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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 9.97356 2
created -9.28 24.9339 5
created -8.8 39.8942 8
created -8.32 29.9207 6
created -7.84 49.8678 10
created -7.36 49.8678 10
created -6.88 29.9207 6
created -6.4 49.8678 10
created -5.92 19.9471 4
created -5.44 49.8678 10
created -4.96 9.97356 2
created -4.48 29.9207 6
created -4 34.9074 7
created -3.52 29.9207 6
created -3.04 4.98678 1
created -2.56 9.97356 2
created -2.08 49.8678 10
created -1.6 49.8678 10
created -1.12 24.9339 5
created -0.64 14.9603 3
created -0.16 4.98678 1
created 0.32 24.9339 5
created 0.8 44.881 9
created 1.28 14.9603 3
created 1.76 19.9471 4
created 2.24 14.9603 3
created 2.72 44.881 9
created 3.2 9.97356 2
created 3.68 24.9339 5
created 4.16 29.9207 6
created 4.64 49.8678 10
created 5.12 49.8678 10
created 5.6 14.9603 3
created 6.08 34.9074 7
created 6.56 9.97356 2
created 7.04 49.8678 10
created 7.52 44.881 9
created 8 9.97356 2
created 8.48 14.9603 3
created 8.96 44.881 9
created 9.44 44.881 9
the total number of created peaks = 41 with sigma = 0.08
the total number of found peaks = 41 with sigma = 0.0800011 (+-2.87904e-05)
fit chi^2 = 5.07211e-06
found -7.84 (+-0.00024831) 49.8679 (+-0.152373) 10.0002 (+-0.00100904)
found -7.36 (+-0.00024831) 49.8679 (+-0.152373) 10.0002 (+-0.00100904)
found -6.4 (+-0.000247527) 49.8676 (+-0.152314) 10.0001 (+-0.00100865)
found -5.44 (+-0.00024678) 49.8674 (+-0.15226) 10.0001 (+-0.0010083)
found -2.08 (+-0.000247557) 49.8677 (+-0.15232) 10.0001 (+-0.00100869)
found -1.6 (+-0.000248162) 49.8679 (+-0.152362) 10.0002 (+-0.00100897)
found 4.64 (+-0.00024831) 49.8679 (+-0.152373) 10.0002 (+-0.00100904)
found 5.12 (+-0.000247798) 49.8678 (+-0.152336) 10.0001 (+-0.0010088)
found 7.04 (+-0.000247454) 49.8677 (+-0.152311) 10.0001 (+-0.00100863)
found 0.8 (+-0.000260717) 44.8808 (+-0.144485) 9.00008 (+-0.000956803)
found 2.72 (+-0.000260059) 44.8807 (+-0.144442) 9.00005 (+-0.000956522)
found 7.52 (+-0.000261123) 44.881 (+-0.144515) 9.00012 (+-0.000957004)
found 8.96 (+-0.000261275) 44.881 (+-0.144523) 9.00012 (+-0.00095706)
found 9.44 (+-0.000259678) 44.8813 (+-0.144438) 9.00018 (+-0.000956493)
found -8.8 (+-0.000277351) 39.8943 (+-0.13627) 8.00011 (+-0.000902404)
found -4 (+-0.000296989) 34.9076 (+-0.127495) 7.00012 (+-0.000844295)
found 6.08 (+-0.000295267) 34.9072 (+-0.127405) 7.00005 (+-0.000843701)
found -8.32 (+-0.000322307) 29.9212 (+-0.11811) 6.00018 (+-0.000782146)
found -6.88 (+-0.000322645) 29.9213 (+-0.118127) 6.0002 (+-0.000782255)
found -3.52 (+-0.000319674) 29.9207 (+-0.117991) 6.00008 (+-0.000781358)
found -4.48 (+-0.000320201) 29.9207 (+-0.118013) 6.00009 (+-0.000781501)
found 4.16 (+-0.000321676) 29.921 (+-0.11808) 6.00015 (+-0.00078195)
found -1.12 (+-0.000352281) 24.9342 (+-0.10779) 5.00013 (+-0.000713804)
found -9.28 (+-0.000351442) 24.9341 (+-0.107757) 5.0001 (+-0.000713587)
found 0.320002 (+-0.00035102) 24.9341 (+-0.107744) 5.0001 (+-0.0007135)
found 3.68 (+-0.000350984) 24.934 (+-0.107738) 5.00008 (+-0.000713463)
found -5.92 (+-0.00039684) 19.9479 (+-0.0965061) 4.0002 (+-0.000639081)
found 1.76 (+-0.000392486) 19.9472 (+-0.0963651) 4.00006 (+-0.000638147)
found 1.28 (+-0.000457255) 14.9608 (+-0.0835532) 3.00013 (+-0.000553304)
found 5.6 (+-0.000458831) 14.961 (+-0.0835924) 3.00017 (+-0.000553564)
found -0.640002 (+-0.000453566) 14.9604 (+-0.0834656) 3.00006 (+-0.000552724)
found 2.24 (+-0.000457255) 14.9608 (+-0.0835532) 3.00013 (+-0.000553304)
found 8.48 (+-0.000455977) 14.9607 (+-0.0835237) 3.00011 (+-0.000553109)
found -4.96 (+-0.000564265) 9.97424 (+-0.0682935) 2.00016 (+-0.000452252)
found 3.2 (+-0.000563257) 9.97414 (+-0.0682759) 2.00014 (+-0.000452136)
found 8 (+-0.000561764) 9.97404 (+-0.0682514) 2.00012 (+-0.000451973)
found 6.56 (+-0.0005648) 9.97429 (+-0.0683028) 2.00017 (+-0.000452313)
found -9.76 (+-0.000557384) 9.97368 (+-0.0681738) 2.00005 (+-0.000451459)
found -2.55999 (+-0.000559733) 9.97399 (+-0.0682218) 2.00011 (+-0.000451777)
found -3.04001 (+-0.000797214) 4.98712 (+-0.0482852) 1.00008 (+-0.000319753)
found -0.159997 (+-0.00079799) 4.98712 (+-0.0482908) 1.00008 (+-0.00031979)
#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:78
Int_t
int Int_t
Signed integer 4 bytes (int)
Definition RtypesCore.h:60
kFALSE
constexpr Bool_t kFALSE
Definition RtypesCore.h:109
Double_t
double Double_t
Double 8 bytes.
Definition RtypesCore.h:74
kRed
@ kRed
Definition Rtypes.h:66
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:417
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:73
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:2119
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:675
TMath::TwoPi
constexpr Double_t TwoPi()
Definition TMath.h:47
Author

Definition in file FitAwmi.C.