class TSpectrum3: public TNamed

THIS CLASS CONTAINS ADVANCED SPECTRA PROCESSING FUNCTIONS.

THREE-DIMENSIONAL BACKGROUND ESTIMATION FUNCTIONS
THREE-DIMENSIONAL SMOOTHING FUNCTIONS
THREE-DIMENSIONAL DECONVOLUTION FUNCTIONS
THREE-DIMENSIONAL PEAK SEARCH FUNCTIONS

These functions were written by:
Miroslav Morhac
Institute of Physics
Slovak Academy of Sciences
Dubravska cesta 9, 842 28 BRATISLAVA
SLOVAKIA

email:fyzimiro@savba.sk,    fax:+421 7 54772479

The original code in C has been repackaged as a C++ class by R.Brun

The algorithms in this class have been published in the following
references:
[1]  M.Morhac et al.: Background elimination methods for
multidimensional coincidence gamma-ray spectra. Nuclear
Instruments and Methods in Physics Research A 401 (1997) 113-
132.

[2]  M.Morhac et al.: Efficient one- and two-dimensional Gold
deconvolution and its application to gamma-ray spectra
decomposition. Nuclear Instruments and Methods in Physics
Research A 401 (1997) 385-408.

[3] M. Morhac et al.: Efficient algorithm of multidimensional
deconvolution and its application to nuclear data processing. Digital
Signal Processing, Vol. 13, No. 1, (2003), 144-171.

[4]  M.Morhac et al.: Identification of peaks in multidimensional
coincidence gamma-ray spectra. Nuclear Instruments and Methods in
Research Physics A  443(2000), 108-125.

These NIM papers are also available as Postscript files from:


   ftp://root.cern.ch/root/SpectrumDec.ps.gz
   ftp://root.cern.ch/root/SpectrumSrc.ps.gz
   ftp://root.cern.ch/root/SpectrumBck.ps.gz



Function Members (Methods)

public:
TSpectrum3()
TSpectrum3(const TSpectrum3&)
TSpectrum3(Int_t maxpositions, Float_t resolution = 1)
virtual~TSpectrum3()
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual const char*Background(const TH1* hist, int niter, Option_t* option = "goff")
const char*Background(float*** spectrum, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t numberIterationsX, Int_t numberIterationsY, Int_t numberIterationsZ, Int_t direction, Int_t filterType)
virtual voidTObject::Browse(TBrowser* b)
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTNamed::Clear(Option_t* option = "")
virtual TObject*TNamed::Clone(const char* newname = "") const
virtual Int_tTNamed::Compare(const TObject* obj) const
virtual voidTNamed::Copy(TObject& named) const
const char*Deconvolution(float*** source, const float*** resp, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t numberIterations, Int_t numberRepetitions, Double_t boost)
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual voidTNamed::FillBuffer(char*& buffer)
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
TH1*GetHistogram() const
virtual const char*TObject::GetIconName() const
virtual const char*TNamed::GetName() const
Int_tGetNPeaks() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
Float_t*GetPositionX() const
Float_t*GetPositionY() const
Float_t*GetPositionZ() const
virtual const char*TNamed::GetTitle() const
virtual UInt_tTObject::GetUniqueID() const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTNamed::Hash() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTNamed::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTNamed::ls(Option_t* option = "") const
voidTObject::MayNotUse(const char* method) const
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
TSpectrum3&operator=(const TSpectrum3&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Pop()
virtual voidPrint(Option_t* option = "") const
virtual Int_tTObject::Read(const char* name)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(basic_ostream<char,char_traits<char> >& out, Option_t* option = "")
virtual Int_tSearch(const TH1* hist, Double_t sigma = 2, Option_t* option = "goff", Double_t threshold = 0.05)
Int_tSearchFast(const float*** source, float*** dest, Int_t ssizex, Int_t ssizey, Int_t ssizez, Double_t sigma, Double_t threshold, Bool_t markov, Int_t averWindow)
Int_tSearchHighRes(const float*** source, float*** dest, Int_t ssizex, Int_t ssizey, Int_t ssizez, Double_t sigma, Double_t threshold, Bool_t backgroundRemove, Int_t deconIterations, Bool_t markov, Int_t averWindow)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
virtual voidTNamed::SetName(const char* name)MENU
virtual voidTNamed::SetNameTitle(const char* name, const char* title)
static voidTObject::SetObjectStat(Bool_t stat)
voidSetResolution(Float_t resolution = 1)
virtual voidTNamed::SetTitle(const char* title = "")MENU
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual Int_tTNamed::Sizeof() const
const char*SmoothMarkov(float*** source, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t averWindow)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
protected:
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTObject::MakeZombie()

Data Members

public:
enum { kBackIncreasingWindow
kBackDecreasingWindow
kBackSuccessiveFiltering
kBackOneStepFiltering
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
protected:
TH1*fHistogramresulting histogram
Int_tfMaxPeaksMaximum number of peaks to be found
Int_tfNPeaksnumber of peaks found
TStringTNamed::fNameobject identifier
Float_t*fPosition[fNPeaks] array of current peak positions
Float_t*fPositionX[fNPeaks] X positions of peaks
Float_t*fPositionY[fNPeaks] Y positions of peaks
Float_t*fPositionZ[fNPeaks] Z positions of peaks
Float_tfResolutionresolution of the neighboring peaks
TStringTNamed::fTitleobject title

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

TSpectrum3(const TSpectrum3& )
 Constructor.
TSpectrum3(Int_t maxpositions, Float_t resolution = 1)
  maxpositions:  maximum number of peaks
  resolution:    determines resolution of the neighboring peaks
                 default value is 1 correspond to 3 sigma distance
                 between peaks. Higher values allow higher resolution
                 (smaller distance between peaks.
                 May be set later through SetResolution.
~TSpectrum3()
 Destructor.
const char * Background(const TH1* hist, int niter, Option_t* option = "goff")
ONE-DIMENSIONAL BACKGROUND ESTIMATION FUNCTION
This function calculates background spectrum from source in h.
The result is placed in the vector pointed by spectrum pointer.

Function parameters:
spectrum:  pointer to the vector of source spectrum
size:      length of spectrum and working space vectors
number_of_iterations, for details we refer to manual


void Print(Option_t* option = "") const
 Print the array of positions
Int_t Search(const TH1* hist, Double_t sigma = 2, Option_t* option = "goff", Double_t threshold = 0.05)
ONE-DIMENSIONAL PEAK SEARCH FUNCTION
This function searches for peaks in source spectrum in hin
The number of found peaks and their positions are written into
the members fNpeaks and fPositionX.

Function parameters:
hin:       pointer to the histogram of source spectrum
sigma:   sigma of searched peaks, for details we refer to manual
Note that sigma is in number of bins
threshold: (default=0.05)  peaks with amplitude less than
threshold*highest_peak are discarded.

if option is not equal to "goff" (goff is the default), then
a polymarker object is created and added to the list of functions of
the histogram. The histogram is drawn with the specified option and
the polymarker object drawn on top of the histogram.
The polymarker coordinates correspond to the npeaks peaks found in
the histogram.
A pointer to the polymarker object can be retrieved later via:
TList *functions = hin->GetListOfFunctions();
TPolyMarker *pm = (TPolyMarker*)functions->FindObject("TPolyMarker")


void SetResolution(Float_t resolution = 1)
  resolution: determines resolution of the neighboring peaks
              default value is 1 correspond to 3 sigma distance
              between peaks. Higher values allow higher resolution
              (smaller distance between peaks.
              May be set later through SetResolution.
const char * Background(float*** spectrum, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t numberIterationsX, Int_t numberIterationsY, Int_t numberIterationsZ, Int_t direction, Int_t filterType)
THREE-DIMENSIONAL BACKGROUND ESTIMATION FUNCTIONS
This function calculates background spectrum from source spectrum.
The result is placed to the array pointed by spectrum pointer.

Function parameters:
spectrum-pointer to the array of source spectrum
ssizex-x length of spectrum
ssizey-y length of spectrum
ssizez-z length of spectrum
numberIterationsX-maximal x width of clipping window
numberIterationsY-maximal y width of clipping window
numberIterationsZ-maximal z width of clipping window
for details we refer to manual
direction- direction of change of clipping window
- possible values=kBackIncreasingWindow
kBackDecreasingWindow
filterType-determines the algorithm of the filtering
-possible values=kBackSuccessiveFiltering
kBackOneStepFiltering




Background estimation

 

Goal: Separation of useful information (peaks) from useless information (background)

         method is based on Sensitive Nonlinear Iterative Peak (SNIP) clipping algorithm [1]

         there exist two algorithms for the estimation of new value in the channel “

 

Algorithm based on Successive Comparisons

It is an extension of one-dimensional SNIP algorithm to another dimension. For details we refer to [2].

 

Algorithm based on One Step Filtering

The algorithm is analogous to that for 2-dimensional data. For details we refer to TSpectrum2. New value in the estimated channel is calculated as

 

 

where p = 1, 2, …, number_of_iterations.

 

Function:

const char* TSpectrum3::Background (float ***fSpectrum, int fSizex, int fSizey, int fSizez, int fNumberIterationsX, int fNumberIterationsY, int fNumberIterationsZ,  int fDirection, int fFilterType) 

 

This function calculates background spectrum from the source spectrum.  The result is placed in the matrix pointed by fSpectrum pointer.  One can also switch the direction of the change of the clipping window and to select one of the two above given algorithms. On successful completion it returns 0. On error it returns pointer to the string describing error.

 

Parameters:

        fSpectrum-pointer to the matrix of source spectrum                 

        fSizex, fSizey, fSizez -lengths of the spectrum matrix                                

        fNumberIterationsX, fNumberIterationsY, fNumberIterationsZ maximal

        widths of clipping window,                                

        fDirection- direction of change of clipping window                 

               - possible values=kBackIncreasingWindow                     

                                            kBackDecreasingWindow                     

        fFilterType-type of the clipping algorithm,                              

                  -possible values=kBack SuccessiveFiltering

                                              kBackOneStepFiltering                             

 

References:

[1]  C. G Ryan et al.: SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. NIM, B34 (1988), 396-402.

[2]  M. Morháč, J. Kliman, V. Matoušek, M. Veselský, I. Turzo.: Background elimination methods for multidimensional gamma-ray spectra. NIM, A401 (1997) 113-132.

 

Example 1– script Back3.c :

 

Fig. 1 Original three-dimensional gamma-gamma-gamma-ray spectrum

Fig. 2 Background estimated from data from Fig. 1 using decreasing clipping window with widths 5, 5, 5 and algorithm based on successive comparisons. The estimate includes not only continuously changing background but also one- and two-dimensional ridges.

 

Fig. 3 Resulting peaks after subtraction of the estimated background (Fig. 2) from original three-dimensional gamma-gamma-gamma-ray spectrum (Fig. 1).

 

 

Script:

// Example to illustrate the background estimator (class TSpectrum3).

// To execute this example, do

// root > .x Back3.C

 

void Back3() {

   Int_t i, j, k;

   Int_t nbinsx = 64;

   Int_t nbinsy = 64;

   Int_t nbinsz = 64;  

   Int_t xmin  = 0;

   Int_t xmax  = nbinsx;

   Int_t ymin  = 0;

   Int_t ymax  = nbinsy;  

   Int_t zmin  = 0;

   Int_t zmax  = nbinsz;     

   float *** source = new float **[nbinsx];

   float *** dest = new float **[nbinsx];     

   for(i=0;i<nbinsx;i++){

      source[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         source[i][j]=new float [nbinsz];

   }          

   for(i=0;i<nbinsx;i++){

      dest[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         dest[i][j]=new float [nbinsz];

   }              

   TH3F *back = new TH3F("back","Background estimation",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);

   TFile *f = new TFile("TSpectrum3.root");

   back=(TH3F*) f->Get("back;1");

   TCanvas *Background = new TCanvas("Background","Estimation of background with decreasing window",10,10,1000,700);

   TSpectrum3 *s = new TSpectrum3();

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

                  for (k = 0; k < nbinsz; k++){

                       source[i][j][k] = back->GetBinContent(i + 1,j + 1,k + 1);

                       dest[i][j][k] = back->GetBinContent(i + 1,j + 1,k + 1);                     

                    }

                 }

   }

   s->Background(dest,nbinsx,nbinsy,nbinsz,5,5,5,s->kBackDecreasingWindow,s->kBackSuccessiveFiltering);

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           back->SetBinContent(i + 1,j + 1,k + 1, dest[i][j][k]);

        }   

     }

   }

 

   FILE *out;

   char PATH[80];  

   strcpy(PATH,"spectra3\\back_output_5ds.spe");  

   out=fopen(PATH,"wb");

   for(i=0;i<nbinsx;i++){

      for(j=0;j<nbinsy;j++){                  

         fwrite(dest[i][j], sizeof(dest[0][0][0]),nbinsz,out);

      }

   }  

   fclose(out);  

  

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           source[i][j][k] = source[i][j][k] - dest[i][j][k];

        }   

     }

   }

  

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           back->SetBinContent(i + 1,j + 1,k + 1, source[i][j][k]);

        }   

     }

   }  

  

   strcpy(PATH,"spectra3\\back_peaks_5ds.spe");  

   out=fopen(PATH,"wb");

   for(i=0;i<nbinsx;i++){

      for(j=0;j<nbinsy;j++){                  

         fwrite(source[i][j], sizeof(source[0][0][0]),nbinsz,out);

      }

   }  

   fclose(out);     

  

   back->Draw(""); 

}

 

 

const char* SmoothMarkov(float*** source, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t averWindow)
THREE-DIMENSIONAL MARKOV SPECTRUM SMOOTHING FUNCTION

This function calculates smoothed spectrum from source spectrum
based on Markov chain method.
The result is placed in the array pointed by spectrum pointer.

Function parameters:
source-pointer to the array of source spectrum
working_space-pointer to the working array
ssizex-x length of spectrum and working space arrays
ssizey-y length of spectrum and working space arrays
ssizey-z length of spectrum and working space arrays
averWindow-width of averaging smoothing window



Smoothing

 

Goal: Suppression of statistical fluctuations

         the algorithm is based on discrete Markov chain, which has very simple invariant distribution

 

                 

          being defined from the normalization condition

         n is the length of the smoothed spectrum and

 

 

 


is the probability of the change of the peak position from channel i to the channel i+1.  is the normalization constant so that  and m is a width of smoothing window. We have extended this algorithm to three dimensions.

 

Function:

const char* TSpectrum3::SmoothMarkov(float ***fSpectrum, int fSizex, int fSizey, int fSizey,  int fAverWindow) 

 

This function calculates smoothed spectrum from the source spectrum based on Markov chain method. The result is placed in the field pointed by source pointer. On successful completion it returns 0. On error it returns pointer to the string describing error.

 

Parameters:

        fSpectrum-pointer to the matrix of source spectrum                 

        fSizex, fSizey, fSizez -lengths of the spectrum matrix                                 

        fAverWindow-width of averaging smoothing window

 

Reference:

[1] Z.K. Silagadze, A new algorithm for automatic photopeak searches. NIM A 376 (1996), 451. 

 

Example 1 – script SmootMarkov3.c :

Fig. 1 Original noisy spectrum.   

Fig. 2 Smoothed spectrum with averaging window m=3.

 

Script:

// Example to illustrate the Markov smoothing (class TSpectrum3).

// To execute this example, do

// root > .x SmoothMarkov3.C

 

void SmoothMarkov3() {

   Int_t i, j, k;

   Int_t nbinsx = 64;

   Int_t nbinsy = 64;

   Int_t nbinsz = 64;  

   Int_t xmin  = 0;

   Int_t xmax  = nbinsx;

   Int_t ymin  = 0;

   Int_t ymax  = nbinsy;  

   Int_t zmin  = 0;

   Int_t zmax  = nbinsz;     

   float *** source = new float **[nbinsx];

   for(i=0;i<nbinsx;i++){

      source[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         source[i][j]=new float [nbinsz];

   }          

   TH3F *sm = new TH3F("Smoothing","Markov smoothing",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);

   TFile *f = new TFile("TSpectrum3.root");

   sm=(TH3F*) f->Get("back;1");

   TCanvas *Background = new TCanvas("Smoothing","Markov smoothing",10,10,1000,700);

   TSpectrum3 *s = new TSpectrum3();

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

                  for (k = 0; k < nbinsz; k++){

                       source[i][j][k] = sm->GetBinContent(i + 1,j + 1,k + 1);

                    }

                 }

   }

   s->SmoothMarkov(source,nbinsx,nbinsy,nbinsz,3);

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           sm->SetBinContent(i + 1,j + 1,k + 1, source[i][j][k]);

        }   

     }

   }

   sm->Draw(""); 

}

const char * Deconvolution(float*** source, const float*** resp, Int_t ssizex, Int_t ssizey, Int_t ssizez, Int_t numberIterations, Int_t numberRepetitions, Double_t boost)
THREE-DIMENSIONAL DECONVOLUTION FUNCTION
This function calculates deconvolution from source spectrum
according to response spectrum
The result is placed in the cube pointed by source pointer.

Function parameters:
source-pointer to the cube of source spectrum
resp-pointer to the cube of response spectrum
ssizex-x length of source and response spectra
ssizey-y length of source and response spectra
ssizey-y length of source and response spectra
numberIterations, for details we refer to manual
numberRepetitions, for details we refer to manual
boost, boosting factor, for details we refer to manual



Deconvolution

 

Goal: Improvement of the resolution in spectra, decomposition of multiplets

 

Mathematical formulation of the 3-dimensional convolution system is

 

 

 

 


where h(i,j,k) is the impulse response function, x, y are input and output fields, respectively, , are the lengths of x and h fields

         let us assume that we know the response and the output fields (spectra) of the above given system.

         the deconvolution represents solution of the overdetermined system of linear equations, i.e.,  the calculation of the field x.

         from numerical stability point of view the operation of deconvolution is extremely critical (ill-posed  problem) as well as time consuming operation.

         the Gold deconvolution algorithm proves to work very well even for 2-dimensional systems. Generalization of the algorithm for 2-dimensional systems was presented in [1], and for multidimensional systems in [2].

         for Gold deconvolution algorithm as well as for boosted deconvolution algorithm we refer also to TSpectrum and TSpectrum2

 

Function:

const char* TSpectrum3::Deconvolution(float ***fSource, const float ***fResp, int fSizex, int fSizey, int fSizez, int fNumberIterations, int fNumberRepetitions, double fBoost)

 

This function calculates deconvolution from source spectrum according to response spectrum using Gold deconvolution algorithm. The result is placed in the field pointed by source pointer. On successful completion it returns 0. On error it returns pointer to the string describing error. If desired after every fNumberIterations one can apply boosting operation (exponential function with exponent given by fBoost coefficient) and repeat it fNumberRepetitions times.

 

Parameters:

        fSource-pointer to the matrix of source spectrum                 

        fResp-pointer to the matrix of response spectrum                 

        fSizex, fSizey, fSizez -lengths of the spectrum matrix                                

        fNumberIterations-number of iterations

        fNumberRepetitions-number of repetitions for boosted deconvolution. It must be

        greater or equal to one.

        fBoost-boosting coefficient, applies only if fNumberRepetitions is greater than one. 

        Recommended range <1,2>.

 

References:

 [1] M. Morháč, J. Kliman, V. Matoušek, M. Veselský, I. Turzo.: Efficient one- and two-dimensional Gold deconvolution and its application to gamma-ray spectra decomposition. NIM, A401 (1997) 385-408.

[2] Morháč M., Matoušek V., Kliman J., Efficient algorithm of multidimensional deconvolution and its application to nuclear data processing, Digital Signal Processing 13 (2003) 144.

 

Example 1 – script Decon.c :

         response function (usually peak) should be shifted to the beginning of the coordinate system (see Fig. 1)

Fig. 1 Three-dimensional response spectrum

 

 

Fig. 2 Three-dimensional input spectrum (before deconvolution)

 

Fig. 3 Spectrum from Fig. 2 after deconvolution (100 iterations)

 

Script:

// Example to illustrate the Gold deconvolution (class TSpectrum3).

// To execute this example, do

// root > .x Decon3.C

 

#include <TSpectrum3>

 

void Decon3() {

   Int_t i, j, k;

   Int_t nbinsx = 32;

   Int_t nbinsy = 32;

   Int_t nbinsz = 32;  

   Int_t xmin  = 0;

   Int_t xmax  = nbinsx;

   Int_t ymin  = 0;

   Int_t ymax  = nbinsy;  

   Int_t zmin  = 0;

   Int_t zmax  = nbinsz;     

   float *** source = new float **[nbinsx];

   float *** resp = new float **[nbinsx];     

   for(i=0;i<nbinsx;i++){

      source[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         source[i][j]=new float [nbinsz];

   }          

   for(i=0;i<nbinsx;i++){

      resp[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         resp[i][j]=new float [nbinsz];

   }              

   TH3F *decon_in = new TH3F("decon_in","Deconvolution",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);

   TH3F *decon_resp = new TH3F("decon_resp","Deconvolution",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);  

   TFile *f = new TFile("TSpectrum3.root");

   decon_in=(TH3F*) f->Get("decon_in;1");

   decon_resp=(TH3F*) f->Get("decon_resp;1");  

   TCanvas *Deconvolution = new TCanvas("Deconvolution","Deconvolution of 3-dimensional spectra",10,10,1000,700);

   TSpectrum3 *s = new TSpectrum3();

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

                  for (k = 0; k < nbinsz; k++){

                       source[i][j][k] = decon_in->GetBinContent(i + 1,j + 1,k + 1);

                       resp[i][j][k] = decon_resp->GetBinContent(i + 1,j + 1,k + 1);                        

                    }

                 }

   }

   s->Deconvolution(source,resp,nbinsx,nbinsy,nbinsz,100,1,1);

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           decon_in->SetBinContent(i + 1,j + 1,k + 1, source[i][j][k]);

        }   

     }

   }

   decon_in->Draw(""); 

}

 

Example 2 – script Decon_hr.c :

This example illustrates repeated Gold deconvolution with boosting. After every 10 iterations we apply power function with exponent = 2 to the spectrum given in Fig. 2.

 

Fig. 4 Spectrum from Fig. 2 after boosted deconvolution (10 iterations repeated 10 times). It decomposes completely cluster of peaks from Fig 2.

 

Script:

// Example to illustrate the Gold deconvolution (class TSpectrum3).

// To execute this example, do

// root > .x Decon3_hr.C

void Decon3_hr() {

   Int_t i, j, k;

   Int_t nbinsx = 32;

   Int_t nbinsy = 32;

   Int_t nbinsz = 32;  

   Int_t xmin  = 0;

   Int_t xmax  = nbinsx;

   Int_t ymin  = 0;

   Int_t ymax  = nbinsy;  

   Int_t zmin  = 0;

   Int_t zmax  = nbinsz;     

   float *** source = new float **[nbinsx];

   float *** resp = new float **[nbinsx];     

   for(i=0;i<nbinsx;i++){

      source[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         source[i][j]=new float [nbinsz];

   }          

   for(i=0;i<nbinsx;i++){

      resp[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         resp[i][j]=new float [nbinsz];

   }              

   TH3F *decon_in = new TH3F("decon_in","Deconvolution",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);

   TH3F *decon_resp = new TH3F("decon_resp","Deconvolution",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);  

   TFile *f = new TFile("TSpectrum3.root");

   decon_in=(TH3F*) f->Get("decon_in;1");

   decon_resp=(TH3F*) f->Get("decon_resp;1");  

   TCanvas *Deconvolution = new TCanvas("Deconvolution","High resolution deconvolution of 3-dimensional spectra",10,10,1000,700);

   TSpectrum3 *s = new TSpectrum3();

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

                  for (k = 0; k < nbinsz; k++){

                       source[i][j][k] = decon_in->GetBinContent(i + 1,j + 1,k + 1);

                       resp[i][j][k] = decon_resp->GetBinContent(i + 1,j + 1,k + 1);                        

                    }

                 }

   }

   s->Deconvolution(source,resp,nbinsx,nbinsy,nbinsz,10,10,2);

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           decon_in->SetBinContent(i + 1,j + 1,k + 1, source[i][j][k]);

        }   

     }

   }

   decon_in->Draw(""); 

}

 

 

Int_t SearchHighRes(const float*** source, float*** dest, Int_t ssizex, Int_t ssizey, Int_t ssizez, Double_t sigma, Double_t threshold, Bool_t backgroundRemove, Int_t deconIterations, Bool_t markov, Int_t averWindow)
THREE-DIMENSIONAL HIGH-RESOLUTION PEAK SEARCH FUNCTION
This function searches for peaks in source spectrum
It is based on deconvolution method. First the background is
removed (if desired), then Markov spectrum is calculated
(if desired), then the response function is generated
according to given sigma and deconvolution is carried out.
It returns number of found peaks.

Function parameters:
source-pointer to the matrix of source spectrum
dest-pointer to the matrix of resulting deconvolved spectrum
ssizex-x length of source spectrum
ssizey-y length of source spectrum
ssizez-z length of source spectrum
sigma-sigma of searched peaks, for details we refer to manual
threshold-threshold value in % for selected peaks, peaks with
amplitude less than threshold*highest_peak/100
are ignored, see manual
backgroundRemove-logical variable, set if the removal of
background before deconvolution is desired
deconIterations-number of iterations in deconvolution operation
markov-logical variable, if it is true, first the source spectrum
is replaced by new spectrum calculated using Markov
chains method.
averWindow-averanging window of searched peaks, for details
we refer to manual (applies only for Markov method)



Peaks searching

 

Goal: to identify automatically the peaks in spectrum with the presence of the continuous background, one- and two-fold coincidences (ridges) and statistical fluctuations - noise.

 

The common problems connected with correct peak identification in three-dimensional coincidence spectra are

  • non-sensitivity to noise, i.e., only statistically relevant peaks should be identified
  • non-sensitivity of the algorithm to continuous background
  • non-sensitivity to one-fold coincidences (coincidences peak – peak – background in all dimensions) and their crossings
  • non-sensitivity to two-fold coincidences (coincidences peak – background – background in all dimensions) and their crossings
  • ability to identify peaks close to the edges of the spectrum region
  • resolution, decomposition of doublets and multiplets. The algorithm should be able to recognize close positioned peaks.

 

Function:

Int_t TSpectrum3::SearchHighRes (const float ***fSource,float ***fDest, int fSizex, int fSizey, int fSizez, float fSigma, double fThreshold, bool fBackgroundRemove,int fDeconIterations, bool fMarkov, int fAverWindow)   

 

This function searches for peaks in source spectrum. It is based on deconvolution method. First the background is removed (if desired), then Markov smoothed spectrum is calculated (if desired), then the response function is generated according to given sigma and deconvolution is carried out. On success it returns number of found peaks.

 

Parameters:

        fSource-pointer to the matrix of source spectrum                 

        fDest-resulting spectrum after deconvolution

        fSizex, fSizey, fSizez -lengths of the source and destination spectra               

        fSigma-sigma of searched peaks

fThreshold- threshold value in % for selected peaks, peaks with amplitude less than threshold*highest_peak/100 are ignored

fBackgroundRemove- background_remove-logical variable, true if the removal of background before deconvolution is desired 

fDeconIterations-number of iterations in deconvolution operation

fMarkov-logical variable, if it is true, first the source spectrum is replaced by new spectrum calculated using Markov chains method

fAverWindow-width of averaging smoothing window

 

References:

[1] M.A. Mariscotti: A method for identification of peaks in the presence of background and its application to spectrum analysis. NIM 50 (1967), 309-320.

[2]  M. Morháč, J. Kliman, V. Matoušek, M. Veselský, I. Turzo.:Identification of peaks in multidimensional coincidence gamma-ray spectra. NIM, A443 (2000) 108-125.

[3] Z.K. Silagadze, A new algorithm for automatic photopeak searches. NIM A 376 (1996), 451.

 

Example of peak searching method

 

SearchHighRes function provides users with the possibility to vary the input parameters and with the access to the output deconvolved data in the destination spectrum. Based on the output data one can tune the parameters.

Example 1 – script Search3.c:

 

Fig. 1 Three-dimensional spectrum with 5 peaks (, threshold=5%, 3 iterations steps in the deconvolution)

 

Fig. 2 Spectrum from Fig. 1 after background elimination and deconvolution

 

Script:

// Example to illustrate high resolution peak searching function (class TSpectrum3).

// To execute this example, do

// root > .x Search3.C

void Search3() {

   Int_t i, j, k, nfound;

   Int_t nbinsx = 32;

   Int_t nbinsy = 32;

   Int_t nbinsz = 32;  

   Int_t xmin  = 0;

   Int_t xmax  = nbinsx;

   Int_t ymin  = 0;

   Int_t ymax  = nbinsy;  

   Int_t zmin  = 0;

   Int_t zmax  = nbinsz;     

   float *** source = new float **[nbinsx];

   float *** dest = new float **[nbinsx];     

   for(i=0;i<nbinsx;i++){

      source[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         source[i][j]=new float [nbinsz];

   }          

   for(i=0;i<nbinsx;i++){

      dest[i]=new float* [nbinsy];

      for(j=0;j<nbinsy;j++)

         dest[i][j]=new float [nbinsz];

   }              

   TH3F *search = new TH3F("Search","Peak searching",nbinsx,xmin,xmax,nbinsy,ymin,ymax,nbinsz,zmin,zmax);

   TFile *f = new TFile("TSpectrum3.root");

   search=(TH3F*) f->Get("search2;1");  

   TCanvas *Search = new TCanvas("Search","Peak searching",10,10,1000,700);

   TSpectrum3 *s = new TSpectrum3();

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

                  for (k = 0; k < nbinsz; k++){

                       source[i][j][k] = search->GetBinContent(i + 1,j + 1,k + 1);

                    }

                 }

   }

   nfound = s->SearchHighRes(source, dest, nbinsx, nbinsy, nbinsz, 2, 5, kTRUE, 3, kFALSE, 3);  

   printf("Found %d candidate peaks\n",nfound);  

   for (i = 0; i < nbinsx; i++){

     for (j = 0; j < nbinsy; j++){

        for (k = 0; k < nbinsz; k++){

           search->SetBinContent(i + 1,j + 1,k + 1, dest[i][j][k]);

        }   

     }

   }

   Float_t *PosX = new Float_t[nfound];        

   Float_t *PosY = new Float_t[nfound];

   Float_t *PosZ = new Float_t[nfound];     

   PosX = s->GetPositionX();

   PosY = s->GetPositionY();        

   PosZ = s->GetPositionZ();           

   for(i=0;i<nfound;i++)

                    printf("posx= %d, posy= %d, posz= %d\n",(int)(PosX[i]+0.5), (int)(PosY[i]+0.5), (int)(PosZ[i]+0.5));          

   search->Draw(""); 

}

Int_t SearchFast(const float*** source, float*** dest, Int_t ssizex, Int_t ssizey, Int_t ssizez, Double_t sigma, Double_t threshold, Bool_t markov, Int_t averWindow)
TSpectrum3(const TSpectrum3& )
TH1 * GetHistogram()
{return fHistogram;}
Int_t GetNPeaks()
{return fNPeaks;}
Float_t * GetPositionX()
{return fPositionX;}
Float_t * GetPositionY()
{return fPositionY;}
Float_t * GetPositionZ()
{return fPositionZ;}

Author: Miroslav Morhac 25/09/2006
Last change: root/spectrum:$Id: TSpectrum3.h 20882 2007-11-19 11:31:26Z rdm $
Last generated: 2008-06-25 08:53
Copyright (C) 1995-2006, Rene Brun and Fons Rademakers. *

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