X-Git-Url: http://git.uio.no/git/?a=blobdiff_plain;f=ITS%2FGetGainModuleLevel.C;h=d9e0fa7047fab606b1e8ecd537fa4f9bcd26d868;hb=a65a7e704a90d2ca4e682c82d8a708900dda04ca;hp=1421fcd2b4a5be3f088f163e0a865adfcb17a189;hpb=1ea19f39c1537bbc6a2eb0d0485c7d846a4ec49b;p=u%2Fmrichter%2FAliRoot.git

diff --git a/ITS/GetGainModuleLevel.C b/ITS/GetGainModuleLevel.C
index 1421fcd2b4a..d9e0fa7047f 100644
--- a/ITS/GetGainModuleLevel.C
+++ b/ITS/GetGainModuleLevel.C
@@ -1,319 +1,319 @@
-#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();
-	
-	
-	
-}
+#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();
+	
+	
+	
+}