mwavepy.calibrationAlgorithms | index /home/alex/docs/python/path/mwavepy/calibrationAlgorithms.py

`Contains calibrations algorithms, used in the Calibration class,`

Modules
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numpy | mwavepy.discontinuities.variationalMethods |  |

Functions
`` |  | abc_2_coefs_dict(abc) `converts an abc ndarry to a dictionarry containing the error coefficients. takes: abc : Nx3 numpy.ndarray, which holds the complex calibration coefficients. the components of abc are a[:] = abc[:,0] b[:] = abc[:,1] c[:] = abc[:,2], a, b and c are related to the error network by a = e01*e10 - e00*e11 b = e00 c = e11 returns: coefsDict: dictionary containing the following 'directivity':e00 'reflection tracking':e01e10 'source match':e11 note: e00 = directivity error e10e01 = reflection tracking error e11 = source match error` guess_length_of_delay_short(aNtwk, tline) `guess length of physical length of a Delay Short given by aNtwk takes: aNtwk: a mwavepy.ntwk type . (note: if this is a measurment it needs to be normalized to the short plane tline: transmission line class of the medium. needed for the calculation of propagation constant` one_port(measured, ideals) `standard algorithm for a one port calibration. If more than three standards are supplied then a least square algorithm is applied. takes: measured - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray, representing a s-matrix or list of 1-port mwavepy.ntwk types. ideals - list of assumed reflection coefficients. can be lists of either a kxnxn numpy.ndarray, representing a s-matrix or list of 1-port mwavepy.ntwk types. returns: (abc, residues) - a tuple. abc is a Nx3 ndarray containing the complex calibrations coefficients,where N is the number of frequency points in the standards that where given. abc: the components of abc are a = abc[:,0] = e01*e10 - e00*e11 b = abc[:,1] = e00 c = abc[:,2] = e11 residuals: a matrix of residuals from the least squared calculation. see numpy.linalg.lstsq() for more info` parameterized_self_calibration(measured, ideals_ps, showProgress=True, **kwargs) `A self-calibration routine which can take any mixture of parameterized standards. The total residual error is minimized by adjusting the parameters of each standard and re-runing the calibration. takes: measured: list of Network types holding actual measurements ideals: list of ParameterizedStandard types showProgress: turn printing progress on/off [boolean] **kwargs: passed to minimization algorithm (scipy.optimize.fmin) returns: a dictionary holding: '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 ParameterizedStandard for more info` unknown_translation_offset(measured, ideals, wb, d, ftol=0.001, xtol=0.001, guessLength=False, solveForLoss=False, showProgress=False, **kwargs) `A one port calibration, which can use a redundent number of delayed shorts to solve for their unknown lengths. !see note at bottom about order! takes: measured - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. ideals - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. see note about order. wb - a mwavepy.workingBand.WorkingBand type. d - vector containing initial guesses for the delay short lengths see note about order. ftol - functional tolerance, passed to the scipy.optimize.fmin function solveForLoss - guessLength - showProgress - returns: (abc, residues) - a tuple. abc is a Nx3 ndarray containing the complex calibrations coefficients,where N is the number of frequency points in the standards that where given. abc: the components of abc are a[:] = abc[:,0] b[:] = abc[:,1] c[:] = abc[:,2], a, b and c are related to the error network by a = e01*e10 - e00*e11 b = e00 c = e11 residues: a matrix of residues from the least squared calculation. see numpy.linalg.lstsq() for more info note: ORDER MATTERS. all standard lists, and d-vector must be in order. The first m-standards are assumed to be delayed shorts, where m is the length of d. Any standards after may be anything.` xds(measured, ideals, wb, d, ftol=0.001, xtol=0.001, guessLength=False, solveForLoss=False, showProgress=False) `A one port calibration, which can use a redundent number of delayed shorts to solve for their unknown lengths. !see note at bottom about order! takes: measured - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. ideals - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. see note about order. wb - a mwavepy.workingBand.WorkingBand type. d - vector containing initial guesses for the delay short lengths see note about order. ftol - functional tolerance, passed to the scipy.optimize.fmin function solveForLoss - guessLength - showProgress - returns: (abc, residues) - a tuple. abc is a Nx3 ndarray containing the complex calibrations coefficients,where N is the number of frequency points in the standards that where given. abc: the components of abc are a[:] = abc[:,0] b[:] = abc[:,1] c[:] = abc[:,2], a, b and c are related to the error network by a = e01*e10 - e00*e11 b = e00 c = e11 residues: a matrix of residues from the least squared calculation. see numpy.linalg.lstsq() for more info note: ORDER MATTERS. all standard lists, and d-vector must be in order. The first m-standards are assumed to be delayed shorts, where m is the length of d. Any standards after may be anything.` xds_xdl(measured, ideals, wb, ds, dl, Gamma0=None, ftol=0.001, xtol=0.001, guessLength=False, solveForLoss=False, solveForLoad=False, showProgress=False) `A one port calibration, which can use a redundent number of delayed shorts to solve for their unknown lengths. !see note at bottom about order! takes: measured - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. ideals - list of measured reflection coefficients. can be lists of either a kxnxn numpy.ndarray. see note about order. wb - a mwavepy.workingBand.WorkingBand type. d - vector containing initial guesses for the delay short lengths see note about order. ftol - functional tolerance, passed to the scipy.optimize.fmin function solveForLoss - guessLength - showProgress - returns: (abc, residues) - a tuple. abc is a Nx3 ndarray containing the complex calibrations coefficients,where N is the number of frequency points in the standards that where given. abc: the components of abc are a[:] = abc[:,0] b[:] = abc[:,1] c[:] = abc[:,2], a, b and c are related to the error network by a = e01*e10 - e00*e11 b = e00 c = e11 residues: a matrix of residues from the least squared calculation. see numpy.linalg.lstsq() for more info note: ORDER MATTERS. all standard lists, and d-vector must be in order. The first m-standards are assumed to be delayed shorts, where m is the length of d. Any standards after may be anything.`