| 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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