.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/tutorials/stimulation/10_proxy_audio_measure.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_generated_tutorials_stimulation_10_proxy_audio_measure.py>`
        to download the full example code.

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

.. _sphx_glr_generated_tutorials_stimulation_10_proxy_audio_measure.py:


.. _tut-proxy-audio-measure:

MISC 006: a proxy measure of the audio stimuli
==============================================

.. include:: ../../../links.inc

.. important::

    This example requires the ``meg_wiki`` package to download the sample dataset. This
    package can be installed with ``pip``:

    .. code-block:: bash

        $ pip install git+https://github.com/fcbg-platforms/meg-wiki

The `Crimson 3`_ audio interface has 2 monitoring feedback outputs, ``Phones 1`` is
connected with a jack to BNC cable to an analogical input of the MEG acquisition system,
``MISC 006``. This measure of the auditory stimuli can be a good proxy for the auditory
waveform, depending on the waveform frequency content. The MEG signal will sample the
analogical channels simultaneously with the MEG channels, at the same sampling
frequency, by default 1 kHz. Thus, any sound that can be sampled at 1 kHz will be
available in the FIF file.

If the auditory stimuli can not be sampled at 1 kHz, this measure can still be a good
proxy to check the waveform, its onset and offset and the delay between the onset and
the trigger.

.. warning::

    If the sound can not be sampled at 1 kHz, extra care should be taken to ensure that
    this proxy measure is representing well-enough the auditory stimuli.

Sentence
--------

Let's start by emulating the result of a 1 kHz downsampling on a 24 kHz wav file
containing a sentence generated with the text-to-speech tool ``Amazon Polly``.

.. GENERATED FROM PYTHON SOURCE LINES 41-48

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from scipy.io import wavfile

    from meg_wiki.datasets import sample








.. GENERATED FROM PYTHON SOURCE LINES 49-60

.. code-block:: Python

    fname = sample.data_path() / "tutorials" / "sentence.wav"
    fs, data = wavfile.read(fname)
    times = np.arange(len(data)) / fs
    step = int(fs / 1000)  # step to downsampling to 1 kHz

    f, ax = plt.subplots(1, 1, layout="constrained")
    ax.plot(times, data, color="teal", label="original")
    ax.plot(times[::step], data[::step], color="orange", label="downsampled")
    ax.legend()
    plt.show()




.. image-sg:: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_001.png
   :alt: 10 proxy audio measure
   :srcset: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 61-63

In this case, the downsampled signal is a good proxy for the original signal.
Importantly, the onset is preserved.

.. GENERATED FROM PYTHON SOURCE LINES 63-71

.. code-block:: Python


    f, ax = plt.subplots(1, 1, layout="constrained")
    ax.plot(times, data, color="teal", label="original")
    ax.plot(times[::step], data[::step], color="orange", label="downsampled")
    ax.legend()
    ax.set_xlim(0.05, 0.15)
    plt.show()




.. image-sg:: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_002.png
   :alt: 10 proxy audio measure
   :srcset: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 72-77

Pure tone
---------

Let's now consider a pure tone at a badly chosen 1 kHz frequency, exacerbing a badly
sampled signal at a sampling frequency of 1 kHz.

.. GENERATED FROM PYTHON SOURCE LINES 77-85

.. code-block:: Python


    data = np.sin(2 * np.pi * 1000 * times)
    f, ax = plt.subplots(1, 1, layout="constrained")
    ax.plot(times, data, color="teal", label="original")
    ax.plot(times[::step], data[::step], color="orange", label="downsampled")
    ax.legend()
    ax.set_xlim(0, 0.01)
    plt.show()



.. image-sg:: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_003.png
   :alt: 10 proxy audio measure
   :srcset: /generated/tutorials/stimulation/images/sphx_glr_10_proxy_audio_measure_003.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 3.002 seconds)

**Estimated memory usage:**  215 MB


.. _sphx_glr_download_generated_tutorials_stimulation_10_proxy_audio_measure.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 10_proxy_audio_measure.ipynb <10_proxy_audio_measure.ipynb>`

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

      :download:`Download Python source code: 10_proxy_audio_measure.py <10_proxy_audio_measure.py>`

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

      :download:`Download zipped: 10_proxy_audio_measure.zip <10_proxy_audio_measure.zip>`


.. only:: html

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

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