def test_classical_significances_trial_correction(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert np.size(pval) == 0
def test_classical_significances_trial_correction(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert np.size(pval) == 0
def test_pvals_is_numpy_array(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 1.0
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert isinstance(pval, np.ndarray)
assert pval.shape[0] == 2
def test_classical_significances_threshold(self):
ps = Powerspectrum(lc = self.lc, norm="leahy")
## change the powers so that just one exceeds the threshold
ps.ps = np.zeros(ps.ps.shape[0])+2.0
index = 1
ps.ps[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=False)
assert pval[0,0] < threshold
assert pval[1,0] == index
def test_pvals_is_numpy_array(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.power = np.zeros_like(ps.power) + 2.0
index = 1
ps.power[index] = 10.0
threshold = 1.0
pval = ps.classical_significances(threshold=threshold,
trial_correction=True)
assert isinstance(pval, np.ndarray)
assert pval.shape[0] == 2
def test_classical_significances_threshold(self):
ps = Powerspectrum(lc=self.lc, norm="leahy")
# change the powers so that just one exceeds the threshold
ps.ps = np.zeros(ps.ps.shape[0])+2.0
index = 1
ps.ps[index] = 10.0
threshold = 0.01
pval = ps.classical_significances(threshold=threshold,
trial_correction=False)
assert pval[0, 0] < threshold
assert pval[1, 0] == index
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
with pytest.raises(ValueError):
ps.classical_significances()
def test_classical_significances_runs(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
ps.classical_significances()
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc = self.lc, norm="rms")
ps.classical_significances()
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
with pytest.raises(ValueError):
ps.classical_significances()
def test_classical_significances_runs(self):
ps = Powerspectrum(lc=self.lc, norm="Leahy")
ps.classical_significances()
def test_classical_significances_fails_in_rms(self):
ps = Powerspectrum(lc=self.lc, norm="rms")
ps.classical_significances()
