QuantileAnalysis

class persalys.QuantileAnalysis(*args)

Create a data analysis of a design of experiments.

Parameters

namestr

Name

designDesignOfExperiment

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 Quantile analysis:

>>> analysis = persalys.QuantileAnalysis('analysis', model)
>>> analysis.setTargetProbabilities([[1e-2]]*3)
>>> analysis.setTailTypes([persalys.QuantileAnalysisResult.Upper]*3)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()

Methods

getCDFThreshold()	CDF threshold accessor.
getClassName()	Accessor to the object's name.
getConfidenceIntervalLevel()	Confidence level accessor.
getDesignOfExperiment()	Design of experiments accessor.
getErrorMessage()	Error message accessor.
getInterestVariables()	Get the variables to analyse.
getName()	Accessor to the object's name.
getParameterSampleSize()	Parameters sample size accessor.
getPythonScript()	Physical model accessor.
getResult()	Result accessor.
getSeed()	Random generator seed.
getTailTypes()	Tail types accessor.
getTargetProbabilities()	Target probabilities accessor.
getThreshold()	Threshold accessor.
getType()	Analysis type 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.
setConfidenceIntervalLevel(ciLevel)	Confidence level accessor.
setInterestVariables(variablesNames)	Set the variables to analyse.
setName(name)	Accessor to the object's name.
setParameterSampleSize(paramSampleSize)	Parameters sample size accessor.
setSeed(seed)	Random generator seed.
setTailTypes(tailTypes)	Tail types accessor.
setTargetProbabilities(targetProbas)	Target probabilities accessor.
setThreshold(threshold)	Threshold accessor.
setType(type)	Analysis type accessor

checkThresholdCompatibility
computeWilksValidity
getDefaultTargetProbability
plotGPD
plotMeanExcess
setDefaultTargetProbability

__init__(*args)

getCDFThreshold()

CDF threshold accessor.

Returns

cdfThresholdopenturns.Sample

CDF threshold sample, dimension: number of marginals, size=2. threshold[0]: lower threshold, threshold[1]: upper threshold.

getClassName()

Accessor to the object’s name.

Returns

class_namestr

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

getConfidenceIntervalLevel()

Confidence level accessor.

Returns

ciLeveldouble

Confidence level for quantile intervals.

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.

getParameterSampleSize()

Parameters sample size accessor.

Returns

sizeint

Number of Generalized Pareto set of parameters used for estimating quantiles confidence interval. It is set by default to 1000.

getPythonScript()

Physical model accessor.

Returns

scriptstr

Python script to replay the analysis

getResult()

Result accessor.

Returns

resultpersalys.QuantileAnalysisResult

Analysis result.

getSeed()

Random generator seed.

Returns

seedint

Random generator seed.

getTailTypes()

Tail types accessor.

Returns

typesopenturns.Indices

Collection of bit-wise tail type for each marginal. Lower = 1, Upper = 2, Bilateral = 4

getTargetProbabilities()

Target probabilities accessor.

Returns

targetProbaopenturns.PointCollection

Collection (size: number of marginals) of points (dimension: number of terget probabilities for each marginals) for which the quantiles are estimated.

getThreshold()

Threshold accessor.

Returns

thresholdopenturns.Sample

Threshold sample, dimension: number of marginals, size=2. threshold[0]: lower threshold, threshold[1]: upper threshold.

getType()

Analysis type accessor

Returns

typeenum

Analysis type, either persalys.QuantileAnalysisResult.MonteCarlo or persalys.QuantileAnalysisResult.GeneralizedPareto.

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.

setConfidenceIntervalLevel(ciLevel)

Confidence level accessor.

Parameters

ciLeveldouble

Confidence level for quantile intervals.

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.

setParameterSampleSize(paramSampleSize)

Parameters sample size accessor.

Parameters

sizeint

Number of Generalized Pareto set of parameters used for estimating quantiles confidence interval.

setSeed(seed)

Random generator seed.

Parameters

seedint

Random generator seed.

setTailTypes(tailTypes)

Tail types accessor.

Parameters

typesopenturns.Indices

Collection of bit-wise tail type for each marginal. Lower = 1, Upper = 2, Bilateral = 4

setTargetProbabilities(targetProbas)

Target probabilities accessor.

Parameters

targetProbaopenturns.PointCollection

Collection (size: number of marginals) of points (dimension: number of terget probabilities for each marginals) for which the quantiles are estimated.

setThreshold(threshold)

Threshold accessor.

Parameters

thresholdopenturns.Sample

Threshold sample, dimension: number of marginals, size=2. threshold[0]: lower threshold, threshold[1]: upper threshold.

setType(type)

Analysis type accessor

Parameters

typeenum

Analysis type, either persalys.QuantileAnalysisResult.MonteCarlo or persalys.QuantileAnalysisResult.GeneralizedPareto.