DataAnalysis

class persalys.DataAnalysis(*args)

Create a data analysis of a design of experiments.

Parameters:

namestr

Name

designDesignOfExperiment

Design of experiments

Methods

getClassName()	Accessor to the object's name.
getDesignOfExperiment()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getInterestVariables()	Get the variables to analyse.
getLevelConfidenceInterval()	Confidence interval level accessor.
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.
isConfidenceIntervalRequired()	Whether a confidence interval is required.
isReliabilityAnalysis()	Whether the analysis involves reliability.
isRunning()	Whether the analysis is running.
run()	Launch the analysis.
setInterestVariables(variablesNames)	Set the variables to analyse.
setIsConfidenceIntervalRequired(isRequired)	Whether a confidence interval is required.
setLevelConfidenceInterval(level)	Confidence interval level accessor.
setName(name)	Accessor to the object's name.

Examples

>>> import openturns as ot
>>> import persalys
>>> ot.RandomGenerator.SetSeed(0)

Create the model:

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

Create the Data analysis:

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

Get the result:

>>> result = analysis.getResult()
>>> mean = result.getMean()

__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

getLevelConfidenceInterval()

Confidence interval level accessor.

Returns:

valuefloat

Confidence interval level.

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:

resultDataAnalysisResult

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

isConfidenceIntervalRequired()

Whether a confidence interval is required.

Returns:

valuebool

Whether a confidence interval is required.

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

setIsConfidenceIntervalRequired(isRequired)

Whether a confidence interval is required.

Parameters:

valuebool

Whether a confidence interval is required

setLevelConfidenceInterval(level)

Confidence interval level accessor.

Parameters:

valuefloat

Confidence interval level

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.