Convert Geohash Precision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import geopandas\n",
    "import shapely.geometry as geom\n",
    "from polygon_geohasher.polygon_geohasher import polygon_to_geohashes, geohashes_to_polygon\n",
    "import pygeohash as gh\n",
    "%matplotlib inline\n",
    "import geohash\n",
    "import geopandas as gp\n",
    "import pandas as pd\n",
    "import math\n",
    "import geojson\n",
    "from geojson import MultiLineString\n",
    "from shapely.geometry import MultiLineString\n",
    "from shapely.geometry import MultiPoint\n",
    "import numpy as np\n",
    "from shapely import geometry\n",
    "from shapely.geometry import Point, Polygon, box,LineString\n",
    "from geopandas import datasets, GeoDataFrame, read_file\n",
    "from geopandas.tools import overlay\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry.polygon import LinearRing, Polygon\n",
    "from numpy import cos,sin,arccos\n",
    "import numpy as np\n",
    "from pylab import *\n",
    "from shapely.ops import cascaded_union\n",
    "from shapely.geometry import Point, LineString, mapping\n",
    "import scipy.interpolate\n",
    "import geopy\n",
    "import geopy.distance\n",
    "import time\n",
    "import networkx as nx\n",
    "from sklearn.model_selection import train_test_split\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_geohash = pd.read_csv('processed_geohash.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_geohash['geohash_source'] = processed_geohash['geohash_source'].astype(str).str[:6]\n",
    "processed_geohash['geohash_dest'] = processed_geohash['geohash_dest'].astype(str).str[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_geohash['timestamp_source'] = pd.to_datetime(processed_geohash['timestamp_source'])\n",
    "processed_geohash['timestamp_dest'] = pd.to_datetime(processed_geohash['timestamp_dest'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>geohash_source</th>\n",
       "      <th>geohash_dest</th>\n",
       "      <th>timestamp_source</th>\n",
       "      <th>timestamp_dest</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>2019-04-16 02:44:15</td>\n",
       "      <td>2019-04-16 02:44:22</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>2019-04-16 02:44:22</td>\n",
       "      <td>2019-04-16 02:44:32</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>2019-04-16 02:44:32</td>\n",
       "      <td>2019-04-16 02:44:37</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>2019-04-16 02:44:37</td>\n",
       "      <td>2019-04-16 02:44:41</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zt5</td>\n",
       "      <td>2019-04-16 02:44:41</td>\n",
       "      <td>2019-04-16 02:44:50</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trj_id geohash_source geohash_dest    timestamp_source      timestamp_dest  \\\n",
       "0     100         w21zth       w21zth 2019-04-16 02:44:15 2019-04-16 02:44:22   \n",
       "1     100         w21zth       w21zth 2019-04-16 02:44:22 2019-04-16 02:44:32   \n",
       "2     100         w21zth       w21zth 2019-04-16 02:44:32 2019-04-16 02:44:37   \n",
       "3     100         w21zth       w21zth 2019-04-16 02:44:37 2019-04-16 02:44:41   \n",
       "4     100         w21zth       w21zt5 2019-04-16 02:44:41 2019-04-16 02:44:50   \n",
       "\n",
       "   time_taken  hour  day  \n",
       "0         7.0     2    1  \n",
       "1        10.0     2    1  \n",
       "2         5.0     2    1  \n",
       "3         4.0     2    1  \n",
       "4         9.0     2    1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_geohash.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trj_id                       int64\n",
       "geohash_source              object\n",
       "geohash_dest                object\n",
       "timestamp_source    datetime64[ns]\n",
       "timestamp_dest      datetime64[ns]\n",
       "time_taken                 float64\n",
       "hour                         int64\n",
       "day                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_geohash.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_arr = []\n",
    "initial_trj_id = ''\n",
    "initial_geohash = ''\n",
    "initial_timestamp = ''\n",
    "time_taken = 0\n",
    "for row in processed_geohash.iterrows():\n",
    "    # if current trip id is the same as the previous id, check for difference in geohash\n",
    "    if row[1]['trj_id'] == initial_trj_id:\n",
    "        if row[1]['geohash_source'] != initial_geohash:\n",
    "            temp_arr = []\n",
    "            temp_arr.append(row[1]['trj_id'])\n",
    "            temp_arr.append(initial_geohash)\n",
    "            temp_arr.append(row[1]['geohash_dest'])\n",
    "            temp_arr.append(initial_timestamp)\n",
    "            temp_arr.append(row[1]['timestamp_dest'])\n",
    "            #print(initial_timestamp, 'initial_timestamp')\n",
    "            #print(row[1]['datetime'], 'row datetime')\n",
    "            #print(row[1]['datetime'] - initial_timestamp, 'difference')\n",
    "            temp_arr.append((row[1]['timestamp_dest'] -initial_timestamp).total_seconds())\n",
    "            temp_arr.append(row[1]['hour'])\n",
    "            temp_arr.append(row[1]['day'])\n",
    "            \n",
    "            initial_timestamp = row[1]['timestamp_source']\n",
    "            initial_geohash = row[1]['geohash_source']\n",
    "            \n",
    "            main_arr.append(temp_arr)\n",
    "            \n",
    "        \n",
    "    elif row[1]['trj_id'] != initial_trj_id:\n",
    "        initial_trj_id = row[1]['trj_id']\n",
    "        initial_timestamp = row[1]['timestamp_source']\n",
    "        initial_geohash = row[1]['geohash_source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
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       "      <td>2019-04-16 02:44:15</td>\n",
       "      <td>2019-04-16 02:44:53</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zt5</td>\n",
       "      <td>w21zsg</td>\n",
       "      <td>2019-04-16 02:44:50</td>\n",
       "      <td>2019-04-16 02:48:14</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>w21zsf</td>\n",
       "      <td>2019-04-16 02:48:06</td>\n",
       "      <td>2019-04-16 02:48:42</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
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       "      <td>w21zsd</td>\n",
       "      <td>2019-04-16 02:48:35</td>\n",
       "      <td>2019-04-16 02:49:07</td>\n",
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       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zsd</td>\n",
       "      <td>w21zs9</td>\n",
       "      <td>2019-04-16 02:49:03</td>\n",
       "      <td>2019-04-16 02:49:37</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>100</td>\n",
       "      <td>w21zs9</td>\n",
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       "      <td>2019-04-16 02:49:36</td>\n",
       "      <td>2019-04-16 02:50:06</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zs3</td>\n",
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       "      <td>2019-04-16 02:49:59</td>\n",
       "      <td>2019-04-16 02:51:05</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zs1</td>\n",
       "      <td>w21zs0</td>\n",
       "      <td>2019-04-16 02:50:58</td>\n",
       "      <td>2019-04-16 02:51:12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zs0</td>\n",
       "      <td>w21zeb</td>\n",
       "      <td>2019-04-16 02:51:10</td>\n",
       "      <td>2019-04-16 02:51:57</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zeb</td>\n",
       "      <td>w21ze8</td>\n",
       "      <td>2019-04-16 02:51:51</td>\n",
       "      <td>2019-04-16 02:53:36</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze8</td>\n",
       "      <td>w21ze9</td>\n",
       "      <td>2019-04-16 02:53:27</td>\n",
       "      <td>2019-04-16 02:54:07</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze9</td>\n",
       "      <td>w21ze6</td>\n",
       "      <td>2019-04-16 02:53:59</td>\n",
       "      <td>2019-04-16 02:54:55</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zed</td>\n",
       "      <td>w21ze6</td>\n",
       "      <td>2019-04-16 02:54:54</td>\n",
       "      <td>2019-04-16 02:55:04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze6</td>\n",
       "      <td>w21ze3</td>\n",
       "      <td>2019-04-16 02:54:55</td>\n",
       "      <td>2019-04-16 02:55:36</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze3</td>\n",
       "      <td>w21ze6</td>\n",
       "      <td>2019-04-16 02:55:34</td>\n",
       "      <td>2019-04-16 02:56:03</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze6</td>\n",
       "      <td>w21ze4</td>\n",
       "      <td>2019-04-16 02:55:53</td>\n",
       "      <td>2019-04-16 02:56:45</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>w21ze4</td>\n",
       "      <td>w21ze5</td>\n",
       "      <td>2019-04-16 02:56:33</td>\n",
       "      <td>2019-04-16 02:59:17</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1000</td>\n",
       "      <td>w23b1v</td>\n",
       "      <td>w23b1s</td>\n",
       "      <td>2019-04-11 00:49:33</td>\n",
       "      <td>2019-04-11 00:50:18</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1000</td>\n",
       "      <td>w23b1u</td>\n",
       "      <td>w23b1s</td>\n",
       "      <td>2019-04-11 00:49:52</td>\n",
       "      <td>2019-04-11 00:50:34</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1000</td>\n",
       "      <td>w23b1s</td>\n",
       "      <td>w23b1e</td>\n",
       "      <td>2019-04-11 00:50:18</td>\n",
       "      <td>2019-04-11 00:51:15</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0       1       2                   3                   4      5  6  7\n",
       "0    100  w21zth  w21zt5 2019-04-16 02:44:15 2019-04-16 02:44:53   38.0  2  1\n",
       "1    100  w21zt5  w21zsg 2019-04-16 02:44:50 2019-04-16 02:48:14  204.0  2  1\n",
       "2    100  w21zsg  w21zsf 2019-04-16 02:48:06 2019-04-16 02:48:42   36.0  2  1\n",
       "3    100  w21zsf  w21zsd 2019-04-16 02:48:35 2019-04-16 02:49:07   32.0  2  1\n",
       "4    100  w21zsd  w21zs9 2019-04-16 02:49:03 2019-04-16 02:49:37   34.0  2  1\n",
       "5    100  w21zs9  w21zs3 2019-04-16 02:49:36 2019-04-16 02:50:06   30.0  2  1\n",
       "6    100  w21zs3  w21zs1 2019-04-16 02:49:59 2019-04-16 02:51:05   66.0  2  1\n",
       "7    100  w21zs1  w21zs0 2019-04-16 02:50:58 2019-04-16 02:51:12   14.0  2  1\n",
       "8    100  w21zs0  w21zeb 2019-04-16 02:51:10 2019-04-16 02:51:57   47.0  2  1\n",
       "9    100  w21zeb  w21ze8 2019-04-16 02:51:51 2019-04-16 02:53:36  105.0  2  1\n",
       "10   100  w21ze8  w21ze9 2019-04-16 02:53:27 2019-04-16 02:54:07   40.0  2  1\n",
       "11   100  w21ze9  w21ze6 2019-04-16 02:53:59 2019-04-16 02:54:55   56.0  2  1\n",
       "12   100  w21zed  w21ze6 2019-04-16 02:54:54 2019-04-16 02:55:04   10.0  2  1\n",
       "13   100  w21ze6  w21ze3 2019-04-16 02:54:55 2019-04-16 02:55:36   41.0  2  1\n",
       "14   100  w21ze3  w21ze6 2019-04-16 02:55:34 2019-04-16 02:56:03   29.0  2  1\n",
       "15   100  w21ze6  w21ze4 2019-04-16 02:55:53 2019-04-16 02:56:45   52.0  2  1\n",
       "16   100  w21ze4  w21ze5 2019-04-16 02:56:33 2019-04-16 02:59:17  164.0  2  1\n",
       "17  1000  w23b1v  w23b1s 2019-04-11 00:49:33 2019-04-11 00:50:18   45.0  0  3\n",
       "18  1000  w23b1u  w23b1s 2019-04-11 00:49:52 2019-04-11 00:50:34   42.0  0  3\n",
       "19  1000  w23b1s  w23b1e 2019-04-11 00:50:18 2019-04-11 00:51:15   57.0  0  3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view = pd.DataFrame(main_arr)\n",
    "view.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "view.columns = ['trj_id', 'geohash_source', 'geohash_dest', 'timestamp_source', 'timestamp_dest', 'time_taken', 'hour','day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trj_id</th>\n",
       "      <th>geohash_source</th>\n",
       "      <th>geohash_dest</th>\n",
       "      <th>timestamp_source</th>\n",
       "      <th>timestamp_dest</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zth</td>\n",
       "      <td>w21zt5</td>\n",
       "      <td>2019-04-16 02:44:15</td>\n",
       "      <td>2019-04-16 02:44:53</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zt5</td>\n",
       "      <td>w21zsg</td>\n",
       "      <td>2019-04-16 02:44:50</td>\n",
       "      <td>2019-04-16 02:48:14</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zsg</td>\n",
       "      <td>w21zsf</td>\n",
       "      <td>2019-04-16 02:48:06</td>\n",
       "      <td>2019-04-16 02:48:42</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zsf</td>\n",
       "      <td>w21zsd</td>\n",
       "      <td>2019-04-16 02:48:35</td>\n",
       "      <td>2019-04-16 02:49:07</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>w21zsd</td>\n",
       "      <td>w21zs9</td>\n",
       "      <td>2019-04-16 02:49:03</td>\n",
       "      <td>2019-04-16 02:49:37</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trj_id geohash_source geohash_dest    timestamp_source      timestamp_dest  \\\n",
       "0     100         w21zth       w21zt5 2019-04-16 02:44:15 2019-04-16 02:44:53   \n",
       "1     100         w21zt5       w21zsg 2019-04-16 02:44:50 2019-04-16 02:48:14   \n",
       "2     100         w21zsg       w21zsf 2019-04-16 02:48:06 2019-04-16 02:48:42   \n",
       "3     100         w21zsf       w21zsd 2019-04-16 02:48:35 2019-04-16 02:49:07   \n",
       "4     100         w21zsd       w21zs9 2019-04-16 02:49:03 2019-04-16 02:49:37   \n",
       "\n",
       "   time_taken  hour  day  \n",
       "0        38.0     2    1  \n",
       "1       204.0     2    1  \n",
       "2        36.0     2    1  \n",
       "3        32.0     2    1  \n",
       "4        34.0     2    1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "view.to_csv('processed_geohash_6.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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