gemseo.algos.doe.scipy.settings.poisson_disk module#

Settings for the Poisson disk DOE from the SciPy library.

Settings PoissonDisk_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, seed=None, optimization=None, radius=0.05, hypersphere=Hypersphere.volume, ncandidates=30)[source]#

Bases: BaseSciPyDOESettings

The settings for the Poisson disk DOE from the SciPy library.

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)]) --

    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, Gt(gt=0)])

  • seed (int | None)

  • optimization (Optimizer | None)

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

    By default it is set to 0.05.

  • hypersphere (Hypersphere) --

    By default it is set to "volume".

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

    By default it is set to 30.

Return type:

None

hypersphere: Hypersphere = Hypersphere.volume#

The sampling strategy to generate potential candidates.

The candidates will be added in the final sample.

ncandidates: PositiveInt = 30#

The number of candidates to sample per iteration.

Constraints:
  • gt = 0

optimization: Optimizer | None = None#

The name of an optimization scheme to improve the DOE's quality.

If None, use the DOE as is. New in SciPy 1.10.0.

radius: NonNegativeFloat = 0.05#

The minimal distance to keep between points when sampling new candidates.

Constraints:
  • ge = 0

model_post_init(context, /)#

We need to both initialize private attributes and call the user-defined model_post_init method.

Parameters:
  • self (BaseModel)

  • context (Any)

Return type:

None