Classes of the ODIN data processing framework (odindata library)

Classes
class	ComplexData< N_rank >
class	Converter
struct	correlationResult
struct	fmriResult
class	Data< T, N_rank >
class	FileIO
class	FileFormat
struct	FileReadOpts
struct	FileWriteOpts
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)
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<typename T >
bool	check_range (T &val, T min, T max)
bool	PolynomialFunction::fit (const Array< float, 1 > &yvals, const Array< float, 1 > &ysigma, const Array< float, 1 > &xvals)
Array< float, 1 >	PolynomialFunction::get_function (const Array< float, 1 > &xvals) const
Array< float, N_rank >	Gridding::init (const TinyVector< int, N_rank > &dst_shape, const TinyVector< float, N_rank > &dst_extent, const STD_vector< GriddingPoint< N_rank > > &src_coords, const JDXfilter &kernel, float kernel_diameter)
template<int N_rank_in>
Array< T, N_rank >	Gridding::operator() (const Array< T, N_rank_in > &src, unsigned int offset=0) const
	CoordTransformation::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::operator() (const Array< T, N_rank > &A) const

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Detailed Description

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

---

Enumeration Type Documentation

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

template<typename T >

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

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

Definition at line 183 of file utils.h.

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
	)			[inherited]

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.

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.

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).

template<int N_rank>

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

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 353 of file fitting.h.

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

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

template<int N_rank>

Array< float, 1 > PolynomialFunction< N_rank >::get_function

(

const Array< float, 1 > &

xvals

)

const [inherited]

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

Definition at line 392 of file fitting.h.

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 JDXfilter &	kernel,
		float	kernel_diameter
	)			[inherited]

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.

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.

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.

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.

template<typename T , int N_rank>

T median

(

const Array< T, N_rank > &

ensemble

)

Returns the median of 'ensemble'.

Definition at line 146 of file statistics.h.

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.

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 [inherited]

Transforms 'A' to new coordinates and returns the result

Definition at line 283 of file gridding.h.

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 [inherited]

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.

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.

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.

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 544 of file fitting.h.

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.

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).

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).

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.

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

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

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.

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 168 of file statistics.h.