gemseo.uncertainty.distributions.openturns.distribution_settings module#

Settings for the OpenTURNS-based probability distributions.

Settings OTDistribution_Settings(*, transformation='', lower_bound=None, upper_bound=None, threshold=0.5, interfaced_distribution='Uniform', parameters=<factory>, standard_parameters=<factory>)[source]#

Bases: BaseDistribution_Settings, _OTDistribution_Settings_Mixin

The settings of an OpenTURNS-based distribution.

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:

  • transformation (str) --

    By default it is set to "".

  • lower_bound (float | None)

  • upper_bound (float | None)

  • threshold (float) --

    By default it is set to 0.5.

  • interfaced_distribution (str) --

    By default it is set to "Uniform".

  • parameters (tuple[float, ...]) --

    By default it is set to <factory>.

  • standard_parameters (Mapping[str, str | int | float]) --

    By default it is set to <factory>.

Return type:

None

interfaced_distribution: str = 'Uniform'#

The name of the probability distribution.

parameters: tuple[float, ...] [Optional]#

The parameters of the probability distribution.

standard_parameters: Mapping[str, str | int | float] [Optional]#

The parameters of the probability distribution used for string representation only.

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