The Core Cosmology Library (CCL) is a standardized library of routines to calculate basic observables used in cosmology. It will be the standard analysis package used by the LSST Dark Energy Science Collaboration (DESC).

The core functions of this package include:

• Matter power spectra P(k) from numerous models including CLASS, the Mira-Titan Emulator and halofit
• Hubble constant H(z) as well as comoving distances \chi(z) and distance moduli \mu(z)
• Growth of structure D(z) and f
• Correlation functions C_\ell for arbitrary combinations of tracers including galaxies, shear and number counts
• Halo mass function {\rm d}n/{\rm d}M and halo bias b(M)
• Approximate baryonic modifications to the matter power spectra \Delta^2_{\rm baryons}
• Simple modified gravity extensions \Delta f(z) and \mu-\Sigma

This software is a publicly released LSST DESC product which was developed within the LSST DESC using LSST DESC resources. DESC users should use it in accordance with the LSST DESC publication policy. External users are welcome to use the code outside DESC in accordance with the licensing information below.

The list of publicly released versions of this package can be found here. The master branch is the most recent (non-released) stable branch, but under development. We recommend using one of the public releases unless working on the development on the library.

See the documentation for more details and installation instructions.

CCL is available as a Python package through PyPi or conda. To install, simply run:

$ conda install -c conda-forge pyccl

or

$ pip install pyccl

For the PyPi installation, you will need CMake installed locally. See Getting CMake for instructions. Note that the code only supports Linux or Mac OS, but no Windows.

Once you have the code installed, you can take it for a spin!

import pyccl as ccl
import numpy as np

# Create new Cosmology object with a given set of parameters. This keeps track
# of previously-computed cosmological functions
cosmo = ccl.Cosmology(
    Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96,
    transfer_function='bbks')

# Define a simple binned galaxy number density curve as a function of redshift
z_n = np.linspace(0., 1., 200)
n = np.ones(z_n.shape)

# Create objects to represent tracers of the weak lensing signal with this
# number density (with has_intrinsic_alignment=False)
lens1 = ccl.WeakLensingTracer(cosmo, dndz=(z_n, n))
lens2 = ccl.WeakLensingTracer(cosmo, dndz=(z_n, n))

# Calculate the angular cross-spectrum of the two tracers as a function of ell
ell = np.arange(2, 10)
cls = ccl.angular_cl(cosmo, lens1, lens2, ell)
print(cls)

This code has been released by DESC, although it is still under active development. It is accompanied by a journal paper that describes the development and validation of CCL, which you can find on the arxiv:1812.05995. If you make use of the ideas or software here, please cite that paper and provide a link to this repository: https://github.com/LSSTDESC/CCL. You are welcome to re-use the code, which is open source and available under terms consistent with our LICENSE (BSD 3-Clause).

External contributors and DESC members wishing to use CCL for non-DESC projects should consult with the TJP working group conveners, ideally before the work has started, but definitely before any publication or posting of the work to the arXiv.

For free use of the CLASS library, the CLASS developers require that the CLASS paper be cited: CLASS II: Approximation schemes, D. Blas, J. Lesgourgues, T. Tram, arXiv:1104.2933, JCAP 1107 (2011) 034. The CLASS repository can be found in http://class-code.net.

The CAMB developers have released CAMB under the LGPL license with a few additional restrictions. Please read their LICENSE for more information.

If you have comments, questions, or feedback, please write us an issue.

The current lead of the LSST DESC CCL Topical Team is Danielle Leonard (c-d-leonard, danielle.leonard at ncl.ac.uk)