#!/usr/bin/env python3
"""
Simulation Tool
"""

import logging
from datetime import datetime
from typing import Any, Dict, Optional

try:
    import numpy as np
    HAS_MONTE_CARLO = True
except ImportError:
    HAS_MONTE_CARLO = False

logger = logging.getLogger(__name__)


class SimulationTool:
    """Simulation for optimization scenarios"""
    
    async def simulate_scenarios(
        self,
        problem_description: str,
        optimization_solution: Optional[Dict] = None,
        scenario_parameters: Optional[Dict] = None,
        simulation_type: str = "monte_carlo",
        num_trials: int = 10000
    ) -> Dict[str, Any]:
        """Simulate different scenarios for optimization analysis"""
        try:
            # REQUIRE a real optimization solution - no fallbacks!
            if not optimization_solution or optimization_solution.get('status') != 'success':
                logger.error("No optimization solution provided - simulate_scenarios requires real optimization results")
                return {
                    "status": "error",
                    "step": "simulation_analysis",
                    "error": "No optimization solution provided - simulate_scenarios requires real optimization results",
                    "message": "Please run solve_optimization_tool first to get optimization results"
                }
            
            # Validate that we have actual results
            result_data = optimization_solution.get('result', {})
            if not result_data or result_data.get('status') != 'optimal':
                return {
                    "status": "error",
                    "step": "simulation_analysis", 
                    "error": "Cannot simulate scenarios: No optimal solution found",
                    "message": "Optimal solution required for scenario simulation"
                }
            
            if simulation_type != "monte_carlo" or not HAS_MONTE_CARLO:
                return {
                    "status": "error",
                    "error": f"Only Monte Carlo supported (NumPy required)",
                    "available_simulations": ["monte_carlo"],
                    "roadmap": ["discrete_event", "agent_based"]
                }
            
            obj_val = result_data.get('objective_value', 0)
            if obj_val == 0:
                return {
                    "status": "error",
                    "step": "simulation_analysis",
                    "error": "Cannot simulate scenarios: Zero objective value",
                    "message": "Valid objective value required for meaningful simulation"
                }
            
            np.random.seed(42)
            scenarios = np.random.normal(obj_val, obj_val * 0.1, num_trials)
            
            return {
                "status": "success",
                "step": "simulation_analysis",
                "timestamp": datetime.now().isoformat(),
                "result": {
                    "simulation_type": "monte_carlo",
                    "num_trials": num_trials,
                    "risk_metrics": {
                        "mean": float(np.mean(scenarios)),
                        "std_dev": float(np.std(scenarios)),
                        "percentile_5": float(np.percentile(scenarios, 5)),
                        "percentile_95": float(np.percentile(scenarios, 95))
                    }
                },
                "message": f"Monte Carlo completed ({num_trials} trials) based on actual optimization results"
            }
        except Exception as e:
            return {"status": "error", "step": "simulation_analysis", "error": str(e)}


async def simulate_scenarios_tool(
    problem_description: str,
    optimization_solution: Optional[Dict] = None,
    scenario_parameters: Optional[Dict] = None,
    simulation_type: str = "monte_carlo",
    num_trials: int = 10000
) -> Dict[str, Any]:
    """Tool wrapper for simulation"""
    # This would be called from the main tools orchestrator
    # For now, return a placeholder
    return {"status": "error", "error": "Tool not initialized"}
