Math methods. More...

#include <mnemath.h>

Public Types
typedef std::pair< int, int >	IdxIntValue

Public Member Functions
virtual	~MNEMath ()

Static Public Member Functions
static int	gcd (int iA, int iB)

static Eigen::VectorXd *	combine_xyz (const Eigen::VectorXd &vec)

static double	getConditionNumber (const Eigen::MatrixXd &A, Eigen::VectorXd &s)

static double	getConditionSlope (const Eigen::MatrixXd &A, Eigen::VectorXd &s)

static void	get_whitener (Eigen::MatrixXd &A, bool pca, QString ch_type, Eigen::VectorXd &eig, Eigen::MatrixXd &eigvec)

static void	get_whitener (Eigen::MatrixXd &A, bool pca, const std::string &ch_type, Eigen::VectorXd &eig, Eigen::MatrixXd &eigvec)

static Eigen::VectorXi	intersect (const Eigen::VectorXi &v1, const Eigen::VectorXi &v2, Eigen::VectorXi &idx_sel)

static bool	issparse (Eigen::VectorXd &v)

static Eigen::MatrixXd	legendre (qint32 n, const Eigen::VectorXd &X, QString normalize=QString("unnorm"))

static Eigen::MatrixXd	legendre (qint32 n, const Eigen::VectorXd &X, std::string normalize="unnorm")

static Eigen::SparseMatrix< double > *	make_block_diag (const Eigen::MatrixXd &A, qint32 n)

static int	nchoose2 (int n)

static qint32	rank (const Eigen::MatrixXd &A, double tol=1e-8)

static Eigen::MatrixXd	rescale (const Eigen::MatrixXd &data, const Eigen::RowVectorXf &times, const QPair< float, float > &baseline, QString mode)

static Eigen::MatrixXd	rescale (const Eigen::MatrixXd &data, const Eigen::RowVectorXf &times, const std::pair< float, float > &baseline, const std::string &mode)

template<typename T >
static Eigen::VectorXi	sort (Eigen::Matrix< T, Eigen::Dynamic, 1 > &v, bool desc=true)

template<typename T >
static Eigen::VectorXi	sort (Eigen::Matrix< T, Eigen::Dynamic, 1 > &v_prime, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &mat, bool desc=true)

template<typename T >
static std::vector< Eigen::Triplet< T > >	sortrows (const std::vector< Eigen::Triplet< T > > &A, qint32 column=0)

template<typename T >
static bool	compareIdxValuePairBiggerThan (const std::pair< int, T > &lhs, const std::pair< int, T > &rhs)

template<typename T >
static bool	compareIdxValuePairSmallerThan (const std::pair< int, T > &lhs, const std::pair< int, T > &rhs)

template<typename T >
static bool	compareTripletFirstEntry (const Eigen::Triplet< T > &lhs, const Eigen::Triplet< T > &rhs)

template<typename T >
static bool	compareTripletSecondEntry (const Eigen::Triplet< T > &lhs, const Eigen::Triplet< T > &rhs)

template<typename T >
static double	log2 (const T d)

template<typename T >
static void	histcounts (const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &matRawData, bool bMakeSymmetrical, int iClassAmount, Eigen::VectorXd &vecResultClassLimits, Eigen::VectorXi &vecResultFrequency, double dGlobalMin=0.0, double dGlobalMax=0.0)

template<typename T >
static void	histcounts (const Eigen::Matrix< T, Eigen::Dynamic, 1 > &matRawData, bool bMakeSymmetrical, int iClassAmount, Eigen::VectorXd &vecResultClassLimits, Eigen::VectorXi &vecResultFrequency, double dGlobalMin=0.0, double dGlobalMax=0.0)

template<typename T >
static void	histcounts (const Eigen::Matrix< T, 1, Eigen::Dynamic > &matRawData, bool bMakeSymmetrical, int iClassAmount, Eigen::VectorXd &vecResultClassLimits, Eigen::VectorXi &vecResultFrequency, double dGlobalMin=0.0, double dGlobalMax=0.0)

template<typename T >
static Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic >	pinv (const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &a)

static bool	compareTransformation (const Eigen::MatrixX4f &mDevHeadT, const Eigen::MatrixX4f &mDevHeadTNew, const float &fThreshRot, const float &fThreshTrans)

Detailed Description

Math methods.

ToDo make this a template class Generalized math methods used by mne methods

Definition at line 87 of file mnemath.h.

Member Typedef Documentation

◆ IdxIntValue

typedef std::pair<int,int> UTILSLIB::MNEMath::IdxIntValue

Typedef of a pair of ints.

Definition at line 90 of file mnemath.h.

Constructor & Destructor Documentation

◆ ~MNEMath()

virtual UTILSLIB::MNEMath::~MNEMath ( )

inlinevirtual

Destroys the MNEMath object

Definition at line 96 of file mnemath.h.

Member Function Documentation

◆ combine_xyz()

VectorXd * MNEMath::combine_xyz ( const Eigen::VectorXd & vec )

static

ToDo make this a template function

mne_combine_xyz

MNE toolbox root function

Compute the three Cartesian components of a vector together

Parameters

[in] vec Input row vector [ x1 y1 z1 ... x_n y_n z_n ].

Returns: Output vector [x1^2+y1^2+z1^2 ... x_n^2+y_n^2+z_n^2 ].

Definition at line 81 of file mnemath.cpp.

◆ compareIdxValuePairBiggerThan()

template<typename T >

bool UTILSLIB::MNEMath::compareIdxValuePairBiggerThan	(	const std::pair< int, T > &	lhs,
		const std::pair< int, T > &	rhs
	)

inlinestatic

Compares two index-value-pairs.

Parameters

[in]	lhs	left hand side of the comparison.
[in]	rhs	right hand side of the comparison.

Returns: true if value of lhs is bigger than value of rhs.

Definition at line 571 of file mnemath.h.

◆ compareIdxValuePairSmallerThan()

template<typename T >

bool UTILSLIB::MNEMath::compareIdxValuePairSmallerThan	(	const std::pair< int, T > &	lhs,
		const std::pair< int, T > &	rhs
	)

inlinestatic

Compares two index-value-pairs.

Parameters

[in]	lhs	left hand side of the comparison.
[in]	rhs	right hand side of the comparison.

Returns: true if value of lhs is smaller than value of rhs.

Definition at line 580 of file mnemath.h.

◆ compareTransformation()

bool MNEMath::compareTransformation	(	const Eigen::MatrixX4f &	mDevHeadT,
		const Eigen::MatrixX4f &	mDevHeadTNew,
		const float &	fThreshRot,
		const float &	fThreshTrans
	)

static

Compare new head position with current head position and update dev_head_t if big displacement occured

Parameters

[in]	mDevHeadTrans	The device to head transformation matrix to compare to.
[in]	mDevHeadTransNew	The device to head transformation matrix to be compared.
[in]	fTreshRot	The threshold for big head rotation in degree.
[in]	fThreshTrans	The threshold for big head movement in m.

Returns: bState The status that shows if devHead is updated or not.

Definition at line 571 of file mnemath.cpp.

◆ compareTripletFirstEntry()

template<typename T >

bool UTILSLIB::MNEMath::compareTripletFirstEntry	(	const Eigen::Triplet< T > &	lhs,
		const Eigen::Triplet< T > &	rhs
	)

inlinestatic

Compares triplet first entry

Parameters

[in]	lhs	left hand side of the comparison.
[in]	rhs	right hand side of the comparison.

Returns: true if value of lhs is smaller than value of rhs.

Definition at line 589 of file mnemath.h.

◆ compareTripletSecondEntry()

template<typename T >

bool UTILSLIB::MNEMath::compareTripletSecondEntry	(	const Eigen::Triplet< T > &	lhs,
		const Eigen::Triplet< T > &	rhs
	)

inlinestatic

Compares triplet second entry

Parameters

[in]	lhs	left hand side of the comparison.
[in]	rhs	right hand side of the comparison.

Returns: true if value of lhs is smaller than value of rhs.

Definition at line 598 of file mnemath.h.

◆ gcd()

int MNEMath::gcd	(	int	iA,
		int	iB
	)

static

Finds the Greatest Common Divisor (GCD) of two integer values

Parameters

[in]	a	First input integer.
[in]	b	Second input integer.

Returns: The Greatest Common Divisor (GCD) of a and b.

Definition at line 70 of file mnemath.cpp.

◆ get_whitener() [1/2]

static void UTILSLIB::MNEMath::get_whitener	(	Eigen::MatrixXd &	A,
		bool	pca,
		const std::string &	ch_type,
		Eigen::VectorXd &	eig,
		Eigen::MatrixXd &	eigvec
	)

static

Returns the whitener of a given matrix.

Parameters

[in]	A	Matrix to compute the whitener from.
[in]	pca	perform a pca.

Returns: rank of matrix A.

◆ get_whitener() [2/2]

static void UTILSLIB::MNEMath::get_whitener	(	Eigen::MatrixXd &	A,
		bool	pca,
		QString	ch_type,
		Eigen::VectorXd &	eig,
		Eigen::MatrixXd &	eigvec
	)

static

Returns the whitener of a given matrix.

Parameters

[in]	A	Matrix to compute the whitener from.
[in]	pca	perform a pca.

Returns: rank of matrix A.

◆ getConditionNumber()

double MNEMath::getConditionNumber	(	const Eigen::MatrixXd &	A,
		Eigen::VectorXd &	s
	)

static

Returns the condition number of a given matrix.

Parameters

[in] A Matrix to compute the condition number from.

Returns: the condition number.

Definition at line 104 of file mnemath.cpp.

◆ getConditionSlope()

double MNEMath::getConditionSlope	(	const Eigen::MatrixXd &	A,
		Eigen::VectorXd &	s
	)

static

Returns the condition slope of a given matrix.

Parameters

[in] A Matrix to compute the condition number from.

Returns: the condition slope.

Definition at line 117 of file mnemath.cpp.

◆ histcounts()

template<typename T >

void UTILSLIB::MNEMath::histcounts	(	const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &	matRawData,
		bool	bMakeSymmetrical,
		int	iClassAmount,
		Eigen::VectorXd &	vecResultClassLimits,
		Eigen::VectorXi &	vecResultFrequency,
		double	dGlobalMin = `0.0`,
		double	dGlobalMax = `0.0`
	)

static

creates a class and frequency distribution from data matrix

Parameters

[in]	matRawData	raw data matrix that needs to be analyzed.
[in]	bMakeSymmetrical	user input to turn the x-axis symmetric.
[in]	iClassCount	user input to determine the amount of classes in the histogram.
[out]	vecResultClassLimits	the upper limit of each individual class.
[out]	vecResultFrequency	the amount of data that fits in the appropriate class ranges.
[in]	dGlobalMin	user input to determine the maximum value allowed in the histogram.
[in]	dGlobalMax	user input to determine the minimum value allowed in the histogram.

Definition at line 647 of file mnemath.h.

◆ intersect()

VectorXi MNEMath::intersect	(	const Eigen::VectorXi &	v1,
		const Eigen::VectorXi &	v2,
		Eigen::VectorXi &	idx_sel
	)

static

Find the intersection of two vectors

Parameters

[in]	v1	Input vector 1.
[in]	v2	Input vector 2.
[out]	idx_sel	Index of intersection based on v1.

Returns: the sorted, unique values that are in both of the input arrays.

Definition at line 194 of file mnemath.cpp.

◆ issparse()

bool MNEMath::issparse ( Eigen::VectorXd & v )

static

Determines if a given data (stored as vector v) are representing a sparse matrix. ToDo: status is experimental -> needs to be increased in speed.

Parameters

[in] v data to be tested.

Returns: true if sparse false otherwise;.

Definition at line 288 of file mnemath.cpp.

◆ legendre() [1/2]

static Eigen::MatrixXd UTILSLIB::MNEMath::legendre	(	qint32	n,
		const Eigen::VectorXd &	X,
		QString	normalize = `QString("unnorm")`
	)

static

LEGENDRE Associated Legendre function.

P = LEGENDRE(N,X) computes the associated Legendre functions of degree N and order M = 0, 1, ..., N, evaluated for each element of X. N must be a scalar integer and X must contain real values between -1 <= X <= 1.

Returns: associated Legendre functions.

◆ legendre() [2/2]

static Eigen::MatrixXd UTILSLIB::MNEMath::legendre	(	qint32	n,
		const Eigen::VectorXd &	X,
		std::string	normalize = `"unnorm"`
	)

static

LEGENDRE Associated Legendre function.

P = LEGENDRE(N,X) computes the associated Legendre functions of degree N and order M = 0, 1, ..., N, evaluated for each element of X. N must be a scalar integer and X must contain real values between -1 <= X <= 1.

Returns: associated Legendre functions.

◆ log2()

template<typename T >

double UTILSLIB::MNEMath::log2 ( const T d )

inlinestatic

Compute log2 of given number

Parameters

[in] d input value.

Returns: double result of log2 operation.

Definition at line 607 of file mnemath.h.

◆ make_block_diag()

SparseMatrix< double > * MNEMath::make_block_diag	(	const Eigen::MatrixXd &	A,
		qint32	n
	)

static

ToDo make this a template function

MNE toolbox root function ###: Definition of the mne_block_diag function - encoding part

Make a sparse block diagonal matrix

Returns a sparse block diagonal, diagonalized from the elements in "A". "A" is ma x na, comprising bdn=(na/"n") blocks of submatrices. Each submatrix is ma x "n", and these submatrices are placed down the diagonal of the matrix.

Parameters

[in,out]	A	Matrix which should be diagonlized.
[in,out]	n	Columns of the submatrices.

Returns: A sparse block diagonal, diagonalized from the elements in "A".

Definition at line 347 of file mnemath.cpp.

◆ nchoose2()

int MNEMath::nchoose2 ( int n )

static

Calculates the combination of n over 2 (nchoosek(n,2))

Parameters

[in] n The number of elements which should be combined with each other (n over 2).

Returns: The number of combinations.

Definition at line 387 of file mnemath.cpp.

◆ pinv()

template<typename T >

Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > UTILSLIB::MNEMath::pinv ( const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > & a )

static

Creates the pseudo inverse of a matrix.

Parameters

[in] a raw data matrix that needs to be analyzed.

Definition at line 755 of file mnemath.h.

◆ rank()

qint32 MNEMath::rank	(	const Eigen::MatrixXd &	A,
		double	tol = `1e-8`
	)

static

ToDo make this a template function

Returns the rank of a matrix A.

Parameters

[in]	A	Matrix to get the rank from.
[in]	tol	realtive threshold: biggest singualr value multiplied with tol is smallest singular value considered non-zero.

Returns: rank of matrix A.

Definition at line 399 of file mnemath.cpp.

◆ rescale() [1/2]

static Eigen::MatrixXd UTILSLIB::MNEMath::rescale	(	const Eigen::MatrixXd &	data,
		const Eigen::RowVectorXf &	times,
		const QPair< float, float > &	baseline,
		QString	mode
	)

static

ToDo: Maybe new processing class

Rescale aka baseline correct data

Parameters

[in]	data	Data Matrix (m x n_time).
[in]	times	Time instants is seconds.
[in]	baseline	If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a and b are equal use interval between the beginning of the data and the time point 0 (stimulus onset).
[in]	mode	Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard. deviatio of power during baseline after substracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)). ("logratio" \| "ratio" \| "zscore" \| "mean" \| "percent")

Returns: rescaled data matrix rescaling.

◆ rescale() [2/2]

static Eigen::MatrixXd UTILSLIB::MNEMath::rescale	(	const Eigen::MatrixXd &	data,
		const Eigen::RowVectorXf &	times,
		const std::pair< float, float > &	baseline,
		const std::string &	mode
	)

static

ToDo: Maybe new processing class

Rescale aka baseline correct data

Parameters

[in]	data	Data Matrix (m x n_time).
[in]	times	Time instants is seconds.
[in]	baseline	If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a and b are equal use interval between the beginning of the data and the time point 0 (stimulus onset).
[in]	mode	Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard. deviatio of power during baseline after substracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)). ("logratio" \| "ratio" \| "zscore" \| "mean" \| "percent")

Returns: rescaled data matrix rescaling.

◆ sort() [1/2]

template<typename T >

Eigen::VectorXi UTILSLIB::MNEMath::sort	(	Eigen::Matrix< T, Eigen::Dynamic, 1 > &	v,
		bool	desc = `true`
	)

static

Sorts a vector (ascending order) in place and returns the track of the original indeces

Parameters

[in,out]	v	vector to sort; it's sorted in place.
[in]	desc	if true its sorted in a descending order, otherwise ascending (optional, default = true).

Returns: Vector of the original indeces in the new order.

Definition at line 499 of file mnemath.h.

◆ sort() [2/2]

template<typename T >

Eigen::VectorXi UTILSLIB::MNEMath::sort	(	Eigen::Matrix< T, Eigen::Dynamic, 1 > &	v_prime,
		Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &	mat,
		bool	desc = `true`
	)

static

Sorts a vector (ascending order) and a corresponding matrix in place and returns the track of the original indeces The matrix is sorted along the columns using the vector values for comparison.

Parameters

[in,out]	v_prime	vector to sort (sorted in place).
[in,out]	mat	matrix to sort (sorted in place).
[in]	desc	if true its sorted in a descending order, otherwise ascending (optional, default = true).

Returns: Vector of the original indeces in the new order.

Definition at line 531 of file mnemath.h.

◆ sortrows()

template<typename T >

std::vector< Eigen::Triplet< T > > UTILSLIB::MNEMath::sortrows	(	const std::vector< Eigen::Triplet< T > > &	A,
		qint32	column = `0`
	)

static

Sort rows in ascending order

Parameters

[in]	A	triplet vector to sort (sorted in place).
[in]	column	sorts the triplet vector based on the column specified.

Returns: Vector of the original indeces in the new order.

Definition at line 552 of file mnemath.h.

The documentation for this class was generated from the following files:

src/libraries/utils/mnemath.h
src/libraries/utils/mnemath.cpp

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Typedef Documentation

◆ IdxIntValue

Constructor & Destructor Documentation

◆ ~MNEMath()

Member Function Documentation

◆ combine_xyz()

MNE toolbox root function

◆ compareIdxValuePairBiggerThan()

◆ compareIdxValuePairSmallerThan()

◆ compareTransformation()

◆ compareTripletFirstEntry()

◆ compareTripletSecondEntry()

◆ gcd()

◆ get_whitener() [1/2]

◆ get_whitener() [2/2]

◆ getConditionNumber()

◆ getConditionSlope()

◆ histcounts()

◆ intersect()

◆ issparse()

◆ legendre() [1/2]

◆ legendre() [2/2]

◆ log2()

◆ make_block_diag()

MNE toolbox root function ###: Definition of the mne_block_diag function - encoding part

◆ nchoose2()

◆ pinv()

◆ rank()

◆ rescale() [1/2]

◆ rescale() [2/2]

◆ sort() [1/2]

◆ sort() [2/2]

◆ sortrows()