DataAnalysis

class persalys.DataAnalysis(*args)

Create a data analysis of a design of experiments.

Parameters:

name : str

Name

design : DesignOfExperiment

Design of experiments

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()

Methods

getClassName()	Accessor to the object’s name.
getDesignOfExperiment()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getId()	Accessor to the object’s id.
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.
getShadowedId()	Accessor to the object’s shadowed id.
getVisibility()	Accessor to the object’s visibility state.
getWarningMessage()	Warning message accessor.
hasName()	Test if the object is named.
hasValidResult()	Whether the analysis has been run.
hasVisibleName()	Test if the object has a distinguishable name.
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.
setShadowedId(id)	Accessor to the object’s shadowed id.
setVisibility(visible)	Accessor to the object’s visibility state.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_name : str

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

getDesignOfExperiment()

Design of experiments accessor.

Returns:

model : DesignOfExperiment

Design of experiments

getErrorMessage()

Error message accessor.

Returns:

message : str

Error message if the analysis failed

getId()

Accessor to the object’s id.

Returns:

id : int

Internal unique identifier.

getInterestVariables()

Get the variables to analyse.

Returns:

variablesNames : sequence of str

Names of the variables to analyse

getLevelConfidenceInterval()

Confidence interval level accessor.

Returns:

value : float

Confidence interval level.

getName()

Accessor to the object’s name.

Returns:

name : str

The name of the object.

getPythonScript()

Physical model accessor.

Returns:

script : str

Python script to replay the analysis

getResult()

Result accessor.

Returns:

result : DataAnalysisResult

Result.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:

id : int

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns:

visible : bool

Visibility flag.

getWarningMessage()

Warning message accessor.

Returns:

message : str

Warning message which can appear during the analysis computation

hasName()

Test if the object is named.

Returns:

hasName : bool

True if the name is not empty.

hasValidResult()

Whether the analysis has been run.

Returns:

hasValidResult : bool

Whether the analysis has already been run

hasVisibleName()

Test if the object has a distinguishable name.

Returns:

hasVisibleName : bool

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

isConfidenceIntervalRequired()

Whether a confidence interval is required.

Returns:

value : bool

Whether a confidence interval is required.

isReliabilityAnalysis()

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysis : bool

Whether the analysis involves a reliability analysis

isRunning()

Whether the analysis is running.

Returns:

isRunning : bool

Whether the analysis is running

run()

Launch the analysis.

setInterestVariables(variablesNames)

Set the variables to analyse.

Parameters:

variablesNames : sequence of str

Names of the variables to analyse

setIsConfidenceIntervalRequired(isRequired)

Whether a confidence interval is required.

Parameters:

value : bool

Whether a confidence interval is required

setLevelConfidenceInterval(level)

Confidence interval level accessor.

Parameters:

value : float

Confidence interval level

setName(name)

Accessor to the object’s name.

Parameters:

name : str

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:

id : int

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:

visible : bool

Visibility flag.