# pymultieis
[**Installation**](#installation)
| [**Examples**](https://github.com/richinex/pymultieis/tree/main/docs/source/examples)
| [**Documentation**](https://pymultieis.readthedocs.io/en/latest/index.html)
| [**Citing this work**](#citation)
A library for fitting a sequence of electrochemical impedance spectra (PyTorch version).
- Implements algorithms for simultaneous and sequential fitting.
- Written in python and based on the [PyTorch](https://pytorch.org/) library.
- Leverages deterministic solvers from [pytorch-minimize](https://pytorch-minimize.readthedocs.io/en/latest/api/index.html) which compute the first- and second-order derivatives via autograd.
## Installation<a id="installation"></a>
Installation of pymultieis should be done via pip:
```bash
pip install pymultieis
```
[Getting started with pymultieis](https://pymultieis.readthedocs.io/en/latest/quick-start-guide.html#) contains a quick start guide to
fitting your data with ``pymultieis``.
## Examples
Jupyter notebooks which cover several aspects of ``pymultieis`` can be found in [Examples](https://github.com/richinex/pymultieis/tree/main/docs/source/examples).
## Documentation
Details about the ``pymultieis`` API, can be found in the [reference documentation](https://pymultieis.readthedocs.io/en/latest/index.html).
## Citing this work<a id="citation"></a>
If you use pymultieis for academic research, you may cite the library as follows:
```
@misc{Chukwu2022,
author = {Chukwu, Richard},
title = {pymultieis: a library for fitting a sequence of electrochemical impedance spectra},
publisher = {GitHub},
year = {2022},
url = {https://github.com/richinex/pymultieis},
}
```
Raw data
{
"_id": null,
"home_page": "https://github.com/richinex/pymultieis",
"name": "pymultieis",
"maintainer": "Richard Chukwu",
"docs_url": null,
"requires_python": ">=3.8,<3.11",
"maintainer_email": "richinex@gmail.com",
"keywords": "electrochemical impedance spectroscopy, eis, batch fitting, multiple spectra, torch",
"author": "Richard Chukwu",
"author_email": "richinex@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/2b/84/b80941607ddbd31f92266cbe7537deb34c1a29a1c0ee4730702040ca21e0/pymultieis-0.1.2.tar.gz",
"platform": null,
"description": "\n# pymultieis\n\n[**Installation**](#installation)\n| [**Examples**](https://github.com/richinex/pymultieis/tree/main/docs/source/examples)\n| [**Documentation**](https://pymultieis.readthedocs.io/en/latest/index.html)\n| [**Citing this work**](#citation)\n\n\nA library for fitting a sequence of electrochemical impedance spectra (PyTorch version).\n\n- Implements algorithms for simultaneous and sequential fitting.\n\n- Written in python and based on the [PyTorch](https://pytorch.org/) library.\n\n- Leverages deterministic solvers from [pytorch-minimize](https://pytorch-minimize.readthedocs.io/en/latest/api/index.html) which compute the first- and second-order derivatives via autograd.\n\n## Installation<a id=\"installation\"></a>\n\nInstallation of pymultieis should be done via pip:\n\n```bash\npip install pymultieis\n```\n\n[Getting started with pymultieis](https://pymultieis.readthedocs.io/en/latest/quick-start-guide.html#) contains a quick start guide to\nfitting your data with ``pymultieis``.\n\n\n## Examples\n\nJupyter notebooks which cover several aspects of ``pymultieis`` can be found in [Examples](https://github.com/richinex/pymultieis/tree/main/docs/source/examples).\n\n## Documentation\n\nDetails about the ``pymultieis`` API, can be found in the [reference documentation](https://pymultieis.readthedocs.io/en/latest/index.html).\n\n\n## Citing this work<a id=\"citation\"></a>\n\nIf you use pymultieis for academic research, you may cite the library as follows:\n\n```\n@misc{Chukwu2022,\n author = {Chukwu, Richard},\n title = {pymultieis: a library for fitting a sequence of electrochemical impedance spectra},\n publisher = {GitHub},\n year = {2022},\n url = {https://github.com/richinex/pymultieis},\n}\n```",
"bugtrack_url": null,
"license": "MIT",
"summary": "A library for fitting a sequence of electrochemical impedance spectra (PyTorch version).",
"version": "0.1.2",
"split_keywords": [
"electrochemical impedance spectroscopy",
" eis",
" batch fitting",
" multiple spectra",
" torch"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "3a5e7b869a1d390be8dc02b5c5776710",
"sha256": "1e3aaa7be9ea8df5d4cf41b5752fadae621cae7e1ee59b0cb92aa433f6ae671f"
},
"downloads": -1,
"filename": "pymultieis-0.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3a5e7b869a1d390be8dc02b5c5776710",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8,<3.11",
"size": 16414,
"upload_time": "2022-12-03T12:40:45",
"upload_time_iso_8601": "2022-12-03T12:40:45.154215Z",
"url": "https://files.pythonhosted.org/packages/94/f1/a70679c4222311055bdfc04099f3d1a6dbd6255aec8b961557280c0f67df/pymultieis-0.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "053d8b0823833f0066e88356d32485b3",
"sha256": "b6096bfa518857b5f1039c30e24d4f351d02e74cd552a95bc00a5a74141b6a2d"
},
"downloads": -1,
"filename": "pymultieis-0.1.2.tar.gz",
"has_sig": false,
"md5_digest": "053d8b0823833f0066e88356d32485b3",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8,<3.11",
"size": 17125,
"upload_time": "2022-12-03T12:40:47",
"upload_time_iso_8601": "2022-12-03T12:40:47.300284Z",
"url": "https://files.pythonhosted.org/packages/2b/84/b80941607ddbd31f92266cbe7537deb34c1a29a1c0ee4730702040ca21e0/pymultieis-0.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-03 12:40:47",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "richinex",
"github_project": "pymultieis",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "pymultieis"
}
JSON
