Now that everything has been setup minimisation can be used to optimise the parameter values.
Firstly a grid search is applied to find a rough starting position for the subsequent optimisation algorithm.
Eleven increments per dimension of the model (in this case the two dimensions {
R_{x}, *I*_{0}}) is sufficient.
The user function for executing the grid search is

[firstnumber=62] # Grid search. minimise.grid_search(inc=11)

The next step is to select one of the minimisation algorithms to optimise the model parameters

[firstnumber=65] # Minimise. minimise.execute('newton', constraints=False)

The relax user manual (PDF), created 2019-02-19.