def test_jet_correction_uncertainty_sources():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = []
levels = []
for name in dir(evaluator):
if 'Summer16_23Sep2016V3_MC_UncertaintySources_AK4PFPuppi' in name:
junc_names.append(name)
levels.append(name.split('_')[-1])
#test for underscore in dataera
if 'Fall17_17Nov2017_V6_MC_UncertaintySources_AK4PFchs_AbsoluteFlavMap' in name:
junc_names.append(name)
levels.append(name.split('_')[-1])
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs):
assert (level in levels)
assert (corrs.shape[0] == test_eta.shape[0])
tic = time.time()
assert (ak.all(corrs == ak.flatten(juncs_jag[i][1])))
toc = time.time()
def test_jet_correction_regrouped_uncertainty_sources():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = []
levels = []
for name in dir(evaluator):
if 'Regrouped_Fall17_17Nov2017_V32_MC_UncertaintySources_AK4PFchs' in name:
junc_names.append(name)
if len(name.split('_')) == 9:
levels.append("_".join(name.split('_')[-2:]))
else:
levels.append(name.split('_')[-1])
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, tpl in enumerate(
list(junc.getUncertainty(JetEta=test_eta, JetPt=test_pt))):
assert (tpl[0] in levels)
assert (tpl[1].shape[0] == test_eta.shape[0])
assert (ak.all(tpl[1] == ak.flatten(juncs_jag[i][1])))
def test_jet_correction_uncertainty():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = ["Summer16_23Sep2016V3_MC_Uncertainty_AK4PFPuppi"]
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs):
assert corrs.shape[0] == test_eta.shape[0]
assert ak.all(corrs == ak.flatten(juncs_jag[i][1]))
test_pt_jag = test_pt_jag[0:3]
test_eta_jag = test_eta_jag[0:3]
counts = counts[0:3]
print("Raw jet values:")
print("pT:", test_pt_jag.tolist())
print("eta:", test_eta_jag.tolist(), "\n")
juncs_jag_ref = ak.unflatten(
np.array([
[1.053504214, 0.946495786],
[1.033343349, 0.966656651],
[1.065159157, 0.934840843],
[1.033140127, 0.966859873],
[1.016858652, 0.983141348],
[1.130199999, 0.869800001],
[1.039968468, 0.960031532],
[1.033100002, 0.966899998],
]),
counts,
)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs_jag):
print("Index:", i)
print("Correction level:", level)
print("Reference Uncertainties (jagged):", juncs_jag_ref)
print("Uncertainties (jagged):", corrs)
assert ak.all(
np.abs(ak.flatten(juncs_jag_ref) - ak.flatten(corrs)) < 1e-6)
def test_jet_correction_uncertainty_sources():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
junc_names = []
levels = []
for name in dir(evaluator):
if 'Summer16_23Sep2016V3_MC_UncertaintySources_AK4PFPuppi' in name:
junc_names.append(name)
levels.append(name.split('_')[-1])
#test for underscore in dataera
if 'Fall17_17Nov2017_V6_MC_UncertaintySources_AK4PFchs_AbsoluteFlavMap' in name:
junc_names.append(name)
levels.append(name.split('_')[-1])
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
for level, corrs in juncs:
assert (level in levels)
assert (corrs.shape[0] == test_eta.shape[0])
def test_jet_correction_uncertainty():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = ['Summer16_23Sep2016V3_MC_Uncertainty_AK4PFPuppi']
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs):
assert (corrs.shape[0] == test_eta.shape[0])
assert (ak.all(corrs == ak.flatten(juncs_jag[i][1])))
def test_jet_correction_uncertainty():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
junc_names = ['Summer16_23Sep2016V3_MC_Uncertainty_AK4PFPuppi']
junc = JetCorrectionUncertainty(**{name: evaluator[name] for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
for level, corrs in juncs:
assert(corrs.shape[0] == test_eta.shape[0])
def test_jet_correction_regrouped_uncertainty_sources():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = []
levels = []
for name in dir(evaluator):
if "Regrouped_Fall17_17Nov2017_V32_MC_UncertaintySources_AK4PFchs" in name:
junc_names.append(name)
if len(name.split("_")) == 9:
levels.append("_".join(name.split("_")[-2:]))
else:
levels.append(name.split("_")[-1])
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, tpl in enumerate(
list(junc.getUncertainty(JetEta=test_eta, JetPt=test_pt))):
assert tpl[0] in levels
assert tpl[1].shape[0] == test_eta.shape[0]
assert ak.all(tpl[1] == ak.flatten(juncs_jag[i][1]))
test_pt_jag = test_pt_jag[0:3]
test_eta_jag = test_eta_jag[0:3]
counts = counts[0:3]
print("Raw jet values:")
print("pT:", test_pt_jag.tolist())
print("eta:", test_eta_jag.tolist(), "\n")
juncs_jag_ref = ak.unflatten(
np.array([
[1.119159088, 0.880840912],
[1.027003404, 0.972996596],
[1.135201275, 0.864798725],
[1.039665259, 0.960334741],
[1.015064503, 0.984935497],
[1.149900004, 0.850099996],
[1.079960600, 0.920039400],
[1.041200001, 0.958799999],
]),
counts,
)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs_jag):
if level != "Total":
continue
print("Index:", i)
print("Correction level:", level)
print("Reference Uncertainties (jagged):", juncs_jag_ref)
print("Uncertainties (jagged):", corrs, "\n")
assert ak.all(
np.abs(ak.flatten(juncs_jag_ref) - ak.flatten(corrs)) < 1e-6)
def test_jet_correction_uncertainty_sources():
from coffea.jetmet_tools import JetCorrectionUncertainty
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_pt_jag = ak.unflatten(test_pt, counts)
test_eta_jag = ak.unflatten(test_eta, counts)
junc_names = []
levels = []
for name in dir(evaluator):
if "Summer16_23Sep2016V3_MC_UncertaintySources_AK4PFPuppi" in name:
junc_names.append(name)
levels.append(name.split("_")[-1])
# test for underscore in dataera
if "Fall17_17Nov2017_V6_MC_UncertaintySources_AK4PFchs_AbsoluteFlavMap" in name:
junc_names.append(name)
levels.append(name.split("_")[-1])
junc = JetCorrectionUncertainty(
**{name: evaluator[name]
for name in junc_names})
print(junc)
juncs = junc.getUncertainty(JetEta=test_eta, JetPt=test_pt)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs):
assert level in levels
assert corrs.shape[0] == test_eta.shape[0]
assert ak.all(corrs == ak.flatten(juncs_jag[i][1]))
test_pt_jag = test_pt_jag[0:3]
test_eta_jag = test_eta_jag[0:3]
counts = counts[0:3]
print("Raw jet values:")
print("pT:", test_pt_jag.tolist())
print("eta:", test_eta_jag.tolist(), "\n")
juncs_jag_ref = ak.unflatten(
np.array([
[1.053504214, 0.946495786],
[1.033343349, 0.966656651],
[1.065159157, 0.934840843],
[1.033140127, 0.966859873],
[1.016858652, 0.983141348],
[1.130199999, 0.869800001],
[1.039968468, 0.960031532],
[1.033100002, 0.966899998],
]),
counts,
)
juncs_jag = list(
junc.getUncertainty(JetEta=test_eta_jag, JetPt=test_pt_jag))
for i, (level, corrs) in enumerate(juncs_jag):
if level != "Total":
continue
print("Index:", i)
print("Correction level:", level)
print("Reference Uncertainties (jagged):", juncs_jag_ref)
print("Uncertainties (jagged):", corrs, "\n")
assert ak.all(
np.abs(ak.flatten(juncs_jag_ref) - ak.flatten(corrs)) < 1e-6)