On Sat, 2018-10-27 at 19:29 +1300, Ralf Gommers wrote:
On Sat, Oct 27, 2018 at 6:37 PM Eric Wieser < wieser.eric+numpy@gmail.com> wrote:
in order to be used prior to calling C or Fortran code that expected at least a 1-d array
<snip>
I'm a big supporter of making a NumPy 2.0 and have been for several years. Now that Python 3 transition has happened, I think we could seriously discuss this.
I think it's more helpful to discuss goals and concrete plans for those, rather than a "NumPy 2.0" label. The latter never worked in the past, and not just because of lack of time/funding - it just means different things to different people. We now have a good start on what our major goals are (http://www.numpy.org/neps/#roadmap), let's build on that.
I agree. I do think that we should not be scared of a major release. But, I would rather see it as a step towards, for example, better dtypes. Aiming for a large cleanup seems like it might be a can of worms [0]. About the asfortranarray/ascontiguousarray thing. I am not sure I like FutureWarnings in the edge cases, it seems likely they arise randomly on functions where the devs may not even be aware of it. I do not like spamming the the API, but if we cannot agree on a nice way forward, maybe this is a point where creating new names is an options: * ascorderarray/asforderarray * asccontiguousarray/asfcontigouousarray * np.asarray(..., order='C'), is somewhat the same I guess not sure I like the names too much, but I think we could find new names here. And then putting warnings is IMO OK, if there is a an easy/nice enough way to avoid them (sure we can start in documentation if it helps). We can wait for the actual removal for very long and at least until the next major release or so, I do not think it matters much as long as visible deprecation warnings exist to push downstream into changing habits/code, the maintenance burden is pretty much zero after all. Discussing how to approach larger changes is important, but I doubt that these particular functions are problematic enough! - Sebastian [0] Happy to be shown wrong, but I seriously fear that aiming too high will hinder progress -- unless maybe there is some very good funding and skilled devs, but even then it might be too ambitious? -- and I am not even sure it is easier on downstream.
I'm trying to raise funding for maintenance and progress for NumPy and SciPy right now via Quansight Labs http://www.quansight.com/labs and I hope to be able to help find grants to support the wonderful efforts that have been happening for some time.
The NumPy grant and having Tyler/Matti/Stefan at BIDS is a great start to funded development; more and more diverse funding sources would be awesome.
Yes, that is very cool news! - Sebastian
Cheers, Ralf
While I'm thrilled and impressed by the number of amazing devs who have kept NumPy and SciPy going in mostly their spare time, it has created challenges that we have not had continuous maintenance funding to allow continuous paid development so that several people who know about the early decisions could not be retained to spend time on helping the transition.
Your bringing the problem of mxnet devs is most appreciated. I will make a documentation PR.
-Travis
Alex.
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
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