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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_surface_analyses_plot_flatmap_distortion.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_surface_analyses_plot_flatmap_distortion.py:


==================
Flatmap Distortion
==================

Creating a flatmap from a folded cortical surface always introduces some
distortion. This is similar to what happens when a map of the globe is flattened
into a 2-D map like a Mercator projection. For the cortical surface the amount
and type of distortion will depend on the curvature of the surface (i.e. whether
it is on a gyrus or a sulcus) and on the distance to the nearest cut.

In general, we recommend examining data both in flattened and original 3-D space
using the interactive webGL viewer, but it is also informative to visualize the
distortion directly.

Here we demonstrate how to compute and display both areal distortion, which is a
measure of how different the area of each triangle in the flattened mesh is from
its area in the original mesh, and metric distortion, which is a measure of how
different the length of each edge in the flattened mesh is from its length in
the original.

.. GENERATED FROM PYTHON SOURCE LINES 23-67



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


    *

      .. image-sg:: /auto_examples/surface_analyses/images/sphx_glr_plot_flatmap_distortion_001.png
         :alt: plot flatmap distortion
         :srcset: /auto_examples/surface_analyses/images/sphx_glr_plot_flatmap_distortion_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/surface_analyses/images/sphx_glr_plot_flatmap_distortion_002.png
         :alt: plot flatmap distortion
         :srcset: /auto_examples/surface_analyses/images/sphx_glr_plot_flatmap_distortion_002.png
         :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    /home/runner/work/pycortex/pycortex/cortex/polyutils/distortion.py:57: RuntimeWarning: invalid value encountered in true_divide
      vertratios /= np.bincount(self.polys.ravel(), minlength=len(self.ref))
    Generating distortion surface info...
    /home/runner/work/pycortex/pycortex/cortex/polyutils/distortion.py:57: RuntimeWarning: invalid value encountered in true_divide
      vertratios /= np.bincount(self.polys.ravel(), minlength=len(self.ref))
    /opt/hostedtoolcache/Python/3.9.12/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:322: SparseEfficiencyWarning: splu requires CSC matrix format
      warn('splu requires CSC matrix format', SparseEfficiencyWarning)
    /home/runner/work/pycortex/pycortex/cortex/polyutils/distortion.py:57: RuntimeWarning: invalid value encountered in true_divide
      vertratios /= np.bincount(self.polys.ravel(), minlength=len(self.ref))
    /opt/hostedtoolcache/Python/3.9.12/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:322: SparseEfficiencyWarning: splu requires CSC matrix format
      warn('splu requires CSC matrix format', SparseEfficiencyWarning)
    Generating distortion surface info...






|

.. code-block:: default


    import cortex
    from cortex.polyutils import Distortion
    import matplotlib.pyplot as plt

    subject = "S1"

    # First let's load the surface and compute the distortion directly using the
    # Distortion class

    # load fiducial (mid-cortical) surfaces
    # we're ignoring the right hemisphere surface here
    # the polys (triangles) are the same for the fiducial and flat surfaces
    (lfidpts, lpolys), _ = cortex.db.get_surf(subject, "fiducial")

    # load flattened surfaces
    (lflatpts, lpolys), _ = cortex.db.get_surf(subject, "flat")

    # Create the Distortion object
    dist = Distortion(lflatpts, lfidpts, lpolys)

    # Compute areal distortion
    # this returns an array of values for each vertex, which we will put into
    # a Vertex object for plotting
    areal_dist = cortex.Vertex(dist.areal, subject, vmin=-2, vmax=2)
    # areal distortion is in log_2 units (e.g. -1 is half the area, 1 is double)

    cortex.quickshow(areal_dist, with_rois=False, with_labels=False)


    # Next compute metric distortion
    metric_dist = cortex.Vertex(dist.metric, subject, vmin=-2, vmax=2)
    # metric distortion is in mm (e.g. -1 means flatmap edge is 1 mm shorter)

    cortex.quickshow(metric_dist, with_rois=False, with_labels=False)


    # Both of these distortion metrics can also be fetched easily via the pycortex
    # database

    # these also return Vertex objects like those we created above
    areal_dist_2 = cortex.db.get_surfinfo(subject, "distortion", dist_type="areal")
    metric_dist_2 = cortex.db.get_surfinfo(subject, "distortion", dist_type="metric")

    plt.show()

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

   **Total running time of the script:** ( 0 minutes  30.161 seconds)


.. _sphx_glr_download_auto_examples_surface_analyses_plot_flatmap_distortion.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_flatmap_distortion.py <plot_flatmap_distortion.py>`



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

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


.. only:: html

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

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