CalibrationAnalysis¶

class persalys.CalibrationAnalysis(*args)¶

Run a calibration analysis.

Parameters

namestr: Name
observationsObservations: Observations of at least one input and one output

Examples

>>> import openturns as ot
>>> import persalys

Create the Chaboche model:

>>> R = persalys.Input('R', 700e6, ot.LogNormalMuSigma(750e6, 11e6).getDistribution(), 'Parameter R')
>>> C = persalys.Input('C', 2500e6, ot.Normal(2750e6, 250e6), 'Parameter C')
>>> gamma = persalys.Input('gam', 8., ot.Normal(10, 2), 'Parameter gamma')
>>> dist_strain = ot.Uniform(0, 0.07)
>>> strain = persalys.Input('strain', 0., dist_strain, 'Strain')
>>> sigma = persalys.Output('sigma', 'Stress (Pa)')

>>> model = persalys.SymbolicPhysicalModel('model1', [R, C, gamma, strain], [sigma], ['R + C / gam * (1 - exp(-gam * strain))'])

Generate observations:

>>> nbObs = 100
>>> strainObs = dist_strain.getSample(nbObs)
>>> strainObs.setDescription(['strain'])
>>> stressSampleNoise = ot.Normal(0., 40.e6).getSample(nbObs)
>>> stressSample = ot.ParametricFunction(model.getFunction(), [0, 1, 2], [750e6, 2750e6, 10.])(strainObs)
>>> stressObs = stressSample + stressSampleNoise

>>> observations = persalys.Observations('obs1', model, strainObs, stressObs)

Process a calibration analysis:

>>> analysis = persalys.CalibrationAnalysis('myAnalysis', observations)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> thetaStar = result.getCalibrationResult().getParameterMAP()

Methods

`GetMethodNames`()	Get the list of available method names.
`getBootStrapSize`(self)	Accessor to the bootstrap size used to sample the posterior distribution.
`getCalibratedInputs`(self)	Inputs to calibrate accessor.
`getClassName`(self)	Accessor to the object’s name.
`getConfidenceIntervalLength`(self)	Confidence interval length accessor.
`getErrorCovariance`(self)	Accessor to the output observations error covariance.
`getErrorMessage`(self)	Error message accessor.
`getFixedInputs`(self)	Fixed inputs accessor.
`getId`(self)	Accessor to the object’s id.
`getInterestVariables`(self)	Get the variables to analyse.
`getMethodName`(self)	Accessor to the method name
`getName`(self)	Accessor to the object’s name.
`getObservations`(self)	Accessor to the data to be fitted.
`getOptimizationAlgorithm`(self)	Accessor to the optimization algorithm used by non linear algorithms.
`getPhysicalModel`(self)	Physical model accessor.
`getPriorDistribution`(self)	Accessor to the prior distribution.
`getPythonScript`(self)	Physical model accessor.
`getResult`(self)	Result accessor.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getVisibility`(self)	Accessor to the object’s visibility state.
`getWarningMessage`(self)	Warning message accessor.
`hasName`(self)	Test if the object is named.
`hasValidResult`(self)	Whether the analysis has been run.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`isReliabilityAnalysis`(self)	Whether the analysis involves reliability.
`isRunning`(self)	Whether the analysis is running.
`run`(self)	Launch the analysis.
`setBootStrapSize`(self, arg2)	Accessor to the bootstrap size used to sample the posterior distribution.
`setCalibratedInputs`(self, *args)	Accessor to the inputs to calibrate and the fixed inputs.
`setConfidenceIntervalLength`(self, arg2)	Confidence interval length accessor.
`setErrorCovariance`(self, matrix)	Accessor to the output observations error covariance.
`setInterestVariables`(self, variablesNames)	Set the variables to analyse.
`setMethodName`(self, name)	Accessor to the method name
`setName`(self, name)	Accessor to the object’s name.
`setOptimizationAlgorithm`(self, solver)	Accessor to the optimization algorithm used by non linear algorithms.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.

canBeLaunched
getElapsedTime
getParentObserver

__init__(self, *args)¶: Initialize self. See help(type(self)) for accurate signature.

static GetMethodNames()¶

Get the list of available method names.

Returns

namesDescription: List of available calibration method names.

getBootStrapSize(self)¶

Accessor to the bootstrap size used to sample the posterior distribution.

Returns

sizeint: Bootstrap size used to sample the posterior distribution by the non linear algorithms. A value of 0 means that no bootstrap has been done but a linear approximation has been used to get the posterior distribution, using the GaussianLinearCalibration or LinearLeastSquaresCalibration algorithm at the maximum a posteriori estimate. The value 1 is not allowed at this time.

getCalibratedInputs(self)¶

Inputs to calibrate accessor.

Returns

inputsopenturns.Description: Names of the input variables to calibrate

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getConfidenceIntervalLength(self)¶

Confidence interval length accessor.

Returns

lengthfloat: Length of the confidence interval of the posterior distribution. It is set by default to 0.95.

getErrorCovariance(self)¶

Accessor to the output observations error covariance.

Returns

matrixopenturns.CovarianceMatrix: The covariance matrix of the gaussian distribution of the output observations error. Used by the gaussian algorithms.

getErrorMessage(self)¶

Error message accessor.

Returns

messagestr: Error message if the analysis failed

getFixedInputs(self)¶

Fixed inputs accessor.

Returns

inputsopenturns.PointWithDescription: Fixed input names and values

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getInterestVariables(self)¶

Get the variables to analyse.

Returns

variablesNamessequence of str: Names of the variables to analyse

getMethodName(self)¶

Accessor to the method name

Returns

methodstr: Method name. Default is ‘LeastSquaresLinear’. Use GetMethodNames() to list available names.

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getObservations(self)¶

Accessor to the data to be fitted.

Returns

dataSample: The data to be fitted.

getOptimizationAlgorithm(self)¶

Accessor to the optimization algorithm used by non linear algorithms.

Returns

algorithmopenturns.OptimizationAlgorithm: Optimization algorithm used by non linear algorithms. The optimization problem and the starting point are not taken into account.

getPhysicalModel(self)¶

Physical model accessor.

Returns

modelPhysicalModel: Physical model

getPriorDistribution(self)¶

Accessor to the prior distribution.

Returns

distributionopenturns.Distribution: Prior distribution

getPythonScript(self)¶

Physical model accessor.

Returns

scriptstr: Python script to replay the analysis

getResult(self)¶

Result accessor.

Returns

resultCalibrationAnalysisResult: Result

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

getWarningMessage(self)¶

Warning message accessor.

Returns

messagestr: Warning message which can appear during the analysis computation

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasValidResult(self)¶

Whether the analysis has been run.

Returns

hasValidResultbool: Whether the analysis has already been run

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

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

isReliabilityAnalysis(self)¶

Whether the analysis involves reliability.

Returns

isReliabilityAnalysisbool: Whether the analysis involves a reliability analysis

isRunning(self)¶

Whether the analysis is running.

Returns

isRunningbool: Whether the analysis is running

run(self)¶: Launch the analysis.

setBootStrapSize(self, arg2)¶

Accessor to the bootstrap size used to sample the posterior distribution.

Parameters

sizeint: Bootstrap size used to sample the posterior distribution by the non linear algorithms. A value of 0 means that no bootstrap has been done but a linear approximation has been used to get the posterior distribution, using the GaussianLinearCalibration or LinearLeastSquaresCalibration algorithm at the maximum a posteriori estimate. The value 1 is not allowed at this time.

setCalibratedInputs(self, *args)¶

Accessor to the inputs to calibrate and the fixed inputs.

Parameters

calibratedInputsequence of str: Names of the input variables to calibrate
priorDistributionopenturns.Distribution: Prior distribution. Its dimension must equal to the number of variables to calibrate
fixedInputssequence of str, optional: Names of the input variables to fix
valuessequence of float, optional: Values of the fixed inputs

setConfidenceIntervalLength(self, arg2)¶

Confidence interval length accessor.

Parameters

lengthfloat: Length of the confidence interval of the posterior distribution. It is set by default to 0.95.

setErrorCovariance(self, matrix)¶

Accessor to the output observations error covariance.

Parameters

matrixopenturns.CovarianceMatrix: The covariance matrix of the gaussian distribution of the output observations error. Used by the gaussian algorithms.

setInterestVariables(self, variablesNames)¶

Set the variables to analyse.

Parameters

variablesNamessequence of str: Names of the variables to analyse

setMethodName(self, name)¶

Accessor to the method name

Parameters

methodstr: Method name. Default is ‘LeastSquaresLinear’. Use GetMethodNames() to list available names.

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setOptimizationAlgorithm(self, solver)¶

Accessor to the optimization algorithm used by non linear algorithms.

Parameters

algorithmopenturns.OptimizationAlgorithm: Optimization algorithm used by non linear algorithms. The optimization problem and the starting point are not taken into account.

setShadowedId(self, id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setVisibility(self, visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

CalibrationAnalysis¶

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