def test_get_network_methods():
import geopandas as gpd
north, south, east, west = 37.79, 37.78, -122.41, -122.43
with httmock.HTTMock(get_mock_response_content('overpass-response-6.json.gz')):
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service')
with httmock.HTTMock(get_mock_response_content()):
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service', truncate_by_edge=True)
location_point = (37.791427, -122.410018)
bbox = ox.bbox_from_point(location_point, project_utm=True)
with httmock.HTTMock(get_mock_response_content('overpass-response-8.json.gz')):
G2 = ox.graph_from_point(location_point, distance=750, distance_type='bbox', network_type='drive')
with httmock.HTTMock(get_mock_response_content('overpass-response-9.json.gz')):
G3 = ox.graph_from_point(location_point, distance=500, distance_type='network')
with httmock.HTTMock(get_mock_response_content('overpass-response-10.json.gz')):
G4 = ox.graph_from_address(address='350 5th Ave, New York, NY', distance=1000, distance_type='network', network_type='bike')
places = ['Los Altos, California, USA', {'city':'Los Altos Hills', 'state':'California'}, 'Loyola, California']
with httmock.HTTMock(get_mock_response_content('overpass-response-11.json.gz')):
G5 = ox.graph_from_place(places, network_type='all', clean_periphery=False)
calif = gpd.read_file('tests/input_data/ZillowNeighborhoods-CA')
mission_district = calif[(calif['CITY']=='San Francisco') & (calif['NAME']=='Mission')]
polygon = mission_district['geometry'].iloc[0]
with httmock.HTTMock(get_mock_response_content('overpass-response-12.json.gz')):
G6 = ox.graph_from_polygon(polygon, network_type='walk')
def test_get_network_methods():
from shapely import wkt
# graph from bounding box
north, south, east, west = 37.79, 37.78, -122.41, -122.43
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service')
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service', truncate_by_edge=True)
# graph from point
location_point = (37.791427, -122.410018)
bbox = ox.bbox_from_point(location_point, project_utm=True)
G2 = ox.graph_from_point(location_point, distance=750, distance_type='bbox', network_type='drive')
G3 = ox.graph_from_point(location_point, distance=500, distance_type='network')
# graph from address
G4 = ox.graph_from_address(address='350 5th Ave, New York, NY', distance=1000, distance_type='network', network_type='bike')
# graph from list of places
places = ['Los Altos, California, USA', {'city':'Los Altos Hills', 'state':'California'}, 'Loyola, California']
G5 = ox.graph_from_place(places, network_type='all', clean_periphery=False)
# graph from polygon
polygon = wkt.loads('POLYGON ((-122.418083 37.754154, -122.418082 37.766028, -122.410909 37.766028, -122.410908 37.754154, -122.418083 37.754154))')
G6 = ox.graph_from_polygon(polygon, network_type='walk')
# test custom query filter
filtr = ('["area"!~"yes"]'
'["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]'
'["foot"!~"no"]'
'["service"!~"private"]'
'["access"!~"private"]')
G = ox.graph_from_point(location_point, network_type='walk', custom_filter=filtr)
def test_get_network_methods():
from shapely import wkt
# graph from bounding box
north, south, east, west = 37.79, 37.78, -122.41, -122.43
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service')
G1 = ox.graph_from_bbox(north, south, east, west, network_type='drive_service', truncate_by_edge=True)
# graph from point
location_point = (37.791427, -122.410018)
bbox = ox.bbox_from_point(location_point, project_utm=True)
G2 = ox.graph_from_point(location_point, distance=750, distance_type='bbox', network_type='drive')
G3 = ox.graph_from_point(location_point, distance=500, distance_type='network')
# graph from address
G4 = ox.graph_from_address(address='350 5th Ave, New York, NY', distance=1000, distance_type='network', network_type='bike')
# graph from list of places
places = ['Los Altos, California, USA', {'city':'Los Altos Hills', 'state':'California'}, 'Loyola, California']
G5 = ox.graph_from_place(places, network_type='all', clean_periphery=False)
# graph from polygon
polygon = wkt.loads('POLYGON ((-122.418083 37.754154, -122.418082 37.766028, -122.410909 37.766028, -122.410908 37.754154, -122.418083 37.754154))')
G6 = ox.graph_from_polygon(polygon, network_type='walk')
# test custom query filter
filtr = ('["area"!~"yes"]'
'["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]'
'["foot"!~"no"]'
'["service"!~"private"]'
'["access"!~"private"]')
G = ox.graph_from_point(location_point, network_type='walk', custom_filter=filtr)
def test_get_network_methods():
# graph from bounding box
_ = ox.utils_geo.bbox_from_point(location_point,
project_utm=True,
return_crs=True)
north, south, east, west = ox.utils_geo.bbox_from_point(location_point,
dist=500)
G = ox.graph_from_bbox(north, south, east, west, network_type="drive")
G = ox.graph_from_bbox(north,
south,
east,
west,
network_type="drive_service",
truncate_by_edge=True)
# truncate graph by bounding box
north, south, east, west = ox.utils_geo.bbox_from_point(location_point,
dist=400)
G = ox.truncate.truncate_graph_bbox(G, north, south, east, west)
# graph from address
G = ox.graph_from_address(address=address,
dist=500,
dist_type="bbox",
network_type="bike")
# graph from list of places
G = ox.graph_from_place([place1],
network_type="drive",
clean_periphery=False)
# graph from polygon
G = ox.graph_from_polygon(polygon,
network_type="walk",
truncate_by_edge=True)
# test custom query filter
cf = (
'["highway"]'
'["area"!~"yes"]'
'["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]'
'["foot"!~"no"]'
'["service"!~"private"]'
'["access"!~"private"]')
G = ox.graph_from_point(location_point,
dist=500,
custom_filter=cf,
dist_type="bbox",
network_type="all")
G = ox.graph_from_point(
location_point,
dist=500,
dist_type="network",
network_type="all_private",
)
def test_get_network_methods():
# graph from bounding box
north, south, east, west = ox.utils_geo.bbox_from_point(location_point,
dist=500)
G = ox.graph_from_bbox(north, south, east, west, network_type="drive")
G = ox.graph_from_bbox(north,
south,
east,
west,
network_type="drive_service",
truncate_by_edge=True)
# graph from address
G = ox.graph_from_address(address=address,
dist=500,
dist_type="bbox",
network_type="bike")
# graph from list of places
G = ox.graph_from_place([place1],
network_type="drive",
clean_periphery=False)
# graph from polygon
G = ox.graph_from_polygon(polygon,
network_type="walk",
truncate_by_edge=True)
# test custom query filter
cf = (
'["area"!~"yes"]'
'["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]'
'["foot"!~"no"]'
'["service"!~"private"]'
'["access"!~"private"]')
G = ox.graph_from_point(location_point,
dist=500,
custom_filter=cf,
dist_type="bbox",
network_type="all")
# test custom settings
cs = '[out:json][timeout:180][date:"2019-10-28T19:20:00Z"]'
G = ox.graph_from_point(
location_point,
dist=500,
custom_settings=cs,
dist_type="network",
network_type="all_private",
)
def load_grid_edges(grid_x, grid_y, crs):
x1 = grid_x - GRID_SIZE / 2
x2 = grid_x + GRID_SIZE + GRID_SIZE / 2
y1 = grid_y - GRID_SIZE / 2
y2 = grid_y + GRID_SIZE + GRID_SIZE / 2
ls = LineString([
[x1, y1],
[x2, y1],
[x2, y2],
[x1, y2],
[x1, y1],
])
t = gpd.GeoDataFrame(geometry=[ls], crs=crs)
tll = ox.project_gdf(t, to_latlong=True)
west, south, east, north = tll.total_bounds
try:
tgp = ox.project_graph(ox.graph_from_bbox(
north, south, east, west,
truncate_by_edge=True, simplify=True, clean_periphery=False, network_type='all', retain_all=True
))
tnp, tep = ox.graph_to_gdfs(tgp, nodes=True, edges=True)
return tep
except ox.core.EmptyOverpassResponse:
pass
return gpd.GeoDataFrame(geometry=[])
def calculateRoutes(self, frm, to):
if isinstance(frm, tuple) and isinstance(to, tuple):
#center = self.GetCenter(frm, to)
north, south, east, west = self.GetBBox(frm, to)
try:
self.G = ox.graph_from_bbox(north,
south,
east,
west,
truncate_by_edge=True)
#self.G = ox.graph_from_point(center, truncate_by_edge=True)
except Exception as e:
print("GraphManager: Error when get graph.\n%s" % (str(e)))
return []
self.calculateWeight()
fromNode = ox.get_nearest_node(self.G, frm)
print("GraphCanvas: From nearest node {}.".format(fromNode))
toNode = ox.get_nearest_node(self.G, to)
print("GraphCanvas: To nearest node {}.".format(toNode))
routes = []
for i in range(4):
routes.append(
nx.shortest_path(self.G,
fromNode,
toNode,
weight='type%d' % (i)))
if self.columsLen >= 10:
routes.append(
nx.shortest_path(self.G, fromNode, toNode,
weight='type10'))
return routes
return []
def get_rivers(bbox, incl_streams=True):
infra = '["waterway"="river"]'
if incl_streams:
infra = infra + '["waterway"="stream"]'
G = ox.graph_from_bbox(*bbox,
custom_filter=infra,
simplify=True,
retain_all=True,
clean_periphery=False)
rivers = []
for u, v, data in G.edges(keys=False, data=True):
name = ""
if "name" in data:
name = data["name"]
if "geometry" in data:
pts = np.array(data["geometry"].xy)
else:
node = G.nodes
pts = [[node[u]['x'], node[v]['x']], [node[u]['y'], node[v]['y']]]
pts = np.array(pts)
line = Line(name, pts, color="g", type="river")
rivers.append(line)
return rivers, G
def get_pistes_osmnx(bbox):
infra = '["piste:type"="downhill"]'
G = ox.graph_from_bbox(*bbox, custom_filter=infra)
clrs = ["g", "g", "b", "r", "k", "w"]
trail_types = ["novice", "easy", "intermediate", "advanced", "expert"]
pistes = []
for u, v, data in G.edges(keys=False, data=True):
name, diff = "", ""
if "name" in data:
name = data["name"]
if "geometry" in data:
pts = np.array(data["geometry"].xy)
else:
node = G.nodes
pts = [[node[u]['x'], node[v]['x']], [node[u]['y'], node[v]['y']]]
pts = np.array(pts)
if "piste:difficulty" in data:
diff = data["piste:difficulty"]
idx = trail_types.index(diff) if diff in trail_types else -1
line = Line(name, pts, color=clrs[idx], type=diff)
pistes.append(line)
return pistes
def download_graph(loc_list, **kwargs):
simplify = kwargs.get("simplify",True) # get rid of non-important nodes?
auto_bbox = kwargs.get("auto_bbox",True) # auto query polygon bbox
show_plot = kwargs.get("show_plot",True)
distance = kwargs.get("distance",20000)
print("osmnx download_graph()...")
if isinstance(loc_list,list) and isinstance(loc_list[0],float) and len(loc_list)>2: # [lats,latn,lngw,lnge]
north=loc_list[1]
south=loc_list[0]
east=loc_list[3]
west=loc_list[2]
G=ox.graph_from_bbox(north,south,east,west,network_type='drive',simplify=simplify)
elif isinstance(loc_list,list) and isinstance(loc_list[0],float) and len(loc_list)==2:# [lat,lng]
G=ox.graph_from_point(loc_list, distance=distance,network_type='drive',simplify=simplify)
elif isinstance(loc_list, str) or (isinstance(loc_list,list) and isinstance(loc_list[0], str)):
if auto_bbox: # addr or [addr1,addr2,] use auto bbox
G=ox.graph_from_place(loc_list,network_type='drive',simplify=simplify)# No-distance arg
else: # addr or [addr1,addr2,] use distance
G=ox.graph_from_address(loc_list,network_type='drive',distance=distance,simplify=simplify)
else:
print(__file__+" download_graph() input error: ",loc_list)
return None
if show_plot and iprint and Has_Display:
if iprint>=2: print("download_graph showing downloaded graph...")
fig, ax = ox.plot_graph(G)
if not simplify:
simplify_graph(G)
if iprint>=2:
print ox.basic_stats(G)
return G
def make_graph_and_trace():
# north, south, east, west = 37.79, 37.78, -122.41, -122.43
p = Polygon([[-122.27465629577637, 37.7983230783235],
[-122.26096630096434, 37.7983230783235],
[-122.26096630096434, 37.80761398306056],
[-122.27465629577637, 37.80761398306056],
[-122.27465629577637, 37.7983230783235]])
west, south, east, north = p.bounds
G1 = ox.graph_from_bbox(north, south, east, west)
# make meter projected
G1 = ox.project_graph(G1)
print('Converting to this projection:', G1.graph['crs'])
project_to_meter = Transformer.from_crs(CRS.from_epsg(4326),
G1.graph['crs'],
always_xy=True)
# create trace path and reproject
trace = LineString([[-122.2701072692871, 37.798865645037765],
[-122.26969957351685, 37.79971339755073],
[-122.26935625076295, 37.800222044388384],
[-122.26892709732056, 37.80079850657009],
[-122.26845502853392, 37.80135800967933],
[-122.26856231689453, 37.801646236899785],
[-122.26939916610718, 37.80208705282608],
[-122.27008581161499, 37.80235832285767],
[-122.27113723754883, 37.8026635004523]])
trace = transform(project_to_meter.transform, LineString(trace))
return G1, trace
def load_graph(place):
"""
load graph networkx
:param place: tuple
(name [string], (north_lat [float], south_lat [float], east_lon [float], west_lon [float]))
:return g: networkx graph
"""
print(' Loading graph ...')
name, bbox = place
try:
with open('./graphs_dir/{0}.pkl'.format(name), 'rb') as f:
graph = pickle.load(f)
except FileNotFoundError:
if bbox is None:
graph = ox.graph_from_place(name, network_type='drive')
else:
graph = ox.graph_from_bbox(bbox[0],
bbox[1],
bbox[3],
bbox[2],
network_type='drive')
graph = max(nx.strongly_connected_component_subgraphs(graph), key=len)
try:
with open('./graphs_dir/{0}.pkl'.format(name), 'wb') as f:
pickle.dump(graph, f, protocol=4)
except MemoryError:
print(' Warning: Dump unsuccessful ...')
print(' Done!')
return graph
def InitialSetup():
custom_walk = ('["area"!~"yes"]["highway"="footway"]["foot"!~"no"]["service"!~"private"]{}').format(ox.settings.default_access)
global factor, img,image_map_horizontal,image_map_vertical ,nodes,edges, fig,ax,G_projected,threshold,G
G = ox.graph_from_bbox(top, bottom,right, left,custom_filter= custom_walk)#network_type="walk")#
## G_projected = ox.core.graph_from_file("centralcampus.xml", network_type = 'walk',simplify=True,retain_all=False)
##Uncomment this for the original stuff
G_projected = ox.project_graph(G)
ox.plot_graph(G_projected,save = True,filename = "maps", show = False,
node_color='#999999',node_edgecolor='#999999',edge_color='#999999',bgcolor = "#000000",
node_zorder=3,dpi=200, edge_linewidth=8,use_geom=True)
ox.simplify.clean_intersections(G,tolerance=100)
nodes, edges = ox.graph_to_gdfs(G)
fig,ax = pyplot.subplots()
ax.axis ('OFF')
img = Image.open('images/maps.png').convert('L').rotate(angle)
img.save('lowered.png')
height_compensation = 0
width_compensation = 0
w,h = img.size
image_size_w,image_size_h=img.size
image_map_horizontal = float(left-right)/image_size_w
image_map_vertical = float(top-bottom)/image_size_h
if img.width >= image_size and img.height >= image_size:
if img.height < img.width:
print "factor denom height is ",str(img.height)
factor = float(image_size) / img.height
else:
print "factor denom width is ",str(img.width)
factor = float(image_size) / img.width
PrintImg(img)
def _get_graph_not_memoized(latitude: float, longitude: float, radius: float,
google_maps_api_key: str) -> igraph.Graph:
# calculate extent of the graph
radius_latlong = radius / 60
north_lat = latitude + radius_latlong
south_lat = latitude - radius_latlong
east_long = longitude + radius_latlong
west_long = longitude - radius_latlong
# download graph
graph = osmnx.graph_from_bbox(north_lat,
south_lat,
east_long,
west_long,
network_type='walk')
graph = osmnx.add_node_elevations(graph, api_key=google_maps_api_key)
graph = osmnx.add_edge_grades(graph)
# convert node labels
graph = networkx.relabel.convert_node_labels_to_integers(graph)
# populate igraph
graph_ig = igraph.Graph(directed=True)
graph_ig.add_vertices(list(graph.nodes()))
graph_ig.add_edges(list(graph.edges()))
for attr in ('osmid', 'x', 'y'):
graph_ig.vs[attr] = list(
networkx.get_node_attributes(graph, attr).values())
for attr in ('length', 'grade'):
graph_ig.es[attr] = list(
networkx.get_edge_attributes(graph, attr).values())
graph_ig.es['streetname'] = [
str(s) for s in networkx.get_edge_attributes(graph, 'name').values()
]
return graph_ig
def _get_cycleways(self):
"""
Creates all cycleways in a city (networkx.MultiDiGraph).
"""
useful_tags = ox.settings.useful_tags_way + ['cycleway']
ox.config(use_cache=True,
log_console=True,
useful_tags_way=useful_tags)
if self.query_type == 'city':
if self.city_limits is True:
cycleways = ox.graph_from_place(query=self.city_name,
network_type='bike',
simplify=False)
else:
cycleways = ox.graph_from_bbox(self.north,
self.south,
self.east,
self.west,
network_type='bike',
simplify=False)
elif self.query_type == 'address':
cycleways = ox.graph_from_address(self.address,
network_type='bike',
dist=self.distance)
non_cycleways = [
(u, v, k) for u, v, k, d in cycleways.edges(keys=True, data=True)
if not ('cycleway' in d or d['highway'] == 'cycleway')
]
cycleways.remove_edges_from(non_cycleways)
cycleways = ox.utils_graph.remove_isolated_nodes(cycleways)
return cycleways
def download_osm_layer(area, mode):
"""Download a single-mode layer from OSM
Parameters
----------
area : str or list
String of geocoded place or list of [north, south, east, west]
mode : str
Mode choice of {‘walk’, ‘bike’, ‘drive’, ‘drive_service’, ‘all’, ‘all_private’, ‘none’}
Returns
-------
layer : networkx multidigraph
OSM map layer of specific mode
"""
if isinstance(area, str):
layer = ox.graph_from_place(area, network_type=mode)
elif isinstance(area, list) and len(area) == 4:
layer = ox.graph_from_bbox(area[0],
area[1],
area[2],
area[3],
network_type=mode)
else:
raise Exception('Graph area not geocoded place nor bounding box')
return layer
def from_osm_bbox(cls, bbox):
"""Grab OpenStreetMap road data within bounding box.
NOTE: Put this in a separate "data" module?
"""
G = ox.graph_from_bbox(*bbox, network_type='walk')
return cls(G)
def generate_graph_from_coords(orig_coords: tuple, dest_coords: tuple):
"""Prüft, ob bereits ein Graph zu den angegebenen Koordinaten existiert und holt diesen oder erstellt diesen je nachdem.
Der Graph enthält mind. die Koordinaten sowie ein wenig Abstand zum Rand von den Koordinaten aus.
"""
key = str(
hash(orig_coords[0] + dest_coords[0] + orig_coords[1] +
dest_coords[1]))
if graphs.get(key, "-1") != "-1":
print("**** graph fetched ****")
return graphs[key]
else:
# using this, the bbox gets slighty bigger resulting in a more usable graph
if orig_coords[0] > dest_coords[0]:
north = orig_coords[0] * 1.00015
south = dest_coords[0] * 0.99985
else:
north = dest_coords[0] * 1.00015
south = orig_coords[0] * 0.99985
if orig_coords[1] > dest_coords[1]:
west = dest_coords[1] * 0.9993
east = orig_coords[1] * 1.0007
else:
west = orig_coords[1] * 0.9993
east = dest_coords[1] * 1.0007
G = ox.graph_from_bbox(north, south, east, west)
graphs[key] = G
print("**** graph created ****")
return G
def __get_from_osmnx(self, tile: Tile) -> gpd.GeoDataFrame:
bbox: LngLatBbox = bounds(tile)
west, south, east, north = bbox
west -= LAT_LNG_BUFFER
east += LAT_LNG_BUFFER
north += LAT_LNG_BUFFER
south -= LAT_LNG_BUFFER
try:
graph = ox.graph_from_bbox(north,
south,
east,
west,
simplify=False,
retain_all=True)
gdfs = ox.graph_to_gdfs(graph)
for gdf in gdfs:
self.cache = self.cache.append(gdf,
ignore_index=True,
sort=False)
except ox.core.EmptyOverpassResponse:
pass
except ValueError as e:
if "not enough values" in str(e):
print(
f"[WARNING] Could not load tile {tile}! Assuming it is empty..."
)
else:
raise e
self.cached_tiles |= {tile}
self.cached_tiles = set(simplify(*self.cached_tiles))
return self.__get_from_cache(tile)
def plot_edge_grades():
files_map = '../data/maps/m43.96267779776494_m19.944747838679202_m43.929659815391865_m19.905049264605925.graphml'
G = ox.load_graphml(files_map)
max_lat = -12.934333867695516
min_lat = -12.961083555974895
max_lon = -38.473331269107426
min_lon = -38.49996781691653
name_geotiff = '../data/maps/19S45_ZN.tif'
G = ox.graph_from_bbox(max_lat,
min_lat,
max_lon,
min_lon,
network_type='all')
G = Graph.set_node_elevation(G, name_geotiff)
# deadends = [(u, v) for u, v, k, data in G.edges(keys=True, data=True) if data['highway'] == '']
# print(deadends)
# G2.remove_nodes_from(deadends)
G = Graph.edge_grades(G)
G_proj = ox.project_graph(G)
ec = ox.plot.get_edge_colors_by_attr(G_proj,
"grade",
cmap="plasma",
num_bins=5,
equal_size=True)
fig, ax = ox.plot_graph(G_proj,
edge_color=ec,
edge_linewidth=0.5,
node_size=0,
bgcolor="w")
def test_nearest_edge():
# test in closest edge section
sheik_sayed_dubai = [25.09, 25.06, 55.16, 55.11]
location_coordinates = (25.071764, 55.138978)
G = ox.graph_from_bbox(*sheik_sayed_dubai, simplify=False, retain_all=True, network_type='drive')
geometry, u, v = ox.get_nearest_edge(G, location_coordinates)
def get_graph(place):
string = place.split(',')[0]
print('Fetching graph data for {}'.format(place))
poly = get_polygon(string)
if poly == -1:
gdf = ox.gdf_from_place('{}'.format(string))
G = ox.graph_from_bbox(gdf.bbox_north, gdf.bbox_south, gdf.bbox_east,
gdf.bbox_west)
val = 0
else:
try:
G = nx.read_gpickle('data/{a}/{a}'.format(a=string))
val = 1
except FileNotFoundError:
G = ox.graph_from_polygon(poly, network_type='drive')
val = 0
G = ox.project_graph(G)
print('Writing graph file')
try:
os.mkdir('data/{}'.format(string))
except FileExistsError:
pass
if val != 1:
nx.write_gpickle(G, path='data/{a}/{a}'.format(a=string))
return G
def test_nearest_edges():
from pyproj import Proj
# test in closest edge section
sheik_sayed_dubai = [25.09, 25.06, 55.16, 55.11]
location_coordinates = (25.071764, 55.138978)
G = ox.graph_from_bbox(*sheik_sayed_dubai,
simplify=False,
retain_all=True,
network_type='drive')
# Unprojected
ne1 = ox.get_nearest_edges(
G,
X=[location_coordinates[1], location_coordinates[1]],
Y=[location_coordinates[0], location_coordinates[0]],
method='balltree',
dist=0.0001)
# Projected
G2 = ox.project_graph(G)
crs = Proj(G2.graph['crs'])
projected_point = crs(location_coordinates[1], location_coordinates[0])
ne2 = ox.get_nearest_edges(G2,
X=[projected_point[0], projected_point[0]],
Y=[projected_point[1], projected_point[1]],
method='kdtree',
dist=10)
assert (ne1 == ne2).all()
def setup_graph_bbox(self, bbox):
print('+' * 15, 'setup graph from bounding box', '+' * 15)
graph_name = 'graph_' + '_'.join(
[str(v) for l in bbox.values()
for v in l]).replace('.', 'p') + '.pkl'
pickle_path = self.path_pickle + os.sep + graph_name
if os.path.isfile(pickle_path):
# Read previously downloaded graph
print('Reading pickle file', os.path.basename(pickle_path))
self.graph = pickle.load(open(pickle_path, 'rb'))
else:
print('setting up graph for bounding box',
bbox,
': downloading',
end=' ... ')
self.graph = osmnx.graph_from_bbox(
south=bbox['lat'][0],
north=bbox['lat'][1],
west=bbox['lon'][0],
east=bbox['lon'][1],
network_type='all'
) # could use bike but this might be to restrictive
print('converting to undirected graph', end=' ... ')
self.graph = osmnx.get_undirected(
self.graph
) # forget the direction, this might be to restrictive
# print('projecting to UTM', end=' ... ')
# self.graph = osmnx.project_graph(self.graph) # convert to x,y projection for faster euclidean calculations
print('saving to', pickle_path)
pickle.dump(self.graph, open(pickle_path, 'bw'))
print('-' * 42)
def convert_graph_from_osmnx(self, bbox, log, start_from = -1):
if log:
logging.basicConfig(filename='create_graph_log.txt', level=logging.INFO)
logging.info('start...')
G = ox.graph_from_bbox(bbox[0], bbox[1], bbox[2], bbox[3])
all_nodes = G.nodes()
count = len(all_nodes)
start = time.time()
for i, osm_id in enumerate(G.nodes()):
if i < start_from:
continue
node = G.nodes[osm_id]
self.add_one_document(node)
for nei_osm_id in G.neighbors(osm_id):
nei_node = G.nodes[nei_osm_id]
self.add_one_document(nei_node)
dist = self.calcDistance(node['x'], node['y'], nei_node['x'], nei_node['y'])
self.add_neighbors(node, nei_node, dist)
if log:
logging.info(f"[{i}/{count}]... {time.time() - start} sec")
if log:
logging.info("create indexes")
self.mongoDB.create_index('id', pymongo.ASCENDING)
self.mongoDB.create_index('coordinates', pymongo.GEOSPHERE)
if log:
logging.info(f"[{count}/{count}]... complete {time.time() - start} sec")
def test_nearest_edge():
# test in closest edge section
sheik_sayed_dubai = [25.09, 25.06, 55.16, 55.11]
location_coordinates = (25.071764, 55.138978)
G = ox.graph_from_bbox(*sheik_sayed_dubai, simplify=False, retain_all=True, network_type='drive')
geometry, u, v = ox.get_nearest_edge(G, location_coordinates)
def graph_scenario(stop_points, geotiff_name, ad_weights, file_name_osm):
max_lat, min_lat, max_lon, min_lon = Coordinates.create_osmnx(stop_points)
# Scenario graph (paths are edges and junctions are nodes)
G = ox.graph_from_bbox(max_lat, min_lat, max_lon, min_lon, network_type='all')
G, nodes_coordinates, nodes_mass_increment = Graph.configure_graph(G, geotiff_name, stop_points, ad_weights, file_name_osm)
return G, nodes_coordinates, nodes_mass_increment
def test_something(self):
osmnx_bellver_graph = osmnx.graph_from_bbox(39.5713, 39.5573, 2.6257, 2.6023)
expected_edges = osmnx_bellver_graph.edges(data=True)
bellver_graph = Graph(39.5713, 39.5573, 2.6257, 2.6023)
edges = bellver_graph.get_edges()
self.assertEqual(type(edges), type(expected_edges))
self.assertEqual(True, True)
def osm_downloader(boundary=None, osm_path=None, regenerating_shp=False):
"""
Download drive network within a certain geographical boundary.
:param boundary:
geodataframe of the area for downloading the network.
:param osm_path:
file path to save the network in .shp and .graphml.
:param regenerating_shp:
if needs to regenerating the shape file.
:return:
None
"""
minx, miny, maxx, maxy = boundary.geometry.total_bounds
new_network = osm_path + 'drive.graphml'
new_network_shp = osm_path + 'drive.shp'
def shp_processor(G, new_network_shp):
print('Processing graphml to GeoDataframe...')
gdf_n = ox.graph_to_gdfs(G)
edge = gdf_n[1]
edge = edge.loc[:, [
'geometry', 'highway', 'junction', 'length', 'maxspeed', 'name',
'oneway', 'osmid', 'u', 'v', 'width', 'lanes'
]]
fields = ['osmid', 'u', 'v', 'length', 'maxspeed', 'oneway']
df_inter = pd.DataFrame()
for f in fields:
df_inter[f] = edge[f].astype(str)
gdf_edge = gpd.GeoDataFrame(df_inter, geometry=edge["geometry"])
gdf_edge = gdf_edge.rename(columns={
'osmid': 'osm_id',
'maxspeed': 'max_speed'
})
print('Saving as shp...')
gdf_edge.to_file(new_network_shp)
if not os.path.exists(new_network):
print('Downloading graphml...')
G = ox.graph_from_bbox(maxy, miny, maxx, minx, network_type='drive')
print('Saving as graphml...')
ox.save_graphml(G, filepath=new_network)
if not os.path.exists(new_network_shp):
shp_processor(G, new_network_shp)
else:
if regenerating_shp:
print('Loading graphml...')
G = ox.load_graphml(new_network)
shp_processor(G, new_network_shp)
if not os.path.exists(new_network_shp):
print('Loading graphml...')
G = ox.load_graphml(new_network)
shp_processor(G, new_network_shp)
else:
print('Drive networks exist. Skip downloading.')
def __init__(self, place):
self.place = place
if str != type(place):
place = json.dumps(place)
_ghsh = utils.calculateMD5ForString(place)
graph_file = os.path.join('cache', '{0}.graph.p'.format(_ghsh))
edges_file = os.path.join('cache', '{0}.edges.p'.format(_ghsh))
# nodes_file = os.path.join('cache', '{0}.nodes.p'.format(_ghsh))
if os.path.exists(graph_file):
Logger.debug("Loading graph from cached file")
self.Graph = pickle.load(open(graph_file, "rb"))
self.Edges = pickle.load(open(edges_file, "rb"))
# self.Nodes = pickle.load( open( nodes_file, "rb" ) )
else:
Logger.debug("Building graph from osm data")
if str == type(self.place):
try:
self.Graph = ox.graph_from_place(self.place,
network_type='drive',
simplify=False,
retain_all=True)
except:
self.Graph = ox.graph_from_place(self.place,
network_type='drive',
simplify=False,
retain_all=True,
which_result=2)
else:
# try:
self.Graph = ox.graph_from_bbox(self.place['north'],
self.place['south'],
self.place['east'],
self.place['west'],
network_type='drive',
simplify=False,
retain_all=True)
# except:
# self.Graph = ox.graph_from_bbox(
# self.place['north'],
# self.place['south'],
# self.place['east'],
# self.place['west'],
# network_type='drive', simplify=False, retain_all=True, which_result=2)
self.Edges = ox.graph_to_gdfs(self.Graph, nodes=False, edges=True)
# self.Nodes = ox.graph_to_gdfs(self.Graph, nodes=True, edges=false)
Logger.debug("Saving graph to cached file")
pickle.dump(self.Graph, open(graph_file, "wb"))
pickle.dump(self.Edges, open(edges_file, "wb"))
def get_map(xmin, ymin, xmax, ymax):
try:
return ox.graph_from_bbox(ymax, ymin, xmax, xmin, network_type='drive')
except Exception:
ymax = ymax * 1.0002
ymin = ymin * 0.9998
xmax = xmax + (xmax * -0.0002)
xmin = xmin * 1.0002
return get_map(xmin, ymin, xmax, ymax)
def map_matching_node(
move_data: Union[PandasMoveDataFrame, DaskMoveDataFrame, PandasDiscreteMoveDataFrame],
inplace: Optional[bool] = True,
bbox: Optional[Tuple[float, float, float, float]] = None,
place: Optional[Text] = None,
G: Optional[MultiDiGraph] = None
) -> Optional[DataFrame]:
"""
Generate Map matching using the graph nodes
Parameters
----------
move_data : MoveDataFrame
The input trajectories data
inplace: bool, optional
if set to true the original dataframe will be altered,
otherwise the alteration will be made in a copy, that will be returned,
by default True
bbox : tuple, optional
The bounding box as (north, east, south, west), by default None
place : string, optional
The query to geocode to get place boundary polygon, by default None
G : MultiDiGraph, optional
The input graph, by default None
Returns
-------
move_data : MoveDataFrame
A copy of the original dataframe or None
"""
if(G is None):
if(bbox is None):
bbox = move_data.get_bbox()
G = ox.graph_from_bbox(
bbox[0], bbox[2], bbox[1], bbox[3], network_type='all_private'
)
elif(place is not None):
G = ox.footprints_from_place(query=place, tags={'network_type': 'all_private'})
if not inplace:
move_data = move_data[:]
nodes = ox.get_nearest_nodes(
G, X=move_data['lon'], Y=move_data['lat'], method='kdtree'
)
gdf_nodes = ox.graph_to_gdfs(G, edges=False)
df_nodes = gdf_nodes.loc[nodes]
move_data['lat'] = list(df_nodes.y)
move_data['lon'] = list(df_nodes.x)
move_data['geometry'] = list(df_nodes.geometry)
if not inplace:
return move_data
def test_nearest_edges():
from pyproj import Proj
# test in closest edge section
sheik_sayed_dubai = [25.09, 25.06, 55.16, 55.11]
location_coordinates = (25.071764, 55.138978)
G = ox.graph_from_bbox(*sheik_sayed_dubai, simplify=False, retain_all=True, network_type='drive')
# Unprojected
ne1 = ox.get_nearest_edges(G, X=[location_coordinates[1], location_coordinates[1]],
Y=[location_coordinates[0], location_coordinates[0]], method='balltree', dist=0.0001)
# Projected
G2 = ox.project_graph(G)
crs = Proj(G2.graph['crs'])
projected_point = crs(location_coordinates[1], location_coordinates[0])
ne2 = ox.get_nearest_edges(G2, X=[projected_point[0], projected_point[0]],
Y=[projected_point[1], projected_point[1]], method='kdtree', dist=10)
assert (ne1 == ne2).all()