pdb.set_trace()
for variable in variables:
cfg_main.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
if w_qcd_mssm_method:
wjets.var = variable # Can put into function but we will not want it by default if we take normalisations from e.g. high MT
qcd.var = variable
wjets_ss.var = variable
if variable.name in ['svfit_mass', 'svfit_transverse_mass']:
if cut.name in ['inclusive', 'nobtag']:
variable.binning = binning_mssm
elif cut.name in ['btag']:
variable.binning = binning_mssm_btag
plot = createHistogram(cfg_main, verbose=False)
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)
HistDrawer.draw(plot, plot_dir='plots/' + cut.name)
if variable.name in ['mvis', 'svfit_transverse_mass', 'svfit_mass']:
plot.WriteDataCard(filename='datacard_{}.root'.format(
variable.name),
dir='mt_' + cut.name,
mode='UPDATE') #mt = mu-tau
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)
# 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 plotDataOverMCEff(hist_mc_tight,
hist_mc_loose,
hist_data_tight,
hist_data_loose,
plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + (
g.GetErrorYlow(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt(
(g_data.GetErrorYhigh(i) / g_data_vals[i])**2 +
(g.GetErrorYhigh(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() /
xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(
g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
# maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2)
g.GetYaxis().SetRangeUser(0.001, 0.2)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.51, 1.49)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
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
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
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 'OS' 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', ['ZZ', 'WZ', 'WW', 'T_tWch', 'TBar_tWch'])
plot.Group('Single t',
['T_tWch', 'TBar_tWch', 'TToLeptons_sch', 'TToLeptons_tch'])
plot.Group('ZLL', ['ZL', 'ZJ'], style=plot.Hist('ZL').style)
plot.Group('Electroweak', ['W', 'VV'])
HistDrawer.draw(plot, plot_dir='plots/' + cut_name, channel='#mu#mu')
# plot.WriteDataCard(filename='datacard_mm.root', dir='mm_' + cut_name)
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,
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
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='',
plot_dir='plot_%s' % cut.name,
blindxmin=blindxmin,
blindxmax=blindxmax)
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)
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}',
plot_dir='plot_%s' % cut.name)
# HistDrawer.drawRatio(plot, channel='#tau_{h}#tau_{h}')
if variable.name == 'mvis':
plot.WriteDataCard(filename='plot_%s/htt_tt.inputs-sm-13TeV.root' %
cut.name,
dir='tt_' + cut.name,
mode='UPDATE')
if variable.name == 'svfit_mass':
plot.WriteDataCard(
filename='plot_%s/htt_tt.inputs-sm-13TeV_svFit.root' %
cut.name,
dir='tt_' + cut.name,
mode='UPDATE')
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf', mc_leg='MC', obs_leg='Observed', ratio_leg='Obs/MC'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Misidentification rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i)/g_data_vals[i])**2 + (g.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((g_data.GetErrorYhigh(i)/g_data_vals[i])**2 + (g.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
maxy = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), mc_leg, 'lep')
legend.AddEntry(g_data.GetName(), obs_leg, 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle(ratio_leg)
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
def plotMCEffs(input_list, plot_name='fakerate.pdf'):
'''input_list expects tuples with structure:
(hist tight, hist loose, legend line, colour)
'''
graphs = []
for (hist_tight, hist_loose, legend, colour, style) in input_list:
g = TGraphAsymmErrors(hist_tight)
g.Divide(hist_tight, hist_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(colour)
g.SetLineStyle(style)
g.SetMarkerColor(colour)
g.SetMarkerStyle(19+style)
g.legend = legend
graphs.append(g)
ratio_graphs = []
base_graph = graphs[0]
for graph in graphs[1:]:
g_vals = base_graph.GetY()
g_data_vals = graph.GetY()
g_ratio = graph.Clone('ratio')
for i in xrange(graph.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, base_graph.GetX()[i], ratio)
rel_y_low = math.sqrt((graph.GetErrorYlow(i)/g_data_vals[i])**2 + (base_graph.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((graph.GetErrorYhigh(i)/g_data_vals[i])**2 + (base_graph.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
ratio_graphs.append(g_ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
for g_ratio in ratio_graphs:
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_ratio.GetXaxis().SetTitle(base_graph.GetXaxis().GetTitle())
for graph in graphs:
graph.GetXaxis().SetLabelColor(0)
graph.GetXaxis().SetLabelSize(0)
maxy = 1.3 * max([gr.GetMaximum() for gr in graphs] + [1./1.25])
for g in graphs:
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
base_graph.Draw('AP')
for graph in graphs[1:]:
graph.Draw('P')
legend = TLegend(0.63, 0.63, 0.93, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend2 = TLegend(0.78, 0.63, 0.93, 0.91)
legend2.SetFillColor(0)
legend2.SetFillStyle(0)
legend2.SetLineColor(0)
legend2.SetLineWidth(0)
for graph in graphs:
if 'opp' in graph.legend:
legend2.AddEntry(graph.GetName(), graph.legend, 'lep')
else:
legend.AddEntry(graph.GetName(), graph.legend, 'lep')
legend.Draw()
legend2.Draw()
padr.cd()
for g_ratio in ratio_graphs:
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle('Ratio to '+base_graph.legend)
g_ratio.Draw('AP' if g_ratio == ratio_graphs[0] else 'P')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
# g_data.Write()
cv.Write()
f.Close()
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_dict[variable.name + '__' + cut.name + '__' +
signal_hist.name + '_'] = ams_hists(
signal_hist.weighted,
plot.BGHist().weighted)
continue
HistDrawer.draw(plot,
channel='#tau_{h}#tau_{h}',
plot_dir='plot_%s' %
cut.name) #, blindxmin=30., blindxmax=50.1)
# HistDrawer.drawRatio(plot, channel='#tau_{h}#tau_{h}')
# if variable.name == 'mvis':
# plot.WriteDataCard(filename='plot_%s/htt_tt.inputs-sm-13TeV.root' %cut.name, dir='tt_' + cut.name, mode='UPDATE')
if variable.name == 'svfit_mass':
plot.WriteDataCard(
filename='plot_%s/htt_tt.inputs-sm-13TeV_svFit.root' %
cut.name,
dir='tt_' + cut.name,
mode='UPDATE')
if optimisation:
print '\nOptimisation results:'
all_vals = ams_dict.items()
['ZTT', 'ZTT1Jets', 'ZTT2Jets', 'ZTT3Jets', 'ZTT4Jets'])
plot.Group('ZJ',
['ZJ', 'ZJ1Jets', 'ZJ2Jets', 'ZJ3Jets', 'ZJ4Jets'])
plot.Group('ZL',
['ZL', 'ZL1Jets', 'ZL2Jets', 'ZL3Jets', 'ZL4Jets'])
plot.Group('W', [
'W', 'WJetsToLNu_HT100to200', 'WJetsToLNu_HT200to400',
'WJetsToLNu_HT400to600', 'WJetsToLNu_HT600toInf'
])
# out_dir = 'fakeplots/'+cut_name if plot is plot_tight else 'fakeplots/loose'+cut_name
# HistDrawer.draw(plot, plot_dir='fakeplots/'+cut_name)
HistDrawer.draw(plot_stack_tight,
plot_dir='fakeplots/stack_' + cut_name +
signal_cut_name + '_tight')
HistDrawer.draw(plot_stack_loose,
plot_dir='fakeplots/stack_' + cut_name +
signal_cut_name + '_loose')
if only_stack:
continue
print '#### MC only tight'
print plot_tight
print '#### MC only loose'
print plot_loose
print '#### Data-MC only tight'
print plot_data_tight
print '#### Data-MC only loose'
