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)
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'].value
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)
def from_ace(cls, ace, idx, ldis):
"""Generate Kalbach-Mann energy-angle distribution from ACE data
Parameters
----------
ace : openmc.data.ace.Table
ACE table to read from
idx : int
Index in XSS array of the start of the energy distribution data
(LDIS + LOCC - 1)
ldis : int
Index in XSS array of the start of the energy distribution block
(e.g. JXS[11])
Returns
-------
openmc.data.KalbachMann
Kalbach-Mann energy-angle distribution
"""
# Read number of interpolation regions and incoming energies
n_regions = int(ace.xss[idx])
n_energy_in = int(ace.xss[idx + 1 + 2*n_regions])
# Get interpolation information
idx += 1
if n_regions > 0:
breakpoints = ace.xss[idx:idx + n_regions].astype(int)
interpolation = ace.xss[idx + n_regions:idx + 2*n_regions].astype(int)
else:
breakpoints = np.array([n_energy_in])
interpolation = np.array([2])
# Incoming energies at which distributions exist
idx += 2*n_regions + 1
energy = ace.xss[idx:idx + n_energy_in]*EV_PER_MEV
# Location of distributions
idx += n_energy_in
loc_dist = ace.xss[idx:idx + n_energy_in].astype(int)
# Initialize variables
energy_out = []
km_r = []
km_a = []
# Read each outgoing energy distribution
for i in range(n_energy_in):
idx = ldis + loc_dist[i] - 1
# intt = interpolation scheme (1=hist, 2=lin-lin)
INTTp = int(ace.xss[idx])
intt = INTTp % 10
n_discrete_lines = (INTTp - intt)//10
if intt not in (1, 2):
warn("Interpolation scheme for continuous tabular distribution "
"is not histogram or linear-linear.")
intt = 2
n_energy_out = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 5*n_energy_out].copy()
data.shape = (5, n_energy_out)
data[0,:] *= EV_PER_MEV
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:]/EV_PER_MEV,
INTERPOLATION_SCHEME[intt],
ignore_negative=True)
eout_continuous.c = data[2][n_discrete_lines:]
if np.any(data[1][n_discrete_lines:] < 0.0):
warn("Kalbach-Mann energy distribution has negative "
"probabilities.")
# If discrete lines are present, create a mixture distribution
if n_discrete_lines > 0:
eout_discrete = Discrete(data[0][:n_discrete_lines],
data[1][:n_discrete_lines])
eout_discrete.c = data[2][:n_discrete_lines]
if n_discrete_lines == n_energy_out:
eout_i = eout_discrete
else:
p_discrete = min(sum(eout_discrete.p), 1.0)
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
else:
eout_i = eout_continuous
energy_out.append(eout_i)
km_r.append(Tabulated1D(data[0], data[3]))
km_a.append(Tabulated1D(data[0], data[4]))
return cls(breakpoints, interpolation, energy, energy_out, km_r, km_a)
def from_hdf5(cls, group):
"""Generate Kalbach-Mann distribution from HDF5 data
Parameters
----------
group : h5py.Group
HDF5 group to read from
Returns
-------
openmc.data.KalbachMann
Kalbach-Mann energy distribution
"""
interp_data = group['energy'].attrs['interpolation']
energy_breakpoints = interp_data[0, :]
energy_interpolation = interp_data[1, :]
energy = group['energy'][()]
data = group['distribution']
offsets = data.attrs['offsets']
interpolation = data.attrs['interpolation']
n_discrete_lines = data.attrs['n_discrete_lines']
energy_out = []
precompound = []
slope = []
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
m = n_discrete_lines[i]
# Create discrete distribution if lines are present
if m > 0:
eout_discrete = Discrete(data[0, j:j+m], data[1, j:j+m])
eout_discrete.c = data[2, j:j+m]
p_discrete = eout_discrete.c[-1]
# Create continuous distribution
if m < n:
interp = INTERPOLATION_SCHEME[interpolation[i]]
eout_continuous = Tabular(data[0, j+m:j+n], data[1, j+m:j+n], interp)
eout_continuous.c = data[2, j+m:j+n]
# If both continuous and discrete are present, create a mixture
# distribution
if m == 0:
eout_i = eout_continuous
elif m == n:
eout_i = eout_discrete
else:
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
# Precompound factor and slope are on rows 3 and 4, respectively
km_r = Tabulated1D(data[0, j:j+n], data[3, j:j+n])
km_a = Tabulated1D(data[0, j:j+n], data[4, j:j+n])
energy_out.append(eout_i)
precompound.append(km_r)
slope.append(km_a)
return cls(energy_breakpoints, energy_interpolation,
energy, energy_out, precompound, slope)
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)
def from_ace(cls, ace, idx, ldis):
"""Generate correlated angle-energy distribution from ACE data
Parameters
----------
ace : openmc.data.ace.Table
ACE table to read from
idx : int
Index in XSS array of the start of the energy distribution data
(LDIS + LOCC - 1)
ldis : int
Index in XSS array of the start of the energy distribution block
(e.g. JXS[11])
Returns
-------
openmc.data.CorrelatedAngleEnergy
Correlated angle-energy distribution
"""
# Read number of interpolation regions and incoming energies
n_regions = int(ace.xss[idx])
n_energy_in = int(ace.xss[idx + 1 + 2*n_regions])
# Get interpolation information
idx += 1
if n_regions > 0:
breakpoints = ace.xss[idx:idx + n_regions].astype(int)
interpolation = ace.xss[idx + n_regions:idx + 2*n_regions].astype(int)
else:
breakpoints = np.array([n_energy_in])
interpolation = np.array([2])
# Incoming energies at which distributions exist
idx += 2*n_regions + 1
energy = ace.xss[idx:idx + n_energy_in]*EV_PER_MEV
# Location of distributions
idx += n_energy_in
loc_dist = ace.xss[idx:idx + n_energy_in].astype(int)
# Initialize list of distributions
energy_out = []
mu = []
# Read each outgoing energy distribution
for i in range(n_energy_in):
idx = ldis + loc_dist[i] - 1
# intt = interpolation scheme (1=hist, 2=lin-lin)
INTTp = int(ace.xss[idx])
intt = INTTp % 10
n_discrete_lines = (INTTp - intt)//10
if intt not in (1, 2):
warn("Interpolation scheme for continuous tabular distribution "
"is not histogram or linear-linear.")
intt = 2
# Secondary energy distribution
n_energy_out = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 4*n_energy_out].copy()
data.shape = (4, n_energy_out)
data[0,:] *= EV_PER_MEV
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:]/EV_PER_MEV,
INTERPOLATION_SCHEME[intt],
ignore_negative=True)
eout_continuous.c = data[2][n_discrete_lines:]
if np.any(data[1][n_discrete_lines:] < 0.0):
warn("Correlated angle-energy distribution has negative "
"probabilities.")
# If discrete lines are present, create a mixture distribution
if n_discrete_lines > 0:
eout_discrete = Discrete(data[0][:n_discrete_lines],
data[1][:n_discrete_lines])
eout_discrete.c = data[2][:n_discrete_lines]
if n_discrete_lines == n_energy_out:
eout_i = eout_discrete
else:
p_discrete = min(sum(eout_discrete.p), 1.0)
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
else:
eout_i = eout_continuous
energy_out.append(eout_i)
lc = data[3].astype(int)
# Secondary angular distributions
mu_i = []
for j in range(n_energy_out):
if lc[j] > 0:
idx = ldis + abs(lc[j]) - 1
intt = int(ace.xss[idx])
n_cosine = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 3*n_cosine]
data.shape = (3, n_cosine)
mu_ij = Tabular(data[0], data[1], INTERPOLATION_SCHEME[intt])
mu_ij.c = data[2]
else:
# Isotropic distribution
mu_ij = Uniform(-1., 1.)
mu_i.append(mu_ij)
# Add cosine distributions for this incoming energy to list
mu.append(mu_i)
return cls(breakpoints, interpolation, energy, energy_out, mu)
def from_hdf5(cls, group):
"""Generate correlated angle-energy distribution from HDF5 data
Parameters
----------
group : h5py.Group
HDF5 group to read from
Returns
-------
openmc.data.CorrelatedAngleEnergy
Correlated angle-energy distribution
"""
interp_data = group['energy'].attrs['interpolation']
energy_breakpoints = interp_data[0, :]
energy_interpolation = interp_data[1, :]
energy = group['energy'].value
offsets = group['energy_out'].attrs['offsets']
interpolation = group['energy_out'].attrs['interpolation']
n_discrete_lines = group['energy_out'].attrs['n_discrete_lines']
dset_eout = group['energy_out'].value
energy_out = []
dset_mu = group['mu'].value
mu = []
n_energy = len(energy)
for i in range(n_energy):
# Determine length of outgoing energy distribution and number of
# discrete lines
offset_e = offsets[i]
if i < n_energy - 1:
n = offsets[i+1] - offset_e
else:
n = dset_eout.shape[1] - offset_e
m = n_discrete_lines[i]
# Create discrete distribution if lines are present
if m > 0:
x = dset_eout[0, offset_e:offset_e+m]
p = dset_eout[1, offset_e:offset_e+m]
eout_discrete = Discrete(x, p)
eout_discrete.c = dset_eout[2, offset_e:offset_e+m]
p_discrete = eout_discrete.c[-1]
# Create continuous distribution
if m < n:
interp = INTERPOLATION_SCHEME[interpolation[i]]
x = dset_eout[0, offset_e+m:offset_e+n]
p = dset_eout[1, offset_e+m:offset_e+n]
eout_continuous = Tabular(x, p, interp, ignore_negative=True)
eout_continuous.c = dset_eout[2, offset_e+m:offset_e+n]
# If both continuous and discrete are present, create a mixture
# distribution
if m == 0:
eout_i = eout_continuous
elif m == n:
eout_i = eout_discrete
else:
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
# Read angular distributions
mu_i = []
for j in range(n):
# Determine interpolation scheme
interp_code = int(dset_eout[3, offsets[i] + j])
# Determine offset and length
offset_mu = int(dset_eout[4, offsets[i] + j])
if offsets[i] + j < dset_eout.shape[1] - 1:
n_mu = int(dset_eout[4, offsets[i] + j + 1]) - offset_mu
else:
n_mu = dset_mu.shape[1] - offset_mu
# Get data
x = dset_mu[0, offset_mu:offset_mu+n_mu]
p = dset_mu[1, offset_mu:offset_mu+n_mu]
c = dset_mu[2, offset_mu:offset_mu+n_mu]
if interp_code == 0:
mu_ij = Discrete(x, p)
else:
mu_ij = Tabular(x, p, INTERPOLATION_SCHEME[interp_code],
ignore_negative=True)
mu_ij.c = c
mu_i.append(mu_ij)
offset_mu += n_mu
energy_out.append(eout_i)
mu.append(mu_i)
return cls(energy_breakpoints, energy_interpolation,
energy, energy_out, mu)
def from_ace(cls, ace, idx, ldis):
"""Generate correlated angle-energy distribution from ACE data
Parameters
----------
ace : openmc.data.ace.Table
ACE table to read from
idx : int
Index in XSS array of the start of the energy distribution data
(LDIS + LOCC - 1)
ldis : int
Index in XSS array of the start of the energy distribution block
(e.g. JXS[11])
Returns
-------
openmc.data.CorrelatedAngleEnergy
Correlated angle-energy distribution
"""
# Read number of interpolation regions and incoming energies
n_regions = int(ace.xss[idx])
n_energy_in = int(ace.xss[idx + 1 + 2*n_regions])
# Get interpolation information
idx += 1
if n_regions > 0:
breakpoints = ace.xss[idx:idx + n_regions].astype(int)
interpolation = ace.xss[idx + n_regions:idx + 2*n_regions].astype(int)
else:
breakpoints = np.array([n_energy_in])
interpolation = np.array([2])
# Incoming energies at which distributions exist
idx += 2*n_regions + 1
energy = ace.xss[idx:idx + n_energy_in]*EV_PER_MEV
# Location of distributions
idx += n_energy_in
loc_dist = ace.xss[idx:idx + n_energy_in].astype(int)
# Initialize list of distributions
energy_out = []
mu = []
# Read each outgoing energy distribution
for i in range(n_energy_in):
idx = ldis + loc_dist[i] - 1
# intt = interpolation scheme (1=hist, 2=lin-lin). When discrete
# lines are present, the value given is 10*n_discrete_lines + intt
n_discrete_lines, intt = divmod(int(ace.xss[idx]), 10)
if intt not in (1, 2):
warn("Interpolation scheme for continuous tabular distribution "
"is not histogram or linear-linear.")
intt = 2
# Secondary energy distribution
n_energy_out = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 4*n_energy_out].copy()
data.shape = (4, n_energy_out)
data[0,:] *= EV_PER_MEV
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:]/EV_PER_MEV,
INTERPOLATION_SCHEME[intt],
ignore_negative=True)
eout_continuous.c = data[2][n_discrete_lines:]
if np.any(data[1][n_discrete_lines:] < 0.0):
warn("Correlated angle-energy distribution has negative "
"probabilities.")
# If discrete lines are present, create a mixture distribution
if n_discrete_lines > 0:
eout_discrete = Discrete(data[0][:n_discrete_lines],
data[1][:n_discrete_lines])
eout_discrete.c = data[2][:n_discrete_lines]
if n_discrete_lines == n_energy_out:
eout_i = eout_discrete
else:
p_discrete = min(sum(eout_discrete.p), 1.0)
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
else:
eout_i = eout_continuous
energy_out.append(eout_i)
lc = data[3].astype(int)
# Secondary angular distributions
mu_i = []
for j in range(n_energy_out):
if lc[j] > 0:
idx = ldis + abs(lc[j]) - 1
intt = int(ace.xss[idx])
n_cosine = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 3*n_cosine]
data.shape = (3, n_cosine)
mu_ij = Tabular(data[0], data[1], INTERPOLATION_SCHEME[intt])
mu_ij.c = data[2]
else:
# Isotropic distribution
mu_ij = Uniform(-1., 1.)
mu_i.append(mu_ij)
# Add cosine distributions for this incoming energy to list
mu.append(mu_i)
return cls(breakpoints, interpolation, energy, energy_out, mu)
def from_hdf5(cls, group):
"""Generate correlated angle-energy distribution from HDF5 data
Parameters
----------
group : h5py.Group
HDF5 group to read from
Returns
-------
openmc.data.CorrelatedAngleEnergy
Correlated angle-energy distribution
"""
interp_data = group['energy'].attrs['interpolation']
energy_breakpoints = interp_data[0, :]
energy_interpolation = interp_data[1, :]
energy = group['energy'][()]
offsets = group['energy_out'].attrs['offsets']
interpolation = group['energy_out'].attrs['interpolation']
n_discrete_lines = group['energy_out'].attrs['n_discrete_lines']
dset_eout = group['energy_out'][()]
energy_out = []
dset_mu = group['mu'][()]
mu = []
n_energy = len(energy)
for i in range(n_energy):
# Determine length of outgoing energy distribution and number of
# discrete lines
offset_e = offsets[i]
if i < n_energy - 1:
n = offsets[i+1] - offset_e
else:
n = dset_eout.shape[1] - offset_e
m = n_discrete_lines[i]
# Create discrete distribution if lines are present
if m > 0:
x = dset_eout[0, offset_e:offset_e+m]
p = dset_eout[1, offset_e:offset_e+m]
eout_discrete = Discrete(x, p)
eout_discrete.c = dset_eout[2, offset_e:offset_e+m]
p_discrete = eout_discrete.c[-1]
# Create continuous distribution
if m < n:
interp = INTERPOLATION_SCHEME[interpolation[i]]
x = dset_eout[0, offset_e+m:offset_e+n]
p = dset_eout[1, offset_e+m:offset_e+n]
eout_continuous = Tabular(x, p, interp, ignore_negative=True)
eout_continuous.c = dset_eout[2, offset_e+m:offset_e+n]
# If both continuous and discrete are present, create a mixture
# distribution
if m == 0:
eout_i = eout_continuous
elif m == n:
eout_i = eout_discrete
else:
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
# Read angular distributions
mu_i = []
for j in range(n):
# Determine interpolation scheme
interp_code = int(dset_eout[3, offsets[i] + j])
# Determine offset and length
offset_mu = int(dset_eout[4, offsets[i] + j])
if offsets[i] + j < dset_eout.shape[1] - 1:
n_mu = int(dset_eout[4, offsets[i] + j + 1]) - offset_mu
else:
n_mu = dset_mu.shape[1] - offset_mu
# Get data
x = dset_mu[0, offset_mu:offset_mu+n_mu]
p = dset_mu[1, offset_mu:offset_mu+n_mu]
c = dset_mu[2, offset_mu:offset_mu+n_mu]
if interp_code == 0:
mu_ij = Discrete(x, p)
else:
mu_ij = Tabular(x, p, INTERPOLATION_SCHEME[interp_code],
ignore_negative=True)
mu_ij.c = c
mu_i.append(mu_ij)
offset_mu += n_mu
energy_out.append(eout_i)
mu.append(mu_i)
return cls(energy_breakpoints, energy_interpolation,
energy, energy_out, mu)
def from_ace(cls, ace_or_filename, name=None):
"""Generate thermal scattering data from an ACE table
Parameters
----------
ace_or_filename : openmc.data.ace.Table or str
ACE table to read from. If given as a string, it is assumed to be
the filename for the ACE file.
name : str
GND-conforming name of the material, e.g. c_H_in_H2O. If none is
passed, the appropriate name is guessed based on the name of the ACE
table.
Returns
-------
openmc.data.ThermalScattering
Thermal scattering data
"""
if isinstance(ace_or_filename, Table):
ace = ace_or_filename
else:
ace = get_table(ace_or_filename)
# Get new name that is GND-consistent
ace_name, xs = ace.name.split('.')
name = get_thermal_name(ace_name)
# Assign temperature to the running list
kTs = [ace.temperature * EV_PER_MEV]
temperatures = [
str(int(round(ace.temperature * EV_PER_MEV / K_BOLTZMANN))) + "K"
]
table = cls(name, ace.atomic_weight_ratio, kTs)
# Incoherent inelastic scattering cross section
idx = ace.jxs[1]
n_energy = int(ace.xss[idx])
energy = ace.xss[idx + 1:idx + 1 + n_energy] * EV_PER_MEV
xs = ace.xss[idx + 1 + n_energy:idx + 1 + 2 * n_energy]
table.inelastic_xs[temperatures[0]] = Tabulated1D(energy, xs)
if ace.nxs[7] == 0:
table.secondary_mode = 'equal'
elif ace.nxs[7] == 1:
table.secondary_mode = 'skewed'
elif ace.nxs[7] == 2:
table.secondary_mode = 'continuous'
n_energy_out = ace.nxs[4]
if table.secondary_mode in ('equal', 'skewed'):
n_mu = ace.nxs[3]
idx = ace.jxs[3]
table.inelastic_e_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2):
n_mu + 2]*EV_PER_MEV
table.inelastic_e_out[temperatures[0]].shape = \
(n_energy, n_energy_out)
table.inelastic_mu_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2)]
table.inelastic_mu_out[temperatures[0]].shape = \
(n_energy, n_energy_out, n_mu+2)
table.inelastic_mu_out[temperatures[0]] = \
table.inelastic_mu_out[temperatures[0]][:, :, 1:]
else:
n_mu = ace.nxs[3] - 1
idx = ace.jxs[3]
locc = ace.xss[idx:idx + n_energy].astype(int)
n_energy_out = \
ace.xss[idx + n_energy:idx + 2 * n_energy].astype(int)
energy_out = []
mu_out = []
for i in range(n_energy):
idx = locc[i]
# Outgoing energy distribution for incoming energy i
e = ace.xss[idx + 1:idx + 1 + n_energy_out[i] *
(n_mu + 3):n_mu + 3] * EV_PER_MEV
p = ace.xss[idx + 2:idx + 2 + n_energy_out[i] *
(n_mu + 3):n_mu + 3] / EV_PER_MEV
c = ace.xss[idx + 3:idx + 3 + n_energy_out[i] *
(n_mu + 3):n_mu + 3]
eout_i = Tabular(e, p, 'linear-linear', ignore_negative=True)
eout_i.c = c
# Outgoing angle distribution for each
# (incoming, outgoing) energy pair
mu_i = []
for j in range(n_energy_out[i]):
mu = ace.xss[idx + 4:idx + 4 + n_mu]
p_mu = 1. / n_mu * np.ones(n_mu)
mu_ij = Discrete(mu, p_mu)
mu_ij.c = np.cumsum(p_mu)
mu_i.append(mu_ij)
idx += 3 + n_mu
energy_out.append(eout_i)
mu_out.append(mu_i)
# Create correlated angle-energy distribution
breakpoints = [n_energy]
interpolation = [2]
energy = table.inelastic_xs[temperatures[0]].x
table.inelastic_dist[temperatures[0]] = CorrelatedAngleEnergy(
breakpoints, interpolation, energy, energy_out, mu_out)
# Incoherent/coherent elastic scattering cross section
idx = ace.jxs[4]
n_mu = ace.nxs[6] + 1
if idx != 0:
n_energy = int(ace.xss[idx])
energy = ace.xss[idx + 1:idx + 1 + n_energy] * EV_PER_MEV
P = ace.xss[idx + 1 + n_energy:idx + 1 + 2 * n_energy]
if ace.nxs[5] == 4:
# Coherent elastic
table.elastic_xs[temperatures[0]] = CoherentElastic(
energy, P * EV_PER_MEV)
# Coherent elastic shouldn't have angular distributions listed
assert n_mu == 0
else:
# Incoherent elastic
table.elastic_xs[temperatures[0]] = Tabulated1D(energy, P)
# Angular distribution
assert n_mu > 0
idx = ace.jxs[6]
table.elastic_mu_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_mu]
table.elastic_mu_out[temperatures[0]].shape = \
(n_energy, n_mu)
# Get relevant nuclides -- NJOY only allows one to specify three
# nuclides that the S(a,b) table applies to. Thus, for all elements
# other than H and Fe, we automatically add all the naturally-occurring
# isotopes.
for zaid, awr in ace.pairs:
if zaid > 0:
Z, A = divmod(zaid, 1000)
element = ATOMIC_SYMBOL[Z]
if element in ['H', 'Fe']:
table.nuclides.append(element + str(A))
else:
if element + '0' not in table.nuclides:
table.nuclides.append(element + '0')
for isotope in sorted(NATURAL_ABUNDANCE):
if re.match(r'{}\d+'.format(element), isotope):
if isotope not in table.nuclides:
table.nuclides.append(isotope)
return table
def from_ace(cls, ace_or_filename, name=None):
"""Generate thermal scattering data from an ACE table
Parameters
----------
ace_or_filename : openmc.data.ace.Table or str
ACE table to read from. If given as a string, it is assumed to be
the filename for the ACE file.
name : str
GND-conforming name of the material, e.g. c_H_in_H2O. If none is
passed, the appropriate name is guessed based on the name of the ACE
table.
Returns
-------
openmc.data.ThermalScattering
Thermal scattering data
"""
if isinstance(ace_or_filename, Table):
ace = ace_or_filename
else:
ace = get_table(ace_or_filename)
# Get new name that is GND-consistent
ace_name, xs = ace.name.split('.')
name = get_thermal_name(ace_name)
# Assign temperature to the running list
kTs = [ace.temperature*EV_PER_MEV]
temperatures = [str(int(round(ace.temperature*EV_PER_MEV
/ K_BOLTZMANN))) + "K"]
table = cls(name, ace.atomic_weight_ratio, kTs)
# Incoherent inelastic scattering cross section
idx = ace.jxs[1]
n_energy = int(ace.xss[idx])
energy = ace.xss[idx+1 : idx+1+n_energy]*EV_PER_MEV
xs = ace.xss[idx+1+n_energy : idx+1+2*n_energy]
table.inelastic_xs[temperatures[0]] = Tabulated1D(energy, xs)
if ace.nxs[7] == 0:
table.secondary_mode = 'equal'
elif ace.nxs[7] == 1:
table.secondary_mode = 'skewed'
elif ace.nxs[7] == 2:
table.secondary_mode = 'continuous'
n_energy_out = ace.nxs[4]
if table.secondary_mode in ('equal', 'skewed'):
n_mu = ace.nxs[3]
idx = ace.jxs[3]
table.inelastic_e_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2):
n_mu + 2]*EV_PER_MEV
table.inelastic_e_out[temperatures[0]].shape = \
(n_energy, n_energy_out)
table.inelastic_mu_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2)]
table.inelastic_mu_out[temperatures[0]].shape = \
(n_energy, n_energy_out, n_mu+2)
table.inelastic_mu_out[temperatures[0]] = \
table.inelastic_mu_out[temperatures[0]][:, :, 1:]
else:
n_mu = ace.nxs[3] - 1
idx = ace.jxs[3]
locc = ace.xss[idx:idx + n_energy].astype(int)
n_energy_out = \
ace.xss[idx + n_energy:idx + 2 * n_energy].astype(int)
energy_out = []
mu_out = []
for i in range(n_energy):
idx = locc[i]
# Outgoing energy distribution for incoming energy i
e = ace.xss[idx + 1:idx + 1 + n_energy_out[i]*(n_mu + 3):
n_mu + 3]*EV_PER_MEV
p = ace.xss[idx + 2:idx + 2 + n_energy_out[i]*(n_mu + 3):
n_mu + 3]/EV_PER_MEV
c = ace.xss[idx + 3:idx + 3 + n_energy_out[i]*(n_mu + 3):
n_mu + 3]
eout_i = Tabular(e, p, 'linear-linear', ignore_negative=True)
eout_i.c = c
# Outgoing angle distribution for each
# (incoming, outgoing) energy pair
mu_i = []
for j in range(n_energy_out[i]):
mu = ace.xss[idx + 4:idx + 4 + n_mu]
p_mu = 1. / n_mu * np.ones(n_mu)
mu_ij = Discrete(mu, p_mu)
mu_ij.c = np.cumsum(p_mu)
mu_i.append(mu_ij)
idx += 3 + n_mu
energy_out.append(eout_i)
mu_out.append(mu_i)
# Create correlated angle-energy distribution
breakpoints = [n_energy]
interpolation = [2]
energy = table.inelastic_xs[temperatures[0]].x
table.inelastic_dist[temperatures[0]] = CorrelatedAngleEnergy(
breakpoints, interpolation, energy, energy_out, mu_out)
# Incoherent/coherent elastic scattering cross section
idx = ace.jxs[4]
n_mu = ace.nxs[6] + 1
if idx != 0:
n_energy = int(ace.xss[idx])
energy = ace.xss[idx + 1: idx + 1 + n_energy]*EV_PER_MEV
P = ace.xss[idx + 1 + n_energy: idx + 1 + 2 * n_energy]
if ace.nxs[5] == 4:
# Coherent elastic
table.elastic_xs[temperatures[0]] = CoherentElastic(
energy, P*EV_PER_MEV)
# Coherent elastic shouldn't have angular distributions listed
assert n_mu == 0
else:
# Incoherent elastic
table.elastic_xs[temperatures[0]] = Tabulated1D(energy, P)
# Angular distribution
assert n_mu > 0
idx = ace.jxs[6]
table.elastic_mu_out[temperatures[0]] = \
ace.xss[idx:idx + n_energy * n_mu]
table.elastic_mu_out[temperatures[0]].shape = \
(n_energy, n_mu)
# Get relevant nuclides -- NJOY only allows one to specify three
# nuclides that the S(a,b) table applies to. Thus, for all elements
# other than H and Fe, we automatically add all the naturally-occurring
# isotopes.
for zaid, awr in ace.pairs:
if zaid > 0:
Z, A = divmod(zaid, 1000)
element = ATOMIC_SYMBOL[Z]
if element in ['H', 'Fe']:
table.nuclides.append(element + str(A))
else:
if element + '0' not in table.nuclides:
table.nuclides.append(element + '0')
for isotope in sorted(NATURAL_ABUNDANCE):
if re.match(r'{}\d+'.format(element), isotope):
if isotope not in table.nuclides:
table.nuclides.append(isotope)
return table
def from_ace(cls, ace_or_filename, name=None):
"""Generate thermal scattering data from an ACE table
Parameters
----------
ace_or_filename : openmc.data.ace.Table or str
ACE table to read from. If given as a string, it is assumed to be
the filename for the ACE file.
name : str
GND-conforming name of the material, e.g. c_H_in_H2O. If none is
passed, the appropriate name is guessed based on the name of the ACE
table.
Returns
-------
openmc.data.ThermalScattering
Thermal scattering data
"""
if isinstance(ace_or_filename, Table):
ace = ace_or_filename
else:
ace = get_table(ace_or_filename)
# Get new name that is GND-consistent
ace_name, xs = ace.name.split('.')
if not xs.endswith('t'):
raise TypeError(
"{} is not a thermal scattering ACE table.".format(ace))
if name is None:
name = get_thermal_name(ace_name)
# Assign temperature to the running list
kTs = [ace.temperature * EV_PER_MEV]
# Incoherent inelastic scattering cross section
idx = ace.jxs[1]
n_energy = int(ace.xss[idx])
energy = ace.xss[idx + 1:idx + 1 + n_energy] * EV_PER_MEV
xs = ace.xss[idx + 1 + n_energy:idx + 1 + 2 * n_energy]
inelastic_xs = Tabulated1D(energy, xs)
energy_max = energy[-1]
# Incoherent inelastic angle-energy distribution
continuous = (ace.nxs[7] == 2)
n_energy_out = ace.nxs[4]
if not continuous:
n_mu = ace.nxs[3]
idx = ace.jxs[3]
energy_out = ace.xss[idx:idx + n_energy * n_energy_out *
(n_mu + 2):n_mu + 2] * EV_PER_MEV
energy_out.shape = (n_energy, n_energy_out)
mu_out = ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2)]
mu_out.shape = (n_energy, n_energy_out, n_mu + 2)
mu_out = mu_out[:, :, 1:]
skewed = (ace.nxs[7] == 1)
distribution = IncoherentInelasticAEDiscrete(
energy_out, mu_out, skewed)
else:
n_mu = ace.nxs[3] - 1
idx = ace.jxs[3]
locc = ace.xss[idx:idx + n_energy].astype(int)
n_energy_out = \
ace.xss[idx + n_energy:idx + 2 * n_energy].astype(int)
energy_out = []
mu_out = []
for i in range(n_energy):
idx = locc[i]
# Outgoing energy distribution for incoming energy i
e = ace.xss[idx + 1:idx + 1 + n_energy_out[i] *
(n_mu + 3):n_mu + 3] * EV_PER_MEV
p = ace.xss[idx + 2:idx + 2 + n_energy_out[i] *
(n_mu + 3):n_mu + 3] / EV_PER_MEV
c = ace.xss[idx + 3:idx + 3 + n_energy_out[i] *
(n_mu + 3):n_mu + 3]
eout_i = Tabular(e, p, 'linear-linear', ignore_negative=True)
eout_i.c = c
# Outgoing angle distribution for each
# (incoming, outgoing) energy pair
mu_i = []
for j in range(n_energy_out[i]):
mu = ace.xss[idx + 4:idx + 4 + n_mu]
p_mu = 1. / n_mu * np.ones(n_mu)
mu_ij = Discrete(mu, p_mu)
mu_ij.c = np.cumsum(p_mu)
mu_i.append(mu_ij)
idx += 3 + n_mu
energy_out.append(eout_i)
mu_out.append(mu_i)
# Create correlated angle-energy distribution
breakpoints = [n_energy]
interpolation = [2]
energy = inelastic_xs.x
distribution = IncoherentInelasticAE(breakpoints, interpolation,
energy, energy_out, mu_out)
table = cls(name, ace.atomic_weight_ratio, energy_max, kTs)
T = table.temperatures[0]
table.inelastic = ThermalScatteringReaction({T: inelastic_xs},
{T: distribution})
# Incoherent/coherent elastic scattering cross section
idx = ace.jxs[4]
n_mu = ace.nxs[6] + 1
if idx != 0:
n_energy = int(ace.xss[idx])
energy = ace.xss[idx + 1:idx + 1 + n_energy] * EV_PER_MEV
P = ace.xss[idx + 1 + n_energy:idx + 1 + 2 * n_energy]
if ace.nxs[5] == 4:
# Coherent elastic
xs = CoherentElastic(energy, P * EV_PER_MEV)
distribution = CoherentElasticAE(xs)
# Coherent elastic shouldn't have angular distributions listed
assert n_mu == 0
else:
# Incoherent elastic
xs = Tabulated1D(energy, P)
# Angular distribution
assert n_mu > 0
idx = ace.jxs[6]
mu_out = ace.xss[idx:idx + n_energy * n_mu]
mu_out.shape = (n_energy, n_mu)
distribution = IncoherentElasticAEDiscrete(mu_out)
table.elastic = ThermalScatteringReaction({T: xs},
{T: distribution})
# Get relevant nuclides -- NJOY only allows one to specify three
# nuclides that the S(a,b) table applies to. Thus, for all elements
# other than H and Fe, we automatically add all the naturally-occurring
# isotopes.
for zaid, awr in ace.pairs:
if zaid > 0:
Z, A = divmod(zaid, 1000)
element = ATOMIC_SYMBOL[Z]
if element in ['H', 'Fe']:
table.nuclides.append(element + str(A))
else:
if element + '0' not in table.nuclides:
table.nuclides.append(element + '0')
for isotope, _ in isotopes(element):
if isotope not in table.nuclides:
table.nuclides.append(isotope)
return table
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)
def from_ace(cls, ace, idx, ldis):
"""Generate Kalbach-Mann energy-angle distribution from ACE data
Parameters
----------
ace : openmc.data.ace.Table
ACE table to read from
idx : int
Index in XSS array of the start of the energy distribution data
(LDIS + LOCC - 1)
ldis : int
Index in XSS array of the start of the energy distribution block
(e.g. JXS[11])
Returns
-------
openmc.data.KalbachMann
Kalbach-Mann energy-angle distribution
"""
# Read number of interpolation regions and incoming energies
n_regions = int(ace.xss[idx])
n_energy_in = int(ace.xss[idx + 1 + 2*n_regions])
# Get interpolation information
idx += 1
if n_regions > 0:
breakpoints = ace.xss[idx:idx + n_regions].astype(int)
interpolation = ace.xss[idx + n_regions:idx + 2*n_regions].astype(int)
else:
breakpoints = np.array([n_energy_in])
interpolation = np.array([2])
# Incoming energies at which distributions exist
idx += 2*n_regions + 1
energy = ace.xss[idx:idx + n_energy_in]*EV_PER_MEV
# Location of distributions
idx += n_energy_in
loc_dist = ace.xss[idx:idx + n_energy_in].astype(int)
# Initialize variables
energy_out = []
km_r = []
km_a = []
# Read each outgoing energy distribution
for i in range(n_energy_in):
idx = ldis + loc_dist[i] - 1
# intt = interpolation scheme (1=hist, 2=lin-lin)
INTTp = int(ace.xss[idx])
intt = INTTp % 10
n_discrete_lines = (INTTp - intt)//10
if intt not in (1, 2):
warn("Interpolation scheme for continuous tabular distribution "
"is not histogram or linear-linear.")
intt = 2
n_energy_out = int(ace.xss[idx + 1])
data = ace.xss[idx + 2:idx + 2 + 5*n_energy_out].copy()
data.shape = (5, n_energy_out)
data[0,:] *= EV_PER_MEV
# Create continuous distribution
eout_continuous = Tabular(data[0][n_discrete_lines:],
data[1][n_discrete_lines:]/EV_PER_MEV,
INTERPOLATION_SCHEME[intt],
ignore_negative=True)
eout_continuous.c = data[2][n_discrete_lines:]
if np.any(data[1][n_discrete_lines:] < 0.0):
warn("Kalbach-Mann energy distribution has negative "
"probabilities.")
# If discrete lines are present, create a mixture distribution
if n_discrete_lines > 0:
eout_discrete = Discrete(data[0][:n_discrete_lines],
data[1][:n_discrete_lines])
eout_discrete.c = data[2][:n_discrete_lines]
if n_discrete_lines == n_energy_out:
eout_i = eout_discrete
else:
p_discrete = min(sum(eout_discrete.p), 1.0)
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
else:
eout_i = eout_continuous
energy_out.append(eout_i)
km_r.append(Tabulated1D(data[0], data[3]))
km_a.append(Tabulated1D(data[0], data[4]))
return cls(breakpoints, interpolation, energy, energy_out, km_r, km_a)
def from_hdf5(cls, group):
"""Generate Kalbach-Mann distribution from HDF5 data
Parameters
----------
group : h5py.Group
HDF5 group to read from
Returns
-------
openmc.data.KalbachMann
Kalbach-Mann energy distribution
"""
interp_data = group['energy'].attrs['interpolation']
energy_breakpoints = interp_data[0, :]
energy_interpolation = interp_data[1, :]
energy = group['energy'].value
data = group['distribution']
offsets = data.attrs['offsets']
interpolation = data.attrs['interpolation']
n_discrete_lines = data.attrs['n_discrete_lines']
energy_out = []
precompound = []
slope = []
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
m = n_discrete_lines[i]
# Create discrete distribution if lines are present
if m > 0:
eout_discrete = Discrete(data[0, j:j+m], data[1, j:j+m])
eout_discrete.c = data[2, j:j+m]
p_discrete = eout_discrete.c[-1]
# Create continuous distribution
if m < n:
interp = INTERPOLATION_SCHEME[interpolation[i]]
eout_continuous = Tabular(data[0, j+m:j+n], data[1, j+m:j+n], interp)
eout_continuous.c = data[2, j+m:j+n]
# If both continuous and discrete are present, create a mixture
# distribution
if m == 0:
eout_i = eout_continuous
elif m == n:
eout_i = eout_discrete
else:
eout_i = Mixture([p_discrete, 1. - p_discrete],
[eout_discrete, eout_continuous])
km_r = Tabulated1D(data[0, j:j+n], data[3, j:j+n])
km_a = Tabulated1D(data[0, j:j+n], data[4, j:j+n])
energy_out.append(eout_i)
precompound.append(km_r)
slope.append(km_a)
return cls(energy_breakpoints, energy_interpolation,
energy, energy_out, precompound, slope)