void fitTools::fillPositionResolution(TH1F* h1_sigmaEta, TH1F* h1_sigmaPhi, TH2D* h2_deltaEta, TH2D* h2_deltaPhi) {
for(int iBin=1; iBin<(h2_deltaEta->GetNbinsX()+1); ++iBin) {
TH1D* h1_projection = h2_deltaEta->ProjectionY("projectiony", iBin, iBin);
TF1* gaussian= new TF1("gaussian", "gaus");
fitProjection(h1_projection, gaussian, 2., "RQLL");
h1_sigmaEta->SetBinContent(iBin, gaussian->GetParameter(2));
h1_sigmaEta->SetBinError(iBin, gaussian->GetParError(2));
delete gaussian;
gaussian = 0;
h1_projection = 0;
} //for bins eta
for(int iBin=1; iBin<(h2_deltaPhi->GetNbinsX()+1); ++iBin) {
TH1D* h1_projection = h2_deltaPhi->ProjectionY("projectiony", iBin, iBin);
TF1* gaussian= new TF1("gaussian", "gaus");
fitProjection(h1_projection, gaussian, 2., "RQLL");
h1_sigmaPhi->SetBinContent(iBin, gaussian->GetParameter(2));
h1_sigmaPhi->SetBinError(iBin, gaussian->GetParError(2));
delete gaussian;
gaussian = 0;
h1_projection = 0;
} //for bins
} //fill position resolution
void GaussianProfile::getTruncatedMeanRMS(TH1* hist, float& mean, float& mean_error, float& rms, float& rms_error) {
int nBins = hist->GetNbinsX();
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
//double binWidth = (xMax - xMin) / (double) nBins; //WARNING: this works only if bins are of the same size
double integral = hist->Integral();
int maxBin = 0;
TF1* gaussian = new TF1("gaussian", "gaus");
fitProjection(hist, gaussian, 1.5, "RQN");
//maxBin = (int) ceil((gaussian->GetParameter(1) - xMin) / binWidth);
maxBin = hist->FindBin(gaussian->GetParameter(1));
delete gaussian;
TH1D* newHisto = new TH1D("newHisto", "", nBins, xMin, xMax);
newHisto->SetBinContent(maxBin, hist->GetBinContent(maxBin));
newHisto->SetBinError(maxBin, hist->GetBinError(maxBin));
int iBin = maxBin;
int delta_iBin = 1;
int sign = 1;
while (newHisto->Integral() < 0.99 * integral) {
iBin += sign * delta_iBin;
newHisto->SetBinContent(iBin, hist->GetBinContent(iBin));
newHisto->SetBinError(iBin, hist->GetBinError(iBin));
delta_iBin += 1;
sign *= -1;
}
rms = newHisto->GetRMS();
rms_error = newHisto->GetRMSError();
while (newHisto->Integral() < 0.99 * integral) {
iBin += sign * delta_iBin;
newHisto->SetBinContent(iBin, hist->GetBinContent(iBin));
newHisto->SetBinError(iBin, hist->GetBinError(iBin));
delta_iBin += 1;
sign *= -1;
}
mean = newHisto->GetMean();
mean_error = newHisto->GetMeanError();
delete newHisto;
}
void fitTools::getTruncatedMeanAndRMS(TH1D* h1_projection, Float_t& mean, Float_t& mean_err, Float_t& rms, Float_t& rms_err, Double_t percentIntegral_MEAN, Double_t percentIntegral_RMS) {
//TCanvas* getTruncatedMeanAndRMS(TH1D* h1_projection, Float_t& mean, Float_t& mean_err, Float_t& rms, Float_t& rms_err, Double_t percentIntegral_MEAN=0.9, Double_t percentIntegral_RMS=0.68) {
bool useMode = false;
if( percentIntegral_MEAN<0. || percentIntegral_MEAN>1. ) {
std::cout << "WARNING! percentIntegral_MEAN is " << percentIntegral_MEAN << "!! Setting it to 90%." << std::endl;
percentIntegral_MEAN = 0.9;
}
if( percentIntegral_RMS<0. || percentIntegral_RMS>1. ) {
std::cout << "WARNING! percentIntegral_RMS is " << percentIntegral_RMS << "!! Setting it to 68%." << std::endl;
percentIntegral_RMS = 0.68;
}
Int_t nBins = h1_projection->GetNbinsX();
Double_t xMin = h1_projection->GetXaxis()->GetXmin();
Double_t xMax = h1_projection->GetXaxis()->GetXmax();
Double_t binWidth = (xMax-xMin)/(Double_t)nBins; //WARNING: this works only if bins are of the same size
Double_t integral = h1_projection->Integral();
// std::cout << "xmax: " << xMax << "\txMin: " << xMin << std::endl;
//first: find maximum
// std::cout << "N: " << gaussian->GetParameter(0) << "\tmu: " << gaussian->GetParameter(1) << "\tsigma: " << gaussian->GetParameter(2) << std::endl;
Int_t maxBin;
if( useMode ) {
maxBin = h1_projection->GetMaximumBin();
} else {
TF1* gaussian = new TF1("gaussian", "gaus");
gaussian->SetLineColor(kGreen);
fitProjection(h1_projection, gaussian, 1.5, "RQN");
maxBin = (Int_t)ceil((gaussian->GetParameter(1)-xMin)/binWidth);
delete gaussian;
}
// std::cout << "maxBin: " << maxBin << "\tbin center: " << h1_projection->GetXaxis()->GetBinCenter(maxBin) << "\t gauss mu: " << gaussian->GetParameter(1) << std::endl;
TH1D* newHisto = new TH1D("newHisto", "", nBins, xMin, xMax);
newHisto->SetBinContent( maxBin, h1_projection->GetBinContent(maxBin) );
newHisto->SetBinError( maxBin, h1_projection->GetBinError(maxBin) );
Int_t iBin = maxBin;
Int_t delta_iBin = 1;
Int_t sign = 1;
// std::cout << "iBin: " << iBin << "\tint: " << newHisto->Integral()/integral << std::endl;
while( newHisto->Integral() < percentIntegral_RMS*integral ) {
iBin += sign*delta_iBin;
// std::cout << "iBin: " << iBin << "\tint: " << newHisto->Integral()/integral << std::endl;
newHisto->SetBinContent( iBin, h1_projection->GetBinContent(iBin) );
newHisto->SetBinError( iBin, h1_projection->GetBinError(iBin) );
delta_iBin += 1;
sign *= -1;
}
// std::cout << "done with rms." << std::endl;
// TCanvas* c1 = new TCanvas("c1", "c1", 800, 600);
// c1->cd();
// h1_projection->Draw();
// newHisto->SetFillColor(kRed);
// newHisto->DrawClone("HISTO same");
rms = newHisto->GetRMS();
rms_err = newHisto->GetRMSError();
//std::cout << "rms: " << rms << std::endl;
while( newHisto->Integral() < percentIntegral_MEAN*integral ) {
// std::cout << "iBin: " << iBin << "\tint: " << newHisto->Integral()/integral << std::endl;
iBin += sign*delta_iBin;
newHisto->SetBinContent( iBin, h1_projection->GetBinContent(iBin) );
newHisto->SetBinError( iBin, h1_projection->GetBinError(iBin) );
delta_iBin += 1;
sign *= -1;
}
// newHisto->SetFillStyle(3004);
// newHisto->SetFillColor(kBlue);
// newHisto->DrawClone("HISTO same");
mean = newHisto->GetMean();
mean_err = newHisto->GetMeanError();
delete newHisto;
// return c1;
}
void fitTools::fillProfile(TH1F* h1_response_FIT, TH1F* h1_resolution_FIT, TH1F* h1_response_MEAN, TH1F* h1_resolution_RMS, TH2D* h2, std::string name) {
std::string fileName = "Projections/"+name+".root";
TFile* projectionFile;
if( name!= "" ) {
projectionFile = TFile::Open(fileName.c_str(), "RECREATE");
projectionFile->cd();
}
for(int iBin=1; iBin<(h2->GetNbinsX()+1); ++iBin) {
char histName[50];
sprintf(histName, "projection_%d",iBin);
TH1D* h1_projection = h2->ProjectionY(histName, iBin, iBin);
TF1* gaussian_LL = new TF1("gaussian_LL", "gaus");
fitProjection(h1_projection, gaussian_LL, 2., "RQLL");
TF1* gaussian_chi = new TF1("gaussian_chi", "gaus");
fitProjection(h1_projection, gaussian_chi, 2., "RQO+");
if( name!="" ) {
h1_projection->Write();
}
Float_t mu = gaussian_LL->GetParameter(1);
Float_t mu_err = gaussian_chi->GetParError(1);
h1_response_FIT->SetBinContent(iBin, mu);
h1_response_FIT->SetBinError(iBin, mu_err);
Float_t sigma = gaussian_LL->GetParameter(2);
Float_t resolution = (mu!=0.) ? sigma/mu : -1.;
h1_resolution_FIT->SetBinContent(iBin, resolution);
Float_t sigma_err = gaussian_chi->GetParError(2);
Float_t res_err = (mu!=0.) ? sqrt( sigma_err*sigma_err/(mu*mu) + mu_err*mu_err*sigma*sigma/(mu*mu*mu*mu) ) : 0.;
h1_resolution_FIT->SetBinError(iBin, res_err);
Float_t n = h1_projection->GetEntries();
Float_t mean = h1_projection->GetMean();
Float_t mean_err = (n!=0) ? h1_projection->GetRMS()/sqrt(n) : 0.;
h1_response_MEAN->SetBinContent(iBin, mean);
h1_response_MEAN->SetBinError(iBin, mean_err);
Float_t rms = h1_projection->GetRMS();
Float_t rms_err = (n!=0) ? h1_projection->GetRMS()/sqrt(n) : 0.;
resolution = (mean!=0.) ? rms/mean : -1.;
res_err = (mean!=0.) ? sqrt( rms_err*rms_err/(mean*mean) + mean_err*mean_err*rms*rms/(mean*mean*mean*mean) ) : 0.;
if( resolution != 0. ) {
h1_resolution_RMS->SetBinContent(iBin, resolution);
h1_resolution_RMS->SetBinError(iBin, res_err);
}
h1_projection = 0;
} //for bins
if(name!="") {
projectionFile->Write();
projectionFile->Close();
delete projectionFile;
}
projectionFile = 0;
} //fill profile
void fitTools::fitProjection_sameArea(TH1D* h1_projection, TF1* gaussian, TH1D* newhisto, Float_t percIntegral, const std::string& option, bool useMode) {
if( percIntegral<0. || percIntegral>1. ) {
std::cout << "WARNING! percIntegral is " << percIntegral << "!! Setting it to 90%." << std::endl;
percIntegral = 0.9;
}
Int_t nBins = h1_projection->GetNbinsX();
Double_t xMin = h1_projection->GetXaxis()->GetXmin();
Double_t xMax = h1_projection->GetXaxis()->GetXmax();
Double_t binWidth = (xMax-xMin)/(Double_t)nBins; //WARNING: this works only if bins are of the same size
Double_t integral = h1_projection->Integral();
//first: find maximum
Int_t maxBin;
if( useMode ) {
maxBin = h1_projection->GetMaximumBin();
} else {
TF1* tmp_gaussian = new TF1("tmp_gaussian", "gaus");
fitProjection(h1_projection, tmp_gaussian, 2.5, "RQN");
maxBin = (Int_t)ceil((tmp_gaussian->GetParameter(1)-xMin)/binWidth);
delete tmp_gaussian;
}
// std::cout << "maxBin: " << maxBin << "\tbin center: " << h1_projection->GetXaxis()->GetBinCenter(maxBin) << "\t gauss mu: " << gaussian->GetParameter(1) << std::endl;
TH1D* newHisto_tmp = new TH1D("newHisto_tmp", "", nBins, xMin, xMax);
newHisto_tmp->SetBinContent( maxBin, h1_projection->GetBinContent(maxBin) );
newHisto_tmp->SetBinError( maxBin, h1_projection->GetBinError(maxBin) );
Int_t iBin = maxBin;
Int_t delta_iBin = 1;
Int_t sign = 1;
Float_t xMin_fit = newHisto_tmp->GetXaxis()->GetBinLowEdge(maxBin);
Float_t xMax_fit = newHisto_tmp->GetXaxis()->GetBinUpEdge(maxBin);
//add bins till percent area is reached:
while( newHisto_tmp->Integral() < percIntegral*integral ) {
iBin += sign*delta_iBin;
newHisto_tmp->SetBinContent( iBin, h1_projection->GetBinContent(iBin) );
newHisto_tmp->SetBinError( iBin, h1_projection->GetBinError(iBin) );
if( newHisto_tmp->GetXaxis()->GetBinLowEdge(iBin) < xMin_fit ) xMin_fit = newHisto_tmp->GetXaxis()->GetBinLowEdge(iBin);
if( newHisto_tmp->GetXaxis()->GetBinUpEdge(iBin) > xMax_fit ) xMax_fit = newHisto_tmp->GetXaxis()->GetBinLowEdge(iBin);
delta_iBin += 1;
sign *= -1; //makes it jump from left to right about max
}
// std::cout << "done with rms." << std::endl;
// TCanvas* c1 = new TCanvas("c1", "c1", 800, 600);
// c1->cd();
// h1_projection->Draw();
// newHisto->SetFillColor(kRed);
// newHisto->DrawClone("HISTO same");
//initialize parameters to likely values:
gaussian->SetParameter( 0, newHisto_tmp->Integral() );
gaussian->SetParameter( 1, newHisto_tmp->GetMean() );
gaussian->SetParameter( 2, newHisto_tmp->GetRMS() );
gaussian->SetRange(xMin_fit, xMax_fit);
newHisto_tmp->Fit(gaussian, option.c_str());
*newhisto = *newHisto_tmp;
delete newHisto_tmp;
newHisto_tmp=0;
// return c1;
}
