| 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, 2000-2004
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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 | //
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| 73 | // Output Container:
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| 74 | // MEffectiveOnTime [MParameterDerr]
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| 75 | // MTimeEffectiveOnTime [MTime]
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| 76 | //
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| 77 | //
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| 78 | // ==========================================================================
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| 79 | // Dear Colleagues,
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| 80 | //
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| 81 | // for the case that we are taking calibration events interleaved with
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| 82 | // cosmics events the calculation of the effective observation time has to
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| 83 | // be modified. I have summarized the proposed procedures in the note at the
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| 84 | // end of this message. The formulas have been checked by a simulation.
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| 85 | //
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| 86 | // Comments are welcome.
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| 87 | //
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| 88 | // Regards, Wolfgang
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| 89 | // --------------------------------------------------------------------------
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| 90 | // Wolfgang Wittek
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| 91 | // 2 Dec. 2004
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| 92 | //
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| 93 | // Calculation of the effective observation time when cosmics and calibration
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| 94 | // events are taken simultaneously.
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| 95 | // --------------------------------
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| 96 | //
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| 97 | // I. Introduction
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| 98 | // ---------------
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| 99 | // It is planned to take light calibration events (at a certain fixed frequency
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| 100 | // lambda_calib) interlaced with cosmics events. The advantages of this
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| 101 | // procedure are :
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| 102 | //
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| 103 | // - the pedestals, which would be determined from the cosmics, could be
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| 104 | // used for both the calibration and the cosmics events
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| 105 | //
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| 106 | // - because calibration and cosmics events are taken quasi simultaneously,
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| 107 | // rapid variations (in the order of a few minutes) of base lines and of the
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| 108 | // photon/ADC conversion factors could be recognized and taken into account
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| 109 | //
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| 110 | // The effective observation time T_eff is defined as that time range, within
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| 111 | // which the recorded number of events N_cosmics would be obtained under ideal
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| 112 | // conditions (only cosmics, no dead time, no calibration events, ...).
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| 113 | //
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| 114 | // In the absence of calibration events the effective observation time can
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| 115 | // be determined from the distribution of time differences 'dt' between
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| 116 | // successive cosmics events (see first figure in the attached ps file).
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| 117 | // The exponential slope 'lambda' of this distribution is the ideal cosmics
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| 118 | // event rate. If 'N_cosmics' is the total number of recorded cosmics events,
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| 119 | // T_eff is obtained by
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| 120 | //
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| 121 | // T_eff = N_cosmics / lambda
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| 122 | //
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| 123 | // In the case of a finite dead time 'dead', the distribution (for dt > dead) is
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| 124 | // still exponential with the same slope 'lambda'. 'lambda' should be determined
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| 125 | // in a region of 'dt' which is not affected by the dead time, i.e. at not too
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| 126 | // low 'dt'.
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| 127 | //
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| 128 | //
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| 129 | //
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| 130 | // II. Problems in the presence of calibration events
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| 131 | // --------------------------------------------------
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| 132 | // If calibration events are taken interlaced with cosmics, and if the dead time
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| 133 | // is negligible, the distribution of time differences 'dt' between cosmics can
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| 134 | // be used for calculating the effective observation time, as if the calibration
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| 135 | // events were not present.
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| 136 | //
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| 137 | // In the case of a non-negligible dead time 'dead', however, the distribution of
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| 138 | // time differences between cosmics is distorted, because a cosmics event may be
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| 139 | // lost due to the dead time after a calibration event. Even if the time
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| 140 | // intervals are ignored which contain a calibration event,
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| 141 | //
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| 142 | //
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| 143 | // ---|---------o--------|---------> t
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| 144 | //
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| 145 | // cosmics calib cosmics
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| 146 | //
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| 147 | // <----------------> <==== time interval to be ignored
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| 148 | //
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| 149 | //
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| 150 | // the distribution of 'dt' is still distorted, because there would be no
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| 151 | // 'dt' with dt > tau_calib = 1/lambda_calib. The distribution would also be
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| 152 | // distorted in the region dt < tau_calib, due to calibration events occuring
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| 153 | // shortly after cosmics events. As a result, the slope of the distribution of
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| 154 | // 'dt' would not reflect the ideal cosmics event rate (see second figure; the
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| 155 | // values assumed in the simulation are lambda = 200 Hz, lambda_calib = 50
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| 156 | // Hz, dead = 0.001 sec, total time = 500 sec, number of generated cosmics
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| 157 | // events = 100 000).
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| 158 | //
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| 159 | //
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| 160 | // Note also that some calibration events will not be recorded due to the dead
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| 161 | // time after a cosmics event.
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| 162 | //
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| 163 | //
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| 164 | // III. Proposed procedures
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| 165 | // ------------------------
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| 166 | //
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| 167 | // A) The ideal event rate 'lambda' may be calculated from the distribution of
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| 168 | // the time difference 'dt_first' between a calibration event and the first
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| 169 | // recorded cosmics event after the calibration event. In the region
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| 170 | //
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| 171 | // dead < dt_first < tau_calib
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| 172 | //
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| 173 | // the probability distribution of dt_first is given by
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| 174 | //
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| 175 | // p(dt_first) = c * exp(-lambda*dt_first)
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| 176 | //
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| 177 | // where c is a normalization constant. 'lambda' can be obtained by a simple
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| 178 | // exponential fit to the experimental distribution of dt_first (see third
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| 179 | // figure). The fit range should start well above the average value of the dead
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| 180 | // time 'dead'.
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| 181 | //
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| 182 | //
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| 183 | // B) One may consider those time intervals between recorded cosmics events, which
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| 184 | // are completely contained in the region
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| 185 | //
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| 186 | // t_calib < t < t_calib + tau_calib
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| 187 | //
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| 188 | // where t_calib is the time of a recorded calibration event.
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| 189 | //
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| 190 | //
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| 191 | // <--------------- tau_calib ----------->
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| 192 | //
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| 193 | //
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| 194 | // 0 1 2 3 4 5 6 7 8 9 10
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| 195 | // --|-o---|-|---|--|-|----|--|---|---|-|----o-|---|-|---------> t
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| 196 | // ^ ^
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| 197 | // | |
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| 198 | // t_calib t_calib + tau_calib
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| 199 | //
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| 200 | //
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| 201 | // In this example, of the time intervals 0 to 10 only the intervals 1 to 9
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| 202 | // should be retained and plotted. The distribution of the length 'dt' of these
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| 203 | // intervals in the region
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| 204 | //
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| 205 | // dead < dt < tau_calib
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| 206 | //
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| 207 | // is given by
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| 208 | //
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| 209 | // p(dt) = c * (tau_calib-dt-dead) * exp(-lambda*dt)
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| 210 | //
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| 211 | // A fit of this expression to the experimental distribution of 'dt' yields
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| 212 | // 'lambda' (see fourth figure). For 'dead' an average value of the dead time
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| 213 | // should be chosen, and the fit range should end well before dt = tau_calib-dead.
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| 214 | //
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| 215 | //
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| 216 | // Method A has the advantage that the p(dt_first) does not depend on 'dead'.
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| 217 | // 'dead' has to be considered when defining the fit range, both in method A and
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| 218 | // in method B. In method B the event statistics is larger leading to a smaller
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| 219 | // fitted error of 'lambda' than method A (see the figures).
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| 220 | //
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| 221 | //
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| 222 | // The effective observation time is again obtained by
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| 223 | //
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| 224 | // T_eff = N_cosmics / lambda
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| 225 | //
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| 226 | // where N_cosmics is the total number of recorded cosmics events. Note that
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| 227 | // N_cosmics is equal to
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| 228 | //
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| 229 | // N_cosmics = N_tot - N_calib
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| 230 | //
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| 231 | // where N_tot is the total number of recorded events (including the calibration
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| 232 | // events) and N_calib is the number of recorded calibration events.
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| 233 | //
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| 234 | // Note that if time intervals are discarded for the determination of lambda,
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| 235 | // the corresponding cosmics events need not and should not be discarded.
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| 236 | //
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| 237 | //
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| 238 | // IV. Procedure if the calibration events are taken in bunches
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| 239 | // ------------------------------------------------------------
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| 240 | // In November 2004 the rate of calibration events is not constant. The events
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| 241 | // are taken in 200 Hz bunches every second, such that the rate is 200 Hz for
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| 242 | // 0.25 sec, followed by a gap of 0.75 sec. Then follows the next 200 Hz bunch.
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| 243 | //
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| 244 | // In this case it is proposed to consider for the calculation of 'lambda' only
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| 245 | // the cosmics events within the gaps of 0.75 sec. For these cosmics events one
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| 246 | // of the methods described in III. can be applied.
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| 247 | //
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| 248 | //
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| 249 | // V. Alternative pocedure
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| 250 | // -----------------------
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| 251 | // The effective observation time can also be determined from the total
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| 252 | // observation time and the total dead time. The latter is written out by the DAQ.
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| 253 | // In this case it has to be made sure that the dead time is available in Mars
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| 254 | // when the effective observation time is calculated.
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| 255 | //
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| 256 | //////////////////////////////////////////////////////////////////////////////
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| 257 | #include "MHEffectiveOnTime.h"
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| 258 |
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| 259 | #include <TF1.h>
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| 260 | #include <TMinuit.h>
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| 261 | #include <TRandom.h>
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| 262 |
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| 263 | #include <TLatex.h>
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| 264 | #include <TGaxis.h>
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| 265 | #include <TCanvas.h>
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| 266 | #include <TPaveStats.h>
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| 267 |
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| 268 | #include "MTime.h"
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| 269 | #include "MParameters.h"
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| 270 | #include "MPointingPos.h"
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| 271 |
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| 272 | #include "MBinning.h"
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| 273 | #include "MParList.h"
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| 274 |
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| 275 | #include "MLog.h"
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| 276 | #include "MLogManip.h"
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| 277 |
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| 278 | ClassImp(MHEffectiveOnTime);
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| 279 |
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| 280 | using namespace std;
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| 281 |
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| 282 | // --------------------------------------------------------------------------
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| 283 | //
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| 284 | // Default Constructor. It initializes all histograms.
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| 285 | //
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| 286 | MHEffectiveOnTime::MHEffectiveOnTime(const char *name, const char *title)
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| 287 | : fPointPos(0), fTime(0), fParam(0), fIsFinalized(kFALSE),
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| 288 | fNumEvents(200*60)/*, fNameProjDeltaT(Form("DeltaT_%p", this)),
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| 289 | fNameProjTheta(Form("Theta_%p", this))*/
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| 290 | {
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| 291 | //
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| 292 | // set the name and title of this object
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| 293 | //
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| 294 | fName = name ? name : "MHEffectiveOnTime";
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| 295 | fTitle = title ? title : "Histogram to determin effective On-Time vs Time and Zenith Angle";
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| 296 |
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| 297 | // Main histogram
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| 298 | fH2DeltaT.SetName("DeltaT");
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| 299 | fH2DeltaT.SetXTitle("\\Delta t [s]");
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| 300 | fH2DeltaT.SetYTitle("\\Theta [\\circ]");
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| 301 | fH2DeltaT.SetZTitle("Count");
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| 302 | fH2DeltaT.UseCurrentStyle();
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| 303 | fH2DeltaT.SetDirectory(NULL);
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| 304 |
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| 305 | // Main histogram
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| 306 | fH1DeltaT.SetName("DeltaT");
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| 307 | fH1DeltaT.SetXTitle("\\Delta t [s]");
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| 308 | fH1DeltaT.SetYTitle("Counts");
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| 309 | fH1DeltaT.UseCurrentStyle();
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| 310 | fH1DeltaT.SetDirectory(NULL);
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| 311 |
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| 312 | // effective on time versus theta
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| 313 | fHThetaEffOn.SetName("EffOnTheta");
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| 314 | fHThetaEffOn.SetTitle("Effective On Time T_{eff}");
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| 315 | fHThetaEffOn.SetXTitle("\\Theta [\\circ]");
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| 316 | fHThetaEffOn.SetYTitle("T_{eff} [s]");
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| 317 | fHThetaEffOn.UseCurrentStyle();
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| 318 | fHThetaEffOn.SetDirectory(NULL);
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| 319 | fHThetaEffOn.GetYaxis()->SetTitleOffset(1.2);
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| 320 | fHThetaEffOn.GetYaxis()->SetTitleColor(kBlue);
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| 321 | fHThetaEffOn.SetLineColor(kBlue);
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| 322 | //fHEffOn.Sumw2();
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| 323 |
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| 324 | // effective on time versus time
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| 325 | fHTimeEffOn.SetName("EffOnTime");
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| 326 | fHTimeEffOn.SetTitle("Effective On Time T_{eff}");
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| 327 | fHTimeEffOn.SetXTitle("Time");
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| 328 | fHTimeEffOn.SetYTitle("T_{eff} [s]");
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| 329 | fHTimeEffOn.UseCurrentStyle();
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| 330 | fHTimeEffOn.SetDirectory(NULL);
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| 331 | fHTimeEffOn.GetYaxis()->SetTitleOffset(1.2);
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| 332 | fHTimeEffOn.GetXaxis()->SetLabelSize(0.033);
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| 333 | fHTimeEffOn.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
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| 334 | fHTimeEffOn.GetXaxis()->SetTimeDisplay(1);
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| 335 | fHTimeEffOn.GetYaxis()->SetTitleColor(kBlue);
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| 336 | fHTimeEffOn.SetLineColor(kBlue);
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| 337 |
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| 338 | // chi2 probability
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| 339 | fHThetaProb.SetName("ProbTheta");
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| 340 | fHThetaProb.SetTitle("\\chi^{2} Probability of Fit");
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| 341 | fHThetaProb.SetXTitle("\\Theta [\\circ]");
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| 342 | fHThetaProb.SetYTitle("p [%]");
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| 343 | fHThetaProb.UseCurrentStyle();
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| 344 | fHThetaProb.SetDirectory(NULL);
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| 345 | fHThetaProb.GetYaxis()->SetTitleOffset(1.2);
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| 346 | fHThetaProb.SetMaximum(101);
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| 347 | fHThetaProb.GetYaxis()->SetTitleColor(kBlue);
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| 348 | fHThetaProb.SetLineColor(kBlue);
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| 349 |
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| 350 | // chi2 probability
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| 351 | fHTimeProb.SetName("ProbTime");
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| 352 | fHTimeProb.SetTitle("\\chi^{2} Probability of Fit");
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| 353 | fHTimeProb.SetXTitle("Time");
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| 354 | fHTimeProb.SetYTitle("p [%]");
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| 355 | fHTimeProb.UseCurrentStyle();
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| 356 | fHTimeProb.SetDirectory(NULL);
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| 357 | fHTimeProb.GetYaxis()->SetTitleOffset(1.2);
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| 358 | fHTimeProb.GetXaxis()->SetLabelSize(0.033);
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| 359 | fHTimeProb.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
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| 360 | fHTimeProb.GetXaxis()->SetTimeDisplay(1);
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| 361 | fHTimeProb.SetMaximum(101);
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| 362 | fHTimeProb.GetYaxis()->SetTitleColor(kBlue);
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| 363 | fHTimeProb.SetLineColor(kBlue);
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| 364 |
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| 365 | // lambda versus theta
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| 366 | fHThetaLambda.SetName("LambdaTheta");
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| 367 | fHThetaLambda.SetTitle("Slope (Rate) vs Theta");
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| 368 | fHThetaLambda.SetXTitle("\\Theta [\\circ]");
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| 369 | fHThetaLambda.SetYTitle("\\lambda [s^{-1}]");
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| 370 | fHThetaLambda.UseCurrentStyle();
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| 371 | fHThetaLambda.SetDirectory(NULL);
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| 372 | fHThetaLambda.SetLineColor(kGreen);
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| 373 |
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| 374 | // lambda versus time
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| 375 | fHTimeLambda.SetName("LambdaTime");
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| 376 | fHTimeLambda.SetTitle("Slope (Rate) vs Time");
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| 377 | fHTimeLambda.SetXTitle("\\Time [\\circ]");
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| 378 | fHTimeLambda.SetYTitle("\\lambda [s^{-1}]");
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| 379 | fHTimeLambda.UseCurrentStyle();
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| 380 | fHTimeLambda.SetDirectory(NULL);
|
|---|
| 381 | fHTimeLambda.GetYaxis()->SetTitleOffset(1.2);
|
|---|
| 382 | fHTimeLambda.GetXaxis()->SetLabelSize(0.033);
|
|---|
| 383 | fHTimeLambda.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
|
|---|
| 384 | fHTimeLambda.GetXaxis()->SetTimeDisplay(1);
|
|---|
| 385 | fHTimeLambda.SetLineColor(kGreen);
|
|---|
| 386 |
|
|---|
| 387 | // NDoF versus theta
|
|---|
| 388 | fHThetaNDF.SetName("NDofTheta");
|
|---|
| 389 | fHThetaNDF.SetTitle("Number of Degrees of freedom vs Theta");
|
|---|
| 390 | fHThetaNDF.SetXTitle("\\Theta [\\circ]");
|
|---|
| 391 | fHThetaNDF.SetYTitle("NDoF [#]");
|
|---|
| 392 | fHThetaNDF.UseCurrentStyle();
|
|---|
| 393 | fHThetaNDF.SetDirectory(NULL);
|
|---|
| 394 | fHThetaNDF.SetLineColor(kGreen);
|
|---|
| 395 |
|
|---|
| 396 | // NDoF versus time
|
|---|
| 397 | /*
|
|---|
| 398 | fHTimeNDF.SetName("NDofTime");
|
|---|
| 399 | fHTimeNDF.SetTitle("Number of Degrees of freedom vs Time");
|
|---|
| 400 | fHTimeNDF.SetXTitle("Time");
|
|---|
| 401 | fHTimeNDF.SetYTitle("NDoF [#]");
|
|---|
| 402 | fHTimeNDF.UseCurrentStyle();
|
|---|
| 403 | fHTimeNDF.SetDirectory(NULL);
|
|---|
| 404 | fHTimeNDF.GetYaxis()->SetTitleOffset(1.2);
|
|---|
| 405 | fHTimeNDF.GetXaxis()->SetLabelSize(0.033);
|
|---|
| 406 | fHTimeNDF.GetXaxis()->SetTimeFormat("%H:%M:%S %F1995-01-01 00:00:00 GMT");
|
|---|
| 407 | fHTimeNDF.GetXaxis()->SetTimeDisplay(1);
|
|---|
| 408 | fHTimeNDF.SetLineColor(kBlue);
|
|---|
| 409 | */
|
|---|
| 410 | // setup binning
|
|---|
| 411 | MBinning btheta("BinningTheta");
|
|---|
| 412 | btheta.SetEdgesASin(67, -0.005, 0.665);
|
|---|
| 413 |
|
|---|
| 414 | MBinning btime("BinningDeltaT");
|
|---|
| 415 | btime.SetEdges(50, 0, 0.1);
|
|---|
| 416 |
|
|---|
| 417 | MH::SetBinning(&fH2DeltaT, &btime, &btheta);
|
|---|
| 418 |
|
|---|
| 419 | btime.Apply(fH1DeltaT);
|
|---|
| 420 |
|
|---|
| 421 | btheta.Apply(fHThetaEffOn);
|
|---|
| 422 | btheta.Apply(fHThetaLambda);
|
|---|
| 423 | btheta.Apply(fHThetaNDF);
|
|---|
| 424 | btheta.Apply(fHThetaProb);
|
|---|
| 425 | //btheta.Apply(fHChi2);
|
|---|
| 426 | }
|
|---|
| 427 |
|
|---|
| 428 | // --------------------------------------------------------------------------
|
|---|
| 429 | //
|
|---|
| 430 | // Set the binnings and prepare the filling of the histogram
|
|---|
| 431 | //
|
|---|
| 432 | Bool_t MHEffectiveOnTime::SetupFill(const MParList *plist)
|
|---|
| 433 | {
|
|---|
| 434 | fPointPos = (MPointingPos*)plist->FindObject("MPointingPos");
|
|---|
| 435 | if (!fPointPos)
|
|---|
| 436 | {
|
|---|
| 437 | *fLog << err << dbginf << "MPointingPos not found... aborting." << endl;
|
|---|
| 438 | return kFALSE;
|
|---|
| 439 | }
|
|---|
| 440 |
|
|---|
| 441 | // FIXME: Remove const-qualifier from base-class!
|
|---|
| 442 | fTime = (MTime*)const_cast<MParList*>(plist)->FindCreateObj("MTime", "MTimeEffectiveOnTime");
|
|---|
| 443 | if (!fTime)
|
|---|
| 444 | return kFALSE;
|
|---|
| 445 | fParam = (MParameterDerr*)const_cast<MParList*>(plist)->FindCreateObj("MParameterDerr", "MEffectiveOnTime");
|
|---|
| 446 | if (!fParam)
|
|---|
| 447 | return kFALSE;
|
|---|
| 448 |
|
|---|
| 449 | const MBinning* binsdtime = (MBinning*)plist->FindObject("BinningDeltaT");
|
|---|
| 450 | const MBinning* binstheta = (MBinning*)plist->FindObject("BinningTheta");
|
|---|
| 451 | if (binsdtime)
|
|---|
| 452 | binsdtime->Apply(fH1DeltaT);
|
|---|
| 453 | if (binstheta)
|
|---|
| 454 | {
|
|---|
| 455 | binstheta->Apply(fHThetaEffOn);
|
|---|
| 456 | binstheta->Apply(fHThetaLambda);
|
|---|
| 457 | binstheta->Apply(fHThetaNDF);
|
|---|
| 458 | binstheta->Apply(fHThetaProb);
|
|---|
| 459 | //binstheta->Apply(fHChi2);
|
|---|
| 460 | }
|
|---|
| 461 | if (binstheta && binsdtime)
|
|---|
| 462 | SetBinning(&fH2DeltaT, binsdtime, binstheta);
|
|---|
| 463 |
|
|---|
| 464 | return kTRUE;
|
|---|
| 465 | }
|
|---|
| 466 |
|
|---|
| 467 | // --------------------------------------------------------------------------
|
|---|
| 468 | //
|
|---|
| 469 | // Fit a single Delta-T distribution. See source code for more details
|
|---|
| 470 | //
|
|---|
| 471 | Bool_t MHEffectiveOnTime::FitH(TH1D *h, Double_t *res, Bool_t paint) const
|
|---|
| 472 | {
|
|---|
| 473 | const Double_t Nm = h->Integral();
|
|---|
| 474 |
|
|---|
| 475 | // FIXME: Do fit only if contents of bin has changed
|
|---|
| 476 | if (Nm<=0 || h->GetBinContent(1)<=0)
|
|---|
| 477 | return kFALSE;
|
|---|
| 478 |
|
|---|
| 479 | // determine range (yq[0], yq[1]) of time differences
|
|---|
| 480 | // where fit should be performed;
|
|---|
| 481 | // require a fraction >=xq[0] of all entries to lie below yq[0]
|
|---|
| 482 | // and a fraction <=xq[1] of all entries to lie below yq[1];
|
|---|
| 483 | // within the range (yq[0], yq[1]) there must be no empty bin;
|
|---|
| 484 | // choose pedestrian approach as long as GetQuantiles is not available
|
|---|
| 485 | Double_t xq[2] = { 0.6, 0.95 }; // previously 0.99
|
|---|
| 486 | Double_t yq[2];
|
|---|
| 487 | h->GetQuantiles(2, yq, xq);
|
|---|
| 488 |
|
|---|
| 489 | //
|
|---|
| 490 | // Setup exponential function for the fit:
|
|---|
| 491 | //
|
|---|
| 492 | // parameter 0 = rate [Hz]
|
|---|
| 493 | // parameter 1 = normalization
|
|---|
| 494 | //
|
|---|
| 495 | TF1 func("Exp", " exp([1]-[0]*x)");
|
|---|
| 496 |
|
|---|
| 497 | func.SetParameter(0, 200); // Hz
|
|---|
| 498 | func.SetParameter(1, log(h->GetBinContent(1))); // Hz
|
|---|
| 499 |
|
|---|
| 500 | // options : N do not store the function, do not draw
|
|---|
| 501 | // I use integral of function in bin rather than value at bin center
|
|---|
| 502 | // R use the range specified in the function range
|
|---|
| 503 | // Q quiet mode
|
|---|
| 504 | // L Use log-likelihood (better for low statistics)
|
|---|
| 505 | h->Fit(&func, "NIQEL", "", h->GetBinLowEdge(3)/*yq[0]*/, yq[1]);
|
|---|
| 506 |
|
|---|
| 507 | const Double_t chi2 = func.GetChisquare();
|
|---|
| 508 | const Double_t prob = func.GetProb();
|
|---|
| 509 | const Int_t NDF = func.GetNDF();
|
|---|
| 510 |
|
|---|
| 511 | // was fit successful ?
|
|---|
| 512 | const Bool_t ok = prob>0.001; //NDF>0 && chi2<3*NDF;
|
|---|
| 513 |
|
|---|
| 514 | if (paint)
|
|---|
| 515 | {
|
|---|
| 516 | func.SetLineWidth(2);
|
|---|
| 517 | func.SetLineColor(ok ? kGreen : kRed);
|
|---|
| 518 | func.Paint("same");
|
|---|
| 519 | }
|
|---|
| 520 |
|
|---|
| 521 | // The effective on time is the "real rate" (slope of the exponential)
|
|---|
| 522 | // divided by the total number of events (histogram integral including
|
|---|
| 523 | // under- and overflows)
|
|---|
| 524 | const Double_t lambda = func.GetParameter(0);
|
|---|
| 525 | const Double_t dldl = func.GetParError(0)*func.GetParError(0);
|
|---|
| 526 | const Double_t teff = Nm / lambda;
|
|---|
| 527 | const Double_t dteff = teff * TMath::Sqrt(dldl/(lambda*lambda) + 1.0/Nm);
|
|---|
| 528 | const Double_t dl = TMath::Sqrt(dldl);
|
|---|
| 529 |
|
|---|
| 530 | // the effective on time is Nm/lambda
|
|---|
| 531 | res[0] = teff;
|
|---|
| 532 | res[1] = dteff;
|
|---|
| 533 |
|
|---|
| 534 | // plot chi2-probability of fit
|
|---|
| 535 | res[2] = prob*100;
|
|---|
| 536 |
|
|---|
| 537 | // lambda of fit
|
|---|
| 538 | res[3] = lambda;
|
|---|
| 539 | res[4] = dl;
|
|---|
| 540 |
|
|---|
| 541 | // NDoF of fit
|
|---|
| 542 | res[5] = NDF;
|
|---|
| 543 |
|
|---|
| 544 | // Chi2
|
|---|
| 545 | res[6] = chi2;
|
|---|
| 546 |
|
|---|
| 547 | return ok;
|
|---|
| 548 | }
|
|---|
| 549 |
|
|---|
| 550 | // --------------------------------------------------------------------------
|
|---|
| 551 | //
|
|---|
| 552 | // Fit a all bins of the distribution in theta. Store the result in the
|
|---|
| 553 | // Theta-Histograms
|
|---|
| 554 | //
|
|---|
| 555 | void MHEffectiveOnTime::FitThetaBins()
|
|---|
| 556 | {
|
|---|
| 557 | fHThetaEffOn.Reset();
|
|---|
| 558 | fHThetaProb.Reset();
|
|---|
| 559 | fHThetaLambda.Reset();
|
|---|
| 560 | fHThetaNDF.Reset();
|
|---|
| 561 |
|
|---|
| 562 | // Use a random name to make sure the object is unique
|
|---|
| 563 | const TString name = Form("CalcTheta%d", (UInt_t)gRandom->Uniform(999999999));
|
|---|
| 564 |
|
|---|
| 565 | // nbins = number of Theta bins
|
|---|
| 566 | const Int_t nbins = fH2DeltaT.GetNbinsY();
|
|---|
| 567 |
|
|---|
| 568 | TH1D *h=0;
|
|---|
| 569 | for (int i=1; i<=nbins; i++)
|
|---|
| 570 | {
|
|---|
| 571 | // TH1D &h = *hist->ProjectionX("Calc-theta", i, i);
|
|---|
| 572 | h = fH2DeltaT.ProjectionX(name, i, i, "E");
|
|---|
| 573 |
|
|---|
| 574 | Double_t res[7] = {0, 0, 0, 0, 0, 0, 0};
|
|---|
| 575 | //if (!FitH(h, res))
|
|---|
| 576 | // continue;
|
|---|
| 577 | FitH(h, res);
|
|---|
| 578 |
|
|---|
| 579 | if (res[0]==0)
|
|---|
| 580 | continue;
|
|---|
| 581 |
|
|---|
| 582 | // the effective on time is Nm/lambda
|
|---|
| 583 | fHThetaEffOn.SetBinContent(i, res[0]);
|
|---|
| 584 | fHThetaEffOn.SetBinError (i, res[1]);
|
|---|
| 585 |
|
|---|
| 586 | // plot chi2-probability of fit
|
|---|
| 587 | fHThetaProb.SetBinContent(i, res[2]);
|
|---|
| 588 |
|
|---|
| 589 | // plot chi2/NDF of fit
|
|---|
| 590 | //fHChi2.SetBinContent(i, res[3]);
|
|---|
| 591 |
|
|---|
| 592 | // lambda of fit
|
|---|
| 593 | fHThetaLambda.SetBinContent(i, res[3]);
|
|---|
| 594 | fHThetaLambda.SetBinError (i, res[4]);
|
|---|
| 595 |
|
|---|
| 596 | // NDoF of fit
|
|---|
| 597 | fHThetaNDF.SetBinContent(i, res[5]);
|
|---|
| 598 |
|
|---|
| 599 | // Rdead (from fit) is the fraction from real time lost by the dead time
|
|---|
| 600 | //fHRdead.SetBinContent(i, Rdead);
|
|---|
| 601 | //fHRdead.SetBinError (i,dRdead);
|
|---|
| 602 | }
|
|---|
| 603 |
|
|---|
| 604 | // Histogram is reused via gROOT->FindObject()
|
|---|
| 605 | // Need to be deleted only once
|
|---|
| 606 | if (h)
|
|---|
| 607 | delete h;
|
|---|
| 608 | }
|
|---|
| 609 |
|
|---|
| 610 | // --------------------------------------------------------------------------
|
|---|
| 611 | //
|
|---|
| 612 | // Fit the single-time-bin histogram. Store the result in the
|
|---|
| 613 | // Time-Histograms
|
|---|
| 614 | //
|
|---|
| 615 | void MHEffectiveOnTime::FitTimeBin()
|
|---|
| 616 | {
|
|---|
| 617 | //
|
|---|
| 618 | // Fit histogram
|
|---|
| 619 | //
|
|---|
| 620 | Double_t res[7];
|
|---|
| 621 | if (!FitH(&fH1DeltaT, res))
|
|---|
| 622 | return;
|
|---|
| 623 |
|
|---|
| 624 | // Reset Histogram
|
|---|
| 625 | fH1DeltaT.Reset();
|
|---|
| 626 |
|
|---|
| 627 | //
|
|---|
| 628 | // Prepare Histogram
|
|---|
| 629 | //
|
|---|
| 630 |
|
|---|
| 631 | // Get number of bins
|
|---|
| 632 | const Int_t n = fHTimeEffOn.GetNbinsX();
|
|---|
| 633 |
|
|---|
| 634 | // Enhance binning
|
|---|
| 635 | MBinning bins;
|
|---|
| 636 | bins.SetEdges(fHTimeEffOn, 'x');
|
|---|
| 637 | bins.AddEdge(fLastTime.GetAxisTime());
|
|---|
| 638 | bins.Apply(fHTimeEffOn);
|
|---|
| 639 | bins.Apply(fHTimeProb);
|
|---|
| 640 | bins.Apply(fHTimeLambda);
|
|---|
| 641 | //bins.Apply(fHTimeNDF);
|
|---|
| 642 |
|
|---|
| 643 | //
|
|---|
| 644 | // Fill histogram
|
|---|
| 645 | //
|
|---|
| 646 | fHTimeEffOn.SetBinContent(n, res[0]);
|
|---|
| 647 | fHTimeEffOn.SetBinError(n, res[1]);
|
|---|
| 648 |
|
|---|
| 649 | fHTimeProb.SetBinContent(n, res[2]);
|
|---|
| 650 |
|
|---|
| 651 | fHTimeLambda.SetBinContent(n, res[3]);
|
|---|
| 652 | fHTimeLambda.SetBinError(n, res[4]);
|
|---|
| 653 |
|
|---|
| 654 | //fHTimeNDF.SetBinContent(n, res[5]);
|
|---|
| 655 |
|
|---|
| 656 | //
|
|---|
| 657 | // Now prepare output
|
|---|
| 658 | //
|
|---|
| 659 | fParam->SetVal(res[0], res[1]);
|
|---|
| 660 | fParam->SetReadyToSave();
|
|---|
| 661 |
|
|---|
| 662 | *fTime = fLastTime;
|
|---|
| 663 |
|
|---|
| 664 | // Include the current event
|
|---|
| 665 | fTime->Plus1ns();
|
|---|
| 666 |
|
|---|
| 667 | *fLog << fLastTime << ": Val=" << res[0] << " Err=" << res[1] << endl;
|
|---|
| 668 | }
|
|---|
| 669 |
|
|---|
| 670 | // --------------------------------------------------------------------------
|
|---|
| 671 | //
|
|---|
| 672 | // Fill the histogram
|
|---|
| 673 | //
|
|---|
| 674 | Bool_t MHEffectiveOnTime::Fill(const MParContainer *par, const Stat_t w)
|
|---|
| 675 | {
|
|---|
| 676 | const MTime *time = dynamic_cast<const MTime*>(par);
|
|---|
| 677 | if (!time)
|
|---|
| 678 | {
|
|---|
| 679 | *fLog << err << "ERROR - MHEffectiveOnTime::Fill without argument or container doesn't inherit from MTime... abort." << endl;
|
|---|
| 680 | return kFALSE;
|
|---|
| 681 | }
|
|---|
| 682 |
|
|---|
| 683 | //
|
|---|
| 684 | // If this is the first call we have to initialize the time-histogram
|
|---|
| 685 | //
|
|---|
| 686 | if (fLastTime==MTime())
|
|---|
| 687 | {
|
|---|
| 688 | MBinning bins;
|
|---|
| 689 | bins.SetEdges(1, time->GetAxisTime()-fNumEvents/200, time->GetAxisTime());
|
|---|
| 690 | bins.Apply(fHTimeEffOn);
|
|---|
| 691 | bins.Apply(fHTimeProb);
|
|---|
| 692 | bins.Apply(fHTimeLambda);
|
|---|
| 693 | //bins.Apply(fHTimeNDF);
|
|---|
| 694 |
|
|---|
| 695 | fParam->SetVal(0, 0);
|
|---|
| 696 | fParam->SetReadyToSave();
|
|---|
| 697 |
|
|---|
| 698 | *fTime = *time;
|
|---|
| 699 |
|
|---|
| 700 | // Make this 1ns before the first event!
|
|---|
| 701 | fTime->Minus1ns();
|
|---|
| 702 | }
|
|---|
| 703 |
|
|---|
| 704 | //
|
|---|
| 705 | // Fill time difference into the histograms
|
|---|
| 706 | //
|
|---|
| 707 | const Double_t dt = *time-fLastTime;
|
|---|
| 708 | fLastTime = *time;
|
|---|
| 709 |
|
|---|
| 710 | fH2DeltaT.Fill(dt, fPointPos->GetZd(), w);
|
|---|
| 711 | fH1DeltaT.Fill(dt, w);
|
|---|
| 712 |
|
|---|
| 713 | //
|
|---|
| 714 | // If we reached the event number limit for the time-bins fit the
|
|---|
| 715 | // histogram - if it fails try again when 1.6% more events available
|
|---|
| 716 | //
|
|---|
| 717 | const Int_t n = (Int_t)fH1DeltaT.GetEntries();
|
|---|
| 718 | if (n>=fNumEvents && n%(fNumEvents/60)==0)
|
|---|
| 719 | FitTimeBin();
|
|---|
| 720 |
|
|---|
| 721 | return kTRUE;
|
|---|
| 722 | }
|
|---|
| 723 |
|
|---|
| 724 | // --------------------------------------------------------------------------
|
|---|
| 725 | //
|
|---|
| 726 | // Fit the theta projections of the 2D histogram and the 1D Delta-T
|
|---|
| 727 | // distribution
|
|---|
| 728 | //
|
|---|
| 729 | Bool_t MHEffectiveOnTime::Finalize()
|
|---|
| 730 | {
|
|---|
| 731 | FitThetaBins();
|
|---|
| 732 | FitTimeBin();
|
|---|
| 733 |
|
|---|
| 734 | fIsFinalized = kTRUE;
|
|---|
| 735 |
|
|---|
| 736 | return kTRUE;
|
|---|
| 737 | }
|
|---|
| 738 |
|
|---|
| 739 | // --------------------------------------------------------------------------
|
|---|
| 740 | //
|
|---|
| 741 | // Paint the integral and the error on top of the histogram
|
|---|
| 742 | //
|
|---|
| 743 | void MHEffectiveOnTime::PaintText(Double_t val, Double_t error, Double_t range) const
|
|---|
| 744 | {
|
|---|
| 745 | TLatex text;
|
|---|
| 746 | text.SetBit(TLatex::kTextNDC);
|
|---|
| 747 | text.SetTextSize(0.04);
|
|---|
| 748 |
|
|---|
| 749 | text.SetText(0.45, 0.94, Form("T_{eff} = %.1fs \\pm %.1fs", val, error));
|
|---|
| 750 | text.Paint();
|
|---|
| 751 |
|
|---|
| 752 | if (range<0)
|
|---|
| 753 | return;
|
|---|
| 754 |
|
|---|
| 755 | text.SetText(0.66, 0.94, Form("T_{axe} = %.1fs", range));
|
|---|
| 756 | text.Paint();
|
|---|
| 757 | }
|
|---|
| 758 |
|
|---|
| 759 | void MHEffectiveOnTime::PaintText(Double_t *res) const
|
|---|
| 760 | {
|
|---|
| 761 | TLatex text(0.27, 0.94, Form("T_{eff}=%.1fs\\pm%.1fs \\lambda=%.1f\\pm%.1fHz p=%.1f%% \\chi^{2}/%d=%.1f",
|
|---|
| 762 | res[0], res[1], res[3], res[4], res[2], (int)res[5], res[6]/res[5]));
|
|---|
| 763 | text.SetBit(TLatex::kTextNDC);
|
|---|
| 764 | text.SetTextSize(0.04);
|
|---|
| 765 | text.Paint();
|
|---|
| 766 | }
|
|---|
| 767 |
|
|---|
| 768 | void MHEffectiveOnTime::PaintProb(TH1 &h) const
|
|---|
| 769 | {
|
|---|
| 770 | Double_t sum = 0;
|
|---|
| 771 | Int_t n = 0;
|
|---|
| 772 | for (int i=0; i<h.GetNbinsX(); i++)
|
|---|
| 773 | if (h.GetBinContent(i+1)>0)
|
|---|
| 774 | {
|
|---|
| 775 | sum += h.GetBinContent(i+1);
|
|---|
| 776 | n++;
|
|---|
| 777 | }
|
|---|
| 778 |
|
|---|
| 779 | if (n==0)
|
|---|
| 780 | return;
|
|---|
| 781 |
|
|---|
| 782 | TLatex text(0.47, 0.94, Form("\\bar{p} = %.1f%%", sum/n));
|
|---|
| 783 | text.SetBit(TLatex::kTextNDC);
|
|---|
| 784 | text.SetTextSize(0.04);
|
|---|
| 785 | text.Paint();
|
|---|
| 786 | }
|
|---|
| 787 |
|
|---|
| 788 | void MHEffectiveOnTime::UpdateRightAxis(TH1 &h)
|
|---|
| 789 | {
|
|---|
| 790 | const Double_t max = h.GetMaximum()*1.1;
|
|---|
| 791 | if (max==0)
|
|---|
| 792 | return;
|
|---|
| 793 |
|
|---|
| 794 | h.SetNormFactor(h.Integral()*gPad->GetUymax()/max);
|
|---|
| 795 |
|
|---|
| 796 | TGaxis *axis = (TGaxis*)gPad->FindObject("RightAxis");
|
|---|
| 797 | if (!axis)
|
|---|
| 798 | return;
|
|---|
| 799 |
|
|---|
| 800 | axis->SetX1(gPad->GetUxmax());
|
|---|
| 801 | axis->SetX2(gPad->GetUxmax());
|
|---|
| 802 | axis->SetY1(gPad->GetUymin());
|
|---|
| 803 | axis->SetY2(gPad->GetUymax());
|
|---|
| 804 | axis->SetWmax(max);
|
|---|
| 805 | }
|
|---|
| 806 |
|
|---|
| 807 | // --------------------------------------------------------------------------
|
|---|
| 808 | //
|
|---|
| 809 | // Prepare painting the histograms
|
|---|
| 810 | //
|
|---|
| 811 | void MHEffectiveOnTime::Paint(Option_t *opt)
|
|---|
| 812 | {
|
|---|
| 813 | TH1D *h=0;
|
|---|
| 814 | TPaveStats *st=0;
|
|---|
| 815 |
|
|---|
| 816 | TString o(opt);
|
|---|
| 817 | if (o==(TString)"fit")
|
|---|
| 818 | {
|
|---|
| 819 | TVirtualPad *pad = gPad;
|
|---|
| 820 |
|
|---|
| 821 | for (int x=0; x<2; x++)
|
|---|
| 822 | for (int y=0; y<3; y++)
|
|---|
| 823 | {
|
|---|
| 824 | TVirtualPad *p=gPad->GetPad(x+1)->GetPad(y+1);
|
|---|
| 825 | if (!(st = dynamic_cast<TPaveStats*>(p->GetPrimitive("stats"))))
|
|---|
| 826 | continue;
|
|---|
| 827 |
|
|---|
| 828 | if (st->GetOptStat()==11)
|
|---|
| 829 | continue;
|
|---|
| 830 |
|
|---|
| 831 | const Double_t y1 = st->GetY1NDC();
|
|---|
| 832 | const Double_t y2 = st->GetY2NDC();
|
|---|
| 833 | const Double_t x1 = st->GetX1NDC();
|
|---|
| 834 | const Double_t x2 = st->GetX2NDC();
|
|---|
| 835 |
|
|---|
| 836 | st->SetY1NDC((y2-y1)/3+y1);
|
|---|
| 837 | st->SetX1NDC((x2-x1)/3+x1);
|
|---|
| 838 | st->SetOptStat(11);
|
|---|
| 839 | }
|
|---|
| 840 |
|
|---|
| 841 | pad->GetPad(1)->cd(1);
|
|---|
| 842 | if ((h = (TH1D*)gPad->FindObject("ProjDeltaT"/*fNameProjDeltaT*/)))
|
|---|
| 843 | {
|
|---|
| 844 | h = fH2DeltaT.ProjectionX("ProjDeltaT"/*fNameProjDeltaT*/, -1, 9999, "E");
|
|---|
| 845 | if (h->GetEntries()>0)
|
|---|
| 846 | gPad->SetLogy();
|
|---|
| 847 | }
|
|---|
| 848 |
|
|---|
| 849 | pad->GetPad(2)->cd(1);
|
|---|
| 850 | if ((h = (TH1D*)gPad->FindObject("ProjTheta"/*fNameProjTheta*/)))
|
|---|
| 851 | fH2DeltaT.ProjectionY("ProjTheta"/*fNameProjTheta*/, -1, 9999, "E");
|
|---|
| 852 |
|
|---|
| 853 | if (!fIsFinalized)
|
|---|
| 854 | FitThetaBins();
|
|---|
| 855 | return;
|
|---|
| 856 | }
|
|---|
| 857 | if (o==(TString)"paint")
|
|---|
| 858 | {
|
|---|
| 859 | if ((h = (TH1D*)gPad->FindObject("ProjDeltaT"/*fNameProjDeltaT*/)))
|
|---|
| 860 | {
|
|---|
| 861 | Double_t res[7];
|
|---|
| 862 | FitH(h, res, kTRUE);
|
|---|
| 863 | PaintText(res);
|
|---|
| 864 | }
|
|---|
| 865 | return;
|
|---|
| 866 | }
|
|---|
| 867 |
|
|---|
| 868 | if (o==(TString)"timendf")
|
|---|
| 869 | {
|
|---|
| 870 | // UpdateRightAxis(fHTimeNDF);
|
|---|
| 871 | // FIXME: first bin?
|
|---|
| 872 | PaintProb(fHTimeProb);
|
|---|
| 873 | }
|
|---|
| 874 |
|
|---|
| 875 | if (o==(TString)"thetandf")
|
|---|
| 876 | {
|
|---|
| 877 | UpdateRightAxis(fHThetaNDF);
|
|---|
| 878 | // FIXME: first bin?
|
|---|
| 879 | PaintProb(fHThetaProb);
|
|---|
| 880 | }
|
|---|
| 881 |
|
|---|
| 882 | h=0;
|
|---|
| 883 |
|
|---|
| 884 | Double_t range=-1;
|
|---|
| 885 | if (o==(TString)"theta")
|
|---|
| 886 | {
|
|---|
| 887 | h = &fHThetaEffOn;
|
|---|
| 888 | UpdateRightAxis(fHThetaLambda);
|
|---|
| 889 | }
|
|---|
| 890 | if (o==(TString)"time")
|
|---|
| 891 | {
|
|---|
| 892 | h = &fHTimeEffOn;
|
|---|
| 893 | UpdateRightAxis(fHTimeLambda);
|
|---|
| 894 | range = h->GetXaxis()->GetXmax() - h->GetXaxis()->GetXmin();
|
|---|
| 895 | }
|
|---|
| 896 |
|
|---|
| 897 | if (!h)
|
|---|
| 898 | return;
|
|---|
| 899 |
|
|---|
| 900 | Double_t error = 0;
|
|---|
| 901 | for (int i=0; i<h->GetXaxis()->GetNbins(); i++)
|
|---|
| 902 | error += h->GetBinError(i);
|
|---|
| 903 |
|
|---|
| 904 | PaintText(h->Integral(), error, range);
|
|---|
| 905 | }
|
|---|
| 906 |
|
|---|
| 907 | void MHEffectiveOnTime::DrawRightAxis(const char *title)
|
|---|
| 908 | {
|
|---|
| 909 | TGaxis *axis = new TGaxis(gPad->GetUxmax(), gPad->GetUymin(),
|
|---|
| 910 | gPad->GetUxmax(), gPad->GetUymax(),
|
|---|
| 911 | 0, 1, 510, "+L");
|
|---|
| 912 | axis->SetName("RightAxis");
|
|---|
| 913 | axis->SetTitle(title);
|
|---|
| 914 | axis->SetTitleOffset(0.9);
|
|---|
| 915 | axis->SetTextColor(kGreen);
|
|---|
| 916 | axis->CenterTitle();
|
|---|
| 917 | axis->SetBit(kCanDelete);
|
|---|
| 918 | axis->Draw();
|
|---|
| 919 | }
|
|---|
| 920 |
|
|---|
| 921 | // --------------------------------------------------------------------------
|
|---|
| 922 | //
|
|---|
| 923 | // Draw the histogram
|
|---|
| 924 | //
|
|---|
| 925 | void MHEffectiveOnTime::Draw(Option_t *opt)
|
|---|
| 926 | {
|
|---|
| 927 | TVirtualPad *pad = gPad ? gPad : MakeDefCanvas(this);
|
|---|
| 928 | pad->SetBorderMode(0);
|
|---|
| 929 |
|
|---|
| 930 | AppendPad("fit");
|
|---|
| 931 |
|
|---|
| 932 | pad->Divide(2, 1, 1e-10, 1e-10);
|
|---|
| 933 |
|
|---|
| 934 | TH1 *h;
|
|---|
| 935 |
|
|---|
| 936 | pad->cd(1);
|
|---|
| 937 | gPad->SetBorderMode(0);
|
|---|
| 938 | gPad->Divide(1, 3, 1e-10, 1e-10);
|
|---|
| 939 | pad->GetPad(1)->cd(1);
|
|---|
| 940 | gPad->SetBorderMode(0);
|
|---|
| 941 | h = fH2DeltaT.ProjectionX("ProjDeltaT"/*fNameProjDeltaT*/, -1, 9999, "E");
|
|---|
| 942 | h->SetTitle("Distribution of \\Delta t [s]");
|
|---|
| 943 | h->SetXTitle("\\Delta t [s]");
|
|---|
| 944 | h->SetYTitle("Counts");
|
|---|
| 945 | h->SetDirectory(NULL);
|
|---|
| 946 | h->SetMarkerStyle(kFullDotMedium);
|
|---|
| 947 | h->SetBit(kCanDelete);
|
|---|
| 948 | h->Draw();
|
|---|
| 949 | AppendPad("paint");
|
|---|
| 950 |
|
|---|
| 951 | pad->GetPad(1)->cd(2);
|
|---|
| 952 | gPad->SetBorderMode(0);
|
|---|
| 953 | fHTimeProb.Draw();
|
|---|
| 954 | AppendPad("timendf");
|
|---|
| 955 | //fHTimeNDF.Draw("same");
|
|---|
| 956 | //DrawRightAxis("NDF");
|
|---|
| 957 |
|
|---|
| 958 | pad->GetPad(1)->cd(3);
|
|---|
| 959 | gPad->SetBorderMode(0);
|
|---|
| 960 | fHTimeEffOn.Draw();
|
|---|
| 961 | AppendPad("time");
|
|---|
| 962 | fHTimeLambda.Draw("same");
|
|---|
| 963 | DrawRightAxis("\\lambda [s^{-1}]");
|
|---|
| 964 |
|
|---|
| 965 | pad->cd(2);
|
|---|
| 966 | gPad->SetBorderMode(0);
|
|---|
| 967 | gPad->Divide(1, 3, 1e-10, 1e-10);
|
|---|
| 968 |
|
|---|
| 969 | pad->GetPad(2)->cd(1);
|
|---|
| 970 | gPad->SetBorderMode(0);
|
|---|
| 971 | h = fH2DeltaT.ProjectionY("ProjTheta"/*fNameProjTheta*/, -1, 9999, "E");
|
|---|
| 972 | h->SetTitle("Distribution of \\Theta [\\circ]");
|
|---|
| 973 | h->SetXTitle("\\Theta [\\circ]");
|
|---|
| 974 | h->SetYTitle("Counts");
|
|---|
| 975 | h->SetDirectory(NULL);
|
|---|
| 976 | h->SetMarkerStyle(kFullDotMedium);
|
|---|
| 977 | h->SetBit(kCanDelete);
|
|---|
| 978 | h->GetYaxis()->SetTitleOffset(1.1);
|
|---|
| 979 | h->Draw();
|
|---|
| 980 |
|
|---|
| 981 | pad->GetPad(2)->cd(2);
|
|---|
| 982 | gPad->SetBorderMode(0);
|
|---|
| 983 | fHThetaProb.Draw();
|
|---|
| 984 | AppendPad("thetandf");
|
|---|
| 985 | fHThetaNDF.Draw("same");
|
|---|
| 986 | DrawRightAxis("NDF");
|
|---|
| 987 |
|
|---|
| 988 | pad->GetPad(2)->cd(3);
|
|---|
| 989 | gPad->SetBorderMode(0);
|
|---|
| 990 | fHThetaEffOn.Draw();
|
|---|
| 991 | AppendPad("theta");
|
|---|
| 992 | fHThetaLambda.Draw("same");
|
|---|
| 993 | DrawRightAxis("\\lambda [s^{-1}]");
|
|---|
| 994 | }
|
|---|
