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

Samuel Garcia sgarcia@olfac.univ-lyon1...
Thu Apr 12 09:14:05 CDT 2012

What algo do you use for the online spike detection (sorting) ?
Do you have a commercial system or a home made system ?

Did you tried some Metroplis Hasting like method :
  * sample a subset of point
  * estimate offset, clockspeed at each step
  * stamp bad point X that have not a pair Y with that estimated offset 
and clockspeed
  * sample again only poitn (that are not bad stamp)
  * check is loglike is better or not and reject or accept the new state


Le 12/04/2012 07:02, Chris Rodgers a écrit :
> 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.
> Thanks!
> Chris
> 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,
>> IIRC.
>> <snipL
>> Chuck
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Samuel Garcia
Lyon Neuroscience
CNRS - UMR5292 -  INSERM U1028 -  Universite Claude Bernard LYON 1
Equipe R et D
50, avenue Tony Garnier
69366 LYON Cedex 07
Tél : 04 37 28 74 24
Fax : 04 37 28 76 01

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