Classes
class	ComplexData< N_rank >

class	Converter

struct	correlationResult

struct	fmriResult

class	Data< T, N_rank >

class	FileIO

class	FileFormat

struct	FileReadOpts

struct	FileWriteOpts

class	FilterChain

struct	fitpar

class	ModelFunction

class	FunctionFitInterface

class	FunctionFitDerivative

struct	ExponentialFunction

struct	ExponentialFunctionWithOffset

struct	GaussianFunction

struct	SinusFunction

struct	GammaVariateFunction

struct	PolynomialFunction< N_rank >

struct	LinearFunction

class	DownhillSimplex

class	FunctionFitDownhillSimplex

struct	GriddingPoint< N_rank >

class	Gridding< T, N_rank >

class	CoordTransformation< T, N_rank, OnPixelRot >

class	Image

class	ImageSet

class	Integrand

class	FunctionIntegral

struct	statisticResult

class	Step< T >

class	StepFactory< T >

Enumerations
enum	dataDim

Functions
template<typename T , int N_rank>
correlationResult	correlation (const Array< T, N_rank > &x, const Array< T, N_rank > &y)

template<typename T , int N_rank>
correlationResult	kendall (const Array< T, N_rank > &x, const Array< T, N_rank > &y)

fmriResult	fmri_eval (const Data< float, 1 > &timecourse, const Data< float, 1 > &designvec)

template<int N_rank>
Array< float, N_rank >	polyniomial_fit (const Array< float, N_rank > &value_map, const Array< float, N_rank > &reliability_map, unsigned int polynom_order, float kernel_size, bool only_zero_reliability=false)

Data< float, 1 >	solve_linear (const Data< float, 2 > &A, const Data< float, 1 > &b, float sv_truncation=0.0)

ComplexData< 1 >	solve_linear (const ComplexData< 2 > &A, const ComplexData< 1 > &b, float sv_truncation=0.0)

Data< float, 1 >	eigenvalues (const Data< float, 2 > &A)

template<typename T , int N_rank>
statisticResult	statistics (const Array< T, N_rank > &ensemble, const Array< T, N_rank > *mask=0)

template<typename T , int N_rank>
T	median (const Array< T, N_rank > &ensemble, const Array< T, N_rank > *mask=0)

template<typename T , int N_rank>
T	weightmean (const Array< T, N_rank > &ensemble, const Array< float, N_rank > &weight)

Data< float, 1 >	unwrap_phase (const Data< float, 1 > &phase, int startindex=0)

template<typename T >
Array< T, 1 >	matrix_product (const Array< T, 2 > &matrix, const Array< T, 1 > &vector)

template<typename T >
Array< T, 2 >	matrix_product (const Array< T, 2 > &matrix1, const Array< T, 2 > &matrix2)

template<typename T >
Array< T, 1 >	vector_product (const Array< T, 1 > &u, const Array< T, 1 > &v)

template<typename T , int N_rank>
bool	operator== (const TinyVector< T, N_rank > &t1, const TinyVector< T, N_rank > &t2)

template<typename T , int N_rank>
bool	operator!= (const TinyVector< T, N_rank > &t1, const TinyVector< T, N_rank > &t2)

template<typename T , int N_rows, int N_columns>
TinyVector< T, N_rows >	operator* (const TinyMatrix< T, N_rows, N_columns > &matrix, const TinyVector< T, N_columns > &vector)

template<typename T , int N_rank>
bool	same_shape (const Array< T, N_rank > &a1, const Array< T, N_rank > &a2, const TinyVector< int, N_rank > &dimmask=1)

template<int N_rank>
bool	on_grid (const TinyVector< int, 3 > &shape, const TinyVector< int, 3 > &index)

template<typename T >
bool	check_range (T &val, T min, T max)

template<typename T , int N_rank>
void	clip_max (Data< T, N_rank > &data, T val)

template<typename T , int N_rank>
void	clip_min (Data< T, N_rank > &data, T val)

bool	PolynomialFunction< N_rank >::fit (const Array< float, 1 > &yvals, const Array< float, 1 > &ysigma, const Array< float, 1 > &xvals)

Array< float, 1 >	PolynomialFunction< N_rank >::get_function (const Array< float, 1 > &xvals) const

Array< float, N_rank >	Gridding< T, N_rank >::init (const TinyVector< int, N_rank > &dst_shape, const TinyVector< float, N_rank > &dst_extent, const STD_vector< GriddingPoint< N_rank > > &src_coords, const LDRfilter &kernel, float kernel_diameter)

template<int N_rank_in>
Array< T, N_rank >	Gridding< T, N_rank >::operator() (const Array< T, N_rank_in > &src, unsigned int offset=0) const

	CoordTransformation< T, N_rank, OnPixelRot >::CoordTransformation (const TinyVector< int, N_rank > &shape, const TinyMatrix< float, N_rank, N_rank > &rotation, const TinyVector< float, N_rank > &offset, float kernel_size=2.5)

Array< T, N_rank >	CoordTransformation< T, N_rank, OnPixelRot >::operator() (const Array< T, N_rank > &A) const

Detailed Description

ODIN data processing framework (odindata library) Input/Output of medical image data (odindata library)

Enumeration Type Documentation

◆ dataDim

enum dataDim

The 4 dimensions of data arrays used in FileIO and Filter components, these dimensions are

timeDim: Time direction
sliceDim: Slice direction
phaseDim: Phase direction
readDim: Read direction

Definition at line 45 of file fileio.h.

Function Documentation

◆ check_range()

template<typename T >

bool check_range	(	T &	val,
		T	min,
		T	max
	)

Limits value 'val' to range (min,max)

Definition at line 197 of file utils.h.

◆ clip_max()

template<typename T , int N_rank>

void clip_max	(	Data< T, N_rank > &	data,
		T	val
	)

Clip all values above maximum value

Definition at line 211 of file utils.h.

◆ clip_min()

template<typename T , int N_rank>

void clip_min	(	Data< T, N_rank > &	data,
		T	val
	)

Clip all values below minimum value

Definition at line 223 of file utils.h.

◆ CoordTransformation()

template<typename T , int N_rank, bool OnPixelRot>

CoordTransformation< T, N_rank, OnPixelRot >::CoordTransformation	(	const TinyVector< int, N_rank > &	shape,
		const TinyMatrix< float, N_rank, N_rank > &	rotation,
		const TinyVector< float, N_rank > &	offset,
		float	kernel_size = `2.5`
	)

Initializes a transformation of an array with shape 'shape' to new coordinates using a gridding algorithm. New coordinates (...z',y',x') are obtained by multiplying (...z,y,x) with 'rotation' and adding 'offset'. Origin is the center of the data set.

Definition at line 251 of file gridding.h.

◆ correlation()

template<typename T , int N_rank>

correlationResult correlation	(	const Array< T, N_rank > &	x,
		const Array< T, N_rank > &	y
	)

Linear correlation between vectors 'x' and 'y'

Definition at line 61 of file correlation.h.

◆ eigenvalues()

Data<float,1> eigenvalues ( const Data< float, 2 > & A )

Computes the eigenvalues of symmetric matrix A using LAPACK (or with GSL if LAPACK is not available).

◆ fit()

template<int N_rank>

bool PolynomialFunction< N_rank >::fit	(	const Array< float, 1 > &	yvals,
		const Array< float, 1 > &	ysigma,
		const Array< float, 1 > &	xvals
	)

polynomial fitting routine. Fits the function to the y-values 'yvals', and optionally the corresponding error bars 'ysigma' and x-values 'xvals'. If no error-bars are given they are all set to 1.0 and if no x-vals are given equidistant points with an increment of one are chosen, i.e. xvals(i)=i; Returns true on success.

Definition at line 362 of file fitting.h.

◆ fmri_eval()

fmriResult fmri_eval	(	const Data< float, 1 > &	timecourse,
		const Data< float, 1 > &	designvec
	)

Returns an fMRI analysis of 'timecourse' using 'designvec'

◆ get_function()

template<int N_rank>

Array< float, 1 > PolynomialFunction< N_rank >::get_function ( const Array< float, 1 > & xvals ) const

Returns the polynomial function values for x-values 'xvals' using the current polynomial coefficients.

Definition at line 401 of file fitting.h.

◆ init()

template<typename T , int N_rank>

Array< float, N_rank > Gridding< T, N_rank >::init	(	const TinyVector< int, N_rank > &	dst_shape,
		const TinyVector< float, N_rank > &	dst_extent,
		const STD_vector< GriddingPoint< N_rank > > &	src_coords,
		const LDRfilter &	kernel,
		float	kernel_diameter
	)

Initializes a gridding object to perform a gridding operation with the following parameters:

dst_shape: The dimensions of the gridded array
dst_extent: The total extent of the gridded array, the gridded array is created symmetrically about the origin
src_coords: The coordinates of the input array: First is tthe coordinate, second is an extra weight for the coordinate
kernel: The gridding kernel used
kernel_diameter: The maximum diameter of the gridding kernel in units of the source coordinates Returns the density of source points on the gridded array

Definition at line 96 of file gridding.h.

◆ kendall()

template<typename T , int N_rank>

correlationResult kendall	(	const Array< T, N_rank > &	x,
		const Array< T, N_rank > &	y
	)

Kendall's rank-correlation between vectors 'x' and 'y' re-implemented from NRC, section 14.6

Definition at line 106 of file correlation.h.

◆ matrix_product() [1/2]

template<typename T >

Array<T,1> matrix_product	(	const Array< T, 2 > &	matrix,
		const Array< T, 1 > &	vector
	)

matrix-vector product

Definition at line 42 of file utils.h.

◆ matrix_product() [2/2]

template<typename T >

Array<T,2> matrix_product	(	const Array< T, 2 > &	matrix1,
		const Array< T, 2 > &	matrix2
	)

matrix-matrix product

Definition at line 72 of file utils.h.

◆ median()

template<typename T , int N_rank>

T median	(	const Array< T, N_rank > &	ensemble,
		const Array< T, N_rank > *	mask = `0`
	)

Returns the median of 'ensemble'.

Definition at line 146 of file statistics.h.

◆ on_grid()

template<int N_rank>

bool on_grid	(	const TinyVector< int, 3 > &	shape,
		const TinyVector< int, 3 > &	index
	)

Checks whether multidimensional index 'index' is within the array with shape 'shape'

Definition at line 181 of file utils.h.

◆ operator!=()

template<typename T , int N_rank>

bool operator!=	(	const TinyVector< T, N_rank > &	t1,
		const TinyVector< T, N_rank > &	t2
	)

Unequal-comparison operator for TinyVectors

Definition at line 139 of file utils.h.

◆ operator()() [1/2]

template<typename T , int N_rank, bool OnPixelRot>

Array< T, N_rank > CoordTransformation< T, N_rank, OnPixelRot >::operator() ( const Array< T, N_rank > & A ) const

Transforms 'A' to new coordinates and returns the result

Definition at line 283 of file gridding.h.

◆ operator()() [2/2]

template<typename T , int N_rank>

template<int N_rank_in>

Array< T, N_rank > Gridding< T, N_rank >::operator()	(	const Array< T, N_rank_in > &	src,
		unsigned int	offset = `0`
	)		const

Put input array 'src' on the grid by stepping linearly through the inidices. Gridding will start at the linear index 'offset' of the 'src_coords' specified in the init() function. Returns gridded array.

Definition at line 194 of file gridding.h.

◆ operator*()

template<typename T , int N_rows, int N_columns>

TinyVector<T,N_rows> operator*	(	const TinyMatrix< T, N_rows, N_columns > &	matrix,
		const TinyVector< T, N_columns > &	vector
	)

Product operator for TinyMatrix*TinyMatrix (defined here because the product(TinyMatrix,TinyMatrix) function is missing in blitz-0.10)

Definition at line 149 of file utils.h.

◆ operator==()

template<typename T , int N_rank>

bool operator==	(	const TinyVector< T, N_rank > &	t1,
		const TinyVector< T, N_rank > &	t2
	)

Equal-comparison operator for TinyVectors

Definition at line 129 of file utils.h.

◆ polyniomial_fit()

template<int N_rank>

Array<float,N_rank> polyniomial_fit	(	const Array< float, N_rank > &	value_map,
		const Array< float, N_rank > &	reliability_map,
		unsigned int	polynom_order,
		float	kernel_size,
		bool	only_zero_reliability = `false`
	)

Fits an N_rank-dimensional polynomial of order 'polynom_order' to each point of the array using the values of its neighbours regarding their reliability (i.e. their relative weight for the fit). Parameters are:

value_map: The array to be fitted
reliability_map: The reliability of each point
polynom_order: Order of the polynom
kernel_size: Size of the neighbourhood of the pixel which is considered for the fit (using a Gaussian kernel with this FWHM)
only_zero_reliability: Fit only pixel with zero reliabiliy

This function returns the fitted array

Definition at line 554 of file fitting.h.

◆ same_shape()

template<typename T , int N_rank>

bool same_shape	(	const Array< T, N_rank > &	a1,
		const Array< T, N_rank > &	a2,
		const TinyVector< int, N_rank > &	dimmask = `1`
	)

Compares array shapes while discarding dimensions with zero value in dimmask. Returns true if equal.

Definition at line 168 of file utils.h.

◆ solve_linear() [1/2]

ComplexData<1> solve_linear	(	const ComplexData< 2 > &	A,
		const ComplexData< 1 > &	b,
		float	sv_truncation = `0.0`
	)

Solves the complex linear system A x = b and returns x. All singular values less than sv_truncation * max_singular_value will be set to zero. The algorithm uses the singular value decomposition from LAPACK (or from GSL if LAPACK is not available).

◆ solve_linear() [2/2]

Data<float,1> solve_linear	(	const Data< float, 2 > &	A,
		const Data< float, 1 > &	b,
		float	sv_truncation = `0.0`
	)

Solves the linear system A x = b and returns x. All singular values less than sv_truncation * max_singular_value will be set to zero. The algorithm uses the singular value decomposition from LAPACK (or from GSL if LAPACK is not available).

◆ statistics()

template<typename T , int N_rank>

statisticResult statistics	(	const Array< T, N_rank > &	ensemble,
		const Array< T, N_rank > *	mask = `0`
	)

Returns a statistical description of 'ensemble'. If mask is non-zero, only values with mask!=0 are considered.

Definition at line 79 of file statistics.h.

◆ unwrap_phase()

Data<float,1> unwrap_phase	(	const Data< float, 1 > &	phase,
		int	startindex = `0`
	)

unwrap phase in one dimension, starting at position 'startindex'

◆ vector_product()

template<typename T >

Array<T,1> vector_product	(	const Array< T, 1 > &	u,
		const Array< T, 1 > &	v
	)

cross (vector) product (always in 3 dimensions)

Definition at line 109 of file utils.h.

◆ weightmean()

template<typename T , int N_rank>

T weightmean	(	const Array< T, N_rank > &	ensemble,
		const Array< float, N_rank > &	weight
	)

Returns the weighted mean of 'ensemble' using 'weight' for the weighting.

Definition at line 180 of file statistics.h.

Classes

Enumerations

Functions

Detailed Description

Enumeration Type Documentation

◆ dataDim

Function Documentation

◆ check_range()

◆ clip_max()

◆ clip_min()

◆ CoordTransformation()

◆ correlation()

◆ eigenvalues()

◆ fit()

◆ fmri_eval()

◆ get_function()

◆ init()

◆ kendall()

◆ matrix_product() [1/2]

◆ matrix_product() [2/2]

◆ median()

◆ on_grid()

◆ operator!=()

◆ operator()() [1/2]

◆ operator()() [2/2]

◆ operator*()

◆ operator==()

◆ polyniomial_fit()

◆ same_shape()

◆ solve_linear() [1/2]

◆ solve_linear() [2/2]

◆ statistics()

◆ unwrap_phase()

◆ vector_product()

◆ weightmean()