void annconvergencetest( TDirectory *lhdir )
{
TCanvas* c = new TCanvas( "MLPConvergenceTest", "MLP Convergence Test", 150, 0, 600, 580*0.8 );
TH1* estimatorHistTrain = (TH1*)lhdir->Get( "estimatorHistTrain" );
TH1* estimatorHistTest = (TH1*)lhdir->Get( "estimatorHistTest" );
Double_t m1 = estimatorHistTrain->GetMaximum();
Double_t m2 = estimatorHistTest ->GetMaximum();
Double_t max = TMath::Max( m1, m2 );
m1 = estimatorHistTrain->GetMinimum();
m2 = estimatorHistTest ->GetMinimum();
Double_t min = TMath::Min( m1, m2 );
estimatorHistTrain->SetMaximum( max + 0.1*(max - min) );
estimatorHistTrain->SetMinimum( min - 0.1*(max - min) );
estimatorHistTrain->SetLineColor( 2 );
estimatorHistTrain->SetLineWidth( 2 );
estimatorHistTrain->SetTitle( TString("MLP Convergence Test") );
estimatorHistTest->SetLineColor( 4 );
estimatorHistTest->SetLineWidth( 2 );
estimatorHistTrain->GetXaxis()->SetTitle( "Epochs" );
estimatorHistTrain->GetYaxis()->SetTitle( "Estimator" );
estimatorHistTrain->GetXaxis()->SetTitleOffset( 1.20 );
estimatorHistTrain->GetYaxis()->SetTitleOffset( 1.65 );
estimatorHistTrain->Draw();
estimatorHistTest ->Draw("same");
// need a legend
TLegend *legend= new TLegend( 1 - c->GetRightMargin() - 0.45, 1-c->GetTopMargin() - 0.20,
1 - c->GetRightMargin() - 0.05, 1-c->GetTopMargin() - 0.05 );
legend->AddEntry(estimatorHistTrain,"Training Sample","l");
legend->AddEntry(estimatorHistTest,"Test sample","l");
legend->Draw("same");
legend->SetMargin( 0.3 );
c->cd();
TMVAGlob::plot_logo(); // don't understand why this doesn't work ... :-(
c->Update();
TString fname = "plots/annconvergencetest";
TMVAGlob::imgconv( c, fname );
}
TH1* GetOne(UShort_t sNN, const TString& trigger)
{
Long_t p = gROOT->ProcessLine(Form("Drawer::GetStack(0, \"pp\", %d, "
"\"%s\", false, true)",
sNN, trigger.Data()));
THStack* s = (THStack*)p;
TList* l = s->GetHists();
TH1* h = 0;
TIter n(l);
l->ls();
while ((h = static_cast<TH1*>(n()))) {
TString m(h->GetName());
if (m.EqualTo("dndetaForward_all")) break;
}
if (h) {
switch (sNN) {
case 900: h->SetTitle("900GeV"); h->SetMarkerColor(kRed+2); break;
case 2760: h->SetTitle("2.76TeV"); h->SetMarkerColor(kGreen+2); break;
case 7000: h->SetTitle("7TeV"); h->SetMarkerColor(kBlue+2); break;
case 8000: h->SetTitle("8TeV"); h->SetMarkerColor(kBlack); break;
}
}
return h;
}
void QAvertex(const Char_t *fdata, const Char_t *fmc)
{
style();
TFile *fdtin = TFile::Open(fdata);
TList *ldtin = (TList *)fdtin->Get("clist");
TH2 *hdtin = (TH2 *)ldtin->FindObject("zv");
TH1 *hdt = (TH1 *)ldtin->FindObject("zvNoSel");
SetHistoStyle(hdt, 20, kRed+1);
hdt->Scale(1. / hdt->Integral());
TH1 *hdt0010 = hdtin->ProjectionX("hdt0010", 1, 4);
SetHistoStyle(hdt0010, 20, kRed+1);
hdt0010->Scale(1. / hdt0010->Integral());
TH1 *hdt7080 = hdtin->ProjectionX("hdt7080", 11, 11);
SetHistoStyle(hdt7080, 25, kAzure-3);
hdt7080->Scale(1. / hdt7080->Integral());
TFile *fmcin = TFile::Open(fmc);
TList *lmcin = (TList *)fmcin->Get("clist");
TH1 *hmc = (TH1 *)lmcin->FindObject("zvNoSel");
SetHistoStyle(hmc, 25, kAzure-3);
hmc->Scale(1. / hmc->Integral());
TCanvas *c = new TCanvas("cVertex", "cVertex", 800, 800);
TH1 * hfr = c->DrawFrame(-20., 0., 20., 0.1);
hfr->SetTitle(";#it{z}_{vtx};");
hdt0010->Draw("same");
hdt7080->Draw("same");
TLegend *legend = new TLegend(0.20, 0.18+0.60, 0.50, 0.30+0.60);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(hdt0010, "0-10%", "p");
legend->AddEntry(hdt7080, "70-80%", "p");
legend->Draw("same");
c->SaveAs(canvasPrefix+"vertex.pdf");
TCanvas *c1 = new TCanvas("cVertexDataMC", "cVertexDataMC", 800, 800);
hfr = c1->DrawFrame(-20., 0., 20., 0.1);
hfr->SetTitle(";#it{z}_{vtx};");
hdt->Draw("same");
hmc->Draw("same");
legend = new TLegend(0.20, 0.18+0.60, 0.50, 0.30+0.60);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(hdt, "data", "p");
legend->AddEntry(hmc, "Monte Carlo", "p");
legend->Draw("same");
c1->SaveAs(canvasPrefix+"vertexDataMC.pdf");
//return 0;
}
void QAcentrality(const Char_t *fdata)
{
style();
TFile *fin = TFile::Open(fdata);
TList *lin = (TList *)fin->Get("clist");
lin->ls();
TH1 *hin = (TH1 *)lin->FindObject("EvCentrDist");
Float_t sum = 1.2 * hin->Integral(hin->FindBin(0.1), hin->FindBin(79.9));
hin->Scale(1. / sum);
SetHistoStyle(hin, 20, kRed+1);
TCanvas *c = new TCanvas("cQAcentrality", "cQAcentrality", 800, 800);
TH1 * hfr = c->DrawFrame(0., 0.005, 100., 0.015);
hfr->SetTitle(";centrality percentile;events");
hin->Draw("same");
c->SaveAs(canvasPrefix+"centrality.pdf");
TH2 *hinv0 = (TH2 *)lin->FindObject("V0");
TCanvas *cv0 = new TCanvas("cQAcentralityV0", "cQAcentralityV0", 800, 800);
cv0->SetLogx();
cv0->SetLogz();
// TH1 * hfrv0 = cv0->DrawFrame(100., -0.5, 50000., 10.5);
// DrawBinLabelsY(hfrv0, kTRUE);
// hfrv0->SetTitle(";V0 signal;");
//hinv0->Draw("same,col");
hinv0->Draw("col");
cv0->SaveAs(canvasPrefix+"centralityV0.pdf");
}
// draw the next waveform that we find associated with an event
void DrawNext(void)
{
static Int_t evno=0;
if (tree == nullptr) {
tree = (TTree*)gROOT->FindObject("FragmentTree");
}
tree->SetBranchAddress("TFragment", &frag);
do {
tree->GetEntry(evno++);
} while (frag->wavebuffer.empty());
cout<<"Event number "<<evno<<endl;
frag->Print();
//printf("wavebuffer.size() = %i\n",wavebuffer.size());
TH1 *his = makeHisto(frag->wavebuffer);
TChannel *chan = TChannel::GetChannel(frag->ChannelAddress);
// if(chan && (strncmp(chan->GetChannelName(),"DSC",3)==0))
// TH1 *his = makeDescantHisto(frag->wavebuffer);
if(chan)
his->SetTitle(chan->GetChannelName());
his->Draw();
}
void SetTitle(TH1& h, TString Xtitle, TString Ytitle, TString title){
h.GetXaxis()->SetTitle(Xtitle);
h.GetYaxis()->SetTitle(Ytitle);
h.GetXaxis()->CenterTitle();
h.GetYaxis()->CenterTitle();
h.SetTitle(title);
}
histoBook* histoBook::set( string param, string p1, string p2, string p3, string p4 ){
// force the param name to lowercase
transform(param.begin(), param.end(), param.begin(), ::tolower);
TH1* h = get( styling );
if ( h ){
if ( "title" == param ){
h->SetTitle( p1.c_str() );
} else if ( "x" == param ){
h->GetXaxis()->SetTitle( p1.c_str() );
} else if ( "y" == param ){
h->GetYaxis()->SetTitle( p1.c_str() );
} else if ( "legend" == param ){
if ( p2 == "")
p2="lpf";
legend->AddEntry( h, p1.c_str(), p2.c_str() );
legend->Draw();
} else if ( "draw" == param ){
drawOption = p1;
}
}
return this;
}
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;
}
void showGraph(double canvasSizeX, double canvasSizeY,
TGraph* graph,
bool useLogScaleX, double xMin, double xMax, const std::string& xAxisTitle, double xAxisOffset,
bool useLogScaleY, double yMin, double yMax, const std::string& yAxisTitle, double yAxisOffset,
const std::string& outputFileName)
{
TCanvas* canvas = new TCanvas("canvas", "canvas", canvasSizeX, canvasSizeY);
canvas->SetFillColor(10);
canvas->SetBorderSize(2);
canvas->SetTopMargin(0.05);
canvas->SetLeftMargin(0.19);
canvas->SetBottomMargin(0.19);
canvas->SetRightMargin(0.05);
canvas->SetLogx(useLogScaleX);
canvas->SetLogy(useLogScaleY);
TH1* dummyHistogram = new TH1D("dummyHistogram", "dummyHistogram", 10, xMin, xMax);
dummyHistogram->SetTitle("");
dummyHistogram->SetStats(false);
dummyHistogram->SetMinimum(yMin);
dummyHistogram->SetMaximum(yMax);
dummyHistogram->Draw("axis");
TAxis* xAxis = dummyHistogram->GetXaxis();
xAxis->SetTitle(xAxisTitle.data());
xAxis->SetTitleOffset(xAxisOffset);
xAxis->SetTitleSize(0.065);
xAxis->SetLabelSize(0.055);
xAxis->SetLabelOffset(0.01);
xAxis->SetTickLength(0.055);
xAxis->SetNdivisions(505);
TAxis* yAxis = dummyHistogram->GetYaxis();
yAxis->SetTitle(yAxisTitle.data());
yAxis->SetTitleOffset(yAxisOffset);
yAxis->SetTitleSize(0.070);
yAxis->SetLabelSize(0.055);
yAxis->SetLabelOffset(0.01);
yAxis->SetTickLength(0.055);
yAxis->SetNdivisions(505);
graph->SetMarkerColor(1);
graph->SetLineColor(1);
graph->Draw("p");
canvas->Update();
size_t idx = outputFileName.find_last_of('.');
std::string outputFileName_plot = std::string(outputFileName, 0, idx);
if ( useLogScaleY ) outputFileName_plot.append("_log");
else outputFileName_plot.append("_linear");
if ( idx != std::string::npos ) canvas->Print(std::string(outputFileName_plot).append(std::string(outputFileName, idx)).data());
canvas->Print(std::string(outputFileName_plot).append(".png").data());
//canvas->Print(std::string(outputFileName_plot).append(".pdf").data());
//canvas->Print(std::string(outputFileName_plot).append(".root").data());
delete dummyHistogram;
delete canvas;
}
void OnlineGUI::TreeDraw(vector <TString> command) {
// Called by DoDraw(), this will plot a Tree Variable
TString var = command[0];
// Combine the cuts (definecuts and specific cuts)
TCut cut = "";
TString tempCut;
if(command.size()>1) {
tempCut = command[1];
vector <TString> cutIdents = fConfig->GetCutIdent();
for(UInt_t i=0; i<cutIdents.size(); i++) {
if(tempCut.Contains(cutIdents[i])) {
TString cut_found = (TString)fConfig->GetDefinedCut(cutIdents[i]);
tempCut.ReplaceAll(cutIdents[i],cut_found);
}
}
cut = (TCut)tempCut;
}
// Determine which Tree the variable comes from, then draw it.
UInt_t iTree;
if(command[4].IsNull()) {
iTree = GetTreeIndex(var);
} else {
iTree = GetTreeIndexFromName(command[4]);
}
TString drawopt = command[2];
Int_t errcode=0;
if(drawopt.IsNull() && var.Contains(":")) drawopt = "box";
if(drawopt=="scat") drawopt = "";
if (iTree <= fRootTree.size() ) {
errcode = fRootTree[iTree]->Draw(var,cut,drawopt,
1000000000,fTreeEntries[iTree]);
TObject *hobj = (TObject*)gROOT->FindObject("htemp");
if(errcode==-1) {
BadDraw(var+" not found");
} else if (errcode!=0) {
if(!command[3].IsNull()) {
TH1* thathist = (TH1*)hobj;
thathist->SetTitle(command[3]);
}
} else {
BadDraw("Empty Histogram");
}
} else {
BadDraw(var+" not found");
if (fConfig->IsMonitor()){
// Maybe we missed it... look again. I dont like the code
// below... maybe I can come up with something better
GetFileObjects();
GetRootTree();
GetTreeVars();
}
}
}
void QAoccupancy(const Char_t *fdata, const Char_t *fmc)
{
style();
TFile *fdtin = TFile::Open(fdata);
TList *ldtin = (TList *)fdtin->Get("clist");
TH2 *hdtin = (TH2 *)ldtin->FindObject("NClustersSPD2");
TProfile *pdtin = hdtin->ProfileY("pdtin_clusters");
pdtin->SetMarkerStyle(20);
pdtin->SetMarkerSize(2);
pdtin->SetMarkerColor(kAzure-3);
TFile *fmcin = TFile::Open(fmc);
TList *lmcin = (TList *)fmcin->Get("clist");
TH2 *hmcin = (TH2 *)lmcin->FindObject("NClustersSPD2");
TProfile *pmcin = hmcin->ProfileY("pmcin_clusters");
pmcin->SetMarkerStyle(25);
pmcin->SetMarkerSize(2);
pmcin->SetMarkerColor(kRed+1);
TCanvas *c = new TCanvas("cOccupancy", "cOccupancy", 800, 800);
c->SetLogy();
TH1 * hfr = c->DrawFrame(-0.5, 2., 10.5, 500.);
DrawBinLabelsX(hfr, kTRUE);
hfr->SetTitle(";;#LT#it{N}_{clusters,SPD-1}#GT");
pdtin->DrawCopy("same");
pmcin->DrawCopy("same");
TLegend *legend = new TLegend(0.20, 0.18, 0.50, 0.30);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(pdtin, "data", "pl");
legend->AddEntry(pmcin, "Monte Carlo", "pl");
legend->Draw("same");
c->SaveAs(canvasPrefix+"occupancy.pdf");
return;
TCanvas *cr = new TCanvas("cOccupancyr", "cOccupancyr", 800, 800);
// hfr = cr->DrawFrame(-0.5, 0.75, 10.5, 1.25);
// DrawBinLabelsX(hfr, kTRUE);
// hfr->SetTitle(";;#LT#it{N}_{clusters,SPD-1}#GT ratio");
pdtin->SetLineColor(kAzure-3);
pdtin->SetLineWidth(3);
pdtin->Divide(pmcin);
pdtin->Draw("same,histo");
legend = new TLegend(0.505025, 0.760673, 0.805276, 0.930142);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(pdtin, "data / Monte Carlo", "l");
legend->Draw("same");
cr->SaveAs(canvasPrefix+"occupancyr.pdf");
}
void compareDYTemplates(TString baseURL="~/scratch0/top-newjec/syst_plotter.root")
{
TString ch[]={"ee","mumu"};
TString categs[]={"eq1jets",""};
for(size_t ich=0; ich<2; ich++)
{
for(size_t icat=0; icat<2; icat++)
{
TObjArray lowMet = getDistributionFromPlotter(ch[ich]+"_"+categs[icat]+"lowmetdilarccosine",baseURL);
TH1 *lowMetH = (TH1 *) ((TList *)lowMet.At(3))->At(3);
formatPlot(lowMetH,1,1,1,24,0,false,false,1,1,1);
lowMetH->SetTitle("E_{T}^{miss}<30 GeV/c^{2}");
lowMetH->Scale(1./lowMetH->Integral());
TObjArray highMet = getDistributionFromPlotter(ch[ich]+"_"+categs[icat]+"dilarccosine",baseURL);
TH1 *highMetH = (TH1 *) ((TList *)highMet.At(3))->At(3);
formatPlot(highMetH,1,1,1,20,0,false,false,1,1,1);
highMetH->SetTitle("E_{T}^{miss}>30 GeV/c^{2}");
highMetH->Scale(1./highMetH->Integral());
TString channelTitle(ich==0 ? "ee" : "#mu#mu");
if(categs[icat]=="eq1jets") channelTitle += "+ 1 jet";
else channelTitle += "+ #geq 2 jets";
//draw
TString plot(ch[ich]+categs[icat]+"_anglecomparison");
TCanvas *cnv = getNewCanvas(plot+"c",plot+"c",false);
cnv->Clear();
cnv->SetWindowSize(600,600);
cnv->cd();
TList *mc = new TList; mc->Add(lowMetH);
TList *data = new TList; data->Add(highMetH);
TList *spimpose = new TList;
TLegend *leg=showPlotsAndMCtoDataComparison(cnv,*mc,*spimpose,*data,false);
TPad *p=(TPad *)cnv->cd(1);
formatForCmsPublic(p,leg,"CMS simulation, " + channelTitle, 4);
cnv->SaveAs(plot+".C");
cnv->SaveAs(plot+".pdf");
cnv->SaveAs(plot+".png");
}
}
}
/**
* Create ratios to other data
*
* @param ib Bin number
* @param res Result
* @param alice ALICE result if any
* @param cms CMS result if any
* @param all Stack to add ratio to
*/
void Ratio2Stack(Int_t ib, TH1* res, TGraph* alice, TGraph* cms, THStack* all)
{
if (!all || !res || !(alice || cms)) return;
Int_t off = 5*ib;
TGraph* gs[] = { (alice ? alice : cms), (alice ? cms : 0), 0 };
TGraph** pg = gs;
while (*pg) {
TGraph* g = *pg;
const char* n = (g == alice ? "ALICE" : "CMS");
TH1* r = static_cast<TH1*>(res->Clone(Form("ratio%s", n)));
TString tit(r->GetTitle());
tit.ReplaceAll("Corrected", Form("Ratio to %s", n));
r->SetTitle(tit);
r->SetMarkerColor(g->GetMarkerColor());
r->SetLineColor(g->GetLineColor());
TObject* tst = r->FindObject("legend");
if (tst) r->GetListOfFunctions()->Remove(tst);
for (Int_t i = 1; i <= r->GetNbinsX(); i++) {
Double_t c = r->GetBinContent(i);
Double_t e = r->GetBinError(i);
Double_t o = g->Eval(r->GetBinCenter(i));
if (o < 1e-12) {
r->SetBinContent(i, 0);
r->SetBinError(i, 0);
continue;
}
r->SetBinContent(i, (c - o) / o + off);
r->SetBinError(i, e / o);
}
all->Add(r);
pg++;
}
TLegend* leg = StackLegend(all);
if (!leg) return;
TString txt = res->GetTitle();
txt.ReplaceAll("Corrected P(#it{N}_{ch}) in ", "");
if (ib == 0) txt.Append(" "); // (#times1)");
// else if (ib == 1) txt.Append(" (#times10)");
else txt.Append(Form(" (+%d)", off));
TObject* dummy = 0;
TLegendEntry* e = leg->AddEntry(dummy, txt, "p");
e->SetMarkerStyle(res->GetMarkerStyle());
e->SetMarkerSize(res->GetMarkerSize());
e->SetMarkerColor(kBlack);
e->SetFillColor(0);
e->SetFillStyle(0);
e->SetLineColor(kBlack);
}
void plotter::draw_output_stat(TH1* output_, TH1* stat_, TH1D* truth_, bool norm, TString file_name){
// std::vector<double> sys = get_sys_errors();
// TH1* output_sys = add_error_bar(output, sys);
TH1* output = (TH1*) output_->Clone("output");
TH1* stat = (TH1*) stat_->Clone("stat");
TH1D* truth = (TH1D*) truth_->Clone("truth");
TCanvas *c = new TCanvas("c","",600,600);
double ymax;
gPad->SetLeftMargin(0.15);
if(truth->GetMaximum() > output->GetMaximum()) ymax = 1.5 * truth->GetMaximum();
else ymax = 1.5 * output->GetMaximum();
TGaxis::SetMaxDigits(3);
output->SetTitle(" ");
output->GetYaxis()->SetRangeUser(0., ymax);
output->GetXaxis()->SetTitle("Leading-jet mass [GeV]");
if(norm) output->GetYaxis()->SetTitle("#frac{1}{#sigma} #frac{d#sigma}{dm_{jet}} [#frac{1}{GeV}]");
else output->GetYaxis()->SetTitle("events");
output->GetYaxis()->SetTitleOffset(1.1);
output->GetXaxis()->SetTitleOffset(0.9);
output->GetYaxis()->SetTitleSize(0.05);
output->GetXaxis()->SetTitleSize(0.05);
output->GetYaxis()->SetNdivisions(505);
output->SetLineColor(kBlack);
output->SetMarkerColor(kBlack);
output->SetMarkerStyle(8);
output->SetMarkerSize(1);
output->Draw("E1");
stat->SetLineColor(kBlack);
stat->SetMarkerColor(kBlack);
stat->SetMarkerStyle(8);
stat->SetMarkerSize(1);
gStyle->SetEndErrorSize(5);
truth->SetLineWidth(3);
truth->SetLineColor(kRed);
truth->SetLineStyle(2);
truth->Draw("HIST SAME");
stat->Draw("E1 SAME");
output->Draw("E1 SAME");
TLegend *l=new TLegend(0.5,0.65,0.85,0.85);
l->SetBorderSize(0);
l->SetFillStyle(0);
l->AddEntry(output,"data unfolded","pl");
l->AddEntry(truth,"MC particle level","pl");
l->SetTextSize(0.04);
l->Draw();
c->SaveAs(directory + file_name + ".pdf");
delete c;
}
void DrawSkippable(TTree* tree, const char* what, const char* where, Bool_t draw = true) {
//cerr << "Doing " << what << " which is " << skip << endl;
if (draw) {
TString cut = what;
cut.Append(">>");
cut.Append(where);
tree->Draw(cut.Data(),"","goff");
TH1* h = (TH1*)gDirectory->FindObject(where);
if (h) h->SetTitle(Form("histo made from T->Draw(\"%s\")",what));
} else {
gSkipped.Add(new TNamed(where,where));
}
};
TH1* makeCDF(TH1* h) {
TString sName(TString(h->GetName())+TString("_CDF"));
TString sTitle(TString(h->GetTitle())+TString(" CDF"));
TH1* hOut = (TH1*) h->Clone(sName);
hOut->SetTitle(sTitle);
hOut->Reset();
double cdf = 0;
for (int ibin=0; ibin < h->GetNbinsX()+2; ++ibin) {
cdf += h->GetBinContent(ibin);
hOut->SetBinContent(ibin,cdf);
}
hOut->Scale(1.0/(h->Integral(0,h->GetNbinsX()+1)));
return hOut;
}
void plotter::draw_delta(TH1* hist_, TString file_name){
TH1* hist = (TH1*) hist_->Clone("hist");
TCanvas *c= new TCanvas("Particle Level","",600,600);
gPad->SetLeftMargin(0.15);
hist->SetTitle(file_name);
hist->GetXaxis()->SetTitle("Leading-jet mass [GeV]");
hist->GetYaxis()->SetTitle("#Delta events");
hist->GetYaxis()->SetTitleOffset(1.5);
hist->GetYaxis()->SetNdivisions(505);
hist->SetFillColor(810);
hist->SetLineColor(810);
hist->Draw("HIST");
gPad->RedrawAxis();
c->SaveAs(directory + file_name + ".pdf");
delete c;
}
//____________________________________________________________________
TH1* One(TDirectory* newDir, TDirectory* oldDir, Double_t c1, Double_t c2)
{
TString name;
name.Form("cent%03dd%02d_%03dd%02d",
Int_t(c1), Int_t(c1*100)%100,
Int_t(c2), Int_t(c2*100)%100);
TDirectory* newSubDir = GetD(newDir, name);
TDirectory* oldSubDir = GetD(oldDir, name);
if (!newSubDir || !oldSubDir) return 0;
Int_t newDim = 0;
if (TString(newDir->GetName()).Contains("etaipz")) newDim = 3;
else if (TString(newDir->GetName()).Contains("eta")) newDim = 2;
else if (TString(newDir->GetName()).Contains("const")) newDim = 1;
Int_t oldDim = 0;
if (TString(oldDir->GetName()).Contains("etaipz")) oldDim = 3;
else if (TString(oldDir->GetName()).Contains("eta")) oldDim = 2;
else if (TString(oldDir->GetName()).Contains("const")) oldDim = 1;
TDirectory* newSubSubDir = GetD(newSubDir, Form("results%dd",newDim));
TDirectory* oldSubSubDir = GetD(oldSubDir, Form("results%dd",oldDim));
if (!newSubSubDir || !oldSubSubDir) return 0;
TH1* newRes = GetH1(newSubSubDir, "result");
TH1* oldRes = GetH1(oldSubSubDir, "result");
if (!newRes || !oldRes) return 0;
TH1* ratio = static_cast<TH1*>(newRes->Clone(name));
ratio->SetDirectory(0);
ratio->SetTitle(Form("%5.1f - %5.1f%%", c1, c2));
ratio->SetYTitle("New / Old");
ratio->Divide(oldRes);
fMin = TMath::Min(fMin, ratio->GetMinimum());
fMax = TMath::Max(fMax, ratio->GetMaximum());
Printf("Calculated %s/%s", newDir->GetName(), oldDir->GetName());
if (!fLegend) return ratio;
TLegendEntry* e =
fLegend->AddEntry("", Form("%3.0f - %3.0f%%", c1, c2), "f");
e->SetFillStyle(1001);
e->SetFillColor(ratio->GetMarkerColor());
return ratio;
}
TH1 *PullHisto(const TList *list, const char *name, Int_t min, Int_t max,
Double_t &mean)
{
THnSparse *hs = list->FindObject(name);
if (!hs) return 0;
TAxis *atmp = hs->GetAxis(1);
atmp->SetRange(min, max);
// !!!!!!!!!!!!!!!!!!!!
hs->GetAxis(2)->SetRangeUser(-0.5, 0.5);
TH1 *hfin = hs->Projection(0);
hfin->SetTitle(Form("p_{t} #in (%4.2f, %4.2f) GeV/c",
atmp->GetBinLowEdge(min),
atmp->GetBinLowEdge(max) + atmp->GetBinWidth(max)));
mean = atmp->GetBinLowEdge(min) +
(atmp->GetBinLowEdge(max) + atmp->GetBinWidth(max) -
atmp->GetBinLowEdge(min))/2.0;
// !!!!!!!!!!!!!!!!!!!!
return hfin;//->Rebin();
}
void DrawHijing50GeV()
{
TCanvas *c1 = new TCanvas();
TFile *fin = TFile::Open("Gamma_Neutron_Hijing_Energy_Graphs.root");
gROOT->cd();
TH1 *hjbkg = (TH1 *) fin->Get("hjbkg")->Clone();
TGraph *anti_neutron50GeV = (TGraph *) fin->Get("anti_neutron50GeV")->Clone();
TGraph *neutron50GeV = (TGraph *) fin->Get("neutron50GeV")->Clone();
hjbkg->SetStats(0);
hjbkg->SetLineColor(6);
hjbkg->SetMaximum(5.5);
hjbkg->SetLineWidth(2);
hjbkg->SetTitle("50 GeV Hadronic Showers with HIJING background");
hjbkg->GetYaxis()->SetTitle("Deposited Energey [GeV]");
hjbkg->GetYaxis()->SetTitleOffset(0.8);
hjbkg->GetXaxis()->SetTitle("cone size (#sqrt{#Delta#Phi^{2}+#Delta#Theta^{2}})");
hjbkg->GetXaxis()->SetTitleOffset(1.2);
hjbkg->Draw();
anti_neutron50GeV->SetLineColor(4);
anti_neutron50GeV->SetLineWidth(2);
anti_neutron50GeV->Draw("same");
neutron50GeV->SetLineColor(2);
neutron50GeV->SetLineWidth(2);
neutron50GeV->Draw("same");
TLine *tl = new TLine();
tl->SetLineStyle(2);
tl->DrawLine(0.1,0,0.1,5.5);
TLegend *legrda = new TLegend(0.67,0.54,0.87,0.74,NULL,"brNDC");
legrda->SetLineColor(1);
legrda->SetLineStyle(1);
legrda->SetLineWidth(1);
legrda->SetFillColor(10);
legrda->SetFillStyle(1001);
legrda->SetBorderSize(0);
// legrda->SetTextSize(labelsize);
legrda->AddEntry(hjbkg,"HIJING bkg");
legrda->AddEntry(anti_neutron50GeV, "50 GeV Anti Neutron","l");
legrda->AddEntry(neutron50GeV, "50 GeV Neutron", "l");
legrda->AddEntry(tl, "HCal tower size","l");
legrda->Draw();
fin->Close();
c1->Print("Hijing50GeV.png");
}
void drawTimeDifference (TDirectory* directory, TH1* refHisto, const char* fname=0)
{
TGraphErrors* graphX = (TGraphErrors*)directory->Get("x");
if ( graphX==0 ) return;
TH1I* hFirst = (TH1I*)directory->Get("firstTime");
TH1I* hLast = (TH1I*)directory->Get("lastTime");
if ( hFirst==0 || hLast==0 ) return;
std::string fullName("cDeltaT");
if ( fname ) fullName += fname;
else fullName += directory->GetName();
TCanvas* c = new TCanvas(fullName.c_str(),fullName.c_str());
TH1* h = refHisto->Clone("DeltaT");
h->Reset();
h->SetTitle("DeltaT");
TGraph* graph = new TGraph();
graph->SetName("gDeltaT");
double xg,yg;
for ( unsigned int i=1; i<=hFirst->GetNbinsX(); ++i ) {
std::time_t t1 = hFirst->GetAt(i);
std::time_t t2 = hLast->GetAt(i);
TTimeStamp ts1(hFirst->GetAt(i));
std::cout << "Fit started at " << ts1.AsString() << std::endl;
graphX->GetPoint(i-1,xg,yg);
graph->SetPoint(i-1,xg,difftime(t2,t1));
}
double xmin,xmax,ymin,ymax;
graph->ComputeRange(xmin,ymin,xmax,ymax);
h->SetMinimum(0.);
h->SetMaximum((ymax+ymin)/2.+2.*(ymax-ymin)/2.);
h->Draw();
graph->SetMarkerStyle(20);
// graph->SetMarkerColor(2);
// graph->SetLineColor(2);
graph->Draw("P");
}
// Called just after the main event loop
// Can be used to write things out, dump a summary etc
// Return non-zero to indicate a problem
int IslandAmplitude::AfterLastEntry(TGlobalData* gData,const TSetupData *setup){
// Print extra info if we're debugging this module:
if(Debug()){
cout<<"-----IslandAmplitude::AfterLastEntry(): I'm debugging!"<<endl;
}
double run_norm = fAmpNorm->Integral(0,-1);
for(mapSH_t::iterator it = fAmpHist.begin(); it != fAmpHist.end(); ++it)
{
TH1F* h = it->second;
TObject* obj = h->Clone((std::string(h->GetName()) + "_RunNorm").c_str());
TH1* hn = static_cast<TH1*>(obj);
hn->SetTitle((std::string(h->GetTitle()) + " (run normalized)").c_str());
hn->Scale(1.0/run_norm);
}
for(mapSH_t::iterator it = fAmpHistNorm.begin(); it != fAmpHistNorm.end(); ++it)
it->second->Scale(1.0/fNProcessed);
return 0;
}
void axesStyle(TH1& hist, const char* titleX, const char* titleY, float yMin, float yMax, float yTitleSize, float yTitleOffset)
{
hist.SetTitle("");
hist.GetXaxis()->SetTitle(titleX);
hist.GetXaxis()->CenterTitle();
hist.GetXaxis()->SetTitleSize ( 0.06);
hist.GetXaxis()->SetTitleColor ( 1);
hist.GetXaxis()->SetTitleOffset( 1.0);
hist.GetXaxis()->SetTitleFont ( 62);
hist.GetXaxis()->SetLabelSize ( 0.05);
hist.GetXaxis()->SetLabelFont ( 62);
hist.GetXaxis()->SetNdivisions ( 505);
hist.GetYaxis()->SetTitle(titleY);
hist.GetYaxis()->SetTitleSize ( yTitleSize );
hist.GetYaxis()->SetTitleColor ( 1);
hist.GetYaxis()->SetTitleOffset(yTitleOffset);
hist.GetYaxis()->SetTitleFont ( 62);
hist.GetYaxis()->SetLabelSize ( 0.04);
hist.GetYaxis()->SetLabelFont ( 62);
hist.GetYaxis()->CenterTitle ( true);
if(yMin!=-123) hist.SetMinimum(yMin);
if(yMax!=-123) hist.SetMaximum(yMax);
}
void drawEventDifference (TDirectory* directory, TH1* refHisto, const char* fname=0)
{
TGraphErrors* graphX = (TGraphErrors*)directory->Get("x");
if ( graphX==0 ) return;
TH1I* hFirst = (TH1I*)directory->Get("firstEvent");
TH1I* hLast = (TH1I*)directory->Get("lastEvent");
if ( hFirst==0 || hLast==0 ) return;
std::string fullName("cDeltaE");
if ( fname ) fullName += fname;
else fullName += directory->GetName();
TCanvas* c = new TCanvas(fullName.c_str(),fullName.c_str());
TH1* h = refHisto->Clone("DeltaE");
h->Reset();
h->SetTitle("DeltaE");
TGraph* graph = new TGraph();
graph->SetName("gDeltaE");
double xg,yg;
for ( unsigned int i=1; i<=hFirst->GetNbinsX(); ++i ) {
int e1 = hFirst->GetAt(i);
int e2 = hLast->GetAt(i);
graphX->GetPoint(i-1,xg,yg);
graph->SetPoint(i-1,xg,e2-e1);
}
double xmin,xmax,ymin,ymax;
graph->ComputeRange(xmin,ymin,xmax,ymax);
h->SetMinimum(0.);
h->SetMaximum((ymax+ymin)/2.+2.*(ymax-ymin)/2.);
h->Draw();
graph->SetMarkerStyle(20);
// graph->SetMarkerColor(2);
// graph->SetLineColor(2);
graph->Draw("P");
}
void QCD_MC_test(){
TString basepath = "/uscms/home/aperloff/nobackup/Summer12ME8TeV/MEInput/";
vector<TString> fnames;
vector<double> scaleFactors;
fnames.push_back("QCD_EM_20to30.root" ); scaleFactors.push_back(2.886e+08/3504052.0);
fnames.push_back("QCD_EM_30to80.root" ); scaleFactors.push_back(7.433e+07/3308873.0);
fnames.push_back("QCD_EM_80to170.root" ); scaleFactors.push_back(1.191e+06/3447258.0);
fnames.push_back("QCD_EM_170to250.root"); scaleFactors.push_back( 30990.0/3169702.0);
fnames.push_back("QCD_EM_250to350.root"); scaleFactors.push_back( 4250.0/3461112.0);
fnames.push_back("QCD_EM_350.root" ); scaleFactors.push_back( 810.0/3408046.0);
TCanvas * c = new TCanvas("sf","sf", 1200,800);
c->Divide(fnames.size()/2,2);
for (int h=0;h<fnames.size();h++){
c->cd(h+1);
TH1* aux = processFile(basepath+fnames[h], scaleFactors[h]);
aux->SetTitle(fnames[h]);
aux->Draw("E");
}
}//QCD_MC_test
void plotter::draw_matrix(TH2* hist_, TString file_name, bool zlog, bool is_migration){
TH1* hist = (TH1*) hist_->Clone("hist");
TCanvas *c= new TCanvas("c","",600,600);
gPad->SetRightMargin(0.15);
gPad->SetLeftMargin(0.15);
if(zlog) gPad->SetLogz();
hist->SetTitle(" ");
hist->GetXaxis()->SetTitleOffset(1.1);
hist->GetYaxis()->SetTitleOffset(1.5);
if(is_migration){
hist->GetXaxis()->SetTitle("generator binning");
hist->GetYaxis()->SetTitle("detector binning");
}
else{
hist->GetXaxis()->SetTitle("Leading-jet mass [GeV]");
hist->GetYaxis()->SetTitle("Leading-jet mass [GeV]");
}
hist->Draw("COLZ");
hist->Draw("BOX SAME");
c->SaveAs(directory + file_name + ".pdf");
delete c;
}
void patBJetTracks_efficiencies()
{
// define proper canvas style
setNiceStyle();
gStyle->SetOptStat(0);
// open file
TFile* file = new TFile("analyzePatBJetTracks.root");
TLegend *legend[3] = { 0, 0, 0 };
// draw canvas with efficiencies
TCanvas *canv;
canv = new TCanvas("canv0", "hand-crafted track counting efficiencies", 800, 300);
canv->Divide(3, 1);
TH1 *total = (TH1*)file->Get(Form("%s/flavours", directory));
TH1 *effVsCutB = 0;
unsigned int i = 0;
for(const char **flavour = flavours; *flavour; flavour++, i++) {
TH1 *h = (TH1*)file->Get(Form("%s/trackIPSig_%s", directory, *flavour));
TH1 *discrShape = (TH1*)h->Clone(Form("%s_discrShape", h->GetName()));
discrShape->Scale(1.0 / discrShape->Integral());
discrShape->SetMaximum(discrShape->GetMaximum() * 5);
TH1 *effVsCut = computeEffVsCut(h, total->GetBinContent(4 - i));
TH1 *effVsBEff = 0;
if (flavour == flavours) // b-jets
effVsCutB = effVsCut;
else
effVsBEff = computeEffVsBEff(effVsCut, effVsCutB);
discrShape->SetTitle("discriminator shape");
effVsCut->SetTitle("efficiency versus discriminator cut");
if (effVsBEff)
effVsBEff->SetTitle("mistag versus b efficiency");
setHistStyle(discrShape);
setHistStyle(effVsCut);
setHistStyle(effVsBEff);
canv->cd(1);
gPad->SetLogy(1);
gPad->SetGridy(1);
discrShape->SetLineColor(i + 1);
discrShape->SetMarkerColor(i + 1);
discrShape->Draw(i > 0 ? "same" : "");
if (!legend[0])
legend[0] = new TLegend(0.5, 0.7, 0.78, 0.88);
legend[0]->AddEntry(discrShape, *flavour);
canv->cd(2);
gPad->SetLogy(1);
gPad->SetGridy(1);
effVsCut->SetLineColor(i + 1);
effVsCut->SetMarkerColor(i + 1);
effVsCut->Draw(i > 0 ? "same" : "");
if (!legend[1])
legend[1] = new TLegend(0.12, 0.12, 0.40, 0.30);
legend[1]->AddEntry(effVsCut, *flavour);
if (!effVsBEff)
continue;
canv->cd(3);
gPad->SetLogy(1);
gPad->SetGridx(1);
gPad->SetGridy(1);
effVsBEff->SetLineColor(i + 1);
effVsBEff->SetMarkerColor(i + 1);
effVsBEff->Draw(i > 1 ? "same" : "");
if (!legend[2])
legend[2] = new TLegend(0.12, 0.7, 0.40, 0.88);
legend[2]->AddEntry(effVsBEff, *flavour);
}
canv->cd(1);
legend[0]->Draw();
canv->cd(2);
legend[1]->Draw();
canv->cd(3);
legend[2]->Draw();
////////////////////////////////////////////
// canvas to compare negative tagger with light flavour mistag
TCanvas *canv;
canv = new TCanvas("canv1", "comparing light flavour mistag with negative tagger", 530, 300);
canv->Divide(2, 1);
TH1 *h1 = (TH1*)file->Get(Form("%s/trackIPSig_udsg", directory));
TH1 *h2 = (TH1*)file->Get(Form("%s/negativeIPSig_all", directory));
h2 = invertHisto(h2); // invert x-axis
TH1 *discrShape1 = (TH1*)h1->Clone("discrShape1");
TH1 *discrShape2 = (TH1*)h2->Clone("discrShape2");
discrShape1->Scale(1.0 / discrShape1->Integral());
discrShape1->SetMaximum(discrShape1->GetMaximum() * 5);
discrShape2->Scale(1.0 / discrShape2->Integral());
TH1 *effVsCut1 = computeEffVsCut(h1, total->GetBinContent(2));
TH1 *effVsCut2 = computeEffVsCut(h2, total->GetBinContent(1));
discrShape1->SetTitle("discriminator shape");
effVsCut1->SetTitle("efficiency versus discriminator cut");
setHistStyle(discrShape1);
setHistStyle(discrShape2);
setHistStyle(effVsCut1);
setHistStyle(effVsCut2);
canv->cd(1);
gPad->SetLogy(1);
gPad->SetGridy(1);
discrShape1->SetLineColor(1);
discrShape1->SetMarkerColor(1);
discrShape2->SetLineColor(2);
discrShape2->SetMarkerColor(2);
discrShape1->Draw();
discrShape2->Draw("same");
TLegend *l = new TLegend(0.5, 0.7, 0.78, 0.88);
l->AddEntry(discrShape1, "udsg");
l->AddEntry(discrShape2, "inv. neg");
l->Draw();
canv->cd(2);
gPad->SetLogy(1);
gPad->SetGridy(1);
effVsCut1->SetLineColor(1);
effVsCut1->SetMarkerColor(1);
effVsCut2->SetLineColor(2);
effVsCut2->SetMarkerColor(2);
effVsCut1->Draw();
effVsCut2->Draw("same");
l = new TLegend(0.5, 0.7, 0.78, 0.88);
l->AddEntry(effVsCut1, "udsg");
l->AddEntry(effVsCut2, "inv. neg");
l->Draw();
}
void FFT()
{
//This tutorial illustrates the Fast Fourier Transforms interface in ROOT.
//FFT transform types provided in ROOT:
// - "C2CFORWARD" - a complex input/output discrete Fourier transform (DFT)
// in one or more dimensions, -1 in the exponent
// - "C2CBACKWARD"- a complex input/output discrete Fourier transform (DFT)
// in one or more dimensions, +1 in the exponent
// - "R2C" - a real-input/complex-output discrete Fourier transform (DFT)
// in one or more dimensions,
// - "C2R" - inverse transforms to "R2C", taking complex input
// (storing the non-redundant half of a logically Hermitian array)
// to real output
// - "R2HC" - a real-input DFT with output in ¡Èhalfcomplex¡É format,
// i.e. real and imaginary parts for a transform of size n stored as
// r0, r1, r2, ..., rn/2, i(n+1)/2-1, ..., i2, i1
// - "HC2R" - computes the reverse of FFTW_R2HC, above
// - "DHT" - computes a discrete Hartley transform
// Sine/cosine transforms:
// DCT-I (REDFT00 in FFTW3 notation)
// DCT-II (REDFT10 in FFTW3 notation)
// DCT-III(REDFT01 in FFTW3 notation)
// DCT-IV (REDFT11 in FFTW3 notation)
// DST-I (RODFT00 in FFTW3 notation)
// DST-II (RODFT10 in FFTW3 notation)
// DST-III(RODFT01 in FFTW3 notation)
// DST-IV (RODFT11 in FFTW3 notation)
//First part of the tutorial shows how to transform the histograms
//Second part shows how to transform the data arrays directly
//Authors: Anna Kreshuk and Jens Hoffmann
//********* Histograms ********//
//prepare the canvas for drawing
TCanvas *myc = new TCanvas("myc", "Fast Fourier Transform", 800, 600);
myc->SetFillColor(45);
TPad *c1_1 = new TPad("c1_1", "c1_1",0.01,0.67,0.49,0.99);
TPad *c1_2 = new TPad("c1_2", "c1_2",0.51,0.67,0.99,0.99);
TPad *c1_3 = new TPad("c1_3", "c1_3",0.01,0.34,0.49,0.65);
TPad *c1_4 = new TPad("c1_4", "c1_4",0.51,0.34,0.99,0.65);
TPad *c1_5 = new TPad("c1_5", "c1_5",0.01,0.01,0.49,0.32);
TPad *c1_6 = new TPad("c1_6", "c1_6",0.51,0.01,0.99,0.32);
c1_1->Draw();
c1_2->Draw();
c1_3->Draw();
c1_4->Draw();
c1_5->Draw();
c1_6->Draw();
c1_1->SetFillColor(30);
c1_1->SetFrameFillColor(42);
c1_2->SetFillColor(30);
c1_2->SetFrameFillColor(42);
c1_3->SetFillColor(30);
c1_3->SetFrameFillColor(42);
c1_4->SetFillColor(30);
c1_4->SetFrameFillColor(42);
c1_5->SetFillColor(30);
c1_5->SetFrameFillColor(42);
c1_6->SetFillColor(30);
c1_6->SetFrameFillColor(42);
c1_1->cd();
TH1::AddDirectory(kFALSE);
//A function to sample
TF1 *fsin = new TF1("fsin", "sin(x)*sin(x)/(x*x)", 0, 4*TMath::Pi());
fsin->Draw();
Int_t n=25;
TH1D *hsin = new TH1D("hsin", "hsin", n+1, 0, 4*TMath::Pi());
Double_t x;
//Fill the histogram with function values
for (Int_t i=0; i<=n; i++){
x = (Double_t(i)/n)*(4*TMath::Pi());
hsin->SetBinContent(i+1, fsin->Eval(x));
}
hsin->Draw("same");
fsin->GetXaxis()->SetLabelSize(0.05);
fsin->GetYaxis()->SetLabelSize(0.05);
c1_2->cd();
//Compute the transform and look at the magnitude of the output
TH1 *hm =0;
TVirtualFFT::SetTransform(0);
hm = hsin->FFT(hm, "MAG");
hm->SetTitle("Magnitude of the 1st transform");
hm->Draw();
//NOTE: for "real" frequencies you have to divide the x-axes range with the range of your function
//(in this case 4*Pi); y-axes has to be rescaled by a factor of 1/SQRT(n) to be right: this is not done automatically!
hm->SetStats(kFALSE);
hm->GetXaxis()->SetLabelSize(0.05);
hm->GetYaxis()->SetLabelSize(0.05);
c1_3->cd();
//Look at the phase of the output
TH1 *hp = 0;
hp = hsin->FFT(hp, "PH");
hp->SetTitle("Phase of the 1st transform");
hp->Draw();
hp->SetStats(kFALSE);
hp->GetXaxis()->SetLabelSize(0.05);
hp->GetYaxis()->SetLabelSize(0.05);
//Look at the DC component and the Nyquist harmonic:
Double_t re, im;
//That's the way to get the current transform object:
TVirtualFFT *fft = TVirtualFFT::GetCurrentTransform();
c1_4->cd();
//Use the following method to get just one point of the output
fft->GetPointComplex(0, re, im);
printf("1st transform: DC component: %f\n", re);
fft->GetPointComplex(n/2+1, re, im);
printf("1st transform: Nyquist harmonic: %f\n", re);
//Use the following method to get the full output:
Double_t *re_full = new Double_t[n];
Double_t *im_full = new Double_t[n];
fft->GetPointsComplex(re_full,im_full);
//Now let's make a backward transform:
TVirtualFFT *fft_back = TVirtualFFT::FFT(1, &n, "C2R M K");
fft_back->SetPointsComplex(re_full,im_full);
fft_back->Transform();
TH1 *hb = 0;
//Let's look at the output
hb = TH1::TransformHisto(fft_back,hb,"Re");
hb->SetTitle("The backward transform result");
hb->Draw();
//NOTE: here you get at the x-axes number of bins and not real values
//(in this case 25 bins has to be rescaled to a range between 0 and 4*Pi;
//also here the y-axes has to be rescaled (factor 1/bins)
hb->SetStats(kFALSE);
hb->GetXaxis()->SetLabelSize(0.05);
hb->GetYaxis()->SetLabelSize(0.05);
delete fft_back;
fft_back=0;
//********* Data array - same transform ********//
//Allocate an array big enough to hold the transform output
//Transform output in 1d contains, for a transform of size N,
//N/2+1 complex numbers, i.e. 2*(N/2+1) real numbers
//our transform is of size n+1, because the histogram has n+1 bins
Double_t *in = new Double_t[2*((n+1)/2+1)];
Double_t re_2,im_2;
for (Int_t i=0; i<=n; i++){
x = (Double_t(i)/n)*(4*TMath::Pi());
in[i] = fsin->Eval(x);
}
//Make our own TVirtualFFT object (using option "K")
//Third parameter (option) consists of 3 parts:
//-transform type:
// real input/complex output in our case
//-transform flag:
// the amount of time spent in planning
// the transform (see TVirtualFFT class description)
//-to create a new TVirtualFFT object (option "K") or use the global (default)
Int_t n_size = n+1;
TVirtualFFT *fft_own = TVirtualFFT::FFT(1, &n_size, "R2C ES K");
if (!fft_own) return;
fft_own->SetPoints(in);
fft_own->Transform();
//Copy all the output points:
fft_own->GetPoints(in);
//Draw the real part of the output
c1_5->cd();
TH1 *hr = 0;
hr = TH1::TransformHisto(fft_own, hr, "RE");
hr->SetTitle("Real part of the 3rd (array) tranfsorm");
hr->Draw();
hr->SetStats(kFALSE);
hr->GetXaxis()->SetLabelSize(0.05);
hr->GetYaxis()->SetLabelSize(0.05);
c1_6->cd();
TH1 *him = 0;
him = TH1::TransformHisto(fft_own, him, "IM");
him->SetTitle("Im. part of the 3rd (array) transform");
him->Draw();
him->SetStats(kFALSE);
him->GetXaxis()->SetLabelSize(0.05);
him->GetYaxis()->SetLabelSize(0.05);
myc->cd();
//Now let's make another transform of the same size
//The same transform object can be used, as the size and the type of the transform
//haven't changed
TF1 *fcos = new TF1("fcos", "cos(x)+cos(0.5*x)+cos(2*x)+1", 0, 4*TMath::Pi());
for (Int_t i=0; i<=n; i++){
x = (Double_t(i)/n)*(4*TMath::Pi());
in[i] = fcos->Eval(x);
}
fft_own->SetPoints(in);
fft_own->Transform();
fft_own->GetPointComplex(0, re_2, im_2);
printf("2nd transform: DC component: %f\n", re_2);
fft_own->GetPointComplex(n/2+1, re_2, im_2);
printf("2nd transform: Nyquist harmonic: %f\n", re_2);
delete fft_own;
delete [] in;
delete [] re_full;
delete [] im_full;
}
///////////////////////////////////////////////////////////////////
//////// Go4 GUI example script addhistos.C
// J.Adamczewski, gsi, May 2006
// JAM May 2015: added support for 2d histograms
// NOTE: to be run in Go4 GUI local command line only!
// NEVER call this script in remote analysis process!!!
/////// Functionality:
// adds histogram of name2 to histogram of name1
/////// Usage:
// histogram name2 will be scaled by factor.
// (e.g. if factor==-1, his2 is substracted from his1)
// The draw flag switches if the results are displayed each time this makro is called
// if display is switched off, the result histogram is just updated in browser and existing displays
///////
Bool_t addhistos(const char* name1, const char* name2, Double_t factor, Bool_t draw)
{
if(TGo4AbstractInterface::Instance()==0 || go4!=TGo4AbstractInterface::Instance()) {
std::cout <<"FATAL: Go4 gui macro executed outside Go4 GUI!! returning." << std::endl;
return kFALSE;
}
TString fullname1 = go4->FindItem(name1);
TObject* ob1=go4->GetObject(fullname1,1000); // 1000=timeout to get object from analysis in ms
TH1 *his1(0), *his2(0);
if(ob1 && ob1->InheritsFrom("TH1"))
his1 = (TH1*) ob1;
if(his1==0) {
std::cout <<"addhistos could not get histogram "<<fullname1 << std::endl;
return kFALSE;
}
TString fullname2 = go4->FindItem(name2);
TObject* ob2=go4->GetObject(fullname2,1000); // 1000=timeout to get object from analysis in ms
if(ob2 && ob2->InheritsFrom("TH1"))
his2 = (TH1*)ob2;
if(his2==0) {
std::cout <<"addhistos could not get histogram "<<fullname2 << std::endl;
return kFALSE;
}
if((his1->GetDimension()) != (his2->GetDimension()))
{
std::cout <<"addhistos could not add histograms of different dimensions "<< std::endl;
return kFALSE;
}
TH1* result = (TH1*) his1->Clone();
TString n1 = his1->GetName();
TString n2 = his2->GetName();
TString t1 = his1->GetTitle();
TString t2 = his2->GetTitle();
TString soper;
if(factor>0)
soper.Form(") + %4.1E * (",factor);
else
soper.Form(") - %4.1E * (",-1*factor);
TString finalname = TString("(")+n1+soper+n2+")";
TString finaltitle = TString("(")+t1+soper+t2+")";
result->SetName(finalname);
result->SetTitle(finaltitle);
result->Sumw2();
result->Add(his2,factor);
result->SetDirectory(0);
TString rname = go4->SaveToMemory("Sums", result, kTRUE);
std::cout<< "Saved result histogram to " << rname.Data() <<std::endl;
if(draw) {
ViewPanelHandle vpanel = go4->StartViewPanel();
if(result->GetDimension()>1)
{
// superimpose mode is not supported for 2d histograms
go4->DrawItem(rname, vpanel);
}
else
{
go4->SetSuperimpose(vpanel,kTRUE);
go4->DrawItem(fullname1, vpanel);
go4->DrawItem(fullname2, vpanel);
go4->DrawItem(rname, vpanel);
}
}
return kTRUE;
}
// 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++;
}
}
}
}
