Pyglet. Panda3D - Free 3D Game Engine. Gensim: Topic modelling for humans. Marisa-trie 0.6. Static memory-efficient & fast Trie-like structures for Python (based on marisa-trie C++ library) Static memory-efficient Trie-like structures for Python (2.x and 3.x).
String data in a MARISA-trie may take up to 50x-100x less memory than in a standard Python dict; the raw lookup speed is comparable; trie also provides fast advanced methods like prefix search. Based on marisa-trie C++ library. Note There are official SWIG-based Python bindings included in C++ library distribution; this package provides an alternative Cython-based pip-installable Python bindings. There are several Trie classes in this package: Context Managers in Python. What are we going to talk about.
Setting up Django with Nginx, Gunicorn, virtualenv, supervisor and PostgreSQL - Michał Karzyński. Django is an efficient, versatile and dynamically evolving web application development framework.
When Django initially gained popularity, the recommended setup for running Django applications was based around Apache with mod_wsgi. The art of running Django advanced and these days the recommended configuration is more efficient and resilient, but also more complex and includes such tools as: Nginx, Gunicorn, virtualenv, supervisord and PostgreSQL.
In this text I will explain how to combine all of these components into a Django server running on Linux. Prerequisites I assume you have a server available on which you have root privileges. If you don’t have a server to play with, I would recommend the inexpensive VPS servers offered by Digital Ocean. Frameworks, microframeworks, too many choices. SampleApps - google-api-python-client - Google APIs Client Library for Python. Creating a Development Environment with pip and virtualenv · zookeepr/zookeepr Wiki. Creating a development environment using virtualenv(wrapper) and pip See also Development-Environment-in-5-minutes (using vagrant) Tested on Ubuntu 10.10 Steps Start with Git, Github.
Green Unicorn - Welcome. Mnot/thor at spdy. Web2py Web Framework. PyPy. CoderBuddy - Create and Publish Apps and Free Web Sites to Google App Engine Easily. Machine Vision made Easy - SimpleCV. 1. Kay tutorial — Kay v1.1.0 documentation. 1.1.
Preparation Install following stuff: Kay-framework - A web framework made specifically for Google App Engine. Kay is a web framework made specifically for Google App Engin The basic design of Kay is based on the Django framework, like middleware, settings and pluggable application, etc.
Kay uses Werkzeug as lower level framework, Jinja2 as template engine, and babel for handling language translations. Welcome to Flask — Flask v0.8-dev documentation. Welcome to Flask’s documentation.
This documentation is divided into different parts. I recommend that you get started with Installation and then head over to the Quickstart. Besides the quickstart, there is also a more detailed Tutorial that shows how to create a complete (albeit small) application with Flask. If you’d rather dive into the internals of Flask, check out the API documentation. Common patterns are described in the Patterns for Flask section.
Flask depends on two external libraries: the Jinja2 template engine and the Werkzeug WSGI toolkit.
Learning. GUI. Packages. Python. Python & semantic. Django. Python Wrapper. "Python API, Documents and Stuff" About.
Python Kinect Webserver. Overview — PyGraphviz v1.1 documentation. 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. 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: Python and XML Processing: Other Software. 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 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 Terminology Return to Top. 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.
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. If you're using Ubuntu you will need to install the pstats library which is in the python-profilers package. 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: Visualization-python - 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. Shah09a. Pbnt.berlios. 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. 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.
Pypingback - Project Hosting on Google Code. 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.