StratifiedExperiment

class StratifiedExperiment(*args)

Base class for stratified experiments.

Available constructor:

StratifiedExperiment(center, levels)

Parameters:
center, levelstwo sequences of float

Sequences which have different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

Notes

A StratifiedExperiment object can be used only through its derived classes:

Methods

generate()

Generate points according to the type of the experiment.

getCenter()

Get the center of the stratified experiment.

getClassName()

Accessor to the object's name.

getId()

Accessor to the object's id.

getLevels()

Get the levels of the stratified experiment.

getName()

Accessor to the object's name.

getShadowedId()

Accessor to the object's shadowed id.

getVisibility()

Accessor to the object's visibility state.

hasName()

Test if the object is named.

hasVisibleName()

Test if the object has a distinguishable name.

setCenter(center)

Set the center of the stratified experiment.

setLevels(levels)

Set the levels of the stratified experiment.

setName(name)

Accessor to the object's name.

setShadowedId(id)

Accessor to the object's shadowed id.

setVisibility(visible)

Accessor to the object's visibility state.

__init__(*args)
generate()

Generate points according to the type of the experiment.

Returns:
sampleSample

The points which constitute the design of experiments. The sampling method is defined by the nature of the experiment.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> myExperiment = ot.Experiment(ot.MonteCarloExperiment(ot.Normal(2),5))
>>> print(myExperiment.generate())
    [ X0        X1        ]
0 : [  0.608202 -1.26617  ]
1 : [ -0.438266  1.20548  ]
2 : [ -2.18139   0.350042 ]
3 : [ -0.355007  1.43725  ]
4 : [  0.810668  0.793156 ]
getCenter()

Get the center of the stratified experiment.

Returns:
centerPoint

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

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.

getLevels()

Get the levels of the stratified experiment.

Returns:
levelsPoint

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:
idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns:
visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleNamebool

True if the name is not empty and not the default one.

setCenter(center)

Set the center of the stratified experiment.

Parameters:
centersequence of float

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

setLevels(levels)

Set the levels of the stratified experiment.

Parameters:
levelssequence of float

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:
idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

Examples using the class

Draw minimum volume level sets

Draw minimum volume level sets

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Use the FORM algorithm in case of several design points

Use the FORM algorithm in case of several design points

Create a domain event

Create a domain event

An illustrated example of a FORM probability estimate

An illustrated example of a FORM probability estimate

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 mixed deterministic and probabilistic designs of experiments

Create mixed deterministic and probabilistic designs of experiments

Deterministic design of experiments

Deterministic design of experiments

Create a deterministic design of experiments

Create a deterministic design of experiments