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
cut=inc_cut,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=total_weight)
samples_mssm_method = samples_non_w + [wjets, qcd]
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = all_vars
# variables = getVars(['_norm_', 'mt', 'mvis', 'l1_pt', 'l2_pt', 'l1_eta', 'l2_eta', 'n_vertices', 'n_jets', 'n_bjets'])
# variables = getVars(['_norm_', 'mt', 'mvis', 'n_vertices', 'met_pt', 'l1_pt', 'l2_pt'])
# variables = getVars(['delta_phi_l2_met', 'met_pt', 'met_phi'])
variables = getVars(['_norm_'])
# variables = getVars(['mvis'])
variables = [
VariableCfg(name='mvis',
binning={
'nbinsx': 35,
'xmin': 0.,
'xmax': 350
},
unit='GeV',
xtitle='m_{vis}')
]
variables = [
VariableCfg(name='svfit_transverse_mass',
binning=binning_mssm,
unit='GeV',
from CMGTools.H2TauTau.proto.plotter.Samples import createSampleLists
from CMGTools.H2TauTau.proto.plotter.Variables import getVars
def ensure_dir(f):
print 'ensure dir', f
d = os.path.dirname(f)
if not os.path.exists(d):
ensure_dir(d)
if not os.path.exists(f):
os.mkdir(f)
analysis_dir = '/data1/steggema/mt/051016/MuTauMC/'
train_vars = getVars([
'mt', 'l2_mt', 'n_jets', 'met_pt', 'pthiggs', 'vbf_mjj', 'vbf_deta', 'vbf_n_central', 'l2_pt', 'l1_pt','mvis', 'l1_eta', 'l2_eta', 'delta_phi_l1_l2', 'delta_eta_l1_l2', 'pt_l1l2', 'delta_phi_j1_met', 'pzeta_disc', 'jet1_pt', 'jet1_eta'
])
split_var = train_vars[10]
# clf0 = joblib.load('/afs/cern.ch/work/s/steggema/GradientBoostingClassifier_clf_0.pkl')
# clf1 = joblib.load('/afs/cern.ch/work/s/steggema/GradientBoostingClassifier_clf_1.pkl')
clf0_0jet = joblib.load('GradientBoostingClassifier_clf_0_0jet.pkl')
clf1_0jet = joblib.load('GradientBoostingClassifier_clf_1_0jet.pkl')
clf0_1jet = joblib.load('GradientBoostingClassifier_clf_0_1jet.pkl')
clf1_1jet = joblib.load('GradientBoostingClassifier_clf_1_1jet.pkl')
clf0_vbf = joblib.load('GradientBoostingClassifier_clf_0_vbf.pkl')
clf1_vbf = joblib.load('GradientBoostingClassifier_clf_1_vbf.pkl')
out_dict = {}
# cut_names = [cut for cut in cuts]
# for cut in cut_names:
# # cuts[cut+'invmu'] = cuts[cut].replace('l1_reliso05<0.1', 'l1_reliso05>0.1')
# cuts[cut+'invtau'] = cuts[cut].replace('l2_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5', 'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits>1.5')
qcd_from_same_sign = False
# -> Command line
analysis_dir = '/afs/cern.ch/user/s/steggema/work/public/mt/NewProd'
tree_prod_name = 'H2TauTauTreeProducerTauMu'
data_dir = analysis_dir
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = getVars([
'l1_pt', 'l2_pt', 'l1_gen_pdgId', 'l2_gen_pdgId', 'l1_reliso05_04',
'l1_reliso05', 'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits'
])
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:
cfg_tight = HistogramCfg(name='tight',
var=None,
cfgs=samples,
cut=inc_cut,
lumi=int_lumi,
def ensure_dir(f):
print 'ensure dir', f
d = os.path.dirname(f)
if not os.path.exists(d):
ensure_dir(d)
if not os.path.exists(f):
os.mkdir(f)
analysis_dir = '/data1/steggema/tt/230816/DiTauNewMC'
train_vars = getVars([
# 'mvis', 'mt2', 'l1_pt', 'l2_pt', 'delta_phi_l1_l2', 'delta_eta_l1_l2', 'met_pt', 'mt_total', 'mt_sum', 'pzeta_vis', 'pzeta_met', 'l2_mt', 'mt', 'pzeta_disc', 'pthiggs', 'jet1_pt', 'n_jets', 'pt_l1l2'
'mt2',
'delta_phi_l1_l2',
'delta_eta_l1_l2',
'mt',
'l2_mt',
'pzeta_disc',
'pt_l1l2'
])
split_var = train_vars[0]
clf0 = joblib.load('GradientBoostingClassifier_clf_stau_mtsum200_0.pkl')
clf1 = joblib.load('GradientBoostingClassifier_clf_stau_mtsum200_1.pkl')
out_dict = {}
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tt',
mode='susy',
# -> Command line
analysis_dir = '/data1/steggema/mt/051016/MuTauMC/'
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*')]
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',
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
# cuts['OS_PU_mZ_eq1jet_tau1_noptouter'] = (
# inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && abs(l2_eta) < 2.1 && mvis>70 && mvis<100 && n_jets==1 && tau1_decayModeFinding>0. && tau1_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5',
# inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && abs(l2_eta) < 2.1 && mvis>70 && mvis<100 && n_jets==1 && tau1_decayModeFinding>0.')
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,
# Subtract background from data
sample.scale = -scale
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', '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}')
# ]
for cut_name in cuts:
if qcd_from_same_sign and not 'SS' in cut_name:
cfg_example = HistogramCfg(name='example',
var=None,
cfgs=samples_qcdfromss,
cut=inc_cut,
lumi=int_lumi,
# 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 ))
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = all_vars
variables = getVars(['mvis', 'svfit_mass']) #, 'l1_pt', 'l2_pt'])
for cut in cuts:
all_samples_qcd = qcd_estimation(
cut.cut & iso_sideband_cut & charge_cut,
cut.cut & iso_cut & (~charge_cut),
cut.cut & iso_sideband_cut & (~charge_cut), all_samples, int_lumi,
total_weight)
# now include charge and isolation too
cut = myCut(cut.name, cut.cut & iso_cut & charge_cut)
for variable in variables:
cfg_total = HistogramCfg(name=cut.name,
var=variable,
scale = 1.06
for sample in samples_ss:
sample.scale = scale
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -scale
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)
# # cuts[cut+'invmu'] = cuts[cut].replace('l1_reliso05<0.1', 'l1_reliso05>0.1')
# cuts[cut+'invtau'] = cuts[cut].replace('tau_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5', 'tau_byCombinedIsolationDeltaBetaCorrRaw3Hits>1.5')
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))')
# )
cuts = cuts.copy()
for cut in cuts:
print cut
print cuts[cut]
analysis_dir = '/data1/steggema/em/06092016/MuEleRealMCPlusTau'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir, channel='em', ztt_cut='(l1_gen_match>2 && l2_gen_match>3)', zl_cut='l2_gen_match==99',zj_cut='l2_gen_match==99', data2016G=data2016G)
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = emu_vars
variables = getVars(['tau1_eta', 'n_jets']) #'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':10, 'xmin':-2.5, 'xmax':2.5}, unit='', xtitle='jet #eta'),
VariableCfg(name='HT_jets', binning=array([0., 100., 200., 300., 500., 1000.]), unit='GeV', xtitle='#Sigma jet p_{T}')
]
for cut_name in cuts:
cfg_tight = HistogramCfg(name='tight', var=None, cfgs=samples, cut=inc_cut, lumi=lumi, weight=total_weight+'*( (abs(tau1_gen_pdgId)!=15) - (abs(tau1_gen_pdgId)==15))')
cfg_loose = HistogramCfg(name='loose', var=None, cfgs=samples, cut=inc_cut, lumi=lumi, weight=total_weight+'*( (abs(tau1_gen_pdgId)!=15) - (abs(tau1_gen_pdgId)==15))')
cfg_tight_data = HistogramCfg(name='tight_data', var=None, cfgs=samples, cut=inc_cut, lumi=lumi, weight=total_weight+'*( (is_data>0.5) - (abs(tau1_gen_pdgId)==15))')
cfg_loose_data = HistogramCfg(name='loose_data', var=None, cfgs=samples, cut=inc_cut, lumi=lumi, weight=total_weight+'*( (is_data>0.5) - (abs(tau1_gen_pdgId)==15))')
cfg_tight.cut = cuts[cut_name][1]
# for pzeta_cut in pzeta_disc_cuts:
# cut_name = 'susy_jan_{c1}_{c2}_{c3}'.format(c1=met_sig_cut, c2=sum_mt_cut, c3=pzeta_cut)
# cut = 'met_pt/sqrt(met_cov00 + met_cov11) > {met_sig_cut} && mvis>100 && mt + mt_leg2 > {sum_mt_cut} && n_bjets==0 && pzeta_disc < {pzeta_disc_cut}'.format(met_sig_cut=met_sig_cut, sum_mt_cut=sum_mt_cut, pzeta_disc_cut=pzeta_cut)
# cuts.append(myCut(cut_name, inc_cut & cut))
# cuts.append(myCut('susy_jan_SS', inc_cut & Cut('met_pt/sqrt(met_cov00 + met_cov11) > 1. && mvis>100 && mt + mt_leg2 > 150. && n_bjets==0 && pzeta_disc < -40.')))
# cuts.append(myCut('1jet' , jet1_cut))
# cuts.append(myCut('vbf' , vbf_cut ))
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = tautau_vars
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='',
qcd_from_same_sign = False
# -> Command line
analysis_dir = '/data1/steggema/mt/051016/MuTauMC/'
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}'),
]