TH2* doSmooth (TH2* hRaw, bool useLog = true) {
//
// smooth histogram
//
TH2* hSmooth = (TH2*)hRaw->Clone("hSmooth");
hSmooth->SetTitle("hSmooth");
if ( useLog ) {
for ( int ix=1; ix<=hSmooth->GetNbinsX(); ++ix ) {
for ( int iy=1; iy<=hSmooth->GetNbinsY(); ++iy ) {
double c = hSmooth->GetBinContent(ix,iy);
c = c>0. ? log(c) : -100.;
hSmooth->SetBinContent(ix,iy,c);
}
}
}
fitLinear(hSmooth,hRaw);
if ( useLog ) {
for ( int ix=1; ix<=hSmooth->GetNbinsX(); ++ix ) {
for ( int iy=1; iy<=hSmooth->GetNbinsY(); ++iy ) {
double craw = hRaw->GetBinContent(ix,iy);
double c = hSmooth->GetBinContent(ix,iy);
c = craw>0. ? exp(c) : 0.;
hSmooth->SetBinContent(ix,iy,c);
}
}
}
hSmooth->SetName(hRaw->GetName());
return hSmooth;
}
void SF_TH2F_And_Eff::init(string filename,string effdata,string effmc,string errordata,string errormc)
{
f = TFile::Open(filename.c_str() ) ;
if (f == NULL){
Log(__FUNCTION__,"ERROR","file '" + filename + "' does not exist");
throw abortException() ;
}
TH2 * hDataEff = getHisto(effdata);
TH2 * hDataErr = NULL;
if (errordata != "" ) hDataErr = getHisto(errordata);
TH2 * hMcEff = getHisto(effmc);
TH2 * hMcErr = NULL;
if (errormc != "" ) hMcErr = getHisto(errormc);
for( int aetabin =1; aetabin <= hDataEff->GetNbinsX() ; ++aetabin)
for( int ptbin =1; ptbin <= hDataEff->GetNbinsY() ; ++ptbin)
{
float ptmin = hDataEff->GetYaxis()->GetBinLowEdge(ptbin);
float ptmax = hDataEff->GetYaxis()->GetBinLowEdge(ptbin+1);
float aetamin = hDataEff->GetXaxis()->GetBinLowEdge(aetabin);
float aetamax = hDataEff->GetXaxis()->GetBinLowEdge(aetabin+1);
float effData = hDataEff->GetBinContent(aetabin,ptbin);
float errData = 0.0 ;
if (hDataErr) err=hDataErr->GetBinContent(aetabin,ptbin);
else errData = hDataEff->GetBinError(aetabin,ptbin);
float effMc = hMcEff->GetBinContent(aetabin,ptbin);
float errMc = 0.0;
if (hMcEff) errMc = hMcEff->GetBinContent(aetabin,ptbin);
else errMc = hMcEff->GetBinError(aetabin,ptbin);
if (ptbin == hDataEff->GetNbinsY() ) ptmax = 8000.; // highest is open, current recommendation
if (aetabin == hDataEff->GetNbinsX() ) aetamax = aetamax+0.0001; // put it slightly larger to get 2.4 as well
add(ptmin,ptmax,aetamin,aetamax,effData,effMc,errData,errMc);
}
f->Close(); // delete?
delete f;
f = NULL;
}
void SetBorders( TH2 &hist, Double_t val=0 )
{
int numx = hist.GetNbinsX();
int numy = hist.GetNbinsY();
for(int i=0; i <= numx+1 ; i++){
hist.SetBinContent(i,0,val);
hist.SetBinContent(i,numy+1,val);
}
for(int i=0; i <= numy+1 ; i++) {
hist.SetBinContent(0,i,val);
hist.SetBinContent(numx+1,i,val);
}
}
// normalize migmatrix column-wise
TH2* normalizeMigMat(TH2* h)
{
TH2* hclone = (TH2*) h->Clone();
const int xbins = hclone->GetNbinsX();
const int ybins = hclone->GetNbinsY();
for(int x=0; x<xbins; x++)
{
double integ = hclone->Integral(x+1, x+1, 1, ybins);
for(int y=0; y<ybins; y++)
{
hclone->SetBinContent(x+1,y+1, hclone->GetBinContent(x+1, y+1)/integ);
}
}
return hclone;
}
void MirrorBorders( TH2& hist ) {
int numx = hist.GetNbinsX();
int numy = hist.GetNbinsY();
Float_t val;
// corner points
hist.SetBinContent(0,0,hist.GetBinContent(1,1));
hist.SetBinContent(numx+1,numy+1,hist.GetBinContent(numx,numy));
hist.SetBinContent(numx+1,0,hist.GetBinContent(numx,1));
hist.SetBinContent(0,numy+1,hist.GetBinContent(1,numy));
for(int i=1; i<=numx; i++){
hist.SetBinContent(i,0, hist.GetBinContent(i,1));
hist.SetBinContent(i,numy+1, hist.GetBinContent(i,numy));
}
for(int i=1; i<=numy; i++) {
hist.SetBinContent(0,i, hist.GetBinContent(1,i));
hist.SetBinContent(numx+1,i, hist.GetBinContent(numx,i));
}
}
TH2* smoothHoles(const TH2* originalHist)
{
TH2* hist = (TH2*)originalHist->Clone("_smoothed");
int xMax = hist->GetNbinsX();
int yMax = hist->GetNbinsY();
int xMin = 0;
for(int xBin = 1; xBin <= xMax; xBin++)
{
for(int yBin = 1; yBin <= yMax; yBin++)
{
if(hist->GetBinContent(xBin,yBin)>0)
{
xMin = xBin;
yBin = yMax+1;
xBin = xMax+1;
}
}
}
// for(unsigned int i = 0; i< 1000; i++) smoothHistAcross(hist,xMin);
for(unsigned int i = 0; i< 1000; i++) interpolateHistAcross(hist,xMin);
return hist;
}
TH1 *
UnfoldMe_MB2(const Char_t *data, const Char_t *mc, const Char_t *anatag, Int_t bin, Bool_t useMBcorr , Bool_t usecorrfit , Bool_t ismc , Float_t smooth , Int_t iter , Int_t regul , Float_t weight , Bool_t bayesian , Int_t nloop )
{
// MF comments:
// usedMBcorr: changes the matrix used for unfonding, from effMatrix to bin matrix (I think this is just to use mult dependent v s mb correction_)
// usecorrfit: if I understand correctly, fits the response matrix and uses fit to extrapolate it
TFile *fdt =0;
if (ismc)
fdt = TFile::Open(data);
else
fdt = TFile::Open(data);
TFile *fmc = TFile::Open(mc);
TList *ldt = (TList *)fdt->Get(Form("%s", anatag));
TList *lmc = (TList *)fmc->Get(Form("%s", anatag));
TH2 *hmatdt = (TH2 *)ldt->FindObject(Form(responseMatrix, bin));
TH2 *hmatmc = 0;
if (useMBcorr){
hmatmc = (TH2 *)lmc->FindObject("effMatrix");
std::cout << "USING MB" << std::endl;
}
else {
hmatmc = (TH2 *)lmc->FindObject(Form(responseMatrix, bin));
}
TH1 *hdata = hmatdt->ProjectionY("hdata");
// TH1 *hdata = hmatdt->ProjectionY("htrue"); // For truth Only Calculations
hdata->Sumw2();
hdata->SetBinContent(1, 0.);
hdata->SetBinError(1, 0.);
// hdata->Scale(1. / hdata->Integral());
hdata->SetMarkerStyle(25);
TH1 *htrue = hmatdt->ProjectionX("htrue");
htrue->Sumw2();
// htrue->Scale(1. / htrue->Integral());
htrue->SetMarkerStyle(7);
htrue->SetMarkerColor(2);
htrue->SetBinContent(1, 0.);
htrue->SetBinError(1, 0.);
TH2 *hcorr = (TH2 *)hmatmc->Clone("hcorr");
TH1 *hinit = (TH1 *)hdata->Clone("hinit");
TH1 *hresu = (TH1 *)hdata->Clone("hresu");
TH1 *hbias = (TH1 *)hdata->Clone("hbias");
hresu->SetMarkerStyle(20);
hresu->SetMarkerColor(4);
hresu->Reset();
TH1 *hnum = hcorr->ProjectionY("hnum");
TH1 *hden = hcorr->ProjectionY("hden");
TH1 *heff = hcorr->ProjectionY("heff");
hnum->Reset();
hnum->Sumw2();
hden->Reset();
hden->Sumw2();
heff->Reset();
for (Int_t i = 0; i < heff->GetNbinsX(); i++) {
Float_t int1 = hcorr->Integral(i + 1, i + 1, 0, -1);
if (int1 <= 0.) continue;
Float_t int2 = hcorr->Integral(i + 1, i + 1, 2, -1);
hnum->SetBinContent(i + 1, int2);
hnum->SetBinError(i + 1, TMath::Sqrt(int2));
hden->SetBinContent(i + 1, int1);
hden->SetBinError(i + 1, TMath::Sqrt(int1));
}
TCanvas *cEfficiency = new TCanvas("cEfficiency", "cEfficiency");
cEfficiency->SetLogx();
cEfficiency->SetLogy();
heff->Divide(hnum, hden, 1., 1., "B");
heff->Draw();
#if 0
for (Int_t ii = 0; ii < heff->GetNbinsX(); ii++) {
heff->SetBinContent(ii + 1, 1.);
heff->SetBinError(ii + 1, 0.);
}
#endif
for (Int_t i = 0; i < hcorr->GetNbinsX(); i++) {
hcorr->SetBinContent(i + 1, 1, 0.);
hcorr->SetBinError(i + 1, 1, 0.);
}
for (Int_t i = 0; i < hcorr->GetNbinsY(); i++) {
hcorr->SetBinContent(1, i + 1, 0.);
hcorr->SetBinError(1, i + 1, 0.);
}
TH2 *hcorrfit = ReturnCorrFromFit(hcorr);
// Docs from AliUnfolding
//Int_t AliUnfolding::Unfold(TH2* correlation, TH1* efficiency, TH1* measured, TH1* initialConditions, TH1* result, Bool_t check)
// unfolds with unfolding method fgMethodType
//
// parameters:
// correlation: response matrix as measured vs. generated
// efficiency: (optional) efficiency that is applied on the unfolded spectrum, i.e. it has to be in unfolded variables. If 0 no efficiency is applied.
// measured: the measured spectrum
// initialConditions: (optional) initial conditions for the unfolding. if 0 the measured spectrum is used as initial conditions.
// result: target for the unfolded result
// check: depends on the unfolding method, see comments in specific functions
for (Int_t iloop = 0; iloop < nloop; iloop++) {
if (bayesian) {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kBayesian);
AliUnfolding::SetBayesianParameters(smooth, iter);
} else {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kChi2Minimization);
AliUnfolding::SetChi2Regularization(AliUnfolding::RegularizationType(regul), weight);
}
AliUnfolding::SetSkip0BinInChi2(kTRUE);
AliUnfolding::SetSkipBinsBegin(1);
AliUnfolding::SetNbins(150, 150);
AliUnfolding::Unfold(usecorrfit ? hcorrfit : hcorr, heff, hdata, hinit, hresu);
hinit = (TH1 *)hresu->Clone(Form("hinit_%d", iloop));
}
printf("hdata->Integral(2, -1) = %f\n", hdata->Integral(2, -1));
printf("hresu->Integral(2, -1) = %f\n", hresu->Integral(2, -1));
TCanvas *cUnfolded = new TCanvas ("cUnfolded", "cUnfolded", 400, 800);
cUnfolded->Divide(1, 2);
cUnfolded->cd(1)->SetLogx();
cUnfolded->cd(1)->SetLogy();
hdata->Draw();
hresu->Draw("same");
htrue->Draw("same");
cUnfolded->cd(2)->SetLogx();
cUnfolded->cd(2)->DrawFrame(1., 0, 300., 10);
TH1 *hrat = (TH1 *)hresu->Clone("hrat");
hrat->Divide(htrue);
hrat->Draw("same");
TH1 *htrig = (TH1 *)hresu->Clone("htrig");
htrig->Multiply(heff);
Float_t dndeta_resu = 0.;
Float_t integr_resu = 0.;
Float_t dndeta_trig = 0.;
Float_t integr_trig = 0.;
for (Int_t i = 1; i < hresu->GetNbinsX(); i++) {
dndeta_resu += hresu->GetBinContent(i + 1) * hresu->GetBinLowEdge(i + 1);
integr_resu += hresu->GetBinContent(i + 1);
dndeta_trig += htrig->GetBinContent(i + 1) * htrig->GetBinLowEdge(i + 1);
integr_trig += htrig->GetBinContent(i + 1);
}
cUnfolded->SaveAs("unfold_efficiency.pdf");
integr_eff = integr_trig / integr_resu;
integr_eff_err = TMath::Sqrt(integr_eff * (1. - integr_eff) / integr_resu);
dndeta_eff = dndeta_trig / dndeta_resu;
dndeta_eff_err = TMath::Sqrt(dndeta_eff * (1. - dndeta_eff) / dndeta_resu);
printf("INEL > 0 efficiency: %.3f +- %.3f\n", integr_eff, integr_eff_err);
printf("dN/dEta correction: %.3f +- %.3f\n", dndeta_eff, dndeta_eff_err);
return hresu;
}
TH1 *
UnfoldMe(Char_t *data, Char_t *mc, Char_t *anatag, Int_t bin, Bool_t useMBcorr = kTRUE, Bool_t usecorrfit = kFALSE, Bool_t ismc = kFALSE, Float_t smooth = 0.001, Int_t iter = 50, Int_t regul = AliUnfolding::kPowerLaw, Float_t weight = 100., Bool_t bayesian = kTRUE, Int_t nloop = 1)
{
if (ismc)
TFile *fdt = TFile::Open(data);
else
TFile *fdt = TFile::Open(data);
TFile *fmc = TFile::Open(mc);
TList *ldt = (TList *)fdt->Get(Form("clist_%s", anatag));
TList *lmc = (TList *)fmc->Get(Form("clist_%s", anatag));
TH2 *hmatdt = (TH2 *)ldt->FindObject(Form("b%d_corrMatrix", bin));
if (useMBcorr)
TH2 *hmatmc = (TH2 *)lmc->FindObject("effMatrix");
else
TH2 *hmatmc = (TH2 *)lmc->FindObject(Form("b%d_corrMatrix", bin));
TH1 *hdata = hmatdt->ProjectionY("hdata");
hdata->Sumw2();
hdata->SetBinContent(1, 0.);
hdata->SetBinError(1, 0.);
// hdata->Scale(1. / hdata->Integral());
hdata->SetMarkerStyle(25);
TH1 *htrue = hmatdt->ProjectionX("htrue");
htrue->Sumw2();
// htrue->Scale(1. / htrue->Integral());
htrue->SetMarkerStyle(7);
htrue->SetMarkerColor(2);
htrue->SetBinContent(1, 0.);
htrue->SetBinError(1, 0.);
TH2 *hcorr = (TH2 *)hmatmc->Clone("hcorr");
TH1 *hinit = (TH1 *)hdata->Clone("hinit");
TH1 *hresu = (TH1 *)hdata->Clone("hresu");
TH1 *hbias = (TH1 *)hdata->Clone("hbias");
hresu->SetMarkerStyle(20);
hresu->SetMarkerColor(4);
hresu->Reset();
TH1 *hnum = hcorr->ProjectionY("hnum");
TH1 *hden = hcorr->ProjectionY("hden");
TH1 *heff = hcorr->ProjectionY("heff");
hnum->Reset();
hnum->Sumw2();
hden->Reset();
hden->Sumw2();
heff->Reset();
for (Int_t i = 0; i < heff->GetNbinsX(); i++) {
Float_t int1 = hcorr->Integral(i + 1, i + 1, 0, -1);
if (int1 <= 0.) continue;
Float_t int2 = hcorr->Integral(i + 1, i + 1, 2, -1);
hnum->SetBinContent(i + 1, int2);
hnum->SetBinError(i + 1, TMath::Sqrt(int2));
hden->SetBinContent(i + 1, int1);
hden->SetBinError(i + 1, TMath::Sqrt(int1));
}
new TCanvas("cEfficiency");
heff->Divide(hnum, hden, 1., 1., "B");
heff->Draw();
#if 0
for (Int_t ii = 0; ii < heff->GetNbinsX(); ii++) {
heff->SetBinContent(ii + 1, 1.);
heff->SetBinError(ii + 1, 0.);
}
#endif
for (Int_t i = 0; i < hcorr->GetNbinsX(); i++) {
hcorr->SetBinContent(i + 1, 1, 0.);
hcorr->SetBinError(i + 1, 1, 0.);
}
for (Int_t i = 0; i < hcorr->GetNbinsY(); i++) {
hcorr->SetBinContent(1, i + 1, 0.);
hcorr->SetBinError(1, i + 1, 0.);
}
TH2 *hcorrfit = ReturnCorrFromFit(hcorr);
for (Int_t iloop = 0; iloop < nloop; iloop++) {
if (bayesian) {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kBayesian);
AliUnfolding::SetBayesianParameters(smooth, iter);
} else {
AliUnfolding::SetUnfoldingMethod(AliUnfolding::kChi2Minimization);
AliUnfolding::SetChi2Regularization(regul, weight);
}
AliUnfolding::SetSkip0BinInChi2(kTRUE);
AliUnfolding::SetSkipBinsBegin(1);
AliUnfolding::SetNbins(150, 150);
AliUnfolding::Unfold(usecorrfit ? hcorrfit : hcorr, heff, hdata, hinit, hresu);
hinit = (TH1 *)hresu->Clone(Form("hinit_%d", iloop));
}
printf("hdata->Integral(2, -1) = %f\n", hdata->Integral(2, -1));
printf("hresu->Integral(2, -1) = %f\n", hresu->Integral(2, -1));
TCanvas *cUnfolded = new TCanvas ("cUnfolded", "", 400, 800);
cUnfolded->Divide(1, 2);
cUnfolded->cd(1)->SetLogx();
cUnfolded->cd(1)->SetLogy();
hdata->Draw();
hresu->Draw("same");
htrue->Draw("same");
cUnfolded->cd(2)->SetLogx();
cUnfolded->cd(2)->DrawFrame(1., 0.75, 300., 1.25);
TH1 *hrat = (TH1 *)hresu->Clone("hrat");
hrat->Divide(htrue);
hrat->Draw("same");
TH1 *htrig = (TH1 *)hresu->Clone("htrig");
htrig->Multiply(heff);
Float_t dndeta_resu = 0.;
Float_t integr_resu = 0.;
Float_t dndeta_trig = 0.;
Float_t integr_trig = 0.;
for (Int_t i = 1; i < hresu->GetNbinsX(); i++) {
dndeta_resu += hresu->GetBinContent(i + 1) * hresu->GetBinLowEdge(i + 1);
integr_resu += hresu->GetBinContent(i + 1);
dndeta_trig += htrig->GetBinContent(i + 1) * htrig->GetBinLowEdge(i + 1);
integr_trig += htrig->GetBinContent(i + 1);
}
// dndeta_resu /= integr_resu;
// dndeta_trig /= integr_trig;
integr_eff = integr_trig / integr_resu;
integr_eff_err = TMath::Sqrt(integr_eff * (1. - integr_eff) / integr_resu);
dndeta_eff = dndeta_trig / dndeta_resu;
dndeta_eff_err = TMath::Sqrt(dndeta_eff * (1. - dndeta_eff) / dndeta_resu);
printf("INEL > 0 efficiency: %.3f +- %.3f\n", integr_eff, integr_eff_err);
printf("dN/dEta correction: %.3f +- %.3f\n", dndeta_eff, dndeta_eff_err);
return hresu;
}
void
TestSPD(const TString& which, Double_t nVar=2)
{
TFile* file = TFile::Open("forward.root", "READ");
if (!file) return;
Bool_t spd = which.EqualTo("spd", TString::kIgnoreCase);
TList* l = 0;
if (spd) l = static_cast<TList*>(file->Get("CentralSums"));
else l = static_cast<TList*>(file->Get("ForwardSums"));
if (!l) {
Warning("", "%sSums not found", spd ? "Central" : "Forward");
return;
}
TList* ei = static_cast<TList*>(l->FindObject("fmdEventInspector"));
if (!l) {
Warning("", "fmdEventInspector not found");
return;
}
TObject* run = ei->FindObject("runNo");
if (!run)
Warning("", "No run number found");
ULong_t runNo = run ? run->GetUniqueID() : 0;
TH2* h = 0;
if (spd) h = static_cast<TH2*>(l->FindObject("nClusterVsnTracklet"));
else {
TList* den = static_cast<TList*>(l->FindObject("fmdDensityCalculator"));
if (!den) {
Error("", "fmdDensityCalculator not found");
return;
}
TList* rng = static_cast<TList*>(den->FindObject(which));
if (!rng) {
Error("", "%s not found", which.Data());
return;
}
h = static_cast<TH2*>(rng->FindObject("elossVsPoisson"));
}
if (!h) {
Warning("", "%s not found", spd ? nClusterVsnTracklet : "elossVsPoisson");
return;
}
gStyle->SetOptFit(1111);
gStyle->SetOptStat(0);
TCanvas* c = new TCanvas("c", Form("Run %u", runNo));
c->Divide(2,2);
TVirtualPad* p = c->cd(1);
if (spd) {
p->SetLogx();
p->SetLogy();
}
p->SetLogz();
h->Draw("colz");
TObjArray* fits = new TObjArray;
h->FitSlicesY(0, 1, -1, 0, "QN", fits);
TF1* mean = new TF1("mean", "pol1");
TF1* var = new TF1("var", "pol1");
// mean->FixParameter(0, 0);
// var->FixParameter(0, 0);
for (Int_t i = 0; i < 3; i++) {
p = c->cd(2+i);
if (spd) {
p->SetLogx();
p->SetLogy();
}
TH1* hh = static_cast<TH1*>(fits->At(i));
hh->Draw();
if (i == 0) continue;
hh->Fit((i == 1? mean : var), "+Q");
}
TGraphErrors* g1 = new TGraphErrors(h->GetNbinsX());
g1->SetFillColor(kBlue-10);
g1->SetFillStyle(3001);
g1->SetLineStyle(1);
TGraph* u1 = new TGraph(h->GetNbinsX());
TGraph* l1 = new TGraph(h->GetNbinsX());
u1->SetLineColor(kBlue+1);
l1->SetLineColor(kBlue+1);
u1->SetName("u1");
l1->SetName("l1");
TGraphErrors* g2 = new TGraphErrors(h->GetNbinsX());
g2->SetFillColor(kRed-10);
g2->SetFillStyle(3001);
g2->SetLineStyle(2);
TGraph* u2 = new TGraph(h->GetNbinsX());
TGraph* l2 = new TGraph(h->GetNbinsX());
u2->SetLineColor(kRed+1);
l2->SetLineColor(kRed+1);
u2->SetName("u2");
l2->SetName("l2");
for (Int_t i = 1; i <= h->GetNbinsX(); i++) {
Double_t x = hh->GetXaxis()->GetBinCenter(i);
Double_t y = mean->Eval(x);
Double_t e = var->Eval(y);
Double_t e1 = nVar * e;
if (spd) e1 *= TMath::Log10(e);
// Printf("%10e -> %10e +/- %10e", x, y, ee);
g1->SetPoint(i-1, x, y);
g1->SetPointError(i-1, 0, e1);
u1->SetPoint(i-1, x, y+e1);
l1->SetPoint(i-1, x, y-e1);
// Printf("%3d: %f -> %f +/- %f", i, x, y, ee);
Double_t e2 = nVar*0.05*x;
g2->SetPoint(i-1, x, x);
g2->SetPointError(i-1, 0, e2);
u2->SetPoint(i-1, x, x+e2);
l2->SetPoint(i-1, x, x-e2);
}
p = c->cd(1);
c->Clear();
c->cd();
c->SetLogz();
h->Draw("colz");
g1->Draw("3 same");
u1->Draw("l same");
l1->Draw("l same");
g2->Draw("3 same");
u2->Draw("l same");
l2->Draw("l same");
Double_t ly = 0.9;
Double_t dy = 0.06;
TLatex* ltx = new TLatex(0.15, ly, Form("#LTy#GT = %f + %f x",
mean->GetParameter(0),
mean->GetParameter(1)));
ltx->SetNDC();
ltx->SetTextSize(dy);
ltx->SetTextAlign(13);
ltx->Draw();
ly -= dy + 0.01;
ltx->DrawLatex(0.15, ly, Form("#sigma_{y} = %f + %f x",
var->GetParameter(0),
var->GetParameter(1)));
ly -= dy + 0.01;
ltx->DrawLatex(0.15, ly, Form("#delta = %f #sigma %s",
nVar, (spd ? "log_{10}(#sigma" : "")));
}
//
// fill missing bins in histogram h fitting a plane to the
// surrounding bins
//
void fitQuadratic (TH2* h, TH2* refHisto, int nmin=9, int nmin2=12) {
//
// prepare histogram
//
TH2* hOrig = (TH2*)h->Clone();
int nbx = hOrig->GetNbinsX();
int nby = hOrig->GetNbinsY();
//
// matrix and vector for fit
//
TMatrixD mat(6,6);
TVectorD cvec(6);
//
// loop over histogram
//
for ( int ix=1; ix<=nbx; ++ix ) {
for ( int iy=1; iy<=nby; ++iy ) {
// clear matrix and vector used for fit
int nn(0);
for ( int i=0; i<6; ++i ) {
cvec(i) = 0.;
for ( int j=0; j<6; ++j ) mat(i,j) = 0.;
}
// loop over neighbours
for ( int jx=-1; jx<2; ++jx ) {
for ( int jy=-1; jy<2; ++jy ) {
if ( (ix+jx)<1 || (ix+jx)>nbx ) continue;
if ( (iy+jy)<1 || (iy+jy)>nby ) continue;
// fill vector and matrix
fillForQuadFit(hOrig,refHisto,ix,iy,jx,jy,nn,mat,cvec);
}
}
for ( int jx=1; jx<6; ++jx ) {
for ( int jy=0; jy<jx; ++jy ) mat(jx,jy) = mat(jy,jx);
}
// if < nmin neighbours in delta_i==1: try to add delta_i==2
if ( nn<nmin ) {
// loop over neighbours
for ( int jx=-2; jx<3; ++jx ) {
for ( int jy=-2; jy<3; ++jy ) {
// skip the central bin (the one to be filled)
if ( abs(jx)<2 && abs(jy)<2 ) continue;
if ( (ix+jx)<1 || (ix+jx)>nbx ) continue;
if ( (iy+jy)<1 || (iy+jy)>nby ) continue;
// fill vector and matrix
fillForQuadFit(hOrig,refHisto,ix,iy,jx,jy,nn,mat,cvec);
}
}
for ( int jx=1; jx<6; ++jx ) {
for ( int jy=0; jy<jx; ++jy ) mat(jx,jy) = mat(jy,jx);
}
// drop bin if <nmin2 in 5x5 area
if ( nn<nmin2 ) continue;
}
// cout << "x / y = " << h->GetXaxis()->GetBinCenter(ix) << " "
// << h->GetYaxis()->GetBinCenter(iy) << endl;
//
// linear 2D fit to neighbours (in units of bin number):
// par(0)*(x-ix)+par(1)*(y-iy)+par(2)
//
double det = mat.Determinant();
if ( det < 1.e-6 ) std::cout << "************* " << det << std::endl;
if ( det < 1.e-6 ) continue;
// TMatrixD mat1(mat);
mat.Invert(&det);
TVectorD par = mat*cvec;
// TVectorD tmp = mat1*par;
h->SetBinContent(ix,iy,par(5));
}
}
delete hOrig;
}
void fullPedestalAnalysis(string inputDIR, string outputDIR, string inputCablingMap, string outputFileName){
gROOT->ProcessLine("gErrorIgnoreLevel = 1");
// open the file and prepare the cluster tree, adding the other trees as frined --> memory consuming
std::cout<<"##################################"<<std::endl;
std::cout<<"###### fullPedestalAnalysis ######"<<std::endl;
std::cout<<"##################################"<<std::endl;
clock_t tStart = clock();
// prepare style and load macros
setTDRStyle();
gROOT->SetBatch(kTRUE);
system(("mkdir -p "+outputDIR).c_str());
ifstream file;
std::cout<<"### Make input file list"<<std::endl;
system(("find "+inputDIR+" -name \"*.root\" > file.temp").c_str());
std::ifstream infile;
string line;
vector<string> fileList;
infile.open("file.temp",ifstream::in);
if(infile.is_open()){
while(!infile.eof()){
getline(infile,line);
if(line != "" and TString(line).Contains(".root") and line !="\n"){
fileList.push_back(line);
}
}
}
system("rm file.temp");
std::sort(fileList.begin(),fileList.end());
TFile* cablingFile = TFile::Open(inputCablingMap.c_str(),"READ");
cablingFile->cd();
TTree* readoutMap = (TTree*) cablingFile->FindObjectAny("readoutMap");
TTreeReader reader(readoutMap);
TTreeReaderValue<uint32_t> detid (reader,"detid");
TTreeReaderValue<uint16_t> fecCrate (reader,"fecCrate");
TTreeReaderValue<uint16_t> fecSlot (reader,"fecSlot");
TTreeReaderValue<uint16_t> fecRing (reader,"fecRing");
TTreeReaderValue<uint16_t> ccuAdd (reader,"ccuAdd");
TTreeReaderValue<uint16_t> ccuChan (reader,"ccuChan");
TTreeReaderValue<uint16_t> lldChannel (reader,"lldChannel");
TTreeReaderValue<uint16_t> fedId (reader,"fedId");
TTreeReaderValue<uint16_t> fedCh (reader,"fedCh");
// output tree
TFile* ouputTreeFile = new TFile((outputDIR+"/"+outputFileName).c_str(),"RECREATE");
ouputTreeFile->cd();
ouputTreeFile->SetCompressionLevel(0);
TTree* outputTree = new TTree("pedestalFullNoise","pedestalFullNoise");
// branches
uint32_t detid_,fedKey_;
uint16_t fecCrate_,fecSlot_, fecRing_, ccuAdd_, ccuChan_, lldChannel_, fedId_, fedCh_, apvId_, stripId_;
float noiseMean_,noiseRMS_, noiseSkewness_, noiseKurtosis_;
float fitChi2_, fitChi2Probab_, fitStatus_;
float fitGausMean_, fitGausSigma_, fitGausNormalization_;
float fitGausMeanError_, fitGausSigmaError_, fitGausNormalizationError_;
float noiseIntegral3Sigma_, noiseIntegral3SigmaFromFit_;
float noiseIntegral4Sigma_, noiseIntegral4SigmaFromFit_;
float noiseIntegral5Sigma_, noiseIntegral5SigmaFromFit_;
float kSValue_, kSProbab_, jBValue_, jBProbab_, aDValue_, aDProbab_;
vector<float> noiseDistribution_, noiseDistributionError_;
float xMin_, xMax_, nBin_ ;
outputTree->Branch("detid",&detid_,"detid/i");
outputTree->Branch("fedKey",&fedKey_,"fedKey/i");
outputTree->Branch("fecCrate",&fecCrate_,"fecCrate/s");
outputTree->Branch("fecSlot",&fecSlot_,"fecSlot/s");
outputTree->Branch("fecRing",&fecRing_,"fecRing/s");
outputTree->Branch("ccuAdd",&ccuAdd_,"ccuAdd/s");
outputTree->Branch("ccuChan",&ccuChan_,"ccuChan/s");
outputTree->Branch("lldChannel",&lldChannel_,"lldChannel/s");
outputTree->Branch("fedId",&fedId_,"fedId/s");
outputTree->Branch("fedCh",&fedCh_,"fedCh/s");
outputTree->Branch("apvId",&apvId_,"apvId/s");
outputTree->Branch("stripId",&stripId_,"stripId/s");
outputTree->Branch("noiseMean",&noiseMean_,"noiseMean/F");
outputTree->Branch("noiseRMS",&noiseRMS_,"noiseRMS/F");
outputTree->Branch("noiseSkewness",&noiseSkewness_,"noiseSkewness/F");
outputTree->Branch("noiseKurtosis",&noiseKurtosis_,"noiseKurtosis/F");
outputTree->Branch("fitGausNormalization",&fitGausNormalization_,"fitGausNormalization/F");
outputTree->Branch("fitGausMean",&fitGausMean_,"fitGausMean/F");
outputTree->Branch("fitGausSigma",&fitGausSigma_,"fitGausSigma/F");
outputTree->Branch("fitGausNormalizationError",&fitGausNormalizationError_,"fitGausNormalizationError/F");
outputTree->Branch("fitGausMeanError",&fitGausMeanError_,"fitGausMeanError/F");
outputTree->Branch("fitGausSigmaError",&fitGausSigmaError_,"fitGausSigmaError/F");
outputTree->Branch("fitChi2",&fitChi2_,"fitChi2/F");
outputTree->Branch("fitChi2Probab",&fitChi2Probab_,"fitChi2Probab/F");
outputTree->Branch("fitStatus",&fitStatus_,"fitStatus_F");
outputTree->Branch("noiseIntegral3Sigma",&noiseIntegral3Sigma_,"noiseIntegral3Sigma/F");
outputTree->Branch("noiseIntegral3SigmaFromFit",&noiseIntegral3SigmaFromFit_,"noiseIntegral3SigmaFromFit/F");
outputTree->Branch("noiseIntegral4Sigma",&noiseIntegral4Sigma_,"noiseIntegral4Sigma/F");
outputTree->Branch("noiseIntegral4SigmaFromFit",&noiseIntegral4SigmaFromFit_,"noiseIntegral4SigmaFromFit/F");
outputTree->Branch("noiseIntegral5Sigma",&noiseIntegral4Sigma_,"noiseIntegral5Sigma/F");
outputTree->Branch("noiseIntegral5SigmaFromFit",&noiseIntegral4SigmaFromFit_,"noiseIntegral5SigmaFromFit/F");
outputTree->Branch("kSValue",&kSValue_,"kSValue/F");
outputTree->Branch("jBValue",&jBValue_,"jBValue/F");
outputTree->Branch("aDValue",&aDValue_,"aDValue/F");
outputTree->Branch("kSProbab",&kSProbab_,"kSProbab/F");
outputTree->Branch("jBProbab",&jBProbab_,"jBProbab/F");
outputTree->Branch("aDProbab",&aDProbab_,"aDProbab/F");
outputTree->Branch("xMin",&xMin_,"xMin/F");
outputTree->Branch("xMax",&xMax_,"xMax/F");
outputTree->Branch("nBin",&nBin_,"nBin/F");
bool histoBranches = false;
// Loop on the file list to extract each histogram 2D DQM histo with full noise distribution
TH1F* histoNoiseStrip = NULL;
TF1* fitFunc = NULL;
TH1F* randomHisto = NULL;
TFitResultPtr result;
for(auto file : fileList){
cout<<"input file: "<<file<<endl;
TFile* inputFile = TFile::Open(file.c_str(),"READ");
inputFile->cd();
// take into account that the DQM file structure for strips is always the same --> use cabling map to browse the histograms
reader.SetEntry(0);
TH2* histoNoise = NULL;
long int iChannel = 0;
int noFitResult = 0;
while(reader.Next()){
cout.flush();
if(iChannel %10 == 0) cout<<"\r"<<"iChannel "<<100*double(iChannel)/(readoutMap->GetEntries()/reductionFactor)<<" % ";
if(iChannel > double(readoutMap->GetEntries())/reductionFactor) break;
iChannel++;
TString objName;
uint32_t fedKey = SiStripFedKey(*fedId,SiStripFedKey::feUnit(*fedCh),SiStripFedKey::feChan(*fedCh)).key();
std::stringstream stream;
stream << std::hex << fedKey;
string fedKeyStr = stream.str();
if(fedKeyStr.size() == 4)
objName = Form("DQMData/SiStrip/ControlView/FecCrate%d/FecSlot%d/FecRing%d/CcuAddr%d/CcuChan%d/ExpertHisto_PedsFullNoise_FedKey0x0000%s_LldChannel%d_Noise2D",*fecCrate,*fecSlot,*fecRing,*ccuAdd,*ccuChan,fedKeyStr.c_str(),*lldChannel);
else if(fedKeyStr.size() == 5)
objName = Form("DQMData/SiStrip/ControlView/FecCrate%d/FecSlot%d/FecRing%d/CcuAddr%d/CcuChan%d/ExpertHisto_PedsFullNoise_FedKey0x000%s_LldChannel%d_Noise2D",*fecCrate,*fecSlot,*fecRing,*ccuAdd,*ccuChan,fedKeyStr.c_str(),*lldChannel);
else
cerr<<"hex number to short "<<fedKeyStr<<" --> please check "<<endl;
inputFile->GetObject(objName.Data(),histoNoise);
// extract single strip noise histogram --> loop on the y-axis
uint16_t apvID = 0;
uint16_t stripID = 0;
if(histoNoiseStrip == 0 or histoNoiseStrip == NULL){
histoNoiseStrip = new TH1F ("histoNoiseStrip","",histoNoise->GetNbinsX(),histoNoise->GetXaxis()->GetXmin(),histoNoise->GetXaxis()->GetXmax());
histoNoiseStrip->Sumw2();
}
for(int iBinY = 0; iBinY < histoNoise->GetNbinsY(); iBinY++){
histoNoiseStrip->Reset();
histoNoiseStrip->SetDirectory(0);
// two multiplexed APV per line
if(iBinY < histoNoise->GetNbinsY()/2) apvID = 1;
else apvID = 2;
// strip id
stripID++;
if(stripID > 128) stripID = 1;
// loop on x-axis bin
for(int iBinX = 0; iBinX < histoNoise->GetNbinsX(); iBinX++){
histoNoiseStrip->SetBinContent(iBinX+1,histoNoise->GetBinContent(iBinX+1,iBinY+1));
histoNoiseStrip->SetBinError(iBinX+1,histoNoise->GetBinError(iBinX+1,iBinY+1));
}
// to initialize branches
detid_ = 0; fedKey_ = 0; fecCrate_ = 0; fecSlot_ = 0; fecRing_ = 0; ccuAdd_ = 0; ccuChan_ = 0; lldChannel_ = 0; fedId_ = 0; fedCh_ = 0; apvId_ = 0; stripId_ = 0;
noiseMean_ = 0.; noiseRMS_ = 0.; noiseSkewness_ = 0.; noiseKurtosis_ = 0.;
fitGausMean_ = 0.; fitGausSigma_ = 0.;fitGausNormalization_ = 0.;
fitGausMeanError_ = 0.; fitGausSigmaError_ = 0.;fitGausNormalizationError_ = 0.;
fitChi2_ = 0.; fitChi2Probab_ = 0.; fitStatus_ = -1.;
noiseIntegral3Sigma_ = 0.; noiseIntegral3SigmaFromFit_ = 0.;
noiseIntegral4Sigma_ = 0.; noiseIntegral4SigmaFromFit_ = 0.;
noiseIntegral5Sigma_ = 0.; noiseIntegral5SigmaFromFit_ = 0.;
kSProbab_ = 0.; jBProbab_ = 0.;
kSValue_ = 0.; jBValue_ = 0.;
aDValue_= 0.; aDProbab_ = 0.;
nBin_ = 0.; xMin_ = 0.; xMax_ = 0.;
// basic info
detid_ = *detid;
fedKey_ = fedKey;
fecCrate_ = *fecCrate;
fecSlot_ = *fecSlot;
fecRing_ = *fecRing;
ccuAdd_ = *ccuAdd;
ccuChan_ = *ccuChan;
lldChannel_ = *lldChannel;
fedId_ = *fedId;
fedCh_ = *fedCh;
apvId_ = apvID;
stripId_ = stripID;
// basic info of nioise distribution
noiseMean_ = histoNoiseStrip->GetMean();
noiseRMS_ = histoNoiseStrip->GetRMS();
noiseSkewness_ = histoNoiseStrip->GetSkewness();
noiseKurtosis_ = histoNoiseStrip->GetKurtosis();
float integral = histoNoiseStrip->Integral();
noiseIntegral3Sigma_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+noiseRMS_*3),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-noiseRMS_*3)))/integral;
noiseIntegral4Sigma_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+noiseRMS_*4),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-noiseRMS_*4)))/integral;
noiseIntegral5Sigma_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+noiseRMS_*5),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-noiseRMS_*5)))/integral;
// make a gaussian fit
if(fitFunc == NULL or fitFunc == 0){
fitFunc = new TF1 ("fitFunc","gaus(0)",histoNoise->GetXaxis()->GetXmin(),histoNoise->GetXaxis()->GetXmax());
}
fitFunc->SetRange(histoNoise->GetXaxis()->GetXmin(),histoNoise->GetXaxis()->GetXmax());
fitFunc->SetParameters(histoNoiseStrip->Integral(),noiseMean_,noiseRMS_);
result = histoNoiseStrip->Fit(fitFunc,"QSR");
if(result.Get()){
fitStatus_ = result->Status();
fitGausNormalization_ = fitFunc->GetParameter(0);
fitGausMean_ = fitFunc->GetParameter(1);
fitGausSigma_ = fitFunc->GetParameter(2);
fitGausNormalizationError_ = fitFunc->GetParError(0);
fitGausMeanError_ = fitFunc->GetParError(1);
fitGausSigmaError_ = fitFunc->GetParError(2);
fitChi2_ = result->Chi2();
fitChi2Probab_ = result->Prob();
noiseIntegral3SigmaFromFit_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+fitGausSigma_*3),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-fitGausSigma_*3)))/histoNoiseStrip->Integral();
noiseIntegral4SigmaFromFit_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+fitGausSigma_*4),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-fitGausSigma_*4)))/histoNoiseStrip->Integral();
noiseIntegral5SigmaFromFit_ = (histoNoiseStrip->Integral(histoNoiseStrip->FindBin(noiseMean_+fitGausSigma_*5),histoNoiseStrip->GetNbinsX()+1) + histoNoiseStrip->Integral(0,histoNoiseStrip->FindBin(noiseMean_-fitGausSigma_*5)))/histoNoiseStrip->Integral();
jBValue_ = (histoNoiseStrip->Integral()/6)*(noiseSkewness_*noiseSkewness_+(noiseKurtosis_*noiseKurtosis_)/4);
jBProbab_ = ROOT::Math::chisquared_cdf_c(jBValue_,2);
if(randomHisto == 0 or randomHisto == NULL)
randomHisto = (TH1F*) histoNoiseStrip->Clone("randomHisto");
randomHisto->Reset();
randomHisto->SetDirectory(0);
if(integral != 0){
if(generateRandomDistribution){
randomHisto->FillRandom("fitFunc",histoNoiseStrip->Integral());
kSValue_ = histoNoiseStrip->KolmogorovTest(randomHisto,"MN");
kSProbab_ = histoNoiseStrip->KolmogorovTest(randomHisto,"N");
aDValue_ = histoNoiseStrip->AndersonDarlingTest(randomHisto,"T");
aDProbab_ = histoNoiseStrip->AndersonDarlingTest(randomHisto);
}
else{
randomHisto->Add(fitFunc);
kSValue_ = histoNoiseStrip->KolmogorovTest(randomHisto,"MN");
kSProbab_ = histoNoiseStrip->KolmogorovTest(randomHisto,"N");
// AD test
ROOT::Fit::BinData data1;
ROOT::Fit::BinData data2;
ROOT::Fit::FillData(data1,histoNoiseStrip,0);
data2.Initialize(randomHisto->GetNbinsX()+1,1);
for(int ibin = 0; ibin < randomHisto->GetNbinsX(); ibin++){
if(histoNoiseStrip->GetBinContent(ibin+1) != 0 or randomHisto->GetBinContent(ibin+1) >= 1)
data2.Add(randomHisto->GetBinCenter(ibin+1),randomHisto->GetBinContent(ibin+1),randomHisto->GetBinError(ibin+1));
}
double probab;
double value;
ROOT::Math::GoFTest::AndersonDarling2SamplesTest(data1,data2,probab,value);
aDValue_ = value;
aDProbab_ = probab;
}
}
}
else
noFitResult++;
if(not histoBranches){
noiseDistribution_.clear();
noiseDistributionError_.clear();
outputTree->Branch("noiseDistribution","vector<float>",&noiseDistribution_);
outputTree->Branch("noiseDistributionError","vector<float>",&noiseDistributionError_);
histoBranches = true;
}
// set histogram
noiseDistribution_.clear();
noiseDistributionError_.clear();
for(int iBin = 0; iBin < histoNoiseStrip->GetNbinsX(); iBin++){
noiseDistribution_.push_back(histoNoiseStrip->GetBinContent(iBin+1));
noiseDistributionError_.push_back(histoNoiseStrip->GetBinError(iBin+1));
}
nBin_ = histoNoiseStrip->GetNbinsX();
xMin_ = histoNoise->GetXaxis()->GetBinLowEdge(1);
xMax_ = histoNoise->GetXaxis()->GetBinLowEdge(histoNoise->GetNbinsX()+1);
// fill all branches for each strip
ouputTreeFile->cd();
outputTree->Fill();
}
}
inputFile->Close();
std::cout<<std::endl;
cout<<"No fit results found for "<<100*double(noFitResult)/iChannel<<endl;
}
outputTree->BuildIndex("detid");
outputTree->Write(outputTree->GetName(),TObject::kOverwrite);
ouputTreeFile->Close();
cablingFile->Close();
/* Do your stuff here */
cout<<"Time taken: "<<(double)(clock() - tStart)/CLOCKS_PER_SEC<<endl;
}