def test_model_units_match(data, t_unit, frequency_unit, y_unit):
t, y, dy = data
t_fit = t[:5]
frequency = 1.0
t = t * t_unit
t_fit = t_fit * t_unit
y = y * y_unit
dy = dy * y_unit
frequency = frequency * frequency_unit
ls = LombScargle(t, y, dy)
y_fit = ls.model(t_fit, frequency)
assert y_fit.unit == y_unit
def test_model_units_match(data, t_unit, frequency_unit, y_unit):
t, y, dy = data
t_fit = t[:5]
frequency = 1.0
t = t * t_unit
t_fit = t_fit * t_unit
y = y * y_unit
dy = dy * y_unit
frequency = frequency * frequency_unit
ls = LombScargle(t, y, dy)
y_fit = ls.model(t_fit, frequency)
assert y_fit.unit == y_unit
def test_model(fit_mean, with_units, freq):
rand = np.random.RandomState(0)
t = 10 * rand.rand(40)
params = 10 * rand.rand(3)
y = np.zeros_like(t)
if fit_mean:
y += params[0]
y += params[1] * np.sin(2 * np.pi * freq * (t - params[2]))
if with_units:
t = t * units.day
y = y * units.mag
freq = freq / units.day
ls = LombScargle(t, y, center_data=False, fit_mean=fit_mean)
y_fit = ls.model(t, freq)
assert_quantity_allclose(y_fit, y)
def test_model(fit_mean, with_units, freq):
rand = np.random.RandomState(0)
t = 10 * rand.rand(40)
params = 10 * rand.rand(3)
y = np.zeros_like(t)
if fit_mean:
y += params[0]
y += params[1] * np.sin(2 * np.pi * freq * (t - params[2]))
if with_units:
t = t * u.day
y = y * u.mag
freq = freq / u.day
ls = LombScargle(t, y, center_data=False, fit_mean=fit_mean)
y_fit = ls.model(t, freq)
assert_quantity_allclose(y_fit, y)
def test_model_parameters(data, nterms, fit_mean, center_data, errors,
with_units):
if nterms == 0 and not fit_mean:
return
t, y, dy = data
frequency = 1.5
if with_units:
t = t * units.day
y = y * units.mag
dy = dy * units.mag
frequency = frequency / t.unit
if errors == 'none':
dy = None
elif errors == 'partial':
dy = dy[0]
elif errors == 'full':
pass
else:
raise ValueError("Unrecognized error type: '{0}'".format(errors))
ls = LombScargle(t,
y,
dy,
nterms=nterms,
fit_mean=fit_mean,
center_data=center_data)
tfit = np.linspace(0, 20, 10)
if with_units:
tfit = tfit * units.day
model = ls.model(tfit, frequency)
params = ls.model_parameters(frequency)
design = ls.design_matrix(frequency, t=tfit)
offset = ls.offset()
assert len(params) == int(fit_mean) + 2 * nterms
assert_quantity_allclose(offset + design.dot(params), model)
def test_model_parameters(data, nterms, fit_mean, center_data,
errors, with_units):
if nterms == 0 and not fit_mean:
return
t, y, dy = data
frequency = 1.5
if with_units:
t = t * u.day
y = y * u.mag
dy = dy * u.mag
frequency = frequency / t.unit
if errors == 'none':
dy = None
elif errors == 'partial':
dy = dy[0]
elif errors == 'full':
pass
else:
raise ValueError("Unrecognized error type: '{0}'".format(errors))
ls = LombScargle(t, y, dy,
nterms=nterms,
fit_mean=fit_mean,
center_data=center_data)
tfit = np.linspace(0, 20, 10)
if with_units:
tfit = tfit * u.day
model = ls.model(tfit, frequency)
params = ls.model_parameters(frequency)
design = ls.design_matrix(frequency, t=tfit)
offset = ls.offset()
assert len(params) == int(fit_mean) + 2 * nterms
assert_quantity_allclose(offset + design.dot(params), model)
def test_absolute_times(data, timedelta):
# Make sure that we handle absolute times correctly. We also check that
# TimeDelta works properly when timedelta is True.
# The example data uses relative times
t, y, dy = data
# FIXME: There seems to be a numerical stability issue in that if we run
# the algorithm with the same values but offset in time, the transit_time
# is not offset by a fixed amount. To avoid this issue in this test, we
# make sure the first time is also the smallest so that internally the
# values of the relative time should be the same.
t[0] = 0.
# Add units
t = t * u.day
y = y * u.mag
dy = dy * u.mag
# We now construct a set of absolute times but keeping the rest the same
start = Time('2019-05-04T12:34:56')
trel = TimeDelta(t) if timedelta else t
t = trel + start
# and we set up two instances of LombScargle, one with absolute and one
# with relative times.
ls1 = LombScargle(t, y, dy)
ls2 = LombScargle(trel, y, dy)
kwargs = dict(samples_per_peak=6,
nyquist_factor=2,
minimum_frequency=2 / u.day,
maximum_frequency=None)
freq1 = ls1.autofrequency(**kwargs)
freq2 = ls2.autofrequency(**kwargs)
assert_quantity_allclose(freq1, freq2)
power1 = ls1.power(freq1)
power2 = ls2.power(freq2)
assert_quantity_allclose(power1, power2)
freq1, power1 = ls1.autopower(**kwargs)
freq2, power2 = ls2.autopower(**kwargs)
assert_quantity_allclose(freq1, freq2)
assert_quantity_allclose(power1, power2)
model1 = ls1.model(t, 2 / u.day)
model2 = ls2.model(trel, 2 / u.day)
assert_quantity_allclose(model1, model2)
# Check model validation
with pytest.raises(TypeError) as exc:
ls1.model(trel, 2 / u.day)
assert exc.value.args[0] == ('t was provided as a relative time but the '
'LombScargle class was initialized with '
'absolute times.')
with pytest.raises(TypeError) as exc:
ls2.model(t, 2 / u.day)
assert exc.value.args[0] == ('t was provided as an absolute time but the '
'LombScargle class was initialized with '
'relative times.')
# Check design matrix
design1 = ls1.design_matrix(2 / u.day, t=t)
design2 = ls2.design_matrix(2 / u.day, t=trel)
assert_quantity_allclose(design1, design2)
# Check design matrix validation
with pytest.raises(TypeError) as exc:
ls1.design_matrix(2 / u.day, t=trel)
assert exc.value.args[0] == ('t was provided as a relative time but the '
'LombScargle class was initialized with '
'absolute times.')
with pytest.raises(TypeError) as exc:
ls2.design_matrix(2 / u.day, t=t)
assert exc.value.args[0] == ('t was provided as an absolute time but the '
'LombScargle class was initialized with '
'relative times.')
def test_absolute_times(data, timedelta):
# Make sure that we handle absolute times correctly. We also check that
# TimeDelta works properly when timedelta is True.
# The example data uses relative times
t, y, dy = data
# FIXME: There seems to be a numerical stability issue in that if we run
# the algorithm with the same values but offset in time, the transit_time
# is not offset by a fixed amount. To avoid this issue in this test, we
# make sure the first time is also the smallest so that internally the
# values of the relative time should be the same.
t[0] = 0.
# Add units
t = t * u.day
y = y * u.mag
dy = dy * u.mag
# We now construct a set of absolute times but keeping the rest the same
start = Time('2019-05-04T12:34:56')
trel = TimeDelta(t) if timedelta else t
t = trel + start
# and we set up two instances of LombScargle, one with absolute and one
# with relative times.
ls1 = LombScargle(t, y, dy)
ls2 = LombScargle(trel, y, dy)
kwargs = dict(samples_per_peak=6, nyquist_factor=2,
minimum_frequency=2 / u.day, maximum_frequency=None)
freq1 = ls1.autofrequency(**kwargs)
freq2 = ls2.autofrequency(**kwargs)
assert_quantity_allclose(freq1, freq2)
power1 = ls1.power(freq1)
power2 = ls2.power(freq2)
assert_quantity_allclose(power1, power2)
freq1, power1 = ls1.autopower(**kwargs)
freq2, power2 = ls2.autopower(**kwargs)
assert_quantity_allclose(freq1, freq2)
assert_quantity_allclose(power1, power2)
model1 = ls1.model(t, 2 / u.day)
model2 = ls2.model(trel, 2 / u.day)
assert_quantity_allclose(model1, model2)
# Check model validation
with pytest.raises(TypeError) as exc:
ls1.model(trel, 2 / u.day)
assert exc.value.args[0] == ('t was provided as a relative time but the '
'LombScargle class was initialized with '
'absolute times.')
with pytest.raises(TypeError) as exc:
ls2.model(t, 2 / u.day)
assert exc.value.args[0] == ('t was provided as an absolute time but the '
'LombScargle class was initialized with '
'relative times.')
# Check design matrix
design1 = ls1.design_matrix(2 / u.day, t=t)
design2 = ls2.design_matrix(2 / u.day, t=trel)
assert_quantity_allclose(design1, design2)
# Check design matrix validation
with pytest.raises(TypeError) as exc:
ls1.design_matrix(2 / u.day, t=trel)
assert exc.value.args[0] == ('t was provided as a relative time but the '
'LombScargle class was initialized with '
'absolute times.')
with pytest.raises(TypeError) as exc:
ls2.design_matrix(2 / u.day, t=t)
assert exc.value.args[0] == ('t was provided as an absolute time but the '
'LombScargle class was initialized with '
'relative times.')