Parameters

Management of model parameters.

Parameter definitions are used to define free and fixed parameters and the allowable ranges of free parameters. They may also be used to define dependent parameters that are defined in terms of other parameters and dynamic parameters that vary based on the data being fitted.

Parameter definitions are stored in Parameters objects. They should not be confused with parameter dictionaries, which only contain specific parameter values, but no metadata.

Parameters may be saved to a JSON file to document the parameter definitions of a model variant, in a form that can be loaded and reused.

Search parameters

Parameters()	Class to manage model parameters.
Parameters.set_fixed(*args, **kwargs)	Set fixed parameter values.
Parameters.set_free(*args, **kwargs)	Set free parameter ranges.

Dependent parameters

Parameters.set_dependent(*args, **kwargs)	Set dependent parameters in terms of other parameters.
Parameters.eval_dependent(param)	Evaluate dependent parameters based on input parameters.

Dynamic parameters

Parameters.set_dynamic(trial_type, scope, ...)	Set dynamic parameters in terms of parameters and data.
Parameters.eval_dynamic(param[, study, recall])	Evaluate dynamic parameters based on data fields.

Parameter sets

Parameters.copy()	Copy the parameters definition.
Parameters.to_json(json_file)	Write parameter definitions to a JSON file.
read_json(json_file)	Read parameters from a JSON file.