from setuptools import setup


setup(
    name="prox_tv",
    version="3.3.0",
    description="Toolbox for fast Total Variation proximity operators",
    long_description="""
proxTV is a toolbox implementing blazing fast implementations of Total Variation
proximity operators. While the core algorithms are implemented in C to achieve high
efficiency, Matlab and Python interfaces are provided for ease of use. The library
provides efficient solvers for a variety of Total Variation proximity problems, with
address input signals of any dimensionality (1d, images, video, ...) and different
norms to apply in the Total Variation term.
""",
    packages=['prox_tv'],
    install_requires=[
        'numpy>=1.11.3',
        'cffi>=1.0.0',
    ],
    setup_requires=[
        'cffi>=1.0.0',
    ],
    package_data={
        'prox_tv': ['src/demos/*']
    },
    cffi_modules=['prox_tv/prox_tv_build.py:ffi'],
    author="Alvaro Barbero, Suvrit Sra, Josip Djolonga (python bindings)",
    author_email="alvaro.barbero@inv.uam.es",
    url='https://github.com/albarji/proxTV',
    license='BSD',
    classifiers=[
        'Development Status :: 5 - Production/Stable',
        'Intended Audience :: Science/Research',
        'Operating System :: POSIX :: Linux',
        'Topic :: Scientific/Engineering :: Mathematics',
        'License :: OSI Approved :: BSD License',
        'Programming Language :: Python :: 2.7',
        'Programming Language :: Python :: 3.4',
        'Programming Language :: Python :: 3.5',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7'
    ],
    keywords='total variation image processing machine learning',
    test_suite="nose.collector",
)
