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
# 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=lumi, weight=total_weight)
else:
cfg_example = HistogramCfg(name='example', var=None, cfgs=samples, cut=inc_cut, lumi=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')
cfg_example.vars = variables
if qcd_from_same_sign:
qcd.vars = variables # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
plots = createHistograms(cfg_example, verbose=True)
for variable in variables:
plot = plots[variable.name]
plot.Group('VV', ['WWTo1L1Nu2Q', 'WZTo1L1Nu2Q', 'WZTo1L3Nu', 'WZTo2L2Q', 'VVTo2L2Nu', 'ZZTo2L2Q', 'ZZTo4L'])
plot.Group('Single t', ['T_tWch', 'TBar_tWch', 'TBarToLeptons_tch_powheg', 'TToLeptons_tch_powheg'])
plot.Group('ZLL', ['ZL', 'ZJ'], style=plot.Hist('ZL').style)
plot.Group('W', ['W1Jets', 'W2Jets', 'W3Jets', 'W4Jets'])
plot.Group('Electroweak', ['W', 'VV', 'Single t'])
base_dir = 'plotsG/' if data2016G else 'plots/'
HistDrawer.draw(plot, plot_dir=base_dir+cut_name, channel='e#mu')
def makePlots(variables,
cuts,
total_weight,
sample_dict,
hist_dict,
qcd_from_same_sign,
w_qcd_mssm_method,
mt_cut,
friend_func,
dc_postfix,
make_plots=True):
for cut in cuts:
if qcd_from_same_sign and not 'SS' in cut.name and not w_qcd_mssm_method:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['samples_qcdfromss'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
elif w_qcd_mssm_method:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['samples_mssm_method'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
hist_dict[
'wjets'].cut = cut.cut # since wjets is a sub-HistogramCfg
else:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['all_samples'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
if qcd_from_same_sign and not 'SS' in cut.name:
hist_dict['qcd'].cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge')
if w_qcd_mssm_method:
hist_dict['stacknow_highmt_os'].name = 'HighMTOS' + cut.name
hist_dict['stacknow_highmt_ss'].name = 'HighMTSS' + cut.name
hist_dict['wjets_incl_os'].name = 'WInclOS' + cut.name
hist_dict['wjets_incl_ss'].name = 'WInclSS' + cut.name
hist_dict['wjets_highmt_os'].name = 'WHighMTOS' + cut.name
hist_dict['wjets_ss'].name = 'WJetsSS' + cut.name
hist_dict['qcd'].cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge')
hist_dict['wjets_ss'].cut = cut.cut.replace(
'l1_charge != l2_charge', 'l1_charge == l2_charge')
hist_dict['stacknow_highmt_os'].cut = cut.cut.replace(
mt_cut, 'mt>70') + '&& mt>70'
hist_dict['stacknow_highmt_ss'].cut = cut.cut.replace(
'l1_charge != l2_charge', 'l1_charge == l2_charge').replace(
mt_cut, 'mt>70') + '&& mt>70'
hist_dict['wjets_incl_os'].cut = cut.cut.replace(mt_cut, '1.')
hist_dict['wjets_incl_ss'].cut = cut.cut.replace(
'l1_charge != l2_charge',
'l1_charge == l2_charge').replace(mt_cut, '1.')
hist_dict['wjets_highmt_os'].cut = cut.cut.replace(
mt_cut, 'mt>70') + '&& mt>70'
plot_w_os = createHistogram(hist_dict['wjets_incl_os'],
verbose=False,
friend_func=friend_func)
plot_w_ss = createHistogram(hist_dict['wjets_incl_ss'],
verbose=False,
friend_func=friend_func)
r_w_os_ss = plot_w_os.GetStack().totalHist.Yield(
) / plot_w_ss.GetStack().totalHist.Yield()
print 'Inclusive W OS/SS ratio:', r_w_os_ss
plot_highmt_os = createHistogram(hist_dict['stacknow_highmt_os'],
all_stack=True,
verbose=False,
friend_func=friend_func)
plot_highmt_ss = createHistogram(hist_dict['stacknow_highmt_ss'],
all_stack=True,
verbose=False,
friend_func=friend_func)
createDefaultGroups(plot_highmt_os)
createDefaultGroups(plot_highmt_ss)
yield_highmt_os = plot_highmt_os.GetStack().totalHist.Yield()
yield_highmt_ss = plot_highmt_ss.GetStack().totalHist.Yield()
plot_w_highmt_os = createHistogram(hist_dict['wjets_highmt_os'],
verbose=False,
friend_func=friend_func)
yield_w_highmt_os = plot_w_highmt_os.GetStack().totalHist.Yield()
if r_w_os_ss < r_qcd_os_ss:
print 'WARNING, OS/SS ratio larger for QCD than for W+jets!', r_w_os_ss, r_qcd_os_ss
yield_estimation = r_w_os_ss * (yield_highmt_os -
r_qcd_os_ss * yield_highmt_ss) / (
r_w_os_ss - r_qcd_os_ss)
print 'High MT W+jets estimated yield', yield_estimation
print 'High MT W+jets MC yield', yield_w_highmt_os
w_sf = 0.
if yield_w_highmt_os:
w_sf = yield_estimation / yield_w_highmt_os
else:
print 'Warning: no MC events in high MT W+jets'
print 'W+jets scale factor:', w_sf, '\n'
if cut.name == 'vbf_highmva0':
print 'VBF very tight category, fixing W+jets SF to 1'
w_sf = 1.
hist_dict['wjets'].total_scale = w_sf
hist_dict['wjets_ss'].total_scale = -w_sf
cfg_main.vars = variables
if qcd_from_same_sign:
hist_dict[
'qcd'].vars = variables # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
if w_qcd_mssm_method:
hist_dict[
'wjets'].vars = variables # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
hist_dict['qcd'].vars = variables
hist_dict['wjets_ss'].vars = variables
for variable in variables:
if variable.name in [
'svfit_mass', 'svfit_transverse_mass', 'mvis'
] and 'mssm' in mode:
if cut.name in ['inclusive', 'nobtag']:
variable.binning = binning_mssm
elif cut.name in ['btag']:
variable.binning = binning_mssm_btag
plots = createHistograms(cfg_main,
verbose=False,
friend_func=friend_func)
for variable in variables:
# for plot in plots.itervalues():
plot = plots[variable.name]
createDefaultGroups(plot)
if not w_qcd_mssm_method:
plot.Group('W', ['W', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets'])
# plot.Group('Electroweak', ['Diboson', 'W'])
# plot.Group('Single t', ['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
# plot.Group('ZLL', ['Ztt_ZL', 'Ztt_ZJ'], style=plot.Hist('Ztt_ZL').style)
if make_plots:
HistDrawer.draw(plot, plot_dir='plots/' + cut.name)
if variable.name in [
'mvis', 'svfit_transverse_mass', 'svfit_mass', 'mva',
'mva2div1', 'mva1', 'mva2', 'l2_nc_ratio'
]:
plot.WriteDataCard(
filename='datacard_{mode}_{var}.root'.format(
mode=mode, var=variable.name),
dir='mt_' + cut.name,
mode='UPDATE',
postfix=dc_postfix) #mt = mu-tau
def makePlots(variables,
cuts,
total_weight,
sample_dict,
hist_dict,
qcd_from_same_sign,
w_qcd_mssm_method,
mt_cut,
friend_func,
dc_postfix,
make_plots=True,
create_trees=False):
ams_dict = {}
sample_names = set()
for cut in cuts:
if qcd_from_same_sign and not 'SS' in cut.name and not w_qcd_mssm_method:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['samples_qcdfromss'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
elif w_qcd_mssm_method:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['samples_mssm_method'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
hist_dict[
'wjets'].cut = cut.cut # since wjets is a sub-HistogramCfg
else:
cfg_main = HistogramCfg(name=cut.name,
var=None,
cfgs=sample_dict['all_samples'],
cut=cut.cut,
lumi=int_lumi,
weight=total_weight)
if qcd_from_same_sign and not 'SS' in cut.name:
hist_dict['qcd'].cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge')
if w_qcd_mssm_method:
estimateQCDWMSSM(hist_dict,
cut,
mt_cut,
friend_func=friend_func,
r_qcd_os_ss=r_qcd_os_ss)
cfg_main.vars = variables
if qcd_from_same_sign:
hist_dict[
'qcd'].vars = variables # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
if w_qcd_mssm_method:
hist_dict[
'wjets'].vars = variables # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
hist_dict['qcd'].vars = variables
hist_dict['wjets_ss'].vars = variables
for variable in variables:
if variable.name in [
'svfit_mass', 'svfit_transverse_mass', 'mvis'
] and 'mssm' in mode:
if cut.name in ['inclusive', 'nobtag']:
variable.binning = binning_mssm
elif cut.name in ['btag']:
variable.binning = binning_mssm_btag
if create_trees:
createTrees(cfg_main, '/data1/steggema/mt/MVATrees', verbose=True)
continue
plots = createHistograms(cfg_main,
verbose=False,
friend_func=friend_func)
for variable in variables:
# for plot in plots.itervalues():
plot = plots[variable.name]
createDefaultGroups(plot)
if not w_qcd_mssm_method:
plot.Group('W', ['W', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets'])
plot.Group('Electroweak', ['VV', 'W'])
# plot.Group('Single t', ['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
plot.Group('ZTT', ['ZTT', 'ZJ'], style=plot.Hist('ZTT').style)
if make_plots:
HistDrawer.draw(plot, plot_dir='plots/' + cut.name)
if variable.name in [
'mvis', 'svfit_transverse_mass', 'svfit_mass', 'mva',
'mva2div1', 'mva1', 'mva2', 'l2_nc_ratio'
]:
plot.WriteDataCard(
filename='datacard_{mode}_{var}.root'.format(
mode=mode, var=variable.name),
dir='mt_' + cut.name,
mode='UPDATE',
postfix=dc_postfix) #mt = mu-tau
for signal_hist in plot.SignalHists():
sample_names.add(signal_hist.name)
ams_dict[variable.name + '__' + cut.name + '__' +
signal_hist.name + '_'] = ams_hists(
signal_hist.weighted,
plot.BGHist().weighted)
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
name='loose_data',
var=None,
cfgs=samples,
cut=inc_cut,
lumi=int_lumi,
weight=total_weight + '*( (is_data>0.5) - (abs(tau1_gen_pdgId)==15))')
cfg_tight.cut = cuts[cut_name][0]
cfg_loose.cut = cuts[cut_name][1]
cfg_tight_data.cut = cuts[cut_name][0]
cfg_loose_data.cut = cuts[cut_name][1]
for cfg in [cfg_tight, cfg_loose, cfg_tight_data, cfg_loose_data]:
cfg.vars = variables
plots_tight = createHistograms(cfg_tight, verbose=True)
plots_loose = createHistograms(cfg_loose, verbose=True)
plots_tight_data = createHistograms(cfg_tight_data,
verbose=True,
all_stack=True)
plots_loose_data = createHistograms(cfg_loose_data,
verbose=True,
all_stack=True)
for variable in variables:
plot_tight = plots_tight[variable.name]
plot_loose = plots_loose[variable.name]
plot_tight_data = plots_tight_data[variable.name]
plot_loose_data = plots_loose_data[variable.name]
for plot in [plot_tight, plot_loose, plot_tight_data, plot_loose_data]:
plot.Group('VV', [
'WWTo1L1Nu2Q', 'WZTo1L1Nu2Q', 'WZTo1L3Nu', 'WZTo2L2Q',
# cuts.append(myCut('bdt0p9' , 'bdt_score > 0.9'))
cuts.append(myCut('mauro_bdt', '1.'))
variables = generic_vars
sample_names = set()
for cut in cuts:
cfg_total = HistogramCfg(name=cut.name,
vars=variables,
cfgs=all_samples,
cut=str(cut.cut),
lumi=int_lumi,
weight=total_weight)
plots = createHistograms(
cfg_total,
verbose=True,
)
for variable in variables:
if variable.name == 'cand_refit_tau_mass' or variable.name == 'cand_tau_mass':
blindxmin = 1.777 - 0.040
blindxmax = 1.777 + 0.040
else:
blindxmin = None
blindxmax = None
plot = plots[variable.name]
# override HTT default style
plot.histPref = histPref
plot._ApplyPrefs()
HistDrawer.draw(plot,
channel='',
cut.cut & iso_cut & (charge_cut if not isSS else ~charge_cut))
# for variable in variables:
cfg_total = HistogramCfg(name=cut.name,
vars=variables,
cfgs=all_samples_qcd,
cut=str(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'])
