def test_measures():
f1 = filters.GaussianFilter(radius=5)
fh = FieldHandler(Ndim=3, Lbox=15, dimensions=16, Pk=(k, Pk), filter=f1)
dx = fh.Lbox / fh.dimensions
sfield = fh.get_smoothed(f1)
# Test density measurement
c = C.DensityConstrain([8, 8, 8], filter=f1, value=1, field_handler=fh)
val = c.measure()
tgt = sfield[8, 8, 8]
assert_allclose(val, tgt)
# Test gradient measurement
c = C.GradientConstrain([8, 8, 8],
filter=f1,
value=[0, 0, 0],
field_handler=fh)
val = c.measure()
tgt = np.array(np.gradient(sfield, dx))[:, 8, 8, 8]
assert_allclose(val, tgt)
# Test hessian measurement
c = C.HessianConstrain([8, 8, 8],
filter=f1,
value=[0] * 6,
field_handler=fh)
val = c.measure()
tgt = np.array(np.gradient(np.gradient(sfield, dx), dx,
axis=(-3, -2, -1)))[:, :, 8, 8, 8]
assert_allclose(val, tgt)
def test_field_sigma():
fh = FieldHandler(Ndim=3, Lbox=100, dimensions=128, Pk=(k, Pk))
smoothing_scales = np.geomspace(5, 10, 20)
sigma_th = [fh.sigma(0, filters.TopHatFilter(R)) for R in smoothing_scales]
sigma_exp = [
fh.get_smoothed(filters.TopHatFilter(R)).std() for R in smoothing_scales
]
# Check the field variance
assert_allclose(sigma_th, sigma_exp, rtol=5e-2)
def test_filtering():
N = 64
v0 = 10
fh = FieldHandler(Ndim=3, Lbox=N, dimensions=N, Pk=(k, Pk))
fh.white_noise[:] = 0
fh.white_noise[N // 2, N // 2, N // 2] = v0
fh.white_noise_fft = np.fft.rfftn(fh.white_noise)
R = 10
field = fh.get_smoothed(R)
x, y, z = (
np.array(np.meshgrid(*[np.linspace(0, N - 1, N)] * 3, indexing="ij")) - N // 2
)
ref = (
1
/ (2 * np.pi * R ** 2) ** (3 / 2)
* np.exp(-(x ** 2 + y ** 2 + z ** 2) / 2 / R ** 2)
* v0
)
assert_allclose(field, ref, atol=1e-5)