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Cleaned up mgxs_library and made sure the mgxs-part-iv notebook was up to date

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Adam Nelson 2016-11-13 15:27:06 -05:00
parent 1a921e7d08
commit df7ae53413
2 changed files with 172 additions and 185 deletions
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217
docs/source/pythonapi/examples/mgxs-part-iv.ipynb
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140
openmc/mgxs_library.py
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@ -1735,108 +1735,82 @@ class XSdata(object):
# Get the sparse scattering data to print to the library
G = self.energy_groups.num_groups
if self.representation == 'isotropic':
g_out_bounds = np.zeros((G, 2), dtype=np.int)
for g_in in range(G):
if self.scatter_format == 'legendre':
nz = np.nonzero(self._scatter_matrix[i][g_in, :, 0])
elif self.scatter_format == 'histogram':
nz = np.nonzero(np.sum(
self._scatter_matrix[i][g_in, :, :], axis=1))
if len(nz[0]) == 0:
g_out_bounds[g_in, 0] = 0
g_out_bounds[g_in, 1] = 0
else:
g_out_bounds[g_in, 0] = nz[0][0]
g_out_bounds[g_in, 1] = nz[0][-1]
# Now create the flattened scatter matrix array
matrix = self._scatter_matrix[i]
flat_scatt = []
for g_in in range(G):
for g_out in range(g_out_bounds[g_in, 0],
g_out_bounds[g_in, 1] + 1):
for l in range(len(matrix[g_in, g_out, :])):
flat_scatt.append(matrix[g_in, g_out, l])
# And write it.
scatt_grp = xs_grp.create_group('scatter_data')
scatt_grp.create_dataset("scatter_matrix",
data=np.array(flat_scatt))
# Repeat for multiplicity
if self._multiplicity_matrix[i] is not None:
# Now create the flattened scatter matrix array
matrix = self._multiplicity_matrix[i][:, :]
flat_mult = []
for g_in in range(G):
for g_out in range(g_out_bounds[g_in, 0],
g_out_bounds[g_in, 1] + 1):
flat_mult.append(matrix[g_in, g_out])
scatt_grp.create_dataset("multiplicity matrix",
data=np.array(flat_mult))
# And finally, adjust g_out_bounds for 1-based group counting
# and write it.
g_out_bounds[:, :] += 1
scatt_grp.create_dataset("g_min", data=g_out_bounds[:, 0])
scatt_grp.create_dataset("g_max", data=g_out_bounds[:, 1])
Np = 1
Na = 1
elif self.representation == 'angle':
Np = self.num_polar
Na = self.num_azimuthal
g_out_bounds = np.zeros((Np, Na, G, 2), dtype=np.int)
for p in range(Np):
for a in range(Na):
for g_in in range(G):
if self.scatter_format == 'legendre':
g_out_bounds = np.zeros((Np, Na, G, 2), dtype=np.int)
for p in range(Np):
for a in range(Na):
for g_in in range(G):
if self.scatter_format == 'legendre':
if self.representation == 'isotropic':
matrix = \
self._scatter_matrix[i][g_in, :, 0]
elif self.representation == 'angle':
matrix = \
self._scatter_matrix[i][p, a, g_in, :, 0]
elif self.scatter_format == 'histogram':
elif self.scatter_format == 'histogram':
if self.representation == 'isotropic':
matrix = \
np.sum(self._scatter_matrix[i][g_in, :, :],
axis=1)
elif self.representation == 'angle':
matrix = \
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]
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]
# Now create the flattened scatter matrix array
flat_scatt = []
for p in range(Np):
for a in range(Na):
if self.representation == 'isotropic':
matrix = self._scatter_matrix[i][:, :, :]
elif self.representation == 'angle':
matrix = self._scatter_matrix[i][p, a, :, :, :]
for g_in in range(G):
for g_out in range(g_out_bounds[p, a, g_in, 0],
g_out_bounds[p, a, g_in, 1] + 1):
for l in range(len(matrix[g_in, g_out, :])):
flat_scatt.append(matrix[g_in, g_out, l])
# And write it.
scatt_grp = xs_grp.create_group('scatter_data')
scatt_grp.create_dataset("scatter_matrix",
data=np.array(flat_scatt))
# Repeat for multiplicity
if self._multiplicity_matrix[i] is not None:
# Now create the flattened scatter matrix array
flat_scatt = []
flat_mult = []
for p in range(Np):
for a in range(Na):
matrix = self._scatter_matrix[i][p, a, :, :, :]
if self.representation == 'isotropic':
matrix = self._multiplicity_matrix[i][:, :]
elif self.representation == 'angle':
matrix = self._multiplicity_matrix[i][p, a, :, :]
for g_in in range(G):
for g_out in range(g_out_bounds[p, a, g_in, 0],
g_out_bounds[p, a, g_in, 1] + 1):
for l in range(len(matrix[g_in, g_out, :])):
flat_scatt.append(matrix[g_in, g_out, l])
flat_mult.append(matrix[g_in, g_out])
# And write it.
scatt_grp = xs_grp.create_group('scatter_data')
scatt_grp.create_dataset("scatter_matrix",
data=np.array(flat_scatt))
scatt_grp.create_dataset("multiplicity_matrix",
data=np.array(flat_mult))
# Repeat for multiplicity
if self._multiplicity_matrix[i] is not None:
# Now create the flattened scatter matrix array
flat_mult = []
for p in range(Np):
for a in range(Na):
matrix = self._multiplicity_matrix[i][p, a, :, :]
for g_in in range(G):
for g_out in range(g_out_bounds[p, a, g_in, 0],
g_out_bounds[p, a, g_in, 1] + 1):
flat_mult.append(matrix[g_in, g_out])
# And write it.
scatt_grp.create_dataset("multiplicity_matrix",
data=np.array(flat_mult))
# And finally, adjust g_out_bounds for 1-based group counting
# and write it.
g_out_bounds[:, :, :, :] += 1
# And finally, adjust g_out_bounds for 1-based group counting
# and write it.
g_out_bounds[:, :, :, :] += 1
if self.representation == 'isotropic':
scatt_grp.create_dataset("g_min", data=g_out_bounds[0, 0, :, 0])
scatt_grp.create_dataset("g_max", data=g_out_bounds[0, 0, :, 1])
elif self.representation == 'angle':
scatt_grp.create_dataset("g_min", data=g_out_bounds[:, :, :, 0])
scatt_grp.create_dataset("g_max", data=g_out_bounds[:, :, :, 1])