7 #include "TProfile.h"
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10 #include <Riostream.h>
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12 #include <TString.h>
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32 Double_t convolution(Double_t* x,Double_t *par)
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35 //par[0]=Width (scale) parameter of Landau density
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36 //par[1]=Most Probable (MP, location) parameter of Landau density
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37 //par[2]=Total area (integral -inf to inf, normalization constant)
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38 //par[3]=Width (sigma) of convoluted Gaussian function
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40 //In the Landau distribution (represented by the CERNLIB approximation),
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41 //the maximum is located at x=-0.22278298 with the location parameter=0.
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42 //This shift is corrected within this function, so that the actual
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43 //maximum is identical to the MP parameter.
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45 // Numeric constants
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46 Double_t invsq2pi = 0.3989422804014; // (2 pi)^(-1/2)
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47 Double_t mpshift = -0.22278298; // Landau maximum location
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49 // Control constants
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50 Double_t np = 200.0; // number of convolution steps
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51 Double_t sc = 5.0; // convolution extends to +-sc Gaussian sigmas
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63 // MP shift correction
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64 mpc = par[1] - mpshift * par[0];
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66 // Range of convolution integral
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67 xlow = x[0] - sc * par[3];
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68 xupp = x[0] + sc * par[3];
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70 step = (xupp-xlow) / np;
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72 // Convolution integral of Landau and Gaussian by sum
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73 for(i=1.0; i<=np/2; i++)
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75 xx = xlow + (i-.5) * step;
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76 fland = TMath::Landau(xx,mpc,par[0]) / par[0];
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77 sum += fland * TMath::Gaus(x[0],xx,par[3]);
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79 xx = xupp - (i-.5) * step;
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80 fland = TMath::Landau(xx,mpc,par[0]) / par[0];
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81 sum += fland * TMath::Gaus(x[0],xx,par[3]);
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84 return (par[2] * step * sum * invsq2pi / par[3]);
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90 void GetGainModuleLevel(TString filename,Bool_t normal=1,Int_t ntofit=500, Bool_t grid=0)
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92 gROOT->SetStyle("Plain");
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93 gStyle->SetPalette(1,0);
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94 gStyle->SetOptStat(111111);
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95 gStyle->SetOptFit(1);
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97 TGrid::Connect("alien://");
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99 TFile* file_data=TFile::Open(filename);
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103 listin=(TList*)file_data->Get("output");
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105 listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/output");
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107 listin=(TList*)file_data->Get("PWGPPdEdxSSDQA/SSDdEdxQA");
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109 listin=(TList*)file_data->Get("SSDdEdxQA");
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114 fHistQ=(TH2F*)listin ->FindObject("QACharge");
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116 fHistQ=(TH2F*)listin ->FindObject("QAChargeCorrected");
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119 TH2F* fHistCR=(TH2F*)listin ->FindObject("QAChargeRatio");
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122 TList *listout1=new TList();
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124 TList *listout2=new TList();
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126 TH1F* fHistMPVs=new TH1F("HistMPVS","HistMPVs;MPV;N",75,70,95);
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127 fHistMPVs->SetDirectory(0);
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128 listout2->Add(fHistMPVs);
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130 TH1F* fHistSL=new TH1F("HistSL","#sigma_{Landau};#sigma_{Landau};N",40,0,16);
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131 fHistSL->SetDirectory(0);
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132 listout2->Add(fHistSL);
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134 TH1F* fHistSG=new TH1F("HistSG","#sigma_{Gaus};#sigma_{Gaus};N",40,0,16);
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135 fHistSG->SetDirectory(0);
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136 listout2->Add(fHistSG);
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138 TH1F* fHistCRmean=new TH1F("HistCRmean","HistCRmean;CRmean;N",200,-1,1);
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139 fHistCRmean->SetDirectory(0);
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140 listout2->Add(fHistCRmean);
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142 TH1F* fHistCRRMS=new TH1F("HistCRRMS","HistCRRMS;CRRMS;N",100,0,1);
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143 fHistCRRMS->SetDirectory(0);
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144 listout2->Add(fHistCRRMS);
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146 TH1F* fHistGainP=new TH1F("HistGainP","HistGainP;CRGainPcorr;N",120,0.5,2.0);
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147 fHistGainP->SetDirectory(0);
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148 listout2->Add(fHistGainP);
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150 TH1F* fHistGainN=new TH1F("HistGainN","HistGainN;CRGainNcorr;N",120,0.5,2.0);
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151 fHistGainN->SetDirectory(0);
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152 listout2->Add(fHistGainN);
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154 TH1F *fMPVGraph = new TH1F("MPVgraph","MPVgraph;Module number;MPV",1698,-0.5,1697.5);
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155 fMPVGraph->SetMarkerColor(kRed);
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156 fMPVGraph->SetMarkerStyle(22);
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157 listout2->Add(fMPVGraph);
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159 TH1F *fCRmeanGraph = new TH1F("CRmeangraph","CRmeangraph;Module number;MPV",1698,-0.5,1697.5);
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160 fCRmeanGraph->SetMarkerColor(kBlue);
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161 fCRmeanGraph->SetMarkerStyle(23);
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162 listout2->Add(fCRmeanGraph);
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164 Float_t gainP[1698];
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165 Float_t gainN[1698];
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169 ofstream outfiletxt;
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170 outfiletxt.open("gain.txt");
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171 outfiletxt.width(10) ;
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172 outfiletxt.setf(outfiletxt.left);
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173 outfiletxt<<"MODULE"<<"\t";
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174 outfiletxt.width(10);
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175 outfiletxt.setf(outfiletxt.left);
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176 outfiletxt<<"FLAG"<<"\t";
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177 outfiletxt.width(10) ;
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178 outfiletxt.setf(outfiletxt.left);
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179 outfiletxt<<"GainPcorr"<<"\t";
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180 outfiletxt.width(10) ;
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181 outfiletxt.setf(outfiletxt.left);
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182 outfiletxt<<"GainNcorr"<<"\t";
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183 outfiletxt.width(10) ;
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184 outfiletxt.setf(outfiletxt.left);
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185 outfiletxt<<"MPV"<<endl;
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188 ofstream outfiletxtbad;
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189 outfiletxtbad.open("badModules.txt");
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194 for (int i =0;i<1698;i++)
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199 TString tmpCR("CR");
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201 TH1D* fHist1DCR= fHistCR->ProjectionY(tmpCR,i+1,i+1);
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202 Double_t mean=fHist1DCR->GetMean();
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203 if(!(TMath::Abs(mean)<1.0)||fHist1DCR->GetEntries()<10)
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211 fHistCRmean->Fill(mean);
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212 fHistCRRMS->Fill(fHist1DCR->GetRMS());
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213 gainN[i]=1.0/(1.0+mean);
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214 gainP[i]=1.0/(1.0-mean);
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215 fHistGainP->Fill(gainP[i]);
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216 fHistGainN->Fill(gainN[i]);
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217 fCRmeanGraph->SetBinContent(i+1,mean);
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218 fCRmeanGraph->SetBinError(i+1,fHist1DCR->GetRMS());
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221 TH1D* fHist1DQ=fHistQ->ProjectionY(tmpQ,i+1,i+1);
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222 fHist1DQ->SetDirectory(0);
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223 listout1->Add(fHist1DQ);
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224 if(fHist1DQ->GetEntries()<ntofit)
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228 outfiletxtbad<<"Low statistic \t module= "<<i<<" netries="<<fHist1DQ->GetEntries()<<endl;
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234 Float_t range=fHist1DQ->GetBinCenter(fHist1DQ->GetMaximumBin());
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235 TF1 *f1 = new TF1(tmpQ,convolution,range*0.45,range*3.0,4);
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236 f1->SetParameters(7.0,range,1.0,5.5);
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237 Float_t normalization=fHist1DQ->GetEntries()*fHist1DQ->GetXaxis()->GetBinWidth(2)/f1->Integral(range*0.45,range*3.0);
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238 f1->SetParameters(7.0,range,normalization,5.5);
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239 //f1->SetParameters(7.0,range,fHist1DQ->GetMaximum(),5.5);
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240 f1->SetParNames("sigma Landau","MPV","N","sigma Gaus");
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241 f1->SetParLimits(0,2.0,100.0);
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242 f1->SetParLimits(3,0.0,100.0);
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243 if(fHist1DQ->Fit(tmpQ,"BRQ")==0)
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245 mpv[i]=f1->GetParameter(1);
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246 fHistMPVs->Fill(mpv[i]);
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247 fHistSL->Fill(f1->GetParameter(0));
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248 fHistSG->Fill(f1->GetParameter(3));
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250 fMPVGraph->SetBinContent(i+1,f1->GetParameter(1));
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251 fMPVGraph->SetBinError(i+1,f1->GetParError(1));
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254 outfiletxtbad<<"MPV lower than 75 \t module="<<i<<endl;
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259 outfiletxtbad<<"MPV higher than 100 \t module="<<i<<endl;
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263 if(f1->GetParError(1)>1.0)
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265 outfiletxtbad<<"MPV high error on MPV \t module="<<i<<endl;
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273 outfiletxtbad<<"BAD FIT \t module="<<i<<endl;
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279 for (int i=0;i<1698;i++)
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281 outfiletxt.setf(outfiletxt.scientific);
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282 outfiletxt.precision(2);
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283 outfiletxt.width(10) ;
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284 outfiletxt.setf(outfiletxt.left);
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285 outfiletxt<<i<<"\t";
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286 outfiletxt.width(10) ;
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287 outfiletxt.setf(outfiletxt.left);
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288 outfiletxt<<flag[i]<<"\t";
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289 outfiletxt.width(10) ;
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290 outfiletxt.setf(outfiletxt.left);
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291 outfiletxt<<gainP[i]<<"\t";
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292 outfiletxt.width(10) ;
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293 outfiletxt.setf(outfiletxt.left);
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294 outfiletxt<<gainN[i]<<"\t";
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295 outfiletxt.width(10) ;
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296 outfiletxt.setf(outfiletxt.left);
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297 outfiletxt<<mpv[i]<<endl;
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300 TCanvas *c1 = new TCanvas("1","1",1200,800);
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303 fHistQ->Draw("colz");
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305 fHistCR->Draw("colz");
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309 TFile* fout1=TFile::Open("gain_all_fits.root","recreate");
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310 listout1->Write("output",TObject::kSingleKey);
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313 TFile* fout2=TFile::Open("gain_control_plots.root","recreate");
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314 listout2->Write("output",TObject::kSingleKey);
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