Open Source Python Software Development Software - Page 63

A high level Python Web framework. The project is under active development and is not ready for production use.

Jacomma is a framework for developing network-aware reactive information agents, using the ICM agent communication infrastructure. It offers transparent ICM bindings for java, and includes a lightweight agent execution environment.

Jepp (Java Embedded Python) embeds CPython in Java. It is stable and thread-safe, and is suitable for many different scripting needs. Note: for most purposes Jep is now at https://github.com/mrj0/jep Releases can be found on pypi: https://pypi.python.org/pypi/jep/3.1.0

Java exception extractor. This utility will parse all files (either plain text or bzipped) and tries to search for various exceptions. It then tries to match exceptions against grouping rules (regexps). It is also able to group unrecognised exceptions.

Simple java preprocessor (no macros for now). its almost fully funcional give it a try

Java Selector is a Red Hat alternatives front-end for switching between installed Java versions.

JPE is a seamless, complete, and efficient integration of Java and standard Python (C Python). JPE provides Java access to Python's native extension, and Java's Swing to Python developpers.

JavaScript Lint is a development tool that validates JavaScript code for best coding practices, finding common mistakes without actually running the script or opening the web page.

Javelina is an advanced code coverage tool that uses dynamic instrumentation to minimize the overhead for collecting coverage data. Additionally, Javelina allows the application of logical operations to code coverage data.

Javer's Makefile Writer is a simple application to help you to write C/C++ makefile.

Jeffy is a Serverless "Application" Framework for Python consisting of a suite of Utilities for Lambda functions that make developing serverless applications easy. There are three main things that Jeffy is focused on: 1. Logging - Jeffy provides easy to see JSON format logging. All events, responses and errors are captured and you can make configurations for any additional attributes you'd like to see on the logs. 2. Decorators - Jeffy has an extensive range of decorators/ event handlers for implementing common things for Lambda functions. 3. Tracing - Events are traceable within related functions and AWS services with generating and passing correlation_id.

Jenkins-Zero-To-Hero is a hands-on learning repository that teaches Jenkins from scratch, starting with installation and moving all the way to building end-to-end CI/CD pipelines. The course is designed around running Jenkins on an AWS EC2 instance, guiding you through installing Java, configuring Jenkins, and exposing it safely via security group rules. From there, it covers installing plugins like Docker Pipeline, configuring Docker as an agent, and wiring up multi-stage and multi-agent pipelines. The folder structure includes practical examples such as java-maven-sonar-argocd-helm-k8s and python-jenkins-argocd-k8s, showing real CI/CD flows that build, test, analyze, containerize, and deploy apps to Kubernetes via Argo CD in a GitOps style. The README walks through detailed step-by-step commands and screenshots, making it accessible to beginners who are unfamiliar with Jenkins, AWS, or pipelines.

GUI for creating shell scripts, written in Python using Tkinter for cross-platform portability.

Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS. Intuitive design pattern for high-performance microservices. Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build.

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype.

An UI editor for j2me developers. You can draw images with this tool and then generate j2me codes which draws the same image by drawLine(), drawRect(), etc.

Joe SDK is an easy to learn and adapt python software development kit for excellent GUI desktop applications. You can create a desktop application using python, html, css, javascript or jquery in minutes! The SDK allows you to: - Run python code, on any javascript event. - Render result of python function dynamically - Create HTML User Interfaces, using standard HTML. The SDK is powered by: - Google Chrome apps - Python anywhere - Cherrypy Web Framework Though it is a desktop application sdk, it is purely web hosted locally. This makes it extremely easy to deploy these applications to web.

Jostraca is a general purpose code generation toolkit for software developers, using a syntax similar to JSP. Jostraca can be extended to generate code in any language, using any language, while placing no restrictions on software design.

Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. Jraph also comes with a model zoo—a collection of forkable reference implementations of common message-passing GNN architectures, such as Graph Networks, Graph Convolutional Networks, and Graph Attention Networks.

It's just like a markdown live preview, but it's Jupyter Notebook live preview. Jupynium uses Selenium to automate Jupyter Notebook, synchronizing everything you type on Neovim. Never leave Neovim. Switch tabs on the browser as you switch files on Neovim. Note that it doesn't sync from Notebook to Neovim so only modify from Neovim.

Dash 2.11 and later supports running Dash apps in classic Jupyter Notebooks and in JupyterLab without the need to update the code or use the additional JupyterDash library. If you are using an earlier version of Dash, you can run Dash apps in a notebook using JupyterDash. This page documents additional options available when running Dash apps in notebooks as well as troubleshooting information.

nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluated notebooks – i.e. notebooks without stored output cells – will be automatically executed during the Sphinx build process.

jupyter-themes brings theme management to classic Jupyter Notebooks with a command-line tool that can restyle the interface, code cells, and UI chrome in seconds. It ships a catalog of popular dark and light themes and lets you customize fonts, font sizes, cell widths, and toolbar visibility so the notebook matches your preferred reading and coding ergonomics. The theming system adjusts CodeMirror syntax highlighting to keep code legible against chosen backgrounds and provides options to harmonize matplotlib/plotly colors for cohesive visuals. Installation and usage are streamlined through a single jt command, with flags to set or preview themes and a built-in restore option to return to defaults. Because it modifies the Notebook’s CSS, the project focuses on lightweight overrides rather than invasive changes, minimizing breakage across notebook versions. It’s especially valued by power users who spend long hours in notebooks and want a consistent, eye-friendly environment.