On Fri, Feb 10, 2023 at 3:31 AM Sebastian Berg
wrote:
> Hi all,
>
> I was wondering if we should introduce a new `np.types` namespace. The
> main reason is that we have the DType classes, that most users don't
> need to worry about. These mirror the scalar classes, but getting them
> is weird currently.
>
> I never wanted to put these in the top-level (because I feel they
> probably won't be used much day to day). That would be thing like:
>
> * np.types.IntDType, np.types.Int64DType (or maybe without dtype)
> * np.types.NumberDType (an abstract DType)
> * np.types.InexactDType
> * ...
> * np.types.DTypeMeta (the metaclass used, just to have it somewhere)
>
>
> Maybe there are some more types that we could use a public entrypoint
> for (e.g. the type used by array-function dispatched functions or
> `np.ufunc` could in principle move also).
>
Small bikeshed: the name np.types indicates to me that it has something to
do with static typing. If this namespace only includes dtype classes, then
np.dtype_classes is a more natural name. If it includes things like
`np.ufunc` then that's not as clear, and I don't have a better idea offhand
than np.types.
>
> What do you think? I don't really have a good idea for an alternative
> but at some point not making these nicely public is not great...
>
Related to your proposal but not orthogonal to it, I still think it would
still be nice to be able to do things like:
>>> np.dtype[numbers.Number]
np.types.NumberDType
I know that currently __class_getitem__ is used by the typing support, but
I think the typing will still work if you also got back a usable dtype
instance at runtime instead of a GenericAlias, which has a confusing repr
and is not useful at runtime.
> (I will note that the DType classes do get printed sometimes in error
> messages.)
>
See also https://github.com/numpy/numpy/issues/22920.
>
> Cheers,
>
> Sebastian
>
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