IPython: Productive Interactive Computing
Project description
IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are:
Powerful interactive Python shells (terminal- and Qt-based).
A web-based interactive notebook environment with all shell features plus support for embedded figures, animations and rich media.
Support for interactive data visualization and use of GUI toolkits.
Flexible, embeddable interpreters to load into your own projects.
A high-performance library for high level and interactive parallel computing that works in multicore systems, clusters, supercomputing and cloud scenarios.
The enhanced interactive Python shells have the following main features:
Comprehensive object introspection.
Input history, persistent across sessions.
Caching of output results during a session with automatically generated references.
Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords.
Extensible system of ‘magic’ commands for controlling the environment and performing many tasks related either to IPython or the operating system.
A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time).
Session logging and reloading.
Extensible syntax processing for special purpose situations.
Access to the system shell with user-extensible alias system.
Easily embeddable in other Python programs and GUIs.
Integrated access to the pdb debugger and the Python profiler.
The parallel computing architecture has the following main features:
Quickly parallelize Python code from an interactive Python/IPython session.
A flexible and dynamic process model that be deployed on anything from multicore workstations to supercomputers.
An architecture that supports many different styles of parallelism, from message passing to task farming.
Both blocking and fully asynchronous interfaces.
High level APIs that enable many things to be parallelized in a few lines of code.
Share live parallel jobs with other users securely.
Dynamically load balanced task farming system.
Robust error handling in parallel code.
The latest development version is always available from IPython’s GitHub site.
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