1 | // Silly macro to run the classes that optimize supercuts
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2 | // using ON and OFF data.
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3 |
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4 | // The user only needs to fill/change the variables that control
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5 | // the optimization procedure.
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6 |
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7 |
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8 | gROOT -> Reset();
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9 |
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10 | void SuperCutsONOFFMacro()
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11 | {
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12 | gLog.SetNoColors();
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13 |
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14 | // File containing the data (ON/OFF DATA and path for files (root/ps))
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15 |
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16 | // From magicserv01
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17 | TString ONDataFilename("/.magic/data16a/mazin/data/Mrk421/2004_04_22/4slices/Hillas_20040422_4sl_time_clean/Mrk421_*_HillasON.root");
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18 |
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19 | TString OFFDataFilename("/.magic/data16a/mazin/data/Mrk421/2004_04_22/4slices/Hillas_20040422_4sl_time_clean/Mrk421_*_HillasOFF.root");
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20 |
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21 |
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22 | TString PathForFiles ("/mnt/magicserv01/scratch/David/SillyTestForCommiting_July20_2004/");
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23 |
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24 |
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25 | // **********************************************
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26 | // Boolean variables defining the job of the
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27 | // macro
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28 | // **********************************************
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29 |
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30 |
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31 |
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32 | // Boolean variable that decides wether data is read from files specified above
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33 | // (ON/OFF) or read from already existing Matrices (which are obviously stored
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34 | // in a root file). The names of the files storing those matrices are produced
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35 | // automatically using information provided by some of the next variables whose
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36 | // values must be specified by user.
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37 |
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38 | // kTRUE reads alredy existing matrices, and kFALSE read data and produce matrices.
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39 |
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40 | Bool_t ReadMatrixFromRootFiles = kTRUE;
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41 |
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42 |
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43 | // Boolean variable that controls wether to use the
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44 | // TRAIN sample or not.
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45 |
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46 |
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47 | Bool_t TrainParams = kTRUE;
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48 |
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49 |
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50 |
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51 | // Variable that allows the user to skip the optimization on the
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52 | // train sample. If optimization is skipped (value kTRUE), the
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53 | // previously optimized supercuts (stored in root file
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54 | // which is called OptSCParametersONOFFThetaRangeXXXXXmRad.root, and located
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55 | // in the directory specified by variable PathForFiles) are used
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56 | // on the train and/or the test sample.
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57 |
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58 | // If value kFALSE, the cuts are optimized.
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59 | // The optimized cuts will be written in root file
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60 | // located in directory specified before. Name of
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61 | // the root files is created automatically.
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62 |
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63 | Bool_t SkipOptimization = kTRUE;
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64 |
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65 |
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66 |
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67 |
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68 |
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69 | // Boolean variable that allows the user to write the initial parameters
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70 | // into the root file that will be used to store the optimum cuts.
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71 | // If ApplyInitialParams = kTRUE , the initial
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72 | // parameters are written into this root file, and they
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73 | // will be applied to the data (TRAIN/TEST )
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74 | // IF NO OPTIMIZATION PROCEDURE IS PERFORMED.
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75 |
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76 | // If cuts are optimized (ie, variable SkipOptimization = kFALSE),
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77 | // the cuts applied to the data are the optimized cuts.
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78 |
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79 | // NOTE: be aware that, if ApplyInitialSCParams = kTRUE and
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80 | // there was a root file with the optimized cuts
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81 | // (previously computed), it will be overwritten with the initial
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82 | // SC parameters.
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83 |
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84 | Bool_t ApplyInitialSCParams = kTRUE;
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85 |
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86 |
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87 |
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88 | // Boolean variable that controls wether to use the
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89 | // TEST sample or not.
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90 |
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91 |
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92 | Bool_t TestParams = kFALSE;
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93 |
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94 |
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95 | // Boolean variable that controls wether to combine, OR not, the
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96 | // alpha distributions computed (after cuts) for the several theta bins
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97 | // in which the TRAIN sample was divided.
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98 |
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99 | Bool_t CombineCosThetaBinsForTrainSample = kFALSE;
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100 |
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101 | // Boolean variable that controls wether to combine, OR not, the
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102 | // alpha distribution computed (after cuts) for the several theta bins
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103 | // in which the TEST sample was divided.
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104 |
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105 | Bool_t CombineCosThetaBinsForTestSample = kFALSE;
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106 |
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107 |
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108 | // Fraction of ON events used for the training/testing
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109 | Double_t whichfractiontrain = 0.999;
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110 | Double_t whichfractiontest = 0.001;
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111 |
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112 | // Fraction of OFF events used for the training/testing
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113 | Double_t whichfractiontrainOFF = 0.999;
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114 | Double_t whichfractiontestOFF = 0.001;
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115 |
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116 |
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117 | // Efficiency for gammas when using this set of dynamical cuts
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118 | // (i.e., fraction of initial gammas that remain after cuts)
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119 |
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120 | // Current value is the first estimation of the efficiency of cuts
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121 | // on Mkn421 at a SIZE > 2000 photons
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122 |
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123 | Double_t gammaeff = 0.6;
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124 |
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125 |
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126 | // Alpha value (degrees) below which signal is expected
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127 |
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128 | Double_t alphasig = 12;
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129 |
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130 | // Definition of alpha bkg region (where no signal is expected)
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131 |
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132 | Double_t alphabkgmin = 30;
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133 | Double_t alphabkgmax = 90;
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134 |
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135 | // Definition of the Size range
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136 |
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137 | Double_t SizeLow = 800;
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138 | Double_t SizeUp = 1200;
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139 | // Double_t SizeUp = 1000000;
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140 |
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141 |
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142 | // Definition of binning of alpha plots
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143 | Int_t NAlphaBins = 35;
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144 | Double_t AlphaBinLow = -9;
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145 | Double_t AlphaBinUp = 96;
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146 |
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147 |
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148 | // Boolean variable used to determine wether the normalization factor is
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149 | // computed from method 1) or 2)
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150 | // 1) Using total number of ON and OFF events before cuts, and tuning the factor
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151 | // correcting for "contamination" of gamma events in ON sample
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152 | // 2) Using number of ON and OFF events after cuts in the background
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153 | // region determined by variables fAlphaBkgMin-fAlphaBkgMax
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154 |
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155 | Bool_t NormFactorFromAlphaBkg = kTRUE; // if kTRUE, method 2) is used
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156 |
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157 |
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158 | // Boolean variable used to disable the usage ("serious" usage) of the
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159 | // quantities computed from fits. This will be useful in those cases
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160 | // where there is too few events to perform a decent fit to the
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161 | // alpha histograms.
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162 |
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163 | Bool_t UseFittedQuantities = kTRUE;
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164 |
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165 |
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166 | // Boolean variable used to control wether to use theta information
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167 | // in the computation of teh dynamical cuts that take place within
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168 | // class MCT1SupercutsCalc
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169 | Bool_t NotUseTheta = kTRUE; // kTRUE renoves theta from the parameterization of cuts
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170 |
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171 | // Boolean variable used to decide wether to use dynamical cuts or static cuts
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172 | // kTRUE means that static cuts are used.
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173 | Bool_t UseStaticCuts = kFALSE;
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174 |
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175 |
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176 |
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177 |
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178 |
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179 | // Name of the Postscript document where all plots
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180 | // will be saved.
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181 | // STORAGE OF PSFILE IS NOT WORKING PROPERLY
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182 | // For the time being, several ps files are produced
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183 | // and saved in the directory specified by PathForFiles
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184 |
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185 | /*
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186 | TString PsFileName = ("PsTest23.ps");
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187 | TString CompletePsFileName = (PathForFiles);
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188 | CompletePsFileName += PsFileName;
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189 | TPostScript* PsFile = new TPostScript(CompletePsFileName, 111);
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190 | */
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191 |
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192 | // Boolean variable used to decide wether initial parameters are
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193 | // read from ascii file or not. If kTRUE, parameters are retrieved
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194 | // from ascii file. Otherwise, default parameters from MSupercuts
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195 | // class are used.
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196 |
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197 | Bool_t ReadInitParamsFromAsciiFile = kTRUE;
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198 |
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199 | // Number of SC parameters. The aim of this variable is to cross check
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200 | // that the number of parameters read from an ascii file
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201 | // is teh one the user wants.
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202 |
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203 | Int_t NInitSCPar = 104;
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204 |
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205 | // Name of the ascii file containing the 2 columns, the first one
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206 | // for initial parameters and the second one for the steps
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207 | // Name must contain also the path.
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208 |
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209 | const char* InitSCParamAsciiFile =
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210 | // {"../InitialSCParametersSteps/InitSCParamsAndStepsDanielModified1.txt"};
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211 | // {"../InitialSCParametersSteps/FixedStaticCutsInLengthWidthDist.txt"};
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212 | // {"../InitialSCParametersSteps/FixedStaticCutsInLengthWidthDist11.txt"};
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213 | // {"../InitialSCParametersSteps/InitSCParamsAndStepsDanielModified1.txt"};
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214 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421.txt"};
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215 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynStaticCutsFixedPol2SizeCut3000.txt"};
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216 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynWithDistParametersFixed.txt"};
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217 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynStaticCutsVariablePol2.txt"};
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218 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynStaticCutsVariablePol2WidthCutLowFixed.txt"};
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219 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynCutsOnSize.txt"};
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220 | // {"../InitialSCParametersSteps/StartingValuesForOptimizationMkn421DynCutsOnSizeAndDist.txt"};
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221 | {"mtemp/mmpi/asciifiles/OptimizedMkn421DynCutsGridWithSelected22pointsMay19.txt"};
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222 |
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223 |
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224 | // Name of the root file where alpha distributions, TTree objects
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225 | // with info about the events and cuts applied and info support histograms
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226 | // will be stored.
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227 | // Write only the name of the file. The Path
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228 | // is the one defined previously
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229 |
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230 | TString RootFilename = ("RootFileDynCuts.root");
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231 |
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232 |
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233 |
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234 |
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235 |
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236 |
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237 | // Vector containing the theta bins in which data (ON/OFF train/test)
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238 | // will be divided. Actually this vector contains the cosinus of
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239 | // these theta bins. The dimension of the vector is N+1, where
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240 | // N is the number of theta bins intended. The first component of the
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241 | // vector is the low bin edge of the first bin, and the last
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242 | // vector component the upper bin edge of the last bin.
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243 |
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244 |
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245 | TArrayD CosThetaRangeVector(2);
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246 | CosThetaRangeVector[0] = 0.0;
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247 | //CosThetaRangeVector[1] = 0.825;
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248 | //CosThetaRangeVector[2] = 0.921;
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249 | //CosThetaRangeVector[3] = 0.961;
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250 | CosThetaRangeVector[1] = 1.0;
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251 |
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252 |
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253 | /*
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254 | TArrayD CosThetaRangeVector(2);
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255 | CosThetaRangeVector[0] = 0.622;
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256 | // CosThetaRangeVector[1] = 0.825;
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257 | //CosThetaRangeVector[2] = 0.921;
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258 | //CosThetaRangeVector[0] = 0.961;
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259 | CosThetaRangeVector[1] = 0.984;
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260 |
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261 | */
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262 | // Object of MCT1FindSupercutsONOFFThetaLoop created, data that was specified
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263 | // above is introduced and ... and the party starts.
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264 |
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265 | MFindSupercutsONOFFThetaLoop FindSupercuts("MFindSupercutsONOFFThetaLoop",
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266 | "Optimizer for the supercuts");
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267 |
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268 |
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269 | FindSupercuts.SetPathForFiles(PathForFiles);
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270 |
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271 | FindSupercuts.SetDataONOFFRootFilenames(ONDataFilename, OFFDataFilename);
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272 |
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273 | FindSupercuts.SetFractionTrainTestOnOffEvents(whichfractiontrain,
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274 | whichfractiontest,
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275 | whichfractiontrainOFF,
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276 | whichfractiontestOFF);
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277 |
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278 |
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279 | FindSupercuts.SetGammaEfficiency(gammaeff);
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280 |
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281 |
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282 | FindSupercuts.SetAlphaSig(alphasig);
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283 |
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284 | // Bkg alpha region is set
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285 | FindSupercuts.SetAlphaBkgMin(alphabkgmin);
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286 | FindSupercuts.SetAlphaBkgMax(alphabkgmax);
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287 |
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288 | // alpha bkg and signal region set in object FindSupercuts
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289 | // are re-checked in order to be sure that make sense
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290 |
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291 | FindSupercuts.CheckAlphaSigBkg();
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292 |
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293 |
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294 | // binning for alpha plots is defined
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295 |
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296 | FindSupercuts.SetAlphaPlotBinining(NAlphaBins, AlphaBinLow,
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297 | AlphaBinUp);
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298 |
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299 |
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300 |
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301 |
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302 | // Size range is defined
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303 |
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304 | FindSupercuts.SetSizeRange(SizeLow, SizeUp);
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305 |
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306 |
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307 |
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308 | FindSupercuts.SetNormFactorFromAlphaBkg(NormFactorFromAlphaBkg);
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309 |
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310 | FindSupercuts.SetUseFittedQuantities(UseFittedQuantities);
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311 |
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312 | FindSupercuts.SetVariableUseStaticCuts(UseStaticCuts);
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313 |
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314 | FindSupercuts.SetVariableNotUseTheta(NotUseTheta);
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315 |
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316 | FindSupercuts.SetReadMatricesFromFile(ReadMatrixFromRootFiles);
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317 |
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318 | FindSupercuts.SetTrainParameters(TrainParams);
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319 | FindSupercuts.SetSkipOptimization(SkipOptimization);
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320 | FindSupercuts.SetUseInitialSCParams(ApplyInitialSCParams);
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321 |
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322 | FindSupercuts.SetTestParameters(TestParams);
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323 |
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324 |
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325 |
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326 | FindSupercuts.SetHadronnessName("MHadSC");
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327 | FindSupercuts.SetHadronnessNameOFF("MHadOFFSC");
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328 |
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329 | FindSupercuts.SetAlphaDistributionsRootFilename (RootFilename);
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330 |
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331 | // FindSupercuts.SetPostScriptFile (PsFile);
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332 |
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333 | FindSupercuts.SetCosThetaRangeVector (CosThetaRangeVector);
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334 |
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335 |
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336 | // Names for all root files (matrices, alpha distributions...)
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337 | // are created
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338 | FindSupercuts.SetALLNames();
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339 |
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340 | if(ReadInitParamsFromAsciiFile)
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341 | {
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342 | // Initial SC Parameters and steps are retrieved from
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343 | // Ascii file
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344 | if(!FindSupercuts.ReadSCParamsFromAsciiFile(InitSCParamAsciiFile,
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345 | NInitSCPar))
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346 | {
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347 | cout << "Initial SC Parameters could not be read from Ascii file "
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348 | << InitSCParamAsciiFile << endl
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349 | << "Aborting execution of macro... " << endl;
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350 | return;
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351 |
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352 | }
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353 | }
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354 |
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355 |
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356 |
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357 |
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358 | // Finally loop over all theta bins defined is executed
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359 |
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360 | if (!FindSupercuts.LoopOverThetaRanges())
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361 | {
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362 | cout << "Function MFindSupercutsONOFFThetaLoop::LoopOverThetaRanges()" << endl
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363 | << "could not be performed" << endl;
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364 |
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365 | }
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366 |
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367 |
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368 |
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369 | // Nex and Significance are computed vs alphasig
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370 |
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371 | if (!FindSupercuts.ComputeNexSignificanceVSAlphaSig())
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372 | {
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373 | cout << "Function MFindSupercutsONOFFThetaLoop::ComputeNexSignificanceVSAlphaSig()" << endl
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374 | << "could not be performed" << endl;
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375 |
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376 |
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377 | }
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378 |
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379 |
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380 |
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381 |
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382 | // Option to store ps files in a single ps document is still not working
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383 | /*
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384 | PsFile -> Close();
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385 | PsFile = NULL;
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386 | */
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387 |
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388 | // Several theta bins are combined to produced a single alpha plot (for train and test)
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389 | // with single Nex and significances
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390 |
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391 | if (CombineCosThetaBinsForTrainSample || CombineCosThetaBinsForTestSample)
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392 | {
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393 | if(!FindSupercuts.ComputeOverallSignificance(CombineCosThetaBinsForTrainSample,
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394 | CombineCosThetaBinsForTestSample))
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395 | {
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396 | cout << "Function MFindSupercutsONOFFThetaLoop::ComputeOverallSignificance" << endl
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397 | << "could not be performed" << endl;
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398 | }
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399 |
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400 |
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401 | }
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402 |
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403 |
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404 |
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405 | }
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406 |
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407 |
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408 |
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409 |
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410 |
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411 |
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412 |
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413 |
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414 |
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415 |
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416 |
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417 |
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418 |
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419 |
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420 |
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421 |
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422 |
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423 |
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424 |
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425 |
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426 |
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