import json

import polars as pl
from pyproj import Transformer

from pipeline.transform.crime_spatial import transform_crime_spatial

_TO_WGS84 = Transformer.from_crs("EPSG:27700", "EPSG:4326", always_xy=True)

_CSV_HEADER = (
    "Crime ID,Month,Reported by,Falls within,Longitude,Latitude,Location,"
    "LSOA code,LSOA name,Crime type,Last outcome category,Context"
)


def _bng_to_wgs84(x: float, y: float) -> tuple[float, float]:
    lon, lat = _TO_WGS84.transform(x, y)
    return lon, lat


def _square_feature(postcode: str, x0: float, y0: float, x1: float, y1: float) -> dict:
    ring = [(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0)]
    coords = [list(_bng_to_wgs84(x, y)) for x, y in ring]
    return {
        "type": "Feature",
        "properties": {"postcodes": postcode, "mapit_code": postcode.replace(" ", "")},
        "geometry": {"type": "Polygon", "coordinates": [coords]},
    }


def _write_boundaries(units_dir, features_by_district: dict[str, list[dict]]) -> None:
    units_dir.mkdir(parents=True)
    for district, features in features_by_district.items():
        collection = {"type": "FeatureCollection", "features": features}
        (units_dir / f"{district}.geojson").write_text(json.dumps(collection))


def _crime_row(month: str, x, y, crime_type: str) -> str:
    if x is None or y is None:
        lon, lat = "", ""
    else:
        lon, lat = _bng_to_wgs84(x, y)
    return f",{month},F,F,{lon},{lat},On or near X,E01000001,L,{crime_type},U,"


def _write_month(crime_dir, month: str, rows: list[str]) -> None:
    month_dir = crime_dir / month
    month_dir.mkdir(parents=True)
    body = "\n".join([_CSV_HEADER, *rows]) + "\n"
    (month_dir / f"{month}-test-force-street.csv").write_text(body)


def test_buffer_overlap_counts_for_each_postcode(tmp_path):
    units = tmp_path / "units"
    # A and B sit 70m apart; their +50m buffers overlap in x in [1030, 1060].
    _write_boundaries(
        units,
        {
            "AB1": [
                _square_feature("AB1 1AA", 1000, 1000, 1010, 1010),
                _square_feature("AB1 1AB", 1080, 1000, 1090, 1010),
                _square_feature("AB1 1AC", 5000, 5000, 5010, 5010),
            ]
        },
    )

    crime = tmp_path / "crime"
    _write_month(
        crime,
        "2024-01",
        [
            # In the overlap: 35m east of A, 35m west of B -> counts for both.
            _crime_row("2024-01", 1045, 1005, "Burglary"),
            # 49m east of C's edge -> inside C's buffer.
            _crime_row("2024-01", 5059, 5005, "Robbery"),
            # 51m east of C's edge -> outside every buffer.
            _crime_row("2024-01", 5061, 5005, "Robbery"),
            # No coordinate -> dropped entirely.
            _crime_row("2024-01", None, None, "Anti-social behaviour"),
        ],
    )

    output = tmp_path / "crime_by_postcode.parquet"
    by_year = tmp_path / "crime_by_postcode_by_year.parquet"
    transform_crime_spatial(crime, units, output, by_year)

    rows = {
        r["postcode"]: r
        for r in pl.read_parquet(output).to_dicts()
    }
    # Single month -> annualised x12.
    assert rows["AB1 1AA"]["Burglary (avg/yr)"] == 12.0
    assert rows["AB1 1AB"]["Burglary (avg/yr)"] == 12.0
    assert rows["AB1 1AA"]["Robbery (avg/yr)"] == 0.0
    # Only the 49m robbery counts for C; the 51m one and the blank row do not.
    assert rows["AB1 1AC"]["Robbery (avg/yr)"] == 12.0
    assert rows["AB1 1AC"]["Burglary (avg/yr)"] == 0.0
    # Anti-social behaviour had no coordinate -> nobody gets it.
    assert all(r["Anti-social behaviour (avg/yr)"] == 0.0 for r in rows.values())


def test_by_year_annualises_and_rolls_up(tmp_path):
    units = tmp_path / "units"
    _write_boundaries(
        units, {"AB1": [_square_feature("AB1 1AA", 1000, 1000, 1010, 1010)]}
    )

    crime = tmp_path / "crime"
    # Point at the centre of AB1 1AA, well inside its buffer.
    _write_month(
        crime,
        "2023-01",
        [
            _crime_row("2023-01", 1005, 1005, "Burglary"),
            _crime_row("2023-01", 1005, 1005, "Robbery"),
        ],
    )
    _write_month(crime, "2024-01", [_crime_row("2024-01", 1005, 1005, "Burglary")])
    _write_month(
        crime,
        "2024-02",
        [
            _crime_row("2024-02", 1005, 1005, "Burglary"),
            _crime_row("2024-02", 1005, 1005, "Anti-social behaviour"),
        ],
    )

    output = tmp_path / "crime_by_postcode.parquet"
    by_year = tmp_path / "crime_by_postcode_by_year.parquet"
    transform_crime_spatial(crime, units, output, by_year)

    by_year_df = pl.read_parquet(by_year)
    assert by_year_df.height == 1
    cols = set(by_year_df.columns)
    assert {"Burglary (by year)", "Serious crime (by year)", "Minor crime (by year)"} <= cols

    row = by_year_df.row(0, named=True)
    burglary = sorted(row["Burglary (by year)"], key=lambda r: r["year"])
    # 2023: 1 burglary in 1 month -> 12/yr; 2024: 2 in 2 months -> 12/yr.
    assert burglary == [
        {"year": 2023, "count": 12.0},
        {"year": 2024, "count": 12.0},
    ]
    serious = {p["year"]: p["count"] for p in row["Serious crime (by year)"]}
    # 2023 serious = Burglary(12) + Robbery(12) = 24; 2024 = Burglary(12).
    assert serious[2023] == 24.0
    assert serious[2024] == 12.0