#include "gstlearn_export.hpp"
#include "geoslib_define.h"
#include "Basic/VectorNumT.hpp"
#include <map>
#include <cmath>
#include <math.h>

Macros
#define	DOUBLE_NA TEST

#define	INT_NA ITEST

#define	STRING_NA "NA"

#define	FLOAT_NA static_cast<float>(TEST)

Functions
GSTLEARN_EXPORT bool	isInteger (double value, double eps=EPSILON10)

GSTLEARN_EXPORT int	getClosestInteger (double value)

GSTLEARN_EXPORT bool	isMultiple (int nbig, int nsmall)

GSTLEARN_EXPORT bool	isOdd (int number)

GSTLEARN_EXPORT bool	isEven (int number)

GSTLEARN_EXPORT double	getMin (double val1, double val2)

GSTLEARN_EXPORT double	getMax (double val1, double val2)

GSTLEARN_EXPORT double	ut_deg2rad (double angle)

GSTLEARN_EXPORT double	ut_rad2deg (double angle)

GSTLEARN_EXPORT int	FFFF (double value)

GSTLEARN_EXPORT int	IFFFF (int value)

GSTLEARN_EXPORT double	getTEST ()

GSTLEARN_EXPORT int	getITEST ()

template<typename T >
T	getNA ()

template<>
double	getNA ()

template<>
int	getNA ()

template<>
String	getNA ()

template<>
float	getNA ()

template<typename T >
bool	isNA (const T &v)

template<>
bool	isNA (const double &v)

template<>
bool	isNA (const int &v)

template<>
bool	isNA (const String &v)

template<>
bool	isNA (const float &v)

GSTLEARN_EXPORT void	ut_tab_unique (int ntab, double tab, int neff)

GSTLEARN_EXPORT void	ut_sort_double (int safe, int nech, int ind, double value)

GSTLEARN_EXPORT void	ut_sort_int (int safe, int nech, int ind, int value)

GSTLEARN_EXPORT void	ut_statistics (int nech, double tab, double sel, double wgt, int nval, double mini, double maxi, double delta, double mean, double *stdv)

GSTLEARN_EXPORT void	ut_stats_mima (int nech, double tab, double sel, int nvalid, double mini, double *maxi)

GSTLEARN_EXPORT void	ut_stats_mima_print (const char title, int nech, double tab, double *sel)

GSTLEARN_EXPORT void	ut_facies_statistics (int nech, double tab, double sel, int nval, int mini, int *maxi)

GSTLEARN_EXPORT void	ut_classify (int nech, double tab, double sel, int nclass, double start, double pas, int nmask, int ntest, int nout, int classe)

GSTLEARN_EXPORT double	ut_median (double *tab, int ntab)

GSTLEARN_EXPORT void	ut_normalize (int ntab, double *tab)

GSTLEARN_EXPORT double	ut_cnp (int n, int k)

GSTLEARN_EXPORT double *	ut_pascal (int ndim)

GSTLEARN_EXPORT int *	ut_combinations (int n, int maxk, int *ncomb)

GSTLEARN_EXPORT void	ut_shuffle_array (int nrow, int ncol, double *tab)

GSTLEARN_EXPORT VectorInt	getListActiveToAbsolute (const VectorDouble &sel)

GSTLEARN_EXPORT std::map< int, int >	getMapAbsoluteToRelative (const VectorDouble &sel, bool verbose=false)

GSTLEARN_EXPORT int	getRankMapAbsoluteToRelative (const std::map< int, int > &map, int iabs)

GSTLEARN_EXPORT int	getRankMapRelativeToAbsolute (const std::map< int, int > &map, int irel)

Macro Definition Documentation

◆ DOUBLE_NA

#define DOUBLE_NA TEST

◆ FLOAT_NA

#define FLOAT_NA static_cast<float>(TEST)

◆ INT_NA

#define INT_NA ITEST

◆ STRING_NA

#define STRING_NA "NA"

Function Documentation

◆ FFFF()

GSTLEARN_EXPORT int FFFF ( double value )

Checks if a double value is TEST

Returns: 1 if a TEST value is encountered; 0 otherwise

Parameters

[in] value Value to be tested

◆ getClosestInteger()

GSTLEARN_EXPORT int getClosestInteger ( double value )

◆ getITEST()

GSTLEARN_EXPORT int getITEST ( )

◆ getListActiveToAbsolute()

GSTLEARN_EXPORT VectorInt getListActiveToAbsolute ( const VectorDouble & sel )

Returns the list of absolute indices for the only active samples A sample is active if its 'sel' value is equal to 1

Parameters

sel	Vector giving the status of all samples (Dimension: absolute)

Returns

◆ getMapAbsoluteToRelative()

GSTLEARN_EXPORT std::map<int, int> getMapAbsoluteToRelative	(	const VectorDouble &	sel,
		bool	verbose
	)

Returns the map such that MAP[iabs] = iact. A sample is active if its 'sel' value is equal to 1

Parameters

sel	Vector giving the status of all samples (Dimension: absolute)
verbose	Verbose flag

Returns: The map (dimension: nrel)

◆ getMax()

GSTLEARN_EXPORT double getMax	(	double	val1,
		double	val2
	)

◆ getMin()

GSTLEARN_EXPORT double getMin	(	double	val1,
		double	val2
	)

◆ getNA() [1/5]

template<typename T >

T getNA ( )

inline

◆ getNA() [2/5]

template<>

double getNA ( )

inline

◆ getNA() [3/5]

template<>

int getNA ( )

inline

◆ getNA() [4/5]

template<>

String getNA ( )

inline

◆ getNA() [5/5]

template<>

float getNA ( )

inline

◆ getRankMapAbsoluteToRelative()

GSTLEARN_EXPORT int getRankMapAbsoluteToRelative	(	const std::map< int, int > &	map,
		int	iabs
	)

Returns the rank of the relative grid node from its absolute index using the Map

Parameters

map	The <int,int> map
iabs	Absolute rank of the grid node

Returns: Rank of the corresponding active (relative) grid node (or -1 is not found)

◆ getRankMapRelativeToAbsolute()

GSTLEARN_EXPORT int getRankMapRelativeToAbsolute	(	const std::map< int, int > &	map,
		int	irel
	)

◆ getTEST()

GSTLEARN_EXPORT double getTEST ( )

◆ IFFFF()

GSTLEARN_EXPORT int IFFFF ( int value )

Checks if an integer value is TEST

Returns: 1 if a ITEST value is encountered; 0 otherwise

Parameters

[in] value Value to be tested

◆ isEven()

GSTLEARN_EXPORT bool isEven ( int number )

◆ isInteger()

GSTLEARN_EXPORT bool isInteger	(	double	value,
		double	eps = `EPSILON10`
	)

◆ isMultiple()

GSTLEARN_EXPORT bool isMultiple	(	int	nbig,
		int	nsmall
	)

◆ isNA() [1/5]

template<typename T >

bool isNA ( const T & v )

inline

◆ isNA() [2/5]

template<>

bool isNA ( const double & v )

inline

◆ isNA() [3/5]

template<>

bool isNA ( const int & v )

inline

◆ isNA() [4/5]

template<>

bool isNA ( const String & v )

inline

◆ isNA() [5/5]

template<>

bool isNA ( const float & v )

inline

◆ isOdd()

GSTLEARN_EXPORT bool isOdd ( int number )

◆ ut_classify()

GSTLEARN_EXPORT void ut_classify	(	int	nech,
		double *	tab,
		double *	sel,
		int	nclass,
		double	start,
		double	pas,
		int *	nmask,
		int *	ntest,
		int *	nout,
		int *	classe
	)

Classify the samples into integer sieves

Parameters

[in]	nech	Number of samples
[in]	tab	Array of values
[in]	sel	Array containing the Selection or NULL
[in]	nclass	Number of sieve classes
[in]	start	Starting sieve value
[in]	pas	Width of the sieve
[out]	nmask	Number of masked values
[out]	ntest	Number of undefined values
[out]	nout	Number of values outside the classes
[out]	classe	Array for number of samples per sieve

◆ ut_cnp()

GSTLEARN_EXPORT double ut_cnp	(	int	n,
		int	k
	)

Compute combinations(n,k)

Returns: Return the number of combinations of 'k' objects amongst 'n'

Parameters

[in]	n	Total number of objects (>= 1)
[in]	k	Selected number of objects (>= 1)

◆ ut_combinations()

GSTLEARN_EXPORT int* ut_combinations	(	int	n,
		int	maxk,
		int *	ncomb
	)

Return all the combinations of k within n

Returns: Return all the combinations of 'k' objects amongst 'n'

Parameters

[in]	n	Total number of objects (>1)
[in]	maxk	Selected number of objects (1<=maxk<n)
[out]	ncomb	Number of combinations

Remarks: The calling function must free the returned array.

◆ ut_deg2rad()

GSTLEARN_EXPORT double ut_deg2rad ( double angle )

Translates from degree to radian

Parameters

[in] angle Angle in degrees

◆ ut_facies_statistics()

GSTLEARN_EXPORT void ut_facies_statistics	(	int	nech,
		double *	tab,
		double *	sel,
		int *	nval,
		int *	mini,
		int *	maxi
	)

Returns the statistics of an array containing the facies

Parameters

[in]	nech	Number of samples
[in]	tab	Array of values
[in]	sel	Array containing the Selection or NULL
[out]	nval	Number of active values
[out]	mini	Minimum value
[out]	maxi	Maximum value

◆ ut_median()

GSTLEARN_EXPORT double ut_median	(	double *	tab,
		int	ntab
	)

Calculate the median from a table of values

Returns: The median value

Parameters

[in]	tab	Array of values
[in]	ntab	Number of samples

◆ ut_normalize()

GSTLEARN_EXPORT void ut_normalize	(	int	ntab,
		double *	tab
	)

Normalize a vector

Parameters

[in]	ntab	Vector dimension
[in,out]	tab	Vector to be normalized

◆ ut_pascal()

GSTLEARN_EXPORT double* ut_pascal ( int ndim )

Create the matrix containing the Pascal Triangle coefficients

Returns: A matrix (Dimension: ndim * ndim) containing the coefficients; or NULL if core allocation problem has been encountered

Parameters

[in] ndim Size of the matrix

Remarks: The calling function must free the returned matrix

◆ ut_rad2deg()

GSTLEARN_EXPORT double ut_rad2deg ( double angle )

Translates from radian to degree

Parameters

[in] angle Angle in radian

◆ ut_shuffle_array()

GSTLEARN_EXPORT void ut_shuffle_array	(	int	nrow,
		int	ncol,
		double *	tab
	)

Shuffle an array (by line)

Parameters

[in]	nrow	Number of rows
[in]	ncol	Number of columns
[in,out]	tab	Array to be suffled

◆ ut_sort_double()

GSTLEARN_EXPORT void ut_sort_double	(	int	safe,
		int	nech,
		int *	ind,
		double *	value
	)

Sorts the (double) array value() and the array ind() in the ascending order of value

Parameters

[in]	safe	1 if the value array if preserved 0 if the value array is also sorted
[in]	nech	number of samples
[out]	ind	output int array
[out]	value	input and output array

Remarks: If ind = NULL, ind is ignored

◆ ut_sort_int()

GSTLEARN_EXPORT void ut_sort_int	(	int	safe,
		int	nech,
		int *	ind,
		int *	value
	)

Sorts the (integer) array value() and the array ind() in the ascending order of value

Parameters

[in]	safe	1 if the value array if preserved 0 if the value array is also sorted
[in]	nech	number of samples
[out]	ind	output int array
[out]	value	input and output array

Remarks: If ind = NULL, ind is ignored

◆ ut_statistics()

GSTLEARN_EXPORT void ut_statistics	(	int	nech,
		double *	tab,
		double *	sel,
		double *	wgt,
		int *	nval,
		double *	mini,
		double *	maxi,
		double *	delta,
		double *	mean,
		double *	stdv
	)

Returns the statistics of an array

Parameters

[in]	nech	Number of samples
[in]	tab	Array of values
[in]	sel	Array containing the Selection or NULL
[in]	wgt	Array containing the Weights or NULL
[out]	nval	Number of active values
[out]	mini	Minimum value
[out]	maxi	Maximum value
[out]	delta	Distance between the minimum and the maximum
[out]	mean	Mean
[out]	stdv	Standard Deviation

◆ ut_stats_mima()

GSTLEARN_EXPORT void ut_stats_mima	(	int	nech,
		double *	tab,
		double *	sel,
		int *	nvalid,
		double *	mini,
		double *	maxi
	)

Returns the minimum and maximum of an array

Parameters

[in]	nech	Number of samples
[in]	tab	Array of values
[in]	sel	Array containing the Selection or NULL
[in,out]	nvalid	Number of valid samples
[in,out]	mini	Minimum value
[in,out]	maxi	Maximum value

Remarks: The calculation starts with the initial values for minimum,; maximum andl nvalid arguments (used in input).; If no default value is defined, set them minimum and maximum to TEST; and set nvalid to 0

◆ ut_stats_mima_print()

GSTLEARN_EXPORT void ut_stats_mima_print	(	const char *	title,
		int	nech,
		double *	tab,
		double *	sel
	)

Print minimum and maximum of an array

Parameters

[in]	title	Title
[in]	nech	Number of samples
[in]	tab	Array of values
[in]	sel	Array containing the Selection or NULL

◆ ut_tab_unique()

GSTLEARN_EXPORT void ut_tab_unique	(	int	ntab,
		double *	tab,
		int *	neff
	)

Returns the unique occurrence of values in a vector of values

Parameters

[in]	ntab	Number of values
[in]	tab	Array of values
[out]	neff	Actual number of different values

Remarks: The 'neff' values are placed at the beginning of 'tab' in output

Macros

Functions

Macro Definition Documentation

◆ DOUBLE_NA

◆ FLOAT_NA

◆ INT_NA

◆ STRING_NA

Function Documentation

◆ FFFF()

◆ getClosestInteger()

◆ getITEST()

◆ getListActiveToAbsolute()

◆ getMapAbsoluteToRelative()

◆ getMax()

◆ getMin()

◆ getNA() [1/5]

◆ getNA() [2/5]

◆ getNA() [3/5]

◆ getNA() [4/5]

◆ getNA() [5/5]

◆ getRankMapAbsoluteToRelative()

◆ getRankMapRelativeToAbsolute()

◆ getTEST()

◆ IFFFF()

◆ isEven()

◆ isInteger()

◆ isMultiple()

◆ isNA() [1/5]

◆ isNA() [2/5]

◆ isNA() [3/5]

◆ isNA() [4/5]

◆ isNA() [5/5]

◆ isOdd()

◆ ut_classify()

◆ ut_cnp()

◆ ut_combinations()

◆ ut_deg2rad()

◆ ut_facies_statistics()

◆ ut_median()

◆ ut_normalize()

◆ ut_pascal()

◆ ut_rad2deg()

◆ ut_shuffle_array()

◆ ut_sort_double()

◆ ut_sort_int()

◆ ut_statistics()

◆ ut_stats_mima()

◆ ut_stats_mima_print()

◆ ut_tab_unique()