uber_berlin_50to30.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import folium\n",
    "import branca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### read input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>segment_id</th>\n",
       "      <th>start_junction_id</th>\n",
       "      <th>end_junction_id</th>\n",
       "      <th>osm_way_id</th>\n",
       "      <th>osm_start_node_id</th>\n",
       "      <th>osm_end_node_id</th>\n",
       "      <th>speed_kph_mean</th>\n",
       "      <th>speed_kph_stddev</th>\n",
       "      <th>speed_kph_p50</th>\n",
       "      <th>speed_kph_p85</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>d0034ae2336f81ef5933a211f2ce2d979f0aff2a</td>\n",
       "      <td>66081f9fe2860af2a498e1248334cb51d6ea5073</td>\n",
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       "      <td>9932085</td>\n",
       "      <td>1236497729</td>\n",
       "      <td>1236497752</td>\n",
       "      <td>41.258</td>\n",
       "      <td>9.239</td>\n",
       "      <td>41.330</td>\n",
       "      <td>46.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>d0034ae2336f81ef5933a211f2ce2d979f0aff2a</td>\n",
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       "      <td>9932085</td>\n",
       "      <td>1236497729</td>\n",
       "      <td>1236497752</td>\n",
       "      <td>35.794</td>\n",
       "      <td>6.706</td>\n",
       "      <td>35.832</td>\n",
       "      <td>41.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>d0034ae2336f81ef5933a211f2ce2d979f0aff2a</td>\n",
       "      <td>66081f9fe2860af2a498e1248334cb51d6ea5073</td>\n",
       "      <td>9c00a9375aeaba9c08f4f2fd507cc1808ba83b89</td>\n",
       "      <td>9932085</td>\n",
       "      <td>1236497729</td>\n",
       "      <td>1236497752</td>\n",
       "      <td>32.766</td>\n",
       "      <td>13.401</td>\n",
       "      <td>35.171</td>\n",
       "      <td>43.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>277640ca389fc7f0fc8a583386e6063df80485f0</td>\n",
       "      <td>ad26106a25d52c3409bd0165f05de3047a4e96b5</td>\n",
       "      <td>9c00a9375aeaba9c08f4f2fd507cc1808ba83b89</td>\n",
       "      <td>9932085</td>\n",
       "      <td>81398215</td>\n",
       "      <td>1236497752</td>\n",
       "      <td>34.507</td>\n",
       "      <td>4.639</td>\n",
       "      <td>35.207</td>\n",
       "      <td>38.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>277640ca389fc7f0fc8a583386e6063df80485f0</td>\n",
       "      <td>ad26106a25d52c3409bd0165f05de3047a4e96b5</td>\n",
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       "      <td>9932085</td>\n",
       "      <td>81398215</td>\n",
       "      <td>1236497752</td>\n",
       "      <td>41.512</td>\n",
       "      <td>6.049</td>\n",
       "      <td>41.128</td>\n",
       "      <td>47.681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter  hour_of_day                                segment_id  \\\n",
       "0  2019        2            0  d0034ae2336f81ef5933a211f2ce2d979f0aff2a   \n",
       "1  2019        2           23  d0034ae2336f81ef5933a211f2ce2d979f0aff2a   \n",
       "2  2019        2           22  d0034ae2336f81ef5933a211f2ce2d979f0aff2a   \n",
       "3  2019        2           23  277640ca389fc7f0fc8a583386e6063df80485f0   \n",
       "4  2019        2            1  277640ca389fc7f0fc8a583386e6063df80485f0   \n",
       "\n",
       "                          start_junction_id  \\\n",
       "0  66081f9fe2860af2a498e1248334cb51d6ea5073   \n",
       "1  66081f9fe2860af2a498e1248334cb51d6ea5073   \n",
       "2  66081f9fe2860af2a498e1248334cb51d6ea5073   \n",
       "3  ad26106a25d52c3409bd0165f05de3047a4e96b5   \n",
       "4  ad26106a25d52c3409bd0165f05de3047a4e96b5   \n",
       "\n",
       "                            end_junction_id  osm_way_id  osm_start_node_id  \\\n",
       "0  9c00a9375aeaba9c08f4f2fd507cc1808ba83b89     9932085         1236497729   \n",
       "1  9c00a9375aeaba9c08f4f2fd507cc1808ba83b89     9932085         1236497729   \n",
       "2  9c00a9375aeaba9c08f4f2fd507cc1808ba83b89     9932085         1236497729   \n",
       "3  9c00a9375aeaba9c08f4f2fd507cc1808ba83b89     9932085           81398215   \n",
       "4  9c00a9375aeaba9c08f4f2fd507cc1808ba83b89     9932085           81398215   \n",
       "\n",
       "   osm_end_node_id  speed_kph_mean  speed_kph_stddev  speed_kph_p50  \\\n",
       "0       1236497752          41.258             9.239         41.330   \n",
       "1       1236497752          35.794             6.706         35.832   \n",
       "2       1236497752          32.766            13.401         35.171   \n",
       "3       1236497752          34.507             4.639         35.207   \n",
       "4       1236497752          41.512             6.049         41.128   \n",
       "\n",
       "   speed_kph_p85  \n",
       "0         46.680  \n",
       "1         41.398  \n",
       "2         43.469  \n",
       "3         38.160  \n",
       "4         47.681  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://movement.uber.com/cities/berlin/downloads/speeds?lang=en-US&tp[y]=2019&tp[q]=2\n",
    "uber_data= pd.read_csv(r\"movement-speeds-quarterly-by-hod-berlin-2019-Q2.csv\\movement-speeds-quarterly-by-hod-berlin-2019-Q2.csv\")\n",
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>@id</th>\n",
       "      <th>cycleway:right</th>\n",
       "      <th>highway</th>\n",
       "      <th>lanes</th>\n",
       "      <th>lit</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>name</th>\n",
       "      <th>oneway</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>...</th>\n",
       "      <th>after_construction:lanes</th>\n",
       "      <th>after_construction:parking:condition:right</th>\n",
       "      <th>after_construction:parking:lane:right</th>\n",
       "      <th>construction</th>\n",
       "      <th>construction:end_date</th>\n",
       "      <th>disused:cycleway:left</th>\n",
       "      <th>protected_bike_lane:right</th>\n",
       "      <th>cycleway:right:buffer</th>\n",
       "      <th>geometry</th>\n",
       "      <th>osm_way_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>way/4045243</td>\n",
       "      <td>way/4045243</td>\n",
       "      <td>track</td>\n",
       "      <td>primary</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>50</td>\n",
       "      <td>Frankfurter Allee</td>\n",
       "      <td>yes</td>\n",
       "      <td>10247</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (13.45421 52.51571, 13.45439 52.515...</td>\n",
       "      <td>4045243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>way/4045656</td>\n",
       "      <td>way/4045656</td>\n",
       "      <td>None</td>\n",
       "      <td>secondary</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>50</td>\n",
       "      <td>Hauptstraße</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (13.37282 52.59334, 13.37281 52.593...</td>\n",
       "      <td>4045656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>way/4054013</td>\n",
       "      <td>way/4054013</td>\n",
       "      <td>track</td>\n",
       "      <td>primary</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>50</td>\n",
       "      <td>Michael-Brückner-Straße</td>\n",
       "      <td>yes</td>\n",
       "      <td>12439</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (13.51476 52.45303, 13.51528 52.452...</td>\n",
       "      <td>4054013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>way/4067882</td>\n",
       "      <td>way/4067882</td>\n",
       "      <td>track</td>\n",
       "      <td>secondary</td>\n",
       "      <td>None</td>\n",
       "      <td>yes</td>\n",
       "      <td>50</td>\n",
       "      <td>Olympische Straße</td>\n",
       "      <td>yes</td>\n",
       "      <td>14052</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (13.24937 52.51535, 13.24967 52.515...</td>\n",
       "      <td>4067882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>way/4067883</td>\n",
       "      <td>way/4067883</td>\n",
       "      <td>None</td>\n",
       "      <td>secondary</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>50</td>\n",
       "      <td>Jafféstraße</td>\n",
       "      <td>None</td>\n",
       "      <td>14052</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (13.26152 52.50771, 13.26179 52.507...</td>\n",
       "      <td>4067883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 431 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id          @id cycleway:right    highway lanes   lit maxspeed  \\\n",
       "0  way/4045243  way/4045243          track    primary     3   yes       50   \n",
       "1  way/4045656  way/4045656           None  secondary     2  None       50   \n",
       "2  way/4054013  way/4054013          track    primary     3   yes       50   \n",
       "3  way/4067882  way/4067882          track  secondary  None   yes       50   \n",
       "4  way/4067883  way/4067883           None  secondary     4   yes       50   \n",
       "\n",
       "                      name oneway postal_code  ... after_construction:lanes  \\\n",
       "0        Frankfurter Allee    yes       10247  ...                     None   \n",
       "1              Hauptstraße   None        None  ...                     None   \n",
       "2  Michael-Brückner-Straße    yes       12439  ...                     None   \n",
       "3        Olympische Straße    yes       14052  ...                     None   \n",
       "4              Jafféstraße   None       14052  ...                     None   \n",
       "\n",
       "  after_construction:parking:condition:right  \\\n",
       "0                                       None   \n",
       "1                                       None   \n",
       "2                                       None   \n",
       "3                                       None   \n",
       "4                                       None   \n",
       "\n",
       "  after_construction:parking:lane:right construction construction:end_date  \\\n",
       "0                                  None         None                  None   \n",
       "1                                  None         None                  None   \n",
       "2                                  None         None                  None   \n",
       "3                                  None         None                  None   \n",
       "4                                  None         None                  None   \n",
       "\n",
       "  disused:cycleway:left protected_bike_lane:right cycleway:right:buffer  \\\n",
       "0                  None                      None                  None   \n",
       "1                  None                      None                  None   \n",
       "2                  None                      None                  None   \n",
       "3                  None                      None                  None   \n",
       "4                  None                      None                  None   \n",
       "\n",
       "                                            geometry osm_way_id  \n",
       "0  LINESTRING (13.45421 52.51571, 13.45439 52.515...    4045243  \n",
       "1  LINESTRING (13.37282 52.59334, 13.37281 52.593...    4045656  \n",
       "2  LINESTRING (13.51476 52.45303, 13.51528 52.452...    4054013  \n",
       "3  LINESTRING (13.24937 52.51535, 13.24967 52.515...    4067882  \n",
       "4  LINESTRING (13.26152 52.50771, 13.26179 52.507...    4067883  \n",
       "\n",
       "[5 rows x 431 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#http://overpass-turbo.eu/s/PVh\n",
    "#osm_links = gpd.read_file('xhain.geojson')\n",
    "#http://overpass-turbo.eu/s/PVN\n",
    "osm_links = gpd.read_file('berlin.geojson')\n",
    "\n",
    "osm_links['osm_way_id']=osm_links['id'].str[4:]\n",
    "osm_links.osm_way_id=osm_links.osm_way_id.astype(int)\n",
    "osm_links.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### produce output interactive html maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper\n",
    "import folium.plugins\n",
    "from folium.features import *\n",
    "\n",
    "class DivIcon(MacroElement):\n",
    "    def __init__(self, html='', size=(30,30), anchor=(0,0), style=''):\n",
    "        \"\"\"TODO : docstring here\"\"\"\n",
    "        super(DivIcon, self).__init__()\n",
    "        self._name = 'DivIcon'\n",
    "        self.size = size\n",
    "        self.anchor = anchor\n",
    "        self.html = html\n",
    "        self.style = style\n",
    "\n",
    "        self._template = Template(u\"\"\"\n",
    "            {% macro header(this, kwargs) %}\n",
    "              <style>\n",
    "                .{{this.get_name()}} {\n",
    "                    {{this.style}}\n",
    "                    }\n",
    "              </style>\n",
    "            {% endmacro %}\n",
    "            {% macro script(this, kwargs) %}\n",
    "                var {{this.get_name()}} = L.divIcon({\n",
    "                    className: '{{this.get_name()}}',\n",
    "                    iconSize: [{{ this.size[0] }},{{ this.size[1] }}],\n",
    "                    iconAnchor: [{{ this.anchor[0] }},{{ this.anchor[1] }}],\n",
    "                    html : \"{{this.html}}\",\n",
    "                    });\n",
    "                {{this._parent.get_name()}}.setIcon({{this.get_name()}});\n",
    "            {% endmacro %}\n",
    "            \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Simon\\Anaconda3\\envs\\geo_julab\\lib\\site-packages\\pyproj\\crs.py:77: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method.\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "#production\n",
    "\n",
    "for h in uber_data.hour_of_day.unique():\n",
    "\n",
    "    #get ready\n",
    "    uber_data_h=uber_data[uber_data.hour_of_day==h].copy()\n",
    "    uber_data_h=uber_data_h.groupby('osm_way_id')['speed_kph_mean'].mean().reset_index().copy()\n",
    "\n",
    "    #merge uber and osm\n",
    "    data_merg= pd.merge(osm_links[['geometry','osm_way_id','name','postal_code', 'maxspeed']],uber_data_h[['speed_kph_mean','osm_way_id']] )\n",
    "\n",
    "    data_merg.maxspeed= data_merg.maxspeed.apply(lambda x: 50 if x=='DE:urban' else x)\n",
    "    data_merg.maxspeed=data_merg.maxspeed.astype(float)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    #plot map\n",
    "    df=data_merg[(data_merg.speed_kph_mean<33) & (data_merg.maxspeed>=50) ]\n",
    "    field='speed_kph_mean'\n",
    "\n",
    "    colorscale = branca.colormap.LinearColormap(['green','lightgreen','orange','red'], \n",
    "                                                index=[20,30,50,55], \n",
    "                                                vmin=0,  \n",
    "                                                vmax=33, \n",
    "                                                caption='')\n",
    "\n",
    "    def style_function(feature):\n",
    "        col=feature['properties'][field]\n",
    "        return {\n",
    "            #'fillOpacity': 0.5,\n",
    "            'weight': 3,\n",
    "            'color': 'grey' if col is None else colorscale(col)\n",
    "        }\n",
    "\n",
    "    def highlight_function(feature):\n",
    "        return {\n",
    "            'color': 'grey',\n",
    "            'weight': 5,\n",
    "            'dashArray': '5, 5'\n",
    "        }\n",
    "\n",
    "    m = folium.Map(location=[52.5, 13.4],\n",
    "                   tiles=\"cartodbpositron\",\n",
    "                   name=\"CartoDB light\",\n",
    "                   zoom_start=13)\n",
    "\n",
    "    folium.GeoJson(\n",
    "        df,\n",
    "        name=str(h)+' Uhr',\n",
    "        style_function=style_function,\n",
    "        highlight_function=highlight_function,\n",
    "        tooltip=folium.GeoJsonTooltip(fields=['name','speed_kph_mean','maxspeed'],\n",
    "                                      aliases=['name','speed_kph_mean','maxspeed']),\n",
    "    ).add_to(m)\n",
    "\n",
    "                                                      \n",
    "                                                      \n",
    "    folium.map.Marker(\n",
    "    #[34.0302, -118.2352],\n",
    "    [52.449463, 13.404811],\n",
    "    icon=DivIcon(\n",
    "        size=(150,36),\n",
    "        anchor=(150,0),\n",
    "        html=str(h)+' Uhr',\n",
    "        style=\"\"\"\n",
    "            font-size:36px;\n",
    "            background-color: transparent;\n",
    "            border-color: transparent;\n",
    "            text-align: right;\n",
    "            \"\"\"\n",
    "        )\n",
    "    ).add_to(m)                                                  \n",
    "                                                      \n",
    "                                                      \n",
    "                                                      \n",
    "\n",
    "    #m\n",
    "    folium.LayerControl(position='bottomright',collapsed=False).add_to(m)\n",
    "    \n",
    "    colorscale.caption = 'Speed'\n",
    "    m.add_child(colorscale)\n",
    "\n",
    "    m.save(\"output/map_\"+str(h)+\".html\")\n",
    "    #m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create plot from htmls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver #requires chromesdriver\n",
    "from PIL import Image\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = webdriver.ChromeOptions()\n",
    "#options.add_argument('--headless')\n",
    "driver = webdriver.Chrome(options=options)\n",
    "\n",
    "for h in uber_data.hour_of_day.unique():\n",
    "    driver.get(os.getcwd() + '\\\\output\\\\map_'+str(h)+'.html')\n",
    "    driver.save_screenshot('output\\\\map_'+str(h)+'.png')\n",
    "    \n",
    "   # newImg = Image.open('map_'+str(h)+'.png')\n",
    "   # newImg.save('map_'+str(h)+'_compressed.png', 'PNG', dpi=[200,200])\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for h in uber_data.hour_of_day.unique():\n",
    "#    newImg = Image.open('map_'+str(h)+'.png')\n",
    "#    newImg = newImg.resize((400,400),Image.ANTIALIAS)\n",
    "#    newImg.save('map_'+str(h)+'_compressed_o.png', 'PNG', optimize=True, dpi=(300,300))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### produce gif from images, finally compress gif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['output\\\\map_0.png',\n",
       " 'output\\\\map_1.png',\n",
       " 'output\\\\map_10.png',\n",
       " 'output\\\\map_11.png',\n",
       " 'output\\\\map_12.png',\n",
       " 'output\\\\map_13.png',\n",
       " 'output\\\\map_14.png',\n",
       " 'output\\\\map_15.png',\n",
       " 'output\\\\map_16.png',\n",
       " 'output\\\\map_17.png',\n",
       " 'output\\\\map_18.png',\n",
       " 'output\\\\map_19.png',\n",
       " 'output\\\\map_2.png',\n",
       " 'output\\\\map_20.png',\n",
       " 'output\\\\map_21.png',\n",
       " 'output\\\\map_22.png',\n",
       " 'output\\\\map_23.png',\n",
       " 'output\\\\map_3.png',\n",
       " 'output\\\\map_4.png',\n",
       " 'output\\\\map_5.png',\n",
       " 'output\\\\map_6.png',\n",
       " 'output\\\\map_7.png',\n",
       " 'output\\\\map_8.png',\n",
       " 'output\\\\map_9.png']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "filenames= glob.glob(\"output\\\\map*.png\")\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['output\\\\map_0.png', 'output\\\\map_1.png', 'output\\\\map_2.png', 'output\\\\map_3.png', 'output\\\\map_4.png', 'output\\\\map_5.png', 'output\\\\map_6.png', 'output\\\\map_7.png', 'output\\\\map_8.png', 'output\\\\map_9.png', 'output\\\\map_10.png', 'output\\\\map_11.png', 'output\\\\map_12.png', 'output\\\\map_13.png', 'output\\\\map_14.png', 'output\\\\map_15.png', 'output\\\\map_16.png', 'output\\\\map_17.png', 'output\\\\map_18.png', 'output\\\\map_19.png', 'output\\\\map_20.png', 'output\\\\map_21.png', 'output\\\\map_22.png', 'output\\\\map_23.png']\n"
     ]
    }
   ],
   "source": [
    "# sort by hour\n",
    "import re\n",
    "\n",
    "def atoi(text):\n",
    "    return int(text) if text.isdigit() else text\n",
    "\n",
    "def natural_keys(text):\n",
    "    '''\n",
    "    alist.sort(key=natural_keys) sorts in human order\n",
    "    http://nedbatchelder.com/blog/200712/human_sorting.html\n",
    "    (See Toothy's implementation in the comments)\n",
    "    '''\n",
    "    return [ atoi(c) for c in re.split(r'(\\d+)', text) ]\n",
    "\n",
    "\n",
    "filenames.sort(key=natural_keys)\n",
    "print(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "\n",
    "images = []\n",
    "for filename in filenames:\n",
    "    images.append(imageio.imread(filename))\n",
    "imageio.mimsave('output\\\\uber_berlin2019Q2_50to30_24h.gif', images, format='GIF', duration=0.2)\n",
    "#imageio.mimsave(exportname, frames, format='GIF', duration=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compress gif with gifsicle (install gifsicle first)\n",
    "os.system(\"C:\\ProgramData\\gifsicle-1.92-win64\\gifsicle-1.92\\gifsicle.exe -O3 output\\\\uber_berlin2019Q2_50to30_24h.gif -o output\\\\uber_berlin2019Q2_50to30_24h.gif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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