-
Notifications
You must be signed in to change notification settings - Fork 4
/
setup.py
109 lines (90 loc) · 3.99 KB
/
setup.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
"""
# SuperScreen
 [](https://superscreen.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/loganbvh/superscreen)  [](https://github.com/psf/black) [](https://zenodo.org/badge/latestdoi/376110557)
`SuperScreen` is a Python package for simulating the magnetic response of thin film superconducting devices. `SuperScreen` solves the coupled Maxwell's and London equations on a triangular mesh using a matrix inversion method described in the following paper:
>SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices, Computer Physics Communications, Volume 280, 2022, 108464 [https://doi.org/10.1016/j.cpc.2022.108464](https://doi.org/10.1016/j.cpc.2022.108464).
The accepted version of the paper can also be found on arXiv: [arXiv:2203.13388](https://doi.org/10.48550/arXiv.2203.13388). The GitHub repository accompanying the paper can be found [here](https://github.com/loganbvh/superscreen-paper).
## Learn `SuperScreen`
The documentation for `SuperScreen` can be found at [superscreen.readthedocs.io](https://superscreen.readthedocs.io/en/latest/).
## Try `SuperScreen`
Click the badge below to try `SuperScreen` interactively online via [Google Colab](https://colab.research.google.com/):
[](https://colab.research.google.com/github/loganbvh/superscreen/blob/main/docs/notebooks/quickstart.ipynb)
"""
from setuptools import find_packages, setup
DESCRIPTION = "SuperScreen: simulate Meissner screening in 2D superconducting devices."
LONG_DESCRIPTION = __doc__
NAME = "superscreen"
AUTHOR = "Logan Bishop-Van Horn"
AUTHOR_EMAIL = "logan.bvh@gmail.com"
URL = "https://github.com/loganbvh/superscreen"
LICENSE = "MIT"
PYTHON_VERSION = ">=3.8, <3.13"
INSTALL_REQUIRES = [
"dill",
"h5py",
"ipython",
"joblib",
"jupyter",
"matplotlib",
"meshpy",
"numba",
"numpy",
"pint",
"pytest",
"scipy",
"shapely",
"tqdm",
]
EXTRAS_REQUIRE = {
"dev": [
"pytest-cov",
"pre-commit",
],
"docs": [
"sphinx<7",
"sphinx_rtd_theme",
"sphinx-autodoc-typehints",
"nbsphinx",
"pillow", # required for image scaling in RTD
],
}
CLASSIFIERS = """\
Development Status :: 4 - Beta
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
Programming Language :: Python :: 3.12
Topic :: Scientific/Engineering
Topic :: Scientific/Engineering :: Physics
"""
CLASSIFIERS = [line for line in CLASSIFIERS.splitlines() if line]
PLATFORMS = ["Linux", "Mac OSX", "Unix", "Windows"]
KEYWORDS = "superconductor meissner screening"
exec(open("superscreen/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,
)