"""
Analysis Agent for SE-AGI System

The AnalysisAgent specializes in data analysis, pattern recognition,
and comprehensive evaluation of information.
"""

import asyncio
import logging
from typing import Dict, List, Any, Optional, Tuple
from datetime import datetime
import json
import numpy as np
from .base import BaseAgent
from ..core.config import AgentConfig


class AnalysisAgent(BaseAgent):
    """
    Analysis Agent specializing in data analysis and pattern recognition.
    
    Capabilities:
    - Statistical analysis
    - Pattern detection
    - Data visualization concepts
    - Trend analysis
    - Comparative analysis
    - Risk assessment
    """
    
    def __init__(self, config: AgentConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.analysis_history = []
        self.pattern_cache = {}
        self.analysis_models = {}
        
    async def initialize(self) -> None:
        """Initialize the analysis agent"""
        await super().initialize()
        
        # Initialize analysis models and tools
        self.analysis_models = {
            "statistical": self._init_statistical_model(),
            "pattern": self._init_pattern_model(),
            "trend": self._init_trend_model(),
            "risk": self._init_risk_model()
        }
        
        self.logger.info("AnalysisAgent initialized with analysis models")
    
    def _init_statistical_model(self) -> Dict[str, Any]:
        """Initialize statistical analysis model"""
        return {
            "methods": ["mean", "median", "std", "correlation", "regression"],
            "confidence_levels": [0.90, 0.95, 0.99],
            "test_types": ["t_test", "chi_square", "anova", "kolmogorov_smirnov"]
        }
    
    def _init_pattern_model(self) -> Dict[str, Any]:
        """Initialize pattern recognition model"""
        return {
            "algorithms": ["clustering", "classification", "anomaly_detection"],
            "features": ["temporal", "spatial", "semantic", "behavioral"],
            "thresholds": {"similarity": 0.8, "confidence": 0.75}
        }
    
    def _init_trend_model(self) -> Dict[str, Any]:
        """Initialize trend analysis model"""
        return {
            "methods": ["linear_regression", "polynomial", "exponential", "seasonal"],
            "window_sizes": [7, 30, 90, 365],
            "forecast_horizons": [1, 7, 30, 90]
        }
    
    def _init_risk_model(self) -> Dict[str, Any]:
        """Initialize risk assessment model"""
        return {
            "categories": ["financial", "operational", "strategic", "compliance"],
            "severity_levels": ["low", "medium", "high", "critical"],
            "probability_thresholds": [0.1, 0.3, 0.6, 0.9]
        }
    
    async def analyze_data(self, data: Any, analysis_type: str = "comprehensive") -> Dict[str, Any]:
        """
        Perform comprehensive data analysis
        
        Args:
            data: Data to analyze
            analysis_type: Type of analysis to perform
            
        Returns:
            Analysis results
        """
        try:
            analysis_id = f"analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
            
            self.logger.info(f"Starting {analysis_type} analysis: {analysis_id}")
            
            if analysis_type == "statistical":
                results = await self._statistical_analysis(data)
            elif analysis_type == "pattern":
                results = await self._pattern_analysis(data)
            elif analysis_type == "trend":
                results = await self._trend_analysis(data)
            elif analysis_type == "risk":
                results = await self._risk_analysis(data)
            else:
                results = await self._comprehensive_analysis(data)
            
            # Store analysis in history
            analysis_record = {
                "id": analysis_id,
                "type": analysis_type,
                "timestamp": datetime.now(),
                "data_summary": self._summarize_data(data),
                "results": results,
                "confidence": results.get("confidence", 0.5)
            }
            
            self.analysis_history.append(analysis_record)
            
            # Update pattern cache if relevant patterns found
            if "patterns" in results:
                await self._update_pattern_cache(results["patterns"])
            
            return results
            
        except Exception as e:
            self.logger.error(f"Analysis failed: {str(e)}")
            return {"error": str(e), "success": False}
    
    async def _statistical_analysis(self, data: Any) -> Dict[str, Any]:
        """Perform statistical analysis"""
        try:
            # Simulate statistical analysis
            stats = {
                "descriptive": {
                    "count": len(data) if hasattr(data, '__len__') else 1,
                    "mean": 0.5,  # Placeholder
                    "median": 0.5,  # Placeholder
                    "std": 0.2,   # Placeholder
                    "min": 0.0,
                    "max": 1.0
                },
                "inferential": {
                    "confidence_interval": [0.4, 0.6],
                    "p_value": 0.05,
                    "test_statistic": 2.3
                },
                "distribution": {
                    "type": "normal",
                    "parameters": {"mu": 0.5, "sigma": 0.2}
                }
            }
            
            return {
                "type": "statistical",
                "statistics": stats,
                "confidence": 0.85,
                "recommendations": ["Data appears normally distributed", "Sample size adequate"],
                "success": True
            }
            
        except Exception as e:
            return {"error": str(e), "success": False}
    
    async def _pattern_analysis(self, data: Any) -> Dict[str, Any]:
        """Perform pattern recognition analysis"""
        try:
            # Simulate pattern analysis
            patterns = [
                {
                    "type": "temporal",
                    "description": "Cyclical pattern detected",
                    "confidence": 0.78,
                    "frequency": "daily",
                    "strength": 0.65
                },
                {
                    "type": "anomaly",
                    "description": "Outliers detected",
                    "confidence": 0.92,
                    "count": 3,
                    "severity": "medium"
                }
            ]
            
            return {
                "type": "pattern",
                "patterns": patterns,
                "pattern_count": len(patterns),
                "confidence": 0.80,
                "recommendations": ["Monitor cyclical patterns", "Investigate anomalies"],
                "success": True
            }
            
        except Exception as e:
            return {"error": str(e), "success": False}
    
    async def _trend_analysis(self, data: Any) -> Dict[str, Any]:
        """Perform trend analysis"""
        try:
            # Simulate trend analysis
            trends = {
                "direction": "increasing",
                "slope": 0.15,
                "r_squared": 0.78,
                "seasonality": {
                    "present": True,
                    "period": 7,
                    "strength": 0.3
                },
                "forecast": {
                    "next_period": 0.62,
                    "confidence_interval": [0.55, 0.69],
                    "horizon": 30
                }
            }
            
            return {
                "type": "trend",
                "trends": trends,
                "forecast_accuracy": 0.82,
                "confidence": 0.79,
                "recommendations": ["Trend is positive", "Consider seasonal adjustments"],
                "success": True
            }
            
        except Exception as e:
            return {"error": str(e), "success": False}
    
    async def _risk_analysis(self, data: Any) -> Dict[str, Any]:
        """Perform risk assessment"""
        try:
            # Simulate risk analysis
            risks = [
                {
                    "category": "operational",
                    "description": "Data quality issues",
                    "probability": 0.3,
                    "impact": "medium",
                    "severity": "medium",
                    "mitigation": "Implement data validation"
                },
                {
                    "category": "strategic",
                    "description": "Market volatility",
                    "probability": 0.6,
                    "impact": "high",
                    "severity": "high",
                    "mitigation": "Diversify portfolio"
                }
            ]
            
            overall_risk = sum(r["probability"] * (0.5 if r["impact"] == "medium" else 0.8) for r in risks) / len(risks)
            
            return {
                "type": "risk",
                "risks": risks,
                "overall_risk_score": overall_risk,
                "risk_level": "medium" if overall_risk < 0.5 else "high",
                "confidence": 0.75,
                "recommendations": ["Monitor operational risks", "Develop contingency plans"],
                "success": True
            }
            
        except Exception as e:
            return {"error": str(e), "success": False}
    
    async def _comprehensive_analysis(self, data: Any) -> Dict[str, Any]:
        """Perform comprehensive analysis combining all methods"""
        try:
            # Run all analysis types
            statistical = await self._statistical_analysis(data)
            pattern = await self._pattern_analysis(data)
            trend = await self._trend_analysis(data)
            risk = await self._risk_analysis(data)
            
            # Combine results
            comprehensive_results = {
                "type": "comprehensive",
                "statistical": statistical,
                "pattern": pattern,
                "trend": trend,
                "risk": risk,
                "overall_confidence": (
                    statistical.get("confidence", 0) +
                    pattern.get("confidence", 0) +
                    trend.get("confidence", 0) +
                    risk.get("confidence", 0)
                ) / 4,
                "key_insights": [
                    "Data shows normal distribution with cyclical patterns",
                    "Positive trend detected with seasonal components",
                    "Medium risk level with operational concerns"
                ],
                "success": True
            }
            
            return comprehensive_results
            
        except Exception as e:
            return {"error": str(e), "success": False}
    
    async def detect_patterns(self, data: Any, pattern_type: str = "all") -> List[Dict[str, Any]]:
        """
        Detect specific patterns in data
        
        Args:
            data: Data to analyze for patterns
            pattern_type: Type of patterns to detect
            
        Returns:
            List of detected patterns
        """
        try:
            patterns = []
            
            if pattern_type in ["all", "temporal"]:
                temporal_patterns = await self._detect_temporal_patterns(data)
                patterns.extend(temporal_patterns)
            
            if pattern_type in ["all", "anomaly"]:
                anomaly_patterns = await self._detect_anomalies(data)
                patterns.extend(anomaly_patterns)
            
            if pattern_type in ["all", "correlation"]:
                correlation_patterns = await self._detect_correlations(data)
                patterns.extend(correlation_patterns)
            
            # Update pattern cache
            await self._update_pattern_cache(patterns)
            
            return patterns
            
        except Exception as e:
            self.logger.error(f"Pattern detection failed: {str(e)}")
            return []
    
    async def _detect_temporal_patterns(self, data: Any) -> List[Dict[str, Any]]:
        """Detect temporal patterns"""
        # Simulate temporal pattern detection
        return [
            {
                "type": "temporal",
                "pattern": "weekly_cycle",
                "confidence": 0.85,
                "period": 7,
                "phase": 2
            }
        ]
    
    async def _detect_anomalies(self, data: Any) -> List[Dict[str, Any]]:
        """Detect anomalies in data"""
        # Simulate anomaly detection
        return [
            {
                "type": "anomaly",
                "pattern": "outlier",
                "confidence": 0.92,
                "severity": "medium",
                "location": "data_point_15"
            }
        ]
    
    async def _detect_correlations(self, data: Any) -> List[Dict[str, Any]]:
        """Detect correlation patterns"""
        # Simulate correlation detection
        return [
            {
                "type": "correlation",
                "pattern": "positive_correlation",
                "confidence": 0.78,
                "strength": 0.65,
                "variables": ["var1", "var2"]
            }
        ]
    
    async def compare_datasets(self, dataset1: Any, dataset2: Any) -> Dict[str, Any]:
        """
        Compare two datasets and identify differences
        
        Args:
            dataset1: First dataset
            dataset2: Second dataset
            
        Returns:
            Comparison results
        """
        try:
            comparison = {
                "size_comparison": {
                    "dataset1_size": len(dataset1) if hasattr(dataset1, '__len__') else 1,
                    "dataset2_size": len(dataset2) if hasattr(dataset2, '__len__') else 1,
                    "size_difference": 0  # Placeholder
                },
                "statistical_comparison": {
                    "mean_difference": 0.1,  # Placeholder
                    "variance_ratio": 1.2,   # Placeholder
                    "correlation": 0.85      # Placeholder
                },
                "similarity_score": 0.78,
                "key_differences": [
                    "Dataset2 has higher variance",
                    "Strong positive correlation detected"
                ],
                "recommendations": [
                    "Datasets are highly similar",
                    "Consider combining for analysis"
                ]
            }
            
            return comparison
            
        except Exception as e:
            self.logger.error(f"Dataset comparison failed: {str(e)}")
            return {"error": str(e), "success": False}
    
    async def _update_pattern_cache(self, patterns: List[Dict[str, Any]]) -> None:
        """Update the pattern cache with new patterns"""
        for pattern in patterns:
            pattern_key = f"{pattern['type']}_{pattern.get('pattern', 'unknown')}"
            if pattern_key not in self.pattern_cache:
                self.pattern_cache[pattern_key] = []
            
            self.pattern_cache[pattern_key].append({
                "timestamp": datetime.now(),
                "confidence": pattern.get("confidence", 0.5),
                "details": pattern
            })
    
    def _summarize_data(self, data: Any) -> Dict[str, Any]:
        """Create a summary of the input data"""
        return {
            "type": type(data).__name__,
            "size": len(data) if hasattr(data, '__len__') else 1,
            "timestamp": datetime.now().isoformat()
        }
    
    async def get_analysis_insights(self) -> List[Dict[str, Any]]:
        """Get insights from analysis history"""
        insights = []
        
        if len(self.analysis_history) > 0:
            # Recent analysis trends
            recent_analyses = self.analysis_history[-10:]
            avg_confidence = sum(a.get("confidence", 0) for a in recent_analyses) / len(recent_analyses)
            
            insights.append({
                "type": "performance",
                "insight": f"Average analysis confidence: {avg_confidence:.2f}",
                "data": {"confidence": avg_confidence, "sample_size": len(recent_analyses)}
            })
            
            # Pattern frequency
            pattern_types = {}
            for analysis in self.analysis_history:
                if "patterns" in analysis.get("results", {}):
                    for pattern in analysis["results"]["patterns"]:
                        ptype = pattern.get("type", "unknown")
                        pattern_types[ptype] = pattern_types.get(ptype, 0) + 1
            
            if pattern_types:
                most_common = max(pattern_types, key=pattern_types.get)
                insights.append({
                    "type": "patterns",
                    "insight": f"Most common pattern type: {most_common}",
                    "data": pattern_types
                })
        
        return insights
    
    async def execute_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """Execute analysis-specific tasks"""
        task_type = task.get("type", "analyze")
        
        if task_type == "analyze":
            return await self.analyze_data(
                task.get("data"),
                task.get("analysis_type", "comprehensive")
            )
        elif task_type == "detect_patterns":
            patterns = await self.detect_patterns(
                task.get("data"),
                task.get("pattern_type", "all")
            )
            return {"patterns": patterns, "success": True}
        elif task_type == "compare":
            return await self.compare_datasets(
                task.get("dataset1"),
                task.get("dataset2")
            )
        else:
            return await super().execute_task(task)
