UCLCHEM is a gas-grain chemical code that propagates the abundances of chemical species through a network of user-defined reactions according to the physical conditions of the gas. We provide several physical models to enable the modelling of different astrophysical environments and a utility script MakeRates to help the user produce a chemical network from simple lists of reactions and species.

Full documentation is available from the website: uclchem.github.io

UCLCHEM is intended to be used as a python module but must be installed from source rather than an online index such as Pypi. This is because users are expected to modify the source code, at least by creating their own networks. To obtain and install the code simply run:

[!IMPORTANT]
From version 3.3 onwards, we need to install in editable mode. This means that the directory that you store UCLCHEM in will directly be used by Python instead of being copied to the site-packages. Keep in mind that code changes you make in the uclchem directory will now affect you directly!

git clone https://github.com/uclchem/UCLCHEM.git
cd UCLCHEM
pip install -e .

You can then import uclchem in any python script. You need to pip install -e . whenever you change your network.

To see the contents of this python module, check our Python API docs. To see some example notebooks, check the tutorial section of the docs or the notebooks in Tutorials/.

If you want to build an executable from the Fortran source, head to src/fortran_src and run make. You can then run the executable with ./uclchem CLOUD input_file.inp where there examples of input files in the examples/ directory. We do not suggest users use the code this way unt

To build UCLCHEM, you'll need gfortran, make and python 3.9+.

To run the python module, you'll need the python modules listed in requirements.txt

See change.log! We've made a large number of improvements for v3.0. The code has been restructured to be Python first in its intended use, different physical models can be accessed without recompilation, and MakeRates is more helpful than ever before.

This is an open source science code for the community and are open to pull requests. We are also happy to work with you to produce a physics module if none of the models available in the python module uclchem.model suit the modelling work you wish to do. If you are contributing, please try to work with our current code style. Feel free to checkout the latest developments with git fetch; git checkout develop We have the following general guidelines:

• Work in a personal branch or fork to your own Github to develop features.
• Make sure you base your new work on the develop branch.
• Pull requests should be opened with the develop branch as target.
• In principle, squash and merge is preferred over keeping the entire git commit history when merging into develop.
• Releases will be coordinated by the core developers, this will consitute a push to main and creating a release.

• Use Black to format your code.
• snake_case variables and functions with self-explanatory names
• Docstrings for all functions, they're used to produce the online docs!

• camelCase variable and subroutines names that are self-explanatory where possible
• CAPITALIZED fortran built in functions to make code structure apparent.
• Modularization, related subroutines should be added as modules. Small tweaks should be inserted into relevant module

If you use UCLCHEM for your research, please cite the following paper (https://ui.adsabs.harvard.edu/abs/2017AJ....154...38H/abstract):

@ARTICLE{2017AJ....154...38H,
       author = {{Holdship}, J. and {Viti}, S. and {Jim{\'e}nez-Serra}, I. and {Makrymallis}, A. and {Priestley}, F.},
        title = "{UCLCHEM: A Gas-grain Chemical Code for Clouds, Cores, and C-Shocks}",
      journal = {\aj},
     keywords = {astrochemistry, ISM: molecules, shock waves, Astrophysics - Astrophysics of Galaxies},
         year = 2017,
        month = jul,
       volume = {154},
       number = {1},
          eid = {38},
        pages = {38},
          doi = {10.3847/1538-3881/aa773f},
archivePrefix = {arXiv},
       eprint = {1705.10677},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017AJ....154...38H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}