gemseo.uncertainty.distributions.openturns.weibull_settings module#
Settings for the OpenTURNS-based Weibull distributions.
- Settings OTWeibullDistribution_Settings(*, transformation='', lower_bound=None, upper_bound=None, threshold=0.5, location=0.0, scale=1.0, shape=1.0, use_weibull_min=True)[source]#
Bases:
BaseWeibullDistribution_Settings,_OTDistribution_Settings_MixinThe settings of an OpenTURNS-based 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:
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.
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
- 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