In [1]:
%%capture
##############################################################################
# IGNORE if you are running on a local machine and have all the dependencies #
##############################################################################
# installing dependencies --- to be able to run on google colab
# it would take 2-3 minutes
!yes | pip3 uninstall pandas
!yes | pip3 uninstall geopandas
!apt-get install libspatialindex-c4v5;
!pip3 install Rtree;
!pip3 install osmnx;
!pip3 install tqdm;
!pip3 install ipyleaflet;
!pip3 install pandas
!pip3 install geopandas==0.7.0
!pip3 install matplotlib==3.1.0
!pip3 install osmapi
!pip3 install geopy
!pip3 install plotly
!pip3 install folium
In [2]:
import osmnx as ox
from ipyleaflet import *
import networkx as nx
import plotly.graph_objects as obj
import numpy as np
import matplotlib.pylab as plt
import pandas as pd
import osmapi as osm
import time
from collections import deque
from tqdm import tqdm
from itertools import islice
In [3]:
!unzip /content/utilities.zip
Archive: /content/utilities.zip ec68c57f03f6058b04cfe8c1108e65923c57b776 creating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/ creating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/.github/ creating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/.github/ISSUE_TEMPLATE/ inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/.github/ISSUE_TEMPLATE/bug_report.md inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/.gitignore inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/LICENSE inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/README.md inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/__init__.py creating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/ inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/__init__.py inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/common.py inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/jupyter.py inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/poi.py inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/problem.py inflating: utilities-ec68c57f03f6058b04cfe8c1108e65923c57b776/src/viz.py
In [5]:
from utilities import *
New Section¶
In [6]:
place = 'North York, Ontario, Canada'
G = ox.graph_from_place(place)
fig, ax = ox.plot_graph(G, node_size=0, edge_color='w', edge_linewidth=0.2) # display the city of North York
In [27]:
nodes, edges = ox.graph_to_gdfs(G) # Convert node and edge GeoDataFrames to a MultiDiGraph.
# print("Nodes:\n", nodes.head(), '\n')
# print("Edges:\n", edges.head(), '\n')
# print("Type:", type(edges))
# print("Nodes:", nodes);
# print("Edges:", edges);
df = pd.DataFrame(edges) # data frame
dic = df.to_dict()
print(len(dic['u']), len(dic['v']))
print(dic['u'][0])
print(dic['v'][0])
print(dic['maxspeed'][0])
print(dic['highway'][0])
print(dic['geometry'][0])
print(dic['oneway'][0])
api = osm.OsmApi()
id_info = api.NodeGet(dic['v'][0])
point = (id_info['lat'], id_info['lon']) # lat (y), lon (x)
print(point)
# all_nodes = list(nodes.to_dict()['osmid'].keys())
# index_element = all_nodes.index(dic['u'][0])
# print(index_element)
# index_element = all_nodes.index(dic['u'][0])
# print(index_element)
# print(dic['u'])
# print(dic['v'])
# print(dic['osmid'])
# print(dic['maxspeed'])
dic.keys()
98389 98389 1497768 1497793 90 motorway LINESTRING (-79.3327357 43.7450436, -79.3328369 43.7454806, -79.33295889999999 43.7459924, -79.3331111 43.7466704, -79.3332228 43.747316, -79.3332826 43.7478616, -79.3333255 43.7484115, -79.333434 43.7496991, -79.33346160000001 43.7501978, -79.3334777 43.750338, -79.3335368 43.7508526, -79.3336887 43.7518932, -79.3337332 43.7521525, -79.3339332 43.7530819, -79.3341564 43.7539499, -79.3345023 43.7550349) True (43.7550349, -79.3345023)
Out[27]:
dict_keys(['osmid', 'oneway', 'lanes', 'ref', 'name', 'highway', 'maxspeed', 'length', 'bridge', 'geometry', 'access', 'tunnel', 'service', 'width', 'junction', 'area', 'u', 'v', 'key'])
Option 1: Using Nominatim API¶
Based on the analysis it does not pickout many points. We can go with option 2 for now.
In [1]:
class Amenity:
def __init__(self, amenity, lat, lon, address):
self.amenity = amenity
self.lat = float(lat)
self.lon = float(lon)
self.address = address
self.coordinates = (float(lon), float(lat))
In [2]:
amenities = []
firestation_link = 'https://nominatim.openstreetmap.org/?addressdetails=1&q=fire+stations+in+north+york&format=json'
hospitals_link = 'https://nominatim.openstreetmap.org/?addressdetails=1&q=hospitals+in+north+york&format=json'
links = [firestation_link, hospitals_link]
for link in links:
response = requests.get(link)
if response.status_code != 200:
raise ValueError("We couldn't decode the address, please make sure you entered it correctly")
response_json = response.json()
for place in response_json:
amenities.append(Amenity(place['type'], place['lat'], place['lon'], place['display_name']))
# print(place['type'], place['display_name'], place['lat'], place['lon'])
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-2-66027f72e9fa> in <module>() 7 8 for link in links: ----> 9 response = requests.get(link) 10 11 if response.status_code != 200: NameError: name 'requests' is not defined
In [ ]:
critical_amenity_nodes = []
# nc = ['w'] * len(nodes)
# ns = [10 if node in amenities else 0 for node in G.nodes()]
for place in amenities:
point = (place.lat, place.lon) # lat (y), lon (x)
pt_nearest_node_euc = ox.get_nearest_node(G, point, method='euclidean')
critical_amenity_nodes.append((pt_nearest_node_euc, place.amenity))
In [ ]:
def node_color_and_size(critical_nodes, nodes):
node_colors = ['w'] * len(nodes)
node_size = [0] * len(nodes)
for place in critical_nodes:
idx = nodes.index(place[0])
if (place[1] == 'fire_station'):
node_colors[idx] = 'b'
elif (place[1] == 'hospital'):
node_colors[idx] = 'r'
node_size[idx] = 10
return node_colors, node_size
print(critical_amenity_nodes)
In [ ]:
all_nodes = list(nodes.to_dict()['osmid'].keys())
nc, ns = node_color_and_size(critical_amenity_nodes, all_nodes)
fig, ax = ox.plot_graph(G, node_color=nc, node_size=ns, edge_linewidth=0.2, bgcolor = 'white')
Option 2/2: Using OpenStreetMap node IDs API¶
This highlights many amenities. Seems to be a good option here.
In [58]:
class Amenity:
def __init__(self, amenity, lat, lon, name, osmid, nearestNode):
self.amenity = amenity
self.lat = float(lat)
self.lon = float(lon)
self.name = name
self.osmid = osmid
self.nearestNode = nearestNode
In [43]:
# Get all the important things we need to highlight on the map
tags = {
'amenity':['hospital', 'fire_station'],
}
emergency_locations = ox.geometries_from_place(place, tags=tags)
print(emergency_locations)
df = pd.DataFrame(emergency_locations) # data frame
dic = df.to_dict()
unique_id osmid ... wheelchair wikipedia:en 0 node/609290577 609290577 ... NaN NaN 1 node/1601962650 1601962650 ... NaN NaN 2 way/27006473 27006473 ... NaN NaN 3 way/31372887 31372887 ... NaN NaN 4 way/43806314 43806314 ... NaN NaN 5 way/60428611 60428611 ... NaN NaN 6 way/68021042 68021042 ... NaN NaN 7 way/108700348 108700348 ... NaN NaN 8 way/109199283 109199283 ... NaN NaN 9 way/111664202 111664202 ... NaN NaN 10 way/120882312 120882312 ... NaN NaN 11 way/124105159 124105159 ... NaN NaN 12 way/130468302 130468302 ... NaN NaN 13 way/134818544 134818544 ... NaN NaN 14 way/134823356 134823356 ... NaN NaN 15 way/191843386 191843386 ... NaN NaN 16 way/207234268 207234268 ... NaN NaN 17 way/231659486 231659486 ... NaN NaN 18 way/280803826 280803826 ... NaN NaN 19 way/432888543 432888543 ... NaN NaN 20 way/671607430 671607430 ... NaN NaN 21 way/673002279 673002279 ... NaN NaN 22 way/678430199 678430199 ... NaN NaN 23 way/678508080 678508080 ... yes Baycrest Health Sciences 24 way/684426041 684426041 ... NaN St. John's Rehab Hospital 25 way/684426043 684426043 ... NaN NaN 26 way/684431220 684431220 ... NaN NaN 27 way/684431221 684431221 ... yes NaN 28 way/684431222 684431222 ... yes NaN [29 rows x 30 columns]
In [ ]:
In [68]:
amenities = []
api = osm.OsmApi() # using this to find lat and lon
for i in range(len(list_osmids)):
if ("node" in dic['unique_id'][i]):
nodeId= dic['osmid'][i]
elif ("way" in dic['unique_id'][i]):
nodeId = dic['nodes'][i][0]
id_info = api.NodeGet(nodeId)
point = (id_info['lat'], id_info['lon']) # lat (y), lon (x)
pt_nearest_node_euc = ox.get_nearest_node(G, point, method='euclidean')
# amenity, lat, lon, name, osmid, nearestNode
amenities.append(Amenity(dic['amenity'][i], id_info['lat'], id_info['lon'],
dic['name'][i], dic['osmid'][i], pt_nearest_node_euc))
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: ResourceWarning:
unclosed <ssl.SSLSocket fd=50, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('172.28.0.2', 49846)>
In [66]:
def find_element_in_list(element, list_element):
try:
index_element = list_element.index(element)
return index_element
except ValueError:
return -1
def node_color_and_size(amenities, nodes):
node_colors = ['w'] * len(nodes)
node_size = [0] * len(nodes)
for i in range(len(amenities)):
idx = find_element_in_list(amenities[i].nearestNode, nodes)
if (idx != -1):
if (amenities[i].amenity == 'fire_station'):
node_colors[idx] = 'b'
if (amenities[i].amenity == 'hospital'):
node_colors[idx] = 'r'
node_size[idx] = 10
return node_colors, node_size
In [67]:
all_nodes = list(nodes.to_dict()['osmid'].keys())
nc, ns = node_color_and_size(amenities, all_nodes)
fig, ax = ox.plot_graph(G, node_color=nc, node_size=ns, edge_linewidth=0.2, bgcolor = 'white')
In [ ]:
Stategy going forward:
- Heat map of traafic data
- Implement the algorithm - currently we are utilizing the algorithm from prof
In [71]:
# first define the origin/source nodes as Node
origin = Node(graph = G, osmid = amenities[0].nearestNode)
destination = Node(graph = G, osmid = amenities[3].nearestNode)
In [72]:
%%time
bar = tqdm(total=len(G))
route = []
frontier = deque([origin])
explored = set()
found = False
while frontier and not found:
bar.update(1); time.sleep(0.05)
node = frontier.pop()
explored.add(node)
for child in node.expand():
if child not in explored and child not in frontier:
if child == destination:
route = child.path()
found = True
continue
frontier.append(child)
bar.close()
print("The route is \n\n",route, "\n\nits cost is\n\n", cost(G, route))
25%|██▌ | 8815/35101 [07:33<22:33, 19.42it/s]
The route is [6624988064, 7368052431, 7127096569, 7127096567, 261631317, 261631491, 7824126406, 1814304153, 1814304152, 261631540, 1333963652, 4315968214, 4315968216, 7127096563, 7127096564, 7127096546, 5777975648, 5777975647, 7127096541, 7368092038, 5670217272, 3447489202, 7368092015, 7368092012, 7368092002, 6474228780, 6474235297, 6474235311, 6842674658, 48490673, 1721319579, 6474235326, 7368091985, 7368091987, 7368091989, 48489536, 7244814028, 7244814019, 7244814018, 48487794, 48494741, 48495722, 48495726, 48496115, 253013816, 430878370, 430878300, 4948911358, 414424283, 430877776, 1245025174, 430878081, 430878425, 430877819, 430877721, 7250327562, 7250327570, 430878136, 809117253, 809232794, 809136544, 809136549, 809130585, 809130579, 5978796615, 5978796611, 809126912, 809117284, 430878477, 809126917, 6225873776, 253012447, 430878326, 430878484, 430878131, 430877969, 430877908, 253013434, 48496770, 4589183201, 4589183203, 4589183202, 4589183206, 5908603285, 4590854797, 48498576, 1578947267, 7238083627, 7238083632, 410798856, 1460723466, 1460723476, 1460723491, 7222707957, 7222707959, 7222707958, 7826097977, 7826097976, 7826097975, 7222707942, 7222707936, 7222707939, 7222707940, 7222707933, 7222712898, 7222712896, 7222707968, 7222712901, 7222712902, 41270527, 7222707919, 7222707930, 41270594, 41270433, 5448637232, 5431805614, 5431809805, 5431809807, 5431809814, 7368042183, 7368112986, 7368112989, 7368112990, 3447489205, 7368112997, 5777975644, 5777975643, 7127096543, 3836524705, 1353178043, 7127096534, 32546846, 4242587599, 7127096509, 3406396868, 7127096444, 7127096441, 7127096431, 7127055059, 7127055061, 7127055062, 3149854693, 84951927, 130033208, 84964060, 2375224224, 2375225374, 2375225399, 2375225408, 59943934, 84959690, 4114799664, 1460808619, 1460808629, 84975546, 84968328, 84957433, 84959092, 84957482, 84957314, 4114799639, 100559405, 7024813367, 7024813375, 7024813364, 7024813362, 7024813360, 7024813358, 100559399, 7024813355, 7024813347, 7024813344, 7024813341, 7024813339, 7024813337, 7024813335, 100559119, 7024813333, 7024813326, 7024813328, 7024813320, 7024813322, 7024813324, 100558594, 4199695351, 4199695369, 4199695366, 4199695368, 262451723, 1416725223, 1416734497, 262451722, 1416725267, 1416725280, 1416725322, 1416725332, 1416741521, 2790910991, 5608375302, 5608375318, 5608375322, 7951337257, 1416725422, 1453363768, 1453363763, 46847678, 59943129, 1453363940, 1453363938, 46850698, 46841067, 4199753646, 4199753644, 4199753642, 4199753641, 4199753640, 4199753634, 59943117, 59943110, 130035113, 432730616, 2704964458, 7232652456, 7232652458, 1416741510, 1354435222, 1416739831, 1416739829, 1416739832, 1416739830, 1416741508, 1354143127, 4199695510, 4199695517, 4199695515, 4199695512, 4199695504, 1354143124, 33070492, 809301530, 809301486, 809301474, 3110478809, 414434936, 809301482, 809301538, 414449620, 1272591111, 1272591194, 2785644525, 1453371490, 1453371494, 1453371498, 432730610, 432730605, 7232652449, 7232652448, 7232652447, 7232652452, 7232652453, 7232652438, 7232652437, 248493768, 248493763, 248493770, 309777925, 248493832, 1453377190, 1453377187, 1453377186, 1453377184, 1453371486, 1453377182, 1453371483, 1453235465, 1453235469, 1453372383, 1453235484, 1453235485, 1453235486, 1453235487, 1453235490, 1453235497, 1453252514, 1453235503, 5742193534, 5667039098, 1453364150, 5667039076, 2179155494, 2179155500, 5627874322, 5667039069, 663792814, 663792828, 1453264892, 1453264885, 1453264882, 1530414641, 1453363753, 1530414640, 2704961964, 2704961941, 2704961932, 2704961909, 2704961924, 2704961927, 2003444822, 1416748411, 363995634, 363995637, 248493690, 2375237240, 248493689, 425760991, 1453235528, 1530413982, 1453235531, 425761047, 2375238203, 46850285, 46847773, 46846526, 410800937, 1453363814, 1955996478, 1453363755, 1453363757, 1453363759, 46844791, 1453363760, 1453363761, 1453361231, 1453361232, 1453361233, 410800986, 6535300849, 6535300839, 6535300838, 1360995448, 36491906, 1453361234, 2449884523, 2449884521, 565543432, 46841102, 5703323777, 5703323778, 5737609848, 4199764164, 5737609910, 5703323666, 5703323759, 5703323756, 5703323765, 5703323764, 5703323665, 873474268, 4368788571, 427541957, 873474326, 5618361721, 1453361240, 1453361245, 561044361, 56922382, 873474252, 1453361247, 7498685755, 7498685753, 56922364, 5567783165, 56922376, 2412554915, 2412554913, 1453361994, 1453361991, 1453361980, 1453361970, 586456371, 586456366, 586456380, 565543477, 550260529, 565532110, 3036279217, 3036279216, 5585411611, 3417386815, 550260054, 5557591047, 5557591064, 5557590915, 5557590857, 5557590859, 5557591038, 5557591035, 5774222313, 5557591066, 5557591065, 5557590579, 5557591049, 2179157199, 2179157194, 5553462847, 5553462827, 927253003, 927253007, 5557591048, 873474281, 147051389, 873474381, 4199764170, 5706241173, 5706236082, 1453364159, 3984377790, 1453364158, 6138113089, 6177326559, 6177326557, 248493833, 5580833303, 5580833302, 2293026514, 927245847, 5580885160, 5580885133, 5580885167, 6168949207, 565543411, 565543400, 927245844, 5580927120, 5580928241, 5580928236, 5780313769, 5780313766, 5580927106, 2087907964, 927245848, 927243493, 770595545, 5591004333, 927243031, 2087907952, 5591004345, 5591004354, 927243034, 565543394, 927243033, 1466077785, 5593007668, 5593007663, 5593007660, 5593007677, 5593006975, 5593006976, 5593236112, 5593236115, 5593236114, 927243032, 927242062, 5593006970, 5593006982, 2250812417, 2250812416, 5595320327, 2250812415, 5595320330, 5595320329, 5595320345, 5595320423, 5595320422, 5595320446, 5595320424, 5595320432, 927242053, 927187748, 196275535, 927241148, 196275524, 859557962, 927238856, 5680059860, 927187849, 2250810373, 5603144536, 5603144542, 5603144541, 5603144540, 5603144539, 2250810374, 2250810377, 2250810380, 5603144554, 2250810392, 259267220, 196325395, 1454610690, 1454597897, 196325419, 1454642792, 1454642793, 1454642794, 1454642795, 1454642796, 1454590242, 1454590251, 1454590254, 1454606871, 1454606873, 1454604914, 1454604907, 1956971996, 1454642800, 1454604901, 1454642798, 1454642799, 5680097357, 5680097356, 5680097353, 5680097352, 5680097349, 5680097348, 5680097344, 1454583305, 1454583309, 1454583310, 1454583311, 5688778133, 760154588, 1454583315, 3861741011, 5688778135, 760154703, 760154711, 760154559, 2136742749, 2136742761, 2136745063, 2136745064, 2136745065, 390160587, 1454610460, 760154637, 760157813, 760157780, 2984993842, 305292732, 390157969, 305292193, 305291836, 1816036381, 1816036375, 1816036378, 254478995, 254478992, 1559896664, 5688513487, 5688778132, 3861741013, 5688513482, 372339364, 372339385, 390362289, 595912748, 595912735, 5688513480, 5688513478, 5688513475, 5688778130, 3033556822, 7190107463, 973478192, 973478193, 5685582315, 5685582316, 808950682, 2337770704, 808950656, 372326749, 372326745, 1138266290, 1138266414, 6581106203, 6581106198, 6581106200, 1138266337, 1138266366, 6581106205, 390361153, 254479964, 2380769237, 254479911, 390361152, 2380769240, 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1585265530, 1585265531, 2353582047, 310847651, 1359997190, 1359997194, 5602888913, 1585265532, 1585265533, 2087910177, 770595550, 927242055, 859557963, 859557959, 859557961, 770595608, 5642023793, 5642023779, 5642023788, 5642023722, 5642023721, 5640873035, 5640873067, 5642023312, 5640872830, 5640872832, 5640872835, 5640872836, 5861141955, 6167301880, 6167301881, 859545666, 3261711535, 5642988810, 6169548261, 5648098472, 5646147822, 436995081, 436995121, 770595593, 770595528, 770581686, 770581521, 2250804308, 5675866657, 5675866652, 5675866655, 5675866656, 5675866625, 5675866616, 5675866591, 3687519812, 5675866558, 5675866553, 5675866554, 3687519807, 770603007, 770581807, 5672983788, 770602997, 770581528, 5630667537, 5630667539, 5630667557, 5630667527, 5630667525, 770581983, 770595552, 5650412653, 1466060447, 3184013815, 3184013816, 3184013819, 3184013820, 5672958651, 5672958661, 5672958624, 5672958625, 5672958676, 5672958516, 5672958445, 5672958456, 859545667, 1453252489, 1453252485, 1453252478, 1453235450, 1416788713, 1453370227, 1453235458, 4306892431, 6168841593, 5723668511, 1453370248, 5723668510, 1453235473, 1453370251, 1453370254, 1453370256, 5749791379, 5749791393, 1453370273, 1453252494, 1453252497, 1453252498, 1453252507, 5667039093, 3782755678, 5667039092, 5667039090, 5667039089, 6182979605, 5667039085, 5667039084, 5667039079, 1453364152, 1453364153, 1453255479, 6108205470, 6108205472, 6108205471, 1453255469, 2353047180, 2620454242, 1453252530, 770595569, 1453252526, 6159420153, 2250808580, 770595634, 6157775187, 6146035489, 2250807489, 6152734902, 6152734899, 6152734994, 6152734991, 6152734996, 1453243684, 252363472, 6152734867, 6152734873, 414442988, 414407473, 6152734966, 6152734894, 6152734892, 6152734898, 6152734882, 6152734879, 6152734876, 6152734852, 6152734851, 6605915765, 6152734903, 6152734859, 6152734860, 6146035524, 5667039424, 5667039425, 6893280954, 5667039421, 5667039428, 5667039439, 3782761646, 3782761647, 3782761652, 5667039101, 6146537991, 6146537990, 6146537988, 6146538024, 6146537993, 6146531584, 6146538049, 6146538048] its cost is 194814.88399999973 CPU times: user 13.9 s, sys: 1.33 s, total: 15.2 s Wall time: 7min 33s
In [73]:
fig, ax = ox.plot_graph_route(G, route=route, node_color=nc, node_size=ns, edge_linewidth=0.2, bgcolor = 'white')
-------- A* -----------¶
In [74]:
# toGoal, toDestination = astar_heuristic(G, origin.osmid, destination.osmid)
toGoal, toDestination = astar_heuristic(G, origin.osmid, destination.osmid)
%time
route = []
frontier = list()
bar = tqdm(total=len(G)) # for the progress bar
frontier.append(origin)
explored = set()
found = False
while frontier and not found:
bar.update(1); time.sleep(0.05)
# choose a node based on its heuristic value
node = min(frontier, key = lambda node : toGoal[node.osmid] + toDestination[node.osmid])
frontier.remove(node)
explored.add(node)
# expand its children
for child in node.expand():
if child not in explored and child not in frontier:
if child == destination:
route = child.path()
found = True
continue
frontier.append(child)
bar.close()
print("The route is \n\n",route, "\n\nits cost is\n\n", cost(G, route))
fig, ax = ox.plot_graph_route(G, route)
0%| | 3/35101 [00:00<20:19, 28.78it/s]
CPU times: user 3 µs, sys: 0 ns, total: 3 µs Wall time: 7.87 µs
1%| | 416/35101 [00:21<29:23, 19.67it/s]
The route is [6624988064, 7368052431, 7368052433, 7368052434, 4550421840, 37016874, 4508806097, 130033865, 59943869, 130033004, 130033753, 130033552, 130033752, 130033751, 130033742, 130033745, 130033439, 84995922, 84997188, 6832060037, 130034184, 1272601850, 6832060049, 130034131, 529593356, 560654489, 560654512, 560654518, 7951336331, 7951336326, 7951336325, 7951336300, 7951336293, 248493828, 248493826, 248493825, 248493820, 1416788711, 248493821, 1416788713, 252363483, 1453370230, 1453370243, 1453370244, 1453370247, 1453370249, 1453370257, 5667681790, 6893295597, 6893280964, 6893295596, 6893280956, 5667039425, 6893280954, 5667039421, 5667039422, 6146537991, 6146537990, 6146537988, 6146538024, 6146537993, 6146531584, 6146538049, 6146538048] its cost is 6629.397000000002
TESTING ONWARDS - Ignore¶
In [ ]:
place = 'University of Toronto'
G = ox.graph_from_place(place)
nodes, edges = ox.graph_to_gdfs(G)
keys = list(nodes.to_dict()['osmid'].keys())
print (keys)
[20979742, 21631731, 24959527, 24959528, 24959535, 24959545, 24959546, 24959547, 24959549, 24959550, 24959551, 24959555, 24959556, 24959559, 24959560, 24960058, 24960060, 24960063, 24960068, 24960070, 24960073, 24960076, 24960080, 50885141, 50885147, 50885160, 50897854, 50897859, 50897869, 50897874, 55808451, 55808512, 55808518, 55808527, 55808564, 55808571, 55808582, 55808839, 59817393, 59817394, 60052037, 60052038, 60052040, 60052042, 60052047, 60052048, 60052049, 60052050, 60654119, 60654120, 60654129, 60654133, 80927418, 80927426, 87475456, 87475463, 123347786, 123347984, 127275360, 127283024, 127284677, 127284680, 127289393, 130170945, 215726254, 239055725, 239055729, 242413453, 243633234, 244213560, 244213561, 249991437, 253815363, 253815365, 253815479, 262732431, 262732432, 262732448, 262817257, 262817259, 299625331, 303255446, 304885460, 304888562, 304890340, 304890341, 304892018, 305862114, 306721038, 306721042, 306721055, 306721057, 306725181, 306725184, 306729816, 306729822, 306729825, 307371155, 307371160, 307371161, 313239199, 316884460, 316884976, 333554741, 333554744, 333554746, 338484439, 340165033, 344202170, 389677889, 389677890, 389677891, 389677892, 389677893, 389677894, 389677896, 389677897, 389677901, 389677902, 389677903, 389677904, 389677905, 389677906, 389677907, 389677908, 389677909, 389677937, 389677942, 389677943, 389677945, 389677946, 389677959, 389677990, 389677993, 389678001, 389678002, 389678003, 389678004, 389678005, 389678007, 389678008, 389678009, 389678012, 389678013, 389678015, 389678016, 389678025, 389678026, 389678027, 389678028, 389678029, 389678032, 389678033, 389678034, 389678036, 389678037, 389678038, 389678039, 389678040, 389678041, 389678042, 389678043, 389678044, 389678054, 389678058, 389678102, 389678103, 389678107, 389678111, 389678112, 389678113, 389678121, 389678122, 389678124, 389678131, 389678133, 389678138, 389678139, 389678140, 389678141, 389678142, 389678145, 389678146, 389678150, 389678151, 389678157, 389678158, 389678159, 389678160, 389678161, 389678175, 389678176, 389678177, 389678180, 389678181, 389678182, 389678183, 389678184, 389678185, 389678186, 389678187, 389678188, 389678189, 389678190, 389678191, 389678192, 389678194, 389678195, 389678197, 389678205, 389678206, 389678207, 389678209, 389678210, 389678211, 389678212, 389678213, 389678214, 389678215, 389678216, 389678220, 389678221, 389678222, 389678226, 390545043, 390545044, 390545045, 390545068, 390545070, 390545071, 390545073, 390545074, 390545921, 390547782, 390547786, 390547788, 390548860, 390548861, 390548862, 390548863, 390548864, 390548865, 390548866, 390548869, 390548870, 390549045, 390549046, 390549047, 390549048, 390550470, 390550471, 393676412, 416737810, 416737812, 416737814, 416737815, 417737948, 516957691, 551186190, 703096142, 703096144, 703096400, 703098888, 728157228, 730029006, 730029007, 730029009, 730029011, 730029013, 733206247, 749951161, 749952029, 751988322, 751988323, 771931704, 771931720, 771931730, 771931732, 771931749, 771950938, 771950946, 771950960, 771950963, 771950967, 771950969, 773001455, 774054378, 774054381, 774054390, 775377001, 775377002, 775377003, 775377006, 779168845, 779168848, 779168853, 779168865, 779168874, 779168877, 779168879, 779168880, 779168881, 783622470, 838289889, 838289891, 838289892, 838289894, 838289896, 854322047, 856114449, 856114454, 856205360, 938003293, 969632817, 969632820, 984911356, 984911358, 1038790590, 1122032146, 1122032154, 1258698109, 1258698113, 1258703092, 1258703099, 1258703122, 1258706664, 1258706665, 1258706666, 1258706668, 1258706670, 1258706673, 1258707987, 1258707990, 1403960963, 1432347915, 1432352439, 1480794706, 1480794735, 1633421933, 1633421938, 1633421950, 1633421951, 1633421956, 1633421968, 1633422235, 1633441264, 1633441265, 1633441266, 1633441971, 1633441972, 1633441974, 1633441975, 1633441977, 1633441979, 1633441980, 1633441982, 1633441983, 1633441985, 1633441986, 1633441987, 1840221676, 1840221682, 1840221686, 1840221692, 1840221695, 2078205535, 2129327538, 2129327576, 2129327665, 2129327680, 2143278000, 2143278003, 2143278005, 2143397656, 2143402264, 2143402265, 2143402266, 2143402267, 2143402268, 2143402269, 2143402270, 2143404199, 2143404200, 2143434271, 2143434279, 2143434334, 2143434335, 2143434338, 2143434369, 2143434370, 2143434372, 2143434375, 2143434377, 2143434379, 2143434860, 2143434862, 2143434863, 2143435225, 2143435226, 2143436381, 2143436406, 2143436407, 2143436415, 2143441662, 2143460109, 2143468182, 2143468186, 2143468192, 2143468197, 2143485199, 2143485660, 2143485661, 2143485662, 2143485664, 2143487624, 2143487625, 2143487626, 2143488443, 2143488445, 2143488528, 2143488529, 2143488609, 2143489692, 2143489693, 2143489694, 2143489695, 2143491963, 2143491965, 2143491966, 2143493381, 2143494205, 2143494207, 2143494214, 2143494216, 2143501174, 2143501175, 2143501176, 2143506365, 2143514056, 2143514059, 2144966781, 2428750571, 2473807790, 2473807791, 2473807793, 2473807798, 2480697684, 2480697688, 2480698620, 2480698622, 2480712845, 2480712846, 2480713262, 2480713264, 2480713273, 2480713275, 2480713279, 2480713553, 2498969982, 2498969984, 2557539818, 2557539819, 2557539823, 2557539827, 2557539829, 2557539839, 2557539841, 2557539842, 2557542523, 2557542526, 2557544302, 2557544304, 3057097224, 3057643410, 3057643411, 3113915725, 3113916167, 3210497978, 3311164627, 3342358872, 3342358873, 3342358875, 3342358877, 3342358878, 3342358879, 3342358885, 3342358888, 3342358889, 3500096539, 3707407638, 3707407641, 3725533837, 3983181527, 3983181528, 3983186028, 3983186029, 3996667045, 3996667046, 3996671921, 3996671922, 3996671926, 3996671928, 3996671930, 4295105603, 4376693530, 4376693531, 4380884142, 4386490228, 4386490229, 4386490231, 4386490232, 4616881038, 4616881039, 4917890706, 4920594800, 4920594801, 4920594802, 4923076695, 5098988924, 5277943137, 5278007117, 5571466141, 5622365694, 5934575891, 5934585972, 5934585973, 5934586688, 6028561921, 6028561924, 6028561925, 6028561926, 6028561927, 6028561928, 6028561929, 6028561930, 6028561931, 6028562355, 6028562356, 6336107765, 6336107772, 6336107774, 6336107775, 6336107777, 6336107778, 6336107779, 6336107780, 6336109108, 6336109110, 6374142747, 6391301132, 6391312709, 6391312713, 6542457318, 7153999189, 7311036242, 7311057930, 7311057931, 7311057935, 7311057936, 7311057937, 7311083154, 7311083157, 7311083158, 7311200695, 7311200696, 7311200697, 7866474269, 7868892239, 7868892244]
In [ ]:
def find_element_in_list(element, list_element):
try:
index_element = list_element.index(element)
return index_element
except ValueError:
return -1
highlight_nodes = [20979742, 21631731, 24959527, 24959528, 24959535, 24959545, 24959546, 24959547, 24959549, 24959550, 24959551, 24959555, 24959556, 24959559, 24959560, 24960058, 24960060, 24960063, 24960068, 24960070, 24960073, 24960076]
colors = ['w'] * len(keys)
size = [0] * len(keys)
for i in range(len(highlight_nodes)):
idx = find_element_in_list(highlight_nodes[i], keys)
if (idx != -1):
colors[idx] = 'r'
size[idx] = 50
fig, ax = ox.plot_graph(G, node_color=colors, node_size=size, edge_linewidth=0.2, bgcolor = 'white')
In [ ]:
api = osm.OsmApi() # this instantiate the OsmApi class,
# it has many function, we will use NodeGet function.
# For more detail about it check the doc
# http://osmapi.metaodi.ch/#osmapi.OsmApi.OsmApi.NodeGet
# for id in size(dic['osmid']):
# id_info = api.NodeGet(dic['osmid'][3])
node = api.NodeGet(dic['nodes'][3])
lat = node['lat']
lon = node['lon']
# print(node)
pt_nearest_node_euc = ox.get_nearest_node(G, (lat, lon), method='euclidean')
print(node)
print(pt_nearest_node_euc)
G.nodes[pt_nearest_node_euc]
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-edf77fb79e4c> in <module>() ----> 1 api = osm.OsmApi() # this instantiate the OsmApi class, 2 # it has many function, we will use NodeGet function. 3 # For more detail about it check the doc 4 # http://osmapi.metaodi.ch/#osmapi.OsmApi.OsmApi.NodeGet 5 NameError: name 'osm' is not defined
In [ ]:
!pip3 install geopy
Requirement already satisfied: geopy in /usr/local/lib/python3.6/dist-packages (1.17.0) Requirement already satisfied: geographiclib<2,>=1.49 in /usr/local/lib/python3.6/dist-packages (from geopy) (1.50)