gemseo.post.variable_influence_settings module#

Settings for post-processing.

Settings VariableInfluence_Settings(*, save=True, show=False, file_path='', directory_path='', file_name='', file_extension='', fig_size=(11.0, 11.0), level=0.99, absolute_value=False, log_scale=False, save_var_files=False)[source]#

Bases: BasePostSettings

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:

  • save (bool) --

    By default it is set to True.

  • show (bool) --

    By default it is set to False.

  • file_path (Path | str) --

    By default it is set to "".

  • directory_path (Path | str) --

    By default it is set to "".

  • file_name (str) --

    By default it is set to "".

  • file_extension (str) --

    By default it is set to "".

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

    By default it is set to (11.0, 11.0).

  • level (Annotated[float, Ge(ge=0.0), Le(le=1.0)]) --

    By default it is set to 0.99.

  • absolute_value (bool) --

    By default it is set to False.

  • log_scale (bool) --

    By default it is set to False.

  • save_var_files (bool) --

    By default it is set to False.

Return type:

None

absolute_value: bool = False#

Whether to plot the absolute value of the influence.

level: float = 0.99#

The proportion of the total sensitivity to use as a threshold to filter the variables.

Constraints:

  • ge = 0.0

  • le = 1.0

log_scale: bool = False#

Whether to set the y-axis as log scale.

save_var_files: bool = False#

Whether to save the influential variables indices to a NumPy file.

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