Installation and References¶
last update: Nov 21st (2022) Hajime Kawahara
At a minimum, you can start to use exojax by
pip install exojax
via pypi.
Alternatively, clone the code from github page and run
python setup.py install
Installation w/ GPU support¶
However, to leverage the power of JAX, numpyro, you need to prepare a GPU environment. For this purpose, jaxlib and numpyro must be linked.
You should check cuda version of your environment as
nvcc -V
Also, check required jaxlib versions by numpyro at NumPyro. Here is an example of installation for jaxlib in linux system. See JAX installation page for the details.
pip install --upgrade pip
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
cd exojax
python setup.py install
References¶
🟢 ExoJAX Paper I: Kawahara, Kawashima, Masuda, Crossfield, Pannier, van den Bekerom (2021) accepted by ApJS: arXiv:2105.14782
Many techniques/databases are used in ExoJAX.
JAX: Bradbury, J., Frostig, R., Hawkins, P., et al. 2018, JAX: composable transformations of Python+NumPy programs, JAX
NumPyro: Phan, D., Pradhan, N., & Jankowiak, M. 2019, arXiv:1912.11554
JAXopt: Blondel, M., Berthet, Q., Cuturi, M. et al. 2021 arXiv:2105.15183
Vaex: Breddels and Veljanoski (2018) arXiv:https://arxiv.org/abs/1801.02638
Algorithm 916: Zaghloul and Ali (2012) arXiv:1106.0151
ExoMol: Tennyson et al. (2016)
HITRAN/HITEMP
VALD3
RADIS, see below.
Other many packages/algorithms. See arXiv:2105.14782 for the details.