double_sb = Plotter.linearize_hist(double_sb_histo)
sig_histo = Plotter.linearize_hist(sig_group[:, :, jmult, 'btagPass', 'Tight'].integrate('jmult').integrate('lepcat').integrate('btag'))
## plot systematic variations after qcd estimation is made
#set_trace()
for sys in systs_to_run:
if sys not in histo.axis('sys')._sorted:
print('\n\n Systematic %s not available, skipping\n\n' % sys)
continue
print('QCD est:', jmult, sys, hname)
#set_trace()
shape_reg = 'BTAG'
for norm in ['Sideband']:
#for norm in ['ABCD', 'Sideband']:
qcd_est_histo = Plotter.QCD_Est(sig_reg=sig_histo, iso_sb=iso_sb, btag_sb=btag_sb, double_sb=double_sb, norm_type=norm, shape_region=shape_reg, norm_region=shape_reg if norm=='Sideband' else None, sys=sys)
if sys == 'nosys':
qcd_name = '%s%s_Norm' % (shape_reg, norm) if norm == 'Sideband' else '%s_Norm' % norm
qcd_dir = os.path.join(outdir, args.lepton, jmult, 'QCD_Est', sys_to_name[sys], qcd_name)
if not os.path.isdir(qcd_dir):
os.makedirs(qcd_dir)
fig, (ax, rax) = plt.subplots(2, 1, gridspec_kw={"height_ratios": (3, 1)}, sharex=True)
fig.subplots_adjust(hspace=.07)
if hname == 'Jets_njets':
print('QCD est:', jmult, sys)
yields_txt, yields_json = Plotter.get_samples_yield_and_frac(qcd_est_histo, data_lumi_year['%ss' % args.lepton]/1000., sys=sys)
frac_name = '%s_yields_and_fracs_QCD_Est_%s' % ('_'.join([sys, jmult, args.lepton]), qcd_name)
plt_tools.print_table(yields_txt, filename='%s/%s.txt' % (qcd_dir, frac_name), print_output=True)
print('%s/%s.txt written' % (qcd_dir, frac_name))
fig.subplots_adjust(hspace=.07)
#set_trace()
iso_sb = histo[:, jmult, 'btagPass',
'Loose'].integrate('jmult').integrate(
'lepcat').integrate('btag')
btag_sb = histo[:, jmult, 'btagFail',
'Tight'].integrate('jmult').integrate(
'lepcat').integrate('btag')
double_sb = histo[:, jmult, 'btagFail',
'Loose'].integrate(
'jmult').integrate(
'lepcat').integrate('btag')
hslice = Plotter.QCD_Est(sig_reg=hslice,
iso_sb=iso_sb,
btag_sb=btag_sb,
double_sb=double_sb,
norm_type='ABCD',
shape_region='BTAG')
if withData:
ax, rax = Plotter.plot_stack1d(ax,
rax,
hslice,
xlabel=xtitle,
xlimits=x_lims,
**mc_opts)
else:
ax = Plotter.plot_mc1d(ax,
hslice,
xlabel=xtitle,
xlimits=x_lims,
def get_bkg_templates(tmp_rname):
'''
Function that writes linearized mtt vs costheta distributions to root file.
'''
## variables that only need to be defined/evaluated once
hdict = plt_tools.add_coffea_files(bkg_fnames) if len(bkg_fnames) > 1 else load(bkg_fnames[0])
## get data lumi and scale MC by lumi
data_lumi_year = prettyjson.loads(open('%s/inputs/lumis_data.json' % proj_dir).read())[args.year]
# get correct hist and rebin
hname_to_use = 'mtt_vs_tlep_ctstar_abs'
if hname_to_use not in hdict.keys():
raise ValueError("%s not found in file" % hname_to_use)
xrebinning, yrebinning = linearize_binning
histo = hdict[hname_to_use] # process, sys, jmult, leptype, btag, lepcat
xaxis_name = histo.dense_axes()[0].name
yaxis_name = histo.dense_axes()[1].name
## rebin x axis
if isinstance(xrebinning, np.ndarray):
new_xbins = hist.Bin(xaxis_name, xaxis_name, xrebinning)
elif isinstance(xrebinning, float) or isinstance(xrebinning, int):
new_xbins = xrebinning
histo = histo.rebin(xaxis_name, new_xbins)
## rebin y axis
if isinstance(yrebinning, np.ndarray):
new_ybins = hist.Bin(yaxis_name, yaxis_name, yrebinning)
elif isinstance(yrebinning, float) or isinstance(yrebinning, int):
new_ybins = yrebinning
rebin_histo = histo.rebin(yaxis_name, new_ybins)
nbins = (len(xrebinning)-1)*(len(yrebinning)-1)
## scale ttJets events, split by reconstruction type, by normal ttJets lumi correction
ttJets_permcats = ['*right', '*matchable', '*unmatchable', '*other']
names = [dataset for dataset in sorted(set([key[0] for key in hdict[hname_to_use].values().keys()]))] # get dataset names in hists
ttJets_cats = [name for name in names if any([fnmatch.fnmatch(name, cat) for cat in ttJets_permcats])] # gets ttJets(_PS)_other, ...
# use ttJets events that don't have PS weights for dedicated sys samples in 2016
if bkg_ttJets_fname is not None:
ttJets_hdict = load(bkg_ttJets_fname)
ttJets_histo = ttJets_hdict[hname_to_use] # process, sys, jmult, leptype, btag, lepcat
## rebin x axis
ttJets_histo = ttJets_histo.rebin(xaxis_name, new_xbins)
## rebin y axis
ttJets_histo = ttJets_histo.rebin(yaxis_name, new_ybins)
only_ttJets_names = [dataset for dataset in sorted(set([key[0] for key in ttJets_hdict[hname_to_use].values().keys()]))] # get dataset names in hists
only_ttJets_cats = [name for name in only_ttJets_names if any([fnmatch.fnmatch(name, cat) for cat in ttJets_permcats])] # gets ttJets(_PS)_other, ...
## make groups based on process
process = hist.Cat("process", "Process", sorting='placement')
process_cat = "dataset"
# need to save coffea hist objects to file so they can be opened by uproot in the proper format
upfout = uproot.recreate(tmp_rname, compression=uproot.ZLIB(4)) if os.path.isfile(tmp_rname) else uproot.create(tmp_rname)
if '3Jets' in njets_to_run:
histo_dict_3j = processor.dict_accumulator({'Muon' : {}, 'Electron' :{}})
if '4PJets' in njets_to_run:
histo_dict_4pj = processor.dict_accumulator({'Muon' : {}, 'Electron' :{}})
for lep in ['Muon', 'Electron']:
lepdir = 'mujets' if lep == 'Muon' else 'ejets'
## make groups based on process
process_groups = plt_tools.make_dataset_groups(lep, args.year, samples=names, gdict='templates')
lumi_correction = load('%s/Corrections/%s/MC_LumiWeights_IgnoreSigEvts.coffea' % (proj_dir, jobid))[args.year]['%ss' % lep]
# scale ttJets events, split by reconstruction type, by normal ttJets lumi correction
if len(ttJets_cats) > 0:
for tt_cat in ttJets_cats:
ttJets_lumi_topo = '_'.join(tt_cat.split('_')[:-1]) # gets ttJets[SL, Had, DiLep] or ttJets_PS
ttJets_eff_lumi = lumi_correction[ttJets_lumi_topo]
lumi_correction.update({tt_cat: ttJets_eff_lumi})
histo = rebin_histo.copy()
histo.scale(lumi_correction, axis='dataset')
histo = histo.group(process_cat, process, process_groups)[:, :, :, lep, :, :].integrate('leptype')
# use ttJets events that don't have PS weights for dedicated sys samples in 2016
if bkg_ttJets_fname is not None:
if len(only_ttJets_cats) > 0:
for tt_cat in only_ttJets_cats:
ttJets_lumi_topo = '_'.join(tt_cat.split('_')[:-1]) # gets ttJets[SL, Had, DiLep] or ttJets_PS
ttJets_eff_lumi = lumi_correction[ttJets_lumi_topo]
lumi_correction.update({tt_cat: ttJets_eff_lumi})
tt_histo = ttJets_histo.copy()
tt_histo.scale(lumi_correction, axis='dataset')
tt_histo = tt_histo.group(process_cat, process, {'TT' : ['ttJets_right', 'ttJets_matchable', 'ttJets_unmatchable', 'ttJets_other']})[:, :, :, lep, :, :].integrate('leptype')
for jmult in njets_to_run:
iso_sb = Plotter.linearize_hist(histo[:, 'nosys', jmult, 'btagPass', 'Loose'].integrate('sys').integrate('jmult').integrate('lepcat').integrate('btag'))
btag_sb = Plotter.linearize_hist(histo[:, 'nosys', jmult, 'btagFail', 'Tight'].integrate('sys').integrate('jmult').integrate('lepcat').integrate('btag'))
double_sb = Plotter.linearize_hist(histo[:, 'nosys', jmult, 'btagFail', 'Loose'].integrate('sys').integrate('jmult').integrate('lepcat').integrate('btag'))
sig_histo = Plotter.linearize_hist(histo[:, :, jmult, 'btagPass', 'Tight'].integrate('jmult').integrate('lepcat').integrate('btag'))
for sys in sys_to_use.keys():
if sys not in histo.axis('sys')._sorted:
print('\n\n Systematic %s not available, skipping\n\n' % sys)
continue
#set_trace()
sysname, onlyTT = sys_to_use[sys]
if 'LEP' in sysname: sysname = sysname.replace('LEP', lepdir[0])
qcd_est_histo = Plotter.QCD_Est(sig_reg=sig_histo, iso_sb=iso_sb, btag_sb=btag_sb, double_sb=double_sb, norm_type='Sideband', shape_region='BTAG', norm_region='BTAG', sys=sys)
## write nominal and systematic variations for each topology to file
for proc in sorted(set([key[0] for key in qcd_est_histo.values().keys()])):
if (proc != 'TT') and onlyTT: continue
if (proc == 'data_obs') and not (sys == 'nosys'): continue
name = proc+lepdir if proc == 'QCD' else proc
print(lep, jmult, sys, name)
outhname = '_'.join([jmult, lepdir, name]) if sys == 'nosys' else '_'.join([jmult, lepdir, name, sysname])
template_histo = qcd_est_histo[proc].integrate('process')
if (('ue' in sys) or ('hdamp' in sys) or ('mtop' in sys)) and (bkg_ttJets_fname is not None):
tt_lin_histo = Plotter.linearize_hist(tt_histo['TT', 'nosys', jmult, 'btagPass', 'Tight'].integrate('jmult').integrate('lepcat').integrate('btag'))
tt_lin_histo = tt_lin_histo['TT', 'nosys'].integrate('process').integrate('sys')
template_histo = substitute_ttJets(sys_histo=template_histo, ttJets_histo=tt_lin_histo, ttJets_PS_histo=sig_histo['TT', 'nosys'].integrate('process').integrate('sys'))
if ((sys == 'mtop1695') or (sys == 'mtop1755')) and (templates_to_smooth[proc]):
template_histo = scale_mtop3gev(nominal=histo_dict_3j[lep][proc] if jmult == '3Jets' else histo_dict_4pj[lep][proc], template=template_histo)
#set_trace()
if (sys != 'nosys') and (args.smooth) and (templates_to_smooth[proc]):
template_histo = smoothing(nominal=histo_dict_3j[lep][proc] if jmult == '3Jets' else histo_dict_4pj[lep][proc], template=template_histo, nbinsx=len(xrebinning)-1, nbinsy=len(yrebinning)-1)#, debug=True if proc=='VV' else False)
#set_trace()
## save template histos to coffea dict
if jmult == '3Jets':
histo_dict_3j[lep][proc if sys == 'nosys' else '%s_%s' % (proc, sys)] = template_histo
if jmult == '4PJets':
histo_dict_4pj[lep][proc if sys == 'nosys' else '%s_%s' % (proc, sys)] = template_histo
## save template histo to root file
upfout[outhname] = hist.export1d(template_histo)
if '3Jets' in njets_to_run:
coffea_out_3j = '%s/templates_lj_3Jets_bkg_smoothed_%s_QCD_Est_%s.coffea' % (outdir, jobid, args.year) if args.smooth else '%s/templates_lj_3Jets_bkg_%s_QCD_Est_%s.coffea' % (outdir, jobid, args.year)
save(histo_dict_3j, coffea_out_3j)
print("%s written" % coffea_out_3j)
if '4PJets' in njets_to_run:
coffea_out_4pj = '%s/templates_lj_4PJets_bkg_smoothed_%s_QCD_Est_%s.coffea' % (outdir, jobid, args.year) if args.smooth else '%s/templates_lj_4PJets_bkg_%s_QCD_Est_%s.coffea' % (outdir, jobid, args.year)
save(histo_dict_4pj, coffea_out_4pj)
print("%s written" % coffea_out_4pj)
upfout.close()
print('%s written' % tmp_rname)