Robert Kern wrote: > On Thu, Mar 12, 2009 at 01:34, Stéfan van der Walt <ste...@sun.ac.za> wrote: > >> 2009/3/12 Robert Kern <robert.k...@gmail.com>: >> >>>> idx = np.array([0,1]) >>>> e = x[0,:,idx] >>>> print e.shape >>>> >>>> #-----> return (2,3). I think the right answer should be (3,2). Is >>>> # it a bug here? my numpy version is 1.2.1. >>>> >>> It's certainly weird, but it's working as designed. Fancy indexing via >>> arrays is a separate subsystem from indexing via slices. Basically, >>> fancy indexing decides the outermost shape of the result (e.g. the >>> leftmost items in the shape tuple). If there are any sliced axes, they >>> are *appended* to the end of that shape tuple. >>> >> This was my understanding, but now I see: >> >> In [31]: x = np.random.random([4,5,6,7]) >> >> In [32]: idx = np.array([1,2]) >> >> In [33]: x[:, idx, idx, :].shape >> Out[33]: (4, 2, 7) >> > > Hmm. Well, your guess is as good as mine at this point. > > Referencing my previous post on this topic. In this case, it is unambiguous to replace dimensions 1 and 2 with the result of broadcasting idx and idx together. Thus the (5,6) dimensions is replaced by the (2,) result of indexing leaving the outer dimensions in-tact, thus (4,2,7) is the result.
I could be persuaded that this attempt to differentiate "unambiguous" from "ambiguous" sub-space replacements was mis-guided and we should have stuck with the simpler rule expressed above. But, it seemed so aesthetically pleasing to swap-out the indexed sub-space when it was possible to do it. -Travis _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
