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