本文整理汇总了C++中TH1D::Delete方法的典型用法代码示例。如果您正苦于以下问题：C++ TH1D::Delete方法的具体用法？C++ TH1D::Delete怎么用？C++ TH1D::Delete使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类TH1D的用法示例。
在下文中一共展示了TH1D::Delete方法的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: fitSlices
void fitSlices(TH2* hCorr, TF1* func){
int nBins = hCorr->GetNbinsX();
TH1D* hMean = new TH1D(Form("%s_1",hCorr->GetName()),"",nBins,hCorr->GetXaxis()->GetXmin(),hCorr->GetXaxis()->GetXmax());
TH1D* hSigma = new TH1D(Form("%s_2",hCorr->GetName()),"",nBins,hCorr->GetXaxis()->GetXmin(),hCorr->GetXaxis()->GetXmax());
for(int i = 1; i < nBins+1; ++i){
int bin = nBins - i;
TH1D* h = hCorr->ProjectionY(Form("%s_bin%d",hCorr->GetName(),bin),i,i);
func->SetParameter(0,h->GetMaximum());
func->SetParameter(1,h->GetMean());
func->SetParameter(2,h->GetRMS());
if(useFits) h->Fit(func);
hMean->SetBinContent(i,func->GetParameter(1));
hMean->SetBinError(i,func->GetParError(1));
hSigma->SetBinContent(i,func->GetParameter(2));
hSigma->SetBinError(i,func->GetParError(2));
if(onlySaveTable){
h->Delete();
}
}
}示例2: plot_point
TGraphErrors* plot_point(
const TString infile,
double R,
double R_sig
)
{
TTree *TData = new TTree();
TData->ReadFile(infile, "t/D:x:y:z:B1:B1Range:B2:B2Range:B3:B3Range:V:T1:T2:T3:T4:T5:T6:T7:T8");
TH1D *h = new TH1D("h", "", 10000, 0, 10000);
TH1D *h2 = new TH1D("h2", "", 10000, 0, 10000);
TData->Draw("TMath::Abs(B2)>>h", "", "goff");
TData->Draw("TMath::Abs(B3)>>h2", "", "goff");
vector<double> B_ext, B_in, Bratio, u, u_err, B_err;
B_in.push_back(h2->GetMean(1));
B_ext.push_back(h->GetMean(1));
Bratio.push_back(B_in[0] / B_ext[0]);
u.push_back( (Bratio[0]*(R**2) + Bratio[0] - 2 - 2*sqrt((Bratio[0]**2)*(R**2) - Bratio[0]*(R**2) - Bratio[0] + 1 ) ) / (Bratio[0]*(R**2) - Bratio[0]) );
B_err.push_back( 0.00 );
u_err.push_back( 0.00 );
TGraphErrors *g_uvB = new TGraphErrors(u.size(), &B_ext[0], &u[0], &B_err[0], &u_err[0]);
h->Delete();
h2->Delete();
return g_uvB;
}示例3: AGY
/*=========================================================*/
Float_t AGY(Char_t *matn,Float_t peak,Float_t width=0.0)
{
Axis_t axmax=axmin+numchx;
setcanvas(2);
TH1D *hist;
hist=(TH1D*)gROOT->FindObject("xtemp");
if(hist!=NULL)hist->Delete();
hist=new TH1D("xtemp","xtemp",NCHX,0,DIMX);
hist->Add(histx,1.0);
Float_t a=GY(matn,peak,width);
histx->Add(hist,1.0);
c1->cd(2);
histx->SetAxisRange(axmin,axmax);
histx->SetLineColor(3);
histx->Draw();
return a;
}示例4: AGX
/*=========================================================*/
Float_t AGX(Char_t *matn,Float_t peak,Float_t width=0.0)
{
Axis_t aymax=aymin+numchy;
setcanvas(2);
TH1D *hist;
hist=(TH1D*)gROOT->FindObject("ytemp");
if(hist!=NULL)hist->Delete();
hist=new TH1D("ytemp","ytemp",NCHY,0,DIMY);
hist->Add(histy,1.0);
Float_t a=GX(matn,peak,width);
histy->Add(hist,1.0);
c1->cd(2);
histy->SetAxisRange(aymin,aymax);
histy->SetLineColor(3);
histy->Draw();
return a;
}示例5: choleskyUncertainty
double FitConfidence::choleskyUncertainty( double xx, double * fCov, TF1 * f, int nSamples ){
int nP = f->GetNpar();
INFO( FitConfidence::classname(), "Num Params : " << nP );
double *fCovSqrt = new double[ nP * nP ];
calcCholesky( nP, fCov, fCovSqrt );
double yerr = 0;
TH1D *hDistributionAtX = new TH1D("hDistributionAtX","",200,f->Eval(xx) - .2,f->Eval(xx) + .2);
for (int n = 0; n < nSamples; n++ ) {
double val = randomSqrtCov(xx,f,nP,fCovSqrt);
hDistributionAtX->Fill( val );
}
yerr = hDistributionAtX->GetRMS();
hDistributionAtX->Delete();
return yerr;
}示例6: makeCentralityTable
//.........这里部分代码省略.........
hNpart->Fill(parameter,npart);
hNcoll->Fill(parameter,ncoll);
hNhard->Fill(parameter,nhard);
hb->Fill(parameter,b);
}
// Fitting Glauber distributions in bins to get mean and sigma values
TF1* fGaus = new TF1("fb","gaus(0)",0,2);
fGaus->SetParameter(0,1);
fGaus->SetParameter(1,0.04);
fGaus->SetParameter(2,0.02);
fitSlices(hNpart,fGaus);
fitSlices(hNcoll,fGaus);
fitSlices(hNhard,fGaus);
fitSlices(hb,fGaus);
/*
hNpart->FitSlicesY();
hNcoll->FitSlicesY();
hNhard->FitSlicesY();
hb->FitSlicesY();
*/
TH1D* hNpartMean = (TH1D*)gDirectory->Get("hNpart_1");
TH1D* hNpartSigma = (TH1D*)gDirectory->Get("hNpart_2");
TH1D* hNcollMean = (TH1D*)gDirectory->Get("hNcoll_1");
TH1D* hNcollSigma = (TH1D*)gDirectory->Get("hNcoll_2");
TH1D* hNhardMean = (TH1D*)gDirectory->Get("hNhard_1");
TH1D* hNhardSigma = (TH1D*)gDirectory->Get("hNhard_2");
TH1D* hbMean = (TH1D*)gDirectory->Get("hb_1");
TH1D* hbSigma = (TH1D*)gDirectory->Get("hb_2");
cout<<"-------------------------------------"<<endl;
cout<<"# Bin NpartMean NpartSigma NcollMean NcollSigma bMean bSigma BinEdge"<<endl;
// Enter values in table
for(int i = 0; i < nbins; ++i){
bins->table_[nbins-i-1].n_part_mean = hNpartMean->GetBinContent(i);
bins->table_[nbins-i-1].n_part_var = hNpartSigma->GetBinContent(i);
bins->table_[nbins-i-1].n_coll_mean = hNcollMean->GetBinContent(i);
bins->table_[nbins-i-1].n_coll_var = hNcollSigma->GetBinContent(i);
bins->table_[nbins-i-1].b_mean = hbMean->GetBinContent(i);
bins->table_[nbins-i-1].b_var = hbSigma->GetBinContent(i);
bins->table_[nbins-i-1].n_hard_mean = hNhardMean->GetBinContent(i);
bins->table_[nbins-i-1].n_hard_var = hNhardSigma->GetBinContent(i);
bins->table_[nbins-i-1].bin_edge = binboundaries[i];
cout<<i<<" "
<<hNpartMean->GetBinContent(i)<<" "
<<hNpartSigma->GetBinContent(i)<<" "
<<hNcollMean->GetBinContent(i)<<" "
<<hNcollSigma->GetBinContent(i)<<" "
<<hbMean->GetBinContent(i)<<" "
<<hbSigma->GetBinContent(i)<<" "
<<binboundaries[i]<<" "
<<endl;
}
cout<<"-------------------------------------"<<endl;
// Save the table in output file
if(onlySaveTable){
TH1D* hh = (TH1D*)gDirectory->Get("hNpart_0");
hh->Delete();
hh = (TH1D*)gDirectory->Get("hNcoll_0");
hh->Delete();
hh = (TH1D*)gDirectory->Get("hNhard_0");
hh->Delete();
hh = (TH1D*)gDirectory->Get("hb_0");
hh->Delete();
hNpart->Delete();
hNpartMean->Delete();
hNpartSigma->Delete();
hNcoll->Delete();
hNcollMean->Delete();
hNcollSigma->Delete();
hNhard->Delete();
hNhardMean->Delete();
hNhardSigma->Delete();
hb->Delete();
hbMean->Delete();
hbSigma->Delete();
}
for(int i = 0; i < runnums.size(); ++i){
CentralityBins* binsForRun = (CentralityBins*) bins->Clone();
binsForRun->SetName(Form("run%d",runnums[i]));
binsForRun->Write();
}
bins->Delete();
outFile->Write();
}示例7: GeneralCorrectionFunction
TF1* GeneralCorrectionFunction(
double ChannelRangeMin, double ChannelRangeMax,double ChannelPeakPos ,
TH2D *h2DInputForCorrection ,
int LineIndex,
TString InputType="T10DeriMaxEnergy",
TString CorrNumber="1",
double XRangeMin=0, double XRangeMax=300,
TString FitFuncCorr="pol2",
double FitCorrRangeMin=10, double FitCorrRangeMax= 270,
double TresholdForCorrection=10,
TString FitFuncSlicesString="gaus(0)+[3]+gaus(4)"
)
{
h2DInputForCorrection->GetYaxis()->SetRangeUser(ChannelRangeMin,ChannelRangeMax);
char buf[60];
sprintf(buf, "hMaxCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
TH1D *hMaxPosManually=new TH1D(buf,"",h2DInputForCorrection->GetNbinsX(),h2DInputForCorrection->GetXaxis()->GetXmin(),h2DInputForCorrection->GetXaxis()->GetXmax());
sprintf(buf, "hMaxFitCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
TH1D *hMaxPosManuallyFit=new TH1D(buf,"",h2DInputForCorrection->GetNbinsX(),h2DInputForCorrection->GetXaxis()->GetXmin(),h2DInputForCorrection->GetXaxis()->GetXmax());
sprintf(buf, "hGausSigmaCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
TH1D *hGausSigmaManually=new TH1D(buf,"",h2DInputForCorrection->GetNbinsX(),h2DInputForCorrection->GetXaxis()->GetXmin(),h2DInputForCorrection->GetXaxis()->GetXmax());
for(int binX = h2DInputForCorrection->GetXaxis()->FindBin(XRangeMin);binX <= h2DInputForCorrection->GetXaxis()->FindBin(XRangeMax);binX++)
//for(int binX = h2DInputForCorrection->GetXaxis()->FindBin(200);binX <= h2DInputForCorrection->GetXaxis()->FindBin(200);binX++)
{
TH1D *hProfileY =h2DInputForCorrection->ProjectionY("_py",binX,binX);
double MaxValue=hProfileY->GetBinCenter(hProfileY->GetMaximumBin());
//hMaxPosManually->SetBinContent(binX, MaxValue);
//h2DInputForCorrection
//cout <<hProfileY->GetEntries()<<endl;
//TF1* FitFuncSlices = new TF1("FitFuncSlices","gaus(0)+[3]",MaxValue-20,MaxValue+20);
//cout << TMath::Max(MaxValue-20,double(ChannelRangeMin)) << "\t" << TMath::Min(MaxValue+20,double(ChannelRangeMax)) << "\t"<<endl;
TF1* FitFuncGausSlices = new TF1("FitFuncGausSlices","gaus(0)",TMath::Max(MaxValue-20,double(ChannelRangeMin)),TMath::Min(MaxValue+20,double(ChannelRangeMax)));
FitFuncGausSlices->SetParameters(hProfileY->GetBinContent(hProfileY->GetMaximumBin()),MaxValue,4);
hProfileY->Fit(FitFuncGausSlices,"RNIQ");
TF1* FitFuncSlices = new TF1("FitFuncSlices",FitFuncSlicesString.Data(),TMath::Max(MaxValue-3*FitFuncGausSlices->GetParameter(2),double(ChannelRangeMin)),TMath::Min(MaxValue+3*FitFuncGausSlices->GetParameter(2),double(ChannelRangeMax)));
FitFuncSlices->SetParameters(FitFuncGausSlices->GetParameter(0),FitFuncGausSlices->GetParameter(1),FitFuncGausSlices->GetParameter(2),10,10,FitFuncGausSlices->GetParameter(1)-5,5);
FitFuncSlices->SetParLimits(0,FitFuncGausSlices->GetParameter(0)*0.8,FitFuncGausSlices->GetParameter(0)*1.5);
FitFuncSlices->SetParLimits(1,TMath::Max(FitFuncGausSlices->GetParameter(1)-10,double(ChannelRangeMin)),TMath::Min(FitFuncGausSlices->GetParameter(1)+10,double(ChannelRangeMax)));
FitFuncSlices->SetParLimits(2,0,FitFuncGausSlices->GetParameter(2)*2);
FitFuncSlices->SetParLimits(3,0,500);
FitFuncSlices->SetParLimits(4,0,FitFuncGausSlices->GetParameter(0)*0.3);
FitFuncSlices->SetParLimits(5,TMath::Max(FitFuncGausSlices->GetParameter(1)-10,double(ChannelRangeMin)),TMath::Min(MaxValue-1,double(ChannelRangeMax)));
FitFuncSlices->SetParLimits(6,0,10);
hProfileY->Fit(FitFuncSlices,"RINQ");
//hProfileY->DrawCopy();
//cout <<MaxValue<<" " << FitFuncSlices->GetParameter(1) << " " << FitFuncSlices->GetParError(1) <<endl;
//cout <<MaxValue<<" " << FitFuncSlices->GetParameter(1) << " " << FitFuncSlices->GetMaximumX() <<endl;
hMaxPosManually->SetBinContent(binX, (FitFuncSlices->GetParameter(1))/ChannelPeakPos);
hMaxPosManually->SetBinError(binX, FitFuncSlices->GetParError(1)/ChannelPeakPos);
hGausSigmaManually->SetBinContent(binX, FitFuncSlices->GetParameter(2));
hGausSigmaManually->SetBinError(binX, FitFuncSlices->GetParError(2));
if(FitFuncSlices->GetParameter(2)<TresholdForCorrection && FitFuncSlices->GetParError(2)<5)
{
hMaxPosManuallyFit->SetBinContent(binX, (FitFuncSlices->GetParameter(1))/ChannelPeakPos);
hMaxPosManuallyFit->SetBinError(binX, FitFuncSlices->GetParError(1)/ChannelPeakPos);
}
//hSpectrumTDeriMax1090Rel_EnergyChannel_MaxPosManually->SetBinError(binX, FitFuncSlices->GetParameter(2)/ChannelPeakPos);
hProfileY->Delete();
//cin.ignore();
}
//write histos to file
//sprintf(buf, "hMaxCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
hMaxPosManually->Write(0,TObject::kOverwrite);
//sprintf(buf, "hGausSigmaCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
hGausSigmaManually->Write(0,TObject::kOverwrite);
hMaxPosManuallyFit->Write(0,TObject::kOverwrite);
sprintf(buf, "funcCorr%s_%sNorm_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
//fit corr function and write it to file
TF1 *funcCorrNorm = new TF1(buf,FitFuncCorr.Data(),FitCorrRangeMin,FitCorrRangeMax);
funcCorrNorm->SetParameters(1,0,-0);
funcCorrNorm->SetParLimits(0,0.8,1);
//funcCorrNorm->SetParLimits(2,-1E5,0);
//if(LineIndex==2)
// hMaxPosManuallyFit->Fit(funcCorrNorm,"RBI");
//else
hMaxPosManuallyFit->Fit(funcCorrNorm,"RBQI");
sprintf(buf, "funcCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
TF1 *funcCorr = new TF1(buf,FitFuncCorr.Data(),FitCorrRangeMin,FitCorrRangeMax);
for(int i= 0; i<funcCorr->GetNpar();i++)
{
funcCorr->SetParameter(i,funcCorrNorm->GetParameter(i)*ChannelPeakPos);
}
//sprintf(buf, "funcCorr%s_%sNorm_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
funcCorrNorm->Write(0,TObject::kOverwrite);
//sprintf(buf, "funcCorr%s_%s_%d",CorrNumber.Data(),InputType.Data(),LineIndex);
funcCorr->Write(0,TObject::kOverwrite);
h2DInputForCorrection->GetYaxis()->UnZoom();
return funcCorr;
}示例8: randomSqrtCov
TGraphErrors *FitConfidence::choleskyBands( TFitResultPtr fitResult, TF1 * f, int nSamples, int nPoints, Reporter* rp, double x1, double x2 ){
int nP = f->GetNpar();
// INFO( FitConfidence::classname(), "Num Params : " << nP );
TMatrixDSym cov = fitResult->GetCovarianceMatrix();
double *covArray = new double[ nP * nP ]; // number of parameters x number of parameters
covArray = cov.GetMatrixArray();
// for ( int i = 0; i < 9; i++ ){
// INFO( FitConfidence::classname(), "[" << i << "] = " << covArray[ i ] );
// }
double *fCov = new double[ nP * nP ];
fCov = cov.GetMatrixArray();
double *fCovSqrt = new double[ nP * nP ];
calcCholesky( nP, fCov, fCovSqrt );
// for ( int i = 0; i < 9; i++ ){
// INFO( FitConfidence::classname(), "[" << i << "] = " << fCovSqrt[ i ] );
// }
// calculate instead
if ( -1.0 == x1 && -1.0 == x2 )
f->GetRange( x1, x2 );
double step = ( x2 - x1 ) / (double) nPoints;
double x[ nPoints + 1 ];
double y[ nPoints + 1 ];
// double yup[ nPoints + 1 ];
// double ydown[ nPoints + 1 ];
double yerr[ nPoints + 1 ];
vector<double> samples;
int i = 0;
for (double xx = x1; xx < x2; xx+= step) {
x[i] = xx;
TH1D *hDistributionAtX = new TH1D("hDistributionAtX","",200,f->Eval(x[i]) - .2,f->Eval(x[i]) + .2);
for (int n = 0; n < nSamples; n++ ) {
double val = randomSqrtCov(x[i],f,nP,fCovSqrt);
hDistributionAtX->Fill( val );
samples.push_back( val );
}
//hDistributionAtX->DrawCopy();
y[i] = f->Eval(x[i]);
yerr[i] = hDistributionAtX->GetRMS();
// cross check - should always give the same result
// but very innefficient
//yerr[i] = choleskyUncertainty( xx, fitResult, f, nSamples );
// rp.savePage();
hDistributionAtX->Delete();
i++;
}
TGraphErrors * g = new TGraphErrors( i - 1, x, y, 0, yerr );
return g;
}示例9: makeTable
//.........这里部分代码省略.........
fitSlices(hb,fGaus);
TH1D* hNpartMean = (TH1D*)gDirectory->Get("hNpart_1");
TH1D* hNpartSigma = (TH1D*)gDirectory->Get("hNpart_2");
TH1D* hNcollMean = (TH1D*)gDirectory->Get("hNcoll_1");
TH1D* hNcollSigma = (TH1D*)gDirectory->Get("hNcoll_2");
TH1D* hNhardMean = (TH1D*)gDirectory->Get("hNhard_1");
TH1D* hNhardSigma = (TH1D*)gDirectory->Get("hNhard_2");
TH1D* hbMean = (TH1D*)gDirectory->Get("hb_1");
TH1D* hbSigma = (TH1D*)gDirectory->Get("hb_2");
cout<<"-------------------------------------"<<endl;
cout<<"# Bin NpartMean NpartSigma NcollMean NcollSigma bMean bSigma BinEdge"<<endl;
// Enter values in table
for(int i = 0; i < nbins; ++i){
int ii = nbins-i;
bins->table_[i].n_part_mean = hNpartMean->GetBinContent(ii);
bins->table_[i].n_part_var = hNpartSigma->GetBinContent(ii);
bins->table_[i].n_coll_mean = hNcollMean->GetBinContent(ii);
bins->table_[i].n_coll_var = hNcollSigma->GetBinContent(ii);
bins->table_[i].b_mean = hbMean->GetBinContent(ii);
bins->table_[i].b_var = hbSigma->GetBinContent(ii);
bins->table_[i].n_hard_mean = hNhardMean->GetBinContent(ii);
bins->table_[i].n_hard_var = hNhardSigma->GetBinContent(ii);
bins->table_[i].bin_edge = binboundaries[ii-1];
cout<<i<<" "
<<hNpartMean->GetBinContent(ii)<<" "
<<hNpartSigma->GetBinContent(ii)<<" "
<<hNcollMean->GetBinContent(ii)<<" "
<<hNcollSigma->GetBinContent(ii)<<" "
<<hbMean->GetBinContent(ii)<<" "
<<hbSigma->GetBinContent(ii)<<" "
<<binboundaries[ii]<<" "
<<endl;
}
cout<<"-------------------------------------"<<endl;
// Save the table in output file
if(onlySaveTable){
hNpart->Delete();
hNpartMean->Delete();
hNpartSigma->Delete();
hNcoll->Delete();
hNcollMean->Delete();
hNcollSigma->Delete();
hNhard->Delete();
hNhardMean->Delete();
hNhardSigma->Delete();
hb->Delete();
hbMean->Delete();
hbSigma->Delete();
}
}else{
cout<<"-------------------------------------"<<endl;
cout<<"# Bin NpartMean NpartSigma NcollMean NcollSigma bMean bSigma BinEdge"<<endl;
// Enter values in table
for(int i = 0; i < nbins; ++i){
int ii = nbins-i;
bins->table_[i].n_part_mean = inputMCtable->NpartMeanOfBin(i);
bins->table_[i].n_part_var = inputMCtable->NpartSigmaOfBin(i);
bins->table_[i].n_coll_mean = inputMCtable->NcollMeanOfBin(i);
bins->table_[i].n_coll_var = inputMCtable->NcollSigmaOfBin(i);
bins->table_[i].b_mean = inputMCtable->bMeanOfBin(i);
bins->table_[i].b_var = inputMCtable->bSigmaOfBin(i);
bins->table_[i].n_hard_mean = inputMCtable->NhardMeanOfBin(i);
bins->table_[i].n_hard_var = inputMCtable->NhardSigmaOfBin(i);
bins->table_[i].bin_edge = binboundaries[ii-1];
cout<<i<<" "
<<bins->table_[i].n_part_mean<<" "
<<bins->table_[i].n_part_var<<" "
<<bins->table_[i].n_coll_mean<<" "
<<bins->table_[i].n_coll_var<<" "
<<bins->table_[i].b_mean<<" "
<<bins->table_[i].b_var<<" "
<<bins->table_[i].n_hard_mean<<" "
<<bins->table_[i].n_hard_var<<" "
<<bins->table_[i].bin_edge<<" "<<endl;
}
cout<<"-------------------------------------"<<endl;
}
outFile->cd();
dir->cd();
bins->SetName(Form("run%d",1));
bins->Write();
nt->Write();
bins->Delete();
outFile->Write();
}示例10: main
//.........这里部分代码省略.........
// --- 2011 Lumi old --- //
//double lumiDY = 29743564.0 / 1626.0;
//double lumiTtJets = 3701947.0 / 94.76;
//double lumiwJets = 81345381.0 / 27770.0;
ttJetsNew->Scale(lumiDY / lumiTtJets);
wJetsNew->Scale(lumiDY / lumiwJets);
dYToMuMuFSRNew->Add(dYToMuMuNonFSRNew);
dYToMuMuFSRNew->Add(ttJetsNew);
dYToMuMuFSRNew->Add(wJetsNew);
dYToMuMuNonFSRNew->Add(ttJetsNew);
dYToMuMuNonFSRNew->Add(wJetsNew);
ttJetsNew->Add(wJetsNew);
c1->Clear();
dYToMuMuFSRNew->GetYaxis()->SetTitle("Events / 0.004"); //FIXME
dYToMuMuFSRNew->GetXaxis()->SetTitle("smearing factors");
/*
dYToMuMuFSR->GetXaxis()->SetLabelFont(42);
dYToMuMuFSR->GetXaxis()->SetTitleFont(42);
dYToMuMuFSR->GetYaxis()->SetLabelFont(42);
dYToMuMuFSR->GetYaxis()->SetTitleFont(42);
dYToMuMuFSR->GetYaxis()->SetTitleOffset(1.65);
*/
dYToMuMuFSRNew->SetFillColor(kGreen-7);
dYToMuMuNonFSRNew->SetFillColor(kAzure-5);
ttJetsNew->SetFillColor(kBlue-1);
wJetsNew->SetFillColor(kCyan+2);
dYToMuMuFSRNew->Draw("");
// --- Fit --- //
TF1 * f1 = new TF1("f1","gaus",xMin,xMax);
//TF1 * f1 = new TF1("f1","gaus",0.96,1.04);
//f1->FixParameter(1,1.0); //FIXME
//f1->FixParameter(2,1.0); //FIXME
dYToMuMuFSRNew->Fit(f1);
f1->SetLineColor(kBlue);
f1->SetLineWidth(2);
f1->Draw("SAMES");
c1->Clear();
dYToMuMuFSRNew->Draw("E");
f1->Draw("SAMES");
dYToMuMuFSRNew->SetMarkerStyle(20);
dYToMuMuFSRNew->SetMarkerSize(0.5);
TLatex latexLabel;
latexLabel.SetTextFont(42);
latexLabel.SetTextSize(0.028);
latexLabel.SetNDC();
//latexLabel.DrawLatex(0.25, 0.96, "CMS Preliminary 2011 #sqrt{s} = 7 TeV L = 4.93 fb^{-1}");
//double Ymin = 0;
//double Ymax = max(dYToMuMuFSR->GetMaximum(),data->GetMaximum()) + max(dYToMuMuFSR->GetMaximum(),data->GetMaximum()) * 0.1;
//cout << "Ymax = "<<Ymax<<endl;
//dYToMuMuFSRNew->GetYaxis()->SetRangeUser(Ymin,Ymax);
plotsRecording(directoryName, fileName, c1);
c1->Clear();
f1->Delete();
f1 = 0;
dYToMuMuFSRNew->Delete();
dYToMuMuFSRNew = 0;
dYToMuMuNonFSRNew->Delete();
dYToMuMuNonFSRNew = 0;
ttJetsNew->Delete();
ttJetsNew = 0;
wJetsNew->Delete();
wJetsNew = 0;
dYToMuMuFSRChainNew->Delete();
dYToMuMuFSRChainNew = 0;
dYToMuMuNonFSRChainNew->Delete();
dYToMuMuNonFSRChainNew = 0;
ttJetsChainNew->Delete();
ttJetsChainNew = 0;
wJetsChainNew->Delete();
wJetsChainNew = 0;
delete c1;
c1 = 0;
return 0;
}