[time-nuts] Re: Timestamping counter techniques : phase computation question

[Erik Kaashoek]

There was a small error in my previous post

Here are the complete and corrected numbers
Below table shows:

Event counter(Y) : The exact count of events since the start of the 
counter captured at the time of the event.
Clock Counter(X) : The clock cycles since the start of the counter 
captured at the time of the event
Calculated clock count: Calculated Y at time of event using the outcome 
of a linear regression using 20 captured (X,Y) pairs starting from the 
time of the event
Wrapped event phase: as the Clock runs at a frequency different from the 
events the calculated clock count is converted to the event frequency 
and wrapped between 0 and 1 to see the variation in the phase

The event and clock periods where constant. As the event period is a 
factor 20 longer than the clock period the phase resolution without 
regression would have been 0.05. With the interpolation over 20 captures 
it is at least a factor 2 better, consistent with the calculated 
accuracy improvement using a regression.
In the actual implementation the regression can run on up to 100000 
captures per second measurement time

Any feedback?


Event Counter(Y)Â Â Â  Clock Counter(X)Â Â Â  Calculated clock count Wrapped 
event phase
280687207Â Â Â  1693025665Â Â Â  1693025665.303Â Â Â  0.061080
280688207Â Â Â  1693046999Â Â Â  1693046998.727Â Â Â  0.065341
280689207Â Â Â  1693068332Â Â Â  1693068331.970Â Â Â  0.061080
280690207Â Â Â  1693089665Â Â Â  1693089665.303Â Â Â  0.061080
280691207Â Â Â  1693110999Â Â Â  1693110998.727Â Â Â  0.065341
280692207Â Â Â  1693132332Â Â Â  1693132331.970Â Â Â  0.061080
280693207Â Â Â  1693153665Â Â Â  1693153665.303Â Â Â  0.061080
280694207Â Â Â  1693174999Â Â Â  1693174998.700Â Â Â  0.064056
280695207Â Â Â  1693196332Â Â Â  1693196331.947Â Â Â  0.059997
280696207Â Â Â  1693217665Â Â Â  1693217665.339Â Â Â  0.062770
280697207Â Â Â  1693238999Â Â Â  1693238998.732Â Â Â  0.065544
280698207Â Â Â  1693260332Â Â Â  1693260332.029Â Â Â  0.063852
280699207Â Â Â  1693281665Â Â Â  1693281665.326Â Â Â  0.062161
280700207Â Â Â  1693302999Â Â Â  1693302998.719Â Â Â  0.064935
280701207Â Â Â  1693324332Â Â Â  1693324332.016Â Â Â  0.063244
280702207Â Â Â  1693345665Â Â Â  1693345665.313Â Â Â  0.061553
280703207Â Â Â  1693366999Â Â Â  1693366998.706Â Â Â  0.064326
280704207Â Â Â  1693388332Â Â Â  1693388332.003Â Â Â  0.062635
280705207Â Â Â  1693409665Â Â Â  1693409665.382Â Â Â  0.064800
280706207Â Â Â  1693430999Â Â Â  1693430998.762Â Â Â  0.066964
280707207Â Â Â  1693452332Â Â Â  1693452332.046Â Â Â  0.064664
280708207Â Â Â  1693473665Â Â Â  1693473665.330Â Â Â  0.062364
280709207Â Â Â  1693494999Â Â Â  1693494998.710Â Â Â  0.064529
280710207Â Â Â  1693516332Â Â Â  1693516331.994Â Â Â  0.062229
280711207Â Â Â  1693537665Â Â Â  1693537665.278Â Â Â  0.059929
280712207Â Â Â  1693558999Â Â Â  1693558998.658Â Â Â  0.062094
280713207Â Â Â  1693580332Â Â Â  1693580331.942Â Â Â  0.059794
280714206Â Â Â  1693601644Â Â Â  1693601643.893Â Â Â  0.057494
280715207Â Â Â  1693622999Â Â Â  1693622998.543Â Â Â  0.056683