#!/usr/bin/env python3
"""
🔬 Advanced Investigation Engine - Enterprise Component
Multi-source investigation and deep behavioral analysis capabilities

This enterprise component provides advanced investigation capabilities including:
- Multi-source data correlation and analysis
- Deep behavioral pattern investigation
- Advanced evidence collection and analysis
- Sophisticated investigation workflow management
"""

from typing import Dict, List, Optional, Any, Callable, Union, Tuple
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import json
import threading
from concurrent.futures import ThreadPoolExecutor, Future
import logging

# ============================================================================
# ADVANCED INVESTIGATION ENGINE (from original advanced_investigation.py)
# ============================================================================

class InvestigationStatus(Enum):
    """Investigation status types"""
    INITIATED = "initiated"
    IN_PROGRESS = "in_progress"
    ANALYZING = "analyzing"
    CORRELATING = "correlating"
    COMPLETED = "completed"
    FAILED = "failed"
    SUSPENDED = "suspended"

class EvidenceType(Enum):
    """Types of evidence"""
    BEHAVIORAL = "behavioral"
    PERFORMANCE = "performance"
    COMMUNICATION = "communication"
    DECISION = "decision"
    ERROR = "error"
    PATTERN = "pattern"
    ANOMALY = "anomaly"

@dataclass
class Evidence:
    """Represents a piece of evidence"""
    evidence_id: str
    evidence_type: EvidenceType
    source: str
    data: Any
    timestamp: datetime
    confidence: float
    metadata: Dict[str, Any] = field(default_factory=dict)
    correlations: List[str] = field(default_factory=list)

@dataclass
class InvestigationProtocol:
    """Investigation protocol configuration"""
    protocol_id: str
    name: str
    description: str
    investigation_steps: List[str]
    evidence_requirements: List[EvidenceType]
    correlation_rules: Dict[str, Any]
    analysis_methods: List[str]
    reporting_format: str = "comprehensive"

class AdvancedInvestigationEngine:
    """
    Enterprise-grade investigation engine for deep AI behavioral analysis
    Provides multi-source investigation and sophisticated evidence correlation
    """
    
    def __init__(self):
        self.active_investigations: Dict[str, Dict[str, Any]] = {}
        self.evidence_store: Dict[str, Evidence] = {}
        self.investigation_protocols: Dict[str, InvestigationProtocol] = {}
        self.correlation_engine = CorrelationEngine()
        self.analysis_engine = AnalysisEngine()
        self.executor = ThreadPoolExecutor(max_workers=4)
        self.investigation_lock = threading.Lock()
        
        # Initialize default protocols
        self._initialize_default_protocols()
    
    def initiate_investigation(self, investigation_id: str, target: str, 
                             protocol_id: str, parameters: Dict[str, Any] = None) -> bool:
        """Initiate a new investigation"""
        try:
            with self.investigation_lock:
                if investigation_id in self.active_investigations:
                    print(f"❌ Investigation {investigation_id} already exists")
                    return False
                
                protocol = self.investigation_protocols.get(protocol_id)
                if not protocol:
                    print(f"❌ Protocol {protocol_id} not found")
                    return False
                
                investigation = {
                    'id': investigation_id,
                    'target': target,
                    'protocol': protocol,
                    'status': InvestigationStatus.INITIATED,
                    'evidence': [],
                    'findings': [],
                    'correlations': [],
                    'started_at': datetime.now(),
                    'parameters': parameters or {},
                    'progress': 0.0
                }
                
                self.active_investigations[investigation_id] = investigation
                
                # Start investigation in background
                future = self.executor.submit(self._execute_investigation, investigation_id)
                investigation['future'] = future
                
                print(f"🔬 Investigation initiated: {investigation_id}")
                return True
                
        except Exception as e:
            print(f"❌ Failed to initiate investigation {investigation_id}: {e}")
            return False
    
    def collect_evidence(self, investigation_id: str, evidence: Evidence) -> bool:
        """Collect evidence for investigation"""
        try:
            if investigation_id not in self.active_investigations:
                return False
            
            # Store evidence
            self.evidence_store[evidence.evidence_id] = evidence
            
            # Add to investigation
            investigation = self.active_investigations[investigation_id]
            investigation['evidence'].append(evidence.evidence_id)
            
            # Trigger correlation analysis
            self._analyze_evidence_correlations(investigation_id, evidence)
            
            print(f"📋 Evidence collected: {evidence.evidence_id}")
            return True
            
        except Exception as e:
            print(f"❌ Failed to collect evidence: {e}")
            return False
    
    def get_investigation_status(self, investigation_id: str) -> Dict[str, Any]:
        """Get current investigation status"""
        investigation = self.active_investigations.get(investigation_id)
        if not investigation:
            return {'error': 'Investigation not found'}
        
        return {
            'id': investigation_id,
            'status': investigation['status'].value,
            'progress': investigation['progress'],
            'evidence_count': len(investigation['evidence']),
            'findings_count': len(investigation['findings']),
            'correlations_count': len(investigation['correlations']),
            'elapsed_time': str(datetime.now() - investigation['started_at'])
        }
    
    def get_investigation_report(self, investigation_id: str) -> Dict[str, Any]:
        """Generate comprehensive investigation report"""
        investigation = self.active_investigations.get(investigation_id)
        if not investigation:
            return {'error': 'Investigation not found'}
        
        # Compile evidence
        evidence_details = []
        for evidence_id in investigation['evidence']:
            evidence = self.evidence_store.get(evidence_id)
            if evidence:
                evidence_details.append({
                    'id': evidence.evidence_id,
                    'type': evidence.evidence_type.value,
                    'source': evidence.source,
                    'confidence': evidence.confidence,
                    'timestamp': evidence.timestamp.isoformat(),
                    'correlations': evidence.correlations
                })
        
        report = {
            'investigation_id': investigation_id,
            'target': investigation['target'],
            'protocol': investigation['protocol'].name,
            'status': investigation['status'].value,
            'duration': str(datetime.now() - investigation['started_at']),
            'evidence': evidence_details,
            'findings': investigation['findings'],
            'correlations': investigation['correlations'],
            'summary': self._generate_investigation_summary(investigation),
            'recommendations': self._generate_recommendations(investigation),
            'generated_at': datetime.now().isoformat()
        }
        
        return report
    
    def _execute_investigation(self, investigation_id: str):
        """Execute investigation protocol"""
        try:
            investigation = self.active_investigations[investigation_id]
            protocol = investigation['protocol']
            
            investigation['status'] = InvestigationStatus.IN_PROGRESS
            
            # Execute investigation steps
            total_steps = len(protocol.investigation_steps)
            for i, step in enumerate(protocol.investigation_steps):
                print(f"🔍 Executing step: {step}")
                
                # Execute step (simplified implementation)
                self._execute_investigation_step(investigation_id, step)
                
                # Update progress
                investigation['progress'] = (i + 1) / total_steps
                
            # Perform final analysis
            investigation['status'] = InvestigationStatus.ANALYZING
            self._perform_final_analysis(investigation_id)
            
            investigation['status'] = InvestigationStatus.COMPLETED
            investigation['completed_at'] = datetime.now()
            
            print(f"✅ Investigation completed: {investigation_id}")
            
        except Exception as e:
            investigation['status'] = InvestigationStatus.FAILED
            investigation['error'] = str(e)
            print(f"❌ Investigation failed: {investigation_id} - {e}")
    
    def _execute_investigation_step(self, investigation_id: str, step: str):
        """Execute individual investigation step"""
        # Simplified step execution
        if step == "collect_behavioral_data":
            self._collect_behavioral_evidence(investigation_id)
        elif step == "analyze_performance_metrics":
            self._analyze_performance_evidence(investigation_id)
        elif step == "correlate_evidence":
            self._correlate_all_evidence(investigation_id)
        elif step == "generate_findings":
            self._generate_findings(investigation_id)
    
    def _collect_behavioral_evidence(self, investigation_id: str):
        """Collect behavioral evidence"""
        # Simulated evidence collection
        evidence = Evidence(
            evidence_id=f"behavioral_{investigation_id}_{datetime.now().timestamp()}",
            evidence_type=EvidenceType.BEHAVIORAL,
            source="behavioral_monitor",
            data={"pattern": "normal", "anomalies": []},
            timestamp=datetime.now(),
            confidence=0.8
        )
        self.collect_evidence(investigation_id, evidence)
    
    def _analyze_performance_evidence(self, investigation_id: str):
        """Analyze performance evidence"""
        # Simulated performance analysis
        evidence = Evidence(
            evidence_id=f"performance_{investigation_id}_{datetime.now().timestamp()}",
            evidence_type=EvidenceType.PERFORMANCE,
            source="performance_monitor",
            data={"metrics": {"response_time": 0.5, "accuracy": 0.95}},
            timestamp=datetime.now(),
            confidence=0.9
        )
        self.collect_evidence(investigation_id, evidence)
    
    def _analyze_evidence_correlations(self, investigation_id: str, new_evidence: Evidence):
        """Analyze correlations with new evidence"""
        investigation = self.active_investigations[investigation_id]
        
        # Find correlations with existing evidence
        correlations = self.correlation_engine.find_correlations(
            new_evidence, 
            [self.evidence_store[eid] for eid in investigation['evidence'] if eid != new_evidence.evidence_id]
        )
        
        if correlations:
            investigation['correlations'].extend(correlations)
    
    def _correlate_all_evidence(self, investigation_id: str):
        """Perform comprehensive evidence correlation"""
        investigation = self.active_investigations[investigation_id]
        all_evidence = [self.evidence_store[eid] for eid in investigation['evidence']]
        
        correlations = self.correlation_engine.correlate_evidence_set(all_evidence)
        investigation['correlations'].extend(correlations)
    
    def _generate_findings(self, investigation_id: str):
        """Generate investigation findings"""
        investigation = self.active_investigations[investigation_id]
        
        # Analyze evidence and correlations to generate findings
        findings = self.analysis_engine.generate_findings(
            investigation['evidence'],
            investigation['correlations'],
            self.evidence_store
        )
        
        investigation['findings'].extend(findings)
    
    def _perform_final_analysis(self, investigation_id: str):
        """Perform final comprehensive analysis"""
        investigation = self.active_investigations[investigation_id]
        
        # Generate final correlations
        self._correlate_all_evidence(investigation_id)
        
        # Generate final findings
        self._generate_findings(investigation_id)
        
        # Perform quality assessment
        investigation['quality_score'] = self._assess_investigation_quality(investigation)
    
    def _assess_investigation_quality(self, investigation: Dict[str, Any]) -> float:
        """Assess investigation quality"""
        evidence_count = len(investigation['evidence'])
        findings_count = len(investigation['findings'])
        correlations_count = len(investigation['correlations'])
        
        # Simple quality score based on evidence and findings
        quality = min(1.0, (evidence_count * 0.3 + findings_count * 0.5 + correlations_count * 0.2) / 10)
        return quality
    
    def _generate_investigation_summary(self, investigation: Dict[str, Any]) -> str:
        """Generate investigation summary"""
        evidence_count = len(investigation['evidence'])
        findings_count = len(investigation['findings'])
        
        return f"Investigation of {investigation['target']} completed with {evidence_count} pieces of evidence and {findings_count} findings."
    
    def _generate_recommendations(self, investigation: Dict[str, Any]) -> List[str]:
        """Generate recommendations based on investigation"""
        recommendations = []
        
        if investigation['quality_score'] > 0.8:
            recommendations.append("High-quality investigation with comprehensive evidence")
        
        if len(investigation['correlations']) > 5:
            recommendations.append("Strong evidence correlations suggest systematic patterns")
        
        return recommendations
    
    def _initialize_default_protocols(self):
        """Initialize default investigation protocols"""
        # Behavioral investigation protocol
        behavioral_protocol = InvestigationProtocol(
            protocol_id="behavioral_analysis",
            name="Behavioral Analysis Protocol",
            description="Comprehensive behavioral pattern investigation",
            investigation_steps=[
                "collect_behavioral_data",
                "analyze_performance_metrics",
                "correlate_evidence",
                "generate_findings"
            ],
            evidence_requirements=[EvidenceType.BEHAVIORAL, EvidenceType.PERFORMANCE],
            correlation_rules={"min_confidence": 0.7},
            analysis_methods=["pattern_analysis", "anomaly_detection"]
        )
        
        self.investigation_protocols["behavioral_analysis"] = behavioral_protocol

class CorrelationEngine:
    """Engine for evidence correlation analysis"""
    
    def find_correlations(self, evidence: Evidence, existing_evidence: List[Evidence]) -> List[Dict[str, Any]]:
        """Find correlations between evidence"""
        correlations = []
        
        for existing in existing_evidence:
            correlation_strength = self._calculate_correlation(evidence, existing)
            if correlation_strength > 0.5:
                correlations.append({
                    'evidence_1': evidence.evidence_id,
                    'evidence_2': existing.evidence_id,
                    'strength': correlation_strength,
                    'type': 'temporal' if abs((evidence.timestamp - existing.timestamp).total_seconds()) < 300 else 'semantic'
                })
        
        return correlations
    
    def correlate_evidence_set(self, evidence_set: List[Evidence]) -> List[Dict[str, Any]]:
        """Correlate entire set of evidence"""
        correlations = []
        
        for i, evidence1 in enumerate(evidence_set):
            for evidence2 in evidence_set[i+1:]:
                correlation_strength = self._calculate_correlation(evidence1, evidence2)
                if correlation_strength > 0.5:
                    correlations.append({
                        'evidence_1': evidence1.evidence_id,
                        'evidence_2': evidence2.evidence_id,
                        'strength': correlation_strength,
                        'type': 'cross_correlation'
                    })
        
        return correlations
    
    def _calculate_correlation(self, evidence1: Evidence, evidence2: Evidence) -> float:
        """Calculate correlation strength between two pieces of evidence"""
        # Simplified correlation calculation
        correlation = 0.0
        
        # Type similarity
        if evidence1.evidence_type == evidence2.evidence_type:
            correlation += 0.3
        
        # Source similarity
        if evidence1.source == evidence2.source:
            correlation += 0.2
        
        # Temporal proximity
        time_diff = abs((evidence1.timestamp - evidence2.timestamp).total_seconds())
        if time_diff < 300:  # 5 minutes
            correlation += 0.3
        
        # Confidence factor
        correlation *= (evidence1.confidence + evidence2.confidence) / 2
        
        return min(1.0, correlation)

class AnalysisEngine:
    """Engine for evidence analysis and finding generation"""
    
    def generate_findings(self, evidence_ids: List[str], correlations: List[Dict[str, Any]], 
                         evidence_store: Dict[str, Evidence]) -> List[Dict[str, Any]]:
        """Generate findings from evidence and correlations"""
        findings = []
        
        # Analyze evidence patterns
        evidence_by_type = {}
        for evidence_id in evidence_ids:
            evidence = evidence_store.get(evidence_id)
            if evidence:
                evidence_type = evidence.evidence_type.value
                if evidence_type not in evidence_by_type:
                    evidence_by_type[evidence_type] = []
                evidence_by_type[evidence_type].append(evidence)
        
        # Generate findings based on evidence patterns
        for evidence_type, evidence_list in evidence_by_type.items():
            if len(evidence_list) > 1:
                finding = {
                    'type': f"{evidence_type}_pattern",
                    'description': f"Multiple {evidence_type} evidence points suggest pattern",
                    'evidence_count': len(evidence_list),
                    'confidence': sum(e.confidence for e in evidence_list) / len(evidence_list),
                    'supporting_evidence': [e.evidence_id for e in evidence_list]
                }
                findings.append(finding)
        
        # Generate findings based on correlations
        if len(correlations) > 3:
            finding = {
                'type': 'correlation_cluster',
                'description': 'Strong evidence correlations indicate systematic behavior',
                'correlation_count': len(correlations),
                'confidence': 0.8,
                'supporting_correlations': correlations[:5]  # Top 5 correlations
            }
            findings.append(finding)
        
        return findings

# ============================================================================
# MULTI-SOURCE ANALYZER
# ============================================================================

class SourceType(Enum):
    """Types of investigation sources"""
    BEHAVIORAL_MONITOR = "behavioral_monitor"
    PERFORMANCE_TRACKER = "performance_tracker"
    COMMUNICATION_LOG = "communication_log"
    ERROR_REPORTER = "error_reporter"
    DECISION_TRACKER = "decision_tracker"
    EXTERNAL_API = "external_api"

class MultiSourceAnalyzer:
    """Analyzer for correlating data from multiple sources"""
    
    def __init__(self):
        self.registered_sources: Dict[str, SourceType] = {}
        self.source_handlers: Dict[SourceType, Callable] = {}
        self.correlation_matrix: Dict[Tuple[SourceType, SourceType], float] = {}
    
    def register_source(self, source_id: str, source_type: SourceType, 
                       handler: Callable = None) -> bool:
        """Register a data source"""
        try:
            self.registered_sources[source_id] = source_type
            if handler:
                self.source_handlers[source_type] = handler
            # print(f"📊 Source registered: {source_id} ({source_type.value})") # Removed print for cleaner output
            return True
        except Exception as e:
            print(f"❌ Failed to register source {source_id}: {e}")
            return False
    
    def analyze_multi_source_data(self, investigation_id: str, 
                                 source_data: Dict[str, Any]) -> List[Evidence]:
        """Analyze data from multiple sources"""
        evidence_list = []
        
        for source_id, data in source_data.items():
            if source_id in self.registered_sources:
                source_type = self.registered_sources[source_id]
                evidence = self._convert_source_data_to_evidence(
                    source_id, source_type, data, investigation_id
                )
                if evidence:
                    evidence_list.append(evidence)
        
        return evidence_list
    
    def _convert_source_data_to_evidence(self, source_id: str, source_type: SourceType, 
                                       data: Any, investigation_id: str) -> Optional[Evidence]:
        """Convert source data to evidence"""
        try:
            evidence_type_map = {
                SourceType.BEHAVIORAL_MONITOR: EvidenceType.BEHAVIORAL,
                SourceType.PERFORMANCE_TRACKER: EvidenceType.PERFORMANCE,
                SourceType.COMMUNICATION_LOG: EvidenceType.COMMUNICATION,
                SourceType.ERROR_REPORTER: EvidenceType.ERROR,
                SourceType.DECISION_TRACKER: EvidenceType.DECISION
            }
            
            evidence_type = evidence_type_map.get(source_type, EvidenceType.PATTERN)
            
            evidence = Evidence(
                evidence_id=f"{source_id}_{investigation_id}_{datetime.now().timestamp()}",
                evidence_type=evidence_type,
                source=source_id,
                data=data,
                timestamp=datetime.now(),
                confidence=0.8,  # Default confidence
                metadata={'source_type': source_type.value}
            )
            
            return evidence
            
        except Exception as e:
            print(f"❌ Failed to convert source data to evidence: {e}")
            return None

class SourceCorrelator:
    """Correlates evidence from different sources"""
    
    def __init__(self):
        self.correlation_rules: Dict[str, Any] = {}
    
    def add_correlation_rule(self, rule_id: str, source_types: List[SourceType], 
                           correlation_function: Callable) -> bool:
        """Add correlation rule for specific source types"""
        try:
            self.correlation_rules[rule_id] = {
                'source_types': source_types,
                'function': correlation_function
            }
            return True
        except Exception as e:
            print(f"❌ Failed to add correlation rule: {e}")
            return False
    
    def correlate_sources(self, evidence_list: List[Evidence]) -> List[Dict[str, Any]]:
        """Apply correlation rules to evidence from different sources"""
        correlations = []
        
        for rule_id, rule in self.correlation_rules.items():
            rule_correlations = self._apply_correlation_rule(rule, evidence_list)
            correlations.extend(rule_correlations)
        
        return correlations
    
    def _apply_correlation_rule(self, rule: Dict[str, Any], 
                              evidence_list: List[Evidence]) -> List[Dict[str, Any]]:
        """Apply specific correlation rule"""
        correlations = []
        
        # Filter evidence by source types in rule
        relevant_evidence = [
            e for e in evidence_list 
            if any(st.value in e.metadata.get('source_type', '') for st in rule['source_types'])
        ]
        
        # Apply correlation function
        if len(relevant_evidence) >= 2:
            try:
                rule_correlations = rule['function'](relevant_evidence)
                correlations.extend(rule_correlations)
            except Exception as e:
                print(f"❌ Error applying correlation rule: {e}")
        
        return correlations
