[SciPy-User] synchronizing timestamps from different systems; unpaired linear regression

jkhilmer@chemistry.montan... jkhilmer@chemistry.montan...
Tue Apr 10 10:31:01 CDT 2012

If these computers are networked, why not have them communicating
while recording the data?  Don't adjust the system clocks during the
run, but record inter-computer timestamps.  At a 10Hz sampling rate,
even a very naive non-NTP should be sufficient to keep a precision
close to your recording interval.


On Tue, Apr 10, 2012 at 8:24 AM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
> On Tue, Apr 10, 2012 at 3:27 AM, Chris Rodgers <xrodgers@gmail.com> wrote:
>> I have what seems like a straightforward problem but it is becoming
>> more difficult than I thought. I have two different computers
>> recording timestamps from the same stream of events. I get lists X and
>> Y from each computer and the question is how to figure out which entry
>> in X corresponds to which entry in Y.
>> Complications:
>> 1) There are an unknown number of missing or spurious events in each
>> list. I do not know which events in X match up to which in Y.
>> 2) The temporal offset between the two lists is unknown, because each
>> timer begins at a different time.
>> 3) The clocks seem to run at slightly different speeds (~0.3%
>> difference adds up to about 10 seconds over my 1hr recording time).
>> I know this problem is solvable because once you find the temporal
>> offset and clock-speed ratio, the matching timestamps agree to within
>> 10ms. That is, there is a strong linear relationship between some
>> unknown X->Y mapping.
>> Basically, the problem is: given list X and list Y, and specifying a
>> certain minimum R**2 value, what is the largest set of matched points
>> from X and Y that satisfy this R**2 value? I have tried googling
>> "unmatched linear regression" but this must not be the right search
>> term.
>> One approach that I've tried is to create an analog trace for X and Y
>> with a Gaussian centered at each timestamp, then finding the lag that
>> optimizes the cross-correlation between the two. This is good for
>> finding the temporal offset but can't handle the clock-speed
>> difference. (Also it takes a really long time because the series are
>> 1hr of data sampled at 10Hz.) Then I can choose the closest matches
>> between X and Y and fit them with a line, which gives me the
>> clock-difference parameter. The problem is that there are a ton of
>> local minima created by how I choose to match up the points in X and
>> Y, so it gets stuck on the wrong answer.
> This is a tricky problem, especially if you need to support windows with
> it's limited tick rate. NTP is a good tool on linux, and you can use it to
> synchronize networked machines to a reference machine, which might well do
> what you need. Much depends on the required time resolution. There are also
> ways to deal with windows machines, but I forget the details. Google around,
> there is a lot of material out there.
> Chuck
> _______________________________________________
> SciPy-User mailing list
> SciPy-User@scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

More information about the SciPy-User mailing list