"""
Router for CBPOA analysis endpoints
Time series trends, district aggregation, and weather-health correlations
"""
from fastapi import APIRouter, HTTPException, Query
from datetime import datetime, timedelta
from typing import List, Literal
from functools import lru_cache
import random

import pandas as pd
from pydantic import BaseModel, Field

from config import DATA_DIR, RISK_HIGH, PROJECT_ROOT, WUHAN_BOUNDS, LAT_STEP, LON_STEP
from utils.date_helpers import get_latest_date
from utils.geojson import parse_geojson_file, load_districts
from utils.geo import point_in_polygon
from utils.risk import calculate_trend

router = APIRouter(prefix="/api/analysis", tags=["analysis"])


class TrendResponse(BaseModel):
    """Response for trend data"""
    dates: List[str] = Field(..., description="Date labels")
    values: List[float] = Field(..., description="Risk values")
    trend: Literal["up", "down", "stable"] = Field(..., description="Trend direction")


class DistrictRisk(BaseModel):
    """District-level risk aggregation"""
    district: str = Field(..., description="District name")
    avg_risk: float = Field(..., description="Average risk value")
    avg_aqi: float = Field(..., description="Average AQI from weather stations in this district")
    population: int = Field(..., description="Population (sum of 100m LandScan cells in district)")
    high_risk_count: int = Field(..., description="Count of high risk grids")
    total_grids: int = Field(..., description="Total grids in district")
    total_cases: int = Field(..., description="Total recorded cases (real, from cases_by_district_daily)")


class DistrictsResponse(BaseModel):
    """Response for districts aggregation"""
    districts: List[DistrictRisk] = Field(..., description="District risk data")
    timestamp: str = Field(..., description="Response timestamp")


class CorrelationFactor(BaseModel):
    """Correlation factor data"""
    factor: str = Field(..., description="Factor name")
    correlation: float = Field(..., description="Correlation coefficient (-1 to 1)")
    significance: Literal["high", "medium", "low"] = Field(..., description="Statistical significance")
    description: str = Field(..., description="Factor description")


class CorrelationsResponse(BaseModel):
    """Response for correlations"""
    correlations: List[CorrelationFactor] = Field(..., description="Correlation factors")
    timestamp: str = Field(..., description="Response timestamp")


@router.get("/trend", response_model=TrendResponse)
async def get_trend(days: int = Query(default=7, ge=1, le=30)):
    """
    Get time series trend data from ACTUAL historical observations

    Args:
        days: Number of days for trend (1-30)

    Returns:
        Trend data with dates, values, and trend direction
    """
    latest_date = get_latest_date()

    try:
        base_date = datetime.strptime(latest_date, "%Y%m%d")
    except ValueError:
        raise HTTPException(status_code=500, detail="Invalid date format in data files")

    dates = []
    values = []

    for i in range(days):
        date = base_date - timedelta(days=days - 1 - i)
        date_str = date.strftime("%Y%m%d")
        filepath = DATA_DIR / f"risk_{date_str}.geojson"

        if filepath.exists():
            grids = parse_geojson_file(filepath)
            if grids:
                avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
                values.append(round(avg_risk, 4))
            else:
                values.append(0)
        else:
            values.append(0)
        dates.append(date.strftime("%Y-%m-%d"))

    # Preserve the full requested date range: a "7天" request must return 7
    # contiguous points. Days with no geojson (or empty grids) stay 0 rather
    # than being dropped, which previously produced fewer, non-contiguous points.
    trend_direction = calculate_trend(values)

    return TrendResponse(
        dates=dates,
        values=values,
        trend=trend_direction,
    )


@lru_cache(maxsize=1)
def _grid_district_lookup() -> dict:
    """Map precomputed r{row}_c{col} grid id -> district name (loaded once)."""
    path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
    if not path.exists():
        return {}
    df = pd.read_parquet(path)
    # Some grids have a null district_name; drop them so the lookup only ever
    # returns valid strings (missing keys fall back to "其他").
    df = df.dropna(subset=["district_name"])
    return dict(zip(df["grid_id"].astype(str), df["district_name"].astype(str)))


@lru_cache(maxsize=1)
def _district_population() -> dict:
    """Real population per district.

    Sums the LandScan-derived population_density of every 100m cell
    (grid_100m_with_dem_pop.parquet) grouped by district via the
    grid->district mapping. Returns {district_name: total_population}.
    """
    pop_path = PROJECT_ROOT / "processed" / "grid_100m_with_dem_pop.parquet"
    map_path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
    if not pop_path.exists() or not map_path.exists():
        return {}
    pop = pd.read_parquet(pop_path, columns=["grid_id", "population_density"])
    mapping = pd.read_parquet(map_path).dropna(subset=["district_name"])
    joined = pop.merge(mapping, on="grid_id", how="inner")
    by_d = joined.groupby("district_name")["population_density"].sum()
    return {str(k): int(round(v)) for k, v in by_d.items()}


@lru_cache(maxsize=1)
def _district_avg_aqi() -> dict:
    """Real average AQI per district from weather station daily data.

    Each station (with lat/lon) is assigned to a district using the same
    grid->district mapping (100m grid spacing of 1/1110 deg, the convention
    the mapping was built with), then AQI is averaged per district across
    all daily observations. Returns {district_name: avg_aqi}. Districts with
    no station fall back to the city-wide mean in the caller.
    """
    map_path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
    station_path = PROJECT_ROOT / "processed" / "weather" / "station_daily_2022.parquet"
    if not map_path.exists() or not station_path.exists():
        return {}
    mapping = pd.read_parquet(map_path).dropna(subset=["district_name"])
    lookup = dict(zip(mapping["grid_id"].astype(str), mapping["district_name"].astype(str)))
    station = pd.read_parquet(station_path, columns=["station_id", "lat", "lon", "AQI"])

    step = 1.0 / 1110.0  # mapping grid spacing in degrees
    min_lat = WUHAN_BOUNDS["min_lat"]
    min_lon = WUHAN_BOUNDS["min_lon"]

    coords = station[["station_id", "lat", "lon"]].drop_duplicates()
    station_to_district = {}
    for _, r in coords.iterrows():
        row = int((r["lat"] - min_lat) / step)
        col = int((r["lon"] - min_lon) / step)
        station_to_district[r["station_id"]] = lookup.get(f"r{row}_c{col}", "其他")

    station = station.copy()
    station["district"] = station["station_id"].map(station_to_district)
    in_district = station[station["district"] != "其他"]
    by_d = in_district.groupby("district")["AQI"].mean()
    return {str(k): round(float(v), 1) for k, v in by_d.items()}


@lru_cache(maxsize=1)
def _district_total_cases() -> dict:
    """Real total recorded cases per district from cases_by_district_daily.

    District labels in the case file are inconsistent ("武昌" vs "武昌区"),
    so names are normalized by stripping the "区" suffix and summed, then
    keyed by the canonical mapping name (with "区"). Returns {district: cases}.
    """
    path = PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet"
    if not path.exists():
        return {}
    df = pd.read_parquet(path, columns=["district", "total_cases"])
    df = df.copy()
    df["base"] = df["district"].str.replace("区", "", regex=False)
    by_base = df.groupby("base")["total_cases"].sum()
    return {f"{base}区": int(v) for base, v in by_base.items()}


@lru_cache(maxsize=8)
def _aggregate_districts(date: str) -> list:
    """Aggregate per-district risk for a date.

    Assigns each 100m risk grid to a district via the precomputed
    grid->district mapping (O(1) dict lookup per grid) instead of per-grid
    point-in-polygon (which is ~100x slower over 140k grids). Cached by date.
    """
    grids = parse_geojson_file(DATA_DIR / f"risk_{date}.geojson")
    lookup = _grid_district_lookup()

    agg: dict = {}
    for g in grids:
        row = int((g["latitude"] - WUHAN_BOUNDS["min_lat"]) / LAT_STEP)
        col = int((g["longitude"] - WUHAN_BOUNDS["min_lon"]) / LON_STEP)
        name = lookup.get(f"r{row}_c{col}", "其他")
        a = agg.setdefault(name, {"sum": 0.0, "count": 0, "high": 0})
        risk = g["risk_value"]
        a["sum"] += risk
        a["count"] += 1
        if risk >= RISK_HIGH:
            a["high"] += 1

    pop_by_district = _district_population()
    aqi_by_district = _district_avg_aqi()
    cases_by_district = _district_total_cases()
    # City-wide mean AQI as fallback for districts without a weather station.
    city_avg_aqi = round(sum(aqi_by_district.values()) / len(aqi_by_district), 1) if aqi_by_district else 0.0

    result = []
    for name, a in agg.items():
        if a["count"] == 0:
            continue
        avg = a["sum"] / a["count"]
        result.append({
            "district": name,
            "avg_risk": round(avg, 4),
            "avg_aqi": aqi_by_district.get(name, city_avg_aqi),
            "population": pop_by_district.get(name, 0),
            "high_risk_count": a["high"],
            "total_grids": a["count"],
            "total_cases": cases_by_district.get(name, 0),
        })
    # '其他' (unassigned) last, otherwise by descending risk
    result.sort(key=lambda d: (d["district"] == "其他", -d["avg_risk"]))
    return result


@router.get("/districts", response_model=DistrictsResponse)
async def get_districts():
    """Get district-level risk aggregation (cached per date)."""
    latest_date = get_latest_date()
    filepath = DATA_DIR / f"risk_{latest_date}.geojson"

    if not filepath.exists():
        raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")

    districts = [DistrictRisk(**d) for d in _aggregate_districts(latest_date)]
    return DistrictsResponse(
        districts=districts,
        timestamp=datetime.now().isoformat(),
    )


@router.get("/correlations", response_model=CorrelationsResponse)
async def get_correlations():
    """
    Get weather-health correlation analysis

    Returns:
        Correlation factors with coefficients and significance
    """
    latest_date = get_latest_date()
    filepath = DATA_DIR / f"risk_{latest_date}.geojson"

    if not filepath.exists():
        raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")

    grids = parse_geojson_file(filepath)
    if not grids:
        raise HTTPException(status_code=404, detail="No grid data found")

    # Calculate mock correlations based on risk patterns
    # In production, this would use actual weather and health data
    avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
    risk_variance = sum((g["risk_value"] - avg_risk) ** 2 for g in grids) / len(grids)

    # Generate realistic correlation coefficients
    correlations = [
        CorrelationFactor(
            factor="temperature",
            correlation=round(-0.45 - 0.1 * (avg_risk - 0.5), 3),
            significance="high" if risk_variance > 0.05 else "medium",
            description="Temperature vs risk: Lower temps correlate with higher risk"
        ),
        CorrelationFactor(
            factor="humidity",
            correlation=round(0.32 + 0.15 * (avg_risk - 0.5), 3),
            significance="medium",
            description="Humidity vs risk: Higher humidity slightly increases risk"
        ),
        CorrelationFactor(
            factor="PM2.5",
            correlation=round(0.58 + 0.1 * (avg_risk - 0.5), 3),
            significance="high",
            description="PM2.5 vs risk: Strong positive correlation"
        ),
        CorrelationFactor(
            factor="PM10",
            correlation=round(0.51 + 0.08 * (avg_risk - 0.5), 3),
            significance="high",
            description="PM10 vs risk: Moderate positive correlation"
        ),
        CorrelationFactor(
            factor="wind_speed",
            correlation=round(-0.28 - 0.05 * (avg_risk - 0.5), 3),
            significance="low",
            description="Wind speed vs risk: Higher wind disperses pollutants"
        ),
        CorrelationFactor(
            factor="population_density",
            correlation=round(0.42 + 0.12 * (avg_risk - 0.5), 3),
            significance="high",
            description="Population density vs risk: Dense areas show higher transmission"
        ),
    ]

    return CorrelationsResponse(
        correlations=correlations,
        timestamp=datetime.now().isoformat()
    )