def lorentz_trans(obt_d):
"""
Assume that X[ievent] only contains (in order) pt, eta, phi, mass
"""
#After chacking the functionality of this function, the matchpattern feature should also be removed.
a0, a1 = obt_d['X'].shape
X_new = np.zeros((a0, a1))
for i, ix in zip(range(a0), obt_d['X']):
for j in range(8): #8 objects in the game
tmp = LorentzVector()
tmp.set_pt_eta_phi_e(ix[5 * j + 0], ix[5 * j + 1], ix[5 * j + 2],
ix[5 * j + 3])
X_new[i][5 * j + 0] = tmp.px
X_new[i][5 * j + 1] = tmp.py
X_new[i][5 * j + 2] = tmp.pz
X_new[i][5 * j + 3] = tmp.e
X_new[i][5 * j + 4] = ix[5 * j + 4]
new_d = {}
for key, value in obt_d.iteritems():
new_d[key] = value
new_d['X'] = X_new
return new_d
def create_tree():
f = TemporaryFile()
tree = Tree("tree", model=create_model())
# fill the tree
for i in xrange(1000):
assert_equal(tree.a_vect, LorentzVector(0, 0, 0, 0))
random_vect = LorentzVector(gauss(.5, 1.), gauss(.5, 1.),
gauss(.5, 1.), gauss(.5, 1.))
tree.a_vect.copy_from(random_vect)
assert_equal(tree.a_vect, random_vect)
tree.a_x = gauss(.5, 1.)
tree.a_y = gauss(.3, 2.)
tree.a_z = gauss(13., 42.)
tree.b_n = randint(1, 5)
for j in xrange(tree.b_n):
vect = LorentzVector(gauss(.5, 1.), gauss(.5, 1.), gauss(.5, 1.),
gauss(.5, 1.))
tree.b_vect.push_back(vect)
tree.b_x.push_back(randint(1, 10))
tree.b_y.push_back(gauss(.3, 2.))
tree.i = i
assert_equal(tree.b_n, tree.b_vect.size())
assert_equal(tree.b_n, tree.b_x.size())
assert_equal(tree.b_n, tree.b_y.size())
tree.fill(reset=True)
tree.write()
# TFile.Close the file but keep the underlying
# tempfile file descriptor open
ROOT.TFile.Close(f)
FILES.append(f)
FILE_PATHS.append(f.GetName())
def fourvect(self):
vect = LorentzVector()
vect.SetPtEtaPhiM(
self.pt,
self.eta,
self.phi,
self.m)
# self._particle.Mass() * GeV)
return vect
def fourvect_vis(self):
vect = LorentzVector()
try:
vect.SetPtEtaPhiM(et2pt(self.vis_Et, self.vis_eta, self.vis_m),
self.eta, self.phi, self.m)
except ValueError:
log.warning("DOMAIN ERROR ON TRUTH 4-VECT: "
"Et: {0} eta: {1} m: {2}".format(
self.vis_Et, self.vis_eta, self.vis_m))
vect.SetPtEtaPhiM(0, self.eta, self.phi, self.m)
return vect
def fourvect(self):
if ((self.nSCTHits + self.nPixHits) < 4):
# electron with low number of tracker hits
eta = self.cl_eta
phi = self.cl_phi
et = self.cl_E / math.cosh(self.cl_eta)
else:
eta = self.tracketa
phi = self.trackphi
et = self.cl_E / math.cosh(self.tracketa)
vect = LorentzVector()
vect.SetPtEtaPhiE(et, eta, phi, self.cl_E)
return vect
def neutrinoPz(lepton_fourVector, neutrino_pt, neutrino_phi):
"""
Calculate the z-component of the nu momentum by using the W-boson mass as the constraint
General idea:
If the discriminant is less than zero, then force it to be zero.
You solve with the discriminant set to zero to get a scaled value
for the term "mu" and "pt". The neutrino Pt will be the
(lepton_pz*scaled_pt)/lepton_pt
"""
m_w = 80.4e3 # mass of the W boson
delta_phi = lepton_fourVector.Phi() - neutrino_phi
# Simplifying term you get when solve for neutrino Pz using transverse mass of W boson
mu = (m_w)**2/2 + np.cos(delta_phi)*lepton_fourVector.Pt()*neutrino_pt
pz_l = lepton_fourVector.Pz() # Lepton Pz
pt_l = lepton_fourVector.Pt() # lepton Pt
e_l = lepton_fourVector.E() # Lepton energy
p_l = sqrt(pt_l**2 + pz_l**2) # Lepton momentum
el_px = lepton_fourVector.Px()
el_py = lepton_fourVector.Py()
nu_px = neutrino_pt*np.cos(neutrino_phi)
nu_py = neutrino_pt*np.sin(neutrino_phi)
if e_l == 0:
nu = LorentzVector()
return nu
discriminant = ((mu**2*pz_l**2)/(e_l**2 - pz_l**2)**2) - ((e_l**2*neutrino_pt**2 - mu**2)/(e_l**2 - pz_l**2))
if discriminant>0:
pZ_nu_A = mu*lepton_fourVector.Pz()/(pt_l**2) + sqrt(discriminant)
pZ_nu_B = mu*lepton_fourVector.Pz()/(pt_l**2) - sqrt(discriminant)
elif discriminant<0:
scaled_mu = sqrt(pt_l**2*e_l**2*neutrino_pt**2/(pz_l**2+pt_l**2))
scaled_pt = m_w**2/(2*pt_l*(1-np.cos(delta_phi)))
pZ_nu_A = pZ_nu_B = (pz_l*scaled_pt)/pt_l
elif discriminant==0:
pZ_nu_A = pZ_nu_B = mu*lepton_fourVector.Pz()/(pt_l**2)
if abs(pZ_nu_A) < abs(pZ_nu_B):
nu_pz = pZ_nu_A
else:
nu_pz = pZ_nu_B
nu = LorentzVector()
nu.SetPxPyPzE(nu_px, nu_py, nu_pz, neutrino_pt)
return nu
def lorentzVecsTop(nom, topIdx0, topIdx1):
'''
Takes the indices of two jets identified to be bjets from top decay, return their LorentzVectors
'''
top0 = LorentzVector()
top0.SetPtEtaPhiE(nom.jet_pt[topIdx0], nom.jet_eta[topIdx0], nom.jet_phi[topIdx0], nom.jet_e[topIdx0])
top1 = LorentzVector()
top1.SetPtEtaPhiE(nom.jet_pt[topIdx1], nom.jet_eta[topIdx1], nom.jet_phi[topIdx1], nom.jet_e[topIdx1])
return (top0, top1)
def fourvect(self):
vect = LorentzVector()
vect.SetPtEtaPhiM(self.pt * GeV, self.eta, self.phi, self.m)
return vect
def __init__(self):
self.fourvect_boosted = LorentzVector()
super(FourMomentum, self).__init__()
def __init__(self):
self.fourvect_boosted = LorentzVector()
### Load data to check ###
### Importing Pyplot ###
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
plt.rcParams["figure.figsize"] = (7,6)
fd = f+"anti-kt_test.npy"
X, _ = np.load(fd)
# In[]:
a1 = []
w1=[]
for i,j in enumerate(X):
constituents = j["content"][j["tree"][:, 0] == -1]
# if len(constituents)>1:
# constituents = np.delete(constituents,0,0)
w1.append([LorentzVector(c).pt() for c in constituents])
w1 = [item for sublist in w1 for item in sublist]
w1=100*np.array(w1)/sum(w1)
a1 = np.vstack(a1)
# In[]:
plt.close()
t=plt.hist2d(a1[:, 0], a1[:, 1], range=[(-0.5,0.5), (-0.5,0.5)],
bins=200, cmap=plt.cm.jet,weights=w1,norm=LogNorm())
cbar = plt.colorbar()
plt.xlabel(r'$\eta$')
plt.ylabel(r'$\varphi$')
cbar.set_label(r'% of p$_t$')
#plt.savefig('tau_pfd_log_bis.png',dpi=600, transparent=True)
plt.show()
def mass(tau1, tau2, METpx, METpy):
"""
Calculate and return the collinear mass and momentum fractions
of tau1 and tau2
TODO: set visible mass of taus. 1.2 GeV for 3p and 0.8 GeV for 1p
"""
recTau1 = LorentzVector()
recTau2 = LorentzVector()
# tau 4-vector; synchronize for MMC calculation
if tau1.nTracks() < 3:
recTau1.SetPtEtaPhiM(tau1.pt(), tau1.eta(), tau1.phi(), 800.) # MeV
else:
recTau1.SetPtEtaPhiM(tau1.pt(), tau1.eta(), tau1.phi(), 1200.) # MeV
if tau2.nTracks() < 3:
recTau2.SetPtEtaPhiM(tau2.pt(), tau2.eta(), tau2.phi(), 800.) # MeV
else:
recTau2.SetPtEtaPhiM(tau2.pt(), tau2.eta(), tau2.phi(), 1200.) # MeV
K = ROOT.TMatrixD(2, 2)
K[0][0] = recTau1.Px()
K[0][1] = recTau2.Px()
K[1][0] = recTau1.Py()
K[1][1] = recTau2.Py()
if K.Determinant() == 0:
return -1., -1111., -1111.
M = ROOT.TMatrixD(2, 1)
M[0][0] = METpx
M[1][0] = METpy
Kinv = K.Invert()
X = Kinv * M
X1 = X(0, 0)
X2 = X(1, 0)
x1 = 1. / (1. + X1)
x2 = 1. / (1. + X2)
p1 = recTau1 * (1. / x1)
p2 = recTau2 * (1. / x2)
m_col = (p1 + p2).M()
m_vis = (recTau1 + recTau2).M()
return m_vis, m_col, x1, x2
def fourvect_clbased(self):
vect = LorentzVector()
tau_numTrack = self.numTrack
tau_nPi0s = self.pi0_n
if tau_nPi0s == 0:
if self.track_n > 0:
sumTrk = LorentzVector()
for trk_ind in xrange(0, self.track_n):
curTrk = LorentzVector()
curTrk.SetPtEtaPhiM(self.track_atTJVA_pt[trk_ind],
self.track_atTJVA_eta[trk_ind],
self.track_atTJVA_phi[trk_ind], 139.8)
sumTrk += curTrk
vect.SetPtEtaPhiM(sumTrk.Pt(), sumTrk.Eta(), sumTrk.Phi(),
sumTrk.M())
else:
vect.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.m)
elif tau_nPi0s == 1 or tau_nPi0s == 2:
if self.pi0_vistau_pt == 0:
vect.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.m)
else:
vect.SetPtEtaPhiM(self.pi0_vistau_pt, self.pi0_vistau_eta,
self.pi0_vistau_phi, self.pi0_vistau_m)
else:
vect.SetPtEtaPhiM(self.pi0_vistau_pt, self.pi0_vistau_eta,
self.pi0_vistau_phi, self.pi0_vistau_m)
return vect
def run_top_mass(inputPath):
topMassesWZ = []
topMassestZ = []
topMassReco = []
f = TFile(inputPath, "READ")
dsid = inputPath.split('/')[-1]
dsid = dsid.replace('.root', '')
#print(inputPath)
nom = f.Get('nominal')
try:
nom.GetEntries()
except:
print('failed for ' + inputPath)
return 0
try:
nom.Mll01
except:
print('failed for ' + inputPath)
return 0
if nom.GetEntries() == 0:
return 0
if hasattr(nom, "topMassReco"):
print('already there', inputPath)
return 0
nEntries = nom.GetEntries()
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
nom.GetEntry(idx)
lep = LorentzVector()
if abs(nom.Mll02 - 91.2e3) < abs(nom.Mll01 - 91.2e3):
lep.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1,
nom.lep_E_1)
else:
lep.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2,
nom.lep_E_2)
met = neutrinoPz(lep, nom.met_met, nom.met_phi)
w = lep + met
jet = LorentzVector()
#jet.SetPtEtaPhiE( nom.jet_Pt_0, nom.jet_Eta_0, nom.jet_Phi_0, nom.jet_E_0 )
if len(nom.jet_pt):
jet.SetPtEtaPhiE(nom.jet_pt[0], nom.jet_eta[0], nom.jet_phi[0],
nom.jet_e[0])
else:
jet.SetPtEtaPhiE(0, 0, 0, 0)
top = LorentzVector()
top = w + jet
topMassReco.append(top.M())
f.Close()
with root_open(inputPath, mode='a') as myfile:
topMassReco = np.asarray(topMassReco)
topMassReco.dtype = [('topMassReco', 'float64')]
topMassReco.dtype.names = ['topMassReco']
root_numpy.array2tree(topMassReco, tree=myfile.nominal)
myfile.write()
myfile.Close()
def transformVars(df):
'''
modifies the variables to create the ones that mv2 uses, inserts default values when needed, saves new variables
in the dataframe
Args:
-----
df: pandas dataframe containing all the interesting variables as extracted from the .root file
Returns:
--------
modified mv2-compliant dataframe
'''
from rootpy.vector import LorentzVector, Vector3
import pandautils as pup
# -- modify features and set default values
df['abs(jet_eta)'] = abs(df['jet_eta'])
# -- create new IPxD features
for (pu, pb, pc) in zip(df['jet_ip2d_pu'], df['jet_ip2d_pb'],
df['jet_ip2d_pc']):
pu[np.logical_or(pu >= 10, pu < -1)] = -1
pb[np.logical_or(pu >= 10, pu < -1)] = -1
pc[np.logical_or(pu >= 10, pu < -1)] = -1
for (pu, pb, pc) in zip(df['jet_ip3d_pu'], df['jet_ip3d_pb'],
df['jet_ip3d_pc']):
pu[pu >= 10] = -1
pb[pu >= 10] = -1
pc[pu >= 10] = -1
df['jet_ip2'] = (df['jet_ip2d_pb'] / df['jet_ip2d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip2_c'] = (df['jet_ip2d_pb'] / df['jet_ip2d_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip2_cu'] = (df['jet_ip2d_pc'] / df['jet_ip2d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3'] = (df['jet_ip3d_pb'] / df['jet_ip3d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3_c'] = (df['jet_ip3d_pb'] / df['jet_ip3d_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3_cu'] = (df['jet_ip3d_pc'] / df['jet_ip3d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
# -- create new IPMP features
for (pu, pb, pc) in zip(df['jet_ipmp_pu'], df['jet_ipmp_pb'],
df['jet_ipmp_pc']):
pu[pu >= 10] = -1
pb[pu >= 10] = -1
pc[pu >= 10] = -1
df['jet_ip'] = (df['jet_ipmp_pb'] / df['jet_ipmp_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip_c'] = (df['jet_ipmp_pb'] / df['jet_ipmp_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip_cu'] = (df['jet_ipmp_pc'] / df['jet_ipmp_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
# -- SV1 features
dx = df['jet_sv1_vtx_x'] - df['PVx']
dy = df['jet_sv1_vtx_y'] - df['PVy']
dz = df['jet_sv1_vtx_z'] - df['PVz']
v_jet = LorentzVector()
pv2sv = Vector3()
sv1_L3d = []
sv1_Lxy = []
dR = []
for index, dxi in enumerate(dx): # loop thru events
sv1_L3d_ev = []
sv1L_ev = []
dR_ev = []
for jet in xrange(len(dxi)): # loop thru jets
v_jet.SetPtEtaPhiM(df['jet_pt'][index][jet],
df['jet_eta'][index][jet],
df['jet_phi'][index][jet],
df['jet_m'][index][jet])
if (dxi[jet].size != 0):
sv1_L3d_ev.append(
np.sqrt(
pow(dx[index][jet], 2) + pow(dy[index][jet], 2) +
pow(dz[index][jet], 2))[0])
sv1L_ev.append(math.hypot(dx[index][jet], dy[index][jet]))
pv2sv.SetXYZ(dx[index][jet], dy[index][jet], dz[index][jet])
jetAxis = Vector3(v_jet.Px(), v_jet.Py(), v_jet.Pz())
dR_ev.append(pv2sv.DeltaR(jetAxis))
else:
dR_ev.append(-1)
sv1L_ev.append(-100)
sv1_L3d_ev.append(-100)
sv1_Lxy.append(sv1L_ev)
dR.append(dR_ev)
sv1_L3d.append(sv1_L3d_ev)
df['jet_sv1_dR'] = dR
df['jet_sv1_Lxy'] = sv1_Lxy
df['jet_sv1_L3d'] = sv1_L3d
# -- add more default values for sv1 variables
sv1_vtx_ok = pup.match_shape(
np.asarray([len(el) for event in df['jet_sv1_vtx_x'] for el in event]),
df['jet_pt'])
for (ok4event, sv1_ntkv4event, sv1_n2t4event, sv1_mass4event,
sv1_efrc4event,
sv1_sig34event) in zip(sv1_vtx_ok, df['jet_sv1_ntrkv'],
df['jet_sv1_n2t'], df['jet_sv1_m'],
df['jet_sv1_efc'], df['jet_sv1_sig3d']):
sv1_ntkv4event[np.asarray(ok4event) == 0] = -1
sv1_n2t4event[np.asarray(ok4event) == 0] = -1
sv1_mass4event[np.asarray(ok4event) == 0] = -1000
sv1_efrc4event[np.asarray(ok4event) == 0] = -1
sv1_sig34event[np.asarray(ok4event) == 0] = -100
# -- JF features
jf_dR = []
for eventN, (etas, phis, masses) in enumerate(
zip(df['jet_jf_deta'], df['jet_jf_dphi'],
df['jet_jf_m'])): # loop thru events
jf_dR_ev = []
for m in xrange(len(masses)): # loop thru jets
if (masses[m] > 0):
jf_dR_ev.append(np.sqrt(etas[m] * etas[m] + phis[m] * phis[m]))
else:
jf_dR_ev.append(-10)
jf_dR.append(jf_dR_ev)
df['jet_jf_dR'] = jf_dR
# -- add more default values for jf variables
for (jf_mass, jf_n2tv, jf_ntrkv, jf_nvtx, jf_nvtx1t, jf_efrc,
jf_sig3) in zip(df['jet_jf_m'], df['jet_jf_n2t'],
df['jet_jf_ntrkAtVx'], df['jet_jf_nvtx'],
df['jet_jf_nvtx1t'], df['jet_jf_efc'],
df['jet_jf_sig3d']):
jf_n2tv[jf_mass <= 0] = -1
jf_ntrkv[jf_mass <= 0] = -1
jf_nvtx[jf_mass <= 0] = -1
jf_nvtx1t[jf_mass <= 0] = -1
jf_mass[jf_mass <= 0] = -1e3
jf_efrc[jf_mass <= 0] = -1
jf_sig3[jf_mass <= 0] = -100
return df
def work(self):
# get argument values
local = self.args.local
syst_terms = self.args.syst_terms
datatype = self.metadata.datatype
year = self.metadata.year
verbose = self.args.student_verbose
very_verbose = self.args.student_very_verbose
redo_selection = self.args.redo_selection
nominal_values = self.args.nominal_values
# get the dataset name
dsname = os.getenv('INPUT_DATASET_NAME', None)
if dsname is None:
# attempt to guess dsname from dirname
if self.files:
dsname = os.path.basename(os.path.dirname(self.files[0]))
# is this a signal sample?
# if so we will also keep some truth information in the output below
is_signal = datatype == datasets.MC and (
'_VBFH' in dsname or '_ggH' in dsname or '_ZH' in dsname
or '_WH' in dsname or '_ttH' in dsname)
log.info("DATASET: {0}".format(dsname))
log.info("IS SIGNAL: {0}".format(is_signal))
# is this an inclusive signal sample for overlap studies?
is_inclusive_signal = is_signal and '_inclusive' in dsname
# is this a BCH-fixed sample? (temporary)
is_bch_sample = 'r5470_r4540_p1344' in dsname
if is_bch_sample:
log.warning("this is a BCH-fixed r5470 sample")
# onfilechange will contain a list of functions to be called as the
# chain rolls over to each new file
onfilechange = []
count_funcs = {}
if datatype != datasets.DATA:
# count the weighted number of events
if local:
def mc_weight_count(event):
return event.hh_mc_weight
else:
def mc_weight_count(event):
return event.TruthEvent[0].weights()[0]
count_funcs = {
'mc_weight': mc_weight_count,
}
if local:
# local means running on the skims, the output of this script
# running on the grid
if datatype == datasets.DATA:
# merge the GRL fragments
merged_grl = goodruns.GRL()
def update_grl(student, grl, name, file, tree):
grl |= str(
file.Get('Lumi/%s' %
student.metadata.treename).GetString())
onfilechange.append((update_grl, (
self,
merged_grl,
)))
if datatype == datasets.DATA:
merged_cutflow = Hist(1, 0, 1, name='cutflow', type='D')
else:
merged_cutflow = Hist(2, 0, 2, name='cutflow', type='D')
def update_cutflow(student, cutflow, name, file, tree):
# record a cut-flow
year = student.metadata.year
datatype = student.metadata.datatype
cutflow[1].value += file.cutflow_event[1].value
if datatype != datasets.DATA:
cutflow[2].value += file.cutflow_event_mc_weight[1].value
onfilechange.append((update_cutflow, (
self,
merged_cutflow,
)))
else:
# NEED TO BE CONVERTED TO XAOD
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # merge TrigConfTrees
# metadirname = '%sMeta' % self.metadata.treename
# trigconfchain = ROOT.TChain('%s/TrigConfTree' % metadirname)
# map(trigconfchain.Add, self.files)
# metadir = self.output.mkdir(metadirname)
# metadir.cd()
# trigconfchain.Merge(self.output, -1, 'fast keep')
# self.output.cd()
if datatype == datasets.DATA:
# merge GRL XML strings
merged_grl = goodruns.GRL()
# for fname in self.files:
# with root_open(fname) as f:
# for key in f.Lumi.keys():
# merged_grl |= goodruns.GRL(
# str(key.ReadObj().GetString()),
# from_string=True)
# lumi_dir = self.output.mkdir('Lumi')
# lumi_dir.cd()
# xml_string= ROOT.TObjString(merged_grl.str())
# xml_string.Write(self.metadata.treename)
# self.output.cd()
self.output.cd()
# create the output tree
model = get_model(datatype,
dsname,
prefix=None if local else 'hh_',
is_inclusive_signal=is_inclusive_signal)
log.info("Output Model:\n\n{0}\n\n".format(model))
outtree = Tree(name=self.metadata.treename, model=model)
if local:
tree = outtree
else:
tree = outtree.define_object(name='tree', prefix='hh_')
#tree.define_object(name='tau', prefix='tau_')
tree.define_object(name='tau1', prefix='tau1_')
tree.define_object(name='tau2', prefix='tau2_')
tree.define_object(name='truetau1', prefix='truetau1_')
tree.define_object(name='truetau2', prefix='truetau2_')
tree.define_object(name='jet1', prefix='jet1_')
tree.define_object(name='jet2', prefix='jet2_')
tree.define_object(name='jet3', prefix='jet3_')
mmc_objects = [
tree.define_object(name='mmc0', prefix='mmc0_'),
tree.define_object(name='mmc1', prefix='mmc1_'),
tree.define_object(name='mmc2', prefix='mmc2_'),
]
for mmc_obj in mmc_objects:
mmc_obj.define_object(name='resonance', prefix='resonance_')
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation = TauTriggerEmulation(
# year=year,
# passthrough=local or datatype != datasets.MC or year > 2011,
# count_funcs=count_funcs)
# if not trigger_emulation.passthrough:
# onfilechange.append(
# (update_trigger_trees, (self, trigger_emulation,)))
# trigger_config = None
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # trigger config tool to read trigger info in the ntuples
# trigger_config = get_trigger_config()
# # update the trigger config maps on every file change
# onfilechange.append((update_trigger_config, (trigger_config,)))
# define the list of event filters
if local and syst_terms is None and not redo_selection:
event_filters = None
else:
tau_ntrack_recounted_use_ntup = False
if year > 2011:
# peek at first tree to determine if the extended number of
# tracks is already stored
with root_open(self.files[0]) as test_file:
test_tree = test_file.Get(self.metadata.treename)
tau_ntrack_recounted_use_ntup = ('tau_out_track_n_extended'
in test_tree)
log.info(self.grl)
event_filters = EventFilterList([
GRLFilter(self.grl,
passthrough=(local
or (datatype not in (datasets.DATA,
datasets.EMBED))),
count_funcs=count_funcs),
CoreFlags(passthrough=local, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingPileupPatch(
# passthrough=(
# local or year > 2011 or datatype != datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD (not a priority)
# PileupTemplates(
# year=year,
# passthrough=(
# local or is_bch_sample or datatype not in (
# datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomSeed(
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHSampleRunNumber(
# passthrough=not is_bch_sample,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomRunNumber(
# tree=tree,
# datatype=datatype,
# pileup_tool=pileup_tool,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation,
# NEED TO BE CONVERTED TO XAOD
# Triggers(
# year=year,
# tree=tree,
# datatype=datatype,
# passthrough=datatype in (datasets.EMBED, datasets.MCEMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupReweight_xAOD(
tree=tree,
passthrough=(local
or (datatype
not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
PriVertex(passthrough=local, count_funcs=count_funcs),
LArError(passthrough=local, count_funcs=count_funcs),
TileError(passthrough=local, count_funcs=count_funcs),
TileTrips(passthrough=(local or datatype
in (datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
JetCalibration(datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
JetResolution(
passthrough=(local
or (datatype
not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
TauCalibration(datatype,
passthrough=local,
count_funcs=count_funcs),
# # truth matching must come before systematics due to
# # TES_TRUE/FAKE
# NEED TO BE CONVERTED TO XAOD
TrueTauSelection(passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
TruthMatching(passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
NvtxJets(tree=tree, count_funcs=count_funcs),
# # PUT THE SYSTEMATICS "FILTER" BEFORE
# # ANY FILTERS THAT REFER TO OBJECTS
# # BUT AFTER CALIBRATIONS
# # Systematics must also come before anything that refers to
# # thing.fourvect since fourvect is cached!
# NEED TO BE CONVERTED TO XAOD
# Systematics(
# terms=syst_terms,
# year=year,
# datatype=datatype,
# tree=tree,
# verbose=verbose,
# passthrough=not syst_terms,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# JetIsPileup(
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
JetCleaning(datatype=datatype,
year=year,
count_funcs=count_funcs),
ElectronVeto(el_sel='Medium', count_funcs=count_funcs),
MuonVeto(count_funcs=count_funcs),
TauPT(2, thresh=20 * GeV, count_funcs=count_funcs),
TauHasTrack(2, count_funcs=count_funcs),
TauEta(2, count_funcs=count_funcs),
TauElectronVeto(2, count_funcs=count_funcs),
TauMuonVeto(2, count_funcs=count_funcs),
TauCrack(2, count_funcs=count_funcs),
# # before selecting the leading and subleading taus
# # be sure to only consider good candidates
TauIDMedium(2, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchIndex(
# # config=trigger_config,
# # year=year,
# # datatype=datatype,
# # passthrough=datatype == datasets.EMBED,
# # count_funcs=count_funcs),
# Select two leading taus at this point
# 25 and 35 for data
# 20 and 30 for MC to leave room for TES uncertainty
TauLeadSublead(lead=(35 * GeV if datatype == datasets.DATA
or local else 30 * GeV),
sublead=(25 * GeV if datatype == datasets.DATA
or local else 20 * GeV),
count_funcs=count_funcs),
# taus are sorted (in decreasing order) by pT from here on
TauIDSelection(count_funcs=count_funcs),
TaudR(3.2, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchThreshold(
# # datatype=datatype,
# # tree=tree,
# # count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTriggerEfficiency(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_systematic=self.args.syst_terms and (
# Systematics.TES_TERMS & self.args.syst_terms),
# passthrough=datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupScale(tree=tree,
year=year,
datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
TauIDScaleFactors(year=year,
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauFakeRateScaleFactors(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_up=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_UP in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_UP in self.args.syst_terms)),
# tes_down=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_DOWN in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_DOWN in self.args.syst_terms)),
# passthrough=datatype in (datasets.DATA, datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
HiggsPT(year=year,
tree=tree,
passthrough=not is_signal or local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTrackRecounting(
# year=year,
# use_ntup_value=tau_ntrack_recounted_use_ntup,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# MCWeight(
# datatype=datatype,
# tree=tree,
# passthrough=local or datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingIsolation(
# tree=tree,
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingCorrections(
# tree=tree,
# year=year,
# passthrough=(
# local or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingTauSpinner(
# year=year,
# tree=tree,
# passthrough=(
# local or datatype not in (
# datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# # put MET recalculation after tau selection but before tau-jet
# # overlap removal and jet selection because of the RefAntiTau
# # MET correction
# NEED TO BE CONVERTED TO XAOD
# METRecalculation(
# terms=syst_terms,
# year=year,
# tree=tree,
# refantitau=not nominal_values,
# verbose=verbose,
# very_verbose=very_verbose,
# count_funcs=count_funcs),
TauJetOverlapRemoval(count_funcs=count_funcs),
JetPreselection(count_funcs=count_funcs),
NonIsolatedJet(tree=tree, count_funcs=count_funcs),
JetSelection(year=year, count_funcs=count_funcs),
RecoJetTrueTauMatching(passthrough=datatype == datasets.DATA
or local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHCleaning(
# tree=tree,
# passthrough=year == 2011 or local,
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
ClassifyInclusiveHiggsSample(
tree=tree,
passthrough=not is_inclusive_signal,
count_funcs=count_funcs),
])
# set the event filters
self.filters['event'] = event_filters
hh_buffer = TreeBuffer()
if local:
chain = TreeChain(
self.metadata.treename,
files=self.files,
# ignore_branches=ignore_branches,
events=self.events,
onfilechange=onfilechange,
filters=event_filters,
cache=True,
cache_size=50000000,
learn_entries=100)
buffer = TreeBuffer()
for name, value in chain._buffer.items():
if name.startswith('hh_'):
hh_buffer[name[3:]] = value
elif name in copied:
buffer[name] = value
outtree.set_buffer(hh_buffer, create_branches=False, visible=True)
outtree.set_buffer(buffer, create_branches=True, visible=False)
else:
root_chain = ROOT.TChain(self.metadata.treename)
for f in self.files:
log.info(f)
root_chain.Add(f)
# if len(self.files) != 1:
# raise RuntimeError('lenght of files has to be 1 for now (no xAOD chaining available)')
# self.files = self.files[0]
# root_chain = ROOT.TFile(self.files)
chain = xAODTree(root_chain,
filters=event_filters,
events=self.events)
define_objects(chain, datatype=datatype)
outtree.set_buffer(hh_buffer, create_branches=True, visible=False)
# create the MMC
mmc = mass.MMC(year=year)
# report which packages have been loaded
# externaltools.report()
self.output.cd()
# The main event loop
# the event filters above are automatically run for each event and only
# the surviving events are looped on
for event in chain:
if local and syst_terms is None and not redo_selection:
outtree.Fill()
continue
# sort taus and jets in decreasing order by pT
event.taus.sort(key=lambda tau: tau.pt(), reverse=True)
event.jets.sort(key=lambda jet: jet.pt(), reverse=True)
# tau1 is the leading tau
# tau2 is the subleading tau
tau1, tau2 = event.taus
tau1.fourvect = asrootpy(tau1.p4())
tau2.fourvect = asrootpy(tau2.p4())
beta_taus = (tau1.fourvect + tau2.fourvect).BoostVector()
tau1.fourvect_boosted = LorentzVector()
tau1.fourvect_boosted.copy_from(tau1.fourvect)
tau1.fourvect_boosted.Boost(beta_taus * -1)
tau2.fourvect_boosted = LorentzVector()
tau2.fourvect_boosted.copy_from(tau2.fourvect)
tau2.fourvect_boosted.Boost(beta_taus * -1)
jets = list(event.jets)
for jet in jets:
jet.fourvect = asrootpy(jet.p4())
jet1, jet2, jet3 = None, None, None
beta = None
if len(jets) >= 2:
jet1, jet2 = jets[:2]
# determine boost of system
# determine jet CoM frame
beta = (jet1.fourvect + jet2.fourvect).BoostVector()
tree.jet_beta.copy_from(beta)
jet1.fourvect_boosted = LorentzVector()
jet1.fourvect_boosted.copy_from(jet1.fourvect)
jet1.fourvect_boosted.Boost(beta * -1)
jet2.fourvect_boosted = LorentzVector()
jet2.fourvect_boosted.copy_from(jet2.fourvect)
jet2.fourvect_boosted.Boost(beta * -1)
tau1.min_dr_jet = min(tau1.fourvect.DeltaR(jet1.fourvect),
tau1.fourvect.DeltaR(jet2.fourvect))
tau2.min_dr_jet = min(tau2.fourvect.DeltaR(jet1.fourvect),
tau2.fourvect.DeltaR(jet2.fourvect))
# tau centrality (degree to which they are between the two jets)
tau1.centrality = eventshapes.eta_centrality(
tau1.fourvect.Eta(), jet1.fourvect.Eta(),
jet2.fourvect.Eta())
tau2.centrality = eventshapes.eta_centrality(
tau2.fourvect.Eta(), jet1.fourvect.Eta(),
jet2.fourvect.Eta())
# boosted tau centrality
tau1.centrality_boosted = eventshapes.eta_centrality(
tau1.fourvect_boosted.Eta(), jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
tau2.centrality_boosted = eventshapes.eta_centrality(
tau2.fourvect_boosted.Eta(), jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
# 3rd leading jet
if len(jets) >= 3:
jet3 = jets[2]
jet3.fourvect_boosted = LorentzVector()
jet3.fourvect_boosted.copy_from(jet3.fourvect)
jet3.fourvect_boosted.Boost(beta * -1)
elif len(jets) == 1:
jet1 = jets[0]
tau1.min_dr_jet = tau1.fourvect.DeltaR(jet1.fourvect)
tau2.min_dr_jet = tau2.fourvect.DeltaR(jet1.fourvect)
RecoJetBlock.set(tree, jet1, jet2, jet3, local=local)
# mass of ditau + leading jet system
if jet1 is not None:
tree.mass_tau1_tau2_jet1 = (tau1.fourvect + tau2.fourvect +
jet1.fourvect).M()
#####################################
# number of tracks from PV minus taus
#####################################
ntrack_pv = 0
ntrack_nontau_pv = 0
for vxp in event.vertices:
# primary vertex
if vxp.vertexType() == 1:
ntrack_pv = vxp.nTrackParticles()
ntrack_nontau_pv = ntrack_pv - tau1.nTracks(
) - tau2.nTracks()
break
tree.ntrack_pv = ntrack_pv
tree.ntrack_nontau_pv = ntrack_nontau_pv
#########################
# MET variables
#########################
MET = event.MET[0]
METx = MET.mpx()
METy = MET.mpy()
METet = MET.met()
MET_vect = Vector2(METx, METy)
MET_4vect = LorentzVector()
MET_4vect.SetPxPyPzE(METx, METy, 0., METet)
MET_4vect_boosted = LorentzVector()
MET_4vect_boosted.copy_from(MET_4vect)
if beta is not None:
MET_4vect_boosted.Boost(beta * -1)
tree.MET_et = METet
tree.MET_etx = METx
tree.MET_ety = METy
tree.MET_phi = MET.phi()
dPhi_tau1_tau2 = abs(tau1.fourvect.DeltaPhi(tau2.fourvect))
dPhi_tau1_MET = abs(tau1.fourvect.DeltaPhi(MET_4vect))
dPhi_tau2_MET = abs(tau2.fourvect.DeltaPhi(MET_4vect))
tree.dPhi_tau1_tau2 = dPhi_tau1_tau2
tree.dPhi_tau1_MET = dPhi_tau1_MET
tree.dPhi_tau2_MET = dPhi_tau2_MET
tree.dPhi_min_tau_MET = min(dPhi_tau1_MET, dPhi_tau2_MET)
tree.MET_bisecting = is_MET_bisecting(dPhi_tau1_tau2,
dPhi_tau1_MET, dPhi_tau2_MET)
sumET = MET.sumet()
tree.MET_sumet = sumET
if sumET != 0:
tree.MET_sig = ((2. * METet / GeV) /
(utils.sign(sumET) * sqrt(abs(sumET / GeV))))
else:
tree.MET_sig = -1.
tree.MET_centrality = eventshapes.phi_centrality(
tau1.fourvect, tau2.fourvect, MET_vect)
tree.MET_centrality_boosted = eventshapes.phi_centrality(
tau1.fourvect_boosted, tau2.fourvect_boosted,
MET_4vect_boosted)
tree.number_of_good_vertices = len(event.vertices)
##########################
# Jet and sum pt variables
##########################
tree.numJets = len(event.jets)
# sum pT with only the two leading jets
tree.sum_pt = sum([tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets[:2]])
# sum pT with all selected jets
tree.sum_pt_full = sum([tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets])
# vector sum pT with two leading jets and MET
tree.vector_sum_pt = sum([tau1.fourvect, tau2.fourvect] +
[jet.fourvect
for jet in jets[:2]] + [MET_4vect]).Pt()
# vector sum pT with all selected jets and MET
tree.vector_sum_pt_full = sum([tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets] +
[MET_4vect]).Pt()
# resonance pT
tree.resonance_pt = sum([tau1.fourvect, tau2.fourvect,
MET_4vect]).Pt()
# #############################
# # tau <-> vertex association
# #############################
tree.tau_same_vertex = (tau1.vertex() == tau2.vertex())
tau1.vertex_prob = ROOT.TMath.Prob(tau1.vertex().chiSquared(),
int(tau1.vertex().numberDoF()))
tau2.vertex_prob = ROOT.TMath.Prob(tau2.vertex().chiSquared(),
int(tau2.vertex().numberDoF()))
# ##########################
# # MMC Mass
# ##########################
mmc_result = mmc.mass(tau1,
tau2,
METx,
METy,
sumET,
njets=len(event.jets))
for mmc_method, mmc_object in enumerate(mmc_objects):
mmc_mass, mmc_resonance, mmc_met = mmc_result[mmc_method]
if verbose:
log.info("MMC (method %d): %f" % (mmc_method, mmc_mass))
mmc_object.mass = mmc_mass
mmc_object.MET_et = mmc_met.Mod()
mmc_object.MET_etx = mmc_met.X()
mmc_object.MET_ety = mmc_met.Y()
mmc_object.MET_phi = math.pi - mmc_met.Phi()
if mmc_mass > 0:
FourMomentum.set(mmc_object.resonance, mmc_resonance)
# ############################
# # collinear and visible mass
# ############################
vis_mass, collin_mass, tau1_x, tau2_x = mass.collinearmass(
tau1, tau2, METx, METy)
tree.mass_vis_tau1_tau2 = vis_mass
tree.mass_collinear_tau1_tau2 = collin_mass
tau1.collinear_momentum_fraction = tau1_x
tau2.collinear_momentum_fraction = tau2_x
# # Fill the tau block
# # This must come after the RecoJetBlock is filled since
# # that sets the jet_beta for boosting the taus
RecoTauBlock.set(event, tree, datatype, tau1, tau2, local=local)
# NEED TO BE CONVERTED TO XAOD
if datatype != datasets.DATA:
TrueTauBlock.set(tree, tau1, tau2)
# fill the output tree
outtree.Fill(reset=True)
# externaltools.report()
# flush any baskets remaining in memory to disk
self.output.cd()
outtree.FlushBaskets()
outtree.Write()
if local:
if datatype == datasets.DATA:
xml_string = ROOT.TObjString(merged_grl.str())
xml_string.Write('lumi')
merged_cutflow.Write()
def create_dict(nom):
current = 0
events = []
bestScores = []
nEntries = nom.GetEntries()
print(nEntries)
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
nom.GetEntry(idx)
higgCand = LorentzVector()
lep4Vecs = []
jet4Vecs = []
btags = []
met = LorentzVector()
met.SetPtEtaPhiE(nom.MET_RefFinal_et, 0, nom.MET_RefFinal_phi,
nom.MET_RefFinal_et)
#for i in range(2):
lep_Pt_0 = nom.lep_Pt_0
lep_Eta_0 = nom.lep_Eta_0
lep_Phi_0 = nom.lep_Phi_0
lep_E_0 = nom.lep_E_0
lepVec_0 = LorentzVector()
lepVec_0.SetPtEtaPhiE(lep_Pt_0, lep_Eta_0, lep_Phi_0, lep_E_0)
lep4Vecs.append(lepVec_0)
lep_Pt_1 = nom.lep_Pt_1
lep_Eta_1 = nom.lep_Eta_1
lep_Phi_1 = nom.lep_Phi_1
lep_E_1 = nom.lep_E_1
lepVec_1 = LorentzVector()
lepVec_1.SetPtEtaPhiE(lep_Pt_1, lep_Eta_1, lep_Phi_1, lep_E_1)
lep4Vecs.append(lepVec_1)
for j in range(len(nom.m_pflow_jet_pt)): #nom.selected_jets'][i]:
jetVec = LorentzVector()
jetVec.SetPtEtaPhiM(nom.m_pflow_jet_pt[j], nom.m_pflow_jet_eta[j],
nom.m_pflow_jet_phi[j], nom.m_pflow_jet_m[j])
jet4Vecs.append(jetVec)
btags.append(nom.m_pflow_jet_flavor_weight_MV2c10[j])
combos = []
combosTop = []
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
t = topDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[0],
lep4Vecs[1], met, btags[i], btags[j],
nom.m_pflow_jet_jvt[i], nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combosTop.append([t, comb])
#loop over combinations, score them in the BDT, figure out the best result
topDF = pd.DataFrame.from_dict([x[0] for x in combosTop])
topMat = xgb.DMatrix(topDF, feature_names=list(topDF))
topPred = topModel.predict(topMat)
topBest = np.argmax(topPred)
bestTopComb = combosTop[topBest][1]
topMatches = bestTopComb[1:]
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
if l == 0:
k = higgsDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[l],
met, btags[i], btags[j], lep4Vecs[1],
nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
else:
k = higgsDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[l],
met, btags[i], btags[j], lep4Vecs[0],
nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combos.append([k, comb])
###Evaluate higgsTop BDT
df = pd.DataFrame.from_dict([x[0] for x in combos])
xgbMat = xgb.DMatrix(df, feature_names=list(df))
pred = xgbModel.predict(xgbMat)
best = np.argmax(pred)
bestScores.append(pred[best])
bestComb = combos[best][1]
lepMatch = bestComb[0]
jetMatches = bestComb[1:]
k = {}
#k['higgs_pt'] = nom.higgs_pt
k['comboScore'] = pred[best]
k['topScore'] = topPred[topBest]
if lepMatch == 0:
k['lep_Pt_H'] = nom.lep_Pt_0
k['lep_Eta_H'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
k['lep_E_H'] = nom.lep_E_0
k['lep_Pt_O'] = nom.lep_Pt_1
k['lep_Eta_O'] = nom.lep_Eta_1
k['lep_Phi_O'] = calc_phi(phi_0, nom.lep_Phi_1)
k['lep_E_O'] = nom.lep_E_1
elif lepMatch == 1:
k['lep_Pt_H'] = nom.lep_Pt_1
k['lep_Eta_H'] = nom.lep_Eta_1
phi_0 = nom.lep_Phi_1
k['lep_E_H'] = nom.lep_E_1
k['lep_Pt_O'] = nom.lep_Pt_0
k['lep_Eta_O'] = nom.lep_Eta_0
k['lep_Phi_O'] = calc_phi(phi_0, nom.lep_Phi_0)
k['lep_E_O'] = nom.lep_E_0
n = 0
for i in jetMatches: #nom.nJets_OR_T):
k['jet_Pt_h' + str(n)] = nom.m_pflow_jet_pt[i]
k['jet_Eta_h' + str(n)] = nom.m_pflow_jet_eta[i]
k['jet_E_h' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
k['jet_Phi_h' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
k['jet_MV2c10_h' +
str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
btags = np.array(btags)
btags[jetMatches[0]] = 0
btags[jetMatches[1]] = 0
bestBtags = np.argpartition(btags, -2)[-2:]
n = 0
for i in topMatches: #bestBtags:#nom.nJets_OR_T):
k['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
k['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
k['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
k['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
k['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
k['MET'] = nom.MET_RefFinal_et
k['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events.append(k)
return events
def calcTopMass(nom, topMasses):
current = 0
for e in nom:
current += 1
if current % 10000 == 0:
print(current)
#if current==200000:
# break
if e.nJets_OR != 1:
continue
if e.nJets_OR_DL1_70 != 1:
continue
if abs(e.Mll01 - 91.2e3) > 10e3 and abs(e.Mll02 - 91.2e3) > 10e3:
continue
if e.trilep_type == 0: continue
lep = LorentzVector()
if abs(e.Mll02 - 91.2e3) < abs(e.Mll01 - 91.2e3):
lep.SetPtEtaPhiE(e.lep_Pt_1, e.lep_Eta_1, e.lep_Phi_1, e.lep_E_1)
else:
lep.SetPtEtaPhiE(e.lep_Pt_2, e.lep_Eta_2, e.lep_Phi_2, e.lep_E_2)
met = neutrinoPz(lep, e.met_met, e.met_phi)
w = lep + met
#w_eta = np.arccosh( abs( np.sqrt(wt.E()**2 - 80e3**2)/ wt.Pt() ) )
#w = LorentzVector()
#w.SetPtEtaPhiE(wt.Pt(), w_eta, wt.Phi(), wt.E())
#print('M', w.M())
jet = LorentzVector()
jet.SetPtEtaPhiE(e.jets_Pt_0, e.jets_Eta_0, e.jets_Phi_0, e.jets_E_0)
top = LorentzVector()
top = w + jet
topMasses.append(top.M())
if top.M() < 0:
print(top.M(), w.M())
#newNom.GetEntry(current)
current+=1
if current%10000==0:
print(current)
#if current==200000:
# break
#if e.nJets_OR_T!=1:
# continue
#if e.nJets_OR_T_MV2c10_70!=1:
# continue
#if abs(e.Mll01 - 91.2e3) > 10e3 and abs(e.Mll02 - 91.2e3) > 10e3:
# continue
#if e.trilep_type==0: continue
lep = LorentzVector()
if abs(e.Mll02 - 91.2e3) < abs(e.Mll01 - 91.2e3):
lep.SetPtEtaPhiE( e.lep_Pt_1, e.lep_Eta_1, e.lep_Phi_1, e.lep_E_1 )
else:
lep.SetPtEtaPhiE( e.lep_Pt_2, e.lep_Eta_2, e.lep_Phi_2, e.lep_E_2 )
met = neutrinoPz(lep, e.met_met, e.met_phi)
w = lep+met
jet = LorentzVector()
jet.SetPtEtaPhiE( e.jets_Pt_0, e.jets_Eta_0, e.jets_Phi_0, e.jets_E_0 )
top = LorentzVector()
def create_dict(nom):
current = 0
events1l = []
events2l = []
decayDicts = []
bestScores = []
nEntries = nom.GetEntries()
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
#if current%100000==0:
#break
nom.GetEntry(idx)
higgCand = LorentzVector()
lep4Vecs = []
jet4Vecs = []
btags = []
met = LorentzVector()
met.SetPtEtaPhiE(nom.MET_RefFinal_et, 0, nom.MET_RefFinal_phi,
nom.MET_RefFinal_et)
lepVec_0 = LorentzVector()
lepVec_0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0,
nom.lep_E_0)
lep4Vecs.append(lepVec_0)
lepVec_1 = LorentzVector()
lepVec_1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1,
nom.lep_E_1)
lep4Vecs.append(lepVec_1)
lepVec_2 = LorentzVector()
lepVec_2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2,
nom.lep_E_2)
lep4Vecs.append(lepVec_2)
for j in range(len(nom.m_pflow_jet_pt)): #nom.selected_jets'][i]:
jetVec = LorentzVector()
jetVec.SetPtEtaPhiM(nom.m_pflow_jet_pt[j], nom.m_pflow_jet_eta[j],
nom.m_pflow_jet_phi[j], nom.m_pflow_jet_m[j])
jet4Vecs.append(jetVec)
btags.append(nom.m_pflow_jet_flavor_weight_MV2c10[j])
combosTop = []
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
t = topDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[0],
lep4Vecs[1], lep4Vecs[2], met, btags[i],
btags[j], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combosTop.append([t, comb])
#loop over combinations, score them in the BDT, figure out the best result
topDF = pd.DataFrame.from_dict([x[0] for x in combosTop])
topMat = xgb.DMatrix(topDF, feature_names=list(topDF))
topPred = topModel.predict(topMat)
topBest = np.argmax(topPred)
bestTopComb = combosTop[topBest][1]
topMatches = bestTopComb[1:]
combos1l = []
for l in range(1, len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
if l == 1:
k = higgs1lDict(
jet4Vecs[i], jet4Vecs[j], lep4Vecs[l], met,
nom.m_pflow_jet_flavor_weight_MV2c10[i],
nom.m_pflow_jet_flavor_weight_MV2c10[j],
lep4Vecs[0], lep4Vecs[2], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j], nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
else:
k = higgs1lDict(
jet4Vecs[i], jet4Vecs[j], lep4Vecs[l], met,
nom.m_pflow_jet_flavor_weight_MV2c10[i],
nom.m_pflow_jet_flavor_weight_MV2c10[j],
lep4Vecs[0], lep4Vecs[1], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j], nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combos1l.append([k, comb])
combos2l = []
possCombs = [[0, 1, 2], [0, 2, 1]]
for comb in possCombs:
k = higgs2lDict(lep4Vecs[comb[0]], lep4Vecs[comb[1]],
lep4Vecs[comb[2]], met)
combos2l.append([k, [comb[0], comb[1]]])
#Run 2l XGB, find best match
df2l = pd.DataFrame.from_dict([x[0] for x in combos2l])
xgbMat2l = xgb.DMatrix(df2l, feature_names=list(df2l))
pred2l = higgs2lModel.predict(xgbMat2l)
best2l = np.argmax(pred2l)
bestComb2l = combos2l[best2l][1]
lepMatch2l = bestComb2l[1]
#Run 1l XGB, find best match
df1l = pd.DataFrame.from_dict([x[0] for x in combos1l])
xgbMat1l = xgb.DMatrix(df1l, feature_names=list(df1l))
pred1l = higgs1lModel.predict(xgbMat1l)
best1l = np.argmax(pred1l)
bestComb1l = combos1l[best1l][1]
lepMatch1l = bestComb1l[0]
jetMatches1l = bestComb1l[1:]
### Add decay dict
k = decayDict(lep4Vecs[0], lep4Vecs[1], lep4Vecs[2], met,
jet4Vecs[topMatches[0]], jet4Vecs[topMatches[1]])
k['nJets'] = nom.nJets_OR_T
k['nJets_MV2c10_70'] = nom.nJets_OR_T_MV2c10_70
k['higgs2l_score'] = pred2l[best2l]
k['higgs1l_score'] = pred1l[best1l]
decayDicts.append(k)
### Add 2l pt prediction dict
q = {}
q['comboScore'] = pred2l[best2l]
if lepMatch2l == 1:
q['lep_Pt_0'] = nom.lep_Pt_0
q['lep_Eta_0'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
q['lep_E_0'] = nom.lep_E_0
q['lep_Pt_1'] = nom.lep_Pt_1
q['lep_Eta_1'] = nom.lep_Eta_1
q['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_1)
q['lep_E_1'] = nom.lep_E_1
q['lep_Pt_2'] = nom.lep_Pt_2
q['lep_Eta_2'] = nom.lep_Eta_2
q['lep_Phi_2'] = calc_phi(phi_0, nom.lep_Phi_2)
q['lep_E_2'] = nom.lep_E_2
elif lepMatch2l == 2:
q['lep_Pt_0'] = nom.lep_Pt_0
q['lep_Eta_0'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
q['lep_E_0'] = nom.lep_E_0
q['lep_Pt_1'] = nom.lep_Pt_2
q['lep_Eta_1'] = nom.lep_Eta_2
q['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_2)
q['lep_E_1'] = nom.lep_E_2
q['lep_Pt_2'] = nom.lep_Pt_1
q['lep_Eta_2'] = nom.lep_Eta_1
q['lep_Phi_2'] = calc_phi(phi_0, nom.lep_Phi_1)
q['lep_E_2'] = nom.lep_E_1
n = 0
for i in topMatches:
q['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
q['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
q['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
q['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
q['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
q['MET'] = nom.MET_RefFinal_et
q['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events2l.append(q)
### Add 1l Pt prediction dict
y = {}
#y['higgs_pt'] = nom.higgs_pt
y['comboScore'] = pred1l[best1l]
if lepMatch1l == 1:
y['lep_Pt_H'] = nom.lep_Pt_1
y['lep_Eta_H'] = nom.lep_Eta_1
phi_0 = nom.lep_Phi_1
y['lep_E_H'] = nom.lep_E_1
y['lep_Pt_0'] = nom.lep_Pt_0
y['lep_Eta_0'] = nom.lep_Eta_0
y['lep_Phi_0'] = calc_phi(phi_0, nom.lep_Phi_0)
y['lep_E_0'] = nom.lep_E_0
y['lep_Pt_1'] = nom.lep_Pt_2
y['lep_Eta_1'] = nom.lep_Eta_2
y['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_2)
y['lep_E_1'] = nom.lep_E_2
elif lepMatch1l == 2:
y['lep_Pt_H'] = nom.lep_Pt_2
y['lep_Eta_H'] = nom.lep_Eta_2
phi_0 = nom.lep_Phi_2
y['lep_E_H'] = nom.lep_E_2
y['lep_Pt_0'] = nom.lep_Pt_0
y['lep_Eta_0'] = nom.lep_Eta_0
y['lep_Phi_0'] = calc_phi(phi_0, nom.lep_Phi_0)
y['lep_E_0'] = nom.lep_E_0
y['lep_Pt_1'] = nom.lep_Pt_1
y['lep_Eta_1'] = nom.lep_Eta_1
y['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_1)
y['lep_E_1'] = nom.lep_E_1
n = 0
for i in jetMatches1l: #nom.nJets_OR_T):
y['jet_Pt_h' + str(n)] = nom.m_pflow_jet_pt[i]
y['jet_Eta_h' + str(n)] = nom.m_pflow_jet_eta[i]
y['jet_E_h' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
y['jet_Phi_h' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
y['jet_MV2c10_h' +
str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
n = 0
for i in topMatches: #bestBtags:#nom.nJets_OR_T):
y['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
y['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
y['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
y['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
y['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
y['MET'] = nom.MET_RefFinal_et
y['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events1l.append(y)
return decayDicts, events1l, events2l
def work(self):
year = self.metadata.year
verbose = self.args.verbose
draw_decays = self.args.draw_decays
args = self.args
# initialize the TreeChain of all input files
# only enable branches I need
chain = TreeChain(self.metadata.treename,
files=self.files,
branches=[
'tau_*',
'mc_*',
'el_*',
'mu_staco_*',
'MET_RefFinal_BDTMedium_*',
'MET_RefFinal_STVF_*',
'EventNumber',
'RunNumber',
'averageIntPerXing',
],
events=self.events,
read_branches_on_demand=True,
cache=True,
verbose=True)
define_objects(chain, year)
self.output.cd()
# this tree will contain info pertaining to true tau decays
# for possible use in the optimization of a missing mass calculator
tree = Tree(name="ditaumass", model=DTMEvent)
tree.define_object(name='resonance', prefix='resonance_')
tree.define_object(name='radiative', prefix='radiative_')
truetaus = [
tree.define_object(name='truetau1', prefix='truetau1_'),
tree.define_object(name='truetau2', prefix='truetau2_')
]
taus = [
tree.define_object(name='tau1', prefix='tau1_'),
tree.define_object(name='tau2', prefix='tau2_')
]
electrons = [
tree.define_object(name='ele1', prefix='ele1_'),
tree.define_object(name='ele2', prefix='ele2_')
]
muons = [
tree.define_object(name='muon1', prefix='muon1_'),
tree.define_object(name='muon2', prefix='muon2_')
]
# get the Z or Higgs
if args.higgs:
resonance_pdgid = 25
else:
resonance_pdgid = 23
if '7TeV' in self.metadata.name:
collision_energy = 7
else:
collision_energy = 8
for event_index, event in enumerate(chain):
try:
tree.reset_branch_values()
# get the Z or Higgs
resonance = tautools.get_particles(event,
resonance_pdgid,
num_expected=1)
if not resonance:
print "could not find resonance"
continue
# get the resonance just before the decay
resonance = resonance[0].last_self
if draw_decays:
resonance.export_graphvis('resonance_%d.dot' %
event.EventNumber)
FourVectModel.set(tree.resonance, resonance)
# collect decay products (taus and photons)
tau_decays = []
mc_photons = []
for child in resonance.iter_children():
if abs(child.pdgId) == pdg.tau_minus:
# ignore status 3 taus in 2012 (something strange in the
# MC record...)
if year == 2012:
if child.status == 3:
continue
tau_decays.append(tautools.TauDecay(child))
elif child.pdgId == pdg.gamma:
mc_photons.append(child)
else:
raise TypeError(
'unexpected particle after resonance:\n%s' % child)
# There should be exactly two taus
if len(tau_decays) != 2:
print "found %i tau decays in MC record" % len(tau_decays)
for decay in tau_decays:
print decay
# skip this event
continue
# check for incomplete tau decays
invalid = False
for decay in tau_decays:
if not decay.valid:
print "invalid tau decay:"
print decay
if draw_decays:
decay.init.export_graphvis('decay_invalid_%d.dot' %
event.EventNumber)
invalid = True
break
if invalid:
# skip this event
continue
radiative_fourvect = LorentzVector()
for photon in mc_photons:
radiative_fourvect += photon.fourvect
radiative_fourvect.fourvect = radiative_fourvect
FourVectModel.set(tree.radiative, radiative_fourvect)
tree.radiative_ngamma = len(mc_photons)
tree.radiative_ngamma_5 = len(
[ph for ph in mc_photons if ph.pt > 5])
tree.radiative_ngamma_10 = len(
[ph for ph in mc_photons if ph.pt > 10])
tree.radiative_et_scalarsum = sum([ph.pt
for ph in mc_photons] + [0])
all_matched = True
matched_objects = []
skip = False
for i, (decay, truetau, tau, electron, muon) in enumerate(
zip(tau_decays, truetaus, taus, electrons, muons)):
if draw_decays:
decay.init.export_graphvis('decay%d_%d.dot' %
(i, event.EventNumber))
TrueTau.set(truetau, decay, verbose=verbose)
# match to reco taus, electrons and muons
if decay.hadronic:
recotau, dr = closest_reco_object(
event.taus, decay.fourvect_visible, dR=0.2)
if recotau is not None:
matched_objects.append(recotau)
recotau.matched = True
recotau.matched_dr = dr
RecoTau.set(tau, recotau, verbose=verbose)
else:
all_matched = False
elif decay.leptonic_electron:
recoele, dr = closest_reco_object(
event.electrons, decay.fourvect_visible, dR=0.2)
if recoele is not None:
matched_objects.append(recoele)
recoele.matched = True
recoele.matched_dr = dr
RecoElectron.set(electron, recoele)
else:
all_matched = False
elif decay.leptonic_muon:
recomuon, dr = closest_reco_object(
event.muons, decay.fourvect_visible, dR=0.2)
if recomuon is not None:
matched_objects.append(recomuon)
recomuon.matched = True
recomuon.matched_dr = dr
RecoMuon.set(muon, recomuon)
else:
all_matched = False
else:
print "unhandled invalid tau decay:"
print decay
if not draw_decays:
decay.init.export_graphvis('decay%d_%d.dot' %
(i, event.EventNumber))
# skip this event
skip = True
break
if skip:
# skip this event
continue
# did both decays match a reco object?
tree.matched = all_matched
# match collision: decays matched same reco object
if all_matched:
tree.match_collision = (
matched_objects[0] == matched_objects[1])
# MET
tree.met_x = event.MET.etx
tree.met_y = event.MET.ety
tree.met_phi = event.MET.phi
tree.met = event.MET.et
tree.sum_et = event.MET.sumet
# set extra event variables
tree.channel = event.mc_channel_number
tree.event = event.EventNumber
tree.run = event.RunNumber
tree.mu = event.averageIntPerXing
tree.collision_energy = collision_energy
tree.Fill()
except:
print "event index: %d" % event_index
print "event number: %d" % event.EventNumber
print "file: %s" % chain.file.GetName()
raise
self.output.cd()
tree.FlushBaskets()
tree.Write()
def lorentzVecs(nom, jetIdx0, jetIdx1, is3l):
'''
Initialize met, lepton, and jet lorentz vectors
Return jet0, jet1, met, lep0, lep1, (lep2 if is3l)
'''
met = LorentzVector()
met.SetPtEtaPhiE(nom.met_met, 0, nom.met_phi, nom.met_met)
lep0 = LorentzVector()
lep0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0, nom.lep_E_0)
lep1 = LorentzVector()
lep1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1, nom.lep_E_1)
if is3l:
lep2 = LorentzVector()
lep2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2, nom.lep_E_2)
jet0 = LorentzVector()
jet0.SetPtEtaPhiE(nom.jet_pt[jetIdx0], nom.jet_eta[jetIdx0], nom.jet_phi[jetIdx0], nom.jet_e[jetIdx0])
jet1 = LorentzVector()
jet1.SetPtEtaPhiE(nom.jet_pt[jetIdx1], nom.jet_eta[jetIdx1], nom.jet_phi[jetIdx1], nom.jet_e[jetIdx1])
if is3l:
return (jet0, jet1, met, lep0, lep1, lep2)
else:
return (jet0, jet1, met, lep0, lep1)
def preprocess(jet, cluster, output="kt", regression=False, R_clustering=0.3):
"""
preprocesses the data to make it usable by the recnn
Preprocessing algorithm:
1. j = the highest pt anti-kt jet (R=1)
2. run kt (R=0.3) on the constituents c of j, resulting in subjets sj1, sj2, ..., sjN
3. phi = sj1.phi(); for all c, do c.rotate_z(-phi)
4. bv = sj1.boost_vector(); bv.set_perp(0); for all c, do c.boost(-bv)
5. deltaz = sj1.pz - sj2.pz; deltay = sj1.py - sj2.py; alpha = -atan2(deltaz, deltay); for all c, do c.rotate_x(alpha)
6. if sj3.pz < 0: for all c, do c.set_pz(-c.pz)
7. finally recluster all transformed constituents c into a single jet
"""
jet = copy.deepcopy(jet)
constituents = jet["content"][jet["tree"][:, 0] == -1]
if regression:
genpt = jet["genpt"]
### run kt (R=0.3) on the constituents c of j, resulting in subjets sj1, sj2, ..., sjN ###
subjets = cluster(constituents, R=R_clustering, jet_algorithm=0)
oldeta = jet["eta"]
oldpt = jet['pt']
### Rot phi ###
# phi = sj1.phi()
# for all c, do c.rotate_z(-phi)
v = subjets[0][1][0]
v = LorentzVector(v)
phi = v.phi()
for _, content, _, _ in subjets:
for i in range(len(content)):
v = LorentzVector(content[i][:4])
v.rotate_z(-phi)
content[i, 0] = v[0]
content[i, 1] = v[1]
content[i, 2] = v[2]
content[i, 3] = v[3]
### boost ###
# bv = sj1.boost_vector()
# bv.set_perp(0)
# for all c, do c.boost(-bv)
v = subjets[0][1][0]
v = LorentzVector(v)
bv = v.boost_vector()
bv.set_perp(0)
for _, content, _, _ in subjets:
for i in range(len(content)):
v = LorentzVector(content[i][:4])
v.boost(-bv)
content[i, 0] = v[0]
content[i, 1] = v[1]
content[i, 2] = v[2]
content[i, 3] = v[3]
### Rot alpha ###
# deltaz = sj1.pz - sj2.pz
# deltay = sj1.py - sj2.py
# alpha = -atan2(deltaz, deltay)
# for all c, do c.rotate_x(alpha)
if len(subjets) >= 2:
deltaz = subjets[0][1][0, 2] - subjets[1][1][0, 2]
deltay = subjets[0][1][0, 1] - subjets[1][1][0, 1]
alpha = -np.arctan2(deltaz, deltay)
for _, content, _, _ in subjets:
for i in range(len(content)):
v = LorentzVector(content[i][:4])
v.rotate_x(alpha)
content[i, 0] = v[0]
content[i, 1] = v[1]
content[i, 2] = v[2]
content[i, 3] = v[3]
### flip if necessary ###
# if sj3.pz < 0: for all c, do c.set_pz(-c.pz)
if len(subjets) >= 3 and subjets[2][1][0, 2] < 0:
for _, content, _, _ in subjets:
for i in range(len(content)):
content[i, 2] *= -1.0
### finally recluster all transformed constituents c into a single jet ###
constituents = []
for tree, content, _, _ in subjets:
constituents.append(content[tree[:, 0] == -1])
constituents = np.vstack(constituents)
if output == "anti-kt":
subjets = cluster(constituents, R=100., jet_algorithm=1)
elif output == "kt":
subjets = cluster(constituents, R=100., jet_algorithm=0)
elif output == "cambridge":
subjets = cluster(constituents, R=100., jet_algorithm=2)
else:
raise
jet["tree"] = subjets[0][0]
jet["content"] = subjets[0][1]
v = LorentzVector(jet["content"][0])
jet["phi"] = v.phi()
jet["eta"] = v.eta()
jet["energy"] = v.E()
jet["mass"] = v.m()
jet["pt"] = v.pt()
jet["root_id"] = 0
jet['oldeta'] = oldeta
jet['oldpt'] = oldpt
if regression:
jet["genpt"] = genpt
return (jet)
# In[]:
### Verification of the formating ###
### Load data to check ###
fd = f+"anti-kt_test.npy"
X, y = np.load(fd)
# In[]:
### Check for signal ###
a1 = []
w1=[]
for i,j in enumerate(X):
constituents = j["content"][j["tree"][:, 0] == -1]
# if len(constituents)>1:
# constituents = np.delete(constituents,0,0)
if y[i]==1:
a1.append(np.array([[LorentzVector(c).eta(),
LorentzVector(c).phi()] for c in constituents]))
w1.append([LorentzVector(c).pt() for c in constituents])
w1 = [item for sublist in w1 for item in sublist]
w1=100*np.array(w1)/sum(w1)
a1 = np.vstack(a1)
# In[]:
plt.close()
t=plt.hist2d(a1[:, 0], a1[:, 1], range=[(-0.5,0.5), (-0.5,0.5)],
bins=200, cmap=plt.cm.jet,weights=w1,norm=LogNorm())
cbar = plt.colorbar()
plt.xlabel(r'$\eta$')
plt.ylabel(r'$\varphi$')
cbar.set_label(r'% of p$_t$')
def getTruthVis4Vector(self):
"""Get the LorentzVector for the visible truth tau """
vector = LorentzVector()
vector.SetPtEtaPhiM(self.vis_Et, self.vis_eta, self.vis_phi,
self.vis_m)
return vector
def work(self):
year = self.metadata.year
verbose = self.args.verbose
draw_decays = self.args.draw_decays
args = self.args
# initialize the TreeChain of all input files
# only enable branches I need
chain = TreeChain(
self.metadata.treename,
files=self.files,
branches=[
'tau_*',
'mc_*',
'el_*',
'mu_staco_*',
'MET_RefFinal_BDTMedium_*',
'MET_RefFinal_STVF_*',
'EventNumber',
'RunNumber',
'averageIntPerXing',
],
events=self.events,
read_branches_on_demand=True,
cache=True,
verbose=True)
define_objects(chain, year)
self.output.cd()
# this tree will contain info pertaining to true tau decays
# for possible use in the optimization of a missing mass calculator
tree = Tree(name="ditaumass", model=DTMEvent)
tree.define_object(name='resonance', prefix='resonance_')
tree.define_object(name='radiative', prefix='radiative_')
truetaus = [
tree.define_object(name='truetau1', prefix='truetau1_'),
tree.define_object(name='truetau2', prefix='truetau2_')]
taus = [
tree.define_object(name='tau1', prefix='tau1_'),
tree.define_object(name='tau2', prefix='tau2_')]
electrons = [
tree.define_object(name='ele1', prefix='ele1_'),
tree.define_object(name='ele2', prefix='ele2_')]
muons = [
tree.define_object(name='muon1', prefix='muon1_'),
tree.define_object(name='muon2', prefix='muon2_')]
# get the Z or Higgs
if args.higgs:
resonance_pdgid = 25
else:
resonance_pdgid = 23
if '7TeV' in self.metadata.name:
collision_energy = 7
else:
collision_energy = 8
for event_index, event in enumerate(chain):
try:
tree.reset_branch_values()
# get the Z or Higgs
resonance = tautools.get_particles(event, resonance_pdgid,
num_expected=1)
if not resonance:
print "could not find resonance"
continue
# get the resonance just before the decay
resonance = resonance[0].last_self
if draw_decays:
resonance.export_graphvis('resonance_%d.dot' %
event.EventNumber)
FourVectModel.set(tree.resonance, resonance)
# collect decay products (taus and photons)
tau_decays = []
mc_photons = []
for child in resonance.iter_children():
if abs(child.pdgId) == pdg.tau_minus:
# ignore status 3 taus in 2012 (something strange in the
# MC record...)
if year == 2012:
if child.status == 3:
continue
tau_decays.append(tautools.TauDecay(child))
elif child.pdgId == pdg.gamma:
mc_photons.append(child)
else:
raise TypeError(
'unexpected particle after resonance:\n%s' %
child)
# There should be exactly two taus
if len(tau_decays) != 2:
print "found %i tau decays in MC record" % len(tau_decays)
for decay in tau_decays:
print decay
# skip this event
continue
# check for incomplete tau decays
invalid = False
for decay in tau_decays:
if not decay.valid:
print "invalid tau decay:"
print decay
if draw_decays:
decay.init.export_graphvis(
'decay_invalid_%d.dot' %
event.EventNumber)
invalid = True
break
if invalid:
# skip this event
continue
radiative_fourvect = LorentzVector()
for photon in mc_photons:
radiative_fourvect += photon.fourvect
radiative_fourvect.fourvect = radiative_fourvect
FourVectModel.set(tree.radiative, radiative_fourvect)
tree.radiative_ngamma = len(mc_photons)
tree.radiative_ngamma_5 = len([
ph for ph in mc_photons if ph.pt > 5])
tree.radiative_ngamma_10 = len([
ph for ph in mc_photons if ph.pt > 10])
tree.radiative_et_scalarsum = sum([
ph.pt for ph in mc_photons] + [0])
all_matched = True
matched_objects = []
skip = False
for i, (decay, truetau, tau, electron, muon) in enumerate(zip(
tau_decays, truetaus, taus, electrons, muons)):
if draw_decays:
decay.init.export_graphvis('decay%d_%d.dot' % (
i, event.EventNumber))
TrueTau.set(truetau, decay, verbose=verbose)
# match to reco taus, electrons and muons
if decay.hadronic:
recotau, dr = closest_reco_object(
event.taus, decay.fourvect_visible, dR=0.2)
if recotau is not None:
matched_objects.append(recotau)
recotau.matched = True
recotau.matched_dr = dr
RecoTau.set(tau, recotau, verbose=verbose)
else:
all_matched = False
elif decay.leptonic_electron:
recoele, dr = closest_reco_object(
event.electrons, decay.fourvect_visible, dR=0.2)
if recoele is not None:
matched_objects.append(recoele)
recoele.matched = True
recoele.matched_dr = dr
RecoElectron.set(electron, recoele)
else:
all_matched = False
elif decay.leptonic_muon:
recomuon, dr = closest_reco_object(
event.muons, decay.fourvect_visible, dR=0.2)
if recomuon is not None:
matched_objects.append(recomuon)
recomuon.matched = True
recomuon.matched_dr = dr
RecoMuon.set(muon, recomuon)
else:
all_matched = False
else:
print "unhandled invalid tau decay:"
print decay
if not draw_decays:
decay.init.export_graphvis('decay%d_%d.dot' % (
i, event.EventNumber))
# skip this event
skip = True
break
if skip:
# skip this event
continue
# did both decays match a reco object?
tree.matched = all_matched
# match collision: decays matched same reco object
if all_matched:
tree.match_collision = (
matched_objects[0] == matched_objects[1])
# MET
tree.met_x = event.MET.etx
tree.met_y = event.MET.ety
tree.met_phi = event.MET.phi
tree.met = event.MET.et
tree.sum_et = event.MET.sumet
# set extra event variables
tree.channel = event.mc_channel_number
tree.event = event.EventNumber
tree.run = event.RunNumber
tree.mu = event.averageIntPerXing
tree.collision_energy = collision_energy
tree.Fill()
except:
print "event index: %d" % event_index
print "event number: %d" % event.EventNumber
print "file: %s" % chain.file.GetName()
raise
self.output.cd()
tree.FlushBaskets()
tree.Write()
def work(self):
# get argument values
local = self.args.local
syst_terms = self.args.syst_terms
datatype = self.metadata.datatype
year = self.metadata.year
verbose = self.args.student_verbose
very_verbose = self.args.student_very_verbose
redo_selection = self.args.redo_selection
nominal_values = self.args.nominal_values
# get the dataset name
dsname = os.getenv('INPUT_DATASET_NAME', None)
if dsname is None:
# attempt to guess dsname from dirname
if self.files:
dsname = os.path.basename(os.path.dirname(self.files[0]))
# is this a signal sample?
# if so we will also keep some truth information in the output below
is_signal = datatype == datasets.MC and (
'_VBFH' in dsname or
'_ggH' in dsname or
'_ZH' in dsname or
'_WH' in dsname or
'_ttH' in dsname)
log.info("DATASET: {0}".format(dsname))
log.info("IS SIGNAL: {0}".format(is_signal))
# is this an inclusive signal sample for overlap studies?
is_inclusive_signal = is_signal and '_inclusive' in dsname
# is this a BCH-fixed sample? (temporary)
is_bch_sample = 'r5470_r4540_p1344' in dsname
if is_bch_sample:
log.warning("this is a BCH-fixed r5470 sample")
# onfilechange will contain a list of functions to be called as the
# chain rolls over to each new file
onfilechange = []
count_funcs = {}
if datatype != datasets.DATA:
# count the weighted number of events
if local:
def mc_weight_count(event):
return event.hh_mc_weight
else:
def mc_weight_count(event):
return event.TruthEvent[0].weights()[0]
count_funcs = {
'mc_weight': mc_weight_count,
}
if local:
# local means running on the skims, the output of this script
# running on the grid
if datatype == datasets.DATA:
# merge the GRL fragments
merged_grl = goodruns.GRL()
def update_grl(student, grl, name, file, tree):
grl |= str(file.Get('Lumi/%s' % student.metadata.treename).GetString())
onfilechange.append((update_grl, (self, merged_grl,)))
if datatype == datasets.DATA:
merged_cutflow = Hist(1, 0, 1, name='cutflow', type='D')
else:
merged_cutflow = Hist(2, 0, 2, name='cutflow', type='D')
def update_cutflow(student, cutflow, name, file, tree):
# record a cut-flow
year = student.metadata.year
datatype = student.metadata.datatype
cutflow[1].value += file.cutflow_event[1].value
if datatype != datasets.DATA:
cutflow[2].value += file.cutflow_event_mc_weight[1].value
onfilechange.append((update_cutflow, (self, merged_cutflow,)))
else:
# NEED TO BE CONVERTED TO XAOD
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # merge TrigConfTrees
# metadirname = '%sMeta' % self.metadata.treename
# trigconfchain = ROOT.TChain('%s/TrigConfTree' % metadirname)
# map(trigconfchain.Add, self.files)
# metadir = self.output.mkdir(metadirname)
# metadir.cd()
# trigconfchain.Merge(self.output, -1, 'fast keep')
# self.output.cd()
if datatype == datasets.DATA:
# merge GRL XML strings
merged_grl = goodruns.GRL()
# for fname in self.files:
# with root_open(fname) as f:
# for key in f.Lumi.keys():
# merged_grl |= goodruns.GRL(
# str(key.ReadObj().GetString()),
# from_string=True)
# lumi_dir = self.output.mkdir('Lumi')
# lumi_dir.cd()
# xml_string= ROOT.TObjString(merged_grl.str())
# xml_string.Write(self.metadata.treename)
# self.output.cd()
self.output.cd()
# create the output tree
model = get_model(datatype, dsname,
prefix=None if local else 'hh_',
is_inclusive_signal=is_inclusive_signal)
log.info("Output Model:\n\n{0}\n\n".format(model))
outtree = Tree(name=self.metadata.treename,
model=model)
if local:
tree = outtree
else:
tree = outtree.define_object(name='tree', prefix='hh_')
#tree.define_object(name='tau', prefix='tau_')
tree.define_object(name='tau1', prefix='tau1_')
tree.define_object(name='tau2', prefix='tau2_')
tree.define_object(name='truetau1', prefix='truetau1_')
tree.define_object(name='truetau2', prefix='truetau2_')
tree.define_object(name='jet1', prefix='jet1_')
tree.define_object(name='jet2', prefix='jet2_')
tree.define_object(name='jet3', prefix='jet3_')
mmc_objects = [
tree.define_object(name='mmc0', prefix='mmc0_'),
tree.define_object(name='mmc1', prefix='mmc1_'),
tree.define_object(name='mmc2', prefix='mmc2_'),
]
for mmc_obj in mmc_objects:
mmc_obj.define_object(name='resonance', prefix='resonance_')
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation = TauTriggerEmulation(
# year=year,
# passthrough=local or datatype != datasets.MC or year > 2011,
# count_funcs=count_funcs)
# if not trigger_emulation.passthrough:
# onfilechange.append(
# (update_trigger_trees, (self, trigger_emulation,)))
# trigger_config = None
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # trigger config tool to read trigger info in the ntuples
# trigger_config = get_trigger_config()
# # update the trigger config maps on every file change
# onfilechange.append((update_trigger_config, (trigger_config,)))
# define the list of event filters
if local and syst_terms is None and not redo_selection:
event_filters = None
else:
tau_ntrack_recounted_use_ntup = False
if year > 2011:
# peek at first tree to determine if the extended number of
# tracks is already stored
with root_open(self.files[0]) as test_file:
test_tree = test_file.Get(self.metadata.treename)
tau_ntrack_recounted_use_ntup = (
'tau_out_track_n_extended' in test_tree)
log.info(self.grl)
event_filters = EventFilterList([
GRLFilter(
self.grl,
passthrough=(
local or (
datatype not in (datasets.DATA, datasets.EMBED))),
count_funcs=count_funcs),
CoreFlags(
passthrough=local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingPileupPatch(
# passthrough=(
# local or year > 2011 or datatype != datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD (not a priority)
# PileupTemplates(
# year=year,
# passthrough=(
# local or is_bch_sample or datatype not in (
# datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomSeed(
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHSampleRunNumber(
# passthrough=not is_bch_sample,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomRunNumber(
# tree=tree,
# datatype=datatype,
# pileup_tool=pileup_tool,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation,
# NEED TO BE CONVERTED TO XAOD
# Triggers(
# year=year,
# tree=tree,
# datatype=datatype,
# passthrough=datatype in (datasets.EMBED, datasets.MCEMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupReweight_xAOD(
tree=tree,
passthrough=(local or (
datatype not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
PriVertex(
passthrough=local,
count_funcs=count_funcs),
LArError(
passthrough=local,
count_funcs=count_funcs),
TileError(
passthrough=local,
count_funcs=count_funcs),
TileTrips(
passthrough=(
local or datatype in (datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
JetCalibration(
datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
JetResolution(
passthrough=(local or (
datatype not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
TauCalibration(
datatype,
passthrough=local,
count_funcs=count_funcs),
# # truth matching must come before systematics due to
# # TES_TRUE/FAKE
# NEED TO BE CONVERTED TO XAOD
TrueTauSelection(
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
TruthMatching(
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
NvtxJets(
tree=tree,
count_funcs=count_funcs),
# # PUT THE SYSTEMATICS "FILTER" BEFORE
# # ANY FILTERS THAT REFER TO OBJECTS
# # BUT AFTER CALIBRATIONS
# # Systematics must also come before anything that refers to
# # thing.fourvect since fourvect is cached!
# NEED TO BE CONVERTED TO XAOD
# Systematics(
# terms=syst_terms,
# year=year,
# datatype=datatype,
# tree=tree,
# verbose=verbose,
# passthrough=not syst_terms,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# JetIsPileup(
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
JetCleaning(
datatype=datatype,
year=year,
count_funcs=count_funcs),
ElectronVeto(
el_sel='Medium',
count_funcs=count_funcs),
MuonVeto(
count_funcs=count_funcs),
TauPT(2,
thresh=20 * GeV,
count_funcs=count_funcs),
TauHasTrack(2,
count_funcs=count_funcs),
TauEta(2,
count_funcs=count_funcs),
TauElectronVeto(2,
count_funcs=count_funcs),
TauMuonVeto(2,
count_funcs=count_funcs),
TauCrack(2,
count_funcs=count_funcs),
# # before selecting the leading and subleading taus
# # be sure to only consider good candidates
TauIDMedium(2,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchIndex(
# # config=trigger_config,
# # year=year,
# # datatype=datatype,
# # passthrough=datatype == datasets.EMBED,
# # count_funcs=count_funcs),
# Select two leading taus at this point
# 25 and 35 for data
# 20 and 30 for MC to leave room for TES uncertainty
TauLeadSublead(
lead=(
35 * GeV if datatype == datasets.DATA or local
else 30 * GeV),
sublead=(
25 * GeV if datatype == datasets.DATA or local
else 20 * GeV),
count_funcs=count_funcs),
# taus are sorted (in decreasing order) by pT from here on
TauIDSelection(
count_funcs=count_funcs),
TaudR(3.2,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchThreshold(
# # datatype=datatype,
# # tree=tree,
# # count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTriggerEfficiency(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_systematic=self.args.syst_terms and (
# Systematics.TES_TERMS & self.args.syst_terms),
# passthrough=datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupScale(
tree=tree,
year=year,
datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
TauIDScaleFactors(
year=year,
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauFakeRateScaleFactors(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_up=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_UP in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_UP in self.args.syst_terms)),
# tes_down=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_DOWN in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_DOWN in self.args.syst_terms)),
# passthrough=datatype in (datasets.DATA, datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# HiggsPT(
# year=year,
# tree=tree,
# passthrough=not is_signal or local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTrackRecounting(
# year=year,
# use_ntup_value=tau_ntrack_recounted_use_ntup,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# MCWeight(
# datatype=datatype,
# tree=tree,
# passthrough=local or datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingIsolation(
# tree=tree,
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingCorrections(
# tree=tree,
# year=year,
# passthrough=(
# local or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingTauSpinner(
# year=year,
# tree=tree,
# passthrough=(
# local or datatype not in (
# datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# # put MET recalculation after tau selection but before tau-jet
# # overlap removal and jet selection because of the RefAntiTau
# # MET correction
# NEED TO BE CONVERTED TO XAOD
# METRecalculation(
# terms=syst_terms,
# year=year,
# tree=tree,
# refantitau=not nominal_values,
# verbose=verbose,
# very_verbose=very_verbose,
# count_funcs=count_funcs),
TauJetOverlapRemoval(
count_funcs=count_funcs),
JetPreselection(
count_funcs=count_funcs),
NonIsolatedJet(
tree=tree,
count_funcs=count_funcs),
JetSelection(
year=year,
count_funcs=count_funcs),
RecoJetTrueTauMatching(
passthrough=datatype == datasets.DATA or local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHCleaning(
# tree=tree,
# passthrough=year == 2011 or local,
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
ClassifyInclusiveHiggsSample(
tree=tree,
passthrough=not is_inclusive_signal,
count_funcs=count_funcs),
])
# set the event filters
self.filters['event'] = event_filters
hh_buffer = TreeBuffer()
if local:
chain = TreeChain(
self.metadata.treename,
files=self.files,
# ignore_branches=ignore_branches,
events=self.events,
onfilechange=onfilechange,
filters=event_filters,
cache=True,
cache_size=50000000,
learn_entries=100)
buffer = TreeBuffer()
for name, value in chain._buffer.items():
if name.startswith('hh_'):
hh_buffer[name[3:]] = value
elif name in copied:
buffer[name] = value
outtree.set_buffer(
hh_buffer,
create_branches=False,
visible=True)
outtree.set_buffer(
buffer,
create_branches=True,
visible=False)
else:
root_chain = ROOT.TChain(self.metadata.treename)
for f in self.files:
log.info(f)
root_chain.Add(f)
# if len(self.files) != 1:
# raise RuntimeError('lenght of files has to be 1 for now (no xAOD chaining available)')
# self.files = self.files[0]
# root_chain = ROOT.TFile(self.files)
chain = xAODTree(root_chain, filters=event_filters, events=self.events)
define_objects(chain, datatype=datatype)
outtree.set_buffer(
hh_buffer,
create_branches=True,
visible=False)
# # create the MMC
# mmc = mass.MMC(year=year)
from ROOT import MissingMassTool
mass_tool = MissingMassTool('mass_tool')
mass_tool.initialize()
# report which packages have been loaded
# externaltools.report()
self.output.cd()
# The main event loop
# the event filters above are automatically run for each event and only
# the surviving events are looped on
for event in chain:
if local and syst_terms is None and not redo_selection:
outtree.Fill()
continue
# sort taus and jets in decreasing order by pT
event.taus.sort(key=lambda tau: tau.pt(), reverse=True)
event.jets.sort(key=lambda jet: jet.pt(), reverse=True)
# tau1 is the leading tau
# tau2 is the subleading tau
tau1, tau2 = event.taus
tau1.fourvect = asrootpy(tau1.p4())
tau2.fourvect = asrootpy(tau2.p4())
beta_taus = (tau1.fourvect + tau2.fourvect).BoostVector()
tau1.fourvect_boosted = LorentzVector()
tau1.fourvect_boosted.copy_from(tau1.fourvect)
tau1.fourvect_boosted.Boost(beta_taus * -1)
tau2.fourvect_boosted = LorentzVector()
tau2.fourvect_boosted.copy_from(tau2.fourvect)
tau2.fourvect_boosted.Boost(beta_taus * -1)
jets = list(event.jets)
for jet in jets:
jet.fourvect = asrootpy(jet.p4())
jet1, jet2, jet3 = None, None, None
beta = None
if len(jets) >= 2:
jet1, jet2 = jets[:2]
# determine boost of system
# determine jet CoM frame
beta = (jet1.fourvect + jet2.fourvect).BoostVector()
tree.jet_beta.copy_from(beta)
jet1.fourvect_boosted = LorentzVector()
jet1.fourvect_boosted.copy_from(jet1.fourvect)
jet1.fourvect_boosted.Boost(beta * -1)
jet2.fourvect_boosted = LorentzVector()
jet2.fourvect_boosted.copy_from(jet2.fourvect)
jet2.fourvect_boosted.Boost(beta * -1)
tau1.min_dr_jet = min(
tau1.fourvect.DeltaR(jet1.fourvect),
tau1.fourvect.DeltaR(jet2.fourvect))
tau2.min_dr_jet = min(
tau2.fourvect.DeltaR(jet1.fourvect),
tau2.fourvect.DeltaR(jet2.fourvect))
# tau centrality (degree to which they are between the two jets)
tau1.centrality = eventshapes.eta_centrality(
tau1.fourvect.Eta(),
jet1.fourvect.Eta(),
jet2.fourvect.Eta())
tau2.centrality = eventshapes.eta_centrality(
tau2.fourvect.Eta(),
jet1.fourvect.Eta(),
jet2.fourvect.Eta())
# boosted tau centrality
tau1.centrality_boosted = eventshapes.eta_centrality(
tau1.fourvect_boosted.Eta(),
jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
tau2.centrality_boosted = eventshapes.eta_centrality(
tau2.fourvect_boosted.Eta(),
jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
# 3rd leading jet
if len(jets) >= 3:
jet3 = jets[2]
jet3.fourvect_boosted = LorentzVector()
jet3.fourvect_boosted.copy_from(jet3.fourvect)
jet3.fourvect_boosted.Boost(beta * -1)
elif len(jets) == 1:
jet1 = jets[0]
tau1.min_dr_jet = tau1.fourvect.DeltaR(jet1.fourvect)
tau2.min_dr_jet = tau2.fourvect.DeltaR(jet1.fourvect)
RecoJetBlock.set(tree, jet1, jet2, jet3, local=local)
# mass of ditau + leading jet system
if jet1 is not None:
tree.mass_tau1_tau2_jet1 = (
tau1.fourvect + tau2.fourvect + jet1.fourvect).M()
#####################################
# number of tracks from PV minus taus
#####################################
ntrack_pv = 0
ntrack_nontau_pv = 0
for vxp in event.vertices:
# primary vertex
if vxp.vertexType() == 1:
ntrack_pv = vxp.nTrackParticles()
ntrack_nontau_pv = ntrack_pv - tau1.nTracks() - tau2.nTracks()
break
tree.ntrack_pv = ntrack_pv
tree.ntrack_nontau_pv = ntrack_nontau_pv
#########################
# MET variables
#########################
MET = event.MET.collection['Final']
METx = MET.mpx()
METy = MET.mpy()
METet = MET.met()
MET_vect = Vector2(METx, METy)
MET_4vect = LorentzVector()
MET_4vect.SetPxPyPzE(METx, METy, 0., METet)
MET_4vect_boosted = LorentzVector()
MET_4vect_boosted.copy_from(MET_4vect)
if beta is not None:
MET_4vect_boosted.Boost(beta * -1)
tree.MET_et = METet
tree.MET_etx = METx
tree.MET_ety = METy
tree.MET_phi = MET.phi()
dPhi_tau1_tau2 = abs(tau1.fourvect.DeltaPhi(tau2.fourvect))
dPhi_tau1_MET = abs(tau1.fourvect.DeltaPhi(MET_4vect))
dPhi_tau2_MET = abs(tau2.fourvect.DeltaPhi(MET_4vect))
tree.dPhi_tau1_tau2 = dPhi_tau1_tau2
tree.dPhi_tau1_MET = dPhi_tau1_MET
tree.dPhi_tau2_MET = dPhi_tau2_MET
tree.dPhi_min_tau_MET = min(dPhi_tau1_MET, dPhi_tau2_MET)
tree.MET_bisecting = is_MET_bisecting(
dPhi_tau1_tau2,
dPhi_tau1_MET,
dPhi_tau2_MET)
sumET = MET.sumet()
tree.MET_sumet = sumET
if sumET != 0:
tree.MET_sig = ((2. * METet / GeV) /
(utils.sign(sumET) * sqrt(abs(sumET / GeV))))
else:
tree.MET_sig = -1.
tree.MET_centrality = eventshapes.phi_centrality(
tau1.fourvect,
tau2.fourvect,
MET_vect)
tree.MET_centrality_boosted = eventshapes.phi_centrality(
tau1.fourvect_boosted,
tau2.fourvect_boosted,
MET_4vect_boosted)
tree.number_of_good_vertices = len(event.vertices)
##########################
# Jet and sum pt variables
##########################
tree.numJets = len(event.jets)
# sum pT with only the two leading jets
tree.sum_pt = sum(
[tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets[:2]])
# sum pT with all selected jets
tree.sum_pt_full = sum(
[tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets])
# vector sum pT with two leading jets and MET
tree.vector_sum_pt = sum(
[tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets[:2]] +
[MET_4vect]).Pt()
# vector sum pT with all selected jets and MET
tree.vector_sum_pt_full = sum(
[tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets] +
[MET_4vect]).Pt()
# resonance pT
tree.resonance_pt = sum(
[tau1.fourvect, tau2.fourvect, MET_4vect]).Pt()
# #############################
# # tau <-> vertex association
# #############################
tree.tau_same_vertex = (
tau1.vertex() == tau2.vertex())
tau1.vertex_prob = ROOT.TMath.Prob(
tau1.vertex().chiSquared(),
int(tau1.vertex().numberDoF()))
tau2.vertex_prob = ROOT.TMath.Prob(
tau2.vertex().chiSquared(),
int(tau2.vertex().numberDoF()))
# ##########################
# # MMC Mass
# ##########################
# OLD USAGE
# mmc_result = mmc.mass(
# tau1, tau2,
# METx, METy, sumET,
# njets=len(event.jets))
# for mmc_method, mmc_object in enumerate(mmc_objects):
# mmc_mass, mmc_resonance, mmc_met = mmc_result[mmc_method]
# if verbose:
# log.info("MMC (method %d): %f" % (mmc_method, mmc_mass))
# mmc_object.mass = mmc_mass
# mmc_object.MET_et = mmc_met.Mod()
# mmc_object.MET_etx = mmc_met.X()
# mmc_object.MET_ety = mmc_met.Y()
# mmc_object.MET_phi = math.pi - mmc_met.Phi()
# if mmc_mass > 0:
# FourMomentum.set(mmc_object.resonance, mmc_resonance)
mass_tool.apply(event.EventInfo, tau1, tau2, MET, len(event.jets))
for i, mmc_object in enumerate(mmc_objects):
mmc_object.mass = event.EventInfo.auxdataConst('double')('mmc%s_mass' % i)
mmc_object.MET_et = mass_tool.GetFittedMetVec(i).Mod()
mmc_object.MET_etx = mass_tool.GetFittedMetVec(i).X()
mmc_object.MET_ety = mass_tool.GetFittedMetVec(i).Y()
mmc_object.MET_phi = math.pi - mass_tool.GetFittedMetVec(i).Phi()
if mmc_object.mass > 0:
FourMomentum.set(mmc_object.resonance, mass_tool.GetResonanceVec(i))
# ############################
# # collinear and visible mass
# ############################
# vis_mass, collin_mass, tau1_x, tau2_x = mass.collinearmass(
# tau1, tau2, METx, METy)
# tree.mass_vis_tau1_tau2 = vis_mass
# tree.mass_collinear_tau1_tau2 = collin_mass
# tau1.collinear_momentum_fraction = tau1_x
# tau2.collinear_momentum_fraction = tau2_x
# # Fill the tau block
# # This must come after the RecoJetBlock is filled since
# # that sets the jet_beta for boosting the taus
RecoTauBlock.set(event, tree, datatype, tau1, tau2, local=local)
# if datatype != datasets.DATA:
# TrueTauBlock.set(tree, tau1, tau2)
# fill the output tree
outtree.Fill(reset=True)
# externaltools.report()
# flush any baskets remaining in memory to disk
self.output.cd()
outtree.FlushBaskets()
outtree.Write()
if local:
if datatype == datasets.DATA:
xml_string = ROOT.TObjString(merged_grl.str())
xml_string.Write('lumi')
merged_cutflow.Write()
from rootpy.io import root_open
from rootpy import stl
from random import gauss
f = root_open("test.root", "recreate")
# define the model
class Event(TreeModel):
x = stl.vector('TLorentzVector')
i = IntCol()
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(100):
tree.x.clear()
for j in xrange(5):
vect = LorentzVector(
gauss(.5, 1.),
gauss(.5, 1.),
gauss(.5, 1.),
gauss(.5, 1.))
tree.x.push_back(vect)
tree.i = i
tree.fill()
tree.write()
f.close()
def lorentzVecsLeps(nom, is3l):
'''
Higgs decays to two jets and one lepton, or two leptons. This returns lorentzVectors for each decay product candidate
For H -> 2j, 1l case (not isF): returns jet0, jet1, met, lep0, lep1, (lep2 if is3l)
For H -> 2l case (isF): Return met, lep0, lep1, lep2
'''
met = LorentzVector()
met.SetPtEtaPhiE(nom.met_met, 0, nom.met_phi, nom.met_met)
lep0 = LorentzVector()
lep0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0, nom.lep_E_0)
lep1 = LorentzVector()
lep1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1, nom.lep_E_1)
if is3l:
lep2 = LorentzVector()
lep2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2, nom.lep_E_2)
if is3l:
return (lep0, lep1, lep2, met)
else:
return (lep0, lep1, met)
def preprocessing(jet):
jet = copy.deepcopy(jet)
jet=jet.reshape(-1,4)
n_consti=len(jet)
# find the jet (eta, phi)
center=jet.sum(axis=0)
v_jet=LorentzVector(center[1], center[2], center[3], center[0])
# centering parameters
phi=v_jet.phi()
bv = v_jet.boost_vector()
bv.set_perp(0)
for i in range(n_consti):
v = LorentzVector(jet[i,1], jet[i,2], jet[i,3], jet[i,0])
v.rotate_z(-phi)
v.boost(-bv)
jet[i, 0]=v[3] #e
jet[i, 1]=v[0] #px
jet[i, 2]=v[1] #py
jet[i, 3]=v[2] #pz
# rotating parameters
weighted_phi=0
weighted_eta=0
for i in range(n_consti):
if jet[i,0]<1e-10: # pass zero paddings
continue
v = LorentzVector(jet[i,1], jet[i,2], jet[i,3], jet[i,0])
r=np.sqrt(v.phi()**2 + v.eta()**2)
if r == 0: # in case there is only one component. In fact these data points should generally be invalid.
continue
weighted_phi += v.phi() * v.E()/r
weighted_eta += v.eta() * v.E()/r
#alpha = np.arctan2(weighted_phi, weighted_eta) # approximately align at eta
alpha = np.arctan2(weighted_eta, weighted_phi) # approximately align at phi
for i in range(n_consti):
v = LorentzVector(jet[i,1], jet[i,2], jet[i,3], jet[i,0])
#v.rotate_x(alpha) # approximately align at eta
v.rotate_x(-alpha) # approximately align at phi
jet[i, 0]=v[3]
jet[i, 1]=v[0]
jet[i, 2]=v[1]
jet[i, 3]=v[2]
#jet=jet.reshape(1,-1)
jet=jet.ravel()
return jet
def preprocessing(
jet
): # every entry would be a sequence of 4-vecs (E, px, py, pz) of jet constituents
jet = copy.deepcopy(jet)
jet = jet.reshape(-1, 4)
n_consti = len(jet)
# find the jet (eta, phi)
center = jet.sum(axis=0)
v_jet = LorentzVector(center[1], center[2], center[3], center[0])
# centering
phi = v_jet.phi()
bv = v_jet.boost_vector()
bv.set_perp(0)
# rotating
weighted_phi = 0
weighted_eta = 0
for i in range(n_consti):
if jet[i, 0] < 1e-10:
continue
v = LorentzVector(jet[i, 1], jet[i, 2], jet[i, 3], jet[i, 0])
r = np.sqrt(v.phi()**2 + v.eta()**2)
weighted_phi += v.phi() * v.E() / r
weighted_eta += v.eta() * v.E() / r
alpha = -np.arctan2(weighted_phi, weighted_eta)
for i in range(n_consti):
v = LorentzVector(jet[i, 1], jet[i, 2], jet[i, 3], jet[i, 0])
v.rotate_z(-phi)
v.boost(-bv)
v.rotate_x(alpha)
jet[i, 0] = v[3]
jet[i, 1] = v[0]
jet[i, 2] = v[1]
jet[i, 3] = v[2]
jet = jet.reshape(1, -1)
return jet
