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Storage"],"sub_categories":["Battery"],"readme":"# galpynostatic\n\n[![galpynostatics CI](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml/badge.svg)](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml)\n[![documentation status](https://readthedocs.org/projects/galpynostatic/badge/?version=latest)](https://galpynostatic.readthedocs.io/en/latest/?badge=latest)\n[![pypi version](https://img.shields.io/pypi/v/galpynostatic)](https://pypi.org/project/galpynostatic/)\n[![python version](https://img.shields.io/badge/python-3.12%2B-4584b6)](https://www.python.org/)\n[![mit license](https://img.shields.io/badge/License-MIT-ffde57)](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE)\n[![doi](https://img.shields.io/badge/doi-10.1016/j.electacta.2023.142951-36abe8)](https://doi.org/10.1016/j.electacta.2023.142951)\n\n**galpynostatic** is a Python/C++ package with physics-based and data-driven \nmodels to predict optimal conditions for fast-charging lithium-ion batteries.\n\n\n## Contact\n\nIf you have any questions, you can contact me at \u003cffernandev@gmail.com\u003e\n\n\n## Requirements\n\nYou need Python 3.12+ to run galpynostatic. All other dependencies, which are the \nusual ones of the scientific computing stack\n([matplotlib](https://matplotlib.org/), [NumPy](https://numpy.org/), \n[pandas](https://pandas.pydata.org/), [scikit-learn](https://scikit-learn.org/) \nand [SciPy](https://scipy.org/)), are installed automatically.\n\n\n## Installation\n\nYou can install the latest stable release of galpynostatic with \n[pip](https://pip.pypa.io/en/latest/)\n\n```\npython -m pip install --upgrade pip\npython -m pip install --upgrade galpynostatic\n```\n\n\n## Usage\n\nTo learn how to use galpynostatic you can start by following the \n[tutorials](https://galpynostatic.readthedocs.io/en/latest/tutorials/index.html)\nand then read the \n[API](https://galpynostatic.readthedocs.io/en/latest/api/index.html).\n\n\n## License\n\ngalpynostatic is licensed under the \n[MIT License](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE).\n\n\n## Citations\n\nIf you use galpynostatic in a scientific publication, we would appreciate it if \nyou could cite the main article of the package:\n\n\u003e F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin, Y. Ein-Eli and \n\u003e E. P. M. Leiva. \"Towards a fast-charging of LIBs electrode materials: a \n\u003e heuristic model based on galvanostatic simulations.\" _Electrochimica Acta 464_\n\u003e (2023): 142951. DOI: https://doi.org/10.1016/j.electacta.2023.142951\n\nFor certain modules of the code, please refer to other works:\n- `galpynostatic.metric`: TODO DOI\n- `galpynostatic.datasets`: https://doi.org/10.1002/cphc.202200665\n\nBibTeX entries can be found in the \n[CITATIONS.bib](https://github.com/fernandezfran/galpynostatic/blob/main/CITATIONS.bib)\nfile.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandezfran%2Fgalpynostatic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffernandezfran%2Fgalpynostatic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandezfran%2Fgalpynostatic/lists"}