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 process(self, file, analysis_type):
output = self.accumulator.identity()
acc = load(file)
Muon_lead_acc = acc['Muon_lead']
Muon_trail_acc = acc['Muon_trail']
Dimu_acc = acc['Dimu']
D0_acc = acc['D0']
D0_trk_acc = acc['D0_trk']
Dstar_acc = acc['Dstar']
Dstar_trk_acc = acc['Dstar_trk']
DimuDstar_acc = acc['DimuDstar']
Primary_vertex_acc = acc['Primary_vertex']
DimuDstar_p4 = build_p4(DimuDstar_acc)
if (analysis_type == 'mc'):
Gen_Part_acc = acc['Gen_particles']
Gen_Jpsi_acc = acc['Gen_Jpsi']
########## Filling histograms
if (analysis_type == 'mc'):
## Gen Particles
print(Gen_Jpsi_acc['mass'].value)
output['GenPart_pdgId'].fill(pdgId=Gen_Part_acc['pdgId'].value)
output['GenJpsi_mass'].fill(mass=Gen_Jpsi_acc['mass'].value)
output['GenJpsi_p'].fill(pt=Gen_Jpsi_acc['pt'].value,
eta=Gen_Jpsi_acc['eta'].value,
phi=Gen_Jpsi_acc['phi'].value)
#print(dir(Gen_Jpsi_acc))
#output['GenJpsi_mass'].fill(pdgId=Gen_Jpsi_acc['pdgId'].value)
# Primary vertex
output['Primary_vertex_npvs'].fill(
npvs=Primary_vertex_acc['npvs'].value)
#Muon
output['Muon_lead_p'].fill(pt=Muon_lead_acc['pt'].value,
eta=Muon_lead_acc['eta'].value,
phi=Muon_lead_acc['phi'].value)
output['Muon_trail_p'].fill(pt=Muon_trail_acc['pt'].value,
eta=Muon_trail_acc['eta'].value,
phi=Muon_trail_acc['phi'].value)
# Upsilon
output['Upsilon_mass'].fill(
mass=Dimu_acc['mass'].value[Dimu_acc['is_ups'].value])
output['Upsilon_p'].fill(
pt=Dimu_acc['pt'].value[Dimu_acc['is_ups'].value],
eta=Dimu_acc['eta'].value[Dimu_acc['is_ups'].value],
phi=Dimu_acc['phi'].value[Dimu_acc['is_ups'].value])
output['Upsilon_rap'].fill(
rap=Dimu_acc['rap'].value[Dimu_acc['is_ups'].value])
output['Upsilon_dl'].fill(
dl=Dimu_acc['dl'].value[Dimu_acc['is_ups'].value])
output['Upsilon_dlSig'].fill(
dlSig=Dimu_acc['dlSig'].value[Dimu_acc['is_ups'].value])
output['Upsilon_chi2'].fill(
chi2=Dimu_acc['chi2'].value[Dimu_acc['is_ups'].value])
output['Upsilon_cosphi'].fill(
cosphi=Dimu_acc['cosphi'].value[Dimu_acc['is_ups'].value])
# Jpsi
output['Jpsi_mass'].fill(
mass=Dimu_acc['mass'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_p'].fill(
pt=Dimu_acc['pt'].value[Dimu_acc['is_jpsi'].value],
eta=Dimu_acc['eta'].value[Dimu_acc['is_jpsi'].value],
phi=Dimu_acc['phi'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_rap'].fill(
rap=Dimu_acc['rap'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_dl'].fill(
dl=Dimu_acc['dl'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_dlSig'].fill(
dlSig=Dimu_acc['dlSig'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_chi2'].fill(
chi2=Dimu_acc['chi2'].value[Dimu_acc['is_jpsi'].value])
output['Jpsi_cosphi'].fill(
cosphi=Dimu_acc['cosphi'].value[Dimu_acc['is_jpsi'].value])
# Psi
output['Psi_mass'].fill(
mass=Dimu_acc['mass'].value[Dimu_acc['is_psi'].value])
output['Psi_p'].fill(
pt=Dimu_acc['pt'].value[Dimu_acc['is_psi'].value],
eta=Dimu_acc['eta'].value[Dimu_acc['is_psi'].value],
phi=Dimu_acc['phi'].value[Dimu_acc['is_psi'].value])
output['Psi_rap'].fill(
rap=Dimu_acc['rap'].value[Dimu_acc['is_psi'].value])
output['Psi_dl'].fill(
dl=Dimu_acc['dl'].value[Dimu_acc['is_psi'].value])
output['Psi_dlSig'].fill(
dlSig=Dimu_acc['dlSig'].value[Dimu_acc['is_psi'].value])
output['Psi_chi2'].fill(
chi2=Dimu_acc['chi2'].value[Dimu_acc['is_psi'].value])
output['Psi_cosphi'].fill(
cosphi=Dimu_acc['cosphi'].value[Dimu_acc['is_psi'].value])
# D0
output['D0_mass12'].fill(mass=D0_acc['mass12'].value)
output['D0_mass21'].fill(mass=D0_acc['mass21'].value)
output['D0_p'].fill(pt=D0_acc['pt'].value,
eta=D0_acc['eta'].value,
phi=D0_acc['phi'].value)
output['D0_rap'].fill(rap=D0_acc['rap'].value)
output['D0_dl'].fill(dl=D0_acc['dl'].value)
output['D0_dlSig'].fill(dlSig=D0_acc['dlSig'].value)
output['D0_chi2'].fill(chi2=D0_acc['chi2'].value)
output['D0_cosphi'].fill(cosphi=D0_acc['cosphi'].value)
output['D0_eta_mass'].fill(eta=D0_acc['eta'].value,
mass=D0_acc['mass'].value)
# D0 trks
output['D0_trk_p'].fill(pt=D0_trk_acc['t1_pt'].value,
eta=D0_trk_acc['t1_eta'].value,
phi=D0_trk_acc['t1_phi'].value)
output['D0_trk_p'].fill(pt=D0_trk_acc['t2_pt'].value,
eta=D0_trk_acc['t2_eta'].value,
phi=D0_trk_acc['t2_phi'].value)
output['D0_trk_chindof'].fill(chindof=D0_trk_acc['t1_chindof'].value)
output['D0_trk_chindof'].fill(chindof=D0_trk_acc['t2_chindof'].value)
output['D0_trk_nValid'].fill(nValid=D0_trk_acc['t1_nValid'].value)
output['D0_trk_nValid'].fill(nValid=D0_trk_acc['t2_nValid'].value)
output['D0_trk_nPix'].fill(nPix=D0_trk_acc['t1_nPix'].value)
output['D0_trk_nPix'].fill(nPix=D0_trk_acc['t2_nPix'].value)
output['D0_trk_dxy'].fill(dxy=D0_trk_acc['t1_dxy'].value)
output['D0_trk_dxy'].fill(dxy=D0_trk_acc['t2_dxy'].value)
output['D0_trk_dz'].fill(dz=D0_trk_acc['t1_dz'].value)
output['D0_trk_dz'].fill(dz=D0_trk_acc['t2_dz'].value)
# Dstar
output['Dstar_p'].fill(
chg='right charge',
pt=Dstar_acc['pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_acc['eta'].value[~Dstar_acc['wrg_chg'].value],
phi=Dstar_acc['phi'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_p'].fill(
chg='wrong charge',
pt=Dstar_acc['pt'].value[Dstar_acc['wrg_chg'].value],
eta=Dstar_acc['eta'].value[Dstar_acc['wrg_chg'].value],
phi=Dstar_acc['phi'].value[Dstar_acc['wrg_chg'].value])
output['Dstar_rap'].fill(
chg='right charge',
rap=Dstar_acc['rap'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_rap'].fill(
chg='wrong charge',
rap=Dstar_acc['rap'].value[Dstar_acc['wrg_chg'].value])
output['Dstar_deltamr'].fill(
chg='right charge',
deltamr=Dstar_acc['deltamr'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_deltamr'].fill(
chg='wrong charge',
deltamr=Dstar_acc['deltamr'].value[Dstar_acc['wrg_chg'].value])
output['Dstar_deltam'].fill(
chg='right charge',
deltam=Dstar_acc['deltam'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_deltam'].fill(
chg='wrong charge',
deltam=Dstar_acc['deltam'].value[Dstar_acc['wrg_chg'].value])
# Dstar trks
output['Dstar_K_p'].fill(
pt=Dstar_trk_acc['K_pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_trk_acc['K_eta'].value[~Dstar_acc['wrg_chg'].value],
phi=Dstar_trk_acc['K_phi'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_chindof'].fill(chindof=Dstar_trk_acc['K_chindof'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_nValid'].fill(nValid=Dstar_trk_acc['K_nValid'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_nPix'].fill(
nPix=Dstar_trk_acc['K_nPix'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_dxy'].fill(
dxy=Dstar_trk_acc['K_dxy'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_dz'].fill(
dz=Dstar_trk_acc['K_dz'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_K_pt_eta'].fill(
pt=Dstar_trk_acc['K_pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_trk_acc['K_eta'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_p'].fill(
pt=Dstar_trk_acc['pi_pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_trk_acc['pi_eta'].value[~Dstar_acc['wrg_chg'].value],
phi=Dstar_trk_acc['pi_phi'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_chindof'].fill(chindof=Dstar_trk_acc['pi_chindof'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_nValid'].fill(nValid=Dstar_trk_acc['pi_nValid'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_nPix'].fill(
nPix=Dstar_trk_acc['pi_nPix'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_dxy'].fill(
dxy=Dstar_trk_acc['pi_dxy'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_dz'].fill(
dz=Dstar_trk_acc['pi_dz'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pi_pt_eta'].fill(
pt=Dstar_trk_acc['pi_pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_trk_acc['pi_eta'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_p'].fill(
pt=Dstar_trk_acc['pis_pt'].value[~Dstar_acc['wrg_chg'].value],
eta=Dstar_trk_acc['pis_eta'].value[~Dstar_acc['wrg_chg'].value],
phi=Dstar_trk_acc['pis_phi'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_chindof'].fill(chindof=Dstar_trk_acc['pis_chindof'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_nValid'].fill(nValid=Dstar_trk_acc['pis_nValid'].
value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_nPix'].fill(
nPix=Dstar_trk_acc['pis_nPix'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_dxy'].fill(
dxy=Dstar_trk_acc['pis_dxy'].value[~Dstar_acc['wrg_chg'].value])
output['Dstar_pis_dz'].fill(
dz=Dstar_trk_acc['pis_dz'].value[~Dstar_acc['wrg_chg'].value])
############# DimuDstar
is_ups = DimuDstar_acc['Dimu']['is_ups'].value
is_jpsi = DimuDstar_acc['Dimu']['is_jpsi'].value
is_psi = DimuDstar_acc['Dimu']['is_psi'].value
wrg_chg = DimuDstar_acc['Dstar']['wrg_chg'].value
# Upsilon
output['UpsilonDstar']['Upsilon_mass'].fill(
mass=DimuDstar_acc['Dimu']['mass'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Upsilon_p'].fill(
pt=DimuDstar_acc['Dimu']['pt'].value[is_ups & ~wrg_chg],
eta=DimuDstar_acc['Dimu']['eta'].value[is_ups & ~wrg_chg],
phi=DimuDstar_acc['Dimu']['phi'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Upsilon_rap'].fill(
rap=DimuDstar_acc['Dimu']['rap'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Dstar_deltamr'].fill(
chg='right charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Dstar_deltamr'].fill(
chg='wrong charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_ups & wrg_chg])
output['UpsilonDstar']['Dstar_deltam'].fill(
chg='right charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Dstar_deltam'].fill(
chg='wrong charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_ups & wrg_chg])
output['UpsilonDstar']['Dstar_p'].fill(
chg='right charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_ups & ~wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_ups & ~wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Dstar_p'].fill(
chg='wrong charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_ups & wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_ups & wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_ups & wrg_chg])
output['UpsilonDstar']['Dstar_rap'].fill(
chg='right charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['Dstar_rap'].fill(
chg='wrong charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_ups & wrg_chg])
output['UpsilonDstar']['UpsilonDstar_deltarap'].fill(
deltarap=DimuDstar_acc['deltarap'].value[is_ups & ~wrg_chg])
output['UpsilonDstar']['UpsilonDstar_mass'].fill(
mass=DimuDstar_p4.mass[is_ups & ~wrg_chg])
# Jpsi
output['JpsiDstar']['Jpsi_mass'].fill(
mass=DimuDstar_acc['Dimu']['mass'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Jpsi_p'].fill(
pt=DimuDstar_acc['Dimu']['pt'].value[is_jpsi & ~wrg_chg],
eta=DimuDstar_acc['Dimu']['eta'].value[is_jpsi & ~wrg_chg],
phi=DimuDstar_acc['Dimu']['phi'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Jpsi_rap'].fill(
rap=DimuDstar_acc['Dimu']['rap'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Dstar_deltamr'].fill(
chg='right charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_jpsi
& ~wrg_chg])
output['JpsiDstar']['Dstar_deltamr'].fill(
chg='wrong charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_jpsi & wrg_chg])
output['JpsiDstar']['Dstar_deltam'].fill(
chg='right charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Dstar_deltam'].fill(
chg='wrong charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_jpsi & wrg_chg])
output['JpsiDstar']['Dstar_p'].fill(
chg='right charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_jpsi & ~wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_jpsi & ~wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Dstar_p'].fill(
chg='wrong charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_jpsi & wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_jpsi & wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_jpsi & wrg_chg])
output['JpsiDstar']['Dstar_rap'].fill(
chg='right charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['Dstar_rap'].fill(
chg='wrong charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_jpsi & wrg_chg])
output['JpsiDstar']['JpsiDstar_deltarap'].fill(
deltarap=DimuDstar_acc['deltarap'].value[is_jpsi & ~wrg_chg])
output['JpsiDstar']['JpsiDstar_mass'].fill(
mass=DimuDstar_p4.mass[is_jpsi & ~wrg_chg])
# Psi
output['PsiDstar']['Psi_mass'].fill(
mass=DimuDstar_acc['Dimu']['mass'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Psi_p'].fill(
pt=DimuDstar_acc['Dimu']['pt'].value[is_psi & ~wrg_chg],
eta=DimuDstar_acc['Dimu']['eta'].value[is_psi & ~wrg_chg],
phi=DimuDstar_acc['Dimu']['phi'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Psi_rap'].fill(
rap=DimuDstar_acc['Dimu']['rap'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Dstar_deltamr'].fill(
chg='right charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Dstar_deltamr'].fill(
chg='wrong charge',
deltamr=DimuDstar_acc['Dstar']['deltamr'].value[is_psi & wrg_chg])
output['PsiDstar']['Dstar_deltam'].fill(
chg='right charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Dstar_deltam'].fill(
chg='wrong charge',
deltam=DimuDstar_acc['Dstar']['deltam'].value[is_psi & wrg_chg])
output['PsiDstar']['Dstar_p'].fill(
chg='right charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_psi & ~wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_psi & ~wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Dstar_p'].fill(
chg='wrong charge',
pt=DimuDstar_acc['Dstar']['pt'].value[is_psi & wrg_chg],
eta=DimuDstar_acc['Dstar']['eta'].value[is_psi & wrg_chg],
phi=DimuDstar_acc['Dstar']['phi'].value[is_psi & wrg_chg])
output['PsiDstar']['Dstar_rap'].fill(
chg='right charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_psi & ~wrg_chg])
output['PsiDstar']['Dstar_rap'].fill(
chg='wrong charge',
rap=DimuDstar_acc['Dstar']['rap'].value[is_psi & wrg_chg])
output['PsiDstar']['PsiDstar_deltarap'].fill(
deltarap=DimuDstar_acc['deltarap'].value[is_psi & ~wrg_chg])
output['PsiDstar']['PsiDstar_mass'].fill(
mass=DimuDstar_p4.mass[is_psi & ~wrg_chg])
return output
def make_trees(args):
filelists = files_by_dataset(args.files)
# The output for each dataset will be written into a separate file
for dataset, files in filelists.items():
# Find region and branch names
datatypes = {}
tree_by_variable = {}
variables = []
regions = []
# Scout out what branches there are
for fname in files:
acc = load(fname)
treenames = [
x for x in map(str, acc.keys()) if x.startswith("tree")
]
for tn in treenames:
datatype = tn.split("_")[-1]
for region in acc[tn].keys():
vars = acc[tn][region].keys()
regions.append(region)
variables.extend(vars)
for v in vars:
datatypes[v] = np.float64 #getattr(np, datatype)
tree_by_variable[v] = tn
# Combine
with uproot.recreate(pjoin(args.outdir, f"tree_{dataset}.root"),
compression=uproot.ZLIB(4)) as f:
for region, fname in tqdm(list(
itertools.product(set(regions), files)),
desc=dataset):
acc = load(fname)
d = {
x: acc[tree_by_variable[x]][region][x].value
for x in variables
}
# Remove empty entries
to_remove = []
for k, v in d.items():
if not len(v):
to_remove.append(k)
for k in to_remove:
d.pop(k)
if not len(d):
continue
if not (region in [
re.sub(";.*", "", x.decode("utf-8")) for x in f.keys()
]):
f[region] = uproot.newtree(
{x: np.float64
for x in d.keys()})
lengths = set()
for k, v in d.items():
lengths.add(len(v))
assert (len(lengths) == 1)
# write
f[region].extend(d)
## Json file reader
with open(metadata) as fin:
datadict = json.load(fin)
filelist = glob.glob(datadict[data_sample])
if isFake:
sample_name = "Fake_Photon"
else:
sample_name = data_sample.split("_")[0]
corr_file = "../Corrections/corrections.coffea"
# corr_file = "corrections.coffea" # Condor-batch
corrections = load(corr_file)
## Read PU weight file
if not isdata:
pu_path_dict = {
"DY": "mcPileupDist_DYToEE_M-50_NNPDF31_TuneCP5_13TeV-powheg-pythia8.npy",
"TTWJets": "mcPileupDist_TTWJetsToLNu_TuneCP5_13TeV-amcatnloFXFX-madspin-pythia8.npy",
"TTZtoLL": "mcPileupDist_TTZToLLNuNu_M-10_TuneCP5_13TeV-amcatnlo-pythia8.npy",
"WW": "mcPileupDist_WW_TuneCP5_DoubleScattering_13TeV-pythia8.npy",
"WZ": "mcPileupDist_WZ_TuneCP5_13TeV-pythia8.npy",
"ZZ": "mcPileupDist_ZZ_TuneCP5_13TeV-pythia8.npy",
"tZq": "mcPileupDist_tZq_ll_4f_ckm_NLO_TuneCP5_13TeV-amcatnlo-pythia8.npy",
"WZG": "mcPileupDist_wza_UL18.npy",
"ZGToLLG": "mcPileupDist_ZGToLLG_01J_5f_TuneCP5_13TeV-amcatnloFXFX-pythia8.npy",
"TTGJets": "mcPileupDist_TTGJets_TuneCP5_13TeV-amcatnloFXFX-madspin-pythia8.npy",
triggers = ["HLT_Mu7_IP4", "HLT_Mu9_IP5", "HLT_Mu9_IP6", "HLT_Mu12_IP6"]
btypes = ["Bu", "Bs", "Bd"]
btype_longnames = {
"Bu": "Bu2KJpsi2KMuMu",
"Bs": "Bs2PhiJpsi2KKMuMu",
"Bd": "Bd2KsJpsi2KPiMuMu"
}
btype_shortnames = {"Bu": "BuToKMuMu", "Bs": "BsToKKMuMu", "Bd": "BdToKPiMuMu"}
vars = ["pt", "y"]
# MC probefilter efficiency
coffea_files = {}
for btype in btypes:
coffea_files[btype] = util.load(
f"{btype_longnames[btype]}/MCEfficiencyHistograms.coffea")
axes = {}
axes["pt"] = {
"probe":
hist.Bin("pt", r"$p_{T}$ [GeV]", np.array([8., 13., 18., 23., 28., 33.])),
"tag":
hist.Bin(
"pt", r"$p_{T}$ [GeV]",
np.array([
10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 20.0, 23.0, 26.0,
29.0, 34.0, 45.0
]))
}
axes["y"] = {
"probe": hist.Bin("y", r"$|y|$", np.array(np.arange(0., 2.25 + 0.25,
}
for year in years_to_run
}
if args.construct_btag:
from copy import deepcopy
btag_contructs_dict = deepcopy(flav_effs)
jet_mults = {
'3Jets': '3 jets',
'4PJets': '4+ jets',
}
flav_to_name = {'bjet': 'bottom', 'cjet': 'charm', 'ljet': 'light'}
hname = 'Jets_pt_eta'
lumi_correction = load(
os.path.join(proj_dir, 'Corrections', jobid, 'MC_LumiWeights.coffea'))
#pt_binning = np.array([30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 105.0, 110.0, 125.0, 150.0,170.0, 200.0, 250.0, 1000.0])
#eta_binning = np.array([-2.5, -1.5, -0.5, 0.0, 0.5, 1.5, 2.5])
pt_binning = np.array([
30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0,
90.0, 95.0, 100.0, 105.0, 110.0, 125.0, 150.0, 170.0, 200.0, 1000.0
])
eta_binning = np.array(
[-2.5, -2., -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 2.5])
pt_bins = hist.Bin('pt', 'pt', pt_binning)
eta_bins = hist.Bin('eta', 'eta', eta_binning)
working_points = []
def test_hist_compat():
from coffea.util import load
test = load("tests/samples/old_hist_format.coffea")
expected_bins = np.array([
-np.inf,
0.0,
20.0,
40.0,
60.0,
80.0,
100.0,
120.0,
140.0,
160.0,
180.0,
200.0,
220.0,
240.0,
260.0,
280.0,
300.0,
320.0,
340.0,
360.0,
380.0,
400.0,
420.0,
440.0,
460.0,
480.0,
500.0,
520.0,
540.0,
560.0,
580.0,
600.0,
620.0,
640.0,
660.0,
680.0,
700.0,
720.0,
740.0,
760.0,
780.0,
800.0,
820.0,
840.0,
860.0,
880.0,
900.0,
920.0,
940.0,
960.0,
980.0,
1000.0,
1020.0,
1040.0,
1060.0,
1080.0,
1100.0,
1120.0,
1140.0,
1160.0,
1180.0,
1200.0,
np.inf,
np.nan,
])
assert np.all(test._axes[2]._interval_bins[:-1] == expected_bins[:-1])
assert np.isnan(test._axes[2]._interval_bins[-1])
parser.add_argument('lepton', choices=['Electron', 'Muon'], help='Choose which lepton to make plots for')
args = parser.parse_args()
proj_dir = os.environ['PROJECT_DIR']
jobid = os.environ['jobid']
base_jobid = os.environ['base_jobid']
analyzer = 'data_hem_comp'
input_dir = os.path.join(proj_dir, 'results', '%s_%s' % (args.year, jobid), analyzer)
f_ext = 'TOT.coffea'
outdir = os.path.join(proj_dir, 'plots', '%s_%s' % (args.year, jobid), analyzer)
if not os.path.isdir(outdir):
os.makedirs(outdir)
fnames = sorted(['%s/%s' % (input_dir, fname) for fname in os.listdir(input_dir) if fname.endswith(f_ext)])
hdict = plt_tools.add_coffea_files(fnames) if len(fnames) > 1 else load(fnames[0])
jet_mults = {
'3Jets' : '3 jets',
'4PJets' : '4+ jets'
}
objtypes = {
'Jets' : 'jets',
'Lep' : {
'Muon' : '$\\mu$',
'Electron' : '$e$',
}
}
btag_cats = {
def process(self, ds):
output = self.accumulator.identity()
acc = load(ds["file"])
############ Histogram definition
# Muons
hist_muon_lead = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,\mu}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{\mu}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{\mu}$"),
)
hist_muon_trail = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,\mu}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{\mu}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{\mu}$"),
)
#Dimu
hist_dimu = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,\mu^+\mu^-}$ [GeV]"),
bh.axis.Regular(80, -2.5, 2.5, metadata=r"$\eta_{\mu^+\mu^-}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{\mu^+\mu^-}$"),
)
hist_dimu_mass = bh.Histogram(
bh.axis.Regular(100, 8.6, 11, metadata=r"$m_{\mu^+\mu^-}$ [GeV]"))
# D0
hist_D0 = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,D^0}$ [GeV]"),
bh.axis.Regular(80, -2.5, 2.5, metadata=r"$\eta_{D^0}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{D^0}$"),
)
hist_D0_mass = bh.Histogram(
bh.axis.Regular(100, 1.7, 2.0, metadata=r"$m_{D^0}$ [GeV]"))
hist_D0_eta_mass = bh.Histogram(
bh.axis.Regular(80, -2.5, 2.5, metadata=r"$\eta_{D^0}$"),
bh.axis.Regular(100, 1.7, 2.0, metadata=r"$m_{D^0}$ [GeV]"))
hist_D0_trk = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,D^0 trks}$ [GeV]"),
bh.axis.Regular(80, -2.5, 2.5, metadata=r"$\eta_{D^0 trks}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{D^0 trks}$"),
)
# Dstar
hist_Dstar = bh.Histogram(
bh.axis.Regular(100, 0, 50, metadata=r"$p_{T,D*}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{D*}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{D*}$"),
)
hist_Dstar_K = bh.Histogram(
bh.axis.Regular(100, 0, 30, metadata=r"$p_{T,D* K}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{D* K}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{D* K}$"),
)
hist_Dstar_pi = bh.Histogram(
bh.axis.Regular(100, 0, 30, metadata=r"$p_{T,D* \pi}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{D* \pi}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{D* \pi}$"),
)
hist_Dstar_pis = bh.Histogram(
bh.axis.Regular(100, 0, 20, metadata=r"$p_{T,\pi_s}$ [GeV]"),
bh.axis.Regular(60, -2.5, 2.5, metadata=r"$\eta_{\pi_s}$"),
bh.axis.Regular(70, -3.5, 3.5, metadata=r"$\phi_{\pi_s}$"),
)
hist_Dstar_mass = bh.Histogram(
bh.axis.Regular(100, 1.8, 2.2, metadata=r"$m_{D*}$ [GeV]"))
hist_Dstar_mass_refit = bh.Histogram(
bh.axis.Regular(100, 1.8, 2.2, metadata=r"$m_{D* refit}$ [GeV]"))
hist_Dstar_deltamr = bh.Histogram(
bh.axis.Regular(50,
0.138,
0.162,
metadata=r"$\Delta m_{refit}$ [GeV]"))
hist_Dstar_deltam = bh.Histogram(
bh.axis.Regular(50, 0.138, 0.162, metadata=r"$\Delta m$ [GeV]"))
# Filling histograms
hist_muon_lead.fill(acc["Muon_lead"]["__fast_pt"].value,
acc["Muon_lead"]["__fast_eta"].value,
acc["Muon_lead"]["__fast_phi"].value)
hist_muon_trail.fill(acc["Muon_trail"]["__fast_pt"].value,
acc["Muon_trail"]["__fast_eta"].value,
acc["Muon_trail"]["__fast_phi"].value)
hist_dimu.fill(acc["Dimu"]["__fast_pt"].value,
acc["Dimu"]["__fast_eta"].value,
acc["Dimu"]["__fast_phi"].value)
hist_dimu_mass.fill(acc["Dimu"]["__fast_mass"].value)
hist_D0.fill(acc["D0"]["__fast_pt"].value,
acc["D0"]["__fast_eta"].value,
acc["D0"]["__fast_phi"].value)
hist_D0_mass.fill(acc["D0"]["__fast_mass"].value)
hist_D0_eta_mass.fill(acc["D0"]["__fast_eta"].value,
acc["D0"]["__fast_mass"].value)
hist_D0_trk.fill(acc["D0_trk"]["t1_pt"].value,
acc["D0_trk"]["t1_eta"].value,
acc["D0_trk"]["t1_phi"].value)
hist_D0_trk.fill(acc["D0_trk"]["t2_pt"].value,
acc["D0_trk"]["t2_eta"].value,
acc["D0_trk"]["t2_phi"].value)
hist_Dstar.fill(acc["Dstar"]["__fast_pt"].value,
acc["Dstar"]["__fast_eta"].value,
acc["Dstar"]["__fast_phi"].value)
hist_Dstar_mass.fill(acc["Dstar"]["__fast_mass"].value)
hist_Dstar_mass_refit.fill(acc["Dstar"]["deltamr"].value +
acc["Dstar_D0"]["D0_mass"].value)
hist_Dstar_K.fill(acc["Dstar_trk"]["K_pt"].value,
acc["Dstar_trk"]["K_eta"].value,
acc["Dstar_trk"]["K_phi"].value)
hist_Dstar_pi.fill(acc["Dstar_trk"]["pi_pt"].value,
acc["Dstar_trk"]["pi_eta"].value,
acc["Dstar_trk"]["pi_phi"].value)
hist_Dstar_pis.fill(acc["Dstar_trk"]["pis_pt"].value,
acc["Dstar_trk"]["pis_eta"].value,
acc["Dstar_trk"]["pis_phi"].value)
hist_Dstar_deltamr.fill(acc["Dstar"]["deltamr"].value)
hist_Dstar_deltam.fill(acc["Dstar"]["deltam"].value)
# Saving histograms
save(hist_muon_lead,
"output/" + ds['analyzer_name'] + "/hist/hist_Muon_lead.hist")
save(hist_muon_trail,
"output/" + ds['analyzer_name'] + "/hist/hist_Muon_trail.hist")
save(hist_dimu,
"output/" + ds['analyzer_name'] + "/hist/hist_Dimu.hist")
save(hist_dimu_mass,
"output/" + ds['analyzer_name'] + "/hist/hist_Dimu_mass.hist")
save(hist_D0, "output/" + ds['analyzer_name'] + "/hist/hist_D0.hist")
save(hist_D0_mass,
"output/" + ds['analyzer_name'] + "/hist/hist_D0_mass.hist")
save(hist_Dstar,
"output/" + ds['analyzer_name'] + "/hist/hist_Dstar.hist")
save(hist_Dstar_mass,
"output/" + ds['analyzer_name'] + "/hist/hist_Dstar_mass.hist")
save(
hist_Dstar_mass_refit, "output/" + ds['analyzer_name'] +
"/hist/hist_Dstar_mass_refit.hist")
save(hist_Dstar_deltamr,
"output/" + ds['analyzer_name'] + "/hist/hist_Dstar_deltamr.hist")
save(hist_Dstar_deltam,
"output/" + ds['analyzer_name'] + "/hist/hist_Dstar_deltam.hist")
# Creating plots 1D
plots_path = "plots/" + ds['analyzer_name'] + "/"
create_plot1d(hist_muon_lead[:, sum, sum],
plots_path + "Muon_lead_pt.png",
log=True)
create_plot1d(hist_muon_lead[sum, :, sum],
plots_path + "Muon_lead_eta.png")
create_plot1d(hist_muon_lead[sum, sum, :],
plots_path + "Muon_lead_phi.png")
create_plot1d(hist_muon_trail[:, sum, sum],
plots_path + "Muon_trail_pt.png",
log=True)
create_plot1d(hist_muon_trail[sum, :, sum],
plots_path + "Muon_trail_eta.png")
create_plot1d(hist_muon_trail[sum, sum, :],
plots_path + "Muon_trail_phi.png")
create_plot1d(hist_dimu[:, sum, sum],
plots_path + "Dimu_pt.png",
log=True)
create_plot1d(hist_dimu[sum, :, sum], plots_path + "Dimu_eta.png")
create_plot1d(hist_dimu[sum, sum, :], plots_path + "Dimu_phi.png")
create_plot1d(hist_dimu_mass, plots_path + "Dimu_mass.png")
create_plot1d(hist_D0[:, sum, sum], plots_path + "D0_pt.png", log=True)
create_plot1d(hist_D0[sum, :, sum], plots_path + "D0_eta.png")
create_plot1d(hist_D0[sum, sum, :], plots_path + "D0_phi.png")
create_plot1d(hist_D0_mass, plots_path + "D0_mass.png")
create_plot1d(hist_D0_trk[:, sum, sum],
plots_path + "D0_trk_pt.png",
log=True)
create_plot1d(hist_D0_trk[sum, :, sum], plots_path + "D0_trk_eta.png")
create_plot1d(hist_D0_trk[sum, sum, :], plots_path + "D0_trk_phi.png")
create_plot1d(hist_Dstar[:, sum, sum],
plots_path + "Dstar_pt.png",
log=True)
create_plot1d(hist_Dstar[sum, :, sum], plots_path + "Dstar_eta.png")
create_plot1d(hist_Dstar[sum, sum, :], plots_path + "Dstar_phi.png")
create_plot1d(hist_Dstar_mass, plots_path + "Dstar_mass.png")
create_plot1d(hist_Dstar_mass_refit,
plots_path + "Dstar_mass_refit.png")
create_plot1d(hist_Dstar_deltamr, plots_path + "Dstar_deltamr.png")
create_plot1d(hist_Dstar_deltam, plots_path + "Dstar_deltam.png")
create_plot1d(hist_Dstar_K[:, sum, sum],
plots_path + "Dstar_K_pt.png",
log=True)
create_plot1d(hist_Dstar_K[sum, :, sum],
plots_path + "Dstar_K_eta.png")
create_plot1d(hist_Dstar_K[sum, sum, :],
plots_path + "Dstar_K_phi.png")
create_plot1d(hist_Dstar_pi[:, sum, sum],
plots_path + "Dstar_pi_pt.png",
log=True)
create_plot1d(hist_Dstar_pi[sum, :, sum],
plots_path + "Dstar_pi_eta.png")
create_plot1d(hist_Dstar_pi[sum, sum, :],
plots_path + "Dstar_pi_phi.png")
create_plot1d(hist_Dstar_pis[:, sum, sum],
plots_path + "Dstar_pis_pt.png",
log=True)
create_plot1d(hist_Dstar_pis[sum, :, sum],
plots_path + "Dstar_pis_eta.png")
create_plot1d(hist_Dstar_pis[sum, sum, :],
plots_path + "Dstar_pis_phi.png")
# Creating plots 2D
create_plot2d(hist_muon_lead[:, sum, :],
plots_path + "Muon_lead_ptXphi")
create_plot2d(hist_muon_trail[:, sum, :],
plots_path + "Muon_trail_ptXphi")
create_plot2d(hist_D0[:, :, sum], plots_path + "D0_ptXeta.png")
create_plot2d(hist_D0[sum, :, :], plots_path + "D0_etaXphi.png")
create_plot2d(hist_D0_eta_mass, plots_path + "D0_etaXmass.png")
# return dummy accumulator
return output
def __init__(self, mcEventYields=None, jetSyst='nominal'):
################################
# INITIALIZE COFFEA PROCESSOR
################################
self.mcEventYields = mcEventYields
if not jetSyst in ['nominal', 'JERUp', 'JERDown', 'JESUp', 'JESDown']:
raise Exception(
f'{jetSyst} is not in acceptable jet systematic types [nominal, JERUp, JERDown, JESUp, JESDown]'
)
self.jetSyst = jetSyst
dataset_axis = hist.Cat("dataset", "Dataset")
lep_axis = hist.Cat("lepFlavor", "Lepton Flavor")
systematic_axis = hist.Cat("systematic", "Systematic Uncertainty")
m3_axis = hist.Bin("M3", r"$M_3$ [GeV]", 200, 0., 1000)
mass_axis = hist.Bin("mass", r"$m_{\ell\gamma}$ [GeV]", 400, 0., 400)
pt_axis = hist.Bin("pt", r"$p_{T}$ [GeV]", 200, 0., 1000)
eta_axis = hist.Bin("eta", r"$\eta_{\gamma}$", 300, -1.5, 1.5)
chIso_axis = hist.Bin("chIso", r"Charged Hadron Isolation",
np.arange(-0.1, 20.001, .05))
## Define axis to keep track of photon category
phoCategory_axis = hist.Bin("category", r"Photon Category",
[1, 2, 3, 4, 5])
phoCategory_axis.identifiers()[0].label = "Genuine Photon"
phoCategory_axis.identifiers()[1].label = "Misidentified Electron"
phoCategory_axis.identifiers()[2].label = "Hadronic Photon"
phoCategory_axis.identifiers()[3].label = "Hadronic Fake"
### Accumulator for holding histograms
self._accumulator = processor.dict_accumulator({
'photon_pt':
hist.Hist("Counts", dataset_axis, pt_axis, phoCategory_axis,
lep_axis, systematic_axis),
'photon_eta':
hist.Hist("Counts", dataset_axis, eta_axis, phoCategory_axis,
lep_axis, systematic_axis),
'photon_chIso':
hist.Hist("Counts", dataset_axis, chIso_axis, phoCategory_axis,
lep_axis, systematic_axis),
'photon_lepton_mass_3j0t':
hist.Hist("Counts", dataset_axis, mass_axis, phoCategory_axis,
lep_axis, systematic_axis),
'M3':
hist.Hist("Counts", dataset_axis, m3_axis, phoCategory_axis,
lep_axis, systematic_axis),
# 3. ADD HISTOGRAMS
## book histograms for photon pt, eta, and charged hadron isolation
#'photon_pt':
#'photon_eta':
#'photon_chIso':
## book histogram for photon/lepton mass in a 3j0t region
#'photon_lepton_mass_3j0t':
## book histogram for M3 variable
#'M3':
'EventCount':
processor.value_accumulator(int)
})
ext = extractor()
ext.add_weight_sets([
f"btag2016 * {cwd}/ScaleFactors/Btag/DeepCSV_2016LegacySF_V1.btag.csv"
])
ext.finalize()
self.evaluator = ext.make_evaluator()
self.ele_id_sf = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/ele_id_sf.coffea')
self.ele_id_err = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/ele_id_err.coffea')
self.ele_reco_sf = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/ele_reco_sf.coffea')
self.ele_reco_err = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/ele_reco_err.coffea')
self.mu_id_sf = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_id_sf.coffea')
self.mu_id_err = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_id_err.coffea')
self.mu_iso_sf = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_iso_sf.coffea')
self.mu_iso_err = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_iso_err.coffea')
self.mu_trig_sf = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_trig_sf.coffea')
self.mu_trig_err = util.load(
f'{cwd}/ScaleFactors/MuEGammaScaleFactors/mu_trig_err.coffea')
if ch in ['eeeSSonZ', 'eeeSSoffZ']:
values = values.eta[:,2]
hout[var].fill(e2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm1pt':
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue
values = values.pt[:,1]
hout[var].fill(m1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm1eta':
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue
values = values.eta[:,1]
hout[var].fill(m1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm2pt':
if ch in ['mmmSSonZ', 'mmmSSoffZ']:
values = values.pt[:,2]
hout[var].fill(m2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm2eta':
if ch in ['mmmSSonZ', 'mmmSSoffZ']:
values = values.eta[:,2]
hout[var].fill(m2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
return hout
def postprocess(self, accumulator):
return accumulator
if __name__ == '__main__':
# Load the .coffea files
outpath= './coffeaFiles/'
samples = load(outpath+'samples.coffea')
topprocessor = AnalysisProcessor(samples)
from .utils.crossSections import *
from .utils.efficiencies import getMuSF, getEleSF
from .utils.genParentage import maxHistoryPDGID
from .utils.updateJets import updateJetP4
import os.path
cwd = os.path.dirname(__file__)
#load lookup tool for btagging efficiencies
with open(f'{cwd}/utils/taggingEfficienciesDenseLookup.pkl', 'rb') as _file:
taggingEffLookup = pickle.load(_file)
#load lookup tools for pileup scale factors
puLookup = util.load(f'{cwd}/ScaleFactors/puLookup.coffea')
puLookup_Down = util.load(f'{cwd}/ScaleFactors/puLookup_Down.coffea')
puLookup_Up = util.load(f'{cwd}/ScaleFactors/puLookup_Up.coffea')
#create and load jet extractor
Jetext = extractor()
Jetext.add_weight_sets([
f"* * {cwd}/ScaleFactors/JEC/Summer16_07Aug2017_V11_MC_L1FastJet_AK4PFchs.jec.txt",
f"* * {cwd}/ScaleFactors/JEC/Summer16_07Aug2017_V11_MC_L2Relative_AK4PFchs.jec.txt",
f"* * {cwd}/ScaleFactors/JEC/Summer16_07Aug2017_V11_MC_Uncertainty_AK4PFchs.junc.txt",
f"* * {cwd}/ScaleFactors/JEC/Summer16_25nsV1_MC_PtResolution_AK4PFchs.jr.txt",
f"* * {cwd}/ScaleFactors/JEC/Summer16_25nsV1_MC_SF_AK4PFchs.jersf.txt",
])
Jetext.finalize()
Jetevaluator = Jetext.make_evaluator()
import numpy as np
import awkward as ak
from coffea.util import load
compiled = load(__file__.replace('.py', '.coffea'))
def _msoftdrop_weight(pt, eta):
gpar = np.array([1.00626, -1.06161, 0.0799900, 1.20454])
cpar = np.array([1.09302, -0.000150068, 3.44866e-07, -2.68100e-10, 8.67440e-14, -1.00114e-17])
fpar = np.array([1.27212, -0.000571640, 8.37289e-07, -5.20433e-10, 1.45375e-13, -1.50389e-17])
genw = gpar[0] + gpar[1]*np.power(pt*gpar[2], -gpar[3])
ptpow = np.power.outer(pt, np.arange(cpar.size))
cenweight = np.dot(ptpow, cpar)
forweight = np.dot(ptpow, fpar)
weight = np.where(np.abs(eta) < 1.3, cenweight, forweight)
return genw*weight
def corrected_msoftdrop(fatjets):
if not isinstance(fatjets, ak.JaggedArray):
raise ValueError
sf_flat = _msoftdrop_weight(fatjets.p4.pt.flatten(), fatjets.p4.eta.flatten())
sf_flat = np.maximum(1e-5, sf_flat)
return fatjets.msoftdrop * fatjets.copy(content=sf_flat)
def n2ddt_shift(fatjets, year='2017'):
return compiled[f'{year}_n2ddt_rho_pt'](fatjets.rho, fatjets.p4.pt)
# ---- Reiterate categories ---- #
ttagcats = ["at"] #, "0t", "1t", "It", "2t"]
btagcats = ["0b", "1b", "2b"]
ycats = ['cen', 'fwd']
list_of_cats = [
t + b + y for t, b, y in itertools.product(ttagcats, btagcats, ycats)
]
from Filesets import filesets
outputs_unweighted = {}
for name, files in filesets.items():
outputs_unweighted[name] = util.load(
'TTbarAllHadUproot/CoffeaOutputs/UnweightedOutputs/TTbarResCoffea_' +
name + '_unweighted_output.coffea')
outputs_unweighted
""" ---------------- CREATE RAW MISTAG PLOTS ---------------- """
# ---- Only Use This When LookUp Tables Were Not In Use for Previous Uproot Job (i.e. UseLookUpTables = False) ---- #
# ---- This Creates Mistag plots for every dataset in every category for debugging if necessary or for curiosity ---- #
# ---- Look up tables are a bit more sophisticated and much more useful to the analysis ---- #
SaveDirectory = maindirectory + '/TTbarAllHadUproot/MistagPlots/'
DoesDirectoryExist(
SaveDirectory) # no need to create the directory several times
# Function sqrt(x)
def forward(x):
return x**(1 / 2)
2017 : 41.53,
2018 : 59.74,
}
file_kind = "CC"
if args.output is None:
template_file = f"templates_{args.identifier}_{file_kind}.root"
template_mu_file = f"templatesmuCR_{args.identifier}_{file_kind}.root"
else:
_base_name = args.output.split(".root")[0]
template_file = f"{_base_name}.root"
template_mu_file = f"{_base_name}_mu.root"
# Load info
print(f'Processing coffea output from: hists_{args.identifier}.coffea')
output = load(f'hists_{args.identifier}.coffea')
xsecs = xSecReader('metadata/xSections_manual.dat')
sumw = getSumW(output)
if args.mergemap is not None:
print(f'Processing with mergemap from: {args.mergemap}')
with open(args.mergemap) as json_file:
merge_map = json.load(json_file)
else:
merge_map = None
if args.cvl not in output[list(output.keys())[0]]['templates'].axes['ddc'].edges:
raise ValueError(
f"args.cvl = {args.cvl} not available. Axis edges are {output[list(output.keys())[0]]['templates'].axes['ddc'].edges}"
)
compiled['2017_pileupweight']._values = np.minimum(
5, compiled['2017_pileupweight']._values)
compiled['2017_pileupweight_puUp']._values = np.minimum(
5, compiled['2017_pileupweight_puUp']._values)
compiled['2017_pileupweight_puDown']._values = np.minimum(
5, compiled['2017_pileupweight_puDown']._values)
compiled['2018_pileupweight']._values = np.minimum(
5, compiled['2018_pileupweight']._values)
compiled['2018_pileupweight_puUp']._values = np.minimum(
5, compiled['2018_pileupweight_puUp']._values)
compiled['2018_pileupweight_puDown']._values = np.minimum(
5, compiled['2018_pileupweight_puDown']._values)
with importlib.resources.path("boostedhiggs.data",
'powhegToMinloPtCC.coffea') as filename:
compiled['powheg_to_nnlops'] = util.load(filename)
class SoftDropWeight(lookup_base):
def _evaluate(self, pt, eta):
gpar = np.array([1.00626, -1.06161, 0.0799900, 1.20454])
cpar = np.array([
1.09302, -0.000150068, 3.44866e-07, -2.68100e-10, 8.67440e-14,
-1.00114e-17
])
fpar = np.array([
1.27212, -0.000571640, 8.37289e-07, -5.20433e-10, 1.45375e-13,
-1.50389e-17
])
genw = gpar[0] + gpar[1] * np.power(pt * gpar[2], -gpar[3])
cenweight = np.polyval(cpar[::-1], pt)
import os
import numpy as np
from coffea.util import load, save
import matplotlib.pyplot as plt
import coffea.hist as hist
import time
#import mplhep
#plt.style.use(mplhep.style.CMS)
filename = "WZ_Run2018_40000.futures"
histo = load(filename)
h1_mass = histo['mass']
h1_Nele = histo['nElectrons']
nWZ = histo['sumw']['WZ']
if not isinstance(h1_mass, hist.Hist):
raise "Error type is not hist"
if not isinstance(h1_Nele, hist.Hist):
raise "Error type is not hist"
hist.plot1d(h1_mass, overlay='dataset')
plt.show()
plt.close()
hist.plot1d(h1_Nele, overlay='dataset')
plt.show()
def plot_bkg_templates(fnames_to_run):
"""
Runs LOWESS smoothing algorithm ntoys times and finds 1 and 2 sigma bands for interpolation
"""
for bkg_file in fnames_to_run:
hdict = load(bkg_file)
jmult = "3Jets" if "3Jets" in os.path.basename(bkg_file) else "4PJets"
for tname, orig_template in hdict[args.lepton].items():
proc = tname.split(
"_")[0] if not "data_obs" in tname else "data_obs"
sys = sorted(filter(None, tname.split(f"{proc}_")))[0]
if proc == "BKG": continue
#if sys not in ["hdampUP", "hdampDOWN", "mtop1665", "mtop1695", "mtop1715", "mtop1735", "mtop1755", "mtop1785", "ueUP", "ueDOWN"]: continue
if sys == "nosys": continue
print(args.lepton, jmult, sys, proc)
nosys_hist = hdict[args.lepton][f"{proc}_nosys"].copy()
orig_smooth_hist = Plotter.smoothing_mttbins(
nosys=nosys_hist,
systematic=orig_template,
mtt_centers=mtt_centers,
nbinsx=nbinsx,
nbinsy=nbinsy)
x_lims = (0, nosys_hist.dense_axes()[0].centers().size)
# get vals and errors of systematic variation
sys_histo_vals, sys_histo_sumw2 = orig_template.values(
sumw2=True)[()]
sys_histo_errs = np.sqrt(sys_histo_sumw2)
# make toys based on Gaussian distribution of mu=bin_val, sigma=bin_error
toy_arrays = np.zeros((nbins, ntoys))
for idx in range(nbins):
toy_arrays[idx] = np.random.normal(sys_histo_vals[idx],
sys_histo_errs[idx],
size=ntoys)
# get smoothed relative deviation distributions from toys
smoothed_rel_dev_arrays = np.zeros((ntoys, nbins))
chi2_pvals = np.zeros((ntoys, 2))
for idx in range(ntoys):
smoothed_array = Plotter.smoothing_mttbins(
nosys=nosys_hist,
systematic=(toy_arrays.T)[idx],
mtt_centers=mtt_centers,
nbinsx=nbinsx,
nbinsy=nbinsy)
chi2_pval = chisquare(
f_obs=smoothed_array, f_exp=orig_smooth_hist.values()[()]
) # convert to expected yields so inputs are greater than 5
chi2_pvals[idx] = np.array(
[chi2_pval.statistic, chi2_pval.pvalue])
smoothed_rel_dev_arrays[idx] = (
smoothed_array -
nosys_hist.values()[()]) / nosys_hist.values()[()]
## find 68% and 95% intervals
plus_one_sigma_smooth_vals, minus_one_sigma_smooth_vals = np.zeros(
nbins), np.zeros(nbins)
plus_two_sigma_smooth_vals, minus_two_sigma_smooth_vals = np.zeros(
nbins), np.zeros(nbins)
for bin in range(nbins):
plus_one_sigma_smooth_vals[bin] = np.sort(
smoothed_rel_dev_arrays[:, bin])[plus_one_sigma_ind]
minus_one_sigma_smooth_vals[bin] = np.sort(
smoothed_rel_dev_arrays[:, bin])[minus_one_sigma_ind]
plus_two_sigma_smooth_vals[bin] = np.sort(
smoothed_rel_dev_arrays[:, bin])[plus_two_sigma_ind]
minus_two_sigma_smooth_vals[bin] = np.sort(
smoothed_rel_dev_arrays[:, bin])[minus_two_sigma_ind]
# plot relative deviation
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
# original relative deviations
orig_masked_vals, orig_masked_bins = Plotter.get_ratio_arrays(
num_vals=orig_template.values()[()] - nosys_hist.values()[()],
denom_vals=nosys_hist.values()[()],
input_bins=nosys_hist.dense_axes()[0].edges())
ax.step(orig_masked_bins,
orig_masked_vals,
where="post",
**{
"color": "k",
"linestyle": "-",
"label": "Original"
})
# original smoothing relative deviations
orig_smoothed_masked_vals, orig_smoothed_masked_bins = Plotter.get_ratio_arrays(
num_vals=orig_smooth_hist.values()[()] -
nosys_hist.values()[()],
denom_vals=nosys_hist.values()[()],
input_bins=nosys_hist.dense_axes()[0].edges())
ax.step(orig_smoothed_masked_bins,
orig_smoothed_masked_vals,
where="post",
**{
"color": "r",
"linestyle": "-",
"label": "Original Smoothing"
})
# plot 68 and 95% intervals for yields
ax.fill_between(nosys_hist.dense_axes()[0].edges(),
np.r_[minus_one_sigma_smooth_vals,
minus_one_sigma_smooth_vals[-1]],
np.r_[plus_one_sigma_smooth_vals,
plus_one_sigma_smooth_vals[-1]],
where=np.r_[plus_one_sigma_smooth_vals,
plus_one_sigma_smooth_vals[-1]] >
np.r_[minus_one_sigma_smooth_vals,
minus_one_sigma_smooth_vals[-1]],
step="post",
**{
"label": "68%",
"facecolor": "#00cc00",
"alpha": 0.5
})
ax.fill_between(nosys_hist.dense_axes()[0].edges(),
np.r_[minus_two_sigma_smooth_vals,
minus_two_sigma_smooth_vals[-1]],
np.r_[plus_two_sigma_smooth_vals,
plus_two_sigma_smooth_vals[-1]],
where=np.r_[plus_two_sigma_smooth_vals,
plus_two_sigma_smooth_vals[-1]] >
np.r_[minus_two_sigma_smooth_vals,
minus_two_sigma_smooth_vals[-1]],
step="post",
**{
"label": "95%",
"facecolor": "#ffcc00",
"alpha": 0.5
})
ax.legend(loc="upper right", title=f"{sys}, {proc}")
ax.axhline(
0, **{
"linestyle": "--",
"color": (0, 0, 0, 0.5),
"linewidth": 1
})
ax.autoscale()
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.15)
ax.set_xlim(x_lims)
ax.set_xlabel(
"$m_{t\\bar{t}}$ $\otimes$ |cos($\\theta^{*}_{t_{l}}$)|")
ax.set_ylabel("Rel. Deviaton from Nominal")
# add lepton/jet multiplicity label
ax.text(0.02,
0.94,
f"{leptypes[args.lepton]}, {jet_mults[jmult]}",
fontsize=rcParams["font.size"] * 0.9,
horizontalalignment="left",
verticalalignment="bottom",
transform=ax.transAxes)
## draw vertical lines for distinguishing different ctstar bins
vlines = [x_lims[1] * ybin / 5 for ybin in range(1, 5)]
for vline in vlines:
ax.axvline(vline, color="k", linestyle="--")
hep.cms.label(ax=ax,
data=False,
paper=False,
year=args.year,
lumi=round(data_lumi_year[f"{args.lepton}s"] / 1000.,
1))
#set_trace()
pltdir = os.path.join(outdir, args.lepton, jmult, sys)
if not os.path.isdir(pltdir):
os.makedirs(pltdir)
figname = os.path.join(
pltdir, "_".join([
jmult, args.lepton, sys, proc,
"SmoothingConfidenceIntervals"
]))
fig.savefig(figname)
print(f"{figname} written")
plt.close()
coffeapath = './coffeaFiles/'
outname = 'plotsTopEFT'
mocapath = 'moca'
mocaScripts = ['corrections', 'functions', 'objects', 'samples', 'selection']
#analysis =
#treeName
nworkers = 8
### (Re)produce inputs...
### Produce/load analysis object
#print("Executing python analysis/topEFT/topeft.py...")
#os.system('python analysis/topEFT/topeft.py')
processor_instance = load(coffeapath + nameProcessor + '.coffea')
### Load samples
samplesdict = load(coffeapath + nameSamples + '.coffea')
flist = {}
xsec = {}
sow = {}
isData = {}
for k in samplesdict.keys():
flist[k] = samplesdict[k]['files']
xsec[k] = samplesdict[k]['xsec']
sow[k] = samplesdict[k]['nSumOfWeights']
isData[k] = samplesdict[k]['isData']
# Run the processor and get the output
tstart = time.time()
def merge(folder, _dataset):
filelist = {}
pd = []
for filename in os.listdir(folder):
if '.pkl.gz' in filename:
if filename.split("____")[0] not in pd:
pd.append(filename.split("____")[0])
for pdi in pd:
files = []
for filename in os.listdir(folder):
if pdi not in filename: continue
if '.pkl.gz' not in filename: continue
files.append(filename)
#print(pdi,'length:',len(files))
split_files = split(files, 100)
#print(pdi,'number of lists:',len(split_files))
for i in range(0, len(split_files)):
filelist[pdi + '___' + str(i) + '_'] = split_files[i]
coffealist = []
for pdi in filelist.keys():
if _dataset not in 'None' and _dataset not in pdi: continue
print(pdi)
#print(filelist[pdi])
hists = {}
for filename in filelist[pdi]:
fin = gzip.open(folder + '/' + filename)
print('Opening:', folder + '/' + filename)
hin = cloudpickle.load(fin)
#print('before',hin['recoil'].integrate('dataset',filename.split(".")[0]).integrate('region','isoneE').integrate('jet_selection','baggy').values())
for k in hin.keys():
if k not in hists: hists[k] = hin[k]
else: hists[k] += hin[k]
#print('middle',hists['recoil'].integrate('dataset',filename.split(".")[0]).integrate('region','isoneE').integrate('jet_selection','baggy').values())
fin.close()
del hin
dataset = hist.Cat("dataset", "dataset", sorting='placement')
dataset_cats = ("dataset", )
dataset_map = OrderedDict()
dataset_map[pdi] = (pdi.split("___")[0] + "*", )
for key in hists.keys():
hists[key] = hists[key].group(dataset_cats, dataset, dataset_map)
#print('after',hists['recoil'].integrate('dataset',pdi).integrate('region','isoneE').integrate('jet_selection','baggy').values())
save(hists, folder + '/' + pdi + '.coffea')
del hists
#coffealist.append(folder+'/'+pdi+'.coffea')
for coffeafile in os.listdir(folder):
if '.coffea' not in coffeafile: continue
coffealist.append(folder + '/' + coffeafile)
print('coffealist', coffealist)
htot = {}
for coffeafile in coffealist:
print('Opening', coffeafile)
hists = load(coffeafile)
#print(hists)
#print(coffeafile.split("/")[1].split(".")[0])
#print('before',hists['recoil'].integrate('dataset',coffeafile.split("/")[1].split(".")[0]).integrate('region','isoneE').integrate('jet_selection','baggy').values())
for k in hists:
if k not in htot: htot[k] = hists[k]
else: htot[k] += hists[k]
#print('after',htot['recoil'].integrate('dataset',coffeafile.split("/")[1].split(".")[0]).integrate('region','isoneE').integrate('jet_selection','baggy').values())
del hists
if _dataset in 'None': _dataset = ''
save(htot, 'condor_hists_' + folder + '.coffea')
def get_bkg_templates(fnames_to_run):
"""
Function that writes linearized mtt vs costheta distributions to root file.
"""
#set_trace()
for bkg_file in fnames_to_run:
hdict = load(bkg_file)
jmult = "3Jets" if "3Jets" in os.path.basename(bkg_file) else "4PJets"
for lep in hdict.keys():
for tname, orig_template in hdict[lep].items():
proc = tname.split(
"_")[0] if not "data_obs" in tname else "data_obs"
sys = sorted(filter(None, tname.split(f"{proc}_")))[0]
#if not ((sys == "ueDOWN") and (proc == "ttJets")): continue
if sys == "nosys": continue
print(lep, jmult, sys, proc)
nominal_hist = hdict[lep][f"{proc}_nosys"].copy()
x_lims = (0, nominal_hist.dense_axes()[0].centers().size)
# perform smoothing
smoothed_histos_list = [(Plotter.smoothing_mttbins(
nosys=nominal_hist,
systematic=orig_template,
mtt_centers=mtt_centers,
nbinsx=len(linearize_binning[0]) - 1,
nbinsy=len(linearize_binning[1]) - 1,
**{"frac": frac_val / 10.}), frac_val / 10.)
for frac_val in np.arange(2, 7, 2)]
#smoothed_histos_chi2 = {frac_val : find_chi2(h_fitted=smooth_histo, h_unc=orig_template) for smooth_histo, frac_val in smoothed_histos_list}
# perform flattening
flattened_histo = Plotter.flatten(nosys=nominal_hist,
systematic=orig_template)
#flat_chi2 = find_chi2(h_fitted=flattened_histo, h_unc=orig_template)
# plot relative deviation
fig, ax = plt.subplots()
fig.subplots_adjust(hspace=.07)
# plot original dist
orig_masked_vals, orig_masked_bins = Plotter.get_ratio_arrays(
num_vals=orig_template.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.fill_between(orig_masked_bins,
orig_masked_vals,
facecolor="k",
step="post",
alpha=0.5,
label="Unsmoothed")
# plot smoothed versions
for smooth_histo, frac_val in smoothed_histos_list:
smooth_masked_vals, smooth_masked_bins = Plotter.get_ratio_arrays(
num_vals=smooth_histo.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.step(smooth_masked_bins,
smooth_masked_vals,
where="post",
**{
"linestyle": "-",
"label": f"Frac={frac_val}",
"linewidth": 2
})
# plot flattened val
flat_masked_vals, flat_masked_bins = Plotter.get_ratio_arrays(
num_vals=flattened_histo.values()[()] -
nominal_hist.values()[()],
denom_vals=nominal_hist.values()[()],
input_bins=nominal_hist.dense_axes()[0].edges())
ax.step(flat_masked_bins,
flat_masked_vals,
where="post",
**{
"linestyle": "-",
"label": "Flat",
"linewidth": 2
})
ax.legend(loc="upper right", title=f"{sys}, {proc}")
ax.axhline(
0, **{
"linestyle": "--",
"color": (0, 0, 0, 0.5),
"linewidth": 1
})
ax.autoscale()
ax.set_xlim(x_lims)
ax.set_xlabel(
"$m_{t\\bar{t}}$ $\otimes$ |cos($\\theta^{*}_{t_{l}}$)|")
ax.set_ylabel("Rel. Deviaton from Nominal")
# add lepton/jet multiplicity label
ax.text(0.02,
0.94,
f"{leptypes[lep]}, {jet_mults[jmult]}",
fontsize=rcParams["font.size"] * 0.9,
horizontalalignment="left",
verticalalignment="bottom",
transform=ax.transAxes)
## draw vertical lines for distinguishing different ctstar bins
vlines = [x_lims[1] * ybin / 5 for ybin in range(1, 5)]
for vline in vlines:
ax.axvline(vline, color="k", linestyle="--")
hep.cms.label(ax=ax,
data=False,
paper=False,
year=args.year,
lumi=round(data_lumi_year[f"{lep}s"] / 1000., 1))
#set_trace()
pltdir = os.path.join(outdir, lep, jmult, sys)
if not os.path.isdir(pltdir):
os.makedirs(pltdir)
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "BinWidths_Comp"]))
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "SmoothValues_Comp"]))
#figname = os.path.join(pltdir, "_".join([jmult, lep, sys, proc, "MttBinWidths_SmoothValues_Comp"]))
figname = os.path.join(
pltdir,
"_".join([jmult, lep, sys, proc, "SmoothedFlatVals_Comp"]))
fig.savefig(figname)
print(f"{figname} written")
plt.close()
def get_sig_templates(tmp_rname):
'''
Function that writes linearized mtt vs costheta distributions to root file.
'''
from rootpy.plotting import Hist2D
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 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 = mtt_ctstar_2d_binning
#xrebinning, yrebinning = 2, 1
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
#set_trace()
histo = histo.rebin(yaxis_name, new_ybins)
rebin_histo = histo[:, :, :, :, 'btagPass', 'Tight'].integrate('lepcat').integrate('btag')
signals = sorted(set([key[0] for key in rebin_histo.values().keys()]))
# create 2D signal hists and write to temp file
with root_open(tmp_rname, 'w') as out:
#for lep in ['Muon']:
for lep in ['Muon', 'Electron']:
lepdir = 'mujets' if lep == 'Muon' else 'ejets'
# scale by lumi
lumi_correction = load('%s/Corrections/%s/MC_LumiWeights_IgnoreSigEvts.coffea' % (proj_dir, jobid))[args.year]['%ss' % lep]
scaled_histo = rebin_histo.copy()
scaled_histo.scale(lumi_correction, axis='dataset')
for jmult in njets_to_run:
histo = scaled_histo[:, :, jmult, lep].integrate('jmult').integrate('leptype')
for signal in signals:
_, mass, width, pI, wt = tuple(signal.split('_'))
samtype = 'int' if pI == 'Int' else 'sgn'
bostype = 'ggA' if _ == 'AtoTT' else 'ggH'
sub_name = '%s_%s-%s-%s-%s' % (bostype, wt, samtype, widthTOname(width).split('W')[-1]+'pc', mass) if pI == 'Int' else '%s_pos-%s-%s-%s' % (bostype, samtype, widthTOname(width).split('W')[-1]+'pc', mass)
#set_trace()
for sys in sys_to_use.keys():
sysname, onlyTT = sys_to_use[sys]
if onlyTT: continue
if sys not in histo.axis('sys')._sorted:
print('\n\n Systematic %s not available, skipping\n\n' % sys)
continue
#set_trace()
if 'LEP' in sysname: sysname = sysname.replace('LEP', lepdir[0])
template_histo = histo[signal, sys].integrate('dataset').integrate('sys')
if wt == 'neg':
template_histo.scale(-1.)
#if (pI == 'Int') and (wt == 'pos'): continue
print(lep, jmult, sub_name, sys)
sumw, sumw2 = template_histo.values(sumw2=True, overflow='all')[()] # get vals and errors for all bins (including under/overflow)
#if args.smooth:
# set_trace()
## create rootpy hist and rename
rtpy_h2d = Hist2D(template_histo.dense_axes()[0].edges(), template_histo.dense_axes()[1].edges())
outhname = '_'.join([jmult, lepdir, sub_name]) if sys == 'nosys' else '_'.join([jmult, lepdir, sub_name, sysname])
rtpy_h2d.name = outhname
# set bin content for rootpy hist
for binx in range(0, rtpy_h2d.GetNbinsX()+2):
for biny in range(0, rtpy_h2d.GetNbinsY()+2):
rtpy_h2d[binx, biny] = sumw[binx, biny], sumw2[binx, biny]
#set_trace()
rtpy_h2d.Write()
print('%s written' % tmp_rname)
input_dir = '/'.join(
[proj_dir, 'results',
'%s_%s' % (args.year, jobid), analyzer])
f_ext = 'TOT.coffea'
outdir = '/'.join([proj_dir, 'plots', '%s_%s' % (args.year, jobid), analyzer])
if not os.path.isdir(outdir):
os.makedirs(outdir)
fnames = sorted([
'%s/%s' % (input_dir, fname) for fname in os.listdir(input_dir)
if fname.endswith(f_ext)
])
#set_trace()
hdict = plt_tools.add_coffea_files(fnames) if len(fnames) > 1 else load(
fnames[0])
jet_mults = {'3Jets': '3 jets', '4PJets': '4+ jets'}
objtypes = {
'Jets': 'jets',
'Lep': {
'Muon': '$\\mu$',
'Electron': '$e$',
}
}
btag_cats = {
'btagFail': '0 btags',
'btagPass': '******',
}
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)
return accumulator
samples = {"default":args.files}
#with open('files_prev.json') as fin:
# samples = json.load(fin)
output = processor.run_uproot_job(samples,
treename='Events',
processor_instance=GenVisualizer(),
executor=processor.futures_executor,
executor_args={'workers': 4},
chunksize=500000,
)
save(output, 'genstuff.coffea')
output = load("genstuff.coffea")
#hmass = output["hmass"]
#bin_contents = hmass.values()[('ZPrimeToQQ_DMsimp_HT400_M50',)]
#edges = hmass.axis('mass').edges()
#edge_pairs = [(edges[i], edges[i+1]) for i in range(len(edges)-1)]
#histd = zip(edge_pairs, bin_contents)
#for thing in histd:
# print(thing)
# In[13]:
for hname, axisname in [("hmass", "mass"), ("hpt", "pt")]:
print(hname)
hists = {k[0]: v for k,v in output[hname].values(sumw2=True, overflow='over').items()}
def getRoc(args):
# vars
vars = args.vars.split(',')
# open hists
hists_unmapped = load('%s.coffea' % args.hists)
print(hists_unmapped)
# map to hists
hists_mapped = {}
for key, val in hists_unmapped.items():
if isinstance(val, hist.Hist):
hists_mapped[key] = processmap.apply(val)
print('hists mapped ', hists_mapped)
# build roc for all vars
vals = {}
rocs = {}
labels = {}
for lep in ['ele', 'mu']:
for jet in ['jet0', 'jet1']:
vals['%s_%s' % (lep, jet)] = {}
rocs['%s_%s' % (lep, jet)] = {}
for var in vars:
for lep in ['ele', 'mu']:
for jet in ['jet0', 'jet1']:
print('getting roc for ', var, lep, jet)
hist_name = 'roc_%ssel%s' % (lep, jet)
if 'lsf' in var:
var_name = jet + '_' + var
var_cut_dir = -1
else:
var_name = lep + '0_' + var
var_cut_dir = 1
# get hist
h = hists_mapped[hist_name]
print(h)
print([
ax for ax in h.axes()
if ax.name not in {'process', var_name}
])
x = h.sum(*[
ax for ax in h.axes()
if ax.name not in {'process', var_name}
])
bkg = 'qcd'
sig = 'h125'
#vals['%s_%s'%(lep,jet)][var], rocs['%s_%s'%(lep,jet)][var] = roc(x, bkg, sig, direction=var_cut_dir)
labels[var] = var
# plot variable
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
print(x)
hist.plot1d(x,
ax=ax,
overlay='process',
clear=False,
density=True)
fig.savefig("plots/rocs/lsf_%s_%s_%s.png" % (var, lep, jet))
return vals, rocs, labels
import mplhep
plt.style.use(mplhep.style.ROOT)
plt.tight_layout()
from brazil.aguapreta import *
figure_directory = "/home/dryu/BFrag/data/figures/"
input_files = [
"/home/dryu/BFrag/data/histograms/condor/job20200513_215445/DataHistograms_Run2018.coffea",
]
hists = {}
subjob_cutflows = {}
for i, input_file in enumerate(input_files):
print(input_file)
this_hists = util.load(input_file)
# Integrate dataset to save space
for key in this_hists.keys():
obj = this_hists[key]
if isinstance(obj, hist.hist_tools.Hist):
if "dataset" in obj.axes():
this_hists[key] = obj.integrate("dataset")
del obj
#if i == 0:
# pprint(this_hists.keys())
for item_name, item in this_hists.items():
if isinstance(item, hist.Hist):
if item_name in hists:
hists[item_name].add(item)
else:
mask = mc_pu > 0.
corr = data_pu.copy()
corr_puUp = data_pu_puUp.copy()
corr_puDown = data_pu_puDown.copy()
corr[mask] /= mc_pu[mask]
corr_puUp[mask] /= mc_pu[mask]
corr_puDown[mask] /= mc_pu[mask]
pileup_corr = lookup_tools.dense_lookup.dense_lookup(corr, fin_pileup["pileup"].edges)
pileup_corr_puUp = lookup_tools.dense_lookup.dense_lookup(corr_puUp, fin_pileup["pileup"].edges)
pileup_corr_puDown = lookup_tools.dense_lookup.dense_lookup(corr_puDown, fin_pileup["pileup"].edges)
corrections['2016_pileupweight'] = pileup_corr
corrections['2016_pileupweight_puUp'] = pileup_corr_puUp
corrections['2016_pileupweight_puDown'] = pileup_corr_puDown
pileup_corr = load('correction_files/pileup_mc.coffea')
with uproot.open("correction_files/pileup_Cert_294927-306462_13TeV_PromptReco_Collisions17_withVar.root") as fin_pileup:
norm = lambda x: x / x.sum()
data_pu = norm(fin_pileup["pileup"].values)
data_pu_puUp = norm(fin_pileup["pileup_plus"].values)
data_pu_puDown = norm(fin_pileup["pileup_minus"].values)
pileup_corr_puUp = {}
pileup_corr_puDown = {}
for k in pileup_corr.keys():
mc_pu = norm(pileup_corr[k].value)
mask = mc_pu > 0.
corr = data_pu.copy()
corr_puUp = data_pu_puUp.copy()
corr_puDown = data_pu_puDown.copy()
corr[mask] /= mc_pu[mask]
def acc_from_dir(indir):
"""Load Coffea accumulator from directory with *.coffea files
:param indir: Directory to search for coffea files
:type indir: string
:return: Sum of all found accumulators
:rtype: dict
"""
files = filter(lambda x: x.endswith(".coffea") and not ('cache' in x),
os.listdir(indir))
files = list(map(lambda x: os.path.abspath(pjoin(indir, x)), files))
listhash = sha256sum(files)
cache = pjoin(indir, f'merged_cache_{listhash}.coffea')
if os.path.exists(cache):
return load(cache)
else:
# Progress bar
t = tqdm(total=len(files), desc='Merging input files')
# Recursive merging
to_merge = files
# Use temporary files to store intermediate
# merger results
tmp_files = []
def load_and_remove(path):
data = load(path)
os.remove(path)
return data
def next():
'''Get next item to merge'''
x = to_merge.pop(0)
if isinstance(x, str):
if x in tmp_files:
tmp_files.remove(x)
x = load_and_remove(x)
else:
x = load(x)
return x
while len(to_merge) > 1:
# Remove first two items from list,
# merge them and insert in the back
t.update()
x = next()
y = next()
tmp = "/tmp/tmp_bucoffea_merge_" + "".join(
random.sample(string.ascii_uppercase + string.digits, 24))
merged = x + y
# clean up to save memory
x = None
y = None
save(merged, tmp)
merged = None
to_merge.append(tmp)
tmp_files.append(tmp)
t.update()
assert (len(to_merge) == 1)
shutil.copy(to_merge[0], cache)
return load(cache)
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")
