RepeatingModel

RepeatingModel = class RepeatingModel(Model, Dynamic)

RepeatingModel(ncomp, model, minComp=0, maxComp=None, fixed=None, same=None, growPrior=None, dynamic=True, copy=None, **kwargs) RepeatingModel is a dynamic model, that calls the same model zero or more times, each time with the next set of parameters. RepeatingModel is a Dynamic model that grows and shrinks according to a growPrior. The growPrior defaults to ExponentialPrior, unless a maxComp (max nr of components) then it defaults to a UniformPrior with limits minComp .. maxComp. When isDynamic is set to False or minComp == maxComp, the RepeatingModel is a normal model with a static number of parameters/components. Priors and/or limits for the RepeatingModel are stored in the (encapsulated) model. It can be arranged that all similar parameters are the same, represented by the same parameters. Use keywords same=. Attributes ---------- ncomp : int number of repetitions model : Model (encapsulated) model to be repeated same : None or int or list of int indices of parameters of model that get identical values index : list of int list of parameter indices not in same. isDyna : bool Whether this is a Dynamic Model. Attributes from Dynamic ----------------------- ncomp, deltaNpar, minComp, maxComp, growPrior Attributes from Model --------------------- parameters, stdevs, npchain _next, _head, _operation xUnit, yUnit (relegated to model) Attributes from FixedModel -------------------------- npmax, fixed, parlist, mlist Attributes from BaseModel -------------------------- npbase, ndim, priors, posIndex, nonZero, tiny, deltaP, parNames Example ------- >>> # Define a model containing between 1 and 6 VoigtModels, starting with 3 >>> # and all with the same widths (for Gauss and Cauchy) >>> vgt = VoigtModel() >>> mdl = RepeatingModel( 3, vgt, minComp=1. maxComp=6, same=[2,3] ) >>> print( mdl.npbase ) # 4 + 2 + 2 >>> 8 >>> # Define a static RepeatingModel of 5 GaussModels >>> gm = GaussModel() >>> mdl = RepeatingModel( 5, gm, isDynamic=False ) >>> print( mdl.npbase ) # 5 * 3 >>> 15 >>> # Define a RepeatingModel with and exponential grow prior with scale 10 >>> mdl = RepeatingModel( 1, gm, growPrior=ExponentialPrior( scale=10 ) ) >>> print( mdl.npbase ) # 3 >>> 3 Author Do Kester

Method resolution order:: RepeatingModel; Model; FixedModel; BaseModel; Dynamic; builtins.object

Constructor:

RepeatingModel( ncomp, model, minComp=0, maxComp=None, fixed=None,
same=None, growPrior=None, dynamic=True, copy=None, **kwargs): Repeating the same model several times. Parameters ---------- ncomp : int number of repetitions model : Model model to be repeated minComp : int (0) minimum number of repetitions maxComp : None or int maximum number of repetitions same : None or int or list of int indices of parameters of model that get identical values growPrior : None or Prior governing the birth and death. ExponentialPrior (scale=2) if maxOrder is None else UniformPrior dynamic : bool (True) Whether this is a Dynamic Model. copy : RepeatingModel model to copy Raises ------ AttributeError when fixed parameters are requested ValueError when order is outside [min..max] range

Methods defined here:

baseDerivative( xdata, params ): Returns the derivative df/dx at the input value. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters

baseName(): Return a string representation of the model.

baseParameterName( k ): Return the name of the indicated parameter. Parameters ---------- k : int parameter number.

baseParameterUnit( k ): Return the unit of the indicated parameter. Parameters ---------- k : int parameter number.

basePartial( xdata, params, parlist=None ): Returns the partials at the input value. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters parlist : array_like list of indices of active parameter

basePrior( k ): Return the prior for parameter k. Parameters ---------- k : int the parameter to be selected.

baseResult( xdata, params ): Returns the result of the model function. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters

changeNComp( dn )

copy(): Copy method.

grow( offset=0, rng=None, **kwargs ): Increase the the number of components by 1 (if allowed by maxComp) Parameters ---------- offset : int index where the dynamic model starts rng : RandomState random numbr generator Return ------ bool : succes

hasPriors(): Return True when the model has priors for all its parameters. Parameters ---------- isBound : bool Also check if the prior is bound.

isDynamic(): Return whether the model can change the number of parameters dynamically.

par2model( k ): Return index in model and repetition nr for param k

setLimits( lowLimits=None, highLimits=None ): Sets the limits for the parameters of the compound model. 1. It is valid to insert for either parameter a None value indicating no lower/upper limits. 2. When a lowerlimit >= upperlimit no limits are enforced. It only works in *Fitter classes which support it. Parameters ---------- lowLimits : array_like lower limits on the parameters highLimits : array_like upper limits on the parameters

setSame( same ): Assign similar parameters the same value. Parameters ---------- same : None or int or [int] similar parameters indicated as an index in encapsulated model.

shrink( offset=0, **kwargs ): Decrease the the number of componenets by 1 (if allowed by minComp) Remove an arbitrary item. Parameters ---------- offset : int index where the dynamic model starts Return ------ bool : succes

shuffle( param, offset, np, rng ): Shuffle the parameters of the components (if they are equivalent) Parameters ---------- param : array-like list of all parameters offset : int index where the dynamic model starts np : int length of the parameters of the dynamic model rng : RNG random number generator

Methods inherited from Model:

Overloaded operators and aliases

Other methods

Methods inherited from FixedModel:

Methods inherited from BaseModel:

Methods inherited from Dynamic: