def test_routing_folium():
G = ox.graph_from_address(address=address,
dist=500,
dist_type="bbox",
network_type="bike")
# give each node a random elevation then calculate edge grades
randm = np.random.random(size=len(G))
elevs = {n: e for n, e in zip(G.nodes(), randm)}
nx.set_node_attributes(G, name="elevation", values=elevs)
G = ox.add_edge_grades(G, add_absolute=True)
# give each edge speed and travel time attributes
G = ox.add_edge_speeds(G)
G = ox.add_edge_travel_times(G)
orig_node = list(G.nodes())[5]
dest_node = list(G.nodes())[-5]
orig_pt = (G.nodes[orig_node]["y"], G.nodes[orig_node]["x"])
dest_pt = (G.nodes[dest_node]["y"], G.nodes[dest_node]["x"])
route = nx.shortest_path(G, orig_node, dest_node, weight="travel_time")
attributes = ox.utils_graph.get_route_edge_attributes(
G, route, "travel_time")
fig, ax = ox.plot_graph_route(G,
route,
save=True,
filename="route",
file_format="png")
fig, ax = ox.plot_graph_route(
G,
route,
origin_point=orig_pt,
destination_point=dest_pt,
save=True,
filename="route",
file_format="png",
)
# test multiple routes
fig, ax = ox.plot_graph_routes(G, [route, route])
# test folium
gm = ox.plot_graph_folium(G, popup_attribute="name")
rm = ox.plot_route_folium(G, route)
# test calling folium plotters with FeatureGroup instead of Map, and extra kwargs
fg = folium.FeatureGroup(name="legend name", show=True)
gm = ox.plot_graph_folium(G, graph_map=fg)
assert isinstance(gm, folium.FeatureGroup)
rm = ox.plot_route_folium(G,
route,
route_color="g",
route_map=fg,
tooltip="x")
assert isinstance(rm, folium.FeatureGroup)
def test_routing():
G = ox.graph_from_address(address=address,
dist=500,
dist_type="bbox",
network_type="bike")
# give each edge speed and travel time attributes
G = ox.add_edge_speeds(G)
G = ox.add_edge_speeds(G, hwy_speeds={"motorway": 100})
G = ox.add_edge_travel_times(G)
orig_x = np.array([-122.404771])
dest_x = np.array([-122.401429])
orig_y = np.array([37.794302])
dest_y = np.array([37.794987])
orig_node = ox.distance.nearest_nodes(G, orig_x, orig_y)[0]
dest_node = ox.distance.nearest_nodes(G, dest_x, dest_y)[0]
route = ox.shortest_path(G, orig_node, dest_node, weight="travel_time")
attributes = ox.utils_graph.get_route_edge_attributes(G, route)
attributes = ox.utils_graph.get_route_edge_attributes(
G, route, "travel_time")
fig, ax = ox.plot_graph_route(G, route, save=True)
# test multiple origins-destinations
n = 5
nodes = np.array(G.nodes)
origs = np.random.choice(nodes, size=n, replace=True)
dests = np.random.choice(nodes, size=n, replace=True)
paths1 = ox.shortest_path(G, origs, dests, weight="length", cpus=1)
paths2 = ox.shortest_path(G, origs, dests, weight="length", cpus=2)
paths3 = ox.shortest_path(G, origs, dests, weight="length", cpus=None)
assert paths1 == paths2 == paths3
# test k shortest paths
routes = ox.k_shortest_paths(G,
orig_node,
dest_node,
k=2,
weight="travel_time")
fig, ax = ox.plot_graph_routes(G, list(routes))
# test folium with keyword arguments to pass to folium.PolyLine
gm = ox.plot_graph_folium(G,
popup_attribute="name",
color="#333333",
weight=5,
opacity=0.7)
rm = ox.plot_route_folium(G, route, color="#cc0000", weight=5, opacity=0.7)
# test calling folium plotters with FeatureGroup instead of Map, and extra kwargs
fg = folium.FeatureGroup(name="legend name", show=True)
gm = ox.plot_graph_folium(G, graph_map=fg)
assert isinstance(gm, folium.FeatureGroup)
rm = ox.plot_route_folium(G, route, route_map=fg, tooltip="x")
assert isinstance(rm, folium.FeatureGroup)
def test_routing():
G = ox.graph_from_address(address=address,
dist=500,
dist_type="bbox",
network_type="bike")
# give each node a random elevation then calculate edge grades
randm = np.random.random(size=len(G))
elevs = {n: e for n, e in zip(G.nodes(), randm)}
nx.set_node_attributes(G, name="elevation", values=elevs)
G = ox.add_edge_grades(G, add_absolute=True)
# give each edge speed and travel time attributes
G = ox.add_edge_speeds(G)
G = ox.add_edge_speeds(G, hwy_speeds={"motorway": 100})
G = ox.add_edge_travel_times(G)
orig_node = list(G.nodes())[5]
dest_node = list(G.nodes())[-5]
orig_pt = (G.nodes[orig_node]["y"], G.nodes[orig_node]["x"])
dest_pt = (G.nodes[dest_node]["y"], G.nodes[dest_node]["x"])
route = ox.shortest_path(G, orig_node, dest_node, weight="travel_time")
attributes = ox.utils_graph.get_route_edge_attributes(G, route)
attributes = ox.utils_graph.get_route_edge_attributes(
G, route, "travel_time")
fig, ax = ox.plot_graph_route(G, route, save=True)
fig, ax = ox.plot_graph_route(G, route, save=True)
# test multiple routes
routes = ox.k_shortest_paths(G,
orig_node,
dest_node,
k=2,
weight="travel_time")
fig, ax = ox.plot_graph_routes(G, list(routes))
# test folium with keyword arguments to pass to folium.PolyLine
gm = ox.plot_graph_folium(G,
popup_attribute="name",
color="#333333",
weight=5,
opacity=0.7)
rm = ox.plot_route_folium(G, route, color="#cc0000", weight=5, opacity=0.7)
# test calling folium plotters with FeatureGroup instead of Map, and extra kwargs
fg = folium.FeatureGroup(name="legend name", show=True)
gm = ox.plot_graph_folium(G, graph_map=fg)
assert isinstance(gm, folium.FeatureGroup)
rm = ox.plot_route_folium(G, route, route_map=fg, tooltip="x")
assert isinstance(rm, folium.FeatureGroup)
def test_routing_folium():
import networkx as nx
with httmock.HTTMock(
get_mock_response_content('overpass-response-3.json.gz')):
G = ox.graph_from_address('398 N. Sicily Pl., Chandler, Arizona',
distance=800,
network_type='drive')
origin = (33.307792, -111.894940)
destination = (33.312994, -111.894998)
origin_node = ox.get_nearest_node(G, origin)
destination_node = ox.get_nearest_node(G, destination)
route = nx.shortest_path(G, origin_node, destination_node)
attributes = ox.get_route_edge_attributes(G, route, 'length')
fig, ax = ox.plot_graph_route(G,
route,
save=True,
filename='route',
file_format='png')
fig, ax = ox.plot_graph_route(G,
route,
origin_point=origin,
destination_point=destination,
save=True,
filename='route',
file_format='png')
graph_map = ox.plot_graph_folium(G, popup_attribute='name')
route_map = ox.plot_route_folium(G, route)
def test_routing_folium():
# calculate shortest path and plot as static image and leaflet web map
import networkx as nx
G = ox.graph_from_address('398 N. Sicily Pl., Chandler, Arizona',
distance=800,
network_type='drive')
origin = (33.307792, -111.894940)
destination = (33.312994, -111.894998)
origin_node = ox.get_nearest_node(G, origin)
destination_node = ox.get_nearest_node(G, destination, method='euclidean')
route = nx.shortest_path(G, origin_node, destination_node)
attributes = ox.get_route_edge_attributes(G, route, 'length')
fig, ax = ox.plot_graph_route(G,
route,
save=True,
filename='route',
file_format='png')
fig, ax = ox.plot_graph_route(G,
route,
origin_point=origin,
destination_point=destination,
save=True,
filename='route',
file_format='png')
graph_map = ox.plot_graph_folium(G, popup_attribute='name')
route_map = ox.plot_route_folium(G, route)
def test_routing_folium():
import networkx as nx
G = ox.graph_from_address('N. Sicily Pl., Chandler, Arizona',
distance=800,
network_type='drive')
origin = (33.307792, -111.894940)
destination = (33.312994, -111.894998)
origin_node = ox.get_nearest_node(G, origin)
destination_node = ox.get_nearest_node(G, destination)
route = nx.shortest_path(G, origin_node, destination_node)
fig, ax = ox.plot_graph_route(G,
route,
save=True,
filename='route',
file_format='png')
fig, ax = ox.plot_graph_route(G,
route,
origin_point=origin,
destination_point=destination,
save=True,
filename='route',
file_format='png')
graph_map = ox.plot_graph_folium(G, popup_attribute='name')
route_map = ox.plot_route_folium(G, route)
def draw_map(G, highlight = None , zoom = 16):
""" draws ipyleaflet map with location as center of the map """
if len(G) >= 1000:
print(f"The graph has {len(G)} which is a lot, we will use basic faster folium instead")
if highlight:
center_osmid = ox.stats.extended_stats(G,ecc=True)['center'][0]
G_gdfs = ox.graph_to_gdfs(G)
nodes_frame = G_gdfs[0]
ways_frame = G_gdfs[1]
m = ox.plot_graph_folium(G = G)
for node_osmid in highlight:
node = nodes_frame.loc[node_osmid]
node_xy = [node['y'], node['x']]
fl.Marker(node_xy).add_to(m)
else:
m = ox.plot_graph_folium(G = G)
return m
center_osmid = ox.stats.extended_stats(G,ecc=True)['center'][0]
G_gdfs = ox.graph_to_gdfs(G)
nodes_frame = G_gdfs[0]
ways_frame = G_gdfs[1]
center_node = nodes_frame.loc[center_osmid]
location = (center_node['y'], center_node['x'])
m = lf.Map(center = location, zoom = zoom)
for _, row in ways_frame.iterrows():
lines = lf.Polyline(
locations = [list(elem)[::-1] for elem in [*row['geometry'].coords]],
color = "black",
fill = False,
weight = 1
)
m.add_layer(lines)
# if we want to mark some specific nodes
if highlight:
for node_osmid in highlight:
node = nodes_frame.loc[node_osmid]
node_xy = (node['y'], node['x'])
marker = lf.Marker(location = node_xy, draggable = False)
m.add_layer(marker)
return m
def buildHTMLWithNetworkx(G):
try:
graphMap = ox.plot_graph_folium(G, edge_width=2)
except (ValueError, KeyError):
raise OsmnxException(
"Probably there are elements without all its nodes or the response is empty. "
"It is not possible to show the results but you can use the option 'Open netedit'."
)
graphMap.save(tilePath)
logging.info("Html built.")
return graphMap.get_root().render()
def get_plot(address):
orig_results, true_results, to_remove, best_improvement, G = get_removable_streets(
address)
cols = vals_to_colors([(orig_results[node_str(node_a, node_b)] if node_str(
node_a, node_b) in orig_results else 0)
for (node_a, node_b) in G.edges()])
for i, (node_a, node_b) in enumerate(G.edges()):
if node_str(node_a, node_b) in to_remove:
cols[i] = '#73db67'
gdf_edges = ox.graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)
gdf_edges['edge_color'] = cols
return ox.plot_graph_folium(gdf_edges), best_improvement
def make_updated_graph_model():
ox.config(log_console=True, use_cache=True)
G_2 = ox.load_graphml('osmnx_graph.graphml')
# plot the street network with folium
graph_map = ox.plot_graph_folium(G_2, edge_width=2)
filepath = 'templates/graph.html'
graph_map.save(filepath)
IFrame(filepath, width=600, height=500)
soup = BeautifulSoup(open('templates/graph.html'), 'html.parser')
js_tag = soup.find_all("script")
js_tag[5].append('{% block script %} {% endblock %}')
with open("templates/graph.html", "w") as file:
file.write(str(soup))
soup.find("div")
div = soup.find("div")
div = str(div).split()
map_id = div[2][4:-8]
fin = open("templates/graph.html", "rt")
# output file to write the result to
fout = open("templates/final_graph.html", "wt")
# for each line in the input file
for line in fin:
# read replace the string and write to output file
fout.write(line.replace(map_id, 'map_id'))
# close input and output files
fin.close()
fout.close()
soup = BeautifulSoup(open('templates/final_graph.html'), 'html.parser')
data_body = '{% block body %} {% endblock %}'
data_script = '{% block script %} {% endblock %}'
data_style = '{% block style %} {% endblock %}'
soup.body.append(data_body)
soup.style.append(data_style)
with open("templates/final_graph.html", "w") as file:
file.write(str(soup))
def mergeNplot(optimal_path, locations):
lat = lon = 0
# Finding the center of all locations, it's purpose is for generating the map
for location in locations:
lat += location[0]
lon += location[1]
lat = lat / len(locations)
lon = lon / len(locations)
mid_point = (lat, lon)
# Calculating the distace from each location to mid_point
distances = [distance.distance(x, mid_point).meters for x in locations]
dist = max(distances) + 200
#Generating the graph
graph = ox.graph_from_point(mid_point, dist)
graph_map = ox.plot_graph_folium(graph,
popup_attribute='name',
edge_width=0)
# Generating the route according to the optimal order of the locations
routes = []
for i in range(len(optimal_path) - 1):
l1 = locations[optimal_path[i]]
l2 = locations[optimal_path[i + 1]]
orig_node = ox.get_nearest_node(graph, l1)
dest_node = ox.get_nearest_node(graph, l2)
route = nx.shortest_path(graph,
source=orig_node,
target=dest_node,
weight='length')
route_graph_map = ox.plot_route_folium(graph,
route,
route_map=graph_map)
routes.append(route)
# Markers addition on the places locations.
i = 0
for location in optimal_path:
folium.Marker(location=locations[location],
popup=i,
icon=folium.Icon(color='blue')).add_to(graph_map)
i += 1
graph_map.save('tsp_graph.html')
def make_folium(self, **kwargs):
# filename = kwargs.get('filename','folium_output_.html')
mapboxtoken = 'pk.eyJ1Ijoid2FsdGVyOTM4OCIsImEiOiJjazhzbmhocGUwMWEyM25uZDd1Z3hwYjA4In0.5JWjuw2ZyuHVzxnvNx1cfQ'
# username_id='walter9388.ck8snlelx2cep1intbso64rza'
username_id = 'mapbox.dark'
self.G.graph = {
'crs': 'WGS84',
'name': 'unnamed',
}
map = folium.Map(
location=[0, 0],
zoom_start=12,
tiles=None,
)
folium.TileLayer(
'http://api.mapbox.com/v4/' + username_id +
'/{z}/{x}/{y}.png?access_token=' + mapboxtoken,
attr=
'Mapbox | Source: InfraSense Labs | Written by Alex Waldron, 2020',
name='Base Map',
).add_to(map)
network_fg = folium.FeatureGroup(name='Distribution Mains')
map.add_child(network_fg)
graph_map = ox.plot_graph_folium(
self.G,
edge_width=1,
edge_color='#FFFFFF',
popup_attribute='name',
# graph_map=map,
)
temp = list(graph_map._children.keys())[1:-1]
[
graph_map._children[temp[i]].add_to(network_fg)
for i in range(len(temp))
]
map.fit_bounds(map.get_bounds())
folium.LayerControl().add_to(map)
self.folium_map = map
def plot_graph(trajectories):
# 求出所有gps点的中心点坐标，获取中心点坐标周围的地图数据
# TODO 利用所有点之间的最大距离作为dist
position_list = [v['positions'] for v in list(trajectories.values())]
all_points = np.concatenate(position_list)
G = ox.graph_from_point(all_points[0], dist=1000)
# ox.plot_graph(G)
# plt.show()
m1 = ox.plot_graph_folium(G, opacity=0)
# 所有轨迹绘制，坐标点，轨迹线路，终点起点等
for track_id, track_values in trajectories.items():
# 去掉异常轨迹点
drop_anormal_points_result = drop_anormal_point(
track_id, track_values, max_speed_threshold=150)
# 去掉停驻轨迹点
remove_stop_points_result, stop_points_info, around_points_info = remove_stop_points(
track_id,
drop_anormal_points_result[track_id],
min_distance_threshold=1e-5,
min_delta_dist=0.5,
min_delta_time=0.8)
# 绘制停留点
# plot_stop_marker(stop_points_info, around_points_info, m1)
# 进行轨迹分段
segments_result = get_trajectory_segments(
remove_stop_points_result[track_id],
segment_angle=90,
segment_distance=0.5)
segments, segments_time = segments_result["segments"], segments_result[
"segments_time"]
# 绘制分段结果
plot_segments(segments, m1, track_id)
# 绘制完整轨迹
# plot_trajectory(remove_stop_points_result[track_id]['positions'], m1, track_id)
filepath = "gps_data_visualize.html"
m1.save(filepath)
def test_routing_folium():
# calculate shortest path and plot as static image and leaflet web map
import networkx as nx
G = ox.graph_from_address('398 N. Sicily Pl., Chandler, Arizona', distance=800, network_type='drive')
origin = (33.307792, -111.894940)
destination = (33.312994, -111.894998)
origin_node = ox.get_nearest_node(G, origin)
destination_node = ox.get_nearest_node(G, destination, method='euclidean')
route = nx.shortest_path(G, origin_node, destination_node)
attributes = ox.get_route_edge_attributes(G, route, 'length')
fig, ax = ox.plot_graph_route(G, route, save=True, filename='route', file_format='png')
fig, ax = ox.plot_graph_route(G, route, origin_point=origin, destination_point=destination,
save=True, filename='route', file_format='png')
# test multiple routes
fig, ax = ox.plot_graph_routes(G, [route, route])
graph_map = ox.plot_graph_folium(G, popup_attribute='name')
route_map = ox.plot_route_folium(G, route)
def plot_corner():
"""
可视化预测可能存在路口的网格点。
:return: None
"""
file_name = "./cache/grid_point_struct_with_labels.pkl"
if not os.path.exists(file_name):
raise IOError('File %s is not found!' % file_name)
with open(file_name, 'rb') as f:
grid_point_struct_with_labels = pickle.load(f)
left_up_point, right_down_point, grid_size = (-10.899355, -37.096252), (
-10.927514, -37.043267), 100
G = ox.graph_from_point(left_up_point, dist=1000)
m1 = ox.plot_graph_folium(G, opacity=0)
plot_points_with_cluster_label(grid_point_struct_with_labels, m1,
left_up_point, grid_size)
filepath = "test.html"
m1.save(filepath)
def test_routing_folium():
# calculate shortest path and plot as static image and leaflet web map
G = ox.graph_from_address(address=address, dist=500, dist_type="bbox", network_type="bike")
# give each node a random elevation then calculate edge grades
randm = np.random.random(size=len(G.nodes()))
elevs = {n: e for n, e in zip(G.nodes(), randm)}
nx.set_node_attributes(G, name="elevation", values=elevs)
G = ox.add_edge_grades(G, add_absolute=True)
orig_node = list(G.nodes())[5]
dest_node = list(G.nodes())[-5]
orig_pt = (G.nodes[orig_node]["y"], G.nodes[orig_node]["x"])
dest_pt = (G.nodes[dest_node]["y"], G.nodes[dest_node]["x"])
route = nx.shortest_path(G, orig_node, dest_node)
attributes = ox.utils_graph.get_route_edge_attributes(G, route, "length")
fig, ax = ox.plot_graph_route(G, route, save=True, filename="route", file_format="png")
fig, ax = ox.plot_graph_route(
G,
route,
origin_point=orig_pt,
destination_point=dest_pt,
save=True,
filename="route",
file_format="png",
)
# test multiple routes
fig, ax = ox.plot_graph_routes(G, [route, route])
graph_map = ox.plot_graph_folium(G, popup_attribute="name")
route_map = ox.plot_route_folium(G, route)
def degreeCentrality(G, outPath, *args):
"""
Compute degree centrality of a node and save the output and road network
data in shapefile format.
Parameters
----------
G: graph
A networkx graph
outpath: string
absolute path to save the output of analysis
*args: string
path to save the output of analysis in html webmap format
Returns
-------
################################################################
"""
G_proj = nx.MultiGraph(ox.project_graph(G))
nodes, edges = ox.graph_to_gdfs(G_proj)
nodes["x"] = nodes["x"].astype(float)
degree_centrality = nx.degree_centrality(G_proj)
degree = gpd.GeoDataFrame(pd.Series(degree_centrality), columns=["degree"])
nodes_dgr = nodes.merge(degree, left_index=True, right_index=True)
argument = [i for i in args]
if any(argument):
linear = cm.LinearColormap(["blue", "yellow", "red"], vmin=0, vmax=5)
nodes_dgr['geoid'] = nodes_dgr.index.astype(str)
lat = list(nodes_dgr["lat"])
lon = list(nodes_dgr["lon"])
dgr = list(nodes_dgr["degree"])
name = list(nodes_dgr["osmid"])
m = ox.plot_graph_folium(G, edge_width=1.5)
fgdgr = folium.FeatureGroup(name="Degrees of Nodes")
classifier = pysal.viz.mapclassify.NaturalBreaks.make(k=5)
classifications = nodes_dgr[["degree"]].apply(classifier)
classifications.columns = ["class"]
nodes_dgr = nodes_dgr.join(classifications)
for lt, ln, dg, nm, cl in zip(lat, lon, dgr, name,
list(nodes_dgr["class"])):
fgdgr.add_child(
folium.CircleMarker(location=[lt, ln],
popup='Degree Centrality of the {}. \
node is {:.5f}'.format(str(nm), dg),
radius=4,
fill_color=linear(cl),
color="grey",
fill_opacity=1,
weight=0.5))
# fgavg.add_child(marker_cluster)
m.add_child(fgdgr)
# m.add_child(linear)
m.save(argument[0])
ox.save_graph_shapefile(G_proj, filename="network", folder=outPath)
nodes_dgr.to_file(outPath + "/degree_centrality.shp")
return "I"
else:
ox.save_graph_shapefile(G_proj, filename="network", folder=outPath)
nodes_dgr.to_file(outPath + "/degree_centrality.shp")
return "G"
import osmnx as ox
import folium
import pickle
G = ox.graph_from_address('Inner Harbor, Baltimore, Maryland, USA', network_type='drive')
pickle.dump(G, open('harbor.pkl', 'wb'))
graph_map = ox.plot_graph_folium(G, popup_attribute='name', edge_width=2)
graph_map.save('harbor.html')
import folium
from api_keys import here_app_id, here_app_code, google_api_key
%load_ext autoreload
%autoreload
streets_w = get_streets('Lörrach, Germany', 'walk')
city_name = 'Lörrach, Germany'
street_type = 'drive'
city_boundary = ox.gdf_from_place(city_name).geometry.iloc[0]
drive_net = ox.graph_from_polygon(city_boundary, network_type=street_type)
# plot strees on folium
graph_folium = ox.plot_graph_folium(drive_net, popup_attribute='highway')
graph_folium.save('map.html')
drive_gpd = ox.graph_to_gdfs(drive_net, nodes=True, edges=False,
fill_edge_geometry=False, node_geometry=True)
drive_gpd_sub = drive_gpd
roads_json = drive_gpd_sub.to_json()
points = folium.features.GeoJson(roads_json)
m = folium.Map([47.6120896, 7.6607218])
m.add_children(points)
m.save('map.html')
streets_w.plot()
streets_w.size
def graphRoute(route, ax=None):
"""check route displaying a graph"""
G = nx.MultiDiGraph()
G.graph['crs'] = {'init': 'epsg:4326'}
G.graph['name'] = "local network"
colorL = [
'black', 'blue', 'red', 'green', 'brown', 'orange', 'purple',
'magenta', 'olive', 'maroon', 'steelblue', 'midnightblue',
'darkslategrey', 'crimson', 'teal', 'darkolivegreen'
]
colorL = colorL + colorL
for i, g in route.iterrows():
G.add_node(i,
pos=(g[['x', 'y']]),
x=g['x'],
y=g['y'],
key=0,
potential=g['potential'],
active=g['active'],
n=g['occupancy'])
for k, g in route.groupby("agent"):
if k == 0: continue
idx = list(g.index)
if len(idx) < 2: continue
pair_idx = [(i, j) for i, j in zip(idx[:-1], idx[1:])]
for i, j in pair_idx:
g1, g2 = route.loc[i], route.loc[j]
i1, i2 = g1['geohash'], g2['geohash']
dist = math.hypot(g1['x'] - g2['x'], g1['y'] - g2['y'])
G.add_edge(i,
j,
key=k,
origin=i1,
destination=i2,
length=dist,
width=1,
speed=1,
color=colorL[k])
G = ox.project_graph(G)
colorL = [cm.get_cmap('plasma')(x) for x in np.linspace(0, 1, 6)]
nc = ['blue' if G.nodes()[x]['active'] else 'red' for x in G.nodes()]
nbins = min(20, len(set(route['potential'])))
cmap = matplotlib.cm.get_cmap('plasma')
max_pot = np.percentile(route['potential'], 95)
nc = [cmap(x['potential'] / max_pot) for i, x in G.nodes(data=True)]
# nc = ox.get_node_colors_by_attr(G,'potential',cmap='plasma',num_bins=nbins-2)
ns = [20. * x['n'] for i, x in G.nodes(data=True)]
# ec = ox.get_edge_colors_by_attr(G, attr='length')
ec = [o['color'] for u, v, k, o in G.edges(data=True, keys=True)]
# if len(ec) == 0: return plt.subplots(1,1)
ew = [200. * o['length'] for u, v, k, o in G.edges(data=True, keys=True)]
pos, lab = {}, {}
for i, x in G.nodes(data=True):
pos[i] = [x['lon'], x['lat']]
for i, x in G.nodes(data=True):
lab[i] = i
# nx.draw_networkx_edge_labels(G,pos,edge_colors=ec)
if ax == None:
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 6)
# nx.draw(G,pos,node_color=nc,node_size=ns,edge_color=ec,edge_cmap=plt.cm.Reds)
nx.draw_networkx_nodes(G,
pos,
node_color=nc,
node_size=ns,
alpha=0.1,
ax=ax)
# nx.draw_networkx_labels(G,pos,lab,font_size=8,ax=ax,alpha=0.3)
nx.draw_networkx_edges(G,
pos,
width=ew,
alpha=0.5,
edge_color=ec,
ax=ax,
edge_cmap=plt.cm.Reds)
ax.set_axis_off()
ax.set_xlim(np.percentile(route['x'], [1, 99]))
ax.set_ylim(np.percentile(route['y'], [1, 99]))
return ax
if False:
gdf_edges = ox.graph_to_gdfs(G, nodes=False, fill_edge_geometry=True)
graph_map = ox.plot_graph_folium(G,
tiles='stamenterrain',
edge_color=ec,
edge_width=2)
origin_node = list(G.nodes())[0]
destination_node = list(G.nodes())[-1]
route = nx.shortest_path(G, origin_node, destination_node)
route_map = ox.plot_route_folium(G, route)
graph_map.save(baseDir + "www/gis/folium.html")
ox.make_folium_polyline(gdf_edges, ew, ew, popup_attribute=None)
plot_graph_folium(gdf_edges,
graph_map=None,
popup_attribute=None,
tiles='cartodbpositron',
zoom=1,
fit_bounds=True,
edge_width=5,
edge_opacity=1)
return fig, ax
def plot_corner_by_cluster(trajectories):
"""
根据轨迹信息挖掘出路口点。
:param trajectories: 轨迹信息
:return: None
"""
position_list = [v['positions'] for v in list(trajectories.values())]
all_points = np.concatenate(position_list)
G = ox.graph_from_point(all_points[0], dist=1000)
m1 = ox.plot_graph_folium(G, opacity=0)
all_segments, all_segments_time = [], []
# 得到所有轨迹的分段结果
for track_id, track_values in trajectories.items():
# 去掉异常轨迹点
drop_anormal_points_result = drop_anormal_point(
track_id, track_values, max_speed_threshold=150)
# 去掉停驻轨迹点
remove_stop_points_result, stop_points_info, around_points_info = remove_stop_points(
track_id,
drop_anormal_points_result[track_id],
min_distance_threshold=1e-5,
min_delta_dist=0.5,
min_delta_time=0.8)
# 进行轨迹分段
segments_result = get_trajectory_segments(
remove_stop_points_result[track_id],
segment_angle=90,
segment_distance=0.5)
segments, segments_time = segments_result["segments"], segments_result[
"segments_time"]
all_segments.extend(segments)
all_segments_time.extend(segments_time)
# 绘制分段结果
# plot_segments(segments, m1, track_id)
# 得到带有速度矢量的点集
points_with_velocity = get_points_with_velocity(all_segments,
all_segments_time)
# plot_points_with_velocity(points_with_velocity, m1)
# 绘制指定区域网格
left_up_point, right_down_point, grid_size = (-10.899355, -37.096252), (
-10.927514, -37.043267), 100
plot_grid(left_up_point, right_down_point, grid_size, m1)
# 按每个网格一个颜色绘制路径点
grid_point_struct = get_grid_point_struct(
left_up_point,
right_down_point,
grid_size=grid_size,
points_with_velocity=points_with_velocity)
if not os.path.exists('./cache/grid_point_struct.pkl'):
with open('./cache/grid_point_struct.pkl', 'wb') as f:
pickle.dump(grid_point_struct, f)
# plot_points_in_which_grid(grid_point_struct, m1)
# 按照轨迹点聚类结果进行绘制
grid_points_struct_with_labels = cluster_grid_points(grid_point_struct)
if not os.path.exists('./cache/grid_point_struct_with_labels.pkl'):
with open('./cache/grid_point_struct_with_labels.pkl', 'wb') as f:
pickle.dump(grid_points_struct_with_labels, f)
plot_points_with_cluster_label(grid_points_struct_with_labels, m1,
left_up_point, grid_size)
filepath = "find_corner.html"
m1.save(filepath)
def plot_html(track,
track_corr=[],
track_color="black",
track_corr_color="#CD473E",
track_size=2,
track_corr_size=2,
route_corr=[],
route_size=2,
route_corr_size=2,
route_color="black",
route_corr_color="#CD473E",
route_opacity=.7,
route_corr_opacity=.7,
proj=False,
proj_color="#CD473E",
proj_size=1,
proj_alpha=.5,
show_graph=False,
graph=None,
file_name="my_map.html",
save=True):
"""
Parameters
----------
track : TYPE
DESCRIPTION.
track_corr : TYPE, optional
DESCRIPTION. The default is [].
track_color : TYPE, optional
DESCRIPTION. The default is "black".
track_corr_color : TYPE, optional
DESCRIPTION. The default is "darkcyan".
track_size : TYPE, optional
DESCRIPTION. The default is 2.
track_corr_size : TYPE, optional
DESCRIPTION. The default is 2.
route_corr : TYPE, optional
DESCRIPTION. The default is [].
route_size : TYPE, optional
DESCRIPTION. The default is 2.
route_corr_size : TYPE, optional
DESCRIPTION. The default is 2.
route_color : TYPE, optional
DESCRIPTION. The default is "black".
route_corr_color : TYPE, optional
DESCRIPTION. The default is "darkcyan".
route_opacity : TYPE, optional
DESCRIPTION. The default is .6.
route_corr_opacity : TYPE, optional
DESCRIPTION. The default is .6.
proj : TYPE, optional
DESCRIPTION. The default is False.
proj_color : TYPE, optional
DESCRIPTION. The default is "skyblue".
proj_size : TYPE, optional
DESCRIPTION. The default is 1.
proj_alpha : TYPE, optional
DESCRIPTION. The default is 1.
show_graph : TYPE, optional
DESCRIPTION. The default is False.
graph : TYPE, optional
DESCRIPTION. The default is None.
file_name : TYPE, optional
DESCRIPTION. The default is "my_map.html".
save : TYPE, optional
DESCRIPTION. The default is True.
Returns
-------
my_map : TYPE
DESCRIPTION.
"""
med_lat = track[len(track) // 2][0]
med_lon = track[len(track) // 2][1]
# Load map centred on central coordinates
my_map = folium.Map(location=[med_lat, med_lon], zoom_start=20)
if show_graph or graph is not None:
if graph is None:
graph = matching.graph_from_track(track, network='drive')
my_map = ox.plot_graph_folium(graph,
popup_attribute='name',
edge_width=1,
edge_color='darkgrey')
if proj:
for i in range(len(track)):
folium.PolyLine([(track[i][0], track[i][1]),
(track_corr[i][0], track_corr[i][1])],
color=proj_color,
weight=proj_size,
opacity=proj_alpha).add_to(my_map)
# If the route is given in input, plot both (original and corrected)
if len(route_corr) > 0:
# add lines
folium.PolyLine(track,
color=route_color,
weight=route_size,
opacity=route_opacity).add_to(my_map)
folium.PolyLine(route_corr,
color=route_corr_color,
weight=route_corr_size,
opacity=route_corr_opacity).add_to(my_map)
# add dots
for i in range(len(track)):
folium.CircleMarker(location=[track[i][0], track[i][1]],
radius=track_size,
weight=1,
color=track_color,
fill=True,
fill_opacity=1).add_to(my_map)
for i in range(len(track_corr)):
folium.CircleMarker(location=[track_corr[i][0], track_corr[i][1]],
radius=track_corr_size,
weight=1,
color=track_corr_color,
fill=True,
fill_opacity=1).add_to(my_map)
# add the OSM light grey background
folium.TileLayer('cartodbpositron').add_to(my_map)
# plot the legend in the HTML page
legend_html = """
<div style="position: fixed;
width: 210px;
top: 10px; right: 10px;
border: 2px solid lightgrey;
border-radius: 4px;
background-color: rgba(255, 255, 255, 0.85);
z-index:9999;
font-size: 15px; color: slategrey;
">
<span style="font-weight: bold">Legend</span>
<br>
Original Point
<i class="fa fa-circle"
style="float: right;
margin-right: 19px; margin-top: 4px;
color: black">
</i>
<br>
Projected Point
<i class="fa fa-circle"
style="float: right;
margin-right: 19px; margin-top: 4px;
color: #CD473E">
</i>
<br>
Projection
<div class="line"
style="float: right;
margin-right: 10px; margin-top: 10px;
width: 30px; height: 2px;
background-color: #CD473E">
</div>
</div>
"""
my_map.get_root().html.add_child(folium.Element(legend_html))
if save:
my_map.save(file_name)
# Plot in new tab
webbrowser.open(file_name, new=2) # open in new tab
return my_map
def submit(self):
city = str(self.ui.comboCity.currentText())
month = str(self.ui.comboMonth.currentText())
day = str(self.ui.comboDay.currentText())
city1, month1 = get_filters(city, month)
global df, df5, df6, gender_mean, female_mean, male_mean, user_subs, user_cust, user
global ut_subs, ut_cust, ut_subs_fem, ut_subs_male, ut_cust_fem, ut_cust_male
df, df5 = load_data(city1, month1, day)
try:
now = datetime.datetime.now()
df['age'] = now.year - df['Birth Year']
except:
df['age'] = 0
pass
try:
female_mean = df[df['Gender'] == 'Female'].groupby(
'Start Station').mean()
female_mean['count'] = df[df['Gender'] == 'Female'].groupby(
'Start Station')['Id'].count()
female_mean.drop(columns=[
'latitude_start', 'longitude_start', 'latitude_end',
'longitude_end'
],
inplace=True)
male_mean = df[df['Gender'] == 'Male'].groupby(
'Start Station').mean()
male_mean['count'] = df[df['Gender'] == 'Male'].groupby(
'Start Station')['Id'].count()
gender_mean = male_mean.merge(female_mean,
how='outer',
left_index=True,
right_index=True,
suffixes=(' male', ' female'))
gender_mean['count total'] = gender_mean[
'count female'] + gender_mean['count male']
gender_mean['female percent'] = (gender_mean['count female'] /
gender_mean['count total']) * 100
except:
pass
try:
user_subs = df[df['User Type'] == 'Subscriber'].groupby(
'Start Station').mean()
user_subs['count'] = df[df['User Type'] == 'Subscriber'].groupby(
'Start Station')['Id'].count()
user_cust = df[df['User Type'] == 'Customer'].groupby(
'Start Station').mean()
user_cust['count'] = df[df['User Type'] == 'Customer'].groupby(
'Start Station')['Id'].count()
user_cust.drop(columns=[
'latitude_start', 'longitude_start', 'latitude_end',
'longitude_end'
],
inplace=True)
user = user_subs.merge(user_cust,
how='outer',
left_index=True,
right_index=True,
suffixes=(' subs', ' cust'))
user.dropna(subset=['latitude_start'], inplace=True)
user['count subs'] = user['count subs'].fillna(0)
user['count cust'] = user['count cust'].fillna(0)
user['count total'] = user['count subs'] + user['count cust']
user['subs percent'] = (user['count subs'] /
user['count total']) * 100
except:
pass
# Statistics
commonMonth, commonDay, commonHour = time_stats(df, city1, month1, day)
commonStartStation, commonEndStation, G, route, dataFr, distRec = station_stats(
df)
ttt, ttm = trip_duration_stats(df)
ut_subs, ut_cust, ut_subs_fem, ut_subs_male, ut_cust_fem, ut_cust_male = user_stats(
df)
df6 = dfStats(df, df5, dataFr)
tstat = []
tstat.append('Statistics on the most frequent times of travel')
tstat.append('The Most Common Month : ' + str(commonMonth))
tstat.append('The Most Common Day : ' + str(commonDay))
tstat.append('The Most Common Hour : ' + str(commonHour))
tstat.append('\nStatistics on the most popular stations and trip')
tstat.append('The Most Common Used Start Station : ' +
str(commonStartStation))
tstat.append('The Most Common Used End Station : ' +
str(commonEndStation))
tstat.append('\nStatistics on the most popular combination stations')
tstat.append('The Most Common Combination of Start-End Station : ' +
str(dataFr['Start Station'].iloc[0]) + ' and ' +
str(dataFr['End Station'].iloc[0]))
tstat.append(
'The Most Common Combination of Start-End Station Track (km): ' +
str(distRec[0]))
tstat.append(
'The Most Common Combination of Start-End Station Lineal Distance (km): '
+ str(dataFr['Dist_lin_km'].iloc[0]))
tstat.append('\nStatistics on the total and average trip duration')
tstat.append('The total travel time : ' + str(ttt))
tstat.append('The mean travel time : ' + str(ttm))
tstat.append('\nDisplays statistics on bikeshare users and gender')
tstat.append('The counts of user types "Subscriber" : ' +
str(len(ut_subs)))
tstat.append('The counts of user types "Customer" : ' +
str(len(ut_cust)))
try:
tstat.append(
'The counts of user types "Subscriber" and Gender "Female": ' +
str(len(ut_subs_fem)))
tstat.append(
'The counts of user types "Subscriber" and Gender "Male": ' +
str(len(ut_subs_male)))
tstat.append(
'The counts of user types "Customer" and Gender "Female": ' +
str(len(ut_cust_fem)))
tstat.append(
'The counts of user types "Customer" and Gender "Male": ' +
str(len(ut_cust_male)))
except:
pass
try:
tstat.append(
'\nDisplay earliest, most recent, and most common year of birth'
)
tstat.append('The earliest year of birt: ' +
str(int(df['Birth Year'].min())))
tstat.append('The most recent year of birt: ' +
str(int(df['Birth Year'].max())))
tstat.append('The most common year of birt: ' +
str(int(df['Birth Year'].mode())))
except:
pass
self.ui.textEdit.setText("\n".join(tstat))
# Route Map
graph_map = ox.plot_graph_folium(G,
popup_attribute='name',
edge_width=2,
edge_color="#85d3de",
zoom=8)
try:
route_graph_map = ox.plot_route_folium(G,
route,
route_map=graph_map,
popup_attribute='length',
zoom=8)
except:
route_graph_map = graph_map
locations = df6[['latitude', 'longitude']]
locationlist = locations.values.tolist()
for point in range(0, len(df6)):
Marker(locationlist[point],
popup=df6[['Station', 'name'
]].iloc[point].values).add_to(route_graph_map)
filepath = 'CommonStation_ShortestPath.html'
route_graph_map.save(filepath)
w = self.ui.webEngineView_1
w.resize(740, 540)
self.url = QtCore.QUrl.fromLocalFile(str(pathname) + '/' + filepath)
w.load(self.url)
w.show()
##Bike Stations
maps = Map([df5['latitude'].mean(), df5['longitude'].mean()],
zoom_start=10,
control_scale=True,
tiles="OpenStreetMap")
locations2 = df5[['latitude', 'longitude']]
locationlist2 = locations2.values.tolist()
group2 = FeatureGroup(name="Bike Stations")
for point in range(0, len(locationlist2)):
Marker(locationlist2[point],
popup=df5[['Station', 'latitude',
'longitude']].iloc[point].values).add_to(group2)
group2.add_to(maps)
LayerControl().add_to(maps)
data = io.BytesIO()
maps.save(data, close_file=False)
m = self.ui.webEngineView_2
m.setHtml(data.getvalue().decode())
m.resize(740, 540)
maps.save("BikeStations.html")
m.show()
maps.save(data, close_file=True)
# initial map tab
maps = Map([df5['latitude'].mean(), df5['longitude'].mean()],
zoom_start=10,
control_scale=True,
tiles="OpenStreetMap")
LayerControl().add_to(maps)
data = io.BytesIO()
maps.save(data, close_file=False)
m = self.ui.webEngineView_3
m.setHtml(data.getvalue().decode())
m.resize(740, 520)
m.show()
maps.save(data, close_file=True)
try:
# initial Graphics
self.figure.clear()
labels = 'Male', 'Female'
sizes = [
len(df[df['Gender'] == 'Male']),
len(df[df['Gender'] == 'Female'])
]
explode = (0, 0.1)
ax1 = self.figure.add_subplot(111)
ax1.pie(sizes,
explode=explode,
labels=labels,
autopct='%1.1f%%',
startangle=90)
ax1.axis('equal')
self.canvas.draw()
self.show
except:
pass
# setting value to progress bar
for i in range(101):
time.sleep(0.04)
self.ui.progressBar.setValue(i)