[fisian]

Great work!

I would've needed this recently for some data analysis, to estimate the mass of an object based on position measurments. I tried calculating the 2nd derivative with a Savitzky-Golay filter, but still had some problems and ended up using a different approach (also using a Kalman filter, but with a physics-based model of my setup).

My main problem was that I had repeated values in my measurements (sensor had a lower, non-integer divisible sampling rate than the acquisition pipeline). This especially made clear that np.gradient wasn't suitable, because it resulted in erratic switches between zero and the calculated derivative. Applying, np.gradient twice made the data look like random noise.

I will try using this library, when I next get the chance.

[hugohadfield]

Thanks so much! Yeah this was also a key reason I like this approach. Quite often we end up with repeated values due to quantisation of signal or timing differences or whatever and we get exactly that problem you describe, either massive gradients or 0 gradient and nothing in betweeen. With the KF approach you can just strip out the repeated values and run the filter with them missing and its fine. In the quantisation case you can approximate the quantisation observation noise by using resolution*1/sqrt(12) and it also all just works nicely. If you have any sample data of some fun problems and don't mind sharing then let me know and we could add some demos to the library!

[radarsat1]

Did you try prefiltering to remove the repeated values?