| import geopandas | |
| import networkx | |
| import numpy | |
| def get_frames(ways: geopandas.GeoDataFrame): | |
| """Extract nodes, edges, and relations from a GeoDataFrame of ways. | |
| Args: | |
| ways (geopandas.GeoDataFrame): A GeoDataFrame containing information about ways. | |
| Returns: | |
| nodes (geopandas.GeoDataFrame): A GeoDataFrame containing node geometries. | |
| edges (geopandas.GeoDataFrame): A GeoDataFrame containing ways information. | |
| rel (geopandas.GeoDataFrame): A GeoDataFrame representing relations between nodes and edges. | |
| """ | |
| edges = ways.explode(index_parts=False)[["id", "name", "geometry"]] | |
| edges.index.name = "way_id" | |
| edges = edges.reset_index() | |
| nodes = geopandas.GeoDataFrame({"geometry": edges.boundary.explode(index_parts=False).geometry.unique()}) | |
| nodes.index.name = "node_id" | |
| nodes.reset_index(inplace=True) | |
| rel = edges.sjoin(nodes).to_crs("epsg:2154") | |
| return nodes, edges, rel | |
| def get(ways: geopandas.GeoDataFrame) -> networkx.Graph: | |
| """ Convert a GeoDataFrame of ways into a networkx Graph. | |
| Args: | |
| ways (geopandas.GeoDataFrame): A GeoDataFrame containing information about ways. | |
| Returns: | |
| networkx.Graph: A networkx Graph representing the geographical network. | |
| """ | |
| nodes, edges, rel = get_frames(ways) | |
| graph = networkx.Graph() | |
| for node in rel['node_id'].unique(): | |
| graph.add_node(node, shape=nodes.loc[node, "geometry"]) | |
| for idx in rel['way_id'].unique(): | |
| node_id1, node_id2 = rel.loc[idx]['node_id'] | |
| way = edges.loc[idx] | |
| graph.add_edge( | |
| node_id1, node_id2, | |
| weight=way.geometry.length, | |
| shape=way.geometry, | |
| id_=idx, | |
| ) | |
| return graph | |
| def weighted_adjacency_matrix(graph: networkx.Graph) -> numpy.ndarray: | |
| """Generate the weighted adjacency matrix from a networkx Graph. | |
| Args: | |
| graph (networkx.Graph): A networkx Graph representing the network. | |
| Returns: | |
| numpy.ndarray: A NumPy array representing the weighted adjacency matrix. | |
| """ | |
| weights = numpy.full((graph.number_of_nodes(),)*2, numpy.inf) | |
| numpy.fill_diagonal(weights, 0., wrap=False) | |
| for i in graph: | |
| for j in graph[i]: | |
| weights[i, j] = graph[i][j]["weight"] | |
| return weights |