1 | /* ======================================================================== *\
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2 | !
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3 | ! *
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4 | ! * This file is part of MARS, the MAGIC Analysis and Reconstruction
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5 | ! * Software. It is distributed to you in the hope that it can be a useful
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6 | ! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
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7 | ! * It is distributed WITHOUT ANY WARRANTY.
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8 | ! *
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9 | ! * Permission to use, copy, modify and distribute this software and its
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10 | ! * documentation for any purpose is hereby granted without fee,
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11 | ! * provided that the above copyright notice appear in all copies and
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12 | ! * that both that copyright notice and this permission notice appear
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13 | ! * in supporting documentation. It is provided "as is" without express
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14 | ! * or implied warranty.
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15 | ! *
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16 | !
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17 | !
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18 | ! Author(s): Wolfgang Wittek 5/2002 <mailto:wittek@mppmu.mpg.de>
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19 | !
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20 | ! Copyright: MAGIC Software Development, 2000-2002
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21 | !
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22 | !
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23 | \* ======================================================================== */
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24 |
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25 | //////////////////////////////////////////////////////////////////////////////
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26 | // //
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27 | // MHEffOnTime //
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28 | // //
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29 | // calculates the effective on time for each bin in the variable Var //
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30 | // (Var may be time or Theta) //
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31 | // //
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32 | // Important : The sample of events used for this should be as close //
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33 | // to the sample of recorded events as possible. //
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34 | // This means that NO additional cuts (be it quality cuts or //
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35 | // gamma/hadron cuts) should be applied (see MAGIC-TDAS 02-02).//
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36 | // //
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37 | //////////////////////////////////////////////////////////////////////////////
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38 |
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39 | #include "MHEffOnTime.h"
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40 |
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41 | #include <TStyle.h>
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42 |
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43 | #include <TMinuit.h>
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44 | #include <TFitter.h>
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45 |
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46 | #include <TF1.h>
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47 | #include <TH2.h>
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48 | #include <TCanvas.h>
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49 |
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50 | #include "MBinning.h"
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51 | #include "MParList.h"
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52 |
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53 | #include "MLog.h"
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54 | #include "MLogManip.h"
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55 |
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56 | #include "MHTimeDiffTheta.h"
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57 |
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58 | ClassImp(MHEffOnTime);
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59 |
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60 | using namespace std;
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61 |
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62 | // --------------------------------------------------------------------------
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63 | //
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64 | // Default Constructor. It sets the name of the variable ("Time" or "Theta")
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65 | // and the units of the variable ("[s]" or "[\\circ])")
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66 | //
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67 | MHEffOnTime::MHEffOnTime(const char *varname, const char *unit)
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68 | : fHEffOn(), fHProb(), fHLambda(), fHRdead()
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69 | {
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70 | fVarname = varname;
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71 | fUnit = unit;
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72 |
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73 | TString strg = fVarname + " " + fUnit;
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74 |
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75 | //
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76 | // set the name and title of this object
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77 | //
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78 | fName = TString("MHEffOnTime-")+fVarname;
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79 | fTitle = "1-D histogram of Eff On Time";
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80 |
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81 | // effective on time versus Var
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82 | fHEffOn.SetName("EffOn");
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83 | fHEffOn.SetTitle(TString("T_{on, eff} vs. ")+fVarname);
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84 |
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85 | fHEffOn.SetDirectory(NULL);
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86 |
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87 | fHEffOn.SetXTitle(strg);
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88 | fHEffOn.SetYTitle("T_{on, eff} [s]");
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89 |
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90 | // chi2-probability versus Var
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91 | fHProb.SetName("Chi2-prob");
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92 | TString strg3("\\chi^{2}-prob of OnTimeFit vs. ");
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93 | strg3 += fVarname;
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94 | fHProb.SetTitle(strg3);
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95 |
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96 | fHProb.SetDirectory(NULL);
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97 |
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98 | fHProb.SetXTitle(strg);
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99 | fHProb.SetYTitle("\\chi^{2}-probability");
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100 |
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101 | // lambda versus Var
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102 | fHLambda.SetName("lambda");
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103 | fHLambda.SetTitle(TString("\\lambda from OnTimeFit vs. ")+fVarname);
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104 |
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105 | fHLambda.SetDirectory(NULL);
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106 |
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107 | fHLambda.SetXTitle(strg);
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108 | fHLambda.SetYTitle("\\lambda [Hz]");
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109 |
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110 | // Rdead versus Var
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111 | // Rdead is the fraction from real time lost by the dead time
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112 | fHRdead.SetName("Rdead");
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113 |
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114 | fHRdead.SetTitle(TString("Rdead of OnTimeFit vs. ")+fVarname);
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115 |
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116 | fHRdead.SetDirectory(NULL);
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117 |
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118 | fHRdead.SetXTitle(strg);
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119 | fHRdead.SetYTitle("Rdead");
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120 |
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121 | }
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122 |
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123 | // --------------------------------------------------------------------
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124 | //
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125 | // determine range (yq[0], yq[1]) of time differences
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126 | // where fit should be performed;
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127 | // require a fraction >=min of all entries to lie below yq[0]
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128 | // and a fraction <=max of all entries to lie below yq[1];
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129 | // within the range (yq[0], yq[1]) there must be no empty bin;
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130 | //
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131 | void MHEffOnTime::GetQuantiles(const TH1 &h, Double_t min, Double_t max, Double_t yq[2]) const
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132 | {
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133 | #if ROOT_VERSION_CODE < ROOT_VERSION(3,02,07)
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134 | // WOrkaround for missing const qualifier of TH1::Integral
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135 | TH1 &h1 = (TH1&)h;
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136 |
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137 | // choose pedestrian approach as long as GetQuantiles is
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138 | // not available
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139 | const Int_t jbins = h1.GetNbinsX();
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140 | const Double_t Nm = h1.Integral();
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141 |
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142 | const Double_t xq[2] = { 0.15*Nm, 0.98*Nm };
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143 |
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144 | yq[0] = yq[1] = h1.GetBinLowEdge(jbins+1);
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145 |
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146 | for (int j=1; j<=jbins; j++)
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147 | if (h1.Integral(2, j) >= xq[0])
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148 | {
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149 | yq[0] = h1.GetBinLowEdge(j);
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150 | break;
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151 | }
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152 |
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153 | for (int j=1; j<=jbins; j++)
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154 | if (h1.Integral(1, j) >= xq[1] || h1.GetBinContent(j)==0)
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155 | {
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156 | yq[1] = h1.GetBinLowEdge(j);
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157 | break;
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158 | }
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159 | #else
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160 | // GetQuantiles doesn't seem to be available in root 3.01/06
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161 | Double_t xq[2] = { min, max };
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162 | ((TH1&)h).GetQuantiles(2, yq, xq);
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163 | #endif
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164 | }
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165 |
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166 | void MHEffOnTime::DrawBin(TH1 &h, Int_t i) const
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167 | {
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168 | TString strg1 = fVarname+"-bin #";
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169 | strg1 += i;
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170 |
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171 | new TCanvas(strg1, strg1);
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172 |
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173 | gPad->SetLogy();
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174 | gStyle->SetOptFit(1011);
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175 |
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176 | TString name="Bin_";
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177 | name += i;
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178 |
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179 | h.SetName(name);
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180 | h.SetXTitle("\\Delta t [s]");
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181 | h.SetYTitle("Counts");
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182 | h.DrawCopy();
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183 | }
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184 |
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185 | Bool_t MHEffOnTime::CalcResults(const TF1 &func, Double_t Nm, Int_t i)
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186 | {
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187 | const Double_t lambda = func.GetParameter(0);
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188 | const Double_t N0 = func.GetParameter(1);
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189 | const Double_t prob = func.GetProb();
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190 | const Int_t NDF = func.GetNDF();
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191 |
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192 | Double_t xmin, xmax;
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193 | ((TF1&)func).GetRange(xmin, xmax);
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194 |
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195 | *fLog << inf;
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196 | *fLog << "Fitted bin #" << i << " from " << xmin << " to " << xmax;
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197 | *fLog << ", got: lambda=" << lambda << "Hz N0=" << N0 << endl;
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198 |
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199 | if (prob<=0.01)
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200 | {
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201 | *fLog << warn << "WARN - Fit bin#" << i << " gives:";
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202 | *fLog << " Chi^2-Probab(" << prob << ")<0.01";
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203 | *fLog << " NoFitPts=" << func.GetNumberFitPoints();
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204 | *fLog << " Chi^2=" << func.GetChisquare();
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205 | }
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206 |
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207 | // was fit successful ?
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208 | if (NDF<=0 || /*prob<=0.001 ||*/ lambda<=0 || N0<=0)
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209 | {
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210 | *fLog << warn << dbginf << "Fit failed bin #" << i << ": ";
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211 | if (NDF<=0)
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212 | *fLog << " NDF(Number of Degrees of Freedom)=0";
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213 | if (lambda<=0)
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214 | *fLog << " Parameter#0(lambda)=0";
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215 | if (N0<=0)
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216 | *fLog << " Parameter#1(N0)=0";
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217 | *fLog << endl;
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218 |
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219 | return kFALSE;
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220 | }
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221 |
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222 | //
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223 | // -------------- start error calculation ----------------
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224 | //
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225 | Double_t emat[2][2];
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226 | gMinuit->mnemat(&emat[0][0], 2);
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227 |
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228 | //
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229 | // Rdead : fraction of real time lost by the dead time
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230 | // kappa = number of observed events (Nm) divided by
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231 | // the number of genuine events (N0)
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232 | // Teff : effective on-time
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233 | //
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234 | const Double_t Teff = Nm/lambda;
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235 | const Double_t kappa = Nm/N0;
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236 | const Double_t Rdead = 1.0 - kappa;
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237 |
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238 | const Double_t dldl = emat[0][0];
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239 | const Double_t dN0dN0 = emat[1][1];
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240 |
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241 | const Double_t dTeff = Teff * sqrt(dldl/(lambda*lambda) + 1.0/Nm);
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242 | const Double_t dl = sqrt(dldl);
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243 | const Double_t dRdead = kappa * sqrt(dN0dN0/(N0*N0) + 1.0/Nm);
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244 | //
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245 | // -------------- end error calculation ----------------
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246 | //
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247 |
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248 | // the effective on time is Nm/lambda
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249 | fHEffOn.SetBinContent(i, Teff);
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250 | fHEffOn.SetBinError (i, dTeff);
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251 |
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252 | // plot chi2-probability of fit
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253 | fHProb.SetBinContent(i, prob);
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254 |
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255 | // lambda from fit
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256 | fHLambda.SetBinContent(i, lambda);
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257 | fHLambda.SetBinError (i, dl);
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258 |
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259 | // Rdead from fit
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260 | fHRdead.SetBinContent(i, Rdead);
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261 | fHRdead.SetBinError (i,dRdead);
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262 |
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263 | return kTRUE;
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264 | }
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265 |
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266 | void MHEffOnTime::ResetBin(Int_t i)
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267 | {
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268 | fHEffOn.SetBinContent (i, 1.e-20);
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269 | fHProb.SetBinContent (i, 1.e-20);
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270 | fHLambda.SetBinContent(i, 1.e-20);
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271 | fHRdead.SetBinContent (i, 1.e-20);
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272 | }
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273 |
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274 | // -----------------------------------------------------------------------
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275 | //
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276 | // Calculate the effective on time by fitting the distribution of
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277 | // time differences
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278 | //
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279 | void MHEffOnTime::Calc(const TH2D *hist, const Bool_t draw)
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280 | {
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281 | // nbins = number of Var bins
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282 | const Int_t nbins = hist->GetNbinsY();
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283 |
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284 | for (int i=1; i<=nbins; i++)
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285 | {
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286 | TH1D &h = *((TH2D*)hist)->ProjectionX(TString("Calc-")+fVarname,
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287 | i, i, "E");
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288 | if (draw)
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289 | DrawBin(h, i);
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290 |
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291 | ResetBin(i);
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292 |
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293 | // Nmdel = Nm * binwidth, with Nm = number of observed events
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294 | const Double_t Nm = h.Integral();
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295 | const Double_t Nmdel = h.Integral("width");
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296 | if (Nm <= 0)
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297 | {
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298 | *fLog << warn << dbginf << "Nm<0 for bin #" << i << endl;
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299 | delete &h;
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300 | continue;
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301 | }
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302 |
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303 | Double_t yq[2];
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304 | GetQuantiles(h, 0.15, 0.95, yq);
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305 |
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306 | //
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307 | // Setup Poisson function for the fit:
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308 | // lambda [Hz], N0 = ideal no of evts, del = bin width of dt
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309 | //
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310 | TF1 func("Poisson", " [1]*[2] * [0] * exp(-[0] *x)", yq[0], yq[1]);
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311 | func.SetParNames("lambda", "N0", "del");
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312 |
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313 | func.SetParameter(0, 1);
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314 | func.SetParameter(1, Nm);
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315 | func.FixParameter(2, Nmdel/Nm);
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316 |
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317 | // For me (TB) it seems that setting parameter limits isn't necessary
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318 |
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319 | // For a description of the options see TH1::Fit
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320 | h.Fit("Poisson", "0IRQ");
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321 |
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322 | // Calc and fill results of fit into the histograms
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323 | const Bool_t rc = CalcResults(func, Nm, i);
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324 |
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325 | // draw the distribution of time differences if requested
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326 | if (rc && draw)
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327 | func.DrawCopy("same");
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328 |
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329 | delete &h;
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330 | }
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331 | }
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332 |
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333 | // -------------------------------------------------------------------------
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334 | //
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335 | // Set the binnings and prepare the filling of the histograms
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336 | //
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337 | Bool_t MHEffOnTime::SetupFill(const MParList *plist)
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338 | {
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339 | TString bn = "Binning"+fVarname;
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340 |
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341 | const MBinning* binsVar = (MBinning*)plist->FindObject(bn);
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342 |
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343 | if (!binsVar)
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344 | {
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345 | *fLog << err << dbginf << bn << " [MBinning] not found... aborting." << endl;
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346 | return kFALSE;
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347 | }
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348 |
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349 | SetBinning(&fHEffOn, binsVar);
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350 | SetBinning(&fHProb, binsVar);
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351 | SetBinning(&fHLambda, binsVar);
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352 | SetBinning(&fHRdead, binsVar);
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353 |
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354 | fHEffOn.Sumw2();
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355 | fHProb.Sumw2();
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356 | fHLambda.Sumw2();
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357 | fHRdead.Sumw2();
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358 |
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359 | return kTRUE;
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360 | }
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361 |
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362 | // -------------------------------------------------------------------------
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363 | //
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364 | // Draw the histogram
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365 | //
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366 | void MHEffOnTime::Draw(Option_t *opt)
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367 | {
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368 | TVirtualPad *pad = gPad ? gPad : MakeDefCanvas(this);
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369 | pad->SetBorderMode(0);
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370 |
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371 | pad->Divide(2,2);
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372 |
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373 | pad->cd(1);
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374 | gPad->SetBorderMode(0);
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375 | fHEffOn.Draw(opt);
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376 |
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377 | pad->cd(2);
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378 | gPad->SetBorderMode(0);
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379 | fHProb.Draw(opt);
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380 |
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381 | pad->cd(3);
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382 | gPad->SetBorderMode(0);
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383 | fHLambda.Draw(opt);
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384 |
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385 | pad->cd(4);
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386 | gPad->SetBorderMode(0);
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387 | fHRdead.Draw(opt);
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388 |
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389 | pad->Modified();
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390 | pad->Update();
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391 | }
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392 |
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393 | void MHEffOnTime::Calc(const MHTimeDiffTheta &hist, const Bool_t Draw)
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394 | {
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395 | Calc(hist.GetHist(), Draw);
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396 | }
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397 |
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