DomainEvent¶

class DomainEvent(*args)¶

Event defined from a domain.

The event occurs when a realization of the underlying random vector belongs to the domain.

Parameters

antecedentRandomVector of dimension 1: Antecedent.
domainDomain: Domain, of same dimension.

See also

ProcessEvent, ThresholdEvent

Examples

>>> import openturns as ot
>>> dim = 2
>>> X = ot.RandomVector(ot.Normal(dim))
>>> model = ot.SymbolicFunction(['x1', 'x2'], ['x1+x2', '2*x1'])
>>> Y = ot.CompositeRandomVector(model, X)
>>> domain = ot.Interval(dim)
>>> event = ot.DomainEvent(Y, domain)

Methods

`getAntecedent`(self)	Accessor to the antecedent RandomVector in case of a composite RandomVector.
`getClassName`(self)	Accessor to the object’s name.
`getCovariance`(self)	Accessor to the covariance of the RandomVector.
`getDescription`(self)	Accessor to the description of the RandomVector.
`getDimension`(self)	Accessor to the dimension of the RandomVector.
`getDistribution`(self)	Accessor to the distribution of the RandomVector.
`getDomain`(self)	Accessor to the domain of the Event.
`getFunction`(self)	Accessor to the Function in case of a composite RandomVector.
`getId`(self)	Accessor to the object’s id.
`getMarginal`(self, \*args)	Get the random vector corresponding to the $i^{th}$ marginal component(s).
`getMean`(self)	Accessor to the mean of the RandomVector.
`getName`(self)	Accessor to the object’s name.
`getOperator`(self)	Accessor to the comparaison operator of the Event.
`getParameter`(self)	Accessor to the parameter of the distribution.
`getParameterDescription`(self)	Accessor to the parameter description of the distribution.
`getProcess`(self)	Get the stochastic process.
`getRealization`(self)	Compute one realization of the RandomVector.
`getSample`(self, size)	Compute realizations of the RandomVector.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getThreshold`(self)	Accessor to the threshold of the Event.
`getVisibility`(self)	Accessor to the object’s visibility state.
`hasName`(self)	Test if the object is named.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`isComposite`(self)	Accessor to know if the RandomVector is a composite one.
`isEvent`(self)	Whether the random vector is an event.
`setDescription`(self, description)	Accessor to the description of the RandomVector.
`setName`(self, name)	Accessor to the object’s name.
`setParameter`(self, parameters)	Accessor to the parameter of the distribution.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.

__init__(self, \*args)¶: Initialize self. See help(type(self)) for accurate signature.

getAntecedent(self)¶

Accessor to the antecedent RandomVector in case of a composite RandomVector.

Returns

antecedentRandomVector: Antecedent RandomVector $\vect{X}$ in case of a CompositeRandomVector such as: $\vect{Y}=f(\vect{X})$ .

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getCovariance(self)¶

Accessor to the covariance of the RandomVector.

Returns

covarianceCovarianceMatrix: Covariance of the considered UsualRandomVector.

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.5], [1.0, 1.5], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getCovariance())
[[ 1    0    ]
 [ 0    2.25 ]]

getDescription(self)¶

Accessor to the description of the RandomVector.

Returns

descriptionDescription: Describes the components of the RandomVector.

getDimension(self)¶

Accessor to the dimension of the RandomVector.

Returns

dimensionpositive int: Dimension of the RandomVector.

getDistribution(self)¶

Accessor to the distribution of the RandomVector.

Returns

distributionDistribution: Distribution of the considered UsualRandomVector.

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.0], [1.0, 1.0], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getDistribution())
Normal(mu = [0,0], sigma = [1,1], R = [[ 1 0 ]
 [ 0 1 ]])

getDomain(self)¶

Accessor to the domain of the Event.

Returns

domainDomain: Describes the domain of an event.

getFunction(self)¶

Accessor to the Function in case of a composite RandomVector.

Returns

functionFunction: Function used to define a CompositeRandomVector as the image through this function of the antecedent $\vect{X}$ : $\vect{Y}=f(\vect{X})$ .

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getMarginal(self, \*args)¶

Get the random vector corresponding to the $i^{th}$ marginal component(s).

Parameters

iint or list of ints, $0\leq i < dim$: Indicates the component(s) concerned. $dim$ is the dimension of the RandomVector.

Returns

vectorRandomVector: RandomVector restricted to the concerned components.

Notes

Let’s note $\vect{Y}=\Tr{(Y_1,\dots,Y_n)}$ a random vector and $I \in [1,n]$ a set of indices. If $\vect{Y}$ is a UsualRandomVector, the subvector is defined by $\tilde{\vect{Y}}=\Tr{(Y_i)}_{i \in I}$ . If $\vect{Y}$ is a CompositeRandomVector, defined by $\vect{Y}=f(\vect{X})$ with $f=(f_1,\dots,f_n)$ , $f_i$ some scalar functions, the subvector is $\tilde{\vect{Y}}=(f_i(\vect{X}))_{i \in I}$ .

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.0], [1.0, 1.0], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getMarginal(1).getRealization())
[0.608202]
>>> print(randomVector.getMarginal(1).getDistribution())
Normal(mu = 0, sigma = 1)

getMean(self)¶

Accessor to the mean of the RandomVector.

Returns

meanPoint: Mean of the considered UsualRandomVector.

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.5], [1.0, 1.5], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getMean())
[0,0.5]

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getOperator(self)¶

Accessor to the comparaison operator of the Event.

Returns

operatorComparisonOperator: Comparaison operator used to define the RandomVector.

getParameter(self)¶

Accessor to the parameter of the distribution.

Returns

parameterPoint: Parameter values.

getParameterDescription(self)¶

Accessor to the parameter description of the distribution.

Returns

descriptionDescription: Parameter names.

getProcess(self)¶

Get the stochastic process.

Returns

processProcess: Stochastic process used to define the RandomVector.

getRealization(self)¶

Compute one realization of the RandomVector.

Returns

aRealizationPoint: Sequence of values randomly determined from the RandomVector definition. In the case of an event: one realization of the event (considered as a Bernoulli variable) which is a boolean value (1 for the realization of the event and 0 else).

See also

getRealization

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.0], [1.0, 1.0], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getSample(3))
    [ X0        X1        ]
0 : [  0.608202 -1.26617  ]
1 : [ -0.438266  1.20548  ]
2 : [ -2.18139   0.350042 ]

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getThreshold(self)¶

Accessor to the threshold of the Event.

Returns

thresholdfloat: Threshold of the RandomVector.

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

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

isComposite(self)¶

Accessor to know if the RandomVector is a composite one.

Returns

isCompositebool: Indicates if the RandomVector is of type Composite or not.

isEvent(self)¶

Whether the random vector is an event.

Returns

isEventbool: Whether it takes it values in {0, 1}.

setDescription(self, description)¶

Accessor to the description of the RandomVector.

Parameters

descriptionstr or sequence of str: Describes the components of the RandomVector.

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setParameter(self, parameters)¶

Accessor to the parameter of the distribution.

Parameters

parametersequence of float: Parameter values.

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.

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An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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