def test_set_values_from_intervals(self):
'''Test behavior of Uncertainy.test_set_values_from_intervals function'''
# Dummy central values and variatons relative to central value
npoints = 100
values = list(range(0, npoints, 1))
uncertainty = [(-random.uniform(0, 1), random.uniform(0, 1)) for _ in range(100)]
# Convert +- error to interval bounds
intervals = [(val + unc_minus, val + unc_plus)
for val, (unc_minus, unc_plus) in zip(values, uncertainty)]
# Reference uses "normal" assignment
refunc = Uncertainty("reference_unc")
refunc.is_symmetric = False
refunc.values = uncertainty
# Test uses new function
testunc = Uncertainty("test_unc")
testunc.is_symmetric = False
testunc.set_values_from_intervals(intervals, nominal=values)
# Check that both agree
self.assertTrue(all((test_utilities.tuple_compare(tup1, tup2) \
for tup1, tup2 in zip(testunc.values, refunc.values))))
def addLimitPlot(submission, config):
table = Table(config["name"])
table.description = config["description"]
table.location = config["location"]
table.keywords["observables"] = ["SIG"]
table.keywords["reactions"] = ["P P --> TOP --> tt + 6j"]
table.add_image(config["image"])
reader = RootFileReader(config["inputData"])
data = reader.read_limit_tree()
stop_pair_Br = np.array([
10.00, 4.43, 2.15, 1.11, 0.609, 0.347, 0.205, 0.125, 0.0783, 0.0500,
0.0326, 0.0216, 0.0145, 0.00991, 0.00683, 0.00476, 0.00335, 0.00238,
0.00170, 0.00122, 0.000887, 0.000646, 0.000473
])
stop_pair_Br1SPpercent = np.array([
6.65, 6.79, 6.99, 7.25, 7.530, 7.810, 8.120, 8.450, 8.8000, 9.1600,
9.5300, 9.9300, 10.3300, 10.76, 11.2, 11.65, 12.12, 12.62, 13.13,
13.66, 14.21, 14.78, 15.37
])
stop_pair_unc = stop_pair_Br * stop_pair_Br1SPpercent / 100.0
stop_pair_up = stop_pair_Br + stop_pair_unc
stop_pair_down = stop_pair_Br - stop_pair_unc
nData = len(data)
for mass_id in range(0, nData):
data[mass_id][1:] = stop_pair_Br[mass_id] * data[mass_id][1:]
#####################################################################################
d = Variable("Top squark mass",
is_independent=True,
is_binned=False,
units="GeV")
d.values = data[:, 0]
sig = Variable("Top squark cross section",
is_independent=False,
is_binned=False,
units="pb")
sig.values = np.array(stop_pair_Br[:nData])
sig.add_qualifier("Limit", "")
sig.add_qualifier("SQRT(S)", 13, "TeV")
sig.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$")
obs = Variable("Observed cross section upper limit at 95% CL",
is_independent=False,
is_binned=False,
units="pb")
obs.values = data[:, 6]
obs.add_qualifier("Limit", "Observed")
obs.add_qualifier("SQRT(S)", 13, "TeV")
obs.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$")
exp = Variable("Expected cross section upper limit at 95% CL",
is_independent=False,
is_binned=False,
units="pb")
exp.values = data[:, 3]
exp.add_qualifier("Limit", "Expected")
exp.add_qualifier("SQRT(S)", 13, "TeV")
exp.add_qualifier("LUMINOSITY", 137, "fb$^{-1}$")
unc_sig = Uncertainty("1 s.d.", is_symmetric=False)
unc_sig.set_values_from_intervals(zip(stop_pair_up[:nData],
stop_pair_down[:nData]),
nominal=sig.values)
sig.add_uncertainty(unc_sig)
# +/- 1 sigma
unc_1s = Uncertainty("1 s.d.", is_symmetric=False)
unc_1s.set_values_from_intervals(zip(data[:, 2], data[:, 4]),
nominal=exp.values)
exp.add_uncertainty(unc_1s)
# +/- 2 sigma
unc_2s = Uncertainty("2 s.d.", is_symmetric=False)
unc_2s.set_values_from_intervals(zip(data[:, 1], data[:, 5]),
nominal=exp.values)
exp.add_uncertainty(unc_2s)
table.add_variable(d)
table.add_variable(sig)
table.add_variable(obs)
table.add_variable(exp)
submission.add_table(table)
