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Merge pull request #764 from nelsonag/from_hdf5_misc
Addition of a from_hdf5 method for a MGXSLibrary class
This commit is contained in:
Paul Romano
GitHub
commit
b6da077ab8
4 changed files with 239 additions and 35 deletions
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@ -818,7 +818,7 @@ class Reaction(EqualityMixin):
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Parameters
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----------
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group : h5py.Group
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HDF5 group to write to
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HDF5 group to read from
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energy : dict
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Dictionary whose keys are temperatures (e.g., '300K') and values are
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arrays of energies at which cross sections are tabulated at.
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@ -1,5 +1,3 @@
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import sys
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from six import string_types
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from openmc.checkvalue import check_type
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@ -45,7 +43,7 @@ class Macroscopic(object):
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return hash((self._name))
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def __repr__(self):
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string = 'Nuclide - {0}\n'.format(self._name)
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string = 'Macroscopic - {0}\n'.format(self._name)
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return string
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@property
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@ -62,6 +62,9 @@ _DOMAINS = (openmc.Cell,
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# Supported ScatterMatrixXS and NuScatterMatrixXS angular distribution types
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MU_TREATMENTS = ('legendre', 'histogram')
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# Maximum Legendre order supported by OpenMC
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_MAX_LEGENDRE = 10
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@add_metaclass(ABCMeta)
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class MGXS(object):
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@ -3475,7 +3478,8 @@ class ScatterMatrixXS(MatrixMGXS):
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cv.check_type('legendre_order', legendre_order, Integral)
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cv.check_greater_than('legendre_order', legendre_order, 0,
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equality=True)
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cv.check_less_than('legendre_order', legendre_order, 10, equality=True)
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cv.check_less_than('legendre_order', legendre_order, _MAX_LEGENDRE,
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equality=True)
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if self.scatter_format == 'legendre':
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if self.correction == 'P0' and legendre_order > 0:
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@ -1,6 +1,6 @@
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from collections import Iterable
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from numbers import Real, Integral
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import sys
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import os
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from six import string_types
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import numpy as np
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@ -16,7 +16,7 @@ from openmc.checkvalue import check_type, check_value, check_greater_than, \
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_REPRESENTATIONS = ['isotropic', 'angle']
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_SCATTER_TYPES = ['tabular', 'legendre', 'histogram']
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_XS_SHAPES = ["[G][G'][Order]", "[G]", "[G']", "[G][G']", "[DG]", "[G][DG]",
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"[G'][DG]"]
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"[G'][DG]", "[G][G'][DG]"]
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class XSdata(object):
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@ -42,7 +42,7 @@ class XSdata(object):
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----------
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name : str
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Unique identifier for the xsdata object
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aromic_weight_ratio : float
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atomic_weight_ratio : float
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Atomic weight ratio of an isotope. That is, the ratio of the mass
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of the isotope to the mass of a single neutron.
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temperatures : numpy.ndarray
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@ -71,50 +71,50 @@ class XSdata(object):
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Number of equal width angular bins that the polar angular domain is
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subdivided into. This only applies when :attr:`XSdata.representation`
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is "angle".
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total : dict of numpy.ndarray
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total : list of numpy.ndarray
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Group-wise total cross section.
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absorption : dict of numpy.ndarray
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absorption : list of numpy.ndarray
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Group-wise absorption cross section.
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scatter_matrix : dict of numpy.ndarray
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scatter_matrix : list of numpy.ndarray
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Scattering moment matrices presented with the columns representing
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incoming group and rows representing the outgoing group. That is,
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down-scatter will be above the diagonal of the resultant matrix.
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multiplicity_matrix : dict of numpy.ndarray
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multiplicity_matrix : list of numpy.ndarray
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Ratio of neutrons produced in scattering collisions to the neutrons
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which undergo scattering collisions; that is, the multiplicity provides
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the code with a scaling factor to account for neutrons produced in
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(n,xn) reactions.
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fission : dict of numpy.ndarray
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fission : list of numpy.ndarray
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Group-wise fission cross section.
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kappa_fission : dict of numpy.ndarray
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kappa_fission : list of numpy.ndarray
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Group-wise kappa_fission cross section.
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chi : dict of numpy.ndarray
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chi : list of numpy.ndarray
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Group-wise fission spectra ordered by increasing group index (i.e.,
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fast to thermal). This attribute should be used if making the common
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approximation that the fission spectra does not depend on incoming
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energy. If the user does not wish to make this approximation, then
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this should not be provided and this information included in the
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:attr:`XSdata.nu_fission` attribute instead.
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chi_prompt : dict of numpy.ndarray
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chi_prompt : list of numpy.ndarray
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Group-wise prompt fission spectra ordered by increasing group index
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(i.e., fast to thermal). This attribute should be used if chi from
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prompt and delayed neutrons is being set separately.
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chi_delayed : dict of numpy.ndarray
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chi_delayed : list of numpy.ndarray
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Group-wise delayed fission spectra ordered by increasing group index
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(i.e., fast to thermal). This attribute should be used if chi from
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prompt and delayed neutrons is being set separately.
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nu_fission : dict of numpy.ndarray
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nu_fission : list of numpy.ndarray
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Group-wise fission production cross section vector (i.e., if ``chi`` is
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provided), or is the group-wise fission production matrix.
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prompt_nu_fission : dict of numpy.ndarray
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prompt_nu_fission : list of numpy.ndarray
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Group-wise prompt fission production cross section vector.
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delayed_nu_fission : dict of numpy.ndarray
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delayed_nu_fission : list of numpy.ndarray
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Group-wise delayed fission production cross section vector.
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beta : dict of numpy.ndarray
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beta : list of numpy.ndarray
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Delayed-group-wise delayed neutron fraction cross section vector.
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decay_rate : dict of numpy.ndarray
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decay_rate : list of numpy.ndarray
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Delayed-group-wise decay rate vector.
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inverse_velocity : dict of numpy.ndarray
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inverse_velocity : list of numpy.ndarray
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Inverse of velocity, in units of sec/cm.
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xs_shapes : dict of iterable of int
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Dictionary with keys of _XS_SHAPES and iterable of int values with the
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@ -303,13 +303,14 @@ class XSdata(object):
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self._xs_shapes["[G][G'][DG]"] = (self.energy_groups.num_groups,
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self.energy_groups.num_groups,
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self.num_delayed_groups)
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self._xs_shapes["[G][G'][Order]"] \
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= (self.energy_groups.num_groups,
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self.energy_groups.num_groups, self.num_orders)
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# If representation is by angle prepend num polar and num azim
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if self.representation == 'angle':
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for key,shapes in self._xs_shapes.items():
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for key, shapes in self._xs_shapes.items():
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self._xs_shapes[key] \
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= (self.num_polar, self.num_azimuthal) + shapes
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@ -337,7 +338,7 @@ class XSdata(object):
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def num_delayed_groups(self, num_delayed_groups):
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# Check validity of num_delayed_groups
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check_type('num_delayed_groups', num_delayed_groups, int)
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check_type('num_delayed_groups', num_delayed_groups, Integral)
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check_less_than('num_delayed_groups', num_delayed_groups,
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openmc.mgxs.MAX_DELAYED_GROUPS, equality=True)
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check_greater_than('num_delayed_groups', num_delayed_groups, 0,
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@ -1652,6 +1653,7 @@ class XSdata(object):
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HDF5 File (a root Group) to write to
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"""
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grp = file.create_group(self.name)
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if self.atomic_weight_ratio is not None:
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grp.attrs['atomic_weight_ratio'] = self.atomic_weight_ratio
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@ -1719,11 +1721,12 @@ class XSdata(object):
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(self._delayed_nu_fission[i] is None or \
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self._prompt_nu_fission[i] is None):
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raise ValueError('nu-fission or prompt-nu-fission and '
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'delayed-nu-fission data must be provided '
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'when writing the HDF5 library')
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'delayed-nu-fission data must be '
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'provided when writing the HDF5 library')
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if self._nu_fission[i] is not None:
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xs_grp.create_dataset("nu-fission", data=self._nu_fission[i])
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xs_grp.create_dataset("nu-fission",
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data=self._nu_fission[i])
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if self._prompt_nu_fission[i] is not None:
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xs_grp.create_dataset("prompt-nu-fission",
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@ -1737,7 +1740,8 @@ class XSdata(object):
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xs_grp.create_dataset("beta", data=self._beta[i])
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if self._decay_rate[i] is not None:
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xs_grp.create_dataset("decay rate", data=self._decay_rate[i])
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xs_grp.create_dataset("decay rate",
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data=self._decay_rate[i])
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if self._scatter_matrix[i] is None:
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raise ValueError('Scatter matrix must be provided when '
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@ -1836,6 +1840,143 @@ class XSdata(object):
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xs_grp.create_dataset("inverse-velocity",
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data=self._inverse_velocity[i])
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@classmethod
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def from_hdf5(cls, group, name, energy_groups, num_delayed_groups):
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"""Generate XSdata object from an HDF5 group
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Parameters
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----------
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group : h5py.Group
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HDF5 group to read from
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name : str
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Name of the mgxs data set.
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energy_groups : openmc.mgxs.EnergyGroups
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Energy group structure
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num_delayed_groups : int
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Number of delayed groups
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Returns
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-------
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openmc.XSdata
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Multi-group cross section data
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"""
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# Get a list of all the subgroups which will contain our temperature
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# strings
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subgroups = group.keys()
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temperatures = []
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for subgroup in subgroups:
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if subgroup != 'kTs':
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temperatures.append(subgroup)
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# To ensure the actual floating point temperature used when creating
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# the new library is consistent with that used when originally creating
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# the file, get the floating point temperatures straight from the kTs
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# group.
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kTs_group = group['kTs']
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float_temperatures = []
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for temperature in temperatures:
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kT = kTs_group[temperature].value
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float_temperatures.append(kT / openmc.data.K_BOLTZMANN)
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attrs = group.attrs.keys()
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if 'representation' in attrs:
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representation = group.attrs['representation'].decode()
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else:
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representation = 'isotropic'
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data = cls(name, energy_groups, float_temperatures, representation,
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num_delayed_groups)
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if 'scatter_format' in attrs:
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data.scatter_format = group.attrs['scatter_format'].decode()
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# Get the remaining optional attributes
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if 'atomic_weight_ratio' in attrs:
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data.atomic_weight_ratio = group.attrs['atomic_weight_ratio']
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if 'order' in attrs:
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data.order = group.attrs['order']
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if data.representation == 'angle':
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data.num_azimuthal = group.attrs['num_azimuthal']
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data.num_polar = group.attrs['num_polar']
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# Read the temperature-dependent datasets
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for temp, float_temp in zip(temperatures, float_temperatures):
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xs_types = ['total', 'absorption', 'fission', 'kappa-fission',
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'chi', 'chi-prompt', 'chi-delayed', 'nu-fission',
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'prompt-nu-fission', 'delayed-nu-fission', 'beta',
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'decay rate', 'inverse-velocity']
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temperature_group = group[temp]
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for xs_type in xs_types:
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set_func = 'set_' + xs_type.replace(' ', '_').replace('-', '_')
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if xs_type in temperature_group:
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getattr(data, set_func)(temperature_group[xs_type].value,
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float_temp)
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scatt_group = temperature_group['scatter_data']
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# Get scatter matrix and 'un-flatten' it
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g_max = scatt_group['g_max']
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g_min = scatt_group['g_min']
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flat_scatter = scatt_group['scatter_matrix'].value
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scatter_matrix = np.zeros(data.xs_shapes["[G][G'][Order]"])
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G = data.energy_groups.num_groups
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if data.representation == 'isotropic':
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Np = 1
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Na = 1
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elif data.representation == 'angle':
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Np = data.num_polar
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Na = data.num_azimuthal
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flat_index = 0
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for p in range(Np):
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for a in range(Na):
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for g_in in range(G):
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if data.representation == 'isotropic':
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g_mins = g_min[g_in]
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g_maxs = g_max[g_in]
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elif data.representation == 'angle':
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g_mins = g_min[p, a, g_in]
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g_maxs = g_max[p, a, g_in]
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for g_out in range(g_mins - 1, g_maxs):
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for ang in range(data.num_orders):
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if data.representation == 'isotropic':
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scatter_matrix[g_in, g_out, ang] = \
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flat_scatter[flat_index]
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elif data.representation == 'angle':
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scatter_matrix[p, a, g_in, g_out, ang] = \
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flat_scatter[flat_index]
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flat_index += 1
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data.set_scatter_matrix(scatter_matrix, float_temp)
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# Repeat for multiplicity
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if 'multiplicity_matrix' in scatt_group:
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flat_mult = scatt_group['multiplicity_matrix'].value
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mult_matrix = np.zeros(data.xs_shapes["[G][G']"])
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flat_index = 0
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for p in range(Np):
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for a in range(Na):
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for g_in in range(G):
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if data.representation == 'isotropic':
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g_mins = g_min[g_in]
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g_maxs = g_max[g_in]
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elif data.representation == 'angle':
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g_mins = g_min[p, a, g_in]
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g_maxs = g_max[p, a, g_in]
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for g_out in range(g_mins - 1, g_maxs):
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if data.representation == 'isotropic':
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mult_matrix[g_in, g_out] = \
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flat_mult[flat_index]
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elif data.representation == 'angle':
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mult_matrix[p, a, g_in, g_out] = \
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flat_mult[flat_index]
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flat_index += 1
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data.set_multiplicity_matrix(mult_matrix, float_temp)
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return data
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class MGXSLibrary(object):
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"""Multi-Group Cross Sections file used for an OpenMC simulation.
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@ -1872,14 +2013,14 @@ class MGXSLibrary(object):
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def num_delayed_groups(self):
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return self._num_delayed_groups
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@property
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def temperatures(self):
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return self._temperatures
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@property
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def xsdatas(self):
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return self._xsdatas
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@property
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def names(self):
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return [xsdata.name for xsdata in self.xsdatas]
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@energy_groups.setter
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def energy_groups(self, energy_groups):
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check_type('energy groups', energy_groups, openmc.mgxs.EnergyGroups)
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@ -1887,7 +2028,7 @@ class MGXSLibrary(object):
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@num_delayed_groups.setter
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def num_delayed_groups(self, num_delayed_groups):
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check_type('num_delayed_groups', num_delayed_groups, int)
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check_type('num_delayed_groups', num_delayed_groups, Integral)
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check_greater_than('num_delayed_groups', num_delayed_groups, 0,
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equality=True)
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check_less_than('num_delayed_groups', num_delayed_groups,
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@ -1916,7 +2057,7 @@ class MGXSLibrary(object):
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self._xsdatas.append(xsdata)
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def add_xsdatas(self, xsdatas):
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"""Add multiple xsdatas to the file.
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"""Add multiple XSdatas to the file.
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Parameters
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----------
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@ -1947,6 +2088,27 @@ class MGXSLibrary(object):
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self._xsdatas.remove(xsdata)
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def get_by_name(self, name):
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"""Access the XSdata objects by name
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Parameters
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----------
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name : str
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Name of openmc.XSdata object to obtain
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Returns
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-------
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result : openmc.XSdata or None
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Provides the matching XSdata object or None, if not found
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"""
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check_type("name", name, str)
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result = None
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for xsdata in self.xsdatas:
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if name == xsdata.name:
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result = xsdata
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return result
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def export_to_hdf5(self, filename='mgxs.h5'):
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"""Create an hdf5 file that can be used for a simulation.
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@ -1969,3 +2131,43 @@ class MGXSLibrary(object):
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xsdata.to_hdf5(file)
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file.close()
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@classmethod
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def from_hdf5(cls, filename=None):
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"""Generate an MGXS Library from an HDF5 group or file
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Parameters
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----------
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filename : str, optional
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Name of HDF5 file containing MGXS data. Default is None.
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If not provided, the value of the OPENMC_MG_CROSS_SECTIONS
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environmental variable will be used
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Returns
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-------
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openmc.MGXSLibrary
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Multi-group cross section data object.
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"""
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# If filename is None, get the cross sections from the
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# OPENMC_CROSS_SECTIONS environment variable
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if filename is None:
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filename = os.environ.get('OPENMC_MG_CROSS_SECTIONS')
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# Check to make sure there was an environmental variable.
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if filename is None:
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raise ValueError("Either path or OPENMC_MG_CROSS_SECTIONS "
|
||||
"environmental variable must be set")
|
||||
|
||||
check_type('filename', filename, str)
|
||||
file = h5py.File(filename, 'r')
|
||||
|
||||
group_structure = file.attrs['group structure']
|
||||
num_delayed_groups = file.attrs['delayed_groups']
|
||||
energy_groups = openmc.mgxs.EnergyGroups(group_structure)
|
||||
data = cls(energy_groups, num_delayed_groups)
|
||||
|
||||
for group_name, group in file.items():
|
||||
data.add_xsdata(openmc.XSdata.from_hdf5(group, group_name,
|
||||
energy_groups,
|
||||
num_delayed_groups))
|
||||
|
||||
return data
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue