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

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

.. _sphx_glr_auto_examples_example_star.py:


Sparse time artifact removal on simulated data
==============================================

This example is similar to test_nt_star.m in Noisetools. Results are equivalent
(within numerical precision) to the Matlab code.

.. GENERATED FROM PYTHON SOURCE LINES 9-17

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    from meegkit import star
    from meegkit.utils import demean, normcol

    rng = np.random.default_rng(9)








.. GENERATED FROM PYTHON SOURCE LINES 18-22

Create simulated data
-----------------------------------------------------------------------------
Simulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,
with temporally local artifacts on each channel.

.. GENERATED FROM PYTHON SOURCE LINES 22-47

.. code-block:: Python


    # Create simulated data
    nchans = 10
    n_samples = 1000
    f = 2
    target = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)
    target = target[:, np.newaxis]
    noise = rng.standard_normal((n_samples, nchans - 3))

    # Create artifact signal
    SNR = np.sqrt(1)
    x0 = normcol(np.dot(noise, rng.standard_normal((noise.shape[1], nchans)))) + \
        SNR * target * rng.standard_normal((1, nchans))
    x0 = demean(x0)
    artifact = np.zeros(x0.shape)
    for k in np.arange(nchans):
        artifact[k * 100 + np.arange(20), k] = 1
    x = x0 + 10 * artifact

    # This is to compare with matlab numerically
    # from scipy.io import loadmat
    # mat = loadmat('/Users/nicolas/Toolboxes/NoiseTools/TEST/X.mat')
    # x = mat['x']
    # x0 = mat['x0']








.. GENERATED FROM PYTHON SOURCE LINES 48-50

Apply STAR
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: Python

    y, w, _ = star.star(x, 2)





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

 .. code-block:: none

    proportion artifact free: 0.70
    proportion artifact free: 0.70
    proportion artifact free: 0.70
    depth: 1
    fixed channels: 10
    fixed samples: 299
    ratio: 1.01
    power ratio: 0.40




.. GENERATED FROM PYTHON SOURCE LINES 53-55

Plot results
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 55-64

.. code-block:: Python

    f, (ax1, ax2, ax3) = plt.subplots(3, 1)
    ax1.plot(x, lw=.5)
    ax1.set_title(f"Signal + Artifacts (SNR = {SNR})")
    ax2.plot(y, lw=.5)
    ax2.set_title("Denoised")
    ax3.plot(demean(y) - x0, lw=.5)
    ax3.set_title("Residual")
    f.set_tight_layout(True)
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_example_star_001.png
   :alt: Signal + Artifacts (SNR = 1.0), Denoised, Residual
   :srcset: /auto_examples/images/sphx_glr_example_star_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.342 seconds)


.. _sphx_glr_download_auto_examples_example_star.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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