A Python/Cython N-body code, implementing the Comoving Lagrangian Acceleration (COLA) method in the temporal and spatial domains.
Project description
pyCOLA is a multithreaded Python/Cython N-body code, implementing the Comoving Lagrangian Acceleration (COLA) method in the temporal and spatial domains.
pyCOLA is based on the following two papers:
Solving Large Scale Structure in Ten Easy Steps with COLA, S. Tassev, M. Zaldarriaga, D. J. Eisenstein, Journal of Cosmology and Astroparticle Physics, 06, 036 (2013), [arXiv:1301.0322](http://arxiv.org/abs/arXiv:1301.0322)
sCOLA: The N-body COLA Method Extended to the Spatial Domain, S. Tassev, D. J. Eisenstein, B. D. Wandelt, M. Zaldarriaga, (2015)
Please cite them if using this code for scientific research.
pyCOLA requires NumPy <http://www.numpy.org/>_, SciPy, pyFFTW, h5py. Note that pyFFTW v0.9.2 does not support large arrays, so one needs to install the development version from github, where the bug has been fixed.
The pyCOLA documentation can be found here, and the source is on bitbucket.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file pycola3-0.0.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
.
File metadata
- Download URL: pycola3-0.0.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
- Upload date:
- Size: 565.6 kB
- Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c8121f77eb8507f5a72ff437343a558e8f0ebf851789ac8064169cd0dd32975b |
|
MD5 | cba0f3f98c4e755f0d188990c31b704b |
|
BLAKE2b-256 | 0d2c361902ae26ff3b58c83d35831d9fa503b370373a3c1ef83a1dd6b182cec5 |