wjets_ss.cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge')
stacknow_highmt_os.cut = cut.cut.replace('mt<30', 'mt>70') + '&& mt>70'
stacknow_highmt_ss.cut = cut.cut.replace(
'l1_charge != l2_charge', 'l1_charge == l2_charge').replace(
'mt<30', 'mt>70') + '&& mt>70'
wjets_incl_os.cut = cut.cut.replace('mt<30', '1.')
wjets_incl_ss.cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge').replace(
'mt<30', '1.')
wjets_highmt_os.cut = cut.cut.replace('mt<30', 'mt>70') + '&& mt>70'
plot_w_os = createHistogram(wjets_incl_os, verbose=False)
plot_w_ss = createHistogram(wjets_incl_ss, verbose=False)
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(stacknow_highmt_os,
all_stack=True,
verbose=False)
plot_highmt_ss = createHistogram(stacknow_highmt_ss,
all_stack=True,
verbose=False)
createDefaultGroups(plot_highmt_os)
createDefaultGroups(plot_highmt_ss)
weight=total_weight + ' * ((genmet_pt < 0.) - (l2_gen_match == 5))')
cfg_tight.cut = cuts[cut_name]
cfg_loose.cut = cuts[cut_name].replace(
'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5', '1.')
cfg_data_tight.cut = cuts[cut_name]
cfg_data_loose.cut = cuts[cut_name].replace(
'l2_byCombinedIsolationDeltaBetaCorrRaw3Hits<1.5', '1.')
for variable in variables:
cfg_tight.var = variable
cfg_loose.var = variable
cfg_data_tight.var = variable
cfg_data_loose.var = variable
plot_tight = createHistogram(cfg_tight, verbose=False)
plot_loose = createHistogram(cfg_loose, verbose=False)
plot_data_tight = createHistogram(cfg_data_tight,
verbose=False,
all_stack=True)
plot_data_loose = createHistogram(cfg_data_loose,
verbose=False,
all_stack=True)
for plot in [plot_tight, plot_loose, plot_data_tight, plot_data_loose]:
plot.Group('VV', ['ZZ', 'WZ', 'WW', 'T_tWch', 'TBar_tWch'])
out_dir = 'fakeplots/' + cut_name if plot is plot_tight else 'fakeplots/loose' + cut_name
# HistDrawer.draw(plot, plot_dir='fakeplots/'+cut_name)
plotDataOverMCEff(
plot_tight.GetStack().totalHist.weighted,
plot_loose.GetStack().totalHist.weighted,
if sample.name != 'Data':
# Subtract background from data
sample.scale = -1.
qcd = HistogramCfg(name='QCD', var=None, cfgs=samples_ss, cut=inc_cut, lumi=40.0)
samples.append(qcd)
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = all_vars
cfg_example = HistogramCfg(name='example', var=None, cfgs=samples, cut=inc_cut, lumi=40.0)
for cut_name in cuts:
cfg_example.cut = cuts[cut_name]
if qcd_from_same_sign and 'OS' in cut_name:
qcd.cut = cuts[cut_name].replace('l1_charge != l2_charge', 'l1_charge == l2_charge')
for variable in variables:
cfg_example.var = variable
if qcd_from_same_sign:
qcd.var = variable # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
plot = createHistogram(cfg_example, verbose=True)
plot.Group('Diboson', ['ZZ', 'WZ', 'WW'])
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)
HistDrawer.draw(plot, plot_dir='plots/'+cut_name)
' * ((l2_gen_match == 6))')
tight_cut = inc_cut + cuts[cut_name] + cut_extra
loose_cut = tight_cut.replace(
'l2_byIsolationMVArun2v1DBoldDMwLT>2.5', '1.')
loose_cut = tight_cut.replace(
'l2_byIsolationMVArun2v1DBoldDMwLT>2.5',
'l2_byIsolationMVArun2v1DBoldDMwLT>0.5')
print 'Tight cut:', tight_cut
print 'Loose cut:', loose_cut
cfg_tight.cut = tight_cut
cfg_loose.cut = loose_cut
cfg_tight.var = variable
cfg_loose.var = variable
plot_tight = createHistogram(cfg_tight, verbose=False)
plot_loose = createHistogram(cfg_loose, verbose=False)
tight_hist = plot_tight.GetStack().totalHist.weighted
loose_hist = plot_loose.GetStack().totalHist.weighted
plot_inputs.append((tight_hist, loose_hist, sample_def['name'],
sample_def['colour'], sample_def['style']))
plotMCEffs(
plot_inputs, 'mcfakeplots/tight_to_loose' + set_name + '_' +
variable.name + cut_name + '.pdf')
def qcd_estimation(B_cut,
C_cut,
D_cut,
all_samples,
int_lumi,
total_weight,
scale=1.,
verbose=True,
friend_func=None):
'''ABCD method.
A | B
-------
C | D
A is the signal region
B is where the shape is taken from
C/D gives the scale factor to be applied to the shape in B
Returns an updated list of samples that includes the QCD HistgramCfg.
'''
norm_var = dict_all_vars['_norm_']
QCD_C_region_cut = C_cut
QCD_D_region_cut = D_cut
QCD_B_region_cut = B_cut
samples_qcd_copy = copy.deepcopy(
[s for s in all_samples if s.name != 'QCD' and not s.is_signal])
samples_qcd_copy = [s for s in samples_qcd_copy if not s.is_signal]
for sample in samples_qcd_copy:
sample.scale = scale if sample.name == 'data_obs' else -scale
qcd_c_region = HistogramCfg(name='QCD_C_region',
var=norm_var,
cfgs=samples_qcd_copy,
cut=str(QCD_C_region_cut),
lumi=int_lumi,
weight=total_weight)
qcd_d_region = HistogramCfg(name='QCD_D_region',
var=norm_var,
cfgs=samples_qcd_copy,
cut=str(QCD_D_region_cut),
lumi=int_lumi,
weight=total_weight)
# samples_qcd = [qcd_c_region, qcd_d_region]
# cfg_qcd = HistogramCfg(name='QCD_aux', var=None, cfgs=samples_qcd, cut=None, lumi=int_lumi, weight=total_weight)
plot_qcd_c = createHistogram(qcd_c_region,
all_stack=True,
friend_func=friend_func)
plot_qcd_d = createHistogram(qcd_d_region,
all_stack=True,
friend_func=friend_func)
if verbose:
print 'Histogram C region'
print plot_qcd_c
print 'Histogram D region'
print plot_qcd_d
yield_c = plot_qcd_c.GetStack().totalHist.Yield()
yield_d = plot_qcd_d.GetStack().totalHist.Yield()
if yield_d == 0.:
print 'WARNING: no events left for the QCD estimation. Set to 0'
qcd_scale = 0.
else:
qcd_scale = yield_c / yield_d
if qcd_scale < 0.:
print 'WARNING: negative QCD scaling; set it to zero'
qcd_scale = 0.
verbose = True
if verbose:
print 'QCD estimation: '
print ' Yield C:', yield_c, ' yield D:', yield_d
print ' Ratio C/D', qcd_scale
qcd_b_region_hist = HistogramCfg(name='QCD',
var=None,
cfgs=samples_qcd_copy,
cut=str(QCD_B_region_cut),
lumi=int_lumi,
weight=total_weight,
total_scale=qcd_scale)
all_samples_qcd = copy.deepcopy(all_samples)
all_samples_qcd = [qcd_b_region_hist] + all_samples_qcd
return all_samples_qcd
var=None,
cfgs=samples_qcdfromss,
cut=inc_cut,
lumi=int_lumi,
weight=total_weight)
else:
cfg_example = HistogramCfg(name='example',
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')
for variable in variables:
cfg_example.var = variable
if qcd_from_same_sign:
qcd.var = variable # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
plot = createHistogram(cfg_example, verbose=True)
plot.Group('VV', ['ZZ', 'WZ', 'WW', 'T_tWch', 'TBar_tWch'])
# 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)
HistDrawer.draw(plot, plot_dir='plots/RemoveLowMET_' + cut_name)
plot.WriteDataCard(filename='datacard_mvis.root', dir='mt_' + cut_name)
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
SampleCfg(name='WJetsToLNu', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=WJetsToLNu.xSection, sumweights=WJetsToLNu.nGenEvents),
SampleCfg(name='TTJets', dir_name='TTLep_pow', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=TT_pow.xSection, sumweights=TT_pow.nGenEvents),
SampleCfg(name='T_tWch', dir_name='T_tWch', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=T_tWch.xSection, sumweights=T_tWch.nGenEvents),
SampleCfg(name='TBar_tWch', dir_name='TBar_tWch', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=TBar_tWch.xSection, sumweights=TBar_tWch.nGenEvents),
SampleCfg(name='TToLeptons_tch', dir_name='TToLeptons_tch', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=TToLeptons_tch.xSection, sumweights=TBar_tWch.nGenEvents),
SampleCfg(name='TToLeptons_sch', dir_name='TToLeptons_sch', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=TToLeptons_sch.xSection, sumweights=TToLeptons_sch.nGenEvents),
SampleCfg(name='ZZ', dir_name='ZZp8', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=ZZp8.xSection, sumweights=ZZp8.nGenEvents),
SampleCfg(name='WZ', dir_name='WZp8', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=WZp8.xSection, sumweights=WZp8.nGenEvents),
SampleCfg(name='WW', dir_name='WWTo2L2Nu', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=WWTo2L2Nu.xSection, sumweights=WWTo2L2Nu.nGenEvents),
SampleCfg(name='QCD', dir_name='QCD_Mu15', ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=QCD_Mu15.xSection),
SampleCfg(name='Data', dir_name='SingleMuon_Run2015B', ana_dir=data_dir, tree_prod_name=tree_prod_name, is_data=True),
]
# -> Can add cross sections for samples either explicitly, or from file, or from cfg
for sample in samples:
setSumWeights(sample)
# Taken from Variables.py, can get subset with e.g. getVars(['mt', 'mvis'])
variables = all_vars
cfg_example = HistogramCfg(name='example', var=None, cfgs=samples, cut=example_cut, lumi=40.0)
for variable in variables:
cfg_example.var = variable
plot = createHistogram(cfg_example)
plot.Group('Diboson', ['ZZ', 'WZ', 'WW'])
plot.Group('Single t', ['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
HistDrawer.draw(plot)
# HistDrawer.drawRatio(plot)
for var_set in var_sets:
print 'Variable set', var_set.name
cfg_signal.cut += var_set.cut_s
cfg_bg.cut += var_set.cut_b
rocs = []
sig_eff = None
bg_eff = None
for i_var, var in enumerate(var_set.vars):
print ' variable:', var
cfg_signal.var = var
cfg_bg.var = var
plot_signal = createHistogram(cfg_signal, verbose=False)
plot_bg = createHistogram(cfg_bg, verbose=False)
h_signal = plot_signal.GetStack().totalHist.weighted
h_bg = plot_bg.GetStack().totalHist.weighted
print 'Working point at cut 1.5:'
if not sig_eff and not bg_eff:
sig_eff = h_signal.Integral(
0, h_signal.FindBin(1.5)) / h_signal.Integral(
0,
h_signal.GetNbinsX() + 1)
bg_eff = h_bg.Integral(0, h_bg.FindBin(1.5)) / h_bg.Integral(
0,
h_bg.GetNbinsX() + 1)
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,
cfgs=all_samples_qcd,
cut=str(cut.cut),
lumi=int_lumi,
weight=total_weight)
all_samples_qcd[-1].var = variable
plot = createHistogram(cfg_total)
plot.Group('VV', [
'ZZTo2L2Q', 'WWTo1L1Nu2Q', 'WZTo1L3Nu', 'ZZTo4L', 'WZTo3L',
'WZTo2L2Q', 'WZTo1L1Nu2Q', 'T_tWch', 'TBar_tWch',
'TToLeptons_tch_powheg', 'TBarToLeptons_tch_powheg'
])
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('WJets', ['WJetsToLNu', 'W1JetsToLNu', 'W4JetsToLNu'])
plot.Group('Single t',
['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
HistDrawer.draw(plot,
channel='#tau_{h}#tau_{h}',
cfgs=all_samples,
cut=inc_cut,
lumi=int_lumi,
weight=total_weight)
cfg_example.cut = cut.cut
if qcd_from_same_sign and not 'SS' in cut.name:
qcd.cut = cut.cut.replace('l1_charge != l2_charge',
'l1_charge == l2_charge')
for variable in variables:
cfg_example.var = variable
if qcd_from_same_sign:
qcd.var = variable # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
plot = createHistogram(cfg_example, verbose=False)
plot.Group('VV', [
'ZZTo2L2Q', 'WWTo1L1Nu2Q', 'WZTo1L3Nu', 'ZZTo4L', 'WZTo3L',
'WZTo2L2Q', 'WZTo1L1Nu2Q', 'T_tWch', 'TBar_tWch',
'TToLeptons_tch_powheg', 'TBarToLeptons_tch_powheg'
])
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('Single t', ['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
# plot.Group('ZLL', ['Ztt_ZL', 'Ztt_ZJ'], style=plot.Hist('Ztt_ZL').style)
HistDrawer.draw(plot, plot_dir='plots/' + cut.name)
if variable.name == 'mvis':
plot.WriteDataCard(filename='datacard_mvis.root',
dir='mt_' + cut.name,
def estimateQCDWMSSM(hist_dict,
cut,
mt_cut,
high_mt_cut='mt>70',
friend_func=None,
r_qcd_os_ss=1.17):
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, high_mt_cut) + '&& ' + high_mt_cut
hist_dict['stacknow_highmt_ss'].cut = cut.cut.replace(
'l1_charge != l2_charge', 'l1_charge == l2_charge').replace(
mt_cut, high_mt_cut) + '&& ' + high_mt_cut
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, high_mt_cut) + '&& ' + high_mt_cut
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)
print plot_w_highmt_os
print plot_highmt_os
print 'Having used cut', hist_dict['stacknow_highmt_os'].cut
print plot_highmt_ss
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 w_sf <= 0.25:
print 'Warning: W+jets scale factor very small or negative', w_sf, 'setting to 1.'
w_sf = 1.
if w_sf > 2.5 and yield_w_highmt_os and yield_w_highmt_os < 10.:
print 'Warning: W+jets scale factor very large', w_sf, ' but low stats, setting to 1.'
w_sf = 1.
# if cut.name in ['vbf_highmva0', '0jet_highmva0']:
# print '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
