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Merge remote-tracking branch 'origin/tile_factor' into tally_fix

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This commit is contained in:
Adam Nelson 2017-03-04 05:14:53 -05:00
commit 42ee464362
1 changed files with 14 additions and 14 deletions
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28
openmc/filter.py
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@ -425,7 +425,7 @@ class Filter(object):
df = pd.DataFrame()
filter_bins = np.repeat(self.bins, self.stride)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
df = pd.concat([df, pd.DataFrame(
{self.short_name.lower(): filter_bins})])
@ -613,7 +613,7 @@ class SurfaceFilter(IntegralFilter):
df = pd.DataFrame()
filter_bins = np.repeat(self.bins, self.stride)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
filter_bins = [_CURRENT_NAMES[x] for x in filter_bins]
df = pd.concat([df, pd.DataFrame(
@ -781,26 +781,26 @@ class MeshFilter(Filter):
nz = 1
# Generate multi-index sub-column for x-axis
filter_bins = np.arange(1, nx+1)
filter_bins = np.arange(1, nx + 1)
repeat_factor = ny * nz * self.stride
filter_bins = np.repeat(filter_bins, repeat_factor)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
filter_dict[(mesh_key, 'x')] = filter_bins
# Generate multi-index sub-column for y-axis
filter_bins = np.arange(1, ny+1)
filter_bins = np.arange(1, ny + 1)
repeat_factor = nz * self.stride
filter_bins = np.repeat(filter_bins, repeat_factor)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
filter_dict[(mesh_key, 'y')] = filter_bins
# Generate multi-index sub-column for z-axis
filter_bins = np.arange(1, nz+1)
filter_bins = np.arange(1, nz + 1)
repeat_factor = self.stride
filter_bins = np.repeat(filter_bins, repeat_factor)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
filter_dict[(mesh_key, 'z')] = filter_bins
@ -1281,7 +1281,7 @@ class DistribcellFilter(Filter):
# Tile the Multi-index columns
for level_key, level_bins in level_dict.items():
level_bins = np.repeat(level_bins, self.stride)
tile_factor = data_size / len(level_bins)
tile_factor = int(data_size / len(level_bins))
level_bins = np.tile(level_bins, tile_factor)
level_dict[level_key] = level_bins
@ -1297,7 +1297,7 @@ class DistribcellFilter(Filter):
# requests Summary geometric information
filter_bins = np.arange(self.num_bins)
filter_bins = np.repeat(filter_bins, self.stride)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
df = pd.DataFrame({self.short_name.lower() : filter_bins})
@ -1402,7 +1402,7 @@ class MuFilter(RealFilter):
# them as necessary to account for other filters.
lo_bins = np.repeat(self.bins[:-1], self.stride)
hi_bins = np.repeat(self.bins[1:], self.stride)
tile_factor = data_size / len(lo_bins)
tile_factor = int(data_size / len(lo_bins))
lo_bins = np.tile(lo_bins, tile_factor)
hi_bins = np.tile(hi_bins, tile_factor)
@ -1505,7 +1505,7 @@ class PolarFilter(RealFilter):
# them as necessary to account for other filters.
lo_bins = np.repeat(self.bins[:-1], self.stride)
hi_bins = np.repeat(self.bins[1:], self.stride)
tile_factor = data_size / len(lo_bins)
tile_factor = int(data_size / len(lo_bins))
lo_bins = np.tile(lo_bins, tile_factor)
hi_bins = np.tile(hi_bins, tile_factor)
@ -1608,7 +1608,7 @@ class AzimuthalFilter(RealFilter):
# them as necessary to account for other filters.
lo_bins = np.repeat(self.bins[:-1], self.stride)
hi_bins = np.repeat(self.bins[1:], self.stride)
tile_factor = data_size / len(lo_bins)
tile_factor = int(data_size / len(lo_bins))
lo_bins = np.tile(lo_bins, tile_factor)
hi_bins = np.tile(hi_bins, tile_factor)
@ -1871,7 +1871,7 @@ class EnergyFunctionFilter(Filter):
out = out[:14]
filter_bins = np.repeat(out, self.stride)
tile_factor = data_size / len(filter_bins)
tile_factor = int(data_size / len(filter_bins))
filter_bins = np.tile(filter_bins, tile_factor)
df = pd.concat([df, pd.DataFrame(
{self.short_name.lower(): filter_bins})])