Commits

Pierre Carbonnelle committed b424805

Bump up version. Update description

Comments (0)

Files changed (3)

2to3.bat

-cd c:\python27\tools\scripts
-2to3.py -w "C:\Users\pcarbonn\Documents\98 Eclipse\pyDatalog"
-pause

pyDatalog/version.py

-__version__ = "0.11.1"
+__version__ = "0.11.2"
     name = "pyDatalog",
     packages = ["pyDatalog", "pyDatalog/examples"],
     version = version.__version__,
-    description = "A pure-python implementation of a subset of prolog, using SLG algorithm with memoization",
+    description = "A pure-python implementation of a truly declarative subset of prolog.  Run logic queries on databases or python objects, and use logic clauses to define python classes.",
     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",
+    keywords = "prolog, logic programming, datalog, object-relational mapping, data integration",
     classifiers = [
         "Programming Language :: Python",
         "Programming Language :: Python :: 2.7",
         "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.  
+pyDatalog adds the logic programming paradigm to python's toolbox, in a pythonic way.  
+You can now run logic queries on databases or python objects, and use logic clauses to define python classes.
 
-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. 
+Datalog is a truly declarative subset of prolog that is best at
+
+* managing large sets of related information (e.g. in data integration or the semantic web).  
+* simulating intelligent behavior (e.g. in games), 
+* performing recursive algorithms (e.g. in network protocol, code and graph analysis)
+* 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. 
+
+* 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.""",