CompositeProcess¶

class CompositeProcess(*args)

Process obtained by transformation.

Parameters: fdyn : FieldFunction A field function. inputProc : Process The input process.

Notes

A composite process is the image of process by the field function :

where and , defined by:

with and .

The process is defined on the domain associated to the mesh .

Examples

Create the process X:

>>> import openturns as ot
>>> amplitude = [1.0, 1.0]
>>> scale = [0.2, 0.3]
>>> myCovModel = ot.ExponentialModel(scale, amplitude)
>>> myMesh = ot.IntervalMesher([100]*2).build(ot.Interval([0.0]*2, [1.0]*2))
>>> myXProcess = ot.GaussianProcess(myCovModel, myMesh)


Create a spatial field function associated to where :

>>> g = ot.SymbolicFunction(['x1', 'x2'],  ['x1^2', 'x1+x2'])
>>> nSpat = 2
>>> gdyn = ot.ValueFunction(g, myMesh)


Create the Y process :

>>> myYProcess = ot.CompositeProcess(gdyn, myXProcess)


>>> f = ot.SymbolicFunction(['x1', 'x2'], ['1+2*x1', '1+3*x2^2'])
>>> fTrend = ot.TrendTransform(f, myMesh)


Create the process :

>>> myYProcess2 = ot.CompositeProcess(fTrend, myXProcess)


Apply the Box Cox transformation where :

>>> h = ot.BoxCoxTransform([3.0, 0.0])
>>> hBoxCox = ot.ValueFunction(h, myMesh)


Create the Y process :

>>> myYProcess3 = ot.CompositeProcess(hBoxCox,  myXProcess)


Methods

 getAntecedent() Get the antecedent process. 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. getFunction() Get the field function. 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) Get the marginal of the random 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. 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.

getAntecedent()

Get the antecedent process.

Returns: XProcess : Process The process .
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.
getFunction()

Get the field function.

Returns: fdyn : FieldFunction The field function .
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)

Get the marginal of the random process.

Parameters: k : int or list of ints Index of the marginal(s) needed. marginals : Process Process defined with marginal(s) of the random process.
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.
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.