def test_jec_txt_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testJEC * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi.jec.txt",
"* * tests/samples/Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs.jec.txt.gz",
"* * tests/samples/Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi.junc.txt",
"* * tests/samples/Autumn18_V8_MC_UncertaintySources_AK4PFchs.junc.txt",
"* * tests/samples/Spring16_25nsV10_MC_SF_AK4PFPuppi.jersf.txt"
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
jec_out = evaluator['testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi'](test_eta,test_pt)
print(evaluator['testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi'])
jec_out = evaluator['Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs'](test_eta,test_pt)
print(evaluator['Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs'])
jersf = evaluator['Spring16_25nsV10_MC_SF_AK4PFPuppi']
print(evaluator['Spring16_25nsV10_MC_SF_AK4PFPuppi'])
junc_out = evaluator['Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi'](test_eta,test_pt)
print(evaluator['Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi'])
assert('Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale' in evaluator.keys())
junc_out = evaluator['Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale'](test_eta,test_pt)
print(evaluator['Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale'])
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_root_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"
])
extractor.finalize(reduce_list=["testSF2d"])
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# test flat eval
test_out = evaluator["testSF2d"](test_eta, test_pt)
# print it
print(evaluator["testSF2d"])
# test structured eval
test_eta_jagged = ak.unflatten(test_eta, counts)
test_pt_jagged = ak.unflatten(test_pt, counts)
test_out_jagged = evaluator["testSF2d"](test_eta_jagged, test_pt_jagged)
assert ak.all(ak.num(test_out_jagged) == counts)
assert ak.all(ak.flatten(test_out_jagged) == test_out)
print(test_out)
diff = np.abs(test_out - _testSF2d_expected_output)
print("Max diff: %.16f" % diff.max())
print("Median diff: %.16f" % np.median(diff))
print("Diff over threshold rate: %.1f %%" %
(100 * (diff >= 1.0e-8).sum() / diff.size))
assert (diff < 1.0e-8).all()
def test_jet_resolution_sf_2d():
from coffea.jetmet_tools import JetResolutionScaleFactor
counts, test_eta, test_pt = dummy_jagged_eta_pt()
resosf = JetResolutionScaleFactor(
**{name: evaluator[name]
for name in ["Autumn18_V7_MC_SF_AK4PFchs"]})
resosfs = resosf.getScaleFactor(JetPt=test_pt, JetEta=test_eta)
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_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_export1d():
import uproot
import os
from coffea.hist import export1d
counts, test_eta, test_pt = dummy_jagged_eta_pt()
h_regular_bins = hist.Hist("regular_joe", hist.Bin("x", "x", 20, 0, 200))
h_regular_bins.fill(x=test_pt)
hout = export1d(h_regular_bins)
filename = 'test_export1d.root'
with uproot.create(filename) as fout:
fout['regular_joe'] = hout
fout.close()
with uproot.open(filename) as fin:
hin = fin['regular_joe']
assert (np.all(hin.edges == hout.edges))
assert (np.all(hin.values == hout.values))
del hin
del fin
if os.path.exists(filename):
os.remove(filename)
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_weights_partial():
from coffea.analysis_tools import Weights
counts, _, _ = dummy_jagged_eta_pt()
w1 = np.random.normal(loc=1.0, scale=0.01, size=counts.size)
w2 = np.random.normal(loc=1.3, scale=0.05, size=counts.size)
weights = Weights(counts.size, storeIndividual=True)
weights.add("w1", w1)
weights.add("w2", w2)
test_exclude_none = weights.weight()
assert np.all(np.abs(test_exclude_none - w1 * w2) < 1e-6)
test_exclude1 = weights.partial_weight(exclude=["w1"])
assert np.all(np.abs(test_exclude1 - w2) < 1e-6)
test_include1 = weights.partial_weight(include=["w1"])
assert np.all(np.abs(test_include1 - w1) < 1e-6)
test_exclude2 = weights.partial_weight(exclude=["w2"])
assert np.all(np.abs(test_exclude2 - w1) < 1e-6)
test_include2 = weights.partial_weight(include=["w2"])
assert np.all(np.abs(test_include2 - w2) < 1e-6)
test_include_both = weights.partial_weight(include=["w1", "w2"])
assert np.all(np.abs(test_include_both - w1 * w2) < 1e-6)
# Check that exception is thrown if arguments are incompatible
error_raised = False
try:
weights.partial_weight(exclude=["w1"], include=["w2"])
except ValueError:
error_raised = True
assert error_raised
error_raised = False
try:
weights.partial_weight()
except ValueError:
error_raised = True
assert error_raised
# Check that exception is thrown if individual weights
# are not saved from the start
weights = Weights(counts.size, storeIndividual=False)
weights.add("w1", w1)
weights.add("w2", w2)
error_raised = False
try:
weights.partial_weight(exclude=["test"], include=["test"])
except ValueError:
error_raised = True
assert error_raised
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_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 test_jet_resolution_sf():
from coffea.jetmet_tools import JetResolutionScaleFactor
counts, test_eta, test_pt = dummy_jagged_eta_pt()
jersf_names = ['Spring16_25nsV10_MC_SF_AK4PFPuppi']
resosf = JetResolutionScaleFactor(**{name: evaluator[name] for name in jersf_names})
print(resosf)
resosfs = resosf.getScaleFactor(JetEta=test_eta)
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_histo_json_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(["testJson * tests/samples/EIDISO_WH_out.histo.json"])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
sf_out = evaluator['testJsonEIDISO_WH/eta_pt_ratio_value'](test_eta, test_pt)
sf_err_out = evaluator['testJsonEIDISO_WH/eta_pt_ratio_error'](test_eta, test_pt)
print(sf_out)
print(sf_err_out)
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_btag_csv_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(["testBTag * tests/samples/testBTagSF.btag.csv"])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# discriminant used for reshaping, zero otherwise
test_discr = np.zeros_like(test_eta)
sf_out = evaluator['testBTagCSVv2_1_comb_up_0'](test_eta, test_pt,
test_discr)
print(sf_out)
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_root_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"
])
extractor.finalize(reduce_list=['testSF2d'])
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# test flat eval
test_out = evaluator["testSF2d"](test_eta, test_pt)
# print it
print(evaluator["testSF2d"])
# test structured eval
test_eta_jagged = ak.unflatten(test_eta, counts)
test_pt_jagged = ak.unflatten(test_pt, counts)
test_out_jagged = evaluator["testSF2d"](test_eta_jagged, test_pt_jagged)
assert ak.all(ak.num(test_out_jagged) == counts)
assert ak.all(ak.flatten(test_out_jagged) == test_out)
# From make_expected_lookup.py
expected_output = np.array([
0.90780139, 0.82748538, 0.86332178, 0.86332178, 0.97981155, 0.79701495,
0.88245934, 0.82857144, 0.91884059, 0.97466666, 0.94072163, 1.00775194,
0.82748538, 1.00775194, 0.97203946, 0.98199672, 0.80655736, 0.90893763,
0.88245934, 0.79701495, 0.82748538, 0.82857144, 0.91884059, 0.90893763,
0.97520661, 0.97520661, 0.82748538, 0.91884059, 0.97203946, 0.88245934,
0.79701495, 0.9458763, 1.00775194, 0.80655736, 1.00775194, 1.00775194,
0.98976982, 0.98976982, 0.86332178, 0.94072163, 0.80655736, 0.98976982,
0.96638656, 0.9458763, 0.90893763, 0.9529984, 0.9458763, 0.9529984,
0.80655736, 0.80655736, 0.80655736, 0.98976982, 0.97466666, 0.98199672,
0.86332178, 1.03286386, 0.94072163, 1.03398061, 0.82857144, 0.80655736,
1.00775194, 0.80655736
])
print(test_out)
diff = np.abs(test_out - expected_output)
print("Max diff: %.16f" % diff.max())
print("Median diff: %.16f" % np.median(diff))
print("Diff over threshold rate: %.1f %%" %
(100 * (diff >= 1.e-8).sum() / diff.size))
assert (diff < 1.e-8).all()
def test_evaluator_exceptions():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(["testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"])
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_eta_jagged = awkward.JaggedArray.fromcounts(counts, test_eta)
test_pt_jagged = awkward.JaggedArray.fromcounts(counts, test_pt)
extractor.finalize()
evaluator = extractor.make_evaluator()
try:
test_out = evaluator["testSF2d"](test_pt_jagged, test_eta)
except Exception as e:
assert(e.args[0] == 'do not mix JaggedArrays and numpy arrays when calling a derived class of lookup_base')
def test_jec_txt_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testJEC * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi.jec.txt"
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
jec_out = evaluator[
'testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi'](test_eta,
test_pt)
print(jec_out)
def test_weights():
from coffea.analysis_tools import Weights
counts, test_eta, test_pt = dummy_jagged_eta_pt()
scale_central = np.random.normal(loc=1.0, scale=0.01, size=counts.size)
scale_up = scale_central * 1.10
scale_down = scale_central * 0.95
scale_up_shift = 0.10 * scale_central
scale_down_shift = 0.05 * scale_central
weight = Weights(counts.size)
weight.add("test", scale_central, weightUp=scale_up, weightDown=scale_down)
weight.add(
"testShift",
scale_central,
weightUp=scale_up_shift,
weightDown=scale_down_shift,
shift=True,
)
var_names = weight.variations
expected_names = ["testShiftUp", "testShiftDown", "testUp", "testDown"]
for name in expected_names:
assert name in var_names
test_central = weight.weight()
exp_weight = scale_central * scale_central
assert np.all(np.abs(test_central - (exp_weight)) < 1e-6)
test_up = weight.weight("testUp")
exp_up = scale_central * scale_central * 1.10
assert np.all(np.abs(test_up - (exp_up)) < 1e-6)
test_down = weight.weight("testDown")
exp_down = scale_central * scale_central * 0.95
assert np.all(np.abs(test_down - (exp_down)) < 1e-6)
test_shift_up = weight.weight("testUp")
assert np.all(np.abs(test_shift_up - (exp_up)) < 1e-6)
test_shift_down = weight.weight("testDown")
assert np.all(np.abs(test_shift_down - (exp_down)) < 1e-6)
def test_jet_resolution_sf():
from coffea.jetmet_tools import JetResolutionScaleFactor
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)
jersf_names = ["Spring16_25nsV10_MC_SF_AK4PFPuppi"]
resosf = JetResolutionScaleFactor(
**{name: evaluator[name]
for name in jersf_names})
print(resosf)
# 0-jet compatibility
assert resosf.getScaleFactor(JetEta=test_eta[:0]).shape == (0, 3)
resosfs = resosf.getScaleFactor(JetEta=test_eta)
resosfs_jag = resosf.getScaleFactor(JetEta=test_eta_jag)
assert ak.all(resosfs == ak.flatten(resosfs_jag))
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)
print("eta:", test_eta_jag, "\n")
resosfs_jag_ref = ak.unflatten(
np.array([
[1.857, 1.928, 1.786],
[1.084, 1.095, 1.073],
[1.364, 1.403, 1.325],
[1.177, 1.218, 1.136],
[1.138, 1.151, 1.125],
[1.364, 1.403, 1.325],
[1.177, 1.218, 1.136],
[1.082, 1.117, 1.047],
]),
counts,
)
resosfs_jag = resosf.getScaleFactor(JetEta=test_eta_jag)
print("Reference Resolution SF (jagged):", resosfs_jag_ref)
print("Resolution SF (jagged):", resosfs_jag)
assert ak.all(
np.abs(ak.flatten(resosfs_jag_ref) - ak.flatten(resosfs_jag)) < 1e-6)
def test_jet_resolution_sf_2d():
from coffea.jetmet_tools import JetResolutionScaleFactor
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)
resosf = JetResolutionScaleFactor(
**{name: evaluator[name]
for name in ["Autumn18_V7_MC_SF_AK4PFchs"]})
print(resosf)
# 0-jet compatibility
assert resosf.getScaleFactor(JetPt=test_pt[:0],
JetEta=test_eta[:0]).shape == (0, 3)
resosfs = resosf.getScaleFactor(JetPt=test_pt, JetEta=test_eta)
resosfs_jag = resosf.getScaleFactor(JetPt=test_pt_jag, JetEta=test_eta_jag)
assert ak.all(resosfs == ak.flatten(resosfs_jag))
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)
print("eta:", test_eta_jag, "\n")
resosfs_jag_ref = ak.unflatten(
np.array([
[1.11904, 1.31904, 1.0],
[1.1432, 1.2093, 1.0771],
[1.16633, 1.36633, 1.0],
[1.17642, 1.37642, 1.0],
[1.1808, 1.1977, 1.1640],
[1.15965, 1.35965, 1.0],
[1.17661, 1.37661, 1.0],
[1.1175, 1.1571, 1.0778],
]),
counts,
)
resosfs_jag = resosf.getScaleFactor(JetPt=test_pt_jag, JetEta=test_eta_jag)
print("Reference Resolution SF (jagged):", resosfs_jag_ref)
print("Resolution SF (jagged):", resosfs_jag)
assert ak.all(
np.abs(ak.flatten(resosfs_jag_ref) - ak.flatten(resosfs_jag)) < 1e-6)
def test_evaluator_exceptions():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"
])
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# test_eta_jagged = ak.unflatten(test_eta, counts)
test_pt_jagged = ak.unflatten(test_pt, counts)
extractor.finalize()
evaluator = extractor.make_evaluator()
try:
evaluator["testSF2d"](test_pt_jagged, test_eta)
except Exception as e:
assert isinstance(e, TypeError)
def test_jec_txt_effareas():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(['* * tests/samples/photon_id.ea.txt'])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
ch_out = evaluator['photon_id_EA_CHad'](test_eta)
print(evaluator['photon_id_EA_CHad'])
nh_out = evaluator['photon_id_EA_NHad'](test_eta)
print(evaluator['photon_id_EA_NHad'])
ph_out = evaluator['photon_id_EA_Pho'](test_eta)
print(evaluator['photon_id_EA_Pho'])
def test_weights():
from coffea.processor import Weights
counts, test_eta, test_pt = dummy_jagged_eta_pt()
scale_central = np.random.normal(loc=1.0, scale=0.01, size=counts.size)
scale_up = scale_central * 1.10
scale_down = scale_central * 0.95
scale_up_shift = 0.10 * scale_central
scale_down_shift = 0.05 * scale_central
weight = Weights(counts.size)
weight.add('test', scale_central, weightUp=scale_up, weightDown=scale_down)
weight.add('testShift',
scale_central,
weightUp=scale_up_shift,
weightDown=scale_down_shift,
shift=True)
var_names = weight.variations
expected_names = ['testShiftUp', 'testShiftDown', 'testUp', 'testDown']
for name in expected_names:
assert (name in var_names)
test_central = weight.weight()
exp_weight = scale_central * scale_central
assert (np.all(np.abs(test_central - (exp_weight)) < 1e-6))
test_up = weight.weight('testUp')
exp_up = scale_central * scale_central * 1.10
assert (np.all(np.abs(test_up - (exp_up)) < 1e-6))
test_down = weight.weight('testDown')
exp_down = scale_central * scale_central * 0.95
assert (np.all(np.abs(test_down - (exp_down)) < 1e-6))
test_shift_up = weight.weight('testUp')
assert (np.all(np.abs(test_shift_up - (exp_up)) < 1e-6))
test_shift_down = weight.weight('testDown')
assert (np.all(np.abs(test_shift_down - (exp_down)) < 1e-6))
def test_packed_selection():
from coffea.processor import PackedSelection
sel = PackedSelection()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
all_true = np.full(shape=counts.shape, fill_value=True, dtype=np.bool)
all_false = np.full(shape=counts.shape, fill_value=False, dtype=np.bool)
ones = np.ones(shape=counts.shape, dtype=np.uint64)
wrong_shape = ones = np.ones(shape=(counts.shape[0] - 5, ), dtype=np.bool)
sel.add('all_true', all_true)
sel.add('all_false', all_false)
assert (np.all(sel.require(all_true=True, all_false=False) == all_true))
assert (np.all(sel.all('all_true', 'all_false') == all_false))
try:
sel.require(all_true=1, all_false=0)
except ValueError:
pass
try:
sel.add('wrong_shape', wrong_shape)
except ValueError:
pass
try:
sel.add('ones', ones)
except ValueError:
pass
try:
overpack = PackedSelection()
for i in range(65):
overpack.add('sel_%d', all_true)
except RuntimeError:
pass
def test_packed_selection():
from coffea.analysis_tools import PackedSelection
sel = PackedSelection()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
all_true = np.full(shape=counts.shape, fill_value=True, dtype=np.bool)
all_false = np.full(shape=counts.shape, fill_value=False, dtype=np.bool)
ones = np.ones(shape=counts.shape, dtype=np.uint64)
wrong_shape = ones = np.ones(shape=(counts.shape[0] - 5, ), dtype=np.bool)
sel.add("all_true", all_true)
sel.add("all_false", all_false)
assert np.all(sel.require(all_true=True, all_false=False) == all_true)
assert np.all(sel.all("all_true", "all_false") == all_false)
try:
sel.require(all_true=1, all_false=0)
except ValueError:
pass
try:
sel.add("wrong_shape", wrong_shape)
except ValueError:
pass
try:
sel.add("ones", ones)
except ValueError:
pass
try:
overpack = PackedSelection()
for i in range(65):
overpack.add("sel_%d", all_true)
except RuntimeError:
pass
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_jec_txt_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets([
"testJEC * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi.jec.txt",
"* * tests/samples/Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs.jec.txt.gz",
"* * tests/samples/Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi.junc.txt",
"* * tests/samples/Autumn18_V8_MC_UncertaintySources_AK4PFchs.junc.txt",
"* * tests/samples/Spring16_25nsV10_MC_SF_AK4PFPuppi.jersf.txt",
"* * tests/samples/Autumn18_V7b_MC_SF_AK8PFchs.jersf.txt.gz",
"* * tests/samples/Fall17_17Nov2017_V32_MC_L2Relative_AK4Calo.jec.txt.gz",
"* * tests/samples/Fall17_17Nov2017_V32_MC_L1JPTOffset_AK4JPT.jec.txt.gz",
"* * tests/samples/Fall17_17Nov2017B_V32_DATA_L2Relative_AK4Calo.txt.gz",
"* * tests/samples/Autumn18_V7b_DATA_SF_AK4PF.jersf.txt",
"* * tests/samples/Autumn18_RunC_V19_DATA_L2Relative_AK8PFchs.jec.txt.gz",
"* * tests/samples/Autumn18_RunA_V19_DATA_L2Relative_AK4Calo.jec.txt",
])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
# test structured eval
test_eta_jagged = ak.unflatten(test_eta, counts)
test_pt_jagged = ak.unflatten(test_pt, counts)
jec_out = evaluator[
"testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi"](test_eta,
test_pt)
jec_out_jagged = evaluator[
"testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi"](
test_eta_jagged, test_pt_jagged)
print(evaluator["testJECFall17_17Nov2017_V32_MC_L2Relative_AK4PFPuppi"])
jec_out = evaluator["Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs"](
test_eta, test_pt)
jec_out_jagged = evaluator[
"Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs"](test_eta_jagged,
test_pt_jagged)
print(jec_out)
print(jec_out_jagged)
print(evaluator["Summer16_07Aug2017_V11_L1fix_MC_L2Relative_AK4PFchs"])
jersf_out = evaluator["Spring16_25nsV10_MC_SF_AK4PFPuppi"](test_eta,
test_pt)
jersf_out_jagged = evaluator["Spring16_25nsV10_MC_SF_AK4PFPuppi"](
test_eta_jagged, test_pt_jagged)
print(jersf_out)
print(jersf_out_jagged)
print(evaluator["Spring16_25nsV10_MC_SF_AK4PFPuppi"])
junc_out = evaluator["Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi"](
test_eta, test_pt)
junc_out_jagged = evaluator[
"Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi"](test_eta_jagged,
test_pt_jagged)
print(junc_out)
print(junc_out_jagged)
print(evaluator["Fall17_17Nov2017_V32_MC_Uncertainty_AK4PFPuppi"])
assert ("Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale"
in evaluator.keys())
junc_out = evaluator[
"Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale"](test_eta,
test_pt)
junc_out_jagged = evaluator[
"Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale"](
test_eta_jagged, test_pt_jagged)
print(junc_out)
print(junc_out_jagged)
print(
evaluator["Autumn18_V8_MC_UncertaintySources_AK4PFchs_AbsoluteScale"])