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Adding a histogram filter test. Testing conversion from Tabulated1D for many interpolation types

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This commit is contained in:
Patrick Shriwise 2022-09-17 11:32:24 -05:00
parent 834450066e
commit 3351918394
2 changed files with 27 additions and 1 deletions
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10
tests/regression_tests/filter_energyfun/inputs_true.dat
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@ -54,6 +54,11 @@
<y>0.2 0.7 0.7 0.2</y>
<interpolation>cubic</interpolation>
</filter>
<filter id="9" type="energyfunction">
<energy>0.0 5000000.0 10000000.0 15000000.0</energy>
<y>0.2 0.7 0.7 0.2</y>
<interpolation>histogram</interpolation>
</filter>
<tally id="1">
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
@ -93,4 +98,9 @@
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
<tally id="9">
<filters>9</filters>
<nuclides>Am241</nuclides>
<scores>(n,gamma)</scores>
</tally>
</tallies>

18
tests/regression_tests/filter_energyfun/test.py
View file

@ -28,6 +28,7 @@ def model():
# Make an EnergyFunctionFilter directly from the x and y lists.
filt1 = openmc.EnergyFunctionFilter(x, y)
# check interpolatoin property setter
assert filt1.interpolation == 'linear-linear'
with pytest.raises(ValueError):
@ -60,7 +61,10 @@ def model():
filt8 = openmc.EnergyFunctionFilter(x, y)
filt8.interpolation = 'cubic'
filters = [filt1, filt3, filt4, filt5, filt6, filt7, filt8]
filt9 = openmc.EnergyFunctionFilter(x, y)
filt9.interpolation = 'histogram'
filters = [filt1, filt3, filt4, filt5, filt6, filt7, filt8, filt9]
# Make tallies
tallies = [openmc.Tally() for _ in range(len(filters) + 1)]
for t in tallies:
@ -72,6 +76,14 @@ def model():
model.tallies.extend(tallies)
interpolation_vals = \
list(openmc.EnergyFunctionFilter.INTERPOLATION_SCHEMES.keys())
for i_val in interpolation_vals:
# breakpoint here is fake and unused
t1d = openmc.data.Tabulated1D(x, y, breakpoints=[1], interpolation=[i_val])
f = openmc.EnergyFunctionFilter.from_tabulated1d(t1d)
assert f.interpolation == openmc.EnergyFunctionFilter.INTERPOLATION_SCHEMES[i_val]
return model
@ -143,6 +155,10 @@ class FilterEnergyFunHarness(PyAPITestHarness):
assert all(contrived_lin_lin_tally.mean < contrived_quadratic_tally.mean)
assert all(contrived_lin_lin_tally.mean < contrived_cubic_tally.mean)
# check that the histogram tally is less than the quadratic/cubic interpolations
histogram_tally = sp.get_tally(id=9)
assert all(histogram_tally.mean < contrived_quadratic_tally.mean)
assert all(histogram_tally.mean < contrived_cubic_tally.mean)
def test_filter_energyfun(model):
harness = FilterEnergyFunHarness('statepoint.5.h5', model)