naginterfaces.library.rand.field_1d_predef_setup¶

naginterfaces.library.rand.
field_1d_predef_setup
(ns, xmin, xmax, var, icov1, params, maxm=None, pad=1, icorr=0)[source]¶ field_1d_predef_setup
performs the setup required in order to simulate stationary Gaussian random fields in one dimension, for a preset variogram, using the circulant embedding method. Specifically, the eigenvalues of the extended covariance matrix (or embedding matrix) are calculated, and their square roots output, for use byfield_1d_generate()
, which simulates the random field.For full information please refer to the NAG Library document for g05zn
https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/g05/g05znf.html
 Parameters
 nsint
The number of sample points to be generated in realizations of the random field.
 xminfloat
The lower bound for the interval over which the random field is to be simulated. Note that if (for simulating fractional Brownian motion), is not referenced and the lower bound for the interval is set to zero.
 xmaxfloat
The upper bound for the interval over which the random field is to be simulated. Note that if (for simulating fractional Brownian motion), the lower bound for the interval is set to zero and so is required to be greater than zero.
 varfloat
The multiplicative factor of the variogram .
 icov1int
Determines which of the preset variograms to use. The choices are given below. Note that , where is the correlation length and is a parameter for most of the variograms, and is the variance specified by .
Symmetric stable variogram
where
, ,
, .
Cauchy variogram
where
, ,
, .
Differential variogram with compact support
where
, .
Exponential variogram
where
, .
Gaussian variogram
where
, .
Nugget variogram
No parameters need be set for this value of .
Spherical variogram
where
, .
Bessel variogram
where
is the Bessel function of the first kind,
, ,
, .
Hole effect variogram
where
, .
WhittleMatérn variogram
where
is the modified Bessel function of the second kind,
, ,
, .
Continuously parameterised variogram with compact support
where
,
is the modified Bessel function of the second kind,
, ,
, (second correlation length),
, .
Generalized hyperbolic distribution variogram
where
is the modified Bessel function of the second kind,
, ,
, no constraint on
, ,
, .
Cosine variogram
where
, .
Used for simulating fractional Brownian motion . Fractional Brownian motion itself is not a stationary Gaussian random field, but its increments can be simulated in the same way as a stationary random field. The variogram for the socalled ‘increment process’ is
where
,
, , is the Hurst parameter,
, , normally is the (fixed) step size.
We scale the increments to set ; let , then
The increments can then be simulated using
field_1d_generate()
, then multiplied by to obtain the original increments for the fractional Brownian motion. paramsfloat, arraylike, shape
The parameters set for the variogram.
 maxmNone or int, optional
Note: if this argument is None then a default value will be used, determined as follows: .
The maximum size of the circulant matrix to use. For example, if the embedding matrix is to be allowed to double in size three times before the approximation procedure is used, then choose where .
 padint, optional
Determines whether the embedding matrix is padded with zeros, or padded with values of the variogram. The choice of padding may affect how big the embedding matrix must be in order to be positive semidefinite.
The embedding matrix is padded with zeros.
The embedding matrix is padded with values of the variogram.
 icorrint, optional
Determines which approximation to implement if required, as described in Notes.
 Returns
 lamfloat, ndarray, shape
Contains the square roots of the eigenvalues of the embedding matrix.
 xxfloat, ndarray, shape
The points at which values of the random field will be output.
 mint
The size of the embedding matrix.
 approxint
Indicates whether approximation was used.
No approximation was used.
Approximation was used.
 rhofloat
Indicates the scaling of the covariance matrix. unless approximation was used with or .
 icountint
Indicates the number of negative eigenvalues in the embedding matrix which have had to be set to zero.
 eigfloat, ndarray, shape
Indicates information about the negative eigenvalues in the embedding matrix which have had to be set to zero. contains the smallest eigenvalue, contains the sum of the squares of the negative eigenvalues, and contains the sum of the absolute values of the negative eigenvalues.
 Raises
 NagValueError
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, , and .
Constraint: .
 (errno )
On entry, and .
Constraint: .
 (errno )
On entry, .
Constraint: the minimum calculated value for is .
 (errno )
On entry, .
Constraint: .
 (errno )
On entry, .
Constraint: and .
 (errno )
On entry, .
Constraint: for , .
 (errno )
On entry, .
Constraint: dependent on .
 (errno )
On entry, .
Constraint: or .
 (errno )
On entry, .
Constraint: , or .
 Notes
A onedimensional random field in is a function which is random at every point , so is a random variable for each . The random field has a mean function and a symmetric positive semidefinite covariance function . is a Gaussian random field if for any choice of and , the random vector follows a multivariate Normal distribution, which would have a mean vector with entries and a covariance matrix with entries . A Gaussian random field is stationary if is constant for all and for all and hence we can express the covariance function as a function of one variable: . is known as a variogram (or more correctly, a semivariogram) and includes the multiplicative factor representing the variance such that .
The functions
field_1d_predef_setup
andfield_1d_generate()
are used to simulate a onedimensional stationary Gaussian random field, with mean function zero and variogram , over an interval , using an equally spaced set of points. The problem reduces to sampling a Normal random vector of size , with mean vector zero and a symmetric Toeplitz covariance matrix . Since is in general expensive to factorize, a technique known as the circulant embedding method is used. is embedded into a larger, symmetric circulant matrix of size , which can now be factorized as , where is the Fourier matrix ( is the complex conjugate of ), is the diagonal matrix containing the eigenvalues of and . is known as the embedding matrix. The eigenvalues can be calculated by performing a discrete Fourier transform of the first row (or column) of and multiplying by , and so only the first row (or column) of is needed – the whole matrix does not need to be formed.As long as all of the values of are nonnegative (i.e., is positive semidefinite), is a covariance matrix for a random vector , two samples of which can now be simulated from the real and imaginary parts of , where and have elements from the standard Normal distribution. Since , this calculation can be done using a discrete Fourier transform of the vector . Two samples of the random vector can now be recovered by taking the first elements of each sample of – because the original covariance matrix is embedded in , will have the correct distribution.
If is not positive semidefinite, larger embedding matrices can be tried; however if the size of the matrix would have to be larger than , an approximation procedure is used. We write , where and contain the nonnegative and negative eigenvalues of respectively. Then is replaced by where and is a scaling factor. The error in approximating the distribution of the random field is given by
Three choices for are available, and are determined by the input argument :
setting sets
setting sets
setting sets .
field_1d_predef_setup
finds a suitable positive semidefinite embedding matrix and outputs its size, , and the square roots of its eigenvalues in . If approximation is used, information regarding the accuracy of the approximation is output. Note that only the first row (or column) of is actually formed and stored.
 References
Dietrich, C R and Newsam, G N, 1997, Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix, SIAM J. Sci. Comput. (18), 1088–1107
Schlather, M, 1999, Introduction to positive definite functions and to unconditional simulation of random fields, Technical Report ST 99–10, Lancaster University
Wood, A T A and Chan, G, 1997, Algorithm AS 312: An Algorithm for Simulating Stationary Gaussian Random Fields, Journal of the Royal Statistical Society, Series C (Applied Statistics) (Volume 46) (1), 171–181