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. 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