def _normalize_multitaper(self, unnorm_power, tseg):
"""Apply one of the allowed normalizations to the periodogram.
Normalize the real part of the multitaper spectrum estimate to Leahy,
absolute rms^2, fractional rms^2 normalization, or not at all.
Parameters
----------
unnorm_power: numpy.ndarray
The unnormalized spectrum estimate.
tseg: int
The length of the Fourier segment, in seconds.
Returns
-------
power: numpy.nd.array
The normalized spectrum estimate (real part of the spectrum).
"""
if self.err_dist == 'poisson':
return normalize_crossspectrum(
unnorm_power, tseg, self.n, self.nphots, self.nphots, self.norm,
self.power_type)
return normalize_crossspectrum_gauss(
unnorm_power, np.sqrt(self.meancounts * self.meancounts),
np.sqrt(self.var * self.var),
dt=self.dt,
N=self.n,
norm=self.norm,
power_type=self.power_type)
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")
def test_norm_frac(self):
power = normalize_crossspectrum(self.cs.power,
self.lc1.tseg,
self.lc1.n,
self.cs.nphots1,
self.cs.nphots2,
norm="frac")
norm = 2. / self.rate1
assert np.isclose(np.mean(power[1:]), norm, rtol=0.1)
def test_norm_leahy(self):
power = normalize_crossspectrum(self.cs.power,
self.lc1.tseg,
self.lc1.n,
self.cs.nphots1,
self.cs.nphots2,
norm="leahy")
leahy_noise = 2.0 # expected Poisson noise level
assert np.isclose(np.mean(power[1:]), leahy_noise, rtol=0.02)
def test_norm_abs(self):
# Testing for a power spectrum of lc1
power = normalize_crossspectrum(self.cs.power,
self.lc1.tseg,
self.lc1.n,
self.cs.nphots1,
self.cs.nphots2,
norm="abs")
abs_noise = 2. * self.rate1 # expected Poisson noise level
assert np.isclose(np.mean(power[1:]), abs_noise, rtol=0.001)
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)
