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test_lookup_tools.py
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test_lookup_tools.py
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from __future__ import print_function, division
import os
from coffea import lookup_tools
import awkward as ak
import pytest
from coffea.util import numpy as np
from coffea.nanoevents import NanoEventsFactory
from dummy_distributions import dummy_jagged_eta_pt
# From make_expected_lookup.py
_testSF2d_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,
]
)
def test_extractor_exceptions():
extractor = lookup_tools.extractor()
# test comments
extractor.add_weight_sets(["#testSF2d asdfgh tests/samples/testSF2d.histo.root"])
# test malformed statement
try:
extractor.add_weight_sets(
["testSF2d testSF2d asdfgh tests/samples/testSF2d.histo.root"]
)
except Exception as e:
assert (
e.args[0]
== '"testSF2d testSF2d asdfgh tests/samples/testSF2d.histo.root" not formatted as "<local name> <name> <weights file>"'
)
# test not existant file entry
try:
extractor.add_weight_sets(["testSF2d asdfgh tests/samples/testSF2d.histo.root"])
except Exception as e:
assert (
e.args[0]
== 'Weights named "asdfgh" not in tests/samples/testSF2d.histo.root!'
)
# test unfinalized evaluator
try:
extractor.add_weight_sets(
["testSF2d scalefactors_Tight_Electron tests/samples/testSF2d.histo.root"]
)
extractor.make_evaluator()
except Exception as e:
assert e.args[0] == "Cannot make an evaluator from unfinalized extractor!"
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_evaluate_noimpl():
from coffea.lookup_tools.lookup_base import lookup_base
try:
lookup_base()._evaluate()
except NotImplementedError:
pass
def test_correctionlib():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(["* * tests/samples/testSF2d.corr.json.gz"])
extractor.finalize()
evaluator = extractor.make_evaluator()
counts, test_eta, test_pt = dummy_jagged_eta_pt()
test_out = evaluator["scalefactors_Tight_Electron"](test_eta, test_pt)
# print it
print(evaluator["scalefactors_Tight_Electron"])
# test structured eval
test_eta_jagged = ak.unflatten(test_eta, counts)
test_pt_jagged = ak.unflatten(test_pt, counts)
test_out_jagged = evaluator["scalefactors_Tight_Electron"](
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_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_btag_csv_scalefactors():
extractor = lookup_tools.extractor()
extractor.add_weight_sets(
[
"testBTag * tests/samples/testBTagSF.btag.csv",
"* * tests/samples/DeepCSV_102XSF_V1.btag.csv.gz",
"* * tests/samples/DeepJet_102XSF_WP_V1.btag.csv.gz",
]
)
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)
print(evaluator["testBTagCSVv2_1_comb_up_0"])
print(evaluator["DeepCSV_1_comb_up_0"])
print(evaluator["btagsf_1_comb_up_0"])
sf_out = evaluator["testBTagCSVv2_1_comb_up_0"](test_eta, test_pt, test_discr)
print(sf_out)
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_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",
"* * tests/samples/Winter14_V8_MC_L5Flavor_AK5Calo.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)
# single jet jersf lookup test:
single_jersf_out_1d = evaluator["Spring16_25nsV10_MC_SF_AK4PFPuppi"](
np.array([1.4]), np.array([44.0])
)
single_jersf_out_0d = evaluator["Spring16_25nsV10_MC_SF_AK4PFPuppi"](
np.array(1.4), np.array(44.0)
)
truth_out = np.array([[1.084, 1.095, 1.073]], dtype=np.float32)
assert np.all(single_jersf_out_1d == truth_out)
assert np.all(single_jersf_out_0d == truth_out)
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"])
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(ch_out)
print(evaluator["photon_id_EA_CHad"])
nh_out = evaluator["photon_id_EA_NHad"](test_eta)
print(nh_out)
print(evaluator["photon_id_EA_NHad"])
ph_out = evaluator["photon_id_EA_Pho"](test_eta)
print(ph_out)
print(evaluator["photon_id_EA_Pho"])
def test_rochester():
rochester_data = lookup_tools.txt_converters.convert_rochester_file(
"tests/samples/RoccoR2018.txt.gz", loaduncs=True
)
rochester = lookup_tools.rochester_lookup.rochester_lookup(rochester_data)
# to test 1-to-1 agreement with official Rochester requires loading C++ files
# instead, preload the correct scales in the sample directory
# the script tests/samples/rochester/build_rochester.py produces these
official_data_k = np.load("tests/samples/nano_dimuon_rochester.npy")
official_data_err = np.load("tests/samples/nano_dimuon_rochester_err.npy")
official_mc_k = np.load("tests/samples/nano_dy_rochester.npy")
official_mc_err = np.load("tests/samples/nano_dy_rochester_err.npy")
mc_rand = np.load("tests/samples/nano_dy_rochester_rand.npy")
# test against nanoaod
events = NanoEventsFactory.from_root(
os.path.abspath("tests/samples/nano_dimuon.root")
).events()
data_k = rochester.kScaleDT(
events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi
)
data_k = np.array(ak.flatten(data_k))
assert all(np.isclose(data_k, official_data_k))
data_err = rochester.kScaleDTerror(
events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi
)
data_err = np.array(ak.flatten(data_err), dtype=float)
assert all(np.isclose(data_err, official_data_err, atol=1e-8))
# test against mc
events = NanoEventsFactory.from_root(
os.path.abspath("tests/samples/nano_dy.root")
).events()
hasgen = ~np.isnan(ak.fill_none(events.Muon.matched_gen.pt, np.nan))
mc_rand = ak.unflatten(mc_rand, ak.num(hasgen))
mc_kspread = rochester.kSpreadMC(
events.Muon.charge[hasgen],
events.Muon.pt[hasgen],
events.Muon.eta[hasgen],
events.Muon.phi[hasgen],
events.Muon.matched_gen.pt[hasgen],
)
mc_ksmear = rochester.kSmearMC(
events.Muon.charge[~hasgen],
events.Muon.pt[~hasgen],
events.Muon.eta[~hasgen],
events.Muon.phi[~hasgen],
events.Muon.nTrackerLayers[~hasgen],
mc_rand[~hasgen],
)
mc_k = np.array(ak.flatten(ak.ones_like(events.Muon.pt)))
hasgen_flat = np.array(ak.flatten(hasgen))
mc_k[hasgen_flat] = np.array(ak.flatten(mc_kspread))
mc_k[~hasgen_flat] = np.array(ak.flatten(mc_ksmear))
assert all(np.isclose(mc_k, official_mc_k))
mc_errspread = rochester.kSpreadMCerror(
events.Muon.charge[hasgen],
events.Muon.pt[hasgen],
events.Muon.eta[hasgen],
events.Muon.phi[hasgen],
events.Muon.matched_gen.pt[hasgen],
)
mc_errsmear = rochester.kSmearMCerror(
events.Muon.charge[~hasgen],
events.Muon.pt[~hasgen],
events.Muon.eta[~hasgen],
events.Muon.phi[~hasgen],
events.Muon.nTrackerLayers[~hasgen],
mc_rand[~hasgen],
)
mc_err = np.array(ak.flatten(ak.ones_like(events.Muon.pt)))
mc_err[hasgen_flat] = np.array(ak.flatten(mc_errspread))
mc_err[~hasgen_flat] = np.array(ak.flatten(mc_errsmear))
assert all(np.isclose(mc_err, official_mc_err, atol=1e-8))
def test_dense_lookup():
from coffea.lookup_tools.dense_lookup import dense_lookup
import numpy
a = ak.Array([[0.1, 0.2], [0.3]])
lookup = dense_lookup(
numpy.ones(shape=(3, 4)), (numpy.linspace(0, 1, 4), numpy.linspace(0, 1, 5))
)
with pytest.raises(ValueError):
lookup(ak.Array([]))
assert ak.to_list(lookup(ak.Array([]), ak.Array([]))) == []
assert lookup(0.1, 0.3) == 1.0
assert numpy.all(lookup(0.1, numpy.array([0.3, 0.5])) == numpy.array([1.0, 1.0]))
assert ak.to_list(lookup(a, a)) == [[1.0, 1.0], [1.0]]
def test_549():
import awkward as ak
from coffea.lookup_tools import extractor
ext = extractor()
f_in = "tests/samples/SFttbar_2016_ele_pt.root"
ext.add_weight_sets(["ele_pt histo_eff_data %s" % f_in])
ext.finalize()
evaluator = ext.make_evaluator()
evaluator["ele_pt"](
ak.Array([[45]]),
)
def test_554():
import uproot
from coffea.lookup_tools.root_converters import convert_histo_root_file
f_in = "tests/samples/PR554_SkipReadOnlyDirectory.root"
rf = uproot.open(f_in)
# check that input file contains uproot.ReadOnlyDirectory
assert any(isinstance(v, uproot.ReadOnlyDirectory) for v in rf.values())
# check that we can do the conversion now and get histograms out of uproot.ReadOnlyDirectories
out = convert_histo_root_file(f_in)
assert out
# check that output does not contain any Directory-like keys
rfkeys = set(k.rsplit(";")[0] for k in rf.keys())
assert all(
not isinstance(rf[k], uproot.ReadOnlyDirectory)
for k, _ in out.keys()
if k in rfkeys
)