.. _sphx_glr_auto_examples_Cochlea_examples_plot_sequence_input.py:


==========================
Repeating pattern sequence
==========================

Run the cochlea on a sequence composed of 1 repeating pattern
This pattern of 50ms appears 10 times and each repetition is separated by a noise segment (i.e. a non-repeating pattern)




.. code-block:: python


    import os
    # import matplotlib
    # matplotlib.use('TkAgg')
    import numpy as np
    from scipy.io import wavfile
    import seaborn as sns
    from simplecochlea import Cochlea
    import simplecochlea
    sns.set_context('paper')








Load the file



.. code-block:: python

    root_dirpath = os.path.dirname(simplecochlea.__file__)
    sample_data_dir = os.path.join(root_dirpath, 'sample_data')
    fs, sequence = wavfile.read(os.path.join(sample_data_dir, 'sample_sequence_10_50ms_1.wav'))







Create the cochlea



.. code-block:: python

    fmin, fmax, freq_scale, n_channels = 200, 8000, 'erbscale', 100
    comp_factor, comp_gain = 0.3, 1.5
    tau, v_thresh, v_spike = np.linspace(0.001, 0.0004, n_channels), np.linspace(0.3, 0.17, n_channels), 0.5

    cochlea = Cochlea(n_channels, fs, fmin, fmax, freq_scale, comp_factor=comp_factor, comp_gain=comp_gain,
                           lif_tau=tau, lif_v_thresh=v_thresh, lif_v_spike=v_spike)







Run the sequence through the cochlea



.. code-block:: python

    spikelist_seq, _ = cochlea.process_input(sequence)





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

 Out::

    Function : process_input - Time elapsed : 0.642064094543457


Plot the spikelist



.. code-block:: python

    spikelist_seq.plot()




.. image:: /auto_examples/Cochlea_examples/images/sphx_glr_plot_sequence_input_001.png
    :align: center




We know the repeating pattern is repeating every 50ms, the sequence starts with a noise segment and in total, there
are 20 segments (10 time the pattern and 10 interleaved noise segments).
Thus we can set the pattern_id of the spikes in the output spikelist, with the set_pattern_id_from_time_limits method.



.. code-block:: python

    chunk_duration, n_chunks = 0.050, 20
    t_start = np.arange(0, chunk_duration*n_chunks, chunk_duration)
    t_end = t_start + chunk_duration
    pattern_id = [1, 2] * 10
    pattern_names = {1: 'Noise', 2: 'Pattern'}

    spikelist_seq.set_pattern_id_from_time_limits(t_start, t_end, pattern_id, pattern_names)







Replot the spikelist to see the results :



.. code-block:: python

    spikelist_seq.plot()












.. image:: /auto_examples/Cochlea_examples/images/sphx_glr_plot_sequence_input_002.png
    :align: center




**Total running time of the script:** ( 0 minutes  2.376 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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