def test_normal_interval():
from coffea.hist.plot import normal_interval
# Reference weighted efficiency and error from ROOTs TEfficiency
denom = np.array([
89.01457591590004,
2177.066076428943,
6122.5256890981855,
0.0,
100.27757990710668,
])
num = np.array([
75.14287743709515,
2177.066076428943,
5193.454723043864,
0.0,
84.97723540536361,
])
denom_sumw2 = np.array([
94.37919737476827, 10000.0, 6463.46795877633, 0.0, 105.90898005417333
])
num_sumw2 = np.array(
[67.2202147680005, 10000.0, 4647.983931785646, 0.0, 76.01275761253757])
ref_hi = np.array([
0.0514643476600107, 0.0, 0.0061403263960343, np.nan, 0.0480731185500146
])
ref_lo = np.array([
0.0514643476600107, 0.0, 0.0061403263960343, np.nan, 0.0480731185500146
])
interval = normal_interval(num, denom, num_sumw2, denom_sumw2)
threshold = 1e-6
lo, hi = interval
assert len(ref_hi) == len(hi)
assert len(ref_lo) == len(lo)
for i in range(len(ref_hi)):
if np.isnan(ref_hi[i]):
assert np.isnan(ref_hi[i])
elif ref_hi[i] == 0.0:
assert hi[i] == 0.0
else:
assert np.abs(hi[i] / ref_hi[i] - 1) < threshold
if np.isnan(ref_lo[i]):
assert np.isnan(ref_lo[i])
elif ref_lo[i] == 0.0:
assert lo[i] == 0.0
else:
assert np.abs(lo[i] / ref_lo[i] - 1) < threshold
def test_hist_compat():
from coffea.util import load
test = load('tests/samples/old_hist_format.coffea')
expected_bins = np.array([ -np.inf, 0., 20., 40., 60., 80., 100., 120., 140.,
160., 180., 200., 220., 240., 260., 280., 300., 320.,
340., 360., 380., 400., 420., 440., 460., 480., 500.,
520., 540., 560., 580., 600., 620., 640., 660., 680.,
700., 720., 740., 760., 780., 800., 820., 840., 860.,
880., 900., 920., 940., 960., 980., 1000., 1020., 1040.,
1060., 1080., 1100., 1120., 1140., 1160., 1180., 1200., np.inf,
np.nan])
assert np.all(test._axes[2]._interval_bins[:-1] == expected_bins[:-1])
assert np.isnan(test._axes[2]._interval_bins[-1])
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 = NanoEvents.from_file(os.path.abspath('tests/samples/nano_dimuon.root'))
data_k = rochester.kScaleDT(events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi)
assert(all(np.isclose(data_k.flatten(), official_data_k)))
data_err = rochester.kScaleDTerror(events.Muon.charge, events.Muon.pt, events.Muon.eta, events.Muon.phi)
data_err = np.array(data_err.flatten(), dtype=float)
assert(all(np.isclose(data_err, official_data_err, atol=1e-8)))
# test against mc
events = NanoEvents.from_file(os.path.abspath('tests/samples/nano_dy.root'))
hasgen = ~np.isnan(events.Muon.matched_gen.pt.fillna(np.nan))
mc_rand = JaggedArray.fromoffsets(hasgen.offsets, mc_rand)
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.ones_like(events.Muon.pt.flatten())
mc_k[hasgen.flatten()] = mc_kspread.flatten()
mc_k[~hasgen.flatten()] = mc_ksmear.flatten()
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.ones_like(events.Muon.pt.flatten())
mc_err[hasgen.flatten()] = mc_errspread.flatten()
mc_err[~hasgen.flatten()] = mc_errsmear.flatten()
assert(all(np.isclose(mc_err, official_mc_err, atol=1e-8)))
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))
