On Tue, Jul 16, 2019 at 5:58 AM Charles R Harris <charlesr.har...@gmail.com> wrote:
> > > On Tue, Jul 16, 2019 at 3:44 AM Kevin Sheppard <kevin.k.shepp...@gmail.com> > wrote: > >> I am trying to make a subclass that never propagates so that when >> interacted with another ndarray, or even itself so that the return type is >> always ndarray. Is this possible? >> >> I got pretty far with >> >> def __array_wrap__(self, out_arr, context=None): >> if out_arr.shape == (): >> return out_arr.item() # if ufunc output is scalar, return it >> else: >> out = super(ArrayLike, self).__array_wrap__(out_arr, context) >> # Never return ArrayLike >> if isinstance(out, ArrayLike): >> out = out.view(np.ndarray) >> return out >> >> Which works well for ufuncs. However, when I try other functions like >> `dot` I get my subclass type returned. >> >> If there a reasonable way to ensure that my subclass doesn't propagate? I >> think I would need some way to override the behavior when .view(MySubClass) >> is called. >> > I think you need to implement __array_finalize__ for this (see e.g. https://docs.scipy.org/doc/numpy-1.13.0/user/basics.subclassing.html#implications-for-subclassing ) >> > I think you will be able to do that with `__array_function__` in the > upcoming 1.17 release. It is also in 1.16, but you need an environmental > variable to activate it. Some documentation can be found at > https://www.numpy.org/devdocs/reference/arrays.classes.html#special-attributes-and-methods > . > That's kind of an orthogonal thing: __array_function__ is for providing your own implementation of functions, which you don't necessarily want to do if you're just building a small subclass. Cheers, Ralf
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