On Wed, Apr 8, 2020 at 1:17 PM Sebastian Berg
But, backward compatibility aside, could we have ONLY Scalars? Well, it is hard to write functions that work on N-Dimensions (where N can be 0), if the 0-D array does not exist.
So as a (silly) example, the following does not generalize to 0d, even though it should:
def weird_normalize_by_trace_inplace(stacked_matrices) """Devides matrices by their trace but retains sign (works in-place, and thus e.g. not for integer arrays)
Parameters ---------- stacked_matrices : (..., N, M) ndarray """ assert stacked_matrices.shape[-1] == stacked_matrices.shape[-2]
trace = np.trace(stacked_matrices, axis1=-2, axis2=-1) trace[trace < 0] *= -1 stacked_matrices /= trace
Sure that function does not make sense and you could rewrite it, but the fact is that in that function you want to conditionally modify trace in-place, but trace can be 0d and the "conditional" modification breaks down.
I guess that's what I'm getting at -- there is always an endpoint to reducing the rank. a function that's designed to work on a "stack" of something doesn't have to work on a single something, when it can, instead, work on a "stack" of hight one. Isn't the trace of a matrix always a scalar? and thus the trace(s) of a stack of matrixes would always be 1-D? So that function should do something like: stacked_matrixes.shape = (-1, M, M) yes? and then it would always work. Again, backwards compatibility, but there is a reason the np.atleast_*() functions exist -- you often need to make sure your inputs have the dimensionality expected. -CHB -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov