picasso-cosmo 1.1.4

Painting intracluster gas on gravity-only simulations

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 picasso-cosmo[jax]

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

pip install picasso-cosmo

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 uv 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
  uv python install
  uv sync --all-groups --all-extras
  uv run pytest

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

uv run pytest --benchmark-enable

Citation

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

@ARTICLE{2024OJAp....7E.116K,
       author = {{K{\'e}ruzor{\'e}}, Florian and {Bleem}, L.~E. and {Frontiere}, N. and {Krishnan}, N. and {Buehlmann}, M. and {Emberson}, J.~D. and {Habib}, S. and {Larsen}, P.},
        title = "{The picasso gas model: Painting intracluster gas on gravity-only simulations}",
      journal = {The Open Journal of Astrophysics},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2024,
        month = dec,
       volume = {7},
          eid = {116},
        pages = {116},
          doi = {10.33232/001c.127486},
archivePrefix = {arXiv},
       eprint = {2408.17445},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024OJAp....7E.116K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}