TF1* checkTotalResolution( const std::string& outputdir, TTree* tree ) {
std::string histoName("h1_tot");
TH1D* h1 = new TH1D(histoName.c_str(), "", 2000, 20000.,900000.);
//TH1D* h1 = new TH1D(histoName.c_str(), "", 200, 0.,4000.);
//tree->Project( histoName.c_str(), "cef3_maxAmpl[0]+cef3_maxAmpl[1]+cef3_maxAmpl[2]+cef3_maxAmpl[3]", "");
tree->Project( histoName.c_str(), "cef3_chaInt[0]+cef3_chaInt[1]+cef3_chaInt[2]+cef3_chaInt[3]", "");
// tree->Project( histoName.c_str(), "cef3_corr[0]+cef3_corr[1]+cef3_corr[2]+cef3_corr[3]", "nClusters_hodoX==1 && nClusters_hodoY==1 && pos_corr_hodoX1<5 && pos_corr_hodoX1>-5 && pos_corr_hodoY1<5 && pos_corr_hodoY1>-5");
//TF1* f1 = new TF1("gaus_tot", "gaus", 100., 4000.);
TF1* f1 = new TF1("gaus_tot", "gaus", 20000.,900000. );
// f1->SetParameter(0, 3000000.);
//f1->SetParameter(1, 3000000.);
//f1->SetParameter(2, 60000.);
f1->SetLineColor(kRed);
doSingleFit( h1, f1, outputdir, "tot" );
std::cout << std::endl;
std::cout << std::endl;
float mean = f1->GetParameter(1);
float sigma = f1->GetParameter(2);
float reso = sigma/mean;
std::cout << "Total " << std::endl;
std::cout << " Mean : " << mean << std::endl;
std::cout << " Sigma : " << sigma << std::endl;
std::cout << " Resolution : " << sigma/mean << std::endl;
std::cout << "Corresponds to a stochastic term of: " << 100.*reso*sqrt(0.5) << " %" << std::endl;
return f1;
}
TF1* checkTotalResolution( const std::string& outputdir, TTree* tree ) {
std::string histoName("h1_tot");
TH1D* h1 = new TH1D(histoName.c_str(), "", 500, 0., 12000.);
tree->Project( histoName.c_str(), "cef3_corr[0]+cef3_corr[1]+cef3_corr[2]+cef3_corr[3]", "(nHodoFibersCorrX==1 && nHodoFibersCorrY==1)");
TF1* f1 = new TF1("gaus_tot", "gaus", 1600., 4800.);
f1->SetParameter(0, 3000.);
f1->SetParameter(1, 3000.);
f1->SetParameter(2, 600.);
doSingleFit( h1, f1, outputdir, "tot" );
std::cout << std::endl;
std::cout << std::endl;
float mean = f1->GetParameter(1);
float sigma = f1->GetParameter(2);
float reso = sigma/mean;
std::cout << "Total " << std::endl;
std::cout << " Mean : " << mean << std::endl;
std::cout << " Sigma : " << sigma << std::endl;
std::cout << " Resolution : " << sigma/mean << std::endl;
std::cout << "Corresponds to a stochastic term of: " << 100.*reso*sqrt(0.5) << " %" << std::endl;
return f1;
}
TF1* fitSingleElectronPeak( const std::string& outputdir, int i, TTree* tree ) {
gStyle->SetOptFit(1);
std::string histoName(Form("h1_%d", i));
//TH1D* h1 = new TH1D(histoName.c_str(), "", 120, 40., 1000.);
TH1D* h1 = new TH1D(histoName.c_str(), "", 1000, 0., 250000.);
//tree->Project( histoName.c_str(), Form("cef3_corr[%d]", i), "");
// tree->Project( histoName.c_str(), Form("cef3[%d]", i), "nClusters_hodoSmallX==1 && nClusters_hodoSmallY==1 && pos_corr_hodoSmallX<5 && pos_corr_hodoSmallX>-5 && pos_corr_hodoSmallY<5 && pos_corr_hodoSmallY>-5");
//tree->Project( histoName.c_str(), Form("cef3_maxAmpl[%d]", i), "abs(cluster_pos_hodoX2)<5 ");
tree->Project( histoName.c_str(), Form("cef3_chaInt_corr[%d]", i), "abs(cluster_pos_hodoX2)<2&& abs(cluster_pos_hodoY2)<2 ");
//tree->Project( histoName.c_str(), Form("cef3_maxAmpl_corr[%d]", i), "abs(cluster_pos_hodoX2)<5&& abs(cluster_pos_hodoY2)<5 ");
// TF1* f1 = new TF1( Form("gaus_%d", i), "gaus", 5., 1000.);
TF1* f1 = new TF1( Form("gaus_%d", i), "gaus", 400., 250000.);
f1->SetParameter(0, 300000.);
f1->SetParameter(1, 250000.);
f1->SetParameter(2, 15000.);
f1->SetLineColor(kRed);
doSingleFit( h1, f1, outputdir, Form("%d", i) );
return f1;
}
TH1F* GetPosteriorPdf( double pars[6] )
{
double TF = pars[0];/*transfer factor*/
double TF_Err = pars[1];/*trabsfer factor Unc.*/
double Nside = pars[2];/*sideband statistics*/
double Higgs = pars[3];/*higgs expected*/
double HiggsErr = pars[4];/*higgs uncertainty*/
double ShapeErr = pars[5];/*shape uncertainty*/
/*ramdomize histo name*/
TRandom3 histoName( 0 );
//--------------------------------------
//define a random var for each nuissance
//--------------------------------------
TRandom3 gausTF( 0 );
TRandom3 poissonNside( 0 );
TRandom3 gausHiggs( 0 );
TRandom3 gausShape( 0 );
//---------------------------------
//pdf for expected observation
//---------------------------------
TRandom3 poissonNobs( 0 );
//histogram to store sampled Nobs
int i_tmp = histoName.Integer( 100000 );
TH1F* h_Nobs = new TH1F( Form("h_Nobs_%d", i_tmp), "h_Nobs", NBINS, -1, 4999. );
for ( int i = 0; i < nsamples; i++ )
{
//--------------------------------
//sample values for each parameter
//--------------------------------
double sTF = gausTF.Gaus( TF, TF_Err );/*sampled value for TF*/
double sNside = poissonNside.Poisson( Nside );
double sHiggs = gausHiggs.Gaus( Higgs, HiggsErr );
double sShape = gausShape.Gaus( 0.0, ShapeErr );
//-----------------------
//expected observation
//using sampled values!!
//-----------------------
double poissonMean = sTF*sNside*( 1.0 + sShape ) + sHiggs;
if ( poissonMean < .0 ) poissonMean = .0;
double sNobs = poissonNobs.PoissonD( poissonMean );
//double sNobs = poissonNobs.Gaus( poissonMean, TMath::Sqrt(poissonMean) );
h_Nobs->Fill( sNobs );
}
return h_Nobs;
};
TF1* fitSingleElectronPeak( const std::string& outputdir, int i, TTree* tree ) {
std::string histoName(Form("h1_%d", i));
TH1D* h1 = new TH1D(histoName.c_str(), "", 100, 0., 3000.);
tree->Project( histoName.c_str(), Form("cef3_corr[%d]", i), "(nHodoFibersCorrX==1 && nHodoFibersCorrY==1)");
TF1* f1 = new TF1( Form("gaus_%d", i), "gaus", 400., 1200.);
f1->SetParameter(0, 3000.);
f1->SetParameter(1, 800.);
f1->SetParameter(2, 150.);
doSingleFit( h1, f1, outputdir, Form("%d", i) );
return f1;
}
TF1* fitSingleElectronPeak( const std::string& outputdir, int i, TTree* tree ) {
std::string histoName(Form("h1_%d", i));
TH1D* h1 = new TH1D(histoName.c_str(), "", 100, 0., 3000.);
tree->Project( histoName.c_str(), Form("cef3[%d]", i), "(nHodoFibersCorrX==1 && nHodoFibersCorrY==1)");
TF1* f1 = new TF1( Form("gaus_%d", i), "gaus", 400., 1200.);
f1->SetParameter(0, 3000.);
f1->SetParameter(1, 800.);
f1->SetParameter(2, 150.);
h1->Fit( f1, "RQN" );
int niter = 4.;
float nSigma = 2.;
for( unsigned iter=0; iter<niter; iter++ ) {
float mean = f1->GetParameter(1);
float sigma = f1->GetParameter(2);
float fitMin = mean - nSigma*sigma;
float fitMax = mean + nSigma*sigma;
f1->SetRange( fitMin, fitMax );
if( iter==(niter-1) )
h1->Fit( f1, "RQN" );
else
h1->Fit( f1, "RQ+" );
}
TCanvas* c1 = new TCanvas("c1", "", 600, 600);
c1->cd();
h1->Draw();
c1->SaveAs( Form("%s/fit_%d.eps", outputdir.c_str(), i) );
c1->SaveAs( Form("%s/fit_%d.png", outputdir.c_str(), i) );
delete c1;
return f1;
}
FitResults fitSingleHisto_sum( TH1D* histo, double pedMin, double pedMax, double xMin, double xMax, bool isConstrained ) {
float integral = histo->Integral();
TF1* f1_ped = new TF1( "ped", "gaus", pedMin, pedMax );
f1_ped->SetParameter(0, integral);
f1_ped->SetParameter(1, 110.);
f1_ped->SetParameter(2, 10.);
f1_ped->SetLineColor(kRed+2);
if(pedMin>10){
histo->Fit( f1_ped, "RQN" );
int nSteps = 2;
for( unsigned iStep=0; iStep<nSteps; iStep++ ) {
float ped_mean = f1_ped->GetParameter(1);
float ped_sigma = f1_ped->GetParameter(2);
float nSigma = 2.;
float newMin = ped_mean-nSigma*ped_sigma;
float newMax = ped_mean+nSigma*ped_sigma;
f1_ped->SetRange( newMin, newMax );
std::string option = (iStep<(nSteps-1)) ? "RQN" : "RQ+";
histo->Fit( f1_ped, option.c_str() );
}
}
TF1* f1;
if(isConstrained){
f1 = new TF1( "func", PMTFunction, xMin, xMax, 8 );
}else{
f1 = new TF1( "func_unconstrained", PMTFunction_unconstrained, xMin, xMax, 8 );
}
f1->SetParameter( 0, integral ); //normalization
f1->SetParameter( 1, 1 ); //poiss mu
f1->SetParameter( 2, 25. ); //gauss step
f1->SetParameter( 3, 10. ); //gauss sigma
f1->SetParameter( 4, 100 ); //offset
f1->SetParameter( 5, 3. ); //sigmaoffset
f1->SetParameter( 6, 0.03 ); //alpha
f1->SetParameter( 7, 0.4 ); //w
f1->FixParameter( 5, 0. ); //sigmaoffset
f1->FixParameter( 6, 0. ); //alpha
f1->FixParameter( 7, 0. ); //w
f1->SetParLimits( 1, 0.5, 2.5 ); //poiss mu
f1->SetParLimits( 2, 10., 40. ); //gauss step
f1->SetParLimits( 3, 3., 12. ); //gauss sigma
//f1->SetParLimits( 4, 90., 110.); //offset
//f1->SetParLimits( 5, 0., 8. ); //gauss sigma
f1->SetLineColor(kRed+2);
if(pedMin<10){
f1->FixParameter( 4, 0 );
}
if(!isConstrained){
std::cout<<"------unconstrained fit"<<std::endl;
f1->FixParameter(1,1);
f1->SetLineColor(kBlue);
}
histo->Fit( f1, "R+" );
TCanvas* c1 = new TCanvas("c1", "c1", 600, 600);
c1->cd();
c1->SetLogy();
TH2D* h2_axes;
if(!(pedMin<10)){
h2_axes= new TH2D("axes", "", 10, 100., 350., 10, 9., 7.*histo->GetMaximum() );
}else{
h2_axes= new TH2D("axes", "", 10, 0., 200., 10, 9., 7.*histo->GetMaximum() );
}
h2_axes->SetXTitle( "ADC Counts" );
h2_axes->Draw();
histo->Draw("same");
TString histoName(histo->GetName());
c1->SaveAs( histoName + ".eps" );
c1->SaveAs( histoName + ".png" );
FitResults fr;
fr.ped_mu = f1_ped->GetParameter(1);
fr.ped_mu_err = f1_ped->GetParError(1);
fr.ped_sigma = f1_ped->GetParameter(2);
fr.ped_sigma_err = f1_ped->GetParError(2);
fr.mu = f1->GetParameter(1);
fr.mu_err = f1->GetParError(1);
fr.offset = f1->GetParameter(4);
fr.offset_err = f1->GetParError(4);
fr.Q1 = f1->GetParameter(2);
fr.Q1_err = f1->GetParError(2);
fr.sigma = f1->GetParameter(3);
fr.sigma_err = f1->GetParError(3);
f1->Clear();
delete c1;
delete f1;
delete f1_ped;
delete h2_axes;
return fr;
}
FitResults fitSingleHisto( TH1D* histo, double pedMin, double pedMax, double xMin, double xMax ) {
float integral = histo->Integral();
TF1* f1_ped = new TF1( "ped", "gaus", pedMin, pedMax );
f1_ped->SetParameter(0, integral);
f1_ped->SetParameter(1, 110.);
f1_ped->SetParameter(2, 10.);
f1_ped->SetLineColor(kRed+2);
if(pedMin>10){
histo->Fit( f1_ped, "RQN" );
int nSteps = 2;
for( unsigned iStep=0; iStep<nSteps; iStep++ ) {
float ped_mean = f1_ped->GetParameter(1);
float ped_sigma = f1_ped->GetParameter(2);
float nSigma = 2.;
float newMin = ped_mean-nSigma*ped_sigma;
float newMax = ped_mean+nSigma*ped_sigma;
f1_ped->SetRange( newMin, newMax );
std::string option = (iStep<(nSteps-1)) ? "RQN" : "RQ+";
histo->Fit( f1_ped, option.c_str() );
}
}
TF1* f1 = new TF1( "func", PMTFunction, xMin, xMax, 8 );
f1->SetParameter( 0, integral ); //normalization
f1->SetParameter( 1, 1. ); //poiss mu
f1->SetParameter( 2, 25. ); //gauss step
f1->SetParameter( 3, 10. ); //gauss sigma
f1->SetParameter( 4, 100 ); //offset
f1->SetParameter( 5, 3. ); //sigmaoffset
f1->SetParameter( 6, 0.03 ); //alpha
f1->SetParameter( 7, 0.4 ); //w
f1->FixParameter( 5, 0. ); //sigmaoffset
f1->FixParameter( 6, 0. ); //alpha
f1->FixParameter( 7, 0. ); //w
f1->SetParLimits( 1, 0.5, 2.5 ); //poiss mu
f1->SetParLimits( 2, 10., 40. ); //gauss step
f1->SetParLimits( 3, 3., 12. ); //gauss sigma
//f1->SetParLimits( 4, 90., 110.); //offset
//f1->SetParLimits( 5, 0., 8. ); //gauss sigma
f1->SetLineColor(kRed+2);
if(pedMin<10){
f1->FixParameter( 4, 0 );
}
histo->Fit( f1, "RN+" );
TString histoName(histo->GetName());
if(histoName=="cef3_pedSubtracted_corr_rebin_2") f1->SetRange(xMin, xMax+3); //plot for the paper
f1->SetLineColor(kRed);
TCanvas* c1 = new TCanvas("c1", "c1", 600, 600);
c1->cd();
// c1->SetLogy();
TH2D* h2_axes;
if(!(pedMin<10)){
h2_axes= new TH2D("axes", "", 10, 100., 350., 10, 9.,1.2*histo->GetMaximum() );
}else{
h2_axes= new TH2D("axes", "", 10, 0., 150., 10, 9., 1.2*histo->GetMaximum() );
}
h2_axes->SetXTitle( "ADC Channel" );
h2_axes->SetYTitle( "Entries / (2 ADC Channels)" );
h2_axes->Draw();
histo->SetLineWidth(2);
histo->SetXTitle( "ADC Counts" );
TPaveText* labelTop = DrawTools::getLabelTop("Cosmic Data");
labelTop->Draw("same");
gPad->RedrawAxis();
float N, mu, Q1, sigma;
float N_err, mu_err, Q1_err, sigma_err;
N=f1->GetParameter(0);
mu=f1->GetParameter(1);
Q1=f1->GetParameter(2);
sigma=f1->GetParameter(3);
N_err=f1->GetParError(0);
mu_err=f1->GetParError(1);
Q1_err=f1->GetParError(2);
sigma_err=f1->GetParError(3);
//longer version
TPaveText* label_fit = new TPaveText(0.52,0.67,0.8,0.85, "brNDC");
label_fit->SetFillColor(kWhite);
label_fit->SetTextSize(0.035);
label_fit->SetTextAlign(10); // align right
label_fit->SetTextFont(62);
label_fit->AddText("W-CeF_{3} Single Tower");
label_fit->AddText("Single photoelectron fit");
std::string N_str=Form("N=%4.0f #pm %2.0f", N,N_err);
// label_fit->AddText(N_str.c_str());
std::string mu_str=Form("#mu=%.2f #pm %.2f", mu,mu_err);
// label_fit->AddText(mu_str.c_str());
std::string Q_1_str=Form("Q_{1} = %.1f #pm %.1f", Q1,Q1_err);
label_fit->AddText(Q_1_str.c_str());
std::string sigma_str=Form("#sigma_{1} = %.1f #pm %.1f", sigma, sigma_err);
label_fit->AddText(sigma_str.c_str());
label_fit->Draw("same");
histo->Draw("same");
f1->Draw("same");
c1->SaveAs( histoName + ".eps" );
c1->SaveAs( histoName + ".png" );
c1->SaveAs( histoName + ".C" );
c1->SaveAs( histoName + ".pdf" );
c1->SetLogy();
c1->SaveAs( histoName + "_log.eps" );
c1->SaveAs( histoName + "_log.png" );
c1->SaveAs( histoName + "_log.C" );
c1->SaveAs( histoName + "_log.pdf" );
FitResults fr;
fr.ped_mu = f1_ped->GetParameter(1);
fr.ped_mu_err = f1_ped->GetParError(1);
fr.ped_sigma = f1_ped->GetParameter(2);
fr.ped_sigma_err = f1_ped->GetParError(2);
fr.mu = f1->GetParameter(1);
fr.mu_err = f1->GetParError(1);
fr.offset = f1->GetParameter(4);
fr.offset_err = f1->GetParError(4);
fr.Q1 = f1->GetParameter(2);
fr.Q1_err = f1->GetParError(2);
fr.sigma = f1->GetParameter(3);
fr.sigma_err = f1->GetParError(3);
delete c1;
delete f1;
delete f1_ped;
delete h2_axes;
return fr;
}
FitResults fitSingleHisto( TH1D* histo, double pedMin, double pedMax, double xMin, double xMax ) {
float integral = histo->Integral();
TF1* f1_ped = new TF1( "ped", "gaus", pedMin, pedMax );
f1_ped->SetParameter(0, integral);
f1_ped->SetParameter(1, 110.);
f1_ped->SetParameter(2, 10.);
f1_ped->SetLineColor(kRed+2);
if(pedMin>10){
histo->Fit( f1_ped, "RQN" );
int nSteps = 2;
for( unsigned iStep=0; iStep<nSteps; iStep++ ) {
float ped_mean = f1_ped->GetParameter(1);
float ped_sigma = f1_ped->GetParameter(2);
float nSigma = 2.;
float newMin = ped_mean-nSigma*ped_sigma;
float newMax = ped_mean+nSigma*ped_sigma;
f1_ped->SetRange( newMin, newMax );
std::string option = (iStep<(nSteps-1)) ? "RQN" : "RQ+";
histo->Fit( f1_ped, option.c_str() );
}
}
TF1* f1 = new TF1( "func", cef3Function, xMin, xMax, 8 );
f1->SetParameter( 0, integral ); //normalization
f1->SetParameter( 1, 1. ); //poiss mu
f1->SetParameter( 2, 3000. ); //gauss step
f1->SetParameter( 3, 500. ); //gauss sigma
f1->SetParameter( 4, 1000 ); //offset
f1->SetParameter( 5, 30. ); //sigmaoffset
f1->SetParameter( 6, 0.3 ); //alpha
f1->SetParameter( 7, 4 ); //w
f1->FixParameter( 5, 0. ); //sigmaoffset
f1->FixParameter( 6, 0. ); //alpha
f1->FixParameter( 7, 0. ); //w
// f1->SetParLimits( 1, 0.5, 2.5 ); //poiss mu
// f1->SetParLimits( 2, 10., 40. ); //gauss step
// f1->SetParLimits( 3, 3., 12. ); //gauss sigma
//f1->SetParLimits( 4, 90., 110.); //offset
//f1->SetParLimits( 5, 0., 8. ); //gauss sigma
f1->SetLineColor(kRed);
f1->SetLineWidth(3);
if(pedMin<10){
f1->FixParameter( 4, 0 );
}
histo->Fit( f1, "R+" );
TCanvas* c1 = new TCanvas("c1", "c1", 600, 600);
c1->cd();
c1->SetLogy();
TLine *lineLow1 = new TLine(f1->GetParameter(2)-2*f1->GetParameter(3),0,f1->GetParameter(2)-2*f1->GetParameter(3),1.2*histo->GetMaximum());
TLine *lineLow2 = new TLine(2*f1->GetParameter(2)-2*sqrt(2)*f1->GetParameter(3),0,2*f1->GetParameter(2)-2*sqrt(2)*f1->GetParameter(3),1.2*histo->GetMaximum());
TLine *lineUp2 = new TLine(2*f1->GetParameter(2)+2*sqrt(2)*f1->GetParameter(3),0,2*f1->GetParameter(2)+2*sqrt(2)*f1->GetParameter(3),1.2*histo->GetMaximum());
lineLow1->SetLineWidth(2);
lineLow2->SetLineWidth(2);
lineUp2->SetLineWidth(2);
lineLow1->SetLineColor(kRed);
lineLow2->SetLineColor(kBlue);
lineUp2->SetLineColor(kBlue);
std::cout<<f1->GetParameter(2)-2*f1->GetParameter(3)<<" "<<2*f1->GetParameter(2)-2*sqrt(2)*f1->GetParameter(3)<<std::endl;
histo->SetXTitle( "ADC Counts" );
histo->Draw();
TString histoName(histo->GetName());
c1->SaveAs( histoName + ".eps" );
c1->SaveAs( histoName + ".png" );
lineLow1->Draw("same");
lineLow2->Draw("same");
lineUp2->Draw("same");
c1->SaveAs( histoName + "_sigma.eps" );
c1->SaveAs( histoName + "_sigma.png" );
FitResults fr;
fr.ped_mu = f1_ped->GetParameter(1);
fr.ped_mu_err = f1_ped->GetParError(1);
fr.ped_sigma = f1_ped->GetParameter(2);
fr.ped_sigma_err = f1_ped->GetParError(2);
fr.mu = f1->GetParameter(1);
fr.mu_err = f1->GetParError(1);
fr.offset = f1->GetParameter(4);
fr.offset_err = f1->GetParError(4);
fr.Q1 = f1->GetParameter(2);
fr.Q1_err = f1->GetParError(2);
fr.sigma = f1->GetParameter(3);
fr.sigma_err = f1->GetParError(3);
delete c1;
delete f1;
delete f1_ped;
return fr;
}
