def test_false_alarm_equivalence(method, normalization, use_errs, units):
# Note: the PSD normalization is not equivalent to the others, in that it
# depends on the absolute errors rather than relative errors. Because the
# scaling contributes to the distribution, it cannot be converted directly
# from any of the three normalized versions.
if not HAS_SCIPY and method in ['baluev', 'davies']:
pytest.skip("SciPy required")
kwds = METHOD_KWDS.get(method, None)
t, y, dy, fmax = make_data(units=units)
if not use_errs:
dy = None
ls = LombScargle(t, y, dy, normalization=normalization)
freq, power = ls.autopower(maximum_frequency=fmax)
Z = np.linspace(power.min(), power.max(), 30)
fap = ls.false_alarm_probability(Z,
maximum_frequency=fmax,
method=method,
method_kwds=kwds)
# Compute the equivalent Z values in the standard normalization
# and check that the FAP is consistent
Z_std = convert_normalization(Z,
len(t),
from_normalization=normalization,
to_normalization='standard',
chi2_ref=compute_chi2_ref(y, dy))
ls = LombScargle(t, y, dy, normalization='standard')
fap_std = ls.false_alarm_probability(Z_std,
maximum_frequency=fmax,
method=method,
method_kwds=kwds)
assert_allclose(fap, fap_std, rtol=0.1)
def test_false_alarm_equivalence(method, normalization, use_errs):
# Note: the PSD normalization is not equivalent to the others, in that it
# depends on the absolute errors rather than relative errors. Because the
# scaling contributes to the distribution, it cannot be converted directly
# from any of the three normalized versions.
if not HAS_SCIPY and method in ['baluev', 'davies']:
pytest.skip("SciPy required")
kwds = METHOD_KWDS.get(method, None)
t, y, dy = make_data()
if not use_errs:
dy = None
fmax = 5
ls = LombScargle(t, y, dy, normalization=normalization)
freq, power = ls.autopower(maximum_frequency=fmax)
Z = np.linspace(power.min(), power.max(), 30)
fap = ls.false_alarm_probability(Z, maximum_frequency=fmax,
method=method, method_kwds=kwds)
# Compute the equivalent Z values in the standard normalization
# and check that the FAP is consistent
Z_std = convert_normalization(Z, len(t),
from_normalization=normalization,
to_normalization='standard',
chi2_ref=compute_chi2_ref(y, dy))
ls = LombScargle(t, y, dy, normalization='standard')
fap_std = ls.false_alarm_probability(Z_std, maximum_frequency=fmax,
method=method, method_kwds=kwds)
assert_allclose(fap, fap_std, rtol=0.1)
def test_convert_normalization(norm_in, norm_out, data):
t, y, dy = data
_, power_in = LombScargle(t, y, dy).autopower(maximum_frequency=5,
normalization=norm_in)
_, power_out = LombScargle(t, y, dy).autopower(maximum_frequency=5,
normalization=norm_out)
power_in_converted = convert_normalization(power_in, N=len(t),
from_normalization=norm_in,
to_normalization=norm_out,
chi2_ref = compute_chi2_ref(y, dy))
assert_allclose(power_in_converted, power_out)
def test_convert_normalization(norm_in, norm_out, data):
t, y, dy = data
_, power_in = LombScargle(t, y, dy).autopower(maximum_frequency=5,
normalization=norm_in)
_, power_out = LombScargle(t, y, dy).autopower(maximum_frequency=5,
normalization=norm_out)
power_in_converted = convert_normalization(power_in, N=len(t),
from_normalization=norm_in,
to_normalization=norm_out,
chi2_ref = compute_chi2_ref(y, dy))
assert_allclose(power_in_converted, power_out)