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

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

.. _sphx_glr_auto_examples_other_plot_simulate_motif.py:


=======================
MoTIF on simulated data
=======================

This example demonstrates MoTIF [1] on simulated data. In the
alphacsc module, we are offering all the alternatives for the users
to try. Please cite our paper [2] if you use this implementation.

[1] Jost, Philippe, et al.
    "MoTIF: An efficient algorithm for learning
    translation invariant dictionaries." Acoustics, Speech and Signal
    Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International
    Conference on. Vol. 5. IEEE, 2006.
[2] Jas, M., Dupr'e La Tour, T., Simsekli, U., & Gramfort, A. (2017).
    Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional
    Sparse Coding. arXiv preprint arXiv:1705.08006.

.. GENERATED FROM PYTHON SOURCE LINES 19-27

.. code-block:: Python


    # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
    #          Tom Dupre La Tour <tom.duprelatour@telecom-paristech.fr>
    #          Umut Simsekli <umut.simsekli@telecom-paristech.fr>
    #          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
    #
    # License: BSD (3-clause)








.. GENERATED FROM PYTHON SOURCE LINES 28-29

Let us first import the modules.

.. GENERATED FROM PYTHON SOURCE LINES 29-34

.. code-block:: Python


    import matplotlib.pyplot as plt
    from alphacsc.simulate import simulate_data
    from alphacsc.other.motif import learn_atoms








.. GENERATED FROM PYTHON SOURCE LINES 35-37

and define the relevant parameters. Note we choose a large n_times
to avoid overlapping atoms which MoTIF cannot handle

.. GENERATED FROM PYTHON SOURCE LINES 37-43

.. code-block:: Python


    n_times_atom = 64  # L
    n_times = 5000  # T
    n_atoms = 2  # K
    n_trials = 10  # N








.. GENERATED FROM PYTHON SOURCE LINES 44-45

simulate the data.

.. GENERATED FROM PYTHON SOURCE LINES 45-55

.. code-block:: Python


    random_state_simulate = 1
    X, ds_true, z_true = simulate_data(n_trials, n_times, n_times_atom,
                                       n_atoms, random_state_simulate,
                                       constant_amplitude=True)

    n_times_atom = n_times_atom * 7  # XXX: hack
    n_iter = 10
    max_shift = 11  # after this, the algorithm breaks








.. GENERATED FROM PYTHON SOURCE LINES 56-62

Note, how we use constant_amplitude=True since
MoTIF cannot handle atoms of varying amplitudes. Check out our examples
on :ref:`vanilla CSC <sphx_glr_auto_examples_plot_simulate_csc.py>` and
:ref:`alphaCSC <sphx_glr_auto_examples_plot_simulate_alphacsc.py>` to learn
how to deal with such cases.
Finally, let us estimate the atoms.

.. GENERATED FROM PYTHON SOURCE LINES 62-67

.. code-block:: Python


    ds_hat = learn_atoms(X, n_atoms, n_times_atom, n_iter=n_iter,
                         max_shift=max_shift, random_state=42)
    plt.plot(ds_hat.T)




.. image-sg:: /auto_examples/other/images/sphx_glr_plot_simulate_motif_001.png
   :alt: plot simulate motif
   :srcset: /auto_examples/other/images/sphx_glr_plot_simulate_motif_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    [seed 42] Atom 0 Iteration 0
    [seed 42] Atom 0 Iteration 1
    [seed 42] Atom 0 Iteration 2
    [seed 42] Atom 0 Iteration 3
    [seed 42] Atom 0 Iteration 4
    [seed 42] Atom 0 Iteration 5
    [seed 42] Atom 0 Iteration 6
    [seed 42] Atom 0 Iteration 7
    [seed 42] Atom 0 Iteration 8
    [seed 42] Atom 0 Iteration 9
    [seed 42] Atom 1 Iteration 0
    [seed 42] Atom 1 Iteration 1
    [seed 42] Atom 1 Iteration 2
    [seed 42] Atom 1 Iteration 3
    [seed 42] Atom 1 Iteration 4
    [seed 42] Atom 1 Iteration 5
    [seed 42] Atom 1 Iteration 6
    [seed 42] Atom 1 Iteration 7
    [seed 42] Atom 1 Iteration 8
    [seed 42] Atom 1 Iteration 9

    [<matplotlib.lines.Line2D object at 0x7f6140e1f520>, <matplotlib.lines.Line2D object at 0x7f6140e1f4f0>]



.. GENERATED FROM PYTHON SOURCE LINES 68-69

Compare this to the original atoms

.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: Python

    plt.figure()
    plt.plot(ds_true.T)
    plt.show()



.. image-sg:: /auto_examples/other/images/sphx_glr_plot_simulate_motif_002.png
   :alt: plot simulate motif
   :srcset: /auto_examples/other/images/sphx_glr_plot_simulate_motif_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.010 seconds)


.. _sphx_glr_download_auto_examples_other_plot_simulate_motif.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_simulate_motif.zip <plot_simulate_motif.zip>`


.. only:: html

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

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