ENH: always store x, y as node attributes in gdf_to_nx, optionally ca…

…st node labels to integers (#546)

* osmnx compat

* tests

* fix the indent level

* let networkx deal with keys in multigraphs

* fix tests

momepy/tests/test_graph.py

@@ -124,7 +124,7 @@ def test_betweenness_centrality(self):
             net2.edges[
                 (1603226.9576840235, 6464160.158361825),
                 (1603039.9632033885, 6464087.491175889),
-                8,
+                0,
             ]["betweenness"]
             == edge
         )
@@ -176,7 +176,7 @@ def test_mean_nodes(self):
             net.edges[
                 (1603226.9576840235, 6464160.158361825),
                 (1603039.9632033885, 6464087.491175889),
-                8,
+                0,
             ]["straightness"]
             == edge
         )

momepy/tests/test_utils.py

@@ -27,7 +27,7 @@ def test_dataset_missing(self):
         with pytest.raises(ValueError, match="The dataset 'sffgkt' is not available."):
             mm.datasets.get_path("sffgkt")
 
-    def test_gdf_to_nx(self):
+    def test_gdf_to_nx_warnings(self):
         with pytest.warns(
             RuntimeWarning, match="The given network does not contain any LineString."
         ):
@@ -39,9 +39,16 @@ def test_gdf_to_nx(self):
         ):
             mm.gdf_to_nx(self.df_points_and_linestring)
 
+    def test_gdf_to_nx(self):
         nx = mm.gdf_to_nx(self.df_streets)
         assert nx.number_of_nodes() == 29
         assert nx.number_of_edges() == 35
+        assert nx.nodes[(1603585.6402153103, 6464428.773867372)] == {
+            "x": 1603585.6402153103,
+            "y": 6464428.773867372,
+        }
+
+    def test_gdf_to_nx_dual(self):
         dual = mm.gdf_to_nx(self.df_streets, approach="dual")
         assert dual.number_of_nodes() == 35
         assert dual.number_of_edges() == 74
@@ -60,6 +67,7 @@ def test_gdf_to_nx(self):
         assert nx.number_of_nodes() == 29
         assert nx.number_of_edges() == 35
 
+    def test_gdf_to_nx_directed(self):
         nx = mm.gdf_to_nx(self.df_streets, multigraph=False, directed=True)
         assert isinstance(nx, networkx.DiGraph)
         assert nx.number_of_nodes() == 29
@@ -78,6 +86,7 @@ def test_gdf_to_nx(self):
         with pytest.raises(ValueError, match="Bidirectional lines"):
             mm.gdf_to_nx(self.df_streets, directed=False, oneway_column="oneway")
 
+    def test_gdf_to_nx_angles(self):
         dual = mm.gdf_to_nx(self.df_streets, approach="dual", angles=False)
         assert (
             dual.edges[
@@ -98,7 +107,7 @@ def test_gdf_to_nx(self):
         dual = mm.gdf_to_nx(
             self.df_streets, approach="dual", angles=False, multigraph=False
         )
-        assert isinstance(nx, networkx.Graph)
+        assert isinstance(dual, networkx.Graph)
         assert (
             dual.edges[
                 (1603499.42326969, 6464328.7520580515),
@@ -108,7 +117,7 @@ def test_gdf_to_nx(self):
         )
 
         dual = mm.gdf_to_nx(self.df_streets, approach="dual", multigraph=False)
-        assert isinstance(nx, networkx.Graph)
+        assert isinstance(dual, networkx.Graph)
         assert dual.edges[
             (1603499.42326969, 6464328.7520580515),
             (1603510.1061735682, 6464204.555117119),
@@ -117,6 +126,14 @@ def test_gdf_to_nx(self):
         with pytest.raises(ValueError, match="Directed graphs are not supported"):
             mm.gdf_to_nx(self.df_streets, approach="dual", directed=True)
 
+    def test_gdf_to_nx_labels(self):
+        nx = mm.gdf_to_nx(self.df_streets, integer_labels=True)
+        assert nx.number_of_nodes() == 29
+        assert nx.number_of_edges() == 35
+        assert nx.nodes[0] == {
+            "x": 1603585.6402153103,
+            "y": 6464428.773867372,
+        }
     def test_nx_to_gdf(self):
         nx = mm.gdf_to_nx(self.df_streets)
         nodes, edges, W = mm.nx_to_gdf(nx, spatial_weights=True)

momepy/utils.py

@@ -71,25 +71,25 @@ def _generate_primal(graph, gdf_network, fields, multigraph, oneway_column=None)
             stacklevel=3,
         )
 
-    key = 0
     for row in gdf_network.itertuples():
         first = row.geometry.coords[0]
         last = row.geometry.coords[-1]
 
         data = list(row)[1:]
         attributes = dict(zip(fields, data, strict=True))
         if multigraph:
-            graph.add_edge(first, last, key=key, **attributes)
-            key += 1
+            graph.add_edge(first, last, **attributes)
 
             if oneway_column:
                 oneway = bool(getattr(row, oneway_column))
                 if not oneway:
-                    graph.add_edge(last, first, key=key, **attributes)
-                    key += 1
+                    graph.add_edge(last, first, **attributes)
         else:
             graph.add_edge(first, last, **attributes)
 
+    node_attrs = {node: {"x": node[0], "y": node[1]} for node in graph.nodes}
+    nx.set_node_attributes(graph, node_attrs)
+
 
 def _generate_dual(graph, gdf_network, fields, angles, multigraph, angle):
     """Generate a dual graph. Helper for ``gdf_to_nx``."""
@@ -150,6 +150,7 @@ def gdf_to_nx(
     angles=True,
     angle="angle",
     oneway_column=None,
+    integer_labels=False,
 ):
     """
     Convert a LineString GeoDataFrame to a ``networkx.MultiGraph`` or other
@@ -188,6 +189,10 @@ def gdf_to_nx(
         path traversal by specifying the boolean column in the GeoDataFrame. Note,
         that the reverse conversion ``nx_to_gdf(gdf_to_nx(gdf, directed=True,
         oneway_column="oneway"))`` will contain additional duplicated geometries.
+    integer_labels : bool, default False
+        Convert node labels to integers. By default, node labels are tuples with (x, y)
+        coordinates. Set to True to encode them as integers. Note that the x, and y
+        coordinates are always preserved as node attributes.
 
     Returns
     -------
@@ -273,6 +278,9 @@ def gdf_to_nx(
             f"Approach '{approach}' is not supported. Use 'primal' or 'dual'."
         )
 
+    if integer_labels:
+        net = nx.convert_node_labels_to_integers(net)
+
     return net