void Draw_KL_Test(){
TChain* ch = new TChain("Tree");
TChain* ch1 = new TChain("Tree");
TH1D* his = new TH1D("Klong6g","Klong6g",20,450,550);
TH1D* his1 = new TH1D("Klong4g","Klong4g",20,450,550);
TH1D* his2 = new TH1D("Klong4gAll","Klong4gAll",60,250,550);
for( int i = 0; i< 68; i++){
ch->Add(Form("klongRootFile/kl%d.root" ,4162+i));
ch1->Add(Form("klongRootFile/ks%d.root",4162+i));
}
ch->Project(his->GetName() ,"KlongMass[0]","CutCondition==0");
ch1->Project(his1->GetName(),"KlongMass[0]","CutCondition==0");
ch1->Project(his2->GetName(),"KlongMass[0]","CutCondition==0");
TF1* func = new TF1("func","gaus(0)+expo(3)",0,550);
func->SetParameter(1,498);
func->SetParameter(2,5);
TF1* func2 = new TF1("func2","gaus(0)",0,550);
func2->SetParameter(1,498);
func2->SetParameter(2,5);
TCanvas* can = new TCanvas("can","",1200,600);
can->Divide(2,1);
can->cd(1);
his2->Fit(func->GetName(),"","",450,550);
his2->Draw();
TF1* func1 = new TF1("Test","gaus",450,550);
func1->SetParameter(0,func->GetParameter(0));
func1->SetParameter(1,func->GetParameter(1));
func1->SetParameter(2,func->GetParameter(2));
can->cd(2);
his1->SetLineColor(2);
his->Draw();
his->Fit(func2->GetName(),"","",450,550);
func->Draw("same");
his1->Draw("same");
std::cout<< func2->GetParameter(0) << " "
<< func->GetParameter(0) << " "
<< func->GetParameter(0)/func2->GetParameter(0)<< std::endl;
std::cout<< func2->Integral(450,550) << " "
<< func1->Integral(450,550) << " "
<< func1->Integral(450,550)/func2->Integral(450,550)
<< std::endl;
//ch->Draw("KlongPt[0]:KlongMass[0]>>(400,200,600,50,0,20)","(CutCondition&(1|2|4|8))==0","colz");
gPad->SetLogz();
TText* text = new TText(0.5,0.5,"");
TText* text1 = new TText(0.5,0.5,"");
text->DrawTextNDC(0.5,0.5,Form("Integral:%2.3lf",func1->Integral(450,550)));
text1->DrawTextNDC(0.5,0.6,Form("Integral:%2.3lf",func2->Integral(450,550)));
}
void FitWithConstant(TH1D* h1, TFile *out)
{
Double_t fitRangeLow = 0.;
Double_t fitRangeHigh = .4;
TF1 *f = new TF1("f","[0]", fitRangeLow, fitRangeHigh);
Int_t binLow = h1->FindBin(fitRangeLow);
Int_t binHigh = h1->FindBin(fitRangeHigh);
cout<<"\t\t********************Fitting with a constant value********************"<<endl<<endl;;
h1->Fit(f, "R0");
TString fitName = h1->GetName();
fitName += "Fit";
f->SetName(fitName);
f->SetTitle(fitName);
// TFile out("Compare.root","update");
TDirectory *dir = out->GetDirectory("Fit");
if(!dir) dir = out->mkdir("Fit");
dir->cd();
// f->SetDirectory(0);
f->Write(f->GetName(), TObject::kOverwrite);
// out->Close();
}
int makeInvMassHistosNoBGKK(){
//Set global style stuff
gROOT->Reset();
gROOT->SetStyle("Plain");
gStyle->SetPalette(1);
gStyle->SetCanvasColor(kWhite);
gStyle->SetCanvasBorderMode(0);
gStyle->SetPadBorderMode(0);
gStyle->SetTitleBorderSize(0);
gStyle->SetOptStat(0);
gStyle->SetOptFit(1);
gStyle->SetErrorX(0);
gStyle->SetTitleW(0.9);
gStyle->SetTitleSize(0.05, "xyz");
gStyle->SetTitleSize(0.06, "h");
int NUM_PT_BINS = 20;
int NUM_MASS_BINS = 1000;
double MASS_LOW = 0.0;
double MASS_HIGH = 2.0;
string particles [8];
particles[0] = "K*^{+} + K*^{0}";
particles[1] = "K*^{-} + #bar{K}*^{0}";
particles[2] = "K*^{+}";
particles[3] = "K*^{-}";
particles[4] = "K*^{0}";
particles[5] = "#bar{K}*^{0}";
particles[6] = "K*^{0} + #bar{K}*^{0}";
particles[7] = "K*^{+} + K*^{-}";
//at decay point
// string folder = "/Users/jtblair/Downloads/kstar_data/decayed/pt02/";
//reconstructed
string folder = "/Users/jtblair/Downloads/kstar_data/reconstructed/pt02/";
string files[20];
files[0] = "invm_[0.0,0.2].dat";
files[1] = "invm_[0.2,0.4].dat";
files[2] = "invm_[0.4,0.6].dat";
files[3] = "invm_[0.6,0.8].dat";
files[4] = "invm_[0.8,1.0].dat";
files[5] = "invm_[1.0,1.2].dat";
files[6] = "invm_[1.2,1.4].dat";
files[7] = "invm_[1.4,1.6].dat";
files[8] = "invm_[1.6,1.8].dat";
files[9] = "invm_[1.8,2.0].dat";
files[10] = "invm_[2.0,2.2].dat";
files[11] = "invm_[2.2,2.4].dat";
files[12] = "invm_[2.4,2.6].dat";
files[13] = "invm_[2.6,2.8].dat";
files[14] = "invm_[2.8,3.0].dat";
files[15] = "invm_[3.0,3.2].dat";
files[16] = "invm_[3.2,3.4].dat";
files[17] = "invm_[3.4,3.6].dat";
files[18] = "invm_[3.6,3.8].dat";
files[19] = "invm_[3.8,4.0].dat";
/*
string files[8];
files[0] = "invm_[0.0,0.5].dat";
files[1] = "invm_[0.5,1.0].dat";
files[2] = "invm_[1.0,1.5].dat";
files[3] = "invm_[1.5,2.0].dat";
files[4] = "invm_[2.0,2.5].dat";
files[5] = "invm_[2.5,3.0].dat";
files[6] = "invm_[3.0,3.5].dat";
files[7] = "invm_[3.5,4.0].dat";
*/
Int_t PARTICLE_NUM = 5;
TFile *output = new TFile("20170721_KKbarAdded2_fixedwidth42_recon_pf100_scaled_error05.root", "RECREATE");
TH1D *kstar0mass = new TH1D("kstar0mass", Form("Fit value of M*_{0} vs. p_{T} for %s", particles[PARTICLE_NUM].c_str()), NUM_PT_BINS, 0.0, 4.0);
TH1D *kstar0width = new TH1D("kstar0width", Form("#Gamma_{tot}(M=M*_{0}) vs p_{T} for %s", particles[PARTICLE_NUM].c_str()), NUM_PT_BINS, 0.0, 4.0);
TH1D *kstar0collWidth = new TH1D("kstar0collWidth", Form("Fit value of #Gamma_{coll} component vs. p_{T} for %s", particles[PARTICLE_NUM].c_str()), NUM_PT_BINS,0.0, 4.0);
TH1D *kstar0decWidth = new TH1D("kstar0decWidth", Form("#Gamma_{dec}(M=M*_{0}) component vs. p_{T} for %s;p_{T} (GeV/c);Width (GeV/c^2)", particles[PARTICLE_NUM].c_str()), NUM_PT_BINS,0.0, 4.0);
kstar0mass->GetXaxis()->SetTitle("p_{T} (GeV/c)");
kstar0mass->GetYaxis()->SetTitle("Mass (GeV/c^{2})");
kstar0width->GetXaxis()->SetTitle("p_{T} (GeV/c)");
kstar0width->GetYaxis()->SetTitle("Width (GeV/c^2)");
kstar0collWidth->GetXaxis()->SetTitle("p_{T} (GeV/c)");
kstar0collWidth->GetYaxis()->SetTitle("Width (GeV/c^2)");
kstar0mass->SetStats(kFALSE);
kstar0width->SetStats(kFALSE);
kstar0collWidth->SetStats(kFALSE);
kstar0decWidth->SetStats(kFALSE);
TF1 *massline = new TF1("massline", "[0]", 0.0, 4.0);
massline->SetParameter(0, 0.892);
massline->SetLineColor(2);
massline->SetLineStyle(7);
TF1 *widthline = new TF1("widthline", "[0]", 0.0, 4.0);
widthline->SetParameter(0, 0.042);
double mass = 0.0, width = 0.0, collWidth = 0.0, massBG=0.0;
double massError = 0.0, widthError= 0.0, collWidthError = 0.0, massBGError=0.0;
TCanvas *canvas[9];
TCanvas *diffCanvas[9];
TPaveStats *st;
TPad *pad;
//ofstream integrals;
//integrals.open("kstarbar_integrals.txt");
for(int nfile = 0; nfile < NUM_PT_BINS; nfile++){
double meanPT = (double)(nfile*2+1)/10.0;
string filename = folder+files[nfile];
string ptLower = filename.substr(filename.find("[")+1, 3);
string ptHigher = filename.substr(filename.find(",")+1, 3);
TH1D* histos[8];
TH1D* newHistos[8];
TH1D* diffHistos[8];
TH1D* bg[8];
for(int i=0; i<8; i++){
if(nfile<5){
histos[i] = new TH1D(Form("ptbin0%dparticle%d",nfile*2+1, i), Form("Invariant Mass for (%s), %s < p_{T} < %s",particles[i].c_str(), ptLower.c_str(), ptHigher.c_str()), NUM_MASS_BINS, MASS_LOW, MASS_HIGH);
newHistos[i] = new TH1D(Form("newptbin0%dparticle%d",nfile*2+1, i), Form("Invariant Mass for (%s), %s < p_{T} < %s",particles[i].c_str(), ptLower.c_str(), ptHigher.c_str()), 250, MASS_LOW, MASS_HIGH);
}else{
histos[i] = new TH1D(Form("ptbin%dparticle%d",nfile*2+1, i), Form("Invariant Mass for (%s), %s < p_{T} < %s",particles[i].c_str(), ptLower.c_str(), ptHigher.c_str()), NUM_MASS_BINS, MASS_LOW, MASS_HIGH);
newHistos[i] = new TH1D(Form("newptbin%dparticle%d",nfile*2+1, i), Form("Invariant Mass for (%s), %s < p_{T} < %s",particles[i].c_str(), ptLower.c_str(), ptHigher.c_str()), 250, MASS_LOW, MASS_HIGH);
}
histos[i]->GetXaxis()->SetTitle("Invariant Mass (GeV/c^{2})");
histos[i]->GetYaxis()->SetTitle("Counts");
}
ifstream input;
input.open(filename.c_str());
string line = "";
if(input.good()){
getline(input, line);
}
double massBin=0.0;
double invMass[8];
for(int i=0; i<8; i++){
invMass[i] = 0.0;
}
int lineNumber = 1;
while(1){
input >> massBin >> invMass[0] >> invMass[1] >> invMass[2] >> invMass[3] >> invMass[4] >> invMass[5] >> invMass[6] >> invMass[7];
if(!input.good())break;
for(int i =0; i<8; i++){
histos[i]->SetBinContent(lineNumber, invMass[i]/500.0);
}
if(lineNumber > 440 && lineNumber < 460 && nfile==6){
// printf("mass: %.12f invMass[6]: %.12f\n", massBin, invMass[6]);
}
lineNumber++;
}
printf("****** Fits for file: %s ******\n", filename.c_str());
for(int i=PARTICLE_NUM; i<PARTICLE_NUM+1; i++){
//add the K*0 distribution to the K*0bar (K*0 = 4 for decay, K*0 = 3 for reconstructed)
histos[i]->Add(histos[3]);
if(nfile==0){
canvas[i] = new TCanvas(Form("c%i", i),Form("c%i", i), 0,0,900,900);
canvas[i]->Divide(5,4);
diffCanvas[i] = new TCanvas(Form("diffC%i", i),Form("diffC%i", i), 0,0,900,900);
diffCanvas[i]->Divide(5,4);
}
//rebin
//histos[i]->Sumw2();
histos[i]->Rebin(4);
//Fixing the errors to a percentage of the signal region:
for(int ibin=1; ibin < histos[i]->GetNbinsX(); ibin++){
histos[i]->SetBinError(ibin, histos[i]->GetBinContent((int)(0.892*(250.0/2.0)))*0.05);
newHistos[i]->SetBinContent(ibin, histos[i]->GetBinContent(ibin));
newHistos[i]->SetBinError(ibin, histos[i]->GetBinError(ibin));
}
pad = (TPad*)canvas[i]->cd(nfile+1);
histos[i]->SetLineColor(1);
histos[i]->SetLineWidth(1);
histos[i]->GetXaxis()->SetRangeUser(0.7, 1.2);
histos[i]->GetYaxis()->SetRangeUser(0, 1.5*histos[i]->GetBinContent(histos[i]->GetMaximumBin()));
//histos[i]->SetStats(kFALSE);
//histos[i]->Draw("HIST");
printf("mean PT: %f\n", meanPT);
TF1 *fit = new TF1(Form("fitPTbin%d00particle%d", nfile*2+1, i), FitFunRelBW, 0.68, 1.05, 5);
//TF1 *fit = new TF1(Form("fitPTbin%d00particle%d", nfile*2+1, i), "gaus(0)", 0.86, 0.92);
fit->SetParNames("BW Area", "Mass", "Width", "PT", "Temp");
fit->SetParameters(TMath::Power(10.0, (float)(nfile)/1.7), 0.89, 0.1, 0.5, 0.130);
//fit->SetParNames("BW Area", "Mass", "Width");
//fit->SetParameters(100, 0.89, 0.0474);
//fit->SetParLimits(0, -10, 1.5e9);
Float_t max = histos[i]->GetXaxis()->GetBinCenter(histos[i]->GetMaximumBin());
//if(max < 0.91 && max > 0.892){
// fit->SetParLimits(1, max-0.001, max+0.001);
//}else{
fit->SetParLimits(1, 0.82, 0.98);
//}
//fit->SetParLimits(2, 0.005, 0.15);
fit->FixParameter(2, 0.042);
fit->FixParameter(3, meanPT);
//fit->SetParLimits(4, 0.05, 0.2);
fit->FixParameter(4, 0.100001);
fit->SetLineColor(2);
printf("%s\n", fit->GetName());
histos[i]->Fit(Form("fitPTbin%d00particle%d", nfile*2+1, i), "BRIM", "SAME");
TVirtualFitter *fitter = TVirtualFitter::GetFitter();
histos[i]->SetStats(1);
histos[i]->Draw();
gPad->Update();
pad->Update();
st = (TPaveStats*)histos[i]->FindObject("stats");
st->SetX1NDC(0.524);
st->SetY1NDC(0.680);
st->SetX2NDC(0.884);
st->SetY2NDC(0.876);
//fit->Draw("SAME");
//histos[i]->Draw();
gPad->Update();
pad->Update();
printf("\n");
diffHistos[i] = (TH1D*)histos[i]->Clone(Form("diffPTbin%d00particl%d", nfile*2+1, i));
diffHistos[i]->Add(fit, -1);
diffCanvas[i]->cd(nfile+1);
diffHistos[i]->Draw("HIST E");
diffHistos[i]->Write();
//counting bins
Float_t integral = histos[i]->Integral(1, 500)*500.0;
//integrals << integral <<" \n";
histos[i]->Write();
fit->Write();
//Do mass and width vs. pT plots just for K*0
if(i==PARTICLE_NUM){
mass = fit->GetParameter(1);
massError = fit->GetParError(1);
collWidth = fit->GetParameter(2);
collWidthError = fit->GetParError(2);
width = Gamma(mass, collWidth);
kstar0mass->SetBinContent(nfile+1, mass);
kstar0mass->SetBinError(nfile+1, massError);
kstar0width->SetBinContent(nfile+1, width);
Double_t widthError = TMath::Sqrt((GammaDerivative(mass)**2)*fitter->GetCovarianceMatrixElement(1,1) + fitter->GetCovarianceMatrixElement(2,2) + 2.0*GammaDerivative(mass)*fitter->GetCovarianceMatrixElement(1,2));
kstar0width->SetBinError(nfile+1, widthError);
kstar0collWidth->SetBinContent(nfile+1, collWidth);
kstar0collWidth->SetBinError(nfile+1, collWidthError);
kstar0decWidth->SetBinContent(nfile+1, width - collWidth);
Double_t decWidthError = TMath::Sqrt((GammaDerivative(mass)**2)*fitter->GetCovarianceMatrixElement(1,1));
kstar0decWidth->SetBinError(nfile+1, decWidthError);
if(nfile==4){
TCanvas *singlecanvas = new TCanvas("singlecanvas", "singlecanvas", 0,0,600,600);
singlecanvas->cd();
printf("Got here! \n");
histos[i]->Draw("HIST E SAME");
fit->SetLineColor(8);
fit->SetLineStyle(1);
fit->Draw("SAME");
if(fitter){
printf("sig11: %f, sig12: %f, sig21: %f, sig22: %f GammaDer(0.8): %f GammaDer(0.85): %f GammaDer(0.9): %f\n", TMath::Sqrt(fitter->GetCovarianceMatrixElement(1,1)), fitter->GetCovarianceMatrixElement(2,1), fitter->GetCovarianceMatrixElement(1,2), TMath::Sqrt(fitter->GetCovarianceMatrixElement(2,2)), GammaDerivative(0.8), GammaDerivative(0.85), GammaDerivative(0.9));
}
}
}
}
printf("************************************************************\n");
}
//integrals.close();
/*
TH2D *gammaPlot = new TH2D("gammaPlot", "#Gamma_{tot}(M_{0}*);M_{0}*;#Gamma_{coll};#Gamma_{tot}", 100, 0.82, 0.9, 100, 0.0, 0.08);
for(int im = 0; im<100; im++){
for(int ig = 0; ig < 100; ig++){
gammaPlot->SetBinContent(im+1, ig+1, Gamma(((0.9-0.82)/(100.0))*((double)(im)) + 0.82, ((0.08)/100.0)*((double)(ig))));
}
}
TH1D *gammaMassDpnd = gammaPlot->ProjectionX("gammaMassDpnd");
*/
TCanvas *masscanvas = new TCanvas("masscanvas", "masscanvas", 50,50, 600, 600);
masscanvas->cd();
kstar0mass->Draw();
massline->Draw("SAME");
masscanvas->Write();
for(int i=PARTICLE_NUM; i<PARTICLE_NUM+1; i++){
canvas[i]->Write();
}
kstar0mass->Write();
kstar0collWidth->Write();
kstar0decWidth->Write();
kstar0width->Write();
// gammaPlot->Write();
// gammaMassDpnd->Write();
}
bool leptonic_fitter_algebraic::fit( const TLorentzVector& B, const TH1& BITF, const TF1& Beff,
const TLorentzVector& lep,
double MEX, double MEY, const TF1& dnuPDF )
{
if( _dbg > 19 ) cout<<"DBG20 Entered leptonic_fitter_algebraic::fit with B mass: "<<B.M()<<", l_m:"<<lep.M()<<", MET: "<<MEX<<" "<<MEY<<endl;
if( B.M() <= 0 ) throw std::runtime_error( "leptonic_fitter_algebraic was given a b-jet with an illegal (non-positive) mass!");
if( lep.M() < 0 ) throw std::runtime_error( "leptonic_fitter_algebraic was given a lepton with an illegal (negative) mass!");
_converged = _swapped = false;
_obsB = B;
_obsL = lep;
_BITF = &BITF;
_Beff = &Beff;
_dnuPDF = dnuPDF;
_b_m2 = B.M2();
double lep_b_angle = lep.Angle( B.Vect() );
double cos_lep_b = TMath::Cos( lep_b_angle );
double sin_lep_b = TMath::Sin( lep_b_angle );
double b_p = B.P();
double b_e = B.E();
_denom = b_e - cos_lep_b * b_p;
_lep_p = lep.P();
_x0 = - _W_m2 / ( 2 * _lep_p );
_y1 = - sin_lep_b * _x0 * b_p / _denom;
_x1_0 = _x0 * b_e / _denom - _y1*_y1 / _x0;
_Z2_0 = _x0*_x0 - _W_m2 - _y1*_y1;
if( _dbg > 219 ) cout<<"DBG220 lfa updated lepton with: "<<lv2str( lep )<<" -> x0:"<<_x0<<", y1: "<<_y1<<", x1_0: "<<_x1_0<<", Z2_0: "<<_Z2_0<<endl;
static double bnums[3];
bnums[0] = B.X();
bnums[1] = B.Y();
bnums[2] = B.Z();
TMatrixD bXYZ( 3, 1, bnums );
_R_T = rotation( 2, lep.Phi() ); // R_z^T
_R_T *= rotation( 1, lep.Theta() - 0.5*TMath::Pi() ); // R_z^T R_y^T
TMatrixD rotation_vect( _R_T, TMatrixD::kTransposeMult, bXYZ ); // R_y R_z
double* rotation_array = rotation_vect.GetMatrixArray();
double phi_x = - TMath::ATan2( rotation_array[2], rotation_array[1] );
if( _dbg > 99 ) cout<<"DBG100 lfa x rotation vector is:"<<rotation_array[0]<<" "<<rotation_array[1]<<" "<<rotation_array[2]<<" -> phi_x:"<<phi_x<<endl;
_R_T *= rotation( 0, - phi_x ); // R_z^T R_y^T R_x^T
// set up _Nu's non-zero elements so that \vec{nu} = Nu \vec{t} for any \vec{t} (since only t's 3nd component is used, and its always 1).
_Nu[0][2] = MEX;
_Nu[1][2] = MEY;
double iVarMET = TMath::Power( TMath::Max( 1., dnuPDF.GetHistogram()->GetRMS() ), -2 );
_invFlatVar[0][0] = _invFlatVar[1][1] = iVarMET; // set up the chi^2 distance with the right order of magnitude (generalizes to rotated covariance matrix)
if( _dbg > 209 ) cout<<"DBG210 lfa "<<dnuPDF.GetName()<<" --> iVarMET:"<<iVarMET<<endl;
// (re)define fit parameter, so all fits start off on an equal footing
_mini->SetPrintLevel( _minimizer_print_level );
_mini->Clear();
_mini->SetFunction( _functor );
leptonic_fitter_algebraic_object = this; // set the function in the functor pointing back to this object. Doubtfull that all this redirection is needed...
_mini->SetTolerance( _tolerance );
bool OK = _mini->SetLimitedVariable( 0, "sB", 1.0, 0.4, 0.1, 6.0 );
//bool OK = _mini->SetVariable( 0, "sB", 1.0, 0.4 );
if( ! OK ) {cerr<<"minimizer (@lfa) failed to SetVariable."<<endl; return false;}
// define 1 sigma in terms of the function
_mini->SetErrorDef( 0.5 ); // since this is a likelihood fit
// do the minimization
OK = _mini->Minimize();
if( _dbg > 19 && ( ! OK || _dbg > 59 ) ) cout<<"DBG INFO: initial fit @lfa returned OK: "<<OK<<", has status: "<<_mini->Status()<<endl;
_converged = OK; // use status somehow? depends on fitter?
// read parameters
const double *xs = _mini->X();
for( int ip = 0; ip < 1; ++ip ) _params[ ip ] = xs[ ip ];
// return all intermediate results to the minimum, in particular, the discriminant
calc_MLL( _params, true );
TMatrixD nu_vec( _Emat, TMatrixD::kMult, _tvec );
update_nu_and_decay_chain( nu_vec );
if( _dbg > 203 ) cout<<"DBG204 lfa finalized _genN: "<<lv2str(_genN)<<", _W: "<<lv2str(_W)<<", & _t: "<<lv2str(_T)<<endl;
_MLL = _mini->MinValue();
return true;
}
void residualAlignment(TH2D* residualX, TH2D* residualY, double& offsetX,
double& offsetY, double& rotation,
double relaxation, bool display)
{
assert(residualX && residualY && "Processors: can't perform residual alignment without histograms");
rotation = 0;
offsetX = 0;
offsetY = 0;
double angleWeights = 0;
double fitChi2 = 0;
for (int axis = 0; axis < 2; axis++)
{
TH2D* hist = 0;
if (axis) hist = residualX;
else hist = residualY;
// Project the histogram and fit with a gaussian to center the sensor
TH1D* project = hist->ProjectionX("ResidualProjetion", 1, hist->GetNbinsY());
project->SetDirectory(0);
double sigma = project->GetBinWidth(1);
double mean = 0;
fitGaussian(project, mean, sigma, false);
if (axis) offsetX = mean;
else offsetY = mean;
delete project;
std::vector<double> ptsX;
std::vector<double> ptsY;
std::vector<double> ptsErr;
const unsigned int numSlices = hist->GetNbinsY();
for (Int_t row = 1; row <= (int)numSlices; row++)
{
TH1D* slice = hist->ProjectionX("ResidualSlice", row, row);
slice->SetDirectory(0);
double mean = 0;
double sigma = 0;
double factor = 0;
double background = 0;
if (slice->Integral() < 1) { delete slice; continue; }
fitGaussian(slice, mean, sigma, factor, background, false);
const double sliceMin = slice->GetBinCenter(1);
const double sliceMax = slice->GetBinCenter(slice->GetNbinsX());
delete slice;
// Quality assurance
// Sigma is contained in the slice's range
if (sigma > (sliceMax - sliceMin)) continue;
// Mean is contained in the slice's range
if (mean > sliceMax || mean < sliceMin) continue;
// Peak is contains sufficient events
if (factor < 100) continue;
// Sufficient signal to noise ratio
if (factor / background < 10) continue;
// Get the total number of events in the gaussian 1 sigma
Int_t sigRangeLow = hist->FindBin(mean - sigma);
Int_t sigRangeHigh = hist->FindBin(mean + sigma);
double sigRangeTotal = 0;
for (Int_t bin = sigRangeLow; bin <= sigRangeHigh; bin++)
sigRangeTotal += hist->GetBinContent(bin);
// 2 * 1 sigma integral shoudl give ~ area under gaussian
sigma /= sqrt(2 * sigRangeTotal);
ptsX.push_back(hist->GetYaxis()->GetBinCenter(row));
ptsY.push_back(mean);
ptsErr.push_back(sigma);
}
if (ptsX.size() < 3) continue;
std::vector<double> yvals = ptsY;
std::sort(yvals.begin(), yvals.end());
const double median = yvals[yvals.size()/2];
double avgDeviation = 0;
for (unsigned int i = 0; i < yvals.size(); i++)
avgDeviation += fabs(yvals[i] - median);
avgDeviation /= (double)yvals.size();
std::vector<double> ptsXGood;
std::vector<double> ptsYGood;
std::vector<double> ptsErrGood;
for (unsigned int i = 0; i < ptsX.size(); i++)
{
if (fabs(ptsY[i] - median) > 1.5*avgDeviation) continue;
ptsXGood.push_back(ptsX[i]);
ptsYGood.push_back(ptsY[i]);
ptsErrGood.push_back(ptsErr[i]);
}
if (ptsXGood.size() < 3) continue;
TGraphErrors* graph = new TGraphErrors(ptsXGood.size(),
&(ptsXGood.at(0)),
&(ptsYGood.at(0)), 0,
&(ptsErrGood.at(0)));
TF1* fitFunc = new TF1("f1", "1 ++ x");
TF1* result = 0;
graph->Fit(fitFunc, "Q0E").Get();
result = graph->GetFunction(fitFunc->GetName());
// Weight the angle by the slope uncertainty and the inverse of the chi2 normalized
double weight = result->GetParError(1);
const double chi2 = result->GetChisquare() / (double)result->GetNDF();
fitChi2 += chi2;
weight *= chi2;
if (weight > 10 * DBL_MIN) weight = 1.0 / weight;
else weight = 1.0;
if (axis)
{
rotation -= weight * atan(result->GetParameter(1));
offsetX = result->GetParameter(0);
}
else
{
rotation += weight * atan(result->GetParameter(1));
offsetY = result->GetParameter(0);
}
angleWeights += weight;
if (display)
{
TCanvas* can = new TCanvas("ResidualAlignment", "Residual Alignment", 900, 600);
can->Divide(2);
can->cd(1);
hist->Draw("COLZ");
can->cd(2);
result->SetLineColor(46);
result->SetLineWidth(2);
graph->Draw("ap");
result->Draw("SAME");
can->Update();
can->WaitPrimitive();
}
delete fitFunc;
delete graph;
}
if (angleWeights > 10 * DBL_MIN)
rotation /= angleWeights;
std::cout << "relaxation: " << relaxation << std::endl;
rotation *= relaxation;
offsetX *= relaxation;
offsetY *= relaxation;
}
// Soft radiation corrections for L3Res
void softrad(double etamin=0.0, double etamax=1.3, bool dodijet=false) {
setTDRStyle();
writeExtraText = false; // for JEC paper CWR
TDirectory *curdir = gDirectory;
// Open jecdata.root produced by reprocess.C
TFile *fin = new TFile("rootfiles/jecdata.root","UPDATE");
assert(fin && !fin->IsZombie());
const int ntypes = 3;
const char* types[ntypes] = {"data", "mc", "ratio"};
const int nmethods = 2;
const char* methods[nmethods] = {"mpfchs1", "ptchs"};
const int nsamples = (dodijet ? 4 : 3);
const char* samples[4] = {"gamjet", "zeejet", "zmmjet", "dijet"};
string sbin = Form("eta%02.0f-%02.0f",10*etamin,10*etamax);
const char* bin = sbin.c_str();
const int nalphas = 4;
const int alphas[nalphas] = {30, 20, 15, 10};
// Z+jet bins
const double ptbins1[] = {30, 40, 50, 60, 75, 95, 125, 180, 300, 1000};
const int npt1 = sizeof(ptbins1)/sizeof(ptbins1[0])-1;
TH1D *hpt1 = new TH1D("hpt1","",npt1,&ptbins1[0]);
TProfile *ppt1 = new TProfile("ppt1","",npt1,&ptbins1[0]);
// gamma+jet bins
const double ptbins2[] = {30, 40, 50, 60, 75, 100, 125, 155, 180,
210, 250, 300, 350, 400, 500, 600, 800};
const int npt2 = sizeof(ptbins2)/sizeof(ptbins2[0])-1;
TH1D *hpt2 = new TH1D("hpt2","",npt2,&ptbins2[0]);
TProfile *ppt2 = new TProfile("ppt2","",npt2,&ptbins2[0]);
// dijet bins
const double ptbins4[] = {20, 62, 107, 175, 242, 310, 379, 467,
628, 839, 1121, 1497, 2000};
const int npt4 = sizeof(ptbins4)/sizeof(ptbins4[0])-1;
TH1D *hpt4 = new TH1D("hpt4","",npt4,&ptbins4[0]);
TProfile *ppt4 = new TProfile("ppt4","",npt4,&ptbins4[0]);
TLatex *tex = new TLatex();
tex->SetNDC();
tex->SetTextSize(0.045);
map<string,const char*> texlabel;
texlabel["gamjet"] = "#gamma+jet";
texlabel["zeejet"] = "Z#rightarrowee+jet";
texlabel["zmmjet"] = "Z#rightarrow#mu#mu+jet";
texlabel["dijet"] = "Dijet";
texlabel["ptchs"] = "p_{T} balance (CHS)";
texlabel["mpfchs"] = "MPF raw (CHS)";
texlabel["mpfchs1"] = "MPF type-I (CHS)";
// overlay of various alpha values
TCanvas *c1 = new TCanvas("c1","c1",ntypes*400,nmethods*400);
c1->Divide(ntypes,nmethods);
TH1D *h1 = new TH1D("h1",";p_{T} (GeV);Response",1270,30,1300);
// extrapolation vs alpha for each pT bin
vector<TCanvas*> c2s(ntypes*nmethods);
for (unsigned int icanvas = 0; icanvas != c2s.size(); ++icanvas) {
TCanvas *c2 = new TCanvas(Form("c2_%d",icanvas),Form("c2_%d",icanvas),
1200,1200);
c2->Divide(3,3);
c2s[icanvas] = c2;
}
TH1D *h2 = new TH1D("h2",";#alpha;Response",10,0.,0.4);
h2->SetMaximum(1.08);
h2->SetMinimum(0.88);
// krad corrections
TCanvas *c3 = new TCanvas("c3","c3",ntypes*400,nmethods*400);
c3->Divide(ntypes,nmethods);
TH1D *h3 = new TH1D("h3",";p_{T,ref} (GeV);FSR sensitivity: -dR/d#alpha [%]",
1270,30,1300);
cout << "Reading in data" << endl << flush;
// Read in plots vs pT (and alpha)
map<string, map<string, map<string, map<int, TGraphErrors*> > > > gemap;
map<string, map<string, map<string, map<int, TGraphErrors*> > > > gamap;
for (int itype = 0; itype != ntypes; ++itype) {
for (int imethod = 0; imethod != nmethods; ++imethod) {
for (int isample = 0; isample != nsamples; ++isample) {
for (int ialpha = 0; ialpha != nalphas; ++ialpha) {
fin->cd();
assert(gDirectory->cd(types[itype]));
assert(gDirectory->cd(bin));
TDirectory *d = gDirectory;
const char *ct = types[itype];
const char *cm = methods[imethod];
const char *cs = samples[isample];
const int a = alphas[ialpha];
// Get graph made vs pT
string s = Form("%s/%s/%s_%s_a%d",types[itype],bin,cm,cs,a);
TGraphErrors *g = (TGraphErrors*)fin->Get(s.c_str());
if (!g) cout << "Missing " << s << endl << flush;
assert(g);
// Clean out empty points
// as well as trigger-biased ones for dijets
// as well as weird gamma+jet high pT point
for (int i = g->GetN()-1; i != -1; --i) {
if (g->GetY()[i]==0 || g->GetEY()[i]==0 ||
(string(cs)=="dijet" && g->GetX()[i]<70.) ||
(string(cs)=="gamjet" && g->GetX()[i]>600. && etamin!=0))
g->RemovePoint(i);
}
gemap[ct][cm][cs][a] = g;
// Sort points into new graphs vs alpha
TH1D *hpt = (isample==0 ? hpt2 : hpt1);
TProfile *ppt = (isample==0 ? ppt2 : ppt1);
if (isample==3) { hpt = hpt4; ppt = ppt4; } // pas-v6
for (int i = 0; i != g->GetN(); ++i) {
double pt = g->GetX()[i];
ppt->Fill(pt, pt);
int ipt = int(hpt->GetBinLowEdge(hpt->FindBin(pt))+0.5);
//int ipt = int(pt+0.5);
TGraphErrors *ga = gamap[ct][cm][cs][ipt];
if (!ga) {
ga = new TGraphErrors(0);
ga->SetMarkerStyle(g->GetMarkerStyle());
ga->SetMarkerColor(g->GetMarkerColor());
ga->SetLineColor(g->GetLineColor());
gamap[ct][cm][cs][ipt] = ga;
}
int n = ga->GetN();
ga->SetPoint(n, 0.01*a, g->GetY()[i]);
ga->SetPointError(n, 0, g->GetEY()[i]);
} // for i
} // for ialpha
} // for isample
} // for imethod
} // for itype
cout << "Drawing plots vs pT for each alpha" << endl << flush;
// 2x6 plots
for (int itype = 0; itype != ntypes; ++itype) {
for (int imethod = 0; imethod != nmethods; ++imethod) {
const char *ct = types[itype];
const char *cm = methods[imethod];
int ipad = ntypes*imethod + itype + 1; assert(ipad<=6);
c1->cd(ipad);
gPad->SetLogx();
h1->SetMaximum(itype<2 ? 1.15 : 1.08);
h1->SetMinimum(itype<2 ? 0.85 : 0.93);
h1->SetYTitle(Form("Response (%s)",ct));
h1->DrawClone("AXIS");
tex->DrawLatex(0.20,0.85,texlabel[cm]);
tex->DrawLatex(0.20,0.80,"|#eta| < 1.3, #alpha=0.1--0.3");
TLegend *leg = tdrLeg(0.60,0.75,0.90,0.90);
for (int isample = 0; isample != nsamples; ++isample) {
for (int ialpha = 0; ialpha != nalphas; ++ialpha) {
const char *cs = samples[isample];
const int a = alphas[ialpha];
TGraphErrors *g = gemap[ct][cm][cs][a]; assert(g);
// Clean out points with very large uncertainty for plot readability
for (int i = g->GetN()-1; i != -1; --i) {
if (g->GetEY()[i]>0.02) g->RemovePoint(i);
}
g->Draw("SAME Pz");
if (ialpha==0) leg->AddEntry(g,texlabel[cs],"P");
}
} // for isample
// Individual plots for JEC paper
if ( true ) { // paper
TH1D *h = new TH1D(Form("h_5%s_%s",ct,cm),
Form(";p_{T} (GeV);Response (%s)",ct),
1270,30,1300);
h->GetXaxis()->SetMoreLogLabels();
h->GetXaxis()->SetNoExponent();
h->SetMinimum(0.88);
h->SetMaximum(1.13);
writeExtraText = true;
extraText = (string(ct)=="mc" ? "Simulation" : "");
lumi_8TeV = (string(ct)=="mc" ? "" : "19.7 fb^{-1}");
TCanvas *c0 = tdrCanvas(Form("c0_%s_%s",cm,ct), h, 2, 11, true);
c0->SetLogx();
TLegend *leg = tdrLeg(0.55,0.68,0.85,0.83);
tex->DrawLatex(0.55,0.85,texlabel[cm]);
tex->DrawLatex(0.55,0.18,"|#eta| < 1.3, #alpha=0.3");
//tex->DrawLatex(0.55,0.18,"Anti-k_{T} R=0.5");
// Loop over Z+jet and gamma+jet (only, no dijet/multijet)
for (int isample = 0; isample != min(3,nsamples); ++isample) {
const char *cs = samples[isample];
TGraphErrors *g = gemap[ct][cm][cs][30]; assert(g);
g->Draw("SAME Pz");
leg->AddEntry(g,texlabel[cs],"P");
} // for isample
if (etamin==0) {
c0->SaveAs(Form("pdf/paper_softrad_%s_%s_vspt.pdf",ct,cm));
c0->SaveAs(Form("pdfC/paper_softrad_%s_%s_vspt.C",ct,cm));
}
else {
c0->SaveAs(Form("pdf/an_softrad_%s_%s_eta%1.0f-%1.0f_vspt.pdf",
ct,cm,10*etamin,10*etamax));
}
} // paper
} // for imethod
} // for itype
c1->cd(0);
//cmsPrel(_lumi, true);
CMS_lumi(c1, 2, 33);
c1->SaveAs("pdf/softrad_2x6_vspt.pdf");
cout << "Drawing plots vs alpha for each pT" << endl << flush;
cout << "...and fitting slope vs alpha" << endl << flush;
map<string, map<string, map<string, TGraphErrors* > > > gkmap;
// 2x6 plots
for (int itype = 0; itype != ntypes; ++itype) {
for (int imethod = 0; imethod != nmethods; ++imethod) {
int icanvas = nmethods*imethod + itype; assert(icanvas<=6);
TCanvas *c2 = c2s[icanvas]; assert(c2);
const char *ct = types[itype];
const char *cm = methods[imethod];
const int npads = 9;
for (int ipad = 0; ipad != npads; ++ipad) {
c2->cd(ipad+1);
h2->SetYTitle(Form("Response (%s)",ct));
h2->DrawClone("AXIS");
tex->DrawLatex(0.20,0.85,texlabel[cm]);
tex->DrawLatex(0.20,0.80,"|#eta| < 1.3");
tex->DrawLatex(0.20,0.75,Form("%1.0f < p_{T} < %1.0f GeV",
hpt1->GetBinLowEdge(ipad+1),
hpt1->GetBinLowEdge(ipad+2)));
TLegend *leg = tdrLeg(0.65,0.75,0.90,0.90);
leg->AddEntry(gemap[ct][cm]["gamjet"][30], texlabel["gamjet"], "P");
leg->AddEntry(gemap[ct][cm]["zeejet"][30], texlabel["zeejet"], "P");
leg->AddEntry(gemap[ct][cm]["zmmjet"][30], texlabel["zmmjet"], "P");
leg->AddEntry(gemap[ct][cm]["dijet"][30], texlabel["dijet"], "P");
}
for (int isample = 0; isample != nsamples; ++isample) {
const char *cs = samples[isample];
map<int, TGraphErrors*> &gam = gamap[ct][cm][cs];
map<int, TGraphErrors*>::iterator itpt;
for (itpt = gam.begin(); itpt != gam.end(); ++itpt) {
int ipt = itpt->first;
int jpt = hpt1->FindBin(ipt);
if (jpt>npads) continue;
assert(jpt<=npads);
c2->cd(jpt);
TGraphErrors *ga = itpt->second; assert(ga);
ga->Draw("SAME Pz");
// Fit slope
TF1 *f1 = new TF1(Form("f1_%s_%s_%s_%d",ct,cm,cs,ipt),
"(x<1)*([0]+[1]*x) + (x>1 && x<2)*[0] +"
"(x>2)*[1]",-1,1);
f1->SetLineColor(ga->GetLineColor());
f1->SetParameters(1,0);
const double minalpha = (isample==0 ? 10./ipt : 5./ipt);
// Constrain slope to within reasonable values
// in the absence of sufficient data using priors
if (true) { // use priors
int n = ga->GetN();
// For response, limit to 1+/-0.02 (we've corrected for L3Res
ga->SetPoint(n, 1.5, 1);
ga->SetPointError(n, 0, 0.02);
n = ga->GetN();
if (imethod==1) { // pT balance
// For pT balance, estimate slope of <vecpT2>/alpha from data
// => 7.5%/0.30 = 25%
// Approximate uncertainty on this to be
// 0.5%/0.30 ~ 1.5% for data, 0.5%/0.30 ~ 1.5% for Z+jet MC, and
// 2%/0.30 ~ 6% for gamma+jet MC (same as slope)
if (itype==0) ga->SetPoint(n, 2.5, -0.250); // DT
if (itype==1 && isample!=0) ga->SetPoint(n, 2.5, -0.250); // MC
if (itype==1 && isample==0) ga->SetPoint(n, 2.5, -0.190);
if (itype==2 && isample!=0) ga->SetPoint(n, 2.5, -0.000); // rt
if (itype==2 && isample==0) ga->SetPoint(n, 2.5, -0.060);
//
// BUG: found 2015-01-08 (no effect on ratio)
//if (itype==1) ga->SetPointError(n, 0, -0.015);
if (itype==0) ga->SetPointError(n, 0, -0.015); // DT
if (itype==1 && isample!=0) ga->SetPointError(n, 0, -0.015); // MC
if (itype==1 && isample==0) ga->SetPointError(n, 0, -0.060);
if (itype==2 && isample!=0) ga->SetPointError(n, 0, -0.015); // rt
if (itype==2 && isample==0) ga->SetPointError(n, 0, -0.060);
}
if (imethod==0) { // MPF
// For MPF, expectation is no slope
// Maximal slope would be approximately
// (<vecpT2>/alpha ~ 25% from pT balance) times
// (response difference between pT1 and vecpT2~10%)
// => 0.25*0.10 = 2.5%
// For data/MC, estimate uncertainty as half of this
// => 1.25%
ga->SetPoint(n, 2.5, 0.);
if (itype!=2) ga->SetPointError(n, 0, 0.025);
if (itype==2) ga->SetPointError(n, 0, 0.0125);
} // MPF
} // use priors
if (ga->GetN()>2) {
f1->SetRange(minalpha, 3.);
ga->Fit(f1,"QRN");
if (f1->GetNDF()>=0) {
f1->DrawClone("SAME");
f1->SetRange(0,0.4);
f1->SetLineStyle(kDashed);
f1->DrawClone("SAME");
// Store results
TGraphErrors *gk = gkmap[ct][cm][cs];
if (!gk) {
gk = new TGraphErrors(0);
gk->SetMarkerStyle(ga->GetMarkerStyle());
gk->SetMarkerColor(ga->GetMarkerColor());
gk->SetLineColor(ga->GetLineColor());
gkmap[ct][cm][cs] = gk;
}
int n = gk->GetN();
TProfile *ppt = (isample==0 ? ppt2 : ppt1);
if (isample==3) { ppt = ppt4; } // pas-v6
double pt = ppt->GetBinContent(ppt->FindBin(ipt));
gk->SetPoint(n, pt, f1->GetParameter(1));
gk->SetPointError(n, 0, f1->GetParError(1));
} // f1->GetNDF()>=0
} // ga->GetN()>2
} // for itpt
} // for isample
c2->SaveAs(Form("pdf/softrad_3x3_%s_%s_vsalpha.pdf",ct,cm));
}
}
cout << "Drawing plots of kFSR vs pT" << endl;
// 2x6 plots
for (int itype = 0; itype != ntypes; ++itype) {
for (int imethod = 0; imethod != nmethods; ++imethod) {
const char *ct = types[itype];
const char *cm = methods[imethod];
TMultiGraph *mgk = new TMultiGraph();
int ipad = ntypes*imethod + itype + 1; assert(ipad<=6);
c3->cd(ipad);
gPad->SetLogx();
h3->SetMaximum(imethod==0 ? 0.05 : (itype!=2 ? 0.1 : 0.25));
h3->SetMinimum(imethod==0 ? -0.05 : (itype!=2 ? -0.4 : -0.25));
h3->SetYTitle(Form("k_{FSR} = dR/d#alpha (%s)",ct));
h3->DrawClone("AXIS");
tex->DrawLatex(0.20,0.85,texlabel[cm]);
tex->DrawLatex(0.20,0.80,"|#eta| < 1.3");
TLegend *leg = tdrLeg(0.60,0.75,0.90,0.90);
for (int isample = 0; isample != nsamples; ++isample) {
const char *cs = samples[isample];
TGraphErrors *gk = gkmap[ct][cm][cs]; assert(gk);
leg->AddEntry(gk,texlabel[cs],"P");
// Fit each sample separately for pT balance
if (true) {
TF1 *fk = new TF1(Form("fk_%s_%s_%s",ct,cm,cs),
"[0]+[1]*log(0.01*x)+[2]*pow(log(0.01*x),2)",
30,1300);
fk->SetParameters(-0.25,-0.5);
fk->SetLineColor(gk->GetLineColor());
gk->Fit(fk, "QRN");
tex->SetTextColor(fk->GetLineColor());
tex->DrawLatex(0.55,0.27-0.045*isample,
Form("#chi^{2}/NDF = %1.1f / %d",
fk->GetChisquare(), fk->GetNDF()));
tex->SetTextColor(kBlack);
// Error band
const int n = fk->GetNpar();
TMatrixD emat(n,n);
gMinuit->mnemat(emat.GetMatrixArray(), n);
TF1 *fke = new TF1(Form("fk_%s_%s_%s",ct,cm,cs),
sr_fitError, 30, 1300, 1);
_sr_fitError_func = fk;
_sr_fitError_emat = &emat;
fke->SetLineStyle(kSolid);
fke->SetLineColor(fk->GetLineColor()-10);
fke->SetParameter(0,-1);
fke->DrawClone("SAME");
fke->SetParameter(0,+1);
fke->DrawClone("SAME");
fk->DrawClone("SAME");
gk->DrawClone("SAME Pz");
// Store soft radiation corrections in fsr subdirectory
assert(fin->cd(ct));
assert(gDirectory->cd(bin));
if (!gDirectory->FindObject("fsr")) gDirectory->mkdir("fsr");
assert(gDirectory->cd("fsr"));
TH1D *hk = (TH1D*)(isample==0 ? hpt2->Clone() : hpt1->Clone());
hk->SetName(Form("hkfsr_%s_%s",cm,cs));
TProfile *ppt = (isample==0 ? ppt2 : ppt1);
if (isample==3) { ppt = ppt4; } // pas-v6
for (int i = 1; i != hk->GetNbinsX()+1; ++i) {
double pt = ppt->GetBinContent(i);
if (pt>30 && pt<1300) {
hk->SetBinContent(i, fk->Eval(pt));
hk->SetBinError(i, fabs(fke->Eval(pt)-fk->Eval(pt)));
}
else {
hk->SetBinContent(i, 0);
hk->SetBinError(i, 0);
}
}
hk->Write(hk->GetName(), TObject::kOverwrite);
// Factorize error matrix into eigenvectors
// Remember: A = Q*Lambda*Q^-1, where
// A is emat, Q is eigmat, and Lambda is a diagonal matrix with
// eigenvalues from eigvec on the diagonal. For eigenmatrix
// Q^-1 = Q^T, i.e. inverse matrix is the original transposed
TVectorD eigvec(n);
TMatrixD eigmat = emat.EigenVectors(eigvec);
// Eigenvectors are the columns and sum of eigenvectors squared
// equals original uncertainty. Calculate histograms from the
// eigenvectors and store them
TF1 *fkeig = (TF1*)fk->Clone(Form("%s_eig",fk->GetName()));
fkeig->SetLineStyle(kDotted);
for (int ieig = 0; ieig != n; ++ieig) {
// Eigenvector functions
for (int i = 0; i != n; ++i) {
fkeig->SetParameter(i, fk->GetParameter(i)
+ eigmat[i][ieig] * sqrt(eigvec[ieig]));
}
fkeig->DrawClone("SAMEL");
// Eigenvector histograms evaluated at bin mean pT
TH1D *hke = (TH1D*)hk->Clone(Form("%s_eig%d",hk->GetName(),ieig));
hke->Reset();
for (int i = 0; i != gk->GetN(); ++i) {
double pt = gk->GetX()[i];
int ipt = hke->FindBin(pt);
// Need to store central value as well, because
// uncertainty sources are signed
hke->SetBinContent(ipt, fkeig->Eval(pt)-fk->Eval(pt));
hke->SetBinError(ipt, fabs(fkeig->Eval(pt)-fk->Eval(pt)));
}
hke->Write(hke->GetName(), TObject::kOverwrite);
}
cout << "." << flush;
} // if tree
} // for isample
} // for imethod
} // for itype
c3->cd(0);
//cmsPrel(_lumi, true);
CMS_lumi(c3, 2, 33);
c3->SaveAs("pdf/softrad_2x6_kfsr.pdf");
fin->Close();
curdir->cd();
} // softrad
