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{
"cells": [
{
"cell_type": "code",
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"execution_count": 34,
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"id": "63f43af5",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
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"execution_count": 35,
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"id": "b0ee2af1",
"metadata": {},
"outputs": [
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Column names and data types after conversion:\n",
"T(sec) float64\n",
"PHASE object\n",
"HR(bpm) float64\n",
"VO2(ml/min) float64\n",
"VCO2(ml/min) float64\n",
"RER float64\n",
"VE(l/min) float64\n",
"FEO2 float64\n",
"FECO2 float64\n",
"FETO2 int64\n",
"FETCO2 int64\n",
"PETO2 (mmHg) int64\n",
"PETCO2(mmHg) int64\n",
"FIO2 float64\n",
"FICO2 float64\n",
"VT(l) float64\n",
"BF(bpm) float64\n",
"EE(kcal/day) float64\n",
"EE(kcal/min) float64\n",
"CARBS(kcal) float64\n",
"CARBS(%) float64\n",
"FAT(kcal) float64\n",
"FAT(%) float64\n",
"MET float64\n",
"CUMULATIVE EE(kcal) float64\n",
"BP(kPa) float64\n",
"Watts float64\n",
"Speed float64\n",
"dtype: object\n",
"\n",
"First few rows:\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_3000/3236648660.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"
]
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}
],
"source": [
"df = pd.read_csv('data/Pnoe_20250729_1550-Moran_Keirstyn.csv', delimiter=';')\n",
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"# Convert all columns to numeric where possible, coercing errors to NaN\n",
"df = df.apply(pd.to_numeric, errors='ignore')\n",
"\n",
"# Display column names and data types to verify conversion\n",
"print(\"Column names and data types after conversion:\")\n",
"print(df.dtypes)\n",
"print(\"\\nFirst few rows:\")"
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]
},
{
"cell_type": "code",
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"execution_count": 36,
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"id": "98da3827",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase transition times: [3.0, 54.0, 233.0, 1733.0]\n"
]
}
],
"source": [
"first_unique_phase = df.drop_duplicates(subset='PHASE')\n",
"phase_times = first_unique_phase['T(sec)'].tolist()\n",
"print(f\"Phase transition times: {phase_times}\")"
]
},
{
"cell_type": "code",
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"execution_count": 37,
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"id": "ef8bc7ac",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18, 5))\n",
"ax1 = plt.subplot()\n",
"\n",
"# Plot VT with step-like appearance\n",
"line1 = sns.lineplot(data=df, x='T(sec)', y='VT(l)', label='VT (L)')\n",
"ax1.set_xlabel('Time (sec)')\n",
"ax1.set_ylabel('VT (L)')\n",
"ax1.set_title('Respiratory')\n",
"ax1.grid(True, alpha=0.1)\n",
"\n",
"# Plot speed as step function on secondary y-axis\n",
"ax2 = ax1.twinx()\n",
"ax1.set_xticks(np.arange(0, df['T(sec)'].max() + 200, 200))\n",
"line2 = sns.lineplot(data=df, x='T(sec)', y='Speed', color='green', ax=ax2, \n",
" drawstyle='steps-post', linewidth=2, label='Speed')\n",
"ax2.set_ylabel('Speed')\n",
"\n",
"# Remove default legends first\n",
"ax1.get_legend().remove()\n",
"ax2.get_legend().remove()\n",
"\n",
"# Combine legends from both axes in the top left\n",
"lines1, labels1 = ax1.get_legend_handles_labels()\n",
"lines2, labels2 = ax2.get_legend_handles_labels()\n",
"ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
"\n",
"# Add colored background regions if you have phase information\n",
"ax1.axvspan(0, phase_times[1], alpha=0.2, color='lightblue')\n",
"ax1.axvspan(phase_times[1], phase_times[2], alpha=0.2, color='purple')\n",
"ax1.axvspan(phase_times[2], phase_times[3], alpha=0.2, color='lightgreen')\n",
"ax1.axvspan(phase_times[3], df['T(sec)'].max(), alpha=0.2, color='blue')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 45,
"id": "033cee9a",
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data grouped by Speed (numeric columns only):\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>T(sec)</th>\n",
" <th>HR(bpm)</th>\n",
" <th>VO2(ml/min)</th>\n",
" <th>VCO2(ml/min)</th>\n",
" <th>RER</th>\n",
" <th>VE(l/min)</th>\n",
" <th>FEO2</th>\n",
" <th>FECO2</th>\n",
" <th>FETO2</th>\n",
" <th>FETCO2</th>\n",
" <th>...</th>\n",
" <th>EE(kcal/day)</th>\n",
" <th>EE(kcal/min)</th>\n",
" <th>CARBS(kcal)</th>\n",
" <th>CARBS(%)</th>\n",
" <th>FAT(kcal)</th>\n",
" <th>FAT(%)</th>\n",
" <th>MET</th>\n",
" <th>CUMULATIVE EE(kcal)</th>\n",
" <th>BP(kPa)</th>\n",
" <th>Watts</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3.5</th>\n",
" <td>142.4</td>\n",
" <td>96.1</td>\n",
" <td>1046.2</td>\n",
" <td>854.9</td>\n",
" <td>0.8</td>\n",
" <td>28.3</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>7230.0</td>\n",
" <td>5.0</td>\n",
" <td>0.2</td>\n",
" <td>39.4</td>\n",
" <td>0.3</td>\n",
" <td>60.6</td>\n",
" <td>5.5</td>\n",
" <td>8.8</td>\n",
" <td>99.5</td>\n",
" <td>95.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>320.8</td>\n",
" <td>109.3</td>\n",
" <td>1423.6</td>\n",
" <td>1271.0</td>\n",
" <td>0.9</td>\n",
" <td>38.7</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>10008.1</td>\n",
" <td>7.0</td>\n",
" <td>0.4</td>\n",
" <td>64.1</td>\n",
" <td>0.2</td>\n",
" <td>35.9</td>\n",
" <td>7.5</td>\n",
" <td>27.3</td>\n",
" <td>99.5</td>\n",
" <td>112.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.5</th>\n",
" <td>501.8</td>\n",
" <td>139.4</td>\n",
" <td>1991.2</td>\n",
" <td>1862.2</td>\n",
" <td>0.9</td>\n",
" <td>56.0</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>14132.4</td>\n",
" <td>9.8</td>\n",
" <td>0.6</td>\n",
" <td>78.0</td>\n",
" <td>0.2</td>\n",
" <td>22.0</td>\n",
" <td>10.5</td>\n",
" <td>52.3</td>\n",
" <td>99.5</td>\n",
" <td>123.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>685.6</td>\n",
" <td>153.2</td>\n",
" <td>2216.6</td>\n",
" <td>2098.0</td>\n",
" <td>0.9</td>\n",
" <td>64.3</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>15771.4</td>\n",
" <td>11.0</td>\n",
" <td>0.7</td>\n",
" <td>80.7</td>\n",
" <td>0.2</td>\n",
" <td>19.3</td>\n",
" <td>11.7</td>\n",
" <td>85.1</td>\n",
" <td>99.5</td>\n",
" <td>135.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.5</th>\n",
" <td>862.6</td>\n",
" <td>163.1</td>\n",
" <td>2303.3</td>\n",
" <td>2198.3</td>\n",
" <td>1.0</td>\n",
" <td>69.9</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>16579.3</td>\n",
" <td>11.5</td>\n",
" <td>0.7</td>\n",
" <td>86.3</td>\n",
" <td>0.1</td>\n",
" <td>13.7</td>\n",
" <td>12.2</td>\n",
" <td>118.3</td>\n",
" <td>99.5</td>\n",
" <td>151.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" T(sec) HR(bpm) VO2(ml/min) VCO2(ml/min) RER VE(l/min) FEO2 \\\n",
"Speed \n",
"3.5 142.4 96.1 1046.2 854.9 0.8 28.3 0.2 \n",
"4.0 320.8 109.3 1423.6 1271.0 0.9 38.7 0.2 \n",
"4.5 501.8 139.4 1991.2 1862.2 0.9 56.0 0.2 \n",
"5.0 685.6 153.2 2216.6 2098.0 0.9 64.3 0.2 \n",
"5.5 862.6 163.1 2303.3 2198.3 1.0 69.9 0.2 \n",
"\n",
" FECO2 FETO2 FETCO2 ... EE(kcal/day) EE(kcal/min) CARBS(kcal) \\\n",
"Speed ... \n",
"3.5 0.0 0.0 0.0 ... 7230.0 5.0 0.2 \n",
"4.0 0.0 0.0 0.0 ... 10008.1 7.0 0.4 \n",
"4.5 0.0 0.0 0.0 ... 14132.4 9.8 0.6 \n",
"5.0 0.0 0.0 0.0 ... 15771.4 11.0 0.7 \n",
"5.5 0.0 0.0 0.0 ... 16579.3 11.5 0.7 \n",
"\n",
" CARBS(%) FAT(kcal) FAT(%) MET CUMULATIVE EE(kcal) BP(kPa) Watts \n",
"Speed \n",
"3.5 39.4 0.3 60.6 5.5 8.8 99.5 95.7 \n",
"4.0 64.1 0.2 35.9 7.5 27.3 99.5 112.4 \n",
"4.5 78.0 0.2 22.0 10.5 52.3 99.5 123.8 \n",
"5.0 80.7 0.2 19.3 11.7 85.1 99.5 135.2 \n",
"5.5 86.3 0.1 13.7 12.2 118.3 99.5 151.9 \n",
"\n",
"[5 rows x 26 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Group by speed and calculate mean for numeric columns only\n",
"speed_groups = df.groupby('Speed').mean(numeric_only=True).round(1)\n",
"print(\"Data grouped by Speed (numeric columns only):\")\n",
"\n",
"speed_groups = speed_groups.iloc[1:-1]\n",
"speed_groups.head()"
]
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},
{
"cell_type": "code",
"execution_count": null,
2025-09-23 17:14:36 +01:00
"id": "25f1a8ef",
2025-09-23 15:53:15 +01:00
"metadata": {},
2025-09-23 17:14:36 +01:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" T(sec) HR(bpm) VO2(ml/min) VCO2(ml/min) RER VE(l/min) FEO2 \\\n",
"Speed \n",
"3.5 142.4 96.1 1046.2 854.9 0.8 28.3 0.2 \n",
"4.0 320.8 109.3 1423.6 1271.0 0.9 38.7 0.2 \n",
"4.5 501.8 139.4 1991.2 1862.2 0.9 56.0 0.2 \n",
"5.0 685.6 153.2 2216.6 2098.0 0.9 64.3 0.2 \n",
"5.5 862.6 163.1 2303.3 2198.3 1.0 69.9 0.2 \n",
"6.0 1042.3 170.8 2282.2 2229.4 1.0 72.0 0.2 \n",
"6.5 1225.8 179.6 2381.8 2345.0 1.0 81.2 0.2 \n",
"7.0 1412.0 187.2 2348.1 2409.4 1.1 88.5 0.2 \n",
"7.5 1596.7 194.0 2519.0 2734.9 1.1 107.0 0.2 \n",
"\n",
" FECO2 FETO2 FETCO2 ... EE(kcal/day) EE(kcal/min) CARBS(kcal) \\\n",
"Speed ... \n",
"3.5 0.0 0.0 0.0 ... 7230.0 5.0 0.2 \n",
"4.0 0.0 0.0 0.0 ... 10008.1 7.0 0.4 \n",
"4.5 0.0 0.0 0.0 ... 14132.4 9.8 0.6 \n",
"5.0 0.0 0.0 0.0 ... 15771.4 11.0 0.7 \n",
"5.5 0.0 0.0 0.0 ... 16579.3 11.5 0.7 \n",
"6.0 0.0 0.0 0.0 ... 16763.2 11.6 0.8 \n",
"6.5 0.0 0.0 0.0 ... 17690.0 12.3 0.8 \n",
"7.0 0.0 0.0 0.0 ... 17600.5 12.2 0.9 \n",
"7.5 0.0 0.0 0.0 ... 18976.5 13.2 0.9 \n",
"\n",
" CARBS(%) FAT(kcal) FAT(%) MET CUMULATIVE EE(kcal) BP(kPa) Watts \n",
"Speed \n",
"3.5 39.4 0.3 60.6 5.5 8.8 99.5 95.7 \n",
"4.0 64.1 0.2 35.9 7.5 27.3 99.5 112.4 \n",
"4.5 78.0 0.2 22.0 10.5 52.3 99.5 123.8 \n",
"5.0 80.7 0.2 19.3 11.7 85.1 99.5 135.2 \n",
"5.5 86.3 0.1 13.7 12.2 118.3 99.5 151.9 \n",
"6.0 93.9 0.1 6.1 12.1 152.8 99.5 163.3 \n",
"6.5 93.8 0.1 6.2 12.6 189.8 99.5 180.0 \n",
"7.0 97.3 0.0 2.7 12.4 227.9 99.5 191.4 \n",
"7.5 99.7 0.0 0.3 13.3 267.2 99.5 208.1 \n",
"\n",
"[9 rows x 26 columns]\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1200x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"speed_groups = df.groupby('Speed').mean(numeric_only=True).round(1)\n",
"# Plot Speed vs Heart Rate from the grouped data\n",
"plt.figure(figsize=(12, 8))\n",
"\n",
"# Drop the first and last row from speed_groups\n",
"speed_groups = speed_groups.iloc[1:-1]\n",
"print(speed_groups)\n",
"# Create speed bins from 3.5 to 7.5 mph\n",
"speed_bins = np.arange(3.5, 8.0, 0.5) # Creates bins: 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5\n",
"stage_labels = [f'Stage {i}' for i in range(1, len(speed_bins) + 1)]\n",
"\n",
"# Filter data to only include speeds in the desired range\n",
"filtered_data = speed_groups[(speed_groups.index >= 3.5) & (speed_groups.index <= 7.5)]\n",
"\n",
"# Create the main axis for heart rate line plot\n",
"ax1 = plt.gca()\n",
"line = ax1.plot(filtered_data.index, filtered_data['HR(bpm)'], marker='o', linewidth=2, markersize=6, color='red', label='Heart Rate')\n",
"ax1.set_xlabel('Speed (mph)', fontsize=12)\n",
"ax1.set_ylabel('Heart Rate (bpm)', fontsize=12, color='red')\n",
"ax1.tick_params(axis='y', labelcolor='red')\n",
"\n",
"# Add heart rate values as text labels on the points\n",
"for i, (speed, hr) in enumerate(zip(filtered_data.index, filtered_data['HR(bpm)'])):\n",
" ax1.text(speed, hr + 2, f'{int(hr)}', ha='center', va='bottom', fontsize=9, fontweight='bold', color='red')\n",
"\n",
"# Create secondary y-axis for EE bar chart\n",
"ax2 = ax1.twinx()\n",
"bars = ax2.bar(filtered_data.index, filtered_data['EE(kcal/min)'], alpha=0.6, width=0.2, color='blue', label='Energy Expenditure')\n",
"ax2.set_ylabel('Energy Expenditure (kcal/min)', fontsize=12, color='blue')\n",
"ax2.tick_params(axis='y', labelcolor='blue')\n",
"\n",
"# Add EE values as text labels on the bars\n",
"for i, (speed, ee) in enumerate(zip(filtered_data.index, filtered_data['EE(kcal/min)'])):\n",
" ax2.text(speed, ee + 0.3, f'{ee:.1f}', ha='center', va='bottom', fontsize=9, fontweight='bold', color='blue')\n",
"\n",
"# Update x-axis with stage labels\n",
"plt.xticks(filtered_data.index, stage_labels)\n",
"plt.title('Heart Rate vs Speed with Energy Expenditure', fontsize=14, fontweight='bold')\n",
"\n",
"# Combine legends\n",
"lines1, labels1 = ax1.get_legend_handles_labels()\n",
"lines2, labels2 = ax2.get_legend_handles_labels()\n",
"ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
"\n",
"plt.grid(True, alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
2025-09-23 15:53:15 +01:00
}
],
"metadata": {
"kernelspec": {
"display_name": "report_generation",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
