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Backport PR #2384: Adapt inline backend to changes in matplotlib...
Backport PR #2384: Adapt inline backend to changes in matplotlib Matplotlib recently merged https://github.com/matplotlib/matplotlib/pull/1125 that makes it simpler to use objective oriented figure creation by automatically creating the right canvas for the backend. To solve that all backends must provide a backend_xxx.FigureCanvas. This is obviosly missing from the inline backend. The change is needed to make the inline backend work with mpl's 1.2.x branch which is due to released soon. Simply setting the default canvas equal to a Agg canvas appears to work for both svg and png figures but I'm not sure weather that is the right approach. Should the canvas depend on the figure format and provide a svg canvas for a svg figure? (Note that before this change to matplotlib the canvas from a plt.figure call seams to be a agg type in all cases) Edit: I made the pull request against 0.13.1 since it would be good to have this in the stable branch for when mpl is released. Just let me know and I can rebase it against master

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pylab.py
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"""Implementation of magic functions for matplotlib/pylab support.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 The IPython Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Our own packages
from IPython.config.application import Application
from IPython.core.magic import Magics, magics_class, line_magic
from IPython.testing.skipdoctest import skip_doctest
#-----------------------------------------------------------------------------
# Magic implementation classes
#-----------------------------------------------------------------------------
@magics_class
class PylabMagics(Magics):
"""Magics related to matplotlib's pylab support"""
@skip_doctest
@line_magic
def pylab(self, parameter_s=''):
"""Load numpy and matplotlib to work interactively.
%pylab [GUINAME]
This function lets you activate pylab (matplotlib, numpy and
interactive support) at any point during an IPython session.
It will import at the top level numpy as np, pyplot as plt, matplotlib,
pylab and mlab, as well as all names from numpy and pylab.
If you are using the inline matplotlib backend for embedded figures,
you can adjust its behavior via the %config magic::
# enable SVG figures, necessary for SVG+XHTML export in the qtconsole
In [1]: %config InlineBackend.figure_format = 'svg'
# change the behavior of closing all figures at the end of each
# execution (cell), or allowing reuse of active figures across
# cells:
In [2]: %config InlineBackend.close_figures = False
Parameters
----------
guiname : optional
One of the valid arguments to the %gui magic ('qt', 'wx', 'gtk',
'osx' or 'tk'). If given, the corresponding Matplotlib backend is
used, otherwise matplotlib's default (which you can override in your
matplotlib config file) is used.
Examples
--------
In this case, where the MPL default is TkAgg::
In [2]: %pylab
Welcome to pylab, a matplotlib-based Python environment.
Backend in use: TkAgg
For more information, type 'help(pylab)'.
But you can explicitly request a different backend::
In [3]: %pylab qt
Welcome to pylab, a matplotlib-based Python environment.
Backend in use: Qt4Agg
For more information, type 'help(pylab)'.
"""
if Application.initialized():
app = Application.instance()
try:
import_all_status = app.pylab_import_all
except AttributeError:
import_all_status = True
else:
import_all_status = True
self.shell.enable_pylab(parameter_s, import_all=import_all_status)