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setup.py
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setup.py
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#!/usr/bin/env python
# -*- coding: utf8 -*-
from setuptools import setup, find_packages
NAME = 'shl_scripts'
import shl_scripts
VERSION = shl_scripts.__version__ # << to change in __init__.py
setup(
name=NAME,
version=VERSION,
# package source directory
package_dir={'shl_scripts': NAME},
packages=find_packages(exclude=['contrib', 'docs', 'probe']),
author='Laurent PERRINET, Institut de Neurosciences de la Timone (CNRS/Aix-Marseille Université)',
description=' This is a collection of python scripts to test learning strategies to efficiently code natural image patches. This is here restricted to the framework of the [SparseNet algorithm from Bruno Olshausen](http://redwood.berkeley.edu/bruno/sparsenet/).',
long_description=open('README.rst', 'r', encoding='utf-8').read(),
license='LICENSE.txt',
keywords="Neural population coding, Unsupervised learning, Statistics of natural images, Simple cell receptive fields, Sparse Hebbian Learning, Adaptive Matching Pursuit, Cooperative Homeostasis, Competition-Optimized Matching Pursuit",
url = 'https://github.com/laurentperrinet/' + NAME, # use the URL to the github repo
download_url = 'https://github.com/laurentperrinet/' + NAME + '/tarball/' + VERSION,
classifiers = ['Development Status :: 3 - Alpha',
'Environment :: Console',
'License :: OSI Approved :: GNU General Public License (GPL)',
'Operating System :: POSIX',
'Topic :: Scientific/Engineering',
'Topic :: Utilities',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
],
install_requires=['SLIP', 'matplotlib', 'numpy'],
extras_require={
'html' : [
'notebook',
'matplotlib'
'jupyter>=1.0']
},
)