PersistentObject¶

class PersistentObject(*args, **kwargs)¶

PersistentObject saves and reloads the object’s internal state.

Methods

`getClassName`()	Accessor to the object's name.
`getName`()	Accessor to the object's name.
`hasName`()	Test if the object is named.
`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__).

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

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

Fitting a distribution with customized maximum likelihood

Get the asymptotic distribution of the estimators

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 a non parametric distribution

Fit a parametric distribution

Fit an extreme value distribution

Fit a distribution by maximum likelihood

Model a singular multivariate distribution

Define a distribution from quantiles

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 scalar ARMA process

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

Import / export a sample via a CSV file

Build and validate a linear model

Estimate quantile confidence intervals from data

Estimate a confidence interval of a quantile

A quick start guide to the Point and Sample classes

Randomize the lines of a Sample

Estimate correlation coefficients

Sample manipulation

Link Pandas and OpenTURNS

Sort a sample

Compare unconditional and conditional histograms

Draw a survival function

Compute squared SRC indices confidence intervals

Draw the empirical CDF

Draw an histogram

Test a discrete distribution

Select fitted distributions

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the statistics

Use the Kolmogorov/Lilliefors test

Draw the QQ-Plot

Test identical distributions

Test the copula

Test independence

Test Normality

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

Create univariate functions

Create a composed function

Create multivariate functions

Increase the input dimension of a function

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

Getting started

Gaussian Process Regression vs KrigingAlgorithm

A quick start guide to graphs

A quick start guide to contours

How to fill an area

Plot the log-likelihood contours of a distribution

Metamodel of a field function

Validation of a Karhunen-Loeve decomposition

Viscous free fall: metamodel of a field function

Create a linear least squares model

Distribution of estimators in linear regression

Mixture of experts

Export a metamodel

Create a general linear model metamodel

Create a linear model

Over-fitting and model selection

Perform stepwise regression

Taylor approximations

Kriging : draw covariance models

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: choose a polynomial trend on the beam model

Gaussian Process Regression : cantilever beam model

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

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a sparse chaos by integration

Conditional expectation of a polynomial chaos expansion

Polynomial chaos expansion cross-validation

Apply a transform or inverse transform on your polynomial chaos

Validate a polynomial chaos

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

Compute grouped indices for the Ishigami function

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

Combinatorial generators

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

Random generator parametrization

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 the ordinal sum of copulas

Create a Joint by Conditioning distribution

Create and draw scalar distributions

Create and draw multivariate distributions

Create an extreme value distribution

Create a random mixture

Create your own distribution given its quantile function

Create a deconditioned distribution

Create a deconditioned random vector

Distribution manipulation

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 a maximum entropy order statistics 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

Aggregate processes

Use the Box-Cox transformation

Create and manipulate an ARMA process

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

Manipulate a time series

Trend computation

Create a custom covariance model

Create a spectral model

Create a white noise process

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

Various design of experiments

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

Optimize an LHS design of experiments

The PlotDesign method

Probabilistic 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

Estimate a process-based event probability

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

Sobol’ sensitivity indices using rank-based algorithm

Sobol' sensitivity indices using rank-based algorithm

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

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Estimate Sobol’ indices for the beam by simulation algorithm

Estimate Sobol' indices for the beam by simulation algorithm

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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PersistentObject¶

Examples using the class¶