Error propagation in kinematics

Hi :slight_smile:

I am currently moving from IDL to Python and desperately seeking for a method that is easy to use in IDL but obviously not available in Python (at least I haven’t found it yet).

I am dealing a lot with time-distance measurements (time series). The measurements are done several times and the resulting standard deviation of each of the measurement points is propagated further.

When building the time derivative of the time-distance track in order to get the speed profile, I was using the IDL function DERIV so far. To propagate the standard deviation as well I was using the IDL function DERIVSIG.

I am currently translating an existing IDL program to Python. To build the time-derivative of my time-distance measurements I use the np.gradient function.

The problem is that I cannot find any function in Python that can calculate the propagated standard deviations for the speed, i.e. an alternative for DERIVSIG.

Is there any solution in SunPy for that? I think there might be other people who need this because kinematic profiles are often done for coronal mass ejections etc.

Thanks in advance for any help!


I believe that this kind of tooling shouldn’t be in SunPy, but in other libraries that specialises in that type of calculations. I’ve found this uncertainties library which may have what you need.
Also, there may be some tooling available within sktime that, even though it’s focused on machine learning with time series, could be useful for your problem.