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Change exception in decay source processing to warning

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This commit is contained in:
Paul Romano 2023-03-03 11:02:48 -06:00
parent b44225b3d5
commit c20f115cb9
2 changed files with 16 additions and 14 deletions
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4
openmc/data/decay.py
View file

@ -558,9 +558,9 @@ class Decay(EqualityMixin):
raise NotImplementedError("Multiple interpolation regions: {name}, {particle}")
interpolation = INTERPOLATION_SCHEME[f.interpolation[0]]
if interpolation not in ('histogram', 'linear-linear'):
raise NotImplementedError(
warn(
f"Continuous spectra with {interpolation} interpolation "
f"({name}, {particle}) not supported")
f"({name}, {particle}) encountered.")
intensity = spectra['continuous_normalization'].n
rates = decay_constant * intensity * f.y

26
openmc/stats/univariate.py
View file

@ -834,10 +834,6 @@ class Tabular(Univariate):
self._interpolation = interpolation
def cdf(self):
if not self.interpolation in ('histogram', 'linear-linear'):
raise NotImplementedError('Can only generate CDFs for tabular '
'distributions using histogram or '
'linear-linear interpolation')
c = np.zeros_like(self.x)
x = self.x
p = self.p
@ -846,15 +842,16 @@ class Tabular(Univariate):
c[1:] = p[:-1] * np.diff(x)
elif self.interpolation == 'linear-linear':
c[1:] = 0.5 * (p[:-1] + p[1:]) * np.diff(x)
else:
raise NotImplementedError('Can only generate CDFs for tabular '
'distributions using histogram or '
'linear-linear interpolation')
return np.cumsum(c)
def mean(self):
"""Compute the mean of the tabular distribution"""
if not self.interpolation in ('histogram', 'linear-linear'):
raise NotImplementedError('Can only compute mean for tabular '
'distributions using histogram '
'or linear-linear interpolation.')
if self.interpolation == 'linear-linear':
mean = 0.0
for i in range(1, len(self.x)):
@ -875,6 +872,10 @@ class Tabular(Univariate):
x_r = self.x[1:]
p_l = self.p[:-1]
mean = (0.5 * (x_l + x_r) * (x_r - x_l) * p_l).sum()
else:
raise NotImplementedError('Can only compute mean for tabular '
'distributions using histogram '
'or linear-linear interpolation.')
# Normalize for when integral of distribution is not 1
mean /= self.integral()
@ -886,10 +887,6 @@ class Tabular(Univariate):
self.p /= self.cdf().max()
def sample(self, n_samples=1, seed=None):
if not self.interpolation in ('histogram', 'linear-linear'):
raise NotImplementedError('Can only sample tabular distributions '
'using histogram or '
'linear-linear interpolation')
np.random.seed(seed)
xi = np.random.rand(n_samples)
@ -942,6 +939,11 @@ class Tabular(Univariate):
m[non_zero] = x_i[non_zero] + (np.sqrt(quad) - p_i[non_zero]) / m[non_zero]
samples_out = m
else:
raise NotImplementedError('Can only sample tabular distributions '
'using histogram or '
'linear-linear interpolation')
assert all(samples_out < self.x[-1])
return samples_out