On Tue, Oct 18, 2016 at 1:30 PM, <josef.p...@gmail.com> wrote: > On Tue, Oct 18, 2016 at 1:25 PM, <josef.p...@gmail.com> wrote: >> On Mon, Oct 17, 2016 at 1:01 PM, Pierre Haessig >> <pierre.haes...@crans.org> wrote: >>> Hi, >>> >>> >>> Le 16/10/2016 à 11:52, Hanno Klemm a écrit : >>>> 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. >>> When estimating the autocorrelation of a signal, it make sense to drop >>> missing pairs of values. Only in this use case, it opens the question of >>> correcting or not correcting for the number of missing elements when >>> computing the mean. I don't remember what R function "acf" is doing. > > as aside: statsmodels has now an option for acf and similar > > missing : str > A string in ['none', 'raise', 'conservative', 'drop'] > specifying how the NaNs > are to be treated.

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aside to the aside: statsmodels was just catching up in this The original for masked array acf including correct counting of "valid" terms is https://github.com/pierregm/scikits.timeseries/blob/master/scikits/timeseries/lib/avcf.py (which I looked at way before statsmodels had any acf) Josef > > Josef > >>> >>> >>> Also, coming back to the initial question, I feel that it is necessary >>> that the name "mask" (or "na" or similar) appears in the parameter name. >>> Otherwise, people will wonder : "what on earth is contagious/being >>> propagated...." >>> >>> just thinking of yet another keyword name : ignore_masked (or drop_masked) >>> >>> If I remember well, in R it is dropna. It would be nice if the boolean >>> switch followed the same logic. >>> >>> Now of course the convolution function is more general than just >>> autocorrelation... >> >> I think "drop" or "ignore" is too generic, for correlation it would be >> for example ignore pairs versus ignore cases. >> >> To me propagate sounds ok to me, but something with `valid` might be >> more explicit for convolution or `correlate`, however `valid` also >> refers to the end points, so maybe valid_na or valid_masked=True >> >> Josef >> >>> >>> best, >>> Pierre >>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion