.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Plots/plot_evoked.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_evoked.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_Plots_plot_evoked.py:


Plot evoked
===========

.. GENERATED FROM PYTHON SOURCE LINES 7-20

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np
    import seaborn as sns
    from systole.detection import ecg_peaks
    from systole.plots import plot_evoked
    from systole.utils import heart_rate, to_epochs

    from systole import import_dataset1

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








.. GENERATED FROM PYTHON SOURCE LINES 21-27

Plot evoked heart rate across two conditions using the Matplotlib backend
Here, for the sake of example, we are going to create the same plot three time using three kind of input data: 
* The raw signal + the triggers timing (or a list of in case of multiple conditions).
* The peaks detection + the triggers timing (or a list of in case of multiple conditions)
* The epoched signal as a 2d NumPy array (or a list of in case of multiple conditions)
--------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 27-73

.. 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]
    ]

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

    # Convert to instantaneous heart rate
    rr, _ = heart_rate(peaks, kind="cubic", unit="bpm", input_type="peaks")

    # Create list epochs arrays for each condition
    hr_epochs, _ = to_epochs(
        signal=rr, triggers_idx=triggers_idx, tmin=-1.0, tmax=10.0,
        apply_baseline=(-1.0, 0.0)
        )

    fig, axs = plt.subplots(ncols=3, figsize=(15, 5), sharey=True)

    # We define a common set of plotting arguments here
    plot_args = {
         "backend": "matplotlib", "figsize": (400, 400),
         "palette": [sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]],
         "tmin": -1.0, "tmax": 10.0, "apply_baseline": (-1.0, 0.0), "decim": 100
     }

    # Using the raw signal and events triggers
    plot_evoked(
         signal=ecg_df.ecg.to_numpy(), triggers_idx=triggers_idx, modality="ecg",
         ax=axs[0], **plot_args
         )

    # Using the detected peaks and events triggers
    plot_evoked(
        rr=peaks, triggers_idx=triggers_idx, input_type="peaks", ax=axs[1],
        **plot_args
        )

    # Using the list of epochs arrays
    plot_evoked(
        epochs=hr_epochs, ax=axs[2], **plot_args
        )



.. image-sg:: /auto_examples/Plots/images/sphx_glr_plot_evoked_001.png
   :alt: plot evoked
   :srcset: /auto_examples/Plots/images/sphx_glr_plot_evoked_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,  2.73it/s]
    Downloading Stim channel:  50%|█████     | 1/2 [00:00<00:00,  2.73it/s]
    Downloading Stim channel: 100%|██████████| 2/2 [00:00<00:00,  2.25it/s]
    Downloading Stim channel: 100%|██████████| 2/2 [00:00<00:00,  2.31it/s]

    <Axes: xlabel='Time (s)', ylabel='Heart rate change (bpm)'>



.. GENERATED FROM PYTHON SOURCE LINES 74-79

Here, for the sake of example, we are going to create the same plot three times using three kind of input data:
* The raw signal + the triggers timing (or a list of in case of multiple conditions).
* The peaks detection + the triggers timing (or a list of in case of multiple conditions)
* The epoched signal as a 2d NumPy array (or a list of in case of multiple conditions)
--------------------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 79-111

.. code-block:: default

    from bokeh.io import output_notebook
    from bokeh.layouts import row
    from bokeh.plotting import show

    output_notebook()

    # We define a common set of plotting arguments here
    plot_args = {
         "backend": "bokeh", "figsize": (300, 300),
         "palette": [sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["pale red"]],
         "tmin": -1.0, "tmax": 10.0, "apply_baseline": (-1.0, 0.0), "decim": 100
     }

    # Using the raw signal and events triggers
    raw_plot = plot_evoked(
         signal=ecg_df.ecg.to_numpy(), triggers_idx=triggers_idx, modality="ecg",
         **plot_args
         )

    # Using the detected peaks and events triggers
    peaks_plot = plot_evoked(
        rr=peaks, triggers_idx=triggers_idx, input_type="peaks", **plot_args
        )

    # Using the list of epochs arrays
    epochs_plots = plot_evoked(epochs=hr_epochs, **plot_args)

    # Create a Bokeh layout and plot the figures side by side
    show(
        row(
            raw_plot, peaks_plot, epochs_plots
            )
        )







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

   **Total running time of the script:** (0 minutes 20.620 seconds)


.. _sphx_glr_download_auto_examples_Plots_plot_evoked.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_evoked.py <plot_evoked.py>`

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

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


.. only:: html

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

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