plot_timing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot performance of physio syncing procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In /Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In /Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In /Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            filename  \\\n",
      "0  func/sub-Blossom_ses-02_task-strangerthings_ru...   \n",
      "1  func/sub-Blossom_ses-02_task-strangerthings_ru...   \n",
      "2  func/sub-Blossom_ses-02_task-strangerthings_ru...   \n",
      "3  func/sub-Blossom_ses-02_task-strangerthings_ru...   \n",
      "4  func/sub-Blossom_ses-02_task-strangerthings_ru...   \n",
      "\n",
      "                     acq_time  duration      onset  phys_onset  index_onset  \\\n",
      "0  2020-05-12 17:06:21.052500     477.0     0.0000    428.2630       856526   \n",
      "1  2020-05-12 17:27:05.957500     382.5  1244.9050   1673.1680      3346336   \n",
      "2  2020-05-12 17:34:22.227500     550.5  1681.1750   2109.4380      4218876   \n",
      "3  2020-05-12 17:44:42.757500     538.5  2301.7050   2729.9680      5459936   \n",
      "4  2020-05-12 17:54:48.270000     450.0  2907.2175   3335.4805      6670961   \n",
      "\n",
      "   index_duration  \n",
      "0          954000  \n",
      "1          765000  \n",
      "2         1101000  \n",
      "3         1077000  \n",
      "4          900000  \n",
      "\n",
      "       onset    index  duration\n",
      "0   181.4695   362939  222.9965\n",
      "1   428.3875   856775  727.1995\n",
      "2  1176.8795  2353759  468.7550\n",
      "3  1673.1680  3346336  412.4035\n",
      "4  2109.5570  4219114  602.9080\n"
     ]
    }
   ],
   "source": [
    "func_df = pd.read_table('/Users/tsalo/Documents/Laird_DIVA/sub-Blossom_synced_func.tsv')\n",
    "physio_df = pd.read_table('/Users/tsalo/Documents/Laird_DIVA/sub-Blossom_updated_physio.tsv')\n",
    "print(func_df.head())\n",
    "print()\n",
    "print(physio_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sync(scan_df, physio_df, onset_col):\n",
    "    max_ = int(1000 * np.ceil(max((physio_df['onset'].max(), scan_df[onset_col].max())) / 1000))\n",
    "    scalar = 10\n",
    "    x = np.linspace(0, max_, (max_*scalar)+1)\n",
    "    physio_timeseries = np.zeros(x.shape)\n",
    "    func_timeseries = np.zeros(x.shape)\n",
    "    for i, row in func_df.iterrows():\n",
    "        func_timeseries[int(row[onset_col] * scalar):int((row[onset_col] + row['duration']) * scalar)] = 1\n",
    "\n",
    "    for i, row in physio_df.iterrows():\n",
    "        physio_timeseries[int(row['onset'] * scalar):int((row['onset'] + row['duration']) * scalar)] = 0.5\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(20, 3))\n",
    "    ax.fill_between(x, func_timeseries, where=func_timeseries>=0,\n",
    "                    interpolate=True, color='red', alpha=0.3,\n",
    "                    label='Functional scans')\n",
    "    ax.fill_between(x, physio_timeseries, where=physio_timeseries>=0,\n",
    "                    interpolate=True, color='blue', alpha=0.3,\n",
    "                    label='Physio triggers')\n",
    "    ax.set_xlim((min(x), max(x)))\n",
    "    ax.set_ylim((0, None))\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.legend()\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_sync(func_df, physio_df, onset_col='onset')\n",
    "ax.set_title('Unsynchronized Times')\n",
    "fig.savefig('/Users/tsalo/Documents/Laird_DIVA/timeseries_unsynchronized.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_sync(func_df, physio_df, onset_col='phys_onset')\n",
    "ax.set_title('Synchronized Times')\n",
    "fig.savefig('/Users/tsalo/Documents/Laird_DIVA/timeseries_synchronized.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:python3]",
   "language": "python",
   "name": "conda-env-python3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}