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setup.py
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setup.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
import re
here = path.abspath(path.dirname(__file__))
# Get the version string
with open(path.join(here, 'zhusuan', '__init__.py'), encoding='utf-8') as f:
version = re.search(r'__version__ = \'(.*?)\'', f.read()).group(1)
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
readme = f.read()
setup(
name='zhusuan',
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# https://packaging.python.org/en/latest/single_source_version.html
version=version,
description='A Library for Bayesian Deep Learning',
long_description=readme,
# The project's main homepage.
url='https://github.com/thu-ml/zhusuan',
# Author details
author='ZhuSuan contributors',
author_email='[email protected]',
# Choose your license
license='MIT',
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
# 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 :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
# What does your project relate to?
keywords='zhusuan generative probabilistic bayesian deep learning',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages().
packages=find_packages(exclude=['tests', 'tests.*',
'examples', 'examples.*']),
# 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>=1.14.0',
'six'],
# 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': [
'Sphinx>=1.7.1',
'sphinx_rtd_theme',
'sphinxcontrib-bibtex>=0.3.6',
'pep8',
'scipy',
'coverage',
'mock'
],
'examples': [
'scipy',
'matplotlib',
'scikit-image',
'progressbar2'
],
},
# 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.
# package_data={
# 'sample': ['package_data.dat'],
# },
# Although 'package_data' is the preferred approach, in some case you may
# need to place data files outside of your packages. See:
# https://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files
# 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=sample:main',
# ],
# },
)