.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_01_plotting_plot_visualize_megatrawls_netmats.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_visualize_megatrawls_netmats.py:


Visualizing Megatrawls Network Matrices from Human Connectome Project
=====================================================================

This example shows how to fetch network matrices data from HCP beta-release
of the Functional Connectivity Megatrawl project.

See :func:`nilearn.datasets.fetch_megatrawls_netmats` documentation for more details.

Fetching the Megatrawls Network matrices
----------------------------------------
Fetching the partial correlation matrices of dimensionality d=300 with
timeseries method 'eigen regression'


.. code-block:: default

    from nilearn import datasets

    netmats = datasets.fetch_megatrawls_netmats(dimensionality=300,
                                                timeseries='eigen_regression',
                                                matrices='partial_correlation')
    # Partial correlation matrices array of size (300, 300) are stored in the name
    # of 'correlation_matrices'
    partial_correlation = netmats.correlation_matrices








Visualization
-------------
Import nilearn plotting modules to use its utilities for plotting
correlation matrices


.. code-block:: default

    from nilearn import plotting

    title = "Partial correlation matrices\n for d=300"
    display = plotting.plot_matrix(partial_correlation, colorbar=True,
                                   title=title)
    plotting.show()



.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_visualize_megatrawls_netmats_001.png
    :alt: plot visualize megatrawls netmats
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.203 seconds)


.. _sphx_glr_download_auto_examples_01_plotting_plot_visualize_megatrawls_netmats.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/01_plotting/plot_visualize_megatrawls_netmats.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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