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. 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. This module uses the pyexpat module to provide access to the Expat parser. This module provides one exception and one type object: exception xml.parsers.expat.ExpatError The exception raised when Expat reports an error. exception xml.parsers.expat.error Alias for ExpatError. xml.parsers.expat.XMLParserType The type of the return values from the ParserCreate() function. The xml.parsers.expat module contains two functions: xml.parsers.expat.ErrorString(errno) Returns an explanatory string for a given error number errno. xml.parsers.expat.ParserCreate([encoding[, namespace_separator]]) Creates and returns a new xmlparser object. encoding, if specified, must be a string naming the encoding used by the XML data. 19.15.1. 19.15.2.
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. 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 tutorial_examples_40.py. Each lesson in tutorial_examples_40.py is encapsulated in a Python function, named exampleN(), where N ranges from 0 to 21 (or more). Prerequisites (Linux) Return to Top The tutorial examples can be run on a Linux system, running AllegroGraph and the examples on the same computer ("localhost"). Terminology Return to Top WebView
PyAIML (a.k.a. Program Y) - A Python AIML Interpreter
Creatures | City in a Bottle
trendrr/whirlwind - GitHub
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. The Subpackage Bayes also offers a tool for parameter estimation of Deterministic and Stochastic Dynamical Models. 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.
pbnt.berlios
App Engine Python Overview - Google App Engine - Google Code
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 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 You specify the runtime element in app.yaml.
jaikuengine - Project Hosting on Google Code
JaikuEngine is a social microblogging platform that runs on AppEngine. JaikuEngine powers Jaiku.com. For the mobile client source, see: Jaiku Mobile client Dependencies Python 2.4 or 2.5 Docutils: Mox: version 0.5.1 Everything else should be included in the checkout via svn:externals. Quickstart Check out the repository (it's somewhat large due to image binaries): svn checkout jaikuengine Copy local_settings.example.py to local_settings.py Run the server with some test data pre-loaded: python manage.py testserver common/fixtures/*.json Browse to localhost:8080 and log in with popular/password Getting Running Jaiku uses the Django framework as well as most of its development process, so most actions go through manage.py. To run the development server: python manage.py runserver python manage.py testserver common/fixtures/*.json Contributing to the project
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. 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
