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- __builtin__.object
-
- ParameterizedStandard
-
- PS_DelayShort_UnknownLength
- PS_Delayed_Termination_TranslationMissalignment
- PS_Delayed_Termination_UnknownLength_TranslationMissalignment
- PS_Match_TranslationMissalignment
- PS_Parameterless
class PS_DelayShort_UnknownLength(ParameterizedStandard) |
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A delay short of unknown length
initial guess for length should be given to constructor |
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- Method resolution order:
- PS_DelayShort_UnknownLength
- ParameterizedStandard
- __builtin__.object
Methods defined here:
- __init__(self, wb, d, **kwargs)
- takes:
wb: a WorkingBand type
d: initial guess for delay short physical length [m]
**kwargs: passed to self.function
Data descriptors inherited from ParameterizedStandard:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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class PS_Delayed_Termination_TranslationMissalignment(ParameterizedStandard) |
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A known Delayed Termination with unknown translation missalignment.
the initial guess for missalignment defaults to [1/10,1/10]*a,
where a is the waveguide width |
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- Method resolution order:
- PS_Delayed_Termination_TranslationMissalignment
- ParameterizedStandard
- __builtin__.object
Methods defined here:
- __init__(self, wb, d, Gamma0, initial_offset=0.10000000000000001, **kwargs)
- takes:
wb: a WorkingBand type, with a RectangularWaveguide object
for its tline property.
d: distance to termination
Gamma0: reflection coefficient off termination at termination
initial_offset: initial offset guess, as a fraction of a,
(the waveguide width dimension)
**kwargs: passed to self.function
Data descriptors inherited from ParameterizedStandard:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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class PS_Delayed_Termination_UnknownLength_TranslationMissalignment(ParameterizedStandard) |
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A known Delayed Termination with unknown translation missalignment.
the initial guess for missalignment defaults to [1/10,1/10]*a,
where a is the waveguide width |
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- Method resolution order:
- PS_Delayed_Termination_UnknownLength_TranslationMissalignment
- ParameterizedStandard
- __builtin__.object
Methods defined here:
- __init__(self, wb, d, Gamma0, initial_offset=0.10000000000000001, **kwargs)
- takes:
wb: a WorkingBand type, with a RectangularWaveguide object
for its tline property.
d: distance to termination
Gamma0: reflection coefficient off termination at termination
initial_offset: initial offset guess, as a fraction of a,
(the waveguide width dimension)
**kwargs: passed to self.function
Data descriptors inherited from ParameterizedStandard:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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class PS_Match_TranslationMissalignment(ParameterizedStandard) |
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A match with unknown translation missalignment.
the initial guess for missalignment is [a/10,a/10], where a is the
waveguide width |
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- Method resolution order:
- PS_Match_TranslationMissalignment
- ParameterizedStandard
- __builtin__.object
Methods defined here:
- __init__(self, wb, initial_offset=0.10000000000000001, **kwargs)
- takes:
wb: a WorkingBand type, with a RectangularWaveguide object
for its tline property.
initial_offset: initial offset guess, as a fraction of a,
(the waveguide width dimension)
**kwargs: passed to self.function
Data descriptors inherited from ParameterizedStandard:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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class PS_Parameterless(ParameterizedStandard) |
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A parameterless standard.
this is needed so that the calibration algorithm doesnt have to
handle more than one type of standard |
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- Method resolution order:
- PS_Parameterless
- ParameterizedStandard
- __builtin__.object
Methods defined here:
- __init__(self, ideal_network)
- takes:
ideal_network: a Network instance of the standard
Data descriptors inherited from ParameterizedStandard:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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class ParameterizedStandard(__builtin__.object) |
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A parameterized standard represents a calibration standard which
has uncertainty in its response. This uncertainty is functionally
known, and represented by a parametric function, where the
uknown quantity is the adjustable parameter. |
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Methods defined here:
- __init__(self, function=None, parameters={}, **kwargs)
- takes:
function: a function which will be called to generate
a Network type, to be used as a ideal response.
parameters: an dictionary holding an list of parameters,
which will be the dependent variables to optimize.
these are passed to the network creating function.
**kwargs: keyword arguments passed to the function, but
not used in parametric optimization.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- network
- a Networks instance generated by calling function(), for
the current set of parameters (and kwargs)
- number_of_parameters
- the number of parameters this standard has
- parameter_array
- This property provides a 1D-array interface to the parameters
dictionary. This is needed to intereface teh optimizing function
because it only takes a 1D-array. Therefore, order must be
preserved with accessing and updating the parameters through this
array. To handle this I make it return and update in alphebetical
order of the parameters dictionary keys.
- parameter_keys
- returns a list of parameter dictionary keys in alphabetical order
- s
- a direct access to the calulated networks' s-matrix
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