picasso-cosmo 1.1.0

No project description provided

picasso

Painting intracluster gas on gravity-only simulations

picasso is a model that allows making predictions for the thermodynamic properties of the gas in massive dark matter halos from gravity-only cosmological simulations. It combines an analytical model of gas properties as a function of gravitational potential with a neural network predicting the parameters of said model. It is released here as a Python package, combining an implementation of the gas model based on JAX and Flax, and models that have been pre-trained to reproduce gas properties from hydrodynamic simulations.

Documentation

See also Kéruzoré et al. (2024).

Installation

picasso can be install via pip:

pip install -e "git+https://github.com/fkeruzore/picasso.git#egg=picasso[jax]"

Alternatively, if you already have JAX and flax installed, you may use

pip install -e "git+https://github.com/fkeruzore/picasso.git#egg=picasso"

The latter option will not install or upgrade any package relying on JAX, which can be useful to avoid messing up an existing install. To install JAX on your system, see JAX's installation page.

Testing and benchmarking

picasso uses Poetry to manage dependencies. To test your installation of picasso, you can install the tests dependency group and run pytest:

git clone [e-mail]:fkeruzore/picasso.git
cd picasso
poetry install --with=tests
poetry run pytest

Some of the test also include basic benchmarking of model predictions using pytest-benchmark:

poetry run pytest --benchmark-enable

Citation

If you use picasso for your research, please cite the picasso original paper:

@article{keruzore_picasso_2024,
  title={The picasso gas model: Painting intracluster gas on gravity-only simulations}, 
  author={F. Kéruzoré and L. E. Bleem and N. Frontiere and N. Krishnan and M. Buehlmann and J. D. Emberson and S. Habib and P. Larsen},
  year={2024},
  eprint={2408.17445},
  archivePrefix={arXiv},
  primaryClass={astro-ph.CO},
  url={https://arxiv.org/abs/2408.17445}, 
}