The latest release of diffstar is available for installation with either pip or conda-forge:
$ conda install -c conda-forge diffstar
Alternatively, to install diffstar into your environment from the source code:
$ cd /path/to/root/diffstar
$ pip install .
For a typical development environment with conda-forge:
$ conda create -c conda-forge -n diffit python=3.9 numpy numba flake8 pytest jax ipython jupyter matplotlib scipy h5py diffmah diffstar
Data for this project can be found at this URL.
The diffstar_fitter_demo.ipynb notebook demonstrates how to fit the SFH of a simulated galaxy with a diffstar approximation.
See history_fitting_script.py for an example of how to fit the SFHs of a large number of simulated galaxies in parallel with mpi4py.
The Diffstar paper has been published in Monthly Notices of the Royal Astronomical Society. Citation information for the paper can be found at this ADS link, copied below for convenience:
@ARTICLE{2023MNRAS.518..562A,
author = {{Alarcon}, Alex and {Hearin}, Andrew P. and {Becker}, Matthew R. and {Chaves-Montero}, Jon{\'a}s},
title = "{Diffstar: a fully parametric physical model for galaxy assembly history}",
journal = {MNRAS},
keywords = {galaxies: evolution, galaxies: fundamental parameters, galaxies: star formation, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
year = 2023,
month = jan,
volume = {518},
number = {1},
pages = {562-584},
doi = {10.1093/mnras/stac3118},
archivePrefix = {arXiv},
eprint = {2205.04273},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023MNRAS.518..562A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}