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

Chris Rodgers xrodgers@gmail....
Thu Apr 12 00:02:47 CDT 2012

The platform is specialized because the data come from equipment in my
lab. One system is a 3KHz real-time operating system detecting voltage
pulses on one input. The other is a data-acquistion card sampling at
30KHz and detecting deflections on another input. The temporal
uncertainty arises not from the raw sampling rates but from the
uncertainties in the pulse detection algorithms.

> Yes, that's why I was suggesting doing something similar to your
> current algorithm, but different in ways that might avoid this problem
I could be wrong but I think your suggestion retains the matching
problem implicitly, because it uses the closest value in X to the
transformed point from Y.

I wrote a new objective function that uses the sum of squared error
between every value in X and every transformed point from Y (but
thresholds any squared error that is above a certain point). I played
around with this function, and the one you suggested, and a couple of
others, using scipy.optimize.brute. It was a fun exercise but the
energy landscape is pathological. There are local minima for every
nearby mismatch error, and the minimum corresponding to the true
solution is extremely narrow and surrounded by peaks. The problem is
that a good guess for one parameter (dilation say) means that a small
error in the other parameter (offset) results in a very bad solution.

It still feels like there should be a way to do this from the
statistics of the input. I tried some crazier things like taking many
subsamples of X and Y, fitting them, and then looking at the
distribution of all the discovered fits. But the problem with this is
that I'm very unlikely to choose useful subsamples, of course. I think
I'll have to use the brute force approach with very fine sampling, or
one of the practical hardware solutions suggested previously.


On Wed, Apr 11, 2012 at 9:53 AM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
> On Wed, Apr 11, 2012 at 10:37 AM, Nathaniel Smith <njs@pobox.com> wrote:
>> On Wed, Apr 11, 2012 at 5:16 AM, Chris Rodgers <xrodgers@gmail.com> wrote:
>> > Re Nathaniel's suggestion:
>> > I think this is pretty similar to the algorithm I'm currently using.
>> > Pseudocode:
>> >
>> > 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!
>> Yes, that's why I was suggesting doing something similar to your
>> current algorithm, but different in ways that might avoid this problem
>> :-).
>> > 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?
>> That also has the problem that the objective function is totally flat
>> whenever the points are all within ERR, so you are guaranteed to get
>> an offset inaccuracy on the same order of magnitude as ERR. It sounds
>> like your clocks have a lot of jitter, though, if you can't do better
>> than 10 ms agreement, so maybe you can't get more accurate than that
>> anyway.
> I'd like to know the platform. The default Window's clock ticks at 15 Hz,
> <snipL
> Chuck
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