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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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config.py
146 lines | 5.4 KiB | text/x-python | PythonLexer
"""Implementation of configuration-related magic functions.
"""
#-----------------------------------------------------------------------------
# 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
#-----------------------------------------------------------------------------
# Stdlib
import re
# Our own packages
from IPython.core.error import UsageError
from IPython.core.magic import Magics, magics_class, line_magic
from IPython.utils.warn import error
#-----------------------------------------------------------------------------
# Magic implementation classes
#-----------------------------------------------------------------------------
@magics_class
class ConfigMagics(Magics):
def __init__(self, shell):
super(ConfigMagics, self).__init__(shell)
self.configurables = []
@line_magic
def config(self, s):
"""configure IPython
%config Class[.trait=value]
This magic exposes most of the IPython config system. Any
Configurable class should be able to be configured with the simple
line::
%config Class.trait=value
Where `value` will be resolved in the user's namespace, if it is an
expression or variable name.
Examples
--------
To see what classes are available for config, pass no arguments::
In [1]: %config
Available objects for config:
TerminalInteractiveShell
HistoryManager
PrefilterManager
AliasManager
IPCompleter
PromptManager
DisplayFormatter
To view what is configurable on a given class, just pass the class
name::
In [2]: %config IPCompleter
IPCompleter options
-----------------
IPCompleter.omit__names=<Enum>
Current: 2
Choices: (0, 1, 2)
Instruct the completer to omit private method names
Specifically, when completing on ``object.<tab>``.
When 2 [default]: all names that start with '_' will be excluded.
When 1: all 'magic' names (``__foo__``) will be excluded.
When 0: nothing will be excluded.
IPCompleter.merge_completions=<CBool>
Current: True
Whether to merge completion results into a single list
If False, only the completion results from the first non-empty
completer will be returned.
IPCompleter.limit_to__all__=<CBool>
Current: False
Instruct the completer to use __all__ for the completion
Specifically, when completing on ``object.<tab>``.
When True: only those names in obj.__all__ will be included.
When False [default]: the __all__ attribute is ignored
IPCompleter.greedy=<CBool>
Current: False
Activate greedy completion
This will enable completion on elements of lists, results of
function calls, etc., but can be unsafe because the code is
actually evaluated on TAB.
but the real use is in setting values::
In [3]: %config IPCompleter.greedy = True
and these values are read from the user_ns if they are variables::
In [4]: feeling_greedy=False
In [5]: %config IPCompleter.greedy = feeling_greedy
"""
from IPython.config.loader import Config
# some IPython objects are Configurable, but do not yet have
# any configurable traits. Exclude them from the effects of
# this magic, as their presence is just noise:
configurables = [ c for c in self.shell.configurables
if c.__class__.class_traits(config=True) ]
classnames = [ c.__class__.__name__ for c in configurables ]
line = s.strip()
if not line:
# print available configurable names
print "Available objects for config:"
for name in classnames:
print " ", name
return
elif line in classnames:
# `%config TerminalInteractiveShell` will print trait info for
# TerminalInteractiveShell
c = configurables[classnames.index(line)]
cls = c.__class__
help = cls.class_get_help(c)
# strip leading '--' from cl-args:
help = re.sub(re.compile(r'^--', re.MULTILINE), '', help)
print help
return
elif '=' not in line:
raise UsageError("Invalid config statement: %r, "
"should be Class.trait = value" % line)
# otherwise, assume we are setting configurables.
# leave quotes on args when splitting, because we want
# unquoted args to eval in user_ns
cfg = Config()
exec "cfg."+line in locals(), self.shell.user_ns
for configurable in configurables:
try:
configurable.update_config(cfg)
except Exception as e:
error(e)