yieldfunctionfitness

YieldFunctionFitness

Bases: FitnessBase

data_get_kwargs `class-attribute` `instance-attribute`

data_get_kwargs: Dict[str, Any] = {}

Keyword arguments for the get method of the state variable exporter

eq_stress `class-attribute` `instance-attribute`

eq_stress: float | None = None

Equivalent stress

error_fitness `class-attribute` `instance-attribute`

error_fitness: float = 10000000000.0

Error fitness.

error_raise `class-attribute` `instance-attribute`

error_raise: bool = False

Whether to raise an error if the fitness is not valid

experiment_excludes `class-attribute` `instance-attribute`

experiment_excludes: List[str] = []

Excluded experiments

experiment_get_kwargs `class-attribute` `instance-attribute`

experiment_get_kwargs: Dict[str, Any] = {}

Keyword arguments for the get method of experiment

experiment_includes `class-attribute` `instance-attribute`

experiment_includes: List[str] | None = None

Included experiments

experiment_weights `class-attribute` `instance-attribute`

experiment_weights: Dict[str, float] = {}

Weights for the experiments

experiments `instance-attribute`

experiments: List[Experiment]

List of experiments to evaluate.

io `instance-attribute`

io: Any

The IO object to create the model.

io_ `property`

io_: 'YieldFunction'

Only for type annotation.

keys `class-attribute` `instance-attribute`

keys: List[str | CalibrationKey] = []

Optimization keys

line_additions `class-attribute` `instance-attribute`

line_additions: Dict[str, Tuple[str, str]] = {}

Additional lines that can be expressions of the existing lines

line_excludes `class-attribute` `instance-attribute`

line_excludes: List[str] = []

Excluded lines

line_experiment_additions `class-attribute` `instance-attribute`

line_experiment_additions: Dict[str, Dict[str, Tuple[str, str]]] = {}

Additional lines that can be expressions of the existing lines for each experiment

line_experiment_excludes `class-attribute` `instance-attribute`

line_experiment_excludes: Dict[str, List[str]] = {}

Excluded lines for each experiment

line_experiment_includes `class-attribute` `instance-attribute`

line_experiment_includes: Dict[str, List[str]] = {}

Included lines for each experiment

line_experiment_weights `class-attribute` `instance-attribute`

line_experiment_weights: Dict[str, Dict[str, float]] = {}

Weights for the lines for each experiment

line_experiment_xys `class-attribute` `instance-attribute`

line_experiment_xys: Dict[str, Dict[str, Tuple[str, str]]] | None = None

Override default calibrated line x-y pairs for each experiment.

line_includes `class-attribute` `instance-attribute`

line_includes: List[str] | None = None

Included lines

line_weights `class-attribute` `instance-attribute`

line_weights: Dict[str, float] = {}

Weights for the lines

line_xys `class-attribute` `instance-attribute`

line_xys: Dict[str, Tuple[str, str]] | None = None

Override default calibrated line x-y pairs.

max_stress `class-attribute` `instance-attribute`

max_stress: float | None = None

Maximum stress

min_stress `class-attribute` `instance-attribute`

min_stress: float | None = None

Minimum stress

origin `class-attribute` `instance-attribute`

origin: Tuple[float, float, float] = (0, 0, 0)

Origin coordinates of the yield surface

plane `class-attribute` `instance-attribute`

plane: Tuple[float, float, float] = (1, 1, 1)

View the yield surface in this plane

evaluate

evaluate(
    x: Iterable | dict, *args, **kwargs
) -> Tuple[Dict[str, Dict[str, float]], Dict[str, Dict[str, float]]]

run

run(x: Iterable | dict, *args, **kwargs) -> float

serialize_keys

serialize_keys(value: List[str | CalibrationKey]) -> List[str]

setModelParameters

setModelParameters(
    yield_function: "YieldFunction", x: Iterable | dict, experiment: Experiment
)

Set the material parameters (for the given experiment) to the model.

validate_keys `classmethod`

validate_keys(value: List[str | CalibrationKey])

yieldfunctionfitness