def update(self, _):
jets = self.source['TopJets']
p4 = self.source['AdjustedP4'.join(jets)]
scale = self.source['ScalingBQN'.join(jets)]
self.value = {}
for iPQB in self.source['HTopCandidateIndices']:
proxy = sum([
p4[i] * (scale['B' if j == 2 else 'Q'][i])
for j, i in enumerate(iPQB) if i != None
], utils.LorentzV())
self.value[iPQB] = proxy.M()
def update(self, _):
reco = self.source["TopReconstruction"][0]
t = reco['top']
tbar = reco['tbar']
q_z = 0.5 * (t + tbar).z()
self.value = {
't':
t,
'tbar':
tbar,
'quark':
utils.LorentzV().SetPxPyPzE(0, 0, q_z, abs(q_z)),
'lepton':
reco['lep'],
'neutrino':
reco['nu'],
'p':
reco['hadP'],
'q':
reco['hadQ'],
'rawW':
reco['hadWraw'],
'metP4':
reco['metP4'],
'key':
reco['key'],
'chi2':
reco['chi2'],
'hadChi2':
reco['hadChi2'],
'ttx':
reco['ttx'],
'fifth':
reco['iX'] != None,
'iPQHL':
reco['iPQHL'],
'iOtherJets': [
i for i in self.source['Indices'.join(self.source['TopJets'])]
if i not in reco['iPQHL']
]
}
def uponAcceptance(self, ev):
ids = list(ev['genPdgId'])
if not (6 in ids and -6 in ids): return
iTop = ids.index(6)
iTbar = ids.index(-6)
iQs = range(2, min(
iTop,
iTbar)) # interested in stuff listed between protons and tops
mom = ev['genMotherIndex']
p4s = ev['genP4']
status = ev['genStatus']
labels = {
1: ' d',
-1: r'/d',
2: ' u',
-2: r'/u',
3: ' s',
-3: r'/s',
4: ' c',
-4: r'/c',
5: ' b',
-5: r'/b',
21: ' g',
6: ' t',
-6: '/t'
}
iGs = filter(lambda i: ids[i] == 21,
range(max(iTop, iTbar) + 1, len(ids)))
iDaughters = filter(lambda i: mom[i] < min(iTop, iTbar),
range(max(iTop, iTbar) + 1, len(ids)))
print '-' * 50
print '\t'.join(['item', 'mom', 'pt', 'm', 'eta', 'phi'])
print
for i in iQs + [iTop, iTbar] + iDaughters:
print '\t'.join(
str(d)[:5] for d in [
"%d(%s)" %
(i, labels[ids[i]] if ids[i] in labels else str(ids[i])),
mom[i], p4s[i].pt(), '-', p4s[i].eta(), p4s[i].phi(),
status[i]
])
print
pD = sum([p4s[i] for i in iDaughters], utils.LorentzV())
print '\t'.join(
str(d)[:5] for d in
["Daught", '-', pD.pt(),
pD.M(), pD.eta(),
pD.phi()])
p4 = p4s[2] + p4s[3]
print '\t'.join(
str(d)[:5] for d in
["[%s,%s]" % (2, 3), '-',
p4.pt(),
p4.M(),
p4.eta(),
p4.phi()])
p4 = p4s[4] + p4s[5]
print '\t'.join(
str(d)[:5] for d in
["[%s,%s]" % (4, 5), '-',
p4.pt(),
p4.M(),
p4.eta(),
p4.phi()])
tt = p4s[iTop] + p4s[iTbar]
print '\t'.join(
str(d)[:5] for d in
["ttbar", '-', tt.pt(),
tt.M(), tt.eta(),
tt.phi()])
print
print
if abs(tt.E() - (p4s[4] + p4s[5]).E()) > 0.5:
print(50 * ' '), "2 -> 3+"
def update(self, _):
self.value = sum(
utils.hackMap(
self.delta,
*[self.source[getattr(self, item)] for item in self.stashed]),
utils.LorentzV())
def __init__(self, nSamples=16, qDirFunc=None):
self.nSamples = nSamples
self.qDirFunc = qDirFunc
self.moreName = "%d samples; %s" % (nSamples, qDirFunc)
self.nu = utils.LorentzV()
def update(self, _):
jets = dict(
(item, self.source[item.join(self.source["TopJets"])])
for item in ["AdjustedP4", "Indices", "Resolution", "ScalingBQN"])
lepton = dict((item, self.source[item.join(self.source['TopLeptons'])][
self.source["SemileptonicTopIndex"]]) for item in ["Charge", "P4"])
topP = self.source["TopComboQQBBProbability"]
TCL = self.source['TopCandidateLikelihood']
recos = []
metP4 = self.source["metAdjustedP4"]
nuXY = np.array([metP4.x(), metP4.y()])
nuErr2 = self.source["metCovariance"]
for iPQHL in TCL.keys()[:self.maxFits]:
iProxyL = [i for i in iPQHL if i != None]
p4s = [jets['AdjustedP4'][i] for i in iProxyL]
scales = [
jets['ScalingBQN']['B' if j > 1 else 'Q'][i]
for j, i in enumerate(iPQHL) if i != None
]
sp4s = [j * s for j, s in zip(p4s, scales)]
hadProxy = sum(sp4s[:3], utils.LorentzV())
iL = iPQHL[-1]
iQQBB = iPQHL[:2] + tuple(sorted(iPQHL[2:]))
nuXY_mod = sum([(1 - s) * np.array([j.x(), j.y()])
for j, s in zip(p4s, scales)], nuXY)
lepFit = utils.fitKinematic.leastsqLeptonicTop2(
sp4s[-1], jets["Resolution"][iL], lepton["P4"], nuXY_mod,
nuErr2)
tt = hadProxy + lepFit.fitT
iX, ttx = (None, tt)
recos.append({
"nu":
lepFit.fitNu,
"hadP":
sp4s[0],
"lep":
lepFit.mu,
"hadQ":
sp4s[1],
"lepB":
lepFit.fitB,
"hadB":
sp4s[2],
"lepW":
lepFit.fitW,
"hadW":
utils.LorentzV(),
"lepTopP4":
lepFit.fitT,
"hadTopP4":
hadProxy,
"lepChi2":
lepFit.chi2,
"hadChi2":
0,
"chi2":
0 + lepFit.chi2,
"probability":
max(self.epsilon, topP[iQQBB]),
"top":
lepFit.fitT if lepton["Charge"] > 0 else hadProxy,
"tbar":
hadProxy if lepton["Charge"] > 0 else lepFit.fitT,
"ttx":
ttx,
"iX":
iX,
"iPQHL":
iPQHL,
"lepCharge":
lepton["Charge"],
"hadTraw":
hadProxy,
"lepTraw":
lepFit.rawT,
"lepBound":
lepFit.bound,
"hadWraw":
utils.LorentzV(),
"lepWraw":
lepFit.rawW,
"metP4":
metP4 + sp4s[-1] - lepFit.fitB,
"nuErr2":
nuErr2,
"residuals":
dict(
zip(["lep" + i for i in "BSLWT"], lepFit.residualsBSLWT) +
zip(["had" + i for i in "PQBWT"], [0] * 5)),
"nuEllipse":
lepFit.Ellipse,
"nuSolutions":
lepFit.solutions,
"nuChi2Matrix":
lepFit.X
})
recos[-1]["key"] = recos[-1]['chi2'] - 2 * math.log(
recos[-1]['probability'])
self.value = sorted(recos, key=lambda x: x["key"])