PersistentObject¶
- class PersistentObject(*args, **kwargs)¶
PersistentObject saves and reloads the object’s internal state.
Methods
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¶
Randomize the lines of a Sample
Import / export a sample via a CSV file
A quick start guide to the Point and Sample classes
Estimate Wilks and empirical quantile
Build and validate a linear model
Estimate correlation coefficients
Compare unconditional and conditional histograms
Compute squared SRC indices confidence intervals
Fit a distribution by maximum likelihood
Model a singular multivariate distribution
Define a distribution from quantiles
Get the asymptotic distribution of the estimators
Estimate a GEV on the Venice sea-levels data
Bandwidth sensitivity in kernel smoothing
Fit an extreme value distribution
Estimate a conditional quantile
Estimate a multivariate distribution
Estimate a GPD on the Wooster temperature data
Estimate a GPD on the Dow Jones Index data
Fit a non parametric distribution
Fitting a distribution with customized maximum likelihood
Estimate a GEV on the Port Pirie sea-levels data
Estimate a GPD on the daily rainfall data
Estimate a GEV on race times data
Estimate a GEV on the Fremantle sea-levels data
Use the Kolmogorov/Lilliefors test
Kolmogorov-Smirnov : understand the p-value
Kolmogorov-Smirnov : understand the statistics
Kolmogorov-Smirnov : get the statistics distribution
Estimate tail dependence coefficients on the wave-surge data
Estimate tail dependence coefficients on the wind data
Estimate a multivariate ARMA process
Estimate a non stationary covariance function
Estimate a scalar ARMA process
Estimate a spectral density function
Estimate a stationary covariance function
Create a conditional random vector
Create the distribution of the maximum of independent distributions
Create a maximum entropy statistics distribution
Create a conditional distribution
Create your own distribution given its quantile function
Create an extreme value distribution
Create and draw scalar distributions
Create and draw multivariate distributions
Generate random variates by inverting the CDF
Overview of univariate distribution management
Quick start guide to distributions
Create a customized distribution or copula
Draw minimum volume level sets
Extract the copula from a distribution
Create the ordinal sum of copulas
Create a functional basis process
Create a parametric spectral density function
Create a stationary covariance model
Create a gaussian process from a cov. model using HMatrix
Use the Box-Cox transformation
Create a stationary covariance model
Create a custom covariance model
Create a process from random vectors and processes
Sample trajectories from a Gaussian Process with correlated outputs
Create a discrete Markov chain process
Create and manipulate an ARMA process
Create a linear least squares model
Create a general linear model metamodel
Distribution of estimators in linear regression
Over-fitting and model selection
Apply a transform or inverse transform on your polynomial chaos
Fit a distribution from an input sample
Compute grouped indices for the Ishigami function
Create a full or sparse polynomial chaos expansion
Create a polynomial chaos metamodel by integration on the cantilever beam
Advanced polynomial chaos construction
Create a polynomial chaos metamodel from a data set
Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos
Polynomial chaos expansion cross-validation
Polynomial chaos is sensitive to the degree
Create a sparse chaos by integration
Compute Sobol’ indices confidence intervals
Kriging: propagate uncertainties
Kriging : multiple input dimensions
Kriging : cantilever beam model
Kriging: choose an arbitrary trend
Kriging the cantilever beam model using HMAT
Example of multi output Kriging on the fire satellite model
Kriging : generate trajectories from a metamodel
Kriging: choose a polynomial trend on the beam model
Kriging with an isotropic covariance function
Kriging: metamodel of the Branin-Hoo function
Sequentially adding new points to a kriging
Kriging: configure the optimization solver
Kriging: choose a polynomial trend
Kriging : draw covariance models
Kriging: metamodel with continuous and categorical variables
Validation of a Karhunen-Loeve decomposition
Viscous free fall: metamodel of a field function
Evaluate the mean of a random vector by simulations
Analyse the central tendency of a cantilever beam
Estimate moments from Taylor expansions
Estimate a probability with Monte Carlo
Use a randomized QMC algorithm
Use the Adaptive Directional Stratification Algorithm
Use the post-analytical importance sampling algorithm
Use the Directional Sampling Algorithm
Specify a simulation algorithm
Estimate a flooding probability
Use the Importance Sampling algorithm
Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability
Estimate a buckling probability
Exploitation of simulation algorithm results
Use the FORM algorithm in case of several design points
Use the FORM - SORM algorithms
Non parametric Adaptive Importance Sampling (NAIS)
Test the design point with the Strong Maximum Test
Time variant system reliability problem
Create unions or intersections of events
Axial stressed beam : comparing different methods to estimate a probability
An illustrated example of a FORM probability estimate
Cross Entropy Importance Sampling
Using the FORM - SORM algorithms on a nonlinear function
Create an event based on a process
Estimate a process-based event probability
Estimate Sobol indices on a field to point function
Sobol’ sensitivity indices using rank-based algorithm
Parallel coordinates graph as sensitivity tool
Estimate Sobol’ indices for a function with multivariate output
Sobol’ sensitivity indices from chaos
The HSIC sensitivity indices: the Ishigami model
Example of sensitivity analyses on the wing weight model
Create a composite design of experiments
Create a Monte Carlo design of experiments
Probabilistic design of experiments
Compute the L2 error between two functions
Create a random design of experiments
Create mixed deterministic and probabilistic designs of experiments
Create a design of experiments with discrete and continuous variables
Deterministic design of experiments
Create a deterministic design of experiments
Generate low discrepancy sequences
Optimize an LHS design of experiments
Merge nodes in Smolyak quadrature
Increase the input dimension of a function
Defining Python and symbolic functions: a quick start introduction to functions
Create a multivariate basis of functions from scalar multivariable functions
Define a connection function with a field output
Generate flooding model observations
Calibrate a parametric model: a quick-start guide to calibration
Generate observations of the Chaboche mechanical model
Calibration without observed inputs
Calibration of the logistic model
Calibration of the deflection of a tube
Calibration of the flooding model
Calibration of the Chaboche mechanical model
Gibbs sampling of the posterior distribution
Sampling from an unnormalized probability density
Posterior sampling using a PythonDistribution
Bayesian calibration of a computer code
Bayesian calibration of the flooding model
Customize your Metropolis-Hastings algorithm
Linear Regression with interval-censored observations
Random generator parametrization
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
Mix/max search and sensitivity from design
Mix/max search using optimization
Multi-objective optimization using Pagmo
Quick start guide to optimization
Optimization of the Rastrigin test function
EfficientGlobalOptimization examples
Estimate threshold exceedance iteratively
Plot the log-likelihood contours of a distribution
A quick start guide to contours