{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plot events\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport seaborn as sns\nfrom bokeh.io import output_notebook\nfrom bokeh.plotting import show\nfrom systole.detection import ecg_peaks\nfrom systole.plots import plot_events, plot_rr\n\nfrom systole import import_dataset1\n\n# Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>\n# Licence: Apache-2.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot events distributions using Matplotlib as plotting backend\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ecg_df = import_dataset1(modalities=['ECG', \"Stim\"])\n\n# Get events triggers\ntriggers_idx = [\n    np.where(ecg_df.stim.to_numpy() == 2)[0],\n    np.where(ecg_df.stim.to_numpy() == 1)[0]\n]\n\nplot_events(\n    triggers_idx=triggers_idx, labels=[\"Disgust\", \"Neutral\"],\n    tmin=-0.5, tmax=10.0, figsize=(13, 3),\n    palette=[sns.xkcd_rgb[\"denim blue\"], sns.xkcd_rgb[\"pale red\"]],\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot events distributions using Bokeh as plotting backend and add the RR time series\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "output_notebook()\n\n# Peak detection in the ECG signal using the Pan-Tompkins method\nsignal, peaks = ecg_peaks(ecg_df.ecg, method='pan-tompkins', sfreq=1000)\n\n# First, we create a RR interval plot\nrr_plot = plot_rr(peaks, input_type='peaks', backend='bokeh', figsize=250)\n\nshow(\n    # Then we add events annotations to this plot using the plot_events function\n    plot_events(triggers_idx=triggers_idx, backend=\"bokeh\", labels=[\"Disgust\", \"Neutral\"],\n                tmin=-0.5, tmax=10.0, palette=[sns.xkcd_rgb[\"denim blue\"], sns.xkcd_rgb[\"pale red\"]],\n                ax=rr_plot.children[0])\n)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}