def test_calibrate_highest_outlier_works(self):
m = 1
nfreq = 100
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
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"
nsim = 5
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
s_all = np.atleast_2d(np.ones(nsim) * 2.0).T
pe = PSDParEst(ps)
pval = pe.calibrate_highest_outlier(loglike, [2.0], sample=s_all,
max_post=False, seed=seed)
assert pval > 0.001
def test_calibrate_highest_outlier_works_with_mvn(self):
m = 1
nfreq = 10000
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
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"
nsim = 10
loglike = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
pe = PSDParEst(ps)
pval = pe.calibrate_highest_outlier(loglike, [2.0], sample=None,
max_post=False, seed=seed,
nsim=nsim)
assert pval > 0.001
def test_calibrate_highest_outlier_works_with_sampling(self):
m = 1
nfreq = 100
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
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"
nsim = 5
lpost = PSDPosterior(ps.freq, ps.power, model, m=1)
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
with catch_warnings(RuntimeWarning):
pval = pe.calibrate_highest_outlier(lpost, [2.0],
sample=None,
max_post=True,
seed=seed,
nsim=nsim,
niter=10,
nwalkers=20,
burnin=10)
assert pval > 0.001
def test_calibrate_highest_outlier_works_with_sampling(self):
m = 1
nfreq = 100000
seed = 100
freq = np.linspace(1, 10, nfreq)
rng = np.random.RandomState(seed)
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"
nsim = 10
lpost = PSDPosterior(ps.freq, ps.power, model, m=1)
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
pval = pe.calibrate_highest_outlier(lpost, [2.0], sample=None,
max_post=True, seed=seed,
nsim=nsim, niter=20, nwalkers=100,
burnin=100)
assert pval > 0.001
priors = {}
priors["alpha_0"] = p_alpha
priors["amplitude_0"] = p_amplitude
priors["amplitude_1"] = p_whitenoise
starting_pars = [3.0, 1.0, 2.4]
lpost = PSDPosterior(ps.freq, ps.power, plc, priors=priors, m=ps.m)
parest = PSDParEst(ps, fitmethod='BFGS', max_post=True)
res = parest.fit(lpost, starting_pars)
sample = parest.sample(lpost,
res.p_opt,
cov=res.cov,
nwalkers=400,
niter=100,
burnin=200,
namestr="psd_modeling_test")
max_power, max_freq, max_ind = parest._compute_highest_outlier(lpost, res)
print(max_power)
pval = parest.calibrate_highest_outlier(lpost,
starting_pars,
sample=sample,
max_post=True,
nsim=100,
niter=100,
nwalkers=400,
burnin=200,
namestr="test")
print(pval)
