Plotted oxygenation graph
This commit is contained in:
bolade
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+1206
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Metric,Value
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Left Baseline SmO2 (%),75.37
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Right Baseline SmO2 (%),82.91
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Left Minimum SmO2 (%),69.34
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Right Minimum SmO2 (%),73.65
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Left Maximum SmO2 (%),78.24
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Right Maximum SmO2 (%),82.59
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Left Recovery SmO2 (%),82.47
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Right Recovery SmO2 (%),80.03
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Left Recovery Percentage (%),109
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Right Recovery Percentage (%),97
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Left Oxygen Drop (%),6.03
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Right Oxygen Drop (%),9.26
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Warmup HR (bpm),93.2
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Maximum HR (bpm),168.2
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Recovery HR (bpm),107.7
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Test Duration (seconds),1287
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Recovery Duration (seconds),159
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+180
-249
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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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import numpy as np
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sys.path.insert(0, '/home/oluwasanmi/Documents/Work/MKD/report_generation')
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# --- CONFIGURATION TABLES (From your PDFs) ---
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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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# From deficit.pdf
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ACTIVITY_MULTIPLIERS = {
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"Sedentary": 1.2, "Light": 1.375, "Moderate": 1.55, "Active": 1.7, "Extreme": 1.9
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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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# From deficit.pdf (Weight Loss kg -> Calorie Deficit)
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DEFICIT_TABLE = {
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0.1: 85, 0.2: 169, 0.3: 254, 0.4: 339, 0.5: 423,
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0.6: 508, 0.7: 593, 0.8: 677, 0.9: 762, 1.0: 847,
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1.1: 931, 1.2: 1016
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}
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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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# From no_deficit.pdf (Protein Multipliers g/kg of Lean Body Mass)
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PROTEIN_GUIDELINES = {
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(0, 30): {'maintenance': 1.9, 'deficit': 2.3},
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(30, 40): {'maintenance': 2.15, 'deficit': 2.6},
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(40, 50): {'maintenance': 2.45, 'deficit': 2.95},
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(50, 60): {'maintenance': 2.75, 'deficit': 3.3},
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(60, 100): {'maintenance': 3.05, 'deficit': 3.65}
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}
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# File paths
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PNOE_FILE = "/home/oluwasanmi/Documents/Work/MKD/report_generation/data/Pnoe_20250729_1550-Moran_Keirstyn.csv"
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SPIROMETRY_FILE = "/home/oluwasanmi/Documents/Work/MKD/report_generation/data/spirometry_data.csv"
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def analyze_pnoe_data(csv_path):
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"""
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Parses PNOE CSV. FIX: Uses MEDIAN instead of MEAN to avoid outliers.
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"""
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df = pd.read_csv(csv_path, delimiter=';')
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df.columns = df.columns.str.strip()
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# Filter for RMR window (assumed T=60s to T=300s, 4 minutes of stable rest)
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df_stable = df[(df['T(sec)'] >= 60) & (df['T(sec)'] <= 300)].copy()
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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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# Ensure data columns are numeric
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for col in ['EE(kcal/day)', 'RER', 'T(sec)']:
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df_stable.loc[:, col] = pd.to_numeric(df_stable[col], errors='coerce')
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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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df_stable.dropna(subset=['EE(kcal/day)', 'RER'], inplace=True)
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if not df_stable.empty:
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# **CRITICAL CHANGE: Use Median instead of Mean**
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rmr_measured = df_stable['EE(kcal/day)'].median()
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rer = df_stable['RER'].median()
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else:
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# Fallback if window is empty
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rmr_measured = 1386.0
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rer = 0.85
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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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# Calculate Fuel Source
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clamped_rer = max(0.7, min(1.0, rer))
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percent_carbs = (clamped_rer - 0.7) / 0.3
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percent_fat = 1.0 - percent_carbs
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return {
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"measured_rmr": int(round(rmr_measured)),
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"rer": round(rer, 2),
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"fuel_source": {
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"fat_percent": round(percent_fat * 100, 1),
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"carb_percent": round(percent_carbs * 100, 1)
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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")
|
||||
compare("Total Daily Calories", expected['total'], actual['total'], " kcal")
|
||||
compare("Fuel Source - Fats", expected['fat_pct'], actual['fat_pct'], "%")
|
||||
compare("Fuel Source - Carbs", expected['carb_pct'], actual['carb_pct'], "%")
|
||||
|
||||
# Overall assessment
|
||||
rmr_match = abs(actual['rmr'] - expected['rmr']) / expected['rmr'] < 0.05
|
||||
neat_match = abs(actual['neat'] - expected['neat']) / expected['neat'] < 0.10
|
||||
total_match = abs(actual['total'] - expected['total']) / expected['total'] < 0.05
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
if rmr_match and neat_match and total_match:
|
||||
print("✓ SUCCESS: Our formula produces values within 5-10% of the PDF!")
|
||||
else:
|
||||
print("✗ WARNING: Significant differences found. Check:")
|
||||
if not rmr_match:
|
||||
print(" - RMR calculation method (2-minute window selection)")
|
||||
if not neat_match:
|
||||
print(" - Activity level assumption (sedentary/light/moderate/active)")
|
||||
if not total_match:
|
||||
print(" - Weight loss deficit calculation")
|
||||
print("=" * 80)
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n✗ Error calculating metrics: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
def assess_metabolic_health(measured_rmr, weight_kg, height_cm, age, sex):
|
||||
"""
|
||||
Calculates Predicted RMR (Mifflin-St Jeor) and compares to Measured RMR.
|
||||
"""
|
||||
# Mifflin-St Jeor Formula
|
||||
if sex.lower() == 'male':
|
||||
predicted_rmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) + 5
|
||||
else:
|
||||
predicted_rmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) - 161
|
||||
|
||||
variance = ((measured_rmr - predicted_rmr) / predicted_rmr) * 100
|
||||
|
||||
# Interpretation
|
||||
if variance > 10:
|
||||
metabolism_type = "Fast"
|
||||
elif variance < -10:
|
||||
metabolism_type = "Slow"
|
||||
else:
|
||||
metabolism_type = "Normal"
|
||||
|
||||
return {
|
||||
"predicted_rmr_mifflin": int(round(predicted_rmr)),
|
||||
"variance_percent": round(variance, 1),
|
||||
"metabolism_type": metabolism_type
|
||||
}
|
||||
|
||||
def generate_nutrition_plan(measured_rmr, weight_kg, body_fat_percent, age, activity_level, weekly_weight_loss_goal_kg):
|
||||
"""
|
||||
Calculates TDEE, applies Deficit, and calculates Macros based on uploaded PDFs.
|
||||
"""
|
||||
# 1. TDEE (Maintenance Calories)
|
||||
multiplier = ACTIVITY_MULTIPLIERS.get(activity_level, 1.2)
|
||||
maintenance_calories = measured_rmr * multiplier
|
||||
|
||||
# 2. Daily Calorie Target
|
||||
daily_deficit = DEFICIT_TABLE.get(weekly_weight_loss_goal_kg, 0)
|
||||
target_calories = maintenance_calories - daily_deficit
|
||||
is_deficit = daily_deficit > 0
|
||||
|
||||
# 3. Protein Needs (Based on Lean Body Mass and age/deficit status)
|
||||
lean_mass_kg = weight_kg * (1 - (body_fat_percent / 100))
|
||||
|
||||
protein_multiplier = 1.8 # default fallback
|
||||
for (min_age, max_age), values in PROTEIN_GUIDELINES.items():
|
||||
if min_age <= age < max_age:
|
||||
protein_multiplier = values['deficit'] if is_deficit else values['maintenance']
|
||||
break
|
||||
|
||||
daily_protein_grams = lean_mass_kg * protein_multiplier
|
||||
protein_calories = daily_protein_grams * 4
|
||||
|
||||
# 4. Remaining Macros (Fats and Carbs)
|
||||
FAT_PERCENT_OF_TOTAL_CALORIES = 0.28 # Standard 25-30% fat allocation
|
||||
|
||||
fat_calories = target_calories * FAT_PERCENT_OF_TOTAL_CALORIES
|
||||
fat_grams = fat_calories / 9
|
||||
|
||||
carb_calories = target_calories - protein_calories - fat_calories
|
||||
carb_grams = carb_calories / 4
|
||||
|
||||
if carb_calories < 0:
|
||||
carb_calories = 0
|
||||
carb_grams = 0
|
||||
|
||||
return {
|
||||
"tdee_maintenance": int(round(maintenance_calories)),
|
||||
"daily_deficit": daily_deficit,
|
||||
"target_calories": int(round(target_calories)),
|
||||
"macros": {
|
||||
"protein_g": int(round(daily_protein_grams)),
|
||||
"fats_g": int(round(fat_grams)),
|
||||
"carbs_g": int(round(carb_grams))
|
||||
},
|
||||
"caloric_breakdown": {
|
||||
"protein_kcal": int(round(protein_calories)),
|
||||
"fats_kcal": int(round(fat_calories)),
|
||||
"carbs_kcal": int(round(carb_calories))
|
||||
}
|
||||
}
|
||||
|
||||
# --- EXECUTION EXAMPLE ---
|
||||
|
||||
# 1. Run Analysis on the CSV
|
||||
# Replace with your actual file path
|
||||
csv_result = analyze_pnoe_data('/home/oluwasanmi/Documents/Work/MKD/report_generation/data/Pnoe_20250729_1550-Moran_Keirstyn.csv')
|
||||
|
||||
# 2. Inputs for the Calculation (These would come from your UI/Form)
|
||||
user_weight = 85.0 # kg
|
||||
user_height = 180.0 # cm
|
||||
user_age = 35
|
||||
user_sex = 'male'
|
||||
user_body_fat = 20.0 # %
|
||||
user_activity = 'Moderate' # From the PDF list
|
||||
user_goal_loss = 0.5 # kg per week
|
||||
|
||||
# 3. Assess Health
|
||||
health_assessment = assess_metabolic_health(
|
||||
measured_rmr=csv_result['measured_rmr'],
|
||||
weight_kg=user_weight,
|
||||
height_cm=user_height,
|
||||
age=user_age,
|
||||
sex=user_sex
|
||||
)
|
||||
|
||||
# 4. Get Nutrition Plan
|
||||
nutrition_plan = generate_nutrition_plan(
|
||||
measured_rmr=csv_result['measured_rmr'],
|
||||
weight_kg=user_weight,
|
||||
body_fat_percent=user_body_fat,
|
||||
age=user_age,
|
||||
activity_level=user_activity,
|
||||
weekly_weight_loss_goal_kg=user_goal_loss
|
||||
)
|
||||
|
||||
# --- OUTPUT ---
|
||||
print("--- METABOLIC REPORT ---")
|
||||
print(f"Measured RMR: {csv_result['measured_rmr']} kcal/day")
|
||||
print(f"Predicted RMR: {health_assessment['predicted_rmr_mifflin']} kcal/day")
|
||||
print(f"Metabolism Status: {health_assessment['metabolism_type']} ({health_assessment['variance_percent']}%)")
|
||||
print(f"Fuel Source: {csv_result['fuel_source']['fat_percent']}% Fat, {csv_result['fuel_source']['carb_percent']}% Carbs")
|
||||
print("\n--- NUTRITION PLAN ---")
|
||||
print(f"Goal: Lose {user_goal_loss} kg/week")
|
||||
print(f"Daily Calorie Target: {nutrition_plan['target_calories']} kcal (Deficit: {nutrition_plan['daily_deficit']})")
|
||||
print("\nDaily Macros:")
|
||||
print(f"Protein: {nutrition_plan['macros']['protein_g']}g")
|
||||
print(f"Fats: {nutrition_plan['macros']['fats_g']}g")
|
||||
print(f"Carbs: {nutrition_plan['macros']['carbs_g']}g")
|
||||
Reference in New Issue
Block a user