GraphGenerator

todo

class blocksnet.preprocessing.graph_generator.GraphNode(*, x: float, y: float)[source]

Bases: BaseModel

x: float

y: float

class blocksnet.preprocessing.graph_generator.GraphEdge(*, geometry: LineString = None, weight: float, transport_type: Literal['walk', 'drive', 'subway', 'tram', 'bus', 'trolleybus'])[source]

Bases: BaseModel

geometry: LineString

weight: float

transport_type: Literal['walk', 'drive', 'subway', 'tram', 'bus', 'trolleybus']

class blocksnet.preprocessing.graph_generator.CityRow(*, geometry: Polygon | MultiPolygon, index: int)[source]

Bases: BaseRow

geometry: Polygon | MultiPolygon

class blocksnet.preprocessing.graph_generator.GraphGenerator(*, territory: GeoDataFrame[CityRow], overpass_url: str = 'http://lz4.overpass-api.de/api/interpreter', speed: dict[str, int] = {'bus': 17, 'drive': 25, 'subway': 12, 'tram': 15, 'trolleybus': 12, 'walk': 4}, waiting_time: dict[str, int] = {'bus': 5, 'subway': 5, 'tram': 5, 'trolleybus': 5})[source]

Bases: BaseModel

territory: GeoDataFrame[CityRow]: City geometry or geometries, may contain blocks or boundaries of the city

overpass_url: str: Overpass url used in OSM queries

speed: dict[str, int]: Average transport type speed in km/h

waiting_time: dict[str, int]: Average waiting time in min

cast_territory()[source]

union_territory()[source]

static to_graphml(graph: MultiDiGraph, file_path: str)[source]: Save graph as OX .graphml

static from_graphml(file_path: str)[source]: Load graph from OX .graphml

property local_crs

classmethod plot(graph: MultiDiGraph)[source]

_get_speed(transport_type: str)[source]: Return transport type speed in meters per minute

_get_basic_graph(network_type: Literal['walk', 'drive'])[source]: Returns walk or drive graph for the city geometry

_get_routes(bounds: DataFrame, public_transport_type: Literal['subway', 'tram', 'trolleybus', 'bus']) → DataFrame[source]: Returns OSM routes for the given geometry shapely geometry bounds and given transport type

static _coordinates_to_linestring(coordinates: list[dict[str, float]]) → LineString[source]: For given route coordinates dicts returns a concated linestring

_ways_to_gdf(df: DataFrame) → GeoDataFrame[source]: Returns GeoDataFrame for the given route ways, converting way’s coordinates to linestring

_nodes_to_gdf(df: DataFrame) → GeoDataFrame[source]: Returns GeoDataFrame for the given route nodes, converting lon and lat columns to geometry column and local CRS

_graph_from_route(nodes: DataFrame, ways: DataFrame, transport_type: str) → list[networkx.classes.multigraph.MultiGraph][source]: Get graph for the given route nodes and ways

_get_pt_graph(pt_type: Literal['subway', 'tram', 'trolleybus', 'bus']) → list[networkx.classes.multigraph.MultiGraph][source]: Get public transport routes graphs for the given transport_type

static validate_graph(graph) → MultiDiGraph[source]: Returns validated copy of the graph, according to `GraphEdge` and `GraphNode` classes

get_graph(graph_type: Literal['intermodal', 'walk', 'drive'])[source]: Returns intermodal graph for the city geometry bounds