Inverse component

The last step to run an inversion with VMOD is to declare an Inverse object. The only attribute in this class stores the dataset in the attribute data. It is also necessary to include the sources that the user wants to retrieve the parameters with the method register_source

import Inverse

inv = Inverse(obs)

inv.register_source(mogi)
inv.register_source(yang)

Non-linear least squares algorithm

The user can choose to run a fast gradient based algorithm to find a good solution that match the observations.

import Inverse

ans=inv.nlsq()
print('Solution', ans.x)

Bayesian algorithm

The user can choose to run a Bayesian algorithm. However, the calculation is substantial more time-consuming than the gradient based algorithm.

import Inverse

trace=inv.mcmc('solution')

The string 'solution' is the filename for a .pkl file that stores the solutions in case the user wants to halt the inversion at some point. The output will give empirical distributions for all the parameters in the model.