bio-performx/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
   "execution_count": 20,
   "id": "b0ee2af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['T(sec)', 'PHASE', 'HR(bpm)', 'VO2(ml/min)', 'VCO2(ml/min)', 'RER',\n",
       "       'VE(l/min)', 'FEO2', 'FECO2', 'FETO2', 'FETCO2', 'PETO2 (mmHg)',\n",
       "       'PETCO2(mmHg)', 'FIO2', 'FICO2', 'VT(l)', 'BF(bpm)', 'EE(kcal/day)',\n",
       "       'EE(kcal/min)', 'CARBS(kcal)', 'CARBS(%)', 'FAT(kcal)', 'FAT(%)', 'MET',\n",
       "       'CUMULATIVE EE(kcal)', 'BP(kPa)', 'Watts', 'Speed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/Pnoe_20250729_1550-Moran_Keirstyn.csv', delimiter=';')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
   "execution_count": 48,
   "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",
   "execution_count": null,
   "id": "241194e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f4e117",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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 },
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