## get data lumi and scale MC by lumi data_lumi_year = prettyjson.loads( open('%s/inputs/lumis_data.json' % proj_dir).read())[args.year] lumi_correction = load( '%s/Corrections/%s/MC_LumiWeights_IgnoreSigEvts.coffea' % (proj_dir, jobid)) for hname in hdict.keys(): if hname == 'cutflow': continue hdict[hname].scale(lumi_correction[args.year]['%ss' % args.lepton], axis='dataset') ## make groups based on process process = hist.Cat("process", "Process", sorting='placement') process_cat = "dataset" process_groups = plt_tools.make_dataset_groups(args.lepton, args.year) #set_trace() for hname in hdict.keys(): if hname == 'cutflow': continue hdict[hname] = hdict[hname].group(process_cat, process, process_groups) ## make plots for hname in variables.keys(): if hname not in hdict.keys(): raise ValueError("%s not found in file" % hname) xtitle, rebinning, x_lims, withData, btag_splitting = variables[hname] if btag_splitting: histo = hdict[hname][:, :, :, args.lepton, :, :].integrate( 'leptype') # process, jmult, lepton, lepcat, btagregion
if any([fnmatch.fnmatch(name, cat) for cat in ttJets_permcats]) ] # gets ttJets(_PS)_other, ... if len(ttJets_cats) > 0: for tt_cat in ttJets_cats: ttJets_lumi_topo = '_'.join( tt_cat.split('_')[:-2]) if 'sl_tau' in tt_cat else '_'.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}) ## make groups based on process process = hist.Cat("process", "Process", sorting='placement') process_cat = "dataset" process_groups = plt_tools.make_dataset_groups(args.lepton, args.year, samples=names) # scale and group hists by process for hname in hdict.keys(): if hname == 'cutflow': continue hdict[hname].scale(lumi_correction, axis='dataset') # scale hists hdict[hname] = hdict[hname].group(process_cat, process, process_groups) # group by process hdict[hname] = hdict[hname][:, :, args.lepton].integrate( 'leptype') # only pick out specified lepton ## make bp plots if ((args.plot == 'all') or (args.plot == 'bp')): for hname in bp_variables.keys(): if hname not in hdict.keys():
if len(ttJets_cats) > 0: for tt_cat in ttJets_cats: ttJets_lumi_topo = '_'.join( tt_cat.split('_')[:-2]) if 'sl_tau' in tt_cat else '_'.join( tt_cat.split('_') [:-1]) # gets ttJets[SL, Had, DiLep] or ttJets_PS mu_lumi = lumi_correction['Muons'][ttJets_lumi_topo] el_lumi = lumi_correction['Electrons'][ttJets_lumi_topo] lumi_correction['Muons'].update({tt_cat: mu_lumi}) lumi_correction['Electrons'].update({tt_cat: el_lumi}) ## make groups based on process process = hist.Cat("process", "Process", sorting='placement') process_cat = "dataset" process_groups = plt_tools.make_dataset_groups('Muon', args.year, samples=names) ## make plots for hname in variables.keys(): if hname not in hdict.keys(): raise ValueError("%s not found in file" % hname) histo = hdict[hname] h_mu = histo[:, :, :, 'Muon', :].integrate('leptype') h_mu.scale(lumi_correction['Muons'], axis='dataset') h_el = histo[:, :, :, 'Electron', :].integrate('leptype') h_el.scale(lumi_correction['Electrons'], axis='dataset') h_tot = h_mu + h_el h_tot = h_tot.group(process_cat, process, process_groups) #set_trace()
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)
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 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][ Plotter. nonsignal_samples] # 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) ## scale ttJets events, split by reconstruction type, by normal ttJets lumi correction ttJets_permcats = [ "*right", "*matchable", "*unmatchable", "*sl_tau", "*other" ] names = [ dataset for dataset in sorted(set([key[0] for key in histo.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, ... ## 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 = uproot3.recreate(tmp_rname, compression=uproot3.ZLIB( 4)) if os.path.isfile(tmp_rname) else uproot3.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"]: orig_lepdir = "muNJETS" if lep == "Muon" else "eNJETS" #set_trace() ## make groups based on process process_groups = plt_tools.make_dataset_groups(lep, args.year, samples=names, gdict="templates") #process_groups = plt_tools.make_dataset_groups(lep, args.year, samples=names, gdict="dataset") lumi_correction = lumi_corr_dict[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( "_")[:-2]) if "sl_tau" in tt_cat else "_".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") #set_trace() systs = sorted(set([key[1] for key in histo.values().keys()])) systs.insert(0, systs.pop( systs.index("nosys"))) # move "nosys" to the front # loop over each jet multiplicity for jmult in njets_to_run: lepdir = orig_lepdir.replace("NJETS", jmult.lower()) # get sideband and signal region hists cen_sb_histo = Plotter.linearize_hist( histo[:, "nosys", jmult, btag_reg_names_dict["Central"]["reg"]].integrate( "jmult").integrate("btag").integrate("sys")) #up_sb_histo = histo[:, "nosys", jmult, btag_reg_names_dict["Up"]["reg"]].integrate("jmult").integrate("btag") #dw_sb_histo = histo[:, "nosys", jmult, btag_reg_names_dict["Down"]["reg"]].integrate("jmult").integrate("btag") sig_histo = Plotter.linearize_hist( histo[:, :, jmult, btag_reg_names_dict["Signal"]["reg"]].integrate( "jmult").integrate("btag")) # loop over each systematic for sys in systs: if sys not in systematics.template_sys_to_name[ args.year].keys(): continue sys_histo = sig_histo[:, sys].integrate( "sys") if sys in systematics.ttJets_sys.values( ) else Plotter.BKG_Est( sig_reg=sig_histo[:, sys].integrate("sys"), sb_reg=cen_sb_histo, norm_type="SigMC", sys=sys, ignore_uncs=True) ## write nominal and systematic variations for each topology to file #for proc in sorted(set([key[0] for key in sig_histo.values().keys()])): for proc in sorted( set([key[0] for key in sys_histo.values().keys()])): if ("tt" not in proc) and ( sys in systematics.ttJets_sys.values()): continue #if (proc != "tt") and (sys in systematics.ttJets_sys.values()): continue if (proc == "data_obs") and not (sys == "nosys"): continue if not sys_histo[proc].values().keys(): #if not sig_histo[proc, sys].values().keys(): print( f"Systematic {sys} for {lep} {jmult} {proc} not found, skipping" ) continue print(args.year, lep, jmult, sys, proc) #set_trace() outhname = "_".join( list( filter(None, [ proc, systematics.template_sys_to_name[ args.year][sys][0], lepdir, (args.year)[-2:] ]))) if "LEP" in outhname: outhname = outhname.replace( "LEP", "muon") if lep == "Muon" else outhname.replace( "LEP", "electron") template_histo = sys_histo[proc].integrate("process") #template_histo = sig_histo[proc, sys].integrate("process").integrate("sys") #set_trace() ## save template histos to coffea dict if jmult == "3Jets": histo_dict_3j[lep][ f"{proc}_{sys}"] = template_histo.copy() if jmult == "4PJets": histo_dict_4pj[lep][ f"{proc}_{sys}"] = template_histo.copy() ## save template histo to root file upfout[outhname] = hist.export1d(template_histo) if "3Jets" in njets_to_run: coffea_out_3j = os.path.join( outdir, f"test_raw_templates_lj_3Jets_bkg_{args.year}_{jobid}.coffea") save(histo_dict_3j, coffea_out_3j) print(f"{coffea_out_3j} written") if "4PJets" in njets_to_run: coffea_out_4pj = os.path.join( outdir, f"test_raw_templates_lj_4PJets_bkg_{args.year}_{jobid}.coffea") save(histo_dict_4pj, coffea_out_4pj) print(f"{coffea_out_4pj} written") upfout.close() print(f"{tmp_rname} written")
def get_sig_templates(tmp_rname): """ Function that writes linearized mtt vs costheta distributions to root file. """ widthTOname = lambda width: str(width).replace(".", "p") nameTOwidth = lambda width: str(width).replace("p", ".") ## variables that only need to be defined/evaluated once hdict = plt_tools.add_coffea_files( sig_fnames) if len(sig_fnames) > 1 else load(sig_fnames[0]) # get correct hist and rebin hname_to_use = "mtt_vs_tlep_ctstar_abs" if hname_to_use not in hdict.keys(): raise ValueError(f"{hname_to_use} not found in file") xrebinning, yrebinning = linearize_binning #xrebinning, yrebinning = mtt_ctstar_2d_binning histo = hdict[hname_to_use] # process, sys, jmult, leptype, btag, lepcat #set_trace() 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 histo = histo.rebin(yaxis_name, new_ybins) rebin_histo = histo[Plotter.signal_samples, :, :, :, "btagPass"].integrate("btag") names = [ dataset for dataset in sorted( set([key[0] for key in rebin_histo.values().keys()])) ] # get dataset names in hists signals = sorted(set([key[0] for key in rebin_histo.values().keys()])) signals = [sig for sig in signals if "TTJetsSL" in sig] # only use SL decays systs = sorted(set([key[1] for key in rebin_histo.values().keys()])) systs.insert(0, systs.pop(systs.index("nosys"))) # move "nosys" to the front # need to save coffea hist objects to file so they can be opened by uproot in the proper format upfout = uproot3.recreate(tmp_rname, compression=uproot3.ZLIB( 4)) if os.path.isfile(tmp_rname) else uproot3.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": {} }) # write signal dists to temp file for lep in ["Muon", "Electron"]: orig_lepdir = "muNJETS" if lep == "Muon" else "eNJETS" # scale by lumi lumi_correction = lumi_corr_dict[args.year]["%ss" % lep] histo = rebin_histo.copy() histo.scale(lumi_correction, axis="dataset") process_groups = plt_tools.make_dataset_groups(lep, args.year, samples=names, gdict="templates") histo = histo.group( "dataset", hist.Cat("process", "Process", sorting="placement"), process_groups) for jmult in njets_to_run: lepdir = orig_lepdir.replace("NJETS", jmult.lower()) #set_trace() lin_histo = Plotter.linearize_hist( histo[:, :, jmult, lep].integrate("jmult").integrate("leptype")) for signal in signals: if "Int" in signal: boson, mass, width, pI, wt = tuple(signal.split("_")) else: boson, mass, width, pI = tuple(signal.split("_")) sub_name = "_".join([ "%s%s" % (boson[0], mass[1:]), "relw%s" % widthTOname(width).split("W")[-1], pI.lower(), wt ]) if pI == "Int" else "_".join([ "%s%s" % (boson[0], mass[1:]), "relw%s" % widthTOname(width).split("W")[-1], pI.lower() ]) #set_trace() for sys in systs: if sys not in systematics.template_sys_to_name[ args.year].keys(): continue if not lin_histo[signal, sys].values().keys(): print( f"Systematic {sys} for {lep} {jmult} {signal} not found, skipping" ) continue print(args.year, lep, jmult, sub_name, sys) outhname = "_".join( list( filter(None, [ sub_name, systematics.template_sys_to_name[ args.year][sys][0], lepdir, (args.year)[-2:] ]))) if "LEP" in outhname: outhname = outhname.replace( "LEP", "muon") if lep == "Muon" else outhname.replace( "LEP", "electron") template_histo = lin_histo[signal, sys].integrate( "process").integrate("sys") ## save template histos to coffea dict if jmult == "3Jets": histo_dict_3j[lep][ f"{signal}_{sys}"] = template_histo.copy() if jmult == "4PJets": histo_dict_4pj[lep][ f"{signal}_{sys}"] = template_histo.copy() ## save template histo to root file upfout[outhname] = hist.export1d(template_histo) if "3Jets" in njets_to_run: coffea_out_3j = os.path.join( outdir, f"test_raw_templates_lj_3Jets_sig_{args.year}_{jobid}.coffea") save(histo_dict_3j, coffea_out_3j) print(f"{coffea_out_3j} written") if "4PJets" in njets_to_run: coffea_out_4pj = os.path.join( outdir, f"test_raw_templates_lj_4PJets_sig_{args.year}_{jobid}.coffea") save(histo_dict_4pj, coffea_out_4pj) print(f"{coffea_out_4pj} written") upfout.close() print(f"{tmp_rname} written")
name for name in names if any([fnmatch.fnmatch(name, cat) for cat in ttJets_permcats]) ] # gets ttJets(_PS)_other, ... if len(ttJets_cats) > 0: for tt_cat in ttJets_cats: ttJets_lumi_topo = '_'.join( tt_cat.split('_')[:-2]) if 'sl_tau' in tt_cat else '_'.join( tt_cat.split('_') [:-1]) # gets ttJets[SL, Had, DiLep] or ttJets_PS mu_lumi = lumi_correction[year]['Muons'][ttJets_lumi_topo] el_lumi = lumi_correction[year]['Electrons'][ttJets_lumi_topo] lumi_correction[year]['Muons'].update({tt_cat: mu_lumi}) lumi_correction[year]['Electrons'].update({tt_cat: el_lumi}) ## make groups based on process process_groups = plt_tools.make_dataset_groups( 'Muon', year, samples=names) # works when only MC present mthad_fit_range = (0.9, 2.2 ) ## chosen because ~95% of events fall in this range #mthad_fit_range = (0.9, 3.5) for hname in variables.keys(): if not hname in hdict.keys(): raise ValueError("Hist %s not found" % hname) #set_trace() histo = hdict[hname] ## rescale hist by lumi for muons and electrons separately and then combine h_mu = histo[:, 'Muon'].integrate('leptype') h_mu.scale(lumi_correction[year]['Muons'], axis='dataset') h_el = histo[:, 'Electron'].integrate('leptype') h_el.scale(lumi_correction[year]['Electrons'], axis='dataset')