def make_fit(labels):
pileup_bin = labels["pileup"]
threshold_bin = labels["threshold"]
efficiency = self.efficiencies.get_bin_contents(
[pileup_bin, threshold_bin])
params = fit_efficiency(
efficiency, self.thresholds.get_bin_center(threshold_bin))
return params
def test_fit_efficiency_symmetric():
in_mean = 35.
in_sigma = 10.
fake_efficiency = FakeEff(in_mean, in_sigma)
params = fit.fit_efficiency(fake_efficiency, 30, 10, False)
mu = params["mu"][0]
sigma = params["sigma"][0]
mu_ratio = mu / in_mean if mu > in_mean else in_mean / mu
sigma_ratio = sigma / in_sigma if sigma > in_sigma else in_sigma / sigma
assert 1.1 > mu_ratio
assert 1.5 > sigma_ratio
assert params["success"]
def test_fit_efficiency_asymmetric():
in_mean = 35.
in_sigma = 10.
fake_efficiency = FakeEff(in_mean, in_sigma)
params = fit.fit_efficiency(fake_efficiency, 30, 10, True)
mu = params["mu"][0]
sigma = params["sigma"][0]
lamda = params["lambda"][0]
mu_ratio = mu / in_mean if mu > in_mean else in_mean / mu
sigma_ratio = sigma / in_sigma if sigma > in_sigma else in_sigma / sigma
assert_greater(1.1, mu_ratio)
assert_greater(1.5, sigma_ratio)
assert_greater(0.5, abs(lamda))
assert_true(params["success"])
