#include "geoslib_enum.h"
#include "geoslib_f.h"
#include "geoslib_old_f.h"
#include "Drifts/DriftFactory.hpp"
#include "Drifts/DriftList.hpp"
#include "Drifts/ADrift.hpp"
#include "Basic/AException.hpp"
#include "Basic/Utilities.hpp"
#include "Basic/File.hpp"
#include "Covariances/CovLMC.hpp"
#include "Covariances/CovLMGradient.hpp"
#include "Covariances/CovLMCTapering.hpp"
#include "Covariances/CovLMCConvolution.hpp"
#include "Covariances/ACovAnisoList.hpp"
#include "Covariances/CovAniso.hpp"
#include "Covariances/CovCalcMode.hpp"
#include "Covariances/CovFactory.hpp"
#include "Covariances/CovGradientNumerical.hpp"
#include "Model/CovInternal.hpp"
#include "Model/Model.hpp"
#include "Model/NoStatArray.hpp"
#include "Anamorphosis/AnamHermite.hpp"
#include "Anamorphosis/AnamDiscreteDD.hpp"
#include "Anamorphosis/AnamDiscreteIR.hpp"
#include "Variogram/Vario.hpp"
#include "Space/SpaceRN.hpp"
#include "Basic/Law.hpp"
#include "Basic/String.hpp"
#include "Db/Db.hpp"
#include "Db/DbGrid.hpp"
#include "Matrix/MatrixSquareGeneral.hpp"
#include "Matrix/NF_Triplet.hpp"
#include <math.h>

Functions
void	model_nostat_update (CovInternal covint, Model model)

void	model_covtab_init (int flag_init, Model model, double covtab)

void	model_calcul_cov (CovInternal covint, Model model, const CovCalcMode mode, int flag_init, double weight, VectorDouble d1, double covtab)

const CovInternal *	get_external_covariance ()

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

int	model_add_cova (Model *model, const ECov &type, int flag_aniso, int flag_rotation, double range, double param, const VectorDouble &aniso_ranges, const VectorDouble &aniso_rotmat, const VectorDouble &sill, double)

double	cova_get_scale_factor (const ECov &type, double param)

int	model_update_coreg (Model model, double aic, double valpro, double vecpro)

int	model_evaluate (Model model, int ivar, int jvar, const CovCalcMode mode, int nh, VectorDouble &codir, const double h, double g)

int	model_evaluate_nostat (Model model, int ivar, int jvar, const CovCalcMode mode, Db db1, int iech1, Db db2, int iech2, int nh, VectorDouble &codir, double h, double g)

int	model_grid (Model model, Db db, int ivar, int jvar, const CovCalcMode mode, double g)

int	model_nfex (Model *model)

double	model_cxx (Model model, Db db1, Db db2, int ivar, int jvar, int seed, double eps, const CovCalcMode mode)

double *	model_covmat_by_ranks (Model model, Db db1, int nsize1, const int ranks1, Db db2, int nsize2, const int ranks2, int ivar0, int jvar0, const CovCalcMode mode)

int	model_drift_mat (Model model, const ECalcMember &member, Db db, double *drfmat)

int	model_drift_vector (Model model, const ECalcMember &member, Db db, int iech, double *vector)

Model *	model_duplicate_for_gradient (const Model *model, double ball_radius)

void	model_covupdt (Model model, double c0, int flag_verbose, int flag_nugget, double nugget)

double	model_drift_evaluate (int, Model model, const Db db, int iech, int ivar, double *coef)

void	model_cova_characteristics (const ECov &type, char cov_name[STRING_LENGTH], int flag_range, int flag_param, int min_order, int max_ndim, int flag_int_1d, int flag_int_2d, int flag_aniso, int flag_rotation, double scale, double parmax)

int	model_sample (Vario vario, Model model, const CovCalcMode *mode)

Model *	model_combine (const Model model1, const Model model2, double r)

int	model_regularize (Model model, Vario vario, DbGrid dbgrid, const CovCalcMode mode)

int	model_covmat_inchol (int verbose, Db db, Model model, double eta, int npivot_max, int nsize1, int ranks1, double center, int flag_sort, int npivot_arg, int Pret, double Gret, const CovCalcMode mode)

double	model_calcul_stdev (Model model, Db db1, int iech1, Db , int iech2, int verbose, double factor, const CovCalcMode mode)

MatrixSparse *	model_covmat_by_ranks_Mat (Model model, Db db1, int nsize1, const VectorInt &ranks1, Db db2, int nsize2, const VectorInt &ranks2, int ivar0, int jvar0, const CovCalcMode mode)

int	model_covmat (Model model, Db db1, Db db2, int ivar0, int jvar0, double covmat, const CovCalcMode *mode)

MatrixSquareSymmetric	model_covmatM (Model model, Db db1, Db db2, int ivar0, int jvar0, const CovCalcMode mode)

Variables
int	NDIM_LOCAL = 0

VectorDouble	X1_LOCAL = VectorDouble()

VectorDouble	X2_LOCAL = VectorDouble()

Function Documentation

double cova_get_scale_factor	(	const ECov &	type,
		double	param
	)

For a given basic structure, get the reduction factor to convert the theoretical to practical scale

Returns: Conversion factor

Parameters

[in]	type	Type of the basic structure
[in]	param	Value of the third parameter

const CovInternal* get_external_covariance ( )

Identify the coordinates of the two end-points (used by the external covariance function)

Returns: A (protected) pointer on the Covariance Internal class

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)

int model_add_cova	(	Model *	model,
		const ECov &	type,
		int	flag_aniso,
		int	flag_rotation,
		double	range,
		double	param,
		const VectorDouble &	aniso_ranges,
		const VectorDouble &	aniso_rotmat,
		const VectorDouble &	sill,
		double
	)

Add a basic covariance

Returns: Error return code

Parameters

[in]	model	Pointer to the Model structure
[in]	type	Type of the basic structure
[in]	flag_aniso	1 if the basic structure is anisotropic
[in]	flag_rotation	1 if the basic structure is rotated (only when anisotropic)
[in]	range	Isotropic range of the basic structure
[in]	param	Auxiliary parameter
[in]	aniso_ranges	Array giving the anisotropy ranges Only used when flag_aniso (Dimension = ndim)
[in]	aniso_rotmat	Anisotropy rotation matrix Only used when flag_aniso && flag_rotation (Dimension = ndim * ndim)
[in]	sill	Sill matrix (optional) (Dimension = nvar * nvar)

void model_calcul_cov	(	CovInternal *	covint,
		Model *	model,
		const CovCalcMode *	mode,
		int	flag_init,
		double	weight,
		VectorDouble	d1,
		double *	covtab
	)

Returns the covariances for an increment This is the generic internal function It can be called for stationary or non-stationary case

Parameters

[in]	covint	Internal structure for non-stationarityAddress for the next term after the drift or NULL (for stationary case)
[in]	model	Model structure
[in]	mode	CovCalcMode structure
[in]	flag_init	Initialize the array beforehand
[in]	weight	Multiplicative weight
[out]	d1	Working array (dimension = ndim) or NULL
[out]	covtab	output covariance (dimension = nvar * nvar)

double model_calcul_stdev	(	Model *	model,
		Db *	db1,
		int	iech1,
		Db *	,
		int	iech2,
		int	verbose,
		double	factor,
		const CovCalcMode *	mode
	)

Returns the standard deviation at a given increment for a given model

Parameters

[in]	model	Structure containing the model
[in]	db1	First Db
[in]	iech1	Rank in the first Db
[in]	iech2	Rank in the second Db
[in]	verbose	Verbose flag
[in]	factor	Multiplicative factor for standard deviation
[in]	mode	CovCalcMode structure

Model* model_combine	(	const Model *	model1,
		const Model *	model2,
		double	r
	)

Combine two monovariate models into a bivariate model (residuals model)

Returns: Pointer to the newly created Model structure

Parameters

[in]	model1	First input Model
[in]	model2	Second input Model
[in]	r	Correlation coefficient

Remarks: : The drift is not copied into the new model; : It has been extended to the case where only one model is defined

void model_cova_characteristics	(	const ECov &	type,
		char	cov_name[STRING_LENGTH],
		int *	flag_range,
		int *	flag_param,
		int *	min_order,
		int *	max_ndim,
		int *	flag_int_1d,
		int *	flag_int_2d,
		int *	flag_aniso,
		int *	flag_rotation,
		double *	scale,
		double *	parmax
	)

Returns the characteristics of the covariance

Parameters

[in]	type	Type of the covariance
[out]	cov_name	Name of the covariance
[out]	flag_range	range definition +1 if the range is defined -1 if the range is redundant with the sill
[out]	flag_param	1 if the third parameter is defined
[out]	min_order	Minimum IRF order for validity
[out]	max_ndim	Maximum dimension for validity
[out]	flag_int_1d	Integral range in 1-D
[out]	flag_int_2d	Integral range in 2-D
[out]	flag_aniso	1 if anisotropy is meaningful
[out]	flag_rotation	1 if an anisotropy rotation is meaningful
[out]	scale	Scaling parameter
[out]	parmax	Maximum value for the third parameter

int model_covmat	(	Model *	model,
		Db *	db1,
		Db *	db2,
		int	ivar0,
		int	jvar0,
		double *	covmat,
		const CovCalcMode *	mode
	)

Establish the covariance matrix between two Dbs

Parameters

[in]	model	Model structure
[in]	db1	First Db
[in]	db2	Second Db
[in]	ivar0	Rank of the first variable (-1: all variables)
[in]	jvar0	Rank of the second variable (-1: all variables)
[in]	mode	CovCalcMode structure
[out]	covmat	The covariance matrix (Dimension = (nactive * nvar) [squared]) nactive: Number of samples active nvar : Number of selected variables (1 or nvar)

Remarks: : If db2 is not provided, it is set to db1

double* model_covmat_by_ranks	(	Model *	model,
		Db *	db1,
		int	nsize1,
		const int *	ranks1,
		Db *	db2,
		int	nsize2,
		const int *	ranks2,
		int	ivar0,
		int	jvar0,
		const CovCalcMode *	mode
	)

Establish the covariance matrix between two Dbs where samples are selected by ranks

Returns: Array containing the covariance matrix

Parameters

[in]	model	Model structure
[in]	db1	First Db
[in]	nsize1	Number of selected samples
[in]	ranks1	Array giving ranks of selected samples (optional)
[in]	db2	Second Db
[in]	nsize2	Number of selected samples
[in]	ranks2	Array giving ranks of selected samples (optional)
[in]	ivar0	Rank of the first variable (-1: all variables)
[in]	jvar0	Rank of the second variable (-1: all variables)
[in]	mode	CovCalcMode structure

Remarks: The covariance matrix (returned) must be freed by calling routine; Its dimension is nsize1 * nsize2 * nvar * nvar; where 'nvar' is the number of active variables (1 or nvar); The covariance matrix is established for the first variable; and returned as a covariance; As the ranks are used, no test is performed on any selection; but only ranks positive or null are considered

MatrixSparse* model_covmat_by_ranks_Mat	(	Model *	model,
		Db *	db1,
		int	nsize1,
		const VectorInt &	ranks1,
		Db *	db2,
		int	nsize2,
		const VectorInt &	ranks2,
		int	ivar0,
		int	jvar0,
		const CovCalcMode *	mode
	)

Establish the covariance matrix between two Dbs where samples are selected by ranks The output is stored in a Sparse Matrix

Returns: Array containing the covariance matrix

Parameters

[in]	model	Model structure
[in]	db1	First Db
[in]	nsize1	Number of selected samples
[in]	ranks1	Array giving ranks of selected samples (optional)
[in]	db2	Second Db
[in]	nsize2	Number of selected samples
[in]	ranks2	Array giving ranks of selected samples (optional)
[in]	ivar0	Rank of the first variable (-1: all variables)
[in]	jvar0	Rank of the second variable (-1: all variables)
[in]	mode	CovCalcMode structure

Remarks: The covariance matrix (returned) must be freed by calling routine; The covariance matrix is established for the first variable; and returned as a covariance; As the ranks are used, no test is performed on any selection; but only ranks positive or null are considered

int model_covmat_inchol	(	int	verbose,
		Db *	db,
		Model *	model,
		double	eta,
		int	npivot_max,
		int	nsize1,
		int *	ranks1,
		double *	center,
		int	flag_sort,
		int *	npivot_arg,
		int **	Pret,
		double **	Gret,
		const CovCalcMode *	mode
	)

Establish and invert a covariance matrix using Incomplete Cholesky method

Parameters

[in]	verbose	Verbose option
[in]	db	Db structure
[in]	model	Model structure
[in]	npivot_max	Maximum number of pivots (or 0)
[in]	eta	Precision (or TEST)
[in]	nsize1	Number of pivots already selected
[in]	ranks1	Ranks of pivots already selected
[in]	center	Optional Centering point (for increments)
[in]	flag_sort	Reordering flag (see remarks)
[in]	mode	CovCalcMode structure
[out]	npivot_arg	Number of pivots
[out]	Pret	Array of indices of the retained samples (from 1) Dimension: nech
[out]	Gret	Rectangular matrix Dimension: nech * npivot_arg

Remarks: The output arrays Pret and Gret should be freed by calling function; The array G contains as many lines as there are samples; If flag_sort = FALSE, the first lines concentrate on pivots,; and the other points are located afterwards; If flag_sort = TRUE, the lines are sorted in the same order as the; initial set of samples; The incomplete Cholsky algorithm stops when either the next pivot; value is below 'eta' or when maximum number of pivots 'npivot_max'; has been reached; If the center point is provided in 'center', the calculations; of covariance of increments are calculated instead. Then 'center'; must provide the coordinates of the origin point.

MatrixSquareSymmetric model_covmatM	(	Model *	model,
		Db *	db1,
		Db *	db2,
		int	ivar0,
		int	jvar0,
		const CovCalcMode *	mode
	)

void model_covtab_init	(	int	flag_init,
		Model *	model,
		double *	covtab
	)

Initializes the covtab array

Parameters

[in]	flag_init	1 If covtab() must be initialized; 0 otherwise
[in]	model	Model structure
[in]	covtab	Array to be initialized

void model_covupdt	(	Model *	model,
		double *	c0,
		int	flag_verbose,
		int *	flag_nugget,
		double *	nugget
	)

Update the model for fitting Covariance or Covariogram

Parameters

[in]	model	Model structure
[in]	c0	Array of variance values at the origin
[in]	flag_verbose	1 for verbose output
[out]	flag_nugget	1 if a nugget component must be added
[out]	nugget	Array of sills for the nugget component

TODO : dead code ?

double model_cxx	(	Model *	model,
		Db *	db1,
		Db *	db2,
		int	ivar,
		int	jvar,
		int	seed,
		double	eps,
		const CovCalcMode *	mode
	)

Evaluate the average model between two Dbs

Returns: Average model value

Parameters

[in]	model	Model structure
[in]	db1	First Db
[in]	db2	Second Db
[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	seed	Seed for the random number generator
[in]	eps	Epsilon used for randomization in calculation of CVV
[in]	mode	CovCalcMode structure

double model_drift_evaluate	(	int	,
		Model *	model,
		const Db *	db,
		int	iech,
		int	ivar,
		double *	coef
	)

Evaluate the drift with a given set of coefficients

Parameters

[in]	model	Model structure
[in]	db	Db structure
[in]	iech	Rank of the sample
[in]	ivar	Rank of the variable
[in]	coef	Array of coefficients (optional)

int model_drift_mat	(	Model *	model,
		const ECalcMember &	member,
		Db *	db,
		double *	drfmat
	)

Establish the drift rectangular matrix for a given Db

Returns: Error return code

Parameters

[in]	model	Model structure
[in]	member	Member of the Kriging System (ECalcMember)
[in]	db	Db structure
[out]	drfmat	The drift matrix (Dimension = nech * nvar * nfeq * nvar)

int model_drift_vector	(	Model *	model,
		const ECalcMember &	member,
		Db *	db,
		int	iech,
		double *	vector
	)

Establish the drift vector for a given sample of the Db

Parameters

[in]	model	Model structure
[in]	member	Member of the Kriging System (ECalcMember)
[in]	db	Db structure
[in]	iech	Rank of the particular sample
[out]	vector	Returned vector (Dimension = nvar * nfeq)

Model* model_duplicate_for_gradient	(	const Model *	model,
		double	ball_radius
	)

Duplicates a Model from another Model for Gradients

Returns: The modified Model structure

Parameters

[in]	model	Input Model
[in]	ball_radius	Radius for Gradient calculation

int model_evaluate	(	Model *	model,
		int	ivar,
		int	jvar,
		const CovCalcMode *	mode,
		int	nh,
		VectorDouble &	codir,
		const double *	h,
		double *	g
	)

Calculate the value of the model for a set of distances

Returns: Error return code

Parameters

[in]	model	Model structure
[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	mode	CovCalcMode structure
[in]	nh	Number of increments
[in]	codir	Array giving the direction coefficients
[in]	h	Vector of increments
[out]	g	Array containing the model values

Remarks: When rank_sel is positive, it indicates the rank of the only; basic structure to be accounted for

int model_evaluate_nostat	(	Model *	model,
		int	ivar,
		int	jvar,
		const CovCalcMode *	mode,
		Db *	db1,
		int	iech1,
		Db *	db2,
		int	iech2,
		int	nh,
		VectorDouble &	codir,
		double *	h,
		double *	g
	)

Calculate the value of the model for a set of distances (non stationary)

Returns: Error return code

Parameters

[in]	model	Model structure
[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	mode	CovCalcMode structure
[in]	db1	First Db structure
[in]	iech1	First sample
[in]	db2	Second Db structure
[in]	iech2	Second sample
[in]	nh	Number of increments
[in]	codir	Array giving the direction coefficients
[in]	h	Vector of increments
[out]	g	Array containing the model values

Remarks: When rank_sel is positive, it indicates the rank of the only; basic structure to be accounted for

int model_grid	(	Model *	model,
		Db *	db,
		int	ivar,
		int	jvar,
		const CovCalcMode *	mode,
		double *	g
	)

Evaluate the model on a regular grid

Parameters

[in]	model	Model structure
[in]	db	Db structure
[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	mode	CovCalcMode structure
[out]	g	Array containing the model values

int model_nfex ( Model * model )

Returns the number of external drift functions

Returns: Number of external drift functions

Parameters

[in] model Model structure

void model_nostat_update	(	CovInternal *	covint,
		Model *	model
	)

Update the Model in the case of Non-stationary parameters This requires the knowledge of the two end-points

Parameters

[in]	covint	Internal structure for non-stationarity or NULL (for stationary case)
[in]	model	Model structure

int model_regularize	(	Model *	model,
		Vario *	vario,
		DbGrid *	dbgrid,
		const CovCalcMode *	mode
	)

Calculate the regularized model as an experimental variogram

Returns: Error return code

Parameters

[in]	model	Model structure
[in]	vario	Vario structure
[in]	dbgrid	Db discretization grid structure
[in]	mode	CovCalcMode structure

int model_sample	(	Vario *	vario,
		Model *	model,
		const CovCalcMode *	mode
	)

Calculates variogram values by sampling a model

Returns: Error return code

Parameters

[in]	vario	Vario structure
[in]	model	Model structure
[in]	mode	CovCalcMode structure

int model_update_coreg	(	Model *	model,
		double *	aic,
		double *	valpro,
		double *	vecpro
	)

Calculate the linear model of coregionalization starting from the coregionalization matrix

Returns: Error return code.

Parameters

[in]	model	Model structure
[out]	aic	array of 'aic' values
[out]	valpro	array of eigen values
[out]	vecpro	array of eigen vectors

Remarks: In case of error, the message is printed by the routine; Warning: in the case of linked drift, the test of definite; positiveness is bypassed as we are not in the scope of the; linear model of coregionalization anymore.; As a consequence the array "aic()" is not evaluated

Variable Documentation

int NDIM_LOCAL = 0

VectorDouble X1_LOCAL = VectorDouble()

VectorDouble X2_LOCAL = VectorDouble()

Functions

Variables

Function Documentation

Variable Documentation