def check_corr_3d(cosmo):
# Scale factor
a = 0.8
# Distances (in Mpc)
r_int = 50
r = 50.
r_lst = np.linspace(50, 100, 10)
# Make sure correlation functions work for valid inputs
corr1 = ccl.correlation_3d(cosmo, a, r_int)
corr2 = ccl.correlation_3d(cosmo, a, r)
corr3 = ccl.correlation_3d(cosmo, a, r_lst)
assert_(all_finite(corr1))
assert_(all_finite(corr2))
assert_(all_finite(corr3))
def test_correlation_3d(model):
Omega_v = [0.7, 0.7, 0.7, 0.65, 0.75]
w_0 = [-1.0, -0.9, -0.9, -0.9, -0.9]
w_a = [0.0, 0.0, 0.1, 0.1, 0.1]
cosmo = ccl.Cosmology(
Omega_c=0.25,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=3.046,
m_nu_type='normal',
Omega_g=0,
Omega_k=1.0 - 0.25 - 0.05 - Omega_v[model],
w0=w_0[model],
wa=w_a[model],
transfer_function='bbks',
matter_power_spectrum='halofit')
data = np.loadtxt("./benchmarks/data/model%d_xi.txt" % (model+1))
N1 = 40
data1 = data[:N1, :]
r1 = data1[:, 0]
data2 = data[N1:, :]
r2 = data2[:, 0]
for z in np.arange(6):
zind = int(z)
a = 1.0 / (1 + z)
xi1 = ccl.correlation_3d(cosmo, a, r1)
err = np.abs(r1*r1*(xi1-data1[:, zind+1]))
assert np.allclose(err, 0, rtol=0, atol=CORR_TOLERANCE1[zind])
xi2 = ccl.correlation_3d(cosmo, a, data2[:, 0])
err = np.abs(r2*r2*(xi2-data2[:, zind+1]))
assert np.allclose(err, 0, rtol=0, atol=CORR_TOLERANCE1[zind])
def test_correlation_3d_smoke(rval):
a = 0.8
corr = ccl.correlation_3d(COSMO, a, rval)
assert np.all(np.isfinite(corr))
assert np.shape(corr) == np.shape(rval)
