Resolving @paulromano comments

examples/python/pincell_multigroup/build-xml.py

@@ -36,7 +36,7 @@ scatter_matrix = np.array(
       [0.0000000, 0.0000000, 0.0000000, 0.0001253, 0.2714010, 0.0102550, 0.0000000],
       [0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0012968, 0.2658020, 0.0168090],
       [0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0085458, 0.2730800]]])
-scatter_matrix = np.swapaxes(np.swapaxes(scatter_matrix, 0, 1), 1, 2)
+scatter_matrix = np.rollaxis(scatter_matrix, 0, 3)
 uo2_xsdata.set_scatter_matrix(scatter_matrix)
 uo2_xsdata.set_fission([7.21206E-03, 8.19301E-04, 6.45320E-03,
                         1.85648E-02, 1.78084E-02, 8.30348E-02,
@@ -62,7 +62,7 @@ scatter_matrix = np.array(
       [0.0000000, 0.0000000, 0.0000000, 0.0000714, 0.1391380, 0.5118200, 0.0612290],
       [0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0022157, 0.6999130, 0.5373200],
       [0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1324400, 2.4807000]]])
-scatter_matrix = np.swapaxes(np.swapaxes(scatter_matrix, 0, 1), 1, 2)
+scatter_matrix = np.rollaxis(scatter_matrix, 0, 3)
 h2o_xsdata.set_scatter_matrix(scatter_matrix)
 
 mg_cross_sections_file = openmc.MGXSLibrary(groups)

openmc/filter.py

@@ -100,10 +100,13 @@ class Filter(object):
     @classmethod
     def _recursive_subclasses(cls):
         """Return all subclasses and their subclasses, etc."""
-        subs = cls.__subclasses__()
-        subsubs = [grand for s in subs for grand in s.__subclasses__()]
-        subsubsubs = [grand for s in subsubs for grand in s.__subclasses__()]
-        return subs + subsubs + subsubsubs
+        all_subclasses = []
+
+        for subclass in cls.__subclasses__():
+            all_subclasses.append(subclass)
+            all_subclasses.extend(subclass._recursive_subclasses())
+
+        return all_subclasses
 
     @classmethod
     def from_hdf5(cls, group, **kwargs):

openmc/mgxs/library.py

@@ -59,7 +59,7 @@ class Library(object):
         The spatial domain(s) for which MGXS in the Library are computed
     correction : {'P0', None}
         Apply the P0 correction to scattering matrices if set to 'P0'
-    scatter_format : {'legendre', or 'histogram'}
+    scatter_format : {'legendre', 'histogram'}
         Representation of the angular scattering distribution (default is
         'legendre')
     legendre_order : int

openmc/mgxs/mgxs.py

@@ -1529,9 +1529,19 @@ class MGXS(object):
         else:
             df = df.drop('score', axis=1)
 
+        # Determine if change-in-angle bins are included in the MGXS to
+        # properly tile the group boundaries
+        if 'mu low' in df:
+            # Find the length of the mu filters indirectly from the number
+            # of times the mu bins repeats.
+            num_mu = int(df.shape[0] /
+                         df[df['mu low'] == df['mu low'][0]].shape[0])
+        else:
+            num_mu = 1
+
         # Override energy groups bounds with indices
         all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
-        all_groups = np.repeat(all_groups, len(query_nuclides))
+        all_groups = np.repeat(all_groups, len(query_nuclides) * num_mu)
         if 'energy low [eV]' in df and 'energyout low [eV]' in df:
             df.rename(columns={'energy low [eV]': 'group in'},
                       inplace=True)
@@ -3791,10 +3801,8 @@ class ScatterMatrixXS(MatrixMGXS):
             # than 1, so try each axis in axes one at a time, catching the
             # ValueError as needed.
             for axis in axes:
-                try:
+                if xs.shape[axis] == 1:
                     xs = np.squeeze(xs, axis=axis)
-                except ValueError:
-                    pass
 
         return xs
 
@@ -3874,9 +3882,12 @@ class ScatterMatrixXS(MatrixMGXS):
                 df = df[df['moment'] == 'P{}'.format(moment)]
 
         elif self.scatter_format == 'histogram':
-            # Add a change-in-angle (mu) column to dataframe
-            ###TODO NOT SURE I NEED TO DO THIS
-            pass
+            # Replace the mu low and mu high columns with a single mu bin
+            del df['mu high']
+            df.rename(columns={'mu low': 'mu bins'}, inplace=True)
+            bins = [i + 1 for i in range(self.histogram_bins)]
+            bins = np.tile(bins, int(df.shape[0] / len(bins)))
+            df['mu bins'] = bins
 
         return df
 

openmc/mgxs_library.py

@@ -1773,8 +1773,14 @@ class XSdata(object):
                                     np.sum(self._scatter_matrix[i][p, a, g_in, :, :],
                                            axis=1)
                         nz = np.nonzero(matrix)
-                        g_out_bounds[p, a, g_in, 0] = nz[0][0]
-                        g_out_bounds[p, a, g_in, 1] = nz[0][-1]
+                        # It is possible that there only zeros in matrix
+                        # and therefore nz will be empty, in that case set
+                        # g_out_bounds to 0s
+                        if len(nz[0]) == 0:
+                            g_out_bounds[p, a, g_in, :] = 0
+                        else:
+                            g_out_bounds[p, a, g_in, 0] = nz[0][0]
+                            g_out_bounds[p, a, g_in, 1] = nz[0][-1]
 
             # Now create the flattened scatter matrix array
             flat_scatt = []

tests/test_tallies/test_tallies.py

@@ -3,13 +3,14 @@
 import os
 import sys
 sys.path.insert(0, os.pardir)
-import numpy as np
+
 from testing_harness import PyAPITestHarness
 from openmc.filter import *
 from openmc import Mesh, Tally, Tallies
 from openmc.source import Source
 from openmc.stats import Box
 
+
 class TalliesTestHarness(PyAPITestHarness):
     def _build_inputs(self):
         # Build default materials/geometry