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

`provides Parameterized Standard class.`

Modules
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numpy |  |  |

Classes
`` |  | __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)
`` | `A delay short of unknown length initial guess for length should be given to constructor`
| 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`
class PS_Delayed_Termination_TranslationMissalignment(ParameterizedStandard)
`` | `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`
| 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`
class PS_Delayed_Termination_UnknownLength_TranslationMissalignment(ParameterizedStandard)
`` | `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`
| 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`
class PS_Match_TranslationMissalignment(ParameterizedStandard)
`` | `A match with unknown translation missalignment. the initial guess for missalignment is [a/10,a/10], where a is the waveguide width`
| 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`
class PS_Parameterless(ParameterizedStandard)
`` | `A parameterless standard. this is needed so that the calibration algorithm doesnt have to handle more than one type of standard`
| 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`
class ParameterizedStandard(__builtin__.object)
`` | `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.`
| 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`