forked from thaarres/boostedWScalefactorProducer
-
Notifications
You must be signed in to change notification settings - Fork 1
/
wtagSFfits_N2DDT_unChanged.py
1192 lines (971 loc) · 75.6 KB
/
wtagSFfits_N2DDT_unChanged.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
from optparse import OptionParser
import ROOT
import sys
import os
import time
import math
import CMS_lumi, tdrstyle
from ROOT import *
parser = OptionParser()
parser.add_option('-b', action='store_true', dest='noX', default=False, help='no X11 windows')
parser.add_option('-c', '--channel',action="store",type="string",dest="channel",default="em")
parser.add_option('--HP', action="store", type="float",dest="tau2tau1cutHP",default=0.45)
parser.add_option('--LP', action="store", type="float",dest="tau2tau1cutLP",default=0.75)
parser.add_option('--sample', action="store",type="string",dest="sample",default="powheg")
parser.add_option('--fitTT', action='store_true', dest='fitTT', default=False, help='Only do ttbar fits')
parser.add_option('--fitMC', action='store_true', dest='fitMC', default=False, help='Only do MC fits')
parser.add_option('--doBinned',dest="doBinnedFit", default=False, action="store_true", help="Do binned fit")
parser.add_option('--76X',dest="use76X", default=False, action="store_true", help="Use 76X samples")
parser.add_option('--useDDT',dest="useDDT", default=False, action="store_true", help="Use DDT tagger")
parser.add_option('--useN2DDT',dest="useN2DDT", default=False, action="store_true", help="Use N_2^DDT tagger")
parser.add_option('--usePuppiSD',dest="usePuppiSD", default=False, action="store_true", help="Use PUPPI+softdrop")
(options, args) = parser.parse_args()
#Added these 2 lines - Michael
ROOT.gSystem.Load("/cvmfs/cms.cern.ch/slc6_amd64_gcc491/lcg/roofit/5.34.22-cms3/lib/libRooFitCore.so")
ROOT.gSystem.Load("/cvmfs/cms.cern.ch/slc6_amd64_gcc491/lcg/roofit/5.34.22-cms3/lib/libRooFit.so")
ROOT.gSystem.Load(".//PlotStyle/Util_cxx.so")
ROOT.gSystem.Load(".//PlotStyle/PlotUtils_cxx.so")
ROOT.gSystem.Load(".//PDFs/PdfDiagonalizer_cc.so")
ROOT.gSystem.Load(".//PDFs/HWWLVJRooPdfs_cxx.so")
ROOT.gSystem.Load(".//PDFs/MakePdf_cxx.so")
ROOT.gSystem.Load(".//BiasStudy/BiasUtils_cxx.so")
ROOT.gSystem.Load(".//FitUtils/FitUtils_cxx.so")
tdrstyle.setTDRStyle()
CMS_lumi.lumi_13TeV = "35.8 fb^{-1}"
if options.use76X: CMS_lumi.lumi_13TeV = "35.8 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
CMS_lumi.lumi_sqrtS = "13 TeV" # used with iPeriod = 0, e.g. for simulation-only plots (default is an empty string)
iPos = 11
if( iPos==0 ): CMS_lumi.relPosX = 0.12
iPeriod = 4
gInterpreter.GenerateDictionary("std::map<std::string,std::string>", "map;string;string")
gStyle.SetOptTitle(0)
RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
if options.noX: gROOT.SetBatch(True)
def getLegend():
legend = TLegend(0.6010112,0.7183362,0.8202143,0.919833)
legend.SetTextSize(0.032)
legend.SetLineColor(0)
legend.SetShadowColor(0)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetMargin(0.35)
return legend
def getPavetext():
# addInfo = TPaveText(0.18,0.3,0.25,0.4,"NDC")
addInfo = TPaveText(0.18,0.74,0.25,0.84,"NDC")
addInfo.SetFillColor(0)
addInfo.SetLineColor(0)
addInfo.SetFillStyle(0)
addInfo.SetBorderSize(0)
addInfo.SetTextFont(42)
addInfo.SetTextSize(0.030)
addInfo.SetTextAlign(12)
return addInfo
def drawFrameGetChi2(variable,fitResult,dataset,pdfModel,isData):
wpForPlotting ="%.2f"%options.tau2tau1cutHP
wpForPlotting = wpForPlotting.replace(".","v")
postfix=""
if options.usePuppiSD: postfix = "_PuppiSD"
if options.useDDT: postfix = "_PuppiSD_DDT"
if options.useN2DDT: postfix = "_PuppiSD_N2DDT"
title = "Pruned jet mass (GeV)"
if options.usePuppiSD or options.useDDT or options.useN2DDT: title = "PUPPI Softdrop Jet Mass (GeV)"
frame = variable.frame()
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
pdfModel.plotOn(frame,RooFit.VisualizeError(fitResult,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.LineColor(kBlack),RooFit.Name(pdfModel.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2 = frame.chiSquare(pdfModel.GetName(), dataset.GetName ())
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 2" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "ErfExp comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kBlue+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 1" ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kRed+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Chebyshev comp." ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
c1 =TCanvas("data_fail","",800,800)
frame.GetYaxis().SetTitleSize(0.05)
frame.GetYaxis().SetTitleOffset(0.90)
frame.SetName("mjjFit")
frame.GetYaxis().SetTitle("Events")
frame.GetXaxis().SetTitle(title)
frame.Draw()
frame.GetYaxis().SetRangeUser(0,1500)
frame.SetMinimum(0.)
legend = getLegend()
if(isData): legend.AddEntry(frame.findObject(dataset.GetName ()),"CMS data","lpe")
else: legend.AddEntry(frame.findObject(dataset.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(pdfModel.GetName()),"Sim. fit","l")
if frame.findObject("Gaussian 1"):
legend.AddEntry(frame.findObject("Gaussian 1"),frame.findObject("Gaussian 1").GetName(),"l")
if frame.findObject("Chebyshev comp."):
legend.AddEntry(frame.findObject("Chebyshev comp."),frame.findObject("Chebyshev comp.").GetName(),"l")
if frame.findObject("Gaussian 2"):
legend.AddEntry(frame.findObject("Gaussian 2"),frame.findObject("Gaussian 2").GetName(),"l")
if frame.findObject("ErfExp comp."):
legend.AddEntry(frame.findObject("ErfExp comp."),frame.findObject("ErfExp comp.").GetName(),"l")
legend.Draw("same")
CMS_lumi.CMS_lumi(c1, iPeriod, iPos)
addInfo = getPavetext()
addInfo.AddText("#chi^{2}/nDOF = %.3f"%chi2)
addInfo.Draw()
c1.Update()
cname = pdfModel.GetName()
c1.SaveAs("plots/%s_%s.pdf"%(cname,options.sample))
c1.SaveAs("plots/%s_%s.root"%(cname,options.sample))
return chi2
def drawDataAndMC(variable,fitResult,dataset,pdfModel,isData,variable2,fitResult2,dataset2,pdfModel2,isData2):
wpForPlotting ="%.2f"%options.tau2tau1cutHP
wpForPlotting = wpForPlotting.replace(".","v")
postfix=""
if options.usePuppiSD: postfix = "_PuppiSD"
if options.useDDT: postfix = "_PuppiSD_DDT"
if options.useN2DDT: postfix = "_PuppiSD_N2DDT"
title = "Pruned jet mass (GeV)"
if options.usePuppiSD or options.useDDT or options.useN2DDT: title = "PUPPI Softdrop Jet mass (GeV)"
frame = variable.frame()
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
# pdfModel.plotOn(frame,RooFit.VisualizeError(fitResult,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.LineColor(kBlack),RooFit.Name(pdfModel.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2 = frame.chiSquare(pdfModel.GetName(), dataset.GetName ())
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 2" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "ErfExp Data comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kBlue+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Gaussian Data comp." ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kRed+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Chebyshev comp." ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
if(isData2): dataset2.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kBlue),RooFit.LineColor(kBlue))
else: dataset2.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kBlue),RooFit.LineColor(kBlue))
# pdfModel2.plotOn(frame,RooFit.VisualizeError(fitResult2,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.LineColor(kGreen),RooFit.Name(pdfModel2.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2_2 = frame.chiSquare(pdfModel2.GetName(), dataset2.GetName ())
pdfModel2.plotOn(frame,RooFit.Name( "Gaussian 2 MC" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "ErfExp MC comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kMagenta+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "Gaussian MC comp." ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kOrange-3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "Chebyshev comp. MC" ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData2): dataset2.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kGreen+3),RooFit.LineColor(kGreen+3))
else: dataset2.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kGreen+3),RooFit.LineColor(kGreen+3))
c1 =TCanvas("data_fail","",800,800)
frame.GetYaxis().SetTitleSize(0.05)
frame.GetYaxis().SetTitleOffset(0.90)
frame.SetName("mjjFit")
frame.GetYaxis().SetTitle("Events")
frame.GetXaxis().SetTitle(title)
frame.Draw()
frame.SetMinimum(0.)
legend = getLegend()
if(isData): legend.AddEntry(frame.findObject(dataset.GetName ()),"CMS data","lpe")
else: legend.AddEntry(frame.findObject(dataset.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(dataset2.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(pdfModel.GetName()),"Sim. Data fit","l")
legend.AddEntry(frame.findObject(pdfModel2.GetName()),"Sim. MC fit","l")
if frame.findObject("Gaussian Data comp."):
legend.AddEntry(frame.findObject("Gaussian Data comp."),frame.findObject("Gaussian Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("Gaussian MC comp."),frame.findObject("Gaussian MC comp.").GetName(),"l")
if frame.findObject("Chebyshev comp."):
legend.AddEntry(frame.findObject("Chebyshev comp."),frame.findObject("Chebyshev comp.").GetName(),"l")
if frame.findObject("Gaussian 2"):
legend.AddEntry(frame.findObject("Gaussian Data comp."),frame.findObject("Gaussian Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("Gaussian MC comp."),frame.findObject("Gaussian MC comp.").GetName(),"l")
if frame.findObject("ErfExp Data comp."):
legend.AddEntry(frame.findObject("ErfExp Data comp."),frame.findObject("ErfExp Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("ErfExp MC comp."),frame.findObject("ErfExp MC comp.").GetName(),"l")
legend.Draw("same")
CMS_lumi.CMS_lumi(c1, iPeriod, iPos)
addInfo = getPavetext()
addInfo.AddText("Data #chi^{2}/nDOF = %.3f"%chi2)
addInfo.AddText("MC #chi^{2}/nDOF = %.3f"%chi2_2)
addInfo.Draw()
c1.Update()
cname = pdfModel.GetName()
c1.SaveAs("plots/CombinedPlot_%s.pdf"%(cname))
c1.SaveAs("plots/CombinedPlot_%s.root"%(cname))
return chi2
def getSF():
print "Getting W-tagging SF for cut " ,options.tau2tau1cutHP
if options.useDDT:
options.usePuppiSD = True
options.use76X = True
boostedW_fitter_sim = doWtagFits()
def doFitsToMatchedTT():
workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_")
ttMC_fitter = initialiseFits("em", options.sample, 40, 130, workspace4fit_)
ttMC_fitter.get_mj_dataset(ttMC_fitter.file_TTbar_mc,"_TTbar_realW")
ttMC_fitter.get_mj_dataset(ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW")
print ttMC_fitter.file_TTbar_mc
print "_TTbar_realW"
print ttMC_fitter.mj_shape["TTbar_realW"]
print ttMC_fitter.channel
print ttMC_fitter.wtagger_label
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_realW",ttMC_fitter.mj_shape["TTbar_realW"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_realW_failN2DDTcut",ttMC_fitter.mj_shape["TTbar_realW_fail"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW",ttMC_fitter.mj_shape["TTbar_fakeW"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW_failN2DDTcut",ttMC_fitter.mj_shape["TTbar_fakeW_fail"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
print "Finished fitting matched tt MC! Plots can be found in plots_*_MCfits. Printing workspace:"
workspace4fit_.Print()
def doFitsToMC():
workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_")
boostedW_fitter_em = initialiseFits("em", options.sample, 40, 130, workspace4fit_)
boostedW_fitter_em.get_datasets_fit_minor_bkg()
print "Finished fitting MC! Plots can be found in plots_*_MCfits. Printing workspace:"
workspace4fit_.Print()
def GetWtagScalefactors(workspace,fitter):
#------------- Calculate scalefactors -------------
### Efficiency in data and MC
rrv_eff_MC_em = workspace.var("eff_ttbar_TotalMC_em_mj")
rrv_mean_MC_em = workspace.var("rrv_mean1_gaus_ttbar_TotalMC_em_mj")
rrv_sigma_MC_em = workspace.var("rrv_sigma1_gaus_ttbar_TotalMC_em_mj")
rrv_eff_data_em = workspace.var("eff_ttbar_data_em_mj")
rrv_mean_data_em = workspace.var("rrv_mean1_gaus_ttbar_data_em_mj")
rrv_sigma_data_em = workspace.var("rrv_sigma1_gaus_ttbar_data_em_mj")
## GET HP SCALEFACTOR AND UNCERTIANTIES
wtagger_sf_em = rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal()
wtagger_sf_em_err = wtagger_sf_em * ( (rrv_eff_data_em.getError()/rrv_eff_data_em.getVal() )**2 + (rrv_eff_MC_em.getError()/rrv_eff_MC_em.getVal())**2 )**0.5
wtagger_mean_shift_em = rrv_mean_data_em.getVal()-rrv_mean_MC_em.getVal()
wtagger_mean_shift_err_em = (rrv_mean_data_em.getError()**2 + rrv_mean_MC_em.getError()**2)**0.5
wtagger_sigma_enlarge_em = rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal()
wtagger_sigma_enlarge_err_em = ((rrv_sigma_data_em.getError()/rrv_sigma_data_em.getVal())**2 + (rrv_sigma_MC_em.getError()/rrv_sigma_MC_em.getVal())**2 )**0.5* wtagger_sigma_enlarge_em
mean_sf_error = (rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal()) * ( (rrv_mean_data_em.getError()/rrv_mean_data_em.getVal())**2 + (rrv_mean_MC_em.getError() /rrv_mean_MC_em.getVal())**2 )**0.5
sigma_sf_error = (rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal()) * ( (rrv_sigma_data_em.getError()/rrv_sigma_data_em.getVal())**2 + (rrv_sigma_MC_em.getError()/rrv_sigma_MC_em.getVal())**2 )**0.5
eff_sf_error = (rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal()) * ( (rrv_eff_data_em.getError()/rrv_eff_data_em.getVal())**2 + (rrv_eff_MC_em.getError() /rrv_eff_MC_em.getVal())**2 )**0.5
## GET EXTREME FAIL NUMBERS IN ORDER TO COMPUTE LP SF:
rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj = workspace.var("rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj")
rrv_number_ttbar_data_extremefailN2DDTcut_em_mj = workspace.var("rrv_number_ttbar_data_extremefailN2DDTcut_em_mj")
rrv_number_total_ttbar_TotalMC_em = workspace.var("rrv_number_ttbar_TotalMC_beforetau2tau1cut_em_mj")
rrv_number_total_ttbar_data_em = workspace.var("rrv_number_ttbar_data_beforetau2tau1cut_em_mj")
eff_MC_em_extremefail = rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getVal() / rrv_number_total_ttbar_TotalMC_em.getVal()
eff_data_em_extremefail = rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getVal() / rrv_number_total_ttbar_data_em.getVal()
eff_MC_em_extremefail_error = 0.1
# eff_MC_em_extremefail_error = eff_MC_em_extremefail * ( (rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getError()/rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getVal() )**2 + (rrv_number_total_ttbar_TotalMC_em.getError()/rrv_number_total_ttbar_TotalMC_em.getVal())**2 )**0.5
eff_data_em_extremefail_error = 0.1
# eff_data_em_extremefail_error = eff_data_em_extremefail * ( (rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getError()/rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getVal() )**2 + (rrv_number_total_ttbar_data_em.getError() /rrv_number_total_ttbar_data_em.getVal() )**2 )**0.5
eff_SF_extremefail = 0.1
eff_SF_extremefail_error = 0.1
# eff_SF_extremefail = eff_data_em_extremefail/eff_MC_em_extremefail
# eff_SF_extremefail_error = eff_SF_extremefail * ( (eff_data_em_extremefail_error/eff_data_em_extremefail)**2 + (eff_MC_em_extremefail_error/eff_MC_em_extremefail)**2 )**0.5
# w/ extreme fail
eff_MC_em_LP = 1.-rrv_eff_MC_em.getVal() - eff_MC_em_extremefail
eff_data_em_LP = 1.-rrv_eff_data_em.getVal() - eff_data_em_extremefail
eff_MC_em_LP_err = TMath.Sqrt( rrv_eff_MC_em.getError() **2 + eff_MC_em_extremefail_error**2 )
eff_data_em_LP_err = TMath.Sqrt( rrv_eff_data_em.getError()**2 + eff_data_em_extremefail_error**2 )
wtagger_sf_em_LP = eff_data_em_LP / eff_MC_em_LP
wtagger_sf_em_LP_err = wtagger_sf_em_LP * ( (eff_data_em_LP_err/eff_data_em_LP)**2 + (eff_MC_em_LP_err/eff_MC_em_LP)**2 )**0.5
# w/o extreme fail
tmpq_eff_MC_em_LP = 1. - rrv_eff_MC_em.getVal()
tmpq_eff_data_em_LP = 1. - rrv_eff_data_em.getVal()
tmpq_eff_MC_em_LP_err = TMath.Sqrt( rrv_eff_MC_em.getError() **2)
tmpq_eff_data_em_LP_err = TMath.Sqrt( rrv_eff_data_em.getError()**2)
pureq_wtagger_sf_em_LP = tmpq_eff_data_em_LP / tmpq_eff_MC_em_LP
pureq_wtagger_sf_em_LP_err = pureq_wtagger_sf_em_LP*TMath.Sqrt( (tmpq_eff_data_em_LP_err/tmpq_eff_data_em_LP)**2 + (tmpq_eff_MC_em_LP_err/tmpq_eff_MC_em_LP)**2 )
#------------- Print results -------------
print "--------------------------------------------------------------------------------------------"
print " HP "
print "--------------------------------------------------------------------------------------------"
print "W-tagging SF : %0.3f +/- %0.3f (rel. error = %0.1f percent)" %(wtagger_sf_em, wtagger_sf_em_err,(wtagger_sf_em_err/wtagger_sf_em*100))
print "Mass shift [GeV] : %0.3f +/- %0.3f" %(wtagger_mean_shift_em, wtagger_mean_shift_err_em)
print "Mass resolution SF: %0.3f +/- %0.3f" %(wtagger_sigma_enlarge_em, wtagger_sigma_enlarge_err_em)
print ""
print "Parameter Data Simulation Data/Simulation"
print " < m > %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_mean_data_em.getVal(),rrv_mean_data_em.getError(),rrv_mean_MC_em.getVal(),rrv_mean_MC_em.getError() , rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal() , mean_sf_error)
print " #sigma %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_sigma_data_em.getVal(),rrv_sigma_data_em.getError(),rrv_sigma_MC_em.getVal(),rrv_sigma_MC_em.getError(), rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal(), sigma_sf_error)
print ""
print "HP W-tag eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_eff_data_em.getVal(),rrv_eff_data_em.getError(),rrv_eff_MC_em.getVal(),rrv_eff_MC_em.getError() , rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal() , eff_sf_error)
print "--------------------------------------------------------------------------------------------"
print " EXTREME FAIL "
print "--------------------------------------------------------------------------------------------"
print "Parameter Data Simulation Data/Simulation"
print "Extreme fail eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_extremefail,eff_data_em_extremefail_error,eff_MC_em_extremefail,eff_MC_em_extremefail_error, eff_SF_extremefail, eff_SF_extremefail_error)
print "--------------------------------------------------------------------------------------------"
print " LP "
print "--------------------------------------------------------------------------------------------"
print "Parameter Data Simulation Data/Simulation"
print "LP W-tag eff+SF ( w/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_LP,eff_data_em_LP_err,eff_MC_em_LP,eff_MC_em_LP_err,wtagger_sf_em_LP,wtagger_sf_em_LP_err)
print "LP W-tag eff+SF (wo/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(tmpq_eff_data_em_LP,tmpq_eff_data_em_LP_err,tmpq_eff_MC_em_LP,tmpq_eff_MC_em_LP_err,pureq_wtagger_sf_em_LP,pureq_wtagger_sf_em_LP_err)
print "--------------------------------------------------------------------------------------------"
print "--------------------------------------------------------------------------------------------"
print ""; print "";
#Write results to file
fitter.file_out_ttbar_control.write("\n HP ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nW-tagging SF : %0.3f +/- %0.3f (rel. error = %.1f percent)" %(wtagger_sf_em, wtagger_sf_em_err, (wtagger_sf_em_err/wtagger_sf_em*100)))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nMass shift [GeV] : %0.3f +/- %0.3f" %(wtagger_mean_shift_em, wtagger_mean_shift_err_em))
fitter.file_out_ttbar_control.write("\nMass resolution SF: %0.3f +/- %0.3f" %(wtagger_sigma_enlarge_em, wtagger_sigma_enlarge_err_em))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\n < m > %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_mean_data_em.getVal(),rrv_mean_data_em.getError(),rrv_mean_MC_em.getVal(),rrv_mean_MC_em.getError() , rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal() , mean_sf_error))
fitter.file_out_ttbar_control.write("\n #sigma %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_sigma_data_em.getVal(),rrv_sigma_data_em.getError(),rrv_sigma_MC_em.getVal(),rrv_sigma_MC_em.getError(), rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal(), sigma_sf_error))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nHP W-tag eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_eff_data_em.getVal(),rrv_eff_data_em.getError(),rrv_eff_MC_em.getVal(),rrv_eff_MC_em.getError(), rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal(), eff_sf_error))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n EXTREME FAIL ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\nExtreme fail eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_extremefail,eff_data_em_extremefail_error,eff_MC_em_extremefail,eff_MC_em_extremefail_error, eff_SF_extremefail, eff_SF_extremefail_error))
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n LP ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\nLP W-tag eff+SF ( w/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_LP,eff_data_em_LP_err,eff_MC_em_LP,eff_MC_em_LP_err,wtagger_sf_em_LP,wtagger_sf_em_LP_err))
fitter.file_out_ttbar_control.write("\nLP W-tag eff+SF (wo/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(tmpq_eff_data_em_LP,tmpq_eff_data_em_LP_err,tmpq_eff_MC_em_LP,tmpq_eff_MC_em_LP_err,pureq_wtagger_sf_em_LP,pureq_wtagger_sf_em_LP_err))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
class doWtagFits:
def __init__(self):
self.workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_") # create workspace
self.boostedW_fitter_em = initialiseFits("em", options.sample, 40, 130, self.workspace4fit_) # Define all shapes to be used for Mj, define regions (SB,signal) and input files.
self.boostedW_fitter_em.get_datasets_fit_minor_bkg() # Loop over intrees to create datasets om Mj and fit the single MCs.
print "Printing workspace:"; self.workspace4fit_.Print(); print ""
self.boostedW_fitter_em.get_sim_fit_components()
#Defining categories
sample_type = RooCategory("sample_type","sample_type")
sample_type.defineType("em_pass")
sample_type.defineType("em_fail")
#Importing fit variables
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
rdataset_data_em_mj = self.workspace4fit_.data("rdataset_data_em_mj")
rdataset_data_em_mj_fail = self.workspace4fit_.data("rdataset_data_failN2DDTcut_em_mj")
#For binned fit (shorter computing time, more presise when no SumW2Error is used!)
if options.doBinnedFit:
#Converting to RooDataHist
rdatahist_data_em_mj = RooDataHist(rdataset_data_em_mj.binnedClone())
rdatahist_data_em_mj_fail = RooDataHist(rdataset_data_em_mj_fail.binnedClone())
#Converting back to RooDataSet
rdataset_data_em_mj_2 = rdataset_data_em_mj.emptyClone()
for i in range(0,rdatahist_data_em_mj.numEntries()):
rdataset_data_em_mj_2.add(rdatahist_data_em_mj.get(i),rdatahist_data_em_mj.weight())
rdataset_data_em_mj_fail_2 = rdataset_data_em_mj_fail.emptyClone()
for i in range(0,rdatahist_data_em_mj_fail.numEntries()):
rdataset_data_em_mj_fail_2.add(rdatahist_data_em_mj_fail.get(i),rdatahist_data_em_mj_fail.weight())
#Combined dataset
combData_data = RooDataSet("combData_data","combData_data",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_data_em_mj_2),RooFit.Import("em_fail",rdataset_data_em_mj_fail_2) )
#For unbinned fit
else:
combData_data = RooDataSet("combData_data","combData_data",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_data_em_mj),RooFit.Import("em_fail",rdataset_data_em_mj_fail) )
#-------------IMPORT MC-------------
#Importing MC datasets
rdataset_TotalMC_em_mj = self.workspace4fit_.data("rdataset_TotalMC_em_mj")
rdataset_TotalMC_em_mj_fail = self.workspace4fit_.data("rdataset_TotalMC_failN2DDTcut_em_mj")
if options.doBinnedFit:
#Converting to RooDataHist
rdatahist_TotalMC_em_mj = RooDataHist(rdataset_TotalMC_em_mj.binnedClone())
rdatahist_TotalMC_em_mj_fail = RooDataHist(rdataset_TotalMC_em_mj_fail.binnedClone())
#Converting back to RooDataSet
rdataset_TotalMC_em_mj_2 = rdataset_TotalMC_em_mj.emptyClone()
for i in range(0,rdatahist_TotalMC_em_mj.numEntries()):
rdataset_TotalMC_em_mj_2.add(rdatahist_TotalMC_em_mj.get(i),rdatahist_TotalMC_em_mj.weight())
rdataset_TotalMC_em_mj_fail_2 = rdataset_TotalMC_em_mj_fail.emptyClone()
for i in range(0,rdatahist_TotalMC_em_mj_fail.numEntries()):
rdataset_TotalMC_em_mj_fail_2.add(rdatahist_TotalMC_em_mj_fail.get(i),rdatahist_TotalMC_em_mj_fail.weight())
#Combined MC dataset
combData_TotalMC = RooDataSet("combData_TotalMC","combData_TotalMC",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_TotalMC_em_mj_2),RooFit.Import("em_fail",rdataset_TotalMC_em_mj_fail_2) )
else:
combData_TotalMC = RooDataSet("combData_TotalMC","combData_TotalMC",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_TotalMC_em_mj),RooFit.Import("em_fail",rdataset_TotalMC_em_mj_fail) )
#-------------Define and perform fit to data-------------
#Import pdf from single fits and define the simultaneous total pdf
model_data_em = self.workspace4fit_.pdf("model_data_em")
model_data_fail_em = self.workspace4fit_.pdf("model_data_failN2DDTcut_em")
simPdf_data = RooSimultaneous("simPdf_data_em","simPdf_data_em",sample_type)
simPdf_data.addPdf(model_data_em,"em_pass")
simPdf_data.addPdf(model_data_fail_em,"em_fail")
#Import Gaussian constraints to propagate error to likelihood
constrainslist_data_em = ROOT.std.vector(ROOT.std.string)()
for i in range(self.boostedW_fitter_em.constrainslist_data.size()):
constrainslist_data_em.push_back(self.boostedW_fitter_em.constrainslist_data.at(i))
print self.boostedW_fitter_em.constrainslist_data.at(i)
pdfconstrainslist_data_em = RooArgSet("pdfconstrainslist_data_em")
for i in range(constrainslist_data_em.size()):
pdfconstrainslist_data_em.add(self.workspace4fit_.pdf(constrainslist_data_em.at(i)) )
pdfconstrainslist_data_em.Print()
# Perform simoultaneous fit to data
if options.doBinnedFit:
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em))#, RooFit.SumW2Error(kTRUE))
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em))#, RooFit.SumW2Error(kTRUE))
else:
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em), RooFit.SumW2Error(kTRUE))
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em), RooFit.SumW2Error(kTRUE))
#Draw
isData = True
chi2FailData = drawFrameGetChi2(rrv_mass_j,rfresult_data,rdataset_data_em_mj_fail,model_data_fail_em,isData)
chi2PassData = drawFrameGetChi2(rrv_mass_j,rfresult_data,rdataset_data_em_mj,model_data_em,isData)
#Print final data fit results
print "FIT parameters (DATA) :"; print ""
print "CHI2 PASS = %.3f CHI2 FAIL = %.3f" %(chi2PassData,chi2FailData)
print ""; print rfresult_data.Print("v"); print ""
#-------------Define and perform fit to MC-------------
# fit TotalMC --> define the simultaneous total pdf
model_TotalMC_em = self.workspace4fit_.pdf("model_TotalMC_em")
model_TotalMC_fail_em = self.workspace4fit_.pdf("model_TotalMC_failN2DDTcut_em")
simPdf_TotalMC = RooSimultaneous("simPdf_TotalMC_em","simPdf_TotalMC_em",sample_type)
simPdf_TotalMC.addPdf(model_TotalMC_em,"em_pass")
simPdf_TotalMC.addPdf(model_TotalMC_fail_em,"em_fail")
#Import Gaussian constraints for fixed paramters to propagate error to likelihood
constrainslist_TotalMC_em = ROOT.std.vector(ROOT.std.string)()
for i in range(self.boostedW_fitter_em.constrainslist_mc.size()):
constrainslist_TotalMC_em.push_back(self.boostedW_fitter_em.constrainslist_mc.at(i))
pdfconstrainslist_TotalMC_em = RooArgSet("pdfconstrainslist_TotalMC_em")
for i in range(constrainslist_TotalMC_em.size()):
pdfconstrainslist_TotalMC_em.add(self.workspace4fit_.pdf(constrainslist_TotalMC_em[i]) )
# Perform simoultaneous fit to MC
if options.doBinnedFit:
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em))#, RooFit.SumW2Error(kTRUE))--> Removing due to unexected behaviour. See https://root.cern.ch/phpBB3/viewtopic.php?t=16917, https://root.cern.ch/phpBB3/viewtopic.php?t=16917
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em))#, RooFit.SumW2Error(kTRUE))
else:
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em), RooFit.SumW2Error(kTRUE))
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em), RooFit.SumW2Error(kTRUE))
isData = False
chi2FailMC = drawFrameGetChi2(rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj_fail,model_TotalMC_fail_em,isData)
chi2PassMC = drawFrameGetChi2(rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj,model_TotalMC_em,isData)
#Print final MC fit results
print "FIT Par. (MC) :"; print ""
print "CHI2 PASS = %.3f CHI2 FAIL = %.3f" %(chi2PassMC,chi2FailMC)
print ""; print rfresult_TotalMC.Print("v"); print ""
drawDataAndMC(rrv_mass_j,rfresult_data,rdataset_data_em_mj_fail,model_data_fail_em,True,rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj_fail,model_TotalMC_fail_em,False)
drawDataAndMC(rrv_mass_j,rfresult_data,rdataset_data_em_mj,model_data_em,True,rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj,model_TotalMC_em,False)
# draw the final fit results
DrawScaleFactorTTbarControlSample(self.workspace4fit_,self.boostedW_fitter_em.color_palet,"","em",self.boostedW_fitter_em.wtagger_label,self.boostedW_fitter_em.AK8_pt_min,self.boostedW_fitter_em.AK8_pt_max,options.sample)
# Get W-tagging scalefactor and efficiencies
GetWtagScalefactors(self.workspace4fit_,self.boostedW_fitter_em)
# wpForPlotting ="%.2f"%options.tau2tau1cutHP
# wpForPlotting = wpForPlotting.replace(".","v")
# pf=""
# if options.usePuppiSD: pf = "_PuppiSD"
# if options.useDDT: pf = "_PuppiSD_DDT"
# postfix = "%s_%s" %(pf,wpForPlotting)
# cmd = "mv plots plots%s" %postfix
# print cmd
# os.system(cmd)
# cmd = "mv WtaggingSF.txt plots%s" %postfix
# print cmd
# os.system(cmd)
self.workspace4fit_.Print()
sys.exit()
wout = ROOT.TFile.Open("Workspace.root","RECREATE")
wout.cd()
self.workspace4fit_.Write()
# Delete workspace
# del self.workspace4fit_
class initialiseFits:
# Constructor: Input is channel (mu,ele,em), range in mj and a workspace
def __init__(self, in_channel, in_sample, in_mj_min=40, in_mj_max=130, input_workspace=None):
RooAbsPdf.defaultIntegratorConfig().setEpsRel(1e-9)
RooAbsPdf.defaultIntegratorConfig().setEpsAbs(1e-9)
# Set channel
self.channel = in_channel
print "CHANNEL = %s" %in_channel
print "Using Tau21 HP cut of " ,options.tau2tau1cutHP
# Map of shapes to be used for the various fits (defined in PDFs/MakePdf.cxx)
self.mj_shape = ROOT.std.map(ROOT.std.string,ROOT.std.string)()
# Fit functions for matched tt MC
self.mj_shape["TTbar_realW"] = "ExpGaus"
# self.mj_shape["TTbar_realW"] = "GausErfExp_ttbar" #before "2Gaus_ttbar"
self.mj_shape["TTbar_realW_fail"] = "GausErfExp_ttbar_failN2DDTcut" #before "GausChebychev_ttbar_failN2DDTcut"
self.mj_shape["TTbar_fakeW"] = "ErfExp_ttbar"
self.mj_shape["TTbar_fakeW_fail"] = "ErfExp_ttbar_failN2DDTcut"
if (options.useDDT):
self.mj_shape["TTbar_realW_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# Fit functions for minor backgrounds
self.mj_shape["VV"] = "ExpGaus"
self.mj_shape["VV_fail"] = "ExpGaus"
self.mj_shape["WJets0"] = "ErfExp"
# self.mj_shape["WJets0"] = "Exp"
# self.mj_shape["WJets0_fail"] = "ErfExp"
self.mj_shape["WJets0_fail"] = "Exp"
self.mj_shape["QCD"] = "ErfExp"
self.mj_shape["QCD_fail"] = "ErfExp"
self.mj_shape["STop"] = "ErfExpGaus_sp"
self.mj_shape["STop_fail"] = "ExpGaus"
if (options.usePuppiSD):
self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
if (options.useN2DDT):
# self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
self.mj_shape["STop_fail"] = "ExpGaus"
if (options.tau2tau1cutHP==0.60):
self.mj_shape["VV_fail"] = "Exp"
self.mj_shape["VV"] = "Exp"
self.mj_shape["WJets0_fail"] = "Exp"
if (options.useDDT):
self.mj_shape["VV"] = "ExpGaus"
self.mj_shape["VV_fail"] = "ErfExpGaus_sp"
self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
self.mj_shape["STop"] = "ExpGaus"
# Fit functions used in simultaneous fit of pass and fail categories
self.mj_shape["bkg_mc_fail"] = "ErfExp_ttbar_failN2DDTcut"
# self.mj_shape["bkg_mc_fail"] = "Exp"
self.mj_shape["bkg_data_fail"] = "ErfExp_ttbar_failN2DDTcut"
self.mj_shape["signal_mc_fail"] = "GausErfExp_ttbar_failN2DDTcut" #Before GausChebychev_ttbar_failN2DDTcut
self.mj_shape["signal_data_fail"] = "GausErfExp_ttbar_failN2DDTcut"
self.mj_shape["bkg_data"] = "ErfExp_ttbar"
self.mj_shape["bkg_mc"] = "ErfExp_ttbar"
# self.mj_shape["signal_data"] = "Gaus"
self.mj_shape["signal_data"] = "GausErfExp_ttbar" #Before 2Gaus_ttbar
# self.mj_shape["signal_mc"] = "Gaus"
self.mj_shape["signal_mc"] = "GausErfExp_ttbar"
if (options.useDDT):
self.mj_shape["signal_mc_fail"] = "GausChebychev_ttbar_failN2DDTcut"
self.mj_shape["signal_data_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# # #TESTS USING OLD METHOD, EG ADDING TWO ADDITIONAL FIT FUNCTIONS
# self.mj_shape["signal_data"] = "2Gaus_ttbar"
# self.mj_shape["signal_mc"] = "2Gaus_ttbar"
# self.mj_shape["signal_mc_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# self.mj_shape["signal_data_fail"] = "GausChebychev_ttbar_failN2DDTcut"
#Set lumi
self.Lumi=35.8
# self.Lumi=2198. #74
if options.use76X: self.Lumi=35.8 #76
self.BinWidth_mj = 5.
self.narrow_factor = 1.
self.BinWidth_mj = self.BinWidth_mj/self.narrow_factor
nbins_mj = int( (in_mj_max - in_mj_min) / self.BinWidth_mj )
in_mj_max = in_mj_min+nbins_mj*self.BinWidth_mj
jetMass = "Pruned jet mass"
if options.usePuppiSD: jetMass = "PUPPI Softdrop Jet Mass"
if options.useN2DDT: jetMass = "PUPPI Softdrop Jet Mass"
rrv_mass_j = RooRealVar("rrv_mass_j", jetMass ,(in_mj_min+in_mj_max)/2.,in_mj_min,in_mj_max,"GeV")
rrv_mass_j.setBins(nbins_mj)
# Create workspace and import fit variable
if input_workspace is None:
self.workspace4fit_ = RooWorkspace("workspace4fit_","Workspace4fit_")
else:
self.workspace4fit_ = input_workspace
getattr(self.workspace4fit_,"import")(rrv_mass_j)
# Signal region between 65 and 105 GeV
self.mj_sideband_lo_min = in_mj_min
self.mj_sideband_lo_max = 65
self.mj_signal_min = 65
self.mj_signal_max = 105
self.mj_sideband_hi_min = 105
self.mj_sideband_hi_max = in_mj_max
# Setting ranges...
rrv_mass_j.setRange("sb_lo",self.mj_sideband_lo_min,self.mj_sideband_lo_max) # 30-65 GeV
rrv_mass_j.setRange("signal_region",self.mj_signal_min,self.mj_signal_max) # 65-105 GeV
rrv_mass_j.setRange("sb_hi",self.mj_sideband_hi_min,self.mj_sideband_hi_max) # 105-135 GeV
rrv_mass_j.setRange("controlsample_fitting_range",40,130) # ---> what is this????
# Directory and input files
self.file_Directory = "/eos/uscms/store/user/mkrohn/WTaggerSF/Trees_N2DDT/v12.02/mu_Tight/"
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag_80X/WWTree_%s/"%(self.channel) #For 80X!!!!
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag/PRUNED/WWTree_%s/"%(self.channel)
# if options.usePuppiSD :
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag_80X/WWTree_%s/"%(self.channel)
# if options.use76X:
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag/PUPPISD_newcorr/WWTree_%s/"%(self.channel)
postfix = ""
if options.use76X: postfix ="_76X"
self.file_data = ("Data_2Trans.root")
self.file_WJets0_mc = ("WJets_2Trans.root")
self.file_VV_mc = ("VV_2Trans.root")
self.file_QCD_mc = ("QCD.root")
self.file_STop_mc = ("ST_2Trans.root")
self.file_TTbar_mc = ("TT_2Trans.root")
self.file_pseudodata = ("PsuedoData_2Trans.root") #Important! ROOT tree containing all backgrounds added together (tt+singleT+VV+Wjets). Used for fit to total MC
# self.file_pseudodata = ("pseudodata_weighted.root")
# Define Tau21 WP
self.wtagger_label = "HP"
self.wtagger_cut = options.tau2tau1cutHP
self.wtagger_cut_min = 0.
if options.usePuppiSD:
postfix = postfix + "_PuppiSD"
if options.useDDT:
postfix = postfix + "_PuppiSD_DDT"
if options.useN2DDT:
postfix = postfix + "_PuppiSD_N2DDT"
# Define label used for plots and choosing fit paramters in PDFs/MakePdf.cxx
wp = "%.2f" %options.tau2tau1cutHP
wp = wp.replace(".","v")
self.wtagger_label = self.wtagger_label + "%s%s%s"%(wp,in_sample,postfix)
#Color pallett for plots
self.color_palet = ROOT.std.map(ROOT.std.string, int) ()
self.color_palet["data"] = 1
self.color_palet["WJets"] = 2
self.color_palet["VV"] = 4
self.color_palet["QCD"] = 5
self.color_palet["STop"] = 7
self.color_palet["TTbar"] = 210
self.color_palet["TTbar_realW"] = 210
self.color_palet["TTbar_fakeW"] = 419
self.color_palet["Signal"] = 1
self.color_palet["Uncertainty"] = 1
self.color_palet["Other_Backgrounds"] = 1
# Cuts (dont need these cuts if they are already implemented in ROOT tree)
self.vpt_cut = 200 # hadronic and leptonic W cut
self.mass_lvj_max = 5000. # invariant mass of 3 body max
self.mass_lvj_min = 0. # invariant mass of 3 body min
self.pfMET_cut = 40. # missing transverse energy
self.lpt_cut = 53. # lepton pT
self.AK8_pt_min = 200
self.AK8_pt_max = 5000
if self.channel == "el":
self.pfMET_cut = 80
self.lpt_cut = 120
# Out .txt file with final SF numbers
self.file_ttbar_control_txt = "WtaggingSF.txt"
self.file_out_ttbar_control = open(self.file_ttbar_control_txt,"w")
setTDRStyle()
def get_datasets_fit_minor_bkg(self):
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
# Build single-t fit pass and fail distributions
print "##################################################"
print "############### Single Top DataSet ###############"
print "##################################################"
print ""
self.get_mj_dataset(self.file_STop_mc,"_STop")
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop" ,self.mj_shape["STop"],self.channel,self.wtagger_label) #Start value and range of parameters defined in PDFs/MakePDF.cxx
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_failN2DDTcut" ,self.mj_shape["STop_fail"],self.channel,self.wtagger_label)
### Build WJet fit pass and fail distributions
print "###########################################"
print "############### WJets Pythia ##############"
print "###########################################"
print ""
self.get_mj_dataset(self.file_WJets0_mc,"_WJets0")
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0",self.mj_shape["WJets0"],self.channel,self.wtagger_label)
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_failN2DDTcut",self.mj_shape["WJets0_fail"],self.channel,self.wtagger_label)
# Build VV fit pass and fail distributions
print "#########################################"
print "############### VV Pythia ###############"
print "#########################################"
print ""
print ""
# self.get_mj_dataset(self.file_VV_mc,"_VV")
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV",self.mj_shape["VV"],self.channel,self.wtagger_label)
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_failN2DDTcut",self.mj_shape["VV_fail"],self.channel,self.wtagger_label)
# # Build QCD fit pass and fail distributions
# print "#########################################"
# print "################## QCD ##################"
# print "#########################################"
# print ""
# print ""
# self.get_mj_dataset(self.file_VV_mc,"_QCD")
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_QCD",self.mj_shape["QCD"],self.channel,self.wtagger_label)
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_QCD_failN2DDTcut",self.mj_shape["QCD_fail"],self.channel,self.wtagger_label)
if options.fitMC:
return
# Get dataset for ttbar
print "#########################################"
print "################ TTbar %s################"%options.sample
print "#########################################"
print ""
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar")
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar_realW")
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar_fakeW")
# Get dataset used for fit to total MC
print "################################################"
print "############## Pseudo Data Powheg ##############"
print "################################################"
print ""
self.get_mj_dataset(self.file_pseudodata,"_TotalMC")
print "#################################"
print "############# Data ##############"
print "#################################"
print ""
print ""
self.get_mj_dataset(self.file_data,"_data")
# print "Saving workspace in myworkspace.root! To save time when debugging use option --WS myworkspace.root to avoid recreating workspace every time"
# filename = "myworkspace.root"
# self.workspace4fit_.writeToFile(filename)
def get_sim_fit_components(self):
# self.print_yields()
self.constrainslist_data = ROOT.std.vector(ROOT.std.string)()
self.constrainslist_mc = ROOT.std.vector(ROOT.std.string)()
#Construct pass/fail models (fix minor backgrounds, create sim. fit total PDFS)
ScaleFactorTTbarControlSampleFit(self.workspace4fit_,self.mj_shape,self.color_palet,self.constrainslist_data,self.constrainslist_mc,"",self.channel,self.wtagger_label,self.AK8_pt_min,self.AK8_pt_max)
#Get data/MC scalefactors
rrv_scale_number = self.workspace4fit_.var("rrv_scale_number_TTbar_STop_VV_WJets").getVal()
rrv_scale_number_fail = self.workspace4fit_.var("rrv_scale_number_TTbar_STop_VV_WJets_fail").getVal()
#Print data/MC scalefactors
print " Pass MC / all data = %.3f" %(rrv_scale_number)
print " Fail MC / all data = %.3f" %(rrv_scale_number_fail)
def print_yields(self):
# Print dataset yields in the signal region
print ""
print ""
print ""
self.workspace4fit_.var("rrv_number_dataset_signal_region_data_" +self.channel+"_mj").Print()
# self.workspace4fit_.var("rrv_number_dataset_signal_region_VV_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_WJets0_" +self.channel+"_mj").Print()
# self.workspace4fit_.var("rrv_number_dataset_signal_region_QCD_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_STop_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_TTbar_" +self.channel+"_mj").Print()
print ""
print ""
print ""
number_dataset_signal_region_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_error2_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_error2_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_error2_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_error2_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_error2_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_error2_TotalMC_"+self.channel+"_mj").getVal()
wtagger_eff_MC = number_dataset_signal_region_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj
wtagger_eff_data = number_dataset_signal_region_data_mj/number_dataset_signal_region_before_cut_data_mj
wtagger_eff_reweight = wtagger_eff_data/wtagger_eff_MC
wtagger_eff_reweight_err = wtagger_eff_reweight*TMath.Sqrt(number_dataset_signal_region_error2_data_mj/number_dataset_signal_region_data_mj/number_dataset_signal_region_data_mj + number_dataset_signal_region_error2_TotalMC_mj/number_dataset_signal_region_TotalMC_mj/number_dataset_signal_region_TotalMC_mj +number_dataset_signal_region_before_cut_error2_data_mj/number_dataset_signal_region_before_cut_data_mj/number_dataset_signal_region_data_mj + number_dataset_signal_region_before_cut_error2_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj)
print ""
print "Nr. data events in signal_region : %s +/- sqrt(%s)"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj**.5)
print ""
print "Nr. MC events in signal_region : %s +/- sqrt(%s)"%(number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj)
print ""
print "Nr. dataevents in signalregion before cut on tau21: %s +/- sqrt(%s)"%(number_dataset_signal_region_before_cut_data_mj, number_dataset_signal_region_before_cut_error2_data_mj)
print ""
print "Nr. MC events in signalregion before cut on tau21 : %s +/- sqrt(%s) "%(number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj)
print ""
print "W-tagging efficiency (pre-fit):"
print "W-tagging eff. MC = %.3f "%(wtagger_eff_MC)
print "W-tagging eff. data = %.3f "%(wtagger_eff_data)
print "W-tagging SF = %.3f +/- %.3f"%(wtagger_eff_reweight, wtagger_eff_reweight_err)
print ""
print ""
self.file_out_ttbar_control.write("%s channel SF: \n"%(self.channel))
self.file_out_ttbar_control.write("Nr. events in signal_region : %s +/- sqrt(%s)\n"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj))
self.file_out_ttbar_control.write("Nr. TotalMC in signal_region : %s +/- sqrt(%s) \n"%(number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj))
self.file_out_ttbar_control.write("event number of data in signalregion before_cut : %s +/- sqrt(%s)\n"%(number_dataset_signal_region_before_cut_data_mj, number_dataset_signal_region_before_cut_error2_data_mj))
self.file_out_ttbar_control.write("event number of TotalMC in signal_region before_cut: %s +/- sqrt(%s) \n"%(number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj))
self.file_out_ttbar_control.write("wtagger_eff_MC = %s \n"%(wtagger_eff_MC ))
self.file_out_ttbar_control.write("wtagger_eff_data = %s \n"%(wtagger_eff_data ))
self.file_out_ttbar_control.write("wtagger_eff_reweight = %s +/- %s\n"%(wtagger_eff_reweight, wtagger_eff_reweight_err))
# Loop over trees
def get_mj_dataset(self,in_file_name, label, jet_mass="Whadr_pruned"):
if options.usePuppiSD or options.useDDT or options.useN2DDT:
jet_mass="AK8Puppijet0_msd_TheaCorr"
print "Using mass variable " ,jet_mass
fileIn_name = TString(self.file_Directory+in_file_name)
print "Using file " ,fileIn_name
fileIn = TFile(fileIn_name.Data())
treeIn = fileIn.Get("myTree")
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
# Mj dataset before tau2tau1 cut : Passed
rdataset_mj = RooDataSet("rdataset" +label+"_"+self.channel+"_mj","rdataset" +label+"_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_mj = RooDataSet("rdataset4fit" +label+"_"+self.channel+"_mj","rdataset4fit"+label+"_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_pass = RooRealVar("rrv_number_ttbar"+label+"_passtau2tau1cut_em_mj","rrv_number_ttbar"+label+"_passtau2tau1cut_em_mj",0.,10000000.) #LUCA
# Mj dataset before tau2tau1 cut : Total
rdataset_beforetau2tau1cut_mj = RooDataSet("rdataset" +label+"_beforetau2tau1cut_"+self.channel+"_mj","rdataset" +label+"_beforetau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_beforetau2tau1cut_mj = RooDataSet("rdataset4fit" +label+"_beforetau2tau1cut_"+self.channel+"_mj","rdataset4fit"+label+"_beforetau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_before = RooRealVar("rrv_number_ttbar"+label+"_beforetau2tau1cut_em_mj","rrv_number_ttbar"+label+"_beforetau2tau1cut_em_mj",0.,10000000.) #LUCA
### Mj dataset failed tau2tau1 cut :
rdataset_failN2DDTcut_mj = RooDataSet("rdataset" +label+"_failN2DDTcut_"+self.channel+"_mj","rdataset" +label+"_failN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_failN2DDTcut_mj = RooDataSet("rdataset4fit" +label+"_failN2DDTcut_"+self.channel+"_mj","rdataset4fit"+label+"_failN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_fail = RooRealVar("rrv_number_ttbar"+label+"_failN2DDTcut_em_mj","rrv_number_ttbar"+label+"_failN2DDTcut_em_mj",0.,10000000.) #LUCA
### Mj dataset extreme failed tau2tau1 cut: > 0.75
rdataset_extremefailN2DDTcut_mj = RooDataSet("rdataset" +label+"_extremefailN2DDTcut_"+self.channel+"_mj","rdataset" +label+"_extremefailN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_extremefailN2DDTcut_mj = RooDataSet("rdataset4fit"+label+"_extremefailN2DDTcut_"+self.channel+"_mj","rdataset4fit" +label+"_extremefailN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_extremefail = RooRealVar("rrv_number_ttbar"+label+"_extremefailN2DDTcut_em_mj","rrv_number_ttbar"+label+"_extremefailN2DDTcut_em_mj",0.,10000000.) #LUCA
# category_cut = RooCategory("category_cut"+"_"+self.channel,"category_cut"+"_"+self.channel) #---->Think this can be removed!!!!
# category_cut.defineType("cut",1)
# category_cut.defineType("beforecut",2)
# combData4cut = RooDataSet("combData4cut"+"_"+self.channel,"combData4cut"+"_"+self.channel,RooArgSet(rrv_mass_j, category_cut, rrv_weight),RooFit.WeightVar(rrv_weight) )
# Define categories
if self.workspace4fit_.cat("category_p_f"+"_"+self.channel):
category_p_f = self.workspace4fit_.cat("category_p_f"+"_"+self.channel)
else:
category_p_f = RooCategory("category_p_f"+"_"+self.channel,"category_p_f"+"_"+self.channel)
category_p_f.defineType("pass")
category_p_f.defineType("fail")
getattr(self.workspace4fit_,"import")(category_p_f)
combData_p_f = RooDataSet("combData_p_f"+label+"_"+self.channel,"combData_p_f"+label+"_"+self.channel,RooArgSet(rrv_mass_j, category_p_f, rrv_weight),RooFit.WeightVar(rrv_weight))
print "N entries: ", treeIn.GetEntries()
hnum_4region = TH1D("hnum_4region" +label+"_"+self.channel,"hnum_4region" +label+"_"+self.channel,4, -1.5, 2.5) # m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_error2 = TH1D("hnum_4region_error2"+label+"_"+self.channel,"hnum_4region_error2" +label+"_"+self.channel,4, -1.5, 2.5) # m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_before_cut = TH1D("hnum_4region_before_cut" +label+"_"+self.channel,"hnum_4region_before_cut" +label+"_"+self.channel,4,-1.5,2.5);# m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_before_cut_error2 = TH1D("hnum_4region_before_cut_error2" +label+"_"+self.channel,"hnum_4region_before_cut_error2"+label+"_"+self.channel,4,-1.5,2.5);# m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_2region = TH1D("hnum_2region" +label+"_"+self.channel,"hnum_2region" +label+"_"+self.channel,2,-0.5,1.5);# m_lvj 0: signal_region; 1: total --> There is only 1 and that is SIGNAL REGION?!
hnum_2region_error2 = TH1D("hnum_2region_error2"+label+"_"+self.channel,"hnum_2region_error2" +label+"_"+self.channel,2,-0.5,1.5);# m_lvj 0: signal_region; 1: total
#-------------------------------------------------------------------------------------------
# Loop over tree entries
tmp_scale_to_lumi = 1
i = 0
for i in range(treeIn.GetEntries()):
if i % 5000 == 0: print "iEntry: ",i
treeIn.GetEntry(i)
if TString(label).Contains("realW") and not getattr(treeIn,"LeadingAK8Jet_MatchedHadW"): #Is a real W, meaning both daughters of W is within jet cone!!
continue
if TString(label).Contains("fakeW") and getattr(treeIn,"LeadingAK8Jet_MatchedHadW"):
continue
if treeIn.AK8Puppijet0_N2DDT_26Per > 0.:
continue
# if treeIn.AK8CHSjet0_pt < 350 or TMath.log(treeIn.AK8Puppijet0_msd_TheaCorr*treeIn.AK8Puppijet0_msd_TheaCorr/(treeIn.AK8CHSjet0_pt*treeIn.AK8CHSjet0_pt)) > -2:
# continue
wtagger = getattr(treeIn,"AK8Puppijet0_tau21")
if options.useN2DDT:
# wtagger = getattr(treeIn,"AK8Puppijet0_N2DDT_26Per")
wtagger = getattr(treeIn,"AK8Puppijet0_doublecsv")
if options.usePuppiSD:
wtagger = getattr(treeIn,"AK8Puppijet0_tau21")
if options.useDDT:
wtagger = getattr(treeIn,"Whadr_puppi_tau2tau1")+ (0.063 * TMath.log( (pow( getattr(treeIn,"Whadr_puppi_softdrop"),2))/getattr(treeIn,"Whadr_puppi_pt") ))
discriminantCut = 0
# if wtagger <= options.tau2tau1cutHP: # HP
# discriminantCut = 2
# elif wtagger > options.tau2tau1cutHP and wtagger <= options.tau2tau1cutLP: #LP
# discriminantCut = 1
# elif wtagger > options.tau2tau1cutLP: # Extreme fail
# discriminantCut = 0
if wtagger >= options.tau2tau1cutHP: # HP
discriminantCut = 2
elif wtagger < options.tau2tau1cutHP and wtagger >= options.tau2tau1cutLP: #LP
discriminantCut = 1