Open Source Python Frameworks - Page 18

Note: latest version can be found at https://github.com/targeted/pythomnic3k Pythomnic3k is a Python 3 framework for service-oriented middleware with hot reloading and fault tolerance. It is used for integrating various systems in enterprise network or writing standalone network services.

Python Automation Suite - a collection of Python programs/modules that forms an integrated Software Test Automation

makes writing python bots easy, and transport independant, allowing the same bot code to run on multiple protocols.

A content generation engine written in Python used for generating content for HTML and textual output. Integrates with Apache to form a web framework that uses XML templates and can embed Python.

Python MyCMS is an application development platform built with robust MySQL integration allowing for easy creation of state and event driven web-based interfaces. Now also featuring a phpMyAdmin-style web interface to MySQL for administration.

Poloniex API wrapper for Python 2.7 & 3.

Python test runner, initial plan is a main process dispatching test to sub process.

This project aims to collect some development tools for Python under windows32 environment. PyGTk and Glade related GUI design tools are my primary concerns.

Python MVC web framework using mod_python with Cheetah template engine

The idea of this framework is to allow the code to tolerate faults by adding redundancy (either by repetition or by different variants of code) and replacing original methods or functions by syntactically identical callable fault-tolerant constructs.

The PyJTF supports the test-driven development of Javascript code using tests written in Python. It executes the code under test in the IE environment under Windows (Win32). Allows greater productivity in testing Javascript code.

NOTE: PythonQt has been moved to https://github.com/MeVisLab/pythonqt PythonQt is a dynamic and lightweight script binding of the Qt framework to the Python language. It can be easily embedded into Qt applications and makes any QObject derived object scriptable via Python without the need of wrapper code generation.

Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format.

Cross-platform and multi-arch ultra lightweight emulator. Supported OS: Linux, MacOS, Windows, FreeBSD, DOS and UEFI. Support Arch: x86(16/32/64), ARM(64) MIPS, EVM and WASM. It also support Linux Kernel Module(.ko) , Windows Driver(.sys) and MacOS Kernel(.kext) via Demigod. Binary instrumentation and API are Qiling Framework's main focus and priority. It is designed for reverse engineers - thus there is no need to rebuild another sand boxing tool. Using Qiling Framework saves you time. The API-rich Qiling Framework brings reverse and instrument binary to the next level quicker. Additionally, Qiling provides API access to register, memory, filesystem, operating system and debugger. Qiling also provides virtual machine-level API such as save and restore execution state. It combines binary instrumentation and binary emulation into one single framework.

QuickDicom is an easy to use dicom medical imaging package for Mac OSX, providing QuickLook, Spotlight, Quartz Composer, Window/Level and a dicom file analyzer. Also included is the iiDicom Framework for image/dictionary usage in Objective C and Python.

Quokka is a Content Management Framework written in Python. A lightweight framework to build CMS (Content Management System) as websites, portals, blogs, applications and anything related to publishing content to the web. Quokka is not limited to CMS area, it is also possible to create Quokka extensions to provide any kind of web application based on Python and Flask. Quokka can also (optionally) generate a static website from the contents generated in its admin interface. Web-based content management admin interface. Multiple content formats (markdown, rst, html, plaintext) Compatibility with any of the Pelican Themes. Flat file NoSQL database TinyDB or optionally MongoDB for scale deployments. Host the Quokka on server or generate a static website. Extensible via modules/plugins. Powered by Python, Flask, Flask-Admin, TinyMongo and Pelican Themes.

QxTransformer is no longer maintained.

JSON Web Token Authentication support for Django REST Framework. This package provides JSON Web Token Authentication support for Django REST framework. Unlike some more typical uses of JWTs, this module only generates authentication tokens that will verify the user who is requesting one of your DRF protected API resources. The actual request parameters themselves are not included in the JWT claims which means they are not signed and may be tampered with. You should only expose your API endpoints over SSL/TLS to protect against content tampering and certain kinds of replay attacks. You can easily test if the endpoint is working by doing the following in your terminal, if you had a user created with the username admin and password password123. Alternatively, you can use all the content types supported by the Django REST framework to obtain the auth token.

Map models in your Django application to XML from an external REST api. Developed to enable building a Django application that sourced some of its data from a Java REST service that supplies XML.

The Related Values Processing Framework helps the integration of Process Control Data Historian Systems.

Radix is a RAD framework for creating native XML web applications. Complete web applications can be created without programming knowledge using XML, XSLT, XPath and related technologies. Radix can be extended using Java, JavaScript, Python, and Tcl.

Use Raiden to enrich your dApp with nearly instant, low-fee and scalable payments. It comes with an easy-to-use API and is compatible with the Ethereum ERC20 token standard. Incentivized, decentralized P2P live streaming with micropayments using Raiden, introducing “Proof-of-Stream-Payment”. The Raiden Network is an infrastructure layer on top of the Ethereum Blockchain. While the basic idea is simple, the underlying protocol is quite complex and the implementation non-trivial. Nonetheless the technicalities can be abstracted away, such that developers can interface with a rather simple API to build scalable decentralized applications based on the Raiden Network. The Raiden Network is an off-chain scaling solution, enabling near-instant, low-fee and scalable payments. It's complementary to the Ethereum Blockchain and works with any ERC20 compatible token. The Raiden project is work in progress.

The goal of this project is to provide a set of Python bindings for the RakNet UDP network library version 3.0. RakNet-Python is implemented with the help of Boost.Python library and tries to mimic RakNet's original C++ interface.

ReactPy provides a React-style component model for Python developers to build web frontends without JavaScript. Components and hooks create declarative, reactive UIs, working across frameworks like Flask, Django, Jupyter, and more. With WebAssembly support for performance, ReactPy aims to empower Python-native frontend development.

RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community.