TUnfoldV17 Class Reference

An algorithm to unfold distributions from detector to truth level. More...

#include <TUnfold.h>

Public Types
enum	EConstraint { kEConstraintNone = 0, kEConstraintArea = 1 }
	type of extra constraint More...
enum	ERegMode { kRegModeNone = 0, kRegModeSize = 1, kRegModeDerivative = 2, kRegModeCurvature = 3, kRegModeMixed = 4 }
	choice of regularisation scheme More...
enum	EHistMap { kHistMapOutputHoriz = 0, kHistMapOutputVert = 1 }
	arrangement of axes for the response matrix (TH2 histogram) More...
Public Member Functions
	TUnfoldV17 (const TH2 *hist_A, EHistMap histmap, ERegMode regmode=kRegModeSize, EConstraint constraint=kEConstraintArea)
	Set up response matrix and regularisation scheme.
	TUnfoldV17 (void)
	only for use by root streamer or derived classes
virtual	~TUnfoldV17 (void)
virtual Int_t	SetInput (const TH1 hist_y, Double_t scaleBias=0.0, Double_t oneOverZeroError=0.0, const TH2 hist_vyy=0, const TH2 *hist_vyy_inv=0)
	Define input data for subsequent calls to DoUnfold(tau).
virtual Double_t	DoUnfold (Double_t tau)
	perform the unfolding for a given regularisation parameter tau
Double_t	DoUnfold (Double_t tau, const TH1 *hist_y, Double_t scaleBias=0.0)
	perform the unfolding for a given input and regularisation
virtual Int_t	ScanLcurve (Int_t nPoint, Double_t tauMin, Double_t tauMax, TGraph lCurve, TSpline logTauX=0, TSpline logTauY=0, TSpline logTauCurvature=0)
	scan the L curve, determine tau and unfold at the final value of tau
Double_t	GetTau (void) const
	return regularisation parameter
void	GetOutput (TH1 output, const Int_t binMap=0) const
	get output distribution, possibly cumulated over several bins
void	GetEmatrix (TH2 ematrix, const Int_t binMap=0) const
	get output covariance matrix, possibly cumulated over several bins
void	GetRhoIJ (TH2 rhoij, const Int_t binMap=0) const
	get correlation coefficiencts, possibly cumulated over several bins
Double_t	GetRhoI (TH1 rhoi, const Int_t binMap=0, TH2 *invEmat=0) const
	get global correlation coefficiencts, possibly cumulated over several bins
void	GetFoldedOutput (TH1 folded, const Int_t binMap=0) const
	get unfolding result on detector level
void	GetProbabilityMatrix (TH2 *A, EHistMap histmap) const
	get matrix of probabilities
void	GetNormalisationVector (TH1 s, const Int_t binMap=0) const
	histogram of truth bins, determined from suming over the response matrix
void	GetInput (TH1 inputData, const Int_t binMap=0) const
	Input vector of measurements.
void	GetInputInverseEmatrix (TH2 *ematrix)
	get inverse of the measurement's covariance matrix
void	GetBias (TH1 bias, const Int_t binMap=0) const
	get bias vector including bias scale
Int_t	GetNr (void) const
	get number of regularisation conditions
void	GetL (TH2 *l) const
	get matrix of regularisation conditions
void	GetLsquared (TH2 *lsquared) const
	get matrix of regularisation conditions squared
Double_t	GetRhoMax (void) const
	get maximum global correlation determined in recent unfolding
Double_t	GetRhoAvg (void) const
	get average global correlation determined in recent unfolding
Double_t	GetChi2A (void) const
	get ²_A contribution determined in recent unfolding
Double_t	GetChi2L (void) const
	get ²_L contribution determined in recent unfolding
virtual Double_t	GetLcurveX (void) const
	get value on x-axis of L-curve determined in recent unfolding
virtual Double_t	GetLcurveY (void) const
	get value on y-axis of L-curve determined in recent unfolding
Int_t	GetNdf (void) const
	get number of degrees of freedom determined in recent unfolding
Int_t	GetNpar (void) const
	get number of truth parameters determined in recent unfolding
void	SetBias (const TH1 *bias)
	set bias vector
void	SetConstraint (EConstraint constraint)
	set type of area constraint
Int_t	RegularizeSize (int bin, Double_t scale=1.0)
	add a regularisation condition on the magnitude of a truth bin
Int_t	RegularizeDerivative (int left_bin, int right_bin, Double_t scale=1.0)
	add a regularisation condition on the difference of two truth bin
Int_t	RegularizeCurvature (int left_bin, int center_bin, int right_bin, Double_t scale_left=1.0, Double_t scale_right=1.0)
	add a regularisation condition on the curvature of three truth bin
Int_t	RegularizeBins (int start, int step, int nbin, ERegMode regmode)
	add regularisation conditions for a group of bins
Int_t	RegularizeBins2D (int start_bin, int step1, int nbin1, int step2, int nbin2, ERegMode regmode)
	add regularisation conditions for 2d unfolding
Double_t	GetEpsMatrix (void) const
	get numerical accuracy for Eigenvalue analysis when inverting matrices with rank problems
void	SetEpsMatrix (Double_t eps)
	set numerical accuracy for Eigenvalue analysis when inverting matrices with rank problems
Static Public Member Functions
static const char *	GetTUnfoldVersion (void)
	return a string describing the TUnfold version
Protected Member Functions
virtual Double_t	DoUnfold (void)
	core unfolding algorithm
virtual void	ClearResults (void)
	reset all results
void	ClearHistogram (TH1 *h, Double_t x=0.) const
	Initialize bin contents and bin errors for a given histogram.
virtual TString	GetOutputBinName (Int_t iBinX) const
	Get bin name of an outpt bin.
TMatrixDSparse *	MultiplyMSparseM (const TMatrixDSparse a, const TMatrixD b) const
	multiply sparse matrix and a non-sparse matrix
TMatrixDSparse *	MultiplyMSparseMSparse (const TMatrixDSparse a, const TMatrixDSparse b) const
	multiply two sparse matrices
TMatrixDSparse *	MultiplyMSparseTranspMSparse (const TMatrixDSparse a, const TMatrixDSparse b) const
	multiply a transposed Sparse matrix with another Sparse matrix
TMatrixDSparse *	MultiplyMSparseMSparseTranspVector (const TMatrixDSparse m1, const TMatrixDSparse m2, const TMatrixTBase< Double_t > *v) const
	calculate a sparse matrix product M1VM2^T where the diagonal matrix V is given by a vector
TMatrixDSparse *	InvertMSparseSymmPos (const TMatrixDSparse A, Int_t rank) const
	get the inverse or pseudo-inverse of a positive, sparse matrix
void	AddMSparse (TMatrixDSparse dest, Double_t f, const TMatrixDSparse src) const
	add a sparse matrix, scaled by a factor, to another scaled matrix
TMatrixDSparse *	CreateSparseMatrix (Int_t nrow, Int_t ncol, Int_t nele, Int_t row, Int_t col, Double_t *data) const
	create a sparse matrix, given the nonzero elements
Int_t	GetNx (void) const
	returns internal number of output (truth) matrix rows
Int_t	GetRowFromBin (int ix) const
	converts truth histogram bin number to matrix row
Int_t	GetBinFromRow (int ix) const
	converts matrix row to truth histogram bin number
Int_t	GetNy (void) const
	returns the number of measurement bins
const TMatrixD *	GetX (void) const
	vector of the unfolding result
const TMatrixDSparse *	GetVxx (void) const
	covariance matrix of the result
const TMatrixDSparse *	GetVxxInv (void) const
	inverse of covariance matrix of the result
const TMatrixDSparse *	GetAx (void) const
	vector of folded-back result
const TMatrixDSparse *	GetDXDY (void) const
	matrix of derivatives dx/dy
const TMatrixDSparse *	GetDXDAM (int i) const
	matrix contributions of the derivative dx/dA
const TMatrixDSparse *	GetDXDAZ (int i) const
	vector contributions of the derivative dx/dA
const TMatrixDSparse *	GetEinv (void) const
	matrix E^-1, using internal bin counting
const TMatrixDSparse *	GetE (void) const
	matrix E, using internal bin counting
const TMatrixDSparse *	GetVyyInv (void) const
	inverse of covariance matrix of the data y
void	ErrorMatrixToHist (TH2 ematrix, const TMatrixDSparse emat, const Int_t *binMap, Bool_t doClear) const
	add up an error matrix, also respecting the bin mapping
Double_t	GetRhoIFromMatrix (TH1 rhoi, const TMatrixDSparse eOrig, const Int_t binMap, TH2 invEmat) const
const TMatrixDSparse *	GetDXDtauSquared (void) const
	vector of derivative dx/dtauSquared, using internal bin counting
Bool_t	AddRegularisationCondition (Int_t i0, Double_t f0, Int_t i1=-1, Double_t f1=0., Int_t i2=-1, Double_t f2=0.)
	add a row of regularisation conditions to the matrix L
Bool_t	AddRegularisationCondition (Int_t nEle, const Int_t indices, const Double_t rowData)
	add a row of regularisation conditions to the matrix L
Static Protected Member Functions
static void	DeleteMatrix (TMatrixD **m)
	delete matrix and invalidate pointer
static void	DeleteMatrix (TMatrixDSparse **m)
	delete sparse matrix and invalidate pointer
Protected Attributes
TMatrixDSparse *	fA
	response matrix A
TMatrixDSparse *	fL
	regularisation conditions L
TMatrixD *	fY
	input (measured) data y
TMatrixDSparse *	fVyy
	covariance matrix Vyy corresponding to y
Double_t	fBiasScale
	scale factor for the bias
TMatrixD *	fX0
	bias vector x0
Double_t	fTauSquared
	regularisation parameter tau squared
TArrayI	fXToHist
	mapping of matrix indices to histogram bins
TArrayI	fHistToX
	mapping of histogram bins to matrix indices
TArrayD	fSumOverY
	truth vector calculated from the non-normalized response matrix
EConstraint	fConstraint
	type of constraint to use for the unfolding
ERegMode	fRegMode
	type of regularisation
Private Member Functions
void	InitTUnfold (void)
	initialize data menbers, for use in constructors
Private Attributes
Int_t	fIgnoredBins
	number of input bins which are dropped because they have error=0
Double_t	fEpsMatrix
	machine accuracy used to determine matrix rank after eigenvalue analysis
TMatrixD *	fX
	unfolding result x
TMatrixDSparse *	fVxx
	covariance matrix Vxx
TMatrixDSparse *	fVxxInv
	inverse of covariance matrix Vxx^-1
TMatrixDSparse *	fVyyInv
	inverse of the input covariance matrix Vyy^-1
TMatrixDSparse *	fAx
	result x folded back A*x
Double_t	fChi2A
	chi**2 contribution from (y-Ax)Vyy^-1(y-Ax)
Double_t	fLXsquared
	chi*2 contribution from (x-sx0)^{TL^TL(x-s*x0)}
Double_t	fRhoMax
	maximum global correlation coefficient
Double_t	fRhoAvg
	average global correlation coefficient
Int_t	fNdf
	number of degrees of freedom
TMatrixDSparse *	fDXDAM [2]
	matrix contribution to the of derivative dx_k/dA_ij
TMatrixDSparse *	fDXDAZ [2]
	vector contribution to the of derivative dx_k/dA_ij
TMatrixDSparse *	fDXDtauSquared
	derivative of the result wrt tau squared
TMatrixDSparse *	fDXDY
	derivative of the result wrt dx/dy
TMatrixDSparse *	fEinv
	matrix E^(-1)
TMatrixDSparse *	fE
	matrix E

Detailed Description

An algorithm to unfold distributions from detector to truth level.

TUnfold is used to decompose a measurement y into several sources x, given the measurement uncertainties and a matrix of migrations A. The method can be applied to a large number of problems, where the measured distribution y is a linear superposition of several Monte Carlo shapes. Beyond such a simple template fit, TUnfold has an adjustable regularisation term and also supports an optional constraint on the total number of events.

For most applications, it is better to use the derived class TUnfoldDensity instead of TUnfold. TUnfoldDensity adds various features to TUnfold, such as: background subtraction, propagation of systematic uncertainties, complex multidimensional arrangements of the bins. For innocent users, the most notable improvement of TUnfoldDensity over TUnfold are the getter functions. For TUnfold, histograms have to be booked by the user and the getter functions fill the histogram bins. TUnfoldDensity simply returns a new, already filled histogram.

If you use this software, please consider the following citation
S.Schmitt, JINST 7 (2012) T10003 [arXiv:1205.6201]
Detailed documentation and updates are available on http://www.desy.de/~sschmitt

Brief recipy to use TUnfold:

a matrix (truth,reconstructed) is given as a two-dimensional histogram as argument to the constructor of TUnfold
a vector of measurements is given as one-dimensional histogram using the SetInput() method
The unfolding is performed
- either once with a fixed parameter tau, method DoUnfold(tau)
- or multiple times in a scan to determine the best chouce of tau, method ScanLCurve()
Unfolding results are retrieved using various GetXXX() methods

Basic formulae:
²_A=(Ax-y)^TV_yy^-1(Ax-y)
²_L=(x-f*x₀)^TL^TL(x-f*x₀)
²_unf=²_A+²²_L+_i(Ax-y)_i
x:result, A:probabilities, y:data, V_yy:data covariance, f:bias scale, x₀:bias, L:regularisation conditions, :regularisation strength, :Lagrangian multiplier
Without area constraint, is set to zero, and ²_unf is minimized to determine x. With area constraint, both x and are determined.

Definition at line 105 of file TUnfold.h.

Member Enumeration Documentation

enum TUnfoldV17::EConstraint

type of extra constraint

Enumerator:

kEConstraintNone	use no extra constraint
kEConstraintArea	enforce preservation of the area

Definition at line 111 of file TUnfold.h.

enum TUnfoldV17::EHistMap

arrangement of axes for the response matrix (TH2 histogram)

Enumerator:

kHistMapOutputHoriz	truth level on x-axis of the response matrix
kHistMapOutputVert	truth level on y-axis of the response matrix

Definition at line 141 of file TUnfold.h.

enum TUnfoldV17::ERegMode

choice of regularisation scheme

Enumerator:

kRegModeNone	no regularisation, or defined later by RegularizeXXX() methods
kRegModeSize	regularise the amplitude of the output distribution
kRegModeDerivative	regularize the 1st derivative of the output distribution
kRegModeCurvature	regularize the 2nd derivative of the output distribution
kRegModeMixed	mixed regularisation pattern

Definition at line 121 of file TUnfold.h.

Constructor & Destructor Documentation

TUnfoldV17::TUnfoldV17	(	const TH2 *	hist_A,
		EHistMap	histmap,
		ERegMode	regmode = `kRegModeSize`,
		EConstraint	constraint = `kEConstraintArea`
	)

Set up response matrix and regularisation scheme.

Parameters:

`[in]`	hist_A	matrix of MC events that describes the migrations
`[in]`	histmap	mapping of the histogram axes
`[in]`	regmode	(default=kRegModeSize) global regularisation mode
`[in]`	constraint	(default=kEConstraintArea) type of constraint

Treatment of overflow bins in the matrix hist_A

Events reconstructed in underflow or overflow bins are counted as inefficiency. They have to be filled properly.
Events where the truth level is in underflow or overflow bins are treated as a part of the generator level distribution. The full truth level distribution (including underflow and overflow) is unfolded.

If unsure, do the following:

store evens where the truth is in underflow or overflow (sometimes called "fakes") in a separate TH1. Ensure that the truth-level underflow and overflow bins of hist_A are all zero.
the fakes are background to the measurement. Use the classes TUnfoldSys and TUnfoldDensity instead of the plain TUnfold for subtracting background

Definition at line 1696 of file TUnfoldV17.cxx.

TUnfoldV17::TUnfoldV17 ( void )

only for use by root streamer or derived classes

Definition at line 235 of file TUnfoldV17.cxx.

virtual TUnfoldV17::~TUnfoldV17 ( void ) [virtual]

Member Function Documentation

void TUnfoldV17::AddMSparse	(	TMatrixDSparse *	dest,
		Double_t	f,
		const TMatrixDSparse *	src
	)			const `[protected]`

add a sparse matrix, scaled by a factor, to another scaled matrix

Parameters:

	inout]	dest destination matrix
`[in]`	f	scaling factor
`[in]`	src	matrix to be added to dest

a replacement for (*dest) += f * (*src) which suffered from a bug in old root versions

Definition at line 912 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), TUnfoldSysV17::GetSummedErrorMatrixXX(), TUnfoldSysV17::GetSummedErrorMatrixYY(), and TUnfoldSysV17::PrepareSysError().

Bool_t TUnfoldV17::AddRegularisationCondition	(	Int_t	nEle,
		const Int_t *	indices,
		const Double_t *	rowData
	)			`[protected]`

add a row of regularisation conditions to the matrix L

Parameters:

`[in]`	nEle	number of valid entries in indeces and rowData
`[in]`	indices	column numbers of L to fill
`[in]`	rowData	data to fill into the new row of L

returns true if a row was added, false otherwise
A new row k is added to the matrix L, its dimension is expanded. The new elements L_ki are filled from the array rowData[] where the indices i which are taken from the array indices[].

Definition at line 1952 of file TUnfoldV17.cxx.

Bool_t TUnfoldV17::AddRegularisationCondition	(	Int_t	i0,
		Double_t	f0,
		Int_t	i1 = `-1`,
		Double_t	f1 = `0.`,
		Int_t	i2 = `-1`,
		Double_t	f2 = `0.`
	)			`[protected]`

add a row of regularisation conditions to the matrix L

Parameters:

`[in]`	i0	truth histogram bin number
`[in]`	f0	entry in the matrix L, column i0
`[in]`	i1	truth histogram bin number
`[in]`	f1	entry in the matrix L, column i1
`[in]`	i2	truth histogram bin number
`[in]`	f2	entry in the matrix L, column i2

the arguments are used to form one row (k) of the matrix L, where
L_k,i0=f0 and L_k,i1=f1 and L_k,i2=f2
negative indexes i0,i1,i2 are ignored

Definition at line 1915 of file TUnfoldV17.cxx.

Referenced by RegularizeCurvature(), RegularizeDerivative(), and RegularizeSize().

void TUnfoldV17::ClearHistogram	(	TH1 *	h,
		Double_t	x = `0.`
	)			const `[protected]`

Initialize bin contents and bin errors for a given histogram.

Parameters:

`[out]`	h	histogram
`[in]`	x	new histogram content

all histgram errors are set to zero, all contents are set to x

Definition at line 3624 of file TUnfoldV17.cxx.

Referenced by GetBias(), GetFoldedOutput(), GetInput(), GetNormalisationVector(), GetOutput(), GetRhoI(), and TUnfoldSysV17::GetRhoItotal().

void TUnfoldV17::ClearResults ( void ) [protected, virtual]

reset all results

Reimplemented in TUnfoldSysV17.

Definition at line 205 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), SetConstraint(), and SetInput().

TMatrixDSparse * TUnfoldV17::CreateSparseMatrix	(	Int_t	nrow,
		Int_t	ncol,
		Int_t	nel,
		Int_t *	row,
		Int_t *	col,
		Double_t *	data
	)			const `[protected]`

create a sparse matrix, given the nonzero elements

Parameters:

`[in]`	nrow	number of rows
`[in]`	ncol	number of columns
`[in]`	nel	number of non-zero elements
`[in]`	row	row indexes of non-zero elements
`[in]`	col	column indexes of non-zero elements
`[in]`	data	non-zero elements data

return pointer to a new sparse matrix

shortcut to new TMatrixDSparse() followed by SetMatrixArray()

Definition at line 576 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), TUnfoldSysV17::PrepareSysError(), SetInput(), and TUnfoldV17().

void TUnfoldV17::DeleteMatrix ( TMatrixDSparse ** m ) [static, protected]

delete sparse matrix and invalidate pointer

Parameters:

inout]

m pointer to a matrix-pointer

if the matrix pointer os non-zero, thematrix id deleted. The matrox pointer is set to zero.

Definition at line 197 of file TUnfoldV17.cxx.

void TUnfoldV17::DeleteMatrix ( TMatrixD ** m ) [static, protected]

delete matrix and invalidate pointer

Parameters:

inout]

m pointer to a matrix-pointer

if the matrix pointer os non-zero, thematrix id deleted. The matrox pointer is set to zero.

Definition at line 185 of file TUnfoldV17.cxx.

Referenced by ClearResults(), TUnfoldSysV17::ClearResults(), TUnfoldSysV17::DoBackgroundSubtraction(), DoUnfold(), TUnfoldSysV17::GetChi2Sys(), GetFoldedOutput(), GetLsquared(), GetRhoIFromMatrix(), TUnfoldSysV17::GetRhoItotal(), TUnfoldSysV17::GetSummedErrorMatrixXX(), TUnfoldSysV17::GetSummedErrorMatrixYY(), TUnfoldSysV17::PrepareSysError(), SetBias(), SetInput(), and TUnfoldSysV17::SetTauError().

Double_t TUnfoldV17::DoUnfold	(	Double_t	tau_reg,
		const TH1 *	input,
		Double_t	scaleBias = `0.0`
	)

perform the unfolding for a given input and regularisation

Parameters:

`[in]`	tau_reg	regularisation parameter
`[in]`	input	input distribution with uncertainties
`[in]`	scaleBias	(default=0.0) scale factor applied to the bias

This is a shortcut for { SetInput(input,scaleBias); DoUnfold(tau); }

Definition at line 2232 of file TUnfoldV17.cxx.

Double_t TUnfoldV17::DoUnfold ( Double_t tau ) [virtual]

perform the unfolding for a given regularisation parameter tau

Parameters:

[in] tau regularisation parameter

this method sets tau and then calls the core unfolding algorithm

Definition at line 2485 of file TUnfoldV17.cxx.

Double_t TUnfoldV17::DoUnfold ( void ) [protected, virtual]

core unfolding algorithm

Definition at line 243 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), and ScanLcurve().

void TUnfoldV17::ErrorMatrixToHist	(	TH2 *	ematrix,
		const TMatrixDSparse *	emat,
		const Int_t *	binMap,
		Bool_t	doClear
	)			const `[protected]`

add up an error matrix, also respecting the bin mapping

Parameters:

	inout]	ematrix error matrix histogram
`[in]`	emat	error matrix stored with internal mapping (member fXToHist)
`[in]`	binMap	mapping of histogram bins
`[in]`	doClear	if true, ematrix is cleared prior to adding elements of emat to it.

the array binMap is explained with the method GetOutput(). The matrix emat must have dimension NxN where N=fXToHist.size() The flag doClear may be used to add covariance matrices from several uncertainty sources.

Definition at line 3323 of file TUnfoldV17.cxx.

Referenced by GetEmatrix().

const TMatrixDSparse* TUnfoldV17::GetAx ( void ) const [inline, protected]

vector of folded-back result

Definition at line 246 of file TUnfold.h.

Referenced by TUnfoldSysV17::GetChi2Sys().

void TUnfoldV17::GetBias	(	TH1 *	out,
		const Int_t *	binMap = `0`
	)			const

get bias vector including bias scale

Parameters:

`[out]`	out	histogram to store the scaled bias vector. The bin contents are overwritten
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

This method returns the bias vector times scaling factor, f*x₀

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 2902 of file TUnfoldV17.cxx.

Int_t TUnfoldV17::GetBinFromRow ( int ix ) const [inline, protected]

converts matrix row to truth histogram bin number

Definition at line 234 of file TUnfold.h.

Double_t TUnfoldV17::GetChi2A ( void ) const [inline]

get ²_A contribution determined in recent unfolding

Definition at line 315 of file TUnfold.h.

Double_t TUnfoldV17::GetChi2L ( void ) const

get ²_L contribution determined in recent unfolding

Definition at line 3175 of file TUnfoldV17.cxx.

const TMatrixDSparse* TUnfoldV17::GetDXDAM ( int i ) const [inline, protected]

matrix contributions of the derivative dx/dA

Definition at line 250 of file TUnfold.h.

Referenced by TUnfoldSysV17::PrepareSysError().

const TMatrixDSparse* TUnfoldV17::GetDXDAZ ( int i ) const [inline, protected]

vector contributions of the derivative dx/dA

Definition at line 252 of file TUnfold.h.

const TMatrixDSparse* TUnfoldV17::GetDXDtauSquared ( void ) const [inline, protected]

vector of derivative dx/dtauSquared, using internal bin counting

Definition at line 263 of file TUnfold.h.

Referenced by TUnfoldSysV17::PrepareSysError().

const TMatrixDSparse* TUnfoldV17::GetDXDY ( void ) const [inline, protected]

matrix of derivatives dx/dy

Definition at line 248 of file TUnfold.h.

const TMatrixDSparse* TUnfoldV17::GetE ( void ) const [inline, protected]

matrix E, using internal bin counting

Definition at line 256 of file TUnfold.h.

const TMatrixDSparse* TUnfoldV17::GetEinv ( void ) const [inline, protected]

matrix E^-1, using internal bin counting

Definition at line 254 of file TUnfold.h.

void TUnfoldV17::GetEmatrix	(	TH2 *	ematrix,
		const Int_t *	binMap = `0`
	)			const

get output covariance matrix, possibly cumulated over several bins

Parameters:

`[out]`	ematrix	histogram to store the covariance. The bin contents are overwritten.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 3390 of file TUnfoldV17.cxx.

Referenced by TUnfoldSysV17::GetEmatrixTotal(), and GetRhoIJ().

Double_t TUnfoldV17::GetEpsMatrix ( void ) const [inline]

get numerical accuracy for Eigenvalue analysis when inverting matrices with rank problems

Definition at line 338 of file TUnfold.h.

void TUnfoldV17::GetFoldedOutput	(	TH1 *	out,
		const Int_t *	binMap = `0`
	)			const

get unfolding result on detector level

Parameters:

`[out]`	out	histogram to store the correlation coefficiencts. The bin contents and errors are overwritten.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

This method returns the unfolding output folded by the response matrix, i.e. the vector Ax.

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 2929 of file TUnfoldV17.cxx.

void TUnfoldV17::GetInput	(	TH1 *	out,
		const Int_t *	binMap = `0`
	)			const

Input vector of measurements.

Parameters:

`[out]`	out	histogram to store the measurements. Bin content and bin errors are overwritte.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

Bins which had an uncertainty of zero in the call to SetInput() maye acquire bin contents or bin errors different from the original settings in SetInput().

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 3013 of file TUnfoldV17.cxx.

void TUnfoldV17::GetInputInverseEmatrix ( TH2 * out )

get inverse of the measurement's covariance matrix

Parameters:

[out] out histogram to store the inverted covariance

Definition at line 3042 of file TUnfoldV17.cxx.

Referenced by DoUnfold().

void TUnfoldV17::GetL ( TH2 * out ) const

get matrix of regularisation conditions

Parameters:

[out] out histogram to store the regularisation conditions. the bincontents are overwritten

The histogram should have dimension nr (x-axis) times nx (y-axis). nr corresponds to the number of regularisation conditions, it can be obtained using the method GetNr(). nx corresponds to the number of histogram bins in the response matrix along the truth axis.

Definition at line 3135 of file TUnfoldV17.cxx.

Double_t TUnfoldV17::GetLcurveX ( void ) const [virtual]

get value on x-axis of L-curve determined in recent unfolding

x=log₁₀(GetChi2A())

Definition at line 3195 of file TUnfoldV17.cxx.

Referenced by ScanLcurve().

Double_t TUnfoldV17::GetLcurveY ( void ) const [virtual]

get value on y-axis of L-curve determined in recent unfolding

y=log₁₀(GetChi2L())

Definition at line 3204 of file TUnfoldV17.cxx.

Referenced by ScanLcurve().

void TUnfoldV17::GetLsquared ( TH2 * out ) const

get matrix of regularisation conditions squared

Parameters:

[out] out histogram to store the squared matrix of regularisation conditions. the bin contents are overwritten

This returns the square matrix L^TL as a histogram

The histogram should have dimension nx times nx, where nx corresponds to the number of histogram bins in the response matrix along the truth axis.

Definition at line 3095 of file TUnfoldV17.cxx.

Int_t TUnfoldV17::GetNdf ( void ) const [inline]

get number of degrees of freedom determined in recent unfolding

This returns the number of valid measurements minus the number of unfolded truth bins. If the area constraint is active, one further degree of freedom is subtracted

Definition at line 325 of file TUnfold.h.

Referenced by ScanLcurve().

void TUnfoldV17::GetNormalisationVector	(	TH1 *	out,
		const Int_t *	binMap = `0`
	)			const

histogram of truth bins, determined from suming over the response matrix

Parameters:

`[out]`	out	histogram to store the truth bins. The bin contents are overwritten
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

This vector is also used to initialize the bias x₀. However, the bias vector may be changed using the SetBias() method.

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 2877 of file TUnfoldV17.cxx.

Int_t TUnfoldV17::GetNpar ( void ) const

get number of truth parameters determined in recent unfolding

empty bins of the response matrix or bins which can not be unfolded due to rank deficits are not counted

Definition at line 3186 of file TUnfoldV17.cxx.

Referenced by GetInputInverseEmatrix().

Int_t TUnfoldV17::GetNr ( void ) const

get number of regularisation conditions

Ths returns the number of regularisation conditions, useful for booking a histogram for a subsequent call of GetL().

Definition at line 3120 of file TUnfoldV17.cxx.

Referenced by GetL().

Int_t TUnfoldV17::GetNx ( void ) const [inline, protected]

returns internal number of output (truth) matrix rows

Definition at line 228 of file TUnfold.h.

Referenced by DoUnfold(), GetBias(), GetLsquared(), GetNormalisationVector(), GetNpar(), GetRhoI(), GetRhoIFromMatrix(), SetBias(), and TUnfoldV17().

Int_t TUnfoldV17::GetNy ( void ) const [inline, protected]

returns the number of measurement bins

Definition at line 236 of file TUnfold.h.

Referenced by TUnfoldSysV17::DoBackgroundSubtraction(), DoUnfold(), GetFoldedOutput(), GetInput(), GetInputInverseEmatrix(), TUnfoldSysV17::PrepareSysError(), and SetInput().

void TUnfoldV17::GetOutput	(	TH1 *	output,
		const Int_t *	binMap = `0`
	)			const

get output distribution, possibly cumulated over several bins

Parameters:

`[out]`	output	existing output histogram. content and errors will be updated.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

If nonzero, the array binMap must have dimension n+2, where n corresponds to the number of bins on the truth axis of the response matrix (the histogram specified with the TUnfold constructor). The indexes of binMap correspond to the truth bins (including underflow and overflow) of the response matrix. The element binMap[i] specifies the histogram number in output where the corresponding truth bin will be stored. It is possible to specify the same output bin number for multiple indexes, in which case these bins are added. Set binMap[i]=-1 to ignore an unfolded truth bin. The uncertainties are calculated from the corresponding parts of the covariance matrix, properly taking care of added truth bins.
If the pointer binMap is zero, the bins are mapped one-to-one. Truth bin zero (underflow) is stored in the output underflow, truth bin 1 is stored in bin number 1, etc.

Definition at line 3233 of file TUnfoldV17.cxx.

TString TUnfoldV17::GetOutputBinName ( Int_t iBinX ) const [protected, virtual]

Get bin name of an outpt bin.

Parameters:

[in] iBinX bin number

Return value: name of the bin
For TUnfold and TUnfoldSys, this function simply returns the bin number as a string. This function really only makes sense in the context of TUnfoldDensity, where binnig schemes are implemented using the class TUnfoldBinning, and non-trivial bin names are returned.

Reimplemented in TUnfoldDensityV17.

Definition at line 1664 of file TUnfoldV17.cxx.

Referenced by SetInput().

void TUnfoldV17::GetProbabilityMatrix	(	TH2 *	A,
		EHistMap	histmap
	)			const

get matrix of probabilities

Parameters:

`[out]`	A	two-dimensional histogram to store the probabilities (normalized response matrix). The bin contents are overwritten
`[in]`	histmap	specify axis along which the truth bins are oriented

Definition at line 2977 of file TUnfoldV17.cxx.

Referenced by TUnfoldDensityV17::GetProbabilityMatrix().

Double_t TUnfoldV17::GetRhoAvg ( void ) const [inline]

get average global correlation determined in recent unfolding

Definition at line 313 of file TUnfold.h.

Double_t TUnfoldV17::GetRhoI	(	TH1 *	rhoi,
		const Int_t *	binMap = `0`,
		TH2 *	invEmat = `0`
	)			const

get global correlation coefficiencts, possibly cumulated over several bins

Parameters:

`[out]`	rhoi	histogram to store the global correlation coefficients. The bin contents are overwritten.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins
`[out]`	invEmat	(default=0) histogram to store the inverted covariance matrix

for a given bin, the global correlation coefficient is defined as
_i=sqrt(1-1/(V_ii*V^-1_ii))
such that the calculation of global correlation coefficients possibly involves the inversion of a covariance matrix.

return value: maximum global correlation coefficient

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 3448 of file TUnfoldV17.cxx.

Double_t TUnfoldV17::GetRhoIFromMatrix	(	TH1 *	rhoi,
		const TMatrixDSparse *	eOrig,
		const Int_t *	binMap,
		TH2 *	invEmat
	)			const `[protected]`

Definition at line 3497 of file TUnfoldV17.cxx.

Referenced by GetRhoI(), and TUnfoldSysV17::GetRhoItotal().

void TUnfoldV17::GetRhoIJ	(	TH2 *	rhoij,
		const Int_t *	binMap = `0`
	)			const

get correlation coefficiencts, possibly cumulated over several bins

Parameters:

`[out]`	rhoij	histogram to store the correlation coefficiencts. The bin contents are overwritten.
`[in]`	binMap	(default=0) array for mapping truth bins to histogram bins

The use of binMap is explained with the documentation of the GetOutput() method

Definition at line 3405 of file TUnfoldV17.cxx.

Double_t TUnfoldV17::GetRhoMax ( void ) const [inline]

get maximum global correlation determined in recent unfolding

Definition at line 311 of file TUnfold.h.

Int_t TUnfoldV17::GetRowFromBin ( int ix ) const [inline, protected]

converts truth histogram bin number to matrix row

Definition at line 232 of file TUnfold.h.

Double_t TUnfoldV17::GetTau ( void ) const

return regularisation parameter

Definition at line 3167 of file TUnfoldV17.cxx.

const char * TUnfoldV17::GetTUnfoldVersion ( void ) [static]

return a string describing the TUnfold version

The version is reported in the form Vmajor.minor Changes of the minor version number typically correspond to bug-fixes. Changes of the major version may result in adding or removing data attributes, such that the streamer methods are not compatible between different major versions.

Definition at line 3661 of file TUnfoldV17.cxx.

const TMatrixDSparse* TUnfoldV17::GetVxx ( void ) const [inline, protected]

covariance matrix of the result

Definition at line 242 of file TUnfold.h.

Referenced by TUnfoldSysV17::GetSummedErrorMatrixXX().

const TMatrixDSparse* TUnfoldV17::GetVxxInv ( void ) const [inline, protected]

inverse of covariance matrix of the result

Definition at line 244 of file TUnfold.h.

const TMatrixDSparse* TUnfoldV17::GetVyyInv ( void ) const [inline, protected]

inverse of covariance matrix of the data y

Definition at line 258 of file TUnfold.h.

Referenced by TUnfoldSysV17::DoBackgroundSubtraction().

const TMatrixD* TUnfoldV17::GetX ( void ) const [inline, protected]

vector of the unfolding result

Definition at line 240 of file TUnfold.h.

void TUnfoldV17::InitTUnfold ( void ) [private]

initialize data menbers, for use in constructors

Definition at line 141 of file TUnfoldV17.cxx.

Referenced by TUnfoldV17().

TMatrixDSparse * TUnfoldV17::InvertMSparseSymmPos	(	const TMatrixDSparse *	A,
		Int_t *	rankPtr
	)			const `[protected]`

get the inverse or pseudo-inverse of a positive, sparse matrix

Parameters:

`[in]`	A	the sparse matrix to be inverted, has to be positive
	inout]	rankPtr if zero, suppress calculation of pseudo-inverse otherwise the rank of the matrix is returned in *rankPtr

return value: 0 or a new sparse matrix

if(rankPtr==0) return the inverse if it exists, or return 0
else return a (pseudo-)inverse and store the rank of the matrix in rankPtr

the matrix inversion is optimized in performance for the case where a large submatrix of A is diagonal

Definition at line 990 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), TUnfoldSysV17::GetChi2Sys(), GetInputInverseEmatrix(), and GetRhoIFromMatrix().

TMatrixDSparse * TUnfoldV17::MultiplyMSparseM	(	const TMatrixDSparse *	a,
		const TMatrixD *	b
	)			const `[protected]`

multiply sparse matrix and a non-sparse matrix

Parameters:

`[in]`	a	sparse matrix
`[in]`	b	matrix

returns a new sparse matrix a*b.
A replacement for: new TMatrixDSparse(a,TMatrixDSparse::kMult,b) the root implementation had problems in older versions of root

Definition at line 757 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), and TUnfoldSysV17::GetChi2Sys().

TMatrixDSparse * TUnfoldV17::MultiplyMSparseMSparse	(	const TMatrixDSparse *	a,
		const TMatrixDSparse *	b
	)			const `[protected]`

multiply two sparse matrices

Parameters:

`[in]`	a	sparse matrix
`[in]`	b	sparse matrix

returns a new sparse matrix a*b.
A replacement for: new TMatrixDSparse(a,TMatrixDSparse::kMult,b) the root implementation had problems in older versions of root

Definition at line 600 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), GetFoldedOutput(), TUnfoldSysV17::GetSummedErrorMatrixYY(), and TUnfoldSysV17::PrepareSysError().

TMatrixDSparse * TUnfoldV17::MultiplyMSparseMSparseTranspVector	(	const TMatrixDSparse *	m1,
		const TMatrixDSparse *	m2,
		const TMatrixTBase< Double_t > *	v
	)			const `[protected]`

calculate a sparse matrix product M1*V*M2^T where the diagonal matrix V is given by a vector

Parameters:

`[in]`	m1	pointer to sparse matrix with dimension I*K
`[in]`	m2	pointer to sparse matrix with dimension J*K
`[in]`	v	pointer to vector (matrix) with dimension K*1

returns a sparse matrix R with elements r_ij=_kM1_ikV_kM2_jk

Definition at line 817 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), TUnfoldSysV17::GetSummedErrorMatrixXX(), and TUnfoldSysV17::GetSummedErrorMatrixYY().

TMatrixDSparse * TUnfoldV17::MultiplyMSparseTranspMSparse	(	const TMatrixDSparse *	a,
		const TMatrixDSparse *	b
	)			const `[protected]`

multiply a transposed Sparse matrix with another Sparse matrix

Parameters:

`[in]`	a	sparse matrix (to be transposed)
`[in]`	b	sparse matrix

returns a new sparse matrix a^T*b
this is a replacement for the root constructors new TMatrixDSparse(TMatrixDSparse(TMatrixDSparse::kTransposed,*a),TMatrixDSparse::kMult,*b)

Definition at line 675 of file TUnfoldV17.cxx.

Referenced by DoUnfold(), GetLsquared(), and SetInput().

Int_t TUnfoldV17::RegularizeBins	(	int	start,
		int	step,
		int	nbin,
		ERegMode	regmode
	)

add regularisation conditions for a group of bins

Parameters:

`[in]`	start	first bin number
`[in]`	step	step size
`[in]`	nbin	number of bins
`[in]`	regmode	regularisation mode (one of: kRegModeSize, kRegModeDerivative, kRegModeCurvature)

add regularisation conditions for a group of equidistant bins. There are nbin bins, starting with bin start and with a distance of step between bins.

Return value: number of regularisation conditions which could not be added.
Conditions which are not added typically correspond to bin numbers where the truth can not be unfolded (either response matrix is empty or the data do not constrain).

Definition at line 2140 of file TUnfoldV17.cxx.

Referenced by RegularizeBins2D(), and TUnfoldV17().

Int_t TUnfoldV17::RegularizeBins2D	(	int	start_bin,
		int	step1,
		int	nbin1,
		int	step2,
		int	nbin2,
		ERegMode	regmode
	)

add regularisation conditions for 2d unfolding

Parameters:

`[in]`	start_bin	first bin number
`[in]`	step1	step size, 1st dimension
`[in]`	nbin1	number of bins, 1st dimension
`[in]`	step2	step size, 2nd dimension
`[in]`	nbin2	number of bins, 2nd dimension
`[in]`	regmode	regularisation mode (one of: kRegModeSize, kRegModeDerivative, kRegModeCurvature)

add regularisation conditions for a grid of bins. The start bin is start_bin. Along the first (second) dimension, there are nbin1 (nbin2) bins and adjacent bins are spaced by step1 (step2) units.

Return value: number of regularisation conditions which could not be added. Conditions which are not added typically correspond to bin numbers where the truth can not be unfolded (either response matrix is empty or the data do not constrain).

Definition at line 2201 of file TUnfoldV17.cxx.

Int_t TUnfoldV17::RegularizeCurvature	(	int	left_bin,
		int	center_bin,
		int	right_bin,
		Double_t	scale_left = `1.0`,
		Double_t	scale_right = `1.0`
	)

add a regularisation condition on the curvature of three truth bin

Parameters:

`[in]`	left_bin	bin number
`[in]`	center_bin	bin number
`[in]`	right_bin	bin number
`[in]`	scale_left	(default=1) scale factor
`[in]`	scale_right	(default=1) scale factor

this adds one row to L, where the element left_bin takes the value -scale_left, the element right_bin takes the value -scale_right and the element center_bin takes the value scale_left+scale_right

return value: 0 if ok, 1 if the condition has not been added. Conditions which are not added typically correspond to bin numbers where the truth can not be unfolded (either response matrix is empty or the data do not constrain).

The RegularizeXXX() methods can be used to set up a custom matrix of regularisation conditions. In this case, start with an empty matrix L (argument regmode=kRegModeNone in the constructor)

Definition at line 2095 of file TUnfoldV17.cxx.

Referenced by RegularizeBins().

Int_t TUnfoldV17::RegularizeDerivative	(	int	left_bin,
		int	right_bin,
		Double_t	scale = `1.0`
	)

add a regularisation condition on the difference of two truth bin

Parameters:

`[in]`	left_bin	bin number
`[in]`	right_bin	bin number
`[in]`	scale	(default=1) scale factor

this adds one row to L, where the element left_bin takes the value -scale and the element right_bin takes the value +scale

return value: 0 if ok, 1 if the condition has not been added. Conditions which are not added typically correspond to bin numbers where the truth can not be unfolded (either response matrix is empty or the data do not constrain).

The RegularizeXXX() methods can be used to set up a custom matrix of regularisation conditions. In this case, start with an empty matrix L (argument regmode=kRegModeNone in the constructor)

Definition at line 2056 of file TUnfoldV17.cxx.

Referenced by RegularizeBins().

Int_t TUnfoldV17::RegularizeSize	(	int	bin,
		Double_t	scale = `1.0`
	)

add a regularisation condition on the magnitude of a truth bin

Parameters:

`[in]`	bin	bin number
`[in]`	scale	(default=1) scale factor

this adds one row to L, where the element bin takes the value scale

return value: 0 if ok, 1 if the condition has not been added. Conditions which are not added typically correspond to bin numbers where the truth can not be unfolded (either response matrix is empty or the data do not constrain).

The RegularizeXXX() methods can be used to set up a custom matrix of regularisation conditions. In this case, start with an empty matrix L (argument regmode=kRegModeNone in the constructor)

Definition at line 2022 of file TUnfoldV17.cxx.

Referenced by RegularizeBins().

Int_t TUnfoldV17::ScanLcurve	(	Int_t	nPoint,
		Double_t	tauMin,
		Double_t	tauMax,
		TGraph **	lCurve,
		TSpline **	logTauX = `0`,
		TSpline **	logTauY = `0`,
		TSpline **	logTauCurvature = `0`
	)			`[virtual]`

scan the L curve, determine tau and unfold at the final value of tau

Parameters:

`[in]`	nPoint	number of points used for the scan
`[in]`	tauMin	smallest tau value to study
`[in]`	tauMax	largest tau value to study. If tauMin=tauMax=0, a scan interval is determined automatically.
`[out]`	lCurve	if nonzero, a new TGraph is returned, containing the L-curve
`[out]`	logTauX	if nonzero, a new TSpline is returned, to parameterize the L-curve's x-coordinates as a function of log10(tau)
`[out]`	logTauY	if nonzero, a new TSpline is returned, to parameterize the L-curve's y-coordinates as a function of log10(tau)
`[out]`	logTauCurvature	if nonzero, a new TSpline is returned of the L-curve curvature as a function of log10(tau)

return value: the coordinate number in the logTauX,logTauY graphs corresponding to the "final" choice of tau

Recommendation: always check logTauCurvature, it should be a peaked function (similar to a Gaussian), the maximum corresponding to the final choice of tau. Also, check the lCurve it should be approximately L-shaped. If in doubt, adjust tauMin and tauMax until the results are satisfactory.

Definition at line 2526 of file TUnfoldV17.cxx.

void TUnfoldV17::SetBias ( const TH1 * bias )

set bias vector

Parameters:

[in] bias histogram with new bias vector

the initial bias vector is determined from the response matrix but may be changed by using this method

Definition at line 1892 of file TUnfoldV17.cxx.

void TUnfoldV17::SetConstraint ( EConstraint constraint )

set type of area constraint

results of a previous unfolding are reset

Definition at line 3155 of file TUnfoldV17.cxx.

Referenced by TUnfoldV17().

void TUnfoldV17::SetEpsMatrix ( Double_t eps )

set numerical accuracy for Eigenvalue analysis when inverting matrices with rank problems

Definition at line 3647 of file TUnfoldV17.cxx.

Int_t TUnfoldV17::SetInput	(	const TH1 *	input,
		Double_t	scaleBias = `0.0`,
		Double_t	oneOverZeroError = `0.0`,
		const TH2 *	hist_vyy = `0`,
		const TH2 *	hist_vyy_inv = `0`
	)			`[virtual]`

Define input data for subsequent calls to DoUnfold(tau).

Parameters:

`[in]`	input	input distribution with uncertainties
`[in]`	scaleBias	(default=0) scale factor applied to the bias
`[in]`	oneOverZeroError	(default=0) for bins with zero error, this number defines 1/error.
`[in]`	hist_vyy	(default=0) if non-zero, this defines the data covariance matrix
`[in]`	hist_vyy_inv	(default=0) if non-zero and hist_vyy is set, defines the inverse of the data covariance matrix. This feature can be useful for repeated unfoldings in cases where the inversion of the input covariance matrix is lengthy

Return value: nError1+10000*nError2

nError1: number of bins where the uncertainty is zero. these bins either are not used for the unfolding (if oneOverZeroError==0) or 1/uncertainty is set to oneOverZeroError.
nError2: return values>10000 are fatal errors, because the unfolding can not be done. The number nError2 corresponds to the number of truth bins which are not constrained by data points.

Reimplemented in TUnfoldSysV17.

Definition at line 2271 of file TUnfoldV17.cxx.

Referenced by DoUnfold().