def test_constrain_correlation_nolag():
f1 = filters.GaussianFilter(radius=5)
fh = FieldHandler(Ndim=3, Lbox=50, dimensions=16, Pk=(k, Pk), filter=f1)
f2 = filters.GaussianFilter(radius=6)
f3 = filters.GaussianFilter(radius=7)
f4 = filters.GaussianFilter(radius=8)
X1 = np.array([0, 0, 0])
c1 = C.DensityConstrain(X1, filter=f1, value=1.69, field_handler=fh)
c2 = C.GradientConstrain(X1, filter=f2, value=[0, 0, 0], field_handler=fh)
c3 = C.HessianConstrain(X1,
filter=f3,
value=[0, 0, 0, 0, 0, 0],
field_handler=fh)
c4 = C.ThirdDerivativeConstrain(X1,
filter=f4,
value=list(range(10)),
field_handler=fh)
fh.add_constrain(c1)
fh.add_constrain(c2)
fh.add_constrain(c3)
fh.add_constrain(c4)
cov = fh.compute_covariance()
# Check positive definite
evals = np.linalg.eigvalsh(cov)
assert_array_less(np.zeros_like(evals), evals)
# Check symmetry
assert_allclose(cov, cov.T)
def test_it(use_cache):
# Note: here we use the old package's k and Pk so that their result agree can be
# be compared
fh = FieldHandler(
Ndim=3,
Lbox=50,
dimensions=16,
Pk=(c.k, c.Pk),
use_covariance_cache=use_cache,
filter=f1,
)
c1 = C.DensityConstrain(X1, filter=f1, value=1.69, field_handler=fh)
c2 = C.GradientConstrain(X2,
filter=f2,
value=[0, 0, 0],
field_handler=fh)
c3 = C.HessianConstrain(X3,
filter=f3,
value=[0, 0, 0, 0, 0, 0],
field_handler=fh)
fh.add_constrain(c1)
fh.add_constrain(c2)
fh.add_constrain(c3)
new = fh.compute_covariance() / S
# Check closeness (note: the order may be different so only
# check det and eigenvalues)
det_ref = np.linalg.det(ref)
det_new = np.linalg.det(new)
assert_allclose(det_ref, det_new)
eval_ref = np.linalg.eigvalsh(ref)
eval_new = np.linalg.eigvalsh(new)
assert_allclose(eval_ref, eval_new)