ProbabilitySimulationResult¶

class ProbabilitySimulationResult(*args)¶

Probability simulation result.

Methods

`drawImportanceFactors`()	Draw the importance factors.
`getBlockSize`()	Accessor to the block size.
`getClassName`()	Accessor to the object's name.
`getCoefficientOfVariation`()	Accessor to the coefficient of variation.
`getConfidenceLength`(*args)	Accessor to the confidence length.
`getEvent`()	Accessor to the event.
`getImportanceFactors`()	Accessor to the importance factors.
`getMeanPointInEventDomain`()	Accessor to the mean point conditioned to the event realization.
`getName`()	Accessor to the object's name.
`getOuterSampling`()	Accessor to the outer sampling.
`getProbabilityDistribution`()	Accessor to the asymptotic probability distribution.
`getProbabilityEstimate`()	Accessor to the probability estimate.
`getStandardDeviation`()	Accessor to the standard deviation.
`getTimeDuration`()	Accessor to the elapsed time.
`getVarianceEstimate`()	Accessor to the variance estimate.
`hasName`()	Test if the object is named.
`setBlockSize`(blockSize)	Accessor to the block size.
`setEvent`(event)	Accessor to the event.
`setName`(name)	Accessor to the object's name.
`setOuterSampling`(outerSampling)	Accessor to the outer sampling.
`setProbabilityEstimate`(probabilityEstimate)	Accessor to the probability estimate.
`setTimeDuration`(time)	Accessor to the elapsed time.
`setVarianceEstimate`(varianceEstimate)	Accessor to the variance estimate.

Notes

Structure created by the method run() of a EventSimulation, and obtained thanks to the method getResult().

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> limitState = ot.SymbolicFunction(['E', 'F', 'L', 'I'], ['-F*L^3/(3.*E*I)'])
>>> # Enable the history mechanism in order to use the getImportanceFactors method
>>> limitState = ot.MemoizeFunction(limitState)
>>> distribution = ot.Normal([50.0, 1.0, 10.0, 5.0], [1.0]*4, ot.IdentityMatrix(4))
>>> output = ot.CompositeRandomVector(limitState, ot.RandomVector(distribution))
>>> event = ot.ThresholdEvent(output, ot.Less(), -3.0)
>>> experiment = ot.MonteCarloExperiment()
>>> algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
>>> algo.run()
>>> result = algo.getResult()
>>> importanceFactors = result.getImportanceFactors()