examples/api_request_deckgl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package loading and basic configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# load dependencies'\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "from envirocar import TrackAPI, DownloadClient, BboxSelector, ECConfig\n",
    "\n",
    "# create an initial but optional config and an api client\n",
    "config = ECConfig()\n",
    "track_api = TrackAPI(api_client=DownloadClient(config=config))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Querying enviroCar Tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell queries tracks from the enviroCar API. It defines a bbox for the area of Münster (Germany) and requests 50 tracks. The result is a GeoDataFrame, which is a geo-extended Pandas dataframe from the GeoPandas library. It contains all information of the track in a flat dataframe format including a specific geometry column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>time</th>\n",
       "      <th>geometry</th>\n",
       "      <th>GPS PDOP.value</th>\n",
       "      <th>GPS PDOP.unit</th>\n",
       "      <th>Speed.value</th>\n",
       "      <th>Speed.unit</th>\n",
       "      <th>GPS Altitude.value</th>\n",
       "      <th>GPS Altitude.unit</th>\n",
       "      <th>GPS Bearing.value</th>\n",
       "      <th>...</th>\n",
       "      <th>Consumption.value</th>\n",
       "      <th>Consumption.unit</th>\n",
       "      <th>track.appVersion</th>\n",
       "      <th>track.touVersion</th>\n",
       "      <th>O2 Lambda Voltage ER.value</th>\n",
       "      <th>O2 Lambda Voltage ER.unit</th>\n",
       "      <th>MAF.value</th>\n",
       "      <th>MAF.unit</th>\n",
       "      <th>O2 Lambda Voltage.value</th>\n",
       "      <th>O2 Lambda Voltage.unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e8b930965b80c5d6b4d7cd1</td>\n",
       "      <td>2020-03-07T12:33:15</td>\n",
       "      <td>POINT (7.64069 51.95733)</td>\n",
       "      <td>1.090631</td>\n",
       "      <td>precision</td>\n",
       "      <td>28.999999</td>\n",
       "      <td>km/h</td>\n",
       "      <td>110.381939</td>\n",
       "      <td>m</td>\n",
       "      <td>124.858622</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5e8b930965b80c5d6b4d7cd3</td>\n",
       "      <td>2020-03-07T12:33:20</td>\n",
       "      <td>POINT (7.64118 51.95712)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>precision</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>km/h</td>\n",
       "      <td>108.260375</td>\n",
       "      <td>m</td>\n",
       "      <td>125.020801</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5e8b930965b80c5d6b4d7cd4</td>\n",
       "      <td>2020-03-07T12:33:26</td>\n",
       "      <td>POINT (7.64162 51.95690)</td>\n",
       "      <td>1.257198</td>\n",
       "      <td>precision</td>\n",
       "      <td>28.000001</td>\n",
       "      <td>km/h</td>\n",
       "      <td>105.826028</td>\n",
       "      <td>m</td>\n",
       "      <td>121.203960</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5e8b930965b80c5d6b4d7cd5</td>\n",
       "      <td>2020-03-07T12:33:31</td>\n",
       "      <td>POINT (7.64210 51.95672)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>precision</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>km/h</td>\n",
       "      <td>104.395998</td>\n",
       "      <td>m</td>\n",
       "      <td>123.412759</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5e8b930965b80c5d6b4d7cd6</td>\n",
       "      <td>2020-03-07T12:33:36</td>\n",
       "      <td>POINT (7.64264 51.95650)</td>\n",
       "      <td>1.026727</td>\n",
       "      <td>precision</td>\n",
       "      <td>31.409419</td>\n",
       "      <td>km/h</td>\n",
       "      <td>101.516865</td>\n",
       "      <td>m</td>\n",
       "      <td>122.170479</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>5dc986e844ea856b702e3e0b</td>\n",
       "      <td>2019-10-28T16:34:55</td>\n",
       "      <td>POINT (7.59523 51.96505)</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>precision</td>\n",
       "      <td>47.999999</td>\n",
       "      <td>km/h</td>\n",
       "      <td>109.652212</td>\n",
       "      <td>m</td>\n",
       "      <td>276.419653</td>\n",
       "      <td>...</td>\n",
       "      <td>3.122268</td>\n",
       "      <td>l/h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>5dc986e844ea856b702e3e0c</td>\n",
       "      <td>2019-10-28T16:35:00</td>\n",
       "      <td>POINT (7.59425 51.96512)</td>\n",
       "      <td>1.497088</td>\n",
       "      <td>precision</td>\n",
       "      <td>48.297297</td>\n",
       "      <td>km/h</td>\n",
       "      <td>110.122771</td>\n",
       "      <td>m</td>\n",
       "      <td>276.271049</td>\n",
       "      <td>...</td>\n",
       "      <td>2.853618</td>\n",
       "      <td>l/h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>5dc986e844ea856b702e3e0d</td>\n",
       "      <td>2019-10-28T16:35:05</td>\n",
       "      <td>POINT (7.59327 51.96518)</td>\n",
       "      <td>1.688911</td>\n",
       "      <td>precision</td>\n",
       "      <td>49.000001</td>\n",
       "      <td>km/h</td>\n",
       "      <td>110.573987</td>\n",
       "      <td>m</td>\n",
       "      <td>275.808021</td>\n",
       "      <td>...</td>\n",
       "      <td>4.657916</td>\n",
       "      <td>l/h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>5dc986e844ea856b702e3e0e</td>\n",
       "      <td>2019-10-28T16:35:10</td>\n",
       "      <td>POINT (7.59225 51.96525)</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>precision</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>km/h</td>\n",
       "      <td>111.140661</td>\n",
       "      <td>m</td>\n",
       "      <td>275.411387</td>\n",
       "      <td>...</td>\n",
       "      <td>3.445271</td>\n",
       "      <td>l/h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>5dc986e844ea856b702e3e0f</td>\n",
       "      <td>2019-10-28T16:35:15</td>\n",
       "      <td>POINT (7.59123 51.96531)</td>\n",
       "      <td>1.423253</td>\n",
       "      <td>precision</td>\n",
       "      <td>50.000001</td>\n",
       "      <td>km/h</td>\n",
       "      <td>111.891658</td>\n",
       "      <td>m</td>\n",
       "      <td>276.124438</td>\n",
       "      <td>...</td>\n",
       "      <td>3.248333</td>\n",
       "      <td>l/h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9944 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id                 time                  geometry  \\\n",
       "0    5e8b930965b80c5d6b4d7cd1  2020-03-07T12:33:15  POINT (7.64069 51.95733)   \n",
       "1    5e8b930965b80c5d6b4d7cd3  2020-03-07T12:33:20  POINT (7.64118 51.95712)   \n",
       "2    5e8b930965b80c5d6b4d7cd4  2020-03-07T12:33:26  POINT (7.64162 51.95690)   \n",
       "3    5e8b930965b80c5d6b4d7cd5  2020-03-07T12:33:31  POINT (7.64210 51.95672)   \n",
       "4    5e8b930965b80c5d6b4d7cd6  2020-03-07T12:33:36  POINT (7.64264 51.95650)   \n",
       "..                        ...                  ...                       ...   \n",
       "283  5dc986e844ea856b702e3e0b  2019-10-28T16:34:55  POINT (7.59523 51.96505)   \n",
       "284  5dc986e844ea856b702e3e0c  2019-10-28T16:35:00  POINT (7.59425 51.96512)   \n",
       "285  5dc986e844ea856b702e3e0d  2019-10-28T16:35:05  POINT (7.59327 51.96518)   \n",
       "286  5dc986e844ea856b702e3e0e  2019-10-28T16:35:10  POINT (7.59225 51.96525)   \n",
       "287  5dc986e844ea856b702e3e0f  2019-10-28T16:35:15  POINT (7.59123 51.96531)   \n",
       "\n",
       "     GPS PDOP.value GPS PDOP.unit  Speed.value Speed.unit  GPS Altitude.value  \\\n",
       "0          1.090631     precision    28.999999       km/h          110.381939   \n",
       "1          1.000000     precision    28.000000       km/h          108.260375   \n",
       "2          1.257198     precision    28.000001       km/h          105.826028   \n",
       "3          1.000000     precision    30.000000       km/h          104.395998   \n",
       "4          1.026727     precision    31.409419       km/h          101.516865   \n",
       "..              ...           ...          ...        ...                 ...   \n",
       "283        1.700000     precision    47.999999       km/h          109.652212   \n",
       "284        1.497088     precision    48.297297       km/h          110.122771   \n",
       "285        1.688911     precision    49.000001       km/h          110.573987   \n",
       "286        1.300000     precision    51.000000       km/h          111.140661   \n",
       "287        1.423253     precision    50.000001       km/h          111.891658   \n",
       "\n",
       "    GPS Altitude.unit  GPS Bearing.value  ... Consumption.value  \\\n",
       "0                   m         124.858622  ...               NaN   \n",
       "1                   m         125.020801  ...               NaN   \n",
       "2                   m         121.203960  ...               NaN   \n",
       "3                   m         123.412759  ...               NaN   \n",
       "4                   m         122.170479  ...               NaN   \n",
       "..                ...                ...  ...               ...   \n",
       "283                 m         276.419653  ...          3.122268   \n",
       "284                 m         276.271049  ...          2.853618   \n",
       "285                 m         275.808021  ...          4.657916   \n",
       "286                 m         275.411387  ...          3.445271   \n",
       "287                 m         276.124438  ...          3.248333   \n",
       "\n",
       "     Consumption.unit track.appVersion  track.touVersion  \\\n",
       "0                 NaN              NaN               NaN   \n",
       "1                 NaN              NaN               NaN   \n",
       "2                 NaN              NaN               NaN   \n",
       "3                 NaN              NaN               NaN   \n",
       "4                 NaN              NaN               NaN   \n",
       "..                ...              ...               ...   \n",
       "283               l/h              NaN               NaN   \n",
       "284               l/h              NaN               NaN   \n",
       "285               l/h              NaN               NaN   \n",
       "286               l/h              NaN               NaN   \n",
       "287               l/h              NaN               NaN   \n",
       "\n",
       "    O2 Lambda Voltage ER.value  O2 Lambda Voltage ER.unit MAF.value  MAF.unit  \\\n",
       "0                          NaN                        NaN       NaN       NaN   \n",
       "1                          NaN                        NaN       NaN       NaN   \n",
       "2                          NaN                        NaN       NaN       NaN   \n",
       "3                          NaN                        NaN       NaN       NaN   \n",
       "4                          NaN                        NaN       NaN       NaN   \n",
       "..                         ...                        ...       ...       ...   \n",
       "283                        NaN                        NaN       NaN       NaN   \n",
       "284                        NaN                        NaN       NaN       NaN   \n",
       "285                        NaN                        NaN       NaN       NaN   \n",
       "286                        NaN                        NaN       NaN       NaN   \n",
       "287                        NaN                        NaN       NaN       NaN   \n",
       "\n",
       "    O2 Lambda Voltage.value  O2 Lambda Voltage.unit  \n",
       "0                       NaN                     NaN  \n",
       "1                       NaN                     NaN  \n",
       "2                       NaN                     NaN  \n",
       "3                       NaN                     NaN  \n",
       "4                       NaN                     NaN  \n",
       "..                      ...                     ...  \n",
       "283                     NaN                     NaN  \n",
       "284                     NaN                     NaN  \n",
       "285                     NaN                     NaN  \n",
       "286                     NaN                     NaN  \n",
       "287                     NaN                     NaN  \n",
       "\n",
       "[9944 rows x 54 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox = BboxSelector([\n",
    "    7.601165771484375, # min_x\n",
    "    51.94807412325402, # min_y\n",
    "    7.648200988769531, # max_x\n",
    "    51.97261482608728  # max_y\n",
    "])\n",
    "\n",
    "# issue a query\n",
    "track_df = track_api.get_tracks(bbox=bbox, num_results=50) # requesting 50 tracks inside the bbox\n",
    "track_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e61090b00>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_df.plot(figsize=(8, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting a single Track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e60d3b160>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_track_id = track_df['track.id'].unique()[5]\n",
    "some_track = track_df[track_df['track.id'] == some_track_id]\n",
    "some_track.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e48fb4b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = some_track['Speed.value'].plot()\n",
    "ax.set_title(\"Speed\")\n",
    "ax.set_ylabel(some_track['Speed.unit'][0])\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Map\n",
    "The following map-based visualization makes use of folium. It allows to visualizate geospatial data based on an interactive leaflet map. Since the data in the GeoDataframe is modelled as a set of Point instead of a LineString, we have to manually create a polyline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_4fc89812f3eb4953a4d031117928f033 {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_4fc89812f3eb4953a4d031117928f033" ></div>
        
</body>
<script>    
    
            var map_4fc89812f3eb4953a4d031117928f033 = L.map(
                "map_4fc89812f3eb4953a4d031117928f033",
                {
                    center: [51.9462096610381, 7.615924958050874],
                    crs: L.CRS.EPSG3857,
                    zoom: 13,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_c349b723295946e78ff6473d0d27408d = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_4fc89812f3eb4953a4d031117928f033);
        
    
            var poly_line_68095eb689ae4e7eb4f8f0d6dd4a2682 = L.polyline(
                [[51.96762650870718, 7.6020395805890315], [51.9669740364003, 7.601865676653752], [51.96638639023848, 7.601748634481659], [51.96590602158017, 7.60167428580957], [51.965626324834936, 7.601646069001132], [51.965538112118146, 7.601638170647878], [51.96553874875201, 7.601635138403548], [51.96554161625535, 7.601635557868345], [51.96554103549518, 7.601635472913449], [51.965538712454496, 7.601635133093867], [51.96553948680139, 7.601635246367061], [51.965540261148284, 7.601635359640255], [51.965541809842065, 7.601635586186643], [51.965535677690184, 7.6016336375411315], [51.96541569485248, 7.601593838547009], [51.965025913851676, 7.601554881216494], [51.9644794999263, 7.601563306873236], [51.96391246379419, 7.601660765252532], [51.96331422450173, 7.601897160950154], [51.96266919713308, 7.602152161449951], [51.96201603311967, 7.602356436157673], [51.96136045667508, 7.602420105665974], [51.96078457057371, 7.602380019867981], [51.96039024374791, 7.602379130186444], [51.96014407877897, 7.602387807157003], [51.959673613518035, 7.6024195727827655], [51.959119157492644, 7.602455322443937], [51.95857309871727, 7.602520071298704], [51.95799228068605, 7.6026136829625175], [51.957398872617844, 7.60268369918402], [51.95673622697998, 7.602750937976581], [51.95606281685737, 7.602718587784212], [51.95536803188655, 7.602637510756393], [51.95469061073045, 7.6026125264252045], [51.954016535974134, 7.602738780681413], [51.953342023548004, 7.603000980680623], [51.95268246726634, 7.603276542720279], [51.95201594203243, 7.60355896209996], [51.95141819871543, 7.603918194072689], [51.950848353193734, 7.604439713497271], [51.95037677299463, 7.605048004188035], [51.950037939818436, 7.605502371122759], [51.949856863161, 7.6057464807452995], [51.94979538099273, 7.605818255285215], [51.94979846654338, 7.605813346166865], [51.94979696754996, 7.6058129519386934], [51.9497985157746, 7.605813178609591], [51.94979696754996, 7.605812951938694], [51.94979928988692, 7.605813291945038], [51.94979696754995, 7.6058129519386934], [51.94979735460611, 7.605813008606418], [51.9497970909286, 7.605812796317358], [51.949714531519845, 7.605927591709827], [51.949448520611526, 7.606297440094917], [51.9490817830121, 7.606853744975769], [51.94888574241686, 7.607505393916186], [51.94889610165799, 7.6081606895098135], [51.94898910762164, 7.608892217906399], [51.94908137822865, 7.609620583804469], [51.949182637237314, 7.61039351627117], [51.949304614758404, 7.611225513209412], [51.94945700558347, 7.612059782106657], [51.94960043920008, 7.612923494225443], [51.94970904820575, 7.613831014153279], [51.94976629245461, 7.614738177284197], [51.949797290213056, 7.61563548322478], [51.949764910175915, 7.616343181086369], [51.949475842110516, 7.61645895684193], [51.94904052870866, 7.616096126000548], [51.948574056365246, 7.615722979250722], [51.94812905733555, 7.615360215010499], [51.94771104130984, 7.615013079969586], [51.94740197882991, 7.614764328493039], [51.947024037352534, 7.614446283501071], [51.946490649876495, 7.614025160241171], [51.94594926681104, 7.61361988774228], [51.9454010015011, 7.613202352322496], [51.94494007837478, 7.612837571632762], [51.944606896862624, 7.612568349968949], [51.94436295050322, 7.6123666856874275], [51.94408704465653, 7.6121415994679165], [51.94359938771518, 7.6117476778462585], [51.94306530324454, 7.611315541712323], [51.942544113960956, 7.6108697246509776], [51.94202877565313, 7.610418267291119], [51.94152227683384, 7.609976174447559], [51.94101829550637, 7.609535704498205], [51.940468373313706, 7.609079060300234], [51.93988411446969, 7.60858797423699], [51.939294362138114, 7.608051736749532], [51.93876877813706, 7.6073365572379785], [51.93834157715532, 7.606584445176662], [51.937904459494746, 7.605801243234412], [51.93741837197971, 7.6049510302738454], [51.93689114018357, 7.6040910041893595], [51.93635486224353, 7.603327965049833], [51.93587844404939, 7.602733018840407], [51.935570568238276, 7.602354232711522], [51.935481230634075, 7.602242370220458], [51.93549452124789, 7.602258471138976], [51.93549664105566, 7.602260230508487], [51.93549431935811, 7.602259890660982], [51.93549509325729, 7.602260003943485], [51.93549509325729, 7.602260003943484], [51.93549664105565, 7.602260230508487], [51.93549509325729, 7.602260003943484], [51.93549509325729, 7.602260003943484], [51.93549509325729, 7.602260003943483], [51.93549509325729, 7.602260003943485], [51.93549664105566, 7.602260230508488], [51.93546184157307, 7.6022177599978775], [51.935159907028414, 7.602166272601844], [51.934961391732344, 7.603052251928237], [51.93497871136707, 7.604634493452149], [51.93506851979857, 7.606445121281519], [51.9349634147539, 7.608039287392449], [51.93475557253699, 7.609697237868671], [51.934695166781076, 7.611360667397755], [51.934753574873284, 7.61297129817179], [51.93487268053354, 7.614575246137643], [51.935028257930156, 7.616121710371439], [51.93521907007221, 7.617640403718126], [51.93545017061726, 7.619099870727214], [51.935713186432054, 7.620500231553487], [51.93598109869627, 7.621891596699944], [51.936225094301236, 7.623303376076733], [51.936482252913336, 7.624731778742431], [51.936744500293486, 7.626170744930674], [51.93701918553901, 7.627517519428386], [51.93726877619952, 7.628865976218938], [51.93748538686623, 7.63015896494411], [51.93768349891607, 7.6315717201046755], [51.93788191415699, 7.633054367704881], [51.93809521558452, 7.634426676177675], [51.93838388591415, 7.635709132749028], [51.93873943960509, 7.63685280620422], [51.93909713741143, 7.637840638071304], [51.939463465457024, 7.638802272583125], [51.93988052685705, 7.639880610195258], [51.94034623395707, 7.64106672590069], [51.94086183781375, 7.642230738598079], [51.94145394042134, 7.643243843521549], [51.941998093041995, 7.64425783006515], [51.94250065149997, 7.645411246138442], [51.942938973854744, 7.646468824420397], [51.94316597219884, 7.647335328191928], [51.942779843270486, 7.647871715073101], [51.94226025097973, 7.648408213227642], [51.941815593354285, 7.648959460422955], [51.941526470690064, 7.649344447405266], [51.94149930110051, 7.649377054922648], [51.941482987544376, 7.649396247698131], [51.94124843425767, 7.649721231693956], [51.940833358232695, 7.650330459534361], [51.940335277553125, 7.651062712149628], [51.93981445410897, 7.651725773549351], [51.939321308121016, 7.652288680811582], [51.939002226133255, 7.6527199793271565], [51.93864999163593, 7.6532312510471], [51.93820034759533, 7.653884523818195], [51.937730405182165, 7.654598430396566], [51.93725616351866, 7.655288242213278], [51.93681924801193, 7.655910659734133], [51.93655448929835, 7.656289607466017], [51.936275205884634, 7.656197293644802], [51.93598830971369, 7.655724440087507]],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 1.0, "smoothFactor": 1.0, "stroke": true, "weight": 3}
            ).addTo(map_4fc89812f3eb4953a4d031117928f033);
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f4e50f874a8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "lats = list(some_track['geometry'].apply(lambda coord: coord.y))\n",
    "lngs = list(some_track['geometry'].apply(lambda coord: coord.x))\n",
    "\n",
    "avg_lat = sum(lats) / len(lats)\n",
    "avg_lngs = sum(lngs) / len(lngs)\n",
    "\n",
    "m = folium.Map(location=[avg_lat, avg_lngs], zoom_start=13)\n",
    "folium.PolyLine([coords for coords in zip(lats, lngs)], color='blue').add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Visualization with pydeck (deck.gl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pydeck library makes use of the basemap tiles from Mapbox. In case you want to visualize the map with basemap tiles, you need to register with MapBox, and configure a specific access token. The service is free until a certain level of traffic is esceeded.\n",
    "\n",
    "You can either configure it via your terminal (i.e. `export MAPBOX_API_KEY=<mapbox-key-here>`), which pydeck will automatically read, or you can pass it as a variable to the generation of pydeck (i.e. `pdk.Deck(mapbox_key=<mapbox-key-here>, ...)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"./tracks_muenster.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f4e508e19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'/home/hafenkran/dev/envirocar/envirocar-py/examples/tracks_muenster.html'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydeck as pdk\n",
    "\n",
    "# for pydeck the attributes have to be flat\n",
    "track_df['lat'] = track_df['geometry'].apply(lambda coord: coord.y)\n",
    "track_df['lng'] = track_df['geometry'].apply(lambda coord: coord.x)\n",
    "vis_df = pd.DataFrame(track_df)\n",
    "vis_df['speed'] = vis_df['Speed.value']\n",
    "\n",
    "# omit unit columns\n",
    "vis_df_cols = [col for col in vis_df.columns if col.lower()[len(col)-4:len(col)] != 'unit']\n",
    "vis_df = vis_df[vis_df_cols]\n",
    "\n",
    "layer = pdk.Layer(\n",
    "    'ScatterplotLayer',\n",
    "    data=vis_df,\n",
    "    get_position='[lng, lat]',\n",
    "    auto_highlight=True,\n",
    "    get_radius=10,          # Radius is given in meters\n",
    "    get_fill_color='[speed < 20 ? 0 : (speed - 20)*8.5, speed < 50 ? 255 : 255 - (speed-50)*8.5, 0, 140]',  # Set an RGBA value for fill\n",
    "    pickable=True\n",
    ")\n",
    "\n",
    "# Set the viewport location\n",
    "view_state = pdk.ViewState(\n",
    "    longitude=7.5963592529296875,\n",
    "    latitude=51.96246168188569,\n",
    "    zoom=10,\n",
    "    min_zoom=5,\n",
    "    max_zoom=15,\n",
    "    pitch=40.5,\n",
    "    bearing=-27.36)\n",
    "\n",
    "r = pdk.Deck(\n",
    "    width=200, \n",
    "    layers=[layer], \n",
    "    initial_view_state=view_state #, mapbox_key=<mapbox-key-here>\n",
    ")\n",
    "r.to_html('tracks_muenster.html', iframe_width=900)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "envirocar",
   "language": "python",
   "name": "envirocar"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}