On Tue, Jan 9, 2018 at 12:53 PM, Tyler Reddy <tyler.je.re...@gmail.com> wrote: > One common issue in computational geometry is the need to operate rapidly on > arrays with "heterogeneous shapes." > > So, an array that has rows with different numbers of columns -- shape (1,3) > for the first polygon and shape (1, 12) for the second polygon and so on. > > This seems like a particularly nasty scenario when the loss of "homogeneity" > in shape precludes traditional vectorization -- I think numpy effectively > converts these to dtype=object, etc. I don't > think is necessarily a BLAS issue since wrapping comp. geo. libraries does > happen in a subset of cases to handle this, but if there's overlap in > utility you could pass it along I suppose.
You might be interested in this discussion of "Batch BLAS": https://docs.google.com/document/d/1DY4ImZT1coqri2382GusXgBTTTVdBDvtD5I14QHp9OE/edit#heading=h.pvsif1mxvaqq I didn't get into it in the draft response, because it didn't seem like something where NumPy/SciPy have any useful experience to offer, but it sounds like there are people worrying about this case. -n -- Nathaniel J. Smith -- https://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion