def test_eisenstein_hu():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="linear",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Test array of scales
k = np.logspace(-4, 2, 512)
# Computing matter power spectrum
pk_ccl = ccl.linear_matter_power(cosmo_ccl, k, 1.0)
pk_jax = (power.linear_matter_power(cosmo_jax, k / cosmo_jax.h, a=1.0) /
cosmo_jax.h**3)
assert_allclose(pk_ccl, pk_jax, rtol=0.5e-2)
def test_growth_rate():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="linear",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Test array of scale factors
a = np.linspace(0.01, 1.0)
fccl = ccl.growth_rate(cosmo_ccl, a)
fjax = bkgrd.growth_rate(cosmo_jax, a)
assert_allclose(fccl, fjax, rtol=1e-2)
def test_growth_rate_gamma():
# We test consistency of both effective growth
# parametrisation for LCDM
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="linear",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
gamma=0.55,
)
# Test array of scale factors
a = np.linspace(0.01, 1.0)
fccl = ccl.growth_rate(cosmo_ccl, a)
fjax = bkgrd.growth_rate(cosmo_jax, a)
assert_allclose(fccl, fjax, rtol=1e-2)
def test_growth():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3, Omega_b=0.05, h=0.7, sigma8 = 0.8, n_s=0.96, Neff=0,
transfer_function='eisenstein_hu', matter_power_spectrum='linear')
cosmo_jax = Cosmology(Omega_c=0.3, Omega_b=0.05, h=0.7, sigma8 = 0.8, n_s=0.96,
Omega_k=0., w0=-1., wa=0.)
# Test array of scale factors
a = np.linspace(0.1, 1.)
gccl = ccl.growth_factor(cosmo_ccl, a)
gjax = bkgrd.growth_factor(cosmo_jax, a)
assert_allclose(gccl, gjax, rtol=1e-3)
def test_clustering_cl():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="halofit",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Define a redshift distribution
nz = smail_nz(1.0, 2.0, 1.0)
# And a bias model
bias = constant_linear_bias(1.0)
z = np.linspace(0, 5.0, 1024)
tracer_ccl = ccl.NumberCountsTracer(cosmo_ccl,
has_rsd=False,
dndz=(z, nz(z)),
bias=(z, bias(cosmo_jax, z)))
tracer_jax = probes.NumberCounts([nz], bias)
# Get an ell range for the cls
ell = np.logspace(0.1, 4)
# Compute the cls
cl_ccl = ccl.angular_cl(cosmo_ccl, tracer_ccl, tracer_ccl, ell)
cl_jax = angular_cl(cosmo_jax, ell, [tracer_jax])
assert_allclose(cl_ccl, cl_jax[0], rtol=0.5e-2)
def test_lensing_cl_IA():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="halofit",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Define a redshift distribution
nz = smail_nz(1.0, 2.0, 1.0)
z = np.linspace(0, 5.0, 1024)
# Pretty big IA to highlight potential differences stemming from IA
# implementation
bias = inverse_growth_linear_bias(10.0)
tracer_ccl = ccl.WeakLensingTracer(cosmo_ccl, (z, nz(z)),
ia_bias=(z, bias(cosmo_jax, z)))
tracer_jax = probes.WeakLensing([nz], bias)
# Get an ell range for the cls
ell = np.logspace(0.1, 4)
# Compute the cls
cl_ccl = ccl.angular_cl(cosmo_ccl, tracer_ccl, tracer_ccl, ell)
cl_jax = angular_cl(cosmo_jax, ell, [tracer_jax])
assert_allclose(cl_ccl, cl_jax[0], rtol=1e-2)
def test_distances_flat():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="linear",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Test array of scale factors
a = np.linspace(0.01, 1.0)
chi_ccl = ccl.comoving_radial_distance(cosmo_ccl, a)
chi_jax = bkgrd.radial_comoving_distance(cosmo_jax, a) / cosmo_jax.h
assert_allclose(chi_ccl, chi_jax, rtol=0.5e-2)
chi_ccl = ccl.comoving_angular_distance(cosmo_ccl, a)
chi_jax = bkgrd.transverse_comoving_distance(cosmo_jax, a) / cosmo_jax.h
assert_allclose(chi_ccl, chi_jax, rtol=0.5e-2)
chi_ccl = ccl.angular_diameter_distance(cosmo_ccl, a)
chi_jax = bkgrd.angular_diameter_distance(cosmo_jax, a) / cosmo_jax.h
assert_allclose(chi_ccl, chi_jax, rtol=0.5e-2)
def test_lensing_cl():
# We first define equivalent CCL and jax_cosmo cosmologies
cosmo_ccl = ccl.Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Neff=0,
transfer_function="eisenstein_hu",
matter_power_spectrum="halofit",
)
cosmo_jax = Cosmology(
Omega_c=0.3,
Omega_b=0.05,
h=0.7,
sigma8=0.8,
n_s=0.96,
Omega_k=0.0,
w0=-1.0,
wa=0.0,
)
# Define a redshift distribution
nz = smail_nz(1.0, 2.0, 1.0)
z = np.linspace(0, 5.0, 1024)
tracer_ccl = ccl.WeakLensingTracer(cosmo_ccl, (z, nz(z)), use_A_ia=False)
tracer_jax = probes.WeakLensing([nz])
# Get an ell range for the cls
ell = np.logspace(0.1, 4)
# Compute the cls
cl_ccl = ccl.angular_cl(cosmo_ccl, tracer_ccl, tracer_ccl, ell)
cl_jax = angular_cl(cosmo_jax, ell, [tracer_jax])
assert_allclose(cl_ccl, cl_jax[0], rtol=0.5e-2)