ProbabilisticDesignOfExperiment

class persalys.ProbabilisticDesignOfExperiment(*args)

Create a probabilistic design of experiments.

Available constructors:

ProbabilisticDesignOfExperiment(name, physicalModel)

ProbabilisticDesignOfExperiment(name, physicalModel, size, designName)

Parameters:

namestr

Name

physicalModelPhysicalModel

Physical model with at least a stochastic input variable.

sizepositive int

Number of points in the design.

designNamestr

Name of the design. Use GetDesignNames() to list available names.

Methods

GetDesignNames()	Accessor to the list of designs provided by ProbabilisticDesignOfExperiment.
getBlockSize()	Block size accessor.
getClassName()	Accessor to the object's name.
getDesignName()	Accessor to the design name.
getErrorDescription()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getFailedInputSample()	Failed input sample accessor.
getInterestVariables()	Get the variables to analyse.
getName()	Accessor to the object's name.
getNotEvaluatedInputSample()	Not evaluated input sample accessor.
getOriginalInputSample()	Input sample accessor.
getPhysicalModel()	Physical model accessor.
getPythonScript()	Physical model accessor.
getSeed()	Seed accessor.
getSize()	Accessor to the size of the generated sample.
getWarningMessage()	Warning message accessor.
hasName()	Test if the object is named.
hasValidResult()	Whether the analysis has been run.
isReliabilityAnalysis()	Whether the analysis involves reliability.
isRunning()	Whether the analysis is running.
run()	Launch the analysis.
setBlockSize(size)	Block size accessor.
setDesignName(name)	Accessor to the design name.
setEvaluations(outputSample)	Add evaluations for the design of experiments
setInterestVariables(variablesNames)	Set the variables to analyse.
setName(name)	Accessor to the object's name.
setSeed(seed)	Initialize the random generator seed.
setSize(size)	Accessor to the size of the generated sample.

GetSpaceFillings
getMCLHSSize
getResult
getSpaceFilling
setSpaceFilling

Examples

>>> import openturns as ot
>>> import persalys

Create the model:

>>> R = persalys.Input('R', 0., ot.LogNormalMuSigma(300., 30., 0.).getDistribution(), 'Yield strength')
>>> F = persalys.Input('F', 0., ot.Normal(75000., 5000.), 'Traction load')
>>> G = persalys.Output('G', 'deviation')
>>> myPhysicalModel = persalys.SymbolicPhysicalModel('myPhysicalModel', [R, F], [G], ['R-F/(pi_*100.0)'])

Create the design of experiments:

>>> myDOE = persalys.ProbabilisticDesignOfExperiment('myDOE', myPhysicalModel, 10, 'MONTE_CARLO')

__init__(*args)

static GetDesignNames()

Accessor to the list of designs provided by ProbabilisticDesignOfExperiment.

Returns:

namesopenturns.Description

List of design names provided by ProbabilisticDesignOfExperiment.

Examples

>>> import persalys
>>> print(persalys.ProbabilisticDesignOfExperiment.GetDesignNames())
[LHS,SALHS,MCLHS,MONTE_CARLO,QUASI_MONTE_CARLO]

getBlockSize()

Block size accessor.

Returns:

blockSizepositive int

Number of terms analysed together. It is set by default to 1.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getDesignName()

Accessor to the design name.

Returns:

designNamestr

The design name.

getErrorDescription()

Design of experiments accessor.

Returns:

errorDescDescription

Description containing messages from failed points.

getErrorMessage()

Error message accessor.

Returns:

messagestr

Error message if the analysis failed

getFailedInputSample()

Failed input sample accessor.

Returns:

sampleopenturns.Sample

Sample with the failed input values

getInterestVariables()

Get the variables to analyse.

Returns:

variablesNamessequence of str

Names of the variables to analyse

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getNotEvaluatedInputSample()

Not evaluated input sample accessor.

Returns:

sampleopenturns.Sample

Points of the design of experiments which were not evaluated

getOriginalInputSample()

Input sample accessor.

Returns:

sampleopenturns.Sample

Input sample.

getPhysicalModel()

Physical model accessor.

Returns:

modelPhysicalModel

Physical model

getPythonScript()

Physical model accessor.

Returns:

scriptstr

Python script to replay the analysis

getSeed()

Seed accessor.

Returns:

seedint

Seed value

getSize()

Accessor to the size of the generated sample.

Returns:

sizepositive int

Number of points in the design.

getWarningMessage()

Warning message accessor.

Returns:

messagestr

Warning message which can appear during the analysis computation

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

hasValidResult()

Whether the analysis has been run.

Returns:

hasValidResultbool

Whether the analysis has already been run

isReliabilityAnalysis()

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysisbool

Whether the analysis involves a reliability analysis

isRunning()

Whether the analysis is running.

Returns:

isRunningbool

Whether the analysis is running

run()

Launch the analysis.

setBlockSize(size)

Block size accessor.

Parameters:

blockSizepositive int

Number of terms analysed together. It is set by default to 1.

setDesignName(name)

Accessor to the design name.

Parameters:

designNamestr

The design name. Use GetDesignNames() to list available names.

setEvaluations(outputSample)

Add evaluations for the design of experiments

Parameters:

outputSample:py:openturns.Sample

sample containing values for the output variables

setInterestVariables(variablesNames)

Set the variables to analyse.

Parameters:

variablesNamessequence of str

Names of the variables to analyse

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setSeed(seed)

Initialize the random generator seed.

Parameters:

seedint

Seed value.

setSize(size)

Accessor to the size of the generated sample.

Parameters:

sizepositive int

Number of points in the design.