ENDF support in openmc.data and resonance reconstruction

.gitignore

@@ -89,3 +89,8 @@ docs/source/pythonapi/examples/mgxs
 docs/source/pythonapi/examples/tracks
 docs/source/pythonapi/examples/fission-rates
 docs/source/pythonapi/examples/plots
+
+# Cython files
+*.c
+*.html
+*.so

docs/source/pythonapi/index.rst

@@ -333,6 +333,7 @@ Functions
 .. autosummary::
    :toctree: generated
    :nosignatures:
+   :template: myfunction.rst
 
    openmc.model.create_triso_lattice
    openmc.model.pack_trisos
@@ -341,16 +342,6 @@ Functions
 :mod:`openmc.data` -- Nuclear Data Interface
 --------------------------------------------
 
-Physical Data
--------------
-
-.. autosummary::
-    :toctree: generated
-    :nosignatures:
-    :template: myfunction.rst
-
-    openmc.data.atomic_mass
-
 Core Classes
 ------------
 
@@ -363,11 +354,23 @@ Core Classes
     openmc.data.Reaction
     openmc.data.Product
     openmc.data.Tabulated1D
+    openmc.data.FissionEnergyRelease
     openmc.data.ThermalScattering
     openmc.data.CoherentElastic
     openmc.data.FissionEnergyRelease
     openmc.data.DataLibrary
 
+Core Functions
+--------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: myfunction.rst
+
+    openmc.data.atomic_mass
+    openmc.data.write_compact_458_library
+
 Angle-Energy Distributions
 --------------------------
 
@@ -381,6 +384,7 @@ Angle-Energy Distributions
     openmc.data.CorrelatedAngleEnergy
     openmc.data.UncorrelatedAngleEnergy
     openmc.data.NBodyPhaseSpace
+    openmc.data.LaboratoryAngleEnergy
     openmc.data.AngleDistribution
     openmc.data.EnergyDistribution
     openmc.data.ArbitraryTabulated
@@ -393,6 +397,24 @@ Angle-Energy Distributions
     openmc.data.LevelInelastic
     openmc.data.ContinuousTabular
 
+Resonance Data
+--------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: myclass.rst
+
+    openmc.data.Resonances
+    openmc.data.ResonanceRange
+    openmc.data.SingleLevelBreitWigner
+    openmc.data.MultiLevelBreitWigner
+    openmc.data.ReichMoore
+    openmc.data.RMatrixLimited
+    openmc.data.ParticlePair
+    openmc.data.SpinGroup
+    openmc.data.Unresolved
+
 ACE Format
 ----------
 
@@ -413,9 +435,37 @@ Functions
 .. autosummary::
     :toctree: generated
     :nosignatures:
+    :template: myfunction.rst
 
     openmc.data.ace.ascii_to_binary
-    openmc.data.write_compact_458_library
+
+ENDF Format
+-----------
+
+Classes
++++++++
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: myclass.rst
+
+    openmc.data.endf.Evaluation
+
+Functions
++++++++++
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: myfunction.rst
+
+    openmc.data.endf.float_endf
+    openmc.data.endf.get_cont_record
+    openmc.data.endf.get_head_record
+    openmc.data.endf.get_tab1_record
+    openmc.data.endf.get_tab2_record
+    openmc.data.endf.get_text_record
 
 .. _Jupyter: https://jupyter.org/
 .. _NumPy: http://www.numpy.org/

openmc/data/__init__.py

@@ -4,6 +4,7 @@
 from .ace import *
 from .angle_distribution import *
 from .function import *
+from .endf import *
 from .energy_distribution import *
 from .product import *
 from .angle_energy import *
@@ -15,3 +16,4 @@
 from .urr import *
 from .library import *
 from .fission_energy import *
+from .resonance import *

openmc/data/angle_distribution.py

@@ -1,12 +1,16 @@
 from collections import Iterable
+from io import StringIO
 from numbers import Real
+from warnings import warn
 
 import numpy as np
 
 import openmc.checkvalue as cv
 from openmc.mixin import EqualityMixin
-from openmc.stats import Univariate, Tabular, Uniform
+from openmc.stats import Univariate, Tabular, Uniform, Legendre
 from .function import INTERPOLATION_SCHEME
+from .endf import get_head_record, get_cont_record, get_tab1_record, \
+    get_list_record, get_tab2_record
 
 
 class AngleDistribution(EqualityMixin):
@@ -199,3 +203,107 @@ def from_ace(cls, ace, location_dist, location_start):
             mu.append(mu_i)
 
         return cls(energy, mu)
+
+    @classmethod
+    def from_endf(cls, ev, mt):
+        """Generate an angular distribution from an ENDF evaluation
+
+        Parameters
+        ----------
+        ev : openmc.data.endf.Evaluation
+            ENDF evaluation
+        mt : int
+            The MT value of the reaction to get angular distributions for
+
+        Returns
+        -------
+        openmc.data.AngleDistribution
+            Angular distribution
+
+        """
+        file_obj = StringIO(ev.section[4, mt])
+
+        # Read HEAD record
+        items = get_head_record(file_obj)
+        lvt = items[2]
+        ltt = items[3]
+
+        # Read CONT record
+        items = get_cont_record(file_obj)
+        li = items[2]
+        nk = items[4]
+        center_of_mass = (items[3] == 2)
+
+        # Check for obsolete energy transformation matrix. If present, just skip
+        # it and keep reading
+        if lvt > 0:
+            warn('Obsolete energy transformation matrix in MF=4 angular '
+                 'distribution.')
+            for _ in range((nk + 5)//6):
+                file_obj.readline()
+
+        if ltt == 0 and li == 1:
+            # Purely isotropic
+            energy = np.array([0., ev.info['energy_max']])
+            mu = [Uniform(-1., 1.), Uniform(-1., 1.)]
+
+        elif ltt == 1 and li == 0:
+            # Legendre polynomial coefficients
+            params, tab2 = get_tab2_record(file_obj)
+            n_energy = params[5]
+
+            energy = np.zeros(n_energy)
+            mu = []
+            for i in range(n_energy):
+                items, al = get_list_record(file_obj)
+                temperature = items[0]
+                energy[i] = items[1]
+                coefficients = np.asarray([1.0] + al)
+                mu.append(Legendre(coefficients))
+
+        elif ltt == 2 and li == 0:
+            # Tabulated probability distribution
+            params, tab2 = get_tab2_record(file_obj)
+            n_energy = params[5]
+
+            energy = np.zeros(n_energy)
+            mu = []
+            for i in range(n_energy):
+                params, f = get_tab1_record(file_obj)
+                temperature = params[0]
+                energy[i] = params[1]
+                if f.n_regions > 1:
+                    raise NotImplementedError('Angular distribution with multiple '
+                                              'interpolation regions not supported.')
+                mu.append(Tabular(f.x, f.y, INTERPOLATION_SCHEME[f.interpolation[0]]))
+
+        elif ltt == 3 and li == 0:
+            # Legendre for low energies / tabulated for high energies
+            params, tab2 = get_tab2_record(file_obj)
+            n_energy_legendre = params[5]
+
+            energy_legendre = np.zeros(n_energy_legendre)
+            mu = []
+            for i in range(n_energy_legendre):
+                items, al = get_list_record(file_obj)
+                temperature = items[0]
+                energy_legendre[i] = items[1]
+                coefficients = np.asarray([1.0] + al)
+                mu.append(Legendre(coefficients))
+
+            params, tab2 = get_tab2_record(file_obj)
+            n_energy_tabulated = params[5]
+
+            energy_tabulated = np.zeros(n_energy_tabulated)
+            for i in range(n_energy_tabulated):
+                params, f = get_tab1_record(file_obj)
+                temperature = params[0]
+                energy_tabulated[i] = params[1]
+                if f.n_regions > 1:
+                    raise NotImplementedError('Angular distribution with multiple '
+                                              'interpolation regions not supported.')
+                mu.append(Tabular(f.x, f.y, INTERPOLATION_SCHEME[f.interpolation[0]]))
+
+            energy = np.concatenate((energy_legendre, energy_tabulated))
+
+        return AngleDistribution(energy, mu)

openmc/data/angle_energy.py

@@ -1,4 +1,5 @@
 from abc import ABCMeta, abstractmethod
+from io import StringIO
 
 import openmc.data
 from openmc.mixin import EqualityMixin
@@ -40,7 +41,7 @@ def from_hdf5(group):
 
     @staticmethod
     def from_ace(ace, location_dist, location_start, rx=None):
-        """Generate an AngleEnergy object from ACE data
+        """Generate an angle-energy distribution from ACE data
 
         Parameters
         ----------

openmc/data/correlated.py

@@ -5,9 +5,11 @@
 import numpy as np
 
 import openmc.checkvalue as cv
-from openmc.stats import Tabular, Univariate, Discrete, Mixture, Uniform
+from openmc.stats import Tabular, Univariate, Discrete, Mixture, \
+    Uniform, Legendre
 from .function import INTERPOLATION_SCHEME
 from .angle_energy import AngleEnergy
+from .endf import get_list_record, get_tab2_record
 
 
 class CorrelatedAngleEnergy(AngleEnergy):
@@ -405,3 +407,52 @@ def from_ace(cls, ace, idx, ldis):
             mu.append(mu_i)
 
         return cls(breakpoints, interpolation, energy, energy_out, mu)
+
+    @classmethod
+    def from_endf(cls, file_obj):
+        """Generate correlated angle-energy distribution from an ENDF evaluation
+
+        Parameters
+        ----------
+        file_obj : file-like object
+            ENDF file positioned at the start of a section for a correlated
+            angle-energy distribution
+
+        Returns
+        -------
+        openmc.data.CorrelatedAngleEnergy
+            Correlated angle-energy distribution
+
+        """
+        params, tab2 = get_tab2_record(file_obj)
+        lep = params[3]
+        ne = params[5]
+        energy = np.zeros(ne)
+        n_discrete_energies = np.zeros(ne, dtype=int)
+        energy_out = []
+        mu = []
+        for i in range(ne):
+            items, values = get_list_record(file_obj)
+            energy[i] = items[1]
+            n_discrete_energies[i] = items[2]
+            # TODO: separate out discrete lines
+            n_angle = items[3]
+            n_energy_out = items[5]
+            values = np.asarray(values)
+            values.shape = (n_energy_out, n_angle + 2)
+
+            # Outgoing energy distribution at the i-th incoming energy
+            eout_i = values[:,0]
+            eout_p_i = values[:,1]
+            energy_out_i = Tabular(eout_i, eout_p_i, INTERPOLATION_SCHEME[lep],
+                                   ignore_negative=True)
+            energy_out.append(energy_out_i)
+
+            # Legendre coefficients used for angular distributions
+            mu_i = []
+            for j in range(n_energy_out):
+                mu_i.append(Legendre(values[j,1:]))
+            mu.append(mu_i)
+
+        return cls(tab2.breakpoints, tab2.interpolation, energy,
+                   energy_out, mu)