fastplotlib
diff --git a/‎examples/selection_tools/fft.py
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+"""
+Explore fourier transform of images
+===================================
+Example showing how to use a `RectangleSelector` to interactively reconstruct
+an image using portions of it fourier transform
+"""
+
+# test_example = false
+# sphinx_gallery_pygfx_docs = 'screenshot'
+
+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()