Remove _smart_set_bins in favor of overriding Filter.bins.setter

openmc/filter.py

@@ -14,7 +14,10 @@
 
 import openmc
 import openmc.checkvalue as cv
+from .cell import Cell
+from .material import Material
 from .mixin import IDManagerMixin
+from .universe import Universe
 
 
 _FILTER_TYPES = ['universe', 'material', 'cell', 'cellborn', 'surface',
@@ -170,7 +173,7 @@ def from_hdf5(cls, group, **kwargs):
         # If the HDF5 'type' variable matches this class's short_name, then
         # there is no overriden from_hdf5 method.  Pass the bins to __init__.
         if group['type'].value.decode() == cls.short_name.lower():
-            out = cls(group['bins'].value, filter_id)
+            out = cls(group['bins'].value, filter_id=filter_id)
             out._num_bins = group['n_bins'].value
             return out
 
@@ -425,12 +428,15 @@ def get_pandas_dataframe(self, data_size, stride, **kwargs):
 
 class WithIDFilter(Filter):
     """Abstract parent for filters of types with ids (Cell, Material, etc.)."""
-    def _smart_set_bins(self, bins, bin_type):
+
+    @Filter.bins.setter
+    def bins(self, bins):
         # Format the bins as a 1D numpy array.
         bins = np.atleast_1d(bins)
 
         # Check the bin values.
-        cv.check_iterable_type('filter bins', bins, (Integral, bin_type))
+        cv.check_iterable_type('filter bins', bins,
+                               (Integral, self.expected_type))
         for edge in bins:
             if isinstance(edge, Integral):
                 cv.check_greater_than('filter bin', edge, 0, equality=True)
@@ -463,13 +469,7 @@ class UniverseFilter(WithIDFilter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
-    def bins(self, bins):
-        self._smart_set_bins(bins, openmc.Universe)
+    expected_type = Universe
 
 
 class MaterialFilter(WithIDFilter):
@@ -493,13 +493,7 @@ class MaterialFilter(WithIDFilter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
-    def bins(self, bins):
-        self._smart_set_bins(bins, openmc.Material)
+    expected_type = Material
 
 
 class CellFilter(WithIDFilter):
@@ -523,13 +517,7 @@ class CellFilter(WithIDFilter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
-    def bins(self, bins):
-        self._smart_set_bins(bins, openmc.Cell)
+    expected_type = Cell
 
 
 class CellFromFilter(WithIDFilter):
@@ -553,13 +541,7 @@ class CellFromFilter(WithIDFilter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
-    def bins(self, bins):
-        self._smart_set_bins(bins, openmc.Cell)
+    expected_type = Cell
 
 
 class CellbornFilter(WithIDFilter):
@@ -583,13 +565,7 @@ class CellbornFilter(WithIDFilter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
-    def bins(self, bins):
-        self._smart_set_bins(bins, openmc.Cell)
+    expected_type = Cell
 
 
 class SurfaceFilter(Filter):
@@ -614,11 +590,7 @@ class SurfaceFilter(Filter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
+    @Filter.bins.setter
     def bins(self, bins):
         # Format the bins as a 1D numpy array.
         bins = np.atleast_1d(bins)
@@ -720,7 +692,7 @@ def from_hdf5(cls, group, **kwargs):
         mesh_obj = kwargs['meshes'][mesh_id]
         filter_id = int(group.name.split('/')[-1].lstrip('filter '))
 
-        out = cls(mesh_obj, filter_id)
+        out = cls(mesh_obj, filter_id=filter_id)
         out._num_bins = group['n_bins'].value
 
         return out
@@ -1169,15 +1141,11 @@ def from_hdf5(cls, group, **kwargs):
 
         filter_id = int(group.name.split('/')[-1].lstrip('filter '))
 
-        out = cls(group['bins'].value, filter_id)
+        out = cls(group['bins'].value, filter_id=filter_id)
         out._num_bins = group['n_bins'].value
 
         return out
 
-    @property
-    def bins(self):
-        return self._bins
-
     @property
     def num_bins(self):
         # Need to handle number of bins carefully -- for distribcell tallies, we
@@ -1188,7 +1156,7 @@ def num_bins(self):
     def paths(self):
         return self._paths
 
-    @bins.setter
+    @Filter.bins.setter
     def bins(self, bins):
         # Format the bins as a 1D numpy array.
         bins = np.atleast_1d(bins)
@@ -1690,11 +1658,7 @@ class DelayedGroupFilter(Filter):
         The number of filter bins
 
     """
-    @property
-    def bins(self):
-        return self._bins
-
-    @bins.setter
+    @Filter.bins.setter
     def bins(self, bins):
         # Format the bins as a 1D numpy array.
         bins = np.atleast_1d(bins)
@@ -1799,7 +1763,7 @@ def from_hdf5(cls, group, **kwargs):
         y = group['y'].value
         filter_id = int(group.name.split('/')[-1].lstrip('filter '))
 
-        return cls(energy, y, filter_id)
+        return cls(energy, y, filter_id=filter_id)
 
     @classmethod
     def from_tabulated1d(cls, tab1d):
@@ -1836,7 +1800,7 @@ def y(self):
 
     @property
     def bins(self):
-        raise RuntimeError('EnergyFunctionFilters have no bins.')
+        raise AttributeError('EnergyFunctionFilters have no bins.')
 
     @property
     def num_bins(self):

openmc/tallies.py

@@ -237,10 +237,10 @@ def sum(self):
 
             # Convert NumPy arrays to SciPy sparse LIL matrices
             if self.sparse:
-                self._sum = \
-                    sps.lil_matrix(self._sum.flatten(), self._sum.shape)
-                self._sum_sq = \
-                    sps.lil_matrix(self._sum_sq.flatten(), self._sum_sq.shape)
+                self._sum = sps.lil_matrix(self._sum.flatten(),
+                                           self._sum.shape)
+                self._sum_sq = sps.lil_matrix(self._sum_sq.flatten(),
+                                              self._sum_sq.shape)
 
             # Indicate that Tally results have been read
             self._results_read = True
@@ -2817,25 +2817,33 @@ def get_slice(self, scores=[], filters=[], filter_bins=[], nuclides=[]):
 
                 # Remove and/or reorder filter bins to user specifications
                 bin_indices = []
-                num_bins = 0
 
                 for filter_bin in filter_bins[i]:
                     bin_index = find_filter.get_bin_index(filter_bin)
-                    if filter_type in [openmc.EnergyFilter,
-                                       openmc.EnergyoutFilter]:
-                        bin_indices.extend([bin_index])
+                    if issubclass(filter_type, openmc.RealFilter):
                         bin_indices.extend([bin_index, bin_index+1])
-                        num_bins += 1
-                    elif filter_type in [openmc.DistribcellFilter,
-                                         openmc.MeshFilter]:
-                        bin_indices = [0]
-                        num_bins = find_filter.num_bins
                     else:
                         bin_indices.append(bin_index)
-                        num_bins += 1
 
-                find_filter.bins = np.unique(find_filter.bins[bin_indices])
-                find_filter._num_bins = num_bins
+                # Set bins for mesh/distribcell filters apart from others
+                if filter_type is openmc.MeshFilter:
+                    bins = find_filter.mesh
+                elif filter_type is openmc.DistribcellFilter:
+                    bins = find_filter.bins
+                else:
+                    bins = np.unique(find_filter.bins[bin_indices])
+
+                # Create new filter
+                new_filter = filter_type(bins)
+
+                # Set number of bins manually for mesh/distribcell filters
+                if filter_type in (openmc.DistribcellFilter, openmc.MeshFilter):
+                    new_filter._num_bins = find_filter._num_bins
+
+                # Replace existing filter with new one
+                for j, test_filter in enumerate(new_tally.filters):
+                    if isinstance(test_filter, filter_type):
+                        new_tally.filters[j] = new_filter
 
         # If original tally was sparse, sparsify the sliced tally
         new_tally.sparse = self.sparse