crp/OpenMC
1
0
Fork 0
mirror of https://github.com/openmc-dev/openmc.git synced 2026-07-28 06:05:58 -04:00
Code Issues Projects Releases Packages Wiki Activity Actions 5

shortened and fixed _align_tally_data routine

Browse source
This commit is contained in:
Sam Shaner 2015-12-07 22:06:56 -05:00
parent b671b1a299
commit 42f438dcab
1 changed files with 17 additions and 63 deletions
Download patch file Download diff file Expand all files Collapse all files

80
openmc/tallies.py
View file

@ -1625,6 +1625,10 @@ class Tally(object):
other_mean = copy.deepcopy(other.mean)
other_std_dev = copy.deepcopy(other.std_dev)
# Initialize list of tile and repeat factors
repeat_factors = [1, 1, 1]
tile_factors = [1, 1, 1]
if self.filters != other.filters:
# Determine the number of paired combinations of filter bins
@ -1634,83 +1638,33 @@ class Tally(object):
# Determine the factors by which each tally operands' data arrays
# must be tiled or repeated for the tally outer product
other_tile_factor = 1
self_repeat_factor = 1
for filter in diff1:
other_tile_factor *= filter.num_bins
tile_factors[0] *= filter.num_bins
for filter in diff2:
self_repeat_factor *= filter.num_bins
# Tile / repeat the tally data for the tally outer product
self_shape = list(self_mean.shape)
other_shape = list(other_mean.shape)
self_shape[0] *= self_repeat_factor
self_mean = np.repeat(self_mean, self_repeat_factor)
self_std_dev = np.repeat(self_std_dev, self_repeat_factor)
if self_repeat_factor == 1:
other_shape[0] *= other_tile_factor
other_mean = np.repeat(other_mean, other_tile_factor, axis=0)
other_std_dev = np.repeat(other_std_dev, other_tile_factor,
axis=0)
else:
other_mean = np.tile(other_mean, (other_tile_factor, 1, 1))
other_std_dev = np.tile(other_std_dev, (other_tile_factor, 1, 1))
# NumPy repeat and tile routines return 1D flattened arrays
# Reshape arrays as 3D with filters, nuclides and scores axes
self_mean.shape = tuple(self_shape)
self_std_dev.shape = tuple(self_shape)
other_mean.shape = tuple(other_shape)
other_std_dev.shape = tuple(other_shape)
repeat_factors[0] *= filter.num_bins
if self.nuclides != other.nuclides:
# Determine the number of paired combinations of nuclides
# between the two tallies and repeat arrays along nuclide axes
self_repeat_factor = other.num_nuclides
other_tile_factor = self.num_nuclides
# Tile / repeat the tally data for the tally outer product
self_shape = list(self_mean.shape)
other_shape = list(other_mean.shape)
self_shape[1] *= self_repeat_factor
other_shape[1] *= other_tile_factor
self_mean = np.repeat(self_mean, self_repeat_factor, axis=1)
other_mean = np.tile(other_mean, (1, other_tile_factor, 1))
self_std_dev = np.repeat(self_std_dev, self_repeat_factor, axis=1)
other_std_dev = np.tile(other_std_dev, (1, other_tile_factor, 1))
# NumPy repeat and tile routines return 1D flattened arrays
# Reshape arrays as 3D with filters, nuclides and scores axes
self_mean.shape = tuple(self_shape)
self_std_dev.shape = tuple(self_shape)
other_mean.shape = tuple(other_shape)
other_std_dev.shape = tuple(other_shape)
repeat_factors[1] = other.num_nuclides
tile_factors[1] = self.num_nuclides
if self.scores != other.scores:
# Determine the number of paired combinations of score bins
# between the two tallies and repeat arrays along score axes
self_repeat_factor = other.num_score_bins
other_tile_factor = self.num_score_bins
repeat_factors[2] = other.num_score_bins
tile_factors[2] = self.num_score_bins
# Tile / repeat the tally data for the tally outer product
self_shape = list(self_mean.shape)
other_shape = list(other_mean.shape)
self_shape[2] *= self_repeat_factor
other_shape[2] *= other_tile_factor
self_mean = np.repeat(self_mean, self_repeat_factor, axis=2)
other_mean = np.tile(other_mean, (1, 1, other_tile_factor))
self_std_dev = np.repeat(self_std_dev, self_repeat_factor, axis=2)
other_std_dev = np.tile(other_std_dev, (1, 1, other_tile_factor))
# Repeat the self tally
for i in range(3):
self_mean = np.repeat(self_mean, repeat_factors[i], axis=i)
self_std_dev = np.repeat(self_std_dev, repeat_factors[i], axis=i)
# NumPy repeat and tile routines return 1D flattened arrays
# Reshape arrays as 3D with filters, nuclides and scores axes
self_mean.shape = tuple(self_shape)
self_std_dev.shape = tuple(self_shape)
other_mean.shape = tuple(other_shape)
other_std_dev.shape = tuple(other_shape)
# Tile the other tally
other_mean = np.tile(other_mean, tile_factors)
other_std_dev = np.tile(other_std_dev, tile_factors)
data = {}
data['self'] = {}