EKO is a Python module to solve the DGLAP equations in N-space in terms of Evolution Kernel Operators in x-space. ## Installation EKO is available via - PyPI: ```bash pip install eko ``` - conda-forge: [](https://anaconda.org/conda-forge/eko) ```bash conda install eko ``` ### Development If you want to install from source you can run ```bash git clone git@github.com:N3PDF/eko.git cd eko poetry install ``` To setup `poetry`, and other tools, see [Contribution Guidelines](https://github.com/N3PDF/eko/blob/master/.github/CONTRIBUTING.md). ## Documentation - The documentation is available here: - To build the documentation from source install [graphviz](https://www.graphviz.org/) and run in addition to the installation commands ```bash poe docs ``` ## Tests and benchmarks - To run unit test you can do ```bash poe tests ``` - Benchmarks of specific part of the code, such as the strong coupling or msbar masses running, are available doing ```bash poe bench ``` - The complete list of benchmarks with external codes is available through `ekomark`: [documentation](https://eko.readthedocs.io/en/latest/development/Benchmarks.html) ## Citation policy When using our code please cite - our DOI: - our paper: [](https://arxiv.org/abs/2202.02338) ## Contributing - Your feedback is welcome! If you want to report a (possible) bug or want to ask for a new feature, please raise an issue: - If you need help, for installation, usage, or anything related, feel free to open a new discussion in the ["Support" section](https://github.com/NNPDF/eko/discussions/categories/support) - Please follow our [Code of Conduct](https://github.com/N3PDF/eko/blob/master/.github/CODE_OF_CONDUCT.md) and read the [Contribution Guidelines](https://github.com/N3PDF/eko/blob/master/.github/CONTRIBUTING.md)