[Numpy-discussion] Depreciating asfortranarray and ascontiguousarray

[Travis Oliphant]

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.rogozhnikov at 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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