def createVariables(mode):
# Taken from Variables.py; can get subset with e.g. getVars(['mt', 'mvis'])
# variables = taumu_vars
# variables = getVars(['_norm_', 'mt', 'mvis', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta', 'n_vertices', 'n_jets', 'n_bjets'])
variables = []
if mode in ['sm', 'pog']:
# MVA training variables, and others
variables += getVars(['mt', 'l2_mt', 'n_jets', 'met_pt', 'pthiggs', 'l2_pt', 'l1_pt','mvis', 'l1_eta', 'l2_eta', 'pzeta_disc', 'jet1_pt', 'jet1_eta', 'n_vertices', 'rho', 'dil_pt', 'l2_byIsolationMVArun2v1DBoldDMwLTraw', 'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits', 'l2_byIsolationMVArun2v1DBoldDMwLTraw_zoom',
'l2_nc_ratio', 'l2_pt_weighted_dr_iso', 'l2_pt_weighted_dr_signal', 'l2_pt_weighted_dphi_strip', 'l2_pt_weighted_deta_strip', 'l2_flightLength', 'l2_flightLengthSig', 'l2_dxy_Sig', 'l2_ip3d', 'l2_ip3d_Sig', 'l2_e_over_h', 'l2_n_photons', ])# 'svfit_transverse_mass', 'delta_phi_l1_l2', 'delta_eta_l1_l2', 'pt_l1l2', 'delta_phi_j1_met',
# variables = taumu_vars
if mode == 'cp':
variables = getVars(['l2_nc_ratio'])
if mode == 'mssm_signal':
variables = [
VariableCfg(name='svfit_transverse_mass', binning=binning_mssm, unit='GeV', xtitle='m_{T,SVFit}'),
VariableCfg(name='svfit_mass', binning=binning_mssm, unit='GeV', xtitle='m_{SVFit}'),
VariableCfg(name='mvis', binning=binning_mssm, unit='GeV', xtitle='m_{vis}'),
]
if mode == 'iso':
variables = getVars(['mt', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta'])
return variables
def getVariables(mode):
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
# variables = tautau_vars
if mode == 'control':
variables = getVars([
'_norm_', 'mvis', 'mt2', 'l1_pt', 'l2_pt', 'delta_phi_l1_l2',
'delta_eta_l1_l2', 'met_pt', 'mt_total', 'mt_total_mssm', 'mt_sum',
'pzeta_met', 'l2_mt', 'mt', 'pzeta_vis', 'pzeta_disc', 'pthiggs',
'jet1_pt', 'n_jets', 'dil_pt',
'l1_byCombinedIsolationDeltaBetaCorrRaw3Hits',
'l1_byIsolationMVArun2v1DBoldDMwLTraw', 'l1_dz_sig', 'l1_log_dz',
'l1_dxy_sig', 'l1_log_dxy', 'l1_decayMode', 'l1_chargedIsoPtSum',
'l1_neutralIsoPtSum', 'l1_puCorrPtSum',
'l1_photonPtSumOutsideSignalCone', 'l1_zImpact', 'l1_jet_charge',
'l1_jet_pt_div_l1_pt'
],
channel='tautau')
if mode == 'mssm':
variables = getVars([
'mt_total', 'mt_total_mssm', 'mt_total_mssm_fine', 'mvis_extended',
'l1_pt', 'dil_pt'
],
channel='tautau')
# variables += [
# VariableCfg(name='mt2', binning={'nbinsx':15, 'xmin':0., 'xmax':150.}, unit='GeV', xtitle='m_{T2}')
# ]
if mode == 'mva':
variables += getVars(['_norm_'])
variables += [
VariableCfg(name='mva1',
binning={
'nbinsx': 10,
'xmin': 0.,
'xmax': 1.
},
unit='',
xtitle='Stau MVA')
]
if mode == 'susy':
variables = getVars([
'l1_pt', '_norm_', 'l2_pt', 'mt2', 'mt', 'mt_leg2',
'mt_total_mssm', 'min_delta_phi_tau1tau2_met'
],
channel='tautau')
return variables
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir)
samples_mc_w = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'W*Jet*')]
samples_mc_tt = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'TT*')]
samples = samples_mc_w + samples_mc_tt
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = getVars(['l1_pt', 'l2_eta'])
variables += [
VariableCfg(name='l2_jet_pt',
binning=array(
[20., 30., 40., 50., 60., 70., 80., 90., 100., 150.,
200.]),
unit='GeV',
xtitle='tau jet p_{T}'),
VariableCfg(name='l2_pt',
binning=array(
[20., 25., 30., 35., 40., 50., 60., 80., 120., 200.]),
unit='GeV',
xtitle='tau p_{T}'),
VariableCfg(name='l2_jet_pt_div_l2_pt',
drawname='l2_jet_pt/l2_pt',
binning=array(
[0., 0.5, 0.8, 1.0, 1.1, 1.2, 1.5, 2.0, 3.0, 4.0]),
unit=None,
xtitle='tau p_{T}'),
]
def createVariables(mode):
# Taken from Variables.py; can get subset with e.g. getVars(['mt', 'mvis'])
# variables = taumu_vars
# variables = getVars(['_norm_', 'mt', 'mvis', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta', 'n_vertices', 'n_jets', 'n_bjets'])
variables = []
if mode == 'sm':
variables = [
# VariableCfg(name='mva', binning={'nbinsx':20, 'xmin':0., 'xmax':1.}, unit='', xtitle='s_{BDT}'),
VariableCfg(name='svfit_mass',
binning={
'nbinsx': 20,
'xmin': 50.,
'xmax': 250
},
unit='GeV',
xtitle='m_{SVFit}'),
VariableCfg(name='mva0',
binning={
'nbinsx': 20,
'xmin': 0.,
'xmax': 1.0001
},
unit='',
xtitle='s_{BDT} (BG)'),
VariableCfg(name='mva1',
binning={
'nbinsx': 20,
'xmin': 0.,
'xmax': 1.0001
},
unit='',
xtitle='s_{BDT} (ZTT)'),
VariableCfg(name='mva2',
binning=binning_mva2,
unit='',
xtitle='s_{BDT} (Higgs)'),
# VariableCfg(name='mva2div1', drawname='mva2/(mva1+mva2)', binning={'nbinsx':20, 'xmin':0., 'xmax':1.0001}, unit='', xtitle='s_{BDT}^{Higgs}/s_{BDT}^{ZTT}'),
VariableCfg(name='mva2div1',
drawname='mva2/(mva1+mva2)',
binning=binning_mva,
unit='',
xtitle='s_{BDT}^{Higgs}/s_{BDT}^{ZTT}'),
]
# MVA training variables
variables += getVars([
'mt', 'n_jets', 'met_pt', 'pthiggs', 'vbf_mjj', 'vbf_deta',
'vbf_n_central', 'l2_pt', 'l1_pt', 'svfit_transverse_mass',
'delta_phi_l1_l2', 'delta_eta_l1_l2', 'mvis'
])
if mode == 'cp':
variables = getVars(['l2_nc_ratio'])
if mode == 'mssm_signal':
variables = [
VariableCfg(name='svfit_transverse_mass',
binning=binning_mssm,
unit='GeV',
xtitle='m_{T,SVFit}'),
VariableCfg(name='svfit_mass',
binning=binning_mssm,
unit='GeV',
xtitle='m_{SVFit}'),
VariableCfg(name='mvis',
binning=binning_mssm,
unit='GeV',
xtitle='m_{vis}'),
]
if mode == 'iso':
variables = getVars(['mt', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta'])
return variables
def createSamples(mode, analysis_dir, total_weight, qcd_from_same_sign,
w_qcd_mssm_method, r_qcd_os_ss):
hist_dict = {}
sample_dict = {}
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir)
if mode == 'mssm_control' or not 'mssm' in mode:
all_samples = [
s for s in all_samples
if not 'ggH' in s.name and not 'bbH' in s.name
]
sample_dict['all_samples'] = all_samples
if qcd_from_same_sign and not w_qcd_mssm_method:
samples_qcdfromss = [s for s in all_samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
samples_ss = [s for s in samples_ss if not s.is_signal]
for sample in samples_ss:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
qcd = HistogramCfg(name='QCD',
var=None,
cfgs=samples_ss,
cut=None,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=total_weight)
samples_qcdfromss.append(qcd)
sample_dict['samples_qcdfromss'] = samples_qcdfromss
if w_qcd_mssm_method:
w_names = ['W', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets']
samples_non_w = [
s for s in all_samples
if s.name != 'QCD' and s.name not in w_names and not s.is_signal
]
samples_non_w_ss = copy.deepcopy(samples_non_w)
samples_signal = [s for s in all_samples if s.is_signal]
samples_w = copy.deepcopy(
[s for s in all_samples if s.name in w_names])
samples_w_ss = copy.deepcopy(samples_w)
# To calculate OS/SS ratio in inclusive W selection
samples_w_incl_os = copy.deepcopy(samples_w)
samples_w_incl_ss = copy.deepcopy(samples_w)
# To calculate W scale factor
samples_w_highmt_os = copy.deepcopy(samples_w)
# Build a high MT region: OS - non-W/QCD OS - (SS - non-W/QCD SS)
samples_non_w_highmt_os = copy.deepcopy(samples_non_w)
samples_non_w_highmt_ss = copy.deepcopy(samples_non_w)
for sample in samples_non_w_highmt_os:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
for sample in samples_non_w_highmt_ss:
if sample.name != 'data_obs':
sample.scale = -1.
for sample in samples_non_w_ss:
if sample.name != 'data_obs':
sample.scale = -1.
var_norm = VariableCfg(name='_norm_',
drawname='1.',
binning={
'nbinsx': 5,
'xmin': -1.5,
'xmax': 3.5
},
unit='',
xtitle='Normalisation')
hist_dict['stacknow_highmt_os'] = HistogramCfg(
name='HighMTOS',
var=var_norm,
cfgs=samples_non_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['stacknow_highmt_ss'] = HistogramCfg(
name='HighMTSS',
var=var_norm,
cfgs=samples_non_w_highmt_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_incl_os'] = HistogramCfg(name='WInclOS',
var=var_norm,
cfgs=samples_w_incl_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_incl_ss'] = HistogramCfg(name='WInclSS',
var=var_norm,
cfgs=samples_w_incl_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_highmt_os'] = HistogramCfg(name='WHighMTOS',
var=var_norm,
cfgs=samples_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets'] = HistogramCfg(name='W',
var=None,
cfgs=samples_w,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_ss'] = HistogramCfg(name='WSS',
var=None,
cfgs=samples_w_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['qcd'] = HistogramCfg(name='QCD',
var=None,
cfgs=samples_non_w_ss +
[hist_dict['wjets_ss']],
cut=None,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=total_weight)
sample_dict['samples_mssm_method'] = samples_non_w + [
hist_dict['wjets'], hist_dict['qcd']
] + samples_signal
return sample_dict, hist_dict
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir,
channel='mm',
ztt_cut='(l1_gen_match == 4 && l2_gen_match == 4)',
zl_cut='(l1_gen_match == 2 && l2_gen_match == 2)',
zj_cut=
'(l1_gen_match != l2_gen_match || (l1_gen_match != 4 && l1_gen_match != 2))',
data2016G=data2016G)
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
# variables = mumu_vars
variables = getVars(['tau1_eta']) #'tau1_pt',
variables += [
VariableCfg(name='jet1_pt',
binning=array(
[30., 40., 50., 60., 80., 100., 120., 160., 200., 350.]),
unit='GeV',
xtitle='jet p_{T}'),
VariableCfg(name='tau1_pt',
binning=array(
[30., 35., 40., 45., 50., 60., 80., 100., 140., 200.]),
unit='GeV',
xtitle='tau p_{T}'),
VariableCfg(name='jet1_eta',
binning={
'nbinsx': 25,
'xmin': -2.5,
'xmax': 2.5
},
unit='',
xtitle='jet #eta'),
int_lumi = 2110.
pt1 = 19
# pt1 = 40
pt2 = 20
inc_cut = '!veto_dilepton && !veto_thirdlepton && !veto_otherlepton && l2_againstMuon3>1.5 && l2_againstElectronMVA5>0.5 && l2_pt>{pt2} && l2_decayModeFinding && l1_pt>{pt1}'.format(
pt1=pt1, pt2=pt2)
vars_tau = [
VariableCfg(
name='l2_byCombinedIsolationDeltaBetaCorrRaw3Hits',
drawname=
'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits + 100 * (l2_photonPtSumOutsideSignalCone/l2_pt>0.1)',
binning={
'nbinsx': 10000,
'xmin': 0.,
'xmax': 150.
},
unit='GeV',
xtitle='db corr. 3-hit iso'),
VariableCfg(
name='l2_byPileupWeightedIsolationRaw3Hits',
drawname=
'l2_byPileupWeightedIsolationRaw3Hits + 100 * (l2_photonPtSumOutsideSignalCone/l2_pt>0.1)',
binning={
'nbinsx': 10000,
'xmin': 0.,
'xmax': 150.
},
unit='GeV',
# Subtract background from data
sample.scale = -1.
for sample in samples_non_w_highmt_ss:
if sample.name != 'data_obs':
sample.scale = -1.
for sample in samples_non_w_ss:
if sample.name != 'data_obs':
sample.scale = -1.
var_norm = VariableCfg(name='_norm_',
drawname='1.',
binning={
'nbinsx': 5,
'xmin': -1.5,
'xmax': 3.5
},
unit='',
xtitle='Normalisation')
stacknow_highmt_os = HistogramCfg(name='HighMTOS',
var=var_norm,
cfgs=samples_non_w_highmt_os,
cut=inc_cut,
lumi=int_lumi,
weight=total_weight)
stacknow_highmt_ss = HistogramCfg(name='HighMTSS',
var=var_norm,
cfgs=samples_non_w_highmt_ss,
cut=inc_cut,
def createQCDWHistograms(samples,
hist_dict,
int_lumi,
weight,
r_qcd_os_ss=1.17):
w_names = ['W', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets']
samples_non_w = [
s for s in samples
if s.name != 'QCD' and s.name not in w_names and not s.is_signal
]
samples_non_w_ss = deepcopy(samples_non_w)
samples_signal = [s for s in samples if s.is_signal]
samples_w = deepcopy([s for s in samples if s.name in w_names])
samples_w_ss = deepcopy(samples_w)
# To calculate OS/SS ratio in inclusive W selection
samples_w_incl_os = deepcopy(samples_w)
samples_w_incl_ss = deepcopy(samples_w)
# To calculate W scale factor
samples_w_highmt_os = deepcopy(samples_w)
# Build a high MT region: OS - non-W/QCD OS - (SS - non-W/QCD SS)
samples_non_w_highmt_os = deepcopy(samples_non_w)
samples_non_w_highmt_ss = deepcopy(samples_non_w)
for sample in samples_non_w_highmt_os:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
for sample in samples_non_w_highmt_ss:
if sample.name != 'data_obs':
sample.scale = -1.
for sample in samples_non_w_ss:
if sample.name != 'data_obs':
sample.scale = -1.
var_norm = VariableCfg(name='_norm_',
drawname='1.',
binning={
'nbinsx': 5,
'xmin': -1.5,
'xmax': 3.5
},
unit='',
xtitle='Normalisation')
hist_dict['stacknow_highmt_os'] = HistogramCfg(
name='HighMTOS',
var=var_norm,
cfgs=samples_non_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['stacknow_highmt_ss'] = HistogramCfg(
name='HighMTSS',
var=var_norm,
cfgs=samples_non_w_highmt_ss,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['wjets_incl_os'] = HistogramCfg(name='WInclOS',
var=var_norm,
cfgs=samples_w_incl_os,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['wjets_incl_ss'] = HistogramCfg(name='WInclSS',
var=var_norm,
cfgs=samples_w_incl_ss,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['wjets_highmt_os'] = HistogramCfg(name='WHighMTOS',
var=var_norm,
cfgs=samples_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['wjets'] = HistogramCfg(name='W',
var=None,
cfgs=samples_w,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['wjets_ss'] = HistogramCfg(name='WSS',
var=None,
cfgs=samples_w_ss,
cut=None,
lumi=int_lumi,
weight=weight)
hist_dict['qcd'] = HistogramCfg(name='QCD',
var=None,
cfgs=samples_non_w_ss +
[hist_dict['wjets_ss']],
cut=None,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=weight)
return samples_non_w + [hist_dict['wjets'], hist_dict['qcd']
] + samples_signal
qcd = HistogramCfg(name='QCD', var=None, cfgs=samples_ss, cut=inc_cut, lumi=int_lumi)
samples_qcdfromss.append(qcd)
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = all_vars
variables = getVars(['_norm_', 'mt', 'met_pt', 'mvis', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta', 'n_vertices', 'n_jets', 'n_bjets'])
# variables = getVars(['_norm_', 'mt', 'mvis', 'n_vertices'])
# variables = getVars(['_norm_', 'met_pt', 'met_phi'])
# variables = getVars(['_norm_'])
variables = [
VariableCfg(name='mvis', binning={'nbinsx':35, 'xmin':0, 'xmax':350}, unit='GeV', xtitle='m_{vis}')
]
# variables += getVars(['_norm_'])
for cut_name in cuts:
if qcd_from_same_sign and not 'SS' in cut_name :
cfg_example = HistogramCfg(name=cut_name, var=None, cfgs=samples_qcdfromss, cut=inc_cut, lumi=int_lumi, weight=total_weight)
else:
cfg_example = HistogramCfg(name=cut_name, var=None, cfgs=samples, cut=inc_cut, lumi=int_lumi, weight=total_weight)
cfg_example.cut = cuts[cut_name]
if qcd_from_same_sign and not 'SS' in cut_name:
qcd.cut = cuts[cut_name].replace('l1_charge != l2_charge', 'l1_charge == l2_charge')
qcd_from_same_sign = False
# -> Command line
analysis_dir = '/data1/steggema/TauFRPU'
data_dir = analysis_dir
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir, channel='tau_fr')
samples_mc_w = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'W*Jet*')]
samples_mc_tt = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'TT*')]
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = getVars(['n_jets'])
variables += [
VariableCfg(name='muon_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':100.}, unit='GeV', xtitle='muon p_{T}'),
VariableCfg(name='tau_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':100.}, unit='GeV', xtitle='tau p_{T}'),
VariableCfg(name='jet_pt', binning=array([20., 30., 40., 50., 60., 70., 80., 90., 100., 150., 200.]), unit='GeV', xtitle='tau jet p_{T}'),
VariableCfg(name='tau_pt', binning=array([20., 25., 30., 35., 40., 50., 60., 80., 120., 200.]), unit='GeV', xtitle='tau p_{T}'),
]
# variables += [v for v in all_vars if 'dxy' in v.name or 'dz' in v.name]
# variables = getVars(['_norm_', 'tau_pt', 'tau_eta', 'tau_mass', 'tau_decayMode', 'mvis', 'mt', 'delta_r_l1_l2', 'tau_gen_pdgId', 'tau_mt'])
# variables = all_vars
for cut_name in cuts:
cfg_tight = HistogramCfg(name='tight', var=None, cfgs=samples_mc_w, cut=inc_cut, lumi=int_lumi, weight=total_weight + ' * ((tau_gen_match == 6))')
cfg_loose = HistogramCfg(name='loose', var=None, cfgs=samples_mc_w, cut=inc_cut, lumi=int_lumi, weight=total_weight + ' * ((tau_gen_match == 6))')
cfg_data_tight = HistogramCfg(name='tight_data', var=None, cfgs=samples_mc_tt, cut=inc_cut, lumi=int_lumi, weight=total_weight + ' * ((tau_gen_match == 6))')
variables = getVars([
'mvis', 'mt2', 'l1_pt', 'l2_pt', 'delta_phi_l1_l2', 'delta_eta_l1_l2',
'met_pt', 'mt_total', 'mt_sum', 'pzeta_met', 'l2_mt', 'mt', 'pzeta_vis',
'pzeta_disc', 'pthiggs', 'jet1_pt', 'n_jets', 'pt_l1l2'
]) #, 'l1_pt', '_norm_', ])
# variables += [
# VariableCfg(name='mt2', binning={'nbinsx':15, 'xmin':0., 'xmax':150.}, unit='GeV', xtitle='m_{T2}')
# ]
if mode == 'mva':
variables += getVars(['_norm_'])
variables += [
VariableCfg(name='mva1',
binning={
'nbinsx': 10,
'xmin': 0.,
'xmax': 1.
},
unit='',
xtitle='Stau MVA')
]
ams_dict = {}
sample_names = set()
for cut in cuts:
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
tree_prod_name = 'H2TauTauTreeProducerTauMu'
data_dir = analysis_dir
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir)
samples_mc_w = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'W*Jet*')]
samples_mc_tt = [s for s in samples_mc if fnmatch.fnmatch(s.name, 'TT*')]
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = getVars(['l1_pt', 'l2_pt', 'l2_eta', 'l2_jet_pt'])
variables += [
VariableCfg(name='l2_jet_pt',
binning=array(
[20., 30., 40., 50., 60., 70., 80., 90., 100., 150.,
200.]),
unit='GeV',
xtitle='tau jet p_{T}'),
VariableCfg(name='l2_pt',
binning=array(
[20., 25., 30., 35., 40., 50., 60., 80., 120., 200.]),
unit='GeV',
xtitle='tau p_{T}'),
]
# variables += [v for v in all_vars if 'dxy' in v.name or 'dz' in v.name]
# variables = getVars(['_norm_', 'l2_pt', 'l2_eta', 'l2_mass', 'l2_decayMode', 'mvis', 'mt', 'delta_r_l1_l2', 'l2_gen_pdgId', 'l2_mt'])
# variables = all_vars
for cut_name in cuts:
from CMGTools.H2TauTau.proto.plotter.PlotConfigs import VariableCfg
from CMGTools.H2TauTau.proto.plotter.binning import binning_svfitMass
all_vars = [
VariableCfg(name='mvis', binning=binning_svfitMass, unit='GeV', xtitle='m_{vis}'),
VariableCfg(name='mt', binning={'nbinsx':20, 'xmin':0., 'xmax':200.}, unit='GeV', xtitle='m_{T}'),
VariableCfg(name='mt_leg2', binning={'nbinsx':20, 'xmin':0., 'xmax':300.}, unit='GeV', xtitle='m_{T} #tau'),
VariableCfg(name='n_vertices', binning={'nbinsx':51, 'xmin':-0.5, 'xmax':50.5}, unit=None, xtitle='N_{vertices}'),
VariableCfg(name='n_jets', binning={'nbinsx':12, 'xmin':-0.5, 'xmax':11.5}, unit=None, xtitle='N_{jets}'),
VariableCfg(name='n_jets_20', binning={'nbinsx':12, 'xmin':-0.5, 'xmax':11.5}, unit=None, xtitle='N_{jets} (20 GeV)'),
VariableCfg(name='n_bjets', binning={'nbinsx':12, 'xmin':-0.5, 'xmax':11.5}, unit=None, xtitle='N_{b jets}'),
VariableCfg(name='met_phi', binning={'nbinsx':12, 'xmin':-3.141593, 'xmax':3.141593}, unit=None, xtitle='E_{T}^{miss} #Phi'),
VariableCfg(name='met_pt', binning={'nbinsx':12, 'xmin':0., 'xmax':200.}, unit='GeV', xtitle='E_{T}^{miss}'),
VariableCfg(name='delta_eta_l1_l2', binning={'nbinsx':12, 'xmin':0, 'xmax':4.5}, unit=None, xtitle='#Delta#eta(#tau, #mu)'),
VariableCfg(name='delta_r_l1_l2', binning={'nbinsx':12, 'xmin':0, 'xmax':4.5}, unit=None, xtitle='#Delta R(#tau, #mu)'),
VariableCfg(name='vbf_mjj', binning={'nbinsx':40, 'xmin':0, 'xmax':1000.}, unit='GeV', xtitle='m_{jj}'),
VariableCfg(name='vbf_deta', binning={'nbinsx':40, 'xmin':-7., 'xmax':7.}, unit=None, xtitle='#Delta#eta (VBF)'),
VariableCfg(name='jet1_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':400.}, unit='GeV', xtitle='jet 1 p_{T}'),
VariableCfg(name='jet2_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':400.}, unit='GeV', xtitle='jet 2 p_{T}'),
VariableCfg(name='jet1_eta', binning={'nbinsx':40, 'xmin':-5., 'xmax':5.}, unit=None, xtitle='jet 1 #eta'),
VariableCfg(name='jet2_eta', binning={'nbinsx':40, 'xmin':-5., 'xmax':5.}, unit=None, xtitle='jet 2 #eta'),
VariableCfg(name='l1_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':400.}, unit='GeV', xtitle='muon p_{T}'),
VariableCfg(name='l2_pt', binning={'nbinsx':40, 'xmin':0., 'xmax':400.}, unit='GeV', xtitle='tau p_{T}'),
VariableCfg(name='l1_eta', binning={'nbinsx':20, 'xmin':-2.5, 'xmax':2.5}, unit=None, xtitle='muon #eta'),
VariableCfg(name='l2_eta', binning={'nbinsx':20, 'xmin':-2.5, 'xmax':2.5}, unit=None, xtitle='tau #eta'),
VariableCfg(name='l2_decayMode', binning={'nbinsx':12, 'xmin':-0.5, 'xmax':11.5}, unit=None, xtitle='tau decay mode'),
VariableCfg(name='l2_mass', binning={'nbinsx':15, 'xmin':0., 'xmax':3.}, unit='GeV', xtitle='tau mass'),
]
dict_all_vars = {}
XSection: sampleDict['WWTo1L1Nu2Q'].xsec,
SumWeights: sampleDict['WWTo1L1Nu2Q'].sumweights
},
#{Name:'ZZTo4L' , DirName:'ZZTo4L' , XSection:sampleDict['ZZTo4L'].xsec , SumWeights:sampleDict['ZZTo4L'].sumweights } ,
]
non_fakes = [
'ZTT', 'ZL', 'W_L', 'TT_L', 'T_tWch_L', 'TBar_tWch_L', 'ZZTo2L2Q_L',
'WZTo3L_L', 'WZTo2L2Q_L', 'WZTo1L3Nu_L', 'WZTo1L1Nu2Q_L', 'VVTo2L2Nu_L',
'WWTo1L1Nu2Q_L'
]
## Variables to use
variables = [
#VariableCfg(name='mvis_stdbins', binning={}, unit='GeV', xtitle='m_{vis}'),
VariableCfg(name='mvis_vs_mt', binning={}, unit='GeV', xtitle='m_{T}'),
]
## Define selections
global_selections = ["{SIGN}Iso_Medium", "{SIGN}MT40_Iso_Medium"]
backgrounds = ['VV', 'TT', 'QCD', 'W', 'ZJ']
shifts = {
'Nom': # no shift
{},
'WStat': # stat shifts
{
'W': 1.
},
'QCDStat': # stat shifts
samples_non_w_highmt_ss = copy.deepcopy(samples_non_w)
for sample in samples_non_w_highmt_os:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
for sample in samples_non_w_highmt_ss:
if sample.name != 'data_obs':
sample.scale = -1.
var_norm = VariableCfg(name='_norm_',
drawname='1.',
binning={
'nbinsx': 5,
'xmin': -1.5,
'xmax': 3.5
},
unit='',
xtitle='Normalisation')
stacknow_highmt_os = HistogramCfg(name='HighMTOS',
var=var_norm,
cfgs=samples_non_w_highmt_os,
cut=inc_cut,
lumi=int_lumi,
weight=total_weight)
stacknow_highmt_ss = HistogramCfg(name='HighMTSS',
var=var_norm,
cfgs=samples_non_w_highmt_ss,
cut=inc_cut,
cut_names = [cut for cut in cuts]
# for cut in cut_names:
# cuts[cut+'_plus'] = cuts[cut] + ' && muon_charge==1'
# cuts[cut+'_minus'] = cuts[cut] + ' && muon_charge==-1'
qcd_from_same_sign = False
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
# variables = getVars(['jet_pt'])
variables = [
VariableCfg(name='jet_pt',
binning=array([
20., 30., 40., 50., 60., 80., 100., 120., 140., 160., 180.,
200., 250., 300., 350.
]),
unit='GeV',
xtitle='jet p_{T}'),
VariableCfg(name='tau_pt',
binning=array([
20., 30., 40., 50., 60., 80., 100., 120., 140., 160., 180.,
200., 250., 300., 350.
]),
unit='GeV',
xtitle='tau p_{T}'),
VariableCfg(name='jet_eta',
binning={
'nbinsx': 25,
'xmin': -2.5,
'xmax': 2.5