lmao

pipeline/utils/postcode_mapping.py

@@ -0,0 +1,40 @@
+"""Map terminated postcodes to their nearest active successor using OS grid coordinates."""
+
+from pathlib import Path
+
+import numpy as np
+import polars as pl
+from scipy.spatial import cKDTree
+
+
+def build_postcode_mapping(arcgis_path: Path) -> pl.DataFrame:
+    """Build a mapping from terminated England postcodes to their nearest active postcode.
+
+    Uses OS National Grid coordinates (oseast1m, osnrth1m) which are Cartesian meters,
+    so Euclidean distance via cKDTree gives accurate results without projection.
+    """
+    arcgis = pl.scan_parquet(arcgis_path).filter(pl.col("ctry") == "E92000001")
+
+    active = arcgis.filter(pl.col("doterm").is_null()).select("pcds", "oseast1m", "osnrth1m").collect()
+    terminated = arcgis.filter(pl.col("doterm").is_not_null()).select("pcds", "oseast1m", "osnrth1m").collect()
+
+    print(f"Active postcodes: {active.height}, terminated postcodes: {terminated.height}")
+
+    if terminated.height == 0:
+        return pl.DataFrame({"old_postcode": pl.Series([], dtype=pl.Utf8), "new_postcode": pl.Series([], dtype=pl.Utf8)})
+
+    active_coords = np.column_stack([active["oseast1m"].to_numpy(), active["osnrth1m"].to_numpy()])
+    terminated_coords = np.column_stack([terminated["oseast1m"].to_numpy(), terminated["osnrth1m"].to_numpy()])
+
+    tree = cKDTree(active_coords)
+    distances, indices = tree.query(terminated_coords)
+
+    active_postcodes = active["pcds"]
+    mapping = pl.DataFrame({
+        "old_postcode": terminated["pcds"],
+        "new_postcode": active_postcodes.gather(indices),
+    })
+
+    print(f"Postcode mapping: max distance = {distances.max():.0f}m, median = {np.median(distances):.0f}m")
+
+    return mapping