-#include "TMath.h"\r
-#include "TList.h"\r
-#include "TH2F.h"\r
-#include "TH1F.h"\r
-#include "TF1.h"\r
-#include "TH1D.h"\r
-#include "TProfile.h"\r
-#include "TFile.h"\r
-#include "TCanvas.h"\r
-#include <Riostream.h>\r
-#include <TROOT.h>\r
-#include <TString.h>\r
-#include "TStyle.h"\r
-#include "TGrid.h"\r
- \r
- \r
-class TList;\r
-class TH2F;\r
-class TH1F;\r
-class TH1D;\r
-class TProfile;\r
-class TFile;\r
-class TCanvas;\r
-class TStyle;\r
-class TF1;\r
-class TGrid;\r
-\r
-\r
-\r
-\r
-\r
-Double_t convolution(Double_t* x,Double_t *par)\r
-{\r
- //Fit parameters:\r
- //par[0]=Width (scale) parameter of Landau density\r
- //par[1]=Most Probable (MP, location) parameter of Landau density\r
- //par[2]=Total area (integral -inf to inf, normalization constant)\r
- //par[3]=Width (sigma) of convoluted Gaussian function\r
- //\r
- //In the Landau distribution (represented by the CERNLIB approximation),\r
- //the maximum is located at x=-0.22278298 with the location parameter=0.\r
- //This shift is corrected within this function, so that the actual\r
- //maximum is identical to the MP parameter.\r
-\r
- // Numeric constants\r
- Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)\r
- Double_t mpshift = -0.22278298; // Landau maximum location\r
-\r
- // Control constants\r
- Double_t np = 200.0; // number of convolution steps\r
- Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas\r
-\r
- // Variables\r
- Double_t xx;\r
- Double_t mpc;\r
- Double_t fland;\r
- Double_t sum = 0.0;\r
- Double_t xlow,xupp;\r
- Double_t step;\r
- Double_t i;\r
-\r
-\r
- // MP shift correction\r
- mpc = par[1] - mpshift * par[0];\r
-\r
- // Range of convolution integral\r
- xlow = x[0] - sc * par[3];\r
- xupp = x[0] + sc * par[3];\r
-\r
- step = (xupp-xlow) / np;\r
-\r
- // Convolution integral of Landau and Gaussian by sum\r
- for(i=1.0; i<=np/2; i++) \r
- {\r
- xx = xlow + (i-.5) * step;\r
- fland = TMath::Landau(xx,mpc,par[0]) / par[0];\r
- sum += fland * TMath::Gaus(x[0],xx,par[3]);\r
-\r
- xx = xupp - (i-.5) * step;\r
- fland = TMath::Landau(xx,mpc,par[0]) / par[0];\r
- sum += fland * TMath::Gaus(x[0],xx,par[3]);\r
- }\r
-\r
- return (par[2] * step * sum * invsq2pi / par[3]);\r
-}\r
-\r
-\r
-\r
-\r
-void GetGainModuleLevel(TString filename,Bool_t normal=1,Int_t ntofit=500, Bool_t grid=0)\r
-{\r
- gROOT->SetStyle("Plain");\r
- gStyle->SetPalette(1,0);\r
- gStyle->SetOptStat(111111);\r
- gStyle->SetOptFit(1); \r
- if(grid)\r
- TGrid::Connect("alien://");\r
- \r
- TFile* file_data=TFile::Open(filename);\r
- if(!file_data)\r
- return;\r
- TList *listin=0x0;\r
- listin=(TList*)file_data->Get("output");\r
- if(!listin)\r
- listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/output");\r
- if(!listin) \r
- listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/SSDdEdxQA");\r
- if(!listin) \r
- listin=(TList*)file_data->Get("SSDdEdxQA");\r
- if(!listin) \r
- return;\r
- TH2F* fHistQ=0x0;\r
- if (normal) \r
- fHistQ=(TH2F*)listin ->FindObject("QACharge");\r
- else\r
- fHistQ=(TH2F*)listin ->FindObject("QAChargeCorrected");\r
- if(!fHistQ)\r
- return;\r
- TH2F* fHistCR=(TH2F*)listin ->FindObject("QAChargeRatio");\r
- if(!fHistCR)\r
- return;\r
- TList *listout1=new TList();\r
- \r
- TList *listout2=new TList();\r
- \r
- TH1F* fHistMPVs=new TH1F("HistMPVS","HistMPVs;MPV;N",75,70,95);\r
- fHistMPVs->SetDirectory(0);\r
- listout2->Add(fHistMPVs);\r
- \r
- TH1F* fHistSL=new TH1F("HistSL","#sigma_{Landau};#sigma_{Landau};N",40,0,16);\r
- fHistSL->SetDirectory(0);\r
- listout2->Add(fHistSL);\r
- \r
- TH1F* fHistSG=new TH1F("HistSG","#sigma_{Gaus};#sigma_{Gaus};N",40,0,16);\r
- fHistSG->SetDirectory(0);\r
- listout2->Add(fHistSG);\r
- \r
- TH1F* fHistCRmean=new TH1F("HistCRmean","HistCRmean;CRmean;N",200,-1,1);\r
- fHistCRmean->SetDirectory(0);\r
- listout2->Add(fHistCRmean);\r
- \r
- TH1F* fHistCRRMS=new TH1F("HistCRRMS","HistCRRMS;CRRMS;N",100,0,1);\r
- fHistCRRMS->SetDirectory(0);\r
- listout2->Add(fHistCRRMS);\r
- \r
- TH1F* fHistGainP=new TH1F("HistGainP","HistGainP;CRGainPcorr;N",120,0.5,2.0);\r
- fHistGainP->SetDirectory(0);\r
- listout2->Add(fHistGainP);\r
- \r
- TH1F* fHistGainN=new TH1F("HistGainN","HistGainN;CRGainNcorr;N",120,0.5,2.0);\r
- fHistGainN->SetDirectory(0);\r
- listout2->Add(fHistGainN);\r
- \r
- TH1F *fMPVGraph = new TH1F("MPVgraph","MPVgraph;Module number;MPV",1698,-0.5,1697.5);\r
- fMPVGraph->SetMarkerColor(kRed);\r
- fMPVGraph->SetMarkerStyle(22);\r
- listout2->Add(fMPVGraph);\r
- \r
- TH1F *fCRmeanGraph = new TH1F("CRmeangraph","CRmeangraph;Module number;MPV",1698,-0.5,1697.5);\r
- fCRmeanGraph->SetMarkerColor(kBlue);\r
- fCRmeanGraph->SetMarkerStyle(23);\r
- listout2->Add(fCRmeanGraph);\r
- \r
- Float_t gainP[1698];\r
- Float_t gainN[1698];\r
- Float_t mpv[1698];\r
- Int_t flag[1698]; \r
- \r
- ofstream outfiletxt;\r
- outfiletxt.open("gain.txt");\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<"MODULE"<<"\t";\r
- outfiletxt.width(10);\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<"FLAG"<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<"GainPcorr"<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<"GainNcorr"<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<"MPV"<<endl;\r
- \r
- \r
- ofstream outfiletxtbad;\r
- outfiletxtbad.open("badModules.txt");\r
- \r
- \r
- \r
- \r
- for (int i =0;i<1698;i++)\r
- {\r
- cout<<i<<endl;\r
- TString tmpQ("Q");\r
- tmpQ+=i;\r
- TString tmpCR("CR");\r
- tmpCR+=i;\r
- TH1D* fHist1DCR= fHistCR->ProjectionY(tmpCR,i+1,i+1);\r
- Double_t mean=fHist1DCR->GetMean();\r
- if(!(TMath::Abs(mean)<1.0)||fHist1DCR->GetEntries()<10)\r
- { \r
- flag[i]=-2;\r
- gainN[i]=1.0;\r
- gainP[i]=1.0;\r
- mpv[i]=1.0;\r
- continue;\r
- }\r
- fHistCRmean->Fill(mean);\r
- fHistCRRMS->Fill(fHist1DCR->GetRMS());\r
- gainN[i]=1.0/(1.0+mean);\r
- gainP[i]=1.0/(1.0-mean);\r
- fHistGainP->Fill(gainP[i]);\r
- fHistGainN->Fill(gainN[i]);\r
- fCRmeanGraph->SetBinContent(i+1,mean);\r
- fCRmeanGraph->SetBinError(i+1,fHist1DCR->GetRMS());\r
- \r
- \r
- TH1D* fHist1DQ=fHistQ->ProjectionY(tmpQ,i+1,i+1);\r
- fHist1DQ->SetDirectory(0);\r
- listout1->Add(fHist1DQ);\r
- if(fHist1DQ->GetEntries()<ntofit)\r
- {\r
- flag[i]=-1;\r
- mpv[i]=1.0;\r
- outfiletxtbad<<"Low statistic \t module= "<<i<<" netries="<<fHist1DQ->GetEntries()<<endl;\r
- continue;\r
- }\r
- else\r
- {\r
- tmpQ+="fit";\r
- Float_t range=fHist1DQ->GetBinCenter(fHist1DQ->GetMaximumBin());\r
- TF1 *f1 = new TF1(tmpQ,convolution,range*0.45,range*3.0,4);\r
- f1->SetParameters(7.0,range,1.0,5.5);\r
- Float_t normalization=fHist1DQ->GetEntries()*fHist1DQ->GetXaxis()->GetBinWidth(2)/f1->Integral(range*0.45,range*3.0);\r
- f1->SetParameters(7.0,range,normalization,5.5);\r
- //f1->SetParameters(7.0,range,fHist1DQ->GetMaximum(),5.5);\r
- f1->SetParNames("sigma Landau","MPV","N","sigma Gaus");\r
- f1->SetParLimits(0,2.0,100.0);\r
- f1->SetParLimits(3,0.0,100.0);\r
- if(fHist1DQ->Fit(tmpQ,"BRQ")==0)\r
- {\r
- mpv[i]=f1->GetParameter(1);\r
- fHistMPVs->Fill(mpv[i]);\r
- fHistSL->Fill(f1->GetParameter(0));\r
- fHistSG->Fill(f1->GetParameter(3));\r
- flag[i]=1; \r
- fMPVGraph->SetBinContent(i+1,f1->GetParameter(1));\r
- fMPVGraph->SetBinError(i+1,f1->GetParError(1));\r
- if(mpv[i]<75.0)\r
- {\r
- outfiletxtbad<<"MPV lower than 75 \t module="<<i<<endl;\r
- flag[i]=0;\r
- } \r
- if(mpv[i]>100.0)\r
- {\r
- outfiletxtbad<<"MPV higher than 100 \t module="<<i<<endl;\r
- flag[i]=0;\r
- \r
- }\r
- if(f1->GetParError(1)>1.0)\r
- {\r
- outfiletxtbad<<"MPV high error on MPV \t module="<<i<<endl; \r
- flag[i]=0;\r
- }\r
- }\r
- else\r
- {\r
- mpv[i]=1;\r
- flag[i]=0;\r
- outfiletxtbad<<"BAD FIT \t module="<<i<<endl;\r
- //continue;\r
- } \r
- } \r
- } \r
- \r
- for (int i=0;i<1698;i++)\r
- { \r
- outfiletxt.setf(outfiletxt.scientific);\r
- outfiletxt.precision(2); \r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<i<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<flag[i]<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<gainP[i]<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<gainN[i]<<"\t";\r
- outfiletxt.width(10) ;\r
- outfiletxt.setf(outfiletxt.left);\r
- outfiletxt<<mpv[i]<<endl;\r
- }\r
- \r
- TCanvas *c1 = new TCanvas("1","1",1200,800);\r
- c1->Divide(2,1);\r
- c1->cd(1);\r
- fHistQ->Draw("colz");\r
- c1->cd(2);\r
- fHistCR->Draw("colz");\r
- \r
- \r
- \r
- TFile* fout1=TFile::Open("gain_all_fits.root","recreate");\r
- listout1->Write("output",TObject::kSingleKey); \r
- fout1->Close();\r
- \r
- TFile* fout2=TFile::Open("gain_control_plots.root","recreate");\r
- listout2->Write("output",TObject::kSingleKey); \r
- fout2->Close();\r
- \r
- \r
- \r
-}\r
+#include "TMath.h"
+#include "TList.h"
+#include "TH2F.h"
+#include "TH1F.h"
+#include "TF1.h"
+#include "TH1D.h"
+#include "TProfile.h"
+#include "TFile.h"
+#include "TCanvas.h"
+#include <Riostream.h>
+#include <TROOT.h>
+#include <TString.h>
+#include "TStyle.h"
+#include "TGrid.h"
+
+
+class TList;
+class TH2F;
+class TH1F;
+class TH1D;
+class TProfile;
+class TFile;
+class TCanvas;
+class TStyle;
+class TF1;
+class TGrid;
+
+
+
+
+
+Double_t convolution(Double_t* x,Double_t *par)
+{
+ //Fit parameters:
+ //par[0]=Width (scale) parameter of Landau density
+ //par[1]=Most Probable (MP, location) parameter of Landau density
+ //par[2]=Total area (integral -inf to inf, normalization constant)
+ //par[3]=Width (sigma) of convoluted Gaussian function
+ //
+ //In the Landau distribution (represented by the CERNLIB approximation),
+ //the maximum is located at x=-0.22278298 with the location parameter=0.
+ //This shift is corrected within this function, so that the actual
+ //maximum is identical to the MP parameter.
+
+ // Numeric constants
+ Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)
+ Double_t mpshift = -0.22278298; // Landau maximum location
+
+ // Control constants
+ Double_t np = 200.0; // number of convolution steps
+ Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas
+
+ // Variables
+ Double_t xx;
+ Double_t mpc;
+ Double_t fland;
+ Double_t sum = 0.0;
+ Double_t xlow,xupp;
+ Double_t step;
+ Double_t i;
+
+
+ // MP shift correction
+ mpc = par[1] - mpshift * par[0];
+
+ // Range of convolution integral
+ xlow = x[0] - sc * par[3];
+ xupp = x[0] + sc * par[3];
+
+ step = (xupp-xlow) / np;
+
+ // Convolution integral of Landau and Gaussian by sum
+ for(i=1.0; i<=np/2; i++)
+ {
+ xx = xlow + (i-.5) * step;
+ fland = TMath::Landau(xx,mpc,par[0]) / par[0];
+ sum += fland * TMath::Gaus(x[0],xx,par[3]);
+
+ xx = xupp - (i-.5) * step;
+ fland = TMath::Landau(xx,mpc,par[0]) / par[0];
+ sum += fland * TMath::Gaus(x[0],xx,par[3]);
+ }
+
+ return (par[2] * step * sum * invsq2pi / par[3]);
+}
+
+
+
+
+void GetGainModuleLevel(TString filename,Bool_t normal=1,Int_t ntofit=500, Bool_t grid=0)
+{
+ gROOT->SetStyle("Plain");
+ gStyle->SetPalette(1,0);
+ gStyle->SetOptStat(111111);
+ gStyle->SetOptFit(1);
+ if(grid)
+ TGrid::Connect("alien://");
+
+ TFile* file_data=TFile::Open(filename);
+ if(!file_data)
+ return;
+ TList *listin=0x0;
+ listin=(TList*)file_data->Get("output");
+ if(!listin)
+ listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/output");
+ if(!listin)
+ listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/SSDdEdxQA");
+ if(!listin)
+ listin=(TList*)file_data->Get("SSDdEdxQA");
+ if(!listin)
+ return;
+ TH2F* fHistQ=0x0;
+ if (normal)
+ fHistQ=(TH2F*)listin ->FindObject("QACharge");
+ else
+ fHistQ=(TH2F*)listin ->FindObject("QAChargeCorrected");
+ if(!fHistQ)
+ return;
+ TH2F* fHistCR=(TH2F*)listin ->FindObject("QAChargeRatio");
+ if(!fHistCR)
+ return;
+ TList *listout1=new TList();
+
+ TList *listout2=new TList();
+
+ TH1F* fHistMPVs=new TH1F("HistMPVS","HistMPVs;MPV;N",75,70,95);
+ fHistMPVs->SetDirectory(0);
+ listout2->Add(fHistMPVs);
+
+ TH1F* fHistSL=new TH1F("HistSL","#sigma_{Landau};#sigma_{Landau};N",40,0,16);
+ fHistSL->SetDirectory(0);
+ listout2->Add(fHistSL);
+
+ TH1F* fHistSG=new TH1F("HistSG","#sigma_{Gaus};#sigma_{Gaus};N",40,0,16);
+ fHistSG->SetDirectory(0);
+ listout2->Add(fHistSG);
+
+ TH1F* fHistCRmean=new TH1F("HistCRmean","HistCRmean;CRmean;N",200,-1,1);
+ fHistCRmean->SetDirectory(0);
+ listout2->Add(fHistCRmean);
+
+ TH1F* fHistCRRMS=new TH1F("HistCRRMS","HistCRRMS;CRRMS;N",100,0,1);
+ fHistCRRMS->SetDirectory(0);
+ listout2->Add(fHistCRRMS);
+
+ TH1F* fHistGainP=new TH1F("HistGainP","HistGainP;CRGainPcorr;N",120,0.5,2.0);
+ fHistGainP->SetDirectory(0);
+ listout2->Add(fHistGainP);
+
+ TH1F* fHistGainN=new TH1F("HistGainN","HistGainN;CRGainNcorr;N",120,0.5,2.0);
+ fHistGainN->SetDirectory(0);
+ listout2->Add(fHistGainN);
+
+ TH1F *fMPVGraph = new TH1F("MPVgraph","MPVgraph;Module number;MPV",1698,-0.5,1697.5);
+ fMPVGraph->SetMarkerColor(kRed);
+ fMPVGraph->SetMarkerStyle(22);
+ listout2->Add(fMPVGraph);
+
+ TH1F *fCRmeanGraph = new TH1F("CRmeangraph","CRmeangraph;Module number;MPV",1698,-0.5,1697.5);
+ fCRmeanGraph->SetMarkerColor(kBlue);
+ fCRmeanGraph->SetMarkerStyle(23);
+ listout2->Add(fCRmeanGraph);
+
+ Float_t gainP[1698];
+ Float_t gainN[1698];
+ Float_t mpv[1698];
+ Int_t flag[1698];
+
+ ofstream outfiletxt;
+ outfiletxt.open("gain.txt");
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<"MODULE"<<"\t";
+ outfiletxt.width(10);
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<"FLAG"<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<"GainPcorr"<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<"GainNcorr"<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<"MPV"<<endl;
+
+
+ ofstream outfiletxtbad;
+ outfiletxtbad.open("badModules.txt");
+
+
+
+
+ for (int i =0;i<1698;i++)
+ {
+ cout<<i<<endl;
+ TString tmpQ("Q");
+ tmpQ+=i;
+ TString tmpCR("CR");
+ tmpCR+=i;
+ TH1D* fHist1DCR= fHistCR->ProjectionY(tmpCR,i+1,i+1);
+ Double_t mean=fHist1DCR->GetMean();
+ if(!(TMath::Abs(mean)<1.0)||fHist1DCR->GetEntries()<10)
+ {
+ flag[i]=-2;
+ gainN[i]=1.0;
+ gainP[i]=1.0;
+ mpv[i]=1.0;
+ continue;
+ }
+ fHistCRmean->Fill(mean);
+ fHistCRRMS->Fill(fHist1DCR->GetRMS());
+ gainN[i]=1.0/(1.0+mean);
+ gainP[i]=1.0/(1.0-mean);
+ fHistGainP->Fill(gainP[i]);
+ fHistGainN->Fill(gainN[i]);
+ fCRmeanGraph->SetBinContent(i+1,mean);
+ fCRmeanGraph->SetBinError(i+1,fHist1DCR->GetRMS());
+
+
+ TH1D* fHist1DQ=fHistQ->ProjectionY(tmpQ,i+1,i+1);
+ fHist1DQ->SetDirectory(0);
+ listout1->Add(fHist1DQ);
+ if(fHist1DQ->GetEntries()<ntofit)
+ {
+ flag[i]=-1;
+ mpv[i]=1.0;
+ outfiletxtbad<<"Low statistic \t module= "<<i<<" netries="<<fHist1DQ->GetEntries()<<endl;
+ continue;
+ }
+ else
+ {
+ tmpQ+="fit";
+ Float_t range=fHist1DQ->GetBinCenter(fHist1DQ->GetMaximumBin());
+ TF1 *f1 = new TF1(tmpQ,convolution,range*0.45,range*3.0,4);
+ f1->SetParameters(7.0,range,1.0,5.5);
+ Float_t normalization=fHist1DQ->GetEntries()*fHist1DQ->GetXaxis()->GetBinWidth(2)/f1->Integral(range*0.45,range*3.0);
+ f1->SetParameters(7.0,range,normalization,5.5);
+ //f1->SetParameters(7.0,range,fHist1DQ->GetMaximum(),5.5);
+ f1->SetParNames("sigma Landau","MPV","N","sigma Gaus");
+ f1->SetParLimits(0,2.0,100.0);
+ f1->SetParLimits(3,0.0,100.0);
+ if(fHist1DQ->Fit(tmpQ,"BRQ")==0)
+ {
+ mpv[i]=f1->GetParameter(1);
+ fHistMPVs->Fill(mpv[i]);
+ fHistSL->Fill(f1->GetParameter(0));
+ fHistSG->Fill(f1->GetParameter(3));
+ flag[i]=1;
+ fMPVGraph->SetBinContent(i+1,f1->GetParameter(1));
+ fMPVGraph->SetBinError(i+1,f1->GetParError(1));
+ if(mpv[i]<75.0)
+ {
+ outfiletxtbad<<"MPV lower than 75 \t module="<<i<<endl;
+ flag[i]=0;
+ }
+ if(mpv[i]>100.0)
+ {
+ outfiletxtbad<<"MPV higher than 100 \t module="<<i<<endl;
+ flag[i]=0;
+
+ }
+ if(f1->GetParError(1)>1.0)
+ {
+ outfiletxtbad<<"MPV high error on MPV \t module="<<i<<endl;
+ flag[i]=0;
+ }
+ }
+ else
+ {
+ mpv[i]=1;
+ flag[i]=0;
+ outfiletxtbad<<"BAD FIT \t module="<<i<<endl;
+ //continue;
+ }
+ }
+ }
+
+ for (int i=0;i<1698;i++)
+ {
+ outfiletxt.setf(outfiletxt.scientific);
+ outfiletxt.precision(2);
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<i<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<flag[i]<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<gainP[i]<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<gainN[i]<<"\t";
+ outfiletxt.width(10) ;
+ outfiletxt.setf(outfiletxt.left);
+ outfiletxt<<mpv[i]<<endl;
+ }
+
+ TCanvas *c1 = new TCanvas("1","1",1200,800);
+ c1->Divide(2,1);
+ c1->cd(1);
+ fHistQ->Draw("colz");
+ c1->cd(2);
+ fHistCR->Draw("colz");
+
+
+
+ TFile* fout1=TFile::Open("gain_all_fits.root","recreate");
+ listout1->Write("output",TObject::kSingleKey);
+ fout1->Close();
+
+ TFile* fout2=TFile::Open("gain_control_plots.root","recreate");
+ listout2->Write("output",TObject::kSingleKey);
+ fout2->Close();
+
+
+
+}
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff