What is the justification for deprecation exactly? These functions have been well documented and have had the intended behavior of producing arrays with dimension at least 1 for some time. Why is it unexpected to produce arrays of at least 1 dimension? For some users this is exactly what is wanted. I don't understand the statement that behavior with 0-d arrays is unexpected.
If the desire is to shrink the API of NumPy, I could see that. But, it seems odd to me to remove a much-used function with an established behavior except as part of a wider API-shrinkage effort. 0-d arrays in NumPy are a separate conversation. At this point, I think it was a mistake not to embrace 0-d arrays in NumPy from day one. In some sense 0-d arrays *are* scalars at least conceptually and for JIT-producing systems that exist now and will be growing in the future, they can be equivalent to scalars. The array scalars should become how you define what is *in* a NumPy array making them true Python types, rather than Python 1-style "instances" of a single "Dtype" object. You would then have 0-d arrays and these Python "memory" types describing what is *in* the array. There is a clear way to do this, some of which has been outlined by Nathaniel, and the rest I have an outline for how to implement. I can advise someone on how to do this. -Travis On Thu, Oct 25, 2018 at 3:17 PM Alex Rogozhnikov <alex.rogozhni...@yandex.ru> wrote: > Dear numpy community, > > I'm planning to depreciate np.asfortranarray and np.ascontiguousarray > functions due to their misbehavior on scalar (0-D tensors) with PR #12244. > > Current behavior (converting scalars to 1-d array with single element) > - is unexpected and contradicts to documentation > - probably, can't be changed without breaking external code > - I believe, this was a cause for poor support of 0-d arrays in mxnet. > - both functions are easily replaced with asarray(..., order='...'), which > has expected behavior > > There is no timeline for removal - we just need to discourage from using > this functions in new code. > > Function naming may be related to how numpy treats 0-d tensors specially, > and those probably should not be called arrays. > https://www.numpy.org/neps/nep-0027-zero-rank-arrarys.html > However, as a user I never thought about 0-d arrays being special and > being "not arrays". > > > Please see original discussion at github for more details > https://github.com/numpy/numpy/issues/5300 > > Your comments welcome, > Alex Rogozhnikov > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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