Open Source ChromeOS Libraries - Page 73
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Libraries for ChromeOS
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PyQt-SiliconUI
A powerful and artistic UI library based on PyQt5
PyQt-SiliconUI is an open source UI framework built on top of PyQt5 that focuses on delivering visually elegant and highly customizable desktop application interfaces. It is designed as a comprehensive collection of UI components, layouts, and utilities that allow developers to build rich graphical user interfaces in Python with a modern and artistic aesthetic. The library includes a wide range of refactored widgets such as buttons, containers, editors, menus, sliders, and progress bars, all structured to work seamlessly with Qt’s layout system. It also provides core modules for animations, event handling, and custom painting, enabling developers to create smooth, interactive desktop experiences beyond standard PyQt capabilities. A key aspect of the project is its ongoing refactoring effort, which aims to modernize components, improve performance, and replace older implementations with more stable and maintainable versions. -
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PyQuil
A Python library for quantum programming using Quil
PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions. PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To run them in a preconfigured execution environment on Binder, click the "launch binder" badge at the top of the README or the link here! To learn more about the tutorials and how you can add your own, visit the rigetti/forest-tutorials repository. If you'd rather set everything up locally, or are interested in contributing to pyQuil, continue to the next section for instructions on installing pyQuil and the Forest SDK. -
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PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research prototyping and production inference. It supports video I/O pipelines, data augmentation, distributed training, and mixed precision computation for large-scale experiments. PyTorchVideo also connects seamlessly with other Meta AI tools such as Detectron2 and PyTorch3D for multimodal video analysis. Designed to accelerate research and deployment, it serves as a unified framework for reproducible, high-performance video AI development. -
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PyUPC-EAN
A barcode library/module for python.
PyUPC-EAN is a barcode library/module for Python. It supports the barcode formats upc-e, upc-a, ean-13, ean-8, ean-2, ean-5, itf14, codabar, code11, code39, code93, and msi. -
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This is an implementation of the Model-View-Controller (MVC) and the Observer patterns for the PyGTK2 graphic toolkit. See the project Homepage for further information.
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Pymunk
Pymunk is a easy-to-use pythonic 2d physics library
Pymunk is an easy-to-use Pythonic 2D physics library that can be used whenever you need 2D rigid body physics from Python. Perfect when you need 2D physics in your game, demo or simulation! It is built on top of the very capable 2D physics library Chipmunk2D. The first version was released in 2007 and Pymunk is still actively developed and maintained today, more than 15 years of active development. Pymunk has been used with success in many projects, big and small. For example: 3 Pyweek game competition winners, dozens of published scientific papers, and even in a self-driving car simulation. -
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Pyrlang
Erlang node implemented in Python 3.5+ (Asyncio-based)
This is a drop-in Erlang node implementation in Python 3, implementing a network Erlang node protocol. It was designed to allow interoperation between existing Python projects and BEAM languages: Erlang, Elixir, Gleam, Luaerl, LFE, Clojerl, and such. With just a few lines of startup code your Python program becomes an Erlang network node, participating in the Erlang cluster. -
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A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.
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Python colorlog
A colored formatter for the python logging module
Add colors to the output of Python's logging module. This library is over a decade old and supported a wide set of Python versions for most of its life, which has made it a difficult library to add new features to. colorlog 6 may break backward compatibility so that newer features can be added more easily, but may still not accept all changes or feature requests. colorlog 4 might accept essential bug fixes but should not be considered actively maintained and will not accept any major changes or new features. -
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Python in neuroscience
Collection of tutorials about methods of computational neuroscience
"Python in neuroscience" is a collection of collaboratively edited tutorials on computational neuroscience methods using Python language. Each tutorial is presented as a literate programming tutorial mixing text, code, and figures. The tutorials offer an introduction to basic methods of computational neuroscience and scientific programming in Python. They are authored in the environment called Jupyter Notebook -- you can even try it in your browser. -
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Python-Spider
Python3 web crawler practice
Python-Spider is a repository intended to teach or provide examples for writing web spiders / crawlers in Python — part of a broader learning and resource collection by its author. The code and documentation are oriented toward beginners or intermediate learners who want to learn how to fetch, parse, and extract data from websites programmatically. As part of the author’s public learning-path repositories, python-spider likely includes examples of HTTP requests, HTML parsing, maybe concurrency or scheduling to crawl multiple pages, and techniques to handle common web-scraping issues. For people wanting to get hands-on with building scrapers, collecting data, or learning how to navigate web programming in Python, this repository acts as a didactic reference or starting point. Because it’s published publicly under an open license, users are free to fork and adapt the code. -
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Python/xarray tutorial
Python/xarray tutorial for GEOS-Chem users
If the page is loaded successfully, you should see a Jupyter notebook interface. Then, click on the first notebook to get started. Jupyter combines Python code, execution results, plots, custom texts, and even Latex formulas in a single page. Besides using the Jupyter program, you can also view the static notebook on GitHub (e.g the first notebook). Python is free & open-source so can be easily installed on any machines. To best way to get the scientific Python environment is using the Conda management system. Please follow the official installation guide for installing on Linux/Mac/Windows. Linux/Mac also comes with a system Python (/usr/bin/python). Don't touch that. Windows users might find the full Anaconda (Conda plus tons of packages) with graphical interface easier to use than the command line. -
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Pythonidae
Curated decibans of scientific programming resources in Python
Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities, offering a structured reference for both academic and applied work. While the primary focus is on Python, some entries also highlight resources implemented in other languages like Julia, R, Go, and Java. The project emphasizes open contribution, allowing the community to continuously expand and refine the index. By gathering these resources in one place, Pythonidae acts as a central hub for scientific and data-driven programming with Python. -
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QDeviceWatcher
Hotpug detection for windows and linux. Only QtCore is required
QDeviceWatcher can detect usb storage add and remove event. Tested on linux (>= 2.6) and windows(mingw and msvc). WinCE is to be tested. -
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QL
Extending standart QLUA
This library is developing by users of QLUA. Main purpose - make it easier and faster to write add-ons for Quik using LUA. -
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QSint
Qt UI Development Library
C++ library for Qt-based UI development. Includes several Qt widgets, charting, property editors etc. -
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QT driver class that supports serial port access on windows and linux / posix compliant systems. This system take advantage of QT's event driven archtecture.
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This is an entity relationship diagram visualization widget programmed on QT4. With this tool you can draw objects (computers, people, whatever), and the relationship between them.
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Qigsaw
Dynamic modularization library which is based on Android App Bundles
Qigsaw is a dynamic modularization library which is based on Android App Bundles. It supports dynamic delivery for split APK without reinstalling the base one. Qigsaw-gradle-plugin will upload split APKs which require dynamic delivery during compilation, so you have to implement SplitApkUploader to upload split APKs to your own CND server. -
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A class to manage Modbus frames and another to make a Modbus server. Both classes are fully portable. An example of a modbus server is given. The code is very simple and comprehensive. Note ; Qt includes some classes related to Modbus since Qt 5.6.
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QuantComponents
Free Java components for Quantitative Finance and Algorithmic Trading
An open-source framework for financial time-series analysis and algorithmic trading, based on Java and OSGi, with an Eclipse front-end. * Highly modular: usable as plain java API, OSGi components, or integrated into Eclipse * Standalone or client-server architecture, depending on performance and reliability needs * Integrated with Interactive Brokers through IB Java API * Generic broker API, it can easily be extended to work with other brokers * It works with historical and/or realtime market data * Backtesting facility * Extensible SWT charting library -
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Quantities.net is a physical quantities library for C#. It performs runtime checking for compatible dimensions in arithmetic operations. it separates the concepts of a dimensioned quantity and a unit, so that units are easily extensible.
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QuantumOptics.jl
Library for the numerical simulation of closed as well as open quantum
QuantumOptics.jl is a numerical framework written in the Julia programming language that makes it easy to simulate various kinds of open quantum systems. It is inspired by the Quantum Optics Toolbox for MATLAB and the Python framework QuTiP. QuantumOptics.jl optimizes processor usage and memory consumption by relying on different ways to store and work with operators. The framework comes with a plethora of pre-defined systems and interactions making it very easy to focus on the physics, not on the numerics. Every function in the framework has been severely tested with all tests and their code coverage presented on the framework's GitHub page.
