First stab at using Partridge as the basis for generating a NetworkX Graph of a GTFS feed (please note this is just a hacky sketch)

peartree.ipynb

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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import partridge as ptg\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "gtfs_loc = '../ac_transit_gtfs.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
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   "source": [
    "# Helper functions\n",
    "def _calculate_average_wait(direction_times):\n",
    "    first = direction_times.arrival_time[1:].values\n",
    "    second = direction_times.arrival_time[:-1].values\n",
    "    wait_seconds = (first - second)\n",
    "    average_wait = np.array(wait_seconds).mean()\n",
    "    return average_wait\n",
    "\n",
    "\n",
    "def _generate_wait_times(trips_and_stop_times: pd.DataFrame):\n",
    "    wait_times = {0: [], 1: []}\n",
    "    for stop_id in trips_and_stop_times.stop_id:\n",
    "\n",
    "        # Handle both inbound and outbound directions\n",
    "        for direction in [0, 1]:\n",
    "            constraint_1 = (trips_and_stop_times.direction_id==direction)\n",
    "            constraint_2 = (trips_and_stop_times.stop_id==stop_id)\n",
    "            direction_subset = trips_and_stop_times[constraint_1 & constraint_2]\n",
    "\n",
    "            # Only run if each direction is contained\n",
    "            # in the same trip id\n",
    "            if direction_subset.empty:\n",
    "                average_wait = None\n",
    "            else:\n",
    "                average_wait = _calculate_average_wait(direction_subset)\n",
    "\n",
    "            # Add according to which direction we are working with\n",
    "            wait_times[direction].append(average_wait)\n",
    "            \n",
    "    return wait_times\n",
    "\n",
    "\n",
    "def _generate_all_observed_edge_costs(trips_and_stop_times):\n",
    "    all_edge_costs = None\n",
    "    for trip_id in trips_and_stop_times.trip_id.unique():\n",
    "        tst_mask = (trips_and_stop_times.trip_id==trip_id)\n",
    "        tst_sub = trips_and_stop_times[tst_mask]\n",
    "\n",
    "        # Just in case both directions are under the same trip id\n",
    "        for direction in [0, 1]:\n",
    "            tst_sub_dir = tst_sub[tst_sub.direction_id==direction]\n",
    "\n",
    "            tst_sub_dir = tst_sub_dir.sort_values('stop_sequence')\n",
    "            deps = tst_sub_dir.departure_time[:-1]\n",
    "            arrs = tst_sub_dir.arrival_time[1:]\n",
    "\n",
    "            # Use .values to strip existing indices\n",
    "            edge_costs = np.subtract(arrs.values, deps.values)\n",
    "\n",
    "            # Now place results in data frame\n",
    "            new_edges = pd.DataFrame({'edge_cost': edge_costs})\n",
    "            new_edges['from_stop_id'] = tst_sub_dir.stop_id[:-1].values\n",
    "            new_edges['to_stop_id'] = tst_sub_dir.stop_id[1:].values\n",
    "\n",
    "            if all_edge_costs is None:\n",
    "                all_edge_costs = new_edges\n",
    "            else:\n",
    "                all_edge_costs = all_edge_costs.append(new_edges)\n",
    "    return all_edge_costs\n",
    "\n",
    "\n",
    "def _summarize_edge_costs(df):\n",
    "    from_stop_id = df.from_stop_id.values[0]\n",
    "    results_mtx = []\n",
    "    for to_stop_id in df.to_stop_id.unique():\n",
    "        to_mask = (df.to_stop_id==to_stop_id)\n",
    "        avg_cost = df[to_mask].edge_cost.mean()\n",
    "        results_mtx.append([avg_cost,\n",
    "                            from_stop_id,\n",
    "                            to_stop_id])\n",
    "    return pd.DataFrame(results_mtx, columns=df.columns)\n",
    "\n",
    "\n",
    "def _generate_summary_edge_costs(all_edge_costs):\n",
    "    summary_groupings = all_edge_costs.groupby('from_stop_id')\n",
    "    summary = summary_groupings.apply(_summarize_edge_costs)\n",
    "    summary = summary.reset_index(drop=True)\n",
    "    return summary\n",
    "\n",
    "\n",
    "def _summarize_waits_at_one_stop(stop_df):\n",
    "    divide_by = len(stop_df) * 2\n",
    "    dir_0_sum = stop_df.wait_dir_0.sum()\n",
    "    dir_1_sum = stop_df.wait_dir_1.sum()\n",
    "    calculated = ((dir_0_sum + dir_1_sum)/divide_by)\n",
    "    \n",
    "    if np.isnan(calculated):\n",
    "        print('stop id', stop_df.stop_id.values[0])\n",
    "        print(stop_df.head(10))\n",
    "        print('---')\n",
    "    \n",
    "    return calculated\n",
    "\n",
    "\n",
    "def _generate_summary_wait_times(df):\n",
    "    df_sub = df[['stop_id',\n",
    "                 'wait_dir_0',\n",
    "                 'wait_dir_1']].reset_index(drop=True)\n",
    "    init_of_stop_ids = df_sub.stop_id.unique()\n",
    "    \n",
    "    # TODO: Use NaN upstream so we don't have this sort of\n",
    "    #       hacky typing (floats support NaNs) and None conditioning\n",
    "    \n",
    "    # First convert all None values to NaN so we can handle them\n",
    "    # in vector format\n",
    "    df_sub.loc[df_sub.wait_dir_0==None, 'wait_dir_0'] = np.nan\n",
    "    df_sub.loc[df_sub.wait_dir_1==None, 'wait_dir_1'] = np.nan\n",
    "    \n",
    "    # Convert anything that is 0 or less seconds to a NaN as well\n",
    "    # as there should not be negative or 0 second waits in the system\n",
    "    df_sub.loc[~(df_sub.wait_dir_0 > 0), 'wait_dir_0'] = np.nan\n",
    "    df_sub.loc[~(df_sub.wait_dir_1 > 0), 'wait_dir_1'] = np.nan\n",
    "    \n",
    "    # Convert to type float (which support float)\n",
    "    df_sub.wait_dir_0 = df_sub.wait_dir_0.astype(float)\n",
    "    df_sub.wait_dir_1 = df_sub.wait_dir_1.astype(float)\n",
    "    \n",
    "    # Clean out the None values\n",
    "    dir_0_mask = ~np.isnan(df_sub.wait_dir_0)\n",
    "    dir_1_mask = ~np.isnan(df_sub.wait_dir_1)\n",
    "    \n",
    "    # We can't include values where both directions\n",
    "    # have NaNs at same time\n",
    "    d0_ids = df_sub[dir_0_mask].stop_id.unique()\n",
    "    d1_ids = df_sub[dir_1_mask].stop_id.unique()\n",
    "    keep_ids = list(d0_ids) + list(d1_ids)\n",
    "    df_sub_clean = df_sub[df_sub.stop_id.isin(keep_ids)]\n",
    "    \n",
    "    orig_len = len(df_sub)\n",
    "    new_len = len(df_sub_clean)\n",
    "    if not new_len == orig_len:\n",
    "        print(('Cleaned out bi-directional NaN values from '\n",
    "               'stop IDs. From {} to {}.'.format(orig_len, new_len)))\n",
    "        # And now replace df_sub\n",
    "        df_sub = df_sub_clean\n",
    "    \n",
    "    # Recheck all for NaNs\n",
    "    dir_0_mask_2 = np.isnan(df_sub.wait_dir_0)\n",
    "    dir_1_mask_2 = np.isnan(df_sub.wait_dir_1)\n",
    "\n",
    "    df_sub.loc[dir_0_mask_2, 'wait_dir_0'] = df_sub.wait_dir_1\n",
    "    df_sub.loc[dir_1_mask_2, 'wait_dir_1'] = df_sub.wait_dir_0\n",
    "\n",
    "    # TODO: All this pruning is a mess, needs to be\n",
    "    #       organized much better\n",
    "    # One more time to drop out the subset that are NaN\n",
    "    # from a given stop id\n",
    "    dir_0_mask_3 = ~np.isnan(df_sub.wait_dir_0)\n",
    "    df_sub = df_sub[dir_0_mask_3]\n",
    "\n",
    "    dir_1_mask_3 = ~np.isnan(df_sub.wait_dir_1)\n",
    "    df_sub = df_sub[dir_1_mask_3]\n",
    "\n",
    "    # Make sure that there are no None values left\n",
    "    dir_0_check_2 = df_sub[np.isnan(df_sub.wait_dir_0)]\n",
    "    dir_1_check_2 = df_sub[np.isnan(df_sub.wait_dir_1)]\n",
    "\n",
    "    if (len(dir_0_check_2) > 0) or (len(dir_1_check_2) > 0):\n",
    "        print('\\ndir_0_check_1 values')\n",
    "        print(dir_0_check_2.head())\n",
    "        print('\\ndir_1_check_2 values')\n",
    "        print(dir_1_check_2.head())\n",
    "        raise Exception('NaN values for both directions on some stop IDs.')\n",
    "    \n",
    "    grouped = df_sub.groupby('stop_id')\n",
    "    summarized = grouped.apply(_summarize_waits_at_one_stop)\n",
    "    \n",
    "    summed_reset = summarized.reset_index(drop=False)\n",
    "    summed_reset.columns = ['stop_id', 'avg_cost']\n",
    "    \n",
    "    end_of_stop_ids = summed_reset.stop_id.unique()\n",
    "    print(f'Original stop id countt: {len(init_of_stop_ids)}')\n",
    "    print(f'After cleaning stop id countt: {len(end_of_stop_ids)}')\n",
    "    \n",
    "    if len(init_of_stop_ids) > len(end_of_stop_ids):\n",
    "        a = set(list(init_of_stop_ids))\n",
    "        b = set(list(end_of_stop_ids))\n",
    "        unresolved_ids = list(a - b)\n",
    "        print('Some unaccounted for stop '\n",
    "              'ids. Resolving {}...'.format(len(unresolved_ids)))\n",
    "        \n",
    "        # TODO: Perhaps these are start/end stops and should adopt\n",
    "        #       a cost that is \"average\" for that route?\n",
    "        # We should think of how to actually do this\n",
    "        # because we do not have enough data, for now let's\n",
    "        # just assign some default high cost connection value\n",
    "        # to these stops\n",
    "        sids = list(summed_reset.stop_id)\n",
    "        acst = list(summed_reset.avg_cost)\n",
    "        for i in unresolved_ids:\n",
    "            sids.append(i)\n",
    "            acst.append(30 * 60)  # 30 minutes, converted to seconds\n",
    "        \n",
    "        # Rebuild the dataframe\n",
    "        summed_reset = pd.DataFrame({'stop_id': sids, 'avg_cost': acst})\n",
    "    \n",
    "    return summed_reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Busiest date: 2017-06-09\n",
      "Number of trips on that date: 6762\n",
      "\n",
      "All related service IDs: \n",
      "\t1703SP-D4-Weekday-04\n",
      "\t1703SP-D4-Saturday-01 -1\n",
      "\t1703SP-D3-Weekday-02\n",
      "\t1703SP-D6-Weekday-01-1110100\n",
      "\t1703SP-D6-Weekday-01-1011100\n",
      "\t1703SP-D2-Weekday-06-1101100\n",
      "\t1703SP-D6-Weekday-01-1100100\n",
      "\t1703SP-D3WACKY-Weekday-01\n",
      "\t1703SP-D3-Weekday-02-1101100\n",
      "\t1703SP-D2-Weekday-06\n",
      "\t1703SP-D6-Saturday-02 -1\n",
      "\t1703SP-D2-Weekday-06-0000100\n",
      "\t1703SP-DBDB1-Weekday-01\n",
      "\t1703SP-D2WACKY-Weekday-01\n",
      "\t1703SP-D4WACKY-Weekday-01\n",
      "\t1703SP-D6-Weekday-01-1101100\n",
      "\t1703SP-D6WACKY-Weekday-01\n",
      "\t1703SP-D4-Weekday-04-1010100\n",
      "\t1703SP-D4-Weekday-04-1101100\n",
      "\t1703SP-D6-Weekday-01\n",
      "\t1703SP-D2-Weekday-06-1110100\n"
     ]
    }
   ],
   "source": [
    "service_ids_by_date = ptg.read_service_ids_by_date(gtfs_loc)\n",
    "trip_counts_by_date = ptg.read_trip_counts_by_date(gtfs_loc)\n",
    "\n",
    "busiest_date, trip_count = max(trip_counts_by_date.items(), key=lambda p: p[1])\n",
    "\n",
    "print(f'Busiest date: {busiest_date}')\n",
    "print(f'Number of trips on that date: {trip_count}')\n",
    "\n",
    "all_service_ids = '\\n\\t'.join(service_ids_by_date[busiest_date])\n",
    "print(f'\\nAll related service IDs: \\n\\t{all_service_ids}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "feed_query = {'trips.txt': {'service_id': service_ids_by_date[busiest_date]}}\n",
    "feed = ptg.feed(gtfs_loc, view=feed_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def _generate_edge_and_wait_values(feed,\n",
    "                                   target_time_start,\n",
    "                                   target_time_end):\n",
    "    all_edge_costs = None\n",
    "    all_wait_times = None\n",
    "    for i, route in feed.routes.iterrows():\n",
    "        print(f'Processing on route {route.route_id}.')\n",
    "        # Now get all the trips for that route\n",
    "        trips = feed.trips[feed.trips.route_id==route.route_id]\n",
    "\n",
    "        # Get just the stop times related to this trip\n",
    "        st_trip_id_mask = feed.stop_times.trip_id.isin(trips.trip_id)\n",
    "        stimes_init = feed.stop_times[st_trip_id_mask]\n",
    "\n",
    "        # Then subset further by just the time period that we care about\n",
    "        start_time_mask = (stimes_init.arrival_time >= target_time_start)\n",
    "        end_time_mask = (stimes_init.arrival_time <= target_time_end)\n",
    "        stimes = stimes_init[start_time_mask & end_time_mask]\n",
    "\n",
    "        # Let user know how it is going\n",
    "        # TODO: Make these logger.info statements\n",
    "        a = len(stimes_init.trip_id.unique())\n",
    "        b = len(stimes.trip_id.unique())\n",
    "        print('\\tReduced trips in consideration from {} to {}.'.format(a,b))\n",
    "\n",
    "        trips_and_stop_times = pd.merge(trips,\n",
    "                                        stimes,\n",
    "                                        how='inner',\n",
    "                                        on='trip_id')\n",
    "\n",
    "        trips_and_stop_times = pd.merge(trips_and_stop_times,\n",
    "                                        feed.stops,\n",
    "                                        how='inner',\n",
    "                                        on='stop_id')\n",
    "\n",
    "        sort_values_list = ['stop_sequence',\n",
    "                            'arrival_time',\n",
    "                            'departure_time']\n",
    "        trips_and_stop_times = trips_and_stop_times.sort_values(sort_values_list)\n",
    "        trips_and_stop_times = pd.merge(trips,\n",
    "                                        stimes,\n",
    "                                        how='inner',\n",
    "                                        on='trip_id')\n",
    "\n",
    "        trips_and_stop_times = pd.merge(trips_and_stop_times,\n",
    "                                        feed.stops,\n",
    "                                        how='inner',\n",
    "                                        on='stop_id')\n",
    "\n",
    "        sort_values_list = ['stop_sequence',\n",
    "                            'arrival_time',\n",
    "                            'departure_time']\n",
    "        trips_and_stop_times = trips_and_stop_times.sort_values(sort_values_list)\n",
    "\n",
    "        wait_times = _generate_wait_times(trips_and_stop_times)\n",
    "        trips_and_stop_times['wait_dir_0'] = wait_times[0]\n",
    "        trips_and_stop_times['wait_dir_1'] = wait_times[1]\n",
    "\n",
    "        tst_sub = trips_and_stop_times[['stop_id',\n",
    "                                        'wait_dir_0',\n",
    "                                        'wait_dir_1']]\n",
    "\n",
    "        if all_wait_times is None:\n",
    "            all_wait_times = tst_sub\n",
    "        else:\n",
    "            all_wait_times = all_wait_times.append(tst_sub)    \n",
    "\n",
    "        # Get all edge costs for this route and add to the running total\n",
    "        edge_costs = _generate_all_observed_edge_costs(trips_and_stop_times)\n",
    "\n",
    "        # Add to the running total\n",
    "        if all_edge_costs is None:\n",
    "            all_edge_costs = edge_costs\n",
    "        else:\n",
    "            all_edge_costs = all_edge_costs.append(edge_costs)\n",
    "            \n",
    "    return (all_edge_costs, all_wait_times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing on route 1-138.\n",
      "\tReduced trips in consideration from 239 to 53.\n",
      "...",
      "\tReduced trips in consideration from 75 to 17.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: Mean of empty slice.\n",
      "/usr/local/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing on route 22-138.\n",
      "\tReduced trips in consideration from 70 to 16.\n",
      "Processing on route 232-138.\n",
      "...",
      "\tReduced trips in consideration from 19 to 6.\n",
      "Processing on route Z-138.\n",
      "\tReduced trips in consideration from 4 to 2.\n"
     ]
    }
   ],
   "source": [
    "# 7 AM to 10 AM\n",
    "target_time_start = 7*60*60\n",
    "target_time_end = 10*60*60\n",
    "(all_edge_costs,\n",
    " all_wait_times) = _generate_edge_and_wait_values(feed,\n",
    "                                                  target_time_start,\n",
    "                                                  target_time_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6522, 8088)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_edge_costs), len(all_wait_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edge_cost</th>\n",
       "      <th>from_stop_id</th>\n",
       "      <th>to_stop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240.0</td>\n",
       "      <td>0100050</td>\n",
       "      <td>0100140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300.0</td>\n",
       "      <td>0100250</td>\n",
       "      <td>9904060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>780.0</td>\n",
       "      <td>0100250</td>\n",
       "      <td>1000350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>360.0</td>\n",
       "      <td>0100340</td>\n",
       "      <td>1011320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0100340</td>\n",
       "      <td>1011370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edge_cost from_stop_id to_stop_id\n",
       "0      240.0      0100050    0100140\n",
       "1      300.0      0100250    9904060\n",
       "2      780.0      0100250    1000350\n",
       "3      360.0      0100340    1011320\n",
       "4      330.0      0100340    1011370"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_edge_costs = _generate_summary_edge_costs(all_edge_costs)\n",
    "\n",
    "summary_edge_costs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned out bi-directional NaN values from stop IDs. From 8088 to 7955.\n",
      "Original stop id countt: 861\n",
      "After cleaning stop id countt: 767\n",
      "Some unaccounted for stop ids. Resolving 94...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_cost</th>\n",
       "      <th>stop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1440.000000</td>\n",
       "      <td>0100250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>834.663462</td>\n",
       "      <td>0100340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3180.000000</td>\n",
       "      <td>0100350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>960.000000</td>\n",
       "      <td>0101010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>0101130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_cost  stop_id\n",
       "0  1440.000000  0100250\n",
       "1   834.663462  0100340\n",
       "2  3180.000000  0100350\n",
       "3   960.000000  0101010\n",
       "4   950.000000  0101130"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wait_times_by_stop = _generate_summary_wait_times(all_wait_times)\n",
    "\n",
    "wait_times_by_stop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "name='ac_transit'\n",
    "G = nx.MultiDiGraph(name=name, crs={'init':'epsg:4326'})\n",
    "for i, row in wait_times_by_stop.iterrows():\n",
    "    sid = str(row.stop_id)\n",
    "    \n",
    "    # TODO: Join tables before hand to make\n",
    "    #       this part go faster\n",
    "    id_mask = (feed.stops.stop_id==sid)\n",
    "    stop_data = feed.stops[id_mask].head(1).T.squeeze()\n",
    "    \n",
    "    G.add_node(sid,\n",
    "               boarding_cost=row.avg_cost,\n",
    "               y=stop_data.stop_lat,\n",
    "               x=stop_data.stop_lon)\n",
    "    \n",
    "for i, row in summary_edge_costs.iterrows():\n",
    "    sid_fr = str(row.from_stop_id)\n",
    "    sid_to = str(row.to_stop_id)\n",
    "    G.add_edge(sid_fr,\n",
    "               sid_to,\n",
    "               length=row.edge_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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UVPB//vQPyS8o4EMf/CAKpZIDBw9is9nIt9moqKggmUoR8PuJRqM4nU4ikQi9vb1nzWrZ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Ixtvb1YLRZCoRB33nkni14ffQ8/ijWT4CdPPUX7wx/CmIrzvT//E5xLSyhVKnRaLXq9XhSDGrUa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Nc//1NC094FXTspKYkcp5PCggJisRgDg4P09/fj8/muDXFVq9Vs37aNsbEx2uQWoBBLlsvt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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb108189198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = ox.plot_graph(G,\n",
    "                        fig_height=12,\n",
    "                        node_color='#8aedfc',\n",
    "                        node_size=5,\n",
    "                        edge_color='#e2dede',\n",
    "                        edge_alpha=0.25,\n",
    "                        bgcolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}