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

.. only:: html

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

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

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

.. _sphx_glr__gallery_selection_tools_fft.py:


Explore fourier transform of images
===================================
Example showing how to use a `RectangleSelector` to interactively reconstruct
an image using portions of it fourier transform

.. GENERATED FROM PYTHON SOURCE LINES 7-101



.. image-sg:: /_gallery/selection_tools/images/sphx_glr_fft_001.webp
   :alt: fft
   :srcset: /_gallery/selection_tools/images/sphx_glr_fft_001.webp
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_image.py:140: ComplexWarning: Casting complex values to real discards the imaginary part
      self.value[key] = value







|

.. code-block:: Python


    # test_example = false

    import numpy as np
    import fastplotlib as fpl
    import imageio.v3 as iio

    image = iio.imread("imageio:camera.png")

    # compute discrete fourier transform of image
    img_fft = np.fft.fftshift(np.fft.fft2(image))

    # image to just visualize absolute magnitudes
    log_abs_img_fft = np.log(np.abs(img_fft + 1))

    # placeholders for displaying fft and inverse fft of selections
    zeros = np.zeros(image.shape)

    # create an ImageWidget to display all the images
    iw = fpl.ImageWidget(
        data=[image, log_abs_img_fft, zeros, zeros, zeros, zeros],
        names=["image", "DFT", "selected", "FFT of selected", "not-selected", "IFFT of not-selected"],
        figure_shape=(3, 2),  # so we can see image and fft side by side
        figure_kwargs={"size": (700, 1024)},
        histogram_widget=False,
    )

    # we don't need the toolbars here, unclutter the figure
    for subplot in iw.figure:
        subplot.toolbar = False

    # viridis cmap for the fft images
    iw.cmap = "viridis"

    # gray for the non-fft images
    iw.managed_graphics[0].cmap = "gray"
    iw.managed_graphics[3].cmap = "gray"
    iw.managed_graphics[-1].cmap = "gray"

    # set contrast limits based on the full DFT for the DFT-selection images
    iw.figure["selected"].graphics[0].vmin, iw.figure["selected"].graphics[0].vmax = log_abs_img_fft.min(), log_abs_img_fft.max()
    iw.figure["not-selected"].graphics[0].vmin, iw.figure["not-selected"].graphics[0].vmax = log_abs_img_fft.min(), log_abs_img_fft.max()

    iw.show()

    # create a rectangle selector
    rs = iw.managed_graphics[1].add_rectangle_selector(edge_color="w", edge_thickness=2.0)


    @rs.add_event_handler("selection")
    def update_images(ev):
        """
        Updates the images when the selection changes
        """

        # get the bbox of the selection
        row_ixs, col_ixs = ev.get_selected_indices()
        row_slice = slice(row_ixs[0], row_ixs[-1] + 1)
        col_slice = slice(col_ixs[0], col_ixs[-1] + 1)

        # fft of the selection
        selected_fft = np.zeros(image.shape, dtype=np.complex64)
        selected_fft[row_slice, col_slice] = img_fft[row_slice, col_slice]

        # update image graphic with the current fft selection
        iw.managed_graphics[2].data = np.log(np.abs(selected_fft + 1))

        # inverse fft to reconstruct image using only the selection
        iw.managed_graphics[3].data = np.fft.ifft2(np.fft.fftshift(selected_fft))
        iw.managed_graphics[3].reset_vmin_vmax()

        # fft of the region outside the selection
        unselected_fft = img_fft.copy()
        unselected_fft[row_slice, col_slice] = 0

        # update image graphic with unselected fft area
        iw.managed_graphics[4].data = np.log(np.abs(unselected_fft + 1))

        # inverse fft to reconstruct image using only the unselected part of the fft
        iw.managed_graphics[5].data = np.fft.ifft2(np.fft.fftshift(unselected_fft))
        iw.managed_graphics[5].reset_vmin_vmax()


    # set initial selection to the center
    rs.selection = (225, 285, 225, 285)


    figure = iw.figure

    # NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
    # please see our docs for using fastplotlib interactively in ipython and jupyter
    if __name__ == "__main__":
        print(__doc__)
        fpl.loop.run()


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

   **Total running time of the script:** (0 minutes 1.455 seconds)


.. _sphx_glr_download__gallery_selection_tools_fft.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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