Real-time signal processing (filtering, averaging, HPI fitting, noise reduction). More...

Classes
struct	FilterObject
class	FilterOverlapAdd
	Filtering with FFT convolution and the overlap add method for continous data streams. More...
class	CosineFilter
	Creates a cosine filter response in the frequency domain. More...
class	FilterIO
	Processes txt files which hold filter coefficients. More...
class	FilterParameter
	The FilterParameter class. More...
class	FilterKernel
	The FilterKernel class provides methods to create/design a FIR filter kernel. More...
class	ParksMcClellan
	The ParksMcClellan class provides the ParksMcClellan filter desing algorithm. More...
class	RtAveragingWorker
	Real-time averaging worker. More...
class	RtAveraging
	Real-time averaging. More...
class	RtConnectivityWorker
	Real-time connectivity worker. More...
class	RtConnectivity
	Real-time connectivity estimation. More...
struct	RtCovComputeResult
class	RtCov
	Real-time covariance worker. More...
class	RtHpiWorker
	Real-time HPI worker. More...
class	RtHpi
	Real-time Head Coil Positions estimation. More...
struct	RtInvOpInput
class	RtInvOpWorker
	Real-time inverse operator worker. More...
class	RtInvOp
	Real-time inverse operator estimation. More...
class	RtNoise
	Real-time Noise estimation. More...

Functions
FIFFLIB::FiffEvoked	computeAverage (const FIFFLIB::FiffRawData &raw, const Eigen::MatrixXi &matEvents, float fTMinS, float fTMaxS, qint32 eventType, bool bApplyBaseline, float fTBaselineFromS, float fTBaselineToS, const QMap< QString, double > &mapReject, const QStringList &lExcludeChs=QStringList(), const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi())
FIFFLIB::FiffEvoked	computeFilteredAverage (const FIFFLIB::FiffRawData &raw, const Eigen::MatrixXi &matEvents, float fTMinS, float fTMaxS, qint32 eventType, bool bApplyBaseline, float fTBaselineFromS, float fTBaselineToS, const QMap< QString, double > &mapReject, const FilterKernel &filterKernel, const QStringList &lExcludeChs=QStringList(), const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi())
QList< Eigen::MatrixXi >	toEventMatrix (QMap< int, QList< QPair< int, double > > > mapTriggers)
QMap< int, QList< QPair< int, double > > >	detectTriggerFlanksMax (const Eigen::MatrixXd &data, const QList< int > &lTriggerChannels, int iOffsetIndex, double dThreshold, bool bRemoveOffset, int iBurstLengthSamp=100)
QList< QPair< int, double > >	detectTriggerFlanksMax (const Eigen::MatrixXd &data, int iTriggerChannelIdx, int iOffsetIndex, double dThreshold, bool bRemoveOffset, int iBurstLengthSamp=100)
QMap< int, QList< QPair< int, double > > >	detectTriggerFlanksGrad (const Eigen::MatrixXd &data, const QList< int > &lTriggerChannels, int iOffsetIndex, double dThreshold, bool bRemoveOffset, const QString &type, int iBurstLengthSamp=100)
QList< QPair< int, double > >	detectTriggerFlanksGrad (const Eigen::MatrixXd &data, int iTriggerChannelIdx, int iOffsetIndex, double dThreshold, bool bRemoveOffset, const QString &type, int iBurstLengthSamp=100)
bool	filterFile (QIODevice &pIODevice, QSharedPointer< FIFFLIB::FiffRawData > pFiffRawData, int type, double dCenterfreq, double dBandwidth, double dTransition, double dSFreq, int iOrder=4096, int designMethod=FilterKernel::m_designMethods.indexOf(FilterParameter("Cosine")), const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi(), bool bUseThreads=true)
bool	filterFile (QIODevice &pIODevice, QSharedPointer< FIFFLIB::FiffRawData > pFiffRawData, const RTPROCESSINGLIB::FilterKernel &filterKernel, const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi(), bool bUseThreads=false)
Eigen::MatrixXd	filterData (const Eigen::MatrixXd &matData, int type, double dCenterfreq, double dBandwidth, double dTransition, double dSFreq, int iOrder=1024, int designMethod=FilterKernel::m_designMethods.indexOf(FilterParameter("Cosine")), const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi(), bool bUseThreads=true, bool bKeepOverhead=false)
Eigen::MatrixXd	filterData (const Eigen::MatrixXd &mataData, const RTPROCESSINGLIB::FilterKernel &filterKernel, const Eigen::RowVectorXi &vecPicks=Eigen::RowVectorXi(), bool bUseThreads=true, bool bKeepOverhead=false)
Eigen::MatrixXd	filterDataBlock (const Eigen::MatrixXd &mataData, const Eigen::RowVectorXi &vecPicks, const RTPROCESSINGLIB::FilterKernel &filterKernel, bool bUseThreads=true)
void	filterChannel (FilterObject &channelDataTime)
bool	performIcp (const QSharedPointer< MNELIB::MNEProjectToSurface > mneSurfacePoints, const Eigen::MatrixXf &matPointCloud, FIFFLIB::FiffCoordTrans &transFromTo, float &fRMSE, bool bScale=false, int iMaxIter=20, float fTol=0.001, const Eigen::VectorXf &vecWeitgths=vecDefaultWeigths)
bool	fitMatchedPoints (const Eigen::MatrixXf &matSrcPoint, const Eigen::MatrixXf &matDstPoint, Eigen::Matrix4f &matTrans, float fScale=1.0, bool bScale=false, const Eigen::VectorXf &vecWeitgths=vecDefaultWeigths)
bool	discard3DPointOutliers (const QSharedPointer< MNELIB::MNEProjectToSurface > mneSurfacePoints, const Eigen::MatrixXf &matPointCloud, const FIFFLIB::FiffCoordTrans &transFromTo, Eigen::VectorXi &vecTake, Eigen::MatrixXf &matTakePoint, float fMaxDist=0.0)
const char *	buildDateTime ()
const char *	buildHash ()
const char *	buildHashLong ()
Eigen::MatrixXd	makeSpharaProjector (const Eigen::MatrixXd &matBaseFct, const Eigen::VectorXi &vecIndices, int iOperatorDim, int iNBaseFct, int iSkip=0)

Variables
const Eigen::VectorXf	vecDefaultWeigths

Detailed Description

Real-time signal processing (filtering, averaging, HPI fitting, noise reduction).

Function Documentation

◆ buildDateTime()

const char * RTPROCESSINGLIB::buildDateTime ( )

Returns build date and time.

Definition at line 46 of file rtprocessing_global.cpp.

◆ buildHash()

const char * RTPROCESSINGLIB::buildHash ( )

Returns abbreviated build git hash.

Definition at line 50 of file rtprocessing_global.cpp.

◆ buildHashLong()

const char * RTPROCESSINGLIB::buildHashLong ( )

Returns full build git hash.

Definition at line 54 of file rtprocessing_global.cpp.

◆ computeAverage()

FIFFLIB::FiffEvoked RTPROCESSINGLIB::computeAverage	(	const FIFFLIB::FiffRawData &	raw,
		const Eigen::MatrixXi &	matEvents,
		float	fTMinS,
		float	fTMaxS,
		qint32	eventType,
		bool	bApplyBaseline,
		float	fTBaselineFromS,
		float	fTBaselineToS,
		const QMap< QString, double > &	mapReject,
		const QStringList &	lExcludeChs = QStringList(),
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi() )

Computes the average for given fiff raw data.

Parameters

[in]	raw	The raw data.
[in]	matEvents	The events provided in samples and event kinds.
[in]	fTMinS	The start time relative to the event in seconds.
[in]	fTMaxS	The end time relative to the event in seconds.
[in]	eventType	The event type.
[in]	bApplyBaseline	Whether to use baseline correction (mode=mean).
[in]	fTBaselineFromS	The start baseline correction time relative to the event in seconds.
[in]	fTBaselineToS	The end baseline correction time relative to the event in seconds.
[in]	mapReject	The thresholds per channel type to reject epochs.
[in]	lExcludeChs	List of channel names to exclude.
[in]	vecPicks	Which channels to pick.

◆ computeFilteredAverage()

FIFFLIB::FiffEvoked RTPROCESSINGLIB::computeFilteredAverage	(	const FIFFLIB::FiffRawData &	raw,
		const Eigen::MatrixXi &	matEvents,
		float	fTMinS,
		float	fTMaxS,
		qint32	eventType,
		bool	bApplyBaseline,
		float	fTBaselineFromS,
		float	fTBaselineToS,
		const QMap< QString, double > &	mapReject,
		const FilterKernel &	filterKernel,
		const QStringList &	lExcludeChs = QStringList(),
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi() )

Computes the filtered average for given fiff raw data.

Parameters

[in]	raw	The raw data.
[in]	matEvents	The events provided in samples and event kinds.
[in]	fTMinS	The start time relative to the event in seconds.
[in]	fTMaxS	The end time relative to the event in seconds.
[in]	eventType	The event type.
[in]	bApplyBaseline	Whether to use baseline correction (mode=mean).
[in]	fTBaselineFromS	The start baseline correction time relative to the event in seconds.
[in]	fTBaselineToS	The end baseline correction time relative to the event in seconds.
[in]	filterKernel	The filter kernel to use when reading the fiff raw data.
[in]	mapReject	The thresholds per channel type to reject epochs.
[in]	lExcludeChs	List of channel names to exclude.
[in]	vecPicks	Which channels to pick.

◆ detectTriggerFlanksGrad() [1/2]

QMap< int, QList< QPair< int, double > > > RTPROCESSINGLIB::detectTriggerFlanksGrad	(	const Eigen::MatrixXd &	data,
		const QList< int > &	lTriggerChannels,
		int	iOffsetIndex,
		double	dThreshold,
		bool	bRemoveOffset,
		const QString &	type,
		int	iBurstLengthSamp = 100 )

detectTriggerFlanksGrad detects flanks from a given data matrix in row wise order. This function uses a simple gradient to locate the triggers.

Parameters

[in]	data	the data used to find the trigger flanks.
[in]	lTriggerChannels	The indeces of the trigger channels.
[in]	iOffsetIndex	the offset index gets added to the found trigger flank index.
[in]	iThreshold	the gradient threshold value used to find the trigger flank.
[in]	bRemoveOffset	remove the first sample as offset.
[in]	type	detect rising or falling flank. Use "Rising" or "Falling" as input.
[in]	iBurstLengthMs	The length in samples which is skipped after a trigger was found.

Returns: This map holds the indices of the channels which are to be read from data. For each index/channel the found triggers and corresponding signal values are written to the value of the map.

◆ detectTriggerFlanksGrad() [2/2]

QList< QPair< int, double > > RTPROCESSINGLIB::detectTriggerFlanksGrad	(	const Eigen::MatrixXd &	data,
		int	iTriggerChannelIdx,
		int	iOffsetIndex,
		double	dThreshold,
		bool	bRemoveOffset,
		const QString &	type,
		int	iBurstLengthSamp = 100 )

detectTriggerFlanksGrad detects flanks from a given data matrix in row wise order. This function uses a simple gradient to locate the triggers.

Parameters

[in]	data	the data used to find the trigger flanks.
[in]	iTriggerChannelIdx	the index of the trigger channel in the matrix.
[in]	iOffsetIndex	the offset index gets added to the found trigger flank index.
[in]	iThreshold	the gradient threshold value used to find the trigger flank.
[in]	bRemoveOffset	remove the first sample as offset.
[in]	type	detect rising or falling flank. Use "Rising" or "Falling" as input.
[in]	iBurstLengthMs	The length in samples which is skipped after a trigger was found.

Returns: This list holds the found trigger indices and corresponding signal values.

◆ detectTriggerFlanksMax() [1/2]

QMap< int, QList< QPair< int, double > > > RTPROCESSINGLIB::detectTriggerFlanksMax	(	const Eigen::MatrixXd &	data,
		const QList< int > &	lTriggerChannels,
		int	iOffsetIndex,
		double	dThreshold,
		bool	bRemoveOffset,
		int	iBurstLengthSamp = 100 )

detectTriggerFlanks detects flanks from a given data matrix in row wise order. This function uses a simple maxCoeff function implemented by eigen to locate the triggers.

Parameters

[in]	data	the data used to find the trigger flanks.
[in]	lTriggerChannels	The indeces of the trigger channels.
[in]	iOffsetIndex	the offset index gets added to the found trigger flank index.
[in]	dThreshold	the signal threshold value used to find the trigger flank.
[in]	bRemoveOffset	remove the first sample as offset.
[in]	iBurstLengthMs	The length in samples which is skipped after a trigger was found.

Returns: This map holds the indices of the channels which are to be read from data. For each index/channel the found triggersand corresponding signal values are written to the value of the map.

◆ detectTriggerFlanksMax() [2/2]

QList< QPair< int, double > > RTPROCESSINGLIB::detectTriggerFlanksMax	(	const Eigen::MatrixXd &	data,
		int	iTriggerChannelIdx,
		int	iOffsetIndex,
		double	dThreshold,
		bool	bRemoveOffset,
		int	iBurstLengthSamp = 100 )

detectTriggerFlanks detects flanks from a given data matrix in row wise order. This function uses a simple maxCoeff function implemented by eigen to locate the triggers.

Parameters

[in]	data	the data used to find the trigger flanks.
[in]	iTriggerChannelIdx	the index of the trigger channel in the matrix.
[in]	iOffsetIndex	the offset index gets added to the found trigger flank index.
[in]	dThreshold	the signal threshold value used to find the trigger flank.
[in]	bRemoveOffset	remove the first sample as offset.
[in]	iBurstLengthMs	The length in samples which is skipped after a trigger was found.

Returns: This list holds the found trigger indices and corresponding signal values.

◆ discard3DPointOutliers()

bool RTPROCESSINGLIB::discard3DPointOutliers	(	const QSharedPointer< MNELIB::MNEProjectToSurface >	mneSurfacePoints,
		const Eigen::MatrixXf &	matPointCloud,
		const FIFFLIB::FiffCoordTrans &	transFromTo,
		Eigen::VectorXi &	vecTake,
		Eigen::MatrixXf &	matTakePoint,
		float	fMaxDist = 0.0 )

Discard outliers compared to a given 3D surface

Parameters

[in]	mneSurfacePoints	The MNEProjectToSurface object that contains the surface triangles etc. (To).
[in]	matPointCloud	The destination point set to be registrated (From).
[in,out]	transFromTo	The forward transformation matrix.
[in]	vecTake	The index of taken digitizers.
[in]	matTakePoint	The the digitizer points to take.
[in]	fMaxDist	The maximum distance to the surface in mm, defaults to 0 mm.

Returns: Wether the discarding was succesfull.

◆ filterChannel()

void RTPROCESSINGLIB::filterChannel ( RTPROCESSINGLIB::FilterObject & channelDataTime )

This function is used to filter row-wise in parallel threads

Parameters

[in] channelDataTime The channel data to perform the filtering on.

Definition at line 376 of file filter.cpp.

◆ filterData() [1/2]

Eigen::MatrixXd RTPROCESSINGLIB::filterData	(	const Eigen::MatrixXd &	mataData,
		const RTPROCESSINGLIB::FilterKernel &	filterKernel,
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi(),
		bool	bUseThreads = true,
		bool	bKeepOverhead = false )

Calculates the filtered version of the raw input data based on a given list filters The data needs to be present all at once. For continous filtering via overlap add use the FilterOverlapAdd class.

Parameters

[in]	mataData	The data which is to be filtered.
[in]	filterKernel	The list of filter kernels to use.
[in]	vecPicks	Channel indexes to filter. Default is filter all channels.
[in]	bUseThreads	Whether to use multiple threads. Default is set to true.
[in]	bKeepOverhead	Whether to keep the delayed part of the data after filtering. Default is set to false .

Returns: The filtered data in form of a matrix.

◆ filterData() [2/2]

Eigen::MatrixXd RTPROCESSINGLIB::filterData	(	const Eigen::MatrixXd &	matData,
		int	type,
		double	dCenterfreq,
		double	dBandwidth,
		double	dTransition,
		double	dSFreq,
		int	iOrder = 1024,
		int	designMethod = FilterKernel::m_designMethods.indexOf(FilterParameter("Cosine")),
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi(),
		bool	bUseThreads = true,
		bool	bKeepOverhead = false )

Creates a user designed filter kernel and filters the raw input data. The data needs to be present all at once. For continous filtering via overlap add use the FilterOverlapAdd class.

Parameters

[in]	matData	The data which is to be filtered.
[in]	type	The type of the filter: LPF, HPF, BPF, NOTCH (from enum FilterType).
[in]	dCenterfreq	The center of the frequency.
[in]	dBandwidth	The filter bandwidth. Ignored if FilterType is set to LPF,HPF. If NOTCH/BPF: bandwidth of stop-/passband.
[in]	dTransition	The transistion band determines the width of the filter slopes (steepness).
[in]	dSFreq	The input data sampling frequency.
[in]	iOrder	Represents the order of the filter, the higher the higher is the stopband attenuation. Default is 1024 taps.
[in]	designMethod	The design method to use. Choose between Cosine and Tschebyscheff. Defaul is set to Cosine.
[in]	vecPicks	Channel indexes to filter. Default is filter all channels.
[in]	bUseThreads	Whether to use multiple threads. Default is set to true.
[in]	bKeepOverhead	Whether to keep the delayed part of the data after filtering. Default is set to false .

Returns: The filtered data in form of a matrix.

◆ filterDataBlock()

Eigen::MatrixXd RTPROCESSINGLIB::filterDataBlock	(	const Eigen::MatrixXd &	mataData,
		const Eigen::RowVectorXi &	vecPicks,
		const RTPROCESSINGLIB::FilterKernel &	filterKernel,
		bool	bUseThreads = true )

Calculates the filtered version of the raw input data block. Always returns the data with half the filter length delay in the front and back.

Parameters

[in]	mataData	The data which is to be filtered.
[in]	vecPicks	The used channel as index in RowVector.
[in]	filterKernel	The FilterKernel to to filter the data with.
[in]	bUseThreads	Whether to use multiple threads.

Returns: The filtered data in form of a matrix with half the filter length delay in the front and back.

◆ filterFile() [1/2]

bool RTPROCESSINGLIB::filterFile	(	QIODevice &	pIODevice,
		QSharedPointer< FIFFLIB::FiffRawData >	pFiffRawData,
		const RTPROCESSINGLIB::FilterKernel &	filterKernel,
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi(),
		bool	bUseThreads = false )

Filters data from an input file based on an exisiting filter kernel and writes the filtered data to a pIODevice.

Parameters

[in]	pIODevice	The IO device to write to.
[in]	pFiffRawData	The fiff raw data object to read from.
[in]	filterKernel	The list of filter kernels to use.
[in]	vecPicks	Channel indexes to filter. Default is filter all channels.
[in]	bUseThreads	hether to use multiple threads. Default is set to true.

Returns: Returns true if successfull, false otherwise.

◆ filterFile() [2/2]

bool RTPROCESSINGLIB::filterFile	(	QIODevice &	pIODevice,
		QSharedPointer< FIFFLIB::FiffRawData >	pFiffRawData,
		int	type,
		double	dCenterfreq,
		double	dBandwidth,
		double	dTransition,
		double	dSFreq,
		int	iOrder = 4096,
		int	designMethod = FilterKernel::m_designMethods.indexOf(FilterParameter("Cosine")),
		const Eigen::RowVectorXi &	vecPicks = Eigen::RowVectorXi(),
		bool	bUseThreads = true )

Creates a user designed filter kernel, filters data from an input file and writes the filtered data to a pIODevice.

Parameters

[in]	pIODevice	The IO device to write to.
[in]	pFiffRawData	The fiff raw data object to read from.
[in]	type	The type of the filter: LPF, HPF, BPF, NOTCH (from enum FilterType).
[in]	dCenterfreq	The center of the frequency.
[in]	dBandwidth	The filter bandwidth. Ignored if FilterType is set to LPF,HPF. If NOTCH/BPF: bandwidth of stop-/passband.
[in]	dTransition	The transistion band determines the width of the filter slopes (steepness).
[in]	dSFreq	The input data sampling frequency.
[in]	iOrder	Represents the order of the filter, the higher the higher is the stopband attenuation. Default is 4096 taps.
[in]	designMethod	The design method to use. Choose between Cosine and Tschebyscheff. Defaul is set to Cosine.
[in]	vecPicks	Channel indexes to filter. Default is filter all channels.
[in]	bUseThreads	hether to use multiple threads. Default is set to true.

Returns: Returns true if successfull, false otherwise.

◆ fitMatchedPoints()

bool RTPROCESSINGLIB::fitMatchedPoints	(	const Eigen::MatrixXf &	matSrcPoint,
		const Eigen::MatrixXf &	matDstPoint,
		Eigen::Matrix4f &	matTrans,
		float	fScale = 1.0,
		bool	bScale = false,
		const Eigen::VectorXf &	vecWeitgths = vecDefaultWeigths )

Corresponding point set registration using quaternions.

Parameters

[in]	matSrcPoint	The source point set.
[in]	matDstPoint	The destination point set.
[in,out]	matTrans	The forward transformation matrix.
[in,out]	fScale	The scaling parameter, defaults to 1.0.
[in]	bScale	Wether to apply scaling or not. Should be false for matching data sets, defaults to false.
[in]	vecWeitgths	The weitghts to apply , defaults to zeros.

Returns: Wether the matching was succesfull.

◆ makeSpharaProjector()

Eigen::MatrixXd RTPROCESSINGLIB::makeSpharaProjector	(	const Eigen::MatrixXd &	matBaseFct,
		const Eigen::VectorXi &	vecIndices,
		int	iOperatorDim,
		int	iNBaseFct,
		int	iSkip = 0 )

Constructs a SPHARA operator.

Parameters

[in]	matBaseFct	The SPHARA basis functions.
[in]	vecIndices	The indices of the positions in the final oeprator which are to be filled with the basis functions weights (i.e. these indices could respond to the indices of gradioemteres in a VectorView system).
[in]	iOperatorDim	The dimensions of the final SPHARA operator. Make sure that these correspond to the dimensions of the data matrix you want tol multiply with the SPHARA operator.
[in]	iNBaseFct	The number of SPHARA basis functions to take.
[in]	iSkip	The value to skip when reading the vecIndices variabel. I.e. use this when dealing with VectorView triplets, which include two gradiometers.

Returns: Returns the final SPHARA operator with dimensions (iOperatorDim,iOperatorDim).

◆ performIcp()

bool RTPROCESSINGLIB::performIcp	(	const QSharedPointer< MNELIB::MNEProjectToSurface >	mneSurfacePoints,
		const Eigen::MatrixXf &	matPointCloud,
		FIFFLIB::FiffCoordTrans &	transFromTo,
		float &	fRMSE,
		bool	bScale = false,
		int	iMaxIter = 20,
		float	fTol = 0.001,
		const Eigen::VectorXf &	vecWeitgths = vecDefaultWeigths )

The ICP algorithm to register a point cloud with a surface.

Parameters

[in]	mneSurfacePoints	The MNEProjectToSurface object that contains the surface triangles etc. (To).
[in]	matPointCloud	The point cloud to be registrated (From).
[in,out]	transFromTo	The forward transformation matrix. It can contain an initial transformatin (e.g. from fiducial alignment).
[in,out]	fRMSE	The resulting Root-Mean-Square-Error in m.
[in]	bScale	Wether to apply scaling or not. Should be false for matching data sets, defaults to false.
[in]	iMaxIter	The maximum number of iterations for the icp algorithms, defaults to 20.
[in]	fTol	The destination point set to be reistrated, defaults to 0.001.
[in]	vecWeitgths	The weitghts to apply, defaults to zeros.

Returns: Wether the registration was succesfull.

◆ toEventMatrix()

QList< MatrixXi > RTPROCESSINGLIB::toEventMatrix ( QMap< int, QList< QPair< int, double > > > mapTriggers )

Transforms QMap with stored information about events per stim channel to a list of event matrices.

Parameters

[in]	mapTriggers	The QMap to be transformed.
[in]	A	list of transformed Eigen matrices.

Definition at line 58 of file detecttrigger.cpp.

Variable Documentation

◆ vecDefaultWeigths

const Eigen::VectorXf RTPROCESSINGLIB::vecDefaultWeigths

Definition at line 78 of file icp.h.

Classes

Functions

Variables

Detailed Description

Function Documentation

◆ buildDateTime()

◆ buildHash()

◆ buildHashLong()

◆ computeAverage()

◆ computeFilteredAverage()

◆ detectTriggerFlanksGrad() [1/2]

◆ detectTriggerFlanksGrad() [2/2]

◆ detectTriggerFlanksMax() [1/2]

◆ detectTriggerFlanksMax() [2/2]

◆ discard3DPointOutliers()

◆ filterChannel()

◆ filterData() [1/2]

◆ filterData() [2/2]

◆ filterDataBlock()

◆ filterFile() [1/2]

◆ filterFile() [2/2]

◆ fitMatchedPoints()

◆ makeSpharaProjector()

◆ performIcp()

◆ toEventMatrix()

Variable Documentation

◆ vecDefaultWeigths