.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_01_plotting_plot_overlay.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_overlay.py:


Visualizing a probablistic atlas: the default mode in the MSDL atlas
=====================================================================

Visualizing a probablistic atlas requires visualizing the different
maps that compose it.

Here we represent the nodes constituting the default mode network in the
`MSDL atlas
<https://team.inria.fr/parietal/18-2/spatial_patterns/spatial-patterns-in-resting-state/>`_.

The tools that we need to leverage are:

 * :func:`nilearn.image.index_img` to retrieve the various maps composing
   the atlas

 * Adding overlays on an existing brain display, to plot each of these
   maps

Alternatively, :func:`nilearn.plotting.plot_prob_atlas` allows to plot the maps in one step that
with less control over the plot (see below)

Fetching probabilistic atlas - MSDL atlas
-----------------------------------------


.. code-block:: default

    from nilearn import datasets

    atlas_data = datasets.fetch_atlas_msdl()
    atlas_filename = atlas_data.maps





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

 Out:

 .. code-block:: none


    Dataset created in /home/varoquau/nilearn_data/msdl_atlas

    Downloading data from https://team.inria.fr/parietal/files/2015/01/MSDL_rois.zip ...
     ...done. (0 seconds, 0 min)
    Extracting data from /home/varoquau/nilearn_data/msdl_atlas/8eaecb9e05c478f565847000d9902a25/MSDL_rois.zip..... done.
    /usr/lib/python3/dist-packages/numpy/lib/npyio.py:2358: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
      output = genfromtxt(fname, **kwargs)




Visualizing a probabilistic atlas with plot_stat_map and add_overlay object
---------------------------------------------------------------------------


.. code-block:: default

    from nilearn import plotting, image

    # First plot the map for the PCC: index 4 in the atlas
    display = plotting.plot_stat_map(image.index_img(atlas_filename, 4),
                                     colorbar=False,
                                     title="DMN nodes in MSDL atlas")

    # Now add as an overlay the maps for the ACC and the left and right
    # parietal nodes
    display.add_overlay(image.index_img(atlas_filename, 5),
                        cmap=plotting.cm.black_blue)
    display.add_overlay(image.index_img(atlas_filename, 6),
                        cmap=plotting.cm.black_green)
    display.add_overlay(image.index_img(atlas_filename, 3),
                        cmap=plotting.cm.black_pink)

    plotting.show()





.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_overlay_001.png
    :alt: plot overlay
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    /usr/lib/python3/dist-packages/numpy/ma/core.py:2785: UserWarning: Warning: converting a masked element to nan.
      _data = np.array(data, dtype=dtype, copy=copy,




Visualizing a probablistic atlas with plot_prob_atlas
=====================================================

Alternatively, we can create a new 4D-image by selecting the 3rd, 4th, 5th and 6th (zero-based) probabilistic map from atlas
via :func:`nilearn.image.index_img` and use :func:`nilearn.plotting.plot_prob_atlas` (added in version 0.2)
to plot the selected nodes in one step.

Unlike :func:`nilearn.plotting.plot_stat_map` this works with 4D images


.. code-block:: default


    dmn_nodes = image.index_img(atlas_filename, [3, 4, 5, 6])
    # Note that dmn_node is now a 4D image
    print(dmn_nodes.shape)




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

 Out:

 .. code-block:: none

    (40, 48, 35, 4)





.. code-block:: default


    display = plotting.plot_prob_atlas(dmn_nodes,
                                       cut_coords=(0, -55, 29),
                                       title="DMN nodes in MSDL atlas")
    plotting.show()



.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_overlay_002.png
    :alt: plot overlay
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  2.943 seconds)


.. _sphx_glr_download_auto_examples_01_plotting_plot_overlay.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/01_plotting/plot_overlay.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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