def test_from_lc_iter_works(self):
pds_ev = AveragedPowerspectrum.from_lc_iterable(
self.events.to_lc_iter(self.dt, self.segment_size),
segment_size=self.segment_size,
dt=self.dt,
norm="leahy",
silent=True,
gti=self.events.gti)
assert np.allclose(self.leahy_pds.power, pds_ev.power)
def test_from_lc_iter_counts_only_works(self):
def iter_lc_counts_only(iter_lc):
for lc in iter_lc:
yield lc.counts
lccs = AveragedPowerspectrum.from_lc_iterable(
iter_lc_counts_only(self.events.to_lc_iter(self.dt, self.segment_size)),
segment_size=self.segment_size, dt=self.dt, norm='leahy', silent=True)
power1 = lccs.power.real
power2 = self.leahy_pds.power.real
assert np.allclose(power1, power2, rtol=0.01)
def test_from_lc_iter_with_err_ignored_with_wrong_err_dist(self):
def iter_lc_with_errs(iter_lc):
for lc in iter_lc:
# Not supposed to have error bars
lc.err_dist = "poisson"
# use a completely wrong error bar, for fun
lc._counts_err = np.zeros_like(lc.counts) + 14.2345425252462
yield lc
lccs = AveragedPowerspectrum.from_lc_iterable(
iter_lc_with_errs(self.events.to_lc_iter(self.dt, self.segment_size)),
segment_size=self.segment_size, dt=self.dt, norm='leahy', silent=True)
power1 = lccs.power.real
power2 = self.leahy_pds.power.real
assert np.allclose(power1, power2, rtol=0.01)
def test_from_lc_iter_with_err_works(self):
def iter_lc_with_errs(iter_lc):
for lc in iter_lc:
# In order for error bars to be considered,
# err_dist has to be gauss.
lc.err_dist = "gauss"
lc._counts_err = np.zeros_like(lc.counts) + lc.counts.mean()**0.5
yield lc
lccs = AveragedPowerspectrum.from_lc_iterable(
iter_lc_with_errs(self.events.to_lc_iter(self.dt, self.segment_size)),
segment_size=self.segment_size, dt=self.dt, norm='leahy', silent=True)
power1 = lccs.power.real
power2 = self.leahy_pds.power.real
assert np.allclose(power1, power2, rtol=0.01)