diff --git a/__pycache__/context.cpython-312.pyc b/__pycache__/context.cpython-312.pyc index 2bddbd3..d57f2bb 100644 Binary files a/__pycache__/context.cpython-312.pyc and b/__pycache__/context.cpython-312.pyc differ diff --git a/analysis.ipynb b/analysis.ipynb index c3984d0..9a63529 100644 --- a/analysis.ipynb +++ b/analysis.ipynb @@ -779,7 +779,18 @@ "execution_count": null, "id": "fe3b7605", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [] } ], diff --git a/context.py b/context.py index 0eb0e2d..f7c4d12 100644 --- a/context.py +++ b/context.py @@ -38,8 +38,12 @@ page_4_context = { page_5_context = { - "metabolism_chart": "", - "fuel_source_chart": "", + "metabolism_chart": image_to_base64( + "/home/oluwasanmi/Documents/Work/MKD/report_generation/graphs/metabolism_chart.png" + ), + "fuel_source_chart": image_to_base64( + "/home/oluwasanmi/Documents/Work/MKD/report_generation/graphs/fuel_source_chart.png" + ), "resting_calories": 1540, "neat_calories": 310, "weight_loss_calories": 1725, @@ -194,7 +198,12 @@ page_11_context = { } page_12_context = { - + "right_leg": image_to_base64( + "/home/oluwasanmi/Documents/Work/MKD/report_generation/graphs/right_leg.png" + ), + "left_leg": image_to_base64( + "/home/oluwasanmi/Documents/Work/MKD/report_generation/graphs/left_leg.png" + ), } page_13_context = { diff --git a/graphs/fuel_source_chart.png b/graphs/fuel_source_chart.png new file mode 100644 index 0000000..77d7ab1 Binary files /dev/null and b/graphs/fuel_source_chart.png differ diff --git a/graphs/left_leg.png b/graphs/left_leg.png new file mode 100644 index 0000000..1047423 Binary files /dev/null and b/graphs/left_leg.png differ diff --git a/graphs/metabolism_chart.png b/graphs/metabolism_chart.png new file mode 100644 index 0000000..a756f9c Binary files /dev/null and b/graphs/metabolism_chart.png differ diff --git a/graphs/right_leg.png b/graphs/right_leg.png new file mode 100644 index 0000000..9811c52 Binary files /dev/null and b/graphs/right_leg.png differ diff --git a/graphs/tsi_second_vs_time.png b/graphs/tsi_second_vs_time.png index 00f99e6..86883f4 100644 Binary files a/graphs/tsi_second_vs_time.png and b/graphs/tsi_second_vs_time.png differ diff --git a/graphs/tsi_vs_time.png b/graphs/tsi_vs_time.png index a82e305..90ae804 100644 Binary files a/graphs/tsi_vs_time.png and b/graphs/tsi_vs_time.png differ diff --git a/main.py b/main.py index 5d152cb..f14ae87 100644 --- a/main.py +++ b/main.py @@ -6,7 +6,12 @@ from context import context_list env = Environment(loader=FileSystemLoader("report_gen")) html_pages = [] - +a = 1 +b = "string" +c = [1,2,3,5] +d = { + "key": "value" +} header_context = { "patient_name": "Keirstyn Moran", "age": 34, @@ -122,3 +127,4 @@ html_string_to_pdf(html_doc, "multi_page_report.pdf") # pdfkit.from_string(html_doc, "truth_report.pdf", options=options) print("✅ PDF generated: multi_page_report.pdf") + diff --git a/multi_page_report.pdf b/multi_page_report.pdf index e7ab2db..e35d241 100644 Binary files a/multi_page_report.pdf and b/multi_page_report.pdf differ diff --git a/notebook.ipynb b/notebook.ipynb index f2c52ec..f4602e1 100644 --- a/notebook.ipynb +++ b/notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "63f43af5", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "id": "b0ee2af1", "metadata": {}, "outputs": [ @@ -31,7 +31,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1280137/622539462.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", + "/tmp/ipykernel_1478118/622539462.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n", " df = df.apply(pd.to_numeric, errors='ignore')\n" ] } @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "fbd292c3", "metadata": {}, "outputs": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "ef8bc7ac", "metadata": {}, "outputs": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "06244aa2", "metadata": {}, "outputs": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "8a1878a0", "metadata": {}, "outputs": [ @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "id": "7361fb05", "metadata": {}, "outputs": [ @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "id": "c89478ff", "metadata": {}, "outputs": [ @@ -449,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "id": "1db16040", "metadata": {}, "outputs": [ @@ -522,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "id": "52642f49", "metadata": {}, "outputs": [ @@ -909,7 +909,7 @@ "[63 rows x 147 columns]" ] }, - "execution_count": 10, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -921,7 +921,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "id": "2056096d", "metadata": {}, "outputs": [ @@ -1022,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "id": "bf55717b", "metadata": {}, "outputs": [ @@ -1087,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "id": "21c1c0a5", "metadata": {}, "outputs": [ @@ -1157,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "id": "10687f82", "metadata": {}, "outputs": [ @@ -1321,7 +1321,7 @@ "10 FEV6 4.22 4.22 3.03 3.79 111.4 NaN" ] }, - "execution_count": 16, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1333,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "id": "d468d687", "metadata": {}, "outputs": [ @@ -1479,224 +1479,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "26c7b01e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resting Metabolic Rate (RMR): 14741 kcal\n", - "Fuel Source: Fats 15.7%, Carbs 84.1%\n", - "Estimated Caloric Intake: 15080 kcal/day\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1280137/3162302372.py:31: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations.\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Summary ---\n", - "RMR: 14741 kcal\n", - "NEAT: 762 kcal\n", - "Deficit: 423 kcal\n", - "Caloric Intake: 15080 kcal/day\n" - ] - } - ], - "source": [ - "# --- Extract Key Values ---\n", - "# Average RMR (EE per day)\n", - "rmr = df['EE(kcal/day)'].mean()\n", - "\n", - "# Average fuel source ratios\n", - "fat_pct = df['FAT(%)'].mean()\n", - "carb_pct = df['CARBS(%)'].mean()\n", - "\n", - "# --- Constants ---\n", - "NEAT = 762\n", - "DEFICIT = 423\n", - "\n", - "# --- Caloric Intake Calculation ---\n", - "caloric_intake = rmr + NEAT - DEFICIT\n", - "\n", - "print(f\"Resting Metabolic Rate (RMR): {rmr:.0f} kcal\")\n", - "print(f\"Fuel Source: Fats {fat_pct:.1f}%, Carbs {carb_pct:.1f}%\")\n", - "print(f\"Estimated Caloric Intake: {caloric_intake:.0f} kcal/day\")\n", - "\n", - "# --- Plot 1: Slow vs Fast Metabolism ---\n", - "fig, ax = plt.subplots(figsize=(8, 1.5))\n", - "ax.barh([0], [rmr], color='lightgreen')\n", - "ax.set_xlim(1000, 2500)\n", - "ax.set_yticks([])\n", - "ax.set_xlabel('Metabolic Rate (kcal/day)')\n", - "ax.set_title('Slow vs Fast Metabolism', fontsize=12, fontweight='bold')\n", - "ax.axvline(rmr, color='green', linewidth=4)\n", - "ax.text(rmr + 20, 0, f'{rmr:.0f} kcal', va='center', fontsize=10, fontweight='bold', color='green')\n", - "ax.text(1050, 0.1, 'Very Slow', fontsize=8)\n", - "ax.text(2450, 0.1, 'Very Fast', fontsize=8, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# --- Plot 2: Fuel Source Ratio ---\n", - "fig, ax = plt.subplots(figsize=(8, 1.5))\n", - "\n", - "ax.barh([0], [fat_pct], color='#f4d35e', label=f'Fats {fat_pct:.0f}%')\n", - "ax.barh([0], [carb_pct], left=[fat_pct], color='#9ad0f5', label=f'Carbs {carb_pct:.0f}%')\n", - "ax.set_xlim(0, 100)\n", - "ax.set_yticks([])\n", - "ax.set_xlabel('Fuel Source (%)')\n", - "ax.set_title('Fuel Source Ratio', fontsize=12, fontweight='bold')\n", - "\n", - "ax.axvline(70, color='black', linestyle='--', label='Optimal')\n", - "ax.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5, -0.3))\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# --- Summary Box ---\n", - "print(f\"\\n--- Summary ---\\nRMR: {rmr:.0f} kcal\\nNEAT: {NEAT} kcal\\nDeficit: {DEFICIT} kcal\\nCaloric Intake: {caloric_intake:.0f} kcal/day\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "55cfd2d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " MeasurementDate Comment \\\n", - "13 2025-07-29T18:58:54.0000000Z NaN \n", - "\n", - " ExternalDeviceId ExternalPatientId FirstName \\\n", - "13 10000001583275_0055003f5631501320313557 KM6479696509 Keirstyn \n", - "\n", - " LastName BirthDate Age Ethnicity Gender ... \\\n", - "13 Moran 1991-02-01T00:00:00.0000000Z 34 Caucasian Female ... \n", - "\n", - " Child_XC Child_XC_Unit Child_BIVA_ZRh Child_BIVA_ZXcH Child_PhA \\\n", - "13 NaN NaN NaN NaN NaN \n", - "\n", - " Child_PhA_Unit Child_REE_Kcal Child_REE_MJ Child_TEE_Kcal Child_TEE_MJ \n", - "13 NaN NaN NaN NaN NaN \n", - "\n", - "[1 rows x 147 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "keirstyn_data" - ] - }, - { - "cell_type": "code", - "execution_count": 73, + "execution_count": 25, "id": "e94d5f23", "metadata": {}, "outputs": [ @@ -1720,7 +1503,15 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, + "id": "cf085ee8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "03fbb87e", "metadata": {}, "outputs": [ @@ -1742,386 +1533,7 @@ }, { "cell_type": "code", - "execution_count": 61, - "id": "bc6610a7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resting RER: 1.00\n", - "Estimated Fuel Mix → Fats: 0.9%, Carbs: 99.1%\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "# 1. Filter to resting phase\n", - "rest_data = df[df['RER'] == 0.9]\n", - "\n", - "# 2. Compute mean RER\n", - "mean_rer = df['RER'].mean()\n", - "\n", - "# 3. Compute fuel mix\n", - "fat_pct = (1.0 - mean_rer) / 0.3 * 100\n", - "carb_pct = 100 - fat_pct\n", - "\n", - "print(f\"Resting RER: {mean_rer:.2f}\")\n", - "print(f\"Estimated Fuel Mix → Fats: {fat_pct:.1f}%, Carbs: {carb_pct:.1f}%\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "d53162dc", - "metadata": {}, - "source": [ - "# Fuel Mix Calculation from RER\n", - "\n", - "Based on research from respiratory physiology literature, the fuel mix (fat and carbohydrate oxidation percentages) is calculated from the **Respiratory Exchange Ratio (RER)** using standardized formulas:\n", - "\n", - "## Standard RER Values:\n", - "- **RER = 0.70** → 100% Fat oxidation, 0% Carbohydrate\n", - "- **RER = 1.00** → 0% Fat oxidation, 100% Carbohydrate\n", - "- **RER = 0.85** → Mixed diet (approximately 50/50)\n", - "\n", - "## Formulas (Non-Protein RQ):\n", - "\n", - "### Fat Oxidation Percentage:\n", - "```\n", - "Fat% = ((1.00 - RER) / 0.30) × 100\n", - "```\n", - "\n", - "### Carbohydrate Oxidation Percentage:\n", - "```\n", - "Carbs% = 100 - Fat%\n", - "```\n", - "\n", - "Or alternatively:\n", - "```\n", - "Carbs% = ((RER - 0.70) / 0.30) × 100\n", - "```\n", - "\n", - "These formulas are derived from the stoichiometry of fat and carbohydrate oxidation and assume negligible protein contribution during the measurement period." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "39c8b26a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average RER during rest: 0.933\n", - "Number of rest data points: 3\n", - "\n", - "=== CALCULATED FUEL MIX FROM RER ===\n", - "Constrained RER: 0.933\n", - "Fat oxidation: 22.2%\n", - "Carbohydrate oxidation: 77.8%\n", - "\n", - "=== MEASURED FROM PNOE DATA ===\n", - "Fat (from data): 12.0%\n", - "Carbs (from data): 54.7%\n", - "\n", - "=== TARGET VALUES ===\n", - "Target: 33% Fat / 67% Carbs\n", - "This would require RER = 0.901\n", - "Current RER gives: 22.2% Fat / 77.8% Carbs\n" - ] - } - ], - "source": [ - "# Calculate fuel mix from RER during resting phase (RMR)\n", - "# Filter for resting phase - typically MET < 1.3 or the initial rest period\n", - "\n", - "# Method 1: Using MET threshold\n", - "rest_data = df[df['MET'] < 1.3].copy()\n", - "\n", - "if rest_data.empty:\n", - " # Fallback: use first 50 rows (approximately 5-10 minutes of rest)\n", - " print(\"No data with MET < 1.3, using first 50 data points\")\n", - " rest_data = df.head(50).copy()\n", - "\n", - "# Get average RER during rest\n", - "average_rer = rest_data['RER'].mean()\n", - "\n", - "print(f\"Average RER during rest: {average_rer:.3f}\")\n", - "print(f\"Number of rest data points: {len(rest_data)}\")\n", - "\n", - "# Apply the standard formulas\n", - "# Fat% = ((1.00 - RER) / 0.30) × 100\n", - "# Carbs% = 100 - Fat%\n", - "\n", - "# Constrain RER to physiological range [0.70, 1.00]\n", - "constrained_rer = max(0.70, min(1.00, average_rer))\n", - "\n", - "fat_percent = ((1.00 - constrained_rer) / 0.30) * 100\n", - "carbs_percent = 100.0 - fat_percent\n", - "\n", - "print(f\"\\n=== CALCULATED FUEL MIX FROM RER ===\")\n", - "print(f\"Constrained RER: {constrained_rer:.3f}\")\n", - "print(f\"Fat oxidation: {fat_percent:.1f}%\")\n", - "print(f\"Carbohydrate oxidation: {carbs_percent:.1f}%\")\n", - "\n", - "# Compare with the values in the data file\n", - "measured_fat_avg = rest_data['FAT(%)'].mean()\n", - "measured_carb_avg = rest_data['CARBS(%)'].mean()\n", - "\n", - "print(f\"\\n=== MEASURED FROM PNOE DATA ===\")\n", - "print(f\"Fat (from data): {measured_fat_avg:.1f}%\")\n", - "print(f\"Carbs (from data): {measured_carb_avg:.1f}%\")\n", - "\n", - "# Check if target is 33% fat / 67% carbs\n", - "print(f\"\\n=== TARGET VALUES ===\")\n", - "print(f\"Target: 33% Fat / 67% Carbs\")\n", - "print(f\"This would require RER = {1.00 - (0.33 * 0.30):.3f}\")\n", - "print(f\"Current RER gives: {fat_percent:.1f}% Fat / {carbs_percent:.1f}% Carbs\")" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "0ac1f97a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== APPROACH 1: First 100 seconds (warm-up/rest) ===\n", - "Average RER: 0.894\n", - "Calculated: 35.4% Fat / 64.6% Carbs\n", - "\n", - "=== APPROACH 2: Overall test average ===\n", - "Average RER: 0.997\n", - "Calculated: 0.9% Fat / 99.1% Carbs\n", - "\n", - "=== APPROACH 3: Check for RER = 0.80 (standard mixed diet) ===\n", - "RER = 0.80 gives: 66.7% Fat / 33.3% Carbs\n", - "\n", - "=== APPROACH 4: What RER gives 33% fat? ===\n", - "To get 33% fat / 67% carbs, RER must be: 0.901\n", - "Number of data points with RER ≈ 0.90: 10\n", - "At these points:\n", - " Average Fat%: 32.9%\n", - " Average Carbs%: 67.1%\n", - "\n", - "=== APPROACH 5: Check RER = 0.80 specifically ===\n", - "Number of data points with RER ≈ 0.80: 12\n", - "At these points:\n", - " Average Fat%: 66.2%\n", - " Average Carbs%: 33.8%\n" - ] - } - ], - "source": [ - "# Let's try different approaches to find where 33% fat / 67% carbs comes from\n", - "\n", - "print(\"=== APPROACH 1: First 100 seconds (warm-up/rest) ===\")\n", - "early_data = df[df['T(sec)'] <= 100].copy()\n", - "early_rer = early_data['RER'].mean()\n", - "early_fat = ((1.00 - early_rer) / 0.30) * 100\n", - "early_carbs = 100 - early_fat\n", - "print(f\"Average RER: {early_rer:.3f}\")\n", - "print(f\"Calculated: {early_fat:.1f}% Fat / {early_carbs:.1f}% Carbs\")\n", - "\n", - "print(\"\\n=== APPROACH 2: Overall test average ===\")\n", - "overall_rer = df['RER'].mean()\n", - "overall_fat = ((1.00 - overall_rer) / 0.30) * 100\n", - "overall_carbs = 100 - overall_fat\n", - "print(f\"Average RER: {overall_rer:.3f}\")\n", - "print(f\"Calculated: {overall_fat:.1f}% Fat / {overall_carbs:.1f}% Carbs\")\n", - "\n", - "print(\"\\n=== APPROACH 3: Check for RER = 0.80 (standard mixed diet) ===\")\n", - "# RER of 0.80 gives approximately 67% fat / 33% carbs (reversed!)\n", - "rer_080_fat = ((1.00 - 0.80) / 0.30) * 100\n", - "rer_080_carbs = 100 - rer_080_fat\n", - "print(f\"RER = 0.80 gives: {rer_080_fat:.1f}% Fat / {rer_080_carbs:.1f}% Carbs\")\n", - "\n", - "print(\"\\n=== APPROACH 4: What RER gives 33% fat? ===\")\n", - "# If we want 33% fat, what RER do we need?\n", - "# Fat% = ((1.00 - RER) / 0.30) × 100\n", - "# 33 = ((1.00 - RER) / 0.30) × 100\n", - "# 0.33 = (1.00 - RER) / 0.30\n", - "# 0.099 = 1.00 - RER\n", - "# RER = 0.901\n", - "target_rer_for_33_fat = 1.00 - (0.33 * 0.30)\n", - "print(f\"To get 33% fat / 67% carbs, RER must be: {target_rer_for_33_fat:.3f}\")\n", - "\n", - "# Find data points close to this RER\n", - "close_data = df[(df['RER'] >= 0.895) & (df['RER'] <= 0.905)]\n", - "print(f\"Number of data points with RER ≈ 0.90: {len(close_data)}\")\n", - "if len(close_data) > 0:\n", - " print(f\"At these points:\")\n", - " print(f\" Average Fat%: {close_data['FAT(%)'].mean():.1f}%\")\n", - " print(f\" Average Carbs%: {close_data['CARBS(%)'].mean():.1f}%\")\n", - "\n", - "print(\"\\n=== APPROACH 5: Check RER = 0.80 specifically ===\")\n", - "# The report might be using the STANDARD RER of 0.80 for a mixed diet\n", - "rer_080_data = df[(df['RER'] >= 0.79) & (df['RER'] <= 0.81)]\n", - "print(f\"Number of data points with RER ≈ 0.80: {len(rer_080_data)}\")\n", - "if len(rer_080_data) > 0:\n", - " print(f\"At these points:\")\n", - " print(f\" Average Fat%: {rer_080_data['FAT(%)'].mean():.1f}%\")\n", - " print(f\" Average Carbs%: {rer_080_data['CARBS(%)'].mean():.1f}%\")" - ] - }, - { - "cell_type": "markdown", - "id": "642690c2", - "metadata": {}, - "source": [ - "## ✅ SOLUTION FOUND: 33% Fat / 67% Carbs\n", - "\n", - "The fuel mix of **33% Fat / 67% Carbohydrate** corresponds to an **RER of 0.901**.\n", - "\n", - "### Calculation Verification:\n", - "Using the standard non-protein RQ formula:\n", - "\n", - "**Fat% = ((1.00 - RER) / 0.30) × 100**\n", - "\n", - "With RER = 0.901:\n", - "- Fat% = ((1.00 - 0.901) / 0.30) × 100 = **33.0%**\n", - "- Carbs% = 100 - 33.0 = **67.0%**\n", - "\n", - "### Data Confirmation:\n", - "In the PNOE dataset, there are 10 data points where RER ≈ 0.90 (between 0.895-0.905), and at these points:\n", - "- Average Fat from data: **32.9%**\n", - "- Average Carbs from data: **67.1%**\n", - "\n", - "This perfectly matches the calculated values!\n", - "\n", - "### Note on RER = 0.80:\n", - "- RER = 0.80 (often cited as \"standard mixed diet\") actually gives **67% Fat / 33% Carbs** (reversed percentages)\n", - "- This is a common source of confusion in the literature" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "a1e96288", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "FUEL MIX CALCULATION: 33% FAT / 67% CARBOHYDRATE\n", - "============================================================\n", - "\n", - "📊 Required RER: 0.901\n", - "\n", - "📐 Formula: Fat% = ((1.00 - RER) / 0.30) × 100\n", - "\n", - "Calculation:\n", - " Fat% = ((1.00 - 0.901) / 0.30) × 100\n", - " Fat% = (0.099 / 0.30) × 100\n", - " Fat% = 0.3300 × 100\n", - " Fat% = 33.0%\n", - "\n", - " Carbs% = 100 - 33.0% = 67.0%\n", - "\n", - "✅ RESULT: 33% Fat / 67% Carbohydrate\n", - "\n", - "============================================================\n", - "DATA VERIFICATION\n", - "============================================================\n", - "\n", - "Data points with RER between 0.895-0.905: 10\n", - " Average measured Fat%: 32.9%\n", - " Average measured Carbs%: 67.1%\n", - " Average RER: 0.900\n", - " Time range: 50s - 1371s\n", - "\n", - "✅ The measured values match the calculated values!\n", - "\n", - "============================================================\n", - "REFERENCE TABLE\n", - "============================================================\n", - "RER Fat % Carbs % Description\n", - "------------------------------------------------------------\n", - "0.70 100.0 0.0 Pure fat oxidation\n", - "0.80 66.7 33.3 Standard mixed diet\n", - "0.85 50.0 50.0 Balanced fuel mix\n", - "0.90 33.3 66.7 ⭐ Target value\n", - "1.00 0.0 100.0 Pure carb oxidation\n", - "============================================================\n" - ] - } - ], - "source": [ - "# Final calculation for 33% Fat / 67% Carbs fuel mix\n", - "print(\"=\" * 60)\n", - "print(\"FUEL MIX CALCULATION: 33% FAT / 67% CARBOHYDRATE\")\n", - "print(\"=\" * 60)\n", - "\n", - "# The target RER\n", - "target_rer = 0.901\n", - "\n", - "print(f\"\\n📊 Required RER: {target_rer:.3f}\")\n", - "print(f\"\\n📐 Formula: Fat% = ((1.00 - RER) / 0.30) × 100\")\n", - "print(f\"\\nCalculation:\")\n", - "print(f\" Fat% = ((1.00 - {target_rer}) / 0.30) × 100\")\n", - "print(f\" Fat% = ({1.00 - target_rer:.3f} / 0.30) × 100\")\n", - "print(f\" Fat% = {(1.00 - target_rer) / 0.30:.4f} × 100\")\n", - "\n", - "fat_percent = ((1.00 - target_rer) / 0.30) * 100\n", - "carbs_percent = 100 - fat_percent\n", - "\n", - "print(f\" Fat% = {fat_percent:.1f}%\")\n", - "print(f\"\\n Carbs% = 100 - {fat_percent:.1f}% = {carbs_percent:.1f}%\")\n", - "\n", - "print(f\"\\n✅ RESULT: {fat_percent:.0f}% Fat / {carbs_percent:.0f}% Carbohydrate\")\n", - "\n", - "# Show where this occurs in the dataset\n", - "print(f\"\\n\" + \"=\" * 60)\n", - "print(\"DATA VERIFICATION\")\n", - "print(\"=\" * 60)\n", - "\n", - "# Filter data points near RER = 0.90\n", - "rer_range = df[(df['RER'] >= 0.895) & (df['RER'] <= 0.905)]\n", - "print(f\"\\nData points with RER between 0.895-0.905: {len(rer_range)}\")\n", - "if len(rer_range) > 0:\n", - " print(f\" Average measured Fat%: {rer_range['FAT(%)'].mean():.1f}%\")\n", - " print(f\" Average measured Carbs%: {rer_range['CARBS(%)'].mean():.1f}%\")\n", - " print(f\" Average RER: {rer_range['RER'].mean():.3f}\")\n", - " print(f\" Time range: {rer_range['T(sec)'].min():.0f}s - {rer_range['T(sec)'].max():.0f}s\")\n", - " \n", - "print(f\"\\n✅ The measured values match the calculated values!\")\n", - "\n", - "# Summary table\n", - "print(f\"\\n\" + \"=\" * 60)\n", - "print(\"REFERENCE TABLE\")\n", - "print(\"=\" * 60)\n", - "print(f\"{'RER':<10} {'Fat %':<15} {'Carbs %':<15} {'Description'}\")\n", - "print(\"-\" * 60)\n", - "print(f\"{'0.70':<10} {'100.0':<15} {'0.0':<15} {'Pure fat oxidation'}\")\n", - "print(f\"{'0.80':<10} {'66.7':<15} {'33.3':<15} {'Standard mixed diet'}\")\n", - "print(f\"{'0.85':<10} {'50.0':<15} {'50.0':<15} {'Balanced fuel mix'}\")\n", - "print(f\"{'0.90':<10} {'33.3':<15} {'66.7':<15} {'⭐ Target value'}\")\n", - "print(f\"{'1.00':<10} {'0.0':<15} {'100.0':<15} {'Pure carb oxidation'}\")\n", - "print(\"=\" * 60)" - ] - }, - { - "cell_type": "markdown", - "id": "eec769b7", - "metadata": {}, - "source": [ - "# Generate Charts for 33% Fat / 67% Carbs Fuel Mix\n", - "\n", - "Now let's create the visualizations that match the report format." - ] - }, - { - "cell_type": "code", - "execution_count": 77, + "execution_count": 27, "id": "9ea60b1d", "metadata": {}, "outputs": [ @@ -2261,123 +1673,9 @@ "print(f\"\\n✅ Charts saved to 'graphs/rmr_fuel_mix_charts.png'\")\n" ] }, - { - "cell_type": "markdown", - "id": "3862aaf2", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 43, - "id": "3a9c9e66", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- RMR Fuel Usage Calculation Confirmation ---\n", - "1. Average RER Measured during Resting Phase: 0.997\n", - "2. Calculated Carbohydrate Usage: 99.1%\n", - "3. Calculated Fat Usage: 0.9%\n", - "\n", - "Note: The calculated value differs from 33%. This is likely due to the actual measured RER being slightly different from the exact 0.80 benchmark.\n" - ] - } - ], - "source": [ - "def calculate_fuel_usage_from_rer(df, phase_name=\"RMR\"):\n", - " \"\"\"\n", - " Calculates the percentage of Carbohydrate and Fat oxidation using the\n", - " measured Respiratory Exchange Ratio (RER) during a specified phase.\n", - "\n", - " This function uses the standard non-protein RER calculation (the RER\n", - " is assumed to be the Non-Protein Respiratory Quotient, or non-protein RQ).\n", - " \n", - " Args:\n", - " df (pd.DataFrame): The DataFrame containing the metabolic data.\n", - " phase_name (str): The name of the phase to analyze (e.g., 'RMR').\n", - " The RMR phase is usually the initial resting period.\n", - "\n", - " Returns:\n", - " tuple: (average_rer, carbs_percent, fat_percent)\n", - " \"\"\"\n", - " \n", - " # 1. Clean the data columns\n", - " # We must ensure RER is numeric and handle potential missing values\n", - " df['RER'] = pd.to_numeric(df['RER'], errors='coerce')\n", - " df['PHASE'] = df['PHASE'].astype(str).str.strip()\n", - "\n", - " # The RMR phase is usually recorded in the 'PHASE' column.\n", - " # If the RMR phase isn't explicitly labeled, we'll try to use the very start of the test.\n", - " \n", - " # First, let's identify the resting data. PNOE often uses an empty string or 'Rest'\n", - " # for the initial RMR phase, or simply the first block of data.\n", - " resting_data = df[df['PHASE'].str.contains(phase_name, case=False, na=False)].copy()\n", - " \n", - " if resting_data.empty:\n", - " # Fallback: If no explicit phase name is found, assume the first 10 minutes (600 seconds)\n", - " # or the first 50 rows of data are the resting phase, which is standard RMR protocol.\n", - " print(f\"Warning: Phase '{phase_name}' not found. Assuming first 50 data points (approx. 5-10 min) are RMR.\")\n", - " resting_data = df.head(50).copy()\n", - " \n", - " if resting_data.empty:\n", - " print(\"Error: Could not find any valid resting data to analyze.\")\n", - " return None, None, None\n", - "\n", - " # Calculate the average RER during the identified resting period\n", - " average_rer = resting_data['RER'].mean()\n", - "\n", - " # 2. Apply the RER to Fuel Conversion Formulas\n", - " # These formulas are based on the non-protein respiratory exchange:\n", - " # RER of 0.70 = 100% Fat, 0% Carb\n", - " # RER of 1.00 = 0% Fat, 100% Carb\n", - " \n", - " # Non-Protein Fat Oxidation Formula:\n", - " # %Fat = ((1.00 - RER) / (1.00 - 0.70)) * 100\n", - " # %Fat = ((1.00 - RER) / 0.30) * 100\n", - " \n", - " # Non-Protein Carbohydrate Oxidation Formula:\n", - " # %Carb = 100 - %Fat\n", - " \n", - " if average_rer is None or pd.isna(average_rer):\n", - " print(\"Error: Average RER calculation resulted in NaN.\")\n", - " return None, None, None\n", - " \n", - " # We constrain the RER to the physiological range [0.70, 1.00] for fuel calculation\n", - " constrained_rer = max(0.70, min(1.00, average_rer))\n", - "\n", - " # Calculate the percentages\n", - " fat_percent = ((1.00 - constrained_rer) / 0.30) * 100\n", - " carbs_percent = 100.0 - fat_percent\n", - " \n", - " return average_rer, carbs_percent, fat_percent\n", - "\n", - "# --- Execution ---\n", - "# file_path = 'Pnoe_20250729_1550-Moran_Keirstyn.csv'\n", - "# df = pd.read_csv(file_path, delimiter=';')\n", - "\n", - "# Run the calculation. Since the RMR is the resting phase, we look for the\n", - "# beginning of the test or a 'Rest' phase.\n", - "avg_rer, carbs, fat = calculate_fuel_usage_from_rer(df, phase_name='')\n", - "\n", - "if avg_rer is not None:\n", - " print(f\"--- RMR Fuel Usage Calculation Confirmation ---\")\n", - " print(f\"1. Average RER Measured during Resting Phase: {avg_rer:.3f}\")\n", - " print(f\"2. Calculated Carbohydrate Usage: {carbs:.1f}%\")\n", - " print(f\"3. Calculated Fat Usage: {fat:.1f}%\")\n", - " \n", - " # This comparison confirms the 33% calculation\n", - " if 32.5 <= carbs <= 34.5:\n", - " print(\"\\n✅ This confirms the reported ~33% fuel usage based on the RER = 0.80 standard conversion.\")\n", - " else:\n", - " print(\"\\nNote: The calculated value differs from 33%. This is likely due to the actual measured RER being slightly different from the exact 0.80 benchmark.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 78, + "execution_count": 28, "id": "7b7056b1", "metadata": {}, "outputs": [], @@ -2387,7 +1685,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 29, "id": "11490430", "metadata": {}, "outputs": [ @@ -2562,7 +1860,7 @@ "9 40 " ] }, - "execution_count": 79, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2577,15 +1875,15 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 34, "id": "8fbbf55d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -2607,33 +1905,214 @@ "oxygenation['Time_minutes'] = oxygenation['Time'] / 60\n", "\n", "# Graph 1: TSI against Time\n", - "plt.figure(figsize=(14, 5))\n", + "plt.figure(figsize=(14, 10))\n", "plt.plot(oxygenation['Time_minutes'], oxygenation['TSI'], linewidth=2, color='purple', \n", " marker='o', markersize=4, label='TSI')\n", - "plt.xlabel('Time (minutes)', fontsize=12)\n", - "plt.ylabel('TSI (%)', fontsize=12)\n", - "plt.title('Tissue Saturation Index (TSI) vs Time', fontsize=14, fontweight='bold')\n", + "plt.xlabel('Time (minutes)', fontsize=16)\n", + "plt.ylabel('TSI (%)', fontsize=16)\n", "plt.ylim(0, 100)\n", "plt.grid(True, alpha=0.3)\n", - "plt.legend(loc='upper right', fontsize=11)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", "plt.tight_layout()\n", - "plt.savefig('graphs/tsi_vs_time.png', dpi=300, bbox_inches='tight')\n", + "plt.savefig('graphs/left_leg.png', dpi=300, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Graph 2: TSI-second against Time\n", - "plt.figure(figsize=(14, 5))\n", + "plt.figure(figsize=(14, 10))\n", "plt.plot(oxygenation['Time_minutes'], oxygenation['TSI-second'], linewidth=2, color='purple', \n", " marker='o', markersize=4, label='TSI-second')\n", - "plt.xlabel('Time (minutes)', fontsize=12)\n", - "plt.ylabel('TSI-second (%)', fontsize=12)\n", - "plt.title('Tissue Saturation Index Second Sensor (TSI-second) vs Time', fontsize=14, fontweight='bold')\n", + "plt.xlabel('Time (minutes)', fontsize=16)\n", + "plt.ylabel('TSI-second (%)', fontsize=16)\n", "plt.ylim(0, 100)\n", "plt.grid(True, alpha=0.3)\n", - "plt.legend(loc='upper right', fontsize=11)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", "plt.tight_layout()\n", - "plt.savefig('graphs/tsi_second_vs_time.png', dpi=300, bbox_inches='tight')\n", + "plt.savefig('graphs/right_leg.png', dpi=300, bbox_inches='tight')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "da9b3f8e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Displaying Metabolism Chart...\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "def plot_metabolism_chart():\n", + " \"\"\"\n", + " Generates and displays the 'Slow vs Fast Metabolism' chart.\n", + " \"\"\"\n", + " fig, ax = plt.subplots(figsize=(10, 2.5))\n", + "\n", + " # --- Chart data and positions ---\n", + " categories = ['Very Slow', 'Slow', 'Average', 'Fast', 'Very Fast']\n", + " positions = [1500, 3000, 4500, 6000, 7500]\n", + " kcal_value = 1386\n", + " # Position the indicator and highlight based on the kcal value\n", + " # For this example, we'll place it in the 'Very Slow' section.\n", + " indicator_pos = kcal_value\n", + " highlight_end = kcal_value\n", + "\n", + " # --- Main Bar (Background) ---\n", + " # Create a rounded rectangle for the main bar\n", + " main_bar = patches.FancyBboxPatch(\n", + " (0, 0.4), 9000, 0.2,\n", + " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", + " ec=\"none\", fc=\"#E0E0E0\",\n", + " )\n", + " ax.add_patch(main_bar)\n", + "\n", + " # --- Highlighted Bar ---\n", + " # Create a rounded rectangle for the highlighted section\n", + " highlight_bar = patches.FancyBboxPatch(\n", + " (0, 0.4), highlight_end, 0.2,\n", + " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", + " ec=\"none\", fc=\"#B2FFC8\",\n", + " )\n", + " ax.add_patch(highlight_bar)\n", + "\n", + " # --- Text and Labels ---\n", + " # Add the kcal text inside the highlighted bar\n", + " ax.text(highlight_end / 2, 0.5, f'{kcal_value}kCals', \n", + " ha='center', va='center', color='#006400', fontsize=14, weight='bold')\n", + "\n", + " # --- Indicator Triangle ---\n", + " ax.plot(indicator_pos, 0.65, 'v', markersize=15, color='#606060', clip_on=False)\n", + "\n", + " # --- Ticks and Labels ---\n", + " # Add category labels and tick marks below the bar\n", + " for pos, label in zip(positions, categories):\n", + " ax.text(pos, 0.15, label, ha='center', va='center', fontsize=12, color='#333333')\n", + " ax.plot([pos, pos], [0.35, 0.39], color='grey', lw=5)\n", + "\n", + "\n", + " # --- Chart Styling ---\n", + " ax.set_title('Slow vs Fast Metabolism', fontsize=18, weight='bold', loc='left')\n", + " ax.set_xlim(0, 9000) # Add padding before and after\n", + " ax.set_ylim(0, 1)\n", + " \n", + " # Hide axes and spines for a cleaner look\n", + " ax.axis('off')\n", + " \n", + " plt.tight_layout()\n", + " plt.savefig('graphs/metabolism_chart.png', bbox_inches='tight', dpi=300)\n", + " plt.show()\n", + "\n", + "\n", + "def plot_fuel_source_chart():\n", + " \"\"\"\n", + " Generates and displays the 'Fuel Source' chart.\n", + " \"\"\"\n", + " fig, ax = plt.subplots(figsize=(10, 2.5))\n", + "\n", + " # --- Chart data and positions ---\n", + " fat_percentage = 33\n", + " carb_percentage = 100 - fat_percentage\n", + " optimal_point = 75\n", + "\n", + " # --- Main Bars (Fats and Carbs) ---\n", + " # Fats bar (yellow)\n", + " fats_bar = patches.FancyBboxPatch(\n", + " (0, 0.4), fat_percentage, 0.2,\n", + " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", + " ec=\"none\", fc=\"#FEEAAB\",\n", + " )\n", + " ax.add_patch(fats_bar)\n", + "\n", + " # Carbs bar (blue) - starts where the fats bar ends\n", + " carbs_bar = patches.FancyBboxPatch(\n", + " (fat_percentage, 0.4), carb_percentage, 0.2,\n", + " boxstyle=\"round,pad=0,rounding_size=0.1\",\n", + " ec=\"none\", fc=\"#A7F5FF\",\n", + " )\n", + " ax.add_patch(carbs_bar)\n", + "\n", + " # --- Text and Labels ---\n", + " # Add percentage labels inside the bars\n", + " ax.text(fat_percentage / 2, 0.5, f'Fats\\n{fat_percentage}%', \n", + " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", + " ax.text(fat_percentage + carb_percentage / 2, 0.5, f'Carbs\\n{100-fat_percentage}%', \n", + " ha='center', va='center', color='#333333', fontsize=12, weight='bold')\n", + " \n", + " # Add 'Optimal' label\n", + " ax.text(optimal_point, 0.75, 'Optimal', ha='center', va='center', fontsize=12)\n", + "\n", + " # --- Indicator Triangle ---\n", + " ax.plot(fat_percentage, 0.65, 'v', markersize=15, color='#606060', clip_on=False)\n", + " \n", + " # --- Ticks and Labels ---\n", + " positions = [0, 25, 50, 75, 100]\n", + " for pos in positions:\n", + " ax.text(pos, 0.15, str(pos), ha='center', va='center', fontsize=12, color='#333333')\n", + " ax.plot([pos, pos], [0.35, 0.39], color='grey', lw=5)\n", + " \n", + " # Add a special tick for the 'Optimal' point\n", + " ax.plot([optimal_point, optimal_point], [0.6, 0.7], color='black', lw=2)\n", + "\n", + " # --- Chart Styling ---\n", + " ax.set_title('Fuel Source', fontsize=18, weight='bold', loc='left')\n", + " # ax.set_xlim(-5, 105) # Add padding\n", + " ax.set_ylim(0, 1)\n", + "\n", + " # Hide axes and spines\n", + " ax.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('graphs/fuel_source_chart.png', bbox_inches='tight', dpi=300)\n", + " plt.show()\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " # Call the functions to display each plot\n", + " print(\"Displaying Metabolism Chart...\")\n", + " plot_metabolism_chart()\n", + " \n", + " print(\"\\nDisplaying Fuel Source Chart...\")\n", + " plot_fuel_source_chart()\n" + ] } ], "metadata": { diff --git a/report_gen/page_12.html b/report_gen/page_12.html index 1546b03..1ac3442 100644 --- a/report_gen/page_12.html +++ b/report_gen/page_12.html @@ -15,7 +15,7 @@
- Right Leg SMO2 Chart + Right Leg SMO2 Chart
@@ -48,7 +48,7 @@
- Left Leg SMO2 Chart + Left Leg SMO2 Chart