"""Fast daily city-wide mean risk_1d with on-disk cache.

Avoids re-parsing ~45MB GeoJSON on every /analysis/trend request.
"""
from __future__ import annotations

import json
import logging
from functools import lru_cache
from pathlib import Path

from config import DATA_DIR, PROJECT_ROOT

logger = logging.getLogger(__name__)

_CACHE_PATH = PROJECT_ROOT / "processed" / "daily_avg_risk.json"


def _read_disk_cache() -> dict[str, float]:
    if not _CACHE_PATH.exists():
        return {}
    try:
        raw = json.loads(_CACHE_PATH.read_text(encoding="utf-8"))
        return {str(k): float(v) for k, v in raw.items()}
    except (OSError, json.JSONDecodeError, TypeError, ValueError):
        return {}


def _write_disk_cache(cache: dict[str, float]) -> None:
    try:
        _CACHE_PATH.parent.mkdir(parents=True, exist_ok=True)
        _CACHE_PATH.write_text(
            json.dumps(cache, ensure_ascii=False, separators=(",", ":")),
            encoding="utf-8",
        )
    except OSError as e:
        logger.warning("Failed to persist daily avg risk cache: %s", e)


def _compute_mean_risk_1d(filepath: Path) -> float:
    """Parse one risk GeoJSON and return mean risk_1d (0 if empty/missing)."""
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            geojson = json.load(f)
    except (OSError, json.JSONDecodeError) as e:
        logger.warning("Failed to parse %s: %s", filepath, e)
        return 0.0

    total = 0.0
    n = 0
    for feature in geojson.get("features", []):
        props = feature.get("properties") or {}
        r = props.get("risk_1d")
        if r is None:
            continue
        total += float(r)
        n += 1
    return round(total / n, 4) if n else 0.0


@lru_cache(maxsize=64)
def daily_avg_risk(date_yyyymmdd: str) -> float:
    """Mean risk_1d for YYYYMMDD. Memory + disk cached."""
    disk = _read_disk_cache()
    if date_yyyymmdd in disk:
        return disk[date_yyyymmdd]

    filepath = DATA_DIR / f"risk_{date_yyyymmdd}.geojson"
    if not filepath.exists():
        return 0.0

    avg = _compute_mean_risk_1d(filepath)
    disk[date_yyyymmdd] = avg
    _write_disk_cache(disk)
    return avg


def warm_daily_avg_risk(dates: list[str]) -> None:
    """Precompute missing dates into the disk cache (blocking)."""
    for d in dates:
        daily_avg_risk(d)