SK_FR_Schule_Landuse_cnt_pyrosm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4baf86e0-50ad-4d07-8ea9-1556c8b6635b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('schulen.json') as f:\n",
    "    schulen = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed24b60b-00f8-4871-8ac0-28a845a75330",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read Boundary\n",
    "# ============\n",
    "from pyrosm import OSM\n",
    "from pyrosm import get_data\n",
    "fp = get_data(\"freiburg_regbez\",directory='pbf/')\n",
    "# Initialize the OSM parser object\n",
    "osm = OSM(fp)\n",
    "bh_bound=osm.get_boundaries(name='Freiburg im Breisgau')\n",
    "#tags = {'boundary': 'administrative'}   \n",
    "lk_bh=bh_bound\n",
    "lk_bh_poly=bh_bound.geometry[0]\n",
    "lk_bh.plot(facecolor=\"none\", edgecolor=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7254c556-4bb3-4a60-b299-d99eea5842d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bh_bound.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "734d0e20-71a4-433d-a392-5a8865c4297e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyrosm\n",
    "# Ebene Gemeinde\n",
    "gem_in_bh_gdf_osm=pyrosm.OSM(filepath=\"pbf/sk_fr_poly.pbf\",bounding_box=lk_bh_poly)\n",
    "gem_in_bh_gdf_of=gem_in_bh_gdf_osm.get_boundaries(custom_filter={'admin_level': ['8']})\n",
    "#Filter notwendig, da einige Grenzen außerhalb enthalten waren\n",
    "in_bh=gem_in_bh_gdf_of.within(lk_bh_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35808443-5aa4-4ac0-92ec-f62eeb993476",
   "metadata": {},
   "outputs": [],
   "source": [
    "gem_in_bh_gdf=gem_in_bh_gdf_of[ in_bh &  (gem_in_bh_gdf_of['admin_level']=='6') ]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47b97e4f-c84a-4488-b849-e5109aef6ac0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gem_in_bh_gdf.plot(facecolor=\"none\",edgecolor=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51c4f59d-e863-40d7-8b80-3c7cc14ec2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gem_in_bh_gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5771ae2b-1bbf-4904-9958-05f4bd26d032",
   "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>name</th>\n",
       "      <th>ref</th>\n",
       "      <th>website</th>\n",
       "      <th>admin_level</th>\n",
       "      <th>boundary</th>\n",
       "      <th>political</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>border_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>version</th>\n",
       "      <th>changeset</th>\n",
       "      <th>geometry</th>\n",
       "      <th>tags</th>\n",
       "      <th>osm_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Freiburg im Breisgau</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>administrative</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>607111</td>\n",
       "      <td>1715031648</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((7.66279 47.96663, 7.66304 47.96698, ...</td>\n",
       "      <td>{\"TMC:cid_58:tabcd_1:Class\":\"Area\",\"TMC:cid_58...</td>\n",
       "      <td>relation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name   ref website admin_level        boundary political  \\\n",
       "0  Freiburg im Breisgau  None    None           6  administrative      None   \n",
       "\n",
       "  postal_code border_type start_date      id   timestamp  version  changeset  \\\n",
       "0        None        None       None  607111  1715031648      176          0   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((7.66279 47.96663, 7.66304 47.96698, ...   \n",
       "\n",
       "                                                tags  osm_type  \n",
       "0  {\"TMC:cid_58:tabcd_1:Class\":\"Area\",\"TMC:cid_58...  relation  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gem_in_bh_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4dc34b5-d6ec-4584-8e5f-e5bcfd01327f",
   "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>name</th>\n",
       "      <th>ref</th>\n",
       "      <th>website</th>\n",
       "      <th>admin_level</th>\n",
       "      <th>boundary</th>\n",
       "      <th>political</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>border_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>version</th>\n",
       "      <th>changeset</th>\n",
       "      <th>geometry</th>\n",
       "      <th>tags</th>\n",
       "      <th>osm_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Oberried</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>administrative</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4179002</td>\n",
       "      <td>1694205952</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((7.90730 47.92321, 7.90778 47.92238, ...</td>\n",
       "      <td>{\"TMC:cid_58:tabcd_1:Class\":\"Area\",\"TMC:cid_58...</td>\n",
       "      <td>relation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Bollschweil</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.bollschweil.de/</td>\n",
       "      <td>8</td>\n",
       "      <td>administrative</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>471469210</td>\n",
       "      <td>1590859208</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((7.87498 47.90358, 7.87332 47.90395, ...</td>\n",
       "      <td>{\"admin_centre:postal_code\":\"79283\",\"de:amtlic...</td>\n",
       "      <td>relation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>March</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>administrative</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>510453409</td>\n",
       "      <td>1580260791</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>MULTIPOLYGON (((7.78693 48.03207, 7.78715 48.0...</td>\n",
       "      <td>{\"admin_centre:postal_code\":\"79232\",\"contact:p...</td>\n",
       "      <td>relation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name   ref                      website admin_level  \\\n",
       "17     Oberried  None                         None           8   \n",
       "66  Bollschweil  None  https://www.bollschweil.de/           8   \n",
       "85        March  None                         None           8   \n",
       "\n",
       "          boundary political postal_code border_type start_date         id  \\\n",
       "17  administrative      None        None        None       None    4179002   \n",
       "66  administrative      None        None        None       None  471469210   \n",
       "85  administrative      None        None        None       None  510453409   \n",
       "\n",
       "     timestamp  version  changeset  \\\n",
       "17  1694205952       56          0   \n",
       "66  1590859208       20          0   \n",
       "85  1580260791       19          0   \n",
       "\n",
       "                                             geometry  \\\n",
       "17  POLYGON ((7.90730 47.92321, 7.90778 47.92238, ...   \n",
       "66  POLYGON ((7.87498 47.90358, 7.87332 47.90395, ...   \n",
       "85  MULTIPOLYGON (((7.78693 48.03207, 7.78715 48.0...   \n",
       "\n",
       "                                                 tags  osm_type  \n",
       "17  {\"TMC:cid_58:tabcd_1:Class\":\"Area\",\"TMC:cid_58...  relation  \n",
       "66  {\"admin_centre:postal_code\":\"79283\",\"de:amtlic...  relation  \n",
       "85  {\"admin_centre:postal_code\":\"79232\",\"contact:p...  relation  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Findet alle Teilorte\n",
    "#in_bh=gem_bh_gdf.within(eki_poly)\n",
    "#ort_in_bh_gdf=gem_in_bh_gdf_of[ in_bh & (gem_in_bh_gdf_of['boundary']=='administrative') &  ((gem_in_bh_gdf_of['admin_level']=='9')|(gem_in_bh_gdf_of['admin_level']=='10') ) ]  \n",
    "#len(ort_in_bh_gdf)\n",
    "gem_in_bh_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d2d9e81-8214-46b4-b630-637009497937",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line 0036: 2024-08-20 16:50:29.005747 - Freiburg im Breisgau\n",
      "Line 0041: 2024-08-20 16:50:29.013039 - Freiburg im Breisgau\n",
      "Line 0049: 2024-08-20 16:51:06.753071 - Freiburg im Breisgau\n",
      "Line 0088: 2024-08-20 16:51:10.843630 - Freiburg im Breisgau\n"
     ]
    }
   ],
   "source": [
    "######\n",
    "#\n",
    "#  Dies ist die Hauptprozedur. \n",
    "#  Hier wird über alle Orte iteriert und dabei jeweils zuerst das osm für ein Bereich eingestellt und danach\n",
    "#  die Wege, Gebäude und Schulen eingetragen und ausgewertet.\n",
    "#\n",
    "######\n",
    "import pandas as pd\n",
    "import numpy\n",
    "from shapely.geometry import Point\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import matplotlib\n",
    "import osmnx as ox\n",
    "import datetime\n",
    "\n",
    "network_type = \"walk\"\n",
    "trip_times = [5, 10, 15, 20, 25,30,40]  # in minutes\n",
    "travel_speed = 3  # walking speed in km/hour\n",
    "# get one color for each isochrone\n",
    "iso_colors = ox.plot.get_colors(n=len(trip_times), cmap=\"plasma\", start=0)\n",
    "cmap = matplotlib.cm.cool\n",
    "norm = matplotlib.colors.BoundaryNorm(trip_times, cmap.N)\n",
    "place='sk_fr'\n",
    "\n",
    "sc=[]\n",
    "for cur in (schulen[\"schools\"]):\n",
    "    p=Point(cur[\"lng\"],cur[\"lat\"])\n",
    "    sc=sc+[[p,cur[\"name\"]]]\n",
    "\n",
    "alle_gem=pd.DataFrame()\n",
    "for index_gem,row_gem in gem_in_bh_gdf.iterrows():\n",
    "    gem_name=row_gem['name']\n",
    "    #if gem_name=='Bollschweil':\n",
    "    if 1==1:\n",
    "\n",
    "        print('Line 0036: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "        #gem_poly=row_gem['geometry'].values[0]\n",
    "        # Initialisierung des Gebiets\n",
    "        gem_poly=row_gem['geometry']\n",
    "        #gem_osm=pyrosm.OSM(filepath=\"pbf/freiburg-regbez-latest.osm.pbf\",bounding_box=gem_poly)\n",
    "        gem_osm=pyrosm.OSM(filepath=\"pbf/sk_fr_poly.pbf\",bounding_box=gem_poly)\n",
    "        print('Line 0041: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "        # Wegenetz\n",
    "        gem_walk_net_nodes, gem_walk_net_edges = gem_osm.get_network(network_type=\"walking\",nodes=True)\n",
    "        gem_walk_net = osm.to_graph(gem_walk_net_nodes, gem_walk_net_edges, graph_type=\"networkx\")\n",
    "        meters_per_minute = travel_speed * 1000 / 60  # km per hour to m per minute\n",
    "        for _, _, _, data in gem_walk_net.edges(data=True, keys=True):\n",
    "            data[\"time\"] = data[\"length\"] / meters_per_minute\n",
    "\n",
    "        print('Line 0049: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "        \n",
    "        # Landuse\n",
    "        tags_lu={'landuse':['residential','farmyard','industrial','commercial']}\n",
    "        gem_gdf_lu=gem_osm.get_landuse(custom_filter=tags_lu)\n",
    "\n",
    "        #tags = {'building': True}  \n",
    "        #gdf_gem_build=ox.features_from_polygon(gem_poly,tags)\n",
    "        tags = {'addr:housenumber': True}\n",
    "        \n",
    "        gdf_gem_hn=gem_osm.get_data_by_custom_criteria(custom_filter=tags)\n",
    "        \n",
    "        gdf_gem_hn['triptime']=120\n",
    "        \n",
    "\n",
    "        in_gem=gem_poly\n",
    "        ort_in_bh_gdf=gem_in_bh_gdf_of[ in_bh & (gem_in_bh_gdf_of['boundary']=='administrative') &  ((gem_in_bh_gdf_of['admin_level']=='9')|(gem_in_bh_gdf_of['admin_level']=='10') ) ]  \n",
    "        \n",
    "        # Ortsteile: \n",
    "        in_gem=gem_in_bh_gdf_of.within(gem_poly)\n",
    "        ort_in_gem_gdf=gem_in_bh_gdf_of[ in_gem & (gem_in_bh_gdf_of['boundary']=='administrative') &  ((gem_in_bh_gdf_of['admin_level']=='9')|(gem_in_bh_gdf_of['admin_level']=='10') ) ]  \n",
    "\n",
    "        # Aufbau Listen:     \n",
    "        t=pd.DataFrame({'GemName':[gem_name]})\n",
    "        x=ort_in_gem_gdf['name'].values\n",
    "        if gem_name not in x:\n",
    "            x=numpy.append(x,[[gem_name]])\n",
    "        ort=pd.DataFrame({'OrtName': x})\n",
    "        #ort=pd.DataFrame[{'Name':x[0]}]\n",
    "        y=numpy.unique(gem_gdf_lu['landuse'].values)\n",
    "        y=numpy.append(y,[['unbekannt']])\n",
    "        #y.append('unbekannt')\n",
    "        landuse_df=pd.DataFrame({'Landuse': y})\n",
    "        tt=pd.DataFrame({'WegeDauer':[0]+trip_times+[120]})\n",
    "        \n",
    "        res=pd.merge(ort,landuse_df, how='cross')\n",
    "        res2=pd.merge(t,res, how='cross')\n",
    "        boxen=pd.merge(res2,tt, how='cross')\n",
    "        boxen=boxen.set_index(['GemName','OrtName','Landuse','WegeDauer'])\n",
    "        boxen['Anzahl']=0\n",
    "            \n",
    "        print('Line 0088: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "        \n",
    "        node_colors = {}\n",
    "        node_time = {}\n",
    "        for cur in (schulen[\"schools\"]):\n",
    "            p=Point(cur[\"lng\"],cur[\"lat\"])\n",
    "            if p.within(gem_poly): \n",
    "                poi=ox.distance.nearest_nodes(gem_walk_net, cur[\"lng\"],cur[\"lat\"])\n",
    "                for trip_time, color in zip(sorted(trip_times, reverse=True), iso_colors):\n",
    "                    subgraph = nx.ego_graph(gem_walk_net, poi, radius=trip_time, distance=\"time\")\n",
    "                    for node in subgraph.nodes():\n",
    "                        col_old=0\n",
    "                        if node in node_colors:\n",
    "                            col_old=sum(node_colors[node])\n",
    "                        if sum(color)>col_old:\n",
    "                            node_colors[node] = color\n",
    "                        col_time=10000000\n",
    "                        if node in node_time:\n",
    "                            col_time=node_time[node]\n",
    "                        if trip_time<col_time:\n",
    "                            node_time[node] = trip_time    \n",
    "        print('Line 0113: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "\n",
    "        for idx_build, row_build in gdf_gem_hn.iterrows():\n",
    "            poly_build=row_build['geometry']\n",
    "            #poly_ort = gpd.GeoSeries(row_orte['geometry'])\n",
    "            p_center=poly_build.centroid\n",
    "            poi=ox.distance.nearest_nodes(gem_walk_net, p_center.x,p_center.y)\n",
    "            WegeDauer=120\n",
    "            if poi in node_time:\n",
    "                WegeDauer=node_time[poi]\n",
    "            if poi in node_time:\n",
    "                gdf_gem_hn.loc[idx_build,['triptime']]=node_time[poi]\n",
    "            \n",
    "            Landuse='unbekannt'\n",
    "            for idx_landuse, row_landuse in gem_gdf_lu.iterrows():\n",
    "                lu_type=row_landuse['landuse']\n",
    "                poly_lu= gpd.GeoSeries(row_landuse['geometry'])\n",
    "                if poly_lu.contains(p_center).any():\n",
    "                    Landuse=lu_type\n",
    "                    break\n",
    "            Ortsteil=gem_name\n",
    "            for idx_ort, row_ort in ort_in_gem_gdf.iterrows():\n",
    "                poly_ortt = gpd.GeoSeries(row_ort['geometry'])\n",
    "                if poly_ortt.contains(p_center).any():\n",
    "                    Ortsteil=row_ort['name']\n",
    "                    break\n",
    "            boxen.at[(gem_name,Ortsteil,Landuse,WegeDauer),'Anzahl']=boxen.at[(gem_name,Ortsteil,Landuse,WegeDauer),'Anzahl']+1\n",
    "        print('Line 0139: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "    \n",
    "        boxen.to_csv('erstellte_daten/'+gem_name+'.csv',index=True)\n",
    "        if len(alle_gem)<5:\n",
    "            alle_gem=boxen\n",
    "        else:\n",
    "            alle_gem=alle_gem+boxen\n",
    "        alle_gem.to_csv('erstellte_daten/'+place+'.csv',index=True)\n",
    "\n",
    "        br=1\n",
    "        anz=1\n",
    "\n",
    "        gp_b=gem_poly.bounds[2]-gem_poly.bounds[0]\n",
    "        gp_l=gem_poly.bounds[3]-gem_poly.bounds[1]\n",
    "        md=max(gp_b,gp_l)\n",
    "        size=60/md\n",
    "\n",
    "\n",
    "        print('Line 0151: {} - {}'.format(datetime.datetime.now(),gem_name))\n",
    "     \n",
    "        fig, axs = plt.subplots(anz,br,figsize=(int(gp_b*size), int(gp_l*size)))\n",
    "        #fig, axs = plt.subplots(anz,br)\n",
    "        axs.set_title(gem_name)\n",
    "        gem_gdf_lu.plot(ax=axs,column='landuse', facecolor='none',legend=True)\n",
    "        for idx_orte, row_orte in ort_in_gem_gdf.iterrows():\n",
    "            poly_ort = gpd.GeoSeries(row_orte['geometry'])\n",
    "            p_center=poly_ort.centroid\n",
    "            if gem_poly.contains(p_center).any():\n",
    "                poly_ort.plot(ax=axs,facecolor='none', edgecolor='green')\n",
    "                axs.annotate(text=row_orte[\"name\"], xy=(p_center[0].x,p_center[0].y), horizontalalignment='center')\n",
    "        if len(ort_in_gem_gdf)==0:\n",
    "            gem_poly_gpd = gpd.GeoSeries(gem_poly)\n",
    "            gem_poly_gpd.plot(ax=axs,facecolor='none', edgecolor='blue') \n",
    "        gdf_gem_hn.plot(ax=axs,column='triptime',markersize =5, cmap=cmap,norm=norm)\n",
    "        #nc = [node_colors[node] if node in node_colors else \"none\" for node in gdf_gem_walk.nodes()]\n",
    "        #ns = [5 if node in node_colors else 0 for node in gdf_gem_walk.nodes()]\n",
    "        #poly_ort.plot(ax=axs,facecolor='none', edgecolor='green')\n",
    "        fig, ax = ox.plot_graph(\n",
    "            gem_walk_net,\n",
    "            ax=axs,\n",
    "            #node_color=nc,\n",
    "            #node_size=ns,\n",
    "            node_alpha=0.8,\n",
    "            edge_linewidth=0.2,\n",
    "            edge_color=\"#999999\",\n",
    "            show=False, \n",
    "            close=False\n",
    "        )\n",
    "        for cur in sc:\n",
    "            axs.scatter(cur[0].x,cur[0].y,color='red',s=80)    \n",
    "            axs.annotate(text=cur[1], xy=(cur[0].x,cur[0].y), horizontalalignment='center')\n",
    "            #axs[sw//br,sw%br].set_ylim(poly_gem.bounds[\"miny\"][0],poly_gem.bounds[\"maxy\"][0])\n",
    "            #axs[sw//br,sw%br].set_xlim(poly_gem.bounds[\"minx\"][0],poly_gem.bounds[\"maxx\"][0])\n",
    "        plt.savefig('images/'+gem_name+'.png')\n",
    "        #plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36bdecfc-a890-4414-9135-c69a364a5389",
   "metadata": {},
   "outputs": [],
   "source": [
    "(int(gp_b*size), int(gp_l*size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c08e7215-bea3-4470-bcf3-2e8b006c125e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hn_colors_series = pd.DataFrame.from_dict(hn_colors,orient='index')\n",
    "#hn_colors_series\n",
    "\n",
    "colors = map(lambda x: hn_colors.get(x, (0.5,0.5,0.5,1)), gdf_gem_hn.index.get_level_values(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f3445b3-1fc6-4d52-9fb0-3b139473c612",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hn_colors\n",
    "for cur in gdf_gem_hn.index:\n",
    "    osmid =cur[1] \n",
    "    #print (cur[1])\n",
    "    if osmid in hn_colors:\n",
    "        print(hn_colors[osmid])"
   ]
  }
 ],
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