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Updating tests to include checks for quadratic/cubic interpolation

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This commit is contained in:
Patrick Shriwise 2022-09-08 22:38:22 -05:00
parent 3b00ed3864
commit b68e6e96e6
3 changed files with 57 additions and 4 deletions
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30
tests/regression_tests/filter_energyfun/inputs_true.dat
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@ -39,6 +39,21 @@
<y>0.1 0.1 0.1333 0.158 0.18467 0.25618 0.4297 0.48 0.48</y>
<interpolation>log-linear</interpolation>
</filter>
<filter id="6" type="energyfunction">
<energy>0.0 5000000.0 10000000.0 15000000.0</energy>
<y>0.2 0.7 0.7 0.2</y>
<interpolation>linear-linear</interpolation>
</filter>
<filter id="7" type="energyfunction">
<energy>0.0 5000000.0 10000000.0 15000000.0</energy>
<y>0.2 0.7 0.7 0.2</y>
<interpolation>quadratic</interpolation>
</filter>
<filter id="8" type="energyfunction">
<energy>0.0 5000000.0 10000000.0 15000000.0</energy>
<y>0.2 0.7 0.7 0.2</y>
<interpolation>cubic</interpolation>
</filter>
<tally id="1">
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
@ -63,4 +78,19 @@
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
<tally id="6">
<filters>6</filters>
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
<tally id="7">
<filters>7</filters>
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
<tally id="8">
<filters>8</filters>
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
</tallies>

2
tests/regression_tests/filter_energyfun/results_true.dat
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@ -6,3 +6,5 @@
0 b4e2ac84068d2d Am241 (n,gamma) 8.19e-02 2.25e-03
energyfunction nuclide score mean std. dev.
0 dacf88242512ea Am241 (n,gamma) 7.95e-02 2.19e-03
energyfunction nuclide score mean std. dev.
0 fe168c70d9e078 Am241 (n,gamma) 1.06e-01 2.96e-03

29
tests/regression_tests/filter_energyfun/test.py
View file

@ -48,9 +48,21 @@ def model():
filt5 = openmc.EnergyFunctionFilter(x, y)
filt5.interpolation = 'log-linear'
filters = [filt1, filt3, filt4, filt5]
# define a trapezoidal function for comparison
x = [0.0, 5e6, 1e7, 1.5e7]
y = [0.2, 0.7, 0.7, 0.2]
filt6 = openmc.EnergyFunctionFilter(x, y)
filt7 = openmc.EnergyFunctionFilter(x, y)
filt7.interpolation = 'quadratic'
filt8 = openmc.EnergyFunctionFilter(x, y)
filt8.interpolation = 'cubic'
filters = [filt1, filt3, filt4, filt5, filt6, filt7, filt8]
# Make tallies
tallies = [openmc.Tally() for i in range(5)]
tallies = [openmc.Tally() for _ in range(len(filters) + 1)]
for t in tallies:
t.scores = ['(n,gamma)']
t.nuclides = ['Am241']
@ -73,7 +85,7 @@ class FilterEnergyFunHarness(PyAPITestHarness):
br_tally = sp.tallies[2] / sp.tallies[1]
dataframes_string += br_tally.get_pandas_dataframe().to_string() + '\n'
for t_id in (3, 4, 5):
for t_id in (3, 4, 5, 6):
ef_tally = sp.tallies[t_id]
dataframes_string += ef_tally.get_pandas_dataframe().to_string() + '\n'
@ -122,7 +134,16 @@ class FilterEnergyFunHarness(PyAPITestHarness):
sp_log_lin_filt = sp_log_lin_tally.find_filter(openmc.EnergyFunctionFilter)
assert sp_log_lin_filt.interpolation == 'log-linear'
# check that the cubic interpolation provides a higher value
# than linear-linear
contrived_lin_lin_tally = sp.get_tally(id=6)
contrived_quadratic_tally = sp.get_tally(id=7)
contrived_cubic_tally = sp.get_tally(id=8)
assert all(contrived_lin_lin_tally.mean < contrived_quadratic_tally.mean)
assert all(contrived_lin_lin_tally.mean < contrived_cubic_tally.mean)
def test_filter_energyfun(model):
harness = FilterEnergyFunHarness('statepoint.5.h5', model)
harness.main()
harness.main()