def makePlots(variables,
cuts,
total_weight,
all_samples,
samples,
friend_func,
mode='control',
dc_postfix='',
make_plots=True,
optimisation=False):
sample_names = set()
ams_dict = {}
from CMGTools.H2TauTau.proto.plotter.cut import Cut
# def_iso_cut = inc_sig_tau1_iso & inc_sig_tau2_iso
iso_cuts = {
# 'vvtight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>5.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>5.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>3.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>3.5')),
'vtight_relax2nd': (Cut('l1_byIsolationMVArun2v1DBoldDMwLT>4.5')
& Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'),
Cut('l1_byIsolationMVArun2v1DBoldDMwLT>4.5')
& Cut('l2_byIsolationMVArun2v1DBoldDMwLT>2.5')),
'loose_not_vtight':
(Cut(
'l1_byIsolationMVArun2v1DBoldDMwLT>1.5 && l1_byIsolationMVArun2v1DBoldDMwLT<4.5'
) & Cut(
'l2_byIsolationMVArun2v1DBoldDMwLT>1.5&&l2_byIsolationMVArun2v1DBoldDMwLT<4.5'
),
Cut('l1_byIsolationMVArun2v1DBoldDMwLT<1.5 && l1_byIsolationMVArun2v1DBoldDMwLT>0.5'
) &
Cut('l2_byIsolationMVArun2v1DBoldDMwLT<1.5 && l2_byIsolationMVArun2v1DBoldDMwLT>0.5'
)),
'one_loose_other_vtight':
(Cut(
'(l1_byIsolationMVArun2v1DBoldDMwLT>4.5 && (l2_byIsolationMVArun2v1DBoldDMwLT>1.5&&l2_byIsolationMVArun2v1DBoldDMwLT<4.5)) || (l2_byIsolationMVArun2v1DBoldDMwLT>4.5 && (l1_byIsolationMVArun2v1DBoldDMwLT>1.5&&l1_byIsolationMVArun2v1DBoldDMwLT<4.5)) '
),
Cut('l1_byIsolationMVArun2v1DBoldDMwLT<1.5 && l1_byIsolationMVArun2v1DBoldDMwLT>0.5'
) &
Cut('l2_byIsolationMVArun2v1DBoldDMwLT<1.5 && l2_byIsolationMVArun2v1DBoldDMwLT>0.5'
)),
# 'vtight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>4.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>2.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>2.5')),
# 'tight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>3.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>3.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>3.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>1.5')),
# 'medium':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>2.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>2.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>0.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>0.5')),
# 'loose':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>1.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>1.5'), Cut('1')),
# 'vloose':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>0.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>0.5'), Cut('1')),
}
# iso_cuts = {
# 'l1_vvtight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>5.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>5.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>3.5')),
# 'l1_vtight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>4.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>4.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>2.5')),
# 'l1_tight':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>3.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>3.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>1.5')),
# 'l1_medium':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>2.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>2.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>0.5')),
# 'l1_loose':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>1.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'), Cut('l1_byIsolationMVArun2v1DBoldDMwLT>1.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>0.5')),
# 'l1_vloose':(Cut('l1_byIsolationMVArun2v1DBoldDMwLT>0.5') & Cut('l2_byIsolationMVArun2v1DBoldDMwLT>4.5'),Cut('l1_byIsolationMVArun2v1DBoldDMwLT>0.5') & Cut('1')),
# }
for cut in cuts:
for iso_cut_name, (iso_cut, max_iso_cut) in iso_cuts.items():
# iso and charge cuts, need to have them explicitly for the QCD estimation
# max_iso_cut = Cut('l1_byIsolationMVArun2v1DBoldDMwLT > 2.5 && l2_byIsolationMVArun2v1DBoldDMwLT > 2.5')
iso_sideband_cut = (~iso_cut) & max_iso_cut
charge_cut = Cut('l1_charge != l2_charge')
isSS = 'SS' in cut.name
all_samples_qcd = qcd_estimation(
cut.cut & iso_sideband_cut &
(charge_cut if not isSS else ~charge_cut), # shape sideband
cut.cut & iso_cut & (~charge_cut), # norm sideband 1
cut.cut & iso_sideband_cut & (~charge_cut), # norm sideband 2
all_samples if mode in ['mssm'] else samples,
int_lumi,
total_weight,
verbose=verbose,
friend_func=friend_func)
# now include charge and isolation too
the_cut = MyCut(
cut.name + iso_cut_name,
cut.cut & iso_cut & (charge_cut if not isSS else ~charge_cut))
# for variable in variables:
cfg_total = HistogramCfg(name=the_cut.name,
vars=variables,
cfgs=all_samples_qcd,
cut=str(the_cut.cut),
lumi=int_lumi,
weight=total_weight)
# all_samples_qcd[-1].vars = variables
if mode == 'mva_train':
createTrees(cfg_total,
'/data1/steggema/tt/MVATrees',
verbose=True)
continue
plots = createHistograms(cfg_total,
verbose=True,
friend_func=friend_func)
for variable in variables:
plot = plots[variable.name]
plot.Group('Single t', [
'T_tWch', 'TBar_tWch', 'TToLeptons_tch_powheg',
'TBarToLeptons_tch_powheg'
]) # 'TToLeptons_sch',
plot.Group('VV', [
'VVTo2L2Nu', 'ZZTo2L2Q', 'WWTo1L1Nu2Q', 'WZTo1L3Nu',
'ZZTo4L', 'WZTo2L2Q', 'WZTo1L1Nu2Q', 'Single t'
]) # 'WZTo3L',
plot.Group(
'ZTT',
['ZTT', 'ZTT1Jets', 'ZTT2Jets', 'ZTT3Jets', 'ZTT4Jets'])
plot.Group('ZJ',
['ZJ', 'ZJ1Jets', 'ZJ2Jets', 'ZJ3Jets', 'ZJ4Jets'])
plot.Group('ZL',
['ZL', 'ZL1Jets', 'ZL2Jets', 'ZL3Jets', 'ZL4Jets'])
plot.Group(
'W',
['WJetsToLNu', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets'])
plot.Group('Electroweak', ['W', 'VV', 'Single t', 'ZJ'])
if optimisation:
plot.DrawStack('HIST')
print plot
for signal_hist in plot.SignalHists():
sample_names.add(signal_hist.name)
ams = ams_hists_rebin(signal_hist.weighted,
plot.BGHist().weighted)
if variable.name == 'mt_total_mssm' and signal_hist.name == 'ggH1800':
print ams_hists_rebin(signal_hist.weighted,
plot.BGHist().weighted,
debug=True)
# import pdb; pdb.set_trace()
ams_dict[variable.name + '__' + the_cut.name + '__' +
signal_hist.name + '_'] = ams
if not make_plots:
continue
blindxmin = 0.7 if 'mva' in variable.name else None
blindxmax = 1.00001 if 'mva' in variable.name else None
if variable.name == 'mt2':
blindxmin = 60.
blindxmax = variable.binning['xmax']
if variable.name == 'mt_sum':
blindxmin = 250.
blindxmax = variable.binning['xmax']
if variable.name == 'mt_total':
blindxmin = 200.
blindxmax = variable.binning['xmax']
plot_dir = 'plot_' + the_cut.name
HistDrawer.draw(plot,
channel='#tau_{h}#tau_{h}',
plot_dir=plot_dir,
blindxmin=blindxmin,
blindxmax=blindxmax)
# HistDrawer.drawRatio(plot, channel='#tau_{h}#tau_{h}')
plot.UnGroup('Electroweak') #, ['W', 'VV', 'Single t', 'ZJ'])
plot.Group('VV', ['VV', 'Single t'])
if variable.name in [
'mt_total', 'svfit_mass', 'mt_total_mssm',
'mt_total_mssm_fine'
]:
plot.WriteDataCard(
filename=plot_dir +
'/htt_tt.inputs-sm-13TeV_{var}{postfix}.root'.format(
var=variable.name, postfix=dc_postfix),
dir='tt_' + cut.name,
mode='UPDATE')
# Save AMS dict
import pickle
pickle.dump(ams_dict, open('opt.pkl', 'wb'))
if optimisation:
print '\nOptimisation results:'
all_vals = ams_dict.items()
for sample_name in sample_names:
vals = [v for v in all_vals if sample_name + '_' in v[0]]
vals.sort(key=itemgetter(1))
for key, item in vals:
print item, key
print '\nBy variable'
for variable in variables:
name = variable.name
print '\nResults for variable', name
for key, item in vals:
if key.startswith(name + '__'):
print item, key
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 18
pt2 = 20
# NEW one - to be implemented as soon as trees are there
inc_sig_tau = Cut(
'!veto_dilepton && !veto_thirdlepton && !veto_otherlepton && l2_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5 && l2_againstMuon3>1.5 && l2_againstElectronMVA5>0.5 && l2_pt>{pt2}'
.format(pt2=pt2))
inc_sig_mu = Cut(
'l1_reliso05<0.1 && l1_muonid_medium>0.5 && l1_pt>{pt1}'.format(pt1=pt1))
inc_sig = inc_sig_mu & inc_sig_tau
cat_Inc_RlxMuIso = str(inc_sig).replace('l1_reliso05<0.1', 'l1_reliso05<1.0')
cat_Inc_RlxTauIso = str(inc_sig).replace('l2_threeHitIso<1.5',
'l2_threeHitIso<10.0')
cat_Inc_RlxMuTauIso = str(inc_sig).replace('l1_reliso05<0.1',
'l1_reliso05<0.5').replace(
'l2_threeHitIso<1.5',
'l2_threeHitIso<10.0')
cat_Inc_AntiMuTauIso = str(inc_sig).replace('l1_reliso05<0.1',
'l1_reliso05>0.1').replace(
'l2_looseMvaIso>0.5',
'l2_looseMvaIso<0.5')
cat_Inc_AntiMuIso = str(inc_sig).replace('l1_reliso05<0.1', 'l1_reliso05>0.1')
cat_Inc_AntiTauIso = str(inc_sig).replace(
'l2_threeHitIso<1.5', 'l2_threeHitIso>1.5 && l2_threeHitIso<5.0')
from CMGTools.H2TauTau.proto.samples.summer16.htt_common import lumi
from CMGTools.H2TauTau.proto.plotter.PlotConfigs import SampleCfg, HistogramCfg, VariableCfg
from CMGTools.H2TauTau.proto.plotter.categories_TauTau import inc_sig_tau1_iso, inc_sig_tau2_iso, inc_sig_no_iso
from CMGTools.H2TauTau.proto.plotter.categories_common import cat_J1, cat_VBF
from CMGTools.H2TauTau.proto.plotter.HistCreator import createHistograms, createTrees
from CMGTools.H2TauTau.proto.plotter.HistDrawer import HistDrawer
from CMGTools.H2TauTau.proto.plotter.Variables import tautau_vars, getVars
from CMGTools.H2TauTau.proto.plotter.Samples import createSampleLists
from CMGTools.H2TauTau.proto.plotter.qcdEstimation import qcd_estimation
from CMGTools.H2TauTau.proto.plotter.cut import Cut
from CMGTools.H2TauTau.proto.plotter.metrics import ams_hists_rebin
MyCut = namedtuple('MyCut', ['name', 'cut'])
inc_sig_no_iso = inc_sig_no_iso & Cut(
'Flag_HBHENoiseFilter && Flag_HBHENoiseIsoFilter && Flag_EcalDeadCellTriggerPrimitiveFilter && Flag_goodVertices && Flag_eeBadScFilter && Flag_globalTightHalo2016Filter && passBadMuonFilter && passBadChargedHadronFilter && badMuonMoriond2017 && badCloneMuonMoriond2017'
)
def prepareCuts(mode):
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# append categories to plot
if mode == 'control':
# cuts.append(MyCut('inclusive', inc_cut & Cut('n_bjets==0')))
# cuts.append(MyCut('dilpt50', inc_cut & Cut('n_bjets==0 && dil_pt>50')))
def prepareCuts(mode):
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# append categories to plot
if mode == 'control':
# cuts.append(MyCut('inclusive', inc_cut & Cut('n_bjets==0')))
# cuts.append(MyCut('dilpt50', inc_cut & Cut('n_bjets==0 && dil_pt>50')))
cuts.append(MyCut('2bjet', inc_cut & Cut('n_bjets>=2')))
cuts.append(MyCut('gr1bjet', inc_cut & Cut('n_bjets>=1')))
# cuts.append(MyCut('sm_dysel_new_mz', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>50 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60. && mvis<100')))
# cuts.append(MyCut('sm_dysel_ptgr100_mz', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>100 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60. && mvis<100')))
# cuts.append(MyCut('sm_dysel_new', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>50 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_ptgr100', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>100 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_ptgr200', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>200 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_pt150_200', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>150 && l1_pt<200 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_pt100_150', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>100 && l1_pt<150 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_pt80_100', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>80 && l1_pt<100 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('sm_dysel_pt50_80', inc_cut & Cut('n_bjets==0 && n_jets>=1 && l1_pt>50 && l1_pt<80 && l2_pt>40 && abs(l1_eta - l2_eta)<1. && pzeta_vis>60.')))
# cuts.append(MyCut('inclusive_SS', inc_cut))
# cuts.append(MyCut('mZ', inc_cut & Cut('mvis < 110.')))
# cuts.append(MyCut('low_deta', inc_cut & Cut('delta_eta_l1_l2 < 1.5')))
# cuts.append(MyCut('high_deta', inc_cut & Cut('delta_eta_l1_l2 > 1.5')))
# cuts.append(MyCut('mZ_0jet', inc_cut & Cut('mvis < 110. && n_jets==0')))
# cuts.append(MyCut('mZ_1jet', inc_cut & Cut('mvis < 110. && n_jets>=1')))
# Next is a failed attempt to get a W+jets-enriched control region
# cuts.append(MyCut('mva_met_sig_1_low_deta', inc_cut & Cut('met_pt/sqrt(met_cov00 + met_cov11) > 1. && delta_eta_l1_l2 < 2.')))
if mode == 'mssm':
cuts.append(MyCut('nobtag', inc_cut & Cut('n_bjets==0')))
cuts.append(MyCut('inclusive', inc_cut & Cut('1')))
cuts.append(MyCut('btag', inc_cut & Cut('n_bjets==1 && n_jets<=1')))
# cuts.append(MyCut('1bjet', inc_cut & Cut('n_bjets==1')))
# # cuts.append(MyCut('0jet', inc_cut & Cut('n_bjets==1 && n_jets==0')))
# # cuts.append(MyCut('inclusive_largedphi', inc_cut & Cut('n_bjets==0 && abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))>1.5')))
# cuts.append(MyCut('inclusive_tau1pt60', inc_cut & Cut('n_bjets==0 && l1_pt>60')))
# cuts.append(MyCut('inclusive_tau1ptl60', inc_cut & Cut('n_bjets==0 && l1_pt<60')))
# cuts.append(MyCut('inclusive_tau1pt75', inc_cut & Cut('n_bjets==0 && l1_pt>75')))
# cuts.append(MyCut('inclusive_tau1ptl75', inc_cut & Cut('n_bjets==0 && l1_pt<75')))
# cuts.append(MyCut('inclusive_tau1pt100', inc_cut & Cut('n_bjets==0 && l1_pt>100')))
# cuts.append(MyCut('inclusive_tau1ptl100', inc_cut & Cut('n_bjets==0 && l1_pt<100')))
# cuts.append(MyCut('inclusive_tau1pt150', inc_cut & Cut('n_bjets==0 && l1_pt>150')))
# cuts.append(MyCut('inclusive_tau1ptl150', inc_cut & Cut('n_bjets==0 && l1_pt<150')))
# cuts.append(MyCut('inclusive_lowdphimetl2', inc_cut & Cut('n_bjets==0 && abs(TVector2::Phi_mpi_pi(l2_phi-met_phi))<0.5')))
# cuts.append(MyCut('inclusive_lowdphimetl2_highdphimetl1', inc_cut & Cut('n_bjets==0 && abs(TVector2::Phi_mpi_pi(l2_phi-met_phi))<0.5 && abs(TVector2::Phi_mpi_pi(l1_phi-met_phi))>2')))
# cuts.append(MyCut('inclusive_mttotal300', inc_cut & Cut('n_bjets==0 && mt_total>300')))
if mode == 'sm':
cuts.append(
MyCut(
'sm_1jet', inc_cut
& Cut('n_bjets==0 && n_jets>=1 && l1_pt>50 && l2_pt>40')))
cuts.append(
MyCut(
'sm_0jet', inc_cut
& Cut('n_bjets==0 && n_jets==0 && l1_pt>50 && l2_pt>40')))
cuts.append(MyCut('1jet', jet1_cut)) # with VBF veto
cuts.append(MyCut('vbf', vbf_cut))
if mode == 'susy':
# cuts.append(MyCut('mva_met_sig_3', inc_cut & Cut('met_pt/sqrt(met_cov00 + met_cov11) > 3.')))
# cuts.append(MyCut('met200', inc_cut & Cut('met_pt > 200.')))
# cuts.append(MyCut('susy_loose_met', inc_cut & Cut('mvis>100 && n_bjets==0 && met_pt>100.')))
# cuts.append(MyCut('susy_loose', inc_cut & Cut('mvis>100 && n_bjets==0 && pzeta_disc < -40.')))
# cuts.append(MyCut('susy_mtsum200', inc_cut & Cut('n_bjets==0 && mt + mt_leg2>200.')))
# cuts.append(MyCut('susy_highmva', inc_cut & Cut('n_bjets==0 && mva1>0.75')))
# cuts.append(MyCut('susy_mva_mtsum200_mt2_20', inc_cut & Cut('n_bjets==0 && mt2>20 && mt + mt_leg2>200. && mva1>0.85')))
# cuts.append(MyCut('susy_mva2_mtsum200_mt2_20', inc_cut & Cut('n_bjets==0 && mt2>20 && mt + mt_leg2>200. && mva1>0.90')))
# cuts.append(MyCut('susy_mva3_mtsum200_mt2_20', inc_cut & Cut('n_bjets==0 && mt2>20 && mt + mt_leg2>200. && mva1>0.95')))
# cuts.append(MyCut('susy_mtsum200_mt2_20', inc_cut & Cut('n_bjets==0 && mt2>20 && mt + mt_leg2>200.')))
# cuts.append(MyCut('pieter_1', inc_cut & Cut('n_bjets==0 && mt2>90. && abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))>1.5')))
# cuts.append(MyCut('pieter_2', inc_cut & Cut('n_bjets==0 && mt2>40. && mt2<90. && mt2>40. && abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))>1.5 && mt + mt_leg2>300. && mt + mt_leg2<300. ')))
# cuts.append(MyCut('pieter_3', inc_cut & Cut('n_bjets==0 && mt2>40. && mt2<90. && mt2>40. && abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))>1.5 && mt + mt_leg2>350.')))
# cuts.append(MyCut('maryam_incl', inc_cut & Cut('n_bjets==0 && mt2>20. && mvis>85. && pfmet_pt>30.')))
# cuts.append(MyCut('maryam_1', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30.')))
# cuts.append(MyCut('maryam_1_0jet', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && n_jets==0')))
# cuts.append(MyCut('maryam_1_1jet', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && n_jets>=1')))
# cuts.append(MyCut('maryam_1_SS', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30.')))
# cuts.append(MyCut('maryam_1_0jet_SS', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && n_jets==0')))
# cuts.append(MyCut('maryam_1_1jet_SS', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && n_jets>=1')))
cuts.append(
MyCut(
'maryam_1_mt2sideband_1jet', inc_cut &
Cut('n_bjets==0 && mt2>75. && mt2<90. && mvis>85. && pfmet_pt>30. && n_jets>=1'
)))
cuts.append(
MyCut(
'maryam_1_mt2sideband_1jet_SS', inc_cut &
Cut('n_bjets==0 && mt2>75. && mt2<90. && mvis>85. && pfmet_pt>30. && n_jets>=1'
)))
# cuts.append(MyCut('maryam_1_tight', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && l1_pt>100.')))
# cuts.append(MyCut('maryam_1_tight_1jet', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && l1_pt>100. && n_jets>=1')))
# cuts.append(MyCut('maryam_1_tight_0jet', inc_cut & Cut('n_bjets==0 && mt2>90. && mvis>85. && pfmet_pt>30. && l1_pt>100. && n_jets==0')))
# cuts.append(MyCut('maryam_2', inc_cut & Cut('n_bjets==0 && mt2<90. && mvis>85. && pfmet_pt>30. && mt + mt_leg2 > 250. && l1_pt>100.')))
# cuts.append(MyCut('susy_onlytaupt', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20.')))
# cuts.append(MyCut('susy_taupt', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50.')))
# cuts.append(MyCut('susy_taupt_pzetamet', inc_cut & Cut(
# 'mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50. && pzeta_met<-50. && delta_eta_l1_l2<3. && (min(abs(TVector2::Phi_mpi_pi(met_phi - jet2_phi)) + 20*(jet2_phi<-50), abs(TVector2::Phi_mpi_pi(met_phi - jet1_phi))+ 20*(jet1_phi<-50)) > 0.8 || jet1_pt<30.)')))
# cuts.append(MyCut('susy_jan_opt', inc_cut & Cut('met_pt/sqrt(met_cov00 + met_cov11) > 2. && mvis>100 && mt + mt_leg2 > 200. && n_bjets==0 && pzeta_disc < -40.')))
# cuts.append(MyCut('susy_jan_tight', inc_cut & Cut(
# 'met_pt/sqrt(met_cov00 + met_cov11) > 1. && mvis>100 && mt + mt_leg2 > 150. && n_bjets==0 && pzeta_disc < -40. && abs(abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))) > 1. && mt_total>300.')))
# cuts.append(MyCut('susy_onlytaupt_0jet', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && n_jets==0')))
# cuts.append(MyCut('susy_taupt_0jet', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50. && n_jets==0')))
# cuts.append(MyCut('susy_taupt_pzetamet_0jet', inc_cut & Cut(
# 'mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50. && pzeta_met<-50. && delta_eta_l1_l2<3. && (min(abs(TVector2::Phi_mpi_pi(met_phi - jet2_phi)) + 20*(jet2_phi<-50), abs(TVector2::Phi_mpi_pi(met_phi - jet1_phi))+ 20*(jet1_phi<-50)) > 0.8 || jet1_pt<30.) && n_jets==0')))
# cuts.append(MyCut('susy_jan_opt_0jet', inc_cut & Cut('met_pt/sqrt(met_cov00 + met_cov11) > 2. && mvis>100 && mt + mt_leg2 > 200. && n_bjets==0 && pzeta_disc < -40. && n_jets==0')))
# cuts.append(MyCut('susy_jan_tight_0jet', inc_cut & Cut(
# 'met_pt/sqrt(met_cov00 + met_cov11) > 1. && mvis>100 && mt + mt_leg2 > 150. && n_bjets==0 && pzeta_disc < -40. && abs(abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))) > 1. && mt_total>300. && n_jets==0')))
# cuts.append(MyCut('susy_onlytaupt_gr1jet', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && n_jets>0')))
# cuts.append(MyCut('susy_taupt_gr1jet', inc_cut & Cut('mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50. && n_jets>0')))
# cuts.append(MyCut('susy_taupt_pzetamet_gr1jet', inc_cut & Cut(
# 'mvis>100 && n_bjets==0 && l1_pt>60 && met_pt>20. && mt>50. && pzeta_met<-50. && delta_eta_l1_l2<3. && (min(abs(TVector2::Phi_mpi_pi(met_phi - jet2_phi)) + 20*(jet2_phi<-50), abs(TVector2::Phi_mpi_pi(met_phi - jet1_phi))+ 20*(jet1_phi<-50)) > 0.8 || jet1_pt<30.) && n_jets>0')))
# cuts.append(MyCut('susy_jan_opt_gr1jet', inc_cut & Cut(
# 'met_pt/sqrt(met_cov00 + met_cov11) > 2. && mvis>100 && mt + mt_leg2 > 200. && n_bjets==0 && pzeta_disc < -40. && n_jets>0')))
# cuts.append(MyCut('susy_jan_tight_gr1jet', inc_cut & Cut(
# 'met_pt/sqrt(met_cov00 + met_cov11) > 1. && mvis>100 && mt + mt_leg2 > 150. && n_bjets==0 && pzeta_disc < -40. && abs(abs(TVector2::Phi_mpi_pi(l1_phi - l2_phi))) > 1. && mt_total>300. && n_jets>0')))
# cuts.append(MyCut('susy_jan_nomet', inc_cut & Cut('mvis>100 && n_bjets==0 && mt + mt_leg2 > 150.')))
return cuts
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 23
pt2 = 23
# NEW one - to be implemented as soon as trees are there
inc_sig = Cut('!veto_dilepton && !veto_thirdlepton && !veto_otherlepton')
inc_sig_mu1 = Cut('l1_reliso05<0.15 && l1_muonid_medium>0.5 && l1_pt>{pt1}'.format(pt1=pt1))
inc_sig_mu2 = Cut('l2_reliso05<0.15 && l2_muonid_medium>0.5 && l2_pt>{pt2}'.format(pt2=pt2))
inc_sig = inc_sig & inc_sig_mu1 & inc_sig_mu2
cat_Inc = str(inc_sig)
categories = {
'Xcat_IncX': cat_Inc,
}
categories.update(categories_common)
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 19
pt2 = 20
inc_sig_tau = Cut(
'!veto_dilepton && !veto_thirdlepton && !veto_otherlepton && l2_byIsolationMVArun2v1DBoldDMwLT>3.5 && l2_againstMuon3>1.5 && l2_againstElectronMVA6>0.5 && l2_pt>{pt2}'
.format(pt2=pt2))
inc_sig_mu = Cut(
'l1_reliso05<0.1 && l1_muonid_medium>0.5 && l1_pt>{pt1}'.format(pt1=pt1))
inc_sig = inc_sig_mu & inc_sig_tau
cat_Inc = str(inc_sig)
categories = {
'Xcat_IncX': cat_Inc,
}
categories.update(categories_common)
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 40
pt2 = 40
inc_event = Cut('!veto_dilepton && !veto_thirdlepton && !veto_otherlepton')
inc_sig_tau1_iso = Cut(
# 'l1_byIsolationMVArun2v1DBoldDMwLT>3.5' # Tight WP
'l1_byIsolationMVArun2v1DBoldDMwLT>4.5' # VTight WP
)
inc_sig_tau2_iso = Cut(
# 'l2_byIsolationMVArun2v1DBoldDMwLT>3.5' # Tight WP
'l2_byIsolationMVArun2v1DBoldDMwLT>4.5' # VTight WP
)
inc_sig_tau1_other = Cut(
'l1_againstMuon3>1.5 && l1_againstElectronMVA6>0.5 && l1_pt>{pt1}'.format(
pt1=pt1))
inc_sig_tau2_other = Cut(
'l2_againstMuon3>1.5 && l2_againstElectronMVA6>0.5 && l2_pt>{pt2}'.format(
pt2=pt2))
inc_sig = inc_event & inc_sig_tau1_iso & inc_sig_tau1_other & inc_sig_tau2_iso & inc_sig_tau2_other
inc_sig_no_iso = inc_event & inc_sig_tau1_other & inc_sig_tau2_other
inc_anti_iso = (~inc_sig_tau1_iso | ~inc_sig_tau2_iso
) & inc_event & inc_sig_tau1_other & inc_sig_tau2_other
from CMGTools.H2TauTau.proto.plotter.cut import Cut
# pt1 = 200
# pt2 = 200
cat_VV = '(njj>0)'
cat_common = '(HLT_HT800||HLT_HT900)&&Flag_goodVertices&&Flag_CSCTightHaloFilter&&Flag_HBHENoiseFilter&&Flag_HBHENoiseIsoFilter&&Flag_eeBadScFilter&&jj_nOtherLeptons==0'
cat_mu = '(HLT_MU||HLT_ELE)&&abs(lnujj_l1_l_pdgId)==13'
cat_e = '(HLT_MU||HLT_ELE)&&abs(lnujj_l1_l_pdgId)==11'
cat_HP = 'lnujj_l2_tau2/lnujj_l2_tau1<0.6'
cat_LP = 'lnujj_l2_tau2/lnujj_l2_tau1>0.6&&lnujj_l2_tau2/lnujj_l2_tau1<0.75'
cat_nob = 'lnujj_nMediumBTags==0'
cat_b = 'lnujj_nMediumBTags>0'
inc_common = Cut(cat_common)
inc_VV = Cut(cat_VV)
inc_sig = inc_common & inc_VV
# inc_sig_mu1 = Cut('l1_reliso05<0.1 && l1_muonid_medium>0.5 && l1_pt>{pt1}'.format(pt1=pt1))
# inc_sig_mu2 = Cut('l2_reliso05<0.1 && l2_muonid_medium>0.5 && l2_pt>{pt2}'.format(pt2=pt2))
# inc_sig = inc_sig & inc_sig_mu1 & inc_sig_mu2
cat_Inc = str(inc_sig)
categories = {
'Inclusive': cat_Inc,
}
# categories.update(categories_common)
int_lumi = 2240. # from Alexei's email
tree_prod_name = 'H2TauTauTreeProducerTauTau'
analysis_dir = '/afs/cern.ch/work/m/manzoni/diTau2015/CMSSW_7_6_3/src/CMGTools/H2TauTau/cfgPython/tt/tt_14march2016'
total_weight = 'weight'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir, channel='tt')
myCut = namedtuple('myCut', ['name', 'cut'])
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# iso and charge cuts, need to have them explicitly for the QCD estimation
iso_cut = inc_sig_tau1_iso & inc_sig_tau2_iso
max_iso_cut = Cut(
'l1_byIsolationMVArun2v1DBoldDMwLT > 3.5 && l2_byIsolationMVArun2v1DBoldDMwLT > 3.5'
)
iso_sideband_cut = (~iso_cut) & max_iso_cut
charge_cut = Cut('l1_charge != l2_charge')
# append categories to plot
cuts.append(myCut('inclusive', inc_cut))
# cuts.append(myCut('1jet' , jet1_cut))
# cuts.append(myCut('vbf' , vbf_cut ))
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
# NEW one - to be implemented as soon as trees are there
inc_sig_e = Cut('!veto_thirdlepton && !veto_otherlepton && l1_reliso05<0.15')
inc_sig_mu = Cut('l2_reliso05<0.2')
inc_pt = Cut('(l2_pt>24 && l1_pt>13) || (l2_pt>9 && l1_pt>24')
inc_sig = inc_sig_mu & inc_sig_e
cat_Inc = str(inc_sig)
categories = {
'Xcat_IncX': cat_Inc,
}
categories.update(categories_common)
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
# NEW one - to be implemented as soon as trees are there
inc_sig_e = Cut('!veto_thirdlepton && !veto_otherlepton && l1_reliso05<0.15')
inc_sig_mu = Cut('l2_reliso05<0.15')
inc_sig = inc_sig_mu & inc_sig_e
cat_Inc_RlxMuIso = str(inc_sig).replace('l1_reliso05<0.1', 'l1_reliso05<1.0')
cat_Inc_RlxTauIso = str(inc_sig).replace('l2_threeHitIso<1.5',
'l2_threeHitIso<10.0')
cat_Inc_RlxMuTauIso = str(inc_sig).replace('l1_reliso05<0.1',
'l1_reliso05<0.5').replace(
'l2_threeHitIso<1.5',
'l2_threeHitIso<10.0')
cat_Inc_AntiMuTauIso = str(inc_sig).replace('l1_reliso05<0.1',
'l1_reliso05>0.1').replace(
'l2_looseMvaIso>0.5',
'l2_looseMvaIso<0.5')
cat_Inc_AntiMuIso = str(inc_sig).replace('l1_reliso05<0.1', 'l1_reliso05>0.1')
cat_Inc_AntiTauIso = str(inc_sig).replace(
'l2_threeHitIso<1.5', 'l2_threeHitIso>1.5 && l2_threeHitIso<5.0')
cat_Inc = str(inc_sig)
categories = {
'Xcat_Inc_RlxMuIsoX': cat_Inc_RlxMuIso,
'Xcat_Inc_RlxTauIsoX': cat_Inc_RlxTauIso,
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 24
pt2 = 20
inc_sig_tau = Cut(
'!veto_dilepton && !veto_thirdlepton && !veto_otherlepton && l2_byCombinedIsolationDeltaBetaCorr3Hits>1.5 && l2_againstElectronMVA5 > 3.5 && l2_againstMuon3 > 0.5&& l2_pt>{pt2}'
.format(pt2=pt2))
inc_sig_ele = Cut(
'l1_reliso05<0.1 && l1_eid_nontrigmva_tight>0.5 && l1_pt>{pt1}'.format(
pt1=pt1))
inc_sig = inc_sig_ele & inc_sig_tau
cat_Inc = str(inc_sig)
categories = {
'Xcat_IncX': cat_Inc,
}
categories.update(categories_common)
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tt',
mode='mssm' if mssm else 'susy',
ztt_cut='(l2_gen_match == 5 && l1_gen_match == 5)',
zl_cut=
'(l1_gen_match < 6 && l2_gen_match < 6 && !(l1_gen_match == 5 && l2_gen_match == 5))',
zj_cut='(l2_gen_match == 6 || l1_gen_match == 6)',
signal_scale=1. if optimisation else 20.)
myCut = namedtuple('myCut', ['name', 'cut'])
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# iso and charge cuts, need to have them explicitly for the QCD estimation
iso_cut = inc_sig_tau1_iso & inc_sig_tau2_iso
max_iso_cut = Cut(
'l1_byIsolationMVArun2v1DBoldDMwLT > 1.5 && l2_byIsolationMVArun2v1DBoldDMwLT > 1.5'
)
iso_sideband_cut = (~iso_cut) & max_iso_cut
charge_cut = Cut('l1_charge != l2_charge')
# append categories to plot
# cuts.append(myCut('inclusive', inc_cut & Cut('n_bjets==0')))
# cuts.append(myCut('inclusive_SS', inc_cut))
from CMGTools.H2TauTau.proto.plotter.categories_common import categories_common
from CMGTools.H2TauTau.proto.plotter.cut import Cut
pt1 = 40
pt2 = 40
inc_event = Cut('!veto_dilepton && !veto_thirdlepton && !veto_otherlepton')
inc_trigger = Cut(
'(trigger_ditau35 && trigger_matched_ditau35) || (trigger_ditau35_combiso && trigger_matched_ditau35_combiso)'
)
inc_sig_tau1_iso = Cut(
# 'l1_byIsolationMVArun2v1DBoldDMwLT>3.5' # Tight WP
'l1_byIsolationMVArun2v1DBoldDMwLT>4.5' # VTight WP
)
inc_sig_tau2_iso = Cut(
# 'l2_byIsolationMVArun2v1DBoldDMwLT>3.5' # Tight WP
'l2_byIsolationMVArun2v1DBoldDMwLT>4.5' # VTight WP
)
inc_sig_tau1_other = Cut(
'l1_againstMuon3>0.5 && l1_againstElectronMVA6>0.5 && l1_pt>{pt1} && abs(l1_eta)<2.1'
.format(pt1=pt1))
inc_sig_tau2_other = Cut(
'l2_againstMuon3>0.5 && l2_againstElectronMVA6>0.5 && l2_pt>{pt2} && abs(l2_eta)<2.1'
.format(pt2=pt2))
inc_sig = inc_event & inc_trigger & inc_sig_tau1_iso & inc_sig_tau1_other & inc_sig_tau2_iso & inc_sig_tau2_other
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tt',
mode='mssm' if mssm else 'susy',
ztt_cut='(l2_gen_match == 5 && l1_gen_match == 5)',
zl_cut=
'(l1_gen_match < 6 && l2_gen_match < 6 && !(l1_gen_match == 5 && l2_gen_match == 5))',
zj_cut='(l2_gen_match == 6 || l1_gen_match == 6)',
signal_scale=1. if optimisation else 20.)
myCut = namedtuple('myCut', ['name', 'cut'])
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# iso and charge cuts, need to have them explicitly for the QCD estimation
iso_cut = inc_sig_tau1_iso & inc_sig_tau2_iso
max_iso_cut = Cut(
'l1_byIsolationMVArun2v1DBoldDMwLT > 1.5 && l2_byIsolationMVArun2v1DBoldDMwLT > 1.5'
)
iso_sideband_cut = (~iso_cut) & max_iso_cut
charge_cut = Cut('l1_charge != l2_charge')
# append categories to plot
# cuts.append(myCut('inclusive', inc_cut))
# cuts.append(myCut('inclusive_SS', inc_cut))