Ccl

CCL — Core Cosmology Library (pyccl)

You're helping a user write code with CCL (pyccl), DESC's Core Cosmology Library for standardized theoretical predictions. CCL is TXPipe's cosmological backbone: every TXPipe theory or simulation stage reads a FiducialCosmology YAML file and converts it to a ccl.Cosmology via FiducialCosmology.to_ccl().

Overview

Cosmology construction

import pyccl as ccl
import numpy as np

# Use sigma8 OR A_s, not both; Omega_* are dimensionless (NOT h²-scaled)
cosmo = ccl.Cosmology(
    Omega_c=0.27, Omega_b=0.045, h=0.67,
    sigma8=0.8, n_s=0.96,
    Omega_k=0.0, w0=-1.0, wa=0.0, Neff=3.046,
    matter_power_spectrum="halofit",   # or "linear", "emu"
    transfer_function="bbks",          # or "boltzmann_camb", "eisenstein_hu"
)
# From YAML (TXPipe's FiducialCosmology.to_ccl() does this internally)
cosmo = ccl.Cosmology.read_yaml("fiducial_cosmology.yml")

Key gotchas: Distances return Mpc (not Mpc/h). Set exactly one of sigma8 / A_s; pass None for the other. For w0waCDM just set w0/wa in the same constructor.

Distances and growth

a = 1.0 / (1 + z)           # CCL uses scale factor a, not redshift z
chi = ccl.comoving_radial_distance(cosmo, a)   # Mpc
D = ccl.growth_factor(cosmo, a)                # normalized to 1 at a=1
f = ccl.growth_rate(cosmo, a)
s8 = ccl.sigma8(cosmo)

Angular power spectra

z = np.linspace(0, 3, 300)
nz = np.exp(-0.5 * ((z - 0.5) / 0.1)**2)

# Tracers
wl = ccl.WeakLensingTracer(cosmo, dndz=(z, nz))
nc = ccl.NumberCountsTracer(cosmo, dndz=(z, nz),
                             has_rsd=False, bias=(z, np.ones_like(z)))
cmb = ccl.CMBLensingTracer(cosmo, z_source=1100.0)

ell = np.arange(2, 2000)
cl_shear   = ccl.angular_cl(cosmo, wl, wl, ell)    # C_ell (dimensionless)
cl_ggl     = ccl.angular_cl(cosmo, nc, wl, ell)
cl_density = ccl.angular_cl(cosmo, nc, nc, ell)

angular_cl returns the standard lensing/clustering angular power spectrum without any ell prefactor.

3D power spectra and Pk2D

k = np.logspace(-4, 2, 200)   # h/Mpc
pk_lin  = ccl.linear_matter_power(cosmo, k, a=1.0)    # (Mpc)^3
pk_nl   = ccl.nonlin_matter_power(cosmo, k, a=1.0)

# Custom power spectrum
pk2d = ccl.Pk2D.from_function(pkfunc=lambda k, a: pk_lin,
                                is_logp=False)

See references/api.md for full parameter tables, halo model components, and units details.

TXPipe usage

Every TXPipe theory or simulation stage reads a FiducialCosmology YAML and converts it to ccl.Cosmology via FiducialCosmology.to_ccl(). See references/txpipe.md for the stage table and utility functions.

Reference