[Numpy-discussion] New DTypes: Are scalars a central concept in NumPy or not?
chris.barker at noaa.gov
Wed Apr 8 18:45:58 EDT 2020
On Wed, Apr 8, 2020 at 1:17 PM Sebastian Berg <sebastian at sipsolutions.net>
> > > > 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)
> 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)
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
Christopher Barker, Ph.D.
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Chris.Barker at noaa.gov
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