"""
Data enrichment pipeline for anomaly detection
"""
import logging
from typing import Dict, List, Any

logger = logging.getLogger(__name__)


class DataEnricher:
    """
    Enriches raw data with additional context and metadata.
    """
    
    def __init__(self):
        """Initialize data enricher."""
        logger.info("DataEnricher initialized")
    
    def enrich(self, data: Dict) -> Dict:
        """
        Enrich data with metadata and context.
        
        Args:
            data: Input data dictionary
            
        Returns:
            Enriched data dictionary
        """
        enriched = data.copy()
        # Add enrichment logic
        return enriched
    
    def add_category_metadata(self, data: Dict, category: str) -> Dict:
        """Add category-specific metadata."""
        logger.info(f"Adding metadata for category: {category}")
        # Implementation
        return data
    
    def add_temporal_features(self, data: Dict) -> Dict:
        """Add temporal features to data."""
        logger.info("Adding temporal features")
        # Implementation
        return data


class EnrichmentPipeline:
    """
    Complete enrichment pipeline combining multiple enrichment steps.
    """
    
    def __init__(self):
        self.enricher = DataEnricher()
    
    def process(self, raw_data: List[Dict]) -> List[Dict]:
        """
        Process raw data through enrichment pipeline.
        
        Args:
            raw_data: List of raw data items
            
        Returns:
            List of enriched data items
        """
        logger.info(f"Processing {len(raw_data)} items through enrichment pipeline")
        
        enriched_data = []
        for item in raw_data:
            enriched_item = self.enricher.enrich(item)
            enriched_data.append(enriched_item)
        
        return enriched_data