.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/specialty_plots/mri_with_eeg.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. meta::
        :keywords: codex

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

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

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

.. _sphx_glr_gallery_specialty_plots_mri_with_eeg.py:


============
MRI with EEG
============

Displays a set of subplots with an MRI image, its intensity
histogram and some EEG traces.

.. redirect-from:: /gallery/specialty_plots/mri_demo

.. GENERATED FROM PYTHON SOURCE LINES 11-58



.. image-sg:: /gallery/specialty_plots/images/sphx_glr_mri_with_eeg_001.png
   :alt: mri with eeg
   :srcset: /gallery/specialty_plots/images/sphx_glr_mri_with_eeg_001.png, /gallery/specialty_plots/images/sphx_glr_mri_with_eeg_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import matplotlib.cbook as cbook

    fig, axd = plt.subplot_mosaic(
        [["image", "density"],
         ["EEG", "EEG"]],
        layout="constrained",
        # "image" will contain a square image. We fine-tune the width so that
        # there is no excess horizontal or vertical margin around the image.
        width_ratios=[1.05, 2],
    )

    # Load the MRI data (256x256 16-bit integers)
    with cbook.get_sample_data('s1045.ima.gz') as dfile:
        im = np.frombuffer(dfile.read(), np.uint16).reshape((256, 256))

    # Plot the MRI image
    axd["image"].imshow(im, cmap="gray")
    axd["image"].axis('off')

    # Plot the histogram of MRI intensity
    im = im[im.nonzero()]  # Ignore the background
    axd["density"].hist(im, bins=np.arange(0, 2**16+1, 512))
    axd["density"].set(xlabel='Intensity (a.u.)', xlim=(0, 2**16),
                       ylabel='MRI density', yticks=[])
    axd["density"].minorticks_on()

    # Load the EEG data
    n_samples, n_rows = 800, 4
    with cbook.get_sample_data('eeg.dat') as eegfile:
        data = np.fromfile(eegfile, dtype=float).reshape((n_samples, n_rows))
    t = 10 * np.arange(n_samples) / n_samples

    # Plot the EEG
    axd["EEG"].set_xlabel('Time (s)')
    axd["EEG"].set_xlim(0, 10)
    dy = (data.min() - data.max()) * 0.7  # Crowd them a bit.
    axd["EEG"].set_ylim(-dy, n_rows * dy)
    axd["EEG"].set_yticks([0, dy, 2*dy, 3*dy], labels=['PG3', 'PG5', 'PG7', 'PG9'])

    for i, data_col in enumerate(data.T):
        axd["EEG"].plot(t, data_col + i*dy, color="C0")

    plt.show()


.. _sphx_glr_download_gallery_specialty_plots_mri_with_eeg.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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