def trigger_selection(selection, df, cfg):
pass_all = np.zeros(df.size) == 0
pass_none = ~pass_all
dataset = df['dataset']
if cfg.RUN.SYNC: # Synchronization mode
selection.add('filt_met', pass_all)
selection.add('trig_met', pass_all)
selection.add('trig_ele', pass_all)
selection.add('trig_mu', pass_all)
selection.add('trig_photon', pass_all)
else:
if df['is_data']:
selection.add('filt_met', mask_and(df, cfg.FILTERS.DATA))
else:
selection.add('filt_met', mask_and(df, cfg.FILTERS.MC))
selection.add('trig_met', mask_or(df, cfg.TRIGGERS.MET))
# Electron trigger overlap
if df['is_data']:
if "SinglePhoton" in dataset:
# Backup photon trigger, but not main electron trigger
trig_ele = mask_or(df, cfg.TRIGGERS.ELECTRON.SINGLE_BACKUP) & (
~mask_or(df, cfg.TRIGGERS.ELECTRON.SINGLE))
elif "SingleElectron" in dataset:
# Main electron trigger, no check for backup
trig_ele = mask_or(df, cfg.TRIGGERS.ELECTRON.SINGLE)
elif "EGamma" in dataset:
# 2018 has everything in one stream, so simple OR
trig_ele = mask_or(
df, cfg.TRIGGERS.ELECTRON.SINGLE_BACKUP) | mask_or(
df, cfg.TRIGGERS.ELECTRON.SINGLE)
else:
trig_ele = pass_none
else:
trig_ele = mask_or(df,
cfg.TRIGGERS.ELECTRON.SINGLE_BACKUP) | mask_or(
df, cfg.TRIGGERS.ELECTRON.SINGLE)
selection.add('trig_ele', trig_ele)
# Photon trigger:
if (not df['is_data']) or ('SinglePhoton' in dataset) or ('EGamma'
in dataset):
trig_photon = mask_or(df, cfg.TRIGGERS.PHOTON.SINGLE)
else:
trig_photon = pass_none
selection.add('trig_photon', trig_photon)
for trgname in cfg.TRIGGERS.HT.GAMMAEFF:
if (not df['is_data']) or ('JetHT' in dataset):
selection.add(trgname, mask_or(df, [trgname]))
else:
selection.add(trgname, np.ones(df.size) == 1)
# Muon trigger
selection.add('trig_mu', mask_or(df, cfg.TRIGGERS.MUON.SINGLE))
return selection
def process(self, df):
self._configure(df)
output = self.accumulator.identity()
dataset = df['dataset']
# Lumi mask
year = extract_year(dataset)
if is_data(dataset):
if year == 2016:
json = bucoffea_path(
'data/json/Cert_271036-284044_13TeV_ReReco_07Aug2017_Collisions16_JSON.txt'
)
elif year == 2017:
json = bucoffea_path(
'data/json/Cert_294927-306462_13TeV_EOY2017ReReco_Collisions17_JSON_v1.txt'
)
elif year == 2018:
json = bucoffea_path(
'data/json/Cert_314472-325175_13TeV_17SeptEarlyReReco2018ABC_PromptEraD_Collisions18_JSON.txt'
)
lumi_mask = LumiMask(json)(df['run'], df['luminosityBlock'])
else:
lumi_mask = np.ones(df.size) == 1
# MET filters
if is_data(dataset):
filt_met = mask_and(df, cfg.FILTERS.DATA)
else:
filt_met = mask_and(df, cfg.FILTERS.MC)
if year == 2016:
trigger = 'HLT_Photon175'
else:
trigger = 'HLT_Photon200'
photons = setup_photons(df)
ak4 = setup_jets(df)
ak4 = ak4[
object_overlap(ak4, photons) \
& ak4.tightId \
& (ak4.pt > 100) \
& (ak4.abseta < 2.4)
]
event_mask = filt_met \
& lumi_mask \
& (ak4.counts > 0) \
& df[trigger] \
& (df['MET_pt'] < 60)
# Generator weight
weights = processor.Weights(size=df.size, storeIndividual=True)
if is_data(dataset):
weights.add('gen', np.ones(df.size))
else:
weights.add('gen', df['Generator_weight'])
photon_kinematics = (photons.pt > 200) & (photons.barrel)
# Medium
vals = photons[photon_kinematics & photons.mediumId].sieie[event_mask]
pt = photons[photon_kinematics & photons.mediumId].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# No Sieie
vals = photons[photon_kinematics
& medium_id_no_sieie(photons)].sieie[event_mask]
pt = photons[photon_kinematics
& medium_id_no_sieie(photons)].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium_nosieie',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# No Sieie, inverted isolation
vals = photons[photon_kinematics
& medium_id_no_sieie_inv_iso(photons)].sieie[event_mask]
pt = photons[photon_kinematics
& medium_id_no_sieie_inv_iso(photons)].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium_nosieie_invertiso',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# Keep track of weight sum
if not is_data(dataset):
output['sumw'][dataset] += df['genEventSumw']
output['sumw2'][dataset] += df['genEventSumw2']
return output