I am in the process of converting some matlab code to python. I working with a 3d volume h x w x d represented as an numpy array, I am extracting smaller 3d patches from this volume using the function from SO here. So if I have 32x32x32 array and extract 16x16x16 patches I end up with a shape (2, 2, 2, 16, 16, 16) After processing each patch I would like to put it back into shape h x w x d basically reverse window_nd What would be the idiomatic numpy way without looping each dimension? Since I also need to work with 2d and 4d data I would like to avoid creating a function for each dimension.
Combining n dimensional arrays
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Normally, writing back to as_strided views is not advised because it can cause race conditions, but since you only made blocks, this should work:
original_shaped_array = windowed_array.transpose(0,3,1,4,2,5).reshape(32,32,32)
Additionally, if you never copied the windowed array, and do calculations in-place, the data should be changed in the original array - a windowed view is simply a new view into the same data. Don't do this if there is any overlap
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