[Numpy-discussion] Revised NEP-18, __array_function__ protocol

Marten van Kerkwijk m.h.vankerkwijk at gmail.com
Sat Jun 30 12:52:19 EDT 2018

Hi Hameer,

I think the override on `dtype` would work - after all, the override is
checked before anything is done, so one can just pass in `self` if one
wishes (or some helper class that contains both `self` and any desired
further information.

But, as you note, it would not cover everything, and your `array_reference`
idea definitely makes things more uniform. Indeed, it would allow one to
implement things like `np.zeros_like` using `np.zero`, which seems quite

Still, I'm not sure whether this should be included in the present NEP or
is best done separately after, with a few concrete examples of where it
would be useful.

All the best,


On Sat, Jun 30, 2018 at 10:40 AM, Hameer Abbasi <einstein.edison at gmail.com>

> Hi Marten,
> Sorry, I had clearly misunderstood. It would indeed be nice for overrides
> to work on functions like `zeros` or `arange` as well, but it seems strange
> to change the signature just for that. As a possible alternative, should we
> perhaps generally check for overrides on `dtype`?
> While this very clearly makes sense for something like astropy, it has a
> few drawbacks:
>    - Other duck arrays such as Dask need more information than just the
>    dtype. For example, Dask needs chunk sizes, XArray needs axis labels, and
>    pydata/sparse needs to know the type of the reference array in order
>    to make one of the same type. The information in a reference array is a
>    strict superset of information in the dtype.
>    - There’s a need for a separate protocol, which might be a lot harder
>    to work with for both NumPy and library authors.
>    - Some things, like numpy.random.RandomState, don’t accept a dtype
>    argument.
> As for your concern about changing the signature, it’s easy enough with a
> decorator. We’ll need a separate decorator for array generation functions.
> Something like:
> def array_generation_function(func):
>     @functools.wraps(func)
>     def wrapped(*args, **kwargs, array_reference=np._NoValue):
>         if array_reference is not np._NoValue:
>             success, result = try_array_function_override(wrapped, [array_reference], args, kwargs)
>             if success:
>                 return result
>         return func(*args, **kwargs)
>     return wrapped
> Hameer Abbasi
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> NumPy-Discussion at python.org
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