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https://github.com/Hestia-Homes/Model.git
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3998ef586c
Add Coordinates value object + GeospatialRepository port + GeospatialS3Repository adapter. Resolves a Property's lon/lat from the partitioned Ordnance Survey Open-UPRN parquet (filename_meta -> partition -> UPRN row). A Repo, not a Fetcher (ADR-0011): no live OS API call. The parquet reader is injected, so it's unit-tested against fixture parquets with no S3/network; returns None when the UPRN is uncovered or absent. pyright strict clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
from __future__ import annotations
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from collections.abc import Callable
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from typing import Optional
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import pandas as pd
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from domain.geospatial.coordinates import Coordinates
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from repositories.geospatial.geospatial_repository import GeospatialRepository
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ParquetReader = Callable[[str], pd.DataFrame]
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_META_KEY = "spatial/filename_meta.parquet"
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class GeospatialS3Repository(GeospatialRepository):
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"""Reads the partitioned Ordnance Survey Open-UPRN parquet dataset.
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`spatial/filename_meta.parquet` maps a UPRN range (lower/upper) to a
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partition file; that partition carries `UPRN`/`LATITUDE`/`LONGITUDE`. The
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parquet reader is injected so the dataset can be sourced from S3 in
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production or a fixture directory in tests — the Repo holds no S3/HTTP code.
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"""
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def __init__(self, read_parquet: ParquetReader) -> None:
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self._read_parquet = read_parquet
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def coordinates_for(self, uprn: int) -> Optional[Coordinates]:
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meta = self._read_parquet(_META_KEY)
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covering = meta[(meta["lower"] <= uprn) & (meta["upper"] >= uprn)]
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if covering.empty:
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return None
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filename = str(covering["filenames"].iloc[0])
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partition = self._read_parquet(f"spatial/{filename}")
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rows = partition[partition["UPRN"] == uprn]
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if rows.empty:
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return None
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row = rows.iloc[0]
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return Coordinates(
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longitude=float(row["LONGITUDE"]),
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latitude=float(row["LATITUDE"]),
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)
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