To help you get started, we've collected the most common ways that pyccl is being used within popular public projects.

LSSTDESC / CLMM / clmm / modbackend / ccl.py View on Github

def eval_sigma_crit (self, z_len, z_src):
        a_len = _get_a_from_z (z_len)
        a_src = np.atleast_1d (_get_a_from_z (z_src))
        cte   = ccl.physical_constants.CLIGHT**2 / (4.0 * np.pi * ccl.physical_constants.GNEWT * ccl.physical_constants.SOLAR_MASS) * ccl.physical_constants.PC_TO_METER

        z_cut = (a_src < a_len)
        if np.isscalar (a_len):
            a_len = np.repeat (a_len, len (a_src))

        res = np.zeros_like (a_src)
        
        if np.any (z_cut):
            Ds  = ccl.angular_diameter_distance (self.cosmo, a_src[z_cut])
            Dl  = ccl.angular_diameter_distance (self.cosmo, a_len)
            Dls = ccl.angular_diameter_distance (self.cosmo, a_len, a_src[z_cut])
        
            res[z_cut] = (cte * Ds / (Dl * Dls)) * self.cor_factor * 1.0e-6 / self.cosmo['h']

        res[~z_cut] = np.Inf

        return np.squeeze (res)

View more ways to use pyccl

CCL

The Core Cosmology Library (CCL) is a public standardized library of routines to calculate basic observables used in cosmology. It will be the standard theoretical prediction package used by the LSST Dark Energy Science Collaboration (DESC), although we hope it will have broader applicability.

The core functions of this package include:

• Background quantities (Hubble parameter $H(z)$, distances etc.).
• Linear growth factor $D(z)$ and growth rate $f(z)$.
• Linear matter power spectra $P(k)$ from Boltzmann codes (CLASS, CAMB), emulators, and approximate fitting functions.
• Non-linear matter power power spectra using a variety of prescriptions, including emulators, and including the impact of baryonic effects.
• Angular power spectra $C_\ell$ and correlation functions $\xi(\theta)$ for arbitrary combinations of tracers including number counts, shear, CMB lensing, ISW, thermal SZ, CIB, as well as custom-made tracers.
• A comprehensive halo model framework able to combine different prescriptions for the halo mass function $dn/dM$, halo bias $b(M)$, concentration-mass relation $c(M)$, mass definitions, and halo profiles, as well as to provide predictions for summary statistics (power spectra, non-Gaussian covariances) of arbitrary quantities.
• Support for $\Lambda$ CDM, and $w_0-w_a$CDM cosmologies with curvature and massive neutrinos, as well as simple modified gravity extensions (e.g. $\mu-\Sigma$ ).

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.

TLDR

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 and SWIG installed locally. See Getting CMake and Installing SWIG 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., 500)
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 = cosmo.angular_cl(lens1, lens2, ell)
print(cls)

License, Credits, Feedback etc

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.

Contact

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

The current lead of the LSST DESC CCL Topical Team is David Alonso (damonge, david.alonso at physics.ox.ac.uk).

Acknowledgements

The DESC acknowledges ongoing support from the Institut National de Physique Nucleaire et de Physique des Particules in France; the Science & Technology Facilities Council in the United Kingdom; and the Department of Energy, the National Science Foundation, and the LSST Corporation in the United States. DESC uses resources of the IN2P3 Computing Center (CC-IN2P3--Lyon/Villeurbanne - France) funded by the Centre National de la Recherche Scientifique; the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; STFC DiRAC HPC Facilities, funded by UK BIS National E-infrastructure capital grants; and the UK particle physics grid, supported by the GridPP Collaboration. This work was performed in part under DOE Contract DE-AC02-76SF00515.

NEC acknowledges support from a Royal Astronomical Society research fellowship and the Delta ITP consortium, a program of the Netherlands Organisation for Scientific Research (NWO) that is funded by the Dutch Ministry of Education, Culture and Science (OCW). DA acknowledges support from the Science and Technology Facilities Council through an Ernest Rutherford Fellowship, grant reference ST/P004474. AL and CG acknowledge support from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement No. [616170] for work on the generic interface for theory inputs.