| Name | magentropy JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | |
| Summary | Perform magnetoentropic mapping of magnetic materials based on DC magnetization data. |
| upload_time | 2023-11-15 09:51:15 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2022-2023 Soren Bear 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 |
magentropy
magnetoentropy
magnetoentropic
magnetocaloric
magnetization
materials
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# MagentroPy
## References
Please cite the following in any published work that makes use of this package:
> [1] J. D. Bocarsly et al.,
> [Phys. Rev. B 97, 100404(R) (2018)](https://doi.org/10.1103/PhysRevB.97.100404)
>
> [2] J. J. Stickel,
> [Comput. Chem. Eng. 34, 467 (2010)](https://dx.doi.org/10.1016/j.compchemeng.2009.10.007)
The first version of the `magentropy` code was included as supplementary
material in [1]. The Tikhonov regularization procedure was described
in [2] and was originally implemented by Stickel in the package
[scikit.datasmooth](https://github.com/jjstickel/scikit-datasmooth).
## Overview
MagentroPy provides a class, `MagentroData`,
that can be used to calculate magnetocaloric quantities from DC magnetization
data supplied as magnetic moment vs. temperature sweeps (monotonic) taken under
several different magnetic fields. The class is set up to work out-of-the-box
with `.dat` data files produced by a Quantum Design Vibrating Sample
Magnetometer or a
[Quantum Design MPMS3 SQUID Magnetometer](https://www.qdusa.com/products/mpms3.html).
However, `pandas.DataFrame`s or delimited files such as `.csv`
are also acceptable inputs.
View the documentation [here](https://magentropy.readthedocs.io/en/stable/).
## Installation
Install MagentroPy with ``pip``:
```{code-block} console
pip install magentropy
```
Or, with ``conda``:
```{code-block} console
conda install -c conda-forge magentropy
```
## License
This project is licensed under the MIT License.
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