allows multiple stats columns

Cargo.toml

@@ -16,11 +16,11 @@ name = "cityseer"
 crate-type = ["cdylib"]
 
 [dependencies]
-atomic_float = "0.1.0"
-ndarray = "0.15.6"
-numpy = "0.18.0"
-petgraph = "0.6.3"
-pyo3 = { version="0.18.3", features=["multiple-pymethods"]}
-rand = "0.8.5"
-rand_distr = "0.4.3"
+atomic_float = "1.1.0"
+ndarray = "0.16.1"
+numpy = "0.23.0"
+petgraph = "0.7.1"
+pyo3 = { version = "0.23.4", features = ["multiple-pymethods"] }
+rand = "0.9.0"
+rand_distr = "0.5.0"
 rayon = "1.7.0"

README.md

@@ -36,5 +36,5 @@ The `cityseer-api` `Python` package addresses a range of issues specific to comp
 
 ## Development
 
-`brew install pdm rust rust-analyzer rustfmt`
-`pdm install`
+`brew install uv rust rust-analyzer rustfmt`
+`uv sync`

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "cityseer"
-version = '4.17.5'
+version = '4.18.0'
 description = "Computational tools for network-based pedestrian-scale urban analysis"
 readme = "README.md"
 requires-python = ">=3.10, <3.14"
@@ -43,12 +43,12 @@ dependencies = [
   "requests>=2.27.1",
   "scikit-learn>=1.0.2",
   "tqdm>=4.63.1",
-  "shapely>=2.0.0",
-  "numpy>=2.0.0",
-  "geopandas>=1.0.0",
+  "shapely>=2.0.6",
+  "numpy>=2.2.2",
+  "geopandas>=1.0.1",
   "rasterio>=1.3.9",
   "fiona>=1.9.6",
-  "osmnx>=2.0.0",
+  "osmnx>=2.0.1",
 ]
 
 [project.urls]

pysrc/cityseer/metrics/layers.py

@@ -480,7 +480,7 @@ def compute_mixed_uses(
 
 def compute_stats(
     data_gdf: gpd.GeoDataFrame,
-    stats_column_label: str,
+    stats_column_labels: list[str],
     nodes_gdf: gpd.GeoDataFrame,
     network_structure: rustalgos.NetworkStructure,
     max_netw_assign_dist: int = 400,
@@ -507,8 +507,8 @@ def compute_stats(
         representing data points. The coordinates of data points should correspond as precisely as possible to the
         location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the
         building entrance.
-    stats_column_label: str
-        The column label corresponding to the column in `data_gdf` from which to take numerical information.
+    stats_column_labels: list[str]
+        The column labels corresponding to the columns in `data_gdf` from which to take numerical information.
     nodes_gdf
         A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe)
         representing nodes. Best generated with the
@@ -598,18 +598,20 @@ def compute_stats(
     :::
 
     """
-    if stats_column_label not in data_gdf.columns:
-        raise ValueError("The specified numerical stats column name can't be found in the GeoDataFrame.")
     data_map, data_gdf = assign_gdf_to_network(data_gdf, network_structure, max_netw_assign_dist, data_id_col)
     if not config.QUIET_MODE:
         logger.info("Computing statistics.")
     # extract landuses
-    stats_map: dict[str, float] = data_gdf[stats_column_label].to_dict()  # type: ignore
+    stats_maps = []
+    for stats_column_label in stats_column_labels:
+        if stats_column_label not in data_gdf.columns:
+            raise ValueError("The specified numerical stats column name can't be found in the GeoDataFrame.")
+        stats_maps.append(data_gdf[stats_column_label].to_dict())  # type: ignore)
     # stats
     partial_func = partial(
         data_map.stats,
         network_structure=network_structure,
-        numerical_map=stats_map,
+        numerical_maps=stats_maps,
         distances=distances,
         betas=betas,
         angular=angular,
@@ -621,26 +623,27 @@ def compute_stats(
     result = config.wrap_progress(total=network_structure.node_count(), rust_struct=data_map, partial_func=partial_func)
     # unpack the numerical arrays
     distances, betas = rustalgos.pair_distances_and_betas(distances, betas)
-    for dist_key in distances:
-        k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=False)
-        nodes_gdf[k] = result.sum[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=True)
-        nodes_gdf[k] = result.sum_wt[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=False)
-        nodes_gdf[k] = result.mean[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=True)
-        nodes_gdf[k] = result.mean_wt[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=False)
-        nodes_gdf[k] = result.count[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=True)
-        nodes_gdf[k] = result.count_wt[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=False)
-        nodes_gdf[k] = result.variance[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=True)
-        nodes_gdf[k] = result.variance_wt[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_max", dist_key, angular=angular)
-        nodes_gdf[k] = result.max[dist_key]  # type: ignore
-        k = config.prep_gdf_key(f"{stats_column_label}_min", dist_key, angular=angular)
-        nodes_gdf[k] = result.min[dist_key]  # type: ignore
+    for idx, stats_column_label in enumerate(stats_column_labels):
+        for dist_key in distances:
+            k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=False)
+            nodes_gdf[k] = result[idx].sum[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=True)
+            nodes_gdf[k] = result[idx].sum_wt[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=False)
+            nodes_gdf[k] = result[idx].mean[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=True)
+            nodes_gdf[k] = result[idx].mean_wt[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=False)
+            nodes_gdf[k] = result[idx].count[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=True)
+            nodes_gdf[k] = result[idx].count_wt[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=False)
+            nodes_gdf[k] = result[idx].variance[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=True)
+            nodes_gdf[k] = result[idx].variance_wt[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_max", dist_key, angular=angular)
+            nodes_gdf[k] = result[idx].max[dist_key]  # type: ignore
+            k = config.prep_gdf_key(f"{stats_column_label}_min", dist_key, angular=angular)
+            nodes_gdf[k] = result[idx].min[dist_key]  # type: ignore
 
     return nodes_gdf, data_gdf

pysrc/cityseer/rustalgos.pyi

@@ -690,15 +690,15 @@ class DataMap:
     def stats(
         self,
         network_structure: NetworkStructure,
-        numerical_map: dict[str, float],
+        numerical_maps: list[dict[str, float]],
         distances: list[int] | None = None,
         betas: list[float] | None = None,
         angular: bool | None = None,
         spatial_tolerance: int | None = None,
         min_threshold_wt: float | None = None,
         jitter_scale: float | None = None,
         pbar_disabled: bool | None = None,
-    ) -> StatsResult: ...
+    ) -> list[StatsResult]: ...
 
 class Viewshed:
     @classmethod

src/centrality.rs

@@ -91,6 +91,7 @@ impl NetworkStructure {
     impedance heuristic - which can be different from metres. Distance map in metres is used for defining max
     distances and computing equivalent distance measures.
     */
+    #[pyo3(signature = (src_idx, max_dist, jitter_scale=None))]
     pub fn dijkstra_tree_shortest(
         &self,
         src_idx: usize,
@@ -114,7 +115,7 @@ impl NetworkStructure {
             distance: 0.0,
         });
         // random number generator
-        let mut rng = rand::thread_rng();
+        let mut rng = rand::rng();
         while let Some(NodeDistance { node_idx, .. }) = active.pop() {
             tree_map[node_idx].visited = true;
             visited_nodes.push(node_idx);
@@ -173,7 +174,7 @@ impl NetworkStructure {
                 // inject jitter
                 let mut jitter: f32 = 0.0;
                 if jitter_scale > 0.0 {
-                    jitter = rng.gen::<f32>() * jitter_scale;
+                    jitter = rng.random::<f32>() * jitter_scale;
                 }
                 /*
                 if impedance less than prior distances for this node then update shortest path
@@ -186,6 +187,7 @@ impl NetworkStructure {
         }
         (visited_nodes, tree_map)
     }
+    #[pyo3(signature = (src_idx, max_dist, jitter_scale=None))]
     pub fn dijkstra_tree_simplest(
         &self,
         src_idx: usize,
@@ -210,7 +212,7 @@ impl NetworkStructure {
             distance: 0.0,
         });
         // random number generator
-        let mut rng = rand::thread_rng();
+        let mut rng = rand::rng();
         while let Some(NodeDistance { node_idx, .. }) = active.pop() {
             tree_map[node_idx].visited = true;
             visited_nodes.push(node_idx);
@@ -269,7 +271,7 @@ impl NetworkStructure {
                 // inject jitter
                 let mut jitter: f32 = 0.0;
                 if jitter_scale > 0.0 {
-                    jitter = rng.gen::<f32>() * jitter_scale;
+                    jitter = rng.random::<f32>() * jitter_scale;
                 }
                 /*
                 if impedance less than prior distances for this node then update shortest path
@@ -284,6 +286,7 @@ impl NetworkStructure {
         }
         (visited_nodes, tree_map)
     }
+    #[pyo3(signature = (src_idx, max_dist, jitter_scale=None))]
     pub fn dijkstra_tree_segment(
         &self,
         src_idx: usize,
@@ -309,7 +312,7 @@ impl NetworkStructure {
             distance: 0.0,
         });
         // random number generator
-        let mut rng = rand::thread_rng();
+        let mut rng = rand::rng();
         while let Some(NodeDistance { node_idx, .. }) = active.pop() {
             tree_map[node_idx].visited = true;
             visited_nodes.push(node_idx);
@@ -363,7 +366,7 @@ impl NetworkStructure {
                 // inject jitter
                 let mut jitter: f32 = 0.0;
                 if jitter_scale > 0.0 {
-                    jitter = rng.gen::<f32>() * jitter_scale;
+                    jitter = rng.random::<f32>() * jitter_scale;
                 }
                 /*
                 if impedance less than prior distances for this node then update shortest path
@@ -384,6 +387,15 @@ impl NetworkStructure {
         }
         (visited_nodes, visited_edges, tree_map, edge_map)
     }
+    #[pyo3(signature = (
+        distances=None,
+        betas=None,
+        compute_closeness=None,
+        compute_betweenness=None,
+        min_threshold_wt=None,
+        jitter_scale=None,
+        pbar_disabled=None
+    ))]
     pub fn local_node_centrality_shortest(
         &self,
         distances: Option<Vec<u32>>,
@@ -532,7 +544,17 @@ impl NetworkStructure {
         });
         Ok(result)
     }
-
+    #[pyo3(signature = (
+        distances=None,
+        betas=None,
+        compute_closeness=None,
+        compute_betweenness=None,
+        min_threshold_wt=None,
+        angular_scaling_unit=None,
+        farness_scaling_offset=None,
+        jitter_scale=None,
+        pbar_disabled=None
+    ))]
     pub fn local_node_centrality_simplest(
         &self,
         distances: Option<Vec<u32>>,
@@ -658,7 +680,15 @@ impl NetworkStructure {
         });
         Ok(result)
     }
-
+    #[pyo3(signature = (
+        distances=None,
+        betas=None,
+        compute_closeness=None,
+        compute_betweenness=None,
+        min_threshold_wt=None,
+        jitter_scale=None,
+        pbar_disabled=None
+    ))]
     pub fn local_segment_centrality(
         &self,
         distances: Option<Vec<u32>>,