In preparing my email, I was looking for a way to identify a ufunc, other than `isinstance`, since `np.vectorize` and `nb.vectorize` make objects that behave like ufuncs (even obeying NEP 13 semantics, checking for `__array_ufunc__`), but they don't inherit from the `np.ufunc` type. I was thinking that a "ufunc" might be defined as a protocol, like Python's `Sequence` or `Mapping`.
I couldn't find a way to do that, particularly because the desired features of a ufunc involve more than having particular methods—they need to check their arguments and call particular methods: `__array_ufunc__`. That's not something one can check with `hasattr`. Sebastian, are you saying that the only definition of "ufunc" is that it inherits from `np.ufunc`? As far as I know, the only way to do that is by implementing it in C (and maybe even depend on a version of the NumPy ABI). But it's useful for libraries other than NumPy to be able to define ufuncs, starting with SciPy, which defines a lot of them. Are there any plans to define "ufunc" as a protocol, rather than a type that must be inherited to be recognized? Its behaviors are well defined at this point. Jim On Thu, Jan 2, 2025, 5:41 AM Sebastian Berg <sebast...@sipsolutions.net> wrote: > Stack is not a generalized ufunc. > > It may behave similar to one in many ways, but implementation wise has > nothing to do with ufuncs. > Also ufuncs do not support an arbitrary number of operands. > > `vectorize` can indeed mimic generalized ufuncs, but (unfortunately) > doesn't create them as such currently. > > - Sebastian > > > On Tue, 2024-12-31 at 10:20 -0600, Jim Pivarski via NumPy-Discussion > wrote: > > I think you're right: the function `stack`, as you've defined it, is > > a > > gufunc. > > > > Here's an implementation using np.vectorize > > < > > https://numpy.org/doc/stable/reference/generated/numpy.vectorize.html> > > (rather than nb.vectorize > > <https://numba.readthedocs.io/en/stable/user/vectorize.html>). Since > > the > > signature is not "()->()" or "(),()->()" (or similar with a different > > number of scalar inputs and scalar outputs), it's a generalized > > ufunc. > > > > > > > def stack(a, b): > > > > ... broadcasts = np.broadcast_arrays(a, b) > > > > ... return np.stack(broadcasts, axis=-1) > > > > ... > > > > > > > stacky = np.vectorize(stack, signature="(),()->(2)") > > > > > > > stacky(np.arange(5), np.arange(5)) > > > > array([[0, 0], > > > > [1, 1], > > > > [2, 2], > > > > [3, 3], > > > > [4, 4]]) > > > > > > > stacky(np.arange(5), np.array([[1], [2], [3], [4], [5]])) > > > > array([[[0, 1], > > > > [1, 1], > > > > [2, 1], > > > > [3, 1], > > > > [4, 1]], > > > > > > [[0, 2], > > > > [1, 2], > > > > [2, 2], > > > > [3, 2], > > > > [4, 2]], > > > > > > [[0, 3], > > > > [1, 3], > > > > [2, 3], > > > > [3, 3], > > > > [4, 3]], > > > > > > [[0, 4], > > > > [1, 4], > > > > [2, 4], > > > > [3, 4], > > > > [4, 4]], > > > > > > [[0, 5], > > > > [1, 5], > > > > [2, 5], > > > > [3, 5], > > > > [4, 5]]]) > > > > > > > > On Tue, Dec 31, 2024 at 2:44 AM john.a.dawson--- via NumPy-Discussion > > < > > numpy-discussion@python.org> wrote: > > > > > Is the function `stack` above a gufunc? > > > _______________________________________________ > > > NumPy-Discussion mailing list -- numpy-discussion@python.org > > > To unsubscribe send an email to numpy-discussion-le...@python.org > > > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > > > Member address: jpivar...@gmail.com > > > > > _______________________________________________ > > NumPy-Discussion mailing list -- numpy-discussion@python.org > > To unsubscribe send an email to numpy-discussion-le...@python.org > > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > > Member address: sebast...@sipsolutions.net > >
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