def test_bls_period_recovery():
"""Can BLS Periodogram recover the period of a synthetic light curve?"""
# Planet parameters
period = 2.0
transit_time = 0.5
duration = 0.1
depth = 0.2
flux_err = 0.01
# Create the synthetic light curve
time = np.arange(0, 20, 0.02)
flux = np.ones_like(time)
transit_mask = (np.abs((time - transit_time + 0.5 * period) % period -
0.5 * period) < 0.5 * duration)
flux[transit_mask] = 1.0 - depth
flux += flux_err * np.random.randn(len(time))
synthetic_lc = LightCurve(time=time, flux=flux)
# Can BLS recover the period?
bls_period = synthetic_lc.to_periodogram("bls").period_at_max_power
assert_almost_equal(bls_period.value, period, decimal=2)
# Does it work if we inject a sneaky NaN?
synthetic_lc.flux[10] = np.nan
bls_period = synthetic_lc.to_periodogram("bls").period_at_max_power
assert_almost_equal(bls_period.value, period, decimal=2)
# Does it work if all errors are NaNs?
# This is a regression test for issue #428
synthetic_lc.flux_err = np.array([np.nan] * len(time))
assert_almost_equal(bls_period.value, period, decimal=2)
def test_bls_period():
"""Regression test for #514."""
lc = LightCurve(time=[1, 2, 3], flux=[4, 5, 6])
period = [1, 2, 3, 4, 5]
pg = lc.to_periodogram(method="bls", period=period)
assert_array_equal(pg.period.value, period)
with pytest.raises(
ValueError) as err: # NaNs should raise a nice error message
lc.to_periodogram(method="bls", period=[1, 2, 3, np.nan, 4])
assert "period" in err.value.args[0]
def test_periodogram_warnings():
"""Tests if warnings are raised for non-normalized periodogram input"""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize(unit="ppm")
# Test amplitude normalization and correct units
pg = lc.to_periodogram(normalization="amplitude")
assert pg.power.unit == u.cds.ppm
pg = lc.to_periodogram(freq_unit=u.microhertz, normalization="psd")
assert pg.power.unit == u.cds.ppm**2 / u.microhertz
def test_assign_periods():
"""Test if you can assign periods and frequencies."""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
periods = np.arange(1, 100) * u.day
lc = lc.normalize()
p = lc.to_periodogram(period=periods)
# Get around the floating point error
assert np.isclose(np.sum(periods - p.period).value, 0, rtol=1e-14)
frequency = np.arange(1, 100) * u.Hz
p = lc.to_periodogram(frequency=frequency)
assert np.isclose(np.sum(frequency - p.frequency).value, 0, rtol=1e-14)
def test_flatten():
npts = 10000
np.random.seed(12069424)
lc = LightCurve(
time=np.arange(npts),
flux=np.random.normal(1, 0.1, npts),
flux_err=np.zeros(npts) + 0.1,
)
lc = lc.normalize()
p = lc.to_periodogram(normalization="psd", freq_unit=1 / u.day)
# Check method returns equal frequency
assert all(p.flatten(method="logmedian").frequency == p.frequency)
assert all(p.flatten(method="boxkernel").frequency == p.frequency)
# Check logmedian flatten of white noise returns mean of ~unity
assert np.isclose(np.mean(p.flatten(method="logmedian").power.value),
1.0,
atol=0.05)
# Check return trend works
s, b = p.flatten(return_trend=True)
assert all(
b.power == p.smooth(method="logmedian", filter_width=0.01).power)
assert all(s.power == p.flatten().power)
str(s)
s.plot()
plt.close()
def test_periodogram_slicing():
"""Tests whether periodograms can be sliced"""
# Fake, noisy data
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize()
p = lc.to_periodogram()
assert len(p[0:200].frequency) == 200
# Test divide
orig = p.power.sum()
p /= 2
assert np.sum(p.power) == orig / 2
# Test multiplication
p *= 0
assert np.sum(p.power) == 0
# Test addition
p += 100
assert np.all(p.power.value >= 100)
# Test subtraction
p -= 100
assert np.sum(p.power) == 0
def test_periodogram_basics():
"""Sanity check to verify that periodogram plotting works"""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize()
pg = lc.to_periodogram()
pg.plot()
plt.close()
pg.plot(view="period")
plt.close()
pg.show_properties()
pg.to_table()
str(pg)
lc[400:500] = np.nan
pg = lc.to_periodogram()
def test_smooth():
"""Test if you can smooth the periodogram and check any pitfalls"""
np.random.seed(42)
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize()
p = lc.to_periodogram(normalization="psd", freq_unit=u.microhertz)
# Test boxkernel and logmedian methods
assert all(p.smooth(method="boxkernel").frequency == p.frequency)
assert all(p.smooth(method="logmedian").frequency == p.frequency)
# Check output units
assert p.smooth().power.unit == p.power.unit
# Check logmedian smooth that the mean of the smoothed power should
# be consistent with the mean of the power
assert np.isclose(
np.mean(p.smooth(method="logmedian").power.value),
np.mean(p.power.value),
atol=0.05 * np.mean(p.power.value),
)
# Can't pass filter_width below 0.
with pytest.raises(ValueError) as err:
p.smooth(method="boxkernel", filter_width=-5.0)
# Can't pass a filter_width in the wrong units
with pytest.raises(ValueError) as err:
p.smooth(method="boxkernel", filter_width=5.0 * u.day)
assert (err.value.args[0] ==
"the `filter_width` parameter must have frequency units.")
# Can't (yet) use a periodogram with a non-evenly spaced frequencies
with pytest.raises(ValueError) as err:
p = np.arange(1, 100)
p = lc.to_periodogram(period=p)
p.smooth()
# Check logmedian doesn't work if I give the filter width units
with pytest.raises(ValueError) as err:
p.smooth(method="logmedian", filter_width=5.0 * u.day)
def test_periodogram_normalization():
"""Tests the normalization options"""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
flux_unit="electron/second",
)
# Test amplitude normalization and correct units
pg = lc.to_periodogram(normalization="amplitude")
assert pg.power.unit == u.electron / u.second
pg = lc.normalize(unit="ppm").to_periodogram(normalization="amplitude")
assert pg.power.unit == u.cds.ppm
# Test PSD normalization and correct units
pg = lc.to_periodogram(freq_unit=u.microhertz, normalization="psd")
assert pg.power.unit == (u.electron / u.second)**2 / u.microhertz
pg = lc.normalize(unit="ppm").to_periodogram(freq_unit=u.microhertz,
normalization="psd")
assert pg.power.unit == u.cds.ppm**2 / u.microhertz
def test_index():
"""Test if you can mask out periodogram"""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize()
p = lc.to_periodogram()
mask = (p.frequency > 0.1 * (1 / u.day)) & (p.frequency < 0.2 *
(1 / u.day))
assert len(p[mask].frequency) == mask.sum()
def test_bin():
"""Test if you can bin the periodogram."""
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
lc = lc.normalize()
p = lc.to_periodogram()
assert len(p.bin(binsize=10,
method="mean").frequency) == len(p.frequency) // 10
assert len(p.bin(binsize=10,
method="median").frequency) == len(p.frequency) // 10
def test_periodogram_can_find_periods():
"""Periodogram should recover the correct period"""
# Light curve that is noisy
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
# Add a 100 day period signal
lc.flux += np.sin(
(lc.time.value / float(lc.time.value.max())) * 20 * np.pi)
lc = lc.normalize()
p = lc.to_periodogram(normalization="amplitude")
assert np.isclose(p.period_at_max_power.value, 100, rtol=1e-3)
def test_periodogram_units():
"""Tests whether periodogram has correct units"""
# Fake, noisy data
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
flux_unit="electron/second",
)
p = lc.to_periodogram(normalization="amplitude")
# Has units
assert hasattr(p.frequency, "unit")
# Has the correct units
assert p.frequency.unit == 1.0 / u.day
assert p.power.unit == u.electron / u.second
assert p.period.unit == u.day
assert p.frequency_at_max_power.unit == 1.0 / u.day
assert p.max_power.unit == u.electron / u.second
def test_error_messages():
"""Test periodogram raises reasonable errors"""
# Fake, noisy data
lc = LightCurve(
time=np.arange(1000),
flux=np.random.normal(1, 0.1, 1000),
flux_err=np.zeros(1000) + 0.1,
)
# Can't specify period range and frequency range
with pytest.raises(ValueError) as err:
lc.to_periodogram(maximum_frequency=0.1, minimum_period=10)
# Can't have a minimum frequency > maximum frequency
with pytest.raises(ValueError) as err:
lc.to_periodogram(maximum_frequency=0.1, minimum_frequency=10)
assert (err.value.args[0] ==
"minimum_frequency cannot be larger than maximum_frequency")
# Can't have a minimum period > maximum period
with pytest.raises(ValueError) as err:
lc.to_periodogram(maximum_period=0.1, minimum_period=10)
assert err.value.args[
0] == "minimum_period cannot be larger than maximum_period"
# Can't specify periods and frequencies
with pytest.raises(ValueError) as err:
lc.to_periodogram(frequency=np.arange(10), period=np.arange(10))
# No unitless periodograms
with pytest.raises(ValueError) as err:
Periodogram([0], [1])
assert err.value.args[
0] == "frequency must be an `astropy.units.Quantity` object."
# No unitless periodograms
with pytest.raises(ValueError) as err:
Periodogram([0] * u.Hz, [1])
assert err.value.args[
0] == "power must be an `astropy.units.Quantity` object."
# No single value periodograms
with pytest.raises(ValueError) as err:
Periodogram([0] * u.Hz, [1] * u.K)
assert err.value.args[
0] == "frequency and power must have a length greater than 1."
# No uneven arrays
with pytest.raises(ValueError) as err:
Periodogram([0, 1, 2, 3] * u.Hz, [1, 1] * u.K)
assert err.value.args[
0] == "frequency and power must have the same length."
# Bad frequency units
with pytest.raises(ValueError) as err:
Periodogram([0, 1, 2] * u.K, [1, 1, 1] * u.K)
assert err.value.args[0] == "Frequency must be in units of 1/time."
# Bad binning
with pytest.raises(ValueError) as err:
Periodogram([0, 1, 2] * u.Hz, [1, 1, 1] * u.K).bin(binsize=-2)
assert err.value.args[0] == "binsize must be larger than or equal to 1"
# Bad binning method
with pytest.raises(ValueError) as err:
Periodogram([0, 1, 2] * u.Hz,
[1, 1, 1] * u.K).bin(method="not-implemented")
assert "method 'not-implemented' is not supported" in err.value.args[0]
# Bad smooth method
with pytest.raises(ValueError) as err:
Periodogram([0, 1, 2] * u.Hz,
[1, 1, 1] * u.K).smooth(method="not-implemented")
assert "method 'not-implemented' is not supported" in err.value.args[0]
def test_bls(caplog):
"""Test that BLS periodogram works and gives reasonable errors"""
lc = LightCurve(
time=np.linspace(0, 10, 200),
flux=np.random.normal(100, 0.1, 200),
flux_err=np.zeros(200) + 0.1,
)
# should be able to make a periodogram
p = lc.to_periodogram(method="bls")
keys = ["period", "power", "duration", "transit_time", "depth", "snr"]
assert np.all([key in dir(p) for key in keys])
p.plot()
plt.close()
# we should be able to specify some keywords
lc.to_periodogram(method="bls",
minimum_period=0.2,
duration=0.1,
maximum_period=0.5)
# Ridiculous BLS spectra should break.
with pytest.raises(ValueError) as err:
lc.to_periodogram(method="bls", frequency_factor=0.00001)
assert err.value.args[0] == (
"`period` contains over 72000001 points.Periodogram is too large to evaluate. Consider setting `frequency_factor` to a higher value."
)
# Some errors should occur
p.compute_stats()
for record in caplog.records:
assert record.levelname == "WARNING"
assert len(caplog.records) == 3
assert "No period specified." in caplog.text
# No more errors
stats = p.compute_stats(1, 0.1, 0)
assert len(caplog.records) == 3
assert isinstance(stats, dict)
# Some errors should occur
p.get_transit_model()
for record in caplog.records:
assert record.levelname == "WARNING"
assert len(caplog.records) == 6
assert "No period specified." in caplog.text
model = p.get_transit_model(1, 0.1, 0)
# No more errors
assert len(caplog.records) == 6
# Model is LC
assert isinstance(model, LightCurve)
# Model is otherwise identical to LC
assert np.in1d(model.time, lc.time).all()
assert np.in1d(lc.time, model.time).all()
mask = p.get_transit_mask(1, 0.1, 0)
assert isinstance(mask, np.ndarray)
assert isinstance(mask[0], np.bool_)
assert mask.sum() < (~mask).sum()
assert isinstance(p.period_at_max_power, u.Quantity)
assert isinstance(p.duration_at_max_power, u.Quantity)
assert isinstance(p.transit_time_at_max_power, Time)
assert isinstance(p.depth_at_max_power, u.Quantity)