Format and lint

pipeline/transform/postcode_boundaries/__main__.py

@@ -4,7 +4,12 @@ from pathlib import Path
 from shapely.geometry import MultiPolygon, Polygon
 from tqdm import tqdm
 
-from .inspire import cache_inspire, get_inspire_candidates, inspire_cache_exists, load_inspire
+from .inspire import (
+    cache_inspire,
+    get_inspire_candidates,
+    inspire_cache_exists,
+    load_inspire,
+)
 from .memory import release_memory
 from .oa_boundaries import load_oa_boundaries
 from .output import merge_fragments, write_district_geojson

pipeline/transform/postcode_boundaries/process_oa.py

@@ -128,6 +128,10 @@ def _extract_polygonal(geom) -> Polygon | MultiPolygon | None:
         if len(polys) == 1:
             return polys[0]
         return MultiPolygon(
-            [p for g in polys for p in (g.geoms if g.geom_type == "MultiPolygon" else [g])]
+            [
+                p
+                for g in polys
+                for p in (g.geoms if g.geom_type == "MultiPolygon" else [g])
+            ]
         )
     return None

pipeline/transform/postcode_boundaries/test_postcode_boundaries.py

@@ -167,9 +167,7 @@ class TestVoronoiDeduplication:
 
     def test_int64_coords_jitter_works(self, square_boundary):
         """Int64 coords (production dtype) must still jitter correctly."""
-        points = np.array(
-            [[500050, 180050], [500050, 180050]], dtype=np.int64
-        )
+        points = np.array([[500050, 180050], [500050, 180050]], dtype=np.int64)
         postcodes = ["A", "B"]
         result = compute_voronoi_regions(points, postcodes, square_boundary)
         assert "A" in result, "Postcode A missing with int64 coords"

pipeline/transform/postcode_boundaries/voronoi.py

@@ -39,7 +39,9 @@ def compute_voronoi_regions(
             else:
                 # Tiny jitter so Voronoi sees distinct points (0.01m per step)
                 jittered = points[i].copy()
-                angle = 2 * np.pi * jitter_idx / max(coord_counts[coord], jitter_idx + 1)
+                angle = (
+                    2 * np.pi * jitter_idx / max(coord_counts[coord], jitter_idx + 1)
+                )
                 jittered[0] += 0.01 * np.cos(angle)
                 jittered[1] += 0.01 * np.sin(angle)
                 unique_pts.append(jittered)