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

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

.. _sphx_glr_auto_examples_timeseries_rgb_timeseries.py:


=============================
Timeseries of RGB Vertex Data
=============================

An RGB dataset carries three timecourses per vertex, one per color channel.
With the timeseries panel open (Open Controls -> movie -> timeseries), click
a vertex and the panel plots all three channels, each with its own checkbox
and color picker, above a strip showing the blended color over time - the
color actually painted on the brain at each volume.

The data is synthetic, built for the example subject(S1). Three patches on
the cortical surface pulse in the red, green and blue channels at different
rates, so each patch blinks in its own color and clicking inside one shows
which channel drives it.

.. GENERATED FROM PYTHON SOURCE LINES 17-51

.. code-block:: Python


    import numpy as np
    import cortex

    np.random.seed(42)
    subject = "S1"
    n_volumes = 120
    t = np.arange(n_volumes)

    (lpts, _), (rpts, _) = cortex.db.get_surf(subject, "wm")
    pts = np.vstack([lpts, rpts])
    n_vertices = len(pts)

    def patch(center_vertex, radius_mm=18.0):
        """Gaussian surface patch (0-1) around one vertex, by 3D distance."""
        d2 = ((pts - pts[center_vertex]) ** 2).sum(1)
        return np.exp(-d2 / (2 * radius_mm ** 2)).astype("float32")

    centers = np.random.choice(n_vertices, 3, replace=False)
    rates = [8.0, 12.0, 4.0]                      # pulse periods
    channels = []
    for center, period in zip(centers, rates):
        pulse = 0.5 + 0.5 * np.sin(2 * np.pi * t / period)          # (t,)
        signal = pulse[:, None] * patch(center)[None, :]            # (t, v)
        noise = 0.08 * np.random.rand(n_volumes, n_vertices)
        channels.append((np.clip(signal + noise, 0, 1) * 255).astype(np.uint8))
    red, green, blue = channels
    alpha = np.ones((n_volumes, n_vertices), dtype="float32")

    rgb = cortex.VertexRGB(red, green, blue, subject, alpha=alpha)

    cortex.webshow({"rgb": rgb})
    # Like the other WebGL examples, run this from ipython/jupyter or with
    # `python -i` so the interpreter (and the viewer's server) stays alive.


.. _sphx_glr_download_auto_examples_timeseries_rgb_timeseries.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: rgb_timeseries.zip <rgb_timeseries.zip>`


.. only:: html

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

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