On Mon, Jul 15, 2013 at 2:33 PM, Gregorio Bastardo <gregorio.basta...@gmail.com> wrote: > Hi, > > On Mon, Jun 10, 2013 at 3:47 PM, Nathaniel Smith <n...@pobox.com> wrote: >> Hi all, >> >> Is there anyone out there using numpy masked arrays, who has an >> opinion on how empty_like (and its friends ones_like, zeros_like) >> should handle the mask? >> >> Right now apparently if you call np.ma.empty_like on a masked array, >> you get a new masked array that shares the original array's mask, so >> modifying one modifies the other. That's almost certainly wrong. This >> PR: >> https://github.com/numpy/numpy/pull/3404 >> makes it so instead the new array has values that are all set to >> empty/zero/one, and a mask which is set to match the input array's >> mask (so whenever something was masked in the original array, the >> empty/zero/one in that place is also masked). We don't know if this is >> the desired behaviour for these functions, though. Maybe it's more >> intuitive for the new array to match the original array in shape and >> dtype, but to always have an empty mask. Or maybe not. None of us >> really use np.ma, so if you do and have an opinion then please speak >> up... > > I recently joined the mailing list, so the message might not reach the > original thread, sorry for that. > > I use masked arrays extensively, and would vote for the first option, > as I use the *_like operations with the assumption that the resulting > array has the same mask as the original. I think it's more intuitive > than selecting between all masked or all unmasked behaviour. If it's > not too late, please consider my use case.
The original submitter of that PR has been silent since then, so so far nothing has happened. So that's 2 votes for copying the mask and 3 against, I guess. That's not very consensus-ful. If there's really a lot of confusion here, then it's possible the answer is that np.ma.empty_like should just raise an error or not be defined. Or can you all agree? -n _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion