def parse_priors(self, priors):
if isinstance(priors, str):
try:
result = joblib.load(priors)
except IOError:
self.logger.error(
f"Priors could not be loaded from path {priors}. Terminating..."
)
sys.exit(1)
else:
result = []
for i, p in enumerate(priors):
prior_str = re.findall(r"(\w+)\(", p)[0]
prior_param_strings = re.findall(r"\((.*?)\)", p)[0].split(",")
keys = [x.split("=")[0].strip() for x in prior_param_strings]
vals = [
float(x.split("=")[1].strip()) for x in prior_param_strings
]
if prior_str == "roundflat":
prior = (lambda x, keys=keys, vals=vals: roundflat(
np.exp(x), **dict(zip(keys, vals))) + x)
else:
dist = getattr(scipy.stats,
prior_str)(**dict(zip(keys, vals)))
if i == 0 or i == len(priors) - 1:
# The signal variance and the signal noise are in positive,
# sqrt domain
prior = (lambda x, dist=dist: dist.logpdf(
np.sqrt(np.exp(x))) + x / 2.0 - np.log(2.0))
else:
# The lengthscale(s) are in positive domain
prior = lambda x, dist=dist: dist.logpdf(np.exp(
x)) + x # noqa: E731
result.append(prior)
return result
def test_roundflat():
assert_almost_equal(roundflat(0.3), 0.0, decimal=0.1)
assert roundflat(0.0) == -np.inf
assert roundflat(-1.0) == -np.inf
def test_roundflat():
assert roundflat(0.3) == approx(0.0, abs=1e-6)
assert roundflat(0.0) == -np.inf
assert roundflat(-1.0) == -np.inf