def background(self, background):
cv.check_type('plot background', background, Iterable, Integral)
cv.check_length('plot background', background, 3)
for rgb in background:
cv.check_greater_than('plot background', rgb, 0, True)
cv.check_less_than('plot background', rgb, 256)
self._background = background
def mask_background(self, mask_background):
check_type('plot mask background', mask_background, Iterable, Integral)
check_length('plot mask background', mask_background, 3)
for rgb in mask_background:
check_greater_than('plot mask background', rgb, 0, True)
check_less_than('plot mask background', rgb, 256)
self._mask_background = mask_background
def num_l(self, num_l):
if num_l is not None:
cv.check_type('num_l', num_l, Integral)
cv.check_greater_than('num_l', num_l, 1, equality=True)
cv.check_less_than('num_l', num_l, 4, equality=True)
# There is an if block in _evaluate that assumes num_l <= 4.
self._num_l = num_l
def num_l(self, num_l):
if num_l is not None:
cv.check_type('num_l', num_l, Integral)
cv.check_greater_than('num_l', num_l, 1, equality=True)
cv.check_less_than('num_l', num_l, 4, equality=True)
# There is an if block in _evaluate that assumes num_l <= 4.
self._num_l = num_l
def mask_background(self, mask_background):
cv.check_type('plot mask background', mask_background, Iterable, Integral)
cv.check_length('plot mask background', mask_background, 3)
for rgb in mask_background:
cv.check_greater_than('plot mask background', rgb, 0, True)
cv.check_less_than('plot mask background', rgb, 256)
self._mask_background = mask_background
def albedo(self, albedo):
check_type('CMFD mesh albedo', albedo, Iterable, Real)
check_length('CMFD mesh albedo', albedo, 6)
for a in albedo:
check_greater_than('CMFD mesh albedo', a, 0, True)
check_less_than('CMFD mesh albedo', a, 1, True)
self._albedo = albedo
def background(self, background):
cv.check_type('plot background', background, Iterable, Integral)
cv.check_length('plot background', background, 3)
for rgb in background:
cv.check_greater_than('plot background',rgb, 0, True)
cv.check_less_than('plot background', rgb, 256)
self._background = background
def get_group_bounds(self, group):
"""Returns the energy boundaries for the energy group of interest.
Parameters
----------
group : int
The energy group index, starting at 1 for the highest energies
Returns
-------
2-tuple
The low and high energy bounds for the group in eV
Raises
------
ValueError
If the group edges have not yet been set.
"""
if self.group_edges is None:
msg = 'Unable to get energy group bounds for group "{0}" since ' \
'the group edges have not yet been set'.format(group)
raise ValueError(msg)
cv.check_greater_than('group', group, 0)
cv.check_less_than('group', group, self.num_groups, equality=True)
lower = self.group_edges[self.num_groups - group]
upper = self.group_edges[self.num_groups - group + 1]
return lower, upper
def meshlines(self, meshlines):
cv.check_type('plot meshlines', meshlines, dict)
if 'type' not in meshlines:
msg = 'Unable to set on plot the meshlines "{0}" which ' \
'does not have a "type" key'.format(meshlines)
raise ValueError(msg)
elif meshlines['type'] not in ['tally', 'entropy', 'ufs', 'cmfd']:
msg = 'Unable to set the meshlines with ' \
'type "{0}"'.format(meshlines['type'])
raise ValueError(msg)
if 'id' in meshlines:
cv.check_type('plot meshlines id', meshlines['id'], Integral)
cv.check_greater_than('plot meshlines id', meshlines['id'], 0,
equality=True)
if 'linewidth' in meshlines:
cv.check_type('plot mesh linewidth', meshlines['linewidth'], Integral)
cv.check_greater_than('plot mesh linewidth', meshlines['linewidth'],
0, equality=True)
if 'color' in meshlines:
cv.check_type('plot meshlines color', meshlines['color'], Iterable,
Integral)
cv.check_length('plot meshlines color', meshlines['color'], 3)
for rgb in meshlines['color']:
cv.check_greater_than('plot meshlines color', rgb, 0, True)
cv.check_less_than('plot meshlines color', rgb, 256)
self._meshlines = meshlines
def get_group_bounds(self, group):
"""Returns the energy boundaries for the energy group of interest.
Parameters
----------
group : Integral
The energy group index, starting at 1 for the highest energies
Returns
-------
2-tuple
The low and high energy bounds for the group in MeV
Raises
------
ValueError
If the group edges have not yet been set.
"""
if self.group_edges is None:
msg = 'Unable to get energy group bounds for group "{0}" since ' \
'the group edges have not yet been set'.format(group)
raise ValueError(msg)
cv.check_greater_than('group', group, 0)
cv.check_less_than('group', group, self.num_groups, equality=True)
lower = self.group_edges[self.num_groups-group]
upper = self.group_edges[self.num_groups-group+1]
return lower, upper
def add_s_alpha_beta(self, name, fraction=1.0):
r"""Add an :math:`S(\alpha,\beta)` table to the material
Parameters
----------
name : str
Name of the :math:`S(\alpha,\beta)` table
fraction : float
The fraction of relevant nuclei that are affected by the
:math:`S(\alpha,\beta)` table. For example, if the material is a
block of carbon that is 60% graphite and 40% amorphous then add a
graphite :math:`S(\alpha,\beta)` table with fraction=0.6.
"""
if self._macroscopic is not None:
msg = 'Unable to add an S(a,b) table to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if not isinstance(name, str):
msg = 'Unable to add an S(a,b) table to Material ID="{}" with a ' \
'non-string table name "{}"'.format(self._id, name)
raise ValueError(msg)
cv.check_type('S(a,b) fraction', fraction, Real)
cv.check_greater_than('S(a,b) fraction', fraction, 0.0, True)
cv.check_less_than('S(a,b) fraction', fraction, 1.0, True)
self._sab.append((name, fraction))
def albedo(self, albedo):
check_type('CMFD mesh albedo', albedo, Iterable, Real)
check_length('CMFD mesh albedo', albedo, 6)
for a in albedo:
check_greater_than('CMFD mesh albedo', a, 0, True)
check_less_than('CMFD mesh albedo', a, 1, True)
self._albedo = albedo
def highlight_domains(self,
geometry,
domains,
seed=1,
alpha=0.5,
background='gray'):
"""Use alpha compositing to highlight one or more domains in the plot.
This routine generates a color scheme and applies alpha compositing
to make all domains except the highlighted ones appear partially
transparent.
Parameters
----------
geometry : openmc.Geometry
The geometry for which the plot is defined
domains : Iterable of Integral
A collection of the domain IDs to highlight in the plot
seed : Integral
The random number seed used to generate the color scheme
alpha : Real in [0,1]
The value to apply in alpha compisiting
background : 3-tuple of Integral or 'white' or 'black' or 'gray'
The background color to apply in alpha compisiting
"""
cv.check_iterable_type('domains', domains, Integral)
cv.check_type('alpha', alpha, Real)
cv.check_greater_than('alpha', alpha, 0., equality=True)
cv.check_less_than('alpha', alpha, 1., equality=True)
# Get a background (R,G,B) tuple to apply in alpha compositing
if isinstance(background, basestring):
if background == 'white':
background = (255, 255, 255)
elif background == 'black':
background = (0, 0, 0)
elif background == 'gray':
background = (160, 160, 160)
else:
msg = 'The background "{}" is not defined'.format(background)
raise ValueError(msg)
cv.check_iterable_type('background', background, Integral)
# Generate a color scheme
self.colorize(geometry, seed)
# Apply alpha compositing to the colors for all domains
# other than those the user wishes to highlight
for domain_id in self.col_spec:
if domain_id not in domains:
r, g, b = self.col_spec[domain_id]
r = int(((1 - alpha) * background[0]) + (alpha * r))
g = int(((1 - alpha) * background[1]) + (alpha * g))
b = int(((1 - alpha) * background[2]) + (alpha * b))
self._col_spec[domain_id] = (r, g, b)
def get_condensed_library(self, coarse_groups):
"""Construct an energy-condensed version of this library.
This routine condenses each of the multi-group cross sections in the
library to a coarse energy group structure. NOTE: This routine must
be called after the load_from_statepoint(...) routine loads the tallies
from the statepoint into each of the cross sections.
Parameters
----------
coarse_groups : openmc.mgxs.EnergyGroups
The coarse energy group structure of interest
Returns
-------
Library
A new multi-group cross section library condensed to the group
structure of interest
Raises
------
ValueError
When this method is called before a statepoint has been loaded
See also
--------
MGXS.get_condensed_xs(coarse_groups)
"""
if self.sp_filename is None:
msg = 'Unable to get a condensed coarse group cross section ' \
'library since the statepoint has not yet been loaded'
raise ValueError(msg)
cv.check_type('coarse_groups', coarse_groups, openmc.mgxs.EnergyGroups)
cv.check_less_than('coarse groups',
coarse_groups.num_groups,
self.num_groups,
equality=True)
cv.check_value('upper coarse energy', coarse_groups.group_edges[-1],
[self.energy_groups.group_edges[-1]])
cv.check_value('lower coarse energy', coarse_groups.group_edges[0],
[self.energy_groups.group_edges[0]])
# Clone this Library to initialize the condensed version
condensed_library = copy.deepcopy(self)
condensed_library.energy_groups = coarse_groups
# Condense the MGXS for each domain and mgxs type
for domain in self.domains:
for mgxs_type in self.mgxs_types:
mgxs = condensed_library.get_mgxs(domain, mgxs_type)
condensed_mgxs = mgxs.get_condensed_xs(coarse_groups)
condensed_library.all_mgxs[
domain.id][mgxs_type] = condensed_mgxs
return condensed_library
def get_condensed_library(self, coarse_groups):
"""Construct an energy-condensed version of this library.
This routine condenses each of the multi-group cross sections in the
library to a coarse energy group structure. NOTE: This routine must
be called after the load_from_statepoint(...) routine loads the tallies
from the statepoint into each of the cross sections.
Parameters
----------
coarse_groups : openmc.mgxs.EnergyGroups
The coarse energy group structure of interest
Returns
-------
Library
A new multi-group cross section library condensed to the group
structure of interest
Raises
------
ValueError
When this method is called before a statepoint has been loaded
See also
--------
MGXS.get_condensed_xs(coarse_groups)
"""
if self.sp_filename is None:
msg = 'Unable to get a condensed coarse group cross section ' \
'library since the statepoint has not yet been loaded'
raise ValueError(msg)
cv.check_type('coarse_groups', coarse_groups, openmc.mgxs.EnergyGroups)
cv.check_less_than('coarse groups', coarse_groups.num_groups,
self.num_groups, equality=True)
cv.check_value('upper coarse energy', coarse_groups.group_edges[-1],
[self.energy_groups.group_edges[-1]])
cv.check_value('lower coarse energy', coarse_groups.group_edges[0],
[self.energy_groups.group_edges[0]])
# Clone this Library to initialize the condensed version
condensed_library = copy.deepcopy(self)
condensed_library.energy_groups = coarse_groups
# Condense the MGXS for each domain and mgxs type
for domain in self.domains:
for mgxs_type in self.mgxs_types:
mgxs = condensed_library.get_mgxs(domain, mgxs_type)
condensed_mgxs = mgxs.get_condensed_xs(coarse_groups)
condensed_library.all_mgxs[domain.id][mgxs_type] = condensed_mgxs
return condensed_library
def highlight_domains(self,
geometry,
domains,
seed=1,
alpha=0.5,
background='gray'):
"""Use alpha compositing to highlight one or more domains in the plot.
This routine generates a color scheme and applies alpha compositing to
make all domains except the highlighted ones appear partially
transparent.
Parameters
----------
geometry : openmc.Geometry
The geometry for which the plot is defined
domains : Iterable of openmc.Cell or openmc.Material
A collection of the domain IDs to highlight in the plot
seed : int
The random number seed used to generate the color scheme
alpha : float
The value between 0 and 1 to apply in alpha compisiting
background : 3-tuple of int or str
The background color to apply in alpha compisiting
"""
cv.check_type('domains', domains, Iterable,
(openmc.Cell, openmc.Material))
cv.check_type('alpha', alpha, Real)
cv.check_greater_than('alpha', alpha, 0., equality=True)
cv.check_less_than('alpha', alpha, 1., equality=True)
cv.check_type('background', background, Iterable)
# Get a background (R,G,B) tuple to apply in alpha compositing
if isinstance(background, str):
if background.lower() not in _SVG_COLORS:
raise ValueError(
"'{}' is not a valid color.".format(background))
background = _SVG_COLORS[background.lower()]
# Generate a color scheme
self.colorize(geometry, seed)
# Apply alpha compositing to the colors for all domains
# other than those the user wishes to highlight
for domain, color in self.colors.items():
if domain not in domains:
if isinstance(color, str):
color = _SVG_COLORS[color.lower()]
r, g, b = color
r = int(((1 - alpha) * background[0]) + (alpha * r))
g = int(((1 - alpha) * background[1]) + (alpha * g))
b = int(((1 - alpha) * background[2]) + (alpha * b))
self._colors[domain] = (r, g, b)
def _check_color(err_string, color):
cv.check_type(err_string, color, Iterable)
if isinstance(color, str):
if color.lower() not in _SVG_COLORS:
raise ValueError("'{}' is not a valid color.".format(color))
else:
cv.check_length(err_string, color, 3)
for rgb in color:
cv.check_type(err_string, rgb, Real)
cv.check_greater_than('RGB component', rgb, 0, True)
cv.check_less_than('RGB component', rgb, 256)
def highlight_domains(self, geometry, domains, seed=1,
alpha=0.5, background='gray'):
"""Use alpha compositing to highlight one or more domains in the plot.
This routine generates a color scheme and applies alpha compositing
to make all domains except the highlighted ones appear partially
transparent.
Parameters
----------
geometry : openmc.Geometry
The geometry for which the plot is defined
domains : Iterable of Integral
A collection of the domain IDs to highlight in the plot
seed : Integral
The random number seed used to generate the color scheme
alpha : Real in [0,1]
The value to apply in alpha compisiting
background : 3-tuple of Integral or 'white' or 'black' or 'gray'
The background color to apply in alpha compisiting
"""
cv.check_iterable_type('domains', domains, Integral)
cv.check_type('alpha', alpha, Real)
cv.check_greater_than('alpha', alpha, 0., equality=True)
cv.check_less_than('alpha', alpha, 1., equality=True)
# Get a background (R,G,B) tuple to apply in alpha compositing
if isinstance(background, basestring):
if background == 'white':
background = (255, 255, 255)
elif background == 'black':
background = (0, 0, 0)
elif background == 'gray':
background = (160, 160, 160)
else:
msg = 'The background "{}" is not defined'.format(background)
raise ValueError(msg)
cv.check_iterable_type('background', background, Integral)
# Generate a color scheme
self.colorize(geometry, seed)
# Apply alpha compositing to the colors for all domains
# other than those the user wishes to highlight
for domain_id in self.col_spec:
if domain_id not in domains:
r, g, b = self.col_spec[domain_id]
r = int(((1-alpha) * background[0]) + (alpha * r))
g = int(((1-alpha) * background[1]) + (alpha * g))
b = int(((1-alpha) * background[2]) + (alpha * b))
self._col_spec[domain_id] = (r, g, b)
def mask_background(self, mask_background):
cv.check_type('plot mask background', mask_background, Iterable)
if isinstance(mask_background, string_types):
if mask_background.lower() not in _SVG_COLORS:
raise ValueError("'{}' is not a valid color.".format(mask_background))
else:
cv.check_length('plot mask_background', mask_background, 3)
for rgb in mask_background:
cv.check_greater_than('plot mask background', rgb, 0, True)
cv.check_less_than('plot mask background', rgb, 256)
self._mask_background = mask_background
def legendre_order(self, legendre_order):
cv.check_type('legendre_order', legendre_order, Integral)
cv.check_greater_than('legendre_order', legendre_order, 0, equality=True)
cv.check_less_than('legendre_order', legendre_order, 10, equality=True)
if self.correction == 'P0' and legendre_order > 0:
msg = 'The P0 correction will be ignored since the scattering ' \
'order {} is greater than zero'.format(self.legendre_order)
warn(msg, RuntimeWarning)
self.correction = None
self._legendre_order = legendre_order
def highlight_domains(self, geometry, domains, seed=1,
alpha=0.5, background='gray'):
"""Use alpha compositing to highlight one or more domains in the plot.
This routine generates a color scheme and applies alpha compositing to
make all domains except the highlighted ones appear partially
transparent.
Parameters
----------
geometry : openmc.Geometry
The geometry for which the plot is defined
domains : Iterable of openmc.Cell or openmc.Material
A collection of the domain IDs to highlight in the plot
seed : int
The random number seed used to generate the color scheme
alpha : float
The value between 0 and 1 to apply in alpha compisiting
background : 3-tuple of int or str
The background color to apply in alpha compisiting
"""
cv.check_type('domains', domains, Iterable,
(openmc.Cell, openmc.Material))
cv.check_type('alpha', alpha, Real)
cv.check_greater_than('alpha', alpha, 0., equality=True)
cv.check_less_than('alpha', alpha, 1., equality=True)
cv.check_type('background', background, Iterable)
# Get a background (R,G,B) tuple to apply in alpha compositing
if isinstance(background, string_types):
if background.lower() not in _SVG_COLORS:
raise ValueError("'{}' is not a valid color.".format(background))
background = _SVG_COLORS[background.lower()]
# Generate a color scheme
self.colorize(geometry, seed)
# Apply alpha compositing to the colors for all domains
# other than those the user wishes to highlight
for domain, color in self.colors.items():
if domain not in domains:
if isinstance(color, string_types):
color = _SVG_COLORS[color.lower()]
r, g, b = color
r = int(((1-alpha) * background[0]) + (alpha * r))
g = int(((1-alpha) * background[1]) + (alpha * g))
b = int(((1-alpha) * background[2]) + (alpha * b))
self._colors[domain] = (r, g, b)
def colors(self, colors):
cv.check_type('plot colors', colors, Mapping)
for key, value in colors.items():
cv.check_type('plot color key', key, (openmc.Cell, openmc.Material))
cv.check_type('plot color value', value, Iterable)
if isinstance(value, string_types):
if value.lower() not in _SVG_COLORS:
raise ValueError("'{}' is not a valid color.".format(value))
else:
cv.check_length('plot color (RGB)', value, 3)
for component in value:
cv.check_type('RGB component', component, Real)
cv.check_greater_than('RGB component', component, 0, True)
cv.check_less_than('RGB component', component, 255, True)
self._colors = colors
def get_group_indices(self, groups="all"):
"""Returns the array indices for one or more energy groups.
Parameters
----------
groups : str, tuple
The energy groups of interest - a tuple of the energy group indices,
starting at 1 for the highest energies (default is 'all')
Returns
-------
numpy.ndarray
The ndarray array indices for each energy group of interest
Raises
------
ValueError
If the group edges have not yet been set, or if a group is requested
that is outside the bounds of the number of energy groups.
"""
if self.group_edges is None:
msg = (
'Unable to get energy group indices for groups "{0}" since '
"the group edges have not yet been set".format(groups)
)
raise ValueError(msg)
if groups == "all":
return np.arange(self.num_groups)
else:
indices = np.zeros(len(groups), dtype=np.int)
for i, group in enumerate(groups):
cv.check_greater_than("group", group, 0)
cv.check_less_than("group", group, self.num_groups, equality=True)
indices[i] = group - 1
return indices
def get_group_indices(self, groups='all'):
"""Returns the array indices for one or more energy groups.
Parameters
----------
groups : str, tuple
The energy groups of interest - a tuple of the energy group indices,
starting at 1 for the highest energies (default is 'all')
Returns
-------
numpy.ndarray
The ndarray array indices for each energy group of interest
Raises
------
ValueError
If the group edges have not yet been set, or if a group is requested
that is outside the bounds of the number of energy groups.
"""
if self.group_edges is None:
msg = 'Unable to get energy group indices for groups "{0}" since ' \
'the group edges have not yet been set'.format(groups)
raise ValueError(msg)
if groups == 'all':
return np.arange(self.num_groups)
else:
indices = np.zeros(len(groups), dtype=np.int)
for i, group in enumerate(groups):
cv.check_greater_than('group', group, 0)
cv.check_less_than('group', group, self.num_groups, equality=True)
indices[i] = group - 1
return indices
def get_condensed_groups(self, coarse_groups):
"""Return a coarsened version of this EnergyGroups object.
This method merges together energy groups in this object into wider
energy groups as defined by the list of groups specified by the user,
and returns a new, coarse EnergyGroups object.
Parameters
----------
coarse_groups : Iterable of 2-tuple
The energy groups of interest - a list of 2-tuples, each directly
corresponding to one of the new coarse groups. The values in the
2-tuples are upper/lower energy groups used to construct a new
coarse group. For example, if [(1,2), (3,4)] was used as the coarse
groups, fine groups 1 and 2 would be merged into coarse group 1
while fine groups 3 and 4 would be merged into coarse group 2.
Returns
-------
EnergyGroups
A coarsened version of this EnergyGroups object.
Raises
------
ValueError
If the group edges have not yet been set.
"""
cv.check_type('group edges', coarse_groups, Iterable)
for group in coarse_groups:
cv.check_type('group edges', group, Iterable)
cv.check_length('group edges', group, 2)
cv.check_greater_than('lower group', group[0], 1, True)
cv.check_less_than('lower group', group[0], self.num_groups, True)
cv.check_greater_than('upper group', group[0], 1, True)
cv.check_less_than('upper group', group[0], self.num_groups, True)
cv.check_less_than('lower group', group[0], group[1], False)
# Compute the group indices into the coarse group
group_bounds = [group[1] for group in coarse_groups]
group_bounds.insert(0, coarse_groups[0][0])
# Determine the indices mapping the fine-to-coarse energy groups
group_bounds = np.asarray(group_bounds)
group_indices = np.flipud(self.num_groups - group_bounds)
group_indices[-1] += 1
# Determine the edges between coarse energy groups and sort
# in increasing order in case the user passed in unordered groups
group_edges = self.group_edges[group_indices]
group_edges = np.sort(group_edges)
# Create a new condensed EnergyGroups object
condensed_groups = EnergyGroups()
condensed_groups.group_edges = group_edges
return condensed_groups
def get_condensed_groups(self, coarse_groups):
"""Return a coarsened version of this EnergyGroups object.
This method merges together energy groups in this object into wider
energy groups as defined by the list of groups specified by the user,
and returns a new, coarse EnergyGroups object.
Parameters
----------
coarse_groups : Iterable of 2-tuple
The energy groups of interest - a list of 2-tuples, each directly
corresponding to one of the new coarse groups. The values in the
2-tuples are upper/lower energy groups used to construct a new
coarse group. For example, if [(1,2), (3,4)] was used as the coarse
groups, fine groups 1 and 2 would be merged into coarse group 1
while fine groups 3 and 4 would be merged into coarse group 2.
Returns
-------
openmc.mgxs.EnergyGroups
A coarsened version of this EnergyGroups object.
Raises
------
ValueError
If the group edges have not yet been set.
"""
cv.check_type('group edges', coarse_groups, Iterable)
for group in coarse_groups:
cv.check_type('group edges', group, Iterable)
cv.check_length('group edges', group, 2)
cv.check_greater_than('lower group', group[0], 1, True)
cv.check_less_than('lower group', group[0], self.num_groups, True)
cv.check_greater_than('upper group', group[0], 1, True)
cv.check_less_than('upper group', group[0], self.num_groups, True)
cv.check_less_than('lower group', group[0], group[1], False)
# Compute the group indices into the coarse group
group_bounds = [group[1] for group in coarse_groups]
group_bounds.insert(0, coarse_groups[0][0])
# Determine the indices mapping the fine-to-coarse energy groups
group_bounds = np.asarray(group_bounds)
group_indices = np.flipud(self.num_groups - group_bounds)
group_indices[-1] += 1
# Determine the edges between coarse energy groups and sort
# in increasing order in case the user passed in unordered groups
group_edges = self.group_edges[group_indices]
group_edges = np.sort(group_edges)
# Create a new condensed EnergyGroups object
condensed_groups = EnergyGroups()
condensed_groups.group_edges = group_edges
return condensed_groups
def add_s_alpha_beta(self, name, fraction=1.0):
r"""Add an :math:`S(\alpha,\beta)` table to the material
Parameters
----------
name : str
Name of the :math:`S(\alpha,\beta)` table
fraction : float
The fraction of relevant nuclei that are affected by the
:math:`S(\alpha,\beta)` table. For example, if the material is a
block of carbon that is 60% graphite and 40% amorphous then add a
graphite :math:`S(\alpha,\beta)` table with fraction=0.6.
"""
if self._macroscopic is not None:
msg = 'Unable to add an S(a,b) table to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if not isinstance(name, string_types):
msg = 'Unable to add an S(a,b) table to Material ID="{}" with a ' \
'non-string table name "{}"'.format(self._id, name)
raise ValueError(msg)
cv.check_type('S(a,b) fraction', fraction, Real)
cv.check_greater_than('S(a,b) fraction', fraction, 0.0, True)
cv.check_less_than('S(a,b) fraction', fraction, 1.0, True)
new_name = openmc.data.get_thermal_name(name)
if new_name != name:
msg = 'OpenMC S(a,b) tables follow the GND naming convention. ' \
'Table "{}" is being renamed as "{}".'.format(name, new_name)
warnings.warn(msg)
self._sab.append((new_name, fraction))
def add_element(self, element, percent, percent_type='ao', enrichment=None):
"""Add a natural element to the material
Parameters
----------
element : str
Element to add
percent : float
Atom or weight percent
percent_type : {'ao', 'wo'}, optional
'ao' for atom percent and 'wo' for weight percent. Defaults to atom
percent.
enrichment : float, optional
Enrichment for U235 in weight percent. For example, input 4.95 for
4.95 weight percent enriched U. Default is None
(natural composition).
"""
if self._macroscopic is not None:
msg = 'Unable to add an Element to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if not isinstance(element, string_types):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-string value "{}"'.format(self._id, element)
raise ValueError(msg)
if not isinstance(percent, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point value "{}"'.format(self._id, percent)
raise ValueError(msg)
if percent_type not in ['ao', 'wo']:
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'percent type "{}"'.format(self._id, percent_type)
raise ValueError(msg)
if enrichment is not None:
if not isinstance(enrichment, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point enrichment value "{}"'\
.format(self._id, enrichment)
raise ValueError(msg)
elif element != 'U':
msg = 'Unable to use enrichment for element {} which is not ' \
'uranium for Material ID="{}"'.format(element, self._id)
raise ValueError(msg)
# Check that the enrichment is in the valid range
cv.check_less_than('enrichment', enrichment, 100./1.008)
cv.check_greater_than('enrichment', enrichment, 0., equality=True)
if enrichment > 5.0:
msg = 'A uranium enrichment of {} was given for Material ID='\
'"{}". OpenMC assumes the U234/U235 mass ratio is '\
'constant at 0.008, which is only valid at low ' \
'enrichments. Consider setting the isotopic ' \
'composition manually for enrichments over 5%.'.\
format(enrichment, self._id)
warnings.warn(msg)
# Add naturally-occuring isotopes
element = openmc.Element(element)
for nuclide in element.expand(percent, percent_type, enrichment):
self._nuclides.append(nuclide)
def verbosity(self, verbosity):
cv.check_type('verbosity', verbosity, Integral)
cv.check_greater_than('verbosity', verbosity, 1, True)
cv.check_less_than('verbosity', verbosity, 10, True)
self._verbosity = verbosity
def search_for_keff(model_builder, initial_guess=None, target=1.0,
bracket=None, model_args=None, tol=None,
bracketed_method='bisect', print_iterations=False,
print_output=False, **kwargs):
"""Function to perform a keff search by modifying a model parametrized by a
single independent variable.
Parameters
----------
model_builder : collections.Callable
Callable function which builds a model according to a passed
parameter. This function must return an openmc.model.Model object.
initial_guess : Real, optional
Initial guess for the parameter to be searched in
`model_builder`. One of `guess` or `bracket` must be provided.
target : Real, optional
keff value to search for, defaults to 1.0.
bracket : None or Iterable of Real, optional
Bracketing interval to search for the solution; if not provided,
a generic non-bracketing method is used. If provided, the brackets
are used. Defaults to no brackets provided. One of `guess` or `bracket`
must be provided. If both are provided, the bracket will be
preferentially used.
model_args : dict, optional
Keyword-based arguments to pass to the `model_builder` method. Defaults
to no arguments.
tol : float
Tolerance to pass to the search method
bracketed_method : {'brentq', 'brenth', 'ridder', 'bisect'}, optional
Solution method to use; only applies if
`bracket` is set, otherwise the Secant method is used.
Defaults to 'bisect'.
print_iterations : bool
Whether or not to print the guess and the result during the iteration
process. Defaults to False.
print_output : bool
Whether or not to print the OpenMC output during the iterations.
Defaults to False.
**kwargs
All remaining keyword arguments are passed to the root-finding
method.
Returns
-------
zero_value : float
Estimated value of the variable parameter where keff is the
targeted value
guesses : List of Real
List of guesses attempted by the search
results : List of 2-tuple of Real
List of keffs and uncertainties corresponding to the guess attempted by
the search
"""
if initial_guess is not None:
cv.check_type('initial_guess', initial_guess, Real)
if bracket is not None:
cv.check_iterable_type('bracket', bracket, Real)
cv.check_length('bracket', bracket, 2)
cv.check_less_than('bracket values', bracket[0], bracket[1])
if model_args is None:
model_args = {}
else:
cv.check_type('model_args', model_args, dict)
cv.check_type('target', target, Real)
cv.check_type('tol', tol, Real)
cv.check_value('bracketed_method', bracketed_method,
_SCALAR_BRACKETED_METHODS)
cv.check_type('print_iterations', print_iterations, bool)
cv.check_type('print_output', print_output, bool)
cv.check_type('model_builder', model_builder, Callable)
# Run the model builder function once to make sure it provides the correct
# output type
if bracket is not None:
model = model_builder(bracket[0], **model_args)
elif initial_guess is not None:
model = model_builder(initial_guess, **model_args)
cv.check_type('model_builder return', model, openmc.model.Model)
# Set the iteration data storage variables
guesses = []
results = []
# Set the searching function (for easy replacement should a later
# generic function be added.
search_function = _search_keff
if bracket is not None:
# Generate our arguments
args = {'f': search_function, 'a': bracket[0], 'b': bracket[1]}
if tol is not None:
args['rtol'] = tol
# Set the root finding method
if bracketed_method == 'brentq':
root_finder = sopt.brentq
elif bracketed_method == 'brenth':
root_finder = sopt.brenth
elif bracketed_method == 'ridder':
root_finder = sopt.ridder
elif bracketed_method == 'bisect':
root_finder = sopt.bisect
elif initial_guess is not None:
# Generate our arguments
args = {'func': search_function, 'x0': initial_guess}
if tol is not None:
args['tol'] = tol
# Set the root finding method
root_finder = sopt.newton
else:
raise ValueError("Either the 'bracket' or 'initial_guess' parameters "
"must be set")
# Add information to be passed to the searching function
args['args'] = (target, model_builder, model_args, print_iterations,
print_output, guesses, results)
# Create a new dictionary with the arguments from args and kwargs
args.update(kwargs)
# Perform the search
zero_value = root_finder(**args)
return zero_value, guesses, results
def add_element(self,
element,
percent,
percent_type='ao',
enrichment=None,
enrichment_target=None,
enrichment_type=None):
"""Add a natural element to the material
Parameters
----------
element : str
Element to add, e.g., 'Zr' or 'Zirconium'
percent : float
Atom or weight percent
percent_type : {'ao', 'wo'}, optional
'ao' for atom percent and 'wo' for weight percent. Defaults to atom
percent.
enrichment : float, optional
Enrichment of an enrichment_taget nuclide in percent (ao or wo).
If enrichment_taget is not supplied then it is enrichment for U235
in weight percent. For example, input 4.95 for 4.95 weight percent
enriched U.
Default is None (natural composition).
enrichment_target: str, optional
Single nuclide name to enrich from a natural composition (e.g., 'O16')
.. versionadded:: 0.12
enrichment_type: {'ao', 'wo'}, optional
'ao' for enrichment as atom percent and 'wo' for weight percent.
Default is: 'ao' for two-isotope enrichment; 'wo' for U enrichment
.. versionadded:: 0.12
Notes
-----
General enrichment procedure is allowed only for elements composed of
two isotopes. If `enrichment_target` is given without `enrichment`
natural composition is added to the material.
"""
cv.check_type('nuclide', element, str)
cv.check_type('percent', percent, Real)
cv.check_value('percent type', percent_type, {'ao', 'wo'})
# Make sure element name is just that
if not element.isalpha():
raise ValueError(
"Element name should be given by the "
"element's symbol or name, e.g., 'Zr', 'zirconium'")
# Allow for element identifier to be given as a symbol or name
if len(element) > 2:
el = element.lower()
element = openmc.data.ELEMENT_SYMBOL.get(el)
if element is None:
msg = 'Element name "{}" not recognised'.format(el)
raise ValueError(msg)
else:
if element[0].islower():
msg = 'Element name "{}" should start with an uppercase ' \
'letter'.format(element)
raise ValueError(msg)
if len(element) == 2 and element[1].isupper():
msg = 'Element name "{}" should end with a lowercase ' \
'letter'.format(element)
raise ValueError(msg)
# skips the first entry of ATOMIC_SYMBOL which is n for neutron
if element not in list(openmc.data.ATOMIC_SYMBOL.values())[1:]:
msg = 'Element name "{}" not recognised'.format(element)
raise ValueError(msg)
if self._macroscopic is not None:
msg = 'Unable to add an Element to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if enrichment is not None and enrichment_target is None:
if not isinstance(enrichment, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point enrichment value "{}"'\
.format(self._id, enrichment)
raise ValueError(msg)
elif element != 'U':
msg = 'Unable to use enrichment for element {} which is not ' \
'uranium for Material ID="{}"'.format(element, self._id)
raise ValueError(msg)
# Check that the enrichment is in the valid range
cv.check_less_than('enrichment', enrichment, 100. / 1.008)
cv.check_greater_than('enrichment', enrichment, 0., equality=True)
if enrichment > 5.0:
msg = 'A uranium enrichment of {} was given for Material ID='\
'"{}". OpenMC assumes the U234/U235 mass ratio is '\
'constant at 0.008, which is only valid at low ' \
'enrichments. Consider setting the isotopic ' \
'composition manually for enrichments over 5%.'.\
format(enrichment, self._id)
warnings.warn(msg)
# Add naturally-occuring isotopes
element = openmc.Element(element)
for nuclide in element.expand(percent, percent_type, enrichment,
enrichment_target, enrichment_type):
self.add_nuclide(*nuclide)
def get_xsdata(self, domain, xsdata_name, nuclide='total', xs_type='macro',
xs_id='1m', order=None, tabular_legendre=None,
tabular_points=33):
"""Generates an openmc.XSdata object describing a multi-group cross section
data set for eventual combination in to an openmc.MGXSLibrary object
(i.e., the library).
Parameters
----------
domain : openmc.Material or openmc.Cell or openmc.Universe
The domain for spatial homogenization
xsdata_name : str
Name to apply to the "xsdata" entry produced by this method
nuclide : str
A nuclide name string (e.g., 'U-235'). Defaults to 'total' to
obtain a material-wise macroscopic cross section.
xs_type: {'macro', 'micro'}
Provide the macro or micro cross section in units of cm^-1 or
barns. Defaults to 'macro'. If the Library object is not tallied by
nuclide this will be set to 'macro' regardless.
xs_ids : str
Cross section set identifier. Defaults to '1m'.
order : int
Scattering order for this data entry. Default is None,
which will set the XSdata object to use the order of the
Library.
tabular_legendre : None or bool
Flag to denote whether or not the Legendre expansion of the
scattering angular distribution is to be converted to a tabular
representation by OpenMC. A value of `True` means that it is to be
converted while a value of `False` means that it will not be.
Defaults to `None` which leaves the default behavior of OpenMC in
place (the distribution is converted to a tabular representation).
tabular_points : int
This parameter is not used unless the ``tabular_legendre``
parameter is set to `True`. In this case, this parameter sets the
number of equally-spaced points in the domain of [-1,1] to be used
in building the tabular distribution. Default is `33`.
Returns
-------
xsdata : openmc.XSdata
Multi-Group Cross Section data set object.
Raises
------
ValueError
When the Library object is initialized with insufficient types of
cross sections for the Library.
See also
--------
Library.create_mg_library()
"""
cv.check_type('domain', domain, (openmc.Material, openmc.Cell,
openmc.Cell))
cv.check_type('xsdata_name', xsdata_name, basestring)
cv.check_type('nuclide', nuclide, basestring)
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
cv.check_type('xs_id', xs_id, basestring)
cv.check_type('order', order, (type(None), Integral))
if order is not None:
cv.check_greater_than('order', order, 0, equality=True)
cv.check_less_than('order', order, 10, equality=True)
cv.check_type('tabular_legendre', tabular_legendre,
(type(None), bool))
if tabular_points is not None:
cv.check_greater_than('tabular_points', tabular_points, 1)
# Make sure statepoint has been loaded
if self._sp_filename is None:
msg = 'A StatePoint must be loaded before calling ' \
'the create_mg_library() function'
raise ValueError(msg)
# If gathering material-specific data, set the xs_type to macro
if not self.by_nuclide:
xs_type = 'macro'
# Build & add metadata to XSdata object
name = xsdata_name
if nuclide is not 'total':
name += '_' + nuclide
name += '.' + xs_id
xsdata = openmc.XSdata(name, self.energy_groups)
if order is None:
# Set the order to the Library's order (the defualt behavior)
xsdata.order = self.legendre_order
else:
# Set the order of the xsdata object to the minimum of
# the provided order or the Library's order.
xsdata.order = min(order, self.legendre_order)
# Set the tabular_legendre option if needed
if tabular_legendre is not None:
xsdata.tabular_legendre = {'enable': tabular_legendre,
'num_points': tabular_points}
if nuclide is not 'total':
xsdata.zaid = self._nuclides[nuclide][0]
xsdata.awr = self._nuclides[nuclide][1]
# Now get xs data itself
if 'nu-transport' in self.mgxs_types and self.correction == 'P0':
mymgxs = self.get_mgxs(domain, 'nu-transport')
xsdata.set_total_mgxs(mymgxs, xs_type=xs_type, nuclide=[nuclide])
elif 'total' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'total')
xsdata.set_total_mgxs(mymgxs, xs_type=xs_type, nuclide=[nuclide])
if 'absorption' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'absorption')
xsdata.set_absorption_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
if 'fission' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'fission')
xsdata.set_fission_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
if 'kappa-fission' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'kappa-fission')
xsdata.set_kappa_fission_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
# For chi and nu-fission we can either have only a nu-fission matrix
# provided, or vectors of chi and nu-fission provided
if 'nu-fission matrix' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'nu-fission matrix')
xsdata.set_nu_fission_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
else:
if 'chi' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'chi')
xsdata.set_chi_mgxs(mymgxs, xs_type=xs_type, nuclide=[nuclide])
if 'nu-fission' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'nu-fission')
xsdata.set_nu_fission_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
# If multiplicity matrix is available, prefer that
if 'multiplicity matrix' in self.mgxs_types:
mymgxs = self.get_mgxs(domain, 'multiplicity matrix')
xsdata.set_multiplicity_mgxs(mymgxs, xs_type=xs_type,
nuclide=[nuclide])
using_multiplicity = True
# multiplicity wil fall back to using scatter and nu-scatter
elif ((('scatter matrix' in self.mgxs_types) and
('nu-scatter matrix' in self.mgxs_types))):
scatt_mgxs = self.get_mgxs(domain, 'scatter matrix')
nuscatt_mgxs = self.get_mgxs(domain, 'nu-scatter matrix')
xsdata.set_multiplicity_mgxs(nuscatt_mgxs, scatt_mgxs,
xs_type=xs_type, nuclide=[nuclide])
using_multiplicity = True
else:
using_multiplicity = False
if using_multiplicity:
nuscatt_mgxs = self.get_mgxs(domain, 'nu-scatter matrix')
xsdata.set_scatter_mgxs(nuscatt_mgxs, xs_type=xs_type,
nuclide=[nuclide])
else:
if 'nu-scatter matrix' in self.mgxs_types:
nuscatt_mgxs = self.get_mgxs(domain, 'nu-scatter matrix')
xsdata.set_scatter_mgxs(nuscatt_mgxs, xs_type=xs_type,
nuclide=[nuclide])
# Since we are not using multiplicity, then
# scattering multiplication (nu-scatter) must be
# accounted for approximately by using an adjusted
# absorption cross section.
if 'total' in self.mgxs_types:
xsdata.absorption = \
np.subtract(xsdata.total,
np.sum(xsdata.scatter[0, :, :], axis=1))
return xsdata
def add_element(self,
element,
percent,
percent_type='ao',
enrichment=None):
"""Add a natural element to the material
Parameters
----------
element : openmc.Element or str
Element to add
percent : float
Atom or weight percent
percent_type : {'ao', 'wo'}, optional
'ao' for atom percent and 'wo' for weight percent. Defaults to atom
percent.
enrichment : float, optional
Enrichment for U235 in weight percent. For example, input 4.95 for
4.95 weight percent enriched U. Default is None
(natural composition).
"""
if self._macroscopic is not None:
msg = 'Unable to add an Element to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if not isinstance(element, string_types + (openmc.Element, )):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-Element value "{}"'.format(self._id, element)
raise ValueError(msg)
if not isinstance(percent, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point value "{}"'.format(self._id, percent)
raise ValueError(msg)
if percent_type not in ['ao', 'wo']:
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'percent type "{}"'.format(self._id, percent_type)
raise ValueError(msg)
# Copy this Element to separate it from same Element in other Materials
if isinstance(element, openmc.Element):
element = deepcopy(element)
else:
element = openmc.Element(element)
if enrichment is not None:
if not isinstance(enrichment, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point enrichment value "{}"'\
.format(self._id, enrichment)
raise ValueError(msg)
elif element.name != 'U':
msg = 'Unable to use enrichment for element {} which is not ' \
'uranium for Material ID="{}"'.format(element.name,
self._id)
raise ValueError(msg)
# Check that the enrichment is in the valid range
cv.check_less_than('enrichment', enrichment, 100. / 1.008)
cv.check_greater_than('enrichment', enrichment, 0., equality=True)
if enrichment > 5.0:
msg = 'A uranium enrichment of {} was given for Material ID='\
'"{}". OpenMC assumes the U234/U235 mass ratio is '\
'constant at 0.008, which is only valid at low ' \
'enrichments. Consider setting the isotopic ' \
'composition manually for enrichments over 5%.'.\
format(enrichment, self._id)
warnings.warn(msg)
self._elements.append((element, percent, percent_type, enrichment))
def get_bin(self, bin_index):
"""Returns the filter bin for some filter bin index.
Parameters
----------
bin_index : Integral
The zero-based index into the filter's array of bins. The bin
index for 'material', 'surface', 'cell', 'cellborn', and 'universe'
filters corresponds to the ID in the filter's list of bins. For
'distribcell' tallies the bin index necessarily can only be zero
since only one cell can be tracked per tally. The bin index for
'energy' and 'energyout' filters corresponds to the energy range of
interest in the filter bins of energies. The bin index for 'mesh'
filters is the index into the flattened array of (x,y) or (x,y,z)
mesh cell bins.
Returns
-------
bin : 1-, 2-, or 3-tuple of Real
The bin in the Tally data array. The bin for 'material', surface',
'cell', 'cellborn', 'universe' and 'distribcell' filters is a
1-tuple of the ID corresponding to the appropriate filter bin.
The bin for 'energy' and 'energyout' filters is a 2-tuple of the
lower and upper energies bounding the energy interval for the filter
bin. The bin for 'mesh' tallies is a 2-tuple or 3-tuple of the x,y
or x,y,z mesh cell indices corresponding to the bin in a 2D/3D mesh.
See also
--------
Filter.get_bin_index()
"""
cv.check_type('bin_index', bin_index, Integral)
cv.check_greater_than('bin_index', bin_index, 0, equality=True)
cv.check_less_than('bin_index', bin_index, self.num_bins)
if self.type == 'mesh':
# Construct 3-tuple of x,y,z cell indices for a 3D mesh
if len(self.mesh.dimension) == 3:
nx, ny, nz = self.mesh.dimension
x = bin_index / (ny * nz)
y = (bin_index - (x * ny * nz)) / nz
z = bin_index - (x * ny * nz) - (y * nz)
filter_bin = (x, y, z)
# Construct 2-tuple of x,y cell indices for a 2D mesh
else:
nx, ny = self.mesh.dimension
x = bin_index / ny
y = bin_index - (x * ny)
filter_bin = (x, y)
# Construct 2-tuple of lower, upper energies for energy(out) filters
elif self.type in ['energy', 'energyout']:
filter_bin = (self.bins[bin_index], self.bins[bin_index + 1])
# Construct 1-tuple of with the cell ID for distribcell filters
elif self.type == 'distribcell':
filter_bin = (self.bins[0], )
# Construct 1-tuple with domain ID (e.g., material) for other filters
else:
filter_bin = (self.bins[bin_index], )
return filter_bin
def search_for_keff(model_builder,
initial_guess=None,
target=1.0,
bracket=None,
model_args=None,
tol=None,
bracketed_method='bisect',
print_iterations=False,
print_output=False,
**kwargs):
"""Function to perform a keff search by modifying a model parametrized by a
single independent variable.
Parameters
----------
model_builder : collections.Callable
Callable function which builds a model according to a passed
parameter. This function must return an openmc.model.Model object.
initial_guess : Real, optional
Initial guess for the parameter to be searched in
`model_builder`. One of `guess` or `bracket` must be provided.
target : Real, optional
keff value to search for, defaults to 1.0.
bracket : None or Iterable of Real, optional
Bracketing interval to search for the solution; if not provided,
a generic non-bracketing method is used. If provided, the brackets
are used. Defaults to no brackets provided. One of `guess` or `bracket`
must be provided. If both are provided, the bracket will be
preferentially used.
model_args : dict, optional
Keyword-based arguments to pass to the `model_builder` method. Defaults
to no arguments.
tol : float
Tolerance to pass to the search method
bracketed_method : {'brentq', 'brenth', 'ridder', 'bisect'}, optional
Solution method to use; only applies if
`bracket` is set, otherwise the Secant method is used.
Defaults to 'bisect'.
print_iterations : bool
Whether or not to print the guess and the result during the iteration
process. Defaults to False.
print_output : bool
Whether or not to print the OpenMC output during the iterations.
Defaults to False.
**kwargs
All remaining keyword arguments are passed to the root-finding
method.
Returns
-------
zero_value : float
Estimated value of the variable parameter where keff is the
targeted value
guesses : List of Real
List of guesses attempted by the search
results : List of 2-tuple of Real
List of keffs and uncertainties corresponding to the guess attempted by
the search
"""
if initial_guess is not None:
cv.check_type('initial_guess', initial_guess, Real)
if bracket is not None:
cv.check_iterable_type('bracket', bracket, Real)
cv.check_length('bracket', bracket, 2)
cv.check_less_than('bracket values', bracket[0], bracket[1])
if model_args is None:
model_args = {}
else:
cv.check_type('model_args', model_args, dict)
cv.check_type('target', target, Real)
cv.check_type('tol', tol, Real)
cv.check_value('bracketed_method', bracketed_method,
_SCALAR_BRACKETED_METHODS)
cv.check_type('print_iterations', print_iterations, bool)
cv.check_type('print_output', print_output, bool)
cv.check_type('model_builder', model_builder, Callable)
# Run the model builder function once to make sure it provides the correct
# output type
if bracket is not None:
model = model_builder(bracket[0], **model_args)
elif initial_guess is not None:
model = model_builder(initial_guess, **model_args)
cv.check_type('model_builder return', model, openmc.model.Model)
# Set the iteration data storage variables
guesses = []
results = []
# Set the searching function (for easy replacement should a later
# generic function be added.
search_function = _search_keff
if bracket is not None:
# Generate our arguments
args = {'f': search_function, 'a': bracket[0], 'b': bracket[1]}
if tol is not None:
args['rtol'] = tol
# Set the root finding method
if bracketed_method == 'brentq':
root_finder = sopt.brentq
elif bracketed_method == 'brenth':
root_finder = sopt.brenth
elif bracketed_method == 'ridder':
root_finder = sopt.ridder
elif bracketed_method == 'bisect':
root_finder = sopt.bisect
elif initial_guess is not None:
# Generate our arguments
args = {'func': search_function, 'x0': initial_guess}
if tol is not None:
args['tol'] = tol
# Set the root finding method
root_finder = sopt.newton
else:
raise ValueError("Either the 'bracket' or 'initial_guess' parameters "
"must be set")
# Add information to be passed to the searching function
args['args'] = (target, model_builder, model_args, print_iterations,
print_output, guesses, results)
# Create a new dictionary with the arguments from args and kwargs
args.update(kwargs)
# Perform the search
zero_value = root_finder(**args)
return zero_value, guesses, results
def verbosity(self, verbosity):
check_type('verbosity', verbosity, Integral)
check_greater_than('verbosity', verbosity, 1, True)
check_less_than('verbosity', verbosity, 10, True)
self._verbosity = verbosity
def expand(self,
percent,
percent_type,
enrichment=None,
enrichment_target=None,
enrichment_type=None,
cross_sections=None):
"""Expand natural element into its naturally-occurring isotopes.
An optional cross_sections argument or the :envvar:`OPENMC_CROSS_SECTIONS`
environment variable is used to specify a cross_sections.xml file.
If the cross_sections.xml file is found, the element is expanded only
into the isotopes/nuclides present in cross_sections.xml. If no
cross_sections.xml file is found, the element is expanded based on its
naturally occurring isotopes.
Parameters
----------
percent : float
Atom or weight percent
percent_type : {'ao', 'wo'}
'ao' for atom percent and 'wo' for weight percent
enrichment : float, optional
Enrichment of an enrichment_taget nuclide in percent (ao or wo).
If enrichment_taget is not supplied then it is enrichment for U235
in weight percent. For example, input 4.95 for 4.95 weight percent
enriched U. Default is None (natural composition).
enrichment_target: str, optional
Single nuclide name to enrich from a natural composition (e.g., 'O16')
.. versionadded:: 0.12
enrichment_type: {'ao', 'wo'}, optional
'ao' for enrichment as atom percent and 'wo' for weight percent.
Default is: 'ao' for two-isotope enrichment; 'wo' for U enrichment
.. versionadded:: 0.12
cross_sections : str, optional
Location of cross_sections.xml file. Default is None.
Returns
-------
isotopes : list
Naturally-occurring isotopes of the element. Each item of the list
is a tuple consisting of a nuclide string, the atom/weight percent,
and the string 'ao' or 'wo'.
Raises
------
ValueError
No data is available for any of natural isotopes of the element
ValueError
If only some natural isotopes are available in the cross-section data
library and the element is not O, W, or Ta
ValueError
If a non-naturally-occurring isotope is requested
ValueError
If enrichment is requested of an element with more than two
naturally-occurring isotopes.
ValueError
If enrichment procedure for Uranium is used when element is not
Uranium.
ValueError
Uranium enrichment is requested with enrichment_type=='ao'
Notes
-----
When the `enrichment` argument is specified, a correlation from
`ORNL/CSD/TM-244 <https://doi.org/10.2172/5561567>`_ is used to
calculate the weight fractions of U234, U235, U236, and U238. Namely,
the weight fraction of U234 and U236 are taken to be 0.89% and 0.46%,
respectively, of the U235 weight fraction. The remainder of the
isotopic weight is assigned to U238.
When the `enrichment` argument is specified with `enrichment_target`, a
general enrichment procedure is used for elements composed of exactly
two naturally-occurring isotopes. `enrichment` is interpreted as atom
percent by default but can be controlled by the `enrichment_type`
argument.
"""
# Check input
if enrichment_type is not None:
cv.check_value('enrichment_type', enrichment_type, {'ao', 'wo'})
if enrichment is not None:
cv.check_less_than('enrichment', enrichment, 100.0, equality=True)
cv.check_greater_than('enrichment', enrichment, 0., equality=True)
# Get the nuclides present in nature
natural_nuclides = {name for name, abundance in natural_isotopes(self)}
# Create dict to store the expanded nuclides and abundances
abundances = OrderedDict()
# If cross_sections is None, get the cross sections from the
# OPENMC_CROSS_SECTIONS environment variable
if cross_sections is None:
cross_sections = os.environ.get('OPENMC_CROSS_SECTIONS')
# If a cross_sections library is present, check natural nuclides
# against the nuclides in the library
if cross_sections is not None:
library_nuclides = set()
tree = ET.parse(cross_sections)
root = tree.getroot()
for child in root.findall('library'):
nuclide = child.attrib['materials']
if re.match(r'{}\d+'.format(self), nuclide) and \
'_m' not in nuclide:
library_nuclides.add(nuclide)
# Get a set of the mutual and absent nuclides. Convert to lists
# and sort to avoid different ordering between Python 2 and 3.
mutual_nuclides = natural_nuclides.intersection(library_nuclides)
absent_nuclides = natural_nuclides.difference(mutual_nuclides)
mutual_nuclides = sorted(list(mutual_nuclides))
absent_nuclides = sorted(list(absent_nuclides))
# If all natural nuclides are present in the library,
# expand element using all natural nuclides
if len(absent_nuclides) == 0:
for nuclide in mutual_nuclides:
abundances[nuclide] = NATURAL_ABUNDANCE[nuclide]
# If no natural elements are present in the library, check if the
# 0 nuclide is present. If so, set the abundance to 1 for this
# nuclide. Else, raise an error.
elif len(mutual_nuclides) == 0:
nuclide_0 = self + '0'
if nuclide_0 in library_nuclides:
abundances[nuclide_0] = 1.0
else:
msg = 'Unable to expand element {0} because the cross '\
'section library provided does not contain any of '\
'the natural isotopes for that element.'\
.format(self)
raise ValueError(msg)
# If some, but not all, natural nuclides are in the library, add
# the mutual nuclides. For the absent nuclides, add them based on
# our knowledge of the common cross section libraries
# (ENDF, JEFF, and JENDL)
else:
# Add the mutual isotopes
for nuclide in mutual_nuclides:
abundances[nuclide] = NATURAL_ABUNDANCE[nuclide]
# Adjust the abundances for the absent nuclides
for nuclide in absent_nuclides:
if nuclide in ['O17', 'O18'] and 'O16' in mutual_nuclides:
abundances['O16'] += NATURAL_ABUNDANCE[nuclide]
elif nuclide == 'Ta180' and 'Ta181' in mutual_nuclides:
abundances['Ta181'] += NATURAL_ABUNDANCE[nuclide]
elif nuclide == 'W180' and 'W182' in mutual_nuclides:
abundances['W182'] += NATURAL_ABUNDANCE[nuclide]
else:
msg = 'Unsure how to partition natural abundance of ' \
'isotope {0} into other natural isotopes of ' \
'this element that are present in the cross ' \
'section library provided. Consider adding ' \
'the isotopes of this element individually.'
raise ValueError(msg)
# If a cross_section library is not present, expand the element into
# its natural nuclides
else:
for nuclide in natural_nuclides:
abundances[nuclide] = NATURAL_ABUNDANCE[nuclide]
# Modify mole fractions if enrichment provided
# Old treatment for Uranium
if enrichment is not None and enrichment_target is None:
# Check that the element is Uranium
if self.name != 'U':
msg = ('Enrichment procedure for Uranium was requested, '
'but the isotope is {} not U'.format(self))
raise ValueError(msg)
# Check that enrichment_type is not 'ao'
if enrichment_type == 'ao':
msg = ('Enrichment procedure for Uranium requires that '
'enrichment value is provided as wo%.')
raise ValueError(msg)
# Calculate the mass fractions of isotopes
abundances['U234'] = 0.0089 * enrichment
abundances['U235'] = enrichment
abundances['U236'] = 0.0046 * enrichment
abundances['U238'] = 100.0 - 1.0135 * enrichment
# Convert the mass fractions to mole fractions
for nuclide in abundances.keys():
abundances[nuclide] /= atomic_mass(nuclide)
# Normalize the mole fractions to one
sum_abundances = sum(abundances.values())
for nuclide in abundances.keys():
abundances[nuclide] /= sum_abundances
# Modify mole fractions if enrichment provided
# New treatment for arbitrary element
elif enrichment is not None and enrichment_target is not None:
# Provide more informative error message for U235
if enrichment_target == 'U235':
msg = ("There is a special procedure for enrichment of U235 "
"in U. To invoke it, the arguments 'enrichment_target'"
"and 'enrichment_type' should be omitted. Provide "
"a value only for 'enrichment' in weight percent.")
raise ValueError(msg)
# Check if it is two-isotope mixture
if len(abundances) != 2:
msg = (
'Element {} does not consist of two naturally-occurring '
'isotopes. Please enter isotopic abundances manually.'.
format(self))
raise ValueError(msg)
# Check if the target nuclide is present in the mixture
if enrichment_target not in abundances:
msg = (
'The target nuclide {} is not one of the naturally-occurring '
'isotopes ({})'.format(enrichment_target,
list(abundances)))
raise ValueError(msg)
# If weight percent enrichment is requested convert to mass fractions
if enrichment_type == 'wo':
# Convert the atomic abundances to weight fractions
# Compute the element atomic mass
element_am = sum(
atomic_mass(nuc) * abundances[nuc] for nuc in abundances)
# Convert Molar Fractions to mass fractions
for nuclide in abundances:
abundances[nuclide] *= atomic_mass(nuclide) / element_am
# Normalize to one
sum_abundances = sum(abundances.values())
for nuclide in abundances:
abundances[nuclide] /= sum_abundances
# Enrich the mixture
# The procedure is more generic that it needs to be. It allows
# to enrich mixtures of more then 2 isotopes, keeping the ratios
# of non-enriched nuclides the same as in natural composition
# Get fraction of non-enriched isotopes in nat. composition
non_enriched = 1.0 - abundances[enrichment_target]
tail_fraction = 1.0 - enrichment / 100.0
# Enrich all nuclides
# Do bogus operation for enrichment target but overwrite immediatly
# to avoid if statement in the loop
for nuclide, fraction in abundances.items():
abundances[nuclide] = tail_fraction * fraction / non_enriched
abundances[enrichment_target] = enrichment / 100.0
# Convert back to atomic fractions if requested
if enrichment_type == 'wo':
# Convert the mass fractions to mole fractions
for nuclide in abundances:
abundances[nuclide] /= atomic_mass(nuclide)
# Normalize the mole fractions to one
sum_abundances = sum(abundances.values())
for nuclide in abundances:
abundances[nuclide] /= sum_abundances
# Compute the ratio of the nuclide atomic masses to the element
# atomic mass
if percent_type == 'wo':
# Compute the element atomic mass
element_am = 0.
for nuclide in abundances.keys():
element_am += atomic_mass(nuclide) * abundances[nuclide]
# Convert the molar fractions to mass fractions
for nuclide in abundances.keys():
abundances[nuclide] *= atomic_mass(nuclide) / element_am
# Normalize the mass fractions to one
sum_abundances = sum(abundances.values())
for nuclide in abundances.keys():
abundances[nuclide] /= sum_abundances
# Create a list of the isotopes in this element
isotopes = []
for nuclide, abundance in abundances.items():
isotopes.append((nuclide, percent * abundance, percent_type))
return isotopes
def add_element(self,
element,
percent,
percent_type='ao',
enrichment=None):
"""Add a natural element to the material
Parameters
----------
element : str
Element to add, e.g., 'Zr'
percent : float
Atom or weight percent
percent_type : {'ao', 'wo'}, optional
'ao' for atom percent and 'wo' for weight percent. Defaults to atom
percent.
enrichment : float, optional
Enrichment for U235 in weight percent. For example, input 4.95 for
4.95 weight percent enriched U. Default is None
(natural composition).
"""
cv.check_type('nuclide', element, str)
cv.check_type('percent', percent, Real)
cv.check_value('percent type', percent_type, {'ao', 'wo'})
if self._macroscopic is not None:
msg = 'Unable to add an Element to Material ID="{}" as a ' \
'macroscopic data-set has already been added'.format(self._id)
raise ValueError(msg)
if enrichment is not None:
if not isinstance(enrichment, Real):
msg = 'Unable to add an Element to Material ID="{}" with a ' \
'non-floating point enrichment value "{}"'\
.format(self._id, enrichment)
raise ValueError(msg)
elif element != 'U':
msg = 'Unable to use enrichment for element {} which is not ' \
'uranium for Material ID="{}"'.format(element, self._id)
raise ValueError(msg)
# Check that the enrichment is in the valid range
cv.check_less_than('enrichment', enrichment, 100. / 1.008)
cv.check_greater_than('enrichment', enrichment, 0., equality=True)
if enrichment > 5.0:
msg = 'A uranium enrichment of {} was given for Material ID='\
'"{}". OpenMC assumes the U234/U235 mass ratio is '\
'constant at 0.008, which is only valid at low ' \
'enrichments. Consider setting the isotopic ' \
'composition manually for enrichments over 5%.'.\
format(enrichment, self._id)
warnings.warn(msg)
# Make sure element name is just that
if not element.isalpha():
raise ValueError("Element name should be given by the "
"element's symbol, e.g., 'Zr'")
# Add naturally-occuring isotopes
element = openmc.Element(element)
for nuclide in element.expand(percent, percent_type, enrichment):
self.add_nuclide(*nuclide)
def verbosity(self, verbosity):
check_type("verbosity", verbosity, Integral)
check_greater_than("verbosity", verbosity, 1, True)
check_less_than("verbosity", verbosity, 10, True)
self._verbosity = verbosity
def get_bin(self, bin_index):
"""Returns the filter bin for some filter bin index.
Parameters
----------
bin_index : Integral
The zero-based index into the filter's array of bins. The bin
index for 'material', 'surface', 'cell', 'cellborn', and 'universe'
filters corresponds to the ID in the filter's list of bins. For
'distribcell' tallies the bin index necessarily can only be zero
since only one cell can be tracked per tally. The bin index for
'energy' and 'energyout' filters corresponds to the energy range of
interest in the filter bins of energies. The bin index for 'mesh'
filters is the index into the flattened array of (x,y) or (x,y,z)
mesh cell bins.
Returns
-------
bin : 1-, 2-, or 3-tuple of Real
The bin in the Tally data array. The bin for 'material', surface',
'cell', 'cellborn', 'universe' and 'distribcell' filters is a
1-tuple of the ID corresponding to the appropriate filter bin.
The bin for 'energy' and 'energyout' filters is a 2-tuple of the
lower and upper energies bounding the energy interval for the filter
bin. The bin for 'mesh' tallies is a 2-tuple or 3-tuple of the x,y
or x,y,z mesh cell indices corresponding to the bin in a 2D/3D mesh.
See also
--------
Filter.get_bin_index()
"""
cv.check_type('bin_index', bin_index, Integral)
cv.check_greater_than('bin_index', bin_index, 0, equality=True)
cv.check_less_than('bin_index', bin_index, self.num_bins)
if self.type == 'mesh':
# Construct 3-tuple of x,y,z cell indices for a 3D mesh
if len(self.mesh.dimension) == 3:
nx, ny, nz = self.mesh.dimension
x = bin_index / (ny * nz)
y = (bin_index - (x * ny * nz)) / nz
z = bin_index - (x * ny * nz) - (y * nz)
filter_bin = (x, y, z)
# Construct 2-tuple of x,y cell indices for a 2D mesh
else:
nx, ny = self.mesh.dimension
x = bin_index / ny
y = bin_index - (x * ny)
filter_bin = (x, y)
# Construct 2-tuple of lower, upper energies for energy(out) filters
elif self.type in ['energy', 'energyout']:
filter_bin = (self.bins[bin_index], self.bins[bin_index+1])
# Construct 1-tuple of with the cell ID for distribcell filters
elif self.type == 'distribcell':
filter_bin = (self.bins[0],)
# Construct 1-tuple with domain ID (e.g., material) for other filters
else:
filter_bin = (self.bins[bin_index],)
return filter_bin
def pin(surfaces, items, subdivisions=None, divide_vols=True, **kwargs):
"""Convenience function for building a fuel pin
Parameters
----------
surfaces : iterable of :class:`openmc.Cylinder`
Cylinders used to define boundaries
between items. All cylinders must be
concentric and of the same orientation, e.g.
all :class:`openmc.ZCylinder`
items : iterable
Objects to go between ``surfaces``. These can be anything
that can fill a :class:`openmc.Cell`, including
:class:`openmc.Material`, or other :class:`openmc.Universe`
objects. There must be one more item than surfaces,
which will span all space outside the final ring.
subdivisions : None or dict of int to int
Dictionary describing which rings to subdivide and how
many times. Keys are indexes of the annular rings
to be divided. Will construct equal area rings
divide_vols : bool
If this evaluates to ``True``, then volumes of subdivided
:class:`openmc.Material` instances will also be divided by the
number of divisions. Otherwise the volume of the
original material will not be modified before subdivision
kwargs:
Additional key-word arguments to be passed to
:class:`openmc.Universe`, like ``name="Fuel pin"``
Returns
-------
:class:`openmc.Universe`
Universe of concentric cylinders filled with the desired
items
"""
if "cells" in kwargs:
raise SyntaxError(
"Cells will be set by this function, not from input arguments.")
check_type("items", items, Iterable)
check_length("surfaces", surfaces, len(items) - 1, len(items) - 1)
# Check that all surfaces are of similar orientation
check_type("surface", surfaces[0], Cylinder)
surf_type = type(surfaces[0])
check_iterable_type("surfaces", surfaces[1:], surf_type)
# Check for increasing radii and equal centers
if surf_type is ZCylinder:
center_getter = attrgetter("x0", "y0")
elif surf_type is YCylinder:
center_getter = attrgetter("x0", "z0")
elif surf_type is XCylinder:
center_getter = attrgetter("z0", "y0")
else:
raise TypeError("Not configured to interpret {} surfaces".format(
surf_type.__name__))
centers = set()
prev_rad = 0
for ix, surf in enumerate(surfaces):
cur_rad = surf.r
if cur_rad <= prev_rad:
raise ValueError(
"Surfaces do not appear to be increasing in radius. "
"Surface {} at index {} has radius {:7.3e} compared to "
"previous radius of {:7.5e}".format(surf.id, ix, cur_rad,
prev_rad))
prev_rad = cur_rad
centers.add(center_getter(surf))
if len(centers) > 1:
raise ValueError(
"Surfaces do not appear to be concentric. The following "
"centers were found: {}".format(centers))
if subdivisions is not None:
check_length("subdivisions", subdivisions, 1, len(surfaces))
orig_indexes = list(subdivisions.keys())
check_iterable_type("ring indexes", orig_indexes, int)
check_iterable_type("number of divisions", list(subdivisions.values()),
int)
for ix in orig_indexes:
if ix < 0:
subdivisions[len(surfaces) + ix] = subdivisions.pop(ix)
# Dissallow subdivision on outer most, infinite region
check_less_than("outer ring",
max(subdivisions),
len(surfaces),
equality=True)
# ensure ability to concatenate
if not isinstance(items, list):
items = list(items)
if not isinstance(surfaces, list):
surfaces = list(surfaces)
# generate equal area divisions
# Adding N - 1 new regions
# N - 2 surfaces are made
# Original cell is not removed, but not occupies last ring
for ring_index in reversed(sorted(subdivisions.keys())):
nr = subdivisions[ring_index]
new_surfs = []
lower_rad = 0.0 if ring_index == 0 else surfaces[ring_index - 1].r
upper_rad = surfaces[ring_index].r
area_term = (upper_rad**2 - lower_rad**2) / nr
for new_index in range(nr - 1):
lower_rad = sqrt(area_term + lower_rad**2)
new_surfs.append(surf_type(r=lower_rad))
surfaces = (surfaces[:ring_index] + new_surfs +
surfaces[ring_index:])
filler = items[ring_index]
if (divide_vols and hasattr(filler, "volume")
and filler.volume is not None):
filler.volume /= nr
items[ring_index:ring_index] = [
filler.clone() for _i in range(nr - 1)
]
# Build the universe
regions = subdivide(surfaces)
cells = [Cell(fill=f, region=r) for r, f in zip(regions, items)]
return Universe(cells=cells, **kwargs)