/usr/include/openturns/swig/WhiteNoise_doc.i is in libopenturns-dev 1.7-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | %feature("docstring") OT::WhiteNoise
"White Noise process.
Parameters
----------
distribution : :class:`~openturns.Distribution`
Distribution of dimension :math:`d` of the white noise process.
mesh : :class:`~openturns.Mesh`, optional
Mesh in :math:`\\\\Rset^n` over which the process is discretized.
By default, the mesh is reduced to one point in :math:`\\\\Rset` which coordinate is equal to 0.
Notes
-----
A second order white noise :math:`\\\\varepsilon: \\\\Omega \\\\times \\\\cD \\\\rightarrow \\\\Rset^d` is a stochastic process of dimension :math:`d` such that the covariance function :math:`C(\\\\vect{s},\\\\vect{t})=\\\\delta(\\\\vect{t}-\\\\vect{s})C(\\\\vect{s},\\\\vect{s})` where :math:`C(\\\\vect{s},\\\\vect{s})` is the covariance matrix of the process at vertex :math:`\\\\vect{s}` and :math:`\\\\delta` the Kroenecker function.
A process :math:`\\\\varepsilon` is a white noise if all finite family of locations :math:`(\\\\vect{t}_i)_{i=1, \\\\dots, n} \\\\in \\\\cD`, :math:`(\\\\varepsilon_{\\\\vect{t}_i})_{i=1, \\\\dots, n}` 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([100]*2).build(ot.Interval([0.0]*2, [1.0]*2))
>>> myWN = ot.WhiteNoise(myDist, myMesh)
Get a realization:
>>> myReal =myWN.getRealization()"
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%feature("docstring") OT::WhiteNoise::getDistribution
"Accessor to the distribution.
Returns
-------
distribution : :class:`~openturns.Distribution`
The distribution of dimension :math:`d` of the white noise."
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%feature("docstring") OT::WhiteNoise::getMarginal
"Accessor to the marginal process.
Parameters
----------
N : integer
The index of the marginal to be extracted.
indices : :class:`~openturns.Indices`, optional
The list of the indexes of the marginal to be extracted.
Returns
-------
wn : :class:`~openturns.WhiteNoise`
The marginal white noise."
// ---------------------------------------------------------------------
%feature("docstring") OT::WhiteNoise::setDistribution
"Accessor to the distribution.
Parameters
----------
distribution : :class:`~openturns.Distribution`
The distribution of dimension :math:`d` of the white noise."
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