def test_norm_abs(self, power_type):
# Testing for a power spectrum of lc1
self.cs.norm = 'abs'
# New lc with the same absolute variance, but mean-subtracted
norm_lc_sub = copy.deepcopy(self.lc1)
norm_lc_sub.counts = norm_lc_sub.counts - np.mean(norm_lc_sub.counts)
norm_lc_sub.err_dist = 'gauss'
cs = Crossspectrum(norm_lc_sub, norm_lc_sub, norm="none")
cs.norm = 'abs'
cs.power_type = power_type
self.cs.power_type = power_type
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
power_norm = cs._normalize_crossspectrum(cs.unnorm_power, self.tseg)
abs_noise = 2. * self.rate1 # expected Poisson noise level
assert np.isclose(np.mean(power[1:]), abs_noise, rtol=0.01)
assert np.allclose(power[1:], power_norm[1:], atol=0.5)
class TestNormalization(object):
def setup_class(self):
tstart = 0.0
self.tseg = 100000.0
dt = 1
time = np.arange(tstart + 0.5 * dt, self.tseg + 0.5 * dt, dt)
np.random.seed(100)
counts1 = np.random.poisson(10000, size=time.shape[0])
counts1_norm = counts1 / 13.4
counts1_norm_err = np.std(counts1) / 13.4
self.lc1_norm = \
Lightcurve(time, counts1_norm, gti=[[tstart, self.tseg]], dt=dt,
err_dist='gauss', err=np.zeros_like(counts1_norm) + counts1_norm_err)
self.lc1 = Lightcurve(time, counts1, gti=[[tstart, self.tseg]], dt=dt)
self.rate1 = np.mean(
counts1) / dt # mean count rate (counts/sec) of light curve 1
with pytest.warns(UserWarning) as record:
self.cs = Crossspectrum(self.lc1, self.lc1, norm="none")
with pytest.warns(UserWarning) as record:
self.cs_norm = Crossspectrum(self.lc1_norm,
self.lc1_norm,
norm="none")
@pytest.mark.parametrize('power_type', ['all', 'real', 'absolute'])
def test_norm_abs(self, power_type):
# Testing for a power spectrum of lc1
self.cs.norm = 'abs'
# New lc with the same absolute variance, but mean-subtracted
norm_lc_sub = copy.deepcopy(self.lc1)
norm_lc_sub.counts = norm_lc_sub.counts - np.mean(norm_lc_sub.counts)
norm_lc_sub.err_dist = 'gauss'
cs = Crossspectrum(norm_lc_sub, norm_lc_sub, norm="none")
cs.norm = 'abs'
cs.power_type = power_type
self.cs.power_type = power_type
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
power_norm = cs._normalize_crossspectrum(cs.unnorm_power, self.tseg)
abs_noise = 2. * self.rate1 # expected Poisson noise level
assert np.isclose(np.mean(power[1:]), abs_noise, rtol=0.01)
assert np.allclose(power[1:], power_norm[1:], atol=0.5)
@pytest.mark.parametrize('power_type', ['all', 'real', 'absolute'])
def test_norm_leahy(self, power_type):
self.cs.norm = 'leahy'
self.cs_norm.norm = 'leahy'
self.cs.power_type = power_type
self.cs_norm.power_type = power_type
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
power_norm = self.cs_norm._normalize_crossspectrum(
self.cs_norm.unnorm_power, self.tseg)
assert np.allclose(power[1:], power_norm[1:], atol=0.5)
leahy_noise = 2.0 # expected Poisson noise level
assert np.isclose(np.mean(power[1:]), leahy_noise, rtol=0.02)
@pytest.mark.parametrize('power_type', ['all', 'real', 'absolute'])
def test_norm_frac(self, power_type):
self.cs.norm = 'frac'
self.cs_norm.norm = 'frac'
self.cs.power_type = power_type
self.cs_norm.power_type = power_type
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
power_norm = self.cs_norm._normalize_crossspectrum(
self.cs_norm.unnorm_power, self.tseg)
assert np.allclose(power[1:], power_norm[1:])
norm = 2. / self.rate1
assert np.isclose(np.mean(power[1:]), norm, rtol=0.1)
def test_failure_when_normalization_not_recognized(self):
with pytest.raises(ValueError):
power = normalize_crossspectrum(self.cs.power,
self.lc1.tseg,
self.lc1.n,
self.cs.nphots1,
self.cs.nphots2,
norm="wrong")
self.cs.norm = 'asdgfasdfa'
self.cs_norm.norm = 'adfafaf'
with pytest.raises(ValueError):
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
with pytest.raises(ValueError):
power = self.cs_norm._normalize_crossspectrum(
self.cs.unnorm_power, self.tseg)
def test_failure_wrong_power_type(self):
self.cs.power_type = 'asdgfasdfa'
self.cs_norm.power_type = 'adfafaf'
self.cs.norm = 'leahy'
self.cs_norm.norm = 'leahy'
with pytest.raises(ValueError):
power = self.cs._normalize_crossspectrum(self.cs.unnorm_power,
self.tseg)
with pytest.raises(ValueError):
power = self.cs_norm._normalize_crossspectrum(
self.cs.unnorm_power, self.tseg)
