geoslib_f.h File Reference

#include "gstlearn_export.hpp"
#include "geoslib_d.h"
#include "Enum/EKrigOpt.hpp"
#include "Enum/ECalcVario.hpp"
#include "Enum/ELoadBy.hpp"
#include "Enum/EConsElem.hpp"
#include "Basic/CSVformat.hpp"
#include "Basic/NamingConvention.hpp"
#include "Db/DbGrid.hpp"
#include "Matrix/MatrixRectangular.hpp"
#include "Model/Constraints.hpp"
#include "Model/Option_AutoFit.hpp"
#include "Model/Option_VarioFit.hpp"
#include "Simulation/SimuBooleanParam.hpp"
#include "Simulation/SimuPartitionParam.hpp"
#include "Simulation/SimuFFTParam.hpp"
#include "Stats/Selectivity.hpp"
#include "Variogram/DirParam.hpp"

Functions
GSTLEARN_EXPORT void	acknowledge_gstlearn (void)
GSTLEARN_EXPORT VectorDouble	util_set_array_double (int ntab, const double *rtab)
GSTLEARN_EXPORT VectorInt	util_set_array_integer (int ntab, const int *itab)
GSTLEARN_EXPORT VectorString	util_set_array_char (int ntab, char **names)
GSTLEARN_EXPORT std::vector< char * >	util_vs_to_vs (VectorString vs)
GSTLEARN_EXPORT int	csv_manage (const char *filename, const CSVformat &csv, int mode, int nitem, bool flag_integer=0, bool verbose=false)
GSTLEARN_EXPORT void	csv_print_double (double value)
GSTLEARN_EXPORT VectorDouble	db_get_grid_axis (DbGrid *dbgrid, int idim)
GSTLEARN_EXPORT VectorDouble	db_get_attribute (Db *db, int iatt, bool verbose=false)
GSTLEARN_EXPORT VectorInt	db_identify_variables_by_name (Db *db, const String &pattern)
GSTLEARN_EXPORT Vario *	variogram_pgs (Db *db, const VarioParam *varioparam, const RuleProp *ruleprop, int flag_rho=false, int opt_correl=2)
GSTLEARN_EXPORT int	model_auto_fit (Vario *vario, Model *model, bool verbose=false, const Option_AutoFit &mauto_arg=Option_AutoFit(), const Constraints &cons_arg=Constraints(), const Option_VarioFit &optvar_arg=Option_VarioFit())
GSTLEARN_EXPORT int	vmap_auto_fit (const DbGrid *dbvmap, Model *model, bool verbose=false, const Option_AutoFit &mauto_arg=Option_AutoFit(), const Constraints &cons_arg=Constraints(), const Option_VarioFit &optvar_arg=Option_VarioFit())
GSTLEARN_EXPORT int	db_model_nostat (Db *db, Model *model, int icov=0, const NamingConvention &namconv=NamingConvention("Nostat"))
GSTLEARN_EXPORT int	is_model_nostat_param (Model *model, const EConsElem &type0)
GSTLEARN_EXPORT void	set_test_discrete (bool flag_discret)
GSTLEARN_EXPORT Vario *	model_pgs (Db *db, const VarioParam *varioparam, const RuleProp *ruleprop, const Model *model1, const Model *model2=nullptr)
GSTLEARN_EXPORT Cheb_Elem *	spde_cheb_manage (int mode, int verbose, double power, const VectorDouble &blin, MatrixSparse *S, Cheb_Elem *cheb_old)
GSTLEARN_EXPORT int	spde_chebychev_operate (MatrixSparse *S, Cheb_Elem *cheb_elem, const VectorDouble &lambda, const VectorDouble &x, VectorDouble &y)
GSTLEARN_EXPORT int	krigsum (Db *dbin, Db *dbout, Model *model, ANeigh *neigh, bool flag_positive=false, const NamingConvention &namconv=NamingConvention("KrigSum"))
GSTLEARN_EXPORT int	declustering (Db *db, Model *model, int method, ANeigh *neigh=nullptr, DbGrid *dbgrid=nullptr, const VectorDouble &radius=VectorDouble(), const VectorInt &ndisc=VectorInt(), int flag_sel=false, bool verbose=false)
GSTLEARN_EXPORT int	simpgs (Db *dbin, Db *dbout, RuleProp *ruleprop, Model *model1, Model *model2=nullptr, ANeigh *neigh=nullptr, int nbsimu=1, int seed=1321421, int flag_gaus=false, int flag_prop=false, int flag_check=false, int flag_show=false, int nbtuba=100, int nboot=10, int niter=100, double percent=5., const NamingConvention &namconv=NamingConvention("Facies", true, true, true, ELoc::fromKey("FACIES")))
GSTLEARN_EXPORT int	simbipgs (Db *dbin, Db *dbout, RuleProp *ruleprop, Model *model11, Model *model12=nullptr, Model *model21=nullptr, Model *model22=nullptr, ANeigh *neigh=nullptr, int nbsimu=1, int seed=43243, int flag_gaus=false, int flag_prop=false, int flag_check=false, int flag_show=false, int nbtuba=100, int nboot=10, int niter=100, double percent=5., const NamingConvention &namconv=NamingConvention("Facies", true, true, true, ELoc::fromKey("FACIES")))
GSTLEARN_EXPORT VectorDouble	simsph_mesh (MeshSpherical *mesh, Model *model, const SimuSphericalParam &sphepar, int seed=54523, int verbose=false)
GSTLEARN_EXPORT DbGrid *	simfine (DbGrid *dbin, Model *model, const SimuRefineParam &param, int seed)
GSTLEARN_EXPORT MatrixRectangular	fluid_extract (DbGrid *dbgrid, const String &name_facies, const String &name_fluid, const String &name_poro, const String &name_date, int nfacies, int nfluids, int facies0, int fluid0, int ntime, double time0, double dtime, bool verbose=false)
GSTLEARN_EXPORT int	simpgs_spde (Db *dbin, Db *dbout, RuleProp *ruleprop, Model *model1, Model *model2, const String &triswitch, const VectorDouble &gext, int flag_gaus, int flag_modif, int flag_check, int flag_show, int nfacies, int seed, int nbsimu, int gibbs_nburn, int gibbs_niter, int ngibbs_int, int verbose, double percent)
GSTLEARN_EXPORT Db *	db_read_csv (const char *filename, const CSVformat &csvfmt, int verbose=0, int ncol_max=-1, int nrow_max=-1, int flag_add_rank=0)
GSTLEARN_EXPORT int	db_write_csv (Db *db, const char *filename, const CSVformat &csv, int flag_allcol=1, int flag_coor=1, bool flag_integer=false)
GSTLEARN_EXPORT int	db_proportion_estimate (Db *dbin, DbGrid *dbout, Model *model, int niter=100, bool verbose=false, const NamingConvention &namconv=NamingConvention("Prop", true, true, true, ELoc::fromKey("P")))
GSTLEARN_EXPORT int	gibbs_sampler (Db *dbin, Model *model, int nbsimu, int seed, int gibbs_nburn, int gibbs_niter, bool flag_moving, bool flag_norm, bool flag_multi_mono, bool flag_propagation, bool flag_sym_neigh, int gibbs_optstats, double percent, bool flag_ce, bool flag_cstd, bool verbose=false, const NamingConvention &namconv=NamingConvention("Gibbs"))
GSTLEARN_EXPORT int	potential_kriging (Db *db, Db *dbgrd, Db *dbtgt, DbGrid *dbout, Model *model, ANeigh *neigh, double nugget_grd=0., double nugget_tgt=0., bool flag_pot=true, bool flag_grad=false, bool flag_trans=false, bool flag_save_data=false, int opt_part=0, bool verbose=false)
GSTLEARN_EXPORT int	potential_cov (Model *model, bool verbose, int type1, const VectorDouble &x10, const VectorDouble &x1p, const VectorDouble &tx1, int type2, const VectorDouble &x20, const VectorDouble &x2p, const VectorDouble &tx2, VectorDouble &covtab)
GSTLEARN_EXPORT int	potential_simulate (Db *dbiso, Db *dbgrd, Db *dbtgt, DbGrid *dbout, Model *model, ANeigh *neigh, double nugget_grd=0., double nugget_tgt=0., double dist_tempere=TEST, bool flag_trans=false, int seed=135674, int nbsimu=1, int nbtuba=100, bool verbose=false)
GSTLEARN_EXPORT int	potential_xvalid (Db *dbiso, Db *dbgrd, Db *dbtgt, Model *model, ANeigh *neigh, double nugget_grd=0., double nugget_tgt=0., int flag_dist_conv=false, bool verbose=false)

Function Documentation

GSTLEARN_EXPORT void acknowledge_gstlearn

(

void

)

Acknowledgment of the authors for gstlearn Library

GSTLEARN_EXPORT int csv_manage	(	const char *	filename,
		const CSVformat &	csv,
		int	mode,
		int	nitem,
		bool	flag_integer,
		bool	verbose
	)

Manage the Utility to write into a CSV file

Returns

Error return code

Parameters

[in]	filename	Name of the CSV file
[in]	csv	CSVFormat description
[in]	mode	1 for opening File; -1 for closing File
[in]	nitem	Number of items per line
[in]	flag_integer	true if the numerical values must be printed as integer
[in]	verbose	Verbose flag

Remarks

: This procedure manages an internal structure (declared as static)

: When opened, you can use csv_print_string() or csv_print_double()

: in order to store items in the file

: Do not forget to use csv_manage(-1,...) to close the file

GSTLEARN_EXPORT void csv_print_double

(

double

value

)

Write a DOUBLE element into the (opened) CSV file

Parameters

[in]

value

Real value to be written

Remarks

: This function uses CSV_ENCODING static structure

: which must have been initiated beforehand

GSTLEARN_EXPORT VectorDouble db_get_attribute	(	Db *	db,
		int	iatt,
		bool	verbose
	)

Returns a vector containing an attribute from the Db

Parameters

[in]	db	Db structure
[in]	iatt	Attribute rank
[in]	verbose	Verbose flag

GSTLEARN_EXPORT VectorDouble db_get_grid_axis	(	DbGrid *	dbgrid,
		int	idim
	)

Returns a vector containing the coordinates of a Grid along one space dimension

Parameters

[in]	dbgrid	Db structure
[in]	idim	Rank of the space dimension

GSTLEARN_EXPORT VectorInt db_identify_variables_by_name	(	Db *	db,
		const String &	pattern
	)

Identify the variables of a Db where names match a criterion

Parameters

[in]	db	Db structure
[in]	pattern	Matching pattern

GSTLEARN_EXPORT int db_model_nostat	(	Db *	db,
		Model *	model,
		int	icov,
		const NamingConvention &	namconv
	)

Calculate and store new variables in the Db which contain the non-stationary Model component

Returns

Distance value

Parameters

[in]	db	Db structure
[in]	model	Model structure
[in]	icov	Rank of the Basic structure
[in]	namconv	Naming convention

Remarks

This procedure automatically creates several fields:

ndim fields for storing the ranges

ndim fields for storing the angles

1 field for storing the sill

GSTLEARN_EXPORT int db_proportion_estimate	(	Db *	dbin,
		DbGrid *	dbout,
		Model *	model,
		int	niter,
		bool	verbose,
		const NamingConvention &	namconv
	)

Standard Kriging

Returns

Error return code

Parameters

[in]	dbin	Input Db structure
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	niter	Number of iterations
[in]	verbose	Verbose flag
[in]	namconv	Naming convention

Remarks

The procedure uses the FIRST covariance of the Model

to describe the spatial structure

GSTLEARN_EXPORT Db* db_read_csv	(	const char *	file_name,
		const CSVformat &	csvfmt,
		int	verbose,
		int	ncol_max,
		int	nrow_max,
		int	flag_add_rank
	)

Read a CSV file and load the results into a Db

Returns

Pointer to the Db descriptor

Parameters

[in]	file_name	Name of the ASCII file
[in]	verbose	Verbose option if the file cannot be opened
[in]	csvfmt	CSVformat structure
[in]	ncol_max	Maximum number of columns (or -1)
[in]	nrow_max	Maximum number of rows (or -1)
[in]	flag_add_rank	1 To add the rank number

GSTLEARN_EXPORT int db_write_csv	(	Db *	db,
		const char *	filename,
		const CSVformat &	csvfmt,
		int	flag_allcol,
		int	flag_coor,
		bool	flag_integer
	)

Write the Data frame into a CSV file. Reserved for numerical data frame.

Returns

Error return code

Parameters

[in]	db	Name of the Db
[in]	filename	Name of the CSV file
[in]	csvfmt	CSVformat structure
[in]	flag_allcol	1 if all the columns available must be dumped out
[in]	flag_coor	1 if the coordinates must be dumped out
[in]	flag_integer	true if the numerical values must be printed as integer

Remarks

: This procedure dumps the Z-variables and optionally the X-variables

GSTLEARN_EXPORT int declustering	(	Db *	dbin,
		Model *	model,
		int	method,
		ANeigh *	neigh,
		DbGrid *	dbgrid,
		const VectorDouble &	radius,
		const VectorInt &	ndiscs,
		int	flag_sel,
		bool	verbose
	)

Perform the Declustering task

Returns

Error return code

Parameters

[in]	dbin	input Db structure
[in]	model	Model structure
[in]	method	Method for declustering
[in]	neigh	ANeigh structure
[in]	dbgrid	Grid auxiliary Db structure
[in]	radius	Array of neighborhood radius
[in]	ndiscs	Array of discretization
[in]	flag_sel	1 to mask off samples with zero weight
[in]	verbose	Verbose option

GSTLEARN_EXPORT MatrixRectangular fluid_extract	(	DbGrid *	dbgrid,
		const String &	name_facies,
		const String &	name_fluid,
		const String &	name_poro,
		const String &	name_date,
		int	nfacies,
		int	nfluids,
		int	facies0,
		int	fluid0,
		int	ntime,
		double	time0,
		double	dtime,
		bool	verbose
	)

Extract time charts from the fluid propagation block

Returns

The returned matrix

Parameters

[in]	dbgrid	Db grid structure
[in]	name_facies	Name of variable containing Facies
[in]	name_fluid	Name of variable containing Fluid
[in]	name_poro	Name of variable containing Porosity (optional)
[in]	name_date	Name of variable containing Date
[in]	nfacies	number of facies (facies 0 excluded)
[in]	nfluids	number of fluids
[in]	facies0	Value of the target facies
[in]	fluid0	Value of the target fluid
[in]	ntime	Number of Time intervals
[in]	time0	Starting time
[in]	dtime	Time interval
[in]	verbose	1 for a verbose option

GSTLEARN_EXPORT int gibbs_sampler	(	Db *	dbin,
		Model *	model,
		int	nbsimu,
		int	seed,
		int	gibbs_nburn,
		int	gibbs_niter,
		bool	flag_moving,
		bool	flag_norm,
		bool	flag_multi_mono,
		bool	flag_propagation,
		bool	flag_sym_neigh,
		int	gibbs_optstats,
		double	percent,
		bool	flag_ce,
		bool	flag_cstd,
		bool	verbose,
		const NamingConvention &	namconv
	)

Perform the Gibbs sampler

Returns

Error return code

Parameters

[in]	dbin	Db structure
[in]	model	Model structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	gibbs_nburn	Initial number of iterations for bootstrapping
[in]	gibbs_niter	Maximum number of iterations
[in]	flag_moving	1 for Moving
[in]	flag_norm	1 if the Model must be normalized
[in]	flag_multi_mono	1 for the Multi_mono algorithm
[in]	flag_propagation	1 for the propagation algorithm
[in]	flag_sym_neigh	Deprecated argument
[in]	gibbs_optstats	0: No stats - 1: Print - 2: Save Neutral file
[in]	percent	Amount of nugget effect added to too continuous model (expressed in percentage of total variance)
[in]	flag_ce	1 if the conditional expectation should be returned instead of simulations
[in]	flag_cstd	1 if the conditional standard deviation should be returned instead of simulations
[in]	verbose	Verbose flag
[in]	namconv	Naming convention

GSTLEARN_EXPORT int is_model_nostat_param	(	Model *	model,
		const EConsElem &	type0
	)

Check if the non-stationary Model has a given non-stationary parameter

Returns

1 if the given non-stationary parameter is defined; 0 otherwise

Parameters

[in]	model	Model structure
[in]	type0	Requested type (EConsElem)

GSTLEARN_EXPORT int krigsum	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_positive,
		const NamingConvention &	namconv
	)

Punctual Multivariate Kriging under a constraint

Returns

Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure (univariate)
[in]	neigh	ANeigh structure
[in]	flag_positive	1 for a positive constraints
[in]	namconv	Naming convention

Remarks

All the variables are estimated using the same model

In this procedure, we assume that:

- the problem is multivariate ("z" variables)

- the constraints is stored in "sum" (only used in dbout)

GSTLEARN_EXPORT int model_auto_fit	(	Vario *	vario,
		Model *	model,
		bool	verbose,
		const Option_AutoFit &	mauto_arg,
		const Constraints &	cons_arg,
		const Option_VarioFit &	optvar_arg
	)

Automatic model fitting

Returns

Error returned code

Parameters

[in]	vario	Vario structure containing the exp. variogram
[in]	model	Model structure containing the basic structures
[in]	verbose	Verbose flag
[in]	mauto_arg	Option_AutoFit structure
[in]	cons_arg	Constraints structure
[in]	optvar_arg	Opt_Vario structure

GSTLEARN_EXPORT Vario* model_pgs	(	Db *	db,
		const VarioParam *	varioparam,
		const RuleProp *	ruleprop,
		const Model *	model1,
		const Model *	model2
	)

Evaluate the experimental variogram of indicators in PluriGaussian case

Returns

Error return code

Parameters

[in]	db	Db descriptor
[in]	varioparam	VarioParam structure
[in]	ruleprop	RuleProp structure
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)

GSTLEARN_EXPORT int potential_cov	(	Model *	model,
		bool	verbose,
		int	type1,
		const VectorDouble &	x10,
		const VectorDouble &	x1p,
		const VectorDouble &	tx1,
		int	type2,
		const VectorDouble &	x20,
		const VectorDouble &	x2p,
		const VectorDouble &	tx2,
		VectorDouble &	covtab
	)

Potential covariance

Returns

Error return code

Parameters

[in]	model	Model structure
[in]	verbose	Verbose flag
[in]	type1	Type of the first point 1 for gradient; 2 for tangent; 3 for isopotential
[in]	x10	Coordinates of the centering for first point
[in]	x1p	Coordinates of the first point
[in]	tx1	Tangent values at the first point
[in]	type2	Type of the second point 1 for gradient; 2 for tangent; 3 for isopotential (Sign is negative for target point)
[in]	x20	Coordinates of the centering for second point
[in]	x2p	Coordinates of the second point
[in]	tx2	Tangent values at the second point
[out]	covtab	Array of returned values (dimensionned to ndim*ndim)

GSTLEARN_EXPORT int potential_kriging	(	Db *	dbiso,
		Db *	dbgrd,
		Db *	dbtgt,
		DbGrid *	dbout,
		Model *	model,
		ANeigh *	neigh,
		double	nugget_grd,
		double	nugget_tgt,
		bool	flag_pot,
		bool	flag_grad,
		bool	flag_trans,
		bool	flag_save_data,
		int	opt_part,
		bool	verbose
	)

Potential estimation

Returns

Error return code

Parameters

[in]	dbiso	Iso-potential Db structure
[in]	dbgrd	Gradient Db structure
[in]	dbtgt	Tangent Db structure (optional)
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	nugget_grd	Nugget effect for Gradients
[in]	nugget_tgt	Nugget effect for Tangents
[in]	flag_pot	True if the Potential must be estimated
[in]	flag_grad	True if the gradient must also be estimated
[in]	flag_trans	True if the estimation result must be translated into layer number
[in]	flag_save_data	True if the Potential / Gradient must be saved on any Information file
[in]	opt_part	Option to exhibit only a part of estimation: 0 : the whole estimation 1 : the gradient contribution only 2 : the tangent contribution only 3 : the isovalues contribution only 4 : the drift contribution only 5 : the external drift contribution only
[in]	verbose	Verbose option

Remarks

The results will be stored in the dbout file

- the estimation in the variable ELoc::Z

- the gradient components in the variables ELoc::GRD

GSTLEARN_EXPORT int potential_simulate	(	Db *	dbiso,
		Db *	dbgrd,
		Db *	dbtgt,
		DbGrid *	dbout,
		Model *	model,
		ANeigh *	neigh,
		double	nugget_grd,
		double	nugget_tgt,
		double	dist_tempere,
		bool	flag_trans,
		int	seed,
		int	nbsimu,
		int	nbtuba,
		bool	verbose
	)

Potential simulations

Returns

Error return code

Parameters

[in]	dbiso	Iso-potential Db structure
[in]	dbgrd	Gradient Db structure
[in]	dbtgt	Tangent Db structure (optional)
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	nugget_grd	Nugget effect for Gradients
[in]	nugget_tgt	Nugget effect for Tangents
[in]	dist_tempere	Distance for tempering simulations (or TEST)
[in]	flag_trans	True if the estimation result must be translated into layer number
[in]	seed	Seed for the random number generator
[in]	nbsimu	Number of simulations
[in]	nbtuba	Number of turning bands
[in]	verbose	Verbose option

Remarks

The simulations will be stored in the dbout file (ELoc::SIMU)

GSTLEARN_EXPORT int potential_xvalid	(	Db *	dbiso,
		Db *	dbgrd,
		Db *	dbtgt,
		Model *	model,
		ANeigh *	neigh,
		double	nugget_grd,
		double	nugget_tgt,
		int	flag_dist_conv,
		bool	verbose
	)

Potential cross-validation

Returns

Error return code

Parameters

[in]	dbiso	Iso-potential Db structure
[in]	dbgrd	Gradient Db structure
[in]	dbtgt	Tangent Db structure (optional)
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	nugget_grd	Nugget effect for Gradients
[in]	nugget_tgt	Nugget effect for Tangents
[in]	flag_dist_conv	Flag for converting into distance
[in]	verbose	Verbose option

GSTLEARN_EXPORT void set_test_discrete

(

bool

flag_discret

)

GSTLEARN_EXPORT int simbipgs	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model11,
		Model *	model12,
		Model *	model21,
		Model *	model22,
		ANeigh *	neigh,
		int	nbsimu,
		int	seed,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nbtuba,
		int	gibbs_nburn,
		int	gibbs_niter,
		double	percent,
		const NamingConvention &	namconv
	)

Perform the conditional or non-conditional Bi Pluri-gaussian simulations

Returns

Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	Ruleprop definition
[in]	model11	First Model structure for First Lithotype Rule
[in]	model12	Second Model structure for First Lithotype Rule
[in]	model21	First Model structure for Second Lithotype Rule
[in]	model22	Second Model structure for Second Lithotype Rule
[in]	neigh	ANeigh structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	flag_gaus	1 gaussian results; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies (only if flag_cond && !flag_gaus)
[in]	nbtuba	Number of turning bands
[in]	gibbs_nburn	Number of bootstrap iterations
[in]	gibbs_niter	Maximum number of iterations
[in]	percent	Amount of nugget effect added to too continuous model (expressed in percentage of the total variance)
[in]	namconv	Naming convention

Remarks

When conditional, the two first variables in the input Db

should correspond to the two facies indices (starting from 1)

The argument 'dbin' is optional: it must be defined only for

conditional simulations

The proportions (nfac1 * nfac2) must be ordered as follows:

f1af2a, f1bf2a, f1cf2a, ..., f1bf2a, f1bf2b, ..., f1nf2m

GSTLEARN_EXPORT DbGrid* simfine	(	DbGrid *	dbin,
		Model *	model,
		const SimuRefineParam &	param,
		int	seed
	)

Refine the simulation

Returns

Newly refined Grid.

Parameters

[in]	dbin	Input grid Db structure
[in]	model	Model structure
[in]	param	SimuRefineParam structure
[in]	seed	Seed for the random number generator

Remarks

For each dimension of the space, if N stands for the number of

nodes in the input grid, the number of nodes of the output grid

will be (N-1) * 2^p + 1 where p is the param.getNmult()

GSTLEARN_EXPORT int simpgs	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model1,
		Model *	model2,
		ANeigh *	neigh,
		int	nbsimu,
		int	seed,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nbtuba,
		int	gibbs_nburn,
		int	gibbs_niter,
		double	percent,
		const NamingConvention &	namconv
	)

Perform the conditional or non-conditional Pluri-gaussian simulations

Returns

Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	RuleProp structure
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)
[in]	neigh	ANeigh structure
[in]	nbsimu	Number of simulations
[in]	seed	Seed for random number generator
[in]	flag_gaus	1 if results must be gaussian; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies (only if flag_cond && !flag_gaus)
[in]	nbtuba	Number of turning bands
[in]	gibbs_nburn	Number of bootstrap iterations
[in]	gibbs_niter	Maximum number of iterations
[in]	percent	Amount of nugget effect added to too much continous model (expressed in percentage of the total variance)
[in]	namconv	Naming convention

Remarks

When conditional, the unique variable in the input Db structure

should correspond to the facies index (starting from 1)

The argument 'dbin' is optional: it must be defined only for

conditional simulations

GSTLEARN_EXPORT int simpgs_spde	(	Db *	dbin,
		Db *	dbout,
		RuleProp *	ruleprop,
		Model *	model1,
		Model *	model2,
		const String &	triswitch,
		const VectorDouble &	gext,
		int	flag_gaus,
		int	flag_prop,
		int	flag_check,
		int	flag_show,
		int	nfacies,
		int	seed,
		int	nbsimu,
		int	gibbs_nburn,
		int	gibbs_niter,
		int	ngibbs_int,
		int	verbose,
		double	percent
	)

Perform the conditional Pluri-gaussian simulations using spde

Returns

Error return code

Parameters

[in]	dbin	Input Db structure (optional)
[in]	dbout	Output Db structure
[in]	ruleprop	RuleProp definition
[in]	model1	First Model structure
[in]	model2	Second Model structure (optional)
[in]	triswitch	Meshing option
[in]	gext	Array of domain dilation
[in]	flag_gaus	1 if results must be gaussian; otherwise facies
[in]	flag_prop	1 for facies proportion
[in]	flag_check	1 if the facies at data must be checked against the closest simulated grid node
[in]	flag_show	1 if the grid node which coincides with the data should be represented with the data facies
[in]	nfacies	Number of facies
[in]	seed	Seed for random number generator
[in]	nbsimu	Number of simulations
[in]	gibbs_nburn	Number of iterations (Burning step)
[in]	gibbs_niter	Maximum number of iterations
[in]	ngibbs_int	Number of iterations internal to Gibbs (SPDE)
[in]	verbose	Verbose flag
[in]	percent	Amount of nugget effect added to too continous model (expressed in percentage of the total variance)

Remarks

When conditional, the unique variable in the input Db structure

should correspond to the facies index (starting from 1)

GSTLEARN_EXPORT VectorDouble simsph_mesh	(	MeshSpherical *	mesh,
		Model *	model,
		const SimuSphericalParam &	sphepar,
		int	seed,
		int	verbose
	)

Simulates the random function on the sphere

Returns

The Vector simulated values

Parameters

[in]	mesh	MeshSpherical object
[in]	model	Model (defined in Euclidean space) to be used
[in]	sphepar	SimuSphericalParam structure
[in]	seed	Seed for random number generation
[in]	verbose	Verbose flag

GSTLEARN_EXPORT Cheb_Elem* spde_cheb_manage	(	int	mode,
		int	verbose,
		double	power,
		const VectorDouble &	blin,
		MatrixSparse *	S,
		Cheb_Elem *	cheb_old
	)

Manage Cheb_Elem structure

Returns

Error return code

Parameters

[in]	mode	1 for allocation; -1 for deallocation
[in]	verbose	Verbose flag
[in]	power	Parameter passed to Chebychev function
[in]	blin	Array of coefficients for Linear combinaison
[in]	S	Shift operator
[in]	cheb_old	Cheb_Elem to be freed (only for mode=-1)

Remarks

Arguments 'power', 'nblin', 'blin' and 'B' are used if mode=1

Argument 'cheb_old' is used if mode=-1

GSTLEARN_EXPORT int spde_chebychev_operate	(	MatrixSparse *	S,
		Cheb_Elem *	cheb_elem,
		const VectorDouble &	lambda,
		const VectorDouble &	x,
		VectorDouble &	y
	)

Perform the Chebychev polynomial procedure on an input vector

Returns

Error return code

Parameters

[in]	S	Shift operator
[in]	cheb_elem	Cheb_Elem structure
[in]	lambda	Scaling vector
[in]	x	Input array (Dimension: nvertex)
[out]	y	Output array (Dimension: nvertex)

GSTLEARN_EXPORT VectorString util_set_array_char	(	int	ntab,
		char **	names
	)

Create a VectorString for storing an array of chars

Returns

The VectorString

Parameters

[in]	ntab	Number of samples
[in]	names	Array of character values to be loaded

GSTLEARN_EXPORT VectorDouble util_set_array_double	(	int	ntab,
		const double *	rtab
	)

Create a VectorDouble for storing an array of double

Returns

The VectorDouble

Parameters

[in]	ntab	Number of samples
[in]	rtab	Array of double values to be loaded

GSTLEARN_EXPORT VectorInt util_set_array_integer	(	int	ntab,
		const int *	itab
	)

Create a VectorInt for storing an array of integer

Returns

The VectorInt

Parameters

[in]	ntab	Number of samples
[in]	itab	Array of integer values to be loaded

GSTLEARN_EXPORT std::vector<char*> util_vs_to_vs

(

VectorString

vs

)

Convert VectorString into a std::vector<char *> structure

Returns

Pointer to the returned array of characters

Parameters

[in]

vs

Input VectorString

GSTLEARN_EXPORT Vario* variogram_pgs	(	Db *	db,
		const VarioParam *	varioparam,
		const RuleProp *	ruleprop,
		int	flag_rho,
		int	opt_correl
	)

Calculate the Gaussian variograms

Returns

Error return code

Parameters

[in]	db	Db structure
[in]	varioparam	VarioParam structure for the GRFs
[in]	ruleprop	RuleProp structure
[in]	flag_rho	1 if the correlation coefficient must be regressed
[in]	opt_correl	0 full model; 1 symmetrical; 2 residuals

Remarks

This is simply a routine dispatching between the stationary function

and the non-stationary one

GSTLEARN_EXPORT int vmap_auto_fit	(	const DbGrid *	dbmap,
		Model *	model,
		bool	verbose,
		const Option_AutoFit &	mauto_arg,
		const Constraints &	cons_arg,
		const Option_VarioFit &	optvar_arg
	)

Automatic model fitting

Returns

Error returned code

Parameters

[in]	dbmap	Db Grid structure containing the Vmap
[in]	model	Model structure containing the basic structures
[in]	verbose	Verbose flag
[in]	mauto_arg	Option_AutoFit structure
[in]	cons_arg	Constraints structure
[in]	optvar_arg	Opt_Vario structure