![](https://secure.gravatar.com/avatar/342bd0a61c7081db529c856d3bcd9545.jpg?s=120&d=mm&r=g)
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:
![](https://secure.gravatar.com/avatar/03f2d50ce2e8d713af6058d2aeafab74.jpg?s=120&d=mm&r=g)
On Thu, Oct 25, 2018 at 11:48 PM Joseph Fox-Rabinovitz <jfoxrabinovitz@gmail.com> wrote:
In that vein, would it be advisable to re-implement them as aliases for the correctly behaving functions instead?
- Joe
Wouldn't "probably, can't be changed without breaking external code" still apply? As I understand the suggestion for _deprecation_ is only because there's (a lot of) code relying on the current behaviour (or at least there's risk). András
![](https://secure.gravatar.com/avatar/93a76a800ef6c5919baa8ba91120ee98.jpg?s=120&d=mm&r=g)
On Thu, Oct 25, 2018 at 3:10 PM Andras Deak <deak.andris@gmail.com> wrote:
I would also advocate for fixing these functions if possible (removing ndim=1). ascontiguousarray(...) is certainly more readable than asarray(... order='C'). The conservative way to handle this would be to do a deprecation cycle, specifically by issuing FutureWarning when scalars or 0d arrays are encountered as inputs. Cheers, Stephan
![](https://secure.gravatar.com/avatar/b4f6d4f8b501cb05fd054944a166a121.jpg?s=120&d=mm&r=g)
On Fri, 2018-10-26 at 13:25 -0700, Stephan Hoyer wrote:
Is there a way to avoid the future warning? An unavoidable warning in a widely used function seems really annoying to me. Unless, the 0d thing happens rarely, but then it might be the downstream users that get the warning for no reason. - Sebastian
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
I see now the original motivation as the unfortunate situation that mxnet authors did not understand that np.ascontiguousarray returned an array of at least one dimension and perhaps used that one API to assume that NumPy did not support 0-d arrays --- which NumPy does indeed support. Certainly that situation would motivate a documentation change to help steer other future users from making the same incorrect assumption, but deprecation is a separate question entirely. I do not agree at all with the trend to remove functions from NumPy API prior to a dedicated NumPy 2.0 effort. This breaks the idea of semantic versioning for NumPy. These functions do, in fact, have a use and were very much intended to produce one-dimensional arrays --- in order to be used prior to calling C or Fortran code that expected at least a 1-d array. A lot of the SciPy wrapping code needed this behavior. It is a misinterpretation to assume this is buggy or unintended. Improving the documentation to warn about the behavior for 0-d arrays could indeed be useful. -Travis On Fri, Oct 26, 2018 at 6:27 PM Stephan Hoyer <shoyer@gmail.com> wrote:
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
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:
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
On Fri, Oct 26, 2018 at 7:14 PM Alex Rogozhnikov <alex.rogozhnikov@yandex.ru> wrote:
If the desire is to shrink the API of NumPy, I could see that.
Very good desire, but my goal was different.
I agree that the old motivation of APIs that would make it easy to create SciPy is no longer a major motivation for most users and even developers and so these reasons would not be very present (as well as why it wasn't even mentioned in the documentation).
I understand the lack of being convinced. This is ultimately a problem of 0-d arrays not being fully embraced and accepted by the Numeric community originally (which NumPy inherited during the early days). Is there a way to document functions that will be removed on a major version increase which don't print warnings on use? I would support this. 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'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. 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
![](https://secure.gravatar.com/avatar/209654202cde8ec709dee0a4d23c717d.jpg?s=120&d=mm&r=g)
in order to be used prior to calling C or Fortran code that expected at least a 1-d array I’d argue that the behavior for these functions should have just been to raise an error saying “this function does not support 0d arrays”, rather than silently inserting extra dimensions. As a bonus, that would push the function developers to add support for 0d. Obviously we can’t make it do that now, but what we can do is have it emit a warning in those cases. I think our options are: 1. Deprecate the entire function 2. Deprecate and eventually(?) throw an error upon calling the function on 0d arrays, with a message like *“in future using ascontiguousarray to promote 0d arrays to 1d arrays will not be supported. If promotion is intentional, use ascontiguousarray(atleast1d(x)) to silence this warning and keep the old behavior, and if not use asarray(x, order='C') to preserve 0d arrays”* 3. Deprecate (future-warning) when passed 0d arrays, and eventually skip the upcast to 1d. If the calling code really needed a 1d array, then it will probably fail, which is not really different to 2, but has the advantage that the names are less surprising. 4. Only improve the documentation My preference would be 3 Eric On Fri, 26 Oct 2018 at 17:35 Travis Oliphant <teoliphant@gmail.com> wrote: On Fri, Oct 26, 2018 at 7:14 PM Alex Rogozhnikov <alex.rogozhnikov@yandex.ru>
![](https://secure.gravatar.com/avatar/5f88830d19f9c83e2ddfd913496c5025.jpg?s=120&d=mm&r=g)
On Sat, Oct 27, 2018 at 6:37 PM Eric Wieser <wieser.eric+numpy@gmail.com> wrote:
I'd go for 4, or alternatively for the warning in 2 (which can be left in place indefinitely). 1 is unwarranted, and 3 will change behavior which is worse than just warning or stopping to support existing behavior (= 2). Eric
No, there's no such thing at the moment - the closest thing is https://github.com/numpy/numpy/wiki/Backwards-incompatible-ideas-for-a-major.... I doubt we want such a thing anyway - removing functions without deprecation warnings first doesn't seem quite right.
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'm trying to raise funding for maintenance and progress for NumPy and
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. Cheers, Ralf
![](https://secure.gravatar.com/avatar/b4f6d4f8b501cb05fd054944a166a121.jpg?s=120&d=mm&r=g)
On Sat, 2018-10-27 at 19:29 +1300, Ralf Gommers wrote:
<snip>
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.
Yes, that is very cool news! - Sebastian
![](https://secure.gravatar.com/avatar/342bd0a61c7081db529c856d3bcd9545.jpg?s=120&d=mm&r=g)
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:
![](https://secure.gravatar.com/avatar/03f2d50ce2e8d713af6058d2aeafab74.jpg?s=120&d=mm&r=g)
On Thu, Oct 25, 2018 at 11:48 PM Joseph Fox-Rabinovitz <jfoxrabinovitz@gmail.com> wrote:
In that vein, would it be advisable to re-implement them as aliases for the correctly behaving functions instead?
- Joe
Wouldn't "probably, can't be changed without breaking external code" still apply? As I understand the suggestion for _deprecation_ is only because there's (a lot of) code relying on the current behaviour (or at least there's risk). András
![](https://secure.gravatar.com/avatar/93a76a800ef6c5919baa8ba91120ee98.jpg?s=120&d=mm&r=g)
On Thu, Oct 25, 2018 at 3:10 PM Andras Deak <deak.andris@gmail.com> wrote:
I would also advocate for fixing these functions if possible (removing ndim=1). ascontiguousarray(...) is certainly more readable than asarray(... order='C'). The conservative way to handle this would be to do a deprecation cycle, specifically by issuing FutureWarning when scalars or 0d arrays are encountered as inputs. Cheers, Stephan
![](https://secure.gravatar.com/avatar/b4f6d4f8b501cb05fd054944a166a121.jpg?s=120&d=mm&r=g)
On Fri, 2018-10-26 at 13:25 -0700, Stephan Hoyer wrote:
Is there a way to avoid the future warning? An unavoidable warning in a widely used function seems really annoying to me. Unless, the 0d thing happens rarely, but then it might be the downstream users that get the warning for no reason. - Sebastian
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
I see now the original motivation as the unfortunate situation that mxnet authors did not understand that np.ascontiguousarray returned an array of at least one dimension and perhaps used that one API to assume that NumPy did not support 0-d arrays --- which NumPy does indeed support. Certainly that situation would motivate a documentation change to help steer other future users from making the same incorrect assumption, but deprecation is a separate question entirely. I do not agree at all with the trend to remove functions from NumPy API prior to a dedicated NumPy 2.0 effort. This breaks the idea of semantic versioning for NumPy. These functions do, in fact, have a use and were very much intended to produce one-dimensional arrays --- in order to be used prior to calling C or Fortran code that expected at least a 1-d array. A lot of the SciPy wrapping code needed this behavior. It is a misinterpretation to assume this is buggy or unintended. Improving the documentation to warn about the behavior for 0-d arrays could indeed be useful. -Travis On Fri, Oct 26, 2018 at 6:27 PM Stephan Hoyer <shoyer@gmail.com> wrote:
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
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:
![](https://secure.gravatar.com/avatar/08bcb178fb791b7c8a5e9ad8d7121bed.jpg?s=120&d=mm&r=g)
On Fri, Oct 26, 2018 at 7:14 PM Alex Rogozhnikov <alex.rogozhnikov@yandex.ru> wrote:
If the desire is to shrink the API of NumPy, I could see that.
Very good desire, but my goal was different.
I agree that the old motivation of APIs that would make it easy to create SciPy is no longer a major motivation for most users and even developers and so these reasons would not be very present (as well as why it wasn't even mentioned in the documentation).
I understand the lack of being convinced. This is ultimately a problem of 0-d arrays not being fully embraced and accepted by the Numeric community originally (which NumPy inherited during the early days). Is there a way to document functions that will be removed on a major version increase which don't print warnings on use? I would support this. 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'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. 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
![](https://secure.gravatar.com/avatar/209654202cde8ec709dee0a4d23c717d.jpg?s=120&d=mm&r=g)
in order to be used prior to calling C or Fortran code that expected at least a 1-d array I’d argue that the behavior for these functions should have just been to raise an error saying “this function does not support 0d arrays”, rather than silently inserting extra dimensions. As a bonus, that would push the function developers to add support for 0d. Obviously we can’t make it do that now, but what we can do is have it emit a warning in those cases. I think our options are: 1. Deprecate the entire function 2. Deprecate and eventually(?) throw an error upon calling the function on 0d arrays, with a message like *“in future using ascontiguousarray to promote 0d arrays to 1d arrays will not be supported. If promotion is intentional, use ascontiguousarray(atleast1d(x)) to silence this warning and keep the old behavior, and if not use asarray(x, order='C') to preserve 0d arrays”* 3. Deprecate (future-warning) when passed 0d arrays, and eventually skip the upcast to 1d. If the calling code really needed a 1d array, then it will probably fail, which is not really different to 2, but has the advantage that the names are less surprising. 4. Only improve the documentation My preference would be 3 Eric On Fri, 26 Oct 2018 at 17:35 Travis Oliphant <teoliphant@gmail.com> wrote: On Fri, Oct 26, 2018 at 7:14 PM Alex Rogozhnikov <alex.rogozhnikov@yandex.ru>
![](https://secure.gravatar.com/avatar/5f88830d19f9c83e2ddfd913496c5025.jpg?s=120&d=mm&r=g)
On Sat, Oct 27, 2018 at 6:37 PM Eric Wieser <wieser.eric+numpy@gmail.com> wrote:
I'd go for 4, or alternatively for the warning in 2 (which can be left in place indefinitely). 1 is unwarranted, and 3 will change behavior which is worse than just warning or stopping to support existing behavior (= 2). Eric
No, there's no such thing at the moment - the closest thing is https://github.com/numpy/numpy/wiki/Backwards-incompatible-ideas-for-a-major.... I doubt we want such a thing anyway - removing functions without deprecation warnings first doesn't seem quite right.
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'm trying to raise funding for maintenance and progress for NumPy and
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. Cheers, Ralf
![](https://secure.gravatar.com/avatar/b4f6d4f8b501cb05fd054944a166a121.jpg?s=120&d=mm&r=g)
On Sat, 2018-10-27 at 19:29 +1300, Ralf Gommers wrote:
<snip>
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.
Yes, that is very cool news! - Sebastian
participants (11)
-
Alex Rogozhnikov
-
Andras Deak
-
Eric Wieser
-
Joe Kington
-
Joseph Fox-Rabinovitz
-
Ralf Gommers
-
Sebastian Berg
-
Stefan van der Walt
-
Stephan Hoyer
-
Sylvain Corlay
-
Travis Oliphant