abc

CalibrationABC

Abstract base class for calibration classes.

Fitness abstractmethod property

Fitness: Type[FitnessBase]

io abstractmethod property

io: Self

parameters abstractmethod property

parameters: List[str]

calibrate

calibrate(
    experiments: Union[
        "Experiment", List["Experiment"], Dict[str, "Experiment"]
    ],
    *,
    includes: List[str | CalibrationKey] | None = None,
    include_additions: List[str | CalibrationKey] | None = None,
    excludes: List[str | CalibrationKey] | None = None,
    experiment_specific_parameters: List[str] | None = None,
    x0: Dict[str | CalibrationKey, float] | None = None,
    bounds: Dict[str | CalibrationKey, Tuple[float, float]] | None = None,
    stds: Dict[str | CalibrationKey, float] | None = None,
    fitness: Union["FitnessBase", Type["FitnessBase"], str, None] = None,
    error_fitness: float = 10000000000.0,
    method: str = "PYCMA-CMAES",
    save_to: str | None = None,
    **kwargs
) -> Tuple[Dict[str, float], OptimizerResult]

Calibrate the yield function to the experiments.

PARAMETER	DESCRIPTION
experiments	Experiments to evaluate the fitness, by default None which means all experiments in the manager TYPE: Experiment | List[Experiment] | Dict[str, Experiment]
includes	Parameters to include in the calibration, by default None which means all parameters TYPE: List[str | CalibrationKey] DEFAULT: None
include_additions	Additional parameters to include in the calibration, by default None TYPE: List[str | CalibrationKey] DEFAULT: None
excludes	Parameters to exclude from the calibration, by default None TYPE: List[str | CalibrationKey] DEFAULT: None
experiment_specific_parameters	Optimization parameters that are specific to each experiment, by default None TYPE: List[str] DEFAULT: None
x0	Initial guess for the parameters, by default None TYPE: Dict[str | CalibrationKey, float] DEFAULT: None
bounds	Bounds for the parameters, by default None TYPE: Dict[str | CalibrationKey, Tuple[float, float]] DEFAULT: None
stds	Standard deviations for the parameters, by default None TYPE: Dict[str | CalibrationKey, float] DEFAULT: None
fitness	Custom fitness object or class or the full name of the class, by default None TYPE: FitnessBase | Type[FitnessBase] | str DEFAULT: None
error_fitness	Fitness value to return if the simulation fails, by default 1e10 TYPE: float DEFAULT: 10000000000.0
method	Optimization method, by default "PYCMA-CMAES" TYPE: str DEFAULT: 'PYCMA-CMAES'
save_to	Save the calibration results to a file, by default None TYPE: str DEFAULT: None
kwargs	Fitness or optimizer keyword arguments, if the fitness has attributes with the same name, the value will be set to the attribute, and other arguments will be passed to the optimizer DEFAULT: {}

RETURNS	DESCRIPTION
Tuple[Dict[str, float], OptimizerResult]	Optimized parameters and the optimization result

fitness

fitness(
    experiments: Union[
        "Experiment", List["Experiment"], Dict[str, "Experiment"]
    ],
    *,
    x0: Dict[str, float] | None = None,
    fitness: Union["FitnessBase", Type["FitnessBase"], str, None] = None,
    error_fitness: float = 10000000000.0,
    **kwargs
) -> float

Evaluate the fitness of the yield function to the experiments.

setParameters

setParameters(params: Dict | None = None, *, force: bool = False, **kwargs)

Set the parameters of the yield function.