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:
>> 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.
>> 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
>> 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
> 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
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