Open Source Python Frameworks - Page 22

This package provides routers and fields to create nested resources in the Django Rest Framework. Nested resources are needed for full REST URL structure, if one resource lives inside another. (optional) If you need hyperlinks for nested relations, you need a custom serializer. There you will inform how to access the parent of the instance being serialized when building the children URL. In order to get started with testing, you will need to install tox. Once installed, you can then run one environment locally, to speed up your development cycle.

portable c/c++ unit test framework

dynamide is a dynamic web application framework for handling the presentation and business layers in a traditional web app. See http://dynamide.com

dynoparse (dynamic option parser) is a python package that implements a flexible and configurable way for cli option handling.

Eggdrop is the most advanced IRC robot available. It has been under development since December 1993, and unlike most other bots, it is still regularly updated.

fgen is a free command line tool that facilitates cross platform c++ development, including header generation, cpp file generation, makefile generation, unit test framework generation, etc.

figusdevpack (fdp), is designed for reducing application development time and acquainting new users with existing resources for multimedia development. It is structured around a wrapper library, example applications, tutorials and documentation.

Freejack is a MVC framework for quickly building dynamic websites using Freemarker as the template engine and Python for the controllers.

gnu4u provides defined set of (GNU-)tools for development C and C++ applications. This tool set is independent of the OS's patch level, is available for different systems and provides the same development and runtime environment for those systems.

Block that receives a " message" and send string values over serial connection, to control hardware devices ( switch ), for example, via Arduino based controller ( see https://sourceforge.net/projects/ardu-switch/ ) Prerequisite: pyserial Build as usual for a Gnuradio block: - download and unpack - cd gr-message_to serial - mkdir build && cd build - cmake .. && make - sudo make install && ldconfig If successful this block is into OOT section... Enjoy ;-)

jToolkit is a Python web application framework built on modpython and Apache.

mgE3D - mgEngine3D is an unfinished 3D Engine based on OpenGL. By now it provided several features like scenetree, mesh/skeleton mesh rendering and animation, terrain, materials, textures, multiple viewports, Python exporter for Blender and so on.

molten is a minimal, extensible, fast and productive framework for building HTTP APIs with Python. molten can automatically validate requests according to predefined schemas, ensuring that your handlers only ever run if given valid input. Schemas are PEP484-compatible, which means mypy and molten go hand-in-hand, making your code more easy to maintain. Schema instances are automatically serializable and you can pick and choose which fields to exclude from responses and requests. Write clean, decoupled code by leveraging DI. Define components for everything from settings to DB access and business logic, test them in isolation and swap them out as needed. Here we’ve declared a Todo manager whose job it is to store and load Todos in the DB. In order for handlers to be able to request manager instances from the DI system, we also define a component that knows how and when to instantiate a TodoManager.

mwclient is a Python framework to interact with MediaWiki wikis using the MediaWiki API. Note: the project is now hosted at github; please go to https://github.com/mwclient/mwclient to get the latest version.

nJES is a complete CPython rewrite of JES (Jython Environment for Students), originally written by Mark Guzdial and Barbara Ericson. The primary objective of the rewrite is to enhance performance while maintaining full forward compatibility with JES.

Nacho is an open-source CLI tool (or library) that streamlines the creation of RESTful APIs, scaffolding, or microservices in a chosen framework, reducing boilerplate and enabling rapid building of backend services. It focuses on productivity by providing commands to generate endpoints, data models, controllers, and routing based on standard patterns (CRUD, REST conventions). The tool also includes support for middleware integration, automatic documentation (such as via OpenAPI/Swagger), and ready hooks for authentication or authorization setup. Developers can customise templates used for generation or plug custom patterns, which makes the tool suitable across small utilities or large service fleets. Because it encourages consistency among services, teams can spin up new microservices with a shared structure and skip repetitive setup. Though the repository may be less active than some major frameworks, it serves as a valuable jump-start for projects where speed and uniformity matter.

The namedtupledefs is a patched release of the standard collection.namedtuple with added support of default values for field. In addition a method _merge is supported for the combination of named tuples. For Python2 see namedtupledefs2.

The namedtupledefs is a patched release of the standard collection.namedtuple with added support of default values for field. In addition a method _merge is supported for the combination of named tuples. For Python3 see namedtupledefs3 or namedtupledefs

The namedtupledefs is a patched release of the standard collection.namedtuple with added support of default values for field. In addition a method _merge is supported for the combination of named tuples. For Python2 see namedtupledefs2.

The namedtupledefs is a patched release of the standard collection.namedtuple with added support of default values for field. In addition a method _merge is supported for the combination of named tuples. For Python2 see namedtupledefs2.

We use nginx in our company lab environment. It often happens that my colleagues have developed an application that is now deployed in our Stage or Prod environment. To make this application accessible nginx has to be adapted. Most of the time my colleagues don't have permission to access the server and change the configuration files and since I don't feel like doing this for everyone anymore I thought a UI could help us all. If you feel the same way I wish you a lot of fun with the application and I am looking forward to your feedback, change requests or even a star. Containerization is now state of the art and therefore the application is delivered in a container. With the menu item Main Config the Nginx specific configuration files can be extracted and updated. These are dynamically read from the Nginx directory. If a file has been added manually, it is immediately integrated into the Nginx UI Main Config menu item.

This package contains type stubs and a custom mypy plugin to provide more precise static types and type inference for Django framework. Django uses some Python "magic" that makes having precise types for some code patterns problematic. This is why we need this project. The final goal is to be able to get precise types for the most common patterns. We are independent from Django at the moment. There's a proposal to merge our project into the Django itself. You can show your support by liking the PR. This project does not affect your runtime at all. It only affects mypy type checking process. The current implementation uses Django's runtime to extract information about models, so it might crash if your installed apps or models.py are broken.

Web RAD for PostgreSQL tool.

Some experiments in constructing analysis framework

Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new video frames and Pipeless takes care of everything else. You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud.