.. only:: html

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

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

    .. _sphx_glr_auto_examples_surface_analyses_plot_geodesic_distance.py:


===========================
Plotting Geodesic Distances
===========================

This plots the distances (in mm) between a vertex or set of vertices and
all other vertices on a surface. These two operations take the exact same
amount of time to run.

To look at the distance to a single point, just supply the index of that
vertex. To look at the distance to a set of points, supply a numpy array of
all of the vertices in the area. In the case of a set of points, the
geodesic distance measure will return the minimum distance to the set of
points as a whole.

The two hemispheres must be run separately.



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


    *

      .. image:: /auto_examples/surface_analyses/images/sphx_glr_plot_geodesic_distance_001.png
          :alt: plot geodesic distance
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/surface_analyses/images/sphx_glr_plot_geodesic_distance_002.png
          :alt: plot geodesic distance
          :class: sphx-glr-multi-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")]

    # Then we will pick one vertex in each hemisphere to find distances to
    vert = 10000
    dists = [s.geodesic_distance(vert) for s in surfs]

    # Now we can plot these distances onto a flatmap
    all_dists = np.hstack((dists[0], dists[1]))
    dist_map = cortex.Vertex(all_dists, subject, cmap="hot")
    cortex.quickshow(dist_map)
    plt.show()

    # Alternatively, you can find the minimum distance from a set of points to the
    # surface
    # Here, we use an example of an ROI
    all_eba = cortex.utils.get_roi_verts(subject, "EBA")["EBA"]

    # We have to then separate these vertices by hemisphere
    numl = surfs[0].pts.shape[0]
    eba_verts = [all_eba[all_eba < numl], all_eba[all_eba >= numl] - numl]

    # Now look at geodesic distances for each hemisphere separately
    dists = [s.geodesic_distance(verts) for s, verts in zip(surfs, eba_verts)]
    all_dists = np.hstack((dists[0], dists[1]))

    # And now plot these distances onto the cortical surface
    dist_map = cortex.Vertex(all_dists, subject, cmap="hot")
    cortex.quickshow(dist_map)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  27.369 seconds)


.. _sphx_glr_download_auto_examples_surface_analyses_plot_geodesic_distance.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_distance.py <plot_geodesic_distance.py>`



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

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


.. only:: html

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

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