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Paul Romano 2019-10-28 11:55:45 -05:00
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from collections.abc import Iterable
from io import StringIO
from numbers import Real
from warnings import warn
import numpy as np
import openmc.checkvalue as cv
from openmc.mixin import EqualityMixin
from openmc.stats import Univariate, Tabular, Uniform, Legendre
from .function import INTERPOLATION_SCHEME
from .data import EV_PER_MEV
from .endf import get_head_record, get_cont_record, get_tab1_record, \
get_list_record, get_tab2_record
class AngleDistribution(EqualityMixin):
"""Angle distribution as a function of incoming energy
Parameters
----------
energy : Iterable of float
Incoming energies in eV at which distributions exist
mu : Iterable of openmc.stats.Univariate
Distribution of scattering cosines corresponding to each incoming energy
Attributes
----------
energy : Iterable of float
Incoming energies in eV at which distributions exist
mu : Iterable of openmc.stats.Univariate
Distribution of scattering cosines corresponding to each incoming energy
"""
def __init__(self, energy, mu):
super().__init__()
self.energy = energy
self.mu = mu
@property
def energy(self):
return self._energy
@property
def mu(self):
return self._mu
@energy.setter
def energy(self, energy):
cv.check_type('angle distribution incoming energy', energy,
Iterable, Real)
self._energy = energy
@mu.setter
def mu(self, mu):
cv.check_type('angle distribution scattering cosines', mu,
Iterable, Univariate)
self._mu = mu
def to_hdf5(self, group):
"""Write angle distribution to an HDF5 group
Parameters
----------
group : h5py.Group
HDF5 group to write to
"""
dset = group.create_dataset('energy', data=self.energy)
# Make sure all data is tabular
mu_tabular = [mu_i if isinstance(mu_i, Tabular) else
mu_i.to_tabular() for mu_i in self.mu]
# Determine total number of (mu,p) pairs and create array
n_pairs = sum([len(mu_i.x) for mu_i in mu_tabular])
pairs = np.empty((3, n_pairs))
# Create array for offsets
offsets = np.empty(len(mu_tabular), dtype=int)
interpolation = np.empty(len(mu_tabular), dtype=int)
j = 0
# Populate offsets and pairs array
for i, mu_i in enumerate(mu_tabular):
n = len(mu_i.x)
offsets[i] = j
interpolation[i] = 1 if mu_i.interpolation == 'histogram' else 2
pairs[0, j:j+n] = mu_i.x
pairs[1, j:j+n] = mu_i.p
pairs[2, j:j+n] = mu_i.c
j += n
# Create dataset for distributions
dset = group.create_dataset('mu', data=pairs)
# Write interpolation as attribute
dset.attrs['offsets'] = offsets
dset.attrs['interpolation'] = interpolation
@classmethod
def from_hdf5(cls, group):
"""Generate angular distribution from HDF5 data
Parameters
----------
group : h5py.Group
HDF5 group to read from
Returns
-------
openmc.data.AngleDistribution
Angular distribution
"""
energy = group['energy'][()]
data = group['mu']
offsets = data.attrs['offsets']
interpolation = data.attrs['interpolation']
mu = []
n_energy = len(energy)
for i in range(n_energy):
# Determine length of outgoing energy distribution and number of
# discrete lines
j = offsets[i]
if i < n_energy - 1:
n = offsets[i+1] - j
else:
n = data.shape[1] - j
interp = INTERPOLATION_SCHEME[interpolation[i]]
mu_i = Tabular(data[0, j:j+n], data[1, j:j+n], interp)
mu_i.c = data[2, j:j+n]
mu.append(mu_i)
return cls(energy, mu)
@classmethod
def from_ace(cls, ace, location_dist, location_start):
"""Generate an angular distribution from ACE data
Parameters
----------
ace : openmc.data.ace.Table
ACE table to read from
location_dist : int
Index in the XSS array corresponding to the start of a block,
e.g. JXS(9).
location_start : int
Index in the XSS array corresponding to the start of an angle
distribution array
Returns
-------
openmc.data.AngleDistribution
Angular distribution
"""
# Set starting index for angle distribution
idx = location_dist + location_start - 1
# Number of energies at which angular distributions are tabulated
n_energies = int(ace.xss[idx])
idx += 1
# Incoming energy grid
energy = ace.xss[idx:idx + n_energies]*EV_PER_MEV
idx += n_energies
# Read locations for angular distributions
lc = ace.xss[idx:idx + n_energies].astype(int)
idx += n_energies
mu = []
for i in range(n_energies):
if lc[i] > 0:
# Equiprobable 32 bin distribution
idx = location_dist + abs(lc[i]) - 1
cos = ace.xss[idx:idx + 33]
pdf = np.zeros(33)
pdf[:32] = 1.0/(32.0*np.diff(cos))
cdf = np.linspace(0.0, 1.0, 33)
mu_i = Tabular(cos, pdf, 'histogram', ignore_negative=True)
mu_i.c = cdf
elif lc[i] < 0:
# Tabular angular distribution
idx = location_dist + abs(lc[i]) - 1
intt = int(ace.xss[idx])
n_points = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 3*n_points]
data.shape = (3, n_points)
mu_i = Tabular(data[0], data[1], INTERPOLATION_SCHEME[intt])
mu_i.c = data[2]
else:
# Isotropic angular distribution
mu_i = Uniform(-1., 1.)
mu.append(mu_i)
return cls(energy, mu)
@classmethod
def from_endf(cls, ev, mt):
"""Generate an angular distribution from an ENDF evaluation
Parameters
----------
ev : openmc.data.endf.Evaluation
ENDF evaluation
mt : int
The MT value of the reaction to get angular distributions for
Returns
-------
openmc.data.AngleDistribution
Angular distribution
"""
file_obj = StringIO(ev.section[4, mt])
# Read HEAD record
items = get_head_record(file_obj)
lvt = items[2]
ltt = items[3]
# Read CONT record
items = get_cont_record(file_obj)
li = items[2]
nk = items[4]
center_of_mass = (items[3] == 2)
# Check for obsolete energy transformation matrix. If present, just skip
# it and keep reading
if lvt > 0:
warn('Obsolete energy transformation matrix in MF=4 angular '
'distribution.')
for _ in range((nk + 5)//6):
file_obj.readline()
if ltt == 0 and li == 1:
# Purely isotropic
energy = np.array([0., ev.info['energy_max']])
mu = [Uniform(-1., 1.), Uniform(-1., 1.)]
elif ltt == 1 and li == 0:
# Legendre polynomial coefficients
params, tab2 = get_tab2_record(file_obj)
n_energy = params[5]
energy = np.zeros(n_energy)
mu = []
for i in range(n_energy):
items, al = get_list_record(file_obj)
temperature = items[0]
energy[i] = items[1]
coefficients = np.asarray([1.0] + al)
mu.append(Legendre(coefficients))
elif ltt == 2 and li == 0:
# Tabulated probability distribution
params, tab2 = get_tab2_record(file_obj)
n_energy = params[5]
energy = np.zeros(n_energy)
mu = []
for i in range(n_energy):
params, f = get_tab1_record(file_obj)
temperature = params[0]
energy[i] = params[1]
if f.n_regions > 1:
raise NotImplementedError('Angular distribution with multiple '
'interpolation regions not supported.')
mu.append(Tabular(f.x, f.y, INTERPOLATION_SCHEME[f.interpolation[0]]))
elif ltt == 3 and li == 0:
# Legendre for low energies / tabulated for high energies
params, tab2 = get_tab2_record(file_obj)
n_energy_legendre = params[5]
energy_legendre = np.zeros(n_energy_legendre)
mu = []
for i in range(n_energy_legendre):
items, al = get_list_record(file_obj)
temperature = items[0]
energy_legendre[i] = items[1]
coefficients = np.asarray([1.0] + al)
mu.append(Legendre(coefficients))
params, tab2 = get_tab2_record(file_obj)
n_energy_tabulated = params[5]
energy_tabulated = np.zeros(n_energy_tabulated)
for i in range(n_energy_tabulated):
params, f = get_tab1_record(file_obj)
temperature = params[0]
energy_tabulated[i] = params[1]
if f.n_regions > 1:
raise NotImplementedError('Angular distribution with multiple '
'interpolation regions not supported.')
mu.append(Tabular(f.x, f.y, INTERPOLATION_SCHEME[f.interpolation[0]]))
energy = np.concatenate((energy_legendre, energy_tabulated))
return AngleDistribution(energy, mu)