/usr/include/openturns/swig/SpectralGaussianProcess_doc.i is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | %feature("docstring") OT::SpectralGaussianProcess
"Spectral normal process.
Available constructors:
SpectralGaussianProcess(*secondOrderModel, timeGrid*)
SpectralGaussianProcess(*spectralModel, timeGrid*)
SpectralGaussianProcess(*secondOrderModel, fmax, N*)
SpectralGaussianProcess(*spectralModel, maxFreq, N*)
Parameters
----------
secondOrderModel : :class:`~openturns.SecondOrderModel`
timeGrid : :class:`~openturns.RegularGrid`
The time grid associated to the process. OpenTURNS has only implemented
the algorithm when the mesh is a regular grid.
spectralModel : :class:`~openturns.SpectralModel`
maxFreq : float
Equal to the maximal frequency minus :math:`\\\\Delta f`.
N : float
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
:math:`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 :math:`(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 :meth:`getRealization` might be time consuming because it
computes :math:`N` hermitian matrices of size :math:`d \\\\times \\\\ d`, where
:math:`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)
Create a *SpectralGaussianProcess* from a second order model and a time grid:
>>> import openturns as ot
>>> amplitude = [1.0]
>>> scale = [4.0, 5.0]
>>> myTimeGrid = ot.RegularGrid(0.0, 0.1, 20)
>>> mySecondOrderModel = ot.ExponentialCauchy(scale, amplitude)
>>> mySpectNormProc2 = ot.SpectralGaussianProcess(mySecondOrderModel, myTimeGrid)
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getFFTAlgorithm
"Get the FFT algorithm used to generate realizations of the spectral normal process.
Returns
-------
fftAlgo : :class:`~openturns.FFT`
FFT algorithm used to generate realizations of the spectral normal process.
By default, it is the :class:`~openturns.KissFFT` algorithm.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::setFFTAlgorithm
"Set the FFT algorithm used to generate realizations of the spectral normal process.
Parameters
----------
fftAlgo : :class:`~openturns.FFT`
FFT algorithm that will be used to generate realizations of the spectral
normal process. OpenTURNS provides the :class:`~openturns.KissFFT`
algorithm. More efficient implementations are provided by the
*openturns-fftw* module.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getFrequencyStep
"Get the frequency step :math:`\\\\Delta f` used to discretize the spectral model.
Returns
-------
freqStep : float
The frequency step :math:`\\\\Delta f` used to discretize the spectral model.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getFrequencyGrid
"Get the frequency grid used to discretize the spectral model.
Returns
-------
freqGrid : :class:`~openturns.RegularGrid`
The frequency grid used to discretize the spectral model.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getMaximalFrequency
"Get the maximal frequency used in the computation.
Returns
-------
freqMax : float
The maximal frequency used in the computation: :math:`(1-1/N)fmax = fmax - \\\\Delta f`.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getNFrequency
"Get the number of points in the frequency grid.
Returns
-------
freqGrid : :class:`~openturns.RegularGrid`
The number :math:`N` of points in the frequency grid, which is equal to the
number of time stamps of the time grid.
"
// ---------------------------------------------------------------------
%feature("docstring") OT::SpectralGaussianProcess::getSpectralModel
"Get the spectral model.
Returns
-------
specMod : :class:`~openturns.SpectralModel`
The spectral model defining the process.
"
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