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Don't call normalize inside Tabular.mean

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This commit is contained in:
Paul Romano 2023-02-03 08:32:05 -06:00
parent 5c672f5ea8
commit dbbad687eb
2 changed files with 14 additions and 8 deletions
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11
openmc/stats/univariate.py
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@ -857,7 +857,6 @@ class Tabular(Univariate):
'or linear-linear interpolation.')
if self.interpolation == 'linear-linear':
mean = 0.0
self.normalize()
for i in range(1, len(self.x)):
y_min = self.p[i-1]
y_max = self.p[i]
@ -872,9 +871,13 @@ class Tabular(Univariate):
mean += exp_val
elif self.interpolation == 'histogram':
mean = 0.5 * (self.x[:-1] + self.x[1:])
mean *= np.diff(self.cdf())
mean = sum(mean)
x_l = self.x[:-1]
x_r = self.x[1:]
p_l = self.p[:-1]
mean = (0.5 * (x_l + x_r) * (x_r - x_l) * p_l).sum()
# Normalize for when integral of distribution is not 1
mean /= self.integral()
return mean

11
tests/unit_tests/test_stats.py
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@ -166,7 +166,7 @@ def test_watt():
def test_tabular():
x = np.array([0.0, 5.0, 7.0])
p = np.array([0.1, 0.2, 0.05])
p = np.array([10.0, 20.0, 5.0])
d = openmc.stats.Tabular(x, p, 'linear-linear')
elem = d.to_xml_element('distribution')
@ -178,19 +178,22 @@ def test_tabular():
# test linear-linear sampling
d = openmc.stats.Tabular(x, p)
n_samples = 100_000
samples = d.sample(n_samples)
assert_sample_mean(samples, d.mean())
# test histogram sampling
d = openmc.stats.Tabular(x, p, interpolation='histogram')
# test linear-linear normalization
d.normalize()
assert d.integral() == pytest.approx(1.0)
# test histogram sampling
d = openmc.stats.Tabular(x, p, interpolation='histogram')
samples = d.sample(n_samples)
assert_sample_mean(samples, d.mean())
d.normalize()
assert d.integral() == pytest.approx(1.0)
def test_legendre():
# Pu239 elastic scattering at 100 keV