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Added an openmc.run mode (summary, which just shows the timing and summary results; fixed the generation of cells from a mesh (incorrect ordering of universes in the lattice), xs_shapes for delayed groups had wrong ordering (fixed) in openmc.MGXSLibrary and openmc.Plotter, fixed error in multi-group plots which only plotted one group not all, fixed issue with setting max order to 0 for Legendre scattering (not legendre converted to tabular w/in OpenMC, the default), removed superfluous xs assignments in MG mode which only slowed things down

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Adam Nelson 2017-02-25 08:17:10 -05:00
parent 60a1f157da
commit fc3df18eab
8 changed files with 50 additions and 52 deletions
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24
openmc/plotter.py
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@ -676,7 +676,7 @@ def calculate_mgxs(this, types, orders=None, temperature=294.,
for line in range(len(types)):
for g in range(library.energy_groups.num_groups):
data[g * 2: g * 2 + 2] = mgxs[line, g]
data[line, g * 2: g * 2 + 2] = mgxs[line, g]
return energy_grid[::-1], data
@ -775,28 +775,28 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
data[i, :] = temp_data[orders[i]]
else:
data[i, :] = np.sum(temp_data[:])
elif shape in (xsdata.xs_shapes["[G'][DG]"],
xsdata.xs_shapes["[G][DG]"]):
elif shape in (xsdata.xs_shapes["[DG][G']"],
xsdata.xs_shapes["[DG][G]"]):
# Then we have an array vs groups with values for each
# delayed group. The user-provided value of orders tells us
# which delayed group we want. If none are provided, then
# we sum all the delayed groups together.
if orders[i]:
if orders[i] < len(shape[1]):
data[i, :] = temp_data[:, orders[i]]
if orders[i] < len(shape[0]):
data[i, :] = temp_data[orders[i], :]
else:
data[i, :] = np.sum(temp_data[:, :], axis=1)
elif shape == xsdata.xs_shapes["[G][G'][DG]"]:
data[i, :] = np.sum(temp_data[:, :], axis=0)
elif shape == xsdata.xs_shapes["[DG][G][G']"]:
# Then we have a delayed group matrix. We will first
# remove the outgoing group dependency
temp_data = np.sum(temp_data, axis=1)
temp_data = np.sum(temp_data, axis=-1)
# And then proceed in exactly the same manner as the
# "[G'][DG]" of "[G][DG]" shapes in the previous block.
# "[DG][G']" or "[DG][G]" shapes in the previous block.
if orders[i]:
if orders[i] < len(shape[1]):
data[i, :] = temp_data[:, orders[i]]
if orders[i] < len(shape[0]):
data[i, :] = temp_data[orders[i], :]
else:
data[i, :] = np.sum(temp_data[:, :], axis=1)
data[i, :] = np.sum(temp_data[:, :], axis=0)
elif shape == xsdata.xs_shapes["[G][G'][Order]"]:
# This is a scattering matrix with angular data
# First remove the outgoing group dependence