def test_fractional_rms_in_frac_norm_is_consistent(self):
time = np.arange(0, 100, 1) + 0.5
poisson_counts = np.random.poisson(100.0, size=time.shape[0])
lc = Lightcurve(time, counts=poisson_counts, dt=1, gti=[[0, 100]])
ps = Powerspectrum(lc=lc, norm="leahy")
rms_ps_l, rms_err_l = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1],
white_noise_offset=0)
ps = Powerspectrum(lc=lc, norm="frac")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1],
white_noise_offset=0)
assert np.allclose(rms_ps, rms_ps_l, atol=0.01)
assert np.allclose(rms_err, rms_err_l, atol=0.01)
def test_fractional_rms_in_frac_norm_is_consistent(self):
time = np.arange(0, 100, 1) + 0.5
poisson_counts = np.random.poisson(100.0,
size=time.shape[0])
lc = Lightcurve(time, counts=poisson_counts, dt=1,
gti=[[0, 100]])
ps = Powerspectrum(lc=lc, norm="leahy")
rms_ps_l, rms_err_l = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1], white_noise_offset=0)
ps = Powerspectrum(lc=lc, norm="frac")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1], white_noise_offset=0)
assert np.allclose(rms_ps, rms_ps_l, atol=0.01)
assert np.allclose(rms_err, rms_err_l, atol=0.01)
def test_fractional_rms_in_leahy_norm(self):
"""
fractional rms should only be *approximately* equal the standard
deviation divided by the mean of the light curve. Therefore, we allow
for a larger tolerance in np.isclose()
"""
ps = Powerspectrum(lc=self.lc, norm="Leahy")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_fractional_rms_in_leahy_norm(self):
"""
fractional rms should only be *approximately* equal the standard
deviation divided by the mean of the light curve. Therefore, we allow
for a larger tolerance in np.isclose()
"""
ps = Powerspectrum(lc=self.lc, norm="Leahy")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_fractional_rms_fails_when_rms_not_leahy(self):
with pytest.raises(Exception):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
def test_fractional_rms_in_frac_norm(self):
ps = Powerspectrum(lc=self.lc, norm="frac")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
def test_compute_rms_wrong_norm(self):
ps = Powerspectrum(self.lc)
ps.norm = 'gibberish'
with pytest.raises(TypeError):
ps.compute_rms(0, 10)
def test_fractional_rms_fails_when_rms_not_leahy(self):
with pytest.raises(Exception):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[0],
max_freq=ps.freq[-1])
def test_fractional_rms_in_rms_norm(self):
ps = Powerspectrum(lc=self.lc, norm="rms")
rms_ps, rms_err = ps.compute_rms(min_freq=ps.freq[1],
max_freq=ps.freq[-1])
rms_lc = np.std(self.lc.counts) / np.mean(self.lc.counts)
assert np.isclose(rms_ps, rms_lc, atol=0.01)
