#!/usr/bin/env python
# -*- coding: utf-8 -*-

from setuptools import find_packages, setup

with open('README.md') as readme_file:
    readme = readme_file.read()

with open('HISTORY.md') as history_file:
    history = history_file.read()

install_requires = [
    'matplotlib>=3.1.0,<4',
    'numpy>=1.16.3,<1.18',
    'pandas>=0.24.2,<0.26',
    'pomegranate>=0.11.0,<0.12',
    'scikit-learn>=0.21.1,<0.22',
    'scipy>=1.3.0,<2',
    'torch>=1.1.0,<2',
    'torchvision>=0.3.0',
]

tests_require = [
    'pytest>=3.4.2',
    'pytest-cov>=2.6.0',
]


setup_requires = [
    'pytest-runner>=2.11.1',
]

development_requires = [
    # general
    'bumpversion>=0.5.3,<0.6',
    'pip>=9.0.1',
    'watchdog>=0.8.3,<0.11',

    # docs
    'm2r>=0.2.0,<0.3',
    'Sphinx>=1.7.1,<3',
    'sphinx_rtd_theme>=0.2.4,<0.5',
    'autodocsumm>=0.1.10,<0.2',

    # style check
    'flake8>=3.7.7,<4',
    'isort>=4.3.4,<5',

    # fix style issues
    'autoflake>=1.1,<2',
    'autopep8>=1.4.3,<2',

    # distribute on PyPI
    'twine>=1.10.0,<4',
    'wheel>=0.30.0',

    # Advanced testing
    'coverage>=4.5.1,<6',
    'tox>=2.9.1,<4',

    # Generate results summary
    'XlsxWriter>=1.2.8,<1.3',
    'tabulate>=0.8.3,<0.9',
]

setup(
    author="MIT Data To AI Lab",
    author_email='dailabmit@gmail.com',
    classifiers=[
        'Development Status :: 2 - Pre-Alpha',
        'Intended Audience :: Developers',
        'License :: OSI Approved :: MIT License',
        'Natural Language :: English',
        'Programming Language :: Python :: 3',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
        'Topic :: Scientific/Engineering :: Artificial Intelligence',
    ],
    description=(
        "A framework to benchmark the performance of synthetic data generators "
        "for non-temporal tabular data"),
    extras_require={
        'dev': development_requires + tests_require,
        'test': tests_require,
    },
    include_package_data=True,
    install_requires=install_requires,
    license="MIT license",
    long_description=readme + '\n\n' + history,
    long_description_content_type='text/markdown',
    keywords='machine learning synthetic data benchmark generative models',
    name='sdgym',
    packages=find_packages(include=['sdgym', 'sdgym.*']),
    python_requires='>=3.6',
    setup_requires=setup_requires,
    test_suite='tests',
    tests_require=tests_require,
    url='https://github.com/sdv-dev/SDGym',
    version='0.2.1',
    zip_safe=False,
)
