> If the desire is to shrink the API of NumPy, I could see that.

Very good desire, but my goal was different.

> For some users this is exactly what is wanted.

Maybe so, but I didn't face such example (and nobody mentioned those so far in the discussion).

The opposite (according to the issue) happened. Mxnet example is sufficient in my opinion.

assert(x.flags.c_contiguous)

assert(np.ascontiguousarray(x).shape == x.shape)

Behavior contradicts to documentation (shape is changed) and to name (flags are saying - it is already c_contiguous)

If you insist, that keeping ndmin=1 is important (I am not yet convinced, but I am ready to believe your autority),

we can add ndmin=1 to functions' signatures, this way explicitly notifying users about expected dimension.

27.10.2018, 02:27, "Travis Oliphant" <teoliphant@gmail.com>:

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@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