CRP_Centrality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import osmnx as ox\n",
    "import networkx as nx\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import geocoder\n",
    "import geopandas as gpd\n",
    "from glob import glob\n",
    "import os\n",
    "%matplotlib inline\n",
    "ox.config(log_console=True, use_cache=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_time(t1, t2):\n",
    "    \n",
    "    diff = t2 - t1\n",
    "    \n",
    "    c = round(diff.total_seconds() / 60, 2)\n",
    "    \n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_graph_old(place):\n",
    "    string = place.split(',')[0]\n",
    "    \n",
    "    print('Fetching graph data for {}'.format(place))\n",
    "    # download and project a street network\n",
    "    if not place == 'Monrovia, Liberia':\n",
    "        try:\n",
    "            G = ox.graph_from_place(place, network_type='drive')\n",
    "        except TypeError:\n",
    "            try:\n",
    "                G = ox.graph_from_place(place, network_type='drive', which_result=2)\n",
    "            except TypeError:\n",
    "                geo = geocoder.osm(place)\n",
    "                lon = geo.geojson['features'][0]['properties']['lng']\n",
    "                lat = geo.geojson['features'][0]['properties']['lng']\n",
    "                G = ox.graph_from_point(center_point=( lon, lat), distance = 20000)\n",
    "            except KeyError:\n",
    "                geo = geocoder.osm(place)\n",
    "                lon = geo.geojson['features'][0]['properties']['lng']\n",
    "                lat = geo.geojson['features'][0]['properties']['lng']\n",
    "                G = ox.graph_from_point(center_point=( lon, lat), distance = 20000)\n",
    "        except:\n",
    "            geo = geocoder.osm(place)\n",
    "            lon = geo.geojson['features'][0]['properties']['lng']\n",
    "            lat = geo.geojson['features'][0]['properties']['lng']\n",
    "            G = ox.graph_from_point(center_point=( lon, lat), distance = 20000)\n",
    "    else:\n",
    "        adm3 = gpd.GeoDataFrame.from_file('Liberia/LBR_adm3.shp')\n",
    "        mnv = adm3[adm3.NAME_3 == 'Monrovia'].geometry.values[0]\n",
    "        G = ox.graph_from_polygon(mnv, network_type='drive')\n",
    "        \n",
    "    G = ox.project_graph(G)\n",
    "    \n",
    "    print('Writing graph file')\n",
    "    \n",
    "    try:\n",
    "        os.mkdir('data/{}'.format(string))\n",
    "    except FileExistsError:\n",
    "        pass\n",
    "    \n",
    "    nx.write_gpickle(G, path='data/{a}/{a}'.format(a=string))\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_polygon(string):\n",
    "    \n",
    "    lis = glob('/nfshome/gb1877/gb1877/WorldBank/CRP/Betweenness_Centrality/data/AOI/*/*.shp')\n",
    "    \n",
    "    city = []\n",
    "    for i in lis:\n",
    "        x = i.split('/')[-2]\n",
    "        city.append(x.split('_')[0])\n",
    "        \n",
    "    if string in city:\n",
    "        l = glob('/nfshome/gb1877/gb1877/WorldBank/CRP/Betweenness_Centrality/data/AOI/{}_AOI/*.shp'.format(string))\n",
    "        adm = gpd.GeoDataFrame.from_file(l[0])\n",
    "        boundary_poly = adm.geometry.values[0]\n",
    "    else:\n",
    "        boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=1)\n",
    "        boundary_poly = boundary_GDF.loc[0,'geometry']\n",
    "        if boundary_poly.geom_type == 'Polygon':\n",
    "            boundary_poly = boundary_poly\n",
    "        else:\n",
    "            try:\n",
    "                boundary_GDF = ox.gdf_from_place('{}'.format(string),which_result=2)\n",
    "                boundary_poly = boundary_GDF.loc[0,'geometry']\n",
    "            except:\n",
    "                print('Polygon not available')\n",
    "    \n",
    "    return boundary_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_graph(place):\n",
    "    string = place.split(',')[0]\n",
    "    \n",
    "    print('Fetching graph data for {}'.format(place))\n",
    "\n",
    "    G = ox.graph_from_polygon(get_polygon(string), network_type='drive')\n",
    "    \n",
    "    G = ox.project_graph(G)\n",
    "    \n",
    "    print('Writing graph file')\n",
    "    \n",
    "    try:\n",
    "        os.mkdir('data/{}'.format(string))\n",
    "    except FileExistsError:\n",
    "        pass\n",
    "    \n",
    "    nx.write_gpickle(G, path='data/{a}/{a}'.format(a=string))\n",
    "    \n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_edge_centrality(place):\n",
    "    \n",
    "    t1 = datetime.datetime.now()\n",
    "    \n",
    "    string = place.split(',')[0]\n",
    "    \n",
    "    # download and project a street network\n",
    "    \n",
    "    G = get_graph(place)\n",
    "    #G = ox.graph_from_place('Davao City, Philippines')\n",
    "    t2 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t2)))\n",
    "    \n",
    "    print('Getting node centrality')\n",
    "    #node_centrality = nx.betweenness_centrality(G)\n",
    "    \n",
    "    \n",
    "    t3 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t3)))\n",
    "    \n",
    "    print('Getting edge centrality')\n",
    "    # edge closeness centrality: convert graph to a line graph so edges become nodes and vice versa\n",
    "    edge_centrality = nx.edge_betweenness_centrality(G)\n",
    "    \n",
    "    t4 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t4)))\n",
    "    \n",
    "    new_edge_centrality = {}\n",
    "\n",
    "    for u,v in edge_centrality:\n",
    "        new_edge_centrality[(u,v,0)] = edge_centrality[u,v]\n",
    "    \n",
    "    print('Saving output gdf')\n",
    "    \n",
    "    #nx.set_node_attributes(G, node_centrality, 'node_centrality')\n",
    "    nx.set_edge_attributes(G, new_edge_centrality, 'edge_centrality')\n",
    "    ox.save_graph_shapefile(G, filename='{}'.format(string))\n",
    "    \n",
    "    t5 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t5)))\n",
    "    \n",
    "    print('Getting extended stats')\n",
    "    \n",
    "    #extended_stats = ox.extended_stats(G, bc=True)\n",
    "    \n",
    "    #dat = pd.DataFrame.from_dict(extended_stats)\n",
    "    #dat.to_csv('data/{a}/Extended_Stats_{b}.csv'.format(a=string, b=string))\n",
    "    \n",
    "    t6 = datetime.datetime.now()\n",
    "    \n",
    "    print('{} minutes elapsed!'.format(get_time(t1, t6)))\n",
    "    \n",
    "    print('Getting basic stats')\n",
    "    \n",
    "    basic_stats = ox.basic_stats(G)\n",
    "    dat = pd.DataFrame.from_dict(basic_stats)\n",
    "    dat.to_csv('data/{a}/Basic_Stats_{b}.csv'.format(a=string, b=string))\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Sfax, Tunisia\n",
    "2. Sousse, Tunisia\n",
    "3. Tunis, Tunisia\n",
    "4. Batroun, Lebanon\n",
    "5. Bint Jbeil, Lebanon\n",
    "6. Joub Jannine, Lebanon\n",
    "7. West Bank\n",
    "8. Montevideo, Uruguay\n",
    "9. Lima, Peru\n",
    "10. Cap-Haitien, Haiti\n",
    "11. Vinh Long, Vietnam\n",
    "12. Khan Hoa, Vietnam\n",
    "13. Semarang, Indonesia\n",
    "14. Penang, Malaysia\n",
    "15. Tashkent, Uzbekistan\n",
    "16. Osh, Kyrgyz \n",
    "17. Turkey (city TBD)\n",
    "18. Monrovia, Liberia \n",
    "19. Johannesburg, South Africa\n",
    "20. Maputo, Mozambique \n",
    "21. Addis Ababa, Ethiopia \n",
    "22. Blantyr, Malawi\n",
    "23. St. Louis, Senegal (TBC)\n",
    "24. Cotonou, Benin (TBC)\n",
    "25. Accra, Ghana (TBC)\n",
    "26. Province of Palawan, Philippines\n",
    "27. Davao City, Philippines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_scan_list = [\n",
    "    'Sfax, Tunisia',\n",
    "'Sousse, Tunisia',\n",
    "'Tunis, Tunisia',\n",
    "'Batroun, Lebanon',\n",
    "'Bint Jbeil, Lebanon',\n",
    "'Joub Jannine, Lebanon',\n",
    "#'West Bank', \n",
    "'Montevideo, Uruguay',\n",
    "'Lima, Peru',\n",
    "'Cap-Haitien, Haiti',\n",
    "'Vinh Long, Vietnam',\n",
    "'Khanh Hoa, Vietnam',\n",
    "'Semarang, Indonesia',\n",
    "'Penang, Malaysia',\n",
    "'Tashkent, Uzbekistan',\n",
    "'Osh,  Kyrgyzstan',\n",
    "#Turkey (city TBD)\n",
    "'Monrovia, Liberia',\n",
    "'Johannesburg, South Africa',\n",
    "'Maputo, Mozambique',\n",
    "'Addis Ababa, Ethiopia',\n",
    "'Blantyr, Malawi',\n",
    "'St. Louis, Senegal',\n",
    "'Cotonou, Benin',\n",
    "'Accra, Ghana',\n",
    "'Province of Palawan, Philippines',\n",
    "'Davao City, Philippines'\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Tashkent, Uzbekistan\n",
      "Writing graph file\n",
      "1.23 minutes elapsed!\n",
      "Getting node centrality\n",
      "25.49 minutes elapsed!\n",
      "Getting edge centrality\n"
     ]
    }
   ],
   "source": [
    "for i in city_scan_list[13:]:\n",
    "    %time get_edge_centrality(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem_cities = ['Bint Jbeil, Lebanon',\n",
    "'Joub Jannine, Lebanon','Khanh Hoa, Vietnam','Semarang, Indonesia',\n",
    "'Penang, Malaysia','Monrovia, Liberia','Johannesburg, South Africa',\n",
    "                 'Addis Ababa, Ethiopia','Blantyr, Malawi','St. Louis, Senegal',\n",
    "                  'Cotonou, Benin', 'Accra, Ghana',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Davao City, Philippines\n",
      "Writing graph file\n",
      "0.02 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.02 minutes elapsed!\n",
      "Getting edge centrality\n",
      "0.02 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.03 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 1.74 s, sys: 28 ms, total: 1.77 s\n",
      "Wall time: 1.94 s\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Davao City, Philippines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Lima, Peru\n",
      "Writing graph file\n",
      "1.04 minutes elapsed!\n",
      "Getting node centrality\n",
      "1.53 minutes elapsed!\n",
      "Getting edge centrality\n",
      "4.96 minutes elapsed!\n",
      "Saving output gdf\n",
      "5.08 minutes elapsed!\n",
      "Getting extended stats\n",
      "6.23 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 5min 24s, sys: 1.08 s, total: 5min 25s\n",
      "Wall time: 6min 14s\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Lima, Peru')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Monrovia, Liberia\n",
      "Writing graph file\n",
      "0.93 minutes elapsed!\n",
      "Getting node centrality\n",
      "4.27 minutes elapsed!\n",
      "Getting edge centrality\n",
      "30.46 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.75 minutes elapsed!\n",
      "Getting extended stats\n",
      "37.97 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 37min 35s, sys: 932 ms, total: 37min 36s\n",
      "Wall time: 38min 2s\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Monrovia, Liberia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for West Bank\n",
      "Writing graph file\n",
      "42.15 minutes elapsed!\n",
      "Getting node centrality\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('West Bank')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Priority cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- El Nido, Philippines\n",
    "- Coron, Philippines \n",
    "- Puerto Princesa, Philippines\n",
    "- Davao City, Philippines\n",
    "- Tashkent, Uzbekistan\n",
    "- West Bank\n",
    "- Lima, Peru\n",
    "- Vinh Long, Vietnam\n",
    "- Khan Hoa, Vietnam\n",
    "- Osh, Kyrgyz\n",
    "- Monrovia, Liberia\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Puerto Princesa',\n",
       " 'West Bank',\n",
       " 'Davao',\n",
       " 'Coron',\n",
       " 'Khan Hoa',\n",
       " 'Bethlehem',\n",
       " 'Monrovia',\n",
       " 'Hebron',\n",
       " 'Osh',\n",
       " 'El Nido',\n",
       " 'Lima',\n",
       " 'Tashkent',\n",
       " 'Penang',\n",
       " 'Vinh Long']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Puerto Princesa\n",
      "Writing graph file\n",
      "0.32 minutes elapsed!\n",
      "Getting node centrality\n",
      "1.33 minutes elapsed!\n",
      "Getting edge centrality\n",
      "10.92 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.07 minutes elapsed!\n",
      "Getting extended stats\n",
      "13.36 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 13min 14s, sys: 720 ms, total: 13min 15s\n",
      "Wall time: 13min 21s\n",
      "Fetching graph data for West Bank\n",
      "Writing graph file\n",
      "0.93 minutes elapsed!\n",
      "Getting node centrality\n",
      "8.34 minutes elapsed!\n",
      "Getting edge centrality\n",
      "86.47 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.86 minutes elapsed!\n",
      "Getting extended stats\n",
      "103.25 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 1h 43min 12s, sys: 1.96 s, total: 1h 43min 14s\n",
      "Wall time: 1h 43min 16s\n",
      "Fetching graph data for Davao\n",
      "Writing graph file\n",
      "0.54 minutes elapsed!\n",
      "Getting node centrality\n",
      "3.88 minutes elapsed!\n",
      "Getting edge centrality\n",
      "36.33 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.59 minutes elapsed!\n",
      "Getting extended stats\n",
      "43.97 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 43min 55s, sys: 1.16 s, total: 43min 56s\n",
      "Wall time: 43min 58s\n",
      "Fetching graph data for Coron\n",
      "Writing graph file\n",
      "0.03 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.03 minutes elapsed!\n",
      "Getting edge centrality\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.04 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 1.51 s, sys: 4 ms, total: 1.51 s\n",
      "Wall time: 2.53 s\n",
      "Fetching graph data for Khan Hoa\n",
      "Writing graph file\n",
      "1.07 minutes elapsed!\n",
      "Getting node centrality\n",
      "5.12 minutes elapsed!\n",
      "Getting edge centrality\n",
      "44.68 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45.0 minutes elapsed!\n",
      "Getting extended stats\n",
      "54.33 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 54min 10s, sys: 1.69 s, total: 54min 12s\n",
      "Wall time: 54min 21s\n",
      "Fetching graph data for Bethlehem\n",
      "Writing graph file\n",
      "0.91 minutes elapsed!\n",
      "Getting node centrality\n",
      "2.25 minutes elapsed!\n",
      "Getting edge centrality\n",
      "10.65 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.81 minutes elapsed!\n",
      "Getting extended stats\n",
      "13.37 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 13min 21s, sys: 1.06 s, total: 13min 22s\n",
      "Wall time: 13min 23s\n",
      "Fetching graph data for Monrovia\n",
      "Writing graph file\n",
      "1.05 minutes elapsed!\n",
      "Getting node centrality\n",
      "8.29 minutes elapsed!\n",
      "Getting edge centrality\n",
      "59.8 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:fiona._env:Normalized/laundered field name: 'node_centrality' to 'node_centr'\n",
      "WARNING:fiona._env:Normalized/laundered field name: 'edge_centrality' to 'edge_centr'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.12 minutes elapsed!\n",
      "Getting extended stats\n",
      "73.85 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 1h 13min 49s, sys: 1.98 s, total: 1h 13min 51s\n",
      "Wall time: 1h 13min 52s\n",
      "Fetching graph data for Hebron\n",
      "Writing graph file\n",
      "4.36 minutes elapsed!\n",
      "Getting node centrality\n"
     ]
    }
   ],
   "source": [
    "for i in city:\n",
    "    %time get_edge_centrality(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Tashkent, Uzbekistan\n",
      "Writing graph file\n",
      "1.33 minutes elapsed!\n",
      "Getting node centrality\n",
      "25.43 minutes elapsed!\n",
      "Getting edge centrality\n",
      "235.26 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235.99 minutes elapsed!\n",
      "Getting extended stats\n",
      "292.0 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 4h 51min 34s, sys: 11.6 s, total: 4h 51min 45s\n",
      "Wall time: 4h 52min 2s\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Tashkent, Uzbekistan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Vinh Long, Vietnam\n",
      "Writing graph file\n",
      "0.06 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.09 minutes elapsed!\n",
      "Getting edge centrality\n",
      "0.41 minutes elapsed!\n",
      "Saving output gdf\n",
      "0.45 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.54 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 30.8 s, sys: 144 ms, total: 30.9 s\n",
      "Wall time: 32.2 s\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Vinh Long, Vietnam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Lima, Peru\n",
      "Writing graph file\n",
      "12.29 minutes elapsed!\n",
      "Getting node centrality\n"
     ]
    }
   ],
   "source": [
    "%time get_edge_centrality('Lima, Peru')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ = nx.read_gpickle('data/Maputo/Maputo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time edge_centrality = nx.betweenness_centrality(nx.line_graph(G_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    }
   ],
   "source": [
    "nx.set_edge_attributes(G_, edge_centrality, 'edge_centrality')\n",
    "ox.save_graph_shapefile(G_, filename='{}'.format('Maputo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_gpickle('data/Penang/Penang.gpickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.multidigraph.MultiDiGraph"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 352.242x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 352.242x432 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f07a064aa20>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ox.plot_graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "edge_centrality = nx.betweenness_centrality(nx.line_graph(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time ec = nx.edge_betweenness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = ox.graph_to_gdfs(G, nodes=False, edges = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>access</th>\n",
       "      <th>bridge</th>\n",
       "      <th>geometry</th>\n",
       "      <th>highway</th>\n",
       "      <th>junction</th>\n",
       "      <th>key</th>\n",
       "      <th>lanes</th>\n",
       "      <th>length</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>name</th>\n",
       "      <th>oneway</th>\n",
       "      <th>osmid</th>\n",
       "      <th>ref</th>\n",
       "      <th>service</th>\n",
       "      <th>tunnel</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LINESTRING (653305.5574910371 607304.237032867...</td>\n",
       "      <td>residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>160842035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1728577536</td>\n",
       "      <td>1728577542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LINESTRING (653305.5574910371 607304.237032867...</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>160842253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1728577536</td>\n",
       "      <td>6039343375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LINESTRING (653305.5574910371 607304.237032867...</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>160842253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1728577536</td>\n",
       "      <td>1728577537</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LINESTRING (653305.5574910371 607304.237032867...</td>\n",
       "      <td>residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>160842260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1728577536</td>\n",
       "      <td>1728577531</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LINESTRING (653328.3562943337 607305.893061060...</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>160842253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1728577537</td>\n",
       "      <td>1728577536</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  access bridge                                           geometry  \\\n",
       "0    NaN    NaN  LINESTRING (653305.5574910371 607304.237032867...   \n",
       "1    NaN    NaN  LINESTRING (653305.5574910371 607304.237032867...   \n",
       "2    NaN    NaN  LINESTRING (653305.5574910371 607304.237032867...   \n",
       "3    NaN    NaN  LINESTRING (653305.5574910371 607304.237032867...   \n",
       "4    NaN    NaN  LINESTRING (653328.3562943337 607305.893061060...   \n",
       "\n",
       "       highway junction  key lanes   length maxspeed name  oneway      osmid  \\\n",
       "0  residential      NaN    0   NaN  117.465      NaN  NaN   False  160842035   \n",
       "1     tertiary      NaN    0   NaN   26.812      NaN  NaN   False  160842253   \n",
       "2     tertiary      NaN    0   NaN   22.836      NaN  NaN   False  160842253   \n",
       "3  residential      NaN    0   NaN   17.772      NaN  NaN   False  160842260   \n",
       "4     tertiary      NaN    0   NaN   22.836      NaN  NaN   False  160842253   \n",
       "\n",
       "   ref service tunnel           u           v width  \n",
       "0  NaN     NaN    NaN  1728577536  1728577542   NaN  \n",
       "1  NaN     NaN    NaN  1728577536  6039343375   NaN  \n",
       "2  NaN     NaN    NaN  1728577536  1728577537   NaN  \n",
       "3  NaN     NaN    NaN  1728577536  1728577531   NaN  \n",
       "4  NaN     NaN    NaN  1728577537  1728577536   NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n. head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LineString'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.geometry.geom_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = nx.read_gpickle('data/Penang/Penang.gpickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 352.242x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 352.242x432 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fddecee4080>)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ox.plot_graph(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time edge_centrality__ = nx.edge_betweenness_centrality(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(1728577536, 1728577542): 0.00020222177181521855,\n",
       " (1728577536, 6039343375): 0.00023912226595279451,\n",
       " (1728577536, 1728577537): 0.00043201701040732933,\n",
       " (1728577536, 1728577531): 7.191211309166181e-05,\n",
       " (1728577537, 1728577536): 0.0005218994863871556,\n",
       " (1728577537, 6039343745): 0.00013134412304812538,\n",
       " (1728577537, 4806220561): 0.0004022029572414075,\n",
       " (1728577539, 1728577535): 8.651720950817094e-05,\n",
       " (1728577539, 6039343642): 5.164909109411773e-06,\n",
       " (1728577539, 6039343648): 7.979660264008269e-05,\n",
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       " (6046614591, 6046614588): 2.6541617731078938e-05,\n",
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       " (1724384323, 6432053462): 3.094833386417535e-05,\n",
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       " (3989570624, 3989570638): 1.904175297571715e-08,\n",
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       " (6046614631, 6046614634): 8.847362632606357e-06,\n",
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       " (3989570655, 4213707779): 6.19282472610635e-05,\n",
       " (3989570655, 3989570654): 8.843052770821731e-06,\n",
       " (6362055999, 6362056014): 3.134444934274428e-10,\n",
       " (6362055999, 6362056007): 4.701667401411643e-10,\n",
       " (6362055999, 6362056011): 1.7689710153317878e-05,\n",
       " (6046614634, 3201479260): 1.864155160014099e-05,\n",
       " (6046614634, 3201479401): 2.8314264013055456e-06,\n",
       " (6046614634, 6046614631): 8.843052770821731e-06,\n",
       " (6046614635, 6046614636): 8.847362632606357e-06,\n",
       " (6046614636, 3201479363): 1.4522880728316248e-05,\n",
       " (6046614636, 6046614635): 8.843052770821731e-06,\n",
       " (6046614636, 3201479378): 1.2802624790608815e-05,\n",
       " (6046614637, 6046614638): 8.847362632606359e-06,\n",
       " (6046614638, 3201567056): 0.0003239408597417733,\n",
       " (6046614638, 6046614637): 8.843052770821731e-06,\n",
       " (6046614638, 3201567130): 0.00022630456116866612,\n",
       " (6046614640, 6046614642): 8.847362632606357e-06,\n",
       " (620495985, 620495999): 3.5513359989678225e-06,\n",
       " (620495985, 613449318): 1.3235213449637346e-05,\n",
       " (6046614642, 6046614640): 8.843052770821731e-06,\n",
       " (6046614642, 6046614643): 2.6541617731078934e-05,\n",
       " (6046614642, 6046614648): 8.843052770821731e-06,\n",
       " (6046614643, 6122117059): 6.521011640090899e-06,\n",
       " (6046614643, 3201567214): 4.18454816375668e-05,\n",
       " (6046614643, 6046614642): 2.6528688145725052e-05,\n",
       " (5525383535, 5525383530): 6.843802991291183e-05,\n",
       " (6046614648, 6046614642): 8.847362632606357e-06,\n",
       " (620495992, 620495994): 1.427865916038521e-07,\n",
       " (620495992, 613449318): 2.2008837705442584e-05,\n",
       " (620495992, 620495999): 5.305829050448454e-06,\n",
       " (620495994, 620495992): 1.3518772192223702e-05,\n",
       " (620495994, 5556316650): 1.210651723069689e-06,\n",
       " (6025380987, 6025380878): 5.48527863498025e-10,\n",
       " (6025380987, 6025380988): 0.00042142256626918,\n",
       " (6025380987, 6025381034): 0.0003047869423004481,\n",
       " (6025380988, 6025381154): 0.0008345927026907359,\n",
       " (6025380988, 6025380878): 0.0002956033664162136,\n",
       " (6025380988, 6025380987): 0.00029698557854036974,\n",
       " (6046614654, 6046614597): 3.2485598966452196e-06,\n",
       " (6046614654, 3201567258): 2.9112332200414454e-07,\n",
       " (6046614654, 3201567186): 5.84642410952855e-06,\n",
       " (620495999, 620495992): 4.4593550933286535e-06,\n",
       " (620495999, 620495985): 4.402119817871964e-06,\n",
       " (6046614661, 3201479425): 0.00014525572378078512,\n",
       " (6046614661, 3201479378): 7.429036769819776e-05,\n",
       " (6046614661, 3201479419): 7.953751531354652e-05,\n",
       " (620496005, 620496006): 1.841876805859097e-05,\n",
       " ...}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_centrality__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 296 ms, sys: 0 ns, total: 296 ms\n",
      "Wall time: 294 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "new_edge_centrality = {}\n",
    "\n",
    "for u,v in edge_centrality__:\n",
    "    new_edge_centrality[(u,v,0)] = edge_centrality__[u,v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272246"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_edge_centrality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    }
   ],
   "source": [
    "nx.set_edge_attributes(p, new_edge_centrality, 'edge_centrality')\n",
    "ox.save_graph_shapefile(p, filename='{}'.format('Penang'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 2 Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase2_list = ['Bethlehem, West Bank',\n",
    "'Hebron, West Bank',\n",
    "'Lima, Peru', #– metropolitan area\n",
    "'Vinh Long, Vietnam',\n",
    "'Khan Hoa, Vietnam', #– focused on the districts of Nha Trang, Cam Ranh, Ninh Hoa and Van Ninh\n",
    "'Semarang, Indonesia',\n",
    "'Osh, Kyrgyz',\n",
    "'Sfax, Tunisia',\n",
    "'Sousse, Tunisia',\n",
    "'Tunis, Tunisia',\n",
    "'Batroun, Lebanon',\n",
    "'Bint Jbeil, Lebanon',\n",
    "'Montevideo, Uruguay',\n",
    "'Cap-Haitien, Haiti',\n",
    "'Porto Alegre, Brazil', #– focused on the 4th district\n",
    "'Ibadan, Nigeria',\n",
    "'Mogadishu, Somalia',\n",
    "'Garowe, Somalia ',\n",
    "'Monrovia, Liberia',\n",
    "\"Abidjan, Cote d’Ivoire\",\n",
    "'Blantyre, Malawi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Bethlehem, West Bank\n",
      "Writing graph file\n",
      "0.94 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.94 minutes elapsed!\n",
      "Getting edge centrality\n",
      "2.5 minutes elapsed!\n",
      "Saving output gdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.67 minutes elapsed!\n",
      "Getting extended stats\n",
      "2.67 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 2min 41s, sys: 1.37 s, total: 2min 43s\n",
      "Wall time: 2min 40s\n",
      "Fetching graph data for Hebron, West Bank\n",
      "Writing graph file\n",
      "3.91 minutes elapsed!\n",
      "Getting node centrality\n",
      "3.91 minutes elapsed!\n",
      "Getting edge centrality\n",
      "44.67 minutes elapsed!\n",
      "Saving output gdf\n",
      "45.49 minutes elapsed!\n",
      "Getting extended stats\n",
      "45.49 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 44min 29s, sys: 3.9 s, total: 44min 33s\n",
      "Wall time: 45min 33s\n",
      "Fetching graph data for Lima, Peru\n",
      "Writing graph file\n",
      "13.43 minutes elapsed!\n",
      "Getting node centrality\n",
      "13.43 minutes elapsed!\n",
      "Getting edge centrality\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Getting edge centrality'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;31m# edge closeness centrality: convert graph to a line graph so edges become nodes and vice versa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0medge_centrality\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_betweenness_centrality\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mt4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m</nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/decorator.py:decorator-gen-231>\u001b[0m in \u001b[0;36medge_betweenness_centrality\u001b[0;34m(G, k, normalized, weight, seed)\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/networkx/utils/decorators.py\u001b[0m in \u001b[0;36m_random_state\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    462\u001b[0m         \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0mnew_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrandom_state_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_random_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/networkx/algorithms/centrality/betweenness.py\u001b[0m in \u001b[0;36medge_betweenness_centrality\u001b[0;34m(G, k, normalized, weight, seed)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;31m# single source shortest paths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# use BFS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_single_source_shortest_path_basic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# use Dijkstra's algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_single_source_dijkstra_path_basic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/networkx/algorithms/centrality/betweenness.py\u001b[0m in \u001b[0;36m_single_source_shortest_path_basic\u001b[0;34m(G, s)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mDv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0msigmav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m                 \u001b[0mQ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/networkx/classes/coreviews.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_atlas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Vinh Long, Vietnam\n",
      "Writing graph file\n",
      "0.05 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.05 minutes elapsed!\n",
      "Getting edge centrality\n",
      "0.1 minutes elapsed!\n",
      "Saving output gdf\n",
      "0.13 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.13 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 6.8 s, sys: 112 ms, total: 6.91 s\n",
      "Wall time: 7.88 s\n",
      "Fetching graph data for Khan Hoa, Vietnam\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Semarang, Indonesia\n",
      "Writing graph file\n",
      "6.75 minutes elapsed!\n",
      "Getting node centrality\n",
      "6.75 minutes elapsed!\n",
      "Getting edge centrality\n",
      "268.87 minutes elapsed!\n",
      "Saving output gdf\n",
      "271.01 minutes elapsed!\n",
      "Getting extended stats\n",
      "271.01 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 4h 30min 49s, sys: 19.5 s, total: 4h 31min 8s\n",
      "Wall time: 4h 31min 8s\n",
      "Fetching graph data for Osh, Kyrgyz\n",
      "Writing graph file\n",
      "0.34 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.34 minutes elapsed!\n",
      "Getting edge centrality\n",
      "2.43 minutes elapsed!\n",
      "Saving output gdf\n",
      "2.63 minutes elapsed!\n",
      "Getting extended stats\n",
      "2.63 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 2min 38s, sys: 1.35 s, total: 2min 39s\n",
      "Wall time: 2min 38s\n",
      "Fetching graph data for Sfax, Tunisia\n",
      "Writing graph file\n",
      "0.57 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.57 minutes elapsed!\n",
      "Getting edge centrality\n",
      "3.43 minutes elapsed!\n",
      "Saving output gdf\n",
      "3.64 minutes elapsed!\n",
      "Getting extended stats\n",
      "3.64 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 3min 30s, sys: 580 ms, total: 3min 31s\n",
      "Wall time: 3min 39s\n",
      "Fetching graph data for Sousse, Tunisia\n",
      "Writing graph file\n",
      "1.36 minutes elapsed!\n",
      "Getting node centrality\n",
      "1.36 minutes elapsed!\n",
      "Getting edge centrality\n",
      "27.75 minutes elapsed!\n",
      "Saving output gdf\n",
      "28.4 minutes elapsed!\n",
      "Getting extended stats\n",
      "28.4 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 28min 17s, sys: 3.55 s, total: 28min 21s\n",
      "Wall time: 28min 26s\n",
      "Fetching graph data for Tunis, Tunisia\n",
      "Writing graph file\n",
      "69.76 minutes elapsed!\n",
      "Getting node centrality\n",
      "69.76 minutes elapsed!\n",
      "Getting edge centrality\n"
     ]
    }
   ],
   "source": [
    "for i in phase2_list:\n",
    "    %time get_edge_centrality(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Batroun, Lebanon\n",
      "Writing graph file\n",
      "0.05 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.05 minutes elapsed!\n",
      "Getting edge centrality\n",
      "0.06 minutes elapsed!\n",
      "Saving output gdf\n",
      "0.08 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.08 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 4.5 s, sys: 36 ms, total: 4.54 s\n",
      "Wall time: 4.59 s\n",
      "Fetching graph data for Bint Jbeil, Lebanon\n",
      "Polygon not available\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Montevideo, Uruguay\n",
      "Writing graph file\n",
      "1.49 minutes elapsed!\n",
      "Getting node centrality\n",
      "1.49 minutes elapsed!\n",
      "Getting edge centrality\n",
      "35.02 minutes elapsed!\n",
      "Saving output gdf\n",
      "35.92 minutes elapsed!\n",
      "Getting extended stats\n",
      "35.92 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 35min 53s, sys: 8.63 s, total: 36min 2s\n",
      "Wall time: 35min 58s\n",
      "Fetching graph data for Cap-Haitien, Haiti\n",
      "Writing graph file\n",
      "0.11 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.11 minutes elapsed!\n",
      "Getting edge centrality\n",
      "0.35 minutes elapsed!\n",
      "Saving output gdf\n",
      "0.43 minutes elapsed!\n",
      "Getting extended stats\n",
      "0.43 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 25.7 s, sys: 224 ms, total: 25.9 s\n",
      "Wall time: 25.7 s\n",
      "Fetching graph data for Porto Alegre, Brazil\n",
      "Writing graph file\n",
      "1.24 minutes elapsed!\n",
      "Getting node centrality\n",
      "1.24 minutes elapsed!\n",
      "Getting edge centrality\n",
      "36.74 minutes elapsed!\n",
      "Saving output gdf\n",
      "37.53 minutes elapsed!\n",
      "Getting extended stats\n",
      "37.53 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 37min 34s, sys: 3.88 s, total: 37min 38s\n",
      "Wall time: 37min 34s\n",
      "Fetching graph data for Ibadan, Nigeria\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Mogadishu, Somalia\n",
      "Polygon not available\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Garowe, Somalia \n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching graph data for Monrovia, Liberia\n",
      "Writing graph file\n",
      "0.93 minutes elapsed!\n",
      "Getting node centrality\n",
      "0.93 minutes elapsed!\n",
      "Getting edge centrality\n",
      "7.25 minutes elapsed!\n",
      "Saving output gdf\n",
      "7.57 minutes elapsed!\n",
      "Getting extended stats\n",
      "7.57 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 7min 36s, sys: 1.2 s, total: 7min 37s\n",
      "Wall time: 7min 35s\n",
      "Fetching graph data for Abidjan, Cote d’Ivoire\n",
      "Writing graph file\n",
      "8.36 minutes elapsed!\n",
      "Getting node centrality\n",
      "8.36 minutes elapsed!\n",
      "Getting edge centrality\n",
      "208.08 minutes elapsed!\n",
      "Saving output gdf\n",
      "209.92 minutes elapsed!\n",
      "Getting extended stats\n",
      "209.92 minutes elapsed!\n",
      "Getting basic stats\n",
      "CPU times: user 3h 29min 53s, sys: 11.3 s, total: 3h 30min 4s\n",
      "Wall time: 3h 30min 1s\n",
      "Fetching graph data for Blantyre, Malawi\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-e8d9cbbbb0ed>\u001b[0m in \u001b[0;36mget_edge_centrality\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# download and project a street network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m#G = ox.graph_from_place('Davao City, Philippines')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-45e789918fe4>\u001b[0m in \u001b[0;36mget_graph\u001b[0;34m(place)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Fetching graph data for {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_polygon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnetwork_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/osmpython/lib/python3.6/site-packages/osmnx/core.py\u001b[0m in \u001b[0;36mgraph_from_polygon\u001b[0;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, name, timeout, memory, max_query_area_size, clean_periphery, infrastructure, custom_filter)\u001b[0m\n\u001b[1;32m   1733\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Shape does not have a valid geometry'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mPolygon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiPolygon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m         raise TypeError('Geometry must be a shapely Polygon or MultiPolygon. If you requested '\n\u001b[0m\u001b[1;32m   1736\u001b[0m                          \u001b[0;34m'graph from place name or address, make sure your query resolves to a '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                          \u001b[0;34m'Polygon or MultiPolygon, and not some other geometry, like a Point. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Geometry must be a shapely Polygon or MultiPolygon. If you requested graph from place name or address, make sure your query resolves to a Polygon or MultiPolygon, and not some other geometry, like a Point. See OSMnx documentation for details."
     ]
    }
   ],
   "source": [
    "for i in phase2_list[10:]:\n",
    "    %time get_edge_centrality(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1d 16h 28min 7s, sys: 21.6 s, total: 1d 16h 28min 29s\n",
      "Wall time: 1d 16h 28min 9s\n"
     ]
    }
   ],
   "source": [
    "#G = nx.read_gpickle('data/Lima/Lima')\n",
    "\n",
    "%time edge_centrality = nx.edge_betweenness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving output gdf\n",
      "CPU times: user 856 ms, sys: 4 ms, total: 860 ms\n",
      "Wall time: 856 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfshome/gb1877/.conda/envs/osmpython/lib/python3.6/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    }
   ],
   "source": [
    "new_edge_centrality = {}\n",
    "\n",
    "for u,v in edge_centrality:\n",
    "    new_edge_centrality[(u,v,0)] = edge_centrality[u,v]\n",
    "\n",
    "print('Saving output gdf')\n",
    "\n",
    "#nx.set_node_attributes(G, node_centrality, 'node_centrality')\n",
    "%time nx.set_edge_attributes(G, new_edge_centrality, 'edge_centrality')\n",
    "ox.save_graph_shapefile(G, filename='{}'.format('Lima'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_stats = ox.basic_stats(G)\n",
    "dat = pd.DataFrame.from_dict(basic_stats)\n",
    "dat.to_csv('data/{a}/Basic_Stats_{b}.csv'.format(a='Lima', b='Lima'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "gp = gpd.read_file('data/Lima/edges/edges.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>access</th>\n",
       "      <th>bridge</th>\n",
       "      <th>edge_centr</th>\n",
       "      <th>from</th>\n",
       "      <th>highway</th>\n",
       "      <th>junction</th>\n",
       "      <th>key</th>\n",
       "      <th>lanes</th>\n",
       "      <th>length</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>name</th>\n",
       "      <th>oneway</th>\n",
       "      <th>osmid</th>\n",
       "      <th>ref</th>\n",
       "      <th>service</th>\n",
       "      <th>to</th>\n",
       "      <th>tunnel</th>\n",
       "      <th>width</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00010904355843800482</td>\n",
       "      <td>273413466</td>\n",
       "      <td>residential</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>56.041</td>\n",
       "      <td>None</td>\n",
       "      <td>Rio Rimac</td>\n",
       "      <td>False</td>\n",
       "      <td>428031671</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1738539013</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (283500.4928064455 -1331334.1747018...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9.71144719271051e-05</td>\n",
       "      <td>273413363</td>\n",
       "      <td>residential</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>81.26</td>\n",
       "      <td>None</td>\n",
       "      <td>Rio Rimac</td>\n",
       "      <td>False</td>\n",
       "      <td>428031671</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1738539013</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (283456.3403705953 -1331204.8290888...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.911921839055536e-05</td>\n",
       "      <td>1273889450</td>\n",
       "      <td>residential</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>99.708</td>\n",
       "      <td>None</td>\n",
       "      <td>Calle los riachuelos</td>\n",
       "      <td>False</td>\n",
       "      <td>571808389</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1738539013</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (283545.2067982325 -1331202.6366488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0004989014275669e-08</td>\n",
       "      <td>197656584</td>\n",
       "      <td>secondary</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.539</td>\n",
       "      <td>40</td>\n",
       "      <td>Calle Bolognesi</td>\n",
       "      <td>True</td>\n",
       "      <td>319802415</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6173803650</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (278435.1870091101 -1340971.4361360...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.072016283336262e-05</td>\n",
       "      <td>197656584</td>\n",
       "      <td>secondary</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.598</td>\n",
       "      <td>40</td>\n",
       "      <td>Calle Bolognesi</td>\n",
       "      <td>True</td>\n",
       "      <td>319802412</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1934706304</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (278435.1870091101 -1340971.4361360...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  access bridge              edge_centr        from      highway junction key  \\\n",
       "0   None   None  0.00010904355843800482   273413466  residential     None   0   \n",
       "1   None   None    9.71144719271051e-05   273413363  residential     None   0   \n",
       "2   None   None   4.911921839055536e-05  1273889450  residential     None   0   \n",
       "3   None   None  1.0004989014275669e-08   197656584    secondary     None   0   \n",
       "4   None   None   3.072016283336262e-05   197656584    secondary     None   0   \n",
       "\n",
       "  lanes  length maxspeed                  name oneway      osmid   ref  \\\n",
       "0  None  56.041     None             Rio Rimac  False  428031671  None   \n",
       "1  None   81.26     None             Rio Rimac  False  428031671  None   \n",
       "2  None  99.708     None  Calle los riachuelos  False  571808389  None   \n",
       "3     1  33.539       40       Calle Bolognesi   True  319802415  None   \n",
       "4     2  70.598       40       Calle Bolognesi   True  319802412  None   \n",
       "\n",
       "  service          to tunnel width  \\\n",
       "0    None  1738539013   None  None   \n",
       "1    None  1738539013   None  None   \n",
       "2    None  1738539013   None  None   \n",
       "3    None  6173803650   None  None   \n",
       "4    None  1934706304   None  None   \n",
       "\n",
       "                                            geometry  \n",
       "0  LINESTRING (283500.4928064455 -1331334.1747018...  \n",
       "1  LINESTRING (283456.3403705953 -1331204.8290888...  \n",
       "2  LINESTRING (283545.2067982325 -1331202.6366488...  \n",
       "3  LINESTRING (278435.1870091101 -1340971.4361360...  \n",
       "4  LINESTRING (278435.1870091101 -1340971.4361360...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "neew_gp = gp[~pd.isnull(gp.edge_centr)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f64fd9ed8d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neew_gp.plot(column='edge_centr', cmap='viridis', figsize=[12,12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tunis, Tunisia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ = nx.read_gpickle('data/Tunis/Tunis')\n",
    "\n",
    "%time edge_centrality = nx.edge_betweenness_centrality(G_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "OSMPython",
   "language": "python",
   "name": "osmpython"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}