def test_3D_jagged(wrapped_tree):
fake_3d = [[np.arange(i + 1) + j for i in range(j % 3)]
for j in range(len(wrapped_tree))]
fake_3d = JaggedArray.fromiter(fake_3d)
wrapped_tree.new_variable("Fake3D", fake_3d)
assert isinstance(fake_3d.count(), JaggedArray)
assert all((fake_3d.copy().count() == fake_3d.count()).all())
aliased = expressions.evaluate(wrapped_tree, "Fake3D")
assert (aliased == fake_3d).all().all().all()
doubled = expressions.evaluate(wrapped_tree, "Fake3D * 2")
assert (doubled == fake_3d * 2).all().all().all()
assert len(doubled[0, :, :]) == 0
assert doubled[1, 0, :] == [2]
assert doubled[2, 0, :] == [4]
assert all(doubled[2, 1, :] == [4, 6])
doubled = expressions.evaluate(wrapped_tree, "Fake3D + Fake3D")
assert (doubled == fake_3d * 2).all().all().all()
assert len(doubled[0, :, :]) == 0
assert doubled[1, 0, :] == [2]
assert doubled[2, 0, :] == [4]
assert all(doubled[2, 1, :] == [4, 6])
fake_3d_2 = [[np.arange(i + 3) + j for i in range(j % 2)]
for j in range(len(wrapped_tree))]
fake_3d_2 = JaggedArray.fromiter(fake_3d_2)
wrapped_tree.new_variable("SecondFake3D", fake_3d_2)
with pytest.raises(RuntimeError) as e:
expressions.evaluate(wrapped_tree, "SecondFake3D + Fake3D")
assert "different jaggedness" in str(e)
def NestNestObjArrayToJagged(objarr):
"""uproot read vector<vector<number>> TBranch
as objectArray, this function convert it
to JaggedJaggedArray
"""
# jaggedArray of lists
jaggedList = JaggedArray.fromiter(objarr)
# flat to 1 level
_jagged = JaggedArray.fromiter(jaggedList.content)
return JaggedArray.fromoffsets(jaggedList.offsets, _jagged)
def __call__(self, *args):
inputs = list(args)
offsets = None
# TODO: check can use offsets (this should always be true for striped)
# Alternatively we can just use starts and stops
for i in range(len(inputs)):
if isinstance(inputs[i], JaggedArray):
if offsets is not None and offsets.base is not inputs[
i].offsets.base:
if type(offsets) is int:
raise Exception(
'Do not mix JaggedArrays and numpy arrays when calling derived class of lookup_base'
)
elif type(offsets
) is np.ndarray and offsets.base is not inputs[
i].offsets.base:
raise Exception(
'All input jagged arrays must have a common structure (offsets)!'
)
offsets = inputs[i].offsets
inputs[i] = inputs[i].content
elif isinstance(inputs[i], np.ndarray):
if offsets is not None:
if type(offsets) is np.ndarray:
raise Exception(
'do not mix JaggedArrays and numpy arrays when calling a derived class of lookup_base'
)
offsets = -1
retval = self._evaluate(*tuple(inputs))
if offsets is not None and type(offsets) is not int:
retval = JaggedArray.fromoffsets(offsets, retval)
return retval
def _kExtra(self, kpt, eta, nl, u, s=0, m=0):
# if it is a jagged array, save the offsets then flatten everything
# needed for the ternary conditions later
offsets = None
if isinstance(kpt, JaggedArray):
offsets = kpt.offsets
kpt = kpt.flatten()
eta = eta.flatten()
nl = nl.flatten()
u = u.flatten()
abseta = abs(eta)
kData = self._kRes[s][m][1](abseta) # type 1 is data
kMC = self._kRes[s][m][0](abseta) # type 0 is MC
mask = kData > kMC
x = np.zeros_like(kpt)
sigma = self._sigma(kpt, eta, nl, s, m)
# Rochester cbA = beta, cbN = m, as well as cbM (always 0?) = loc and cbS = scale to transform y = (x-loc)/scale in the pdf method
cbA = self._cbA[s][m](abseta, nl)
cbN = self._cbN[s][m](abseta, nl)
loc = np.zeros_like(u)
cbS = self._cbS[s][m](abseta, nl)
invcdf = doublecrystalball.ppf(u, cbA, cbA, cbN, cbN, loc, cbS)
x[mask] = (np.sqrt(kData[mask] * kData[mask] - kMC[mask] * kMC[mask]) *
sigma[mask] * invcdf[mask])
result = np.ones_like(kpt)
result[(x > -1)] = 1.0 / (1.0 + x[x > -1])
if offsets is not None:
result = JaggedArray.fromoffsets(offsets, result)
return result
def test_jagged_nth_3D(jagged_1):
fake_3d = [[np.arange(i + 1) + j for i in range(j % 3)] for j in range(5)]
fake_3d = JaggedArray.fromiter(fake_3d)
get_second = reductions.JaggedNth(1, np.nan)
reduced = get_second(fake_3d)
assert len(reduced[0]) == 0
assert len(reduced[1]) == 1
assert np.isnan(reduced[1])
assert len(reduced[2]) == 2
assert np.isnan(reduced[2][0])
assert reduced[2][1] == 3
assert len(reduced[3]) == 0
assert len(reduced[4]) == 1
assert np.isnan(reduced[4])
get_first = reductions.JaggedNth(0, np.nan)
reduced = get_first(fake_3d)
assert len(reduced[0]) == 0
assert len(reduced[1]) == 1
assert reduced[1][0] == 1
assert len(reduced[2]) == 2
assert reduced[2][0] == 2
assert reduced[2][1] == 2
assert len(reduced[3]) == 0
assert len(reduced[4]) == 1
assert reduced[4] == 4
def passLooseJetSel(jet):
outs = np.ones_like(jet.pt.content,dtype=np.bool)
absEta = np.abs(jet.eta.content)
etaVFor = (absEta <= 3.0)
etaFor = (absEta <= 2.7)
etaCen = (absEta <= 2.4)
#forward jets
outs[etaFor] &= ( (jet.neuHadFrac.content[etaFor] < 0.99) &
(jet.neuEmFrac.content[etaFor] < 0.99) &
(jet.nParticles.content[etaFor] > 1 ) )
#central jets
outs[etaCen] &= ( (jet.chHadFrac.content[etaCen] > 0.0 ) &
(jet.nCharged.content[etaCen] > 0 ) &
(jet.chEmFrac.content[etaCen] < 0.99 ) )
#2.7-3.0
etaHE = etaVFor & ~etaFor
outs[etaHE] &= ( (jet.neuEmFrac.content[etaHE] > 0.01) &
(jet.neuHadFrac.content[etaHE] < 0.98) &
(jet.nNeutrals.content[etaHE] > 2 ) )
# > 3.0
etaHF = ~etaVFor
outs[etaHF] &= ( (jet.neuEmFrac.content[etaHF] > 0.90) &
(jet.nNeutrals.content[etaHF] > 10 ) )
outs = JaggedArray.fromoffsets(jet.pt.offsets,outs)
return outs
def getvar(events, name, default=None, parents="run"):
if name in events:
return events[name]
if parents not in events:
return None
else:
if isinstance(events[parents], np.ndarray):
return np.full_like(events[parents], default)
if isinstance(events[parents], JaggedArray):
content = [default] * events[parents].flatten().shape[0]
return JaggedArray.fromoffsets(events[parents].offsets, content)
def setup_gen_candidates(df):
# Find first ancestor with different PDG ID
# before defining the gen candidates
mothers = JaggedArray.fromcounts(df['nGenPart'],
df['GenPart_genPartIdxMother'])
pdgids = JaggedArray.fromcounts(df['nGenPart'], df['GenPart_pdgId'])
parent_index = find_first_parent(mothers, pdgids)
gen = JaggedCandidateArray.candidatesfromcounts(
df['nGenPart'],
pt=df['GenPart_pt'],
eta=df['GenPart_eta'],
phi=df['GenPart_phi'],
mass=df['GenPart_mass'],
charge=df['GenPart_pdgId'],
pdg=df['GenPart_pdgId'],
status=df['GenPart_status'],
flag=df['GenPart_statusFlags'],
mother=df['GenPart_genPartIdxMother'],
parentIndex=parent_index.flatten())
return gen
def get_lepton_values(zl, key):
val = np.zeros_like(zl.flatten(), dtype=float)
if len(val) == 0:
return JaggedArray.fromoffsets(zl.offsets, val)
for i in range(2):
mask = (i == zl.flatten())
if key == 'pt':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].pt
elif key == 'eta':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].eta
elif key == 'phi':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].phi
elif key == 'mass':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].mass
else:
val[mask] = z_cands[passZCand][str(i)].flatten()[mask][key]
return JaggedArray.fromoffsets(zl.offsets, val)
def event(self, chunk):
for output, expression, reduction, fill_missing in self._variables:
branches = get_branches(expression, chunk.tree.allkeys())
data = chunk.tree.pandas.df(branches)
result = data.eval(expression)
if reduction:
groups = result.groupby(level=0)
result = reduction(groups)
array = result.values
else:
events = result.index.get_level_values(0).values
events -= events[0]
array = JaggedArray.fromparents(events, result.values)
chunk.tree.new_variable(output, array)
return True
def passJetTightLepVetoSel(jet):
outs = np.ones_like(jet.pt.content,dtype=np.bool)
absEta = np.abs(jet.eta.content)
etaFor = (absEta <= 2.7)
etaCen = (absEta <= 2.4)
#forward jets
outs[etaFor] &= ( (jet.neuHadFrac.content[etaFor] < 0.90) &
(jet.neuEmFrac.content[etaFor] < 0.90) &
(jet.nParticles.content[etaFor] > 1 ) &
(jet.muonFrac.content[etaFor] < 0.8 ) )
#central jets
outs[etaCen] &= ( (jet.chHadFrac.content[etaCen] > 0.0 ) &
(jet.nCharged.content[etaCen] > 0 ) &
(jet.chEmFrac.content[etaCen] < 0.9 ) )
outs = JaggedArray.fromoffsets(jet.pt.offsets,outs)
return outs
def test_rochester():
rochester_data = lookup_tools.txt_converters.convert_rochester_file('tests/samples/RoccoR2018.txt.gz',loaduncs=True)
rochester = lookup_tools.rochester_lookup.rochester_lookup(rochester_data)
# to test 1-to-1 agreement with official Rochester requires loading C++ files
# instead, preload the correct scales in the sample directory
# the script tests/samples/rochester/build_rochester.py produces these
official_data_k = np.load('tests/samples/nano_dimuon_rochester.npy')
official_data_err = np.load('tests/samples/nano_dimuon_rochester_err.npy')
official_mc_k = np.load('tests/samples/nano_dy_rochester.npy')
official_mc_err = np.load('tests/samples/nano_dy_rochester_err.npy')
mc_rand = np.load('tests/samples/nano_dy_rochester_rand.npy')
# test against nanoaod
events = NanoEvents.from_file(os.path.abspath('tests/samples/nano_dimuon.root'))
data_k = rochester.kScaleDT(events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi)
assert(all(np.isclose(data_k.flatten(), official_data_k)))
data_err = rochester.kScaleDTerror(events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi)
data_err = np.array(data_err.flatten(), dtype=float)
assert(all(np.isclose(data_err, official_data_err, atol=1e-8)))
# test against mc
events = NanoEvents.from_file(os.path.abspath('tests/samples/nano_dy.root'))
hasgen = ~np.isnan(events.Muon.matched_gen.pt.fillna(np.nan))
mc_rand = JaggedArray.fromoffsets(hasgen.offsets, mc_rand)
mc_kspread = rochester.kSpreadMC(events.Muon.charge[hasgen], events.Muon.pt[hasgen], events.Muon.eta[hasgen], events.Muon.phi[hasgen],
events.Muon.matched_gen.pt[hasgen])
mc_ksmear = rochester.kSmearMC(events.Muon.charge[~hasgen], events.Muon.pt[~hasgen], events.Muon.eta[~hasgen], events.Muon.phi[~hasgen],
events.Muon.nTrackerLayers[~hasgen], mc_rand[~hasgen])
mc_k = np.ones_like(events.Muon.pt.flatten())
mc_k[hasgen.flatten()] = mc_kspread.flatten()
mc_k[~hasgen.flatten()] = mc_ksmear.flatten()
assert(all(np.isclose(mc_k, official_mc_k)))
mc_errspread = rochester.kSpreadMCerror(events.Muon.charge[hasgen], events.Muon.pt[hasgen], events.Muon.eta[hasgen], events.Muon.phi[hasgen],
events.Muon.matched_gen.pt[hasgen])
mc_errsmear = rochester.kSmearMCerror(events.Muon.charge[~hasgen], events.Muon.pt[~hasgen], events.Muon.eta[~hasgen], events.Muon.phi[~hasgen],
events.Muon.nTrackerLayers[~hasgen], mc_rand[~hasgen])
mc_err = np.ones_like(events.Muon.pt.flatten())
mc_err[hasgen.flatten()] = mc_errspread.flatten()
mc_err[~hasgen.flatten()] = mc_errsmear.flatten()
assert(all(np.isclose(mc_err, official_mc_err, atol=1e-8)))
def getSubCorrections(self, **kwargs):
"""
Returns the set of corrections for all input jets broken down by level
use like:
jecs = corrector.getSubCorrections(JetProperty1=jet.property1,...)
'jecs' will be formatted like [[jec_jet1 jec_jet2 ...] ...]
"""
localargs = kwargs
firstarg = localargs[self._signature[0]]
cumulativeCorrection = 1.0
offsets = None
if isinstance(firstarg, JaggedArray):
offsets = firstarg.offsets
cumulativeCorrection = firstarg.ones_like().content
for key in localargs.keys():
localargs[key] = localargs[key].content
else:
cumulativeCorrection = np.ones_like(firstarg)
corrVars = []
if 'JetPt' in localargs.keys():
corrVars.append('JetPt')
if 'JetE' in localargs.keys():
corrVars.append('JetE')
if len(corrVars) == 0:
raise Exception(
'No variable to correct, need JetPt or JetE in inputs!')
corrections = []
for i, func in enumerate(self._funcs):
sig = func.signature
args = []
for input in sig:
args.append(localargs[input])
corr = func(*tuple(args))
for var in corrVars:
localargs[var] *= corr
cumulativeCorrection *= corr
corrections.append(cumulativeCorrection)
if offsets is not None:
for i in range(len(corrections)):
corrections[i] = JaggedArray.fromoffsets(
offsets, corrections[i])
return corrections
def convert_effective_area_file(eaFilePath):
ea_f = open(eaFilePath, 'r')
layoutstr = ea_f.readline().strip().strip('{}')
ea_f.close()
name = eaFilePath.split('/')[-1].split('.')[0]
layout = layoutstr.split()
if not layout[0].isdigit():
raise Exception(
'First column of Effective Area File Header must be a digit!')
#setup the file format
nBinnedVars = int(layout[0])
nBinColumns = 2 * nBinnedVars
nEvalVars = int(layout[nBinnedVars + 1])
minMax = ['Min', 'Max']
columns = []
dtypes = []
offset = 1
for i in range(nBinnedVars):
columns.extend(['%s%s' % (layout[i + offset], mm) for mm in minMax])
dtypes.extend(['<f8', '<f8'])
offset += nBinnedVars + 1
for i in range(nEvalVars):
columns.append('%s' % (layout[i + offset]))
dtypes.append('<f8')
pars = np.genfromtxt(eaFilePath,
dtype=tuple(dtypes),
names=tuple(columns),
skip_header=1,
unpack=True,
encoding='ascii')
bins = {}
offset_col = 0
offset_name = 1
bin_order = []
for i in range(nBinnedVars):
binMins = None
binMaxs = None
if i == 0:
binMins = np.unique(pars[columns[0]])
binMaxs = np.unique(pars[columns[1]])
bins[layout[i + offset_name]] = np.union1d(binMins, binMaxs)
else:
counts = np.zeros(0, dtype=np.int)
allBins = np.zeros(0, dtype=np.double)
for binMin in bins[bin_order[0]][:-1]:
binMins = np.unique(pars[np.where(
pars[columns[0]] == binMin)][columns[i + offset_col]])
binMaxs = np.unique(pars[np.where(
pars[columns[0]] == binMin)][columns[i + offset_col + 1]])
theBins = np.union1d(binMins, binMaxs)
allBins = np.append(allBins, theBins)
counts = np.append(counts, theBins.size)
bins[layout[i + offset_name]] = JaggedArray.fromcounts(
counts, allBins)
bin_order.append(layout[i + offset_name])
offset_col += 1
# again this is only for one dimension of binning, fight me
# we can figure out a 2D EA when we get there
offset_name += 1
wrapped_up = {}
lookup_type = 'dense_lookup'
dims = bins[layout[1]]
for i in range(nEvalVars):
ea_name = '_'.join([name, columns[offset_name + i]])
values = pars[columns[offset_name + i]]
wrapped_up[(ea_name, lookup_type)] = (values, dims)
return wrapped_up
def process(self, df):
output = self.accumulator.identity()
# ---- Define dataset ---- #
dataset = df['dataset'] #coffea.processor.LazyDataFrame
Dataset_info = df.available #list of available columns in LazyDataFrame object (Similar to 'Events->Show()' command in ROOT)
# ---- Get triggers from Dataset_info ---- #
#triggers = [itrig for itrig in Dataset_info if 'HLT_PFHT' in itrig]
#AK8triggers = [itrig for itrig in Dataset_info if 'HLT_AK8PFHT' in itrig]
# ---- Find numeric values in trigger strings ---- #
#triggers_cut1 = [sub.split('PFHT')[1] for sub in triggers] # Remove string characters from left of number
#triggers_cut2 = [sub.split('_')[0] for sub in triggers_cut1] # Remove string characters from right of number
#isTriggerValue = [val.isnumeric() for val in triggers_cut2] # Boolean -> if string is only a number
#triggers_cut2 = np.where(isTriggerValue, triggers_cut2, 0) # If string is not a number, replace with 0
#triggers_vals = [int(val) for val in triggers_cut2] # Convert string numbers to integers
#AK8triggers_cut1 = [sub.split('HT')[1] for sub in AK8triggers]
#AK8triggers_cut2 = [sub.split('_')[0] for sub in AK8triggers_cut1]
#isAK8TriggerValue = [val.isnumeric() for val in AK8triggers_cut2]
#AK8triggers_cut2 = np.where(isAK8TriggerValue, AK8triggers_cut2, 0)
#AK8triggers_vals = [int(val) for val in AK8triggers_cut2]
# ---- Find Largest and Second Largest Value ---- #
#triggers_vals.sort(reverse = True)
#AK8triggers_vals.sort(reverse = True)
#triggers_vals1 = str(triggers_vals[0])
#triggers_vals2 = str(triggers_vals[1])
#AK8triggers_vals1 = str(AK8triggers_vals[0])
#AK8triggers_vals2 = str(AK8triggers_vals[1])
# ---- Define strings for the selected triggers ---- #
#HLT_trig1_str = [itrig for itrig in triggers if (triggers_vals1) in itrig][0]
#HLT_trig2_str = [itrig for itrig in triggers if (triggers_vals2) in itrig][0]
#HLT_AK8_trig1_str = [itrig for itrig in AK8triggers if (AK8triggers_vals1) in itrig][0]
#HLT_AK8_trig2_str = [itrig for itrig in AK8triggers if (AK8triggers_vals2) in itrig][0]
# ---- Define HLT triggers to be used ---- #
#HLT_trig1 = df[HLT_trig1_str]
#HLT_trig2 = df[HLT_trig2_str]
#HLT_AK8_trig1 = df[HLT_AK8_trig1_str]
#HLT_AK8_trig2 = df[HLT_AK8_trig2_str]
# ---- Define AK8 Jets as FatJets ---- #
FatJets = JaggedCandidateArray.candidatesfromcounts(
df['nFatJet'],
pt=df['FatJet_pt'],
eta=df['FatJet_eta'],
phi=df['FatJet_phi'],
mass=df['FatJet_mass'],
area=df['FatJet_area'],
msoftdrop=df['FatJet_msoftdrop'],
jetId=df['FatJet_jetId'],
tau1=df['FatJet_tau1'],
tau2=df['FatJet_tau2'],
tau3=df['FatJet_tau3'],
tau4=df['FatJet_tau4'],
n3b1=df['FatJet_n3b1'],
btagDeepB=df['FatJet_btagDeepB'],
btagCSVV2=df['FatJet_btagCSVV2'],
deepTag_TvsQCD=df['FatJet_deepTag_TvsQCD'],
deepTagMD_TvsQCD=df['FatJet_deepTagMD_TvsQCD'],
subJetIdx1=df['FatJet_subJetIdx1'],
subJetIdx2=df['FatJet_subJetIdx2'])
# ---- Define AK4 jets as Jets ---- #
Jets = JaggedCandidateArray.candidatesfromcounts(df['nJet'],
pt=df['Jet_pt'],
eta=df['Jet_eta'],
phi=df['Jet_phi'],
mass=df['Jet_mass'],
area=df['Jet_area'])
# ---- Define SubJets ---- #
SubJets = JaggedCandidateArray.candidatesfromcounts(
df['nSubJet'],
pt=df['SubJet_pt'],
eta=df['SubJet_eta'],
phi=df['SubJet_phi'],
mass=df['SubJet_mass'],
btagDeepB=df['SubJet_btagDeepB'],
btagCSVV2=df['SubJet_btagCSVV2'])
# ---- Get event weights from dataset ---- #
if 'JetHT' in dataset: # If data is used...
evtweights = np.ones(FatJets.size) # set all "data weights" to one
else: # if Monte Carlo dataset is used...
evtweights = df["Generator_weight"].reshape(-1, 1).flatten()
# ---- Show all events ---- #
output['cutflow']['all events'] += FatJets.size
# ---- Apply Trigger(s) ---- #
#FatJets = FatJets[HLT_AK8_trig1]
#evtweights = evtweights[HLT_AK8_trig1]
#Jets = Jets[HLT_AK8_trig1]
#SubJets = SubJets[HLT_AK8_trig1]
# ---- Jets that satisfy Jet ID ---- #
jet_id = (FatJets.jetId > 0) # Loose jet ID
FatJets = FatJets[jet_id]
output['cutflow']['jet id'] += jet_id.any().sum()
# ---- Apply pT Cut and Rapidity Window ---- #
jetkincut_index = (FatJets.pt > self.ak8PtMin) & (np.abs(
FatJets.p4.rapidity) < 2.4)
FatJets = FatJets[jetkincut_index]
output['cutflow']['jet kin'] += jetkincut_index.any().sum()
# ---- Find two AK8 Jets ---- #
twoFatJetsKin = (FatJets.counts == 2)
FatJets = FatJets[twoFatJetsKin]
evtweights = evtweights[twoFatJetsKin]
Jets = Jets[twoFatJetsKin]
SubJets = SubJets[twoFatJetsKin]
output['cutflow']['two FatJets and jet kin'] += twoFatJetsKin.sum()
# ---- Apply HT Cut ---- #
hT = Jets.pt.sum()
passhT = (hT > self.htCut)
evtweights = evtweights[passhT]
FatJets = FatJets[passhT]
SubJets = SubJets[passhT]
# ---- Randomly Assign AK8 Jets as TTbar Candidates 0 and 1 --- #
if self.RandomDebugMode == True: # 'Sudo' randomizer for consistent results
highPhi = FatJets.phi[:, 0] > FatJets.phi[:, 1]
highRandIndex = np.where(highPhi, 0, 1)
index = JaggedArray.fromcounts(np.ones(len(FatJets), dtype='i'),
highRandIndex)
else: # Truly randomize
index = JaggedArray.fromcounts(
np.ones(len(FatJets), dtype='i'),
self.prng.randint(2, size=len(FatJets)))
jet0 = FatJets[index] #J0
jet1 = FatJets[1 - index] #J1
ttbarcands = jet0.cross(jet1) #FatJets[:,0:2].distincts()
# ---- Make sure we have at least 1 TTbar candidate pair and re-broadcast releveant arrays ---- #
oneTTbar = (ttbarcands.counts >= 1)
output['cutflow']['>= one oneTTbar'] += oneTTbar.sum()
ttbarcands = ttbarcands[oneTTbar]
evtweights = evtweights[oneTTbar]
FatJets = FatJets[oneTTbar]
SubJets = SubJets[oneTTbar]
# ---- Apply Delta Phi Cut for Back to Back Topology ---- #
dPhiCut = (ttbarcands.i0.p4.delta_phi(ttbarcands.i1.p4) >
2.1).flatten()
output['cutflow']['dPhi > 2.1'] += dPhiCut.sum()
ttbarcands = ttbarcands[dPhiCut]
evtweights = evtweights[dPhiCut]
FatJets = FatJets[dPhiCut]
SubJets = SubJets[dPhiCut]
# ---- Identify subjets according to subjet ID ---- #
hasSubjets0 = ((ttbarcands.i0.subJetIdx1 > -1) &
(ttbarcands.i0.subJetIdx2 > -1))
hasSubjets1 = ((ttbarcands.i1.subJetIdx1 > -1) &
(ttbarcands.i1.subJetIdx2 > -1))
GoodSubjets = ((hasSubjets0) & (hasSubjets1)).flatten()
ttbarcands = ttbarcands[GoodSubjets]
SubJets = SubJets[GoodSubjets]
evtweights = evtweights[GoodSubjets]
SubJet01 = SubJets[ttbarcands.i0.subJetIdx1] # FatJet i0 with subjet 1
SubJet02 = SubJets[ttbarcands.i0.subJetIdx2] # FatJet i0 with subjet 2
SubJet11 = SubJets[ttbarcands.i1.subJetIdx1] # FatJet i1 with subjet 1
SubJet12 = SubJets[ttbarcands.i1.subJetIdx2] # FatJet i1 with subjet 2
# ---- Define Rapidity Regions ---- #
cen = np.abs(ttbarcands.i0.p4.rapidity -
ttbarcands.i1.p4.rapidity) < 1.0
fwd = (~cen)
# ---- CMS Top Tagger Version 2 (SD and Tau32 Cuts) ---- #
tau32_i0 = np.where(ttbarcands.i0.tau2 > 0,
ttbarcands.i0.tau3 / ttbarcands.i0.tau2, 0)
tau32_i1 = np.where(ttbarcands.i1.tau2 > 0,
ttbarcands.i1.tau3 / ttbarcands.i1.tau2, 0)
taucut_i0 = tau32_i0 < self.tau32Cut
taucut_i1 = tau32_i1 < self.tau32Cut
mcut_i0 = (self.minMSD < ttbarcands.i0.msoftdrop) & (
ttbarcands.i0.msoftdrop < self.maxMSD)
mcut_i1 = (self.minMSD < ttbarcands.i1.msoftdrop) & (
ttbarcands.i1.msoftdrop < self.maxMSD)
ttag_i0 = (taucut_i0) & (mcut_i0)
ttag_i1 = (taucut_i1) & (mcut_i1)
# ---- Define "Top Tag" Regions ---- #
antitag = (~taucut_i0) & (mcut_i0
) #Probe will always be ttbarcands.i1 (at)
antitag_probe = np.logical_and(
antitag, ttag_i1
) #Found an antitag and ttagged probe pair for mistag rate (Pt)
pretag = ttag_i0 # Only jet0 (pret)
ttag0 = (~ttag_i0) & (~ttag_i1) # No tops tagged (0t)
ttag1 = ttag_i0 ^ ttag_i1 # Exclusively one top tagged (1t)
ttagI = ttag_i0 | ttag_i1 # At least one top tagged ('I' for 'inclusive' tagger; >=1t; 1t+2t)
ttag2 = ttag_i0 & ttag_i1 # Both jets top tagged (2t)
Alltags = ttag0 | ttagI #Either no tag or at least one tag (0t+1t+2t)
# ---- Pick FatJet that passes btag cut based on its subjet with the highest btag value ---- #
btag_i0 = (np.maximum(SubJet01.btagCSVV2, SubJet02.btagCSVV2) >
self.bdisc)
btag_i1 = (np.maximum(SubJet11.btagCSVV2, SubJet12.btagCSVV2) >
self.bdisc)
# --- Define "B Tag" Regions ---- #
btag0 = (~btag_i0) & (~btag_i1) #(0b)
btag1 = btag_i0 ^ btag_i1 #(1b)
btag2 = btag_i0 & btag_i1 #(2b)
# ---- Get Analysis Categories ---- #
# ---- They are (central, forward) cross (0b,1b,2b) cross (At,at,0t,1t,>=1t,2t) ---- #
regs = [cen, fwd]
btags = [btag0, btag1, btag2]
ttags = [
antitag_probe, antitag, pretag, ttag0, ttag1, ttagI, ttag2, Alltags
]
cats = [(t & b & y).flatten()
for t, b, y in itertools.product(ttags, btags, regs)]
labels_and_categories = dict(zip(self.anacats, cats))
# ---- Variables for Kinematic Histograms ---- #
# ---- "i0" is the control jet, "i1" is the probe jet ---- #
ttbarmass = ttbarcands.p4.sum().mass.flatten()
jetpt = ttbarcands.i1.pt.flatten()
jeteta = ttbarcands.i1.eta.flatten()
jetphi = ttbarcands.i1.phi.flatten()
jety = ttbarcands.i1.p4.rapidity.flatten()
jetmass = ttbarcands.i1.p4.mass.flatten()
SDmass = ttbarcands.i1.msoftdrop.flatten()
jetdy = np.abs(ttbarcands.i0.p4.rapidity.flatten() -
ttbarcands.i1.p4.rapidity.flatten())
Tau32 = (ttbarcands.i1.tau3 / ttbarcands.i1.tau2).flatten()
# ---- Variables for Deep Tagger Analysis ---- #
deepTag = ttbarcands.i1.deepTag_TvsQCD.flatten()
deepTagMD = ttbarcands.i1.deepTagMD_TvsQCD.flatten()
weights = evtweights.flatten()
# ---- Define the SumW2 for MC Datasets ---- #
output['cutflow']['sumw'] += np.sum(weights)
output['cutflow']['sumw2'] += np.sum(weights**2)
# ---- Define Momentum p of probe jet as the Mistag Rate variable; M(p) ---- #
# ---- Transverse Momentum pT can also be used instead; M(pT) ---- #
pT = ttbarcands.i1.pt.flatten()
eta = ttbarcands.i1.eta.flatten()
pz = np.sinh(eta) * pT
p = np.absolute(np.sqrt(pT**2 + pz**2))
# ---- Define the Numerator and Denominator for Mistag Rate ---- #
numerator = np.where(
antitag_probe, p,
-1) # If no antitag and tagged probe, move event to useless bin
denominator = np.where(antitag, p,
-1) # If no antitag, move event to useless bin
df = pd.DataFrame({"momentum":
p}) # Used for finding values in LookUp Tables
for ilabel, icat in labels_and_categories.items():
### ------------------------------------ Mistag Scaling ------------------------------------ ###
if self.UseLookUpTables == True:
# ---- Weight ttbar M.C. and data by mistag from data (corresponding to its year) ---- #
if 'TTbar_' in dataset:
file_df = self.lu['JetHT' + dataset[-4:] + '_Data'][
'at' + str(
ilabel[-5:]
)] #Pick out proper JetHT year mistag for TTbar sim.
elif dataset == 'TTbar':
file_df = self.lu['JetHT']['at' + str(
ilabel[-5:])] # All JetHT years mistag for TTbar sim.
else:
file_df = self.lu[dataset]['at' + str(
ilabel[-5:])] # get mistag (lookup) filename for 'at'
bin_widths = file_df[
'p'].values # collect bins as written in .csv file
mtr = file_df[
'M(p)'].values # collect mistag rate as function of p as written in file
wgts = mtr # Define weights based on mistag rates
BinKeys = np.arange(
bin_widths.size
) # Use as label for BinNumber column in the new dataframe
#Bins = pd.interval_range(start=0, periods=100, freq=100, closed='left') # Recreate the momentum bins from file_df as something readable for pd.cut()
Bins = np.array(manual_bins)
df['BinWidth'] = pd.cut(p, bins=Bins) # new dataframe column
df['BinNumber'] = pd.cut(p, bins=Bins, labels=BinKeys)
BinNumber = df[
'BinNumber'].values # Collect the Bin Numbers into a numpy array
BinNumber = BinNumber.astype(
'int64') # Insures the bin numbers are integers
WeightMatching = wgts[
BinNumber] # Match 'wgts' with corresponding p bin using the bin number
Weights = weights * WeightMatching # Include 'wgts' with the previously defined 'weights'
else:
Weights = weights # No mistag rates, no change to weights
###---------------------------------------------------------------------------------------------###
### ----------------------------------- Mod-mass Procedure ------------------------------------ ###
if self.ModMass == True:
QCD_unweighted = util.load(
'TTbarResCoffea_QCD_unweighted_output.coffea')
# ---- Extract event counts from QCD MC hist in signal region ---- #
QCD_hist = QCD_unweighted['jetmass'].integrate(
'anacat',
'2t' + str(ilabel[-5:])).integrate('dataset', 'QCD')
data = QCD_hist.values() # Dictionary of values
QCD_data = [
i for i in data.values()
][0] # place every element of the dictionary into a numpy array
# ---- Re-create Bins from QCD_hist as Numpy Array ---- #
bins = np.arange(
510
) #Re-make bins from the jetmass_axis starting with the appropriate range
QCD_bins = bins[::
10] #Finish re-making bins by insuring exactly 50 bins like the jetmass_axis
# ---- Define Mod Mass Distribution ---- #
ModMass_hist_dist = ss.rv_histogram([QCD_data, QCD_bins])
jet1_modp4 = copy.copy(
jet1.p4
) #J1's Lorentz four vector that can be safely modified
jet1_modp4["fMass"] = ModMass_hist_dist.rvs(
size=jet1_modp4.size
) #Replace J1's mass with random value of mass from mm hist
ttbarcands_modmass = jet0.p4.cross(
jet1_modp4) #J0's four vector x modified J1's four vector
# ---- Apply Necessary Selections to new modmass version ---- #
ttbarcands_modmass = ttbarcands_modmass[oneTTbar]
ttbarcands_modmass = ttbarcands_modmass[dPhiCut]
ttbarcands_modmass = ttbarcands_modmass[GoodSubjets]
# ---- Manually sum the modmass p4 candidates (Coffea technicality) ---- #
ttbarcands_modmass_p4_sum = (ttbarcands_modmass.i0 +
ttbarcands_modmass.i1)
# ---- Re-define Mass Variables for ModMass Procedure (pt, eta, phi are redundant to change) ---- #
ttbarmass = ttbarcands_modmass_p4_sum.flatten().mass
jetmass = ttbarcands_modmass.i1.mass.flatten()
###---------------------------------------------------------------------------------------------###
output['cutflow'][ilabel] += np.sum(icat)
output['ttbarmass'].fill(dataset=dataset,
anacat=ilabel,
ttbarmass=ttbarmass[icat],
weight=Weights[icat])
output['jetpt'].fill(dataset=dataset,
anacat=ilabel,
jetpt=jetpt[icat],
weight=Weights[icat])
output['probept'].fill(dataset=dataset,
anacat=ilabel,
jetpt=pT[icat],
weight=Weights[icat])
output['probep'].fill(dataset=dataset,
anacat=ilabel,
jetp=p[icat],
weight=Weights[icat])
output['jeteta'].fill(dataset=dataset,
anacat=ilabel,
jeteta=jeteta[icat],
weight=Weights[icat])
output['jetphi'].fill(dataset=dataset,
anacat=ilabel,
jetphi=jetphi[icat],
weight=Weights[icat])
output['jety'].fill(dataset=dataset,
anacat=ilabel,
jety=jety[icat],
weight=Weights[icat])
output['jetdy'].fill(dataset=dataset,
anacat=ilabel,
jetdy=jetdy[icat],
weight=Weights[icat])
output['numerator'].fill(dataset=dataset,
anacat=ilabel,
jetp=numerator[icat],
weight=Weights[icat])
output['denominator'].fill(dataset=dataset,
anacat=ilabel,
jetp=denominator[icat],
weight=Weights[icat])
output['jetmass'].fill(dataset=dataset,
anacat=ilabel,
jetmass=jetmass[icat],
weight=Weights[icat])
output['SDmass'].fill(dataset=dataset,
anacat=ilabel,
jetmass=SDmass[icat],
weight=Weights[icat])
output['tau32'].fill(dataset=dataset,
anacat=ilabel,
tau32=Tau32[icat],
weight=Weights[icat])
output['tau32_2D'].fill(dataset=dataset,
anacat=ilabel,
jetpt=pT[icat],
tau32=Tau32[icat],
weight=Weights[icat])
output['deepTag_TvsQCD'].fill(dataset=dataset,
anacat=ilabel,
jetpt=pT[icat],
tagger=deepTag[icat],
weight=Weights[icat])
output['deepTagMD_TvsQCD'].fill(dataset=dataset,
anacat=ilabel,
jetpt=pT[icat],
tagger=deepTagMD[icat],
weight=Weights[icat])
return output
def process(self, df):
output = self.accumulator.identity()
datasetFull = df['dataset']
dataset = datasetFull.replace('_2016', '')
isData = 'Data' in dataset
year = 2016
yearStr = "2016"
muTrigger = df['HLT_IsoMu24'] | df['HLT_IsoTkMu24']
eleTrigger = df['HLT_Ele27_WPTight_Gsf']
photonBitMapName = 'Photon_cutBased'
#### These are already applied in the skim
# filters = (df['Flag_goodVertices'] &
# df['Flag_globalSuperTightHalo2016Filter'] &
# df['Flag_HBHENoiseFilter'] &
# df['Flag_HBHENoiseIsoFilter'] &
# df['Flag_EcalDeadCellTriggerPrimitiveFilter'] &
# df['Flag_BadPFMuonFilter']
# )
# if year > 2016:
# filters = (filters &
# df['Flag_ecalBadCalibFilterV2']
# )
muons = JaggedCandidateArray.candidatesfromcounts(
df['nMuon'],
pt=df['Muon_pt'],
eta=df['Muon_eta'],
phi=df['Muon_phi'],
mass=df['Muon_mass'],
charge=df['Muon_charge'],
relIso=df['Muon_pfRelIso04_all'],
tightId=df['Muon_tightId'],
isPFcand=df['Muon_isPFcand'],
isTracker=df['Muon_isTracker'],
isGlobal=df['Muon_isGlobal'],
)
electrons = JaggedCandidateArray.candidatesfromcounts(
df['nElectron'],
pt=df['Electron_pt'],
eta=df['Electron_eta'],
phi=df['Electron_phi'],
mass=df['Electron_mass'],
charge=df['Electron_charge'],
cutBased=df['Electron_cutBased'],
d0=df['Electron_dxy'],
dz=df['Electron_dz'],
)
jets = JaggedCandidateArray.candidatesfromcounts(
df['nJet'],
pt=df['Jet_pt'],
eta=df['Jet_eta'],
phi=df['Jet_phi'],
mass=df['Jet_mass'],
jetId=df['Jet_jetId'],
btag=df['Jet_btagDeepB'],
hadFlav=df['Jet_hadronFlavour']
if not isData else np.ones_like(df['Jet_jetId']),
genIdx=df['Jet_genJetIdx']
if not isData else np.ones_like(df['Jet_jetId']),
)
photons = JaggedCandidateArray.candidatesfromcounts(
df['nPhoton'],
pt=df['Photon_pt'],
eta=df['Photon_eta'],
phi=df['Photon_phi'],
mass=np.zeros_like(df['Photon_pt']),
isEE=df['Photon_isScEtaEE'],
isEB=df['Photon_isScEtaEB'],
photonId=df[photonBitMapName],
passEleVeto=df['Photon_electronVeto'],
pixelSeed=df['Photon_pixelSeed'],
sieie=df['Photon_sieie'],
chIso=df['Photon_pfRelIso03_chg'] * df['Photon_pt'],
vidCuts=df['Photon_vidNestedWPBitmap'],
genFlav=df['Photon_genPartFlav']
if not isData else np.ones_like(df['Photon_electronVeto']),
genIdx=df['Photon_genPartIdx']
if not isData else np.ones_like(df['Photon_electronVeto']),
)
if not isData:
genPart = JaggedCandidateArray.candidatesfromcounts(
df['nGenPart'],
pt=df['GenPart_pt'],
eta=df['GenPart_eta'],
phi=df['GenPart_phi'],
mass=df['GenPart_mass'],
pdgid=df['GenPart_pdgId'],
motherIdx=df['GenPart_genPartIdxMother'],
status=df['GenPart_status'],
statusFlags=df['GenPart_statusFlags'],
)
genmotherIdx = genPart.motherIdx
genpdgid = genPart.pdgid
## TTbar vs TTGamma Overlap Removal (work in progress, still buggy)
doOverlapRemoval = False
if 'TTbar' in dataset:
doOverlapRemoval = True
overlapPt = 10.
overlapEta = 5.
overlapDR = 0.1
if re.search("^W[1234]jets$", dataset):
doOverlapRemoval = True
overlapPt = 10.
overlapEta = 2.5
overlapDR = 0.05
if 'DYjetsM' in dataset:
doOverlapRemoval = True
overlapPt = 15.
overlapEta = 2.6
overlapDR = 0.05
if doOverlapRemoval:
overlapPhoSelect = ((genPart.pt >= overlapPt) &
(abs(genPart.eta) < overlapEta) &
(genPart.pdgid == 22) & (genPart.status == 1))
OverlapPhotons = genPart[overlapPhoSelect]
idx = OverlapPhotons.motherIdx
maxParent = maxHistoryPDGID(idx.content, idx.starts, idx.stops,
genpdgid.content, genpdgid.starts,
genpdgid.stops, genmotherIdx.content,
genmotherIdx.starts,
genmotherIdx.stops)
isNonPrompt = (maxParent > 37).any()
finalGen = genPart[(
(genPart.status == 1) | (genPart.status == 71)) & ~(
(abs(genPart.pdgid) == 12) | (abs(genPart.pdgid) == 14) |
(abs(genPart.pdgid) == 16))]
genPairs = OverlapPhotons['p4'].cross(finalGen['p4'], nested=True)
##remove the case where the cross produce is the gen photon with itself
genPairs = genPairs[~(genPairs.i0 == genPairs.i1)]
dRPairs = genPairs.i0.delta_r(genPairs.i1)
isOverlap = ((dRPairs.min() > overlapDR) & (maxParent < 37)).any()
passOverlapRemoval = ~isOverlap
else:
passOverlapRemoval = np.ones_like(df['event']) == 1
muonSelectTight = ((muons.pt > 30) & (abs(muons.eta) < 2.4) &
(muons.tightId) & (muons.relIso < 0.15))
muonSelectLoose = ((muons.pt > 15) & (abs(muons.eta) < 2.4) &
((muons.isPFcand) &
(muons.isTracker | muons.isGlobal)) &
(muons.relIso < 0.25) & np.invert(muonSelectTight))
eleEtaGap = (abs(electrons.eta) < 1.4442) | (abs(electrons.eta) >
1.566)
elePassD0 = ((abs(electrons.eta) < 1.479) & (abs(electrons.d0) < 0.05)
| (abs(electrons.eta) > 1.479) &
(abs(electrons.d0) < 0.1))
elePassDZ = ((abs(electrons.eta) < 1.479) & (abs(electrons.dz) < 0.1) |
(abs(electrons.eta) > 1.479) & (abs(electrons.dz) < 0.2))
electronSelectTight = ((electrons.pt > 35) & (abs(electrons.eta) < 2.1)
& eleEtaGap & (electrons.cutBased >= 4)
& elePassD0 & elePassDZ)
electronSelectLoose = ((electrons.pt > 15) & (abs(electrons.eta) < 2.4)
& eleEtaGap & (electrons.cutBased >= 1)
& elePassD0 & elePassDZ
& np.invert(electronSelectTight))
tightMuon = muons[muonSelectTight]
looseMuon = muons[muonSelectLoose]
tightElectron = electrons[electronSelectTight]
looseElectron = electrons[electronSelectLoose]
oneMuon = (tightMuon.counts == 1)
muVeto = (tightMuon.counts == 0)
oneEle = (tightElectron.counts == 1)
eleVeto = (tightElectron.counts == 0)
looseMuonSel = (looseMuon.counts == 0)
looseElectronSel = (looseElectron.counts == 0)
#### Calculate deltaR between photon and nearest muon
####### make combination pairs
phoMu = photons['p4'].cross(tightMuon['p4'], nested=True)
####### check delta R of each combination, if min is >0.1 it is okay, or if there are no tight muons it passes
dRphomu = (phoMu.i0.delta_r(phoMu.i1) > 0.4).all() | (tightMuon.counts
== 0)
phoEle = photons['p4'].cross(tightElectron['p4'], nested=True)
dRphoele = ((phoEle.i0.delta_r(phoEle.i1)).min() >
0.4) | (tightElectron.counts == 0)
#photon selection (no ID requirement used here)
photonSelect = ((photons.pt > 20) & (abs(photons.eta) < 1.4442) &
(photons.isEE | photons.isEB) & (photons.passEleVeto)
& np.invert(photons.pixelSeed) & dRphomu & dRphoele)
#split out the ID requirement, enabling Iso and SIEIE to be inverted for control regions
photonID = photons.photonId >= 2
#parse VID cuts, define loose photons (not used yet)
photon_MinPtCut = (photons.vidCuts >> 0 & 3) >= 2
photon_PhoSCEtaMultiRangeCut = (photons.vidCuts >> 2 & 3) >= 2
photon_PhoSingleTowerHadOverEmCut = (photons.vidCuts >> 4 & 3) >= 2
photon_PhoFull5x5SigmaIEtaIEtaCut = (photons.vidCuts >> 6 & 3) >= 2
photon_ChIsoCut = (photons.vidCuts >> 8 & 3) >= 2
photon_NeuIsoCut = (photons.vidCuts >> 10 & 3) >= 2
photon_PhoIsoCut = (photons.vidCuts >> 12 & 3) >= 2
photonID_NoChIsoSIEIE = (photon_MinPtCut & photon_PhoSCEtaMultiRangeCut
& photon_PhoSingleTowerHadOverEmCut
& photon_PhoFull5x5SigmaIEtaIEtaCut
& photon_NeuIsoCut & photon_PhoIsoCut)
tightPhotons = photons[photonSelect & photonID]
loosePhotons = photons[photonSelect & photonID_NoChIsoSIEIE
& photon_PhoFull5x5SigmaIEtaIEtaCut]
loosePhotonsSideband = photons[photonSelect & photonID_NoChIsoSIEIE &
(photons.sieie > 0.012)]
##medium jet ID cut
jetIDbit = 1
if year > 2016: jetIDbit = 2
##check dR jet,lepton & jet,photon
jetMu = jets['p4'].cross(tightMuon['p4'], nested=True)
dRjetmu = (
(jetMu.i0.delta_r(jetMu.i1)).min() > 0.4) | (tightMuon.counts == 0)
jetEle = jets['p4'].cross(tightElectron['p4'], nested=True)
dRjetele = ((jetEle.i0.delta_r(jetEle.i1)).min() >
0.4) | (tightElectron.counts == 0)
jetPho = jets['p4'].cross(tightPhotons['p4'], nested=True)
dRjetpho = ((jetPho.i0.delta_r(jetPho.i1)).min() >
0.1) | (tightPhotons.counts == 0)
jetSelect = ((jets.pt > 30) & (abs(jets.eta) < 2.4) &
((jets.jetId >> jetIDbit & 1) == 1) & dRjetmu & dRjetele
& dRjetpho)
tightJets = jets[jetSelect]
bTagWP = 0.6321 #2016 DeepCSV working point
btagged = tightJets.btag > bTagWP
bJets = tightJets[btagged]
## Define M3, mass of 3-jet pair with highest pT
triJet = tightJets['p4'].choose(3)
triJetPt = (triJet.i0 + triJet.i1 + triJet.i2).pt
triJetMass = (triJet.i0 + triJet.i1 + triJet.i2).mass
M3 = triJetMass[triJetPt.argmax()]
leadingMuon = tightMuon[::1]
leadingElectron = tightElectron[::1]
leadingPhoton = tightPhotons[:, :1]
leadingPhotonLoose = loosePhotons[:, :1]
leadingPhotonSideband = loosePhotonsSideband[:, :1]
# egammaMass = (leadingElectron['p4'] + leadingPhoton['p4']).mass
egamma = leadingElectron['p4'].cross(leadingPhoton['p4'])
mugamma = leadingMuon['p4'].cross(leadingPhoton['p4'])
egammaMass = (egamma.i0 + egamma.i1).mass
mugammaMass = (mugamma.i0 + mugamma.i1).mass
if not isData:
#### Photon categories, using genIdx branch
# reco photons really generated as electrons
idx = leadingPhoton.genIdx
matchedPho = (genpdgid[idx] == 22).any()
isMisIDele = (abs(genpdgid[idx]) == 11).any()
maxParent = maxHistoryPDGID(idx.content, idx.starts, idx.stops,
genpdgid.content, genpdgid.starts,
genpdgid.stops, genmotherIdx.content,
genmotherIdx.starts,
genmotherIdx.stops)
hadronicParent = maxParent > 25
isGenPho = matchedPho & ~hadronicParent
isHadPho = matchedPho & hadronicParent
isHadFake = ~(isMisIDele | isGenPho | isHadPho) & (
leadingPhoton.counts == 1)
#define integer definition for the photon category axis
phoCategory = 1 * isGenPho + 2 * isMisIDele + 3 * isHadPho + 4 * isHadFake
isMisIDeleLoose = (leadingPhotonLoose.genFlav == 13).any()
matchedPhoLoose = (leadingPhotonLoose.genFlav == 1).any()
# look through parentage to find if any hadrons in genPhoton parent history
idx = leadingPhotonLoose.genIdx
maxParent = maxHistoryPDGID(idx.content, idx.starts, idx.stops,
genpdgid.content, genpdgid.starts,
genpdgid.stops, genmotherIdx.content,
genmotherIdx.starts,
genmotherIdx.stops)
hadronicParent = maxParent > 25
isGenPhoLoose = matchedPhoLoose & ~hadronicParent
isHadPhoLoose = matchedPhoLoose & hadronicParent
isHadFakeLoose = ~(isMisIDeleLoose | isGenPhoLoose
| isHadPhoLoose) & (leadingPhotonLoose.counts
== 1)
#define integer definition for the photon category axis
phoCategoryLoose = 1 * isGenPhoLoose + 2 * isMisIDeleLoose + 3 * isHadPhoLoose + 4 * isHadFakeLoose
isMisIDeleSideband = (leadingPhotonSideband.genFlav == 13).any()
matchedPhoSideband = (leadingPhotonSideband.genFlav == 1).any()
# look through parentage to find if any hadrons in genPhoton parent history
idx = leadingPhotonSideband.genIdx
maxParent = maxHistoryPDGID(idx.content, idx.starts, idx.stops,
genpdgid.content, genpdgid.starts,
genpdgid.stops, genmotherIdx.content,
genmotherIdx.starts,
genmotherIdx.stops)
hadronicParent = maxParent > 25
isGenPhoSideband = matchedPhoSideband & ~hadronicParent
isHadPhoSideband = matchedPhoSideband & hadronicParent
isHadFakeSideband = ~(isMisIDeleSideband | isGenPhoSideband
| isHadPhoSideband) & (
leadingPhotonSideband.counts == 1)
#define integer definition for the photon category axis
phoCategorySideband = 1 * isGenPhoSideband + 2 * isMisIDeleSideband + 3 * isHadPhoSideband + 4 * isHadFakeSideband
else:
phoCategory = np.ones_like(df['event'])
phoCategoryLoose = np.ones_like(df['event'])
phoCategorySideband = np.ones_like(df['event'])
### remove filter selection
### This is already applied in the skim, and is causing data to fail for some reason (the flag branches are duplicated in NanoAOD for data, is it causing problems???)
# mu_noLoose = (muTrigger & filters & passOverlapRemoval &
# oneMuon & eleVeto &
# looseMuonSel & looseElectronSel)
# ele_noLoose = (eleTrigger & filters & passOverlapRemoval &
# oneEle & muVeto &
# looseMuonSel & looseElectronSel)
mu_noLoose = (muTrigger & passOverlapRemoval & oneMuon & eleVeto
& looseMuonSel & looseElectronSel)
ele_noLoose = (eleTrigger & passOverlapRemoval & oneEle & muVeto
& looseMuonSel & looseElectronSel)
lep_noLoose = mu_noLoose | ele_noLoose
lep_jetSel = (lep_noLoose & (tightJets.counts >= 4) &
(bJets.counts >= 1))
lep_zeropho = (lep_jetSel & (tightPhotons.counts == 0))
lep_phosel = (lep_jetSel & (tightPhotons.counts == 1))
lep_phoselLoose = (lep_jetSel & (loosePhotons.counts == 1))
lep_phoselSideband = (lep_jetSel & (loosePhotonsSideband.counts == 1))
lep_phosel_3j0t = (lep_noLoose & (tightJets.counts >= 3) &
(bJets.counts == 0) & (tightPhotons.counts == 1))
lepFlavor = -0.5 * ele_noLoose + 0.5 * mu_noLoose
evtWeight = np.ones_like(df['event'], dtype=np.float64)
if not 'Data' in dataset:
nMCevents = self.mcEventYields[datasetFull]
xsec = crossSections[dataset]
evtWeight *= xsec * lumis[year] / nMCevents
#btag key name
#name / working Point / type / systematic / jetType
# ... / 0-loose 1-medium 2-tight / comb,mujets,iterativefit / central,up,down / 0-b 1-c 2-udcsg
bJetSF_b = self.evaluator['btag%iDeepCSV_1_comb_central_0' % year](
tightJets[tightJets.hadFlav == 5].eta,
tightJets[tightJets.hadFlav == 5].pt,
tightJets[tightJets.hadFlav == 5].btag)
bJetSF_c = self.evaluator['btag%iDeepCSV_1_comb_central_1' % year](
tightJets[tightJets.hadFlav == 4].eta,
tightJets[tightJets.hadFlav == 4].pt,
tightJets[tightJets.hadFlav == 4].btag)
bJetSF_udcsg = self.evaluator[
'btag%iDeepCSV_1_incl_central_2' %
year](tightJets[tightJets.hadFlav == 0].eta,
tightJets[tightJets.hadFlav == 0].pt,
tightJets[tightJets.hadFlav == 0].btag)
bJetSF = JaggedArray(content=np.ones_like(tightJets.pt.content,
dtype=np.float64),
starts=tightJets.starts,
stops=tightJets.stops)
bJetSF.content[(tightJets.hadFlav == 5).content] = bJetSF_b.content
bJetSF.content[(tightJets.hadFlav == 4).content] = bJetSF_c.content
bJetSF.content[(
tightJets.hadFlav == 0).content] = bJetSF_udcsg.content
## mc efficiency lookup, data efficiency is eff* scale factor
btagEfficiencies = taggingEffLookup(datasetFull, tightJets.hadFlav,
tightJets.pt, tightJets.eta)
btagEfficienciesData = btagEfficiencies * bJetSF
##probability is the product of all efficiencies of tagged jets, times product of 1-eff for all untagged jets
## https://twiki.cern.ch/twiki/bin/view/CMS/BTagSFMethods#1a_Event_reweighting_using_scale
pMC = btagEfficiencies[btagged].prod() * (
1. - btagEfficiencies[np.invert(btagged)]).prod()
pData = btagEfficienciesData[btagged].prod() * (
1. - btagEfficienciesData[np.invert(btagged)]).prod()
btagWeight = pData / pMC
btagWeight[pData == 0] = 0
evtWeight *= btagWeight
eleID = self.ele_id_sf(tightElectron.eta, tightElectron.pt)
eleIDerr = self.ele_id_err(tightElectron.eta, tightElectron.pt)
eleRECO = self.ele_reco_sf(tightElectron.eta, tightElectron.pt)
eleRECOerr = self.ele_reco_err(tightElectron.eta, tightElectron.pt)
eleSF = (eleID * eleRECO).prod()
eleSFup = ((eleID + eleIDerr) * (eleRECO + eleRECOerr)).prod()
eleSFdo = ((eleID - eleIDerr) * (eleRECO - eleRECOerr)).prod()
evtWeight *= eleSF
muID = self.mu_id_sf(tightMuon.eta, tightMuon.pt)
muIDerr = self.mu_id_err(tightMuon.eta, tightMuon.pt)
muIso = self.mu_iso_sf(tightMuon.eta, tightMuon.pt)
muIsoerr = self.mu_iso_err(tightMuon.eta, tightMuon.pt)
muTrig = self.mu_iso_sf(abs(tightMuon.eta), tightMuon.pt)
muTrigerr = self.mu_iso_err(abs(tightMuon.eta), tightMuon.pt)
muSF = (muID * muIso * muTrig).prod()
muSF_up = ((muID + muIDerr) * (muIso + muIsoerr) *
(muTrig + muTrigerr)).prod()
muSF_down = ((muID - muIDerr) * (muIso - muIsoerr) *
(muTrig - muTrigerr)).prod()
evtWeight *= muSF
output['photon_pt'].fill(
dataset=dataset,
pt=tightPhotons.p4.pt[:, :1][lep_phosel].flatten(),
category=phoCategory[lep_phosel].flatten(),
lepFlavor=lepFlavor[lep_phosel],
weight=evtWeight[lep_phosel].flatten())
output['photon_eta'].fill(
dataset=dataset,
eta=tightPhotons.eta[:, :1][lep_phosel].flatten(),
category=phoCategory[lep_phosel].flatten(),
lepFlavor=lepFlavor[lep_phosel],
weight=evtWeight[lep_phosel].flatten())
output['photon_chIsoSideband'].fill(
dataset=dataset,
chIso=loosePhotonsSideband.chIso[:, :1]
[lep_phoselSideband].flatten(),
category=phoCategorySideband[lep_phoselSideband].flatten(),
lepFlavor=lepFlavor[lep_phoselSideband],
weight=evtWeight[lep_phoselSideband].flatten())
output['photon_chIso'].fill(
dataset=dataset,
chIso=loosePhotons.chIso[:, :1][lep_phoselLoose].flatten(),
category=phoCategoryLoose[lep_phoselLoose].flatten(),
lepFlavor=lepFlavor[lep_phoselLoose],
weight=evtWeight[lep_phoselLoose].flatten())
output['photon_lepton_mass'].fill(
dataset=dataset,
mass=egammaMass[lep_phosel & ele_noLoose].flatten(),
category=phoCategory[lep_phosel & ele_noLoose].flatten(),
lepFlavor=lepFlavor[lep_phosel & ele_noLoose],
weight=evtWeight[lep_phosel & ele_noLoose].flatten())
output['photon_lepton_mass'].fill(
dataset=dataset,
mass=mugammaMass[lep_phosel & mu_noLoose].flatten(),
category=phoCategory[lep_phosel & mu_noLoose].flatten(),
lepFlavor=lepFlavor[lep_phosel & mu_noLoose],
weight=evtWeight[lep_phosel & mu_noLoose].flatten())
output['photon_lepton_mass_3j0t'].fill(
dataset=dataset,
mass=egammaMass[lep_phosel_3j0t & ele_noLoose].flatten(),
category=phoCategory[lep_phosel_3j0t & ele_noLoose].flatten(),
lepFlavor=lepFlavor[lep_phosel_3j0t & ele_noLoose],
weight=evtWeight[lep_phosel_3j0t & ele_noLoose].flatten())
output['photon_lepton_mass_3j0t'].fill(
dataset=dataset,
mass=mugammaMass[lep_phosel_3j0t & mu_noLoose].flatten(),
category=phoCategory[lep_phosel_3j0t & mu_noLoose].flatten(),
lepFlavor=lepFlavor[lep_phosel_3j0t & mu_noLoose],
weight=evtWeight[lep_phosel_3j0t & mu_noLoose].flatten())
output['M3'].fill(dataset=dataset,
M3=M3[lep_phosel].flatten(),
category=phoCategoryLoose[lep_phosel].flatten(),
lepFlavor=lepFlavor[lep_phosel],
weight=evtWeight[lep_phosel].flatten())
output['M3Presel'].fill(dataset=dataset,
M3=M3[lep_zeropho].flatten(),
lepFlavor=lepFlavor[lep_zeropho],
weight=evtWeight[lep_zeropho].flatten())
output['EventCount'] = len(df['event'])
return output
def convert_jec_txt_file(jecFilePath):
jec_f = open(jecFilePath,'r')
layoutstr = jec_f.readline().strip().strip('{}')
jec_f.close()
name = jecFilePath.split('/')[-1].split('.')[0]
layout = layoutstr.split()
if not layout[0].isdigit():
raise Exception('First column of JEC descriptor must be a digit!')
#setup the file format
nBinnedVars = int(layout[0])
nBinColumns = 2*nBinnedVars
nEvalVars = int(layout[nBinnedVars+1])
formula = layout[nBinnedVars+nEvalVars+2]
nParms = 0
while( formula.count('[%i]'%nParms) ):
formula = formula.replace('[%i]'%nParms,'p%i'%nParms)
nParms += 1
#protect function names with vars in them
funcs_to_cap = ['max','exp']
for f in funcs_to_cap:
formula = formula.replace(f,f.upper())
templatevars = ['x','y','z','w','t','s']
varnames = [layout[i+nBinnedVars+2] for i in range(nEvalVars)]
for find,replace in zip(templatevars,varnames):
formula = formula.replace(find,replace)
#restore max
for f in funcs_to_cap:
formula = formula.replace(f.upper(),f)
nFuncColumns = 2*nEvalVars + nParms
nTotColumns = nFuncColumns + 1
#parse the columns
minMax = ['Min','Max']
columns = []
dtypes = []
offset = 1
for i in range(nBinnedVars):
columns.extend(['%s%s'%(layout[i+offset],mm) for mm in minMax])
dtypes.extend(['<f8','<f8'])
columns.append('NVars')
dtypes.append('<i8')
offset += nBinnedVars + 1
for i in range(nEvalVars):
columns.extend(['%s%s'%(layout[i+offset],mm) for mm in minMax])
dtypes.extend(['<f8','<f8'])
for i in range(nParms):
columns.append('p%i'%i)
dtypes.append('<f8')
pars = np.genfromtxt(jecFilePath,
dtype=tuple(dtypes),
names=tuple(columns),
skip_header=1,
unpack=True,
encoding='ascii'
)
#the first bin is always usual for JECs
#the next bins may vary in number, so they're jagged arrays... yay
bins = {}
offset_col = 0
offset_name = 1
bin_order = []
for i in range(nBinnedVars):
binMins = None
binMaxs = None
if i == 0:
binMins = np.unique(pars[columns[0]])
binMaxs = np.unique(pars[columns[1]])
bins[layout[i+offset_name]] = np.union1d(binMins,binMaxs)
else:
counts = np.zeros(0,dtype=np.int)
allBins = np.zeros(0,dtype=np.double)
for binMin in bins[bin_order[0]][:-1]:
binMins = np.unique(pars[np.where(pars[columns[0]] == binMin)][columns[i+offset_col]])
binMaxs = np.unique(pars[np.where(pars[columns[0]] == binMin)][columns[i+offset_col+1]])
theBins = np.union1d(binMins,binMaxs)
allBins = np.append(allBins,theBins)
counts = np.append(counts,theBins.size)
bins[layout[i+offset_name]] = JaggedArray.fromcounts(counts,allBins)
bin_order.append(layout[i+offset_name])
offset_col += 1
#skip nvars to the variable columns
#the columns here define clamps for the variables defined in columns[]
# ----> clamps can be different from bins
# ----> if there is more than one binning variable this array is jagged
# ----> just make it jagged all the time
binshapes = tuple([bins[thebin].size-1 for thebin in bin_order])
clamp_mins = {}
clamp_maxs = {}
var_order = []
offset_col = 2*nBinnedVars+1
offset_name = nBinnedVars + 2
jagged_counts = np.ones(bins[bin_order[0]].size-1,dtype=np.int)
if len(bin_order) > 1:
jagged_counts = np.maximum(bins[bin_order[1]].counts - 1,0) #need counts-1 since we only care about Nbins
for i in range(nEvalVars):
clamp_mins[layout[i+offset_name]] = JaggedArray.fromcounts(jagged_counts,np.atleast_1d(pars[columns[i+offset_col]]))
clamp_maxs[layout[i+offset_name]] = JaggedArray.fromcounts(jagged_counts,np.atleast_1d(pars[columns[i+offset_col+1]]))
var_order.append(layout[i+offset_name])
offset_col += 1
#now get the parameters, which we will look up with the clamps
parms = []
parm_order = []
offset_col = 2*nBinnedVars+1 + 2*nEvalVars
for i in range(nParms):
parms.append(JaggedArray.fromcounts(jagged_counts,pars[columns[i+offset_col]]))
parm_order.append('p%i'%(i))
wrapped_up = {}
wrapped_up[(name,'jet_energy_corrector')] = (formula,
(bins,bin_order),
(clamp_mins,clamp_maxs,var_order),
(parms,parm_order))
return wrapped_up
def jagged_1():
boundaries = [0, 3, 5, 6, 9, 12, 12]
return JaggedArray(
boundaries[:-1], boundaries[1:],
[0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.0, 11.0])
loose_muons = muons[loose_muon_selection]
loose_photons = photons[loose_photon_selection]
selected_taus = taus[tau_selection]
selected_jets = jets[jet_selection]
# end seletion
# clean leptons
e_combinations = loose_electrons.p4.cross(selected_jets.p4, nested=True)
mask = (e_combinations.i0.delta_r(e_combinations.i1) < 0.3).any()
clean_electrons = loose_electrons[~mask]
m_combinations = loose_muons.p4.cross(selected_jets.p4, nested=True)
mask = (m_combinations.i0.delta_r(m_combinations.i1) < 0.3).any()
clean_muons = loose_muons[mask]
clean_leptons = JaggedArray.fromiter([clean_electrons, clean_muons])
# once merge is done
# mask = loose_electrons.p4.match(selected_jets.p4, 0.3)
# clean electrons = loose_electrons[~mask]
# end cleaning
# weights evaluation
e_counts = clean_electrons.counts
e_sfTrigg = np.ones(clean_electrons.size)
e_sfTrigg[e_counts > 0] = 1 - evaluator["hEffEtaPt"](
clean_electrons.eta[e_counts > 0, 0], clean_electrons.pt[e_counts > 0,
0])
e_sfTrigg[e_counts > 1] = 1 - (1 - evaluator["hEffEtaPt"](
clean_electrons.eta[e_counts > 1, 0],
clean_electrons.pt[e_counts > 1, 0])) * (
def process(self, events):
logging.debug('starting process')
output = self.accumulator.identity()
dataset = events.metadata['dataset']
self._isData = dataset in [
'SingleMuon', 'DoubleMuon', 'SingleElectron', 'DoubleEG', 'EGamma',
'MuonEG'
]
selection = processor.PackedSelection()
# TODO: instead of cutflow, use processor.PackedSelection
output['cutflow']['all events'] += events.size
logging.debug('applying lumi mask')
if self._isData:
lumiMask = lumi_tools.LumiMask(self._corrections['golden'])
events['passLumiMask'] = lumiMask(np.array(events.run),
np.array(events.luminosityBlock))
else:
events['passLumiMask'] = np.ones_like(events.run, dtype=bool)
passLumiMask = events.passLumiMask
selection.add('lumiMask', passLumiMask)
logging.debug('adding trigger')
self._add_trigger(events)
passHLT = events.passHLT
selection.add('trigger', passHLT)
output['cutflow']['pass trigger'] += passHLT.sum()
# if no trigger: fast return
if passHLT.sum() == 0:
return output
# require one good vertex
logging.debug('checking vertices')
passGoodVertex = (events.PV.npvsGood > 0)
output['cutflow']['good vertex'] += passGoodVertex.sum()
selection.add('goodVertex', passGoodVertex)
# run rochester
rochester = self._rochester
_muon_offsets = events.Muon.pt.offsets
_charge = events.Muon.charge
_pt = events.Muon.pt
_eta = events.Muon.eta
_phi = events.Muon.phi
if self._isData:
_k = rochester.kScaleDT(_charge, _pt, _eta, _phi)
# _kErr = rochester.kScaleDTerror(_charge, _pt, _eta, _phi)
else:
# for default if gen present
_gpt = events.Muon.matched_gen.pt
# for backup w/o gen
_nl = events.Muon.nTrackerLayers
_u = JaggedArray.fromoffsets(_muon_offsets,
np.random.rand(*_pt.flatten().shape))
_hasgen = (_gpt.fillna(-1) > 0)
_kspread = rochester.kSpreadMC(_charge[_hasgen], _pt[_hasgen],
_eta[_hasgen], _phi[_hasgen],
_gpt[_hasgen])
_ksmear = rochester.kSmearMC(_charge[~_hasgen], _pt[~_hasgen],
_eta[~_hasgen], _phi[~_hasgen],
_nl[~_hasgen], _u[~_hasgen])
_k = np.ones_like(_pt.flatten())
_k[_hasgen.flatten()] = _kspread.flatten()
_k[~_hasgen.flatten()] = _ksmear.flatten()
_k = JaggedArray.fromoffsets(_muon_offsets, _k)
# _kErrspread = rochester.kSpreadMCerror(_charge[_hasgen], _pt[_hasgen], _eta[_hasgen], _phi[_hasgen],
# _gpt[_hasgen])
# _kErrsmear = rochester.kSmearMCerror(_charge[~_hasgen], _pt[~_hasgen], _eta[~_hasgen], _phi[~_hasgen],
# _nl[~_hasgen], _u[~_hasgen])
# _kErr = np.ones_like(_pt.flatten())
# _kErr[_hasgen.flatten()] = _kErrspread.flatten()
# _kErr[~_hasgen.flatten()] = _kErrsmear.flatten()
# _kErr = JaggedArray.fromoffsets(_muon_offsets, _kErr)
mask = _pt.flatten() < 200
rochester_pt = _pt.flatten()
rochester_pt[mask] = (_k * _pt).flatten()[mask]
events.Muon['pt'] = JaggedArray.fromoffsets(_muon_offsets,
rochester_pt)
logging.debug('adding muon id')
self._add_muon_id(events.Muon)
logging.debug('adding electron id')
self._add_electron_id(events.Electron)
logging.debug('selecting muons')
muonId = (events.Muon.passId > 0)
muons = events.Muon[muonId]
logging.debug('selecting electrons')
electronId = (events.Electron.passId > 0)
electrons = events.Electron[electronId]
passTwoLeptons = (muons.counts >= 2) | (electrons.counts >= 2)
output['cutflow']['two leptons'] += passTwoLeptons.sum()
selection.add('twoLeptons', passTwoLeptons)
# build cands
# remake z to have same columns
# pt eta phi mass charge pdgId
logging.debug('rebuilding leptons')
def rebuild(leptons):
return JaggedCandidateArray.candidatesfromoffsets(
leptons.offsets,
pt=leptons.pt.flatten(),
eta=leptons.eta.flatten(),
phi=leptons.phi.flatten(),
mass=leptons.mass.flatten(),
charge=leptons.charge.flatten(),
pdgId=leptons.pdgId.flatten(),
# needed for electron SF
etaSC=leptons.etaSC.flatten()
if hasattr(leptons, 'etaSC') else leptons.eta.flatten(),
)
newMuons = rebuild(muons)
newElectrons = rebuild(electrons)
logging.debug('building 2 leptons')
ee_cands = newElectrons.choose(2)
mm_cands = newMuons.choose(2)
# combine them
z_cands = JaggedArray.concatenate([ee_cands, mm_cands], axis=1)
def bestcombination(zcands):
good_charge = sum(zcands[str(i)]['charge'] for i in range(2)) == 0
# this keeps the first z cand in each event
# should instead sort the best first
# TODO: select best
zcands = zcands[good_charge][:, :1]
return zcands
logging.debug('selecting best combinations')
z_cands = bestcombination(z_cands)
z1 = np.zeros_like(z_cands['p4'].pt.flatten(), dtype='i')
z2 = np.ones_like(z_cands['p4'].pt.flatten(), dtype='i')
z1[(z_cands['0']['p4'].pt.flatten() <
z_cands['1']['p4'].pt.flatten())] = 1
z2[(z_cands['0']['p4'].pt.flatten() <
z_cands['1']['p4'].pt.flatten())] = 0
z1 = JaggedArray.fromoffsets(z_cands.offsets, z1)
z2 = JaggedArray.fromoffsets(z_cands.offsets, z2)
passZCand = (z_cands.counts > 0)
output['cutflow']['z cand'] += passZCand.sum()
selection.add('zCand', passZCand)
passMassWindow = (passZCand & z_cands[(
(z_cands.p4.mass > 60) & (z_cands.p4.mass < 120))].counts > 0)
output['cutflow']['mass window'] += passMassWindow.sum()
selection.add('massWindow', passMassWindow)
# im sure there is a better way, but for now just do this
def get_lepton_values(zl, key):
val = np.zeros_like(zl.flatten(), dtype=float)
if len(val) == 0:
return JaggedArray.fromoffsets(zl.offsets, val)
for i in range(2):
mask = (i == zl.flatten())
if key == 'pt':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].pt
elif key == 'eta':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].eta
elif key == 'phi':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].phi
elif key == 'mass':
val[mask] = z_cands[passZCand][str(
i)].flatten()[mask]['p4'].mass
else:
val[mask] = z_cands[passZCand][str(i)].flatten()[mask][key]
return JaggedArray.fromoffsets(zl.offsets, val)
z1pt = get_lepton_values(z1, 'pt')
z2pt = get_lepton_values(z2, 'pt')
passPt = ((z1pt > 30) & (z2pt > 20)).counts > 0
output['cutflow']['pt threshold'] += passPt.sum()
selection.add('ptThreshold', passPt)
chanSels = {}
z1pdg = get_lepton_values(z1, 'pdgId')
z2pdg = get_lepton_values(z2, 'pdgId')
for chan in ['ee', 'mm']:
if chan == 'ee':
pdgIds = (11, 11)
if chan == 'mm':
pdgIds = (13, 13)
chanSels[chan] = ((abs(z1pdg) == pdgIds[0])
& (abs(z2pdg) == pdgIds[1]))
weights = processor.Weights(events.run.size)
if self._isData:
output['sumw'][dataset] = 0 # always set to 0 for data
else:
output['sumw'][dataset] += events.genWeight.sum()
weights.add('genWeight', events.genWeight)
weights.add(
'pileupWeight',
self._corrections['pileupWeight'](events.Pileup.nPU),
self._corrections['pileupWeightUp'](events.Pileup.nPU),
self._corrections['pileupWeightDown'](events.Pileup.nPU),
)
zls = [z1, z2]
# electron sf
for ei, zl in enumerate(zls):
ei = str(ei)
eta = get_lepton_values(zl, 'etaSC')
pt = get_lepton_values(zl, 'pt')
electronRecoSF = self._corrections['electron_reco'](eta, pt)
electronIdSF = self._corrections['electron_id_MVA90'](eta, pt)
electronSF = np.ones_like(electronRecoSF.prod())
if ei in ['0', '1']:
chans = ['ee']
else:
chans = []
for chan in chans:
# turns empty arrays into 0's, nonempty int 1's
chanSel = (chanSels[chan].ones_like().sum() > 0)
electronSF[chanSel] *= electronRecoSF[chanSel].prod()
electronSF[chanSel] *= electronIdSF[chanSel].prod()
weights.add('electronSF' + ei, electronSF)
# muon SF
for mi, zl in enumerate(zls):
mi = str(mi)
eta = get_lepton_values(zl, 'eta')
pt = get_lepton_values(zl, 'pt')
if self._year == '2016':
idSF = self._corrections['muon_id_MediumID'](eta, pt)
isoSF = self._corrections['muon_iso_TightRelIso_MediumID'](
eta, pt)
else:
idSF = self._corrections['muon_id_MediumPromptID'](
pt, abs(eta))
isoSF = self._corrections['muon_iso_TightRelIso_MediumID'](
pt, abs(eta))
muonSF = np.ones_like(idSF.prod())
if mi in ['0', '1']:
chans = ['mm']
else:
chans = []
for chan in chans:
# turns empty arrays into 0's, nonempty int 1's
chanSel = (chanSels[chan].ones_like().sum() > 0)
muonSF[chanSel] *= idSF[chanSel].prod()
muonSF[chanSel] *= isoSF[chanSel].prod()
weights.add('muonSF' + mi, muonSF)
logging.debug('filling')
for sel in self._selections:
if sel == 'massWindow':
cut = selection.all('lumiMask', 'trigger', 'goodVertex',
'twoLeptons', 'zCand', 'massWindow',
'ptThreshold')
for chan in ['ee', 'mm']:
chanSel = chanSels[chan]
weight = chanSel.astype(float) * weights.weight()
output[sel + '_zmass'].fill(
dataset=dataset,
channel=chan,
mass=z_cands[cut].p4.mass.flatten(),
weight=weight[cut].flatten(),
)
output[sel + '_met'].fill(
dataset=dataset,
channel=chan,
met=events.MET.pt[cut],
weight=weight[cut].flatten(),
)
output[sel + '_pileup'].fill(
dataset=dataset,
channel=chan,
npvs=events.PV.npvs[cut],
weight=weight[cut].flatten(),
)
return output
treename,
branches=branches,
namedecode='utf-8',
entrysteps=200000)):
charge = arrays['Muon_charge']
pt = arrays['Muon_pt']
eta = arrays['Muon_eta']
phi = arrays['Muon_phi']
if not isData:
# for default if gen present
gid = arrays['Muon_genPartIdx']
gpt = arrays['GenPart_pt']
# for backup w/o gen
nl = arrays['Muon_nTrackerLayers']
u = np.random.rand(*pt.flatten().shape)
u = JaggedArray.fromoffsets(pt.offsets, u)
fullu += [u]
for ie in range(len(pt)):
subres = []
suberr = []
for im in range(len(pt[ie])):
if isData:
subres += [
roccor.kScaleDT(int(charge[ie][im]), float(pt[ie][im]),
float(eta[ie][im]), float(phi[ie][im]))
]
suberr += [
roccor.kScaleDTerror(int(charge[ie][im]),
float(pt[ie][im]),
float(eta[ie][im]),
float(phi[ie][im]))
def jagged_1():
return JaggedArray.fromiter([[0.0, 1.1, 2.2], [3.3, 4.4], [5.5],
[6.6, 7.7, 8.8], [9.9, 10.0, 11.0], []])
def _build_standard_jme_lookup(name,
layout,
pars,
nBinnedVars,
nBinColumns,
nEvalVars,
formula,
nParms,
columns,
dtypes,
interpolatedFunc=False):
#the first bin is always usual for JECs
#the next bins may vary in number, so they're jagged arrays... yay
bins = {}
offset_col = 0
offset_name = 1
bin_order = []
for i in range(nBinnedVars):
binMins = None
binMaxs = None
if i == 0:
binMins = np.unique(pars[columns[0]])
binMaxs = np.unique(pars[columns[1]])
bins[layout[i + offset_name]] = np.union1d(binMins, binMaxs)
else:
counts = np.zeros(0, dtype=np.int)
allBins = np.zeros(0, dtype=np.double)
for binMin in bins[bin_order[0]][:-1]:
binMins = np.unique(pars[np.where(
pars[columns[0]] == binMin)][columns[i + offset_col]])
binMaxs = np.unique(pars[np.where(
pars[columns[0]] == binMin)][columns[i + offset_col + 1]])
theBins = np.union1d(binMins, binMaxs)
allBins = np.append(allBins, theBins)
counts = np.append(counts, theBins.size)
bins[layout[i + offset_name]] = JaggedArray.fromcounts(
counts, allBins)
bin_order.append(layout[i + offset_name])
offset_col += 1
#skip nvars to the variable columns
#the columns here define clamps for the variables defined in columns[]
# ----> clamps can be different from bins
# ----> if there is more than one binning variable this array is jagged
# ----> just make it jagged all the time
binshapes = tuple([bins[thebin].size - 1 for thebin in bin_order])
clamp_mins = {}
clamp_maxs = {}
var_order = []
offset_col = 2 * nBinnedVars + 1
offset_name = nBinnedVars + 2
jagged_counts = np.ones(bins[bin_order[0]].size - 1, dtype=np.int)
if len(bin_order) > 1:
jagged_counts = np.maximum(
bins[bin_order[1]].counts - 1,
0) #need counts-1 since we only care about Nbins
for i in range(nEvalVars):
var_order.append(layout[i + offset_name])
if not interpolatedFunc:
clamp_mins[layout[i + offset_name]] = JaggedArray.fromcounts(
jagged_counts, np.atleast_1d(pars[columns[i + offset_col]]))
clamp_maxs[layout[i + offset_name]] = JaggedArray.fromcounts(
jagged_counts,
np.atleast_1d(pars[columns[i + offset_col + 1]]))
offset_col += 1
#now get the parameters, which we will look up with the clamped values
parms = []
parm_order = []
offset_col = 2 * nBinnedVars + 1 + (interpolatedFunc
== False) * 2 * nEvalVars
for i in range(nParms):
parms.append(
JaggedArray.fromcounts(jagged_counts,
pars[columns[i + offset_col]]))
parm_order.append('p%i' % (i))
wrapped_up = {}
wrapped_up[(name, 'jme_standard_function')] = (formula, (bins, bin_order),
(clamp_mins, clamp_maxs,
var_order), (parms,
parm_order))
return wrapped_up
