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Currently, we only produce filters for the spatial frequencies specified by the dictionary returned by the frequency preference function. If your frequencies are very low (e.g., 0.2 or 0.4), the convolutions can take a very long time (several hours, as opposed to several minutes) to run. In order to make this more reasonable, we probably want to do something like shrinking the image first (losing the high frequency signal, but a lowpass filter doesn't care about that anyway) and then applying the filter, so there are fewer points to convolute it with. There are probably efficient implementations of this in python somewhere.
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