skrf.calibration.calibrationAlgorithms.parameterized_self_calibration_nls

skrf.calibration.calibrationAlgorithms.parameterized_self_calibration_nls(measured, ideals_ps, showProgress=True, **kwargs)

An iterative, general self-calibration routine.

A self calibration routine based off of residual error minimization which can take any mixture of parameterized standards. Uses a non-linear least squares estimator to calculate the residuals.

Parameters :

measured : list of Network objects

a list of the measured networks

ideals : list of Network objects

a list of the ideal networks

showProgress : Boolean

turn printing progress on/off

**kwargs : key-word arguments

passed to minimization algorithm (scipy.optimize.fmin)

Returns :

output : a dictionary

a dictionary containing the following keys:

• ‘error_coefficients’ : dictionary of error coefficients

• ‘residuals’: residual matrix (shape depends on #stds)

• ‘parameter_vector_final’: final results for parameter vector

• ‘mean_residual_list’: the mean, magnitude of the residuals at each

  iteration of calibration. this is the variable being minimized.

See also

parametricStandard

sub-module for more info on them

parameterized_self_calibration_nls

similar algorithm, but uses a non-linear least-squares estimator