I spent the day refactoring the code I wrote (months ago) on geometric (scalar, vector, and tensor) convolution filters for convolutions on geometric (scalar, vector, and tensor) images. I refactored so that all kinds of geometric objects can be operated on transparently. The good thing is that we have provable tests that test our code for correctness (yay properties of groups!), but the bad thing is that the operations aren't trivial to implement correctly for arbitrary dimensions. I learned a lot about numpy.einsum().
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