On Wed, Apr 8, 2020 at 1:17 PM Sebastian Berg <sebast...@sipsolutions.net> wrote:
> > > > 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.bar...@noaa.gov
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