Hi Jaime, Very belated reply, but only with the semester over I seem to have regained some time to think.
The behaviour of reduceat always has seemed a bit odd to me, logical for dividing up an array into irregular but contiguous pieces, but illogical for more random ones (where one effectively passes in pairs of points, only to remove the unwanted calculations after the fact by slicing with [::2]; indeed, the very first example in the documentation does exactly this [1]). I'm not sure any of your proposals helps all that much for the latter case, while it risks breaking existing code in unexpected ways. For me, for irregular pieces, it would be much nicer to simply pass in pairs of points. I think this can be quite easily done in the current API, by expanding it to recognize multidimensional index arrays (with last dimension of 2; maybe 3 for step as well?). These numbers would just be the equivalent of start, end (and step?) of `slice`, so I think one can allow any integer with negative values having the usual meaning and clipping at 0 and length. So, specifically, the first example in the documentation would change from: np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2] to np.add.reduceat(np.arange(8),[(0, 4), (1, 5), (2, 6), (3,7)]) (Or an equivalent ndarray. Note how horrid the example is: really, you'd want 4,8 as a pair too, but in the current API, you'd get that by adding a 4.) What do you think? Would this also be easy to implement? All the best, Marten [1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.reduceat.html
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