On Sun, Nov 6, 2016 at 11:44 AM, Charles R Harris

wrote:

Hi All,

For those interested in continuing the __numpy_ufunc__ saga, there is a pull request enabling it https://github.com/numpy/numpy/pull/8247. Likely we will want to make some changes up front before merging that, so some discussion is in order.

As a first order of business, let's decide whether to remove the index and rename `__numpy_ufunc__`. The motivation for this is discussed in issue #5986. https://github.com/numpy/numpy/issues/5986 If we decide positive on that (I'm in favor), I would be happy with the proposed name `__array_ufunc__`, although something more descriptive like `__handle_ufunc__` might be better. This is a wonderful opportunity for bike shedding for those so inclined ;) Chuck

On Mon, Nov 7, 2016 at 9:10 AM, Charles R Harris wrote:

On Sun, Nov 6, 2016 at 11:44 AM, Charles R Harris < charlesr.harris@gmail.com> wrote:

...

Hi All,

For those interested in continuing the __numpy_ufunc__ saga, there is a pull request enabling it https://github.com/numpy/numpy/pull/8247. Likely we will want to make some changes up front before merging that, so some discussion is in order.

As a first order of business, let's decide whether to remove the index and rename `__numpy_ufunc__`. The motivation for this is discussed in issue #5986. https://github.com/numpy/numpy/issues/5986 If we decide positive on that (I'm in favor),

It seems like everyone on that issue is in favor or at least +0. So +1 from me too.

I would be happy with the proposed name `__array_ufunc__`, although something more descriptive like `__handle_ufunc__` might be better.

This is a wonderful opportunity for bike shedding for those so inclined ;)

pass :) Ralf

On Mon, Nov 7, 2016 at 1:08 AM, Ralf Gommers wrote:

On Mon, Nov 7, 2016 at 9:10 AM, Charles R Harris < charlesr.harris@gmail.com> wrote:

...

On Sun, Nov 6, 2016 at 11:44 AM, Charles R Harris < charlesr.harris@gmail.com> wrote:

...

Hi All,

For those interested in continuing the __numpy_ufunc__ saga, there is a pull request enabling it https://github.com/numpy/numpy/pull/8247. Likely we will want to make some changes up front before merging that, so some discussion is in order.

As a first order of business, let's decide whether to remove the index and rename `__numpy_ufunc__`. The motivation for this is discussed in issue #5986. https://github.com/numpy/numpy/issues/5986 If we decide positive on that (I'm in favor),

It seems like everyone on that issue is in favor or at least +0. So +1 from me too.

...

I would be happy with the proposed name `__array_ufunc__`, although something more descriptive like `__handle_ufunc__` might be better.

...

This is a wonderful opportunity for bike shedding for those so inclined ;)

Let me try to break things down an bit. *Uncontroversial* - Rename __numpy_ufunc__ to __array_ufunc__ - Remove index - Out is always a tuple I think this much is useful even if nothing else is done. *Goals* - Deprecate __array_priority__ - Ufuncs should succeed or error, never return NotImplemented - Add __array_ufunc__ stub to ndarray. I don't think these are controversial either, but they are longer term except possibly the last. Note that never returning NotImplemented disentangles ufuncs from ndarray binops, which I think is a good thing. *Binops* Here we come to the crux of the last arguments. The functions used for binops can currently be set dynamically, the method that is currently used to set them when the ufunc module is loaded. I think we want to do away with that at some point and fix a set of ufuncs with which they are implemented. This allows folks to overide the binop behavior using __array_ufunc__. I think that is mostly of interest to people who are subclassing ndarray and with that restriction doesn't bother me except that it entangles ufuncs with binops. However, what I'd like to see as well is an opt out for objects that don't care about ufuncs, but want to override the python numeric operators, something simple like `__me_me_me__`, or, more seriously, `__array_opt_out__` that will only come into play if the defining object is on the right hand side of an instance of ndarray. In that case the binop would return NotImplemented so as to defer to the Python machinery. Note that __array_priority__ is currently (ab)used for this. *Numpy scalars* Numpy scalars default to the corresponding PyArray_Type or PyGenericArrType_Type unless both arguments can be converted to the same c type as the calling scalar, so I don't think there is a problem there. Note that they do check _array_priority_ before trying to convert unknown objects to array scalars in a fallback case. Chuck

On Wed, Feb 22, 2017 at 6:31 AM, Marten van Kerkwijk < m.h.vankerkwijk@gmail.com> wrote:

It seems to me entirely logical (but then it would, I suggested it before...) that we allow opting out by setting `__array_ufunc__` to None; in that case, binops return NotImplemented and ufuncs raise errors. (In addtion, or alternatively, one could allow setting `__array__` to None, which would generally disable something to be turned into an array object).

This is indeed appealing, but I recall this was still a point of contention because it leaves intact two different ways to override arithmetic involving numpy arrays. Mimicking all this logic on classes designed to wrap well-behaved array-like classes (e.g., xarray, which can wrap NumPy or Dask arrays) could be painful -- it's easier to just call np.add and let it handle all the dispatching rather than also worrying about NotImplemented. That said, I think the opt-out is probably OK, as long we make it clear that defining __array_ufunc__ to do arithmetic overriding is the preferred solution (and provide appropriate Mixin classes to make this easier). Just to be clear: if __array__ = None but __array_ufunc__ is defined, this would be a class that defines array-like operations but can't be directly converted into a NumPy array? For example, a scipy.sparse matrix?