def test_citations(data):
pm = pytest.importorskip("pymc3")
x, diag, y, t = data
with pm.Model() as model:
term = terms.SHOTerm(S0=1.0, w0=0.5, Q=3.0)
gp = GaussianProcess(term, t=x, diag=diag)
gp.marginal("obs", observed=y)
assert model.__citations__["celerite2"] == CITATIONS
def test_consistency(name, args, mean, data):
x, diag, y, t = data
term = getattr(terms, name)(**args)
gp = GaussianProcess(term, mean=mean)
gp.compute(x, diag=diag)
pyterm = getattr(pyterms, name)(**args)
pygp = celerite2.GaussianProcess(pyterm, mean=mean)
pygp.compute(x, diag=diag)
check_gp_models(lambda x: x.eval(), gp, pygp, y, t)
def test_marginal(data):
pm = pytest.importorskip("pymc3")
x, diag, y, t = data
with pm.Model() as model:
term = terms.SHOTerm(S0=1.0, w0=0.5, Q=3.0)
gp = GaussianProcess(term, t=x, diag=diag)
gp.marginal("obs", observed=y)
assert np.allclose(
model.fastfn(model.logpt)(model.test_point),
model.fastfn(gp.log_likelihood(y))(model.test_point),
)
def test_errors(data):
x, diag, y, t = data
term = terms.SHOTerm(S0=1.0, w0=0.5, Q=3.0)
gp = GaussianProcess(term)
# Need to call compute first
with pytest.raises(RuntimeError):
gp.log_likelihood(y)
# Sorted
with pytest.raises(AssertionError):
gp.compute(x[::-1], diag=diag)
gp._d.eval()
# 1D
with pytest.raises(ValueError):
gp.compute(np.tile(x[:, None], (1, 5)), diag=diag)
# Only one of diag and yerr
with pytest.raises(ValueError):
gp.compute(x, diag=diag, yerr=np.sqrt(diag))
# Not positive definite
with pytest.raises(celerite2.backprop.LinAlgError):
gp.compute(x, diag=-10 * diag)
gp._d.eval()
# Not positive definite with `quiet`
gp.compute(x, diag=-10 * diag, quiet=True)
ld = gp._log_det.eval()
assert np.isinf(ld)
assert ld < 0
# Compute correctly
gp.compute(x, diag=diag)
gp.log_likelihood(y).eval()
# Dimension mismatch
with pytest.raises(ValueError):
gp.log_likelihood(y[:-1]).eval()
with pytest.raises(ValueError):
gp.log_likelihood(np.tile(y[:, None], (1, 5)))
with pytest.raises(ValueError):
gp.predict(y, t=np.tile(t[:, None], (1, 5)))