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.

## Advertising

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
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