pybop.parameters.parameter#

Attributes#

`ArrayLike`
`BoundsPair`
`Inputs`
`NumericValue`

Exceptions#

`ParameterError`	Base exception for parameter-related errors.
`ParameterNotFoundError`	Raised when a parameter is not found.
`ParameterValidationError`	Raised when parameter validation fails.

Classes#

`Bounds`	Immutable bounds representation with validation.
`Parameter`	Represents a parameter within the PyBOP framework.
`Parameters`	Container for managing multiple Parameter objects with additional functionality.

Module Contents#

exception pybop.parameters.parameter.ParameterError[source]#

Bases: Exception

Base exception for parameter-related errors.

exception pybop.parameters.parameter.ParameterNotFoundError[source]#

Bases: ParameterError

Raised when a parameter is not found.

exception pybop.parameters.parameter.ParameterValidationError[source]#

Bases: ParameterError

Raised when parameter validation fails.

class pybop.parameters.parameter.Bounds[source]#

Immutable bounds representation with validation.

lower#

Lower bound (inclusive)

Type:: float

upper#

Upper bound (inclusive)

Type:: float

__post_init__() → None[source]#

clip(value: NumericValue) → float[source]#: Clip value to bounds.

clip_array(values: ArrayLike) → numpy.typing.NDArray[numpy.floating][source]#: Clip array values to bounds.

contains(value: NumericValue) → bool[source]#: Check if value is within bounds.

contains_array(values: ArrayLike) → bool[source]#: Check if all values in array are within bounds.

width() → float[source]#: Return the width of the bounds.

lower: float#

upper: float#

class pybop.parameters.parameter.Parameter(distribution: scipy.stats.distributions.rv_frozen | pybop.parameters.distributions.Distribution | None = None, bounds: BoundsPair | None = None, initial_value: float = None, transformation: pybop.transformation.base_transformation.Transformation | None = None)[source]#

Represents a parameter within the PyBOP framework.

This class encapsulates the definition of a parameter, including its initial value, bounds.

Parameters:

distribution (stats.distribution.rv_frozen | Distribution, optional) – Distribution of the parameter
bounds (tuple[float, float], optional) – Parameter bounds as (lower, upper)
initial_value (NumericValue, optional) – Initial parameter value
transformation (Transformation, optional) – Parameter transformation

__call__(*unused_args, **unused_kwargs) → float | None[source]#: Return the initial value. The unused arguments are to pass pybamm.ParameterValues checks.

__repr__() → str[source]#: String representation of the parameter.

_check_compatible_transformation() → None[source]#: Raise an error if the transformation is not compatible with the distribution.

_validate_initial_value_within_bounds() → None[source]#: Validate that the initial value is within the bounds.

get_initial_value(transformed: bool = False) → numpy.typing.NDArray | None[source]#: Get initial value in either the model space or the transformed search space.

get_mean(transformed: bool = False)[source]#: Get the mean of each parameter, or its initial value.

get_std(transformed: bool = False)[source]#: Get the standard deviation, or an estimate of it.

sample_from_distribution(n_samples: int = 1, *, random_state: int | None = None, transformed: bool = False) → numpy.typing.NDArray[numpy.floating] | None[source]#

Sample from parameter’s distribution.

Parameters:

n_samples (int) – Number of samples to draw (default: 1).
random_state (int, optional) – Random seed for reproducibility.
transformed (bool, optional) – Whether to apply transformation to samples (default: False).

Returns:

Array of samples, or None if no distribution exists exists

Return type:

NDArray[np.floating] or None

update_initial_value(value: NumericValue | None) → None[source]#: Update the initial parameter value.

_bounds = None#

_distribution = None#

_initial_value = None#

_transformation#

property bounds: tuple | None#: Parameter bounds as (lower, upper) tuple.

property distribution: Any | None#

property initial_value: float | None#

property transformation: pybop.transformation.base_transformation.Transformation#

class pybop.parameters.parameter.Parameters(parameters: dict | Parameters | None = None)[source]#

Container for managing multiple Parameter objects with additional functionality.

This class provides a comprehensive interface for parameter management including validation, transformation, serialisation, and bulk operations.

__contains__(name: str) → bool[source]#

__getitem__(name: str) → Parameter[source]#

__iter__() → collections.abc.Iterator[Parameter][source]#

__len__() → int[source]#

__repr__() → str[source]#

__setitem__(name: str, param: Parameter) → None[source]#

_add(name: str, parameter: Parameter, update_transform: bool = True, update_distribution: bool = True) → None[source]#

Internal method to add a parameter to the collection.

Parameters:

name (str) – Name of the parameter.
parameter (pybop.Parameter) – The parameter to add.
update_transform (bool, optional) – Whether to update the transformation after adding (default: True).
update_distribution (bool, optional) – Whether to update the joint or multivariate distribution (default: True).

_bulk_update_initial_values(values: ArrayLike | Inputs) → None[source]#: Update initial values in bulk.

_collect_parameters(parameters)[source]#

add(name: str, parameter: Parameter, update_distribution: bool = True) → None[source]#: Add a parameter to the collection.

construct_transformation() → pybop.transformation.base_transformation.Transformation[source]#: Create a ComposedTransformation object from the individual parameter transformations.

convert_grad_to_array(grad: dict[str, numpy.ndarray]) → numpy.ndarray[source]#: Get an array of sensitivities with the parameters in the expected order.

copy() → Parameters[source]#: Create a deep copy of the Parameters object.

get(name: str) → Parameter[source]#: Get a parameter by name.

get_bounds(transformed: bool = False) → dict[source]#

Get bounds for each parameter as a dictionary.

Parameters:: transformed (bool) – If True, the transformation is applied to the output (default: False).

get_bounds_array(transformed: bool = False) → numpy.ndarray[source]#

Retrieve parameter bounds in numpy format.

Returns:: bounds – An array of shape (n_parameters, 2) containing the bounds for each parameter.
Return type:: numpy.ndarray

get_bounds_for_plotly(transformed: bool = False) → numpy.ndarray[source]#

Retrieve parameter bounds in the format expected by Plotly.

Returns:: bounds – An array of shape (n_parameters, 2) containing the bounds for each parameter.
Return type:: numpy.ndarray

get_covariance(transformed: bool = False)[source]#

Get the covariance matrix, or an estimate of it.

Parameters:: transformed (bool, optional) – If True, the transformation is applied to the output (default: False).

get_initial_values(*, transformed: bool = False) → numpy.typing.NDArray[numpy.floating][source]#

Get initial values as array.

Parameters:: transformed (bool, default=False) – Whether to apply transformations to bounds
Returns:: Array of initial values
Return type:: NDArray[np.floating]

get_mean(transformed: bool = False)[source]#

Get the mean of each parameter, or its initial value.

Parameters:: transformed (bool, optional) – If True, the transformation is applied to the output (default: False).

get_std(transformed: bool = False) → list[source]#

Get the standard deviation, or an estimate of it, for each parameter.

Parameters:: transformed (bool, optional) – If True, the transformation is applied to the output (default: False).

items() → collections.abc.Iterator[tuple[str, Parameter]][source]#: Iterate over (name, parameter) pairs.

join(parameters=None)[source]#

Join two Parameters objects into the first by copying across each Parameter.

Parameters:: parameters (pybop.Parameters)

keys() → collections.abc.Iterator[str][source]#: Iterate over parameter names.

sample_from_distribution(n_samples: int = 1, *, random_state: int | None = None, transformed: bool = False) → numpy.typing.NDArray[numpy.floating] | None[source]#

Sample from a joint or multivariate distribution.

Parameters:

n_samples (int) – The number of samples to draw (default: 1).
random_state (int, optional) – The random state seed for reproducibility (default: None).
transformed (bool, optional) – If True, the transformation is applied to the output (default: False).

Returns:

Array of shape (n_samples, n_parameters) or None if any distribution is missing

Return type:

NDArray[np.floating] or None

set(name: str, param: Parameter, update_distribution: bool = True) → None[source]#: Set a parameter by name.

to_dict(values: str | ArrayLike | None = None) → Inputs[source]#

Return values as a dictionary of inputs.

Parameters:: values (str or array-like, optional) – Which values to use (‘initial’) or custom array. Default is “initial”.
Returns:: Dictionary mapping parameter names to values
Return type:: Inputs

to_inputs_list(values: numpy.ndarray | list[numpy.ndarray]) → list[Inputs][source]#: Return parameter values as a list of dictionaries, as required for multiprocessing.

update(*, initial_values: ArrayLike | Inputs | None = None, **individual_updates: dict[str, Any]) → None[source]#

Update multiple parameters efficiently.

Parameters:

initial_values (array-like or dict, optional) – New initial values (by position or name)
bounds (sequence or dict, optional) – New bounds (by position or name)
**individual_updates (dict) – Individual parameter updates with parameter names as keys

update_distribution()[source]#

Method to determine whether to construct a JointDistribution or a MultivariateDistribution and to set up the distribution.

Multivariate distributions are passed to individual parameters via the corresponding marginal distribution. The pybop.MarginalDistribution class retains the underlying pybop.MultivariateDistribution in the parent_distribution property.

values() → collections.abc.Iterator[Parameter][source]#: Iterate over parameters.

verify_inputs(inputs: Inputs) → bool[source]#: Check if the inputs are valid parameters.

_distribution = None#

_multivariate = None#

_parameters = None#

_transform = None#

property distribution: pybop.parameters.multivariate_distributions.BaseMultivariateDistribution | pybop.parameters.distributions.JointDistribution | None#: Return the joint or multivariate distribution.

property names: list[str]#

property transformation: pybop.transformation.base_transformation.Transformation#: Get the transformation for the parameters.

property transformed_distribution_properties#

pybop.parameters.parameter.ArrayLike#

pybop.parameters.parameter.BoundsPair#

pybop.parameters.parameter.Inputs#

pybop.parameters.parameter.NumericValue#