In that vein, would it be advisable to re-implement them as aliases for the correctly behaving functions instead? - Joe On Thu, Oct 25, 2018 at 5:01 PM Joe Kington <joferkington@gmail.com> wrote:
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.rogozhnikov@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
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