SpectralGaussianProcess

(Source code, png, hires.png, pdf)

../../_images/openturns-SpectralGaussianProcess-1.png
class SpectralGaussianProcess(*args)

Spectral Gaussian process.

Available constructors:

SpectralGaussianProcess(spectralModel, timeGrid)

SpectralGaussianProcess(spectralModel, maxFreq, N)

Parameters
timeGridRegularGrid

The time grid associated to the process. The algorithm is only implemented when the mesh is a regular grid.

spectralModelSpectralModel
maxFreqfloat

Equal to the maximal frequency minus \Delta f.

Nfloat

The number of points in the frequency grid, which is equal to the number of time stamps of the time grid.

Notes

  • In the first usage, we fix the time grid and the second order model (spectral density model) which implements the process. The frequency discretization is deduced from the time discretization by the formulas f_{max} = \frac{1}{\Delta t}, \quad \Delta f = \frac{1}{t_{max}}, N = \frac{f_{max}}{\Delta f}= \frac{t_{max}}{\Delta t}

  • In the second usage, the process is fixed in the frequency domain. fmax value and N induce the time grid. Care: the maximal frequency used in the computation is not fmax but (1-1/N)fmax = fmax - \Delta f.

  • In the third usage, the spectral model is given and the other arguments are the same as the first usage.

  • In the fourth usage, the spectral model is given and the other arguments are the same as the second usage.

The first call of getRealization() might be time consuming because it computes N hermitian matrices of size d \times \ d, where d is the dimension of the spectral model. These matrices are factorized and stored in order to be used for each call of the getRealization method.

Examples

Create a SpectralGaussianProcess from a spectral model and a time grid:

>>> import openturns as ot
>>> amplitude = [1.0, 2.0]
>>> scale = [4.0, 5.0]
>>> spatialCorrelation = ot.CorrelationMatrix(2)
>>> spatialCorrelation[0,1] = 0.8
>>> myTimeGrid =  ot.RegularGrid(0.0, 0.1, 20)
>>> mySpectralModel = ot.CauchyModel(scale, amplitude, spatialCorrelation)
>>> mySpectNormProc1 = ot.SpectralGaussianProcess(mySpectralModel, myTimeGrid)
Attributes
thisown

The membership flag

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.

getFFTAlgorithm()

Get the FFT algorithm used to generate realizations of the spectral Gaussian process.

getFrequencyGrid()

Get the frequency grid used to discretize the spectral model.

getFrequencyStep()

Get the frequency step \Delta f used to discretize the spectral model.

getFuture(*args)

Prediction of the N future iterations of the process.

getId()

Accessor to the object’s id.

getInputDimension()

Get the dimension of the domain \cD.

getMarginal(*args)

Get the k^{th} marginal of the random process.

getMaximalFrequency()

Get the maximal frequency used in the computation.

getMesh()

Get the mesh.

getNFrequency()

Get the number of points in the frequency grid.

getName()

Accessor to the object’s name.

getOutputDimension()

Get the dimension of the domain \cD.

getRealization()

Get a realization of the process.

getSample(size)

Get n realizations of the process.

getShadowedId()

Accessor to the object’s shadowed id.

getSpectralModel()

Get the spectral model.

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.

setFFTAlgorithm(fft)

Set the FFT algorithm used to generate realizations of the spectral Gaussian 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.

AdaptGrid

__init__(*args)

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

getClassName()

Accessor to the object’s name.

Returns
class_namestr

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

getContinuousRealization()

Get a continuous realization.

Returns
realizationFunction

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 \cM: in dimension d=1, a linear interpolation and in dimension d \geq 2, 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_modelCovarianceModel

Covariance model, if any.

getDescription()

Get the description of the process.

Returns
descriptionDescription

Description of the process.

getFFTAlgorithm()

Get the FFT algorithm used to generate realizations of the spectral Gaussian process.

Returns
fftAlgoFFT

FFT algorithm used to generate realizations of the spectral Gaussian process. By default, it is the KissFFT algorithm.

getFrequencyGrid()

Get the frequency grid used to discretize the spectral model.

Returns
freqGridRegularGrid

The frequency grid used to discretize the spectral model.

getFrequencyStep()

Get the frequency step \Delta f used to discretize the spectral model.

Returns
freqStepfloat

The frequency step \Delta f used to discretize the spectral model.

getFuture(*args)

Prediction of the N future iterations of the process.

Parameters
stepNumberint, N \geq 0

Number of future steps.

sizeint, size \geq 0, optional

Number of futures needed. Default is 1.

Returns
predictionProcessSample or TimeSeries

N future iterations of the process. If size = 1, prediction is a TimeSeries. Otherwise, it is a ProcessSample.

getId()

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getInputDimension()

Get the dimension of the domain \cD.

Returns
nint

Dimension of the domain \cD: n.

getMarginal(*args)

Get the k^{th} marginal of the random process.

Parameters
kint or list of ints 0 \leq k < d

Index of the marginal(s) needed.

Returns
marginalsProcess

Process defined with marginal(s) of the random process.

getMaximalFrequency()

Get the maximal frequency used in the computation.

Returns
freqMaxfloat

The maximal frequency used in the computation: (1-1/N)fmax = fmax - \Delta f.

getMesh()

Get the mesh.

Returns
meshMesh

Mesh over which the domain \cD is discretized.

getNFrequency()

Get the number of points in the frequency grid.

Returns
freqGridRegularGrid

The number N of points in the frequency grid, which is equal to the number of time stamps of the time grid.

getName()

Accessor to the object’s name.

Returns
namestr

The name of the object.

getOutputDimension()

Get the dimension of the domain \cD.

Returns
dint

Dimension of the domain \cD.

getRealization()

Get a realization of the process.

Returns
realizationField

Contains a mesh over which the process is discretized and the values of the process at the vertices of the mesh.

getSample(size)

Get n realizations of the process.

Parameters
nint, n \geq 0

Number of realizations of the process needed.

Returns
processSampleProcessSample

n realizations of the random process. A process sample is a collection of fields which share the same mesh \cM \in \Rset^n.

getShadowedId()

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

getSpectralModel()

Get the spectral model.

Returns
specModSpectralModel

The spectral model defining the process.

getTimeGrid()

Get the time grid of observation of the process.

Returns
timeGridRegularGrid

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 \Rset 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
trendTrendTransform

Trend, if any.

getVisibility()

Accessor to the object’s visibility state.

Returns
visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns
hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

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

isComposite()

Test whether the process is composite or not.

Returns
isCompositebool

True if the process is composite (built upon a function and a process).

isNormal()

Test whether the process is normal or not.

Returns
isNormalbool

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 k \in \Nset and I_k \in \Nset^*, with cardI_k=k, there is \vect{m}_1, \dots, \vect{m}_k \in \Rset^d and \mat{C}_{1,\dots,k}\in\mathcal{M}_{kd,kd}(\Rset) such that:

\Expect{\exp\left\{i\Tr{\vect{X}}_{I_k} \vect{U}_{k}  \right\}} =
\exp{\left\{i\Tr{\vect{U}}_{k}\vect{M}_{k}-\frac{1}{2}\Tr{\vect{U}}_{k}\mat{C}_{1,\dots,k}\vect{U}_{k}\right\}}

where \Tr{\vect{X}}_{I_k} = (\Tr{X}_{\vect{t}_1}, \hdots, \Tr{X}_{\vect{t}_k}), \\Tr{vect{U}}_{k} = (\Tr{\vect{u}}_{1}, \hdots, \Tr{\vect{u}}_{k}) and \Tr{\vect{M}}_{k} = (\Tr{\vect{m}}_{1}, \hdots, \Tr{\vect{m}}_{k}) and \mat{C}_{1,\dots,k} is the symmetric matrix:

\mat{C}_{1,\dots,k} = \left(
\begin{array}{cccc}
  C(\vect{t}_1, \vect{t}_1) &C(\vect{t}_1, \vect{t}_2) & \hdots & C(\vect{t}_1, \vect{t}_{k}) \\
  \hdots & C(\vect{t}_2, \vect{t}_2)  & \hdots & C(\vect{t}_2, \vect{t}_{k}) \\
  \hdots & \hdots & \hdots & \hdots \\
  \hdots & \hdots & \hdots & C(\vect{t}_{k}, \vect{t}_{k})
\end{array}
\right)

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

isStationary()

Test whether the process is stationary or not.

Returns
isStationarybool

True if the process is stationary.

Notes

A process X is stationary if its distribution is invariant by translation: \forall k \in \Nset, \forall (\vect{t}_1, \dots, \vect{t}_k) \in \cD, \forall \vect{h}\in \Rset^n, we have:

(X_{\vect{t}_1}, \dots, X_{\vect{t}_k})
\stackrel{\mathcal{D}}{=} (X_{\vect{t}_1+\vect{h}}, \dots, X_{\vect{t}_k+\vect{h}})

setDescription(description)

Set the description of the process.

Parameters
descriptionsequence of str

Description of the process.

setFFTAlgorithm(fft)

Set the FFT algorithm used to generate realizations of the spectral Gaussian process.

Parameters
fftAlgoFFT

FFT algorithm that will be used to generate realizations of the spectral Gaussian process. The KissFFT is provided. More efficient implementations are provided by the openturns-fftw module.

setMesh(mesh)

Set the mesh.

Parameters
meshMesh

Mesh over which the domain \cD is discretized.

setName(name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters
idint

Internal unique identifier.

setTimeGrid(timeGrid)

Set the time grid of observation of the process.

Returns
timeGridRegularGrid

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 \Rset 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
visiblebool

Visibility flag.

thisown

The membership flag