CopulaInferenceAnalysis

class persalys.CopulaInferenceAnalysis(*args)

Perform a dependence inference analysis.

Parameters:

namestr

Name

designDesignOfExperiment

Design of experiments

Methods

getClassName()	Accessor to the object's name.
getDesignOfExperiment()	Design of experiments accessor.
getDistributionsFactories(variablesNames)	Get the sequence of distributions to test for a set of variables.
getErrorMessage()	Error message accessor.
getInterestVariables()	Get the variables to analyse.
getName()	Accessor to the object's name.
getPythonScript()	Physical model accessor.
getResult()	Result accessor.
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.
setDistributionsFactories(variablesNames, ...)	Set the sequence of distributions to test for a set of variables.
setInterestVariables(variablesNames)	Set the variables to analyse.
setName(name)	Accessor to the object's name.

getVariablesGroups

Examples

Create the model:

>>> import openturns as ot
>>> import persalys
>>> filename = 'data.csv'
>>> ot.RandomGenerator.SetSeed(0)
>>> sample = ot.Normal(3).getSample(100)
>>> sample.exportToCSVFile(filename)
>>> model = persalys.DataModel('myDataModel', 'data.csv', [0, 1, 2])

Create the dependence inference analysis:

>>> analysis = persalys.CopulaInferenceAnalysis('analysis', model)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> resultForSet = result.getCopulaInferenceSetResultCollection()

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getDesignOfExperiment()

Design of experiments accessor.

Returns:

modelDesignOfExperiment

Design of experiments

getDistributionsFactories(variablesNames)

Get the sequence of distributions to test for a set of variables.

Parameters:

variablessequence of str

Names of the variables

Returns:

factoriessequence of openturns.DistributionFactory

Distributions to test for the given set of variables

getErrorMessage()

Error message accessor.

Returns:

messagestr

Error message if the analysis failed

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.

getPythonScript()

Physical model accessor.

Returns:

scriptstr

Python script to replay the analysis

getResult()

Result accessor.

Returns:

resultCopulaInferenceResult

Result.

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.

setDistributionsFactories(variablesNames, distributionsFactories)

Set the sequence of distributions to test for a set of variables.

Parameters:

variablessequence of str

Names of the variables

factoriessequence of openturns.DistributionFactory

Distributions to test

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.