.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Plots/plot_events.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_Plots_plot_events.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Plots_plot_events.py:


Plot events
===========

.. GENERATED FROM PYTHON SOURCE LINES 7-19

.. code-block:: default

    import numpy as np
    import seaborn as sns
    from bokeh.io import output_notebook
    from bokeh.plotting import show
    from systole.detection import ecg_peaks
    from systole.plots import plot_events, plot_rr

    from systole import import_dataset1

    # Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
    # Licence: GPL v3








.. GENERATED FROM PYTHON SOURCE LINES 20-22

Plot events distributions using Matplotlib as plotting backend
--------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 22-36

.. code-block:: default


    ecg_df = import_dataset1(modalities=['ECG', "Stim"])

    # Get events triggers
    triggers_idx = [
        np.where(ecg_df.stim.to_numpy() == 2)[0],
        np.where(ecg_df.stim.to_numpy() == 1)[0]
    ]

    plot_events(
        triggers_idx=triggers_idx, labels=["Disgust", "Neutral"],
        tmin=-0.5, tmax=10.0, figsize=(13, 3),
        palette=[sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]],
    )



.. image-sg:: /auto_examples/Plots/images/sphx_glr_plot_events_001.png
   :alt: plot events
   :srcset: /auto_examples/Plots/images/sphx_glr_plot_events_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


      0%|          | 0/2 [00:00<?, ?it/s]
    Downloading ECG channel:   0%|          | 0/2 [00:00<?, ?it/s]
    Downloading ECG channel:  50%|█████     | 1/2 [00:00<00:00,  2.62it/s]
    Downloading Stim channel:  50%|█████     | 1/2 [00:00<00:00,  2.62it/s]
    Downloading Stim channel: 100%|██████████| 2/2 [00:00<00:00,  2.78it/s]
    Downloading Stim channel: 100%|██████████| 2/2 [00:00<00:00,  2.75it/s]

    <Axes: xlabel='Time'>



.. GENERATED FROM PYTHON SOURCE LINES 37-39

Plot events distributions using Bokeh as plotting backend and add the RR time series
------------------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 39-53

.. code-block:: default


    output_notebook()

    # Peak detection in the ECG signal using the Pan-Tompkins method
    signal, peaks = ecg_peaks(ecg_df.ecg, method='pan-tompkins', sfreq=1000)

    # First, we create a RR interval plot
    rr_plot = plot_rr(peaks, input_type='peaks', backend='bokeh', figsize=250)

    show(
        # Then we add events annotations to this plot using the plot_events function
        plot_events(triggers_idx=triggers_idx, backend="bokeh", labels=["Disgust", "Neutral"],
                    tmin=-0.5, tmax=10.0, palette=[sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]],
                    ax=rr_plot.children[0])
    )







.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.838 seconds)


.. _sphx_glr_download_auto_examples_Plots_plot_events.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_events.py <plot_events.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_events.ipynb <plot_events.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_