gemseo.uncertainty.distributions.openturns.log_normal_settings module#

Settings for the OpenTURNS-based log-normal distributions.

Settings OTLogNormalDistribution_Settings(*, transformation='', lower_bound=None, upper_bound=None, threshold=0.5, mu=1.0, sigma=1.0, location=0.0, set_log=False)[source]#

Bases: BaseLogNormalDistribution_Settings, _OTDistribution_Settings_Mixin

The settings of an OpenTURNS-based log-normal 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 (Annotated[float, Ge(ge=0.0), Le(le=1.0)]) --

    By default it is set to 0.5.

  • mu (float) --

    By default it is set to 1.0.

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

    By default it is set to 1.0.

  • location (float) --

    By default it is set to 0.0.

  • set_log (bool) --

    By default it is set to False.

Return type:

None

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