.. only:: html

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

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

    .. _sphx_glr_auto_examples_datasets_plot_volumeRGB.py:


====================
Plot RGB Volume Data
====================

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

The cortex.VolumeRGB object is instantiated with three cortex.Volume objects,
one for each of the three color channels. Ideally, the data in the Volume
objects will be scaled to be between 0-255 and be np.uint8 type. The objects
can also be 1D numpy arrays of equal sizes, in which case the subject and
transform will need to be given as arguments.

Here, two datasets are generated to look like gradients across the brain and
a third dataset makes a series of stripes across the brain.

By default, VolumeRGB maps each data channels on to the red, green, and blue
channels in RGB color space. You can also specify custom colors for each data
channel, and the colors will be linearly combined. Custom colors can be specified
by the `channel1color`, `channel2color`, and `channel2color` arguments, using
3-ples of uint8 to specify RGB colors for each data channel. `cortex.Colors` provides
a set of named colors that can be used.

Also by default, each data channel is normalized separately on to its respective
color. Doing so is not necessarily good for quantitative comparisons in cases in
which the range of values in each data channel differs. The argument `shared_range`,
if set to `True`, will force all three data channels to be on the same scale for
equitable comparisons. When `shared_range` is true, the arguments `shared_vmin` and
shared_vmax` will allow you to specify minimum and maximum values manually.



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


    *

      .. image:: /auto_examples/datasets/images/sphx_glr_plot_volumeRGB_001.png
          :alt: plot volumeRGB
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/datasets/images/sphx_glr_plot_volumeRGB_002.png
          :alt: plot volumeRGB
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/datasets/images/sphx_glr_plot_volumeRGB_003.png
          :alt: plot volumeRGB
          :class: sphx-glr-multi-img






.. code-block:: default


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

    subject = "S1"
    xfm = "fullhead"

    # Creating three test datasets that are the same shape as this transform with
    # one entry for this voxel
    # The first two are gradients going in different directions across the brain
    # and the third is stripes across certain slices of the brain
    test1 = np.arange(31. * 100 * 100).reshape((31, 100, 100), order='C')
    test2 = np.arange(31. * 100 * 100).reshape((31, 100, 100), order='F')
    test3 = np.zeros((31, 100, 100))
    test3[::3, :, :] = 1

    # Scaling the three datasets to be between 0-255
    test1_scaled = test1 / np.max(test1) * 255
    test2_scaled = test2 / np.max(test2) * 255
    test3_scaled = test3 / np.max(test3) * 255

    # Creating three cortex.Volume objects with the test data as np.uint8
    red = cortex.Volume(test1_scaled.astype(np.uint8), 'S1', 'fullhead')
    green = cortex.Volume(test2_scaled.astype(np.uint8), 'S1', 'fullhead')
    blue = cortex.Volume(test3_scaled.astype(np.uint8), 'S1', 'fullhead')

    # This creates an RGB Volume from the three different color channels for
    # this subject using the default RGB mappings
    # Note that you do not need to specify the transform when creating this as it
    # is already specified in the red, green, and blue channels
    vol_data = cortex.VolumeRGB(red, green, blue, subject)
    cortex.quickshow(vol_data, with_colorbar=False)
    plt.show()

    # This creates an RGB Volume from the three different color channels for
    # this subject using custom colors.
    # Note that you do not need to specify the transform when creating this as it
    # is already specified in the red, green, and blue channels
    vol_data = cortex.VolumeRGB(red, green, blue, subject,
    							channel1color=cortex.Colors.RoseRed,
    							channel2color=cortex.Colors.LimeGreen,
    							channel3color=cortex.Colors.DodgerBlue)
    cortex.quickshow(vol_data, with_colorbar=False)
    plt.show()

    # This creates an RGB Volume from the three different color channels for
    # this subject using custom colors, and shared value ranges.
    # Note that you do not need to specify the transform when creating this as it
    # is already specified in the red, green, and blue channels
    vol_data = cortex.VolumeRGB(red, green, blue, subject,
    							channel1color=cortex.Colors.RoseRed,
    							channel2color=cortex.Colors.LimeGreen,
    							channel3color=cortex.Colors.DodgerBlue,
    							shared_range=True, shared_vmin=10, shared_vmax=100)
    cortex.quickshow(vol_data, with_colorbar=False)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  20.196 seconds)


.. _sphx_glr_download_auto_examples_datasets_plot_volumeRGB.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_volumeRGB.py <plot_volumeRGB.py>`



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

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


.. only:: html

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

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