naginterfaces.library.rand.field_1d_user_setup(ns, xmin, xmax, var, cov1, maxm=None, pad=1, icorr=0, data=None)[source]

field_1d_user_setup performs the setup required in order to simulate stationary Gaussian random fields in one dimension, for a user-defined 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 by field_1d_generate(), which simulates the random field.

For full information please refer to the NAG Library document for g05zm


The number of sample points to be generated in realizations of the random field.


The lower bound for the interval over which the random field is to be simulated.


The upper bound for the interval over which the random field is to be simulated.


The multiplicative factor of the variogram .

cov1callable gamma = cov1(x, data=None)

must evaluate the variogram , without the multiplicative factor , for all .

The value returned in is multiplied internally by .


The value at which the variogram is to be evaluated.

dataarbitrary, optional, modifiable in place

User-communication data for callback functions.


The value of 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.

dataarbitrary, optional

User-communication data for callback functions.

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.


The size of the embedding matrix.


Indicates whether approximation was used.

No approximation was used.

Approximation was used.


Indicates the scaling of the covariance matrix. unless approximation was used with or .


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.

(errno )

On entry, .

Constraint: .

(errno )

On entry, and .

Constraint: .

(errno )

On entry, .

Constraint: the minimum calculated value for is .

(errno )

On entry, .

Constraint: .

(errno )

On entry, .

Constraint: or .

(errno )

On entry, .

Constraint: , or .


A one-dimensional 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_user_setup and field_1d_generate() are used to simulate a one-dimensional stationary Gaussian random field, with mean function zero and variogram , over an interval , using an equally spaced set of points on the interval. 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 non-negative (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 non-negative 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_user_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.


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, 1994, Simulation of stationary Gaussian processes in , Journal of Computational and Graphical Statistics (3(4)), 409–432