Metadata-Version: 2.1
Name: tdgl
Version: 0.7.1
Summary: pyTDGL: Time-dependent Ginzburg-Landau in Python.
Home-page: https://github.com/loganbvh/py-tdgl
Author: Logan Bishop-Van Horn
Author-email: logan.bvh@gmail.com
License: MIT
Keywords: superconductor vortex Ginzburg-Landau
Platform: Linux
Platform: Mac OSX
Platform: Unix
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Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
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Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
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# pyTDGL

Time-dependent Ginzburg-Landau in Python

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## Motivation
`pyTDGL` solves a 2D generalized time-dependent Ginzburg-Landau (TDGL) equation, enabling simulations of vortex and phase dynamics in thin film superconducting devices.

## Learn `pyTDGL`

The documentation for `pyTDGL` can be found at [py-tdgl.readthedocs.io](https://py-tdgl.readthedocs.io/en/latest/).

## Try `pyTDGL`

Click the badge below to try `pyTDGL` interactively online via [Google Colab](https://colab.research.google.com/):

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/loganbvh/py-tdgl/blob/main/docs/notebooks/quickstart.ipynb)

## About `pyTDGL`

### Authors

- Primary author and maintainer: [@loganbvh](https://github.com/loganbvh/).

### Citing `pyTDGL`

`pyTDGL` is described in the following paper:

>*pyTDGL: Time-dependent Ginzburg-Landau in Python*, Computer Physics Communications **291**, 108799 (2023), DOI: [10.1016/j.cpc.2023.108799](https://doi.org/10.1016/j.cpc.2023.108799).

If you use `pyTDGL` in your research, please cite the paper linked above.

    % BibTeX citation
    @article{
        Bishop-Van_Horn2023-wr,
        title    = "{pyTDGL}: Time-dependent {Ginzburg-Landau} in Python",
        author   = "Bishop-Van Horn, Logan",
        journal  = "Comput. Phys. Commun.",
        volume   =  291,
        pages    = "108799",
        month    =  may,
        year     =  2023,
        url      = "http://dx.doi.org/10.1016/j.cpc.2023.108799",
        issn     = "0010-4655",
        doi      = "10.1016/j.cpc.2023.108799"
    }


### Acknowledgments

Parts of this package have been adapted from [`SuperDetectorPy`](https://github.com/afsa/super-detector-py), a GitHub repo authored by [Mattias Jönsson](https://github.com/afsa). Both `SuperDetectorPy` and `py-tdgl` are released under the open-source MIT License. If you use either package in an academic publication or similar, please consider citing the following in addition to the `pyTDGL` paper:

- Mattias Jönsson, Theory for superconducting few-photon detectors (Doctoral dissertation), KTH Royal Institute of Technology (2022) ([Link](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-312132))
- Mattias Jönsson, Robert Vedin, Samuel Gyger, James A. Sutton, Stephan Steinhauer, Val Zwiller, Mats Wallin, Jack Lidmar, Current crowding in nanoscale superconductors within the Ginzburg-Landau model, Phys. Rev. Applied 17, 064046 (2022) ([Link](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.17.064046))

The user interface is adapted from [`SuperScreen`](https://github.com/loganbvh/superscreen).
