pybop._problem#

Classes#

`BaseProblem`	Base class for defining a problem within the PyBOP framework, compatible with PINTS.
`DesignProblem`	Problem class for design optimization problems.
`FittingProblem`	Problem class for fitting (parameter estimation) problems.

Module Contents#

class pybop._problem.BaseProblem(parameters, model=None, check_model=True, signal=['Voltage [V]'], additional_variables=[], init_soc=None, x0=None)[source]#

Base class for defining a problem within the PyBOP framework, compatible with PINTS.

Parameters:

parameters (list) – List of parameters for the problem.
model (object, optional) – The model to be used for the problem (default: None).
check_model (bool, optional) – Flag to indicate if the model should be checked (default: True).
signal (List[str]) – The signal to observe.
additional_variables (List[str], optional) – Additional variables to observe and store in the solution (default: []).
init_soc (float, optional) – Initial state of charge (default: None).
x0 (np.ndarray, optional) – Initial parameter values (default: None).

abstract evaluate(x)[source]#

Evaluate the model with the given parameters and return the signal.

Parameters:: x (np.ndarray) – Parameter values to evaluate the model at.
Raises:: NotImplementedError – This method must be implemented by subclasses.

abstract evaluateS1(x)[source]#

Evaluate the model with the given parameters and return the signal and its derivatives.

Parameters:: x (np.ndarray) – Parameter values to evaluate the model at.
Raises:: NotImplementedError – This method must be implemented by subclasses.

target()[source]#

Return the target dataset.

Returns:: The target dataset array.
Return type:: np.ndarray

time_data()[source]#

Returns the time data.

Returns:: The time array.
Return type:: np.ndarray

property model[source]#

class pybop._problem.DesignProblem(model, parameters, experiment, check_model=True, signal=['Voltage [V]'], additional_variables=[], init_soc=None, x0=None)[source]#

Bases: BaseProblem

Problem class for design optimization problems.

Extends BaseProblem with specifics for applying a model to an experimental design.

Parameters:

model (object) – The model to apply the design to.
parameters (list) – List of parameters for the problem.
experiment (object) – The experimental setup to apply the model to.
check_model (bool, optional) – Flag to indicate if the model parameters should be checked for feasibility each iteration (default: True).
signal (str, optional) – The signal to fit (default: “Voltage [V]”).
additional_variables (List[str], optional) – Additional variables to observe and store in the solution (default additions are: [“Time [s]”, “Current [A]”]).
init_soc (float, optional) – Initial state of charge (default: None).
x0 (np.ndarray, optional) – Initial parameter values (default: None).

evaluate(x)[source]#

Evaluate the model with the given parameters and return the signal.

Parameters:: x (np.ndarray) – Parameter values to evaluate the model at.
Returns:: y – The model output y(t) simulated with inputs x.
Return type:: np.ndarray

class pybop._problem.FittingProblem(model, parameters, dataset, check_model=True, signal=['Voltage [V]'], additional_variables=[], init_soc=None, x0=None)[source]#

Bases: BaseProblem

Problem class for fitting (parameter estimation) problems.

Extends BaseProblem with specifics for fitting a model to a dataset.

Parameters:

model (object) – The model to fit.
parameters (list) – List of parameters for the problem.
dataset (Dataset) – Dataset object containing the data to fit the model to.
signal (str, optional) – The variable used for fitting (default: “Voltage [V]”).
additional_variables (List[str], optional) – Additional variables to observe and store in the solution (default additions are: [“Time [s]”]).
init_soc (float, optional) – Initial state of charge (default: None).
x0 (np.ndarray, optional) – Initial parameter values (default: None).

evaluate(x)[source]#

Evaluate the model with the given parameters and return the signal.

Parameters:: x (np.ndarray) – Parameter values to evaluate the model at.
Returns:: y – The model output y(t) simulated with inputs x.
Return type:: np.ndarray

evaluateS1(x)[source]#

Evaluate the model with the given parameters and return the signal and its derivatives.

Parameters:: x (np.ndarray) – Parameter values to evaluate the model at.
Returns:: A tuple containing the simulation result y(t) and the sensitivities dy/dx(t) evaluated with given inputs x.
Return type:: tuple