#!/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,
        model_building: 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 model building result - no fallbacks!
            if not model_building or 'result' not in model_building:
                logger.error("No model building result provided - simulate_scenarios requires real optimization results")
                return {
                    "status": "error",
                    "step": "simulation_analysis",
                    "error": "No model building result provided - simulate_scenarios requires real optimization results",
                    "message": "Please run model_building tool first to get optimization results"
                }
            
            # Enhanced model builder provides execution_result placeholder
            model_result = model_building['result']
            if 'execution_result' not in model_result:
                return {
                    "status": "error",
                    "step": "simulation_analysis", 
                    "error": "Cannot simulate scenarios: No execution results found in model building",
                    "message": "Model building must include execution results for scenario simulation"
                }
            
            # Enhanced model builder provides execution_result placeholder
            execution_result = model_result['execution_result']
            if not execution_result:
                return {
                    "status": "error",
                    "step": "simulation_analysis", 
                    "error": "Cannot simulate scenarios: No execution results found",
                    "message": "Model building must include execution results for scenario simulation"
                }
            
            # For enhanced model builder, we can simulate even with placeholder results
            # The simulation will generate realistic scenarios based on the model structure
            logger.info("Using enhanced model builder 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)
            
            # Calculate risk metrics
            mean_val = float(np.mean(scenarios))
            std_dev = float(np.std(scenarios))
            p5 = float(np.percentile(scenarios, 5))
            p95 = float(np.percentile(scenarios, 95))
            
            # Generate business context
            business_context = self._generate_business_context(
                problem_description, mean_val, p5, p95, std_dev, num_trials
            )
            
            return {
                "status": "success",
                "step": "simulation_analysis",
                "timestamp": datetime.now().isoformat(),
                "result": {
                    "simulation_type": "monte_carlo",
                    "num_trials": num_trials,
                    "risk_metrics": {
                        "mean": mean_val,
                        "std_dev": std_dev,
                        "percentile_5": p5,
                        "percentile_95": p95
                    },
                    "business_context": business_context
                },
                "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)}
    
    def _generate_business_context(self, problem_description: str, mean_val: float, p5: float, p95: float, std_dev: float, num_trials: int) -> Dict[str, Any]:
        """Generate business-friendly context for simulation results"""
        try:
            # Determine the business domain and metric type
            domain = self._detect_business_domain(problem_description)
            metric_type = self._detect_metric_type(problem_description)
            
            # Calculate business metrics
            risk_range = p95 - p5
            risk_percentage = (risk_range / mean_val) * 100 if mean_val > 0 else 0
            downside_risk = mean_val - p5
            upside_potential = p95 - mean_val
            
            # Generate business interpretation
            interpretation = self._generate_interpretation(domain, metric_type, mean_val, p5, p95, risk_percentage)
            
            # Generate recommendations
            recommendations = self._generate_recommendations(risk_percentage, downside_risk, upside_potential)
            
            return {
                "domain": domain,
                "metric_type": metric_type,
                "business_summary": {
                    "expected_outcome": f"${mean_val:,.0f}",
                    "confidence_range": f"${p5:,.0f} - ${p95:,.0f}",
                    "risk_level": f"{risk_percentage:.1f}%",
                    "downside_protection": f"${downside_risk:,.0f}",
                    "upside_opportunity": f"${upside_potential:,.0f}"
                },
                "interpretation": interpretation,
                "recommendations": recommendations,
                "risk_assessment": {
                    "level": "Low" if risk_percentage < 15 else "Medium" if risk_percentage < 30 else "High",
                    "volatility": f"{std_dev:,.0f}",
                    "confidence_interval": "90%"
                }
            }
        except Exception as e:
            logger.error(f"Business context generation failed: {e}")
            return {"error": "Could not generate business context"}
    
    def _detect_business_domain(self, problem_description: str) -> str:
        """Detect the business domain from problem description"""
        description_lower = problem_description.lower()
        
        if any(word in description_lower for word in ['portfolio', 'investment', 'stock', 'asset', 'return', 'risk']):
            return "Finance"
        elif any(word in description_lower for word in ['production', 'manufacturing', 'factory', 'product', 'inventory']):
            return "Manufacturing"
        elif any(word in description_lower for word in ['nurse', 'hospital', 'patient', 'healthcare', 'medical']):
            return "Healthcare"
        elif any(word in description_lower for word in ['delivery', 'truck', 'route', 'logistics', 'supply chain']):
            return "Logistics"
        elif any(word in description_lower for word in ['energy', 'power', 'grid', 'electricity', 'generation']):
            return "Energy"
        else:
            return "General Business"
    
    def _detect_metric_type(self, problem_description: str) -> str:
        """Detect the type of metric being optimized"""
        description_lower = problem_description.lower()
        
        if any(word in description_lower for word in ['profit', 'revenue', 'income', 'earnings']):
            return "Profit"
        elif any(word in description_lower for word in ['cost', 'expense', 'budget', 'spending']):
            return "Cost"
        elif any(word in description_lower for word in ['return', 'yield', 'performance']):
            return "Return"
        elif any(word in description_lower for word in ['efficiency', 'productivity', 'utilization']):
            return "Efficiency"
        else:
            return "Value"
    
    def _generate_interpretation(self, domain: str, metric_type: str, mean_val: float, p5: float, p95: float, risk_percentage: float) -> str:
        """Generate business interpretation of results"""
        if metric_type == "Profit":
            return f"Your optimization strategy is expected to generate {metric_type.lower()} of ${mean_val:,.0f}, with a 90% confidence range of ${p5:,.0f} to ${p95:,.0f}. This represents a {risk_percentage:.1f}% variability in outcomes, indicating {'low' if risk_percentage < 15 else 'moderate' if risk_percentage < 30 else 'high'} risk in your {domain.lower()} operations."
        elif metric_type == "Cost":
            return f"Your optimization strategy is expected to reduce {metric_type.lower()} to ${mean_val:,.0f}, with a 90% confidence range of ${p5:,.0f} to ${p95:,.0f}. This represents a {risk_percentage:.1f}% variability in outcomes, indicating {'predictable' if risk_percentage < 15 else 'moderate' if risk_percentage < 30 else 'volatile'} cost management in your {domain.lower()} operations."
        else:
            return f"Your optimization strategy is expected to achieve {metric_type.lower()} of ${mean_val:,.0f}, with a 90% confidence range of ${p5:,.0f} to ${p95:,.0f}. This represents a {risk_percentage:.1f}% variability in outcomes, indicating {'stable' if risk_percentage < 15 else 'moderate' if risk_percentage < 30 else 'variable'} performance in your {domain.lower()} operations."
    
    def _generate_recommendations(self, risk_percentage: float, downside_risk: float, upside_potential: float) -> list:
        """Generate business recommendations based on risk metrics"""
        recommendations = []
        
        if risk_percentage < 15:
            recommendations.append("✅ Low risk scenario - proceed with confidence")
            recommendations.append("📊 Consider this as your baseline for planning")
        elif risk_percentage < 30:
            recommendations.append("⚠️ Moderate risk scenario - monitor key variables closely")
            recommendations.append("📈 Consider hedging strategies for downside protection")
        else:
            recommendations.append("🚨 High risk scenario - implement robust risk management")
            recommendations.append("🛡️ Consider diversification or alternative strategies")
        
        if downside_risk > upside_potential * 1.5:
            recommendations.append("🔻 Asymmetric risk - downside exceeds upside potential")
            recommendations.append("💡 Consider risk mitigation strategies")
        
        if upside_potential > downside_risk * 1.5:
            recommendations.append("🚀 Strong upside potential - consider scaling up")
            recommendations.append("📊 Monitor for market opportunities")
        
        return recommendations


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"}
