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Fix crossspectrum all default #762

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merged 5 commits into from
Oct 5, 2023
Merged

Fix crossspectrum all default #762

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9fb746a
The power type of Crossspectrum should be all by default
matteobachetti Sep 29, 2023
9e1e9f2
Use power type real where it should be used
matteobachetti Sep 29, 2023
7b284ac
Fix tests using the Crossspectrum to calculate lags
matteobachetti Sep 29, 2023
5245470
Add changelog
matteobachetti Sep 29, 2023
5324211
blacken
matteobachetti Oct 1, 2023
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1 change: 1 addition & 0 deletions docs/changes/762.bugfix.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
Crossspectrum had "real" as default value. This meant that, for example, lags could not be calculated. Now the default value is "all", as it should be.
2 changes: 1 addition & 1 deletion stingray/crossspectrum.py
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Expand Up @@ -547,7 +547,7 @@ def __init__(
gti=None,
lc1=None,
lc2=None,
power_type="real",
power_type="all",
dt=None,
fullspec=False,
skip_checks=False,
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10 changes: 5 additions & 5 deletions stingray/modeling/gpmodeling.py
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Expand Up @@ -37,8 +37,7 @@
tfp_available = False


__all__ = ["get_kernel", "get_mean", "get_prior",
"get_log_likelihood", "GPResult", "get_gp_params"]
__all__ = ["get_kernel", "get_mean", "get_prior", "get_log_likelihood", "GPResult", "get_gp_params"]


def get_kernel(kernel_type, kernel_params):
Expand Down Expand Up @@ -575,8 +574,7 @@
self.counts = lc.counts
self.result = None

def sample(self, prior_model=None, likelihood_model=None, max_samples=1e4,
num_live_points=500):
def sample(self, prior_model=None, likelihood_model=None, max_samples=1e4, num_live_points=500):
"""
Makes a Jaxns nested sampler over the Gaussian Process, given the
prior and likelihood model
Expand Down Expand Up @@ -619,7 +617,9 @@
nsmodel = Model(prior_model=self.prior_model, log_likelihood=self.log_likelihood_model)
nsmodel.sanity_check(random.PRNGKey(10), S=100)

self.exact_ns = ExactNestedSampler(nsmodel, num_live_points=num_live_points, max_samples=max_samples)
self.exact_ns = ExactNestedSampler(

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nsmodel, num_live_points=num_live_points, max_samples=max_samples
)

termination_reason, state = self.exact_ns(
random.PRNGKey(42), term_cond=TerminationCondition(live_evidence_frac=1e-4)
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6 changes: 3 additions & 3 deletions stingray/simulator/tests/test_simulator.py
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Expand Up @@ -39,7 +39,7 @@ def calculate_lag(self, lc, h, delay):

lc1 = Lightcurve(time, s)
lc2 = Lightcurve(time, output)
cross = Crossspectrum(lc1, lc2)
cross = Crossspectrum(lc2, lc1)
cross = cross.rebin(0.0075)

return np.angle(cross.power) / (2 * np.pi * cross.freq)
Expand Down Expand Up @@ -559,7 +559,7 @@ def test_position_varying_channels(self):
outputs.append(lc2)

with pytest.warns(UserWarning, match="Your lightcurves have different statistics"):
cross = [Crossspectrum(lc, lc2).rebin(0.0075) for lc2 in outputs]
cross = [Crossspectrum(lc2, lc).rebin(0.0075) for lc2 in outputs]
lags = [np.angle(c.power) / (2 * np.pi * c.freq) for c in cross]

v_cutoffs = [1.0 / (2.0 * 5), 1.0 / (2.0 * 10)]
Expand Down Expand Up @@ -588,7 +588,7 @@ def test_intensity_varying_channels(self):
outputs.append(lc2)

with pytest.warns(UserWarning, match="Your lightcurves have different statistics"):
cross = [Crossspectrum(lc, lc2).rebin(0.0075) for lc2 in outputs]
cross = [Crossspectrum(lc2, lc).rebin(0.0075) for lc2 in outputs]
lags = [np.angle(c.power) / (2 * np.pi * c.freq) for c in cross]

v_cutoff = 1.0 / (2.0 * 5)
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8 changes: 4 additions & 4 deletions stingray/tests/test_crossspectrum.py
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Expand Up @@ -869,7 +869,7 @@ def test_classical_significances_fails_in_rms(self):
@pytest.mark.slow
def test_classical_significances_threshold(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy")
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy", power_type="real")

# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
Expand All @@ -886,7 +886,7 @@ def test_classical_significances_threshold(self):
@pytest.mark.slow
def test_classical_significances_trial_correction(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy")
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy", power_type="real")
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0
index = 1
Expand All @@ -897,15 +897,15 @@ def test_classical_significances_trial_correction(self):

def test_classical_significances_with_logbinned_psd(self):
with pytest.warns(UserWarning) as record:
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy")
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy", power_type="real")
cs_log = cs.rebin_log()
pval = cs_log.classical_significances(threshold=1.1, trial_correction=False)

assert len(pval[0]) == len(cs_log.power)

@pytest.mark.slow
def test_pvals_is_numpy_array(self):
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy")
cs = Crossspectrum(self.lc1, self.lc2, norm="leahy", power_type="real")
# change the powers so that just one exceeds the threshold
cs.power = np.zeros_like(cs.power) + 2.0

Expand Down