Parameter

class ionworkspipeline.Parameter(name, initial_value=None, bounds=None, prior=None, normalize=None, check_bounds=None, check_initial_value=None, initial_guess_distribution=None)

Parameter object for use in data fits, stores the initial value and bounds

Inherits from pybamm.InputParameter for compatibility with PyBaMM models.

Parameters

namestr

The name of the parameter.

initial_valuefloat or int, optional

The initial value to be assigned to the parameter. If not provided and bounds are given, will be set to the mean of finite bounds. Defaults to 1 if no bounds are given.

boundstuple, optional

A tuple defining the lower and upper bounds for the parameter. Defaults to (-np.inf, np.inf).

prioriwp.stats.Distribution, optional

The prior distribution for the parameter.

normalizebool, optional

Whether to normalize the parameter by its initial value. Defaults to True.

fitting_scalefloat, optional

The scale factor for the parameter. If not provided, the parameter will be normalized by its initial value.

check_boundsbool, optional

Whether to check the bounds of the parameter. Defaults to True.

check_initial_valuebool, optional

Whether to check the initial value of the parameter. Defaults to True.

initial_guess_distributioniwp.stats.Distribution, optional

The initial guess distribution for the parameter. Defaults to a uniform distribution between the bounds. The provided distribution must be either a iwp.stats.Uniform or iwp.stats.PointMass.

Extends: ionworkspipeline.controllers.ConfigMixin

property base_parameter

Returns the base parameter

property bounds

Returns the corrected parameter bounds

get_bounds()

Returns the unmodified bounds

get_initial_guess_distribution() → Distribution | None

Returns the unmodified initial guess distribution for the parameter

get_initial_value()

Returns the unmodified initial value

property initial_guess_distribution: Distribution

Returns the initial guess distribution for the parameter

property initial_value

Returns the corrected initial value

property is_transformed

Returns whether the parameter represents a transformed variable.

Returns

bool

True if the parameter is a transformed variable (e.g., “Log10(Diffusion Coefficient)”) False if the parameter is in its original form (e.g., “Diffusion Coefficient”)

Examples

>>> from ionworkspipeline.data_fits.parameter import Parameter
>>> from ionworkspipeline.data_fits.transforms import Log10
>>> param = Parameter("Diffusion Coefficient", 1e-9, (1e-10, 1e-8))
>>> param.is_transformed
False
>>> transformed_param = Log10(param)
>>> transformed_param.is_transformed
True

property prior

Returns the prior

set_bounds(value, check_initial_value=True, check_bounds=True)

Sets the parameter bounds

set_fitting_scale(value=None)

Sets the fitting scale for the parameter

Parameters

valuefloat, optional

If the parameter is not normalized, the value to set the fitting scale to.

set_initial_guess_distribution(initial_guess_distribution: Distribution | None)

Sets the initial guess distribution for the parameter

set_prior(prior)

Sets the prior for the parameter

to_config() → dict

Convert the Parameter to a JSON-serializable configuration dictionary.

Returns a dictionary that can be used with parse_parameters() to reconstruct the parameter. The name field is excluded since it comes from the dictionary key in the parser.

Returns

dict

Configuration dictionary with all initialization parameters, excluding validation-only parameters (check_bounds, check_initial_value) and name (which comes from the parser’s dict key).

to_dict(name)

Convert the parameter to a dictionary for fitting.

This method is used by the DataFit class to process parameters for fitting. For a basic Parameter object, it simply returns a dictionary with one entry mapping the given name to this parameter object.

Parameters

namestr

The name to use for the parameter in the model. This may be different from the parameter’s internal name.

Returns

dict

A dictionary with a single entry mapping the given name to this parameter.