model.cpp File Reference

#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 "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 "Anamorphosis/AnamHermite.hpp"
#include "Anamorphosis/AnamDiscreteIR.hpp"
#include "Variogram/Vario.hpp"
#include "Space/SpaceRN.hpp"
#include "Basic/String.hpp"
#include "Db/Db.hpp"
#include "Basic/Memory.hpp"
#include <math.h>

Functions
Model *	model_duplicate_for_gradient (const Model *model, double ball_radius)
void	model_covupdt (Model *model, const double *c0, int flag_verbose, int *flag_nugget, double *nugget)
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)
Model *	model_combine (const Model *model1, const Model *model2, double r)
int	model_covmat_inchol (int verbose, Db *db, Model *model, double eta, int npivot_max, int nsize1, const int *ranks1, const double *center, int flag_sort, int *npivot_arg, int **Pret, double **Gret, const CovCalcMode *mode)

Variables
int	NDIM_LOCAL = 0
VectorDouble	X1_LOCAL = VectorDouble()
VectorDouble	X2_LOCAL = VectorDouble()

Function Documentation

model_combine()

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

model_cova_characteristics()

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

model_covmat_inchol()

int model_covmat_inchol	(	int	verbose,
		Db *	db,
		Model *	model,
		double	eta,
		int	npivot_max,
		int	nsize1,
		const int *	ranks1,
		const 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.

model_covupdt()

void model_covupdt	(	Model *	model,
		const 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 ?

model_duplicate_for_gradient()

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

Variable Documentation

NDIM_LOCAL

int NDIM_LOCAL = 0

X1_LOCAL

VectorDouble X1_LOCAL = VectorDouble()

X2_LOCAL

VectorDouble X2_LOCAL = VectorDouble()