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)
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)
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)