Example #1
0
TH1F * MakeOnePSE(TH1F * sig, int nsig, TH1F * back, int nback){

  //threw one data PSE  and fill it
  TH1F * data  = (TH1F*) sig ->Clone("data");
  data->Reset();
  data->FillRandom(sig,nsig);
  data->FillRandom(back,nback);

  return data;

}//MakeOnePSE
Example #2
0
vector<double> one_fit(bool do_fit)
{
    TH1F* h = new TH1F("h","h", 20, -10, 10);
    h->FillRandom("gaus");
    vector<double> ret;
    if (do_fit) {
        h->Fit("gaus","");
        TF1* g = h->GetFunction("gaus");
        h->Draw();
        canvas->Modified();
        canvas->Update();
        for (int ind=0; ind < 3; ++ind) {
            ret.push_back(g->GetParameter(ind));
        }
    }
    else {
        for (int ind=0; ind < 3; ++ind) {
            ret.push_back(0.0);
        }
    }
    
    ret.push_back(h->GetMean());
    ret.push_back(h->GetRMS());
    delete h;
    return ret;
}
Example #3
0
File: fitcont.C Project: Y--/root
void fitcont()
{
   //be sure default is Minuit since we will use gMinuit
   TVirtualFitter::SetDefaultFitter("Minuit");

   TCanvas *c1 = new TCanvas("c1");
   TH1F *h = new TH1F("h","My histogram",100,-3,3);
   h->FillRandom("gaus",6000);
   h->Fit("gaus");
   c1->Update();

   TCanvas *c2 = new TCanvas("c2","contours",10,10,600,800);
   c2->Divide(1,2);
   c2->cd(1);
   /*get first contour for parameter 1 versus parameter 2*/
   TGraph *gr12 = (TGraph*)gMinuit->Contour(40,1,2);
   gr12->Draw("alp");
   c2->cd(2);
   /*Get contour for parameter 0 versus parameter 2  for ERRDEF=2*/
   gMinuit->SetErrorDef(4); //note 4 and not 2!
   TGraph *gr2 = (TGraph*)gMinuit->Contour(80,0,2);
   gr2->SetFillColor(42);
   gr2->Draw("alf");
   /*Get contour for parameter 0 versus parameter 2 for ERRDEF=1*/
   gMinuit->SetErrorDef(1);
   TGraph *gr1 = (TGraph*)gMinuit->Contour(80,0,2);
   gr1->SetFillColor(38);
   gr1->Draw("lf");
}
Example #4
0
void transp()
{
   //1. Try to find free indices for our custom colors.
   //We can use hard-coded indices like 1001, 1002, 1003, ... but
   //I prefer to find free indices in a ROOT's color table
   //to avoid possible conflicts with other tutorials.
   Int_t indices[2] = {};
   if (ROOT::GLTutorials::FindFreeCustomColorIndices(indices) != 2) {
      Error("transp", "failed to create new custom colors");
      return;
   }
   
   //2. Now that we have indices, create our custom colors.
   const Int_t redIndex = indices[0], greeIndex = indices[1];
   
   new TColor(redIndex, 1., 0., 0., "red", 0.85);
   new TColor(greeIndex, 0., 1., 0., "green", 0.5);

   gStyle->SetCanvasPreferGL(kTRUE);
   TCanvas * const cnv = new TCanvas("trasnparency", "transparency demo", 600, 400);

   TH1F * hist = new TH1F("a5", "b5", 10, -2., 3.);
   TH1F * hist2 = new TH1F("c6", "d6", 10, -3., 3.);
   hist->FillRandom("landau", 100000);
   hist2->FillRandom("gaus", 100000);

   hist->SetFillColor(redIndex);
   hist2->SetFillColor(greeIndex);
   
   cnv->cd();
   hist2->Draw();
   hist->Draw("SAME");
}
Example #5
0
File: peaks.C Project: Y--/root
void peaks(Int_t np=10) {
   npeaks = TMath::Abs(np);
   TH1F *h = new TH1F("h","test",500,0,1000);
   //generate n peaks at random
   Double_t par[3000];
   par[0] = 0.8;
   par[1] = -0.6/1000;
   Int_t p;
   for (p=0;p<npeaks;p++) {
      par[3*p+2] = 1;
      par[3*p+3] = 10+gRandom->Rndm()*980;
      par[3*p+4] = 3+2*gRandom->Rndm();
   }
   TF1 *f = new TF1("f",fpeaks,0,1000,2+3*npeaks);
   f->SetNpx(1000);
   f->SetParameters(par);
   TCanvas *c1 = new TCanvas("c1","c1",10,10,1000,900);
   c1->Divide(1,2);
   c1->cd(1);
   h->FillRandom("f",200000);
   h->Draw();
   TH1F *h2 = (TH1F*)h->Clone("h2");
   //Use TSpectrum to find the peak candidates
   TSpectrum *s = new TSpectrum(2*npeaks);
   Int_t nfound = s->Search(h,2,"",0.10);
   printf("Found %d candidate peaks to fit\n",nfound);
   //Estimate background using TSpectrum::Background
   TH1 *hb = s->Background(h,20,"same");
   if (hb) c1->Update();
   if (np <0) return;

   //estimate linear background using a fitting method
   c1->cd(2);
   TF1 *fline = new TF1("fline","pol1",0,1000);
   h->Fit("fline","qn");
   //Loop on all found peaks. Eliminate peaks at the background level
   par[0] = fline->GetParameter(0);
   par[1] = fline->GetParameter(1);
   npeaks = 0;
   Double_t *xpeaks = s->GetPositionX();
   for (p=0;p<nfound;p++) {
      Double_t xp = xpeaks[p];
      Int_t bin = h->GetXaxis()->FindBin(xp);
      Double_t yp = h->GetBinContent(bin);
      if (yp-TMath::Sqrt(yp) < fline->Eval(xp)) continue;
      par[3*npeaks+2] = yp;
      par[3*npeaks+3] = xp;
      par[3*npeaks+4] = 3;
      npeaks++;
   }
   printf("Found %d useful peaks to fit\n",npeaks);
   printf("Now fitting: Be patient\n");
   TF1 *fit = new TF1("fit",fpeaks,0,1000,2+3*npeaks);
   //we may have more than the default 25 parameters
   TVirtualFitter::Fitter(h2,10+3*npeaks);
   fit->SetParameters(par);
   fit->SetNpx(1000);
   h2->Fit("fit");
}
Example #6
0
void rebin() {
   //create a fix bin histogram
   TH1F *h = new TH1F("h","test rebin",100,-3,3);
   Int_t nentries = 1000;
   h->FillRandom("gaus",nentries);
   Double_t xbins[1001];
   Int_t k=0;
   TAxis *axis = h->GetXaxis();
   for (Int_t i=1;i<=100;i++) {
      Int_t y = (Int_t)h->GetBinContent(i);
      if (y <=0) continue;
      Double_t dx = axis->GetBinWidth(i)/y;
      Double_t xmin = axis->GetBinLowEdge(i);
      for (Int_t j=0;j<y;j++) {
         xbins[k] = xmin +j*dx;
         k++;
      }
   }
   xbins[k] = axis->GetXmax();
   //create a variable bin-width histogram out of fix bin histogram
   //new rebinned histogram should have about 10 entries per bin
   TH1F *hnew = new TH1F("hnew","rebinned",k,xbins);
   hnew->FillRandom("gaus",10*nentries);

   //rebin hnew keeping only 50% of the bins
   Double_t xbins2[501];
   Int_t kk=0;
   for (Int_t j=0;j<k;j+=2) {
      xbins2[kk] = xbins[j];
      kk++;
   }
   xbins2[kk] = xbins[k];
   TH1F *hnew2 = (TH1F*)hnew->Rebin(kk,"hnew2",xbins2);

   //draw the 3 histograms
   TCanvas *c1 = new TCanvas("c1","c1",800,1000);
   c1->Divide(1,3);
   c1->cd(1);
   h->Draw();
   c1->cd(2);
   hnew->Draw();
   c1->cd(3);
   hnew2->Draw();
}
Example #7
0
File: exec3.C Project: MycrofD/root
void exec3()
{
   // Temporary work around the lack of automatic refresh of the list
   // when a script is reloaded.
   gROOT->GetListOfGlobalFunctions()->Delete();

   TH1F *h = new TH1F("h","h",100,-3,3);
   h->FillRandom("gaus",1000);
   TCanvas *c1=new TCanvas("c1");
   h->Draw();
   c1->Update();
   c1->Connect("ProcessedEvent(Int_t,Int_t,Int_t,TObject*)", 0, 0,
               "exec3event(Int_t,Int_t,Int_t,TObject*)");
}
Example #8
0
void pad2png()
{
   TCanvas *c = new TCanvas;
   TH1F *h = new TH1F("gaus", "gaus", 100, -5, 5);
   h->FillRandom("gaus", 10000);
   h->Draw();

   gSystem->ProcessEvents();

   TImage *img = TImage::Create();

   img->FromPad(c);

   img->WriteImage("canvas.png");
}
Example #9
0
void fill_local_ocdb()
{
  AliceO2::CDB::Manager* cdb = AliceO2::CDB::Manager::Instance();
  cdb->setDefaultStorage("local://O2CDB");
  TH1F* h = 0;
  for (int run = 2000; run < 2100; run++) {
    cdb->setRun(run);
    h = new TH1F(Form("histo for %d run", run), "gauss", 100, -5, 5);
    h->FillRandom("gaus", 1000);
    AliceO2::CDB::ConditionId* id = new AliceO2::CDB::ConditionId("DET/Calib/Histo", run, run, 1, 0);
    AliceO2::CDB::ConditionMetaData* md = new AliceO2::CDB::ConditionMetaData();
    cdb->putObject(h, *id, md);
    h = 0;
  }
}
Example #10
0
void exec3()
{
   // Example of using signal/slot in TCanvas/TPad to get feedback
   // about processed events. Note that slots can be either functions
   // or class methods. Compare this with tutorials exec1.C and exec2.C.
   //Author: Ilka Antcheva
   
   // Temporary work around the lack of automatic refresh of the list
   // when a script is reloaded.
   gROOT->GetListOfGlobalFunctions()->Delete();

   TH1F *h = new TH1F("h","h",100,-3,3);
   h->FillRandom("gaus",1000);
   TCanvas *c1=new TCanvas("c1");
   h->Draw();
   c1->Update();
   c1->Connect("ProcessedEvent(Int_t,Int_t,Int_t,TObject*)", 0, 0,
               "exec3event(Int_t,Int_t,Int_t,TObject*)");
}
Example #11
0
void lochanroot()

     {
                  char name[10], title[20];
                  TObjArray Hlist(0);       // create an array of Histograms
                  TH1F* g;                  // create a pointer to a histogram
  // make and fill 15 histograms and add them to the object array
          for (Int_t i = 0; i < 15; i++) {
                      sprintf(name,"lochan%d",i);
                      sprintf(title,"histo number:%d",i);
                      g = new TH1F(name,title,100,-4,4);
                      Hlist.Add(g); //changed from -> to .
                      g->FillRandom("gaus",1000);
                     }
  // open a file and write the array to the file
  TFile f("lochan.root","recreate");
  Hlist.Write(); //changed from -> to .
  f.Close();
}
Example #12
0
void customContextMenu()
{
   TH1F *h;
   TH1F *h2;
   TClassMenuItem *n;
   TList *l;

   // Create test histograms
   h = new TH1F("h","Schtroumpf",100,-4,4);
   h->FillRandom("gaus");
   h->Draw();

   h2 = new TH1F("h2","h2",1000,-4,4);
   h2->FillRandom("gaus",30000);

   // Retrieve menu list from TClass
   TClass *cl = h->IsA();
   l = cl->GetMenuList();

   // Add some items to the popup menus
   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "Test object, draw a second h","Draw",h2,"Option_t*");
   l->AddFirst(n);
   n = new TClassMenuItem(TClassMenuItem::kPopupSeparator,cl);
   l->AddFirst(n);

   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "test no 4","poptest4",0,"const char*");
   l->AddFirst(n);
   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "test no 3","poptest3",0,"");
   l->AddFirst(n);
   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "test no 2 bis","poptest2bis",0,"TObject*",2);
   l->AddFirst(n);
   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "test no 2","poptest2",0,"int,int,TObject*",2);
   l->AddFirst(n);
   n = new TClassMenuItem(TClassMenuItem::kPopupUserFunction,cl,
                          "test no 1","poptest1",0,"int,int");
   l->AddFirst(n);
}
Example #13
0
/*
   * From here on, the code can also be used as a macro
   * Note though, that CINT may report errors where there are none
     in C++. E.g. this happens here where CINT says that f1 is
     out of scope ...

     ==>>  put your code here
      (remember to update the name of you Macro in the
       lines above if you intend to comile the code)
                                                                     */
void ExampleMacro() {
  // Create a histogram, fill it with random gaussian numbers
  TH1F *h = new TH1F ("h", "example histogram", 100, -5.,5.);
  h->FillRandom("gaus",1000);

  // draw the histogram
  h->DrawClone();

/* - Create a new ROOT file for output
   - Note that this file may contain any kind of ROOT objects, histograms,
     pictures, graphics objects etc.
   - the new file is now becoming the current directory */
  TFile *f1 = new TFile("ExampleMacro.root","RECREATE","ExampleMacro");

  // write Histogram to current directory (i.e. the file just opened)
  h->Write();

  // Close the file.
  //   (You may inspect your histogram in the file using the TBrowser class)
  f1->Close();
}
void billtw(Int_t compress) {
   //write N histograms to a Tree
   timer.Start();
   TFile f("billt.root","recreate","bill benchmark with trees",compress);
   TH1F *h = new TH1F("h","h",1000,-3,3);
   h->FillRandom("gaus",50000);
   TTree *T = new TTree("T","test bill");
   T->Branch("event","TH1F",&h,64000,0);
   for (Int_t i=0;i<N;i++) {
      char name[20];
      sprintf(name,"h%d",i);
      h->SetName(name);
      h->Fill(2*gRandom->Rndm());
      T->Fill();
   }
   T->Write();
   delete T;
   timer.Stop();
   printf("billtw%d : RT=%7.3f s, Cpu=%7.3f s, File size= %9d bytes, CX= %g\n",compress,timer.RealTime(),timer.CpuTime(),
                    (Int_t)f.GetBytesWritten(),f.GetCompressionFactor());
}
Example #15
0
void transp()
{
   //1. Try to find free indices for our custom colors.
   //We can use hard-coded indices like 1001, 1002, 1003, ... but
   //I prefer to find free indices in a ROOT's color table
   //to avoid possible conflicts with other tutorials.
   Int_t indices[2] = {};
   if (ROOT::CocoaTutorials::FindFreeCustomColorIndices(indices) != 2) {
      Error("transp", "failed to create new custom colors");
      return;
   }
   
   //2. Now that we have indices, create our custom colors.
   const Int_t redIndex = indices[0], greeIndex = indices[1];
   
   new TColor(redIndex, 1., 0., 0., "red", 0.85);
   new TColor(greeIndex, 0., 1., 0., "green", 0.5);

   //3. Test that back-end is TGCocoa.
   TCanvas * const cnv = new TCanvas("trasnparency", "transparency demo", 600, 400);
   //After we created a canvas, gVirtualX in principle should be initialized
   //and we can check its type:
   if (gVirtualX && !gVirtualX->InheritsFrom("TGCocoa")) {
      Warning("transp", "You can see the transparency ONLY in a pdf or png output (\"File\"->\"Save As\" ->...)\n"
                        "To have transparency in a canvas graphics, you need MacOSX version with cocoa enabled");
   }

   TH1F * hist = new TH1F("a5", "b5", 10, -2., 3.);
   TH1F * hist2 = new TH1F("c6", "d6", 10, -3., 3.);
   hist->FillRandom("landau", 100000);
   hist2->FillRandom("gaus", 100000);

   hist->SetFillColor(redIndex);
   hist2->SetFillColor(greeIndex);
   
   cnv->cd();
   hist2->Draw();
   hist->Draw("SAME");
}
Example #16
0
void fitcont()
{
   // Example illustrating how to draw the n-sigma contour of a Minuit fit.
   // To get the n-sigma contour the ERRDEF parameter in Minuit has to set
   // to n^2. The fcn function has to be set before the routine is called.
   //
   // WARNING!!! This test works only with TMinuit
   //
   // The TGraph object is created via the interpreter. The user must cast it
   // to a TGraph*
   // Author:  Rene Brun

   //be sure default is Minuit since we will use gMinuit 
   TVirtualFitter::SetDefaultFitter("Minuit");  
      
   TCanvas *c1 = new TCanvas("c1");
   TH1F *h = new TH1F("h","My histogram",100,-3,3);
   h->FillRandom("gaus",6000);
   h->Fit("gaus");
   c1->Update();
   
   TCanvas *c2 = new TCanvas("c2","contours",10,10,600,800);
   c2->Divide(1,2);
   c2->cd(1);
   //get first contour for parameter 1 versus parameter 2
   TGraph *gr12 = (TGraph*)gMinuit->Contour(40,1,2);
   gr12->Draw("alp");
   c2->cd(2);
   //Get contour for parameter 0 versus parameter 2  for ERRDEF=2 
   gMinuit->SetErrorDef(4); //note 4 and not 2!
   TGraph *gr2 = (TGraph*)gMinuit->Contour(80,0,2);
   gr2->SetFillColor(42);
   gr2->Draw("alf");
   //Get contour for parameter 0 versus parameter 2 for ERRDEF=1  
   gMinuit->SetErrorDef(1);
   TGraph *gr1 = (TGraph*)gMinuit->Contour(80,0,2);
   gr1->SetFillColor(38);
   gr1->Draw("lf");
}
Example #17
0
void pad2png()
{
   // Create a canvas and save as png.
   //Author: Valeriy Onuchin

   TCanvas *c = new TCanvas;
   TH1F *h = new TH1F("gaus", "gaus", 100, -5, 5);
   h->FillRandom("gaus", 10000);
   h->Draw();

   gSystem->ProcessEvents();

   TImage *img = TImage::Create();

   //img->FromPad(c, 10, 10, 300, 200);
   img->FromPad(c);

   img->WriteImage("canvas.png");

   delete h;
   delete c;
   delete img;
}
Example #18
0
void fitExclude() {
   //Create a source function
   TF1 *f1 = new TF1("f1","[0] +[1]*x +gaus(2)",0,5);
   f1->SetParameters(6,-1,5,3,0.2);
   // create and fill histogram according to the source function
   TH1F *h = new TH1F("h","background + signal",100,0,5);
   h->FillRandom("f1",2000);
   TF1 *fl = new TF1("fl",fline,0,5,2);
   fl->SetParameters(2,-1);
   //fit only the linear background excluding the signal area
   reject = kTRUE;
   h->Fit(fl,"0");
   reject = kFALSE;
   //store 2 separate functions for visualization
   TF1 *fleft = new TF1("fleft",fline,0,2.5,2);
   fleft->SetParameters(fl->GetParameters());
   h->GetListOfFunctions()->Add(fleft);
   gROOT->GetListOfFunctions()->Remove(fleft);
   TF1 *fright = new TF1("fright",fline,3.5,5,2);
   fright->SetParameters(fl->GetParameters());
   h->GetListOfFunctions()->Add(fright);
   gROOT->GetListOfFunctions()->Remove(fright);
   h->Draw();
}
Example #19
0
//___________________________________________________________________________
Double_t* IfitBin(TH1F* dataInput, TH1F* sigTemplate, TH1F* bkgTemplate, 
	       int fit_data=1)
{

  cout << "Input files are " << dataInput->GetName() << "\t" << sigTemplate->GetName() << "\t" << bkgTemplate->GetName() << endl;

  TCanvas *c1 = new TCanvas("HF1", "Histos1", 0, 0, 600, 600);
  dataCollBin.clear();
  sigCollBin.clear();
  bkgCollBin.clear();

  Double_t* fitted = new Double_t[8];
  fitted[0] = fitted[1] = fitted[2] = fitted[3] = fitted[4] = fitted[5] = fitted[6] = fitted[7] = 0.0;

  TH1F *hsum = new TH1F();
  float ntemplate = 1.;
  float sigfrac = 0.1;
  TH1F *hsum_norm = new TH1F();
  TH1F *hdata;
  TH1F *hsig  = (TH1F*)sigTemplate->Clone();
  hsig->SetName("hsig");
  hsig->Rebin(6);

  TH1F *hbkg  = (TH1F*)bkgTemplate->Clone();
  hbkg->SetName("hbkg");
  hbkg->Rebin(6);

  float ndata=0;
  if ( fit_data>0 ) {
    hdata = (TH1F*)dataInput->Clone();
    hdata -> SetName("hdata");
    ndata = hdata->Integral();
  }else {
    hsum = (TH1F*)hsig->Clone();
    hsum->Add(hbkg,1);
    cout << "For histogram " << sigTemplate->GetName() << " and " << bkgTemplate->GetName() << " sum = " << 
      hsum->Integral() << endl;

    if (hsum->Integral()>1.) ntemplate = hsum->Integral();
    sigfrac = hsig->Integral()/ntemplate;

    hsum_norm = (TH1F*)hsum->Clone();  
    hsum_norm->Scale(1./hsum->Integral());

    hdata = (TH1F*)hsum_norm->Clone();
    hdata -> SetName("hdata");
    ndata=ntemplate;
    hdata->FillRandom(hsum_norm, ndata);
  }
  if(ndata==0) {
    printf(" ---  no events in the fit \n");
    return fitted;
  }
    
  printf(" --------- before the fit ------------- \n");
  printf("Nsig %2.3f, Nbg %2.3f, Ntemplate %3.3f \n", hsig->Integral(), hbkg->Integral(), ntemplate);
//   printf("Purity %2.3f, init size %4.3f,  test sample size %4d\n", hsig->Integral()/hsum->Integral(), hsum->Integral(), ndata);
  printf(" -------------------------------------- \n");

  hdata->Rebin(6);
  int nbins = hdata->GetNbinsX();

  hsig->Scale(1./hsig->Integral());
  hbkg->Scale(1./hbkg->Integral());  

  for (int ibin=1; ibin<=nbins; ibin++) {
    dataCollBin.push_back(hdata->GetBinContent(ibin));
    sigCollBin.push_back(hsig->GetBinContent(ibin));
    bkgCollBin.push_back(hbkg->GetBinContent(ibin));    
  }
  printf( " -----  Got %d, %d, %d events for fit ----- \n ", dataCollBin.size(),
	  sigCollBin.size(), bkgCollBin.size() );  
  if ( dataCollBin.size() != sigCollBin.size() || sigCollBin.size()!=bkgCollBin.size() ) {
    printf(" error ...  inconsistent hit collection size \n");
    return fitted;
  }

  //--------------------------------------------------
  //init parameters for fit
  Double_t vstart[10] = {1., 1.};
  vstart[0] = sigfrac*ndata;
  vstart[1] = (1-sigfrac)*ndata;
 
  TMinuit *gMinuit = new TMinuit(NPARBIN);  
  gMinuit->Command("SET STR 1");
  gMinuit->SetFCN(fcnBin);
  Double_t arglist[10];
  Int_t ierflg = 0;
  
  arglist[0] = 1;
  gMinuit->mnexcm("SET ERR", arglist ,1,ierflg);
  arglist[0] = 1;
  gMinuit->mnexcm("SET PRINT", arglist ,1,ierflg);

  Double_t step[] = { 0.1, 0.1,};

  gMinuit->mnparm(0,  "Signal yield"  , vstart[0],  step[0], 0., ndata*2.  , ierflg);
  gMinuit->mnparm(1,  "background yield"  , vstart[1],  step[1], 0., ndata*2. , ierflg);
  
  printf(" --------------------------------------------------------- \n");
  printf(" Now ready for minimization step \n --------------------------------------------------------- \n");
  
  arglist[0] = 2000; // number of iteration
  arglist[1] = 1.;
  gMinuit->mnexcm("MIGRAD", arglist ,2,ierflg);
  printf (" -------------------------------------------- \n");
  printf("Finished.  ierr = %d \n", ierflg);

  infoBin.clear();
  infoBin_err.clear();

  double para[NPARBIN+1],errpara[NPARBIN+1];
  if ( ierflg == 0 ) 
    {
      for(int j=0; j<=NPARBIN-1;j++) {
        gMinuit->GetParameter(j, para[j],errpara[j]);
        para[NPARBIN] = dataCollBin.size();
        infoBin.push_back(para[j]);
        infoBin_err.push_back(errpara[j]);
        printf("Parameter (yeild) %d = %f +- %f\n",j,para[j],errpara[j]);
	
      }
      printf(" fitted yield %2.3f \n", (para[0]+para[1])/ndata );
      infoBin.push_back(sigCollBin.size());

    }
  else {
    printf(" *********** Fit failed! ************\n");
    gMinuit->GetParameter(0, para[0],errpara[0]);
    gMinuit->GetParameter(1, para[1],errpara[1]);
    para[0]=0.; errpara[0]=0.;
  }

  
  // Print results
  Double_t amin,edm,errdef;
  Int_t nvpar,nparx,icstat;
  gMinuit->mnstat(amin,edm,errdef,nvpar,nparx,icstat);
  gMinuit->mnprin(1,amin);  
  gMinuit->mnmatu(1);
  printf(" ========= happy ending !? =========================== \n");
  
  printf("FCN =  %3.3f \n", amin);

  double yerr[20];
  for(int i=0;i<20;i++){
    yerr[i] = 0.;
  }

  hsig->Scale(para[0]);
  hbkg->Scale(para[1]);
  TH1F *hfit = (TH1F*)hsig->Clone();
  hfit->Add(hbkg);


  hsig->SetLineColor(1);
  hsig->SetFillColor(5);
  hsig->SetFillStyle(3001);

  hbkg->SetLineWidth(2);
  // plot
  c1->Draw();  
  //gPad->SetLogy();
  hdata->SetLineColor(1);
  hdata->SetNdivisions(505,"XY");
  hdata->SetXTitle("Iso_{ECAL}+Iso_{HCAL}+Iso_{TRK} (GeV)");
  hdata->SetYTitle("Entries");
  hdata->SetTitle("");
  hdata->SetMarkerStyle(8);
  hdata->SetMinimum(0.);
  hdata->SetMaximum(hdata->GetMaximum()*1.5);
  hdata->Draw("p e");
  hsig->Draw("hist same");
  hbkg->SetMarkerStyle(0);
  hbkg->SetFillColor(8);
  hbkg->SetLineWidth(1);
  hbkg->SetFillStyle(3013);
  hbkg->SetError(yerr);
  hbkg->Draw("hist same");
  hfit->SetMarkerStyle(0);
  hfit->SetLineColor(1);
  hfit->SetLineWidth(2);
  hfit->SetError(yerr);
  hfit->Draw("hist same");

  double chi2ForThisBin=0;
  int nbinForThisBin=0;
  chi2NbinsHisto(hfit, hdata, chi2ForThisBin, nbinForThisBin);
  TPaveText *pavetex = new TPaveText(0.43, 0.87, 0.90, 0.92,"NDCBR");
  pavetex->SetBorderSize(0);
  pavetex->SetFillColor(0);
  pavetex->SetFillStyle(0);
  pavetex->SetLineWidth(3);
  pavetex->SetTextAlign(12);
  pavetex->SetTextSize(0.03);
  pavetex->AddText(Form("#chi^{2}/NDF=%.1f/%d",chi2ForThisBin, nbinForThisBin));
  pavetex->Draw();


  char text[1000];
  TLegend *tleg = new TLegend(0.43, 0.60, 0.90, 0.87);
  tleg->SetHeader(dataInput->GetTitle());

  tleg->SetTextSize(0.03);
  tleg->SetFillColor(0);
  tleg->SetShadowColor(0);
  tleg->SetBorderSize(0);
  sprintf(text,"Data %5.1f events",hdata->Integral());
  tleg->AddEntry(hdata,text,"pl");
  sprintf(text,"Fitted %5.1f events",hfit->Integral());
  tleg->AddEntry(hfit,text,"l");
  sprintf(text,"SIG %5.1f #pm %5.1f events",para[0], errpara[0]);
  tleg->AddEntry(hsig,text,"f");
  sprintf(text,"BKG %5.1f #pm %5.1f events",para[1], errpara[1]);
  tleg->AddEntry(hbkg,text,"f");
  tleg->Draw();

  gPad->RedrawAxis();


  cout << dataInput->GetName() << endl;
  char fname[300];
  sprintf(fname,"plots/Ifit_%s.eps",dataInput->GetName());
  c1->SaveAs(fname);
  sprintf(fname,"plots/Ifit_%s.gif",dataInput->GetName());
  c1->SaveAs(fname);

  printf("----- fit results with signal projection   ----------- \n");

  //   ftemplate->Close();
  
  int purityMaxBin = hsig->FindBin(5.0)-1;
  Double_t scale_signal = hsig->Integral(1,purityMaxBin)/hsig->Integral();
  Double_t scale_background = hbkg->Integral(1,purityMaxBin)/hbkg->Integral();
  
  fitted[0] = para[0];
  fitted[1] = errpara[0];
  fitted[2] = para[1];
  fitted[3] = errpara[1];

  // for integral up to 5 GeV
  fitted[4] = para[0]*scale_signal;
  fitted[5] = errpara[0]*scale_signal;
  fitted[6] = para[1]*scale_background;
  fitted[7] = errpara[1]*scale_background;

  return fitted;
}
void compareCorrRCSpike(){
	

  char tmp[1000];
  setTDRStyle();
  // settings for purity TGraphAsymmetryErrors
  Double_t fBinsPtMidPoint[nPtBin]={0};
  Double_t fBinsPtError[nPtBin]={0};
  
  for(int ipt=0; ipt < nPtBin; ipt++)
    {
      fBinsPtMidPoint[ipt] = 0.5*(fBinsPt[ipt+1]+fBinsPt[ipt]);
      fBinsPtError[ipt] = 0.5*(fBinsPt[ipt+1]-fBinsPt[ipt]);
    }


  TH1F* hTemplate_S[nEtaBin][nPtBin];
  TH1F* hdata_S[nEtaBin][nPtBin];

  
  std::string dataFile     = "SBDataTemplate_131511_139239.root";
  std::string templateFile = "spike_131511_139239.root";

  TFile* inf_data = new TFile(dataFile.data());
  TFile* inf_template = new TFile(templateFile.data());

  
  char* dec[2] = {"EB","EE"};
  for(int ieta=0; ieta<1; ieta++){
    for(int ipt=0; ipt < nPtBin; ipt++){

      // getting histograms from data root file

      sprintf(tmp,"h_%s_comb3Iso_sig_pt%d",dec[ieta],(int)fBinsPt[ipt]);
      cout << "looking for histogram " << tmp << " in file " << 
	inf_data->GetName() << endl;
      hdata_S[ieta][ipt] = (TH1F*)inf_data->FindObjectAny(tmp);
      hdata_S[ieta][ipt]->SetName(Form("hdata_S_%d_%d",ieta,ipt));
      hdata_S[ieta][ipt]->Rebin(REBINNINGS);

      // getting histogram for template root file
      sprintf(tmp,"h_EB_comb3Iso_EGdata_SIG");
      cout << "looking for histogram " << tmp << " in file " << 
	inf_template->GetName() << endl;
      hTemplate_S[ieta][ipt] = (TH1F*)inf_template->FindObjectAny(tmp);
      hTemplate_S[ieta][ipt]->SetName(Form("hTemplate_S_%d_%d",ieta,ipt));
      hTemplate_S[ieta][ipt]->Rebin(REBINNINGS);

    }
  }


  TH1F* hfit_sig;
  TH1F* hfit_spike;

  TH1F* hsig = new TH1F("hsig","",120,-1,11);
  hsig->SetXTitle("Iso (GeV)");
  hsig->SetYTitle("A.U.");
  hsig->GetYaxis()->SetDecimals();
  hsig->SetYTitle("Iso (GeV)");

  hsig->SetLineColor(2);
  TH1F* hspike = new TH1F("hspike","",120,-1,11);
  hspike->SetLineColor(4);

  TCanvas* c1 = new TCanvas("c1","",500,500);

  for(int ieta=0; ieta< 1; ieta++){
    for(int ipt=0; ipt < nPtBin; ipt++){


      hsig->Reset();
      hspike->Reset();

      // first obtain fit for the random cone corrected values
      hfit_sig  = (TH1F*)hdata_S[ieta][ipt]->Clone();
      hfit_sig  -> SetName("hfit_sig");
      hfit_sig  -> Rebin(4);

      double sigPar[20] = {hfit_sig->GetMaximum(), 1., 0.5, 0.3,
			   1.,-.3,-1., 0.01, 0.5, 0.01, 1., 1.};

      TF1 *fsig = new TF1("fsig", exp_conv, -1., 11., 12);
      fsig->SetParameters(sigPar);
      fsig->SetNpx(2500);
      fsig->SetLineColor(2);
      hfit_sig->Fit(fsig,"","",-1,5.0);

      c1->Print(Form("hfit_sig_pt%d.gif",(int)fBinsPt[ipt]));
      
      fsig->SetParameter(0,2);
      fsig->SetParameter(1,fsig->GetParameter(1)*8.39614e-01/6.83080e-01);//correction from RC
      fsig->SetParameter(2,4.83182e-01);
      fsig->SetParameter(3,fsig->GetParameter(3)*2.33769e-01/2.26323e-01);
    
      cout << "fsig Integral = " << fsig->Integral(-1,11) << endl;
      for(int i=0;i<4;i++)
	cout << "fsig par " << i << "= " << fsig->GetParameter(i) << endl;

      // second obtain fit for the spikes
      hfit_spike  = (TH1F*)hTemplate_S[ieta][ipt]->Clone();
      hfit_spike  -> SetName("hfit_spike");
      hfit_spike  -> Rebin(4);

      TF1 *fspike = new TF1("fspike", exp_conv, -1., 11., 12);
      fspike->SetParameters(sigPar);
      fspike->SetParameter(0,hfit_spike->GetMaximum());
      fspike->SetLineColor(4);
      fspike->SetNpx(2500);
      hfit_spike->Fit(fspike,"","",-1,5.0);

      c1->Print(Form("hfit_spike_pt%d.gif",(int)fBinsPt[ipt]));
      
      fspike->SetParameter(0,2.0);
      cout << "fspike Integral = " << fspike->Integral(-1,11) << endl;
      float scale = (float)fsig->Integral(-1,11)/(float)fspike->Integral(-1,11);
      fspike->SetParameter(0,2.0*scale);
      cout << "now fspike Integral = " << fspike->Integral(-1,11) << endl;

      for(int i=0;i<4;i++)
	cout << "fspike par " << i << "= " << fspike->GetParameter(i) << endl;

    
      hsig->FillRandom("fsig",10000);
      hspike->FillRandom("fspike",10000);

      cout << "KS test for sideband data and sideband MC = " << 
	hsig->KolmogorovTest(hspike,"X") << endl;
      cout << "KS test 2 for sideband data and sideband MC = " << 
	hsig->KolmogorovTest(hspike) << endl;
     
      hsig->Reset();
      hsig->SetMaximum(1.2);
      hsig->Draw();
//       hspike->Draw("same");
      fsig->Draw("same");
      fspike->Draw("same");

      TLegend* leg = new TLegend(0.35,0.70,0.55,0.85);
      leg->SetHeader(Form("p_{T}[%d,%d]",(int)fBinsPt[ipt],(int)fBinsPt[ipt+1]));
      leg->SetFillColor(0);
      leg->SetFillStyle(0);
      leg->SetTextSize(0.04);
      leg->SetBorderSize(0);
      leg->AddEntry(fsig,"Random-cone-corrected MC","f");
      leg->AddEntry(fspike,"Spike","f");   
      leg->Draw("same");
      
      c1->Print(Form("CorrRCSpike_pt%d.eps",(int)fBinsPt[ipt]));
      c1->Print(Form("CorrRCSpike_pt%d.gif",(int)fBinsPt[ipt]));
      c1->Print(Form("CorrRCSpike_pt%d.C",(int)fBinsPt[ipt]));
      
    } 
  }

    
   

}
Example #21
0
void quantiles() {
   // demo for quantiles
   // Authors: Rene Brun, Eddy Offermann
   
   const Int_t nq = 10000;
   const Int_t nshots = 10000;
   Double_t xq[nq];  // position where to compute the quantiles in [0,1]
   Double_t yq[nq];  // array to contain the quantiles
   for (Int_t i=0;i<nq;i++) xq[i] = Float_t(i+1)/nq;

   TGraph *gr70 = new TGraph(nshots);
   TGraph *gr90 = new TGraph(nshots);
   TGraph *gr98 = new TGraph(nshots);
   TH1F *h = new TH1F("h","demo quantiles",500,-4,4);
   
   for (Int_t shot=0;shot<nshots;shot++) {
      h->FillRandom("gaus",50);
      h->GetQuantiles(nq,yq,xq);
      gr70->SetPoint(shot,shot+1,yq[70]);
      gr90->SetPoint(shot,shot+1,yq[90]);
      gr98->SetPoint(shot,shot+1,yq[98]);
   }
   
   //show the original histogram in the top pad
   TCanvas *c1 = new TCanvas("c1","demo quantiles",10,10,600,900);
   c1->SetFillColor(41);
   c1->Divide(1,3);
   c1->cd(1);
   h->SetFillColor(38);
   h->Draw();
   
   // show the final quantiles in the middle pad
   c1->cd(2);
   gPad->SetFrameFillColor(33);
   gPad->SetGrid();
   TGraph *gr = new TGraph(nq,xq,yq);
   gr->SetTitle("final quantiles");
   gr->SetMarkerStyle(21);
   gr->SetMarkerColor(kRed);
   gr->SetMarkerSize(0.3);
   gr->Draw("ap");
   
   // show the evolution of some  quantiles in the bottom pad
   c1->cd(3);
   gPad->SetFrameFillColor(17);
   gPad->DrawFrame(0,0,nshots+1,3.2);
   gPad->SetGrid();
   gr98->SetMarkerStyle(22);
   gr98->SetMarkerColor(kRed);
   gr98->Draw("lp");
   gr90->SetMarkerStyle(21);
   gr90->SetMarkerColor(kBlue);
   gr90->Draw("lp");
   gr70->SetMarkerStyle(20);
   gr70->SetMarkerColor(kMagenta);
   gr70->Draw("lp");
   // add a legend
   TLegend *legend = new TLegend(0.85,0.74,0.95,0.95);
   legend->SetTextFont(72);
   legend->SetTextSize(0.05);
   legend->AddEntry(gr98," q98","lp");
   legend->AddEntry(gr90," q90","lp");
   legend->AddEntry(gr70," q70","lp");
   legend->Draw();
}
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;  
}