Installation and References
----------------------------------

*last update: June 11st (2023) Hajime Kawahara*

Linux, Windows WSL, Mac
============================

At the very least, you can start using exojax through `pypi <https://pypi.org/project/exojax/>`_.

.. code:: sh
	  
	  pip install exojax


Alternatively, clone the code from `github page <https://github.com/HajimeKawahara/exojax>`_ and run

.. code:: sh
	  
	  python setup.py install

	  
However, to take advantage of the power of JAX, you need to prepare a GPU environment (if you have). For this, jaxlib need to be linked.

You should check the cuda version of your environment as

.. code:: sh

	  nvcc -V

Here is an example of installation for jaxlib in linux system. See `JAX installation page <https://github.com/google/jax#installation>`_ for the details.

.. code:: sh

		pip install --upgrade pip
		pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Windows Anaconda
===================

not supported yet.
		

		
References
=================

|:green_circle:| **ExoJAX Paper I**:  Kawahara, Kawashima, Masuda, Crossfield, Pannier, van den Bekerom (2021) accepted by ApJS: `arXiv:2105.14782 <http://arxiv.org/abs/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 <http://github.com/google/jax>`_
- NumPyro: Phan, D., Pradhan, N., & Jankowiak, M. 2019, `arXiv:1912.11554 <http://arxiv.org/abs/1912.11554>`_
- JAXopt: Blondel, M., Berthet, Q., Cuturi, M. et al. 2021 `arXiv:2105.15183 <http://arxiv.org/abs/2105.15183>`_
- Vaex: Breddels and Veljanoski (2018) `arXiv:https://arxiv.org/abs/1801.02638 <https://arxiv.org/abs/1801.02638>`_
- Algorithm 916: Zaghloul and Ali (2012) `arXiv:1106.0151 <https://arxiv.org/abs/1106.0151>`_
- DIT: `van den Bekerom and Pannier (2021) <https://www.sciencedirect.com/science/article/abs/pii/S0022407320310049>`_ 
- ExoMol: `Tennyson et al. (2016) <https://www.sciencedirect.com/science/article/abs/pii/S0022285216300807?via%3Dihub>`_
- HITRAN/HITEMP
- VALD3
- RADIS, see below.
- Other many packages/algorithms. See `arXiv:2105.14782 <http://arxiv.org/abs/2105.14782>`_ for the details.


Related Projects
=====================
  
- `RADIS <https://github.com/radis/radis>`_

 | ExoJAX draws a lot of inspiration from a fast line-by-line code for high-resolution infrared molecular spectra `RADIS <https://github.com/radis/radis>`_, including DIT, the use of Vaex, and so on. 
 | Since version 1.2 we have been using a common molecular database I/O API in Radis.

- `REACH <http://secondearths.sakura.ne.jp/reach/>`_
  
 | ExoJAX was originally developed to interpret data from a new high-dispersion coronagraphic capability at the Subaru telescope, the `REACH <http://secondearths.sakura.ne.jp/reach/>`_ project (SCExAO+IRD). REACH is supported by `RESCEU <http://www.resceu.s.u-tokyo.ac.jp/top.php>`_, ABC and `JSPS KAKENHI JP20H00170 <https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20H00170/>`_ (Kawahara). See also `Lozi et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018SPIE10703E..59L/abstract>`_ for SCExAO, `Kotani et al. (2018) <https://ui.adsabs.harvard.edu/abs/2018SPIE10702E..11K/abstract>`_ for IRD, `Jovanovic et al. (2017) <https://ui.adsabs.harvard.edu/abs/2017arXiv171207762J/abstract>`_ for post-coronagraphic injection, and `Kawahara et al. (2014) <https://ui.adsabs.harvard.edu/abs/2014ApJS..212...27K/abstract>`_ for high dispersion coronagraphy.