Metadata-Version: 2.1
Name: tdgl
Version: 0.2.1
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
Platform: Windows
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8, <3.11
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: jax
License-File: LICENSE


# 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 and navigate to `docs/notebooks/` to try `pyTDGL` interactively online via [Binder](https://mybinder.org/)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/loganbvh/py-tdgl/HEAD)

## 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:

- 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).
