SORMAnalysis

class persalys.SORMAnalysis(*args)

Run a reliability analysis using the SORM method.

Parameters
namestr

Name

limitStateLimitState

Limit state

Examples

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

Create the model:

>>> R = persalys.Input('R', 0., ot.LogNormalMuSigma(300., 30., 0.).getDistribution(), 'Yield strength')
>>> F = persalys.Input('F', 0., ot.Normal(75000., 5000.), 'Traction load')
>>> G = persalys.Output('G', 'deviation')
>>> myPhysicalModel = persalys.SymbolicPhysicalModel('myPhysicalModel', [R, F], [G], ['R-F/(pi_*100.0)'])

Create the limit state:

>>> limitState = persalys.LimitState('ls1', myPhysicalModel, 'G', ot.Less(), 0.)

Process a reliability analysis using the SORM method:

>>> analysis = persalys.SORMAnalysis('myAnalysis', limitState)
>>> analysis.run()

Get the result:

>>> result = analysis.getResult().getSORMResult()
>>> pf = result.getEventProbabilityBreitung()
>>> designPoint = result.getStandardSpaceDesignPoint()

Methods

getClassName(self)

Accessor to the object’s name.

getErrorMessage(self)

Error message accessor.

getId(self)

Accessor to the object’s id.

getInterestVariables(self)

Get the variables to analyse.

getLimitState(self)

Limit-state accessor.

getName(self)

Accessor to the object’s name.

getOptimizationAlgorithm(self)

Accessor to the optimization algorithm used to find the design point.

getPhysicalModel(self)

Physical model accessor.

getPhysicalStartingPoint(self)

Physical starting point accessor.

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.

setInterestVariables(self, outputsNames)

Set the variables to analyse.

setName(self, name)

Accessor to the object’s name.

setOptimizationAlgorithm(self, solver)

Accessor to the optimization algorithm used to find the design point.

setPhysicalStartingPoint(self, point)

Physical starting point accessor.

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.

getClassName(self)

Accessor to the object’s name.

Returns
class_namestr

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

getErrorMessage(self)

Error message accessor.

Returns
messagestr

Error message if the analysis failed

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

getLimitState(self)

Limit-state accessor.

Returns
limitStateLimitState

The Limit-state.

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getOptimizationAlgorithm(self)

Accessor to the optimization algorithm used to find the design point.

Returns
algorithmopenturns.OptimizationAlgorithm

Optimization algorithm used to research the design point. Cobyla is the default used algorithm.

getPhysicalModel(self)

Physical model accessor.

Returns
modelPhysicalModel

Physical model

getPhysicalStartingPoint(self)

Physical starting point accessor.

Returns
pointopenturns.Point

Physical starting point. By default it set to the mean of the composed distribution of the physical model

getPythonScript(self)

Physical model accessor.

Returns
scriptstr

Python script to replay the analysis

getResult(self)

Result accessor.

Returns
resultSORMAnalysisResult

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.

setInterestVariables(self, outputsNames)

Set the variables to analyse.

Parameters
variablesNamessequence of str

Names of the variables to analyse

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 to find the design point.

Parameters
algorithmopenturns.OptimizationAlgorithm

Optimization algorithm used to research the design point. Cobyla is the default used algorithm.

setPhysicalStartingPoint(self, point)

Physical starting point accessor.

Parameters
pointsequence of float

Physical starting point. By default it set to the mean of the composed distribution of the physical model

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.