// 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++;
}
}
}
}
//------------------------------------------------------------------------------
// DrawIt
//------------------------------------------------------------------------------
void DrawIt(TString filename,
TString hname,
TString cname,
TString title)
{
TFile* inputfile = TFile::Open("../AuxiliaryFilesWZXS8TeV/" + filename + ".root");
TH2F* h = (TH2F*)inputfile->Get(hname)->Clone(cname);
h->SetDirectory(0);
inputfile->Close();
TString name = h->GetName();
TCanvas* canvas = new TCanvas(name, name, 600, 600);
if (name.Contains("PR")) canvas->SetLogx();
if (name.Contains("SF")) canvas->SetLogx();
canvas->SetLeftMargin (0.9 * canvas->GetLeftMargin());
canvas->SetRightMargin(3.5 * canvas->GetRightMargin());
canvas->SetTopMargin (1.2 * canvas->GetTopMargin());
TH2FAxisFonts(h, "x", "p_{T} [GeV]");
TH2FAxisFonts(h, "y", "#eta");
h->Draw("colz");
h->SetTitle("");
DrawTLatex(42, 0.940, 0.976, _bigLabelSize, 33, title);
if (!title.Contains("trigger")) {
Double_t hmin = h->GetMinimum();
Double_t hmax = h->GetMaximum();
for (Int_t i=1; i<=h->GetNbinsX(); i++) {
for (Int_t j=1; j<=h->GetNbinsY(); j++) {
Double_t value = h->GetBinContent(i,j);
Double_t ypos = h->GetYaxis()->GetBinCenter(j);
Double_t xpos = h->GetXaxis()->GetBinCenter(i);
if (gPad->GetLogx()) xpos = h->GetXaxis()->GetBinCenterLog(i);
TLatex* latex = new TLatex(xpos, ypos, Form("%.2f", value));
latex->SetTextAlign( 22);
latex->SetTextFont ( 42);
latex->SetTextSize (0.027);
if (value < hmin + 0.3*(hmax - hmin)) latex->SetTextColor(kWhite);
latex->Draw();
}
}
}
// Set the palette font
//----------------------------------------------------------------------------
canvas->Update();
TPaletteAxis* palette = (TPaletteAxis*)h->GetListOfFunctions()->FindObject("palette");
palette->SetLabelFont(42);
// Save the plot
//----------------------------------------------------------------------------
canvas->Update();
canvas->Modified();
canvas->GetFrame()->DrawClone();
canvas->SaveAs("pdf/scale_factors/" + name + ".pdf");
canvas->SaveAs("png/scale_factors/" + name + ".png");
}
void allInOneLifetime(double lumi=4560., double maxInstLumi=5000.) {
ExtraLimitPlots plots(lumi);
plots.calculateCrossSections(4,6,3,39,9);
// graphs - observed
TGraph* g_obs = plots.getObsLimit();
TGraph* g_exp = plots.getExpLimit();
TGraphAsymmErrors* g_exp_1sig = plots.getExpLimit1Sig();
TGraphAsymmErrors* g_exp_2sig = plots.getExpLimit2Sig();
TGraph* g_obs_gluino = plots.getLimitGluino();
double gluino2ref = g_obs_gluino->GetY()[0] / g_obs->GetY()[0];
TGraph* g_obs_stop = plots.getLimitStop();
double stop2ref = g_obs_stop->GetY()[0] / g_obs->GetY()[0];
TGraph* g_obs_stau = plots.getLimitStau();
double stau2ref = g_obs_stau->GetY()[0] / g_obs->GetY()[0];
cout << "scales: " << g_obs->GetY()[0]
<< '/' <<g_obs_gluino->GetY()[0]
<< '/' <<g_obs_stop->GetY()[0]
<< '/' <<g_obs_stau->GetY()[0]
<<endl;
TCanvas *canvas = new TCanvas("allLifetime", "allLifetime", 1000, 600);
canvas->SetLogx();
canvas->SetLogy();
canvas->SetRightMargin(0.8*canvas->GetLeftMargin());
canvas->SetLeftMargin(1.2*canvas->GetLeftMargin());
canvas->SetTicks (canvas->GetTickx(), 0);
TH1F* h = new TH1F ("h", "", 1, 7.5e-8, 1e6);
h->SetStats (0);
h->SetMinimum (.0001);
h->SetMaximum (0.99e1);
// TH1* h = canvas->DrawFrame(7.5e-8, .001, 1e6, 1e2, "Y+");
h->SetTitle("Beamgap Expt");
// h->GetXaxis()->SetTitle("#tau_{#tilde{g},#tilde{t},#tilde{#tau}} [s]");
h->GetXaxis()->SetTitle("#tau [s]");
h->GetYaxis()->SetTitle("#sigma #times BF #times #varepsilon_{stopping} #times #varepsilon_{reco} [pb] ");
h->Draw ("Y+");
ExtraAxis aGluino = anotherScale (h, gluino2ref, kRed+2, "#sigma(pp #rightarrow #tilde{g}#tilde{g}) #times BF(#tilde{g} #rightarrow g#tilde{#chi}^{0}) [pb] ", 0.0);
ExtraAxis aStop = anotherScale (h, stop2ref, kBlue+2, "#sigma(pp #rightarrow #tilde{t}#tilde{t}) #times BF(#tilde{t} #rightarrow t#tilde{#chi}^{0}) [pb] ", 0.2);
ExtraAxis aStau = anotherScale (h, stau2ref, kGreen+2, "#sigma(pp #rightarrow #tilde{#tau}#tilde{#tau}) #times BF(#tilde{#tau} #rightarrow #tau#tilde{#chi}^{0}) [pb] ", 0.4);
TPaveText* blurb = new TPaveText(0.25, 0.57, 0.50, 0.87, "NDC");
blurb->AddText("CMS Preliminary 2015");
// std::stringstream label;
// label<<"#int L dt = "<<lumi<<" pb^{-1}";
// blurb->AddText(label.str().c_str());
// double peakInstLumi=maxInstLumi;
// int exponent=30;
// while (peakInstLumi>10) {
// peakInstLumi/=10;
// ++exponent;
// }
// std::stringstream label2;
// label2<<"L^{max}_{inst} = "<<peakInstLumi<<" x 10^{"<<exponent<<"} cm^{-2}s^{-1}";
// blurb->AddText(label2.str().c_str());
//blurb->AddText("CMS 2011");
blurb->AddText("#int L dt = 2.46 fb^{-1}");//, #int L_{eff} dt = 935 pb^{-1}");
//blurb->AddText("L^{max}_{inst} = 3.5 #times 10^{33} cm^{-2}s^{-1}");
blurb->AddText("#sqrt{s} = 13 TeV");
blurb->AddText("E_{g} > 120 GeV, E_{t} > 150 GeV");
blurb->AddText("E_{jet} > 70 GeV");
//blurb->AddText("m_{#tilde{g}} = 300 GeV/c^{2}");
//blurb->AddText("m_{#tilde{#chi}^{0}} = 200 GeV/c^{2}");
blurb->SetTextFont(42);
blurb->SetBorderSize(0);
blurb->SetFillColor(0);
blurb->SetShadowColor(0);
blurb->SetTextAlign(12);
blurb->SetTextSize(0.033);
blurb->Draw();
// 2 sigma band
if (g_exp_2sig) {
g_exp_2sig->SetLineColor(0);
g_exp_2sig->SetLineStyle(0);
g_exp_2sig->SetLineWidth(0);
g_exp_2sig->SetFillColor(kYellow);
g_exp_2sig->SetFillStyle(1001);
g_exp_2sig->Draw("3");
}
// 1 sigma band
if (g_exp_1sig) {
// g_exp_1sig->SetLineColor(8);
g_exp_1sig->SetLineColor(0);
g_exp_1sig->SetLineStyle(0);
g_exp_1sig->SetLineWidth(0);
// g_exp_1sig->SetFillColor(8);
g_exp_1sig->SetFillColor(kGreen);
g_exp_1sig->SetFillStyle(1001);
// g_exp_1sig->SetFillStyle(3005);
g_exp_1sig->Draw("3");
// g_exp_1sig->Draw("lX");
}
// GLUINO LIMIT
if (g_exp) {
g_exp->SetLineColor(1);
g_exp->SetLineStyle(4);
g_exp->SetLineWidth(2);
g_exp->Draw("l3");
}
TLine *l;
l = new TLine(7.5e-8, 1.49/gluino2ref, 1e6, 1.49/gluino2ref); //600 GeV
l->SetLineColor(kRed);
l->SetLineWidth(2);
l->Draw();
TLatex *t1;
t1 = new TLatex(0.1, 0.7/gluino2ref, "#sigma_{theory} (m_{#tilde{g}} = 800 GeV)");
t1->SetTextColor(kRed);
t1->SetTextFont(42);
t1->SetTextSize(0.035);
t1->Draw();
// STOP LIMIT
TLine *ltop = new TLine(7.5e-8, 0.028/stop2ref, 1e6, 0.028/stop2ref); //600 GeV
ltop->SetLineColor(kBlue);
ltop->SetLineWidth(2);
ltop->Draw();
TLatex *t1top;
t1top = new TLatex(0.1, 0.015/stop2ref, "#sigma_{theory} (m_{#tilde{t}} = 800 GeV)");
t1top->SetTextColor(kBlue);
t1top->SetTextFont(42);
t1top->SetTextSize(0.035);
t1top->Draw();
// observed limit
if (g_obs) {
g_obs->SetLineColor(1);
g_obs->SetLineStyle(1);
g_obs->SetLineWidth(2);
g_obs->Draw("l");
}
TLegend* leg = new TLegend(0.67, 0.65, 0.82, 0.87,"95% CL Limits:","NDC");
leg->SetTextSize(0.033);
leg->SetBorderSize(0);
leg->SetTextFont(42);
leg->SetFillColor(0);
TGraph* expectedStyle1 = new TGraph (*g_exp);
expectedStyle1->SetFillColor (g_exp_1sig->GetFillColor());
TGraph* expectedStyle2 = new TGraph (*g_exp);
expectedStyle2->SetFillColor (g_exp_2sig->GetFillColor());
cout << "colors: " << g_exp_1sig->GetFillColor() << ':' << g_exp_2sig->GetFillColor() << endl;
leg->AddEntry(g_obs, "Observed", "l");
leg->AddEntry(expectedStyle1, "Expected #pm1#sigma", "lf");
leg->AddEntry(expectedStyle2, "Expected #pm2#sigma", "lf");
//leg->AddEntry(g_obs_stop,"Obs.: Counting Exp. (#tilde{t})", "l");
//leg->AddEntry(g_obs_nb, "Obs.: Counting Exp. (Neutral R-Baryon)", "l");
//leg->AddEntry(g_obs_em, "Observed: Counting Exp. (EM only)", "l");
//leg->AddEntry(g_obs_tp, "Observed: Timing Profile", "l");
leg->Draw();
h->Draw("sameaxis y+");
aGluino.Draw();
aStop.Draw();
//aStau.Draw();
canvas->Print("allInOneLifetime.png");
canvas->Print("allInOneLifetime.pdf");
}
// input: - Input file (result from TMVA)
// - use of TMVA plotting TStyle
void probas( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
{
// set style and remove existing canvas'
TMVAGlob::Initialize( useTMVAStyle );
// switches
const Bool_t Draw_CFANN_Logy = kFALSE;
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;
// list of existing MVAs
const Int_t nveto = 1;
TString suffixSig = "_tr_S";
TString suffixBgd = "_tr_B";
// search for the right histograms in full list of keys
TList methods;
UInt_t nmethods = TMVAGlob::GetListOfMethods( methods );
if (nmethods==0) {
cout << "--- Probas.C: no methods found!" << endl;
return;
}
TIter next(&methods);
TKey *key, *hkey;
char fname[200];
TH1* sig(0);
TH1* bgd(0);
while ((key = (TKey*)next())) {
TDirectory * mDir = (TDirectory*)key->ReadObj();
TList titles;
UInt_t ni = TMVAGlob::GetListOfTitles( mDir, titles );
TString methodName;
TMVAGlob::GetMethodName(methodName,key);
if (ni==0) {
cout << "+++ No titles found for classifier: " << methodName << endl;
return;
}
TIter nextTitle(&titles);
TKey *instkey;
TDirectory *instDir;
while ((instkey = (TKey *)nextTitle())) {
instDir = (TDirectory *)instkey->ReadObj();
TString instName = instkey->GetName();
TList h1hists;
UInt_t nhists = TMVAGlob::GetListOfKeys( h1hists, "TH1", instDir );
if (nhists==0) cout << "*** No histograms found!" << endl;
TIter nextInDir(&h1hists);
TString methodTitle;
TMVAGlob::GetMethodTitle(methodTitle,instDir);
while (hkey = (TKey*)nextInDir()) {
TH1 *th1 = (TH1*)hkey->ReadObj();
TString hname= th1->GetName();
if (hname.Contains( suffixSig ) && !hname.Contains( "Cut") &&
!hname.Contains("original") && !hname.Contains("smoothed")) {
// retrieve corresponding signal and background histograms
TString hnameS = hname;
TString hnameB = hname;
hnameB.ReplaceAll("_S","_B");
sig = (TH1*)instDir->Get( hnameS );
bgd = (TH1*)instDir->Get( hnameB );
if (sig == 0 || bgd == 0) {
cout << "*** probas.C: big troubles in probas.... histogram: " << hname << " not found" << endl;
return;
}
TH1* sigF(0);
TH1* bkgF(0);
for (int i=0; i<= 5; i++) {
TString hspline = hnameS + Form("_smoothed_hist_from_spline%i",i);
sigF = (TH1*)instDir->Get( hspline );
if (sigF) {
bkgF = (TH1*)instDir->Get( hspline.ReplaceAll("_tr_S","_tr_B") );
break;
}
}
if ((sigF == NULL || bkgF == NULL) &&!hname.Contains("hist") ) {
cout << "*** probas.C: big troubles - did not found histogram " << hspline.Data() << " "
<< sigF << " " << bkgF << endl;
return;
}
else {
// remove the signal suffix
// check that exist
if (NULL != sigF && NULL != bkgF && NULL!=sig && NULL!=bgd) {
TString hname = sig->GetName();
// chop off useless stuff
sig->SetTitle( TString("TMVA output for classifier: ") + methodTitle );
// create new canvas
cout << "--- Book canvas no: " << countCanvas << endl;
char cn[20];
sprintf( cn, "canvas%d", countCanvas+1 );
c = new TCanvas( cn, Form("TMVA Output Fit Variables %s",methodTitle.Data()),
countCanvas*50+200, countCanvas*20, width, width*0.78 );
// set the histogram style
TMVAGlob::SetSignalAndBackgroundStyle( sig, bgd );
TMVAGlob::SetSignalAndBackgroundStyle( sigF, bkgF );
// frame limits (choose judicuous x range)
Float_t nrms = 4;
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 ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*1.5;
if (Draw_CFANN_Logy && mvaName[imva] == "CFANN") ymin = 0.01;
// build a frame
Int_t nb = 500;
TH2F* frame = new TH2F( TString("frame") + sig->GetName() + "_proba", sig->GetTitle(),
nb, xmin, xmax, nb, ymin, ymax );
frame->GetXaxis()->SetTitle(methodTitle);
frame->GetYaxis()->SetTitle("Normalized");
TMVAGlob::SetFrameStyle( frame );
// eventually: draw the frame
frame->Draw();
if (Draw_CFANN_Logy && mvaName[imva] == "CFANN") c->SetLogy();
// overlay signal and background histograms
sig->SetMarkerColor( TMVAGlob::c_SignalLine );
sig->SetMarkerSize( 0.7 );
sig->SetMarkerStyle( 20 );
sig->SetLineWidth(1);
bgd->SetMarkerColor( TMVAGlob::c_BackgroundLine );
bgd->SetMarkerSize( 0.7 );
bgd->SetMarkerStyle( 24 );
bgd->SetLineWidth(1);
sig->Draw("samee");
bgd->Draw("samee");
sigF->SetFillStyle( 0 );
bkgF->SetFillStyle( 0 );
sigF->Draw("samehist");
bkgF->Draw("samehist");
// redraw axes
frame->Draw("sameaxis");
// Draw legend
TLegend *legend= new TLegend( c->GetLeftMargin(), 1 - c->GetTopMargin() - 0.2,
c->GetLeftMargin() + 0.4, 1 - c->GetTopMargin() );
legend->AddEntry(sig,"Signal data","P");
legend->AddEntry(sigF,"Signal PDF","L");
legend->AddEntry(bgd,"Background data","P");
legend->AddEntry(bkgF,"Background PDF","L");
legend->Draw("same");
legend->SetBorderSize(1);
legend->SetMargin( 0.3 );
// save canvas to file
c->Update();
TMVAGlob::plot_logo();
sprintf( fname, "plots/mva_pdf_%s_c%i", methodTitle.Data(), countCanvas+1 );
if (Save_Images) TMVAGlob::imgconv( c, fname );
countCanvas++;
}
}
}
}
}
}
}
TCanvas* makeNiceCanvasByPixMargins(Int_t pixelPerBinX, Int_t pixelPerBinY, Int_t nbinx, Int_t nbiny, Int_t top, Int_t bottom, Int_t left, Int_t right) {
Int_t rubaX = 4; //determinato sperimentalmente
Int_t rubaY = 28; //determinato sperimentalmente
TString name = generateRandomName();
Int_t plotBaseDimX = pixelPerBinX*nbinx;
Int_t plotBaseDimY = pixelPerBinY*nbiny;
Int_t XX = (Int_t)(plotBaseDimX+left+right);
Int_t YY = (Int_t)(plotBaseDimY+top+bottom);
TCanvas* can = new TCanvas(name,name,XX+rubaX,YY+rubaY);
can->SetTopMargin((1.*top)/(1.*YY));
can->SetBottomMargin((1.*bottom)/(1.*YY));
can->SetRightMargin(right/(1.*XX));
can->SetLeftMargin(left/(1.*XX));
can->SetBorderMode(0);
std::cout << "Nice canvas " << XX << " * " << YY << " Margin: t " << can->GetTopMargin() << " b " << can->GetBottomMargin() << " l " << can->GetLeftMargin() << " r " << can->GetRightMargin() << std::endl;
return can;
}
void DrawMLPoutputMovie( TFile* file, const TString& methodType, const TString& methodTitle )
{
gROOT->SetBatch( 1 );
// define Canvas layout here!
const Int_t width = 600; // size of canvas
// this defines how many canvases we need
TCanvas* c = 0;
Float_t nrms = 4;
Float_t xmin = -1.2;
Float_t xmax = 1.2;
Float_t ymin = 0;
Float_t ymax = 0;
Float_t maxMult = 6.0;
Int_t countCanvas = 0;
Bool_t first = kTRUE;
TString dirname = methodType + "/" + methodTitle + "/" + "EpochMonitoring";
TDirectory *epochDir = (TDirectory*)file->Get( dirname );
if (!epochDir) {
cout << "Big troubles: could not find directory \"" << dirname << "\"" << endl;
exit(1);
}
// now read all evolution histograms
TIter keyItTit(epochDir->GetListOfKeys());
TKey *titkeyTit;
while ((titkeyTit = (TKey*)keyItTit())) {
if (!gROOT->GetClass(titkeyTit->GetClassName())->InheritsFrom("TH1F")) continue;
TString name = titkeyTit->GetName();
if (!name.BeginsWith("convergencetest___")) continue;
if (!name.Contains("_train_")) continue; // only for training so far
if (name.EndsWith( "_B")) continue;
// must be signal histogram
if (!name.EndsWith( "_S")) {
cout << "Big troubles with histogram: " << name << " -> should end with _S" << endl;
exit(1);
}
// create canvas
countCanvas++;
TString ctitle = Form("TMVA response %s",methodTitle.Data());
c = new TCanvas( Form("canvas%d", countCanvas), ctitle, 0, 0, width, (Int_t)width*0.78 );
TH1F* sig = (TH1F*)titkeyTit->ReadObj();
sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) );
TString dataType = (name.Contains("_train_") ? "(training sample)" : "(test sample)");
// find background
TString nbn = sig->GetName(); nbn[nbn.Length()-1] = 'B';
TH1F* bgd = dynamic_cast<TH1F*>(epochDir->Get( nbn ));
if (bgd == 0) {
cout << "Big troubles with histogram: " << bgd << " -> cannot find!" << endl;
exit(1);
}
cout << "sig = " << sig->GetName() << endl;
cout << "bgd = " << bgd->GetName() << endl;
// set the histogram style
TMVAGlob::SetSignalAndBackgroundStyle( sig, bgd );
// normalise both signal and background
TMVAGlob::NormalizeHists( sig, bgd );
// set only first time, then same for all plots
if (first) {
if (xmin == 0 && xmax == 0) {
xmin = TMath::Max( TMath::Min(sig->GetMean() - nrms*sig->GetRMS(),
bgd->GetMean() - nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmin() );
xmax = TMath::Min( TMath::Max(sig->GetMean() + nrms*sig->GetRMS(),
bgd->GetMean() + nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmax() );
}
ymin = 0;
ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*maxMult;
first = kFALSE;
}
// build a frame
Int_t nb = 100;
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 + " response" );
frame->GetYaxis()->SetTitle("(1/N) dN^{ }/^{ }dx");
TMVAGlob::SetFrameStyle( frame );
// find epoch number (4th token)
TObjArray* tokens = name.Tokenize("_");
TString es = ((TObjString*)tokens->At(4))->GetString();
if (!es.IsFloat()) {
cout << "Big troubles in epoch parsing: \"" << es << "\" is not float" << endl;
exit(1);
}
Int_t epoch = es.Atoi();
// 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() + 0.5, 1 - c->GetTopMargin() );
legend->SetFillStyle( 1 );
legend->AddEntry(sig,TString("Signal ") + dataType, "F");
legend->AddEntry(bgd,TString("Background ") + dataType, "F");
legend->SetBorderSize(1);
legend->SetMargin( 0.15 );
legend->Draw("same");
TText* t = new TText();
t->SetTextSize( 0.04 );
t->SetTextColor( 1 );
t->SetTextAlign( 31 );
t->DrawTextNDC( 1 - c->GetRightMargin(), 1 - c->GetTopMargin() + 0.015, Form( "Epoch: %i", epoch) );
// overlay signal and background histograms
sig->Draw("samehist");
bgd->Draw("samehist");
// save to file
TString dirname = "movieplots";
TString foutname = dirname + "/" + name;
foutname.Resize( foutname.Length()-2 );
foutname.ReplaceAll("convergencetest___","");
foutname += ".gif";
cout << "storing file: " << foutname << endl;
c->Update();
c->Print(foutname);
}
}
