perfect-postcode/pipeline/transform/postcode_boundaries/loader.py

105 lines
3.8 KiB
Python

"""Load per-district postcode boundary GeoJSONs as EPSG:27700 polygons.
The postcode-boundary pipeline (:mod:`output`) writes one WGS84 GeoJSON per
postcode district under ``units/{district}.geojson``, each feature carrying a
``postcodes`` (full unit string, e.g. "AL1 1AG") property. Spatial transforms
that test points against postcode geometry want those polygons back in British
National Grid (EPSG:27700) so buffers/distances are in metres.
:func:`load_postcode_polygons` reads the files, reprojects WGS84→27700, repairs
invalid rings, and returns parallel ``(postcodes, polygons)`` arrays sorted by
postcode so callers can use the array index as a stable postcode id -- the same
"buffer index == postcode index" convention used by ``tree_density``.
"""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import shapely
from pyproj import Transformer
def _read_district(
path: Path, transformer: Transformer
) -> tuple[np.ndarray, np.ndarray]:
"""Return (postcodes, polygons_27700) for one district GeoJSON."""
with path.open() as file:
collection = json.load(file)
features = collection.get("features", [])
if not features:
return np.empty(0, dtype=object), np.empty(0, dtype=object)
postcodes = np.array(
[feature["properties"]["postcodes"] for feature in features], dtype=object
)
geom_json = np.array(
[json.dumps(feature["geometry"]) for feature in features], dtype=object
)
geoms = shapely.from_geojson(geom_json)
# Reproject every vertex in a single pyproj call, then rebuild the polygons.
coords = shapely.get_coordinates(geoms)
if coords.size:
x, y = transformer.transform(coords[:, 0], coords[:, 1])
geoms = shapely.set_coordinates(geoms, np.column_stack([x, y]))
invalid = ~shapely.is_valid(geoms)
if invalid.any():
geoms[invalid] = shapely.make_valid(geoms[invalid])
return postcodes, geoms
def load_postcode_polygons(
units_dir: Path, max_postcodes: int | None = None
) -> tuple[np.ndarray, np.ndarray]:
"""Load all postcode polygons under ``units_dir`` reprojected to EPSG:27700.
Returns ``(postcodes, polygons)`` parallel object arrays sorted by postcode.
``max_postcodes`` (testing) keeps only the lexicographically-first N
postcodes, reading just enough district files to reach the cap.
"""
units_dir = Path(units_dir)
files = sorted(units_dir.glob("*.geojson"))
if not files:
raise FileNotFoundError(f"No postcode-boundary GeoJSONs found in {units_dir}")
transformer = Transformer.from_crs("EPSG:4326", "EPSG:27700", always_xy=True)
postcode_chunks: list[np.ndarray] = []
geom_chunks: list[np.ndarray] = []
total = 0
for path in files:
postcodes, geoms = _read_district(path, transformer)
if len(postcodes) == 0:
continue
postcode_chunks.append(postcodes)
geom_chunks.append(geoms)
total += len(postcodes)
if max_postcodes is not None and total >= max_postcodes:
break
if not postcode_chunks:
raise ValueError(f"No postcode features found in {units_dir}")
postcodes = np.concatenate(postcode_chunks)
geoms = np.concatenate(geom_chunks)
# Stable postcode order makes "index == postcode id" deterministic; dedupe
# defensively (a postcode lives in exactly one district file).
order = np.argsort(postcodes, kind="stable")
postcodes = postcodes[order]
geoms = geoms[order]
_, first = np.unique(postcodes, return_index=True)
postcodes = postcodes[first]
geoms = geoms[first]
if max_postcodes is not None and len(postcodes) > max_postcodes:
postcodes = postcodes[:max_postcodes]
geoms = geoms[:max_postcodes]
print(f"Loaded {len(postcodes):,} postcode polygons from {units_dir}")
return postcodes, geoms