rNets 24.6

Rnets software for the visualization of reaction networks

A python tool for the generation of graphs of reaction network

rNets is an innovative python tool designed for visualization of reaction networks with a simple and robust command-line interface. rNets was conceptualized with the core principles of modularity, and easy integration with existing software packages by reducing dependencies to the minimum. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.

• Getting Started

  • Prerequisites

  • Installing the dependencies

  • Installing rNets

  • Uninstalling rNets

• Developed with

• Examples and Docs

• Authors

• License

• How to cite

Getting Started

These instructions will get you a copy of the project up and running on your local machine.

Prerequisites

• python >= 3.12

Installing the dependencies

rNets only depends on the python standard library that usually comes with the cpython interpreter, the default one adopted by the python community. This means the the only requisite for installing rNets is python. The python community has placed a lot of effort into the documentation and actively maintains it so for further instructions on how to install python into your computer we highly recommend to check the official python documentation.

Although for being able to install and run rNets only python is required, rNets relies heavily on graphviz for the actual rendering of the images, as the output of rNets is tipically a .dot file. Installing graphviz is tipically a straightforward and quick process. Graphviz may be downloaded from their official webpage.

Installing rNets

rNets can be directly installed through pip as a version of the package is hosted at the Python Package Index (PyPI):

$ python -m pip install rNets

Warning

Warning: The rNets code is not available at the Python Package Index, but will soon be available, please use the other installation options.

However, if the user does prefer it, it can also be easily installed from the source code. For that we will start by downloading the source code using git.

$ git clone https://codeberg.org/spgarcica/rNets.git rnets-source

Next we proceed to install it using pip

$ python -m pip install rnets-source/

If you do not have git or do prefer to download manually the source code as a .zip or .tar.gz do it install it.

$ python -m pip install rNets.tar.gz

Note

It is possible to unpack it and install it as indicated in the previous step but it is not required.

Uninstalling rNets

Completely uninstalling rNets is also quite easy due to its lack of dependencies. All that is needed is to command pip to uninstall the package

$ python -m pip uninstall rNets

Developed with

• python 3.12

Examples and Docs

The examples folders contains with corresponding readme files the instructions and necessary data to generate a variety of different reaction networks and animations.

The documentation of rNets can be accessed at https://spgarcica.github.io/rNets/ . Here a more detailed description of the examples can also be found.

Authors

List of main developers and contact emails:

• Sergio Pablo-García [ ORCID , Github ]

• Raúl Pérez-Soto [ ORCID , Github ]

• Albert Sabadell-Rendón [ ORCID , Github ]

• Diego Garay-Ruiz [ ORCID , Github ]

• Vladyslav Nosylevskyi [ ORCID Github ]

• Nuria Lopez [ ORCID ]

License

rNets is freely available under an MIT License

How to cite

To cite rNets please cite the following publications:

@misc{pablo-garcia_rnets_2024,
    title = {{rNets}: {A} standalone package to visualize reaction networks.},
    shorttitle = {{rNets}},
    url = {https://chemrxiv.org/engage/chemrxiv/article-details/660c5ccae9ebbb4db9378cc1},
    doi = {10.26434/chemrxiv-2024-l7gf5},
    language = {en},
    urldate = {2024-04-04},
    publisher = {ChemRxiv},
    author = {Pablo-García, Sergio and Pérez-Soto, Raúl and Sabadell-Rendón, Albert and Garay-Ruiz, Diego and Nosylevskyi, Vladyslav and López, Nuria},
    month = apr,
    year = {2024},
    keywords = {catalysis, computational chemistry, density functional theory, dft, graphviz, microkinetics, python, reaction networks, visualization},
}