[u/mrichter/AliRoot.git] / ITS / GetGainModuleLevel.C

#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
Commit	Line	Data
2d9ed4be	1	#include "TMath.h"\r
	2	#include "TList.h"\r
	3	#include "TH2F.h"\r
	4	#include "TH1F.h"\r
	5	#include "TF1.h"\r
	6	#include "TH1D.h"\r
	7	#include "TProfile.h"\r
	8	#include "TFile.h"\r
	9	#include "TCanvas.h"\r
	10	#include <Riostream.h>\r
	11	#include <TROOT.h>\r
	12	#include <TString.h>\r
	13	#include "TStyle.h"\r
	14	#include "TGrid.h"\r
	15	\r
	16	\r
	17	class TList;\r
	18	class TH2F;\r
	19	class TH1F;\r
	20	class TH1D;\r
	21	class TProfile;\r
	22	class TFile;\r
	23	class TCanvas;\r
	24	class TStyle;\r
	25	class TF1;\r
	26	class TGrid;\r
	27	\r
	28	\r
	29	\r
	30	\r
	31	\r
	32	Double_t convolution(Double_t* x,Double_t *par)\r
	33	{\r
	34	//Fit parameters:\r
	35	//par[0]=Width (scale) parameter of Landau density\r
	36	//par[1]=Most Probable (MP, location) parameter of Landau density\r
	37	//par[2]=Total area (integral -inf to inf, normalization constant)\r
	38	//par[3]=Width (sigma) of convoluted Gaussian function\r
	39	//\r
	40	//In the Landau distribution (represented by the CERNLIB approximation),\r
	41	//the maximum is located at x=-0.22278298 with the location parameter=0.\r
	42	//This shift is corrected within this function, so that the actual\r
	43	//maximum is identical to the MP parameter.\r
	44	\r
	45	// Numeric constants\r
	46	Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)\r
	47	Double_t mpshift = -0.22278298; // Landau maximum location\r
	48	\r
	49	// Control constants\r
	50	Double_t np = 200.0; // number of convolution steps\r
	51	Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas\r
	52	\r
	53	// Variables\r
	54	Double_t xx;\r
	55	Double_t mpc;\r
	56	Double_t fland;\r
	57	Double_t sum = 0.0;\r
	58	Double_t xlow,xupp;\r
	59	Double_t step;\r
	60	Double_t i;\r
	61	\r
	62	\r
	63	// MP shift correction\r
	64	mpc = par[1] - mpshift * par[0];\r
65	\r
66	// Range of convolution integral\r
67	xlow = x[0] - sc * par[3];\r
68	xupp = x[0] + sc * par[3];\r
69	\r
70	step = (xupp-xlow) / np;\r
71	\r
72	// Convolution integral of Landau and Gaussian by sum\r
73	for(i=1.0; i<=np/2; i++) \r
74	{\r
75	xx = xlow + (i-.5) * step;\r
76	fland = TMath::Landau(xx,mpc,par[0]) / par[0];\r
77	sum += fland * TMath::Gaus(x[0],xx,par[3]);\r
78	\r
79	xx = xupp - (i-.5) * step;\r
80	fland = TMath::Landau(xx,mpc,par[0]) / par[0];\r
81	sum += fland * TMath::Gaus(x[0],xx,par[3]);\r
82	}\r
83	\r
84	return (par[2] * step * sum * invsq2pi / par[3]);\r
85	}\r
86	\r
87	\r
88	\r
89	\r
90	void GetGainModuleLevel(TString filename,Bool_t normal=1,Int_t ntofit=500, Bool_t grid=0)\r
91	{\r
92	gROOT->SetStyle("Plain");\r
93	gStyle->SetPalette(1,0);\r
94	gStyle->SetOptStat(111111);\r
95	gStyle->SetOptFit(1); \r
96	if(grid)\r
97	TGrid::Connect("alien://");\r
98	\r
99	TFile* file_data=TFile::Open(filename);\r
100	if(!file_data)\r
101	return;\r
102	TList *listin=0x0;\r
103	listin=(TList*)file_data->Get("output");\r
104	if(!listin)\r
2bfe5463	105	listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/output");\r
2d9ed4be	106	if(!listin) \r
2bfe5463	107	listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/SSDdEdxQA");\r
2d9ed4be	108	if(!listin) \r
	109	listin=(TList*)file_data->Get("SSDdEdxQA");\r
	110	if(!listin) \r
	111	return;\r
	112	TH2F* fHistQ=0x0;\r
	113	if (normal) \r
	114	fHistQ=(TH2F*)listin ->FindObject("QACharge");\r
	115	else\r
	116	fHistQ=(TH2F*)listin ->FindObject("QAChargeCorrected");\r
	117	if(!fHistQ)\r
	118	return;\r
	119	TH2F* fHistCR=(TH2F*)listin ->FindObject("QAChargeRatio");\r
	120	if(!fHistCR)\r
	121	return;\r
	122	TList *listout1=new TList();\r
	123	\r
	124	TList *listout2=new TList();\r
	125	\r
	126	TH1F* fHistMPVs=new TH1F("HistMPVS","HistMPVs;MPV;N",75,70,95);\r
	127	fHistMPVs->SetDirectory(0);\r
	128	listout2->Add(fHistMPVs);\r
	129	\r
	130	TH1F* fHistSL=new TH1F("HistSL","#sigma_{Landau};#sigma_{Landau};N",40,0,16);\r
	131	fHistSL->SetDirectory(0);\r
	132	listout2->Add(fHistSL);\r
	133	\r
	134	TH1F* fHistSG=new TH1F("HistSG","#sigma_{Gaus};#sigma_{Gaus};N",40,0,16);\r
	135	fHistSG->SetDirectory(0);\r
	136	listout2->Add(fHistSG);\r
	137	\r
	138	TH1F* fHistCRmean=new TH1F("HistCRmean","HistCRmean;CRmean;N",200,-1,1);\r
	139	fHistCRmean->SetDirectory(0);\r
	140	listout2->Add(fHistCRmean);\r
	141	\r
	142	TH1F* fHistCRRMS=new TH1F("HistCRRMS","HistCRRMS;CRRMS;N",100,0,1);\r
	143	fHistCRRMS->SetDirectory(0);\r
	144	listout2->Add(fHistCRRMS);\r
	145	\r
	146	TH1F* fHistGainP=new TH1F("HistGainP","HistGainP;CRGainPcorr;N",120,0.5,2.0);\r
	147	fHistGainP->SetDirectory(0);\r
	148	listout2->Add(fHistGainP);\r
	149	\r
	150	TH1F* fHistGainN=new TH1F("HistGainN","HistGainN;CRGainNcorr;N",120,0.5,2.0);\r
	151	fHistGainN->SetDirectory(0);\r
	152	listout2->Add(fHistGainN);\r
	153	\r
	154	TH1F *fMPVGraph = new TH1F("MPVgraph","MPVgraph;Module number;MPV",1698,-0.5,1697.5);\r
	155	fMPVGraph->SetMarkerColor(kRed);\r
	156	fMPVGraph->SetMarkerStyle(22);\r
	157	listout2->Add(fMPVGraph);\r
	158	\r
	159	TH1F *fCRmeanGraph = new TH1F("CRmeangraph","CRmeangraph;Module number;MPV",1698,-0.5,1697.5);\r
	160	fCRmeanGraph->SetMarkerColor(kBlue);\r
	161	fCRmeanGraph->SetMarkerStyle(23);\r
	162	listout2->Add(fCRmeanGraph);\r
	163	\r
	164	Float_t gainP[1698];\r
	165	Float_t gainN[1698];\r
	166	Float_t mpv[1698];\r
	167	Int_t flag[1698]; \r
	168	\r
	169	ofstream outfiletxt;\r
	170	outfiletxt.open("gain.txt");\r
	171	outfiletxt.width(10) ;\r
172	outfiletxt.setf(outfiletxt.left);\r
173	outfiletxt<<"MODULE"<<"\t";\r
174	outfiletxt.width(10);\r
175	outfiletxt.setf(outfiletxt.left);\r
176	outfiletxt<<"FLAG"<<"\t";\r
177	outfiletxt.width(10) ;\r
178	outfiletxt.setf(outfiletxt.left);\r
179	outfiletxt<<"GainPcorr"<<"\t";\r
180	outfiletxt.width(10) ;\r
181	outfiletxt.setf(outfiletxt.left);\r
182	outfiletxt<<"GainNcorr"<<"\t";\r
183	outfiletxt.width(10) ;\r
184	outfiletxt.setf(outfiletxt.left);\r
185	outfiletxt<<"MPV"<<endl;\r
186	\r
187	\r
188	ofstream outfiletxtbad;\r
189	outfiletxtbad.open("badModules.txt");\r
190	\r
191	\r
192	\r
193	\r
194	for (int i =0;i<1698;i++)\r
195	{\r
196	cout<<i<<endl;\r
197	TString tmpQ("Q");\r
198	tmpQ+=i;\r
199	TString tmpCR("CR");\r
200	tmpCR+=i;\r
201	TH1D* fHist1DCR= fHistCR->ProjectionY(tmpCR,i+1,i+1);\r
202	Double_t mean=fHist1DCR->GetMean();\r
203	if(!(TMath::Abs(mean)<1.0)\|\|fHist1DCR->GetEntries()<10)\r
204	{ \r
205	flag[i]=-2;\r
206	gainN[i]=1.0;\r
207	gainP[i]=1.0;\r
208	mpv[i]=1.0;\r
209	continue;\r
210	}\r
211	fHistCRmean->Fill(mean);\r
212	fHistCRRMS->Fill(fHist1DCR->GetRMS());\r
213	gainN[i]=1.0/(1.0+mean);\r
214	gainP[i]=1.0/(1.0-mean);\r
215	fHistGainP->Fill(gainP[i]);\r
216	fHistGainN->Fill(gainN[i]);\r
217	fCRmeanGraph->SetBinContent(i+1,mean);\r
218	fCRmeanGraph->SetBinError(i+1,fHist1DCR->GetRMS());\r
219	\r
220	\r
221	TH1D* fHist1DQ=fHistQ->ProjectionY(tmpQ,i+1,i+1);\r
222	fHist1DQ->SetDirectory(0);\r
223	listout1->Add(fHist1DQ);\r
224	if(fHist1DQ->GetEntries()<ntofit)\r
225	{\r
226	flag[i]=-1;\r
227	mpv[i]=1.0;\r
228	outfiletxtbad<<"Low statistic \t module= "<<i<<" netries="<<fHist1DQ->GetEntries()<<endl;\r
229	continue;\r
230	}\r
231	else\r
232	{\r
233	tmpQ+="fit";\r
234	Float_t range=fHist1DQ->GetBinCenter(fHist1DQ->GetMaximumBin());\r
235	TF1 f1 = new TF1(tmpQ,convolution,range0.45,range*3.0,4);\r
236	f1->SetParameters(7.0,range,1.0,5.5);\r
237	Float_t normalization=fHist1DQ->GetEntries()fHist1DQ->GetXaxis()->GetBinWidth(2)/f1->Integral(range0.45,range*3.0);\r
238	f1->SetParameters(7.0,range,normalization,5.5);\r
239	//f1->SetParameters(7.0,range,fHist1DQ->GetMaximum(),5.5);\r
240	f1->SetParNames("sigma Landau","MPV","N","sigma Gaus");\r
241	f1->SetParLimits(0,2.0,100.0);\r
242	f1->SetParLimits(3,0.0,100.0);\r
243	if(fHist1DQ->Fit(tmpQ,"BRQ")==0)\r
244	{\r
245	mpv[i]=f1->GetParameter(1);\r
246	fHistMPVs->Fill(mpv[i]);\r
247	fHistSL->Fill(f1->GetParameter(0));\r
248	fHistSG->Fill(f1->GetParameter(3));\r
249	flag[i]=1; \r
250	fMPVGraph->SetBinContent(i+1,f1->GetParameter(1));\r
251	fMPVGraph->SetBinError(i+1,f1->GetParError(1));\r
252	if(mpv[i]<75.0)\r
253	{\r
254	outfiletxtbad<<"MPV lower than 75 \t module="<<i<<endl;\r
255	flag[i]=0;\r
256	} \r
257	if(mpv[i]>100.0)\r
258	{\r
259	outfiletxtbad<<"MPV higher than 100 \t module="<<i<<endl;\r
260	flag[i]=0;\r
261	\r
262	}\r
263	if(f1->GetParError(1)>1.0)\r
264	{\r
265	outfiletxtbad<<"MPV high error on MPV \t module="<<i<<endl; \r
266	flag[i]=0;\r
267	}\r
268	}\r
269	else\r
270	{\r
271	mpv[i]=1;\r
272	flag[i]=0;\r
273	outfiletxtbad<<"BAD FIT \t module="<<i<<endl;\r
274	//continue;\r
275	} \r
276	} \r
277	} \r
278	\r
279	for (int i=0;i<1698;i++)\r
280	{ \r
281	outfiletxt.setf(outfiletxt.scientific);\r
282	outfiletxt.precision(2); \r
283	outfiletxt.width(10) ;\r
284	outfiletxt.setf(outfiletxt.left);\r
285	outfiletxt<<i<<"\t";\r
286	outfiletxt.width(10) ;\r
287	outfiletxt.setf(outfiletxt.left);\r
288	outfiletxt<<flag[i]<<"\t";\r
289	outfiletxt.width(10) ;\r
290	outfiletxt.setf(outfiletxt.left);\r
291	outfiletxt<<gainP[i]<<"\t";\r
292	outfiletxt.width(10) ;\r
293	outfiletxt.setf(outfiletxt.left);\r
294	outfiletxt<<gainN[i]<<"\t";\r
295	outfiletxt.width(10) ;\r
296	outfiletxt.setf(outfiletxt.left);\r
297	outfiletxt<<mpv[i]<<endl;\r
298	}\r
299	\r
300	TCanvas *c1 = new TCanvas("1","1",1200,800);\r
301	c1->Divide(2,1);\r
302	c1->cd(1);\r
303	fHistQ->Draw("colz");\r
304	c1->cd(2);\r
305	fHistCR->Draw("colz");\r
306	\r
307	\r
308	\r
309	TFile* fout1=TFile::Open("gain_all_fits.root","recreate");\r
310	listout1->Write("output",TObject::kSingleKey); \r
311	fout1->Close();\r
312	\r
313	TFile* fout2=TFile::Open("gain_control_plots.root","recreate");\r
314	listout2->Write("output",TObject::kSingleKey); \r
315	fout2->Close();\r
316	\r
317	\r
318	\r
319	}\r