"""Compute Ofsted-rated school proximity counts per postcode."""

import argparse
from pathlib import Path

import polars as pl

from pipeline.utils.poi_counts import count_pois_per_postcode

SCHOOL_GROUPS = {
    "good_primary": ["good_primary", "outstanding_primary"],
    "good_secondary": ["good_secondary", "outstanding_secondary"],
    "outstanding_primary": ["outstanding_primary"],
    "outstanding_secondary": ["outstanding_secondary"],
}


def main():
    parser = argparse.ArgumentParser(
        description="Count good+ and outstanding primary/secondary schools near each postcode"
    )
    parser.add_argument(
        "--ofsted", type=Path, required=True, help="Ofsted inspection parquet"
    )
    parser.add_argument(
        "--arcgis", type=Path, required=True, help="ArcGIS postcode parquet"
    )
    parser.add_argument(
        "--output", type=Path, required=True, help="Output parquet path"
    )
    args = parser.parse_args()

    # Load Ofsted data: filter to good+ (1, 2) primary/secondary schools.
    # Post-2025 reform the single "Overall effectiveness" grade was retired;
    # the legacy 1–4 scale is now carried forward under "Latest OEIF overall
    # effectiveness" (OEIF = the previous Ofsted Education Inspection
    # Framework). The new report-card columns use text judgements instead.
    ofsted = pl.read_parquet(args.ofsted).filter(
        pl.col("Ofsted phase").is_in(["Primary", "Secondary"])
        & pl.col("Latest OEIF overall effectiveness").is_in(["1", "2"])
    )

    print(f"Good+ schools: {len(ofsted):,}")
    print(
        "Outstanding schools: "
        f"{ofsted.filter(pl.col('Latest OEIF overall effectiveness') == '1').height:,}"
    )

    # Assign category based on phase and rating. Good+ groups include both
    # category variants; outstanding groups count grade 1 only.
    ofsted = ofsted.with_columns(
        pl.when(pl.col("Ofsted phase") == "Primary")
        .then(
            pl.when(pl.col("Latest OEIF overall effectiveness") == "1")
            .then(pl.lit("outstanding_primary"))
            .otherwise(pl.lit("good_primary"))
        )
        .otherwise(
            pl.when(pl.col("Latest OEIF overall effectiveness") == "1")
            .then(pl.lit("outstanding_secondary"))
            .otherwise(pl.lit("good_secondary"))
        )
        .alias("category")
    ).select(
        pl.col("Postcode").alias("postcode"),
        "category",
    )

    # Join with arcgis to get lat/lng for each school's postcode
    arcgis = pl.read_parquet(args.arcgis).select(
        pl.col("pcds").alias("postcode"),
        "lat",
        pl.col("long").alias("lng"),
    )

    schools = ofsted.join(arcgis, on="postcode", how="inner")
    print(f"Schools with coordinates: {len(schools):,}")

    # Load all postcodes for proximity counting
    postcodes = arcgis.rename({"lng": "lon"})

    counts_5km = count_pois_per_postcode(
        postcodes, schools, radius_km=5, groups=SCHOOL_GROUPS
    )
    counts_2km = count_pois_per_postcode(
        postcodes, schools, radius_km=2, groups=SCHOOL_GROUPS
    )

    result = counts_5km.join(counts_2km, on="postcode")

    result.write_parquet(args.output)
    size_mb = args.output.stat().st_size / (1024 * 1024)
    print(f"Wrote {args.output} ({size_mb:.1f} MB)")


if __name__ == "__main__":
    main()