%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([10]*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."

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%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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