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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