[time-nuts] Long Wave Radio-Frequency standard testing

Tue Jan 19 22:19:30 UTC 2021

Answers inline

> On Jan 19, 2021, at 16:27, Bob kb8tq <kb8tq at n1k.org> wrote:
> 
> Hi
> 
> Assuming the goal is a normal ADEV or xDEV sort of calculation:
> 
> If you replace the raw phase values with zero that can mess things up
> 
> 0 seconds +20 ns
> 1 seconds +22 ns
> 2 seconds +23 ns
> 3 seconds +25 ns
> 4 seconds +27ns
> 5 seconds +29 ns
> 
> If you “loose” one of those 20 to 30 ns values and replace it with zero, you have significantly
> changed the data set.

Ok

> 
> Even if you are looking at deltas,

Nope. It doesn’t work for deltas. 

> zero stuffing would be problematic with that
> (contrived) phase data set. 
> 
> 1 seconds +2
> 2 seconds +1
> 3 seconds +2
> 4 seconds +2
> 5 seconds +2
> 
> If the objective is something like a PLL then “hold at the last value” is the only practical
> answer to the question. You don’t *have* the next value and you need to stuff something
> into the control loop computation.

For a control loop, certainly. For just generating the waveform after DAC using interploation, it works well.

So, my suggestion doesn’t cover all use cases, and I learned something. That makes it a good day.

DaveD
> 
> Bob
> 
> 
>> On Jan 19, 2021, at 1:37 PM, Dave Daniel <kc0wjn at gmail.com> wrote:
>> 
>> 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 <kb8tq at n1k.org> 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 <clemgill at gmail.com> 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 <kb8tq at n1k.org> 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 <clemgill at gmail.com> 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. 
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> 
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>>>>> 
>>>>> 
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