def cal_errors(params_file):
config = ConfigLoader("config.yml")
config.set_params(params_file)
fcn = config.get_fcn()
fcn.model.Amp.vm.rp2xy_all(
) # we can use this to transform all complex parameters to xy-coordinates, since the Hesse errors of xy are more statistically reliable
params = config.get_params()
errors, config.inv_he = cal_hesse_error(
fcn, params, check_posi_def=True, save_npy=True
) # obtain the Hesse errors and the error matrix (inverse Hessian)
print("\n########## fit parameters in XY-coordinates:")
errors = dict(zip(fcn.model.Amp.vm.trainable_vars, errors))
for key, value in config.get_params().items():
print(key, error_print(value, errors.get(key, None)))
print("\n########## correlation matrix:")
print("Matrix index:\n", fcn.model.Amp.vm.trainable_vars)
print("Correlation Coefficients:\n", corr_coef_matrix(config.inv_he)
) # obtain the correlation matrix using the inverse Hessian
def test_cfit(gen_toy):
config = ConfigLoader(f"{this_dir}/config_cfit.yml")
config.set_params(f"{this_dir}/gen_params.json")
fcn = config.get_fcn()
fcn({})
fcn.nll_grad({})