gemseo.uncertainty.distributions.base_settings.weibull_settings module

Base settings for defining a uniform distribution.

Settings BaseWeibullDistribution_Settings(*, location=0.0, scale=1.0, shape=1.0, use_weibull_min=True)

Bases: BaseDistribution_Settings

The base settings of a uniform 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:

• location (float) --

  By default it is set to 0.0.

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

  By default it is set to 1.0.

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

  By default it is set to 1.0.

• use_weibull_min (bool) --

  By default it is set to True.

Return type:

None

location: float = 0.0

The location parameter \(\gamma\) of the Weibull distribution.

scale: PositiveFloat = 1.0

The scale parameter of the Weibull distribution.

Constraints:

• gt = 0

shape: PositiveFloat = 1.0

The shape parameter of the Weibull distribution.

Constraints:

• gt = 0

use_weibull_min: bool = True

Whether to use the Weibull minimum extreme value distribution (the support of the random variable is \([\gamma,+\infty[\)) or the Weibull maximum extreme value distribution (the support of the random variable is \(]-\infty[,\gamma]\)).

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