health_data/record_merger.py

"""
Health Record Merger
Merge and aggregate health records from multiple days
"""

from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from collections import defaultdict

from .pydantic_models import (
    HealthRecord, NutritionRecord, ExerciseRecord,
    SymptomRecord, MentalHealthRecord, RecordType
)


class HealthRecordMerger:
    """Merge and aggregate health records"""
    
    @staticmethod
    def merge_records(
        records: List[HealthRecord],
        merge_strategy: str = 'latest'
    ) -> Dict[str, Any]:
        """
        Merge multiple health records into aggregated data
        
        Args:
            records: List of health records to merge
            merge_strategy: 'latest', 'average', 'all'
            
        Returns:
            Merged data dictionary
        """
        if not records:
            return {}
        
        # Group records by type
        records_by_type = defaultdict(list)
        for record in records:
            records_by_type[record.record_type].append(record)
        
        merged = {
            'total_records': len(records),
            'date_range': {
                'start': min(r.timestamp for r in records).isoformat(),
                'end': max(r.timestamp for r in records).isoformat()
            },
            'by_type': {}
        }
        
        # Merge each type
        for record_type, type_records in records_by_type.items():
            if record_type == RecordType.NUTRITION:
                merged['by_type']['nutrition'] = HealthRecordMerger._merge_nutrition_records(
                    type_records, merge_strategy
                )
            elif record_type == RecordType.EXERCISE:
                merged['by_type']['exercise'] = HealthRecordMerger._merge_exercise_records(
                    type_records, merge_strategy
                )
            elif record_type == RecordType.SYMPTOM:
                merged['by_type']['symptom'] = HealthRecordMerger._merge_symptom_records(
                    type_records, merge_strategy
                )
            elif record_type == RecordType.MENTAL_HEALTH:
                merged['by_type']['mental_health'] = HealthRecordMerger._merge_mental_health_records(
                    type_records, merge_strategy
                )
        
        # Extract common health metrics
        merged['health_metrics'] = HealthRecordMerger._extract_health_metrics(
            records, merge_strategy
        )
        
        return merged
    
    @staticmethod
    def _merge_nutrition_records(
        records: List[HealthRecord],
        strategy: str
    ) -> Dict[str, Any]:
        """Merge nutrition records"""
        if strategy == 'latest':
            latest = max(records, key=lambda r: r.timestamp)
            return {
                'latest_record': latest.model_dump(),
                'total_records': len(records)
            }
        
        elif strategy == 'average':
            # Calculate averages
            total_calories = sum(r.data.get('calories', 0) for r in records if r.data.get('calories'))
            total_protein = sum(r.data.get('protein', 0) for r in records if r.data.get('protein'))
            total_carbs = sum(r.data.get('carbs', 0) for r in records if r.data.get('carbs'))
            total_fat = sum(r.data.get('fat', 0) for r in records if r.data.get('fat'))
            
            count = len(records)
            
            return {
                'average_daily': {
                    'calories': round(total_calories / count, 1) if count > 0 else 0,
                    'protein': round(total_protein / count, 1) if count > 0 else 0,
                    'carbs': round(total_carbs / count, 1) if count > 0 else 0,
                    'fat': round(total_fat / count, 1) if count > 0 else 0
                },
                'total': {
                    'calories': round(total_calories, 1),
                    'protein': round(total_protein, 1),
                    'carbs': round(total_carbs, 1),
                    'fat': round(total_fat, 1)
                },
                'total_records': count
            }
        
        else:  # 'all'
            return {
                'all_records': [r.model_dump() for r in records],
                'total_records': len(records)
            }
    
    @staticmethod
    def _merge_exercise_records(
        records: List[HealthRecord],
        strategy: str
    ) -> Dict[str, Any]:
        """Merge exercise records"""
        if strategy == 'latest':
            latest = max(records, key=lambda r: r.timestamp)
            return {
                'latest_record': latest.model_dump(),
                'total_records': len(records)
            }
        
        elif strategy == 'average':
            total_duration = sum(r.data.get('duration_minutes', 0) for r in records)
            total_calories = sum(r.data.get('calories_burned', 0) for r in records)
            
            # Count by exercise type
            exercise_types = defaultdict(int)
            for r in records:
                ex_type = r.data.get('exercise_type', 'unknown')
                exercise_types[ex_type] += 1
            
            count = len(records)
            
            return {
                'total_workouts': count,
                'total_duration_minutes': total_duration,
                'total_calories_burned': round(total_calories, 1),
                'average_duration': round(total_duration / count, 1) if count > 0 else 0,
                'average_calories': round(total_calories / count, 1) if count > 0 else 0,
                'exercise_types': dict(exercise_types)
            }
        
        else:  # 'all'
            return {
                'all_records': [r.model_dump() for r in records],
                'total_records': len(records)
            }
    
    @staticmethod
    def _merge_symptom_records(
        records: List[HealthRecord],
        strategy: str
    ) -> Dict[str, Any]:
        """Merge symptom records"""
        if strategy == 'latest':
            latest = max(records, key=lambda r: r.timestamp)
            return {
                'latest_record': latest.model_dump(),
                'total_records': len(records)
            }
        
        # Collect all symptoms
        all_symptoms = []
        symptom_counts = defaultdict(int)
        
        for r in records:
            symptoms = r.data.get('symptoms', [])
            if isinstance(symptoms, list):
                all_symptoms.extend(symptoms)
                for symptom in symptoms:
                    symptom_counts[symptom] += 1
        
        return {
            'total_reports': len(records),
            'unique_symptoms': len(set(all_symptoms)),
            'most_common_symptoms': sorted(
                symptom_counts.items(),
                key=lambda x: x[1],
                reverse=True
            )[:5],
            'all_symptoms': list(set(all_symptoms))
        }
    
    @staticmethod
    def _merge_mental_health_records(
        records: List[HealthRecord],
        strategy: str
    ) -> Dict[str, Any]:
        """Merge mental health records"""
        if strategy == 'latest':
            latest = max(records, key=lambda r: r.timestamp)
            return {
                'latest_record': latest.model_dump(),
                'total_records': len(records)
            }
        
        # Calculate averages
        stress_levels = [r.data.get('stress_level') for r in records if r.data.get('stress_level')]
        sleep_hours = [r.data.get('sleep_hours') for r in records if r.data.get('sleep_hours')]
        sleep_quality = [r.data.get('sleep_quality') for r in records if r.data.get('sleep_quality')]
        
        return {
            'total_records': len(records),
            'average_stress_level': round(sum(stress_levels) / len(stress_levels), 1) if stress_levels else None,
            'average_sleep_hours': round(sum(sleep_hours) / len(sleep_hours), 1) if sleep_hours else None,
            'average_sleep_quality': round(sum(sleep_quality) / len(sleep_quality), 1) if sleep_quality else None,
            'stress_trend': 'improving' if len(stress_levels) >= 2 and stress_levels[-1] < stress_levels[0] else 'stable'
        }
    
    @staticmethod
    def _extract_health_metrics(
        records: List[HealthRecord],
        strategy: str
    ) -> Dict[str, Any]:
        """Extract common health metrics from records"""
        weights = [r.weight for r in records if r.weight]
        heights = [r.height for r in records if r.height]
        bmis = [r.bmi for r in records if r.bmi]
        
        metrics = {}
        
        if weights:
            metrics['weight'] = {
                'latest': weights[-1],
                'average': round(sum(weights) / len(weights), 1),
                'min': min(weights),
                'max': max(weights),
                'change': round(weights[-1] - weights[0], 1) if len(weights) >= 2 else 0
            }
        
        if heights:
            metrics['height'] = {
                'latest': heights[-1],
                'average': round(sum(heights) / len(heights), 1)
            }
        
        if bmis:
            metrics['bmi'] = {
                'latest': bmis[-1],
                'average': round(sum(bmis) / len(bmis), 1),
                'change': round(bmis[-1] - bmis[0], 1) if len(bmis) >= 2 else 0
            }
        
        return metrics
    
    @staticmethod
    def merge_by_date_range(
        records: List[HealthRecord],
        start_date: datetime,
        end_date: datetime,
        merge_strategy: str = 'average'
    ) -> Dict[str, Any]:
        """
        Merge records within a specific date range
        
        Args:
            records: All health records
            start_date: Start of date range
            end_date: End of date range
            merge_strategy: How to merge data
            
        Returns:
            Merged data for the date range
        """
        # Filter records by date range
        filtered = [
            r for r in records
            if start_date <= r.timestamp <= end_date
        ]
        
        return HealthRecordMerger.merge_records(filtered, merge_strategy)
    
    @staticmethod
    def get_weekly_summary(
        records: List[HealthRecord],
        weeks_back: int = 1
    ) -> Dict[str, Any]:
        """
        Get weekly summary of health records
        
        Args:
            records: All health records
            weeks_back: Number of weeks to look back
            
        Returns:
            Weekly summary
        """
        end_date = datetime.now()
        start_date = end_date - timedelta(weeks=weeks_back)
        
        return HealthRecordMerger.merge_by_date_range(
            records,
            start_date,
            end_date,
            merge_strategy='average'
        )
    
    @staticmethod
    def get_monthly_summary(
        records: List[HealthRecord],
        months_back: int = 1
    ) -> Dict[str, Any]:
        """
        Get monthly summary of health records
        
        Args:
            records: All health records
            months_back: Number of months to look back
            
        Returns:
            Monthly summary
        """
        end_date = datetime.now()
        start_date = end_date - timedelta(days=30 * months_back)
        
        return HealthRecordMerger.merge_by_date_range(
            records,
            start_date,
            end_date,
            merge_strategy='average'
        )


def merge_records(
    records: List[HealthRecord],
    strategy: str = 'latest'
) -> Dict[str, Any]:
    """
    Convenience function to merge health records
    
    Args:
        records: List of health records
        strategy: 'latest', 'average', or 'all'
        
    Returns:
        Merged data dictionary
    """
    return HealthRecordMerger.merge_records(records, strategy)