gemseo.algos.doe.morris_doe.settings.morris_doe_settings module#

The settings of the Morris DOE.

Settings MorrisDOE_Settings(*, enable_progress_bar=None, eq_tolerance=0.01, ineq_tolerance=0.0001, log_problem=True, max_time=0.0, normalize_design_space=False, reset_iteration_counters=True, round_ints=True, use_database=True, use_one_line_progress_bar=False, store_jacobian=True, eval_func=True, eval_jac=False, n_processes=1, wait_time_between_samples=0.0, callbacks=(), n_samples=0, doe_algo_name='PYDOE_LHS', doe_algo_settings=<factory>, step=0.05)[source]#

Bases: BaseDOESettings

The MorrisDOE settings.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • enable_progress_bar (bool | None)

  • eq_tolerance (Annotated[float, Ge(ge=0), Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.01.

  • ineq_tolerance (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.0001.

  • log_problem (bool) --

    By default it is set to True.

  • max_time (Annotated[float, Ge(ge=0), Ge(ge=0)]) --

    By default it is set to 0.0.

  • normalize_design_space (bool) --

    By default it is set to False.

  • reset_iteration_counters (bool) --

    By default it is set to True.

  • round_ints (bool) --

    By default it is set to True.

  • use_database (bool) --

    By default it is set to True.

  • use_one_line_progress_bar (bool) --

    By default it is set to False.

  • store_jacobian (bool) --

    By default it is set to True.

  • eval_func (bool) --

    By default it is set to True.

  • eval_jac (bool) --

    By default it is set to False.

  • n_processes (Annotated[int, Gt(gt=0)]) --

    By default it is set to 1.

  • wait_time_between_samples (Annotated[float, Ge(ge=0)]) --

    By default it is set to 0.0.

  • callbacks (Sequence[Annotated[Callable[[int, tuple[dict[str, float | ndarray[Any, dtype[floating[Any]]]], dict[str, ndarray[Any, dtype[floating[Any]]]]]], Any], WithJsonSchema(json_schema={}, mode=None)]]) --

    By default it is set to ().

  • n_samples (Annotated[int, Ge(ge=0)]) --

    By default it is set to 0.

  • doe_algo_name (str) --

    By default it is set to "PYDOE_LHS".

  • doe_algo_settings (Mapping[str, Any]) --

    By default it is set to <factory>.

  • step (Annotated[float, Gt(gt=0)]) --

    By default it is set to 0.05.

Return type:

None

doe_algo_name: str = 'PYDOE_LHS'#

The name of the DOE algorithm to repeat the OAT DOE.

doe_algo_settings: StrKeyMapping [Optional]#

The options of the DOE algorithm.

n_samples: NonNegativeInt = 0#

The maximum number of samples required by the user.

If 0, deduce it from the design space dimension and n_replicates.

Constraints:

  • ge = 0

step: PositiveFloat = 0.05#

The relative step of the OAT DOE.

Constraints:

  • gt = 0

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None