On Wed, 2020-07-22 at 16:23 -0600, Aaron Meurer wrote: > Why does fancy indexing have this behavior? > > > > > a = np.empty((0, 1, 2)) > > > > b = np.empty((1, 1, 2)) > > > > a[np.array([10, 10])] > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > IndexError: index 10 is out of bounds for axis 0 with size 0 > > > > a[:, np.array([10, 10])] > array([], shape=(0, 2, 2), dtype=float64) > > > > a[:, :, np.array([10, 10])] > array([], shape=(0, 1, 2), dtype=float64) > > > > b[np.array([10, 10])] > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > IndexError: index 10 is out of bounds for axis 0 with size 1 > > > > b[:, np.array([10, 10])] > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > IndexError: index 10 is out of bounds for axis 1 with size 1 > > > > b[:, :, np.array([10, 10])] > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > IndexError: index 10 is out of bounds for axis 2 with size 2 > > As far as I can tell, the behavior is that if an array has a 0 > dimension and an integer array index indexes an axis that isn't 0, > there are no bounds checks. Why does it do this? It seems to be > inconsistent with the behavior of shape () fancy indices (integer > indices). I couldn't find any reference to this behavior in > https://numpy.org/doc/stable/reference/arrays.indexing.html. >
The reason is because we used to not do this when there are *two* advanced indices: arr = np.ones((5, 6)) arr[[], [10, 10]] giving an empty result. If you check on master (and maybe on 1.19.x, I am not sure). You should see that all of your examples give a deprecation warning to be turned into an error (except the example I gave above, which can be argued to be correct). - Sebastian > Aaron Meurer > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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