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"""
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)
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