.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/01_plotting/plot_visualization.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_01_plotting_plot_visualization.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_01_plotting_plot_visualization.py:


NeuroImaging volumes visualization
==================================

Simple example to show Nifti data visualization.

.. GENERATED FROM PYTHON SOURCE LINES 9-11

Fetch data
----------

.. GENERATED FROM PYTHON SOURCE LINES 11-24

.. code-block:: Python

    from nilearn import datasets

    # By default 2nd subject will be fetched
    haxby_dataset = datasets.fetch_haxby()

    # print basic information on the dataset
    print(
        f"First anatomical nifti image (3D) located is at: {haxby_dataset.anat[0]}"
    )
    print(
        f"First functional nifti image (4D) is located at: {haxby_dataset.func[0]}"
    )





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

 .. code-block:: none

    First anatomical nifti image (3D) located is at: /home/himanshu/nilearn_data/haxby2001/subj2/anat.nii.gz
    First functional nifti image (4D) is located at: /home/himanshu/nilearn_data/haxby2001/subj2/bold.nii.gz




.. GENERATED FROM PYTHON SOURCE LINES 25-27

Visualization
-------------

.. GENERATED FROM PYTHON SOURCE LINES 27-37

.. code-block:: Python

    from nilearn.image.image import mean_img

    # Compute the mean EPI: we do the mean along the axis 3, which is time
    func_filename = haxby_dataset.func[0]
    mean_haxby = mean_img(func_filename)

    from nilearn.plotting import plot_epi, show

    plot_epi(mean_haxby, colorbar=True, cbar_tick_format="%i")




.. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_001.png
   :alt: plot visualization
   :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x73326bf7fc20>



.. GENERATED FROM PYTHON SOURCE LINES 38-41

Extracting a brain mask
-----------------------
Simple computation of a mask from the :term:`fMRI` data

.. GENERATED FROM PYTHON SOURCE LINES 41-50

.. code-block:: Python

    from nilearn.masking import compute_epi_mask

    mask_img = compute_epi_mask(func_filename)

    # Visualize it as an ROI
    from nilearn.plotting import plot_roi

    plot_roi(mask_img, mean_haxby)




.. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_002.png
   :alt: plot visualization
   :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x733251b024e0>



.. GENERATED FROM PYTHON SOURCE LINES 51-53

Applying the mask to extract the corresponding time series
----------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 53-72

.. code-block:: Python

    from nilearn.masking import apply_mask

    masked_data = apply_mask(func_filename, mask_img)

    # masked_data shape is (timepoints, voxels). We can plot the first 150
    # timepoints from two voxels

    # And now plot a few of these
    import matplotlib.pyplot as plt

    plt.figure(figsize=(7, 5))
    plt.plot(masked_data[:150, :2])
    plt.xlabel("Time [TRs]", fontsize=16)
    plt.ylabel("Intensity", fontsize=16)
    plt.xlim(0, 150)
    plt.subplots_adjust(bottom=0.12, top=0.95, right=0.95, left=0.12)

    show()




.. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_003.png
   :alt: plot visualization
   :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_visualization_003.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 16.719 seconds)

**Estimated memory usage:**  1360 MB


.. _sphx_glr_download_auto_examples_01_plotting_plot_visualization.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nilearn/nilearn/main?urlpath=lab/tree/notebooks/auto_examples/01_plotting/plot_visualization.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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