ccl

name

ccl

description

Guide to CCL (pyccl), the DESC Core Cosmology Library for theoretical cosmological calculations. Use this skill whenever the user imports pyccl, works with Cosmology objects, computes angular power spectra (angular_cl), uses tracers (WeakLensingTracer, NumberCountsTracer, CMBLensingTracer), computes 3D power spectra (linear_matter_power, nonlin_matter_power), uses Pk2D, calls scalar functions like sigma8/growth_factor/comoving_radial_distance, configures halo model components, or asks about CCL cosmological calculations, units conventions, or parameter naming in any DESC pipeline context.

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

Topic	Reference
Full API reference (parameters, tracers, Pk2D, halo model, units, gotchas)	references/api.md
TXPipe stage table and utilities	references/txpipe.md