[Numpy-discussion] Revised NEP-18, array_function protocol (original) (raw)
Hameer Abbasi einstein.edison at gmail.com
Sat Jun 30 10:40:29 EDT 2018
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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)
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