Metadata-Version: 2.1
Name: exo-k
Version: 1.1.1
Summary: Library to handle radiative opacities from various sources for atmospheric applications
Home-page: https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public
Author: Jeremy Leconte
Author-email: jeremy.leconte@u-bordeaux.fr
License: GPLv3
Keywords: opacities,cross sections,correlated-k,spectra,atmosphere,atmospheric,exopanet,radiative transfer
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries
Provides: exo_k
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: numba (>=0.49)
Requires-Dist: astropy
Requires-Dist: h5py
Requires-Dist: matplotlib
Requires-Dist: pandas

# Exo_k

Author: Jeremy Leconte (CNRS/LAB/Univ. Bordeaux)

`Exo_k` is a Python 3 based library to handle radiative opacities from various sources for atmospheric applications.
It enables you to:

* Interpolate efficiently and easily in correlated-k and cross section tables.
* Convert easily correlated-k and cross section tables from one format to another
  (hdf5, LMDZ GCM, Exomol, Nemesis, PetitCode, TauREx, ExoREM, ARCIS, etc.).
* Adapt precomputed correlated-k tables to your needs by changing:

  * the resolution and quadrature (g) grid,
  * the pressure/temperature grid.
* Create tables for a mix of gases using tables for individual gases.
* Create your own tables from high-resolution spectra (for example from K-spectrum, Helios-K, etc.).
* Use your data in an integrated radiative transfer framework to simulate planetary atmospheres.

For a complete online documentation, checkout:
http://perso.astrophy.u-bordeaux.fr/~jleconte/exo_k-doc/index.html

In this repository, you'll find a [tutorial jupyter notebook](https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public/-/blob/public/tutorial-exo_k.ipynb) that will show you how to do all that
with concrete examples that you can run on your own machine. Many important concepts and options are
presented along the way.

Enjoy!

J. Leconte

# Acknowledgements

If you use this library in your research, please acknowledge it by citing
[Leconte (2021)](https://ui.adsabs.harvard.edu/abs/2021A%26A...645A..20L/abstract):

  * Spectral binning of precomputed correlated-k coefficients. **Astronomy and Astrophysics** 645. Leconte, J. 2021. doi:10.1051/0004-6361/202039040

This project has received funding from the European Research Council (ERC)
under the European Union's Horizon 2020 research and innovation programme
(grant agreement n° 679030/WHIPLASH).

The framework for this documentation has been developped by Aurelien Falco using Sphinx. 

# last release (see past releases below)

v1.0.2 (June 2021): Adds a few missing dependencies. Enables computation of thermal
emission spectra with scattering through the two-stream method (full documentation pending). 
Enables creating Xtables for a mix of gases (CIA can be added as well). Solves some issues
with the 2018 Hitran CIA format.

# Installation

Exo_k can be installed using pip (without cloning the repository;
dependencies should be downloaded automatically):
```
pip install exo_k
```
Or by running the [setup.py](https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public/-/blob/public/setup.py) script in the cloned repository:
```
python setup.py install
```

# Usage

To learn how to use `exo_k`, you can follow the [tutorial jupyter notebook](https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public/-/blob/public/tutorial-exo_k.ipynb).

Have fun!

# Links

* Project homepage: http://perso.astrophy.u-bordeaux.fr/~jleconte/
* Code repository: https://forge.oasu.u-bordeaux.fr/jleconte/exo_k-public
* Documentation: http://perso.astrophy.u-bordeaux.fr/~jleconte/exo_k-doc/index.html
* Contact: jeremy.leconte at u-bordeaux.fr


# past releases

v1.0.1 (Jan 2021): Solves a binary/string conversion issue introduced by version 3 of h5py.
Enables linear interpolation in pressure (default is log). Enables creation of
empty tables to be filled later and extension of the spectral range of existing tables. 

v1.0.0 (Dec 2020): Finally our first official version. Creation of a
'examples' notebook with fully worked out use cases for the `Exo_k`. 

v0.0.5 (Oct 2020): Ensures compatibility with latest Exomol correlated-k and cross-section tables.

