Classes of the ODIN data processing framework (odindata library)


Classes
class	ComplexData< N_rank >
class	OdinData
class	Converter
struct	correlationResult
struct	fmriResult
struct	FileMapHandle
class	Data< T, N_rank >
class	FileIO
class	FileFormat
struct	FileReadOpts
struct	FileWriteOpts
struct	Filter
class	FilterChain
struct	fitpar
class	ModelFunction
class	FunctionFit
struct	ExponentialFunction
struct	ExponentialFunctionWithOffset
struct	GaussianFunction
struct	SinusFunction
struct	LinearFunction
class	Gridding< T, N_rank >
class	CoordTransformation< T, N_rank >
class	Image
class	ImageSet
class	Integrand
class	FunctionIntegral
struct	statisticResult
class	Step< T >
class	StepFactory< T >
struct	regressResult
Enumerations
enum	autoscaleOption
enum	dataDim
Functions
template<typename T>
correlationResult	correlation (const Array< T, 1 > &x, const Array< T, 1 > &y)
template<typename T>
correlationResult	kendall (const Array< T, 1 > &x, const Array< T, 1 > &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)
bool	pseudo_inverse (ComplexData< 2 > &A, float sv_truncation=0.0)
Data< float, 1 >	eigenvalues (const Data< float, 2 > &A)
template<int N_rank>
Array< STD_complex, N_rank >	smooth (const Array< STD_complex, N_rank > &source, float smoothing_kernel)
template<int N_rank>
Array< float, N_rank >	smooth (const Array< float, N_rank > &source, float smoothing_kernel, bool use_abs=false)
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)
float	wrap_from_to (const float from, const float to)
float	interpolate (float xs, float xe, float ys, float ye, float xnow)
unsigned long	calc_max_pts (Array< float, 1 > a, float limit)
unsigned long	GetNextMaxAreaSize (Array< float, 1 > a, float limit)
void	correct_lin_phase (Array< float, 1 > &x, Array< float, 1 > &ph, Array< float, 1 > &amp, float limit, unsigned int sig_pts=2)
template<int N_rank>
Array< float, N_rank >	truncate_float (Array< float, N_rank > a)
template<int N_rank>
void	wrapPhase (Array< float, N_rank > ph)
template<int N_rank>
void	unwrapPhase1d (Data< float, N_rank > ph)
template<typename type>
regressResult	regress1d (const Array< type, 1 > &x, const Array< type, 1 > &y, const Array< type, 1 > &y_err=Array< type, 1 >())
template<typename T>
Array< T, 1 >	matrix_product (const Array< T, 2 > &matrix, const Array< T, 1 > &vector)
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_rank>
bool	same_shape (const Array< T, N_rank > &a1, const Array< T, N_rank > &a2)
template<typename T>
bool	check_range (T &val, T min, T max)
template<typename T, int N_length>
STD_string	tostr (const TinyVector< T, N_length > &tv)
template<class T>
STD_vector< T >	list2vector (const STD_list< T > &src)
Array< float, N_rank >	Gridding::init (const TinyVector< int, N_rank > &dst_shape, const TinyVector< float, N_rank > &dst_extent, const Array< TinyVector< float, N_rank >, 1 > &src_coords, const JDXfilter &kernel, float kernel_diameter)
Array< T, N_rank >	Gridding::operator() (const Array< T, 1 > &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=1.5)
void	CoordTransformation::operator() (Array< T, N_rank > &A) const
Variables
static Array< T, N_rank >	Data::defaultArray

Detailed Description

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

Enumeration Type Documentation

enum autoscaleOption

Scaling strategy when converting data to integer types:

noscale: No scaling
fulllscale scale values so they will fit into the numeric limits of the destination

Definition at line 46 of file converter.h.

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

unsigned long calc_max_pts	(	Array< float, 1 >	a,
		float	limit
	)

calculates number of points, which are neighboured to the maximum of 'a' without points in between that are less than 'limit' (function for correct_lin_phase)

template<typename T>

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

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

Definition at line 253 of file utils.h.

template<typename T, int N_rank>

CoordTransformation< T, N_rank >::CoordTransformation	(	const TinyVector< int, N_rank > &	shape,
		const TinyMatrix< float, N_rank, N_rank > &	rotation,
		const TinyVector< float, N_rank > &	offset,
		float	kernel_size = `1.5`
	)			`[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. 'kernel_size' is the width of the regridding kernel in units of pixels.

Definition at line 225 of file gridding.h.

void correct_lin_phase	(	Array< float, 1 > &	x,
		Array< float, 1 > &	ph,
		Array< float, 1 > &	amp,
		float	limit,
		unsigned int	sig_pts = `2`
	)

prepares phase 'ph' and its index 'x' for linear regression. 'ph' is linearly where the amplitude 'amp' is less than 'limit'

template<typename T>

correlationResult correlation	(	const Array< T, 1 > &	x,
		const Array< T, 1 > &	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).

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

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

Definition at line 160 of file correlation.h.

unsigned long GetNextMaxAreaSize	(	Array< float, 1 >	a,
		float	limit
	)

returns the number of points from the beginning of 'a' to the next value that is less than 'limit' (function for correct_lin_phase)

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 Array< TinyVector< float, N_rank >, 1 > &	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
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 80 of file gridding.h.

float interpolate	(	float	xs,
		float	xe,
		float	ys,
		float	ye,
		float	xnow
	)

calculates linear interpolate of 'xnow' between the points (xs,ys) and (xe,ye) (function for correct_lin_phase)

template<typename T>

correlationResult kendall	(	const Array< T, 1 > &	x,
		const Array< T, 1 > &	y
	)

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

Definition at line 96 of file correlation.h.

template<class T>

STD_vector<T> list2vector ( const STD_list< T > & src )

Converts a list into a vector

Definition at line 284 of file utils.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 170 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 231 of file utils.h.

template<typename T, int N_rank>

void CoordTransformation< T, N_rank >::operator() ( Array< T, N_rank > & A ) const [inherited]

Transforms 'A' to new coordinates

Definition at line 255 of file gridding.h.

template<typename T, int N_rank>

Array< T, N_rank > Gridding< T, N_rank >::operator()	(	const Array< T, 1 > &	src,
		unsigned int	offset = `0`
	)			const `[inherited]`

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

Definition at line 166 of file gridding.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 221 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 322 of file fitting.h.

bool pseudo_inverse	(	ComplexData< 2 > &	A,
		float	sv_truncation = `0.0`
	)

Calculates the pseudo inverse of a complex matrix with a singular value decomposition. The input matrix will be tranposed and it will store the values of the pseudo inverse matrix. All singular values less than sv_truncation * max_singular_value will be set to zero. The algorithm uses the singular value decomposition from LAPACK.

template<typename type>

regressResult regress1d	(	const Array< type, 1 > &	x,
		const Array< type, 1 > &	y,
		const Array< type, 1 > &	y_err = `Array<type,1>()`
	)

1d linear regression giving back a member of regressResult

Definition at line 128 of file utils.h.

template<typename T, int N_rank>

bool same_shape	(	const Array< T, N_rank > &	a1,
		const Array< T, N_rank > &	a2
	)

Compares array shapes, returns true if equal

Definition at line 241 of file utils.h.

template<int N_rank>

Array<float,N_rank> smooth	(	const Array< float, N_rank > &	source,
		float	smoothing_kernel,
		bool	use_abs = `false`
	)

Smoothes the float array 'source' by convoluting it with a gaussian function with a FWHM of 'smoothing_kernel' pixels; Returns the smoothed array. If 'use_abs' is true, the magnitude is taken after smoothing, otherwise the real part.

Definition at line 70 of file smooth.h.

template<int N_rank>

Array<STD_complex,N_rank> smooth	(	const Array< STD_complex, N_rank > &	source,
		float	smoothing_kernel
	)

Smoothes the complex array 'source' by convoluting it with a gaussian function with a FWHM of 'smoothing_kernel' pixels; Returns the smoothed array

Definition at line 35 of file smooth.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.

template<typename T, int N_length>

STD_string tostr ( const TinyVector< T, N_length > & tv )

Converts tiny vector to string (for debugging tracing)

Definition at line 266 of file utils.h.

template<int N_rank>

Array<float,N_rank> truncate_float ( Array< float, N_rank > a )

returns integer next to zero of float arrays

Definition at line 65 of file utils.h.

template<int N_rank>

void unwrapPhase1d ( Data< float, N_rank > ph )

unwraps phase of a float array in last dimension

Definition at line 84 of file utils.h.

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 201 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.

float wrap_from_to	(	const float	from,
		const float	to
	)

calculates the multiples of 2PI, which have to be added to 'from' that 'to - from' is minimal (function for correct_lin_phase)

template<int N_rank>

void wrapPhase ( Array< float, N_rank > ph )

wraps phase of a float array (in rad !!!)

Definition at line 75 of file utils.h.

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1.5.1

Classes of the ODIN data processing framework (odindata library)

Classes

Enumerations

Functions

Variables

Detailed Description

Enumeration Type Documentation

Function Documentation