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Graph. Graph Description In mathematics and computer science, graph theory studies networks of connected nodes and their properties.


A graph can be used to visualize related data, or to find the shortest path from one node to another node for example. Central concepts in graph theory are: Node: a block of information in the network.Edge: a connection between two nodes (can have a direction and a weight).Centrality: determining the relative importance of a node.Clustering: partitioning nodes into groups. The NodeBox Graph library includes algorithms from NetworkX for betweenness centrality and eigenvector centrality, Connelly Barnes' implementation of Dijksta shortest paths (here) and the spring layout for JavaScript by Aslak Hellesoy and Dave Hoover (here).

For those of you looking for the old Graph library built on Boost, it can still be found here. Download Documentation The library has a cool example of a visual browser for WordNet. How to get the library up and running graph = ximport("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.

Python API Tutorial for AllegroGraph 4.0

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 Overview Return to Top The Python client tutorial rests on a simple architecture involving AllegroGraph, disk-based data files, Python, and a file of Python examples called Each lesson in is encapsulated in a Python function, named exampleN(), where N ranges from 0 to 21 (or more). Prerequisites (Linux) Return to Top. Shah09a. Pbnt.berlios. App Engine Python Overview - Google App Engine - Google Code. Welcome to Google App Engine for Python!

App Engine Python Overview - Google App Engine - Google Code

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 executes your Python application code using a pre-loaded Python interpreter in a safe "sandboxed" environment. Your app receives web requests, performs work, and sends responses by interacting with this environment.

A Python web app interacts with the App Engine web server using the WSGI protocol, so apps can use any WSGI-compatible web application framework. The Python interpreter can run any Python code, including Python modules you include with your application, as well as the Python standard library. Selecting the Python runtime. Category:Programming language:Python. Jaikuengine - Project Hosting on Google Code. JaikuEngine is a social microblogging platform that runs on AppEngine.

jaikuengine - Project Hosting on Google Code

JaikuEngine powers 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 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 Pebl Documentation includes installation instructions, tutorial and API reference.

Pebl has been developed at the Systems Biology Lab at the University of Michigan and is available with a permissive MIT-style license. Update 3/6/2009. 19.5. xml.parsers.expat — Fast XML parsing using Expat — Python v2.7.1 documentation. Warning The pyexpat module is not secure against maliciously constructed data.

19.5. xml.parsers.expat — Fast XML parsing using Expat — Python v2.7.1 documentation

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. The module provides a single extension type, xmlparser, that represents the current state of an XML parser. This module uses the pyexpat module to provide access to the Expat parser. This module provides one exception and one type object: RDFLib. City in a Bottle. PyAIML (a.k.a. Program Y) - A Python AIML Interpreter. Pydev. Trendrr/whirlwind - GitHub. 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.

bayesian-inference - Project Hosting on Google Code

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. This tool is fully described in this paper: Coelho FC, Codeço CT, Gomes MGM (2011) A Bayesian Framework for Parameter Estimation in Dynamical Models.

To install, download the latest version from this page, unpack and follow instructions on README file. I hope that in time it will mature into an useful tool for general use. Visualization-python - Project Hosting on Google Code. Overview — Official Grok v1.2.1 documentation. Overview — PyGraphviz v1.1 documentation.