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

[Hameer Abbasi]

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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