generic_edges_geopandas_from_nx

pysrc/cityseer/tools/graphs.py

@@ -670,7 +670,7 @@ def nx_consolidate_nodes(
         The buffer distance to be used for consolidating nearby nodes. Defaults to 5.
     neighbour_policy: str
         Whether all nodes within the buffer distance are merged, or only "direct" or "indirect" neighbours. Defaults to
-        None.
+        None which will consider all nodes.
     crawl: bool
         Whether the algorithm will recursively explore neighbours of neighbours if those neighbours are within the
         buffer distance from the prior node. Defaults to False.

pysrc/cityseer/tools/io.py

@@ -955,6 +955,51 @@ def nx_from_cityseer_geopandas(
     return g_multi_copy
 
 
+def generic_edges_geopandas_from_nx(
+    nx_multigraph: nx.MultiGraph,
+    crs: str | int,
+) -> gpd.GeoDataFrame:
+    """
+    Transpose a `cityseer` `networkX` `MultiGraph` into a `gpd.GeoDataFrame` representing the network edges.
+
+    Converts the `geom` attribute attached to each edge into a GeoPandas GeoDataFrame. This is useful when
+    inspecting or cleaning the network in QGIS. It can then be reimported with
+    [`nx_from_generic_geopandas`](#nx-from-generic-geopandas)
+
+    Parameters
+    ----------
+    nx_multigraph: nx.MultiGraph
+        A `networkX` `MultiGraph` in a projected coordinate system, containing `x` and `y` node attributes, and `geom`
+        edge attributes containing `LineString` geoms.
+    crs: str | int
+        CRS for initialising the returned structures. This is used for initialising the GeoPandas
+        [`GeoDataFrame`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html#geopandas-geodataframe).  # pylint: disable=line-too-long
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        A `gpd.GeoDataFrame` with `edge_idx` and `geom` attributes.
+
+    """
+    if not isinstance(nx_multigraph, nx.MultiGraph):
+        raise TypeError("This method requires an undirected networkX MultiGraph.")
+    logger.info("Preparing node and edge arrays from networkX graph.")
+    agg_edge_data = []
+    # set edges
+    for start_nd_key, end_nd_key, edge_idx, edge_data in nx_multigraph.edges(keys=True, data=True):
+        agg_edge_data.append(
+            {
+                "start_nd_key": start_nd_key,
+                "end_nd_key": end_nd_key,
+                "edge_idx": edge_idx,
+                "geom": edge_data["geom"],
+            }
+        )
+    edges_gdf = gpd.GeoDataFrame(agg_edge_data, crs=crs, geometry="geom")
+
+    return edges_gdf
+
+
 def nx_from_generic_geopandas(
     gdf_network: gpd.GeoDataFrame,  # type: ignore
 ) -> nx.MultiGraph:

tests/tools/test_io.py

@@ -705,6 +705,17 @@ def test_nx_from_cityseer_geopandas(primal_graph):
     assert G_round_trip_decomp.edges == G_decomposed.edges
 
 
+def test_generic_edges_geopandas_from_nx(primal_graph):
+    """ """
+    edges_gdf = io.generic_edges_geopandas_from_nx(primal_graph, 3395)
+    assert len(edges_gdf) == len(primal_graph.edges)
+    for _idx, row_data in edges_gdf.iterrows():
+        assert (
+            row_data["geom"]
+            == primal_graph[row_data["start_nd_key"]][row_data["end_nd_key"]][row_data["edge_idx"]]["geom"]
+        )
+
+
 def test_nx_from_generic_geopandas(primal_graph):
     """ """
     # generate a GDF for testing with