CalibrationResult

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../../_images/CalibrationResult.png
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)
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.