void pidHistogramMaker::distributionReport( string pType ){
uint nBinsPt = ptBins.size() - 1;
string rName = speciesName( pType, 0 );
taskProgress tp( pType + " distribution report", nBinsPt );
book->cd( "tof" );
for ( uint i = 0; i < nBinsPt; i ++ ){
tp.showProgress( i );
// momentum value used for finding nice range
double p = ptBins[ i ];
double p2 = ptBins[ i + 1 ];
double avgP = 0.2;
avgP = (ptBins[ i ] + ptBins[ i + 1])/2.0;
string name = speciesName( pType, 0, i, 0 );
book->cd( "dedx_tof" );
TH2 * pTof = book->get2D( name );
book->cd( "scratch" );
TH2 * pDedx = (TH2*)pTof->Clone( "pDedx__" );
// start a new page on the report file
pReport[ rName ]->newPage( 2, 2 );
// get information on plot ranges
double tofLow, tofHigh, dedxLow, dedxHigh;
autoViewport( pType, p, &tofLow, &tofHigh, &dedxLow, &dedxHigh, tofPadding, dedxPadding, tofScalePadding, dedxScalePadding );
if ( true ) { // show the tof proj
string title = "#beta^{-1} : " + ts(ptBins[ i ], 4) + " < pT < " + ts(ptBins[i+1], 4);
vector<string> others = otherSpecies( pType );
vector< double > tofMean = enhanceTof( pType, others, avgP );
vector< double > dedxMean = enhanceDedx( pType, others, avgP );
pReport[ rName ]->cd( 1, 1 );
//hdt->GetXaxis()->SetRangeUser( -.06, .06 );
// Make the all tof tracks histogram
string hName = sTofName( pType, 0, i );
book->cd( "scratch" );
TH1* hTof = (TH1D*)pTof->ProjectionY( "_py" );
book->cd( "tof" );
book->add( hName, (TH1*)hTof->Clone( hName.c_str() ) );
book->style( hName )->set( "style.tof" )
->set( "title", title )->draw();
TLine * l1 = new TLine( tofMean[ 0 ], hTof->GetMinimum(), tofMean[ 0 ], hTof->GetMaximum() );
l1->Draw();
TLine * l2 = new TLine( tofMean[ 1 ], hTof->GetMinimum(), tofMean[ 1 ], hTof->GetMaximum() );
l2->Draw();
pReport[ rName ]->cd( 2, 1 );
pTof->GetXaxis()->SetRangeUser( -.06, .06 );
// Make the all tof tracks histogram
hName = sTofName( pType, 0, i, 0, pType );
book->cd( "scratch" );
hTof = (TH1D*)pTof->ProjectionY( "_py" );
book->cd( "tof" );
book->add( hName, (TH1*)hTof->Clone( hName.c_str() ) );
book->style( hName )->set( "style.tof" )
->set( "title", title + " " + pType + " enhanced" )->draw();
for ( int j = 0; j < dedxMean.size(); j++ ){
pReport[ rName ]->cd( j+1, 2 );
pTof->GetXaxis()->SetRangeUser( dedxMean[j]-0.06, dedxMean[j]+0.06 );
// Make the all tof tracks histogram
hName = sTofName( pType, 0, i, 0, others[ j ] );
book->cd( "scratch" );
hTof = (TH1D*)pTof->ProjectionY( "_py" );
book->cd( "tof" );
book->add( hName, (TH1*)hTof->Clone( hName.c_str() ) );
book->style( hName )->set( "style.tof" )
->set( "title", title + " " + others[ j ] + " enhanced" )->draw();
}
}
pReport[ rName ]->savePage();
pReport[ rName ]->newPage( 2, 2 );
if ( true ) { // show the dedx proj
string title = "dEdx : " + ts(ptBins[ i ], 4) + " < pT < " + ts(ptBins[i+1], 4);
pTof->GetXaxis()->SetRange( 1, pTof->GetXaxis()->GetNbins() );
pTof->GetYaxis()->SetRange( 1, pTof->GetYaxis()->GetNbins() );
vector<string> others = otherSpecies( pType );
vector< double > tofMean = enhanceTof( pType, others, avgP );
vector< double > dedxMean = enhanceDedx( pType, others, avgP );
pReport[ rName ]->cd( 1, 1 );
// Make the all dedx tracks histogram
string hName = sDedxName( pType, 0, i );
book->cd( "scratch" );
TH1* hDedx = (TH1D*)pTof->ProjectionX( "_px" );
book->cd( "dedx" );
book->add( hName, (TH1*)hDedx->Clone( hName.c_str() ) );
book->style( hName )->set( "style.dedx" )
->set( "title", title )->draw();
TLine * l1 = new TLine( dedxMean[ 0 ], hDedx->GetMinimum(), dedxMean[ 0 ], hDedx->GetMaximum() );
l1->Draw();
TLine * l2 = new TLine( dedxMean[ 1 ], hDedx->GetMinimum(), dedxMean[ 1 ], hDedx->GetMaximum() );
l2->Draw();
pReport[ rName ]->cd( 2, 1 );
pTof->GetYaxis()->SetRangeUser( -.012, .012 );
// Make the all tof tracks histogram
hName = sDedxName( pType, 0, i, 0, pType );
book->cd( "scratch" );
hDedx = (TH1D*)pTof->ProjectionX( "_px" );
book->cd( "dedx" );
book->add( hName, (TH1*)hDedx->Clone( hName.c_str() ) );
book->style( hName )->set( "style.dedx" )
->set( "title", title + " " + pType + " enhanced" )->draw();
for ( int j = 0; j < dedxMean.size(); j++ ){
pReport[ rName ]->cd( j+1, 2 );
pTof->GetYaxis()->SetRangeUser( tofMean[j]-0.012, tofMean[j]+0.012 );
// Make the all tof tracks histogram
hName = sDedxName( pType, 0, i, 0, others[ j ] );
book->cd( "scratch" );
hDedx = (TH1D*)pTof->ProjectionX( "_px" );
book->cd( "dedx" );
book->add( hName, (TH1*)hDedx->Clone( hName.c_str() ) );
book->style( hName )->set( "style.dedx" )
->set( "title", title + " " + others[ j ] + " enhanced" )->draw();
}
}
pReport[ rName ]->savePage();
}
}
TH1 *
UnfoldMe(Char_t *data, Char_t *mc, Char_t *anatag, Int_t bin, Bool_t useMBcorr = kTRUE, Bool_t usecorrfit = kFALSE, Bool_t ismc = kFALSE, Float_t smooth = 0.001, Int_t iter = 50, Int_t regul = AliUnfolding::kPowerLaw, Float_t weight = 100., Bool_t bayesian = kTRUE, Int_t nloop = 1)
{
if (ismc)
TFile *fdt = TFile::Open(data);
else
TFile *fdt = TFile::Open(data);
TFile *fmc = TFile::Open(mc);
TList *ldt = (TList *)fdt->Get(Form("clist_%s", anatag));
TList *lmc = (TList *)fmc->Get(Form("clist_%s", anatag));
TH2 *hmatdt = (TH2 *)ldt->FindObject(Form("b%d_corrMatrix", bin));
if (useMBcorr)
TH2 *hmatmc = (TH2 *)lmc->FindObject("effMatrix");
else
TH2 *hmatmc = (TH2 *)lmc->FindObject(Form("b%d_corrMatrix", bin));
TH1 *hdata = hmatdt->ProjectionY("hdata");
hdata->Sumw2();
hdata->SetBinContent(1, 0.);
hdata->SetBinError(1, 0.);
// hdata->Scale(1. / hdata->Integral());
hdata->SetMarkerStyle(25);
TH1 *htrue = hmatdt->ProjectionX("htrue");
htrue->Sumw2();
// htrue->Scale(1. / htrue->Integral());
htrue->SetMarkerStyle(7);
htrue->SetMarkerColor(2);
htrue->SetBinContent(1, 0.);
htrue->SetBinError(1, 0.);
TH2 *hcorr = (TH2 *)hmatmc->Clone("hcorr");
TH1 *hinit = (TH1 *)hdata->Clone("hinit");
TH1 *hresu = (TH1 *)hdata->Clone("hresu");
TH1 *hbias = (TH1 *)hdata->Clone("hbias");
hresu->SetMarkerStyle(20);
hresu->SetMarkerColor(4);
hresu->Reset();
TH1 *hnum = hcorr->ProjectionY("hnum");
TH1 *hden = hcorr->ProjectionY("hden");
TH1 *heff = hcorr->ProjectionY("heff");
hnum->Reset();
hnum->Sumw2();
hden->Reset();
hden->Sumw2();
heff->Reset();
for (Int_t i = 0; i < heff->GetNbinsX(); i++) {
Float_t int1 = hcorr->Integral(i + 1, i + 1, 0, -1);
if (int1 <= 0.) continue;
Float_t int2 = hcorr->Integral(i + 1, i + 1, 2, -1);
hnum->SetBinContent(i + 1, int2);
hnum->SetBinError(i + 1, TMath::Sqrt(int2));
hden->SetBinContent(i + 1, int1);
hden->SetBinError(i + 1, TMath::Sqrt(int1));
}
new TCanvas("cEfficiency");
heff->Divide(hnum, hden, 1., 1., "B");
heff->Draw();
#if 0
for (Int_t ii = 0; ii < heff->GetNbinsX(); ii++) {
heff->SetBinContent(ii + 1, 1.);
heff->SetBinError(ii + 1, 0.);
}
#endif
for (Int_t i = 0; i < hcorr->GetNbinsX(); i++) {
hcorr->SetBinContent(i + 1, 1, 0.);
hcorr->SetBinError(i + 1, 1, 0.);
}
for (Int_t i = 0; i < hcorr->GetNbinsY(); i++) {
hcorr->SetBinContent(1, i + 1, 0.);
hcorr->SetBinError(1, i + 1, 0.);
}
TH2 *hcorrfit = ReturnCorrFromFit(hcorr);
for (Int_t iloop = 0; iloop < nloop; iloop++) {
if (bayesian) {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kBayesian);
AliUnfolding::SetBayesianParameters(smooth, iter);
} else {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kChi2Minimization);
AliUnfolding::SetChi2Regularization(regul, weight);
}
AliUnfolding::SetSkip0BinInChi2(kTRUE);
AliUnfolding::SetSkipBinsBegin(1);
AliUnfolding::SetNbins(150, 150);
AliUnfolding::Unfold(usecorrfit ? hcorrfit : hcorr, heff, hdata, hinit, hresu);
hinit = (TH1 *)hresu->Clone(Form("hinit_%d", iloop));
}
printf("hdata->Integral(2, -1) = %f\n", hdata->Integral(2, -1));
printf("hresu->Integral(2, -1) = %f\n", hresu->Integral(2, -1));
TCanvas *cUnfolded = new TCanvas ("cUnfolded", "", 400, 800);
cUnfolded->Divide(1, 2);
cUnfolded->cd(1)->SetLogx();
cUnfolded->cd(1)->SetLogy();
hdata->Draw();
hresu->Draw("same");
htrue->Draw("same");
cUnfolded->cd(2)->SetLogx();
cUnfolded->cd(2)->DrawFrame(1., 0.75, 300., 1.25);
TH1 *hrat = (TH1 *)hresu->Clone("hrat");
hrat->Divide(htrue);
hrat->Draw("same");
TH1 *htrig = (TH1 *)hresu->Clone("htrig");
htrig->Multiply(heff);
Float_t dndeta_resu = 0.;
Float_t integr_resu = 0.;
Float_t dndeta_trig = 0.;
Float_t integr_trig = 0.;
for (Int_t i = 1; i < hresu->GetNbinsX(); i++) {
dndeta_resu += hresu->GetBinContent(i + 1) * hresu->GetBinLowEdge(i + 1);
integr_resu += hresu->GetBinContent(i + 1);
dndeta_trig += htrig->GetBinContent(i + 1) * htrig->GetBinLowEdge(i + 1);
integr_trig += htrig->GetBinContent(i + 1);
}
// dndeta_resu /= integr_resu;
// dndeta_trig /= integr_trig;
integr_eff = integr_trig / integr_resu;
integr_eff_err = TMath::Sqrt(integr_eff * (1. - integr_eff) / integr_resu);
dndeta_eff = dndeta_trig / dndeta_resu;
dndeta_eff_err = TMath::Sqrt(dndeta_eff * (1. - dndeta_eff) / dndeta_resu);
printf("INEL > 0 efficiency: %.3f +- %.3f\n", integr_eff, integr_eff_err);
printf("dN/dEta correction: %.3f +- %.3f\n", dndeta_eff, dndeta_eff_err);
return hresu;
}
TH1 *
UnfoldMe_MB2(const Char_t *data, const Char_t *mc, const Char_t *anatag, Int_t bin, Bool_t useMBcorr , Bool_t usecorrfit , Bool_t ismc , Float_t smooth , Int_t iter , Int_t regul , Float_t weight , Bool_t bayesian , Int_t nloop )
{
// MF comments:
// usedMBcorr: changes the matrix used for unfonding, from effMatrix to bin matrix (I think this is just to use mult dependent v s mb correction_)
// usecorrfit: if I understand correctly, fits the response matrix and uses fit to extrapolate it
TFile *fdt =0;
if (ismc)
fdt = TFile::Open(data);
else
fdt = TFile::Open(data);
TFile *fmc = TFile::Open(mc);
TList *ldt = (TList *)fdt->Get(Form("%s", anatag));
TList *lmc = (TList *)fmc->Get(Form("%s", anatag));
TH2 *hmatdt = (TH2 *)ldt->FindObject(Form(responseMatrix, bin));
TH2 *hmatmc = 0;
if (useMBcorr){
hmatmc = (TH2 *)lmc->FindObject("effMatrix");
std::cout << "USING MB" << std::endl;
}
else {
hmatmc = (TH2 *)lmc->FindObject(Form(responseMatrix, bin));
}
TH1 *hdata = hmatdt->ProjectionY("hdata");
// TH1 *hdata = hmatdt->ProjectionY("htrue"); // For truth Only Calculations
hdata->Sumw2();
hdata->SetBinContent(1, 0.);
hdata->SetBinError(1, 0.);
// hdata->Scale(1. / hdata->Integral());
hdata->SetMarkerStyle(25);
TH1 *htrue = hmatdt->ProjectionX("htrue");
htrue->Sumw2();
// htrue->Scale(1. / htrue->Integral());
htrue->SetMarkerStyle(7);
htrue->SetMarkerColor(2);
htrue->SetBinContent(1, 0.);
htrue->SetBinError(1, 0.);
TH2 *hcorr = (TH2 *)hmatmc->Clone("hcorr");
TH1 *hinit = (TH1 *)hdata->Clone("hinit");
TH1 *hresu = (TH1 *)hdata->Clone("hresu");
TH1 *hbias = (TH1 *)hdata->Clone("hbias");
hresu->SetMarkerStyle(20);
hresu->SetMarkerColor(4);
hresu->Reset();
TH1 *hnum = hcorr->ProjectionY("hnum");
TH1 *hden = hcorr->ProjectionY("hden");
TH1 *heff = hcorr->ProjectionY("heff");
hnum->Reset();
hnum->Sumw2();
hden->Reset();
hden->Sumw2();
heff->Reset();
for (Int_t i = 0; i < heff->GetNbinsX(); i++) {
Float_t int1 = hcorr->Integral(i + 1, i + 1, 0, -1);
if (int1 <= 0.) continue;
Float_t int2 = hcorr->Integral(i + 1, i + 1, 2, -1);
hnum->SetBinContent(i + 1, int2);
hnum->SetBinError(i + 1, TMath::Sqrt(int2));
hden->SetBinContent(i + 1, int1);
hden->SetBinError(i + 1, TMath::Sqrt(int1));
}
TCanvas *cEfficiency = new TCanvas("cEfficiency", "cEfficiency");
cEfficiency->SetLogx();
cEfficiency->SetLogy();
heff->Divide(hnum, hden, 1., 1., "B");
heff->Draw();
#if 0
for (Int_t ii = 0; ii < heff->GetNbinsX(); ii++) {
heff->SetBinContent(ii + 1, 1.);
heff->SetBinError(ii + 1, 0.);
}
#endif
for (Int_t i = 0; i < hcorr->GetNbinsX(); i++) {
hcorr->SetBinContent(i + 1, 1, 0.);
hcorr->SetBinError(i + 1, 1, 0.);
}
for (Int_t i = 0; i < hcorr->GetNbinsY(); i++) {
hcorr->SetBinContent(1, i + 1, 0.);
hcorr->SetBinError(1, i + 1, 0.);
}
TH2 *hcorrfit = ReturnCorrFromFit(hcorr);
// Docs from AliUnfolding
//Int_t AliUnfolding::Unfold(TH2* correlation, TH1* efficiency, TH1* measured, TH1* initialConditions, TH1* result, Bool_t check)
// unfolds with unfolding method fgMethodType
//
// parameters:
// correlation: response matrix as measured vs. generated
// efficiency: (optional) efficiency that is applied on the unfolded spectrum, i.e. it has to be in unfolded variables. If 0 no efficiency is applied.
// measured: the measured spectrum
// initialConditions: (optional) initial conditions for the unfolding. if 0 the measured spectrum is used as initial conditions.
// result: target for the unfolded result
// check: depends on the unfolding method, see comments in specific functions
for (Int_t iloop = 0; iloop < nloop; iloop++) {
if (bayesian) {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kBayesian);
AliUnfolding::SetBayesianParameters(smooth, iter);
} else {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kChi2Minimization);
AliUnfolding::SetChi2Regularization(AliUnfolding::RegularizationType(regul), weight);
}
AliUnfolding::SetSkip0BinInChi2(kTRUE);
AliUnfolding::SetSkipBinsBegin(1);
AliUnfolding::SetNbins(150, 150);
AliUnfolding::Unfold(usecorrfit ? hcorrfit : hcorr, heff, hdata, hinit, hresu);
hinit = (TH1 *)hresu->Clone(Form("hinit_%d", iloop));
}
printf("hdata->Integral(2, -1) = %f\n", hdata->Integral(2, -1));
printf("hresu->Integral(2, -1) = %f\n", hresu->Integral(2, -1));
TCanvas *cUnfolded = new TCanvas ("cUnfolded", "cUnfolded", 400, 800);
cUnfolded->Divide(1, 2);
cUnfolded->cd(1)->SetLogx();
cUnfolded->cd(1)->SetLogy();
hdata->Draw();
hresu->Draw("same");
htrue->Draw("same");
cUnfolded->cd(2)->SetLogx();
cUnfolded->cd(2)->DrawFrame(1., 0, 300., 10);
TH1 *hrat = (TH1 *)hresu->Clone("hrat");
hrat->Divide(htrue);
hrat->Draw("same");
TH1 *htrig = (TH1 *)hresu->Clone("htrig");
htrig->Multiply(heff);
Float_t dndeta_resu = 0.;
Float_t integr_resu = 0.;
Float_t dndeta_trig = 0.;
Float_t integr_trig = 0.;
for (Int_t i = 1; i < hresu->GetNbinsX(); i++) {
dndeta_resu += hresu->GetBinContent(i + 1) * hresu->GetBinLowEdge(i + 1);
integr_resu += hresu->GetBinContent(i + 1);
dndeta_trig += htrig->GetBinContent(i + 1) * htrig->GetBinLowEdge(i + 1);
integr_trig += htrig->GetBinContent(i + 1);
}
cUnfolded->SaveAs("unfold_efficiency.pdf");
integr_eff = integr_trig / integr_resu;
integr_eff_err = TMath::Sqrt(integr_eff * (1. - integr_eff) / integr_resu);
dndeta_eff = dndeta_trig / dndeta_resu;
dndeta_eff_err = TMath::Sqrt(dndeta_eff * (1. - dndeta_eff) / dndeta_resu);
printf("INEL > 0 efficiency: %.3f +- %.3f\n", integr_eff, integr_eff_err);
printf("dN/dEta correction: %.3f +- %.3f\n", dndeta_eff, dndeta_eff_err);
return hresu;
}
void smooth(std::string model){
TFile *f_xsec = new TFile("xsec_susy_13tev.root", "READ");
TH1F *h_xsec = (TH1F*)f_xsec->Get("h_xsec_c1n2");//FIXME for Gluino models
//TH1F *h_xsec = (TH1F*)f_xsec->Get("h_xsec_stop");// for stop/sbottom models
TFile *f = new TFile(Form("r-values_%s.root", model.c_str()), "UPDATE");
f->cd();
TH2 *hExp = (TH2*)f->Get("hExp");
TH2 *hObs = (TH2*)f->Get("hObs");
TH2 *hExp1m = (TH2*)f->Get("hExp1m");
TH2 *hExp2m = (TH2*)f->Get("hExp2m");
TH2 *hExp1p = (TH2*)f->Get("hExp1p");
TH2 *hExp2p = (TH2*)f->Get("hExp2p");
if(!hExp) std::cout << "hist not found" << std::endl;
TH2* hXsec_obs = (TH2*)hObs->Clone("hXsec_obs");
TH1D* proj_x = hXsec_obs->ProjectionX();
for(int i=1; i<=hXsec_obs->GetNbinsX(); i++){
for(int j=1; j<=hXsec_obs->GetNbinsY(); j++){
float xsec_limit = hXsec_obs->GetBinContent(i,j);
if(xsec_limit == 0){
continue;
}
xsec_limit *= GetValue(proj_x->GetBinCenter(i), *h_xsec);
hXsec_obs->SetBinContent(i,j,xsec_limit);
}
}
TH2* hObs1m = (TH2*)hObs->Clone("hObs1m");
TH1D* proj_x_1m = hObs1m->ProjectionX();
for(int i=1; i<=hObs1m->GetNbinsX(); i++){
for(int j=1; j<=hObs1m->GetNbinsY(); j++){
float xsec_limit = hObs1m->GetBinContent(i,j);
if(xsec_limit == 0){
continue;
}
xsec_limit *= (GetValueMinus(proj_x_1m->GetBinCenter(i), *h_xsec)/GetValue(proj_x_1m->GetBinCenter(i), *h_xsec));
hObs1m->SetBinContent(i,j,xsec_limit);
}
}
TH2* hObs1p = (TH2*)hObs->Clone("hObs1p");
TH1D* proj_x_1p = hObs1p->ProjectionX();
for(int i=1; i<=hObs1p->GetNbinsX(); i++){
for(int j=1; j<=hObs1p->GetNbinsY(); j++){
float xsec_limit = hObs1p->GetBinContent(i,j);
if(xsec_limit == 0){
continue;
}
xsec_limit *= (GetValuePlus(proj_x_1p->GetBinCenter(i), *h_xsec)/GetValue(proj_x_1p->GetBinCenter(i), *h_xsec));
hObs1p->SetBinContent(i,j,xsec_limit);
}
}
TGraph2D *hExp_graph = new TGraph2D(hExp);
TGraph2D *hObs_graph = new TGraph2D(hObs);
TGraph2D *hObs1m_graph = new TGraph2D(hObs1m);
TGraph2D *hObs1p_graph = new TGraph2D(hObs1p);
TGraph2D *hExp1m_graph = new TGraph2D(hExp1m);
TGraph2D *hExp2m_graph = new TGraph2D(hExp2m);
TGraph2D *hExp1p_graph = new TGraph2D(hExp1p);
TGraph2D *hExp2p_graph = new TGraph2D(hExp2p);
TGraph2D *hXsec_obs_graph = new TGraph2D(hXsec_obs);
//hExp_graph->SetNpx(2*hExp_graph->GetNpx());
//hObs_graph->SetNpx(2*hObs_graph->GetNpx());
//hObs1m_graph->SetNpx(2*hObs1m_graph->GetNpx());
//hObs1p_graph->SetNpx(2*hObs1p_graph->GetNpx());
//hExp1m_graph->SetNpx(2*hExp1m_graph->GetNpx());
//hExp2m_graph->SetNpx(2*hExp2m_graph->GetNpx());
//hExp1p_graph->SetNpx(2*hExp1p_graph->GetNpx());
//hExp2p_graph->SetNpx(2*hExp2p_graph->GetNpx());
//hXsec_obs_graph->SetNpx(2*hXsec_obs_graph->GetNpx());
//hExp_graph->SetNpy(2*hExp_graph->GetNpy());
//hObs_graph->SetNpy(2*hObs_graph->GetNpy());
//hObs1m_graph->SetNpy(2*hObs1m_graph->GetNpy());
//hObs1p_graph->SetNpy(2*hObs1p_graph->GetNpy());
//hExp1m_graph->SetNpy(2*hExp1m_graph->GetNpy());
//hExp2m_graph->SetNpy(2*hExp2m_graph->GetNpy());
//hExp1p_graph->SetNpy(2*hExp1p_graph->GetNpy());
//hExp2p_graph->SetNpy(2*hExp2p_graph->GetNpy());
//hXsec_obs_graph->SetNpy(2*hXsec_obs_graph->GetNpy());
TH2D *hExp_smooth = hExp_graph->GetHistogram();
TH2D *hObs_smooth = hObs_graph->GetHistogram();
TH2D *hObs1m_smooth = hObs1m_graph->GetHistogram();
TH2D *hObs1p_smooth = hObs1p_graph->GetHistogram();
TH2D *hExp1m_smooth = hExp1m_graph->GetHistogram();
TH2D *hExp2m_smooth = hExp2m_graph->GetHistogram();
TH2D *hExp1p_smooth = hExp1p_graph->GetHistogram();
TH2D *hExp2p_smooth = hExp2p_graph->GetHistogram();
TH2D *hXsec_obs_smooth = hXsec_obs_graph->GetHistogram();
hExp_smooth->Write("hExp_smooth",TObject::kOverwrite);
hObs_smooth->Write("hObs_smooth",TObject::kOverwrite);
hObs1m_smooth->Write("hObs1m_smooth",TObject::kOverwrite);
hObs1p_smooth->Write("hObs1p_smooth",TObject::kOverwrite);
hExp1m_smooth->Write("hExp1m_smooth",TObject::kOverwrite);
hExp2m_smooth->Write("hExp2m_smooth",TObject::kOverwrite);
hExp1p_smooth->Write("hExp1p_smooth",TObject::kOverwrite);
hExp2p_smooth->Write("hExp2p_smooth",TObject::kOverwrite);
hXsec_obs_smooth->Write("hXsec_obs_smooth",TObject::kOverwrite);
f->Close();
delete f;
f_xsec->Close();
delete f_xsec;
}