CalibrationResult

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class CalibrationResult(*args)

Calibration result.

Returned by calibration algorithms, see CalibrationAlgorithm.

Parameters

parameterPriorDistribution

The prior distribution of the parameter.

parameterPosteriorDistribution

The posterior distribution of the parameter.

parameterMapsequence of float

The maximum a posteriori estimate of the parameter.

observationsErrorDistribution

The distribution of the observations error.

inputObservationsSample

The sample of input observations.

outputObservationsSample

The sample of output observations.

residualFunctionFunction

The residual function.

Notes

The residual function returns model(inputObservations) - outputObservations.

Examples

>>> import openturns as ot

# assume we obtained a result from CalibrationAlgorithm

>>> result = ot.CalibrationResult()
>>> pmap = result.getParameterMAP()
>>> prior = result.getParameterPrior()
>>> posterior = result.getParameterPosterior()
>>> graph1 = result.drawParameterDistributions()  
>>> graph2 = result.drawResiduals()  
>>> graph3 = result.drawObservationsVsInputs()  
>>> graph4 = result.drawObservationsVsPredictions()  

Methods

drawObservationsVsInputs()	Draw observations/inputs.
drawObservationsVsPredictions()	Draw observations/predictions.
drawParameterDistributions()	Draw parameter prior/posterior.
drawResiduals()	Draw residuals.
getClassName()	Accessor to the object’s name.
getId()	Accessor to the object’s id.
getInputObservations()	Accessor to the input observations.
getName()	Accessor to the object’s name.
getObservationsError()	Accessor to the observations error distribution.
getOutputAtPosteriorMean()	Accessor to the output observations.
getOutputAtPriorMean()	Accessor to the output observations.
getOutputObservations()	Accessor to the output observations.
getParameterMAP()	Accessor to the maximum a posteriori parameter estimate.
getParameterPosterior()	Accessor to the parameter posterior distribution.
getParameterPrior()	Accessor to the parameter prior distribution.
getResidualFunction()	Accessor to the residual function.
getShadowedId()	Accessor to the object’s shadowed id.
getVisibility()	Accessor to the object’s visibility state.
hasName()	Test if the object is named.
hasVisibleName()	Test if the object has a distinguishable name.
setInputObservations(inputObservations)	Accessor to the input observations.
setName(name)	Accessor to the object’s name.
setObservationsError(observationsError)	Accessor to the observations error distribution.
setOutputAtPriorAndPosteriorMean(…)	Accessor to the output at prior/posterior mean.
setOutputObservations(outputObservations)	Accessor to the output observations.
setParameterMAP(parameterMAP)	Accessor to the maximum a posteriori parameter estimate.
setParameterPosterior(parameterPosterior)	Accessor to the parameter posterior distribution.
setParameterPrior(parameterPrior)	Accessor to the parameter prior distribution.
setResidualFunction(residualFunction)	Accessor to the residual function.
setShadowedId(id)	Accessor to the object’s shadowed id.
setVisibility(visible)	Accessor to the object’s visibility state.

__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

drawObservationsVsInputs()

Draw observations/inputs.

Plot the observed output of the model depending on the observed input before and after calibration.

Returns

graphGraph

Graph.

Examples

>>> import openturns as ot
>>> result = ot.CalibrationResult()

drawObservationsVsPredictions()

Draw observations/predictions.

Plots the output of the model depending on the output observations before and after calibration.

Returns

graphGraph

Graph.

Examples

>>> import openturns as ot
>>> result = ot.CalibrationResult()

drawParameterDistributions()

Draw parameter prior/posterior.

Plots the prior and posterior distribution of the calibrated parameter theta.

Returns

graphGraph

Graph.

Examples

>>> import openturns as ot
>>> result = ot.CalibrationResult()

drawResiduals()

Draw residuals.

Plot the distribution of the residuals and the distribution of the observation errors.

Returns

graphGraph

Graph.

Examples

>>> import openturns as ot
>>> result = ot.CalibrationResult()

getClassName()

Accessor to the object’s name.

Returns

class_namestr

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

getId()

Accessor to the object’s id.

Returns

idint

Internal unique identifier.

getInputObservations()

Accessor to the input observations.

Returns

inputObservationsSample

The sample of input observations.

getName()

Accessor to the object’s name.

Returns

namestr

The name of the object.

getObservationsError()

Accessor to the observations error distribution.

Returns

observationsErrorDistribution

The observations error distribution.

getOutputAtPosteriorMean()

Accessor to the output observations.

Returns

outputAtPosteriorSample

Output at posterior mean.

getOutputAtPriorMean()

Accessor to the output observations.

Returns

outputAtPriorSample

Output at prior mean.

getOutputObservations()

Accessor to the output observations.

Returns

outputObservationsSample

The sample of output observations.

getParameterMAP()

Accessor to the maximum a posteriori parameter estimate.

Returns

parameterPosteriorPoint

The maximum a posteriori parameter estimate.

getParameterPosterior()

Accessor to the parameter posterior distribution.

Returns

parameterPosteriorDistribution

The posterior distribution of the parameter.

getParameterPrior()

Accessor to the parameter prior distribution.

Returns

parameterPriorDistribution

The prior distribution of the parameter.

getResidualFunction()

Accessor to the residual function.

Returns

residualFunctionFunction

The residual function.

getShadowedId()

Accessor to the object’s shadowed id.

Returns

idint

Internal unique identifier.

getVisibility()

Accessor to the object’s visibility state.

Returns

visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns

hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool

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

setInputObservations(inputObservations)

Accessor to the input observations.

Parameters

inputObservationsSample

The sample of input observations.

setName(name)

Accessor to the object’s name.

Parameters

namestr

The name of the object.

setObservationsError(observationsError)

Accessor to the observations error distribution.

Parameters

observationsErrorDistribution

The observations error distribution.

setOutputAtPriorAndPosteriorMean(outputAtPriorMean, outputAtPosteriorMean)

Accessor to the output at prior/posterior mean.

Parameters

outputAtPriorSample

The sample of output at prior mean.

outputAtPosteriorSample

The sample of output at posterior mean.

setOutputObservations(outputObservations)

Accessor to the output observations.

Parameters

outputObservationsSample

The sample of output observations.

setParameterMAP(parameterMAP)

Accessor to the maximum a posteriori parameter estimate.

Parameters

parameterPosteriorsequence of float

The maximum a posteriori parameter estimate.

setParameterPosterior(parameterPosterior)

Accessor to the parameter posterior distribution.

Parameters

parameterPosterior: Distribution

The posterior distribution of the parameter.

setParameterPrior(parameterPrior)

Accessor to the parameter prior distribution.

Parameters

parameterPrior: Distribution

The prior distribution of the parameter.

setResidualFunction(residualFunction)

Accessor to the residual function.

Parameters

residualFunctionFunction

The residual function.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters

idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters

visiblebool

Visibility flag.