def test_simple_example(ak_HZZ_example):
assert (
repr(ak_HZZ_example)
== "<Array [[{x: -52.9, y: -11.7, ... t: 69.6}]] type='2421 * var * LorentzXYZT[\"x\":...'>"
)
# This new array understands that data labeled "LorentzXYZT" should have the above methods.
example2 = ak.Array(ak_HZZ_example, behavior=behavior)
assert (
repr(example2)
== "<LorentzXYZTArray [[Lxyz(-52.9 -11.7 -8.16 54.8), ... ] type='2421 * var * Loren...'>"
)
assert repr(example2[0, 0]) == "Lxyz(-52.9 -11.7 -8.16 54.8)"
assert type(example2[0, 0]) is vector.awkward.lorentz.xyzt.LorentzXYZT
assert example2[0, 0].mag == approx(0.10559298741436905)
assert (
repr(example2.mass)
== "<Array [[0.106, 0.105], ... [0.104]] type='2421 * var * float64'>"
)
# We need a "ak.sizes" function with a simpler interface than this...
hastwo = ak.count(example2, axis=-1).x >= 2
assert (
str(example2[hastwo, 0] + example2[hastwo, 1])
== "[Lxyz(-15.2 -11 -19.5 94.2), Lxyz(49.8 8.08 48.1 102), ... Lxyz(2.94 18.4 -262 273)]"
)
assert (
str((example2[hastwo, 0] + example2[hastwo, 1]).mag)
== "[90.2, 74.7, 89.8, 94.9, 92.1, 53.4, 89.8, ... 91.7, 88.8, 101, 91.5, 92.1, 85.4, 76]"
)
def test():
nums = [[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
17, 11, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]]
sample = []
for outer in nums:
sample.append([])
for inner in outer:
sample[-1].append([0] * inner)
assert awkward1.is_valid(awkward1.count(sample, axis=0))
#HadW is mask for Quark deacying W boson
hadW = ak.num(W_Events, axis=2) == 2
#filters out t_events that have a hadronically decayign W Boson
hadB = t_Events[hadW]
hadB = ak.flatten(hadB, axis=2)
W_quarks = W_Events[hadW]
W_quarks = ak.flatten(W_quarks, axis=2)
#concatentating these two arrays make an array of events with the correctly decaying GenParticles.
qqb = ak.concatenate([hadB, W_quarks], axis=1)
print("qqb Genparts matched")
#Filtering Out events with extra tops
final = final[(ak.count(qqb.pdgId, axis=1) == 3)]
finaljets = final.Jet
qqb = qqb[(ak.count(qqb.pdgId, axis=1) == 3)]
#Implementing Tight Jet Cuts on Training Data
finaljetSel = (abs(finaljets.eta) < 2.4) & (finaljets.pt > 30)
finalJets = finaljets[finaljetSel]
#Use nearest to match Jets
matchedGenJets = qqb.nearest(final.GenJet)
matchedJets = matchedGenJets.nearest(finalJets)
print("matched genpart to genjet and finally to reco jets")
#Assigning True false to sets of 3 jets
test = matchedJets.genJetIdx
combs = ak.combinations(finalJets, 3, replacement=False)
def process(self, events):
output = self.accumulator.identity()
dataset = events.metadata['dataset']
output['sumw'][dataset] += ak.sum(events.genWeight)
##############
# Trigger level
triggers = [
"HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ",
"HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ",
]
trig_arrs = [events.HLT[_trig.strip("HLT_")] for _trig in triggers]
req_trig = np.zeros(len(events), dtype='bool')
for t in trig_arrs:
req_trig = req_trig | t
############
# Event level
## Muon cuts
# muon twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2
events.Muon = events.Muon[(events.Muon.pt > 30) & (abs(events.Muon.eta < 2.4))] # & (events.Muon.tightId > .5)
events.Muon = ak.pad_none(events.Muon, 1, axis=1)
req_muon =(ak.count(events.Muon.pt, axis=1) == 1)
## Electron cuts
# electron twiki: https://twiki.cern.ch/twiki/bin/viewauth/CMS/CutBasedElectronIdentificationRun2
events.Electron = events.Electron[(events.Electron.pt > 30) & (abs(events.Electron.eta) < 2.4)]
events.Electron = ak.pad_none(events.Electron, 1, axis=1)
req_ele = (ak.count(events.Electron.pt, axis=1) == 1)
## Jet cuts
events.Jet = events.Jet[(events.Jet.pt > 25) & (abs(events.Jet.eta) <= 2.5)]
req_jets = (ak.count(events.Jet.pt, axis=1) >= 2)
req_opposite_charge = events.Electron[:, 0].charge * events.Muon[:, 0].charge == -1
event_level = req_trig & req_muon & req_ele & req_opposite_charge & req_jets
# Selected
selev = events[event_level]
#########
# Per electron
el_eta = (abs(selev.Electron.eta) <= 2.4)
el_pt = selev.Electron.pt > 30
el_level = el_eta & el_pt
# Per muon
mu_eta = (abs(selev.Muon.eta) <= 2.4)
mu_pt = selev.Muon.pt > 30
mu_level = mu_eta & mu_pt
# Per jet
jet_eta = (abs(selev.Jet.eta) <= 2.4)
jet_pt = selev.Jet.pt > 25
jet_pu = ( ((selev.Jet.puId > 6) & (selev.Jet.pt < 50)) | (selev.Jet.pt > 50) )
jet_id = selev.Jet.jetId >= 2
#jet_id = selev.Jet.isTight() == 1 & selev.Jet.isTightLeptonVeto() == 0
jet_level = jet_pu & jet_eta & jet_pt & jet_id
# b-tag twiki : https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation102X
bjet_disc_t = selev.Jet.btagDeepB > 0.7264 # L=0.0494, M=0.2770, T=0.7264
bjet_disc_m = selev.Jet.btagDeepB > 0.2770 # L=0.0494, M=0.2770, T=0.7264
bjet_disc_l = selev.Jet.btagDeepB > 0.0494 # L=0.0494, M=0.2770, T=0.7264
bjet_level_t = jet_level & bjet_disc_t
bjet_level_m = jet_level & bjet_disc_m
bjet_level_l = jet_level & bjet_disc_l
sel = selev.Electron[el_level]
smu = selev.Muon[mu_level]
sjets = selev.Jet[jet_level]
sbjets_t = selev.Jet[bjet_level_t]
sbjets_m = selev.Jet[bjet_level_m]
sbjets_l = selev.Jet[bjet_level_l]
# output['pt'].fill(dataset=dataset, pt=selev.Jet.pt.flatten())
# Fill histograms dynamically
for histname, h in output.items():
if (histname not in self.jet_hists) and (histname not in self.deepcsv_hists): continue
# Get valid fields perhistogram to fill
fields = {k: ak.flatten(sjets[k], axis=None) for k in h.fields if k in dir(sjets)}
h.fill(dataset=dataset, **fields)
def flatten(ar): # flatten awkward into a 1d array to hist
return ak.flatten(ar, axis=None)
def num(ar):
return ak.num(ak.fill_none(ar[~ak.is_none(ar)], 0), axis=0)
output['njet'].fill(dataset=dataset, njet=flatten(ak.num(sjets)))
output['nbjet_t'].fill(dataset=dataset, nbjet_t=flatten(ak.num(sbjets_t)))
output['nbjet_m'].fill(dataset=dataset, nbjet_m=flatten(ak.num(sbjets_m)))
output['nbjet_l'].fill(dataset=dataset, nbjet_l=flatten(ak.num(sbjets_l)))
output['nel'].fill(dataset=dataset, nel=flatten(ak.num(sel)))
output['nmu'].fill(dataset=dataset, nmu=flatten(ak.num(smu)))
output['lelpt'].fill(dataset=dataset, lelpt=flatten(selev.Electron[:, 0].pt))
output['lmupt'].fill(dataset=dataset, lmupt=flatten(selev.Muon[:, 0].pt))
output['ljpt'].fill(dataset=dataset, ljpt=flatten(selev.Jet[:, 0].pt))
output['sljpt'].fill(dataset=dataset, sljpt=flatten(selev.Jet[:, 1].pt))
return output