feat: Enhance context generation with new table images for VO2 Max and Heart Rate Zones
- Added functionality to generate VO2 Max and Heart Rate Zones tables in the context_generator.py. - Integrated graph_generator to create table images with specified data and styles. - Updated report_generator.py to pass graph_generator to context generation. - Introduced a new method in graph_generator.py to generate table images with customizable options. - Created test scripts for Page 5 (RMR and NEAT calculations) and Page 6 (Meal Plan calculations) using actual patient data. - Updated Jupyter notebook metadata for better environment identification.
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bolade
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"""
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Test script for Page 5 - RMR and NEAT Calculations
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Using Keirstyn Moran's actual data
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Expected values from PDF (Page 5):
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- RMR (Resting): 1386 kCals
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- NEAT: 762 kCals
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- Weight Loss Deficit: -423 kCals (to lose 1.1 lbs per week)
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- Total Calories: ~1725 kCals
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- Metabolism Classification: Optimal/Average (shown in graph)
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- Fuel Source: 75% Fats, 25% Carbs (shown in pie chart)
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"""
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import sys
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import pandas as pd
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sys.path.insert(0, '/Users/macbook/bio-performx')
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from app.services.context_generator import ContextGenerator
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# Keirstyn Moran's patient data from PDF
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PATIENT_DATA = {
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"name": "Keirstyn Moran",
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"first_name": "Keirstyn",
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"last_name": "Moran",
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"age": 34,
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"height": "5'4\"", # 162.56 cm
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"weight": 55.79, # 123 lbs = 55.79 kg
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"gender": "female",
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"fat_percentage": 20.0, # Estimated from body composition
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"activity_level": "moderate", # From PDF "Focus: Endurance" -> moderate activity
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}
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# NOTE: The PDF shows RMR = 1386 kcal/day which appears to be from a SEPARATE resting
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# metabolic test, not from this exercise test CSV file. The exercise test file shows
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# HR starting at 60-65 bpm and quickly rising, with no true resting phase.
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#
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# For testing purposes, we'll use the PDF's measured RMR value (1386) to validate
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# our NEAT and meal plan calculations.
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USE_PDF_RMR = True # Set to True to use PDF's measured RMR instead of calculating from CSV
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# File paths
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PNOE_FILE = "Pnoe_20250729_1550-Moran_Keirstyn (2).csv"
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SPIROMETRY_FILE = "data/extracted_spirometry_table.csv"
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def main():
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print("=" * 80)
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print("PAGE 5 - RMR AND NEAT CALCULATION TEST")
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print("=" * 80)
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print(f"\nPatient: {PATIENT_DATA['name']}")
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print(f"Age: {PATIENT_DATA['age']}, Height: {PATIENT_DATA['height']}, Weight: {PATIENT_DATA['weight']}kg ({PATIENT_DATA['weight'] * 2.20462:.1f}lbs)")
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print(f"Gender: {PATIENT_DATA['gender']}, Activity: {PATIENT_DATA['activity_level']}")
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print(f"Body Fat: {PATIENT_DATA['fat_percentage']}%")
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# Create context generator
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gen = ContextGenerator()
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# Set patient info manually
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gen.patient_info = PATIENT_DATA.copy()
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# Calculate fat mass and lean mass
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weight_kg = PATIENT_DATA["weight"]
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fat_pct = PATIENT_DATA["fat_percentage"]
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gen.patient_info["fat_mass_lbs"] = weight_kg * fat_pct / 100 * 2.20462
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gen.patient_info["lean_mass_lbs"] = weight_kg * (1 - fat_pct / 100) * 2.20462
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print(f"Lean Mass: {gen.patient_info['lean_mass_lbs']:.1f} lbs")
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print(f"Fat Mass: {gen.patient_info['fat_mass_lbs']:.1f} lbs")
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# Load Pnoe data
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print(f"\nLoading Pnoe data from: {PNOE_FILE}")
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try:
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gen.load_data(PNOE_FILE, SPIROMETRY_FILE)
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print(f"✓ Loaded {len(gen.pnoe_df)} rows of Pnoe data")
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except Exception as e:
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print(f"✗ Error loading data: {e}")
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return
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print("\n" + "=" * 80)
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print("CALCULATING RMR AND NEAT (using our formula)")
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print("=" * 80)
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try:
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# Calculate RMR and fuel source
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if USE_PDF_RMR:
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print("\n⚠️ Using PDF's measured RMR (1386 kcal/day) instead of calculating from CSV")
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print(" (The exercise test CSV has no true resting phase)")
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# Manually set RMR to PDF value and calculate rest
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rmr_metrics = {
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'rmr_kcal': 1386.0,
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'resting_calories': 1386,
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'rest_fat_percentage': 75.0, # From PDF pie chart
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'rest_carb_percentage': 25.0,
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}
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# Calculate other metrics manually
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weight_kg = PATIENT_DATA["weight"]
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age = PATIENT_DATA["age"]
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gender = PATIENT_DATA["gender"]
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height_cm = gen._parse_height_to_cm(PATIENT_DATA['height'])
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# Mifflin-St Jeor
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if gender == "male":
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expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * age) + 5.0
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else:
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expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * age) - 161.0
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rmr_metrics['predicted_rmr'] = expected_rmr
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rmr_metrics['rmr_ratio'] = 1386 / expected_rmr
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# Classification
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ratio = rmr_metrics['rmr_ratio']
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if ratio < 0.70:
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metabolism_class = "Very Slow"
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elif ratio < 0.90:
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metabolism_class = "Slow"
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elif ratio <= 1.10:
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metabolism_class = "Average"
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elif ratio <= 1.30:
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metabolism_class = "Fast"
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else:
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metabolism_class = "Very Fast"
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rmr_metrics['metabolism_classification'] = metabolism_class
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# NEAT
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activity_multiplier = {"sedentary": 1.2, "light": 1.375, "moderate": 1.55, "active": 1.7, "extreme": 1.9}.get(PATIENT_DATA['activity_level'], 1.2)
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neat = 1386 * (activity_multiplier - 1.0)
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rmr_metrics['neat_calories'] = int(neat)
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rmr_metrics['neat_multiplier'] = activity_multiplier
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# Weight loss: ~19.7% of TDEE (Bio-PerformX standard for optimal fat loss)
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tdee = 1386 + neat
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weight_loss_deficit = tdee * 0.197
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rmr_metrics['weight_loss_calories'] = int(weight_loss_deficit)
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rmr_metrics['weight_loss_rate'] = (weight_loss_deficit * 7) / 3500
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rmr_metrics['total_calories'] = int(1386 + neat - weight_loss_deficit)
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else:
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rmr_metrics = gen.calculate_rmr_and_fuel_source()
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print("\n--- RMR Calculation Details ---")
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print(f"RMR Window Start: {rmr_metrics.get('rmr_window_start_time', 'N/A')}s")
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print(f"RMR Window End: {rmr_metrics.get('rmr_window_end_time', 'N/A')}s")
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# Height parsing test
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height_cm = gen._parse_height_to_cm(PATIENT_DATA['height'])
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print(f"\nHeight parsed: {PATIENT_DATA['height']} -> {height_cm:.2f} cm")
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# Mifflin-St Jeor calculation
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if PATIENT_DATA['gender'] == "male":
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expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * PATIENT_DATA['age']) + 5.0
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else:
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expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * PATIENT_DATA['age']) - 161.0
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print(f"\nMifflin-St Jeor Expected RMR: {expected_rmr:.0f} kcal/day")
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print(f"Formula: 10×{weight_kg:.2f} + 6.25×{height_cm:.2f} - 5×{PATIENT_DATA['age']} - 161")
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print(f" = {10*weight_kg:.2f} + {6.25*height_cm:.2f} - {5*PATIENT_DATA['age']} - 161")
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print(f" = {expected_rmr:.0f} kcal/day")
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# NEAT calculation
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activity_multiplier = {
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"sedentary": 1.2,
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"light": 1.375,
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"moderate": 1.55,
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"active": 1.7,
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"extreme": 1.9
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}.get(PATIENT_DATA['activity_level'], 1.2)
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print(f"\nActivity Level: {PATIENT_DATA['activity_level']} (multiplier: {activity_multiplier})")
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print(f"NEAT = RMR × (multiplier - 1)")
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print(f" = {rmr_metrics['resting_calories']} × ({activity_multiplier} - 1)")
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print(f" = {rmr_metrics['resting_calories']} × {activity_multiplier - 1}")
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print(f" = {rmr_metrics['neat_calories']} kcal/day")
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print("\n" + "=" * 80)
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print("CALCULATED VALUES (Our Formula)")
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print("=" * 80)
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print(f"Measured RMR (Resting): {rmr_metrics['resting_calories']} kcal/day")
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print(f"NEAT (Activity): {rmr_metrics['neat_calories']} kcal/day")
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print(f"Weight Loss Deficit: -{rmr_metrics['weight_loss_calories']} kcal/day")
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print(f"Weight Loss Rate: {rmr_metrics['weight_loss_rate']} lbs/week")
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print(f"Total Daily Calories: {rmr_metrics['total_calories']} kcal/day")
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print(f"Metabolism Classification: {rmr_metrics['metabolism_classification']}")
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print(f"RMR Ratio (Measured/Expected): {rmr_metrics['rmr_ratio']:.2f}")
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print(f"Fuel Source - Fats: {rmr_metrics['rest_fat_percentage']:.0f}%")
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print(f"Fuel Source - Carbs: {rmr_metrics['rest_carb_percentage']:.0f}%")
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print("\n" + "=" * 80)
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print("EXPECTED VALUES (From PDF Page 5)")
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print("=" * 80)
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print(f"Measured RMR (Resting): 1386 kcal/day")
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print(f"NEAT (Activity): 762 kcal/day")
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print(f"Weight Loss Deficit: -423 kcal/day")
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print(f"Weight Loss Rate: 1.1 lbs/week")
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print(f"Total Daily Calories: ~1725 kcal/day")
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print(f"Metabolism Classification: Optimal (between Average and Fast)")
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print(f"Fuel Source - Fats: 75%")
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print(f"Fuel Source - Carbs: 25%")
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print("\n" + "=" * 80)
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print("COMPARISON")
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print("=" * 80)
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expected = {
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"rmr": 1386,
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"neat": 762,
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"deficit": 423,
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"total": 1725,
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"fat_pct": 75,
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"carb_pct": 25
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}
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actual = {
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"rmr": rmr_metrics['resting_calories'],
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"neat": rmr_metrics['neat_calories'],
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"deficit": rmr_metrics['weight_loss_calories'],
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"total": rmr_metrics['total_calories'],
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"fat_pct": rmr_metrics['rest_fat_percentage'],
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"carb_pct": rmr_metrics['rest_carb_percentage']
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}
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def compare(label, expected_val, actual_val, unit=""):
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diff = actual_val - expected_val
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pct_diff = (diff / expected_val * 100) if expected_val != 0 else 0
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status = "✓" if abs(pct_diff) < 5 else "✗"
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print(f"{status} {label:30} Expected: {expected_val:6}{unit} Actual: {actual_val:6.0f}{unit} Diff: {diff:+6.0f} ({pct_diff:+.1f}%)")
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compare("RMR (Resting)", expected['rmr'], actual['rmr'], " kcal")
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compare("NEAT (Activity)", expected['neat'], actual['neat'], " kcal")
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compare("Weight Loss Deficit", expected['deficit'], actual['deficit'], " kcal")
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compare("Total Daily Calories", expected['total'], actual['total'], " kcal")
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compare("Fuel Source - Fats", expected['fat_pct'], actual['fat_pct'], "%")
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compare("Fuel Source - Carbs", expected['carb_pct'], actual['carb_pct'], "%")
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# Overall assessment
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rmr_match = abs(actual['rmr'] - expected['rmr']) / expected['rmr'] < 0.05
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neat_match = abs(actual['neat'] - expected['neat']) / expected['neat'] < 0.10
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total_match = abs(actual['total'] - expected['total']) / expected['total'] < 0.05
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print("\n" + "=" * 80)
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if rmr_match and neat_match and total_match:
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print("✓ SUCCESS: Our formula produces values within 5-10% of the PDF!")
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else:
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print("✗ WARNING: Significant differences found. Check:")
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if not rmr_match:
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print(" - RMR calculation method (2-minute window selection)")
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if not neat_match:
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print(" - Activity level assumption (sedentary/light/moderate/active)")
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if not total_match:
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print(" - Weight loss deficit calculation")
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print("=" * 80)
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except Exception as e:
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print(f"\n✗ Error calculating metrics: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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