def test_compute_lrt_works(self):
m = 1
nfreq = 100000
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(100)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
s_all = np.atleast_2d(np.ones(10) * 2.0).T
model2 = models.PowerLaw1D() + models.Const1D()
model2.x_0_0.fixed = True
loglike2 = PSDLogLikelihood(ps.freq, ps.power, model2, 1)
pe = PSDParEst(ps)
lrt_obs, res1, res2 = pe.compute_lrt(loglike, [2.0],
loglike2, [2.0, 1.0, 2.0],
neg=True)
lrt_sim = pe.simulate_lrts(s_all,
loglike, [2.0],
loglike2, [2.0, 1.0, 2.0],
max_post=False,
seed=100)
assert (lrt_obs > 0.4) and (lrt_obs < 0.6)
assert np.all(lrt_sim < 10.0) and np.all(lrt_sim > 0.01)
def test_compute_lrt_works(self):
m = 1
nfreq = 100000
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(100)
noise = rng.exponential(size=nfreq)
model = models.Const1D()
model.amplitude = 2.0
p = model(freq)
power = noise * p
ps = Powerspectrum()
ps.freq = freq
ps.power = power
ps.m = m
ps.df = freq[1] - freq[0]
ps.norm = "leahy"
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
s_all = np.atleast_2d(np.ones(10) * 2.0).T
model2 = models.PowerLaw1D() + models.Const1D()
model2.x_0_0.fixed = True
loglike2 = PSDLogLikelihood(ps.freq, ps.power, model2, 1)
pe = PSDParEst(ps)
lrt_obs, res1, res2 = pe.compute_lrt(loglike, [2.0], loglike2,
[2.0, 1.0, 2.0], neg=True)
lrt_sim = pe.simulate_lrts(s_all, loglike, [2.0], loglike2,
[2.0, 1.0, 2.0],
seed=100)
assert (lrt_obs > 0.4) and (lrt_obs < 0.6)
assert np.all(lrt_sim < 10.0) and np.all(lrt_sim > 0.01)
def test_compute_lrt_fails_with_wrong_input(self):
pe = PSDParEst(self.ps)
with pytest.raises(AssertionError):
lrt_sim = pe.simulate_lrts(np.arange(5), self.lpost, [1, 2, 3, 4],
[1, 2, 3, 4], [1, 2, 3, 4])
def test_compute_lrt_fails_with_wrong_input(self):
pe = PSDParEst(self.ps)
with pytest.raises(AssertionError):
lrt_sim = pe.simulate_lrts(np.arange(10), self.lpost, [1, 2, 3, 4],
[1, 2, 3, 4], [1, 2, 3, 4])
