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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 9.97356 2
created -8.8 14.9603 3
created -8.32 44.881 9
created -7.84 34.9074 7
created -7.36 29.9207 6
created -6.88 49.8678 10
created -6.4 49.8678 10
created -5.92 29.9207 6
created -5.44 44.881 9
created -4.96 9.97356 2
created -4.48 44.881 9
created -4 24.9339 5
created -3.52 44.881 9
created -3.04 49.8678 10
created -2.56 4.98678 1
created -2.08 49.8678 10
created -1.6 19.9471 4
created -1.12 34.9074 7
created -0.64 34.9074 7
created -0.16 39.8942 8
created 0.32 34.9074 7
created 0.8 39.8942 8
created 1.28 19.9471 4
created 1.76 49.8678 10
created 2.24 4.98678 1
created 2.72 9.97356 2
created 3.2 4.98678 1
created 3.68 9.97356 2
created 4.16 24.9339 5
created 4.64 44.881 9
created 5.12 39.8942 8
created 5.6 34.9074 7
created 6.08 39.8942 8
created 6.56 44.881 9
created 7.04 9.97356 2
created 7.52 9.97356 2
created 8 39.8942 8
created 8.48 14.9603 3
created 8.96 44.881 9
created 9.44 24.9339 5
the total number of created peaks = 41 with sigma = 0.08
the total number of found peaks = 41 with sigma = 0.0800011 (+-2.15411e-05)
fit chi^2 = 2.86239e-06
found -6.88 (+-0.000186537) 49.8679 (+-0.114467) 10.0002 (+-0.000758018)
found -6.4 (+-0.000186537) 49.8679 (+-0.114467) 10.0002 (+-0.000758018)
found -3.04 (+-0.000185645) 49.8676 (+-0.114404) 10.0001 (+-0.000757604)
found -2.08 (+-0.000185141) 49.8674 (+-0.114366) 10 (+-0.00075735)
found 1.76 (+-0.000185141) 49.8674 (+-0.114366) 10 (+-0.00075735)
found -8.32 (+-0.000196086) 44.8809 (+-0.108556) 9.0001 (+-0.000718879)
found -5.44 (+-0.000195779) 44.8808 (+-0.108536) 9.00008 (+-0.000718747)
found -4.48 (+-0.000195659) 44.8808 (+-0.108528) 9.00007 (+-0.000718693)
found -3.52 (+-0.000196662) 44.8812 (+-0.108595) 9.00015 (+-0.000719138)
found 4.64 (+-0.000196485) 44.8811 (+-0.108583) 9.00013 (+-0.000719054)
found 6.56 (+-0.000195986) 44.8809 (+-0.108551) 9.0001 (+-0.000718842)
found 8.96 (+-0.000195857) 44.8808 (+-0.10854) 9.00008 (+-0.000718775)
found -0.16 (+-0.00020873) 39.8944 (+-0.102392) 8.00014 (+-0.000678062)
found 0.8 (+-0.00020832) 39.8942 (+-0.102368) 8.00011 (+-0.000677898)
found 5.12 (+-0.000208942) 39.8945 (+-0.102406) 8.00016 (+-0.00067815)
found 6.08 (+-0.000208942) 39.8945 (+-0.102406) 8.00016 (+-0.00067815)
found 8 (+-0.000207338) 39.8939 (+-0.10231) 8.00005 (+-0.000677514)
found -7.84 (+-0.00022348) 34.9077 (+-0.0957982) 7.00015 (+-0.000634393)
found -1.12 (+-0.000222918) 34.9075 (+-0.0957677) 7.00011 (+-0.000634191)
found -0.64 (+-0.000223503) 34.9077 (+-0.0957992) 7.00015 (+-0.0006344)
found 0.32 (+-0.000223627) 34.9078 (+-0.0958061) 7.00016 (+-0.000634445)
found 5.6 (+-0.000223627) 34.9078 (+-0.0958061) 7.00016 (+-0.000634445)
found -7.36 (+-0.000241982) 29.9211 (+-0.0887204) 6.00017 (+-0.000587523)
found -5.92 (+-0.000242257) 29.9212 (+-0.0887336) 6.00019 (+-0.00058761)
found -4 (+-0.000265715) 24.9345 (+-0.0810161) 5.00018 (+-0.000536504)
found 9.44 (+-0.000262404) 24.9343 (+-0.0809033) 5.00014 (+-0.000535757)
found 4.16 (+-0.000264164) 24.9341 (+-0.0809562) 5.00011 (+-0.000536107)
found -1.6 (+-0.000297554) 19.9477 (+-0.072479) 4.00017 (+-0.000479969)
found 1.28 (+-0.000297757) 19.9478 (+-0.0724857) 4.00018 (+-0.000480014)
found 8.48 (+-0.000344724) 14.961 (+-0.0627975) 3.00017 (+-0.000415857)
found -8.8 (+-0.000342542) 14.9607 (+-0.0627451) 3.00011 (+-0.000415509)
found -4.96 (+-0.000424682) 9.97434 (+-0.0513175) 2.00018 (+-0.000339834)
found 7.04 (+-0.000421252) 9.97399 (+-0.0512603) 2.00011 (+-0.000339455)
found -9.76 (+-0.000416897) 9.97353 (+-0.0511836) 2.00002 (+-0.000338947)
found -9.28 (+-0.000418644) 9.97368 (+-0.051215) 2.00005 (+-0.000339155)
found 2.72 (+-0.000415827) 9.97353 (+-0.0511715) 2.00002 (+-0.000338867)
found 3.68 (+-0.000418684) 9.97373 (+-0.0512176) 2.00006 (+-0.000339172)
found 7.52 (+-0.000420925) 9.97394 (+-0.0512543) 2.0001 (+-0.000339415)
found -2.56 (+-0.000608071) 4.98773 (+-0.0363564) 1.00021 (+-0.000240758)
found 2.23999 (+-0.000601327) 4.98733 (+-0.0362965) 1.00012 (+-0.000240362)
found 3.2 (+-0.000594797) 4.98692 (+-0.0362368) 1.00004 (+-0.000239967)
#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.