Visualization_Newport.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "cdict = {'red'  :  ((0., 0., 0.), (0.2, 1., 1.), (0.6, 1., 1.), (1., 0., 0.)),\n",
    "         'green':  ((0., 0., 0.), (0.2, 0., 0.), (0.6, 1., 1.), (1., 1., 1.)),\n",
    "         'blue' :  ((0., 0., 0.), (0.2, 0., 0.), (0.6, 0., 0.), (1., 0., 0.))}\n",
    "\n",
    "my_cmap = colors.LinearSegmentedColormap('my_colormap', cdict, 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_1_07012018 = 'Data/Probe_Data/2018_07_01_NewportBeach_basic_probe.csv'\n",
    "d_2_07022018 = 'Data/Probe_Data/2018_07_02_NewportBeach_basic_probe.csv'\n",
    "d_3_07032018 = 'Data/Probe_Data/2018_07_03_NewportBeach_basic_probe.csv'\n",
    "d_4_07042018 = 'Data/Probe_Data/2018_07_04_NewportBeach_basic_probe.csv'\n",
    "d_5_07052018 = 'Data/Probe_Data/2018_07_05_NewportBeach_basic_probe.csv'\n",
    "d_6_07062018 = 'Data/Probe_Data/2018_07_06_NewportBeach_basic_probe.csv'\n",
    "d_7_07072018 = 'Data/Probe_Data/2018_07_07_NewportBeach_basic_probe.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>PROBE_ID</th>\n",
       "      <th>SAMPLE_DATE</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>HEADING</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>PROBE_DATA_PROVIDER</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>LOCAL_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>2018-07-04 00:00:00</td>\n",
       "      <td>33.615262</td>\n",
       "      <td>-117.927329</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>413977.562793</td>\n",
       "      <td>3.719884e+06</td>\n",
       "      <td>2018-07-03 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>2018-07-04 00:00:01</td>\n",
       "      <td>33.615269</td>\n",
       "      <td>-117.927325</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>413977.940815</td>\n",
       "      <td>3.719885e+06</td>\n",
       "      <td>2018-07-03 17:00:01 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>2018-07-04 00:00:02</td>\n",
       "      <td>33.615287</td>\n",
       "      <td>-117.927314</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>413978.979134</td>\n",
       "      <td>3.719886e+06</td>\n",
       "      <td>2018-07-03 17:00:02 -0700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 PROBE_ID          SAMPLE_DATE        LAT  \\\n",
       "480  285613c2-f71d-55f6-bb2b-8f16528c0a2c  2018-07-04 00:00:00  33.615262   \n",
       "479  285613c2-f71d-55f6-bb2b-8f16528c0a2c  2018-07-04 00:00:01  33.615269   \n",
       "445  285613c2-f71d-55f6-bb2b-8f16528c0a2c  2018-07-04 00:00:02  33.615287   \n",
       "\n",
       "           LONG  HEADING  SPEED PROBE_DATA_PROVIDER              X  \\\n",
       "480 -117.927329     34.0   14.0          CONSUMER14  413977.562793   \n",
       "479 -117.927325     19.0   12.0          CONSUMER14  413977.940815   \n",
       "445 -117.927314     19.0   17.0          CONSUMER14  413978.979134   \n",
       "\n",
       "                Y                 LOCAL_TIME  \n",
       "480  3.719884e+06  2018-07-03 17:00:00 -0700  \n",
       "479  3.719885e+06  2018-07-03 17:00:01 -0700  \n",
       "445  3.719886e+06  2018-07-03 17:00:02 -0700  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = pd.read_csv('crop_data_Newport.csv') #changed .csv here\n",
    "\n",
    "data = pd.read_csv(d_4_07042018,\n",
    "                   names = [\"PROBE_ID\",\"SAMPLE_DATE\", \"LAT\", \"LONG\", \"HEADING\",\\\n",
    "                           \"SPEED\",\"PROBE_DATA_PROVIDER\",\"X\",\"Y\", \"LOCAL_TIME\"])\n",
    "#Newport data is here: https://drive.google.com/open?id=1hIIFrDH2Y8MRH_95lgRx2iiQxls9G1pf\n",
    "data.sort_values(by='SAMPLE_DATE', inplace=True, ascending=True) # This now sorts in date order\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique data provider count 14\n",
      "unique Probe_ID count 374\n",
      "unique time 21156\n",
      "raw data shape (25632, 10)\n"
     ]
    }
   ],
   "source": [
    "print('unique data provider count',len(data.PROBE_DATA_PROVIDER.unique()))\n",
    "print('unique Probe_ID count',len(data.PROBE_ID.unique()))\n",
    "print('unique time',len(data.LOCAL_TIME.unique()))\n",
    "print('raw data shape',data.shape)\n",
    "# - Got 18 unique IDs vs 21 in the other set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw data shape (0, 10)\n"
     ]
    }
   ],
   "source": [
    "## I think this is where the Newport data is filtered out, in the original .csv the raw data shape is 40,11 here now it is 0,11\n",
    "t1_series = data[data.LOCAL_TIME == '2018-06-01 16:49:26 -0700' ]\n",
    "t1_series.head()\n",
    "print('raw data shape',t1_series.shape)\n",
    "\n",
    "# t2_series = data[data.LOCAL_TIME.isin'2018-06-01 16:49:26 -0700' ]\n",
    "# print('raw data shape',t1_series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw data shape (0, 10)\n",
      "raw data shape (1096, 10)\n"
     ]
    }
   ],
   "source": [
    "# Trajectory\n",
    "ID_series = data[data.PROBE_ID == '19793987'] #'24046157' ] - I changed to pick a probe ID from the data list, but did not find anything still\n",
    "ID_series.head()\n",
    "print('raw data shape',ID_series.shape)\n",
    "\n",
    "fleet_series = data[data.PROBE_DATA_PROVIDER == 'FLEET51' ]\n",
    "\n",
    "print('raw data shape',fleet_series.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to set specific regions of interest\n",
    "intersection_center = data[data['edge_id'].isin(intersection_center_edges)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_heatmap(data,title):\n",
    "    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(14, 10))\n",
    "\n",
    "    scatter = ax.scatter(data['X'].values,\n",
    "                               data['Y'].values)\n",
    "    scatter_heat = ax.scatter(data['X'].values,\n",
    "                               data['Y'].values,\n",
    "                               c=data['SPEED'], s=45, alpha=0.9,cmap=my_cmap)\n",
    "    cbar = fig.colorbar(scatter_heat)\n",
    "    cbar.set_label(\"Speed (m/s)\",fontsize=20)\n",
    "    cbar.ax.tick_params(labelsize=18)\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.axis('off')\n",
    "    plt.show()   \n",
    "\n",
    "def route_map(data,title):\n",
    "    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(14, 10))\n",
    "    scatter = ax.plot(data['X'].values, data['Y'].values)\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.axis('off')\n",
    "    plt.show() \n",
    "    \n",
    "def mean_speed(data,title):\n",
    "    plt.figure(figsize = (14,10))\n",
    "    sns.lineplot(x=data['time'],y=data['speed'])\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.xlabel('Time (s)',fontsize=20)\n",
    "    plt.ylabel('Mean Speed (m/s)',fontsize=20)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>PROBE_ID</th>\n",
       "      <th>SAMPLE_DATE</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>HEADING</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>PROBE_DATA_PROVIDER</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>LOCAL_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PROBE_ID, SAMPLE_DATE, LAT, LONG, HEADING, SPEED, PROBE_DATA_PROVIDER, X, Y, LOCAL_TIME]\n",
       "Index: []"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = route_map(ID_series,'Routemap of Speed') #Changed to routemap from heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pos_heatmap(ID_series,'Heatmap of Speed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Populations and Speeds in Each Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import folium\n",
    "from shapely.geometry import Polygon, Point, LineString\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "plt.scatter(data.LONG, data.LAT, s = 0.5, c = 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read shape files into geopandas\n",
    "right_half_tri_SF = gpd.read_file(\"shapefiles/right_half_triangle/right_half_triangle.shp\")[['Name', 'geometry']]\n",
    "left_half_tri_SF = gpd.read_file(\"shapefiles/left_half_triangle/left_half_triangle.shp\")[['Name', 'geometry']]\n",
    "island_SF = gpd.read_file(\"shapefiles/island/island.shp\")[['Name', 'geometry']]\n",
    "newport_SF = gpd.read_file(\"shapefiles/inundation_map/inundation_map.shp\")[['Name', 'geometry']]\n",
    "# Read Street Shape Files\n",
    "balboa_blvd = gpd.read_file(\"shapefiles/Streets/Balboa/Balboa_Blvd-polygon.shp\")[['Name', 'geometry']]\n",
    "w_balboa_blvd = gpd.read_file(\"shapefiles/Streets/W_Balboa/W_Balboa-polygon.shp\")[['Name', 'geometry']]\n",
    "newport = gpd.read_file(\"shapefiles/Streets/Newport/Newport_Blvd-polygon.shp\")[['Name', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape_df = right_half_tri_SF.append([left_half_tri_SF, island_SF,newport_SF])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn coordinates into points\n",
    "def create_point(row):\n",
    "    return Point(row['LONG'],row['LAT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['coord'] = data.apply(create_point, axis=1)\n",
    "data['section'] = 'N/A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "section = []\n",
    "\n",
    "for i in data.index.values:\n",
    "    if right_half_tri_SF['geometry'][0].contains(data['coord'][i]):\n",
    "        section.append('right_half_tri')\n",
    "    elif left_half_tri_SF['geometry'][0].contains(data['coord'][i]):\n",
    "        section.append('left_half_tri')\n",
    "    elif island_SF['geometry'][0].contains(data['coord'][i]):\n",
    "        section.append('island')\n",
    "    elif newport_SF['geometry'][0].contains(data['coord'][i]):\n",
    "        section.append('rest_of_new_port')\n",
    "    else:\n",
    "        section.append('N/A')\n",
    "        \n",
    "data['section'] = section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "street = []\n",
    "\n",
    "for i in data.index.values:\n",
    "    if balboa_blvd['geometry'][0].contains(data['coord'][i]):\n",
    "        street.append('balboa')\n",
    "    elif w_balboa_blvd['geometry'][0].contains(data['coord'][i]):\n",
    "        street.append('w_balboa')\n",
    "    elif newport['geometry'][0].contains(data['coord'][i]):\n",
    "        street.append('newport')\n",
    "    else:\n",
    "        street.append('N/A')\n",
    "        \n",
    "data['street'] = street"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data[data.section != 'N/A'][['PROBE_ID','LAT','LONG',\\\n",
    "                                            'HEADING',\\\n",
    "                                            'SPEED',\\\n",
    "                                            'PROBE_DATA_PROVIDER',\\\n",
    "                                            'LOCAL_TIME',\\\n",
    "                                            'coord',\\\n",
    "                                            'section','street']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique data provider count 11\n",
      "unique Probe_ID count 171\n",
      "unique time 11888\n",
      "raw data shape (12893, 10)\n"
     ]
    }
   ],
   "source": [
    "print('unique data provider count',len(data_cleaned.PROBE_DATA_PROVIDER.unique()))\n",
    "print('unique Probe_ID count',len(data_cleaned.PROBE_ID.unique()))\n",
    "print('unique time',len(data_cleaned.LOCAL_TIME.unique()))\n",
    "print('raw data shape',data_cleaned.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "plt.scatter(data_cleaned.LONG, data_cleaned.LAT, s = 0.5, c = 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unique Probes per area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "rht_uniq_probe = data_cleaned.loc[data_cleaned['section'] == 'right_half_tri'].PROBE_ID.unique()\n",
    "lht_uniq_probe = data_cleaned.loc[data_cleaned['section'] == 'left_half_tri'].PROBE_ID.unique()\n",
    "isl_uniq_probe = data_cleaned.loc[data_cleaned['section'] == 'island'].PROBE_ID.unique()\n",
    "rest_uniq_probe = data_cleaned.loc[data_cleaned['section'] == 'rest_of_new_port'].PROBE_ID.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned['LOCAL_TIME'] = pd.to_datetime(data_cleaned['LOCAL_TIME']).dt.tz_localize('UTC').dt.tz_convert('America/Los_Angeles')\n",
    "# Separate to hours\n",
    "data_cleaned['HOUR'] = data_cleaned['LOCAL_TIME'].dt.hour  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For calcualting heading related info on Balboa and Newport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make life easier when dealing with headings\n",
    "heading_fixed = []\n",
    "for i in data_cleaned.index.values:\n",
    "    if data_cleaned['HEADING'][i] >180:\n",
    "        heading_fixed.append(-(360 - data_cleaned['HEADING'][i]))\n",
    "    else:\n",
    "        heading_fixed.append(data_cleaned['HEADING'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned['HEADING_FIXED'] = heading_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "balboa_df = data_cleaned.loc[data_cleaned['street']=='balboa'].copy()\n",
    "newport_df = data_cleaned.loc[data_cleaned['street']=='newport'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes\n",
    "- For vehicle speeds, assume all 0 speeds means stopping at a light and is ignored\n",
    "\n",
    "## Headings\n",
    "- Uses fixed headings\n",
    "### For Newport\n",
    "- Northbound: -90 <= heading <= 45\n",
    "- Southbound: 145 <= heading <= -150\n",
    "\n",
    "### For Balboa\n",
    "- Northbound: -140 <= heading <= 0\n",
    "- Sounthbound: 90 <= heading <= -160\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_heading_info(df, lower_head, upper_head):\n",
    "    \n",
    "    '''\n",
    "    Inputs:\n",
    "        df - cleaned up dataframe with hours and streets \n",
    "        lower_head - lower heading limits for traffic\n",
    "        upper_head - upper heading limits for traffic\n",
    "        \n",
    "    Outputs:\n",
    "        head_speed - list of 24 north heading speeds averaged over an hour\n",
    "        head_unique_probe_vehicle - list of 24 counts of unique probe ids in that hour - vehicles\n",
    "        head_unique_probe_ped - list of 24 counts of unique probe ids in that hour - pedestrian\n",
    "    '''\n",
    "    head_speed_veh = []\n",
    "    head_speed_ped = []\n",
    "    head_unique_probe_vehicle = []\n",
    "    head_unique_probe_ped = []\n",
    "    \n",
    "    \n",
    "    for i in range(24):\n",
    "        head_df = df.loc[(df['HOUR']==i) & \\\n",
    "                         ((df['HEADING_FIXED'] >= lower_head) | \\\n",
    "                         (df['HEADING_FIXED'] <= upper_head))][['PROBE_ID','SPEED']]\n",
    "        \n",
    "        head_df = head_df[head_df['SPEED'] != 0]\n",
    "        unique_probe_list = list(set(head_df['PROBE_ID']))\n",
    "\n",
    "        ped_count = 0\n",
    "        veh_count = 0\n",
    "        ped_speed = []\n",
    "        veh_speed = []\n",
    "        for j in unique_probe_list:\n",
    "\n",
    "            probe_df = head_df.loc[head_df['PROBE_ID'] == j]\n",
    "            mean_speed = np.mean(probe_df['SPEED'])\n",
    "            \n",
    "            if mean_speed <= 15:\n",
    "                ped_count += 1\n",
    "                ped_speed.append(mean_speed)\n",
    "                \n",
    "            else:\n",
    "                veh_count += 1\n",
    "                veh_speed.append(mean_speed)\n",
    "        \n",
    "        head_unique_probe_vehicle.append(veh_count)\n",
    "        head_unique_probe_ped.append(ped_count)\n",
    "        \n",
    "        if not ped_speed:\n",
    "            head_speed_ped.append([])\n",
    "        else:\n",
    "            head_speed_ped.append(np.mean(ped_speed))\n",
    "            \n",
    "        if not veh_speed:\n",
    "            head_speed_veh.append([])\n",
    "        else:\n",
    "            head_speed_veh.append(np.mean(veh_speed))\n",
    "        \n",
    "    return head_speed_veh, head_speed_ped, head_unique_probe_vehicle, head_unique_probe_ped\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Balboa\n",
    "bal_north_limits = [-90,45]\n",
    "bal_south_limits = [145,-150]\n",
    "balboa_north_speed_veh, balboa_north_speed_ped, balboa_north_unique_veh,balboa_north_unique_ped  = get_heading_info(balboa_df,\\\n",
    "                                                           bal_north_limits[0],\\\n",
    "                                                           bal_north_limits[1])\n",
    "balboa_south_speed_veh, balboa_south_speed_ped,balboa_south_unique_veh,balboa_south_unique_ped  = get_heading_info(balboa_df,\\\n",
    "                                                           bal_south_limits[0],\\\n",
    "                                                           bal_south_limits[1])\n",
    "\n",
    "# Newport\n",
    "newport_north_limits = [-140,0]  \n",
    "newport_south_limits = [90,-160]\n",
    "newport_north_speed_veh, newport_north_speed_ped, newport_north_unique_veh, newport_north_unique_ped = get_heading_info(newport_df,\\\n",
    "                                                             newport_north_limits[0],\\\n",
    "                                                             newport_north_limits[1])\n",
    "newport_south_speed_veh, newport_south_speed_ped, newport_south_unique_veh, newport_south_unique_ped = get_heading_info(newport_df,\\\n",
    "                                                             newport_south_limits[0],\\\n",
    "                                                             newport_south_limits[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe of road information per hour\n",
    "road_info_df = pd.DataFrame({'Hour':range(0,24),\\\n",
    "                'Newport_North_Speed_Veh':newport_north_speed_veh,\\\n",
    "                'Newport_South_Speed_Veh':newport_south_speed_veh,\\\n",
    "                'Newport_North_Probe_Veh':newport_north_unique_veh,\\\n",
    "                'Newport_South_Probe_Veh':newport_south_unique_veh,\\\n",
    "                'Newport_North_Speed_Ped':newport_north_speed_ped,\\\n",
    "                'Newport_South_Speed_Ped':newport_south_speed_ped,\\\n",
    "                'Newport_North_Probe_Ped':newport_north_unique_ped,\\\n",
    "                'Newport_South_Probe_Ped':newport_south_unique_ped,\\\n",
    "                             \n",
    "                'Balboa_North_Speed_Veh':balboa_north_speed_veh,\\\n",
    "                'Balboa_South_Speed_Veh':balboa_south_speed_veh,\\\n",
    "                'Balboa_North_Probe_Veh':balboa_north_unique_veh,\\\n",
    "                'Balboa_South_Probe_Veh':balboa_south_unique_veh,\\\n",
    "                'Balboa_North_Speed_Ped':balboa_north_speed_ped,\\\n",
    "                'Balboa_South_Speed_Ped':balboa_south_speed_ped,\\\n",
    "                'Balboa_North_Probe_Ped':balboa_north_unique_ped,\\\n",
    "                'Balboa_South_Probe_Ped':balboa_south_unique_ped})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>Hour</th>\n",
       "      <th>Newport_North_Speed_Veh</th>\n",
       "      <th>Newport_South_Speed_Veh</th>\n",
       "      <th>Newport_North_Probe_Veh</th>\n",
       "      <th>Newport_South_Probe_Veh</th>\n",
       "      <th>Newport_North_Speed_Ped</th>\n",
       "      <th>Newport_South_Speed_Ped</th>\n",
       "      <th>Newport_North_Probe_Ped</th>\n",
       "      <th>Newport_South_Probe_Ped</th>\n",
       "      <th>Balboa_North_Speed_Veh</th>\n",
       "      <th>Balboa_South_Speed_Veh</th>\n",
       "      <th>Balboa_North_Probe_Veh</th>\n",
       "      <th>Balboa_South_Probe_Veh</th>\n",
       "      <th>Balboa_North_Speed_Ped</th>\n",
       "      <th>Balboa_South_Speed_Ped</th>\n",
       "      <th>Balboa_North_Probe_Ped</th>\n",
       "      <th>Balboa_South_Probe_Ped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>41.329114</td>\n",
       "      <td>45.3291</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.8125</td>\n",
       "      <td>25.7778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>27.358780</td>\n",
       "      <td>27.3588</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>27.285714</td>\n",
       "      <td>15.5714</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.73434</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30.577460</td>\n",
       "      <td>30.5775</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.7204</td>\n",
       "      <td>8.96632</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8567</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>41.038170</td>\n",
       "      <td>15.2887</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.9057</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>29.504567</td>\n",
       "      <td>39.4288</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.24203</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>47.054110</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.24203</td>\n",
       "      <td>9.65604</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>37.823529</td>\n",
       "      <td>37.8235</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.3043</td>\n",
       "      <td>10.3043</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0833</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>32.171290</td>\n",
       "      <td>28.5828</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.95</td>\n",
       "      <td>18.7778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>23.520513</td>\n",
       "      <td>31.3615</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.17857</td>\n",
       "      <td>10.9226</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22.5308</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.62821</td>\n",
       "      <td>9.24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>28.270196</td>\n",
       "      <td>22.2017</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6.46534</td>\n",
       "      <td>3.13178</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>26.5</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8.16135</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>24.345755</td>\n",
       "      <td>[]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10.7993</td>\n",
       "      <td>10.5425</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24.588</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.22222</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>22.8333</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.31096</td>\n",
       "      <td>7.86447</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9.59721</td>\n",
       "      <td>9.20377</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>21.7141</td>\n",
       "      <td>19.7707</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5.96953</td>\n",
       "      <td>6.44267</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>17.756836</td>\n",
       "      <td>15.0205</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6812</td>\n",
       "      <td>7.3169</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>26.4451</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7.95965</td>\n",
       "      <td>8.01552</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>25.138549</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.27164</td>\n",
       "      <td>6.28802</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>20.3125</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7.4409</td>\n",
       "      <td>[]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>25.853276</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.1356</td>\n",
       "      <td>9.29464</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.65604</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>35.876380</td>\n",
       "      <td>40.3415</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>9.484</td>\n",
       "      <td>10.1296</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>35.1915</td>\n",
       "      <td>20.9214</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5909</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>27.515702</td>\n",
       "      <td>25.4559</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0357</td>\n",
       "      <td>8.35714</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21.3357</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>31.127807</td>\n",
       "      <td>36.3472</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.7244</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>27.851176</td>\n",
       "      <td>25.7709</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33.7738</td>\n",
       "      <td>17.6667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>32.837025</td>\n",
       "      <td>36.9452</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.67568</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.6367</td>\n",
       "      <td>37.4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>28.636675</td>\n",
       "      <td>22.8649</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0817</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20.9214</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5714</td>\n",
       "      <td>13.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>27.352941</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.61271</td>\n",
       "      <td>8.39655</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>30.475</td>\n",
       "      <td>30.4444</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hour  Newport_North_Speed_Veh Newport_South_Speed_Veh  \\\n",
       "0      0                41.329114                 45.3291   \n",
       "1      1                27.358780                 27.3588   \n",
       "2      2                27.285714                 15.5714   \n",
       "3      3                30.577460                 30.5775   \n",
       "4      4                41.038170                 15.2887   \n",
       "5      5                29.504567                 39.4288   \n",
       "6      6                47.054110                      41   \n",
       "7      7                37.823529                 37.8235   \n",
       "8      8                32.171290                 28.5828   \n",
       "9      9                23.520513                 31.3615   \n",
       "10    10                28.270196                 22.2017   \n",
       "11    11                24.345755                      []   \n",
       "12    12                19.000000                      []   \n",
       "13    13                22.000000                      16   \n",
       "14    14                17.756836                 15.0205   \n",
       "15    15                25.138549                      []   \n",
       "16    16                25.853276                      []   \n",
       "17    17                35.876380                 40.3415   \n",
       "18    18                27.515702                 25.4559   \n",
       "19    19                31.127807                 36.3472   \n",
       "20    20                27.851176                 25.7709   \n",
       "21    21                32.837025                 36.9452   \n",
       "22    22                28.636675                 22.8649   \n",
       "23    23                27.352941                      []   \n",
       "\n",
       "    Newport_North_Probe_Veh  Newport_South_Probe_Veh Newport_North_Speed_Ped  \\\n",
       "0                         2                        2                      []   \n",
       "1                         1                        1                      []   \n",
       "2                         2                        1                      []   \n",
       "3                         1                        1                 10.7204   \n",
       "4                         1                        1                 11.9057   \n",
       "5                         1                        1                      []   \n",
       "6                         2                        1                      []   \n",
       "7                         1                        1                 10.3043   \n",
       "8                         4                        3                      []   \n",
       "9                         3                        1                 8.17857   \n",
       "10                        7                        4                 6.46534   \n",
       "11                        3                        0                 10.7993   \n",
       "12                        1                        0                 5.22222   \n",
       "13                        1                        2                 9.59721   \n",
       "14                        3                        1                  8.6812   \n",
       "15                        2                        0                 6.27164   \n",
       "16                        2                        0                 12.1356   \n",
       "17                        7                        4                   9.484   \n",
       "18                        4                        3                 10.0357   \n",
       "19                        7                        4                    10.5   \n",
       "20                        5                        3                     6.7   \n",
       "21                        3                        2                 7.67568   \n",
       "22                        3                        1                 14.0817   \n",
       "23                        1                        0                 9.61271   \n",
       "\n",
       "   Newport_South_Speed_Ped  Newport_North_Probe_Ped  Newport_South_Probe_Ped  \\\n",
       "0                       []                        0                        0   \n",
       "1                       []                        0                        0   \n",
       "2                       []                        0                        0   \n",
       "3                  8.96632                        2                        1   \n",
       "4                       []                        2                        0   \n",
       "5                       []                        0                        0   \n",
       "6                       []                        0                        0   \n",
       "7                  10.3043                        1                        1   \n",
       "8                       []                        0                        0   \n",
       "9                  10.9226                        1                        2   \n",
       "10                 3.13178                        5                        3   \n",
       "11                 10.5425                        2                        2   \n",
       "12                       3                        3                        2   \n",
       "13                 9.20377                        5                        4   \n",
       "14                  7.3169                        4                        2   \n",
       "15                 6.28802                        5                        5   \n",
       "16                 9.29464                        3                        2   \n",
       "17                 10.1296                        7                        2   \n",
       "18                 8.35714                        2                        2   \n",
       "19                       6                        2                        1   \n",
       "20                      []                        1                        0   \n",
       "21                      []                        2                        0   \n",
       "22                      []                        2                        0   \n",
       "23                 8.39655                        2                        2   \n",
       "\n",
       "   Balboa_North_Speed_Veh Balboa_South_Speed_Veh  Balboa_North_Probe_Veh  \\\n",
       "0                 32.8125                25.7778                       1   \n",
       "1                      []                     []                       0   \n",
       "2                      []                     []                       0   \n",
       "3                      []                     []                       0   \n",
       "4                      []                     []                       0   \n",
       "5                      []                     []                       0   \n",
       "6                    31.5                     []                       1   \n",
       "7                 17.0833                     18                       2   \n",
       "8                   24.95                18.7778                       1   \n",
       "9                 22.5308                     []                       1   \n",
       "10                   26.5                     26                       4   \n",
       "11                 24.588                     []                       1   \n",
       "12                22.8333                     []                       1   \n",
       "13                21.7141                19.7707                       6   \n",
       "14                26.4451                     40                       2   \n",
       "15                20.3125                     27                       2   \n",
       "16                   23.5                     20                       1   \n",
       "17                35.1915                20.9214                       7   \n",
       "18                21.3357                   23.5                       2   \n",
       "19                22.7244                   17.5                       2   \n",
       "20                33.7738                17.6667                       3   \n",
       "21                29.6367                   37.4                       4   \n",
       "22                20.9214                     []                       1   \n",
       "23                 30.475                30.4444                       1   \n",
       "\n",
       "    Balboa_South_Probe_Veh Balboa_North_Speed_Ped Balboa_South_Speed_Ped  \\\n",
       "0                        1                     []                     []   \n",
       "1                        0                     []                     []   \n",
       "2                        0                5.73434                    7.5   \n",
       "3                        0                10.8567                     []   \n",
       "4                        0                     []                     []   \n",
       "5                        0                7.24203                     []   \n",
       "6                        0                7.24203                9.65604   \n",
       "7                        2                   12.5                     12   \n",
       "8                        1                      5                      5   \n",
       "9                        0                9.62821                   9.24   \n",
       "10                       1                8.16135                      6   \n",
       "11                       0                     13                     13   \n",
       "12                       0                8.31096                7.86447   \n",
       "13                       3                5.96953                6.44267   \n",
       "14                       1                7.95965                8.01552   \n",
       "15                       1                 7.4409                     []   \n",
       "16                       1                9.65604                     []   \n",
       "17                       1                 8.5909                      6   \n",
       "18                       1                     []                     []   \n",
       "19                       1                     []                     15   \n",
       "20                       1                     10                     []   \n",
       "21                       1                     []                     []   \n",
       "22                       0                13.5714                   13.2   \n",
       "23                       1                     []                     []   \n",
       "\n",
       "    Balboa_North_Probe_Ped  Balboa_South_Probe_Ped  \n",
       "0                        0                       0  \n",
       "1                        0                       0  \n",
       "2                        2                       1  \n",
       "3                        2                       0  \n",
       "4                        0                       0  \n",
       "5                        1                       0  \n",
       "6                        2                       1  \n",
       "7                        1                       1  \n",
       "8                        1                       1  \n",
       "9                        2                       2  \n",
       "10                       2                       1  \n",
       "11                       1                       1  \n",
       "12                       7                       4  \n",
       "13                       8                       6  \n",
       "14                       5                       2  \n",
       "15                       3                       0  \n",
       "16                       1                       0  \n",
       "17                       4                       1  \n",
       "18                       0                       0  \n",
       "19                       0                       1  \n",
       "20                       1                       0  \n",
       "21                       0                       0  \n",
       "22                       1                       1  \n",
       "23                       0                       0  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "road_info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Residents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>PROBE_ID</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>HEADING</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>PROBE_DATA_PROVIDER</th>\n",
       "      <th>LOCAL_TIME</th>\n",
       "      <th>coord</th>\n",
       "      <th>section</th>\n",
       "      <th>street</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>HEADING_FIXED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>33.615262</td>\n",
       "      <td>-117.927329</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>2018-07-03 17:00:00-07:00</td>\n",
       "      <td>POINT (-117.927329 33.615262)</td>\n",
       "      <td>right_half_tri</td>\n",
       "      <td>N/A</td>\n",
       "      <td>17</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>33.615269</td>\n",
       "      <td>-117.927325</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>2018-07-03 17:00:01-07:00</td>\n",
       "      <td>POINT (-117.927325 33.615269)</td>\n",
       "      <td>right_half_tri</td>\n",
       "      <td>N/A</td>\n",
       "      <td>17</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>33.615287</td>\n",
       "      <td>-117.927314</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>2018-07-03 17:00:02-07:00</td>\n",
       "      <td>POINT (-117.927314 33.615287)</td>\n",
       "      <td>right_half_tri</td>\n",
       "      <td>N/A</td>\n",
       "      <td>17</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>6c247e2bf8f447b2b31b7450261a1e3c</td>\n",
       "      <td>33.618990</td>\n",
       "      <td>-117.930160</td>\n",
       "      <td>189.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CONSUMER21</td>\n",
       "      <td>2018-07-03 17:00:03-07:00</td>\n",
       "      <td>POINT (-117.93016 33.61899)</td>\n",
       "      <td>right_half_tri</td>\n",
       "      <td>newport</td>\n",
       "      <td>17</td>\n",
       "      <td>-171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>285613c2-f71d-55f6-bb2b-8f16528c0a2c</td>\n",
       "      <td>33.615319</td>\n",
       "      <td>-117.927299</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>2018-07-03 17:00:03-07:00</td>\n",
       "      <td>POINT (-117.927299 33.615319)</td>\n",
       "      <td>right_half_tri</td>\n",
       "      <td>N/A</td>\n",
       "      <td>17</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  PROBE_ID        LAT        LONG  HEADING  \\\n",
       "480   285613c2-f71d-55f6-bb2b-8f16528c0a2c  33.615262 -117.927329     34.0   \n",
       "479   285613c2-f71d-55f6-bb2b-8f16528c0a2c  33.615269 -117.927325     19.0   \n",
       "445   285613c2-f71d-55f6-bb2b-8f16528c0a2c  33.615287 -117.927314     19.0   \n",
       "494       6c247e2bf8f447b2b31b7450261a1e3c  33.618990 -117.930160    189.0   \n",
       "2276  285613c2-f71d-55f6-bb2b-8f16528c0a2c  33.615319 -117.927299     18.0   \n",
       "\n",
       "      SPEED PROBE_DATA_PROVIDER                LOCAL_TIME  \\\n",
       "480    14.0          CONSUMER14 2018-07-03 17:00:00-07:00   \n",
       "479    12.0          CONSUMER14 2018-07-03 17:00:01-07:00   \n",
       "445    17.0          CONSUMER14 2018-07-03 17:00:02-07:00   \n",
       "494     4.0          CONSUMER21 2018-07-03 17:00:03-07:00   \n",
       "2276   20.0          CONSUMER14 2018-07-03 17:00:03-07:00   \n",
       "\n",
       "                              coord         section   street  HOUR  \\\n",
       "480   POINT (-117.927329 33.615262)  right_half_tri      N/A    17   \n",
       "479   POINT (-117.927325 33.615269)  right_half_tri      N/A    17   \n",
       "445   POINT (-117.927314 33.615287)  right_half_tri      N/A    17   \n",
       "494     POINT (-117.93016 33.61899)  right_half_tri  newport    17   \n",
       "2276  POINT (-117.927299 33.615319)  right_half_tri      N/A    17   \n",
       "\n",
       "      HEADING_FIXED  \n",
       "480            34.0  \n",
       "479            19.0  \n",
       "445            19.0  \n",
       "494          -171.0  \n",
       "2276           18.0  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_data = data_cleaned.loc[data_cleaned['street'] == \"N/A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniqe_res_probe = list(set(data_cleaned[\"PROBE_ID\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_list = []\n",
    "for i in uniqe_res_probe:\n",
    "    speed = np.mean(res_data.loc[res_data[\"PROBE_ID\"]==i][\"SPEED\"])\n",
    "    if speed == 0:\n",
    "        speed_list.append(speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(speed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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