# WhiteNoise¶

class WhiteNoise(*args)

White Noise process.

Parameters: distribution : Distribution Distribution of dimension of the white noise process. mesh : Mesh, optional Mesh in over which the process is discretized. By default, the mesh is reduced to one point in which coordinate is equal to 0.

Notes

A second order white noise is a stochastic process of dimension such that the covariance function where is the covariance matrix of the process at vertex and the Kroenecker function.

A process is a white noise if all finite family of locations , is independent and identically distributed.

Examples

Create a normal normal white noise of dimension 1:

>>> import openturns as ot
>>> myDist = ot.Normal()
>>> myMesh = ot.IntervalMesher([10]*2).build(ot.Interval([0.0]*2, [1.0]*2))
>>> myWN = ot.WhiteNoise(myDist, myMesh)


Get a realization:

>>> myReal =myWN.getRealization()


Methods

 getClassName() Accessor to the object’s name. getContinuousRealization() Get a continuous realization. getCovarianceModel() Accessor to the covariance model. getDescription() Get the description of the process. getDistribution() Accessor to the distribution. getFuture(*args) Prediction of the future iterations of the process. getId() Accessor to the object’s id. getInputDimension() Get the dimension of the domain . getMarginal(indices) Accessor to the marginal process. getMesh() Get the mesh. getName() Accessor to the object’s name. getOutputDimension() Get the dimension of the domain . getRealization() Get a realization of the process. getSample(size) Get realizations of the process. getShadowedId() Accessor to the object’s shadowed id. getTimeGrid() Get the time grid of observation of the process. getTrend() Accessor to the trend. getVisibility() Accessor to the object’s visibility state. hasName() Test if the object is named. hasVisibleName() Test if the object has a distinguishable name. isComposite() Test whether the process is composite or not. isNormal() Test whether the process is normal or not. isStationary() Test whether the process is stationary or not. setDescription(description) Set the description of the process. setDistribution(distribution) Accessor to the distribution. setMesh(mesh) Set the mesh. setName(name) Accessor to the object’s name. setShadowedId(id) Accessor to the object’s shadowed id. setTimeGrid(timeGrid) Set the time grid of observation of the process. setVisibility(visible) Accessor to the object’s visibility state.
__init__(*args)

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

getClassName()

Accessor to the object’s name.

Returns: class_name : str The object class name (object.__class__.__name__).
getContinuousRealization()

Get a continuous realization.

Returns: realization : Function According to the process, the continuous realizations are built: either using a dedicated functional model if it exists: e.g. a functional basis process. or using an interpolation from a discrete realization of the process on : in dimension , a linear interpolation and in dimension , a piecewise constant function (the value at a given position is equal to the value at the nearest vertex of the mesh of the process).
getCovarianceModel()

Accessor to the covariance model.

Returns: cov_model : CovarianceModel Covariance model, if any.
getDescription()

Get the description of the process.

Returns: description : Description Description of the process.
getDistribution()

Accessor to the distribution.

Returns: distribution : Distribution The distribution of dimension of the white noise.
getFuture(*args)

Prediction of the future iterations of the process.

Parameters: stepNumber : int, Number of future steps. size : int, , optional Number of futures needed. Default is 1. prediction : future iterations of the process. If , prediction is a TimeSeries. Otherwise, it is a ProcessSample.
getId()

Accessor to the object’s id.

Returns: id : int Internal unique identifier.
getInputDimension()

Get the dimension of the domain .

Returns: n : int Dimension of the domain : .
getMarginal(indices)

Accessor to the marginal process.

Parameters: N : integer The index of the marginal to be extracted. indices : Indices, optional The list of the indexes of the marginal to be extracted. wn : WhiteNoise The marginal white noise.
getMesh()

Get the mesh.

Returns: mesh : Mesh Mesh over which the domain is discretized.
getName()

Accessor to the object’s name.

Returns: name : str The name of the object.
getOutputDimension()

Get the dimension of the domain .

Returns: d : int Dimension of the domain .
getRealization()

Get a realization of the process.

Returns: realization : Field Contains a mesh over which the process is discretized and the values of the process at the vertices of the mesh.
getSample(size)

Get realizations of the process.

Parameters: n : int, Number of realizations of the process needed. processSample : ProcessSample realizations of the random process. A process sample is a collection of fields which share the same mesh .
getShadowedId()

Accessor to the object’s shadowed id.

Returns: id : int Internal unique identifier.
getTimeGrid()

Get the time grid of observation of the process.

Returns: timeGrid : RegularGrid Time grid of a process when the mesh associated to the process can be interpreted as a RegularGrid. We check if the vertices of the mesh are scalar and are regularly spaced in but we don’t check if the connectivity of the mesh is conform to the one of a regular grid (without any hole and composed of ordered instants).
getTrend()

Accessor to the trend.

Returns: trend : TrendTransform Trend, if any.
getVisibility()

Accessor to the object’s visibility state.

Returns: visible : bool Visibility flag.
hasName()

Test if the object is named.

Returns: hasName : bool True if the name is not empty.
hasVisibleName()

Test if the object has a distinguishable name.

Returns: hasVisibleName : bool True if the name is not empty and not the default one.
isComposite()

Test whether the process is composite or not.

Returns: isComposite : bool True if the process is composite (built upon a function and a process).
isNormal()

Test whether the process is normal or not.

Returns: isNormal : bool True if the process is normal.

Notes

A stochastic process is normal if all its finite dimensional joint distributions are normal, which means that for all and , with , there is and such that:

where , and and is the symmetric matrix:

A Gaussian process is entirely defined by its mean function and its covariance function (or correlation function ).

isStationary()

Test whether the process is stationary or not.

Returns: isStationary : bool True if the process is stationary.

Notes

A process is stationary if its distribution is invariant by translation: , , , we have:

setDescription(description)

Set the description of the process.

Parameters: description : sequence of str Description of the process.
setDistribution(distribution)

Accessor to the distribution.

Parameters: distribution : Distribution The distribution of dimension of the white noise.
setMesh(mesh)

Set the mesh.

Parameters: mesh : Mesh Mesh over which the domain is discretized.
setName(name)

Accessor to the object’s name.

Parameters: name : str The name of the object.
setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters: id : int Internal unique identifier.
setTimeGrid(timeGrid)

Set the time grid of observation of the process.

Returns: timeGrid : RegularGrid Time grid of observation of the process when the mesh associated to the process can be interpreted as a RegularGrid. We check if the vertices of the mesh are scalar and are regularly spaced in but we don’t check if the connectivity of the mesh is conform to the one of a regular grid (without any hole and composed of ordered instants).
setVisibility(visible)

Accessor to the object’s visibility state.

Parameters: visible : bool Visibility flag.