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Changes in filter.py from PullRequest Inc. review
This commit is contained in:
Paul Romano
parent
e92044bc4b
commit
a5605ea3a1
1 changed files with 36 additions and 61 deletions
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@ -36,13 +36,16 @@ _PARTICLES = {'neutron', 'photon', 'electron', 'positron'}
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class FilterMeta(ABCMeta):
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"""Metaclass for filters that ensures class names are appropriate."""
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def __new__(cls, name, bases, namespace, **kwargs):
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# Check the class name.
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if not name.endswith('Filter'):
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required_suffix = 'Filter'
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if not name.endswith(required_suffix):
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raise ValueError("All filter class names must end with 'Filter'")
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# Create a 'short_name' attribute that removes the 'Filter' suffix.
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namespace['short_name'] = name[:-6]
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namespace['short_name'] = name[:-len(required_suffix)]
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# Subclass methods can sort of inherit the docstring of parent class
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# methods. If a function is defined without a docstring, most (all?)
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@ -51,7 +54,7 @@ class FilterMeta(ABCMeta):
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# use that docstring. However, Sphinx does not have that functionality.
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# This chunk of code handles this docstring inheritance manually so that
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# the autodocumentation will pick it up.
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if name != 'Filter':
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if name != required_suffix:
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# Look for newly-defined functions that were also in Filter.
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for func_name in namespace:
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if func_name in Filter.__dict__:
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@ -72,6 +75,12 @@ class FilterMeta(ABCMeta):
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return super().__new__(cls, name, bases, namespace, **kwargs)
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def _repeat_and_tile(bins, repeat_factor, data_size):
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filter_bins = np.repeat(bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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return np.tile(filter_bins, tile_factor)
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class Filter(IDManagerMixin, metaclass=FilterMeta):
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"""Tally modifier that describes phase-space and other characteristics.
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@ -292,8 +301,8 @@ class Filter(IDManagerMixin, metaclass=FilterMeta):
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if type(self) is not type(other):
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return False
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for bin in other.bins:
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if bin not in self.bins:
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for b in other.bins:
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if b not in self.bins:
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return False
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return True
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@ -389,8 +398,7 @@ class WithIDFilter(Filter):
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# Extract ID values
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bins = np.array([b if isinstance(b, Integral) else b.id
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for b in bins])
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self.bins = bins
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self.id = filter_id
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super().__init__(bins, filter_id)
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def check_bins(self, bins):
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# Check the bin values.
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@ -804,28 +812,16 @@ class MeshFilter(Filter):
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ny = nz = 1
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# Generate multi-index sub-column for x-axis
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filter_bins = np.arange(1, nx + 1)
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repeat_factor = stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'x')] = filter_bins
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filter_dict[mesh_key, 'x'] = _repeat_and_tile(
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np.arange(1, nx + 1), stride, data_size)
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# Generate multi-index sub-column for y-axis
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filter_bins = np.arange(1, ny + 1)
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repeat_factor = nx * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'y')] = filter_bins
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filter_dict[mesh_key, 'y'] = _repeat_and_tile(
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np.arange(1, ny + 1), nx * stride, data_size)
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# Generate multi-index sub-column for z-axis
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filter_bins = np.arange(1, nz + 1)
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repeat_factor = nx * ny * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'z')] = filter_bins
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filter_dict[mesh_key, 'z'] = _repeat_and_tile(
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np.arange(1, nz + 1), nx * ny * stride, data_size)
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# Initialize a Pandas DataFrame from the mesh dictionary
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df = pd.concat([df, pd.DataFrame(filter_dict)])
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@ -933,37 +929,22 @@ class MeshSurfaceFilter(MeshFilter):
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ny = nz = 1
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# Generate multi-index sub-column for x-axis
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filter_bins = np.arange(1, nx + 1)
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repeat_factor = n_surfs * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'x')] = filter_bins
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filter_dict[mesh_key, 'x'] = _repeat_and_tile(
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np.arange(1, nx + 1), n_surfs * stride, data_size)
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# Generate multi-index sub-column for y-axis
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if len(self.mesh.dimension) > 1:
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filter_bins = np.arange(1, ny + 1)
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repeat_factor = n_surfs * nx * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'y')] = filter_bins
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filter_dict[mesh_key, 'y'] = _repeat_and_tile(
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np.arange(1, ny + 1), n_surfs * nx * stride, data_size)
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# Generate multi-index sub-column for z-axis
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if len(self.mesh.dimension) > 2:
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filter_bins = np.arange(1, nz + 1)
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repeat_factor = n_surfs * nx * ny * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'z')] = filter_bins
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filter_dict[mesh_key, 'z'] = _repeat_and_tile(
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np.arange(1, nz + 1), n_surfs * nx * ny * stride, data_size)
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# Generate multi-index sub-column for surface
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repeat_factor = stride
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filter_bins = np.repeat(_CURRENT_NAMES[:n_surfs], repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'surf')] = filter_bins
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filter_dict[mesh_key, 'surf'] = _repeat_and_tile(
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_CURRENT_NAMES[:n_surfs], stride, data_size)
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# Initialize a Pandas DataFrame from the mesh dictionary
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return pd.concat([df, pd.DataFrame(filter_dict)])
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@ -1386,8 +1367,8 @@ class DistribcellFilter(Filter):
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Returns
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-------
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pandas.DataFrame
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A Pandas DataFrame with columns describing distributed cells. The
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for will be either:
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A Pandas DataFrame with columns describing distributed cells. The
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dataframe will have either:
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1. a single column with the cell instance IDs (without summary info)
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2. separate columns for the cell IDs, universe IDs, and lattice IDs
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@ -1479,10 +1460,8 @@ class DistribcellFilter(Filter):
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# Tile the Multi-index columns
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for level_key, level_bins in level_dict.items():
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level_bins = np.repeat(level_bins, stride)
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tile_factor = data_size // len(level_bins)
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level_bins = np.tile(level_bins, tile_factor)
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level_dict[level_key] = level_bins
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level_dict[level_key] = _repeat_and_tile(
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level_bins, stride, data_size)
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# Initialize a Pandas DataFrame from the level dictionary
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if level_df is None:
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@ -1494,10 +1473,8 @@ class DistribcellFilter(Filter):
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# Create DataFrame column for distribcell instance IDs
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# NOTE: This is performed regardless of whether the user
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# requests Summary geometric information
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filter_bins = np.arange(self.num_bins)
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filter_bins = np.repeat(filter_bins, stride)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_bins = _repeat_and_tile(
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np.arange(self.num_bins), stride, data_size)
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df = pd.DataFrame({self.short_name.lower() : filter_bins})
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# Concatenate with DataFrame of distribcell instance IDs
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@ -1905,9 +1882,7 @@ class EnergyFunctionFilter(Filter):
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# hex characters) of the digest are probably sufficient.
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out = out[:14]
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filter_bins = np.repeat(out, stride)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_bins = _repeat_and_tile(out, stride, data_size)
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df = pd.concat([df, pd.DataFrame(
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{self.short_name.lower(): filter_bins})])
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