def test_compute_stats(data, with_units, with_err):
t, y, dy, params = data
y_unit = 1
if with_units:
y_unit = u.mag
t = t * u.day
y = y * u.mag
dy = dy * u.mag
params["period"] = params["period"] * u.day
params["duration"] = params["duration"] * u.day
params["transit_time"] = params["transit_time"] * u.day
params["depth"] = params["depth"] * u.mag
if not with_err:
dy = None
model = BoxLeastSquares(t, y, dy)
results = model.power(params["period"],
params["duration"],
oversample=1000)
stats = model.compute_stats(params["period"], params["duration"],
params["transit_time"])
# Test the calculated transit times
tt = params["period"] * np.arange(int(t.max() / params["period"]) + 1)
tt += params["transit_time"]
assert_quantity_allclose(tt, stats["transit_times"])
# Test that the other parameters are consistent with the periodogram
assert_allclose(stats["per_transit_count"], [9, 7, 7, 7, 8])
assert_quantity_allclose(np.sum(stats["per_transit_log_likelihood"]),
results["log_likelihood"])
assert_quantity_allclose(stats["depth"][0], results["depth"])
# Check the half period result
results_half = model.power(0.5 * params["period"],
params["duration"],
oversample=1000)
assert_quantity_allclose(stats["depth_half"][0], results_half["depth"])
# Skip the uncertainty tests when the input errors are None
if not with_err:
assert_quantity_allclose(stats["harmonic_amplitude"],
0.029945029964964204 * y_unit)
assert_quantity_allclose(stats["harmonic_delta_log_likelihood"],
-0.5875918155223113 * y_unit * y_unit)
return
assert_quantity_allclose(stats["harmonic_amplitude"],
0.033027988742275853 * y_unit)
assert_quantity_allclose(stats["harmonic_delta_log_likelihood"],
-12407.505922833765)
assert_quantity_allclose(stats["depth"][1], results["depth_err"])
assert_quantity_allclose(stats["depth_half"][1], results_half["depth_err"])
for f, k in zip((1.0, 1.0, 1.0, 0.0),
("depth", "depth_even", "depth_odd", "depth_phased")):
res = np.abs((stats[k][0] - f * params["depth"]) / stats[k][1])
assert res < 1, f'f={f}, k={k}, res={res}'
def test_compute_stats(data, with_units, with_err):
t, y, dy, params = data
y_unit = 1
if with_units:
y_unit = u.mag
t = t * u.day
y = y * u.mag
dy = dy * u.mag
params["period"] = params["period"] * u.day
params["duration"] = params["duration"] * u.day
params["transit_time"] = params["transit_time"] * u.day
params["depth"] = params["depth"] * u.mag
if not with_err:
dy = None
model = BoxLeastSquares(t, y, dy)
results = model.power(params["period"], params["duration"],
oversample=1000)
stats = model.compute_stats(params["period"], params["duration"],
params["transit_time"])
# Test the calculated transit times
tt = params["period"] * np.arange(int(t.max() / params["period"]) + 1)
tt += params["transit_time"]
assert_quantity_allclose(tt, stats["transit_times"])
# Test that the other parameters are consistent with the periodogram
assert_allclose(stats["per_transit_count"], np.array([9, 7, 7, 7, 8]))
assert_quantity_allclose(np.sum(stats["per_transit_log_likelihood"]),
results["log_likelihood"])
assert_quantity_allclose(stats["depth"][0], results["depth"])
# Check the half period result
results_half = model.power(0.5*params["period"], params["duration"],
oversample=1000)
assert_quantity_allclose(stats["depth_half"][0], results_half["depth"])
# Skip the uncertainty tests when the input errors are None
if not with_err:
assert_quantity_allclose(stats["harmonic_amplitude"],
0.029945029964964204 * y_unit)
assert_quantity_allclose(stats["harmonic_delta_log_likelihood"],
-0.5875918155223113 * y_unit * y_unit)
return
assert_quantity_allclose(stats["harmonic_amplitude"],
0.033027988742275853 * y_unit)
assert_quantity_allclose(stats["harmonic_delta_log_likelihood"],
-12407.505922833765)
assert_quantity_allclose(stats["depth"][1], results["depth_err"])
assert_quantity_allclose(stats["depth_half"][1], results_half["depth_err"])
for f, k in zip((1.0, 1.0, 1.0, 0.0),
("depth", "depth_even", "depth_odd", "depth_phased")):
assert np.abs((stats[k][0]-f*params["depth"]) / stats[k][1]) < 1.0
def test_fast_method(data, objective, offset):
t, y, dy, params = data
if offset:
t = t - params["transit_time"] + params["period"]
model = BoxLeastSquares(t, y, dy)
periods = np.exp(np.linspace(np.log(params["period"]) - 1,
np.log(params["period"]) + 1, 10))
durations = params["duration"]
results = model.power(periods, durations, objective=objective)
assert_allclose_blsresults(results, model.power(periods, durations,
method="slow",
objective=objective))
def test_shapes(data, shape):
t, y, dy, params = data
duration = params["duration"]
model = BoxLeastSquares(t, y, dy)
period = np.empty(shape)
period.flat = np.linspace(params["period"]-1, params["period"]+1,
period.size)
if len(period.shape) > 1:
with pytest.raises(ValueError):
results = model.power(period, duration)
else:
results = model.power(period, duration)
for k, v in results.items():
if k == "objective":
continue
assert v.shape == shape
def test_autopower(data):
t, y, dy, params = data
duration = params["duration"] + np.linspace(-0.1, 0.1, 3)
model = BoxLeastSquares(t, y, dy)
period = model.autoperiod(duration)
results1 = model.power(period, duration)
results2 = model.autopower(duration)
assert_allclose_blsresults(results1, results2)
def test_correct_model(data, objective):
t, y, dy, params = data
model = BoxLeastSquares(t, y, dy)
periods = np.exp(np.linspace(np.log(params["period"]) - 0.1,
np.log(params["period"]) + 0.1, 1000))
results = model.power(periods, params["duration"], objective=objective)
ind = np.argmax(results.power)
for k, v in params.items():
assert_allclose(results[k][ind], v, atol=0.01)
chi = (results.depth[ind]-params["depth"]) / results.depth_err[ind]
assert np.abs(chi) < 1
def test_results_units(data, method, with_err, t_unit, y_unit, objective):
t, y, dy, params = data
periods = np.linspace(params["period"] - 1.0, params["period"] + 1.0, 3)
if t_unit is not None:
t = t * t_unit
if y_unit is not None:
y = y * y_unit
dy = dy * y_unit
if not with_err:
dy = None
model = BoxLeastSquares(t, y, dy)
results = model.power(periods,
params["duration"],
method=method,
objective=objective)
if t_unit is None:
assert not has_units(results.period)
assert not has_units(results.duration)
assert not has_units(results.transit_time)
else:
assert results.period.unit == t_unit
assert results.duration.unit == t_unit
assert results.transit_time.unit == t_unit
if y_unit is None:
assert not has_units(results.power)
assert not has_units(results.depth)
assert not has_units(results.depth_err)
assert not has_units(results.depth_snr)
assert not has_units(results.log_likelihood)
else:
assert results.depth.unit == y_unit
assert results.depth_err.unit == y_unit
assert results.depth_snr.unit == units.one
if dy is None:
assert results.log_likelihood.unit == y_unit * y_unit
if objective == "snr":
assert results.power.unit == units.one
else:
assert results.power.unit == y_unit * y_unit
else:
assert results.log_likelihood.unit == units.one
assert results.power.unit == units.one
def test_32bit_bug():
rand = np.random.RandomState(42)
t = rand.uniform(0, 10, 500)
y = np.ones_like(t)
y[np.abs((t + 1.0) % 2.0 - 1) < 0.08] = 1.0 - 0.1
y += 0.01 * rand.randn(len(t))
model = BoxLeastSquares(t, y)
results = model.autopower(0.16)
assert np.allclose(results.period[np.argmax(results.power)],
1.9923406038842544)
periods = np.linspace(1.9, 2.1, 5)
results = model.power(periods, 0.16)
assert np.allclose(
results.power,
np.array([0.01421067, 0.02842475, 0.10867671, 0.05117755, 0.01783253]))
def test_32bit_bug():
rand = np.random.default_rng(42)
t = rand.uniform(0, 10, 500)
y = np.ones_like(t)
y[np.abs((t + 1.0) % 2.0 - 1) < 0.08] = 1.0 - 0.1
y += 0.01 * rand.standard_normal(len(t))
model = BoxLeastSquares(t, y)
results = model.autopower(0.16)
assert_allclose(results.period[np.argmax(results.power)],
2.000412388152837)
periods = np.linspace(1.9, 2.1, 5)
results = model.power(periods, 0.16)
assert_allclose(results.power,
[0.01723948, 0.0643028, 0.1338783, 0.09428816, 0.03577543],
rtol=1.1e-7)
def test_32bit_bug():
rand = np.random.RandomState(42)
t = rand.uniform(0, 10, 500)
y = np.ones_like(t)
y[np.abs((t + 1.0) % 2.0-1) < 0.08] = 1.0 - 0.1
y += 0.01 * rand.randn(len(t))
model = BoxLeastSquares(t, y)
results = model.autopower(0.16)
assert np.allclose(results.period[np.argmax(results.power)],
1.9923406038842544)
periods = np.linspace(1.9, 2.1, 5)
results = model.power(periods, 0.16)
assert np.allclose(
results.power,
np.array([0.01421067, 0.02842475, 0.10867671, 0.05117755, 0.01783253])
)
def test_results_units(data, method, with_err, t_unit, y_unit, objective):
t, y, dy, params = data
periods = np.linspace(params["period"]-1.0, params["period"]+1.0, 3)
if t_unit is not None:
t = t * t_unit
if y_unit is not None:
y = y * y_unit
dy = dy * y_unit
if not with_err:
dy = None
model = BoxLeastSquares(t, y, dy)
results = model.power(periods, params["duration"], method=method,
objective=objective)
if t_unit is None:
assert not has_units(results.period)
assert not has_units(results.duration)
assert not has_units(results.transit_time)
else:
assert results.period.unit == t_unit
assert results.duration.unit == t_unit
assert results.transit_time.unit == t_unit
if y_unit is None:
assert not has_units(results.power)
assert not has_units(results.depth)
assert not has_units(results.depth_err)
assert not has_units(results.depth_snr)
assert not has_units(results.log_likelihood)
else:
assert results.depth.unit == y_unit
assert results.depth_err.unit == y_unit
assert results.depth_snr.unit == u.one
if dy is None:
assert results.log_likelihood.unit == y_unit * y_unit
if objective == "snr":
assert results.power.unit == u.one
else:
assert results.power.unit == y_unit * y_unit
else:
assert results.log_likelihood.unit == u.one
assert results.power.unit == u.one
