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

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

.. _sphx_glr_auto_examples_example_dss.py:


DSS example
===========

Find the linear combinations of multichannel data that maximize repeatability
over trials.

Uses meegkit.dss0().

.. GENERATED FROM PYTHON SOURCE LINES 10-18

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    from meegkit import dss
    from meegkit.utils import fold, rms, tscov, unfold

    rng = np.random.default_rng(5)








.. GENERATED FROM PYTHON SOURCE LINES 19-21

Create simulated data
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-47

.. code-block:: Python


    # Data are time * channel * trials.
    n_samples = 100 * 3
    n_chans = 30
    n_trials = 100
    noise_dim = 20  # dimensionality of noise

    # Source signal
    source = np.hstack((
        np.zeros((n_samples // 3,)),
        np.sin(2 * np.pi * np.arange(n_samples // 3) / (n_samples / 3)).T,
        np.zeros((n_samples // 3,))))[np.newaxis].T
    s = source * rng.standard_normal((1, n_chans))  # 300 * 30
    s = s[:, :, np.newaxis]
    s = np.tile(s, (1, 1, 100))

    # Noise
    noise = np.dot(
        unfold(rng.standard_normal((n_samples, noise_dim, n_trials))),
        rng.standard_normal((noise_dim, n_chans)))
    noise = fold(noise, n_samples)

    # Mix signal and noise
    SNR = 0.1
    data = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten())








.. GENERATED FROM PYTHON SOURCE LINES 48-50

Apply DSS to clean them
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 50-65

.. code-block:: Python


    # Compute original and biased covariance matrices
    c0, _ = tscov(data)

    # In this case the biased covariance is simply the covariance of the mean over
    # trials
    c1, _ = tscov(np.mean(data, 2))

    # Apply DSS
    [todss, _, pwr0, pwr1] = dss.dss0(c0, c1)
    z = fold(np.dot(unfold(data), todss), epoch_size=n_samples)

    # Find best components
    best_comp = np.mean(z[:, 0, :], -1)








.. GENERATED FROM PYTHON SOURCE LINES 66-68

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

.. GENERATED FROM PYTHON SOURCE LINES 68-74

.. code-block:: Python

    f, (ax1, ax2, ax3) = plt.subplots(3, 1)
    ax1.plot(source, label="source")
    ax2.plot(np.mean(data, 2), label="data")
    ax3.plot(best_comp, label="recovered")
    plt.legend()
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_example_dss_001.png
   :alt: example dss
   :srcset: /auto_examples/images/sphx_glr_example_dss_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.257 seconds)


.. _sphx_glr_download_auto_examples_example_dss.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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