"""Analysis service for processing government data."""

from typing import Dict, List, Optional


class AnalysisService:
    """Service for data analysis operations."""
    
    def __init__(self):
        self._analysis_cache = {}
    
    async def analyze_spending_patterns(self, data: List[Dict]) -> Dict:
        """Analyze spending patterns in government data."""
        if not data:
            return {"error": "No data provided for analysis"}
        
        # Basic analysis stub
        total_value = sum(float(item.get("valor", 0)) for item in data)
        avg_value = total_value / len(data) if data else 0
        
        return {
            "total_items": len(data),
            "total_value": total_value,
            "average_value": avg_value,
            "analysis_type": "spending_patterns",
            "status": "stub_implementation"
        }
    
    async def detect_anomalies(self, data: List[Dict]) -> List[Dict]:
        """Detect anomalies in government data."""
        # TODO: Integrate with ML anomaly detection
        return []
    
    async def generate_insights(self, data: List[Dict]) -> List[str]:
        """Generate insights from data analysis."""
        if not data:
            return ["Nenhum dado disponível para análise"]
        
        insights = [
            f"Analisados {len(data)} registros de dados governamentais",
            "Análise detalhada em desenvolvimento",
            "Sistema de detecção de anomalias será implementado"
        ]
        
        return insights
    
    async def compare_periods(self, current_data: List[Dict], previous_data: List[Dict]) -> Dict:
        """Compare data between different periods."""
        current_total = sum(float(item.get("valor", 0)) for item in current_data)
        previous_total = sum(float(item.get("valor", 0)) for item in previous_data)
        
        change = current_total - previous_total
        change_pct = (change / previous_total * 100) if previous_total > 0 else 0
        
        return {
            "current_total": current_total,
            "previous_total": previous_total,
            "absolute_change": change,
            "percentage_change": change_pct,
            "trend": "increase" if change > 0 else "decrease" if change < 0 else "stable"
        }
    
    async def rank_entities(self, data: List[Dict], by: str = "valor") -> List[Dict]:
        """Rank entities by specified criteria."""
        # TODO: Implement entity ranking
        return []