naginterfaces.library.rand.field_fracbm_generate¶
- naginterfaces.library.rand.field_fracbm_generate(ns, s, xmax, h, lam, rho, statecomm)[source]¶
field_fracbm_generate
produces realizations of a fractional Brownian motion, using the circulant embedding method. The square roots of the eigenvalues of the extended covariance matrix (or embedding matrix) need to be input, and can be calculated usingfield_1d_predef_setup()
.For full information please refer to the NAG Library document for g05zt
https://support.nag.com/numeric/nl/nagdoc_30.3/flhtml/g05/g05ztf.html
- Parameters
- nsint
The number of steps (points) to be generated in realizations of the increments of the fractional Brownian motion. This must be the same value as supplied to
field_1d_predef_setup()
when calculating the eigenvalues of the embedding matrix.Note: in the context of fractional Brownian motion, represents the number of steps from a zero starting state. Realizations returned in include this starting state and so values are returned for each realization.
- sint
, the number of realizations of the fractional Brownian motion to simulate.
- xmaxfloat
The upper bound for the interval over which the fractional Brownian motion is to be simulated, as input to
field_1d_user_setup()
orfield_1d_predef_setup()
.- hfloat
The Hurst parameter, , for the fractional Brownian motion. This must be the same value as supplied to
field_1d_predef_setup()
in , when the eigenvalues of the embedding matrix were calculated.- lamfloat, array-like, shape
Contains the square roots of the eigenvalues of the embedding matrix, as returned by
field_1d_user_setup()
orfield_1d_predef_setup()
.- rhofloat
Indicates the scaling of the covariance matrix, as returned by
field_1d_user_setup()
orfield_1d_predef_setup()
.- statecommdict, RNG communication object, modified in place
RNG communication structure.
This argument must have been initialized by a prior call to
init_repeat()
orinit_nonrepeat()
.
- Returns
- zfloat, ndarray, shape
Contains the realizations of the fractional Brownian motion, . The th realization, for the th point , is stored in , for , for .
- xxfloat, ndarray, shape
The points at which values of the fractional Brownian motion are output. The first point is always zero, and the subsequent points represent the equispaced steps towards the last point, . Note that in
field_1d_user_setup()
andfield_1d_predef_setup()
, the returned sample points are the mid-points of the grid returned in here.
- Raises
- NagValueError
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, , and .
Constraint: .
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, at least one element of was negative.
Constraint: all elements of must be non-negative.
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, [‘state’] vector has been corrupted or not initialized.
- Notes
The functions
field_1d_predef_setup()
andfield_fracbm_generate
are used to simulate a fractional Brownian motion process with Hurst parameter over an interval , using a set of equally spaced points. Fractional Brownian motion itself cannot be simulated directly using this method, since it is not a stationary Gaussian random field; however its increments can be simulated like a stationary Gaussian random field. The circulant embedding method is described in the documentation forfield_1d_predef_setup()
.field_fracbm_generate
takes the square roots of the eigenvalues of the embedding matrix as returned byfield_1d_predef_setup()
when , and its size , as input and outputs realizations of the fractional Brownian motion in .One of the initialization functions
init_repeat()
(for a repeatable sequence if computed sequentially) orinit_nonrepeat()
(for a non-repeatable sequence) must be called prior to the first call tofield_fracbm_generate
.
- 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, 1994, Simulation of stationary Gaussian processes in , Journal of Computational and Graphical Statistics (3(4)), 409–432