Open Source Python Frameworks - Page 24

pyGLFW is a set of python bindings to the successful cross-platform GLFW. It is intended as a powerful and lightweight alternative to pygame for python OpenGL application development.

pyHed its a framework created to help developers to create desktop aplications cross-plataform using Python. Using pyQT and sqlAlchemy, the pyHed framework transforms the job of creating and support aplications much more easy and fast. pyHed Team

Simple SVN server management tool based on Pylons Web Framework.

Python interface to ECLiPSe Constraint Logic Programming.

pycpp is a free C++ class library that will help you to easily write C++ code for both, embedding and extending C++ and Python. It comes along with a code generator that uses Python itself as interface language between C++ and your Python code.

PyGWA is a GPGPU library for Python. It contains Python bindings for AMD CAL and PyGWA.DP - a toy data-parallel programming API.

pylaunchy is an extension to Launchy's (www.launchy.net) plugin system that allows developing Launchy plugins in the Python language.

Pypes is a framework which allows users to break complex data processing logic down into a series of smaller less complex tasks. These tasks, referred to as components, can then be connected so that the output of one becomes the input to another.

The ‘pythonids‘ package provides the enumeration of Python syntaxes and the categorization of Python implementations. This enables the development of fast and easy portable generic code for arbitrary platforms in IT and IoT landscapes consisting of heterogeneous physical and virtual runtime environments. The current supported syntaxes are Python2.7+ and Python3 for the Python implementations: CPython IPython (based on CPython) IronPython Jython PyPy

A Python library and collection of tools that automate work on MediaWiki sites. Originally designed for Wikipedia, it is now used throughout the Wikimedia Foundation's projects and on many other MediaWiki wikis including wikidata service. We do not use sourceforge.net anymore, but are very grateful for their support in the past. Please see our website at https://www.mediawiki.org/wiki/Pywikibot Our pypi package could be found at https://pypi.org/project/pywikibot/

CSS parser and utilities for CSS2.1 and CSS3

repro is a Python package for automating reproducible research in scientific computing. It works in combination with SCons, a next-generation build tool (www.scons.org).

A lightweight REST mini framework for Python. Works great with Django, Flask, Pyramid & Tornado, but should be useful for many other Python web frameworks. Based on the lessons learned from Tastypie & other REST libraries. Restless tries to be RESTful by default, but flexible enough. The main Resource class has data methods (that you implement) for all the main RESTful actions. It also uses HTTP status codes as correctly as possible. Restless is BYOD (bring your own data) and hence, works with almost any ORM/data source. If you can import a module to work with the data & can represent it as JSON, Restless can work with it. Restless is small & easy to keep in your head. Common usages involve overriding just a few easily remembered method names. Total source code is an under a thousand lines of code.

Small Python library with various things such as Configuration file parsing (in Python syntax), HTML and PDF parsing. Used in others of my projects.

SAAFL is a language to define statements about assembly functions and can be used to match functions.

Screwdriver is an Simple Dis-Integrated Development Environment (SDIDE) used for programming in Python, PySide, Bash and Other object-oriented programming technologies. Source code management Interface can Push Up to 97 branches into the Git server. Reclaim your favourite Linux text editor.

seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. Researchers used it as a baseline to reproduce classic results and to prototype new attention variants and training tricks. It also offered scripts for data preprocessing, evaluation, and exporting models for serving. Although now historical as newer frameworks have emerged, seq2seq remains a clear, pedagogical implementation that documents the core ideas behind modern encoder-decoder systems.

The ‘setupjavax‘ package provides extension modules for the build of Java and Jython packages. The current release supports the following commands, additional are coming soon: - build_java - Java build and packaging for Python. Supports Java packaging for Python projects, adds native Java modules for Jython. - build_jy - Integrated Python and Java builds for Jython. Supports combined Java and Python / Jython packaging. Calls build_py and build_java.

The *setuplib* package provides core functions for the query of meta information and installation repositories of *Python* packages. It provides query and filter options on the installed packages and the available information, while displaying the result data in various formats, e.g. as table, list, or JSON, XML, YAML, CSV, etc. The provided commands and extension points integrate seamless into the standard *setuptools* and/or *distutils*. The *setuplib* is member of the group *setuplib* of extension components for the common *setup.py* installer. The targeted users are mainly developers, advanced users, and system administrators.

The 'setuptestx' supports a common call interface for the major implementations and releases of Python on various operating systems and their variants of distributions. The current provided implementations are: - CPython, IPython, PyPY : Python2.7, and Python3.5+ - IronPython, Jython : Python2.7 For the OS see *Tested Platforms* in the manuals.

Our goal is to find out useful and funny ways for expanding the possibilities of many FOSS by integrating them with other FOSS and proprietary systems. You'll find here many open source plugins and extensions useful to put those FOSS hermits together.

A simple messaging system designed for rapid deployment on robotic platforms. Based on the venerable Inter-Process Communication Package, simpleIPC offers several key extensions: units, auto-discovery, message descriptors, and a Python wrapper. See Soto.

Small and simple component-based python web framework. Compatible with GAE.

spaCy supports a number of transfer and multi-task learning workflows that can often help improve your pipeline’s efficiency or accuracy. Transfer learning refers to techniques such as word vector tables and language model pretraining. These techniques can be used to import knowledge from raw text into your pipeline, so that your models are able to generalize better from your annotated examples. You can convert word vectors from popular tools like FastText and Gensim, or you can load in any pre trained transformer model if you install spacy-transformers. You can also do your own language model pretraining via the spacy pre train command. You can even share your transformer or another contextual embedding model across multiple components, which can make long pipelines several times more efficient. To use transfer learning, you’ll need at least a few annotated examples for what you’re trying to predict.