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| """ | |
| Seed demo data for ZenVolt platform. | |
| Creates realistic assets based on NASA battery data. | |
| """ | |
| import pandas as pd | |
| import json | |
| from datetime import datetime, timedelta | |
| from database import SessionLocal, Asset, HealthPrediction, init_db | |
| from predict_soh import predict_soh | |
| import uuid | |
| def seed_demo_assets(): | |
| """Create demo assets from NASA battery data.""" | |
| init_db() | |
| db = SessionLocal() | |
| # Clear existing data | |
| db.query(HealthPrediction).delete() | |
| db.query(Asset).delete() | |
| db.commit() | |
| print("🌱 Seeding demo data...") | |
| # Demo user IDs | |
| demo_users = ["demo-user-1", "demo-user-2"] | |
| # NASA battery configurations with realistic metadata | |
| nasa_configs = [ | |
| { | |
| "battery": "B0005", | |
| "project_name": "NASA B0005 - 18650 Li-ion Cell", | |
| "asset_id": "NASA_B0005", | |
| "location": "NASA Ames Research Center, Moffett Field, CA", | |
| "bms_provider": "NASA Battery Testing Station (Arbin BT2000)", | |
| "category": ["Research", "18650", "Li-ion", "Validated"], | |
| "stage": "Certified", | |
| "owner": demo_users[0], | |
| "specs": { | |
| "chemistry": "LiCoO2/Graphite", | |
| "format": "18650 cylindrical", | |
| "rated_capacity": "1.85 Ah", | |
| "nominal_voltage": "3.7V", | |
| "test_period": "2008", | |
| "cycles_tested": "168" | |
| } | |
| }, | |
| { | |
| "battery": "B0006", | |
| "asset_id": "NASA_B0006", | |
| "project_name": "NASA B0006 - 18650 Li-ion Cell", | |
| "location": "NASA Ames Research Center, Moffett Field, CA", | |
| "bms_provider": "NASA Battery Testing Station (Arbin BT2000)", | |
| "category": ["Research", "18650", "Li-ion", "Validated"], | |
| "stage": "Certified", | |
| "owner": demo_users[0], | |
| "specs": { | |
| "chemistry": "LiCoO2/Graphite", | |
| "format": "18650 cylindrical", | |
| "rated_capacity": "2.04 Ah", | |
| "nominal_voltage": "3.7V", | |
| "test_period": "2008", | |
| "cycles_tested": "168" | |
| } | |
| }, | |
| { | |
| "battery": "B0007", | |
| "asset_id": "NASA_B0007", | |
| "project_name": "NASA B0007 - 18650 Li-ion Cell (Validation)", | |
| "location": "NASA Ames Research Center, Moffett Field, CA", | |
| "bms_provider": "NASA Battery Testing Station (Arbin BT2000)", | |
| "category": ["Research", "18650", "Li-ion", "Validated", "Test-Set"], | |
| "stage": "Certified", | |
| "owner": demo_users[0], | |
| "specs": { | |
| "chemistry": "LiCoO2/Graphite", | |
| "format": "18650 cylindrical", | |
| "rated_capacity": "1.89 Ah", | |
| "nominal_voltage": "3.7V", | |
| "test_period": "2008", | |
| "cycles_tested": "168", | |
| "validation_role": "Independent test set for model validation" | |
| } | |
| } | |
| ] | |
| created_assets = [] | |
| for config in nasa_configs: | |
| print(f"\n📦 Processing {config['battery']}...") | |
| try: | |
| # Load NASA data | |
| data_path = f"./data/nasa_converted/{config['battery']}.csv" | |
| df = pd.read_csv(data_path) | |
| if len(df) == 0: | |
| print(f" ⚠️ No data found for {config['battery']}") | |
| continue | |
| # Get initial capacity | |
| initial_capacity = df['capacity'].max() | |
| # Generate predictions at different cycle points for historical data | |
| cycle_points = [ | |
| int(len(df) * 0.25), # 25% through lifecycle | |
| int(len(df) * 0.50), # 50% through lifecycle | |
| int(len(df) * 0.75), # 75% through lifecycle | |
| len(df) - 1 # Latest | |
| ] | |
| # Create asset | |
| asset_id = str(uuid.uuid4()) | |
| asset = Asset( | |
| id=asset_id, | |
| owner_id=config['owner'], | |
| project_name=config['project_name'], | |
| asset_id=config['asset_id'], | |
| location=config['location'], | |
| capacity=round(initial_capacity, 2), | |
| bms_provider=config['bms_provider'], | |
| category=json.dumps(config['category']), | |
| project_stage=config['stage'], | |
| is_online=1, | |
| created_at=datetime.utcnow() - timedelta(days=90), # 3 months ago | |
| updated_at=datetime.utcnow() | |
| ) | |
| # Generate predictions for historical timeline | |
| predictions = [] | |
| latest_prediction = None | |
| for i, cycle_idx in enumerate(cycle_points): | |
| try: | |
| # Get data up to this cycle | |
| data_subset = df.iloc[:cycle_idx+1] | |
| # Run prediction | |
| result = predict_soh( | |
| battery_data=data_subset, | |
| initial_capacity=initial_capacity, | |
| forecast_horizons=[10, 50, 100] | |
| ) | |
| # Calculate prediction date (spread over last 3 months) | |
| days_ago = 90 - (i * 30) # 90, 60, 30, 0 days ago | |
| pred_date = datetime.utcnow() - timedelta(days=days_ago) | |
| # Create historical prediction | |
| pred = HealthPrediction( | |
| asset_id=asset_id, | |
| prediction_date=pred_date, | |
| cycle_index=result['current_cycle'], | |
| health_score=result['health_score'], | |
| current_soh=result['current_soh']['point_estimate'], | |
| soh_lower_95=result['current_soh']['lower_95'], | |
| soh_upper_95=result['current_soh']['upper_95'], | |
| remaining_life_years=result['remaining_life']['years'], | |
| remaining_life_cycles=result['remaining_life']['cycles'], | |
| degradation_rate=result['degradation_rate']['per_year_pct'], | |
| confidence=result['confidence'], | |
| data_quality=result['data_quality']['quality_score'], | |
| shap_top_features=json.dumps( | |
| result.get('shap_explanation', {}).get('top_features', [])[:5] | |
| ) if result.get('shap_explanation') else None | |
| ) | |
| predictions.append(pred) | |
| latest_prediction = result | |
| print(f" ✓ Prediction at cycle {cycle_idx}: Health={result['health_score']:.1f}/100") | |
| except Exception as e: | |
| print(f" ⚠️ Error at cycle {cycle_idx}: {e}") | |
| continue | |
| # Update asset with latest metrics | |
| if latest_prediction: | |
| asset.health_score = latest_prediction['health_score'] | |
| asset.remaining_life = latest_prediction['remaining_life']['years'] | |
| asset.degradation_rate = latest_prediction['degradation_rate']['per_year_pct'] | |
| asset.confidence = latest_prediction['confidence'] | |
| asset.last_prediction_date = datetime.utcnow() | |
| # Add some operational data | |
| asset.state_of_charge = 75.0 + (len(predictions) * 5) % 30 | |
| asset.voltage = 700.0 + (len(predictions) * 10) % 100 | |
| asset.temperature = 20.0 + (len(predictions) * 2) % 10 | |
| # Save to database | |
| db.add(asset) | |
| for pred in predictions: | |
| db.add(pred) | |
| db.commit() | |
| db.refresh(asset) | |
| # Store asset info before session closes | |
| asset_info = { | |
| "project_name": asset.project_name, | |
| "asset_id": asset.asset_id, | |
| "health_score": asset.health_score, | |
| "remaining_life": asset.remaining_life, | |
| "predictions_count": len(predictions) | |
| } | |
| created_assets.append(asset_info) | |
| print(f" ✅ Created asset: {asset_info['project_name']}") | |
| print(f" Health: {asset_info['health_score']:.1f}/100") | |
| print(f" RUL: {asset_info['remaining_life']:.1f} years") | |
| print(f" Historical predictions: {asset_info['predictions_count']}") | |
| except Exception as e: | |
| print(f" ❌ Error processing {config['battery']}: {e}") | |
| db.rollback() | |
| continue | |
| print(f"\n✅ Seed complete! Created {len(created_assets)} assets") | |
| print("\nAssets:") | |
| for asset_info in created_assets: | |
| print(f" - {asset_info['project_name']} ({asset_info['asset_id']}): Health={asset_info['health_score']:.1f}/100") | |
| db.close() | |
| return created_assets | |
| if __name__ == "__main__": | |
| seed_demo_assets() | |