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setup.py
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setup.py
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from setuptools import setup
meta = {}
with open("athena/meta.py") as fp:
exec(fp.read(), meta)
# Package meta-data.
IMPORTNAME = meta['__title__']
PIPNAME = meta['__packagename__']
DESCRIPTION = (
"Advanced Techniques for High dimensional parameter spaces to Enhance "
"Numerical Analysis"
)
URL = 'https://github.com/mathLab/ATHENA'
MAIL = meta['__mail__']
AUTHOR = meta['__author__']
VERSION = meta['__version__']
KEYWORDS = (
"parameter-space-reduction active-subspaces kernel-active-subspaces "
"model-reduction sensitivity-analysis nonlinear-level-set-learning"
)
REQUIRED = [
'numpy', 'scipy', 'matplotlib', 'torch', 'GPyOpt', 'scikit-learn', 'scikit-learn-extra'
]
EXTRAS = {
'docs': ['Sphinx>=1.4', 'sphinx_rtd_theme'],
'formatting': ['yapf'],
'tutorials': ['pyro', 'pyhmc'],
'test': ['pytest', 'pytest-cov'],
}
LDESCRIPTION = (
'ATHENA is a Python package for reduction of high dimensional '
'parameter spaces in the context of numerical analysis. It allows '
'the use of several dimensionality reduction techniques such as '
'Active Subspaces (AS), Kernel-based Active Subspaces (KAS), and '
'Nonlinear Level-set Learning (NLL).\n'
'\n'
'It is particularly suited for the study of parametric PDEs, for '
'sensitivity analysis, and for the approximation of engineering '
'quantities of interest. It can handle both scalar and vectorial '
'high dimensional functions, making it a useful tool also to reduce '
'the burden of computational intensive optimization tasks.'
)
setup(
name=PIPNAME,
version=VERSION,
description=DESCRIPTION,
long_description=LDESCRIPTION,
classifiers=[
'Development Status :: 5 - Production/Stable',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3.8',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Mathematics'
],
keywords=KEYWORDS,
url=URL,
author=AUTHOR,
author_email=MAIL,
license='MIT',
packages=[IMPORTNAME],
install_requires=REQUIRED,
extras_require=EXTRAS,
include_package_data=True,
zip_safe=False
)