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

.. only:: html

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

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

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

.. _sphx_glr__gallery_compression_pmd_compression.py:


PMD Compression
===============

Simple PMD compression, the hello world of masknmf

.. GENERATED FROM PYTHON SOURCE LINES 7-76



.. image-sg:: /_gallery/compression/images/sphx_glr_pmd_compression_001.webp
   :alt: pmd compression
   :srcset: /_gallery/compression/images/sphx_glr_pmd_compression_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    [25-10-25 22:10:20]: Starting compression
    [25-10-25 22:10:20]: sampled from the following regions: [0]
    [25-10-25 22:10:20]: We are initializing on a total of 2000 frames
    [25-10-25 22:10:20]: Loading data to estimate complete spatial basis
    [25-10-25 22:10:20]: skipping the pruning step for frame cutoff
    [25-10-25 22:10:20]: Finding spatiotemporal roughness thresholds

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    [25-10-25 22:10:26]: Running Blockwise Decompositions
    [25-10-25 22:10:26]: Constructed U matrix. Rank of U is 182
    [25-10-25 22:10:26]: PMD Objected constructed
    /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/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 = true

    import masknmf
    import torch
    import fastplotlib as fpl
    from urllib.request import urlretrieve
    import tifffile


    urlretrieve(
        "https://github.com/flatironinstitute/CaImAn/raw/refs/heads/main/example_movies/demoMovie.tif",
        "./demo.tif"
    )

    # always lazy load raw data by memmaping or other methods
    data = tifffile.imread("./demo.tif")

    block_sizes = [32, 32]
    max_components = 20

    # it's recommended to use masknmf on a machine with a GPU
    # device = "cuda" if torch.cuda.is_available() else "cpu"
    device = "cpu"
    # number of frames used to estimate the spatial basis in PMD
    num_frames_for_spatial_fit = data.shape[0]

    # perform PMD
    pmd_result = masknmf.compression.pmd_decomposition(
        data,
        block_sizes,
        num_frames_for_spatial_fit,
        max_components=max_components,
        device=device,
        frame_batch_size=1024
    )

    # get the residual
    pmd_residual = masknmf.PMDResidualArray(data, pmd_result)

    # view the movies, note that all these array are LAZY evaluated, allowing you to view extremely large datasets!
    iw = fpl.ImageWidget(
        data=[data, pmd_result, pmd_residual],
        names=["raw", "pmd", "residual"],
        figure_kwargs={"size": (1000, 340), "shape": (1, 3)},
        cmap="gnuplot2",
    )

    iw.show()

    # use the time slider or set the frame index programmatically
    iw.current_index = {"t": 1610}

    # manually set vmin-vmax to emphasize noise in raw video
    # you can also adjust the vmin-vmax using the histogram tool
    # reset the vmin-vmax by clicking the buttons under "ImageWidget Controls"
    for image in iw.managed_graphics:
        image.vmax = 3_200

    # remove toolbar to reduce clutter
    for subplot in iw.figure:
        subplot.toolbar = False


    # ignore the remaining lines these are just for docs generation
    figure = iw.figure
    if __name__ == "__main__":
        print(__doc__)
        fpl.loop.run()


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

   **Total running time of the script:** (0 minutes 42.963 seconds)


.. _sphx_glr_download__gallery_compression_pmd_compression.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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