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"""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 [] |