InterfaceObject¶
- class InterfaceObject(*args, **kwargs)¶
Methods
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¶
![](../../_images/sphx_glr_plot_quick_start_point_and_sample_thumb.png)
A quick start guide to the Point and Sample classes
![](../../_images/sphx_glr_plot_kolmogorov_distribution_thumb.png)
Kolmogorov-Smirnov : get the statistics distribution
![](../../_images/sphx_glr_plot_estimate_dependence_wavesurge_thumb.png)
Estimate tail dependence coefficients on the wave-surge data
![](../../_images/sphx_glr_plot_estimate_dependence_wind_thumb.png)
Estimate tail dependence coefficients on the wind data
![](../../_images/sphx_glr_plot_create_your_own_dist_thumb.png)
Create your own distribution given its quantile function
![](../../_images/sphx_glr_plot_mix_rv_process_thumb.png)
Create a process from random vectors and processes
![](../../_images/sphx_glr_plot_kronecker_covmodel_thumb.png)
Sample trajectories from a Gaussian Process with correlated outputs
![](../../_images/sphx_glr_plot_chaos_distribution_transformation_thumb.png)
Apply a transform or inverse transform on your polynomial chaos
![](../../_images/sphx_glr_plot_functional_chaos_database_thumb.png)
Create a full or sparse polynomial chaos expansion
![](../../_images/sphx_glr_plot_functional_chaos_thumb.png)
Create a polynomial chaos metamodel from a data set
![](../../_images/sphx_glr_plot_chaos_ishigami_thumb.png)
Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos
![](../../_images/sphx_glr_plot_kriging_multioutput_firesatellite_thumb.png)
Example of multi output Kriging on the fire satellite model
![](../../_images/sphx_glr_plot_kriging_categorical_thumb.png)
Kriging: metamodel with continuous and categorical variables
![](../../_images/sphx_glr_plot_expectation_simulation_algorithm_thumb.png)
Evaluate the mean of a random vector by simulations
![](../../_images/sphx_glr_plot_estimate_probability_adaptive_directional_sampling_thumb.png)
Use the Adaptive Directional Stratification Algorithm
![](../../_images/sphx_glr_plot_post_analytical_importance_sampling_thumb.png)
Use the post-analytical importance sampling algorithm
![](../../_images/sphx_glr_plot_axial_stressed_beam_quickstart_thumb.png)
Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability
![](../../_images/sphx_glr_plot_multi_form_thumb.png)
Use the FORM algorithm in case of several design points
![](../../_images/sphx_glr_plot_nais_thumb.png)
Non parametric Adaptive Importance Sampling (NAIS)
![](../../_images/sphx_glr_plot_strong_maximum_test_thumb.png)
Test the design point with the Strong Maximum Test
![](../../_images/sphx_glr_plot_axial_stressed_beam_thumb.png)
Axial stressed beam : comparing different methods to estimate a probability
![](../../_images/sphx_glr_plot_form_explained_thumb.png)
An illustrated example of a FORM probability estimate
![](../../_images/sphx_glr_plot_estimate_probability_form_oscillator_thumb.png)
Using the FORM - SORM algorithms on a nonlinear function
![](../../_images/sphx_glr_plot_field_fca_sobol_thumb.png)
Estimate Sobol indices on a field to point function
![](../../_images/sphx_glr_plot_sensitivity_sobol_multivariate_thumb.png)
Estimate Sobol’ indices for a function with multivariate output
![](../../_images/sphx_glr_plot_sensitivity_sobol_thumb.png)
![](../../_images/sphx_glr_plot_sensitivity_wingweight_thumb.png)
Example of sensitivity analyses on the wing weight model
![](../../_images/sphx_glr_plot_mixed_design_thumb.png)
Create mixed deterministic and probabilistic designs of experiments
![](../../_images/sphx_glr_plot_design_of_experiment_continuous_discrete_thumb.png)
Create a design of experiments with discrete and continuous variables
![](../../_images/sphx_glr_plot_quick_start_functions_thumb.png)
Defining Python and symbolic functions: a quick start introduction to functions
![](../../_images/sphx_glr_plot_multidimensional_basis_thumb.png)
Create a multivariate basis of functions from scalar multivariable functions
![](../../_images/sphx_glr_plot_calibration_quickstart_thumb.png)
Calibrate a parametric model: a quick-start guide to calibration
![](../../_images/sphx_glr_plot_generate_chaboche_thumb.png)
Generate observations of the Chaboche mechanical model
![](../../_images/sphx_glr_plot_gibbs_simus_thumb.png)
Linear Regression with interval-censored observations
![](../../_images/sphx_glr_plot_pce_design_thumb.png)
Compute leave-one-out error of a polynomial chaos expansion
![](../../_images/sphx_glr_plot_regression_interval_thumb.png)
Compute confidence intervals of a regression model from data
![](../../_images/sphx_glr_plot_regression_sinus_thumb.png)
Compute confidence intervals of a univariate noisy function
![](../../_images/sphx_glr_plot_graphs_loglikelihood_contour_thumb.png)
Plot the log-likelihood contours of a distribution