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_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()
