.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/01_plotting/plot_multiscale_parcellations.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_multiscale_parcellations.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_multiscale_parcellations.py:


Visualizing multiscale functional brain parcellations
=====================================================

This example shows how to download and fetch brain parcellations of
multiple networks using
:func:`~nilearn.datasets.fetch_atlas_basc_multiscale_2015`
and visualize them using plotting function :func:`~nilearn.plotting.plot_roi`.

We show here only three different networks of 'symmetric' version. For more
details about different versions and different networks, please refer to its
documentation.

.. GENERATED FROM PYTHON SOURCE LINES 16-18

Retrieving multiscale group brain parcellations
-----------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 18-33

.. code-block:: Python


    # import datasets module and use `fetch_atlas_basc_multiscale_2015` function
    from nilearn.datasets import fetch_atlas_basc_multiscale_2015

    parcellations = [
        fetch_atlas_basc_multiscale_2015(version="sym", resolution=64),
        fetch_atlas_basc_multiscale_2015(version="sym", resolution=197),
        fetch_atlas_basc_multiscale_2015(version="sym", resolution=444),
    ]

    # We show here networks of 64, 197, 444
    networks_64 = parcellations[0]["maps"]
    networks_197 = parcellations[1]["maps"]
    networks_444 = parcellations[2]["maps"]





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

 .. code-block:: none

    [fetch_atlas_basc_multiscale_2015] Dataset created in 
    /home/runner/nilearn_data/basc_multiscale_2015
    [fetch_atlas_basc_multiscale_2015] Downloading data from 
    https://ndownloader.figshare.com/files/1861819 ...
    [fetch_atlas_basc_multiscale_2015]  ...done. (1 seconds, 0 min)

    [fetch_atlas_basc_multiscale_2015] Extracting data from 
    /home/runner/nilearn_data/basc_multiscale_2015/5fd26f33e54da6efc0bc7eca321ad622/
    1861819...
    [fetch_atlas_basc_multiscale_2015] .. done.

    [fetch_atlas_basc_multiscale_2015] Dataset found in 
    /home/runner/nilearn_data/basc_multiscale_2015
    [fetch_atlas_basc_multiscale_2015] Dataset found in 
    /home/runner/nilearn_data/basc_multiscale_2015




.. GENERATED FROM PYTHON SOURCE LINES 34-36

Visualizing brain parcellations
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 36-49

.. code-block:: Python


    # import plotting module and use `plot_roi` function, since the maps are in 3D
    from nilearn.plotting import plot_roi, show

    # The coordinates of all plots are selected automatically by itself
    # We manually change the colormap of our choice
    plot_roi(networks_64, cmap="bwr", title="64 regions of brain clusters")

    plot_roi(networks_197, cmap="bwr", title="197 regions of brain clusters")

    plot_roi(networks_444, cmap="bwr_r", title="444 regions of brain clusters")

    show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_001.png
         :alt: plot multiscale parcellations
         :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_002.png
         :alt: plot multiscale parcellations
         :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_003.png
         :alt: plot multiscale parcellations
         :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_multiscale_parcellations_003.png
         :class: sphx-glr-multi-img






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

   **Total running time of the script:** (0 minutes 4.654 seconds)

**Estimated memory usage:**  108 MB


.. _sphx_glr_download_auto_examples_01_plotting_plot_multiscale_parcellations.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/0.12.0?urlpath=lab/tree/notebooks/auto_examples/01_plotting/plot_multiscale_parcellations.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

      :download:`Download zipped: plot_multiscale_parcellations.zip <plot_multiscale_parcellations.zip>`


.. only:: html

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

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