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FitAwmi.C File Reference
Tutorials » Legacy tutorials » Spectrum tutorials

Detailed Description

This macro fits the source spectrum using the AWMI algorithm from the "TSpectrumFit" class ("TSpectrum" class is used to find peaks).

To try this macro, in a ROOT (5 or 6) prompt, do:

root > .x FitAwmi.C

or:

root > .x FitAwmi.C++
root > FitAwmi(); // re-run with another random set of peaks
created -9.76 34.9074 7
created -9.28 14.9603 3
created -8.8 14.9603 3
created -8.32 29.9207 6
created -7.84 4.98678 1
created -7.36 9.97356 2
created -6.88 14.9603 3
created -6.4 14.9603 3
created -5.92 9.97356 2
created -5.44 49.8678 10
created -4.96 49.8678 10
created -4.48 14.9603 3
created -4 14.9603 3
created -3.52 34.9074 7
created -3.04 39.8942 8
created -2.56 44.881 9
created -2.08 49.8678 10
created -1.6 4.98678 1
created -1.12 29.9207 6
created -0.64 34.9074 7
created -0.16 44.881 9
created 0.32 19.9471 4
created 0.8 19.9471 4
created 1.28 19.9471 4
created 1.76 29.9207 6
created 2.24 19.9471 4
created 2.72 44.881 9
created 3.2 29.9207 6
created 3.68 9.97356 2
created 4.16 49.8678 10
created 4.64 19.9471 4
created 5.12 4.98678 1
created 5.6 49.8678 10
created 6.08 29.9207 6
created 6.56 24.9339 5
created 7.04 4.98678 1
created 7.52 19.9471 4
created 8 29.9207 6
created 8.48 49.8678 10
created 8.96 19.9471 4
created 9.44 49.8678 10
the total number of created peaks = 41 with sigma = 0.08
the total number of found peaks = 41 with sigma = 0.080001 (+-3.17431e-05)
fit chi^2 = 5.63004e-06
found -5.44 (+-0.000260817) 49.8677 (+-0.160479) 10.0001 (+-0.00106272)
found -4.96 (+-0.000261072) 49.8678 (+-0.160496) 10.0001 (+-0.00106283)
found -2.08 (+-0.00026036) 49.8676 (+-0.160447) 10.0001 (+-0.00106251)
found 4.16 (+-0.000259999) 49.8674 (+-0.160416) 10.0001 (+-0.00106231)
found 5.6 (+-0.00025998) 49.8675 (+-0.160418) 10.0001 (+-0.00106232)
found 8.48 (+-0.000260786) 49.8676 (+-0.160473) 10.0001 (+-0.00106268)
found 9.44 (+-0.000258742) 49.8678 (+-0.160344) 10.0001 (+-0.00106183)
found -2.56 (+-0.000276277) 44.8813 (+-0.152333) 9.00018 (+-0.00100877)
found -0.16 (+-0.00027523) 44.881 (+-0.152261) 9.00011 (+-0.0010083)
found 2.72 (+-0.000275077) 44.8809 (+-0.15225) 9.0001 (+-0.00100823)
found -3.04 (+-0.000293034) 39.8945 (+-0.14362) 8.00016 (+-0.00095108)
found -9.76 (+-0.000311604) 34.9071 (+-0.134245) 7.00003 (+-0.000888994)
found -3.52 (+-0.000312524) 34.9076 (+-0.134306) 7.00011 (+-0.000889397)
found -0.639999 (+-0.000313423) 34.9078 (+-0.134353) 7.00015 (+-0.000889712)
found 3.2 (+-0.00033773) 29.9208 (+-0.124353) 6.00011 (+-0.000823485)
found 6.08 (+-0.000338907) 29.921 (+-0.124405) 6.00015 (+-0.000823835)
found -8.32 (+-0.000335752) 29.9205 (+-0.124263) 6.00004 (+-0.000822889)
found -1.12 (+-0.000336797) 29.9207 (+-0.124311) 6.00008 (+-0.000823212)
found 1.76 (+-0.000337345) 29.9207 (+-0.124332) 6.00008 (+-0.000823346)
found 8 (+-0.000338629) 29.921 (+-0.124393) 6.00014 (+-0.000823751)
found 6.56 (+-0.000369131) 24.9339 (+-0.113487) 5.00007 (+-0.00075153)
found 0.319998 (+-0.000416012) 19.9475 (+-0.101607) 4.00013 (+-0.000672861)
found 2.24 (+-0.000416756) 19.9476 (+-0.101631) 4.00015 (+-0.000673017)
found 4.64 (+-0.000414399) 19.9474 (+-0.101561) 4.00011 (+-0.000672557)
found 8.96 (+-0.000418097) 19.9479 (+-0.101676) 4.00021 (+-0.000673313)
found 0.8 (+-0.00041443) 19.9473 (+-0.101555) 4.00008 (+-0.000672518)
found 1.28 (+-0.000415165) 19.9474 (+-0.101579) 4.0001 (+-0.000672674)
found 7.52 (+-0.000413327) 19.9472 (+-0.101525) 4.00007 (+-0.000672316)
found -4.48 (+-0.000481452) 14.9608 (+-0.0880227) 3.00013 (+-0.000582902)
found -9.28 (+-0.000480463) 14.9606 (+-0.0879969) 3.0001 (+-0.000582731)
found -8.8 (+-0.000480071) 14.9606 (+-0.0879869) 3.00009 (+-0.000582665)
found -6.4 (+-0.000477808) 14.9604 (+-0.0879326) 3.00005 (+-0.000582306)
found -4 (+-0.000480463) 14.9607 (+-0.0879969) 3.0001 (+-0.000582731)
found -6.88 (+-0.000477808) 14.9604 (+-0.0879326) 3.00005 (+-0.000582306)
found -5.92 (+-0.000592282) 9.97409 (+-0.0719151) 2.00013 (+-0.000476235)
found 3.68 (+-0.000594489) 9.97424 (+-0.0719517) 2.00016 (+-0.000476477)
found -7.36 (+-0.000585664) 9.97363 (+-0.071805) 2.00004 (+-0.000475505)
found -1.60001 (+-0.000849264) 4.98753 (+-0.0509555) 1.00016 (+-0.000337436)
found -7.84001 (+-0.000839918) 4.98712 (+-0.0508716) 1.00008 (+-0.000336881)
found 5.12001 (+-0.000846829) 4.98743 (+-0.0509338) 1.00014 (+-0.000337293)
found 7.04 (+-0.000842257) 4.98717 (+-0.0508908) 1.00009 (+-0.000337008)
#include "TROOT.h"
#include "TMath.h"
#include "TRandom.h"
#include "TH1.h"
#include "TF1.h"
#include "TCanvas.h"
#include "TSpectrum.h"
#include "TSpectrumFit.h"
#include "TPolyMarker.h"
#include "TList.h"
#include <iostream>
TH1F *FitAwmi_Create_Spectrum(void)
{
Int_t nbins = 1000;
Double_t xmin = -10., xmax = 10.;
delete gROOT->FindObject("h"); // prevent "memory leak"
TH1F *h = new TH1F("h", "simulated spectrum", nbins, xmin, xmax);
h->SetStats(kFALSE);
TF1 f("f", "TMath::Gaus(x, [0], [1], 1)", xmin, xmax);
// f.SetParNames("mean", "sigma");
gRandom->SetSeed(0); // make it really random
// create well separated peaks with exactly known means and areas
// note: TSpectrumFit assumes that all peaks have the same sigma
Double_t sigma = (xmax - xmin) / Double_t(nbins) * Int_t(gRandom->Uniform(2., 6.));
Int_t npeaks = 0;
while (xmax > (xmin + 6. * sigma)) {
npeaks++;
xmin += 3. * sigma; // "mean"
f.SetParameters(xmin, sigma);
Double_t area = 1. * Int_t(gRandom->Uniform(1., 11.));
h->Add(&f, area, ""); // "" ... or ... "I"
std::cout << "created " << xmin << " " << (area / sigma / TMath::Sqrt(TMath::TwoPi())) << " " << area
<< std::endl;
xmin += 3. * sigma;
}
std::cout << "the total number of created peaks = " << npeaks << " with sigma = " << sigma << std::endl;
return h;
}
void FitAwmi(void)
{
TH1F *h = FitAwmi_Create_Spectrum();
TCanvas *cFit = ((TCanvas *)(gROOT->GetListOfCanvases()->FindObject("cFit")));
if (!cFit)
cFit = new TCanvas("cFit", "cFit", 10, 10, 1000, 700);
else
cFit->Clear();
h->Draw("L");
Int_t i, nfound, bin;
Int_t nbins = h->GetNbinsX();
Double_t *source = new Double_t[nbins];
Double_t *dest = new Double_t[nbins];
for (i = 0; i < nbins; i++)
source[i] = h->GetBinContent(i + 1);
TSpectrum *s = new TSpectrum(); // note: default maxpositions = 100
// searching for candidate peaks positions
nfound = s->SearchHighRes(source, dest, nbins, 2., 2., kFALSE, 10000, kFALSE, 0);
// filling in the initial estimates of the input parameters
Bool_t *FixPos = new Bool_t[nfound];
Bool_t *FixAmp = new Bool_t[nfound];
for (i = 0; i < nfound; i++)
FixAmp[i] = FixPos[i] = kFALSE;
Double_t *Pos, *Amp = new Double_t[nfound]; // ROOT 6
Pos = s->GetPositionX(); // 0 ... (nbins - 1)
for (i = 0; i < nfound; i++) {
bin = 1 + Int_t(Pos[i] + 0.5); // the "nearest" bin
Amp[i] = h->GetBinContent(bin);
}
TSpectrumFit *pfit = new TSpectrumFit(nfound);
pfit->SetFitParameters(0, (nbins - 1), 1000, 0.1, pfit->kFitOptimChiCounts, pfit->kFitAlphaHalving, pfit->kFitPower2,
pfit->kFitTaylorOrderFirst);
pfit->SetPeakParameters(2., kFALSE, Pos, FixPos, Amp, FixAmp);
// pfit->SetBackgroundParameters(source[0], kFALSE, 0., kFALSE, 0., kFALSE);
pfit->FitAwmi(source);
Double_t *Positions = pfit->GetPositions();
Double_t *PositionsErrors = pfit->GetPositionsErrors();
Double_t *Amplitudes = pfit->GetAmplitudes();
Double_t *AmplitudesErrors = pfit->GetAmplitudesErrors();
Double_t *Areas = pfit->GetAreas();
Double_t *AreasErrors = pfit->GetAreasErrors();
delete gROOT->FindObject("d"); // prevent "memory leak"
TH1F *d = new TH1F(*h);
d->SetNameTitle("d", "");
d->Reset("M");
for (i = 0; i < nbins; i++)
d->SetBinContent(i + 1, source[i]);
Double_t x1 = d->GetBinCenter(1), dx = d->GetBinWidth(1);
Double_t sigma, sigmaErr;
pfit->GetSigma(sigma, sigmaErr);
// current TSpectrumFit needs a sqrt(2) correction factor for sigma
sigma /= TMath::Sqrt2();
sigmaErr /= TMath::Sqrt2();
// convert "bin numbers" into "x-axis values"
sigma *= dx;
sigmaErr *= dx;
std::cout << "the total number of found peaks = " << nfound << " with sigma = " << sigma << " (+-" << sigmaErr << ")"
<< std::endl;
std::cout << "fit chi^2 = " << pfit->GetChi() << std::endl;
for (i = 0; i < nfound; i++) {
bin = 1 + Int_t(Positions[i] + 0.5); // the "nearest" bin
Pos[i] = d->GetBinCenter(bin);
Amp[i] = d->GetBinContent(bin);
// convert "bin numbers" into "x-axis values"
Positions[i] = x1 + Positions[i] * dx;
PositionsErrors[i] *= dx;
Areas[i] *= dx;
AreasErrors[i] *= dx;
std::cout << "found " << Positions[i] << " (+-" << PositionsErrors[i] << ") " << Amplitudes[i] << " (+-"
<< AmplitudesErrors[i] << ") " << Areas[i] << " (+-" << AreasErrors[i] << ")" << std::endl;
}
d->SetLineColor(kRed);
d->SetLineWidth(1);
d->Draw("SAME L");
TPolyMarker *pm = ((TPolyMarker *)(h->GetListOfFunctions()->FindObject("TPolyMarker")));
if (pm) {
h->GetListOfFunctions()->Remove(pm);
delete pm;
}
pm = new TPolyMarker(nfound, Pos, Amp);
h->GetListOfFunctions()->Add(pm);
pm->SetMarkerStyle(23);
pm->SetMarkerColor(kRed);
pm->SetMarkerSize(1);
// cleanup
delete pfit;
delete[] Amp;
delete[] FixAmp;
delete[] FixPos;
delete s;
delete[] dest;
delete[] source;
return;
}
d
#define d(i)
Definition RSha256.hxx:102
f
#define f(i)
Definition RSha256.hxx:104
h
#define h(i)
Definition RSha256.hxx:106
Bool_t
bool Bool_t
Boolean (0=false, 1=true) (bool)
Definition RtypesCore.h:77
Int_t
int Int_t
Signed integer 4 bytes (int)
Definition RtypesCore.h:59
kFALSE
constexpr Bool_t kFALSE
Definition RtypesCore.h:108
Double_t
double Double_t
Double 8 bytes.
Definition RtypesCore.h:73
kRed
@ kRed
Definition Rtypes.h:67
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:359
dest
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t dest
Definition TGWin32VirtualXProxy.cxx:164
x1
Option_t Option_t TPoint TPoint const char x1
Definition TGWin32VirtualXProxy.cxx:70
xmin
float xmin
Definition THbookFile.cxx:95
xmax
float xmax
Definition THbookFile.cxx:95
gROOT
#define gROOT
Definition TROOT.h:411
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62
TCanvas
The Canvas class.
Definition TCanvas.h:23
TF1
1-Dim function class
Definition TF1.h:182
TH1F
1-D histogram with a float per channel (see TH1 documentation)
Definition TH1.h:878
TPolyMarker
A PolyMarker is defined by an array on N points in a 2-D space.
Definition TPolyMarker.h:31
TRandom::SetSeed
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition TRandom.cxx:614
TRandom::Uniform
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Definition TRandom.cxx:681
TSpectrumFit
Advanced 1-dimensional spectra fitting functions.
Definition TSpectrumFit.h:18
TSpectrum
Advanced Spectra Processing.
Definition TSpectrum.h:18
TSpectrum::SearchHighRes
Int_t SearchHighRes(Double_t *source, Double_t *destVector, Int_t ssize, Double_t sigma, Double_t threshold, bool backgroundRemove, Int_t deconIterations, bool markov, Int_t averWindow)
One-dimensional high-resolution peak search function.
Definition TSpectrum.cxx:2118
TSpectrum::GetPositionX
Double_t * GetPositionX() const
Definition TSpectrum.h:58
sigma
const Double_t sigma
Definition h1analysisProxy.h:11
TMath::Sqrt2
constexpr Double_t Sqrt2()
Definition TMath.h:89
TMath::Sqrt
Double_t Sqrt(Double_t x)
Returns the square root of x.
Definition TMath.h:673
TMath::TwoPi
constexpr Double_t TwoPi()
Definition TMath.h:47
Author

Definition in file FitAwmi.C.