setup.py

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

here = path.abspath(path.dirname(__file__))

# Get the long description from the README file
with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
    long_description = f.read()

setup(name='astrodash',
    version='1.0.20',
    description='Deep Learning for Automated Spectral Classification of Supernovae',
    # long_description=long_description,
    url='https://github.com/daniel-muthukrishna/DASH',
    author='Daniel Muthukrishna',
    author_email='daniel.muthukrishna@gmail.com',
    license='MIT',
    classifiers=[
        # How mature is this project? Common values are
        #   3 - Alpha
        #   4 - Beta
        #   5 - Production/Stable
        'Development Status :: 4 - Beta',

        # Indicate who your project is intended for
        'Intended Audience :: Science/Research',
        'Topic :: Scientific/Engineering :: Astronomy',

        # Pick your license as you wish (should match "license" above)
        'License :: OSI Approved :: MIT License',

        # Specify the Python versions you support here. In particular, ensure
        # that you indicate whether you support Python 2, Python 3 or both.
        'Programming Language :: Python :: 2',
        'Programming Language :: Python :: 3',
    ],

    keywords='supernova spectral classification deep machine learning',

    # You can just specify the packages manually here if your project is
    # simple. Or you can use find_packages().
    packages=find_packages(exclude=['contrib', 'docs', 'tests']),

    # Alternatively, if you want to distribute just a my_module.py, uncomment
    # this:
    #   py_modules=["my_module"],

    # List run-time dependencies here.  These will be installed by pip when
    # your project is installed. For an analysis of "install_requires" vs pip's
    # requirements files see:
    # https://packaging.python.org/en/latest/requirements.html
    install_requires=['numpy', 'scipy', 'tensorflow', 'pyqtgraph', 'astropy'],

    # List additional groups of dependencies here (e.g. development
    # dependencies). You can install these using the following syntax,
    # for example:
    # $ pip install -e .[dev,test]
    # extras_require={
    #     'dev': ['check-manifest'],
    #     'test': ['coverage'],
    # },

    # If there are data files included in your packages that need to be
    # installed, specify them here.  If using Python 2.6 or less, then these
    # have to be included in MANIFEST.in as well.


    # Although 'package_data' is the preferred approach, in some case you may
    # need to place data files outside of your packages. See:
    # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa
    # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
    # data_files=[('my_data', ['data/data_file'])],

    # To provide executable scripts, use entry points in preference to the
    # "scripts" keyword. Entry points provide cross-platform support and allow
    # pip to create the appropriate form of executable for the target platform.
    entry_points={
        'console_scripts': [
            'sample=astrodash:main',
            'astrodash=astrodash:run_gui'
        ],
    },
)