"""
Station utilities for basin assignment and metadata handling.
Single Responsibility: Station-related helper functions.
"""
import pandas as pd
from pathlib import Path
from typing import Dict, List, Optional
from ..config.settings import STATION_LIST, BASIN_IDS


def assign_basin_id(station_name: str) -> int:
    """
    Assign basin ID based on station name.

    Args:
        station_name: Station name (e.g., "La Risle à Rai", "L'Eure à Chartres")

    Returns:
        Basin ID: 0 for La Risle, 1 for La Eure
    """
    station_name_lower = station_name.lower()

    if "risle" in station_name_lower:
        return BASIN_IDS["LA_RISLE"]
    elif "eure" in station_name_lower or "bras de l'eure" in station_name_lower:
        return BASIN_IDS["LA_EURE"]
    else:
        raise ValueError(f"Cannot determine basin for station: {station_name}")


def load_station_metadata(
    filepath: Optional[Path] = None,
    add_basin_id: bool = True
) -> pd.DataFrame:
    """
    Load station metadata from CSV.

    Args:
        filepath: Path to station_list.csv (default: from settings)
        add_basin_id: Whether to add basin_id column

    Returns:
        DataFrame with station metadata
    """
    if filepath is None:
        filepath = STATION_LIST

    df = pd.read_csv(filepath)

    # Add basin_id column if requested
    if add_basin_id and "basin_id" not in df.columns:
        df["basin_id"] = df["station_name"].apply(assign_basin_id)

    return df


def get_stations_by_basin(basin_id: int, filepath: Optional[Path] = None) -> List[str]:
    """
    Get list of station codes for a specific basin.

    Args:
        basin_id: Basin identifier (0=Risle, 1=Eure)
        filepath: Path to station_list.csv (optional)

    Returns:
        List of station codes
    """
    df = load_station_metadata(filepath, add_basin_id=True)
    return df[df["basin_id"] == basin_id]["station_code"].tolist()


def get_basin_name(basin_id: int) -> str:
    """
    Get basin name from basin ID.

    Args:
        basin_id: Basin identifier

    Returns:
        Basin name
    """
    basin_map = {v: k for k, v in BASIN_IDS.items()}
    return basin_map.get(basin_id, "UNKNOWN")


def validate_station_code(station_code: str, filepath: Optional[Path] = None) -> bool:
    """
    Check if station code exists in station list.

    Args:
        station_code: Station code to validate
        filepath: Path to station_list.csv (optional)

    Returns:
        True if valid
    """
    df = load_station_metadata(filepath, add_basin_id=False)
    return station_code in df["station_code"].values


def get_station_coordinates(
    station_code: str,
    filepath: Optional[Path] = None,
    crs: str = "wgs84"
) -> tuple:
    """
    Get coordinates for a station.

    Args:
        station_code: Station code
        filepath: Path to station_list.csv (optional)
        crs: Coordinate system ("wgs84" for lat/lon, "lambert93" for X/Y)

    Returns:
        Tuple of (x, y) or (lon, lat) coordinates
    """
    df = load_station_metadata(filepath, add_basin_id=False)
    station = df[df["station_code"] == station_code]

    if len(station) == 0:
        raise ValueError(f"Station code not found: {station_code}")

    station = station.iloc[0]

    if crs.lower() == "wgs84":
        return (station["lon"], station["lat"])
    elif crs.lower() == "lambert93":
        return (station["X"], station["Y"])
    else:
        raise ValueError(f"Unknown CRS: {crs}. Use 'wgs84' or 'lambert93'")


def get_station_summary() -> Dict:
    """
    Get summary statistics of stations.

    Returns:
        Dictionary with station summary
    """
    df = load_station_metadata(add_basin_id=True)

    risle_stations = df[df["basin_id"] == BASIN_IDS["LA_RISLE"]]
    eure_stations = df[df["basin_id"] == BASIN_IDS["LA_EURE"]]

    return {
        "total_stations": len(df),
        "risle_stations": len(risle_stations),
        "eure_stations": len(eure_stations),
        "risle_codes": risle_stations["station_code"].tolist(),
        "eure_codes": eure_stations["station_code"].tolist(),
        "bbox": {
            "lon_min": df["lon"].min(),
            "lon_max": df["lon"].max(),
            "lat_min": df["lat"].min(),
            "lat_max": df["lat"].max()
        }
    }