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NNTP-Posting-Date: Tue, 18 Nov 2014 20:28:54 -0600
From: Dennis Lee Bieber <wlfraed@ix.netcom.com>
Newsgroups: comp.lang.ada
Subject: Re: how to analyze clock drift
Date: Tue, 18 Nov 2014 21:28:57 -0500
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Date: 2014-11-18T21:28:57-05:00
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On Tue, 18 Nov 2014 23:12:04 +0100, Emanuel Berg <embe8573@student.uu.se>
declaimed the following:

>I have a long list of samples of clock readings. It
>"should" be periodical, by 1 ms, but of course it
>isn't. The data is in nanoseconds.
>

	By what criteria? That is, how are the readings being produced? An
interrupt handler with an external millisecond clock source? A
multi-tasking system with variable load, round-robin scheduling?

>Anyone knows how I can apply some math/stat method to
>find out the average drift, and/or if the drift will
>average out, or just about any conclusions you can get
>out of the material?
>

	Feed the data to "R" and play around with various commands? (Maybe the
ones that run cyclic differences... compare deltas of each pair, each
third, etc.)


>Also, do you know of any rule-of-thumb how many
>readings I will need to make it "good science"? I will
>of course tell how many readings were used in the
>examination, but what would you say is a good
>benchmark were patterns will (almost foolproof) be
>visible?
>
	Other than "as many as you can get"... I've never understood how polls
work out their "margin of error".

>If you know of some tool that'd be great, or some

	As mentioned - the R system is just made for statistical operations.

>The file with the readings looks like this:

	What defines "truth" -- I see a significant delta between the first
two, but small change between 2&3...

>
>85033101286784
>85033108461718
>85033109544537
>85033110621490
>85033111714366
>85033112794112
>85033113871903
>85033114934049
>85033116009605
>85033117089909
>85033118169656
>85033119256945
>85033120336411
>85033121409174
>
>and so on.
>
>Hope to hear from you Ada real-time experts :)

	Just some games with Excel... Column 2 is the difference between each
original row. Subsequent columns are differences skipping the appropriate
number of items. The bottom row is the mean, ignoring the extreme (first
difference of the column)

85033101286784									
85033108461718	7174934								
85033109544537	1082819	-6092115							
85033110621490	1076953	-5866	-6097981						
85033111714366	1092876	15923	10057	-6082058					
85033112794112	1079746	-13130	2793	-3073	-6095188				
85033113871903	1077791	-1955	-15085	838	-5028	-6097143			
85033114934049	1062146	-15645	-17600	-30730	-14807	-20673	-6112788
85033116009605	1075556	13410	-2235	-4190	-17320	-1397	-7263
-6099378	
85033117089909	1080304	4748	18158	2513	558	-12572	3351	-2515
-6094630
85033118169656	1079747	-557	4191	17601	1956	1	-13129	2794
-3072
85033119256945	1087289	7542	6985	11733	25143	9498	7543 -5587
10336
85033120336411	1079466	-7823	-281	-838	3910	17320	1675 -280
-13410
85033121409174	1072763	-6703	-14526	-6984	-7541	-2793	10617 -5028
-6983
									
	1078955	-914	-754	-1459	-1641	-1517	466	-2123	-3282
-- 
	Wulfraed                 Dennis Lee Bieber         AF6VN
    wlfraed@ix.netcom.com    HTTP://wlfraed.home.netcom.com/