"""
Enhanced ROI calculators and business logic
"""

from typing import Dict
from core.data_models import IncidentScenario

class EnhancedROICalculator:
    """Investor-grade ROI calculator with sensitivity analysis"""
    
    def calculate_comprehensive_roi(self, monthly_incidents: int, 
                                   avg_impact: float, team_size: int) -> Dict:
        """Calculate multi-scenario ROI analysis"""
        # Base scenario (realistic)
        base = self._calculate_scenario(monthly_incidents, avg_impact, team_size, 
                                       savings_rate=0.82, efficiency_gain=0.85)
        
        # Best case (aggressive adoption)
        best = self._calculate_scenario(monthly_incidents, avg_impact, team_size,
                                       savings_rate=0.92, efficiency_gain=0.92)
        
        # Worst case (conservative)
        worst = self._calculate_scenario(monthly_incidents, avg_impact, team_size,
                                        savings_rate=0.72, efficiency_gain=0.78)
        
        # Generate recommendation
        recommendation = self._get_recommendation(base['roi_multiplier'])
        
        return {
            "summary": {
                "your_annual_impact": f"${base['annual_impact']:,.0f}",
                "potential_savings": f"${base['savings']:,.0f}",
                "enterprise_cost": f"${base['enterprise_cost']:,.0f}",
                "roi_multiplier": f"{base['roi_multiplier']:.1f}×",
                "payback_months": f"{base['payback_months']:.1f}",
                "annual_roi_percentage": f"{base['roi_percentage']:.0f}%"
            },
            "scenarios": {
                "base_case": {
                    "roi": f"{base['roi_multiplier']:.1f}×",
                    "payback": f"{base['payback_months']:.1f} months",
                    "confidence": "High"
                },
                "best_case": {
                    "roi": f"{best['roi_multiplier']:.1f}×",
                    "payback": f"{best['payback_months']:.1f} months",
                    "confidence": "Medium"
                },
                "worst_case": {
                    "roi": f"{worst['roi_multiplier']:.1f}×",
                    "payback": f"{worst['payback_months']:.1f} months",
                    "confidence": "Medium"
                }
            },
            "comparison": {
                "industry_average": "5.2× ROI",
                "top_performers": "8.7× ROI",
                "your_position": f"Top {self._get_percentile(base['roi_multiplier'])}%"
            },
            "recommendation": recommendation
        }
    
    def _calculate_scenario(self, monthly_incidents: int, avg_impact: float,
                           team_size: int, savings_rate: float, 
                           efficiency_gain: float) -> Dict:
        """Calculate specific scenario"""
        annual_impact = monthly_incidents * 12 * avg_impact
        enterprise_cost = team_size * 125000  # Conservative $125k/engineer
        savings = annual_impact * savings_rate * efficiency_gain
        
        roi_multiplier = savings / enterprise_cost if enterprise_cost > 0 else 0
        roi_percentage = (roi_multiplier - 1) * 100
        payback_months = (enterprise_cost / (savings / 12)) if savings > 0 else 0
        
        return {
            "annual_impact": annual_impact,
            "enterprise_cost": enterprise_cost,
            "savings": savings,
            "roi_multiplier": roi_multiplier,
            "roi_percentage": roi_percentage,
            "payback_months": payback_months
        }
    
    def _get_recommendation(self, roi_multiplier: float) -> Dict:
        """Get recommendation based on ROI"""
        if roi_multiplier >= 5.0:
            return {
                "action": "🚀 Deploy ARF Enterprise",
                "reason": "Exceptional ROI (>5×) with quick payback",
                "timeline": "30-day implementation",
                "expected_value": ">$1M annual savings",
                "priority": "High"
            }
        elif roi_multiplier >= 2.0:
            return {
                "action": "✅ Implement ARF Enterprise",
                "reason": "Strong ROI (2-5×) with operational benefits",
                "timeline": "60-day phased rollout",
                "expected_value": ">$500K annual savings",
                "priority": "Medium"
            }
        else:
            return {
                "action": "🆓 Start with ARF OSS",
                "reason": "Validate value before Enterprise investment",
                "timeline": "14-day evaluation",
                "expected_value": "Operational insights + clear upgrade path",
                "priority": "Low"
            }
    
    def _get_percentile(self, roi_multiplier: float) -> str:
        """Calculate percentile vs industry"""
        if roi_multiplier >= 8.0:
            return "10"
        elif roi_multiplier >= 5.0:
            return "25"
        elif roi_multiplier >= 3.0:
            return "50"
        elif roi_multiplier >= 2.0:
            return "75"
        else:
            return "90"