"""Run-up forecast endpoints"""

from fastapi import APIRouter, HTTPException, Query, Path, Body
from typing import Dict, List, Optional
from datetime import datetime
import uuid

router = APIRouter()

@router.post("/runup", response_model=Dict)
async def forecast_runup(
    zone: str = Query(..., description="Coastal zone name"),
    source: Optional[str] = Query(None, description="Source region (e.g., 'sumatra', 'chile')"),
    parameters: Optional[Dict] = Body(None, description="Parameter values for forecast")
):
    """Generate run-up forecast for a coastal zone"""
    
    # Placeholder for different zones
    zone_data = {
        "hilo_bay_hawaii": {
            "becf": 4.8,
            "typical_beach_slope": 0.05,
            "shelf_width_km": 22,
            "population": 45000
        },
        "khao_lak_thailand": {
            "becf": 4.1,
            "typical_beach_slope": 0.03,
            "shelf_width_km": 65,
            "population": 20000
        },
        "banda_aceh_indonesia": {
            "becf": 7.3,
            "typical_beach_slope": 0.04,
            "shelf_width_km": 65,
            "population": 250000
        }
    }
    
    if zone not in zone_data:
        # Return generic forecast
        runup = 5.0  # default
    else:
        data = zone_data[zone]
        # Simplified forecast based on BECF
        runup = 1.0 * (data['becf'] ** 0.5) * 2
    
    return {
        "forecast_id": str(uuid.uuid4()),
        "zone": zone,
        "timestamp": datetime.utcnow().isoformat(),
        "runup_m": runup,
        "confidence_interval": [runup * 0.88, runup * 1.12],
        "lead_time_min": 67,
        "parameters_used": parameters or {},
        "zone_data": zone_data.get(zone, {}),
        "method": "physical_scaling"
    }

@router.post("/ensemble", response_model=Dict)
async def ensemble_forecast(
    zone: str = Query(..., description="Coastal zone name"),
    n_samples: int = Query(100, ge=10, le=1000, description="Number of ensemble samples")
):
    """Generate ensemble forecast with uncertainty quantification"""
    
    import numpy as np
    
    # Generate ensemble
    base_runup = 5.0
    uncertainties = np.random.normal(0, 0.5, n_samples)
    ensemble = base_runup + uncertainties
    
    return {
        "forecast_id": str(uuid.uuid4()),
        "zone": zone,
        "timestamp": datetime.utcnow().isoformat(),
        "ensemble_statistics": {
            "mean": float(np.mean(ensemble)),
            "median": float(np.median(ensemble)),
            "std": float(np.std(ensemble)),
            "p10": float(np.percentile(ensemble, 10)),
            "p90": float(np.percentile(ensemble, 90)),
            "min": float(np.min(ensemble)),
            "max": float(np.max(ensemble)),
            "n_samples": n_samples
        },
        "ensemble": ensemble.tolist()[:10]  # sample first 10
    }

@router.get("/zone/{zone_name}", response_model=Dict)
async def get_zone_forecast_history(
    zone_name: str = Path(...),
    days: int = Query(7, ge=1, le=30, description="Number of days of history")
):
    """Get forecast history for a zone"""
    
    # Placeholder history
    import numpy as np
    from datetime import datetime, timedelta
    
    history = []
    for i in range(days):
        date = datetime.utcnow() - timedelta(days=i)
        history.append({
            "date": date.isoformat(),
            "forecast_runup": 5.0 + np.random.normal(0, 0.3),
            "observed_runup": None  # would be filled if observed
        })
    
    return {
        "zone": zone_name,
        "history": history
    }

@router.post("/validate", response_model=Dict)
async def validate_forecast(
    forecast_id: str = Query(..., description="Forecast ID"),
    observed_runup: float = Query(..., description="Observed run-up height [m]")
):
    """Validate a forecast against observation"""
    
    # Placeholder validation
    error = abs(5.0 - observed_runup) / observed_runup * 100
    
    return {
        "forecast_id": forecast_id,
        "observed_runup": observed_runup,
        "forecast_runup": 5.0,
        "error_percent": error,
        "within_15_percent": error < 15,
        "within_30_percent": error < 30,
        "bias": 5.0 - observed_runup
    }

@router.get("/scenarios", response_model=List[Dict])
async def get_forecast_scenarios():
    """Get pre-computed forecast scenarios"""
    
    return [
        {
            "id": "tohoku_2011",
            "name": "Tōhoku 2011",
            "description": "2011 Tōhoku earthquake and tsunami",
            "source": "Japan Trench",
            "magnitude": 9.0,
            "runup_m": 40.5
        },
        {
            "id": "indian_ocean_2004",
            "name": "Indian Ocean 2004",
            "description": "2004 Sumatra-Andaman earthquake and tsunami",
            "source": "Sumatra",
            "magnitude": 9.1,
            "runup_m": 30.0
        },
        {
            "id": "hokkaido_1993",
            "name": "Hokkaido 1993",
            "description": "1993 Hokkaido Nansei-Oki earthquake",
            "source": "Sea of Japan",
            "magnitude": 7.8,
            "runup_m": 31.0
        }
    ]