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- 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.
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