InterfaceObject

class InterfaceObject(*args, **kwargs)

Methods

getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getName()	Accessor to the object's name.
setName(name)	Accessor to the object's name.

__init__(*args, **kwargs)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

Estimate moments from sample

Estimate moments from sample

Sample manipulation

Sample manipulation

A quick start guide to the Point and Sample classes

A quick start guide to the Point and Sample classes

Estimate Wilks and empirical quantile

Estimate Wilks and empirical quantile

Compare unconditional and conditional histograms

Compare unconditional and conditional histograms

Compute squared SRC indices confidence intervals

Compute squared SRC indices confidence intervals

Model a singular multivariate distribution

Model a singular multivariate distribution

Estimate a GEV on the Venice sea-levels data

Estimate a GEV on the Venice sea-levels data

Bandwidth sensitivity in kernel smoothing

Bandwidth sensitivity in kernel smoothing

Fit an extreme value distribution

Fit an extreme value distribution

Estimate a conditional quantile

Estimate a conditional quantile

Estimate a multivariate distribution

Estimate a multivariate distribution

Estimate a GPD on the Wooster temperature data

Estimate a GPD on the Wooster temperature data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the Dow Jones Index data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the daily rainfall data

Estimate a GEV on race times data

Estimate a GEV on race times data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Test independence

Test independence

Test the copula

Test the copula

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : get the statistics distribution

Fit a parametric copula

Fit a parametric copula

Fit a non parametric copula

Fit a non parametric copula

Estimate tail dependence coefficients on the wave-surge data

Estimate tail dependence coefficients on the wave-surge data

Estimate tail dependence coefficients on the wind data

Estimate tail dependence coefficients on the wind data

Estimate a multivariate ARMA process

Estimate a multivariate ARMA process

Estimate a non stationary covariance function

Estimate a non stationary covariance function

Estimate a spectral density function

Estimate a spectral density function

Estimate a stationary covariance function

Estimate a stationary covariance function

Visualize pairs

Visualize pairs

Visualize clouds

Visualize clouds

Visualize sensitivity

Visualize sensitivity

Create a conditional random vector

Create a conditional random vector

Create a conditional distribution

Create a conditional distribution

Create your own distribution given its quantile function

Create your own distribution given its quantile function

Create a Bayes distribution

Create a Bayes distribution

Truncate a distribution

Truncate a distribution

Create and draw multivariate distributions

Create and draw multivariate distributions

Generate random variates by inverting the CDF

Generate random variates by inverting the CDF

Transform a distribution

Transform a distribution

Overview of univariate distribution management

Overview of univariate distribution management

Quick start guide to distributions

Quick start guide to distributions

Create a customized distribution or copula

Create a customized distribution or copula

Draw minimum volume level sets

Draw minimum volume level sets

Create a copula

Create a copula

Extract the copula from a distribution

Extract the copula from a distribution

Assemble copulas

Assemble copulas

Composite random vector

Composite random vector

Create a random vector

Create a random vector

Create a random vector

Create a random vector

Create a functional basis process

Create a functional basis process

Add a trend to a process

Add a trend to a process

Create a parametric spectral density function

Create a parametric spectral density function

Export a field to VTK

Export a field to VTK

Create a stationary covariance model

Create a stationary covariance model

Use the Box-Cox transformation

Use the Box-Cox transformation

Create a stationary covariance model

Create a stationary covariance model

Create a normal process

Create a normal process

Create a custom covariance model

Create a custom covariance model

Create a random walk process

Create a random walk process

Create a spectral model

Create a spectral model

Draw a field

Draw a field

Create a process from random vectors and processes

Create a process from random vectors and processes

Sample trajectories from a Gaussian Process with correlated outputs

Sample trajectories from a Gaussian Process with correlated outputs

Draw fields

Draw fields

Create a discrete Markov chain process

Create a discrete Markov chain process

Trend computation

Trend computation

Compare covariance models

Compare covariance models

Create a mesh

Create a mesh

Create a linear least squares model

Create a linear least squares model

Create a general linear model metamodel

Create a general linear model metamodel

Taylor approximations

Taylor approximations

Create a linear model

Create a linear model

Mixture of experts

Mixture of experts

Perform stepwise regression

Perform stepwise regression

Distribution of estimators in linear regression

Distribution of estimators in linear regression

Over-fitting and model selection

Over-fitting and model selection

Apply a transform or inverse transform on your polynomial chaos

Apply a transform or inverse transform on your polynomial chaos

Fit a distribution from an input sample

Fit a distribution from an input sample

Polynomial chaos exploitation

Polynomial chaos exploitation

Polynomial chaos graphs

Polynomial chaos graphs

Create a full or sparse polynomial chaos expansion

Create a full or sparse polynomial chaos expansion

Advanced polynomial chaos construction

Advanced polynomial chaos construction

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Plot enumeration rules

Plot enumeration rules

Polynomial chaos expansion cross-validation

Polynomial chaos expansion cross-validation

Polynomial chaos is sensitive to the degree

Polynomial chaos is sensitive to the degree

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Kriging: propagate uncertainties

Kriging: propagate uncertainties

Kriging : multiple input dimensions

Kriging : multiple input dimensions

Kriging : draw the likelihood

Kriging : draw the likelihood

Kriging: choose an arbitrary trend

Kriging: choose an arbitrary trend

Example of multi output Kriging on the fire satellite model

Example of multi output Kriging on the fire satellite model

Kriging : generate trajectories from a metamodel

Kriging : generate trajectories from a metamodel

Kriging with an isotropic covariance function

Kriging with an isotropic covariance function

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Kriging : quick-start

Kriging : quick-start

Sequentially adding new points to a kriging

Sequentially adding new points to a kriging

Kriging: configure the optimization solver

Kriging: configure the optimization solver

Kriging: choose a polynomial trend

Kriging: choose a polynomial trend

Advanced Kriging

Advanced Kriging

Kriging: metamodel with continuous and categorical variables

Kriging: metamodel with continuous and categorical variables

Metamodel of a field function

Metamodel of a field function

Evaluate the mean of a random vector by simulations

Evaluate the mean of a random vector by simulations

Analyse the central tendency of a cantilever beam

Analyse the central tendency of a cantilever beam

Estimate moments from Taylor expansions

Estimate moments from Taylor expansions

Simulate an Event

Simulate an Event

Estimate a probability with Monte Carlo

Estimate a probability with Monte Carlo

Use a randomized QMC algorithm

Use a randomized QMC algorithm

Use the Adaptive Directional Stratification Algorithm

Use the Adaptive Directional Stratification Algorithm

Use the post-analytical importance sampling algorithm

Use the post-analytical importance sampling algorithm

Use the Directional Sampling Algorithm

Use the Directional Sampling Algorithm

Create a threshold event

Create a threshold event

Specify a simulation algorithm

Specify a simulation algorithm

Estimate a flooding probability

Estimate a flooding probability

Use the Importance Sampling algorithm

Use the Importance Sampling algorithm

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a buckling probability

Estimate a buckling probability

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Use the FORM algorithm in case of several design points

Use the FORM algorithm in case of several design points

Subset Sampling

Subset Sampling

Use the FORM - SORM algorithms

Use the FORM - SORM algorithms

Non parametric Adaptive Importance Sampling (NAIS)

Non parametric Adaptive Importance Sampling (NAIS)

Create a domain event

Create a domain event

Test the design point with the Strong Maximum Test

Test the design point with the Strong Maximum Test

Time variant system reliability problem

Time variant system reliability problem

Create unions or intersections of events

Create unions or intersections of events

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

An illustrated example of a FORM probability estimate

An illustrated example of a FORM probability estimate

Cross Entropy Importance Sampling

Cross Entropy Importance Sampling

Using the FORM - SORM algorithms on a nonlinear function

Using the FORM - SORM algorithms on a nonlinear function

Create an event based on a process

Create an event based on a process

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

Parallel coordinates graph as sensitivity tool

Parallel coordinates graph as sensitivity tool

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

Use the ANCOVA indices

Use the ANCOVA indices

Estimate Sobol’ indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

Estimate Sobol' indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

The HSIC sensitivity indices: the Ishigami model

The HSIC sensitivity indices: the Ishigami model

Example of sensitivity analyses on the wing weight model

Example of sensitivity analyses on the wing weight model

Create a composite design of experiments

Create a composite design of experiments

Create a Monte Carlo design of experiments

Create a Monte Carlo design of experiments

Create a Gauss product design

Create a Gauss product design

Compute the L2 error between two functions

Compute the L2 error between two functions

Create a random design of experiments

Create a random design of experiments

Create mixed deterministic and probabilistic designs of experiments

Create mixed deterministic and probabilistic designs of experiments

Create a design of experiments with discrete and continuous variables

Create a design of experiments with discrete and continuous variables

Deterministic design of experiments

Deterministic design of experiments

Create a deterministic design of experiments

Create a deterministic design of experiments

Plot Smolyak multi-indices

Plot Smolyak multi-indices

Generate low discrepancy sequences

Generate low discrepancy sequences

Plot the Smolyak quadrature

Plot the Smolyak quadrature

Merge nodes in Smolyak quadrature

Merge nodes in Smolyak quadrature

Use the Smolyak quadrature

Use the Smolyak quadrature

Create univariate functions

Create univariate functions

Create a composed function

Create a composed function

Create an aggregated function

Create an aggregated function

Create a linear combination of functions

Create a linear combination of functions

Create a symbolic function

Create a symbolic function

Create a quadratic function

Create a quadratic function

Increase the output dimension of a function

Increase the output dimension of a function

Create a parametric function

Create a parametric function

Create a Python function

Create a Python function

Increase the input dimension of a function

Increase the input dimension of a function

Defining Python and symbolic functions: a quick start introduction to functions

Defining Python and symbolic functions: a quick start introduction to functions

Create multivariate functions

Create multivariate functions

Create a multivariate basis of functions from scalar multivariable functions

Create a multivariate basis of functions from scalar multivariable functions

Value function

Value function

Vertex value function

Vertex value function

Define a connection function with a field output

Define a connection function with a field output

Logistic growth model

Logistic growth model

Function manipulation

Function manipulation

Link to a computer code with coupling tools

Link to a computer code with coupling tools

Generate flooding model observations

Generate flooding model observations

Calibrate a parametric model: a quick-start guide to calibration

Calibrate a parametric model: a quick-start guide to calibration

Generate observations of the Chaboche mechanical model

Generate observations of the Chaboche mechanical model

Calibration without observed inputs

Calibration without observed inputs

Calibration of the logistic model

Calibration of the logistic model

Calibration of the deflection of a tube

Calibration of the deflection of a tube

Calibration of the flooding model

Calibration of the flooding model

Calibration of the Chaboche mechanical model

Calibration of the Chaboche mechanical model

Gibbs sampling of the posterior distribution

Gibbs sampling of the posterior distribution

Sampling from an unnormalized probability density

Sampling from an unnormalized probability density

Posterior sampling using a PythonDistribution

Posterior sampling using a PythonDistribution

Bayesian calibration of a computer code

Bayesian calibration of a computer code

Bayesian calibration of the flooding model

Bayesian calibration of the flooding model

Customize your Metropolis-Hastings algorithm

Customize your Metropolis-Hastings algorithm

Linear Regression with interval-censored observations

Linear Regression with interval-censored observations

Estimate an integral

Estimate an integral

Save/load a study

Save/load a study

Iterated Functions System

Iterated Functions System

Compute leave-one-out error of a polynomial chaos expansion

Compute leave-one-out error of a polynomial chaos expansion

Compute confidence intervals of a regression model from data

Compute confidence intervals of a regression model from data

Compute confidence intervals of a univariate noisy function

Compute confidence intervals of a univariate noisy function

Mix/max search and sensitivity from design

Mix/max search and sensitivity from design

Optimization with constraints

Optimization with constraints

Optimization using NLopt

Optimization using NLopt

Mix/max search using optimization

Mix/max search using optimization

Control algorithm termination

Control algorithm termination

Optimization using bonmin

Optimization using bonmin

Multi-objective optimization using Pagmo

Multi-objective optimization using Pagmo

Quick start guide to optimization

Quick start guide to optimization

Optimization of the Rastrigin test function

Optimization of the Rastrigin test function

Optimization using dlib

Optimization using dlib

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples

Estimate moments iteratively

Estimate moments iteratively

Estimate extrema iteratively

Estimate extrema iteratively

Estimate threshold exceedance iteratively

Estimate threshold exceedance iteratively

Plot the log-likelihood contours of a distribution

Plot the log-likelihood contours of a distribution

A quick start guide to contours

A quick start guide to contours

A quick start guide to graphs

A quick start guide to graphs