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def test_pickle(with_general, seed=42):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.rand(500))
diag = np.random.uniform(0.1, 0.5, len(t))
y = np.sin(t)
if with_general:
U = np.vander(t - np.mean(t), 4).T
V = U * np.random.rand(4)[:, None]
A = np.sum(U * V, axis=0) + 1e-8
else:
A = np.empty(0)
U = np.empty((0, 0))
V = np.empty((0, 0))
alpha_real = np.array([1.3, 1.5])
beta_real = np.array([0.5, 0.2])
def test_dot(with_general, seed=42):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.rand(500))
b = np.random.randn(len(t), 5)
alpha_real = np.array([1.3, 0.2])
beta_real = np.array([0.5, 0.8])
alpha_complex_real = np.array([0.1])
alpha_complex_imag = np.array([0.0])
beta_complex_real = np.array([1.5])
beta_complex_imag = np.array([0.1])
K = get_kernel_value(
alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
beta_complex_real, beta_complex_imag, t[:, None] - t[None, :]
)
def test_carma(seed=42):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.uniform(0, 5, 100))
yerr = 0.1 + np.zeros_like(t)
y = np.sin(t) + yerr * np.random.randn(len(t))
carma_solver = CARMASolver(-0.5, np.array([0.1, 0.05, 0.01]),
np.array([0.2, 0.1]))
carma_ll = carma_solver.log_likelihood(t, y, yerr)
params = carma_solver.get_celerite_coeffs()
solver.compute(
0.0, params[0], params[1], params[2], params[3], params[4], params[5],
np.empty(0), np.empty((0, 0)), np.empty((0, 0)),
t, yerr**2
)
celerite_ll = -0.5*(
def _test_solve(alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
beta_complex_real, beta_complex_imag, seed=42,
with_general=False):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.rand(500))
diag = np.random.uniform(0.1, 0.5, len(t))
b = np.random.randn(len(t))
with pytest.raises(RuntimeError):
solver.log_determinant()
with pytest.raises(RuntimeError):
solver.dot_solve(b)
if with_general:
U = np.vander(t - np.mean(t), 4).T
V = U * np.random.rand(4)[:, None]
A = np.sum(U * V, axis=0) + 1e-8
else:
A = np.empty(0)
def _test_log_determinant(alpha_real, beta_real, alpha_complex_real,
alpha_complex_imag, beta_complex_real,
beta_complex_imag, seed=42):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.rand(5))
diag = np.random.uniform(0.1, 0.5, len(t))
solver.compute(
0.0, alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
beta_complex_real, beta_complex_imag,
np.empty(0), np.empty((0, 0)), np.empty((0, 0)),
t, diag
)
K = get_kernel_value(
alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
beta_complex_real, beta_complex_imag, t[:, None] - t[None, :]
)
K[np.diag_indices_from(K)] += diag
assert np.allclose(solver.log_determinant(), np.linalg.slogdet(K)[1])
def test_dot_L(with_general, seed=42):
solver = celerite.CholeskySolver()
np.random.seed(seed)
t = np.sort(np.random.rand(5))
b = np.random.randn(len(t), 5)
yerr = np.random.uniform(0.1, 0.5, len(t))
alpha_real = np.array([1.3, 0.2])
beta_real = np.array([0.5, 0.8])
alpha_complex_real = np.array([0.1])
alpha_complex_imag = np.array([0.0])
beta_complex_real = np.array([1.5])
beta_complex_imag = np.array([0.1])
K = get_kernel_value(
alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
beta_complex_real, beta_complex_imag, t[:, None] - t[None, :]
)