def __init__(self, output, ev, weight, selection=None):
'''
output: the accumulator object
ev: NanoEvent
weight: coffea analysis_tools Weights object
'''
self.ev = ev
self.weight = weight.weight()
self.output = output
self.selection = None
self.addRow('entry', (ak.ones_like(self.weight) == 1))
def mask_or(ev, collection, masks):
"""Returns the OR of the masks in the list
:param ev: NanoEvents
:type ev: NanoEvents
:param collection: HLT or Filter
"type collection: string
:param masks: Mask names as saved in the df
:type masks: List
:return: OR of all masks for each event
:rtype: array
"""
# Start with array of False
decision = (ak.ones_like(ev.MET.pt) == 0)
coll = getattr(ev, collection)
# Flip to true if any is passed
for t in masks:
try:
decision = decision | getattr(coll, t)
except KeyError:
continue
return decision
def build_weight_like(weight, selection, like):
return ak.flatten(weight[selection] * ak.ones_like(like[selection]))
def __init__(self, ev, obj, wp, year=2018, verbose=0):
self.obj = obj
self.wp = wp
if self.wp == None:
self.selection_dict = {}
else:
self.selection_dict = obj_def[self.obj][self.wp]
self.v = verbose
self.year = year
id_level = None
if wp.lower().count('veto'):
id_level = 0
elif wp.lower().count('fake'):
id_level = 1
elif wp.lower().count('tight'):
id_level = 2
if self.obj == "Muon":
# collections are already there, so we just need to calculate missing ones
ev['Muon', 'absMiniIso'] = ev.Muon.miniPFRelIso_all * ev.Muon.pt
ev['Muon', 'ptErrRel'] = ev.Muon.ptErr / ev.Muon.pt
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso) otherwise
mask_close = (ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet),
99) < 0.4) * 1
mask_far = ~(ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet), 99)
< 0.4) * 1
deepJet = ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB,
0) * mask_close + 0 * mask_far
jetRelIsoV2 = ev.Muon.jetRelIso * mask_close + ev.Muon.pfRelIso03_all * mask_far # default to 0 if no match
conePt = 0.9 * ak.fill_none(
ev.Muon.matched_jet.pt,
0) * mask_close + (ev.Muon.pt *
(1 + ev.Muon.miniPFRelIso_all)) * mask_far
#conePt = 0.8 * ak.fill_none(ev.Muon.matched_jet.pt,0) * mask_close + (ev.Muon.pt/(1 + ev.Muon.miniPFRelIso_all))*mask_far
ev['Muon', 'deepJet'] = ak.copy(deepJet)
ev['Muon', 'jetRelIsoV2'] = jetRelIsoV2
ev['Muon', 'conePt'] = conePt
ev['Muon', 'id'] = ak.ones_like(conePt) * id_level
self.cand = ev.Muon
elif self.obj == "Electron":
# calculate new variables. asignment is awkward, but what can you do.
ev['Electron',
'absMiniIso'] = ev.Electron.miniPFRelIso_all * ev.Electron.pt
ev['Electron', 'etaSC'] = ev.Electron.eta + ev.Electron.deltaEtaSC
# the following line is only needed if we do our own matching.
# right now, we keep using the NanoAOD match, but check the deltaR distance
# jet_index, mask_match, mask_nomatch = self.matchJets(ev.Electron, ev.Jet)
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso) otherwise
mask_close = (ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
mask_far = ~(ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
deepJet = ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB,
0) * mask_close
jetRelIsoV2 = ev.Electron.jetRelIso * mask_close + ev.Electron.pfRelIso03_all * mask_far # default to 0 if no match
conePt = 0.9 * ak.fill_none(
ev.Electron.matched_jet.pt, 0) * mask_close + (
ev.Electron.pt *
(1 + ev.Electron.miniPFRelIso_all)) * mask_far
#conePt = 0.8 * ak.fill_none(ev.Electron.matched_jet.pt,0) * mask_close + (ev.Electron.pt/(1 + ev.Electron.miniPFRelIso_all))*mask_far
ev['Electron', 'deepJet'] = ak.copy(deepJet)
ev['Electron', 'jetRelIsoV2'] = jetRelIsoV2
ev['Electron', 'conePt'] = conePt
ev['Electron', 'id'] = ak.ones_like(conePt) * id_level
self.cand = ev.Electron
self.getSelection()
if self.obj == "Electron" and self.wp == "tight":
self.selection = self.selection & self.getElectronMVAID(
) & self.getIsolation(0.07, 0.78, 8.0) & self.isTriggerSafeNoIso()
if self.v > 0: print(" - custom ID and multi-isolation")
if self.obj == "Muon" and self.wp == "tight":
self.selection = self.selection & self.getIsolation(
0.11, 0.74, 6.8)
if self.v > 0: print(" - custom multi-isolation")
#self.selection = self.selection & ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Muon.matched_jet.btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == "Electron" and (self.wp == "tightTTH"
or self.wp == 'fakeableTTH'
or self.wp == "tightSSTTH"
or self.wp == 'fakeableSSTTH'):
self.selection = self.selection & self.getSigmaIEtaIEta()
if self.v > 0: print(" - SigmaIEtaIEta")
#self.selection = self.selection & ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Electron.matched_jet.btagDeepFlavB<0.2770)
#self.selection = self.selection & (ev.Jet[ev.Electron.jetIdx].btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == 'Muon' and (self.wp == 'fakeableTTH'
or self.wp == 'fakeableSSTTH'):
#self.selection = self.selection & (self.cand.deepJet < self.getThreshold(self.cand.conePt, min_pt=20, max_pt=45, low=0.2770, high=0.0494))
self.selection = self.selection & (ak.fill_none(
ev.Muon.matched_jet.btagDeepFlavB, 0) < self.getThreshold(
self.cand.conePt, min_pt=20, max_pt=45))
if self.v > 0: print(" - interpolated deepJet")
def process(self, events):
output = self.accumulator.identity()
# we can use a very loose preselection to filter the events. nothing is done with this presel, though
presel = ak.num(events.Jet) > 0
ev = events[presel]
dataset = ev.metadata['dataset']
# load the config - probably not needed anymore
cfg = loadConfig()
output['totalEvents']['all'] += len(events)
output['skimmedEvents']['all'] += len(ev)
## Muons
muon = ev.Muon
## Electrons
electron = Collections(ev, "Electron", "tight").get()
#electron = electron[(ak.nan_to_num(electron.eta, 99))]
electron = electron[(electron.miniPFRelIso_all < 0.12)
& (electron.pt > 20) & (abs(electron.eta) < 2.4)]
gen_matched_electron = electron[((electron.genPartIdx >= 0) & (abs(
electron.matched_gen.pdgId) == 11))]
is_flipped = ((gen_matched_electron.matched_gen.pdgId *
(-1) == gen_matched_electron.pdgId) &
(abs(gen_matched_electron.pdgId) == 11))
#(abs(ev.GenPart[gen_matched_electron.genPartIdx].pdgId) ==abs(gen_matched_electron.pdgId))&(ev.GenPart[gen_matched_electron.genPartIdx].pdgId/abs(ev.GenPart[gen_matched_electron.genPartIdx].pdgId) != gen_matched_electron.pdgId/abs(gen_matched_electron.pdgId))
flipped_electron = gen_matched_electron[is_flipped]
flipped_electron = flipped_electron[(ak.fill_none(
flipped_electron.pt, 0) > 0)]
n_flips = ak.num(flipped_electron)
dielectron = choose(electron, 2)
SSelectron = ak.any(
(dielectron['0'].charge * dielectron['1'].charge) > 0, axis=1)
leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1))
leading_electron = electron[leading_electron_idx]
leading_flipped_electron_idx = ak.singletons(
ak.argmax(flipped_electron.pt, axis=1))
leading_flipped_electron = electron[leading_flipped_electron_idx]
## MET -> can switch to puppi MET
met_pt = ev.MET.pt
met_phi = ev.MET.phi
# setting up the various weights
weight = Weights(len(ev))
if not dataset == 'MuonEG':
# generator weight
weight.add("weight", ev.genWeight)
#selections
filters = getFilters(ev, year=self.year, dataset=dataset)
electr = ((ak.num(electron) >= 1))
gen_electr = ((ak.num(gen_matched_electron) >= 1))
ss = (SSelectron)
flip = (n_flips >= 1)
selection = PackedSelection()
selection.add('filter', (filters))
selection.add('electr', electr)
selection.add('ss', ss)
selection.add('flip', flip)
selection.add('gen_electr', gen_electr)
bl_reqs = ['filter', 'electr', 'gen_electr']
bl_reqs_d = {sel: True for sel in bl_reqs}
baseline = selection.require(**bl_reqs_d)
f_reqs = bl_reqs + ['flip']
f_reqs_d = {sel: True for sel in f_reqs}
flip_sel = selection.require(**f_reqs_d)
#adjust weights to prevent length mismatch
ak_weight_gen = ak.ones_like(
gen_matched_electron[baseline].pt) * weight.weight()[baseline]
ak_weight_flip = ak.ones_like(
flipped_electron[flip_sel].pt) * weight.weight()[flip_sel]
output['N_ele'].fill(dataset=dataset,
multiplicity=ak.num(electron)[baseline],
weight=weight.weight()[baseline])
output['electron_flips'].fill(dataset=dataset,
multiplicity=n_flips[baseline],
weight=weight.weight()[baseline])
output["electron"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].pt)),
eta=abs(ak.to_numpy(ak.flatten(
gen_matched_electron[baseline].eta))),
#phi = ak.to_numpy(ak.flatten(leading_electron[baseline].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_gen))
)
output["electron2"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].pt)),
eta=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].eta)),
#phi = ak.to_numpy(ak.flatten(leading_electron[baseline].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_gen))
)
output["flipped_electron"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)),
eta=abs(ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta))),
#phi = ak.to_numpy(ak.flatten(flipped_electron[flip_sel].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_flip))
)
output["flipped_electron2"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)),
eta=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta)),
#phi = ak.to_numpy(ak.flatten(flipped_electron[flip_sel].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_flip))
)
return output
def test():
array = awkward1.Array([[{
"x": 0.0,
"y": []
}, {
"x": 1.1,
"y": [1]
}, {
"x": 2.2,
"y": [1, 2]
}], [],
[{
"x": 3.3,
"y": [1, 2, None, 3]
}, False, False, True, {
"x": 4.4,
"y": [1, 2, None, 3, 4]
}]])
assert awkward1.full_like(array,
12.3).tolist() == [[{
"x": 12.3,
"y": []
}, {
"x": 12.3,
"y": [12]
}, {
"x": 12.3,
"y": [12, 12]
}], [],
[{
"x": 12.3,
"y": [12, 12, None, 12]
}, True, True, True, {
"x": 12.3,
"y":
[12, 12, None, 12, 12]
}]]
assert awkward1.zeros_like(array).tolist() == [[{
"x": 0.0,
"y": []
}, {
"x": 0.0,
"y": [0]
}, {
"x": 0.0,
"y": [0, 0]
}], [],
[{
"x": 0.0,
"y": [0, 0, None, 0]
}, False, False, False, {
"x": 0.0,
"y": [0, 0, None, 0, 0]
}]]
assert awkward1.ones_like(array).tolist() == [[{
"x": 1.0,
"y": []
}, {
"x": 1.0,
"y": [1]
}, {
"x": 1.0,
"y": [1, 1]
}], [],
[{
"x": 1.0,
"y": [1, 1, None, 1]
}, True, True, True, {
"x": 1.0,
"y": [1, 1, None, 1, 1]
}]]
array = awkward1.Array([["one", "two", "three"], [], ["four", "five"]])
assert awkward1.full_like(
array, "hello").tolist() == [["hello", "hello", "hello"], [],
["hello", "hello"]]
assert awkward1.full_like(array, 1).tolist() == [["1", "1", "1"], [],
["1", "1"]]
assert awkward1.full_like(array, 0).tolist() == [["0", "0", "0"], [],
["0", "0"]]
assert awkward1.ones_like(array).tolist() == [["1", "1", "1"], [],
["1", "1"]]
assert awkward1.zeros_like(array).tolist() == [["", "", ""], [], ["", ""]]
array = awkward1.Array([[b"one", b"two", b"three"], [], [b"four",
b"five"]])
assert awkward1.full_like(
array, b"hello").tolist() == [[b"hello", b"hello", b"hello"], [],
[b"hello", b"hello"]]
assert awkward1.full_like(array, 1).tolist() == [[b"1", b"1", b"1"], [],
[b"1", b"1"]]
assert awkward1.full_like(array, 0).tolist() == [[b"0", b"0", b"0"], [],
[b"0", b"0"]]
assert awkward1.ones_like(array).tolist() == [[b"1", b"1", b"1"], [],
[b"1", b"1"]]
assert awkward1.zeros_like(array).tolist() == [[b"", b"", b""], [],
[b"", b""]]