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Use integral() methods instead of _intensity hidden attribute

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This commit is contained in:
Paul Romano 2022-05-24 12:48:06 -05:00
parent 6388c8f994
commit d4989ae642
1 changed files with 8 additions and 11 deletions
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19
openmc/data/decay.py
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@ -511,6 +511,7 @@ class Decay(EqualityMixin):
"""
sources = {}
name = self.nuclide['name']
decay_constant = self.decay_constant.n
for particle, spectra in self.spectra.items():
# Set particle type based on 'particle' above
particle_type = {
@ -538,10 +539,9 @@ class Decay(EqualityMixin):
energies.append(discrete_data['energy'].n)
intensities.append(discrete_data['intensity'].n)
energies = np.array(energies)
intensities = np.array(intensities)
intensities *= spectra['discrete_normalization'].n
dist_discrete = Discrete(energies, intensities) # <-- not normalized yet
dist_discrete._intensity = intensities.sum()
intensity = spectra['discrete_normalization'].n
rates = decay_constant * intensity * np.array(intensities)
dist_discrete = Discrete(energies, rates)
sources[particle_type].append(dist_discrete)
# Create distribution for continuous
@ -553,9 +553,9 @@ class Decay(EqualityMixin):
if interpolation not in ('histogram', 'linear-linear'):
raise NotImplementedError("Continuous spectra with {interpolation} interpolation ({name}, {particle}) not supported")
# TODO: work normalization into tabular itself
dist_continuous = Tabular(f.x, f.y, interpolation)
dist_continuous._intensity = spectra['continuous_normalization'].n
intensity = spectra['continuous_normalization'].n
rates = decay_constant * intensity * f.y
dist_continuous = Tabular(f.x, rates, interpolation)
sources[particle_type].append(dist_continuous)
# Combine discrete distributions
@ -595,14 +595,12 @@ def combine_distributions(dists, probs):
for i in cont_index:
dist = dist_list[i]
dist.p *= probs[i]
dist._intensity *= probs[i]
if discrete_index:
# Create combined discrete distribution
dist_discrete = [dist_list[i] for i in discrete_index]
discrete_probs = [probs[i] for i in discrete_index]
combined_dist = Discrete.merge(dist_discrete, discrete_probs)
combined_dist._intensity = np.sum(combined_dist.p)
# Replace multiple discrete distributions with merged
for idx in reversed(discrete_index):
@ -611,8 +609,7 @@ def combine_distributions(dists, probs):
# Combine discrete and continuous if present
if len(dist_list) > 1:
probs = [d._intensity for d in dist_list]
probs = [d.integral() for d in dist_list]
dist_list[:] = [Mixture(probs, dist_list.copy())]
dist_list[0]._intensity = sum(probs)
return dist_list[0]