def test_mcmc():
x0 = generic_params
bounds = generic_bounds
f = np.linspace(0, 288, 100)
model = asy_peakbag.asymp_spec_model(f, 2)
snr = np.ones(len(f))
prior = asy_peakbag.Prior(generic_bounds, generic_gaussian)
mcmc = asy_peakbag.mcmc(f, snr, model, x0, prior)
def test_prior_call():
prior = asy_peakbag.Prior(generic_bounds, generic_gaussian)
lp_bound = prior.pbound(generic_params)
assert lp_bound == 0
lp_gauss = prior.pgaussian(generic_params)
assert lp_gauss >= 0
p = generic_params
lp = prior(generic_params)
assert lp > 1e-3
def test_prior_bounds():
prior = asy_peakbag.Prior([(10, 5000), (1, 200), (-3, 2)], [(0, 0), (0, 0),
(0, 0)])
lp = prior.pbound([120.0, 10.0, 1.0])
assert_almost_equal(lp, 0.0, 2)
lp = prior.pbound([1.0, 10.0, 1.0])
assert_almost_equal(lp, -np.inf, 1)
lp = prior.pbound([100.0, 1000.0, 1.0])
assert_almost_equal(lp, -np.inf, 1)
def test_mcmc_call():
x0 = generic_params
bounds = generic_bounds
f = np.linspace(0, 288, 100)
model = asy_peakbag.asymp_spec_model(f, 2)
snr = model(x0)
prior = asy_peakbag.Prior(generic_bounds, generic_gaussian)
mcmc = asy_peakbag.mcmc(f, snr, model, x0, prior)
samples = mcmc(10, 20)
def test_prior():
prior = asy_peakbag.Prior([], [])