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): Thomas Bretz, 8/2002 <mailto:tbretz@astro.uni-wuerzburg.de>
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19 | ! Author(s): Wolfgang Wittek, 1/2002 <mailto:wittek@mppmu.mpg.de>
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20 | !
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21 | ! Copyright: MAGIC Software Development, 2000-2008
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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 | //
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28 | // MHEffectiveOnTime
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29 | //
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30 | // Filling this you will get the effective on-time versus theta and
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31 | // observation time.
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32 | //
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33 | // From this histogram the effective on-time is determined by a fit.
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34 | // The result of the fit (see Fit()) and the fit-parameters (like chi^2)
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35 | // are stored in corresponding histograms
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36 | //
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37 | // To determin the efective on time a poisson fit is done. For more details
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38 | // please have a look into the source code of FitH() it should be simple
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39 | // to understand. In this function a Delta-T distribution is fitted, while
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40 | // Delta-T is the time between two consecutive events.
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41 | //
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42 | // The fit is done for projections of a 2D histogram in Theta and Delta T.
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43 | // So you get the effective on time versus theta.
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44 | //
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45 | // To get the effective on-time versus time a histogram is filled with
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46 | // the Delta-T distribution of a number of events set by SetNumEvents().
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47 | // The default is 12000 (roughly 1min at 200Hz)
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48 | //
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49 | // For each "time-bin" the histogram is fitted and the resulting effective
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50 | // on-time is stored in the fHTimeEffOn histogram. Each entry in this
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51 | // histogram is the effective observation time between the upper and
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52 | // lower edges of the bins.
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53 | //
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54 | // In addition the calculated effective on time is stored in a
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55 | // "MEffectiveOnTime [MParameterDerr]" and the corresponding time-stamp
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56 | // (the upper edge of the bin) "MTimeEffectiveOnTime [MTime]"
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57 | //
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58 | // The class takes two binnings from the Parameter list; if these binnings
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59 | // are not available the defaultbinning is used:
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60 | // MBinning("BinningDeltaT"); // Units of seconds
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61 | // MBinning("BinningTheta"); // Units of degrees
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62 | //
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63 | //
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64 | // Usage:
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65 | // ------
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66 | // MFillH fill("MHEffectiveOnTime", "MTime");
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67 | // tlist.AddToList(&fill);
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68 | //
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69 | //
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70 | // Input Container:
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71 | // MPointingPos
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72 | // MRawRunHeader
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73 | // MTime
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74 | //
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75 | // Output Container:
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76 | // MEffectiveOnTime [MParameterDerr]
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77 | // MTimeEffectiveOnTime [MTime]
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78 | //
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79 | //
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80 | // Class version 2:
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81 | // ----------------
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82 | // + UInt_t fFirstBin;
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83 | // + UInt_t fNumEvents;
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84 | // - Int_t fNumEvents;
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85 | //
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86 | // Class version 3:
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87 | // ----------------
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88 | // + Double_t fTotalTime;
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89 | //
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90 | // Class version 4:
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91 | // ----------------
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92 | // + Double_t fEffOnTime; //[s] On time as fitted from the DeltaT distribution
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93 | // + Double_t fEffOnTimeErr; //[s] On time error as fitted from the DeltaT distribution
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94 | //
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95 | // ==========================================================================
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96 | // Dear Colleagues,
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97 | //
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98 | // for the case that we are taking calibration events interleaved with
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99 | // cosmics events the calculation of the effective observation time has to
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100 | // be modified. I have summarized the proposed procedures in the note at the
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101 | // end of this message. The formulas have been checked by a simulation.
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102 | //
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103 | // Comments are welcome.
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104 | //
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105 | // Regards, Wolfgang
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106 | // --------------------------------------------------------------------------
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107 | // Wolfgang Wittek
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108 | // 2 Dec. 2004
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109 | //
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110 | // Calculation of the effective observation time when cosmics and calibration
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111 | // events are taken simultaneously.
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112 | // --------------------------------
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113 | //
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114 | // I. Introduction
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115 | // ---------------
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116 | // It is planned to take light calibration events (at a certain fixed frequency
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117 | // lambda_calib) interlaced with cosmics events. The advantages of this
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118 | // procedure are :
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119 | //
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120 | // - the pedestals, which would be determined from the cosmics, could be
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121 | // used for both the calibration and the cosmics events
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122 | //
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123 | // - because calibration and cosmics events are taken quasi simultaneously,
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124 | // rapid variations (in the order of a few minutes) of base lines and of the
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125 | // photon/ADC conversion factors could be recognized and taken into account
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126 | //
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127 | // The effective observation time T_eff is defined as that time range, within
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128 | // which the recorded number of events N_cosmics would be obtained under ideal
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129 | // conditions (only cosmics, no dead time, no calibration events, ...).
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130 | //
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131 | // In the absence of calibration events the effective observation time can
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132 | // be determined from the distribution of time differences 'dt' between
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133 | // successive cosmics events (see first figure in the attached ps file).
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134 | // The exponential slope 'lambda' of this distribution is the ideal cosmics
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135 | // event rate. If 'N_cosmics' is the total number of recorded cosmics events,
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136 | // T_eff is obtained by
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137 | //
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138 | // T_eff = N_cosmics / lambda
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139 | //
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140 | // In the case of a finite dead time 'dead', the distribution (for dt > dead) is
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141 | // still exponential with the same slope 'lambda'. 'lambda' should be determined
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142 | // in a region of 'dt' which is not affected by the dead time, i.e. at not too
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143 | // low 'dt'.
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144 | //
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145 | //
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146 | //
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147 | // II. Problems in the presence of calibration events
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148 | // --------------------------------------------------
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149 | // If calibration events are taken interlaced with cosmics, and if the dead time
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150 | // is negligible, the distribution of time differences 'dt' between cosmics can
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151 | // be used for calculating the effective observation time, as if the calibration
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152 | // events were not present.
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153 | //
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154 | // In the case of a non-negligible dead time 'dead', however, the distribution of
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155 | // time differences between cosmics is distorted, because a cosmics event may be
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156 | // lost due to the dead time after a calibration event. Even if the time
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157 | // intervals are ignored which contain a calibration event,
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158 | //
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159 | //
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160 | // ---|---------o--------|---------> t
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161 | //
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162 | // cosmics calib cosmics
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163 | //
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164 | // <----------------> <==== time interval to be ignored
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165 | //
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166 | //
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167 | // the distribution of 'dt' is still distorted, because there would be no
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168 | // 'dt' with dt > tau_calib = 1/lambda_calib. The distribution would also be
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169 | // distorted in the region dt < tau_calib, due to calibration events occuring
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170 | // shortly after cosmics events. As a result, the slope of the distribution of
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171 | // 'dt' would not reflect the ideal cosmics event rate (see second figure; the
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172 | // values assumed in the simulation are lambda = 200 Hz, lambda_calib = 50
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173 | // Hz, dead = 0.001 sec, total time = 500 sec, number of generated cosmics
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174 | // events = 100 000).
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175 | //
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176 | //
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177 | // Note also that some calibration events will not be recorded due to the dead
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178 | // time after a cosmics event.
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179 | //
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180 | //
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181 | // III. Proposed procedures
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182 | // ------------------------
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183 | //
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184 | // A) The ideal event rate 'lambda' may be calculated from the distribution of
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185 | // the time difference 'dt_first' between a calibration event and the first
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186 | // recorded cosmics event after the calibration event. In the region
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187 | //
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188 | // dead < dt_first < tau_calib
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189 | //
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190 | // the probability distribution of dt_first is given by
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191 | //
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192 | // p(dt_first) = c * exp(-lambda*dt_first)
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193 | //
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194 | // where c is a normalization constant. 'lambda' can be obtained by a simple
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195 | // exponential fit to the experimental distribution of dt_first (see third
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196 | // figure). The fit range should start well above the average value of the dead
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197 | // time 'dead'.
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198 | //
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199 | //
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200 | // B) One may consider those time intervals between recorded cosmics events, which
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201 | // are completely contained in the region
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202 | //
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203 | // t_calib < t < t_calib + tau_calib
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204 | //
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205 | // where t_calib is the time of a recorded calibration event.
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206 | //
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207 | //
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208 | // <--------------- tau_calib ----------->
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209 | //
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210 | //
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211 | // 0 1 2 3 4 5 6 7 8 9 10
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212 | // --|-o---|-|---|--|-|----|--|---|---|-|----o-|---|-|---------> t
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213 | // ^ ^
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214 | // | |
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215 | // t_calib t_calib + tau_calib
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216 | //
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217 | //
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218 | // In this example, of the time intervals 0 to 10 only the intervals 1 to 9
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219 | // should be retained and plotted. The distribution of the length 'dt' of these
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220 | // intervals in the region
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221 | //
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222 | // dead < dt < tau_calib
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223 | //
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224 | // is given by
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225 | //
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226 | // p(dt) = c * (tau_calib-dt-dead) * exp(-lambda*dt)
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227 | //
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228 | // A fit of this expression to the experimental distribution of 'dt' yields
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229 | // 'lambda' (see fourth figure). For 'dead' an average value of the dead time
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230 | // should be chosen, and the fit range should end well before dt = tau_calib-dead.
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231 | //
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232 | //
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233 | // Method A has the advantage that the p(dt_first) does not depend on 'dead'.
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234 | // 'dead' has to be considered when defining the fit range, both in method A and
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235 | // in method B. In method B the event statistics is larger leading to a smaller
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236 | // fitted error of 'lambda' than method A (see the figures).
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237 | //
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238 | //
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239 | // The effective observation time is again obtained by
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240 | //
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241 | // T_eff = N_cosmics / lambda
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242 | //
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243 | // where N_cosmics is the total number of recorded cosmics events. Note that
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244 | // N_cosmics is equal to
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245 | //
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246 | // N_cosmics = N_tot - N_calib
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247 | //
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248 | // where N_tot is the total number of recorded events (including the calibration
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249 | // events) and N_calib is the number of recorded calibration events.
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250 | //
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251 | // Note that if time intervals are discarded for the determination of lambda,
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252 | // the corresponding cosmics events need not and should not be discarded.
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253 | //
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254 | //
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255 | // IV. Procedure if the calibration events are taken in bunches
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256 | // ------------------------------------------------------------
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257 | // In November 2004 the rate of calibration events is not constant. The events
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258 | // are taken in 200 Hz bunches every second, such that the rate is 200 Hz for
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259 | // 0.25 sec, followed by a gap of 0.75 sec. Then follows the next 200 Hz bunch.
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260 | //
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261 | // In this case it is proposed to consider for the calculation of 'lambda' only
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262 | // the cosmics events within the gaps of 0.75 sec. For these cosmics events one
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263 | // of the methods described in III. can be applied.
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264 | //
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265 | //
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266 | // V. Alternative pocedure
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267 | // -----------------------
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268 | // The effective observation time can also be determined from the total
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269 | // observation time and the total dead time. The latter is written out by the DAQ.
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270 | // In this case it has to be made sure that the dead time is available in Mars
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271 | // when the effective observation time is calculated.
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272 | //
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273 | //////////////////////////////////////////////////////////////////////////////
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274 | #include "MHEffectiveOnTime.h"
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275 |
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276 | #include <TF1.h>
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277 | #include <TMinuit.h>
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278 | #include <TRandom.h>
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279 |
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280 | #include <TLatex.h>
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281 | #include <TGaxis.h>
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282 | #include <TCanvas.h>
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283 | #include <TPaveStats.h>
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284 |
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285 | #include "MTime.h"
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286 | #include "MString.h"
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287 | #include "MParameters.h"
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288 | #include "MPointingPos.h"
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289 | #include "MRawRunHeader.h"
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290 |
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291 | #include "MBinning.h"
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292 | #include "MParList.h"
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293 |
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294 | #include "MLog.h"
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295 | #include "MLogManip.h"
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296 |
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297 | ClassImp(MHEffectiveOnTime);
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298 |
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299 | using namespace std;
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300 |
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301 | // --------------------------------------------------------------------------
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302 | //
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303 | // Default Constructor. It initializes all histograms.
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304 | //
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305 | MHEffectiveOnTime::MHEffectiveOnTime(const char *name, const char *title)
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306 | : fPointPos(0), fTime(0), fParam(0), fIsFinalized(kFALSE),
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307 | fNumEvents(200*60), fFirstBin(3), fTotalTime(-1),
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308 | fEffOnTime(-1), fEffOnTimeErr(0)
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309 | //fNumEvents(2*60), fFirstBin(1)
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310 | {
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311 | //
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312 | // set the name and title of this object
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313 | //
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314 | fName = name ? name : "MHEffectiveOnTime";
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315 | fTitle = title ? title : "Histogram to determin effective On-Time vs Time and Zenith Angle";
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316 |
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317 | // Main histogram
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318 | fH2DeltaT.SetName("DeltaT");
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319 | fH2DeltaT.SetXTitle("\\Delta t [s]");
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320 | fH2DeltaT.SetYTitle("\\Theta [\\circ]");
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321 | fH2DeltaT.SetZTitle("Count");
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322 | fH2DeltaT.UseCurrentStyle();
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323 | fH2DeltaT.SetDirectory(NULL);
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324 |
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325 | // Main histogram
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326 | fH1DeltaT.SetName("DeltaT");
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327 | fH1DeltaT.SetXTitle("\\Delta t [s]");
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328 | fH1DeltaT.SetYTitle("Counts");
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329 | fH1DeltaT.UseCurrentStyle();
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330 | fH1DeltaT.SetDirectory(NULL);
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331 |
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332 | // effective on time versus theta
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333 | fHThetaEffOn.SetName("EffOnTheta");
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334 | fHThetaEffOn.SetTitle("Effective On Time T_{eff}");
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335 | fHThetaEffOn.SetXTitle("\\Theta [\\circ]");
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336 | fHThetaEffOn.SetYTitle("T_{eff} [s]");
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337 | fHThetaEffOn.UseCurrentStyle();
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338 | fHThetaEffOn.SetDirectory(NULL);
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339 | fHThetaEffOn.GetYaxis()->SetTitleOffset(1.2);
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340 | fHThetaEffOn.GetYaxis()->SetTitleColor(kBlue);
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341 | fHThetaEffOn.SetLineColor(kBlue);
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342 | //fHEffOn.Sumw2();
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343 |
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344 | // effective on time versus time
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345 | fHTimeEffOn.SetName("EffOnTime");
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346 | fHTimeEffOn.SetTitle("Effective On Time T_{eff}");
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347 | fHTimeEffOn.SetXTitle("Time");
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348 | fHTimeEffOn.SetYTitle("T_{eff} [s]");
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349 | fHTimeEffOn.UseCurrentStyle();
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350 | fHTimeEffOn.SetDirectory(NULL);
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351 | fHTimeEffOn.GetYaxis()->SetTitleOffset(1.2);
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352 | fHTimeEffOn.GetXaxis()->SetLabelSize(0.033);
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353 | fHTimeEffOn.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
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354 | fHTimeEffOn.GetXaxis()->SetTimeDisplay(1);
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355 | fHTimeEffOn.GetYaxis()->SetTitleColor(kBlue);
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356 | fHTimeEffOn.SetLineColor(kBlue);
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357 |
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358 | // chi2 probability
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359 | fHThetaProb.SetName("ProbTheta");
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360 | fHThetaProb.SetTitle("\\chi^{2} Probability of Fit");
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361 | fHThetaProb.SetXTitle("\\Theta [\\circ]");
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362 | fHThetaProb.SetYTitle("p [%]");
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363 | fHThetaProb.UseCurrentStyle();
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364 | fHThetaProb.SetDirectory(NULL);
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365 | fHThetaProb.GetYaxis()->SetTitleOffset(1.2);
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366 | fHThetaProb.SetMaximum(101);
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367 | fHThetaProb.GetYaxis()->SetTitleColor(kBlue);
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368 | fHThetaProb.SetLineColor(kBlue);
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369 |
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370 | // chi2 probability
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371 | fHTimeProb.SetName("ProbTime");
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372 | fHTimeProb.SetTitle("\\chi^{2} Probability of Fit");
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373 | fHTimeProb.SetXTitle("Time");
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374 | fHTimeProb.SetYTitle("p [%]");
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375 | fHTimeProb.UseCurrentStyle();
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376 | fHTimeProb.SetDirectory(NULL);
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377 | fHTimeProb.GetYaxis()->SetTitleOffset(1.2);
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378 | fHTimeProb.GetXaxis()->SetLabelSize(0.033);
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379 | fHTimeProb.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
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380 | fHTimeProb.GetXaxis()->SetTimeDisplay(1);
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381 | fHTimeProb.SetMaximum(101);
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382 | fHTimeProb.GetYaxis()->SetTitleColor(kBlue);
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383 | fHTimeProb.SetLineColor(kBlue);
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384 |
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385 | // lambda versus theta
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386 | fHThetaLambda.SetName("LambdaTheta");
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387 | fHThetaLambda.SetTitle("Slope (Rate) vs Theta");
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388 | fHThetaLambda.SetXTitle("\\Theta [\\circ]");
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389 | fHThetaLambda.SetYTitle("\\lambda [s^{-1}]");
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390 | fHThetaLambda.UseCurrentStyle();
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391 | fHThetaLambda.SetDirectory(NULL);
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392 | fHThetaLambda.SetLineColor(kGreen);
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393 |
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394 | // lambda versus time
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395 | fHTimeLambda.SetName("LambdaTime");
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396 | fHTimeLambda.SetTitle("Slope (Rate) vs Time");
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397 | fHTimeLambda.SetXTitle("\\Time [\\circ]");
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398 | fHTimeLambda.SetYTitle("\\lambda [s^{-1}]");
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399 | fHTimeLambda.UseCurrentStyle();
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400 | fHTimeLambda.SetDirectory(NULL);
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401 | fHTimeLambda.GetYaxis()->SetTitleOffset(1.2);
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402 | fHTimeLambda.GetXaxis()->SetLabelSize(0.033);
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403 | fHTimeLambda.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
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404 | fHTimeLambda.GetXaxis()->SetTimeDisplay(1);
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405 | fHTimeLambda.SetLineColor(kGreen);
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406 |
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407 | // NDoF versus theta
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408 | fHThetaNDF.SetName("NDofTheta");
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409 | fHThetaNDF.SetTitle("Number of Degrees of freedom vs Theta");
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410 | fHThetaNDF.SetXTitle("\\Theta [\\circ]");
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411 | fHThetaNDF.SetYTitle("NDoF [#]");
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412 | fHThetaNDF.UseCurrentStyle();
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413 | fHThetaNDF.SetDirectory(NULL);
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414 | fHThetaNDF.SetLineColor(kGreen);
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415 |
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416 | // NDoF versus time
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417 | /*
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418 | fHTimeNDF.SetName("NDofTime");
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419 | fHTimeNDF.SetTitle("Number of Degrees of freedom vs Time");
|
---|
420 | fHTimeNDF.SetXTitle("Time");
|
---|
421 | fHTimeNDF.SetYTitle("NDoF [#]");
|
---|
422 | fHTimeNDF.UseCurrentStyle();
|
---|
423 | fHTimeNDF.SetDirectory(NULL);
|
---|
424 | fHTimeNDF.GetYaxis()->SetTitleOffset(1.2);
|
---|
425 | fHTimeNDF.GetXaxis()->SetLabelSize(0.033);
|
---|
426 | fHTimeNDF.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
|
---|
427 | fHTimeNDF.GetXaxis()->SetTimeDisplay(1);
|
---|
428 | fHTimeNDF.SetLineColor(kBlue);
|
---|
429 | */
|
---|
430 | // setup binning
|
---|
431 | MBinning btheta("BinningTheta");
|
---|
432 | btheta.SetEdgesASin(67, -0.005, 0.665);
|
---|
433 |
|
---|
434 | MBinning btime("BinningDeltaT");
|
---|
435 | btime.SetEdges(50, 0, 0.1);
|
---|
436 |
|
---|
437 | MH::SetBinning(fH2DeltaT, btime, btheta);
|
---|
438 |
|
---|
439 | btime.Apply(fH1DeltaT);
|
---|
440 |
|
---|
441 | btheta.Apply(fHThetaEffOn);
|
---|
442 | btheta.Apply(fHThetaLambda);
|
---|
443 | btheta.Apply(fHThetaNDF);
|
---|
444 | btheta.Apply(fHThetaProb);
|
---|
445 | //btheta.Apply(fHChi2);
|
---|
446 | }
|
---|
447 |
|
---|
448 | // --------------------------------------------------------------------------
|
---|
449 | //
|
---|
450 | // Set the binnings and prepare the filling of the histogram
|
---|
451 | //
|
---|
452 | Bool_t MHEffectiveOnTime::SetupFill(const MParList *plist)
|
---|
453 | {
|
---|
454 | fPointPos = (MPointingPos*)plist->FindObject("MPointingPos");
|
---|
455 | if (!fPointPos)
|
---|
456 | {
|
---|
457 | *fLog << err << dbginf << "MPointingPos not found... aborting." << endl;
|
---|
458 | return kFALSE;
|
---|
459 | }
|
---|
460 |
|
---|
461 | // FIXME: Remove const-qualifier from base-class!
|
---|
462 | fTime = (MTime*)const_cast<MParList*>(plist)->FindCreateObj("MTime", "MTimeEffectiveOnTime");
|
---|
463 | if (!fTime)
|
---|
464 | return kFALSE;
|
---|
465 | fParam = (MParameterDerr*)const_cast<MParList*>(plist)->FindCreateObj("MParameterDerr", "MEffectiveOnTime");
|
---|
466 | if (!fParam)
|
---|
467 | return kFALSE;
|
---|
468 |
|
---|
469 | const MBinning* binsdtime = (MBinning*)plist->FindObject("BinningDeltaT");
|
---|
470 | const MBinning* binstheta = (MBinning*)plist->FindObject("BinningTheta");
|
---|
471 | if (binsdtime)
|
---|
472 | binsdtime->Apply(fH1DeltaT);
|
---|
473 | if (binstheta)
|
---|
474 | {
|
---|
475 | binstheta->Apply(fHThetaEffOn);
|
---|
476 | binstheta->Apply(fHThetaLambda);
|
---|
477 | binstheta->Apply(fHThetaNDF);
|
---|
478 | binstheta->Apply(fHThetaProb);
|
---|
479 | //binstheta->Apply(fHChi2);
|
---|
480 | }
|
---|
481 | if (binstheta && binsdtime)
|
---|
482 | SetBinning(fH2DeltaT, *binsdtime, *binstheta);
|
---|
483 |
|
---|
484 | fTotalTime = 0;
|
---|
485 |
|
---|
486 | fEffOnTime = -1;
|
---|
487 | fEffOnTimeErr = 0;
|
---|
488 |
|
---|
489 | return kTRUE;
|
---|
490 | }
|
---|
491 |
|
---|
492 | Bool_t MHEffectiveOnTime::ReInit(MParList *pList)
|
---|
493 | {
|
---|
494 | MRawRunHeader *header = (MRawRunHeader*)pList->FindObject("MRawRunHeader");
|
---|
495 | if (!header)
|
---|
496 | {
|
---|
497 | *fLog << err << "MRawRunHeader not found... aborting." << endl;
|
---|
498 | return kFALSE;
|
---|
499 | }
|
---|
500 |
|
---|
501 | fTotalTime += header->GetRunLength();
|
---|
502 |
|
---|
503 | return kTRUE;
|
---|
504 | }
|
---|
505 |
|
---|
506 | // --------------------------------------------------------------------------
|
---|
507 | //
|
---|
508 | // Fit a single Delta-T distribution. See source code for more details
|
---|
509 | //
|
---|
510 | Bool_t MHEffectiveOnTime::FitH(TH1D *h, Double_t *res, Bool_t paint) const
|
---|
511 | {
|
---|
512 | // Count also events in under-/overflowbins
|
---|
513 | const Double_t Nm = h->Integral(0, h->GetNbinsX()+1);
|
---|
514 |
|
---|
515 | // FIXME: Do fit only if contents of bin has changed
|
---|
516 | if (Nm<=0 || h->GetBinContent(1)<=0)
|
---|
517 | return kFALSE;
|
---|
518 |
|
---|
519 | // determine range (yq[0], yq[1]) of time differences
|
---|
520 | // where fit should be performed;
|
---|
521 | // require a fraction >=xq[0] of all entries to lie below yq[0]
|
---|
522 | // and a fraction <=xq[1] of all entries to lie below yq[1];
|
---|
523 | // within the range (yq[0], yq[1]) there must be no empty bin;
|
---|
524 | // choose pedestrian approach as long as GetQuantiles is not available
|
---|
525 | Double_t xq[2] = { 0.6, 0.95 }; // previously 0.99
|
---|
526 | Double_t yq[2];
|
---|
527 | h->GetQuantiles(2, yq, xq);
|
---|
528 |
|
---|
529 | //
|
---|
530 | // Determine a good starting value for the slope
|
---|
531 | //
|
---|
532 | const TAxis &axe = *h->GetXaxis();
|
---|
533 | const UInt_t ibin = axe.FindFixBin(yq[1]);
|
---|
534 | const Double_t x1 = axe.GetBinCenter(ibin<=fFirstBin?fFirstBin+1:ibin);
|
---|
535 | const Double_t x0 = axe.GetBinCenter(fFirstBin);
|
---|
536 | const Double_t y1 = h->GetBinContent(ibin)>1 ? TMath::Log(h->GetBinContent(ibin)) : 0;
|
---|
537 | const Double_t y0 = TMath::Log(h->GetBinContent(fFirstBin));
|
---|
538 |
|
---|
539 | // Estimated slope
|
---|
540 | const Float_t m = -(y1-y0)/(x1-x0);
|
---|
541 |
|
---|
542 | //
|
---|
543 | // Setup exponential function for the fit:
|
---|
544 | //
|
---|
545 | // parameter 0 = rate [Hz]
|
---|
546 | // parameter 1 = normalization
|
---|
547 | //
|
---|
548 | TF1 func("Exp", " exp([1]-[0]*x)");
|
---|
549 |
|
---|
550 | func.SetParameter(0, m); // Hz
|
---|
551 | func.SetParameter(1, log(h->GetBinContent(1))); // Hz
|
---|
552 |
|
---|
553 | // We set a limit to make sure that almost empty histograms which
|
---|
554 | // are fitted dont't produce hang ups or crashes
|
---|
555 | func.SetParLimits(0, 0, 15000); // Hz
|
---|
556 |
|
---|
557 | // options : N do not store the function, do not draw
|
---|
558 | // I use integral of function in bin rather than value at bin center
|
---|
559 | // R use the range specified in the function range
|
---|
560 | // Q quiet mode
|
---|
561 | // L Use log-likelihood (better for low statistics)
|
---|
562 | h->Fit(&func, "NIQEL", "", h->GetBinLowEdge(fFirstBin)/*yq[0]*/, yq[1]);
|
---|
563 |
|
---|
564 | const Double_t chi2 = func.GetChisquare();
|
---|
565 | const Double_t prob = func.GetProb();
|
---|
566 | const Int_t NDF = func.GetNDF();
|
---|
567 |
|
---|
568 | // was fit successful ?
|
---|
569 | const Bool_t ok = prob>0.001; //NDF>0 && chi2<3*NDF;
|
---|
570 |
|
---|
571 | if (paint)
|
---|
572 | {
|
---|
573 | func.SetLineWidth(2);
|
---|
574 | func.SetLineColor(ok ? kGreen : kRed);
|
---|
575 | func.Paint("same");
|
---|
576 | }
|
---|
577 |
|
---|
578 | // The effective on time is the "real rate" (slope of the exponential)
|
---|
579 | // divided by the total number of events (histogram integral including
|
---|
580 | // under- and overflows)
|
---|
581 | const Double_t lambda = func.GetParameter(0);
|
---|
582 | const Double_t dldl = func.GetParError(0)*func.GetParError(0);
|
---|
583 | const Double_t teff = lambda==0 ? 0 : Nm / lambda;
|
---|
584 | const Double_t dteff = lambda==0 ? 0 : teff * TMath::Sqrt(dldl/(lambda*lambda) + 1.0/Nm);
|
---|
585 | const Double_t dl = TMath::Sqrt(dldl);
|
---|
586 |
|
---|
587 | // the effective on time is Nm/lambda
|
---|
588 | res[0] = teff;
|
---|
589 | res[1] = dteff;
|
---|
590 |
|
---|
591 | // plot chi2-probability of fit
|
---|
592 | res[2] = prob*100;
|
---|
593 |
|
---|
594 | // lambda of fit
|
---|
595 | res[3] = lambda;
|
---|
596 | res[4] = dl;
|
---|
597 |
|
---|
598 | // NDoF of fit
|
---|
599 | res[5] = NDF;
|
---|
600 |
|
---|
601 | // Chi2
|
---|
602 | res[6] = chi2;
|
---|
603 |
|
---|
604 | return ok;
|
---|
605 | }
|
---|
606 |
|
---|
607 | // --------------------------------------------------------------------------
|
---|
608 | //
|
---|
609 | // Fit a all bins of the distribution in theta. Store the result in the
|
---|
610 | // Theta-Histograms
|
---|
611 | //
|
---|
612 | void MHEffectiveOnTime::FitThetaBins()
|
---|
613 | {
|
---|
614 | fHThetaEffOn.Reset();
|
---|
615 | fHThetaProb.Reset();
|
---|
616 | fHThetaLambda.Reset();
|
---|
617 | fHThetaNDF.Reset();
|
---|
618 |
|
---|
619 | // Use a random name to make sure the object is unique
|
---|
620 | const TString name = MString::Format("CalcTheta%d", (UInt_t)gRandom->Uniform(999999999));
|
---|
621 |
|
---|
622 | // nbins = number of Theta bins
|
---|
623 | const Int_t nbins = fH2DeltaT.GetNbinsY();
|
---|
624 |
|
---|
625 | TH1D *h=0;
|
---|
626 | for (int i=1; i<=nbins; i++)
|
---|
627 | {
|
---|
628 | // TH1D &h = *hist->ProjectionX("Calc-theta", i, i);
|
---|
629 | h = fH2DeltaT.ProjectionX(name, i, i, "E");
|
---|
630 |
|
---|
631 | Double_t res[7] = {0, 0, 0, 0, 0, 0, 0};
|
---|
632 | //if (!FitH(h, res))
|
---|
633 | // continue;
|
---|
634 | FitH(h, res);
|
---|
635 |
|
---|
636 | if (res[0]==0)
|
---|
637 | continue;
|
---|
638 |
|
---|
639 | // the effective on time is Nm/lambda
|
---|
640 | fHThetaEffOn.SetBinContent(i, res[0]);
|
---|
641 | fHThetaEffOn.SetBinError (i, res[1]);
|
---|
642 |
|
---|
643 | // plot chi2-probability of fit
|
---|
644 | fHThetaProb.SetBinContent(i, res[2]);
|
---|
645 |
|
---|
646 | // plot chi2/NDF of fit
|
---|
647 | //fHChi2.SetBinContent(i, res[3]);
|
---|
648 |
|
---|
649 | // lambda of fit
|
---|
650 | fHThetaLambda.SetBinContent(i, res[3]);
|
---|
651 | fHThetaLambda.SetBinError (i, res[4]);
|
---|
652 |
|
---|
653 | // NDoF of fit
|
---|
654 | fHThetaNDF.SetBinContent(i, res[5]);
|
---|
655 |
|
---|
656 | // Rdead (from fit) is the fraction from real time lost by the dead time
|
---|
657 | //fHRdead.SetBinContent(i, Rdead);
|
---|
658 | //fHRdead.SetBinError (i,dRdead);
|
---|
659 | }
|
---|
660 |
|
---|
661 | // Histogram is reused via gROOT->FindObject()
|
---|
662 | // Need to be deleted only once
|
---|
663 | if (h)
|
---|
664 | delete h;
|
---|
665 | }
|
---|
666 |
|
---|
667 | // --------------------------------------------------------------------------
|
---|
668 | //
|
---|
669 | // Fit the single-time-bin histogram. Store the result in the
|
---|
670 | // Time-Histograms
|
---|
671 | //
|
---|
672 | void MHEffectiveOnTime::FitTimeBin()
|
---|
673 | {
|
---|
674 | //
|
---|
675 | // Fit histogram
|
---|
676 | //
|
---|
677 | Double_t res[7];
|
---|
678 | if (!FitH(&fH1DeltaT, res))
|
---|
679 | return;
|
---|
680 |
|
---|
681 | // Reset Histogram
|
---|
682 | fH1DeltaT.Reset();
|
---|
683 |
|
---|
684 | //
|
---|
685 | // Prepare Histogram
|
---|
686 | //
|
---|
687 |
|
---|
688 | // Get number of bins
|
---|
689 | const Int_t n = fHTimeEffOn.GetNbinsX();
|
---|
690 |
|
---|
691 | // Enhance binning
|
---|
692 | MBinning bins;
|
---|
693 | bins.SetEdges(fHTimeEffOn, 'x');
|
---|
694 | bins.AddEdge(fLastTime.GetAxisTime());
|
---|
695 | bins.Apply(fHTimeEffOn);
|
---|
696 | bins.Apply(fHTimeProb);
|
---|
697 | bins.Apply(fHTimeLambda);
|
---|
698 | //bins.Apply(fHTimeNDF);
|
---|
699 |
|
---|
700 | //
|
---|
701 | // Fill histogram
|
---|
702 | //
|
---|
703 | fHTimeEffOn.SetBinContent(n, res[0]);
|
---|
704 | fHTimeEffOn.SetBinError(n, res[1]);
|
---|
705 |
|
---|
706 | fHTimeProb.SetBinContent(n, res[2]);
|
---|
707 |
|
---|
708 | fHTimeLambda.SetBinContent(n, res[3]);
|
---|
709 | fHTimeLambda.SetBinError(n, res[4]);
|
---|
710 |
|
---|
711 | //fHTimeNDF.SetBinContent(n, res[5]);
|
---|
712 |
|
---|
713 | //
|
---|
714 | // Now prepare output
|
---|
715 | //
|
---|
716 | fParam->SetVal(res[0], res[1]);
|
---|
717 | fParam->SetReadyToSave();
|
---|
718 |
|
---|
719 | *fTime = fLastTime;
|
---|
720 |
|
---|
721 | // Include the current event
|
---|
722 | fTime->Plus1ns();
|
---|
723 |
|
---|
724 | *fLog << inf2 << fLastTime << ": Val=" << res[0] << " Err=" << res[1] << endl;
|
---|
725 | }
|
---|
726 |
|
---|
727 | // --------------------------------------------------------------------------
|
---|
728 | //
|
---|
729 | // Fill the histogram
|
---|
730 | //
|
---|
731 | Int_t MHEffectiveOnTime::Fill(const MParContainer *par, const Stat_t w)
|
---|
732 | {
|
---|
733 | const MTime *time = dynamic_cast<const MTime*>(par);
|
---|
734 | if (!time)
|
---|
735 | {
|
---|
736 | *fLog << err << "ERROR - MHEffectiveOnTime::Fill without argument or container doesn't inherit from MTime... abort." << endl;
|
---|
737 | return kERROR;
|
---|
738 | }
|
---|
739 |
|
---|
740 | //
|
---|
741 | // If this is the first call we have to initialize the time-histogram
|
---|
742 | //
|
---|
743 | if (fLastTime==MTime())
|
---|
744 | {
|
---|
745 | MBinning bins;
|
---|
746 | bins.SetEdges(1, time->GetAxisTime()-fNumEvents/200, time->GetAxisTime());
|
---|
747 | bins.Apply(fHTimeEffOn);
|
---|
748 | bins.Apply(fHTimeProb);
|
---|
749 | bins.Apply(fHTimeLambda);
|
---|
750 | //bins.Apply(fHTimeNDF);
|
---|
751 |
|
---|
752 | fParam->SetVal(0, 0);
|
---|
753 | fParam->SetReadyToSave();
|
---|
754 |
|
---|
755 | *fTime = *time;
|
---|
756 |
|
---|
757 | // Make this 1ns before the first event!
|
---|
758 | fTime->Minus1ns();
|
---|
759 | }
|
---|
760 |
|
---|
761 | //
|
---|
762 | // Fill time difference into the histograms
|
---|
763 | //
|
---|
764 | const Double_t dt = *time-fLastTime;
|
---|
765 | fLastTime = *time;
|
---|
766 |
|
---|
767 | fH2DeltaT.Fill(dt, fPointPos->GetZd(), w);
|
---|
768 | fH1DeltaT.Fill(dt, w);
|
---|
769 |
|
---|
770 | //
|
---|
771 | // If we reached the event number limit for the time-bins fit the
|
---|
772 | // histogram - if it fails try again when 1.6% more events available
|
---|
773 | //
|
---|
774 | const UInt_t n = (UInt_t)fH1DeltaT.GetEntries();
|
---|
775 | if (n>=fNumEvents && n%(fNumEvents/60)==0)
|
---|
776 | FitTimeBin();
|
---|
777 |
|
---|
778 | return kTRUE;
|
---|
779 | }
|
---|
780 |
|
---|
781 | // --------------------------------------------------------------------------
|
---|
782 | //
|
---|
783 | // Fit the theta projections of the 2D histogram and the 1D Delta-T
|
---|
784 | // distribution
|
---|
785 | //
|
---|
786 | Bool_t MHEffectiveOnTime::Finalize()
|
---|
787 | {
|
---|
788 | FitThetaBins();
|
---|
789 | FitTimeBin();
|
---|
790 |
|
---|
791 |
|
---|
792 | TH1D *h = fH2DeltaT.ProjectionX("FinalizeProjDeltaT", -1, -1, "E");
|
---|
793 | Double_t res[7];
|
---|
794 | if (FitH(h, res))
|
---|
795 | {
|
---|
796 | fEffOnTime = res[0];
|
---|
797 | fEffOnTimeErr = res[1];
|
---|
798 | }
|
---|
799 | delete h;
|
---|
800 |
|
---|
801 |
|
---|
802 | fIsFinalized = kTRUE;
|
---|
803 |
|
---|
804 | return kTRUE;
|
---|
805 | }
|
---|
806 |
|
---|
807 | // --------------------------------------------------------------------------
|
---|
808 | //
|
---|
809 | // Paint the integral and the error on top of the histogram
|
---|
810 | //
|
---|
811 | void MHEffectiveOnTime::PaintText(Double_t val, Double_t error, Double_t range) const
|
---|
812 | {
|
---|
813 | TLatex text;
|
---|
814 | text.SetBit(TLatex::kTextNDC);
|
---|
815 | text.SetTextSize(0.04);
|
---|
816 |
|
---|
817 | TString txt = MString::Format("T_{eff} = %.1fs \\pm %.1fs", val, error);
|
---|
818 | if (range>0)
|
---|
819 | txt += MString::Format(" T_{axe} = %.1fs", range);
|
---|
820 | if (fTotalTime>0)
|
---|
821 | txt += MString::Format(" T_{max} = %.1fs", fTotalTime);
|
---|
822 |
|
---|
823 | text.SetText(0.35, 0.94, txt);
|
---|
824 | text.Paint();
|
---|
825 | }
|
---|
826 |
|
---|
827 | void MHEffectiveOnTime::PaintText(Double_t *res) const
|
---|
828 | {
|
---|
829 | TLatex text(0.27, 0.94, MString::Format("T_{eff}=%.1fs\\pm%.1fs \\lambda=%.1f\\pm%.1fHz p=%.1f%% \\chi^{2}/%d=%.1f",
|
---|
830 | res[0], res[1], res[3], res[4], res[2], (int)res[5], res[6]/res[5]));
|
---|
831 | text.SetBit(TLatex::kTextNDC);
|
---|
832 | text.SetTextSize(0.04);
|
---|
833 | text.Paint();
|
---|
834 | }
|
---|
835 |
|
---|
836 | void MHEffectiveOnTime::PaintProb(TH1 &h) const
|
---|
837 | {
|
---|
838 | Double_t sum = 0;
|
---|
839 | Int_t n = 0;
|
---|
840 | for (int i=0; i<h.GetNbinsX(); i++)
|
---|
841 | if (h.GetBinContent(i+1)>0)
|
---|
842 | {
|
---|
843 | sum += h.GetBinContent(i+1);
|
---|
844 | n++;
|
---|
845 | }
|
---|
846 |
|
---|
847 | if (n==0)
|
---|
848 | return;
|
---|
849 |
|
---|
850 | TLatex text(0.47, 0.94, MString::Format("\\bar{p} = %.1f%%", sum/n));
|
---|
851 | text.SetBit(TLatex::kTextNDC);
|
---|
852 | text.SetTextSize(0.04);
|
---|
853 | text.Paint();
|
---|
854 | }
|
---|
855 |
|
---|
856 | void MHEffectiveOnTime::UpdateRightAxis(TH1 &h)
|
---|
857 | {
|
---|
858 | const Double_t max = h.GetMaximum()*1.1;
|
---|
859 | if (max==0)
|
---|
860 | return;
|
---|
861 |
|
---|
862 | h.SetNormFactor(h.Integral()*gPad->GetUymax()/max);
|
---|
863 |
|
---|
864 | TGaxis *axis = (TGaxis*)gPad->FindObject("RightAxis");
|
---|
865 | if (!axis)
|
---|
866 | return;
|
---|
867 |
|
---|
868 | axis->SetX1(gPad->GetUxmax());
|
---|
869 | axis->SetX2(gPad->GetUxmax());
|
---|
870 | axis->SetY1(gPad->GetUymin());
|
---|
871 | axis->SetY2(gPad->GetUymax());
|
---|
872 | axis->SetWmax(max);
|
---|
873 | }
|
---|
874 |
|
---|
875 | // --------------------------------------------------------------------------
|
---|
876 | //
|
---|
877 | // Prepare painting the histograms
|
---|
878 | //
|
---|
879 | void MHEffectiveOnTime::Paint(Option_t *opt)
|
---|
880 | {
|
---|
881 | TH1D *h=0;
|
---|
882 | TPaveStats *st=0;
|
---|
883 |
|
---|
884 | TString o(opt);
|
---|
885 | if (o==(TString)"fit")
|
---|
886 | {
|
---|
887 | TVirtualPad *pad = gPad;
|
---|
888 |
|
---|
889 | for (int x=0; x<2; x++)
|
---|
890 | for (int y=0; y<3; y++)
|
---|
891 | {
|
---|
892 | TVirtualPad *p=gPad->GetPad(x+1)->GetPad(y+1);
|
---|
893 | if (!(st = dynamic_cast<TPaveStats*>(p->GetPrimitive("stats"))))
|
---|
894 | continue;
|
---|
895 |
|
---|
896 | if (st->GetOptStat()==11)
|
---|
897 | continue;
|
---|
898 |
|
---|
899 | const Double_t y1 = st->GetY1NDC();
|
---|
900 | const Double_t y2 = st->GetY2NDC();
|
---|
901 | const Double_t x1 = st->GetX1NDC();
|
---|
902 | const Double_t x2 = st->GetX2NDC();
|
---|
903 |
|
---|
904 | st->SetY1NDC((y2-y1)/3+y1);
|
---|
905 | st->SetX1NDC((x2-x1)/3+x1);
|
---|
906 | st->SetOptStat(11);
|
---|
907 | }
|
---|
908 |
|
---|
909 | pad->GetPad(1)->cd(1);
|
---|
910 | if ((h = (TH1D*)gPad->FindObject("ProjDeltaT"/*fNameProjDeltaT*/)))
|
---|
911 | {
|
---|
912 | h = fH2DeltaT.ProjectionX("ProjDeltaT"/*fNameProjDeltaT*/, -1, -1, "E");
|
---|
913 | if (h->GetEntries()>0)
|
---|
914 | gPad->SetLogy();
|
---|
915 | }
|
---|
916 |
|
---|
917 | pad->GetPad(2)->cd(1);
|
---|
918 | if ((h = (TH1D*)gPad->FindObject("ProjTheta"/*fNameProjTheta*/)))
|
---|
919 | fH2DeltaT.ProjectionY("ProjTheta"/*fNameProjTheta*/, -1, -1, "E");
|
---|
920 |
|
---|
921 | if (!fIsFinalized)
|
---|
922 | FitThetaBins();
|
---|
923 | return;
|
---|
924 | }
|
---|
925 | if (o==(TString)"paint")
|
---|
926 | {
|
---|
927 | if ((h = (TH1D*)gPad->FindObject("ProjDeltaT"/*fNameProjDeltaT*/)))
|
---|
928 | {
|
---|
929 | Double_t res[7];
|
---|
930 | if (FitH(h, res, kTRUE))
|
---|
931 | PaintText(res);
|
---|
932 | }
|
---|
933 | return;
|
---|
934 | }
|
---|
935 |
|
---|
936 | if (o==(TString)"timendf")
|
---|
937 | {
|
---|
938 | // UpdateRightAxis(fHTimeNDF);
|
---|
939 | // FIXME: first bin?
|
---|
940 | PaintProb(fHTimeProb);
|
---|
941 | }
|
---|
942 |
|
---|
943 | if (o==(TString)"thetandf")
|
---|
944 | {
|
---|
945 | UpdateRightAxis(fHThetaNDF);
|
---|
946 | // FIXME: first bin?
|
---|
947 | PaintProb(fHThetaProb);
|
---|
948 | }
|
---|
949 |
|
---|
950 | h=0;
|
---|
951 |
|
---|
952 | Double_t range=-1;
|
---|
953 | if (o==(TString)"theta")
|
---|
954 | {
|
---|
955 | h = &fHThetaEffOn;
|
---|
956 | UpdateRightAxis(fHThetaLambda);
|
---|
957 | }
|
---|
958 | if (o==(TString)"time")
|
---|
959 | {
|
---|
960 | h = &fHTimeEffOn;
|
---|
961 | UpdateRightAxis(fHTimeLambda);
|
---|
962 | range = h->GetXaxis()->GetXmax() - h->GetXaxis()->GetXmin();
|
---|
963 | }
|
---|
964 |
|
---|
965 | if (!h)
|
---|
966 | return;
|
---|
967 |
|
---|
968 | Double_t error = 0;
|
---|
969 | for (int i=0; i<h->GetXaxis()->GetNbins(); i++)
|
---|
970 | error += h->GetBinError(i);
|
---|
971 |
|
---|
972 | PaintText(h->Integral(), error, range);
|
---|
973 | }
|
---|
974 |
|
---|
975 | void MHEffectiveOnTime::DrawRightAxis(const char *title)
|
---|
976 | {
|
---|
977 | TGaxis *axis = new TGaxis(gPad->GetUxmax(), gPad->GetUymin(),
|
---|
978 | gPad->GetUxmax(), gPad->GetUymax(),
|
---|
979 | 0, 1, 510, "+L");
|
---|
980 | axis->SetName("RightAxis");
|
---|
981 | axis->SetTitle(title);
|
---|
982 | axis->SetTitleOffset(0.9);
|
---|
983 | axis->SetTextColor(kGreen);
|
---|
984 | axis->CenterTitle();
|
---|
985 | axis->SetBit(kCanDelete);
|
---|
986 | axis->Draw();
|
---|
987 | }
|
---|
988 |
|
---|
989 | // --------------------------------------------------------------------------
|
---|
990 | //
|
---|
991 | // Draw the histogram
|
---|
992 | //
|
---|
993 | void MHEffectiveOnTime::Draw(Option_t *opt)
|
---|
994 | {
|
---|
995 | TVirtualPad *pad = gPad ? gPad : MakeDefCanvas(this);
|
---|
996 | pad->SetBorderMode(0);
|
---|
997 |
|
---|
998 | AppendPad("fit");
|
---|
999 |
|
---|
1000 | pad->Divide(2, 1, 1e-10, 1e-10);
|
---|
1001 |
|
---|
1002 | TH1 *h;
|
---|
1003 |
|
---|
1004 | pad->cd(1);
|
---|
1005 | gPad->SetBorderMode(0);
|
---|
1006 | gPad->Divide(1, 3, 1e-10, 1e-10);
|
---|
1007 | pad->GetPad(1)->cd(1);
|
---|
1008 | gPad->SetBorderMode(0);
|
---|
1009 | h = fH2DeltaT.ProjectionX("ProjDeltaT"/*fNameProjDeltaT*/, -1, -1, "E");
|
---|
1010 | h->SetTitle("Distribution of \\Delta t [s]");
|
---|
1011 | h->SetXTitle("\\Delta t [s]");
|
---|
1012 | h->SetYTitle("Counts");
|
---|
1013 | h->SetDirectory(NULL);
|
---|
1014 | h->SetMarkerStyle(kFullDotMedium);
|
---|
1015 | h->SetBit(kCanDelete);
|
---|
1016 | h->Draw();
|
---|
1017 | AppendPad("paint");
|
---|
1018 |
|
---|
1019 | pad->GetPad(1)->cd(2);
|
---|
1020 | gPad->SetBorderMode(0);
|
---|
1021 | fHTimeProb.Draw();
|
---|
1022 | AppendPad("timendf");
|
---|
1023 | //fHTimeNDF.Draw("same");
|
---|
1024 | //DrawRightAxis("NDF");
|
---|
1025 |
|
---|
1026 | pad->GetPad(1)->cd(3);
|
---|
1027 | gPad->SetBorderMode(0);
|
---|
1028 | fHTimeEffOn.Draw();
|
---|
1029 | AppendPad("time");
|
---|
1030 | fHTimeLambda.Draw("same");
|
---|
1031 | DrawRightAxis("\\lambda [s^{-1}]");
|
---|
1032 |
|
---|
1033 | pad->cd(2);
|
---|
1034 | gPad->SetBorderMode(0);
|
---|
1035 | gPad->Divide(1, 3, 1e-10, 1e-10);
|
---|
1036 |
|
---|
1037 | pad->GetPad(2)->cd(1);
|
---|
1038 | gPad->SetBorderMode(0);
|
---|
1039 | h = fH2DeltaT.ProjectionY("ProjTheta"/*fNameProjTheta*/, -1, -1, "E");
|
---|
1040 | h->SetTitle("Distribution of \\Theta [\\circ]");
|
---|
1041 | h->SetXTitle("\\Theta [\\circ]");
|
---|
1042 | h->SetYTitle("Counts");
|
---|
1043 | h->SetDirectory(NULL);
|
---|
1044 | h->SetMarkerStyle(kFullDotMedium);
|
---|
1045 | h->SetBit(kCanDelete);
|
---|
1046 | h->GetYaxis()->SetTitleOffset(1.1);
|
---|
1047 | h->Draw();
|
---|
1048 |
|
---|
1049 | pad->GetPad(2)->cd(2);
|
---|
1050 | gPad->SetBorderMode(0);
|
---|
1051 | fHThetaProb.Draw();
|
---|
1052 | AppendPad("thetandf");
|
---|
1053 | fHThetaNDF.Draw("same");
|
---|
1054 | DrawRightAxis("NDF");
|
---|
1055 |
|
---|
1056 | pad->GetPad(2)->cd(3);
|
---|
1057 | gPad->SetBorderMode(0);
|
---|
1058 | fHThetaEffOn.Draw();
|
---|
1059 | AppendPad("theta");
|
---|
1060 | fHThetaLambda.Draw("same");
|
---|
1061 | DrawRightAxis("\\lambda [s^{-1}]");
|
---|
1062 | }
|
---|
1063 |
|
---|
1064 | // --------------------------------------------------------------------------
|
---|
1065 | //
|
---|
1066 | // The following resources are available:
|
---|
1067 | //
|
---|
1068 | // MHEffectiveOnTime.FistBin: 3
|
---|
1069 | // MHEffectiveOnTime.NumEvents: 12000
|
---|
1070 | //
|
---|
1071 | Int_t MHEffectiveOnTime::ReadEnv(const TEnv &env, TString prefix, Bool_t print)
|
---|
1072 | {
|
---|
1073 | Bool_t rc = kFALSE;
|
---|
1074 | if (IsEnvDefined(env, prefix, "FirstBin", print))
|
---|
1075 | {
|
---|
1076 | rc = kTRUE;
|
---|
1077 | SetFirstBin(GetEnvValue(env, prefix, "FirstBin", (Int_t)fFirstBin));
|
---|
1078 | }
|
---|
1079 | if (IsEnvDefined(env, prefix, "NumEvents", print))
|
---|
1080 | {
|
---|
1081 | rc = kTRUE;
|
---|
1082 | SetNumEvents(GetEnvValue(env, prefix, "NumEvents", (Int_t)fNumEvents));
|
---|
1083 | }
|
---|
1084 | return rc;
|
---|
1085 | }
|
---|