def test_max_likelihood_parameter_sign(data, no):
def _modify_initial_parameters_mock(fd):
return fd
load_cycle_limit = 1e6
if hasattr(data, "N_threshold"):
load_cycle_limit = data.N_threshold
fatdat = woehler.determine_fractures(data,
load_cycle_limit=load_cycle_limit)
ml = woehler.MaxLikeFull(fatigue_data=fatdat.fatigue_data)
wl = ml.analyze()
print("Data set number {}".format(no))
print("Woehler parameters: {}".format(wl))
def assert_positive_or_nan_but_not_zero(x):
if np.isfinite(x):
assert x >= 0
assert not np.isclose(x, 0.0)
assert_positive_or_nan_but_not_zero(wl['SD_50'])
assert_positive_or_nan_but_not_zero(wl['1/TS'])
assert_positive_or_nan_but_not_zero(wl['k_1'])
assert_positive_or_nan_but_not_zero(wl['ND_50'])
assert_positive_or_nan_but_not_zero(wl['1/TN'])
def test_max_likelihood_full_method_with_all_fixed_params():
"""
Test of woehler curve parameters evaluation with the maximum likelihood method
"""
fp = {
'k_1': 15.7,
'1/TN': 1.2,
'SD_50': 280,
'1/TS': 1.2,
'ND_50': 10000000
}
fd = woehler.determine_fractures(data, 1e7).fatigue_data
with pytest.raises(
AttributeError,
match=r'You need to leave at least one parameter empty!'):
(woehler.MaxLikeFull(fd).analyze(fixed_parameters=fp))
def test_max_likelihood_one_mixed_horizon():
expected = pd.Series({
'SD_50': 489.3,
'1/TS': 1.147,
'k_1': 7.99,
'ND_50': 541e3,
'1/TN': 2.51
}).sort_index()
fd = woehler.determine_fractures(data_01, 1e7).fatigue_data
ml = woehler.MaxLikeFull(fatigue_data=fd)
with pytest.warns(UserWarning,
match=r"^.*less than two mixed load levels.*"):
wc = ml.analyze().sort_index()
bic = ml.bayesian_information_criterion()
pd.testing.assert_index_equal(wc.index, expected.index)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy(), rtol=1e-1)
def test_max_likelihood_full_with_fixed_params():
expected = pd.Series({
'SD_50': 335,
'1/TS': 1.19,
'k_1': 8.0,
'ND_50': 520000.,
'1/TN': 6.0
}).sort_index()
fd = woehler.determine_fractures(data, 1e7).fatigue_data
wc = (woehler.MaxLikeFull(fd).analyze(fixed_parameters={
'1/TN': 6.0,
'k_1': 8.0
}).sort_index())
pd.testing.assert_index_equal(wc.index, expected.index)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy(), rtol=1e-1)
assert wc['1/TN'] == 6.0
assert wc['k_1'] == 8.0
def test_woehler_max_likelihood_full_without_fixed_params():
expected = pd.Series({
'SD_50': 335,
'1/TS': 1.19,
'k_1': 6.94,
'ND_50': 463000.,
'1/TN': 4.7
}).sort_index()
bic = 45.35256860035525
fd = woehler.determine_fractures(data, 1e7).fatigue_data
we = woehler.MaxLikeFull(fd)
wc = we.analyze().sort_index()
pd.testing.assert_index_equal(wc.index, expected.index)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy(), rtol=1e-1)
np.testing.assert_almost_equal(we.bayesian_information_criterion(),
bic,
decimal=2)
def test_woehler_max_likelihood_full_without_fixed_params_no_runouts():
expected = pd.Series({
'SD_50': 0,
'1/TS': 1.,
'k_1': 6.94,
'ND_50': 4.4e30,
'1/TN': 5.7
}).sort_index()
bic = np.inf
fd = woehler.determine_fractures(data_no_runouts, 1e7).fatigue_data
we = woehler.MaxLikeFull(fd)
with pytest.warns(UserWarning, match=r"^.*no runouts are present.*"):
wc = we.analyze().sort_index()
pd.testing.assert_index_equal(wc.index, expected.index)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy(), rtol=1e-1)
np.testing.assert_almost_equal(we.bayesian_information_criterion(),
bic,
decimal=2)
def test_max_likelihood_min_three_fractures_on_two_load_levels(invalid_data):
fd = woehler.determine_fractures(invalid_data, 1e7).fatigue_data
ml = woehler.MaxLikeFull(fatigue_data=fd)
with pytest.raises(ValueError, match=r"^.*[N|n]eed at least.*"):
ml.analyze()
def test_bic_without_analysis():
fd = woehler.determine_fractures(data, 1e7).fatigue_data
we = woehler.MaxLikeFull(fd)
with pytest.raises(ValueError, match="^.*BIC.*"):
we.bayesian_information_criterion()
