def test_compute_highest_outlier_works(self):
mp_ind = 5
max_power = 1000.0
ps = Powerspectrum()
ps.freq = np.arange(10)
ps.power = np.ones_like(ps.freq)
ps.power[mp_ind] = max_power
ps.m = 1
ps.df = ps.freq[1]-ps.freq[0]
ps.norm = "leahy"
model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost = PSDPosterior(ps.freq, ps.power, model, 1)
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
res = pe.fit(lpost, [1.0])
res.mfit = np.ones_like(ps.freq)
max_y, max_x, max_ind = pe._compute_highest_outlier(lpost, res)
assert np.isclose(max_y[0], 2*max_power)
assert np.isclose(max_x[0], ps.freq[mp_ind])
assert max_ind == mp_ind
def test_compute_highest_outlier_works(self):
mp_ind = 5
max_power = 1000.0
ps = Powerspectrum()
ps.freq = np.arange(10)
ps.power = np.ones_like(ps.freq)
ps.power[mp_ind] = max_power
ps.m = 1
ps.df = ps.freq[1]-ps.freq[0]
ps.norm = "leahy"
model = models.Const1D()
p_amplitude = lambda amplitude: \
scipy.stats.norm(loc=1.0, scale=1.0).pdf(
amplitude)
priors = {"amplitude": p_amplitude}
lpost = PSDPosterior(ps.freq, ps.power, model, 1)
lpost.logprior = set_logprior(lpost, priors)
pe = PSDParEst(ps)
res = pe.fit(lpost, [1.0])
res.mfit = np.ones_like(ps.freq)
max_y, max_x, max_ind = pe._compute_highest_outlier(lpost, res)
assert np.isclose(max_y[0], 2*max_power)
assert np.isclose(max_x[0], ps.freq[mp_ind])
assert max_ind == mp_ind
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)
