from distutils.core import setup
from pyDatalog import __version__
setup(
    name = "pyDatalog",
    packages = ["pyDatalog"],
    version = __version__,
    description = "A pure-python implementation of a subset of prolog, using SLG algorithm with memoization",
    author = "Pierre Carbonnelle",
    author_email = "pierre.carbonnelle@gmail.com",
    url = "https://bitbucket.org/pcarbonn/pydatalog/wiki/Home",
    download_url = "http://pypi.python.org/pypi?name=pyDatalog&:action=display",
    classifiers = [
        "Programming Language :: Python",
        "Programming Language :: Python :: 2.7",
        "Programming Language :: Python :: 3",
        "Programming Language :: Prolog",
        "Development Status :: 4 - Beta",
        "Environment :: Other Environment",
        "Intended Audience :: Developers",
        "Intended Audience :: Education",
        "Topic :: Database",
        "Topic :: Database :: Database Engines/Servers",
        "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
        "Operating System :: OS Independent",
        "Topic :: Software Development :: Libraries :: Python Modules",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
        ],
    long_description = """\
pyDatalog embeds logic programming in python.  Assert facts and insert logic clauses directly in your python code, 
and use the inference engine to resolve complex, recursive queries fast.  

Datalog is a subset of prolog that is best 
at managing large sets of related information (e.g. in data integration or the semantic web).  
at simulating intelligent behavior (e.g. in games), 
at performing recursive algorithms (e.g. in network protocol, code and graph analysis)
or at solving discrete constraint problems. 

In particular, pyDatalog can be an alternative to SQL: 
it is more expressive, with a cleaner syntax; 
it facilitates re-use of code snippet (e.g. for frequent joins or formula); 
it offloads the database server by performing joins on the application tier; 
it can perform multi-database queries (from memory datastore, noSQL database, and/or relational databases, with appropriate connectors).

Datalog excels at accelerated development : Datalog programs are often shorter than their python equivalent, 
and Datalog statements can be specified in any order, as simply as formula in a spreadsheet.""",
    setup_requires = ["six"],
    install_requires = ["six"]
)
from distutils.core import setup
from pyDatalog import version
setup(
    name = "pyDatalog",
    packages = ["pyDatalog"],
    version = version.__version__,
    description = "A pure-python implementation of a subset of prolog, using SLG algorithm with memoization",
    author = "Pierre Carbonnelle",
    author_email = "pierre.carbonnelle@gmail.com",
    url = "https://bitbucket.org/pcarbonn/pydatalog/wiki/Home",
    download_url = "http://pypi.python.org/pypi?name=pyDatalog&:action=display",
    classifiers = [
        "Programming Language :: Python",
        "Programming Language :: Python :: 2.7",
        "Programming Language :: Python :: 3",
        "Programming Language :: Prolog",
        "Development Status :: 4 - Beta",
        "Environment :: Other Environment",
        "Intended Audience :: Developers",
        "Intended Audience :: Education",
        "Topic :: Database",
        "Topic :: Database :: Database Engines/Servers",
        "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
        "Operating System :: OS Independent",
        "Topic :: Software Development :: Libraries :: Python Modules",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
        ],
    long_description = """\
pyDatalog embeds logic programming in python.  Assert facts and insert logic clauses directly in your python code, 
and use the inference engine to resolve complex, recursive queries fast.  

Datalog is a subset of prolog that is best 
at managing large sets of related information (e.g. in data integration or the semantic web).  
at simulating intelligent behavior (e.g. in games), 
at performing recursive algorithms (e.g. in network protocol, code and graph analysis)
or at solving discrete constraint problems. 

In particular, pyDatalog can be used for object-relational mapping: 
it can perform multi-database queries (from memory datastore,  relational databases, and noSQL database with appropriate connectors)
it is more expressive than SQL, with a cleaner syntax; 
it facilitates re-use of SQL code snippet (e.g. for frequent joins or formula); 
it offloads the database server by performing joins on the application tier. 

Datalog excels at accelerated development : Datalog programs are often shorter than their python equivalent, 
and Datalog statements can be specified in any order, as simply as formula in a spreadsheet.""",
    setup_requires = ["six"],
    install_requires = ["six"]
)
use version.py file. Update pyDatalog description.