You're right. I was confusing the broadcasting logic for boolean arrays. However, I did find this example
>>> np.arange(10).reshape((2, 5))[np.array([[0, 0, 0, 0, 0]], dtype=np.int64), >>> False] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (1,5) (0,) That certainly seems to imply there is some broadcasting being done. Aaron Meurer On Wed, Aug 19, 2020 at 6:55 PM Sebastian Berg <sebast...@sipsolutions.net> wrote: > > On Wed, 2020-08-19 at 18:07 -0600, Aaron Meurer wrote: > > > > 3. If you have multiple advanced indexing you get annoying > > > > broadcasting > > > > of all of these. That is *always* confusing for boolean > > > > indices. > > > > 0-D should not be too special there... > > > > OK, now that I am learning more about advanced indexing, this > > statement is confusing to me. It seems that scalar boolean indices do > > not broadcast. For example: > > Well, broadcasting means you broadcast the *nonzero result* unless I am > very confused... There is a reason I dismissed it. We could (and > arguably should) just deprecate it. And I have doubts anyone would > even notice. > > > > > > > > np.arange(2)[False, np.array([True, False])] > > array([], dtype=int64) > > > > > np.arange(2)[tuple(np.broadcast_arrays(False, np.array([True, > > > > > False])))] > > Traceback (most recent call last): > > File "<stdin>", line 1, in <module> > > IndexError: too many indices for array: array is 1-dimensional, but 2 > > were indexed > > > > And indeed, the docs even say, as you noted, "the nonzero equivalence > > for Boolean arrays does not hold for zero dimensional boolean > > arrays," > > which I guess also applies to the broadcasting. > > I actually think that probably also holds. Nonzero just behave weird > for 0D because arrays (because it returns a tuple). > But since broadcasting the nonzero result is so weird, and since 0-D > booleans require some additional logic and don't generalize 100% (code > wise), I won't rule out there are differences. > > > From what I can tell, the logic is that all integer and boolean > > arrays > > Did you try that? Because as I said above, IIRC broadcasting the > boolean array without first calling `nonzero` isn't really whats going > on. And I don't know how it could be whats going on, since adding > dimensions to a boolean index would have much more implications? > > - Sebastian > > > > (and scalar ints) are broadcast together, *except* for boolean > > scalars. Then the first boolean scalar is replaced with and(all > > boolean scalars) and the rest are removed from the index. Then that > > index adds a length 1 axis if it is True and 0 if it is False. > > > > So they don't broadcast, but rather "fake broadcast". I still contend > > that it would be much more useful, if True were a synonym for newaxis > > and False worked like newaxis but instead added a length 0 axis. > > Alternately, True and False scalars should behave exactly like all > > other boolean arrays with no exceptions (i.e., work like > > np.nonzero(), > > broadcast, etc.). This would be less useful, but more consistent. > > > > Aaron Meurer > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
