'\nCONV POINTS: ' + str(conv_points) + \
'\nTIME: ' + str(comp_time)
# specify path
path = 'results_and_plots/optimization/logicle5_1/'
# file name is equal to starting points
file_name = str(n_starts)
# save
save_optimization_to_file(result=result,
n_start=n_starts,
nominal_par=petab_problem.x_nominal,
par_names=model.getParameterIds()[:22],
opt_lh=obj(petab_problem.x_nominal),
file_name=file_name,
conv_points=conv_points,
comp_time=comp_time,
opt_interval=[problem.lb, problem.ub],
startpoints=startpoints,
options=options,
path=path)
# SAVE GUESSES _________________________________________________________________________________________________________
# save converged points to use them as starting points for
# the regularization
path_guess = 'guesses/logicle5_1/'
save_guesses(result=result,
n_starts=n_starts,
# save the startpoints
save_startpoints(result=result, path='startpoints/', file_name='log10')
# SAVE OPTIMIZATION RESULTS ____________________________________________________________________________________________
options = 'MODEL: caro model, ' \
'\nSCALE: log10' + \
'\nSTARTS: ' + str(n_starts) + \
'\nCONV POINTS: ' + str(conv_points) + \
'\nTIME: ' + str(comp_time)
# specify path
path = 'results_and_plots/optimization/log10/'
# file name is equal to starting points
file_name = str(n_starts)
# save
save_optimization_to_file(result=result,
n_start=n_starts,
nominal_par=petab_problem.x_nominal,
par_names=['$\\xi_1$', '$\\xi_2$', '$\\xi_3$', '$\sigma$'],
opt_lh=obj(petab_problem.x_nominal),
file_name=file_name,
conv_points=conv_points,
comp_time=comp_time,
opt_interval=[problem.lb, problem.ub],
startpoints=startpoints,
options=options,
path=path)