DataSensitivityAnalysis

class persalys.DataSensitivityAnalysis(*args)

Perform a sensitivity analysis on a design of experiments. see openturns.experimental.RankSobolSensitivityAlgorithm for more details.

Parameters:

namestr

Name of the analysis.

designDesignOfExperiment

Design of experiments to analyze.

Examples

>>> import openturns as ot
>>> import persalys
>>> from openturns.usecases import ishigami_function

Create the csv:

>>> im = ishigami_function.IshigamiModel()
>>> x = im.inputDistribution.getSample(100)
>>> y = im.model(x)
>>> data_sample = x
>>> data_sample.stack(y)
>>> data_sample.exportToCSVFile('data.csv')

Create the model:

>>> model = persalys.DataModel('myDataModel', 'data.csv', [0, 1, 2], [3])

Create and run the analysis:

>>> analysis = persalys.DataSensitivityAnalysis('sensitivity', model)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> if result.isIndependent():
...    # The inputs need to be independant for the result to be valid
...    firstOrderIndices = result.getFirstOrderIndices()
...    firstOrderIndicesInterval = result.getFirstOrderIndicesInterval()
... else:
...    independenceWarning = result.getIndependenceWarningMessage()

Methods

getClassName()	Accessor to the object's name.
getDesignOfExperiment()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getInterestVariables()	Get the variables to analyse.
getName()	Accessor to the object's name.
getPythonScript()	Physical model accessor.
getResult()	Get the result of the sensitivity analysis.
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.
setInterestVariables(variablesNames)	Set the variables to analyse.
setName(name)	Accessor to the object's name.

__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

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()

Get the result of the sensitivity analysis.

Returns:

resultDataSensitivityAnalysisResult

Result of the sensitivity analysis.

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.

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.