source: trunk/MagicSoft/Mars/mjtrain/MJTrainSeparation.cc@ 7675

Last change on this file since 7675 was 7675, checked in by tbretz, 19 years ago
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1/* ======================================================================== *\
2!
3! *
4! * This file is part of MARS, the MAGIC Analysis and Reconstruction
5! * Software. It is distributed to you in the hope that it can be a useful
6! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
7! * It is distributed WITHOUT ANY WARRANTY.
8! *
9! * Permission to use, copy, modify and distribute this software and its
10! * documentation for any purpose is hereby granted without fee,
11! * provided that the above copyright notice appear in all copies and
12! * that both that copyright notice and this permission notice appear
13! * in supporting documentation. It is provided "as is" without express
14! * or implied warranty.
15! *
16!
17!
18! Author(s): Thomas Bretz 11/2005 <mailto:tbretz@astro.uni-wuerzburg.de>
19!
20! Copyright: MAGIC Software Development, 2006
21!
22!
23\* ======================================================================== */
24
25/////////////////////////////////////////////////////////////////////////////
26//
27// MJTrainSeparation
28//
29////////////////////////////////////////////////////////////////////////////
30#include "MJTrainSeparation.h"
31
32#include <TF1.h>
33#include <TH2.h>
34#include <TChain.h>
35#include <TGraph.h>
36#include <TCanvas.h>
37#include <TVirtualPad.h>
38
39#include "MHMatrix.h"
40
41#include "MLog.h"
42#include "MLogManip.h"
43
44// tools
45#include "MMath.h"
46#include "MDataSet.h"
47#include "MTFillMatrix.h"
48#include "MChisqEval.h"
49#include "MStatusDisplay.h"
50
51// eventloop
52#include "MParList.h"
53#include "MTaskList.h"
54#include "MEvtLoop.h"
55
56// tasks
57#include "MReadMarsFile.h"
58#include "MContinue.h"
59#include "MFillH.h"
60#include "MRanForestCalc.h"
61#include "MParameterCalc.h"
62
63// container
64#include "MMcEvt.hxx"
65#include "MParameters.h"
66
67// histograms
68#include "MBinning.h"
69#include "MH3.h"
70#include "MHHadronness.h"
71
72// filter
73#include "MF.h"
74#include "MFEventSelector.h"
75#include "MFilterList.h"
76
77ClassImp(MJTrainSeparation);
78
79using namespace std;
80
81void MJTrainSeparation::DisplayResult(MH3 &h31, MH3 &h32)
82{
83 TH2D &g = (TH2D&)h32.GetHist();
84 TH2D &h = (TH2D&)h31.GetHist();
85
86 h.SetMarkerColor(kRed);
87 g.SetMarkerColor(kGreen);
88
89 TH2D res1(g);
90 TH2D res2(g);
91
92 h.SetTitle("Hadronness-Distribution vs. Size");
93 res1.SetTitle("Significance Li/Ma");
94 res1.SetXTitle("Size [phe]");
95 res1.SetYTitle("Hadronness");
96 res2.SetTitle("Significance-Distribution");
97 res2.SetXTitle("Size-Cut [phe]");
98 res2.SetYTitle("Hadronness-Cut");
99 res1.SetContour(50);
100 res2.SetContour(50);
101
102 const Int_t nx = h.GetNbinsX();
103 const Int_t ny = h.GetNbinsY();
104
105 gROOT->SetSelectedPad(NULL);
106
107 TGraph gr1;
108 TGraph gr2;
109 for (int x=0; x<nx; x++)
110 {
111 TH1 *hx = h.ProjectionY("H_py", x+1, x+1);
112 TH1 *gx = g.ProjectionY("G_py", x+1, x+1);
113
114 Double_t max1 = -1;
115 Double_t max2 = -1;
116 Int_t maxy1 = 0;
117 Int_t maxy2 = 0;
118 for (int y=0; y<ny; y++)
119 {
120 const Float_t s = gx->Integral(1, y+1);
121 const Float_t b = hx->Integral(1, y+1);
122 const Float_t sig1 = MMath::SignificanceLiMa(s+b, b);
123 const Float_t sig2 = s<1 ? 0 : MMath::SignificanceLiMa(s+b, b)*TMath::Log10(s);
124 if (sig1>max1)
125 {
126 maxy1 = y;
127 max1 = sig1;
128 }
129 if (sig2>max2)
130 {
131 maxy2 = y;
132 max2 = sig2;
133 }
134
135 res1.SetBinContent(x+1, y+1, sig1);
136 }
137
138 gr1.SetPoint(x, h.GetXaxis()->GetBinCenter(x+1), h.GetYaxis()->GetBinCenter(maxy1+1));
139 gr2.SetPoint(x, h.GetXaxis()->GetBinCenter(x+1), h.GetYaxis()->GetBinCenter(maxy2+1));
140
141 delete hx;
142 delete gx;
143 }
144
145 for (int x=0; x<nx; x++)
146 {
147 TH1 *hx = h.ProjectionY("H_py", x+1);
148 TH1 *gx = g.ProjectionY("G_py", x+1);
149 for (int y=0; y<ny; y++)
150 {
151 const Float_t s = gx->Integral(1, y+1);
152 const Float_t b = hx->Integral(1, y+1);
153 const Float_t sig = MMath::SignificanceLiMa(s+b, b);
154 res2.SetBinContent(x+1, y+1, sig);
155 }
156 delete hx;
157 delete gx;
158 }
159
160 TGraph gr3;
161 TGraph gr4;
162 gr4.SetTitle("Significance Li/Ma vs. Hadronness-cut");
163
164 TH1 *hx = h.ProjectionY("H_py");
165 TH1 *gx = g.ProjectionY("G_py");
166 for (int y=0; y<ny; y++)
167 {
168 const Float_t s = gx->Integral(1, y+1);
169 const Float_t b = hx->Integral(1, y+1);
170 const Float_t sig1 = MMath::SignificanceLiMa(s+b, b);
171 const Float_t sig2 = s<1 ? 0 : MMath::SignificanceLiMa(s+b, b)*TMath::Log10(s);
172
173 gr3.SetPoint(y, h.GetYaxis()->GetBinLowEdge(y+2), sig1);
174 gr4.SetPoint(y, h.GetYaxis()->GetBinLowEdge(y+2), sig2);
175 }
176 delete hx;
177 delete gx;
178
179 TCanvas &c = fDisplay->AddTab("OptCut");
180 c.SetBorderMode(0);
181 c.Divide(2,2);
182
183 c.cd(1);
184 gPad->SetBorderMode(0);
185 gPad->SetFrameBorderMode(0);
186 gPad->SetLogx();
187 gPad->SetGridx();
188 gPad->SetGridy();
189 h.DrawCopy();
190 g.DrawCopy("same");
191 gr1.SetMarkerStyle(kFullDotMedium);
192 gr1.DrawClone("LP")->SetBit(kCanDelete);
193 gr2.SetLineColor(kBlue);
194 gr2.SetMarkerStyle(kFullDotMedium);
195 gr2.DrawClone("LP")->SetBit(kCanDelete);
196
197 c.cd(3);
198 gPad->SetBorderMode(0);
199 gPad->SetFrameBorderMode(0);
200 gPad->SetGridx();
201 gPad->SetGridy();
202 gr4.SetMarkerStyle(kFullDotMedium);
203 gr4.DrawClone("ALP")->SetBit(kCanDelete);
204 gr3.SetLineColor(kBlue);
205 gr3.SetMarkerStyle(kFullDotMedium);
206 gr3.DrawClone("LP")->SetBit(kCanDelete);
207
208 c.cd(2);
209 gPad->SetBorderMode(0);
210 gPad->SetFrameBorderMode(0);
211 gPad->SetLogx();
212 gPad->SetGridx();
213 gPad->SetGridy();
214 gPad->AddExec("color", "gStyle->SetPalette(1, 0);");
215 res1.SetMaximum(7);
216 res1.DrawCopy("colz");
217
218 c.cd(4);
219 gPad->SetBorderMode(0);
220 gPad->SetFrameBorderMode(0);
221 gPad->SetLogx();
222 gPad->SetGridx();
223 gPad->SetGridy();
224 gPad->AddExec("color", "gStyle->SetPalette(1, 0);");
225 res2.SetMaximum(res2.GetMaximum()*1.075);
226 res2.DrawCopy("colz");
227}
228
229/*
230Bool_t MJSpectrum::InitWeighting(const MDataSet &set, MMcSpectrumWeight &w) const
231{
232 fLog->Separator("Initialize energy weighting");
233
234 if (!CheckEnv(w))
235 {
236 *fLog << err << "ERROR - Reading resources for MMcSpectrumWeight failed." << endl;
237 return kFALSE;
238 }
239
240 TChain chain("RunHeaders");
241 set.AddFilesOn(chain);
242
243 MMcCorsikaRunHeader *h=0;
244 chain.SetBranchAddress("MMcCorsikaRunHeader.", &h);
245 chain.GetEntry(1);
246
247 if (!h)
248 {
249 *fLog << err << "ERROR - Couldn't read MMcCorsikaRunHeader from DataSet." << endl;
250 return kFALSE;
251 }
252
253 if (!w.Set(*h))
254 {
255 *fLog << err << "ERROR - Initializing MMcSpectrumWeight failed." << endl;
256 return kFALSE;
257 }
258
259 w.Print();
260 return kTRUE;
261}
262
263Bool_t MJSpectrum::ReadOrigMCDistribution(const MDataSet &set, TH1 &h, MMcSpectrumWeight &weight) const
264{
265 // Some debug output
266 fLog->Separator("Compiling original MC distribution");
267
268 weight.SetNameMcEvt("MMcEvtBasic");
269 const TString w(weight.GetFormulaWeights());
270 weight.SetNameMcEvt();
271
272 *fLog << inf << "Using weights: " << w << endl;
273 *fLog << "Please stand by, this may take a while..." << flush;
274
275 if (fDisplay)
276 fDisplay->SetStatusLine1("Compiling MC distribution...");
277
278 // Create chain
279 TChain chain("OriginalMC");
280 set.AddFilesOn(chain);
281
282 // Prepare histogram
283 h.Reset();
284
285 // Fill histogram from chain
286 h.SetDirectory(gROOT);
287 if (h.InheritsFrom(TH2::Class()))
288 {
289 h.SetNameTitle("ThetaEMC", "Event-Distribution vs Theta and Energy for MC (produced)");
290 h.SetXTitle("\\Theta [\\circ]");
291 h.SetYTitle("E [GeV]");
292 h.SetZTitle("Counts");
293 chain.Draw("MMcEvtBasic.fEnergy:MMcEvtBasic.fTelescopeTheta*TMath::RadToDeg()>>ThetaEMC", w, "goff");
294 }
295 else
296 {
297 h.SetNameTitle("ThetaMC", "Event-Distribution vs Theta for MC (produced)");
298 h.SetXTitle("\\Theta [\\circ]");
299 h.SetYTitle("Counts");
300 chain.Draw("MMcEvtBasic.fTelescopeTheta*TMath::RadToDeg()>>ThetaMC", w, "goff");
301 }
302 h.SetDirectory(0);
303
304 *fLog << "done." << endl;
305 if (fDisplay)
306 fDisplay->SetStatusLine2("done.");
307
308 if (h.GetEntries()>0)
309 return kTRUE;
310
311 *fLog << err << "ERROR - Histogram with original MC distribution empty..." << endl;
312
313 return h.GetEntries()>0;
314}
315*/
316
317Bool_t MJTrainSeparation::GetEventsProduced(MDataSet &set, Double_t &num, Double_t &min, Double_t &max) const
318{
319 TChain chain("OriginalMC");
320 set.AddFilesOn(chain);
321
322 min = chain.GetMinimum("MMcEvtBasic.fEnergy");
323 max = chain.GetMaximum("MMcEvtBasic.fEnergy");
324
325 num = chain.GetEntries();
326
327 if (num<100)
328 *fLog << err << "ERROR - Less than 100 entries in OriginalMC-Tree of MC-Train-Data found." << endl;
329
330 return num>=100;
331}
332
333Double_t MJTrainSeparation::GetDataRate(MDataSet &set, Double_t &num) const
334{
335 TChain chain1("Events");
336 set.AddFilesOff(chain1);
337
338 num = chain1.GetEntries();
339 if (num<100)
340 {
341 *fLog << err << "ERROR - Less than 100 entries in Events-Tree of Train-Data found." << endl;
342 return -1;
343 }
344
345 TChain chain("EffectiveOnTime");
346 set.AddFilesOff(chain);
347
348 chain.Draw("MEffectiveOnTime.fVal", "MEffectiveOnTime.fVal", "goff");
349
350 TH1 *h = dynamic_cast<TH1*>(gROOT->FindObject("htemp"));
351 if (!h)
352 {
353 *fLog << err << "ERROR - Weird things are happening (htemp not found)!" << endl;
354 return -1;
355 }
356
357 const Double_t ontime = h->Integral();
358 delete h;
359
360 if (ontime<1)
361 {
362 *fLog << err << "ERROR - Less than 1s of effective observation time found in Train-Data." << endl;
363 return -1;
364 }
365
366 return num/ontime;
367}
368
369Double_t MJTrainSeparation::GetNumMC(MDataSet &set) const
370{
371 TChain chain1("Events");
372 set.AddFilesOn(chain1);
373
374 const Double_t num = chain1.GetEntries();
375 if (num<100)
376 {
377 *fLog << err << "ERROR - Less than 100 entries in Events-Tree of Train-Data found." << endl;
378 return -1;
379 }
380
381 return num;
382}
383
384Bool_t MJTrainSeparation::AutoTrain(MDataSet &set, UInt_t &seton, UInt_t &setoff)
385{
386 Double_t num, min, max;
387 if (!GetEventsProduced(set, num, min, max))
388 return kFALSE;
389
390 *fLog << inf << "Using build-in radius of 300m to calculate collection area!" << endl;
391
392 // Target spectrum
393 TF1 flx("Flux", "[0]/1000*(x/1000)^(-2.6)", min, max);
394 flx.SetParameter(0, 2e-7);
395
396 // Number n0 of events this spectrum would produce per s and m^2
397 const Double_t n0 = flx.Integral(min, max); //[#]
398
399 // Area produced in MC
400 const Double_t A = TMath::Pi()*300*300; //[m²]
401
402 // Rate R of events this spectrum would produce per s
403 const Double_t R = n0*A; //[Hz]
404
405 *fLog << "Gamma rate from the source inside the MC production area: " << R << "Hz" << endl;
406
407 // Number N of events produced (in trainings sample)
408 const Double_t N = num; //[#]
409
410 *fLog << "Events produced by MC inside the production area: " << TMath::Nint(num) << endl;
411
412 // This correponds to an observation time T [s]
413 const Double_t T = N/R; //[s]
414
415 *fLog << "Total time produced by the Monte Carlo: " << T << "s" << endl;
416
417 // With an average data rate after star of
418 Double_t data=0;
419 const Double_t r = GetDataRate(set, data); //[Hz]
420
421 *fLog << "Events measured per second effective on time: " << r << "Hz" << endl;
422 *fLog << "Total effective on time: " << data/r << "s" << endl;
423
424 // this yields a number of n events to be read for training
425 const Double_t n = r*T; //[#]
426
427 *fLog << "Events to be read from the data sample: " << TMath::Nint(n) << endl;
428 *fLog << "Events available in data sample: " << data << endl;
429
430 if (r<0)
431 return kFALSE;
432
433 Double_t nummc = GetNumMC(set);
434
435 *fLog << "Events available in MC sample: " << nummc << endl;
436
437 *fLog << "MC read probability: " << data/n << endl;
438
439 // more data requested than available => Scale down num MC events
440 Double_t on, off;
441 if (data<n)
442 {
443 on = TMath::Nint(nummc*data/n);
444 off = TMath::Nint(data);
445 *fLog << warn << "Not enough data events available... scaling by " << data/n << endl;
446 }
447 else
448 {
449 on = TMath::Nint(nummc);
450 off = TMath::Nint(n);
451 }
452
453 if (seton>0 && seton<on)
454 {
455 setoff = TMath::Nint(off*seton/on);
456 *fLog << "Less MC events requested... scaling by " << seton/on << endl;
457 }
458 else
459 {
460 seton = TMath::Nint(on);
461 setoff = TMath::Nint(off);
462 }
463
464 *fLog << "Target number of MC events: " << seton << endl;
465 *fLog << "Target number of data events: " << setoff << endl;
466
467 /*
468 An event rate dependent selection?
469 ----------------------------------
470 Total average data rate: R
471 Goal number of events: N
472 Number of data events: N0
473 Rate assigned to single evt: r
474
475 Selection probability: N/N0 * r/R
476
477 f := N/N0 * r
478
479 MF f("f * MEventRate.fRate < rand");
480 */
481
482 return kTRUE;
483}
484
485Bool_t MJTrainSeparation::Train(const char *out)
486{
487 if (!fDataSetTrain.IsValid())
488 {
489 *fLog << err << "ERROR - DataSet for training invalid!" << endl;
490 return kFALSE;
491 }
492 if (!fDataSetTest.IsValid())
493 {
494 *fLog << err << "ERROR - DataSet for testing invalid!" << endl;
495 return kFALSE;
496 }
497
498 // ----------------------- Auto Train? ----------------------
499
500 if (fAutoTrain)
501 {
502 fLog->Separator("Auto-Training -- Train-Data");
503 if (!AutoTrain(fDataSetTrain, fNumTrainOn, fNumTrainOff))
504 return kFALSE;
505 fLog->Separator("Auto-Training -- Test-Data");
506 if (!AutoTrain(fDataSetTest, fNumTestOn, fNumTestOff))
507 return kFALSE;
508 }
509
510 // --------------------- Setup files --------------------
511 MReadMarsFile read1("Events");
512 MReadMarsFile read2("Events");
513 MReadMarsFile read3("Events");
514 MReadMarsFile read4("Events");
515 read1.DisableAutoScheme();
516 read2.DisableAutoScheme();
517 read3.DisableAutoScheme();
518 read4.DisableAutoScheme();
519
520 fDataSetTrain.AddFilesOn(read1);
521 fDataSetTrain.AddFilesOff(read3);
522
523 fDataSetTest.AddFilesOff(read2);
524 fDataSetTest.AddFilesOn(read4);
525
526 // ----------------------- Setup RF ----------------------
527 MHMatrix train("Train");
528 train.AddColumns(fRules);
529 train.AddColumn("MHadronness.fVal");
530
531 // ----------------------- Fill Matrix RF ----------------------
532
533 MParameterD had("MHadronness");
534
535 MParList plistx;
536 plistx.AddToList(&had);
537 plistx.AddToList(this);
538
539 MTFillMatrix fill;
540 fill.SetLogStream(fLog);
541 fill.SetDisplay(fDisplay);
542 fill.AddPreCuts(fPreCuts);
543 fill.AddPreCuts(fTrainCuts);
544
545 // Set classifier for gammas
546 had.SetVal(0);
547 fill.SetName("FillGammas");
548 fill.SetDestMatrix1(&train, fNumTrainOn);
549 fill.SetReader(&read1);
550 if (!fill.Process(plistx))
551 return kFALSE;
552
553 const Int_t numgammastrn = train.GetNumRows();
554 if (numgammastrn==0)
555 {
556 *fLog << err << "ERROR - No gammas available for training... aborting." << endl;
557 return kFALSE;
558 }
559
560 // Set classifier for hadrons
561 had.SetVal(1);
562 fill.SetName("FillBackground");
563 fill.SetDestMatrix1(&train, fNumTrainOff);
564 fill.SetReader(&read3);
565 if (!fill.Process(plistx))
566 return kFALSE;
567
568 const Int_t numbackgrndtrn = train.GetNumRows()-numgammastrn;
569 if (numbackgrndtrn==0)
570 {
571 *fLog << err << "ERROR - No background available for training... aborting." << endl;
572 return kFALSE;
573 }
574
575 // ------------------------ Train RF --------------------------
576
577 MRanForestCalc rf;
578 rf.SetNumTrees(fNumTrees);
579 rf.SetNdSize(fNdSize);
580 rf.SetNumTry(fNumTry);
581 rf.SetNumObsoleteVariables(1);
582 rf.SetDebug(fDebug);
583 rf.SetDisplay(fDisplay);
584 rf.SetLogStream(fLog);
585 rf.SetFileName(out);
586 rf.SetNameOutput("MHadronness");
587
588 if (fUseRegression)
589 {
590 if (!rf.TrainSingleRF(train)) // regression
591 return kFALSE;
592 }
593 else
594 {
595 MBinning b(2, -0.5, 1.5, "BinningHadronness", "lin");
596 if (!rf.TrainSingleRF(train, b.GetEdgesD())) // classification
597 return kFALSE;
598 }
599
600 //if (!rf.TrainMultiRF(train, b.GetEdgesD())) // classification
601 // return;
602
603 //fDisplay = rf.GetDisplay();
604
605
606 *fLog << all;
607 fLog->Separator("The forest was tested with...");
608
609 *fLog << "Training method:" << endl;
610 *fLog << " * " << (fUseRegression?"regression":"classification") << endl;
611 *fLog << endl;
612 *fLog << "Events used for training:" << endl;
613 *fLog << " * Gammas: " << numgammastrn << endl;
614 *fLog << " * Background: " << numbackgrndtrn << endl;
615 *fLog << endl;
616 *fLog << "Gamma/Background ratio:" << endl;
617 *fLog << " * Requested: " << (float)fNumTrainOn/fNumTrainOff << endl;
618 *fLog << " * Result: " << (float)numgammastrn/numbackgrndtrn << endl;
619
620 if (!fDataSetTest.IsValid())
621 return kTRUE;
622
623 // --------------------- Display result ----------------------
624 fLog->Separator("Test");
625
626 MParList plist;
627 MTaskList tlist;
628 plist.AddToList(this);
629 plist.AddToList(&tlist);
630
631 MMcEvt mcevt;
632 plist.AddToList(&mcevt);
633
634 // ----- Setup histograms -----
635 MBinning binsy(50, 0 , 1, "BinningMH3Y", "lin");
636 MBinning binsx(40, 10, 100000, "BinningMH3X", "log");
637
638 plist.AddToList(&binsx);
639 plist.AddToList(&binsy);
640
641 MH3 h31("MHillas.fSize", "MHadronness.fVal");
642 MH3 h32("MHillas.fSize", "MHadronness.fVal");
643 MH3 h40("MMcEvt.fEnergy", "MHadronness.fVal");
644 h31.SetTitle("Background probability vs. Size:Size [phe]:Hadronness");
645 h32.SetTitle("Background probability vs. Size:Size [phe]:Hadronness");
646 h40.SetTitle("Background probability vs. Energy:Energy [GeV]:Hadronness");
647
648 MHHadronness hist;
649
650 // ----- Setup tasks -----
651 MFillH fillh0(&hist, "", "FillHadronness");
652 MFillH fillh1(&h31);
653 MFillH fillh2(&h32);
654 MFillH fillh4(&h40);
655 fillh1.SetNameTab("Background");
656 fillh2.SetNameTab("GammasH");
657 fillh4.SetNameTab("GammasE");
658 fillh0.SetBit(MFillH::kDoNotDisplay);
659
660 // ----- Setup filter -----
661 MFilterList precuts;
662 precuts.AddToList(fPreCuts);
663 precuts.AddToList(fTestCuts);
664
665 MContinue c0(&precuts);
666 c0.SetName("PreCuts");
667 c0.SetInverted();
668
669 MFEventSelector sel;
670 sel.SetNumSelectEvts(fNumTestOff);
671
672 MContinue c1(&sel);
673 c1.SetInverted();
674
675 // ----- Setup tasklist -----
676 tlist.AddToList(&read2);
677 tlist.AddToList(&c0);
678 tlist.AddToList(&c1);
679 tlist.AddToList(&rf);
680 tlist.AddToList(&fillh0);
681 tlist.AddToList(&fillh1);
682
683 // ----- Run eventloop on gammas -----
684 MEvtLoop loop;
685 loop.SetDisplay(fDisplay);
686 loop.SetLogStream(fLog);
687 loop.SetParList(&plist);
688
689 if (!loop.Eventloop())
690 return kFALSE;
691
692 // ----- Setup and run eventloop on background -----
693 sel.SetNumSelectEvts(fNumTestOn);
694 fillh0.ResetBit(MFillH::kDoNotDisplay);
695
696 tlist.Replace(&read4);
697 tlist.Replace(&fillh2);
698 tlist.AddToListAfter(&fillh4, &fillh2);
699
700 if (!loop.Eventloop())
701 return kFALSE;
702
703 DisplayResult(h31, h32);
704
705 if (!WriteDisplay(out))
706 return kFALSE;
707
708 *fLog << all;
709 fLog->Separator("The forest was tested with...");
710
711 const Double_t numgammastst = h32.GetHist().GetEntries();
712 const Double_t numbackgrndtst = h31.GetHist().GetEntries();
713
714 *fLog << "Events used for test:" << endl;
715 *fLog << " * Gammas: " << numgammastst << endl;
716 *fLog << " * Background: " << numbackgrndtst << endl;
717 *fLog << endl;
718 *fLog << "Gamma/Background ratio:" << endl;
719 *fLog << " * Requested: " << (float)fNumTestOn/fNumTestOff << endl;
720 *fLog << " * Result: " << (float)numgammastst/numbackgrndtst << endl;
721
722 return kTRUE;
723}
724
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