[SciPy-User] synchronizing timestamps from different systems; unpaired linear regression
Charles R Harris
Wed Apr 11 10:31:41 CDT 2012
On Tue, Apr 10, 2012 at 10:16 PM, Chris Rodgers <firstname.lastname@example.org> wrote:
> Dear all
> Thanks very much for the suggestions!
> Re a new hardware implementation: I bet this would totally work and
> honestly is probably the fastest way to get it working. I think even a
> rough system clock would do the trick. The downsides are 1) many data
> have already been collected with the old setup; 2) I'm getting
> stubbornly interested in this problem for its own sake since it feel
> so solvable. So perhaps I'll change the hardware for future data and
> keep working on algorithms for the old data. (I'd never heard of
> Lamport timestamps. The wikipedia article is really interesting. If I
> understand it correctly, it would still require a hardware change
> Re Nathaniel's suggestion:
> I think this is pretty similar to the algorithm I'm currently using.
> current_guess = estimate_from_correlation(x, y)
> for timescale in decreasing_order:
> xm, ym = find_matches(
> x, y, current_guess, within=timescale)
> current_guess = linfit(xm, ym)
> The problem is the local minima caused by mismatch errors. If the
> clockspeed estimate is off, then late events are incorrectly matched
> with a delay of one event. Then the updated guess moves closer to this
> incorrect solution. So by killing off the points that disagree, we
> reinforce the current orthodoxy!
> Actually the truest objective function would be the number of matches
> within some specified error.
> ERR = .1
> def objective(offset, clockspeed):
> # adjust parametrization to suit
> adj_y_times = y_times * clockspeed + offset
> closest_x_times = np.searchsorted(x_midpoints, adj_y_times)
> pred_err = abs(adj_y_times - x_midpoints[closest_x_times])
> closest_good = closest_x_times[pred_err < ERR]
> return len(unique(closest_good))
> That function has some ugly non-smoothness due to the
> len(unique(...)). Would something like optimize.brent work for this or
> am I on the wrong track?
You can also get some useful information from NTP (or chrony). For instance
on Fedora 16, which uses chrony instead of the ntp utilities to synchronize
Reference ID : 126.96.36.199 (mx1.mailfighter.net)
Stratum : 3
Ref time (UTC) : Wed Apr 11 15:17:33 2012
System time : 0.000000349 seconds fast of NTP time
Frequency : 41.465 ppm fast
Residual freq : 0.001 ppm
Skew : 0.104 ppm
Root delay : 0.063986 seconds
Root dispersion : 0.023441 seconds
The Frequency might be useful to you.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the SciPy-User