void plotTreeNorms(TTree *tree_, std::string selectString, bool do7TeV){
// Create a map for plotting the pullsummaries:
std::map < const char*, std::pair <double,double> > pullSummaryMap;
int nPulls=0;
TObjArray *l_branches = tree_->GetListOfBranches();
int nBranches = l_branches->GetEntries();
gStyle->SetPadTopMargin(0.01);
TCanvas *c = new TCanvas("c","",960,800);
std::string treename = tree_->GetName();
c->SaveAs(Form("%s_normresiduals.pdf[",treename.c_str()));
// File to store plots in
TFile *fOut = new TFile(Form("%s_normresiduals.root",treename.c_str()),"RECREATE");
TH1F *bHd = new TH1F("bHd","",50,-1.0,1.0);
TH1F *bHfd = new TH1F("bHfd","",50,-1.0,1.0);
for (int iobj=0;iobj<nBranches;iobj++){
TBranch *br =(TBranch*) l_branches->At(iobj);
// Draw the normal histogram
const char* name = br->GetName();
// select only the normalizations
string namestr(name);
if(namestr.find("n_exp")==string::npos) continue;
bool fitPull=true;
bool fitPullf=true;
double p_mean =0;
int nToysInTree = tree_->GetEntries();
// Find out if paramter is fitted value or constraint term.
bool isFitted = true;
p_mean = prenorms_[name].first; // toy initial parameters from the datacards
std::cout << "******* "<< name << " *******"<<std::endl;
std::cout << p_mean << std::endl;
std::cout << "******************************" <<std::endl;
TH1F* bH = (TH1F*)bHd->Clone(Form("%s",name));
TH1F* bHf = (TH1F*)bHfd->Clone(Form("%s_fail",name));
const char* drawInput = Form("(%s-%f)/%f",name,p_mean,p_mean);
tree_->Draw(Form("%s>>%s",drawInput,name),"");
tree_->Draw(Form("%s>>%s_fail",drawInput,name),selectString.c_str(),"same");
fitPull = true;
fitPullf = true;
bHf->SetLineColor(2);
bH->GetXaxis()->SetTitle(bH->GetTitle());
bH->GetYaxis()->SetTitle(Form("no toys (%d total)",nToysInTree));
bH->GetYaxis()->SetTitleOffset(1.05);
bH->GetXaxis()->SetTitleOffset(0.9);
bH->GetYaxis()->SetTitleSize(0.05);
bH->GetXaxis()->SetTitleSize(0.05);
bH->GetXaxis()->SetTitle(Form("%s",name));
bH->SetTitle("");
if ( bH->Integral() <=0 ) fitPull = false;
if (fitPull) {bH->Fit("gaus"); bH->GetFunction("gaus")->SetLineColor(4);}
if ( bHf->Integral() <=0 ) fitPullf = false;
if (fitPullf) {bHf->Fit("gaus"); bHf->GetFunction("gaus")->SetLineColor(2);}
c->Clear();
TPad pad1("t1","",0.01,0.01,0.66,0.95);
TPad pad2("t2","",0.70,0.20,0.98,0.80);
pad1.SetNumber(1); pad2.SetNumber(2);
if ( isFitted ) {pad2.Draw();}
pad1.Draw();
pad1.SetGrid(true);
TLatex *titletext = new TLatex();titletext->SetNDC();
c->cd(1); bH->Draw(); bHf->Draw("same");
TLegend *legend = new TLegend(0.6,0.8,0.9,0.89);
legend->SetFillColor(0);
legend->AddEntry(bH,"All Toys","L");
legend->AddEntry(bHf,selectString.c_str(),"L");
legend->Draw();
if (fitPull){
c->cd(2);
double gap;
TLatex *tlatex = new TLatex(); tlatex->SetNDC();
if (fitPullf) {tlatex->SetTextSize(0.09); gap=0.12;}
else {tlatex->SetTextSize(0.11);gap=0.14;}
tlatex->SetTextColor(4);
tlatex->DrawLatex(0.11,0.80,Form("Mean : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParError(1)));
tlatex->DrawLatex(0.11,0.80-gap,Form("Sigma : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(2),bH->GetFunction("gaus")->GetParError(2)));
if (fitPullf){
tlatex->SetTextColor(2);
tlatex->DrawLatex(0.11,0.60,Form("Mean : %.3f #pm %.3f",bHf->GetFunction("gaus")->GetParameter(1),bHf->GetFunction("gaus")->GetParError(1)));
tlatex->DrawLatex(0.11,0.60-gap,Form("Sigma : %.3f #pm %.3f",bHf->GetFunction("gaus")->GetParameter(2),bHf->GetFunction("gaus")->GetParError(2)));
}
tlatex->SetTextSize(0.10);
tlatex->SetTextColor(1);
tlatex->DrawLatex(0.11,0.33,Form("Pre-fit: %.3f",p_mean));
pullSummaryMap[name]=std::make_pair<double,double>(bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParameter(2));
nPulls++;
}
// double titleSize = isFitted ? 0.1 : 0.028;
//titletext->SetTextSize(titleSize);titletext->SetTextAlign(21); titletext->DrawLatex(0.55,0.92,name);
c->SaveAs(Form("%s_normresiduals_%s.pdf",treename.c_str(),(do7TeV ? "7TeV": "8TeV")));
c->SaveAs(Form("mlfit/%s_residual_%s_%s.pdf",name,treename.c_str(),(do7TeV ? "7TeV": "8TeV")));
fOut->WriteObject(c,Form("%s_%s",treename.c_str(),name));
}
if (nPulls>0){
std::cout << "Generating Pull Summaries" <<std::endl;
int nRemainingPulls = nPulls;
TCanvas *hc = new TCanvas("hc","",3000,2000); hc->SetGrid(0);
std::map < const char*, std::pair <double,double> >::iterator pull_it = pullSummaryMap.begin();
std::map < const char*, std::pair <double,double> >::iterator pull_end = pullSummaryMap.end();
int pullPlots = 1;
while (nRemainingPulls > 0){
int nThisPulls = min(maxPullsPerPlot,nRemainingPulls);
TH1F pullSummaryHist("pullSummary","",nThisPulls,0,nThisPulls);
for (int pi=1;pull_it!=pull_end && pi<=nThisPulls ;pull_it++,pi++){
pullSummaryHist.GetXaxis()->SetBinLabel(pi,(*pull_it).first);
pullSummaryHist.SetBinContent(pi,((*pull_it).second).first);
pullSummaryHist.SetBinError(pi,((*pull_it).second).second);
nRemainingPulls--;
}
pullSummaryHist.SetMarkerStyle(21);pullSummaryHist.SetMarkerSize(1.5);pullSummaryHist.SetMarkerColor(2);pullSummaryHist.SetLabelSize(pullLabelSize);
pullSummaryHist.GetYaxis()->SetRangeUser(-1,1);pullSummaryHist.GetYaxis()->SetTitle("residual summary (relative)");pullSummaryHist.Draw("E1");
hc->SaveAs(Form("%s_normresiduals_%s.pdf",treename.c_str(),(do7TeV ? "7TeV": "8TeV")));
hc->SaveAs(Form("mlfit/residual_summary_%d_%s_%s.pdf",pullPlots,treename.c_str(),(do7TeV ? "7TeV": "8TeV")));
fOut->WriteObject(hc,Form("comb_pulls_%s_%d",treename.c_str(),pullPlots));
// hc->SaveAs(Form("comb_pulls_%s_%d.pdf",treename.c_str(),pullPlots));
pullPlots++;
}
delete hc;
}
c->SaveAs(Form("%s_normresiduals_%s.pdf]",treename.c_str(),(do7TeV ? "7TeV": "8TeV")));
fOut->Close();
delete c;
return;
}
void plotTree(TTree *tree_, std::string whichfit, std::string selectString){
// Create a map for plotting the pullsummaries:
std::map < const char*, std::pair <double,double> > pullSummaryMap;
int nPulls=0;
TObjArray *l_branches = tree_->GetListOfBranches();
int nBranches = l_branches->GetEntries();
TCanvas *c = new TCanvas("c","",960,800);
std::string treename = tree_->GetName();
c->SaveAs(Form("%s.pdf[",treename.c_str()));
for (int iobj=0;iobj<nBranches;iobj++){
TBranch *br =(TBranch*) l_branches->At(iobj);
// Draw the normal histogram
const char* name = br->GetName();
bool fitPull=false;
bool plotLH=false;
TGraph *gr=0;
double p_mean =0;
double p_err =0;
int nToysInTree = tree_->GetEntries();
if (doPull && findNuisancePre(name)){
p_mean = bfvals_[name].first; // toy constrainits thrown about best fit to data
p_err = prevals_[name].second; // uncertainties taken from card
const char* drawInput = Form("(%s-%f)/%f",name,p_mean,p_err);
tree_->Draw(Form("%s>>%s",drawInput,name),"");
tree_->Draw(Form("%s>>%s_fail",drawInput,name),selectString.c_str(),"same");
fitPull = true;
if (doLH) {
gr = graphLH(name,p_err,whichfit);
plotLH=true;
}
}
else{
tree_->Draw(Form("%s>>%s",name,name),"");
tree_->Draw(Form("%s>>%s_fail",name,name),"mu<0","same");
}
TH1F* bH = (TH1F*) gROOT->FindObject(Form("%s",name))->Clone();
TH1F* bHf = (TH1F*) gROOT->FindObject(Form("%s_fail",name))->Clone();
bHf->SetLineColor(2);
bH->GetXaxis()->SetTitle(bH->GetTitle());
bH->GetYaxis()->SetTitle(Form("no toys (%d total)",nToysInTree));
bH->GetYaxis()->SetTitleOffset(1.32);
bH->SetTitle("");
if (fitPull) bH->Fit("gaus");
c->Clear();
TPad pad1("t1","",0.01,0.02,0.59,0.98);
TPad pad2("t2","",0.59,0.04,0.98,0.62);
TPad pad3("t3","",0.59,0.64,0.98,0.90);
pad1.SetNumber(1); pad2.SetNumber(2); pad3.SetNumber(3);
pad1.Draw(); pad2.Draw();pad3.Draw();
pad2.SetGrid(true);
c->cd(1); bH->Draw(); bHf->Draw("same");
TLatex *titletext = new TLatex();titletext->SetNDC();titletext->SetTextSize(0.04); titletext->DrawLatex(0.1,0.95,name);
TLegend *legend = new TLegend(0.6,0.8,0.9,0.89);
legend->SetFillColor(0);
legend->AddEntry(bH,"All Toys","L");
legend->AddEntry(bHf,selectString.c_str(),"L");
legend->Draw();
if (doPull && plotLH) {
c->cd(2); gr->Draw("ALP");
}
if (fitPull){
c->cd(3);
TLatex *tlatex = new TLatex(); tlatex->SetNDC(); tlatex->SetTextSize(0.12);
tlatex->DrawLatex(0.15,0.75,Form("Mean : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParError(1)));
tlatex->DrawLatex(0.15,0.60,Form("Sigma : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(2),bH->GetFunction("gaus")->GetParError(2)));
tlatex->DrawLatex(0.15,0.35,Form("Pre-fit : %.3f ",prevals_[name].first));
tlatex->DrawLatex(0.15,0.2,Form("Best-fit (B) : %.3f ",p_mean));
tlatex->DrawLatex(0.15,0.05,Form("Best-fit (S+B): %.3f ",bfvals_sb_[name].first));
pullSummaryMap[name]=std::make_pair<double,double>(bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParameter(2));
nPulls++;
}
c->SaveAs(Form("%s.pdf",treename.c_str()));
}
if (doPull && nPulls>0){
int nRemainingPulls = nPulls;
TCanvas *hc = new TCanvas("hc","",3000,2000); hc->SetGrid(0);
std::map < const char*, std::pair <double,double> >::iterator pull_it = pullSummaryMap.begin();
std::map < const char*, std::pair <double,double> >::iterator pull_end = pullSummaryMap.end();
while (nRemainingPulls > 0){
int nThisPulls = min(15,nRemainingPulls);
TH1F pullSummaryHist("pullSummary","",nThisPulls,0,nThisPulls);
for (int pi=1;pull_it!=pull_end && pi<=nThisPulls ;pull_it++,pi++){
pullSummaryHist.GetXaxis()->SetBinLabel(pi,(*pull_it).first);
pullSummaryHist.SetBinContent(pi,((*pull_it).second).first);
pullSummaryHist.SetBinError(pi,((*pull_it).second).second);
nRemainingPulls--;
}
pullSummaryHist.SetMarkerStyle(21);pullSummaryHist.SetMarkerSize(1.5);pullSummaryHist.SetMarkerColor(2);pullSummaryHist.SetLabelSize(0.018);
pullSummaryHist.GetYaxis()->SetRangeUser(-3,3);pullSummaryHist.GetYaxis()->SetTitle("pull summary");pullSummaryHist.Draw("E1");
hc->SaveAs(Form("%s.pdf",treename.c_str()));
}
delete hc;
}
c->SaveAs(Form("%s.pdf]",treename.c_str()));
delete c;
return;
}
void plotTree(TTree *tree_, std::string whichfit, std::string selectString){
// Create a map for plotting the pullsummaries:
std::map < const char*, std::pair <double,double> > pullSummaryMap;
int nPulls=0;
TObjArray *l_branches = tree_->GetListOfBranches();
int nBranches = l_branches->GetEntries();
gStyle->SetPadTopMargin(0.01);
TCanvas *c = new TCanvas("c","",960,800);
std::string treename = tree_->GetName();
c->SaveAs(Form("%s.pdf[",treename.c_str()));
// File to store plots in
TFile *fOut = new TFile(Form("%s.root",treename.c_str()),"RECREATE");
for (int iobj=0;iobj<nBranches;iobj++){
TBranch *br =(TBranch*) l_branches->At(iobj);
// Draw the normal histogram
const char* name = br->GetName();
// names with - are not allowed
string namestr(name);
if(namestr.find("-")!=string::npos) {
std::cout << "Variable " << name << " contains a bad character: -. Skipping. " << std::endl;
continue;
}
bool fitPull=false;
bool fitPullf=false;
bool plotLH=false;
TGraph *gr=NULL;
double p_mean =0;
double p_err =0;
int nToysInTree = tree_->GetEntries();
// Find out if paramter is fitted value or constraint term
bool isFitted = findNuisancePre(name);
if (doPull && isFitted){
p_mean = bfvals_[name].first; // toy constrainits thrown about best fit to data
if(namestr.find("n_exp")==string::npos) p_err = prevals_[name].second; // uncertainties taken from card
std::cout << "******* "<< name << " *******"<<std::endl;
std::cout << p_mean << " " << p_err << std::endl;
std::cout << "******************************" <<std::endl;
const char* drawInput;
// if the parameter is a normalization, the error is not available. Do the residual instead of the pull
if(namestr.find("n_exp")!=string::npos) drawInput = Form("(%s-%f)/%f",name,p_mean,p_mean);
else drawInput = Form("(%s-%f)/%f",name,p_mean,p_err);
tree_->Draw(Form("%s>>%s",drawInput,name),"");
tree_->Draw(Form("%s>>%s_fail",drawInput,name),selectString.c_str(),"same");
fitPull = true;
fitPullf = true;
if (doLH) {
gr = graphLH(name,p_err,whichfit);
if (gr) plotLH=true;
}
}
else{
tree_->Draw(Form("%s>>%s",name,name),"");
tree_->Draw(Form("%s>>%s_fail",name,name),selectString.c_str(),"same");
}
TH1F* bH = (TH1F*) gROOT->FindObject(Form("%s",name))->Clone();
TH1F* bHf = (TH1F*) gROOT->FindObject(Form("%s_fail",name))->Clone();
bHf->SetLineColor(2);
bH->GetXaxis()->SetTitle(bH->GetTitle());
bH->GetYaxis()->SetTitle(Form("no toys (%d total)",nToysInTree));
bH->GetYaxis()->SetTitleOffset(1.05);
bH->GetXaxis()->SetTitleOffset(0.9);
bH->GetYaxis()->SetTitleSize(0.05);
bH->GetXaxis()->SetTitleSize(0.05);
if (isFitted) {bH->GetXaxis()->SetTitle(Form("(%s-#theta_{B})/#sigma_{#theta}",name));}
else {bH->GetXaxis()->SetTitle(Form("%s",name));}
bH->SetTitle("");
if ( bH->Integral() <=0 ) fitPull = false;
if (fitPull) {bH->Fit("gaus"); bH->GetFunction("gaus")->SetLineColor(4);}
if ( bHf->Integral() <=0 ) fitPullf = false;
if (fitPullf) {bHf->Fit("gaus"); bHf->GetFunction("gaus")->SetLineColor(2);}
c->Clear();
//TPad pad1("t1","",0.01,0.02,0.59,0.98);
// Pad 1 sizes depend on the parameter type ...
double pad1_x1,pad1_x2,pad1_y1,pad1_y2;
if ( !isFitted ) {
pad1_x1 = 0.01;
pad1_x2 = 0.98;
pad1_y1 = 0.045;
pad1_y2 = 0.98;
} else {
pad1_x1 = 0.01;
pad1_x2 = 0.59;
pad1_y1 = 0.56;
pad1_y2 = 0.98;
}
TPad pad1("t1","",pad1_x1,pad1_y1,pad1_x2,pad1_y2);
TPad pad1a("t1a","",0.01,0.045,0.59,0.522);
TPad pad2("t2","",0.59,0.04,0.98,0.62);
TPad pad3("t3","",0.55,0.64,0.96,0.95);
pad1.SetNumber(1); pad2.SetNumber(2); pad3.SetNumber(3); pad1a.SetNumber(4);
if ( isFitted ) {pad1a.Draw();pad2.Draw();pad3.Draw();}
pad1.Draw();
pad2.SetGrid(true);
TLatex *titletext = new TLatex();titletext->SetNDC();
if ( isFitted ){
c->cd(4);
tree_->Draw(Form("%s:%s_In>>%s_%s_2d",name,name,name,tree_->GetName()),"");
//TH2D *h2d_corr = (TH2D*)gROOT->FindObject(Form("%s_2d",name));
//h2d_corr->SetMarkerColor(4);
//h2d_corr->SetTitle("");
//h2d_corr->GetXaxis()->SetTitle(Form("%s_In",name));
//h2d_corr->GetYaxis()->SetTitle(Form("%s",name));
titletext->SetTextAlign(11);
titletext->SetTextSize(0.05);
titletext->DrawLatex(0.05,0.02,Form("%s_In",name));
titletext->SetTextAngle(90);
titletext->DrawLatex(0.04,0.06,Form("%s",name));
titletext->SetTextAngle(0);
}
c->cd(1); bH->Draw(); bHf->Draw("same");
TLegend *legend = new TLegend(0.6,0.8,0.9,0.89);
legend->SetFillColor(0);
legend->AddEntry(bH,"All Toys","L");
legend->AddEntry(bHf,selectString.c_str(),"L");
legend->Draw();
if (doPull && plotLH) {
c->cd(2); gr->Draw("ALP");
}
if (fitPull){
c->cd(3);
double gap;
TLatex *tlatex = new TLatex(); tlatex->SetNDC();
if (fitPullf) {tlatex->SetTextSize(0.09); gap=0.12;}
else {tlatex->SetTextSize(0.11);gap=0.14;}
tlatex->SetTextColor(4);
tlatex->DrawLatex(0.11,0.80,Form("Mean : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParError(1)));
tlatex->DrawLatex(0.11,0.80-gap,Form("Sigma : %.3f #pm %.3f",bH->GetFunction("gaus")->GetParameter(2),bH->GetFunction("gaus")->GetParError(2)));
if (fitPullf){
tlatex->SetTextColor(2);
tlatex->DrawLatex(0.11,0.60,Form("Mean : %.3f #pm %.3f",bHf->GetFunction("gaus")->GetParameter(1),bHf->GetFunction("gaus")->GetParError(1)));
tlatex->DrawLatex(0.11,0.60-gap,Form("Sigma : %.3f #pm %.3f",bHf->GetFunction("gaus")->GetParameter(2),bHf->GetFunction("gaus")->GetParError(2)));
}
tlatex->SetTextSize(0.10);
tlatex->SetTextColor(1);
if(namestr.find("n_exp")!=string::npos) tlatex->DrawLatex(0.11,0.33,Form("Pre-fit: %.3f",prevals_[name].first));
else tlatex->DrawLatex(0.11,0.33,Form("Pre-fit #pm #sigma_{#theta}: %.3f #pm %.3f",prevals_[name].first, p_err));
tlatex->DrawLatex(0.11,0.18,Form("Best-fit (#theta_{B}) : %.3f ",p_mean));
tlatex->DrawLatex(0.11,0.03,Form("Best-fit (#theta_{S+B}): %.3f ",bfvals_sb_[name].first));
pullSummaryMap[name]=std::make_pair<double,double>(bH->GetFunction("gaus")->GetParameter(1),bH->GetFunction("gaus")->GetParameter(2));
nPulls++;
}
double titleSize = isFitted ? 0.1 : 0.028;
titletext->SetTextSize(titleSize);titletext->SetTextAlign(21); titletext->DrawLatex(0.55,0.92,name);
c->SaveAs(Form("%s.pdf",treename.c_str()));
fOut->WriteObject(c,Form("%s_%s",treename.c_str(),name));
//c->SaveAs(Form("%s_%s.pdf",treename.c_str(),name));
}
if (doPull && nPulls>0){
std::cout << "Generating Pull Summaries" <<std::endl;
int nRemainingPulls = nPulls;
TCanvas *hc = new TCanvas("hc","",3000,2000); hc->SetGrid(0);
std::map < const char*, std::pair <double,double> >::iterator pull_it = pullSummaryMap.begin();
std::map < const char*, std::pair <double,double> >::iterator pull_end = pullSummaryMap.end();
int pullPlots = 1;
while (nRemainingPulls > 0){
int nThisPulls = min(maxPullsPerPlot,nRemainingPulls);
TH1F pullSummaryHist("pullSummary","",nThisPulls,0,nThisPulls);
for (int pi=1;pull_it!=pull_end && pi<=nThisPulls ;pull_it++,pi++){
pullSummaryHist.GetXaxis()->SetBinLabel(pi,(*pull_it).first);
pullSummaryHist.SetBinContent(pi,((*pull_it).second).first);
pullSummaryHist.SetBinError(pi,((*pull_it).second).second);
nRemainingPulls--;
}
pullSummaryHist.SetMarkerStyle(21);pullSummaryHist.SetMarkerSize(1.5);pullSummaryHist.SetMarkerColor(2);pullSummaryHist.SetLabelSize(pullLabelSize);
pullSummaryHist.GetYaxis()->SetRangeUser(-3,3);pullSummaryHist.GetYaxis()->SetTitle("pull summary (n#sigma)");pullSummaryHist.Draw("E1");
hc->SaveAs(Form("%s.pdf",treename.c_str()));
fOut->WriteObject(hc,Form("comb_pulls_%s_%d",treename.c_str(),pullPlots));
// hc->SaveAs(Form("comb_pulls_%s_%d.pdf",treename.c_str(),pullPlots));
pullPlots++;
}
delete hc;
}
c->SaveAs(Form("%s.pdf]",treename.c_str()));
fOut->Close();
delete c;
return;
}
