Thank you for your help! Sebastian, I couldn't agree more with someone's bug being someone else's feature! A fast identity ufunc would be useful, though.
Actually, numpy.frompyfunc(operator.is_,2,1) is much faster than the numpy.vectorize approach - only about 35% slower on quick measurement than the direct ==, as opposed to 62% slower with vectorize (with otypes hint). Robert, yes, that's what I already did provisionally. Eric, that is a nice puzzle - but I agree with Robert about understanding by code maintainers. Thanks again, and best regards, Martin On Mon, 17 Jul 2017 11:41 Sebastian Berg <sebast...@sipsolutions.net> write > Yes, I guess ones bug is someone elses feature :(, if it is very bad, we > could delay the deprecation probably. For a solutions, maybe > we could add a ufunc for elementwise `is` on object arrays (dunno about the > name, maybe `object_identity`. > Just some quick thoughts. > - Sebastian On Mon, 17 Jul 2017 at 17:45 Robert Kern <robert.k...@gmail.com> wrote: > Wrap the clumsiness up in a documented, tested utility function with a > descriptive name and use that function everywhere instead. > Robert Kern On Mon, Jul 17, 2017 at 10:52 AM, Eric Wieser <wieser.eric+nu...@gmail.com> wrote: > Here's a hack that lets you keep using ==: > > class IsCompare: > __array_priority__ = 999999 # needed to make it work on either side of > `==` > def __init__(self, val): self._val = val > def __eq__(self, other): return other is self._val > def __neq__(self, other): return other is not self._val > > a == IsCompare(None) # a is None > a == np.array(IsCompare(None)) # broadcasted a is None > > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
