def test_classical_significances_trial_correction(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
cs.power[index] = 10.0
threshold = 0.01
pval = cs.classical_significances(threshold=threshold,
trial_correction=True)
assert np.size(pval) == 0
def test_pvals_is_numpy_array(self):
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
cs.power[index] = 10.0
threshold = 1.0
pval = cs.classical_significances(threshold=threshold,
trial_correction=True)
assert isinstance(pval, np.ndarray)
assert pval.shape[0] == 2
def test_classical_significances_fails_in_rms(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='frac')
with pytest.raises(ValueError):
cs.classical_significances()
def test_classical_significances_runs(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm='leahy')
cs.classical_significances()
