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

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

.. _sphx_glr_auto_examples_example_asr.py:


ASR example
===========

Denoise data using Artifact Subspace Reconstruction.

Uses meegkit.ASR().

.. GENERATED FROM PYTHON SOURCE LINES 9-21

.. code-block:: Python

    import os

    import matplotlib.pyplot as plt
    import numpy as np

    from meegkit.asr import ASR
    from meegkit.utils.matrix import sliding_window

    # THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
    raw = np.load(os.path.join("..", "tests", "data", "eeg_raw.npy"))
    sfreq = 250








.. GENERATED FROM PYTHON SOURCE LINES 22-24

Calibration and processing
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 24-39

.. code-block:: Python


    # Train on a clean portion of data
    asr = ASR(method="euclid")
    train_idx = np.arange(0 * sfreq, 30 * sfreq, dtype=int)
    _, sample_mask = asr.fit(raw[:, train_idx])

    # Apply filter using sliding (non-overlapping) windows
    X = sliding_window(raw, window=int(sfreq), step=int(sfreq))
    Y = np.zeros_like(X)
    for i in range(X.shape[1]):
        Y[:, i, :] = asr.transform(X[:, i, :])

    raw = X.reshape(8, -1)  # reshape to (n_chans, n_times)
    clean = Y.reshape(8, -1)








.. GENERATED FROM PYTHON SOURCE LINES 40-46

Plot the results
-----------------------------------------------------------------------------

Data was trained on a 40s window from 5s to 45s onwards (gray filled area).
The algorithm then removes portions of this data with high amplitude
artifacts before running the calibration (hatched area = good).

.. GENERATED FROM PYTHON SOURCE LINES 46-67

.. code-block:: Python


    times = np.arange(raw.shape[-1]) / sfreq
    f, ax = plt.subplots(8, sharex=True, figsize=(8, 5))
    for i in range(8):
        ax[i].fill_between(train_idx / sfreq, 0, 1, color="grey", alpha=.3,
                           transform=ax[i].get_xaxis_transform(),
                           label="calibration window")
        ax[i].fill_between(train_idx / sfreq, 0, 1, where=sample_mask.flat,
                           transform=ax[i].get_xaxis_transform(),
                           facecolor="none", hatch="...", edgecolor="k",
                           label="selected window")
        ax[i].plot(times, raw[i], lw=.5, label="before ASR")
        ax[i].plot(times, clean[i], label="after ASR", lw=.5)
        ax[i].set_ylim([-50, 50])
        ax[i].set_ylabel(f"ch{i}")
        ax[i].set_yticks([])
    ax[i].set_xlabel("Time (s)")
    ax[0].legend(fontsize="small", bbox_to_anchor=(1.04, 1), borderaxespad=0)
    plt.subplots_adjust(hspace=0, right=0.75)
    plt.suptitle("Before/after ASR")
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_example_asr_001.png
   :alt: Before/after ASR
   :srcset: /auto_examples/images/sphx_glr_example_asr_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.834 seconds)


.. _sphx_glr_download_auto_examples_example_asr.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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