PDI supports loose coupling of simulation codes with data handling the simulation code is annotated in a library-agnostic way, libraries are used from the specification tree.

Provides bindings to libxdo for manipulating X11 via simulated input. (Note that this is mostly a legacy library; you may wish to look at python-xdo for newer bindings.)

CLE loads binaries and their associated libraries, resolves imports and provides an abstraction of process memory the same way as if it was loader by the operating system's loader.

This library provides a low-level implementation of the IRC protocol for Python. It provides an event-driven IRC client framework with support for the basic IRC protocol, CTCP, and DCC connections.

python-mpv is a ctypes-based python interface to the mpv media player. It gives you more or less full control of all features of the player, just as the lua interface does.

python-gql is a GraphQL client which plays nicely with other graphql implementations compatible with the spec. GQL supports multiple transports to communicate with the backend such as Websockets, HTTP, Absinthe, and more.

Python Build Reasonableness (PBR) is a library that injects some useful and sensible default behaviors into your setuptools run. It will set versions, process requirements files and generate AUTHORS and ChangeLog file from git information.

This package provides an implementation of TRX, a tractography file format designed to facilitate dataset exchange, interoperability, and state-of-the-art analyses, acting as a community-driven replacement for the myriad existing file formats.

This package defines the can module, which provides controller area network (CAN) support for Python developers; providing common abstractions to different hardware devices, and a suite of utilities for sending and receiving messages on a CAN bus.

Python-RSA is a pure-Python RSA implementation. It supports encryption and decryption, signing and verifying signatures, and key generation according to PKCS#1 version 1.5. It can be used as a Python library as well as on the command line.

This package includes two Python modules:

1. fiu is a wrapper for libfiu, the fault injection C library.

2. fiu_ctrl provide an easy way run a command with libfiu enabled, and controlling the failure points dynamically.

NumPy is a large library used everywhere in scientific computing. That's why breaking backwards-compatibility comes at a significant cost and is almost always avoided, even if the API of some methods is arguably lacking. This package provides drop-in wrappers "fixing" those.

Tox is a generic virtualenv management and test command line tool. It can be used to check that a package installs correctly with different Python versions and interpreters, or run tests in each type of supported environment, or act as a frontend to continuous integration servers.

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

PythonSCAD is a programmatic 3D modeling application. It allows you to turn simple code into 3D models suitable for 3D printing. It is a fork of OpenSCAD which not only adds support for using Python as a native language, but also adds new features and improves existing ones.

The SGE Game Engine ("SGE", pronounced like "Sage") is a general-purpose 2D game engine. It takes care of several details for you so you can focus on the game itself. This makes more rapid game development possible, and it also makes the SGE easy to learn.

This library computes and verifies Cyclic Redundancy Check checksums, using predefined and custom CRC configurations.

Configurations:

CRC8

CCITT AUTOSAR SAEJ1850 SAEJ1850_ZERO BLUETOOTH MAXIM-DOW

CRC16

XMODEM GSM PROFIBUS MODBUS IBM-3740 KERMIT

CRC32

CRC32 AUTOSAR BZIP2 POSIX

CRC64

CRC64

TBB is a C++ runtime library that abstracts the low-level threading details necessary for optimal multi-core performance. It uses common C++ templates and coding style to eliminate tedious threading implementation work. It provides parallel loop constructs, asynchronous tasks, synchronization primitives, atomic operations, and more.

This library provides a low-level implementation of the IRC protocol for Python. It provides an event-driven IRC client framework. It has a fairly thorough support for the basic IRC protocol, CTCP, and DCC connections.

RAD is package which defines schemas for the Nancy Grace Roman Space Telescope shared attributes for processing and archive. These schemas are schemas for the ASDF file file format, which are used by ASDF to serialize and deserialize data for the Nancy Grace Roman Space Telescope.

This is a little HTTP/1.1 library written from scratch in Python, heavily inspired by hyper-h2. It's a bring-your-own-I/O library; h11 contains no IO code whatsoever. This means you can hook h11 up to your favorite network API, and that could be anything you want.

Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. Six supports every Python version since 2.5. It is contained in only one Python file, so it can be easily copied into your project.

This is a package meant primarily for documenting histogram indexing and the PlottableHistogram Protocol and any future cross-library standards. It also contains the code for the PlottableHistogram Protocol, to be used in type checking libraries wanting to conform to the protocol. It is not usually a runtime dependency, but only a type checking, testing, and/or docs dependency in support of other libraries.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

• Data: Scalable datasets for ML;

• Train: Distributed training;

• Tune: Scalable hyperparameter tuning;

• RLlib: Scalable reinforcement learning;

• Serve: Scalable and programmable serving.