setup.py

# Copyright (c) 2016-2018, The University of Texas at Austin 
# & University of California--Merced.
# Copyright (c) 2019-2020, The University of Texas at Austin 
# University of California--Merced, Washington University in St. Louis.
#
# All Rights reserved.
# See file COPYRIGHT for details.
#
# This file is part of the hIPPYlib library. For more information and source code
# availability see https://hippylib.github.io.
#
# hIPPYlib is free software; you can redistribute it and/or modify it under the
# terms of the GNU General Public License (as published by the Free
# Software Foundation) version 2.0 dated June 1991.


from setuptools import setup, find_packages
from os import path
from io import open


try:
    from dolfin import __version__ as dolfin_version
    from dolfin import has_linear_algebra_backend, has_slepc
except ImportError:
    raise
else:
    fenics_version_major = int(dolfin_version[0:4])
    if (fenics_version_major < 2019 or fenics_version_major >= 2020):
        raise Exception(
            'SOUPy requires FEniCS versions 2019.x.x')
    if not has_linear_algebra_backend("PETSc"):
        raise Exception(
            'SOUPy requires FEniCS to be installed with PETSc support')
    if not has_slepc():
        raise Exception(
            'SOUPy requires FEniCS to be installed with SLEPc support')


try:
    from hippylib import version as hippylib_version
except ImportError:
    raise
else:
    hippylib_version_major = int(hippylib_version.version_info[0])
    if (hippylib_version_major < 3 or hippylib_version_major >= 4):
        raise Exception(
            'SOUPy requires hIPPylib versions 3.x.x')


here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()

version = {}
with open(path.join(here, 'soupy/version.py')) as f:
    exec(f.read(), version)

VERSION = version['__version__']

REQUIREMENTS = [
    'mpi4py',
    'numpy',
    'matplotlib',
    'scipy',
]

EXTRAS = {
    'Notebook':  ["jupyter"],
}

KEYWORDS = """
    Optimization under uncertainty, stochastic programming,
    adjoint-based methods, numerical optimization, 
    uncertainty quantification, sampling, PDE-constrained optimization
    """

setup(
    name='soupy',
    version=VERSION,
    description='Stochastic PDE-Constrained optimization under high-dimensional uncertainty in Python', 
    long_description=long_description,
    long_description_content_type='text/markdown',
    author='Peng Chen, Omar Ghattas, Dingcheng Luo, Thomas O\'Leary-Roseberry, Umberto Villa',
    author_email='dc.luo@utexas.edu',
    classifiers=[
        'Development Status :: 5 - Production/Stable',
        'Intended Audience :: Science/Research',
        'Topic :: Scientific/Engineering',
        'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
        'Programming Language :: Python :: 3',
    ],
    keywords=KEYWORDS,
    packages=find_packages(exclude=['examples', 'doc', 'test']),
    install_requires=REQUIREMENTS,
    extras_require=EXTRAS,
    python_requires=">=3.6,<4",
    project_urls={
        'Bug Reports': 'https://github.com/hippylib/soupy/issues',
        'Source': 'https://github.com/hippylib/soupy',
    },
)