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

.. only:: html

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

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

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

.. _sphx_glr_howto_howto_examples_plot_recobundles.py:


=========================================
Using RecoBundles for bundle recognition
=========================================

For bundle recognition, pyAFQ defaults to use the waypoint ROI approach
described in [Yeatman2012]_. However, as an alternative approach, pyAFQ also
supports using the RecoBundles algorithm [Garyfallidis2017]_, which uses an
atlas of bundles in streamlines. This example shows how to
use RecoBundles for bundle recognition.

The code closely resembles the code used in :ref:`sphx_glr_tutorial_examples_plot_001-plot_afq_api.py`.

.. GENERATED FROM PYTHON SOURCE LINES 15-27

.. code-block:: Python


    import os.path as op
    import AFQ.data.fetch as afd
    from AFQ.api.group import GroupAFQ

    afd.organize_stanford_data(clear_previous_afq="track")

    tracking_params = dict(n_seeds=25000,
                           random_seeds=True,
                           rng_seed=42)









.. GENERATED FROM PYTHON SOURCE LINES 28-35

Defining the segmentation params
--------------------------------
We also refer to bundle recognition as the "segmentation" of the tractogram.
Parameters of this process are set through a dictionary input to the
`segmentation_params` argument of the GroupAFQ object. In this case, we
set the `seg_algo` to "reco", which tells pyAFQ to use the RecoBundles
algorithm for bundle recognition.

.. GENERATED FROM PYTHON SOURCE LINES 35-48

.. code-block:: Python


    myafq = GroupAFQ(
        output_dir=op.join(afd.afq_home, 'stanford_hardi', 'derivatives',
                           'recobundles'),
        bids_path=op.join(afd.afq_home, 'stanford_hardi'),
        # Set the algorithm to use RecoBundles for bundle recognition:
        segmentation_params={'seg_algo': 'reco'},
        preproc_pipeline='vistasoft',
        tracking_params=tracking_params,
        viz_backend_spec='plotly_no_gif')

    fig_files = myafq.export_all()





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

 .. code-block:: none


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     81%|████████  | 10424/12916 [00:04<00:00, 2555.54 MB/s]
     83%|████████▎ | 10719/12916 [00:04<00:00, 2641.78 MB/s]
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    100%|██████████| 12916/12916 [00:05<00:00, 2380.13 MB/s]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]

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.. GENERATED FROM PYTHON SOURCE LINES 49-57

References:
-------------------------
 .. [Garyfallidis2017] Garyfallidis, Eleftherios, Marc-Alexandre Côté,
                     Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent
                     Petit, David Fortin, Stephen Cunanne, and Maxime
                     Descoteaux. 2017.“Recognition of White Matter Bundles
                     Using Local and Global Streamline-Based Registration and
                     Clustering.”NeuroImage 170: 283-295.


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

   **Total running time of the script:** (15 minutes 29.493 seconds)

**Estimated memory usage:**  2192 MB


.. _sphx_glr_download_howto_howto_examples_plot_recobundles.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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