def test_inverse_bootstrap(null_data, normalization, use_errs, fmax=5): t, y, dy = null_data if not use_errs: dy = None fap = np.linspace(0, 1, 10) method = 'bootstrap' method_kwds = METHOD_KWDS['bootstrap'] ls = LombScargle(t, y, dy, normalization=normalization) z = ls.false_alarm_level(fap, maximum_frequency=fmax, method=method, method_kwds=method_kwds) fap_out = ls.false_alarm_probability(z, maximum_frequency=fmax, method=method, method_kwds=method_kwds) # atol = 1 / n_bootstraps assert_allclose(fap, fap_out, atol=0.05)
def test_inverses(method, normalization, use_errs, N, T=5, fmax=5): if not HAS_SCIPY and method in ['baluev', 'davies']: pytest.skip("SciPy required") t, y, dy = make_data(N, rseed=543) if not use_errs: dy = None method_kwds = METHOD_KWDS.get(method, None) fap = np.logspace(-10, 0, 10) ls = LombScargle(t, y, dy, normalization=normalization) z = ls.false_alarm_level(fap, maximum_frequency=fmax, method=method, method_kwds=method_kwds) fap_out = ls.false_alarm_probability(z, maximum_frequency=fmax, method=method, method_kwds=method_kwds) assert_allclose(fap, fap_out)