.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_gallery/image_volume/image_volume_4d.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download__gallery_image_volume_image_volume_4d.py>`
        to download the full example code.

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

.. _sphx_glr__gallery_image_volume_image_volume_4d.py:


Volume movie
============

View 4D data of a volume over time by updating the volume data.

.. GENERATED FROM PYTHON SOURCE LINES 7-115



.. image-sg:: /_gallery/image_volume/images/sphx_glr_image_volume_4d_001.webp
   :alt: image volume 4d
   :srcset: /_gallery/image_volume/images/sphx_glr_image_volume_4d_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    gaussing filtering
    finishing
    /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:400: RuntimeWarning: divide by zero encountered in divide
      screen_full = (ndc_full[:, :2] / ndc_full[:, 3:4]) * half_canvas_size
    /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:400: RuntimeWarning: invalid value encountered in divide
      screen_full = (ndc_full[:, :2] / ndc_full[:, 3:4]) * half_canvas_size
    /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:412: RuntimeWarning: invalid value encountered in divide
      screen_sel = (ndc_sel[:, :2] / ndc_sel[:, 3:4]) * half_canvas_size
    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/features/_base.py:18: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = false

    import numpy as np
    from scipy.ndimage import gaussian_filter
    import fastplotlib as fpl


    def generate_data(
        p=1,
        noise=0.5,
        T=128,
        framerate=10,
        firerate=2.0,
    ):
        gamma = np.array([0.9])
        dims = (128, 128, 30)  # size of image
        sig = (4, 4, 2)  # neurons size
        bkgrd = 10
        N = 150  # number of neurons
        np.random.seed(0)
        centers = np.asarray(
            [[np.random.randint(s, x - s) for x, s in zip(dims, sig)] for i in range(N)]
        )
        Y = np.zeros((T,) + dims, dtype=np.float32)
        trueSpikes = np.random.rand(N, T) < firerate / float(framerate)
        trueSpikes[:, 0] = 0
        truth = trueSpikes.astype(np.float32)
        for i in range(2, T):
            if p == 2:
                truth[:, i] += gamma[0] * truth[:, i - 1] + gamma[1] * truth[:, i - 2]
            else:
                truth[:, i] += gamma[0] * truth[:, i - 1]
        for i in range(N):
            Y[:, centers[i, 0], centers[i, 1], centers[i, 2]] = truth[i]
        tmp = np.zeros(dims)
        tmp[tuple(np.array(dims) // 2)] = 1.0
        print("gaussing filtering")
        z = np.linalg.norm(gaussian_filter(tmp, sig).ravel())

        print("finishing")
        Y = (
            bkgrd
            + noise * np.random.randn(*Y.shape)
            + 10 * gaussian_filter(Y, (0,) + sig) / z
        )

        return Y


    voldata = generate_data()

    figure = fpl.Figure(cameras="3d", controller_types="orbit", size=(700, 560))

    volume = figure[0, 0].add_image_volume(
        voldata[0],
        vmin=10,
        vmax=15,
        cmap="gnuplot2",
        alpha_mode="add",
    )

    hlut = fpl.HistogramLUTTool(voldata, volume)

    figure[0, 0].docks["right"].size = 100
    figure[0, 0].docks["right"].controller.enabled = False
    figure[0, 0].docks["right"].add_graphic(hlut)
    figure[0, 0].docks["right"].auto_scale(maintain_aspect=False)

    figure.show()

    # load a pre-saved camera state
    state = {
        "position": np.array([-70, 90, 150]),
        "rotation": np.array([-0.09210227, -0.47460177, -0.05001713, 0.87393857]),
        "scale": np.array([1.0, 1.0, 1.0]),
        "reference_up": np.array([0.0, 1.0, 0.0]),
        "fov": 50.0,
        "width": 27.605629518746266,
        "height": 117.78401927998402,
        "depth": 183.4884192530962,
        "zoom": 0.75,
        "maintain_aspect": True,
        "depth_range": None,
    }

    figure[0, 0].camera.set_state(state)


    i = 0
    def update():
        global i

        volume.data = voldata[i]

        i += 1
        if i == voldata.shape[0]:
            i = 0


    figure.add_animations(update)


    # NOTE: fpl.loop.run() should not be used for interactive sessions
    # See the "JupyterLab and IPython" section in the user guide
    if __name__ == "__main__":
        print(__doc__)
        fpl.loop.run()


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

   **Total running time of the script:** (0 minutes 21.058 seconds)


.. _sphx_glr_download__gallery_image_volume_image_volume_4d.py:

.. only:: html

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

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

      :download:`Download Python source code: image_volume_4d.py <image_volume_4d.py>`

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

      :download:`Download zipped: image_volume_4d.zip <image_volume_4d.zip>`


.. only:: html

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

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