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¶

Customize your Metropolis-Hastings algorithm

Bayesian calibration of a computer code

Bayesian calibration of the flooding model

Gibbs sampling of the posterior distribution

Linear Regression with interval-censored observations

Bayesian calibration of hierarchical fission gas release models

Sampling from an unscaled probability density

Posterior sampling using a PythonDistribution

Calibration of the Chaboche mechanical model

Calibration of the deflection of a tube

Calibration of the flooding model

Calibration of the logistic model

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

Calibration without observed inputs

Generate observations of the Chaboche mechanical model

Generate flooding model observations

Estimate a conditional quantile

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on race times data

Estimate a GEV on the Venice sea-levels data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the Wooster temperature data

Estimate a multivariate distribution

Fit an extreme value distribution

Model a singular multivariate distribution

Bandwidth sensitivity in kernel smoothing

Fit a parametric copula

Estimate tail dependence coefficients on the wave-surge data

Estimate tail dependence coefficients on the wind data

Fit a non parametric copula

Estimate a multivariate ARMA process

Estimate a non stationary covariance function

Estimate a spectral density function

Estimate a stationary covariance function

Visualize sensitivity

Visualize clouds

Visualize pairs between two samples

Visualize pairs

Estimate moments from sample

Estimate a confidence interval of a quantile

A quick start guide to the Point and Sample classes

Sample manipulation

Compare unconditional and conditional histograms

Compute squared SRC indices confidence intervals

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the statistics

Test the copula

Test independence

Function manipulation

Logistic growth model

Create a process sample from a sample

Value function

Vertex value function

Define a connection function with a field output

Create a multivariate basis of functions from scalar multivariable functions

Link to a computer code with coupling tools

Create univariate functions

Create an aggregated function

Create a composed function

Create multivariate functions

Get the gradient and hessian as functions

Increase the input dimension of a function

Increase the output dimension of a function

Create a linear combination of functions

Create a parametric function

Create a Python function

Create a quadratic function

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

Create a symbolic function

Getting started

Gaussian Process Regression vs KrigingAlgorithm

A quick start guide to graphs

A quick start guide to contours

Plot the log-likelihood contours of a distribution

Metamodel of a field function

Create a linear least squares model

Distribution of estimators in linear regression

Mixture of experts

Create a general linear model metamodel

Create a linear model

Over-fitting and model selection

Perform stepwise regression

Taylor approximations

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression : quick-start

Gaussian Process-based active learning for reliability

Advanced Gaussian process regression

Gaussian Process Regression: choose an arbitrary trend

Gaussian Process Regression: surrogate model with continuous and categorical variables

Gaussian Process Regression: choose a polynomial trend

Gaussian process fitter: configure the optimization solver

Gaussian Process Regression: use an isotropic covariance kernel

Gaussian process regression: draw the likelihood

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression: metamodel of the Branin-Hoo function

Example of multi output Gaussian Process Regression on the fire satellite model

Sequentially adding new points to a Gaussian Process metamodel

Gaussian Process Regression: propagate uncertainties

Polynomial chaos is sensitive to the degree

Fit a distribution from an input sample

Conditional expectation of a polynomial chaos expansion

Polynomial chaos expansion cross-validation

Apply a transform or inverse transform on your polynomial chaos

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Plot enumeration rules

Create a polynomial chaos metamodel from a data set

Advanced polynomial chaos construction

Create a full or sparse polynomial chaos expansion

Polynomial chaos exploitation

Polynomial chaos graphs

Integrate a function with Gauss-Kronrod algorithm

Estimate a multivariate integral with IteratedQuadrature

Iterated Functions System

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

Compute confidence intervals of a regression model from data

Compute confidence intervals of a univariate noisy function

Save/load a study

Estimate extrema iteratively

Estimate moments iteratively

Estimate threshold exceedance iteratively

Control algorithm termination

EfficientGlobalOptimization examples

Mix/max search and sensitivity from design

Mix/max search using optimization

Optimization using bonmin

Optimization with constraints

Optimization using dlib

Optimization using NLopt

Multi-objective optimization using Pagmo

Optimization of the Rastrigin test function

Quick start guide to optimization

Assemble copulas

Create a copula

Extract the copula from a distribution

Create a Joint by Conditioning distribution

Create and draw multivariate distributions

Create your own distribution given its quantile function

Create a deconditioned distribution

Create a deconditioned random vector

Transform a distribution

Generate random variates by inverting the CDF

Draw minimum volume level sets

Create a mixture of distributions

Overview of univariate distribution management

Create a Point Conditional Distribution

Compare frequentist and Bayesian estimation

Create a customized distribution or copula

Quick start guide to distributions

Truncate a distribution

Create the distribution of the maximum of distributions

Compute the joint distribution of order statistics

Composite random vector

Create a random vector

Use the Ratio of Uniforms algorithm to sample a distribution

Add a trend to a process

Use the Box-Cox transformation

Create a mesh

Create a Gaussian process

Create a stationary covariance model

Create a discrete Markov chain process

Export a field to VTK

Draw a field

Create a functional basis process

Compare covariance models

Sample trajectories from a Gaussian Process with correlated outputs

Create a process from random vectors and processes

Create a parametric spectral density function

Manipulate stochastic processes

Create a random walk process

Trend computation

Create a custom covariance model

Create a spectral model

Analyse the central tendency of a cantilever beam

Estimate moments from Taylor expansions

Evaluate the mean of a random vector by simulations

Create a composite design of experiments

Compute the L2 error between two functions

Create a deterministic design of experiments

Create a random design of experiments

Create a design of experiments with discrete and continuous variables

Deterministic design of experiments

Create a Gauss product design

LOLA-Voronoi sequential design of experiment

Generate low discrepancy sequences

Create mixed deterministic and probabilistic designs of experiments

Create a Monte Carlo design of experiments

Plot the Smolyak quadrature

Plot Smolyak multi-indices

Merge nodes in Smolyak quadrature

Use the Smolyak quadrature

Axial stressed beam : comparing different methods to estimate a probability

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

Create a domain event

Create a threshold event

Cross Entropy Importance Sampling

Use the Adaptive Directional Stratification Algorithm

Use the Directional Sampling Algorithm

Use the FORM - SORM algorithms

Using the FORM - SORM algorithms on a nonlinear function

Use the Importance Sampling algorithm

Estimate a probability with Monte Carlo

Use a randomized QMC algorithm

Simulate an Event

Create unions or intersections of events

Estimate a flooding probability

An illustrated example of a FORM probability estimate

Estimate a probability using Line Sampling

Use the FORM algorithm in case of several design points

Non parametric Adaptive Importance Sampling (NAIS)

Use the post-analytical importance sampling algorithm

Time variant system reliability problem

Specify a simulation algorithm

Exploitation of simulation algorithm results

Estimate a buckling probability

Test the design point with the Strong Maximum Test

Subset Sampling

Create an event based on a process

Estimate Sobol indices on a field to point function

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

The HSIC sensitivity indices: the Ishigami model

Use the ANCOVA indices

Parallel coordinates graph as sensitivity tool

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Example of sensitivity analyses on the wing weight model

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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InterfaceObject¶

Examples using the class¶