For what it's worth, these are fairly widely used functions. From a user standpoint, I'd gently argue against deprecating them. Documenting the inconsistency with scalars seems like a less invasive approach.
In particular ascontiguousarray is a very common check to make when working with C libraries or low-level file formats. A significant advantage over asarray(..., order='C') is readability. It makes the intention very clear. Similarly, asfortranarray is quite readable for folks that aren't deeply familiar with numpy. Given that the use-cases they're primarily used for are likely to be read by developers working in other languages (i.e. ascontiguousarray gets used at a lot of "boundaries" with other systems), keeping function names that make intention very clear is important. Just my $0.02, anyway. Cheers, -Joe 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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