setup.py
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",
)