CalibrationAnalysis¶

class persalys.CalibrationAnalysis(*args)¶

Run a calibration analysis.

Parameters:

name : str

Name

observations : Observations

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`()	Accessor to the bootstrap size used to sample the posterior distribution.
`getCalibratedInputs`()	Inputs to calibrate accessor.
`getClassName`()	Accessor to the object’s name.
`getConfidenceIntervalLength`()	Confidence interval length accessor.
`getErrorCovariance`()	Accessor to the output observations error covariance.
`getErrorMessage`()	Error message accessor.
`getFixedInputs`()	Fixed inputs accessor.
`getId`()	Accessor to the object’s id.
`getInterestVariables`()	Get the variables to analyse.
`getMethodName`()	Accessor to the method name
`getName`()	Accessor to the object’s name.
`getObservations`()	Accessor to the data to be fitted.
`getOptimizationAlgorithm`()	Accessor to the optimization algorithm used by non linear algorithms.
`getPhysicalModel`()	Physical model accessor.
`getPriorDistribution`()	Accessor to the prior distribution.
`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.
`isReliabilityAnalysis`()	Whether the analysis involves reliability.
`isRunning`()	Whether the analysis is running.
`run`()	Launch the analysis.
`setBootStrapSize`(arg2)	Accessor to the bootstrap size used to sample the posterior distribution.
`setCalibratedInputs`(*args)	Accessor to the inputs to calibrate and the fixed inputs.
`setConfidenceIntervalLength`(arg2)	Confidence interval length accessor.
`setErrorCovariance`(matrix)	Accessor to the output observations error covariance.
`setInterestVariables`(variablesNames)	Set the variables to analyse.
`setMethodName`(name)	Accessor to the method name
`setName`(name)	Accessor to the object’s name.
`setOptimizationAlgorithm`(solver)	Accessor to the optimization algorithm used by non linear algorithms.
`setShadowedId`(id)	Accessor to the object’s shadowed id.
`setVisibility`(visible)	Accessor to the object’s visibility state.

__init__(*args)¶

static GetMethodNames()¶

Get the list of available method names.

Returns:

names : Description

List of available calibration method names.

getBootStrapSize()¶

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

Returns:

size : int

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()¶

Inputs to calibrate accessor.

Returns:

inputs : openturns.Description

Names of the input variables to calibrate

getClassName()¶

Accessor to the object’s name.

Returns:

class_name : str

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

getConfidenceIntervalLength()¶

Confidence interval length accessor.

Returns:

length : float

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

getErrorCovariance()¶

Accessor to the output observations error covariance.

Returns:

matrix : openturns.CovarianceMatrix

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

getErrorMessage()¶

Error message accessor.

Returns:

message : str

Error message if the analysis failed

getFixedInputs()¶

Fixed inputs accessor.

Returns:

inputs : openturns.PointWithDescription

Fixed input names and values

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

getMethodName()¶

Accessor to the method name

Returns:

method : str

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

getName()¶

Accessor to the object’s name.

Returns:

name : str

The name of the object.

getObservations()¶

Accessor to the data to be fitted.

Returns:

data : Sample

The data to be fitted.

getOptimizationAlgorithm()¶

Accessor to the optimization algorithm used by non linear algorithms.

Returns:

algorithm : openturns.OptimizationAlgorithm

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

getPhysicalModel()¶

Physical model accessor.

Returns:

model : PhysicalModel

Physical model

getPriorDistribution()¶

Accessor to the prior distribution.

Returns:

distribution : openturns.Distribution

Prior distribution

getPythonScript()¶

Physical model accessor.

Returns:

script : str

Python script to replay the analysis

getResult()¶

Result accessor.

Returns:

result : CalibrationAnalysisResult

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.

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.

setBootStrapSize(arg2)¶

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

Parameters:

size : int

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(*args)¶

Accessor to the inputs to calibrate and the fixed inputs.

Parameters:

calibratedInput : sequence of str

Names of the input variables to calibrate

priorDistribution : openturns.Distribution

Prior distribution. Its dimension must equal to the number of variables to calibrate

fixedInputs : sequence of str, optional

Names of the input variables to fix

values : sequence of float, optional

Values of the fixed inputs

setConfidenceIntervalLength(arg2)¶

Confidence interval length accessor.

Parameters:

length : float

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

setErrorCovariance(matrix)¶

Accessor to the output observations error covariance.

Parameters:

matrix : openturns.CovarianceMatrix

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

setInterestVariables(variablesNames)¶

Set the variables to analyse.

Parameters:

variablesNames : sequence of str

Names of the variables to analyse

setMethodName(name)¶

Accessor to the method name

Parameters:

method : str

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

setName(name)¶

Accessor to the object’s name.

Parameters:

name : str

The name of the object.

setOptimizationAlgorithm(solver)¶

Accessor to the optimization algorithm used by non linear algorithms.

Parameters:

algorithm : openturns.OptimizationAlgorithm

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

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.

CalibrationAnalysis¶

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