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Re-run flaky tests when needed (#3604)

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Paul Romano 2025-10-14 07:47:26 -05:00 • committed by GitHub
parent 3dfa34d2c6
commit 58c7fbeac6
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3 changed files with 19 additions and 1 deletions
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9
pyproject.toml
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@ -48,7 +48,14 @@ docs = [
"sphinxcontrib-svg2pdfconverter",
"sphinx-rtd-theme"
]
test = ["packaging", "pytest", "pytest-cov>=4.0", "colorama", "openpyxl"]
test = [
"packaging",
"pytest",
"pytest-cov>=4.0",
"pytest-rerunfailures",
"colorama",
"openpyxl",
]
ci = ["coverage>=7.4", "gcovr>=7.2"]
vtk = ["vtk"]

1
tests/unit_tests/test_deplete_activation.py
View file

@ -49,6 +49,7 @@ ENERGIES = np.logspace(log10(1e-5), log10(2e7), 100)
("flux", {'energies': ENERGIES, 'reactions': ['(n,gamma)']}, 1e-5),
("flux", {'energies': ENERGIES, 'reactions': ['(n,gamma)'], 'nuclides': ['W186', 'H3']}, 1e-2),
])
@pytest.mark.flaky(reruns=1)
def test_activation(run_in_tmpdir, model, reaction_rate_mode, reaction_rate_opts, tolerance):
# Determine (n.gamma) reaction rate using initial run
sp = model.run()

10
tests/unit_tests/test_stats.py
View file

@ -16,6 +16,7 @@ def assert_sample_mean(samples, expected_mean):
assert np.abs(expected_mean - samples.mean()) < 4*std_dev
@pytest.mark.flaky(reruns=1)
def test_discrete():
x = [0.0, 1.0, 10.0]
p = [0.3, 0.2, 0.5]
@ -104,6 +105,7 @@ def test_clip_discrete():
d.clip(5)
@pytest.mark.flaky(reruns=1)
def test_uniform():
a, b = 10.0, 20.0
d = openmc.stats.Uniform(a, b)
@ -127,6 +129,7 @@ def test_uniform():
assert_sample_mean(samples, exp_mean)
@pytest.mark.flaky(reruns=1)
def test_powerlaw():
a, b, n = 10.0, 100.0, 2.0
d = openmc.stats.PowerLaw(a, b, n)
@ -148,6 +151,7 @@ def test_powerlaw():
assert_sample_mean(samples, exp_mean)
@pytest.mark.flaky(reruns=1)
def test_maxwell():
theta = 1.2895e6
d = openmc.stats.Maxwell(theta)
@ -171,6 +175,7 @@ def test_maxwell():
assert samples_2.mean() != samples.mean()
@pytest.mark.flaky(reruns=1)
def test_watt():
a, b = 0.965e6, 2.29e-6
d = openmc.stats.Watt(a, b)
@ -194,6 +199,7 @@ def test_watt():
assert_sample_mean(samples, exp_mean)
@pytest.mark.flaky(reruns=1)
def test_tabular():
# test linear-linear sampling
x = np.array([0.0, 5.0, 7.0, 10.0])
@ -270,6 +276,7 @@ def test_legendre():
d.to_xml_element('distribution')
@pytest.mark.flaky(reruns=1)
def test_mixture():
d1 = openmc.stats.Uniform(0, 5)
d2 = openmc.stats.Uniform(3, 7)
@ -425,6 +432,7 @@ def test_point():
assert d.xyz == pytest.approx(p)
@pytest.mark.flaky(reruns=1)
def test_normal():
mean = 10.0
std_dev = 2.0
@ -444,6 +452,7 @@ def test_normal():
assert_sample_mean(samples, mean)
@pytest.mark.flaky(reruns=1)
def test_muir():
mean = 10.0
mass = 5.0
@ -463,6 +472,7 @@ def test_muir():
assert_sample_mean(samples, mean)
@pytest.mark.flaky(reruns=1)
def test_combine_distributions():
# Combine two discrete (same data as in test_merge_discrete)
x1 = [0.0, 1.0, 10.0]