QuantileAnalysis¶

class persalys.QuantileAnalysis(*args)¶

Create a data analysis of a design of experiments.

Parameters:

namestr: Name
designDesignOfExperiment: Design of experiments

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
computeSampleSizeValidity
getDefaultTargetProbability
plotGPD
plotMeanExcess
setDefaultTargetProbability

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()

__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:

targetProbasequence of openturns.Point: 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:

targetProbasequence of openturns.Point: 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.

QuantileAnalysis¶

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