Or one can replace those values with zero. That eliminates them; averaging then proceeds without those values altering the most probable correct average.
DaveD > On Jan 19, 2021, at 08:49, Bob kb8tq <[email protected]> wrote: > > Hi > > The normal approach to filling a gap is to put in a point that is the average > of the two adjacent points. The assumption is that this is a “safe” value that > will not blow up the result. That’s probably ok if it is done rarely. The > risk is > that you are running a filter process (averaging is a low pass filter). > > If you pull out a *lot* of outliers and replace them, you are doing a lot of > filtering. > Since you are measuring noise, filtering is very likely to improve the > result. > The question becomes - how representative is the result after a lot of this > or > that has been done? > > Obviously the answer to all this depends on what you are trying to do. If you > are running a control loop and the output improves, that’s fine. If you are > trying to provide an accurate measure of noise …. maybe not so much :) > > Bob > >> On Jan 19, 2021, at 2:15 AM, Gilles Clement <[email protected]> wrote: >> >> Hi, >> Yes outliers removal creates gap in Stable32. >> The « fill » function can fills gaps with interpolated values. >> It does not change much the graphs, except in the low Tau area (see >> attached). >> Do you know a discussion of impact of outliers removal ? >> Gilles. >> >> >> >>> Le 18 janv. 2021 à 22:06, Bob kb8tq <[email protected]> a écrit : >>> >>> Hi >>> >>> As you throw away samples that are far off the mean, you reduce the sample >>> rate ( or at least create gaps in the record). Dealing with that could be >>> difficult. >>> >>> Bob >>> >>>>> On Jan 18, 2021, at 1:33 PM, Gilles Clement <[email protected]> wrote: >>>>> >>>>> Hi >>>>> >>>>> Very cool !!! >>>>> >>>>> The red trace is obviously the one to focus on. Some sort of digital loop >>>>> that >>>>> only operates under the “known good” conditions would seem to make sense. >>>>> >>>>> Thanks for sharing >>>>> >>>>> Bob >>>> >>>> Hi, >>>> I tried something with the idea to consider night records fluctuations as >>>> « outliers » as compared to day records. >>>> Indeed the 3 days record mean value is flat and the histogram quite >>>> gaussian. >>>> So I processed the 3 days record (green trace) with Stable32’s « Check >>>> Function », >>>> while removing outliers with decreasing values of the Sigma Factor. The >>>> graph below shows the outcome. >>>> The graph with Sigma=0.8 (blue trace) connects rather well with the 1Day >>>> record (red trace). >>>> Would this be a workable approach ? >>>> Best, >>>> Gilles. >>>> >>>> >>>> >>>> >>>> >>>> _______________________________________________ >>>> time-nuts mailing list -- [email protected] >>>> To unsubscribe, go to >>>> http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com >>>> and follow the instructions there. >>> >>> >>> _______________________________________________ >>> time-nuts mailing list -- [email protected] >>> To unsubscribe, go to >>> http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com >>> and follow the instructions there. >> >> _______________________________________________ >> time-nuts mailing list -- [email protected] >> To unsubscribe, go to >> http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com >> and follow the instructions there. > > > _______________________________________________ > time-nuts mailing list -- [email protected] > To unsubscribe, go to > http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com > and follow the instructions there. _______________________________________________ time-nuts mailing list -- [email protected] To unsubscribe, go to http://lists.febo.com/mailman/listinfo/time-nuts_lists.febo.com and follow the instructions there.
