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version5.rst
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5.x Series

Released June, 2016

IPython 5.0 now uses promt-toolkit for the command line interface, thus allowing real multi-line editing and syntactic coloration as you type.

When using IPython as a subprocess, like for emacs inferior-shell, IPython can be started with --simple-prompt flag, which will bypass the prompt_toolkit input layer. In this mode completion, prompt color and many other features are disabled.

Backwards incompatible changes

The install_ext magic function which was deprecated since 4.0 have now been deleted. You can still distribute and install extension as packages on PyPI.

Update IPython event triggering to ensure callback registration and unregistration only affects the set of callbacks the next time that event is triggered. See :ghissue:`9447` and :ghpull:`9453`.

This is a change to the existing semantics, wherein one callback registering a second callback when triggered for an event would previously be invoked for that same event.

Integration with pydb has been removed since pydb development has been stopped since 2012, and pydb is not installable from PyPI

IPython 5.0 now uses prompt_toolkit, so any setting that affects readline will have no effect, and has likely been replaced by a configuration option on IPython itself.

the PromptManager class have been removed, and the prompt machinery simplified. See TerminalINteractiveShell.prompts configurable for how to setup your prompts.

Provisional Changes

Provisional changes are in experimental functionality that may, or may not make it to future version of IPython, and which API may change without warnings. Activating these feature and using these API is at your own risk, and may have security implication for your system, especially if used with the Jupyter notebook,

When running via the Jupyter notebook interfaces, or other compatible client, you can enable rich documentation experimental functionality:

When the docrepr package is installed setting the boolean flag InteractiveShell.sphinxify_docstring to True, will process the various object through sphinx before displaying them (see the docrepr package documentation for more information.

You need to also enable the IPython pager display rich HTML representation using the InteractiveShell.enable_html_pager boolean configuration option. As usual you can set these configuration options globally in your configuration files, alternatively you can turn them on dynamically using the following snippet:

ip = get_ipython()
ip.sphinxify_docstring = True
ip.enable_html_pager = True

You can test the effect of various combinations of the above configuration in the Jupyter notebook, with things example like :

import numpy as np
np.histogram?

This is part of an effort to make Documentation in Python richer and provide in the long term if possible dynamic examples that can contain math, images, widgets... As stated above this is nightly experimental feature with a lot of (fun) problem to solve. We would be happy to get your feedback and expertise on it.