def test_sampler_runs(self):
pe = PSDParEst(self.ps)
sample_res = pe.sample(self.lpost, [2.0, 0.1, 100, 2.0], nwalkers=50,
niter=10, burnin=15, print_results=True,
plot=True)
assert os.path.exists("test_corner.pdf")
os.unlink("test_corner.pdf")
assert sample_res.acceptance > 0.25
assert isinstance(sample_res, SamplingResults)
p_whitenoise = lambda white_noise: scipy.stats.norm(2.0, 0.1).pdf(white_noise)
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")
