def test_jet_transformer():
from coffea.analysis_objects import JaggedCandidateArray as CandArray
from coffea.jetmet_tools import (FactorizedJetCorrector,
JetResolution,
JetResolutionScaleFactor,
JetCorrectionUncertainty,
JetTransformer)
counts, test_px, test_py, test_pz, test_e = dummy_four_momenta()
test_Rho = np.full(shape=(np.sum(counts),), fill_value=100.)
test_A = np.full(shape=(np.sum(counts),), fill_value=5.)
jets = CandArray.candidatesfromcounts(counts, px=test_px, py=test_py, pz=test_pz, energy=test_e)
jets.add_attributes(ptRaw=jets.pt,
massRaw=jets.mass,
rho=test_Rho,
area=test_A)
jec_names = ['Summer16_23Sep2016V3_MC_L1FastJet_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L2Relative_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L2L3Residual_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L3Absolute_AK4PFPuppi']
corrector = FactorizedJetCorrector(**{name: evaluator[name] for name in jec_names})
junc_names = []
for name in dir(evaluator):
if 'Summer16_23Sep2016V3_MC_UncertaintySources_AK4PFPuppi' in name:
junc_names.append(name)
junc = JetCorrectionUncertainty(**{name: evaluator[name] for name in junc_names})
jer_names = ['Spring16_25nsV10_MC_PtResolution_AK4PFPuppi']
reso = JetResolution(**{name: evaluator[name] for name in jer_names})
jersf_names = ['Spring16_25nsV10_MC_SF_AK4PFPuppi']
resosf = JetResolutionScaleFactor(**{name: evaluator[name] for name in jersf_names})
xform = JetTransformer(jec=corrector, junc=junc, jer=reso, jersf=resosf)
print(xform.uncertainties)
xform.transform(jets)
print(jets.columns)
assert('pt_jer_up' in jets.columns)
assert('pt_jer_down' in jets.columns)
assert('mass_jer_up' in jets.columns)
assert('mass_jer_down' in jets.columns)
for unc in xform.uncertainties:
assert('pt_'+unc+'_up' in jets.columns)
assert('pt_'+unc+'_down' in jets.columns)
assert('mass_'+unc+'_up' in jets.columns)
assert('mass_'+unc+'_down' in jets.columns)
def test_analysis_objects():
counts, px, py, pz, energy = dummy_four_momenta()
thep4 = np.stack((px, py, pz, energy)).T
#test JaggedTLorentzVectorArray
tlva1 = uproot_methods.TLorentzVectorArray(px, py, pz, energy)
tlva2 = uproot_methods.TLorentzVectorArray(thep4[:, 0], thep4[:, 1],
thep4[:, 2], thep4[:, 3])
jtlva1 = JaggedTLorentzVectorArray.fromcounts(counts, tlva1)
jtlva2 = JaggedTLorentzVectorArray.fromcounts(counts, tlva2)
jtlva1_selection1 = jtlva1[jtlva1.counts > 0]
jtlva1_selection2 = jtlva1_selection1[jtlva1_selection1.pt > 5]
jtlva2_selection1 = jtlva2[jtlva2.counts > 0]
jtlva2_selection2 = jtlva1_selection1[jtlva2_selection1.pt > 5]
diffx = np.abs(jtlva1.x - jtlva2.x)
diffy = np.abs(jtlva1.y - jtlva2.y)
diffz = np.abs(jtlva1.z - jtlva2.z)
difft = np.abs(jtlva1.t - jtlva2.t)
assert (diffx < 1e-8).flatten().all()
assert (diffy < 1e-8).flatten().all()
assert (diffz < 1e-8).flatten().all()
assert (difft < 1e-8).flatten().all()
#test JaggedCandidateArray
jca1 = JaggedCandidateArray.candidatesfromcounts(counts, p4=thep4)
jca2 = JaggedCandidateArray.candidatesfromcounts(counts, p4=thep4)
assert ((jca1.offsets == jca2.offsets).all())
addon1 = jca1.zeros_like()
addon2 = jca2.ones_like()
jca1['addon'] = addon1
jca2['addon'] = addon2
jca1.add_attributes(addonFlat=addon1.flatten(), addonJagged=addon1)
diffm = np.abs(jca1.p4.mass - jca2.p4.mass)
assert ((jca1.offsets == jca2.offsets).all())
diffpt = np.abs(jca1.p4.pt - jca2.p4.pt)
assert ((jca1.offsets == jca2.offsets).all())
eta2 = jca2.p4.eta
eta1 = jca1.p4.eta
print(np.sum(eta1.counts), np.sum(eta2.counts))
diffeta_temp = np.abs(eta1 - eta2)
diffeta = np.abs(jca1.p4.eta - jca2.p4.eta)
assert ((jca1.offsets == jca2.offsets).all())
assert (diffm < 1e-8).flatten().all()
assert (diffpt < 1e-8).flatten().all()
assert (diffeta < 1e-8).flatten().all()
#test fast functions
fastfs = ['pt', 'eta', 'phi', 'mass']
for func in fastfs:
func1 = getattr(jca1, func)
func2 = getattr(jca1.p4, func)
dfunc = np.abs(func1 - func2)
assert (dfunc < 1e-8).flatten().all()
adistinct = jca1.distincts()
apair = jca1.pairs()
across = jca1.cross(jca2)
assert 'p4' in adistinct.columns
assert 'p4' in apair.columns
assert 'p4' in across.columns
admsum = (adistinct.i0.p4 + adistinct.i1.p4).mass
apmsum = (apair.i0.p4 + apair.i1.p4).mass
acmsum = (across.i0.p4 + across.i1.p4).mass
diffadm = np.abs(adistinct.p4.mass - admsum)
diffapm = np.abs(apair.p4.mass - apmsum)
diffacm = np.abs(across.p4.mass - acmsum)
assert (diffadm < 1e-8).flatten().all()
assert (diffapm < 1e-8).flatten().all()
assert (diffacm < 1e-8).flatten().all()
selection11 = jca1[jca1.counts > 0]
selection12 = selection11[selection11.p4.pt > 5]
selection21 = jca2[jca2.counts > 0]
selection22 = selection21[selection21.p4.pt > 5]
diffcnts = selection12.counts - jtlva1_selection2.counts
diffm = np.abs(selection12.p4.mass - jtlva1_selection2.mass)
diffaddon = selection12.addon - selection22.addon
assert (diffcnts == 0).flatten().all()
assert (diffm < 1e-8).flatten().all()
assert (diffaddon == -1).flatten().all()
#test gen-reco matching
gen, reco = gen_reco_TLV()
flat_gen = gen.flatten()
gen_px, gen_py, gen_pz, gen_e = flat_gen.x, flat_gen.y, flat_gen.z, flat_gen.t
flat_reco = reco.flatten()
reco_px, reco_py, reco_pz, reco_e = flat_reco.x, flat_reco.y, flat_reco.z, flat_reco.t
jca_gen = JaggedCandidateArray.candidatesfromcounts(gen.counts,
px=gen_px,
py=gen_py,
pz=gen_pz,
energy=gen_e)
jca_reco = JaggedCandidateArray.candidatesfromcounts(reco.counts,
px=reco_px,
py=reco_py,
pz=reco_pz,
energy=reco_e)
print('gen eta: ', jca_gen.p4.eta, '\n gen phi:', jca_gen.p4.phi)
print('reco eta: ', jca_reco.p4.eta, '\n reco phi:', jca_reco.p4.phi)
print('match mask: ', jca_reco.match(jca_gen, deltaRCut=0.3))
print('arg matches: ', jca_reco.argmatch(jca_gen, deltaRCut=0.3))
argmatch_nocut = jca_gen.argmatch(jca_reco).flatten()
argmatch_dr03 = jca_gen.argmatch(jca_reco, deltaRCut=0.3).flatten()
argmatch_dr03_dpt01 = jca_gen.argmatch(jca_reco,
deltaRCut=0.3,
deltaPtCut=0.1).flatten()
assert (argmatch_nocut.size == 5)
assert (argmatch_dr03[argmatch_dr03 != -1].size == 3)
assert (argmatch_dr03_dpt01[argmatch_dr03_dpt01 != -1].size == 2)
assert (jca_gen.match(jca_reco,
deltaRCut=0.3).flatten().flatten().sum() == 3)
assert (jca_gen.match(jca_reco, deltaRCut=0.3,
deltaPtCut=0.1).flatten().flatten().sum() == 2)
def test_jet_transformer():
import numpy as np
import awkward as ak
import math
from coffea.analysis_objects import JaggedCandidateArray as CandArray
from coffea.jetmet_tools import (FactorizedJetCorrector, JetResolution,
JetResolutionScaleFactor,
JetCorrectionUncertainty, JetTransformer)
counts, test_px, test_py, test_pz, test_e = dummy_four_momenta()
test_Rho = np.full(shape=(np.sum(counts), ), fill_value=100.)
test_A = np.full(shape=(np.sum(counts), ), fill_value=5.)
jets = CandArray.candidatesfromcounts(counts,
px=test_px,
py=test_py,
pz=test_pz,
energy=test_e)
jets.add_attributes(ptRaw=jets.pt,
massRaw=jets.mass,
rho=test_Rho,
area=test_A)
fakemet = np.random.exponential(scale=1.0, size=counts.size)
metphi = np.random.uniform(low=-math.pi, high=math.pi, size=counts.size)
syst_up = 0.001 * fakemet
syst_down = -0.001 * fakemet
met = CandArray.candidatesfromcounts(
np.ones_like(counts),
pt=fakemet,
eta=np.zeros_like(counts),
phi=metphi,
mass=np.zeros_like(counts),
MetUnclustEnUpDeltaX=syst_up * np.cos(metphi),
MetUnclustEnUpDeltaY=syst_down * np.sin(metphi))
jec_names = [
'Summer16_23Sep2016V3_MC_L1FastJet_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L2Relative_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L2L3Residual_AK4PFPuppi',
'Summer16_23Sep2016V3_MC_L3Absolute_AK4PFPuppi'
]
corrector = FactorizedJetCorrector(
**{name: evaluator[name]
for name in jec_names})
junc_names = []
for name in dir(evaluator):
if 'Summer16_23Sep2016V3_MC_UncertaintySources_AK4PFPuppi' in name:
junc_names.append(name)
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
jer_names = ['Spring16_25nsV10_MC_PtResolution_AK4PFPuppi']
reso = JetResolution(**{name: evaluator[name] for name in jer_names})
jersf_names = ['Spring16_25nsV10_MC_SF_AK4PFPuppi']
resosf = JetResolutionScaleFactor(
**{name: evaluator[name]
for name in jersf_names})
xform = JetTransformer(jec=corrector, junc=junc, jer=reso, jersf=resosf)
print(xform.uncertainties)
xform.transform(jets, met=met)
print('jets', jets.columns)
print('met', met.columns)
assert ('pt_jer_up' in jets.columns)
assert ('pt_jer_down' in jets.columns)
assert ('mass_jer_up' in jets.columns)
assert ('mass_jer_down' in jets.columns)
assert ('pt_UnclustEn_up' in met.columns)
assert ('pt_UnclustEn_down' in met.columns)
assert ('phi_UnclustEn_up' in met.columns)
assert ('phi_UnclustEn_down' in met.columns)
for unc in xform.uncertainties:
assert ('pt_' + unc + '_up' in jets.columns)
assert ('pt_' + unc + '_down' in jets.columns)
assert ('mass_' + unc + '_up' in jets.columns)
assert ('mass_' + unc + '_down' in jets.columns)
assert ('pt_' + unc + '_up' in met.columns)
assert ('phi_' + unc + '_up' in met.columns)
def test_analysis_objects():
counts, px, py, pz, energy = dummy_four_momenta()
thep4 = np.stack((px, py, pz, energy)).T
#test JaggedTLorentzVectorArray
tlva1 = uproot_methods.TLorentzVectorArray(px, py, pz, energy)
tlva2 = uproot_methods.TLorentzVectorArray(thep4[:, 0], thep4[:, 1],
thep4[:, 2], thep4[:, 3])
jtlva1 = JaggedTLorentzVectorArray.fromcounts(counts, tlva1)
jtlva2 = JaggedTLorentzVectorArray.fromcounts(counts, tlva2)
jtlva1_selection1 = jtlva1[jtlva1.counts > 0]
jtlva1_selection2 = jtlva1_selection1[jtlva1_selection1.pt > 5]
jtlva2_selection1 = jtlva2[jtlva2.counts > 0]
jtlva2_selection2 = jtlva1_selection1[jtlva2_selection1.pt > 5]
diffx = np.abs(jtlva1.x - jtlva2.x)
diffy = np.abs(jtlva1.y - jtlva2.y)
diffz = np.abs(jtlva1.z - jtlva2.z)
difft = np.abs(jtlva1.t - jtlva2.t)
assert (diffx < 1e-8).flatten().all()
assert (diffy < 1e-8).flatten().all()
assert (diffz < 1e-8).flatten().all()
assert (difft < 1e-8).flatten().all()
#test JaggedCandidateArray
jca1 = JaggedCandidateArray.candidatesfromcounts(counts, p4=thep4)
jca2 = JaggedCandidateArray.candidatesfromcounts(counts, p4=thep4)
assert ((jca1.offsets == jca2.offsets).all())
addon1 = jca1.zeros_like()
addon2 = jca2.ones_like()
jca1['addon'] = addon1
jca2['addon'] = addon2
diffm = np.abs(jca1.p4.mass - jca2.p4.mass)
assert ((jca1.offsets == jca2.offsets).all())
diffpt = np.abs(jca1.p4.pt - jca2.p4.pt)
assert ((jca1.offsets == jca2.offsets).all())
eta2 = jca2.p4.eta
eta1 = jca1.p4.eta
print(np.sum(eta1.counts), np.sum(eta2.counts))
diffeta_temp = np.abs(eta1 - eta2)
diffeta = np.abs(jca1.p4.eta - jca2.p4.eta)
assert ((jca1.offsets == jca2.offsets).all())
assert (diffm < 1e-8).flatten().all()
assert (diffpt < 1e-8).flatten().all()
assert (diffeta < 1e-8).flatten().all()
#test fast functions
fastfs = ['pt', 'eta', 'phi', 'mass']
for func in fastfs:
func1 = getattr(jca1, func)
func2 = getattr(jca1.p4, func)
dfunc = np.abs(func1 - func2)
assert (dfunc < 1e-8).flatten().all()
adistinct = jca1.distincts()
apair = jca1.pairs()
across = jca1.cross(jca2)
assert 'p4' in adistinct.columns
assert 'p4' in apair.columns
assert 'p4' in across.columns
admsum = (adistinct.i0.p4 + adistinct.i1.p4).mass
apmsum = (apair.i0.p4 + apair.i1.p4).mass
acmsum = (across.i0.p4 + across.i1.p4).mass
diffadm = np.abs(adistinct.p4.mass - admsum)
diffapm = np.abs(apair.p4.mass - apmsum)
diffacm = np.abs(across.p4.mass - acmsum)
assert (diffadm < 1e-8).flatten().all()
assert (diffapm < 1e-8).flatten().all()
assert (diffacm < 1e-8).flatten().all()
selection11 = jca1[jca1.counts > 0]
selection12 = selection11[selection11.p4.pt > 5]
selection21 = jca2[jca2.counts > 0]
selection22 = selection21[selection21.p4.pt > 5]
diffcnts = selection12.counts - jtlva1_selection2.counts
diffm = np.abs(selection12.p4.mass - jtlva1_selection2.mass)
diffaddon = selection12.addon - selection22.addon
assert (diffcnts == 0).flatten().all()
assert (diffm < 1e-8).flatten().all()
assert (diffaddon == -1).flatten().all()
