1.3-GTFS_to_networkx.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "inspired by https://github.com/Data-Monkey/GTFS-NetworkX/blob/master/GTFStoGraph.py\n",
    "\n",
    "GTFS API: https://developers.google.com/transit/gtfs/reference#agencytxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the GTFS Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROUTES = pd.read_csv('cta_data/routes.txt')\n",
    "STOPS = pd.read_csv('cta_data/stops.txt')\n",
    "TRIPS = pd.read_csv('cta_data/trips.txt')\n",
    "STOP_TIMES = pd.read_csv('cta_data/stop_times.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the Data for \"L\" Train info only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We (for now) only want to model subways/metros, which use `route_type=1`, so we get a list of the `route_id`s that correspond ot the subway (\"L\") lines"
   ]
  },
  {
   "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>route_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>route_type</th>\n",
       "      <th>route_url</th>\n",
       "      <th>route_color</th>\n",
       "      <th>route_text_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Red</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Red Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/redline/</td>\n",
       "      <td>C60C30</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Purple Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/purpleline/</td>\n",
       "      <td>522398</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yellow Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/yellowline/</td>\n",
       "      <td>F9E300</td>\n",
       "      <td>000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blue Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/blueline/</td>\n",
       "      <td>00A1DE</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Pink</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pink Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/pinkline/</td>\n",
       "      <td>E27EA6</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Green Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/greenline/</td>\n",
       "      <td>009B3A</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Org</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orange Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/orangeline/</td>\n",
       "      <td>F9461C</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Brn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown Line</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.transitchicago.com/brownline/</td>\n",
       "      <td>62361B</td>\n",
       "      <td>FFFFFF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    route_id route_short_name route_long_name  route_type  \\\n",
       "126      Red              NaN        Red Line           1   \n",
       "127        P              NaN     Purple Line           1   \n",
       "128        Y              NaN     Yellow Line           1   \n",
       "129     Blue              NaN       Blue Line           1   \n",
       "130     Pink              NaN       Pink Line           1   \n",
       "131        G              NaN      Green Line           1   \n",
       "132      Org              NaN     Orange Line           1   \n",
       "133      Brn              NaN      Brown Line           1   \n",
       "\n",
       "                                     route_url route_color route_text_color  \n",
       "126     http://www.transitchicago.com/redline/      C60C30           FFFFFF  \n",
       "127  http://www.transitchicago.com/purpleline/      522398           FFFFFF  \n",
       "128  http://www.transitchicago.com/yellowline/      F9E300           000000  \n",
       "129    http://www.transitchicago.com/blueline/      00A1DE           FFFFFF  \n",
       "130    http://www.transitchicago.com/pinkline/      E27EA6           FFFFFF  \n",
       "131   http://www.transitchicago.com/greenline/      009B3A           FFFFFF  \n",
       "132  http://www.transitchicago.com/orangeline/      F9461C           FFFFFF  \n",
       "133   http://www.transitchicago.com/brownline/      62361B           FFFFFF  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROUTES_subway = ROUTES[ROUTES['route_type']==1]\n",
    "# record subway route_ids\n",
    "route_ids_subway = ROUTES_subway['route_id'].values.tolist()\n",
    "ROUTES_subway"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we will filter the `TRIPS` data so that it only includes trips for subway routes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([60164372393, 60164283124, 60164283213, 60166141078, 60166141628, 60161923601, 60166204913, 60166204634])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter to only include trips with subway route_ids\n",
    "TRIPS_subway = TRIPS[TRIPS['route_id'].isin(route_ids_subway)]\n",
    "\n",
    "# # only include one direction to avoid redundancies\n",
    "# TRIPS_subway = TRIPS_subway[TRIPS_subway['direction_id']==1]\n",
    "trip_ids_subway = list(TRIPS_subway['trip_id'].values)\n",
    "\n",
    "\n",
    "trip_id_dict = {}\n",
    "\n",
    "for route_id in route_ids_subway:\n",
    "    trip_id_dict[route_id] = TRIPS_subway[TRIPS_subway['route_id']==route_id].iloc[0]['trip_id']\n",
    "\n",
    "trip_id_dict.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>index</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_sequence</th>\n",
       "      <th>stop_headsign</th>\n",
       "      <th>pickup_type</th>\n",
       "      <th>shape_dist_traveled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14608</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>30003</td>\n",
       "      <td>1</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14609</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:02:00</td>\n",
       "      <td>04:02:00</td>\n",
       "      <td>30263</td>\n",
       "      <td>2</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>2558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14610</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:03:30</td>\n",
       "      <td>04:03:30</td>\n",
       "      <td>30119</td>\n",
       "      <td>3</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>5317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14611</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:05:00</td>\n",
       "      <td>04:05:00</td>\n",
       "      <td>30243</td>\n",
       "      <td>4</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>7912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14612</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:06:30</td>\n",
       "      <td>04:06:30</td>\n",
       "      <td>30054</td>\n",
       "      <td>5</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>10223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>136359</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:14:00</td>\n",
       "      <td>04:14:00</td>\n",
       "      <td>30023</td>\n",
       "      <td>20</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>90716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>136360</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:18:00</td>\n",
       "      <td>04:18:00</td>\n",
       "      <td>30061</td>\n",
       "      <td>21</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>101776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>136361</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:20:00</td>\n",
       "      <td>04:20:00</td>\n",
       "      <td>30220</td>\n",
       "      <td>22</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>136362</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:22:00</td>\n",
       "      <td>04:22:00</td>\n",
       "      <td>30186</td>\n",
       "      <td>23</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>113177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>136363</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:24:30</td>\n",
       "      <td>04:24:30</td>\n",
       "      <td>30182</td>\n",
       "      <td>24</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>120101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index      trip_id arrival_time departure_time  stop_id  stop_sequence  \\\n",
       "0     14608  60161923601     04:00:00       04:00:00    30003              1   \n",
       "1     14609  60161923601     04:02:00       04:02:00    30263              2   \n",
       "2     14610  60161923601     04:03:30       04:03:30    30119              3   \n",
       "3     14611  60161923601     04:05:00       04:05:00    30243              4   \n",
       "4     14612  60161923601     04:06:30       04:06:30    30054              5   \n",
       "..      ...          ...          ...            ...      ...            ...   \n",
       "209  136359  60166204913     04:14:00       04:14:00    30023             20   \n",
       "210  136360  60166204913     04:18:00       04:18:00    30061             21   \n",
       "211  136361  60166204913     04:20:00       04:20:00    30220             22   \n",
       "212  136362  60166204913     04:22:00       04:22:00    30186             23   \n",
       "213  136363  60166204913     04:24:30       04:24:30    30182             24   \n",
       "\n",
       "     stop_headsign  pickup_type  shape_dist_traveled  \n",
       "0    Cottage Grove            0                    0  \n",
       "1    Cottage Grove            0                 2558  \n",
       "2    Cottage Grove            0                 5317  \n",
       "3    Cottage Grove            0                 7912  \n",
       "4    Cottage Grove            0                10223  \n",
       "..             ...          ...                  ...  \n",
       "209         Midway            0                90716  \n",
       "210         Midway            0               101776  \n",
       "211         Midway            0               107300  \n",
       "212         Midway            0               113177  \n",
       "213         Midway            0               120101  \n",
       "\n",
       "[213 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STOP_TIMES_subway = STOP_TIMES[STOP_TIMES['trip_id'].isin(trip_id_dict.values())].reset_index()\n",
    "STOP_TIMES_subway[STOP_TIMES_subway['pickup_type'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only want to track `stations` (`location_type=1`), but `STOP_TIMES` gives information for `platforms`(`location_type=0`). So first we need to assign the parent for each of the stops in `STOP_TIMES`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOPS_stations = STOPS[STOPS['location_type']==1]\n",
    "# stop_ids_stations = list(STOPS_stations['stop_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_parent(stop_id):\n",
    "    parent = STOPS[STOPS['stop_id']==stop_id]['parent_station'].values\n",
    "    if len(parent) == 0:\n",
    "        return int(stop_id)\n",
    "    else:\n",
    "        return int(parent[0])\n",
    "\n",
    "STOP_TIMES_subway['station_id'] = STOP_TIMES_subway['stop_id'].apply(get_parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>trip_id</th>\n",
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       "      <th>departure_time</th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_sequence</th>\n",
       "      <th>stop_headsign</th>\n",
       "      <th>pickup_type</th>\n",
       "      <th>shape_dist_traveled</th>\n",
       "      <th>station_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14608</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>30003</td>\n",
       "      <td>1</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14609</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:02:00</td>\n",
       "      <td>04:02:00</td>\n",
       "      <td>30263</td>\n",
       "      <td>2</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>2558</td>\n",
       "      <td>41350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14610</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:03:30</td>\n",
       "      <td>04:03:30</td>\n",
       "      <td>30119</td>\n",
       "      <td>3</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>5317</td>\n",
       "      <td>40610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14611</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:05:00</td>\n",
       "      <td>04:05:00</td>\n",
       "      <td>30243</td>\n",
       "      <td>4</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>7912</td>\n",
       "      <td>41260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14612</td>\n",
       "      <td>60161923601</td>\n",
       "      <td>04:06:30</td>\n",
       "      <td>04:06:30</td>\n",
       "      <td>30054</td>\n",
       "      <td>5</td>\n",
       "      <td>Cottage Grove</td>\n",
       "      <td>0</td>\n",
       "      <td>10223</td>\n",
       "      <td>40280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>136359</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:14:00</td>\n",
       "      <td>04:14:00</td>\n",
       "      <td>30023</td>\n",
       "      <td>20</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>90716</td>\n",
       "      <td>40120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>136360</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:18:00</td>\n",
       "      <td>04:18:00</td>\n",
       "      <td>30061</td>\n",
       "      <td>21</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>101776</td>\n",
       "      <td>40310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>136361</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:20:00</td>\n",
       "      <td>04:20:00</td>\n",
       "      <td>30220</td>\n",
       "      <td>22</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>107300</td>\n",
       "      <td>41150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>136362</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:22:00</td>\n",
       "      <td>04:22:00</td>\n",
       "      <td>30186</td>\n",
       "      <td>23</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>113177</td>\n",
       "      <td>40960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>136363</td>\n",
       "      <td>60166204913</td>\n",
       "      <td>04:24:30</td>\n",
       "      <td>04:24:30</td>\n",
       "      <td>30182</td>\n",
       "      <td>24</td>\n",
       "      <td>Midway</td>\n",
       "      <td>0</td>\n",
       "      <td>120101</td>\n",
       "      <td>40930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index      trip_id arrival_time departure_time  stop_id  stop_sequence  \\\n",
       "0     14608  60161923601     04:00:00       04:00:00    30003              1   \n",
       "1     14609  60161923601     04:02:00       04:02:00    30263              2   \n",
       "2     14610  60161923601     04:03:30       04:03:30    30119              3   \n",
       "3     14611  60161923601     04:05:00       04:05:00    30243              4   \n",
       "4     14612  60161923601     04:06:30       04:06:30    30054              5   \n",
       "..      ...          ...          ...            ...      ...            ...   \n",
       "209  136359  60166204913     04:14:00       04:14:00    30023             20   \n",
       "210  136360  60166204913     04:18:00       04:18:00    30061             21   \n",
       "211  136361  60166204913     04:20:00       04:20:00    30220             22   \n",
       "212  136362  60166204913     04:22:00       04:22:00    30186             23   \n",
       "213  136363  60166204913     04:24:30       04:24:30    30182             24   \n",
       "\n",
       "     stop_headsign  pickup_type  shape_dist_traveled  station_id  \n",
       "0    Cottage Grove            0                    0       40020  \n",
       "1    Cottage Grove            0                 2558       41350  \n",
       "2    Cottage Grove            0                 5317       40610  \n",
       "3    Cottage Grove            0                 7912       41260  \n",
       "4    Cottage Grove            0                10223       40280  \n",
       "..             ...          ...                  ...         ...  \n",
       "209         Midway            0                90716       40120  \n",
       "210         Midway            0               101776       40310  \n",
       "211         Midway            0               107300       41150  \n",
       "212         Midway            0               113177       40960  \n",
       "213         Midway            0               120101       40930  \n",
       "\n",
       "[214 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STOP_TIMES_subway"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use trip information to determine graph edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_stops = []\n",
    "dest_stops = []\n",
    "trip_ids = []\n",
    "\n",
    "edges_list = []\n",
    "\n",
    "for trip_id, group in STOP_TIMES_subway.groupby('trip_id'):\n",
    "        for i in group.index[:-1]:\n",
    "            origin_stop = STOP_TIMES_subway['station_id'][i]\n",
    "    #         print(i)\n",
    "            dest_stop = STOP_TIMES_subway['station_id'][i+1]\n",
    "            if (STOP_TIMES_subway['stop_sequence'][i+1] - STOP_TIMES_subway['stop_sequence'][i]) == 1:\n",
    "                origin_stops.append(origin_stop)\n",
    "                dest_stops.append(dest_stop)\n",
    "                trip_ids.append(trip_id)\n",
    "                edges_list.append((int(origin_stop), int(dest_stop)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(STOP_TIMES_subway['trip_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_stop</th>\n",
       "      <th>dest_stop</th>\n",
       "      <th>trip_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40020</td>\n",
       "      <td>41350</td>\n",
       "      <td>60161923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41350</td>\n",
       "      <td>40610</td>\n",
       "      <td>60161923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40610</td>\n",
       "      <td>41260</td>\n",
       "      <td>60161923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41260</td>\n",
       "      <td>40280</td>\n",
       "      <td>60161923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40280</td>\n",
       "      <td>40700</td>\n",
       "      <td>60161923601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>41060</td>\n",
       "      <td>40120</td>\n",
       "      <td>60166204913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>40120</td>\n",
       "      <td>40310</td>\n",
       "      <td>60166204913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>40310</td>\n",
       "      <td>41150</td>\n",
       "      <td>60166204913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>41150</td>\n",
       "      <td>40960</td>\n",
       "      <td>60166204913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>40960</td>\n",
       "      <td>40930</td>\n",
       "      <td>60166204913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     origin_stop  dest_stop      trip_id\n",
       "0          40020      41350  60161923601\n",
       "1          41350      40610  60161923601\n",
       "2          40610      41260  60161923601\n",
       "3          41260      40280  60161923601\n",
       "4          40280      40700  60161923601\n",
       "..           ...        ...          ...\n",
       "201        41060      40120  60166204913\n",
       "202        40120      40310  60166204913\n",
       "203        40310      41150  60166204913\n",
       "204        41150      40960  60166204913\n",
       "205        40960      40930  60166204913\n",
       "\n",
       "[206 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges = pd.DataFrame({'origin_stop':origin_stops, 'dest_stop':dest_stops, 'trip_id':trip_ids})\n",
    "edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's play around with just stops data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_stops_raw = pd.read_csv(\"cta_data/CTA_-_System_Information_-_List_of__L__Stops_-_Map.csv\")\n",
    "# convert coords to a readable dict\n",
    "locations = l_stops_raw.Location.apply(lambda x: x[1:-1].split(\",\"))\n",
    "coords = pd.DataFrame(locations.tolist(), columns=['lat', 'long']).astype('float')\n",
    "\n",
    "l_stops = l_stops_raw.drop(['Location'], axis=1)\n",
    "l_stops = l_stops.join(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_stops['STATION_ID'] = l_stops['STOP_ID'].apply(get_parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in stop colors\n",
    "stop_colors = l_stops[['STATION_ID', 'RED', 'BLUE', 'G', 'BRN', 'P', 'Pexp', 'Y','Pnk', 'O']]\n",
    "\n",
    "color_conversion = {'RED':'red', 'BLUE':'blue', 'G':'green', 'BRN':'brown', 'P':'purple', 'Pexp':'purple', 'Y':'yellow','Pnk':'pink', 'O':'orange'}\n",
    "\n",
    "colors_dict = {}\n",
    "for i in stop_colors.index:\n",
    "    colors_list = []\n",
    "    for c in stop_colors.columns.astype('str'):\n",
    "        if stop_colors[c][i] == True:\n",
    "            colors_list.append(color_conversion[c])\n",
    "    if len(colors_list) == 0:\n",
    "        colors_list.append('k')\n",
    "    colors_dict[l_stops['STATION_ID'][i]] = colors_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_coords = {}\n",
    "for i in coords.index:\n",
    "    node_coords[l_stops['STATION_ID'][i]] = (coords.iloc[i].long, \n",
    "                      coords.iloc[i].lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "\n",
    "G.add_edges_from(edges_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in G.nodes:\n",
    "    stop_row = STOPS[STOPS['stop_id']==n]\n",
    "    G.nodes[n]['pos'] = (stop_row['stop_lon'].values[0],\n",
    "                         stop_row['stop_lat'].values[0])\n",
    "    G.nodes[n]['nodecolor'] = colors_dict[n][0]\n",
    "    G.nodes[n]['allnodecolors'] = colors_dict[n]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=150)\n",
    "nx.draw(G, node_coords, \n",
    "        ax=ax, alpha=0.4, \n",
    "        node_color=[G.nodes[i]['nodecolor'] for i in G.nodes],\n",
    "        node_size=10)\n",
    "plt.axis('equal')\n",
    "plt.grid(which='both')\n",
    "# plt.show()t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.write_gml(G, 'cta.gml')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:alyssa] *",
   "language": "python",
   "name": "conda-env-alyssa-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}