.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/datasets/plot_vertex2D.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_datasets_plot_vertex2D.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_datasets_plot_vertex2D.py:


===================
Plot 2D Vertex Data
===================

This plots example 2D vertex data onto an example subject, S1, onto a flatmap
using quickflat. In order for this to run, you have to have a flatmap for this
subject in the pycortex filestore.

The cortex.Vertex2D object is instantiated with two numpy arrays of the same
size as the total number of vertices in that subject's flatmap. Each pixel is
colored according to both values given for the nearest vertex in the flatmap.

Instead of random test data, you can replace these with any arrays that are
the length of the all the vertices in the subject.

.. GENERATED FROM PYTHON SOURCE LINES 17-48



.. image-sg:: /auto_examples/datasets/images/sphx_glr_plot_vertex2D_001.png
   :alt: plot vertex2D
   :srcset: /auto_examples/datasets/images/sphx_glr_plot_vertex2D_001.png
   :class: sphx-glr-single-img


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 .. code-block:: none

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    Area 0:0:1960.5:1024 exported to 1960 x 1024 pixels (96 dpi)






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.. code-block:: Python


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

    subject = 'S1'

    # In order to get the number of vertices in this subject's cortical surface
    # we have to load in their surfaces and get the number of points in each
    surfs = [cortex.polyutils.Surface(*d)
             for d in cortex.db.get_surf(subject, "fiducial")]

    # This is the total number of vertices in the left and right hemispheres
    num_verts = [s.pts.shape[0] for s in surfs]

    # Creating one random dataset that is basically a gradient across each
    # hemisphere based on vertex number
    test_data1 = np.hstack((np.arange(num_verts[0]), np.arange(num_verts[1])))

    # Picking a different vertex in each hemisphere to create another fake
    # gradient away from that vertex
    second_verts = [n / 4 for n in num_verts]
    test_data2 = np.hstack((np.abs(np.arange(num_verts[0]) - second_verts[0]),
                            np.abs(np.arange(num_verts[1]) - second_verts[1])))

    # This creates a 2D Vertex object with both of our test datasets for the
    # given subject
    vertex_data = cortex.Vertex2D(test_data1, test_data2, subject)
    cortex.quickshow(vertex_data, with_colorbar=False)
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.433 seconds)


.. _sphx_glr_download_auto_examples_datasets_plot_vertex2D.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_vertex2D.ipynb <plot_vertex2D.ipynb>`

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

      :download:`Download Python source code: plot_vertex2D.py <plot_vertex2D.py>`


.. only:: html

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

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