On 10/17/2016 01:01 PM, Pierre Haessig 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. > > > 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.
There is an old unimplemented NEP which uses similar language, like "ignorena", and np.NA. http://docs.scipy.org/doc/numpy/neps/missing-data.html But right now that isn't part of numpy, so I think it would be confusing to use that terminology. Allan _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
