def get_footway_data(x_data, city_data):
"""
Get footway data if applicable for the intersection and crop within the radius.
:param x_data: dictionary
:param city_data: dictionary
:return: list of railway paths
"""
nodes_dict = city_data['nodes']
footway_jsons = get_box_data(x_data,
city_data['raw_data'],
network_type='all')
footway_paths = [
e for e in footway_jsons[0]['elements'] if e['type'] == 'way'
and 'highway' in e['tags'] and 'foot' in e['tags']['highway']
]
x_data['footway_paths'] = copy.deepcopy(footway_paths)
add_nodes_to_dictionary(
[e for e in footway_jsons[0]['elements'] if e['type'] == 'node'],
nodes_dict,
paths=footway_paths)
paths_with_borders = add_borders_to_paths(footway_paths,
nodes_dict,
width=1.8)
cropped_paths = remove_elements_beyond_radius(paths_with_borders,
nodes_dict,
x_data['center_x'],
x_data['center_y'],
x_data['crop_radius'])
set_direction(cropped_paths, x_data, nodes_dict)
return sorted(cropped_paths, key=lambda p: p['id'])
def get_street_data(x_data, city_data):
"""
Get a list of paths related to the intersection and a list of data matching the osmnx format.
:param x_data: dictionary
:param city_data: dictionary
:return: a tuple of lists
"""
nodes_dict = city_data['nodes']
intersection_jsons = get_box_data(x_data,
city_data['raw_data'],
infrastructure='way["highway"]',
network_type='drive')
x_data['raw_data'] = copy.deepcopy(intersection_jsons)
intersection_paths = [
e for e in intersection_jsons[0]['elements'] if e['type'] == 'way'
]
add_nodes_to_dictionary(
[e for e in intersection_jsons[0]['elements'] if e['type'] == 'node'],
nodes_dict,
paths=intersection_paths)
manual_correction(intersection_paths)
split_x_streets = repeat_street_split(intersection_paths, nodes_dict,
x_data['streets'])
oneway_paths = split_bidirectional_paths(split_x_streets, nodes_dict)
set_direction(oneway_paths, x_data, nodes_dict)
correct_paths(oneway_paths)
oneway_paths_with_borders = add_borders_to_paths(oneway_paths, nodes_dict)
cropped_paths = remove_elements_beyond_radius(oneway_paths_with_borders,
nodes_dict,
x_data['center_x'],
x_data['center_y'],
x_data['crop_radius'])
cleaned_intersection_paths = sorted(clean_paths(cropped_paths,
x_data['streets']),
key=lambda p: p['id'])
node_subset = get_node_subset(intersection_jsons,
cleaned_intersection_paths, nodes_dict)
intersection_selection = [{
'version':
intersection_jsons[0]['version'],
'osm3s':
intersection_jsons[0]['osm3s'],
'generator':
intersection_jsons[0]['generator'],
'elements':
node_subset + cleaned_intersection_paths
}]
return cleaned_intersection_paths, intersection_selection, intersection_jsons
def get_public_transit_data(x_data, city_data):
"""
Get public transit data if applicable for the intersection and crop within the radius.
:param x_data: dictionary
:param city_data: dictionary
:return: list of railway paths
"""
nodes_dict = city_data['nodes']
public_transit_jsons = get_box_data(x_data,
city_data['raw_data'],
network_type='all',
infrastructure='node["highway"]')
public_transit_nodes = [
e for e in public_transit_jsons[0]['elements'] if e['type'] == 'node'
and 'tags' in e and 'stop' in ' '.join(e['tags'].values())
]
add_nodes_to_dictionary(public_transit_nodes, nodes_dict, paths=None)
return public_transit_nodes
def get_railway_data(x_data, city_data):
"""
Get railway data if applicable for the intersection and crop within the radius.
:param x_data: dictionary
:param city_data: dictionary
:return: list of railway paths
"""
nodes_dict = city_data['nodes']
railway_jsons = get_box_data(x_data,
city_data['raw_data'],
network_type='all',
infrastructure='way["railway"]')
railway_paths = [
e for e in railway_jsons[0]['elements'] if e['type'] == 'way'
]
x_data['railway_paths'] = copy.deepcopy(railway_paths)
referenced_nodes = {}
referenced_nodes = get_node_dict_subset_from_list_of_lanes(
x_data['merged_lanes'], nodes_dict, referenced_nodes)
split_railway_paths = split_railways(remove_subways(railway_paths),
referenced_nodes)
add_nodes_to_dictionary(
[e for e in railway_jsons[0]['elements'] if e['type'] == 'node'],
nodes_dict,
paths=railway_paths)
paths_with_borders = add_borders_to_paths(split_railway_paths,
nodes_dict,
width=2.0)
cropped_paths = remove_elements_beyond_radius(paths_with_borders,
nodes_dict,
x_data['center_x'],
x_data['center_y'],
x_data['crop_radius'])
set_direction(cropped_paths, x_data, nodes_dict)
return sorted(cropped_paths, key=lambda p: p['id'])