@@ -61,7 +61,7 @@ def makeMCDirectory(var=''):
6161
6262# # Overall 30% normalization
6363# nuisances['fake_syst_0j'] = {
64- # 'name' : 'CMS_WH_hww_fake_syst_0j_2016 ',
64+ # 'name' : 'CMS_WH_hww_fake_syst_0j_2016HIPM ',
6565# 'kind' : 'weight',
6666# 'type' : 'lnN',
6767# 'samples' : {
@@ -77,7 +77,7 @@ def makeMCDirectory(var=''):
7777# }
7878
7979nuisances ['fake_syst_2j' ] = {
80- 'name' : 'CMS_WH_hww_fake_syst_2j_2016 ' ,
80+ 'name' : 'CMS_WH_hww_fake_syst_2j_2016HIPM ' ,
8181 'kind' : 'weight' ,
8282 'type' : 'lnN' ,
8383 'samples' : {
@@ -87,7 +87,7 @@ def makeMCDirectory(var=''):
8787}
8888
8989nuisances ['fake_syst_1j' ] = {
90- 'name' : 'CMS_WH_hww_fake_syst_1j_2016 ' ,
90+ 'name' : 'CMS_WH_hww_fake_syst_1j_2016HIPM ' ,
9191 'kind' : 'weight' ,
9292 'type' : 'lnN' ,
9393 'samples' : {
@@ -97,7 +97,7 @@ def makeMCDirectory(var=''):
9797}
9898
9999nuisances ['fake_syst_sssf' ] = {
100- 'name' : 'CMS_WH_hww_fake_syst_sssf_2016 ' ,
100+ 'name' : 'CMS_WH_hww_fake_syst_sssf_2016HIPM ' ,
101101 'kind' : 'weight' ,
102102 'type' : 'lnN' ,
103103 'samples' : {
@@ -110,7 +110,7 @@ def makeMCDirectory(var=''):
110110}
111111
112112# # nuisances['fake_syst_sssf_minus'] = {
113- # # 'name' : 'CMS_WH_hww_fake_syst_sssf_minus_2016 ',
113+ # # 'name' : 'CMS_WH_hww_fake_syst_sssf_minus_2016HIPM ',
114114# # 'kind' : 'weight',
115115# # 'type' : 'lnN',
116116# # 'samples' : {
@@ -122,7 +122,7 @@ def makeMCDirectory(var=''):
122122# # }
123123
124124nuisances ['fake_syst_ossf' ] = {
125- 'name' : 'CMS_WH_hww_fake_syst_ossf_2016 ' ,
125+ 'name' : 'CMS_WH_hww_fake_syst_ossf_2016HIPM ' ,
126126 'kind' : 'weight' ,
127127 'type' : 'lnN' ,
128128 'samples' : {
@@ -135,7 +135,7 @@ def makeMCDirectory(var=''):
135135}
136136
137137# # nuisances['fake_syst_ossf_minus'] = {
138- # # 'name' : 'CMS_WH_hww_fake_syst_ossf_minus_2016 ',
138+ # # 'name' : 'CMS_WH_hww_fake_syst_ossf_minus_2016HIPM ',
139139# # 'kind' : 'weight',
140140# # 'type' : 'lnN',
141141# # 'samples' : {
@@ -146,40 +146,56 @@ def makeMCDirectory(var=''):
146146# # ],
147147# # }
148148
149- # Statistical and systematic uncertainty on the fake rates
149+ # Statistical and systematic uncertainties on the fake rates
150150nuisances ['fake_ele' ] = {
151- 'name' : 'CMS_WH_hww_fake_e_2016 ' ,
151+ 'name' : 'CMS_WH_hww_fake_e_2016HIPM ' ,
152152 'kind' : 'weight' ,
153153 'type' : 'shape' ,
154154 'samples' : {
155155 'Fake' : ['fakeWEleUp' , 'fakeWEleDown' ],
156156 }
157157}
158158nuisances ['fake_ele_stat' ] = {
159- 'name' : 'CMS_WH_hww_fake_stat_e_2016 ' ,
159+ 'name' : 'CMS_WH_hww_fake_stat_e_2016HIPM ' ,
160160 'kind' : 'weight' ,
161161 'type' : 'shape' ,
162162 'samples' : {
163163 'Fake' : ['fakeWStatEleUp' , 'fakeWStatEleDown' ]
164164 }
165165}
166+ nuisances ['fake_ele_EWK' ] = {
167+ 'name' : 'CMS_WH_hww_fake_EWK_sub_e_2016HIPM' ,
168+ 'kind' : 'weight' ,
169+ 'type' : 'shape' ,
170+ 'samples' : {
171+ 'Fake' : ['fakeWEWKEleUp' , 'fakeWEWKEleDown' ]
172+ }
173+ }
166174
167175nuisances ['fake_mu' ] = {
168- 'name' : 'CMS_WH_hww_fake_m_2016 ' ,
176+ 'name' : 'CMS_WH_hww_fake_m_2016HIPM ' ,
169177 'kind' : 'weight' ,
170178 'type' : 'shape' ,
171179 'samples' : {
172180 'Fake' : ['fakeWMuUp' , 'fakeWMuDown' ],
173181 }
174182}
175183nuisances ['fake_mu_stat' ] = {
176- 'name' : 'CMS_WH_hww_fake_stat_m_2016 ' ,
184+ 'name' : 'CMS_WH_hww_fake_stat_m_2016HIPM ' ,
177185 'kind' : 'weight' ,
178186 'type' : 'shape' ,
179187 'samples' : {
180188 'Fake' : ['fakeWStatMuUp' , 'fakeWStatMuDown' ],
181189 }
182190}
191+ nuisances ['fake_mu_EWK' ] = {
192+ 'name' : 'CMS_WH_hww_fake_EWK_sub_m_2016HIPM' ,
193+ 'kind' : 'weight' ,
194+ 'type' : 'shape' ,
195+ 'samples' : {
196+ 'Fake' : ['fakeWEWKMuUp' , 'fakeWEWKMuDown' ]
197+ }
198+ }
183199
184200###### B-tagger
185201for shift in ['lf' , 'hf' , 'hfstats1' , 'hfstats2' , 'lfstats1' , 'lfstats2' , 'cferr1' , 'cferr2' ]:
@@ -509,7 +525,7 @@ def makeMCDirectory(var=''):
509525
510526# Vg and VgS scale uncertainty
511527nuisances ['VgStarScale0j' ] = {
512- 'name' : 'CMS_hww_VgStarScale0j_2016 ' ,
528+ 'name' : 'CMS_hww_VgStarScale0j_2016HIPM ' ,
513529 'type' : 'lnN' ,
514530 'samples' : {
515531 'VgS' : '1.25'
@@ -518,7 +534,7 @@ def makeMCDirectory(var=''):
518534}
519535
520536nuisances ['VgScale0j' ] = {
521- 'name' : 'CMS_hww_VgScale0j_2016 ' ,
537+ 'name' : 'CMS_hww_VgScale0j_2016HIPM ' ,
522538 'type' : 'lnN' ,
523539 'samples' : {
524540 'Vg' : '1.25'
@@ -611,7 +627,7 @@ def makeMCDirectory(var=''):
611627
612628# WZ normalization from control region
613629nuisances ['WZ2jnorm' ] = {
614- 'name' : 'CMS_hww_WZ3l2jnorm_2016 ' ,
630+ 'name' : 'CMS_hww_WZ3l2jnorm_2016HIPM ' ,
615631 'samples' : {
616632 'WZ' : '1.00' ,
617633 },
@@ -621,7 +637,7 @@ def makeMCDirectory(var=''):
621637}
622638
623639nuisances ['WZ1jnorm' ] = {
624- 'name' : 'CMS_hww_WZ3l1jnorm_2016 ' ,
640+ 'name' : 'CMS_hww_WZ3l1jnorm_2016HIPM ' ,
625641 'samples' : {
626642 'WZ' : '1.00' ,
627643 },
@@ -632,7 +648,7 @@ def makeMCDirectory(var=''):
632648}
633649
634650nuisances ['WZ3lnorm' ] = {
635- 'name' : 'CMS_hww_WZ0j3lnorm_2016 ' ,
651+ 'name' : 'CMS_hww_WZ0j3lnorm_2016HIPM ' ,
636652 'samples' : {
637653 'WZ' : '1.00' ,
638654 },
@@ -646,76 +662,76 @@ def makeMCDirectory(var=''):
646662 ]
647663}
648664
649- ### Charge asymmetry uncertainty
650-
651- # # 0 jet plus
652- # nuisances['charge_plus_0j'] = {
653- # 'name' : 'CMS_WH_hww_charge_0j_plus_2016',
654- # 'kind' : 'weight',
655- # 'type' : 'lnN',
656- # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
657- # 'cuts' : [
658- # 'wh3l_13TeV_sssf_plus_pt2ge20',
659- # 'wh3l_13TeV_ossf_plus_pt2ge20',
660- # ],
661- # }
665+ # ### Charge asymmetry uncertainty
662666
663- # # 0 jet minus
664- # nuisances['charge_minus_0j '] = {
665- # 'name' : 'CMS_WH_hww_charge_0j_minus_2016 ',
666- # 'kind' : 'weight',
667- # 'type' : 'lnN',
668- # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
669- # 'cuts' : [
670- # 'wh3l_13TeV_sssf_minus_pt2ge20 ',
671- # 'wh3l_13TeV_ossf_minus_pt2ge20 ',
672- # ],
673- # }
667+ # # # 0 jet plus
668+ # # nuisances['charge_plus_0j '] = {
669+ # # 'name' : 'CMS_WH_hww_charge_0j_plus_2016HIPM ',
670+ # # 'kind' : 'weight',
671+ # # 'type' : 'lnN',
672+ # # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
673+ # # 'cuts' : [
674+ # # 'wh3l_13TeV_sssf_plus_pt2ge20 ',
675+ # # 'wh3l_13TeV_ossf_plus_pt2ge20 ',
676+ # # ],
677+ # # }
674678
675- # SSSF plus
676- nuisances ['charge_plus_0j_sssf' ] = {
677- 'name' : 'CMS_WH_hww_charge_0j_plus_sssf_2016' ,
678- 'kind' : 'weight' ,
679- 'type' : 'lnN' ,
680- 'samples' : dict ((skey , '1.10' ) for skey in samples if skey not in ['DATA' ]),
681- 'cuts' : [
682- 'wh3l_13TeV_sssf_plus_pt2ge20' ,
683- ],
684- }
679+ # # # 0 jet minus
680+ # # nuisances['charge_minus_0j'] = {
681+ # # 'name' : 'CMS_WH_hww_charge_0j_minus_2016HIPM',
682+ # # 'kind' : 'weight',
683+ # # 'type' : 'lnN',
684+ # # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
685+ # # 'cuts' : [
686+ # # 'wh3l_13TeV_sssf_minus_pt2ge20',
687+ # # 'wh3l_13TeV_ossf_minus_pt2ge20',
688+ # # ],
689+ # # }
685690
686- # # SSSF minus
687- # nuisances['charge_minus_0j_sssf '] = {
688- # 'name' : 'CMS_WH_hww_charge_0j_minus_sssf_2016 ',
691+ # # SSSF plus
692+ # nuisances['charge_plus_0j_sssf '] = {
693+ # 'name' : 'CMS_WH_hww_charge_0j_plus_sssf_2016HIPM ',
689694# 'kind' : 'weight',
690695# 'type' : 'lnN',
691696# 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
692697# 'cuts' : [
693- # 'wh3l_13TeV_sssf_minus_pt2ge20 ',
698+ # 'wh3l_13TeV_sssf_plus_pt2ge20 ',
694699# ],
695700# }
696701
697- # OSSF plus
698- nuisances ['charge_plus_0j_ossf ' ] = {
699- 'name' : 'CMS_WH_hww_charge_0j_plus_ossf_2016 ' ,
700- 'kind' : 'weight' ,
701- 'type' : 'lnN' ,
702- 'samples' : dict ((skey , '1.10' ) for skey in samples if skey not in ['DATA' ]),
703- 'cuts' : [
704- 'wh3l_13TeV_ossf_plus_pt2ge20 ' ,
705- ],
706- }
702+ # # # SSSF minus
703+ # # nuisances['charge_minus_0j_sssf '] = {
704+ # # 'name' : 'CMS_WH_hww_charge_0j_minus_sssf_2016HIPM ',
705+ # # 'kind' : 'weight',
706+ # # 'type' : 'lnN',
707+ # # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
708+ # # 'cuts' : [
709+ # # 'wh3l_13TeV_sssf_minus_pt2ge20 ',
710+ # # ],
711+ # # }
707712
708- # # OSSF minus
709- # nuisances['charge_minus_0j_ossf '] = {
710- # 'name' : 'CMS_WH_hww_charge_0j_minus_ossf_2016 ',
713+ # # OSSF plus
714+ # nuisances['charge_plus_0j_ossf '] = {
715+ # 'name' : 'CMS_WH_hww_charge_0j_plus_ossf_2016HIPM ',
711716# 'kind' : 'weight',
712717# 'type' : 'lnN',
713718# 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
714719# 'cuts' : [
715- # 'wh3l_13TeV_ossf_minus_pt2ge20 ',
720+ # 'wh3l_13TeV_ossf_plus_pt2ge20 ',
716721# ],
717722# }
718723
724+ # # # OSSF minus
725+ # # nuisances['charge_minus_0j_ossf'] = {
726+ # # 'name' : 'CMS_WH_hww_charge_0j_minus_ossf_2016HIPM',
727+ # # 'kind' : 'weight',
728+ # # 'type' : 'lnN',
729+ # # 'samples' : dict((skey, '1.10') for skey in samples if skey not in ['DATA']),
730+ # # 'cuts' : [
731+ # # 'wh3l_13TeV_ossf_minus_pt2ge20',
732+ # # ],
733+ # # }
734+
719735# Use the following if you want to apply the automatic combine MC stat nuisances.
720736nuisances ['stat' ] = {
721737 'type' : 'auto' ,
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