def test_factorized_jet_corrector():
from coffea.jetmet_tools import FactorizedJetCorrector
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_Rho = np.full_like(test_eta, 100.)
test_A = np.full_like(test_eta, 5.)
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})
print(corrector)
pt_copy = np.copy(test_pt)
corrs = corrector.getCorrection(JetEta=test_eta,
Rho=test_Rho,
JetPt=test_pt,
JetA=test_A)
assert ((np.abs(pt_copy - test_pt) < 1e-6).all())
def test_factorized_jet_corrector():
from coffea.jetmet_tools import FactorizedJetCorrector
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_Rho = np.full_like(test_eta, 100.)
test_A = np.full_like(test_eta, 5.)
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})
print(corrector)
pt_copy = np.copy(test_pt)
corrs = corrector.getCorrection(JetEta=test_eta,
Rho=test_Rho,
JetPt=test_pt,
JetA=test_A)
assert ((np.abs(pt_copy - test_pt) < 1e-6).all())
# Test for bug #320
def make_starts_stops(counts, data, padding):
cumcounts = np.cumsum(counts)
first_nonempty_event = cumcounts[np.nonzero(counts > 1)[0][0]]
data_pad = np.empty(data.size + padding)
data_pad[:first_nonempty_event] = data[:first_nonempty_event]
data_pad[first_nonempty_event + padding:] = data[first_nonempty_event:]
starts = np.r_[0, cumcounts[:-1]]
starts[starts >= first_nonempty_event] += padding
return awkward.JaggedArray(starts, starts + counts, data_pad)
test_pt_jag = make_starts_stops(counts, test_pt, 5)
test_eta_jag = make_starts_stops(counts, test_eta, 3)
test_Rho_jag = awkward.JaggedArray.fromcounts(counts, test_Rho)
test_A_jag = awkward.JaggedArray.fromcounts(counts, test_A)
corrs_jag = corrector.getCorrection(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag,
JetA=test_A_jag)
assert ((np.abs(pt_copy - test_pt_jag.flatten()) < 1e-6).all())
assert ((np.abs(corrs - corrs_jag.flatten()) < 1e-6).all())
def test_jet_resolution():
from coffea.jetmet_tools import JetResolution
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_Rho = np.full_like(test_eta, 10.0)
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
test_Rho_jag = ak.unflatten(test_Rho, counts)
jer_names = ["Spring16_25nsV10_MC_PtResolution_AK4PFPuppi"]
reso = JetResolution(**{name: evaluator[name] for name in jer_names})
print(reso)
resos = reso.getResolution(JetEta=test_eta, Rho=test_Rho, JetPt=test_pt)
resos_jag = reso.getResolution(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag)
assert ak.all(np.abs(resos - ak.flatten(resos_jag)) < 1e-6)
test_pt_jag = test_pt_jag[0:3]
test_eta_jag = test_eta_jag[0:3]
test_Rho_jag = test_Rho_jag[0:3]
test_Rho_jag = ak.concatenate(
[test_Rho_jag[:-1], [ak.concatenate([test_Rho_jag[-1, :-1], 100.0])]])
counts = counts[0:3]
print("Raw jet values:")
print("pT:", test_pt_jag)
print("eta:", test_eta_jag)
print("rho:", test_Rho_jag, "\n")
resos_jag_ref = ak.unflatten(
np.array([
0.21974642,
0.32421591,
0.33702479,
0.27420327,
0.13940689,
0.48134521,
0.26564994,
1.0,
]),
counts,
)
resos_jag = reso.getResolution(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag)
print("Reference Resolution (jagged):", resos_jag_ref)
print("Resolution (jagged):", resos_jag)
# NB: 5e-4 tolerance was agreed upon by lgray and aperloff, if the differences get bigger over time
# we need to agree upon how these numbers are evaluated (double/float conversion is kinda random)
assert ak.all(
np.abs(ak.flatten(resos_jag_ref) - ak.flatten(resos_jag)) < 5e-4)
def dummy_four_momenta():
np.random.seed(12345)
nrows = 1000
counts = np.minimum(np.random.exponential(0.5, size=nrows).astype(int), 20)
px = np.random.normal(loc=20.0, scale=5.0, size=np.sum(counts))
py = np.random.normal(loc=20.0, scale=5.0, size=np.sum(counts))
pz = np.random.normal(loc=0, scale=55, size=np.sum(counts))
m_pi = np.full_like(px, fill_value=0.135)
energy = np.sqrt(px * px + py * py + pz * pz + m_pi * m_pi)
return (counts, px, py, pz, energy)
def test_jet_resolution():
from coffea.jetmet_tools import JetResolution
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_Rho = np.full_like(test_eta, 100.)
jer_names = ['Spring16_25nsV10_MC_PtResolution_AK4PFPuppi']
reso = JetResolution(**{name: evaluator[name] for name in jer_names})
print(reso)
resos = reso.getResolution(JetEta=test_eta, Rho=test_Rho, JetPt=test_pt)
def test_factorized_jet_corrector():
from coffea.jetmet_tools import FactorizedJetCorrector
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_Rho = np.full_like(test_eta, 100.0)
test_A = np.full_like(test_eta, 5.0)
# Check that the FactorizedJetCorrector is functional
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})
print(corrector)
pt_copy = np.copy(test_pt)
# Check that the corrector can be evaluated for flattened arrays
corrs = corrector.getCorrection(JetEta=test_eta,
Rho=test_Rho,
JetPt=test_pt,
JetA=test_A)
assert (np.abs(pt_copy - test_pt) < 1e-6).all()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
test_Rho_jag = ak.unflatten(test_Rho, counts)
test_A_jag = ak.unflatten(test_A, counts)
# Check that the corrector can be evaluated for jagges arrays
corrs_jag = corrector.getCorrection(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag,
JetA=test_A_jag)
assert ak.all(np.abs(pt_copy - ak.flatten(test_pt_jag)) < 1e-6)
assert ak.all(np.abs(corrs - ak.flatten(corrs_jag)) < 1e-6)
# Check that the corrector returns the correct answers for each level of correction
# Use a subset of the values so that we can check the corrections by hand
test_pt_jag = test_pt_jag[0:3]
test_eta_jag = test_eta_jag[0:3]
test_Rho_jag = test_Rho_jag[0:3]
test_A_jag = test_A_jag[0:3]
counts = counts[0:3]
print("Raw jet values:")
print("pT:", test_pt_jag)
print("eta:", test_eta_jag)
print("rho:", test_Rho_jag)
print("area:", test_A_jag, "\n")
# Start by checking the L1 corrections
corrs_L1_jag_ref = ak.full_like(test_pt_jag, 1.0)
corrector = FactorizedJetCorrector(
**{name: evaluator[name]
for name in jec_names[0:1]})
corrs_L1_jag = corrector.getCorrection(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag,
JetA=test_A_jag)
print("Reference L1 corrections:", corrs_L1_jag_ref)
print("Calculated L1 corrections:", corrs_L1_jag)
assert ak.all(
np.abs(ak.flatten(corrs_L1_jag_ref) - ak.flatten(corrs_L1_jag)) < 1e-6)
# Apply the L1 corrections and save the result
test_ptL1_jag = test_pt_jag * corrs_L1_jag
print("L1 corrected pT values:", test_ptL1_jag, "\n")
assert ak.all(
np.abs(ak.flatten(test_pt_jag) - ak.flatten(test_ptL1_jag)) < 1e-6)
# Check the L2 corrections on a subset of jets
# Look up the parameters for the L2 corrections by hand and calculate the corrections
# [(1.37906,35.8534,-0.00829227,7.96644e-05,5.18988e-06),
# (1.38034,17.9841,-0.00729638,-0.000127141,5.70889e-05),
# (1.74466,18.6372,-0.0367036,0.00310864,-0.000277062),
# (1.4759,24.8882,-0.0155333,0.0020836,-0.000198039),
# (1.14606,36.4215,-0.00174801,-1.76393e-05,1.91863e-06),
# (0.999657,4.02981,1.06597,-0.619679,-0.0494)],
# [(1.54524,23.9023,-0.0162807,0.000665243,-4.66608e-06),
# (1.48431,8.68725,0.00642424,0.0252104,-0.0335696)]])
corrs_L2_jag_ref = ak.unflatten(
np.array([
1.37038741364,
1.37710384514,
1.65148641108,
1.46840446827,
1.1328319784,
1.0,
1.50762056349,
1.48719866989,
]),
counts,
)
corrector = FactorizedJetCorrector(
**{name: evaluator[name]
for name in jec_names[1:2]})
corrs_L2_jag = corrector.getCorrection(JetEta=test_eta_jag,
JetPt=test_pt_jag)
print("Reference L2 corrections:", corrs_L2_jag_ref.tolist())
print("Calculated L2 corrections:", corrs_L2_jag.tolist())
assert ak.all(
np.abs(ak.flatten(corrs_L2_jag_ref) - ak.flatten(corrs_L2_jag)) < 1e-6)
# Apply the L2 corrections and save the result
test_ptL1L2_jag = test_ptL1_jag * corrs_L2_jag
print("L1L2 corrected pT values:", test_ptL1L2_jag, "\n")
# Apply the L3 corrections and save the result
corrs_L3_jag = ak.full_like(test_pt_jag, 1.0)
test_ptL1L2L3_jag = test_ptL1L2_jag * corrs_L3_jag
print("L1L2L3 corrected pT values:", test_ptL1L2L3_jag, "\n")
# Check that the corrections can be chained together
corrs_L1L2L3_jag_ref = ak.unflatten(
np.array([
1.37038741364,
1.37710384514,
1.65148641108,
1.46840446827,
1.1328319784,
1.0,
1.50762056349,
1.48719866989,
]),
counts,
)
corrector = FactorizedJetCorrector(
**{name: evaluator[name]
for name in (jec_names[0:2] + jec_names[3:])})
corrs_L1L2L3_jag = corrector.getCorrection(JetEta=test_eta_jag,
Rho=test_Rho_jag,
JetPt=test_pt_jag,
JetA=test_A_jag)
print("Reference L1L2L3 corrections:", corrs_L1L2L3_jag_ref)
print("Calculated L1L2L3 corrections:", corrs_L1L2L3_jag)
assert ak.all(
np.abs(
ak.flatten(corrs_L1L2L3_jag_ref) -
ak.flatten(corrs_L1L2L3_jag)) < 1e-6)
# Apply the L1L2L3 corrections and save the result
test_ptL1L2L3chain_jag = test_pt_jag * corrs_L1L2L3_jag
print("Chained L1L2L3 corrected pT values:", test_ptL1L2L3chain_jag, "\n")
assert ak.all(
np.abs(
ak.flatten(test_ptL1L2L3_jag) -
ak.flatten(test_ptL1L2L3chain_jag)) < 1e-6)