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()