Remove segment_centrality, add segment_weighted param, rename centrality functions

Replace the separate segment_centrality code path (edge-based continuous
integrals, dedicated Dijkstra, CentralitySegmentResult) with a
segment_weighted boolean on the existing centrality functions. On dual
graphs, this sets node weights to primal edge lengths so that closeness
measures reflect total reachable street length and betweenness weights
pairs by both endpoint segment lengths.

- Rename node_centrality_shortest → centrality_shortest
- Rename node_centrality_simplest → centrality_simplest
- Add segment_weighted param (Rust + Python) with _SegmentWeightContext
- Add set_node_weight method on NetworkStructure
- Replace pair_distances_betas_time with pair_distances_and_time
- Clean up betas machinery from Python callers and log_thresholds
- Remove dead code: EdgeVisit, dijkstra_tree_segment, origin_seg/last_seg,
  unchecked edge helpers, CentralitySegmentResult
- Update all docs, tests, type stubs, and references

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: songololo

RELEASE_NOTES.md

@@ -12,7 +12,7 @@ Supported in all IO methods: `nx_from_osm_nx`, `nx_from_open_roads`, `nx_from_ge
 
 ### Adaptive sampling (experimental)
 
-`node_centrality_shortest` and `node_centrality_simplest` accept `sample=True` to use distance-based Hoeffding/Eppstein-Wang sampling for approximate centrality, achieving 2-3x speedup while maintaining ρ ≥ 0.95. Sampling probability is derived deterministically from each distance threshold using a canonical grid network model.
+`centrality_shortest` and `centrality_simplest` accept `sample=True` to use distance-based Hoeffding/Eppstein-Wang sampling for approximate centrality, achieving 2-3x speedup while maintaining ρ ≥ 0.95. Sampling probability is derived deterministically from each distance threshold using a canonical grid network model.
 
 ### QGIS plugin updates
 
@@ -22,31 +22,31 @@ New accessibility and statistics processing algorithms. Expanded centrality algo
 
 ### Angular (simplest-path) analysis now requires a dual graph
 
-`node_centrality_simplest` (and the convenience wrappers `closeness_simplest`, `betweenness_simplest`) now raises `ValueError` if the input `NetworkStructure` was not ingested from a dual graph. Angular routing uses endpoint-aware dual-graph traversal instead of the previous bearing-based angular costs. Convert primal graphs with `graphs.nx_to_dual()` before calling `network_structure_from_nx()`.
+`centrality_simplest` (and the convenience wrappers `closeness_simplest`, `betweenness_simplest`) now raises `ValueError` if the input `NetworkStructure` was not ingested from a dual graph. Angular routing uses endpoint-aware dual-graph traversal instead of the previous bearing-based angular costs. Convert primal graphs with `graphs.nx_to_dual()` before calling `network_structure_from_nx()`.
 
 ### `tolerance` parameter semantics changed
 
-The `tolerance` parameter on `node_centrality_shortest`, `node_centrality_simplest`, `betweenness_shortest`, `betweenness_simplest`, and `betweenness_od` now uses **relative percentage** semantics (e.g. `1.0` = 1%) instead of the previous absolute fraction. The default changed from `0.0` to `None`. A tiny internal epsilon is always enforced for floating-point stability. To migrate: multiply old values by 100 (e.g. old `0.05` → new `5.0`).
+The `tolerance` parameter on `centrality_shortest`, `centrality_simplest`, `betweenness_shortest`, `betweenness_simplest`, and `betweenness_od` now uses **relative percentage** semantics (e.g. `1.0` = 1%) instead of the previous absolute fraction. The default changed from `0.0` to `None`. A tiny internal epsilon is always enforced for floating-point stability. To migrate: multiply old values by 100 (e.g. old `0.05` → new `5.0`).
 
-### `tolerance` parameter reordered in `node_centrality_simplest`
+### `tolerance` parameter reordered in `centrality_simplest`
 
 `tolerance` now appears before `angular_scaling_unit` and `farness_scaling_offset`. Code using positional arguments for these parameters will need updating.
 
 ### `betweenness_beta` removed from angular (simplest) results
 
-`CentralitySimplestResult` no longer exposes `node_betweenness_beta`. The `node_centrality_simplest` function no longer writes `cc_betweenness_beta_*` columns. Only `cc_betweenness_*` columns are produced.
+`CentralitySimplestResult` no longer exposes `node_betweenness_beta`. The `centrality_simplest` function no longer writes `cc_betweenness_beta_*` columns. Only `cc_betweenness_*` columns are produced.
 
 ### `cycles` metric changed
 
-The `cycles` output from `node_centrality_shortest` now measures the **circuit rank** of the locally reachable subgraph (m − n + c), providing a more stable measure of network meshedness than the older tree-cycle heuristic.
+The `cycles` output from `centrality_shortest` now measures the **circuit rank** of the locally reachable subgraph (m − n + c), providing a more stable measure of network meshedness than the older tree-cycle heuristic.
 
 ### Sampling functions moved from `config` to `sampling` module
 
 `compute_distance_p`, `compute_hoeffding_p`, `HOEFFDING_EPSILON`, `HOEFFDING_DELTA`, and `GRID_SPACING` have moved from `cityseer.config` to `cityseer.sampling`. The `config` module is still importable via lazy-loading but no longer contains sampling functions. Update imports accordingly.
 
 ## Other Changes
 
-- All result arrays (`CentralityShortestResult`, `CentralitySimplestResult`, `CentralitySegmentResult`, `Stats`, etc.) now return `np.float64` instead of `np.float32`.
+- All result arrays (`CentralityShortestResult`, `CentralitySimplestResult`, `Stats`, etc.) now return `np.float64` instead of `np.float32`.
 - `betweenness_od` now accepts an optional `tolerance` parameter.
 - `closeness_shortest` and `closeness_simplest` now accept an optional `tolerance` parameter.
 - Bug fix: `is_dual` graph attribute was incorrectly cast via `CRS()` instead of `bool()` in `nx_remove_dangling_nodes` and `nx_merge_parallel_edges`.

analysis/od/prototype_od_weighted.py

@@ -96,7 +96,7 @@
 
 # %% Run standard centrality
 
-nodes_std = networks.node_centrality_shortest(
+nodes_std = networks.centrality_shortest(
     net,
     nodes_gdf.copy(),
     distances=DISTANCES,

analysis/sampling/paper/main.tex

@@ -429,7 +429,7 @@ \section{Technical Details}
 \begin{verbatim}
 from cityseer.metrics import networks
 
-gdf = networks.node_centrality_shortest(
+gdf = networks.centrality_shortest(
     network_structure,
     nodes_gdf,
     distances=[500,1000,2000,5000,10000,20000],

docs/plots/plots.py

@@ -24,7 +24,7 @@
 G = graphs.nx_simple_geoms(G)
 G = graphs.nx_decompose(G, 20)
 nodes_gdf, _edges_gdf, network_structure = io.network_structure_from_nx(G)
-nodes_gdf = networks.segment_centrality(network_structure=network_structure, nodes_gdf=nodes_gdf, distances=[400, 800])
+nodes_gdf = networks.centrality_shortest(network_structure=network_structure, nodes_gdf=nodes_gdf, distances=[400, 800])
 data_gdf = mock.mock_landuse_categorical_data(G, random_seed=25)
 nodes_gdf, data_gdf = layers.compute_mixed_uses(
     data_gdf,
@@ -34,7 +34,7 @@
     distances=[400, 800],
 )
 # custom colourmap
-segment_harmonic_vals = nodes_gdf["cc_seg_harmonic_800"]
+segment_harmonic_vals = nodes_gdf["cc_harmonic_800"]
 mixed_uses_vals = nodes_gdf["cc_hill_q0_800"]
 cmap = colors.LinearSegmentedColormap.from_list("cityseer", ["#64c1ff", "#d32f2f"])
 segment_harmonic_vals = colors.Normalize()(segment_harmonic_vals)
@@ -157,7 +157,7 @@ def simple_plot(_G, _path, plot_geoms=True):
 G = graphs.nx_simple_geoms(G)
 G = graphs.nx_decompose(G, 50)
 nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(G)
-networks.node_centrality_shortest(network_structure=network_structure, nodes_gdf=nodes_gdf, distances=[800])
+networks.centrality_shortest(network_structure=network_structure, nodes_gdf=nodes_gdf, distances=[800])
 G_after = io.nx_from_cityseer_geopandas(nodes_gdf, edges_gdf)
 # let's extract and normalise the values
 vals = []

docs/src/pages/api/network.md

@@ -1073,7 +1073,7 @@ print(cn_restored.nodes_gdf["cc_harmonic_800"])
 </div>
 
 
- Compute shortest-path (metric) node centrality. Wraps [`node_centrality_shortest`](/metrics/networks#node-centrality-shortest). All keyword arguments are forwarded; see that function for the full parameter list including ``distances``, ``minutes``, ``compute_closeness``, ``compute_betweenness``, ``decay_fn``, ``sample``, and ``epsilon``.
+ Compute shortest-path (metric) centrality. Wraps [`centrality_shortest`](/metrics/networks#centrality-shortest). All keyword arguments are forwarded; see that function for the full parameter list including ``distances``, ``minutes``, ``compute_closeness``, ``compute_betweenness``, ``decay_fn``, ``sample``, and ``epsilon``.
 ### Returns
 <div class="param-set">
   <div class="def">
@@ -1146,7 +1146,7 @@ cn.centrality_shortest(minutes=[5, 10, 20])
 </div>
 
 
- Compute simplest-path (angular) node centrality. Wraps [`node_centrality_simplest`](/metrics/networks#node-centrality-simplest). All keyword arguments are forwarded; see that function for the full parameter list.
+ Compute simplest-path (angular) centrality. Wraps [`centrality_simplest`](/metrics/networks#centrality-simplest). All keyword arguments are forwarded; see that function for the full parameter list.
 
  This method does not accept a ``decay_fn`` parameter; angular centralities use angular cost rather than distance-based decay.
 ### Returns
@@ -1180,44 +1180,6 @@ cn.centrality_simplest(distances=[400, 800, 1600])
 
 
 
-<div class="function">
-
-## segment_centrality
-
-
-<div class="content">
-<span class="name">segment_centrality</span><div class="signature multiline">
-  <span class="pt">(</span>
-  <div class="param">
-    <span class="pn">self</span>
-  </div>
-  <div class="param">
-    <span class="pn">**kwargs</span>
-  </div>
-  <span class="pt">)-&gt;[</span>
-  <span class="pr">CityNetwork</span>
-  <span class="pt">]</span>
-</div>
-</div>
-
-
- Compute segment-based centrality. Wraps [`segment_centrality`](/metrics/networks#segment-centrality). All keyword arguments are forwarded; see that function for the full parameter list.
-### Returns
-<div class="param-set">
-  <div class="def">
-    <div class="name">self</div>
-    <div class="type">CityNetwork</div>
-  </div>
-  <div class="desc">
-
- Returns self for method chaining. Results are written to ``nodes_gdf``.</div>
-</div>
-
-
-</div>
-
- 
-
 <div class="function">
 
 ## build_od_matrix

docs/src/pages/guide.md

@@ -154,7 +154,7 @@ Centrality metrics quantify the structural importance of each location in the st
 
 ### Shortest-path centrality
 
-[`centrality_shortest`](/api/network#centrality-shortest) (or [`node_centrality_shortest`](/metrics/networks#node-centrality-shortest) in the lower-level API) computes the following metrics for each distance threshold `d`:
+[`centrality_shortest`](/api/network#centrality-shortest) (or [`centrality_shortest`](/metrics/networks#centrality-shortest) in the lower-level API) computes the following metrics for each distance threshold `d`:
 
 | Column | Description |
 | --- | --- |
@@ -169,7 +169,7 @@ Centrality metrics quantify the structural importance of each location in the st
 
 ### Simplest-path centrality
 
-[`centrality_simplest`](/api/network#centrality-simplest) (or [`node_centrality_simplest`](/metrics/networks#node-centrality-simplest)) computes angular centrality metrics. Note the `_ang` suffix:
+[`centrality_simplest`](/api/network#centrality-simplest) (or [`centrality_simplest`](/metrics/networks#centrality-simplest) in the lower-level API) computes angular centrality metrics. Note the `_ang` suffix:
 
 | Column | Description |
 | --- | --- |
@@ -372,13 +372,7 @@ The [`add_gtfs`](/api/network#add-gtfs) method integrates public transport stops
 
 ## Performance and Scale
 
-The underlying algorithms are parallelised in Rust and scale to large networks. Typical performance on a modern laptop (8 cores):
-
-- **10,000 edges** at 3 distance thresholds: seconds
-- **50,000 edges** at 5 distance thresholds: under a minute
-- **200,000+ edges** at long distances: minutes; consider [adaptive sampling](#adaptive-sampling) for 5 km+ thresholds
-
-Computation time scales with the number of edges, the number of distance thresholds, and the reachability at each threshold. Simplest-path (angular) centrality is typically faster than shortest-path because angular routing produces sparser traversal trees.
+The underlying algorithms are parallelised in Rust and scale to large networks. Computation scales with the number of edges, the number of distance thresholds, and the reachability at each threshold. Simplest-path (angular) centrality is typically faster than shortest-path because angular routing produces sparser traversal trees. For large networks at long distance thresholds, consider [adaptive sampling](#adaptive-sampling).
 
 ## Adaptive Sampling