Graph Description In mathematics and computer science, graph theory studies networks of connected nodes and their properties. NodeBox | Graph
Python API Tutorial for AllegroGraph 4.0 This is an introduction to the Python client API to AllegroGraph RDFStore™ version 4.2 from Franz Inc. The Python Sesame API offers convenient and efficient access to an AllegroGraph server from a Python-based application. This API provides methods for creating, querying and maintaining RDF data, and for managing the stored triples. The Python Sesame API deliberately emulates the Aduna Sesame API to make it easier to migrate from Sesame to AllegroGraph. The Python Sesame API has also been extended in ways that make it easier and more intuitive than the Sesame API. Contents
pbnt.berlios PBNT is a general purpose Bayesian Network library implemented in Python. Currently it is in its first release. It supports static Bayesian Networks with discrete variables. For a simple example of how to create Bayesian Networks with the toolbox, please see dist/examples/ExampleModels.py, and an example of how to use this network for inference is contained in dist/examples/exampleinference.py. I are currently compiling more complete documentation.
Welcome to Google App Engine for Python! With App Engine, you can build web applications using the Python programming language, and take advantage of the many libraries, tools and frameworks for Python that professional developers use to build world-class web applications. Your Python application runs on Google's scalable infrastructure, and uses large-scale persistent storage and services. Introduction App Engine Python Overview - Google App Engine - Google Code
Category:Programming language:Python - LiteratePrograms
JaikuEngine is a social microblogging platform that runs on AppEngine. JaikuEngine powers Jaiku.com. For the mobile client source, see: Jaiku Mobile client
pypingback - Project Hosting on Google Code
pebl-project - Project Hosting on Google Code Update 11/15/2011 Pebl source code and issues are now hosted at http://github.com/abhik/pebl. This site is only for historical purposes. Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: Can learn with observational and interventional data Handles missing values and hidden variables using exact and heuristic methods Provides several learning algorithms; makes creating new ones simple Has facilities for transparent parallel execution using several cluster/grid resources Calculates edge marginals and consensus networks Presents results in a variety of formats
Warning The pyexpat module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities. New in version 2.0. The xml.parsers.expat module is a Python interface to the Expat non-validating XML parser. 19.5. xml.parsers.expat — Fast XML parsing using Expat — Python v2.7.1 documentation
We have released our game development engine. It is available in the new "Download" section on the website. NodeBox for OpenGL is a free, cross-platform library for generating 2D animations with Python programming code. It is built on Pyglet and adopts the drawing API from NodeBox for Mac OS X ... Read more → Creatures | City in a Bottle
A Python AIML Interpreter by Cort Stratton Another Fine Product from Your Friends At Dangerware Latest Stable Version: None Latest Unstable Version: 0.8.5 Download Now! Your generous donations are appreciated! Table of Contents Latest News PyAIML (a.k.a. Program Y) - A Python AIML Interpreter
trendrr/whirlwind - GitHub README.markdown Welcome to whirlwind Whirlwind is an easy-to-use MVC framework written in Python that builds on top MongoDB and Tornado (and others) to be super fast and scalable. The code base of whirlwind was originally developed as the underlying web framework for the social media metrics and analytics platform Trendrr.
Python and XML Processing: Other Software
bayesian-inference - Project Hosting on Google Code This package is a collection of useful classes for basic Bayesian inference. Currently, its main goal is to be a tool for learning and exploration of Bayesian probabilistic calculations. Currently it also includes subpackages for stochastic simulation tools which are not strictly related to Bayesian inference, but are currently being developed within BIP. One such package is the BIP.SDE which contains a parallelized solver for stochastic differential equations, an implementation of the Gillespie direct algorithm. The Subpackage Bayes also offers a tool for parameter estimation of Deterministic and Stochastic Dynamical Models.
visualization-python - Project Hosting on Google Code
Overview — Official Grok v1.2.1 documentation
Overview — PyGraphviz v1.1 documentation