.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_surface_analyses_plot_geodesic_path.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_surface_analyses_plot_geodesic_path.py:


=======================
Plotting Geodesic Paths
=======================

This will plot a geodesic path between two vertices on the cortical surface.
This path is based on geodesic distances across the surface. The path starts
at the given endpoint and selects the neighbor of that point in the surface
map that is closest to the other endpoint. This process continues iteratilvely
until the last vertex in the path is the endpoint you gave to it.

All you need to do is supply a surface object and two vertices on that surface
and you can find the geodesic path. This script additionally makes a plot to
show all of the vertices listed in the path.



.. image:: /auto_examples/surface_analyses/images/sphx_glr_plot_geodesic_path_001.png
    :alt: plot geodesic path
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    /home/travis/virtualenv/python3.8.0/lib/python3.8/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:318: SparseEfficiencyWarning: splu requires CSC matrix format
      warn('splu requires CSC matrix format', SparseEfficiencyWarning)






|


.. code-block:: default

    import cortex
    import cortex.polyutils
    import numpy as np
    import matplotlib.pyplot as plt

    subject = "S1"

    # First we need to import the surfaces for this subject
    surfs = [cortex.polyutils.Surface(*d)
             for d in cortex.db.get_surf(subject, "fiducial")]
    numl = surfs[0].pts.shape[0]

    # Now we need to pick the start and end points of the line we will draw
    pt_a = 100
    pt_b = 50000

    # Then we find the geodesic path between these points
    path = surfs[0].geodesic_path(pt_a, pt_b)

    # In order to plot this on the cortical surface, we need an array that is the
    # same size as the number of vertices in the left hemisphere
    path_data = np.zeros(numl)
    for v in path:
        path_data[v] = 1

    # And now plot these distances onto the cortical surface
    path_verts = cortex.Vertex(path_data, subject, cmap="Blues_r")
    cortex.quickshow(path_verts, with_colorbar=False)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  15.470 seconds)


.. _sphx_glr_download_auto_examples_surface_analyses_plot_geodesic_path.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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