void dominik()
{
TH1* matHistogramRoman = static_cast<TH1*>(extractObjectFromFile("lyRoman.root", "lightYieldProjectionY")->At(0));
TList* objects = extractObjectFromFile("c.root", "chargeBins");
TH1* matHistogramDominik = new TH1D("matHistogramDominik", ";channel;light yield / pixels", 512, -0.5, 512-0.5);
int sipmIt = 0;
for (int i = 0; i < objects->GetSize(); ++i) {
TH1* h = static_cast<TH1*>(objects->At(i));
if (h->GetLineColor() == 8) {
for (int bin = 1; bin <= 128; ++bin) {
matHistogramDominik->SetBinContent(512 - (sipmIt * 128 + bin - 1), h->GetBinContent(bin));
if (h->GetBinError(bin) > 0)
matHistogramDominik->SetBinError(512 - (sipmIt * 128 + bin - 1), h->GetBinError(bin));
}
++sipmIt;
}
}
TCanvas* c = new TCanvas;
c->Divide(1, 2);
c->cd(1);
matHistogramDominik->Draw();
matHistogramRoman->Draw("SAME");
c->cd(2);
TH1* h = static_cast<TH1*>(matHistogramDominik->Clone());
h->Add(matHistogramRoman, -1);
h->Draw();
}
//________________________________________________________
void GFHistManager::ColourFuncs(GFHistArray *hists) const
{
// adjust colour of funcs to match hist, but only if exactly one function per hist
if (!hists) return;
for (Int_t iH = 0; iH < hists->GetEntriesFast(); ++iH) {
TH1 *h = hists->At(iH);
if (!h) continue;
// look for _the_ TF1 (not > 1!)
TF1 *func = NULL;
TIter nextprim(h->GetListOfFunctions());
while (TObject* next = nextprim()) {
if (next->InheritsFrom(TF1::Class())) {
if (func) { // there is already a TF1, so...
func = NULL; // remove it again...
break; // ...and stop searching for more!
} else {
func = static_cast<TF1*>(next);
}
}
}
// if exactly 1 found, adjust line style/colour
if (func) {
func->SetLineColor(h->GetLineColor());
func->SetLineStyle(h->GetLineStyle());
}
}
}
void DrawReport(const char* psname, TObjArray* harr)
{
gStyle->SetOptFit(1);
if (!harr) harr = &histoArr;
TCanvas* cnv = new TCanvas("cl","cl",900,600);
//
TString psnm1 = psname;
if (psnm1.IsNull()) psnm1 = "clusters.ps";
TString psnm0 = psnm1.Data();
psnm0 += "[";
TString psnm2 = psnm1.Data();
psnm2 += "]";
cnv->Print(psnm0.Data());
//
TH1* clall = GetHistoClSize(0,kNPixAll,harr);
clall->SetLineColor(kRed);
clall->Draw();
TH1* clSpl = GetHistoClSize(0,kNPixSPL,harr);
clSpl->SetLineColor(kBlue);
clSpl->Draw("sames");
gPad->Modified();
gPad->Update();
SetStPadPos(clall,0.75,0.97,0.8,1.,-1,clall->GetLineColor());
SetStPadPos(clSpl,0.75,0.97,0.6,0.8,-1,clSpl->GetLineColor());
gPad->Modified();
gPad->Update();
gPad->SetLogy(1);
//
cnv->cd();
cnv->Print(psnm1.Data());
//
// plot cluster sized from 1 to 10
for (int i=1;i<=10;i++) {
if (clall->GetBinContent(clall->FindBin(i))<100) continue;
DrawNP(i,harr,cnv);
cnv->Print(psnm1.Data());
}
cnv->Print(psnm2.Data());
}
TCanvas* DrawNP(int np, TObjArray* harr, TCanvas* cnv)
{
if (!harr) harr = &histoArr;
if (!cnv) cnv = new TCanvas(Form("cnv%d",np),Form("cnv%d",np),900,700);
cnv->Clear();
cnv->Divide(2,1);
cnv->cd(1);
//
TH1* dxodd = (TH1*)harr->At(np*10+kDTXodd);
TH1* dxevn = (TH1*)harr->At(np*10+kDTXeven);
TH1* dxoddS =(TH1*)harr->At(np*10+kDTXoddSPL);
TH1* dxevnS =(TH1*)harr->At(np*10+kDTXevenSPL);
double max = TMath::Max(dxodd->GetMaximum(),dxevn->GetMaximum());
dxodd->SetMaximum(1.1*max);
dxodd->GetXaxis()->SetTitle("#DeltaX, #mum");
dxodd->SetTitle(Form("#DeltaX for clSize=%d",np));
dxodd->Fit("gaus","","");
dxevn->Fit("gaus","","sames");
//
dxoddS->Draw("sames");
dxevnS->Draw("sames");
//
gPad->Modified();
gPad->Update();
SetStPadPos(dxodd,0.75,0.97,0.8,1., -1,dxodd->GetLineColor());
SetStPadPos(dxevn,0.75,0.97,0.6,0.8, -1,dxevn->GetLineColor());
SetStPadPos(dxoddS,0.75,0.97,0.4,0.6, -1,dxoddS->GetLineColor());
SetStPadPos(dxevnS,0.75,0.97,0.2,0.4, -1,dxevnS->GetLineColor());
//
cnv->cd(2);
TH1* dz = (TH1*)harr->At(np*10+kDTZ);
dz->SetTitle(Form("#DeltaZ for clSize=%d",np));
dz->GetXaxis()->SetTitle("#DeltaZ, #mum");
dz->Fit("gaus");
TH1* dzS = (TH1*)harr->At(np*10+kDTZSPL);
dz->Draw("sames");
gPad->Modified();
gPad->Update();
SetStPadPos(dz,0.75,0.97,0.8,1., -1, dz->GetLineColor());
SetStPadPos(dzS,0.75,0.97,0.5,0.7, -1, dzS->GetLineColor());
gPad->Modified();
gPad->Update();
//
cnv->cd();
return cnv;
}
// input: - Input file (result from TMVA)
// - use of TMVA plotting TStyle
void mvas( TString fin = "TMVA.root", HistType htype = MVAType, Bool_t useTMVAStyle = kTRUE )
{
// set style and remove existing canvas'
TMVAGlob::Initialize( useTMVAStyle );
// switches
const Bool_t Save_Images = kTRUE;
// checks if file with name "fin" is already open, and if not opens one
TFile* file = TMVAGlob::OpenFile( fin );
// define Canvas layout here!
Int_t xPad = 1; // no of plots in x
Int_t yPad = 1; // no of plots in y
Int_t noPad = xPad * yPad ;
const Int_t width = 600; // size of canvas
// this defines how many canvases we need
TCanvas *c = 0;
// counter variables
Int_t countCanvas = 0;
// search for the right histograms in full list of keys
TIter next(file->GetListOfKeys());
TKey *key(0);
while ((key = (TKey*)next())) {
if (!TString(key->GetName()).BeginsWith("Method_")) continue;
if( ! gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory") ) continue;
TString methodName;
TMVAGlob::GetMethodName(methodName,key);
TDirectory* mDir = (TDirectory*)key->ReadObj();
TIter keyIt(mDir->GetListOfKeys());
TKey *titkey;
while ((titkey = (TKey*)keyIt())) {
if (!gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory")) continue;
TDirectory *titDir = (TDirectory *)titkey->ReadObj();
TString methodTitle;
TMVAGlob::GetMethodTitle(methodTitle,titDir);
cout << "--- Found directory for method: " << methodName << "::" << methodTitle << flush;
TString hname = "MVA_" + methodTitle;
if (htype == ProbaType ) hname += "_Proba";
else if (htype == RarityType ) hname += "_Rarity";
TH1* sig = dynamic_cast<TH1*>(titDir->Get( hname + "_S" ));
TH1* bgd = dynamic_cast<TH1*>(titDir->Get( hname + "_B" ));
if (sig==0 || bgd==0) {
if (htype == MVAType)
cout << "mva distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == ProbaType)
cout << "probability distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == RarityType)
cout << "rarity distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == CompareType)
cout << "overtraining check not available (this is normal for Cut classifier)" << endl;
else cout << endl;
}
else {
cout << endl;
// chop off useless stuff
sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) );
if (htype == ProbaType)
sig->SetTitle( Form("TMVA probability for classifier: %s", methodTitle.Data()) );
else if (htype == RarityType)
sig->SetTitle( Form("TMVA Rarity for classifier: %s", methodTitle.Data()) );
else if (htype == CompareType)
sig->SetTitle( Form("TMVA overtraining check for classifier: %s", methodTitle.Data()) );
// create new canvas
TString ctitle = ((htype == MVAType) ?
Form("TMVA response %s",methodTitle.Data()) :
(htype == ProbaType) ?
Form("TMVA probability %s",methodTitle.Data()) :
(htype == CompareType) ?
Form("TMVA comparison %s",methodTitle.Data()) :
Form("TMVA Rarity %s",methodTitle.Data()));
TString cname = ((htype == MVAType) ?
Form("output_%s",methodTitle.Data()) :
(htype == ProbaType) ?
Form("probability_%s",methodTitle.Data()) :
(htype == CompareType) ?
Form("comparison_%s",methodTitle.Data()) :
Form("rarity_%s",methodTitle.Data()));
c = new TCanvas( Form("canvas%d", countCanvas+1), ctitle,
countCanvas*50+200, countCanvas*20, width, (Int_t)width*0.78 );
// set the histogram style
TMVAGlob::SetSignalAndBackgroundStyle( sig, bgd );
// normalise both signal and background
TMVAGlob::NormalizeHists( sig, bgd );
// frame limits (choose judicuous x range)
Float_t nrms = 4;
cout << "--- Mean and RMS (S): " << sig->GetMean() << ", " << sig->GetRMS() << endl;
cout << "--- Mean and RMS (B): " << bgd->GetMean() << ", " << bgd->GetRMS() << endl;
Float_t xmin = TMath::Max( TMath::Min(sig->GetMean() - nrms*sig->GetRMS(),
bgd->GetMean() - nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmin() );
Float_t xmax = TMath::Min( TMath::Max(sig->GetMean() + nrms*sig->GetRMS(),
bgd->GetMean() + nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmax() );
Float_t ymin = 0;
Float_t maxMult = (htype == CompareType) ? 1.3 : 1.2;
Float_t ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*maxMult;
// build a frame
Int_t nb = 500;
TString hFrameName(TString("frame") + methodTitle);
TObject *o = gROOT->FindObject(hFrameName);
if(o) delete o;
TH2F* frame = new TH2F( hFrameName, sig->GetTitle(),
nb, xmin, xmax, nb, ymin, ymax );
frame->GetXaxis()->SetTitle( methodTitle + ((htype == MVAType || htype == CompareType) ? " response" : "") );
if (htype == ProbaType ) frame->GetXaxis()->SetTitle( "Signal probability" );
else if (htype == RarityType ) frame->GetXaxis()->SetTitle( "Signal rarity" );
frame->GetYaxis()->SetTitle("Normalized");
TMVAGlob::SetFrameStyle( frame );
// eventually: draw the frame
frame->Draw();
c->GetPad(0)->SetLeftMargin( 0.105 );
frame->GetYaxis()->SetTitleOffset( 1.2 );
// Draw legend
TLegend *legend= new TLegend( c->GetLeftMargin(), 1 - c->GetTopMargin() - 0.12,
c->GetLeftMargin() + (htype == CompareType ? 0.40 : 0.3), 1 - c->GetTopMargin() );
legend->SetFillStyle( 1 );
legend->AddEntry(sig,TString("Signal") + ((htype == CompareType) ? " (test sample)" : ""), "F");
legend->AddEntry(bgd,TString("Background") + ((htype == CompareType) ? " (test sample)" : ""), "F");
legend->SetBorderSize(1);
legend->SetMargin( (htype == CompareType ? 0.2 : 0.3) );
legend->Draw("same");
// overlay signal and background histograms
sig->Draw("samehist");
bgd->Draw("samehist");
if (htype == CompareType) {
// if overtraining check, load additional histograms
TH1* sigOv = 0;
TH1* bgdOv = 0;
TString ovname = hname += "_Train";
sigOv = dynamic_cast<TH1*>(titDir->Get( ovname + "_S" ));
bgdOv = dynamic_cast<TH1*>(titDir->Get( ovname + "_B" ));
if (sigOv == 0 || bgdOv == 0) {
cout << "+++ Problem in \"mvas.C\": overtraining check histograms do not exist" << endl;
}
else {
cout << "--- Found comparison histograms for overtraining check" << endl;
TLegend *legend2= new TLegend( 1 - c->GetRightMargin() - 0.42, 1 - c->GetTopMargin() - 0.12,
1 - c->GetRightMargin(), 1 - c->GetTopMargin() );
legend2->SetFillStyle( 1 );
legend2->SetBorderSize(1);
legend2->AddEntry(sigOv,"Signal (training sample)","P");
legend2->AddEntry(bgdOv,"Background (training sample)","P");
legend2->SetMargin( 0.1 );
legend2->Draw("same");
}
Int_t col = sig->GetLineColor();
sigOv->SetMarkerColor( col );
sigOv->SetMarkerSize( 0.7 );
sigOv->SetMarkerStyle( 20 );
sigOv->SetLineWidth( 1 );
sigOv->SetLineColor( col );
sigOv->Draw("e1same");
col = bgd->GetLineColor();
bgdOv->SetMarkerColor( col );
bgdOv->SetMarkerSize( 0.7 );
bgdOv->SetMarkerStyle( 20 );
bgdOv->SetLineWidth( 1 );
bgdOv->SetLineColor( col );
bgdOv->Draw("e1same");
ymax = TMath::Max( ymax, TMath::Max( sigOv->GetMaximum(), bgdOv->GetMaximum() )*maxMult );
frame->GetYaxis()->SetLimits( 0, ymax );
// for better visibility, plot thinner lines
sig->SetLineWidth( 1 );
bgd->SetLineWidth( 1 );
// perform K-S test
cout << "--- Perform Kolmogorov-Smirnov tests" << endl;
Double_t kolS = sig->KolmogorovTest( sigOv );
Double_t kolB = bgd->KolmogorovTest( bgdOv );
cout << "--- Goodness of signal (background) consistency: " << kolS << " (" << kolB << ")" << endl;
TString probatext = Form( "Kolmogorov-Smirnov test: signal (background) probability = %5.3g (%5.3g)", kolS, kolB );
TText* tt = new TText( 0.12, 0.74, probatext );
tt->SetNDC(); tt->SetTextSize( 0.032 ); tt->AppendPad();
}
// redraw axes
frame->Draw("sameaxis");
// text for overflows
Int_t nbin = sig->GetNbinsX();
Double_t dxu = sig->GetBinWidth(0);
Double_t dxo = sig->GetBinWidth(nbin+1);
TString uoflow = Form( "U/O-flow (S,B): (%.1f, %.1f)%% / (%.1f, %.1f)%%",
sig->GetBinContent(0)*dxu*100, bgd->GetBinContent(0)*dxu*100,
sig->GetBinContent(nbin+1)*dxo*100, bgd->GetBinContent(nbin+1)*dxo*100 );
TText* t = new TText( 0.975, 0.115, uoflow );
t->SetNDC();
t->SetTextSize( 0.030 );
t->SetTextAngle( 90 );
t->AppendPad();
// update canvas
c->Update();
// save canvas to file
TMVAGlob::plot_logo(1.058);
if (Save_Images) {
if (htype == MVAType) TMVAGlob::imgconv( c, Form("plots/mva_%s", methodTitle.Data()) );
else if (htype == ProbaType) TMVAGlob::imgconv( c, Form("plots/proba_%s", methodTitle.Data()) );
else if (htype == CompareType) TMVAGlob::imgconv( c, Form("plots/overtrain_%s", methodTitle.Data()) );
else TMVAGlob::imgconv( c, Form("plots/rarity_%s", methodTitle.Data()) );
}
countCanvas++;
}
}
}
}
void makePlot(const std::string& inputFilePath, const std::string& canvasName, const std::string& sample, int massPoint, const std::string& channel, double k,
const std::string& inputFileName, const std::string& outputFilePath, const std::string& outputFileName)
{
std::string inputFileName_full = Form("%s%s", inputFilePath.data(), inputFileName.data());
TFile* inputFile = new TFile(inputFileName_full.data());
if ( !inputFile ) {
std::cerr << "Failed to open input file = " << inputFileName_full << " !!" << std::endl;
assert(0);
}
inputFile->ls();
TCanvas* canvas = dynamic_cast<TCanvas*>(inputFile->Get(canvasName.data()));
if ( !canvas ) {
std::cerr << "Failed to load canvas = " << canvasName << " !!" << std::endl;
assert(0);
}
int idxPad = -1;
if ( massPoint == 90 ) idxPad = 1;
if ( massPoint == 125 ) idxPad = 2;
if ( massPoint == 200 ) idxPad = 3;
if ( massPoint == 300 ) idxPad = 4;
if ( massPoint == 500 ) idxPad = 5;
if ( massPoint == 800 ) idxPad = 6;
if ( !(idxPad >= 1 && idxPad <= 6) ) {
std::cerr << "Invalid sample = " << sample << " !!" << std::endl;
assert(0);
}
TVirtualPad* pad = canvas->GetPad(idxPad);
std::cout << "pad = " << pad << ": ClassName = " << pad->ClassName() << std::endl;
TCanvas* canvas_new = new TCanvas("canvas_new", "canvas_new", 900, 800);
canvas_new->SetFillColor(10);
canvas_new->SetBorderSize(2);
canvas_new->SetTopMargin(0.065);
canvas_new->SetLeftMargin(0.17);
canvas_new->SetBottomMargin(0.165);
canvas_new->SetRightMargin(0.015);
canvas_new->SetLogx(true);
canvas_new->SetLogy(true);
canvas_new->Draw();
canvas_new->cd();
//TList* pad_primitives = canvas->GetListOfPrimitives();
TList* pad_primitives = pad->GetListOfPrimitives();
TH1* histogramCA = 0;
TH1* histogramSVfit = 0;
TH1* histogramSVfitMEMkEq0 = 0;
TH1* histogramSVfitMEMkNeq0 = 0;
TIter pad_nextObj(pad_primitives);
while ( TObject* obj = pad_nextObj() ) {
std::string objName = "";
if ( dynamic_cast<TNamed*>(obj) ) objName = (dynamic_cast<TNamed*>(obj))->GetName();
std::cout << "obj = " << obj << ": name = " << objName << ", type = " << obj->ClassName() << std::endl;
TH1* tmpHistogram = dynamic_cast<TH1*>(obj);
if ( tmpHistogram ) {
std::cout << "tmpHistogram:"
<< " fillColor = " << tmpHistogram->GetFillColor() << ", fillStyle = " << tmpHistogram->GetFillStyle() << ","
<< " lineColor = " << tmpHistogram->GetLineColor() << ", lineStyle = " << tmpHistogram->GetLineStyle() << ", lineWidth = " << tmpHistogram->GetLineWidth() << ","
<< " markerColor = " << tmpHistogram->GetMarkerColor() << ", markerStyle = " << tmpHistogram->GetMarkerStyle() << ", markerSize = " << tmpHistogram->GetMarkerSize() << ","
<< " integral = " << tmpHistogram->Integral() << std::endl;
std::cout << "(mean = " << tmpHistogram->GetMean() << ", rms = " << tmpHistogram->GetRMS() << ": rms/mean = " << (tmpHistogram->GetRMS()/tmpHistogram->GetMean()) << ")" << std::endl;
if ( tmpHistogram->GetLineColor() == 416 ) histogramCA = tmpHistogram;
if ( tmpHistogram->GetLineColor() == 600 ) histogramSVfit = tmpHistogram;
if ( tmpHistogram->GetLineColor() == 616 ) histogramSVfitMEMkEq0 = tmpHistogram;
if ( tmpHistogram->GetLineColor() == 632 ) histogramSVfitMEMkNeq0 = tmpHistogram;
}
}
if ( !(histogramCA && histogramSVfit && histogramSVfitMEMkEq0 && histogramSVfitMEMkNeq0) ) {
std::cerr << "Failed to load histograms !!" << std::endl;
assert(0);
}
//gStyle->SetLineStyleString(2,"40 10 10 10 10 10 10 10");
//gStyle->SetLineStyleString(3,"25 15");
//gStyle->SetLineStyleString(4,"60 25");
//int colors[4] = { kBlack, kGreen - 6, kBlue - 7, kMagenta - 7 };
int colors[4] = { 28, kGreen - 6, kBlue - 7, kBlack };
//int lineStyles[4] = { 2, 3, 4, 1 };
int lineStyles[4] = { 7, 1, 1, 1 };
//int lineWidths[4] = { 3, 3, 4, 3 };
int lineWidths[4] = { 3, 3, 1, 1 };
int markerStyles[4] = { 20, 25, 21, 24 };
int markerSizes[4] = { 2, 2, 2, 2 };
histogramCA->SetFillColor(0);
histogramCA->SetFillStyle(0);
histogramCA->SetLineColor(colors[0]);
histogramCA->SetLineStyle(lineStyles[0]);
histogramCA->SetLineWidth(lineWidths[0]);
histogramCA->SetMarkerColor(colors[0]);
histogramCA->SetMarkerStyle(markerStyles[0]);
histogramCA->SetMarkerSize(markerSizes[0]);
histogramSVfit->SetFillColor(0);
histogramSVfit->SetFillStyle(0);
histogramSVfit->SetLineColor(colors[1]);
histogramSVfit->SetLineStyle(lineStyles[1]);
histogramSVfit->SetLineWidth(lineWidths[1]);
histogramSVfit->SetMarkerColor(colors[1]);
histogramSVfit->SetMarkerStyle(markerStyles[1]);
histogramSVfit->SetMarkerSize(markerSizes[1]);
histogramSVfitMEMkEq0->SetFillColor(0);
histogramSVfitMEMkEq0->SetFillStyle(0);
histogramSVfitMEMkEq0->SetLineColor(colors[2]);
histogramSVfitMEMkEq0->SetLineStyle(lineStyles[2]);
histogramSVfitMEMkEq0->SetLineWidth(lineWidths[2]);
histogramSVfitMEMkEq0->SetMarkerColor(colors[2]);
histogramSVfitMEMkEq0->SetMarkerStyle(markerStyles[2]);
histogramSVfitMEMkEq0->SetMarkerSize(markerSizes[2]);
// CV: fix pathological bins at high mass for which dN/dm increases
int numBins = histogramSVfitMEMkEq0->GetNbinsX();
for ( int idxBin = 1; idxBin <= numBins; ++idxBin ) {
double binCenter = histogramSVfitMEMkEq0->GetBinCenter(idxBin);
if ( (channel == "#tau_{h}#tau_{h}" && massPoint == 500 && binCenter > 1500.) ||
(channel == "#tau_{h}#tau_{h}" && massPoint == 800 && binCenter > 2000.) ||
(channel == "#mu#tau_{h}" && massPoint == 500 && binCenter > 1500.) ||
(channel == "#mu#tau_{h}" && massPoint == 800 && binCenter > 2500.) ) {
histogramSVfitMEMkEq0->SetBinContent(idxBin, 0.);
}
}
histogramSVfitMEMkNeq0->SetFillColor(0);
histogramSVfitMEMkNeq0->SetFillStyle(0);
histogramSVfitMEMkNeq0->SetLineColor(colors[3]);
histogramSVfitMEMkNeq0->SetLineStyle(lineStyles[3]);
histogramSVfitMEMkNeq0->SetLineWidth(lineWidths[3]);
histogramSVfitMEMkNeq0->SetMarkerColor(colors[3]);
histogramSVfitMEMkNeq0->SetMarkerStyle(markerStyles[3]);
histogramSVfitMEMkNeq0->SetMarkerSize(markerSizes[3]);
TAxis* xAxis = histogramCA->GetXaxis();
xAxis->SetTitle("m_{#tau#tau} [GeV]");
xAxis->SetTitleOffset(1.15);
xAxis->SetTitleSize(0.070);
xAxis->SetTitleFont(42);
xAxis->SetLabelOffset(0.010);
xAxis->SetLabelSize(0.055);
xAxis->SetLabelFont(42);
xAxis->SetTickLength(0.040);
xAxis->SetNdivisions(510);
//double xMin = 20.;
//double xMax = xAxis->GetXmax();
//xAxis->SetRangeUser(xMin, xMax);
TAxis* yAxis = histogramCA->GetYaxis();
yAxis->SetTitle("dN/dm_{#tau#tau} [1/GeV]");
yAxis->SetTitleOffset(1.20);
yAxis->SetTitleSize(0.070);
yAxis->SetTitleFont(42);
yAxis->SetLabelOffset(0.010);
yAxis->SetLabelSize(0.055);
yAxis->SetLabelFont(42);
yAxis->SetTickLength(0.040);
yAxis->SetNdivisions(505);
double massPoint_double = 0.;
if ( massPoint == 90 ) massPoint_double = 91.2;
else massPoint_double = massPoint;
double dLog = (TMath::Log(5.*massPoint_double) - TMath::Log(50.))/25.; // xMin = 50, xMax = 5*massPoint, numBins = 25
double binWidth = TMath::Exp(TMath::Log(massPoint_double) + 0.5*dLog) - TMath::Exp(TMath::Log(massPoint_double) - 0.5*dLog);
double sf_binWidth = 1./binWidth;
std::cout << "massPoint = " << massPoint << ": sf_binWidth = " << sf_binWidth << std::endl;
histogramCA->SetTitle("");
histogramCA->SetStats(false);
histogramCA->SetMaximum(sf_binWidth*0.79);
histogramCA->SetMinimum(sf_binWidth*1.1e-4);
histogramCA->Draw("hist");
histogramSVfit->Draw("histsame");
//histogramSVfitMEMkEq0->Draw("histsame");
histogramSVfitMEMkEq0->Draw("epsame");
//histogramSVfitMEMkNeq0->Draw("histsame");
histogramSVfitMEMkNeq0->Draw("epsame");
histogramCA->Draw("axissame");
//TPaveText* label_sample = new TPaveText(0.21, 0.86, 0.46, 0.94, "NDC");
TPaveText* label_sample = new TPaveText(0.1700, 0.9475, 0.4600, 1.0375, "NDC");
label_sample->SetFillStyle(0);
label_sample->SetBorderSize(0);
label_sample->AddText(sample.data());
label_sample->SetTextFont(42);
label_sample->SetTextSize(0.055);
label_sample->SetTextColor(1);
label_sample->SetTextAlign(13);
label_sample->Draw();
//TLegend* legend_new = new TLegend(0.225, 0.52, 0.41, 0.82, NULL, "brNDC");
TLegend* legend_new = new TLegend(0.30, 0.30, 0.80, 0.80, NULL, "brNDC");
legend_new->SetFillColor(10);
legend_new->SetFillStyle(0);
legend_new->SetBorderSize(0);
legend_new->SetTextFont(42);
legend_new->SetTextSize(0.055);
legend_new->SetTextColor(1);
legend_new->SetMargin(0.20);
legend_new->AddEntry(histogramCA, "CA", "l");
legend_new->AddEntry(histogramSVfit, "SVfit", "l");
//legend_new->AddEntry(histogramSVfitMEMkEq0, "SVfitMEM (k=0)", "l");
legend_new->AddEntry(histogramSVfitMEMkEq0, "SVfitMEM (k=0)", "p");
//legend_new->AddEntry(histogramSVfitMEMkNeq0, Form("SVfitMEM(k=%1.0f)", k), "l");
legend_new->AddEntry(histogramSVfitMEMkNeq0, Form("SVfitMEM (k=%1.0f)", k), "p");
//legend_new->Draw();
double label_channel_y0;
if ( channel == "e#mu" ) label_channel_y0 = 0.9275;
else if ( channel == "#mu#tau_{h}" ) label_channel_y0 = 0.9400;
else if ( channel == "#tau_{h}#tau_{h}" ) label_channel_y0 = 0.9350;
else {
std::cerr << "Invalid channel = " << channel << " !!" << std::endl;
assert(0);
}
TPaveText* label_channel = new TPaveText(0.895, label_channel_y0, 0.975, label_channel_y0 + 0.055, "NDC");
label_channel->SetFillStyle(0);
label_channel->SetBorderSize(0);
label_channel->AddText(channel.data());
label_channel->SetTextFont(62);
label_channel->SetTextSize(0.055);
label_channel->SetTextColor(1);
label_channel->SetTextAlign(31);
label_channel->Draw();
canvas_new->Update();
std::string outputFileName_full = Form("%s%s", outputFilePath.data(), outputFileName.data());
size_t idx = outputFileName_full.find_last_of('.');
std::string outputFileName_plot = std::string(outputFileName_full, 0, idx);
canvas_new->Print(std::string(outputFileName_plot).append(".pdf").data());
canvas_new->Print(std::string(outputFileName_plot).append(".root").data());
std::string channel_string;
if ( channel == "e#mu" ) channel_string = "emu";
else if ( channel == "#mu#tau_{h}" ) channel_string = "muhad";
else if ( channel == "#tau_{h}#tau_{h}" ) channel_string = "hadhad";
else {
std::cerr << "Invalid channel = " << channel << " !!" << std::endl;
assert(0);
}
std::string outputFileName_legend = Form("makeSVfitMEM_PerformancePlots_legend_%s.pdf", channel_string.data());
makePlot_legend(legend_new, outputFilePath, outputFileName_legend);
delete label_sample;
delete legend_new;
delete label_channel;
delete canvas_new;
delete inputFile;
}
void makePlot(const std::string& inputFilePath, const std::string& canvasName, const std::string& histogram, const std::string& channel, const std::string& xAxisTitle, const std::string& yAxisTitle,
const std::string& inputFileName, const std::string& outputFilePath, const std::string& outputFileName)
{
std::string inputFileName_full = Form("%s%s", inputFilePath.data(), inputFileName.data());
TFile* inputFile = new TFile(inputFileName_full.data());
if ( !inputFile ) {
std::cerr << "Failed to open input file = " << inputFileName_full << " !!" << std::endl;
assert(0);
}
inputFile->ls();
TCanvas* canvas = dynamic_cast<TCanvas*>(inputFile->Get(canvasName.data()));
if ( !canvas ) {
std::cerr << "Failed to load canvas = " << canvasName << " !!" << std::endl;
assert(0);
}
int idxPad = -1;
if ( histogram == "mVis" ) idxPad = 1;
if ( histogram == "mTauTau" ) idxPad = 2;
if ( !(idxPad >= 1 && idxPad <= 2) ) {
std::cerr << "Invalid histogram = " << histogram << " !!" << std::endl;
assert(0);
}
TVirtualPad* pad = canvas->GetPad(idxPad);
std::cout << "pad = " << pad << ": ClassName = " << pad->ClassName() << std::endl;
TCanvas* canvas_new = new TCanvas("canvas_new", "canvas_new", 900, 850);
canvas_new->SetFillColor(10);
canvas_new->SetBorderSize(2);
canvas_new->SetTopMargin(0.065);
canvas_new->SetLeftMargin(0.17);
canvas_new->SetBottomMargin(0.155);
canvas_new->SetRightMargin(0.045);
canvas_new->SetLogx(false);
canvas_new->SetLogy(false);
canvas_new->Draw();
canvas_new->cd();
//TList* pad_primitives = canvas->GetListOfPrimitives();
TList* pad_primitives = pad->GetListOfPrimitives();
TH1* histogramDYJets = 0;
TH1* histogramHiggs125 = 0;
TH1* histogramHiggs200 = 0;
TH1* histogramHiggs300 = 0;
TIter pad_nextObj(pad_primitives);
while ( TObject* obj = pad_nextObj() ) {
std::string objName = "";
if ( dynamic_cast<TNamed*>(obj) ) objName = (dynamic_cast<TNamed*>(obj))->GetName();
std::cout << "obj = " << obj << ": name = " << objName << ", type = " << obj->ClassName() << std::endl;
TH1* tmpHistogram = dynamic_cast<TH1*>(obj);
if ( tmpHistogram ) {
std::cout << "tmpHistogram:"
<< " fillColor = " << tmpHistogram->GetFillColor() << ", fillStyle = " << tmpHistogram->GetFillStyle() << ","
<< " lineColor = " << tmpHistogram->GetLineColor() << ", lineStyle = " << tmpHistogram->GetLineStyle() << ", lineWidth = " << tmpHistogram->GetLineWidth() << ","
<< " markerColor = " << tmpHistogram->GetMarkerColor() << ", markerStyle = " << tmpHistogram->GetMarkerStyle() << ", markerSize = " << tmpHistogram->GetMarkerSize() << ","
<< " integral = " << tmpHistogram->Integral() << std::endl;
if ( tmpHistogram->GetFillColor() == 0 ) histogramDYJets = tmpHistogram;
if ( tmpHistogram->GetFillColor() == 632 ) histogramHiggs125 = tmpHistogram;
if ( tmpHistogram->GetFillColor() == 616 ) histogramHiggs200 = tmpHistogram;
if ( tmpHistogram->GetFillColor() == 600 ) histogramHiggs300 = tmpHistogram;
}
}
if ( !(histogramDYJets && histogramHiggs125 && histogramHiggs200 && histogramHiggs300) ) {
std::cerr << "Failed to load histograms !!" << std::endl;
assert(0);
}
int lineColors[4] = { 1, 2, 6, 4 };
int lineStyles[4] = { 1, 1, 1, 1 };
int lineWidths[4] = { 2, 2, 2, 2 };
int fillColors[4] = { 10, 2, 6, 4 };
int fillStyles[4] = { 0, 3002, 3004, 3005 };
histogramDYJets->SetFillColor(fillColors[0]);
histogramDYJets->SetFillStyle(fillStyles[0]);
histogramDYJets->SetLineColor(lineColors[0]);
histogramDYJets->SetLineStyle(lineStyles[0]);
histogramDYJets->SetLineWidth(lineWidths[0]);
histogramHiggs125->SetFillColor(fillColors[1]);
histogramHiggs125->SetFillStyle(fillStyles[1]);
histogramHiggs125->SetLineColor(lineColors[1]);
histogramHiggs125->SetLineStyle(lineStyles[1]);
histogramHiggs125->SetLineWidth(lineWidths[1]);
histogramHiggs200->SetFillColor(fillColors[2]);
histogramHiggs200->SetFillStyle(fillStyles[2]);
histogramHiggs200->SetLineColor(lineColors[2]);
histogramHiggs200->SetLineStyle(lineStyles[2]);
histogramHiggs200->SetLineWidth(lineWidths[2]);
histogramHiggs300->SetFillColor(fillColors[3]);
histogramHiggs300->SetFillStyle(fillStyles[3]);
histogramHiggs300->SetLineColor(lineColors[3]);
histogramHiggs300->SetLineStyle(lineStyles[3]);
histogramHiggs300->SetLineWidth(lineWidths[3]);
TAxis* xAxis = histogramHiggs300->GetXaxis();
if ( histogram == "mVis" ) xAxis->SetRangeUser(0,350);
else xAxis->SetRangeUser(0,450);
xAxis->SetTitle(xAxisTitle.data());
xAxis->SetTitleOffset(1.00);
xAxis->SetTitleSize(0.070);
xAxis->SetTitleFont(42);
xAxis->SetLabelOffset(0.010);
xAxis->SetLabelSize(0.050);
xAxis->SetLabelFont(42);
xAxis->SetTickLength(0.040);
xAxis->SetNdivisions(505);
//double xMin = 20.;
//double xMax = xAxis->GetXmax();
//xAxis->SetRangeUser(xMin, xMax);
TAxis* yAxis = histogramHiggs300->GetYaxis();
yAxis->SetTitle(yAxisTitle.data());
yAxis->SetTitleOffset(1.20);
yAxis->SetTitleSize(0.070);
yAxis->SetTitleFont(42);
yAxis->SetLabelOffset(0.010);
yAxis->SetLabelSize(0.055);
yAxis->SetLabelFont(42);
yAxis->SetTickLength(0.040);
yAxis->SetNdivisions(505);
histogramHiggs300->SetTitle("");
histogramHiggs300->SetStats(false);
histogramHiggs300->SetMaximum(1.2*histogramDYJets->GetMaximum());
histogramHiggs300->SetMinimum(0.);
histogramHiggs300->Draw("hist");
histogramHiggs200->Draw("histsame");
histogramHiggs125->Draw("histsame");
histogramDYJets->Draw("histsame");
histogramHiggs300->Draw("axissame");
TLegend* legend_new = new TLegend(0.50, 0.62, 0.87, 0.92, NULL, "brNDC");
legend_new->SetFillColor(10);
legend_new->SetFillStyle(0);
legend_new->SetBorderSize(0);
legend_new->SetTextFont(42);
legend_new->SetTextSize(0.055);
legend_new->SetTextColor(1);
legend_new->SetMargin(0.20);
legend_new->AddEntry(histogramDYJets, "Z/#gamma* #rightarrow #tau#tau", "f");
legend_new->AddEntry(histogramHiggs125, "H(125 GeV) #rightarrow #tau#tau", "f");
legend_new->AddEntry(histogramHiggs200, "H(200 GeV) #rightarrow #tau#tau", "f");
legend_new->AddEntry(histogramHiggs300, "H(300 GeV) #rightarrow #tau#tau", "f");
legend_new->Draw();
double label_channel_y0;
if ( channel == "e#mu" ) label_channel_y0 = 0.9275;
else if ( channel == "#mu#tau_{h}" ) label_channel_y0 = 0.9400;
else if ( channel == "#tau_{h}#tau_{h}" ) label_channel_y0 = 0.9350;
else {
std::cerr << "Invalid channel = " << channel << " !!" << std::endl;
assert(0);
}
TPaveText* label_channel = new TPaveText(0.855, label_channel_y0, 0.945, label_channel_y0 + 0.055, "NDC");
label_channel->SetFillStyle(0);
label_channel->SetBorderSize(0);
label_channel->AddText(channel.data());
label_channel->SetTextFont(62);
label_channel->SetTextSize(0.055);
label_channel->SetTextColor(1);
label_channel->SetTextAlign(31);
label_channel->Draw();
canvas_new->Update();
std::string outputFileName_full = Form("%s%s", outputFilePath.data(), outputFileName.data());
size_t idx = outputFileName_full.find_last_of('.');
std::string outputFileName_plot = std::string(outputFileName_full, 0, idx);
canvas_new->Print(std::string(outputFileName_plot).append(".pdf").data());
//canvas_new->Print(std::string(outputFileName_plot).append(".root").data());
delete legend_new;
delete label_channel;
delete canvas_new;
delete inputFile;
}
void AnalysisSparse(Bool_t save_output = kFALSE)
{
gStyle->SetGridColor(kGray);
// TString tmpstr(fname);
// if (tmpstr.Contains("data")) {
// Printf("!!! Real Data !!!");
// mc = kFALSE;
// }
TString gtitle = Form("Monte Carlo, %s", graph_name.Data());
grapht = graph_name.Data();
Double_t grx[999], gry[999], gry2[999], gry3[999], gry4[999],
gry_eff[999], gry_fix[999], grxE[999];
Double_t gry22[999], gry22E[999], grx22E[999];
Double_t gry_true[999], gry_true_eff[999], gry_true_effE[999];
TH1::AddDirectory(kFALSE);
TFile::SetCacheFileDir(gSystem->HomeDirectory());
TFile *f = TFile::Open(fname.Data(), "CACHEREAD");
if (!f) return;
TList *l; f->GetObject(lname.Data(), l);
if (!l) return;
Int_t bf[999], bl[999];
Int_t nn = FindExactRange(((THnSparse *)(l->FindObject(s1name.Data())))->
Projection(1), del_step, bf, bl);
// Int_t nn = FindRange5(bf, bl);
Bool_t binhaluska = kFALSE;
if (binAnders) {
nn = 8;
bf[0] = 6;bf[1] = 9;bf[2] = 11;bf[3] = 16;bf[4] = 21;bf[5] = 26;
bl[0] = 8;bl[1] = 10;bl[2] = 15;bl[3] = 20;bl[4] = 25;bl[5] = 30;
bf[6] = 31;bf[7] = 41;
bl[6] = 40;bl[7] = 50;
}
Printf("number of intervals = %d =>", nn);
Int_t count = 0;
Double_t ptmean = 0, value = 0;
Int_t fitStatus = -1;
gStyle->SetOptStat(0);
TCanvas *c = new TCanvas("c", "Signal & Background");
c->Divide(5, 5); c->Modified(); c->Draw();
TCanvas *c2 = (TCanvas *)c->DrawClone("c2");
c2->SetTitle("Phi mesons (raw)"); c2->Modified(); c2->Draw();
TCanvas *c3, *c4;
if (mc) {
c3 = (TCanvas *)c->DrawClone("c3");
c3->SetTitle("Phi mesons (gen)"); c3->Modified(); c3->Draw();
c4 = (TCanvas *)c->DrawClone("c4");
c4->SetTitle("Phi mesons (true)"); c4->Modified(); c4->Draw();
}
for (Int_t i = 0; i < nn; i++) {
c->cd(count + 1)->SetGrid();
h1 = (TH1D *)PullHisto(l, s1name.Data(), bf[i], bl[i], ptmean);
h1->SetLineColor(kRed);
h1->GetXaxis()->SetTitle("inv. mass, GeV/c^2");
h1->Draw("hist");
h3_p = (TH1D *)PullHisto(l, s3name_p.Data(), bf[i], bl[i], ptmean);
h3_m = (TH1D *)PullHisto(l, s3name_m.Data(), bf[i], bl[i], ptmean);
// !!!!!!!!!!!!!!!!!!!!!!!!
if (count==0) h3_p = h1;
// !!!!!!!!!!!!!!!!!!!!!!!!
else {
h3_p->Add(h3_m);
// h3_p->Add((TH1D *)PullHisto(l, smix.Data(), bf[i], bl[i], ptmean));
// h3_p->Add((TH1D *)PullHisto(l, smixpp.Data(), bf[i], bl[i], ptmean));
// h3_p->Add((TH1D *)PullHisto(l, smixmm.Data(), bf[i], bl[i], ptmean));
Norm(h1, h3_p, norm[0], norm[1]);
}
h3_p->SetLineColor(kBlue);
h3_p->Draw("hist, same");
if (mc) {
c3->cd(count + 1)->SetGrid();
Printf("%s", s1namegen.Data());
hg = (TH1D *)PullHisto(l, s1namegen.Data(), bf[i], bl[i], ptmean);
hg->SetLineColor(kMagenta);
hg->GetXaxis()->SetTitle("inv. mass, GeV/c^2");
hg->Draw("hist");
c4->cd(count + 1)->SetGrid();
ht = (TH1D *)PullHisto(l, s1nametrue.Data(), bf[i], bl[i], ptmean);
ht->SetLineColor(kMagenta-5);
ht->GetXaxis()->SetTitle("inv. mass, GeV/c^2");
ht->Draw("hist");
}
c2->cd(count + 1)->SetGrid();
TH1 *hh = (TH1 *)h1->Clone("hh");
hh->SetLineColor(kRed+1);
hh->Add(h3_p, -1);
/// !!!!!!!!!!!!!!!!!!!!!!
////////// if ((ilist == 3) && (count < 2)) hh->Reset();
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!
hh->Draw("hist");
// !!!!!!!!!!!!!!!!!!
ff->SetParameters(0.1, 1.02, 0.004, -25000., 0., 0., 0.);
ff->SetLineColor(hh->GetLineColor());
ff->SetLineWidth(1);
// ff->SetLineStyle(kDashed);
// where fit
Double_t fmin = 1.02-2*0.004;
Double_t fmax = 1.02+2*0.004;
// Double_t fmin = 0.995;
// Double_t fmax = 1.185;
// !!!!!!!!!!!!!!!!!!
Bool_t hisfun = kFALSE; // kFALSE = integral from function
Double_t hisfun_k = 1.0/hh->GetBinWidth(10);
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
if (binhaluska)
if (i > 9) hisfun_k = 0.5/hh->GetBinWidth(10);
Printf("======= %f", hisfun_k);
// !!!!!!!!!!!!!!!!!!
// wehere integral (his or fun)
Double_t fmini = 1.02-2*0.004;
Double_t fmaxi = 1.02+2*0.004;
hh->Fit(ff, "Q", "", fmin, fmax);
hh->Fit(ff, "Q", "", fmin, fmax);
fitStatus = hh->Fit(ff, "Q", "", fmin, fmax);
TF1 *pp3 = new TF1("pp3", "[0]+x*[1]+x*x*[2]+x*x*x*[3]", fmin, fmax);
pp3->SetParameters(ff->GetParameter(3), ff->GetParameter(4),
ff->GetParameter(5), ff->GetParameter(6));
pp3->SetLineWidth(1);
pp3->SetLineColor(h3_p->GetLineColor());
pp3->Draw("same");
// ff->SetRange(fmin, fmax);
// ff->DrawCopy("same");
value = hh->Integral(hh->FindBin(fmini), hh->FindBin(fmaxi));
if (!hisfun) value = ff->Integral(fmini, fmaxi)*hisfun_k -
pp3->Integral(fmini, fmaxi)*hisfun_k;
if (value < 0) value = 0;
if ((fitStatus != 0) || (ff->GetParameter(2) > 0.1)) {
printf(" SKIP Data");
value = 0;
}
grx[count] = ptmean;
if (binhaluska) {
if (count < 10) grxE[count] = 0.25; // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
else grxE[count] = 0.50; // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
}
else
// grxE[count] = (1.30-1.10)/2.0; // !!!!!!!!!!!!!!!!!!!!!!!!!!
grxE[count] = 0.05;
gry[count] = value;
Double_t tmp1 = h1->Integral(h1->FindBin(fmini), h1->FindBin(fmaxi));
Double_t tmp2 = h3_p->Integral(h3_p->FindBin(fmini), h3_p->FindBin(fmaxi));
Double_t tmp_sg = tmp1 - tmp2;
Double_t tmp_bg = tmp2;
// if ((tmp_sg <= -tmp_bg) || (tmp_bg < 33.0)) {
// gry3[count] = 0.0;
// gry4[count] = 0.0;
// }
// else {
gry3[count] = tmp_sg/tmp_bg;
gry4[count] = tmp_sg/TMath::Sqrt(tmp_sg + tmp_bg);
// }
// Printf("%4.2f, %10f, %10f, %10f", ptmean, tmp1, tmp2, gry3[count]);
if (mc) {
c3->cd(count + 1);
// !!!!!!!!!!!!!!!!
ff->SetParameters(1, 1.02, 0.004, 0., 0., 0., 0.);
hg->Fit(ff, "Q", "", fmin, fmax);
hg->Fit(ff, "Q", "", fmin, fmax);
fitStatus = hg->Fit(ff, "Q", "", fmin, fmax);
/* TF1 *pp3 = new TF1("pp3", "[0]+x*[1]+x*x*[2]+x*x*x*[3]", fmin, fmax);
pp3->SetParameters(ff->GetParameter(3), ff->GetParameter(4),
ff->GetParameter(5), ff->GetParameter(6));
pp3->SetLineWidth(1);
pp3->SetLineColor(h3_p->GetLineColor());
pp3->Draw("same");
*/
value = hg->Integral(hg->FindBin(fmini), hg->FindBin(fmaxi));
if (!hisfun) value = ff->Integral(fmini, fmaxi)*hisfun_k;
//!!!!!!!!!!!!!!!!!!!pp3->Integral(fmini, fmaxi)*hisfun_k;
if (value <= 0) value = -1;
if ((fitStatus != 0) || (ff->GetParameter(2) > 0.1)) {
printf(" SKIP MC");
value = -1;
}
gry2[count] = value;
Double_t superfactor = CalculateFactor(l, 0.1);
if (useCF) {
gry22E[i] = TMath::Sqrt(gry2[i])*superfactor;
// gry22E[i] = 0.0001;
gry22[i] = gry2[i]*superfactor;
grx22E[i] = 0.05;
}
gry_eff[count] = gry[count]/gry2[count];
c4->cd(count + 1);
// !!!!!!!!!!!!!!!!
ff->SetParameters(1, 1.02, 0.004, 0., 0., 0., 0.);
ht->Fit(ff, "Q", "", fmin, fmax);
ht->Fit(ff, "Q", "", fmin, fmax);
fitStatus = ht->Fit(ff, "Q", "", fmin, fmax);
/* TF1 *pp3 = new TF1("pp3", "[0]+x*[1]+x*x*[2]+x*x*x*[3]", fmin, fmax);
pp3->SetParameters(ff->GetParameter(3), ff->GetParameter(4),
ff->GetParameter(5), ff->GetParameter(6));
pp3->SetLineWidth(1);
pp3->SetLineColor(h3_p->GetLineColor());
pp3->Draw("same");
*/
value = ht->Integral(ht->FindBin(fmini), ht->FindBin(fmaxi));
if (!hisfun) value = ff->Integral(fmini, fmaxi)*hisfun_k;
//!!!!!!!!!!!!!!!!!!!pp3->Integral(fmini, fmaxi)*hisfun_k;
if (value <= 0) value = -1;
if ((fitStatus != 0) || (ff->GetParameter(2) > 0.1)) {
printf(" SKIP true");
value = -1;
}
gry_true[count] = value;
gry_true_eff[count] = gry_true[count]/gry2[count];
// Propagation of uncertainty (A/B)
Double_t AAA = gry_true[count];
Double_t AAAE = TMath::Sqrt(AAA);
Double_t BBB = gry2[count];
Double_t BBBE = TMath::Sqrt(BBB);
Double_t EEE = TMath::Sqrt((AAAE/AAA)*(AAAE/AAA)+(BBBE/BBB)*(BBBE/BBB));
EEE = EEE*gry_true_eff[count];
gry_true_effE[count] = EEE;
}
Printf("=> %6.4f", ptmean);
count++;
}
new TCanvas();
TGraph *gr = new TGraph(count, grx, gry);
gr->SetMarkerStyle(8);
gr->SetMarkerColor(hh->GetLineColor());
gr->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr->SetTitle(Form("raw phi, %s", gtitle.Data()));
gr->Draw("AP");
cc3 = new TCanvas();
TGraph *gr3 = new TGraph(count, grx, gry3);
gr3->SetMarkerStyle(22);
gr3->SetMarkerColor(kBlue+1);
gr3->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr3->SetTitle(Form("SIG / BKG, %s", gtitle.Data()));
gr3->SetMinimum(0);
gr3->Draw("AP");
cc4 = new TCanvas();
TGraph *gr4 = new TGraph(count, grx, gry4);
gr4->SetMarkerStyle(23);
gr4->SetMarkerColor(kBlue-1);
gr4->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr4->SetTitle(Form("Significance, %s", gtitle.Data()));
gr4->SetMinimum(0);
gr4->Draw("AP");
ccc = new TCanvas("ccc","ccc",0,0,900,300);
ccc->Divide(2, 1, 0.001, 0.001);
ccc->cd(1); gr3->Draw("AP");
ccc->cd(2); gr4->Draw("AP");
TString blabla = "mc";
if (!mc) blabla = "data";
// gr3->SaveAs(Form("SB_%s_%s.C", blabla.Data(), grapht.Data()));
// gr4->SaveAs(Form("Sig_%s_%s.C", blabla.Data(), grapht.Data()));
// ccc->SaveAs(Form("%s_%s_2.eps", blabla.Data(), grapht.Data()));
// c->SaveAs(Form("%s_%s_0.eps", blabla.Data(), grapht.Data()));
// c2->SaveAs(Form("%s_%s_1.eps", blabla.Data(), grapht.Data()));
// cc3->SaveAs(Form("%s_%s_2.eps", blabla.Data(), grapht.Data()));
// gr3->SaveAs(Form("sig_bck_%s_%s.C", blabla.Data(), grapht.Data()));
if (mc) {
new TCanvas();
TGraph *gr2 = new TGraph(count, grx, gry2);
gr2->SetMarkerStyle(8);
gr2->SetMarkerColor(hg->GetLineColor());
gr2->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr2->SetTitle(Form("gen phi, %s", gtitle.Data()));
gr2->Draw("AP");
new TCanvas();
TGraphErrors *gr22 = new TGraphErrors(count, grx, gry22, grx22E, gry22E);
gr22->SetMarkerStyle(8);
gr22->SetMarkerColor(kCyan);
gr22->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr22->SetTitle(Form("gen phi, %s", gtitle.Data()));
gr22->Draw("AP");
c = new TCanvas();
c->SetGrid();
TGraph *gr_e = new TGraph(count, grx, gry_eff);
gr_e->SetMarkerStyle(22);
gr_e->SetMarkerColor(kBlack);
gr_e->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr_e->SetTitle(Form("efficiency (raw), %s", grapht.Data()));
gr_e->Draw("AP");
Printf("Save as '\033[1meffi_raw_%s\033[0m' file", grapht.Data());
for (Int_t i = 0; i < gr_e->GetN(); i++)
Printf("%f %f", gr_e->GetX()[i], gr_e->GetY()[i]);
cvb = new TCanvas();
cvb->cd();
TGraph *gr_true = new TGraph(count, grx, gry_true);
gr_true->SetMarkerStyle(8);
gr_true->SetMarkerColor(ht->GetLineColor());
gr_true->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr_true->SetTitle(Form("true phi, %s", gtitle.Data()));
gr_true->Draw("AP");
c = new TCanvas();
c->cd();
c->SetGrid();
TGraphErrors *gr_true_eff = new TGraphErrors(count, grx, gry_true_eff,
grxE, gry_true_effE);
gr_true_eff->SetMarkerStyle(20);
// gr_true_eff->SetMarkerSize(0.75);
gr_true_eff->SetMarkerColor(kBlack);
gr_true_eff->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr_true_eff->SetTitle(Form("efficiency (true), %s", grapht.Data()));
gr_true_eff->Draw("AEP");
m_gr->Add(gr_true_eff);
Printf("Save as '\033[1meffi_true_%s\033[0m' file", grapht.Data());
TString tout;
Double_t oux, ouy, ouxe, ouye;
for (Int_t i = 0; i < gr_true_eff->GetN(); i++) {
oux = gr_true_eff->GetX()[i];
ouy = gr_true_eff->GetY()[i];
ouy = MinusCheck(ouy);
ouxe = gr_true_eff->GetErrorX(i);
ouye = gr_true_eff->GetErrorY(i);
ouye = NanCheck(ouye);
Printf("%f %f %f %f", gr_true_eff->GetX()[i], gr_true_eff->GetY()[i],
gr_true_eff->GetErrorX(i), gr_true_eff->GetErrorY(i));
if (!save_output) continue;
gSystem->mkdir(dir_prefix.Data());
tout = Form("%f %f %f %f", oux, ouy, ouxe, ouye);
if (i == 0)
tout = Form("Printf(\"%s\"); > %s/effi_%s", tout.Data(),
dir_prefix.Data(), grapht.Data());
else
tout = Form("Printf(\"%s\"); >> %s/effi_%s", tout.Data(),
dir_prefix.Data(), grapht.Data());
// Printf(":::::: %s", tout.Data());
gROOT->ProcessLine(tout.Data());
}
// ------------------
c = new TCanvas("cfinal", "mc_effi", 1200, 450);
c->Divide(2, 1, 0.001, 0.001); c->Modified(); c->Draw();
c->cd(1);
gr_true->SetMinimum(0);
gr_true->SetTitle(Form("phi (true & raw), %s", gtitle.Data()));
gr_true->SetMarkerColor(kGreen+1);
gr_true->Draw("AP");
gr->SetMarkerColor(kRed+1);
gr->Draw("P");
c->cd(2)->SetGrid();
gr_true_eff->SetMinimum(0);
gr_true_eff->SetTitle(Form("efficiency, %s", grapht.Data()));
gr_true_eff->SetMarkerColor(kGreen+1);
gr_true_eff->Draw("AP");
gr_e->SetMarkerColor(kRed+1);
gr_e->Draw("P");
// c->SaveAs(Form("%s_%s.eps", blabla.Data(), grapht.Data()));
return;
}
// TGraph *geff = new TGraph(Form("effi_raw_%s", grapht.Data()));
// TGraph *geff = new TGraph(Form("effi_true_%s", grapht.Data()));
// TGraph *geff = new TGraph("effi_true_Phi2010_qualityonly");
TGraph *geff = new TGraph("effi_true_PhiNsigma_qualityonly");
if (geff->IsZombie()) return;
geff->SetMarkerStyle(22);
geff->SetMarkerColor(kBlack);
geff->GetXaxis()->SetTitle("p_{t}, GeV/c");
geff->SetTitle(Form("efficiency, %s", grapht.Data()));
c = new TCanvas();
c->SetGrid();
geff->Draw("AP");
Double_t tpcsigma = 9999.9;
if (ilist == 1) tpcsigma = 1.0;
if (ilist == 2) tpcsigma = 1.5;
if (ilist == 3) tpcsigma = 2.0;
if (ilist == 4) tpcsigma = 2.5;
if (ilist == 5) tpcsigma = 3.0;
Double_t sss = TMath::Erf(tpcsigma/TMath::Sqrt(2.0));
if (noSigma) sss = 1.0;
Printf("sigma = %10f", sss);
// for (Int_t i = 0; i < count; i++)
// geff->GetY()[i] = (sss*sss)/(geff->GetY()[i]);
// geff->SetMaximum(1.0);
// geff->Draw("AP");
for (Int_t i = 0; i < count; i++) {
Double_t deno = geff->Eval(grx[i])*sss*sss;
if (deno < 0.00001) deno = 1;
gry_fix[i] = gry[i]/deno;
}
new TCanvas;
TGraph *gr_fix = new TGraph(count, grx, gry_fix);
gr_fix->SetMarkerStyle(21);
gr_fix->SetMarkerColor(hh->GetLineColor());
gr_fix->GetXaxis()->SetTitle("p_{t}, GeV/c");
gr_fix->SetTitle(Form("corrected phi * #sigma^{2}, %s", gtitle.Data()));
if (noSigma)
gr_fix->SetTitle(Form("corrected phi (no #sigma), %s", gtitle.Data()));
gr_fix->Draw("AP");
//---------------------
c = new TCanvas("cfinald", "data_correct", 1200, 450);
c->Divide(2, 1, 0.001, 0.001); c->Modified(); c->Draw();
c->cd(1);
gr->SetMinimum(0);
gr->SetMarkerColor(kBlack);
gr->Draw("AP");
c->cd(2);
gr_fix->SetMinimum(0);
gr_fix->SetMarkerColor(kGreen+3);
gr_fix->Draw("AP");
TString bla9 = Form("qualityonly_PID2_%s", grapht.Data());
if (noSigma) bla9 = Form("%s_noSig.C", bla9.Data());
else bla9 = Form("%s.C", bla9.Data());
// gr_fix->SaveAs(bla9.Data());
// TPad *cp = new TPad("cpf", "", 0.45,0.45,0.99,0.92);
TPad *cp = new TPad("cpf", "", 0.60,0.55,0.99,0.93);
cp->SetLogy(); cp->Draw(); cp->cd();
TGraph *cloneg = ((TGraph *)gr_fix->Clone());
cloneg->SetTitle(); cloneg->SetMarkerSize(0.8);
cloneg->Draw("AP");
// c->SaveAs(Form("%s_%s.eps", blabla.Data(), grapht.Data()));
f->Close();
}
