13 <input>CPVToolsOutput.root</input>
14 <output>test6_CPVFlavorTaggerEfficiency.root</output>
15 <contact>Yo Sato; yosato@post.kek.jp</contact>
16 <description>This file calculates the effective efficiency of the category based flavor tagger considering the two
17 standard combiners and the individual categories. Validation plots are also produced. </description>
22 from array
import array
24 ROOT.gROOT.SetBatch(
True)
26 workingFiles = [
"../CPVToolsOutput.root"]
30 workingDirectory =
'.'
38 r_subsample = array(
'd', [
47 r_size = len(r_subsample)
53 'IntermediateElectron',
57 'IntermediateKinLepton',
71 """Context handler class to quiet errors in a 'with' statement"""
74 """Class constructor"""
79 """Enter the context"""
82 ROOT.gErrorIgnoreLevel = self.
levellevel
85 """Exit the context"""
86 ROOT.gErrorIgnoreLevel = self.
oldleveloldlevel
89 tree = ROOT.TChain(treeName)
91 mcstatus = array(
'd', [-511.5, 0.0, 511.5])
92 ROOT.TH1.SetDefaultSumw2()
94 for iFile
in workingFiles:
98 for branch
in tree.GetListOfBranches():
99 totalBranches.append(branch.GetName())
101 if 'FBDT_qrCombined' in totalBranches:
102 methods.append(
"FBDT")
105 for cat
in categories:
106 catBranch =
'qp' + cat
107 if catBranch
in totalBranches:
108 usedCategories.append(cat)
110 categoriesNtupleList =
''
111 for category
in usedCategories:
112 categoriesNtupleList = categoriesNtupleList +
"Eff_%s:" % category
116 outputFile = ROOT.TFile(
"test6_CPVFlavorTaggerEfficiency.root",
"RECREATE")
119 outputNtuple = ROOT.TNtuple(
121 "Effective efficiencies of the flavor tagger combiners as well as of the individual tagging categories.",
122 "Eff_FBDT:DeltaEff_FBDT:" + categoriesNtupleList)
124 outputNtuple.SetAlias(
'Description',
"These are the effective efficiencies of the flavor tagger combiners as well as of " +
125 "the individual tagging efficiencies.")
126 outputNtuple.SetAlias(
128 "These values should not change drastically. Since the nightly reconstruction validation runs" +
129 "on the same input file (which changes only from release to release), the values between builds should be the same.")
130 outputNtuple.SetAlias(
'Contact',
"yosato@post.kek.jp")
132 efficienciesForNtuple = []
136 for method
in methods:
138 histo_avr_r = ROOT.TH1F(
'Average_r',
'Average r in each of 7 bins (B0 and B0bar)', 7,
140 histo_avr_rB0 = ROOT.TH1F(
'Average_rB0',
'Average r in each of 7 bins (B0)', 7,
142 histo_avr_rB0bar = ROOT.TH1F(
'Average_rB0bar',
'Average r in each of 7 bins (B0bar)', 7,
145 histo_entries_per_bin = ROOT.TH1F(
147 'Events binned in r_subsample according to their r-value for B0 and B0bar prob',
150 histo_entries_per_binB0 = ROOT.TH1F(
'entries_per_binB0',
'Events binned in r_subsample according '
151 'to their r-value for B0 prob', 7, r_subsample)
152 histo_entries_per_binB0bar = ROOT.TH1F(
'entries_per_binB0bar',
153 'Events binned in r_subsample according to their r-value '
154 'for B0bar prob', 7, r_subsample)
156 histo_Cnet_output_B0 = ROOT.TH1F(
'Comb_Net_Output_B0',
'Combiner network output [not equal to r] '
157 'for true B0 (binning 100)', 100, 0.0, 1.0)
159 histo_Cnet_output_B0bar = ROOT.TH1F(
'Comb_Net_Output_B0bar',
'Combiner network output [not equal to r] '
160 'for true B0bar (binning 100)', 100, 0.0, 1.0)
162 histo_belleplotB0 = ROOT.TH1F(
'qr_' + method +
'_B0',
163 'BellePlot for true B0 (binning 50)', 50,
166 histo_belleplotB0bar = ROOT.TH1F(
'qr_' + method +
'_B0Bar',
167 'BellePlot for true B0Bar (binning 50)',
171 histo_belleplotBoth = ROOT.TH1F(
'qr_' + method +
'_B0Both',
172 'qr-tagger output (binning 50)',
177 histo_calib_B0 = ROOT.TH1F(
'Calibration_' + method +
'_B0',
'CalibrationPlot for true B0', 100, -1.0, 1.0)
179 histo_calib_B0bar = ROOT.TH1F(
'Calibration_' + method +
'_B0Bar',
180 'CalibrationPlot for true B0Bar', 100, -1.0,
183 hallo12 = ROOT.TH1F(
'BellePlot_NoCut',
'BellePlot_NoCut (binning 100)',
187 diag = ROOT.TF1(
'diag',
'pol1', -1, 1)
191 histo_m0 = ROOT.TH1F(
'BellePlot_B0_m0',
192 'BellePlot_m for true B0 (binning 50)', 50, -1.0, 1.0)
193 histo_m1 = ROOT.TH1F(
'BellePlot_B0_m1',
194 'BellePlot_m for true B0 (binning 50)', 50, -1.0, 1.0)
195 histo_m2 = ROOT.TH1F(
'BellePlot_B0_m2',
196 'BellePlot_m for true B0Bar (binning 50)', 50, -1.0,
201 tree.Draw(method +
'_qrCombined>>qr_' + method +
'_B0',
'qrMC == 1')
202 tree.Draw(method +
'_qrCombined>>qr_' + method +
'_B0Bar',
'qrMC == -1')
203 tree.Draw(method +
'_qrCombined>>BellePlot_NoCut',
'abs(qrMC) == 1')
204 tree.Draw(method +
'_qrCombined>>qr_' + method +
'_B0Both',
'abs(qrMC) == 1')
206 tree.Draw(method +
'_qrCombined>>Calibration_' + method +
'_B0',
'qrMC == 1')
207 tree.Draw(method +
'_qrCombined>>Calibration_' + method +
'_B0Bar',
'qrMC == -1')
210 tree.Draw(method +
'_qrCombined>>BellePlot_B0_m0',
211 'qrMC == 1 && ' + method +
'_qrCombined>0')
212 tree.Draw(method +
'_qrCombined>>BellePlot_B0_m1',
213 'qrMC == 1 && ' + method +
'_qrCombined<0')
214 tree.Draw(method +
'_qrCombined>>BellePlot_B0_m2',
215 'qrMC == -1 && ' + method +
'_qrCombined>0 ')
220 tree.Project(
'Average_r',
'abs(' + method +
'_qrCombined)',
221 'abs(' + method +
'_qrCombined)')
222 tree.Project(
'Average_rB0',
'abs(' + method +
'_qrCombined)',
'abs(' + method +
'_qrCombined)*(qrMC==1)')
223 tree.Project(
'Average_rB0bar',
'abs(' + method +
'_qrCombined)',
'abs(' + method +
'_qrCombined)*(qrMC==-1)')
226 tree.Project(
'entries_per_bin',
'abs(' + method +
'_qrCombined)',
'abs(qrMC) == 1')
227 tree.Project(
'entries_per_binB0',
'abs(' + method +
'_qrCombined)',
'qrMC == 1')
228 tree.Project(
'entries_per_binB0bar',
'abs(' + method +
'_qrCombined)',
'qrMC == -1')
231 histo_avr_r.Divide(histo_entries_per_bin)
232 histo_avr_rB0.Divide(histo_entries_per_binB0)
233 histo_avr_rB0bar.Divide(histo_entries_per_binB0bar)
237 histo_calib_B0.Divide(hallo12)
238 histo_calib_B0bar.Divide(hallo12)
242 print(
'****************** CALIBRATION CHECK FOR COMBINER USING ' + method +
' ***************************************')
244 print(
'Fit polynomial of first order to the calibration plot. Expected value ~0.5')
246 histo_calib_B0.Fit(diag,
'TEST')
248 print(
'****************** MEASURED EFFECTIVE EFFICIENCY FOR COMBINER USING ' + method +
' ***************************')
251 total_entries = histo_entries_per_bin.GetEntries()
252 total_entries_B0 = histo_entries_per_binB0.GetEntries()
253 total_entries_B0bar = histo_entries_per_binB0bar.GetEntries()
256 event_fractionB0 = array(
'f', [0] * r_size)
257 event_fractionB0bar = array(
'f', [0] * r_size)
258 event_fractionTotal = array(
'f', [0] * r_size)
259 event_fractionDiff = array(
'f', [0] * r_size)
260 rvalueB0 = array(
'f', [0] * r_size)
261 rvalueB0bar = array(
'f', [0] * r_size)
262 rvalueB0Average = array(
'f', [0] * r_size)
263 wvalue = array(
'f', [0] * r_size)
264 wvalueB0 = array(
'f', [0] * r_size)
265 wvalueB0bar = array(
'f', [0] * r_size)
266 wvalueDiff = array(
'f', [0] * r_size)
267 entries = array(
'f', [0] * r_size)
268 entriesB0 = array(
'f', [0] * r_size)
269 entriesB0bar = array(
'f', [0] * r_size)
270 iEffEfficiency = array(
'f', [0] * r_size)
271 iDeltaEffEfficiency = array(
'f', [0] * r_size)
274 for i
in range(1, r_size):
276 rvalueB0[i] = histo_avr_rB0.GetBinContent(i)
277 rvalueB0bar[i] = histo_avr_rB0bar.GetBinContent(i)
278 rvalueB0Average[i] = (rvalueB0[i] + rvalueB0bar[i]) / 2
280 wvalue[i] = (1 - rvalueB0Average[i]) / 2
281 wvalueB0[i] = (1 - rvalueB0[i]) / 2
282 wvalueB0bar[i] = (1 - rvalueB0bar[i]) / 2
283 wvalueDiff[i] = wvalueB0[i] - wvalueB0bar[i]
284 entries[i] = histo_entries_per_bin.GetBinContent(i)
285 entriesB0[i] = histo_entries_per_binB0.GetBinContent(i)
286 entriesB0bar[i] = histo_entries_per_binB0bar.GetBinContent(i)
288 event_fractionTotal[i] = (entriesB0[i] + entriesB0bar[i]) / total_entries
289 event_fractionDiff[i] = (entriesB0[i] - entriesB0bar[i]) / total_entries
290 event_fractionB0[i] = entriesB0[i] / total_entries_B0
291 event_fractionB0bar[i] = entriesB0bar[i] / total_entries_B0bar
292 iEffEfficiency[i] = (event_fractionB0[i] * rvalueB0[i] * rvalueB0[i] +
293 event_fractionB0bar[i] * rvalueB0bar[i] * rvalueB0bar[i]) / 2
294 iDeltaEffEfficiency[i] = event_fractionB0[i] * rvalueB0[i] * \
295 rvalueB0[i] - event_fractionB0bar[i] * rvalueB0bar[i] * rvalueB0bar[i]
297 tot_eff_effB0 = tot_eff_effB0 + event_fractionB0[i] * rvalueB0[i] * rvalueB0[i]
298 tot_eff_effB0bar = tot_eff_effB0bar + event_fractionB0bar[i] * rvalueB0bar[i] * rvalueB0bar[i]
300 average_eff = (tot_eff_effB0 + tot_eff_effB0bar) / 2
301 diff_eff = tot_eff_effB0 - tot_eff_effB0bar
302 print(
'* ------------------------------------------------------------------------------------------------ *')
304 print(
'* __________________________________________________________________________________________ *')
306 print(
'* | TOTAL NUMBER OF TAGGED EVENTS = ' +
307 '{:<24}'.format(
"%.0f" % total_entries) +
'{:>36}'.format(
'| *'))
309 print(
'* | TOTAL AVERAGE EFFECTIVE EFFICIENCY (q=+-1)= ' +
'{:.2f}'.format(average_eff * 100) +
312 print(
'* | B0-TAGGER TOTAL EFFECTIVE EFFICIENCIES: ' +
313 '{:.2f}'.format(tot_eff_effB0 * 100) +
' % (q=+1) ' +
314 '{:.2f}'.format(tot_eff_effB0bar * 100) +
' % (q=-1) EffDiff=' +
315 '{:^5.2f}'.format(diff_eff * 100) +
' % | *')
317 print(
'* | FLAVOR PERCENTAGE (MC): ' +
318 '{:.2f}'.format(total_entries_B0 / total_entries * 100) +
' % (q=+1) ' +
319 '{:.2f}'.format(total_entries_B0bar / total_entries * 100) +
' % (q=-1) Diff=' +
320 '{:^5.2f}'.format((total_entries_B0 - total_entries_B0bar) / total_entries * 100) +
' % | *')
321 print(
'* |__________________________________________________________________________________________| *')
323 print(
'****************************************************************************************************')
326 efficienciesForNtuple.append(float(average_eff * 100))
327 efficienciesForNtuple.append(float(diff_eff * 100))
329 maxB0 = histo_belleplotB0.GetBinContent(histo_belleplotB0.GetMaximumBin())
330 maxB0bar = histo_belleplotB0bar.GetBinContent(histo_belleplotB0bar.GetMaximumBin())
331 maxB0Both = histo_belleplotBoth.GetBinContent(histo_belleplotBoth.GetMaximumBin())
333 Ymax = max(maxB0, maxB0bar, maxB0Both)
334 Ymax = Ymax + Ymax / 12
336 if YmaxForQrPlot < Ymax:
340 ROOT.gStyle.SetOptStat(0)
341 with Quiet(ROOT.kError):
342 Canvas1 = ROOT.TCanvas(
'Bla',
'Final Output', 1200, 800)
344 Canvas1.SetLeftMargin(0.13)
345 Canvas1.SetRightMargin(0.04)
346 Canvas1.SetTopMargin(0.03)
347 Canvas1.SetBottomMargin(0.14)
348 histo_belleplotB0.SetFillColorAlpha(ROOT.kBlue, 0.2)
349 histo_belleplotB0.SetFillStyle(1001)
350 histo_belleplotB0.GetXaxis().SetLabelSize(0.04)
351 histo_belleplotB0.GetYaxis().SetLabelSize(0.04)
352 histo_belleplotB0.GetYaxis().SetTitleOffset(0.9)
353 histo_belleplotB0.GetXaxis().SetTitleSize(0.06)
354 histo_belleplotB0.GetYaxis().SetTitleSize(0.06)
355 histo_belleplotB0.GetYaxis().SetLimits(0, YmaxForQrPlot)
356 histo_belleplotB0.SetLineColor(ROOT.kBlue)
357 histo_belleplotB0bar.SetFillColorAlpha(ROOT.kRed, 1.0)
358 histo_belleplotB0bar.SetFillStyle(3005)
359 histo_belleplotB0bar.SetLineColor(ROOT.kRed)
362 histo_belleplotB0.SetTitle(
'; #it{qr}_{' + method +
'} ; Events (Total = ' +
'{:<1}'.format(
"%.0f" % total_entries) +
')'
364 histo_belleplotB0.SetMinimum(0)
365 histo_belleplotB0.SetMaximum(YmaxForQrPlot)
366 histo_belleplotB0.Draw(
'hist')
367 histo_belleplotB0bar.Draw(
'hist same')
369 leg = ROOT.TLegend(0.2, 0.7, 0.9, 0.95)
373 ' #varepsilon_{eff}(B^{0}) = ' +
377 '% #frac{n_{B^{0}}}{n} = ' +
384 histo_belleplotB0bar,
385 'true #bar{B}^{0} ' +
386 ' #varepsilon_{eff}(#bar{B}^{0}) = ' +
390 '% #frac{n_{#bar{B}^{0}}}{n} = ' +
392 total_entries_B0bar /
396 leg.AddEntry(
"",
"Avrg. #bf{ #varepsilon_{eff} = " +
'{:.2f}'.format(average_eff * 100) +
397 '%} #Delta#varepsilon_{eff} = ' +
'{:^5.2f}'.format(diff_eff * 100) +
'%')
398 leg.SetTextSize(0.045)
403 with Quiet(ROOT.kError):
404 Canvas1.SaveAs(workingDirectory +
'/' +
'test6_CPVFTqr' + method +
'_both.pdf')
407 histo_belleplotBoth.GetXaxis().SetLabelSize(0.04)
408 histo_belleplotBoth.GetYaxis().SetLabelSize(0.04)
409 histo_belleplotBoth.GetYaxis().SetTitleOffset(0.7)
410 histo_belleplotBoth.GetXaxis().SetTitleOffset(0.7)
411 histo_belleplotBoth.GetXaxis().SetTitleSize(0.06)
412 histo_belleplotBoth.GetYaxis().SetTitleSize(0.06)
414 histo_belleplotBoth.GetListOfFunctions().Add(ROOT.TNamed(
'MetaOptions',
'nostats'))
415 histo_belleplotBoth.GetListOfFunctions().Add(ROOT.TNamed(
'Description',
'Output of the flavor tagger combiner ' + method))
416 histo_belleplotBoth.GetListOfFunctions().Add(
419 'Shape should not change drastically. E.g. Warning if the peak at 0 increases or if the peaks at +-1 decrease.'))
420 histo_belleplotBoth.GetListOfFunctions().Add(ROOT.TNamed(
'Contact',
'yosato@post.kek.jp'))
422 histo_belleplotBoth.SetTitle(
423 'Flavor tagger output for combiner ' +
427 '} ; Events (Total = ' +
432 histo_belleplotBoth.SetMinimum(0)
433 histo_belleplotBoth.SetMaximum(YmaxForQrPlot)
434 histo_belleplotBoth.SetStats(
False)
435 histo_belleplotBoth.Write()
438 histo_belleplotB0.GetYaxis().SetTitleOffset(0.7)
439 histo_belleplotB0.GetXaxis().SetTitleOffset(0.7)
440 histo_belleplotB0.SetLineColor(ROOT.kBlue + 2)
441 histo_belleplotB0.SetTitle(
442 'Flavor tagger output for combiner ' +
444 ' for true B^{0}s; #it{qr}_{' +
446 '} ; Events (Total = ' +
449 histo_belleplotB0.GetEntries()) +
451 histo_belleplotB0.SetStats(
False)
453 histo_belleplotB0.GetListOfFunctions().Add(ROOT.TNamed(
'MetaOptions',
'nostats'))
454 histo_belleplotB0.GetListOfFunctions().Add(
455 ROOT.TNamed(
'Description',
'Output of the flavor tagger combiner ' + method +
' for true B0s'))
456 histo_belleplotB0.GetListOfFunctions().Add(
459 'Shape should not change drastically. E.g. Warning if the peak at 0 increases or if the peak at +1 decreases.'))
460 histo_belleplotB0.GetListOfFunctions().Add(ROOT.TNamed(
'Contact',
'yosato@post.kek.jp'))
461 histo_belleplotB0.Write()
464 histo_belleplotB0bar.GetXaxis().SetLabelSize(0.04)
465 histo_belleplotB0bar.GetYaxis().SetLabelSize(0.04)
466 histo_belleplotB0bar.GetYaxis().SetTitleOffset(0.7)
467 histo_belleplotB0bar.GetXaxis().SetTitleOffset(0.7)
468 histo_belleplotB0bar.GetXaxis().SetTitleSize(0.06)
469 histo_belleplotB0bar.GetYaxis().SetTitleSize(0.06)
470 histo_belleplotB0bar.SetLineColor(ROOT.kBlue + 2)
471 histo_belleplotB0bar.SetTitle(
472 'Flavor tagger output for combiner ' +
474 ' for true #bar{B}^{0}s; #it{qr}_{' +
476 '} ; Events (Total = ' +
479 histo_belleplotB0bar.GetEntries()) +
481 histo_belleplotB0bar.SetMinimum(0)
482 histo_belleplotB0bar.SetMaximum(YmaxForQrPlot)
483 histo_belleplotB0bar.SetStats(
False)
485 histo_belleplotB0bar.GetListOfFunctions().Add(ROOT.TNamed(
'MetaOptions',
'nostats'))
486 histo_belleplotB0bar.GetListOfFunctions().Add(
489 'Output of the flavor tagger combiner ' +
492 histo_belleplotB0bar.GetListOfFunctions().Add(ROOT.TNamed(
493 'Check',
'Shape should not change drastically. E.g. Warning if the peak at 0 increases or if the peak at -1 decreases.'))
494 histo_belleplotB0bar.GetListOfFunctions().Add(ROOT.TNamed(
'Contact',
'yosato@post.kek.jp'))
495 histo_belleplotB0bar.Write()
500 with Quiet(ROOT.kError):
501 Canvas2 = ROOT.TCanvas(
'Bla2',
'Calibration plot for true B0', 1200, 800)
503 Canvas2.SetLeftMargin(0.13)
504 Canvas2.SetRightMargin(0.04)
505 Canvas2.SetTopMargin(0.03)
506 Canvas2.SetBottomMargin(0.14)
507 histo_calib_B0.GetXaxis().SetLabelSize(0.04)
508 histo_calib_B0.GetYaxis().SetLabelSize(0.04)
509 histo_calib_B0.GetYaxis().SetTitleOffset(0.9)
510 histo_calib_B0.GetXaxis().SetTitleSize(0.06)
511 histo_calib_B0.GetYaxis().SetTitleSize(0.06)
512 histo_calib_B0.SetFillColorAlpha(ROOT.kBlue, 0.2)
513 histo_calib_B0.SetFillStyle(1001)
514 histo_calib_B0.GetYaxis().SetTitleOffset(0.9)
515 histo_calib_B0.SetLineColor(ROOT.kBlue)
517 histo_calib_B0.SetTitle(
'; #it{qr}_{' + method +
'} ; Calibration '
519 histo_calib_B0.Draw(
'hist')
522 leg2 = ROOT.TLegend(0.2, 0.75, 0.63, 0.93)
523 leg2.SetHeader(
" y = #it{m}#it{x} + #it{c}",
"")
524 leg2.GetListOfPrimitives().First().SetTextAlign(22)
529 diag.GetParameter(
"p1")) +
532 diag.GetParameter(
"p0")))
533 leg2.SetTextSize(0.05)
537 with Quiet(ROOT.kError):
538 Canvas2.SaveAs(workingDirectory +
'/' +
'test6_CPVFTCalibration_' + method +
'_B0.pdf')
542 histo_calib_B0.GetYaxis().SetTitleOffset(0.7)
543 histo_calib_B0.GetXaxis().SetTitleOffset(0.7)
544 histo_calib_B0.SetLineColor(ROOT.kBlue + 2)
545 histo_calib_B0.SetTitle(
'Calibration plot for the flavor tagger combiner ' +
546 method +
' ; #it{qr}_{' + method +
'} ; Calibration')
547 histo_calib_B0.SetMinimum(-0.2)
548 histo_calib_B0.SetMaximum(+1.2)
549 histo_calib_B0.SetStats(
False)
551 histo_calib_B0.GetListOfFunctions().Add(ROOT.TNamed(
'MetaOptions',
'nostats'))
552 histo_calib_B0.GetListOfFunctions().Add(
555 'Calibration plot for the flavor tagger combiner ' +
558 histo_calib_B0.GetListOfFunctions().Add(
559 ROOT.TNamed(
'Check',
'Shape should not change drastically. E.g. warning if the shape stops being linear.'))
560 histo_calib_B0.GetListOfFunctions().Add(ROOT.TNamed(
'Contact',
'yosato@post.kek.jp'))
561 histo_calib_B0.Write()
563 histo_belleplotBoth.Delete()
565 histo_avr_rB0.Delete()
566 histo_avr_rB0bar.Delete()
567 histo_entries_per_bin.Delete()
568 histo_entries_per_binB0.Delete()
569 histo_entries_per_binB0bar.Delete()
570 histo_Cnet_output_B0.Delete()
571 histo_Cnet_output_B0bar.Delete()
572 histo_belleplotB0.Delete()
573 histo_belleplotB0bar.Delete()
574 histo_calib_B0.Delete()
575 histo_calib_B0bar.Delete()
588 print(
'************************* MEASURED EFFECTIVE EFFICIENCY FOR INDIVIDUAL CATEGORIES *********************************')
593 for category
in usedCategories:
595 hist_signal = ROOT.TH1F(
'Signal_' + category,
'Input Signal (B0)' +
596 category +
' (binning 50)', 50, -1.0, 1.0)
598 hist_background = ROOT.TH1F(
'Background_' + category,
'Input Background (B0bar)' +
599 category +
' (binning 50)', 50, -1.0, 1.0)
600 hist_both = ROOT.TH1F(
'qp_' + category,
'Input Background (B0bar)' +
601 category +
' (binning 50)', 100, -1, 1)
605 hist_probB0 = ROOT.TH1F(
'ProbabilityB0_' + category,
606 'Transformed to probability (B0) (' + category +
')',
608 hist_probB0bar = ROOT.TH1F(
'ProbabilityB0bar_' + category,
609 'Transformed to probability (B0bar) (' + category +
')',
612 hist_qpB0 = ROOT.TH1F(
'QRB0_' + category,
'Transformed to qp (B0)(' +
613 category +
')', 50, -1.0, 1.0)
614 hist_qpB0bar = ROOT.TH1F(
'QRB0bar_' + category,
'Transformed to qp (B0bar) (' +
615 category +
')', 50, -1.0, 1.0)
618 hist_absqpB0 = ROOT.TH1F(
'AbsQRB0_' + category,
'Abs(qp)(B0) (' + category +
')', 7, r_subsample)
619 hist_absqpB0bar = ROOT.TH1F(
'AbsQRB0bar_' + category,
'Abs(qp) (B0bar) (' + category +
')', 7, r_subsample)
622 hist_aver_rB0 = ROOT.TH1F(
'AverageRB0_' + category,
'A good one (B0)' +
623 category, 7, r_subsample)
624 hist_aver_rB0bar = ROOT.TH1F(
'AverageRB0bar_' + category,
'A good one (B0bar)' +
625 category, 7, r_subsample)
628 hist_all = ROOT.TH1F(
'All_' + category,
'Input Signal (B0) and Background (B0Bar)' +
629 category +
' (binning 50)', 50, 0.0, 1.0)
630 tree.Draw(
'qp' + category +
'>>All_' + category,
'qrMC!=0')
631 hist_calib_B0 = ROOT.TH1F(
'Calib_B0_' + category,
'Calibration Plot for true B0' +
632 category +
' (binning 50)', 50, 0.0, 1.0)
633 tree.Draw(
'qp' + category +
'>>Calib_B0_' + category,
'qrMC == 1.0')
634 hist_calib_B0.Divide(hist_all)
637 tree.Draw(
'qp' + category +
'>>Signal_' + category,
'qrMC == 1.0')
639 tree.Draw(
'qp' + category +
'>>Background_' + category,
'qrMC == -1.0'
642 tree.Draw(
'qp' + category +
'>>qp_' + category,
'abs(qrMC) == 1.0'
648 purityB0 = array(
'd', [0] * 51)
649 dilutionB02 = array(
'd', [0] * 51)
650 purityB0bar = array(
'd', [0] * 51)
651 dilutionB0bar2 = array(
'd', [0] * 51)
652 signal = array(
'd', [0] * 51)
653 back = array(
'd', [0] * 51)
654 weight = array(
'd', [0] * 51)
656 for i
in range(1, 51):
658 signal[i] = hist_signal.GetBinContent(i)
659 back[i] = hist_background.GetBinContent(i)
661 weight[i] = signal[i] + back[i]
664 if signal[i] + back[i] == 0:
668 dilutionB0bar2[i] = 0
670 purityB0[i] = signal[i] / (signal[i] + back[i])
671 dilutionB02[i] = -1 + 2 * signal[i] / (signal[i] + back[i])
673 purityB0bar[i] = back[i] / (signal[i] + back[i])
674 dilutionB0bar2[i] = -1 + 2 * back[i] / (signal[i] + back[i])
677 hist_probB0.Fill(purityB0[i], signal[i])
678 hist_probB0bar.Fill(purityB0bar[i], back[i])
681 hist_qpB0.Fill(dilutionB02[i], signal[i])
682 hist_qpB0bar.Fill(dilutionB0bar2[i], back[i])
685 hist_absqpB0.Fill(abs(dilutionB02[i]), signal[i])
686 hist_absqpB0bar.Fill(abs(dilutionB0bar2[i]), back[i])
688 hist_aver_rB0.Fill(abs(dilutionB02[i]), abs(dilutionB02[i]) * signal[i])
689 hist_aver_rB0bar.Fill(abs(dilutionB0bar2[i]), abs(dilutionB0bar2[i]) * back[i])
692 hist_aver_rB0.Divide(hist_absqpB0)
693 hist_aver_rB0bar.Divide(hist_absqpB0bar)
699 for i
in range(1, r_size):
700 tot_entriesB0 = tot_entriesB0 + hist_absqpB0.GetBinContent(i)
701 tot_entriesB0bar = tot_entriesB0bar + hist_absqpB0bar.GetBinContent(i)
705 event_fractionB0 = array(
'f', [0] * r_size)
706 event_fractionB0bar = array(
'f', [0] * r_size)
707 rvalueB0 = array(
'f', [0] * r_size)
708 rvalueB0bar = array(
'f', [0] * r_size)
710 entriesB0 = array(
'f', [0] * r_size)
711 entriesB0bar = array(
'f', [0] * r_size)
713 for i
in range(1, r_size):
714 rvalueB0[i] = hist_aver_rB0.GetBinContent(i)
715 rvalueB0bar[i] = hist_aver_rB0bar.GetBinContent(i)
717 entriesB0[i] = hist_absqpB0.GetBinContent(i)
718 entriesB0bar[i] = hist_absqpB0bar.GetBinContent(i)
719 event_fractionB0[i] = entriesB0[i] / tot_entriesB0
720 event_fractionB0bar[i] = entriesB0bar[i] / tot_entriesB0bar
724 tot_eff_effB0 = tot_eff_effB0 + event_fractionB0[i] * rvalueB0[i] \
726 tot_eff_effB0bar = tot_eff_effB0bar + event_fractionB0bar[i] * rvalueB0bar[i] \
728 effDiff = tot_eff_effB0 - tot_eff_effB0bar
729 effAverage = (tot_eff_effB0 + tot_eff_effB0bar) / 2
732 '{:<25}'.format(
"* " +
745 '{: 8.2f}'.format(effAverage * 100) +
' %' +
747 '{: 8.2f}'.format(effDiff * 100) +
' % *')
751 efficienciesForNtuple.append(float(effAverage * 100))
753 maxSignal = hist_signal.GetBinContent(hist_signal.GetMaximumBin())
754 maxBackground = hist_background.GetBinContent(hist_background.GetMaximumBin())
756 Ymax = max(maxSignal, maxBackground)
757 Ymax = Ymax + Ymax / 12
759 ROOT.gStyle.SetOptStat(0)
760 with Quiet(ROOT.kError):
761 Canvas = ROOT.TCanvas(
'Bla',
'TITEL BLA', 1200, 800)
764 Canvas.SetLeftMargin(0.13)
765 Canvas.SetRightMargin(0.04)
766 Canvas.SetTopMargin(0.03)
767 Canvas.SetBottomMargin(0.14)
768 hist_signal.SetFillColorAlpha(ROOT.kBlue, 0.2)
769 hist_signal.SetFillStyle(1001)
770 hist_signal.SetTitleSize(0.1)
771 hist_signal.GetXaxis().SetLabelSize(0.04)
772 hist_signal.GetYaxis().SetLabelSize(0.04)
773 hist_signal.GetXaxis().SetTitleSize(0.06)
774 hist_signal.GetYaxis().SetTitleSize(0.06)
775 hist_signal.GetXaxis().SetLabelSize(0.04)
776 hist_signal.GetYaxis().SetLabelSize(0.04)
777 hist_signal.GetXaxis().SetTitleSize(0.05)
778 hist_signal.GetYaxis().SetTitleSize(0.05)
779 hist_signal.GetXaxis().SetTitleOffset(0.95)
780 hist_signal.GetYaxis().SetTitleOffset(1.1)
781 hist_signal.GetXaxis().SetTitleOffset(1.15)
782 hist_signal.GetYaxis().SetLimits(0, Ymax)
783 hist_signal.SetLineColor(ROOT.kBlue)
784 hist_background.SetFillColorAlpha(ROOT.kRed, 1.0)
785 hist_background.SetFillStyle(3005)
786 hist_background.GetYaxis().SetLimits(0, Ymax)
787 hist_background.SetLineColor(ROOT.kRed)
790 if category ==
'MaximumPstar':
791 catName =
'MaximumP*'
793 hist_signal.SetTitle(
'; (#it{qp})^{' + catName +
'} ; Events')
795 hist_signal.SetMaximum(Ymax)
797 hist_background.SetMaximum(Ymax)
799 hist_signal.Draw(
'hist')
800 hist_background.Draw(
'hist same')
802 l0 = ROOT.TLegend(0.13, 0.65, 0.33, 0.97)
803 l0.SetFillColorAlpha(ROOT.kWhite, 0)
804 l0.AddEntry(hist_signal,
' #varepsilon_{eff}(B^{0}) = ' +
'{:.2f}'.format(tot_eff_effB0 * 100) +
"%")
805 l0.AddEntry(hist_background,
' #varepsilon_{eff}(#bar{B}^{0}) = ' +
'{:.2f}'.format(tot_eff_effB0bar * 100) +
"%")
806 l0.AddEntry(
"",
"#bf{#varepsilon_{eff} = " +
'{:.2f}'.format(effAverage * 100) +
'%}')
807 l0.AddEntry(
"",
'#Delta#varepsilon_{eff} = ' +
'{:^5.2f}'.format(effDiff * 100) +
'%')
809 l0.SetTextSize(0.045)
812 l1 = ROOT.TLegend(0.85, 0.7, 0.96, 0.97)
813 l1.SetFillColorAlpha(ROOT.kWhite, 0.35)
814 l1.AddEntry(hist_signal,
'B^{0}_{MC}')
815 l1.AddEntry(hist_background,
'#bar{B}^{0}_{MC}')
816 l1.SetTextSize(0.045)
820 with Quiet(ROOT.kError):
821 Canvas.SaveAs(workingDirectory +
'/' +
'test6_CPVFTqp_' + category +
'_both.pdf')
824 hist_both.GetXaxis().SetLabelSize(0.04)
825 hist_both.GetYaxis().SetLabelSize(0.04)
826 hist_both.GetYaxis().SetTitleOffset(0.7)
827 hist_both.GetXaxis().SetTitleOffset(0.7)
828 hist_both.GetXaxis().SetTitleSize(0.06)
829 hist_both.GetYaxis().SetTitleSize(0.06)
831 hist_both.GetListOfFunctions().Add(ROOT.TNamed(
'MetaOptions',
'nostats, logy'))
832 hist_both.GetListOfFunctions().Add(ROOT.TNamed(
'Description',
'Output of the flavor tagger category ' + catName))
833 hist_both.GetListOfFunctions().Add(
834 ROOT.TNamed(
'Check',
'Shape should not change drastically. E.g. Warning if there is only a peak at 0.'))
835 hist_both.GetListOfFunctions().Add(ROOT.TNamed(
'Contact',
'yosato@post.kek.jp'))
838 'Flavor tagger output of the category ' +
842 '} ; Events (Total = ' +
845 hist_both.GetEntries()) +
852 outputNtuple.Fill(array(
'f', efficienciesForNtuple))
857 print(
'*******************************************************************************************************************')
oldlevel
the previously set level to be ignored
def __exit__(self, type, value, traceback)
def __init__(self, level=ROOT.kInfo+1)