| Name | galpynostatic JSON |
| Version |
0.5.12
JSON |
| download |
| home_page | None |
| Summary | A Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries. |
| upload_time | 2024-10-29 14:17:26 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License Copyright (c) 2022-2023 Francisco Fernandez Copyright (c) 2024 Francisco Fernandez, Maximiliano Gavilán, Andrés Ruderman Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
battery
physics-based
data-driven
heuristic-algorithm
regression-models
fast-charging
predictions
metrics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# galpynostatic
[](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml)
[](https://galpynostatic.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/galpynostatic/)
[](https://www.python.org/)
[](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE)
[](https://doi.org/10.1016/j.electacta.2023.142951)
**galpynostatic** is a Python/C++ package with physics-based and data-driven
models to predict optimal conditions for fast-charging lithium-ion batteries.
## Contact
If you have any questions, you can contact me at <ffernandev@gmail.com>
## Requirements
You need Python 3.12+ to run galpynostatic. All other dependencies, which are the
usual ones of the scientific computing stack
([matplotlib](https://matplotlib.org/), [NumPy](https://numpy.org/),
[pandas](https://pandas.pydata.org/), [scikit-learn](https://scikit-learn.org/)
and [SciPy](https://scipy.org/)), are installed automatically.
## Installation
You can install the latest stable release of galpynostatic with
[pip](https://pip.pypa.io/en/latest/)
```
python -m pip install --upgrade pip
python -m pip install --upgrade galpynostatic
```
## Usage
To learn how to use galpynostatic you can start by following the
[tutorials](https://galpynostatic.readthedocs.io/en/latest/tutorials/index.html)
and then read the
[API](https://galpynostatic.readthedocs.io/en/latest/api/index.html).
## License
galpynostatic is licensed under the
[MIT License](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE).
## Citations
If you use galpynostatic in a scientific publication, we would appreciate it if
you could cite the main article of the package:
> F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin, Y. Ein-Eli and
> E. P. M. Leiva. "Towards a fast-charging of LIBs electrode materials: a
> heuristic model based on galvanostatic simulations." _Electrochimica Acta 464_
> (2023): 142951. DOI: https://doi.org/10.1016/j.electacta.2023.142951
For certain modules of the code, please refer to other works:
- `galpynostatic.metric`: TODO DOI
- `galpynostatic.datasets`: https://doi.org/10.1002/cphc.202200665
BibTeX entries can be found in the
[CITATIONS.bib](https://github.com/fernandezfran/galpynostatic/blob/main/CITATIONS.bib)
file.
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"description": "# galpynostatic\n\n[](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml)\n[](https://galpynostatic.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.org/project/galpynostatic/)\n[](https://www.python.org/)\n[](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE)\n[](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 <ffernandev@gmail.com>\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> F. Fernandez, E. M. Gavil\u00e1n-Arriazu, D. E. Barraco, A. Visintin, Y. Ein-Eli and \n> E. P. M. Leiva. \"Towards a fast-charging of LIBs electrode materials: a \n> heuristic model based on galvanostatic simulations.\" _Electrochimica Acta 464_\n> (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",
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