H8XGyEAGHzkyhEcPx9t5Emzfj+le03H2qyKj3q8kQWIpNQaEMSsh4XQYcYxDKWBW/X0A\nPcf71UqUBImUzb3Xhb17xUFJkAIANUr+AoDvMib1LVcBxGnsPu4bDlJzebtuSUneY0oDnd8JfvJa\n24XleokB2mqlLVexjowU5ERio3MHs50X8OUnCf7mU8dh6pqc8HD+gTc4MTAkcRLC0GyylMVjR4YA\nwsv/vjm/B8dz8ZWLS/jjN+axUQsCsnLThuWGG2BQqpdi11rGncZbsFm/OkQf7z+6KUHSSriqSBoa\n9puARif9wXHpqWCU8aorlTaffI0PZPGlR8YxNcHTXcotG3/47lexXF0PlZUEuHS9ruSUTyirRyJV\nrhtsj6KTkHLTsnkf0/JqWGlfx469hG1rEQCfCHKxmDJBi31eF8vta2Cg2OjM7kse2S7FC7e28Ob8\nXtf97veA3ceDgXD59uRnTK0MtbzHx6yk55qycFzjuCxZCcKsuHzfn9BGlSA2DY+RlteCS1koFeaM\nb6Y413wbs82L0JQW6ioLHGlTeQYKxhSyRlk1bnqV0L6EEFBxTv/DZnRNXsfxOOESraSiaxpypgGd\nEOlTUvGVZMPmodDkPzU48ncJAjoGaBYACpvxIGDQHE0NnERQQkhgkCowlj0SO4bfW0CCLNWXYu+H\nSZB4gN2t2xJzxrntJijhxx4azklzxLgy6cHqA5M8QZhveBqFx+LtibJ4mkzSPF22b43PYQUJAgCP\njj2KC6P8Wam529ITBADqHRdtx5O/SsFXgqy0r0sy8+zgEymERfzzqsgr3iIUQLRCCfE9bkLbUtLI\ngHBVpej+oXSYSNtTS+FGj0tWjfROYiQaq/ZCgpD06++HfEaHpiVXrOk1nSYtlSl+/XgKk7qfmnok\n+v2k7ySj+XM7LZwOI5QdtstAtCDe0rLc4yjqCxJXgvxkkSCrAB4BAELIUfD0lxeV90cBdOKH9XG/\nkJbj2wvUVJinTo1itMAnIarJmksZ/uq9jcR8yNtbQT7ssZG8nICsd2ZAQbHcvibflytNJN5kCdFi\nsju1EzwzUcTRUb5KVOs4+OaVVfzuN6/LwVoM3gzAei2cxy9yM9uOg6X2VdTcbcw330n/Uvro4x4R\nDZjVzj6aI6yiYm9iz14FYww/urODF25uyQnXfmk0GSNtlTVANBWMMeDGeg2U8GsMF0wYOvCpc2OY\nGuQDX9N28Rc3vyfzOU2/bQtJOsDJS1Wm2ws+Oht4AAAgAElEQVRcjyWufIs86oazh6blYbdho2Lz\ndDZuhqgSIIErcsv24Hq8ULaKDg0r3KKf/92lMtYrHcxuNbBVSx/CHqzpfx/3C71ksarkZWA8nLyv\n2r24lPmeIH5qqd9GXRas6LkexXbdQsez4icA0PF4+xELFA13F4wxmQpDwCvDMEalwSdRVmSprPjS\nJR2GMUliAGESJAqCoKqA6HEyhgbPV4c4HoNO9JAiht8/l8rnMzoYKOrujlSYjWQOAQCGC8F1k4Ih\nETSoAR3R2yCahw7jSoqhzEiqV4G6/XD+fOgaSakwAJDTiyjo/HqL9YXY++EyuUnpMN1K5PL/3t6q\ngwgSZCiHAZOrCqNzym7n+jAiMR2GJaeexytfeFwZFdke9QShlPF0GOKCEf4MFxQShBCCk4MnAQBt\nr4GMHvwmDcsNlZ4fyBigzMNSS5TFLeBo4Vy0ajU/r/I8J0FdkGQp6TCxc3bx6EgkQWQfQWP773fe\npH2TjU05VHLlXtJpkq77QRimJiGpHHoS4ukwsR1S9+9GSGT0vL9/OB1GlIW2XQ9QSRBzDyAu9lpO\naOIUrS61Xzry+42DkiBfB/D3CCH/DMBXAbQAfFN5/3EAca1eH/cNaatISYguGotUGDO7i6FSFeen\n+crOerWDtpJn2rK9xEnbzGYQaBwdzcPQ0h830diSOixTi9fkNnQtYGgJ8FPHh+TAL9zMTQP46NFh\n/NqTwWrKRpQE8WeNLSdorE0n2YMgmsfZRx93g2hqmjq+pCm2du1VzLfewWLrCrbbG1jYaWG92sGV\nFb4Sml4lwkPZXofHkgN4FlGCtL06rtdexnKLZzdeWa2C+MTBSCEDChe6Bjx9agznfT+fcsvG24vc\nC0i0bbVqRNurYqu9HFOLbNY6eHepnEjgeJQl+vM4HgVlDHW7jhdubeKV29tY3uWr2Qw0QeLJsFJu\n47vXNvDK7R24Xnj1oel2LxW+uBesdLcTlCkCfSVIHx8Eehlr1BV5EZiq7frMRBA0RUlWhwYKC1OW\n+eTpMB3Hw/M3t/Dq7A7+w6u3UW07caNhBOXrAZ6C5rgUd7b5AsmhoRwKWSN8nCL7EH0CIYGSIgqH\ndeCwgFQ1tfTS1UnxS0ZRmALAn9z4E/ybS3+AXWtFbhPVWQyNgIGiYgelyYfNaRgawZnxARkYppkh\nAuGA7tg48ORJE2K2P5gZSQ3k1HPm9CKOF8/J11FTVBVjWU7SrDZWY+akpqFJwqdpx9NhupWhFN/Y\n7FYDIC6KWQPFrIlilpto2i4NBTEPWg+okiBBOgiD4xuS2jSo5uRGCCLqK0GsSMUdLxIINmye0kL0\nYA6d1wdC+xwuHpbXPjEZzK0bHTdcGSZrYLMzB4vytnk0/6hPXsTn42q1piREA+SDGqPmjFzqe92U\nIGnGqtFt0f1DN5qyz90oWboRRtF97xXR6wvyq9fr9FxFJ0HJIpUgMn0ogExP1NxwdRif4LY9CkIU\n5T1h0DK7sF0PLWVOFSUAf9KUIP8HgG8A+O8BnATwdxhjuwBACCmB+4F8/4DX6OMAcCkFYwyXVyp4\nc34PG9VO6sRd7dBcj2JupwHoTUxOLmPVeg9HxoXxKLCw20w8h4qZDU4mEADHhgsYypsx0xsxGRJK\nEC1hTmRo8RJZuq8EEe3V0IGPnxrlfh6E4vxUEb/100dxamIAIwVT5rNuKWaPGrSABLHj5nJxZ+8+\n+rh3xDxBIquzUay2b2KpFRiZ7dnB5H27bsfOoWKzM4eF1iW8u/tq4vtRY9SF5iVYtIkdexk2beO9\n1SpAeNscypsB2aAR/NoTR6SE/qWZLbRsT/roCCUIZR5mGxdxs3IJN+uvytXVtu3h/355Dl97ewXf\nubaBKFzKElODXMpAKcNafU8Su++t1OXkPzr+e4zihl+hqtp2sFFryrKSAND2fKKTuVht38SutRx8\nN2A9V45K+/5dj4Ykon300Ss2a52evLzU51KMVephaupHtO/hyirevsWKosc81CwHr93ZlYsca9UG\n/u2Ls9iqx8v0AkG7o8zDppI+e2qigNnGm7jdeF3uqwrY1cpv3UKEW/Wg/+q2GpsUuKlmsNvWIr42\n8zWUrTJmG2+GPwMIcqYOxpgssU2gYTgzhbGBDHSdKEFAktSeX0f1NxgtUZSKwVxjKDOWGkxFZezP\nTv0sJvNTOFF4HHk9vlIuMJbjJAhlFDU3Xha9mOOLQo0EJUg38QZlDJv1OvasHUBzMFnKgRCglMnL\n99Xn6UFTgoh0GILguWNgcFzfdL/2EnYsnqLkRaX+4GR+jASJKBWrLX9cU6oLqUoQQCVBAKY3pPdH\nveOgqfjADGR0OY8g0HCs8Bj/u4cqKrH3QyRIQtWWhPOKcxokbvoJ8NQegxj4+PTHQ9dQibpY2yfx\n++1a3WWfMrFJ9xs9RxIGMgOJ2+8VvRBIj47xssY9V4dJSGUJv5++f5oxKgBkBQlCHO7/4cOVSpBw\nOgwAaBneT5WbwfZoylj09QeN5KeyRzDGGgD+y5S32wDOAIhbV/dx3+B6DFfXavizi8EkP2dqOD5a\nwPHRATw0VcIR3zzR1AlE4ZfFvRYcj0HL1jBZ4g97cXATAB/o53eauHCou5HPzGYdIC6GR+cx38zi\nNx/9HFaqFRQyOgyNoNZxsWXNY9iclowj9/qIrBy31/Dc4isosDOYzJ0CEKzgEGUQGsyb+OKFKdiu\nhs8cOomV9jV0bF5d5tBQHne2G6i0bTguhWloICoJ4gQkCANfifMoQ8drYLMzh9HMYTB2FN2nbH08\nqFjabWG53MJHjg5hMGdKU+FC1sCR4WDyqEI88zfWa3h3qRI7pzAeTIJ4btVzMkZB/MnChjXL92MW\nKPOgRWSrNESCeKH0EI+5uLZaA4iHwZwJXSc+CeKvauYNPHFsCBcXynCYjcvLZfz8Y4dAEJgT2rQD\nj7nQtRws2kDTrWLQHMc7S2WprHh9bhefOjsuK0gItBOqGbgehcc07LSC76ncbuPOTg3IipJ3ABj/\nXud3GmgqVaxWKg0cnQrOJz7LensG2/YiAKBojCGrF2IBYzclSBIJYrsU37y8BsoYfunxw6E86j76\n6IZq28HzN+IBLcDb+nK5BduhODdViqTDxJUgWmTVFuDqrYKpw/Wo3y8EJIjrOfiD52clkWHoBK7m\noOFt4ru37uDJ48M4NhopqelfwmMuZrfqABiIUcPoiI26UmoWgD90ilVeT95j0qQ8iQIiSDdRjY7L\nBjEA3fXvzcGN2kvyvbKzDpu2kdHyUk06PZRDq0NR9U1RB81x6MRUzp5MgqhpuaoSxKJNNN1gej2c\nGU0POiObpwvH8E8//S/wlSvJBLbAuK8EAYB3ys/BIBkAooqNAT1/DmgeQtN2QSnjVfZ8dAs0PErx\n+uZLMIobYDSHycEBEABDueC3tz2KvP5g9mueFzy7oo0xMLjMlqb7y+1rGDInYcZMZD1QxqRaWW6P\nLLiJNqgZNbktSoIcKQYqoUqngmKuhKbtom65MsjXCIFLaij7z/V07gxy+gBPQ0toS5rWrY2FV80Z\n+GJk/PgwxHNvaIY0wVTbwiNjj+DC6IVUojG6f9o+kiiJiT4SPEFU1QMLH78faaDuUzSLqNv8N7f8\n1MF78QTpdq20dJW4KC3lOpHN+1XKCZXIFQr9hPvKinSYmCcIf3ZtL5wOAwBadgeoh+dNLKIEud+r\nzQdVgqSCMeYyxjYZS9Fj93Ff4FKKG+u10LaOQzGz2cD3b2zi3744i7cXufmfmq5y209lYTSLSd8H\nwDTbGPIDlrmd9Jx6gDeeuZ0mtOw6igMtbFur2Oms40uPjOGxI0OyU1rv3MaN+itSIaIhbKxkeS18\n/c5/xM0yL8fnCmdkwnMXxZ5CSZLL6Bgu6CCEKGarBEeGgwFktynOoSskSPgxdSk3Wp1pvI49ZxWz\nzYsPnAt6H72BUoYfzu5gcbeFtxd4msXsVgMXF8p46da2LFUXTU0TA0ESAbIf1JVfyjzcrL2Kb819\nC1fXw+oKXSOhFBV5z2p1mIjyomVbWNxtAsTDcIG391C5OhCcGh/AeDELolnYrFmY32lCI0Tm4HvM\n4Y7gcpXZAWUMb8wHgZFLGV6a4X4B6kquSl4IOB4nJSutgKwkxMXr81v+38rOjOGdpXC6y2q1LuX/\n/k4AgF07IIeFjFk1dgUCUoYxhnonLC1Pyl+d2azDcikcjyvw+uijV8xtp4+r37m2gf/rpTn80WsL\n2K5bofHI9RgYC6pOaASYr1/HrfqP0PEa8BjDjfUa/tl3buEff/sm/uKdFViuEyIPX5zZwHV/rjCU\nN/Fzj0zjp06UYAzMgjKGtxfLuLpaDU1gg1VbD7PbTWiZbZjFmzDycZUXD0b43zKdhgBGzO/r7lZp\n/TdCyBt5gHC/sFXrXbQ8dQ7E5Eq9SL/NGBpOT1G0fa+TYfOQvL9u11FJjAFzQAYQHa+Blhu0/eHs\nWGLwoREtvppO4kFbEkaykyiaPEXZZTY6tIEOraPlVdFwd9E034bo56J9Ku0SaZTtLWzU+NqlplkY\nL3JPlCgJIs/1wClBlOow8mdisbTPhluWnlnKXmAMsJyoOWT49xEkCFFIkLyZrgQpW2WUskL540oz\n3IGsjrq7I/ebzvFUK6IQkirSiMYnJp5QjuOgFNC1hBK5aUoQzYhtSzomKeCOK07iAXuSOkScr5cS\nuer+I74P0HDeDKm9ohDtDwAajvBDuncSJCkVJF2pcW9KkChxJb7uuymRC4SVIKH+wB+EbJeCaOHn\nnBgVgNjYVHzWaEQF5XVJ1fsgcGAShBAySAj5PULIJUJI1f93yd82/H7cZB/3Dsej0sh0vJjBzzw8\nibMTRSkVZQC+dWUdlZYdMtqZ9U1Ni1kDgzl/RYQAp8Z5R7xe6SSu2ApU2w5atgeitzGYN6ETgopV\ngUPj+alAII/VNT0gNhjD9dqLaLn8/l1mY6V93X8vWeYGBAGbIFYMTcfRkWAA3677jC0IPMZl9p2E\nqjGex6SMH+jn//eRDLVKw3qVd+5X1wIB3K5fMpIyhl1rBSut6/CYG3uerq1VY4SlCjXgFsdSxrBr\nr+DW9gbeWtrCn12+iJd9YgEAdJ3AofHyu+J4bpAYfm+t2gADQAiVkwFBMhIAvIoewZPHhmEY/Nyv\nz/E0FYcGJIi6ykyZhztbDVk+U+Diwh6qbSdUxUascKseRTytD6h21BxTD3e2q2h03FDQsNOwsFoJ\nV69YrzVCFazEZEMld7btRbjUjlVTEEqQN+b38M3L66H3kohRNZWhWypNH31EkVYl6tZGDa/c5sEM\nZcCV1Ti55lImn+sOrWO5cQctr4q1zgwohexbLJfiL95ZxVfemsfibosbIW/UcGuLnzOf0fHMmTGY\nhoZPnhvCx44Hpp6zWw28tbAnV+vE5NilDua2G9AL8xgZyMBIqEqlEp0yYCRxw9O0+F8EM0L23w15\ng69SeloFN6tvAQDG8+PyfmveIi4cKkkSBAAWagvyeGGKql5b/bzqdtVLQKhBOl4DTY+PAToxUTCK\niSaSakqvgJ5QYSMJGtHxjz/9j/HkxJOYyJ7AeOY4xjLHkNe5QpfCkZVF1LLeQFx5oGKns4qtBj9u\nrMh/y7ZXxYt7/wbG4LsAwiag9zuH/68bwhNE0yDjUMZYLJizaTuWUi2UIHakLGhUmROQIAEpWjCK\noX0m8hOSWOBKEJ8EsRQSJGOEKikVjRH/r2QD1KQ59RMTT2CyMCnfF0hSgnTz4wh5+nR5vLuqPJT7\njO2T5tuTcLm0Si5i25mJAZyfKuHMZDH2vnqPI7kRuU14ZPSiJEnDfqagRDlfXN3S2zX28w5RfyeV\nuIpCfl4COJ46rxNzSxZTghDCq8Ss19pyPhb9zD9RniCEkJMArgD4XwEUAbzq/yv62y75+/RxlyCE\n6IQQEwdMWdqqBXm6D02V8MULU/g7nzqF/+XLx/EzjxEAHiyX4uvvrmCzM4ttawENy8WaH8ydHCsE\nDCshODHGH3wG8NXitOvWhau8jlLOAAGXiwnJWBQiGDE0XTbm9c4MNq250H4LzcugjMv0+QQkGby0\nnh9MEQ1DeVNWidlpWP5noGhZHmyPhsgOgBvJ9ZKX3ceDifVqG9++uoGFnaZUFgFBHroa/Irc/JbT\nxlL7PWzbi1ht3wx1/ct7LfzJG0v4/15f7NquBFxF/l51trAkjTwZvn1tQ/rx6CRZCSLGmVpHrb7g\nq7OkeTCTShCBYPwkGMgZ+OhxrhKzXA+XlstSCcKrOpCABIGL1+cCFcgXL/BJlUs9PHf7Jcy13pZt\nUHx3WSMIGhzfY6PmkyBZQweIBwYPc9vNUGrclZUKiM6/w+nBnH8/Fqrt+PegoupsYqF1Ga3Iqqko\n2Tu3Hf9d9lu56fcgfdwNkspDV9sOvvb2SmjbtdU4Wep6gRKk6Vbkg9j2aqCMyXFPoGU7uLiwh3/9\n/CxubdQB4iFv6vjk6Qnk/LFyrTOD42MFfPrcOHJ+e1yttLFW4aop0eb2Wm1pxjhRzCIRgWgi8AAD\ngaH1pgQRo/25lMBERc7IgTGG5+aek6uZf//Jv4+jJV5tZb29iEJGC6WzqCSIUIKIO04zTYxuEySI\nRZto+SRIQR+SVWi6fS4VvShBCIDPHP0M/u5H/y5+auSX8dOjv4KnRn8VZ4sfD87je0o0Iuq2bnHG\ncqUix5dJv8T5jepb2LYWYQwsAHorNL51U5V8GCF8PjRCYPpBIgOL+d05tBPzBOEkCOLpMBHFSECC\nBJ4gA5F0GF3TMZYbA8CVIIIE8SiTJNVA1pCKJAIiCTJ/Qwz8WYwrGcQzfng4MDI9OTaQoAQhCaTm\n/kqQpP1VpBmjJh3Xzben6/FKW9c0guGCKecvMcLIP/x46TimC9MoZUp4ePThxPs/sBIkSX2G7r4m\nKuLlhVP6V3/78cHjyBt5GMTAE5NPJJ4DAHJ+OgyAxDmm7XnSGLVkjAfXz+zA9RgaVvKC+P1ebD6o\nEuRfApgE8DcYY2cZY7/g/zsL4Nf99/7lQW/yAcU/AmADeOMgJ7m1GXSip8b55KFsr2Om+UOMjS/j\n2CEuJ79TXsQLC+9gpX0DVzcW5TEnx4KOVwNwfIwHRVp2Fe/uvsQnWwkQZSUZNVHyCQvbs2H7K8XR\nli3ywgxfCdLxGrhRe9nfZuLJyScB8BUuI7eGjG4krqLI8/n/A7jhq040TPgDerXtwHZ4icCra1VY\nTpgEIdDgeSxujPpgjfV9dMELN7ex17Txozu72FWCi9EB3j7UVGCRcqUSgLv2cuh5WikHyoUrK/vb\nKAWeIMBOeycg9hifaFxfr+HWRo2nw1DxHsNq+ybW27cDEqTtyIE376+wrtcaABgIiEx/EwhkqPz1\n6SlT7rO418J6tQXGKDxm83QYv4FWWh3c9ImZ81NFfO6hSUyUsiBGAzN7i9hubWDJr0wjJnCqOsTx\nGFYrbVlF58RYAaU8AdEcLO41Ybse7zccD3PVOWiZXYwUMrKSDdEcbDfiqxVR1N0dtO1wu+9eHSa+\nTe2THrRV0j4Ohuiz5lGGP7u4hJYtKjXxtrZR68RIDduvoATwykxisjugD8OjTKqwTowW8IULkzAM\nvm/FJwc1jeK/efYkhvJxEmN0IINPnRuXE+Urq1W4HpX9wHpV9F8E46X48ST2h/+SAKaeRrSGIXzA\nMqaGExFvkihMzcSl7UtYrvN0t4dGHsKzh5/FQyMPAeB98WJ90b8lfsHFGn89YAwiJypxdJGKy5Vn\nZZswR+14TbT9FJyCPuj3hXEliJag+tBI9zKlKlQlisCAHqxOiyA6qm7baXTwjUureHsxXiVrtRKQ\nvROlLDzmYqk5E5xT68BxQ7K6nu71w4KgOgxBRj67cSWIRVsxhQeFl2yMGiFLKhFjVJNkoUdW5QkI\nxvKcBKl0KjIdRkUhq0slSF4flN5gBMBwId5OtYSFxVD1IlPH40eH8PjRIRQyRmLAXzKTzXxVhUG3\nFKpelCBJ95bm29Nrelk3tVe3Yz599NP48skvo2B275PSoBrG9mJWGxCy8XvpBfuV+zU0Az9/8ufx\nS6d/SZK6Sb9Jzgg+r1rFS8yJ1BK5g+Y4CvoQv36GK5VVc1QVP1FKEAA/A+BfMcb+U/QNxthfAvgD\nf58+7h6/ByAD4OmDnESUrCMATo7zh7bq+MZrBHjmbBEZXYOW2cOb83to2x5u767K44+PBWwfIcBg\nHijlCPT8CjYaZVl7PIotJeWklDVACJ942F7yg68qQQDgWu0F2bB+8eSv4lfP/KqcRLy786Y8d1rD\np4yG0mEIIRhXVqh2GhYoKKotB5W2Lb1GAD65cSmNeTj002H6SII6wTQTZOBCURStFqJ29qqa5Pp6\nbd+BgDJ+vO1ZWCkHktmPHC3C1Plqzo31Ol6e2Ybj2zLt2IvYsuaxYc1iu8Nz9msdV7L4soJSvQWA\nYShvhsz0gKQAxeNyef/Vu8sVVDudmBLkyuqepB0+cWoMGiH4wsOTALF52tt6DRVngyu4/B11LVDR\nuB7Dne1g9XswZ+LMZB5Eb8KjDJeW+URvfrspyc+zk0UMFzL8HoiN3UaQh3o3Lbnj0PTfI2Fz2JDy\nLi7UxwOPaIrpD25uYmGXEwwPTZXwD754Xr53bTVMllquF5AgtCrbpEEyqHUc2U8dGs7hCw9P4b94\nchoPTZdk4PPJsyMx41MVxZyBC4f8dA/Hw82Nuhx/N2q8D9JgYHQgk3qOuLFoUtWIdCXIfhN4gabT\nxPcXeWFCUzPx5VNfhkY0uVoLALf3bkv5v+Va2GjyPnEsezh2viQPgqSgSQQNFK5cWCkYQyDQEu89\nqToM90Xr4UMq1z87EahjgpQHwMzw3yVKgizttdC0PNzaqMeMoNerXOVj6hqGCxlsWwuhlEqiWbCV\nudGDJph1pKkvQkqQaGDv0I4c88ViAGWeP27vUx1GGqNyEiSjFRLJCaEEabkt5DNxYqFg6nKhcsAI\nuxOUsmbouRFIqrSkPuM5U5dKMT3SdglJUEIkKEGiHihp14qeI7pPqDpMtxSXHoiNNLVXUpzRVckS\nY3pTdwXA1VymZuJI8YhM4+t6ApK4FSQlnE8yc94PuqaHyOlEY1QtUAVRZsfmOrZny37MJDmMZbgK\nTzPrYIyg3PpwKEEsAMtd3l8C0DdGvQcwxjzGmAMgvbfoAcJobWowh4K/0quqHgZyBn7usWnwOucU\nl5YrWNnjP9nh4VyosgEhBBQuTkzwx6bStlF3kj0MBAlSyhNeYg6ARYN0mGiTCkrk6pitX8W2xVdl\nRszD+MLxL2E4O4zHJx4HAKw313GncseXsiaDwQspQYiiBOH3zr8DCg+VliM9SfwPyj1BaJ8E6WN/\nqNLgpEdETIRsmp4HvKuoFKptR3qLdANlXOq+UuaTVkMjeOrUMP72syehgw9gL81s4wdz76DpVFBx\ngqoTNZtPjGptBy6zoROCrKHBdimqFj/f8ICJKDQ50fDvARRDBRNPHOMTrI7j4U/fXITt2nwy5e93\nY5OvOI4UTBQGl3C1+gJOTwFjRd4n3d5soG17sGg7dC3TJ0EcSjG3E6jaBvMGjo8UkM3x/u2tuSZc\nj0n/o7yp49BQHpoGjA1kQAjFTsNWfEHS23KoLwD/TdO8GvbrE/bL8e2jDxWW0pfMbjXw4i2+albK\nGvivnzmBJ44NybTOq2vhsff717dklRhhTAzwsXVVUZqJxYCsyfDsmXH8wy8/hJ99dBrHRvl2mmJM\nd2qsgKdOjEqy9M5WA9W2A0qB7QZvt2MDhZC3WBRxo1ESyztPPZwoE/ougQVjDH9680/Rdvk9fe7Y\n5zCUHQIBwUR+AiNZThLMlGcAxs+50liRbXVcIUG6xS9JQZNaJlcgrw+BEJJsIgkCkkA09+QJonwX\no8UMpvz5jaFlkNW4kkU3ef/YjJAgar8U9S0S6YClHF+8Wm3fDN+fZoc9QR6wPo7SgAQxNJ7qzRKM\nUW0aKBfleMkoLIfGCJOoEiTqCZLVCwmxMMFEYSLYoId9sAAgm3Hg+ouJBT0gQYS/zmgxTFjyuXKc\nMEgL+nsJqIX/jKoESfMGFNeLXSclHSVJCRI7XwKJIdpOIonSC2HSrX0m/E7dMJYfw6+c+RU8e/jZ\n5NOlnC+p30g+vjcS5K6IHQA5hbAhmivnuWL8sJW5nKnlMOqTIHx/C+XWh0MJ8p8A/Doh8W/V3/br\n/j59/DVgpdySbNupiSCthaqTfMbw9KlRTA/xznCz1kHTn/CfmyxBDRYIeK7/4ZFgW6URnzAxxrBV\n50HccMGQB7ecVqoSRKDp1PHm1gsAOMv8kaEvIKPzdJpPHPqE3O/5ped5w0xp+WqOpqHp0ImGfEaX\npmpbNUt+F+WWHcppY8xL9AR5sIb6PnqFHZoQ8hVZFSIwia5+NJ1AwbHbDEvbr3cxSBWgjGG9sSMH\nk0NDeWiE4fR4EZ86Ny6DkVubdby2dhFUIT8132qoZXtwqYWsyXPjKy0bhLgAGEbycRIkmCiE8dSp\nUem/sVxp4OXbQXWItXIblsuv/dTJIew6y3BYB3PNi3jqVEl+lpnNupywAdxUVVSscj2G+d26vHYx\na0DXCU5N8sBit27i9bk9+VucnihCFLsSQZ9HmZT+l511rLVvJX6vFm2BMg+LzStYa3MZeFpKzH59\nQp837WM/CLJdJd3rHQdffWuZGxQD+K2njmG4YCJj6LI0/WqlHZMUC9Nvylw5cWUAVsoBqSo8Ozzm\nQdOAUt5EIaPLVWqWQoLomgZDJ3j86LA875vzu6i2bV/5BUz5leSmB8NS+7RmQBAnQdIQClgS3meM\nYr19G3+1+sd4ZfUVfh+FaTx9iItpherj/ChX05StMtaaayAgMm0GCJMgAmnlM6NQy+QKFPRBECQH\nH0m+Zr3L9xG6J/UQoQZhOh9H2o4HL6RsTSdBxPiVNXTYtC0r6UhoFgCGAZ8Mi3phfNjhSmKDE1sa\nIUCCMaoHV6Z/i9+T+h580X2jBEqt7ehKHKUAACAASURBVABgILpPgiQpQRAoQQDAJfHKUkwLtg0o\nCiFxfBQkbXsKERCvNML3e3z88eC6flUbtZ3fNQkSuY5Qj4X7hBQSA+ntiYXim3QlSa8KtLTr74d7\nOf/deI2Er5VMntwNsQMAeV1RDRJHKdPONzk0GHNMLYexbECC6Pkl1NpOpE/iuN8eQwclQf5PAMMA\nvk8I+VVCyMP+v18D8DyAQQD/nBByWP130Jvuoze8ficwIjyleHt4oQ6XQSMEz5weVVZwBAlSDD2O\nGuErTJPD6gp2fHWj1nHlyunIgOis+HVV87EoGGP41uLXZIWJh0qfRMEYgu6z1hOFCZwb5iW+ru5e\nxVJ9KfUBpozKyZyumJ+Jcm+Vti0Z+WrbCRFDFBSOS+F40fzsB2uw76M3WCElCEMn4ikhJk1ORAIr\nVFEeZSg3w5OCblViBChjuLEZkA1HR/NycjVWNPDJs4EZ1XJ1O1QmUshYKWNwmAXhr1NpOQDxADAM\nJ8jatZQB09A0fPb8BCcZiYeZ7TJmfD+i2e0GAApDI/jYiWA1yoOLkxNZlHxDt4XdFvZafNK22ZnD\n5b2X0aZcQeJ6VBrGFnOGTNM5NT7AV+K8AtZ8GbeuEW7o7ENVgO3UA5IlarosYHstrLZvYs9ZxaZ1\nBy2vlkqCJClB1G0PmlS8j7vDrY06vvbWMq6uVkMkyHPvrcsUhs89NIkzE0VohMDy2njkUPBsq1Wo\nBCjzQEGVoJj76QgIzw6PuVKmrxECD26IAIkqMsT5podymB7iq4CbNQuXl6vgQTXFRIm35eOjAzg6\nEpd2R+fZYjW92z5yO4hCRoQ/72z1Kn648x9xufodVGyunjE1E7905pdCBAYhBOdHgpSitzffBggk\nCZLVsxg0x5SzhwOO/aT2ySSIrwRJqQ6TVPkiGlwmIX5PwX2IgJeSDuCbE6plctXgz1LmOa5HJZGc\n0Qn3j4qQYkSzkDX0xApADwLcUDqMCULEolt8jBD+IYLsosyD5Xox4iiqDKm0bUDrgGj8N8tqA4lk\n2WR+Ur52aB3MK4Ax/pzlTR1tGnj2DahKEHRTOMSD+DQj0SR1EwCcHzmPp6aewuePfV6mVbyf6TBR\nHyGgu29IWopOL+hmotrT8Xdxrf2ur6rE7jVF5yBKEPWSYSWIA0eWe+EVNylRCgZoOWS0PEoG71v1\nwiIYmFTkq7jfJbcP2otdA/BRAJ8D8HX/9TUAfwHgswCeBHAVPGVG/dfHfcDr83vyb1HaFuD5qgJi\nMCzmdDx7hj+ghHjI6BqOjxZCS5mEEHjMQTZrSaf4rWq84xcqEAAYygdGTCqSmtuOvYQ7VS69HMsc\nxbH8YwB4oxUN95nDz8j9/3L2L7sYo1L52QzB1gMh07adhgUPLpqWJ1eyBGzPhe2Ft93vxtnHTwZU\n7xgGBIOBDylRp3HTQ8YYqm0Hnt/OAqPBDlpWuiEnwJvm3A4nNnKmjo+fHJUrShQeJopZjPhERrUd\nnnSI/FHGAIdaIIQHQuWWAxAXGUNDKZukBEm+F40AuYyOp0+PImMAhLh4e7GMF29xA1kQDx89Nox8\nJjzkMLh4eHrQvxeG1+Y5qbPWuYU2beBW7XUAQMel0rBPluwGL+d5eCQPsKBe4fHRAkwjuM5QPiMJ\n3qiZZBJcZmPHXkLGn+Q7NL0ceLlp41tX1vDOUmAwqCrIotJO2mdF+lDw9mKZl7xdqUryrNKycdX3\n+zg6ksfPPMwDnbZXxQ/Xvwcn/w6yvqnptbU4WSrKU4tJMmOUV3MhNgzdkUbGlDmh1BXKvFDA221S\n/PjRIXmsUFdlTWCwoImDY6VsPzL+EQxEynxWrV38+/f+Peaab8q2Er1qRtdwbrIYkeXz/9acHbyy\n8yf44ca3pQGkBh1fOvEl/PZHfxuHi0pqi3/88dJxZHU+D3hr8y1QSrFS59V3jpWOdZW/72faGE2H\nISDI6UVoaZ4gSPYb6IVgYF1WhIvGaHC+hAoxar+kKkG4cpi/lzV1rHW4Ws4gmYBM0mxp1As8ePMi\nkQ6zVbPwP37lit+G40oQQE3z5uOrUIJEiSUvoTqMplSGyeoFJJET4/lgoaPp1gBnCqB8zB/Ihsvj\nRj1BejUgvZs0CXWR5OTQydD99ZwO08N9iXOF0mFSyAFTM2O+O0mfSJaejYTFd+NFJPbv9vpuESeO\n08ie7ne13/Hdj46fPGQEqyhBPMY4kaqQICbh/e2o9AWpgeitVHPU+4kDlV8F8E/RzxL4scUbfknK\nqcEsBhTn6GjOO8AHsiePj+DGeh1btoePHB3iA7Gr/LyEH+sxB2OlDFbLbew1LViOh6wZMMIi1QQA\nhgr+9ujKsR6WtlLGMFN/zX9N8MjgZ2WnpGsaCOV/nxg8gUMDh7DeXMcPln6AXz52EkA8WNu2FiXb\nrmuaXDmeiJijCvY+SoLsNNuIzkP6SpA+9gVDzEtG5Eo6sXJ53ARUTYV59PAQ3lutQM8vYr3aidWp\nd2gHHvOQ0wdwc72GcsuBZgJnJ4oYHchgt+pJIkTXgCODJZSbu6h1HDAWNENBulDG4DLbXxmCnw5D\ncHK8INNJVBAQPDTyEG6sXYy+AQJgMG/ilx+fxjduzQIAvnvNV6oQik+cHovlj7vMweHhPEYHMthr\n2pjZ2pWKGQ0ANA0bnTvItY7BpRSmfw0VZyaKWFojodcqNI0rwDZrFnab3Bck6bMJCM+kjKHB9ihc\n5iSWLgWAiwuc/Ki163j8CO8zKWXYsZb84OAklvdaODZawBtzu1jcbeHZs2M4OnJvTvJ9fHgh+o3X\n5/a4gkhr4bPnjvoVnhzcrryK8WIGlDg4c8jG9eUslvZaqLYDYoMxhrX2DF8x9M/LAKxWqzAHL2Mw\nb8ChDyOj5UBBAyWIBngeDQVjfFtwf2pwUcjoeGiqFErbOz5mghAmr1vIRkvfanIxgjGKhdZlzG6+\nIecjLvVwvvQM1Am7oRE8cZwHbyHCgPCc83fKz6FDebCowcCxwqP42MTH8bef+EW8s/VO6PrieF3T\ncWb4DK7vXsfM3gxmK7MyKDtWOhY5Jjg2+h0E9xjMraJKkJxegkZ0EJJcHYanvoS3aV08GFSICi1J\nPilqhRjNqMNzxmLmqAJhEsSGnNJrdVSdTQDAmcFHsNlZQMWqAJqFtu2Fnq8HCa7/vF5fr6HSyqPm\nNkFAcPhcfH4YTXNhzINH4/4hSSSIWh43oyV4ghCCQqaAnJ5Dx+ugYlcwUsig7PDBrZDRZXlcnRjS\nJ0Ycm0b2RYn7NCVDt8IESehZCdJDOookQXpQaZi6CS0y4O9X8WW/63clhmIq2YOG2cnXvSs1CoJ2\nqkfuL8n0OXZ8wnciK2iBK0HEPJeB9ylEU0gQ30R1LHMUi63L/HqZLdQ6iqeNj/sdZx3o12GM/c77\ndSN9vL9Yr7ax7BsmqioQIOwJIoISl9nQNa4GsY4M4ampw6H3uUiOwGEOKPMwXsxitdwGZQy3Nusy\nTxgITFEBYDCno4M483pkJM87ehA8criElxbeRd3dAQA8Mvp4KH9R14JVFEIInjn8DL5+++twqIOZ\n2iUczT4V+/y79nLkeH4HuYyOYtZAw3Kx27Dk4ONFSJCbG7X4QNUnQfrYBwxxLxkR3ETTYRgoPMq4\nKSpxYBSvY/zQEHLNKjx0sF7NhkgQjzm4XnsZFB4eKn0Sz73XAAg/97mpolxpEitzhGg4OlLC1fVd\neJTXZS/5KgphVkYZ40oQnZvn8bQPitPjyUG6RoCiWUxYnQjybs9M5vFoI4vr64oPQUnHkeE8LC9s\n3uZR7iD+8HQJP7qzC5taWCpXAYOTMhoD1jsz0JslEP+zqkoQgBMcTxwbxns7yzgykkcxFx/Wxoo5\nbNYs6QvSrYJFx+OKE6EEcakVSodhjKLqbKNgDCKjxSX/u+1tLLevAQBMLYtXbgN/6+njslLXyzM7\n+FtPH0+9fh8PJlzKYLsUFxf2oOVWUBrcQG7IAPBTmG++DWIE4+i5qQFcX+b9ybW1Kp49w1dca+42\n9pxVDGT0kBnjdmcVyFAUszpW2zdwvPARAJCLA7ztspA3ltgmQOT/cZydLGKl0vb9C4DjY37qq79P\ntFIW8WcRLbeK96rfR9lZD70/13wbRWMMjxc/IlM3poeCCgRq4MYYxeXKdyUBcrp0ASfzzyKj5ZHX\nM+mqC/8DnB85j+u710FB8dz8c3KfY6VjaDdjh8rPnRR4qCvchmagYAyg5fKTyLKQKUEnL5Hbe4Cm\nQlRoSfJpUudPwlxTJUEoPJTtdRT0IVhuMHfbU1ZmW9q8yIzGQ0NPoMPKqFgVrgTpePKHftAM48XY\nyVWFfG49t9PA5fwuJsbD+wrFB09j4Gborhf3BIkuDlRbDkhGUYKkeIIQEAznhrHR3EClw0mQvQrX\nPQxkDWz7SpCCPhx6rqIqLfWcMbIlhexI8q5Jq1ACAFOFKfn3hdELqfsllYeOkSB6nARRY4TQvpqZ\nShekpahFNiTul4bo8XdzbC/nu7f91O/p7u8nUQmiVrIhjjRL5tWPmCyPCwQkyEjmMAQlo2V3EhW2\n0bawvNdC1tQwWcrF9n0/8GOd1EcIuUAI+Qoh5DYhpEEIqRFC3iWE/A+EkExkX50Q8juEkFlCiOX/\n93cIif/iPw77ftB4Y05NhQkCKcZoxP1dSJh4hQhNI5go6YmlPgl4IObBxVQp50+KGJ67sh5yHxfp\nMCOFDHTdf6BjOX0aPnp0GI8fHQIhDDdqXAViaiY+dfgzoX0NEnasfmTsEYzmuNxzpnoZlhfMWopZ\nQ66KyWtpRqhjFUaJtY6L5cZCSAasljKj0SoRD9yaRx93C8YQM3sSOdZu5HniK0IMe00bxKiD6B0M\n5jRMDfFncLdhhVbpml5FTp6Wm9fwV1c3ADDkTE4wEELQ8mrYsRcBcCXI8dFgZVJNiVEr0zjMAiEE\nm0LBRSDLaUchclLjEu6gidu0hfNTxZAvx9kp/ndUBiwUWKeFeoO4uL1d8a9F5DnX6jWIGflghOQg\nIPjff/kxfPb8JD52PGz+JjChuOCrviBJEIGgrhHohMBldmiwXu/MYr71Dm7UXgkdJ371nc6m3CYC\nvX4aTB9RREtmi+psbceDnlvF6fEiai6v6NTwyj5hwRvE8bGCrJy0vBcQi3Vnx/8raJ9Ny4Hj8tfF\nnMGrVvh9kU6IbzLI3/eUUvFJwUAo6NAInj0z5qsrCE5PZmLHCYPUsWIGjDFc3nkDr+7+qWwXWT2H\n3zz/m3J8vlp9HjudDTw0XcKJsQKmB5W8c4XEeGPrFbnQMWxO45OHvhwiJFNNSP0O5ezwWXmuxdqi\nPOZI8UjkmJTvQkHUn2AwE5AKggQh0GLBkDhndGVWQ2/yeyfBVFAgqxVg+FNkI6FM7mbnDhZal3Cr\n/ipsxch7t2EBhAJgqLE7/rkGcKx4FsNZ/3NpnBQWd+14VFYzeRDgMYqO46FpMYAFv90rt7ewGanq\nJhckECh+HOrGlMcqkbSw00St44aUIFltIDaHFs+zqHZUtso8/ZX5ShBTR8vlaXXRVJjpoVziM62R\neApnmieIeK9XFMwCPn/s8/jEoU/EFFeRk8bvCylKEDUdRqi1okoQzYy1J/F6emBabttp78TOKc53\nEE+RNN+UXpH63cfuoYs6RXkvrgS5t3vgcZVPfGtBOgxlFLbrhZUgJOcfk8WIydM7tcw2Wm5cEaQ+\nf7NbdbxyewfP39hKTUk+KO6KBCGEfJwQ8vHo6/3+HeD+jgEYBfAVAP8TgN8BcB3AvwDw1ci+/xrA\n7wN4GcDf8//7+wD+VcJ5fxz2fd9BKcPtzTo2ax28MR+Yoo4PtzAyvIuxohmT3THGU1woqMzx9RKU\nIoLJdn0lSCGr4/w0rx5Tt1z85aVl7HSWUHN2ZDrMoaGcMggkwJ/TXdq+hL0OJ22+eOKLGMmFO2xN\nC+fTakTDz538OQCATTt4u/wcXF/OOpgzQ6tHAPDmxmv4szt/KMvuqkaJc+UlWDSYRAryh8KLsfVu\nihJkr2nj21fXezKz7OPDD4dSMMaway2j5uxwsoOyWO40ZRSMiRUlCkMnyBgaDg3z55eBV2tKwnaj\n5ZfGZTg6nIemEZnisd65DYCvPp5USJCaMlEV90IZg0stEAAb8lqM+wElINXgL4jP0KENgADPnh3H\nJ8+O46NHh3F0JOOfOaKS8SeDh4dzyOgaiGbjjk+CqDLxrVoLIAy6RlDIRkpqakApm8HRkfTynHfj\nCyJIEELgpyLYoRSnTYsHBxRhgzsxdlNlYize9yITy3rnwQka+kiGHSFB2raLH93hE3FdIzjhk4ii\njahKDKK5OHtIg168jqs711ETvhy+RFk10Kt0bDDGg4ZizoDNOvB8XzBNI8joGdnW1AWSaMpYUss6\nNJTHzz06hS9cmEIhF5+kHhsZwKOHBnF6rIg/uv5H+N7KN+X8YiJ7Av/V+d/Gbz30W3h2+kv+9T28\ntP4N6LqFqcFc6B6Ep8eN3Rt4d5t7BWW1Ap4Y/nKoTwp7h8SPB3hAFg3EpgvTiYaL/LMLwiK4juhD\nRaUKgRAJYgQeIWmS+ni1hriyo9s9RX1SxDmEGkQE003FY0p4RXhw0XKCYEUoQYi5B5vx4w7lzsPQ\ndAxlfUJHs2G5njKe8YWwtJTBn3RU2+F0SArKDcTBfahOiLGSeLi4sBcaZ8NKEP77uNSDF/PECMaH\n79/gJHrUE0R9HtTfXpBTDnX+f/beO8iS5D4T+zKr6nnTvnump8ebnZ31u1i/C3AXjiAPAHkBErog\nRV6IIYoSKZ4UoYu7uNDpQhfHiIs48XgSeTxI0CloQAMCIECQAAkCtxbrd2d3drzv7mnvnjflUn9k\nZVaWe/16DDBY9G9jdubVy8rKV1Xpvt/3+34oZkW7ACPVlmtYNT1uWqfQqZbITGLK/CXGnCRBYI2G\nwYXeb+1IdiRRdyf82wLH+gAdkspwECQKbAAcDBW2q7ALcUZAYtkpie0ItT88Ptwq65sx0kNAtkfl\nseV1T+sDxI4JhxHzFpVC/AAwkuHjLtG66LobCJtYI7ouwxtX+PeMAfXurVkzbfXpvAaAEUKyjDFT\nfO5RnnjfXxcUxhj7DoDvhA7/R0LIBoD/gRByhDF2jhByN4D/DsD/yRj7Ta/cFwkhdQC/QQj5T4yx\n9wHgdih7q+zcUh3HZ/jk9qqXGWakxLBovQu9msWIbsFFOXQWk4wHsRAKaoaIcBihbu1nUjkyXsLy\nRgfLDeDs6iWU5qsYLabRtncASGG8lJaDcFL3shwLL86+CABIaSl85sBnMFdfDJSJG4SemXoG59fP\n4+3lt1Gzl3Gi+h3cP/CTnsCj13LGcLHxBi4tcv2C9c4Gnh75RQyrXuFGJ8AkEUAHY24kHCZJAOx7\nZ5ZgOQzrzYpMX7htP37GGIPraYKsm3OYaZ8EAOwoDsJh0ThgBg5CrDVNEOKgkDIAAoyXMnLgXKh0\nMBUDSEyv1wGMgYBhcjArnUSU+BlJNAqMl3IwNOp56/zFrtjUuy5ngmQIwaLnxdI1YKyUAqLzk/Q+\nhfuzQVOgXl/vONzzaGgUP3v/XpyaX0Ol5Xj3KJ4JolOK0VIa19ZsLNQasGzXY5fwKy3X2wBclDJG\nbCjOZvP/VnRBbM8bLqq0mQ+CmG4QlGJgkUuri0hhYZ2YjaYfmrRtP57WtYJ94ZVL6zKUVBX3FeFZ\nar+z3S52jVVxsVEH9DremD+Fjx64H5T42dhEP6m2TIglWDGtw1HmcEoQBEGUMSrOzxh3rJTRYTpM\n9mW1fxIK5DM6Zuuz+OalbwLgIpt3FJ/EZPYoSqkBEBDcN3o/ZmoLmGmdQNtp4M/P/Tl+6dgvBeLp\nCQhWWiv4xsVveJ8p7hv4JDJaUAMokb4f8mgfGjyEmbqf/nWqlOydFvVlFQq4AIzCnt6BlM9Gy8pw\nmCgt3qfvI3SceBtMEgmtVE3omyWBJgV9EFVrCYw0ATjo2py1EWb5thUQZK3JNUG0rB9OPJk9AkIo\nyikBgtgAHJi2SInJ/16qdbBnOBh6/aNuK/Uu/v70EgyN4LP3T8LQKFzX4YKOjIKB4J6pARQzBk5c\ndWC7DK9eWsOHj4wiY2iyP6lPyGVOJPxaMEHqHUt6vIkmUsJTGCSTDIIoTsNSoQUwTwZUU9PjDgTO\nTjKVfQlsrk9zo6Kf/dbZK/NL+LxIOIxmRDf+XpFyuoxHJh7Bemcd+8r7Yq/Fw362wBcINf+GmSAJ\njp1IuT4fRQSoEsD6Fp8zAYVBUzCdJgg1o+EwnjCqQTKBZzKSnsKF+lu8rL4Cx7kDmqIRKcaTekjD\n6FZF3W01HOa/9/5Yoc9Jf37N+/tm21Xvb9GzPw/+KH8nVO53vOM/rxy7HcreEhMASK1j4eoaZzfs\nGhGLHYKF9uWIKCrz/gMgxYPUATrw4hGfNcLLA08cHIKhEdDMPN6brWC51oFRPg6iNTBaTIMxF2vd\na/iDs/8RXz73Zay316Ham0tvom7xwf6xHY9hID0APYIu08jAltJS+FeP/ysMpHhGm+XuFZytf5+X\nBxd3OlN/EZeavoBj26lhw5pHxtBkSMxyvYu6VRM/D4W0SB3qRDatSZogKi01vNnZtg+ehemiADDT\nOokT1b/HencJtsOw2L0ov9swV+PF0FwHpuN62VNc5DN8skzpFMPe+7lU70TeKcaAmXW+wClmdAzm\nUlL9XJ3gCKEwqIHBHN9sq+EwwkNlu1zomIBJEGQwZyTGeVPwxXy4P6b1tJyEBQhCwLMlEOqzJsLh\nMDIMjRKMlzIAcQFiY7neBaF86nUchkq7A4ChmNFjhb0o6KYLspECZ9g4LkOllaxK7jNBiMd+68Jl\nDLOtkzhVey7xvLhwOfH7ws8wzALYth8/U1NrA8BX3vY3nqq4b9flIIia8cVmFnaUNcluurDmnSuy\nTFF/LV5VWEdifhPrAEIIUjQl643btElL6F7+uaLO4PeO6+BvLv+N7B8PDP4UduXuDGwuBnMpPDj8\nYYx42QPmGnP4q4t/hUuVS5iuTWO+MY/Llcv4z6f+M0wPpDxSfMKLMQ8u0tVscsHmB8ERNVUuEBVF\nVX+yqD9vbL7JL6d9EESGw3jgcZxHORIm44G6SV71IxNF7ChnsNcLc04a9/KalyGGMKkL0uxE2Rpt\ni78fC9U2Liw1ANjQsjxbTkEfRtEYASUIbLZB/VDND3KY8GtecgHLYZjzNPZcuJ6ALEHe0FDOGjg4\nVsDhCe6saFsOXr+8DstxA2tln20VBUHEmmKx2pH6U+KZpWg2Mt+q+heDyvt2ZNLFs0cn8Oj+YXSZ\nnz47nB6X/x0DNoBgKJdC2sv+eHg8nJVJMRKtYzMmSD8W13f7ASGSyqSoD/IKUz/uLu3GfWP3IaVF\nw/mS6t4KYHCjmiD9Wq9br/bRG0mRGz6qMkH8cBgWCIcReiDCRtM7IbRjqLER0FsT5wOQoIqwW7W3\n2hIThDH2n3p9vlVGCMkByIGrED0M4J8CWABwwivyEIAlxtiVUPuuEEKWve9xG5W9pXZl1Wc27Brk\nLyAhPHVcWOei4zZQMTnzQjgIHNhgjHkDryqMRiKxjMWMjk/etQPfvsKF3d6d5QOvXjyF0eK9WIeD\nmdb7qFs1nF2v4fzGeTy641E8NfkUGBi+P8eBi5yew6M7H+ULhRjkMw6dLaaK+Niun8VfXf0Sum4L\n0633cHpjBI9lP4T3qt/BohcWINSzAWCufQZDqUkcHB3EamMRLdPBn711Hg8e5GKvU0M5VOaqfKJC\nf0wQ1WzXvWHkd9tubwsPxl2nJePTrzZO487RPQHGg+sQDoKEAACXMSxVOchBiYuCIla2o5zhKZxd\nhpV6NxDitdboom3zfrhnOOvRVfl3qvdQI/BAkBSW6110LAddy0XaoDI8w/I2FC3TkWKEQzlDMqLa\nTh0Os2TKRY3yjUu4h6ao4TPJ4G+wCkZBAjQtp4a2U0ecEQJMlDIgpMJBkFrH27Qw1Ds2n8QJ85kg\nyiPgC8woUAoA6+Y8cloJGa2AkQADrIuhQrw4qgyD86qzXBMuA1YVsWVhHOQI9nfXjXo1TDs8dnxw\nNw7b1p91Q1oMb3mZhg6PFwLivoIJAoXwZDMTug7sHc7j0koDi/UGWl0b/nvlv4P1tgUgg5SuSXaJ\n2IQJJogoLd59GvII8xp7b3B8EfVgudcXXsdyi2ub3DvyIQzpvu6GEAwlFLh71yD2jX4e/9+p/xcb\n3Q2cXDuJk2snZdkv4ovy34cHjmFP+h75eaKcxrIXzrd7OJe4kVGPj2RHZKY5IB4Ekb/duxmBtJAJ\ndv/Iw3h5/gUU9CEU9WHvfCrb4AtXx99PlQkSk3UV5ayBctaQmjCSCRKqrhAQR62D2WU0ujYG8kEG\nWsfuwnZcPHd2Bdc2WtAyC5LKPpk9AoBv5NTNNqEmTNuBplEg4bl/EEx1BogNvus6PJUwS+PAeAEa\n7QIEeHjfAKrmMlYaXWy0TLx4fgXPHCqBk7P4XAZwNmR4HS36Tl2Kk0OGMaUpf+eSNrkqONVw1vCr\nT38E3zzdwFurfnrcnMIEUUNzwibAursnS7BdhpROIwwqWRbxa/UbtSQWV2xZ5XgvTZCwYOtWNvwE\n0RDgrYTz3Gh2mJt9h8MhTNdbPyUEBvXYaNSCZQkmiLfe0fjaMhUCQTSqo6gPo2avAFobbcsJzHei\nyzku42Hl5iw0YsBxQ6rDN8muWxiVEJIjhMwRQv7nm9mgBPunAFbAGSBfBjAN4FOMsbb3/U4Acwnn\nzgFQ1a5uh7KxRgjZQQh5wPvzkKencmyz84DgxuzKig+CTHr6AhT8pQ1rggDAXOcsL6N0bOHBVBFE\nQqJZVACGR/cNSQE0ddIYKaThMgdNx+fVu8zFK/Ov4Hff/V385YW/RNvmj/DJySeR1tKxXpy4Y1xo\niqJoDOCBwZ+SMWevLf0X/Om5P5QASJrm8M8e/mfYkeMLnMXOJdiuiU8enZICqnWrjhfPr+DsYh1p\nXcNQLsXFUiNpzDbfuNg9xMq2rvKtKgAAIABJREFU7YNhYUC6YvnhW5bbhe26gX7Tsmz83anFmHAY\nF9NrQo/GDaSx3lH2adcLlbY4AYvVDt69VgEhvH/uHhYLJN53M4aqnUOgU52LpXkmQmJEW0R4x5qi\nkzGQ1+G4LjpOEy+v/gleXv0TvL3x16hba0jr1AMcgvcgpRmxYqmG5oMj5xuvyqwpYSMgyKU0lDKa\nZIJQr45ax4IQ6ytldZyuvoQ317+OtlOT58aF6FxuvIM31r+G19a+CofZKGdT0L3GrGyiCyLqBbgw\nahz7Bwgy5eY22pirtAPjhBhHw8yPH7OECtsWYyoT5JXLa3LEOLqnih3ljGRtSCaI8obbrgmXOTg8\nztkADDZOL9RC2dy4VTs8xKGgpKwVWZoo8TRBvGEj7CQJWwQYUbzB/ube/77SqeD5a88D4Kywj039\nVOD8MN2+kM7h83d8HmktjSQbz43jmV2fDIw3hkZx39QA7pkqI61ryZsc5TAhBB/bw7VIpopTkfS2\nwdM8EETfHAQZy03g6ZFfxIODPx0JVVEdJGJNE8ds4yyZ3teRbIAkJkhchhgzGldvM1uOT03TkSwQ\ngOuBiPYLTRAAILSLrqIBAACm4/Cx+gNk6npWhpd1up6XmuLQeMm/+8TGw/uGMJjj8229Y+Pbp+ax\n0TQD/VFlgshjntOh2bX9cBidz29pLco+8t8rXxMEABabi3LD3vTS4xokE9mIivVzpF6vbkq5Ppko\nm7xTjr67t8L6YVMk9Qe+Bgm1s1czI2NcfHhdv3arssPEgTXJpoB5Cdv+Xr/RdKPMWUIIdOqtLYmf\nItf1wmGSmCCEAMMZnhqXaO2IlpBYm1qOi/nOOcy2T+Fq6100rAZuhV03RMUYaxFCMgDiEordbPtD\nAC8DGAbwDIC74IfCAJwlEu9iBDoASrdZ2ST7VQD/Wx/lIqaqcwsmyEghjVyaYr0NwKOeRkEM39TJ\n2GWul0pJEUaNOYd52Ske2T+Eb59qSgpT1tCQ1jnzRChUj+fGYTomNrobaFpNXKhwoKKUKuGhCU6U\niUOdwx4c3h6vJAHKxjjuKX8cxyvfAgPDQmuet0Er4UODn8Ge0h7cN/IhLMzMwmEWFjuXMFa8G5+5\nbydevLCKKysdMPBUg1986TIe3jeEQsqJhA71kyK3Vwzvtn0wLBwq0rD9EK+iMewh2AoThNnoWNG0\neC5jmF4XXl5XbnoAIJfmNNtq28JCrYupRhen52uBFIaHxgooZyvouH7fTGka4DExXDAY1MCwAoLU\n2jbGSgpDweFgwFK9C8YoCHExmDPguC5WulfleLHSvYqV7jTW3bvx8YPRlNSGZiT2235iWgk4y2Ss\nmEGlxrMPVNomihkdtbYFAhcgBJpRx+XquwCAU9Xn8dDQp0FJdIzoOi0ZCtdx66iYixhO78JwIY2l\nWgfrTROuy3p6sqS2EFyYTvy4yeCgYq4jqxXx2mV+rNLuKt8nMUG27cfdxDvRsRy8M80dBSMDbaRy\naxjIFmE5DI2uLTWriML4slkXOlI4MFoAAQEjNs4v1bF3h8IOINw50vQE5YrK+GJ6YuAiHEZsIFQg\nI9wzkr3H/G8/1I0fYIzhW1e+Bdvl49Gv3P0rKOhFAKvyXI1G5/bR3Ch+7d5fw0JzAbZrw3ZtWK6F\nPaU9WGotYV95H0zTABAEMjWNQBMriDhvckw4yucOfw5TxalNWZ6ijUnCqeGy0fCFqJfaF0YNs1yp\nt0m9sQ1lViuBgILBBdXrcBAfDqOuc1pdG6TEl7ElfVTqrVBCA0wQaF10LRd8v8/HOCFi+NE7x25Z\nOssftKnYNZUAuqcLxQgOjxWxavL32WEODJ3iw0dG8frldSxU2+haNl66sApKCCYHuWPDZY5kgnDH\npO8y8dPUO6A676OCCaKaqn+hUx3FVBF1s47F5qJ0Ugjx23BmmF5MkKT5MJEJEgEXbhwE2Yowqun4\n6yEBnEY0QWhUR6wXChJ3/aTMTrHnh+r+QTHD+731kXspxu8enpmuE3UaERAYRDBBHJiOp/sGhq5t\nAYS/42EAjoJgojCOK43TIFonkvVFbKG6loXlrh9Y0bLauBV2oylyvwvg2ZvRkF7GGLvMGPsuY+zP\nGWO/CuArAL5DCBHJplsAklwHGQDq3bsdyibZFwA86P35EIBHAPzXfZwnKXT1jiW9nPtG8n6MLyGg\nlEa80aqpnYNBvNBqgeg5DC4MneKJgyO4b8ofbIsZHYy5aDl1ufk7PHgYv3bfr+GZ3c/IFFcA8OFd\nH5aUMTHY5FL877QuFgQhdghowPs7ntmPO4pPye8L+jAeGfqHyOllUEJx9/D9khI31z6DfConvUeP\nHRhAyosFurrWxDfenUet247qF/QRDrOtCfLBtzAIogKLBDx+WA19Ed+H3x/XZZjx0lsSrYlcOjhZ\nihAY03bw8oVVX7kfwN7hPP7xk/v863odQXhvAMCyXc4EyRm+V9gDSwWgZ7p8rLi23gbcFLKGhlxK\ng8MY1kzfI8iN4czGCfzCt38B7669HFg865RGJnsSA04kmfAoj5XS0oMwu94GCPGYIA6yBoUFP855\n1ZzBSnfa0z8I7touNd8MPBfxW4QWkOMyvHV1A6sNs2dkiqhS3KewLXUu83S59ZfkIkIFu1pOFevm\nvPT6izLbo8S2Ce/729Mb8v24Z7cOEATmNtPlSwd1f+IyBw5sZFMadg1lQYiLiys12a+555mg0bXl\nu1ZQhHhN1pV1GpohGVJ+OEyfP0IBZlQWCgCcWT+DixWujbS3tBfPTj0bGQlEOEzYSukSjgwdwbGR\nY7h37F48NPEQPnf4c3hm6plNdTmSNAziQuY0qmFPaU8yuEH8Ovu1XnT+xMxaaju98kmZrsJ1Jgmj\nUkLlBlgzOLDR6EaZPgKkAjhThFD+bqSUzTchBIOZYDiMeGdtZuJK87j87pWLfmbCH3VT53qR5nyt\nyUEQBopDY0X5/ASLSqcED+8bwsGxAkBcuIzhuXPLODVXBRi/X2rWGMCfF2ptE2vdadCMP/f64TAK\ngBYC1QQbZLHFmSAOs2XoqZoZhp8j2htdzyY5HOMsLKKq1n0jFnc9neoyy8qdw3fK45aSZSdJ04Nn\nh7k+ECNJbHVL4TA3mB1GvVTw39fHwknSBAmHa6sWB4JQlQkCzoIGvBS5ble2VYTMqO3eXdrh/dtB\nyw5ujcU8UjWDzA/HvQ1S5MbY/wTgTkLI/0UIOUzIDygXEPAnAAwAv+B9nkdyuMkkgmEqt0PZWGOM\nLTDG3vH+vMUYewNAPH9cMcdlmPbYH6oeyP6RvI84A9AIjWzsVVNDxdT0Z4C3oYlvMwgAXeNI95Hx\nIrIpDQfHCnARDIUZzg5DpzqenHwSv37/r+OJySfw7O5ncd/YfX4bvIXK4fEidg/lcMdEKSCgFi6n\n9vu9+XvxzORP4+nJp/HI0M8g49EIKaEopAsYz+wHAGxY86h0N2QIzUQ5g2eOjmGixDedXdvB+eWN\nmJSmfYTD9MEW2bYfbXMZZxostC+g7dQD8b2MMc4ECYAgjndeOLzKwex6G1r2KoxUAxk9OFHvVEJi\nhE0OZvHs0XHct3sAPBo7GF+eUpT/TZtBpzo0jaLoxVz64TA8ba/ldtHsOthomWAsxYEXAliOg3UP\nOCgb43ho8NMo6ZzCaDomTlXewInKd+TiTSMa9DAIAgFgbn5POUgLDBfS0DS+mJzZaIGAoNaxAcIw\nUjKkd0vY2drLYMwNLN6a9gZmW8FhU2i2jJf8CXm+2sbLF1bwvbNLuLzSjAhxEfiLjq6TkKrYnAbA\nn4PYQIbBrunWezBtF7OeeG7VWpbhMBUvdnx2vYVt+/Ey14t5FuKLWUPDHRM85EDdAAuNHRAigUYG\nX2j5oCei2rEtLNXaoigIgEbHBgGDEBUWZnsLVgFU6iI1vOI0iWzOY/qxCjcwhUXSsTv42yt/y38L\n0fCp/Z8CoVHPcb8gqd+m+E1JuE2JwqjhDV4CayTuXGGP7ngURaOIhyce3rSs2nbAF58H/M1InDed\nr3k2AUFi2CVhy2secKE1ADCeZjK0jPFBeoZW1wJoNJafEoQ0QbowFU2birWIusUZER8koVTVOy4c\nXGseEySfMjBa9DNfiL5DPWDwrskyHtozIJ/O8dkKpteacrMI+POLCwbTdrHSWcSacx564YwsI8Ao\n9SmHn7l4NsutZTDG0LRrEA86zAQRPSEuNL6X/kec0etL/NnT4sI1CAg+vvfjeGzHYzg6dFQeV3+D\nDKELAzOEIJQQqWffimNrbcV+WEyQXhCU2iO1BBCkFxMk/moEOvFBEJFVjzEE3nGDhJgghGKqtFN+\nblnBAAoxj3SdYAhOeH12s+xGQZBpAEfAM8CcAdAlhJihP5sHX2/dxO5AjMpvAxgnhOxTC3mfx7zv\ncRuVval2fGZDZoNRQRDOBPEV23t16P2jQmmcW1gTJOlMBhZYLB3dWcInjk1grJSBy1wZlwhwEERY\nMVXEs7ufxROTT0QQbgIejzhRziBt0PiFComn4x0s34mP7P5IIA6NEILBXBqTWX/wfHX+ZaR1vwNn\nDA2PHfDbt95qRgCjuHAYxhhMt42Z1klUreVtJsiPgTkuw8XmG1jsXsTZepAR4YIDCKqJPhgNhwFm\nN1qg6SUU0jr3HClWzhoY8kJZxksZ/MQdY/jQ3iEpIuUypX963WAg53s0dw1mpYhpOevHKYtMNQyc\nVr/obZzgpjBRyoDBxWJrRnqgh1O7MJLejceGfw4/c/BnMJYbAwAsdS9joXPeuz6BERIA0yiJ3UzF\nGffWcO/naJHXc229i7ZpezGjLkbyBpp2MG9v09nApbrQx+YXOl9/TfbdrMbj/KvWMiy3i1LWwKP7\nhzGc98GQesfGiWsV/O3JRSxVfbBD3UglMUFUk9oIMUCz6bhYNWfhwsHlpj8VPHduGdc22njpwmrk\nnG37YJvtMlTblpeWFLhvakARu4xjCBBFk4L3YUoIDohxg9i4uu6HuRDCmSDwBBnVcDtLMkGIB2AG\nN3JAfL9N8Al7LfLHoudmn5Mx3E9OPomR7Ig3FgWXnFpSnG2C+eKHPcoksEtiQZAE1khcncKmilP4\n5L5PYk9pT2zZOEeTWKeoTBA/HCYKDAHcsdSzTfJeJJcTgtYgDojWgu2wSGieYIJYDoONNoSApxEA\nQbRIdpiuHVzrCGfAB8kPpC7nHMZgOyIzDNftUkXCww4JANg7ksFjB4YlqDlXacvsY4D/7BhjaJk2\nmk6Fp4fX/PlGaoLEsADEuyKejctcrHfX0bD8eTIuHAbgwsBhS+w3CcdvRThM3OtMCEHeyGNXcVci\nkyGjZ2S7hAlAJTzu9LItpcPdxJIA2a3WklR3P+XCFnlm3t+CSbOVdqksDzGnuIzBdv11VEQTBAQT\n+Qn5ueMGGR9i+DDtYAiydZsyQb4KHpryZeXPV0N/vna9lRNCxhK+Eml3X/f+/nNwsOufhMr9E+/4\nnyvHboeyN9XOL/GXyHEZTs1zMaWRQhqlrBFIhVe11jDvbVrCNhLKlCDRfKYei5ooF4ciusxBS/Hc\nDmeGI2XCliiMGnpVKWhsloqJXJSMoxENBqW4d+ww0pRPKC/PvwyDBCmwKV1DKcPvw0arFfHc26HP\n71+r4itvX8PV5rtYM2dxufn2tjDqj4EJ4EuYq+rsMBZAsDeaJl65soRax4pNkSvS7uVTemTTsXs4\nhycPjuKTd03gsQPDUsg32Bj+12hmFGktDUOnuHNHCQdHCxjKp2Q6W3EugweEMAaXcSbIUrUDxnSk\nNB0jRT6pXW36WRn2FPfyaxQzuHv0bvzOT/yOZFGdqb2IjtOA1kPQuJ/Y9rTmh+yMlXndlk3leAbi\nYrhgSGZZhuZlPOr7G6+hbtZBCFAxF7HUvQQAGDR24kBe6JcwrJucjDdRzuCpwyN45o4x7BspyA2g\n4zKcW/K9Ehk94zNB7M1BEH8sjIYUtEOChDWz6h1XtGO2AdQfK3NdhvmKP47sGc7JdyiOH0EIZBip\n6zGPCAF2DmSQ0igIcTCz5qen1oiGRofP/5T4IaaA7/0nxGNxaQLAU9/dOEZD6LNyTLTddk28vcSB\nvuHMMJ6YfMIrG2WFxc3tSRbYjPUYUpK82byxfZZVvu/HMpqy0O9xijpGit8SydbgATOTg9mIaGqo\ncYF64q6rboBFtpGGGQyJsZkNxoBapwOa8kNZUtRnIoqwKfE7eTjMrdmU3E7GGFCzVtF26nBdhovL\nDcn23VnOQBWw9Rkw/oNgcDFWymCHF9q63jRhKyEcPmjAYDlcS8xyGAj1N5HpLTBBAGC1tRoEQbQw\nEwSSXf3kzidjvwt/TmSCRFgXscW2ZNfDvgAQK6YsgJEtCaMGyt3YD7oZ6XGv934ETRX4DY43I9lR\n3Dt6L7J6lHncyyihASYIgwXGuK6NYJMB0XAYBq4PKcxkQVlRsZfsOmFNxlsz3txQ+Apj7PM3qyEJ\n9gVCyDCA5wHMgouhfgJch+RlAF/y2vEeIeT/BvA/EkKKAL4P4AkA/xjAFxhjwlV4W5S9VXZhqS5j\nPu+b4rRaW1nsEMRldwma6CqCkqR6NeLYUgxuYid14UjPbU7PywGpl9GY5V8cmqoKo6qmES2SVU6U\n3TWYx10j9+Dt5Vex2l7FQnsGPqmID+jjxSJqnTVUO1FNkHA4zPtzfCOjUvSX6y0M5AwUM5sLqG3b\nD98cl+GNK+tI6RQP7hnc/ARwD+5SrYN8Skchowc8/y5j6HgbZtdleO3yOtptG93aHB48Fnyfqm0L\nXduFAaCQ0SMTteaFiGR60CnFO6pRDfsHDuLU2qlAujGNaiAASln/WLXFY5JdxtC2O1hpmICbwbGd\nZWiUb6IECEJAcWx8H4qpjEyvube0Fw+OPIU3Vp6Dxbo4VXseP4MnIjHsfM8RHw6jERLIopLSDCnK\nNlr0JlZGcdLrYwQOyjkNzQrva+XUGIaMXThTfwmm28Efnf4jZNhhnKu/Ius8UnwCac3v32vmrAyJ\nA4BS1sC9U2Uc21nEm1c3pGBqx3KQMTRk9CwAfj0rRh09akIThD8TnVKp+9C1gxN6zdwAsCtwrGu7\nyKZ+UNTZbfthm8MY5ir+ZmfnQBZdMV/TGMCBRL11HOwgGC2lMV21sVTrwLRdHuZCPSYIdAzk9ITU\n83xuDTNBYlkgCWtxedhbM1TMZRlK+vCOh6XeV6xg6BZSbKrCpr3OSkqXTRAVRt0sHEaCUpt4cx/f\n+TheuPYCyqkyClo58r2fCUZhgiRpeXhtMjSK+3YPoNKycGklmhkhzASJW4dJJgg8EKQ7wYUIFVkV\nx7XgMoaz9VegF/0wwlSATcvXZTkjh47T4dlhrHjKx1ahXMdlm+qf/LCsai1L5t5D1s/i7EJHMqsm\nB3gqZiH87b8r/vliLthRzuLaRhu2y7DaaEPsC0VZ1wujtViXzxlaFARRLfysVZbOSnsFdYWBndOD\n76OYl/n1o/0hHM7Uu49E1+o3akmhbJtZHAgiNvZb0c/oJxym3/NvND3uZtdSrVcXUp9pOBzm4YlH\nUM701lmKb1cI4KAmbMflIXcqCBIKhwELMkFs1gRjKpgu1kzBNZd9uzBBCCH/hRByy8VQPfsz8Owz\n/w2A3wPwLwGUAfwvAD7KWCB9x68D+BcAPgLg972//4V3PGy3Q9mbbm/PcMCBALh/N9/QyXAYbA2l\ndeEERBaTJjbG+IIrLg7UYZYMhxnqgwUCIKLzIY7FDWJxCHVYlwDwRVRBgEd23i+Pn628E7oOMFnm\nE0bb7qJjBQGj4zMbESXjsJ2ar+BvTixsWm7bbg87OVfFldUmzi3WA17ZJKt1LPz1iXm8emkNL5xf\ngRlaCDIwWB69eLne5d4yYuPCciMSJrPa8Bc6+ZQWBfQ2WRjykBYhtMnwzUvfxImVEwFWFmeC+OEw\nAFDt2HAZA2PAfLXllad4aA/vo4y5uNbiMckDxgRSNCUBEID3vbsGH8agweM6V7pX8dbyqzFMEC3R\nixRyfvI0nd4NKGUMZAwNAEHb9H4LcZFOtySVOK8NYCp3l4x5/8alb+BM9XVsWDwz1ETmIAZS48hq\nJeS9ReBaNyz0CowW0tA1it1D/kJz0QuJUb27Vh/hMMLERlKls7dD1M440kc4Vdy2fbDNdhnmKnUQ\nYw1pw8FQPiXnawoSmbApIUjRILgu5rXxYgYgXAR1pd4FAZ/36l0LAMNgPl6znYLrjPhMED6eNawN\nnK++g3VzTr7PA+kBDKXHA+erYTtiLNowl+T3Ows7lbJ8Yx/+Tf3SxQmia4OkcnHsktjN1c1wXYOH\n+v6DA/8Az+x+Jna8C4cviHYCiGTPUpukUSIBqkidfdwMVRRTMEHC44zt2mjZbTSsuhSlBgBDYYJo\n3jieM7xxMoYJooZ29GvL9Q6++vY1PHd2ue9zetnNZtNda5+W/15pL+PENR9c2DmQjZ3f4l6pibLf\n/xbr7UhZxhgsx4Xt8vS7QpwWSNAECWnjqEyQlfYK6pZgTBagkWSHXD+Cn0n9M06r7wedHUY1ARCr\nGWMkE2QLYM2N/gb1/JvCBEkEn6NOp/7q2zpw9dD4Q5FjjBDoxH+vCbFhOVxXiBD/GYSzw7gMGM2O\nAsx7h7V2YCyR2WFu43CYjwAY36zQzTAvG8xPMsYmGWMpxliRMfYhxti/Y4x1Q2VtxthvMcb2e2X3\ne58jcti3Q9mbbc2ujbMLfJLbP5qXucodRRNkK8YYw0ghBQYu3rbSmUHVrETKuXBwpvp6TA1cPFLE\new1novGHcRZHj1U9QGq5JCYIABweL2Igl8KdO0oBz9BIdgSTBR4yc6F6Uor5AHwQmRr0MhlTC+ut\n4MaHgeG4BzQlifQ4zIbLgIvLtyan9bbdXFuo+guSZoxyvmrvzlbw1+8t4Htn+ILNclzMbgQFLcVi\nBgBm1kR8vgPbZVisBkGW1ab/fuXSOt5Y/D4uN96Wi8jNvKQqCPLqwgv44zN/jK9f/Dr+7urfBQRL\nCSFIG9QDFoBqy4LjuhwEqfjt/9A+vpCq2avoeik0h9NBtgLA+55GKe4uPyvDYv7iwp+iERK4IsQD\nIEO/w3YttJ1a4JihGb4ngwBjxTQAAub1+1yaoOX6uhl5fRCUaLij9KR3L1y8u/G8dzrFocKjsux4\nZjcArh/Scfx+mdE17BvlHpCxUlqOEQseCGJQXY4n/WiCiLW/2EiqVPa2ZYbKupHNQufHgF6+bb65\nLsNC9zT0/EUMj8x56TL98NVw7ycAdE2PHKPwsioRBwBnqRFC0DJdGZ45lIvfCBHK+6juoZKMOWg7\ndTy/9GUcX38Bb6z/Jb63/P/grfVv4v3VE1jtLEbeW9FvhAdPgCCU0ADtGQCK6SCTZSvx8qqIas+R\nkcRvZJLi8/vZxPYTssNTcSYJS3KL0wQJe2Yp1fq6L+GNcFy7dWogQ7kuEvVAkLCDxmE2wLyUzTR+\n8yLAqrzOx0tCuzI7jLCqtYSlzuVYwc0k++7pZdguw0K1EwBnZtdbuLTS2BKgMl9p4ytvX5NCwzfD\ngtcnOHGtCoAhl9JQzKRimY5x7+ZEKSePC/FitSwDZ4LYzITpuDIcRiMGdAF8KhWHRXGLqaJ8t5aa\nS2h4DOyoKCoC+oCUUKlDJOoLv7+i3jiGdvjdvSkgSELf3cwE66KjiJgLDaXrze6SJPLb7++82aKo\nPS/bZ5siGkR9sLD2lffh0wc+jazm69YRBIVRQSxYLmeCBMHUKAiiUQ068QBVrY2OpUYc8HtuOkEQ\n5HbNDrNtt4m9d60i6eUP7PZRYRn7GzNY9zIGV4Z0zLXP4LW1b+AbV74U6xGtWauBVFXCRAw+AAyH\nRJj2FPdgKDMUPkVu2lQT+h+qSYpexFvGB52BnIHD4wUvzCAYYiOy0djMwmLnol8nodgzWPb+7WCj\nFUrdxFyeqQKIiIsJE0r+m41HLdPeFlG9DcxUNFzCXsqwnfb0KZZq/iR7ZbUZ0s1hsF0blu1iQZQ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EzbTMwMA4QyC8UwQQYzQecAAUVWKyFsRDmvH4ZEYnaYPvvOVq0fsGXTOkLtMrQQO7zH1vdG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\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SG/fTc0+DEipKurZ+iMdXXsEnN4qoWRyvXdlCmzbwyDG4MwWSEsSJK0G82vDR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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08a610f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(timedf.index.values, timedf.trip_duration, alpha=0.4, color='C0', label='Trips per day')\n",
    "plt.plot(timedf_wk.index.values, timedf_wk.trip_duration/7., color='C0', alpha=1, label='One week average of trips per day')\n",
    "\n",
    "plt.plot(timedf2.index.values, timedf2.trip_duration, alpha=0.4, color='C1', label='Guests Trips per day')\n",
    "plt.plot(timedf2_wk.index.values, timedf2_wk.trip_duration/7., color='C1', alpha=1, label='Guests One week average of trips per day')\n",
    "\n",
    "plt.plot(timedf3.index.values, timedf3.trip_duration, alpha=0.4, color='C2', label='Subscriber Trips per day')\n",
    "plt.plot(timedf3_wk.index.values, timedf3_wk.trip_duration/7., color='C2', alpha=1, label='Subscriber One week average of trips per day')\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel(\"Trips Per Day\")\n",
    "plt.gcf().set_size_inches(10, 5)\n",
    "\n",
    "# years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "yearsFmt = mdates.DateFormatter('%b %Y')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(yearsFmt)\n",
    "\n",
    "ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(10000))\n",
    "ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(2500))\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.xlim('2013-07-01', '2017-01-01')\n",
    "# plt.xlim('2016-06-01', '2016-08-01')\n",
    "# ax.xaxis.set_major_locator(mdates.WeekdayLocator())\n",
    "# ax.xaxis.set_minor_locator(mdates.WeekdayLocator())\n",
    "# ax.xaxis.set_major_formatter(yearsFmt)\n",
    "\n",
    "\n",
    "plt.ylim(0, 75000)\n",
    "# ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "plt.gcf().autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_df = pd.read_csv('../central_park_weather.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weather_df['DATE'] = pd.to_datetime(weather_df.DATE, format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "merged_counts = timedf.merge(weather_df['DATE PRCP SNWD SNOW TMAX TMIN AWND'.split()], left_index=True, right_on='DATE', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "merged_counts.columns = ['N', 'DATE', 'PRCP', 'SNWD', 'SNOW', 'TMAX', 'TMIN',\n",
    "       'AWND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api\n",
    "import statsmodels.formula.api\n",
    "from patsy import standardize as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>N</td>        <th>  R-squared:         </th> <td>   0.549</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.547</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   308.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 19 May 2017</td> <th>  Prob (F-statistic):</th> <td>1.81e-216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:09:19</td>     <th>  Log-Likelihood:    </th> <td> -13493.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1276</td>      <th>  AIC:               </th> <td>2.700e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1270</td>      <th>  BIC:               </th> <td>2.703e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 2.892e+04</td> <td>  265.571</td> <td>  108.898</td> <td> 0.000</td> <td> 2.84e+04  2.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(PRCP)</th>  <td>-3210.2392</td> <td>  273.517</td> <td>  -11.737</td> <td> 0.000</td> <td>-3746.835 -2673.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(SNWD)</th>  <td>-2102.8777</td> <td>  299.407</td> <td>   -7.023</td> <td> 0.000</td> <td>-2690.265 -1515.490</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(SNOW)</th>  <td> -182.5400</td> <td>  284.662</td> <td>   -0.641</td> <td> 0.521</td> <td> -741.000   375.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(TMAX)</th>  <td> 8357.3333</td> <td> 1012.308</td> <td>    8.256</td> <td> 0.000</td> <td> 6371.353  1.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(TMIN)</th>  <td>  422.5314</td> <td> 1020.685</td> <td>    0.414</td> <td> 0.679</td> <td>-1579.882  2424.945</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>126.581</td> <th>  Durbin-Watson:     </th> <td>   0.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 168.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.797</td>  <th>  Prob(JB):          </th> <td>2.26e-37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.798</td>  <th>  Cond. No.          </th> <td>    8.23</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      N   R-squared:                       0.549\n",
       "Model:                            OLS   Adj. R-squared:                  0.547\n",
       "Method:                 Least Squares   F-statistic:                     308.9\n",
       "Date:                Fri, 19 May 2017   Prob (F-statistic):          1.81e-216\n",
       "Time:                        15:09:19   Log-Likelihood:                -13493.\n",
       "No. Observations:                1276   AIC:                         2.700e+04\n",
       "Df Residuals:                    1270   BIC:                         2.703e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2.892e+04    265.571    108.898      0.000      2.84e+04  2.94e+04\n",
       "st(PRCP)   -3210.2392    273.517    -11.737      0.000     -3746.835 -2673.644\n",
       "st(SNWD)   -2102.8777    299.407     -7.023      0.000     -2690.265 -1515.490\n",
       "st(SNOW)    -182.5400    284.662     -0.641      0.521      -741.000   375.920\n",
       "st(TMAX)    8357.3333   1012.308      8.256      0.000      6371.353  1.03e+04\n",
       "st(TMIN)     422.5314   1020.685      0.414      0.679     -1579.882  2424.945\n",
       "==============================================================================\n",
       "Omnibus:                      126.581   Durbin-Watson:                   0.431\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              168.763\n",
       "Skew:                           0.797   Prob(JB):                     2.26e-37\n",
       "Kurtosis:                       3.798   Cond. No.                         8.23\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = statsmodels.formula.api.ols(formula=\"N ~ st(PRCP) + st(SNWD) + st(SNOW) + st(TMAX) + st(TMIN)\", data=merged_counts)\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>N</td>        <th>  R-squared:         </th> <td>   0.549</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.547</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   308.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 19 May 2017</td> <th>  Prob (F-statistic):</th> <td>1.81e-216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:10:17</td>     <th>  Log-Likelihood:    </th> <td> -13493.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1276</td>      <th>  AIC:               </th> <td>2.700e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1270</td>      <th>  BIC:               </th> <td>2.703e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 2.892e+04</td> <td>  265.571</td> <td>  108.898</td> <td> 0.000</td> <td> 2.84e+04  2.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(PRCP)</th>  <td>-3210.2392</td> <td>  273.517</td> <td>  -11.737</td> <td> 0.000</td> <td>-3746.835 -2673.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(SNWD)</th>  <td>-2102.8777</td> <td>  299.407</td> <td>   -7.023</td> <td> 0.000</td> <td>-2690.265 -1515.490</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(SNOW)</th>  <td> -182.5400</td> <td>  284.662</td> <td>   -0.641</td> <td> 0.521</td> <td> -741.000   375.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(TMAX)</th>  <td> 8357.3333</td> <td> 1012.308</td> <td>    8.256</td> <td> 0.000</td> <td> 6371.353  1.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>st(TMIN)</th>  <td>  422.5314</td> <td> 1020.685</td> <td>    0.414</td> <td> 0.679</td> <td>-1579.882  2424.945</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>126.581</td> <th>  Durbin-Watson:     </th> <td>   0.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 168.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.797</td>  <th>  Prob(JB):          </th> <td>2.26e-37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.798</td>  <th>  Cond. No.          </th> <td>    8.23</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      N   R-squared:                       0.549\n",
       "Model:                            OLS   Adj. R-squared:                  0.547\n",
       "Method:                 Least Squares   F-statistic:                     308.9\n",
       "Date:                Fri, 19 May 2017   Prob (F-statistic):          1.81e-216\n",
       "Time:                        15:10:17   Log-Likelihood:                -13493.\n",
       "No. Observations:                1276   AIC:                         2.700e+04\n",
       "Df Residuals:                    1270   BIC:                         2.703e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2.892e+04    265.571    108.898      0.000      2.84e+04  2.94e+04\n",
       "st(PRCP)   -3210.2392    273.517    -11.737      0.000     -3746.835 -2673.644\n",
       "st(SNWD)   -2102.8777    299.407     -7.023      0.000     -2690.265 -1515.490\n",
       "st(SNOW)    -182.5400    284.662     -0.641      0.521      -741.000   375.920\n",
       "st(TMAX)    8357.3333   1012.308      8.256      0.000      6371.353  1.03e+04\n",
       "st(TMIN)     422.5314   1020.685      0.414      0.679     -1579.882  2424.945\n",
       "==============================================================================\n",
       "Omnibus:                      126.581   Durbin-Watson:                   0.431\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              168.763\n",
       "Skew:                           0.797   Prob(JB):                     2.26e-37\n",
       "Kurtosis:                       3.798   Cond. No.                         8.23\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = statsmodels.formula.api.ols(formula=\"N ~ st(PRCP) + st(SNWD) + st(SNOW) + st(TMAX) + st(TMIN)\", data=merged_counts)\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class OLS in module statsmodels.regression.linear_model:\n",
      "\n",
      "class OLS(WLS)\n",
      " |  A simple ordinary least squares model.\n",
      " |  \n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  endog : array-like\n",
      " |      1-d endogenous response variable. The dependent variable.\n",
      " |  exog : array-like\n",
      " |      A nobs x k array where `nobs` is the number of observations and `k`\n",
      " |      is the number of regressors. An intercept is not included by default\n",
      " |      and should be added by the user. See\n",
      " |      :func:`statsmodels.tools.add_constant`.\n",
      " |  missing : str\n",
      " |      Available options are 'none', 'drop', and 'raise'. If 'none', no nan\n",
      " |      checking is done. If 'drop', any observations with nans are dropped.\n",
      " |      If 'raise', an error is raised. Default is 'none.'\n",
      " |  hasconst : None or bool\n",
      " |      Indicates whether the RHS includes a user-supplied constant. If True,\n",
      " |      a constant is not checked for and k_constant is set to 1 and all\n",
      " |      result statistics are calculated as if a constant is present. If\n",
      " |      False, a constant is not checked for and k_constant is set to 0.\n",
      " |  \n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  weights : scalar\n",
      " |      Has an attribute weights = array(1.0) due to inheritance from WLS.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  GLS\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> import numpy as np\n",
      " |  >>>\n",
      " |  >>> import statsmodels.api as sm\n",
      " |  >>>\n",
      " |  >>> Y = [1,3,4,5,2,3,4]\n",
      " |  >>> X = range(1,8)\n",
      " |  >>> X = sm.add_constant(X)\n",
      " |  >>>\n",
      " |  >>> model = sm.OLS(Y,X)\n",
      " |  >>> results = model.fit()\n",
      " |  >>> results.params\n",
      " |  array([ 2.14285714,  0.25      ])\n",
      " |  >>> results.tvalues\n",
      " |  array([ 1.87867287,  0.98019606])\n",
      " |  >>> print(results.t_test([1, 0])))\n",
      " |  <T test: effect=array([ 2.14285714]), sd=array([[ 1.14062282]]), t=array([[ 1.87867287]]), p=array([[ 0.05953974]]), df_denom=5>\n",
      " |  >>> print(results.f_test(np.identity(2)))\n",
      " |  <F test: F=array([[ 19.46078431]]), p=[[ 0.00437251]], df_denom=5, df_num=2>\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  No constant is added by the model unless you are using formulas.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      OLS\n",
      " |      WLS\n",
      " |      RegressionModel\n",
      " |      statsmodels.base.model.LikelihoodModel\n",
      " |      statsmodels.base.model.Model\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, endog, exog=None, missing='none', hasconst=None, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  loglike(self, params)\n",
      " |      The likelihood function for the clasical OLS model.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      params : array-like\n",
      " |          The coefficients with which to estimate the log-likelihood.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      The concentrated likelihood function evaluated at params.\n",
      " |  \n",
      " |  whiten(self, Y)\n",
      " |      OLS model whitener does nothing: returns Y.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from RegressionModel:\n",
      " |  \n",
      " |  fit(self, method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)\n",
      " |      Full fit of the model.\n",
      " |      \n",
      " |      The results include an estimate of covariance matrix, (whitened)\n",
      " |      residuals and an estimate of scale.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      method : str\n",
      " |          Can be \"pinv\", \"qr\".  \"pinv\" uses the Moore-Penrose pseudoinverse\n",
      " |          to solve the least squares problem. \"qr\" uses the QR\n",
      " |          factorization.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A RegressionResults class instance.\n",
      " |      \n",
      " |      See Also\n",
      " |      ---------\n",
      " |      regression.RegressionResults\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The fit method uses the pseudoinverse of the design/exogenous variables\n",
      " |      to solve the least squares minimization.\n",
      " |  \n",
      " |  fit_regularized(self, method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs)\n",
      " |      Return a regularized fit to a linear regression model.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      method : string\n",
      " |          Only the coordinate descent algorithm is implemented.\n",
      " |      maxiter : integer\n",
      " |          The maximum number of iteration cycles (an iteration cycle\n",
      " |          involves running coordinate descent on all variables).\n",
      " |      alpha : scalar or array-like\n",
      " |          The penalty weight.  If a scalar, the same penalty weight\n",
      " |          applies to all variables in the model.  If a vector, it\n",
      " |          must have the same length as `params`, and contains a\n",
      " |          penalty weight for each coefficient.\n",
      " |      L1_wt : scalar\n",
      " |          The fraction of the penalty given to the L1 penalty term.\n",
      " |          Must be between 0 and 1 (inclusive).  If 0, the fit is\n",
      " |          ridge regression.  If 1, the fit is the lasso.\n",
      " |      start_params : array-like\n",
      " |          Starting values for ``params``.\n",
      " |      cnvrg_tol : scalar\n",
      " |          If ``params`` changes by less than this amount (in sup-norm)\n",
      " |          in once iteration cycle, the algorithm terminates with\n",
      " |          convergence.\n",
      " |      zero_tol : scalar\n",
      " |          Any estimated coefficient smaller than this value is\n",
      " |          replaced with zero.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A RegressionResults object, of the same type returned by\n",
      " |      ``fit``.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The approach closely follows that implemented in the glmnet\n",
      " |      package in R.  The penalty is the \"elastic net\" penalty, which\n",
      " |      is a convex combination of L1 and L2 penalties.\n",
      " |      \n",
      " |      The function that is minimized is: ..math::\n",
      " |      \n",
      " |          0.5*RSS/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)\n",
      " |      \n",
      " |      where RSS is the usual regression sum of squares, n is the\n",
      " |      sample size, and :math:`|*|_1` and :math:`|*|_2` are the L1 and L2\n",
      " |      norms.\n",
      " |      \n",
      " |      Post-estimation results are based on the same data used to\n",
      " |      select variables, hence may be subject to overfitting biases.\n",
      " |      \n",
      " |      References\n",
      " |      ----------\n",
      " |      Friedman, Hastie, Tibshirani (2008).  Regularization paths for\n",
      " |      generalized linear models via coordinate descent.  Journal of\n",
      " |      Statistical Software 33(1), 1-22 Feb 2010.\n",
      " |  \n",
      " |  initialize(self)\n",
      " |      Initialize (possibly re-initialize) a Model instance. For\n",
      " |      instance, the design matrix of a linear model may change\n",
      " |      and some things must be recomputed.\n",
      " |  \n",
      " |  predict(self, params, exog=None)\n",
      " |      Return linear predicted values from a design matrix.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      params : array-like\n",
      " |          Parameters of a linear model\n",
      " |      exog : array-like, optional.\n",
      " |          Design / exogenous data. Model exog is used if None.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      An array of fitted values\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If the model has not yet been fit, params is not optional.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from RegressionModel:\n",
      " |  \n",
      " |  df_model\n",
      " |      The model degree of freedom, defined as the rank of the regressor\n",
      " |      matrix minus 1 if a constant is included.\n",
      " |  \n",
      " |  df_resid\n",
      " |      The residual degree of freedom, defined as the number of observations\n",
      " |      minus the rank of the regressor matrix.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from statsmodels.base.model.LikelihoodModel:\n",
      " |  \n",
      " |  hessian(self, params)\n",
      " |      The Hessian matrix of the model\n",
      " |  \n",
      " |  information(self, params)\n",
      " |      Fisher information matrix of model\n",
      " |      \n",
      " |      Returns -Hessian of loglike evaluated at params.\n",
      " |  \n",
      " |  score(self, params)\n",
      " |      Score vector of model.\n",
      " |      \n",
      " |      The gradient of logL with respect to each parameter.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from statsmodels.base.model.Model:\n",
      " |  \n",
      " |  from_formula(formula, data, subset=None, *args, **kwargs) from builtins.type\n",
      " |      Create a Model from a formula and dataframe.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      formula : str or generic Formula object\n",
      " |          The formula specifying the model\n",
      " |      data : array-like\n",
      " |          The data for the model. See Notes.\n",
      " |      subset : array-like\n",
      " |          An array-like object of booleans, integers, or index values that\n",
      " |          indicate the subset of df to use in the model. Assumes df is a\n",
      " |          `pandas.DataFrame`\n",
      " |      args : extra arguments\n",
      " |          These are passed to the model\n",
      " |      kwargs : extra keyword arguments\n",
      " |          These are passed to the model with one exception. The\n",
      " |          ``eval_env`` keyword is passed to patsy. It can be either a\n",
      " |          :class:`patsy:patsy.EvalEnvironment` object or an integer\n",
      " |          indicating the depth of the namespace to use. For example, the\n",
      " |          default ``eval_env=0`` uses the calling namespace. If you wish\n",
      " |          to use a \"clean\" environment set ``eval_env=-1``.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      model : Model instance\n",
      " |      \n",
      " |      Notes\n",
      " |      ------\n",
      " |      data must define __getitem__ with the keys in the formula terms\n",
      " |      args and kwargs are passed on to the model instantiation. E.g.,\n",
      " |      a numpy structured or rec array, a dictionary, or a pandas DataFrame.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from statsmodels.base.model.Model:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  endog_names\n",
      " |  \n",
      " |  exog_names\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(statsmodels.api.OLS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>is_bus_day</th>\n",
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       "      <th>start_station_id</th>\n",
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       "      <th>517</th>\n",
       "      <td>145338</td>\n",
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       "      <th>537</th>\n",
       "      <td>144539</td>\n",
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       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>140362</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>825</td>\n",
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       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>754</td>\n",
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       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>713</td>\n",
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       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>698</td>\n",
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       "    <tr>\n",
       "      <th>3348</th>\n",
       "      <td>667</td>\n",
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       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>658</td>\n",
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       "    <tr>\n",
       "      <th>3338</th>\n",
       "      <td>645</td>\n",
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       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>561</td>\n",
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       "    <tr>\n",
       "      <th>3333</th>\n",
       "      <td>509</td>\n",
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       "    <tr>\n",
       "      <th>3440</th>\n",
       "      <td>478</td>\n",
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       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>453</td>\n",
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       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>420</td>\n",
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       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>393</td>\n",
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       "    <tr>\n",
       "      <th>3394</th>\n",
       "      <td>325</td>\n",
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       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>303</td>\n",
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       "      <th>3326</th>\n",
       "      <td>284</td>\n",
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       "      <th>3245</th>\n",
       "      <td>177</td>\n",
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       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>170</td>\n",
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       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3371</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>3432</th>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3036</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3240</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>659 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  is_bus_day\n",
       "start_station_id            \n",
       "519                   356749\n",
       "497                   235838\n",
       "521                   233325\n",
       "435                   228568\n",
       "293                   214210\n",
       "402                   208014\n",
       "490                   206323\n",
       "477                   199371\n",
       "426                   196368\n",
       "285                   196145\n",
       "151                   178778\n",
       "444                   177460\n",
       "284                   176299\n",
       "379                   174838\n",
       "523                   173904\n",
       "459                   170351\n",
       "382                   166108\n",
       "327                   164709\n",
       "168                   161661\n",
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       "446                   140362\n",
       "...                      ...\n",
       "3302                     825\n",
       "3332                     754\n",
       "3401                     713\n",
       "3424                     698\n",
       "3348                     667\n",
       "3229                     658\n",
       "3338                     645\n",
       "3340                     561\n",
       "3333                     509\n",
       "3440                     478\n",
       "3395                     453\n",
       "3330                     420\n",
       "3393                     393\n",
       "3394                     325\n",
       "3342                     303\n",
       "3326                     284\n",
       "3130                     201\n",
       "3245                     177\n",
       "3219                     170\n",
       "3014                     100\n",
       "3371                      40\n",
       "3250                      25\n",
       "3432                      24\n",
       "3239                      18\n",
       "3017                      10\n",
       "3036                      10\n",
       "3040                       8\n",
       "3266                       7\n",
       "3240                       3\n",
       "3385                       1\n",
       "\n",
       "[659 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['is_bus_day', 'start_station_id']][df2.is_bus_day == True].groupby('start_station_id') \\\n",
    "    .count().compute().sort_values('is_bus_day', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>402</th>\n",
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       "      <td>260880</td>\n",
       "      <td>260880</td>\n",
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       "    <tr>\n",
       "      <th>151</th>\n",
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       "      <td>247791</td>\n",
       "      <td>247791</td>\n",
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       "      <td>245801</td>\n",
       "      <td>245801</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>236954</td>\n",
       "      <td>236954</td>\n",
       "      <td>236954</td>\n",
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       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>229820</td>\n",
       "      <td>229820</td>\n",
       "      <td>229820</td>\n",
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       "      <th>444</th>\n",
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       "      <td>229799</td>\n",
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       "      <th>459</th>\n",
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       "      <td>227610</td>\n",
       "      <td>227610</td>\n",
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       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>219669</td>\n",
       "      <td>219669</td>\n",
       "      <td>219669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>215974</td>\n",
       "      <td>215974</td>\n",
       "      <td>215974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>215419</td>\n",
       "      <td>215419</td>\n",
       "      <td>215419</td>\n",
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       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>213026</td>\n",
       "      <td>213026</td>\n",
       "      <td>213026</td>\n",
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       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>204413</td>\n",
       "      <td>204413</td>\n",
       "      <td>204413</td>\n",
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       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>200189</td>\n",
       "      <td>200189</td>\n",
       "      <td>200189</td>\n",
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       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>196410</td>\n",
       "      <td>196410</td>\n",
       "      <td>196410</td>\n",
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       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>191457</td>\n",
       "      <td>191457</td>\n",
       "      <td>191457</td>\n",
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       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>189364</td>\n",
       "      <td>189364</td>\n",
       "      <td>189364</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>187794</td>\n",
       "      <td>187794</td>\n",
       "      <td>187794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>186694</td>\n",
       "      <td>186694</td>\n",
       "      <td>186694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>185525</td>\n",
       "      <td>185525</td>\n",
       "      <td>185525</td>\n",
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       "    <tr>\n",
       "      <th>446</th>\n",
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       "      <td>181832</td>\n",
       "      <td>181832</td>\n",
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       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>180320</td>\n",
       "      <td>180320</td>\n",
       "      <td>180320</td>\n",
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       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>179485</td>\n",
       "      <td>179485</td>\n",
       "      <td>179485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>929</td>\n",
       "      <td>929</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>916</td>\n",
       "      <td>916</td>\n",
       "      <td>916</td>\n",
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       "    <tr>\n",
       "      <th>3338</th>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>897</td>\n",
       "      <td>897</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>794</td>\n",
       "      <td>794</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3333</th>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>610</td>\n",
       "      <td>610</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3440</th>\n",
       "      <td>588</td>\n",
       "      <td>588</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3395</th>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3394</th>\n",
       "      <td>450</td>\n",
       "      <td>450</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>429</td>\n",
       "      <td>429</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3130</th>\n",
       "      <td>351</td>\n",
       "      <td>351</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3257</th>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3253</th>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3371</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3432</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3252</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3036</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3240</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>662 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  is_bus_day  start_time  start_station_name\n",
       "start_station_id                                            \n",
       "519                   397813      397813              397813\n",
       "497                   315510      315510              315510\n",
       "435                   299698      299698              299698\n",
       "426                   280868      280868              280868\n",
       "293                   279981      279981              279981\n",
       "521                   268807      268807              268807\n",
       "285                   266162      266162              266162\n",
       "402                   260880      260880              260880\n",
       "151                   247791      247791              247791\n",
       "490                   245801      245801              245801\n",
       "284                   236954      236954              236954\n",
       "477                   229820      229820              229820\n",
       "444                   229799      229799              229799\n",
       "459                   227690      227690              227690\n",
       "368                   227610      227610              227610\n",
       "382                   219669      219669              219669\n",
       "327                   215974      215974              215974\n",
       "499                   215419      215419              215419\n",
       "358                   213026      213026              213026\n",
       "168                   204413      204413              204413\n",
       "379                   200189      200189              200189\n",
       "523                   196410      196410              196410\n",
       "387                   191457      191457              191457\n",
       "2006                  189364      189364              189364\n",
       "127                   187794      187794              187794\n",
       "514                   186694      186694              186694\n",
       "3002                  185525      185525              185525\n",
       "446                   181832      181832              181832\n",
       "472                   180320      180320              180320\n",
       "504                   179485      179485              179485\n",
       "...                      ...         ...                 ...\n",
       "3424                     929         929                 929\n",
       "3229                     916         916                 916\n",
       "3338                     900         900                 900\n",
       "255                      897         897                 897\n",
       "3340                     794         794                 794\n",
       "3333                     669         669                 669\n",
       "3330                     610         610                 610\n",
       "3440                     588         588                 588\n",
       "3395                     567         567                 567\n",
       "3393                     562         562                 562\n",
       "3394                     450         450                 450\n",
       "3326                     429         429                 429\n",
       "3342                     410         410                 410\n",
       "3130                     351         351                 351\n",
       "3014                     324         324                 324\n",
       "3219                     180         180                 180\n",
       "3257                     178         178                 178\n",
       "3245                     177         177                 177\n",
       "3253                      62          62                  62\n",
       "3371                      53          53                  53\n",
       "3250                      25          25                  25\n",
       "3432                      24          24                  24\n",
       "3252                      20          20                  20\n",
       "3239                      18          18                  18\n",
       "3017                      12          12                  12\n",
       "3040                      11          11                  11\n",
       "3036                      10          10                  10\n",
       "3266                       7           7                   7\n",
       "3240                       3           3                   3\n",
       "3385                       1           1                   1\n",
       "\n",
       "[662 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz = df2[['start_station_id', 'is_bus_day', 'start_time', 'start_station_name']].groupby('start_station_id') \\\n",
    " .count().compute().sort_values('is_bus_day', ascending=False)\n",
    "zz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 519,  497,  435,  426,  293,  521,  285,  402,  151,  490,\n",
       "            ...\n",
       "            3424, 3229, 3338,  255, 3340, 3333, 3330, 3440, 3395, 3393],\n",
       "           dtype='int64', name='start_station_id', length=642)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz[zz.is_bus_day>500].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3 = df2[df2.start_station_id.isin(zz[zz.is_bus_day>500].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time                 36899280\n",
       "trip_duration              36899280\n",
       "stop_time                  36899280\n",
       "start_station_id           36899280\n",
       "start_station_name         36899280\n",
       "start_station_latitude     36899280\n",
       "start_station_longitude    36899280\n",
       "end_station_id             36899280\n",
       "end_station_name           36899280\n",
       "end_station_latitude       36899280\n",
       "end_station_longitude      36899280\n",
       "bike_id                    36899280\n",
       "user_type                  36863421\n",
       "birth_year                 32541201\n",
       "gender                     36899280\n",
       "start_taxizone_id          36899266\n",
       "end_taxizone_id            36898966\n",
       "is_bus_day                 36899280\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trip_duration              36902025\n",
       "stop_time                  36902025\n",
       "start_station_id           36902025\n",
       "start_station_name         36902025\n",
       "start_station_latitude     36902025\n",
       "start_station_longitude    36902025\n",
       "end_station_id             36902025\n",
       "end_station_name           36902025\n",
       "end_station_latitude       36902025\n",
       "end_station_longitude      36902025\n",
       "bike_id                    36902025\n",
       "user_type                  36866154\n",
       "birth_year                 32543206\n",
       "gender                     36902025\n",
       "start_taxizone_id          36902006\n",
       "end_taxizone_id            36901708\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_stations = pd.DataFrame(json.load(open(\"../15_dataframe_analysis/stations.2017.04.20.09.43.json\"))['stationBeanList'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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