On Thu, Aug 19, 2010 at 10:12 AM, Angus McMorland <[email protected]> wrote: > Another rank-generic approach is to use apply_over_axes (you get a > different shape to the result this way): > > a = np.random.randint(20, size=(4,3,5)) > b = np.apply_over_axes(np.sum, a, [1,2]).flat > assert( np.all( b == a.sum(axis=2).sum(axis=1) ) ) >
Thanks for the responses! This looks like what I've been looking for. There's really a lot to numpy. It's very powerful and I feel like I've only scratched the surface. _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
