openturns¶

Package Contents¶

Classes¶

class Widget_Metamodel_PC_Monotonicity¶

Bases: PyQt5.QtWidgets.QWidget

Provides an interface to visualize the impact of a single parameter. The other parameters are frozen using their central point.

set_metamodel_PC(theMetamodel_PC, objectiveName)¶

_generate_points(name_selected, N=100)¶: Generate points along variant selected attribute, with others fixed

update_graphs()¶

class Widget_Metamodel_PC_Tuner¶

Bases: PyQt5.QtWidgets.QWidget

Class to tune a OpenTURNS Polynomial Chaos fit. Provides an interface to interactively select fit degree and display information about its quality

_copy_metamodel()¶

set_metamodel_PC(theMetamodel_PC)¶

Set metamodel to fit

Parameters:	theMetamodel_PC – Metamodel_PC_Openturns

do_fit()¶

update()¶

class Widget_Collection_Metamodels(collection_metamodels)¶

Bases: PyQt5.QtWidgets.QWidget

Higher level widget to tune optimeed.consolidate.OpenTURNS_interface.name_collection.

Embeds the underlying Widget_Metamodel_PC_Tuner and Widget_Metamodel_PC_Monotonicity.

update_available_attributes()¶

update_tune_window()¶

update_monotonicity_windows()¶

class Widget_simplifyMetamodel(master_sensitivityAnalysis, controled_metamodel)¶

Bases: PyQt5.QtWidgets.QWidget

This widget works using SALib and restrains the number of parameters used to perform the metamodel fit to the first N most influencials. Usage: - Instantiates the widget using the base sensitivty parameters - Set the slave sensitivity analysis using set_slave_SA - Update the slave with the selected limited number of parameters using update_SA

set_nb_fit()¶

set_controled_metamodel(theMetamodel_PC)¶

set_master_SA(theSensitivityAnalysis)¶

update_controled_metamodel()¶