def Realization(M, C, *args, **kwargs):
    """
    f = Realization(M, C[, init_mesh, init_vals, check_repeats = True, regularize = True])


    Returns a realization from a Gaussian process.


    :Arguments:

        -   `M`: A Gaussian process mean function.

        -   `C`: A Covariance instance.

        -   `init_mesh`: An optional ndarray giving mesh at which f's initial value will be specified.

        -   `init_vals`: An ndarray giving the value of f over init_mesh.

        -   `regularize`: If init_mesh is not shaped as (n, ndim), where ndim is the dimension of
            the space, regularize should be True.

        -   `check_repeats: Determines whether calls to the GP realization will be checked against
            previous calls before evaluation.

    :SeeAlso: Mean, Covariance, BasisCovariance, observe, GP
    """
    if isinstance(C, BasisCovariance):
        return BasisRealization(M, C, *args, **kwargs)
    else:
        return StandardRealization(M, C, *args, **kwargs)
def Realization ( M , C , * args , ** kwargs ) : if isinstance ( C , BasisCovariance ) : return BasisRealization ( M , C , * args , ** kwargs ) else : return StandardRealization ( M , C , * args , ** kwargs )
f = Realization(M, C[, init_mesh, init_vals, check_repeats = True, regularize = True])


    Returns a realization from a Gaussian process.


    :Arguments:

        -   `M`: A Gaussian process mean function.

        -   `C`: A Covariance instance.

        -   `init_mesh`: An optional ndarray giving mesh at which f's initial value will be specified.

        -   `init_vals`: An ndarray giving the value of f over init_mesh.

        -   `regularize`: If init_mesh is not shaped as (n, ndim), where ndim is the dimension of
            the space, regularize should be True.

        -   `check_repeats: Determines whether calls to the GP realization will be checked against
            previous calls before evaluation.

    :SeeAlso: Mean, Covariance, BasisCovariance, observe, GP