"""Download Defra Round 4 (2022) strategic noise data for England.

Downloads modelled noise levels (road, rail, airport) as GeoTIFF rasters via
WCS, then samples the local maximum around each postcode representative point.
Outputs a parquet file with postcode-level noise in dB for each source.

Uses smaller 20km tiles at native 10m resolution so values are not understated
by a single coarse centroid sample.

Data source: Defra Strategic Noise Mapping Round 4 (2022)
  - Lden = day-evening-night 24h weighted average (the EU standard metric)
  - 10m grid, modelled at 4m above ground
License: Open Government Licence v3.0

Note: Road/rail use WCS 1.0.0; airport requires WCS 2.0.1 (Defra's 1.0.0
endpoint is broken for that coverage).
"""

import argparse
import math
import tempfile
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path

import httpx
import numpy as np
import polars as pl
import rasterio
from pyproj import Transformer
from rasterio.transform import rowcol
from scipy.ndimage import maximum_filter

# Noise sources:
# (label, column_name, WCS base URL, coverage ID, WCS version, allow_missing_tiles)
# Road/rail work with WCS 1.0.0; airport requires WCS 2.0.1 and returns 500
# for many sparse/no-coverage tiles, which should become nulls.
NOISE_SOURCES = [
    (
        "Road",
        "road_noise_lden_db",
        "https://environment.data.gov.uk/spatialdata/road-noise-all-metrics-england-round-4/wcs",
        "Road_Noise_Lden_England_Round_4_All",
        "1.0.0",
        False,
    ),
    (
        "Rail",
        "rail_noise_lden_db",
        "https://environment.data.gov.uk/spatialdata/noise-data/wcs",
        "Rail_Noise_Lden_England_Round_4_All",
        "1.0.0",
        False,
    ),
    (
        "Airport",
        "airport_noise_lden_db",
        "https://environment.data.gov.uk/spatialdata/airport-noise-all-metrics-england-round-4/wcs",
        "dac9cba4-abe7-43bd-b8e9-8a83da52edd8__Airport_Noise_ALL_Lden",
        "2.0.1",
        True,
    ),
]

# England extent in EPSG:27700 (British National Grid), rounded outward
BNG_MIN_E = 80_000
BNG_MAX_E = 660_000
BNG_MIN_N = 0
BNG_MAX_N = 660_000

# Tile size in metres. At 10m resolution, 20km tiles are ~4M pixels each,
# small enough to process one at a time without mosaicking all England.
TILE_SIZE = 20_000

# Max concurrent tile downloads
MAX_WORKERS = 4

# Native raster resolution (10m grid)
NATIVE_RESOLUTION = 10

# Request pixel resolution in metres.
RESOLUTION = NATIVE_RESOLUTION

# The pipeline has postcode representative points rather than complete unit
# polygons here. Use a small local footprint and take the maximum 10m cell so
# postcode-level noise is not understated by centroid rounding.
POSTCODE_NOISE_RADIUS_M = 50

# Retry/split behaviour for slow Defra WCS requests. Some 100km eastern tiles
# intermittently return 504s; smaller fallback requests usually succeed.
MAX_RETRIES = 3
RETRY_BACKOFF_SECONDS = 5
MIN_TILE_SIZE = 5_000

type Tile = tuple[int, int, int, int]


class NoGeoTiffError(RuntimeError):
    """Raised when WCS returns an XML/HTML exception or another non-raster body."""


def _wcs_get_coverage_url(
    wcs_base: str,
    coverage_id: str,
    min_e: int,
    min_n: int,
    max_e: int,
    max_n: int,
    wcs_version: str = "1.0.0",
) -> str:
    """Build a WCS GetCoverage URL for a BNG bounding box."""
    if wcs_version == "2.0.1":
        return (
            f"{wcs_base}?"
            f"service=WCS&version=2.0.1&request=GetCoverage"
            f"&coverageId={coverage_id}"
            f"&format=image/tiff"
            f"&subsettingCRS=EPSG:27700"
            f"&subset=E({min_e},{max_e})"
            f"&subset=N({min_n},{max_n})"
            f"&scaleFactor={NATIVE_RESOLUTION / RESOLUTION}"
        )
    width = (max_e - min_e) // RESOLUTION
    height = (max_n - min_n) // RESOLUTION
    return (
        f"{wcs_base}?"
        f"service=WCS&version=1.0.0&request=GetCoverage"
        f"&coverage={coverage_id}"
        f"&CRS=EPSG:27700"
        f"&BBOX={min_e},{min_n},{max_e},{max_n}"
        f"&width={width}&height={height}"
        f"&format=GeoTIFF"
    )


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


def _bng_from_latlon(lat: np.ndarray, lon: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    """Convert WGS84 lat/lon to British National Grid easting/northing."""
    return _TO_BNG.transform(lon, lat)  # pyproj takes (x=lon, y=lat)


def _looks_like_tiff(response: httpx.Response) -> bool:
    content_type = response.headers.get("content-type", "")
    return "tiff" in content_type or response.content[:4] in (b"II*\x00", b"MM\x00*")


def _fetch_tile_bytes(
    wcs_base: str,
    coverage_id: str,
    min_e: int,
    min_n: int,
    max_e: int,
    max_n: int,
    wcs_version: str = "1.0.0",
) -> bytes:
    """Fetch one WCS tile."""
    url = _wcs_get_coverage_url(
        wcs_base, coverage_id, min_e, min_n, max_e, max_n, wcs_version
    )
    with httpx.Client(timeout=300, follow_redirects=True) as client:
        resp = client.get(url)
        resp.raise_for_status()

    if not _looks_like_tiff(resp):
        content_type = resp.headers.get("content-type", "<missing>")
        body_preview = resp.text[:200].replace("\n", " ")
        raise NoGeoTiffError(
            f"WCS returned non-GeoTIFF response ({content_type}): {body_preview}"
        )
    return resp.content


def _split_tile(min_e: int, min_n: int, max_e: int, max_n: int) -> list[Tile]:
    e_edges = [min_e, max_e]
    n_edges = [min_n, max_n]
    if max_e - min_e > MIN_TILE_SIZE:
        e_edges.insert(1, (min_e + max_e) // 2)
    if max_n - min_n > MIN_TILE_SIZE:
        n_edges.insert(1, (min_n + max_n) // 2)

    subtiles: list[Tile] = []
    for e0, e1 in zip(e_edges, e_edges[1:]):
        for n0, n1 in zip(n_edges, n_edges[1:]):
            if e1 > e0 and n1 > n0:
                subtiles.append((e0, n0, e1, n1))
    return subtiles


def _tile_path(tile_dir: Path, min_e: int, min_n: int, max_e: int, max_n: int) -> Path:
    return tile_dir / f"tile_{min_e}_{min_n}_{max_e}_{max_n}.tif"


def _download_tile(
    wcs_base: str,
    coverage_id: str,
    min_e: int,
    min_n: int,
    max_e: int,
    max_n: int,
    tile_dir: Path,
    wcs_version: str = "1.0.0",
    split_failures: bool = True,
) -> tuple[list[Path], list[Tile]]:
    """Download a WCS tile, splitting on repeated server failures."""
    tile_path = _tile_path(tile_dir, min_e, min_n, max_e, max_n)
    if tile_path.exists() and tile_path.stat().st_size > 0:
        return [tile_path], []

    last_error: Exception | None = None
    for attempt in range(1, MAX_RETRIES + 1):
        try:
            content = _fetch_tile_bytes(
                wcs_base, coverage_id, min_e, min_n, max_e, max_n, wcs_version
            )
            tile_path.write_bytes(content)
            return [tile_path], []
        except (
            NoGeoTiffError,
            httpx.HTTPStatusError,
            httpx.TimeoutException,
            httpx.ConnectError,
        ) as e:
            last_error = e
            if attempt < MAX_RETRIES:
                sleep_for = RETRY_BACKOFF_SECONDS * attempt
                print(
                    f"  Retrying tile ({min_e},{min_n})-({max_e},{max_n}) "
                    f"after {type(e).__name__} ({attempt}/{MAX_RETRIES})"
                )
                time.sleep(sleep_for)

    subtiles = _split_tile(min_e, min_n, max_e, max_n) if split_failures else []
    if len(subtiles) > 1:
        print(
            f"  Splitting tile ({min_e},{min_n})-({max_e},{max_n}) "
            f"into {len(subtiles)} smaller requests after: {last_error}"
        )
        paths: list[Path] = []
        failures: list[Tile] = []
        for e0, n0, e1, n1 in subtiles:
            child_paths, child_failures = _download_tile(
                wcs_base,
                coverage_id,
                e0,
                n0,
                e1,
                n1,
                tile_dir,
                wcs_version,
                split_failures,
            )
            paths.extend(child_paths)
            failures.extend(child_failures)
        return paths, failures

    print(
        f"  Failed to download tile ({min_e},{min_n})-({max_e},{max_n}): {last_error}"
    )
    return [], [(min_e, min_n, max_e, max_n)]


def download_raster(
    tile_dir: Path,
    wcs_base: str,
    coverage_id: str,
    label: str,
    wcs_version: str = "1.0.0",
    allow_missing_tiles: bool = False,
) -> list[Path]:
    """Download noise GeoTIFF raster covering England, returning paths to saved files."""
    tiles = []
    for min_e in range(BNG_MIN_E, BNG_MAX_E, TILE_SIZE):
        for min_n in range(BNG_MIN_N, BNG_MAX_N, TILE_SIZE):
            max_e = min(min_e + TILE_SIZE, BNG_MAX_E)
            max_n = min(min_n + TILE_SIZE, BNG_MAX_N)
            tiles.append((min_e, min_n, max_e, max_n))

    print(
        f"[{label}] Downloading {len(tiles)} tiles at {RESOLUTION}m resolution ({MAX_WORKERS} workers)..."
    )
    paths: list[Path] = []
    failures: list[Tile] = []
    completed = 0

    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
        futures = {}
        for min_e, min_n, max_e, max_n in tiles:
            fut = executor.submit(
                _download_tile,
                wcs_base,
                coverage_id,
                min_e,
                min_n,
                max_e,
                max_n,
                tile_dir,
                wcs_version,
                not allow_missing_tiles,
            )
            futures[fut] = (min_e, min_n)

        for fut in as_completed(futures):
            completed += 1
            result_paths, result_failures = fut.result()
            paths.extend(result_paths)
            failures.extend(result_failures)
            print(
                f"\r  [{completed}/{len(tiles)}] Downloaded {len(paths)} GeoTIFFs",
                end="",
                flush=True,
            )

    if failures:
        preview = ", ".join(
            f"({e0},{n0})-({e1},{n1})" for e0, n0, e1, n1 in failures[:5]
        )
        suffix = "..." if len(failures) > 5 else ""
        if allow_missing_tiles:
            print(
                f"\n[{label}] WARNING: skipped {len(failures)} missing/no-data "
                f"tile requests: {preview}{suffix}"
            )
            print(f"[{label}] Downloaded {len(paths)} GeoTIFFs from {len(tiles)} tiles")
            return paths
        raise RuntimeError(
            f"[{label}] Failed to download {len(failures)} tile requests after "
            f"retries and splitting: {preview}{suffix}"
        )

    print(f"\n[{label}] Downloaded {len(paths)} GeoTIFFs from {len(tiles)} tiles")
    return paths


def sample_noise_at_postcodes(
    tile_paths: list[Path],
    easting: np.ndarray,
    northing: np.ndarray,
    label: str,
    col_name: str,
) -> pl.Series:
    """Sample max noise values from 10m tiles around postcode representative points."""
    print(f"[{label}] Sampling max noise values from {len(tile_paths)} tiles...")
    noise_db = np.full(len(easting), np.nan, dtype=np.float32)
    radius_cells = max(0, math.ceil(POSTCODE_NOISE_RADIUS_M / RESOLUTION))
    filter_size = radius_cells * 2 + 1

    for path in tile_paths:
        with rasterio.open(path) as dataset:
            bounds = dataset.bounds
            candidate_mask = (
                (easting >= bounds.left - POSTCODE_NOISE_RADIUS_M)
                & (easting <= bounds.right + POSTCODE_NOISE_RADIUS_M)
                & (northing >= bounds.bottom - POSTCODE_NOISE_RADIUS_M)
                & (northing <= bounds.top + POSTCODE_NOISE_RADIUS_M)
            )
            candidate_indices = np.flatnonzero(candidate_mask)
            if len(candidate_indices) == 0:
                continue

            grid = dataset.read(1).astype(np.float32, copy=False)
            invalid = ~np.isfinite(grid) | (grid == 0)
            if dataset.nodata is not None:
                invalid |= np.isclose(
                    grid, np.float32(dataset.nodata), rtol=1e-5, atol=1e-5
                )
            grid = grid.copy()
            grid[invalid] = -np.inf
            if filter_size > 1:
                grid = maximum_filter(
                    grid, size=filter_size, mode="constant", cval=-np.inf
                )

            rows, cols = rowcol(
                dataset.transform,
                easting[candidate_indices],
                northing[candidate_indices],
            )
            rows = np.asarray(rows)
            cols = np.asarray(cols)
            h, w = grid.shape
            in_bounds = (rows >= 0) & (rows < h) & (cols >= 0) & (cols < w)
            if not np.any(in_bounds):
                continue

            sampled_indices = candidate_indices[in_bounds]
            sampled = grid[rows[in_bounds], cols[in_bounds]]
            valid = sampled != -np.inf
            if not np.any(valid):
                continue

            sampled_indices = sampled_indices[valid]
            sampled = sampled[valid]
            existing = noise_db[sampled_indices]
            noise_db[sampled_indices] = np.where(
                np.isnan(existing), sampled, np.maximum(existing, sampled)
            )

    valid_count = int(np.sum(~np.isnan(noise_db)))
    print(
        f"[{label}] Sampled {valid_count:,} / {len(easting):,} postcodes with noise data"
    )

    # Return as masked Series: use null (not NaN) so that Polars max_horizontal
    # correctly ignores missing values instead of propagating NaN.
    return pl.Series(col_name, noise_db).fill_nan(None)


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Download Defra noise data (road, rail, airport) and sample at postcode centroids"
    )
    parser.add_argument(
        "--arcgis",
        type=Path,
        required=True,
        help="ArcGIS postcode data parquet (for lat/lon coordinates)",
    )
    parser.add_argument(
        "--output", type=Path, required=True, help="Output parquet file path"
    )
    args = parser.parse_args()

    args.output.parent.mkdir(parents=True, exist_ok=True)

    print("Loading postcode coordinates...")
    postcodes = pl.read_parquet(args.arcgis, columns=["pcds", "lat", "long"]).rename(
        {"pcds": "postcode", "long": "lon"}
    )

    lat = postcodes["lat"].to_numpy()
    lon = postcodes["lon"].to_numpy()

    print("Converting lat/lon to BNG...")
    easting, northing = _bng_from_latlon(lat, lon)

    result = postcodes.select("postcode")

    with tempfile.TemporaryDirectory() as tmp:
        for (
            label,
            col_name,
            wcs_base,
            coverage_id,
            wcs_version,
            allow_missing_tiles,
        ) in NOISE_SOURCES:
            tile_dir = Path(tmp) / label.lower()
            tile_dir.mkdir()
            tile_paths = download_raster(
                tile_dir,
                wcs_base,
                coverage_id,
                label,
                wcs_version,
                allow_missing_tiles,
            )

            if not tile_paths:
                print(
                    f"[{label}] WARNING: No tiles downloaded — column will be all null"
                )
                series = pl.Series(col_name, [None] * len(lat), dtype=pl.Float32)
            else:
                series = sample_noise_at_postcodes(
                    tile_paths, easting, northing, label, col_name
                )

            result = result.with_columns(series)

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


if __name__ == "__main__":
    main()