> On 16 Oct 2016, at 03:21, Allan Haldane <allanhald...@gmail.com> wrote: > >> On 10/14/2016 07:49 PM, Juan Nunez-Iglesias wrote: >> +1 for propagate_mask. That is the only proposal that immediately makes >> sense to me. "contagious" may be cute but I think approximately 0% of >> users would guess its purpose on first use. >> >> Can you elaborate on what happens with the masks exactly? I didn't quite >> get why propagate_mask=False was unintuitive. My expectation is that any >> mask present in the input will not be set in the output, but the mask >> will be "respected" by the function. > > Here's an illustration of how the PR currently works with convolve, using the > name "propagate_mask": > > >>> m = np.ma.masked > >>> a = np.ma.array([1,1,1,m,1,1,1,m,m,m,1,1,1]) > >>> b = np.ma.array([1,1,1]) > >>> > >>> print np.ma.convolve(a, b, propagate_mask=True) > [1 2 3 -- -- -- 3 -- -- -- -- -- 3 2 1] > >>> print np.ma.convolve(a, b, propagate_mask=False) > [1 2 3 2 2 2 3 2 1 -- 1 2 3 2 1] > > Allan >
Given this behaviour, I'm actually more concerned about the logic ma.convolve uses in the propagate_mask=False case. It appears that the masked values are essentially replaced by zero. Is my interpretation correct and if so does this make sense? When I have similar situations, I usually interpolate between the valid values. I assume there are a lot of use cases for convolutions but I have difficulties imagining that ignoring a missing value and, for the purpose of the computation, treating it as zero is useful in many of them. Hanno _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
