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Improved testing for manually set source strengths

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This commit is contained in:
Patrick Shriwise 2022-12-26 22:57:37 -06:00
parent bc7c8cd892
commit 996930f7f1
1 changed files with 27 additions and 23 deletions
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50
tests/unit_tests/test_source_mesh.py
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@ -102,11 +102,8 @@ def test_unstructured_mesh_sampling(model, test_cases):
strengths = None
elif test_cases['source_strengths'] == 'manual':
vol_norm = False
strengths = np.zeros(n_cells*TETS_PER_VOXEL)
# set non-zero strengths only for the tets corresponding to the
# first two geometric hex cells
strengths[0:TETS_PER_VOXEL] = 10
strengths[TETS_PER_VOXEL:2*TETS_PER_VOXEL] = 2
# assign random weights
strengths = np.random.rand(n_cells*TETS_PER_VOXEL)
# create the spatial distribution based on the mesh
space = openmc.stats.MeshSpatial(uscd_mesh, strengths, vol_norm)
@ -120,9 +117,10 @@ def test_unstructured_mesh_sampling(model, test_cases):
n_cells = len(model.geometry.get_all_cells())
n_samples = 50000
n_measurements = 100
n_samples = 1000
cell_counts = np.zeros(n_cells)
cell_counts = np.zeros((n_cells, n_measurements))
# This model contains 1000 geometry cells. Each cell is a hex
# corresponding to 12 of the tets. This test runs 10000 particles. This
@ -130,25 +128,31 @@ def test_unstructured_mesh_sampling(model, test_cases):
average_in_hex = n_samples / n_cells
openmc.lib.init([])
sites = openmc.lib.sample_external_source(n_samples)
cells = [openmc.lib.find_cell(s.r) for s in sites]
# perform many sets of samples and track counts for each cell
for m in range(n_measurements):
sites = openmc.lib.sample_external_source(n_samples)
cells = [openmc.lib.find_cell(s.r) for s in sites]
for c in cells:
cell_counts[c[0]._index, m] += 1
openmc.lib.finalize()
for c in cells:
cell_counts[c[0]._index] += 1
# normalize cell counts to get sampling frequency per particle
cell_counts /= n_samples
if strengths is not None:
assert(cell_counts[0] > 0 and cell_counts[1] > 0)
assert(cell_counts[0] > cell_counts[1])
# get the mean and std. dev. of the cell counts
mean = cell_counts.mean(axis=1)
std_dev = cell_counts.std(axis=1)
# counts for all other cells should be zero
for i in range(2, len(cell_counts)):
assert(cell_counts[i] == 0)
if test_cases['source_strengths'] == 'uniform':
exp_vals = np.ones(n_cells) / n_cells
else:
# check that the average number of source sites in each cell
# is within the expected deviation
diff = np.abs(cell_counts - average_in_hex)
# sum up the source strengths for each tet, these are the expected true mean
# of the sampling frequency for that cell
exp_vals = strengths.reshape(-1, 12).sum(axis=1) / sum(strengths)
assert(np.average(cell_counts) == average_in_hex) # this probably shouldn't be exact???
assert((diff < 2*cell_counts.std()).sum() / diff.size >= 0.75)
assert((diff < 6*cell_counts.std()).sum() / diff.size >= 0.97)
diff = np.abs(mean - exp_vals)
assert((diff < 2*std_dev).sum() / diff[:10].size >= 0.95)
assert((diff < 6*std_dev).sum() / diff.size >= 0.97)