#!/usr/bin/env python3
"""
Post-Event Analysis and Reporting Tools
Comprehensive post-event analysis and automated reporting system
"""

import json
import math
import base64
import hashlib
import datetime
import re
import string
import itertools
import collections
import dataclasses
import random
from typing import Dict, List, Optional, Any, Union, Tuple
from dataclasses import dataclass, field
from collections import defaultdict
from enum import Enum

class ReportType(Enum):
    EXECUTIVE_SUMMARY = "executive_summary"
    FINANCIAL_ANALYSIS = "financial_analysis"
    OPERATIONAL_REVIEW = "operational_review"
    CUSTOMER_INSIGHTS = "customer_insights"
    TECHNICAL_PERFORMANCE = "technical_performance"
    SAFETY_SECURITY = "safety_security"
    MARKETING_ANALYSIS = "marketing_analysis"

class ReportFormat(Enum):
    PDF = "pdf"
    HTML = "html"
    JSON = "json"
    EXCEL = "excel"
    POWERPOINT = "powerpoint"

class InsightType(Enum):
    KEY_FINDING = "key_finding"
    RECOMMENDATION = "recommendation"
    ANOMALY = "anomaly"
    TREND = "trend"
    OPPORTUNITY = "opportunity"
    CONCERN = "concern"

@dataclass
class EventData:
    """Comprehensive event data structure"""
    event_id: str
    event_name: str
    venue_id: str
    artist: str
    date: datetime.datetime
    expected_attendance: int
    actual_attendance: int
    total_revenue: float
    operational_cost: float
    weather_conditions: str
    marketing_channels: List[str]
    staffing_levels: Dict[str, int]

@dataclass
class Insight:
    """Automated insight generated from data analysis"""
    insight_id: str
    insight_type: InsightType
    title: str
    description: str
    impact_level: str  # high, medium, low
    confidence_score: float
    supporting_data: Dict[str, Any]
    recommendations: List[str]

@dataclass
class Report:
    """Automated report structure"""
    report_id: str
    report_type: ReportType
    event_id: str
    title: str
    generated_at: datetime.datetime
    insights: List[Insight]
    metrics_summary: Dict[str, Any]
    visualizations: List[Dict[str, Any]]
    format_options: List[ReportFormat]

class PostEventAnalyzer:
    """
    Post-event analysis and reporting system
    Generates comprehensive insights and automated reports
    """
    
    def __init__(self):
        self.events = {}
        self.reports = {}
        self.analysis_templates = self._initialize_analysis_templates()
        self.benchmarks = self._initialize_benchmarks()
        self.insight_patterns = self._initialize_insight_patterns()
    
    def _initialize_analysis_templates(self) -> Dict[str, Dict[str, Any]]:
        """Initialize analysis templates for different report types"""
        templates = {
            ReportType.EXECUTIVE_SUMMARY.value: {
                "focus_areas": ["financial_performance", "attendance", "overall_success"],
                "metrics": ["revenue_vs_target", "occupancy_rate", "profit_margin"],
                "visualization_types": ["summary_dashboard", "key_metrics"]
            },
            ReportType.FINANCIAL_ANALYSIS.value: {
                "focus_areas": ["revenue_breakdown", "cost_analysis", "profitability"],
                "metrics": ["ticket_revenue", "merchandise_sales", "operational_costs", "roi"],
                "visualization_types": ["revenue_chart", "cost_breakdown", "profit_trend"]
            },
            ReportType.OPERATIONAL_REVIEW.value: {
                "focus_areas": ["staff_efficiency", "venue_operations", "logistics"],
                "metrics": ["staff_productivity", "entry_wait_times", "incident_rate"],
                "visualization_types": ["staff_performance", "operational_timeline"]
            },
            ReportType.CUSTOMER_INSIGHTS.value: {
                "focus_areas": ["satisfaction", "demographics", "behavior"],
                "metrics": ["satisfaction_score", "social_engagement", "repeat_attendance"],
                "visualization_types": ["satisfaction_chart", "demographic_breakdown"]
            }
        }
        return templates
    
    def _initialize_benchmarks(self) -> Dict[str, float]:
        """Initialize industry benchmarks"""
        return {
            "average_occupancy_rate": 78.5,
            "average_ticket_price": 65.0,
            "average_profit_margin": 15.2,
            "average_satisfaction_score": 4.2,
            "average_staff_ratio": 0.02,  # 2% of attendance
            "average_incident_rate": 1.5,  # per 1000 attendees
            "average_marketing_roi": 3.2
        }
    
    def _initialize_insight_patterns(self) -> Dict[str, Dict[str, Any]]:
        """Initialize patterns for automated insight generation"""
        patterns = {
            "high_performance": {
                "condition": "occupancy_rate > 90 AND satisfaction_score > 4.5",
                "insight_type": InsightType.KEY_FINDING,
                "template": "Exceptional performance achieved with {occupancy_rate:.1f}% occupancy and {satisfaction_score:.1f}/5 satisfaction"
            },
            "cost_overrun": {
                "condition": "operational_cost > budget * 1.1",
                "insight_type": InsightType.CONCERN,
                "template": "Operational costs exceeded budget by {cost_overrun:.1f}%, review efficiency measures"
            },
            "staff_optimization": {
                "condition": "staff_ratio > benchmark * 1.5",
                "insight_type": InsightType.OPPORTUNITY,
                "template": "Staffing levels {staff_ratio:.2f}% are higher than industry average, potential optimization opportunity"
            },
            "low_engagement": {
                "condition": "social_engagement_rate < 50",
                "insight_type": InsightType.RECOMMENDATION,
                "template": "Social media engagement below expectations at {engagement_rate:.1f} per 1000 attendees"
            }
        }
        return patterns
    
    def add_event_data(self, event_data: EventData) -> bool:
        """Add event data for analysis"""
        try:
            self.events[event_data.event_id] = event_data
            return True
        except Exception as e:
            return False
    
    def analyze_event(self, event_id: str) -> Dict[str, Any]:
        """Perform comprehensive event analysis"""
        if event_id not in self.events:
            return {"error": "Event not found"}
        
        event = self.events[event_id]
        analysis = {
            "event_id": event_id,
            "analysis_timestamp": datetime.datetime.now().isoformat(),
            "performance_metrics": self._calculate_performance_metrics(event),
            "comparative_analysis": self._perform_comparative_analysis(event),
            "insights": self._generate_insights(event),
            "recommendations": self._generate_recommendations(event),
            "key_trends": self._identify_trends(event)
        }
        
        return analysis
    
    def _calculate_performance_metrics(self, event: EventData) -> Dict[str, Any]:
        """Calculate comprehensive performance metrics"""
        metrics = {}
        
        # Attendance metrics
        occupancy_rate = (event.actual_attendance / event.expected_attendance * 100) if event.expected_attendance > 0 else 0
        metrics["occupancy_rate"] = occupancy_rate
        metrics["attendance_variance"] = event.actual_attendance - event.expected_attendance
        metrics["attendance_variance_percent"] = ((event.actual_attendance - event.expected_attendance) / event.expected_attendance * 100) if event.expected_attendance > 0 else 0
        
        # Financial metrics
        revenue_per_attendee = event.total_revenue / event.actual_attendance if event.actual_attendance > 0 else 0
        profit = event.total_revenue - event.operational_cost
        profit_margin = (profit / event.total_revenue * 100) if event.total_revenue > 0 else 0
        roi = (profit / event.operational_cost * 100) if event.operational_cost > 0 else 0
        
        metrics["revenue_per_attendee"] = revenue_per_attendee
        metrics["total_profit"] = profit
        metrics["profit_margin"] = profit_margin
        metrics["roi"] = roi
        
        # Operational metrics
        staff_ratio = sum(event.staffing_levels.values()) / event.actual_attendance if event.actual_attendance > 0 else 0
        cost_per_attendee = event.operational_cost / event.actual_attendance if event.actual_attendance > 0 else 0
        
        metrics["staff_ratio"] = staff_ratio
        metrics["cost_per_attendee"] = cost_per_attendee
        
        # Performance scoring
        metrics["overall_performance_score"] = self._calculate_overall_score(metrics)
        
        return metrics
    
    def _calculate_overall_score(self, metrics: Dict[str, Any]) -> float:
        """Calculate overall performance score (0-100)"""
        score = 0.0
        
        # Attendance weight (25%)
        if metrics["occupancy_rate"] >= 90:
            score += 25
        elif metrics["occupancy_rate"] >= 75:
            score += 20
        elif metrics["occupancy_rate"] >= 60:
            score += 15
        else:
            score += 10
        
        # Financial performance weight (35%)
        if metrics["profit_margin"] >= 20:
            score += 35
        elif metrics["profit_margin"] >= 15:
            score += 28
        elif metrics["profit_margin"] >= 10:
            score += 21
        elif metrics["profit_margin"] >= 5:
            score += 14
        else:
            score += 7
        
        # Operational efficiency weight (25%)
        staff_benchmark = self.benchmarks["average_staff_ratio"]
        staff_efficiency = min(25, 25 * (staff_benchmark / max(metrics["staff_ratio"], staff_benchmark)))
        score += staff_efficiency
        
        # ROI weight (15%)
        if metrics["roi"] >= 30:
            score += 15
        elif metrics["roi"] >= 20:
            score += 12
        elif metrics["roi"] >= 10:
            score += 8
        else:
            score += 4
        
        return min(100, score)
    
    def _perform_comparative_analysis(self, event: EventData) -> Dict[str, Any]:
        """Compare event performance against benchmarks"""
        metrics = self._calculate_performance_metrics(event)
        comparison = {}
        
        for metric_name, benchmark_value in self.benchmarks.items():
            if metric_name in metrics:
                current_value = metrics[metric_name]
                variance = current_value - benchmark_value
                variance_percent = (variance / benchmark_value * 100) if benchmark_value != 0 else 0
                
                comparison[metric_name] = {
                    "current_value": current_value,
                    "benchmark_value": benchmark_value,
                    "variance": variance,
                    "variance_percent": variance_percent,
                    "performance": "above" if variance > 0 else "below" if variance < 0 else "at_benchmark"
                }
        
        return comparison
    
    def _generate_insights(self, event: EventData) -> List[Insight]:
        """Generate automated insights from data analysis"""
        metrics = self._calculate_performance_metrics(event)
        insights = []
        
        # Apply insight patterns
        for pattern_name, pattern_config in self.insight_patterns.items():
            insight = self._evaluate_pattern(pattern_config, metrics, event)
            if insight:
                insights.append(insight)
        
        # Additional custom insights
        insights.extend(self._generate_custom_insights(metrics, event))
        
        return insights
    
    def _evaluate_pattern(self, pattern: Dict[str, Any], metrics: Dict[str, Any], event: EventData) -> Optional[Insight]:
        """Evaluate a pattern and generate insight if conditions are met"""
        try:
            # Simplified pattern evaluation (in production, this would be more sophisticated)
            if pattern["condition"] == "occupancy_rate > 90 AND satisfaction_score > 4.5":
                if metrics.get("occupancy_rate", 0) > 90:  # Assuming high satisfaction
                    insight = Insight(
                        insight_id=f"auto_{datetime.datetime.now().timestamp()}",
                        insight_type=pattern["insight_type"],
                        title="Exceptional Performance",
                        description=pattern["template"].format(
                            occupancy_rate=metrics["occupancy_rate"],
                            satisfaction_score=4.6  # Placeholder
                        ),
                        impact_level="high",
                        confidence_score=0.85,
                        supporting_data=metrics,
                        recommendations=["Consider scaling up for future events"]
                    )
                    return insight
            
            elif pattern["condition"] == "operational_cost > budget * 1.1":
                # Simulate budget calculation
                estimated_budget = event.operational_cost * 0.9  # Assume cost was 10% over budget
                if event.operational_cost > estimated_budget:
                    insight = Insight(
                        insight_id=f"auto_{datetime.datetime.now().timestamp()}",
                        insight_type=pattern["insight_type"],
                        title="Cost Overrun Detected",
                        description=pattern["template"].format(
                            cost_overrun=((event.operational_cost - estimated_budget) / estimated_budget * 100)
                        ),
                        impact_level="medium",
                        confidence_score=0.75,
                        supporting_data={"operational_cost": event.operational_cost, "estimated_budget": estimated_budget},
                        recommendations=["Review staffing levels", "Optimize procurement process"]
                    )
                    return insight
        
        except Exception as e:
            pass  # Skip pattern if evaluation fails
        
        return None
    
    def _generate_custom_insights(self, metrics: Dict[str, Any], event: EventData) -> List[Insight]:
        """Generate custom insights based on specific conditions"""
        insights = []
        
        # High revenue per attendee insight
        if metrics["revenue_per_attendee"] > 100:
            insights.append(Insight(
                insight_id=f"custom_{datetime.datetime.now().timestamp()}",
                insight_type=InsightType.OPPORTUNITY,
                title="High Revenue Per Attendee",
                description=f"Event achieved ${metrics['revenue_per_attendee']:.2f} revenue per attendee, above industry average",
                impact_level="medium",
                confidence_score=0.8,
                supporting_data={"revenue_per_attendee": metrics["revenue_per_attendee"]},
                recommendations=["Consider premium pricing strategies", "Analyze what drove high spend"]
            ))
        
        # Low occupancy insight
        if metrics["occupancy_rate"] < 60:
            insights.append(Insight(
                insight_id=f"custom_{datetime.datetime.now().timestamp()}",
                insight_type=InsightType.CONCERN,
                title="Low Occupancy Rate",
                description=f"Occupancy rate of {metrics['occupancy_rate']:.1f}% is below target",
                impact_level="high",
                confidence_score=0.9,
                supporting_data={"occupancy_rate": metrics["occupancy_rate"]},
                recommendations=["Review marketing strategy", "Adjust pricing", "Improve promotion timing"]
            ))
        
        return insights
    
    def _generate_recommendations(self, event: EventData) -> List[Dict[str, Any]]:
        """Generate actionable recommendations based on analysis"""
        metrics = self._calculate_performance_metrics(event)
        recommendations = []
        
        # Financial recommendations
        if metrics["profit_margin"] < 10:
            recommendations.append({
                "category": "Financial",
                "priority": "high",
                "recommendation": "Implement cost control measures to improve profit margins",
                "expected_impact": "Increase profit margin by 3-5%"
            })
        
        # Operational recommendations
        staff_benchmark = self.benchmarks["average_staff_ratio"]
        if metrics["staff_ratio"] > staff_benchmark * 1.5:
            recommendations.append({
                "category": "Operational",
                "priority": "medium",
                "recommendation": "Optimize staffing levels to align with industry benchmarks",
                "expected_impact": "Reduce operational costs by 10-15%"
            })
        
        # Marketing recommendations
        if metrics["occupancy_rate"] < 75:
            recommendations.append({
                "category": "Marketing",
                "priority": "high",
                "recommendation": "Enhance promotional activities and marketing channels",
                "expected_impact": "Increase occupancy rate by 10-15%"
            })
        
        return recommendations
    
    def _identify_trends(self, event: EventData) -> List[Dict[str, Any]]:
        """Identify key trends from the event data"""
        trends = []
        
        # Simulate trend identification
        # In production, this would analyze historical data
        trends.append({
            "trend_type": "performance",
            "description": "Consistent improvement in revenue per attendee over recent events",
            "direction": "increasing",
            "confidence": 0.8
        })
        
        trends.append({
            "trend_type": "operational",
            "description": "Staff efficiency showing positive trend with better ratio management",
            "direction": "improving",
            "confidence": 0.7
        })
        
        return trends
    
    def generate_report(self, event_id: str, report_type: ReportType, 
                        format_options: Optional[List[ReportFormat]] = None) -> Optional[Report]:
        """Generate automated report for event"""
        if event_id not in self.events:
            return None
        
        event = self.events[event_id]
        analysis = self.analyze_event(event_id)
        
        if format_options is None:
            format_options = [ReportFormat.JSON, ReportFormat.HTML]
        
        # Create report based on template
        template = self.analysis_templates.get(report_type.value, {})
        
        report = Report(
            report_id=f"report_{event_id}_{report_type.value}_{datetime.datetime.now().timestamp()}",
            report_type=report_type,
            event_id=event_id,
            title=f"{event.event_name} - {report_type.value.replace('_', ' ').title()}",
            generated_at=datetime.datetime.now(),
            insights=analysis["insights"],
            metrics_summary=analysis["performance_metrics"],
            visualizations=self._generate_visualizations(template, analysis),
            format_options=format_options
        )
        
        self.reports[report.report_id] = report
        return report
    
    def _generate_visualizations(self, template: Dict[str, Any], analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate visualization specifications"""
        visualizations = []
        
        viz_types = template.get("visualization_types", ["summary_dashboard"])
        
        for viz_type in viz_types:
            if viz_type == "summary_dashboard":
                visualizations.append({
                    "type": "dashboard",
                    "title": "Performance Summary",
                    "components": [
                        {"metric": "occupancy_rate", "chart": "gauge"},
                        {"metric": "profit_margin", "chart": "progress"},
                        {"metric": "revenue_per_attendee", "chart": "metric"}
                    ]
                })
            elif viz_type == "revenue_chart":
                visualizations.append({
                    "type": "chart",
                    "title": "Revenue Analysis",
                    "chart_type": "bar",
                    "data": analysis["performance_metrics"]
                })
        
        return visualizations
    
    def export_report(self, report_id: str, format: ReportFormat) -> Dict[str, Any]:
        """Export report in specified format"""
        if report_id not in self.reports:
            return {"error": "Report not found"}
        
        report = self.reports[report_id]
        
        if format == ReportFormat.JSON:
            return self._export_json_report(report)
        elif format == ReportFormat.HTML:
            return self._export_html_report(report)
        else:
            return {"error": "Format not supported"}
    
    def _export_json_report(self, report: Report) -> Dict[str, Any]:
        """Export report as JSON"""
        return {
            "report_id": report.report_id,
            "title": report.title,
            "event_id": report.event_id,
            "report_type": report.report_type.value,
            "generated_at": report.generated_at.isoformat(),
            "metrics_summary": report.metrics_summary,
            "insights": [
                {
                    "insight_id": insight.insight_id,
                    "type": insight.insight_type.value,
                    "title": insight.title,
                    "description": insight.description,
                    "impact_level": insight.impact_level,
                    "confidence_score": insight.confidence_score,
                    "recommendations": insight.recommendations
                }
                for insight in report.insights
            ],
            "visualizations": report.visualizations
        }
    
    def _export_html_report(self, report: Report) -> Dict[str, Any]:
        """Export report as HTML structure"""
        html_content = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <title>{report.title}</title>
            <style>
                body {{ font-family: Arial, sans-serif; margin: 40px; }}
                .header {{ background-color: #f0f0f0; padding: 20px; border-radius: 5px; }}
                .metric {{ margin: 10px 0; padding: 10px; border-left: 4px solid #007cba; }}
                .insight {{ margin: 20px 0; padding: 15px; background-color: #f9f9f9; border-radius: 5px; }}
                .high {{ border-left-color: #d32f2f; }}
                .medium {{ border-left-color: #f57c00; }}
                .low {{ border-left-color: #388e3c; }}
            </style>
        </head>
        <body>
            <div class="header">
                <h1>{report.title}</h1>
                <p>Generated: {report.generated_at.strftime('%Y-%m-%d %H:%M')}</p>
            </div>
            
            <h2>Performance Summary</h2>
            """
        
        # Add metrics
        for metric_name, metric_value in report.metrics_summary.items():
            html_content += f'<div class="metric"><strong>{metric_name.replace("_", " ").title()}:</strong> {metric_value}</div>'
        
        html_content += "<h2>Key Insights</h2>"
        
        # Add insights
        for insight in report.insights:
            impact_class = insight.impact_level
            html_content += f"""
            <div class="insight {impact_class}">
                <h3>{insight.title}</h3>
                <p>{insight.description}</p>
                <p><strong>Impact:</strong> {insight.impact_level} | <strong>Confidence:</strong> {insight.confidence_score:.1%}</p>
                """
            
            if insight.recommendations:
                html_content += "<p><strong>Recommendations:</strong></p><ul>"
                for rec in insight.recommendations:
                    html_content += f"<li>{rec}</li>"
                html_content += "</ul>"
            
            html_content += "</div>"
        
        html_content += """
        </body>
        </html>
        """
        
        return {
            "format": "html",
            "content": html_content,
            "filename": f"report_{report.event_id}_{datetime.datetime.now().strftime('%Y%m%d')}.html"
        }

def create_sample_event() -> EventData:
    """Create sample event data for testing"""
    return EventData(
        event_id="event_001",
        event_name="Summer Music Festival 2024",
        venue_id="venue_main_stadium",
        artist="Various Artists",
        date=datetime.datetime(2024, 7, 15, 19, 0),
        expected_attendance=5000,
        actual_attendance=4250,
        total_revenue=350000.0,
        operational_cost=280000.0,
        weather_conditions="Clear",
        marketing_channels=["social_media", "radio", "email"],
        staffing_levels={"security": 50, "concessions": 30, "medical": 8, "technical": 12}
    )

if __name__ == "__main__":
    # Initialize analyzer
    analyzer = PostEventAnalyzer()
    
    # Add sample event
    event = create_sample_event()
    analyzer.add_event_data(event)
    
    print("=== Post-Event Analysis and Reporting ===")
    
    # Perform analysis
    print("\n=== Event Analysis ===")
    analysis = analyzer.analyze_event(event.event_id)
    print(json.dumps(analysis, indent=2, default=str))
    
    # Generate reports
    print("\n=== Generating Reports ===")
    report_types = [ReportType.EXECUTIVE_SUMMARY, ReportType.FINANCIAL_ANALYSIS, ReportType.OPERATIONAL_REVIEW]
    
    for report_type in report_types:
        report = analyzer.generate_report(event.event_id, report_type)
        if report:
            print(f"\nGenerated {report_type.value.replace('_', ' ').title()}: {report.report_id}")
            print(f"Insights: {len(report.insights)}")
            print(f"Visualizations: {len(report.visualizations)}")
    
    # Export sample report
    print("\n=== Exporting Report ===")
    if analyzer.reports:
        first_report_id = list(analyzer.reports.keys())[0]
        json_export = analyzer.export_report(first_report_id, ReportFormat.JSON)
        html_export = analyzer.export_report(first_report_id, ReportFormat.HTML)
        
        print("JSON Export:")
        print(json.dumps(json_export, indent=2, default=str))
        
        print(f"\nHTML Export Filename: {html_export['filename']}")