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setup.py
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"""
# pyTDGL
Time-dependent Ginzburg-Landau in Python
![PyPI](https://img.shields.io/pypi/v/tdgl)
![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/loganbvh/py-tdgl/lint-and-test.yml?branch=main)
[![Documentation Status](https://readthedocs.org/projects/py-tdgl/badge/?version=latest)](https://py-tdgl.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/loganbvh/py-tdgl/branch/main/graph/badge.svg?token=VXdxJKP6Ag)](https://codecov.io/gh/loganbvh/py-tdgl)
![GitHub](https://img.shields.io/github/license/loganbvh/py-tdgl)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![DOI](https://zenodo.org/badge/535746543.svg)](https://zenodo.org/badge/latestdoi/535746543)
## 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).
"""
from setuptools import find_packages, setup
DESCRIPTION = "pyTDGL: Time-dependent Ginzburg-Landau in Python."
LONG_DESCRIPTION = __doc__
NAME = "tdgl"
AUTHOR = "Logan Bishop-Van Horn"
AUTHOR_EMAIL = "[email protected]"
URL = "https://github.com/loganbvh/py-tdgl"
LICENSE = "MIT"
PYTHON_VERSION = ">=3.8, <3.12"
INSTALL_REQUIRES = [
"cloudpickle",
"h5py",
"joblib",
"jupyter",
"matplotlib",
"meshpy",
"numba",
"numpy",
"pint",
"pytest",
"pytest-cov",
"scipy<1.11",
"shapely",
"tqdm",
]
EXTRAS_REQUIRE = {
"dev": [
"black",
"isort",
"pre-commit",
],
"docs": [
"IPython",
# https://github.com/readthedocs/sphinx_rtd_theme/issues/1115
"sphinx==5.3.0",
"sphinx-rtd-theme>=0.5.2",
"sphinx-autodoc-typehints",
"nbsphinx",
"pillow",
"sphinx_toolbox",
"enum_tools",
"sphinx-argparse",
"sphinxcontrib-bibtex",
],
"umfpack": [
"swig",
"scikit-umfpack",
],
"pardiso": [
"pypardiso",
],
}
CLASSIFIERS = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Operating System :: MacOS",
"Operating System :: POSIX",
"Operating System :: Unix",
"Operating System :: Microsoft :: Windows",
"Programming Language :: Python",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Physics",
]
PLATFORMS = ["Linux", "Mac OSX", "Unix", "Windows"]
KEYWORDS = "superconductor vortex Ginzburg-Landau"
exec(open("tdgl/version.py").read())
setup(
name=NAME,
version=__version__, # noqa: F821
author=AUTHOR,
author_email=AUTHOR_EMAIL,
url=URL,
license=LICENSE,
packages=find_packages(),
include_package_data=True,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
long_description_content_type="text/markdown",
keywords=KEYWORDS,
classifiers=CLASSIFIERS,
platforms=PLATFORMS,
python_requires=PYTHON_VERSION,
install_requires=INSTALL_REQUIRES,
extras_require=EXTRAS_REQUIRE,
)